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diff --git a/examples/acolite/index.html b/examples/acolite/index.html
index ae4affee..14941241 100644
--- a/examples/acolite/index.html
+++ b/examples/acolite/index.html
@@ -22,7 +22,7 @@
       
       
       <link rel="icon" href="../../assets/images/favicon.png">
-      <meta name="generator" content="mkdocs-1.6.1, mkdocs-material-9.5.39">
+      <meta name="generator" content="mkdocs-1.6.1, mkdocs-material-9.5.40">
     
     
       
diff --git a/examples/aviris/index.html b/examples/aviris/index.html
index e57c192b..7bd6f0ba 100644
--- a/examples/aviris/index.html
+++ b/examples/aviris/index.html
@@ -22,7 +22,7 @@
       
       
       <link rel="icon" href="../../assets/images/favicon.png">
-      <meta name="generator" content="mkdocs-1.6.1, mkdocs-material-9.5.39">
+      <meta name="generator" content="mkdocs-1.6.1, mkdocs-material-9.5.40">
     
     
       
diff --git a/examples/chlorophyll_a/index.html b/examples/chlorophyll_a/index.html
index 585eb893..25b95cea 100644
--- a/examples/chlorophyll_a/index.html
+++ b/examples/chlorophyll_a/index.html
@@ -22,7 +22,7 @@
       
       
       <link rel="icon" href="../../assets/images/favicon.png">
-      <meta name="generator" content="mkdocs-1.6.1, mkdocs-material-9.5.39">
+      <meta name="generator" content="mkdocs-1.6.1, mkdocs-material-9.5.40">
     
     
       
@@ -2279,7 +2279,7 @@ <h1 id="visualizing-pace-chlorophyll-a-data-interactively-with-hypercoast">Visua
     display_scale:  log
     display_min:    0.01
     display_max:    20.0
-    date:           ['2024-06-01', '2024-06-02', '2024-06-03', '2024-06-04', ...</pre><div class="xr-wrap" style="display:none"><div class="xr-header"><div class="xr-obj-type">xarray.DataArray</div><div class="xr-array-name">'chlor_a'</div><ul class="xr-dim-list"><li><span class="xr-has-index">lat</span>: 1800</li><li><span class="xr-has-index">lon</span>: 3600</li><li><span class="xr-has-index">date</span>: 17</li></ul></div><ul class="xr-sections"><li class="xr-section-item"><div class="xr-array-wrap"><input checked="" class="xr-array-in" id="section-187970bd-507e-4c91-ac23-e84e132355aa" type="checkbox"/><label for="section-187970bd-507e-4c91-ac23-e84e132355aa" title="Show/hide data repr"><svg class="icon xr-icon-database"><use xlink:href="#icon-database"></use></svg></label><div class="xr-array-preview xr-preview"><span>dask.array&lt;chunksize=(512, 1024, 1), meta=np.ndarray&gt;</span></div><div class="xr-array-data"><table>
+    date:           ['2024-06-01', '2024-06-02', '2024-06-03', '2024-06-04', ...</pre><div class="xr-wrap" style="display:none"><div class="xr-header"><div class="xr-obj-type">xarray.DataArray</div><div class="xr-array-name">'chlor_a'</div><ul class="xr-dim-list"><li><span class="xr-has-index">lat</span>: 1800</li><li><span class="xr-has-index">lon</span>: 3600</li><li><span class="xr-has-index">date</span>: 17</li></ul></div><ul class="xr-sections"><li class="xr-section-item"><div class="xr-array-wrap"><input checked="" class="xr-array-in" id="section-c951c9f3-ff2a-4cdb-8473-9d9291e9e7b7" type="checkbox"/><label for="section-c951c9f3-ff2a-4cdb-8473-9d9291e9e7b7" title="Show/hide data repr"><svg class="icon xr-icon-database"><use xlink:href="#icon-database"></use></svg></label><div class="xr-array-preview xr-preview"><span>dask.array&lt;chunksize=(512, 1024, 1), meta=np.ndarray&gt;</span></div><div class="xr-array-data"><table>
 <tr>
 <td>
 <table style="border-collapse: collapse;">
@@ -2389,12 +2389,12 @@ <h1 id="visualizing-pace-chlorophyll-a-data-interactively-with-hypercoast">Visua
 </svg>
 </td>
 </tr>
-</table></div></div></li><li class="xr-section-item"><input checked="" class="xr-section-summary-in" id="section-bb888332-43f6-429d-a846-f18744113c04" type="checkbox"/><label class="xr-section-summary" for="section-bb888332-43f6-429d-a846-f18744113c04">Coordinates: <span>(4)</span></label><div class="xr-section-inline-details"></div><div class="xr-section-details"><ul class="xr-var-list"><li class="xr-var-item"><div class="xr-var-name"><span class="xr-has-index">lat</span></div><div class="xr-var-dims">(lat)</div><div class="xr-var-dtype">float32</div><div class="xr-var-preview xr-preview">89.95 89.85 89.75 ... -89.85 -89.95</div><input class="xr-var-attrs-in" id="attrs-85c73701-e6ed-4237-8ead-2aa280423fbf" type="checkbox"/><label for="attrs-85c73701-e6ed-4237-8ead-2aa280423fbf" title="Show/Hide attributes"><svg class="icon xr-icon-file-text2"><use xlink:href="#icon-file-text2"></use></svg></label><input class="xr-var-data-in" id="data-214980a2-f7f3-45de-859c-5c0e60801a0e" type="checkbox"/><label for="data-214980a2-f7f3-45de-859c-5c0e60801a0e" title="Show/Hide data repr"><svg class="icon xr-icon-database"><use xlink:href="#icon-database"></use></svg></label><div class="xr-var-attrs"><dl class="xr-attrs"><dt><span>long_name :</span></dt><dd>Latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>valid_min :</span></dt><dd>-90.0</dd><dt><span>valid_max :</span></dt><dd>90.0</dd></dl></div><div class="xr-var-data"><pre>array([ 89.95    ,  89.85    ,  89.75    , ..., -89.75    , -89.850006,
-       -89.950005], dtype=float32)</pre></div></li><li class="xr-var-item"><div class="xr-var-name"><span class="xr-has-index">lon</span></div><div class="xr-var-dims">(lon)</div><div class="xr-var-dtype">float32</div><div class="xr-var-preview xr-preview">-179.9 -179.9 ... 179.9 180.0</div><input class="xr-var-attrs-in" id="attrs-71d7e09d-3f86-49cf-bf55-7a000ef74bd8" type="checkbox"/><label for="attrs-71d7e09d-3f86-49cf-bf55-7a000ef74bd8" title="Show/Hide attributes"><svg class="icon xr-icon-file-text2"><use xlink:href="#icon-file-text2"></use></svg></label><input class="xr-var-data-in" id="data-b7a43980-737e-4819-8b6d-bd31e2769e6d" type="checkbox"/><label for="data-b7a43980-737e-4819-8b6d-bd31e2769e6d" title="Show/Hide data repr"><svg class="icon xr-icon-database"><use xlink:href="#icon-database"></use></svg></label><div class="xr-var-attrs"><dl class="xr-attrs"><dt><span>long_name :</span></dt><dd>Longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>valid_min :</span></dt><dd>-180.0</dd><dt><span>valid_max :</span></dt><dd>180.0</dd></dl></div><div class="xr-var-data"><pre>array([-179.95   , -179.85   , -179.75   , ...,  179.75   ,  179.85   ,
-        179.95001], dtype=float32)</pre></div></li><li class="xr-var-item"><div class="xr-var-name"><span class="xr-has-index">date</span></div><div class="xr-var-dims">(date)</div><div class="xr-var-dtype">&lt;U10</div><div class="xr-var-preview xr-preview">'2024-06-01' ... '2024-06-17'</div><input class="xr-var-attrs-in" disabled="" id="attrs-25243614-03ff-4b89-bcae-fc005c8eeb8d" type="checkbox"/><label for="attrs-25243614-03ff-4b89-bcae-fc005c8eeb8d" title="Show/Hide attributes"><svg class="icon xr-icon-file-text2"><use xlink:href="#icon-file-text2"></use></svg></label><input class="xr-var-data-in" id="data-11ce84d4-49e7-4a25-8a31-0689e64c01da" type="checkbox"/><label for="data-11ce84d4-49e7-4a25-8a31-0689e64c01da" title="Show/Hide data repr"><svg class="icon xr-icon-database"><use xlink:href="#icon-database"></use></svg></label><div class="xr-var-attrs"><dl class="xr-attrs"></dl></div><div class="xr-var-data"><pre>array(['2024-06-01', '2024-06-02', '2024-06-03', '2024-06-04', '2024-06-05',
+</table></div></div></li><li class="xr-section-item"><input checked="" class="xr-section-summary-in" id="section-9003234d-fbe8-4ce5-bdb9-eec8b29cd1e1" type="checkbox"/><label class="xr-section-summary" for="section-9003234d-fbe8-4ce5-bdb9-eec8b29cd1e1">Coordinates: <span>(4)</span></label><div class="xr-section-inline-details"></div><div class="xr-section-details"><ul class="xr-var-list"><li class="xr-var-item"><div class="xr-var-name"><span class="xr-has-index">lat</span></div><div class="xr-var-dims">(lat)</div><div class="xr-var-dtype">float32</div><div class="xr-var-preview xr-preview">89.95 89.85 89.75 ... -89.85 -89.95</div><input class="xr-var-attrs-in" id="attrs-ec86d1df-7d1c-4b48-9beb-6a77a28c23e9" type="checkbox"/><label for="attrs-ec86d1df-7d1c-4b48-9beb-6a77a28c23e9" title="Show/Hide attributes"><svg class="icon xr-icon-file-text2"><use xlink:href="#icon-file-text2"></use></svg></label><input class="xr-var-data-in" id="data-d23d7786-fdad-43a3-a2f3-aa007bcaa27b" type="checkbox"/><label for="data-d23d7786-fdad-43a3-a2f3-aa007bcaa27b" title="Show/Hide data repr"><svg class="icon xr-icon-database"><use xlink:href="#icon-database"></use></svg></label><div class="xr-var-attrs"><dl class="xr-attrs"><dt><span>long_name :</span></dt><dd>Latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>valid_min :</span></dt><dd>-90.0</dd><dt><span>valid_max :</span></dt><dd>90.0</dd></dl></div><div class="xr-var-data"><pre>array([ 89.95    ,  89.85    ,  89.75    , ..., -89.75    , -89.850006,
+       -89.950005], dtype=float32)</pre></div></li><li class="xr-var-item"><div class="xr-var-name"><span class="xr-has-index">lon</span></div><div class="xr-var-dims">(lon)</div><div class="xr-var-dtype">float32</div><div class="xr-var-preview xr-preview">-179.9 -179.9 ... 179.9 180.0</div><input class="xr-var-attrs-in" id="attrs-a67de0c6-f6eb-4e1c-ba6a-32be30067b2b" type="checkbox"/><label for="attrs-a67de0c6-f6eb-4e1c-ba6a-32be30067b2b" title="Show/Hide attributes"><svg class="icon xr-icon-file-text2"><use xlink:href="#icon-file-text2"></use></svg></label><input class="xr-var-data-in" id="data-7e477ce3-0834-4831-9b47-1c05c45e42a3" type="checkbox"/><label for="data-7e477ce3-0834-4831-9b47-1c05c45e42a3" title="Show/Hide data repr"><svg class="icon xr-icon-database"><use xlink:href="#icon-database"></use></svg></label><div class="xr-var-attrs"><dl class="xr-attrs"><dt><span>long_name :</span></dt><dd>Longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>valid_min :</span></dt><dd>-180.0</dd><dt><span>valid_max :</span></dt><dd>180.0</dd></dl></div><div class="xr-var-data"><pre>array([-179.95   , -179.85   , -179.75   , ...,  179.75   ,  179.85   ,
+        179.95001], dtype=float32)</pre></div></li><li class="xr-var-item"><div class="xr-var-name"><span class="xr-has-index">date</span></div><div class="xr-var-dims">(date)</div><div class="xr-var-dtype">&lt;U10</div><div class="xr-var-preview xr-preview">'2024-06-01' ... '2024-06-17'</div><input class="xr-var-attrs-in" disabled="" id="attrs-a64d96d8-ff3c-489f-8c01-30a30405bfa6" type="checkbox"/><label for="attrs-a64d96d8-ff3c-489f-8c01-30a30405bfa6" title="Show/Hide attributes"><svg class="icon xr-icon-file-text2"><use xlink:href="#icon-file-text2"></use></svg></label><input class="xr-var-data-in" id="data-d887b5ab-7fef-4019-8e1d-c8b981aefef2" type="checkbox"/><label for="data-d887b5ab-7fef-4019-8e1d-c8b981aefef2" title="Show/Hide data repr"><svg class="icon xr-icon-database"><use xlink:href="#icon-database"></use></svg></label><div class="xr-var-attrs"><dl class="xr-attrs"></dl></div><div class="xr-var-data"><pre>array(['2024-06-01', '2024-06-02', '2024-06-03', '2024-06-04', '2024-06-05',
        '2024-06-06', '2024-06-07', '2024-06-08', '2024-06-09', '2024-06-10',
        '2024-06-11', '2024-06-12', '2024-06-13', '2024-06-14', '2024-06-15',
-       '2024-06-16', '2024-06-17'], dtype='&lt;U10')</pre></div></li><li class="xr-var-item"><div class="xr-var-name"><span>spatial_ref</span></div><div class="xr-var-dims">()</div><div class="xr-var-dtype">int64</div><div class="xr-var-preview xr-preview">0</div><input class="xr-var-attrs-in" id="attrs-bc03e649-dda8-4339-b0d9-c1056303f0d7" type="checkbox"/><label for="attrs-bc03e649-dda8-4339-b0d9-c1056303f0d7" title="Show/Hide attributes"><svg class="icon xr-icon-file-text2"><use xlink:href="#icon-file-text2"></use></svg></label><input class="xr-var-data-in" id="data-c770f10b-af2f-42e1-b2ee-d3491d3535ce" type="checkbox"/><label for="data-c770f10b-af2f-42e1-b2ee-d3491d3535ce" title="Show/Hide data repr"><svg class="icon xr-icon-database"><use xlink:href="#icon-database"></use></svg></label><div class="xr-var-attrs"><dl class="xr-attrs"><dt><span>crs_wkt :</span></dt><dd>GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0,AUTHORITY["EPSG","8901"]],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]],AXIS["Latitude",NORTH],AXIS["Longitude",EAST],AUTHORITY["EPSG","4326"]]</dd><dt><span>semi_major_axis :</span></dt><dd>6378137.0</dd><dt><span>semi_minor_axis :</span></dt><dd>6356752.314245179</dd><dt><span>inverse_flattening :</span></dt><dd>298.257223563</dd><dt><span>reference_ellipsoid_name :</span></dt><dd>WGS 84</dd><dt><span>longitude_of_prime_meridian :</span></dt><dd>0.0</dd><dt><span>prime_meridian_name :</span></dt><dd>Greenwich</dd><dt><span>geographic_crs_name :</span></dt><dd>WGS 84</dd><dt><span>horizontal_datum_name :</span></dt><dd>World Geodetic System 1984</dd><dt><span>grid_mapping_name :</span></dt><dd>latitude_longitude</dd><dt><span>spatial_ref :</span></dt><dd>GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0,AUTHORITY["EPSG","8901"]],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]],AXIS["Latitude",NORTH],AXIS["Longitude",EAST],AUTHORITY["EPSG","4326"]]</dd></dl></div><div class="xr-var-data"><pre>array(0)</pre></div></li></ul></div></li><li class="xr-section-item"><input class="xr-section-summary-in" id="section-71f90df5-5a2e-4d14-a392-36c90348edb7" type="checkbox"/><label class="xr-section-summary" for="section-71f90df5-5a2e-4d14-a392-36c90348edb7">Indexes: <span>(3)</span></label><div class="xr-section-inline-details"></div><div class="xr-section-details"><ul class="xr-var-list"><li class="xr-var-item"><div class="xr-index-name"><div>lat</div></div><div class="xr-index-preview">PandasIndex</div><div></div><input class="xr-index-data-in" id="index-cb203c4b-c030-4a24-a37c-359ba5c6882c" type="checkbox"><label for="index-cb203c4b-c030-4a24-a37c-359ba5c6882c" title="Show/Hide index repr"><svg class="icon xr-icon-database"><use xlink:href="#icon-database"></use></svg></label><div class="xr-index-data"><pre>PandasIndex(Index([ 89.94999694824219,   89.8499984741211,              89.75,
+       '2024-06-16', '2024-06-17'], dtype='&lt;U10')</pre></div></li><li class="xr-var-item"><div class="xr-var-name"><span>spatial_ref</span></div><div class="xr-var-dims">()</div><div class="xr-var-dtype">int64</div><div class="xr-var-preview xr-preview">0</div><input class="xr-var-attrs-in" id="attrs-a7fff5fa-0e33-4795-841f-2ec90c644ca8" type="checkbox"/><label for="attrs-a7fff5fa-0e33-4795-841f-2ec90c644ca8" title="Show/Hide attributes"><svg class="icon xr-icon-file-text2"><use xlink:href="#icon-file-text2"></use></svg></label><input class="xr-var-data-in" id="data-80b0cc21-7e7f-47b1-b008-bc399efb5484" type="checkbox"/><label for="data-80b0cc21-7e7f-47b1-b008-bc399efb5484" title="Show/Hide data repr"><svg class="icon xr-icon-database"><use xlink:href="#icon-database"></use></svg></label><div class="xr-var-attrs"><dl class="xr-attrs"><dt><span>crs_wkt :</span></dt><dd>GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0,AUTHORITY["EPSG","8901"]],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]],AXIS["Latitude",NORTH],AXIS["Longitude",EAST],AUTHORITY["EPSG","4326"]]</dd><dt><span>semi_major_axis :</span></dt><dd>6378137.0</dd><dt><span>semi_minor_axis :</span></dt><dd>6356752.314245179</dd><dt><span>inverse_flattening :</span></dt><dd>298.257223563</dd><dt><span>reference_ellipsoid_name :</span></dt><dd>WGS 84</dd><dt><span>longitude_of_prime_meridian :</span></dt><dd>0.0</dd><dt><span>prime_meridian_name :</span></dt><dd>Greenwich</dd><dt><span>geographic_crs_name :</span></dt><dd>WGS 84</dd><dt><span>horizontal_datum_name :</span></dt><dd>World Geodetic System 1984</dd><dt><span>grid_mapping_name :</span></dt><dd>latitude_longitude</dd><dt><span>spatial_ref :</span></dt><dd>GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0,AUTHORITY["EPSG","8901"]],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]],AXIS["Latitude",NORTH],AXIS["Longitude",EAST],AUTHORITY["EPSG","4326"]]</dd></dl></div><div class="xr-var-data"><pre>array(0)</pre></div></li></ul></div></li><li class="xr-section-item"><input class="xr-section-summary-in" id="section-fc4af9ab-129d-4c40-89cb-1d1cd90f386f" type="checkbox"/><label class="xr-section-summary" for="section-fc4af9ab-129d-4c40-89cb-1d1cd90f386f">Indexes: <span>(3)</span></label><div class="xr-section-inline-details"></div><div class="xr-section-details"><ul class="xr-var-list"><li class="xr-var-item"><div class="xr-index-name"><div>lat</div></div><div class="xr-index-preview">PandasIndex</div><div></div><input class="xr-index-data-in" id="index-8c7ec458-fcbe-4ad5-b468-0106c109e9a5" type="checkbox"><label for="index-8c7ec458-fcbe-4ad5-b468-0106c109e9a5" title="Show/Hide index repr"><svg class="icon xr-icon-database"><use xlink:href="#icon-database"></use></svg></label><div class="xr-index-data"><pre>PandasIndex(Index([ 89.94999694824219,   89.8499984741211,              89.75,
          89.6500015258789,  89.55000305175781,  89.44999694824219,
          89.3499984741211,              89.25,   89.1500015258789,
         89.05000305175781,
@@ -2403,7 +2403,7 @@ <h1 id="visualizing-pace-chlorophyll-a-data-interactively-with-hypercoast">Visua
        -89.35000610351562, -89.45000457763672, -89.55000305175781,
         -89.6500015258789,             -89.75, -89.85000610351562,
        -89.95000457763672],
-      dtype='float32', name='lat', length=1800))</pre></div></input></li><li class="xr-var-item"><div class="xr-index-name"><div>lon</div></div><div class="xr-index-preview">PandasIndex</div><div></div><input class="xr-index-data-in" id="index-ad3cc3cb-d90a-423a-afec-562de9ae84e4" type="checkbox"><label for="index-ad3cc3cb-d90a-423a-afec-562de9ae84e4" title="Show/Hide index repr"><svg class="icon xr-icon-database"><use xlink:href="#icon-database"></use></svg></label><div class="xr-index-data"><pre>PandasIndex(Index([ -179.9499969482422, -179.85000610351562,             -179.75,
+      dtype='float32', name='lat', length=1800))</pre></div></input></li><li class="xr-var-item"><div class="xr-index-name"><div>lon</div></div><div class="xr-index-preview">PandasIndex</div><div></div><input class="xr-index-data-in" id="index-f905fab4-1dc1-4869-b427-d2502b2d4229" type="checkbox"><label for="index-f905fab4-1dc1-4869-b427-d2502b2d4229" title="Show/Hide index repr"><svg class="icon xr-icon-database"><use xlink:href="#icon-database"></use></svg></label><div class="xr-index-data"><pre>PandasIndex(Index([ -179.9499969482422, -179.85000610351562,             -179.75,
        -179.64999389648438,  -179.5500030517578,  -179.4499969482422,
        -179.35000610351562,             -179.25, -179.14999389648438,
         -179.0500030517578,
@@ -2412,11 +2412,11 @@ <h1 id="visualizing-pace-chlorophyll-a-data-interactively-with-hypercoast">Visua
         179.35000610351562,  179.45001220703125,   179.5500030517578,
         179.65000915527344,              179.75,  179.85000610351562,
         179.95001220703125],
-      dtype='float32', name='lon', length=3600))</pre></div></input></li><li class="xr-var-item"><div class="xr-index-name"><div>date</div></div><div class="xr-index-preview">PandasIndex</div><div></div><input class="xr-index-data-in" id="index-4b5b1572-e65a-483d-a6b4-10f8c8ee3430" type="checkbox"><label for="index-4b5b1572-e65a-483d-a6b4-10f8c8ee3430" title="Show/Hide index repr"><svg class="icon xr-icon-database"><use xlink:href="#icon-database"></use></svg></label><div class="xr-index-data"><pre>PandasIndex(Index(['2024-06-01', '2024-06-02', '2024-06-03', '2024-06-04', '2024-06-05',
+      dtype='float32', name='lon', length=3600))</pre></div></input></li><li class="xr-var-item"><div class="xr-index-name"><div>date</div></div><div class="xr-index-preview">PandasIndex</div><div></div><input class="xr-index-data-in" id="index-9fdca58a-7073-4b58-8a06-c8f050dd4b36" type="checkbox"><label for="index-9fdca58a-7073-4b58-8a06-c8f050dd4b36" title="Show/Hide index repr"><svg class="icon xr-icon-database"><use xlink:href="#icon-database"></use></svg></label><div class="xr-index-data"><pre>PandasIndex(Index(['2024-06-01', '2024-06-02', '2024-06-03', '2024-06-04', '2024-06-05',
        '2024-06-06', '2024-06-07', '2024-06-08', '2024-06-09', '2024-06-10',
        '2024-06-11', '2024-06-12', '2024-06-13', '2024-06-14', '2024-06-15',
        '2024-06-16', '2024-06-17'],
-      dtype='object', name='date'))</pre></div></input></li></ul></div></li><li class="xr-section-item"><input class="xr-section-summary-in" id="section-a65807b8-e341-4afa-acef-442d9f46e2cf" type="checkbox"/><label class="xr-section-summary" for="section-a65807b8-e341-4afa-acef-442d9f46e2cf">Attributes: <span>(10)</span></label><div class="xr-section-inline-details"></div><div class="xr-section-details"><dl class="xr-attrs"><dt><span>long_name :</span></dt><dd>Chlorophyll Concentration, OCI Algorithm</dd><dt><span>units :</span></dt><dd>lg(mg m^-3)</dd><dt><span>standard_name :</span></dt><dd>mass_concentration_of_chlorophyll_in_sea_water</dd><dt><span>valid_min :</span></dt><dd>0.001</dd><dt><span>valid_max :</span></dt><dd>100.0</dd><dt><span>reference :</span></dt><dd>Hu, C., Lee Z., and Franz, B.A. (2012). Chlorophyll-a algorithms for oligotrophic oceans: A novel approach based on three-band reflectance difference, J. Geophys. Res., 117, C01011, doi:10.1029/2011JC007395.</dd><dt><span>display_scale :</span></dt><dd>log</dd><dt><span>display_min :</span></dt><dd>0.01</dd><dt><span>display_max :</span></dt><dd>20.0</dd><dt><span>date :</span></dt><dd>['2024-06-01', '2024-06-02', '2024-06-03', '2024-06-04', '2024-06-05', '2024-06-06', '2024-06-07', '2024-06-08', '2024-06-09', '2024-06-10', '2024-06-11', '2024-06-12', '2024-06-13', '2024-06-14', '2024-06-15', '2024-06-16', '2024-06-17']</dd></dl></div></li></ul></div></div>
+      dtype='object', name='date'))</pre></div></input></li></ul></div></li><li class="xr-section-item"><input class="xr-section-summary-in" id="section-b16b1af8-ed02-4c8a-a249-2af512a3fb35" type="checkbox"/><label class="xr-section-summary" for="section-b16b1af8-ed02-4c8a-a249-2af512a3fb35">Attributes: <span>(10)</span></label><div class="xr-section-inline-details"></div><div class="xr-section-details"><dl class="xr-attrs"><dt><span>long_name :</span></dt><dd>Chlorophyll Concentration, OCI Algorithm</dd><dt><span>units :</span></dt><dd>lg(mg m^-3)</dd><dt><span>standard_name :</span></dt><dd>mass_concentration_of_chlorophyll_in_sea_water</dd><dt><span>valid_min :</span></dt><dd>0.001</dd><dt><span>valid_max :</span></dt><dd>100.0</dd><dt><span>reference :</span></dt><dd>Hu, C., Lee Z., and Franz, B.A. (2012). Chlorophyll-a algorithms for oligotrophic oceans: A novel approach based on three-band reflectance difference, J. Geophys. Res., 117, C01011, doi:10.1029/2011JC007395.</dd><dt><span>display_scale :</span></dt><dd>log</dd><dt><span>display_min :</span></dt><dd>0.01</dd><dt><span>display_max :</span></dt><dd>20.0</dd><dt><span>date :</span></dt><dd>['2024-06-01', '2024-06-02', '2024-06-03', '2024-06-04', '2024-06-05', '2024-06-06', '2024-06-07', '2024-06-08', '2024-06-09', '2024-06-10', '2024-06-11', '2024-06-12', '2024-06-13', '2024-06-14', '2024-06-15', '2024-06-16', '2024-06-17']</dd></dl></div></li></ul></div></div>
 </div>
 </div>
 </div>
@@ -2466,7 +2466,7 @@ <h1 id="visualizing-pace-chlorophyll-a-data-interactively-with-hypercoast">Visua
 <div class="jp-OutputArea-child jp-OutputArea-executeResult">
 <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[7]:</div>
 <div class="jp-RenderedText jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/plain" tabindex="0">
-<pre>&lt;matplotlib.collections.QuadMesh at 0x7fc7244e82f0&gt;</pre>
+<pre>&lt;matplotlib.collections.QuadMesh at 0x7fc64a6f7ef0&gt;</pre>
 </div>
 </div>
 <div class="jp-OutputArea-child">
@@ -2522,7 +2522,7 @@ <h1 id="visualizing-pace-chlorophyll-a-data-interactively-with-hypercoast">Visua
 <div class="jp-OutputArea-child jp-OutputArea-executeResult">
 <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[8]:</div>
 <div class="jp-RenderedText jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/plain" tabindex="0">
-<pre>&lt;matplotlib.collections.QuadMesh at 0x7fc723ebbfb0&gt;</pre>
+<pre>&lt;matplotlib.collections.QuadMesh at 0x7fc64a5f9ee0&gt;</pre>
 </div>
 </div>
 <div class="jp-OutputArea-child">
@@ -2962,7 +2962,7 @@ <h1 id="visualizing-pace-chlorophyll-a-data-interactively-with-hypercoast">Visua
     display_scale:  log
     display_min:    0.01
     display_max:    20.0
-    date:           ['2024-06-01', '2024-06-02', '2024-06-03', '2024-06-04', ...</pre><div class="xr-wrap" style="display:none"><div class="xr-header"><div class="xr-obj-type">xarray.DataArray</div><div class="xr-array-name">'chlor_a'</div><ul class="xr-dim-list"><li><span class="xr-has-index">lat</span>: 1800</li><li><span class="xr-has-index">lon</span>: 3600</li></ul></div><ul class="xr-sections"><li class="xr-section-item"><div class="xr-array-wrap"><input checked="" class="xr-array-in" id="section-a366338f-85b9-408c-acba-b9af866dc817" type="checkbox"/><label for="section-a366338f-85b9-408c-acba-b9af866dc817" title="Show/hide data repr"><svg class="icon xr-icon-database"><use xlink:href="#icon-database"></use></svg></label><div class="xr-array-preview xr-preview"><span>dask.array&lt;chunksize=(512, 1024), meta=np.ndarray&gt;</span></div><div class="xr-array-data"><table>
+    date:           ['2024-06-01', '2024-06-02', '2024-06-03', '2024-06-04', ...</pre><div class="xr-wrap" style="display:none"><div class="xr-header"><div class="xr-obj-type">xarray.DataArray</div><div class="xr-array-name">'chlor_a'</div><ul class="xr-dim-list"><li><span class="xr-has-index">lat</span>: 1800</li><li><span class="xr-has-index">lon</span>: 3600</li></ul></div><ul class="xr-sections"><li class="xr-section-item"><div class="xr-array-wrap"><input checked="" class="xr-array-in" id="section-adb89e03-9fab-41e5-a130-5e406ab50e93" type="checkbox"/><label for="section-adb89e03-9fab-41e5-a130-5e406ab50e93" title="Show/hide data repr"><svg class="icon xr-icon-database"><use xlink:href="#icon-database"></use></svg></label><div class="xr-array-preview xr-preview"><span>dask.array&lt;chunksize=(512, 1024), meta=np.ndarray&gt;</span></div><div class="xr-array-data"><table>
 <tr>
 <td>
 <table style="border-collapse: collapse;">
@@ -3017,9 +3017,9 @@ <h1 id="visualizing-pace-chlorophyll-a-data-interactively-with-hypercoast">Visua
 </svg>
 </td>
 </tr>
-</table></div></div></li><li class="xr-section-item"><input checked="" class="xr-section-summary-in" id="section-00be9fa6-4c7f-4453-a628-daa4abde6313" type="checkbox"/><label class="xr-section-summary" for="section-00be9fa6-4c7f-4453-a628-daa4abde6313">Coordinates: <span>(4)</span></label><div class="xr-section-inline-details"></div><div class="xr-section-details"><ul class="xr-var-list"><li class="xr-var-item"><div class="xr-var-name"><span class="xr-has-index">lat</span></div><div class="xr-var-dims">(lat)</div><div class="xr-var-dtype">float32</div><div class="xr-var-preview xr-preview">89.95 89.85 89.75 ... -89.85 -89.95</div><input class="xr-var-attrs-in" id="attrs-b113c1e2-3df1-40a3-ba22-50b8f1a1cab2" type="checkbox"/><label for="attrs-b113c1e2-3df1-40a3-ba22-50b8f1a1cab2" title="Show/Hide attributes"><svg class="icon xr-icon-file-text2"><use xlink:href="#icon-file-text2"></use></svg></label><input class="xr-var-data-in" id="data-d45dd3d9-f08f-4a60-b625-8946f8b79b70" type="checkbox"/><label for="data-d45dd3d9-f08f-4a60-b625-8946f8b79b70" title="Show/Hide data repr"><svg class="icon xr-icon-database"><use xlink:href="#icon-database"></use></svg></label><div class="xr-var-attrs"><dl class="xr-attrs"><dt><span>long_name :</span></dt><dd>Latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>valid_min :</span></dt><dd>-90.0</dd><dt><span>valid_max :</span></dt><dd>90.0</dd></dl></div><div class="xr-var-data"><pre>array([ 89.95    ,  89.85    ,  89.75    , ..., -89.75    , -89.850006,
-       -89.950005], dtype=float32)</pre></div></li><li class="xr-var-item"><div class="xr-var-name"><span class="xr-has-index">lon</span></div><div class="xr-var-dims">(lon)</div><div class="xr-var-dtype">float32</div><div class="xr-var-preview xr-preview">-179.9 -179.9 ... 179.9 180.0</div><input class="xr-var-attrs-in" id="attrs-e32fc12e-843a-4198-9de6-2b73af1cca21" type="checkbox"/><label for="attrs-e32fc12e-843a-4198-9de6-2b73af1cca21" title="Show/Hide attributes"><svg class="icon xr-icon-file-text2"><use xlink:href="#icon-file-text2"></use></svg></label><input class="xr-var-data-in" id="data-1182a92c-df98-483b-9123-d75166d600c9" type="checkbox"/><label for="data-1182a92c-df98-483b-9123-d75166d600c9" title="Show/Hide data repr"><svg class="icon xr-icon-database"><use xlink:href="#icon-database"></use></svg></label><div class="xr-var-attrs"><dl class="xr-attrs"><dt><span>long_name :</span></dt><dd>Longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>valid_min :</span></dt><dd>-180.0</dd><dt><span>valid_max :</span></dt><dd>180.0</dd></dl></div><div class="xr-var-data"><pre>array([-179.95   , -179.85   , -179.75   , ...,  179.75   ,  179.85   ,
-        179.95001], dtype=float32)</pre></div></li><li class="xr-var-item"><div class="xr-var-name"><span>date</span></div><div class="xr-var-dims">()</div><div class="xr-var-dtype">&lt;U10</div><div class="xr-var-preview xr-preview">'2024-06-01'</div><input class="xr-var-attrs-in" disabled="" id="attrs-722bda4d-1228-4b1d-8e20-fe4d68714519" type="checkbox"/><label for="attrs-722bda4d-1228-4b1d-8e20-fe4d68714519" title="Show/Hide attributes"><svg class="icon xr-icon-file-text2"><use xlink:href="#icon-file-text2"></use></svg></label><input class="xr-var-data-in" id="data-088fb6dc-67f5-4c5f-baeb-64221d837179" type="checkbox"/><label for="data-088fb6dc-67f5-4c5f-baeb-64221d837179" title="Show/Hide data repr"><svg class="icon xr-icon-database"><use xlink:href="#icon-database"></use></svg></label><div class="xr-var-attrs"><dl class="xr-attrs"></dl></div><div class="xr-var-data"><pre>array('2024-06-01', dtype='&lt;U10')</pre></div></li><li class="xr-var-item"><div class="xr-var-name"><span>spatial_ref</span></div><div class="xr-var-dims">()</div><div class="xr-var-dtype">int64</div><div class="xr-var-preview xr-preview">0</div><input class="xr-var-attrs-in" id="attrs-268c8904-4f06-46cd-8544-f74dcf07f709" type="checkbox"/><label for="attrs-268c8904-4f06-46cd-8544-f74dcf07f709" title="Show/Hide attributes"><svg class="icon xr-icon-file-text2"><use xlink:href="#icon-file-text2"></use></svg></label><input class="xr-var-data-in" id="data-e5a00b7d-0fe3-4170-966b-149c68620c9c" type="checkbox"/><label for="data-e5a00b7d-0fe3-4170-966b-149c68620c9c" title="Show/Hide data repr"><svg class="icon xr-icon-database"><use xlink:href="#icon-database"></use></svg></label><div class="xr-var-attrs"><dl class="xr-attrs"><dt><span>crs_wkt :</span></dt><dd>GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0,AUTHORITY["EPSG","8901"]],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]],AXIS["Latitude",NORTH],AXIS["Longitude",EAST],AUTHORITY["EPSG","4326"]]</dd><dt><span>semi_major_axis :</span></dt><dd>6378137.0</dd><dt><span>semi_minor_axis :</span></dt><dd>6356752.314245179</dd><dt><span>inverse_flattening :</span></dt><dd>298.257223563</dd><dt><span>reference_ellipsoid_name :</span></dt><dd>WGS 84</dd><dt><span>longitude_of_prime_meridian :</span></dt><dd>0.0</dd><dt><span>prime_meridian_name :</span></dt><dd>Greenwich</dd><dt><span>geographic_crs_name :</span></dt><dd>WGS 84</dd><dt><span>horizontal_datum_name :</span></dt><dd>World Geodetic System 1984</dd><dt><span>grid_mapping_name :</span></dt><dd>latitude_longitude</dd><dt><span>spatial_ref :</span></dt><dd>GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0,AUTHORITY["EPSG","8901"]],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]],AXIS["Latitude",NORTH],AXIS["Longitude",EAST],AUTHORITY["EPSG","4326"]]</dd></dl></div><div class="xr-var-data"><pre>array(0)</pre></div></li></ul></div></li><li class="xr-section-item"><input class="xr-section-summary-in" id="section-cc5038e0-f52f-42be-a4b2-de5aceef09a3" type="checkbox"/><label class="xr-section-summary" for="section-cc5038e0-f52f-42be-a4b2-de5aceef09a3">Indexes: <span>(2)</span></label><div class="xr-section-inline-details"></div><div class="xr-section-details"><ul class="xr-var-list"><li class="xr-var-item"><div class="xr-index-name"><div>lat</div></div><div class="xr-index-preview">PandasIndex</div><div></div><input class="xr-index-data-in" id="index-3aa7c056-c45e-4083-b89d-9f10cfab18b7" type="checkbox"><label for="index-3aa7c056-c45e-4083-b89d-9f10cfab18b7" title="Show/Hide index repr"><svg class="icon xr-icon-database"><use xlink:href="#icon-database"></use></svg></label><div class="xr-index-data"><pre>PandasIndex(Index([ 89.94999694824219,   89.8499984741211,              89.75,
+</table></div></div></li><li class="xr-section-item"><input checked="" class="xr-section-summary-in" id="section-5e2d9bae-6f0b-4d65-8c33-c96106261f55" type="checkbox"/><label class="xr-section-summary" for="section-5e2d9bae-6f0b-4d65-8c33-c96106261f55">Coordinates: <span>(4)</span></label><div class="xr-section-inline-details"></div><div class="xr-section-details"><ul class="xr-var-list"><li class="xr-var-item"><div class="xr-var-name"><span class="xr-has-index">lat</span></div><div class="xr-var-dims">(lat)</div><div class="xr-var-dtype">float32</div><div class="xr-var-preview xr-preview">89.95 89.85 89.75 ... -89.85 -89.95</div><input class="xr-var-attrs-in" id="attrs-9d2e1948-b937-4c57-9cca-2e180393c55c" type="checkbox"/><label for="attrs-9d2e1948-b937-4c57-9cca-2e180393c55c" title="Show/Hide attributes"><svg class="icon xr-icon-file-text2"><use xlink:href="#icon-file-text2"></use></svg></label><input class="xr-var-data-in" id="data-e56ead7b-d3ec-46b1-a6ba-a8c9e9f4dabe" type="checkbox"/><label for="data-e56ead7b-d3ec-46b1-a6ba-a8c9e9f4dabe" title="Show/Hide data repr"><svg class="icon xr-icon-database"><use xlink:href="#icon-database"></use></svg></label><div class="xr-var-attrs"><dl class="xr-attrs"><dt><span>long_name :</span></dt><dd>Latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>valid_min :</span></dt><dd>-90.0</dd><dt><span>valid_max :</span></dt><dd>90.0</dd></dl></div><div class="xr-var-data"><pre>array([ 89.95    ,  89.85    ,  89.75    , ..., -89.75    , -89.850006,
+       -89.950005], dtype=float32)</pre></div></li><li class="xr-var-item"><div class="xr-var-name"><span class="xr-has-index">lon</span></div><div class="xr-var-dims">(lon)</div><div class="xr-var-dtype">float32</div><div class="xr-var-preview xr-preview">-179.9 -179.9 ... 179.9 180.0</div><input class="xr-var-attrs-in" id="attrs-31a01541-d748-4a8c-8ad8-397fa5b1a09e" type="checkbox"/><label for="attrs-31a01541-d748-4a8c-8ad8-397fa5b1a09e" title="Show/Hide attributes"><svg class="icon xr-icon-file-text2"><use xlink:href="#icon-file-text2"></use></svg></label><input class="xr-var-data-in" id="data-6a771bdc-469c-492d-b6ea-a24f02f7ab6c" type="checkbox"/><label for="data-6a771bdc-469c-492d-b6ea-a24f02f7ab6c" title="Show/Hide data repr"><svg class="icon xr-icon-database"><use xlink:href="#icon-database"></use></svg></label><div class="xr-var-attrs"><dl class="xr-attrs"><dt><span>long_name :</span></dt><dd>Longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>valid_min :</span></dt><dd>-180.0</dd><dt><span>valid_max :</span></dt><dd>180.0</dd></dl></div><div class="xr-var-data"><pre>array([-179.95   , -179.85   , -179.75   , ...,  179.75   ,  179.85   ,
+        179.95001], dtype=float32)</pre></div></li><li class="xr-var-item"><div class="xr-var-name"><span>date</span></div><div class="xr-var-dims">()</div><div class="xr-var-dtype">&lt;U10</div><div class="xr-var-preview xr-preview">'2024-06-01'</div><input class="xr-var-attrs-in" disabled="" id="attrs-3d0e2aac-92d4-496e-9c1b-235908eb76b8" type="checkbox"/><label for="attrs-3d0e2aac-92d4-496e-9c1b-235908eb76b8" title="Show/Hide attributes"><svg class="icon xr-icon-file-text2"><use xlink:href="#icon-file-text2"></use></svg></label><input class="xr-var-data-in" id="data-9e537588-1152-4786-9d11-7dabc152518c" type="checkbox"/><label for="data-9e537588-1152-4786-9d11-7dabc152518c" title="Show/Hide data repr"><svg class="icon xr-icon-database"><use xlink:href="#icon-database"></use></svg></label><div class="xr-var-attrs"><dl class="xr-attrs"></dl></div><div class="xr-var-data"><pre>array('2024-06-01', dtype='&lt;U10')</pre></div></li><li class="xr-var-item"><div class="xr-var-name"><span>spatial_ref</span></div><div class="xr-var-dims">()</div><div class="xr-var-dtype">int64</div><div class="xr-var-preview xr-preview">0</div><input class="xr-var-attrs-in" id="attrs-d47e584c-4079-4904-a42e-106f6e08dfa8" type="checkbox"/><label for="attrs-d47e584c-4079-4904-a42e-106f6e08dfa8" title="Show/Hide attributes"><svg class="icon xr-icon-file-text2"><use xlink:href="#icon-file-text2"></use></svg></label><input class="xr-var-data-in" id="data-9e6860ea-f79b-4da1-83f0-638842d09d4a" type="checkbox"/><label for="data-9e6860ea-f79b-4da1-83f0-638842d09d4a" title="Show/Hide data repr"><svg class="icon xr-icon-database"><use xlink:href="#icon-database"></use></svg></label><div class="xr-var-attrs"><dl class="xr-attrs"><dt><span>crs_wkt :</span></dt><dd>GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0,AUTHORITY["EPSG","8901"]],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]],AXIS["Latitude",NORTH],AXIS["Longitude",EAST],AUTHORITY["EPSG","4326"]]</dd><dt><span>semi_major_axis :</span></dt><dd>6378137.0</dd><dt><span>semi_minor_axis :</span></dt><dd>6356752.314245179</dd><dt><span>inverse_flattening :</span></dt><dd>298.257223563</dd><dt><span>reference_ellipsoid_name :</span></dt><dd>WGS 84</dd><dt><span>longitude_of_prime_meridian :</span></dt><dd>0.0</dd><dt><span>prime_meridian_name :</span></dt><dd>Greenwich</dd><dt><span>geographic_crs_name :</span></dt><dd>WGS 84</dd><dt><span>horizontal_datum_name :</span></dt><dd>World Geodetic System 1984</dd><dt><span>grid_mapping_name :</span></dt><dd>latitude_longitude</dd><dt><span>spatial_ref :</span></dt><dd>GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0,AUTHORITY["EPSG","8901"]],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]],AXIS["Latitude",NORTH],AXIS["Longitude",EAST],AUTHORITY["EPSG","4326"]]</dd></dl></div><div class="xr-var-data"><pre>array(0)</pre></div></li></ul></div></li><li class="xr-section-item"><input class="xr-section-summary-in" id="section-1342dae2-0a38-44c2-824c-32cbe821cbb9" type="checkbox"/><label class="xr-section-summary" for="section-1342dae2-0a38-44c2-824c-32cbe821cbb9">Indexes: <span>(2)</span></label><div class="xr-section-inline-details"></div><div class="xr-section-details"><ul class="xr-var-list"><li class="xr-var-item"><div class="xr-index-name"><div>lat</div></div><div class="xr-index-preview">PandasIndex</div><div></div><input class="xr-index-data-in" id="index-d92afb5f-66e9-4437-8d00-508506c4edfc" type="checkbox"><label for="index-d92afb5f-66e9-4437-8d00-508506c4edfc" title="Show/Hide index repr"><svg class="icon xr-icon-database"><use xlink:href="#icon-database"></use></svg></label><div class="xr-index-data"><pre>PandasIndex(Index([ 89.94999694824219,   89.8499984741211,              89.75,
          89.6500015258789,  89.55000305175781,  89.44999694824219,
          89.3499984741211,              89.25,   89.1500015258789,
         89.05000305175781,
@@ -3028,7 +3028,7 @@ <h1 id="visualizing-pace-chlorophyll-a-data-interactively-with-hypercoast">Visua
        -89.35000610351562, -89.45000457763672, -89.55000305175781,
         -89.6500015258789,             -89.75, -89.85000610351562,
        -89.95000457763672],
-      dtype='float32', name='lat', length=1800))</pre></div></input></li><li class="xr-var-item"><div class="xr-index-name"><div>lon</div></div><div class="xr-index-preview">PandasIndex</div><div></div><input class="xr-index-data-in" id="index-b2c6ea80-7004-4c1c-882f-d225027337bd" type="checkbox"><label for="index-b2c6ea80-7004-4c1c-882f-d225027337bd" title="Show/Hide index repr"><svg class="icon xr-icon-database"><use xlink:href="#icon-database"></use></svg></label><div class="xr-index-data"><pre>PandasIndex(Index([ -179.9499969482422, -179.85000610351562,             -179.75,
+      dtype='float32', name='lat', length=1800))</pre></div></input></li><li class="xr-var-item"><div class="xr-index-name"><div>lon</div></div><div class="xr-index-preview">PandasIndex</div><div></div><input class="xr-index-data-in" id="index-647ea0bf-4643-4e7b-968b-498edd54998e" type="checkbox"><label for="index-647ea0bf-4643-4e7b-968b-498edd54998e" title="Show/Hide index repr"><svg class="icon xr-icon-database"><use xlink:href="#icon-database"></use></svg></label><div class="xr-index-data"><pre>PandasIndex(Index([ -179.9499969482422, -179.85000610351562,             -179.75,
        -179.64999389648438,  -179.5500030517578,  -179.4499969482422,
        -179.35000610351562,             -179.25, -179.14999389648438,
         -179.0500030517578,
@@ -3037,7 +3037,7 @@ <h1 id="visualizing-pace-chlorophyll-a-data-interactively-with-hypercoast">Visua
         179.35000610351562,  179.45001220703125,   179.5500030517578,
         179.65000915527344,              179.75,  179.85000610351562,
         179.95001220703125],
-      dtype='float32', name='lon', length=3600))</pre></div></input></li></ul></div></li><li class="xr-section-item"><input class="xr-section-summary-in" id="section-df2dc7fc-6ea1-45d0-8ddf-f331a1863dfd" type="checkbox"/><label class="xr-section-summary" for="section-df2dc7fc-6ea1-45d0-8ddf-f331a1863dfd">Attributes: <span>(10)</span></label><div class="xr-section-inline-details"></div><div class="xr-section-details"><dl class="xr-attrs"><dt><span>long_name :</span></dt><dd>Chlorophyll Concentration, OCI Algorithm</dd><dt><span>units :</span></dt><dd>lg(mg m^-3)</dd><dt><span>standard_name :</span></dt><dd>mass_concentration_of_chlorophyll_in_sea_water</dd><dt><span>valid_min :</span></dt><dd>0.001</dd><dt><span>valid_max :</span></dt><dd>100.0</dd><dt><span>reference :</span></dt><dd>Hu, C., Lee Z., and Franz, B.A. (2012). Chlorophyll-a algorithms for oligotrophic oceans: A novel approach based on three-band reflectance difference, J. Geophys. Res., 117, C01011, doi:10.1029/2011JC007395.</dd><dt><span>display_scale :</span></dt><dd>log</dd><dt><span>display_min :</span></dt><dd>0.01</dd><dt><span>display_max :</span></dt><dd>20.0</dd><dt><span>date :</span></dt><dd>['2024-06-01', '2024-06-02', '2024-06-03', '2024-06-04', '2024-06-05', '2024-06-06', '2024-06-07', '2024-06-08', '2024-06-09', '2024-06-10', '2024-06-11', '2024-06-12', '2024-06-13', '2024-06-14', '2024-06-15', '2024-06-16', '2024-06-17']</dd></dl></div></li></ul></div></div>
+      dtype='float32', name='lon', length=3600))</pre></div></input></li></ul></div></li><li class="xr-section-item"><input class="xr-section-summary-in" id="section-0f011d3b-2e75-49f4-b2f0-a0fc69e2f678" type="checkbox"/><label class="xr-section-summary" for="section-0f011d3b-2e75-49f4-b2f0-a0fc69e2f678">Attributes: <span>(10)</span></label><div class="xr-section-inline-details"></div><div class="xr-section-details"><dl class="xr-attrs"><dt><span>long_name :</span></dt><dd>Chlorophyll Concentration, OCI Algorithm</dd><dt><span>units :</span></dt><dd>lg(mg m^-3)</dd><dt><span>standard_name :</span></dt><dd>mass_concentration_of_chlorophyll_in_sea_water</dd><dt><span>valid_min :</span></dt><dd>0.001</dd><dt><span>valid_max :</span></dt><dd>100.0</dd><dt><span>reference :</span></dt><dd>Hu, C., Lee Z., and Franz, B.A. (2012). Chlorophyll-a algorithms for oligotrophic oceans: A novel approach based on three-band reflectance difference, J. Geophys. Res., 117, C01011, doi:10.1029/2011JC007395.</dd><dt><span>display_scale :</span></dt><dd>log</dd><dt><span>display_min :</span></dt><dd>0.01</dd><dt><span>display_max :</span></dt><dd>20.0</dd><dt><span>date :</span></dt><dd>['2024-06-01', '2024-06-02', '2024-06-03', '2024-06-04', '2024-06-05', '2024-06-06', '2024-06-07', '2024-06-08', '2024-06-09', '2024-06-10', '2024-06-11', '2024-06-12', '2024-06-13', '2024-06-14', '2024-06-15', '2024-06-16', '2024-06-17']</dd></dl></div></li></ul></div></div>
 </div>
 </div>
 </div>
@@ -3148,12 +3148,12 @@ <h1 id="visualizing-pace-chlorophyll-a-data-interactively-with-hypercoast">Visua
 <div class="jp-OutputArea jp-Cell-outputArea">
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-<div class="jupyter-widgets jp-OutputArea-output jp-OutputArea-executeResult" id="c57b6c09-f9ab-4414-9932-2ea912002820" tabindex="0">
+<div class="jupyter-widgets jp-OutputArea-output jp-OutputArea-executeResult" id="19276346-4f3c-4656-a5fb-6e670d0b8002" tabindex="0">
 <script type="text/javascript">
-var element = document.getElementById('c57b6c09-f9ab-4414-9932-2ea912002820');
+var element = document.getElementById('19276346-4f3c-4656-a5fb-6e670d0b8002');
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-{"version_major": 2, "version_minor": 0, "model_id": "e0777c3693254f06855574e3f96eaaf8"}
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 </script>
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+      <meta name="generator" content="mkdocs-1.6.1, mkdocs-material-9.5.40">
     
     
       
@@ -2183,9 +2183,9 @@ <h1 id="visualizing-neon-aop-hyperspectral-data-interactively-with-hypercoast">V
     scale_factor:   10000.0
     no_data_value:  -9999.0
     crs:            EPSG:32618
-    transform:      (1.0, 0.0, 368000.0, 0.0, -1.0, 4307000.0)</pre><div class="xr-wrap" style="display:none"><div class="xr-header"><div class="xr-obj-type">xarray.Dataset</div></div><ul class="xr-sections"><li class="xr-section-item"><input class="xr-section-summary-in" disabled="" id="section-9a9964ce-e9fc-4958-811a-1e867d3b9f2a" type="checkbox"/><label class="xr-section-summary" for="section-9a9964ce-e9fc-4958-811a-1e867d3b9f2a" title="Expand/collapse section">Dimensions:</label><div class="xr-section-inline-details"><ul class="xr-dim-list"><li><span class="xr-has-index">y</span>: 1000</li><li><span class="xr-has-index">x</span>: 1000</li><li><span class="xr-has-index">wavelength</span>: 426</li></ul></div><div class="xr-section-details"></div></li><li class="xr-section-item"><input checked="" class="xr-section-summary-in" id="section-0fe917d2-5b25-4996-80aa-07fd80a84de9" type="checkbox"/><label class="xr-section-summary" for="section-0fe917d2-5b25-4996-80aa-07fd80a84de9">Coordinates: <span>(3)</span></label><div class="xr-section-inline-details"></div><div class="xr-section-details"><ul class="xr-var-list"><li class="xr-var-item"><div class="xr-var-name"><span class="xr-has-index">y</span></div><div class="xr-var-dims">(y)</div><div class="xr-var-dtype">float64</div><div class="xr-var-preview xr-preview">4.307e+06 4.307e+06 ... 4.306e+06</div><input class="xr-var-attrs-in" disabled="" id="attrs-30874193-ed8c-4ed8-9a0a-d56b29efc388" type="checkbox"/><label for="attrs-30874193-ed8c-4ed8-9a0a-d56b29efc388" title="Show/Hide attributes"><svg class="icon xr-icon-file-text2"><use xlink:href="#icon-file-text2"></use></svg></label><input class="xr-var-data-in" id="data-a17498b6-2fa6-4963-a400-90324a0c15e2" type="checkbox"/><label for="data-a17498b6-2fa6-4963-a400-90324a0c15e2" title="Show/Hide data repr"><svg class="icon xr-icon-database"><use xlink:href="#icon-database"></use></svg></label><div class="xr-var-attrs"><dl class="xr-attrs"></dl></div><div class="xr-var-data"><pre>array([4307000.      , 4306998.998999, 4306997.997998, ..., 4306002.002002,
-       4306001.001001, 4306000.      ])</pre></div></li><li class="xr-var-item"><div class="xr-var-name"><span class="xr-has-index">x</span></div><div class="xr-var-dims">(x)</div><div class="xr-var-dtype">float64</div><div class="xr-var-preview xr-preview">3.68e+05 3.68e+05 ... 3.69e+05</div><input class="xr-var-attrs-in" disabled="" id="attrs-cfa21d2a-9274-4f33-9b8a-649c356023b4" type="checkbox"/><label for="attrs-cfa21d2a-9274-4f33-9b8a-649c356023b4" title="Show/Hide attributes"><svg class="icon xr-icon-file-text2"><use xlink:href="#icon-file-text2"></use></svg></label><input class="xr-var-data-in" id="data-85ff567e-695e-4326-a71a-2e7c4801f247" type="checkbox"/><label for="data-85ff567e-695e-4326-a71a-2e7c4801f247" title="Show/Hide data repr"><svg class="icon xr-icon-database"><use xlink:href="#icon-database"></use></svg></label><div class="xr-var-attrs"><dl class="xr-attrs"></dl></div><div class="xr-var-data"><pre>array([368000.      , 368001.001001, 368002.002002, ..., 368997.997998,
-       368998.998999, 369000.      ])</pre></div></li><li class="xr-var-item"><div class="xr-var-name"><span class="xr-has-index">wavelength</span></div><div class="xr-var-dims">(wavelength)</div><div class="xr-var-dtype">float64</div><div class="xr-var-preview xr-preview">383.9 388.9 ... 2.507e+03 2.512e+03</div><input class="xr-var-attrs-in" disabled="" id="attrs-75c180ff-e615-4e39-9f65-242eb004f54f" type="checkbox"/><label for="attrs-75c180ff-e615-4e39-9f65-242eb004f54f" title="Show/Hide attributes"><svg class="icon xr-icon-file-text2"><use xlink:href="#icon-file-text2"></use></svg></label><input class="xr-var-data-in" id="data-a8b081a7-1ba1-4f03-9838-adb1f1b0dcaf" type="checkbox"/><label for="data-a8b081a7-1ba1-4f03-9838-adb1f1b0dcaf" title="Show/Hide data repr"><svg class="icon xr-icon-database"><use xlink:href="#icon-database"></use></svg></label><div class="xr-var-attrs"><dl class="xr-attrs"></dl></div><div class="xr-var-data"><pre>array([ 383.88,  388.89,  393.9 , ..., 2502.17, 2507.17, 2512.18])</pre></div></li></ul></div></li><li class="xr-section-item"><input checked="" class="xr-section-summary-in" id="section-94cb0b74-5b7d-49af-92fa-8a29f160a7a1" type="checkbox"/><label class="xr-section-summary" for="section-94cb0b74-5b7d-49af-92fa-8a29f160a7a1">Data variables: <span>(1)</span></label><div class="xr-section-inline-details"></div><div class="xr-section-details"><ul class="xr-var-list"><li class="xr-var-item"><div class="xr-var-name"><span>reflectance</span></div><div class="xr-var-dims">(y, x, wavelength)</div><div class="xr-var-dtype">float64</div><div class="xr-var-preview xr-preview">0.1569 0.1206 0.1034 ... nan nan</div><input class="xr-var-attrs-in" id="attrs-e2a54552-5e50-4611-926b-36cddcf08826" type="checkbox"/><label for="attrs-e2a54552-5e50-4611-926b-36cddcf08826" title="Show/Hide attributes"><svg class="icon xr-icon-file-text2"><use xlink:href="#icon-file-text2"></use></svg></label><input class="xr-var-data-in" id="data-b3c09129-43a9-4da1-8f34-dfcbd0dd9819" type="checkbox"/><label for="data-b3c09129-43a9-4da1-8f34-dfcbd0dd9819" title="Show/Hide data repr"><svg class="icon xr-icon-database"><use xlink:href="#icon-database"></use></svg></label><div class="xr-var-attrs"><dl class="xr-attrs"><dt><span>scale_factor :</span></dt><dd>10000.0</dd><dt><span>no_data_value :</span></dt><dd>-9999.0</dd><dt><span>crs :</span></dt><dd>EPSG:32618</dd><dt><span>transform :</span></dt><dd>(1.0, 0.0, 368000.0, 0.0, -1.0, 4307000.0)</dd></dl></div><div class="xr-var-data"><pre>array([[[0.1569, 0.1206, 0.1034, ..., 0.1512,    nan,    nan],
+    transform:      (1.0, 0.0, 368000.0, 0.0, -1.0, 4307000.0)</pre><div class="xr-wrap" style="display:none"><div class="xr-header"><div class="xr-obj-type">xarray.Dataset</div></div><ul class="xr-sections"><li class="xr-section-item"><input class="xr-section-summary-in" disabled="" id="section-7d76cd1e-d9ad-4f65-b7d2-97b3191d2a66" type="checkbox"/><label class="xr-section-summary" for="section-7d76cd1e-d9ad-4f65-b7d2-97b3191d2a66" title="Expand/collapse section">Dimensions:</label><div class="xr-section-inline-details"><ul class="xr-dim-list"><li><span class="xr-has-index">y</span>: 1000</li><li><span class="xr-has-index">x</span>: 1000</li><li><span class="xr-has-index">wavelength</span>: 426</li></ul></div><div class="xr-section-details"></div></li><li class="xr-section-item"><input checked="" class="xr-section-summary-in" id="section-846f5345-da02-43ad-b86d-e571b6c8c77d" type="checkbox"/><label class="xr-section-summary" for="section-846f5345-da02-43ad-b86d-e571b6c8c77d">Coordinates: <span>(3)</span></label><div class="xr-section-inline-details"></div><div class="xr-section-details"><ul class="xr-var-list"><li class="xr-var-item"><div class="xr-var-name"><span class="xr-has-index">y</span></div><div class="xr-var-dims">(y)</div><div class="xr-var-dtype">float64</div><div class="xr-var-preview xr-preview">4.307e+06 4.307e+06 ... 4.306e+06</div><input class="xr-var-attrs-in" disabled="" id="attrs-a8c68332-9200-493d-926f-ca92334d8e84" type="checkbox"/><label for="attrs-a8c68332-9200-493d-926f-ca92334d8e84" title="Show/Hide attributes"><svg class="icon xr-icon-file-text2"><use xlink:href="#icon-file-text2"></use></svg></label><input class="xr-var-data-in" id="data-aef1e393-27a8-48b0-98c8-8fa1bf3a6eb6" type="checkbox"/><label for="data-aef1e393-27a8-48b0-98c8-8fa1bf3a6eb6" title="Show/Hide data repr"><svg class="icon xr-icon-database"><use xlink:href="#icon-database"></use></svg></label><div class="xr-var-attrs"><dl class="xr-attrs"></dl></div><div class="xr-var-data"><pre>array([4307000.      , 4306998.998999, 4306997.997998, ..., 4306002.002002,
+       4306001.001001, 4306000.      ])</pre></div></li><li class="xr-var-item"><div class="xr-var-name"><span class="xr-has-index">x</span></div><div class="xr-var-dims">(x)</div><div class="xr-var-dtype">float64</div><div class="xr-var-preview xr-preview">3.68e+05 3.68e+05 ... 3.69e+05</div><input class="xr-var-attrs-in" disabled="" id="attrs-00c475f2-a1a4-4302-9b56-89f38050a517" type="checkbox"/><label for="attrs-00c475f2-a1a4-4302-9b56-89f38050a517" title="Show/Hide attributes"><svg class="icon xr-icon-file-text2"><use xlink:href="#icon-file-text2"></use></svg></label><input class="xr-var-data-in" id="data-74c16831-ecab-4858-a469-6855f3d63a55" type="checkbox"/><label for="data-74c16831-ecab-4858-a469-6855f3d63a55" title="Show/Hide data repr"><svg class="icon xr-icon-database"><use xlink:href="#icon-database"></use></svg></label><div class="xr-var-attrs"><dl class="xr-attrs"></dl></div><div class="xr-var-data"><pre>array([368000.      , 368001.001001, 368002.002002, ..., 368997.997998,
+       368998.998999, 369000.      ])</pre></div></li><li class="xr-var-item"><div class="xr-var-name"><span class="xr-has-index">wavelength</span></div><div class="xr-var-dims">(wavelength)</div><div class="xr-var-dtype">float64</div><div class="xr-var-preview xr-preview">383.9 388.9 ... 2.507e+03 2.512e+03</div><input class="xr-var-attrs-in" disabled="" id="attrs-1a965043-225d-403a-a329-39d988c9890a" type="checkbox"/><label for="attrs-1a965043-225d-403a-a329-39d988c9890a" title="Show/Hide attributes"><svg class="icon xr-icon-file-text2"><use xlink:href="#icon-file-text2"></use></svg></label><input class="xr-var-data-in" id="data-7737c60d-e10d-44af-b0b7-30243992a24c" type="checkbox"/><label for="data-7737c60d-e10d-44af-b0b7-30243992a24c" title="Show/Hide data repr"><svg class="icon xr-icon-database"><use xlink:href="#icon-database"></use></svg></label><div class="xr-var-attrs"><dl class="xr-attrs"></dl></div><div class="xr-var-data"><pre>array([ 383.88,  388.89,  393.9 , ..., 2502.17, 2507.17, 2512.18])</pre></div></li></ul></div></li><li class="xr-section-item"><input checked="" class="xr-section-summary-in" id="section-daa64ca6-f2bc-4d57-aee0-9aa575658e53" type="checkbox"/><label class="xr-section-summary" for="section-daa64ca6-f2bc-4d57-aee0-9aa575658e53">Data variables: <span>(1)</span></label><div class="xr-section-inline-details"></div><div class="xr-section-details"><ul class="xr-var-list"><li class="xr-var-item"><div class="xr-var-name"><span>reflectance</span></div><div class="xr-var-dims">(y, x, wavelength)</div><div class="xr-var-dtype">float64</div><div class="xr-var-preview xr-preview">0.1569 0.1206 0.1034 ... nan nan</div><input class="xr-var-attrs-in" id="attrs-ad89bd08-d3c2-49d5-99d8-c7bf0eced4c2" type="checkbox"/><label for="attrs-ad89bd08-d3c2-49d5-99d8-c7bf0eced4c2" title="Show/Hide attributes"><svg class="icon xr-icon-file-text2"><use xlink:href="#icon-file-text2"></use></svg></label><input class="xr-var-data-in" id="data-8e3e0bcb-ec8f-4a62-86ae-3d47dcc34546" type="checkbox"/><label for="data-8e3e0bcb-ec8f-4a62-86ae-3d47dcc34546" title="Show/Hide data repr"><svg class="icon xr-icon-database"><use xlink:href="#icon-database"></use></svg></label><div class="xr-var-attrs"><dl class="xr-attrs"><dt><span>scale_factor :</span></dt><dd>10000.0</dd><dt><span>no_data_value :</span></dt><dd>-9999.0</dd><dt><span>crs :</span></dt><dd>EPSG:32618</dd><dt><span>transform :</span></dt><dd>(1.0, 0.0, 368000.0, 0.0, -1.0, 4307000.0)</dd></dl></div><div class="xr-var-data"><pre>array([[[0.1569, 0.1206, 0.1034, ..., 0.1512,    nan,    nan],
         [0.1593, 0.118 , 0.105 , ...,    nan,    nan,    nan],
         [0.1415, 0.1147, 0.1061, ..., 0.    , 0.    ,    nan],
         ...,
@@ -2225,7 +2225,7 @@ <h1 id="visualizing-neon-aop-hyperspectral-data-interactively-with-hypercoast">V
         ...,
         [0.082 , 0.0593, 0.0511, ...,    nan,    nan,    nan],
         [0.0883, 0.0693, 0.0518, ...,    nan,    nan,    nan],
-        [0.1128, 0.1   , 0.0844, ..., 0.5833,    nan,    nan]]])</pre></div></li></ul></div></li><li class="xr-section-item"><input class="xr-section-summary-in" id="section-fa49d363-9c09-4158-a114-063a247bd03f" type="checkbox"/><label class="xr-section-summary" for="section-fa49d363-9c09-4158-a114-063a247bd03f">Indexes: <span>(3)</span></label><div class="xr-section-inline-details"></div><div class="xr-section-details"><ul class="xr-var-list"><li class="xr-var-item"><div class="xr-index-name"><div>y</div></div><div class="xr-index-preview">PandasIndex</div><div></div><input class="xr-index-data-in" id="index-c4a21217-e1f3-421a-9edf-2c88a3c929dd" type="checkbox"><label for="index-c4a21217-e1f3-421a-9edf-2c88a3c929dd" title="Show/Hide index repr"><svg class="icon xr-icon-database"><use xlink:href="#icon-database"></use></svg></label><div class="xr-index-data"><pre>PandasIndex(Index([        4307000.0, 4306998.998998999, 4306997.997997998,
+        [0.1128, 0.1   , 0.0844, ..., 0.5833,    nan,    nan]]])</pre></div></li></ul></div></li><li class="xr-section-item"><input class="xr-section-summary-in" id="section-6a87b701-47ef-4b47-8bab-ecf42e3f9c3c" type="checkbox"/><label class="xr-section-summary" for="section-6a87b701-47ef-4b47-8bab-ecf42e3f9c3c">Indexes: <span>(3)</span></label><div class="xr-section-inline-details"></div><div class="xr-section-details"><ul class="xr-var-list"><li class="xr-var-item"><div class="xr-index-name"><div>y</div></div><div class="xr-index-preview">PandasIndex</div><div></div><input class="xr-index-data-in" id="index-b5d7ba68-0330-4ad9-814b-b0854dac8b31" type="checkbox"><label for="index-b5d7ba68-0330-4ad9-814b-b0854dac8b31" title="Show/Hide index repr"><svg class="icon xr-icon-database"><use xlink:href="#icon-database"></use></svg></label><div class="xr-index-data"><pre>PandasIndex(Index([        4307000.0, 4306998.998998999, 4306997.997997998,
        4306996.996996997, 4306995.995995996, 4306994.994994995,
        4306993.993993994, 4306992.992992993, 4306991.991991992,
        4306990.990990991,
@@ -2234,7 +2234,7 @@ <h1 id="visualizing-neon-aop-hyperspectral-data-interactively-with-hypercoast">V
        4306006.006006006, 4306005.005005005, 4306004.004004004,
        4306003.003003003, 4306002.002002002, 4306001.001001001,
                4306000.0],
-      dtype='float64', name='y', length=1000))</pre></div></input></li><li class="xr-var-item"><div class="xr-index-name"><div>x</div></div><div class="xr-index-preview">PandasIndex</div><div></div><input class="xr-index-data-in" id="index-54cf9dec-69e4-4683-8c47-ba44bbe44a2f" type="checkbox"><label for="index-54cf9dec-69e4-4683-8c47-ba44bbe44a2f" title="Show/Hide index repr"><svg class="icon xr-icon-database"><use xlink:href="#icon-database"></use></svg></label><div class="xr-index-data"><pre>PandasIndex(Index([          368000.0,   368001.001001001, 368002.00200200203,
+      dtype='float64', name='y', length=1000))</pre></div></input></li><li class="xr-var-item"><div class="xr-index-name"><div>x</div></div><div class="xr-index-preview">PandasIndex</div><div></div><input class="xr-index-data-in" id="index-15d21e84-838b-4b0f-92f7-c2df410ea0d7" type="checkbox"><label for="index-15d21e84-838b-4b0f-92f7-c2df410ea0d7" title="Show/Hide index repr"><svg class="icon xr-icon-database"><use xlink:href="#icon-database"></use></svg></label><div class="xr-index-data"><pre>PandasIndex(Index([          368000.0,   368001.001001001, 368002.00200200203,
          368003.003003003,   368004.004004004,   368005.005005005,
        368006.00600600604,   368007.007007007,   368008.008008008,
          368009.009009009,
@@ -2243,12 +2243,12 @@ <h1 id="visualizing-neon-aop-hyperspectral-data-interactively-with-hypercoast">V
        368993.99399399396,   368994.994994995,   368995.995995996,
          368996.996996997,   368997.997997998,   368998.998998999,
                  369000.0],
-      dtype='float64', name='x', length=1000))</pre></div></input></li><li class="xr-var-item"><div class="xr-index-name"><div>wavelength</div></div><div class="xr-index-preview">PandasIndex</div><div></div><input class="xr-index-data-in" id="index-1343ef6c-de33-4858-8945-512cb4e2de2a" type="checkbox"><label for="index-1343ef6c-de33-4858-8945-512cb4e2de2a" title="Show/Hide index repr"><svg class="icon xr-icon-database"><use xlink:href="#icon-database"></use></svg></label><div class="xr-index-data"><pre>PandasIndex(Index([ 383.88,  388.89,   393.9,  398.91,  403.92,  408.92,  413.93,  418.94,
+      dtype='float64', name='x', length=1000))</pre></div></input></li><li class="xr-var-item"><div class="xr-index-name"><div>wavelength</div></div><div class="xr-index-preview">PandasIndex</div><div></div><input class="xr-index-data-in" id="index-da55060c-bb60-4f6c-99c5-bf3a3406fd23" type="checkbox"><label for="index-da55060c-bb60-4f6c-99c5-bf3a3406fd23" title="Show/Hide index repr"><svg class="icon xr-icon-database"><use xlink:href="#icon-database"></use></svg></label><div class="xr-index-data"><pre>PandasIndex(Index([ 383.88,  388.89,   393.9,  398.91,  403.92,  408.92,  413.93,  418.94,
         423.95,  428.95,
        ...
        2467.11, 2472.12, 2477.13, 2482.13, 2487.14, 2492.15, 2497.16, 2502.17,
        2507.17, 2512.18],
-      dtype='float64', name='wavelength', length=426))</pre></div></input></li></ul></div></li><li class="xr-section-item"><input checked="" class="xr-section-summary-in" id="section-57fcea54-457d-498b-a02a-9774718e87b1" type="checkbox"/><label class="xr-section-summary" for="section-57fcea54-457d-498b-a02a-9774718e87b1">Attributes: <span>(4)</span></label><div class="xr-section-inline-details"></div><div class="xr-section-details"><dl class="xr-attrs"><dt><span>scale_factor :</span></dt><dd>10000.0</dd><dt><span>no_data_value :</span></dt><dd>-9999.0</dd><dt><span>crs :</span></dt><dd>EPSG:32618</dd><dt><span>transform :</span></dt><dd>(1.0, 0.0, 368000.0, 0.0, -1.0, 4307000.0)</dd></dl></div></li></ul></div></div>
+      dtype='float64', name='wavelength', length=426))</pre></div></input></li></ul></div></li><li class="xr-section-item"><input checked="" class="xr-section-summary-in" id="section-d88605db-2ef6-4264-b901-0b8c6c1cc660" type="checkbox"/><label class="xr-section-summary" for="section-d88605db-2ef6-4264-b901-0b8c6c1cc660">Attributes: <span>(4)</span></label><div class="xr-section-inline-details"></div><div class="xr-section-details"><dl class="xr-attrs"><dt><span>scale_factor :</span></dt><dd>10000.0</dd><dt><span>no_data_value :</span></dt><dd>-9999.0</dd><dt><span>crs :</span></dt><dd>EPSG:32618</dd><dt><span>transform :</span></dt><dd>(1.0, 0.0, 368000.0, 0.0, -1.0, 4307000.0)</dd></dl></div></li></ul></div></div>
 </div>
 </div>
 </div>
@@ -2301,12 +2301,12 @@ <h1 id="visualizing-neon-aop-hyperspectral-data-interactively-with-hypercoast">V
 <div class="jp-OutputArea jp-Cell-outputArea">
 <div class="jp-OutputArea-child jp-OutputArea-executeResult">
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-<div class="jupyter-widgets jp-OutputArea-output jp-OutputArea-executeResult" id="48cbb1c1-c7f5-4b12-9d4a-976ecdd00add" tabindex="0">
+<div class="jupyter-widgets jp-OutputArea-output jp-OutputArea-executeResult" id="c0b30dff-efee-47e1-bc13-7ef562747827" tabindex="0">
 <script type="text/javascript">
-var element = document.getElementById('48cbb1c1-c7f5-4b12-9d4a-976ecdd00add');
+var element = document.getElementById('c0b30dff-efee-47e1-bc13-7ef562747827');
 </script>
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         <style>
diff --git a/examples/pace_cyano/index.html b/examples/pace_cyano/index.html
index 21a6347e..05912db2 100644
--- a/examples/pace_cyano/index.html
+++ b/examples/pace_cyano/index.html
@@ -22,7 +22,7 @@
       
       
       <link rel="icon" href="../../assets/images/favicon.png">
-      <meta name="generator" content="mkdocs-1.6.1, mkdocs-material-9.5.39">
+      <meta name="generator" content="mkdocs-1.6.1, mkdocs-material-9.5.40">
     
     
       
@@ -835,15 +835,6 @@
     </span>
   </a>
   
-</li>
-      
-        <li class="md-nav__item">
-  <a href="#visualize-the-selected-region-based-on-band-ratios" class="md-nav__link">
-    <span class="md-ellipsis">
-      Visualize the selected region based on band ratios
-    </span>
-  </a>
-  
 </li>
       
         <li class="md-nav__item">
@@ -865,18 +856,18 @@
 </li>
       
         <li class="md-nav__item">
-  <a href="#keans-applied-to-selected-pixels" class="md-nav__link">
+  <a href="#k-means-applied-to-selected-pixels" class="md-nav__link">
     <span class="md-ellipsis">
-      Keans applied to selected pixels
+      K-means applied to selected pixels
     </span>
   </a>
   
 </li>
       
         <li class="md-nav__item">
-  <a href="#pca-sam-applied-to-whole-image" class="md-nav__link">
+  <a href="#principal-component-analysis-pca" class="md-nav__link">
     <span class="md-ellipsis">
-      PCA + SAM applied to whole image
+      Principal Component Analysis (PCA)
     </span>
   </a>
   
@@ -889,6 +880,48 @@
     </span>
   </a>
   
+    <nav class="md-nav" aria-label="Spectral Angle Mapper (SAM)">
+      <ul class="md-nav__list">
+        
+          <li class="md-nav__item">
+  <a href="#apply-sam-to-the-whole-image" class="md-nav__link">
+    <span class="md-ellipsis">
+      Apply SAM to the whole image
+    </span>
+  </a>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#apply-sam-to-selected-pixels" class="md-nav__link">
+    <span class="md-ellipsis">
+      Apply SAM to selected pixels
+    </span>
+  </a>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#apply-sam-with-a-filtering-condition" class="md-nav__link">
+    <span class="md-ellipsis">
+      Apply SAM with a filtering condition
+    </span>
+  </a>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#use-spectral-library" class="md-nav__link">
+    <span class="md-ellipsis">
+      Use spectral library
+    </span>
+  </a>
+  
+</li>
+        
+      </ul>
+    </nav>
+  
 </li>
       
     </ul>
@@ -1363,15 +1396,6 @@
     </span>
   </a>
   
-</li>
-      
-        <li class="md-nav__item">
-  <a href="#visualize-the-selected-region-based-on-band-ratios" class="md-nav__link">
-    <span class="md-ellipsis">
-      Visualize the selected region based on band ratios
-    </span>
-  </a>
-  
 </li>
       
         <li class="md-nav__item">
@@ -1393,18 +1417,18 @@
 </li>
       
         <li class="md-nav__item">
-  <a href="#keans-applied-to-selected-pixels" class="md-nav__link">
+  <a href="#k-means-applied-to-selected-pixels" class="md-nav__link">
     <span class="md-ellipsis">
-      Keans applied to selected pixels
+      K-means applied to selected pixels
     </span>
   </a>
   
 </li>
       
         <li class="md-nav__item">
-  <a href="#pca-sam-applied-to-whole-image" class="md-nav__link">
+  <a href="#principal-component-analysis-pca" class="md-nav__link">
     <span class="md-ellipsis">
-      PCA + SAM applied to whole image
+      Principal Component Analysis (PCA)
     </span>
   </a>
   
@@ -1417,6 +1441,48 @@
     </span>
   </a>
   
+    <nav class="md-nav" aria-label="Spectral Angle Mapper (SAM)">
+      <ul class="md-nav__list">
+        
+          <li class="md-nav__item">
+  <a href="#apply-sam-to-the-whole-image" class="md-nav__link">
+    <span class="md-ellipsis">
+      Apply SAM to the whole image
+    </span>
+  </a>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#apply-sam-to-selected-pixels" class="md-nav__link">
+    <span class="md-ellipsis">
+      Apply SAM to selected pixels
+    </span>
+  </a>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#apply-sam-with-a-filtering-condition" class="md-nav__link">
+    <span class="md-ellipsis">
+      Apply SAM with a filtering condition
+    </span>
+  </a>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#use-spectral-library" class="md-nav__link">
+    <span class="md-ellipsis">
+      Use spectral library
+    </span>
+  </a>
+  
+</li>
+        
+      </ul>
+    </nav>
+  
 </li>
       
     </ul>
@@ -1988,9 +2054,23 @@ <h2 id="import-libraries">Import libraries<a class="anchor-link" href="#import-l
 </div>
 <div class="highlight-ipynb hl-python"><pre><span></span><span class="kn">import</span> <span class="nn">earthaccess</span>
 <span class="kn">import</span> <span class="nn">hypercoast</span>
+<span class="kn">from</span> <span class="nn">hypercoast.pace</span> <span class="kn">import</span> <span class="p">(</span>
+    <span class="n">cyano_band_ratios</span><span class="p">,</span>
+    <span class="n">apply_kmeans</span><span class="p">,</span>
+    <span class="n">apply_pca</span><span class="p">,</span>
+    <span class="n">apply_sam</span><span class="p">,</span>
+    <span class="n">apply_sam_spectral</span><span class="p">,</span>
+<span class="p">)</span>
 </pre></div>
 <div class="clipboard-copy-txt" id="cell-2">import earthaccess
-import hypercoast</div>
+import hypercoast
+from hypercoast.pace import (
+    cyano_band_ratios,
+    apply_kmeans,
+    apply_pca,
+    apply_sam,
+    apply_sam_spectral,
+)</div>
 </div>
 </div>
 </div>
@@ -2040,7 +2120,7 @@ <h2 id="download-pace-data">Download PACE data<a class="anchor-link" href="#down
 <div class="jp-OutputArea-child jp-OutputArea-executeResult">
 <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[3]:</div>
 <div class="jp-RenderedText jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/plain" tabindex="0">
-<pre>&lt;earthaccess.auth.Auth at 0x7f479064f260&gt;</pre>
+<pre>&lt;earthaccess.auth.Auth at 0x7fb3f8723bc0&gt;</pre>
 </div>
 </div>
 </div>
@@ -2137,34 +2217,34 @@ <h2 id="download-pace-data">Download PACE data<a class="anchor-link" href="#down
 <div class="jp-OutputArea jp-Cell-outputArea">
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 <script type="text/javascript">
-var element = document.getElementById('0fe24a08-6577-4695-bd90-9fa8ebadd074');
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 </script>
 </div>
 </div>
@@ -2235,8 +2315,8 @@ <h2 id="compute-band-ratios">Compute band ratios<a class="anchor-link" href="#co
 </div>
 </div>
 </div>
-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs">
-<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs">
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell">
 <div class="jp-Cell-inputWrapper" tabindex="0">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
@@ -2254,21 +2334,21 @@ <h2 id="compute-band-ratios">Compute band ratios<a class="anchor-link" href="#co
 </div>
 </clipboard-copy>
 </div>
-<div class="highlight-ipynb hl-python"><pre><span></span><span class="n">da</span> <span class="o">=</span> <span class="n">dataset</span><span class="p">[</span><span class="s2">"Rrs"</span><span class="p">]</span>
-<span class="n">data</span> <span class="o">=</span> <span class="p">(</span>
-    <span class="p">(</span><span class="n">da</span><span class="o">.</span><span class="n">sel</span><span class="p">(</span><span class="n">wavelength</span><span class="o">=</span><span class="mi">650</span><span class="p">)</span> <span class="o">&gt;</span> <span class="n">da</span><span class="o">.</span><span class="n">sel</span><span class="p">(</span><span class="n">wavelength</span><span class="o">=</span><span class="mi">620</span><span class="p">))</span>
-    <span class="o">&amp;</span> <span class="p">(</span><span class="n">da</span><span class="o">.</span><span class="n">sel</span><span class="p">(</span><span class="n">wavelength</span><span class="o">=</span><span class="mi">701</span><span class="p">)</span> <span class="o">&gt;</span> <span class="n">da</span><span class="o">.</span><span class="n">sel</span><span class="p">(</span><span class="n">wavelength</span><span class="o">=</span><span class="mi">681</span><span class="p">))</span>
-    <span class="o">&amp;</span> <span class="p">(</span><span class="n">da</span><span class="o">.</span><span class="n">sel</span><span class="p">(</span><span class="n">wavelength</span><span class="o">=</span><span class="mi">701</span><span class="p">)</span> <span class="o">&gt;</span> <span class="n">da</span><span class="o">.</span><span class="n">sel</span><span class="p">(</span><span class="n">wavelength</span><span class="o">=</span><span class="mi">450</span><span class="p">))</span>
-<span class="p">)</span>
-<span class="c1"># data</span>
+<div class="highlight-ipynb hl-python"><pre><span></span><span class="n">da</span> <span class="o">=</span> <span class="n">cyano_band_ratios</span><span class="p">(</span><span class="n">dataset</span><span class="p">,</span> <span class="n">plot</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
 </pre></div>
-<div class="clipboard-copy-txt" id="cell-7">da = dataset["Rrs"]
-data = (
-    (da.sel(wavelength=650) &gt; da.sel(wavelength=620))
-    &amp; (da.sel(wavelength=701) &gt; da.sel(wavelength=681))
-    &amp; (da.sel(wavelength=701) &gt; da.sel(wavelength=450))
-)
-# data</div>
+<div class="clipboard-copy-txt" id="cell-7">da = cyano_band_ratios(dataset, plot=True)</div>
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell-outputWrapper">
+<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
+</div>
+<div class="jp-OutputArea jp-Cell-outputArea">
+<div class="jp-OutputArea-child">
+<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
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+<img alt="No description has been provided for this image" class="" 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"/>
 </div>
 </div>
 </div>
@@ -2292,7 +2372,21 @@ <h2 id="the-spectra-of-cyanobacteria-bloom">The spectra of cyanobacteria bloom:<
 </div>
 <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
 </div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
-<h2 id="visualize-the-selected-region-based-on-band-ratios">Visualize the selected region based on band ratios<a class="anchor-link" href="#visualize-the-selected-region-based-on-band-ratios">¶</a></h2>
+<h2 id="cyanobacteria-and-spectral-angle-mapper">Cyanobacteria and Spectral Angle Mapper<a class="anchor-link" href="#cyanobacteria-and-spectral-angle-mapper">¶</a></h2><p>Spectral Angle Mapper: Spectral similarity
+Input: library of Cyanobacteria bloom Rrs spectra with Chla at different levels</p>
+<p>Spectral Mixture Analysis: unmix different cyanobacteria species based on spectral difference.</p>
+<p><img alt="" src="https://i.imgur.com/xLaLMA4.png"/></p>
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
+<h2 id="k-means-applied-to-the-whole-image">K-means applied to the whole image<a class="anchor-link" href="#k-means-applied-to-the-whole-image">¶</a></h2>
 </div>
 </div>
 </div>
@@ -2315,77 +2409,9 @@ <h2 id="visualize-the-selected-region-based-on-band-ratios">Visualize the select
 </div>
 </clipboard-copy>
 </div>
-<div class="highlight-ipynb hl-python"><pre><span></span><span class="kn">import</span> <span class="nn">xarray</span> <span class="k">as</span> <span class="nn">xr</span>
-<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
-<span class="kn">import</span> <span class="nn">cartopy.crs</span> <span class="k">as</span> <span class="nn">ccrs</span>
-<span class="kn">import</span> <span class="nn">cartopy.feature</span> <span class="k">as</span> <span class="nn">cfeature</span>
-
-<span class="c1"># Create a plot</span>
-<span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">12</span><span class="p">,</span> <span class="mi">6</span><span class="p">),</span> <span class="n">subplot_kw</span><span class="o">=</span><span class="p">{</span><span class="s2">"projection"</span><span class="p">:</span> <span class="n">ccrs</span><span class="o">.</span><span class="n">PlateCarree</span><span class="p">()})</span>
-
-<span class="c1"># ax.set_extent([-93, -87, 28, 32], crs=ccrs.PlateCarree())</span>
-
-<span class="c1"># Plot the data</span>
-<span class="n">data</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span>
-    <span class="n">ax</span><span class="o">=</span><span class="n">ax</span><span class="p">,</span>
-    <span class="n">transform</span><span class="o">=</span><span class="n">ccrs</span><span class="o">.</span><span class="n">PlateCarree</span><span class="p">(),</span>
-    <span class="n">cmap</span><span class="o">=</span><span class="s2">"coolwarm"</span><span class="p">,</span>
-    <span class="n">cbar_kwargs</span><span class="o">=</span><span class="p">{</span><span class="s2">"label"</span><span class="p">:</span> <span class="s2">"Cyano"</span><span class="p">},</span>
-<span class="p">)</span>
-
-<span class="c1"># Add coastlines</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">coastlines</span><span class="p">()</span>
-
-<span class="c1"># Add state boundaries</span>
-<span class="n">states_provinces</span> <span class="o">=</span> <span class="n">cfeature</span><span class="o">.</span><span class="n">NaturalEarthFeature</span><span class="p">(</span>
-    <span class="n">category</span><span class="o">=</span><span class="s2">"cultural"</span><span class="p">,</span>
-    <span class="n">name</span><span class="o">=</span><span class="s2">"admin_1_states_provinces_lines"</span><span class="p">,</span>
-    <span class="n">scale</span><span class="o">=</span><span class="s2">"50m"</span><span class="p">,</span>
-    <span class="n">facecolor</span><span class="o">=</span><span class="s2">"none"</span><span class="p">,</span>
-<span class="p">)</span>
-
-<span class="n">ax</span><span class="o">.</span><span class="n">add_feature</span><span class="p">(</span><span class="n">states_provinces</span><span class="p">,</span> <span class="n">edgecolor</span><span class="o">=</span><span class="s2">"gray"</span><span class="p">)</span>
-
-<span class="c1"># Optionally, add gridlines, labels, etc.</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">gridlines</span><span class="p">(</span><span class="n">draw_labels</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-
-<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
+<div class="highlight-ipynb hl-python"><pre><span></span><span class="n">cluster_labels</span><span class="p">,</span> <span class="n">latitudes</span><span class="p">,</span> <span class="n">longitudes</span> <span class="o">=</span> <span class="n">apply_kmeans</span><span class="p">(</span><span class="n">dataset</span><span class="p">,</span> <span class="n">n_clusters</span><span class="o">=</span><span class="mi">6</span><span class="p">)</span>
 </pre></div>
-<div class="clipboard-copy-txt" id="cell-8">import xarray as xr
-import matplotlib.pyplot as plt
-import cartopy.crs as ccrs
-import cartopy.feature as cfeature
-
-# Create a plot
-fig, ax = plt.subplots(figsize=(12, 6), subplot_kw={"projection": ccrs.PlateCarree()})
-
-# ax.set_extent([-93, -87, 28, 32], crs=ccrs.PlateCarree())
-
-# Plot the data
-data.plot(
-    ax=ax,
-    transform=ccrs.PlateCarree(),
-    cmap="coolwarm",
-    cbar_kwargs={"label": "Cyano"},
-)
-
-# Add coastlines
-ax.coastlines()
-
-# Add state boundaries
-states_provinces = cfeature.NaturalEarthFeature(
-    category="cultural",
-    name="admin_1_states_provinces_lines",
-    scale="50m",
-    facecolor="none",
-)
-
-ax.add_feature(states_provinces, edgecolor="gray")
-
-# Optionally, add gridlines, labels, etc.
-ax.gridlines(draw_labels=True)
-
-plt.show()</div>
+<div class="clipboard-copy-txt" id="cell-8">cluster_labels, latitudes, longitudes = apply_kmeans(dataset, n_clusters=6)</div>
 </div>
 </div>
 </div>
@@ -2397,25 +2423,11 @@ <h2 id="visualize-the-selected-region-based-on-band-ratios">Visualize the select
 <div class="jp-OutputArea-child">
 <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
 <div class="jp-RenderedImage jp-OutputArea-output" tabindex="0">
-<img alt="No description has been provided for this image" class="" 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"/>
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-<h2 id="cyanobacteria-and-spectral-angle-mapper">Cyanobacteria and Spectral Angle Mapper<a class="anchor-link" href="#cyanobacteria-and-spectral-angle-mapper">¶</a></h2><p>Spectral Angle Mapper: Spectral similarity
-Input: library of Cyanobacteria bloom Rrs spectra with Chla at different levels</p>
-<p>Spectral Mixture Analysis: unmix different cyanobacteria species based on spectral difference.</p>
-<p><img alt="" src="https://i.imgur.com/xLaLMA4.png"/></p>
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@@ -2424,12 +2436,12 @@ <h2 id="cyanobacteria-and-spectral-angle-mapper">Cyanobacteria and Spectral Angl
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-<h2 id="k-means-applied-to-the-whole-image">K-means applied to the whole image<a class="anchor-link" href="#k-means-applied-to-the-whole-image">¶</a></h2>
+<h2 id="k-means-applied-to-selected-pixels">K-means applied to selected pixels<a class="anchor-link" href="#k-means-applied-to-selected-pixels">¶</a></h2>
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@@ -2447,14 +2459,59 @@ <h2 id="k-means-applied-to-the-whole-image">K-means applied to the whole image<a
 </div>
 </clipboard-copy>
 </div>
-<div class="highlight-ipynb hl-python"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-<span class="kn">from</span> <span class="nn">sklearn.cluster</span> <span class="kn">import</span> <span class="n">KMeans</span>
+<div class="highlight-ipynb hl-python"><pre><span></span><span class="n">da</span> <span class="o">=</span> <span class="n">dataset</span><span class="p">[</span><span class="s2">"Rrs"</span><span class="p">]</span>
+
+<span class="n">filter_condition</span> <span class="o">=</span> <span class="p">(</span>
+    <span class="p">(</span><span class="n">da</span><span class="o">.</span><span class="n">sel</span><span class="p">(</span><span class="n">wavelength</span><span class="o">=</span><span class="mi">650</span><span class="p">)</span> <span class="o">&gt;</span> <span class="n">da</span><span class="o">.</span><span class="n">sel</span><span class="p">(</span><span class="n">wavelength</span><span class="o">=</span><span class="mi">620</span><span class="p">))</span>
+    <span class="o">&amp;</span> <span class="p">(</span><span class="n">da</span><span class="o">.</span><span class="n">sel</span><span class="p">(</span><span class="n">wavelength</span><span class="o">=</span><span class="mi">701</span><span class="p">)</span> <span class="o">&gt;</span> <span class="n">da</span><span class="o">.</span><span class="n">sel</span><span class="p">(</span><span class="n">wavelength</span><span class="o">=</span><span class="mi">681</span><span class="p">))</span>
+    <span class="o">&amp;</span> <span class="p">(</span><span class="n">da</span><span class="o">.</span><span class="n">sel</span><span class="p">(</span><span class="n">wavelength</span><span class="o">=</span><span class="mi">701</span><span class="p">)</span> <span class="o">&gt;</span> <span class="n">da</span><span class="o">.</span><span class="n">sel</span><span class="p">(</span><span class="n">wavelength</span><span class="o">=</span><span class="mi">450</span><span class="p">))</span>
+<span class="p">)</span>
+<span class="n">extent</span> <span class="o">=</span> <span class="p">[</span><span class="o">-</span><span class="mi">95</span><span class="p">,</span> <span class="o">-</span><span class="mi">85</span><span class="p">,</span> <span class="mi">27</span><span class="p">,</span> <span class="mi">33</span><span class="p">]</span>
+<span class="n">colors</span> <span class="o">=</span> <span class="p">[</span><span class="s2">"#e41a1c"</span><span class="p">,</span> <span class="s2">"#377eb8"</span><span class="p">,</span> <span class="s2">"#4daf4a"</span><span class="p">,</span> <span class="s2">"#f781bf"</span><span class="p">,</span> <span class="s2">"#a65628"</span><span class="p">,</span> <span class="s2">"#984ea3"</span><span class="p">]</span>
+
+<span class="n">cluster_labels</span><span class="p">,</span> <span class="n">latitudes</span><span class="p">,</span> <span class="n">longitudes</span> <span class="o">=</span> <span class="n">apply_kmeans</span><span class="p">(</span>
+    <span class="n">da</span><span class="p">,</span> <span class="n">n_clusters</span><span class="o">=</span><span class="mi">6</span><span class="p">,</span> <span class="n">filter_condition</span><span class="o">=</span><span class="n">filter_condition</span><span class="p">,</span> <span class="n">extent</span><span class="o">=</span><span class="n">extent</span><span class="p">,</span> <span class="n">colors</span><span class="o">=</span><span class="n">colors</span>
+<span class="p">)</span>
 </pre></div>
-<div class="clipboard-copy-txt" id="cell-9">import numpy as np
-from sklearn.cluster import KMeans</div>
+<div class="clipboard-copy-txt" id="cell-9">da = dataset["Rrs"]
+
+filter_condition = (
+    (da.sel(wavelength=650) &gt; da.sel(wavelength=620))
+    &amp; (da.sel(wavelength=701) &gt; da.sel(wavelength=681))
+    &amp; (da.sel(wavelength=701) &gt; da.sel(wavelength=450))
+)
+extent = [-95, -85, 27, 33]
+colors = ["#e41a1c", "#377eb8", "#4daf4a", "#f781bf", "#a65628", "#984ea3"]
+
+cluster_labels, latitudes, longitudes = apply_kmeans(
+    da, n_clusters=6, filter_condition=filter_condition, extent=extent, colors=colors
+)</div>
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell-outputWrapper">
+<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
+</div>
+<div class="jp-OutputArea jp-Cell-outputArea">
+<div class="jp-OutputArea-child">
+<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+<div class="jp-RenderedImage jp-OutputArea-output" tabindex="0">
+<img alt="No description has been provided for this image" class="" 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"/>
+</div>
+</div>
+</div>
 </div>
 </div>
 </div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
+<h2 id="principal-component-analysis-pca">Principal Component Analysis (PCA)<a class="anchor-link" href="#principal-component-analysis-pca">¶</a></h2>
+</div>
 </div>
 </div>
 </div><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
@@ -2476,11 +2533,9 @@ <h2 id="k-means-applied-to-the-whole-image">K-means applied to the whole image<a
 </div>
 </clipboard-copy>
 </div>
-<div class="highlight-ipynb hl-python"><pre><span></span><span class="c1"># Get the shape of the DataArray</span>
-<span class="nb">print</span><span class="p">(</span><span class="s2">"Shape of da:"</span><span class="p">,</span> <span class="n">da</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
+<div class="highlight-ipynb hl-python"><pre><span></span><span class="n">pca_data</span> <span class="o">=</span> <span class="n">apply_pca</span><span class="p">(</span><span class="n">dataset</span><span class="p">,</span> <span class="n">n_components</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">x_component</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">y_component</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
 </pre></div>
-<div class="clipboard-copy-txt" id="cell-10"># Get the shape of the DataArray
-print("Shape of da:", da.shape)</div>
+<div class="clipboard-copy-txt" id="cell-10">pca_data = apply_pca(dataset, n_components=3, x_component=0, y_component=1)</div>
 </div>
 </div>
 </div>
@@ -2491,9 +2546,8 @@ <h2 id="k-means-applied-to-the-whole-image">K-means applied to the whole image<a
 <div class="jp-OutputArea jp-Cell-outputArea">
 <div class="jp-OutputArea-child">
 <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
-<pre>Shape of da: (1710, 1272, 184)
-</pre>
+<div class="jp-RenderedImage jp-OutputArea-output" tabindex="0">
+<img alt="No description has been provided for this image" class="" 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"/>
 </div>
 </div>
 </div>
@@ -2518,11 +2572,9 @@ <h2 id="k-means-applied-to-the-whole-image">K-means applied to the whole image<a
 </div>
 </clipboard-copy>
 </div>
-<div class="highlight-ipynb hl-python"><pre><span></span><span class="c1"># Get the dimension names of the DataArray</span>
-<span class="nb">print</span><span class="p">(</span><span class="s2">"Dimension names:"</span><span class="p">,</span> <span class="n">da</span><span class="o">.</span><span class="n">dims</span><span class="p">)</span>
+<div class="highlight-ipynb hl-python"><pre><span></span><span class="n">pca_data</span> <span class="o">=</span> <span class="n">apply_pca</span><span class="p">(</span><span class="n">dataset</span><span class="p">,</span> <span class="n">n_components</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">x_component</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">y_component</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
 </pre></div>
-<div class="clipboard-copy-txt" id="cell-11"># Get the dimension names of the DataArray
-print("Dimension names:", da.dims)</div>
+<div class="clipboard-copy-txt" id="cell-11">pca_data = apply_pca(dataset, n_components=3, x_component=1, y_component=2)</div>
 </div>
 </div>
 </div>
@@ -2533,12 +2585,22 @@ <h2 id="k-means-applied-to-the-whole-image">K-means applied to the whole image<a
 <div class="jp-OutputArea jp-Cell-outputArea">
 <div class="jp-OutputArea-child">
 <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
-<pre>Dimension names: ('latitude', 'longitude', 'wavelength')
-</pre>
+<div class="jp-RenderedImage jp-OutputArea-output" tabindex="0">
+<img alt="No description has been provided for this image" class="" 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"/>
+</div>
+</div>
+</div>
 </div>
 </div>
 </div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
+<h2 id="spectral-angle-mapper-sam">Spectral Angle Mapper (SAM)<a class="anchor-link" href="#spectral-angle-mapper-sam">¶</a></h2><h3 id="apply-sam-to-the-whole-image">Apply SAM to the whole image<a class="anchor-link" href="#apply-sam-to-the-whole-image">¶</a></h3>
+</div>
 </div>
 </div>
 </div><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
@@ -2560,11 +2622,17 @@ <h2 id="k-means-applied-to-the-whole-image">K-means applied to the whole image<a
 </div>
 </clipboard-copy>
 </div>
-<div class="highlight-ipynb hl-python"><pre><span></span><span class="c1"># Get the size of each dimension</span>
-<span class="nb">print</span><span class="p">(</span><span class="s2">"Size of each dimension:"</span><span class="p">,</span> <span class="n">da</span><span class="o">.</span><span class="n">sizes</span><span class="p">)</span>
+<div class="highlight-ipynb hl-python"><pre><span></span><span class="n">data</span><span class="p">,</span> <span class="n">latitudes</span><span class="p">,</span> <span class="n">longitudes</span> <span class="o">=</span> <span class="n">apply_sam</span><span class="p">(</span>
+    <span class="n">dataset</span><span class="p">,</span>
+    <span class="n">n_components</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span>
+    <span class="n">n_clusters</span><span class="o">=</span><span class="mi">6</span><span class="p">,</span>
+<span class="p">)</span>
 </pre></div>
-<div class="clipboard-copy-txt" id="cell-12"># Get the size of each dimension
-print("Size of each dimension:", da.sizes)</div>
+<div class="clipboard-copy-txt" id="cell-12">data, latitudes, longitudes = apply_sam(
+    dataset,
+    n_components=3,
+    n_clusters=6,
+)</div>
 </div>
 </div>
 </div>
@@ -2575,16 +2643,26 @@ <h2 id="k-means-applied-to-the-whole-image">K-means applied to the whole image<a
 <div class="jp-OutputArea jp-Cell-outputArea">
 <div class="jp-OutputArea-child">
 <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
-<pre>Size of each dimension: Frozen({'latitude': 1710, 'longitude': 1272, 'wavelength': 184})
-</pre>
+<div class="jp-RenderedImage jp-OutputArea-output" tabindex="0">
+<img alt="No description has been provided for this image" class="" 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"/>
 </div>
 </div>
 </div>
 </div>
 </div>
-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs">
-<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs">
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
+<h3 id="apply-sam-to-selected-pixels">Apply SAM to selected pixels<a class="anchor-link" href="#apply-sam-to-selected-pixels">¶</a></h3>
+</div>
+</div>
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell">
 <div class="jp-Cell-inputWrapper" tabindex="0">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
@@ -2602,38 +2680,50 @@ <h2 id="k-means-applied-to-the-whole-image">K-means applied to the whole image<a
 </div>
 </clipboard-copy>
 </div>
-<div class="highlight-ipynb hl-python"><pre><span></span><span class="n">reshaped_data</span> <span class="o">=</span> <span class="n">da</span><span class="o">.</span><span class="n">values</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="n">da</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span>
+<div class="highlight-ipynb hl-python"><pre><span></span><span class="n">extent</span> <span class="o">=</span> <span class="p">[</span><span class="o">-</span><span class="mi">95</span><span class="p">,</span> <span class="o">-</span><span class="mi">85</span><span class="p">,</span> <span class="mi">27</span><span class="p">,</span> <span class="mi">33</span><span class="p">]</span>
+<span class="n">colors</span> <span class="o">=</span> <span class="p">[</span><span class="s2">"#377eb8"</span><span class="p">,</span> <span class="s2">"#ff7f00"</span><span class="p">,</span> <span class="s2">"#4daf4a"</span><span class="p">,</span> <span class="s2">"#f781bf"</span><span class="p">,</span> <span class="s2">"#a65628"</span><span class="p">,</span> <span class="s2">"#984ea3"</span><span class="p">]</span>
+<span class="n">data</span><span class="p">,</span> <span class="n">latitudes</span><span class="p">,</span> <span class="n">longitudes</span> <span class="o">=</span> <span class="n">apply_sam</span><span class="p">(</span>
+    <span class="n">dataset</span><span class="p">,</span>
+    <span class="n">n_components</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span>
+    <span class="n">n_clusters</span><span class="o">=</span><span class="mi">6</span><span class="p">,</span>
+    <span class="n">extent</span><span class="o">=</span><span class="n">extent</span><span class="p">,</span>
+    <span class="n">colors</span><span class="o">=</span><span class="n">colors</span><span class="p">,</span>
+<span class="p">)</span>
 </pre></div>
-<div class="clipboard-copy-txt" id="cell-13">reshaped_data = da.values.reshape(-1, da.shape[-1])</div>
+<div class="clipboard-copy-txt" id="cell-13">extent = [-95, -85, 27, 33]
+colors = ["#377eb8", "#ff7f00", "#4daf4a", "#f781bf", "#a65628", "#984ea3"]
+data, latitudes, longitudes = apply_sam(
+    dataset,
+    n_components=3,
+    n_clusters=6,
+    extent=extent,
+    colors=colors,
+)</div>
 </div>
 </div>
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"/>
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-<div class="jp-InputPrompt jp-InputArea-prompt">In [14]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
-<div class="CodeMirror cm-s-jupyter">
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-<svg aria-hidden="true" class="clipboard-copy-icon" data-view-component="true" height="20" version="1.1" viewbox="0 0 16 16" width="20">
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-<div class="highlight-ipynb hl-python"><pre><span></span><span class="n">reshaped_data_no_nan</span> <span class="o">=</span> <span class="n">reshaped_data</span><span class="p">[</span><span class="o">~</span><span class="n">np</span><span class="o">.</span><span class="n">isnan</span><span class="p">(</span><span class="n">reshaped_data</span><span class="p">)</span><span class="o">.</span><span class="n">any</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)]</span>
-</pre></div>
-<div class="clipboard-copy-txt" id="cell-14">reshaped_data_no_nan = reshaped_data[~np.isnan(reshaped_data).any(axis=1)]</div>
 </div>
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+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
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+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
+<h3 id="apply-sam-with-a-filtering-condition">Apply SAM with a filtering condition<a class="anchor-link" href="#apply-sam-with-a-filtering-condition">¶</a></h3>
 </div>
 </div>
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@@ -2643,10 +2733,10 @@ <h2 id="k-means-applied-to-the-whole-image">K-means applied to the whole image<a
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-<div class="jp-InputPrompt jp-InputArea-prompt">In [15]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+<div class="jp-InputPrompt jp-InputArea-prompt">In [14]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
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 <svg aria-hidden="true" class="clipboard-copy-icon" data-view-component="true" height="20" version="1.1" viewbox="0 0 16 16" width="20">
@@ -2656,15 +2746,43 @@ <h2 id="k-means-applied-to-the-whole-image">K-means applied to the whole image<a
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-<div class="highlight-ipynb hl-python"><pre><span></span><span class="c1"># Apply K-means clustering to classify into 5-6 water types.</span>
-<span class="n">n_clusters</span> <span class="o">=</span> <span class="mi">6</span>
-<span class="n">kmeans</span> <span class="o">=</span> <span class="n">KMeans</span><span class="p">(</span><span class="n">n_clusters</span><span class="o">=</span><span class="n">n_clusters</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
-<span class="n">kmeans</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">reshaped_data_no_nan</span><span class="p">)</span>
+<div class="highlight-ipynb hl-python"><pre><span></span><span class="n">da</span> <span class="o">=</span> <span class="n">dataset</span><span class="p">[</span><span class="s2">"Rrs"</span><span class="p">]</span>
+
+<span class="n">filter_condition</span> <span class="o">=</span> <span class="p">(</span>
+    <span class="p">(</span><span class="n">da</span><span class="o">.</span><span class="n">sel</span><span class="p">(</span><span class="n">wavelength</span><span class="o">=</span><span class="mi">650</span><span class="p">)</span> <span class="o">&gt;</span> <span class="n">da</span><span class="o">.</span><span class="n">sel</span><span class="p">(</span><span class="n">wavelength</span><span class="o">=</span><span class="mi">620</span><span class="p">))</span>
+    <span class="o">&amp;</span> <span class="p">(</span><span class="n">da</span><span class="o">.</span><span class="n">sel</span><span class="p">(</span><span class="n">wavelength</span><span class="o">=</span><span class="mi">701</span><span class="p">)</span> <span class="o">&gt;</span> <span class="n">da</span><span class="o">.</span><span class="n">sel</span><span class="p">(</span><span class="n">wavelength</span><span class="o">=</span><span class="mi">681</span><span class="p">))</span>
+    <span class="o">&amp;</span> <span class="p">(</span><span class="n">da</span><span class="o">.</span><span class="n">sel</span><span class="p">(</span><span class="n">wavelength</span><span class="o">=</span><span class="mi">701</span><span class="p">)</span> <span class="o">&gt;</span> <span class="n">da</span><span class="o">.</span><span class="n">sel</span><span class="p">(</span><span class="n">wavelength</span><span class="o">=</span><span class="mi">450</span><span class="p">))</span>
+<span class="p">)</span>
+<span class="n">extent</span> <span class="o">=</span> <span class="p">[</span><span class="o">-</span><span class="mi">95</span><span class="p">,</span> <span class="o">-</span><span class="mi">85</span><span class="p">,</span> <span class="mi">27</span><span class="p">,</span> <span class="mi">33</span><span class="p">]</span>
+<span class="n">colors</span> <span class="o">=</span> <span class="p">[</span><span class="s2">"#e41a1c"</span><span class="p">,</span> <span class="s2">"#377eb8"</span><span class="p">,</span> <span class="s2">"#4daf4a"</span><span class="p">,</span> <span class="s2">"#f781bf"</span><span class="p">,</span> <span class="s2">"#a65628"</span><span class="p">,</span> <span class="s2">"#984ea3"</span><span class="p">]</span>
+
+<span class="n">data</span><span class="p">,</span> <span class="n">latitudes</span><span class="p">,</span> <span class="n">longitudes</span> <span class="o">=</span> <span class="n">apply_sam</span><span class="p">(</span>
+    <span class="n">dataset</span><span class="p">,</span>
+    <span class="n">n_components</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span>
+    <span class="n">n_clusters</span><span class="o">=</span><span class="mi">6</span><span class="p">,</span>
+    <span class="n">filter_condition</span><span class="o">=</span><span class="n">filter_condition</span><span class="p">,</span>
+    <span class="n">extent</span><span class="o">=</span><span class="n">extent</span><span class="p">,</span>
+    <span class="n">colors</span><span class="o">=</span><span class="n">colors</span><span class="p">,</span>
+<span class="p">)</span>
 </pre></div>
-<div class="clipboard-copy-txt" id="cell-15"># Apply K-means clustering to classify into 5-6 water types.
-n_clusters = 6
-kmeans = KMeans(n_clusters=n_clusters, random_state=0)
-kmeans.fit(reshaped_data_no_nan)</div>
+<div class="clipboard-copy-txt" id="cell-14">da = dataset["Rrs"]
+
+filter_condition = (
+    (da.sel(wavelength=650) &gt; da.sel(wavelength=620))
+    &amp; (da.sel(wavelength=701) &gt; da.sel(wavelength=681))
+    &amp; (da.sel(wavelength=701) &gt; da.sel(wavelength=450))
+)
+extent = [-95, -85, 27, 33]
+colors = ["#e41a1c", "#377eb8", "#4daf4a", "#f781bf", "#a65628", "#984ea3"]
+
+data, latitudes, longitudes = apply_sam(
+    dataset,
+    n_components=3,
+    n_clusters=6,
+    filter_condition=filter_condition,
+    extent=extent,
+    colors=colors,
+)</div>
 </div>
 </div>
 </div>
@@ -2673,503 +2791,23 @@ <h2 id="k-means-applied-to-the-whole-image">K-means applied to the whole image<a
 <div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
 </div>
 <div class="jp-OutputArea jp-Cell-outputArea">
-<div class="jp-OutputArea-child jp-OutputArea-executeResult">
-<div class="jp-OutputPrompt jp-OutputArea-prompt">Out[15]:</div>
-<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html" tabindex="0">
-<style>#sk-container-id-1 {
-  /* Definition of color scheme common for light and dark mode */
-  --sklearn-color-text: black;
-  --sklearn-color-line: gray;
-  /* Definition of color scheme for unfitted estimators */
-  --sklearn-color-unfitted-level-0: #fff5e6;
-  --sklearn-color-unfitted-level-1: #f6e4d2;
-  --sklearn-color-unfitted-level-2: #ffe0b3;
-  --sklearn-color-unfitted-level-3: chocolate;
-  /* Definition of color scheme for fitted estimators */
-  --sklearn-color-fitted-level-0: #f0f8ff;
-  --sklearn-color-fitted-level-1: #d4ebff;
-  --sklearn-color-fitted-level-2: #b3dbfd;
-  --sklearn-color-fitted-level-3: cornflowerblue;
-
-  /* Specific color for light theme */
-  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));
-  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));
-  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));
-  --sklearn-color-icon: #696969;
-
-  @media (prefers-color-scheme: dark) {
-    /* Redefinition of color scheme for dark theme */
-    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));
-    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));
-    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));
-    --sklearn-color-icon: #878787;
-  }
-}
-
-#sk-container-id-1 {
-  color: var(--sklearn-color-text);
-}
-
-#sk-container-id-1 pre {
-  padding: 0;
-}
-
-#sk-container-id-1 input.sk-hidden--visually {
-  border: 0;
-  clip: rect(1px 1px 1px 1px);
-  clip: rect(1px, 1px, 1px, 1px);
-  height: 1px;
-  margin: -1px;
-  overflow: hidden;
-  padding: 0;
-  position: absolute;
-  width: 1px;
-}
-
-#sk-container-id-1 div.sk-dashed-wrapped {
-  border: 1px dashed var(--sklearn-color-line);
-  margin: 0 0.4em 0.5em 0.4em;
-  box-sizing: border-box;
-  padding-bottom: 0.4em;
-  background-color: var(--sklearn-color-background);
-}
-
-#sk-container-id-1 div.sk-container {
-  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`
-     but bootstrap.min.css set `[hidden] { display: none !important; }`
-     so we also need the `!important` here to be able to override the
-     default hidden behavior on the sphinx rendered scikit-learn.org.
-     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */
-  display: inline-block !important;
-  position: relative;
-}
-
-#sk-container-id-1 div.sk-text-repr-fallback {
-  display: none;
-}
-
-div.sk-parallel-item,
-div.sk-serial,
-div.sk-item {
-  /* draw centered vertical line to link estimators */
-  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));
-  background-size: 2px 100%;
-  background-repeat: no-repeat;
-  background-position: center center;
-}
-
-/* Parallel-specific style estimator block */
-
-#sk-container-id-1 div.sk-parallel-item::after {
-  content: "";
-  width: 100%;
-  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);
-  flex-grow: 1;
-}
-
-#sk-container-id-1 div.sk-parallel {
-  display: flex;
-  align-items: stretch;
-  justify-content: center;
-  background-color: var(--sklearn-color-background);
-  position: relative;
-}
-
-#sk-container-id-1 div.sk-parallel-item {
-  display: flex;
-  flex-direction: column;
-}
-
-#sk-container-id-1 div.sk-parallel-item:first-child::after {
-  align-self: flex-end;
-  width: 50%;
-}
-
-#sk-container-id-1 div.sk-parallel-item:last-child::after {
-  align-self: flex-start;
-  width: 50%;
-}
-
-#sk-container-id-1 div.sk-parallel-item:only-child::after {
-  width: 0;
-}
-
-/* Serial-specific style estimator block */
-
-#sk-container-id-1 div.sk-serial {
-  display: flex;
-  flex-direction: column;
-  align-items: center;
-  background-color: var(--sklearn-color-background);
-  padding-right: 1em;
-  padding-left: 1em;
-}
-
-
-/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is
-clickable and can be expanded/collapsed.
-- Pipeline and ColumnTransformer use this feature and define the default style
-- Estimators will overwrite some part of the style using the `sk-estimator` class
-*/
-
-/* Pipeline and ColumnTransformer style (default) */
-
-#sk-container-id-1 div.sk-toggleable {
-  /* Default theme specific background. It is overwritten whether we have a
-  specific estimator or a Pipeline/ColumnTransformer */
-  background-color: var(--sklearn-color-background);
-}
-
-/* Toggleable label */
-#sk-container-id-1 label.sk-toggleable__label {
-  cursor: pointer;
-  display: block;
-  width: 100%;
-  margin-bottom: 0;
-  padding: 0.5em;
-  box-sizing: border-box;
-  text-align: center;
-}
-
-#sk-container-id-1 label.sk-toggleable__label-arrow:before {
-  /* Arrow on the left of the label */
-  content: "▸";
-  float: left;
-  margin-right: 0.25em;
-  color: var(--sklearn-color-icon);
-}
-
-#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {
-  color: var(--sklearn-color-text);
-}
-
-/* Toggleable content - dropdown */
-
-#sk-container-id-1 div.sk-toggleable__content {
-  max-height: 0;
-  max-width: 0;
-  overflow: hidden;
-  text-align: left;
-  /* unfitted */
-  background-color: var(--sklearn-color-unfitted-level-0);
-}
-
-#sk-container-id-1 div.sk-toggleable__content.fitted {
-  /* fitted */
-  background-color: var(--sklearn-color-fitted-level-0);
-}
-
-#sk-container-id-1 div.sk-toggleable__content pre {
-  margin: 0.2em;
-  border-radius: 0.25em;
-  color: var(--sklearn-color-text);
-  /* unfitted */
-  background-color: var(--sklearn-color-unfitted-level-0);
-}
-
-#sk-container-id-1 div.sk-toggleable__content.fitted pre {
-  /* unfitted */
-  background-color: var(--sklearn-color-fitted-level-0);
-}
-
-#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {
-  /* Expand drop-down */
-  max-height: 200px;
-  max-width: 100%;
-  overflow: auto;
-}
-
-#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
-  content: "▾";
-}
-
-/* Pipeline/ColumnTransformer-specific style */
-
-#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
-  color: var(--sklearn-color-text);
-  background-color: var(--sklearn-color-unfitted-level-2);
-}
-
-#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
-  background-color: var(--sklearn-color-fitted-level-2);
-}
-
-/* Estimator-specific style */
-
-/* Colorize estimator box */
-#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
-  /* unfitted */
-  background-color: var(--sklearn-color-unfitted-level-2);
-}
-
-#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
-  /* fitted */
-  background-color: var(--sklearn-color-fitted-level-2);
-}
-
-#sk-container-id-1 div.sk-label label.sk-toggleable__label,
-#sk-container-id-1 div.sk-label label {
-  /* The background is the default theme color */
-  color: var(--sklearn-color-text-on-default-background);
-}
-
-/* On hover, darken the color of the background */
-#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {
-  color: var(--sklearn-color-text);
-  background-color: var(--sklearn-color-unfitted-level-2);
-}
-
-/* Label box, darken color on hover, fitted */
-#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {
-  color: var(--sklearn-color-text);
-  background-color: var(--sklearn-color-fitted-level-2);
-}
-
-/* Estimator label */
-
-#sk-container-id-1 div.sk-label label {
-  font-family: monospace;
-  font-weight: bold;
-  display: inline-block;
-  line-height: 1.2em;
-}
-
-#sk-container-id-1 div.sk-label-container {
-  text-align: center;
-}
-
-/* Estimator-specific */
-#sk-container-id-1 div.sk-estimator {
-  font-family: monospace;
-  border: 1px dotted var(--sklearn-color-border-box);
-  border-radius: 0.25em;
-  box-sizing: border-box;
-  margin-bottom: 0.5em;
-  /* unfitted */
-  background-color: var(--sklearn-color-unfitted-level-0);
-}
-
-#sk-container-id-1 div.sk-estimator.fitted {
-  /* fitted */
-  background-color: var(--sklearn-color-fitted-level-0);
-}
-
-/* on hover */
-#sk-container-id-1 div.sk-estimator:hover {
-  /* unfitted */
-  background-color: var(--sklearn-color-unfitted-level-2);
-}
-
-#sk-container-id-1 div.sk-estimator.fitted:hover {
-  /* fitted */
-  background-color: var(--sklearn-color-fitted-level-2);
-}
-
-/* Specification for estimator info (e.g. "i" and "?") */
-
-/* Common style for "i" and "?" */
-
-.sk-estimator-doc-link,
-a:link.sk-estimator-doc-link,
-a:visited.sk-estimator-doc-link {
-  float: right;
-  font-size: smaller;
-  line-height: 1em;
-  font-family: monospace;
-  background-color: var(--sklearn-color-background);
-  border-radius: 1em;
-  height: 1em;
-  width: 1em;
-  text-decoration: none !important;
-  margin-left: 1ex;
-  /* unfitted */
-  border: var(--sklearn-color-unfitted-level-1) 1pt solid;
-  color: var(--sklearn-color-unfitted-level-1);
-}
-
-.sk-estimator-doc-link.fitted,
-a:link.sk-estimator-doc-link.fitted,
-a:visited.sk-estimator-doc-link.fitted {
-  /* fitted */
-  border: var(--sklearn-color-fitted-level-1) 1pt solid;
-  color: var(--sklearn-color-fitted-level-1);
-}
-
-/* On hover */
-div.sk-estimator:hover .sk-estimator-doc-link:hover,
-.sk-estimator-doc-link:hover,
-div.sk-label-container:hover .sk-estimator-doc-link:hover,
-.sk-estimator-doc-link:hover {
-  /* unfitted */
-  background-color: var(--sklearn-color-unfitted-level-3);
-  color: var(--sklearn-color-background);
-  text-decoration: none;
-}
-
-div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,
-.sk-estimator-doc-link.fitted:hover,
-div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,
-.sk-estimator-doc-link.fitted:hover {
-  /* fitted */
-  background-color: var(--sklearn-color-fitted-level-3);
-  color: var(--sklearn-color-background);
-  text-decoration: none;
-}
-
-/* Span, style for the box shown on hovering the info icon */
-.sk-estimator-doc-link span {
-  display: none;
-  z-index: 9999;
-  position: relative;
-  font-weight: normal;
-  right: .2ex;
-  padding: .5ex;
-  margin: .5ex;
-  width: min-content;
-  min-width: 20ex;
-  max-width: 50ex;
-  color: var(--sklearn-color-text);
-  box-shadow: 2pt 2pt 4pt #999;
-  /* unfitted */
-  background: var(--sklearn-color-unfitted-level-0);
-  border: .5pt solid var(--sklearn-color-unfitted-level-3);
-}
-
-.sk-estimator-doc-link.fitted span {
-  /* fitted */
-  background: var(--sklearn-color-fitted-level-0);
-  border: var(--sklearn-color-fitted-level-3);
-}
-
-.sk-estimator-doc-link:hover span {
-  display: block;
-}
-
-/* "?"-specific style due to the `<a>` HTML tag */
-
-#sk-container-id-1 a.estimator_doc_link {
-  float: right;
-  font-size: 1rem;
-  line-height: 1em;
-  font-family: monospace;
-  background-color: var(--sklearn-color-background);
-  border-radius: 1rem;
-  height: 1rem;
-  width: 1rem;
-  text-decoration: none;
-  /* unfitted */
-  color: var(--sklearn-color-unfitted-level-1);
-  border: var(--sklearn-color-unfitted-level-1) 1pt solid;
-}
-
-#sk-container-id-1 a.estimator_doc_link.fitted {
-  /* fitted */
-  border: var(--sklearn-color-fitted-level-1) 1pt solid;
-  color: var(--sklearn-color-fitted-level-1);
-}
-
-/* On hover */
-#sk-container-id-1 a.estimator_doc_link:hover {
-  /* unfitted */
-  background-color: var(--sklearn-color-unfitted-level-3);
-  color: var(--sklearn-color-background);
-  text-decoration: none;
-}
-
-#sk-container-id-1 a.estimator_doc_link.fitted:hover {
-  /* fitted */
-  background-color: var(--sklearn-color-fitted-level-3);
-}
-</style><div class="sk-top-container" id="sk-container-id-1"><div class="sk-text-repr-fallback"><pre>KMeans(n_clusters=6, random_state=0)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br/>On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden=""><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input checked="" class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-1" type="checkbox"/><label class="sk-toggleable__label fitted sk-toggleable__label-arrow fitted" for="sk-estimator-id-1">  KMeans<a class="sk-estimator-doc-link fitted" href="https://scikit-learn.org/1.5/modules/generated/sklearn.cluster.KMeans.html" rel="noreferrer" target="_blank">?<span>Documentation for KMeans</span></a><span class="sk-estimator-doc-link fitted">i<span>Fitted</span></span></label><div class="sk-toggleable__content fitted"><pre>KMeans(n_clusters=6, random_state=0)</pre></div> </div></div></div></div>
-</div>
-</div>
-</div>
-</div>
-</div>
-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs">
-<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs">
-<div class="jp-Cell-inputWrapper" tabindex="0">
-<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
-</div>
-<div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [16]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
-<div class="CodeMirror cm-s-jupyter">
-<div class="zeroclipboard-container">
-<clipboard-copy ,="" aria-label="Copy to Clipboard" for="cell-16">
-<div>
-<span class="notice" hidden="">Copied!</span>
-<svg aria-hidden="true" class="clipboard-copy-icon" data-view-component="true" height="20" version="1.1" viewbox="0 0 16 16" width="20">
-<path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill="currentColor" fill-rule="evenodd"></path>
-<path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path>
-</svg>
-</div>
-</clipboard-copy>
-</div>
-<div class="highlight-ipynb hl-python"><pre><span></span><span class="c1"># Initialize an array for cluster labels with NaN</span>
-<span class="n">labels</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">full</span><span class="p">(</span><span class="n">reshaped_data</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span><span class="p">)</span>
-</pre></div>
-<div class="clipboard-copy-txt" id="cell-16"># Initialize an array for cluster labels with NaN
-labels = np.full(reshaped_data.shape[0], np.nan)</div>
-</div>
-</div>
-</div>
-</div>
-</div>
-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs">
-<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs">
-<div class="jp-Cell-inputWrapper" tabindex="0">
-<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
-</div>
-<div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [17]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
-<div class="CodeMirror cm-s-jupyter">
-<div class="zeroclipboard-container">
-<clipboard-copy ,="" aria-label="Copy to Clipboard" for="cell-17">
-<div>
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"/>
 </div>
-<div class="highlight-ipynb hl-python"><pre><span></span><span class="c1"># Assign the computed cluster labels to the non-NaN positions</span>
-<span class="n">labels</span><span class="p">[</span><span class="o">~</span><span class="n">np</span><span class="o">.</span><span class="n">isnan</span><span class="p">(</span><span class="n">reshaped_data</span><span class="p">)</span><span class="o">.</span><span class="n">any</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)]</span> <span class="o">=</span> <span class="n">kmeans</span><span class="o">.</span><span class="n">labels_</span>
-</pre></div>
-<div class="clipboard-copy-txt" id="cell-17"># Assign the computed cluster labels to the non-NaN positions
-labels[~np.isnan(reshaped_data).any(axis=1)] = kmeans.labels_</div>
 </div>
 </div>
 </div>
 </div>
 </div>
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-<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs">
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
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-<div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [18]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
-<div class="CodeMirror cm-s-jupyter">
-<div class="zeroclipboard-container">
-<clipboard-copy ,="" aria-label="Copy to Clipboard" for="cell-18">
-<div>
-<span class="notice" hidden="">Copied!</span>
-<svg aria-hidden="true" class="clipboard-copy-icon" data-view-component="true" height="20" version="1.1" viewbox="0 0 16 16" width="20">
-<path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill="currentColor" fill-rule="evenodd"></path>
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-<div class="highlight-ipynb hl-python"><pre><span></span><span class="c1"># Reshape the labels back to the original spatial dimensions</span>
-<span class="n">cluster_labels</span> <span class="o">=</span> <span class="n">labels</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">da</span><span class="o">.</span><span class="n">shape</span><span class="p">[:</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span>
-</pre></div>
-<div class="clipboard-copy-txt" id="cell-18"># Reshape the labels back to the original spatial dimensions
-cluster_labels = labels.reshape(da.shape[:-1])</div>
-</div>
-</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
+<h3 id="use-spectral-library">Use spectral library<a class="anchor-link" href="#use-spectral-library">¶</a></h3>
 </div>
 </div>
 </div>
@@ -3179,10 +2817,10 @@ <h2 id="k-means-applied-to-the-whole-image">K-means applied to the whole image<a
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-<div class="jp-InputPrompt jp-InputArea-prompt">In [19]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+<div class="jp-InputPrompt jp-InputArea-prompt">In [15]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
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-<clipboard-copy ,="" aria-label="Copy to Clipboard" for="cell-19">
+<clipboard-copy ,="" aria-label="Copy to Clipboard" for="cell-15">
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@@ -3192,15 +2830,17 @@ <h2 id="k-means-applied-to-the-whole-image">K-means applied to the whole image<a
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-<div class="highlight-ipynb hl-python"><pre><span></span><span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
-<span class="kn">import</span> <span class="nn">matplotlib.colors</span> <span class="k">as</span> <span class="nn">mcolors</span>
-<span class="kn">import</span> <span class="nn">cartopy.crs</span> <span class="k">as</span> <span class="nn">ccrs</span>
-<span class="kn">import</span> <span class="nn">cartopy.feature</span> <span class="k">as</span> <span class="nn">cfeature</span>
+<div class="highlight-ipynb hl-python"><pre><span></span><span class="n">filepath</span> <span class="o">=</span> <span class="s2">"data/PACE_OCI.20240730T181157.L2.OC_AOP.V2_0.NRT.nc"</span>
+<span class="n">dataset</span> <span class="o">=</span> <span class="n">hypercoast</span><span class="o">.</span><span class="n">read_pace</span><span class="p">(</span><span class="n">filepath</span><span class="p">)</span>
+<span class="n">url</span> <span class="o">=</span> <span class="s2">"https://github.com/opengeos/datasets/releases/download/hypercoast/SAM_spectral_library.zip"</span>
+<span class="n">hypercoast</span><span class="o">.</span><span class="n">download_file</span><span class="p">(</span><span class="n">url</span><span class="p">)</span>
+<span class="n">spectral_library</span> <span class="o">=</span> <span class="s2">"./SAM_spectral_library/*.csv"</span>
 </pre></div>
-<div class="clipboard-copy-txt" id="cell-19">import matplotlib.pyplot as plt
-import matplotlib.colors as mcolors
-import cartopy.crs as ccrs
-import cartopy.feature as cfeature</div>
+<div class="clipboard-copy-txt" id="cell-15">filepath = "data/PACE_OCI.20240730T181157.L2.OC_AOP.V2_0.NRT.nc"
+dataset = hypercoast.read_pace(filepath)
+url = "https://github.com/opengeos/datasets/releases/download/hypercoast/SAM_spectral_library.zip"
+hypercoast.download_file(url)
+spectral_library = "./SAM_spectral_library/*.csv"</div>
 </div>
 </div>
 </div>
@@ -3212,10 +2852,10 @@ <h2 id="k-means-applied-to-the-whole-image">K-means applied to the whole image<a
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-<div class="jp-InputPrompt jp-InputArea-prompt">In [20]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+<div class="jp-InputPrompt jp-InputArea-prompt">In [16]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
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+<clipboard-copy ,="" aria-label="Copy to Clipboard" for="cell-16">
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@@ -3225,117 +2865,19 @@ <h2 id="k-means-applied-to-the-whole-image">K-means applied to the whole image<a
 </div>
 </clipboard-copy>
 </div>
-<div class="highlight-ipynb hl-python"><pre><span></span><span class="c1"># Assume 'cluster_labels' contains the K-means classification results reshaped to the original spatial dimensions</span>
-<span class="c1"># Also assume that 'da' has the original latitude and longitude data</span>
-<span class="n">latitudes</span> <span class="o">=</span> <span class="n">da</span><span class="o">.</span><span class="n">coords</span><span class="p">[</span><span class="s2">"latitude"</span><span class="p">]</span><span class="o">.</span><span class="n">values</span>
-<span class="n">longitudes</span> <span class="o">=</span> <span class="n">da</span><span class="o">.</span><span class="n">coords</span><span class="p">[</span><span class="s2">"longitude"</span><span class="p">]</span><span class="o">.</span><span class="n">values</span>
-
-<span class="c1"># Create a custom discrete color map for K-means clusters</span>
-<span class="n">cmap</span> <span class="o">=</span> <span class="n">mcolors</span><span class="o">.</span><span class="n">ListedColormap</span><span class="p">(</span>
-    <span class="p">[</span><span class="s2">"#377eb8"</span><span class="p">,</span> <span class="s2">"#ff7f00"</span><span class="p">,</span> <span class="s2">"#4daf4a"</span><span class="p">,</span> <span class="s2">"#f781bf"</span><span class="p">,</span> <span class="s2">"#a65628"</span><span class="p">,</span> <span class="s2">"#984ea3"</span><span class="p">]</span>
-<span class="p">)</span>
-<span class="n">bounds</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="o">-</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">n_clusters</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
-<span class="n">norm</span> <span class="o">=</span> <span class="n">mcolors</span><span class="o">.</span><span class="n">BoundaryNorm</span><span class="p">(</span><span class="n">bounds</span><span class="p">,</span> <span class="n">cmap</span><span class="o">.</span><span class="n">N</span><span class="p">)</span>
-
-<span class="c1"># Create a figure and axis with the correct map projection</span>
-<span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span>
-    <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">6</span><span class="p">,</span> <span class="mi">4</span><span class="p">),</span> <span class="n">dpi</span><span class="o">=</span><span class="mi">200</span><span class="p">,</span> <span class="n">subplot_kw</span><span class="o">=</span><span class="p">{</span><span class="s2">"projection"</span><span class="p">:</span> <span class="n">ccrs</span><span class="o">.</span><span class="n">PlateCarree</span><span class="p">()}</span>
-<span class="p">)</span>
-
-<span class="c1"># Plot the K-means classification results on the map</span>
-<span class="n">im</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">pcolormesh</span><span class="p">(</span>
-    <span class="n">longitudes</span><span class="p">,</span>
-    <span class="n">latitudes</span><span class="p">,</span>
-    <span class="n">cluster_labels</span><span class="p">,</span>
-    <span class="n">cmap</span><span class="o">=</span><span class="n">cmap</span><span class="p">,</span>
-    <span class="n">norm</span><span class="o">=</span><span class="n">norm</span><span class="p">,</span>
-    <span class="n">transform</span><span class="o">=</span><span class="n">ccrs</span><span class="o">.</span><span class="n">PlateCarree</span><span class="p">(),</span>
-<span class="p">)</span>
-
-<span class="c1"># Add geographic features for context</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">add_feature</span><span class="p">(</span><span class="n">cfeature</span><span class="o">.</span><span class="n">COASTLINE</span><span class="p">)</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">add_feature</span><span class="p">(</span><span class="n">cfeature</span><span class="o">.</span><span class="n">BORDERS</span><span class="p">,</span> <span class="n">linestyle</span><span class="o">=</span><span class="s2">":"</span><span class="p">)</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">add_feature</span><span class="p">(</span><span class="n">cfeature</span><span class="o">.</span><span class="n">STATES</span><span class="p">,</span> <span class="n">linestyle</span><span class="o">=</span><span class="s2">"--"</span><span class="p">)</span>
-
-<span class="c1"># Add gridlines</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">gridlines</span><span class="p">(</span><span class="n">draw_labels</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-
-<span class="c1"># Set the extent to zoom in to the specified region</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">set_extent</span><span class="p">([</span><span class="o">-</span><span class="mi">95</span><span class="p">,</span> <span class="o">-</span><span class="mi">85</span><span class="p">,</span> <span class="mi">27</span><span class="p">,</span> <span class="mi">33</span><span class="p">],</span> <span class="n">crs</span><span class="o">=</span><span class="n">ccrs</span><span class="o">.</span><span class="n">PlateCarree</span><span class="p">())</span>
-
-<span class="c1"># Add color bar with labels</span>
-<span class="n">cbar</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">colorbar</span><span class="p">(</span>
-    <span class="n">im</span><span class="p">,</span>
-    <span class="n">ax</span><span class="o">=</span><span class="n">ax</span><span class="p">,</span>
-    <span class="n">orientation</span><span class="o">=</span><span class="s2">"vertical"</span><span class="p">,</span>
-    <span class="n">pad</span><span class="o">=</span><span class="mf">0.02</span><span class="p">,</span>
-    <span class="n">fraction</span><span class="o">=</span><span class="mf">0.05</span><span class="p">,</span>
-    <span class="n">ticks</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">n_clusters</span><span class="p">),</span>
+<div class="highlight-ipynb hl-python"><pre><span></span><span class="n">extent</span> <span class="o">=</span> <span class="p">[</span><span class="o">-</span><span class="mi">95</span><span class="p">,</span> <span class="o">-</span><span class="mi">85</span><span class="p">,</span> <span class="mi">27</span><span class="p">,</span> <span class="mi">33</span><span class="p">]</span>
+<span class="n">data</span><span class="p">,</span> <span class="n">latitudes</span><span class="p">,</span> <span class="n">longitudes</span> <span class="o">=</span> <span class="n">apply_sam_spectral</span><span class="p">(</span>
+    <span class="n">dataset</span><span class="p">,</span>
+    <span class="n">spectral_library</span><span class="o">=</span><span class="n">spectral_library</span><span class="p">,</span>
+    <span class="n">extent</span><span class="o">=</span><span class="n">extent</span><span class="p">,</span>
 <span class="p">)</span>
-<span class="n">cbar</span><span class="o">.</span><span class="n">ax</span><span class="o">.</span><span class="n">set_yticklabels</span><span class="p">([</span><span class="sa">f</span><span class="s2">"Class </span><span class="si">{</span><span class="n">i</span><span class="o">+</span><span class="mi">1</span><span class="si">}</span><span class="s2">"</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">n_clusters</span><span class="p">)])</span>
-<span class="n">cbar</span><span class="o">.</span><span class="n">set_label</span><span class="p">(</span><span class="s2">"Water Types"</span><span class="p">,</span> <span class="n">rotation</span><span class="o">=</span><span class="mi">270</span><span class="p">,</span> <span class="n">labelpad</span><span class="o">=</span><span class="mi">20</span><span class="p">)</span>
-
-<span class="c1"># Add title</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s2">"Water Type Classification using K-means"</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">16</span><span class="p">)</span>
-
-<span class="c1"># Show the plot</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
 </pre></div>
-<div class="clipboard-copy-txt" id="cell-20"># Assume 'cluster_labels' contains the K-means classification results reshaped to the original spatial dimensions
-# Also assume that 'da' has the original latitude and longitude data
-latitudes = da.coords["latitude"].values
-longitudes = da.coords["longitude"].values
-
-# Create a custom discrete color map for K-means clusters
-cmap = mcolors.ListedColormap(
-    ["#377eb8", "#ff7f00", "#4daf4a", "#f781bf", "#a65628", "#984ea3"]
-)
-bounds = np.arange(-0.5, n_clusters, 1)
-norm = mcolors.BoundaryNorm(bounds, cmap.N)
-
-# Create a figure and axis with the correct map projection
-fig, ax = plt.subplots(
-    figsize=(6, 4), dpi=200, subplot_kw={"projection": ccrs.PlateCarree()}
-)
-
-# Plot the K-means classification results on the map
-im = ax.pcolormesh(
-    longitudes,
-    latitudes,
-    cluster_labels,
-    cmap=cmap,
-    norm=norm,
-    transform=ccrs.PlateCarree(),
-)
-
-# Add geographic features for context
-ax.add_feature(cfeature.COASTLINE)
-ax.add_feature(cfeature.BORDERS, linestyle=":")
-ax.add_feature(cfeature.STATES, linestyle="--")
-
-# Add gridlines
-ax.gridlines(draw_labels=True)
-
-# Set the extent to zoom in to the specified region
-ax.set_extent([-95, -85, 27, 33], crs=ccrs.PlateCarree())
-
-# Add color bar with labels
-cbar = plt.colorbar(
-    im,
-    ax=ax,
-    orientation="vertical",
-    pad=0.02,
-    fraction=0.05,
-    ticks=np.arange(n_clusters),
-)
-cbar.ax.set_yticklabels([f"Class {i+1}" for i in range(n_clusters)])
-cbar.set_label("Water Types", rotation=270, labelpad=20)
-
-# Add title
-ax.set_title("Water Type Classification using K-means", fontsize=16)
-
-# Show the plot
-plt.show()</div>
+<div class="clipboard-copy-txt" id="cell-16">extent = [-95, -85, 27, 33]
+data, latitudes, longitudes = apply_sam_spectral(
+    dataset,
+    spectral_library=spectral_library,
+    extent=extent,
+)</div>
 </div>
 </div>
 </div>
@@ -3347,20 +2889,15 @@ <h2 id="k-means-applied-to-the-whole-image">K-means applied to the whole image<a
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"/>
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"/>
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"/>
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-<h2 id="keans-applied-to-selected-pixels">Keans applied to selected pixels<a class="anchor-link" href="#keans-applied-to-selected-pixels">¶</a></h2>
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@@ -3370,10 +2907,10 @@ <h2 id="keans-applied-to-selected-pixels">Keans applied to selected pixels<a cla
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@@ -3383,137 +2920,33 @@ <h2 id="keans-applied-to-selected-pixels">Keans applied to selected pixels<a cla
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-<div class="highlight-ipynb hl-python"><pre><span></span><span class="c1"># Assume 'cluster_labels' contains the K-means classification results reshaped to the original spatial dimensions</span>
-<span class="c1"># Also assume that 'da' has the original latitude and longitude data</span>
-<span class="n">latitudes</span> <span class="o">=</span> <span class="n">da</span><span class="o">.</span><span class="n">coords</span><span class="p">[</span><span class="s2">"latitude"</span><span class="p">]</span><span class="o">.</span><span class="n">values</span>
-<span class="n">longitudes</span> <span class="o">=</span> <span class="n">da</span><span class="o">.</span><span class="n">coords</span><span class="p">[</span><span class="s2">"longitude"</span><span class="p">]</span><span class="o">.</span><span class="n">values</span>
-
-<span class="c1"># Filtering condition based on wavelength values</span>
+<div class="highlight-ipynb hl-python"><pre><span></span><span class="n">da</span> <span class="o">=</span> <span class="n">dataset</span><span class="p">[</span><span class="s2">"Rrs"</span><span class="p">]</span>
+<span class="n">extent</span> <span class="o">=</span> <span class="p">[</span><span class="o">-</span><span class="mi">95</span><span class="p">,</span> <span class="o">-</span><span class="mi">85</span><span class="p">,</span> <span class="mi">27</span><span class="p">,</span> <span class="mi">33</span><span class="p">]</span>
 <span class="n">filter_condition</span> <span class="o">=</span> <span class="p">(</span>
     <span class="p">(</span><span class="n">da</span><span class="o">.</span><span class="n">sel</span><span class="p">(</span><span class="n">wavelength</span><span class="o">=</span><span class="mi">650</span><span class="p">)</span> <span class="o">&gt;</span> <span class="n">da</span><span class="o">.</span><span class="n">sel</span><span class="p">(</span><span class="n">wavelength</span><span class="o">=</span><span class="mi">620</span><span class="p">))</span>
     <span class="o">&amp;</span> <span class="p">(</span><span class="n">da</span><span class="o">.</span><span class="n">sel</span><span class="p">(</span><span class="n">wavelength</span><span class="o">=</span><span class="mi">701</span><span class="p">)</span> <span class="o">&gt;</span> <span class="n">da</span><span class="o">.</span><span class="n">sel</span><span class="p">(</span><span class="n">wavelength</span><span class="o">=</span><span class="mi">681</span><span class="p">))</span>
     <span class="o">&amp;</span> <span class="p">(</span><span class="n">da</span><span class="o">.</span><span class="n">sel</span><span class="p">(</span><span class="n">wavelength</span><span class="o">=</span><span class="mi">701</span><span class="p">)</span> <span class="o">&gt;</span> <span class="n">da</span><span class="o">.</span><span class="n">sel</span><span class="p">(</span><span class="n">wavelength</span><span class="o">=</span><span class="mi">450</span><span class="p">))</span>
 <span class="p">)</span>
-
-<span class="c1"># Apply the filtering condition to the K-means classification results</span>
-<span class="n">filtered_cluster_labels</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">filter_condition</span><span class="p">,</span> <span class="n">cluster_labels</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span><span class="p">)</span>
-
-<span class="c1"># Create a custom discrete color map for K-means clusters</span>
-<span class="n">cmap</span> <span class="o">=</span> <span class="n">mcolors</span><span class="o">.</span><span class="n">ListedColormap</span><span class="p">(</span>
-    <span class="p">[</span><span class="s2">"#e41a1c"</span><span class="p">,</span> <span class="s2">"#377eb8"</span><span class="p">,</span> <span class="s2">"#4daf4a"</span><span class="p">,</span> <span class="s2">"#f781bf"</span><span class="p">,</span> <span class="s2">"#a65628"</span><span class="p">,</span> <span class="s2">"#984ea3"</span><span class="p">]</span>
-<span class="p">)</span>
-<span class="n">bounds</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="o">-</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">n_clusters</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
-<span class="n">norm</span> <span class="o">=</span> <span class="n">mcolors</span><span class="o">.</span><span class="n">BoundaryNorm</span><span class="p">(</span><span class="n">bounds</span><span class="p">,</span> <span class="n">cmap</span><span class="o">.</span><span class="n">N</span><span class="p">)</span>
-
-<span class="c1"># Create a figure and axis with the correct map projection</span>
-<span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span>
-    <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">6</span><span class="p">,</span> <span class="mi">4</span><span class="p">),</span> <span class="n">dpi</span><span class="o">=</span><span class="mi">200</span><span class="p">,</span> <span class="n">subplot_kw</span><span class="o">=</span><span class="p">{</span><span class="s2">"projection"</span><span class="p">:</span> <span class="n">ccrs</span><span class="o">.</span><span class="n">PlateCarree</span><span class="p">()}</span>
-<span class="p">)</span>
-
-<span class="c1"># Plot the filtered K-means classification results on the map</span>
-<span class="n">im</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">pcolormesh</span><span class="p">(</span>
-    <span class="n">longitudes</span><span class="p">,</span>
-    <span class="n">latitudes</span><span class="p">,</span>
-    <span class="n">filtered_cluster_labels</span><span class="p">,</span>
-    <span class="n">cmap</span><span class="o">=</span><span class="n">cmap</span><span class="p">,</span>
-    <span class="n">norm</span><span class="o">=</span><span class="n">norm</span><span class="p">,</span>
-    <span class="n">transform</span><span class="o">=</span><span class="n">ccrs</span><span class="o">.</span><span class="n">PlateCarree</span><span class="p">(),</span>
-<span class="p">)</span>
-
-<span class="c1"># Add geographic features for context</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">add_feature</span><span class="p">(</span><span class="n">cfeature</span><span class="o">.</span><span class="n">COASTLINE</span><span class="p">)</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">add_feature</span><span class="p">(</span><span class="n">cfeature</span><span class="o">.</span><span class="n">BORDERS</span><span class="p">,</span> <span class="n">linestyle</span><span class="o">=</span><span class="s2">":"</span><span class="p">)</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">add_feature</span><span class="p">(</span><span class="n">cfeature</span><span class="o">.</span><span class="n">STATES</span><span class="p">,</span> <span class="n">linestyle</span><span class="o">=</span><span class="s2">"--"</span><span class="p">)</span>
-
-<span class="c1"># Add gridlines</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">gridlines</span><span class="p">(</span><span class="n">draw_labels</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-
-<span class="c1"># Set the extent to zoom in to the specified region</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">set_extent</span><span class="p">([</span><span class="o">-</span><span class="mi">95</span><span class="p">,</span> <span class="o">-</span><span class="mi">85</span><span class="p">,</span> <span class="mi">27</span><span class="p">,</span> <span class="mi">33</span><span class="p">],</span> <span class="n">crs</span><span class="o">=</span><span class="n">ccrs</span><span class="o">.</span><span class="n">PlateCarree</span><span class="p">())</span>
-
-<span class="c1"># Add color bar with labels</span>
-<span class="n">cbar</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">colorbar</span><span class="p">(</span>
-    <span class="n">im</span><span class="p">,</span>
-    <span class="n">ax</span><span class="o">=</span><span class="n">ax</span><span class="p">,</span>
-    <span class="n">orientation</span><span class="o">=</span><span class="s2">"vertical"</span><span class="p">,</span>
-    <span class="n">pad</span><span class="o">=</span><span class="mf">0.02</span><span class="p">,</span>
-    <span class="n">fraction</span><span class="o">=</span><span class="mf">0.05</span><span class="p">,</span>
-    <span class="n">ticks</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">n_clusters</span><span class="p">),</span>
+<span class="n">data</span><span class="p">,</span> <span class="n">latitudes</span><span class="p">,</span> <span class="n">longitudes</span> <span class="o">=</span> <span class="n">apply_sam_spectral</span><span class="p">(</span>
+    <span class="n">da</span><span class="p">,</span>
+    <span class="n">spectral_library</span><span class="o">=</span><span class="n">spectral_library</span><span class="p">,</span>
+    <span class="n">filter_condition</span><span class="o">=</span><span class="n">filter_condition</span><span class="p">,</span>
+    <span class="n">extent</span><span class="o">=</span><span class="n">extent</span><span class="p">,</span>
 <span class="p">)</span>
-<span class="n">cbar</span><span class="o">.</span><span class="n">ax</span><span class="o">.</span><span class="n">set_yticklabels</span><span class="p">([</span><span class="sa">f</span><span class="s2">"Class </span><span class="si">{</span><span class="n">i</span><span class="o">+</span><span class="mi">1</span><span class="si">}</span><span class="s2">"</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">n_clusters</span><span class="p">)])</span>
-<span class="n">cbar</span><span class="o">.</span><span class="n">set_label</span><span class="p">(</span><span class="s2">"Water Types"</span><span class="p">,</span> <span class="n">rotation</span><span class="o">=</span><span class="mi">270</span><span class="p">,</span> <span class="n">labelpad</span><span class="o">=</span><span class="mi">20</span><span class="p">)</span>
-
-<span class="c1"># Add title</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s2">"Water Type Classification using K-means"</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">16</span><span class="p">)</span>
-
-<span class="c1"># Show the plot</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
 </pre></div>
-<div class="clipboard-copy-txt" id="cell-21"># Assume 'cluster_labels' contains the K-means classification results reshaped to the original spatial dimensions
-# Also assume that 'da' has the original latitude and longitude data
-latitudes = da.coords["latitude"].values
-longitudes = da.coords["longitude"].values
-
-# Filtering condition based on wavelength values
+<div class="clipboard-copy-txt" id="cell-17">da = dataset["Rrs"]
+extent = [-95, -85, 27, 33]
 filter_condition = (
     (da.sel(wavelength=650) &gt; da.sel(wavelength=620))
     &amp; (da.sel(wavelength=701) &gt; da.sel(wavelength=681))
     &amp; (da.sel(wavelength=701) &gt; da.sel(wavelength=450))
 )
-
-# Apply the filtering condition to the K-means classification results
-filtered_cluster_labels = np.where(filter_condition, cluster_labels, np.nan)
-
-# Create a custom discrete color map for K-means clusters
-cmap = mcolors.ListedColormap(
-    ["#e41a1c", "#377eb8", "#4daf4a", "#f781bf", "#a65628", "#984ea3"]
-)
-bounds = np.arange(-0.5, n_clusters, 1)
-norm = mcolors.BoundaryNorm(bounds, cmap.N)
-
-# Create a figure and axis with the correct map projection
-fig, ax = plt.subplots(
-    figsize=(6, 4), dpi=200, subplot_kw={"projection": ccrs.PlateCarree()}
-)
-
-# Plot the filtered K-means classification results on the map
-im = ax.pcolormesh(
-    longitudes,
-    latitudes,
-    filtered_cluster_labels,
-    cmap=cmap,
-    norm=norm,
-    transform=ccrs.PlateCarree(),
-)
-
-# Add geographic features for context
-ax.add_feature(cfeature.COASTLINE)
-ax.add_feature(cfeature.BORDERS, linestyle=":")
-ax.add_feature(cfeature.STATES, linestyle="--")
-
-# Add gridlines
-ax.gridlines(draw_labels=True)
-
-# Set the extent to zoom in to the specified region
-ax.set_extent([-95, -85, 27, 33], crs=ccrs.PlateCarree())
-
-# Add color bar with labels
-cbar = plt.colorbar(
-    im,
-    ax=ax,
-    orientation="vertical",
-    pad=0.02,
-    fraction=0.05,
-    ticks=np.arange(n_clusters),
-)
-cbar.ax.set_yticklabels([f"Class {i+1}" for i in range(n_clusters)])
-cbar.set_label("Water Types", rotation=270, labelpad=20)
-
-# Add title
-ax.set_title("Water Type Classification using K-means", fontsize=16)
-
-# Show the plot
-plt.show()</div>
+data, latitudes, longitudes = apply_sam_spectral(
+    da,
+    spectral_library=spectral_library,
+    filter_condition=filter_condition,
+    extent=extent,
+)</div>
 </div>
 </div>
 </div>
@@ -3525,1683 +2958,13 @@ <h2 id="keans-applied-to-selected-pixels">Keans applied to selected pixels<a cla
 <div class="jp-OutputArea-child">
 <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
 <div class="jp-RenderedImage jp-OutputArea-output" tabindex="0">
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vQC5Sk8D9PS0pR6j5XNXxrxdIr6+PEja9q0qVIxGBkZsQULFii8D2nHODs7m/36668K77NKlSrszZs3Cu+zIDRR6bV161ZmampaoHNh1apVcvNWRaVXamoq69Onj8IxNW7cmMXExCj+pjHGXr9+zdzc3BTeR7du3VhKSorWVHpFR0ez+vXrS8TZtWtXlp6erraYREk7dgkJCczX11eh99TY2JgdPXqUl+fTp08VuqkCgJUtW5Y9f/5c6biDg4NZzZo1lTrvzczM2Pbt2xXeR9++fQv0+QLAfv/9d6m/N2QR354xxt6+fctq166t8D5HjBih8LWbsp8d0b969eop/LoIIZpBg4UUMYFAwBuLC8h/MPuAgABkZ2dzj0W3F13OycmRmO5enPi+WrRoIbXLUWJiosw8zMzMYGtrK7ObxPnz59GyZUuJsWmKwpcvX9CqVSvs2rVLajchExMTlC5dWmq3nKNHj8LHxwffv39Xer9JSUlo06YNAgICeOuNjIxgZWWldH5FxcjICCNHjuSt27hxo9L5hIeH49q1a9xjAwMDjBgxQuHtJ0yYgE2bNvHWGRgYyOx6+vbtW/j6+kqMgSXPy5cv0bRpU/z3339Snzc3N5e6P8YY1q9fj+7du/M+h6ok3oXDysoK9evXV8u+VEXWxBl6enqwtraGtbW11LIlOzsbv/76KxYsWKDQfm7evIkePXrg27dvEs8JBAKUKlVKbnkkz8uXL+Hr64vIyEipz1taWsLOzg7GxsZK512cHDhwAC1btkRoaKjU5w0NDWFrawtDQ0Opz0sra4XkTbBiYWEBGxsbmfnu3LkTXbt25Y1nqa2eP3+OZs2ayZyRuVSpUlLP0czMTMyaNQvDhw8v8Ov85ZdfsHbtWt46IyMjmd91b968Qfv27bmhEXTBvn37MGLECKmvSV9fH9bW1nLPNXXLzs5G9+7dcfjwYd56Y2Njmd9zwcHB6Nmzp8Jdnt+8eQNvb288f/5c6vNWVlYS5+DJkyfRr18/rehW/fnzZ3h5eSEkJIS3vm/fvjh69KjGyuGMjAx07twZV65c4a23tLSUOgRCRkYGfvrpJ+51hIeHw8fHB2/fvuXSCAQCWFtbSy0Tvnz5gg4dOih1DX3ixAl4eXkhLCxM4jmBQAArKyuYm5tLPJeamophw4Zh6dKlCu1HVnku/C4uXbq0zG6TS5YskbjeVManT5/g5eWFZ8+e8dZbWlpKfW0AsHXrVsyZMyffvGNjY9GqVSuZnx0LCwvY2dnB1NRU+cAJIdpBo1VuJdSqVat4dwgsLCzk3omYOXMml1YgELC4uDjuueTkZF4rsBkzZsjMJzs7m5UqVYq377/++ktq2iVLljAgrzvZ0KFD2b59+9izZ89YZmYmL93Hjx/Z/v37mbe3t8Sdj+HDh8uMJTg4mG3cuJFt3LiRderUibddp06duOek/Z08eVJqnunp6czd3Z2Xl0AgYF27dmUnT57kvW+ZmZns9u3bbNCgQRJdUPr27SszbiHxO4GiLSQqV67MNm/ezOsukZKSwi5cuMDOnz+fb96qoGhLL8byjqHoOaSvr88+fvyo1P4mTpzI21/Pnj1lphVv2SLaYsjIyIhNnTqVPXv2jLsjmJaWxs6cOcPryiv8q1KlikKt82JjY1mFChV42xoYGLCBAweyixcv8lpIpqWlsUuXLrFu3bpJ7G/69OlKvS+Ksra25u3Hx8dHLfuRpSB3+YVdGhs2bMhmz57Nzp8/zyIjI3ld3bKystjjx4/ZwoULJbol6unp5dslIDc3l7m6uvK2s7KyYrNmzWIPHz6UuPP/7ds3duvWLbZ8+XLm5eXFBAKB3JZebdu25eVtbGzMfvvtNxYQECBxXiUnJ7N79+6xf/75h7Vv354ZGhqqpKWXaFm4ceNGiZZCgwYNkloO7tmzp0D7E3f58mWp3WB8fHzY3r17WVRUFC99VFQUO3PmDPPz8+OOqWjXLXGjRo1iAJizszPz8/NjR48eZeHh4RLfeW/evGFbt26VKMMByG0JdfXqVaXfO+Hf1atXpeYpvv/8JCcns2rVqkls5+HhwY4cOcKSkpIYY3nns3DYAPHvYgBs0aJF+e5L/BiLdmd0dHRka9asYW/fvuXSp6ens3PnzrHGjRtL7O/PP//Md38FVZQtvZKSkiRa0JctW5YtX76chYaGSly3REdHs2vXrrF58+Zx74u6W3qJHqcaNWqwbdu28bqWJSUlsUOHDkkdGmHr1q35vV0sKyuLNWzYUGLbFi1asBMnTrDk5GQubUREBFuxYgXvPRNvuVvULb0iIiJYlSpVJOIfOnSoUq2DVEH82IkPL3LkyBGWmJjIpY+IiGAzZ85khoaGvO2aN2/O0tPTudZXAoGA9evXj127do1lZGRw2z98+JD16tVL4rX/8ccfCsV7584d3jUcAFa6dGk2Y8YMdu/ePd75Hxsby/bt28fq1avHSy8QCNh///2X777atWvHADA3Nzc2ZcoUdvr0afbu3Tve935OTg57/vw5W7NmjdRycffu3Qq9LvHtRD9DrVu3ZqdOneJdu0VFRbGVK1dKDGliYGCQb8u5X375ReL6ZOjQoezq1au8Y81Y3jXiw4cP2datW1nPnj2ZmZkZtfQipBigSi8NePjwoURhHhQUJDN9ixYtuHS1a9eWeF70C7lZs2Yy85E2bpCs/Z46dYodPnxYqS5dW7Zs4f140tPTU6gLhbwxnpQxduxYXj42NjbswoUL+W534cIFZmlpydv28OHDcrcRvygS/vXp06fImuDLo0ylF2NM4oJr1qxZCu8rLS1NYoyKS5cuyUwvqzuXra2t3HEfcnNz2dy5cyW2++233/KNUbxitVKlSiw4ODjf7Xbt2sW7mBQIBOzevXv5bqeMuLg4ideUX5c8VStIpdeiRYvYs2fPFN5HYmIi69KlC28/+XVZunv3rsSFfHh4uML7fP36Nbty5YrU5z5//szrAm1kZMQCAgIUzvvz58/s9OnTctMU5Id/YSoLlN328+fPzM7OjreNmZlZvuWfUGZmJvP392ehoaEy0+zcuVOhclgoJyeHzZs3jxeTpaWlxA8PaQpb0SIk/nnMj7BiT/Rv6tSpcn+sf/jwQeIHoYGBQb7dmmV15+zYsSNXuSZNRkaGRCWvvb29RIWQqhRlpdeBAwd4aStXrsyio6MV3ldISEi+5XphK72Ef8OHD2dZWVkyt/v27ZtEhUT9+vXzfQ3Lli2T2NfMmTPljrcXHR0tsS9lvgMKSvy9/Omnn1jFihUlYvDz8yvwGKOFIevYTZkyRW48+/btk9ima9euDAAzMTFhx44dk7vfESNG8LYtU6ZMvp/PuLg4iRt6vr6+7MuXL3K3y8rKkrhmtre3z/cm4tq1a1lgYKDcNKLS09MlKpQqV66sUEWmtGNgYGDA/v33X7nbPX78WOKaXt51YkZGhsRNiCNHjij8GuPj4xX+ziSEaA5VemlATk6OxJ0IWS2u0tLSeIPIjx07ViLN5MmTuecNDQ1lfmmtXLlS4oeEqscpEv+xMmXKlHy3UUWl18uXL3kttoyMjJSqnDh69CgvhvwmBZB2UeTh4aHWcZ+UoWyl19WrV3npy5cvL/fCXNTOnTt521arVk3uhaG0Si+BQMBu3bql0P6GDh3K29bY2FjuuCeXLl3ipbe1tVVqMPUVK1bwtu/Vq5fC2yri6dOnEu+HrPHQ1KWoxnPJyMiQGPReXsWZ+NiC8lqyKuu///7j5d2vXz+V5S2k7ZVe4uNA6evra82AvOKf83Xr1uW7jSYqvT59+iTRymLgwIEK7ef9+/cSP7Z69+4tdxtplV4NGjRQ6GbLp0+fJCalkXeDojCKstJr2rRpvLSbNm0qXPBSqKLSq3PnzgpV4gQHB0ts++rVK5npMzMzJcbm/OmnnxR6XV++fJE6sHpRVnpJ+5s2bZra9p8facdOkR4AjDGZ437lV1HDWN6NIfHfBvl9PkV7ggB5raHyGwdRKDc3l6uUE/79888/Cm2rLPGW+mfOnMl3G2nv4/LlyxXa319//cXbztHRUWbasLAwXtqmTZsq/LoIIcUHjemlAXp6egqP63Xv3j1kZGRwj8W3E1+XlZWFwMBAqXmJjxvUokULlU+ZPmHCBN4YBaLjPKnT33//zRsPZdKkSWjSpInC2/fs2RNeXl7c4/v37+Phw4dKxbB+/Xq1TUGvbj4+PrwpzD9//oyTJ08qtK34GGCjR4+WOo6MPIMGDUKLFi0USvvXX3/xxknLyMjA9u3bZaZfvnw57/GiRYtQpUoVhWP79ddfUblyZe7x8ePHERMTo/D2+UlOTpZYJ2uMl+LOyMgIEyZM4K2TV0aIj+Pl4uKisljUmXdxEBcXh61bt/LWTZ8+nVcOatLUqVN5j4vqu0RZmzZt4o31Z21tjdWrVyu0bcWKFTF//nzeuuPHj+Pjx49KxbB+/XqFxjtydHREp06deOsePHig1L60UXH4LBsYGGD9+vUKfTc2atQI7u7uvHXyjtPJkyfx9etX7rGZmRlWrVqlUFwODg6YN2+eQmmLSvXq1bFw4UJNh8ExMjJS+DP9008/SayrX7++QmNZlSpVCl27duWtk3fck5OTsWHDBu6xvr4+tm7dChMTE4ViFQgEWLFiBe+69d9//1VoW2Wpojx3dXXFlClTFEo7bNgwGBgYcI8jIyN5nxFRxaH8IIQUHlV6aYj4D4vbt29LHcRWvDKsVatWEmlatmzJu5ASr9wCAMYYbt26JTcGVbCysoKrqyv3+PHjx2ofLDc3NxeHDh3iHuvp6WHcuHFK59OvXz/e4/wmGBDl7u6Ohg0bKr1PbeLn58d7rMiA9o8fP+YN3GxiYoIhQ4YUet/y2NraShyrU6dOSU0bGxuLy5cvc4+trKwwePBgpWIzMDBAnz59uMe5ubm4ffu2UnnIk5mZKbFO1qCsuqBp06a8x7IG/gYkK//u37+vsjjUmXdx8N9///EGSjY1NcXkyZM1GBGfm5sbSpUqxT2Wd55okvjEGAMGDICtra3C2w8fPpz3ec/JycHFixcV3r5evXrw9PRUOH3z5s15j1+8eKHwttqqOHyWO3XqhIoVKyqcXpnjdOHCBd7jHj16oEyZMgrva9CgQVo1QHd4eDj69++vtoljlNW5c2eULVtWobSNGzeWWKfMpD7i28saWB3IO+6iFTa+vr6oWbOmwvsCgKpVq6JRo0bc42fPniE+Pl6pPBShzPe+LKNGjVL4hqqNjQ3c3Nx462R9hsTLj4cPHxaLyVMIIcoxyD8JUQfxCqeEhAQ8efJEYsY20QqsKlWqoHz58hJ52djYoGbNmtzMW9Iqa0JDQxEXFyc3hvxkZ2cjPDwcERER+P79O5KSkpCTkyORTrSSKzs7G1++fFHrnZOQkBDebJN16tSBo6Oj0vmI31kNDAzExIkTFdrWx8dH6f1pm59//hkzZszgZq+8evUqXr16hWrVqsncRrxi7Mcff4SNjY1S+y1XrpzUC0V5unfvjs2bN3OPHz58iKysLImZuW7evMmblcrb21vhu6CipJ0bPXr0UDofaaTN3pSSkqKSvItaYmIiQkNDERMTg+/fvyM1NVViVjDxckheqxbx1prbt29H/fr1MXr0aKkzQyqjcePGEAgEXHyXLl3CrFmzMGvWrALNBFncXL9+nfe4Q4cOSn92CyojIwMvXrzAx48fkZSUhOTkZKnfJaJ36j9//ozc3NxCH3dVSklJwZMnT3jrevXqpVQeFhYWaN++PY4ePcqtCwgIwPDhwxXaXtnvcdFWq4D82TWLC/FyYtGiRahevTp69+6toYgkqfM4iVcgdOjQQal9lSpVCs2aNZOYnbCo9OjRA9evX+dV4Bw9ehQDBw7E3r17Nd6CXtrNZlmkVWxK66Gh6Pbyjrv4tb6yx13I3d0d9+7dA5B3g/zu3bvo2LGjwtunpqYiNDQUUVFRSEpKQkpKSr6VRsq2ZgUK9hkSneFb1ntZo0YNWFlZcb8jXr58iZEjR2L16tWwtLRUOk5CiHaiSi8NcXd3h6WlJZKSkrh1N27c4FV6iXdVlPfFKzrdvLBLpGh3B/HWX+bm5ry7O7KkpqZi79692L9/P+7cuSO1VUp+1H1RLX7Bp6enh02bNimdT3R0NO9xVFSUwtvWqVNH6f1pGwsLCwwePBj//PMPgLyLn02bNmHFihVS0yclJWHv3r28dWPGjFF6v+IVSgXZJi0tDeHh4bwumoDkuZGSklKgc0P8bqsy50Z+LCwsJNaJVuJqu/DwcOzYsQNHjhzB69evld5eXvnQqFEjNGzYkOvikZOTAz8/P/z111/o168fOnToAA8PjwJNZV+mTBn07NmTV9mwcOFC/Pvvv+jXrx86duyI5s2bSz0+ukC8+3azZs3Uur/4+Hjs3LkTBw8exIMHD5RuxcEYQ2JiIkqXLq2mCJUXFhbGex0CgaBALX4bNWrEOw/FK9LkcXZ2Vmpf4j/ihDc5irPOnTvD0dERkZGRAPKuW/r06YOaNWuib9++aN++Pdzd3XmVqEVNncfp5cuXvMcNGjRQal/CbTRV6VW3bl3MnDkTvr6+vO++gwcPwsDAALt27dJoZXelSpUUTiutlbYy24t/34j+RhAnfn3z6tWrAl3fvHv3jvdYkeubyMhI7NixA4cOHUJoaKjSLaMK8rtAXZ8hfX19jBgxgnetu337dhw9ehR9+vRB586d0apVK6367iGEKI8qvTREX18fzZs3x/nz57l1N2/e5I13c//+fV73E3l3i1q2bMl92aWnpyMoKIiXXvyOUPPmzfO9ADx9+jT8/PwKdEdGlLwvbVUQ76f/6NGjAlW+iFOmibcy3Vm02dixY7Fu3Tqu9Yu/vz8WLVoktXXU3r17eeNR1a9fX6IJuyKUuSAUsre3h6mpKa9VoXgLIkDy3Lh8+TKvu2NBqbL5f7ly5dSav7pkZmZixowZ+OeffwrVDSW/8mHXrl1o0aIFrxVAREQEli5diqVLl8LY2BgNGjRAixYt4OXlBR8fH4W7h65btw7379/H+/fvuXXR0dFYu3Yt1q5dCwMDA9SpUwfNmzeHl5cXfvjhhyJrDaVu4uPSKTPOnbK2b9+OqVOnFvq8TkpK0qofHuKvp3Tp0rzxBhUl3hJamfdJ2fH/xFvNSGthV9yYmJhg9+7d6NChA28M1LCwMMyZMwdz5szhbvS1aNEC3t7eaNWqVZG26FTXcUpOTkZWVhZvnbTvlPwo2n1PXRo2bIgLFy6gbdu2vMqJvXv3wtDQENu3b8+3a9u1a9ckKgDl+emnnxRqyaPMZ1paqzTRbtrKbi/v8yl+fSM6vldhyCt/GGNYunQpFi5cyPt9oqyC/C5QZ1k3b948XLt2jXczKDExEVu3bsXWrVshEAjg5uaGZs2awcvLC76+vgX6nBFCNIcqvTTIy8tLotKLMcZ9sYu3zsqvpZeoGzdu8NaJ55VfM+GdO3di2LBhKunXru6+8eqqIFDmC11XWoO4urrC19eXqxiKj4/HoUOHMGjQIIm04ncUR48eXaB9KnNBKMrKyopX6SU+GCmgHedGfmxtbWFtbc2786lMSw9NyMzMRPfu3XHu3LlC55Vf+VCzZk0EBwdj5MiRUge/zcjIwN27d3H37l38/fffMDMzQ7du3TBt2jSJ7uLiypYti6CgIIwdO5bX0kYoOzsbjx49wqNHj7Bu3ToYGhqiffv2mDx5stYM+F5Q4pXE6po8YcGCBZg9e7ZK8tK2cVbEy5zClGWilCm3lJ00RFf5+Pjgzp07GDlyJB49eiTxfEpKCm7cuIEbN25g0aJFsLa2xo8//ohp06aptcJXSF3HSVqr4IJ0ySrouatKHh4eOHfuHNq1a8e7oebv7w9DQ0Ns3rxZ7vu4c+dO7Ny5U+H9tW/fXqH3qrDHTl3HXhPXNyNHjsS2bdsKvQ/xYQ8Uoc6yztzcHDdv3sSkSZOwbds2iQoyxhjCwsIQFhaGrVu3Qk9PD15eXpgwYQK6deumtrgIIaqjPYNjlEDiP5piY2MRFhbGPRZtnVW+fHm5F2YVKlTgtZgR3TY8PBxfvnyRu29Rr1+/xqhRo3g/MPT19dG5c2esXbsWN2/exNu3b/H9+3dkZGSAMcb7K+ofgwXpcqmIgnwp6wLxSQCkDWgfGBiIx48fc48tLS2lzlqkacXl3BCvnHnw4IFWn39Lly6VqPCys7PDuHHjcODAATx48ABfvnxBcnIysrOzeeWDeFcKRVSpUgVXr17FnTt3MGrUKLkDQqempmL//v1wd3fH+PHj8z0H7O3tceTIETx58gQTJ06UO4ZdVlYWTp8+DW9vb/z444860TVMnW7evClR4WVsbIx+/fph8+bNCAwMxIcPH/D9+3dkZmZKfJcUpBUoKbmEXaHPnz+PgQMHwsHBQWbahIQE/Pvvv3B1dcXChQu1uryVR1rX7oJ876nru1JZzZo1w3///SfRWnfLli1KTXhTEhT19c2uXbskKrwsLCwwbNgw7Ny5E0FBQYiMjERSUhKysrIkynNtZ25ujs2bNyM8PByzZs1C3bp1ZVa05ebm4tq1a+jevTt8fHxUOuQFIUQ9qKWXBjVq1AhmZma8uyo3b95ErVq1kJubizt37nDrFRkIs1WrVti9ezeAvEqJ7OxsGBgYSLTyMjMzkztw+Pz583ldBCpWrIjTp0+jbt26Cr0u0Tt0RUG8u9GPP/6IgwcPFmkMuqRz586oVKkS1+Xr7t27ePz4MerVq8elEW/l9fPPPxe4tVtBKw7E73BL6/Ykfm5MmzYNy5YtK9D+1KlVq1a8gcUTExMREhJSoLFZ1C05ORlLly7lrevfvz+2bt0KMzMzhbYvqGbNmnFjT0VERODWrVu4ffs2bt26JTHuGmMM69atw7dv37Bnz558865Tpw5WrlyJlStXIioqisv79u3bCAkJkbhoP3z4MKKjo3HlyhWND7RcELa2ttwYSIB6xl78888/eY/r1auHkydPKlyZVdTfJcoSL3NUVZbpShfawihoqz6BQIB27dqhXbt2APJmbBMtJ8Qr3bOzszFr1iykp6dj4cKFhY67qElroZmYmKhQWSy+jbZo2bIlTp8+jU6dOvFac2/cuBGGhoZYs2aNBqPTHjY2Nrwb2v/991+BB7PPD2MMs2bN4q3z9fXFwYMHFRreQ9vLclGVK1fG/PnzMX/+fMTHx+PWrVu4c+cObt++jeDgYInhHK5fvw5fX1/cvXtXK1pMEkKko5ZeGmRoaCgxeLCwhVZISAjvAlqR2WNEK8ZSUlK4abvFK708PT1ljmWRnZ2N06dP89bt3LlT4QovIK/FWlESn5pb2thORHH6+voSXRVFW3sJuzyKKmjXRgC88ZQUFR0dzbsYBqSPq1Zczo0uXbpIrNu+fbsGIsnf+fPnee99tWrV4O/vr/CPLFWVD87Ozvj555+xefNmhIWFISIiAosWLYKdnR0v3d69e5Uex61cuXL48ccfsXbtWjx8+BBRUVFYu3atRGXNjRs3sGPHjkK/Fk0Q/2wUZBICeaKjo3k3bvT19XH06FGFK7yys7O1fmZB8TLn27dvBao8EK+I0YVKr8KOHaaqY+/q6oqRI0di586dePv2LcLCwjB9+nSJlkRLly5VajwobWFgYCBxHr548ULpfAqyjTr5+Pjg5MmTEuOJrl27FpMnT5a6jb+/v0TrInl/yg6Mrm2K8vrmwYMH+PDhA/fY2toahw8fVng826L+XaAqNjY26NatG5YvX46AgADExMRgx44dqF27Ni/d8+fPsXz5cg1FSQhRBFV6aZh4V0BhBZX4wPPKVnqJ5iGel7zuhx8+fOBdbDo5OcHb2zvffQtFR0cXqBKjMMS7hj1+/LhYNKXWZiNGjOB1m9i7dy838Ki/vz/S09O555o3b16o2SvFZ5EryDampqaoXr26RDrxc0PaWC/aoFGjRhIXUXv27FH7JBAFIT7eWN++fZUaEDo4OFjVIQHImxDhjz/+wLNnzyQGBhefZVRZDg4OGD9+PEJDQ9GkSROV5q0p4rMMBgQEqDT/Z8+e8VrreHp6KjV20qNHj7R+kHU3NzfehDCMsQKVZ8IbVELK3GTSVuItf5Vt6aGu6wg3NzcsXboUDx8+5LWSysnJwYEDB9SyT3UTn4lbfFY/Rdy7d09V4ahMmzZtcPz4cYkunCtXrsTvv/+uoai0R1Fe34h/73fu3FmpSUXU9b1f1KytrTFkyBA8evQIXbt25T1XXK8FCCkpqNJLw8QroKKiovDq1Ste6ywbGxvUqlUr37xcXV15d35u3ryJ9+/f8+7OSNunqOjoaN7jChUq5LtfUWfOnFEqPQCJWSSV/aHTvHlzmJqaco9jY2O18gKuOLGzs0Pfvn25x8nJydizZw8YY9i8eTMvbWFnyoyKilL6gujEiRO8x+7u7jA0NJRI16ZNG97jkJAQfP78WekYi8KkSZN4jxMSEjBlyhSV5a+qQcALW0aItyRVNQcHB0ybNo23TlUTA5ibm2PevHlqybuoid/MOH/+vEpbCmjiPCnsd4myzM3NJSqojh8/rlQeKSkpuHDhAm+deAvw4ki8252yY/mJ36xTterVq2PUqFG8dcX1syx+vihbeffgwQOEh4erMiSVad++PY4cOSJxY2Xp0qUqmyCjuBK/vjl37pzabvhq+/d+UTMwMJAYKiMiIkIrb1QSQvJQpZeGNWnSRKL59vXr13Hr1i3ucYsWLRSetaRFixbc8u3bt3H16lXe8yYmJvDw8JC5vfiFhTJdDHJycrBq1SqF0wuJz56jbPcQY2Njie5hS5YsUToOwic+oP2mTZtw9epV3sWxnZ0devfuXeh9KTPVdlxcnMRFvbTugUDehZloy5zc3FytHNMLAAYNGiTR2uvff/9VycViUlISBgwYUOh8gMKVEffu3eOVbeoi3tIrJSWlWORdlDp27Mjr4pWWloaVK1eqLP/CnCdJSUkS4wYqorDfJQXRqVMn3uO9e/dKnUlWlu3bt/NaQenr63PjURVnrq6uvMeiXV3z8+nTJ/z333+qDkmCrnyW+/fvz7tGfPLkicSNIXnmz5+vhqhUp3Pnzjh48KBEpfaCBQuwYMECDUWleR06dOANK/D8+XOljrsyClOef/z4USfH2RUvP4DiW4YQUhJQpZeGGRsbo2nTprx1Gzdu5N1xV6Rro5BoF8fv37/jn3/+4T3ftGlTqbP9CDk6OvIev3jxAm/evFFo3/PmzcOzZ88UjlWobNmyvMfiA1IrYtasWbyLvlOnThXoR5MQdY8EGjduzJvw4MmTJxIVYUOHDpV7Pilq586dCv8omjp1Ku/HrJGREYYNGyYzvfjd4PXr1+Ps2bMFCxTqOzf09fWxY8cOiRZrvXv3xtGjRwuc74MHD9CwYUOcP3++sCECyOvyLErR1p0pKSkYMmSIUvsSHzBWUeJliHgZo+68iwMbGxv88ssvvHXLli2TGAOyoMTPk9u3bytcCeXn54eYmBil96mK7xJljR49mveZjY+PV7iF5qdPnyTKp549e0q8d8WReDfg+/fv82b8lWfs2LHIyspSeF8l/bNcrVo1tG7dmrfOz88PERER+W7777//4tSpU2qKTHW6d++O/fv3S4wVN3v2bImJVUoKOzs7jB07lrduzJgxheoaLOv6RrxMunDhgkKfu5ycHAwaNEhrZgeVRlXlh76+vsQ4a4QQ7UGVXlpAvLuheL98ZSq9xNOK5yWvayOQ1zXIzc2Ne8wYw8iRI3mzOYpjjGHx4sUFvuMmPkPdjRs3lK48q127NkaMGMFbN27cOMybN0+pLi7CikJ3d3el9q+rxCu5RAe7FQgEEt1DCooxhu7du+f7o2j+/PkSA4ePHj1a7oVGp06d0LZtW+5xTk4OevfurXSlaExMDBYtWoT27dsrtZ0yGjVqhPXr1/PWZWZmom/fvkpXBHz8+BFjxoyBh4cHXr16pbIYxbvF3bp1C//++6/cbWJjY9GmTRulB0seNGgQRo8ejbCwMIW3effunURrPh8fH4l0v//+OwYMGKBUV+jY2FjMnDkz37yLi2nTpvE+Ozk5OejQoQOOHTum0PbZ2dnYtWuX1OPj7u7Om8kqKSkJfn5+crvZZmdnY+zYsdwsxMoS/y45duyY2rszly9fXqLSffv27Zg9e7bcCvLIyEi0adOG12LCwMBAZ8YqqlKlisSYQyNGjJDb/Sc7OxujRo1SunVrmzZtMG3aNKV+7N+/fx9btmzhrSvOn+W///6bV/n6+fNneHl54cqVK1LTZ2ZmYt68edzwBOI9DrRR7969sXv3bomKr99//12lrVSLk+nTp6N8+fLc469fv6Jly5ZKt6gOCwvDmDFjMGPGDKnPe3l58W4sv337Nt/upSkpKejVqxdvZmpttGHDBnTu3BmXL19W+KZmamoqJk6cyFvXsmXLYjmTMyElBiMad/XqVQZA6p+FhQXLzs5WOK/s7GxmaWkpM7+rV6/mm8eaNWsktqtTpw47fPgwS0xM5NLFx8ezAwcOsMaNG3Pp7OzsWM2aNXnbXrt2Ld99urq68rYxMzNjAwYMYEuWLGHr169nGzdu5P5OnjwpNY/09HTm6ekpEbuzszNbunQpu3//PsvMzORt8+3bN3br1i22du1a1r59e2ZkZMQAMHNzc7nxenl5Kf0ai1KlSpV48c2ZM6dA+aSnpzM7Ozup51K7du0KlOecOXN4+TRp0oRbNjIyYtOmTWOhoaEsNzeXMcZYWloaO3v2LPPx8ZGIwcXFhSUnJ+e7z7i4OFa1alWJ7WvXrs3++ecf9vTpU4nPWXR0NLty5Qpbvnw5a9WqFdPX12cAWK1atQr0upXx119/SX3PS5UqxYYOHcoOHTrEPn36xDufMzMz2fPnz9m2bdtYly5dmIGBAW9bKysrmfvbsWMHL+3gwYNlps3NzWXu7u4SsQ0dOpTdv3+fex9zc3PZ8+fP2cKFC1np0qW5dOKfnUqVKsncV7du3XjHatasWezs2bMsMjKSOz+Er/3x48ds7ty5zNraWqIM/fDhg0TeEyZM4NJUrlyZTZkyhR0/fpxFRESwnJwcLl12djZ78eIF+/vvv1n58uV5eevr67Pg4GCZ8Q8ePJiXfseOHTLTFmabwmx7+fJl7twW/fvhhx/Yvn372JcvX3jpv3z5ws6ePcvGjRvHypUrJ7f8mzhxokS+LVu2ZOfOnWOpqalcuqioKLZt2zZWo0YNLl316tVZ2bJledu+e/dO7mtJTU2VOP6lS5dmw4YNY8uXL2cbNmzgfZfI+j4Ujzk/ycnJrFq1ahLbNW/enB0/fpylpKRwad++fcuWLFnCrKysJNIvXrw4330V5vxgjLFr167xtvfy8lJqe2Vs27ZN4jXWrFmTHT16lPeeREVFse3btzM3NzcuXbNmzRSOs169egwAEwgErEmTJmzRokXs0qVLLCYmhpcuLS2N3bt3j02aNImZmpry8nd0dGRJSUky9yH+nZrfuVjYawRlymShxYsXS/3ecHd3Z9OmTWOrV69mS5YsYUOHDmVlypThlZGzZs3ibTNkyBCl4lVGYa9Pdu3axfT09CRe59q1a1USX2GPnbLlh6iCfD7v3bvHTExMeNsJBALWvn17tm/fPvb+/Xve92VOTg6LiIhgp06dYr///jvvmt3Pz0/mfnr06CHx2rp27cpu3rzJuxaJiIhga9asYY6OjjK/9xV5XwrzPjKmeFm5atUqLk25cuXY2LFj2cGDB1l4eDjLysri0uXm5rJ3796xTZs2serVq0vEd/z4caVjJIQUHar00gKpqalcZYv4X5s2bZTOr23btlLzMjY2Zmlpaflun56eLvVHrfCL1Nrampmbm0s8Z2hoyC5cuFCgC4bdu3dL3Z+0P3kXATExMVK/XEX/LC0tmY2NjUSlgOgfVXr9z4wZM6S+RwX9ghev9JozZw4bN26c1PNJtLJE/M/W1paFhIQovN83b96wOnXqyMxPIBAwKysrZm1tLbUSQPhXFJVejDF26NAhuRXYwj9ra+t8YwbAvL29Ze5L2R9Yd+7ckVlmGRoaMltbW6mfrzp16rDHjx/z1ila6SX+p6enx0qXLs1Kly4t87Xr6emx3bt3S81btNJL1rlga2vLDA0NZabLr5KiOFR6McbYvn37mLGxsczXaWRkxGxtbWUec1nlX2xsrERZJHpsbGxsJH6sAXmVuyEhIUpXNDDG2IIFC/L9zOR3nounU0RYWBhzcnKSuS8rKyu57/GwYcN4la2yFKdKr9zcXObt7S3zM1a6dGmJyicArG3btuzSpUsKxyms9JL2Z2BgwGxtbZm1tbXUihIAzMTEhF25ckXuaykOlV6MMTZ16lSFz38g77rw7NmzbPv27bz1o0ePVipeZaji+mT79u1MIBBIvJ4NGzYUOr7iVunFGGMXLlyQe72kr6/PbGxsmKWlpdT3Tfgnr9Lr1atXEjcVxD9n0r4jHB0d2cePH5V+XwrzPjJWsEovaX+WlpbMzs5O5vcfAPbLL78oHR8hpGhR90YtYGpqKjH+hZAyXRuFRMf1EiVt0HxpjI2Ncfr0aanTpjPGkJCQIDFYY6lSpXD8+HFeNzJlDBw4EAsXLpQYqFRZdnZ2uHz5MmbMmCFzrKmkpCTEx8fL7ccv3k2mJBszZoxEk20nJyeZg8cXxOrVqzF69GjeuqysLJkDQjs7O+Py5cuoV6+ewvuoXLky7t69i+HDh0ttgs4YQ2JiIhISEmR2iRUIBErtszD69OmDsLAw9O/fH3p6sovqhIQEuTG7urri8OHDuHbtmspia9asGfbs2cObNVUoKysLcXFxEp+vZs2a4fLly7wub/mRN4FHbm4uvn37hm/fvkl97XZ2djhy5AgGDhyodN7CcyEuLk7q2ELm5ubYsGGDznRF69+/P65fv47q1atLfT4zMxNxcXEyx2WRdX7a2tri7NmzqFSpksRzubm5iI+PR3p6Om99uXLlcOnSpQJ/zv744w+JcW6KgpubGwIDAyXG6BRKTEyUOkyAkZER5s+fj23btsn9nBdHAoEAR44ckXp9wxjDt2/fkJaWxlvfs2dPHD9+XKlrAXmf5ezsbMTFxSEhIUFq19qKFSvi0qVL+OGHHxTenzZbvnw5/P39FRpbqHLlyrhy5Qo6duyI79+/854Tn31T2wwdOhSbN2+WOPZ+fn7YunWrhqLSnLZt2+LBgwcyfzPk5OQgPj4eSUlJMrvwmZiYSExAIapq1ao4efIkbG1tJZ4Tfs7EvyNcXV1x/fp1rR6nML+JwpKSkhAbGyv1+8/Q0BCzZs0q1BjChJCioVtXWMWYrLG2VFnpld94XqLKly+Pu3fvYs6cOVK/4ITMzc3xyy+/4Pnz5xKzWClr5syZePnyJebPn4927dqhYsWKsLCwUPqHgIGBAZYsWYJ3795h6tSpqFKlSr7b6Ovro2nTppg1axaePXtWJDPMFRcVK1bkjfMGACNHjlTp2AX6+vrYuHEjTp8+jYYNG8pMZ29vj99//x2hoaES48UowszMDFu3bsXLly8xevRoiYkbpDE2Noa3tzeWLFmCt2/fYu/evUrvt6CcnJywb98+vHz5Er///jtq1Kih0HYVKlTAyJEjcfv2bTx//lwlM2yK69OnD+7fv48ePXrI/YxWrVoV69evx82bN2Fvb6/UPvbu3YsTJ05g5MiRqFGjhkKz2FarVg1z587Fq1ev0KNHD5npli1bhgsXLuDXX39FvXr1FDqfHR0dMXnyZISHh3Nj4eiKpk2bIiwsDNu2bYOHh0e+5W7ZsmUxdOhQ3L59W+73VK1atfDw4UNMmDCBN1ukOBsbG0ydOhVhYWEybwIpQk9PD+vXr8eTJ08wY8YM+Pj4oHz58jA3N1d4FuSCcnJyQmBgIA4dOgRPT0+572Hp0qUxdOhQvHjxArNmzVJrXJpka2uL69evY/HixXKvJWrUqIHdu3fj6NGjvBnpFHHt2jXs27cPP//8M5ydnRXapl69elixYgVevnzJm/VaFwwePBjh4eHYvHkz2rdvD2dnZ5iYmMDExAQuLi7o2bMndu/ejefPn6N58+YAgOjoaF4epUuX1kToShk5ciTWrVvHW8cYwy+//AJ/f3/NBKVBLi4uuHHjBq5fv47u3bsrdIOpdOnS6NGjB7Zs2YIvX75IjOMqrlWrVnj06BGGDBkiMemOqPLly2PRokV49OgRqlatqvRrKUrjxo3DrVu3MH36dDRp0kTu6xKytbXFqFGj8OzZM8yfP1/t3y2EkMITMFlV/oT8v6ysLNy/fx9Pnz5FfHw8GGOwtbWFm5sbmjRpopLZ+9Tt48ePePjwIWJiYhAXF4fc3FxYWlrCzs4O1atXh6urq9IX2iXFixcveJVeBgYGeP/+PW/wVGXMnTsX8+bN4x7PmTMHc+fO5aV5+/Yt7t27h0+fPiE7OxsODg6oVq0amjVrpvKBQl+9eoUnT54gLi4OcXFx0NPTg6WlJezt7VGjRg1Ur15dq87xmJgYPH78GBEREfj27RvS09NhYWGB0qVLw8HBAe7u7nBwcCjSmOLj43Hr1i28f/8e379/h4mJCRwdHdGgQQO5d46V9e3bNzx//hzv3r1DTEwMUlJSYGBgAEtLS1SsWBF169ZFxYoVC5R3cnIynj9/jjdv3uDr169ISUmBQCBAqVKlUK5cOdStWxdVqlQpMRe33759w927dxEVFYXY2Fjk5OTA0tISTk5OqFmzJqpVq6b0e5Geno7AwEC8ePEC3759g56eHuzt7VGrVi00atRI5wYBjo+PR0BAAL58+YLY2FgYGxujTJkyqFq1Kho3bqxzrzc/OTk5CA4OxrNnzxAXFwfGGMqWLYtGjRqhdu3aKttPdHQ0Xrx4gXfv3iEuLg6pqakwNjZGqVKl4OzsjHr16hXbmRrVpW3btrh06RL3+NSpUyptzU2KXk5ODh49eoTXr19zrR2NjY25ctzV1RUuLi4FbmGalJSEO3fu4M2bN0hISIChoSHKli2LunXrol69esX2uzI9PZ27Fvjy5QvXOs7S0hIODg6oXbs2atSoUeLKb0KKO6r0IoTI9dtvv2HNmjXc4549e+Lo0aMFzk+RSi9CCCGEqF98fDycnJx4XU2/fPlS5DdPCCGEEHWh7o2EEJmSkpIkugn4+flpJhhCCCGEqNSKFSt4FV61a9emCi9CCCE6hSq9CCEyrVq1ComJidzj2rVr68yAv4QQQoiuyMrKkjlIuSxnz57F0qVLeet0bbxCQgghhCq9CCFSBQQEYMmSJbx1M2bM0FA0hBBCCJElNDQUtWrVwqZNmxATEyM3bXx8PP78809069aNN6tlhQoVMGjQIHWHSgghhBQpxeeEJoTorJcvX+LatWsA8gawfvToEY4ePcq7GK5Xrx769++vqRAJIYQQIsfz588xZswY+Pn5oWHDhqhfvz4qVaoEKysrZGRkIDY2Fvfv38edO3d4XRqBvJlPd+/eDQsLCw1FTwghhKgHVXoRQhAYGCi3S4OhoSG2b99e4Fl+CCGEEFI0cnNzERwcjODgYIXSm5qawt/fH15eXmqOjBBCCCl69AuWECKXqakp9u3bB3d3d02HQgghhBAprKysUL58eaW3a9++PQIDA/Hjjz+qISpCCCFE86ilFyFEgqmpKZydndGmTRv8+uuvqFKliqZDIoQQQogMLi4u+PjxIwIDA3Hjxg0EBwfj7du3iIyMRHJyMrKzs2FlZQUbGxtUq1YNLVu2RKdOnVC3bl1Nh06IzsrOzsaXL180GkPZsmVhYEA/+UnJJmDKTvVCCCGEEEIIIYQQmT59+oQKFSpoNIZq1arB0NCQty40NFRD0RCiGdS9kRBCCCGEEEIIIYToHGrrSAghhBBCCCGEqMmMlvNgZWJdJPtKTE/A0ltzAABXr16Fk5NTkeyXEG1FlV6EEEIIIYQQQoiaWJlYo7SpjabDIKREou6NhBBCCCGEEEIIIUTnUKUXIYQQQgghhBBCCNE5VOlFCCGEEEIIIYQQQnQOVXoRQgghhBBCCCGEEJ1DlV6EEEIIIYQQQgghROdQpZeWSU9Px4YNG+Dr64syZcrAyMgI5cuXR8eOHXHgwIEC5ZmbmwtPT08IBALur7C8vb15+cn7y8/58+fRrFkzmJqaokyZMhg2bBg+f/4skS44OJjLc9KkSXLzfPz4MS+Gt2/fyk0/dOhQLu3z58/zjVkbFebcef/+PbZs2YKRI0eiSZMmqFSpEszMzGBqaooKFSqga9eu2L17N7KysvKNIyMjA7Nnz4aLiwuMjY1Rs2ZNrFmzBowxibR+fn7c+/7kyRO5+U6cOJFL26pVq3xjMDU1hUAggIeHR74xa6vClgevX7/G/v37MXHiRDRv3hxmZmbce+jv76+yOKk80D6q+i4JCAjAwIEDUalSJZiYmKBs2bJo164d9u/fr9D2VB6oTmZmJrZu3Yp27dqhXLlyMDY2hoWFBWrUqIGhQ4ciICAg3zwiIiIwffp0NGzYENbW1jA0NISNjQ2aNWuG+fPnIzo6utBxOjs7K1QWODs755vXnj17UL9+fZiYmMDR0RG//fYbEhMTJdIdPnyYy3ft2rVy8zx58iSX1tjYGOnp6XLT+/j4QCAQwMDAAMnJyfnGrG0Ket5EREQoXK4rckw/f/6MIUOGoEyZMjA1NUWLFi1w/vx5qWlr1aoFgUAAW1tbqWWFqAYNGnD7nz17tty0N27c4NJOnz5dblptVpiywN/fX+HjWdjrBCoLCCFagxGt8eLFC1ajRg0GQOZf27ZtWVJSklL5/vPPPxL5FJaXl5fcOBXdl7+/PxMIBBLbODk5scjISF7arKwsZmFhwQCwRo0aKfWa/f395aavUqUKA8DKlCmj2BugZQp77sycOVOhY1mnTh329u1bmXFkZ2ezNm3aSN126NChEun379/PPb9u3Tq5r7Fhw4ZcWmNjY5aeni4z7a1bt7i0U6ZMkZuvtirsMb1+/brcbXfs2KGyWKk80C6q+i6ZM2cO09PTk5lHp06dWFpamsztqTxQnYiICFarVq18P1/jx49nubm5UvPYtWsXMzU1lbu9jY0Nu3jxYqFirVSpkkJlQaVKleTmM2/ePKnb1a1bV+LcjYqK4p7v3bu33HwnT57My+/69esy02ZmZnLvWX7ljDYqzHnz7t07hct10XJFmsjISObk5CSRXiAQSP0uGjVqFJfm2bNnMl9fQkICr4zy9vaW+34sWLCAS3vmzJn830AtVNiyYMeOHQofz8JeJ1BZwNjHjx+5/Je0WcM2dd1dJH9L2qzh9vvx40eF4yVEV1FLLy0RHR2NNm3a4OXLlwCAPn364MyZM3j48CHOnDmDPn36AAAuXryIfv36KZxvZGQk/vjjDwgEAtjZ2ak87kaNGuHp06dy/2SJjY3l7uxPnjwZt2/fxqlTp+Dh4YFPnz5h4sSJvPQGBgbw9PQEADx69EjuXZZbt24BAPT19XmPpYmKisKbN28AAC1btlTshWsRVZw7enp6qFevHvz8/LBlyxacOXMGwcHBuHr1KrZs2YJmzZoBAJ4+fYrWrVsjJSVFaj7//vsvLl26BEdHR+zYsQN3797F6tWrYWVlhR07duDcuXO89KItNOQdo6SkJISEhADIO6YZGRkIDg6WmV40r/xagWgjVRxTJnJ3XE9PD7Vq1UKTJk3UGjeVB5qnqu+SzZs3Y968ecjNzUWVKlWwbds2BAUF4cSJE/Dx8QEAnD17FsOGDZOZB5UHqpGVlYVOnTohNDQUAFC3bl34+/sjMDAQFy9exOzZs2Fubg4A+Oeff7Bs2TKJPO7cuYMhQ4YgLS0Nenp6GDp0KE6cOIGgoCAcOXIEXbp0AQDEx8ejW7du+baGVES3bt3klgUXL16UuW1oaCjmzZsHExMTLFiwAIGBgTh48CBq1KiBJ0+eYMGCBbz0ZcuWRbVq1QDIP3dEn1ekPLh//z7S0tIAFL9zp7DnjaOjY77l+dOnTzFgwABum8GDB0uNZeLEifj06RM8PT1x+vRp3L59myvTx40bh9jYWF56RcuCgIAA5Obmcsfy3r17clukC/PS09NDixYtZKbTVqooC0RduHBB7rHt3r27SuKmsoAQonGarnUjefz8/Lga+Tlz5khNM3v2bC7N4cOHFcq3e/fuDAAbNmwYrzVGYQnz8vLyKnAe/v7+DACbNGkSb31SUhJzcnJixsbGLCMjg/ec6F26CxcuyMy7XLlyDADr168fA8CqVasmM+3Bgwe5PFevXl3g16Mpqjh3srKy8t3PhAkTuDzWrFkjNY23tzcDwJ48ecJbf/z4cQaADRkyRGIbYaua8uXLy9z3+fPnuVYIbdu2ZQDYokWLZKbv0KEDA8D09PRYfHx8vq9N26jimIaHh7O//vqLXb9+nbsTKnqHVx0tvag80DxVnDtxcXHMysqKAWAVK1ZkMTExvOezs7NZly5duDyuXbsmdT9UHqjG4cOHuffa09OTZWdnS6S5f/8+MzQ0ZACYtbW1RJneqVMnLo/169dL3c+kSZO4NH5+fgWOV9i6Y/DgwQXOY+7cuQwAW7t2LW/9p0+fmJmZGXN2dpbYZvjw4Vz8L1++lJpvcnIyMzAw4JUHbdq0kRnHsmXLuDxPnDhR4NejCao4b/KTnZ3NypcvzwAwS0tLlpqaKpEmPT2dGRsbswoVKrDk5GTec8LrCvHWt6KtY/r37y9z/zNmzGAAWOfOnbmWv3fu3JEZq6WlJQPA6tevr9Tr1BaqOKai1wHv3r1Ta7xUFlBLL0K0BbX00gI5OTnYs2cPAKBSpUqYNWuW1HSzZ89GxYoVAQBLly7NN99jx47hxIkTsLOzw/Lly1UXsIpERkYCANdqQMjCwgJNmjRBRkaGxN0/0ZYXsu7IvH79GlFRUQDAjdnw6tUrfP36VWr64twKQFXnjoGBQb77mjFjBrcs672PjIyEra0t6tSpw1vv6+vLPS9OeEw/f/7MtbARJ9xfs2bNuPSyYsjNzeXGs6hduzZKly4t8zVpI1Ud02rVqmHKlCnw8vKChYWF+gJWESoPCk9V587WrVu5sVKWLVsm0UpYX18fGzZs4O6O//XXX1L3Q+WBaoiOz/P7779z77uohg0bonPnzgCAhIQEibHohHnY2tpi7NixUvcjOh5SYGBgoeMuDFnlgaOjI1xdXeWeO4Ds8yEwMBDZ2dmwsrLCmDFjuHU5OTlS0wvzEQgExa7lpyrOm/xcvnyZG3Oxd+/eMDU1lUgTFxeHjIwMNGnShGuFJCSrLHBycuLGeZLX+kb4nLe3Nzden6z0ISEhSEpKAlD8ynahojim2obKAkKIKlCllxZ49eoV9wOjTZs2Ur/EgLwfGm3atAEAPHjwAO/evZOZ5/fv3zF+/HgAeT9IbG1tVRx14dnb2wPIG1hUVEpKCu7fvw8jIyOJuD08PGBsbAxA9heZcL2bmxvq16+PSpUqKZTeysoK9erVK+Cr0Qx1nDuyWFpacsuyBvu0t7dHXFwc1/Re6Pr16wCAcuXKSWyjSDcG4fqWLVtyXRKE3RrEPXnyhHtPiuOFbVEeU21C5UHhqercOXHiBACgVKlS6Nmzp9Q8nJyc0Lp1awDAlStXuB+Toqg8UI3MzExuuXLlyjLTValSReo2oo9dXFxkbm9lZcVVcIpvX9RklQdfvnzBy5cvC33uNGvWjCs/kpOT8ejRI4m0jDHcuXMHQN7A6jY2NgV7MRqiivMmP7t27eKWZXVtLF26NAwMDHD//n2kpqbynlOkLPj06RMiIiIknk9PT+e6NYuWBfkde9G8i5uiOKbahsoCQogqUKWXFoiLi+OWHRwc5KYVfV7e3a8ZM2bg8+fPaNWqFYYMGVLoGNWhQ4cOMDIywsqVKzF9+nQEBATg7NmzaNu2LT58+IAuXbpwP2iFTExM0LhxYwB5YzdI+zK/efMmAHAXQML/wvWiEhMTuXGGmjdvDj294vWRUMe5I4vojG+urq5S03Tr1g1A3rHduXMngoKCsG7dOu5iWDiekCjRixNpxygjIwNBQUEA8o6lh4cHDA0N8f37d25cH1HF/cK2KI+pNqHyoPBUce5kZmZynzdPT08YGRnJzMPLywtA3mf0/v37Es9TeaAaNWrU4JbljbUlbBknEAi4MW3E88jvZpmwNaXoPjVBeO5MnToVS5Yswd27d3HkyBH4+voiJSVF6rnj4uICJycnANLPHdH1LVq0gLGxMRo1aiQz/bNnz/Dt2zcAxfPcUcV5I09SUhJXQe7s7CzzPTI1NUXbtm3x/v17tG3bFmfPnkVAQACmTp2K1atXw8zMDJ06dZLYLr+yQFjmm5mZwd3dnSvb79y5I7UCnMqC4onKAkKIKhSvK3odJdr1SNr0u6JEnw8LC5OaJjAwEJs2bYKhoSE2btyomiBlePHiBTw8PGBtbQ0TExM4OTmhW7du2LVrl9zBRIG8psmLFy9Gbm4uli9fjubNm6Nz584ICAhA+fLlsXr1aqnbCb9wRO/yiRJe2Ij/yJVWKSB6cVQcv8hUfe6I+/btGx49eoRJkyZxzb+NjIy4ZXHjx49Ho0aN8PHjRwwZMgQeHh4YP348vn37hkGDBnFN7kVVqVIF5cuXByD9GAUHByM9PR0mJiZo1KgRTE1N4e7uLjN9cb+wVfcxVScqDzRLFedOeHg4171DVuW2kOjz0rrQUHmgGv3790epUqUA5HU3ldb95tGjRzh79iwAYMCAAVx6odGjRwPIqxjdtGmT1P2IDggtTF8YN2/eRP369WFpaQkzMzO4uLigb9++OHHiBG+iDWkaN26McePGIS0tDX/88Qc8PT3Rp08fhIWFoXbt2ryumKKEx/jdu3cS3Z6ysrJw7949AIqVB8X93FHFeSPPkSNHuJZbP//8MwQCgcy0q1atgo2NDe7cuYPOnTujefPm+Pvvv8EYw9q1a7nWPKLya60jXOfh4QEDAwM0bdoU+vr6SEhIkDppyu3btwHklVtlypRR+HVqE1Uf06FDh6J8+fIwMjKCnZ0dmjZtij///FNql8HCoLKAEKJpVOmlBapWrQpDQ0MAsu9ICIk+/+HDB4nns7KyMHLkSDDGMGXKFNSsWVO1wYr5+vUrgoKCkJiYiIyMDERGRuLUqVMYPHgw6tevn+9YApMnT8bBgwfRsGFDGBsbw9bWFoMGDUJQUBB3l0acvL760mZeE36RiXZzkbZ9cfwiU+W5IzRkyBAIBAIIBALY2NjA3d0dq1atQlZWFszMzLB//36ZzepNTExw9epVTJo0CY6OjjA0NESNGjWwcuVK7NixQ+Y+hcfq9evX+PLlC+854TFq0qQJ1+pEkYuT6tWr59vaRRup45gWFSoPNEsV586nT5+4ZVnvuVCFChW45Y8fP0o8T+WBatjZ2WH37t0wMzPDnTt30LhxY+zatQt3797F5cuXMW/ePHh5eSEzMxPu7u5YsWKFRB7Dhg3DoEGDAAB+fn4YOXIkTp8+jfv37+PYsWPo0aMH/v77bwDAzJkzua6rhfHu3Ts8fvwYycnJSEtLQ0REBA4dOoQePXqgZcuW+f6wXrt2LdavX4+aNWvC0NAQZcuWxfjx43Hr1i2ZP+TllQfC2deMjIy4mWyF546wQkRUcS8PVHHeyCPatVF4bslSvXp13Lt3Dz/++CN3U8TT0xNnzpzB8OHDpW5TrVo1lC1bFoD8z7bwmFtYWKB+/fpS0798+RLR0dEAiuexFFL1Mb1+/TqioqKQlZWFuLg43Lt3D4sWLULVqlWxefNmlcVNZQEhROM0OYo++Z927dpxs2zs27dPapp9+/ZxafD/s9WIE85m5uLiIjGLjipnb/Tx8WG+vr5sxYoV7PLly+zRo0fs5s2bbPXq1czNzY3bj4ODA3v//n2h9yfq+/fvTF9fnwFgHTt25D0nnHnN0dGRW5ebm8tKly7NALD//vuPl7558+YMADMzM2OZmZkqjbOoqOrcERo8eDAvrfCvX79+7MOHD2p5DevXr+f2c+jQId5zwpnX/vjjD26dcPY3BwcHXtpXr15x+YwYMUItsRYFVR9TIXXN3kjlgfYo7Llz6NAhbv3GjRvl7issLIxLO27cOJW9BioPpHv+/DkbMWIEEwgEEuWzg4MDW716NUtJSZGbx+HDh1mDBg2klvE+Pj7s0qVLhY6zWrVqrGvXrmzdunXs+vXr7NGjR+zatWts8eLFrEKFCtz+3NzcWEJCQqH3Jyo0NJTLf+zYsbznhLOvNWvWjFsXHx/PvZ9hYWG89I6OjgwAq1q1qkpjLGqqOG/EvX//nstP9P1UtT59+nCxfv36lVsvOhPjxYsXufXC2SB//PFHXj5btmzh8tmzZ4/a4i0qhTmmO3bsYJUrV2ZTpkxhR48eZUFBQSwoKIgdOHCA9enTh5fn5s2bCxUnlQU0eyMh2oJaemmJuXPncjPoDR48GAsXLsSHDx+QlZWFDx8+YOHChRg8eDBvfJW0tDReHq9evcKiRYsAAOvWrZM6i46qHDt2DJcvX8akSZPg6+uL+vXro2XLlpgwYQIeP37Mjdny9etX/Pbbbyrdt6WlJXc3T3zsBmHrhebNm3PrBAIBmjVrxnse4HeHatq0KddCorhRxbkjatGiRXj69CmePn2KO3fuYOPGjXB3d8eBAwcwYMAAvHr1SuWvQdbYHbm5udzgocK7cKLLX79+xcuXL7n1unI3TtXHVN2oPNAehT13RCepkDeeFwDeGGuqPP+oPJCUmZmJXbt24eTJk1K7A339+hV79uzB5cuXZebx/Plz7Nq1S2rXLyBvaIRt27YVumtTUFAQTp48CT8/P3h5eaF+/frw9vbG77//jtDQULRt25aLZ968eYXalzg3NzduMH7x1o7i4/sBeYOsC1vEi6YX7RJVnM8dVZw30uzZs4fLL79WXoUhq4vjo0ePkJSUBH19fXh6enLrZbX6pLLgf3r06IHXr1/jr7/+Qs+ePdG4cWM0btwYffv2xaFDh3Dq1Cnu+2/ixIkSrW2VQWUBIURbUKWXlmjatCk2b94MAwMDZGVlYdasWahUqRKMjIy4qecNDAywcuVKbhvR2fQAYNSoUUhPT0evXr3QsWNHtcZrbW0t8zlDQ0Ns3bqVG3Dz+PHjKh8fQNhsOTExEU+ePOHWizd3F08veuETFBTEDXxdnKcfVsW5I8rR0RG1a9dG7dq10axZM4wePRpBQUEYNWoUbt++DQ8PDzx+/Filr0F0NhzRY/T48WN8//4denp6XEUFkNfEXziekGh60WU6pkWHygPtUdhzx8TEhFvOb9avjIwMblmVN1moPOBLSUlB69atsWTJEsTHx2PatGl4/vw5MjIykJiYiIsXL6JFixa4f/8+unfvzju2Qrdu3YKnpydOnz4NR0dH7N69G1++fEFmZiY+fvyI9evXw8zMDAcOHECTJk0kZtxUhrzywNLSEocOHeKO77///qvS2eUEAgH3QzY0NBTx8fEA+BWmipQHunDuqOK8kWX37t0A8iq++/btq66XILOLmnC5fv36vLEMhemjoqLw+vVrifSVKlXidcsublRxTK2srOSOv9a5c2dunKzU1FRs27atwPFSWUAI0RZU6aVFhg0bhnv37qFHjx4wNzfn1hsYGKBr1654+PAhN7sIkHdXQmj79u24du0aLC0tsWbNmiKNWxoDAwPeOA3iUw0XlrS7fwkJCXj27BkA/t0b0cfCQZBFtxPPrzgqzLmjCH19faxduxYVKlTAt2/fZA5kX1CiFydPnz7lxloSHqM6derAysqKt420O7rC5QoVKsDZ2VmlMRY1dR/TokTlQdEqzLkjWgGWnJwsdz8pKSncsugPz8Ki8oBv7ty53GvZtm0bli1bBldXVxgZGaFUqVJo06YNrl27Bh8fHzDGMHXqVN6NiYyMDPTv3x+JiYkoW7Ys7t69i4EDB8LBwQGGhoZwcnLC2LFjcfPmTZiYmODz589c60x1sLKyQr9+/QDknUPSZv4sDOHnlzHG/bh99uwZEhISIBAIeC0/Afnnjmh+xU1hzxtZgoKC8OLFCwBA165d5VZsFFadOnW4/KUdH/Gy3cHBAVWrVuWliYyM5GYtLa7HUkhdx1TcL7/8wlWMqfr7WhSVBYSQokKVXlrG3d0dx44dQ0JCAj58+IDXr18jKSkJJ0+ehKurK69rWa1atbjlZcuWAcibQv7WrVs4cOCAxJ9wEE8A3LrTp0+r7bWIDqKvjpYdwi9kYTPk27dvIzc3F6VKlULdunV56Rs1agRjY2NkZmZyM7YIv8gMDQ15zeOLq4KeO4oyMjJC+/btAeR1g1H1MRVeTOTm5nIDiUprgi4kXCdM8+XLF+7Orq5cmKj7mBYlKg+KVkHPHdHB60UHtZdGdPB6VbeeoPIgD2MM27dvB5A3GLisyigDAwNu9sXc3Fz4+/tzz50/f577zI0fP54bHFxcrVq1MHDgQADAgwcPVN6iV5Q6ywNp3WOF/2vWrClxg0B47nz48AHv378H8L/ywMnJSebELdpMFeeNLMoMYF9Yenp63PERtvQE/jfYuCJlga5UWqjzmIqzt7eHra0tANV/PsVRWUAIKQpU6aWlDAwMUKFCBVSpUoXX3eTBgwfcsnDGEeB/3UzOnDmD/v37S/0TnTlNuG78+PFqew3ymk8Xlp2dHdzc3AD87wtJ+L9Zs2bQ0+Of2sbGxmjcuDGXLicnBwEBAQDATXuvK5Q9d5QhOs23qmcLlNZaR3hhK61JufDiJCIiAp8+feKNwVCcL2ylUecxLSpUHmiGsudO9erVoa+vDwBcaw5ZRJ8Xvv+qQuVBnq9fv3Ldcho0aCA3bcOGDbll0WMj+t3v7u5eoDxUTZ3lQf369bkWi+LlgbRzp1KlSlxl761btxAdHc2NDVdcuzOp4ryRJisrCwcOHACQVzEivBGmTsLPr7Ccfv78OWJiYgDILwuEx5zKgoJ9jtX5GS2q/VBZQAgRokqvYiQnJwfHjh0DkHdXXXRME20UFhbGLZcvX17l+Qu/gL5+/YpXr17JbO4uJHohFBISgqSkJADF+yJIUao6d0TvwqmyOxOQdxEnzPPWrVsIDw/H169fAUg/plWqVEG5cuW49LpyN1dRVB7wUXmgOHnnjugU7oGBgXLHWBF2ezE2NuZ1l1QFKg/yCCclAIDs7Gy5abOysqRup4o8VE2d5YG+vj53Tj98+BCpqalKlQe6cO6o65ifPXsWcXFxAIABAwao9RwREh/XS3h8qlatCgcHB4n0wmP55s0bREVFcenLli2L6tWrqz1edSnKz3FMTAxiY2MBqOf7WhSVBYSQokCVXsXItm3buNY1o0aN4u7GA3l3txljcv+8vLy49MJ1ERERaok1Ozuba4YNqOfLQjTPCxcucGMB5PdFFhAQgGvXrqk1Nm0j79xRVEpKCs6dOwcgb9Bq4bgZqmJgYMB1K7t//z4uXLgAAHB2doajo6PUbYTjMdy8eZO7OLG3t+cGtdZlqjimRYXKA+2S37nTvXt3AMD379+5yjFxnz594mYH8/X1VflEClQe5LGxsUGpUqUA5FVCyvuxKzr2jouLi9Rl8VntFM1DlRITE7nWQmZmZiqvMAX+9znOysrC7t27ERUVBSD/8kD03BHNp7hRxXkjjWjXRnWO+yaqYcOGMDMzA5B3fOR1cwaAGjVqcK3ST5w4wU3KUNxb6qjrmErz77//cjNDiv52UDUqCwghRYUqvbSIvL7sV69exW+//QYgr/vJ5MmT1RbHkCFDIBAIIBAIcP36dYnnr127hoSEBJnbZ2VlYcSIEVyXii5duqhlthzRL6BVq1YhKysLRkZG8PDwkJq+efPmEAgESE5OxsaNGwHwx4sozgpz7sTGxuLo0aNy809PT8ewYcO4ceF69+6tli5gwmOamZmJ1atXA5B/oSo8dufOncPTp0/zTV+cUHmgHCoP/qew586IESO4geJnzJjBtewQysnJwdixY5GTkwMAmDp1qooi56PyIO+c7NSpEwDg8+fPWLRokdR03759w/Tp07nHnTt35pZ9fX25SoONGzdy7424c+fO4fjx4wDyZvGtX7++RBpvb2+uPJB20+z8+fNIS0uT+XqSk5Px448/cufU8OHDYWxsLDN9QYmWB8uXLweQ16qxUqVKUtMLz50XL17g1KlTAPK6TYuON1ScqOK8ERcfH4+zZ88CyBtgXtr5oQ6i4ywGBwdzNynkfbaFFeB///03V3lT3CstVHFMIyIi8OjRI7n7OXPmDObPnw8g7wbn0KFDpaajsoAQUpyov10yUVjt2rXh5eWFTp06oVatWjA2NsaHDx9w/Phx7N27F7m5ubCxscGhQ4d4Y7MUtZ07d6Jr167o2rUrvL29UaNGDZQqVQrJycl48OAB/v33X665sr29vdpmk3RycoKzszMiIiLw9u1bAHl3BGW9N9bW1qhVqxaePXvGpa9Xrx5356w4K8y5k5ycjN69e6Nq1aro1asXmjRpAkdHRxgbGyM2NhZBQUHYtm0b9545OjpyEyeomuhFrHB/8iohhM8JBxwFiv+FrZAqyoMjR47wZuATjokkvgzkdf0oyPgsVB5on8KeOzY2Nli2bBlGjx6N9+/fw8PDAzNnzkSdOnXw+fNnrF69mvvh2b9/f3h7e6vldVB5kGf27Nk4efIkUlNTMXfuXDx48ACDBw9G5cqVkZ6ejrt372L16tVc6z1fX1+0bduW297a2hozZszA7NmzkZSUhGbNmmH8+PFo06YNSpcuja9fv+LkyZPYsmULcnNzAQBLly6VGAtPEUuXLsVPP/2Enj17okWLFqhSpQosLCyQmJiIgIAAbNq0iYuzRo0amDt3buHfICkaN24MExMTpKenK3TuCGcETUxM5Gb6K+4VpoU9b8QdOHCA6+5cVK28hFq2bIkrV64gIyMDnz9/BpB/WXDixAnu2ANUFgB5lV4+Pj7w9PREly5dUK9ePdjb2wPIK2OPHDmCI0eOcBWFf//9t8yWtfmhsoAQolUY0Rrm5uYMgMy/WrVqsZCQkALn7+XlxeUlz+DBg7l0165dk/u8vL86deqw0NDQAseriEGDBvH2OXXqVLnpR48ezUs/YcIEtcZXVApz7rx7906h4wmAeXp6sjdv3qjtdaSlpTFjY2PePuWdQ9nZ2czCwoKX/tGjR2qLryipojyoVKmSwsfWy8tLah5UHhQ/qvoumT17NhMIBDLz6dixI0tLS1Pb66Dy4H8uXbrE7Ozs8v2c/fDDDyw+Pl5i+9zcXPbbb7/JPZ4AmKGhIfvrr79kxiF6HfHu3Tu5z+dX3nz69EmVb5GEVq1a8fa5fv16uenbt2/PS79q1Sq1xlcUCnveiPLw8GAAmL6+PouKiiqiV5Dn6tWrvHjt7e3lpr979y4vfenSpVlubm4RRatehTmm165dU+jzaWZmxjZv3iw3DioLFPPx40cunyVt1rBNXXcXyd+SNmu4/X78+LHA8ROiK6illxbZunUrLl68iKCgIERFRSE5ORllypRB3bp10adPHwwcOBCGhoaaDhPTp09H/fr1ERgYiLCwMMTExCA+Ph7GxsZwcHBAo0aN0Lt3b/To0UPt4wy1atWKN8ZEfl2TWrZsiU2bNvG21wWFOXcqVqyIW7ducdu/f/8eX79+RVJSEiwsLFCxYkU0atQIffr0Qbt27dQ6046JiQkaN27MtUKytbWVOyucvr4+PD09cenSJQCAlZUV6tatq7b4ihKVB8qj8iCPqs6defPmoV27dli/fj1u3bqFr1+/wtraGvXq1cPQoUPRv39/tb4OKg/+p3Xr1njx4gW2bduGc+fOITQ0FAkJCTAwMEDZsmXRuHFjDBgwAF27dpVaRgsEAqxatQoDBw7E1q1bcfv2bbx//x6pqamwsLBA1apV4eXlhVGjRhVqsO+///4bV65cQWBgIF6+fInY2FgkJCTAzMwM5cuXh4eHB/r374+2bduqfXa4Vq1a8WbuU6Q8OH/+PG/74q6w543Qq1evcO/ePQBAmzZtULZs2aJ6CQCApk2bwsjIiGtpJuy+KIu7uzvMzMyQmpoKIO/YF9VshOpWmGPasGFD7NmzB4GBgbh//z6ioqIQGxuL7OxslC5dGrVq1YKvry9GjBjBtQArKCoLCCHaRMDY/7dhJYQQQgghhBBCSKF9+vSJG8d0SZs1KG1qUyT7/ZYWj98vTQAAfPz4EU5OTkWyX0K0FQ1kTwghhBBCCCGEEEJ0DlV6EUIIIYQQQgghhBCdQ5VehBBCCCGEEEIIIUTnUKUXIYQQQgghhBBCCNE5VOlFCCGEEEIIIYQQQnQOVXoRQgghhBBCCCGEEJ1DlV6EEEIIIYQQQgghROdQpRchhBBCCCGEEEII0TlU6UUIIYQQQgghhBBCdA5VehFCCCGEEEIIIYQQnUOVXoQQQgghhBBCCClyAoEAAoEAc+fO1XQoREdRpRchhBBCCCGEEEKUlpmZif3792PQoEFwdXWFra0tDA0NYWdnh4YNG2LMmDG4fPkycnNzNR2qVvvw4QPmzJmDRo0aoUyZMjAxMUGFChXQsmVLzJ49G8+ePdN0iMWWgaYDIIQQQgghhBBCSPFy7NgxTJ48GRERERLPxcXFIS4uDg8fPsSmTZtQvXp1rFy5Ep06dSr6QLXcP//8g99//x0pKSm89Z8+fcKnT59w+/ZtfP/+HatXr9ZMgMUcVXoRQgghhBBCCCFEYQsWLMDs2bO5x23atEHXrl1Rs2ZNWFtbIz4+Hi9fvsTp06dx6dIlhIeHY+bMmVTpJWbhwoWYNWsWAKB69eoYOXIkGjduDCsrK8TFxeHRo0c4fvw49PSok15BUaUXIYQQQgghhBBCFLJjxw6uwsve3h6HDh2Cl5eXRLrWrVvDz88Pz549w8SJExETE1PUoWq1K1eucBVegwYNwtatW2FoaMhL4+vriylTpiAzM1MTIeoEqvQihBBCCCGEEEJIviIjIzFu3DgAgLm5OW7cuAFXV1e529SuXRsXLlzAvn37iiLEYiE3NxdjxowBANSrVw/btm2DgYHs6hkjI6OiCk3nUBs5QgghhBBCCCGE5GvVqlVITU0FAMyfPz/fCi8hPT09DBw4UOn9vX37FitWrECXLl3g7OwMU1NTmJqaolKlSujbty/Onz+fbx4JCQlYtGgRPD09Ubp0aRgaGqJMmTKoWbMmevTogY0bN+Lr169St7169Sr69+8PFxcXmJqawszMDJUqVULTpk0xZcoUXL16VenXBAAXL17Eq1evAADTp0+XW+FFCofeWS2VnZ2NL1++aDoMQgghhBBCCClxypYtSxURYhhj2LlzJ4C8Vl4jR45U6/7evXuHKlWqSH3uw4cP+PDhAw4dOoSBAwdix44dUo/X8+fP0bp1a3z+/Jm3PjY2FrGxsXj+/DlOnDiBnJwcrgWb0MSJE6UOHi/c97179+Dv74/Y2FilX9vhw4cBAAKBAJ07d+bWx8fHIy4uDra2trCxsVE6XyKJPsVa6suXL6hQoYKmwyCEEEIIIYSQEufjx49wcnLSdBhaJTQ0lKvgadmyJSwtLdW6v5ycHBgZGaFdu3Zo06YNatasCRsbG8THxyM8PBzr169HaGgo9uzZg8qVK2PevHkSefz888/4/PkzDA0NMXLkSHTo0AFly5ZFbm4uPn36hLt37+L48eMS2505c4ar8Kpbty7GjBkDNzc3WFlZISEhAaGhobh8+TKCgoIK9Nru3r0LAHB2doalpSX27duHJUuW4NmzZ1wa4cD248ePh7GxcYH2Q6jSixBCCCGEEEII0TlRUVH5plGmYu/x48fccsOGDQsUkzLKlSuHiIgIlCtXTuI5X19fjB49GsOGDYO/vz9WrFiBSZMmwcrKikvz9u1bPHjwAACwcuVKiZZcTZo0Qc+ePbFs2TIkJCTwnjt06BAAoFKlSrhz5w4sLCx4z3t7e8PPzw/x8fFKv67c3Fy8ePECAGBnZ4cJEyZg7dq1EunCw8MxdepUHD9+HGfPnoW1tbXS+yJU6VUsBAUFSf2ga4u0tDTcvHkTANCqVSuYmppqOCK+n3/+GdevXwcAjBkzBn/88YdmA9JR2n4ekKKhifOgVatWePfuHcqVKwcnJyd06tQJVatWhaurKxwcHNS+f8JHZQEBCn4ezJo1C/7+/gCAY8eOoXHjxuoKkRQBKg8IUDTnQVpaGgICAhASEoJy5cphwIABBconKioKTZo0UXF0mqPIa2GMKZxfXFwct2xvb1+gmJRhbm4Oc3Nzmc8LBAKsWLECu3fvRkpKCi5fvoxevXpxz4sOF9SqVSu5+ZQuXZq3Tritu7u7RIWXqIJ0QUxMTERubi4A4OnTpwgODka5cuXw119/oWPHjjAxMUFwcDCmT5+Ou3fvIiAgAMOGDcOxY8eU3hehSq9iQfhDTlulpaXBzs4OQN6dAm27oDExMeGWLS0ttfq9LM60/TwgRUMT58HgwYMxd+5cREVFISoqCsHBwQDyLmBsbGxw+vRpeHp6qj0OkofKAgIU/DwIDQ3llj08PFC+fHm1xEeKBpUHBCia8yAuLg5DhgwBADRv3hzTpk1T+T4IkJSUxC3Lq4xSl6ysLHz9+hVJSUnIycnh1tva2iI6OhqPHz/mVXqJNhzx9/fHypUrFd6XcNubN2/izZs3MscWK4iUlBRuOT09HWZmZrh27Rpq1KjBrW/VqhWuXr0KT09PPH78GMePH8e9e/fg4eGhsjhKCpq9kZQoAoFA0yEQQlRszpw5WL9+PapWrcpbzxhDXFwcmjdvjr1792ooOkKIMkS7zpQtW1aDkRBCihNbW1uu5VFAQADS09M1HJF2CAoKwsePH+X+KUN0DC/Riht1ysrKwvr169G0aVNYWFigQoUKqFmzJurUqcP9RUdHA4DEgPIuLi5o2bIlgLxZJ2vVqoXZs2fj6tWr3AyUsgwaNAhAXoVq7dq10a9fP+zYsQOvX78u9GsSbZQBACNGjOBVeAmZmppi0aJF3OODBw8Wet8lEVV6EZ0n2mSXKr0I0U1jx45FeHg4njx5gg0bNmDy5MncDD6MMfz88884deqUhqMkhMiTnZ2NzMxMAICxsTH09OgylRCiOGErJMYYIiMjNRyNdhD2GJL3pwxbW1tu+evXr6oOV0J8fDw8PT0xbtw43Lt3j/uOkCUtLU1i3f79+7kW/2FhYViwYAF8fX1hbW2NVq1aYdOmTVIrSX19fbFu3TqYmpoiPT0dBw8exLBhw1CtWjU4OTlh9OjRvBs1yhCfAKBt27Yy0/r6+nLXtMLeDEQ5dDVBCCFEJwgEAtSpUwdjxozB33//jfDwcO6igjGGXr165XtXjxCiOR8+fOCWHR0dNRgJIaQ4ElYMACjwjHpEvnr16nHLDx8+VPv+JkyYwA1E3717d5w6dQoRERFITU1Fbm4uGGNgjKFChQoApI9P5ujoiICAAFy+fBljx45FrVq1IBAIkJWVhVu3bmHMmDGoXbs2wsPDJbb18/NDREQEVq1ahY4dO3KD5EdGRmLz5s1o0KAB/vzzT6Vfl7GxMcqUKcM9FsYvjYmJCdc9OCYmRul9Ear0IiWAq6srt6zKvtiEEO3m4uKCa9eucY+zs7ORnJyswYgIIfJ8/vyZW+7cubMGIyGEFEei5QZNXKUetWrV4ipgbt26he/fv6ttX9+/f+e68/300084fvw4unTpgkqVKsHU1JTXg+fbt2/55ufr64v169fj2bNniImJwYEDB/DDDz8AAN68eYO+fftK3c7e3h6//fYbzp49i/j4eDx48AB//vknrK2twRjDokWLcPLkSaVfX61atbhl0fHJpBE+L1qxSxRHlV5E54nO3lYUs4wQQrRDdnY2pk+fzj1u3749lQGEaLG3b99yy5UrV9ZgJISQ4mjt2rVcRUhERATu3bun4Yh0j0AgwODBgwHkjem1detWte3r1atXyMrKAgCZFVIA8OLFC6Vvatra2qJv3764cuUKunbtCgAICQnBq1ev5G6np6cHd3d3LFiwAFeuXOHWHzp0SKn9A/zZJEW//8R9//6dG6uMWkEXDFV6EZ2nzDS8hBDdkJ2djVGjRnEXJPb29ti5c6eGoyKEyCN60e/i4qLBSAghxZGdnR03aDmQ1zWNqN7EiRNhZmYGAJg9ezZevHih0Ha5ublKTSyUnZ3NLcsbNH/Tpk0K5ymNr68vtyw+EL487u7uKF26tNLbCYnOMnn8+HGZ6Y4fP879nhU9v4niqNKLlCg0kD0hui8gIABeXl7Yvn07gLym4EeOHKFWXoRoubCwMG6ZWnoRQgpiw4YN3PKDBw94YwUS1XB0dMS6desA5FVGeXl54caNG3K3CQsLQ/v27fHXX38pvJ+qVatyv9127twptSHD6dOnuVikCQkJQUhIiMznGWO4fPkygLzfic7OztxzBw8elDowvtD9+/e5bpUFuVFTt25ddOjQAUDeYPuiLceEvnz5wo0ZZmRkhKFDhyq9HwJQp1Ci83766SeuVtzNzU3D0RBC1Om///5Dt27dkJ2dDT09Pejp6WHPnj10Z4yQYuD8+fPcMnXhIIQURK1ateDq6sq1Plq4cCH+/fdfDUele4YOHYpPnz5h9uzZiI6Ohre3N9q2bYtu3brBzc0N1tbWiI+PR3h4OM6ePYvz588jJyeHNxB+fmxtbdGxY0du+7Zt22LMmDGoVKkSoqOjcfToUfj7+6Ny5cpISEiQOsh7SEgIhg4disaNG6NLly5wd3dH2bJlkZWVhXfv3mHHjh24dOkSAKBr164oV64ct+306dMxevRodOvWDa1atUL16tVhbm6OuLg43L59G//88w8AQF9fHyNGjCjQ+7h69WoEBgYiISEBnTt3xm+//YaOHTvC1NQUQUFBWLJkCT59+gQAWLBgAX03FhBVehGdt2fPHq6G/PTp0zQ4LiE6KisrC0OHDuWaw1tZWWHx4sVyx4EghGgPYfcVPT09rssIIYQoa/HixejZsycAID4+XsPR6K5Zs2ahVq1amDx5MiIiInDx4kVcvHhRZvpatWph+fLlSu1j48aNaNGiBT58+IDLly9zrbKEKlasiBMnTqBjx45y8wkODkZwcLDM55s1a4Zt27ZJrE9ISMDOnTtlDpFhbGyMTZs2oVGjRgq8GknVq1fH6dOn0bt3b3z9+hVLly7F0qVLeWkEAgFmzpyJadOmFWgfhCq9SAlAY3oRUjJcuXIF0dHRAAAvLy9cunQJhoaGGo6KEKKIpKQk5ObmAgA3VgwhhBSEq6srbGxsEB8fDxsbG02Ho9N69uyJzp0748iRIzh37hyCg4MRHR2NpKQklCpVCs7OzmjatCl69+4Nb29vpYeaqVChAh4+fIhly5bh5MmTeP/+PUxMTODs7Izu3btjwoQJcm+S9O/fHw4ODrh06RKCg4MRGRmJr1+/Ijs7G/b29nB3d0ffvn3Rr18/6OnxR366du0aTp8+jZs3byI8PBxfvnzBt2/fYGZmhipVqsDX1xdjxowpdHf8Fi1aIDQ0FP/88w9OnDiBd+/eITMzE+XKlYO3tzfGjx+PBg0aFGofJR1VepEShcb0IkR3iY7p8Ntvv1GFFyHFiLD7BgBujBNCCCkINzc3xMXFIT09HRkZGZoOR+cZGRlhwIABGDBgQIG2z6+Bgq2tLZYvXy63lVhERITU9cbGxujQoUOBvldcXFzw66+/4tdff1V6W2XZ2tpi7ty5mDt3rtr3VRLRQPZE54nOBhUZGanBSAgh6pKQkICzZ88CyLv4at++vYYjIoQoQ/S7ukaNGhqMhBCiK0xMTPD9+3dNh0EI0TCq9CI67/3799xyVFSUBiMhhKjLypUrueUKFSrAxMREg9EQQpQlWulVpUoVDUZCCNEFOTk5WLhwISpXrsy7RiCElDxU6UV0Ho3pRYjuu3btGrfco0cPDUZCCCkI0Uqvgkz9Tgghoo4dO4ZZs2YhOzsbkydPhre3NzduICGkZKFKL1Ki0JhehOim8PBwbrlLly4ajIQQUhCnT5/mlkWnjCeEkILo3bs3ZsyYwT2+ceMGOnXqpMGICCGaQpVeROdRSy9CdN+3b9+45RYtWmgwEkJIQYgOZO/s7Ky5QAghOkEgEGDJkiWoW7cut+7ixYsajIgQoilU6UVKFPGpaAkhukHYZcHU1FTDkRBClJWbm8vNsGZoaAgjIyMNR0QI0RVVq1bllql7IyElE9UAEJ1HLb0I0X3C2d7S0tJQqlQpVKhQAf3798ecOXNw9epVDUdHCJEnJiaGWxZtlUEIIYXVt29f3uPly5drKBJCiKZQpRfRec2bN+eW69evr7lACCFqM3PmTG45JSUF0dHROHDgAObPnw9fX1/07dsXcXFxGoyQECLLu3fvuOUmTZpoMBJCiK758ccf0bhxY+7xH3/8geTkZA1GRAgpalTpRXSegYEBt2xoaKjBSAgh6jJgwABcu3YNHTp0QMWKFSUmrTh06BAGDx6soegIIfKIztxYuXJlDUZCCNFFAQEBsLS0BADk5OTgxIkTmg2IEFKkqNKLlCg0eyMhusvb2xv//fcf3r9/j7i4OISGhmLLli3che7Zs2fx6tUrDUdJCBEn2tKLKr0IIapmYGCADh06cI+/fPmiwWgIIUWNKr2IzqtYsSKaN2+O5s2bw9raWtPhEEKKgLm5OWrWrIkff/wRSUlJ3Prz589rMCpCiDSXL1/mlh0dHTUYCSFEVw0dOpRb/v79uwYjIYQUNYP8kxBSvFWtWpW7u1OmTBkNR0MIUYecnBzcvHkTT58+xatXr5CcnIwPHz7g5s2bXBpDQ0P89NNPGoySECJNSEgIt1yuXDnNBUII0Vn169dHrVq18Pvvv0sMbk8I0W1U6UV03uXLl7Fw4UIAgIeHB1xcXDQcESFElb59+4ZGjRrxxgWSZs+ePbCxsSmiqAghihK2xhQIBKhYsaKGoyGE6KKyZcvi6dOnNNQJISUQVXoRnccY03QIhBA1mjlzJt6+fQs9PT3k5uZKPG9ubo7Zs2fjxx9/1EB0hBB5MjMzuc+tvb29hqMhhOiyrKwsREZGIikpCVZWVqhUqZKmQyKEFAGq9CIlCt3dIUT3HDt2DEBe90U/Pz/07NkTNjY20NPTg4ODA43lR4gW+/DhA3dzqlWrVhqOhhCiyy5duoTOnTsDAH7//XcsXrxYwxERQooCVXoRnXf//n1u+cWLF/D19dVgNIQQVTMxMQEAlCpVCitWrNBwNIQQZYh2S6aZGwkh6vTt2zduee3atfjxxx9Rv359zQVECCkSNHsj0XmiM7elp6drMBJCiDo4OTkBAGJiYhAVFaXhaAghynj37h23TJVehBB1atasGdfrIyUlBY0aNcLDhw81HBUhRN2o0ovoPBrTixDd5u3tzS1TSy9CihfRmRup0osQok6VK1fG1KlTucc5OTmYPHmyBiMihBQFqvQihBBSrHl6enLLq1evxqdPnzQYDSFEGadPn+aWjY2NNRgJIaQkWLZsGW/SDNHWpoQQ3USVXqREoYHsCdE9nTp14i5gc3JyUL9+fXz48EHDURFCFCE6xk7Dhg01GAkhpKRo1qwZt/z+/Xv8888/GoyGEKJuVOlFdB51byRE9+3fv5+r1I6Li4OLiwvWrVuH7OxsDUdGCJEnKysLQN7sq2ZmZhqOhhBSEhw+fBguLi7c419//RVHjhzRYESEEHWiSi+i8ywtLbllc3NzDUZCCFGXH374AStXruQe5+bmYvz48fD09ER8fLwGIyOEyJKQkMBVerVs2VLD0RBCSgoDAwM8e/YMdnZ23Lpx48ZRV0dCdBRVehGd5+7uzi3XqFFDg5EQQtTpt99+w61bt1ChQgVu3f379zFp0iQNRkUIkeXt27fcsmirC0IIUTczMzOEhobCwcEBAPD161fs379fw1ERQtSBKr1IiUJjehGi21q0aIEPHz7g1q1b3LrLly9rMCJCiCyilV5VqlTRYCSEkJLI3t4ez549g6urKxYuXIjff/9d0yERQtTAQNMBEEIIIapWu3ZtGBkZITMzEwYG9FVHiDYSrfSqXLmyBiMhhJRUdnZ2ePDgATemYHJysoYjIoSoGv0SIDqvTZs23Fhezs7Omg2GEFIkzpw5g8zMTOjr66Ns2bKaDocQIsWePXu4ZRpzkxCiKcIKr5iYGPTt21fD0RBCVI26NxKd9+rVK1y6dAmXLl3iTY1OCNFdt2/fBgDk5ORgzpw5Go6GECLN58+fueXq1atrMBJCCAHCwsLw5MkTTYdBCFExaulFdN67d++48X2+f/+u4WgIIUXh69ev3HLdunU1GAlJn38dAGAy21ujcRDtI/qdXLVqVQ1GQgghgJeXFxo2bIgHDx5oOhRCiApRSy+i8xhjmg6BEFLEkpKSuGVLS0sNRkIIkSY7O5v7fq5atSr09OiSlBCieR4eHpoOgRCiYnSFQXReVlYWt5ybm6vBSAghRSUxMZFbtrCw0GAkJVvG6kBNh0C01KdPn5CdnQ0AqFOnjoajIYSQPI0bN9Z0CIQQFaPujUTnCcf2AYDQ0FB4e3trLhhCSJEICwvjluPj42FnZ6fBaEou4988YWJqqukwiBZ69+4dt0wzNxJCtAW19CJE91BLL1KiCAQCTYdACCkCTk5O3PLPP/+swUhKHmrdRRTx9u1bbpkqvQgh2oKGRCBE91ClFyGEEJ0zfPhwbvn8+fOwtbXlJrQg6pE+/zo3aD0h+Tl79iy3TC0xCSGEEKIuVOlFdB4NZE9IyTNt2jReV+b4+Hi0atUKPj4+2L59O2/ML1J4VNlFlCU6Oxq1rCCEEEKIulClFylRqHsjISXHlStXMGbMGBgaGnLrrl+/juHDh8PBwQE//fQTTW5BiIbExcVxy56enhqMhBBCCCG6jCq9CCGE6CQ9PT1s2LABSUlJ2Lp1K6pWrco9l5GRgX379qFnz55ISkrSYJTFG3VpJAVlYJA3l1KpUqVgbW2t2WAIIYQQorOo0ovovAoVKnDLDg4OGoyEEKIJxsbGGD58OMLDw3H37l0MHDgQ+vr6AICTJ09i/PjxGo6w+DKZ7S2xzvg3arVD5EtKSuK6GNevX1+zwRBCCCFEp1GlF9F5Li4u3HK5cuU0GAkhRJMEAgE8PDywe/dunDlzBubm5gCAY8eOIScnR8PRFV8ms72lVn4RIsu7d++4ZdHvaEIIIYQQVaNKL6LzaCB7Qoi49u3bo0OHDgDyWp2EhYVpOKLijyq/iKLevn3LLVeuXFmDkRBCCCFE11GlFylRaCB7QohQo0aNuOUlS5ZoMBJCSpaAgABu2dnZWXOBEEKIjvvhhx9Qq1Yt3h8hJY2BpgMgRN3Gjh2LHj16AABq1qyp4WgIIdrC1taWW96/fz9OnDgBBwcHtGnTBl5eXmjXrh3s7Ow0GCEhuun8+fPcclpamgYjIYQQPnXN6mxhvBalTAzzT6gCWblZRbIfQooLaulFdN769evh5eUFLy8vPH/+XNPhEEK0xLBhw3gVX2lpaYiIiMCWLVswcOBAlC1bFu3bt8fHjx81GCUhuufLly/csoeHhwYjIYQQvpSUFE2HoFJXr15FaGgo74+QkoYqvYjOozG9CCHS6OnpITw8HG3btoWJiYnE8zk5Obhw4QKmT5+ugegI0V3C72WBQIC6detqOBpCCPkfS0tLTYdACFExqvQiOk+00ovG9CKEiLKxscGFCxeQlpaGmJgYnDx5EqdOncLUqVO5NAcOHOC1TCGEFFxubi6SkpIA5A05oKdHl6KEEEIIUR8a04vovOjoaG45MTFRg5EQQrSZnZ0dunbtCgBwcXHBqlWrwBhDTk4OjTtEiIpERUUhIyMDQN7njBBCCCFEnajSi+i8sLAwbvnz588ajIQQUhykp6ejU6dOyM7OBgBUrFiRfpwToiJv377llitXrqzBSAghhBBSElCbclKiUPdGQog8z549g6enJz58+MCt8/f311xAhOiY169fc8tU6UUIIYQQdaNKL0IIIQTA0aNHUb9+fYSEhAAAzMzMcPLkSfj4+Gg2MEJ0iGglMk00QwghhBB1o+6NpEShll6EEGn279+PAQMGcI9NTU1x+/ZtNGjQQINREaJ7Pn78yC1Tt2FCCCGEqBu19CIlClV6EUKk8fPz45bNzMyowosQNfn+/Tu33Lx5cw1GQgghhJCSgFp6EZ1H3ScIIfJs2rQJ3759AwAYGBjg/fv3sLOz03BUhOgmQ0NDAHmzpdLnjBBCCCHqRi29iM6rV68et1ypUiUNRkII0UZHjx7lln/++Wf6IU6ImqSmpuLLly8AgOrVq2s4GkIIIYSUBFTpRXReqVKluGVzc3MNRkII0TZ3797F5cuXAQDOzs5Yv369hiMiRHdFRERwyzRzIyGEEEKKAlV6kRKFxvQihIiaP38+tzxz5kyYmppqMBpCdNvbt2+5Zar0IoQQQkhRoDG9iM4zNTWFtbU1AEBfX1+zwRBCtMaBAwdw7tw5AHldnwcNGqThiAjRbaJdiamCmRCibRISErB161ZNh0EIUTGq9CI67+eff0bLli0BABUrVtRwNIQQbZCYmAg/Pz/o6+sjJycHs2bNgpGRkabDIkSnhYSEcMs03AAhRJvs2rULQ4cORW5urqZDIYSoGHVvJDpv06ZN6NGjB3r06MEbT4QQUnJNmDAB8fHxyMnJQY0aNTB06FBNh0SIzouKiuKWmzZtqsFICCHkf758+YJhw4ZRhRchOopaehGdxxjTdAiEEA3JzMzErl27kJKSAgsLC1haWuLTp0/YuXMngLyJLi5evAg9PboHRIi6lSpVCl+/foW+vj4aNGig6XAIIQQAMHz4cOTk5AAAjIyMkJmZqeGICCGqRJVepEShgewJKTk+fPgANzc3pKamykyzZs0a6vZMSBFgjCEyMhIAUKVKFRgY0CUoIUTzYmNjufE9AWDz5s3U+psQHaO2W9vfv3/HgQMHMHnyZHh5eaFq1aqwsrKCkZER7O3t4e3tjeXLlyMuLk5mHllZWTh//jwmTpyIZs2awc7ODoaGhrC2toa7uzumTp3KmwlInsTEREyYMAHly5eHiYkJ3N3dsXfvXrnbCAQC7q9ixYr51vr7+/tz6a9fv65QXET9Xr58yS1/+fJFg5EQQorSn3/+KbfCq0uXLhg8eHARRkRIyRUdHc19HmnmRkKIthg/fjzXK8TJyQmtW7dWSb7fv3/HyZMnVZKXKtBvYVKSqe02W1BQEPr37y/1uZiYGNy4cQM3btzAX3/9hT179qBdu3YSadzc3KRWiiUmJuLRo0d49OgR1q5di+XLl2PChAkyY0lOTkarVq3w5MkTbt2jR48wcOBAvHnzBrNnz8739Xz8+BFbtmyBn59fvmmJdvn69Su3nJSUpMFICCFFhTGG48ePc49r164NfX19ZGRkwNraGp6enpg9eza1/iSkiIjepKRKL0KItrh16xa3vGjRIpXlGxQUhHHjxqksv8Kg38KkpFPrICYVKlTAoEGDsGbNGhw7dgyBgYG4c+cODh48iD59+kBfXx+xsbHo2rUrHj9+zNs2IyODq/CqX78+5syZg//++w8PHjzA1atXMXXqVJiYmCAzMxO//fYb/v33X5lxLFiwAE+ePIGbmxsOHTqEwMBAzJ8/H8bGxpg7dy5CQ0MVej2LFy9Genp6wd8QohGiY3rRD1xCSoasrCwkJycDACwsLBAcHIyQkBA8f/4cgYGBWLlyJaytrTUbJCEliOgPSycnJw1GQggh/2NmZsYt//DDDyrNu1y5cirNr6DotzAp6dTW0svHxwcfPnyQ+fyPP/6IEydOoEePHsjMzMS8efNw7Ngx7nmBQIA2bdpg/vz5Umf48fHxQa9eveDj44O0tDRMmzYN/fv3h6WlpUTaQ4cOwdzcHFeuXOEKn6ZNm8LS0hITJ07EkSNHUKtWLZmx2tnZITY2Fp8/f8bGjRsxceJEZd4KQgghRczQ0BBVq1bF69evkZycjNatW+PkyZOwtbXVdGiElEgXL17klhMSEjQXCCGEiKhevTpevXoFADA2NkZGRoZK8vXx8UFQUBAqVKigkvwKg34Lk5JObS299PX1803TvXt31KhRAwD/DiAAODo64uLFi3KntPbw8MDYsWMB5HV5vHTpktR0kZGRcHV1laht9/X15Z6Xp0OHDqhduzYAYNmyZXLHiCHaR7SlF83QRkjJIBAIsH37dpibmwMA7t27h549eyI7O1vDkRFSMoneCHV3d9dgJIQQ8j/Tp0/HwoUL0bt3b6mNJwpKkd/CRYV+C5OSTuM1AMLCpaBNJX18fLjlN2/eSE1jb2+Ply9f8sZ2AsANsJdf01M9PT3MmzcPQN74UOvWrStQrIQQQopOy5YtcfbsWZiZmSE7Oxs3b97kzdBECCk6hoaG3LKnp6cGIyGEkP9p2bIlZs6cicOHD8PExETT4agF/RYmJZ1GK71evnyJkJAQAICrq2uB8hBtgiqrRr1bt25ITk6Gr68vjhw5grt372Lx4sWYNm0aBAIBevXqle9+evTogfr16wMAli9fTgOiFyOirbu06a4LIUT9vLy8sGzZMu4xzeBKiGYIx9iztbVFxYoVNRwNIYQAN2/exLNnz3S+5RL9FiYlXZFXeqWmpuLVq1dYuXIlvLy8uK4mv/32W4Hyu3HjBrfs5uYmNc28efPg4uKC0NBQ9OnTB56enpg5cybS09Mxa9Ys1K1bN9/9CAQCzJ8/HwAQFxeHNWvWFCheUvSaNWvGLVetWlWDkRBCNEH0zi1NZkFI0cvMzMTHjx8B0MyNhBDt0bt3b9SpU4cbbkdX0W9hUtIVSaWXv78/BAIBBAIBzM3NUb16dUyePJlrYjljxgwMGDBA6XyjoqKwY8cOAECZMmV4XR1F2dnZITAwEMOHD0eZMmVgZGSEevXqYefOnVxTTUV06dIFjRs3BgCsXLkSiYmJSsdMip7omF6EkJJHdABtGtePkKIXGRnJfRdTpRchRBtkZ2cjJiYGQMGH2Sku6LcwKek0evVfv359BAUFYcmSJUrffWeMYdSoUVzTylmzZsnth+3g4ICtW7ciOjoaGRkZCAkJwaBBg5SOWVjD/e3bN6xcuVLp7YlmUSsPQkqWq1ev4vDhwwDyPv9NmjTRcESElDwvXrzglqnSixCiDQwMDLjfjrGxsQgODtZwROpFv4VJSVYklV7du3fH06dP8fTpUwQFBWH//v3o0aMHQkJC0L9/f5w5c0bpPBcvXozTp08DyBvM3s/PT9VhS9W+fXuuu9zq1asRHx9fJPslBdekSRP07dsXffv2RalSpTQdDiGkiCQnJ/PGqahduzY3+xAhpOiIXuclJCRoLhBCCBEhOpPs/v37NRhJ8UG/hUlxZFAUO7G2toa1tTX3uHHjxujXrx92796NwYMHo1u3bti2bRuGDBmiUH579+7FrFmzAAAuLi7Yt29fkXZZWbBgAXx9ffH9+3f89ddfWLJkiVr3l5aWhrS0NLXuozBEmwRrY/NgMzMzlC1bFkBeU2Ztfi+LM20/D0jR0Jbz4L///sP48eOhr68PW1tb6Onp4fTp0/T5LwLacg4QzRI99rGxsbC1tQUAVKtWjT6HJQiVBwTQ3vPA0NCQK5tcXFy0/jeXtijq38KEFJaAaXjAo759++LQoUMwNzfHhw8fYGNjIzf92bNn0aNHD2RlZaFs2bK4ffs2qlSpopbYhF3hBg8eDH9/f95zPj4+uH79OiwsLPD27VuUKVMG/v7+GDp0KADg2rVr8Pb2LvC+P336hAoVKgAAtm7dCjs7uwLnRQghhBBCCCFEvtjYWIwYMQIA8PHjRzg5ORU4L9Hfc/90rgJbM0OVxJifuNQsjD/zBkDhXoMmfwsTokoaH9G3W7duAICUlBScP39ebtrr16+jd+/eyMrKQunSpXHhwgW1VXjlR9ifOTk5GcuWLdNIDIQQQgghhBBCSFGi38KkOCmS7o3ylClThlt+//69zHRBQUHo0qUL0tPTYWFhgXPnzik0vaq6tGzZEm3atMGlS5ewYcMGTJkyRW37atWqVaHuMqhbeno6bt68CSAvVnkTCmjC+/fv0bFjR3z//p1bZ29vj0OHDmms0lQXaft5QIpGUZ8HHz58QNeuXREXF8dbb25ujmnTpmHQoEEwMND4V12JQmUBAfjnwezZsxEZGYmKFSvizp07Go6MFCUqDwigfefBtWvXeIO4jxs3DtOnTweQ1zqL5K8ofwsTUlga/yUQGRnJLVtYWEhN8+TJE7Rv3x7JyckwMTHB6dOn4eHhUVQhyjR//nxcunQJaWlpWLJkCRo0aKCW/ZiamsLU1FQteauaiYmJ1sXq6uqKs2fPokOHDlzFalxcHJo0aYLo6GiZ5x0pOG08D0jRK4rzYNq0aQgPDwcAVKhQAV27dkXr1q3h4+MDKysrte6b5I/KAgLkXevFxcWhQYMGdD6UYFQeEEDz50FmZiaGDx/O3SyrWrUqZs+ezY0PTeeo4orqtzAhhaXx7o3CqeQBoE6dOhLPh4eHo23btvj27RsMDQ1x9OhRrekf3LRpU3Tq1AkAsHnzZrozoMXc3NwQHh6OMWPGcP3T09LS0L17d80GRggpsCdPnuDUqVMAgHLlyuHRo0dYt24dunfvThVehGghFxcXTYdACCnh/vzzT3z58gVAXgVcQEBAkU6IpkvotzApLtT2Cff39893do5Vq1bhv//+A5B3IdSyZUve8x8+fEDr1q3x9etX6OvrY9++fejYsaO6Qi4QYX/mjIwMrFmzRsPREHmMjIywYcMG3pTEV65cwYULFzQYFSGkIJ49e4YOHTpwj6dNm8bNwEQI0U7U1ZgQommdO3dGhQoVYGRkhNu3b/OG2lElRX4L6wL6LUyKA7VdfcydOxeTJ09Gr1690KJFC1SpUgUWFhZISkrC06dPsXfvXm5cByMjI/z777/Q19fnto+Li0Pr1q3x8eNHAMDkyZPh6uqKZ8+eydxn6dKl4ejoqK6XJJW7uzu6d++OEydOIDY2tkj3TQqmb9+++Pvvv3H//n0AgJ+fH/z9/dG8eXOuFRghRHs9efIEjRs3RmZmJgCgZs2a+OWXXzQcFSEkP5oex4cQQlq1aoXHjx8jICAADRs2VNt+5s6di8TERLXlry3otzApDtR6yy0+Ph5btmzBli1bZKZxcnLC9u3b0bp1a976p0+f4tWrV9zj5cuXY/ny5XL3J2061aIwb948nDx5EoyxIt83KZizZ8+iQoUKyMzMxJs3b9CyZUu4u7vjyJEj1P2CEC0WEhICDw8PrsJLX18fhw4dgpmZmYYjI4Tkp379+poOgRBSQj169Ajp6enw9PRE6dKluW556pSQkKD2fWgD+i1MtJ3aujdeuHABK1asQM+ePVG3bl04ODjAwMAAlpaWqFKlCnr16oUdO3bg5cuXaNOmjbrCKBJ169ZFnz59NB0GUYK9vT2CgoJQvXp1bt3nz5+RmZnJa2FICNEe0iq8rl69ilq1amk4MkKIInx8fDQdAiGkhJo8eTKaNWuG9u3bIzo6Wu37u3DhAmbNmqX2/WgD+i1MtJ3aWnrVqFEDNWrUwKRJkwq0vbe3t8Zri5XZ/8GDB3Hw4EE1RkNUrV69enj69CkOHjyINWvWoGPHjhg2bBgCAgLg6emJc+fOSZ1cgRBS9M6fP49hw4bxKryuX7+OFi1aaDgyQogirKys4OTkpOkwCCEl0NOnT3Ht2jUAwOvXr2FjY6P2fdaoUQO//PILFixYoPZ9qUtR/RYWDi8zZ84czJ07t0B5ECIPTVVBSjQjIyP8/PPPCA4OxpAhQ7jZXCIjI9GqVStcvXpVwxESUvLk5uYiPDwcGzZswPDhw1GnTh106NABUVFRAPLGBaIKL0KKl4oVK9K4mYQQjdi2bRu3LOx9RFQnMzMT+/fvx6BBg+Dq6gpbW1sYGhrCzs4ODRs2xJgxY3D58mXk5uZqOlStIxAIFPrz9vbWdKjFGn3iCUFegVO5cmUEBgaiU6dOuH//PhISEtC2bVuEhISgdu3amg6REJ336dMnzJ07FwcPHkRycrLUNK1bt8aOHTuoxQghxUx2dramQyCElFCiN7HbtWunwUh0z7FjxzB58mRERERIPBcXF4e4uDg8fPgQmzZtQvXq1bFy5coiGU+NEFFU6UWICHt7e1y9ehVVq1ZFdHQ0cnJy0K5dO0RGRmo6NEJ02j///IMZM2YgNTWVt15fXx85OTlo2rQpRo8ejUGDBlFrEUKKoc+fP2s6BEJICZSZmYnQ0FDu8ZAhQzQXjI5ZsGABZs+ezT1u06YNunbtipo1a8La2hrx8fF4+fIlTp8+jUuXLiE8PBwzZ86kSi8pxowZg7Fjx8p83tzcvAij0T1U6UWIGEtLS1y5cgV169YFYwyfP3/GmjVrMGHCBE2HRohOOnLkCH799VfucalSpfDDDz+gQYMGKFWqFPr164eyZctqMEJCSGElJiZqOgRCSAm0a9curludg4MDKlasqOGIdMOOHTu4Ci97e3scOnQIXl5eEulat24NPz8/PHv2DBMnTkRMTExRh1os2NvbU88iNaJKL0KkqF27NoYPH46tW7cCABYvXkyVXoSoQWZmJn7++WfuccWKFfHw4UPY2tpqMCpCiDpkZmbCyMhI02EQQkqQ48ePc8s0LpJqREZGYty4cQDyWiDduHEDrq6ucrepXbs2Lly4gH379hVFiITw0ED2hMjwzz//QF9fHwAQHR2NlStXajgiQnTPhAkTkJ6eDuD/2LvvsCayrw/g34RepYkFEBAREDuiooAoYmHtvbD2tXddy9r1p64Fe29gF2zYCxYEFMWGCjZEkSYgINJbkvcPXmYTQ9UkA3I+z8PjzWTKCcRk5sy95wIqKip4+vQpJbwI+U19+/aN7RAIIdVMYGAg0x47diyLkfw+Nm/ezJSjWLlyZZkJryJcLhdubm4VPt7Hjx/h7u6Onj17wsTEBCoqKlBRUYGxsTEGDx6M69evl7mP1NRUrF69GnZ2dtDW1oaCggJq1qyJRo0aoW/fvti9ezcSEhKK3fbOnTsYOnQoTE1NoaKiAlVVVRgbG6Nt27aYO3cuTXxWBVBPL0JKoKysjCFDhuD48eMAgOXLl2PIkCGoW7cuy5ER8vsQvuP3v//9D3p6eixGQwiRptzcXLZDIIRUM0U31gDAysqKxUh+DwKBAIcPHwZQ2Mvrr7/+kurxPn36BDMzs2Kfi4qKQlRUFLy9veHm5gYPD49iZ+Z88+YNOnfuLFZbMikpCUlJSXjz5g18fHzA4/GYHmxFZs2ahS1btpR47EePHsHT0xNJSUk//yKJ1FFPL0JKcejQIdSsWRMAkJ6ejgEDBtBJOyESEhkZibS0NACAkpISZs6cyW5AhBCporpehBBZs7CwYNrCQx3JzwkLC2MSPA4ODtDQ0JDq8Xg8HhQVFdGzZ09s27YNt27dwrNnz3Dr1i3s2rUL1tbWAIBjx45h1apVxe7jzz//RFxcHBQUFDB58mRcunQJjx8/xqNHj3D27Fn8/fffaNCggdh2ly9fZhJeTZs2xe7du+Hn54fnz5/j7t272LFjB/r06QMlJaVffp2nT59Go0aNoKqqCg0NDZibm2PkyJG4e/fuL++bUE8vQkqlqKiI169fw8bGBlFRUQgKCsLKlSuxevVqtkMjpMo7ffo0054yZQq4XLoPQ8jvjJJehBBZW7FiBfr16wcACAkJYTcYFnz58qXMdQwNDcu9vxcvXjBtGxubn4qpIurUqYPIyEjUqVNH7DlnZ2dMnDgRY8aMgaenJ9zd3TF79mzUqFGDWefjx494+vQpAGDTpk1iPblat26Nfv36Yd26dUhNTRV5ztvbGwBgbGyM+/fvQ11dXeR5JycnTJkyBSkpKb/8Ol+/fi3y+MOHD/jw4QOOHDmCPn36wNPTU+R1kYqhKwxCyqCnp4dz584xF+RHjx5lOSJCqr6UlBRs3LiReTxp0iQWoyGEyIIkLgwIIaQiunfvjiZNmmD8+PFM8qs6ad26NYyMjEr9qYjk5GSmra+vL+lwxaipqRWb8CrC4XDg7u4OOTk5ZGZm4tatWyLPx8fHM21HR8dS96OtrV3sti1bthRLeAnT0dEp9TWURlVVFUOGDMH+/fsREBCA58+f4+bNm1i0aBFT49bHxwe9e/dGfn7+Tx+nuqOeXoSUg42NDc6ePYsaNWpAUVERmZmZUFNTYzssQqqsuXPnIjExEQDQt2/fYruVE0J+L7du3UKvXr3YDoMQUo0oKyvj5cuXbIfx20hPT2fabFwL5efnIyEhAenp6eDxeMxyXV1dJCYm4sWLF+jfvz+zXDhh5unpWaGJyYq29ff3R0RERIm1xX5FbGwstLS0xJa7uLhg2rRp6N69O54/f4579+5h9+7dmD59usRjqA6opxch5fTmzRt06dIF9vb2ePToEdvhEFJleXt748iRIwAATU1NbN++neWICCGyQD29CCFsysvLE+mpVB0EBwcjOjq61J+KEK7hlZmZKelwi5Wfn4+dO3eibdu2UFdXh5GRERo1aoQmTZowP0U3Un8sKG9qagoHBwcAhbNOWltbY+nSpbhz5w4zA2VJRowYAaCwd1vjxo0xZMgQeHh44MOHDxJ7bcUlvIrUqlULZ86cgYKCAgDQ+fIvoKQXIeVUs2ZNFBQUAABevXrFcjSEVE1ZWVlYuHAheDwe5OXlMXPmTBgYGLAdFiFEBoq+QwkhRJY+f/4MZ2dnaGlpYcGCBWyHI1N16tSBoaFhqT8VUTTkDgASEhIkHa6YlJQU2NnZYerUqXj06BHy8vJKXT87O1ts2cmTJ2FnZwegsHbWqlWrmPeDo6Mj9uzZIzLLZxFnZ2fs2LEDKioqyMnJgZeXF8aMGQNzc3MYGhpi4sSJIjXOpKF+/fpwcXEBUFjn68cZKEn5UNKLkHJq3rw5075z5w57gRBShZ05cwYfP34EALRr1w5Lly5lOSJCiKyYm5uzHQIhpBqqWbMmAgICkJ2djatXr7IdTpXWrFkzpv3s2TOpH2/GjBlMIfo+ffrg4sWLiIyMRFZWFvh8PgQCAQQCAVObTCAQiO3DwMAADx48wK1btzB58mRYW1uDw+EgPz8fAQEBmDRpEho3boz379+LbTtlyhRERkZi8+bNcHV1ZYrJx8bGYu/evWjRogUWL14sxd8A0KhRI6YdGxsr1WP9rijpRUg5tWjRArVq1QIAXLx4EYsWLWI5IkKqHj8/P6a9YsUKyMnJsRcMIUSmaPZGQggbVFVVmWFkcXFxMknW/K6sra2hp6cHAAgICEBaWprUjpWWlgYvLy8AwPDhw3H+/Hn07NkTxsbGUFFRAYfDYdb99u1bmftzdnbGzp07ERoaiq9fv+LUqVPo1KkTACAiIgKDBw8udjt9fX3MnDkTV65cQUpKCp4+fYrFixdDS0sLAoEAq1evxoULFyTwiosn/DrJz6GkFyHlJCcnJzI177p161iMhpCqSfguWps2bViMhBAia9K8OCKEkNI0bNiQaXt4eLAYSdXG4XAwcuRIAIU1vQ4cOCC1Y4WHhzMzFpaUkAKAt2/fIiMjo0L71tXVxeDBg3H79m1mgpWQkBCEh4eXuh2Xy0XLli2xatUq3L59m1nu7e1doeNXxOvXr5l23bp1pXac3xklvQipgAsXLjA9U3g8Xpnjygkhooq+uJWUlKCiosJyNIQQWaKeXoQQtvTo0YNpCycrSMXNmjULqqqqAIClS5fi7du35dqOz+fj+PHj5T6OcB3I0orm79mzp9z7LI6zszPT/rEQfmlatmwJbW3tCm9XEZ8+fYKvry8AwMzMjOrg/iRKehFSAREREcz0uEpKSlBUVGQ5IkKqjri4OKb7OZdLXz+EVDdFdVkIIUTWxowZw7TfvXuH1NRU9oKp4gwMDLBjxw4AhcmoDh064N69e6Vu8/r1a3Tr1g0bNmwo93EaNGjADO07fPhwsfW6Ll26xMRSnJCQEISEhJT4vEAgwK1btwAU9mIzMTFhnvPy8iq2MH6RJ0+eMOe1pqampb2UYl26dKnUCV4SEhLQv39/ppPF5MmTK3wMUkie7QAIqUpOnz7NtBs3bsxiJIRUPSdPnmTaP3NyQAip2pKTk9kOgRBSTenr66Nu3bqIi4sDn89Hnz59ROqMkooZPXo0YmJisHTpUiQmJsLJyQldunRB7969YWVlBS0tLaSkpOD9+/e4cuUKrl+/Dh6PJ1IIvyy6urpwdXVltu/SpQsmTZoEY2NjJCYm4uzZs/D09ET9+vWRmpqKr1+/iu0jJCQEo0ePhq2tLXr27ImWLVuidu3ayM/Px6dPn+Dh4cH0pOrVqxfq1KnDbDt//nxMnDgRvXv3hqOjIxo2bAg1NTUkJycjMDAQ27dvB1BYAmfcuHEV/h1OmzYN+fn56N+/P+zs7GBiYgIVFRUkJSXBz88Pe/fuZXqQ2dvbY8qUKRU+BilESS9CKiAyMpJpN2jQgL1ACKmCbty4wbTbt2/PYiSEEDYU1WYhhBA2rF+/Hm5ubgCAe/fuYcaMGdi6dSvLUVVdS5YsgbW1NebMmYPIyEjcvHkTN2/eLHF9a2trrF+/vkLH2L17N+zt7REVFYVbt24xvbKK1KtXDz4+PnB1dS11P48fP8bjx49LfL5du3Y4ePCg2PLU1FQcPnwYhw8fLnY7JSUl7NmzB61atSrHqxEXFxeH7du3Mwm04vTv3x8HDhyAkpLSTx2DUNKLkAoRHs4o3P2VEFK2ly9fMu3+/fuzGAkhRJZ0dXWRnJzMTPVOCCFsGD58OI4dO4br168DALZv347+/fvD0dGR5ciqrn79+qFHjx44c+YMrl27hsePHyMxMRHp6enQ1NSEiYkJ2rZtiwEDBsDJyanCMxEaGRnh2bNnWLduHS5cuIDPnz9DWVkZJiYm6NOnD2bMmMHU1SrO0KFDUatWLfj6+uLx48eIjY1FQkICCgoKoK+vj5YtW2Lw4MEYMmSIWOmNu3fv4tKlS/D398f79+8RHx+Pb9++QVVVFWZmZnB2dsakSZNQv379n/rdHT58GPfu3UNQUBA+fvyIpKQkpKWlQV1dHUZGRmjXrh1GjhwJOzu7n9o/+Q8lvQipAOET9o4dO7IYCSFVi0AgYOoiyMvLw8XFheWICCGyoqenh/fv35daiJgQQmThypUrsLS0RHh4OAQCAQYMGIAnT56gXr16bIdWZSkqKmLYsGEYNmzYT21fXK0uYbq6uli/fn2pvcSER+MIU1JSQvfu3dG9e/cKx2Vqaorp06dj+vTpFd62PDp06IAOHTpIZd9EFFUSJqSc8vLycOzYMeaxtbU1i9EQUrV8/PgRaWlpAAAXFxcqZE9INaKurg4AyM7OpiGOhBBWcblcPHv2jOnd5eDggJycHJajIoRIE/X0IqScrl+/jpiYGABA9+7dYWhoyHJEhFQdDx8+ZNrUTZuQ6kVTU5Npp6enQ0dHh8VoCCHVnbq6Os6fP4/4+Hg0atSI7XAIIVJGSS9CyqlTp0548eIFPnz4AAsLC7bDIaRKeffuHdNu0aIFi5EQQtgUHR1NSS9CCOt0dHTos4iQaoLGlxBSTt7e3mjWrBn69++PM2fOsB0OIVVKcnIy0y4a6kQIqR6ioqKYtnACnBBC2BQSEoK+ffvCysqKZnEk5DdGSS9Cyqlz587MjCObN2/G58+fWY6IkKpDeHhjUlISi5EQQmRNTU2NaScmJrIYCSGE/Ofz58/w8fHB27dvkZWVxXY4hBApoaQXIeVUr149/PnnnwCA79+/o3Xr1nTHmpAypKSkYOzYsXj27BkAwNzcHH369GE3KEKITBkbGzNtRUVFFiMhhJD/CJ/HX7lyhcVICCHSREkvQipgy5YtqF+/PoDCu9XNmjVDSEgIu0ERUkktWLAADRo0wKFDh5hlw4YNg7w8lZMkpDqxsbFh2hoaGixGQggh/+nUqRPTfvjwIc3iSMhvipJehFSAtrY29uzZAy638L9Obm4ubG1tce3aNZYjI6TyOX78OL59+wagsI7X5s2bsWTJEpajIoTImnAdv7S0NBYjIYSQ/7Rq1Qo1a9YEAPB4PCxbtozliAgh0kBJL0IqyMXFBb6+vpCTkwMAFBQUwNXVFfXr18eOHTvw5csXliMkhB18Ph9Dhw4VW+7g4IC3b99i5syZzP8bQkj1Idy76/v37yxGQgghosaNG8e0/f39WYyEECItlPQi5Cd06tQJQUFBUFJSYpZ9+vQJ06ZNg6GhISZMmICCggIWIyRE9mbOnInAwEDmsZycHFatWgV/f38YGBiwGBkhhE3CPb1SUlJYjIQQQkQJ1xmk4Y2E/J4o6UXIT7K1tUVMTAxsbW1FlvP5fOzbtw9HjhxhKTJCZC8oKAi7d+8WWRYSEoLFixezFBEhpLL4+vUr07569SqLkRBCiCjhAvaOjo4sRkIIkRZKehHyC/T09BAcHIzv379j586daNeuHXr06AEAiIyMZDc4QmSkaFjjj70btbS02AmIEFKpaGtrM+2srCwWIyGEkP+EhITgyZMnzOO//vqLxWgIIdJCU2gRIgGamprw9PTE48ePcfnyZTRp0gQDBw5kOyxCZCImJgafP38GADRq1IjlaAghlU1RoWgAyM/PZzESQgj5z6JFi5i2lZUVGjdujJiYGBYjIoRIAyW9CJGAgoICPH78GAAwYcIE+sIk1Yquri7TFq7dQwghAGBqasq0GzRowGIkhBDyn3fv3gEA5OXl4ePjw24whBCpoeGNhEiAvLw8rK2tAQCxsbFQUlKCra0tIiIiWI6MEOlTU1ODsrIygP9OIAkhpIiamhq43MJTzrS0NJajIYQQ4Nu3b4iOjkafPn2wfft2NGzYkO2QCCFSQj29CJGQJUuWYMiQIQCAvLw8PHnyBE2bNsW3b99EZoYh5HdkaGiIDx8+ID09ne1QCCGVDIfDgaamJlJTUynpRQipFDQ1NZGQkAAtLS0IBAK2wyGESBH19CJEQgYPHozjx49DQ0ODWZaVlQV3d3cWoyJENtq3bw+gsKg9IYT8SFNTEwDw/ft3liMhhBBATk4O7969w4ULF8DhcNgOhxAiRZT0IkSChg0bhrS0NEycOJFZRtOzk+pg2bJlIjO0EUKIsLy8PABAUlISy5EQQgjw4MED9OjRA/369cPOnTvZDocQIkWU9CJECoRnbnzz5g2LkRAiG6ampjh79qzIsvDwcJaiIYRUNpmZmQAAHo+HrKwslqMhhFRneXl56NSpE5KSksDn83HmzBnweDy2wyKESAklvQiRAicnJ8jJyQEAkpOTcefOHZYjIkT6OnbsCHt7e+bxli1b2AuGEFKpFE12ARRO+EIIIWzx8fFBbm4uAEBDQwPnz59nztsJIb8fSnoRIgVcLhfdunVjHvft2xf+/v4sRkSIbAjPfkRF7QkhRZo2bcq0qfYfIYRN169fZ9qOjo7Q0tJiLxhCiNRR0osQKTl//jxcXV0BFE7R3rVrV0RHR7McFSHS9erVK6atp6fHYiSEkMrEzMyMaefk5LAYCSGkunv69CnT7tChA4uREEJkgZJehEiJgoICTp8+zfT46tq1K75+/YoDBw6wHBkh0nHhwgU8fvyYeUwnkoSQIkWzNwKFN4IIIYQtwkOse/TowWIkhBBZoKQXIVKkqqqKCxcuwMbGBgoKCmjdujUuXLjAdliESMWtW7dEHvfu3ZulSAghlY1w0uv79+8sRkIIqe6EJ9OwsLBgMRJCiCzIsx0AIb+7u3fv4unTp3j69Cm4XC6mT5/OdkiESMWHDx/YDoEQUklxOBymTZ8VhBA25eXlASiswcvlUh8QQn539L+cECn7559/mPb27dvh4uLCYjSESIdAIEBwcDDbYRBCKqnIyEim/ejRI/YCIYRUa3w+HzweD0BhKRJCyO+Pkl6ESBGfz8ezZ88AFE6JPHHiRJYjIkQ6IiIikJKSwnYYhJBKSkdHh2mnpqayFwghpFoTrimora3NYiSEEFmhpBchMpKRkcEkwAj53Vy8eJHtEAghlZiJiQnTVldXZy8QQki1JlxT0N7ensVICCGyQkkvQqSIy+XCyMgIQOHwr9atW2Pp0qUsR0WI5D18+BAAqDYGIaRYtra2TLt27dosRkIIqc5UVFSwYMECjBs3Dp07d2Y7HEKIDFAhe0Kk7OzZs7CzswOPx4NAIMCqVatw+/ZtBAYGihT2JaQyEwgESElJwZcvX6CpqQkdHR2oqqoiJycH165dg4+PDwBAWVmZ3UAJIZUSzd5ICGEbj8eDvr4+1q5dy3YohBAZolvyhEiZra0t3rx5I3Jn+8GDB1iwYAGLURFSPteuXUOnTp2goqICPT09NGnSBMbGxtDQ0ICcnBzU1NQwYMAA5OfnAwAGDRrEcsSEkMqoRo0aTFu4pg4hhMjKwoULMW3aNOachRBSPVDSixAZMDc3R2xsLHr37s0s27BhA16/fs1iVISU7tq1a3B1dcXdu3eRm5tb5vrOzs6YP3++DCIjhFQ11NOLEMK248ePY8eOHejWrRsEAgHb4RBCZISSXoTICJfLhY+PD1avXg2gcLjY6dOnWY6KkJI9ePBA5HHr1q1Rq1Yt6OjoQFNTE6qqqlBVVcWwYcNw8OBBXL16lQpUE0KKpaKiwrQfP37MYiSEkOoqPT0dAODn54eEhASWoyGEyAolvQiRMTc3N6YdHx9Pd5pIpZWTkwOgMGG7YMECPHr0CPHx8UhOTsb379+RmZmJzMxMHD9+HGPGjIGioiLLERNCKisOh8PUsSxPz1FCCJG0WrVqAQD4fD6mTJnCcjSEEFmhpBchMlavXj2MHTsWPj4+MDQ0xL///st2SIQUq0WLFgAKTw6PHDmC+Ph4liMihFRlcnJyAEA3ewghrFi/fj3T9vX1ZTESQogsUdKLEBbs3r0b27dvx+LFi3Hz5k22wyGkWMOGDUOPHj0AAHFxcXBxccGbN29YjooQUlVZWVkB+C/5RQghsiRcWzc7O5vFSAghskRJL0JYoKCggBcvXgAAwsPDWY6GkJLt3bsXxsbGAIDQ0FBYW1uLDNElhJDy0tLSAgDk5eXREEdCiMy9e/eOaQtPrkEI+b1R0osQljRs2BAAEBsbiyZNmkBdXR3q6upo2rQpLl++zHJ0hBSqW7cubty4ASMjIwCFw5KOHz+OjIwMliMjhFQ1wheZaWlpLEZCCKmO/Pz8mLaBgQF7gRBCZEqe7QAIqa66d+/OzI4XGhrKLH/16hX69u2L7OxsyMvTf1HCPgsLC7x8+RLa2trMMmVlZRYjIoRURTVq1GDaaWlpqFmzJovREEKqm8jISKZdXT5/OnXqBAUFBZFlYWFhLEVDCDvoipoQlsyaNQvbt29HYmKi2HMFBQXYv38/Jk2axEJkhIhbuXIl09bX16eELCGkwlJSUph2aGgozMzMWIyGEFLdFJVrAIBHjx7h06dPMDU1lcmxuzVcC8Maskm0xXz/CmCQTI5FSFVAwxsJYYmamhq+fPmCw4cPw93dHR8/fsSff/7JPP/hwwcWoyNE1PHjx5k2JWMJIT/j27dvTPvTp08sRkIIqY5GjBjB3LTLzMzEX3/9xXJE0nfnzh2EhYWJ/BBS3VDSixAWcblcjBgxArNnz4apqSmaN2/OPNe6dWv2AiNEyK1bt5geiRwOB7Nnz2Y5IkJIVSRc0+vr168sRkIIqY7U1dVx7do16OrqAgCmTp3KckSEEFmgpBchlYjwXXDh+kmEsMnb25tpT58+nWY8IoT8FBsbG6Zdu3ZtFiMhhFRXnTt3xps3b7Bx40b07NkTABASEoJt27axHBkhRFqklvRKS0vDqVOnMGfOHHTo0AENGjRAjRo1oKioCH19fTg5OWH9+vVITk4udT8fPnzAyZMnMWvWLLRv3x6qqqrgcDjgcDjw9PQsdzzfv3/HjBkzULduXSgrK6Nly5Yiw3WKU3QcDoeDevXqIS8vr9T1PT09mfWFZwchpLy+f//OtIumdieEbTwej2nb2dmxGAkhpCqzsrJi2lwu3XclhLCjZs2amDNnDuTk5HDx4kXY29tjxowZ8PLyktgx0tLScOHCBYnt71fRtTCpzqRWiTg4OBhDhw4t9rmvX7/i3r17uHfvHjZs2IBjx46ha9euYuvdu3cPTk5OvxxLRkYGHB0d8fLlS2bZ8+fP4ebmhoiICCxdurTMfURHR2P//v2YMmXKL8dDSHF4PB64XC4uXbqEqKgomRXWJKQsAwcOxKFDhwAAu3fvxuDBg1mOiBBSFQn3Ek1LS2MxEkIIKfT+/XtkZmYCAIYPH44nT55IZL/BwcGVZvgkXQuT6k6qt9mMjIwwYsQIbN26FefOnUNQUBDu378PLy8vDBw4EHJyckhKSkKvXr3w4sULse0FAsF/gXK5sLa2/qk6R6tWrcLLly9hZWUFb29vBAUFYeXKlVBSUsLy5cvLXdBvzZo1yMnJqfDxCSmPAwcOYOvWrRg2bBj09fWrzVTKpPLr0qULGjRoAADw9/fHrFmzWI6IEFIV1ahRg2kL92wmhBC2zJkzBwYGBgAKb0BPmDBBYvuuU6eOxPb1K+hamFR3Uuvp1bFjR0RFRZX4/KBBg+Dj44O+ffsiLy8PK1aswLlz50TWMTAwwIYNG2BrawsbGxuoq6vD09MTwcHBFYrF29sbampquH37NvPh07ZtW2hoaGDWrFk4c+YMrK2tS9xeT08PSUlJiIuLw+7du+mCj0jF5cuXAQDp6elQUlJiORpC/sPlcrF8+XKMGDECfD4fW7ZsgbGxMWbOnMl2aISQKkRFRYVpR0ZGshcIIYT8Pw6Hg4MHD6Jbt24AUOHrzJJ07NgRwcHBMDIyksj+fgVdC5PqTmo9veTk5Mpcp0+fPrCwsAAABAQEiD1vbm6OuXPnokOHDlBXV//pWGJjY2FpaSmWbXd2dmaeL0337t3RuHFjAMC6deuQlZX107EQUhIdHR2mvXnzZhYjIUTc8OHDsWDBAubxrFmzJFr7ghDy+8vIyGDajx49YjESQgj5T9euXUUm2pCE8lwLywpdC5PqjvUqohoaGgAg1a6S+vr6ePfuHRISEkSWFxXYK6vrKZfLxYoVKwAACQkJ2LFjh1TiJNXbggULmB5ed+/exeLFi1mOiBBRq1evxrJly5jH69evZzEaQkhVI3y+RRdNhJDK5OzZs2yHIDV0LUyqO1aTXu/evUNISAgAwNLSUmrH6d27NzIyMuDs7IwzZ87g4cOHWLNmDebNmwcOh4P+/fuXuY++ffuiefPmAAov9NLT06UWL6merKysRN6La9aswb59+1iMiBBxy5YtYz6vQ0JCEBcXx3JEhJCqwtDQkGmrqamxGAkhhIgyNjaGvr4+22FIBV0Lk+pO5kmvrKwshIeHY9OmTejQoQMKCgoAQKq1YVasWAFTU1OEhYVh4MCBsLOzw6JFi5CTk4MlS5agadOmZe6Dw+Fg5cqVAIDk5GRs3bpVavGS6uvo0aPM3RaBQIAJEybA3d2d5agI+c/06dPx9u1bAACfz8fVq1dZjogQUlXUqFEDioqKTJsQQioTe3t7tkOQCroWJtWdTJJenp6e4HA44HA4UFNTQ8OGDTFnzhymi+WCBQswbNgwqR1fT08PQUFBGDt2LGrWrAlFRUU0a9YMhw8fZrpqlkfPnj1ha2sLANi0aRPNPEQkjsvl4v79+1BVVWWWFX3BEMKmmJgY1K9fX6RLu4KCAlP4lRBCykNTUxMAzd5ICKl8Ro8ezXYIUkHXwqS6Y3V4Y/PmzREcHIy1a9eCw+FI9Vi1atXCgQMHkJiYiNzcXISEhGDEiBEV3k9RAuLbt2/YtGmTpMMk1diDBw8we/ZsuLm5idQ6UVZWZjEqQoCgoCDY2Njg06dPzDITExN8+PBBZLgSIYSUpaiHV1paGsuREEKIKFdXV6Y36u+GroVJdSaTpFefPn3w6tUrvHr1CsHBwTh58iT69u2LkJAQDB06FJcvX5ZFGBLRrVs3tGvXDgCwZcsWpKSksBwRqeoEAgEuXLiAe/fuYfPmzXjw4IHI88uXL2cnMFLtFRQUYOXKlXBwcEBiYiKzfNKkSfj06RPq1avHYnSEkKpIuKeXQCBgORpCCPkPl8vFsWPH2A6jUqNrYVIVycviIFpaWtDS0mIe29raYsiQITh69ChGjhyJ3r174+DBgxg1apQswvllq1atgrOzM9LS0rBhwwasXbtWqsfLzs5Gdna2VI/xK4Rn3pTmLJy/q+DgYIwdOxZ79uyBs7MzQkJCYGZmBgcHB3Tp0gUODg6V+u9fhN4Hv5f8/Hz8+eefuH//PvP5zeVywefzcfv27WKLverq6qJGjRpYuHAhAKBt27bIzc0Fl/vf/ZXs7GxkZmaKbKegoCBS34fD4SAnJ0esl+P379+Rl5cnskxVVVWkIDafz0dWVhbU1dVF1vv69atYvNra2lBQUGAe5+TkgMvlitzlzcvLQ2pqqsh2HA4HNWvWFFmWlpbGXMwXSU9PF/u/q6ysLLZeVlaWyJBmoLBeBo/HE1mmqakp8jvJz89Hfn6+yLY8Hg/Jyclir1VXV1dk+vTMzEwoKSlBXv6/04Ds7GyxwrTy8vLQ0dERWZadnQ0VFRWRZWlpacz/e21tbWZmz6LvSqAwwZ+RkcHM2lzk69evYskPbW1tkb9Dbm4uADAz3GppaWHlypVo27at2GsllUNJ3wlZWVnQ1dUFAMTHx4ucH5JCfn5++Pfff5GZmQl5eXnk5+eLPP/t2zexz1EAqF27tsj/aSMjI0RHR4usEx8fz9TTLaKgoIBatWqJLDM0NBTZtqCgQGzmN6BwQoIf/4YcDof5P62hoYHZs2cDKBzhIfx5qqurK/KZpqurK/b5lZycXOw5Rd26dUVGiZiamuLjx48i6xQ30YqSkhLz/iuir68v8tpyc3OLvZDX0NAQ+W7h8/ki329A4WdhcX8bPT09ke8bQ0NDxMTEiKzz9etXsb8Nl8sV+9sYGxsjMjJSJI7i/jYqKipifxtlZWWR32d2drbYdxxQ+Bkr/DmvoaEh9v2Qmppa7Pmpvr6+yPeNiYkJkpOT8ffffwMAXFxcEB4eLva5r6CgIPZ9U7duXZG/Y35+frF/G1VVVZHvlqJ9C79HMjIyiv3b/Ph98+N3NBEn62thQn4VR8DybbbBgwfD29sbampqiIqKEvuw+5Gnpycz3trDw0OqibKiD8qRI0fC09NT5LmOHTvCz88P6urq+PjxI2rWrCkS2927d+Hk5PTTx46JiYGRkREA4MCBA9DT0/vpfRFCCCGEEEIIKV1SUhLGjRsHAIiOjv6lMg7C13Phs71hWKNmGVtIRsz3rzDfNAjAr70GNq+FCZEkVmt6AYVTqAKFd52vX7/OcjTlVzSeOSMjA+vWrWM5GkIIIYQQQgghRProWphUJTIZ3lga4SEinz9/ZjGSinFwcICLiwt8fX2xa9cuzJ07V2rHcnR0rNTFonNycuDv7w+gMFYqvF5xZ8+excyZMwEUdmX38vKqcsN26H1AAHofEOm+B/h8PkxMTCAQCMDlcqvUeUN1U9L7YO7cufDy8gIAXLlyBU2bNmUtxsro0KFDWLZsGYDCmeSq+gzO9J1AgKrzPsjLy6ty599skeW1MCG/ivWkV2xsLNP+sQZLZbdy5Ur4+voiOzsba9euRYsWLaRyHBUVFbH6KZWVsrJylYm1Mhk+fDi2bduGx48fAwC6dOmCT58+VepkZ2nofUAAeh8Qyb8HAgMDkZSUBKDwphm9v6oG4feBpqYmU7cpMzOT/oY/iIuLY34/ISEhv9Xvh74TCFC53wcqKirYsWMHOnfuzHYoVYKsroUJ+VWsD288ffo0027SpAmLkVRc27Zt8ccffwAA9u7dK1aQkpDy4nA4OH36NFNIs6CgAP3792c5KkIIqVyKeggBQMOGDVmMhPws4Ukrvn//zmIklZNw4fHiim4TQqTLwsKC7RCqDLoWJtLA5/Mlvk+pJb08PT3LnMFt8+bNuHr1KoDCWVccHBykFY7UFHU7z83NxdatW1mOhlRlxsbGmDhxIvM4LCyMxWgIIaTyEb5RVlQTlFQtwjOj/TgbHIHI7H00EzIhVVd5roV/B3QtTH5VXl4e7t+/j7Vr16J79+5SmdVZasMbly9fjjlz5qB///6wt7eHmZkZ1NXVkZ6ejlevXuH48eO4f/8+AEBRURH79u0Tmd62yJkzZ5CRkcE8DgwMLLYNFE7V3K1bNym9ouK1bNkSffr0gY+PDzPkgpCfsWTJEmzbto153L59exajIYSQyiUxMRGJiYkACmsfzpgxg+WIyM8Q7r30+PFjjBgxgsVoKp/t27czbSsrKxYjIYT8iuXLl1eL3qx0LUwqKjMzE0FBQfD394e/vz+Cg4OlniCWak2vlJQU7N+/H/v37y9xHUNDQxw6dKjEsdNz584tsVDtwYMHcfDgQeZxhw4dZJ70AoAVK1bgwoULEAgEMj82+T34+Pjgf//7H/NYVVVVZBgPIYRUd3v37mW+Z//8809mODipWoQvAt++fctiJJXPvXv38OXLFwCFiV3qNUFI1Zaamsp2CDJB18KkNN++fUNAQAACAgLg7++P58+fi/Rq5nA44HA4AAq/+5o1aybxGKSW9Lpx4wauXLmC+/fv48OHD0hISEBycjJUVFSgr6+P5s2bo0ePHhg0aBBUVVWlFYZMNG3aFAMHDoS3tzfboZAqasmSJUzb2toafn5+UunaSQghVVF4eDhWr14NoPCEaP78+SxHRH6W8KzdaWlpLEZSuQgEAixfvpx5PH78eOjp6bEXECHkl9y4cQPHjx/HqlWr2A5F6uhamAj78uUL/P39mSRXWFiYSEJUOMmlpKQEW1tbODg4wNHREe3atZPK5IZSS3pZWFjAwsICs2fP/qX9REZGSiagn1CRbLWXlxf1zCE/RSAQ4PXr1wAKez6+evWK+SAghJDqTiAQYOLEicjNzQUAzJw5k4Z9VWGNGjVi2sbGxixGUrk8ffoUfn5+AABzc3ORcgeEkKrHwsIC48ePr9JJL7oWJuXx8eNHZqhiQEAAIiIiRJ4XTnJpaGigXbt2cHBwgIODA9q0aQMFBQWpxyjV4Y2EkLJxOBzUrVsXMTEx0NXVZTscQgipVBYsWIA7d+4AKEySrFixguWIyK+oX78+05bFiW5Vcf78eaY9d+5c+t0QQgipEszNzZkEqXCCCyjs3V2U4HJwcEDz5s1Z6dxBSS9CKgFPT09oaGggPDwcHz58gLm5OdshEUJIpRAVFQUAUFFRwf/+9z+pdHsnsiM8eyMNb/zPp0+fAABaWlqwsbFhORpCCCGkYoo6cnTs2BEdOnSAg4MDGjZsyHZYAAAu2wEQQgov6tq0aQM3Nzc8fPiQ7XAIIaRS4PP5TC8veXl5DB48mOWIyK8STnpVh5nNyqtJkyZo2bIl0tLSsH79eioITQghpEoRCARISkrCp0+f8PHjR3z8+BHp6elshwWAkl6EVArC07TyeDwWIyGEkMrj1atXSExMBAA4OTnRkK/fgJKSEvN3jI+PZzmaymPs2LGIiIgAn8+Ht7c31q9fz3ZIhBBCSJlmz56N1q1bQ0FBAXl5eXjw4AHWrl0LV1dXaGtrw8bGBjNnzsTZs2fx9etXVmKk4Y2EVAJhYWFMu3bt2ixGQgghlYevry/TdnFxYTESIklFU5UXDekjgL6+Pg4dOoT+/fsDKKxlFx8fD3d3d3C5dI+aEEJI5bRhwwYAhZ04Hj58iICAAAQGBuLhw4fIyMhASEgInj9/zkzQ0rBhQ6bGl6OjI0xMTKQeIyW9CGFZUlIS9u7dC6CwqG/btm1ZjogQQtiXmJiINWvWMI+dnZ1ZjIZIkpycHAoKCqhn8w/69euH1atXY9GiRQCALVu24NKlSwgJCaFadoQQQio1ZWVlODk5wcnJCUDhcMfnz58jMDCQ+UlISEB4eDjev3+PgwcPAgAMDAyYBJiDgwOsra0lHhslvQhhWa9evZi73np6etDS0mI3IEIIYVlKSgosLS3x7ds3AIU3BCwtLVmOikiKhoYG87cVCASszORUWS1cuBA8Hg9Lly4FAERERMDQ0BBPnz6FmZkZy9ERQggh5cPhcNCyZUu0bNkS06dPB1D4nRYYGMj0BgsPD0dcXBy8vLxw6tQpAIC2tjaSk5MlGgv1lyaERWfPnkVQUBDz+MCBAyxGQwghlUPTpk2ZpAgAHDp0iIZ4/UaaNWsGoDDhlZ2dzXI0lQuHw8GSJUswe/ZsZtn3799hZWWFwMBAFiMjhBBCfo2ZmRlGjhyJAwcO4O3bt4iPj8eZM2cwZMgQyMvLg8PhiJz/SQr19CKEJQUFBRg1ahTzuEOHDnB1dWUvIEIIqQTc3d0RGxvLPN6/fz/c3NxYjIhIWo0aNZh2WloaVFVVWYymcnJ3d0fjxo0xbtw48Pl85Ofno0uXLkhJSYGysjLb4RFCCCE/7d27dyLDHiMiIgBAaj2/KelFCEtGjhyJjIwMAIVDd3x8fNgNiBBCKgHh3q/jx4/HuHHjWIyGSIOmpibTTktLowlcSjB69Gg0bNgQHTt2RH5+PrKzs7FlyxYsWLCA7dAIIYSQcikoKMCzZ8+YIY33799HUlISgP+SXBwOB2ZmZkyBe0mjpBchLMjIyMCZM2eYx//++y/V8iKEEIjOYOvo6MhiJERahJNe379/ZzGSyq99+/a4dOkSXF1dwefzsWLFCgwaNAj169dnOzRCCCFETHp6OoKCgpheXMHBwcjKygLwX5KLy+WicePGTPF6BwcHqd4Ao6QXISx4+vQp8vLyAABWVlYitTsIIaQ6q1evHtOOiYlhMRIiLZGRkUz7/v37sLW1ZS2Wz58/Izk5Gc2bN6+0deO6du2K6dOnY8uWLcjJycG4ceNw8+ZNyMvTaTwhhBB2ffnyhenFFRgYiFevXjGzMxcluRQVFWFjY8Mkudq3by9S6kDa6NuSEBaEhoYy7ZkzZ7IXCCGEVCIZGRlwd3dnHnfu3JnFaIi0FJ0MA4Uny2zZtGkT5syZA6DwrvPVq1fRtWtX1uIpzcqVK3HmzBnExMTg3r17mDdvHtatWwcFBQW2QyOEEFKNGRoaQiAQAPgvyaWuro62bdvCwcEBjo6OaNu2LZSUlFiLsXLe0iLkNyec9GrcuDGLkRBCSOUxadIkJCYmAgAGDhwIGxsbliMi0iA8nF/S05JXxP3795k2n8/HoEGDWIulLBoaGjh58iT09PRgamqKzZs3o02bNrhz5w7boRFCCCHgcDgwNTXF7t27kZSUhJs3b2LJkiXo0KEDqwkvgJJehLDC0NAQ3bt3h5GREaytrdkOhxBCWMfj8eDr6wugsBv8P//8w3JERFqcnZ2ZdpMmTViLY9myZSKP09LSmLojlZG9vT3WrVvHDA99/vw5nJ2dYWdnhyNHjrAbHCGEkGpNIBDg48ePmDhxImrVqoU//vgD69atw4MHD5Cfn89qbJT0IkSGBAIBNm7ciOXLl8POzg4REREyHc9MCCGV1b1795CQkAAAcHV1RfPmzdkNiEiNcLHa9PR01uIICQkRWxYbGyv7QCpgzJgxCAoKEkkWPnz4ECNHjoShoSGuXr3KYnSEEFJxHA4HHA4Hy5cvZzsU8hOmT5+O5s2bQ05OjvlbpqWl4dq1a1i4cCHs7e1Ro0YNODk5YcmSJbh58yYyMjJkGiMlvQiRkdDQULi6uuLvv/9GQUEBli5dihs3brAdFiGEVAoBAQFMe+DAgSxGQqStMszeuGTJEowcOVJs+aJFi7BlyxY8e/aMhajKx9bWFs+fP8fRo0dRq1YtZnlsbCz++OMPWFhY4NGjRyxGSAipTvLy8nDy5EmMGDEClpaW0NXVhYKCAvT09GBjY4NJkybh1q1b4PP5bIdaZcyfP59JIHE4HPj5+bEdUok2b96MZ8+eITk5GZcuXcLff/+NNm3aQFFRkYk/NzcX/v7+WL16Nbp16wZtbW20atUKs2bNwrlz5/D161epxkiF7AmRAQ8PD4wdO5Yp8gcAc+bMwR9//MFiVIQQUnkIn/CoqamxGAmRNuGkV1paGisxHD16tNjlp0+fxunTpwEARkZGOHHiBOzt7WUZWrnIycnBzc0Nffr0wdixY3HmzBnmgvL9+/do164dvLy8MGDAAJYjJYT8zs6dO4c5c+aIzMpbJDk5GcnJyXj27Bn27NmDhg0bYtOmTXT9U4aQkBBs2rSJ7TAqTFNTE66urnB1dQUAZGdnIygoCP7+/vD398ejR4+Qk5MDoLCO5rNnz/Ds2TNs3boVANCwYUM4ODjAwcEBI0aMkGhs1NOLECnLy8vDpEmTmISXnp4erly5go0bNzIzXBBCSHXXsGFDpj106FDExMSwGA2RJjk5Oab94sULVmLQ0dFh2r179y62vmZ0dDScnJzw+PFjWYZWIerq6vDy8sKXL1/g4uLCLOfz+Rg6dCiioqJYjI4Q8jtbtWoV+vfvzyS8XFxcsH37dty+fRtPnz6Fr68vduzYga5du4LL5eL9+/dYtGgRu0FXcnw+H+PHj0dBQQH09fXZDueXqKiooFOnTli+fDnu3LmD79+/IzAwEGvWrEG3bt1Qo0YNkd5s79+/x8GDBzFq1CiJx0JJL0KkbPHixcjNzQUAKCsrIzAwkMmAE0IIKTRu3DgoKysDKLw7aGVlhZSUFJajItJQ9HcGgI8fP8r8+K9fv8abN2+Yx1OnTsXLly/x5s0bBAYGonv37sxNKR6PVyW+s/X19XHz5k18+PABLVq0AAAUFBQgMDCQ5cgIIb8jDw8PLF26FEDh54+fnx9u3ryJqVOnolOnTmjZsiU6d+6MKVOm4Pr163jx4gU6d+7MctSV37Zt2/D48WNYWlpi7NixbIcjUfLy8rCzs8P8+fNx5coVpKSk4MmTJ9i0aRP69OmDmjVrSq1DCCW9CJGys2fPMu0FCxbAwsKCxWgIIaRyUlVVxbFjx5gTnoyMDIwZM4blqIg01K1bl2kX3RSSpZ07dzJDLNzc3NC5c2dwuVxYWlqiffv2uHr1Kl6+fMn0SEtKSsKdO3dkHufPMDMzE7lQ4vF4LEZDCPkdxcbGYurUqQAKyxHcu3cPHTp0KHWbxo0b48aNG5g7d64sQqySoqKisGTJEgDAnj17oKioyHJE0sXhcNCiRQvMmDEDZ8+eRUJCAkJDQ7F7926JH4uSXoRIUUFBAT59+sQ8njVrFovREEJI5da/f394enoyj69cucJeMERq1NTUIC9fWFZWS0tL5sf39fUFACgoKGD79u3FrtO4cWOR4YKLFy+WSWyS8PnzZ6Zds2ZNFiMhhPyONm/ejKysLADAypUrYWlpWa7tuFwu3NzcKny8jx8/wt3dHT179oSJiQlUVFSgoqICY2NjDB48GNevXy9zH6mpqVi9ejXs7Oygra0NBQUF1KxZE40aNULfvn2xe/duZgbpH925cwdDhw6FqakpVFRUoKqqCmNjY7Rt2xZz586V2E2RKVOmICMjAyNHjiwziVgZxcbGYvPmzZg8eTKmT5+OvXv3Ijk5uUL7sLKywoQJEyQeGxWyJ0SKDh8+zNTyqlOnjkjxXkIIIeKMjY2ZNo/HA5/PB5dL9+h+N7q6ukhISGCltmVERAQAQFtbu9Sk24YNG5iLqUePHiErKwuqqqqyCPGXxMTEoFWrVkhPT0eTJk3YDocQ8hsRCAQ4fPgwgMIbGH/99ZdUj/fp0yeYmZkV+1xUVBSioqLg7e0NNzc3eHh4MDdUhL158wadO3dGXFycyPKkpCQkJSXhzZs38PHxAY/HY3qwFZk1axa2bNlS4rEfPXoET09PJCUl/fyLBODt7Y3Lly9DR0cHGzdu/KV9scHT0xOTJk0S6729cOFCHD9+HN27d2eWRUVFYf/+/ZCXl8fSpUtlch5ASS9CpOjmzZtMu0uXLixGQgghVcP//vc/pj179mxKeP2mNDU1kZCQgO/fv8v0uGFhYcwsh2UNrWzcuDHq1q2LuLg48Pl8rFixAuvWrZNFmD9NIBDg5s2bSE5ORq1atWBgYMBKHF27doWfnx/s7e0xffp0VmIghEheWFgYk+BxcHCAhoaGVI/H4/GgqKiIrl27wsXFBY0aNYKOjg5SUlLw/v177Ny5E2FhYTh27Bjq16+PFStWiO3jzz//RFxcHBQUFPDXX3+he/fuqF27Nvh8PmJiYvDw4UOcP39ebLvLly8zCa+mTZti0qRJsLKyQo0aNZCamoqwsDDcunULwcHBv/QaU1NTMWPGDADAunXroKen90v7k7WHDx/ir7/+Ao/HA4fDgaqqKpSVlZGSkoLv379j4MCBeP36NerVqwcAMDAwgIeHB+Li4uDo6IiOHTtKPUZKehEiBVFRUfjjjz8QGhrKLFu5ciWLERFCSNUgfLJHQ7N+X0U9n9PS0iAQCGTW42vYsGFM28rKqsz1J0+ezAxt9PDwqPRJrxcvXjDDSWxsbCS23/z8fHz79q1chYb5fD5z04+t2TkJIYW+fPlS5jqGhobl3p/w/2lJfsaUpE6dOoiMjESdOnXEnnN2dsbEiRMxZswYeHp6wt3dHbNnz0aNGjWYdT5+/IinT58CADZt2iTWk6t169bo168f1q1bh9TUVJHnvL29ART2QL9//z7U1dVFnndycsKUKVN+edKdefPmIT4+Hu3bt6+Sxes3bNgAPp8PZWVleHp6YtCgQQCAu3fvok+fPkhPT8eGDRuYcgJycnLo168fdu7ciatXr8ok6UW3TwmRsAsXLiAgIECkYH3Dhg1hZGTEYlSEEFI1LFq0iOndtXbt2grXgyBVQ9HFA5/PR1pamkyO2bNnT7x8+ZJ5XDTzWGnmz5/PFLT/+vUrnj9/LrX4JOHChQtM+48//vjl/QkEAkydOhWqqqqoVasW9PX14eHhUeo2XC632IR1QUHBL8dDCKmY1q1bw8jIqNSfihD+TtbX15d0uGLU1NSKTXgV4XA4cHd3h5ycHDIzM3Hr1i2R5+Pj45m2o6NjqfvR1tYudtuWLVuKJbyE6ejolPoaShMQEIADBw5AXl4ee/bsYWXI/6+6f/8+BAIBZsyYwSS8AKBjx474+++/weFwcPnyZZFt2rZtCwAICgqSSYyU9CJEgjIzMzFu3DiMGzcO06dPR79+/bBkyRLcunWrSn6IEUKIrDVu3BgjR44EUNgLaNy4cSxHRKTh/fv3TFt4whdpCQkJETnpHjp0qEiNkZLIy8uLrHf79m2pxCcJYWFhWL16NfO4V69ev7zP0NBQ7Ny5k0lYJSUlYezYsSLF8ovTo0cPsWVmZmYwNjbGX3/9hfz8/F+OjRAie+np6UxbTU1N5sfPz89HTEwM3rx5g9DQUISGhiIuLg66uroAxHuXCifMhCfKKY+ibf39/ZlakJKUl5eH8ePHQyAQYNasWWjcuLHEjyELRT3khCd/KVJU3ufz588i75369esDAN69eyf9AEFJL0IkysPDA0lJScjJycG+fftw9uxZrFy5knp5EUJIBSxcuBCKiooQCAS4evUqfHx82A6JSJiKigrT/rG4sDScPn2aaTds2BAnTpwo97YbNmxg2jdu3JBoXJLk6urKJJMcHR0rNGSpJMJlGooIBAKYmJjAyMgI4eHhxT5///59seV8Ph9RUVE4cOAAtm3b9suxEULKFhwcjOjo6FJ/KkK4hldmZqakwy1Wfn4+du7cibZt20JdXR1GRkZo1KgRmjRpwvwkJiYCgFhBeVNTUzg4OAAonHXS2toaS5cuxZ07d5gZKEsyYsQIAIW92xo3bowhQ4bAw8MDHz58kMjrWrNmDd6+fYt69eph2bJlEtknG4rKUggPKy0iPAmBcC9BZWVlAJBZXU9KehEiQWFhYUx74sSJLEZCCCFVl7m5OebMmQOg8E5o3759YW9vjz///LPYi3BS9QgPiynrwkMScnJymHaDBg0qtG1R8V1AdKhMZeLh4YGoqCgAhcN0du3aJZH9Cv+dfuy9FRMTg3/++UdsG39/f6YnX9EQlh/9auFnQkj51KlTB4aGhqX+VERRjyoASEhIkHS4YlJSUmBnZ4epU6fi0aNHyMvLK3X97OxssWUnT56EnZ0dAOD169dYtWoVnJ2doaWlBUdHR+zZs0fkO6KIs7MzduzYARUVFeTk5MDLywtjxoyBubk5DA0NMXHixJ+uW/j27VusXbsWALB9+3ZWes1JSrNmzQCIXgcXEU6ECX/XFyVbZfW6KelFiAQJd/cvumAjhBBSccuXL8fw4cOZx/fv38exY8fQpEkTdO7cWSaJEiI9zs7OTFtLS0vqxxP+fi7roulHY8aMYdotW7aUWEyStHPnTqY9aNAgWFtbS2S/wr06Xr9+jf3794s8f+bMGWzdulVkmfA6wjXUiqioqDA9KAghVUtRggMAnj17JvXjzZgxgylE36dPH1y8eBGRkZHIysoCn8+HQCCAQCBgRtUIBAKxfRgYGODBgwe4desWJk+eDGtra3A4HOTn5yMgIACTJk1C48aNRYbdF5kyZQoiIyOxefNmuLq6Mkmc2NhY7N27Fy1atGAmO6mIzZs3Iy8vD/Xr10dWVhZOnTol9iN8k+/OnTvMcln1sCuvMWPGgMPhYNu2bczsyEXk5f+bN1H4b/PgwQMAgImJiUxipNkbCZGgunXrMu3g4GAcPnyYqU1DCCGk/BQVFXHkyBG0adMGe/bswevXr5nnbt++DUdHRzx58oTFCMmvKJq9EZD+8IaXL1/i7NmzzGPhhFtZ9u7dCy8vLwCFJ+/Tp0+XeHySIHwxYWtrK7H9WlpaMu1Pnz4xvSOEZzmbNWsWevXqBVNTU3z58kVkKKlwctrc3BzLli2Dm5ubWMHoymTLli148+YNVq1aBX19faSkpMDX1xc5OTlQU1NDr169oKioyHaYhLDC2toaenp6SEpKQkBAANLS0kQ+zyUpLS2N+fwdPnw4jh07VuK63759K3N/zs7OzOd/cnIybt26hX379uHOnTuIiIjA4MGDi52sRF9fHzNnzsTMmTPB5/MREhKC8+fPY8eOHUhNTcXq1atha2uL3r17l/u15ebmAiicXXLo0KFlrr9q1Sqm/enTp0rVM6x///4YMmQITp06BTc3Nxw8eFCkhMGP4uLisGvXLggEAnTu3FkmMVJPL0IkaN++fahVqxbzOCAggMVoCCGkauNyuZg2bRrCwsJw6dIlNG3alHnu6dOnUiksS2RD+CJJ2rM3Cs/mZWBggHnz5pVrOz6fj9mzZzOPBw0aBBsbG4nHJwnCPbvu3bsnsf1qamrC2NgYQGFizd7eHv/88w9UVVWZdQQCATPhxKRJk0rsSXfr1i1MmzatUie8IiMjMWvWLOzbtw9WVlYYPnw4dHV1MWTIEIwaNQoDBw6Ejo6OWG+Giqpob0NCKgsOh8Pc0M/MzMSBAwekdqzw8HCmTuHgwYNLXO/t27fIyMio0L51dXUxePBg3L59m5n0IyQkpNg6hcK4XC5atmyJVatWiUxs4u3tXaHj/07u3buHsWPHon379jh16hTMzc2xcOFCnD17Fnfv3mXWCwoKwrZt29CmTRukp6dDVVVVZjeSKOlFiARxuVxmzDhQ+OFJCCHk1/Xo0QMvXrwQGVqxZMkSFiMiv0K4zoe0k15fvnxh2h4eHuByy3f6u2vXLqankrKyMg4ePCiV+CThn3/+gZycHADg4cOHEk2qeHl5MTNQx8XFITQ0FJmZmSJF/b99+4a4uDhcvHiRWSY8rAUoHBoqi0kLfoVwXZ+UlJRiJzzIzMwU6ekmjM/nw8/PDytXrsTIkSOxZs0avHnzhvl7JCYmomHDhlBSUoK9vb1UXgMh0jZr1iwm8b106VK8ffu2XNvx+XwcP3683McpmjUWKL1o/p49e8q9z+II9/79sRB+aVq2bMkk8SuyHVA4k2TR0MySfoSL29+9e5dZLqshgeXVqVMndO7cGYGBgQAKvyfWrVuHgQMHwtnZmemJPH78eMycORNxcXFQUVHBsWPHZDbZGyW9CJEw4Q9OKrhMCCGS5e7uzrRPnjyJfv36MQW8SdUhXAD55s2bUj2WcN2Zhg0blns74TpZo0aNYmabqowsLS3Rv39/AMDXr19Fkk+/qk2bNli0aBHzuGioqIuLC2rWrAmgsIBxnz59mIsbIyMjTJo0SWQ/ycnJcHR0lFhc0mBqalrmOq6urtDR0UFBQQEuXryI5cuXw9XVFXXq1IG8vDw6duyIZcuW4ciRI1i0aBEaNWoEVVVVGBkZwcTEhOlJcv/+ffj7+zP7zcvLQ0hIyC/3IiNE2gwMDLBjxw4AhcmoDh06lNnD9PXr1+jWrZvIbLhladCgAZNwP3z4cLH1ui5dusTEUpyQkJBSOyEIBAKmNzCHwxFJKHl5eRVbGL/IkydPmGGV5fns+F1xOJxy/9SoUQNubm549uwZ+vTpI7MYqaYXIRI2efJkzJkzB3l5ecjNzcXFixeZbrOEEEJ+jbOzM6ZMmcIkJM6fP4+7d+8iODgY5ubmLEdHyktBQYFpS3MGsIyMDObus4mJichMjGUR7iEmnPSprOzs7JghNuPHj0ezZs0k9n9i1apV+Pfff1FQUIDMzExkZGRAXV0d1tbW8PPzQ15eHh4/fsysf/DgQbi4uIgUwgeAiIgINGjQACNGjEDLli1hZ2cnMhsc20q7wAUAMzMzXLhwAX379sWlS5fA4/HKtV8ej4eYmBix5aNGjcLRo0exatUq3Lp1CzweD23atMHDhw9/Kn5CZGX06NGIiYnB0qVLkZiYCCcnJ3Tp0gW9e/eGlZUVtLS0kJKSgvfv3+PKlSu4fv06eDyeSG/tsujq6sLV1ZXZvkuXLpg0aRKMjY2RmJiIs2fPwtPTE/Xr10dqaiq+fv0qto+QkBCMHj0atra26NmzJ1q2bInatWsjPz8fnz59goeHB3x9fQEAvXr1Qp06dZht58+fj4kTJ6J3795wdHREw4YNoaamhuTkZAQGBmL79u0AADk5OWaId3V0586dUp/ncDhQUVGBnp4ea73UKOlFiIRxuVy4ubnh0KFDAAoL6FLSixBCJGf79u2ws7PDnDlzkJCQgNTUVPTo0QNv375l7gqTyk24/mVFa7FUxNGjR5mhZd26davQ+0M4MTdixIgyT+zZ9u7dO6b97ds3WFhYYOzYsVBWVkZGRgYaN2780zNLf/r0SWSoUVEh9+KSRAoKCnBxcQEALF68WKwnX0REBDNsh8PhoGvXrhgwYABGjx5d7qGnksLn87Fr1y5cuHABkZGRYnUC3759CysrK6aHyciRI+Hi4gI/P79i98fhcKCnpwdzc3PUq1cP2traSElJwbt37xAXFwcNDQ18/fqVGdL76dMnsWGOZdUUIqSyWLJkCaytrTFnzhxERkbi5s2bpfbctba2xvr16yt0jN27d8Pe3h5RUVG4deuWSI1GAKhXrx58fHzg6upa6n4eP34skpj/Ubt27Yodwp6amorDhw/j8OHDxW6npKSEPXv2oFWrVuV4Nb+nyt6DF6CkFyFS4eLiwiS9ZH0CRwghvzsOh4Phw4fD1dUVFhYW+PbtG96/f4+jR49ixIgRbIdHyqFx48ZMW7gIu6QJzyQofAe/PObNm8cUvb979y7Onz+Pvn37SjQ+SRozZoxIbRuBQCBWZPrFixc4cuRIhffdo0cPpt2gQQMm6fX+/XuxdYVnshZmb2+PCxcuiCwTCAS4fv06rl+/ji1btuD58+ditcCkhc/nw9LSssQk0+TJk2FhYQFNTU1mhtGlS5eKrKOrqwtra2uYm5ujV69ecHV1LVf8PXv2xOXLl4t9rn79+hV8JYSwp1+/fujRowfOnDmDa9eu4fHjx0hMTER6ejo0NTVhYmKCtm3bYsCAAXBycqrwjSkjIyM8e/YM69atw4ULF/D582coKyvDxMQEffr0wYwZM0qdHGPo0KGoVasWfH198fjxY8TGxiIhIQEFBQXQ19dHy5YtMXjwYAwZMkTsmu3u3bu4dOkS/P398f79e8THx+Pbt29QVVWFmZkZnJ2dMWnSJPo/WwVQ0osQKRC+y1GR+iGEEELKT1tbGxs3bmRmkvr333+rZdLr69evOHr0KN69eweBQAA3NzfY29tX6psu+vr6TLuoWLw0CBdOr+hQv7///huXLl1iZmJeu3ZtpU562draYvbs2di0aVOJ6xw9ehTLli2DmZlZufbp6emJffv24fXr18yykydPMu309HSxbUq663/y5ElMmTIFHh4exT4fGhqKoUOHiiQqJW3Hjh3YtWsXtLW1ERoaWuwkCkZGRti6dSvztw4NDUW/fv1EeokYGRlh/fr1GDJkiMi2WVlZePToEezs7ET+/2VlZSEtLQ21a9cGUFiH6J9//sG+ffvA4XBEimCPHTtWoq+ZEGlTVFTEsGHDMGzYsJ/avrhaXcJ0dXWxfv36UnuJRUZGFrtcSUkJ3bt3R/fu3Sscl6mpKaZPny6zGQZ/tHz5cixfvpyVY0tbUlISU6ZCuGC/tFTesyFCqqi7d+9i+PDhqFGjBjQ1NdGpUye2QyKEkN/WiBEj0Lx5cwAQmSXtd/bu3TsMGTIE06dPR9++fWFoaIg5c+Zg37592L9/Pzp06ABFRUWYmJhg6NChOH/+vMjQtMpAuNaTNGdvFC4K/jOF6Hft2sW0P3z4IJGYpGnDhg1iNbJGjRol0vtq5cqV5drXgwcPMHr0aAQFBTHLevfuLTKMp2PHjiLbaGtri/zOhP31118lJryKlHcWuJ+RmZmJmTNn4s2bN3jw4IHI+65bt27w9PTEt2/fEBUVJZLcNDQ0xP3793HlyhVs27YNx44dw/v378USXo8ePYKamhrs7e2hoqICPp+PnJwctG7dGmpqakyh+/79+yMjIwNr1qxBUlISvn79ivbt2zP7OXToUKX7/0oIIZKUmJiIlStXYsWKFTI5HvX0IkSCrly5gj59+qB9+/a4cuUK3r17Bx0dHbbDIoSQ35qcnByAwuHkshoaxaZ27dohJSWl1HV4PB4+f/6Mz58/49SpU+BwOBgwYABOnDhRKX5HCgoKUFVVZXrASIuqqirTLut3VpxGjRoxbQ6HA4FAIDY8Jy0tDYqKiqzP7piRkYFWrVohOTlZZPmjR49EeryVNXFAXl4eXr58iYULF4osb9KkCby8vESWXbx4EcOHD0dsbCwsLS2xZcsWqKurF7vf69evM20jIyOsWrUK69evF+lFZmdnV+y2YWFhmDp1Kh49egRVVVWMGDECrVq1Qnx8POLj4xEUFISHDx9CIBCAz+eL9BxRVFSEhoYGFBQUxArPc7lcjB07Fvv27UNSUhJu3LgBBwcHsSGaCgoKZdYM+vPPP5l2Xl4ePDw8cPfuXZEeYjweD+fOncPVq1dx7NgxZsbNhQsXMkNIHz9+jEaNGiE0NJQZRkoIIZWZQCDAnTt38ODBA3z58gU5OTml9uBLTU1l2qNHjxZ5rqybIz+D/bMeQn4Tb9++xcCBA1FQUIB79+4hICAACxYsYDssQgj57RXV2xEuPP47K25IWVkEAgFOnz6Nmzdv4ty5c5WiF7KioiKysrIQGxsLAMjPz8fNmzfh7+8PNTU1zJw5E8rKyigoKBBJXlWE8HbCJ9nlxeVy0aFDB9y7dw8pKSk4ceIEhg8fzjz/999/Y+PGjeBwOJg1axbc3d1/Ks5flZSUBBcXF5Fi9kXevHkj8tjBwUFsndTUVIwePRqXL18utpfRgAED8O+//0JJSUlkeUFBAbp37w4fHx88efIE7dq1A5/PZ35q1qwpci6krq6OsWPHYsSIEfj8+TOWLVuG4OBgHD58GKmpqbh16xZq1aoFgUAAgUCAvLw8ZGRkiPTYy87OxubNm8v9u8nLyxNLBNaoUQOurq5o3bo1TE1N8ezZMzg5OTH/t+Tl5cHlcsHlciEnJwd5eXnIy8tDQUEBTk5OcHV1hY2NDSwsLCAnJ4d3796J1QabPHmySNJUWE5ODoYMGYIpU6agS5cucHR0xLRp05jZ4MLDw9GuXTs8efKk3K+TEELYkJmZCRcXl5+edfbHSQIo6UVIJVVQUIAhQ4YwsxgNHjwYf//9N8tREUJIxcXHx+PVq1fQ0dGBtbU1671XyqMo6ZWbm4t169aJ9VD53UyYMAE7duz4qW2/f/+OLl26wN3dHdOmTfvpul9hYWGYPHkylJSUsHv37nLXiBKWmZkJoHCmwTVr1mDXrl1MAgwA3N3dkZubi9zcXJibm2Pr1q0VrstS1AsQKH6mwfKYPn067t27B6BwqODr168xfvx4nD9/Hhs3bgRQmFTctGkTunXrxsxcKCs8Hg89e/ZESEhIqespKytj/PjxWLRokcjylJQU1K1bF7m5ucVuZ2dnx9TZ4vP5CAgIwPr16/HgwYMyE4k/Pp+RkYGtW7di69atxa7/6dOnUvcnKd+/f8fJkydF6pMJK2144alTp3Dq1CkAgJqaGkxNTUV6qxXJy8sr9W9SUFDA/C7k5OSgp6cHFRUV5n369OlTuLu7//SMm4QQIgvr16/Ho0ePwOFwwOVyoa+vL3aD5Ef5+fmIi4uDQCCAiYmJ1GOkml6ESMDChQvx4sULAICxsTEOHTokcqJNCCGV2e3bt1G3bl1wOBzUqVMHXbp0QatWraCiooI6derAzs4OgwYNwsiRI3Ht2jWRXheVgfCwoqVLlyImJkYmx+Xz+Th//jx27dqFr1+/Sv14ycnJGDx4MHbv3l2h7ZSUlDCpdV/IcQu/l3g8HmbOnAlHR8diZ98rS1paGlq3bg1/f3/4+vqiQ4cOFd4HUHjSW2TRokUiCa+i4xQlYsLDw+Hq6gpVVVUMGjQIa9euxfXr18scqie8zyZNmvxUnH379mWGXxQUFGDNmjUwMTHBrFmzxNb9cfifLOzcuZO5w/5jb0cOh4OJEyciNjYW2dnZxSabtm/fLpLwUlVVRc2aNWFnZ4fDhw/jwYMHSE5Ohru7OywtLeHk5ISrV6/+VM+5301mZiZCQ0NFPhM3btxYaq9TRUVFTJ8+XeQ8kcfjISEhQSwxe+zYMckHTQghEnTp0iUIBAIMHToUqampiI2NxcePH0v9EZ707cfnpIGSXoRIwI0bN5j2wIEDf3oYBiGEyBKfz4eLiws6d+6ML1++FLtOfHw8Hj58iNOnT+PIkSNwdXWFvLw8jIyMMG/evEpx4bt69WqoqakBKExKFA2xkqYLFy5AS0sL/fr1w5QpU6Cvr4+mTZvC399fIvsvKCjAkSNHsGDBAvTv3x82NjaoU6cOvL29xeoSlcbAwAAPHjzApj+m48Nsb/Rt9F+C6v79+2jWrBnmzZtX7gkA8vLy0K9fP5EZF2NjY9GuXbvyvzgUvq9+xOFw4OrqWuLsf0Bhb63Tp0/jn3/+Qffu3VG7dm3o6OjA3t4eZ86cEVtfOBkpXCy8IjgcDvbt24e5c+eWuW5xQwelKScnB2vXrmUeCycSlZSUEBAQgN27d4vVqBImnBhs1KgRMjMzkZiYiAcPHqBevXrQ1dVFrVq1MHfuXLEhfL8TZWVlDBkyBCoqKj+9j0aNGqF58+Z4+/YtJkyYgDFjxsDa2pqpA9ekSRO8ePECW7duRXx8PE6fPo0pU6bA1NS02P1RXVhCSGUXEREBAJg2bRpzLlaWsmbslDQa3kiIBAifMI4bN47FSAghpHxycnLQuHFj5mQFKLy4L5p5tm3btoiOjkZkZKRYQkwgECAmJgYbNmzAhg0bYGFhgT179sDJyUnGr6KQsrIyrl69yvQ4io6OxsaNGzFv3jypHG/Dhg3F7vvVq1fo0KEDpk6dilWrVkFLS6tC+w0PD8e2bdtw9epVfPr0qdSTwlq1amHgwIEwNTVFeno6MjIymH8VFRVhaGiI7t27o1WrVlBQUEDOZT/U1tDBicHLERAZgkn+OxAREYGcnBxs2LABu3btwokTJ9CrVy+xY+Xk5CAgIADe3t44c+ZMsYnOoKAgHD9+XKTeVWlevXol8njr1q34448/mGGSffv2hY+PD4DC+kqdO3dGQEAAcnNzxYaeffv2Dffv38f9+/cxduxYHDhwgHmuaAgll8uFnp5euWIrjry8PDZs2IA+ffpg6tSpCA8PR3Z2tkhyVUVFRaTXYUXx+XxMmDABoaGhmDdvnsgMgj/KysrCxYsXsWDBAiaBqKSkJNJj68WLF7CwsCjzuObm5iKvQTgeV1fXnx4WyhZjY2M0bNgQhoaGMDQ0hLq6OnJycmBtbQ0bGxvUq1ePGdYbHx+P4OBgmJmZwdraGkDhOd2FCxcQExODuLg4XL16FWlpacjLyyvzQu3169fo3Lkz5OXl0bRpU3A4HPTu3RtHjx5FjRo1YGJiwhxbT08PAwYMwIABAwAA/v7+GDp0KDPpAJfLxT///COtXxMhhEhE0ffsr3zHShslvQiRAOGZsGiaaUJIZZeXl4f69euLJLNat26NCxcuoHbt2mLrZ2dnw8vLC15eXnj69KnYUL53796hY8eOmDlzJtasWSP1+Ivj6OiIuXPnMjWW/ve//2Hq1KkS73mbkpKC+fPniywzMTFBjRo1mGHuO3bswI4dO9CrVy94eXmVWBft48eP8PDwwI0bN/D69WvmxLE4HA4HlpaW6Nq1K0aPHo2mTZtWKG7lpU5M2wVOCF5sieV3DmJ7UGHvqMzMTPTu3Rvy8vLM7HvZ2dmlXuhv3LgR+/fvZ4qnjxs3Dr6+vtDX18fIkSOL3SYlJQVOTk4iSS8zMzNMnz5dZL2zZ89iyZIleP/+PRYuXIiWLVsCKPyOff/+PZ4/f47nz5/j+vXrCAsLY7Z79uwZ005ISGBiL2lGwYpq3749nj9/zjy+f/8+7O3tAQCGhobg8Xg/XSfN3d2dSdj169cPL1++FBuS+fjxY7i5uYkNS+VwOFi8eDGWLFnCLAsKCiox6fX69Wt4enri1atXuHXrFrNcuBehn59fpUp4cTgcKCoqQl1dHbq6uqhTpw6T4GrcuDFatGgBQ0PDCv3+a9euLZboNTAwwOTJk4tdPzU1FaGhoQgLC8OHDx/w4cMHPH/+HDExMSK/u4KCAua9+PTpU6xZswZKSkowMzODk5MTRowYgTZt2ojs29HREdHR0Vi+fDmCgoLg7u5e4f/nhBAia7LutfUzKOlFiATUqFEDiYmJAAov/oruFhJCSGXUoUMHkYTX7NmzS511TkVFBaNGjcKoUaMAFPb82bJlC/bv3y9Sf2HLli0ICwvDlClTpBZ7aTZs2ICYmBicOnUK6enp8PPzg6urq0SP8fjxY7ETvMjISPSytIdbtyn4+/pOZvnFixfRqVMnPHjwAEBhj6S7d+/izp078PLyQlJSUqnHql27Nrp27YpFixbB2NgYioqKEnkNsQZG0J1wFOu7TYG5rhHmXNuOfF7hDZuCgoJSh6yqqamhd+/eGDt2LDp16gRnZ2e0aNECQOH7omgWpkOHDuHgwYMACmuRGRoags/no1WrVmLFyosbEsjlcrF69Wqx5fLy8mjUqBEaNWqE4cOHY+PGjVBRUUFOTg4AQF9fH+fPn4empiY0NDSY7aTVC7FZs2aoXbs24uPjER4ejq5du+L06dPQ1dWt8L6+ffsm8riogHxR8f6YmBi0a9eu2JtrU6ZMwZw5c0SSXufPn2f+zwqLi4uDjY0N8zsTxufz4ezsjJCQkGKflyYlJSVoaGhAV1cXTZo0Qffu3ZneWoaGhtDU1JRpPMXR0tKCvb09k+gUFhkZicDAQAQEBCAgIEBs1szc3Fy8fv0ar1+/xq5duyAnJwcTExO4uLhgyZIlqFu3LrhcLlauXCmrl0MIIb+Mw+H8VOLrZ7f7GZT0IkQChGsuCA8VIoSQysbDw0NkWuk1a9ZUeLZDZWVlLFiwAAsWLEBMTAwcHBwQGRkJQLSnDRu6d+/OzKz27t07iSe9OnToAFVVVZGaVgBw5+NTnBy8Akv8DookC0JCQtCuXTvk5ubi+fPnpZ7gKSkpoXHjxhg8eDAmTJggtYt83QlHmfZftr3Q39oJwx5uQnBwMHJyckRilJeXh5KSEmrWrIm1a9eiZ8+eIjU7mjdvjqFDh5Y4Cx4A2NvbQ0FBgSmmXkRLSwtjxozBhg0bfun1FNVLAgprbArX2SxiY2PzS8coibq6Og4cOIB+/fohLy8Pd+/ehaGhIU6cOFHq8MTi/FgXLTMzE/369UNwcDBev36NCRMmiCS8uFwuM7yyqHehsJ49exZ7nBMnTpSY0Hr58mWFYq6Ijh074tu3bxAIBEwPraZNm6J58+al1hyrKkxMTGBiYgI3NzcAwNu3b3H48GH4+vri7du3Yj05eTweIiIiEBERgT179sDa2hqdO3dG586d0b59e2hra7PxMn4Kn8/HlClT8O7dO6xevRp2dnZsh0QIkZGfGeXUqFEjmY6OoqQXIRIgfLL2/ft3FiMhhJCSvXnzRmQYWd++fSuc8PqRoaEhIiIiMHbsWJw4cULkuZs3b6J3796/tP+KEq6xVNKwwl+hrKyM5ORk7NmzB2/fvsXevXsBADw+H/y5rZGzQjSZkJ2djaCgILH9cDgcqKiowMLCAi4uLhg1ahSsrKwkHm+xr+H/hzrmrPRD8t7CGlR+sdHM83FxceByucUOdY01MEIqAAOh9U+cOIEFCxbg5MmTiIqKQq1atZi6RACQkZGB5ORkkf1MnDixwrNQlqR9+/YiQ/SK07VrV4kcqzh//PEH/Pz80KdPHyQmJiInJwf9+vXDP//8U2xvtZK4urpCR0cHKSkpzLKcnJxih7gdOXIEI0aMKHFfLi4uGDFiBK5evYobN27g8ePHiIiIQHJycoUmQigLh8Nhemjp6emhbt26MDExgYWFBRo3bgwbGxvm/X/kyJFfKhJf1VhaWmLt2rXMRAPx8fE4cuQIrly5gpcvX4r1qAwLC0NYWBgzw6aGhgZatmyJQYMGYdSoUTKZJGnatGnYuXMnrK2t8eLFi3INFeXz+bC0tGQmOWjXrh327NmDCRMmSDtcQggpF0p6ESIBy5Ytg4+PDwQCATZs2ABTU1OMHTuW7bAIIYTx/ft39O3bFxkZGQCAwYMHiyWpfhaXy4WHhwfmzp2L0aNHM8v//fdf/PHHHyJ1D6UtJCSEaf+YaJEUZWVlzJgxA/v372eWaSqrQV1dHRoaGkhPTy92u6ZNm6Jz585wdnaGvb0968O1lJc6wWBpNGINjBBrYMQksira6ybWwAi6ANYKJcKys7OZKcmFL5yVlZUxfvx45sJeEm7cuIGdO3fi7NmzSEhIwOfPn0V6lA0fPlysfpKk2dnZ4caNG2jfvj3TC3DNmjVwdXUt96yRXC4X+vr6Ikmv4owaNarUSRp0dXXx4MEDKCkplf8FlEBZWRlNmzZFy5YtmWGGNWvWhJycHFq2bImaNWuWun1lqgnGttq1a2PevHnM3y4tLQ2HDh1iatQFBweLJO3T09Nx79493Lt3j5nhce3atRg8eLBU4nv//j327t0LgUCA0NBQLFq0SGRm0JJMmDBBbFbP5cuXY9iwYSJDjAkh1YOvry8uXryI58+fIzk5GXJyctDX10erVq3Qq1evYoeHSxslvQiRgGbNmmHMmDE4ePAg8vLyMGnSJHTo0AENGjRgOzRCCEFWVhb69+/PFBxv2rQpDh069NMFt0tibW0NX19f+Pn5ASicjXD06NE4evRo6RtKkHDCRngmO0kpKCjAuXPnsHbtWpEE29xlhT3mAgMDMXv2bISFhSEvLw/6+vpwcXHBhAkTKm29R+FeW7+ybqyBUbHrPHz4ECdOnIClpSUzU50kcblcTJs2DdOmTWOWXb58GUeOHEGXLl2KrWslDc2bN0d0dDSaNGnC9HRbvnw5fH19y70P4R5ywjgcDpydnWFiYiIyO2Vxykr21qhRA05OTli3bp1IofuMjAw8e/YMubm5aNGiRaWeiet3oKmpiZkzZzKPU1NT4efnh6NHj+Ly5cvIy8sTWf/Tp08YMmQIJk+ejLlz52L+/Pk//RmelZWFrVu34tKlS5CXlweHw0FwcDDy8/OZdTZs2IBx48YxM6oK4/P54PP5CAkJKfb9GB8fj61bt2Lx4sU/FR8hpOr5+PEjhg0bhuDgYGZZUfmB169fw8/PDxs3boSdnR0OHz4s0+tkyZ7tElKN7d69G1OnToWCggJOnTqF5cuX48qVK2yHRQip5vh8Plq0aIHbt28DKOwFcv78eakNlfmx2Prx48cRExMjlWMVp23btkx7165dSEhIkMh+CwoK8Pfff0NDQwODBw8WSXgNGTIEM2bMAFCYULx16xa+fPmC5ORkvHnzBtu2bau0Ca9fVZToKk2dOnWwePFiqSS8StKjRw94e3tj3LhxCAwMrFDi6Vfo6OjA29ubeezv7y/Se6csw4YNE6lRBhQOc8vOzkZERESZCS9hCgoKMDAwQKdOnfDPP/8gMDAQPB4Pqamp8PHxEZvZUV1dHY6OjnBxcaGEFwu0tLTQp08fnD17Frm5uXj48CHGjh0LExMTkfdESkoK/vnnH6irq+PQoUMiiaqy8Pl8jBw5Epqamvjnn38QFBSEgIAA+Pv7i9V54/F4aN26NZN8EwgEePr0Kfr16wclJSUoKCjA1ta2xGNt3LixxF6vhJDfy4cPH9CmTRsEBweDw+GAy+WiQYMGaNasGbOOpaUlOBwOgoKCYGtry8x4LQuU9CJEQhQUFLB9+3Y8evQIZ86cwfHjx0VOfAkhRNaSkpJgYWGB9+/fAyi84+bj44P69evLLAaBQIC2bdsiJCRE5OI/JycHL168wN27d8VmrfsVjo6OzPCflJQUkd4/P6OgoACzZ8+Guro6Nm7cKHJhaGNjg4sXL+LEiRNQUFD4peMQyUtJSYGlpSU6duyILl26wMXFhZlJszz4fD4uX77M9Fwsj5ycHJGC8nl5ecXWdCtOaGgoEhISROpeaWtr4+3bt3jx4oXYrJfCNDQ00LRpU6xfvx5BQUFISEhAXl4eYmJicPv2baxevRrt27eXeO9OIj1t2rTBgQMH8OnTJyQmJmLw4MEiQ8Wzs7MxduxYmJmZYdOmTeVKMDk7O+PIkSPF1nXT1NTEiBEj4OPjwyTZUlJSMHLkSBw6dAiNGzdGq1atcP78+XIVoP7+/TtiY2Mr8IoJIVWRQCBA//79kZKSAnl5eSxYsABxcXF4//49jh07xqz3+vVrvHr1CjY2NkhLS0Pv3r2l0iO/ODS8kRAJa9SoEby8vABApHsnIYTI0tWrV9GvXz+RE4oZM2awUkshNjYWLVq0AACoqalBTk4OaWlpzPNycnJwcnLC8uXLJRLf9u3bcfv2bSQlJeH06dM4e/Ys+vfvX+H9nD17FsOHDxc7KVNTU8PmzZsxbtw4sV45knL27FmcO3cOx48fl8r+heXn5+PgwYM4deoUIiMjwePxwOPxoKSkBHNzc7Rq1QrDhg1D48aNxbYtGsoo3OMr1sAIOh/eSyVWgUCAL1++4MOHDwgPD8eDBw/g6+sr1tuloKAAycnJIjNR3rp1C7du3ULHjh3h6emJevXqlXiclJQUNG3alLlo79KlC65duyaWNMrJyUFQUBACAwNx5coVPHr0SOR5OTk5GBsbF3uMvLw8vHjxAkeOHIGXlxe+fv0qts6OHTtQt25dKCkpQUVFhamR1a5dO8TExMDS0hL79u0r8Rjk96Cnp4dTp04hLy8PCxcuxN69e5nZIKOjozFnzhysWLECzZo1w5YtW9CyZUuxfXh7e4skcPX09DBs2DAMGDAALVu2FJmVdePGjZgzZw4A4NSpU8yMuOXRsGFDNGjQAB06dIClpeVPvmJCSFXh7e2N0NBQCAQCnDhxotRe3Y0aNcLNmzdRv359REVFYf/+/Zg6darUY6SkFyESlpiYyJxkS3KGJEIIKa8pU6Zg165dIsumTp2KzZs3yzSOoUOHivR6AcBcqAnj8Xi4ffs27ty5AxcXF1y6dElkmGReXh7Cw8Nhbm4uNnyyODVr1sSECROYmfNmzZr1U0mv8ePHiyS81NTUMG/ePCxevFiqPWbc3d0xd+5cAIX1l/r27QsXFxepHCsxMRHt27fHhw8fin0+MjISvr6+WLt2LWxsbHD48OFih2oaxEaXa6hjRT179gyzZ89GeHg4UlJSUFBQ8MvTnN+9excrVqzAwYMHmWW5ubm4efMm7t+/j+DgYPj7+4t8h9+8eROampowNTWFlZUVuFwuoqOj8fz58xKLtdeqVQvu7u4ICQnBxYsXER0dDV9fX4SHhyMzM7PUcwQdHR1s27YNw4YNA1A4LPnly5eIioqCvb19uf4fkN+PoqIi3N3dsWHDBgQGBmLDhg24fPkygMLC+AEBAbCxsYGxsTGWLFkiMqnS58+fRfYVGxuLhIQEPH78GLt27YKSkhJsbGzQtm1bzJ49G4sXL67wRARNmjRBSEgI9SgkpBo5e/YsAKBnz57lKmOgra2NqVOnYs2aNfDx8aGkFyFV0fDhw5mklzSnSCeEkB+lpqaiXbt2ePPmDbNMSUkJ586dg6urq8zjWb9+PRo0aAAPDw/ExMQgNTWVudDncrmwtbVFs2bN4Ovri0+fPkEgEODmzZuoVasW3NzckJGRgdu3byMmJob5XNXS0kJISEiZPVtmz56NtWvXgs/nIzo6Gjdu3KjwZ7LwkEVtbW0kJSVJ7GLu8OHDmDBhgkhSTVlZGQKBQGTZ7t270aBBA6klve7evVtiwutHT58+RePGjWFubg4bGxtwuVxwOBzIycmBw+HAtEYlywAA4kxJREFUdPw49Nu9Bzpykjm9nDNnDjZt2iSRfQmzsbFhZhmNiorCqFGj8OLFizJnTczMzERoaChCQ0PLdZyEhAS4ubmVOy45OTm0bt0aCxYsQK9evcSeb9CgAU2QQwAUfn46OjrC0dERr1+/xsaNG+Hp6cl8Tn7+/Bnjxo3DjBkzMGLECKxfvx7Tpk3DggULmGHmJc3uWb9+fRw7dgyqqqoiSa8GDRogKipKrMB+kYYNG+LZs2eU8CKkmnn8+DEEAkGFZpYtqgcYFhYmrbBEUNKLEAm7f/8+0x4yZAiLkRBCqpM7d+6gR48eYhcp9+/fh76+PmtxzZgxgynyDoDprSMcE4/Hw6RJk7B//34Ahcm7H3uIFUlNTcWYMWOYwvwl0dHRQdeuXXHt2jUAwLRp05jaZuXx+fNnJCUlMY/79Okj0Yu5JUuWiA2b/LGQNFB4cSs8w9uPUlNT8fLlSzx79gxOTk5o3rx5heJo1aoVlJWViz12ScLDwxEeHl7sc8tROKPxKaFeJU5OTlBRUYGhoSFMTU1hYWEBGxsbWFlZlXqcvXv3ljumilBRUYG9vT0iIiJgaWn5yz3HfgaHw4GysjJq1KiBunXrYsSIEZgyZYpIzSZCyqNRo0Y4dOgQpk2bhqlTpyIoKIhJfmVmZmL37t3Yu3cvHB0d0atXL/j4+JS6v48fP8LR0RFr1qzB6tWr8f37d+jr6+P79+9MwktRURGWlpYICwsDj8dDw4YN8erVK3r/ElINFQ3NNzc3L/c2RkaFPcPLutkkKfTJRIgEJSUlMXfQVFRU0L59e5YjIoRUB0lJSWIJr9GjR+PQoUMsRlU8HR0dsWVycnLYt28fmjRpgpkzZxY7252mpiZTB+zFixcQCARl1tPat28f6tWrB4FAgPDwcLx69QpNmjQpM0aBQAAbGxumV5qcnBz+/fff8ry8ctPX10d0dHSZ6zk4OBSbbDt8+DBGjRoltnzJkiVYuXJlueMwMzPDhw8fEBAQgIiICERERODOnTuIjo6u0KyDwl68eIGJEydi1qxZAICIiAgkJyeLrde1a1dcv369xP2MHDkS+/btY5JSysrKUFBQgIKCAmrWrIlmzZpBRUUFqqqqUFFRwYsXL8DhcKCqqgpVVVWoq6szP5qamqhRowY6duzInJjPnTv3lxNeJb0H5eXloa6uDmdnZ1hbW8PIyAj16tWDnp4ejIyMaHZEInEtWrTA/fv3kZKSgrlz5+LkyZNMMpvP5zP1vOrUqYPk5GTk5eVBSUkJs2fPRpMmTZCcnIy5c+ciNzcXBQUFWLp0KQICAmBkZAR7e3umR6itrS2uXr0KPT09pict1ZQjpPoSPlcqr6JzAi0tLWmEJIaSXoRIkI6ODrhcLvh8PrKzs3HkyBGMGDGC7bAIIb+xoinoixJeCgoKOHny5E/VsGLbtGnT0LdvX6xfvx5RUVHIzc1Fr169MHr0aCgpKUFfXx9JSUlIT08Hn88v8wTL0NAQ9vb2CAgIAFBYjLx169a4cuUK6tWrB3V1dYwcORLTpk2DvLw8nJ2dERAQAFNTU+aETEFBAZcuXZJ4bzkfHx/Mnz8fiYmJaN26NdLS0ort3bZhw4Zit1+8eHGxy589e1bhWAwMDIrtmZyUlMTUuAoNDcXHjx8RFRUlMglBcTQ0NDB9+vQyj1vWrIY7d+7Ezp07y9zPzxo+fDguXbokUluLw+FATU0NtWrVQv369dGkSRO4uLjA0dERqqqqUouFEEnR0dHBoUOHcODAAezatQv//vuvyCyKX758gbW1NQ4cOIC2bduKbNumTRu0bt0aQGHPU1tbW6ipqTG1GK2srJiEF1DYE5USXoRUb7q6uoiLi0NiYmK5t7l48SIAoGnTptIKSwQlvQiRIC6Xi06dOuHWrVsACosnU9KLECJN7u7uuHr1KoDCAu6PHj2Cqakpy1H9PENDQ2zbtk1seVxcHDPcUE5ODo8fP0bbtm1RUFCAxMRE6OnpiRT35vF4uHPnDmxsbPD06VNkZWUhPj4eFy9ehLKyMj59+gSgsLfP/PnzweVymRkAhWtcLVq0SCr1GQ0NDcVmZvwx6dW2bVum7sWPmjRpgpiYGJFljRs3Fpke/Ffp6emhd+/e6N27t9hzOTk5eP/+PRITE5keYhkZGWjdujWGDRsGHo+Hmzdvlrr/ZcuWSSzWnzFgwADk5OQgNTUVfD4fXC6XemCR3waXy8XUqVOZIY9TpkxBSEgIBAIBwsLC0K5dO0ydOhWrV6+GhoYGABTbE7Yo4WVkZIQbN27Q/xFCiAgLCwvExcXh+fPn6NatW5nr37hxA3v37oVAIMDQoUNlECElvQiRuAsXLkBTUxM8Hg8pKSnMiTQhhEiaQCDAmjVrABT2UDl+/HiVTniVRvhCKzs7G3Z2dmLryMvLQ0dHB6qqqoiOjhbpwSMnJ8c8/rF+FY/HK3EmPUdHR0mEXy49evRgZmLjcrnw8vIqcd2rV68iLCwMioqKMDMzk/n3jLKyMnOHtnPnzmLPCw+1/fz5M5KTkxEZGYn379/j+/fvcHR0LDGhJ0vy8vJ0EU9+e3Z2dnj27BmeP3+Ov/76C0+fPoVAIMD27duxf/9+/P3331i5ciXOnz9f4j6WLFnC1OEhhJAi9vb28PPzw8WLF7Fw4cIS19u1axf8/f1x5swZ8Pl82NraYuTIkTKJka7ECZEwVVVV1K9fn3kcFxfHYjSEkN9ZcnIyUlNTAQDOzs5Sm+GvMhDuxVWSol5fkZGRYkmskpJawn48KXJzc4OTk1MFoixZTEwM2rRpAyUlJcjLy2Pt2rVi61y6dAlHjx7F8OHDcfv2bdSrV6/UfVpbW8Pc3LzS31jhcrmoV68eHB0dMW7cOMyZM6dSJLwIqW5atGiBhw8fYuPGjVBRUQFQeBNg1apVqFevHurXr19iMfrx48dDT08PK1asYGXyB0JI5dS3b18AwKNHj0qd3Xjq1Knw9vaGQCBA27ZtcenSpQrVAfsVlfssiZAqqkePHkz7Z+qrEEJIeRTVqgJQZp2lyig6Ohr9+vVD79694eTkhF27dpW4rjRm+BmsooqYuoZ4W7suQvRrI6quIW7W1Iebqhp2a+tg/vz5eP78+S8fx9fXF8bGxggODkZeXh54PB48PDyKXdfNzQ3Hjh2TWLKNEEKEycvLY86cOXj16hVq1qzJLI+OjoadnR0aNGhQ4rbJyclYvnw5tLS0mML4hJDqrXnz5li5ciXmzp2LyMhIsec5HA44HA4UFRXRvn177Nu3DwEBATKdWZyGNxIiBa1atWLa79+/ZzESQsjv7O3bt0y7qk0VLxAI8Oeff+LevXvMsnv37sHf3x+nTp0SW7+iBc3d3d2xePFikWF2Rcx0DHB80HI0q1N4cacr9JzLUic0MjDCjexstGjSBAUA9uzZgwkTJlTo+EUCAwPRpUsXseV9+vT5qf0RQogkmJmZIT4+HhMnTsSBAwcgEAggEAhEvldKkpmZiY4dO6JevXpo06YNBg4ciJ49e0JZWVls3Tt37sDDwwMDBgwAAOTl5TG9zAghv4dFixYVu9zCwgIfP36EqqoqM+EbG6R21LS0NJw6dQpz5sxBhw4d0KBBA9SoUQOKiorQ19eHk5MT1q9fX+wU1sV58OAB3NzcYGxsDGVlZdSuXRtdu3bFyZMny7V9bm4uli5dClNTUygpKaFRo0bYunUrBAJBiduYmJgwmUkNDQ2mgG5J/Pz8mPU9PT3LFRf5Peno6EBBQQFA4Zc7IYRIw9evX5l2ccmdyszLy0sk4VXE29sbfD5fZNnjx4+xevXqCu1/7ty5iIuLQ6tWrSAvLw8DAwP0sXLERbd1CJ1xDIYXV0B5qRPzUyRnpR9utP0TY78loyiKAwcOVPTlASisX+Hg4CC2/N9//8X//ve/n9onIYRICpfLxb59+xAWFgYTE5MS19PT04OamprY8qioKJw+fRqDBg2ClZUV1q9fj1q1aoHL5UJfXx+6urpwdnbGtWvXmG0uXLggjZdS6aSlpVWq10rXwoQNcnJyqFevHvT09FgtxSC1IwcHB2Po0KHYtGkT/P39ERERgbS0NOTn5+Pr16+4d+8e5s+fD0tLS9y4caPUfS1fvhwODg44fvw4M4V5QkICbt68iWHDhqFHjx5iRWmF8Xg89OzZE6tWrUJkZCTy8vLw5s0bzJw5E2PHji3X68nIyMC6desq9Dsg1Refz8ft27dx/vx59OzZk+1wCCG/qWHDhjHt58+fIywsjMVoyi8kJARTp05lHu/du5e5USAQCLBgwQLmuY8fP6JHjx7Izc0tc7/CJ1QqKirQ1NTE48ePkZ+fj5iYGJx/fQ89j85D8t4/xbYVToCFmRQeqyjpJTxkvTyePXsGSwUFTJkyRWR5/fr18axWHbht21GuGmWEECILVlZW+PTpE1asWFFsjZ0BAwYwsziWJDIyEvPnz0diYiIEAgG+fv1a7LD0L1++SCzuyiw4OFjke45NdC1MqjupptuMjIwwYsQIbN26FefOnUNQUBDu378PLy8vDBw4EHJyckhKSkKvXr3w4sWLYvexd+9erFixAnw+H2ZmZjh48CCCg4Ph4+ODjh07AgCuXLmCMWPGlBjHvn374OvrCwMDA3h4eODhw4fYsmULatSoAQ8PD5G7D6XZuXMn4uPjK/6LINVKZGQkBg4cCEdHR8yYMQNWVlZsh0QI+U21atVKZIr54cOHsxhN+Tx58gStW7dmenr36NED48ePh5ubG7POhg0bYGZmhu3bt8Pe3h6JiYml7pPD4QCASA+x06dPl3hX0SA2Ggax0SXub+3atejWrRs0NDTg6uqKZcuWleu13blzB23atIGNjQ3e/VDoWZvDwY3sXOj//wVlrIERYg1oJjRCSOWxdOlSxMXFYdq0aTA3N4e2tjaGDh2KnTt3QktLSyLHiI2Nlch+qoI6deqwHQIAuhYmRGoFQDp27IioqKgSnx80aBB8fHzQt29f5OXlYcWKFTh37pzIOikpKZg/fz4AoF69enj48KHItNI9evRA3759cenSJZw8eRLjx48vtvCrt7c3AODatWvMxUGbNm1gbGyMvn37wtvbG927dy8xVj09PSQlJSE7Oxtr167F1q1by/17INXP9OnTkZWVBQD4448/qlydHUJI1XLkyBG0aNECAPDixQvY2NggICAAqqqqLEcm7tq1a+jVqxcz85eCggJ27NgBoHAo4IULF5ieAR8/fsT06dNL3Z+rqyvq1q2Lc+fOMdtxOBysW7cOrq6uPx2noqJimRcBRQkr4eTZMBcXJPwwNBMoPNl6XKsOlP8/OUcIIZWVvr4+tm3bJrY8ICAAtra2IqNr6tSpU2LPLRMTE8ybNw9HjhxBeHg4s/zEiRMYM2YMmjdvLvHYK5OOHTsiODgYRkbs39yga2EiTaampj+9rUAgKLb4vaRJradXeaaf7NOnDywsLACIzkBV5MCBA/j+/TsAYN26dSIJr6Jj7Nq1iznWhg0bij1ObGwsdHV1Re6GA4XTuxc9Xxpra2vm5Hnfvn3V6g4FqZjc3FxcuXKFefzvv/+yGA0hpDpo3rw5830GFA6tq127drH1stiSmpoKJycnuLq6MgkveXl53Lt3D8bGxgAAZWVlfPnyBd26dWN6bgkbMGCASPHjhw8f4sqVK1i2bJnIEJqAgAD8/fffFY4xMTERHh4eIhdnxfmxh1asgREu19RHC0XFYhNeAFAAoHnCFwTl5iAsn+o8EkKqnsaNGyM8PBw6OjrMstKGKkZGRmLq1KnFFq3fv3+/VGKsTMpzLSwrdC1MpCkqKgqfP39GVFRUqT+fP38udj1ZYK+a2P/T0NAAgGJrcvn4+AAANDU10a9fv2K3NzQ0ROfOnQEAt2/fRnp6utg6+vr6SE5OFqt1UjTVbnm6nq5cuZKJs6LFdEn1oaioKDK85v79+yxGQwipLm7evImBAwcyj9PT0+Hk5AQHBwfs3r0bsbGx4PF4rMS2adMm6OvriyThFBUV8eDBA9jZ2YmsW9TDKi4uDqNGjULt2rWhoqICBwcHeHl54cmTJ+jbty8OHDiAJk2aMDU/i1hYWKB9+/YVjvHq1auoU6cOxowZg4YNG6Jx48bYu3evWEF94L+eXVl8Pq5lZ2POtxT0TvqKkPz8Uo+RIRBgYHIStv9wnkLDHAkhVYWhoSGio6Nhbm4u9pyJiYlYBwU+n4+7d++KFcFPSEgAAERHR+Pw4cP4+PGj9IImdC1MpGrUqFEYNWoURo4cWeyPm5sb/vjjD9SrVw8cDgcCgQBWVlYYOXIkRo0aJZMYWU16vXv3DiEhIQAAS0tLkefy8vIQHBwMALCzsyu14GuHDh0AFPayefLkidjzvXv3BgB0794dhw8fRnBwMHbs2IGRI0cCgMiFQklsbGyY/Rw8eBCfP38ucxtS/fTv31/kcXlnJyWEkF/B5XLh7e2NAwcOiNxdDgwMxOTJk2FoaAh5eXlwuVwoKSlBS0sLBgYGaNasGY4dOya1uNq3b485c+YgXygh1KZNG8TGxsLW1rbE7WrXrg0PDw98+fIFWVlZ8Pf3B5fLRaNGjXDu3Dl069YNhoaGWLFihUi3+KJhnhU1ZcoUkQRXWFgYJk6cCDU5Oeb3s01bB31VVLGqhhY+5OfDKj4Of31Lhld2Forv31W8yznZMIyLQcQPSTJpJb4ePHhQrkkACCGkPFRVVfH27Vu4uLiILI+MjESfPn0QFxeHLVu2MB0bADBlP4ro6OggICAAFhYWGDVqFMzMzNCzZ0+MHj0acXFxMnkd1QldCxNpOnjwIA4dOlTiz+HDh3Hx4kVERkYiICAAzZo1w7t379CmTRscOnRIJjHKPOmVlZWF8PBwbNq0CR06dGCGOcycOVNkvffv3zN3pX9MiP1I+Pk3b96IPT9t2jS0atUK0dHRGDVqFNq0aYNp06bh27dvGDFiRLlnZVqxYgU4HA7y8vKwatWqcm1Dqo/evXvj/PnzzOP27duLFGYmhBBpGzt2LN69e4fBgwcXO4mGQCBAXl4evn//jri4OLx8+RJ//vknLCwsEBoa+svH5/P5ePfuHfNYuNu6hoYGzpw5I1afszhJSUkYOnQoatSoAVVVVWhoaKBevXqYP38+0tLSsGDBAnz79k1kGyMjoxLLHJSlqJTCj3IA/Pnnn1DkcDAj9Rt8crKxNO07nL4m4Ff7zaUJKpIq+zk8Hg/t27eHsrIyZs+eLfXjEUKqBy6Xi5s3b+LGjRvMxGJAYWma+vXrQ1tbGzExMdDV1QVQ+N0jTElJCZMnT0Z2djaz7PLly/D09ISZmVmxsz6Sn0fXwqSyaNeuHVPaYtq0aXj69KlMjiuTpJenpyc4HA44HA7U1NTQsGFDzJkzh+naumDBApFp1wEgJiaGaRsaGpa6f+ECgdHR4rMxKSsr486dO5g9ezYMDAygoKAACwsLbNq0CR4eHuV+Hc2aNcOAAQMAAIcPH0ZERES5tyW/tz/++AMXL15kHnfo0AH+/v4sRkQIqa7MzMxw6tQphIWFISQkBPPnz0f37t2hr69fYo2R9+/fo0mTJjA3N8eqVavw8eNHsYsUoHBYw4EDB9C/f384ODjA2toaRkZG0NbWhqGhIbS0tJiSA0U4HA6GDx+OlJQUsd6wxdm7dy/09fVx6tQppKWlITs7GxkZGYiOjsb69euhpaUl0jtt5syZSEhIQFRUVJnnCz+KNTDCWb2aIgm027dvY/To0SLrlT5wURyHwym1nguXy0UTeYVi45Fkj6+mTZsy7c2bN+Pw4cMS2zchhHTp0gW3b9/GwYMHmclTcnJyMHLkSPTu3Rv37t0r9rNwx44dJd5oycnJKfeMuaR86FqYVCaampqYO3cueDwetm/fLpNjsjq8sXnz5ggODsbatWvFitYK1+ZSV1cvdT/C48QzMjKKXUdDQwPu7u6IiYlBXl4e3r59i1mzZpU4nXlJli9fDi6Xi4KCAmZsM6ne1qxZg6tXrzKPO3bsCD8/vwq/twghRJI4HA6aNWuGf//9F1evXkVCQgIKCgogEAiQlJSEBw8eoFevXiLbfPjwAUuXLoWZmRnq1auH4cOHY9SoUejSpQsMDQ2hqqqKv/76C+fOnUNgYCBev36NmJgYpKamIjY2Vqyupp2dHV68eIFjx46VayZbPz8/TJo0qdiEW5Efnxs+fDj09fUr8Jv5TzKPhwnf/utR0LJlS3Tq1AkHDhwoc1sdLhfCZy56XC4Ca9bCnTt38ObNGwQHB5f4PdC9e3cYf4kVmflRGn6smSar2hmEkOqDw+FgzJgxuHPnDpSVlZnlfn5+aN68Of73v/+JFL8vDxqSLXl0LUwqExsbGwCQWScRmVyV9+nTB69evcKrV68QHByMkydPom/fvggJCcHQoUNx+fJlsW2EC9uXVs8LKOwiW0S4m6w0NGrUCEOGDAEAHD9+XGQYB6l+PD09sWjRIuZxly5dcOfOHRYjIoSQsunq6sLOzg4XLlxAaGhosWUEYmJicOLECRw+fBi+vr6IjY0tNRnF5XJhbGyMbt26Mcu8vb3FZosqzapVq5hjaGpq4tixYxAIBMjMzMT//vc/1K5dW2R9DoCcP3qWq4dU0ToBtWpjoroGmikqwibhC5L/v5YXh8PBuXPnABQOryyNPIAzunqIqF0XN/X0EVm7LkJq14WJggI6duwICwsLtGzZEkeOHBHZTg5APxUVpmdwrIERDGKjxZJfkurxdeDAAUycOFFk2Y0bN355v4QQ8qM2bdqITeBUUFCAhQsXom/fvuXah5ycHFxdXbFnzx5phEgkgK6FiSQUlbiSVQ2/sm+7SoCWlha0tLSYx7a2thgyZAiOHj3KdH89ePCgyB1I4TsFeXmlT+8tfDeguGlxJW3ZsmXw8vICj8fD8uXLcfLkSakeLzs7W+rJvF8hnKAsbhbO31VCQgJWrFjB1CtYtGgRJk6cWKn/VtJUXd8HRBS9D6qe+vXr49mzZ0hLS8O1a9cQHx+PR48e4enTp2LFhzkcDmrXro02bdrA3NwchoaGMDIygomJCXR1dcHlcpGTk8PcuavIeyA7Oxtv3ryBrq4uNDU1cfPmTRgYGCA7OxscDgezZ8/GzJkzMW7cOPj6+kJBQQELVFRhoKIKnSfBzD6KE9+qNaClhdVpqbjCK0C+shKgrIQaQq9r0aJF0NfXR3Z2Ns6cOcN8tgOAGoeDPHl5KCoqwtLSEisjo6D//z3XGgDgA8gDUPtJMBNDfKvWaAfgsnlDnMnKgr2SEjopF56jfG7SrHDHWlr4ZN2EaQsT3tevWLVqFU6fPs083rlzJxwdHX95v2WhzwIC0PugurGwsEDNmjXFZr49d+4c2rVrB6Cwx6mnp6fI8/Ly8li2bBlGjBgBLpfLak+v6noeXxGyvhYmv5+AgACmPpwscASl3baVgcGDB8Pb2xtqamqIiopiur9ev34d3bt3BwBs2LABc+fOLXEfT548YWaBWrBgAdauXSuR2ExMTPD582d06NCBmdK1yOjRo+Hp6Qkul4uXL1/C2toafn5+TDFHDw+PXxpGEBMTw9QqO3DgQJlFfwkhhBBCCCGE/LykpCSMGzcOQGGt6IrWihQmfD0XPtsbhjVqSiTGMo/7/SvMNw0CAJibm0NBQbSGZFhYWLn2w+a1MCGSJJOeXqXp3bs3vL29kZmZievXrzMF7YU/YISL2hdHuHi9cFF7aVqyZAmOHTuGgoICLFu2DGfOnJHJcQkhhBBCCCGEVB2TrHZDRU/6I5IAIDtJ+r3V6FqYVCWsJ71q1vwv4/3582em3bBhQ8jJyYHH4+Ht27el7kP4+eKmaJeG+vXrY/To0di/fz/OnTuHkJAQqR3L0dHxl+4ySJvwUBZHR0eRoam/g7///hunTp0qcz1lZWW8efOmXMWaf0e/+/uAlA+9D8ivvAf++usvXL9+HQAwf/58TJ069ZfjiW/VGgBgl/AFBf+/7NOnTyV+Vru4uJR43qGoqIgxY8aI1HIskpOTA0tLS/B4POjo6ODBgwfMRDtFMZRX7f8frikpFy9exJQpU0SWXb58Gc2aNZPocYTRZwEB6H1QXRUUFGDMmDG4e/cuAEBbWxsbN24EAMydOxcGBga4du0amyGWqKzOFlXNnTt3pHIdKctrYUJ+FetX57GxsUxbeJZGRUVFtG7dGkFBQQgKCkJeXl6JBe3v3bsHoLCgfatWraQbsJDFixfj8OHDyMvLw7JlyzBr1iypHEdFRUUmtcokQVlZucrEWl4+Pj5ITk5mHrdu3Rq2trYICgpCdHQ0vn79yjy3aNEibNu2jY0wK5Xf8X1AKo7eB6Si74GePXvi+PHjAIClS5di2rRpYhfJQUFBOHv2LHx8fKCkpIRt27bB2dm5xH2ahr0CAKRwOMj//2UaGholrp+cnCzymf+j1atXQ0VFRSzxNXPmTCQmJjL7mDZtGjb6BwIASp+ORxxnwyORx8pLnSq4B1Gamppiryk0NBRt27b9pf2WF30WEIDeB9XN6dOncebMGcybNw/fvn1jln/79g0fPnyAubk5ZsyYgVmzZlWqG8b0Hi0/WV0LE/KrZDJ7Y2mEi6v+OMNTnz59AABpaWnMjEo/iomJwa1btwAAzs7OpZ7ISlq9evXw119/ASi8i/r48WOZHZvIxrFjx5jELIfDQXJyMh49eoQdO3bg6dOnSExMxLx585j1fxzvTgghpPwGDhwIVVVVAIU9RHR0dNCxY0f06NEDXbp0ga6uLtq1awd3d3dERETg9evX5Z4VDP9f04TD4ZS62r1799CmTZtS11m7dq1IEsnX1xf79u0TWUdbWxsdE+KxLyO9fPEJSd77Z4W3KU3Xrl3FpqWnCztCiLQNGDAA4eHh8PDwEHsuNjYW8+bNg6qqKnr27Inw8HAWIiS/gq6FSVUhtaSXp6dnmbO0bN68GVevXgUAmJqawsHBQeT5cePGoUaNwrmVFixYIHaXksfjYfLkyeDxeAAKh6HJ2j///MPchV6/fr3Mj0+kJyMjg/kgBwrrzxVNtCDsy5cvTNvY2FgmsRFCyO+Iy+Vi3bp1zOPs7Gz4+fnhypUr8PX1RUpKitg2xX0u/6igoAD5+fnMMUqjr6+Phw8fMjVGi5OZmYkPHz4AKLwx98cff4its3v3boTzCrAy7TueVmAmMt0JR6E74ajIspyVfuXevjhcLlcsxtTU1F/aJyGElIecnBw6d+7MPG7durXI53B+fj4uX76Mhg0bwsrKClFRUWyEKXHluRb+HdC1MKkKpJb0Wr58OQwMDDB+/HgcOXIE9+/fx4sXLxAYGIjdu3fD3t4es2fPBlA4lHHfvn2Qk5MT2YeOjg5z8vv582e0adMGHh4eePLkCS5evAgXFxdcunQJADB06FA4OTlJ6+WUqG7dupg4cSKAwtk+yO+je/fuzJeVsrIyjh49Wux6Dx8+ZNqurq4yiY0QQn5XU6dOxf/+9z+xc4KS7Nixo8x1vLy8mLa+vn6Z62dlZeHEiRMlPq+jowNbW1sUFBRAT0+PSaiVpHfy11Kf/1FxwxlzVvr9UvLrx6H3nz59+ul9EULIzzp79iy+f/+OWbNmQVtbW+S5t2/fwszMjBnmXpUtX74ctra2bIchdXQtTKoCqQ6gTklJwf79+7F///4S1zE0NMShQ4dE7gAImzBhAuLi4rBq1SpERERgzJgxYuu4urri0KFDEou7ohYsWIB9+/YhKyuLtRiIZB05cgSBgYHMYw8PD5Gac8KE70gNHjxY6rERQsjvbtGiRVi4cCGuXr2KR48eISsrC/n5+XB2dsapU6dEJhfp1q1bmfubP38+027fvn2Z6ysqKoLL5YLP5xf7fEpKCoKDg7Ggg1OZCS8A0CxjSGWRH3t4FfnVml4AxHrJFddrjhBCZEFdXR2bNm3Cpk2bcO3aNSxevBjPnj0DUNgz183NDenp6UwypaqqLj1q6VqYVHZS6+l148YNuLu7o1+/fmjatClq1aoFeXl5aGhowMzMDP3794eHhwfevXv3f+zddVhUaRsG8HvoUpEyADGxCxUbMUCxMLFF1o61d1fX7gZdu7HWFru7axUDA1dEBRQERKRj5vuDb87OMEMJQ3n/rovrmjnxnnfgzDDnOc/7vHBwcEi3rTlz5uDmzZvo27cvLC0toaWlBTMzMzg4OODvv//GqVOn8nQ2mBIlSuTIDFOUP8TFxWHs2LHCc3t7e/Tu3Vvpth8+fED8/4et6OnpZWqYDRERZUxNTQ0dO3bEvHnzsGLFCvz1119wdnaGk5OT3Ha1a9dGUlJSGq0AkyZNEmozqqmpYf369RkeW0NDA+PHj093m8OHD+OlYTGl64qIRKisoYFGmlropKOLy6Yl0mxHOpzRPPAjdGbaCwGunAh0ydLW1pZ7LjsJCxFRXnFycsI///yDGzduCDPeAsC4ceNw/37OzmSbm86dO4cZM2bkdTdyBa+FKSckJSXh/fv3eP/+fY63rbJMr8qVK6Ny5crCEMbsatKkCZo0aZIjbWWWv79/prddsmSJXB0SKrhmz56Nb9++AQD09fVx6tSpNLd9/fq18Dgz2QNERJQ9AwcOhLe3Nzw8PAAAL168gIWFBe7fv48yZcoI20kkEuzbt09u+GPv3r1hYmKSqeOsWLEC27dvl5t1TNby5cvRunVrvH//HlZWVlj6zAd3EuJRV0sbVmnMRCbN5JItVJ9ecCsnA18JCQlyzwMCAnKsbSKi7GrWrBk+fPiABg0awM/PDwkJCejRowceP34MY2PjvO5ellWuXBnDhg3DvHnz8rorP4zXwpSbfH19UbNmTUgkkjQz7X9Uns/eSJSf3L17F8uWLQMAaGpq4ubNm8JMYso8fvxYeJxe0WMiIso57u7uGDVqlPA8ODgYVlZWKFGiBCpXroyOHTuicuXK6Nu3rxDsqVu3bpq1GdMSFBSEkiVLprn+0qVL+Pfff9GtWzeU0NBAFz19IeBlHvgR5oEfASgWp5fN7sotqWdG48QrRJSfvH79GtOmTcOJEyeERIePHz9mqm4jEVF6GPQi+r+4uDgMGjRIiCzPmTMHderUSXefhw8fCo9/hmKVRET5xdq1a7Fw4UKIZOplhYSEwNfXF6dOnZIL8ri4uODOnTsZztyYmo6ODj59+oQuXbqku922bdvkglypyWZ2AZAbxpiW7BauTy117bHKlSvnWNtERNkREBCAGjVqYMOGDWjZsqVcIfuDBw/meNYHEf1cGPQi+j9PT09huKKtrS1+++23dLcPCQnBtWvXAKTU86pSpYrK+0hERP+ZOnUqrl69imrVqskFv4CUYvRNmjSBl5cX9u/fr1DTKiuWLl2KcuXKwdLSEqVLl5Zbp6mpibVr1wrPpcGvQHNLBJpbKmR5ZWbIYk4Gu6RSD2+sVatWjh+DiOhHtG7dWqjNGBISgqJFi6Jhw4YAAB8fH8yfPz8vu0dEBZxKZ28kKkhWr14tPB4+fDg00qjJAgD37t1Dy5YtERsbCwBo2LAh1NXVVd5HImUiIyOxYcMGfP78GVZWVmjSpAnq1q2b7jlMlJpsoCWni6irkp2dHXx8fJCQkIBr164hKioKOjo6aNWqVbYCXbIqVaoEPz8/nD9/Hh07dpRbt2HDBuHiTJY08CUrq7/XnPw7HDlyRO65ra1tjrVNRPSj7ty5A19fX7llCQkJaN68Oe7duwcgpd5uw4YN0bZt27zoIhEVcLwiIvo/Pz8/4XG7du2UbpOQkICNGzdi/PjxQqq1mpoaFi5cmCt9pJ+Xn58fXF1d8fr1ayQnJ8PJyQmzZs3ChQsXMH78eIWhS0DKbG21a9fGzp07OZSJCj0tLa0MZ4NOS0JCAipWrIiPHz9CJBKhdOnSmDFjBoYPHy5sExISgvbt2yM5OVlYVqJECYVJTKSBLtnhjlnJ3IqbezXHg47h4eE4c+aM3LIKFSrk6DGIiH5EcHCwwjIjIyMsWLAA27ZtQ3h4OCQSCTp27IjHjx+jRo0aedBLIirIOLyRCMCHDx8QFxcHANDV1RWGr0RFRWHq1KkoU6YMtLW1oa2tjbFjxwoBLw0NDXh5eaFRo0Z51nf6OSxevBg3b97Ely9fEB4ejj179sDa2hqjR49WGvACgPj4eNy/fx/169dHeHh4LveYCpqClN2V054+fYqPH1MCVBKJBIGBgRgxYgQaNmwofN4fPnxYLuB18+ZNXBOpQ2LfCgCE4Yzvk5KQJFN/JnW2V0ZU8XdwcXGR67u2tnaW65sREalCtWrVFJZpaWlBS0sLjx8/ho6ODgAgKSkJDRs2RERERC73kIgKOn7jIQLkZvSqWLEixGIxHB0dUbRoUSxevBgfP35UqIdSrFgxPH/+HJ07d87t7tJP6OTJk+mu19PTg6OjI+rUqQMzMzO5dVFRUQV6ymzKPZkpsF4YKbvoAoD79+/DxcUFADBgwAC54ZKrVq1CveBPqPo5CJUrV0atz0Go9CkQTUM+o3zwJ7x8+VLYVlrIXhW1ujLy6NEjXLp0SW5Z3bp1c70fRETKFC1aVO65bH3GMmXK4Pr160IJkZiYGAwZMiRX+0dE6VNXV4eamlq2f1SZxcmgFxEgFKQHgGbNmuHSpUu4cOECJBKJ3Hba2tooVaoUnJ2dERAQwCFjpFJXr15Fp06dULRoUXz69AlAypfBDx8+oGvXrlBXV4e6ujo6d+6Mr1+/4ty5c3jw4AEMDAwU2pI9x4noP1++fEHz5s3TXH/48GF06dIFrVu3Rnx8vLD84MGDiP7//whfX1+Ei8WI/f9zsUSCxYsXA4BCIfvc1qtXL4Vls2fPzv2OEBEpkV7QC0iZHX3btm3C82PHjglF74ko/xCJRNn+URXW9CrEXr9+jalTp8LBwQEjR44EALx8+RJz5szBmzdvYGlpidmzZ6NOnTp529F8oEiRIsLjyMhInD17VnhevHhxTJw4EZMnTxZSrAu65ORkJCcnQ0tLK6+7QjLEYjGOHTuG1atX4+7du8JECbI6deoES0tLhaLUUhMmTJCrTycVExOT4/0lKuiePHmCbt26KX3PyDp27FiW25ZmU+Vl5tzNmzfx77//yi0rU6YMi0ETUb6hq6sLPT094XuKsqHXAwcOxLhx4xAREYGkpCTcvHkT9vb2udxTIlLG3t5eIVHkR0RHR+Phw4c50CNFDHoVUrt374arqyvEYjG8vLwwadIkqKmpITo6Wtjm0aNHOHbsGH7//XcsWbIkD3ub9+zs7IQgwu3bt9G+fXth3ZYtW9CtW7dc60tSUhJevXqFZ8+ewdfXF35+foiIiEBcXBzi4uJQvXp1lCxZEsWLF0fx4sURGxsLPz8/6OjooEqVKmjevDlKly4NsViMR48e4fjx43j48CEiIyOhp6eHd+/e4f3790hMTIS6ujokEgk0NTWhra2NEiVKoF69emjbti26du2KYsWK5drr/hm8fPkSw4YNg4GBAdzc3IRhU2FhYVi8eDFWrVqVZn0ubW1t/PLLL1izZk2a7YvFYmzatEnpuurVq2f/BRAVIu/fv0e9evXkal3lFE1NTYwZMybH280qsUxtMQAwMDDAqVOn8qg3RESKRCIRihYtKgS90sri0tPTE+p56evr51b3iCgDqUso/KgXL16gZs2aOdJWagx6FUKPHj3CwIED5SKuyjJGpNatW4cpU6agePHiudG9fKlLly4YP348AODdu3c4fPiwsO7vv//O8aCXRCJBQEAAXr16hVevXgkByMjIyAwvwK5fv55h+9L00Iyi7tJjxcfHIz4+HpGRkXjz5g327dsHNzc36OrqwsrKCg4ODhg0aBBq1KjB7LAs+PTpE9q1a4fk5GRMnjwZo0aNEt6LZ8+exa5duzBs2DAMGDAA3759U9hfX18fzZo1w7hx4+Dk5JTh8W7evCnUnitSpAiOHj2KsWPHIigoKNMX4BKJBImJidDU1FRpmjFRXhKLxejQoUOWA15dunTBrl27sH//fpw5cwZNmjSBvb09WrZsicjISGG7jh07QkMje1+xcmIWRzs7O7nnc+bM4cxnRJTvlC1bFp8/fxaef/nyBaampnLbyF7LWFlZ5VrfiKjgY9CrEHr+/HmawQ5tbW3UqVMHHTt2xPr16xEUFISoqCiMHz8eO3bsyOWe5h9WVlbo0KGDcAdc9h/vsWPH8Pr16yzX7woPD8fNmzfx+PFj+Pj4wM/PD58/f0Z4eDiSk5MVCuPnpPSCXQYGBihfvjyKFCmCZ8+eISoqSiEbQCo2NlYIzK1evRpaWlpo0KAB2rZtCysrK/Tu3ZtBsHTcvHkTT58+BQC4ubkprD958qRCgXpDQ0O0bt0av/32Gxo2bJil412+fFl4HBcXh1atWgmfByKRCJGRkViwYIEQYJUGO5OSkpCUlASxWAyRSASxWAxtbW2ULl0ajRs3hp2dHVxdXQvN8F76uSQlJcHBwQHXr18Xzm2xWJxmVmV6FixYAAMDAwwePBiDBw8WlgcHB8Pe3h737t2DhoYGVq5cmSN9z07gKzIyEqNGjUKdOnXg7e0NKysrjB07Nkf6RUSUk9q0aYO7d+8Kz0+fPg1XV1e5bYoVK4avX79CJBLBxMQkt7tIRAUYg16F0L1794THGzduROfOnRESEgILCwsYGRkJ61xdXVG5cmXExsZi9+7dWLdu3U+dLnzy5Em4uLjg4MGDcsuTkpJQv359PH36FOXKlUtzfx8fHwwePBivX7/G9+/ff2jIjEgkgo6ODooUKQIjIyOYmZnBwsICZmZmQs2D6tWrQ1NTExEREYiIiMDHjx/x5MkTxMXFISgoCMHBwYiKioJEIkHx4sVRsWJF1K9fHw0aNED79u1hamqqNIMnJCQER48exfnz5+Ht7Y2AgAC5os0AkJCQgFu3buHWrVsAgEGDBsHc3BytW7cWMuXoP1m5E9mtWzd4eHigTJkyP3w82ayOxMREnD59Gu3bt0d0dDT69++P48ePZ5j9J10fHx+Pd+/e4d27d/j7778xefJkvH37VmFmSKL8LCkpCTVr1sSrV6+EZak/15ydnXH27FmF5bLq168PT0/PNGd51NHRwd27d/H69WuYm5srnUwis3JihseEhAS54elr1qzBoEGDsp19RkSkCv3798f8+fOF5+vXr8eAAQPk6ntJHxsbGyut+0VEBcvXr1+xe/du/Prrryo/Fr/9FDKHDh3CunXrAKR8CXd0dETJkiVRsmRJhW0tLS3h5OSEI0eOQCwWw9/f/6ev+3PgwAGsW7cOY8eOlQtaRUVFoVq1anjw4IHc0JCQkBAcPnwYu3fvxu3btzN9HH19fbRs2RJVqlRB5cqVUaVKFVhYWKBs2bI5+XKyxMzMDMOGDcOwYcOEZZ8/f8aRI0fw7ds3+Pj44N69e3JFkaXDNHfs2IGTJ09i69atAFLS0rMTvCksateuDQMDA0RFRQFI+cKmLKtu7Nix8PDwyPaXuFatWqFp06ZCUHLOnDl49uwZPDw8EBwcnO6+ampqUFdXh6mpKcqVK4dv377B399f6HtUVBR69eqFK1euZKuPRLlBGjiaG3deLuClrq4ufLYbGBhg6NChcHd3h7W1Nd68eQMg5X/nL7/8IvwvBVIyNTPz/zEzGcHSvmU2gyur24vFYoVyBb169fqpb2oRUf5WuXJlaGpqChm49+7dw/z58zFz5kxhG2k9L0NDwyy3n5SUhP379+Pu3bv4/v07oqKiEBMTg+LFi8PKygoVKlRAlSpVhAlIiEiRdCb4hg0bZmv0x5UrV7BlyxYcOXIE8fHxDHpRilGjRmHJkiWoWrVqutsFBQWhd+/ewvMlS5ZkGERp0KCBUMB90aJF2L17d7b7W9CNGjUKZcqUQc+ePREXFycsj4uLg62tLTZt2oSdO3fi/v37iIyMVJo5IxKJoKenBxMTE1haWqJChQqoWbMm6tWrB1tbW+jp6eXmS/phJUuWxKhRo+SWvX//HitXrsTBgwcRGBiodD8bGxsUKVIEQ4YMwR9//PHTZhdoa2ujYsWK8Pb2BgCMHz8ejx49wtWrV+W2u3btGvz8/FCxYsVsH9PW1lYIet2/fx/379+XW29ubo4RI0agffv2MDMzg5mZWZpDVBMSEjB58mSsXr1aaI8oP3n58iXu3r2Lrl27yl0ISQNEb3utF5b17NkTHrfuQCwWo9TH93KfS3369MHcuXNRqVIlbNiwAa1atcLgwYOxYsUKlC5dWuFzUFXSy/LK7FDHRo0ayc3W2rp1aw4FIqJ8z8rKSu7G6pw5c9CzZ0/h+uf79+8AlM/uqExYWBjOnz+P9evX4+bNm5meXU52Rnci+k+rVq0gkUjw5s0bVKhQIUv7fvr0CZ6enti2bRvevn0LALlaO5i5oQXAiRMnUK1aNZQuXRpnzpxJc7sxY8YId7BLliyZqcLVskGy48ePZ7+zhUTHjh1x7tw5hSh2bGwsBgwYgAsXLuDbt29y/0CrV6+OFStW4Pnz5xCLxYiKioK/vz9u3LgBT09PTJo0Cfb29gUm4JUWKysreHh4ICAgALGxsdi4cSMaN26s8CXE398f06dPh7m5OcaPH48HDx7kyHS2BU2PHj2Ex+7u7nj+/LnCNk+ePEGTJk3w9evXbB/vzz//VHoXtFu3bjhx4gQCAgIwffp02NjYwMLCIt2abFpaWpg9e7bw/GcNXlL+FBERgRo1auCXX35B8eLFYWZmhpEjR8rVS5TNbho9ejTMAz9CTU0NwVbyQ9WHbdmGf0qUwqtXr9CqVSsAKcH7PXv2YNmyZQg0t0SguWW6/YmbezVTQxN1Ztpnu0B9Wvr164cHDx7ILbtw4YJKjkVElJMaN24s91wsFqN58+ZC3VFpFpivr69w0SwrKSkJ27dvx++//47GjRvDzMwMffv2xY0bN7L0/VMaXCMiedIgleyNtfSIxWKcOHECzs7OKFOmDKZNmwY/Pz+IRCIYGhpi+PDhcrX8ZI+R03gFU4B8+vQJHTp0wC+//IIFCxagRIkSwrqkpCS5gtgbN27M1J2QsmXLQiQSQSKRIDo6WiX9zg8SEhIQEhICHR0d6OjoQFdXF+rq6unuY2dnhwYNGuDGjRtpbmNkZIThw4fDxcUFtWvX/ulmu9PR0RGGRH7//l0opC77ewgJCcGqVauwatUqWFtbo1atWpg/f36WJwYoqKZOnYoFCxYIsw6FhoYq3e7bt2/p1hTKLBMTE3h7e2PevHnQ1dVFzZo10bx58wwzRdMi+8UyO3WKiHLa169f5YYLf/nyBRs2bICnpydWrlyJ4cOHo1mzZti+fTsAYERPV3So3ARqkZF4n5wE9b59UaxYMYwZMwaGAEqoq+d4nZjUQbCMgl2p12dlaOOKFSvw999/yy37/v37T/d/iYgKplmzZmH37t1yAaqwsDCYm5sjJCREbtsaNWqgRIkSKFWqFPT09ODn5wd/f/9MH8vU1BRly5aFtbU1goODERwcjOjoaJQuXRofP37E+/fvc+plERUapUqVQmBgILy8vFCzZs00t/Pz88PWrVvh6emJT58+AUi5NlRXV0erVq3g5uaGrl27QltbW26/atWqISkpSSV9Z9CrgJDWApJIJNi6dSu+ffsmV3B98eLFwh0QQ0NDdO7cOVPtSmdrk0gkhSYLRywW49ixY9ixYwdu376NsLAwpXWU1NTUoKmpCU1NTWhoaCApKQkxMTFC9Ll+/fppzmpYpUoVrF+/Hvb29ip+NQWHbBaQj48PPDw88OLFC5w7d04I5vj6+sLX1xeHDh1C2bJlMWnSJIwaNapQFyRVU1PDokWL0i30b2hoiL///ltp7b0fYWVlhS1btuRIW35+fsLjokWL5kibRDmhXLlyaNu2Lc6dOye3PC4uDiNGjMCRI0fwxx9/wMTEBKGhoXj15T1efZG5kNm7FwCwYcMG/PXXX+i2eCkCzS1hHvhR4VjKlqWWVsAqO3Rm2mc45DFs4wAcio7G5G/ymaIfPnxgoJqICowKFSpg/Pjx8PDwkFueOuAFpHzOv3///oeDU1++fMGXL1+QmJiIx48fy60LCAiApWX6mb1EP6PWrVtj165dmDNnDvz9/TFlyhRYW1sDSJkk6MiRI9iyZQuuXr0qzBwvEolQvnx5DBo0CK6urrCwsMiTvjPoVUB07NgRGhoaQv2tkydPIjY2Furq6tizZ4/cECQ3N7dMt3vv3j0hsPMjhSHzE19fX0yfPh2HDx9OM1glSywWIz4+Xml2TVhYmMKFlLW1NQ4ePIiAgAC0b98+x/pdGBUpUgSLFi0CkDIE6fDhw9i1a5dQABFIGf7466+/YvLkyejYsSNWrFiRpdkOC5Jx48ZBU1MTixYtgoWFhVwqr5OTEw4fPgxdXd087GHa/vnnH+ExZ26k/Ob06dMYM2YMNm3apDBj7vnz53H+/PlMtTN27Fg0MysJMyVDeKXDGjMT+ALSDnaFbRwAbPyxdlLX85KuC9s4AGKxGONTBby2bNnCizYiKnDc3d3x9etXeHp65njburq6Qta9lLe3NxISEtIt9UBEKebPn49r167hw4cP8PT0hKenJ5o0aYIaNWrg4MGD+Pr1q5BdbmBggB49esDNzQ3NmzfP456zpleBERUVhcOHD8PV1RVAyh0OPT09YZYp6Zf9IkWKYPHixZlud+//73QDkJuVsCC5du0aHBwcULlyZRw8eFAh4CUtKm9kZITixYujaNGiMDAwQO3atVG7dm1Uq1YNlSpVgrGxMdTV1ZUOBSlevDgePHiAWrVqMeCVRYaGhhg8eDCuXr2K8+fPo379+nK/4/j4eBw+fFhIM9+6dWumgpYFzahRo/Dx40fcuXMHDg4OwvLHjx/n24AXANy5c0d43LBhwzzsCZEiNTU1rFu3Du/evct2W2fjYtNdn5m6XsB/NbvCNg6Q+/mRdmSlVTNM9OCe3HNPT08MHjw4w/aJiPKj7du3Y8OGDWjbtq2wTENDAzt37kR0dDQkEgm+ffuGY8eOYcmSJZg1axbWrl0LX1/fNINXmzZtQnR0NBITE+UK1YtEokI92oAoJ1lYWMDb2xvjxo2Drq4uRCIRbt++jU2bNiEiIgIikQglSpTA5s2b8fnzZ2zbti1fBLwABr0KhPLly+Pw4cMAgOHDh8sFDGSHJKqpqeHy5ctZulshm3nj6OiYA73NHWKxGIsXL0a1atVgb2+PixcvCutEIhFMTU3RpUsXHDlyBElJSYiOjkZYWBjCw8Px7ds3fP/+Hd7e3vD29oaPjw98fX0RGhoqFMs8e/YsBgwYgCZNmmDQoEHw8/Pj0K4c4ODggAcPHiA8PBwjR45U+J2+efMGQ4YMQalSpbB27Vp8+fIlj3qqWn///bfwJevz589Ys2bND7UzcuRI6OjoQCQSQVdXF5UqVUKPHj2wfv16hIeH50hfZTO9unbtiuTkZCxatAhly5aFnp4eKlWqJMxOSZQbJBIJPn36hAcPHiAyMhIAULp0aXTo0EFuO5FIBBcXF7n6l+mZFvkNK79H4mPpnEm9l83oMh6+S+HnR0kDX+LJtnDv7YL169djpL4B6mpq4fTp08LNMSKigmr48OE4e/YsQkNDERYWhsTERAwYMECYDKpo0aLo3Lkzfv/9d8yePRujRo1CpUqVEB8fjwcPHqBDhw5y10uzZs2Ct7c3bt++LVeoftmyZZykhygLihUrBnd3dwQEBGDFihWoXr268F6TSCT4/PkzlixZgpUrVyIoKCiPe/sfkaSwFHIqZGTHk3/8+FFu/Ovu3bsxcOBAYaxs6dKl0aJFC8ydOzdL04eKxWJoa2sLBeP8/f1/aHhZbGysMITE0dFRpVkrYrEY8+bNw/LlyxEVFSW3rkKFChgxYgQGDhzIYVh54EfPg2PHjmHmzJl4+vSpwjoNDQ20bdsWPXr0QMeOHWFiYpKjfc5LgwYNwo4dOwCkpAD/9ddfWL16NSIjI/Hu3TuIxWIhc3PkyJEKGYiTJk2Cu7t7mu2rqamhX79+WLhw4Q+Pn4+IiEDx4sWF540bN8bbt28V6msYGhoKM0/m5ucB5U+qOgdWrlyJOXPmICIiQlimpqaGYsWKwdLSEq1atUJMTAw2bdokrG/ZsiUuXryIiIgIPKhcFUHJybiXEI9/EhLwWJysMCQSAOYVM4SbfkotLOPhu4QsLWmgSjZrC0h/uGJ69bhSt6ts+GJa+yWIxSj/+b8vk8uXL8ekSZPS3Ccv8LOAAJ4HlCIvzoO9e/eib9++aa5v3Lgxbt++rbA8vWuwrJJtq83mltA1yZ3zPzY0FheHXgGQ/ddAlJH79+9j+/bt2Ldvn3AzUiKRQE1NDY6OjnBzc4Ozs3OeDiNmaLsA6t+/P+rWrYuIiAg0btz4h9Ny//77byHgZWRklK/rKcXExGDOnDlYs2aNwjSppUqVwrJly9CrVy/erSmAnJ2d4ezsjNDQUPz+++84fvw4wsLCAKTMSnrq1CmcOnUKQErW45AhQzBhwgTo6OjkZbezbdOmTdi3bx/i4+MRFRWFX375RWGb79+/Y/To0UhMTISlpSV8fX1x48YNXLp0Sa4WnXQyCllisRi7du3CsWPHcOjQIbkhlZlVtGhR6OvrCzO7yg51lBUREQGxWMwhAqRSf/75p0I9FrFYjK9fv+Lr169KA+dXrlxBRz19bDY2QQ0tLdQA4Pj/i63ApCS0/RKMiFTvndeJiUIgS3YYYtjGAVnO0Eo9RFHZsEZp8Mt85n/Bs7QK2Eu31VJTQ3l1dfj9P2jH+l1ERPL69OmDp0+fYvHixcKEVbLu3LkDHR0dbNmyBf379xeWBwQE5HZXiQo0W1tb2NrawsPDA4cPH8bWrVtx/fp1SCQSnD17FmfPnoWRkRH69OmDX375BXXr1s31PvIKpQCYMGGCEDWVql69Opo2bZqti0zZIVVOTk4/3I6qREVFYc6cObC2toaBgQGWLl0qF/DS1tbG+PHj4e/vj379+jHgVcCZmJhg27ZtCA0NxdOnT/HHH38oXMj5+fnhzz//hL6+PurWrYsdO3aobGpbVdPS0kL37t0zte2ECRPQvXt3TJ06FadPn5YLeJUtWxZJSUkICwvDtm3b0KdPH9ja2sLIyAgAEBkZifbt22PevHlZ/l2pqanhwIEDKF++vHBXtlixYnB2dsa8efOgr68vbKtsdqWccuHCBVhaWsLAwADVq1fHokWLEBcXp7LjUf7UokWLH9rveVKi0uW1Ru/F++ln0UlHFxoARACKikQYcvqUsI2yIYmpl0lrbWU0W2Pc3KtKhzlK25XW+pK2Ja0NJiWbYRaSlIT3/w941atXDy4uLln/xRARFXKLFi3CgwcPMG/ePNSvX19hfXx8PAYMGCDMMicSidC4ceM86ClRwaejo4N+/frh8uXLePPmDaZPnw5LS0uIRCJ8/foVa9euRb169VC3bl2sWrVKSHLIDRzemE+lni63UaNGaWZZ/IjQ0FCUK1dOGCL44sULVK1a9YfaysmU5fj4eJw/fx6HDh3Cnj17lA490dXVxa+//ooFCxYw0JVPBJpbIsHQEE8XzgeQc6nrYrEYV69exejRo/H69WuFbCYgJcvJzMwM9erVg5ubGzp37lxgZuH58OEDypcvr/Q8z4i+vj7atGmDAwcOKH29X79+hZubG44dOyYs09DQgJ2dHZYtWwYbG5ssHzMxMREaGhrCUMuKFSvi7du3AABXV1d4enqqZAiD7FBQqbZt2+LUqVNQV1fPdvuUs1Q5jMXHxwd2dnZyNevc3NxQtmxZnDt3Dj4+Pvj27ZuwztLSEsePH0edOnWELKu0srUkEomQjv+jUmd2AekPVZRKPWRS2ZBHQD5T7N3+vShfvjxKly6d5X7mBg5rI4DnAaXIL+dBVFQUjh49igEDBmS8MTi8kSgnnD9/Htu3b8fRo0eRkJAgXM9pamqiY8eOQu1yVWLEoIAIDQ3N0famTJkiBLyaNWv2wwGvnBAeHo41a9bA19cXx48flyswKcvKygq//PILpk+fzmFUPwk1NTW0atUKL1++RGRkJBYsWIDdu3fLFUaUSCQIDg7G6dOncfr0aejq6qJhw4Zo3rw5bG1tYWNjk68uCh89eoT9+/cjODgYMTExaN26tTBltmytIlkGBgYYMmQImjRpAn19fVSuXDnD+n3FixfH4cOHMXr0aGzcuBFAynDRy5cvo169etDS0kKJEiVgaGgIY2NjNGvWDL/99lu6EzZoamrKPR83bhzGjh0LANi5cyeWL18ul/2VU2RnWpI6d+4cvLy80KNHjxw/HqWQ1o3MT6pXr47Dhw+jZcuWwrLY2FjMnDkTM2fOBJCS3XjlyhXUq1dP7ou+eeBHBJpbpjlMUXqX/0cI9bmwS2F5ZoZEprWNbMBMZ6a98BqAlP/dRIWF9FxXFjgmyikGBgbo378/XFxcsHjxYsyaNSuvu0RU6Dk6OsLR0RFfv37F33//ja1bt+LJkydISkqCl5dXrvSBmV75VOpMr5wsUnvjxg3Y2dkBSKnZ8+rVK5QqVSpT+wYFBWH79u3Q0NBAv379YGFh8UN3b8RiMbZs2YLly5fjzZs3SrfR19eHqakp+vTpg8mTJwvDtSh/ys27eO/evcPs2bNx/vx5BAcHK80Ak6Wnpwdra2u0bNkSbdq0QatWrXK1JlhQUBBGjRqF8+fPK9QkyowTJ06gY8eOP3z8adOmYePGjRmmEevo6MDX1zdL9YFKly6NT58+AQCWLFmCX3/9NcfPgzt37qBJkyYKy+vVq4eHDx9mu31SdPToUbi6uqJRo0bYvn17lgLHufFZYG9vLzf78NixY7Fq1Sq5baTBIdlAkVR2Zk/ML/J7cCC/ZHZQ3srKeSAd1kuFT379PEhKSsLNmzfx559/Ii4uDsWLFxdGGQDM9CJSFW9vb2zfvh179uzJ8eQeZZguUwA0bdo0xwJe/v7+6NOnj/B8/vz5mQp4+fj4oEGDBrCwsMD06dMxZcoUlC1bFrNmzYJYLBa2q1KlCmbMmJFmO2/fvkX37t2hp6eH4cOHKwS8ihUrBldXV5w4cQJhYWF49+4dFi5cyIAXySlXrhx27NiBT58+ISEhAceOHYObmxv69++vdEKGmJgYeHt7w8PDAx06dICuri50dHRQsmRJ1K1bFwMHDoSXlxdev36dozXCHj16hIYNG8Lc3BzHjh3LdMCrWLFics+7dOmC5s2b4/Llyz/UjwULFiA0NBRHjx5F9erV0/yyGRcXh+3bt2ep7bZt2wqP0xuCvWrVKhgbG6NevXoIDAzMdPsbN25E8+bNla6TTl1OOevRo0fo0aMHIiMjcf78edjY2OD169d53S05ly9fRu3atYXnf/31V5oBUGXF4wsCiUSCoV6LYbW0G0YfX66wPjN1xIiIKG0aGhqwt7fH7du38ejRI1y6dAm7dhX8myJE+V2dOnWwatUqudE7qsThjQXAvn37srW/WCzGixcvULZsWTRu3BifP38GADRv3hwjR45U2P779++4evUqbt26BW9vb7x58wZ+fn4K2yUnJ2Pu3Lnw8vLCvHnzAADR0dFYtmyZ8Fx6/LVr18LDwwPv3r1T2sdKlSph2LBhGDt2bIGpx0R5T3oxW+//P1JBJUrifnwCtkV/xwt1daVFz+Pj4xEcHIzg4GB4e3sLX3K0tbVRtWpVJCUlwdzcHLa2tmjTpg2aNGmS6RpyXl5e+P333/Hvv/8qrNPT00O9evUwfvx41K9fH2pqalBTU0NsbCxEIhGKFi0KExMTLF68GFOnTgWQ8l67efMmWrdujWLFiqFnz55YsGABzMzMsvT7ks6UCaTUtbh06RJWrlwp3NEEgJIlS2apzRo1agiPU2eSff/+HStXrsTGjRvx/v17ACnDmWvUqIF79+7B2to63bZHjhyJDRs2pLk+O9lvpFxQUBCaNm0qV2cuODgY3bt3x7179xAQEIAnT56gbt26qFSpUp71U01NDY8ePULt2rXx/PlzAICnp6fSQsXKpB52mFZNrZwWtnEAzAM/ZhisEovFaLxhGJ4Gp9TM2/bPKazuOFHp0H4OC6PCgucwEdHPJbeu+xn0+gk4Ojri0qVLcsvU1dWFdMJr167h/v378PLyQkBAABITlc90JVWnTh00aNAAW7duhVgsVojQSmeW+/LlC3bs2IF58+YpzD4JpNTpcXFxwcKFC7N88U6UntLqGuiip4Eu/88Eep+UhIMx0biXkICPZqb48uWL3AyIsuLj4+Ht7Q0AeP78Oc6dOycEcfX19VG6dGlUrVoVjRo1QtOmTWFlZQUdHR18+/YNx44dw8KFC5XW5qpUqRKWL1+Ozp07Z+o1TJkyBbVq1YKbm5vczIjfvn3Dli1b4OnpCVdXV7i5uaFJkyZZqkWUlJSEfv364cKFC3LZZzVr1oSbm1um2wkNDcXu3buF5xYWFpg1axaaNm0KAKhWrZrSIZURERG4evVqukGvqKgooRaZMlZWVpg8eXKm+0qZ07NnT6VBYh8fHxgYGMgt09TUhL29PdasWZNhAFMV1NTUcPPmTZQoUQLx8fE4ffq03HplwxqVSR3wkl2WneCXtA3zwI8A/gvSB5pbptuuWCxGqcWdERkfLSwrVcSYtSyJiIiIfgCDXj8BZUOi1NXV0bZtW7x8+TLT7VSoUAEDBw6EqakpDAwMoKGhgfXr1yvdtlevXvDy8lIIoIlEItSuXRuzZ88WMk6IsuJHhipZaWhgctH/DxkUAzA2RZJYjM9Hj+DBgweIjY1FTEwMfHx88Pz5c7x+/Vpu2K5UdHQ03rx5gzdv3uD48eMZHlckEqFRo0ZYt24d6tSpk+V+t2/fHsHBwXj06BGmTZuGy5cvIyEhAUBK4Grr1q3YunUrrK2tMX/+fPTs2TPDNhMSEmBlZSVkfEr72atXL6xYsUKhWH16fv/9dyFACAB79+6FsbGxEPRKT0a/Dx8fnzRrtTk4OOD48eMMAqjA7du3FZapq6srnWE0MTERFy5cQJ06dbB06VKMGDEi12fULVasGJo0aYIrV67g3bt3uHPnjtx089KAkyzpZ4hssEs2QCYbpMpsIXopnZn2Cp9R0kwsaTvKgmxSSclJKLm4E6IT5AOP14aszfDYrIdEREREqpKVG+NZkdXSKj+CQa8CYOLEiViyZAnKli37QzNLmZqaymWKACkXvmkFvHR1dWFmZoZy5cqhZs2aaNy4Mf7++2+cPHky07OcHDhwQO65kZERevfujQULFsDQ0DDLr4Eop2moqaFhw4Zo2LChwrqoqCicPXsW165dw6NHj+Dn54fQ0NBM1/vS0dGBk5MT1q1bl+XhgsrY2NjgzJkzwgQQ69atw9u3b4UZWH19feHi4gIrKyusWbMm3WF/U6dOlQt42dvbY/369ahSpUqW+2VqapruepFIhAoVKqBz584oUqQI5s6dK6xr27YtdHR0ULVqVTg7O2Po0KFyNbpk6zXJmj17NmdbUpE///xT6fIeWtrYHxuT5n6xsbH49ddfsXHjRqxatUou6JQbOnfujCtXUgr2Nm3aFCVKlICDgwPmz5+PMmXKKGyfVgZY6gBZ6gyttIJfqTO6UrefViaZbBBM+rj1tnEKAS/fCftgaVhC6bGJiIgKOun17axZszB79uy87QylaefOnRlOHvYjGPQiAMDBgwdx8OBBlC5dGjY2NtDQ0ECXLl3Qs2fPTBVyvnTpEmxtbRWKaKupqcHW1hb29vZo0aIFrK2tUbZsWbnsiaSkJDRs2BCPHj3Kcr9NTEzg5uaGIUOG5MnQFyqcMjtkKTMCzS2VZoIYGBigR48e6NGjh9zykJAQnD17Fjdu3IC3tzeCg4NRs2ZNIbulUaNG6NGjR4a1jmT7r+z4aVFTU8OwYcMwbNgwREdH48iRI9i0aRNu3rwJAHj//j06deqE2rVrw8vLC+XKlVNoQ3Zq4FGjRmHt2owzSNKyePFi2Nvb48GDB9izZw9EIhGqV68urPfz80ORIkUAAKdOnZLbVzoE9PPnz7hy5QomTJiAmjVrYuLEiRgwYAC0tbUVjmdmZsaAlwq9ePFC6fK5xQyhIQL2xKQd+AJShgO3bt0adnZ2mDhxoiq6qNTw4cOxefNmvHjxAhKJBJ8/f8auXbuwe/dudO3aFbVq1cKnT5/QokULNJs4We5/XGbef5kNfmX2c0m6/+AlHWAkUseUYsWEwNejIPkJAxjwIiKi/C4hIQGHDx/GmTNncP/+fXz58gWRkZEoVqwYrKysYGtri+7du6NVq1bM0pcRGRmJ06dP48GDB3j48CECAwPx5csXxMbGwtDQENWqVUP79u0xePBgGBsb53V3UbZs2XSDXjExMQgJCRGCmFpaWjA2NoZEIkF4eLgwWkUikcDMzCxXJ6QSSVQRrqNsk53iNj1WVla4f/9+hjWxkpKScOLECRw4cADh4eFo2bIlXF1d0525MS4uDjVq1MDbt2+FZba2tmjWrBk2bNiAmP9fABkbG2Pr1q0AgMGDB0MkEmHt2rXo0qULi9L/RHJzOuqcnI0tK0GnrEoruPWjQS9lJBIJhg0bhq1bt8r9IxKJRHBxcYGnpyc0NDRw+/ZtvHz5EiNGjBC2CQ4OzvF6eumdBz179sTRo0czzJjT0tKCra2tEMyT3T91FinlnJCQEJQvXx7R0dFyy/U0dXDebSV2PDqNzQ8zHtYr+z+hfv36MDc3V0l/ZUVERMDR0REPHz5M9wtZEW09vPJ7g9KlS2f5GKk/d1IPV0wdHFO2DQCIJRLsjY6GR1QkPovFWF+8ODrp6sN4+C5MOLUK+55ehJFuUdwZsRFFdeTrqGVGfhnimJv/Eyj/4nlAQME6D2SvwT5+/AgLC4scaavN5pbQNcmd1x0bGouLQ1MyoLP7GjJy5MgRTJo0Cf7+/hlua21tDXd3d3To0EFu+c+a6XXx4kU4ODhkuJ2JiQl2794tN2N6fiOd+TsoKAgjR46Eq6srateuLfxtJRIJnjx5Ak9PT2zYsAGlS5fGwYMHUa9evQxazhkMtRYAEyZMgIODg5AxIev9+/dp1tWSpaGhga5du2Lv3r04d+4cpkyZojTgFRISgv3796Nnz54wNDSUC3iVLVsW/v7+cHd3FwJeqR04cABfvnyBi4sLA16kMuaBH+V+CoJAc0vhJyeJRCJs3rwZoaGhaNWqlbBcIpFg//790NfXh6amJlq0aKEwW6uRkVGO9iUjBw8eRGJiIhITE/HlyxdcunQJQ4YMUfgylpCQoBDwAlJmnCXVMTMzwz///IORI0eiRYsWwmd4TGIc7DaNQERclNz2Ojo6GdaAq1u3LiZNmqRQIy85OVlumC0AHDt2DHXr1hUmWVBWVy8thoaGuH//PhISErBlyxZYWVkp3e57fAwaN24szCaaFVn9rJHNCDMevgvGw3fhVnwcxn8Nxx+REfj8/9d3QWbiAI8O4/Bp6gn4jN/zQwEvIiKi3DJv3jx0795dCHg5ODhg9erVuHTpEv755x9cuHABa9asQdu2baGmpgZfX19MmzYtbzudz1haWmLgwIFYtWoVjhw5gjt37uDWrVvC9bi6ujpCQ0PRuXNnPHnyJK+7q1RgYCCcnJzw+fNnXL58GR4eHqhTp45cWSaRSIQ6depg5cqVuHTpEj5//oz27dsrTIinKhzeWACMHz8eK1asUDrEUE1NDQMHDsxW+4mJifD09MS4ceMUhkDKUhbBNzU1lbuQzu1aLvTzUvUQx8weT1l2h7L1aW2T2eNnxMjICJcuXcK9e/fQq1cv4aJeNnAgmwFTo0aNXC86LqWhoQETExO0atVKCNS9f/8eM2fOhJeXF75//650P3V19dzs5k+pcuXKWLduHYCUu7e9evVCUlISxAAOPpefFEXZLI+picViuLu7Y9OmTWjdujUcHR1x+fJl3L17F4GBgXBxccGOHTsQFxeHrl27QiKRwNvbG3///TfU1NQwY8aMLN311dDQwODBgzF48GBs374dmzZtgpqamlyB/g8fPqBSpUro3r07goKCMH78eHTt2jXTxwCUv+9Tv7+lGVeh006jzbZxePL5X6VtOemkpPfnxIyRREREuWH79u2YOXMmgJSbZgcOHECLFi0UtmvTpg1Gjx6N58+fY8KECfjy5UtudzXfatmyJT58+JDmehcXFxw9ehRdu3ZFQkIC5syZgyNHjuRiDzNn+fLl+PLlC6ZOnYomTZpkuH3Tpk0xceJELFq0CMuXL4e7u7vK+8hMrwJg9+7d+OuvvxAWFqawbvXq1Urr9mTW7du3YW1tjWHDhqUb8JKlrq6OFi1a4Pbt2wgJCcGUKVN++PhE+VlOZ2elzlBTRaZaw4YN4e/vjw0bNgjZoZqamihZsiQqV64MJycnzJs3D48fP87R42aXlZUVduzYgYiICHh5eaF69eowNTXFX3/9JWxz4cKFPOzhz6dbt254+vQpihUrlu22oqKicOzYMYwePRqHDx9GYGAggJTsYCcnJ5w5c0ZhWKJYLMaSJUt++Jhubm7CHdMDBw7A3t4eBgYp2VOJiYnYt28frl+/jh49emQqgJceaSaXlOz7ute+mWkGvEqrqcMp1TCf9GZ3zAzpbJFERESqEBgYiDFjxgAA9PX1ce3aNaUBL1k1atTAuXPnMHny5NzoYoGQmZu5Xbp0QeXKlQEAN27cUHWXfoi0bm/79u0zvY+Tk5PcvqrGTK8C4ONHxYviUqVKoU2bNhg1atQPt7tv3z4MGjQI8fHxGW6rrq6Ojh07wtnZGf369ePQRcozmQlAZZRdlVlZqcOVusB+Xg+7HD58OIYPHw6xWFygioaqqamhS5cu6NKlCyQSCeLi4jB79myEh4fj6NGjOHTokMIEA6Q6VatWRUBAANq1a4dbt27lWLtqamrQ0tJCXFwcrl69iqtXr+ZY28r07NkTPXv2RHh4ODw8PDB//nxhnVgsRlRUFHR0dDJsJ633ddjGASnrNiquK1tc+QyuXXR0sdyweOZeABERUT7h4eEhlLqZO3dupmcAV1NTQ//+/bN8PD8/P3h5eeHq1at49uwZgoODAaRkmDVq1Ahubm5o165dum1ERERg7dq1OHnyJF69eoWoqCgYGhrC1NQUlStXhqOjI7p164YSJRQnj7l8+TI2b96Mu3fv4vPnzxCJRDA1NUWpUqXQrFkztG/fXq7ESE6T3sTO7g06VZHeyFQ2CVVapNsGBASopE+pMehVAAwdOhRXrlzB69f/zer06dMn7Nq1C5aWlliwYEGW2hOLxXBychIKSgKAubk5goODFQpMlypVCqNHj4abm9sPFf4lymmy2RRpZURkNdCVmSGGWZnlLT8pSAGv1EQiEXR1deHu7o5BgwYBSAle6OvrY9myZQo1ykg1DAwMcPPmTYSGhmL/L4vwb1gAgr6H4lnSJ7x58ybDiQmqamjgtyLFsN+2Pl6/fo06depgwYIFiIqKgpOTE8LDw9Pcd8iQITn6WoyMjDBv3jzs27cP//77X/bVj97IycxsslsfnoSdkQmuJcTBVKSO5jraqK2hqbL3Zn4pZE9ERIWPRCLBjh07AKRkeQ0dOlSlx3v37h0qVKigdN2HDx/w4cMHHDhwAP3798f27duVlu54+fIl2rRpo1A/KjQ0FKGhoXj58iWOHj2K5ORkIYNNasKECVi5cmWax7537x48PT0RGhr64y8yHa9fv4a3tzcAZDq4mNv09fURGxuLe/fuoX79+pna5969e8K+uYFBrwLAzMwML168gI6ODhITE+XWLVy4EH369EGNGjUy1VZUVBTq1KkjV6De0NBQiNBKmZqaYsKECfjtt9/yrO4PUUaMh++Su8DL6SLxlD8MHDgQa9euxYMHDwAA0dHRGD16NJydnRmMz0UmJiYYfXyF3DKJRILDhw/jjz/+gJ+fn9L9hugXgaOuLhyf+aQsuP8QcGiLYgA2QQ19ACQq2c/Q0BCrVq1CUFAQTp48CRsbm0x/mcpI6hoaHTt2xPXr13+oLdlgd1qB7za6umiThdnKwjYOyHJtLwa7iIhI1Xx8fIQAT/PmzZVOtJaTkpOToaWlhbZt28LBwQHVqlWDkZERwsPD4evri7Vr18LHxwe7d+9G+fLlMWfOHIU2BgwYgKCgIGhqamLo0KFwcnJCyZIlIRaLERAQgLt378LLy0thv5MnTwoBr1q1amHkyJGoWrUqihUrhoiICPj4+ODixYu4f/9+jr7mmJgYBAYG4sSJE1i6dKlwc3H8+PE5epyc0qBBA5w9exaLFi1C9+7dUbKk8gx3qU+fPmHRokWQSCSwtbXNlT4ymlFAqKmpoWvXrjhw4IDCOkdHRwQEBGR41/jt27eoX78+IiIihGUlSpQQUkSlx5k8eXK26qgQqRIv7H4+IpEIZ8+eRffu3YVhcBKJBGfOnMHgwYPztnM/OZFIhB49emDevHlK169duxbNJqZdv6ORjg5elyyNPTHReJSQgK6bNmLQoEEQi8WIiIiAmZmZXD3Ls2fP5siU3alnhkwv2ywnZCYjLLvi5l7l5yMREcn59OlThtuknkU7PbIzCNarV++H+pQVpUqVgr+/P0qVKqWwrnXr1hgxYgR++eUXeHp6YsWKFZg4caJcLVI/Pz/8888/AAB3d3eFTC5bW1t069YNS5YskbtGBiBcd1tZWeHWrVtCXVApe3t7jB49Oke+Q3h6esLNzS3N9VOmTEHfvn2zfRxVGDduHM6ePYugoCA0atQIy5YtQ7du3RRqliUlJcHLywuTJk0Ssu5+/fXXXOljwR338hNK643w6dOnDC/8Ll++jKpVq8q9mWvVqiUX8DI3N4efnx8DXpTvyRaYz+6FpCqKyVPOMzIywpUrV1CxYkVhWeoMVcobd+7cwdOnT4Xnsl9yOnfunOH7S0tNDW4GRbDayBitpvyJMXr/pbqnnsBl4sSJ2e7vggULFIZkmpqaYtGiRejQoQPKlSsHAwMD6Ovro1ixYujWrRtCQkKyfdysftZkt6A9ERGRra0tLC0t0/3JCtn/y2ZmZjndXQX6+vpKA15SIpEIK1asgLq6OqKjo3Hx4kW59Z8/fxYe29nZpdtO8eLydTal+9rY2CgEvGQZGRml+xqyo06dOrh//z4WLVoEkUiksuNkh6OjI8aNGweRSIQPHz6gV69eMDMzQ+vWrdG3b1/07dsXrVu3hpmZGXr16oWAgACIRCJMmjQpR25kZgYzvQqQdu3a4ebNm9i6dSu+ffuGJ0+eCMMUPT09cePGDXTr1g1DhgyBtbW1sN+xY8eEqeCBlDf1oEGDsH37dmGb0qVL499//81UIV+i/Cr1BWVaAbGsDhui/KNUqVJCLaZZs2bh+vXr2LlzJ4c55qGTJ08Kj0uoqeGGkQle/P95/Mo7ECWqZWko8q8GRbA9OgrfU83mCAAvXrxAhw4dsG3bNqXFZjNDNkAnlV4xfS8vL3h5eUFdXR1lypSBvr4+Xr9+DWNjY1SqVAkDBw7M8dpjP/IZxSwvIiJSte/fvwuPc6sek6zExEQEBwfj+/fvSE5OFpYbGxsjJCQET548Qffu3YXlsgEzT09PuLu7Z/pY0n2vX7+Ot2/fpllbLCd06dJFKOEQGxuLt2/f4sCBA/Dy8kKfPn2wcuVKdOzYUWXHzy53d3dYWVlh+vTpiImJQUREBK5cuSK3jUgkgkgkgr6+PhYsWJBrWV4Ag14FTtOmTdG0aVPhuYWFhZDt8PbtWyxbtgzLli1DnTp1MGTIELi6uqJv375CwEtLSwsrV67En3/+KbRhamqK169fM+BFBUbqoUI/ay2v/DRbpKrdunUL48aNw5s3b+SWX7p0CRYWFujduzd2795doAv3F1SyswmFSCTQu3geePgw3X3Sm2FVV00NV0xL4M9vEUgCcDlefrai06dPY3i5cjj6/5mjssrd3R1nzpyR++KeGcnJyXj37p3w/PPnz/j8+TNu3LiBpk2bomrVqj/UH1kMyBMRUU66f/9+uplSWSVbwys6OjrH2k1PYmIiNm3ahF27duHx48dISEhIc9vUBeXLlSuH5s2b48aNG/Dw8MC5c+fQvXt32Nvbo1GjRtDT00uzrYEDB2Lnzp0ICwtDjRo14OzsjLZt26J58+ZyIw9ygqGhIQwNDYXnDRo0QO/evbFr1y64urrC2dkZW7duFSZ2yo/GjRuHfv36YceOHbh48SKeP38uDP00MjJCzZo10aZNG7i6usLY2DhX+8agVwGX1gWet7c3xowZg99++w2xsbHC8ooVK2LMmDFCPZPq1avj+vXr6aZsEuVHmZnFMS2yRaILWh2cnzHAd+jQIbi4uAjB+9QkEgn27t2L6OhoeHp6KqSnk2r169cPHh4ekEgkkEgkGNvAFgPXr8twP+m5LH0vyr6PS2poYJuxCQCgYlAAUk/SfQOamX4vpA4Im5ub49q1a5gzZw7++ecfJCcnw9raGt+/f4eNjQ3atGkDJycnFC1aFF5eXhg+fDi+fPmS7jGioqIy1RdlMhPokv5uGBQjIqKsKFWqVJZqdmVENlghWyZHVcLDw+Ho6CjU5cqI7HWv1N69e9GzZ0/cuXMHL168wIsXLzBv3jxoamqiUaNG6Nu3LwYNGqSQANK6dWusWbNGuJ7ev38/9u/fDyDlu0THjh0xcuRI1K5dO/svNA0DBgzAyZMnceDAAYwZMwadO3dW6XDK7DIxMcGkSZMwadKkvO6KHN4SL8AePnyIjx/Tz+5I/cZ/8eKFEPBq3Lgx7t69m6/fOERp0ZlpL/wU9iyn3JZT9dJyQlJSEgYMGKA04HXu3DlYWVkJz48fP466devi7t27udnFPBMVFYUKFSpAV1cXtWrVUpiRMLfs3btX7u/zIum/uRi1xzcW3qepSWtchW0ckG7gekrRYlAHYKGuDun8h9GxkWkGQTNTO6tu3bo4evQoPn78iKCgIFy9ehX//PMPNm/ejF69eqFo0aIAgK5du+Lz588ICwvD2LFjFdrR1NREnz594Ovri3nz5sHJyQk2Njbo0qWLMNuolI+PD3777Tf4bN8K4+G7hJ/MyMq2REREqiIb4Hn06JHKjzdu3Dgh4NWlSxccP34c/v7+iImJgVgsFm64SWuTKftuYG5ujtu3b+PixYsYNWoUqlevDpFIhMTERNy4cQMjR45EjRo14Ovrq7Dv6NGj4e/vDw8PD7Rv314okh8YGIiNGzeibt26mD59ugp/A4CzszOAlMy6s2fPqvRYhRWDXgXYggULhMcNGjTA+/fvYWNjk+F+RYsWxcSJE3H+/HlmeNFPSzZoVhCkFYQqLIX4xWIxXr16pZCyPmnSJGH4nJaWlty6pKQk+Pv7y9UnfP/+PZo3b445c+YoFCsvbEaMGAE/Pz/ExcXh2bNnqF69ep685s2bN8s9T5KZNSmrUp/LxsN3YYhBEbwvbYHampqQ3sZp3b69UNBV1e8BNTU1GBkZoXz58grrkpKSsHfvXvTv3x8zZ87E2bNn8fjxYxw7dgyNGjWCj48PACAhIQH169fH8uXL0bZtW8yKPqP0M0h2WUbBwNTi5l7N7kslIiJKV/Xq1WFikpKJfePGDURGRqrsWJGRkUJmVb9+/eDl5YVOnTrBysoKurq6coXdv379mmF7rVu3xtq1a/H8+XN8+fIF+/btQ6tWrQCklAnq1auX0v3MzMwwfvx4nDp1CuHh4fjnn38wffp0GBoaQiKRYMGCBTh27FgOvGLlTE1Nhcfv379X2XEKMwa9CjDZNM9JkyahTJkyqFatWob7RUZGwt3dHcWLF0e3bt0Upm4nKoikF76Z/SnMUs9umdWsrbz4Pc2cORNVq1ZFgwYNhMDXnTt3sGbNGmGbP//8E6tXrxaed+jQAbq6ugqz1yYlJWH27NmZuglQkF24cEHueVRUFNq0aZPr/Ug9nPTbt29Z2l/2fJOep9LnH/uYY4FzJzRWF+GUTO2w8ePHK5yjGT3PrnHjxqFPnz7Q0PivMkRa2WZASiB3/PjxAFLqgsjWPnN3d5fLxE4d/IqbexXGw3fJ9T+9QH1BCuATEVHBJRKJ4OrqCiAl82jLli0qO9abN2+QmJiSPZ5WQAoAXr16leUyA8bGxujVqxcuXbqEzp07A0gpD5S6dmxqampqsLGxwbx583Dp0iVh+YEDB7J0/KyQna08vyesJCQkYPPmzejUqRMsLCygq6sLAwMDlC9fHi4uLti9e3ee3KBl0KsAOXToENq3b4+VK1ciMDBQmDJWQ0NDSHscOXKkwn5mZmZKpzhNSkqCl5cXbt++rdqOE9EP+5EML1UFq1Q57PH48eMAUmbWq1y5MqysrNCkSRMhKF+yZEnMmDEDI0eOhL29vbBfXFycXOBeOiwNAJ49e6bSLyGq8Pr1a/z2228YMWKEXJZbagkJCUrrTF27dg07d+5UdTflNGjQQO5548aN09w2rfNHulx6Xj99+hRlypSBtbU11q9fLzeUv1q1anBwcEi3Tz8a8M3I33//jejoaHTv3l24y1ykSBHMmjULR44cwYsXL7Bu3X/1zC5evIiqVasKd6qlkpKSoK+vj5cvX2Z4zPSCXAx2ERFRbpswYYJQAH7mzJl49epVpvYTi8XYs2dPpo8jGxxJr2j+hg0bMt2mMq1btxYepy6Enx4bGxvhxl9W9suqgwcPCo9r1qypsuNk171791CpUiUMHz4cp06dwqdPn5CQkIDY2Fi8f/8ehw4dwsCBA1GpUiXcunUrV/vGoFcB8fr1a/Ts2RNnzpzBhAkTYGFhgZj/z1w1ZMgQ6Ojo4NixY5g3bx50dXWF/cqVK4fg4GAkJSVh9erV0NTUlGtXWguGiPIX2Yv1nMpYye7Fvyozv8qVKyc89vf3l6tPpa6ujlOnTkFNTQ3q6uo4d+4cfv/9d6irqyu006tXL7Rr1054PnHixEz3IS/rmJ07dw7Ozs6oUqUKli9fjo0bN8Ld3R0DBvw3vG3Pnj2YOHEi3r9/j4ULFwpZRiVKlECLFi2E7YYOHYqIiAhERkaie/fuMDU1RalSpVRSe2P37t1CwFLq9evXGe6X+vcsPbcCzS0REhKCunXrplmzslu3bti8eTMOHTqk0mEVadHS0sKhQ4eEeiKRkZGYPXs2unbtiqpVqwp3wKXSuhiQSCRo0aKF8L9cSnaYYtzcqwVusg0iIirczM3NhUz86OhotGjRAteuXUt3nxcvXqBdu3ZYtmxZpo9TsWJFIXFjx44dSrOrT5w4ITcqIDVvb294e3unuV4ikeDixYsAUrLYypYtK6zbv3+/0sL4Ug8fPhSGVcp+j80sT0/PNG9uSnl4eOD06dPCMZo3b57l4+SG+/fvo2XLlvj48SNEIhEMDQ3RunVrODk5AUj5Pbdr1w66urr48OEDWrVqJZcpp2qcvbEACA8PR4cOHZSuMzExwYIFCzBkyBBs3bpVYf2CBQsQFxeHefPmYcmSJUhOThbW2djY4MyZM3KZEUSUu2QvcHPiwjajoE1G65UFtmT3UVXgq3379grBEwCoUqUKTp8+LfdlQktLC0uWLMGsWbPw8OFDLF68GGfOnAEAaGtry2V+5dZ02tlx9epVuUCdrEOHDmH+/PnYtGmTEATy8PCQ22br1q1o2bIlTE1NERMTg4SEBAwbNgznzp2TCwpNmTIF58+fz7F++/v7Y/jw4YiPj5dbntbdzsyee1fMSqQ77H7+/Plyz/X19VG+fHlER0fjw4cP0E1ORn1NbQwx0EcLHV3h2Lk1XFdPTw+NGzfGnTt35JY7OzvDxsYGs2bNEpZ9+fIF+vr66NKlC3bVGg0N9f++lunMtGedLiIiypfc3NwQEBCAmTNnIiQkBPb29nB0dISzszOqVq0KQ0NDhIeHw9fXF6dOncLZs2eRnJycpZkOjY2N0b59e2F/R0dHjBw5ElZWVggJCcHhw4fh6emJ8uXLIyIiQmkGvLe3N9zc3NCgQQN06tQJNjY2KFmyJBITE/Hu3Tts375dKBfRuXNnlCpVStj3jz/+wIgRI+Ds7Aw7OztYW1tDX18fYWFhuHnzplByQ11dHUOGDMny73D27NmYNGkSunfvjmbNmqFChQowMDDA9+/f8ezZM+zZs0fIiNLS0sKmTZuU3vDNa4mJiXBxcUF8fDyKFCmC5cuXw83NDRoaGnjx4oXwHf306dMIDw/HwIEDcebMGfTq1Qt+fn65Eotg0KsAsLGxkQtWSdna2mLjxo0IDg5WCHhpampi7ty5iIiIgIWFhTAUEkgpQLhp0yY0adJE5X0nIuWkF7Nyhao3Kt82reFgGZHO9paVYtgZyakAWOp2BgwYgBEjRgjL2rVrBy8vL4Xpo2Xp6enBzs4OAwcOFJZ9+/ZNLrCTlTuKeVXr7a+//kp3/YwZM9Jct2TJEuGmyMaNG4XMMNlUeCk/Pz8kJSXJ1aT6UcePH8fIkSMVspTSYzx8F2Zd3IJDt/fBLzkZOiIRzpmWQIVUGcjWmlr4s0hRbI2OwlexGMnq6pBIJGkGwqKjo/Hs2TPh+XcAVxLicCU8DjU0NPFPdJTCJAiqdv36dSxbtgyXL1+GpaUlZs2aBSsrK+FOp5eXFxYvXixsf/ToUbT5xxdXh6yVa0dnpn3Ke2Vj5s/P1J8thb2GIRER5Y0ZM2agevXqmDRpEvz9/XH+/Pl0b65Vr14dS5cuzdIx1q9fj2bNmuHDhw+4ePGikJUlVaZMGRw9ehTt27dPt50HDx4ozKosq0mTJkoTSCIiIrBjxw7s2LFD6X7a2trYsGED6tevn4lXoyg8PBybN29WmBRIloWFBbZt25YndVszw9PTEx8/foSamhpOnjyZbjaakZERDh06hPLly+Pz589Yu3Ytpk6dqvI+MuhVAMgGvMaPH4/p06fjw4cPqFOnDg4ePIh+/foJ601NTbFr1y7UrFkT7dq1k7sQ0NTUxLRp0zB16tRcvwAgIuWMh+/KkaCUNMCV3WNkZXhfVrJnUge5Uu+np6eHSpUqCQVEz549i/bt2+P8+fPpBmnWr18vN5NN6iLqgwYNylT/8tKPFiVdsGABfv/9d+F5//794erqmmZw6O3btxgwYAB27doFDQ0NXL16FXfu3EGPHj1QqVKlTB/3w4cPQh3JjMSvvIPwNa54nBCPVd8jcVEmKywOwMURw1Bh63aF/UYVKYpRRf6781fq43uEhoYiMDAQgYGBePz4MXbs2IGPHz8qzPgp63lSIvr27YtDhw5l+vXlBA0NDUydOlXhi5xIJIKtrS1sbW2xbds2hISECOvufXyBq6t7oaaWltz7I733NhERUV7q1q0bOnbsiEOHDuHMmTN48OABQkJC8P37dxQtWhRly5ZFo0aN0KNHD9jb2yutM50eS0tLPHr0CEuWLMGxY8fw/v176OjooGzZsujSpQvGjRunMKGOrD59+qBEiRK4cOECHjx4gMDAQKH0j5mZGWxsbNCrVy/07t0bamrylZ+uXLmCEydO4Pr16/D19cXnz5/x9etX6OnpoUKFCmjdujVGjhypdHbnzDh37hxOnTqFW7du4d9//0VwcDDCwsKgq6sLMzMz1KlTBx07doSLi4tQQy0/OnHiBCQSCXr37p2p4Zc6Ojr49ddfMX36dJw6dYpBL/pP0aJFsW/fPmFcbJEiRTB9+nQsWrRIGN8sEolw/vx5nDhxAh06dJALlhkZGeHatWuoUaNGnvSfiFIozfDKAbLtyV4wp1UEP611eSXQ3BJ7k5LQuXRpBAUFAUj5smFpaYljx47B1tZW6X6enp5yzz9//iz3fPDgwWnencsvli9fjsuXL8vNzlOiRAkcO3YMa9euxa1bt2BiYoIlS5bAzMwMCxYsQKtWreDq6orRo0fj2LFjKF26NO7evQt1dXWFoNdYgyJYH/UdiQD27duHxMREuLq6CrMV/fnnnzh9+rTw/yUjyrLIDA0N0ahRI/j6+qJ79+7C8u/x0bAOCUFckvLAlIuLC8znzlVYLlvPTsrMzAxmZmaoW7cuOnbsKGTAvX//HodHrsDbr4Go+PIKSqirYbV1Jdy9excA4OPjk2GG24cPH4TfX9WqVeVmQpa+Z3O6rpanpyfGjBkDPz8/YVnH0BAcNTGFucx2WT1u6mGRyn6XREREOUVLSwt9+/ZF3759f2j/9GZCBlKGOS5dujTdLDF/f3+ly7W1teHk5JTp7ziyypUrh7Fjx2Ls2LFZ3jczKleujMqVK2ep/mx+9PjxYwBAly5dMr2PdJirr6+vKrqkgEGvAqBq1ap4+vSp8IU9OjoarVu3xr1794RtihYtCnd3d3Tv3l3uCzSQcgLu37+f2V1E+UhOZXjJtpfR+tQXz+aBH7Md+Eq9f4KhIbAwpebS5/q2KOfzLMN9pEpqaOCuWIyhbdvi3LlzKW18/oyGDRvC1NQUY8aMwdSpU+Um5Jg1axZ69uwpDLO7f/++XJtZncI6L5iZmcHPzw8mJib4/v07gJQisQ0bNkTDhg0VtpfOfNSvXz/8/fffAFKms96yZYswtbdUEZEIvxcthnpaWhga9R0JCQk4fPgwDh8+LLfdwIEDldbCUEYaTAJSZjz6/fffYWtrC0NDQwBAbGysMLyh3ho3pQEvS3V1/GVolOaNmLQCs8oCN1ZWVhjVqBsAIOxDyt+/xYcAOGto4p+kRLx69QomWlpooqWN6Ia2MDExQaULF+Gmb4DKnwIxefJkeHh4yAULGzRoAE9PT1SrVk1lReSdnJzw9u1bHDlyRAgUJgNYXNkaHdPYJys1AHP6M4aIiIgoNWkt16wU8y9dujQAxREaqsKgVwFw+vRpuTvUM2fOFAJeGhoamDVrFoKDgzF06FC5SLmRkRGOHDkiN6sXEeUt2QtVY/wXqMruxWlatcHSCoblpywvWWpqajh79iwWLFiAGTNmCJ9pX758waxZs7B+/Xr069cPderUQenSpVGlShVER0ejYcOGCgGvihUrYteu/D007NatWxg9ejSePHkitzwzNS/27t0r9zx1fS2RSASvCxdg3ro1BgIo8f8ZIlMXngdSvrB8/vwZJUuWzPC4skPyDA0N4ejoKDx/+PAh5s2bh19++QUAkHqgpZWVVZp3YzMjrSG1yt4/MwwN0TX0CyQAvkkkOBMfB1y/Lqxf8j0SRsbGCA8PV9j3wYMH6NWrl1yJgJwmkUjg5uamkImY+m50oLml0vdxWrM6plX8PjcmpCAiIqKfi3RYaHqTEKUmHdVhbGyskj6lxqBXASAb8Prtt9+Embt0dHSwZcsWTJ48WWFIj5ubG7Zs2aIwNpmI8l5OD3FUlhUje5EsvTDOTKAro9kbs+pH9g00t8QgALbGplhUszquX78uDNf+/PkzVqxYIbe9SCRSSI13dnbGkSNHlH4Gvn37FgcPHoSBgQG6desm3G3Kba9fv4adnZ3ClwSRSITWrVtnuK/sa9bV1cWwYcMwadIkob2//vpLrp22bduiQ4cOOHLkiNI27927l6laXX379sX1/wePjhw5gjp16iAkJAQmJiYICAiAmpqaEPSSVbx4caUzdKYnO8GZ+lra2FTcGKujIvEyMRGJSrZRFvCSkh3iqAr//vuv0qG3RkZGmW4jdYBLVVlpRERERMqYmpriw4cP+PTpU6b32b9/PyQSyQ9PAJBVjIgUEJ8/f0aDBg2wfPly4UInLi4OQ4cOlQt4lS5dGo8ePcK2bdsY8CLK53K6QLW0QLx54EfozLQXfuLmXk0z+yOr7eamalpa2PX6Dd6YlsD8ooZw0tFVeqdGWS2IY8eOQVdXV2H2xu3bt6NixYqYOnUqfv31V5ibm6Ns2bJ4+/atil5F2oYMGaL0rphEIkl3hiHgv1RyqX/++Qd6enoYOnQoDA0NMWfOHIwZM0Zhv+3btwtD3UUikVxmV2aHwA8fPhzm5uZCX588eYJPnz7h2bNn+Pr1q8L22tramDhxIkJDQ1GrVq1MHSOrUhd+l763nHR1cdq0BF6WMscGQyNMMiiC3rp6sMxgym9NTU3s3r1bJX2VKlmyJPT19RWWjxo1ChuNjDG7WDGU0dCAY8hnOC5ohz/OrkPAt/SHoAaaWyJs4wAhoM4i+ERERKRKVatWBZDyXTQztmzZInzHcnV1VVm/ZDEqUgC4u7vDwsICDx8+VFgXGxsLAFBXV8fkyZMRGBiIunXr5nYXiegH5URASRrUSh3YCjS3TDP7S/bY+Z2WmhoGGRhgs5ExHpYohe1GxlixYgWaNWsGQ0NDqKcRwEhISMDvv/+ODh06CIExZcVC379/j9q1ayMpKUmlr0NWUlIS7ty5o3Rd8eLFM/wcr127tnBjo1q1asIXjg0bNuDr16+YOXOm0v2KFi2KR48ewcnJCYcPHxZ+L9ra2lkaCi+tJZaRiRMnIioqCitWrFD5jRjp+Swb9JHSEYnQUU8PE4oWw/LiRrhTohSCp57AdHtXWBQzg1qq2aQSExOFunKqUqRIETx69AhjxoxBkSJF5NaN+BqOOZGR+JicjBdJSbiREI+/7hxEnTUDcdXvUabaV/Z7kJJ+NqT+jCAiIiLKihYtWkAkEqU5kkDq999/R6NGjTBs2DAAQPv27eUmP1IlBr0KAA8PD7mZGGVpaGhg7Nix+PTpk0JGAxHlT7JZWACynYkl215GF7LGw3cpBLsyE3jLLxfHJurqcNDRRa8VHtjn54/negZ4X6IUvM1KYn3x4hiqb4A2bdrIZdCcPn0aZcuWhY2NDSIiIpS2Gx0djSVLlihkUKmKp6en3Of6/v37Ua1aNdja2uLRo0fpzjQIAAYGBjhy5Aj69u2b5eBM9erVcfr0aTRo0ADBwcEAgJYtW2ZpOmw7OztcvHhRrhB90aJFFbabMGFChq8lryTuGIkRvpfwZuJ+RM++DIlEgiZNmgjr+/Xrp/JAqLW1NVavXo3IyEhh9qP0RCfEoe/+2QiOSntYZlYVhMA3ERER5U/SwNXz589x69atNLdbvnw5Hjx4AJFIhG7duuHAgQO51UUGvQqybt264cWLF1i1ahVMTU3zujtElMuUBaqkQ7tks7pSZ3vklwBWTjLR0EAnXX3MKmYIzxev4FOkGBpq/jdc78OHD3JBBR0dHYU2pk+fDlNTU5QrVw5r1qxJ81gnT57E+PHjMXv27HRrQqVHOtOhVI8ePeDj44N79+6hbNmymWrD2dkZe/bsgYWFxQ/1QXYWxooVK2Z5fxsbG7nh9cePH4evry9Onz79Q/3JKWkNdcxI3NyrOHHihBCki4yMxNChQ9PdJykpCT169EDVqlUVas1lVZ06dXD37l1YWqb/3vwa9x0X/32QbiYXERERUW6oVKkSduzYgTVr1ihN1BGJRNDT00P58uXRt29fnD9/HgcPHszSzdbsyp+3Xyld+vr68PT0RI8ePfK6K0SUA6QZX+YzPyodjih7YZvRBbw02yszbaQ1+1tWKCuinx9oqKnhsKkZnm/bgjlz5uDRo0eIj49HmTJlMHToUHz69Anr1q1Tuq+/vz9+/fVXLF26FNeuXZObgjk5ORldunQR/qnPmzcPw4cPh7u7O3bv3o2AgADMnDkz3aF8MTExsLe3F56rq6vneg3GhIQEjBo1Snjevn37LO0vFovx+++/C5lxvXv3FoZHxsbGZqmYqSpIz8vMZFCGbRwgvCeMjIwwd+5c/PnnnwCAHTt2YPLkyahevbrSfVu0aIHbt28DACZPnox27dqluW1mNGzYENu3b4eDg4PSWnVSiVc3Abp6Qv+B/wJ8DIQRERFRburfv7/S5dWqVcvV8iFpYdCrABGJROjbty88PT3z7XARIsoeZUONzGd+lLt4z0qgSvYCODvDmMwD/wvIybaT34JdUtI+mgOo8csQwNgUSRIJNJLEMJ8+HU+fPsWGDRsgFovRtm1bODk54f3799ixY4eQvfXx40dUrlwZR44cQceOHQGkfA7L3sUSi8VYv3491q9fLyy7ceMGLl26pNCnxMREDBo0CAcPHkRi4n9zCZqYmKjiV5CujRs34suXlKLolpaWaNeuXab3DQgIQJMmTfDxY8rvWE9Pr8ANr08reBw39yomoDE2lS0Lf39/SCQSjB49GleuXIEoVd2vmJgYIeAlVaNGDdSvXx+NGzfGwoULYWBgkKV+rVmzBhMnTkw34AUAPv36YsTq1Vl+/6X3GSD9jOEMkERERFSYMHJSQJQrVw7Hjx+Xq59CRD+PH70QzcnZ2wpS7R9lwQCroADhca1atfDt2zckJSXJDTVcvHgxBg8ejD179kAikSAxMRGdOnXCjBkzYGVlhfnz52d47CtXriAyMlKhxtXRo0eVFoD/kaGF2SU7a+THjx+xefNmobBoej58+IBq1aohOjpaWDZ//vwfHmKpStJzIKMhjsqyIE+ePIm6desiMTER165dg5WVFYYOHYqpU6cKN53SymZ7+PAhHj58iEaNGqFv375Z6vPUqVPlAqLK1KxZE6MOHUHgkaMKr0P2tWY3eJUTmaBEREREeY1BrwJg6tSpWLhwYV53g4gKiNwMTqU+VmxsLJ6eP59rx8+MtH4fyrJwtLS0sPjyVXQ3MkGfsC+I+//yefPmKWzr6uqKSpUqYfr06XLLJRIJ9u7dC11dXRgaGsLR0RE6OjpYunSp0n7kRdr3yJEjMXXqVGEG4OHDh+PLly+YNm2a0u3FYjE2b96MX3/9VQjKiEQi/Prrr5gwYUKu9ftHpB7ulzooZD7zvyG60m0NN6bMhDl48GAAKYHBmTNn4vPnz1i7di2AlJtR+vr6cgFAWfHx8Vnua1RUVIbbDC/rgHI9OskNZc7Mez6rk2WEbRwAbCxYwW4iIiLKXS9fvhRKO+zatQv9+vVT2Gb79u3YsWMHPn36hJIlS6Jnz54YNWpUrpX3UOlRHj58iLlz58LR0REWFhbQ1taGgYEBrK2t4ebmhps3b2a6rXfv3mHChAmoUaMGihQpAn19fVSqVAmjRo2Cj49Pptr49u0bxo0bh9KlS0NHRwc2NjbYs2dPuvuIRCLhp0yZMkhISEh3e09PT2H7q1evZvblpUu27goRUX72ub5tXndBQUYzWirTQFsbd81KKs1g0tPTw7Zt27BlyxZ06tRJYdibSCTCyJEj4erqCmdnZxgYGEBNTQ0PHz4UtpEdom5jY/MDryp7tLS08OTJE7kstxkzZigU5o+Li0P//v2hra2NESNGyAW8Dh48iFWrVuVmt7Mk9UQPsjWvMlP3qu2MWdhc3Bgmov++Knl7ewuP1dTU0Lx58zT3L1OmTNb7bG6e4TYNZ/b5L2CXwayrGZH+LqQ/spld2W2biOhn9+TJk7zugpyCei1M+dupU6cgEolQokQJ9O7dW2H9jBkzMHjwYFy/fh3//vsvbty4gbFjxyrdVlVUFvSys7NDgwYNMGvWLFy4cAGBgYFISEhAdHQ03rx5A09PTzRv3hyurq4Zvnk2bdqEKlWqYOXKlfDx8UFUVBRiYmLw77//Yv369bCxsUl3pi0g5e6pnZ0d/vrrL3z69Anx8fF4/Pgx+vfvj7lz52bqNUmHgBARUeElvdivHfwJ79+/R+vWrYV1ampqOHr0KNzc3KChoYETJ04o1F+SSCRyy5KTk+Wea2tr4+nTp2jRogXq16+f6f9BOa1SpUoIDAxE8eLFAaT0e+vWrcL6BQsWwNDQEHv27JHLRtPT08OFCxeEKarzO+nfU2emPXRm2mcpmOOkq4uW1VsIz1PPupl6CKuUrq4umjdvjtDQ0Cxl8qXODuvWrRscHByE5/Xq1UP9+vUz3Z4s6euXfZ7691AYZ3YlIsoLdnZ2Qj3Q/IDXwqQqN2/ehEQiQbdu3aCuri637uPHj8JIh0qVKmHChAlo1KgRRCIRDh06BC8vr1zpo8qCXkFBQQCA0qVLY9y4cTh06BDu37+PO3fuwN3dXbibuXPnTgwaNCjNdvbt24fhw4cjISEBxYoVw9y5c3Hz5k08ePAAmzZtQsWKFZGQkICxY8fiwIEDabYzb948PH36FFWrVsWBAwdw584dzJ07F9ra2pg9e3ams8UWLlyIuLi4jDckIvoJlXx4X+5xTmSKZFSTKSuymvWlpqaGixcv4tixY1i5ciWCg4PlghAuLi6wtbVVyPaS0tHRkXtua2uLyMhIVK1aFVevXsWDBw/ypJC9lJ6entyXcm9vbzx8+BBt27bF9OnT5YIwxYsXR9++fREcHCwXCCwspOdZ6nPN0v+/DD1ptl5MTAz69++f5veOmjVrolu3bjA1NYW2tjZGjx6d4fGTkpKE2TCljhw5ggsXLqT0z9gYp0+fztJrUiZ18EsaBJT9ISKi7JFeC+cXvBYmVXn79i0AoEmTJgrrpDdOixUrhtu3b2P58uW4efOmkCm/c+fOXOmjymp6ValSBQsXLkT37t0VIn6NGjXCgAED0LRpU/j6+mLv3r0YMWIE7Ozs5LaLiYnBuHHjAKTUXrl586ZcIff69eujV69eaNasGZ49e4axY8eiffv2Suu0HDhwAPr6+rh06RJKlSol9KNIkSKYMGECDh06lO404yYmJggNDUVQUBDWr1+f72uYEBHlF7IzP/6IzAxF+xGB5paZvsDv3Lmz0uWVKlXCvXv3IBaL8c8//2DTpk3Yt28fkpOTMXjwYKxevRq7d+/Gtm3b0LVrV/z66685+RKy7PXr19i/fz9CQ0PRr18/NGzYEPfv/xeo/PvvvxWK7evr62Pp0qWFfqi9XCBIps7XKIMiWBUbg+TkZISEhKBIkSKIjY2Vm8XT0tJSmM1SJBJh27ZtqFmzJoCUemjr1q3Dt2/fsHv37jSPn9Gd9nnz5sHMzOxHX16OST3LIwveExEpqlKlCiZNmpRv/nfyWphUJTg4GABQoUIFhXWXLl0SssCMjY0BpNxQHjx4MG7cuCFX+kOVVJbpdfLkSbi4uCgEvKRMTEywYsUK4fmhQ4cUtjl9+jRCQkIAAOPGjVM6c2HRokXh7u4OIOUX7unpqfR4gYGBqFKlivAml5LerQ4MDEz39Tg5OQnHX7JkCWJiYtLdnoiI/qMsiyQvM0ty+rhqampo0KABNm/ejO/fvyMmJgarV68GAPTv3x+XL19WCHi9fv0a79+/z1T77dq1g7GxMZydnbF06VKcPXsWnz59UhhamZanT5+ibNmyqFKlCmbNmoXVq1ejUaNGePz4MV6/fq10HysrK8ycORMRERH55ku7qqXOAjQP/IgKnwLlMtuioqLkAl4WFhbChABAypDEypUrK/xt9uzZgzNnzig97rt375ROliBVo0YNDB06NMuvJ6fJFsOPm3tVeJ7VIvlERIXdyZMn0alTp7zuhoDXwqQqERERABRHNyQnJ+Pu3bsAIDdKAvgvQPblyxfVdxAqLmSfkZYtWwqPpWlxsmQjf05OTmm2Y29vL/ySlQXPAMDMzAyvX78WIpFS0gJ7qT8AUlNTU8OcOXMApATXMqohRkREymU24JRRoCy9YVnZGbKVlJSE+/fvZ/sLnVgsxo4dO9C8eXO0adMGx44dg4ODA/T09CASiVClShWUK1cuwyEF169fx7lz5xAeHo7jx4/jjz/+gJOTE0qXLg0NDQ1Uq1YNq1evhlgsVrr/7NmzUbt2baUBtuPHj6Nhw4Zyyxo3boyNGzfi1atXmDNnjlzR/cIurXPl8OHDCoXpRSIRihYtiu/fvwvDEtXV1bFly5Y0/xa9e/dWqPF19epVhUx3WZUrV8aJEydy5e8gG8hKj2zxeynWAyMiyr94LUyqUqRIEQBQOLcePHggfJdOPfGPvr4+gJSJlXJDnga9ZGuFKMsICwsLEx6XKFEizXY0NDRgZGQEALhz547SorHOzs6IiopC69atcejQIdy9excLFy7E77//DpFIlKmCvF27dkWdOnUAAEuXLsX3798z3IeIiDKWUZAqq4GyjI6RnidPnqBhw4bQ19cXZiBSU1NDx44dM1WUPCQkBH/99Rf09fUxaNAg3Lx5E5cuXUKXLl1w8eJFuawgiUSC58+fp9ueqalpmuvEYjFevnyJsWPHwsDAALdu3ZJbf+fOHeFLamqampoYNGgQ7t69iydPnmDfvn149eoVbt++jWHDhincsfuZGRgY4N27d9i1axcOHjyIjh07QiKRIDIyEt++fRO2S05ORvXq1YUvc1LSKbkjIyPRt29fYfnu3bvRqlUrBAQEAEgZJim9+2lsbIzx48fj2rVrKFu2rIpfYYrU9b7SIlv/LLOzYRIRUd7htTCpSpUqVQAA58+fl1surXtaoUIFhdmppXGe9L7j5qQ8DXpdu3ZNeFy1alWF9bK1uWS/VKYm/eIJAAkJCfj3338VtpkzZ45wR71nz55o3Lgxpk2bhri4OMyYMQO1atXKsL8ikUiouREWFpavp2onIqIfs3//foVlEokEp06dQuPGjdPcLzY2Fvb29ihRogTGjRuXqUKvBgYG6NmzZ7rbVK1aFfv371daKyH18Vu0aCF3XGX9rVSpEmbNmoXQ0FBYWVkBAGrVqoVevXqhcuXKGfb5Z6Wmpob+/fujR48eaQ5TBFKKF8sGR8eOHYtly5YJzw8ePIh3797h6tWrGDBggDAMUhq0/PfffxEdHY3Q0FB4eHike9MvN6WVAZa6+D8zvoiI8h9eC5OqtGvXDiKRCBs2bMChQ4fw/ft3HD16FBs3boREIoGzs7PCPk+fPgUAhWCYquRZ0EssFmPx4sXCcxcXF4VtZANhsgGy1B4/foyoqCjh+YcPHxS2MTExwZ07dzB48GCYmppCS0sLtWvXxo4dO9K8C65Mp06d0KBBAwCAu7t7usE4IiIqeKKjo9NcJ/u/JrUnT56k+79KVpkyZXDw4EF8//5dyAJKj4uLC/7991+EhYVh4sSJaQ5DSE5OVgi2PX78GNra2jh9+jSio6Ph6+uL2bNno2jRopnqKykqXbq08Lh48eJy66TfLwYMGIBTp05h5cqVmDhxIipWrChsM23aNLnvHkWKFIG3tzcsLVOCRXp6eip+BTlDNssrp2ZYJSKinMdrYVKVESNGoFixYoiNjYWLiwuKFSuGbt26ITY2FpqamkpnsD579iwkEgns7e1zpY95VqjDw8NDmDGqW7duqFevnsI2Tk5O0NDQQFJSEtzd3TFw4ECFqd3FYjGmTZsmtyytVMsSJUpgy5Yt2e773Llz4eTkhK9fv8Ld3T1LHxRERJS/rV27Fh4eHli3bh2uXbuGyMhIPHv2DA4ODnIZO6nVrFkTFhYWwlA1qaJFi6JSpUqws7ND9+7d0bhx40wFupQxMjLCihUrsGLFCiQlJeH8+fM4ceIE7ty5g/DwcPTo0QOGhoZy+9SpU4fTi+cgiUQiN/Tz69evAFLugG/duhUDBgxQWn9rw4YNcHBwgEQiwaFDh+TOgdWrV2eYyZdbpFlaaQWxlA1lVLbsc31bYOF84XE5n2c52Mv0SV9DXkySQUSUX/FamFTB1NQU+/fvR48ePeRuDotEIvz1118oV66c3PYRERGIjIxE/fr1c22yhzwJel27dg1TpkwBkFJUb/369Uq3s7S0xIgRI7BmzRoEBgaiadOmWLp0KVq2bAktLS14e3tj9uzZOHfuHLS0tJCQkAAAcvVSVKFdu3Zo0qQJbt++jZUrV2LcuHFCTTFViI2NVflryg7ZiyleWP28eB4QULjOg+HDh2P48OEKy9P6PFZTU8OtW7fw4MEDiMViGBkZoV69etDW1pbbTraeZXa1bNlSblKY9PqXWwrTOaDMsmXLEB4eLky9LVWmTBn07t0biYmJSExMVNivSZMmmDZtmtx3HmkZBw8PD6UZ73kh4f9B0zhNxWL8X3ePAwwNUbz/qpTH6bUjk0mYULSoys/Lz/VtAQAlH94XXkNevxeo8H8eUOYUpPOAnxsZy+1rYcr/HBwc8OLFC+zZswfv3r2DiYkJevTogdq1aytsa2hoiNu3b+dq/0SSzM53nkN8fHzQvHlzfP36FTo6Ojh37ly6sxbFx8ejW7duOH36dJrb1K9fHw0aNBC+SB49elTp2NEfIRKJAACurq7w9PQUll++fFmY4nXKlClYtGgRAMDT0xNubm4AgCtXrvxwyl5AQIAwzGHLli0KGW5ERERERESUc0JDQzFkyBAAwMePH2FhYfHDbclez7XZ3BK6Jro50seMxIbG4uLQKwCy/xry6lqYKCflak2vd+/ewdHREV+/foW6ujr27duXbsALALS1tXHixAls3rwZderUEd54QEqW2LRp03Djxg3Ixu5S19dQhVatWglv4jVr1uDLly8qPyYRERERERFRXuK1MBUkuTa8MSgoCG3atEFQUBBEIhG2bduW6WwsNTU1DBkyBEOGDMH3798RHBwMPT09lCxZUqiJ8ebNG2H7atWqqeQ1pDZ37lzY2dkhKioKS5YswfLly1VyHDs7u2xF6FUtLi4O169fB5DSV05z/3PieUAAzwMqfOdAUlISunXrhsePH6e5zd69e9GsWbNMt/n9+3fY29sjJCREWNawYUMcOnQoW33NafEr7ygdxli8f8qMXekNcTS8eT3XzwPZIY6UPxS2zwP6MQXpPEhdl5PSllvXwkTZlStBr9DQUDg4OMDPzw9ASsHWgQMH/lBbRYoUQZEiReSWJScnw9vbGwBQvnz5XBsK2Lx5czg4OODChQtYt24dJk+erJLj6OrqQlc3d9Jhs0tHR6fA9JVUh+cBATwPKONzoCAUHO/QoQMuXrwoPJetIQqkDOtwcHDIUpu6urq4f/8+evbsiTdv3sDNzQ1Tp0794QkOVEV3aisYTpUvQB9obonoNa4AAC0o/9vFzb2KT83shEL2ufVZIC2WXxDOq58R/ycQkP/Pg/zct/wmt66FibJL5UGvb9++oW3btnjx4gUAYPHixUqnrcyOK1euICwsDADQq1evHG07I3PnzsWFCxcQGxuLRYsWoW7durl6fCIiooKqIAQlLl26JDxu2bIlXr16hU+fPgEAWrRoIdQxySoDAwOcOXMmR/qYm9L7m0mDTQCAVLOI5qaCcF4RERUGvBamgkCltxRjYmLQoUMHPHr0CAAwbdo0/PHHHzl6DIlEgtmzZwMANDU1MXTo0BxtPyONGjVChw4dAAAbN25kSiwREVEhkZSUJDfbZnBwsBDwsrOzk8sA+1kFmlsKP0RE9J+fYSZIXgtTQaCyoFdCQgK6du2KW7duAQDGjRuH+fPnZ7mdsLCwNKd3T05OxpgxY4RjTJ06FeXKlfvxTv+guXPnAkiZaXLVqlW5fnwiIiLKea9evZJ7Ls1aL1GiBPbt2wcNjVwrjZpvmQd+VJpZxbpaRPQzS0hIEGaBLOx4LUz5ncq+rfXp0wfnz58HkDK7w+DBg/H8+fM0t9fS0oK1tbXC8itXrmDMmDHo3bs3WrRogTJlyiAuLg5Pnz7Fpk2bhFpeTk5OmDZtmkpeS0ZsbGzQpUsXHD16FKGhoXnSByIiIspZb9++VVhWo0YNHDp0CKVKlcqDHuVfqQNfP0OGAxFRWvr06SMU7y/seC1M+Z3Kgl5HjhwRHl++fBm1atVKd3srKyv4+/srXRccHIxVq1YpjRyLRCK4ublh3bp10NLSylafs2POnDk4duwYJBJJnvWBiIiIck7Lli2hp6eHmJgY6Ovro3Hjxjh69Cj09fXzumtERJSPyV4L/wx4LUz5Wf6aJkiJ5s2bY9myZXByckK5cuWgp6cHAwMDWFtbY/jw4bhz5w62bt0KbW3tPO1nrVq10LNnzzztAxEREeWcokWLIjo6GvHx8YiKisKFCxcY8CIiIkqF18KUn6ks0yunorwlSpTA5MmT82wK1Ky8jv3792P//v0q7A0RERHltrzMJCciooJHIpEgICAAlpYFe5IPXgtTYZDvM72IiIiIiIiIiIiyikEvIiIiIiIiIiIqdBj0IiIiIiIiIiKiQodBLyIiIiIiIiIiKnQY9CIiIiIiIiIiokKHQS8iIiIiIiIiIip0NPK6A0RERERERESUs1q1agVNTU25ZT4+PnnUG6K8waAXERERERERkYqE+vwC7aKmuXKs+MgvAK7kyrGICgIGvYiIiIiIiIgKmcuXL8PCwiKvu0GUp1jTi4iIiIiIiIiICh0GvYiIiIiIiIiIqNBh0IuIiIiIiIiIiAodBr2IiIiIiIiIiKjQYdCLiIiIKB2B5pYINLfM624QERERURYx6EVERESUBmmwyzzwYx73hME3IiIioqzSyOsOEBEREeVX+SXYRURERERZx0wvIiIiIhlxc68ibu7VfJNZlTrwlh/6RERERFQQMNOLiIiISEbYxgF53YUM5adhl0RERET5FYNeRERERDKkgaS4uVfztiMypH1KneXF4BcRERFR2ji8kYiIiEgJnZn20Jlpn2+GOQKKwS3j4bsY8CIiIiJKAzO9iIiIiNKRX4NKxsN35XUXiIiIiPI1Br2IiIiICpD8GoQjIiIiym84vJGIiIiIiIiIiAodBr2IiIiIiIiIiKjQYdCLiIiIiIiIiIgKHQa9iIiIiIiIiIio0GHQi4iIiIiIiIiICh0GvYiIiIiIiIiIqNBh0IuIiIiIiIiIiAodBr2IiIiIiIiIiKjQYdCLiIiIiIiIiIgKHQa9iIiIiIiIiIio0GHQi4iIiIiIiIiICh0GvYiIiIiIiIiIqNBh0IuIiIiIiIiIiAodBr2IiIiIiIiIiKjQYdCLiIiIiIiIiIgKHQa9iIiIiIiIiIio0GHQi4iIiIiIiIiICh0GvYiIiIiIiIiIqNBh0IuIiIiIiIiIiAodBr2IiIiIiIiIiKjQYdCLiIiIiIiIiIgKHQa9iIiIiIiIiIio0GHQi4iIiIiIiIiICh0GvYiIiIiIiIiIqNBh0IuIiIiIiIiIiAodBr2IiIiIiIiIKNeJRCKIRCLMnj07r7tChRSDXkRERERERESUZQkJCdi7dy8GDhyIKlWqwNjYGJqamjAxMUG9evUwcuRIXLx4EWKxOK+7mu88fPgQc+fOhaOjIywsLKCtrQ0DAwNYW1vDzc0NN2/ezOsuFgoaed0BIiIiIiIiIipYjhw5gkmTJsHf319hXVhYGMLCwvDo0SNs2LAB1tbWcHd3R4cOHXK/o/mQnZ0dbty4obA8ISEBb968wZs3b+Dp6YmBAwdi8+bN0NLSyoNeFg4MehERERERERFRps2bNw8zZ84Unjs4OKBz586oVq0aDA0NER4ejtevX+PEiRO4cOECfH19MW3aNAa9/i8oKAgAULp0afTs2RPNmzdHmTJlkJycjDt37mDFihUIDAzEzp07kZiYiL///juPe1xwMehFRERERERERJmyfft2IeBlZmaGAwcOoEWLFgrbtWnTBqNHj8bz588xYcIEfPnyJbe7mm9VqVIFCxcuRPfu3aGuri63rlGjRhgwYACaNm0KX19f7N27FyNGjICdnV0e9bZgY00vIiIiIiIiIspQYGAgxowZAwDQ19fHtWvXlAa8ZNWoUQPnzp3D5MmTc6OLBcLJkyfh4uKiEPCSMjExwYoVK4Tnhw4dyq2uFToMehERERERERFRhjw8PBATEwMAmDt3LqpUqZKp/dTU1NC/f/8sH8/Pzw8rVqxAp06dULZsWejq6kJXVxdWVlbo1asXzp49m2EbERERWLBgARo3bozixYtDU1MTpqamqFatGrp27Yr169cjODhY6b6XL19Gnz59UK5cOejq6kJPTw9WVlZo1KgRJk+ejMuXL2f5NWVWy5Ythcdv375V2XEKOw5vJCIiIiIiIqJ0SSQS7NixA0BKltfQoUNVerx3796hQoUKStd9+PABHz58wIEDB9C/f39s374dGhqK4Y2XL1+iTZs2Qg0tqdDQUISGhuLly5c4evQokpOThQw2qQkTJmDlypVpHvvevXvw9PREaGjoj7/IdMTHxwuP08oIo4wx6EVERERERERE6fLx8RECPM2bN0eRIkVUerzk5GRoaWmhbdu2cHBwQLVq1WBkZITw8HD4+vpi7dq18PHxwe7du1G+fHnMmTNHoY0BAwYgKCgImpqaGDp0KJycnFCyZEmIxWIEBATg7t278PLyUtjv5MmTQsCrVq1aGDlyJKpWrYpixYohIiICPj4+uHjxIu7fv6+y13/t2jXhcdWqVVV2nMKOQS8iIiIiIiKiQubTp08ZbmNhYZHp9p48eSI8rlev3g/1KStKlSoFf39/lCpVSmFd69atMWLECPzyyy/w9PTEihUrMHHiRBQrVkzYxs/PD//88w8AwN3dXSGTy9bWFt26dcOSJUsQEREht+7AgQMAACsrK9y6dQsGBgZy6+3t7TF69GiEh4fnxEtVIBaLsXjxYuG5i4uLSo7zM2DQi4iIiIiIiKiQsbW1zXAbiUSS6fbCwsKEx2ZmZj/Up6zQ19eHvr5+mutFIhFWrFiBXbt2ITo6GhcvXkT37t2F9Z8/fxYepzfzoUgkQvHixeWWSfe1sbFRCHjJMjIyyvB1/AgPDw8hi6xbt265EmQsrFjInoiIiIiIiIjS9f37d+FxesEoVUlMTERAQABevnyJ58+f4/nz5wgKCoKxsTEA+Uw0AHIZYp6enlk6lnTf69ev53oR+WvXrmHKlCkAUoKL69evz9XjFzbM9CIiIiIiIiIqZO7fv690aOCPkq3hFR0dnWPtpicxMRGbNm3Crl278PjxYyQkJKS5beqC8uXKlUPz5s1x48YNeHh44Ny5c+jevTvs7e3RqFEj6OnppdnWwIEDsXPnToSFhaFGjRpwdnZG27Zt0bx5c1SsWDHHXl9qPj4+6Nq1K5KSkqCjo4ODBw/mSlZdYcagFxEREREREVEhU6pUqSzV7MqINKMKAIKDg3Os3bSEh4fD0dFRqMuVkdjYWIVle/fuRc+ePXHnzh28ePECL168wLx586CpqYlGjRqhb9++GDRoEHR0dOT2a926NdasWYPffvsNsbGx2L9/P/bv3w8AMDc3R8eOHTFy5EjUrl07+y/0/969ewdHR0d8/foV6urq2LdvX7rDMilzOLyRiIiIiIiIiNIlG+B59OiRyo83btw4IeDVpUsXHD9+HP7+/oiJiYFYLIZEIoFEIoGlpSUA5fXJzM3Ncfv2bVy8eBGjRo1C9erVIRKJkJiYiBs3bmDkyJGoUaMGfH19FfYdPXo0/P394eHhgfbt2wtF8gMDA7Fx40bUrVsX06dPz5HXGhQUhDZt2iAoKAgikQjbtm2Ds7NzjrT9s2PQi4iIiIiIiIjSVb16dZiYmAAAbty4gcjISJUdKzIyUsis6tevH7y8vNCpUydYWVlBV1cXIpFI2Pbr168Ztte6dWusXbsWz58/x5cvX7Bv3z60atUKAPD27Vv06tVL6X5mZmYYP348Tp06hfDwcPzzzz+YPn06DA0NIZFIsGDBAhw7dixbrzU0NBQODg7w8/MDAKxevRoDBw7MVpv0Hwa9iIiIiIiIiChdIpEIrq6uAFJqem3ZskVlx3rz5g0SExMBIM2AFAC8evUKUVFRWWrb2NgYvXr1wqVLl9C5c2cAgLe3N968eZPufmpqarCxscG8efNw6dIlYfmBAweydHxZ3759Q9u2bfHixQsAwOLFizF69Ogfbo8UqTTo9fDhQ8ydOxeOjo6wsLCAtrY2DAwMYG1tDTc3N9y8eTPTbfn7++OPP/5AvXr1YGhoCE1NTRgZGaFJkyaYO3cuQkJCMmwjKCgIgwYNgqmpKXR1ddGsWTOcPXs23WOKRCLhp1GjRhkeY/bs2cL2/v7+mX59REREREREVDiknkkwr+XUtfCECROEAvAzZ87Eq1evhH3SuxYWi8XYs2dPpvublJQkPE6vaP6GDRsy3aYyrVu3Fh6nLoSfHhsbGxQvXjzL+8mKiYlBhw4dhKGi06ZNwx9//PFDbVHaVBb0srOzQ4MGDTBr1ixcuHABgYGBSEhIQHR0NN68eQNPT080b94crq6u6c7AAAC7du1CtWrVsHTpUjx69Ajfvn1DUlISvn79ijt37mDWrFmoWrUqLly4kGYbQUFBaNiwIXbs2IHQ0FDExcXh1q1baN++faanL7137x5OnTqVlV8DERERERER/UTs7OzQsWPHvO6GICevhc3NzbFmzRoAKcGoFi1a4Nq1a+nu++LFC7Rr1w7Lli3LdJ8rVqwoDGHcsWOH0npdJ06cEPqijLe3N7y9vdNcL5FIcPHiRQApWWxly5YV1u3fv19pYXyphw8fCsMqy5Url95LUSohIQFdu3bFrVu3AKTUL5s/f36W26GMqWz2xqCgIABA6dKl0bNnTzRv3hxlypRBcnIy7ty5gxUrViAwMBA7d+5EYmIi/v77b6Xt3Lp1C4MGDYJYLIaamhpcXV3h7OyM0qVL48OHD9ixYwdOnDiB8PBwODs74/nz5yhfvrxCOxMmTEBAQAAaN26MP//8E8WLF8fhw4excuVKjBkzBh07dhTGJ6dn5syZ6NChQ/Z+OURERERERFQoSa+F84ucvhZ2c3NDQEAAZs6ciZCQENjb28PR0REaGv+FF27duoUzZ87g1KlTOHv2LJKTk7M006GxsTHat28v7O/o6IiRI0fCysoKISEhOHz4MDw9PVG+fHlERETgy5cvCm14e3vDzc0NDRo0QKdOnWBjY4OSJUsiMTER7969w/bt24XEmc6dO6NUqVLCvn/88QdGjBgBZ2dn2NnZwdraGvr6+ggLC8PNmzexevVqAIC6ujqGDBmS6dcl1adPH5w/fx4A0KpVKwwePBjPnz9Pc3stLS1YW1tn+TikwqBXlSpVsHDhQnTv3h3q6upy6xo1aoQBAwagadOm8PX1xd69ezFixAil03EuWrQIYrEYQEpBt1GjRgnrGjRogO7du2PSpElwd3dHbGws3N3dFaK98fHxOHbsGCwtLXHhwgXo6+sDAJo2bQqxWIxVq1bh1KlTwvhkZUxMTBAaGopHjx7By8sLXbt2/eHfDRERERERERVOVapUwaRJk+SuXfOKqq6FZ8yYgerVq2PSpEnw9/cXAjhS/fv3l3tevXp1LF26NEt9X79+PZo1a4YPHz7g4sWLQlaWVJkyZXD06FG0b98+3XYePHiABw8epLm+SZMm2Lp1q8LyiIgI7NixAzt27FC6n7a2NjZs2ID69etn4tXIO3LkiPD48uXLqFWrVrrbW1lZsXzSD1LZ8MaTJ0/CxcVFIeAlZWJighUrVgjPDx06pHS727dvA0iJ9Kb1oTFz5kzh8Z07dxTWh4WFIT4+Hra2tsKbXEo6hjcwMDCdVwNh/DOQMlZZWXolERERERER/dxOnjyJTp065XU3AKj2Wrhbt254/fo19uzZg/79+8PY2FjYx9DQEDY2Nhg1ahQuX76MZ8+ewdHRMUt9t7S0xKNHj/Dbb7/B2toa2traKFasGGrXro1Zs2bB29sb1apVS3P/Pn364PTp05gwYQKaNWuGcuXKQU9PD1paWrCwsEDnzp2xZ88e3LhxQ67vAHDlyhWsWrUK3bt3R82aNWFqagoNDQ0ULVoUdevWxeTJk/HixQsMGjQoS6+Jcp/KMr0yo2XLlsLjt2/fKt1GWu8rvXGyxYoVE6LPyuqDFS9eHBoaGnj48CFiYmKEwnsAcPXqVQCQS2VURl9fH1OmTMGkSZPw9OlTHDx4EC4uLunuQ0RERERERJRXVH0trKWlhb59+6Jv376YPXs25syZAwB4/PixXI2stGSUTGJsbIylS5emmyWWVgaUtrY2nJyc4OTklGE/UitXrhzGjh2LsWPHZnnfzGASTe5R6eyNGYmPjxcep5URVrlyZQDAu3fv0mwnMjJSmDFBur0sXV1dODo64v3793B0dMSpU6dw+/Zt/Pbbb1i5ciX09PQyVadr5MiRwgfC7NmzhWGXRERERERERPkNr4XpZ5enQS/ZWR6qVq2qdJsRI0YASEnLTGs60nnz5ilsn5qHhweMjIxw69YtdOzYEU2bNsXy5cshkUjw119/wczMLMP+6urqYurUqQCAly9fpll8n4iIiIiIiCg/4LUw/czyLOglFouxePFi4XlaQwV/+eUXDBw4EAAwevRoDB06FCdOnMDDhw9x5MgRdO3aFcuXLwcATJs2DW3atFHajrW1Ne7duwcXFxcYGhpCR0cHjRs3xsmTJzF48OBM93vYsGGwsLAAAMydOxfJycmZ3peIiIiIiIgoN/FamH5meVbTy8PDA/fv3weQUgCvXr16SrdTV1fHjh070KlTJyxcuBBbtmzBli1b5LZp2bIl/vzzzzQDXlIVK1bE/v37s9VvbW1tTJs2DSNHjsSbN2+wc+dOuLm5ZatNIiIiIiIiIlXhtTD9rPIk0+vatWuYMmUKAMDMzAzr169Pd/uXL19i586dePbsmdL1d+7cwdatWzOcdSKnDB48WCjKN2/ePCQmJubKcYmIiIiIiIjyCq+FqaDJ9UwvHx8fdO3aFUlJSdDR0cHBgwfTHUN848YNdOrUCd++fYOVlRXmz58PBwcHGBkZITg4GMePH8eMGTOwb98+XL9+HefPn0f16tVV+ho0NTUxY8YMDB48GO/evcO2bdswfPhwlR0vNjYWsbGxKms/u+Li4pQ+pp8LzwMCeB4QzwFKwfOAAJ4HlKIgnQf5+Zorv8jta2Gi7MrVoNe7d+/g6OiIr1+/Ql1dHfv27YOdnV2a28fHx6NPnz749u0bSpYsibt376JkyZLCegsLC4waNQotWrRA/fr1ERQUBFdXVzx8+FDlr2XgwIFYtGgR/v33XyxYsACDBg2Ctra2So51/fp1mJiYqKTtnHb9+vW87gLlAzwPCOB5QDwHKAXPAwJ4HlCK/H4ehIaG5nUXCoTcvBYmyq5cG94YFBSENm3aICgoCCKRCNu2bYOzs3O6+5w9e1YYsvjrr7/KBbxkVa9eHf379wcA/PPPP3jy5EnOdl4JDQ0NzJw5EwDw8eNHbN68WeXHJCIiIiIiIspLvBamgiRXMr1CQ0Ph4OAAPz8/AMDq1auFGRnT8/LlS+GxjY1NutvWq1dPKHD/6tUr1K5dOxs9zpy+ffti4cKFePXqFRYuXIghQ4ao5Dh2dnbCLBn5UVxcnHDXxs7ODjo6OnncI8oLPA8I4HlAPAcoBc8DAngeUIqCdB4EBATkdRcKjNy6FibKLpUHvb59+4a2bdvixYsXAIDFixdj9OjRmdpXQ+O/7iUlJaW7rWwBPdn9VEldXR2zZ89G79698enTpwwL8v8oXV1d6OrqqqTtnKajo1Ng+kqqw/OAAJ4HxHOAUvA8IIDnAaXI7+dBfu5bfpNb18JE2aXS4Y0xMTHo0KEDHj16BACYNm0a/vjjj0zvX65cOeHxjRs30t322rVrSvdTNRcXF9SsWRNASkAvOjo6145NRERERERE+c/PUBSf18JUEKgs6JWQkICuXbvi1q1bAIBx48Zh/vz5WWqjdevW0NPTAwCsX78ez549U7rdmTNn4OXlBQAwNzdHnTp1frzjWSQSiTB79mwAQEhICDw9PXPt2ERERERERJS/JCQk/BTD/XgtTAWBysYB9unTB+fPnwcAtGrVCoMHD8bz58/T3F5LSwvW1tZyywwNDTFlyhTMnDkT379/R5MmTfDrr7/CwcEBxYsXR3BwMI4dO4bNmzdDLBYDSIkwq6nlWn1+AEDXrl1Rt25dPH78mDN+EBERERER/cT69OmT72eqzCm8Fqb8TmVBryNHjgiPL1++jFq1aqW7vZWVFfz9/RWWT58+HeHh4Vi1ahWioqKwaNEiLFq0SGE7TU1NLFy4UJjFMTeJRCLMnTsXnTp1yvVjExERERERUf4hey1c2PFamPK73E2J+gEikQgeHh548OABRowYgRo1aqBIkSJQV1dHsWLFUK9ePUycOBHPnz/H5MmT86yfHTt2hK2tbZ4dn4iIiIiIiCi38VqY8jOVZXpJJJIcba9evXqoV69ejraZkbJly2bpddy7d0+FvSEiIiIiIqL8TiKRICAgAJaWlnndlR/Ga2EqLPJ9phcREREREREREVFWMehFRERERERERESFDoNeRERERERERERU6DDoRUREREREREREhQ6DXkREREREREREVOgw6EVERERERERERIUOg15ERERERERERFToMOhFRERERERERESFDoNeRERERERERERU6DDoRUREREREREREhQ6DXkREREREREREVOgw6EVERERERERERIUOg15ERERERERERFToMOhFRERERERERESFDoNeRERERERERERU6DDoRUREREREREREhQ6DXkREREREREREVOgw6EVERERERERERIUOg15ERERERERERFToMOhFRERERERERESFDoNeRERERERERERU6DDoRUREREREREREhY5GXneAiIiIiIiIiHJWq/+1d+dxUVX//8Bfw76ooCAgi4C74ha44YZGYrlXmkvmVn3SzIdL2fqN7OOnPpllVprmkriUuVSa+76gYLhRKporKKKoIIob25zfH/zuaYZZQGYGhvm8no8HD8d7zz333HvOnJl5z5lznnwSjo6OWttOnTpVSaUhqhwMehERERERERFZyIKVU+FvXzEfvTOKCtGuQs5EVDUw6EVERERERERkY3bv3o3AwMDKLgZRpeKcXkREREREREREZHMY9CIiIiIiIiIiIpvDoBcREREREREREdkcBr2IiIiIiIiIiMjmMOhFREREREREREQ2h0EvIiIiIiIiIiKyOQx6ERERERERERGRzWHQi4iIiIiIiIiIbA6DXkREREREREREZHMY9CIiIiIiIiIiIpvDoBcREREREREREdkcBr2IiIiIiIiIiMjmMOhFREREREREREQ2h0EvIiIiIiIiIiKyOQx6ERERERERERGRzWHQi4iIiIiIiIiIbA6DXkREREREREREZHMY9CIiIiIiIiIiIpvDoBcREREREREREdkcBr2IiIiIiIiIiMjmMOhFREREREREREQ2h0EvIiIiIiIiIiKyOQx6ERERERERERGRzWHQi4iIiIiIiIiIbA6DXkREREREREREZHMY9CIiIiIiIiIiIpvDoBcREREREREREdkcBr2IiIiIiIiIiMjmMOhFREREREREREQ2h0EvIiIiIiIiIiKyOQx6ERERERERERGRzWHQi4iIiIiIiIiIbA6DXkREREREREREZHMY9CIiIiIiIiIiIpvDoBcREREREREREdkcBr2IiIiIiIiIiMjmMOhFREREREREREQ2h0EvIiIiIiIiIiKyOQx6ERERERERERGRzWHQi4iIiIiIiIgqnEqlgkqlwrRp0yq7KGSjGPQiIiIiIiIioseWn5+PlStXYsSIEWjSpAm8vLzg6OgIb29vREREYNy4cdi5cyfUanVlF9Xq3LhxAxs3bkRsbCyeeeYZeHt7yyDgqFGjKrt4NsOhsgtARERERERERFXLr7/+ijfffBOpqak6+7KyspCVlYVjx45h/vz5aNSoEWbNmoXevXtXfEGtlK+vb2UX4X8Cg15EREREREREVGbTp09HbGys/H+PHj3Qr18/NGvWDJ6ensjOzsbff/+NDRs2YMeOHTh79iw++OADBr0MqFu3Lpo0aYLt27dXdlFsDoNeRERERERERFQmS5YskQEvHx8frF69GlFRUTrpnnrqKYwfPx4nT57E5MmTcfPmzYouqlWLjY1F27Zt0bZtW/j6+iI1NRWhoaGVXSybw6AXEREREREREZXq6tWreOONNwAA7u7u2LdvH5o0aWL0mObNm2Pbtm346aefKqKIVcbHH39c2UX4n8CJ7ImIiIiIiIioVF999RUePHgAAPj3v/9dasBLYWdnh+HDhz/2+S5evIgvv/wSffv2RUhICFxdXeHq6org4GAMHjwYW7duLTWPnJwcfPLJJ4iMjETNmjXh6OiI2rVro1mzZnj22Wcxb948ZGZm6j129+7dGDp0KEJDQ+Hq6go3NzcEBwejQ4cOeOutt7B79+7HviaqWBzpRURERERERERGCSGwdOlSAMWjvF599VWLnu/SpUuoX7++3n2XL1/G5cuXsXr1agwfPhxLliyBg4NueOP06dN46qmnkJGRobX91q1buHXrFk6fPo1169ahqKhIjmBTTJ48GbNnzzZ47j/++ANxcXG4detW+S+SLI5BLyIiIiIiIiIy6tSpUzLA06VLF1SvXt2i5ysqKoKTkxN69uyJHj16oFmzZqhVqxays7Nx9uxZzJ07F6dOncKKFStQr149vT8XfOmll5CRkQFHR0e8+uqreOaZZ+Dn5we1Wo309HQcOnQIv/32m85xGzdulAGvli1bYty4cWjatCk8PDyQk5ODU6dOYefOnUhKSrLoPSDTMehFREREREREZGOuXbtWaprAwMAy5/fnn3/KxxEREeUq0+OoU6cOUlNTUadOHZ190dHRGDt2LMaMGYO4uDh8+eWXmDJlCjw8PGSaixcv4ujRowCAWbNm6YzkateuHZ577jnMmDEDOTk5WvtWr14NAAgODsbBgwdRrVo1rf3dunXD+PHjkZ2dbY5LJQti0IuIiIiIiIjIxrRr167UNEKIMueXlZUlH/v4+JSrTI/D3d0d7u7uBverVCp8+eWXWL58Oe7fv4+dO3fi+eefl/uvX78uH3ft2tVoPjVr1tTaphwbHh6uE/DSVKtWrVKvgyoXg15VQFki9JXp4cOHcphreno6XF1dK7lEVBnYDghgOyC2ASrGdkAA2wEVq0rtwNo/d1W23Nxc+dhYMMpSCgoKkJmZidzcXBQVFcntXl5euHHjBv7880+toJfmCLG4uDjMmjWrzOdSjt2/fz8uXLhgcG4xsn4MelUBZYnQExERERERESmSkpL0/jSwvDTn8Lp//77Z8jWmoKAACxYswPLly3H8+HHk5+cbTFtyQvnQ0FB06dIF8fHx+Oqrr7Bt2zY8//zz6NatGzp06AA3NzeDeY0YMQLLli1DVlYWmjdvjv79+6Nnz57o0qULGjRoYLbrI8tj0IuIiIiIiIjIxtSpU+ex5uwqjZeXl3ycmZlptnwNyc7ORkxMjJyXqzQPHz7U2bZy5UoMGjQIiYmJSElJQUpKCqZPnw5HR0d06NABw4YNw6hRo+Di4qJ1XHR0NObMmYOpU6fi4cOHWLVqFVatWgUACAgIQJ8+fTBu3Di0atXK9Asli2LQy0r5+fnhypUrlV0MIiIiIiKi/zl+fn6VXQSroxngOXbsmMXPN3HiRBnwGjBgAMaMGYOWLVvCx8cHLi4uUKlUAIC6deviypUreucnCwgIQEJCAnbt2oVff/0V+/btQ0pKCgoKChAfH4/4+Hh88cUX2Lx5Mxo1aqR17Pjx4zFo0CD89NNP2LFjBw4ePIg7d+7g6tWr+P7777FgwQK8//77+M9//mPxe0Hlx6CXlXJwcDBrVJ6IiIiIiIiovMLCwuDt7Y1bt24hPj4ed+/eRY0aNSxyrrt378qRVS+++CJWrFhhMO3t27dLzS86OhrR0dEAiifk37lzJxYsWIDdu3fjwoULGDx4MI4fP65znI+PDyZNmoRJkyZBrVYjOTkZv/32G+bMmYOcnBx88sknaNu2Lfr371/OKyVLs6vsAhARERERERGRdVOpVBg5ciSA4jm9Fi1aZLFznTt3DgUFBQCAwYMHG0x35swZ3Lt377Hy9vLywuDBg7Fr1y7069cPAJCcnIxz584ZPc7Ozg7h4eGYPn06du3aJbevXr36sc5PFYtBLyIiIiIiIiIq1eTJk+UE8LGxsThz5kyZjlOr1fjxxx/LfJ7CwkL52Nik+fPnzy9znvooo78A3YnwjQkPD0fNmjUf+ziqeAx6EREREREREVGpAgICMGfOHADFwaioqCjs27fP6DEpKSl4+umnMXPmzDKfp0GDBnLOrqVLl+qdr2vDhg2yLPokJycjOTnZ4H4hBHbu3AmgeBRbSEiI3Ldq1Sq9E+Mrjhw5In9WGRoaauxSqJJxTi8iIiIiIiIiKpPRo0cjPT0dsbGxuHHjBrp164aYmBj0798fTZs2haenJ7Kzs3H27Fls2rQJW7duRVFR0WOtdOjl5YVevXrJ42NiYjBu3DgEBwfjxo0b+OWXXxAXF4d69eohJycHN2/e1MkjOTkZo0ePRtu2bdG3b1+Eh4fDz88PBQUFuHTpEpYsWYIdO3YAAPr164c6derIY9955x2MHTsW/fv3R9euXdGoUSO4u7sjKysLBw4cwLfffgsAsLe3xyuvvFKu+3jgwAGcP39e/l9zxNj58+cRFxenlX7UqFHlOs//Oga9iIiIiIiIiKjMPvzwQ4SFheHNN99Eamoqtm/fju3btxtMHxYWhs8///yxzjFv3jx07twZly9fxs6dO+WoLEXdunWxbt069OrVy2g+hw8fxuHDhw3u79ixIxYvXqyzPScnB0uXLsXSpUv1Hufs7Iz58+ejTZs2ZbgaXYsWLTKY98GDB3Hw4EGtbQx6lQ+DXkRERERERET0WJ577jn06dMHa9euxZYtW3D48GHcuHEDubm5qFGjBkJCQtChQwcMHDgQ3bp1kz9XLKugoCAcO3YMM2bMwPr165GWlgYXFxeEhIRgwIABmDhxopxXS5+hQ4fC19cXO3bswOHDh3H16lVkZmaisLAQPj4+CA8Px+DBgzFkyBDY2WnP/LRnzx5s2LAB+/fvx9mzZ3H9+nXcvn0bbm5uqF+/PqKjozFu3DjUq1evXPeOKo5K6PtxLBERERERERGVS3p6OoKCggAASb5+8LevmPEmGUWFaJd5HQBw5coVBAYGVsh5iawVJ7InIiIiIiIiIiKbw6AXERERERERERHZHAa9iIiIiIiIiIjI5jDoRURERERERERENodBLyIiIiIiIiIisjkMelmZR48e4bvvvkN0dDRq164NJycn+Pv7o1evXvj555/LladarUZkZCRUKpX8M5Wy5GxZ/kqzdetWdOzYEa6urqhduzbGjBmDjIwMnXSHDx+WeU6ZMsVonn/++adWGS5evGg0/ejRo2Xa06dPl1pma2RK20lLS8PChQvx6quvol27dggODoabmxtcXV0RFBSEfv36Yfny5SgoKCi1HHl5eYiNjUVoaCicnZ3RrFkzfP3119C3UOz48ePlff/rr7+M5jt58mSZtmvXrqWWwdXVFSqVCu3bty+1zNbK1P7g/PnzWLlyJSZPnoxOnTrBzc1N3sO4uDizlZP9gfUx12tJQkIChg8fjuDgYLi4uMDPzw89e/bEypUry3Q8+wPzyc/Px6JFi9CzZ0/UqVMHzs7OqFatGho3bozRo0cjISGh1DxSU1PxzjvvICIiAp6ennB0dEStWrXQsWNH/Pvf/8aNGzdMLmdISEiZ+oKQkJBS81qxYgVat24NFxcXBAQEYNKkSbhz545OujVr1sh8v/nmG6N5rl+/XqZ1dnbGo0ePjKbv3r07VCoVHBwccO/evVLLbG3K225SU1PL3K+XpU4zMjIwatQo1K5dG66urujcuTO2bt2qN21YWBhUKhW8vLz09hWannjiCXn+2NhYo2n37dsn077zzjtG01ozU/qCuLi4Mtenqe8T2BcQkdUQZDXOnDkjGjduLAAY/IuJiRG5ubmPle+3336rk4+poqKijJazrOeKi4sTKpVK55jAwEBx9epVrbQFBQWiWrVqAoBo06bNY11zXFyc0fT169cXAETt2rXLdgOsjKlt54MPPihTXbZo0UJcvHjRYDkKCwtFjx499B47evRonfQrV66U++fMmWP0GiMiImRaZ2dn8ejRI4Np4+PjZdq33nrLaL7WytQ63bt3r9FjlyxZYraysj+wLuZ6Lfnoo4+EnZ2dwTx69+4tHj58aPB49gfmk5qaKsLCwkp9fk2YMEGo1Wq9eSxbtky4uroaPb5WrVpi+/btJpU1ODi4TH1BcHCw0Xw+/vhjvce1bNlSp+1eu3ZN7h84cKDRfN98802t/Pbu3WswbX5+vrxnpfUz1siUdnPp0qUy9+ua/Yo+V69eFYGBgTrpVSqV3tei1157TaY5efKkwevLycnR6qO6detm9H5Mnz5dpt24cWPpN9AKmdoXLFmypMz1aer7BPYFQly5ckXmn+TrJ9L9AyvkL8nXT573ypUrZS4vka1i0MtKZGZmiqCgINlBDRo0SGzcuFEcO3ZMbNy4UQwaNEjrg0ZZpaeni+rVqwuVSiW8vb1lHqZSPuS2adNGnDhxwuifITdv3hTu7u7Czs5OvPnmm+LAgQPi999/F+3btxcAxAsvvKBzjPIByt7e3ugHthdeeEGmAyBefvllg2kzMjLkfXnuuece70ZYAXO0nQ8//FC0atVKjB8/XixcuFBs3LhRHD58WOzevVssXLhQdOzYUeZRr149ce/ePb35fPfddwKACAgIEEuWLBGHDh0Ss2fPFh4eHgKA2Lx5s1b6q1evynwHDx5s8Brv3r0r61L5Nz4+3mD6Tz/9VOb7+++/l+EuWhdz1OmePXtkGjs7OxEWFibatWtntjezmtgfWA9zvZbMnz9fpqtfv75YvHixSEpKEuvWrRPdu3eX+4YOHWowD/YH5pGfn6/1Ibdly5YiLi5OJCYmiu3bt4vY2Fjh7u4u9//3v//VyePAgQMyOGBnZydGjx4t1q1bJ5KSksTatWtF37595fGurq7iwoUL5S6v8kG3f//+RvuCv//+22AeJ0+eFHZ2dsLFxUVMnz5dJCYmilWrVslg7ttvv61zTMOGDQUA4evra7R8Sj+otJ3p06cbTJuQkCDvy5QpU8p+E6yAqe0mPz+/1P78xIkTYtiwYTKPH3/8UW9ZlD44MjJSbNiwQRw4cEBMnjxZqFQq4e7uLm7evKmV/scff5R5zps3z+A1bt68WasuXV1dRX5+vsH0MTEx8jmQk5PzGHfTOpijL9AMem3bts1o3d6+fduk8rIvYNCLyFow6GUlxo8fLzunjz76SG+a2NhYmWbNmjVlynfAgAECgBgzZozWaAxTKXlFRUWVO4+4uDi9Lx65ubkiMDBQODs7i7y8PK19mt/Sbdu2zWDederUEQDEkCFDBADRsGFDg2lXrVol85w9e3a5r6eymKPtFBQUlHqeiRMnyjy+/vprvWm6desmAIi//vpLa/tvv/0mAIhRo0bpHKOMqvH39zd47q1btwqgeBSC8qb1k08+MZj+mWeekW9ss7OzS702a2OOOj179qyYOXOm2Lt3rwwIab7ZtUTQi/1B5TNH28nKypKBqbp16+p8IC0sLNQKkuzZs0fvedgfmMeaNWvkvY6MjBSFhYU6aY4cOSIcHR0FAOHp6anTp/fu3VvmMXfuXL3nmTJlikwzfvz4cpdX+aA7cuTIcucxbdo0AUB88803WtvT09OFm5ubCAkJ0Tnm5ZdfluU39CH63r17wsHBQas/6NGjh8FyzJgxQ+a5bt26cl9PZTBHuylNYWGh8Pf3FwBE9erVxYMHD3TSPHr0SDg7O4ugoCCdL8yU9xUlR99qBgqMBdbfffddAUD06dNHjvw9ePCgwbJWr15dABCtW7d+rOu0FuaoU833AZcuXbJoedkXMOhFZC04p5cVKCoqwooVKwAAwcHB+PDDD/Wmi42NRd26dQEAn332Wan5/vrrr1i3bh28vb3x+eefm6/AZnL16lUAxb+R11StWjW0a9cOeXl5uHXrlta+Ll26yMfx8fF68z1//jyuXbsGAHLOhnPnziEzM1Nves18SpsbxtqYq+04ODiUeq53331XPjZ0769evQovLy+0aNFCa3t0dLTcX5JSpxkZGbhw4YLefJXzdezYUaY3VAa1Wi3ns2jevDlq1qxp8JqskbnqtGHDhnjrrbcQFRWFatWqWa7AZsL+wHTmajuLFi2Sc6XMmDED3t7eWvvt7e3x3Xffwd7eHgAwc+ZMvedhf2AemvPzvPfee/K+a4qIiECfPn0AADk5OTpz0Sl5eHl54fXXX9d7Hs35kBITE00utykM9QcBAQFo0qSJ0bYDGG4PiYmJKCwshIeHB8aNGye3FRUV6U2v5KNSqbTyrwrM0W5Ks3PnTjnn4sCBA+Hq6qqTJisrC3l5eWjXrh3c3d219hnqCwIDA+U8T4bqUnNft27d5Hx9htInJycjNzcXQNXr2xUVUafWhn0BEZkDg15W4Ny5c/IDRo8ePfS+iAHFHzR69OgBADh69CguXbpkMM+7d+9iwoQJAIo/kHh5eZm51Kbz8fEBUDyxqKb79+/jyJEjcHJy0il3+/bt4ezsDMDwC5myvWnTpmjdujWCg4PLlN7DwwOtWrUq59VUDku0HUOqV68uHxua7NPHxwdZWVk4deqU1va9e/cCAOrUqaNzjOabz9LqqEuXLujcuTOA4jd/arVaJ+1ff/0l70lVfGNbkXVqTdgfmM5cbWfdunUAgBo1auC5557Tm0dgYCCeeuopAMCuXbvkh0lN7A/MIz8/Xz6uV6+ewXT169fXe4zm/0NDQw0e7+HhIQOcJY+vaIb6g+vXr+Pvv/82ue107NhR9h/37t3D8ePHddIKIXDw4EEAxROr16pVq3wXU0nM0W5Ks2zZMvl45MiRetPUrFkTDg4OOHLkCB48eKC1ryx9QXp6OlJTU3X2P3r0CIcPHwag3ReUVveaeVc1FVGn1oZ9ARGZA4NeViArK0s+9vX1NZpWc7+xb7/effddZGRkoGvXrhg1apTJZbSEZ555Bk5OTpg1axbeeecdJCQkYNOmTYiJicHly5fRt29f+YFW4eLigrZt2wIA/vjjD70v5vv37wcA+QZI+VfZrunOnTs4ceIEAKBTp06ws6taTwlLtB1DNFd8a9Kkid40/fv3B1Bct0uXLkVSUhLmzJkj3wwPGjRI5xjNNyf66igvLw9JSUkAiuuyffv2cHR0xN27d5GcnKyTvqq/sa3IOrUm7A9MZ462k5+fL59vkZGRcHJyMphHVFQUgOLn6JEjR3T2sz8wj8aNG8vHxlYeVUbGqVQqNGzYUG8epX1Zpoym1DxnZVDaztSpU/Hf//4Xhw4dwtq1axEdHY379+/rbTuhoaEIDAwEoL/taG7v3LkznJ2d0aZNG4PpT548idu3bwOomm3HHO3GmNzcXBkgDwkJMXiPXF1dERMTg7S0NMTExGDTpk1ISEjA1KlTMXv2bLi5uaF37946x5XWFyh9vpubG8LDw2XffvDgQb0BcPYFVRP7AiIyh6r1jt5Gaf70SN/yu5o096ekpOhNk5iYiPnz58PR0RHz5s0zTyENOHPmDNq3bw9PT0+4uLggMDAQ/fv3x7Jly1BQUGD02ICAAHz66adQq9X4/PPP0alTJ/Tp0wcJCQnw9/fH7Nmz9R6nvOBofsunSXljU/JDrr6ggOabo6r4QmbutlPS7du3cfz4cUyZMkUO/3ZycpKPS5owYQLatGmDK1euYNSoUWjfvj0mTJiA27dvY8SIEXLIvab69evD398fgP46Onz4MB49egQXFxe0adMGrq6uCA8PN5i+qr+xtXSdWhL7g8pljrZz9uxZ+fMOQ8FtheZ+fT+hYX9gHkOHDkWNGjUAFP/cVN/Pb44fP45NmzYBAIYNGybTK8aOHQugODA6f/58veeZPn26TnpT7N+/H61bt0b16tXh5uaG0NBQDB48GOvWrYMQwuixbdu2xRtvvIGHDx/i/fffR2RkJAYNGoSUlBQ0b95c66eYmpQ6vnTpks7PngoKCvDHH38AKFt/UNXbjjnajTFr166VI7deeuklqFQqg2m/+uor1KpVCwcPHkSfPn3QqVMnfPHFFxBC4JtvvpGjeTSVNlpH2da+fXs4ODigQ4cOsLe3R05OjvzyQtOBAwcAFPdbtWvXLvN1WhNz1+no0aPh7+8PJycneHt7o0OHDvi///s/vT8ZNAX7AiKqdJU6oxgJIYonU1QmnWzZsqXRtC1atDA6uafmyi7vvfee1j5LTGRv7K9Zs2YiJSWl1LxWrVolIiIihLOzs/Dy8hIjRowQ6enpBtNv2bJFnqPkyjSaK69dvHhRCCHEiRMn5CTGJVfrUSZBBSASEhLKcScqlznbjmLkyJEG69TNzU388ssvRs9z9+5dMWXKFBEQECAcHR1F48aNxaxZs0RRUZHBYwYPHizPce3aNa19ysprXbt2lduUZaaff/55nbz8/Ion72zUqJHRclorS9SpwtIT2bM/qFzmaDua93PmzJlG8zh8+LBM++677+pNw/7APNavXy/c3NwEAPHEE0+IpUuXisTERLFjxw4xbdo0OUF3eHi4uH79us7xhYWFYsSIEbLtv/LKK+L3338Xhw8fFr/88otc9AaA+OCDD0wqqzJ5tbG/Tp06GX1eCyGEWq0Wc+fOFc2aNROOjo7Cz89PTJgwweiKcvPmzZPnWLlypdY+ZfU1Jycn8fDhQyGEEBs2bBAAhLe3t05eyuTWAERGRsbj3wgrYGq7MUZZqAKAOHfuXKnpz507J1544QXh6ekpXFxcRGRkpNi0aZPRY5Tnb+PGjXX2KYtYxMbGym0RERECgPj222+10p45c0aW9V//+lcZr9A6mVqnmu8DDP25uLiI+fPnm1xW9gWcyJ7IWjDoZSV69uwpO6effvpJb5qffvpJ64WiT58+OmmU1cxCQ0N1VtExZ9Cre/fuIjo6Wnz55Zdi586d4vjx42L//v1i9uzZomnTpvI8vr6+Ii0tzeTzadJcrr5Xr15a+5SV1wICAuQ2tVotatasKQCIzZs3a6Xv1KmTDOYYW+bampmr7SgMBb2GDBkiLl++bJFrmDt3rjzP6tWrtfYpK6+9//77cpuy+lvJ5ajPnTsn83nllVcsUtaKYO46VVgq6MX+wHqY2nZWr14tt8+bN8/ouVJSUmTaN954w2zXwP5Av9OnT4tXXnlFqFQqnf7Z19dXzJ49W9y/f99oHmvWrBFPPPGE3j6+e/fuYseOHSaXs2HDhqJfv35izpw5Yu/eveL48eNiz5494tNPPxVBQUHyfE2bNtUJPJvq1KlTMv/XX39da5+y+lrHjh3ltuzsbHk/SwblAwICBADRoEEDs5axopmj3ZSUlpYm89O8n+Y2aNAgWdbMzEy5XXMlxu3bt8vtymqQL7zwglY+CxculPmsWLHCYuWtKKbU6ZIlS0S9evXEW2+9JX755ReRlJQkkpKSxM8//ywGDRqklef3339vUjnZFzDoRWQtGPSyEomJiXLpXEdHRzF9+nSRlpYm8vPzRVpampg+fbpwdHQUTk5OshOLjo7WyuPs2bPCxcVFAND77Zk5g17Gvl3Jz8/XCpw8++yzJp+vJOXbPA8PD60RA+PHj9f7hkdZrl1zNMLDhw/l/XzyySfNXsaKYo62oyk9PV2cOHFCnDhxQhw8eFDMmzdPhIeHCwCic+fO4uzZs2a/BmX0TckPz0VFRaJGjRo6AYqbN2/K9GfOnJHbf/jhB7l92bJlZi9nRTF3nSosFfRif2A9TG07y5Ytk9sXL15s9FwXLlyQaV9++WWzXQP7A115eXnivffeE7Vr19YbsAIg2rRpI9avX28wj5SUFNG3b1/ZPkr+ubi4iCFDhpQ66qI0xvqDu3fvyhE6AMTkyZNNOldJarVaeHt7CwCiefPmWvuU5/3bb7+ttV0ZHa85suXixYuyjGPGjDFrGSuSOdqNPp988ok83hwjggz59ttv5XnWrl0rtyujTO3t7UVubq7cvmbNGgFA1KlTRysfZZQjAIt9eVdRTK3TnJwcoVarDea/YcMGOWLYzc1NZ7Tt42BfwKAXkbVg0MuKLF682OCbUQDC1dVVzJkzR/5/wIABWsd3795dAPp/4iGEeYNepSkoKBCNGzeW5zP1TXRJkyZNknkfP35cbm/ZsqUAdIe2f/bZZwIoHkat2Ldvn8zjo48+Mmv5Kpqpbac0hYWF4rXXXhMARM2aNUVycrJZy69Wq0WtWrUEANGqVSu5/dixYwLQ/1O0Jk2aCABi4cKFctvo0aPlNV66dMmsZaxolqhTSwW9SsP+oGKZ0nasYaQX+wNt9+7dE126dJEf8t9++21x+vRpkZeXJ+7cuSO2b98uOnfuLAAIlUolvvzyS5089u/fLzw8PAQAERwcLJYvXy6uX78u8vPzxZUrV8TcuXPlPff39xcnT5602PXk5OTIc7m7u4u8vDyz5q/8VFOlUomsrCwhRHHA1NPTUwAQGzZs0Eo/duxYAUC8+OKLctvSpUsrpa80J3O0G0OU55uzs7PRwIapkpOTZT1MnDhRbp81a5YAICIiIrTSX79+XabX/MllaGiobPtVmSXrVJPyqxEA4j//+Y+Zr+If/wt9AYNeRNaBE9lbkTFjxuCPP/7As88+C3d3d7ndwcEB/fr1w7Fjx+TqIkDxMtCKH374AXv27EH16tXx9ddfV2i59XFwcMDLL78s/19yqWFT6ZvgNCcnBydPngTwz4SUCuX/yiTImseVzK8qMqXtlIW9vT2++eYbBAUF4fbt2wYnsi8vlUol6+jEiRNykm2ljlq0aAEPDw+tY/RNOqo8DgoKQkhIiFnLWNEsXacVif1BxTKl7VSvXl0+vnfvntHz3L9/Xz7WnETfVOwPtE2bNk1ey+LFizFjxgw0adIETk5OqFGjBnr06IE9e/age/fuEEJg6tSp+PPPP+XxeXl5GDp0KO7cuQM/Pz8cOnQIw4cPh6+vLxwdHREYGIjXX38d+/fvh4uLCzIyM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-<h2 id="pca-sam-applied-to-whole-image">PCA + SAM applied to whole image<a class="anchor-link" href="#pca-sam-applied-to-whole-image">¶</a></h2>
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-<div class="highlight-ipynb hl-python"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.decomposition</span> <span class="kn">import</span> <span class="n">PCA</span>
-</pre></div>
-<div class="clipboard-copy-txt" id="cell-22">from sklearn.decomposition import PCA</div>
-</div>
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-<div class="highlight-ipynb hl-python"><pre><span></span><span class="c1"># Check the dimensions and coordinates</span>
-<span class="nb">print</span><span class="p">(</span><span class="n">da</span><span class="o">.</span><span class="n">dims</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="n">da</span><span class="o">.</span><span class="n">coords</span><span class="p">)</span>  <span class="c1"># This will show the available spectral bands</span>
-</pre></div>
-<div class="clipboard-copy-txt" id="cell-23"># Check the dimensions and coordinates
-print(da.dims)
-print(da.coords)  # This will show the available spectral bands</div>
-</div>
-</div>
-</div>
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-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
-<pre>('latitude', 'longitude', 'wavelength')
-Coordinates:
-    longitude   (latitude, longitude) float32 9MB -92.88 -92.84 ... -70.54
-    latitude    (latitude, longitude) float32 9MB 15.71 15.72 ... 43.91 43.92
-  * wavelength  (wavelength) float64 1kB 339.0 341.0 344.0 ... 714.0 717.0 719.0
-</pre>
-</div>
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-</div>
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-<div class="jp-InputPrompt jp-InputArea-prompt">In [24]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
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-<path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path>
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-<div class="highlight-ipynb hl-python"><pre><span></span><span class="c1"># Reshape data to (n_pixels, n_bands)</span>
-<span class="n">reshaped_data</span> <span class="o">=</span> <span class="n">da</span><span class="o">.</span><span class="n">values</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="n">da</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span>
-
-<span class="c1"># Handle NaNs by removing them</span>
-<span class="n">reshaped_data_no_nan</span> <span class="o">=</span> <span class="n">reshaped_data</span><span class="p">[</span><span class="o">~</span><span class="n">np</span><span class="o">.</span><span class="n">isnan</span><span class="p">(</span><span class="n">reshaped_data</span><span class="p">)</span><span class="o">.</span><span class="n">any</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)]</span>
-</pre></div>
-<div class="clipboard-copy-txt" id="cell-24"># Reshape data to (n_pixels, n_bands)
-reshaped_data = da.values.reshape(-1, da.shape[-1])
-
-# Handle NaNs by removing them
-reshaped_data_no_nan = reshaped_data[~np.isnan(reshaped_data).any(axis=1)]</div>
-</div>
-</div>
-</div>
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"/>
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-<div class="highlight-ipynb hl-python"><pre><span></span><span class="c1"># Apply PCA to reduce dimensionality</span>
-<span class="n">pca</span> <span class="o">=</span> <span class="n">PCA</span><span class="p">(</span><span class="n">n_components</span><span class="o">=</span><span class="mi">3</span><span class="p">)</span>
-<span class="n">pca_data</span> <span class="o">=</span> <span class="n">pca</span><span class="o">.</span><span class="n">fit_transform</span><span class="p">(</span><span class="n">reshaped_data_no_nan</span><span class="p">)</span>
-
-<span class="c1"># Visualize PCA components to manually identify endmembers</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">8</span><span class="p">))</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">pca_data</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">pca_data</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">c</span><span class="o">=</span><span class="s2">"blue"</span><span class="p">,</span> <span class="n">s</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s2">"PCA of Spectral Data"</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s2">"Principal Component 1"</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s2">"Principal Component 2"</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
-</pre></div>
-<div class="clipboard-copy-txt" id="cell-25"># Apply PCA to reduce dimensionality
-pca = PCA(n_components=3)
-pca_data = pca.fit_transform(reshaped_data_no_nan)
-
-# Visualize PCA components to manually identify endmembers
-plt.figure(figsize=(10, 8))
-plt.scatter(pca_data[:, 0], pca_data[:, 1], c="blue", s=1)
-plt.title("PCA of Spectral Data")
-plt.xlabel("Principal Component 1")
-plt.ylabel("Principal Component 2")
-plt.show()</div>
-</div>
-</div>
-</div>
-</div>
-<div class="jp-Cell-outputWrapper">
-<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
-</div>
-<div class="jp-OutputArea jp-Cell-outputArea">
-<div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedImage jp-OutputArea-output" tabindex="0">
-<img alt="No description has been provided for this image" class="" 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j35fPj5+aGsrEwqdgHgypUrcHBwYIH1E/UkHw0NDe0KqbYCWAjRe8FSh3g9H3j27t2LhQsXYu/evZg9e3bv7qxXHqdBA05wcLCYMGGCOHPmjPj888/FqFGjRHh4uLT+m2++Ee7u7uLMmTNCCCHu378vfHx8hJeXlygrKxNVVVXSq7m5ub8O42cpPT1dqFQqkZaWJr766iuxePFiYWlpKaqrq4UQQkRGRorY2FipfX5+vjAyMhLJycmipKRExMXFCWNjY3Hx4sX+OoRBRdd8JCUlCaVSKT755BOtz0FdXV1/HcKgoms+HsenC8pL13zcuHFDqNVqsXz5clFaWioyMzOFra2tePPNN/vrEAYVXfMRFxcn1Gq12Lt3rygvLxf/+c9/xMiRI0VYWFh/HcKgUldXJ4qKikRRUZEAIN577z1RVFQkvv76ayGEELGxsSIyMlJqX15eLoYMGSLWrVsnSkpKREpKijA0NBRHjx7tr0MYVHTNx549e4SRkZFISUnRup7fu3evV+JjkaUnbt++LcLDw4W5ubnQaDRi4cKFWl8Sr1+/LgCInJwcIYQQOTk5AkCHr+vXr/fPQfyMbd26VTg7OwulUikmT54sTp8+La0LCAgQCxYs0Gq/f/9+MXr0aKFUKsXYsWPF4cOH+zjiwU2XfLi4uHT4OYiLi+v7wAcpXT8fP8YiS3665uPUqVPCx8dHqFQqMWLECPHWW2/xj3Ey0iUfTU1NIj4+XowcOVKYmJgIJycnsWzZMnH37t2+D3wQ6uy7UVsOFixYIAICAtpt8+yzzwqlUilGjBgh/v73v/d53IOVrvkICAjosr3cFEJw/JiIiIiIiEguvCeLiIiIiIhIRiyyiIiIiIiIZMQii4iIiIiISEYssoiIiIiIiGTEIouIiIiIiEhGLLKIiIiIiIhkxCKLiIiIiIhIRiyyiIiIiIiIZMQii4iIus3V1RUffPCBbP1FRUVhzpw5svUHALm5uVAoFLh3756s/RIREXUXiywiIj0UFRUFhUIBhUIBpVIJNzc3JCQkoLm5ucvtzp07h8WLF8sWx4cffoi0tDTZ+tNFUVER5s6dCzs7O5iYmGDUqFF4+eWXceXKlX6JZ6DqbmG9fft2TJs2DRqNhkUuEek9FllERHoqODgYVVVVuHr1KtasWYP4+Hi8++67HbZtbGwEANjY2GDIkCGyxWBhYQFLS0vZ+uuuzMxMTJkyBY8ePcKePXtQUlKCf/zjH7CwsMDGjRv7PJ7BoKGhAcHBwdiwYUN/h0JE1O9YZBER6SmVSgV7e3u4uLhg6dKlCAwMREZGBoD/n8b31ltvwdHREe7u7gDaj2ooFArs2LEDv/nNbzBkyBCMGjVK6qPNl19+iV//+tfQaDRQq9Xw9/fHtWvXtPbTZtq0aVi+fDmWL18OCwsLWFtbY+PGjRBCSG12796NSZMmQa1Ww97eHvPnz8d3333X7eNuaGjAwoULMWvWLGRkZCAwMBBPP/00fHx8kJycjL/97W9S2//+97+YPHkyVCoVHBwcEBsbqzXaN23aNKxYsQKrVq3C0KFDYWdnh9TUVDx48AALFy6EWq2Gm5sbjhw5Im3TNp3x8OHDGDduHExMTDBlyhRcunRJK85//vOfGDt2LFQqFVxdXbFlyxat9a6urti8eTNeeuklqNVqODs7Y/v27Vptbt68ibCwMFhaWsLKygqhoaGoqKiQ1red/+TkZDg4OGDYsGGIiYlBU1OTdHxff/01Xn31VWnkszOrVq1CbGwspkyZ0u1cEBENViyyiIgIAGBqaiqNWAFAdnY2SktLcfz4cWRmZna63aZNmxAWFob//e9/mDVrFiIiInDnzh0AQGVlJZ5//nmoVCqcOHEChYWFeOmll7qclrhr1y4YGRnh7Nmz+PDDD/Hee+9hx44d0vqmpiYkJibiwoUL+PTTT1FRUYGoqKhuH+exY8dQU1OD9evXd7i+bWStsrISs2bNwi9+8QtcuHAB27Ztw86dO/Hmm2+2i9fa2hpnz57FihUrsHTpUsydOxdTp07F+fPnMXPmTERGRqKhoUFru3Xr1mHLli04d+4cbGxsEBISIhU3hYWFCAsLw4svvoiLFy8iPj4eGzdubDe1csuWLZg0aRKKioqwbNkyLF26FKWlpdJ5CgoKglqtxsmTJ5Gfnw9zc3MEBwdr5TknJwfXrl1DTk4Odu3ahbS0NGk/Bw8exPDhw5GQkICqqipUVVV1+zwTEek1QUREemfBggUiNDRUCCFEa2urOH78uFCpVGLt2rXSejs7O/Ho0SOt7VxcXMT7778vvQcg3njjDel9fX29ACCOHDkihBDij3/8o3j66adFY2PjE+MQQoiAgADh6ekpWltbpWWvvfaa8PT07PRYzp07JwCIuro6IYQQOTk5AoC4e/duh+3//Oc/CwDizp07nfYphBAbNmwQ7u7uWrGkpKQIc3Nz0dLSIsX73HPPSeubm5uFmZmZiIyMlJZVVVUJAKKgoEArvvT0dKnN7du3hampqdi3b58QQoj58+eLGTNmaMWzbt06MWbMGOm9i4uL+N3vfie9b21tFba2tmLbtm1CCCF2797dLv5Hjx4JU1NTcezYMSHED+ffxcVFNDc3S23mzp0r5s2bp7WfH+f8SZ50/omI9AFHsoiI9FRmZibMzc1hYmKCX/3qV5g3bx7i4+Ol9V5eXlAqlU/sZ9y4cdLPZmZm0Gg00vS94uJi+Pv7w9jYuNtxTZkyRWtamq+vL65evYqWlhYAP4zyhISEwNnZGWq1GgEBAQCAGzdudKt/8aOph10pKSmBr6+vVix+fn6or6/HN998Iy378fEbGhpi2LBh8PLykpbZ2dkBQLspjb6+vtLPVlZWcHd3R0lJibRvPz8/rfZ+fn5a5+HxfSsUCtjb20v7uXDhAsrKyqBWq2Fubg5zc3NYWVnh4cOH0nRNABg7diwMDQ2l9w4ODjpNvyQiovaM+jsAIiLqH9OnT8e2bdugVCrh6OgIIyPtS4KZmVm3+nm8gFIoFGhtbQXwwxREOT148ABBQUEICgrCnj17YGNjgxs3biAoKEhrClxXRo8eDQC4fPmyVqHTUx0d/4+XtRVpbedETl2d+/r6enh7e2PPnj3ttrOxselWH0RE1DMcySIi0lNmZmZwc3ODs7NzuwJLLuPGjcPJkyele42648yZM1rvT58+jVGjRsHQ0BCXL1/G7du3kZSUBH9/f3h4eOg86jJz5kxYW1vjnXfe6XB926PHPT09UVBQoDXylZ+fD7VajeHDh+u0z46cPn1a+vnu3bu4cuUKPD09pX3n5+drtc/Pz8fo0aO1Rp26MnHiRFy9ehW2trZwc3PTellYWHQ7TqVSqTV6RkRET8Yii4iIes3y5ctRW1uLF198EV988QWuXr2K3bt3Sw9n6MiNGzewevVqlJaWYu/evdi6dStWrlwJAHB2doZSqcTWrVtRXl6OjIwMJCYm6hSTmZkZduzYgcOHD+OFF17AZ599hoqKCnzxxRdYv349lixZAgBYtmwZbt68iRUrVuDy5cv417/+hbi4OKxevRoGBj/98pmQkIDs7GxcunQJUVFRsLa2lp60uGbNGmRnZyMxMRFXrlzBrl278Je//AVr167tdv8RERGwtrZGaGgoTp48ievXryM3NxevvPKK1nTHJ3F1dUVeXh4qKytRU1PTabvq6moUFxejrKwMAHDx4kUUFxdLD0EhItInLLKIiKjXDBs2DCdOnEB9fT0CAgLg7e2N1NTULu/R+v3vf4/vv/8ekydPRkxMDFauXCn9A2QbGxukpaXhwIEDGDNmDJKSkpCcnKxzXKGhoTh16hSMjY0xf/58eHh4IDw8HPfv35eeHvjUU08hKysLZ8+exfjx47FkyRJER0fjjTfe6NnJeExSUhJWrlwJb29vVFdX49///rd0D9zEiROxf/9+pKen45lnnsGf/vQnJCQk6PQUxSFDhiAvLw/Ozs747W9/C09PT0RHR+Phw4fQaDTd7ichIQEVFRUYOXKk1jTDx3300UeYMGECXn75ZQDA888/jwkTJrR7pD8RkT5QiO7eAUxERNTLpk2bhmeffVbrf3ENNrm5uZg+fTru3r3bL/+ImYiIeh9HsoiIiIiIiGTEIouIiIiIiEhGnC5IREREREQkI45kERERERERyYhFFhERERERkYxYZBEREREREcmIRRYREREREZGMWGQRERERERHJiEUWERERERGRjFhkERERERERyYhFFhERERERkYz+DxCdKxWMwsQRAAAAAElFTkSuQmCC"/>
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-<div class="highlight-ipynb hl-python"><pre><span></span><span class="c1"># Apply K-means to find clusters representing endmembers</span>
-<span class="n">n_clusters</span> <span class="o">=</span> <span class="mi">6</span>  <span class="c1"># Number of endmembers you want to find</span>
-<span class="n">kmeans</span> <span class="o">=</span> <span class="n">KMeans</span><span class="p">(</span><span class="n">n_clusters</span><span class="o">=</span><span class="n">n_clusters</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
-<span class="n">kmeans</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">pca_data</span><span class="p">)</span>
-
-<span class="c1"># The cluster centers in the original spectral space are your endmembers</span>
-<span class="n">endmembers</span> <span class="o">=</span> <span class="n">pca</span><span class="o">.</span><span class="n">inverse_transform</span><span class="p">(</span><span class="n">kmeans</span><span class="o">.</span><span class="n">cluster_centers_</span><span class="p">)</span>
-</pre></div>
-<div class="clipboard-copy-txt" id="cell-26"># Apply K-means to find clusters representing endmembers
-n_clusters = 6  # Number of endmembers you want to find
-kmeans = KMeans(n_clusters=n_clusters, random_state=0)
-kmeans.fit(pca_data)
-
-# The cluster centers in the original spectral space are your endmembers
-endmembers = pca.inverse_transform(kmeans.cluster_centers_)</div>
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-<div class="highlight-ipynb hl-python"><pre><span></span><span class="k">def</span> <span class="nf">spectral_angle_mapper</span><span class="p">(</span><span class="n">pixel</span><span class="p">,</span> <span class="n">reference</span><span class="p">):</span>
-    <span class="n">norm_pixel</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">norm</span><span class="p">(</span><span class="n">pixel</span><span class="p">)</span>
-    <span class="n">norm_reference</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">norm</span><span class="p">(</span><span class="n">reference</span><span class="p">)</span>
-    <span class="n">cos_theta</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">pixel</span><span class="p">,</span> <span class="n">reference</span><span class="p">)</span> <span class="o">/</span> <span class="p">(</span><span class="n">norm_pixel</span> <span class="o">*</span> <span class="n">norm_reference</span><span class="p">)</span>
-    <span class="n">angle</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arccos</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">clip</span><span class="p">(</span><span class="n">cos_theta</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">))</span>
-    <span class="k">return</span> <span class="n">angle</span>
-
-
-<span class="c1"># Apply SAM for each pixel and each endmember</span>
-<span class="n">angles</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="n">reshaped_data_no_nan</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">endmembers</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]))</span>
-
-<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">reshaped_data_no_nan</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]):</span>
-    <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">endmembers</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]):</span>
-        <span class="n">angles</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">]</span> <span class="o">=</span> <span class="n">spectral_angle_mapper</span><span class="p">(</span>
-            <span class="n">reshaped_data_no_nan</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="p">:],</span> <span class="n">endmembers</span><span class="p">[</span><span class="n">j</span><span class="p">,</span> <span class="p">:]</span>
-        <span class="p">)</span>
-
-<span class="c1"># Find the minimum angle (best match) for each pixel</span>
-<span class="n">best_match</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">argmin</span><span class="p">(</span><span class="n">angles</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
-
-<span class="c1"># Reshape best_match back to the original spatial dimensions</span>
-<span class="n">original_shape</span> <span class="o">=</span> <span class="n">da</span><span class="o">.</span><span class="n">shape</span><span class="p">[:</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>  <span class="c1"># Get the spatial dimensions</span>
-<span class="n">best_match_full</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">full</span><span class="p">(</span><span class="n">reshaped_data</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span><span class="p">)</span>
-<span class="n">best_match_full</span><span class="p">[</span><span class="o">~</span><span class="n">np</span><span class="o">.</span><span class="n">isnan</span><span class="p">(</span><span class="n">reshaped_data</span><span class="p">)</span><span class="o">.</span><span class="n">any</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)]</span> <span class="o">=</span> <span class="n">best_match</span>
-<span class="n">best_match_full</span> <span class="o">=</span> <span class="n">best_match_full</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">original_shape</span><span class="p">)</span>
-</pre></div>
-<div class="clipboard-copy-txt" id="cell-27">def spectral_angle_mapper(pixel, reference):
-    norm_pixel = np.linalg.norm(pixel)
-    norm_reference = np.linalg.norm(reference)
-    cos_theta = np.dot(pixel, reference) / (norm_pixel * norm_reference)
-    angle = np.arccos(np.clip(cos_theta, -1, 1))
-    return angle
-
-
-# Apply SAM for each pixel and each endmember
-angles = np.zeros((reshaped_data_no_nan.shape[0], endmembers.shape[0]))
-
-for i in range(reshaped_data_no_nan.shape[0]):
-    for j in range(endmembers.shape[0]):
-        angles[i, j] = spectral_angle_mapper(
-            reshaped_data_no_nan[i, :], endmembers[j, :]
-        )
-
-# Find the minimum angle (best match) for each pixel
-best_match = np.argmin(angles, axis=1)
-
-# Reshape best_match back to the original spatial dimensions
-original_shape = da.shape[:-1]  # Get the spatial dimensions
-best_match_full = np.full(reshaped_data.shape[0], np.nan)
-best_match_full[~np.isnan(reshaped_data).any(axis=1)] = best_match
-best_match_full = best_match_full.reshape(original_shape)</div>
-</div>
-</div>
-</div>
-</div>
-</div>
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-<div class="highlight-ipynb hl-python"><pre><span></span><span class="c1"># Assume 'best_match_full' contains the SAM classification results reshaped to the original spatial dimensions</span>
-<span class="c1"># Also assume that 'da' has the original latitude and longitude data</span>
-<span class="n">latitudes</span> <span class="o">=</span> <span class="n">da</span><span class="o">.</span><span class="n">coords</span><span class="p">[</span><span class="s2">"latitude"</span><span class="p">]</span><span class="o">.</span><span class="n">values</span>
-<span class="n">longitudes</span> <span class="o">=</span> <span class="n">da</span><span class="o">.</span><span class="n">coords</span><span class="p">[</span><span class="s2">"longitude"</span><span class="p">]</span><span class="o">.</span><span class="n">values</span>
-
-<span class="c1"># Create a custom discrete color map</span>
-<span class="n">cmap</span> <span class="o">=</span> <span class="n">mcolors</span><span class="o">.</span><span class="n">ListedColormap</span><span class="p">(</span>
-    <span class="p">[</span><span class="s2">"#377eb8"</span><span class="p">,</span> <span class="s2">"#ff7f00"</span><span class="p">,</span> <span class="s2">"#4daf4a"</span><span class="p">,</span> <span class="s2">"#f781bf"</span><span class="p">,</span> <span class="s2">"#a65628"</span><span class="p">,</span> <span class="s2">"#984ea3"</span><span class="p">]</span>
-<span class="p">)</span>
-<span class="n">bounds</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="o">-</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">n_clusters</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
-<span class="n">norm</span> <span class="o">=</span> <span class="n">mcolors</span><span class="o">.</span><span class="n">BoundaryNorm</span><span class="p">(</span><span class="n">bounds</span><span class="p">,</span> <span class="n">cmap</span><span class="o">.</span><span class="n">N</span><span class="p">)</span>
-
-<span class="c1"># Create a figure and axis with the correct map projection</span>
-<span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">8</span><span class="p">),</span> <span class="n">subplot_kw</span><span class="o">=</span><span class="p">{</span><span class="s2">"projection"</span><span class="p">:</span> <span class="n">ccrs</span><span class="o">.</span><span class="n">PlateCarree</span><span class="p">()})</span>
-
-<span class="c1"># Plot the SAM classification results</span>
-<span class="n">im</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">pcolormesh</span><span class="p">(</span>
-    <span class="n">longitudes</span><span class="p">,</span>
-    <span class="n">latitudes</span><span class="p">,</span>
-    <span class="n">best_match_full</span><span class="p">,</span>
-    <span class="n">cmap</span><span class="o">=</span><span class="n">cmap</span><span class="p">,</span>
-    <span class="n">norm</span><span class="o">=</span><span class="n">norm</span><span class="p">,</span>
-    <span class="n">transform</span><span class="o">=</span><span class="n">ccrs</span><span class="o">.</span><span class="n">PlateCarree</span><span class="p">(),</span>
-<span class="p">)</span>
-
-<span class="c1"># Add geographic features for context</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">add_feature</span><span class="p">(</span><span class="n">cfeature</span><span class="o">.</span><span class="n">COASTLINE</span><span class="p">)</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">add_feature</span><span class="p">(</span><span class="n">cfeature</span><span class="o">.</span><span class="n">BORDERS</span><span class="p">,</span> <span class="n">linestyle</span><span class="o">=</span><span class="s2">":"</span><span class="p">)</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">add_feature</span><span class="p">(</span><span class="n">cfeature</span><span class="o">.</span><span class="n">STATES</span><span class="p">,</span> <span class="n">linestyle</span><span class="o">=</span><span class="s2">"--"</span><span class="p">)</span>
-
-<span class="c1"># Add gridlines</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">gridlines</span><span class="p">(</span><span class="n">draw_labels</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-
-<span class="c1"># Add color bar with labels</span>
-<span class="n">cbar</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">colorbar</span><span class="p">(</span>
-    <span class="n">im</span><span class="p">,</span>
-    <span class="n">ax</span><span class="o">=</span><span class="n">ax</span><span class="p">,</span>
-    <span class="n">orientation</span><span class="o">=</span><span class="s2">"vertical"</span><span class="p">,</span>
-    <span class="n">pad</span><span class="o">=</span><span class="mf">0.02</span><span class="p">,</span>
-    <span class="n">fraction</span><span class="o">=</span><span class="mf">0.05</span><span class="p">,</span>
-    <span class="n">ticks</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">n_clusters</span><span class="p">),</span>
-<span class="p">)</span>
-<span class="n">cbar</span><span class="o">.</span><span class="n">ax</span><span class="o">.</span><span class="n">set_yticklabels</span><span class="p">([</span><span class="sa">f</span><span class="s2">"Class </span><span class="si">{</span><span class="n">i</span><span class="o">+</span><span class="mi">1</span><span class="si">}</span><span class="s2">"</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">n_clusters</span><span class="p">)])</span>
-<span class="n">cbar</span><span class="o">.</span><span class="n">set_label</span><span class="p">(</span><span class="s2">"Water Types"</span><span class="p">,</span> <span class="n">rotation</span><span class="o">=</span><span class="mi">270</span><span class="p">,</span> <span class="n">labelpad</span><span class="o">=</span><span class="mi">20</span><span class="p">)</span>
-
-<span class="c1"># Add title</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s2">"Spectral Angle Mapper (SAM) Water Type Classification"</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">16</span><span class="p">)</span>
-
-<span class="c1"># Show the plot</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
-</pre></div>
-<div class="clipboard-copy-txt" id="cell-28"># Assume 'best_match_full' contains the SAM classification results reshaped to the original spatial dimensions
-# Also assume that 'da' has the original latitude and longitude data
-latitudes = da.coords["latitude"].values
-longitudes = da.coords["longitude"].values
-
-# Create a custom discrete color map
-cmap = mcolors.ListedColormap(
-    ["#377eb8", "#ff7f00", "#4daf4a", "#f781bf", "#a65628", "#984ea3"]
-)
-bounds = np.arange(-0.5, n_clusters, 1)
-norm = mcolors.BoundaryNorm(bounds, cmap.N)
-
-# Create a figure and axis with the correct map projection
-fig, ax = plt.subplots(figsize=(10, 8), subplot_kw={"projection": ccrs.PlateCarree()})
-
-# Plot the SAM classification results
-im = ax.pcolormesh(
-    longitudes,
-    latitudes,
-    best_match_full,
-    cmap=cmap,
-    norm=norm,
-    transform=ccrs.PlateCarree(),
-)
-
-# Add geographic features for context
-ax.add_feature(cfeature.COASTLINE)
-ax.add_feature(cfeature.BORDERS, linestyle=":")
-ax.add_feature(cfeature.STATES, linestyle="--")
-
-# Add gridlines
-ax.gridlines(draw_labels=True)
-
-# Add color bar with labels
-cbar = plt.colorbar(
-    im,
-    ax=ax,
-    orientation="vertical",
-    pad=0.02,
-    fraction=0.05,
-    ticks=np.arange(n_clusters),
-)
-cbar.ax.set_yticklabels([f"Class {i+1}" for i in range(n_clusters)])
-cbar.set_label("Water Types", rotation=270, labelpad=20)
-
-# Add title
-ax.set_title("Spectral Angle Mapper (SAM) Water Type Classification", fontsize=16)
-
-# Show the plot
-plt.show()</div>
-</div>
-</div>
-</div>
-</div>
-<div class="jp-Cell-outputWrapper">
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-<div class="highlight-ipynb hl-python"><pre><span></span><span class="c1"># Assume 'best_match_full' contains the SAM classification results reshaped to the original spatial dimensions</span>
-<span class="c1"># Also assume that 'da' has the original latitude and longitude data</span>
-<span class="n">latitudes</span> <span class="o">=</span> <span class="n">da</span><span class="o">.</span><span class="n">coords</span><span class="p">[</span><span class="s2">"latitude"</span><span class="p">]</span><span class="o">.</span><span class="n">values</span>
-<span class="n">longitudes</span> <span class="o">=</span> <span class="n">da</span><span class="o">.</span><span class="n">coords</span><span class="p">[</span><span class="s2">"longitude"</span><span class="p">]</span><span class="o">.</span><span class="n">values</span>
-
-<span class="c1"># Create a custom discrete color map</span>
-<span class="n">cmap</span> <span class="o">=</span> <span class="n">mcolors</span><span class="o">.</span><span class="n">ListedColormap</span><span class="p">(</span>
-    <span class="p">[</span><span class="s2">"#377eb8"</span><span class="p">,</span> <span class="s2">"#ff7f00"</span><span class="p">,</span> <span class="s2">"#4daf4a"</span><span class="p">,</span> <span class="s2">"#f781bf"</span><span class="p">,</span> <span class="s2">"#a65628"</span><span class="p">,</span> <span class="s2">"#984ea3"</span><span class="p">]</span>
-<span class="p">)</span>
-<span class="n">bounds</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="o">-</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">n_clusters</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
-<span class="n">norm</span> <span class="o">=</span> <span class="n">mcolors</span><span class="o">.</span><span class="n">BoundaryNorm</span><span class="p">(</span><span class="n">bounds</span><span class="p">,</span> <span class="n">cmap</span><span class="o">.</span><span class="n">N</span><span class="p">)</span>
-
-<span class="c1"># Create a figure and axis with the correct map projection</span>
-<span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">8</span><span class="p">),</span> <span class="n">subplot_kw</span><span class="o">=</span><span class="p">{</span><span class="s2">"projection"</span><span class="p">:</span> <span class="n">ccrs</span><span class="o">.</span><span class="n">PlateCarree</span><span class="p">()})</span>
-
-<span class="c1"># Plot the SAM classification results</span>
-<span class="n">im</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">pcolormesh</span><span class="p">(</span>
-    <span class="n">longitudes</span><span class="p">,</span>
-    <span class="n">latitudes</span><span class="p">,</span>
-    <span class="n">best_match_full</span><span class="p">,</span>
-    <span class="n">cmap</span><span class="o">=</span><span class="n">cmap</span><span class="p">,</span>
-    <span class="n">norm</span><span class="o">=</span><span class="n">norm</span><span class="p">,</span>
-    <span class="n">transform</span><span class="o">=</span><span class="n">ccrs</span><span class="o">.</span><span class="n">PlateCarree</span><span class="p">(),</span>
-<span class="p">)</span>
-
-<span class="c1"># Add geographic features for context</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">add_feature</span><span class="p">(</span><span class="n">cfeature</span><span class="o">.</span><span class="n">COASTLINE</span><span class="p">)</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">add_feature</span><span class="p">(</span><span class="n">cfeature</span><span class="o">.</span><span class="n">BORDERS</span><span class="p">,</span> <span class="n">linestyle</span><span class="o">=</span><span class="s2">":"</span><span class="p">)</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">add_feature</span><span class="p">(</span><span class="n">cfeature</span><span class="o">.</span><span class="n">STATES</span><span class="p">,</span> <span class="n">linestyle</span><span class="o">=</span><span class="s2">"--"</span><span class="p">)</span>
-
-<span class="c1"># Add gridlines</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">gridlines</span><span class="p">(</span><span class="n">draw_labels</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-
-<span class="c1"># Set the extent to zoom in to the specified region</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">set_extent</span><span class="p">([</span><span class="o">-</span><span class="mi">95</span><span class="p">,</span> <span class="o">-</span><span class="mi">85</span><span class="p">,</span> <span class="mi">27</span><span class="p">,</span> <span class="mi">33</span><span class="p">],</span> <span class="n">crs</span><span class="o">=</span><span class="n">ccrs</span><span class="o">.</span><span class="n">PlateCarree</span><span class="p">())</span>
-
-<span class="c1"># Add color bar with labels</span>
-<span class="n">cbar</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">colorbar</span><span class="p">(</span>
-    <span class="n">im</span><span class="p">,</span>
-    <span class="n">ax</span><span class="o">=</span><span class="n">ax</span><span class="p">,</span>
-    <span class="n">orientation</span><span class="o">=</span><span class="s2">"vertical"</span><span class="p">,</span>
-    <span class="n">pad</span><span class="o">=</span><span class="mf">0.02</span><span class="p">,</span>
-    <span class="n">fraction</span><span class="o">=</span><span class="mf">0.05</span><span class="p">,</span>
-    <span class="n">ticks</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">n_clusters</span><span class="p">),</span>
-<span class="p">)</span>
-<span class="n">cbar</span><span class="o">.</span><span class="n">ax</span><span class="o">.</span><span class="n">set_yticklabels</span><span class="p">([</span><span class="sa">f</span><span class="s2">"Class </span><span class="si">{</span><span class="n">i</span><span class="o">+</span><span class="mi">1</span><span class="si">}</span><span class="s2">"</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">n_clusters</span><span class="p">)])</span>
-<span class="n">cbar</span><span class="o">.</span><span class="n">set_label</span><span class="p">(</span><span class="s2">"Water Types"</span><span class="p">,</span> <span class="n">rotation</span><span class="o">=</span><span class="mi">270</span><span class="p">,</span> <span class="n">labelpad</span><span class="o">=</span><span class="mi">20</span><span class="p">)</span>
-
-<span class="c1"># Add title</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s2">"Spectral Angle Mapper (SAM) Water Type Classification"</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">16</span><span class="p">)</span>
-
-<span class="c1"># Show the plot</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
-</pre></div>
-<div class="clipboard-copy-txt" id="cell-29"># Assume 'best_match_full' contains the SAM classification results reshaped to the original spatial dimensions
-# Also assume that 'da' has the original latitude and longitude data
-latitudes = da.coords["latitude"].values
-longitudes = da.coords["longitude"].values
-
-# Create a custom discrete color map
-cmap = mcolors.ListedColormap(
-    ["#377eb8", "#ff7f00", "#4daf4a", "#f781bf", "#a65628", "#984ea3"]
-)
-bounds = np.arange(-0.5, n_clusters, 1)
-norm = mcolors.BoundaryNorm(bounds, cmap.N)
-
-# Create a figure and axis with the correct map projection
-fig, ax = plt.subplots(figsize=(10, 8), subplot_kw={"projection": ccrs.PlateCarree()})
-
-# Plot the SAM classification results
-im = ax.pcolormesh(
-    longitudes,
-    latitudes,
-    best_match_full,
-    cmap=cmap,
-    norm=norm,
-    transform=ccrs.PlateCarree(),
-)
-
-# Add geographic features for context
-ax.add_feature(cfeature.COASTLINE)
-ax.add_feature(cfeature.BORDERS, linestyle=":")
-ax.add_feature(cfeature.STATES, linestyle="--")
-
-# Add gridlines
-ax.gridlines(draw_labels=True)
-
-# Set the extent to zoom in to the specified region
-ax.set_extent([-95, -85, 27, 33], crs=ccrs.PlateCarree())
-
-# Add color bar with labels
-cbar = plt.colorbar(
-    im,
-    ax=ax,
-    orientation="vertical",
-    pad=0.02,
-    fraction=0.05,
-    ticks=np.arange(n_clusters),
-)
-cbar.ax.set_yticklabels([f"Class {i+1}" for i in range(n_clusters)])
-cbar.set_label("Water Types", rotation=270, labelpad=20)
-
-# Add title
-ax.set_title("Spectral Angle Mapper (SAM) Water Type Classification", fontsize=16)
-
-# Show the plot
-plt.show()</div>
-</div>
-</div>
-</div>
-</div>
-<div class="jp-Cell-outputWrapper">
-<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
-</div>
-<div class="jp-OutputArea jp-Cell-outputArea">
-<div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedImage jp-OutputArea-output" tabindex="0">
-<img alt="No description has been provided for this image" class="" 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-</div>
-<div class="highlight-ipynb hl-python"><pre><span></span><span class="c1"># Assume 'best_match_full' contains the SAM classification results reshaped to the original spatial dimensions</span>
-<span class="c1"># Also assume that 'da' has the original latitude and longitude data</span>
-<span class="n">latitudes</span> <span class="o">=</span> <span class="n">da</span><span class="o">.</span><span class="n">coords</span><span class="p">[</span><span class="s2">"latitude"</span><span class="p">]</span><span class="o">.</span><span class="n">values</span>
-<span class="n">longitudes</span> <span class="o">=</span> <span class="n">da</span><span class="o">.</span><span class="n">coords</span><span class="p">[</span><span class="s2">"longitude"</span><span class="p">]</span><span class="o">.</span><span class="n">values</span>
-
-<span class="c1"># Filtering condition based on wavelength values</span>
-<span class="n">filter_condition</span> <span class="o">=</span> <span class="p">(</span>
-    <span class="p">(</span><span class="n">da</span><span class="o">.</span><span class="n">sel</span><span class="p">(</span><span class="n">wavelength</span><span class="o">=</span><span class="mi">650</span><span class="p">)</span> <span class="o">&gt;</span> <span class="n">da</span><span class="o">.</span><span class="n">sel</span><span class="p">(</span><span class="n">wavelength</span><span class="o">=</span><span class="mi">620</span><span class="p">))</span>
-    <span class="o">&amp;</span> <span class="p">(</span><span class="n">da</span><span class="o">.</span><span class="n">sel</span><span class="p">(</span><span class="n">wavelength</span><span class="o">=</span><span class="mi">701</span><span class="p">)</span> <span class="o">&gt;</span> <span class="n">da</span><span class="o">.</span><span class="n">sel</span><span class="p">(</span><span class="n">wavelength</span><span class="o">=</span><span class="mi">681</span><span class="p">))</span>
-    <span class="o">&amp;</span> <span class="p">(</span><span class="n">da</span><span class="o">.</span><span class="n">sel</span><span class="p">(</span><span class="n">wavelength</span><span class="o">=</span><span class="mi">701</span><span class="p">)</span> <span class="o">&gt;</span> <span class="n">da</span><span class="o">.</span><span class="n">sel</span><span class="p">(</span><span class="n">wavelength</span><span class="o">=</span><span class="mi">450</span><span class="p">))</span>
-<span class="p">)</span>
-
-<span class="c1"># Apply the filtering condition to the SAM classification results</span>
-<span class="n">filtered_best_match_full</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">filter_condition</span><span class="p">,</span> <span class="n">best_match_full</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span><span class="p">)</span>
-
-<span class="c1"># Create a custom discrete color map</span>
-<span class="n">cmap</span> <span class="o">=</span> <span class="n">mcolors</span><span class="o">.</span><span class="n">ListedColormap</span><span class="p">(</span>
-    <span class="p">[</span><span class="s2">"#e41a1c"</span><span class="p">,</span> <span class="s2">"#377eb8"</span><span class="p">,</span> <span class="s2">"#4daf4a"</span><span class="p">,</span> <span class="s2">"#f781bf"</span><span class="p">,</span> <span class="s2">"#a65628"</span><span class="p">,</span> <span class="s2">"#984ea3"</span><span class="p">]</span>
-<span class="p">)</span>
-<span class="n">bounds</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="o">-</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">n_clusters</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
-<span class="n">norm</span> <span class="o">=</span> <span class="n">mcolors</span><span class="o">.</span><span class="n">BoundaryNorm</span><span class="p">(</span><span class="n">bounds</span><span class="p">,</span> <span class="n">cmap</span><span class="o">.</span><span class="n">N</span><span class="p">)</span>
-
-<span class="c1"># Create a figure and axis with the correct map projection</span>
-<span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">8</span><span class="p">),</span> <span class="n">subplot_kw</span><span class="o">=</span><span class="p">{</span><span class="s2">"projection"</span><span class="p">:</span> <span class="n">ccrs</span><span class="o">.</span><span class="n">PlateCarree</span><span class="p">()})</span>
-
-<span class="c1"># Plot the filtered SAM classification results</span>
-<span class="n">im</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">pcolormesh</span><span class="p">(</span>
-    <span class="n">longitudes</span><span class="p">,</span>
-    <span class="n">latitudes</span><span class="p">,</span>
-    <span class="n">filtered_best_match_full</span><span class="p">,</span>
-    <span class="n">cmap</span><span class="o">=</span><span class="n">cmap</span><span class="p">,</span>
-    <span class="n">norm</span><span class="o">=</span><span class="n">norm</span><span class="p">,</span>
-    <span class="n">transform</span><span class="o">=</span><span class="n">ccrs</span><span class="o">.</span><span class="n">PlateCarree</span><span class="p">(),</span>
-<span class="p">)</span>
-
-<span class="c1"># Add geographic features for context</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">add_feature</span><span class="p">(</span><span class="n">cfeature</span><span class="o">.</span><span class="n">COASTLINE</span><span class="p">)</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">add_feature</span><span class="p">(</span><span class="n">cfeature</span><span class="o">.</span><span class="n">BORDERS</span><span class="p">,</span> <span class="n">linestyle</span><span class="o">=</span><span class="s2">":"</span><span class="p">)</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">add_feature</span><span class="p">(</span><span class="n">cfeature</span><span class="o">.</span><span class="n">STATES</span><span class="p">,</span> <span class="n">linestyle</span><span class="o">=</span><span class="s2">"--"</span><span class="p">)</span>
-
-<span class="c1"># Add gridlines</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">gridlines</span><span class="p">(</span><span class="n">draw_labels</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-
-<span class="c1"># Set the extent to zoom in to the specified region</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">set_extent</span><span class="p">([</span><span class="o">-</span><span class="mi">95</span><span class="p">,</span> <span class="o">-</span><span class="mi">85</span><span class="p">,</span> <span class="mi">27</span><span class="p">,</span> <span class="mi">33</span><span class="p">],</span> <span class="n">crs</span><span class="o">=</span><span class="n">ccrs</span><span class="o">.</span><span class="n">PlateCarree</span><span class="p">())</span>
-
-<span class="c1"># Add color bar with labels</span>
-<span class="n">cbar</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">colorbar</span><span class="p">(</span>
-    <span class="n">im</span><span class="p">,</span>
-    <span class="n">ax</span><span class="o">=</span><span class="n">ax</span><span class="p">,</span>
-    <span class="n">orientation</span><span class="o">=</span><span class="s2">"vertical"</span><span class="p">,</span>
-    <span class="n">pad</span><span class="o">=</span><span class="mf">0.02</span><span class="p">,</span>
-    <span class="n">fraction</span><span class="o">=</span><span class="mf">0.05</span><span class="p">,</span>
-    <span class="n">ticks</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">n_clusters</span><span class="p">),</span>
-<span class="p">)</span>
-<span class="n">cbar</span><span class="o">.</span><span class="n">ax</span><span class="o">.</span><span class="n">set_yticklabels</span><span class="p">([</span><span class="sa">f</span><span class="s2">"Class </span><span class="si">{</span><span class="n">i</span><span class="o">+</span><span class="mi">1</span><span class="si">}</span><span class="s2">"</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">n_clusters</span><span class="p">)])</span>
-<span class="n">cbar</span><span class="o">.</span><span class="n">set_label</span><span class="p">(</span><span class="s2">"Water Types"</span><span class="p">,</span> <span class="n">rotation</span><span class="o">=</span><span class="mi">270</span><span class="p">,</span> <span class="n">labelpad</span><span class="o">=</span><span class="mi">20</span><span class="p">)</span>
-
-<span class="c1"># Add title</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s2">"PCA + Spectral Angle Mapper (SAM) Water Type Classification"</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">16</span><span class="p">)</span>
-
-<span class="c1"># Show the plot</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
-</pre></div>
-<div class="clipboard-copy-txt" id="cell-30"># Assume 'best_match_full' contains the SAM classification results reshaped to the original spatial dimensions
-# Also assume that 'da' has the original latitude and longitude data
-latitudes = da.coords["latitude"].values
-longitudes = da.coords["longitude"].values
-
-# Filtering condition based on wavelength values
-filter_condition = (
-    (da.sel(wavelength=650) &gt; da.sel(wavelength=620))
-    &amp; (da.sel(wavelength=701) &gt; da.sel(wavelength=681))
-    &amp; (da.sel(wavelength=701) &gt; da.sel(wavelength=450))
-)
-
-# Apply the filtering condition to the SAM classification results
-filtered_best_match_full = np.where(filter_condition, best_match_full, np.nan)
-
-# Create a custom discrete color map
-cmap = mcolors.ListedColormap(
-    ["#e41a1c", "#377eb8", "#4daf4a", "#f781bf", "#a65628", "#984ea3"]
-)
-bounds = np.arange(-0.5, n_clusters, 1)
-norm = mcolors.BoundaryNorm(bounds, cmap.N)
-
-# Create a figure and axis with the correct map projection
-fig, ax = plt.subplots(figsize=(10, 8), subplot_kw={"projection": ccrs.PlateCarree()})
-
-# Plot the filtered SAM classification results
-im = ax.pcolormesh(
-    longitudes,
-    latitudes,
-    filtered_best_match_full,
-    cmap=cmap,
-    norm=norm,
-    transform=ccrs.PlateCarree(),
-)
-
-# Add geographic features for context
-ax.add_feature(cfeature.COASTLINE)
-ax.add_feature(cfeature.BORDERS, linestyle=":")
-ax.add_feature(cfeature.STATES, linestyle="--")
-
-# Add gridlines
-ax.gridlines(draw_labels=True)
-
-# Set the extent to zoom in to the specified region
-ax.set_extent([-95, -85, 27, 33], crs=ccrs.PlateCarree())
-
-# Add color bar with labels
-cbar = plt.colorbar(
-    im,
-    ax=ax,
-    orientation="vertical",
-    pad=0.02,
-    fraction=0.05,
-    ticks=np.arange(n_clusters),
-)
-cbar.ax.set_yticklabels([f"Class {i+1}" for i in range(n_clusters)])
-cbar.set_label("Water Types", rotation=270, labelpad=20)
-
-# Add title
-ax.set_title("PCA + Spectral Angle Mapper (SAM) Water Type Classification", fontsize=16)
-
-# Show the plot
-plt.show()</div>
-</div>
-</div>
-</div>
-</div>
-<div class="jp-Cell-outputWrapper">
-<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
-</div>
-<div class="jp-OutputArea jp-Cell-outputArea">
-<div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedImage jp-OutputArea-output" tabindex="0">
-<img alt="No description has been provided for this image" class="" 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-<h2 id="spectral-angle-mapper-sam">Spectral Angle Mapper (SAM)<a class="anchor-link" href="#spectral-angle-mapper-sam">¶</a></h2>
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-<div class="highlight-ipynb hl-python"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
-<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
-<span class="kn">import</span> <span class="nn">matplotlib.colors</span> <span class="k">as</span> <span class="nn">mcolors</span>
-<span class="kn">from</span> <span class="nn">scipy.interpolate</span> <span class="kn">import</span> <span class="n">interp1d</span>
-</pre></div>
-<div class="clipboard-copy-txt" id="cell-31">import numpy as np
-import pandas as pd
-import matplotlib.pyplot as plt
-import matplotlib.colors as mcolors
-from scipy.interpolate import interp1d</div>
-</div>
-</div>
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-<div class="highlight-ipynb hl-python"><pre><span></span><span class="c1"># Load the netCDF data</span>
-<span class="n">file_path</span> <span class="o">=</span> <span class="s2">"data/PACE_OCI.20240730T181157.L2.OC_AOP.V2_0.NRT.nc"</span>
-<span class="n">dataset</span> <span class="o">=</span> <span class="n">hypercoast</span><span class="o">.</span><span class="n">read_pace</span><span class="p">(</span><span class="n">file_path</span><span class="p">)</span>
-<span class="n">da</span> <span class="o">=</span> <span class="n">dataset</span><span class="p">[</span><span class="s2">"Rrs"</span><span class="p">]</span>  <span class="c1"># Assuming 'Rrs' contains the reflectance data</span>
-</pre></div>
-<div class="clipboard-copy-txt" id="cell-32"># Load the netCDF data
-file_path = "data/PACE_OCI.20240730T181157.L2.OC_AOP.V2_0.NRT.nc"
-dataset = hypercoast.read_pace(file_path)
-da = dataset["Rrs"]  # Assuming 'Rrs' contains the reflectance data</div>
-</div>
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-</div>
-<div class="highlight-ipynb hl-python"><pre><span></span><span class="c1"># Extract PACE wavelengths</span>
-<span class="n">pace_wavelengths</span> <span class="o">=</span> <span class="n">da</span><span class="p">[</span><span class="s2">"wavelength"</span><span class="p">]</span><span class="o">.</span><span class="n">values</span>
-<span class="nb">print</span><span class="p">(</span><span class="n">pace_wavelengths</span><span class="p">)</span>
-</pre></div>
-<div class="clipboard-copy-txt" id="cell-33"># Extract PACE wavelengths
-pace_wavelengths = da["wavelength"].values
-print(pace_wavelengths)</div>
-</div>
-</div>
-</div>
-</div>
-<div class="jp-Cell-outputWrapper">
-<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
-</div>
-<div class="jp-OutputArea jp-Cell-outputArea">
-<div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
-<pre>[339. 341. 344. 346. 348. 351. 353. 356. 358. 361. 363. 366. 368. 371.
- 373. 375. 378. 380. 383. 385. 388. 390. 393. 395. 398. 400. 403. 405.
- 408. 410. 413. 415. 418. 420. 422. 425. 427. 430. 432. 435. 437. 440.
- 442. 445. 447. 450. 452. 455. 457. 460. 462. 465. 467. 470. 472. 475.
- 477. 480. 482. 485. 487. 490. 492. 495. 497. 500. 502. 505. 507. 510.
- 512. 515. 517. 520. 522. 525. 527. 530. 532. 535. 537. 540. 542. 545.
- 547. 550. 553. 555. 558. 560. 563. 565. 568. 570. 573. 575. 578. 580.
- 583. 586. 588. 591. 593. 596. 598. 601. 603. 605. 608. 610. 613. 615.
- 618. 620. 623. 625. 627. 630. 632. 635. 637. 640. 641. 642. 643. 645.
- 646. 647. 648. 650. 651. 652. 653. 655. 656. 657. 658. 660. 661. 662.
- 663. 665. 666. 667. 668. 670. 671. 672. 673. 675. 676. 677. 678. 679.
- 681. 682. 683. 684. 686. 687. 688. 689. 691. 692. 693. 694. 696. 697.
- 698. 699. 701. 702. 703. 704. 706. 707. 708. 709. 711. 712. 713. 714.
- 717. 719.]
-</pre>
-</div>
-</div>
-</div>
-</div>
-</div>
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-</svg>
-</div>
-</clipboard-copy>
-</div>
-<div class="highlight-ipynb hl-python"><pre><span></span><span class="c1"># Function to load and resample a single CSV spectral library file</span>
-<span class="k">def</span> <span class="nf">load_and_resample_spectral_library</span><span class="p">(</span><span class="n">csv_path</span><span class="p">,</span> <span class="n">target_wavelengths</span><span class="p">):</span>
-    <span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="n">csv_path</span><span class="p">)</span>
-    <span class="n">original_wavelengths</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">iloc</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">values</span>  <span class="c1"># First column is wavelength</span>
-    <span class="n">spectra_values</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">iloc</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">values</span>  <span class="c1"># Second column is spectral values</span>
-
-    <span class="c1"># Interpolation function</span>
-    <span class="n">interp_func</span> <span class="o">=</span> <span class="n">interp1d</span><span class="p">(</span>
-        <span class="n">original_wavelengths</span><span class="p">,</span> <span class="n">spectra_values</span><span class="p">,</span> <span class="n">kind</span><span class="o">=</span><span class="s2">"linear"</span><span class="p">,</span> <span class="n">fill_value</span><span class="o">=</span><span class="s2">"extrapolate"</span>
-    <span class="p">)</span>
-
-    <span class="c1"># Resample to the target (PACE) wavelengths</span>
-    <span class="n">resampled_spectra</span> <span class="o">=</span> <span class="n">interp_func</span><span class="p">(</span><span class="n">target_wavelengths</span><span class="p">)</span>
-
-    <span class="k">return</span> <span class="n">resampled_spectra</span>
-</pre></div>
-<div class="clipboard-copy-txt" id="cell-34"># Function to load and resample a single CSV spectral library file
-def load_and_resample_spectral_library(csv_path, target_wavelengths):
-    df = pd.read_csv(csv_path)
-    original_wavelengths = df.iloc[:, 0].values  # First column is wavelength
-    spectra_values = df.iloc[:, 1].values  # Second column is spectral values
-
-    # Interpolation function
-    interp_func = interp1d(
-        original_wavelengths, spectra_values, kind="linear", fill_value="extrapolate"
-    )
-
-    # Resample to the target (PACE) wavelengths
-    resampled_spectra = interp_func(target_wavelengths)
-
-    return resampled_spectra</div>
-</div>
-</div>
-</div>
-</div>
-</div>
-</div>
-<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
-<div class="jp-Cell-inputWrapper" tabindex="0">
-<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
-</div>
-<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
-</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
-<p>Download the SAM spectral library:</p>
-</div>
-</div>
-</div>
-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
-<div class="jp-Cell jp-CodeCell jp-Notebook-cell">
-<div class="jp-Cell-inputWrapper" tabindex="0">
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-</div>
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-<div class="jp-InputPrompt jp-InputArea-prompt">In [35]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
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-<path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path>
-</svg>
-</div>
-</clipboard-copy>
-</div>
-<div class="highlight-ipynb hl-python"><pre><span></span><span class="n">url</span> <span class="o">=</span> <span class="s2">"https://github.com/opengeos/datasets/releases/download/hypercoast/SAM_spectral_library.zip"</span>
-<span class="n">hypercoast</span><span class="o">.</span><span class="n">download_file</span><span class="p">(</span><span class="n">url</span><span class="p">)</span>
-</pre></div>
-<div class="clipboard-copy-txt" id="cell-35">url = "https://github.com/opengeos/datasets/releases/download/hypercoast/SAM_spectral_library.zip"
-hypercoast.download_file(url)</div>
-</div>
-</div>
-</div>
-</div>
-<div class="jp-Cell-outputWrapper">
-<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
-</div>
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-<div class="jp-OutputArea-child jp-OutputArea-executeResult">
-<div class="jp-OutputPrompt jp-OutputArea-prompt">Out[35]:</div>
-<div class="jp-RenderedText jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/plain" tabindex="0">
-<pre>'/home/runner/work/HyperCoast/HyperCoast/docs/examples/SAM_spectral_library.zip'</pre>
-</div>
-</div>
-</div>
-</div>
-</div>
-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs">
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-<path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path>
-</svg>
-</div>
-</clipboard-copy>
-</div>
-<div class="highlight-ipynb hl-python"><pre><span></span><span class="c1"># Load and resample all 6 endmembers</span>
-<span class="n">endmember_paths</span> <span class="o">=</span> <span class="p">[</span>
-    <span class="s2">"./SAM_spectral_library/Dataset_1.csv"</span><span class="p">,</span>
-    <span class="s2">"./SAM_spectral_library/Dataset_2.csv"</span><span class="p">,</span>
-    <span class="s2">"./SAM_spectral_library/Dataset_3.csv"</span><span class="p">,</span>
-    <span class="s2">"./SAM_spectral_library/Dataset_4.csv"</span><span class="p">,</span>
-    <span class="s2">"./SAM_spectral_library/Dataset_5.csv"</span><span class="p">,</span>
-    <span class="s2">"./SAM_spectral_library/Dataset_6.csv"</span><span class="p">,</span>
-<span class="p">]</span>
-
-<span class="n">endmembers</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span>
-    <span class="p">[</span>
-        <span class="n">load_and_resample_spectral_library</span><span class="p">(</span><span class="n">path</span><span class="p">,</span> <span class="n">pace_wavelengths</span><span class="p">)</span>
-        <span class="k">for</span> <span class="n">path</span> <span class="ow">in</span> <span class="n">endmember_paths</span>
-    <span class="p">]</span>
-<span class="p">)</span>
-</pre></div>
-<div class="clipboard-copy-txt" id="cell-36"># Load and resample all 6 endmembers
-endmember_paths = [
-    "./SAM_spectral_library/Dataset_1.csv",
-    "./SAM_spectral_library/Dataset_2.csv",
-    "./SAM_spectral_library/Dataset_3.csv",
-    "./SAM_spectral_library/Dataset_4.csv",
-    "./SAM_spectral_library/Dataset_5.csv",
-    "./SAM_spectral_library/Dataset_6.csv",
-]
-
-endmembers = np.array(
-    [
-        load_and_resample_spectral_library(path, pace_wavelengths)
-        for path in endmember_paths
-    ]
-)</div>
-</div>
-</div>
-</div>
-</div>
-</div>
-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
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-<div class="jp-InputPrompt jp-InputArea-prompt">In [37]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
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-<path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path>
-</svg>
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-</clipboard-copy>
-</div>
-<div class="highlight-ipynb hl-python"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="n">endmembers</span><span class="p">)</span>
-</pre></div>
-<div class="clipboard-copy-txt" id="cell-37">print(endmembers)</div>
-</div>
-</div>
-</div>
-</div>
-<div class="jp-Cell-outputWrapper">
-<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
-</div>
-<div class="jp-OutputArea jp-Cell-outputArea">
-<div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
-<pre>[[0.00871842 0.00865469 0.00855908 ... 0.00442423 0.00389996 0.00375134]
- [0.01088283 0.01082291 0.01073303 ... 0.01052424 0.00998495 0.00957979]
- [0.0169974  0.0168173  0.01654716 ... 0.01991858 0.01881549 0.01798281]
- [0.0227546  0.022496   0.0221081  ... 0.0312176  0.03006733 0.02898674]
- [0.02859302 0.02826977 0.02778489 ... 0.05700807 0.05723728 0.05712533]
- [0.03042756 0.03000733 0.02937699 ... 0.07612622 0.08101502 0.08450761]]
-</pre>
-</div>
-</div>
-</div>
-</div>
-</div>
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-<div class="jp-InputPrompt jp-InputArea-prompt">In [38]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
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-<path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path>
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-<div class="highlight-ipynb hl-python"><pre><span></span><span class="c1"># Plot sample spectra from the CSV files and their resampled versions</span>
-<span class="k">def</span> <span class="nf">plot_sample_spectra</span><span class="p">(</span><span class="n">csv_paths</span><span class="p">,</span> <span class="n">pace_wavelengths</span><span class="p">):</span>
-    <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">14</span><span class="p">,</span> <span class="mi">8</span><span class="p">))</span>
-
-    <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">csv_path</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">csv_paths</span><span class="p">):</span>
-        <span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="n">csv_path</span><span class="p">)</span>
-        <span class="n">original_wavelengths</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">iloc</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">values</span>
-        <span class="n">spectra_values</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">iloc</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">values</span>
-        <span class="n">resampled_spectra</span> <span class="o">=</span> <span class="n">load_and_resample_spectral_library</span><span class="p">(</span>
-            <span class="n">csv_path</span><span class="p">,</span> <span class="n">pace_wavelengths</span>
-        <span class="p">)</span>
-
-        <span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span>
-            <span class="n">original_wavelengths</span><span class="p">,</span>
-            <span class="n">spectra_values</span><span class="p">,</span>
-            <span class="n">label</span><span class="o">=</span><span class="sa">f</span><span class="s2">"Original Spectra </span><span class="si">{</span><span class="n">i</span><span class="o">+</span><span class="mi">1</span><span class="si">}</span><span class="s2">"</span><span class="p">,</span>
-            <span class="n">linestyle</span><span class="o">=</span><span class="s2">"--"</span><span class="p">,</span>
-        <span class="p">)</span>
-        <span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">pace_wavelengths</span><span class="p">,</span> <span class="n">resampled_spectra</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="sa">f</span><span class="s2">"Resampled Spectra </span><span class="si">{</span><span class="n">i</span><span class="o">+</span><span class="mi">1</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
-
-    <span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s2">"Wavelength (nm)"</span><span class="p">)</span>
-    <span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s2">"Spectral Reflectance"</span><span class="p">)</span>
-    <span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s2">"Comparison of Original and Resampled Spectra"</span><span class="p">)</span>
-    <span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>
-    <span class="n">plt</span><span class="o">.</span><span class="n">grid</span><span class="p">(</span><span class="kc">True</span><span class="p">)</span>
-    <span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
-
-
-<span class="n">plot_sample_spectra</span><span class="p">(</span><span class="n">endmember_paths</span><span class="p">,</span> <span class="n">pace_wavelengths</span><span class="p">)</span>
-</pre></div>
-<div class="clipboard-copy-txt" id="cell-38"># Plot sample spectra from the CSV files and their resampled versions
-def plot_sample_spectra(csv_paths, pace_wavelengths):
-    plt.figure(figsize=(14, 8))
-
-    for i, csv_path in enumerate(csv_paths):
-        df = pd.read_csv(csv_path)
-        original_wavelengths = df.iloc[:, 0].values
-        spectra_values = df.iloc[:, 1].values
-        resampled_spectra = load_and_resample_spectral_library(
-            csv_path, pace_wavelengths
-        )
-
-        plt.plot(
-            original_wavelengths,
-            spectra_values,
-            label=f"Original Spectra {i+1}",
-            linestyle="--",
-        )
-        plt.plot(pace_wavelengths, resampled_spectra, label=f"Resampled Spectra {i+1}")
-
-    plt.xlabel("Wavelength (nm)")
-    plt.ylabel("Spectral Reflectance")
-    plt.title("Comparison of Original and Resampled Spectra")
-    plt.legend()
-    plt.grid(True)
-    plt.show()
-
-
-plot_sample_spectra(endmember_paths, pace_wavelengths)</div>
-</div>
-</div>
-</div>
-</div>
-<div class="jp-Cell-outputWrapper">
-<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
-</div>
-<div class="jp-OutputArea jp-Cell-outputArea">
-<div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedImage jp-OutputArea-output" tabindex="0">
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lCtWjV8fHy4c+eO9qtVq1YA2qVleTWvfvnll0KLlxsaGmJiYgI8rv117949cnNzCQgIKPA6dOvWDVtbW+3rvHv6o48+0qlX1aBBA7Kzs/Mt4XRycqJLly7a1zY2NgQHB3Py5En+/PPPAmOMj4/n8uXL9OzZk7t372rHm5GRQevWrdm3bx8ajQa1Ws3WrVvp3Lmzzs+datWqlbge1KRJk/j3v/+Nv78/W7du5YsvvqBu3brUqVOH8+fP52sfHBysc3917doVR0dH7c/Qksau0Wj4+eefCQoKIiAgIN95nl7WWRh3d/cCx/rk5/3BgwfcuXOH5s2bc+XKFR48eFCivoUQQrx8kjQSQgihFzY2NsD/kgjFuXr1KgYGBvmezOXg4ECZMmW0vyjnefIXNED7S6Wzs3OB21NTU3W2GxgY4OHhobOtatWqADo1ZjZu3EjDhg0xMzOjXLly2Nvbs2DBggJ/6XF3dy9umMDjWk9nzpzB2dmZ+vXrM3HiRJ1kRd5Yvb298x3r4+OT71qYmZlhb2+vs61s2bL5xvy0ws5jYmKCh4dHvvOURN4vs8W973/99Ve+xJKRkRGVK1cu9hxXr17F0dExX72kkjzVLc/Tnx/If80yMzMZP348zs7OmJqaUr58eezt7bl///5z/dL7rP09HWNe0fC8GPPeHy8vL5129vb2RRYYf1qnTp3Yvn07mzZt0taAevjwoU4i8fLly5w9exZ7e3udr7x7Jq9g8wcffECTJk0YMGAAFStW5MMPP+Snn37Kl0BasWIFfn5+mJmZYWdnh729PZs2bSrRdXjWe93T0zNfAqSge/1Jly9fBqBPnz75xvzdd9+RlZXFgwcPuH37NpmZmfneAyj4/i1Mjx49+O2330hNTWXbtm307NmTkydPEhQUxKNHj3TaPn0ulUqFp6endizPEntaWho1atQocZwFKezn3oEDB2jTpg2WlpaUKVMGe3t7bW0rSRoJIcTrQx4XIoQQQi9sbGxwcnLizJkzz3RcSf+6bWho+EzblacKXJfEb7/9xj/+8Q/eeecd5s+fj6OjI8bGxixbtox///vf+dqX9IlB3bt3p1mzZqxfv55t27bx1VdfMWPGDNatW0eHDh2eOc7CxqwPnp6eGBkZ8fvvvxfaJisri4sXL+ab3WBqaqqTqHiZSvI5+ec//8myZcsYMmQIjRo1wtbWFpVKxYcffljoLJqiPGt/L/KzXJTKlSvTpk0bADp27Ej58uUJCwujZcuW/L//9/+AxzOCatasyezZswvsIy+BY25uzr59+9i9ezebNm0iNjaW1atX06pVK7Zt24ahoSGrVq0iJCSEzp07M3LkSCpUqIChoSHTp0/PV3wbXs29/rS89+Orr76idu3aBbaxsrLKVwT877KxsaFt27a0bdsWY2NjVqxYwZEjR2jevHmJ+yhp7E8XDX9eBf3cS0xMpHXr1vj4+DB79mycnZ0xMTFh8+bNREZGPtf9I4QQ4uWQpJEQQgi9ee+99/j22285dOiQzlKygri6uqLRaLh8+TLVqlXTbr958yb379/H1dX1hcam0Wi4cuWKdsYBwKVLlwC0T+r6z3/+g5mZGVu3bsXU1FTbbtmyZX/7/I6OjgwaNIhBgwZx69Yt6tSpw9SpU+nQoYN2rBcvXtQu/8lz8eLFF3YtnjzPk7OusrOzSUpK0iYSnoWlpSUtW7Zk165dXL16tcBYf/rpJ7KysnjvvfeeO+7du3fz8OFDndlGCQkJz9VfYdauXUufPn34+uuvtdsePXqk82Q5ffaXd20vX76s8/7dvn272FlmRfnkk0+IjIxk3LhxdOnSBZVKRZUqVTh16hStW7cuNrFrYGBA69atad26NbNnz2batGl88cUX7N69mzZt2rB27Vo8PDxYt26dTl95T+h70RISElAURedcT9/rT8tbumhjY1PkfWBvb4+5ubl2ds+TLl68+DeihoCAAFasWMGNGzd0tj99LkVRSEhIwM/P75ljt7GxKTaxX9JE/pN+/fVXsrKy2LBhg85MsaefkCeEEEL/ZHmaEEIIvRk1ahSWlpYMGDCAmzdv5tufmJhIVFQU8HiGA8CcOXN02uTNbHj33XdfeHzR0dHa7xVFITo6GmNjY1q3bg08nsmgUql0Hl2fnJzMzz///NznVKvV+ZZmVKhQAScnJ+2shYCAACpUqMDChQt1ZjJs2bKF8+fPv7Br0aZNG0xMTJg7d67O7IwlS5bw4MGD5z7PuHHjUBSFkJAQMjMzdfYlJSUxatQoHB0d+eSTT56r/8DAQHJycli8eLF2m0aj4Ztvvnmu/gpjaGiYb9bKvHnzdD4P+uyvTZs2GBsbM2/ePJ1+n76HnpWRkRHDhw/n/Pnz/PLLL8Dj2XHXr1/XueZ5MjMzycjIAChw9krebJe8z3LeDKEnYz5y5AiHDh36W3EX5o8//mD9+vXa12lpaXz//ffUrl0bBweHAo+pW7cuVapUYdasWaSnp+fbf/v2beDxWAIDA/n5559JSUnR7j9//jxbt24tNraHDx8WOu4tW7YA+Ze5ff/99zrLP9euXcuNGze0sxRLGruBgQGdO3fm119/5fjx4/na5b0/lpaWAM+U3CzoPX7w4MELSbgLIYR4sWSmkRBCCL2pUqUK//73v/nggw+oVq0awcHB1KhRg+zsbA4ePMiaNWsICQkBoFatWvTp04dvv/2W+/fv07x5c44ePcqKFSvo3LkzLVu2fKGxmZmZERsbS58+fWjQoAFbtmxh06ZNjB07Vlsf6N1332X27Nm0b9+enj17cuvWLb755hs8PT2LXH5VlL/++ovKlSvTtWtXatWqhZWVFTt27ODYsWPaGSjGxsbMmDGDvn370rx5c3r06MHNmzeJiorCzc2NoUOHvpBrYG9vz5gxY5g0aRLt27fnH//4BxcvXmT+/PnUq1ePjz766Ln6feedd5g1axbDhg3Dz8+PkJAQHB0duXDhAosXL0aj0bB58+ZnqrvzpM6dO1O/fn2GDx9OQkICPj4+bNiwQZuweJ6ZEQV57733WLlyJba2tvj6+nLo0CF27NiBnZ3da9Gfvb09I0aMYPr06bz33nt07NiRkydPsmXLFsqXL/9cfeYJCQlh/PjxzJgxg86dO9O7d29++uknPv30U3bv3k2TJk1Qq9VcuHCBn376ia1btxIQEMCXX37Jvn37ePfdd3F1deXWrVvMnz+fypUr07RpU+11WLduHV26dOHdd98lKSmJhQsX4uvrW2CS4++qWrUq/fv359ixY1SsWJGlS5dy8+bNIhMYBgYGfPfdd3To0IHq1avTt29fKlWqxPXr19m9ezc2Njb8+uuvwONC1rGxsTRr1oxBgwaRm5vLvHnzqF69erE/Jx4+fEjjxo1p2LAh7du3x9nZmfv37/Pzzz/z22+/0blzZ/z9/XWOKVeuHE2bNqVv377cvHmTOXPm4OnpqS0M/yyxT5s2jW3bttG8eXMGDhxItWrVuHHjBmvWrGH//v2UKVOG2rVrY2hoyIwZM3jw4AGmpqa0atWKChUqFDqudu3aYWJiQlBQEJ988gnp6eksXryYChUq5Js5JYQQQs/08MQ2IYQQQselS5eUjz/+WHFzc1NMTEwUa2trpUmTJsq8efN0Ho+dk5OjTJo0SXF3d1eMjY0VZ2dnZcyYMTptFOXxY57ffffdfOcB8j3KPu+x3U8+PrxPnz6KpaWlkpiYqLRr106xsLBQKlasqEyYMEHn0fOKoihLlixRvLy8FFNTU8XHx0dZtmxZoY9xf/rcT+6bMGGCoiiKkpWVpYwcOVKpVauWYm1trVhaWiq1atVS5s+fn++41atXK/7+/oqpqalSrlw5pVevXsp///tfnTZ5Y3laQTEWJjo6WvHx8VGMjY2VihUrKp999pmSmppaYH9FPUr9afv27VM6deqklC9fXjE2NlZcXFyUjz/+WElOTs7XtrBx5O1zdXXV2Xb79m2lZ8+eirW1tWJra6uEhIQoBw4cUAAlJiYmX9xPKuy9cnV11XlMfWpqqtK3b1+lfPnyipWVlRIYGKhcuHAhX7unH9demJL2t2zZMgXI9xj0gs6jVquVSZMmKY6Ojoq5ubnSokUL5cyZM/n6LExRn9uJEyfqnC87O1uZMWOGUr16dcXU1FQpW7asUrduXWXSpEnKgwcPFEVRlJ07dyqdOnVSnJycFBMTE8XJyUnp0aOHcunSJW2/Go1GmTZtmuLq6qqYmpoq/v7+ysaNG/O9zwXdu09ehzVr1uhsL+i65f2s2Lp1q+Ln56e9j58+trD38OTJk8r/+3//T7Gzs1NMTU0VV1dXpXv37srOnTt12u3du1epW7euYmJionh4eCgLFy4s0T2Yk5OjLF68WOncubP2elhYWCj+/v7KV199pWRlZeWL8ccff1TGjBmjVKhQQTE3N1feffdd5erVq/n6LmnsV69eVYKDgxV7e3vF1NRU8fDwUEJDQ3XOvXjxYsXDw0MxNDTUuU6F/SxWFEXZsGGD4ufnp5iZmSlubm7KjBkzlKVLlyqAkpSUVOR1EUII8eqoFOUFV0sUQgghSrmQkBDWrl37UmY1CP35+eef6dKlC/v376dJkyb6Dke8Btzc3KhRowYbN27Udyh/2549e2jZsiVr1qyha9eu+g5HCCHEG0JqGgkhhBDijfN0rSS1Ws28efOwsbGhTp06eopKCCGEEKJ0kZpGQgghhHjj/POf/yQzM5NGjRqRlZXFunXrOHjwINOmTSvwEeBCCCGEECI/SRoJIYQQ4o3TqlUrvv76azZu3MijR4/w9PRk3rx5hIWF6Ts0IYQQQohSQ2oaCSGEEEIIIYQQQoh8pKaREEIIIYQQQgghhMhHkkZCCCGEEEIIIYQQIh+paVQAjUbDH3/8gbW1NSqVSt/hCCGEEEIIIYQQQrwQiqLw119/4eTkhIFB0XOJJGlUgD/++ANnZ2d9hyGEEEIIIYQQQgjxUly7do3KlSsX2UaSRgWwtrYGHl9AGxsbPUcjhH7l5OSwbds22rVrh7Gxsb7DEUKUkNy7QpRecv8KUXrJ/StKg7S0NJydnbW5j6JI0qgAeUvSbGxsJGkk3no5OTlYWFhgY2Mj//AJUYrIvStE6SX3rxCll9y/ojQpSTkeKYQthBBCCCGEEEIIIfKRpJEQQgghhBBCCCGEyEeSRkIIIYQQQgghhBAiH6lp9JwURSE3Nxe1Wq3vUIR4qXJycjAyMuLRo0cFft4NDQ0xMjIq0XpYIYQQQgghhBClhySNnkN2djY3btzg4cOH+g5FiJdOURQcHBy4du1aoYkhCwsLHB0dMTExecXRCSGEEEIIIYR4WSRp9Iw0Gg1JSUkYGhri5OSEiYmJzLAQbzSNRkN6ejpWVlYYGOiuaFUUhezsbG7fvk1SUhJeXl752gghhBBCCCGEKJ0kafSMsrOz0Wg0ODs7Y2Fhoe9whHjpNBoN2dnZmJmZFZgQMjc3x9jYmKtXr2rbCSGEEEIIIYQo/WRKwHOS2RRC/I/cD0IIIYQQQgjx5pHf9IQQQgghhBBCCCFEPpI0EkIIIYQQQgghhBD5SNJIlEhycjIqlYr4+PgSH7N8+XLKlCmj9ziEEEIIIYQQQgjx7CRp9Ba5du0a/fr10z71zdXVlcGDB3P37t1ij3V2dubGjRvUqFGjxOf74IMPuHTp0t8J+bkkJSXRs2dPnJycMDMzo3LlynTq1IkLFy68shheRXJr3bp1tGvXDjs7O0mkCSGEEEIIIYR44SRp9Ja4cuUKAQEBXL58mR9//JGEhAQWLlzIzp07adSoEffu3Sv02OzsbAwNDXFwcMDIqOQP3DM3N6dChQovIvwSy8nJoW3btjx48IB169Zx8eJFVq9eTc2aNbl///4rjaUksrOzn/vYjIwMmjZtyowZM15gREIIIYQQQgghxGOSNHqBHmbnFvr1KEf9Qts+q9DQUExMTNi2bRvNmzfHxcWFDh06sGPHDq5fv84XX3yhbevm5sbkyZMJDg7GxsaGgQMHFjhzZsOGDXh5eWFmZkbLli1ZsWIFKpVKm5x5ennaxIkTqV27NitXrsTNzQ1bW1s+/PBD/vrrL22b2NhYmjZtSpkyZbCzs+O9994jMTGxxOM8e/YsiYmJzJ8/n4YNG+Lq6kqTJk2YMmUKDRs2BP43CygmJobGjRtjZmZGjRo12Lt3r05fZ86coUOHDlhZWVGxYkV69+7NnTt3tPs1Gg0zZ87E09MTU1NTXFxcmDp1KgDu7u4A+Pv7o1KpaNGiBQAhISF07tyZqVOn4uTkhLe3NwArV64kICAAa2trHBwc6NmzJ7du3SpyrL1792b8+PG0adOmxNdHCCGEEEIIIYQoqZJPGxHF8h2/tdB9Lb3tWda3vvZ13ck7yHwqOZSngXs5Vn/SSPu66Yzd3MvQnZGSHPFuieO6d+8eW7duZerUqZibm+vsc3BwoFevXqxevZr58+ejUqkAmDVrFuPHj2fChAkF9pmUlETXrl0ZPHgwAwYM4OTJk4wYMaLYWBITE/n555/ZuHEjqampdO/enYiICG2yJSMjg2HDhuHn50d6ejrjx4+nS5cuxMfHl+ix7vb29hgYGLB27VqGDBmCoaFhoW1HjhzJnDlz8PX1Zfbs2QQFBZGUlISdnR3379+nVatWDBgwgMjISDIzMxk9ejTdu3dn165dAIwZM4bFixcTGRlJ06ZNuXHjhnYJ3NGjR6lfvz47duygevXqmJiYaM+7c+dObGxs2L59u3ZbTk4OkydPxtvbm1u3bjFs2DBCQkLYvHlzsWMWQgghhBBCCCFeBkkavQUuX76MoihUq1atwP3VqlUjNTWV27dva5eTtWrViuHDh2vbJCcn6xyzaNEivL29+eqrrwDw9vbmzJkz2uRPYTQaDcuXL8fa2hp4PFtm586d2uPef/99nfZLly7F3t6ec+fOlaieUqVKlZg7dy6jRo1i0qRJBAQE0LJlS3r16oWHh4dO27CwMO35FixYQGxsLEuWLGHUqFFER0fj7+/PtGnTdGJxdnbm0qVLODo6EhUVRXR0NH369AGgSpUqNG3aFHicvAKws7PDwcFB57yWlpZ89913Oomkfv36ab/38PBg7ty51KtXj/T0dKysrIodtxBCCCGEEEII8aJJ0ugFOvdlYKH7DP5vBk+euPDClxQ93Xb/6JZ/L7D/oyhKidsGBAQUuf/ixYvUq1dPZ1v9+vULaf0/bm5u2oQRgKOjo84yrMuXLzN+/HiOHDnCnTt30Gg0AKSkpJS4CHdoaCjBwcHs2bOHw4cPs2bNGqZNm8aGDRto27attl2jRv+bzWVkZERAQADnz58H4NSpU+zevbvAhE1iYiL3798nKyuL1q1blyimJ9WsWVMnYQQQFxfHxIkTOXXqFKmpqTrj9vX1feZzCCGEEEIIIYQQf5ckjV4gC5OSX86X1bYgnp6eqFQqzp8/T5cuXfLtP3/+PGXLltXOjoHHs2FeBmNjY53XKpVKmyABCAoKwtXVlcWLF+Pk5IRGo6FGjRrPXDDa2tqaoKAggoKCmDJlCoGBgUyZMkUnaVSU9PR0goKCCiwy7ejoyJUrV54pnic9fW0zMjIIDAwkMDCQH374AXt7e1JSUggMDPxbhbKFEEIIIYQQQoi/QwphvwXs7Oxo27Yt8+fPJzMzU2ffn3/+yQ8//MAHH3ygrWdUEt7e3hw/flxn27Fjx/5WnHfv3uXixYuMGzeO1q1ba5fN/V0qlQofHx8yMjJ0th8+fFj7fW5uLnFxcdolfHXq1OHs2bO4ubnh6emp82VpaYmXlxfm5ubs3LmzwHPmzSRSqwuuW/WkCxcucPfuXSIiImjWrBk+Pj7FFsEWQgghhBBCCCFeNkkavSWio6PJysoiMDCQffv2ce3aNWJjY2nbti2VKlUqthbR0z755BMuXLjA6NGjuXTpEj/99BPLly8HeKbk05PKli2LnZ0d3377LQkJCezatYthw4Y9Ux/x8fF06tSJtWvXcu7cORISEliyZAlLly6lU6dOOm2/+eYb1q9fz4ULFwgNDSU1NVVbWyg0NJR79+7Ro0cPjh07RmJiIlu3bqVv376o1WrMzMwYPXo0o0aN4vvvvycxMZHDhw+zZMkSACpUqIC5uTmxsbHcvHmTBw8eFBqzi4sLJiYmzJs3jytXrrBhwwYmT55c7Fjv3btHfHw8586dAx4vGYyPj+fPP/98pmsmhBBCCCGEEEIURJJGbwkvLy+OHz+Oh4cH3bt3p0qVKgwcOJCWLVty6NAhypUr90z9ubu7s3btWtatW4efnx8LFizgiy++AMDU1PS5YjQwMCAmJoa4uDhq1KjB0KFDtYW2S6py5cq4ubkxadIkGjRoQJ06dYiKimLSpEna+PJEREQQERFBrVq12L9/Pxs2bKB8+fIAODk5ceDAAdRqNe3ataNmzZoMGTKEMmXKaJ/iFh4ezvDhwxk/fjzVqlXjgw8+0M4QMjIyYu7cuSxatAgnJ6d8Casn2dvbs3z5ctasWYOvry8RERHMmjWr2LFu2LABf39/3n338ZP0PvzwQ/z9/Vm4cOEzXTMhhBBCCCGEEKIgKuVZqiO/JdLS0rC1teXBgwfY2Njo7Hv06BFJSUm4u7tjZmampwhfT1OnTmXhwoVcu3ZN36EUKTk5GXd3d06ePEnt2rX1Hc5rT6PRkJaWho2NjTZh9jS5L4R4/eTk5LB582Y6duyYr56cEOL1JvevEKWX3L+iNCgq5/E0KYQtntv8+fOpV68ednZ2HDhwgK+++oqwsDB9hyWEEEIIIYQQQogXQJJG4rldvnyZKVOmcO/ePVxcXBg+fDhjxozRd1hCCCGEEEIIIfTs/sNsbv2VRdWK1voORfwNkjQSzy0yMpLIyEh9h/HM3NzckFWZQgghhBBCCPFynPsjjd5LjnA3I5u5Pfz5Ry0nfYcknpMUwhZCCCGEEEIIIcQLEXc1lQ+/PcTdjGwARq/9nfM30vQclXhekjQSQgghhBBCCCHE33Yw4Q69lxwh7VEuAa5laepZnswcNdM2n9d3aOI5yfI0IYQQQgghhBBC/G0Xb/7Fw2w1zbzKs6h3XbJyNERsucCYjj76Dk08J0kaCSGEEEIIIYQQ4m/r28SdCtZmtPGtgKmRIRYmMKOrn77DEn+DLE8TQgghhBBCCCHEc9ly+gYPHuZoX7/r54ipkWG+doqi8MORq2w7++erDE/8TZI0EkIIIYQQQgghxDNbeSiZz344Qd/lR3mUoy6y7a+/3+CL9WcY9tMpEm+nv6IIxd8lSSNR6iUnJ6NSqYiPj/9b/bRo0YIhQ4a8kJiEEEIIIYQQ4k12Nz2LyZseF7gOcCuHqVHR6YUONRyo71aO9KxcPlkZR3pW7qsIU/xNkjR6S4SEhKBSqVCpVBgbG+Pu7s6oUaN49OiRvkMrNdRqNREREfj4+GBubk65cuVo0KAB33333SuN42Unt27cuEHPnj2pWrUqBgYGDB069KWdSwghhBBCCFE6xRy7RnauBr/Ktozp4INKpSqyvbGhAdG9/KloY0rCrXRG/HQKRVFeUbTieUnS6C3Svn17bty4wZUrV4iMjGTRokVMmDBB32GVGpMmTSIyMpLJkydz7tw5du/ezcCBA7l//76+Q8tHURRyc58vc5+VlYW9vT3jxo2jVq1aLzgyIYQQQgghRGmXq9aw6vBVAPo0cis2YZSngrUZCz6qi7Ghitizf7Lm+H9fZpjiBZCk0YugKJCd8eq/njEra2pqioODA87OznTu3Jk2bdqwfft27X6NRsP06dNxd3fH3NycWrVqsXbtWu3+1NRUevXqhb29Pebm5nh5ebFs2TLt/tGjR1O1alUsLCzw8PAgPDycnJz/FUSbOHEitWvXZunSpbi4uGBlZcWgQYNQq9XMnDkTBwcHKlSowNSpU3XiVqlULFiwgA4dOmBubo6Hh4dOXAU5c+YMHTp0wMrKiooVK9K7d2/u3Lmj3Z+RkUFwcDBWVlY4Ojry9ddfF3v9NmzYwKBBg+jWrRvu7u7UqlWL/v37M2LECG2bFi1aEBYWRlhYGLa2tpQvX57w8HCdDHpWVhYjRoygUqVKWFpa0qBBA/bs2aNzrgMHDtCiRQssLCwoW7YsgYGBpKamEhISwt69e4mKitLOHEtOTmbPnj2oVCq2bNlC3bp1MTU1Zf/+/SQmJtKpUycqVqyIlZUV9erVY8eOHUWO083NjaioKIKDg7G1tS32ugghhBBCCCHeLtvO3eTGg0fYWZrwXi3HZzq2jktZRgZ6AxARe4H7D7NfRojiBTHSdwBvhJyHMM3p1Z937B9gYvlch545c4aDBw/i6uqq3TZ9+nRWrVrFwoUL8fLyYt++fXz00UfY29vTvHlzwsPDOXfuHFu2bKF8+fIkJCSQmZmpPd7a2prly5fj5OTE6dOn+fjjj7G2tmbUqFHaNomJiWzZsoXY2FgSExPp2rUrV65coWrVquzdu5eDBw/Sr18/2rRpQ4MGDbTHhYeHExERQVRUFCtXruTDDz/k9OnTVKtWLd/Y7t+/T6tWrRgwYACRkZFkZmYyevRounfvzq5duwAYOXIke/fu5ZdffqFChQqMHTuWEydOULt27UKvmYODA7t27WLQoEHY29sX2m7FihX079+fo0ePcvz4cQYOHIiLiwsff/wxAGFhYZw7d46YmBicnJxYv3497du35/Tp03h5eREfH0/r1q3p168fUVFRGBkZsXv3btRqNVFRUVy6dIkaNWrw5ZdfAmBvb09ycjIAn3/+ObNmzcLDw4OyZcty7do1OnbsyNSpUzE1NeX7778nKCiIixcv4uLiUsQnRAghhBBCCCEKtvxgMgA96rsU+KS04vRt4s7auP9y+VY6ey/dplPtSi84QvGiSNLoLbJx40asrKzIzc0lKysLAwMDoqOjgcezX6ZNm8aOHTto1KgRAB4eHuzfv59FixbRvHlzUlJS8Pf3JyAgAHg8I+VJ48aN037v5ubGiBEjiImJ0UkaaTQali5dirW1Nb6+vrRs2ZKLFy+yefNmDAwM8Pb2ZsaMGezevVsnadStWzcGDBgAwOTJk9m+fTvz5s1j/vz5+cYZHR2Nv78/06ZN025bunQpzs7OXLp0CScnJ5YsWcKqVato3bo18DjRU7ly5SKv3+zZs+natSsODg5Ur16dxo0b06lTJzp06KDTztnZmcjISFQqFd7e3pw+fZrIyEg+/vhjUlJSWLZsGSkpKTg5PU40jhgxgtjYWJYtW8a0adOYOXMmAQEBOmOrXr269nsTExMsLCxwcHDIF+OXX35J27Ztta/LlSuns8Rs8uTJrF+/ng0bNhAWFlbkeIUQQgghhBDiadm5GipYm2JqZECvhs/3h2hjQwNmdq2FCqjlXOaFxideLEkavQjGFo9n/ejjvM+gZcuWLFiwgIyMDCIjIzEyMuL9998HICEhgYcPH+okHACys7Px9/cH4LPPPuP999/nxIkTtGvXjs6dO9O4cWNt29WrVzN37lwSExNJT08nNzcXGxsbnf7c3NywtrbWvq5YsSKGhoYYGBjobLt165bOcXmJrCdfF/a0tFOnTrF7926srKzy7UtMTCQzM5Ps7GydpFS5cuXw9vYusL88vr6+nDlzhri4OA4cOMC+ffsICgoiJCREpxh2w4YNddb0NmrUiK+//hq1Ws3p06dRq9VUrVpVp++srCzs7OwAiI+Pp1u3bkXGUpi8hF6e9PR0Jk6cyKZNm7hx4wa5ublkZmaSkpLyXP0LIYQQQggh3m4mRgZE96xD2qMcbMyMn7uf2pIsKhUkafQiqFTPvUzsVbK0tMTT0xN4PPOmVq1aLFmyhP79+5Oeng7Apk2bqFRJd2qgqakpAB06dODq1ats3ryZ7du307p1a0JDQ5k1axaHDh2iV69eTJo0icDAQGxtbYmJiclXK8jYWPeHSt7T3J7eptFonnuc6enpBAUFMWPGjHz7HB0dSUhIeO6+DQwMqFevHvXq1WPIkCGsWrWK3r1788UXX+Du7l6i2AwNDYmLi8PQUHcaZ16Sy9zc/Lnjs7TU/RyOGDGC7du3M2vWLDw9PTE3N6dr165kZ8u6YSGEEEIIIcTz+zsJo6cl3ckg+U4GLX0qvLA+xYshhbDfUgYGBowdO5Zx48aRmZmJr68vpqampKSk4OnpqfPl7OysPc7e3p4+ffqwatUq5syZw7fffgugrY/0xRdfEBAQgJeXF1evXn1h8R4+fDjf64LqGQHUqVOHs2fP4ubmlm8slpaWVKlSBWNjY44cOaI9JjU1lUuXLj1zXL6+vsDjwtp5nuw3L1YvLy8MDQ3x9/dHrVZz69atfLHlLTfz8/Nj586dhZ7TxMQEtVpdovgOHDhASEgIXbp0oWbNmjg4OGjrHwkhhBBCCCHEsziQcIeEW+kvtM+4q6kERu5jyOp47mXIH7dfN5I0eot169YNQ0NDvvnmG6ytrRkxYgRDhw5lxYoVJCYmcuLECebNm8eKFSsAGD9+PL/88gsJCQmcPXuWjRs3ahM3Xl5epKSkEBMTQ2JiInPnzmX9+vUvLNY1a9awdOlSLl26xIQJEzh69GihNXlCQ0O5d+8ePXr04NixYyQmJrJ161b69u2LWq3GysqK/v37M3LkSHbt2sWZM2cICQnRWSJXkK5duxIZGcmRI0e4evUqe/bsITQ0lKpVq+Lj46Ntl5KSwrBhw7h48SI//vgj8+bNY/DgwQBUrVqVXr16ERwczLp160hKSuLo0aNMnz6dTZs2ATBmzBiOHTvGoEGD+P3337lw4QILFizQPv3Nzc2NI0eOkJyczJ07d4qcleXl5cW6deuIj4/n1KlT9OzZs0SzuOLj44mPjyc9PZ3bt29z+vRpzp07V+xxQgghhBBCiDeTWqMw+j+/02b2XnZfuFX8ASVUq7ItHvaWPMjMYWbshRfWr3gxJGn0FjMyMiIsLIyZM2eSkZHB5MmTCQ8PZ/r06VSrVo327duzadMm7bIrExMTxowZg5+fH++88w6GhobExMQA8I9//IOhQ4cSFhZG7dq1OXjwIOHh4S8s1kmTJhETE4Ofnx/ff/89P/74o3aWz9OcnJw4cOAAarWadu3aUbNmTYYMGUKZMmW0iaGvvvqKZs2aERQURJs2bWjatCl169YtMobAwEB+/fVXgoKCqFq1Kn369MHHx4dt27ZhZPS/lZ7BwcFkZmZSv359QkNDGTx4MAMHDtTuX7ZsGcHBwQwfPhxvb286d+7MsWPHtE8zq1q1Ktu2bePUqVPUr1+fRo0a8csvv2jPMWLECAwNDfH19cXe3r7I+kSzZ8+mbNmyNG7cmKCgIAIDA6lTp06x19vf3x9/f3/i4uL48ccfeeedd3jvvfeKPU4IIYQQQgjxZtp5/ib/Tc2krIUxjarYvbB+jQwNmNK5BgAxx65xIiX1hfUt/j6VoiiKvoN43aSlpWFra8uDBw/yFXJ+9OgRSUlJuLu7Y2ZmpqcI3y4qlYr169fTuXNnfYdSrBYtWlC7dm3mzJmj71BeGI1GQ1paGjY2NoXOxpL7QojXT05ODps3b6Zjx475ascJIV5vcv8KUXq9yfdvz8WHOZh4l0+bV+HzDj7FH/CMRqw5xdq4/1LdyYYNYU0xNFAVf5B4LkXlPJ4mM42EEEIIIYQQQghRqEs3/+Jg4l0MVPBRQ5eXco7PO/hgY2bE2T/S+PGoPO35dSFJIyGEEEIIIYQQQhRqxcFkANr6VqRyWYuXco7yVqYMa1sVgMjtl0h7lPNSziOejVHxTYTQr9K0gnLPnj36DkEIIYQQQgghXpgHmTmsO3EdgD6N3V7quXo1dGX9yeu0q+6AiaHMcXkdSNJICCGEEEIIIYQQBbp88y/MjA1wKWdBI48XVwC7IMaGBqwf1AQDqWf02pCkkRBCCCGEEEIIIQoU4FaOQ2Na88f9TFSql5/MeTJhpNEokkDSM5nvJYQQQgghhBBCiEKZGRviYW/1Ss95IOEO787bz9Gke6/0vEKXJI2EEEIIIYQQQgiRz6lr99Fo9FNjdtPpG5y/kcbkjef0FoOQpJEQQgghhBBCCCGecuV2Op2+OUC7Oft4lKN+5ecf2qYqVqZGnL7+gPUnr7/y84vHJGkkhBBCCCGEEEIIHasOpwDgUs4CM2PDV35+e2tTQlt6AjBz6wXSs3JfeQxCkkaihJKTk1GpVMTHx5f4mOXLl1OmTBm9xyGEEEIIIYQQouQeZueyJu4aAL0bueotjr5N3Khc1pybaVkERu5jx7mbeovlbSVJo7fItWvX6NevH05OTpiYmODq6srgwYO5e/duscc6Oztz48YNatSoUeLzffDBB1y6dOnvhPxckpKS6NmzJ05OTpiZmVG5cmU6derEhQsXXlkMLzu5lZOTw+jRo6lZsyaWlpY4OTkRHBzMH3/88VLOJ4QQQgghhHh7/BL/B389ysXVzoLmXvZ6i8PM2JC5PfypVMac6/czGfD9cc7+8UBv8byNJGn0lrhy5QoBAQFcvnyZH3/8kYSEBBYuXMjOnTtp1KgR9+4VXpE+OzsbQ0NDHBwcMDIyKvE5zc3NqVChwosIv8RycnJo27YtDx48YN26dVy8eJHVq1dTs2ZN7t+//0pjKYns7OznOu7hw4ecOHGC8PBwTpw4oR3rP/7xjxccoRBCCCGEEOJtoigK3x+6CsBHDVz1/sj7Oi5l2T7sHT5tXoWudStT3clWr/G8bSRp9AI9zHlY6FeWOqvEbR/lPiq27bMKDQ3FxMSEbdu20bx5c1xcXOjQoQM7duzg+vXrfPHFF9q2bm5uTJ48meDgYGxsbBg4cGCBM2c2bNiAl5cXZmZmtGzZkhUrVqBSqbTJmaeXp02cOJHatWuzcuVK3NzcsLW15cMPP+Svv/7StomNjaVp06aUKVMGOzs73nvvPRITE0s8zrNnz5KYmMj8+fNp2LAhrq6uNGnShClTptCwYUPgf7OAYmJiaNy4MWZmZtSoUYO9e/fq9HXmzBk6dOiAlZUVFStWpHfv3ty5c0e7X6PRMHPmTDw9PTE1NcXFxYWpU6cC4O7uDoC/vz8qlYoWLVoAEBISQufOnZk6dSpOTk54e3sDsHLlSgICArC2tsbBwYGePXty69atQsdpa2vL9u3b6d69O97e3jRs2JDo6Gji4uJISUkp8fUSQgghhBBCiCedSEnl/I00TI0M6BZQWd/hAGBhYsTnHXz4qqufdtsf9zPpMv8AR5MKnwAh/r6STxsRxWrw7waF7mtWqRnz28zXvm7xUwsyczMLbBtQMYBl7ZdpX7f/T3tSs1J12pzuc7rEcd27d4+tW7cydepUzM3NdfY5ODjQq1cvVq9ezfz581GpHmeRZ82axfjx45kwYUKBfSYlJdG1a1cGDx7MgAEDOHnyJCNGjCg2lsTERH7++Wc2btxIamoq3bt3JyIiQptsycjIYNiwYfj5+ZGens748ePp0qUL8fHxGBgUn+O0t7fHwMCAtWvXMmTIEAwNCy/YNnLkSObMmYOvry+zZ88mKCiIpKQk7OzsuH//Pq1atWLAgAFERkaSmZnJ6NGj6d69O7t27QJgzJgxLF68mMjISJo2bcqNGze0S+COHj1K/fr12bFjB9WrV8fExER73p07d2JjY8P27du123Jycpg8eTLe3t7cunWLYcOGERISwubNm4sdc54HDx6gUqleeB0pIYQQQgghxNtj69nHdYM61XaijIVJMa1frbzfVwFmb7/EyZT7dF90iK51KzOmgw92VqZ6jO7NJEmjt8Dly5dRFIVq1aoVuL9atWqkpqZy+/Zt7XKyVq1aMXz4cG2b5ORknWMWLVqEt7c3X331FQDe3t6cOXNGm/wpjEajYfny5VhbWwPQu3dvdu7cqT3u/fff12m/dOlS7O3tOXfuXInqKVWqVIm5c+cyatQoJk2aREBAAC1btqRXr154eHjotA0LC9Oeb8GCBcTGxrJkyRJGjRpFdHQ0/v7+TJs2TScWZ2dnLl26hKOjI1FRUURHR9OnTx8AqlSpQtOmTYHHySsAOzs7HBwcdM5raWnJd999p5NI6tevn/Z7Dw8P5s6dS7169UhPT8fKyqrYcT969IjRo0fTo0cPbGxsim0vhBBCCCGEEAUZ08GHVj4VsLd+vRMwX3SshrGhih+PXmNt3H/Zfu4mzavaU8elDAFu5ahR6fmWseWoNVy9m8Glm+lc/PMvLt/6C7/KZfi0eZUXPILSQZJGL9CRnkcK3WdooDvjZU/3PYW2NVDpzqiJfT/2b8WVR1GUErcNCAgocv/FixepV6+ezrb69esX26+bm5s2YQTg6Oioswzr8uXLjB8/niNHjnDnzh00Gg0AKSkpJS7CHRoaSnBwMHv27OHw4cOsWbOGadOmsWHDBtq2batt16hRI+33RkZGBAQEcP78eQBOnTrF7t27C0zYJCYmcv/+fbKysmjdunWJYnpSzZo1dRJGAHFxcUycOJFTp06RmpqqM25fX98i+8vJyaF79+4oisKCBQueOR4hhBBCCCGEyKNSqWjoYafvMIpV1tKE6f/Pj651nfli/Wku/PkXG079wYZTf1DXtSz/+ayxtu2+S7fxdrCmoo2Zdptao3Dt3kMsTY20CbK9l27z8YrjZKs1OudKy8yVpJH4+yyMLfTetiCenp6oVCrOnz9Ply5d8u0/f/48ZcuW1c6OgcezYV4GY2NjndcqlUqbIAEICgrC1dWVxYsX4+TkhEajoUaNGs9cMNra2pqgoCCCgoKYMmUKgYGBTJkyRSdpVJT09HSCgoKYMWNGvn2Ojo5cuXLlmeJ50tPXNiMjg8DAQAIDA/nhhx+wt7cnJSWFwMDAYsedlzC6evUqu3btkllGQgghhBBCiOeSq9aQrdZgYVK60gR1Xcuy8Z9NOZJ0j7irqZxIScXfuax2f3pWLn2WHUVRoFIZc6o5WvNn2iMSbqXzKEfD5x18tAkhJ1uz/7sGhnhVtKZqBSu8Hayp+Zyzlt4EpevTIJ6LnZ0dbdu2Zf78+QwdOlSnrtGff/7JDz/8QHBwsM760OJ4e3vnq7dz7NixvxXn3bt3uXjxIosXL6ZZs2YA7N+//2/1CY8TUz4+Phw8eFBn++HDh3nnnXcAyM3NJS4ujrCwMADq1KnDf/7zH9zc3Ap8YpyXlxfm5ubs3LmTAQMG5NufN5NIrVYXG9+FCxe4e/cuERERODs7A3D8+PFij8tLGF2+fJndu3djZ/f6/zVACCGEEEII8Xracf4WI9eeYkBTDwa38dJ3OM/EyNCAJp7laeJZPt++m2mP8HGw4eKfaVy/n8n1+/+rLWxiZED6o1zta/fylvw2qiWVypjr/alxrwtJGr0loqOjady4sXbGjbu7O2fPnmXkyJFUqlSp2FpET/vkk0+YPXs2o0ePpn///sTHx7N8+XKAZ0o+Pals2bLY2dnx7bff4ujoSEpKCp9//vkz9REfH8+ECRPo3bs3vr6+mJiYsHfvXpYuXcro0aN12n7zzTd4eXlRrVo1IiMjSU1N1dYWCg0NZfHixfTo0YNRo0ZRrlw5EhISiImJ4bvvvsPMzIzRo0czatQoTExMaNKkCbdv3+bs2bP079+fChUqYG5uTmxsLJUrV8bMzAxb24Kz0y4uLpiYmDBv3jw+/fRTzpw5w+TJk4scZ05ODl27duXEiRNs3LgRtVrNn3/+CUC5cuXyLX8TQgghhBBCiKKsPJzMX49yeZRb/B++S5Mq9lZsGdyM9Kxcfv/vfS79+RcOtuZ4O1jjUs4CwyeSQ0aGBjiX+3srfd40xT+OSrwRvLy8OH78OB4eHnTv3p0qVaowcOBAWrZsyaFDhyhXrtwz9efu7s7atWtZt24dfn5+LFiwgC+++AIAU9PnK5hmYGBATEwMcXFx1KhRg6FDh2oLbZdU5cqVcXNzY9KkSTRo0IA6deoQFRXFpEmTtPHliYiIICIiglq1arF//342bNhA+fKPM9NOTk4cOHAAtVpNu3btqFmzJkOGDKFMmTLap7iFh4czfPhwxo8fT7Vq1fjggw+09ZmMjIyYO3cuixYtwsnJiU6dOhUas729PcuXL2fNmjX4+voSERHBrFmzihzn9evX2bBhA//973+pXbs2jo6O2q+nZ1QJIYQQQgghRFESbqVzIOEuBiro1cBF3+G8FFamRjSuUp6QJu60r+GAe3lLnYSRKJhKeZbqyG+JtLQ0bG1tefDgQb4aMY8ePSIpKQl3d3fMzMwK6eHtNHXqVBYuXMi1a9f0HUqRkpOTcXd35+TJk9SuXVvf4bz2NBoNaWlp2NjYaBNmT5P7QojXT05ODps3b6Zjx4756skJIV5vcv8KUXqV1vt34oazLD+YTJtqFfmuT9EPRRKlX1E5j6fJ8jTx3ObPn0+9evWws7PjwIEDfPXVV9qaQEIIIYQQQgghXn9ZuWr+E/dfAIIbueo5GvG6kaSReG6XL19mypQp3Lt3DxcXF4YPH86YMWP0HZYQQgghhBBCiBI6+0caf2XlYmdpQtMCCkmLt5skjcRzi4yMJDIyUt9hPDM3NzdkVaYQQgghhBBCwI1Tu+hoEI+ZYxN5YpjIR5JGQgghhBBCCCHEW6rpg1941+QXLtgAtNd3OOI1I09PE0IIIYQQQggh3lK2d+IB8KnbSr+BiNeSJI2EEEIIIYQQQoi3UfptuH8VUEGlOvqORryGJGkkhBBCCCGEEEK8hf44+xsAOeWqgpmtnqMRryNJGgkhhBBCCCGEEG+hlN/3AnBCU0XPkYjXlSSNhBBCCCGEEEKIt5D1/9Uz0lQK0G8g4rUlSSNR6iUnJ6NSqYiPj/9b/bRo0YIhQ4a8kJiEEEIIIYQQ4nWmyc3FLesCAOW9m+g5GvG6kqTRWyIkJASVSoVKpcLY2Bh3d3dGjRrFo0eP9B1aqaFWq4mIiMDHxwdzc3PKlStHgwYN+O67715pHC87ubVu3Tratm2Lvb09NjY2NGnShJ07d7608wkhhBBCCCFevf9ePoklj8hQTHGrVlff4YjXlJG+AxCvTvv27Vm2bBk5OTnExcXRp08fVCoVM2bM0HdopcKkSZNYtGgR0dHRBAQEkJaWxvHjx0lNTdV3aPkoioJarcbI6Nlv8X379tG2bVumTZtGmTJlWLp0KT169ODQoUPUrSv/mAghhBBCCPEmuHV+Py5AkqkPNYyN9R2OeE3JTKMXQFEUNA8fvvIvRVGeKU5TU1McHBxwdnamc+fOtGnThu3bt2v3azQapk+fjru7O+bm5tSqVYu1a9dq96emptKrVy/s7e0xNzfHy8uLZcuWafePHj2aqlWrYmFhgYeHB+Hh4eTk5Gj3T5w4kdq1a7N06VJcXFywsrJi0KBBqNVqZs6ciYODAxUqVGDq1Kk6catUKhYsWECHDh0wNzfHw8NDJ66CnDlzhg4dOmBlZUXFihXp3bs3d+7c0e7PyMggODgYKysrHB0d+frrr4u9fhs2bGDQoEF069YNd3d3atWqRf/+/RkxYoS2TYsWLQgLCyMsLAxbW1vKly9PeHi4znuVlZXFiBEjqFSpEpaWljRo0IA9e/bonOvAgQO0aNECCwsLypYtS2BgIKmpqYSEhLB3716ioqK0M8eSk5PZs2cPKpWKLVu2ULduXUxNTdm/fz+JiYl06tSJihUrYmVlRb169dixY0eR45wzZw6jRo2iXr16eHl5MXXqVKpUqcLGjRuLvUZCCCGEEEKIUuK/xwF4YFdLz4GI15nMNHoBlMxMLtZ59TMwvE/EobKweK5jz5w5w8GDB3F1ddVumz59OqtWrWLhwoV4eXmxb98+PvroI+zt7WnevDnh4eGcO3eOLVu2UL58eRISEsjMzNQeb21tzfLly3FycuL06dN8/PHHWFtbM2rUKG2bxMREtmzZQmxsLImJiXTt2pUrV65QtWpV9u7dy8GDB+nXrx9t2rShQYMG2uPCw8OJiIggKiqKlStX8uGHH3L69GmqVauWb2z379+nVatWDBgwgMjISDIzMxk9ejTdu3dn165dAIwcOZK9e/fyyy+/UKFCBcaOHcuJEyeoXbt2odfMwcGBXbt2MWjQIOzt7Qttt2LFCvr378/Ro0c5fvw4AwcOxMXFhY8//hiAsLAwzp07R0xMDE5OTqxfv5727dtz+vRpvLy8iI+Pp3Xr1vTr14+oqCiMjIzYvXs3arWaqKgoLl26RI0aNfjyyy8BsLe3Jzk5GYDPP/+cWbNm4eHhQdmyZbl27RodO3Zk6tSpmJqa8v333xMUFMTFixdxcXEp4hPyPxqNhr/++oty5cqVqL0QQgghhBDi9Vf+we8AmLo2KKaleJtJ0ugtsnHjRqysrMjNzSUrKwsDAwOio6OBx7Nfpk2bxo4dO2jUqBEAHh4e7N+/n0WLFtG8eXNSUlLw9/cnIOBxZX03Nzed/seNG6f93s3NjREjRhATE6OTNNJoNCxduhRra2t8fX1p2bIlFy9eZPPmzRgYGODt7c2MGTPYvXu3TtKoW7duDBgwAIDJkyezfft25s2bx/z58/ONMzo6Gn9/f6ZNm6bdtnTpUpydnbl06RJOTk4sWbKEVatW0bp1a+Bxoqdy5cpFXr/Zs2fTtWtXHBwcqF69Oo0bN6ZTp0506NBBp52zszORkZGoVCq8vb05ffo0kZGRfPzxx6SkpLBs2TJSUlJwcnICYMSIEcTGxrJs2TKmTZvGzJkzCQgI0Blb9erVtd+bmJhgYWGBg4NDvhi//PJL2rZtq31drlw5atX6318OJk+ezPr169mwYQNhYWFFjjfP119/TUZGBt26dStReyGEEEIIIcRr7lEaruprAHj4v6PnYMTrTJJGL4DK3BzvE3F6Oe+zaNmyJQsWLCAjI4PIyEiMjIx4//33AUhISODhw4c6CQeA7Oxs/P39Afjss894//33OXHiBO3ataNz5840btxY23b16tXMnTuXxMRE0tPTyc3NxcbGRqc/Nzc3rK2tta8rVqyIoaEhBgYGOttu3bqlc1xeIuvJ14U9Le3UqVPs3r0bKyurfPsSExPJzMwkOztbJylVrlw5vL29C+wvj6+vL2fOnCEuLo4DBw6wb98+goKCCAkJ0SmG3bBhQ1QqlU6sX3/9NWq1mtOnT6NWq6latapO31lZWdjZ2QEQHx//3AmavIRenvT0dCZOnMimTZu4ceMGubm5ZGZmkpKSUqL+/v3vf/Pll1/yww8/UKFCheeKSQghhBBCCPGa+eMEKhQo40K5iiVbgSDeTpI0egFUKtVzLxN7lSwtLfH09AQez7ypVasWS5YsoX///qSnpwOwadMmKlWqpHOcqakpAB06dODq1ats3ryZ7du307p1a0JDQ5k1axaHDh2iV69eTJo0icDAQGxtbYmJiclXK8j4qQJreU9ze3qbRqN57nGmp6cTFBRUYIFvR0dHEhISnrtvAwMD6tWrR7169RgyZAirVq2id+/efPHFF7i7u5coNkNDQ+Li4jA0NNTZl5fkMn/GZOCTLC0tdV6PGDGC7du3M2vWLDw9PTE3N6dr165kZ2cX21dMTAwDBgxg9erVNGvW7LljEkIIIYQQQrxm/nvs8X8r19NvHOK1J4Ww31IGBgaMHTuWcePGkZmZia+vL6ampqSkpODp6anz5ezsrD3O3t6ePn36sGrVKubMmcO3334LoK2P9MUXXxAQEICXlxdXr159YfEePnw43+uC6hkB1KlTh7Nnz+Lm5pZvLJaWllSpUgVjY2OOHDmiPSY1NZVLly49c1y+vr7A48LaeZ7sNy9WLy8vDA0N8ff3R61Wc+vWrXyx5S038/PzK/IR9yYmJqjV6hLFd+DAAUJCQujSpQs1a9bEwcFBW/+oKD/++CN9+/blxx9/5N133y3RuYQQQgghhBClw9VT+wBILeun50jE606SRm+xbt26YWhoyDfffIO1tTUjRoxg6NChrFixgsTERE6cOMG8efNYsWIFAOPHj+eXX34hISGBs2fPsnHjRm3ixsvLi5SUFGJiYkhMTGTu3LmsX7/+hcW6Zs0ali5dyqVLl5gwYQJHjx4ttCZPaGgo9+7do0ePHhw7dozExES2bt1K3759UavVWFlZ0b9/f0aOHMmuXbs4c+YMISEhOkvkCtK1a1ciIyM5cuQIV69eZc+ePYSGhlK1alV8fHy07VJSUhg2bBgXL17kxx9/ZN68eQwePBiAqlWr0qtXL4KDg1m3bh1JSUkcPXqU6dOns2nTJgDGjBnDsWPHGDRoEL///jsXLlxgwYIF2qe/ubm5ceTIEZKTk7lz506Rs7K8vLxYt24d8fHxnDp1ip49exY7i+vf//43wcHBfP311zRo0IA///yTmzdv8uDBgyKPE0IIIYQQQpQCioLN3XgArllWL7qteOtJ0ugtZmRkRFhYGDNnziQjI4PJkycTHh7O9OnTqVatGu3bt2fTpk3aZVcmJiaMGTMGPz8/3nnnHQwNDYmJiQHgH//4B0OHDiUsLIzatWtz8OBBwsPDX1iskyZNIiYmBj8/P77//nt+/PFH7Syfpzk5OXHgwAHUajXt2rWjZs2aDBkyhDJlymgTQ1999RXNmjUjKCiINm3a0LRpU+rWLfoJeIGBgfz6668EBQVRtWpV+vTpg4+PD9u2bcPI6H8rPYODg8nMzKR+/fqEhoYyePBgBg4cqN2/bNkygoODGT58ON7e3nTu3Jljx45pn2ZWtWpVtm3bxqlTp6hfvz6NGjXil19+0Z5jxIgRGBoa4uvri729fZH1iWbPnk3ZsmVp3LgxQUFBBAYGUqdOnSLH+e2335Kbm0toaCiOjo5UqlQJHx8fhgwZUuRxQgghhBBCiNffnWsXKUsaWYoRHjUbF3+AeKupFEVR9B3E6yYtLQ1bW1sePHiQr5Dzo0ePSEpKwt3dHTMzMz1F+HZRqVSsX7+ezp076zuUYrVo0YLatWszZ84cfYfywmg0GtLS0rCxsSl0NpbcF0K8fnJycti8eTMdO3bMVztOCPF6k/tXiNKrNNy/v29ZjN+REVwwrIpP+DF9hyP0oKicx9NkppEQQgghhBBCCPGWyL56FIC7ZaSekSieJI2EEEIIIYQQQoi3RJn/q2ekcpYnp4niGRXfRAj9Kk0rKPfs2aPvEIQQQgghhBCiQOrsTFyzE0EFFX2b6jscUQrITCMhhBBCCCGEEOItcOvSUYxVau4qNrhVKfjBQkI8SZJGQgghhBBCCCHEW8Ax7TQApm71MTSUdIAonnxKhBBCCCGEEEKIt8G1wwBYeTbRcyCitJCkkRBCCCGEEEII8aZTFEg58vh7l0b6jUWUGnpPGn3zzTe4ublhZmZGgwYNOHr0aJHt16xZg4+PD2ZmZtSsWZPNmzfr7E9PTycsLIzKlStjbm6Or68vCxcufJlDEEIIIYQQQgghXmuXzv8OGbfIVRmDk7++wxGlhF6TRqtXr2bYsGFMmDCBEydOUKtWLQIDA7l161aB7Q8ePEiPHj3o378/J0+epHPnznTu3JkzZ85o2wwbNozY2FhWrVrF+fPnGTJkCGFhYWzYsOFVDUsIIYQQQgghhHit3Di9G4Akk6pgbKbnaERpodek0ezZs/n444/p27evdkaQhYUFS5cuLbB9VFQU7du3Z+TIkVSrVo3JkydTp04doqOjtW0OHjxInz59aNGiBW5ubgwcOJBatWoVO4NJFC05ORmVSkV8fHyJj1m+fDllypTRexxCCCGEEEII8bYz+L96RhkVA/QciShNjPR14uzsbOLi4hgzZox2m4GBAW3atOHQoUMFHnPo0CGGDRumsy0wMJCff/5Z+7px48Zs2LCBfv364eTkxJ49e7h06RKRkZGFxpKVlUVWVpb2dVpaGgA5OTnk5OTotM3JyUFRFDQaDRqNpsTjfR1cu3aNiRMnsnXrVu7cuYOjoyOdOnUiPDwcOzu7Io+tVKkS169fp3z58iUed7du3Wjfvv0LvU55fRV1/ZOSkhg3bhx79+7l3r17lC9fnjp16hAREYGPj88Li6UoycnJVKlShbi4OGrXrv1SzjFp0iRWr17NtWvXMDExoW7dukyePJkGDRq80PMoiqL9b2HXXKPRoCgKOTk5GBoavtDzCyGeT96/X0//OyaEeP3J/StE6fW63r9qjUKl9N8BsPZq8trFJ16tZ3n/9ZY0unPnDmq1mooVK+psr1ixIhcuXCjwmD///LPA9n/++af29bx58xg4cCCVK1fGyMgIAwMDFi9ezDvvvFNoLNOnT2fSpEn5tm/btg0LCwudbUZGRjg4OJCenk52dnax43xdJCcn065dO6pUqcLixYtxcXHhwoULjB8/ns2bN7N9+3bKli1b4LHZ2dmYmJhgYWHBw4cPn+m8ZmZm2iTci5Ceng5ARkZGgf3m5OTQtm1bPD09WbFiBRUrVuSPP/5gx44dXL9+HScnpxcWy9+JM0/etX0elStXJiIiAjc3NzIzM1mwYAGBgYGcOHGC8uXLP1efRfnrr78K3ZednU1mZib79u0jNzf3hZ9bCPH8tm/fru8QhBDPSe5fIUqv1+3+vX0/jQFcB+DCzRzOPFUbWLxdnuX3er0ljV6WefPmcfjwYTZs2ICrqyv79u0jNDQUJycn2rRpU+AxY8aM0ZnBlJaWhrOzM+3atcPGxkan7aNHj7h27RpWVlaYmemuA83JUhcal8oAjIwNS9ZWBUYmRbc1Nn222Ryff/45pqam7NixA3NzcwCqV69OkyZN8PLyYsaMGcyfPx8ADw8P+vXrx+XLl/nll1/o0qULEyZMyDdzZsOGDYwcOZJr167RqFEjgoOD6devH3fv3qVMmTIsX76cYcOGce/ePeDxzJhffvmFoUOHMmHCBFJTU2nfvj3ffvst1tbWAMTGxjJt2jTOnDmDoaEhDRs2ZM6cOVSpUgUAKysrACwtLfO9NwDx8fEkJSWxc+dOXF1dAahRowbt2rXTtsmbBfTDDz8QHR3NiRMn8PT0ZN68eTRv3lzb7syZM4waNYr9+/djaWlJ27ZtmT17tjYho9Fo+Prrr1m8eDHXrl2jYsWKDBw4kLFjx1KrVi0AbbKyefPm7Nq1i759+3L//n3q1avH/PnzMTU1JTExkZUrVzJv3jwuXryIpaUlLVu2JDIykgoVKhT6nvbv31/ndbVq1Vi5ciVJSUl4eHgU+5koKUVR+Ouvv7C2tkalUhXY5tGjR5ibm/POO+/kuy+EEPqRk5PD9u3badu2LcbGxvoORwjxDOT+FaL0el3v3x0/LwfgurEL7Tp9oN9ghN49y8QOvSWNypcvj6GhITdv3tTZfvPmTRwcHAo8xsHBocj2mZmZjB07lvXr1/Puu+8C4OfnR3x8PLNmzSo0aWRqaoqpqWm+7cbGxvludLVajUqlwsDAAAMD3ZJQ3w3dU+h4XWvY8V5YLe3r5aP3kZtd8FIfJ68ydBleR/t6VfgBHqXrTh8LXdiq0HM97d69e2zbto2pU6diaWmpey4nJ3r16sVPP/3EggULtEmBr7/+mvHjxzNx4kQA7Vjzxp2UlET37t0ZPHgwAwYM4OTJk4wYMUKnzZPHAKhUKhITE9mwYQMbN24kNTWV7t27M3PmTKZOnQo8fg+HDRuGn58f6enpjB8/nvfff5/4+Ph8fT59/eHxzDMDAwPWrVvHkCFDClwqlXfc6NGjmTNnDr6+vsyePZtOnTqRlJSEnZ0d9+/fp02bNgwYMIA5c+aQmZnJ6NGj+fDDD9m1axfwONm4ePFiIiMjadq0KTdu3ODChQsYGBhw9OhR6tevz44dO6hevTomJiYYGBigUqnYtWsXtra22r8+GBgYoFarmTx5Mt7e3ty6dYthw4bRr1+/fE8HLEx2djbfffcdtra2+Pv7F3htnlfekrS8z31B8sZW0D0jhNAvuS+FKL3k/hWi9Hrt7t9rj2v8ptkHUOl1ikvoxbN8NvWWNMqrwbJz5046d+4MPP7ldOfOnYSFhRV4TKNGjdi5cydDhgzRbtu+fTuNGjUC/leD6OlfbA0NDUtd/aEX6fLlyyiKQrVq1QrcX61aNVJTU7l9+7Z2ZkurVq0YPny4tk1ycrLOMYsWLcLb25uvvvoKAG9vb86cOaNN/hRGo9GwfPly7cyi3r17s3PnTu1x77//vk77pUuXYm9vz7lz56hRo0axY61UqRJz585l1KhRTJo0iYCAAFq2bEmvXr3yzb4JCwvTnm/BggXExsayZMkSRo0aRXR0NP7+/kybNk0nFmdnZy5duoSjoyNRUVFER0fTp08fAKpUqULTpk0BsLe3B8DOzi5fEtTS0pLvvvtOZ1lav379tN97eHgwd+5c6tWrR3p6unZ2VUE2btzIhx9+yMOHD3F0dGT79u0vZWmaEEIIIYQQovSqoT4HgLVXUz1HIkobvS5PGzZsGH369CEgIID69eszZ84cMjIy6Nu3LwDBwcFUqlSJ6dOnAzB48GCaN2/O119/zbvvvktMTAzHjx/n22+/BcDGxobmzZszcuRIzM3NcXV1Ze/evXz//ffMnj37pY9nYFTzQvepnpqg0e+rZoW3fWoFUPDUxn8nLK28gsYlERBQdEX9ixcvUq9ePZ1t9evXL7ZfNzc3bcIIwNHRkVu3bmlfX758mfHjx3PkyBHu3LmjTfalpKSUKGkEEBoaSnBwMHv27OHw4cOsWbOGadOmsWHDBtq2battl5dshMe1qgICAjh//jwAp06dYvfu3QUmbBITE7l//z5ZWVm0bt26RDE9qWbNmvnqGMXFxTFx4kROnTpFamqqzrh9fX0L7atly5bEx8dz584dFi9eTPfu3Tly5EiRy9qEEEIIIYQQb5GcTJwzLwJQqVbJV6wIAXpOGn3wwQfcvn2b8ePH8+eff1K7dm1iY2O1xa5TUlJ0Zg01btyYf//734wbN46xY8fi5eXFzz//rJNMiImJYcyYMfTq1Yt79+7h6urK1KlT+fTTT1/6eJ6lztDLalsQT09PVCoV58+fp0uXLvn2nz9/nrJly2pnxwD5lrG9KE9Pg1OpVDqzwIKCgnB1dWXx4sU4OTmh0WioUaPGMxcdt7a2JigoiKCgIKZMmUJgYCBTpkzRSRoVJT09naCgIGbMmJFvn6OjI1euXHmmeJ709LXNyMggMDCQwMBAfvjhB+zt7UlJSSEwMLDYcVtaWuLp6YmnpycNGzbEy8uLJUuW6DyVUAghhBBCCPEWu34CNDlgVRFVWTd9RyNKGb0Xwg4LCyt0OdqePXvybevWrRvdunUrtD8HBweWLVv2osJ7I9jZ2dG2bVvmz5/P0KFDtYWw4fET6X744QeCg4MLLXJcEG9v73z1do4dO/a34rx79y4XL15k8eLFNGv2eCbW/v37/1af8Dgx5ePjw8GDB3W2Hz58WFuoOjc3l7i4OO1nsU6dOvznP//Bzc0NI6P8t4mXlxfm5ubs3LmTAQMG5NufN5NIrS684HmeCxcucPfuXSIiInB2dgbg+PHjzzbI/6PRaMjKynquY4UQQgghhBBvntQLv1EWwKVh/mUtQhTjxVXLFa+16OhosrKyCAwMZN++fVy7do3Y2Fjatm1LpUqViq1F9LRPPvmECxcuMHr0aC5dusRPP/3E8uXLAZ4p+fSksmXLYmdnx7fffktCQgK7du3SeapdScTHx9OpUyfWrl3LuXPnSEhIYMmSJSxdupROnTrptP3mm29Yv349Fy5cIDQ0lNTUVG1todDQUO7du0ePHj04duwYiYmJbN26lb59+6JWqzEzM2P06NGMGjWK77//nsTERA4fPsySJUsAqFChAubm5sTGxnLz5k0ePHhQaMwuLi6YmJgwb948rly5woYNG5g8eXKR48zIyGDs2LEcPnyYq1evEhcXR79+/bh+/XqRSVUhhBBCCCHE2yNXreH3Q7EApNrV1XM0ojSSpNFbwsvLi+PHj+Ph4UH37t2pUqUKAwcOpGXLlhw6dIhy5co9U3/u7u6sXbuWdevW4efnx4IFC/jiiy8ACnwSXUkYGBgQExNDXFwcNWrUYOjQodpC2yVVuXJl3NzcmDRpEg0aNKBOnTpERUUxadIkbXx5IiIiiIiIoFatWuzfv58NGzZoi0g7OTlx4MAB1Go17dq1o2bNmgwZMoQyZcpol0yGh4czfPhwxo8fT7Vq1fjggw+09ZmMjIyYO3cuixYtwsnJKV/C6kn29vYsX76cNWvW4OvrS0REBLNmzSpynIaGhly4cIH333+fqlWrEhQUxN27d/ntt9+oXr36M10zIYQQQgghxJvpzPX7+PO4npGN9zt6jkaURirlWaojvyXS0tKwtbXlwYMH2NjY6Ox79OgRSUlJuLu7Y2ZmpqcIX09Tp05l4cKFXLt2Td+hFCk5ORl3d3dOnjxJ7dq19R3Oa0+j0ZCWloaNjU2+JxPmkftCiNdPTk4OmzdvpmPHjq/XI3+FEMWS+1eI0ut1u39Xb4zlg+Mf8Ehlhtm462Co9wo14jVQVM7jafKJEc9t/vz51KtXDzs7Ow4cOMBXX31VaH0qIYQQQgghhBCvVmbiAQDulq1FJUkYiecgnxrx3C5fvsyUKVO4d+8eLi4uDB8+XJ7aJYQQQgghhBCvgRy1Brt7J0AFJm6N9R2OKKUkaSSeW2RkJJGRkfoO45m5ubkhqzKFEEIIIYQQb7LzN9Lw5wIAdr7N9RyNKK2kELYQQgghhBBCCPGGSUi4SGXVHTQYYOAcoO9wRCklSSMhhBBCCCGEEOINU9/wMgAPbH3A1FrP0YjSSpJGQgghhBBCCCHEG6byX6cAKOvzjp4jEaWZJI2EEEIIIYQQQog3Tcqhx/91aajfOESpJkkjIYQQQgghhBDiDfLfGzdR/jz7+IWzJI3E85OkkRBCCCGEEEII8QY5c3QHKjTcNnIEG0d9hyNKMUkaiVIvOTkZlUpFfHz83+qnRYsWDBky5IXEJIQQQgghhBD6oko5DMCdcv56jkSUdpI0ekuEhISgUqlQqVQYGxvj7u7OqFGjePTokb5DKzXUajURERH4+Phgbm5OuXLlaNCgAd99990rjeNlJ7f2799PkyZNsLOzw9zcHF9fX+bPn//SzieEEEIIIYR4sSrcPwmAgWsjPUciSjsjfQcgXp327duzbNkycnJyiIuLo0+fPqhUKmbMmKHv0EqFSZMmsWjRIqKjowkICCAtLY3jx4+Tmpqq79DyURQFtVqNkdGz3+KWlpaEhYXh5+eHpaUl+/bt47PPPqNcuXJ8+umnLyFaIYQQQgghxIvyIP0h3rmXQAUONVroOxxRyslMoxdAURRyHj165V+KojxTnKampjg4OODs7Eznzp1p06YN27dv1+7XaDRMnz4dd3d3zM3NqVWrFmvXrtXuT01NpVevXtjb22Nubo6XlxfLli3T7h89ejRVq1bFwsICDw8PwsPDycnJ0e6fOHEitWvXZunSpbi4uGBlZcWgQYNQq9XMnDkTBwcHKlSowNSpU3XiVqlULFiwgA4dOmBubo6Hh4dOXAU5c+YMHTp0wMrKiooVK9K7d2/u3Lmj3Z+RkUFwcDBWVlY4Ojry9ddfF3v9NmzYwKBBg+jWrRvu7u7UqlWL/v37M2LECG2bFi1aEBYWRlhYGLa2tpQvX57w8HCd9yorK4sRI0ZQqVIlLC0tadCgAXv27NE514EDB2jRogUWFhaULVuWwMBAUlNTCQkJYe/evURFRWlnjiUnJ7Nnzx5UKhVbtmyhbt26mJqasn//fhITE+nUqRMVK1bEysqKevXqsWPHjiLH6e/vT48ePahevTpubm589NFHtGrViv379xd7jYQQQgghhBD6dfXMYSxUWaRhha1zDX2HI0o5mWn0AuRmZTG3T9dXft5/rViLsZnZcx175swZDh48iKurq3bb9OnTWbVqFQsXLsTLy4t9+/bx0UcfYW9vT/PmzQkPD+fcuXNs2bKF8uXLk5CQQGZmpvZ4a2trli9fjpOTE6dPn+bjjz/G2tqaUaNGadskJiayZcsWYmNjSUxMpGvXrly5coWqVauyd+9eDh48SL9+/WjTpg0NGjTQHhceHk5ERARRUVGsXLmSDz/8kNOnT1OtWrV8Y7t//z6tWrViwIABREZGkpmZyejRo+nevTu7du0CYOTIkezdu5dffvmFChUqMHbsWE6cOEHt2rULvWYODg7s2rWLQYMGYW9vX2i7FStW0L9/f44ePcrx48cZOHAgLi4ufPzxxwCEhYVx7tw5YmJicHJyYv369bRv357Tp0/j5eVFfHw8rVu3pl+/fkRFRWFkZMTu3btRq9VERUVx6dIlatSowZdffgmAvb09ycnJAHz++efMmjULDw8PypYty7Vr1+jYsSNTp07F1NSU77//nqCgIC5evIiLi0sRn5D/OXnyJEePHmXy5Mklai+EEEIIIYTQn78u/wbAVcua1DSQeSLi75Gk0Vtk48aNWFlZkZubS1ZWFgYGBkRHRwOPZ79MmzaNHTt20KjR43WvHh4e7N+/n0WLFtG8eXNSUlLw9/cnICAAADc3N53+x40bp/3ezc2NESNGEBMTo5M00mg0LF26FGtra3x9fWnZsiUXL15k8+bNGBgY4O3tzYwZM9i9e7dO0qhbt24MGDAAgMmTJ7N9+3bmzZtXYK2d6Oho/P39mTZtmnbb0qVLcXZ25tKlSzg5ObFkyRJWrVpF69atgceJnsqVKxd5/WbPnk3Xrl1xcHCgevXqNG7cmE6dOtGhQwedds7OzkRGRqJSqfD29ub06dNERkby8ccfk5KSwrJly0hJScHJyQmAESNGEBsby7Jly5g2bRozZ84kICBAZ2zVq1fXfm9iYoKFhQUODg75Yvzyyy9p27at9nW5cuWoVauW9vXkyZNZv349GzZsICwsrMjxVq5cmdu3b5Obm8vnn3+uvf5CCCGEEEKI15fZn8cByKgYoOdIxJtAkkYvgJGpKf9aUfRyqZd13mfRsmVLFixYQEZGBpGRkRgZGfH+++8DkJCQwMOHD3USDgDZ2dn4+z+uuP/ZZ5/x/vvvc+LECdq1a0fnzp1p3Lixtu3q1auZO3cuiYmJpKenk5ubi42NjU5/bm5uWFtba19XrFgRQ0NDDJ7IgFesWJFbt27pHJeXyHrydWFPSzt16hS7d+/Gysoq377ExEQyMzPJzs7WSUqVK1cOb2/vAvvL4+vry5kzZ4iLi+PAgQPs27ePoKAgQkJCdIphN2zYEJVKpRPr119/jVqt5vTp06jVaqpWrarTd1ZWFnZ2dgDEx8fTrVu3ImMpTF5CL096ejoTJ05k06ZN3Lhxg9zcXDIzM0lJSSm2r99++4309HQOHjzImDFj8PX1pVevXs8VlxBCCCGEEOLVqGVwBQCXms30HIl4E0jS6AVQqVTPvUzsVbK0tMTT0xN4PPOmVq1aLFmyhP79+5Oeng7Apk2bqFSpks5xpv+XnOrQoQNXr15l8+bNbN++ndatWxMaGsqsWbM4dOgQvXr1YtKkSQQGBmJra0tMTEy+WkHGxsY6r/Oe5vb0No1G89zjTE9PJygoqMAC346OjiQkJDx33wYGBtSrV4969eoxZMgQVq1aRe/evfniiy9wd3cvUWyGhobExcVhaGiosy8vyWVubv7c8VlaWuq8HjFiBNu3b2fWrFl4enpibm5O165dyc7OLravvPFUr16da9eu8eWXX0rSSAghhBBCiNdZxh2M/vovAE7V5Mlp4u+TBY5vKQMDA8aOHcu4cePIzMzE19cXU1NTUlJS8PT01PlydnbWHmdvb0+fPn1YtWoVc+bM4dtvvwXQ1kf64osvCAgIwMvLi6tXr76weA8fPpzvdUH1jADq1KnD2bNncXNzyzcWS0tLqlSpgrGxMUeOHNEek5qayqVLl545Ll9fX+BxYe08T/abF6uXlxeGhob4+/ujVqu5detWvtjylpv5+fmxc+fOQs9pYmKCWq0uUXwHDhwgJCSELl26ULNmTRwcHLT1j56FRqMhKyvrmY8TQgghhBBCvELXTzz+r50XmNkU3VaIEpCk0VusW7duGBoa8s0332Btbc2IESMYOnQoK1asIDExkRMnTjBv3jxWrFgBwPjx4/nll19ISEjg7NmzbNy4UZu48fLyIiUlhZiYGBITE5k7dy7r169/YbGuWbOGpUuXcunSJSZMmMDRo0cLrckTGhrKvXv36NGjB8eOHSMxMZGtW7fSt29f1Go1VlZW9O/fn5EjR7Jr1y7OnDlDSEiIzhK5gnTt2pXIyEiOHDnC1atX2bNnD6GhoVStWhUfHx9tu5SUFIYNG8bFixf58ccfmTdvHoMHDwagatWq9OrVi+DgYNatW0dSUhJHjx5l+vTpbNq0CYAxY8Zw7NgxBg0axO+//86FCxdYsGCB9ulvbm5uHDlyhOTkZO7cuVPkrCwvLy/WrVtHfHw8p06domfPnsXO4vrmm2/49ddfuXz5MpcvX2bJkiVER0fLLCMhhBBCCCFec5fj9wGQZldTz5GIN4Ukjd5iRkZGhIWFMXPmTDIyMpg8eTLh4eFMnz6datWq0b59ezZt2qRdpmRiYsKYMWPw8/PjnXfewdDQkJiYGAD+8Y9/MHToUMLCwqhduzYHDx4kPDz8hcU6adIkYmJi8PPz4/vvv+fHH3/UzvJ5mpOTEwcOHECtVtOuXTtq1qzJkCFDKFOmjDYx9NVXX9GsWTOCgoJo06YNTZs2pW7dukXGEBgYyK+//kpQUBBVq1alT58++Pj4sG3bNoyM/rfSMzg4mMzMTOrXr09oaCiDBw9m4MCB2v3Lli0jODiY4cOH4+3tTefOnTl27Jj2aWZVq1Zl27ZtnDp1ivr169OoUSN++eUX7TlGjBiBoaEhvr6+2NvbF1mfaPbs2ZQtW5bGjRsTFBREYGAgderUKXKcGo2GMWPGULt2bQICAliwYAETJkxg0qRJRR4nhBBCCCGE0K+MpGMAnMNTz5GIN4VKURRF30G8btLS0rC1teXBgwf5Cjk/evSIpKQk3N3dMSsFdYzeBCqVivXr19O5c2d9h1KsFi1aULt2bebMmaPvUF4YjUZDWloaNjY2hc7GkvtCiNdPTk4OmzdvpmPHjvlqxwkhXm9y/wpReunz/lU0Gu5+6U557nPh3f/gU6/NKz2/KD2Kynk8TWYaCSGEEEIIIYQQpdytP5Ioz31yFQNcfRsUf4AQJSBJIyGEEEIIIYQQopS7fvYgAFeNXDG3tNZzNOJNYVR8EyH0qzStoNyzZ4++QxBCCCGEEEK8hbJTHtczumtTnSp6jkW8OWSmkRBCCCGEEEIIUcpZ3jkNgOLkr+dIxJtEkkZCCCGEEEIIIUQppmg0uGRdAsCuakM9RyPeJJI0EkIIIYQQQgghSjHVvSvYko7G0ATXagH6Dke8QaSmkRBCCCGEEEIIUZpdOwKAQaW6GJiY6TkY8SaRmUZCCCGEEEIIIURp9n9JI5zr6zcO8caRpJEokeTkZFQqFfHx8SU+Zvny5ZQpU0bvcQghhBBCCCHEm+yPM3sBuFtOimCLF0uSRm+Ra9eu0a9fP5ycnDAxMcHV1ZXBgwdz9+7dYo91dnbmxo0b1KhRo8Tn++CDD7h06dLfCfm5JCUl0bNnT5ycnDAzM6Ny5cp06tSJCxcuvLIYXnVy69NPP0WlUjFnzpxXcj4hhBBCCCHE6yH9wV0csq4CoK4k9YzEiyVJo7fElStXCAgI4PLly/z4448kJCSwcOFCdu7cSaNGjbh3716hx2ZnZ2NoaIiDgwNGRiUvg2Vubk6FChVeRPgllpOTQ9u2bXnw4AHr1q3j4sWLrF69mpo1a3L//v1XGktJZGdn/+0+1q9fz+HDh3FycnoBEQkhhBBCCCFKk6un9mKgUrimcqSCg7O+wxFvGEkavSVCQ0MxMTFh27ZtNG/eHBcXFzp06MCOHTu4fv06X3zxhbatm5sbkydPJjg4GBsbGwYOHFjgzJkNGzbg5eWFmZkZLVu2ZMWKFahUKm1y5unlaRMnTqR27dqsXLkSNzc3bG1t+fDDD/nrr7+0bWJjY2natCllypTBzs6O9957j8TExBKP8+zZsyQmJjJ//nwaNmyIq6srTZo0YcqUKTRs+PjRk3ljiYmJoXHjxpiZmVGjRg327t2r09eZM2fo0KEDVlZWVKxYkd69e3Pnzh3tfo1Gw8yZM/H09MTU1BQXFxemTp0KgLu7OwD+/v6oVCpatGgBQEhICJ07d2bq1Kk4OTnh7e0NwMqVKwkICMDa2hoHBwd69uzJrVu3ih3v9evX+ec//8kPP/yAsbFxia+TEEIIIYQQ4s2QnnAQgBvWNfUciXgTSdLoBdJkqwv9UnI0z9BWXWzbZ3Hv3j22bt3KoEGDMDc319nn4OBAr169WL16NYqiaLfPmjWLWrVqcfLkScLDw/P1mZSURNeuXencuTOnTp3ik08+0Uk8FSYxMZGff/6ZjRs3snHjRvbu3UtERIR2f0ZGBsOGDeP48ePs3LkTAwMDunTpgkajKaLX/7G3t8fAwIC1a9eiVhd9nUaOHMnw4cM5efIkjRo1IigoSLtU7/79+7Rq1Qp/f3+OHz9ObGwsN2/epHv37trjx4wZQ0REBOHh4Zw7d45///vfVKxYEYCjR48CsGPHDm7cuMG6deu0x+3cuZOLFy+yfft2Nm7cCDyeITV58mROnTrFzz//THJyMiEhIUXGr9Fo6N27NyNHjqR69eoluj5CCCGEEEKIN4vlrTgANJXr6TkS8SYq+VojUaw/xh8sdJ+Zd1nK9/1fPaAbkw/nSyTlMXG3pcInftrXf844iiYjV6dN5YhmJY7r8uXLKIpCtWrVCtxfrVo1UlNTuX37tnY5WatWrRg+fLi2TXJyss4xixYtwtvbm6+++goAb29vzpw5o51pUxiNRsPy5cuxtrYGoHfv3uzcuVN73Pvvv6/TfunSpdjb23Pu3LkS1VOqVKkSc+fOZdSoUUyaNImAgABatmxJr1698PDw0GkbFhamPd+CBQuIjY1lyZIljBo1iujoaPz9/Zk2bZpOLM7Ozly6dAlHR0eioqKIjo6mT58+AFSpUoWmTZsCj5NXAHZ2djg4OOic19LSku+++w4TExPttn79+mm/9/DwYO7cudSrV4/09HSsrKwKHOuMGTMwMjLiX//6V7HXRQghhBBCCPHm0eTm4v7oHAB2Pu/oORrxJpKZRm+RJ2cSFScgoOgCahcvXqRePd1Mdv36xT/e0c3NTZswAnB0dNRZhnX58mV69OiBh4cHNjY2uLm5AZCSklLi2ENDQ/nzzz/54YcfaNSoEWvWrKF69eps375dp12jRo203xsZGREQEMD58+cBOHXqFLt378bKykr75ePjAzyeLXX+/HmysrJo3bp1iePKU7NmTZ2EEUBcXBxBQUG4uLhgbW1N8+bNixx3XFwcUVFRLF++HJVK9cwxCCGEEEIIIUq/lItxWPKIdMUc92p19R2OeAPJTKMXyOnLxoXue/oXe8fwhkW01X3tMLr4ZExRPD09UalUnD9/ni5duuTbf/78ecqWLaudHQOPZ8O8DE/X3VGpVDpLz4KCgnB1dWXx4sU4OTmh0WioUaPGMxeMtra2JigoiKCgIKZMmUJgYCBTpkyhbdu2JTo+PT2doKAgZsyYkW+fo6MjV65ceaZ4nvT0tc3IyCAwMJDAwEB++OEH7O3tSUlJITAwsNBx//bbb9y6dQsXFxftNrVazfDhw5kzZ06+mWFCCCGEEEKIN4/66hEArphVw09qnIqXQGYavUAGJoaFfqmMDZ6hrWGxbZ+FnZ0dbdu2Zf78+WRmZursy5uR88EHHzzTjBVvb2+OHz+us+3YsWPPFNfT7t69y8WLFxk3bhytW7fWLpv7u1QqFT4+PmRkZOhsP3z4sPb73Nxc4uLitEv46tSpw9mzZ3Fzc8PT01Pny9LSEi8vL8zNzdm5c2eB58ybSVRcXSWACxcucPfuXSIiImjWrBk+Pj7FFsHu3bs3v//+O/Hx8dovJycnRo4cydatW4s9pxBCCCGEEKL0q/LoLAC+9Z59BYQQJSFJo7dEdHQ0WVlZBAYGsm/fPq5du0ZsbCxt27alUqVKxdYietonn3zChQsXGD16NJcuXeKnn35i+fLlQP5ZVSVVtmxZ7Ozs+Pbbb0lISGDXrl0MGzbsmfqIj4+nU6dOrF27lnPnzpGQkMCSJUtYunQpnTp10mn7zTffsH79ei5cuEBoaCipqana2kKhoaHcu3ePHj16cOzYMRITE9m6dSt9+/ZFrVZjZmbG6NGjGTVqFN9//z2JiYkcPnyYJUuWAFChQgXMzc21BbQfPHhQaMwuLi6YmJgwb948rly5woYNG5g8eXKR47Szs6NGjRo6X8bGxjg4OGifyCaEEEIIIYR4w117PNPIyK3wlSxC/B2SNHpLeHl5cfz4cTw8POjevTtVqlRh4MCBtGzZkkOHDlGuXLln6s/d3Z21a9eybt06/Pz8WLBggfbpaaamps8Vo4GBATExMcTFxVGjRg2GDh2qLbRdUpUrV8bNzY1JkybRoEED6tSpQ1RUFJMmTcr3dLeIiAgiIiKoVasW+/fvZ8OGDZQvXx4AJycnDhw4gFqtpl27dtSsWZMhQ4ZQpkwZDAwe3zbh4eEMHz6c8ePHU61aNT744APtDCEjIyPmzp3LokWLcHJyypewepK9vT3Lly9nzZo1+Pr6EhERwaxZs55p3EIIIYQQQoi3S+6DPyE1CVBBpaJr0grxvFTKs1RHfkukpaVha2vLgwcPsLGx0dn36NEjkpKScHd3x8zMTE8Rvp6mTp3KwoULuXbtmr5DKVJycjLu7u6cPHmS2rVr6zuc155GoyEtLQ0bGxttwuxpcl8I8frJyclh8+bNdOzYMV89OSHE603uXyFKr1d5/57avopaB0L508wDh89PvtRziTdLUTmPp0khbPHc5s+fT7169bCzs+PAgQN89dVXhIWF6TssIYQQQgghhHjjPbpyCICr5jVw0HMs4s0lSSPx3C5fvsyUKVO4d+8eLi4uDB8+nDFjxug7LCGEEEIIIYR449neOfH4G5cG+g1EvNEkaSSeW2RkJJGRkfoO45m5ubkhqzKFEEIIIYQQpVVO9iPcsy+DChyqN9N3OOINJoWwhRBCCCGEEEKIUiT59CFMVTmkYo1zlZr6Dke8wSRpJIQQQgghhBBClCL3LvwGQLJ5DQwM5dd68fLIp0sIIYQQQgghhChFjG8cB+CRQ109RyLedJI0EkIIIYQQQgghSgtFwTvnHABlvJvqORjxppOkkRBCCCGEEEIIUVo8uIZl1m0wMKJaneb6jka84SRpJIQQQgghhBBClBbXjj7+r0NNMLHQbyzijSdJI1HqJScno1KpiI+P/1v9tGjRgiFDhryQmIQQQgghhBDiZUi9tP/xN84N9BuIeCtI0ugtERISgkqlQqVSYWxsjLu7O6NGjeLRo0f6Dq3UUKvVRERE4OPjg7m5OeXKlaNBgwZ89913rzSOV5ncOnDgACYmJjRr1uyVnE8IIYQQQghRtLvnHz857ZTKW8+RiLeBkb4DEK9O+/btWbZsGTk5OcTFxdGnTx9UKhUzZszQd2ilwqRJk1i0aBHR0dEEBASQlpbG8ePHSU1N1Xdo+SiKglqtxsjo+W/x+/fvExwcTKtWrbhx48YLjE4IIYQQQgjxPDLT03DLuQIqqOArf9gVL5/MNHqLmJqa4uDggLOzM507d6ZNmzZs375du1+j0TB9+nTc3d0xNzenVq1arF27Vrs/NTWVXr16YW9vj7m5OV5eXixbtky7f/To0VStWhULCws8PDwIDw8nJydHu3/ixInUrl2bpUuX4uLigpWVFYMGDUKtVjNz5kwcHByoUKECU6dO1YlbpVKxYMECOnTogLm5OR4eHjpxFeTMmTN06NABKysrKlasSO/evblz5452f0ZGBsHBwVhZWeHo6MjXX39d7PXbsGEDgwYNolu3bri7u1OrVi369+/PiBEjtG1atGhBWFgYYWFh2NraUr58ecLDw1EURdsmKyuLESNGUKlSJSwtLWnQoAF79uzROdeBAwdo0aIFFhYWlC1blsDAQFJTUwkJCWHv3r1ERUVpZ44lJyezZ88eVCoVW7ZsoW7dupiamrJ//34SExPp1KkTFStWxMrKinr16rFjx45ixwrw6aef0rNnTxo1alSi9kIIIYQQQoiXK+n33zBSabiJHQ7OnvoOR7wFJGn0AiiKQnZ29iv/ejIR8azOnDnDwYMHMTEx0W6bPn0633//PQsXLuTs2bMMHTqUjz76iL179wIQHh7OuXPn2LJlC+fPn2fBggWUL19ee7y1tTXLly/n3LlzREVFsXjxYiIjI3XOm5iYyJYtW4iNjeXHH39kyZIlvPvuu/z3v/9l7969zJgxg3HjxnHkyBGd48LDw3n//fc5deoUvXr14sMPP+T8+fMFju3+/fu0atUKf39/jh8/TmxsLDdv3qR79+7aNiNHjmTv3r388ssvbNu2jT179nDixIkir5mDgwO7du3i9u3bRbZbsWIFRkZGHD16lKioKGbPnq2zhC0sLIxDhw4RExPD77//Trdu3Wjfvj2XL18GID4+ntatW+Pr68uhQ4fYv38/QUFBqNVqoqKiaNSoER9//DE3btzgxo0bODs7a/v+/PPPiYiI4Pz58/j5+ZGenk7Hjh3ZuXMnJ0+epH379gQFBZGSklLkGJYtW8aVK1eYMGFCke2EEEIIIYQQr07a5QMA/NeqJiqVSs/RiLeBLE97AXJycpg2bdorP+/YsWN1kj7F2bhxI1ZWVuTm5pKVlYWBgQHR0dHA49kv06ZNY8eOHdqZJR4eHuzfv59FixbRvHlzUlJS8Pf3JyAgAAA3Nzed/seNG6f93s3NjREjRhATE8OoUaO02zUaDUuXLsXa2hpfX19atmzJxYsX2bx5MwYGBnh7/3/27js8inJv4/h3d9Mr6QUCCS0QaqjSRaRqBI9igSOgHhtwjoqAoKIiSBNBEEVFQH31iEdFRZogUpReQ2+BEEog9JCe7O77R2Q1EkKCSTYh9+e6cpGZeWb2nk0GyC9PiWTixImsXLmSli3/mNitd+/e/Otf/wJgzJgxLF++nHfffZf333//mvucMWMG0dHReb4mc+bMISwsjIMHDxIaGsrs2bP5/PPP6dSpE5Bb6KlSpUqB79+UKVO4//77CQ4Opl69erRu3ZqePXvSvXv3PO3CwsKYOnUqBoOByMhIdu3axdSpU3niiSdISEhg7ty5JCQkEBoaCsDQoUNZunQpc+fOZdy4cUyaNIlmzZrlubd69erZPndycsLNzY3g4OBrMr7xxht07tzZtu3r60ujRo1s22PGjOG7775jwYIFDB48ON/7PHToECNGjODXX3/9W8PbRERERESkeLme3gpAVkgzOyeRikI/EVYgHTt2ZObMmaSmpjJ16lQcHBy47777ADh8+DBpaWl5Cg4AWVlZREdHA/DMM89w3333sW3bNrp06UKvXr1o3bq1re1XX33F9OnTiYuLIyUlhZycHLy8vPJcLzw8HE9PT9t2UFAQJpMJo9GYZ19SUlKe8/46RKpVq1bXXS0tNjaWlStX4uHhcc2xuLg40tPTycrKylOU8vX1JTKy4InkoqKi2L17N1u3bmXt2rWsWbOGmJgYBgwYkKcn0W233Zan6t+qVSvefvttzGYzu3btwmw2U7t27TzXzszMxM/PD8jtadS7d+8Cs1zP1YLeVSkpKbz++ussWrSIxMREcnJySE9Pv25PI7PZTJ8+fRg9evQ1GUVERERExH6sFgvV0ncD4FunrZ3TSEWholExcHR05KWXXrLL6xaFu7s7NWvmjnudM2cOjRo1Yvbs2Tz++OOkpKQAsGjRIipXrpznPGdnZwC6d+/OsWPHWLx4McuXL6dTp04MGjSIyZMns379evr27cvo0aPp2rUr3t7ezJs375q5gv6a+epqbn/dZ7FYinRvf5aSkkJMTEy+E3yHhIRw+PDhm7620WikefPmNG/enOeee47PP/+cRx55hJdffpmIiIhCZTOZTGzduhWTyZTn2NUil6ur603nc3d3z7M9dOhQli9fzuTJk6lZsyaurq7cf//9ZGVl5Xv+lStX2LJlC9u3b7f1RLJYLFitVpycnFi2bBl33HHHTecTEREREZGbc+LwTsJIIcPqSER9zTsqpUNFo2JgMBiKNEysLDAajbz00ksMGTKEPn36EBUVhbOzMwkJCXTo0OG65wUEBNC/f3/69+9Pu3btGDZsGJMnT2bdunVUq1aNl19+2db22LFjxZZ3w4YN9OvXL8/21R5Qf9WkSRO+/fZbwsPD8x1eVaNGDRwdHdm4cSNVq1YFcif5PnjwYIH3np+oqCggd2Ltq/46H9OGDRuoVasWJpOJ6OhozGYzSUlJ113GvmHDhqxYsYLRo0fne9zJyQmz2VyofGvXrmXAgAHce++9QG7RKj4+/rrtvby82LVrV5597733HitWrOCbb76hRo0ahXpdEREREREpXn4XYwG4UKk+oc4udk4jFYWKRhVY7969GTZsGO+99x5Dhw5l6NChPP/881gsFtq2bcvly5dZu3YtXl5e9O/fn1dffZWmTZtSr149MjMzWbhwIXXr1gWgVq1aJCQkMG/ePJo3b86iRYv47rvvii3r119/TbNmzWjbti1ffPEFmzZtYvbs2fm2HTRoELNmzeLhhx9m+PDh+Pr6cvjwYebNm8fHH3+Mh4cHjz/+OMOGDcPPz4/AwEBefvnlPEPk8nP//ffTpk0bWrduTXBwMEePHmXkyJHUrl2bOnXq2NolJCQwZMgQnnrqKbZt28a7775r63FVu3Zt+vbtS79+/Xj77beJjo7m7NmzrFixgoYNG3LXXXcxcuRIGjRowMCBA3n66adxcnJi5cqV9O7dG39/f8LDw9m4cSPx8fF4eHjg6+t73cy1atVi/vz5xMTEYDAYGDVqVIG9uIxGI/Xr18+zLzAwEGdnZ+rXr3/D90hEREREREqG25ktAITWL9ovukX+Dv0EWIE5ODgwePBgJk2aRGpqKmPGjGHUqFGMHz+eunXr0q1bNxYtWmQbduXk5MTIkSNp2LAh7du3x2QyMW/ePADuuecenn/+eQYPHkzjxo1Zt24do0aNKraso0ePZt68eTRs2JDPPvuML7/80tbL569CQ0NZu3YtZrOZLl260KBBA5577jkqVapkK3q89dZbtGvXjpiYGO68807atm1L06ZNC8zQtWtXfvzxR2JiYqhduzb9+/enTp06LFu2LE+Ppn79+pGenk6LFi0YNGgQzz77LE8++aTt+Ny5c+nXrx8vvPACkZGR9OrVi82bN9t6PdWuXZtly5YRGxtLixYtaNWqFT/88IPtNYYOHYrJZCIqKoqAgIACV0KbMmUKPj4+tG7dmpiYGLp27UqTJk0K96aLiIiIiEjZcXxT7p9hLQtuJ1KMDNa/s277LSo5ORlvb28uX758zUTOGRkZHD16lIiICFxc1CWwNBgMBr777jt69epl7yg3dPvtt9O4cWPeeecde0cpNhaLheTkZLy8vK7b00jPhUjZk52dzeLFi+nRo0eR58ATEfvS8ytSfpXU85t86Rxe7/w+VcTQw+ARUGzXloqnoJrHX6mnkYiIiIiIiEgZFr99FQAnDCEqGEmpUtFIREREREREpAxLi1sHQKJXQzsnkYpGE2FLmVeeRlCuWrXK3hFEREREROQW43F2GwCWyi3snEQqGvU0EhERERERESmjzDnZRGTsAyAgqp2d00hFo6KRiIiIiIiISBkVv3cL7oYMrlhdqVan4BWfRYqbikYiIiIiIiIiZdS5/b8CEO9SF5ODZpiR0qWikYiIiIiIiEgZZTqxCYDUwCZ2TiIVkYpGIiIiIiIiImVUE1McABHRHe2cRCoiFY1EREREREREyqK0CxgvHgEgqE5rO4eRikhFIymU+Ph4DAYDO3bsKPQ5n3zyCZUqVbJ7DhERERERkXLpxJbcP/1qgZuvfbNIhaSiUQVy/PhxHnvsMUJDQ3FycqJatWo8++yznD9//obnhoWFkZiYSP369Qv9eg8++CAHDx78O5FvytGjR+nTpw+hoaG4uLhQpUoVevbsyf79+0stQ2kUtwYMGIDBYMjz0a1btxJ7PRERERERKV07N/4MwHmfBnZOIhWVpl6vII4cOUKrVq2oXbs2X375JREREezZs4dhw4axZMkSNmzYgK9v/pXrrKwsnJycCA4OLtJrurq64urqWhzxCy07O5vOnTsTGRnJ/PnzCQkJ4cSJEyxZsoRLly6VapbCuPre3qxu3boxd+5c27azs3NxxBIRERERkTLAemIzAEddovCzcxapmNTTqIIYNGgQTk5OLFu2jA4dOlC1alW6d+/Ozz//zMmTJ3n55ZdtbcPDwxkzZgz9+vXDy8uLJ598Mt+eMwsWLKBWrVq4uLjQsWNHPv30UwwGg60489fhaa+//jqNGzfm//7v/wgPD8fb25uHHnqIK1eu2NosXbqUtm3bUqlSJfz8/Lj77ruJi4sr9H3u2bOHuLg43n//fW677TaqVatGmzZtGDt2LLfddhvwRy+gefPm0bp1a1xcXKhfvz6rV6/Oc63du3fTvXt3PDw8CAoK4pFHHuHcuXO24xaLhUmTJlGzZk2cnZ2pWrUqb775JgAREREAREdHYzAYuP3224Hc3kG9evXizTffJDQ0lMjISAD+7//+j2bNmuHp6UlwcDB9+vQhKSnphvfr7OxMcHCw7cPHx6fQ75WIiIiIiJRdZrOZ8IwDAPhHtrFzGqmoVDQqRmZzWgEfmUVom3HDtkVx4cIFfvrpJwYOHHhNz5/g4GD69u3LV199hdVqte2fPHkyjRo1Yvv27YwaNeqaax49epT777+fXr16ERsby1NPPZWn8HQ9cXFxfP/99yxcuJCFCxeyevVqJkyYYDuemprKkCFD2LJlCytWrMBoNHLvvfdisVgKda8BAQEYjUa++eYbzGZzgW2HDRvGCy+8wPbt22nVqhUxMTG2oXqXLl3ijjvuIDo6mi1btrB06VLOnDnDAw88YDt/5MiRTJgwgVGjRrF3717++9//EhQUBMCmTbnLYv78888kJiYyf/5823krVqzgwIEDLF++nIULFwK5PaTGjBlDbGws33//PfHx8QwYMOCG97tq1SoCAwOJjIzkmWeeKdRQQxERERERKfuOHYzF25BKutWJsDrN7B1HKigNTytGq1Zff5ypn9/tNG4027a95tcWWCzp+batVKklTZv817a9dl0HsrMv5GnT6Y7C9745dOgQVquVunXr5nu8bt26XLx4kbNnzxIYGAjAHXfcwQsvvGBrEx8fn+ecDz/8kMjISN566y0AIiMj2b17t62nzfVYLBY++eQTPD09AXjkkUdYsWKF7bz77rsvT/s5c+YQEBDA3r17CzWfUuXKlZk+fTrDhw9n9OjRNGvWjI4dO9K3b1+qV6+ep+3gwYNtrzdz5kyWLl3K7NmzGT58ODNmzCA6Oppx48blyRIWFsbBgwcJCQlh2rRpzJgxg/79+wNQo0YN2rZtC+QWrwD8/PyuGdbn7u7Oxx9/nGdY2mOPPWb7vHr16kyfPp3mzZuTkpKCh4dHvvfarVs3/vGPfxAREUFcXBwvvfQS3bt3Z/369ZhMphu+VyIiIiIiUnYl7VtLdeCYcy3qON78lBYif4d6GlUgf+5JdCPNmhVcyT5w4ADNmzfPs69FixY3vG54eLitYAQQEhKSZxjWoUOHePjhh6levTpeXl6Eh4cDkJCQUOjsgwYN4vTp03zxxRe0atWKr7/+mnr16rF8+fI87Vq1amX73MHBgWbNmrFv3z4AYmNjWblyJR4eHraPOnXqALm9pfbt20dmZiadOnUqdK6rGjRocM08Rlu3biUmJoaqVavi6elJhw4dbnjfDz30EPfccw8NGjSgV69eLFy4kM2bN7Nq1aoiZxIRERERkbLl6nxGl/0a2zeIVGjqaVSMbu+wq4CjeXt+tG+3qYC2eWt5bVqvvk67wqlZsyYGg4F9+/Zx7733XnN83759+Pj42HrHQG5vmJLg6OiYZ9tgMOQZehYTE0O1atWYNWsWoaGhWCwW6tevT1ZWVpFex9PTk5iYGGJiYhg7dixdu3Zl7NixdO7cuVDnp6SkEBMTw8SJE685FhISwpEjR4qU58/++t6mpqbStWtXunbtyhdffEFAQAAJCQl07dq1SPddvXp1/P39OXz48E0Vs0REREREpOwIuJT786Vz+I1/OS9SUtTTqBiZTG4FfDgXoa3LDdsWhZ+fH507d+b9998nPT3vkLirPXIefPBBDAZDoa8ZGRnJli1b8uzbvHlzkXL91fnz5zlw4ACvvPIKnTp1sg2b+7sMBgN16tQhNTU1z/4NGzbYPs/JyWHr1q22IXxNmjRhz549hIeHU7NmzTwf7u7u1KpVC1dXV1asWJHva17tSXSjeZUA9u/fz/nz55kwYQLt2rWjTp06hZoE+69OnDjB+fPnCQkJKfK5IiIiIiJSdmSlXSHcEg9Alfrt7RtGKjQVjSqIGTNmkJmZSdeuXVmzZg3Hjx9n6dKldO7cmcqVK99wLqK/euqpp9i/fz8vvvgiBw8e5H//+x+ffPIJQJGKT3/m4+ODn58fH330EYcPH+aXX35hyJAhRbrGjh076NmzJ9988w179+7l8OHDzJ49mzlz5tCzZ888bd977z2+++479u/fz6BBg7h48aJtbqFBgwZx4cIFHn74YTZv3kxcXBw//fQTjz76KGazGRcXF1588UWGDx/OZ599RlxcHBs2bGD27Nx5qwIDA3F1dbVNoH358uXrZq5atSpOTk68++67HDlyhAULFjBmzJgC7zMlJYVhw4axYcMG4uPjWbFiBT179qRmzZp07dq1SO+ZiIiIiIiULU5JO3HAgtk9GP/K1W98gkgJUdGogqhVqxZbtmyhevXqPPDAA9SoUYMnn3ySjh07sn79enx9fYt0vYiICL755hvmz59Pw4YNmTlzpm31NGdn5xucnT+j0ci8efPYunUr9evX5/nnn7dNtF1YVapUITw8nNGjR9OyZUuaNGnCtGnTGD169DWru02YMIEJEybQqFEjfvvtNxYsWIC/vz8AoaGhrF27FrPZTJcuXWjQoAHPPfcclSpVwmjMfWxGjRrFCy+8wKuvvkrdunV58MEHbT2EHBwcmD59Oh9++CGhoaHXFKz+LCAggE8++YSvv/6aqKgoJkyYwOTJkwu8T5PJxM6dO7nnnnuoXbs2jz/+OE2bNuXXX3+96fdfRERERETKiJO5ozpMVZvfoKFIyTJYizI7cgWRnJyMt7c3ly9fxsvLK8+xjIwMjh49SkREBC4uLte5QsX05ptv8sEHH3D8+HF7RylQfHw8ERERbN++ncaNG9s7TplnsVhITk7Gy8vLVjD7Kz0XImVPdnY2ixcvpkePHtfMJyciZZueX5Hyq9ie36/+Cft+hM5vQJtniy+gCAXXPP5KE2HLTXv//fdp3rw5fn5+rF27lrfeeovBgwfbO5aIiIiIiEi5ZbVaObd/HQHA+UoN8LN3IKnQVDSSm3bo0CHGjh3LhQsXqFq1Ki+88AIjR460dywREREREZFy6/SJI4RYz5NjNeJWrZm940gFp6KR3LSpU6cydepUe8cosvDwcDQqU0REREREyqJTu38lBDjmEE4Nj4KHDomUNE2ELSIiIiIiIlJGZB3bCMC5Sg3tnERERSMRERERERGRMsP7/E4AjGEamib2p6KRiIiIiIiISBmQnZVJRNZBAALrtrNzGhEVjURERERERETKhGP7tuBqyCIZN8JqNrB3HBEVjURERERERETKAsfT2wA44VoXo8lk5zQiKhqJiIiIiIiIlAnVMg4AENWso52TiORS0UjKvfj4eAwGAzt27Phb17n99tt57rnniiWTiIiIiIhIkSXuyP0zpJFdY4hcpaJRBTFgwAAMBgMGgwFHR0ciIiIYPnw4GRkZ9o5WbpjNZiZMmECdOnVwdXXF19eXli1b8vHHH5dqjpIubq1atcr2vWIwGDCZTPj4+HD69OkSe00RERERkYouMyMNa9K+3I3QxnbNInKVg70DSOnp1q0bc+fOJTs7m61bt9K/f38MBgMTJ060d7RyYfTo0Xz44YfMmDGDZs2akZyczJYtW7h48aK9o13DarViNptxcLj5R/zAgQN4eXlhsVi4cuUKgYGBxZhQRERERET+bO+ODURbcrhi9MLTO8zecUQA9TSqUJydnQkODiYsLIxevXpx5513snz5cttxi8XC+PHjiYiIwNXVlUaNGvHNN9/Yjl+8eJG+ffsSEBCAq6srtWrVYu7cubbjL774IrVr18bNzY3q1aszatQosrOzbcdff/11GjduzJw5c6hatSoeHh4MHDgQs9nMpEmTCA4OJjAwkDfffDNPboPBwMyZM+nevTuurq5Ur149T6787N69m+7du+Ph4UFQUBCPPPII586dsx1PTU2lX79+eHh4EBISwttvv33D92/BggUMHDiQ3r17ExERQaNGjXj88ccZOnSorc3tt9/O4MGDGTx4MN7e3vj7+zNq1CisVqutTWZmJkOHDqVy5cq4u7vTsmVLVq1alee11q5dy+23346bmxs+Pj507dqVixcvMmDAAFavXs20adNsPYHi4+NtvYOWLFlC06ZNcXZ25rfffiMuLo6ePXsSFBSEh4cHzZs35+eff77hvQIEBgYSHBxMcHAwQUFBGI3660JEREREpKQkH9kMwAmX2mAw2DmNSC79FFgMrFYrqWZzqX/8uRBRVLt372bdunU4OTnZ9o0fP57PPvuMDz74gD179vD888/zz3/+k9WrVwMwatQo9u7dy5IlS9i3bx8zZ87E39/fdr6npyeffPIJe/fuZdq0acyaNYupU6fmed24uDiWLFnC0qVL+fLLL5k9ezZ33XUXJ06cYPXq1UycOJFXXnmFjRs35jlv1KhR3HfffcTGxtK3b18eeugh9u3bl++9Xbp0iTvuuIPo6Gi2bNnC0qVLOXPmDA888ICtzbBhw1i9ejU//PADy5YtY9WqVWzbtq3A9yw4OJhffvmFs2fPFtju008/xcHBgU2bNjFt2jSmTJmSZwjb4MGDWb9+PfPmzWPnzp307t2bbt26cejQIQB27NhBp06diIqKYv369fz222/ExMRgNpuZNm0arVq14oknniAxMZHExETCwv74LcSIESOYMGEC+/bto2HDhqSkpNCjRw9WrFjB9u3b6datGzExMSQkJBR4DwCNGzcmJCSELl26sGHDhhu2FxERERGRm2c6swuAdP8Gdk4i8gcNTysGaRYLNdbsKvXXjWvfAPciLMO4cOFCPDw8yMnJITMzE6PRyIwZM4Dc3i/jxo3j559/plWrVgBUr16d3377jQ8//JAOHTqQkJBAdHQ0zZo1AyA8PDzP9V955RXb5+Hh4QwdOpR58+YxfPhw236LxcKcOXPw9PQkKiqKjh07cuDAARYvXozRaCQyMpKJEyeycuVKWrZsaTuvd+/e/Otf/wJgzJgxLF++nHfffZf333//mvucMWMG0dHRjBs3zrZvzpw5hIWFcfDgQUJDQ5k9ezaff/45nTp1AnILPVWqVCnw/ZsyZQr3338/wcHB1KtXj9atW9OzZ0+6d++ep11YWBhTp07FYDAQGRnJrl27mDp1Kk888QQJCQnMnTuXhIQEQkNDARg6dChLly5l7ty5jBs3jkmTJtGsWbM891avXj3b505OTri5uREcHHxNxjfeeIPOnTvbtn19fWnU6I9J9MaMGcN3333HggULGDx4cL73GRISwgcffECzZs3IzMxk1qxZxMTEsH79etvXXkREREREilfAlb0AOFdtYuckIn9Q0agC6dixIzNnziQ1NZWpU6fi4ODAfffdB8Dhw4dJS0vLU3AAyMrKIjo6GoBnnnmG++67j23bttGlSxd69epF69atbW2/+uorpk+fTlxcHCkpKeTk5ODl5ZXneuHh4Xh6etq2g4KCMJlMeYY+BQUFkZSUlOe8q4WsP29fb7W02NhYVq5ciYeHxzXH4uLiSE9PJysrK09RytfXl8jIyHyvd1VUVBS7d+9m69atrF27ljVr1hATE8OAAQPy9CS67bbbMPypO2mrVq14++23MZvN7Nq1C7PZTO3atfNcOzMzEz8/PyC3p1Hv3r0LzHI9fy3qpKSk8Prrr7No0SISExPJyckhPT29wJ5GkZGRed6L2267jYMHD/LOO+/w+eef31QuERERERG5vrT0NMLNx8AAwXVus3ccERsVjYqBm9FIXPvS70LoVsQ5Ztzd3alZsyaQ2/OmUaNGzJ49m8cff5yUlBQAFi1aROXKlfOc5+zsDED37t05duwYixcvZvny5XTq1IlBgwYxefJk1q9fT9++fRk9ejRdu3bF29ubefPmXTNXkKOjY57tq6u5/XWfxWIp0r39WUpKCjExMflO8B0SEsLhw4dv+tpGo5HmzZvTvHlznnvuOT7//HMeeeQRXn75ZSIiIgqVzWQysXXrVkx/6SV2tcjl6up60/nc3d3zbA8dOpTly5czefJkatasiaurK/fffz9ZWVlFum6TJk3YsmXLTecSEREREZHrO7ZvK3UNOVzGA7/KtewdR8RGRaNiYDAYijRMrCwwGo289NJLDBkyhD59+hAVFYWzszMJCQl06NDhuucFBATQv39/+vfvT7t27Rg2bBiTJ09m3bp1VKtWjZdfftnW9tixY8WWd8OGDfTr1y/P9tUeUH/VpEkTvv32W8LDw/NdPaxGjRo4OjqyceNGqlatCuRO8n3w4MEC7z0/UVFRQO7E2lf9dT6mDRs2UKtWLUwmE9HR0ZjNZpKSkmjXrl2+12zYsCErVqxg9OjR+R53cnLCbDYXKt/atWsZMGAA9957L5BbtIqPjy/UuX+2e/fufIfDiYiIiIjI33cp7uok2LXw1iTYUoZoIuwKrHfv3phMJt577z08PT0ZOnQozz//PJ9++ilxcXFs27aNd999l08//RSAV199lR9++IHDhw+zZ88eFi5cSN26dQGoVasWCQkJzJs3j7i4OKZPn853331XbFm//vpr5syZw8GDB3nttdfYtGnTdefkGTRoEBcuXODhhx9m8+bNxMXF8dNPP/Hoo49iNpvx8PDg8ccfZ9iwYfzyyy/s3r2bAQMG3HB1sPvvv5+pU6eyceNGjh07xqpVqxg0aBC1a9emTp06tnYJCQkMGTKEAwcO8OWXX/Luu+/y7LPPAlC7dm369u1Lv379mD9/PkePHmXTpk2MHz+eRYsWATBy5Eg2b97MwIED2blzJ/v372fmzJm21d/Cw8PZuHEj8fHxnDt3rsBeWbVq1WL+/Pns2LGD2NhY+vTpc8NeXO+8847t67x7926ef/551qxZw8CBAws8T0REREREbk5EVu6iONbgRjdoKVK6VDSqwBwcHBg8eDCTJk0iNTWVMWPGMGrUKMaPH0/dunXp1q0bixYtsg27cnJyYuTIkTRs2JD27dtjMpmYN28eAPfccw/PP/88gwcPpnHjxqxbt45Ro0YVW9bRo0czb948GjZsyGeffcaXX35p6+XzV6Ghoaxduxaz2UyXLl1o0KABzz33HJUqVbIVht566y3atWtHTEwMd955J23btqVp06YFZujatSs//vgjMTEx1K5dm/79+1OnTh2WLVuWp0dTv379SE9Pp0WLFgwaNIhnn32WJ5980nZ87ty59OvXjxdeeIHIyEh69erF5s2bbb2eateuzbJly4iNjaVFixa0atWKH374wfYaQ4cOxWQyERUVRUBAQIHzE02ZMgUfHx9at25NTEwMXbt2pUmTgifWy8rK4oUXXqBBgwZ06NCBnTt38v3339smDRcRERERkeIVnHYAgPrN2ts5iUheBuvfWbf9FpWcnIy3tzeXL1++ZiLnjIwMjh49SkREBC4uLnZKWLEYDAa+++47evXqZe8oN3T77bfTuHFj3nnnHXtHKTYWi4Xk5GS8vLyu2xtLz4VI2ZOdnc3ixYvp0aPHNXPHiUjZpudXpPy6qefXYoZxlSEnHQZvAX/NaSQlq6Cax1+pp5GIiIiIiIiInSSfOgg56VgdXMG3ur3jiOShopGIiIiIiIiIneyPXQ9AnCEMjOVrgSW59Wn1NCnzytMIylWrVtk7goiIiIiIlCNZJ3cBcMmztp2TiFxLPY1ERERERERE7MTlwj4ADMH17ZxE5FoqGomIiIiIiIjYgdVqJTgjDoBK4Y3tG0YkHyoaiYiIiIiIiNjBmbNnqUISAJUjm9k5jci1VDQSERERERERsYMTB7YCcNbgh4t3gJ3TiFxLRSMRERERERERO7hybAcAZ91q2jeIyHXYvWj03nvvER4ejouLCy1btmTTpk0Ftv/666+pU6cOLi4uNGjQgMWLF1/TZt++fdxzzz14e3vj7u5O8+bNSUhIKKlbEBERERERESmyhk6nAHALa2jnJCL5s2vR6KuvvmLIkCG89tprbNu2jUaNGtG1a1eSkpLybb9u3ToefvhhHn/8cbZv306vXr3o1asXu3fvtrWJi4ujbdu21KlTh1WrVrFz505GjRqFi4tLad3WLSk+Ph6DwcCOHTsKfc4nn3xCpUqV7J5DRERERESkLPJLOQRAeFQLOycRyZ9di0ZTpkzhiSee4NFHHyUqKooPPvgANzc35syZk2/7adOm0a1bN4YNG0bdunUZM2YMTZo0YcaMGbY2L7/8Mj169GDSpElER0dTo0YN7rnnHgIDA0vrtsqs48eP89hjjxEaGoqTkxPVqlXj2Wef5fz58zc8NywsjMTEROrXL/wykA8++CAHDx78O5FvytGjR+nTpw+hoaG4uLhQpUoVevbsyf79+0stQ2kVt9SrTkRERESknLJa4cye3M+D6tk3i8h1ONjrhbOysti6dSsjR4607TMajdx5552sX78+33PWr1/PkCFD8uzr2rUr33//PQAWi4VFixYxfPhwunbtyvbt24mIiGDkyJH06tXrulkyMzPJzMy0bScnJwOQnZ1NdnZ2nrbZ2dlYrVYsFgsWi6Uot2xXR44coU2bNtSuXZsvvviCiIgI9uzZw4svvsiSJUtYt24dvr6++Z6blZWFk5OTrfBW2Pt2dnbG2dm5WN+nq9e63vufnZ1N586dqV27Nt988w0hISGcOHGCpUuXcuHChVL7mt0o51VX39ubcbVX3WOPPcZrr72Gl5cXe/bswcnJqVjv02q12v683nUtFgtWq5Xs7GxMJlOxvbaI3Lyr/3799d8xESn79PyKlF9FeX4TEw5SNTMZq9GRHO9w0DMvpaQo/77YrWh07tw5zGYzQUFBefYHBQVdt0fI6dOn821/+vRpAJKSkkhJSWHChAmMHTuWiRMnsnTpUv7xj3+wcuVKOnTokO91x48fz+jRo6/Zv2zZMtzc3PLsc3BwIDg4mJSUFLKysgp9v/b29NNP4+DgwP/+9z9cXV0BaNOmDd9++y1NmjRh+PDhTJkyBYCGDRvyyCOPEBcXx+LFi7n77rsZMWIEjRo1Ys2aNTRo0ACAxYsXM2rUKE6ePEnz5s3p06cPAwcOJD4+Hm9vb/773/8ycuRIjh07BsCECRNYtGgRgwYNYty4cVy6dIk777yTadOm4enpCcDPP//M5MmT2bdvHyaTiebNmzNhwgQiIiIASElJASA1NdVW3PuzXbt2ERcXx/z586latSoAPj4+tszJyckkJCTQqFEjPv74Yz788EN27txJREQEkydPpk2bNrZr7d27l1dffZUNGzbg5uZGx44dGTduHH5+fkBuoeTdd9/l008/5eTJkwQEBDBgwACGDh1KjRo1AGjatKntvV64cCEDBw7k8uXLREdHM3v2bJycnIiNjWXevHl8+OGHHD58GDc3N9q1a8f48eMJCLj+Cgovvvgid955Jy+//LJt3+233267z+J25cqV6x7LysoiPT2dNWvWkJOTU+yvLSI3b/ny5faOICI3Sc+vSPlVmOf33NHtPA7EW0PY+dPPJR9K5HdpaWmFbmu3olFJuNoLomfPnjz//PMANG7cmHXr1vHBBx9ct2g0cuTIPD2YkpOTCQsLo0uXLnh5eeVpm5GRwfHjx/Hw8LhmnqS0rOv/sGwyGHB2NBWqrdFgwOUGbd2cCv+lu3DhAr/88gtjx469pujm5eVFnz59+Pbbb5k1axYGgwGj0ciMGTMYNWoUY8aMyc3/e+8Rd3d3vLy8OHr0KAMGDOA///mPbY6p4cOHA+Dp6YmXlxcuLi4YDAbbe+js7Ex8fDzLli1j4cKFXLx4kYceeoiZM2cyduxYILc3y9ChQ2nYsCEpKSm89tpr9O/fn23btmE0GvHw8MiT46/Cw8MxGo0sW7aMZ599Nt9eL1ev8frrrzNlyhSioqKYOnUqDz/8MHFxcfj5+XHp0iV69erF448/zvTp00lPT2fEiBE88cQT/Pxz7l/oI0aM4OOPP+btt9+mbdu2JCYmsn//fry8vNiwYQO33XYby5Yto169ejg5OeHl5YWjoyNr1qzB19eXZcuW2b4GDg4OjB07lsjISJKSkhg6dCj/+c9/WLRoUb5fU4vFwvLlyxk2bBgPPPAAO3bsICIighdffLHAXnU3w2q1cuXKFTw9PTEYDPm2ycjIwNXVlfbt22v+MJEyIjs7m+XLl9O5c2ccHR3tHUdEikDPr0j5VZTn9+eP1gCQ5hdFjx49SiOeCFC0TgZ2Kxr5+/tjMpk4c+ZMnv1nzpwhODg433OCg4MLbO/v74+DgwNRUVF52tStW5fffvvtulmuDqP6K0dHx2sedLPZbCusGI15p4Sq//r1q8kdIwOY++gfk5s1f/MX0rPN+bZtGeHLV0+1sm23f2s1F1Lz9mqKn3DXdV/rr+Li4rBarURFRV2TGSAqKoqPP/6Y8+fP24ag3XHHHQwdOvSP14uPB7Dd96xZs4iMjGTy5MlA7nu8d+9e3nzzTVubq6919U+DwYDFYuHTTz+19Sx65JFH+OWXX2xtevfunSfb3LlzCQgIYP/+/dSvXz/PNfO7l7CwMKZPn87w4cN54403aNasGR07dqRv375Ur149T57BgwfbXu+DDz7gp59+Yu7cuQwfPpz333+f6Ohoxo8fnydLWFgYhw8fJiQkhOnTpzNjxgweffRRAGrVqkX79u0BbMW5gIAAQkNDbdcwGAy4u7vbehld9a9//cv2ec2aNZk+fTrNmzcnLS3NVuT6s6u96iZOnMjYsWOZNGkSS5cu5f777y+wV93NuFqMvfp9nx+j0YjBYMj3mRER+9JzKVJ+6fkVKb8K8/y6Xz4AgCmkgZ51KVVF+X6z20TYTk5ONG3alBUrVtj2WSwWVqxYQatWrfI9p1WrVnnaQ263v6vtnZycaN68OQcOHMjT5uDBg1SrVq2Y76D8uTo3TWE0a9aswOMHDhygefPmefa1aHHjGf/Dw8NtBSOAkJCQPKvlHTp0iIcffpjq1avj5eVFeHg4QJEmdx40aBCnT5/miy++oFWrVnz99dfUq1fvmi6if/4+c3BwoFmzZuzbtw+A2NhYVq5ciYeHh+2jTp06QG4Rbt++fWRmZtKpU6dC57qqQYMG18xjtHXrVmJiYqhatSqenp62os/17vuvveoaN27MiBEjuPvuu/nggw+KnElEREREREpPVo6FyplxAPhVj7ZzGpHrs+vwtCFDhtC/f3+aNWtGixYteOedd0hNTbX13OjXrx+VK1e29fZ49tln6dChA2+//TZ33XUX8+bNY8uWLXz00Ue2aw4bNowHH3yQ9u3b07FjR5YuXcqPP/7IqlWrSvx+9r7R9brHjH8Z1rN11J2Fbvvbix3/Vq6aNWtiMBjYt28f99577zXH9+3bh4+PT575c9zd3f/Wa17PXyuaV3sfXRUTE0O1atWYNWsWoaGhWCwW6tevX+T5ozw9PYmJiSEmJoaxY8fStWtXxo4dS+fOnQt1fkpKCjExMUycOPGaYyEhIRw5cqRIef7sr+9tamoqXbt2pWvXrnzxxRcEBASQkJBA165dr3vfN9urTkRERERE7O9I4llqkQiAf40mdk4jcn1262kEuUuyT548mVdffZXGjRuzY8cOli5dahvak5CQQGJioq1969at+e9//8tHH31Eo0aN+Oabb/j+++/zLAN/77338sEHHzBp0iQaNGjAxx9/zLfffkvbtm1L/H7cnByu+/HnOYqKo21R+Pn50blzZ95//33S09PzHLvaI+fBBx+87nw1+YmMjGTLli159m3evLlIuf7q/PnzHDhwgFdeeYVOnTpRt25dLl68+LeuCbmFqTp16pCamppn/4YNG2yf5+TksHXrVurWrQtAkyZN2LNnD+Hh4dSsWTPPh7u7O7Vq1cLV1fWanm9XXe1JZDbnPwTxz/bv38/58+eZMGEC7dq1o06dOnl6X13v+upVJyIiIiJSPp08uAOTwUqy0RuDZ/7Ts4iUBXafCHvw4MEMHjw432P59Q7q3bv3NfPe/NVjjz3GY489VhzxbhkzZsygdevWth43ERER7Nmzh2HDhlG5cmXefPPNIl3vqaeeYsqUKbz44os8/vjj7Nixg08++QSgSMWnP/Px8cHPz4+PPvqIkJAQEhISGDFiRJGusWPHDl577TUeeeQRoqKicHJyYvXq1cyZM4cXX3wxT9v33nuPWrVqUbduXaZOncrFixdt3zeDBg1i1qxZPPzwwwwfPhxfX18OHz7MvHnz+Pjjj3FxceHFF19k+PDhODk50aZNG86ePcuePXt4/PHHCQwMxNXVlaVLl1KlShVcXFzw9vbON3PVqlVxcnLi3Xff5emnn2b37t22CcgLYs9edSIiIiIicvMyT+4E4JxbTbxu8ucnkdJg155GUnpq1arFli1bqF69Og888AA1atTgySefpGPHjqxfvx5fX98iXS8iIoJvvvmG+fPn07BhQ2bOnGlb+j2/ScULw2g0Mm/ePLZu3Ur9+vV5/vnneeutt4p0jSpVqhAeHs7o0aNp2bIlTZo0Ydq0aYwePTrP0vQAEyZMYMKECTRq1IjffvuNBQsW4O/vD0BoaChr167FbDbTpUsXGjRowHPPPUelSpVsk0GPGjWKF154gVdffZW6devy4IMP2noIOTg4MH36dD788ENCQ0Pp2bPndTMHBATwySef8PXXXxMVFcWECRNsE4wXxJ696kRERERE5Oa188pd4MmzWmP7BhG5AYO1KLMjVxDJycl4e3tz+fLla5Z1z8jI4OjRo0RERGhp8b948803+eCDDzh+/Li9oxQoPj6eiIgItm/fTuPGje0dp8yzWCwkJyfj5eV13dXT9FyIlD3Z2dksXryYHj16aEUWkXJGz69I+VXo5/fTGDi6Bnq+B9H/LL2AIhRc8/gruw9Pk/Lr/fffp3nz5vj5+bF27Vreeuut6w41FBEREREREcBqhTN7cj8PjCq4rYidqWgkN+3QoUOMHTuWCxcuULVqVV544QVGjhxp71giIiIiIiJl1pnEBILSzmM1GDEE1LF3HJECqWgkN23q1KlMnTrV3jGKLDw8HI3KFBERERERe0jYu5kg4KQxlCpObvaOI1IgTYQtIiIiIiIiUkqyTv2xcppIWXdTRaO4uDheeeUVHn74YdtqUUuWLGHPnj3FGk5ERERERETkVuJ0fj8AGb4amiZlX5GLRqtXr6ZBgwZs3LiR+fPnk5KSAkBsbCyvvfZasQcUERERERERuVV4px4FwDlUk2BL2VfkotGIESMYO3Ysy5cvx8nJybb/jjvuYMOGDcUaTkRERERERORWYbVYCM0+DoBv1fp2TgM5OSmYzZn2jiFlWJEnwt61axf//e9/r9kfGBjIuXPniiWUiIiIiIiIyK3mbOIxAg3p5FiNhFSvV6qvfTHjMnvSrMSl5+Dv5IDThW/JOTkZL5cAoht/hptbeKnmkfKhyEWjSpUqkZiYSERERJ7927dvp3LlysUWTERERERERORWknRkJ4FAojGYMGeXEnudixnJbDp7gG0Xz7AzJYsDWR6csgb+pVVTDHyBX8Y5Qjet5+maWfyjSu0SyyTlU5GHpz300EO8+OKLnD59GoPBgMViYe3atQwdOpR+/fqVREaRAsXHx2MwGNixY8ffus7tt9/Oc889VyyZRERERERE/irS4TQA7pVLZj6j9IwkBq79kKh1h+h/2JFp56uwMrO6rWAU7JBNJ18voj3d8DIZsRqMnDMEstNalx1HPiI19XCJ5JLyq8hFo3HjxlGnTh3CwsJISUkhKiqK9u3b07p1a1555ZWSyCjFYMCAARgMBgwGA46OjkRERDB8+HAyMjLsHa3cMJvNTJgwgTp16uDq6oqvry8tW7bk448/LtUcpVHcyszM5OWXX6ZatWq4urrSsGFD5syZU6KvKSIiIiJyq3O8mFuU8a1W/EPTsiwWntlzkPlZLbEaTPhzgdaOR3nK5xgfRKSyvUUoO9o154tG1VnSrDYH2jVgd5v6fNsgkP84fUOdnHVs3daHlJQDxZ5Nyq8iD09zcnJi1qxZvPrqq+zatYuUlBSio6OpVatWSeSTYtStWzfmzp1LdnY2W7dupX///hgMBiZOnGjvaOXC6NGj+fDDD5kxYwbNmjUjOTmZLVu2cPHiRXtHu4bVasVsNuPgUORHHIAHHniAM2fOMHv2bKpXr87hw4dxdXUt5pQiIiIiIhXM2d8LMv7FOwwsw2zh0d1HWZnsgQNWJlZ3oW+1Owo8x2Aw4O/kgL9/KM29hrB9RywpKXvZEfsYrW5bgclUcsPnpPwock+jq8LCwujRowcPPPCACkblhLOzM8HBwYSFhdGrVy/uvPNOli9fbjtusVgYP348ERERuLq60qhRI7755hvb8YsXL9K3b18CAgJwdXWlVq1azJ0713b8xRdfpHbt2ri5uVG9enVGjRpFdna27fjrr79O48aNmTNnDlWrVsXDw4OBAwdiNpuZNGkSwcHBBAYG8uabb+bJbTAYmDlzJt27d8fV1ZXq1avnyZWf3bt30717dzw8PAgKCuKRRx7JM1F7amoq/fr1w8PDg5CQEN5+++0bvn8LFixg4MCB9O7dm4iICBo1asTjjz/O0KFDbW1uv/12Bg8ezODBg/H29sbf359Ro0ZhtVptbTIzMxk6dCiVK1fG3d2dli1bsmrVqjyvtXbtWm6//Xbc3Nzw8fGha9euXLx4kQEDBrB69WqmTZtm6zkWHx/PqlWrMBgMLFmyhKZNm+Ls7Mxvv/1GXFwcPXv2JCgoCA8PD5o3b87PP/9c4H0uXbqU1atXs3jxYu68807Cw8Np0aIFbdq0ueF7JCIiIiIi15d8Yi8AqV41iu2aOZYcBu2NY+WFK7iZjPxfwxr0rVa3SNdwcvKlSfT/4e0VTWTt0SoYiU2Ri0b33Xdfvj1TJk2aRO/evYslVLljtUJWaul//KkQUVS7d+9m3bp1ODk52faNHz+ezz77jA8++IA9e/bw/PPP889//pPVq1cDMGrUKPbu3cuSJUvYt28fM2fOxN/f33a+p6cnn3zyCXv37mXatGnMmjWLqVOn5nnduLg4lixZwtKlS/nyyy+ZPXs2d911FydOnGD16tVMnDiRV155hY0bN+Y5b9SoUdx3333ExsbSt29fHnroIfbt25fvvV26dIk77riD6OhotmzZwtKlSzlz5gwPPPCArc2wYcNYvXo1P/zwA8uWLWPVqlVs27atwPcsODiYX375hbNnzxbY7tNPP8XBwYFNmzYxbdo0pkyZkmcI2+DBg1m/fj3z5s1j586d9O7dm27dunHo0CEAduzYQadOnYiKimL9+vX89ttvxMTEYDabmTZtGq1ateKJJ54gMTGRxMREwsLCbNceMWIEEyZMYN++fTRs2JCUlBR69OjBihUr2L59O926dSMmJoaEhITr5l+wYAHNmjVj0qRJVK5cmTp16jBq1CjS09MLvG8REREREbm+K5cv4JWd+7NEjm/NYrmmxWJh8OYFLDqXipMBPmsQQUc/r5u6lqNjJZo2/R8BAXfa9lmtlmLJKeVXkceurFmzhtdff/2a/d27dy9Ub41bUnYajAst/dd96RQ4uRe6+cKFC/Hw8CAnJ4fMzEyMRiMzZswAcnu/jBs3jp9//plWrVoBUL16dX777Tc+/PBDOnToQEJCAtHR0TRr1gyA8PDwPNf/85xW4eHhDB06lHnz5jF8+HDbfovFwpw5c/D09CQqKoqOHTty4MABFi9ejNFoJDIykokTJ7Jy5UpatmxpO693797861//AmDMmDEsX76cd999l/fff/+a+5wxYwbR0dGMGzfOtm/OnDmEhYVx8OBBQkNDmT17Np9//jmdOnUCcgs9VapUKfD9mzJlCvfffz/BwcHUq1eP1q1b07NnT7p3756nXVhYGFOnTsVgMBAZGcmuXbuYOnUqTzzxBAkJCcydO5eEhARCQ3O/Z4YOHcrSpUuZO3cu48aNY9KkSTRr1izPvdWr98eYZycnJ9zc3AgODr4m4xtvvEHnzp1t276+vjRq1Mi2PWbMGL777jsWLFjA4MGD873PI0eO8Ntvv+Hi4sJ3331HUlISAwcO5MqVK3zyyScFvkciIiIiIpK/xLjdeALnqYSfT0CxXPP12B/5Pq06BquFN8Ou0NbH829dz2D4o19Jevpxdu58ijp1xuHt3fhvJpXyqshFo5SUlDy9U65ydHQkOTm5WEJJyejYsSMzZ84kNTWVqVOn4uDgwH333QfA4cOHSUtLy1NwAMjKyiI6OhqAZ555hvvuu49t27bRpUsXevXqRevWrW1tv/rqK6ZPn05cXBwpKSnk5OTg5ZW3yh0eHo6n5x9/kQUFBWEymTAajXn2JSUl5TnvaiHrz9vXWy0tNjaWlStX4uHhcc2xuLg40tPTycrKylOU8vX1JTIyMt/rXRUVFcXu3bvZunUra9euZc2aNcTExDBgwIA8PYluu+02DAZDnqxvv/02ZrOZXbt2YTabqV077xjmzMxM/Pz8gNyeRjfba+9qQe+qlJQUXn/9dRYtWkRiYiI5OTmkp6cX2NPIYrFgMBj44osv8Pb2xmKx8Oabb9K/f39mzpypuY1ERERERG7C5eO7ATjtVBW/Yrjeu3uW8NGlagAMDTjFIzXvLoar/uHIkamkpB5g+47+NG48h0reTYv1+lI+FLlo1KBBA7766iteffXVPPvnzZtHVFTJLBtY5jm65fb6scfrFoG7uzs1a+Z2g5wzZw6NGjVi9uzZPP7446SkpACwaNEiKleunOc8Z2dnILc32bFjx1i8eDHLly+nU6dODBo0iMmTJ7N+/Xr69u3L6NGj6dq1K97e3sybN++a3meOjo55tq+u5vbXfRbLzXeDTElJISYmJt9hlCEhIRw+fPPLSBqNRpo3b07z5s157rnn+Pzzz3nkkUd4+eWXiYiIKFQ2k8nE1q1bMZlMeY5dLXL9naKMu3venmdDhw5l+fLlTJ48mZo1a+Lq6sr9999PVlbWda8REhJC5cqV8fb2tu2rXbs2VquVEydOaA4zEREREZGbkJOUOwl2imf1v32tr+JWMe5MEBigv9dRXmhw79++5l9FRo4hI/M0ly5tZMeOx2jZYhGurgWPzpBbT5GLRqNGjeIf//gHcXFx3HFH7mzsK1as4Msvv+Trr78u9oDlgsFQpGFiZYHRaOSll15iyJAh9OnTh6ioKJydnUlISKBDhw7XPS8gIID+/fvTv39/2rVrx7Bhw5g8eTLr1q2jWrVqvPzyy7a2x44dK7a8GzZsoF+/fnm2r/aA+qsmTZrw7bffEh4enu/qYTVq1MDR0ZGNGzdStWpVIHeS74MHDxZ47/m5WihNTU217fvrfEwbNmygVq1amEwmoqOjMZvNJCUl0a5du3yv2bBhQ1asWMHo0aPzPe7k5ITZbC5UvrVr1zJgwADuvTf3H5GUlBTi4+MLPKdNmzZ8/fXXpKSk2ApZcXFxGI3GGw7hExERERGR/Dlfisv9xP/v/RL2wNnNjExwxGow0sP1COOje/39cPlwcHCncaPZbNv+CMnJ29m//2UaN/4kz6gKufUVeSLsmJgYvv/+ew4fPszAgQN54YUXOHHiBD///DO9evUqgYhSUnr37o3JZOK9997D09OToUOH8vzzz/Ppp58SFxfHtm3bePfdd/n0008BePXVV/nhhx84fPgwe/bsYeHChdStmzsrf61atUhISGDevHnExcUxffp0vvvuu2LL+vXXXzNnzhwOHjzIa6+9xqZNm647J8+gQYO4cOECDz/8MJs3byYuLo6ffvqJRx99FLPZjIeHB48//jjDhg3jl19+Yffu3QwYMCDPELn83H///UydOpWNGzdy7NgxVq1axaBBg6hduzZ16tSxtUtISGDIkCEcOHCAL7/8knfffZdnn30WyO2x07dvX/r168f8+fM5evQomzZtYvz48SxatAiAkSNHsnnzZgYOHMjOnTvZv38/M2fOtK3+Fh4ezsaNG4mPj+fcuXMF9sqqVasW8+fPZ8eOHcTGxtKnT58b9uLq06cPfn5+PProo+zdu5c1a9bw6quv8uijj2pomoiIiIjITfJNjwfALaRoK5v9mdmcxog9saThTm1TEh80j7nhzzF/h8nkSr2otzAanblw8TcSEytoR5EK7Ka+u+666y7Wrl1Lamoq586d45dffilyDw2xPwcHBwYPHsykSZNITU1lzJgxjBo1ivHjx1O3bl26devGokWLbMOunJycGDlyJA0bNqR9+/aYTCbmzZsHwD333MPzzz/P4MGDady4MevWrWPUqFHFlnX06NHMmzePhg0b8tlnn/Hll19edzhkaGgoa9euxWw206VLFxo0aMBzzz1HpUqVbH+hvvXWW7Rr146YmBjuvPNO2rZtS9OmBY/R7dq1Kz/++CMxMTHUrl2b/v37U6dOHZYtW5anR1O/fv1IT0+nRYsWDBo0iGeffZYnn3zSdnzu3Ln069ePF154gcjISHr16sXmzZttvZ5q167NsmXLiI2NpUWLFrRq1YoffvjB9hpDhw7FZDIRFRVFQEBAgfMTTZkyBR8fH1q3bk1MTAxdu3alSZMmBd6nh4cHy5cv59KlSzRr1oxHHnmEbt26MW3atALPExERERGR/OVkZxFqzp3SJKB6g5u+zspLOay3NsOEhXcbNcfJ5Hjjk/4mN7cIqlcfAsDBQ2+SkXm6xF9Tyg6D1Xpz67ZnZWWRlJR0Ta+Fqz/4lmfJycl4e3tz+fLlayZyzsjI4OjRo0RERODi4mKnhBWLwWDgu+++Kxc92W6//XYaN27MO++8Y+8oxcZisZCcnIyXl9d1f4uh50Kk7MnOzmbx4sX06NHjmrnjRKRs0/MrUn5d9/k9HwfvNsFicoGXTmH8y/ymhZFqNtNh035OZGTzdFgAr9esfOOTionVambL1gcwmdyIqjsRFxc7rB4uxaagmsdfFXlOo0OHDvHYY4+xbt26PPutVisGg6HQc62IiIiIiIiIVAgXjgBg9KsBN1EwslqtjDuwhxMZFio7OzIsIri4ExbIYDDRuNFsHBy8NadRBVPkotGAAQNwcHBg4cKFhISE6BtGREREREREpCDnf1/B2a/GTZ3+a8IvzD1dCQwmJtSugvtNFJ7+LkfHSnm2rVYzBkPp55DSVeSi0Y4dO9i6dWueiX9FStJNjqC0i1WrVtk7goiIiIiIlDH7dm+jLnDasTJF7SOUbc7kpaOpWAx+3OGWRGf/xiWQsPBycq5wOG4SWVkXaFB/hjqS3OKKPBF2VFSUbRUnERERERERESlYVlJuT6NTxqLPQzR97zIOW6viSjpvNWhR3NGKLD3jJKdO/Y+zZ5eSlLTY3nGkhBW5aDRx4kSGDx/OqlWrOH/+PMnJyXk+Kory1PtFpKTpeRARERERub6ArOMAeFYp2oidc+kX+OBcJQD+E3iZym6VijlZ0Xl61CG82kAADhx8nays83ZOJCWpyMPT7rzzTgA6deqUZ39FmQj76gz4aWlpuLq62jmNSNmQlpYGoBVeRERERET+IjU1hWDrOTBAULV6RTp3/J6VXKEGVQxnGFSn041PKCXh4c9w9uxPpKQe4ODBN6hff5q9I0kJKXLRaOXKlSWRo9wwmUxUqlSJpKQkANzc3DSGU25pFouFrKwsMjIyMBrzdk60Wq2kpaWRlJREpUqVMNlhQj4RERERkbIs8eheahqsXMENL/+QQp8Xd+ko/7sSBsCIah44mcrOL2iNRifq1p3Ilq33cSZpIUFn7yIgoIu9Y0kJKHLRqEOHDiWRo1wJDs6duuxq4UjkVma1WklPT8fV1fW6BdJKlSrZngsREREREfnDpeP7ATjtUBnPInQ4mHzsPNk4Ud90gn9U61FS8W6al1cDqlZ9gmPHPmD/gVfx8WmDg4O7vWNJMSty0eiqtLQ0EhISyMrKyrO/YcOGfztUWWcwGAgJCSEwMJDs7Gx7xxEpUdnZ2axZs4b27dvnO/zM0dFRPYxERERERK4jK+kQAMlu1Qp9zt6UdL6/kPvj+tiohtf0+C8rIsL/w5kzi8jKOseVlL34VGpu70hSzIpcNDp79iyPPvooS5Ysyff4rT6n0Z+ZTCb9sCy3PJPJRE5ODi4uLpqzSERERESkiJwuHwEgu1L1Qp8zJu4UVuCewErc5l+1hJL9fSaTM40azcLVpQomk+b8vRUVuVz53HPPcenSJTZu3IirqytLly7l008/pVatWixYsKAkMoqIiIiIiIiUS808LgDQoGGTQrX/MX4NKy9cwdFg4KXqhZ8DyV483GupYHQLK3JPo19++YUffviBZs2aYTQaqVatGp07d8bLy4vx48dz1113lUROERERERERkfLnQhwA7iGRN2yaY85gXPxlwIt/eJ8l3LVRCYcrXmZzBiaTi71jSDEqck+j1NRUAgMDAfDx8eHs2bMANGjQgG3bthVvOhEREREREZHyKiMZUs7kfu5X44bN58X9ylFrGK6k83KdFiUcrvhcuLCWDRu7sXfvUHtHkWJW5KJRZGQkBw4cAKBRo0Z8+OGHnDx5kg8++ICQkLLfdU5ERERERESkNMQf2g1AqqMvuHjfsP2Sc8kA3Ot5hkBXrxLNVpwcHL1JTT3EufOrMJvT7B1HilGRi0bPPvssiYmJALz22mssWbKEqlWrMn36dMaNG1fsAUVERERERETKo3PH9gCQwI07WJgtOWzKzB3V0y2oconmKm6eHvVwcQnDYknn/Pk19o4jxajIcxr985//tH3etGlTjh07xv79+6latSr+/v7FGk5ERERERESkvMo6m7tyWqpblRu2XXdmN1fwxJU0OoQ0K+loxcpgMBAY2I2EhFkkJS0hMLCbvSNJMSlyT6M33niDtLQ/upu5ubnRpEkT3N3deeONN4o1nIiIiIiIiEh5Zb18IvdP7xsXjX46fQyApk5ncHZwKtFcJSEosAcA587/gtmcYec0UlyKXDQaPXo0KSkp1+xPS0tj9OjRxRJKREREREREpLxzSsud2sXFr+oN265PdQfgdp/yuXy9p2cDXJxDMZvTuHBBQ9RuFUUuGlmtVgwGwzX7Y2Nj8fX1LZZQIiIiIiIiIuWdV1YSAN7BEQW2u5idw77s3Ole7qnWssRzlYTcIWrdAUhKWmrnNFJcCj2nkY+PDwaDAYPBQO3atfMUjsxmMykpKTz99NMlElJERERERESkPLmUlkWQ9RwYILBKjQLbrr5wBQtQx92Fqu4+pROwBAQF3U1W9gWCg+6xdxQpJoUuGr3zzjtYrVYee+wxRo8ejbf3H8sFOjk5ER4eTqtWrUokpIiIiIiIiEh5knT+ArUNqcCNh6f9cv4yAB19PUs8V0ny8mpIvajJ9o4hxajQRaP+/fsDEBERQZs2bXBwKPLCayIiIiIiIiIVQm2X3EKQ1dkTg4vXddtZLGZ+PnMU8KGtZ3YppRMpnCLPaZSamsqKFSuu2f/TTz+xZMmSYgklIiIiIiIiUq79vnKawTuswGZ7z+/jAj44kM1tvkGlkazEpabGcfDgGFJSD9k7ivxNRS4ajRgxArPZfM1+q9XKiBEjiiWUiIiIiIiISLmWfDL3T6/KBTZbeXo/AJEOZ3F3LJ8rp/1V3JHJHD/xCSdPfmHvKPI3FblodOjQIaKioq7ZX6dOHQ4fPlwsoURERERERETKs8W/bQbgkmNgge02XE4HoIXnrTMFTOXKfQFITPyOnJxUO6eRv6PIRSNvb2+OHDlyzf7Dhw/j7u5eLKFEREREREREyiur1Urm+QQAcjxDr9suJ+cKO7Nzh6TdHlTwCmvlia9Pa1xdwzGbUzhzZoG948jfUOSiUc+ePXnuueeIi4uz7Tt8+DAvvPAC99yjZfVERERERESkYruQmoW/5RwAXkHh12235/R6zhKIETNtA67frrwxGIxUqdwHgBMn/4vVarVzIrlZRS4aTZo0CXd3d+rUqUNERAQRERHUrVsXPz8/Jk/W0noiIiIiIiJSsR27kE6o4TwATr5Vr9tuVVLuKJ46jsm4O5hKJVtpCQm5D6PRmZSUvSQnb7d3HLlJRR406e3tzbp161i+fDmxsbG4urrSsGFD2rdvXxL5RERERERERMqV+HMpNDBcyN0oYCLs/dQDoFWlW2+qF0fHSgQF3k3i6W85cfILvL2b2DuS3ISbmmnLYDDQpUsX2rdvj7OzMwaDobhziYiIiIiIiJRLF84l4WbIzN0ooGi0I8sPyOL24MjSCVbKKlfpy9lzK3BxKXgFOSm7ijw8zWKxMGbMGCpXroyHhwdHjx4FYNSoUcyePbvYA4qIiIiIiIiUJw6ppwBIdfABR5d825zOzOZoehZGoGUlj1JMV3q8PBvSru06alQfYu8ocpOKXDQaO3Ysn3zyCZMmTcLJycm2v379+nz88cfFGk5ERERERESkvPHJTgIgxSX4um1+ObULgHoernjdYvMZXWUwGDAane0dQ/6GIheNPvvsMz766CP69u2LyfTHN3ajRo3Yv39/sYYTERERERERKW/urZ77Z1CVGtdt89Px9QBEu1wujUh2ZbFkcunSFtLTT9g7ihRRkYtGJ0+epGbNmtfst1gsZGdnF0soERERERERkXIr+ffiyHXmM8rOvsiunNxV1doHVCmtVHazb99Itm57kMTT39k7ihRRkYtGUVFR/Prrr9fs/+abb4iOji6WUCIiIiIiIiLllSH5ZO4n3vkXjY5c2MkpQxgArf0CSyuW3Xh7NwXg0qVNdk4iRVXk1dNeffVV+vfvz8mTJ7FYLMyfP58DBw7w2WefsXDhwpLIKCIiIiIiIlJuHD16iBrAsRxfquVz/LezJwFvIkwX8XW8qUXNy5VKlZoBcPnydiyWbIxGRzsnksIqck+jnj178uOPP/Lzzz/j7u7Oq6++yr59+/jxxx/p3LlzSWQUERERERERKTc80nNXT7tynYmwNyZnAdDcw1xqmezJ3b0WDg6VsFjSuXJlj73jSBHcVEmzXbt2LF++vLiziIiIiIiIiJRrRksWQdZzAJj8rp0I22q1EpvpB0AbP/9SzWYvBoORSpWace7cz1y6tAlv78b2jiSFVOSeRiIiIiIiIiKSP/fMswAkW11x8b52vqLEK/EkWHPnOuoYVKtUs9mTT6UWAFy6tNnOSaQoCtXTyMfHB4PBUKgLXrhw4W8FEhERERERESmv3DLPAHDMGkSQy7U/cm9Pd8VquEy4YwaBLu6lHc9uKlVqDsCly5uxWs0YDCY7J5LCKFTR6J133inhGCIiIiIiIiLln0tGEgDHrMHUcL72R+5NV3LnMWofkP/KarcqD48oatYcQSXvZkDhOqWI/RWqaBQbG8uYMWNwd3dnzZo1tG7dGgeHW3+GdxEREREREZGicM34o6eRq+O1vWk2XUoFoKV3xellBGA0OlCt6hP2jiFFVKg5jd59911SUlIA6Nixo4agiYiIiIiIiOTDPSu3aHTaIfSaaV5SsjPZeSX3Z+vm3q6lnk2kqArVXSg8PJzp06fTpUsXrFYr69evx8fHJ9+27du3L9aAIiIiIiIiIuWFT3bu8LQ3Btx9zbH1SQcwY8SPC1Rxdi7taHZnNmdy9uxSkq/splbNlwo9d7LYT6GKRm+99RZPP/0048ePx2AwcO+99+bbzmAwYDabizWgiIiIiIiISLlgzsItK3f1NINvjWsOrz2fCARRz/EsRmPFXMx8774RWK1ZVKncBze3CHvHkRso1Hdpr169OH36NMnJyVitVg4cOMDFixev+dCwNREREREREamwLh/HgBWrgyt4Bl9zeNvvk2A38aiYPWxMJme8vRoBcOnSZjunkcIoUmnTw8ODlStXEhERgbe3d74fIiIiIiIiIhWR4WI8AMcNwUz9+VCeY2arld1ZudO8tPa7tqBUUVSq1ByAi5c22TmJFEaR+8N16NCBY8eO8corr/Dwww+TlJQ7XnPJkiXs2bOn2AOKiIiIiIiIlAeGC0cB2Jvhz55TyXmO7Uk+TxquuFjTaRYYZY94ZUKlSi0A9TQqL4pcNFq9ejUNGjRg48aNzJ8/37aqWmxsLK+99lqxBxQREREREREpFy7mFo3irUF4OJvyHPrtTBwAtY0JuDn7l3q0ssLbOxqDwURGxgkyMk7ZO47cQJGLRiNGjGDs2LEsX74cJycn2/477riDDRs2FGs4ERERERERkfLCcPEIAAnWILxdHfMc23gpt+dRI9e0Us9Vljg4eODpWR+A8+dX2zmN3EiRi0a7du3Kd/W0wMBAzp07VyyhRERERERERMobw596GkUGe+U5ticnFIBOVZqXeq6yJiCgKwApqQfsnERuxKGoJ1SqVInExEQiIvIujbd9+3YqV65cbMFEREREREREyg2LGS4eA+CYJYj6lf8oGsWlZXAiMxsHA7QNqmuvhGVGaEhvggLvwtW1ir2jyA0UuafRQw89xIsvvsjp06cxGAxYLBbWrl3L0KFD6devX0lkFBERERERESnbkk9isGSTaXXgrNGfyGBP26HFZy8D0LaSJx4OputdocJwcvJVwaicKHLRaNy4cdSpU4ewsDBSUlKIioqiffv2tG7dmpdffrkkMoqIiIiIiIiUbRdy5zM6RSA1Ar1w/lNx6Ntj2wG4s5LFLtHKspycK/aOIAUo8vA0JycnZs2axauvvsquXbtISUkhOjqaWrVqlUQ+ERERERERkbLv96KRq3cg/3uyhW13fPJJ9puDMVgtdPHzsFe6MsdqNbNz10DOn1/NbS2X4uYWbu9Iko8i9zS6KiwsjB49evDAAw9Qq1Yt5s+fT8OGDYszm4iIiIiIiEj58HvRKNU5CBfHP/UyStgBQKTpJFU9Q+yRrEwyGExYzBlYrdmcObPQ3nHkOopUNPrwww+5//776dOnDxs3bgTgl19+ITo6mkceeYQ2bdqUSEgRERERERGRssxqKxoF5tm//GIOAHdWspZ6prIuKCgGgDNJC7Fa9f6URYUuGk2YMIF///vfxMfHs2DBAu644w7GjRtH3759efDBBzlx4gQzZ84syawiIiIiIiIiZVLO2TgAvjoTjMWSWwBJSjvPzpzcCZ//EVbPbtnKqoCALhgMTqSmHiIl9YC940g+Cl00mjt3LrNmzWLLli0sWbKE9PR01q1bx+HDhxkxYgQ+Pj4lmVNERERERESkbLJaMV6KB+BgdjBGowGA7xO2YsFENcNponxr2DFg2eTo6IW//+0AnDm9wL5hJF+FLholJCRwxx13ANCuXTscHR0ZPXo07u7uJRZOREREREREpMy7chqTOYMcqxGTh59t99JzKQB08sqwV7IyT0PUyrZCF40yMzNxcXGxbTs5OeHr61sioURERERERETKjd/nMzphDSDUI3cS7FSzma051QDoWbmm3aKVdf5+d2AyuZORcZLLydvsHUf+wqEojUeNGoWbmxsAWVlZjB07Fm9v7zxtpkyZUnzpRERERERERMq634tGx6xBVHHP7S2z6sIVMq0mwlycaBFY157pyjSTyYXwak9jcvDE3a26vePIXxS6aNS+fXsOHPhjYqrWrVtz5MiRPG0MBkPxJRMREREREREpB1JPH8QdSCCIKr/P4LLk7GUAevh762flGwgPH2jvCHIdhS4arVq1qgRjiIiIiIiIiJRPqYmHcAeuuIbhZYLMnCx+OnseMNI9wPtGp4uUWUUaniYiIiIiIiIiebmlJADgGVobgBWJO7hicceLKzTzcrNntHIjO/siZ5KWYLVkERY2wN5x5HeFnghbRERERERERP7CasUj7TgAD3VtD8CipNMAtHNNwsGoH7sL48qVvRw4MIqj8e9hseTYO478Tt+9IiIiIiIiIjcr7TxkJgMGqFQVsLAp3ROA7kFBdo1WnlSq1AJHRx+ysy9w6dIme8eR36loJCIiIiIiInKTss4eyv3Euwo4uJBlPMlpAgHoEBJtx2Tli9HoSIB/ZwCSzi61cxq5SkUjERERERERkZt09MAuAPak+wKQ6Jg7NM3PmEaAi7vdcpVHgYHdADh79iesVrOd0wgUciLsnTt3FvqCDRs2vOkwIiIiIiIiIuXJlVMHAbjoWgWr1cpJxywA6riqj0ZR+fi0xsHBm6ysc1y6tAUfn5b2jlThFapo1LhxYwwGA1arNd/jV48ZDAbMZlUDRUREREREpGKwXIgHwMGvOunp8Rw3+AHQyCfYjqnKp9whaneSePpbks4uUdGoDChU0ejo0aMlnUNERERERESk3HFMTQTAJ7g6bm4RxFvagAkaennbOVn5FBjYndNnvicn54q9owiFLBpVq1atpHOIiIiIiIiIlCuX07OplHMWjBBarQYWq5WTRjcAojxc7ZyufPL1bUPbNhtwcvK1dxShkEWj/Ozdu5eEhASysrLy7L/nnnv+digRERERERGRsm7vyctEGy4A4BlYjYMZWWQajDgbDVR3dbZzuvLJaHRSwagMKXLR6MiRI9x7773s2rUrzzxHBoMBQHMaiYiIiIiISIVwOCGBVoZsAKwewfyy+xPgNiJdHXEwGuya7VaQmXkGJ6cADAZNKm4vRS4aPfvss0RERLBixQoiIiLYtGkT58+f54UXXmDy5MklkVFERERE/iInJxWjyY0TGVkcTstkQ8Ji9ief56Q1kHNWH6zk/WHF2zWICDc3Ilyd8TMfxjdzJ3V9a1A3oCluLkF2ugsRkfIt0jV33p0MZz+M1lR2XDwOhtuo665eRn+H1WoldufjnD+/hmZN/4e3dxN7R6qwilw0Wr9+Pb/88gv+/v4YjUaMRiNt27Zl/Pjx/Oc//2H79u0lkVNEREREgJSsVMbvXsbyyw6cMYSRaVvctk6B5yWl53AoPfn3LW+gHVwA06EEQozbaeiSRisfT+4IrkMN77ASvAMRkVtHC990AFx8q3Ap7SgJhANQz8PdjqnKP4PBgIODN2AlKWmpikZ2VOSikdlsxtPTEwB/f39OnTpFZGQk1apV48CBA8UeUERERETAYrEw78gaxh23cI6I3J1WcDIYiHBzJsLZQrhTFhEuEOYMjn8ZFZHjWJWETAtH0jM5eDmRI2npnMxxJcfgyAlrMCfSYXE6jDp1njDny3Tyr8SLEcH4ON70FJgiIre+5JO5f3pVJi3tKMd+LxrVdXexX6ZbRGBgN86cWUBS0hJq1hxpmxJHSleR/xdQv359YmNjiYiIoGXLlkyaNAknJyc++ugjqlevXhIZRURERCq0XecO8eK+vWzLyV3R1p8LPFfZRKcqLanq6oypyP+RrgyA2WolIeUCW8/u5bcLZ9ma6kycJYTjmTl8cvIcy85d5p2aXrQPVM8jEZH8XD5zDG/A6hnK6ZQEzhtqASoaFQc/3w6YTG5kZJ7iypVdeHk1tHekCqnIRaNXXnmF1NRUAN544w3uvvtu2rVrh5+fH1999VWxBxQRERGpqFJyzLwWu4h5yaGYqYYD2fT1PsGo+l3wcPr7Qx9MBgMRnn5EeLbj/t9/95eclcmG5AxeP3yKI+mZPLjnLAOOb2J04xicTE5/+zVFRG4lq7fs4B7gvMmfPVdy5zfys6Tg7WCyb7BbgMnkgp9fR5KSFpGUtERFIzsp8hTkXbt25R//+AcANWvWZP/+/Zw7d46kpCTuuOOOYg8oIiIiUhEtSLpE2437+SK5KmYcaOEYz7JGvkxscm+xFIyux8vJmS7+3ixvVpseHmexYmRucg26r11K3KVjJfa6IiLlTbbZgq/5HAAuflXZl57b6zPMkmbPWLeUwMDuACQlLbWt3C6lq0hFo+zsbBwcHNi9e3ee/b6+vhpfKCIiIlIMEi/v56nYnTy5J57TWdlUc3FkRngKC9r2Isq3RqnlcHcwMad5Z8ZXvowr6ewxV6XL9uN8eXhlqWUQESnLLqZmEWK4AICrX2Xisr0BqGw22zPWLcXfrwNGowvpGQmkpOy1d5wKqUjD0xwdHalatSpmPQQiIiIixSon5woL9n/Ka2drcZYgjMCz1YJ4tloQLqYidw4vNo/W7kCrgGM8sXMPhyyhPH8cVp2fz9Qm3XBzdLNbLhERezt3JZNqvxeNTJXCuHAhCFIyqWK239/ZtxqTyY3qEf/BySkQV9eq9o5TIRX5u/nll1/mpZde4sKFCyWRR0RERKTCSc88x9B10xmc1IqzBBFkTGZ+o8q8WD3ErgWjq+r4VGN5m0708YwH4Ie06jy1YzMWDRUQkQrs8sWzuBsyAcjxCOFgWhYAYeYce8a65VSr9hQhIffi4OBp7ygVUpEnwp4xYwaHDx8mNDSUatWq4e6ed0z9tm3bii2ciIiIyK3OYrHQd/Nq1pnvAgPcXSmbKQ3a4FXGJlF1cXBmSrNetD66jufinVme4s2YuFO8VrOyvaOJiNhF+vnjAFwxenEyx0CmxYq7yYifVUUjuXUUuWjUs2dPzV8kIiIiUkze3rOIddk1cCSL8RHu/DO8cbFde++pZBb8soaQM6up5ZaCU9UmVK7fnuCwWhiMN9eD6f6I1lhdL/DvfQnMPH6Wys6OPFa5EkajVlYTkYol60Ju0eiyYwBrjv0EhFPLOafow3nkhjIyEjlz5kccHL2pHPqgveNUKEUuGr3++uslEENERESk4tl8ZjfTzwUB8FzAWf4Z3uLvX9RihoQNcHAJwdsXMCL99xXPrgBn/gub4Sw+nHCvR7VGt+Mb2QZCo8HRtdAv0TvYl8TMbMYdSWTU4ZPknP2Cp5s8//ezi4iUIzVckgEweldh58UzQDg1Ha/YN9Qt6tKlzRyOm4ibWw1CQx5QR5ZSVOSiUfXq1dm8eTN+fn559l+6dIkmTZpw5MiRYgsnIiIicqtKM1sYctRKNk60dIzn+ah7/tb1UlOusHPhTBqf/ALXK/EA+AI5OHDevxkJhFDp0m7Cs48QYLhIQOpvsO43WAc4uLCz5lMs9nqAf3eqg7vzjf+L+O+qgRxJTmTeOQNvXmpN+JGldKve7W/dg4hIeVLr96JRaNUaHM72AKCehwdwyX6hblH+/h0xGJxIS4sjJfUAnh517B2pwihy0Sg+Pj7f1dMyMzM5ceJEsYQSERERudWNPnySQ+lmAp0c+Ci6I8abHC52OvEEhxZOpd7Jr2jF77/hdvaGyO4Q2Q2HGncQ5OJN0O/t01NTOLx7LWlx62lqOgzHN0PKaRrun0aWZQkjjoxgwhM9b1g4MhgMTK7XiOObfmFtuh//PubOUr/j1PAOu6n7EBEpd5JPAmD2CCDeEgoGaOgdzDn0c3Fxc3DwxM+vPefO/czOnU/RuNFc3N2r2ztWhVDootGCBQtsn//00094e3vbts1mMytWrCAiIqJ404mIiIjcghYmHufTU+cBeLduNYLcir4izIE9sZxZ9jbNLy2hnSF3xZ5EQyAnIgfQ7N7/YHDO/5qu7h7UbdkVWnbN3WG1QuyXZC8cSrOcg9Q9+wyfv7edvgNfw8PFscAMDkYDnzZtR7d1P3PYEsozsdtY0jYEk7HIv5cUESl30s8fxxU472DiUrYPBizU9/Znlb2D3aJq13qF1NRDpKcfY8vW3jRq+CGVKjWzd6xbXqH/Re/VqxeQ+1ul/v375znm6OhIeHg4b7/9drGGExEREbnVHL2cwHP7EwAPnq7iSwffIhaMLh5j9+dDiTq3nEiDFQxwxLEWqc0GUq/TI4Q4FFzouYbBAI374FitDSlfPYnH6Q08lfwuW6ZspM7g/+Hh5VPg6R6OTnzUoA7dY8+y01yNt3YtYkSjnkXLICJSDp0+HkcEsOaiETwgxHgJN5OmwS4prq5hNGv6NbE7nyA5OZbtOx6hUcOP8fVtY+9ot7RCf0dbLBYsFgtVq1YlKSnJtm2xWMjMzOTAgQPcfffdJZlVREREpFzLMWfzVOxWUvCghjGRERFBNz7pqvSL8NPLMKMZ9c8vw2iwssf9No7e/RXVX9pMg66PYSxqwejPfKrh8eQSTrV8hUwcaZa1iRPv9uDK5Qs3PDXKtzovBF0C4L0LIWxL2n3zOUREygGr1UqAJbfH6FFHNwBqOqbYM1KF4OTkR5PoL/D374SzcygeHnXtHemWV+Qy6NGjR/H39y/WEO+99x7h4eG4uLjQsmVLNm3aVGD7r7/+mjp16uDi4kKDBg1YvHjxdds+/fTTGAwG3nnnnWLNLCIiIlJU43YuYKc5Amcy+bBBXVwcnG94TmZGGhu+eIOsKQ1h/QwwZ2GNuJ1j9y+l3rCfiGjWLbe3UHEwGgntPozjPeeTjDt1svdy6aMYyEi+4amD63SmheMxsnHihSPZZFksxZNJRKQMSrt8Hg9DOgB7XQMAqOtusmekCsNkcqVB/fdp2mQeTk6+tv1Wq9WOqW5dRS4a/ec//2H69OnX7J8xYwbPPfdckQN89dVXDBkyhNdee41t27bRqFEjunbtSlJSUr7t161bx8MPP8zjjz/O9u3b6dWrF7169WL37mt/o/Xdd9+xYcMGQkNDi5xLREREpDj9enIzH16sBsCIkBTq+4YX2N5qsbB18WzOTWzMbYfexik7GUtAXej7LYZ+31OtfqsSy1ozuj1Jvb4i1ehJWOpu+PwfkHG5wHOMRiPvR7fBx8HIvnQDb8efKbF8IiL2duXUQQDOWH046tIAgJZBTewZqUIxGh1wdg6wbZ88+SV79jyHxZJpx1S3piIXjb799lvatLl2zGDr1q355ptvihxgypQpPPHEEzz66KNERUXxwQcf4Obmxpw5c/JtP23aNLp168awYcOoW7cuY8aMoUmTJsyYMSNPu5MnT/Lvf/+bL774AkfHv9FVW0RERORvupRxkX8fvIjZ4EB75wSejryjwPaxu3ayb1xbmm4aQmXrGc7iw6YGo7E8+SvUurP4ehYVoGbjdrj/ayG4VIITm7H+3z/IuHKxwHOquPvzVmRVAN49doaNl27cQ0lEpDxKP5NbNDplDOFwem6homElP3tGqrAyM89y8NAYziQtZPuOR8nO1r89xanIS1ucP38+z8ppV3l5eXHu3LkiXSsrK4utW7cycuRI2z6j0cidd97J+vXr8z1n/fr1DBkyJM++rl278v3339u2LRYLjzzyCMOGDaNevXo3zJGZmUlm5h8VyeTk3G+y7OxssrOzi3JLIrecq8+AngWR8kXPbtny7LZlnCYSfy7ybsM25OTk5Nsu4UIay779mL5nJuNlSCPN6syOqv2o948RRHt4Y6WUv6YB9aDvfBz+ex+Gk1s4Nq0LAU8uwNPn+lMVdPVx574Ab749e5knd2xmedMG+LgUPJm25KXnV6Tsy0k6DMBpx1ByrOBtMhJo1PNrD0ZjJepFzWTvvn9z6dJGtmx9gPr1P8LFOcTe0cqsonx/FrloVLNmTZYuXcrgwYPz7F+yZAnVq1cv0rXOnTuH2WwmKCjvJJBBQUHs378/33NOnz6db/vTp0/btidOnIiDgwP/+c9/CpVj/PjxjB49+pr9y5Ytw83NrVDXELnVLV++3N4RROQm6Nm1v80ObvzkFonBauHR9AQ2rMj/N6BGSxbhR7/gmeSVYIBDpprsqv4MJo8AVq1ZW8qp8zKFDKFt3EQicw6y7/0e/BI1DEdn9+u2b4eZVZ7enMGPf29cRJ9U3+u2levT8ytSdlWJ3w5AvDW3Q0VwViJLlhy1HdfzW/qMxqdxcZ1FWtohNmzoSWbGA5jNdYGS751b3qSlpRW6bZGLRkOGDGHw4MGcPXuWO+7I7Vq9YsUK3n777TIx2fTWrVuZNm0a27Ztw1DIrtsjR47M03spOTmZsLAwunTpgpeXV0lFFSkXsrOzWb58OZ07d9ZQT5FyRM9u2XAiI4th2+PAbGFwFV/+E9En/4bnDuEw/zEMyfuwYuB8o6cJ7/4q4aay87U7ursJ1h8eoi5HcDgwBf9nFuLhff2hGN6nYnkszsJqUzT31j3PfRHtSzFt+abnV6TsSz/xDqTDpYDcDg0NvNLo0eFuPb92lpFxF7v3PEla2mFc3T7G17cj9evNtHesMufq6KrCKHLR6LHHHiMzM5M333yTMWPGABAeHs7MmTPp169fka7l7++PyWTizJm8EyWeOXOG4ODgfM8JDg4usP2vv/5KUlISVatWtR03m8288MILvPPOO8THx19zTWdnZ5ydr129xNHRUQ+6yO/0PIiUT3p27SfLnMW/Dxwj2WyhqZcbw2tWw9F47S+0MtJTcZr3EIbLx8A9EMM/PsS/RsFzHtlD7ei2HHH8Br65n1rmQ8TOfoT6LyzB5JD/fye7V2vGg2d/YN6Varx20pEOlS8S4h5YyqnLNz2/ImWXY9pxABIq5/4cWt/LM8/zqufXPhwdq9G82TccjX+Pkye/wN+vne3rYLWaAQMGQ5Gndr7lFOV786berWeeeYYTJ05w5swZkpOTOXLkSJELRgBOTk40bdqUFStW2PZZLBZWrFhBq1b5rwjSqlWrPO0ht+vf1faPPPIIO3fuZMeOHbaP0NBQhg0bxk8//VTkjCIiIiI3Y3TsIrZcycLdaOX9qPwLRgDb/jcB4+VjZLgEwjNroQwWjK6qXv82zvf6kgyrI43SN7Fp7tAC249r3IVqhtNcxoshsau1HLKI3BoykiH1LADrneoC0Ninsj0TyZ84OHhSq+YIWrdaTWjog7b9Z84sYuOmHpw+veD3ApIUxk0VjXJycvj555+ZP3++7R//U6dOkZKSUuRrDRkyhFmzZvHpp5+yb98+nnnmGVJTU3n00UcB6NevX56Jsp999lmWLl3K22+/zf79+3n99dfZsmWLbY4lPz8/6tevn+fD0dGR4OBgIiMjb+Z2RURERIrklxNbmHOpGgCvhFygmuu1PZoBkk4fp8GRWQDsjXoePMp+T5yajduxu+lYAFqdnMvJtV9et62bgytT6lTBgIWVmbX4Lk5zfIhI+We9cASAbFdfTjsEYsRMQ78IO6eSv3Jy8sVkcrFtnzjxGamph9iz93k2bOxKYuK3pKefwGLJLOAqUuThaceOHaNbt24kJCSQmZlJ586d8fT0ZOLEiWRmZvLBBx8U6XoPPvggZ8+e5dVXX+X06dM0btyYpUuX2ia7TkhIwGj8o7bVunVr/vvf//LKK6/w0ksvUatWLb7//nvq169f1FsRERERKXaXMi7z3KFLWA3+dHY5wqO1/3Hdtoe+eok2hnQOO9Qk+u6nSjHl39PsnqfZnLiT5olfUHnVC1CzIQTlv2Jtm+D69D6xiP9dqczYRC+6RVhwM2logIiUX8mnDuINxKb6g9lKFYdzuDm43PA8sa/Gjedy/MRnJCTMIS3tKHv3Dbcd8/VpS3T0p7bt+GMfYjK54uwUiLNzIM4uobg45z+Fzq2uyEWjZ599lmbNmhEbG4uf3x+TH95777088cQTNxVi8ODB16zGdtWqVauu2de7d2969+5d6OvnN4+RiIiISEkYun0pSUQSwAWmR3e6brt9sRu57cKPuYu6dBmHwWgqvZDFoPm/psMXJ+HIKpjXB55YCW75r5I2tmEnft20n1PZLrwdf5pRNUJLN6yISDG6ePwA3sApUxCYDNRyLvxKVGI/Dg6eRIQPIqxKf06c/IJTp/5HZuYpLJYsTA5/rJputVo5cuQdrNYs2z5f33ZEN/7EDqntr8hFo19//ZV169bh5OSUZ394eDgnT54stmAiIiIi5c38wz+xMCN3OPzkWpXwcfHOt53FYiVt4UhMBis7PdvTsEXX0oxZPEwOcP9c+Oh2uBjP0Q8eJOzfi3BwdLqmqZeTCxPrRNBv11E+OJ7EvYGVqO/pdu01RUTKgcyzhwE46ZLb86SuW5F/rBY7cnDwILzaU4RXewqr1UpOzmUslj8KRFZrNpUrP0RmZhJZmWfIzDqLi0vFnbOqyN/dFosFs/naSaNOnDiBp6dnsYQSERERKW9OXznOK8dzVyPp7XmSrlXuum7b9cvm0SZ7K9lWE5Xvn1RaEYufmy85D3xO9kd3EpG8iQ2zn+O2p9/Pt2kXf2/uDvBm4dnLDNqxjmW33Yazo0cpBxYR+fscLx0F4Kh77jxGrSu3tWcc+RsMBgOOjpXy7DManYis/Zp9ApVBRR5Q3qVLF9555x3btsFgICUlhddee40ePXoUZzYRERGRcmNMQhYX8CXUeJ7xjTpfv6E5h9qxEwHYHfYwftXqllLCkuEQ2pC9LXLv57bTX7Bl4UfXbTu2ZihupHMgx5+pe5aUVkQRkWLlnXECgN1uub1P6nuqAC63riIXjd5++23Wrl1LVFQUGRkZ9OnTxzY0beLEiSWRUURERKRMW3z2Et8mXcEIzGzYBI98hmjZbPuUgPSj5Dj7UPfBMaWWsSQ17fEo60P7A1Bv88scjl2bb7tgF2eGhOQOAfjgYhXikzW1gYiUL5aMFPws5wE44lMVX0cTAU4ania3riIXjapUqUJsbCwvvfQSzz//PNHR0UyYMIHt27cTGFj2l4kVERERKU6nUk4x7MBxAAZVDaSlj8/1G2dchpXjAHC44yVcPPOfOLo8avHYFGJdmuNqyMLju/5cSMq/IPRM7XbUMZ0gA1dG791QyilFRP6epIQDAFy0enDJy4dqxnMYDAY7pxIpOTdVEnVwcOCf//xncWcRERERKVeys68wcOtyzlsaEenmwNCIgpfj3fnlqzRMOwf+taHZo6WUsnSYHBwIf2oex6e3I8x6ij0fP4zn0OU4OjnnbWc0MrpmFR48AEvTIth8ZjfNg+rbKbWISNG4pyYAcMYlEIwGwh2S7ZxIpGQVuacRwIEDBxg8eDCdOnWiU6dODB48mP379xd3NhEREZEy7YNd/8cGSyNMmHmndhDOxuv/12rf3p1Exn8OQHK718DkWFoxS423jz+WBz4n1epCvaxYspa8nG+7DqGN6eB8BKvByOsHD2G1Wks5qYjIzfFMOQLA6Uq5o2zqeLjbM45IiSty0ejbb7+lfv36bN26lUaNGtGoUSO2bdtGgwYN+Pbbb0sio4iIiEiZs//MOt651ACAZ4KtRPsEFNg+ZdErOBtyOODeFK+G119ZrbyrVrcpiXdMA8B9+yzY8d98271RtwkmctiaE8GCY+tKM6KIyM1Lyu0sscOtOgD1KxXcw1SkvCty0Wj48OGMHDmS9evXM2XKFKZMmcK6det46aWXGD58eElkFBERESlzhh88RarBk0jHi7wY2bTAtnE719E8dTUWqwG3uyfALT7/Rc0OD0GHEbkbPz5HRvzma9pE+oTzgNcZAN5J8sWs3kYiUg4kH98FwDbXegA08ouwZxyRElfkolFiYiL9+vW7Zv8///lPEhMTiyWUiIiISFm27PhmNuXUxEQO79ariaOx4CLQ5WXjAdjmdQdhdVuURkT76/Ai1sjuYM4k+ZMHOXf6+DVNXm3YFW8HE/tSM/nq9AU7hBQRKbzs7GycLx4G4KBDVSpxBX9nDU+TW1uRi0a33347v/766zX7f/vtN9q1a1csoURERETKsqnxpwDo4ZZAQ5+wAtse27eVxldy/+/k131kiWcrM4xGMu6eyTFjFQI5z+k5fcjKysrTxMfRgeerBQEw8UgiV7LS7JFURKRQTh3di7Mhm3SrE8e8KxPhmGLvSCIlrsirp91zzz28+OKLbN26ldtuuw2ADRs28PXXXzN69GgWLFiQp62IiIjIrWTj+SS251TDRA7Daxc8LA3g7JJxVDNY2ebejiZRzUshYdnh6umD8aEvSP2iK/WzdrL64+fpMPC9PG0ereLPx8dPcCIrh4m7lzC2yX12SisiUrCzcbFUA46aKmM1mqjtdlOLkYuUK0X+Lh84cCAA77//Pu+//36+xwAMBgNms/lvxhMREREpW949mfub5Z5+Jmr5VCuwrfXcYZok/wKAV5cK1MvoT8JqN2Z3mwnUX/ccHZI+Z82PLWgf09923Nlo5NmgKww77s3/Xa7Mk8nHqepVcO8tERF7yEzcDcBR59xJsJsFR9szjkipKPLwNIvFUqgPFYxERETkVrPzSho/n0/GCAytGXXD9obfpmDEgrlmF2o2alPyAcuo+l0eJbbyQwA03jKCXbu25znet3o7Ik0nycSF0XvX2yOiiMgNOV04CMA+t3AAIt1c7JhGpHQUuWgkIiIiUlFNPJj7W+ZeQT5Ud3MuuPHFYxA7DwDT7S+WdLQyr+Gj0zniEoWXIQ2nbx/l0uXLtmNGo5HXa1YBYHFaDbYm7bJXTBGR6/JLOwLADo9wACLdVTSSW1+hi0br169n4cKFefZ99tlnREREEBgYyJNPPklmZmaxBxQREREpC7Ym7WNFshMGLAyu7HnD9vu/eQOsZqzVO0KVZqWQsGwzODgT/Pg8Lhu8iOQo3qteznO8Y2gj2jofw2ow8fqBQ3ZKKSKSv4yMDKqYTwCw37cGfoYreDtqTiO59RW6aPTGG2+wZ88e2/auXbt4/PHHufPOOxkxYgQ//vgj48ePL5GQIiIiIvb21uF9ALR1OkaUt2+Bbc+ciKP6ie8BiIsaWGDbisQtoBrufT4FDBi2/x/sy/sLyTfqNMZEDptzqrP4mIapiUjZYbwYh5PBTLrRhRMeIVQzXbB3JJFSUeii0Y4dO+jUqZNte968ebRs2ZJZs2YxZMgQpk+fzv/+978SCSkiIiJiT7vOHWJ1RjgAw2rWvmH7+B/G4WTIYa9TA2o261LC6coXh1p3QNvnALAuHkpGykXbsSjfavR0TwDgrePpWK1We0QUEbnG1fmMzniFgtFIDWfN4SsVQ6GLRhcvXiQoKMi2vXr1arp3727bbt68OcePHy/edCIiIiJlwIRDu7EajNzmGE+LoHoFtj1/fD/RSd8BYG47rDTilT8dXiTdsyqGK4ns+3x4nkMvR7XB1WhlX7YvS85dvs4FRERKWVJub9NDbrnzr9V0v8G8diK3iEIXjYKCgjh69CgAWVlZbNu2jdtuu812/MqVKzg6OhZ/QhERERE7WnLqMCsyIjBYLQyrEXHD9mfnj8DJYGa7UxPqt7unFBKWQ46u7Gr0OgCNEr/m5J7fbIcqewTxdFgwAOOPJGJWbyMRKQMSD+Wu+rjdsToAdTwLHqYscqsodNGoR48ejBgxgl9//ZWRI0fi5uZGu3btbMd37txJjRo1SiSkiIiIiD1czs5h2KEkAHq67KBNSKOC2+9fTZ2LKzFbDWTf8QYGg6E0YpZLzTv9g9/cOmE0WDF//2+sOVm2Y89UDcTHwcShtEz+7+hOO6YUEcmVcfoAADusNQGIqhRmzzgipabQRaMxY8bg4OBAhw4dmDVrFrNmzcLJycl2fM6cOXTpojH7IiIicut47fBJzlncCLIm8lqdhgU3tlhI/uFFAJa7dKF5y7alkLD8MhgMhD38DhetHlTNPsLBHybajnk5mPhXUA4Ak4+dJz0n3V4xRUSwWKz4ms8BcNIrBHdSqewRYudUIqWj0EUjf39/1qxZw8WLF7l48SL33ntvnuNff/01r732WrEHFBEREbGHFeeTmXf6IgZgelR1QnybFXzC7m8JS99HGi4E9RyjXkaFUC2sKhtrPp/7+a7ppCfF2Y49FV4bHy5xDl9m7PvFXhFFREg8dx5vQyoAJz2DqeqQhtFosnMqkdJR6KLRVd7e3phM1z4gvr6+eXoeiYiIiJRXl7NzGLo/d4GPJ6oE0CE4suATstPh59cBcL79BaKjbtBebDo88BzbjPVxIYvELwbC73MYeTi580xgGgCzznlyKeOKPWOKSAV2+kTu3L6puJDi5EFDf/0dLxVHkYtGIiIiIre6kXu3k5iVTbiLAyOq33gIgnX9+5B8AryqYGrz71JIeOtwdXYgvctkMq0OVL+8Aeuub2zHnoq8kxDDWZLx4m31NhIRO7mQGA/AWaMfADXdtHKaVBwqGomIiIj8ydLTx5l/wTF3tTSv33AzFfzfpbQLp0j/5S0AzB1fAUfX0oh5S2nd8jbORecW2ww/jYS0CwA4OzjxbEjuML/PLwVwJu2c3TKKSMWVdi635+lpR39ARSOpWFQ0EhEREfnd5exsXtgfD8DdjuvoVWfADc859NVLuJHOPkNNsuvdX7IBb1EGg4HKd48E/0hIPQs//zFP5iO1OlLdeIp03Bi9Z60dU4pIRZVz6SQAJxx9AQixnrRnHJFSpaKRiIiIyO+G7viF81ZPgjnN+EZdMZlcCmx/+tA26p/+HoDL7V/HxcmxFFLeohycIWZa7ufbPmPP2oUAmIwmXqnmCcAPKWEcTM2wV0IRqaDurGIG4JRnMCZrDpGVwuycSKT0qGgkIiIiAiw4tpUfU4IwWC28WTUHf69aNzzn/HcvYjJY2ejShpa3310KKW9x1VqxLSB3hd6A5f/m0rnTAHSv1pbOPk6YMTL68Cl7JhSRCsg7+ywAp9yDCDKcw9XJy86JREqPikYiIiJS4V3KOM/II8kA/MP9AD2q33XDc/b+Op96aZvIsprw6zUeg8FQ0jErhDr9p3HcEEogFzg65zGsFgsGg4HXa1fHwQArLiSz6kKyvWOKSEWSnFusTnQOoKqD/v6RikVFIxEREanwpsaf4Tw+BBnOM6Hx3TcsAOVkZ+G2Mnfenc2B91OzTqPSiFkhuHl4k9nrY7KsDkSnrWXTN5MBqOHmwmOVAwB4aW8s2WaLPWOKSAVx6MwVUq5OhO3sT4Sz2c6JREqXikYiIiJSoR1KzWD26SwAxtaqiqez5w3PObFsOuGWBC7hQb0Hx5R0xAqnZqM2bKv9LACN9kzi6N7NAPynijsepHAk25MPD/1qz4giUkFsikvCNTN35cZTzgHUcnezcyKR0qWikYiIiFRYZnMWLx86QY4V7vTzIqbyjecx4soZwnfmTth8qskwKvkHlXDKiqnFQy8T69IcF0M2hm8eIyMtBX/XSvzLJxGA6YlGLmWm2jmliNzqzp85jslgJRsT5xx9iParbu9IIqVKRSMRERGpkHJyrvDOhpGsuZiCs8HA2FqVC3fiz69BZjKENCbq7n+XbMgKzGgyUfnRTzhHJcItCWQsfgmA5+r1IJTTJOPJm3tW2jmliNzqriTlDk07Y/LFzZhN88C6dk4kUrpUNBIREZEK6cCxT5mT2Q2Ap6tUItzV+YbnnNy5EmK/zN24620wmkoyYoXnH1SF5G7vAlBp96cQvxYXR3dGVMn9L+yXl4M4dPmEPSOKyC0u51Lu3zGnnfxp5RuAg9HBzolESpeKRiIiIlLhWCw5zDiRynlDACGOOTwbEXbDc6zmHLIXDAHg/9m76/AorvWB499ZSTbubhAlAgSCt1CKe9EWK1r3e2u3vfVb760LVLBSoJRSihcp7ppAlIQQd/dkZX5/bH+0XKCEeML5PE+fp9k5M+fdkNmZfeec96R4TwHPXs0dpgD49psAvRYYf9jxIrJBz1S/YXRXXkKHmpdjTrVugIIgdFh6g4yiwjglNlvjRH9by1aOSBBankgaCYIgCIJwyzmddYBN+iEAvBXYCXPljW+JYjd/QiddMqWyBeoRrzVzhMIVBr+IbGIF2ZGs/Oa/KBQK/hPoiyTr2Vfbib1ZUa0doSAIHVB2aTVOchEAWWbODBBJI+EWJJJGgiAIgiDcct5OKUEvqemnyWO0k8MN21cW5+AV+REAp/wexcPTu7lDbDKyLCPr5dYOo3EsnSjv/QQAw7IXcyYpkz6u3RlnYZw28n6WBoPczt+jIAhtTmphFa5SIQCFGltCLdStHJEgtDwxIVMQBEEQhFvKheI0jtV1AgnjaBVJuuE+SaueoTuVXJA6c9s9zzZ/kI1QU1ZF+vlkClJzKcotoKS0lHJVDQpnDa6urri5ueFsZo+7vxdqjUlrh1tv1oOfoPjEEty1Oez/9T16PP0pb4WPYc/xOM6WV7Myq5C5Ho6tHaYgCB1IhI8dmQ7FUAIqMxUmSpE0Em49ImkkCIIgCMIt5ZvkM8hSJ3qqUujqMPGG7dOj9tG9YDMAZUPeQWPathItdXV1pKelE7/zDGmFWeTpSpCl/xl1owOyICsr6/JLClnCXmWNq40T7u7uhISHYdPJEUnVRgeiqzUw7FXY/jATyn/kaNSDDAgP5bnOrryalMWrSZn0tlITYm3T2pEKgtBBaNRKzGqN09Oc7URSWrg1iaSRIAiCIAi3jFqDga1VnQCY425/w/ayXod2y9MAHLYcwW0DRzdnePWWFZtK9LnzZFTlkZmZiV6v/3OjBBaSBnszG+zs7LB3c8TBwwmFiYrs7Gyy0jLITMukVtJSoC+loKiU6KIkdp8/iI/sTJhrAOEP3olS2fZWhrPrM4PMA5/jURlL2fbXGxTwZQABAABJREFUMXRby/2eTuzKTedQuZKFUef5fcCAetWoEgRBuBG9Xod9bTEAXTyCWzkaQWgdImkkCIIgCMItY0teCcU6cDdVM7XzwBu2l0+vwFebRDnmdJr+3xaI8O+lRV1k/849XKzMvOJ1KysrvGzd8HHxxC+8C46eztfcPzQ0FACDwUBRWh7pcZfISs/kUn46BdpSLkm5XMrLZf+n5+jZsycDBgzAxKQNjaySJKwmvA9rxjG8ZicHD+/njoGDedOjiIlxKi7p7Hj+/HE+D+/f2pEKgtABLN6zj0cNdQD06DyolaMRhNYhkkaCIAiCINwyVmQZC5rOdndApbhBLaOqIhR7/wOAZsTLWHn6NHd415VyJpF9u/eQUmVc+hkZOlu6E9yvG34hAdjb29erNtP/UygUOHZyxbGTKz3+eC0rK4szx04RfSGWsrIy9u3bR9TpSMbeNhz/fqFN/6YayDpoIIkOQwko/B3zfa+iv20fXdyG83L+lzxT0J91xWYMSotlmndIa4cqCEI7dzwqkUeBYqUVdhrr1g5HEFqFSBoJgiAIgnBLOF1wiROllaiQmeV24xXT5N2vI1UXg0sY6r4PtECEV7t0PpG9W38nrSYHAEmGABsfBo8egntw0yax3N3dcZ88gZHa0cTFxbFr1y6Ky0v4Yfs6wk+fZ9Tcu9BYmjVpnw3lPu09dIv70VsfCRd/h4BhzAh7gGPHlrKuti/PXyyhp20uftYurR2qIAjtmFmN8UFDuakddq0cy/WUZBdx8UQsfqGB2Ppfe5SpIDSGSBoJgiAIgnBL+OZSDODJAPUFXEx7/G3bS+cO4nNmBRLAmA9A2bK3TOXl5WzatInExEQAJFkiyNaHwWOH4hro1ax9q9VqunXrhr+3L1uW/0JsSTKR+fEkf5jG+OFjCBgQ1qz914eFawD0fwiOfgE7XwLfwSiUat6LmEbU0QNckDuxMPIUvw0YhkZl2trhCoLQDsmyjJ2+zPj/lh6tHM2VyotKidp9irikODJrC0AC8zMHmXTHWAKGdGvt8IQORiSNBEEQBEHo8ErravitwrjyzVwPp79tKxv06DY/jQKZY5bD6OczoCVCNPYtyyQcj2bzwd+orKxEoVAQ6hnIwMGDcPZ1b7E4AMxtLbn7qTnE7otk677fKJOrWLXzZ7qdOceYuZPQWLXyqKNBz0DkKsiPQ3tqBeq+CzE3tefrbsGMjSogXu/BP87uZlHvsa0bpyAI7dKFqlpcDMaEjIV959YOh6rKKuIT4omOjubSpUvI8h+rZEpgipoqqZbV+zcwODubgTOGo1CIBQGEpiGSRoIgCIIgdHjLLx6nFhs8yGaU99C/bXvm18+J0CZQIZvhM+PDFooQtNV1/PbtBk4XxQHg7OzM1KlTcXZu3ekGIYPD6dwjgK0rNhBdlMS5ggtc+ugzxkwcR5duITdVS6lJmdlRd/tzmOx6gfLtr6PuMgkrG3uC7YN43yefx1NlNlR40D+zgDkeYqlsQRBuztGSClzlIpBAad2ySfv/V1VSwfl9Z4hNiCW9OhcD8uVtzmb2dHH3p9ugCCydbfjlmzVcKE5lb+JRKrfqGDV2tEgcCU1CJI0EQRAEQejQZFlmdZ4WgLvtSlEqr78aWFlRHn7njKukRfo/zO0enVoiRApSclm38kdy9calncN9Qhg7exJqtbpF+r8RMxsLpj4xm9ADkWzd8xvlcjVrN6zDN8qXUaNGtVpiS9F7ARm7P8dTzuLc6n/Q7eEVAEz1vZ1MRQ7vXMrhxcQMAiw09Le1bJUYBUFon3ZlnOU+uQgAla1ni/Wrq9YS9fsJomNjSK3MxiD9mShycXImtGsYoaGhODhcWZtvxhPz2Lv6Nw4kHufE6ZOUV1UwefLkNnMdEdovkTQSBEEQBKFD2597kVSDI6ZyDfP8/n6qWcLq5+hNOZcU3vS5+18tEl/8wSg2/L6FWrSYoGLsHSPpfmfvFun7ZgUPCqdTeACHDh/m2KnjJCcns2jRIro5BTL8njFYOti0aDwqEw0Zt7+N+4H5dMv9lbKzv2DdYzIAT/i4EFdZw695Jcw/F8/mrvYE2Pm2aHyCILRPBoOBU2VqXpKMSSMT+5apaZRz6hIbNm8kVyoxviCBrcKSLh7+dL8tArcu169pJ0kSQ2aNxjnaiw0bNhAXF8fqZT8w/Z7pmNq0jUUMhPZJJI0EQRAEQejQvk1JBNwYYpqIi1W/67ZLijpERP6vIEHF0HcxMW3eAsoGg4EDa3ay/8IxZAmcVDbcPWcmTt5te8UvM2sLho8eQUTfXuzcuZP4+Hii8hKI/zyZwcH96Tv5DhRqZYvF03fIRH45uZkpNetRbnkK/PqDtRuSJPFRF2/iSjJJqLPg3qh4dvZ3wNq0ZRNbgiC0P7FFFylV2OCqLgE9mNg1f9IoLi6OX7dvoFaqQ42KcI9guvfviXuIz01NMwsLC8PCwoLVq1ZxKSuVHz5byqwH56JxFqMthYYRkxwFQRAEQeiw8mq17K82JmEWeP3NTb8sY7rrBRSSzCmrIXS9rXmLJ2u1WtavWMu+RGPCKNimM/f/85E2nzD6K3t7e6ZPn86MIZOwV1hRi5YdcQdY8s5XZB5J+rNIazOTJAm3Sf8hxuCDhb6U3JULwGAAwFypYHnXzthQRorszsJTuzAYdC0SlyAI7dfe3CQ0ch12+nIAJOvmTRqdPn2atWvXUquvw93OhYcfe5ix90/BM6xzg+oSde7cmXvGTEGFgnR9PqsWraAqtbgZIhduBSJpJAiCIAhCh7U6uxAdCnpZm3O71x3Xb5h6GK+Kc+iVGrynf9SsMZWUlLB06VJiUhOQkBgS1J9pT96LiXn7XBo+aFB3Hn3xKe7o0g8lCjINhSzdsZqd//2Z6uSW+ZIyIMiD4z3ep0ZW45J/hPQdH1/e1tnancVBNijRcbDOn1cif22RmARBaL+Ol1bhWpdv/EFtDprmG6F4YP1uNm/eDEBERAQLH3sAe0eHG+x1Y/49g5k+5R5UKEmX81mzdBUVsXmNPq5w6xFJo44q5TD8/h+oLW/tSARBEAShVehlmZVZhQDM83BEkv5mytTRrwBQhs/A2aP5lla+cDSab77+huzsbMzNzZkzdw6DZoxs9yvcKFVK7pw+iocffhhvGzf0koGjlTEsXb+SrKysFolh3l0jWe/4MAAeJ9+F3JjL2+50786/XUsB+K7UnzWJO1skJkEQ2h9Zljlfa417aS4AFSZO0AyrRBoMBnYs3cie84cA6N+/P+PGjUOpbLrpvf5dg5g+fTpKFKRLBfz040+UnWyZz2Sh42jfdyjCtRkMsPPfcPC/8FkPOPkd6LWtHZUgCIIgtKitWclk1mqxUykZ52R73XYX4qKQE7YZf+j3SLPEYqxftIM1v62nqroKV1dXHnjgATp3br4EVWtwdHFi/lMPMGHkOEyVJuRXFvHtt9+yY8cOdLrmnRamUEjcdf8rVPoMRWGog/X3QV3V5e2PBA9limU6AC9kWHG2IKFZ4xEEoX1KK71ELk64l2cDkFzX9KOMDAYDW7/5maNpZwEY4NOT4cOHIzVDcsq/SwAzZ85EKSlJUxSwfvMvVKeUNHk/QsclkkYdkSTBwGfAwR8q82Hr0/BVf4jfBi1UX0AQBEEQWtu3KfEAjDCJRKO89i2P3iAT+fN7SMiUeg4Gp8Amj0NXq+WXz1ezJ+EosiQTZO3DgrnzsbW1bfK+2gJJkujZvxeP/+MJwsLCkGWZo0eP8uv3P2P4o9ZQc7HUqLGYthgsnCAvlppfHkX+S58f9xxDd1U6NZjxWEIxVfrmjUcQhPbnaJExWeRXlwlAqcqpSY+v1+n55YvVnM6JBWBwYD9GzJ/QrCNO/QL9mTFrBkpJQaqigF8Pb0ev1zdbf0LHIpJGHZEkQfA4eOQYjPkvmDtAYSL8OAOWj4XM060doSAIgiA0qw2XDnGyzgNJNnC/b9frtjsck8wY3e8AmA18vMnjKM8vZemHi4kuTgIZBvn34Z6n5mJi1j7rF90MS0tLpk6dyrRp05CA6LR4Dv20uwU6doZpyzFIKjTxv3D0h9cubzJRKlnSow+OKpmLdRpeScxs/ngEQWhXEgydAPDHOFKx3NS5yY6tq9Py02ffE11kvCaM7HoHg2eOarLj/x1/f3+mz5iOUqkkITGBbdu2tUi/QvsnkkYdmVINfe6HJyJh4NOg0kDqYfh2CPy8EIpTWztCQRAEQWhyxTVlvJxSB8A0qxTCnMKv2zZ339dYSjXkanwxCRzapHFkxqbwzVeLyaorRC0rmXrHBIbMHtPu6xfdrNDQUAb59wFgb9xREg6fb/5OO93O6eDnAOh38TNO//7T5U2eli4sCvVHAn7ILuTXXLGikCAIfzpVakwWeeqNnw3VTZQ00mq1rP58BQllqUiyxPg+I+g/5c4mOXZ9BQQGcvfddwPGFdtiTp5r0f6F9unWumu5VWmsYegr8Php6D4TkCD6Z/iiF+x8CarFzZIgCILQcbxwbjcF2ONCAW93H3HddnklFQwoWA+A3O/hJi10eiHhAst/Wkm5XI21ZM786XMIG9KzyY7f3twxcxSB1j7IksyGXZsoTMtt9j57T3uO0w7jUUgyAQee5GJ85OVtA+2teMrHBYB/xF4gtuhis8cjCELbV2swEFVuTBo51hUAUGPW+KRRXV0da9asIbk8AyUKJg8cS8TYAY0+bkMEBQXRv3c/ADZv3UxpTlGrxCG0HyJpdCux8YRJi+DBA9D5DtDXwZHP4dNw46oxurrWjlAQBEEQGmV3xil+rfQF4F0/CyxNLK/b9szOlXhIBZRK1rjeNqfJYoiJieHHtT+iRY+Hxon7H30Q92CfJjt+e6RQKJjy4AwclTbUoGXNitXUVtY0b6eSRLcHviXBJARrqQrl2pkUFxVc3vx0J1fCVJlUY8r95xOo1lU3bzyCILR5hzOPUyfL2CrqsK7LB0Br4daoY1ZXV7Ny5UqSk5NRq9XMnDmTrsN6NUW4DTZk6BAclNbUoOWX5T81e705oX0TSaNbkVs3mLMRZv0MziFQUwI7XoAve0PMBlEsWxAEQWiXqnR6nksqAWCsWRKjvftft63BIOMRvwyATP+ZoNY0un9Za+DMnuP8/LOx4HPXrl1Z8OxDWDk2/co77ZGphYbpc2aiQU2BvpRfvl7T7F9U1KZmuCz8iTzJgU5yJslfz6CuzriirEoh8W237lhRzkWDO8+c2d6ssQiC0PYdLcoBIEyZhaW2EABDI5JGZfklLPlwMenp6Wg0GubMmYNfoH+TxNoYao0JU6ZOQSkrSK3J4fC631s7JKENE0mjW5UkQcBweOgQTPgcLF2hOAXWzYMlwyHtWGtHKAiCIAg35aPUXLJkRxykCt7rPuRv216K2k9XQwJ1sopOo59odN+Gah0HP9vMpv3bkWWZ8PBwJk2ahFKpbPSxOxJHHxcmDZ+AJEsklKWycd2GZl/Bx9bFi5rJ31Mjq4moPcH5H567vK2zjSfv/jEIbH2lLz9e3NessQiC0LadqTAmsm9XV6BEjywp6d89uEHHKskqZNmi7yjQlaKRTJg7Zy5eXl5NGW6juAf7MKhLXwD2xR4lOz69lSMS2iqRNLrVKZTQcw48cQYGvwhqC8g4CUtHwtrZUCjm+AuCIAhtX1R5FYvS8wD4KKwrjmb2f9veL2k5AIWdx2Nu79GovmWtgf2Lt7Cn/CxIEB7QlQkTmnf55PYs6LaujOoxGEmSiIo7z88//4xOp2vWPr273s7Ffm8DEJG2FKLXX942xXcg0y2TAfh3mprEkrRmjUUQhLbJYDAQU+cIwG0mxhp3kpUrwR52N30sda3E+hVrKTZUYIEpc2fci5t746a5NYeBdw/HW+OCXjKwft3P6Gq1rR2S0AaJuxnByMQCBj8PT5yFiHkgKSBuM3zZB7Y9C5UFNzyEIAiCILSGWp2Wp2IS0csw0dmWkTeaDpafADG/AuA26plG9S0bZPZ+vZn9pZEA9ArtwV0zJ4uE0Q30nXgH06ZNQ6lUEhcXx+pVq6itrW3WPkNHPwADHjf+8OujkBtzedu7PcYSqMikEgvuj4qiVtR5FIRbzsWyDEqwQYmOMNUfU5atGpbosUiHHEMxEjB3zjzcAtvOCKO/UigVTJ4zDdM/pg3vWLGptUMS2iBxR9NBVer0aA0NqE1k5QLjP4WHj0DASDDo4MQ38FkPOPgRaEWRSEEQBKFteSf6N+KqZWwUWv4TcONRQ/q97wIydBkHrmEN7leWZfZ8u5kDBWcB6BMSwdipE5CacBW2jiwkJISZM2eiVqlJvnSJFd8spaqqqnk7HfY6+A0BXTUlP8yltsbYn0ZlynfdQjGniniDF68kpiGLGo+CcEs5mp8EQGdFLoVZxqla+dLfj1q9FlmWKSo1rkjm4+KNs2/bG2H0V7bujozpPww7yZJufcJbOxyhDRJJow7qi7Q8eh2N4cNLOeQ1ZJihczDM+gnmbALXblBbBr+/Dp/3gqgfQVTYFwRBENqAc4UX+a7YFYBnXQpxMlH/bXs5Lw4pdgMAOT2ealTfu5dv4WD2GQD6d+nF6GnjRMLoJvn5+XGX8yBMZRVZhbks+eIbykrLmq9DhRImfU250hbb8kTOLv9zpFmgXSf+62eNBKzIqeDztLzmi0MQhDbnTEkJAKGaWnIzjFNWYyqsbvo42swKUjB+foT36dFk8TWn7iP78ti/nsIr3K+1QxHaIJE06oBkWWZnYSm5dTo+SMkh4mgsj8Smcrq08uafmvneAQ/sh0nfgLUnlGXAhgfh28GQvL9Z4hcEQRCE+tAZDDwRE48ONb1VF1kQOOKG+xRuexMFMjvlPlh2avjN/IkTJzicehqA2/x7MXK6SBg1VOiC25jWZRTmsimFVSV899nX5Gc0Y8LG0vlyfaM+2auJObLt8qbJ3mGXR6u9nZzNyvTk5otDEIQ2JU5nrGfU08YKqfgSAKYO3jd9nMz4NEoVVSglBV1CG1ZEuzUoTVWX/19XWC1GWwqXiaRRByRJEtsjAvkqxIcIa3O0sswvucWMPZPIqNMX+CmniNqbGSmkUED3e+DxUzDsNTC1huwo+H4CrLob8uKb7b0IgiAIwvV8EruLeL0HZlTzSdeeN64jlBeHfcpWAOICHsbyLzfINyM2NpZt24yJhgEhvRk+e1yDjiMYSSoF/jN6MWvYVKxlM8r0lSz7bgkpZxKbrc/w4bM4aTcWhSTjsPMJykoKL2+7z9OJx7ycAHg+qZjNGTHXO4wgCB2EziBzoc5YD+9Or4E4V14AwNLn5h8uJMlZAAT4+qPRaJouyBZScTybnI9PU3ksu7VDEdoIkTTqoEwUCia72LE1IpDfIgK529UOE0kiqryaJ+LS6HkklneTs8mquYlCj2ozuP0f8EQk9HkQFCpI3AGL+sOmJ6A8p9nejyAIgiD8VWJpBp/n2wLwhGM+fradb7hP3Z53USCzXd+bwXcMaVC/SZEJrF9vXHkrIiKC4dPGNOg4wtU8BgYwf+48HBRWVFHL9xtXc+z3w832tDt4/pdkSS64kk/Csoev2PavTnYMUUViQMljiRUcyolrlhgEQWgb4iurqTbIWCkVuGgr8JSN32s8Q/re1HEMBgOxsbEAdO3ZvcnjbAmy1gA6mZKtyWhzKls7HKENEEmjW0C4tTmfBftwZkAoL3R2w91UTaFWxyepufQ+Fsv90SkcK6mo/02ZhQOMeR8ePQHB40E2wJkV8FlP2Pcu1IkPF0EQBKH5GAwGnjx/llpMCVWm8kRoPRI3eXGo4zcCsMl2Dt08b7DC2jWkn09m7YZ16PV6ugR1YezYsWJKWhOz83Vh4RMP4mvhgUGS+e3gLjZs2EBdXdOvZmZpbUfZqC/QyxK9S3dwZvuyy9tUKnO+6zOJHspEajFlflwBZwsSmjwGQRDahlMlxsLV4dbmZMafAiAHR+wcXW/qOJeSk6moqECpVOLn1z7rA1ne5o4myA50MoVr4pG1+tYOSWhlIml0C3E0UfFkJxdO9Avh29BO9LOxQC/D5vwSJp5NYtipBFZlFVKlr+fUNQc/uOcHWLADPHuDthL2vWNMHp1eAQbxASMIgiA0vbU5xZzReqGmjk9DAlEqbjzNTN7/HhIy2/R96D9g0E0ne/Iv5bB6/Vq0kg53tQNTJk6+8XQ4oUHMbS2Z/fRChg8dhiRJnDt3ju++/Y7cpMwm76tL3xGc8JwLgO/xl6gp+rMPc1Mb1vQZSoAig3KsmH4+h3MFF5o8BkEQWt/+zBMA+EuXKLtkrFeXbR5w08c5ue4AAA6WdiiVyqYLsAVJkoTdtEAUlmp0uVWUbL3U2iEJrUzc7dyCVAqJ8c62/NozgD29g5jt5oCZQiKmooanE9KJOBLDG0lZpFXX1u+A3v1g4S6YthzsOkFFDmx+AhbdBom7QBRREwRBEJpIbq2W1y4a60U819mdMMd6FBnNjYWYXwFYzBTuCve4qT7L8or54fuVVFOLvcKK2Y/MR21mcrOhCzdBoVBw28DbmTt3LhYWFuTl57Fk5TJSjjZ9HcWIOe+RbhqALRVotj1xxX2Lrcae9b370VmRTSk23HM+i+jCpCaPQRCE1hVTYw5AuKUJ6rxoAGocQm/qGDV5FVysMSae7eztmjbAFqa0NMH+niAAKo9lUx1d0MoRCa1JJI1ucSGWZvy3ixdnBoTyip87XhoTinV6vkrPo9+xOOadT+ZgUfmNp65JEoROMk5ZG/kOmNlBfhysmgrf3wXZ51rmDQmCIAgd2gsX0ijV6elmZcbD3vVM/hx4HwmZdNfh9Ow9EBszdb37qymvZuU3KyiVK7HEjDn3zcXczrKB0Qs3q1OnTjyw4D5c1PbUSTp+/G09WVFN+9TbxFSD18IfQKWBpN1w8rsrtjubO7KhVy98pGyKsWVGbAmXqur5YE0QhDavpKaMdIMzAP2dA+lpkgZAcI/bbuo4MQcj0Up6LBVmmNlYNHmcLU0TYIflIE8AitYnoi9r+mnCQvsgkkYCAHZqFY94O3OsXzArunZmkJ0lBuC3gjKmRV1k0Il4lmUWUKm7wZQzlSn0fwSeOAsDHgelCVzaD18Pgg0PQWlGi7wfQRAEoeNZffEQ2wrKUSHzSRdvVIp6TDH7yygjr0mv89qE+j851tVqWfXlMvJ1JZiiZvbMmdi6OzYweqGhbBzsmP+P+3EysaNG0rJqw1oKLzbxqj7OXWDY6wAYdrxE+oXIKza7Wjjxa+8I/Ex15OtUTIlMIqW+I7IFQWjTjuXFIUsKHCnC28weKd84otHWt9dNHSfmgrEAdhcv/w5T785mhA8m3lZYDfJAYVn/By5CxyKSRsIVlJLESEcbfgr350CfLsz3cMRCqSCxqpYXLmQQfiSGlxMzSL7REzYzOxjxJjx2CsKmAjJErYHPI2D361BT1iLvRxAEQegY8qtLeD3NWHNvlvVFQizN6rVf+c63ABlC7gKX+ieMDAYDv6xdT3pNHipZwYwJ03AN9GpI6EIT0JibMefh+dgqLamkhpUrf6Asu6hpO+nzAPnO/VHoa2DtbLSVxVdsdrNw5tde3QkwNyWrVsvkMxe4UJzStDEIgtDiThXnAhBsUgr58WDQgcYWbOr/mV+ZX0ZKjTGZ3bV/j+YIs1VIKgVOD3bH+k5vpPo8qBE6JJE0Eq4r0ELDO4GenB0QypsBHviamVKuN/BtRgEDjscxM+oivxeWYfi7qWt2PjB1Cdy/B3xuB10NHPoIPguHE9+CXtti70cQBEFov56J2kcp1nhKObzabXS99qnKOI/VxS0AJIU8Wu++ZFlm586dxCbHo5AUTB48jk49AxsUt9B0rOysmXv/PCwkDSVUsvLbFVQVVzRdBwoF8l1fk4s9Xvp0Mr6bedWiHk4mataH++NnpiKrTs+UyIskFqc2XQyCILS48xXG7yPdLFWcP3UQgHzLIGP5jXo6t/80BknGTmmFm69ns8TZWiTln78HWatHm1/VitEIrUEkjYQbslYpuc/TiUN9u7C6my9D7a0B2FNUzqxzydx+PJ5v0/Mp+7upax4RMG8LTF8DDgFQVQjbnoGv+kHcFlEsWxAEQbiujSnH2FHdCUk28L6/A+bq+tWKuLjuJQD2KPrj5Nez3v0dPniIY8eOAXDXxLsIubP++wrNy87VkXvvvRcNavINpaz9aS06na7Jju/s4UPC4G+olk3oXHyE3F+ev7qNqZo1oQ64Sfnk48CUyCSSS9ObLAZBEFpWfJ3xu02EnQtVqWcAuKj0valjxCTFARDiHdhhpqb9L11RDblfRFKwJBpDddN97gptn0gaCfWmkCSGOFizqrsvR/sG84CnE1ZKBcnVtbyclEn4kRj+dSGDC5U11z6AJEGXMfDIURj7IVg4QWESrJ0Fy0ZDxqmWfUOCIAhCm1daW8G/Lxmfak6xTGaIZ+967Re5azVdS/dhkCXsx7xc7+LXx3/dz+49vwMwYsQIunfv3rDAhWbj6uvBjCnTMVGrSc1OZ/369ej1N6i5eBMGDR7Ojx4vAOAS/S3Fh5dd1cbbyo0NPUNwk/LJw4F7zkaTWyHqNgpCe1NUV0cuTgD0cQrEutSY/FF51P+zv6KigsyafAC63xbR9EG2EQpzFbLWgL6kluINiTdeKEnoMETSSGiQzuamvBHgQeSAUN4N9CTQXEOV3sDyzAIGnYhnWmQSv+WXor/Wh4lSDb3vMxbLHvQsqMwg7Sh8NxTWzYeipl0VRRAEQWi/XojaRQH2OFPI291H1Gufwtw0fA4bR4gcd51OeK/6rYATvecMv53dB0CEexgDBgxoUMxC8/Pp6sf0GTNQKpXExcWxecMmDAZDkx1/8uzHWaG+GwCLXc+QG73vqjadrD1Y3yMYe0pIl92YffoolbVNXGdJEIRmdb7CWKe1k5kJ9iZWeNclA+AYUL8HFABxcXHIyLi7u+Ps794scbYFCo0K++lBoJCoPldA1anc1g5JaCEiaSQ0ioVKyTwPR/b3CeLncD9GO9qgAA4WVzAv+hL9jsXxZVoexdprDGE0tYIhL8HjpyF8NiBBzC/wRW/Y8W+oEjdegiAIt7JjxWVsqOwEwNudNVibWt9wH9lgIHP5fdhRRrKiEz3mfVSvvi6ejGPD/i3IkkyQdSfGLpzcmNCFFuDr68vUqVORJInI6Cg2fbOuyZ5825irGfHoJ+xX9sMEHVYb50NR8tUx2HiysltnNNRw3hDAgye3odeLZakFob2IKjeOZO1uZU7WxfNYSDVUyaZ4+ner9zFiYmIACA2t/2IL7ZWptzXWI3wAKNl0UdQ3ukWIpJHQJCRJ4nY7K5Z17cyxfsE86u2MnUpJek0d/7mYRc8jMTwdn0ZsRfXVO9t4wMQv4aGD4DcEDFo4+gV81gOOfA46saStIAjCraZGb+CZC5nISEx1kBnXqX6jhSI3fES36uPUymrkyd+gMTO/4T4ZsSms3boevWTAW+PKtMdmoVCKW6T2IDg4mFHdBgMQmRPHlqXrmyxx5GZrQZdHVlNgGYi5tgiWj7/maOgIBx++DLBGgZ7d2jD+mywKYwtCe3G6yLjiWXdLMwoSDgNwSe2PSm1Sr/1Li0pISUkBbo2kEYDVIE9M/WyQtQaK1sQj65pulKfQNok7IqHJeZuZ8rKfO2cGhPJRkBehlhqqDTKrsosYcjKBiWcS2ZRXgtbwPzd1rl3h3g0wez04h0JNCex8yTjy6PzPoli2IAjCLeTDlBySqmpxMVHxVnDX+u1UkEh43H8BOBXwBH5hfW+4S15KNqt+Wk0dOlxV9sx6bB4qk/rVPxLahr6T7mBYgHEq4en0aLYt29BkiSMXBwccH9wMjoFQloG8fCyZyXFXtRvrGcKrnsYvTh9nVPJTjhgtLQjtwZmSQgD8FBkYMk4DUGhbz2sOcP6AcR9nhS02NjZNH2AbJCkk7O8JQmGuQptVSdnutNYOSWhmImkkNBszpYKZ7g7s7hXExh7+THC2RSnBsdJKHohJoc+xWD5JySG/Tnvljv7DjKOOJnwBVm5QkgrrFxprHqUeaZ03IwiCILSYfdnn+TItB4B3Az2xUatuvJNeC+vvQ9JVI/sOZsDMf99wl9LSUlb+sJJq6nBQWHPvQ/MwtdQ0NnyhFdw2czhDOvcD4GTaOX5bvrHpDm7lCnM3Y3AIQCrLRPn9OC4lRl/V7MGA3jzm7QzAP+PTOFhY2HQxCILQ5LIqc8nHAYA+TgG4lhunmeHZq97HiIkzJpG7uPt32FXTrkVpbYrd1EBMOltj0c+ttcMRmplIGgnNTpIk+tpa8k1oJ071D+EfPi44qlVk12p591IOEUdieTwulciyv8yJVSih573Gekd3vgQmlpB52rjK2o+zoCCx9d6QIAiC0GzKtTU8lZCPAQUjzFIY7WRbr/0Kt74B2ZGgsUWauAhJofzb9pWVlaxcuZJyXRW2akvuXTAXC8cb10xqz/RaA4VZFSSdziPheA6VpR1n+rckSQyaO4rBvsbRZcdTI9mxtGkTR+X3/EKawhNXCjBbdRcXL5y/qtmLvm7c5WyLTob55+KIK05puhgEQWhSJ/IuAOAh5WGnNMG9JgmAPrcNr9f+2fHpZNcWgAzdh9W/cHZHYRbigNMD3VDZmrZ2KEIzq8ejO0FoOm6mJjzv68ZTnVzYlFfCkowCIsurWJdTzLqcYiKszVno6cQ4JxtMFAowsYA7noWIubDvHTi9AuK3QMJ26LUABv8LLBxb+20JgiAITeSZyH3kyK44UshH3eq3elny6V34nP4cJKgb8zEm1n+/ek1VeSUrV62koKAAa2tr5i1YgK2tbRNE3zaUFZRRkpNOaX4mRZkZZMRdJD81Db2uBklhgSRZIiksQWGJtaMjoQMD8Ovph4OnN5KifT9PHDxnNPL3MvuTT3A07Syq3RYMGTq0SUYA2Dh7w0PbSf96NF76DLJX30Xi9I0EdPlzKotCkvgo0I24gmguGDyZHXWBnf3scNDcGtNWBKE9MU5Ns6KLSQXknAeDDiyc0Dj61Gv/Qzv2AeBr6YFDJ5fmC7QN++tna21qGSZeVkiKW2fE1a1CJI2EVmGqUDDN1Z5prvacKatkaUYBG/NKOF1WxenYVF41UXGvuwNz3R1xMVWDpTOM+xj6PgS7XoUL2+HktxD1I9z+FPR7BExuXOxUEARBaLs2ZcSxscIVgHe8tTiaO99wn5qKYsy2PIJSkjlqNZJ+XSf9bfvyglJWLFpCgb4Mc3Nz7r333nabMKqp1JIek0F63AXyUpIpyUmluiwTg674uvvI+mpkCi7/XJwBh9YY/zO3scUtsDv2HqH0HHk7lvbtc+TVnXPGIK1Tsi/mKAcPHUKn1zNixIgmSxxJD/5G+tejjImjH+8i4Z5fCQr+c6UlC7UpP/TswehTCWTKztx76gAb+49Brfz70W+CILSs85XG2mddrUyQM08hAXhEQD0+K0pziogrSgYJbrvj9uYNtB0o3X6J8v0Z2IzpjNUgz9YOR2hiImkktLqe1hb0DLHgVX93VmYV8n1mITl1Wj5KyeWz1FzGOdmy0NOJXtbmSE5BMPNHuHTQWCQ7OxL2/AdOLYUhL0G36dDOn5IKgiDcivJra3kuqRCwZLLmLON859Vrv/hljxAu55GJM0HzF/1tYqA8v5Tli5dQqC/DDBPmTL8XJyenpnkDLagwI419P/xEatQpZEPFNduYmNvg6tcZBw8vrJ3ckJT2OHk7oddWUFFcSEVRISU5+RSk54BcQXFONlWlJVw8uZ+LJ/dz8tfFmNl0xqdrb3qMGoJ7wN+P3mprBk8biZm3Ldu3b+fo0aPU5FcwfuYkFE1wj2Dt7IX08G9kLBqNpz4d1k4kc85WPHyDL7fxtvJgSZd87omv4YzWiyfPbOer3uMa3bcgCE1DlmVitcZ6Rr3sXDm/ZzndgARlIEH12P/Iln0YJBkXlT2+vbs0a6ztgdLeWA+wdEcKmiA71C4WrRyR0JRE0khoM5xM1PyzkyuPe7uwNb+EpZkFnCit5Ne8En7NK6GbpRkLPB2Z6GyHpvNAuH8vRK+H31+H0nT49WE49hUM/w/43dnab0cQBEGoJ1mWeSzqCCWyHZ5k8Hb3kfUaFRKzawXhhdvQyxK5wz+lp73Dddv+b8Lo3ntm4erdfop3GgwGkk6c4vyeLaREnblim4mZA9ZO3jh6d8ajSyCduwdj43z938W16HVaMuJiOPbL72QnRqHXFlFdepH4QxeJP/QjajMv3AMjGLZwPLYu7WMaRt++fVGr1WzatImzSeep+bKKqY/MRNkEI36sHL2QHt5O+iLjiKPKTXPg4b1ganm5TT+3cN4p38HTGSb8UuFJYNxengoW9yeC0BbEl2ZTijVquY7+zsGUlhqL25fY33jltLq6OqJyEgDo16vPLVUA+3os+rhSE1dETXwRJRsv4nh/V/F76UBE0khoc9QKiYkudkx0seN8eRVLMgrYkFfMuYpqnopP542LWcx2c2COhyOe3aZB8Hg48TUc+NA4H3nlROMKbMP/Ay4hrf12BEEQhBv4MTuf/ZV2KGUt7/losTW/8aiWouwUPA+/CMBhtzkMun3MdduW55eyfNESCg1/jjBy6+LVZPE3p5rKKvb/sJn4w9vR1f4xrUyS8O/VD/8+Q/Hv1RVT88Y/0VWq1Ph0DcenazgAqeeTiNy5j/TYU9RWZKCtTic1Kp0lT/yKm38QAf1uI2zwcMysrBrdd3Pq2bMn+pRKtkXtIa7wImu/+J67H70Xlarxt8CWjl5UL9iCbtUILEouGB9e3f39FVNbZgaO5ELFWhaXBvFejg3BNhcY6R7Y6L4FQWicU38M0gzV1KCorsXdkA2AV9cbTzWLjIykRleLnY0t3W7BAtjXIkkSthP8yL1YQm1yKdVR+ZiH33iKudA+iKSR0KZ1tTLnk2BvXvZzZ3V2IcszC8is1fJZWh5fpOUx2smGBR6ODBjwBFKPe2H/+3DyO0jaDRf3QPgsuPPfYN1+niYLgiDcStKqa3k5KQeAB+xzGOI78cY7GQzkfD+fECpIUPrTZ977121allfM8sVLKTKUY4Ypc2bMxi2o7SeMCtJz2bPiJzJiDiAbqo0vSmq6DhlBn7smYevi2qz9+3T1x6erP3AfhRnZnN62h8z4UxRlJZGdlEB2UgIHV6/Cu9swRj04q03XP+o9eSAqlZLNp3dxoTiV1Z8tZ8ajc1Gbqht9bCePzjD9B1g+FuI2UbvvA0zvfO6KNq+ETyHp6Dp21wXzWFINW21qCLTQNLpvQRAa7mS5FoA7XANIiz5IEJCKG94uf/+dwWAwcPToUQD63zYApUrUKvt/KnsNVnd6UbYzlZKtyWi62KPQiHRDRyCKvwjtgoOJisd9XDjeL4SlYZ24zdYSA7A1v5QpkRcZcjKBlSUGKke8BY8eh5C7QDbA2ZXweU/Y+zbUXrvugyAIgtA69LLME3FpVOgN9La24KXu4+o3nP34YkKqz1CDCaqp36HRXPsLeEVFBd9/v/LPhNHMtp8wSjoVw9J/vsqKZx4g/fwOZEM1CpU1/n0mc/8XyxjxwMPNnjD6Xw6ebox4YBbzP/qYhxZ/z9CFj2Dp4IFsqCU1civfPLqAX97/hoqS8haN62b0mDCASX1Go5QVJJdl8P0n31FbWdM0B/fuC2P/C4B6/9tE7fnxis0KhYrv+k2lr40F5XoD955LJr9O2zR9C4LQIMdLKgGM52XScQCyLUNueA2KPnSW4uJizMzMCA8Pb+4w2x2rQZ6oHM0wlGsp25Xa2uEITUQkjYR2RaWQGONky/oe/uztHcQcdwfMFAriKmt4NiGDnkdiea1YQ+r4b2HBTvDsA9oq2P8efNYDTi0Dva6134YgCIIAfBC7j2OllZgrFXwe4o2yPgmj3FjY/RoAJmPexS+4xzWb1dXVsXLlSgoqirAwMWfuzNm4BbbdhFF6zDl+euNFNn7wPMWZpwE9phae9JrwMI8vW8FdTy/A2tG2tcPEwtaO8BFjuP+zr4gY/yBKEwdkQw2XTm/im4cXsumjZVSVV7V2mNfUdWwfpg4aj0pWkF6dy5KPv6aivIkeKEXM44TjRBTI+O5/irQLkVds1ijVLAnrjLfGhNSaOoYePcrB7MhrHkoQ/iqvqoAVCTv58fwiyisutHY4HUJOrZbUmjoUyERYmWCaFwmA1uXa15O/OnzgMADd3YMxMTFpzjDbJUmlwHaCHwpLNSZebXv6slB/ImkktFvBlma8H+TF2QEhvO7vjo/GhFKdnsXp+fQ7Fse9JU7sm/Qz8rQVYO8LlXmw5SlYfBtc2AGy3NpvQRAE4Za1/MJuPs21AeA51zI6mZnecJ/amirqfloI+loIGImi94JrttNX1LFj+2/k5uZiYWHB/AcW4NoGE0ayQebsjjjWvPoGP73xIukx55AUCuzcwxn58Os8tnQxd8wai8qk8dOomppCpWTw7PE8tmwJ4SMXoFTbIRuqSDy+nsUPzufExl/R1dW1dphXCR7agxljp2GKmjxdMUuWLqGwsLBJjh1+/9fEq0OwkqqRf5xFeUnBFdsdTVSs7u6Lh6qSPIM10+N1fBi9DYPB0CT9Cx2DLMtcqKzhg/gTDD2wme7H0ng+y5mnCvoz7uRZtibvaO0Q271DBVkAeMkpWCr0eFTGAmDj3+9v97t4Ip5cXREKWaL/8BvXPrpVaQLtcH2ut6hp1IGIpJHQ7tmqVTzo5czRfsGs7NqZO+2tkIFdhWVMP3eJgRVBLLtrC7Uj3gEze8iPh9V3w4rxkBXZ2uELgiDcclYm7uHFTDtkScFYs4s84N+/XvudXf4MJoWx1JrYw11fXFFw+P/JsszJ5Xs4fda4wtjkyZNxdHRs0vibQmlBJd+/sJg9S/9NVvwJJElB9+FjuO/z71jw8ZuEDY5o7RDrRaVSMXTBZB5bupSwoXNQqGyQ9ZUcXP0dS556gKhd29Bp21byyK9PMAsWLsTW1pbi4mKWLFlCelp6o49rYqrBceFa8rDHx5BB7qIJGGqunLLnb65hV+9Qbje5hB4VH+S7M/PYr5TUlDW6f6H9qtXVsT3tOC/ExtL/eByDTsTzYbYJMXovZEmBt5SLCVoSCGJhqguPnFhPja62tcNut44UGoteh6mz0JfkY08pOpR07vr3SaMj+w8B0MXeFxtX+2aPsz1TmPxZ60kWD+rbPZE0EjoMhSQx3NGGNd39ONS3Cws9HLFUKkiqquWF5HxCdbfzn7FbKe7zGChNIeUgfHMH/PIAlDT+ZlEQBEG4sTVJe3k+wwYDSkZqLvJNn4koFDcuJHr+wEb65awCILbP22B57SeY2Ycusif/FAD9e/TFz8+v6YJvArIsc+zXIyx96gkKUrYCdVg5ejP9jf8y7L5HsHZsn09mVSZqRj5wN48uWcqg2Q9i6eBIRWEBu7/7is/nzWfH1z+h17ad6eEuXq4sXLgQNzc3qqqqWLF0Oed2nWz0cR1dvSmeuIpS2QL/2hgyFk0EbfUVbew1tvzU/y6ecsxAiY59tb4MPXaMM3nxje5faD/0+ho2X9zBjCO/EHzwFPMvmrIst46U6jpMJInBdpY855TFwR72nBg8kuP9uzLRMgOAXyr9mBKVSlZN20rIthcny4yfRREWekxyjA8YVG5dsbK8/nSqwpQ8kisyAbh9xKDmD7IDkGWZqsg88j47i6G67Xz+CzdPJI2EDsnfXMNbgZ5EDgjlrQAP/M1NqdAb+DK/jmCzaTw+bD1ZQZOMjc+thc8jYNerUFPauoELgiB0YD9d3Mcz6VYYUDJMk8ySvnehrEfCqCQ/C9c9TwJw3GEiPYbNuGY7bUkNG3/fQp2kw9XKiWHjRjRp/I1VnFvEsqff5PCatzFoc5GUpgyYdh/3ff457oEdYxl2E40pvcePZ+Gn3zJk/oOoNdYYdKVE7/meb594gcqStrMohZWVFfPmzaOzrQc69Gw4tJWDP+1q9HGDwgdweuB3VMgavEtPUbRsOuiu/HKvUCj4V9dxrAyUsKeYTNmZiTGlLEs62uj+hbZvd/pxRh7azP1pLuyt9aUKc6woZ7RFFkvCOhF7exg/hvvzz7AxBNh6A+Cm0bC49zi+7FSJjVLB6bIqhp1KYH+hGKV2M0q1OpK0FgD0t3OAzNPGDZ69/na/k78dQpZk3E0dcQ/2ae4wOwaDTNnvaWizKyndmdLa0QiNIJJGQodmqVKy0NOJg326sLa7HyMcrJGAdTVW9HR9irn9lpHp2tdYH+PwJ/BpOBz/+qqbO0EQBKFxtmYl8480S/SoGGJ6ieV9J6BS3HgpXtlgIHXZApwoJkXhRbcFX1y7nSyzY/lGcinBRFJx99zpKJVtZynk+MNHWfbUwxRnGlfpcfHtzf1ffEP/qfUbadXeqNRqeowazwNffod/30mAksqiOJY8+RTZFzNaO7zLTE1Nmf3ofELt/JAl+D32MFu++bnRdYaGDBvDusAPqZNMsc/aB+sXXnMhjiEeEfzeJ5QeqlTqMOWFdDOeTUinRtQ56pBKtToWHv+V2UmmRBsCUKFlomU6qwP1xN3Rn2V9xjDWyRbLv1nGfUrn29jZO4iulmYUafVMP3eRVyM3YjDoW/CdtF8nSkqRkXCRs/F3DKcm9Y8Rhh7XnxKsLa/lfHYCABG9/z65JPxJUiqwvcsfgMpj2WhzK1s5IqGhRNJIuCVIksQd9lZ8382XY/2CecjLCRuVkh2mvkQEvsfCru+Sa+0L1UWw/Tn4qi/EbhTFsgVBEJrA3sIyHr5Qjh4Vg0wvsbzvuHoljADOrP+A7lVHqZXVaCd+h5nFtacPxP8eyaniOABGDxmFvaNDk8XfGNqaGnZ/9yVbP3sL2VCJ0sSBEQ++zOx3XsXKvm3E2Jw0lubc9c+FjHzkZSSFOdqaHNa8/AyxhyJbO7TLlGoVUx6fxW0+PQE4lRXNj5+sQNvIqT9zZsxCPXM1KE0gbhPyxkfgGskgNwtnNt82lic9zJCAlVmFTIq6RL4kHmB1JNvySxh0Ip6tVZ0AGGN+icO9/VjcezxDPCLq/ZkI4GNmyqaeAUyx1yEj8XWxD7OO/kxFbUnzBN+BHMhLASBYcREzjT+GzEgAsixCrrtPQlQslVItGsmEbne0j3pzbYXG3xZNiAPIUH4gs7XDERpIJI2EW46PmSmv+XtwZkAIHwR50sXSjK32/ekZ/i3PBjxNsakDFCXDT3Ng6UhIb3yNA0EQhFvV/sIy5kVfok6WGetkww9978JEWb/VwLIvnCEs+gMATgU+RUC3axcpLc8rYfOh30CCUOcAegxsG0+Cz+87w4pnHydq13YAwkeM56FFi+g6pG8rR9bywu7oxfQ3PkBl6oSsr2D7569x/NfGTwVrKgqFguHzJzC6+51IssSFslSWf/wtVY2YTqdUSEgBw2DacmRJiXRuLYnLH7zmAymVQsULgUGs6e6LvVpJdGUNb1u6sz7lcGPeltAGpJfnMDfyPAuiU8it0+FrZsIPATJL+07Cx7LhxZTNlAq+7N6Ll1xyUaBnb10QE47uJq34fBNG3/EcLzWe070sIeNCJOZSLRWyGS6+Xa+7T1SqcXW1Hj17oFa3vdUs2zqrwZ4AVEXmoS8TyfD2SCSNhFuWhVLJve6O7O0dxC/h/oxydmCV+wR69fqBj7znUK3QQPpxWDIMfpprTCQJgiAI9bYt9TBzziVQa5AZ6WjNohAfTJT1vPXQVuO66xFMJS2Rmj70n/7iNZvJsszGbZupohZbhSUT5k1pwnfQMNpaLT/95wt2LnqV0rxsLO0dmPrvNxm68EE0luatHV6rcQ/wYcEnn2Bh5w/oOPTjZ5zctL5NrazTd9IdTLvzLtSykszafJatXE5JSUnjDtplLGci3sMgSwSk/UTkkseQrzP9bLC9NTsjAumizKJKsuCpDFv+eWoDlW1sBTrhxgwGA1/F7WDwqYvsKNajBJ7wdmZP7y4M8+zRZP08FjKS7wJMMKOGWNmfiZFpnM7Y0WTH70hq9Abi62wBGOUzhLw4Y1I2xTToutOZi4uLSUxMBKDXgD4tEmdHY+ptjYmPNehlKo5ktXY4QgOIpJFwy5MkiQF2liwJ68yJ/iEs9O3Ed4EP0K/PKla5jsWABLG/YviiD/z2AlQVtXbIgiAIbd7OtKM8nKykFjUDNHl8E9oJE8VN3HbsegUpPw4snOn66CoU10k2HT9+nKSUiyiVSqbNmIapuaaJ3kHDJJ+9wKIHHyU9+jdAxtatO7Pe+QyfbuGtGldbYWVvw32f/5ewO0eBLHNg1TJ2fv0ZdbVtZ/nwkMHhzJk8E0sLS/ILC1iyZAnZ2dmNOmbPsfexw8+Y+AzP+IHjXz9y3cSRp5kpm3sPYoTuLACryzvT7dBpnow8SFRJQaPiEFpGQvElxhzezBs5LlRigZ8ii03dHXjRzx1NfRPnN2GMZ1c29vDDSVFOFh7MSNSwJ/1Ik/fT3p0tr6JOlnE2URHqGHy5CHa5Q7fr7nNyr7E4va+vLw4OHX9KcXOxGuQBQMWxbAx1ov5WeyOSRh1USUkJZWViNYWb5akx4UU/d870D+WF7j1YEfEqQyKWsMeuDwqDFo59hfaTbugPfQramtYOVxAEoU3anX6M+y9K1KKhlzqNH3oPwvQmEkY5JzfAiW+MP0xahNLq2svQZ2VlsWuXcYrTiBEj8AhovRVt9HoDmz/9gQ3vPoe2OgskU3pNeJCFn7yFpa1Nq8XVFqnUKkY+9Bh3znsQSVIQvXcX3z76DMU5ha0d2mVe3f24/4H7cXJyory8nGVLlhJ/KKrBx5MkidFznuNw4PMA9Mtdw6nPZqK7zggijcqUKVX2fOBegCMFVGLG2mIrRp7N4M6jJ1iakU9erbbB8QjNQ6vX8c65zQyPzCNS54OaOh6zT2fv7cOJsPeq1zHiTu1j+4p32L7mC/Zs/oGj+7Zy7uxxUi4lUlJSdN1kYzdbJ3b27YO/qoAybLgvWUNUeVVTvr127/gf00372lgiSRIupcZz2rTTtUcQ1eZVcDYqEoCI8KYbHXYr0gQ7YNHHFcc5IUhqkYJob+pfcU1oVw4cOMCZM2fw9vYmLCyMkJAQLC0tWzusdkOjVDDdzYF7XO05VebJEt8Ivk3Yxb8vLiasMgl2v0Lp0a+Rhr2Mdfd74GaenguCIHRgezJOcF+STC1m9FClsbbvcMxV9R/9U5qbhmbr4wCU93wQK/9h12xXW1vLT8vXoNfrCQoMok+f1ps2kJ+Ww7o336O61DiFwdzWlykv/AvnTu6tFlN70HP0eMxtHNn62QfUlF9ixTP/YOJzr9Cpm39rhwaAjY0NCxYs4McVq0nNSWftrl8ZXVRGnwkDG3zM22a+yMkNlvSMfIXeJds5+/FdBD+2Do35te/RpvvewSSvbDYl72Z9kZJjuhDiakx4MTGTfydm0t1cz+2aLCZ7hRBi79fguITG252XxUtx50kxGJND3ZSpfBLWlRD7en42pZ+EfW8TfHEPwX/TzCCpkCwcwcKJQqy5VG1GgU1XMgNmMa1PJ7b3u417oxI4Vq5gVlQyWyIC6GRm2vg32AHszY4F7OmuKaOqMAMfQzoGWcIzfOg125/fc5pqqQ5zhYYuodcvlC3cmKSQsJsc0NphCA0kkkYdVEWFMZOelpZGWloa27dvp1OnToSGhhIcHIyFhUUrR9g+SJJEbxsLettYkOs/j5UZo1lzZjWPJn6De2UmbHyI1AOfUzvsDQJDr/3FRhAE4VZxIPMUCxN11GBOd1U66/oNx0JtVu/9ZYOerOXzCKacCwpfPIa+et22m9f8SkldORayKaP7DEWSpKZ4Czct8eRRdn79OTXlZYCSLgMnM/rh2SiuUx9DuFKXAf1RmbzJ5o/fRK8t4pe3/8Xgef+k56gBrR0aAGZmZsyeO5t1X67iQkUa2878TmlJCUNnj0XRwAdGvSc9QaSlA8GHnqRH1RHKv5+MZt460Fx7RJqluTczwxYwE8goTWZbmSUbcks4W15FZJWSyCovvigqx0fazkibau72DCTUMbTVzolbTXxlNa8nZbG3qBxwwYIKnnap4KEu4+v1N5Jwag+e5z7DIm0vALJCRbwmHBNJj1pbjqm+Ao2+Egu5EpVkQCHroCIHKnJwABwAynbz/aVoFkT/g58eGsDK8K5MOptEdEU195y9wPquTnhauTbnr6HN0xkMnKsx1pQLNy0h88wJAoAEhS/BLlcn+GVZ5myisah4N7+Q69Y8EhpGlmXxGdWOiKRRBzVz5kxKS0uJiYkhOjqarKwsLl26xKVLl9i6dSu+vr6EhYXRpUsXzMzqf0N/K3MxVfOMnyd1nZ9he9ZsSg59zuTE5fgUx8C6KRw9MIjSwa9wZ1DETU3DEARB6AgiyyqYf6GOaswJU2Xwc99hWN5Ewgjg7No36Vl9mirZFHnykus+4Ig6fZbolDgkGUZ3GYyt/7WnrzWn2qoa9q38hug9OwGwc/PhzvlP0Ll7UIvH0t759wrh3nc/Zs0rr1BXlcneZe9SkH4vI+6f1tqhAaA2M2X6P+ax7Zv1nMqN4XDyaUq/KmXSQzNQqhr2RTJ8+CxiLewI3HMfVjnHYflYmLIEnP7+78fTxpcHbOABL2eya+v4MWk/O4qqOKfzJFV245sS+KZEh7+0iZHWFSwMmYq7RowyaQ7ZVcW8FnOEzRUeGAC1JHGvi4YnvVxwsXS74f7xkUeo2fEa4dXHjS9ISgifgTTwGYLtO1+9gyxTW12OurYURXUhVBaQkpZCcVos4alLmaPaRXGWFSuOujP/ts6s7ubL2NOxpNbquefMabb2vR3b6yQmbwVnCpOpRoMZVfR27Yn28PcAVHlee+RgbnwGmfoCkKHPsLaRxO4I9BV1lO/LQJtTiePCMJE4aickuS0tWdFGlJWVYWNjQ2lpKdbW1q0dTpMoKioiJiaGmJgYcnJyLr+uVCrx8/MjLCyMoKAgTE3FjcXNiM5Jo2j3mwxI+hkVenQoWe8xnrwBzzDVPwg3U5PWDrHRtFot27ZtY8yYMWKZUUFoR1ry3I0ur2Jq5EVKdHrC1Hn80ud2rE1ubpWw1OjDuK0bj4mk50CXlxk0/ZlrtissKGTxl4vQyjoi1AGMfXY6CpOWfQKcnZTBujff+KN2kUTv8ZMZcPdsVOIzslFqKqr44cU3Kc09B4CL/wDueeWfqE1bt7j5/5NlmUNrdrEn4QiyBD7mbsx8dA6mFo14+JYdBT9Mgcp8ZIWK8vD7sR7xIlql2U2dv3lVRaxPPc3mwhrOaj2Q/yhbKgF9bSyY5GLHJCcrrE3EfV5jVWpr+TBuL0sLbanB+Lc51smGl3zd6Wx+499vbEwUpVtfo2/lXhSSjE5WcNZuFD1nv4XS0bdhQZ38DrY+DcBbhnnc++Q7eDuYE12YzKRzmZRjRQ9VChv6j0JzE9OFO5IPzm/nwwI3eqkusuX2yfBRMJRnw5yN4Dv4qvabv/6Z09nR+Ji7Mf+5B2+qL3HvfH36ijqy3z0BOhmnB7th2vnWTWS2tpvJeYik0TV0xKTRXxUUFFwegZSfn3/5dZVKRUBAAKGhoQQGBmJi0v4THi2lKCuW4u0v4Zf+OwAVSjO+8ppJWo/7udfHiz42Fu02ky4ufILQPrXUuXu+tIR7zqdTpNXTy9qcH7v7YXmToy9qKksp+LAfnoYsTpjdTq9nNl9ztTSdTsc3H35FXnURrgZb5s6fh1ln2yZ6J/Vz5rcj7FvxCbKhCklhxl3P/Au/iIgWjaEjM+j0/PzuItLP/waAg6c3Y594Fiefa4y8aCVRvx1n09Ed6CUDLjZOzL5vDlZWVg0/YHEq1ZufwSzZOGpNZ+4Mw15ja5oFY8aOvenzN6e6nJ9SI/mtVMOZqj/3NaOa0RZZPOzbja6OorbIzdIbDKy4eJCPMg0UyHYA+EvpvNzZhpE+g264f3ziBbI2vcHAsm2oJePqUWeth+A84XU8/K+/eld9Gfa9h2Lf2wB8Zf88jzxhXK3vcM55ZsZVUYspQ02T+L7fRJSKW2+yyeRDGzii7czDdqm86tEFvuwDKg08nwrqKxNpBr2BD//zHpXUMrH/aMJH9r2pvsS9898r/iWRyhM5aILtcZwb2trh3LJE0qiROnrS6K/y8vKIjo4mJiaGwsI/Vy1Rq9UEBgYSFhaGv7+/+MCrJ92lQ5RvfxG7PONqDNkmjrzXeSGxfhOZ5+nCJBc7zJphqdXmJC58gtA+tcS5ezovhhkxRZRhRQ8rc9aG+2HdgOk6kV/MIrxgCzk4oHzkME7O157asfnbdZzOjMFUVjF/zExc+zbwqXwDGAwGtn62kgtHfwZk1BpXprz4Mh5BrbdiW0cWtfswR9ctprKkGKVaTf8pc+h9110NriPU1JKPxbFu90aqdTXY2toyc+ZMnJ0bPk0yt6yGzxd/xcKKxXRW5AKQqQnEedZi1F4NT0pm1tSxMa+E79NTSKn782FgT1Uq93nYM7FTfxS3YALhZv2eGclrSdkkGoyfTQ4U8ZRLFfODRqBS3uAha3UxHP4U3ZFFqAzGlXfjLPpiM+4N3IP7NV2QskzZr09jHbUEWVIhzVgDgSMA2JhyjEcuqdCj4h6LRD7pPbXdPsxsiDq9lqADJ6nGnPVdJAJj9uJ06FW0nQajnrfxqvaJx2NYtX0dJqh49vnnUJvd3IN0ce/897T5VeR+eBoAl6cjUDvd3MhkoWmIpFEj3UpJo/8nyzI5OTmXRyCVlJRc3mZiYkKXLl0IDQ3Fz88PlUrcXPwtgwFifqFu12uYlKUDEGPhxxu+D3POuR8z3R2Y5+GIl6Z9jOQSFz5BaJ+a+9yNzI/jnuh8SrHGX5nNln7DsTVpwPUh+hf4eT4GJKKG/kCPgeOu2ezkyZNs3boVgIk9RhB+V8vVmKipqGL1y+9SnHUGAFu3Hsz4zwuYW4kb3eZUVVbKjkWfkHzmJACWDl2Y8uJzOHq2fA2raykqKuKHH36gqKgItVrNqB53EjGm4X+XJVV1PLT8CD0zV/OY6lfMpVr0KIhxm0KnCS9i7dbwJKnBYGBr+nGWpOdwvM4HWTIm33yldB5yqWWG/wjU6lvjnvdmJFfV8ubFLLYVlAKgoZq5tlk8EzIcK9Mb/76qozZgtvNZqDSO7E+z6Ip6xOu4db/2al2NZjAgb3gQ6fxPYGIJjxwDW+Nqbt8l7OGlLHsAHre7xL/DJzVPDG3QnszTzLygxJIK4u/ox6XPJxJQfJCNTg9y16PvX9V+06ZNnDlzhq4+wUyZf89N9yfunW+sYEUMNXFFWPRxFauqtRKRNGqkWzFp9FeyLJOVlXV5BFJZWdnlbRqNhi5duhAWFkbnzp3FSgJ/R1cLJ77BcOADFDXGm429dr15w/dhEiz9GOlow0JPR26ztWzTT3vEhU8Q2qfmPHfPF1xg2vlsSrChsyKbTX3642Rme/MHKkmDRbdDbSnywGeQhr58zWaxsbGsW7cOWZa5vc8Aho0Z0bg3cDMh5uTw/b9eRludDUgE9p/C2CfmtJkRLx2dLMvsXvoj53b+COiRFGb0Gj+fQTPHtHZoAFRWVrJ+/XqSk5MB6O4UxPj7pqIybdg5pzfIHL1YyO6jp+iT+BFjlMf/3NhpIITPQg4eh2Ta8OlwF4ov8WVSJL9WuFH7R02eYHMVz/h6MtrRBkUbvidpKXlVhXycWsDK3Gp0MiiAseYXeSW4L17Wnjfcvygvk0srHyWi3LgiGo5BMPx1CBwFzf371WuNhdXTj1PtPRjNvA1If3xevXVuG58XGlcK+zzInWnubSMB29zeiDnGV3ka7jBNZm3f8VT9xwtzqtlzxzqG3Hnl9USn0/Hf//6Xmpoa5syZg6/vzSdrG3v9PbfnJCc3b8HM0g6/Xj3pOrgX5jYd6yFF7aVS8r8+ByoJt2d7o7QR9dZamkgaNdKtnjT6K4PBQEZGxuUi2hUVFZe3mZmZERISQmhoKJ06dRI30NdTVQQH/ot84hskgxYDEmtdR/Fep4XkmDoRZKFhgYcjU13ssGjgKizNSSSNBKF9aq5zN7YoiSlRGRRjSydFDpt698HZ3P6mj6PXaan+ZhSWeafAszfM3w7Kq+NMPB7Djzt/Qa/X07NnT8aPH99iifa06HNs+eRdqsvLkBRm3DHnKSJG39YifQtXunAiht++/AhtjXHqlo1rOFNf/Ce2Ljf/t9fU9Do9O5Zt5ETmHwW8VXbcM3cm9l5ODT6mVqtlw+ZtOJnV4R//NV6lJy9vq5E0RFkPRtNrNmEDxjT4AV5+TTkfJRznx2JbqmXjKMFgCw0zzU5wT+d+WFsGNjj+9kqWZdYkbOLVbHvKMSbmhthb8Yq/O13qWfD89LZldD7xCvaUoZMVZHd9CK+Jr4GqBb8UFySi+2oAKkMdJ7q/SZ9JjwPG9/d01D5WF9uhkuCHbr4Mtu/433XGn07kZFklHwS6MrY8Efsfx1EkW2J4JglHqyv/XRMSElizZg2Wlpb885//bND3m4Zefwsy8tj88SKKMk5euUFS4h0ainfXHoQNHoa5jW2bfuBcH7Isk78oirq0csx7OmN/t1h5tKWJpFEjiaTRtRkMBtLS0oiOjiY2NpaqqqrL2ywsLAgJCSEsLAwvLy+RQLqWomT4/Q2I2QBAnVLD11738InHdCpV5lirFEx3dWC+h2O9Vt9oKSJpJAjtU3Ocu/FFyUyJSqUQO7ylXDb1icDV3LFBxzqx/Dn6pHxNrdIC00cPwzWWmM6IvsSKn1ehRUeQbwB3z5reIiNcDQYDJzZu4MhPK5ANBpw7+zHiwedw6ezR7H0L11dXU8uG978mI2YXICMpLek/7X76T2qmqT436fzvp9h88Dfq0KFBzYQ7xxByR48GHeuq87ckDaLWoj3zA+rSlMvtsnAm2X08XkMW4OMf1qC+irU6vknP57uMfMr1BgC8yWBVeFcC7G6dml1lNYX888x2ttQaf4/eUi7vhIYz1MmlXvsX5maQuvJhelYcAOCSohO6CV8QEH7tJd2b27HvX6Zf8meUYUHN/Udw9ugEgEGWeSQ2lV/zSrBQKvipqzMRdq6tEmNLKNPpCT50Hr0MJ/uHUPbLq4Re+IqDJgMZ+OKWq9qv/ngZF0pT6dMtgjGTxzeoz5u9/hoMenZ9+xPRe38GuRYAO/ceSAqJkuwLGPR/PrRXqk1Qm3XFp9twIsZ0xc2v/a4+piuspnRnKnaT/FFoRPmTliaSRo0kkkY3ptfrSUlJISYmhri4OKqrqy9vs7KyIjQ0lNDQUDw9Pdt9JrzJpZ+EnS9B+jEAqjQOfOG7gE8dR6GXVEjAEHtrFno6MtjeqtWHiYukkSC0T0197qZV1zL+5Bly9WZ4Snls7BWOh2XDpjZcOLkLvy3TUEoyx8Lfo9/Eh65qU5CSw9Lly6iiFje1A/P/cT8m5s2/VHRVeRVrXn6HkuyzAIQMGsKw+x9FLZYqbzOi959m93efoa8zLuDRdcgI7rj3PkzNW3/6Rn5yNmtX/UiB3jgtvZdnV0bNvQuV+ua+EF3v/JUNBpLO/E7p0RUEFe7Gij/vv2JNuqLqNZfAO2eDun6jYv6qRKvj69RUvknPoxIz3KU8tvUOx9Wi409hOpp1kscTCsjAWOh6pm0eb3e7E801Rj/+L1mWOb1tKX4nX8WOcrSyklPe84mY/RYmpq23vL1OW0fye7cRqLtApFl/uj+77fI0tVqDgdnnkjlYXIE1ZfzS1Ykwx+BWi7U5bc7J5P64fPzMTDncL5ik927Hv/o8O/1eZMS9z1/Rtqa8mg/++wF6ycD8CbPw6dmwWjs3c/3NTkxg95KvyLt0EQC1xpU75z1E1zt7Aca/r4L0dDLizhF3YC/ZSQl/7KlAqQlm6Px76Tq4YQnjtkiWZfHdsYWIpFEjiaTRzdHr9SQnJxMdHU18fDy1tbWXt9nY2BAaGkpYWBhubm7iQ+D/yTLEb4Fdr0KR8SJRaefPoqBH+a9Jj8vz3X3NTFng6cg9rvZYtdLUNZE0EoT2qSnP3YyaOiadTSK9pg4vVSXre/jjbVm/p+//q7ykkIpP++Im53Pcajh9/rnuqmtDSU4RS7/+jjK5CnuFFQsfux+LFphCkZmQyvq3/4O2JgeQ6D5yJkPnTxfXrjaopqKKDR98RVbCfpBlrJ2cGXbfo3QOb/hKY01FW13L5m9/5lxRIgAebu5MvXsadnZ29T9GPc7fuuoK4vauQXluDSHVZ1BIf9zSa2yg2z3oe8xB6db1puNPKctk3OkECrAnUJHJ5n6DsGlEDaW2TG/Q8d65jXxV5I1OUmNHGR8G2DPGs55fwmvLkbc9ixS1BoCLys7IE77Ev3vbmMaaEnsS97UjMZH0nOr1Ab3GPXB5W2ldDWOO7uOiwRUX8tkSEVyvek3tzUPH1/NrlR932xTyUZdeyO93Ro2e81MP0DWs+xVtT246xNYzu7GRLHjy5acbPHOiPudvUXYBB1evIOnkPpBlTM0tCOw/kSELpl130SFZlkk9H8mhNWvITY7941UFve96qM3UeWsoWZYp35+BvrQW2wl+4rrbAkTSqJFE0qjhtFotFy9eJDo6moSEBLRa7eVt9vb2l0cgubi4iA8DMBYrPLUM9r8LVcYnptVeA1gR+hQf1rldHiZuoVRwt6s98z0cCbRo2adWImkkCO1TU527qeW53BNdSEpNHZ3NTPilhz9upg1b/VE2GDjz8RQiyveQKblg+eQxbGyvrElTUVzOki++oVhfjpVkxoL7FmLn0bApcDfj1NZDHPjhU2RDNZLCnDvnPUWPkS23QpvQMOmx5/ntq08oyzfWOnLw7sXkfz2JtUP9EzTN5eymI+yI3k9NXS2mpqZMmDCB0NDQeu17s+dvbsZFUnZ/Q5/irUil6Zdfv6AKpDR4JiEj5mNhZVvv2M8VXGDy+TwqsCRClcIv/cdgqmofq77WV16tlsfjLrG/2FhuoZ9JBl/3GIiLef2m+8iZZ5HWL4Sii8iSghNeC+kx681WHV10LceWPU+/1MUUY4X+4WM4uvyZGMqsLGDMyShyZQd8pQy29u2PnZlDK0bbtGRZpsf+38mRHfnMu4w+ufl02rmANFxwfzkelfLKpNCydxeTWpNDP69wRi2c2OB+/+78Nej07Pz2J2IPrEc21AAQesdQBs6ch4Vt/T+3MuNj2fLpN1QUJQESPUbfx5B5dzU45tZWl1VB3udnQQbr4T5YD/Vu7ZA6PJE0aiSRNGoadXV1JCYmEhMTw4ULF9DpdJe3OTo6Xh6B5OTU8EKRHUZNKRz6GI4tAp3xAqILncqvXR/ns1INF6pqLjcdZGfJQk8nhjlYo2yBxJtIGglC+9QU525aeRZ3nY4mW3bGW6NmQ48APDQN/+J48tcv6B35b7SykqTx6wjudWUtmurKKpZ++i35dcWYY8q8WXNwDmjeOkIGg4Etn3xP4vH1gIyJmTtTX3oFN/+O98S9o6qrrmLnN0tIOLIDAElpTu/xc7h9+thWf0BVXFzM+vXrycjIAKCbWxfGzZ2MyQ3OowafvwY9JO9FPr0CfdxWVOgBqJQ1xDgMx27g/fh3H3h5mtLfOZB1lnsTaqlFwzDTZFb0uwulou0t2NEQvxeW8URcGoVaHaYS/NO5gCeCh9br70Wr03Hg+9cYnL4IpawDa0+Y8i34tM0ks7aulrT3+uKnv8QpqyH0enrDFdvjitO5KzKFMqwIVyazof9IzNQWrRRt0zqSl8zkmDLU1HGufxB2e9+B44vI9p+B2+zFV7Qtyyvm4y8/Q5ZkHpq5ENdArwb3e73zNzU6nc0fvU1tpTGxqzJ1ZuRDj9JlQMNGSOp0OlY+/9blwtk9Rs9nyLwpDY67tVUczqRks3ElSrvJAVj06bi1ttqCm8l5tIlqxV9++SWdOnVCo9HQt29fTpw48bft161bR5cuXdBoNHTt2pVt27Zd3qbVann++efp2rUrFhYWuLu7M2fOHLKyspr7bQj/w8TEhNDQUO6++26effZZpkyZQlBQEEqlkoKCAvbv38+XX37JV199xYEDBygsLGztkFuPxgaGvQaPnYJu0wFQxfzM1HUj2F+6il+6ODLK0RoFcKC4grnnL9H/WBxfpeVRrNX97aEFQRBulsFgYEXiPkadSiBbdsaJQtaE2DcqYVSYFkfo2TcAONn5oasSRnV1daxevZr8umI0sppZk6c3e8JIW1fLqn+/SeLxnwEZO48I7v/yE5EwamdMzMwZ9+Tj3LngZZRqR2R9FSd+XczXjzxDRnxKq8ZmZ2fH/PnzGdC7PwDnsuNZ/MEXZMY2U1wKJfgPQ7pnJSUPRXHM9wkyJDcspBr6FG0mYOMEkt/qyZl170N1yd8eapB7Dz7ppEWBnt21vjx/IZPMmrrmibuF1OhqeerUBmadS6ZQqyPEQsOO3kE8GTKsXgmjzIxUzr83gqFpn6OUdZR3Hg0PHWyzCSMAtYkp8oQv0MsSvcr3IF/cd8X2YDsvVoQ4Y0oNkXpfFpzYgk7fvv+d/9+K9DQAblcnYKexgeR9ALj1GHVV26i9p5AlGSelTaMSRtdzasspfn7reWPCSDIhcMA0Hv3u6wYnjABUKhVz33sJp07Gv7+z25cRuWNrU4Xc4ixv88DqTuPvvnhDItUxt/B3wzam1UcarV27ljlz5rB48WL69u3LJ598wrp160hISMDZ+erCe0eOHGHQoEG88847jBs3jtWrV/Pee+9x5swZwsLCKC0tZerUqdx///10796d4uJinnzySfR6PadOnapXTGKkUfOqqakhPj6emJgYLl68iMFguLzNzc3t8hS2m5n73+FkRcKul+GScQUOzOzgjudJC5vNitxyVmcVUqwzPj00U0hMcbFnoacjwZY3X/jyRsRII0Fonxp67p7Mi+Nf8UnE6I03bq7k81N4IIGNWUUpOwr5x1lIpenEa7oT8MwelH+p2aDValmzZg3JycloTDXMHDEF74iGFSCtr7KCfDZ9+Da5yYmARNBtUxnz2L1i9c92rrqims0fLyU9eiegB1T4dB/B+CcXYNrC07v/SpZlYnacZuuxnVRTh0KW6NepB0NmjUZlcvX52ZTXXtlgIPbodqqPL6Vr6X5MpT9KB2hsYPh/oOecy7UUr2VlahzPJv9Zr7KvjQUTXewY52SD0zVib6sSilO4/1wMFwzGZPQcV3PeCPRHo6zfOX9i10/4HX4GB0qpQc3Fnv8mdPxTf/u7a0tqNz+D6elvwSEAHj4C/zPdcEv6GR5MMqBHxRyHSt7v1jbqMjVUuU5P2MEz1KJmsWsUEz1HwodBgATPJYP5lVOjF7/1GTnaIu4I6Muds0Y3qu//PX+jD5xhx1dvglyHiZkDk198DY/Aq1cMbSiDwcCe5d8RtWMTAHfcu5Be4yY12fFbkizLFK9PpOpULqgUOC0Mw7Rz+10hri1rV9PT+vbtS+/evfniiy8A4x+9l5cXjz/+OP/617+uan/PPfdQWVnJli1/LpHYr18/wsPDWbx48VXtAU6ePEmfPn1ITU3F2/vG8yNF0qjlVFVVER8fT3R0NJcuXeKvf44eHh6EhYURGhp6a/47yDIk7jImj/Ljja/ZdYZhr1IVNIFf80pYkplPTMWfU9f621qw0MOJUY42qBRNcxMjkkaC0D7d7LmbX13Gy+d/Z2OlDzIKTKhljk0m/wobgaWJZYPjkM+tQ9r0OOiqwd4Xee5mJJs/R/LotDp+XLGapIxk1Go1c+bMwcur6Z/y/lXCsTPsWfoRVaUlaKysGf3oM/j26NmsfQotKznyAr99+QXVZcapDjbOrgxd8BCde/Rq1bhKs4vYsHIdKVXZADgqbZg4aSKeYVd+gWyua29pYQ7xO7+je84GNKVJANR5DuBiv7cIDrv+ObAlr4TvMvI5Vlp5+TUFMNDOirucrehnqadSr6dSsqVYq6NIqyM9/zAVujqq9Fqq9Hqq9AZqDFAraVCprPG2C8VRrcLRRIVJTQIOJipcNFa4mdvjbOaARtXwFQv1skxBnY7cOi05tVqi8iL5KldNNeZYUMmb3lpm+A2u17Fqaqo5ueQfDMw3FrtOVfpgMn0FbgE9Ghxfq6gugS96Q2UeDH0VBv7zqiZLEw/zYoZxatqrfu487N1+V8779lI8L6fU4C6ns6+XL5cOHKT7qecpsQ3D9qnDV7QtLi7m008/BeDJ+x9rdB29v56/teVlLHnqMXS15ZjZ+DDvg7cxt2n6JIgsyxxeu5LjG34CoFPP8Ux5/sEm76clyHqZwh9iqYkrQtKocH0mAqVlx6qp1ha0m6RRXV0d5ubm/Pzzz0ycOPHy63PnzqWkpISNGzdetY+3tzf//Oc/eeqppy6/9uqrr/Lrr78SFRV1zX52797NiBEjKCkpueYvpLa29ooVv8rKyvDy8qKgoODWTFa0ksrKSuLj44mLiyM1NfWKbV5eXoSEhNClSxcsLRv+5aVdMuiQolaj3P8uUmWe8SWP3hiGvY7BozcnyqpYnl3E9oKyPyoXgJuJinvd7JnpaofDTS7z+7+0Wi27du1i+PDhImkkCO1Ifc9dvSyzKqeYD1JzKdEZR34OMknijaAe+Nk0ohClQUfp5pdwjP7O+KPfMPR3LQYz2z/71ulZv2g1iWVpKBVKps+YTqdOnRre5w3Issxvi9aSeGQdYMDRuxNjn/oXNs4NWwlOaNsMBgOHf/yNuIPrqCkvAcC3Zx8iJszGzb95E5M3iity+3H2RB6kFi2SLHFbv/4MuOP2y+dqs197DToUJ79Bsf9dJG0VtbKaw+7ziZjxEuZm5tfcRa+vZN+5Z9hVac0JxRASDc07jdOCSjRoMVUqsTBxRK2QMJEktDWXUMl1KCQDf308JgMV2FCqcKKgTofhGsfsokhncWgofjb1+/eXC5NI+242/jpjgu208xSC7/0ItaZ91vyRzv+EatMj1GBKwZz9uHj5X9VmcUYBb6UYC8t/GuDOZJf2OfL/zmMHSdLZ84DZQV6OeIioz+6hV/nvHHG9l94LP76i7cGDBzlw4AA+Pj7Mnj270X3///l75+A72PTe6+ReTMTc1o3pb76HpW3zrkS4b8UPnNu1HgCP0BFMev7BdjmCVq7TU7wyHk1XB8xFbaNmUVZWhqOjY9tPGmVlZeHh4cGRI0fo37//5defe+459u/fz/Hjx6/ax8TEhBUrVjBjxozLr3311Ve8/vrr5ObmXtW+pqaG2267jS5durBq1aprxvHaa6/x+uuvX/X66tWrMTe/9oVTaF5arZaSkhKKi4uprKy8YpulpSV2dnbY2tped0nKjkipr8E/bzv+eVtRGYxzzbNsexPrfjeVpi4US0oOmFhyUG1F+R+FKlWyTG9tJXfWleNj6Bjz0wVBaDpJqhLWmHYhQ2l8ou9hKGdGbQIB2sYtUKDSluN/4SuC6mIAWKscj6brFJD+vHGVDTLFsVmkavNQyBJhToEovZrvmmuo05P5+3Fqi40xmdj44znydhQqkQzv6AzaOoqiz1ISf944ihcVFp4RuPQLRWHSeoWd5UodeUlZZBmMdTvUajUuLi44ODi02Jc80+p8XBKX00N/HoAk2ZOj7guxdvW7RmsDpprVqNVnAMjFhaPczlFuIxNPzKnFwqDGQjZgIeuxUKRjihYTGUxkCVNZgVqWMKUOUFMmu1IuKSlXKKhU5lAumVGGJWVYIUuNf/+SLGNDKXYUYEsJ/jotd1a7oKR+/+ZehYfolrEClaGWEtmS313uQ+XRvkckygYZ33Nv001O4IiyN/ndHr+6DbDO1I7fTa1RynqeqD1Kl7rWS7I2RIrChHcs3VDJej6oPI2Z1oEhUY9hLVWxxv1lzF3+nP4syzKxsbHU1dXh4+ODvb393xy5/gwGmfzj+ym/lIjCxBSvkRNRW7XMYIScQ9FUpB0FwMy5B25DIlA00QyEFiUD7TDs9qKqqoqZM2fWK2nUob9xa7Va7r77bmRZZtGiRddt98ILL/DPf/45RPP/RxqNGDFCjDRqA8rKyoiLiyM2NpasrCwqKiqoqKggIyODzp07ExISQmBgIGZmTV/Pp+2ZjFyeg+HAe0hRq3AvOYlb2VkMEQsw3P40s8wdqDUY2FJQxrKsIqIqqjlqYslRE0sirMyY5+7AGAcrTG7iZlSMNBKE9unvzt1LZVm8En+afXXdAbBRKXnG24nZbiGopH6N6rfg4hmU657DWZ9DlWzKMqdnmTzrYRwt/5xqYtAb2Pz1OlK1eUgyjO01jG6j+jSq37+Tl5LDL2+/RV2VcQUrv953MfrxOe3y6avQQHdNpCA9lQ3vfkJ1aQqVGcdJ25zC4HmPEjKwa6uFZTAYiIuNY+++vZSWlpKRkUFZfgm3Dbyd7IIcRowY0fzXXnkO53cvw/PEm/hLGfhmvcGRmol0mfk+Nv+zBLgsj6ak5AQGuZZwlS1jVTao1bYolVYoFP/7taJ3g8LRG/QU1pSQW11EmbYUvaxGbeZPnSyjlWUKSs5Qa9BfcySRndoMP4dwnE1UOKpVFBfuRm+QsDDvg6VlcL36r8q9iPz7G9imGQsKG7wHII/6nAlOjajr1oak+Dqj+2UsA/QnOWtZS9igq2vfjJZl7jt7gF1Vjnyl6cWykDoGuve/xtHapgWxaVBUzgRne6YGzSflxBaspSoKZBsmzH4Uk7+Mwr9wMpbIyEg0phruueeeJjnftFotK1/7lIrURCSFgrv++SJeYd0afdx6GzOG7V+uIvHoz1TnnaX8uDn3vPoPFKr2u/qhoUpHxe/pWI30RmrFZH9HUlZWVu+2HXZ62v8njJKTk9mzZw8ODg71jkvUNGq7iouLiYmJITo6mpycnMuvKxQK/P39CQ0NJSgoCI2m9Ypdtpi8ONj1CiTuNP5sag0Dn4a+D4Ha+P7PlFayJLOATXklaP841Z1NVNzr7sAcd0dcTG98YRQ1jQShfbrWuVupreb9mF0sL3ahFlMk2cAEywzeDh+Dg0njnyOd2fodwSdfxIxa0mRnzg/8ijFDr1yVyGAwsOHLHzlfeAFkGBsxlN4TBja67+s5v/c0u779AFlfAZIpg2Y9Tu/xg5utP6FtMxgM7Fu5icjffkA21ABKvMJGM/HZBZg0YnXAxtLpdJw+dZp9O/ZQLRtLJlgpzBg6eCjdB0bUa2WvxqooziNx5RP0KNoOQA4O1Ax/n063TW32vtuE0kzyt72FXcKPqNAjS0qkwS8Ya/8oWudLqr5SizarAtNO1kjqpovh2KKH6Je7hgzJDcfnTqMxu3q6Xa1ex6SjOzij9cCGUnb1CsLbqu1PEzpZnM/4yEwUwP4+XQiw0HDu64V0y/6ZA1ZjGfT06iva//DhEpLK0+nuGMSkx2Zc+6A36djGvRxe/REgEzZkOiMfbPyUt4b4bdEaYvYZZ9o4ePVh9rsvtstZGrIsk//1OepSytAE2eEwJwSpngXshetrNzWNwFgIu0+fPnz++eeA8WLu7e3NY489dt1C2FVVVWzevPnyawMGDKBbt26XC2H/f8IoMTGRvXv34uR0c8PsRdKofSgoKCAmJoaYmBjy8vIuv65UKgkICCAsLIzAwEBMTDp44bTkfbDzJcgxDi3HxguGvAxdp8EfT9HzarWszCrk+6wCcut0AKgliXFONiz0dCLC2vy6N6QiaSQI7dNfz12lUsnPlw7xVnodubKxwGeIMo23AzvRz7UJnn7WlJGx5nE8U38F4Iy6J3Zzvqfz/xS0NhgMbPxqLVEFCSDD6B5D6DtxUOP7v45Tm7ex/4evAT0qU0cm/+sVvEJ8m60/of0oys7jl3c+oDQ3DgATcy/G/+MZOnW71rSsllNyNptjOw9wtiqRWsl4vXZS2dK/Zx+6Det9zZXWmlrCkU1Y7XoWd/mPh3MhE2H0+2DVMWt/6cvzSN/0Fu6JqzDBuLLccSkcp4lv4dv99laJqS6ngtz9F8k5n06leR363tbYOzrQrVs3lMrGJ4/KS4uo+bgnThRz1Och+s9/75rtSuuqGXl0PykGV/qqL7FhwF1tfoTm6IMbOavzYZKDgkXduoHBQNGbftgbitjb60vuHPdnAqc8v4SPv/gUgyRz36Q5eHZv/PUhLSaZn//zHLJcg51HL+b995VW/Z3tXrqeqB3LARmvsAFMffF5FE3wN9TSalPLKPjuPLLWgHlPZ+ymBbZIMr0ja1dJo7Vr1zJ37ly+/vpr+vTpwyeffMJPP/1EfHw8Li4uzJkzBw8PD9555x0Ajhw5wh133MG7777L2LFj+fHHH3n77bc5c+YMYWFhaLVapk6dypkzZ9iyZQsuLn9e4Ozt7euVQBBJo/YnLy+P6OhoYmJiKCwsvPy6Wq0mMDCQ0NBQAgICOm7Sw2CAc2thz3+gLNP4mlt3GPEmdP7zC1mdwcC2/FKWZhZw4i8roHSzMmOhhxN3OdtetfSsSBoJQvv0/+du4OChPB97jBM1xmSRHSU8565lbsDQprmRTTsOv9wPJanoUXDScx4Rc9+/6vNClmV+++03Y71CGUaFD6bfpMGN7/8a9Dod+39YwtntxgdMVo4hzHrrZSyauQCp0L7Issy+lb9wZtsqkOtQmZhy59z76Tp0ZKt+GZFlmaK4HHZt2EZSXRY6ybjMhYXSjD6D+hEREdHsi4LUVpdTtfMt7CK/AVmPrLEhvsujBA6dj9Kq/a6o9VeVJQUkbXybgEurMMe4Eu0JQxdO+T7CzLtnYGvesg8dDQYD8QfOcezYMTKr89BLV0/Ac3Bw4I7gfoQNiWj05/eprd/S6+Qz1MhqiuYdwr1zl2u2O5MXx4SYCnSoedU1l4eDRzaq3+a0I/0kc5PUKNGxN9yRQLtO5MQcwnXdWMplM6qfTMDZ/s+Vy/as2saBxBM4K215+KUnG33eVxSVseTJJ9DVFaA0deX+zz/Bwqb1F/DZ98NmTm/5FmQDgf1uZ8zjz6BshyOOquOLKPw+BgxgeYcntqM733gn4braVdII4IsvvuCDDz4gJyeH8PBwPvvsM/r27QvA4MGD6dSpE8uXL7/cft26dbz00kukpKQQEBDA+++/z5gxYwBISUmhc+dr/wHt3buXwYMH3zAekTRqv2RZJicn5/IUtpKSksvbTExMCAoKIiwsDD8/v3Y5PPOGtNVwbBEc/Ajqyo2vBYyE4W+A85U3A+fKq1iaUcCGvGJqDcaPAXu1knvdHZnr7oD7H8P0RdJIENqfMp2ei2WVfHTiLHs1NuhkUKFlhnUGL4UNxca08de2uro6Yn58ifBL3yLJBrDxRjdxMarOt13VVq/Xs337dk6dOgXAmEEj6DNkQKNjuJbCzHx2Lv4vWReMBa/7TZlJ/yn3tMsnq0LLyE1OZ/tXn1CYngBA5/AIhix4FFuX1kuO/P+1d2DEAI7/dohzGXFUY1zQQqlUEtIlmG4+Ifj3Dm7eBFd2FGx6ArIjAdCjIMW8KzX+Y/G+7W6sXNrhl7aSNDj+NYbTK1D8ca8UjR+xXZ5g0Kh7cLVt2RqZWq2WU9uPcCLyJMWGisuvS0jYWFnj4OyIjY0N8fHxVFVVAeCudmDE6FF06hlwvcPekGwwEPvuYELroki0G0jAk1uu2/aNqE18VeSNGVXs7ulV79XnWpLBYGDooW3E6T2ZYJ7MN30nA5C/4V84RS3imPlg+j238Yr2n775IaWGSkaEDmLAtCGN61+n59snnqeiMB5JaYnniIlMmjWlzdw7J548ypaP38Og1+Hbsy9jHn8OU3PTG+/YxlSeyqX45wsA2Iz1xWqgRytH1H61u6RRWyOSRh2DLMtkZWVdHoH012JfpqamBAcHExoaiq+vb5MM9W1TKgtg/3twaikYdMYVi3rOgcEvXjW8vLBOx+rsQpZnFpBZaxyWrZRgtKMNCzyc6GVhwvbt20XSSBDaCFmWKdXpya7VEl8QSWplKek1WjLqJLK0JuToLSnnylXIRjhY81InSwKtm+ZLcGpSNNU/LqSLLt74Qrd7YMwHoLG5qm1FRQU/Ll1FRlE2AOPGjaNXr15NEsf/ij96ju1fvIdBV4paY8aYx57Gv3fjCnsLtwbZYOD0to0c+vF79FotkkJDzzFzGXzv+FaJ538f2Ghr6oiNi+XEqZNkZmZebmejsKB7UFd6jxyAlW0z3bPqdZxY/yFmsWvpysUrNl1UB1LkPYIug2dh5RXSPP03kfiTu1GdWIx/we8gG0fx5Jn7Exv0OH1HzcbMtGUfJpaXl3Py5ElOnTp1ORmklpWEugTQZ9gAXPw8rrg/rampYc/q7ZxOPX95FFKAlTcjJo/BqXPDag2lxp/Ba+1wFLIOZqyFoFHXbKcz6Bh68DcSDJ4MsFbwc8+uKNrY1KDvk47yXLoZptSwP8KLTtYextUSv+gNhYnoJy9B2e3P+lyJx2NYtX0dKlnJ0//4J2a2V9d1uhnr3vyStPPbASXDH36ZS6WZbe7e+dLZU2z88C30Wi3mdgHM/+9baCzb30rhZfvSKfstBQD7GV0w7964FV9vVSJp1EgiadTxGAwGMjIyLtdAqqj480mOmZkZwcHBhIWF0alTpzY/V/umFCTB7lch/o+nR2oLuO1JGPAYmFx5cdQZZHYUlrI0o4DDJX/+foLNTYkozOKVIbdjrWl/TyQEoT2r1lVzMi+Bo4UZnCmvI1lnRyEOVOmvtW7Qlawpw0VnwsvdAhnh0jRLCMsGA8c3fE7Xc29jIdVQhjnJfd4gfMz912yfmZnJjytWU15XiVpSMXnaFIJD6reC0c3a98MWTm/+DtChVNsx+YVX8A5t+FN44dZUkJ7KT2+8TfUfU71tXLsx9d9PY+tc/wVVmsLfjfLNzMzk6MZ9xOcmX566JiHhYeqIi70zbu5uePUKwNHZsUkfimn1BqJioik6uR6XrF101cWikP7yNcLeFwJGUuBxJ1ZBgzA1bf1VbXUFyVzY+wOmCRvx0yX9ucF3MPR7FPyHXa7/2FLSzl3kyO8HuVCWhuGP5JWNjQ3hzl3oO3Yg5rZ/P52pMD2f3T9vI67kEkigkCV6uIUwatZ41FYNWAhm1ytw+FOw9YaHj4DptafxxpZkMCaqkBqDzDuBnsz3cLz5vppJpU5P30OHKJBtWGidwFsR9xg35JyHxbeD0hSeTQLNn9/r1ny8nITSFEJt/Zj21L2N6j8jLpq1r78Askz4qAUMmj2+zY7Sj9p9hN3fvg/oMLcNYMEn72Jq1r7u72VZpnRLMtWxhTguCEPtZH75dVHnqP5E0qiRRNKoYzMYDKSlpREdHU1sbOzlpzsAFhYWhISEEBoaire3d8dJIKUehZ3/hszTxp8tXWHIvyF81jVXBImrqGZZZgHrcoqpNvxxQ6NSMtPNnnkejvi0s4uLILQXWTV17M08zvGifM5VqblocEHLtetq2KuV2MuFOErFeKi1eGoU+GhM6WxhTSdLe6zU9vy++1ST3bQWF+SSvGwhEZUHAYgz6YrDvctw9rp2Yiby9Bk2b9mCXjZgYzBn8sCx+AwPbXQc/8tgkNn86U8kHfsBkDG3C2DWm69i7Wjb5H0Jt4a62jo2fvANaed3ADIKpSW3z3yI3uMGt1gM9ZkaXl1UwdnfjhGVGE2uXHLVdqVSibOzMw4Ka5ws7PH288EzpFPDEgvXkJ6eQtqRn3HJ3Il/5VnQ113eVilrSLbojsI9HI/gftj6RhiTEi3xha4omZqoXyg/sw6n8vjLL9fJKiLtRuA95hlcAyOaP46/0Ov0xOw9w7GTx8mqK7j8upeXF/369aNLly43neBLP5fMjq3byajNB8BN48isx+bdfL2r2gr4qj+UppHlew/uc765btPvMvJ5KTETM4WC33sF4mvRNlYrfj36MIvyLXAknyP9wrE2M448yf75Odyiv4bgCXDPysvtq6qq+PCD/6KXDcwdO4POvYMa3Hd1RTkrn3uC8sJ8ggbcybgnn27zpR0idx/j92/fBXTYuIYz/8PXUara16wL2SBjqNahtDD+fmWdgZwPT2Hqa4tZV0c0/rZIqg7yPa6ZiKRRI4mk0a1Dr9eTkpJCTEwMcXFxVFdXX95mZWVFSEgIYWFheHp6tv/MtSxDzC+w+3UoSTW+5hwCw/8D/kOveSNXotWxKjOfRRczKFAYP5QlYISjNQs8nBhkZ9n+fy+C0ArqDAbya6q4UHyB48V5JBHE6bIqsv+YIvpXVpQToi6gh6WCCDtXQp3CcTNVY3aD5Wab8qY14chmbHc+gQtFaGUlZ/0eodfM11BcozacXq9n+7rNnIqPBMBL78hdQ8fiOLjpa5/UVGpZ/+4qci6sB2ScO/dlxpsvdMyadUKLi9l/hl3ffoJeWwSAs29/pr74FGZWjZvGUh83c/7KegPZMWlcSrhIbm4u+dXFFGpLqauru6qtUlbgpLDBzdqZwB7BBA3q1jQPyGrLIXkf+oTfKI7cgiMlVzWpUlhRbheMS2AfcA4GSxcuVmmwsHfD1tHtmsu+X0tdTTVlRbmUFeVSVZJLbVkejlIFPiblkLgTcs5dbquXJU5JYZT7jSN8xL04urRs/ZPy/FJO7zrKmcRzlMnGh5SSLBFg7UX/wbfROaLhyQowPgg9v/c0Ww/uoA4dNjY2TJ8+HTc3t5s6TmHMHuzWTUaBTMLQJQQNnHrNdgZZZlpkEodLKglSZrN9wDDMVa2bFMmoqeO2o+eoRcUbDud4oNscY6x6PXn/CcSVAhLu+IqgO2dd3uf48eP/x95ZhslxnGv77mHagWVmBjFZFllmZnacGBLHDicn+IWTkxPmOOAYkxhiZluyZVtgMS4zM8wOc/f3Y+RdrcVaXs19XXtJ013TXbM71VX11FvPy5tvvkl8TByf/dz9Z9wGRFHk5V//jKa9O7AkJXPHz/+ASqOd8aIRwO5X32Pzv38HiMRnr+b2//36rF4s99QMMfhY5chrQaNAWxyNdl5cREA6DhHRaJxERKOzk1AoRFNTExUVFdTU1ODz+UbOmUwmSkpKKCkpITk5eXYLJUEf7P4nfPBL8A6Hj2WvC4tHSUen3g4EArz2xhtoVqzmiR4r7w05Rs7l6dTclRLLTYnRGGbZCkWECGeCJElYgyGsgSDN/R/SYavDLqoO/yhxiwokws8Ho3EecrkGb0iiy9VNv9eBNaRmWFThlI4dPSQXIFflpUjexWJTFOfGZ1NozjqjgdyEDFqDPnj3x7D9zwC0y1LwX/03cuavOWZxh9XOM//8Dx2uXgAWyXK44PpL0ZVMzjaGp37wOF01zwES6WWrueE7X0eYxYPeCDMPt93Fcz/7E/3NWwHQGmO56qv/Q2pR6aTed7ztVxRFhoeH6enpoXV7HT0DPfR4BvExVpiOU5pZf93FFBYWTtjYRgyFaCz/kI5DHxDsPECyp448oQPV4a10x8MpaRmWmXDKLXhUFrTmBArjNOAaIOgcoK+3C6NoxyB4TngdSZCzNVjEXsNass69mYuXl6JRTt0YRRRFmpub2bdvH9VV1SNb0NQoKEsqZOVla4hOm1iT9f7+fp566imGhoZQKpVcuf4y5p2z8JTfL0kSH/zpM6wb+i+DmJF9bgeWuGMLT/XDHVy8vxU3elbphnhq6XkoZdM3Lv5sZQsv9Q2zSOvlpUUFqFQWAKq3v0nR27fgkLQov9U4IkpKksRf//pX+vr6uPTSS0eSL50J7zz6IgffehhBJuf2//0NCdm5wOxJIvPeEy+z7/WHAMhadCXXffO+aa7RmSOJEv4WG+7yATwVA4iO0WedoJETfWM+2kkai8xWIqLROImIRhGCwSANDQ1UVlZSW1s7ZrXOYrFQUlJCaWkpCQkJs1dA8lhh869h1z8Oh5QLMP9WWP9dMI2uxH2842twe3m0Y4BneoZwHvZViZLLuCnRws0WO/PiJn77SYQI002Ho4dHm3bzhiebZs/R0UBngiCFMAt2CpXDrE4sYkVMKvONWvQT5EEy3kHrcMtBzG9+DnrLAegvuA3Dlb9Aazh2v1hdXc3rr7+O0+lEKcm5KOtcFt+yBplmcqJ+qra8x1t/+S2SJJG3fD1XfvnLEcEowqSx86UP2PniQwS8wyAILLniWs696Q4UqslJyz4Zk05Jkujv7KO1qpGWuiZq+5sIHjZUTkhIYM2aNRQVFiE7SRTj6dLn8LKjthtvdyXFQjOlQisMNRJ09DPU14lZsp1UUPo4QUmGTYjCITPilptRGWPJzUiHlEVQeCW7+wWWZFimdIw21NHPno3bqRxqxOawjRyPlZtYUFDGkkvPRRM1eT5PHo+H5557jsbGsFn5itQFXHT3Vae86OByOen/9XIypQ4Oapcz7+tvHfeZ+mrDKzzQlkBAUHOJrpuHl16E/Bh2B5PNrmEnV+1vQAA2LMmnLGrU1HnXn+5k2eDL7DJfxrIvPzVyvK2miUeefgKFQsHXvvY1tNoz+5v0Nnfx729/ASTfUYLLbBGNAF79w+PUffgsAGvvvJ8ll18+zTUaP2EByY67vB9PxSCiw0/CVxejjA9/Pzw1Q/jb7GjyLajSjAjyWTqXGycR0WicRESjCEcSCASor6+noqKCuro6gsHgyLnY2NgRASkubpY691tbwpEEFc+HXys0cM7n4Nwvg8Z43I7PEQzx354hHu0coME9GpW1SN7AXclGrs1ai0Ie8T6KMHsJikFea9vJvzt72e5PJ8So+GGQyzDJAhgEF0ZZAJMsgFEWRC8LIiPcrRqN85HLtSgFAW2gFZWvFossgFkRIEWfSlp0GTpt+qRNas500CqJIjuf+QULan6DRgiALgau+hMUHnsgaesZ4u0PNlJVXQ1AtMnC9WuvIGVRzoR8jmNRtXkTbz74O5Akys6/mAvv/VxEMDoNQmKI7oFmuur3k2JXou23E+zvZ9hjpX64/rjvS4/KIEEfjpCwix526Xowli1g8aLLSYpKnqrqTxs+t4v3Hn+IyvffAUCtT+Diz36FvGUTH3U0FZNOe5+Vnft3s3vvnpHFMYssilWLz2HBxcunzONEEkXstiHsA504h3rxDPcSsPcSL3OQlRgdfgbpYqh1KNGa4jHFJBJlikE2AzLfBv0BKt7bx/4D+2lz9yAdfpyr1WrmzZvHwoULSU6eurYRCoV447GX2NseFvvnxxZw9f03n7IQ2Fi+g9TnrkAtBNiR91VW3P6DY5aTJInHKx7jO/3zEAU556qaeWTJBZiOY6I9GYiSxEW7DlHhlrg9KZrfFKaPnPP7vLj/LwczTsrXP0HZmqtHzj37h39RaW2kOCmXm+6748zuLYr8/YGv47bWotIlc//f/4JCNdpOZ5NoBPDcz/5E68G3EQQZV371W+QtWzndVZowJFEi0OlElTb63Rx8shrPobC3mKCWo841o8mzoIzXITepkEdrZm9QwGkQEY3GSUQ0inA8fD4fdXV1VFZWUl9fTyg0ujIWHx9PaWkpJSUlxMRMbZaVCaFjL2z4LrR9GH6ti4V13yIw73beeHvjcTs+UZLYbHXw5/r9bHNHIREemCTQx80WO/fmrSJef2apYCNEmA6abR083LSXl4ajGGA061iBrIM7kqO5LWs5+lmwHfNMBq0DPW10PnY38727Aag1LKPgvn9B1NFtWBRFdr28mfcObsVHEEEQOPfcc1m7du2kDZIlSeLl3z5F466nAIl5F1zCBfc8EBGMjoHo9xPo7KS7/gDVlZvxd7RDdx+aPjvmIR9G98mvcaq4VdCbokUszCZ+7YWUXnAzOoN54m4ww2jYvYM3/vR7Aj4nICNr8eVc9ZV7UCgnLqpuKiedbrebHTt2sGPLh/il8MKYSdCzqGQBSy9Zic4w+R5Os42eunZ2b9pBVU89Hkaj0ZNVsSyav5D5Fy2bVrFg63/f5Z3KLSBAkSmLGz5/O/JT/H7u/O8vWV71v/glOS1Xv0D+onXHLftY7dt8tyuaIEqyZN08Mb+EPHP6cctPFE6/m+8eeoenHelo8bBpfhRZ0aPi7YF3n2bBlvsYwIzlu43ID/vceexufvOb3xAUQtx+8Q3knXNmgu/b/3iOincfA+Rc++1fkr1grDfVbBONJEliw9//RMX7G7ngnvspXX8J8gmOOJxJuA/146kcxFdvRXQHx5wTlDKSf7xyRDSyv9dOyOZDblIjN6tRxmrHCFCzmYhoNE4iolGEU8Hr9VJbW0tFRQWNjY2I4mgK7KSkpBEPJIvFMo21PE0kCWrfCKdfHQynppWic9hluoJFt34X5UnC8Ott3TzYsJdX7NG4CIeAqvFyoaaFrxZfRLFpYtJ+R4gw0QREiXcH7TzR0cl7Vi+SEB4s6XFyib6PuzOLWBw/OaniJ4vTGrSGglS8/TDJu35KNHZ8kpIDhV9h2c3fQjjGloOm8no2vvoW3f5BAOIUZq67+2aSkk/PfPV0efvvz1Gx6TEAilZfxKUPfP6sF4x6HN1UVryHreogwfpGVM3d5AwoUHT1h5/pJ8CllSFPSSYupwRFQjy2oJMG6wkijYwZxOvCkUY2aw8Dh3ZjaBtEGRx7H78CxPmFpJ9/FfpV56LOy5tzq7YD7X08/3+/xjlYBYBan8KVX/06GaW5E3L96Zh0uq1Otrz4LnvbyvETnkjJkZEfncmSlcvIXlww5/6Op4Pf76eqqop9e/fR1t42clyLitLkfJasP4eE3Kk12j4Ru1/dwht7NiEJEjm6VG794p0oNCffTimJIvt+czWLXZvplSeS8PVdoDEdt/z7nfu4r86BDRPRcpHH5xew1DR5QuOzTZv5SWuAPsILtJ/VbeZ7Sz6DXD6ayW3Pb65jieNddsTfzIoHRrPBffjcJjZUbMYs6Pni9752Rn6BXXUdPPX9L4HkI3fZtVz9tXuOKjPbRCMIe5F11VYjU6byzmNVXP7APGJSTjMT3yzjoygkb70VX7ONkNWHoJSR8KVFI2V6/7SfQKdz5LU630Lc3ZPraTdVRESjcRIRjSKcLh6Ph+rqaiorK2lqauLIZpWSkkJpaSnFxcWYTMfvdGcUoQDsfQze/zm4w+GbYtoKZBf/DFJPnqbWGfDxeOOHPNEboFUcNXtcZTZwT2osF8YYUZzlE70I00NIkrAHQ9iCIayBEL2ubrYMdPKaPZpe/+hqU5m8jVsSdNyctRKDSneCK85cTmnQGgriP/AM9g0/I9bXAUCTLBPZDf8ks3jpUcV7unvY8PzrNA20A+FMTOfmLmHtLRcjn2Sj2T2vbeaDf/0KkEguXM0tP/zGWTmBFb1e2re+Tfnr/0ZR2Uh8jwft0UmyABC0WoSkBJr1LqSkeDRp6Rgzc4nPKSMhdx4q4/j7JCkYZLDmENXbX8O2awfRB9uw2Mf60wRjTLQWmIhatZrSS28nJmniM+lNB6Io8u4jL3LonSdB8gEKClddz6UP3DburVPTOen02Fzsfm0rB+orGGI0+UVsbCxlZWWUlpbOzojqM0AURdoPNbF3+25qh1tGkqQICGRoE1m0cBHF6xaM2Zo0kzj4zm5e3vIGoiCRrk7gji/dhUqnOen7bNYBQn9dRbS/G4qvhhsfP2aW3Y9otLVzd2UXtT41KkHgp3kp3JYUg2ICDbLrrC18vXI/OwPh50cMVr6TBrdmrx0j/gQ8doK/yEWLj9qrXqFg0dqRcw/+9Pf0BYdZk7OM9Z+47LTrIIZE/nb/V/HYGlDrU7n/H38eiWI6ktkoGkE44uiVPxygo8aK3qTium8sxhgzeT5cswHXvl6C/R5CNh8hux9VehSmizKnu1oTQkQ0GicR0SjCeHC5XFRXV1NRUUFLS8uYc+np6ZSUlFBcXExU1CwIbfTaCW3+LWz/M3LpsPlv6fVw/vfBknnSt0uSxLtdFTze6+ddm5yPYrHiGOIG8yCfyT2HpKjUSat+hLmHJEn0+YPUu73s7z1Anb2XPn+QwaDAkKjHhhn34Sg3QThSmJSQJGkkguhYxCgV3JwYzS0JBvKjZv+z/4SD1lAQyv8Lm38FQ00AWCUDB1JvZ+UnfoBaM3aV2Gq18u5rG6hoDPsWCZJAsT6T82+6lOjMic0CdCxayxt47n+/CZIPS/IiPvWbH87q1MCngyiKNOzbhLhrP7q9tbj37EE6IrsnQFAuYEsxEshMRpGfQ8qClaTPX4U8NnbKhTVRFGk6tBnLgVZcH27DvWs3ktc7eh7oSdXiXVxE0tqLKF55JRrz7I5C7apv56Vf/gqPPdyWTIn53PS9b2GMPfO2MRMmnWJQpHlnDXt37abe2U4gNCqsx6ssFOcVsWD9Uswxsyii+hRw21zU766ioaaOlsEOHNJotjaz2cyiRYuYP3/+rFkIrNl6iOc2vkRQEMnIyODWW29Fozm5cETHXnjkIhCDcPlvYenRETVH4gqF+GJ1G6/3h03A4+VePpWWwR3JscSrz/w77A2J/L6xjr902gmgQk6QW6La+X7ZcTyUDj0LL9yLU5+O/msHR6JR28ubePj5J5BJAl/+3Bcxxp/+9/bAhrd49+E/A3Ju/N5vSD9OZOFMaL9nitcV4MXf7GOoy4U5Qcd1X1+E1jA5hv8RppeIaDRO5oJoJEnSWbkCO9NwOBxUVVVRWVlJW9toKLMgCGRkZFBaWkpRURF6/cz1CwgEAmx66d9cKN+B7NAzgARyFSz7DKz5H9CeWqfb4fXzeOcA/+rsZjgUXoFVSn7O17bw1bwFzIvNn8RPEWG2IYoB2u2t7B3qpMJuo9EToMWvpltIxx4ST36Bk6CWPBhwosdFsirEHRllXJWSi2oOCRHHGrQG/T7qNj5MasWDGD3haCFJG827lpvQrfosK4vHRoA4HA62bNnCnj17RrbgZpPAeWvOI/W8qdmqMtxn5bGvfYmQfwi1PpV7//Q7NPq5vfI52N9OxZv/wfHB+8QcasfsGPudVyQk0FRgRLd8OYUrLiO2oAzhGKvdMwHR5+PAxqfofu9NtPtqSer2HVVGmZGBtqQETWkpruwEYhcsQ2eeXamRxWCINx58ktoPnwcpiEqrY9WtdzL/wkuRnUFWqZk26fR6vdTU1FBeXj42olqCFG0cRTmFFC4rJTYjYXoregaIokh3dze1eyqpr6ylxzeEJIxOj+SSjBxjKkvXrSBnYeGsFKybyut55rXn8Pl8JCcnc8cdd6DTnUIU7Yd/gg3fJSRT0XHJo2Qsu+KExUVJ4i+tPfy5uREb4a1NckTO0w1ySbSCYnMSRZYMtIqTP8MdwRBv9Nv4XWsPLZ5wOOV8RSu/LCpi/onGjP+5CerfhrXfhPO+M3L46T88Ro21hVxDGnf8z4kFsGPhdTl5+Iufxut0sOK6T3DuzTcft+xMa7+ni9Pq5flf7sVp9RGfaeSaryxEqZ75Xo4RTo+IaDRO5oJotPk/j9JWcYiUwmJSC0tILihCb55bK0GzDZvNRmVlJZWVlXR2do4cFwSB7OxsSkpKKCoqOuPUn5PFmI5voBo2fg+a3g+f1Jhhzddh2adBcWqZ0lwBP082b+fxbg8NYthcV5BELtA28+2CBRRHT17GpQgzm1aPj0dq3uSA3UmDmMggx540yoBMrZp0hYNEOknWGEjRx5CsjyVWqcCkkCEAKqUZQQgPcoIhJ6GQDxlgVMhQHQ6Zl8s1KBSzIOrvDDiy7Q50NdG68e9kd7xIPEMASNoYhHO/CEvvBfVY34LOyha2bdxMra2FkHRYLMrOZlXWEjIX5yHTTc0gOOgP8NAXvo57uAGZ3Mgnfvl7YlMnP7JpqpEkCW9dHe88/QsUOw+R1uxCfsTozKeA/oI4Fl15N4ZVq1Dl5MzahaHe1mqq3n4a99YPMdV1ETN8bBF4IFaFIzseZXEhsQuWkbv8IqIsM1+Q6G9rY+M//kB3fS0AUbHprP/UZ8ldOu+0rjOTJ522jkEObtpNVUstPaJ1zDmToCevpID8eYVkZGSgVs/MLKrD3UPU7iyn1d5NS08bbvdYZ3iToCMzOpXcgjzyVpSgMc7ObcpH0tXVxb///W/cbjcxGhN33HoHloyTZP4VRZr/egNZ/e/iQ4V4y1NoCy846b3au1/jsZqX2SCdT71QOOacjBCZWh25OjV5eg2JoSby9DpKozMxqMxsGrLzbFcX7w658R/OWpqoUvKD7BiuTog/oWgnuQYQflMQjo763G6IC4tLjXWN/OvJf4EEd152C9nLC497jeOx5cnH2PXyc8SkpnPnL/90wi2oM7n9nipD3S5e+PVefK4g6SUxXPZA2Zw2xz4biYhG42QuiEb/+c5X6Gkca2ZpSUohpbCYlMISUgqLMSckzdpB52zHarVSWVlJRUUFPT09I8dlMhk5OTmUlpZSUFBwauHDk8xRHZ8kQcO7YfGoL2wAKpkzKC/6CmUXfvKUTWklSWJzTyV/aKzjw0A2EB5I3BBv4H+yM0nXzsyBZoSJp9rp4c9tfbzUZyV0RI8kIJIiDJKvcpKrlZFvMLIgrpQ8YwzqWbjSO9W4nXZee+xXFDq2Uerdh+zwyvkQRvalfoJF1/8P0ZbRbUFiSKTqgwPs2LmDDl/fyPGUhGTOv/gCsrOzp/wzvPvI3znw9quAgiu+8hMKVpRNeR0mi7buWlrff43cKhvOLVsIHtEXAPTFq3Auzif+vIspveAmtLrZOR45EZIkYe1pRdXQgbeyEk9lBa273yNmOHTM8oOJOnLWXYl+2TJ0S5fiN+vRKWfeZF4UQxzc+CZb/vM4AV94a1Ni3rlc/bX7MVjMp3SN2TDplCSJgZouDu06QH1HI71+65gIHUEQSDDFkWKIJ7Moh6x5eRiipsdYN+D107inlvrKWlr62hgM2cecV6lUZGdlkyqPpWBJCbFZiXNyjNzX18fjDz2KK+DBiI47b7+D2LzkE75nyO6k8ndXs1raQ0BQofzEs5C97qT38vn6sdsPsG+omxesGqp9etrFaDwcf3FUTpAQo1GT6XI7t6Xnc29qHIZTyFr6Uea3YXMx5i9vByAYDPLXv/6VwcFB5qcVc+09N530Oh+np6mTJ//f55DEINd843vkLF5+wvKzof2eCj1NNl7+3X6CAZFzrsth0UUZ012lCBNIRDQaJ3NBNLIP9NNZW0VndSWdtVUMtLcelUVFb7aMCEgphSXEZWSeUQh1hPExODhIRUUFlZWV9PWNTtTkcjl5eXmUlJRQUFCA6iSZyyaL43Z8YggO/Ac2/S84w5OdWkUB4oU/oWj5xad1jw+7D/HzxiZ2BTIBUAhwW1IMn081kK6PRMjNVT7o2sfvmlrZERjdErXGrGOZpo+lMakssqQQNYsHW9OCzwkN70D1q/iq30Qdco2cqlAvwDfvE5SefxtqzehEu7e1i33v76KypRbnYe8OQYKcqFTOWbOKrCX507IdY9+br/DeY+GsN+ff+zUWXHjelNdhommp2sG+5/6KfMchslu8KI4IshE0GtzzcrAvyqXwsltJzJ8/fRWdRiRJoqezjuZd72I9uAeptgFzy9AxhaTuGBlN2ToC8/M5/5ovkZ+7bBpqfHw6azt57Y8P4hw4CIAg17Lw0ttYd/vVJ11gmY2TTrfVSf2BGtqc3TQ2NjI8PHxUGYvKSEpMIsmxiRijTZjzEtDr9ej1+jMe54iiiM/hwTnkYHhgCJvDhkPwYrVaGbZaGezqxxXyHvW+OLmJ7LQsitYtIC0tDfk4zctnCwPtvTz+6OM4RDd6NNx+/S0kl2We8D276rtwPHEb58v3E5RpkN/xDMIpCEcfRxRFur1Omn0C9W4fdU4nB/oO0hY0Mkh4vBctDXAOW7k4aphLC+8nKqrgJFc9zFAz/X++iDixjx25X2HFHT8EYPPmzWzatAm9Xs/nP//5M4rof/RrP2KoYzeaqAweeOjPJxUUZ2P7PR4t5QPU7erl/DuLkCsjC3ZziYhoNE7mgmj0cbxOJ1111XTUVNJZU0VvYx2hYHBMGZVWS3J+ESkFxaQUlZCYm49SFYn2mEr6+vpGIpAGBwdHjisUCvLz8yktLSUvL29KO6CTdnx+Fwee+Qn5DY+gE8JeFfv1q4i95mek5Z3epGf38DC/aRnkfWs4W4uCAFfq2/l20QrSo068EhZhdiCKIq+17eBPbQOUh9KBcETRFXEWvpCRwLyomRc1MNMJOgcZ2PsyoapXSBr4EFlo1DOmTzLTkHwVGRd+lpTskpHjTqeTiooKDh06RFdX18hxpaSgLDGflZeuITYzcUo/x0eIYoiN/3iIivdeA+Dcm+5gxfW3TEtdJoKA38uu5x7E8d9nyagZHnPOGqsmbv3FJF14BbqlS5HNgOjSmcpAVyPO/fvQHmrEtXsXvuqaMYthogAt52ax6Lu/Iimz5ARXmlokSWLHS1vY+fwjhALhbKQaYzqXPvAFshcWHfd9c2HSOTw8TN2mg7Q1t9Jp78UquE5YXoEcDSp0CjVapQadWotMKUeUSYhykAxyQqEQwWAQb48Df8CPV/TjkwKIwsmnM1pUZJiSyc3NJX95yRkZIc8VhnsHefwfj2ENOdBKKm695mbSF57YHuAvGysp3Pw5zpfvx4eajkseJmfFlRNXJ6+dNnszMaFmNKpooqNXn3q0V38toceuQu7qoUVKQPPA+yQmJNPf1sPfH3uIoBjiuuuuY96809smCtCwt5qXf/kNQOLi+39I6bolJ33PXGi/EeY+EdFonMxF0ejjBPw+ehvr6ayporOmks7aavyesfu5ZXIFCTm5pBQUk1pUQnJBMVrD3PT+mGlIkkRvb+9IBJLVOuoZoFKpKCgooKSkhNzcXBSTbH56qh3fQE8bjc9+lyUDryAXJAKSnH1xV5N300+Jjk85rXvuGHby4+p97POG258SP9caOvhm0bmkGGa+p0WEowmKQZ5u3MLfutw0iOHvg5wgF2tb+WruPEpj86a5hrMD0e+lqXoP1obdiF0HMduqyAnUoxCOCFmxZELRlQTzLuPVA71cccUVKJVKvE4PlR/s41BFBW3enhEjW0EQSFfFU5pfwryLlqKOmj5fNb/HzUu//gXtFXsBWHbtray6+bZZuU0k0NvH8LPP0vXU46gGw0K4CHQWWDCet57CK27HnHt80SDCiQnZbAzu2Er3lo3Yd35IbHv4d+xTQPcVi1n9jd9giJ45/YXH6eX1Pz1B64E3gQAgsOjSK1l50x2oj2FIPNcmnVJQZLiul+ZDDXT0dzHotuIlgFcVxOVyEfzYQuaZIJdk6AUNJrWB+NI0zGYzFosFnUOOJT4GU1bsrDSxniwcVjuPP/gwAwEbKknBLZffQPay43v9hESJP22oYP6HX+A82X4AxJLrkV3wA7BM47al7oPwr2vBPUitmMrvkn7B3+6/AkmSePRXf6fN3UO6MYm7vvKZM+pL/nb/13AN1WKMK+XTf/75Kb1nrrXfj5BEiW0vNBCXFkXB8ulZWIowcUREo3FyNohGH0cUQwy0tdJZU0nHYSHJZR06qlxMajqpRSUj0UjjSScb4dSQJImurq4RE22bzTZyTq1WU1hYSGlpKdnZ2ZMSWn26HV9r9V6sr3yHBZ4dAHgEHdr1/wMrHgDl6U1G32zbwa9a+qkKpQGgxsv1UV18Kns5ZZbkWTmRPNvwiyLP91r5bX0F7aFwemI1Xq7Sd/HVgqVkmdKmuYYzF4/LQWv1LhS95eQGG6D7IFJfNYIYOKpsrZTOQcNqggVXcOsVlyDIZAQCAV5/5TUy9ElUVVbRbO8geIS4lJyczLx58ygtLcVgmB6fkSOx9/fxws9/xGBHKyAnJuNa7vjpJ1CoZs+WEVEUOfDmv1C98i7yrfsgFN5S5dDJ6D+/jLJ7vkZ64dJpruXc5OCm/9Lzq1+R3uwEQDJFkfj5L2K5+SaEadrefSw6atp4/Y9/wTlYCYDeEs3aO+6hYOXqMYLGXJ10HgtJkvD7/di7rDgGbbiGHThtDlxOF6IviBAApVZF1PwEFAoFcrkcsd+LSqFEb4pCZ4nCEB2FWh+J1Dtd3HYX//rzI3T7B1Ei5/ZP3EFmTtYJ39M5MEzHv+5nme1NhMMZdcVl9+FY9iVMlpMYa080bTsJ/ut6FAEHh8Rs7vR/k29et5Jbl6Wzb8MOXvnwLeSSjE/fcheJRac/3jiwYTvvPvy/gMAN3/0dGWW5p/S+udp+a3f28M6jVchkApd/bh7pJTHTXaUI4yAiGo2Ts1E0+jiSJGHr6w1HIR3e0jbU1XFUuaiYuBFPpNTCYmJS00/ZCDnC6SOKIp2dnVRUVFBVVYXD4Rg5p9VqKSoqoqSkhMzMzAkTkM6046vY+iqa935AbqgxfMCYin/td1AsuOWEGSc+Tng703Z+3TpMnTgasRSrVLDSYmCV2cBSvZ8CY2JkBXEG0eW2899eG//qttPpC4scelzcZOrjSwWrSNRP8cBypiJJ4B7E2dNId0s1w10NMFBLnKOGtFA78mNsuXDKomhR5uC0lKBIW0h8wUpSs4uRHc4I5/P5aGho4NDG3TRZOwgIoyv4RkFHYXIuC1ctIakofco+5snoqqvhhZ//GJ/LDoIeY+L13PbDq9GbZ8cWaftAF7se+QWqV94nbsA/cly7eDGWW25Bf9EFKNSRCe1kI4oi2575HZp/PIuhO7zAokxPh/tuI/vaT8yYPkKSJFoO7mPTo39juKcbAK0pm/M++UmKzl0MzN1JZ4SZh9fp4d9/fpQObx9KpZI77riDjIxTiBzqPggbvgvNmwEYxkBN/mdZdP3XUU32884zDAeehE0/gYCbXWIBd/u/zvLCTP582yIkr5c///aPuPGxImU+l3z62tO+hSiK/OWe+/G7O4nLXMmdv/jOKb93rrZfSZTY+GgV9bt7UajlXPPlhSRknZ1z5blARDQaJxHR6Ni47bZRc+2aSnqbG5HEsalyNXoDyQVFhw22S0jIzkUxhx6WMwlRFGlra6OyspKqqipcrlGvAL1eT1FREaWlpaSnp49roDyejk8SQwjlz4U7dVs7ADWKIqJu/ScpOaWndS1RFHmxZRsPd/RSGcrEx9htedFYWageZKVJzfqEbApjCkfSrUeYGkKSxJudlTze3sY2bwIi4d9/gkrBfalx3JagxayZe8/UQEjE4fbhs3WTJLOB1wZeOxVNbThtg+Cxgc+O3G9HGXCgDDrQSy4ydUFwD0Lg+D4fg5jo1BZQtngNQvJ8SJoP5nT4WJSdbdhG1a5DNPS20NLSQig0ahqsR01BQg7zli8kfUHOjJk4f8S+tzby/uN/QRKDCPI4jAk3cNWXVxOfMfO/K7XbXqfx0QdJ3tGE+rA251FBx7m5rP/SzzEUzhxvnbMJKRhk+Lnn6f/znwkNhH2EujIMJP7P1ym58PQzJ00WQb+fnS8/x47nngHCbdYQk8ma228hZ8lS3nzrrTk36YwwM/H7/Tz99NM0NTWhVCq5/bbbyMw6ccQREF74qN9Ix7NfJzXQAkCHkEjfkq+z8OI7ERQTG+XXcHArQx/8laX2dxGC4cQNgwmreDDhh9x8biH5CWEbjZcefJoDfTWYBB2f+/qXUOlOfwFi81NvsPulB0FQ8slf/53Y1FPfXTFXRSOAUFDk9b8cpL3aisag5PqvL8acEPGjnI1ERKNxEhGNTo2A10tXfc2IL1JXfQ1Bn29MGYVSRWJu/kiWtuT8omPu3Y8wPkKhEK2trVRUVFBdXY3H4xk5FxUVRXFxMaWlpaSmpp72lq4J6fgCXgLb/4pv0y8w4MEtqaku+waLrvvqGUWm+UJBDji8bLU62TzYx16HjyBj6xZPHwtU/ayNTeKyzFUkqWfO9oS5RpvLysONu3lhSEW/ZB45XiRr5768lVyTYEEjn1lCxWkT8MJQI+UHd2Pvqido60Lp7Mbg7yeOIeIYHusrdJoMyWNpF+Nw61MRorPRZCwipWgFsYlHR28G/UF6Gzroauigp6ubtsFO+gPDY8pER0eTk5yJb8jN5Xdei1oz8yJ2Aj4v259/mt0vPweATJnDwkvv5ZxrC1HrZu4gW/R4sL/+OtX//D3mltGEBV2JKoJXn8/yT30dsyVpGmsY4SNEl4tdv/8e6qffRHN4V2f7vAQK/9//kj7/3Omt3BG0Vzfz9t+fwNa9j4/EI01UHLqsIm758ufQ6vXTW8EIZwWBQICnnnqKpqYmFMi5+eLryDvn1ITvYMDPvlf+Qnb574llGIAhWTTesttJXv9ZMKWecb28HhflGx7HWP44BcGa0RPxJbD0Hlj4CThCnGo90MBjL/4bSYCb111L0brTz0QZCgZ57KsPMNzbRebCS7n+W587rffPZdEIwO8N8vLv9tPX6iAqWsP131g8ayKDI4wSEY3GSUQ0OjNCwSD9LU101lbRUV1JZ20VHrttTBlBkBGbkUnqYREppbAEgyV6mmo8NwmFQjQ1NVFZWUl1dTW+I4Q8k8lESUkJJSUlJCefmifQRHZ83a11DP7nXkr94fTD5bplpH7yYSwJ49sm4/S72dxTweaBHnY5FdSG4gl9LBIpR6tmpUlNEeVckraMZENkUjcegqLEu0N2/la3nR2+eCTCwoYBBxdpW/lkWhbLklfMrmgvScI+0E1300EcHTWE+msxOpspUvaAtRU4cXcZRIbCEA9aC2iMNDsVDIa0hFRGRFUUgsaEoDWj0JtR6syU5aQj6KLBmEJQpkJxDGHNNWinZ7CfvsE+ent76WrqYMA2eHSmIAkSMFNYVkTp2kXExsYSDAZn5KBVFEXqtm9j85OP4BjoB8CSspqrvnI/sWkzt89tLf8Q4aUNeF55A/Hw1uCAHFqWJJN8x90sOv/WGRfFFSFMZ2slu//v6+R+0IxcCmdaa12dQ9n//Ii0/MXTXb0RarY38N4T/8Vt3QtSuO9WaqJYfs21lJ53IXrz2ZvtK8LUEAgE+NfvH6bN1YNCkoWFo5WnHhnucgxz8Nmfkdf6NHFCeA4gIkNWcCmUXkcwcQHymOxTWjDsbKqmbeOfKex+GQvhZ65fknPIuA7TmvvJW3LBUVG3oVCIv/3fn+gPDlMQlcGtX7vrND79KAc3vsk7//wLWqOJe//4ECrt6S14z3XRCMDj8PP8L/di6/cQm2bgxm8tQTbbFwjPMiKi0TiJiEYTgyRJWLs76aiupKu2io6aSmy9PUeVMyUkHhaRwkKSJSklYnA8QQSDQRobG6moqKC2tha/f9Rrw2KxUFJSQmlpKQkJCcf9nU90xxcKhdj51E9ZXP8n1EKAYQx0rvoZJRd8ctzX/ghHwMcHvbV80N/Nfn8SlW5xzHRfkESKFZ1capFxU+ZC0qOSJ+zec50Gey/P9od4psdKj3/UkLlU1shNsQK35KzBqImdxhqeHKdtCIOrDazNMNRETeUBhMF6EgPtmHAe/41qEz3qdDqFJARTCrqYVEwJmehj09DFpqIwJoLszESyUDBEf3M3XQ1t9HT00DvUz4DHigvvMcsrJTkxKhNxxliSk5MoKC3Ekp2AoJzZRroNe6rZ8PcH8dibgbAv3rpP3kv+8pkT9XEkAZ+H3c8+iPO/z5FWNzxyXJmaivGmGxAvP4/ElPzpq2CE06Jq30bqf/4j8g+FI8RCApiuvIL4z96POjt7mmsXJhQUadjbwa6XX6OveTOI4WeSIJORUrCAxLzlLL9mPRr99GU5jDC3CXj8/OsPD9Pm7UUhybjxomspOLfstK7RZ7Wz8YVHyG59hnNkVWPO2dHRrsrDEV2KMm0RiUXnkJxZFBaSxBA0vENo50MIDe8gO7xA0kMsLZk3k3vJ/cQmHt/Qevv27bz99tuoBCWf+8z9mJJOf2Ha6/Lw2FfvwzU8xHmf+gyLLr3qtK8xE/vfycA+4OGl3+5n5fW55C6OJEeabUREo3ESEY0mD+fQYNgXqSYsIvW3Nof3Qx+B1mgipaB4JEtbfFbOaRknRzg2gUCA+vp6KioqqKurG5PeNiYmhtLSUkpKSoiPjz/qfZPR8dWX74IX7yNPbALAV3wD6it/A1rzhN3jI4YDQXYMu3inu4qtVjst4mgaZkESKVJ0colF4Jasc0g3RDJBfBxfKMDzLbv4T/cQ+wIpI1FFMUoF18fKuM7iZX78wpkj9koS1v5OepurcHTXExxsQmlrIcrdTnywGwv2475VlAS6hXgGNGm4jTkIsfksWLgUTVIR6OOOWtU8XYL+AAOtvVhdw1jddgYGBuhu66Svv5/Qcba3mTRRJGWmkJCQQHxMHHGGaGIzEk+6ojeTBq22fiuv/u7v9DZuIxyxpWDJldex8sabUM5Ag+iuxnIOPvxrLBv2YHKG/y6iAF3zkznncz9Ef+65kaQPsxRJktj37lP0/elPZNYOhw8KAlEXXkjgjivJWnbBtNbvIwKBAK+++Bq5sXrK332Lnoa60ZOCmtj0hSy85GJK1y2ORLhFmHCOFI7kkoybLryWglWnJxwBtA66iPO2oKt4Etq2E+wqRyH5jyrnQ4lTYSFaK0dwdI8cP6RZgrj4HkrX3XhSj9S9e/fy6quvAnD55ZezdOmZZap87v/+RuuB19Bb4rj3T/84I2/WmdT/TjahgIhcGXkGzUYiotE4iYhGU4fP7aKrrmYkQ1t3Qy2hwNh00kq1hqS8glFfpLxClJqZN8mYTfh8Purq6qisrKS+vn6MaW58fPxIBFJMTMykdnxer4ddj32TVT3/QoYIxlS45kHIXjuh9/k4jbZ2nm09yFtWiRpx7IrVEqOOtdFRLNGHWGaJR688e72QqoZaebj5IK/ZzdgYfRYu01i5N2chl8QaUU3zZCUYEtl5qJqeqs1cE9uBvGM39FaC/wQRQ0BIG4s8Jhuis2gXEulWpmNOKyE5pxSDIWpcdRJFEdvgML3V7Qx09zM4OMCQYxir145D9CAdIyMagFySEaM0EhcVQ2JCAkkZqSQXpaMzG86oHjNh0BoKBtn4z2ep+uB5JDEcNRUVW8JlX3iA1MJTyM4zhUiiiHPbNrb9+XukHepFdvjPZNML9F8wn7J7vkZm/pLprWSECcV18ABDD/0T5zvvjhxrLY4h5XNfouz8G6exZke338HOdjY9+iIdVTsQQ6PCt1xpIbX4HFZcdxmphZnTV+EIc46Ax8+///gIrZ4eBElgXnYx5119IWaz+cyv6ffRVrOXwfpdSF37sdiqyAw0oToiyycaMyy8g9Diu5HH5pzSdXfs2MFbb70FwNKlS7nsssvOaCFrsHOAx752H0g+Fl3+ac678+rTvgbMjP53OnAMeand2cPiSzJmzkJihOMSEY3GSUQ0mj6CgQC9TQ2HRaSwL5LPNTazkEwuJz4rh5SCYlIORyPpjKZpqvHsx+v1UltbS0VFBY2NjYhHZMRLTEykqKiI3t5err322snr+NrDUUcMhaOODqXeRsknfoNcPfmm6c22Dp5tO8AGm4aKwNhtVXKCFMh7WWIIsTomnrVJpRhVZzaBny14QyKv9nTwz6aDHAyOCmoWhrkiqo+7s8ooiimaxhqGaTz0If3bHie1dxOp9B11XkKgh1gGVSm4DWmEzFmo43MwJecTn1FIlCkcsh70B3F0D+EddOG1eQiGAgSCQQKhIMFQEFEnQxkb3gYihST87XZCoogv6Mcf8OML+vH6ffjcHvwKEZ8QYGhoaEwk38dRSDIsejNxmYnExMSQkJBArMJEXE4ycuXERVVO96C1+sM9bPzHXwl4egFQqOI49+a7WXLF6imvy4mw9rYhvvYO1meeIdDWNnK8NTcK7Q3XsOLmL6HWRoyI5zLeujp2/epbxGytHhELO3KMxN53HwuuvGtaJj/Ha79iMMT+jds5uHEj1q5DII0utKWVzGPx5VeTvXBpJBIuwoQQ8Pp55o9P0ODuAEAmk7F48WJWrVqFyTQxY2+/z0tzUz1tbS3kmAWyF50PylPffrllyxbefTcs/C7JmMfln7r2jNvsv7/zK3obP0CpTeDzD//jjHc6THf/Ox0E/CGe+uFOHENell2ZxdLLTyH7XoRpJSIajZOIaDRzkESRwY42Og5naOusqcIx2H9Uuejk1BFj7ZTCEkzxx/foiXB8PB4P1dXVVFZW0tTUxJGPh+Tk5JEtbBM1UBiDz0ngrf+Hcv9jAHQo0lHe+BAJBSsm/l7HodvnZ+OAne3DTj7oa2EI85jzcoLkynpYYpC4JGMNy80GjIq5sXWy0tbPM31+nu0ZwhoMR54JUoilylZuTYriuszVqBXTG+E32NNG/TuPEN/0Itliy8hxEYFeTTbGvHPR56yElEVI5gwEpQZRFHH22Rho62WwdwDrwBAOmRe76GJ4eBi73c5kdIMymQwjWswaI9FRFmJiY4hNjic+MwlTUvSUbCeZrkGrfaCfzf9+hNrtW8IHBDV5K67i0vtvQ6meGYNnURQ58N4zdP7rEdJ3d6A6HGwpMxiQXbYe4dpLyF143vRWMsKU01i5jco//JSsrS0oDq+fdKfqMHz6Uyy54YEp3Sp/Ku3XZXOy44WN1G5/H4+tiY/M+i1JKcRnr2XdHVdhiJ7bCx0RJh8pKFL9/G52Oapo6WgFQC6XU2zO5pwLVpFcND1Ro5IksfGlt/jw4E4AFgWyWLNqNeZLz8yfrL2qlf/+6ItAiLV3/g9LLl93xnU7G0UjgIoPOvjgqfBW2rW3FVC6JmWaaxThREREo3ESEY1mNvaBPjqrK+k4LCINdrQdVcZgiSa5sITUw0JSbHoGsjM0qD1bcblcVFdXU15eTmtr65hzaWlplJaWUlxcTFTU+LbyHIkkSWx540mKdn2HOGGYgCRnb+onKL35hxiMU5s1RhRDVA3V8UFvEzvsHg54LfQz1u9IBpQatBRRzXKThnWJxSQbx5cJbipx+t38p2kbz/R5qQqNRhWlqJVcY3ZxW3IcOebxrRR5AyFsngAWnQqV4gyEEq8d6jfAwaeQGjchSOGZnF9SUBG1Evn8W8hbcAEur4TN58RqtTLYM0BfVTs2nwN7yE1ACJ3wFoIECkGBQiZHKVOgEOQoBTkKQYHSoEFhOZxGVpQIdLkQBAG1TIlKpkQlKFHLlWgMWvSJZkwF8URHR2MymZBPsxfbVA9afR4P7/zzPzTseoOg3w+CQM7i81h16yeITY2b9PufClZrNzsf/xXKlzeR3D2aWdKZFU/u3Z/HdPnlyHSTH+EYYWbT1rCfA3/8EembalEfDhpU5eQQe99nMF52GYJCceILTACn235t/b0c3PAGh955C587HKEtCFqSClaz/lM3kZCVONlVjnAW0NLSwnvvvTdmXJiqjmPZ4mWUrF+EfAoW0kRRpG5rOVu2baXTF15IXhbIZXnpEizX5yFTnVkdHv7yDxju3ovWlMX9f//juBafz1bRCGDnK03seaMFQYCLP1NKzsKIQfZMJSIajZOIaDS78DjsdNVV01Ed3s7W29iAGBq7NUSl1ZFSUDTii5SYk49CdfZ61ZwOgUCAl19+meTkZGpqamhrGyvSZWZmUlJSQnFxMXr9xGzhaO9oo+Nf93OObysAg5hoKP48i6/9Mopp8hiSJIkGWzvv9dRy0Gtin9dAs+doM8cMoYvFOg/nxcZxUcpCTOqJE9Umil19VTzaUsPbrkTchCfIMinEerPAXRm5rIuOQj7OSD2v28n+Z3+Gpek1BCmEiAxJJkcmU4BSQ2F2ZthYWhdLjUONS2lBY0okyqAj2l6Nt2U3oY69xPvaEI7IfVcjy6dWdS5uRSFOXxB7wIVT8iCdqLoS6AUNJpUBk85ITGIs8cWpmM1mLBYLer1+ThrJTtWgVZIkdrywgZ0vPkEoEE6xnFJYwvq77iM+c2ZkpOqv2Me2v3yP9G1NaA83W78COldkkfnJ+yhadVUkOjXCUfR01rHzD98j55065O6wJ5ciNYXea1ey/J5vo9RMXgazM22/fo+bzU++RMV7r4+0R5ATnbqY1bfeTO6SgsmpcIQJQxRFOms78Dp6sfd3MtjRRn9bOwGfkWu+/gDmhOlN2CFJEvXbK9mxdTvNrs6R/teAloXZpSy7bBVRsRMfkS6KIrW1tbz/2rv0ugYAECSBtUmLOfe69Sjjz1zwr91RxWu/+yYgcfmXfkLhyoXjquvZLBpJksT7/6mlamsXcoWMK784n5T8qV34jXBqRESjcRIRjWY3AZ+XnsZ6Og+LSF111fg9njFl5AoFCTn5pBQWk1pYQnJ+ERpDJIT7WHy847PZbFRVVVFRUUFnZ+dIOUEQyMrKorS0lMLCQnTjXK2XRJH9G/9D3I6fkSZ1AeA25qC77KdQcOm4s1hNBN0+P5v7O9nUVc4+j5Z2cWw0hZwg8zRuLk0uYF10FKUGLbJpqrcrGOLJ1oP8q6ObOnE0XDieAa41O7kndynpURMQRiyKcOgZBl79HrGho7eSnglWzByigIMUMcSxBx4KQY4lNpro6OiwEORUEB0bTUxKPDHp8SjVZ59IPBWD1uaDtbz14IO4hxsBEOQGFl12O2tvv2LaRRgpGMSxYQNDTz6JZ8/ekeMDsSr8V65jyd3fwBQXCZ2PcHJCDgfWJ59i6PHHCQ0NAWAzynHfcAEr7/8hmijzhN9zvO03FAyy44UN7H/rZXyu0b5abyng4vvvJHPevGlvoxHCiKJIR3ULVVt20VFdjr2/ASnkOmZZQW5g1S2fZdlV66a2ksdhoLWXHW9toby7Fh9hfy05MkrmlZKbm0t2djaGcY6vg/4A5QcOsW3XdgYGwmKRXJJRFp/H6ivWE5ORcJIrnJyHv/RNhnsqMSWUcu8ffz7u653NohGAGBJ56x8VNB8cQKWRc/N3l2GMnTyRPcKZERGNxklENJpbiKEQ/W0tYU+kw9va3LbhsYUEgdi0jBFz7dTCEqJiYo95vbONE3V8VquVyspKKisr6e4eTZEqk8lIU8dTXFBM2bpFZ5z9CcDv87Hvhd9S2vBXDKHDq6aWTDz5V6NdeCMklM4IAQmgx23l3a5DbB4cZIfbSC9jv0PRClggb2KlJYq1icWUmJMnVUTyBEPsGh7mtUE3L/RacYXCW7vkBDlX1cadKYlcmr4c+QRt3ZQa30PY+D3oKQeghzi6F3yReWXzcHt8ODxeHB4fUsBLYZQPydlPsLODQ40NyMVhjNjRCD5qxTQqpCx6iMcnRKGXiQgCaAU1JoUes96I2WQhOi6amKR4YjPiMcaYIxOgjzGZg1bH4DCv/v4fdNdtIeyjIie5cD1XfvluDJbpja6z9rez8+8/xfLaDozDh8OK5HLcK0pRXH85ZRffNqXeNBHmDqLHw3sPfhfNM28RbQ8/T506AeuV53LOF35MVGzShN1rotqvJElUfrCbD597Dkd/1cjxhOxcFl16FblLz0GljUzmphJJkrD19dKwex+HNu3E1lOHGHJ8rJSAzhRPSkEOMWnpQBS7X32BkH8QgPicc7nhO19Ca5gZ22n9bi/73trBnsr9DHw0VjtMjMpEZmwqaRnpJGanEJeZdEpJHxxWO3ve3MbeuoM4CUf6qdVqli1bxrKFS4iKnphops6aKp7+wTdAELjxe78jvSR33Nc820UjgKA/xEu/208wIHLhXcXEpEQW52caEdFonEREo7mNJEkM93bTeYS5trW786hyxrj48Ha2gmJSi0qITk49K7ORnGrHNzg4SGVlJRUVFfT1jWazkksy0vWJlBSXULpuERrDGQ5OvTbY+jvY+XcIuEfrZ8lBOe8GKLkO4gvP7NqTRNVQE9udajbbfGy1OkdEm4+Iwk2paoiFBpHSKAuL4kvJ0J+5+OEP+dnbX897/d186AhxyBeNn9G/WY5WzZWGTj6RWUaKYfwrcx/RXLUb+yvfZr53d/iA2oi06qv4Fn8aje7oQYIkSXR3d3Nw/0HKdx/Azai3jE5QE62yYDIYiU+IJWFeOhaLBYvFgiqypfS0mIxBqyiGqHhvI+8+8ghiMNwO9dHFXPrAZ8kom96taM2V26l88OekbK5DczihVMhkIOH2OzHffBPKhIn7zkc4u3G5bWx+6Mfon3mbuKGwZ5pHBb2XLOL8r/8OVdz4PTwmo/22VTZwcONrNO3ZTDAQFlQFQUlsxjzmX3ghpetWIJ8Cv6azDceglbpd5bSVV+G0tuLob8XjsH+slAy1IY34rCJylyyiePVCNPqx4yW33c1zP/sj/c3hrftyVTQX3/9VilYumJoPcgqIokhHWwc1dTU0NTXR09NzVBm5JMOiMBCjt5CUm0pSQTrx8fEjSVaaahvZtelDGvpbEA9vT9eiYuX61SxdthSNZuKSckiSxDM//CadNVWUrb+Ii+774oRcNyIahfE4/Kh1CmTys2/+NBuIiEbjJCIanX24hq101VYfNteupK+5CUkaO8HXGKLCGdoKwubaCdk5yBVzvyM4k46vp76dA5v3UtPZwLDoHDkul2RkRSUzf/USCheVnFlH6nex8+0nse56hvNkB1ALo+mGO1VZ9ClTKEw0olWGO6guu48mhwKn3IRTbgJdDNlZeRQtvxCtfuoiIvyiyJaeSjZ217PXBbXBFPyC+qhyBrmMAr2GTIWNdFkvRcYY8kwZZBgS0B1hMBkUJerdXg443Gzp2EalW0aTGE+AscKKBSsXJmRyS1Ic55j1ExqNM9DZTPNz/49FQ28gFySCyBGW3Yt87TdBf7TngsfmYsebW6jor2dwcHDkuFquoriwiPlLFpKekT4n/YWmg4ketHbWVLHp0b/T1xLeiiZXxbLi+k+x/Oq10xblJYoi+995kp6H/0HmwX4++ub0JKqRbr6Ccz75DbS6SD8eYXLw+zxs/tcvkP3rRZJ6D4swajXmG28k5u67UCYnn/G1J3PS6bbbOLjxDfa98TZe58DIcZlcR2LuYhZdehH5KxZEojfPAL/PS8POSpoPVdLbWId9oJWQ33pUOZlcQUJ2Dip9Jhll8ylZsxid8dR8IXe+9D7b/vs3pJATECg571ou+vQnZ2QE5XD7IPU7KmjpbKPPMcBQ0EFIEI9ZVqlQohRluMXRhaQ4wcTC4nksufxcVLqJz+B6YOMHvPvPX6FQqrj7j/8gKnpidhlERKMIs4GIaDROIqJRBL/HTVd97Ug0Und9LUG/b0wZhUpNUm4+KUXhaKTk/EJU2pkRJjyRjKfjE0WRrqpWDm3fT01XA3ZpNEJIqVRSUFBAUUY+eaUFqLRHCygnoqLTxu9e24OxdSNXyHewWnYI1UmyZB2JV1JSq1tE6vJriVl4JZhST+v+48XlHWBH70F2Wm0cckOzX0cniQRP8EQ2Cm7i5V4UBGkVE/CIRxfW4WKeoovlhgBrYywsSVyOShU9sXW3DVH57I8oa38SrRCeKO3TryHu2p+Rllt2VPnhriG2vraJg101BAib1CsUCgoKCigrKyM3NxdFZHV7wpmoQWtvSxev/+GvWLv2A6DW6Vl5422Unn8pqmnyipKCQRwbN9LxjwcRqhtGjreUxBB7190svuxTEfExwpQRDAbY/tyfiHnmvdHvo0JB/ap0Sr76A3Lyl532Nadi0un3Bdn35i6qPngPa/dBOKKPlivNlKw9j0WXXkhM6uzJCjqViMEQLRUNdNXW4HV00N1Qx0BbC2Lo6LGITBGNITqdxJw8lly+griMrHElZBnqHuS5//01jv7wdvDE3Hwu/dzXiE6e2T5toWCIwbZeeho76e3sYVjtYWB4kIGBAUKHf29KSU6+Oo3FK5eRtboIQT454mUgEOAvd3+GkL+fkrVXcskD903otSOi0SjBQIj9G9qQyQUWX5I53dWJcJiIaDROIqJRhI8TCgbpa26ks6aSjpoqOmur8H4stFgQZMRlZpF6OENbSmEJevPszxYwUR2fKIq0H2qi4lA5dYMt2Gyje97lyEjRx5Obm0vhkhLiUhNPaYVTkiR2NA3R2O9EFbCT3L8VZdBJWYoJ3eGUqx1DTgYG+lD7raj9w4ScA0Q5mkjkYybNCWV0JazGk3kh2fPXIMinXsQIiBJNHh81Lg+7e/ZS43DQEtDRL5rwCUevsBnkMsqitOQrBihSuVgal0mhOWvCPIqOIuij/vXfE7f/j5gJR5BVK4vhwh9TtOzCo4q3VzWzfeNWaoaaEIVwV2OWGVixeBkLzl82oSHmEY5mvG3X5/Lyxl+eoGnfmyCFI/qK117I2ts/ic5knuDanhoDg+20/OdhzC9tIdAVNsgPKgTaz82h4P7/IWfB2mmpV4QIEO6T3Dt2MPC3v+PeuRMAvxxq12ZQ8uXvUpK/6pSvNdWTTrfdy57Xt1Cz7QMc/ZXAaBRvXGY2acXLKVq1lsScqV1gmUn4PF7K39tLw+69DHbU43V0gnR0FlW50oDakEJMag5pxYXkLi0jNjV2wiO3RFGkfNMmtvznIXxuFwqVinkX3Mq6O2+YdVFioVCI/rZerE19pCQnE1UQhyCb3M+w4R/PUf7uYwiCmnv/8jDGGPOEXTsiGo2l6UA/b/6tHLlCxq0/WI4pLuKjNhOIiEbjJCIaRTgZkiQx1Nlx2BMpLCTZ+3uPKmdOTAr7Ih3O0mZOTJ51HflkdHySJNHR0UHFoQoqd48aHH5ElExLZkwaecX55J9TOuHigiSKNFfvwVX+OmWuHdCxC47YjmjFSLNlJaqiy8hecSU648RG6pwuwaCXHkcjTbZW2lxDiMoElictJc9gmppsbKIIFc/Bpp/AcBsArUIq/Su+zeILbxvj9eXxeDi09wB7Nu+k3z88cjxJGcM5y1ZQun5xZG/7FHGmbVcURba/8C67X3qCUCC8rUKlT2X9XZ+lZPWCSartiWmo28mhB39O+ns16A8HfcotFiy33Yb51ltQxkYSF0SYWZRveJq+P/6B5IZhAHwKOLQ2lflf/j7z81af9P3TOem0Dzoof3cLfS17aDmwd0zkjEqXTFrJYhZeso70kvxZN6Y5XTxOB837drPjxXewdlePCOijKFAbUihZs5Dk/EKScvOJio2b0t+LfaCft//6O9oqDgEQFVvADd/9JtFJ4/fWmqt4nB7+9pl7EEN2cpdfy9VfvWdCrx8RjcYiSRKv/OEAHTVWMspiuPyBSPbGmUBENBonEdEowpngGBwIi0i1VXRWV9Lf3gofa146k3lEQEopLCEuI2tG7kE/ksnu+MSQSNfBZmr3VtHU00p3cHAkKgVAEASSk5PJzMwkVR1H1qKCMzfTPh6uQcT6jex95ykKHLswCkcYbUty6jSleDPOY/H6G2ZUtrapoGXnqxi3/pRoR034QFQStUWfJ+uCz4wYU4uiSMOhOg41VFBdXT0SYi6TBLINqZyz9lxyls0sk/KzgTNpu63l9bzx5wdxD9cD4fTO8y+8mfPuvHrKxT5Jkti39Tna//EXcvb2ojis6w7EqUm859Pk3HIPski0WoQZjCRJ1G14lt7f/5645rAA61FB2yXzuOL7D6M4QSrymTLp9DjsVG/bwvbn3sDraB1zTq4yk5C9gNJ1qylZs2TGj2dOBUmSaC1v5uA7H+B11NFZU4kkji4qCTId5sR8kgtKyV5USvaCAhSq6RcFJFHk5d8+RuPul4EQgkzLkis/xapbLo1s1T0GL//mURp2PY9MHsUDDz2CWj+x48qZ0n5nEtYeF0//ZBdiSOKy+8vImh833VU664mIRuMkIhpFmAi8LiddddUjvkg9DXWEgsExZZQaLcn5hYcNtktIystHqZ5Zk6Cp7vg8g04adlfRUN9Aq7eXYdfY1K2CJGBWGIgxRROfnEBCRhJxyQnExsVOSHYtr9dL9c6NOCteI31gCxnSxzLrGRKQstdxUL2EtCWXEZMwN0P1++t2MvTytylw7QVAVEUhW/VlWPEAqMLeXZ1VLRz6cB9VnfU4JM/IexMSEijLKKKsrAxT2tGG2BGmhlNtu5Ik0VVXQ/m7b1O15T0kMQTISSlaxxVfuheDZeoM4yE8+al+/T+0/O2PZDWOGul35luIvetu5l1115yYnEY4e5AkidY3n6fz978mui3cpykSE0n84Q+IWrfumO+ZiZPOvtZe9rz2Hi0Hd+OxNQCjEUgaQxTZC5eQs3QFqUXz0RlnT3rtYCBE5Qf7qdy8jb7mA4T8Y7evx6Znklq0iOSCRRSsKJvRz5/qDyvZ8LffEvSFo98NMUVc9bUvk5Qzs72OphJb/zAPf+FeJMlL2QV3ctGnb5rwe8zE9jsT2P5SI/veaiUqWsOtP1yOUjVz29LZQEQ0GicR0SjCZBD0++lpqqezOhyN1FVbjc/tGlNGJpeTkJU7Yq6dUliMNmp6v4PT3fHZbDaam5tpPFRLc1PzUVvZjsRkMhEVFYVGpkJpk9BqtGi1WnR6HTqDDp3RgM6oRx9vRKFVIQgCMpnsuP8CdDRW0LXnVfIcu4np3wkB95h7Nshz6E84F2PJJeQtOR/VDBP9Tpeupir6Xvk+C4Y3AuCX5GyLvpaim39EYmIqvQ0dHNiyl5r2eqxHZMZTSQpKCotZunYFSUlJkbDjGcDJ2q7TOsz2F16nYed7uG2jaZHjs+ax9hP3kl6SPZXVRfT5sL38MkOPPY6/qQmAkACdyzPJv/9rZC2/YErrEyHCRCOJIvv/+1cMD71IqDO8IKG85HzM3/gKsck5Y8pOd997Mmz9dva9tZXGPTvx2Grxe5xHnJWjj84lvXQhhSvnk1aSj1J1eskuJptQMEjVll3sfeM9hjrKkcQj6y+gNWVStOpcFl68DnNC4rTV80zweby8/OuHaK/YCIggqJh3wY1ccPfNY7aTn60886M/0lG1AYUqhs898jAK5cR7WM709jtdBHwhnvzRDpxDPpZclsnyq6Z2nBFhLBHRaJxERKMIU4Eohhhsb6OjpnIkGsk5NHhUuZjU9LCAdFhIMsbFT+mEfCZ1fKIoYm3rp7uqjd6WLgYGBrAGHQwLLrzCx30GJgZBEFAoFJhMJixaDam2VozBJgzuCvJoGVPWLamp1y3EVHYJmcuvgujsWbOVbaBhLy0v/y8L7ZuQH94euFVzHjFX/5iEhFQqKys5tPsA/fbR76hMEsjQJ1FSXELZusWoDbNbMJtrHKvtiqEQB9/ZwYG332Sos5yPIgXkShWFK9cw74KLSc4vmtJ69nbWs+vBH5O64RAaR9hUVmYwYLtkGVn3fp64zKmtT4QIk43odtP/pz8z9PjjIIo4tQKuB25izT3fH1mwmEl978kQQyG6aqtp2LODqi3b8Ng/lmgCAbU+AVN8BjlLSsmaXzzu7GFnQjAYpOXAQZr3badu14djE5oISswJheQuXcGiy9YSFW2e0rpNBg17qnjzwT/id3UAkJRfyEWf+QKxaRnTXLPpw2kd4qHP3YMYCrD8+i+w6qaLJ+U+s6n9TjVN+/t58+/lWBJ13Pr95ZNueB7h+EREo3ESEY0iTAeSJGHv7ztsrB0WkoY6248qZ4iJJaWgeCRLW2xaxqSuHM30jk8KiojuIB7Rx5DditPpxNlrw94+iMftweP14PF78QZ8eEM+vKIfvyyEKImM9/Enk7zES33kS+3Ml9UQI4zNqIc5HVvyGhpNK8hdfhlG8wzcqtW2A7b8FurfHjl0SLMU99Kv4O6VUTfQTM9Q38g5QRJI08ZTUlBM2dpF6KKndutShFPnyLbbXtnIrpfeort+N2LwiMyFygRSS1az7hNXEJs6tWbS9fvfo/pvvyJjWzOqj3buJsYR/8m7Md94A/IT+L1EiDAX6Nmzlaqvf56k7rC7e09GFDnf/D7p66+Y8X3v8RBFkca9NRx6dzM9jTV4HV0guY8qJwgy9JZ4BHkM5sQ04jMzSS3MJqUwHe0Etn2Pw8GhTTuo/XAXA22HkMTRCG+t0YQxroSClecw/4KVqDQzKxpqIggFg2z774scePu/BLweZHIF+edcxvq7PoF2ov0hZwHv/PNBDm58g7iMPO74+W8mze9ptrbfqUCSJDY9Uc2iizOwJOqnuzpnNRHRaJxERKMIMwW33UZXbTUdNZV01VTR29wwJosJgFqvJzm/iJTCElILS0jIyUMxgR3UXO/4RH+IoMuPJElhIUmURgQlURSRVAJBKYTNZmOovY+Bhm5sDjt2twN7wIVL8iIJgCShlRzkaIa4wNCMabgSQRyNfgpKMupVhQwnryG2ZD3Z81cjV+um5TNLokj5By9g3PsnMp0HwscQaDKuoVx5IT0O6PGPRhQJgkBmZialpaUUZOZiiDFNS70jnB4DnR28+vjDCIO9DHa0jZ4QVMSkLWbhxZdQdt7CKTW4FkWR/W//i96HHyKrYvQ71p2mR3PHTSy/7cvIlVMbfRAhwnTi97l59+dfIuH5rWgPZ2+3z8uk8Js/5f2Ojlnf93ocfjqq22mrrKanqQG1Zoi+lkY8dttx3yOT61BqLaQWZGCKj0UTFUXQryIUUmEwmzBEmzHGWTDGmdEZ9WMm/mIoRFd9DVWbd9K8fx/OoVbgiOQaMi0l61ZTeM4a0kpmtj/RRGIf6OfdR/5K095dAMiV0ay+/T4WX3ruNNds6rB2d/LY1x5ADIW46Qf/R1px2aTda66PnSeavlY7celREWuDKSYiGo2TiGgUYaYS8HrpbqgbydLWVVtNwDfW40euVJKYkz+SpS0pvxCN/sxX7SId34lxOZxU7Cun/OAhOoa6R45HKQTWWERU8g6iez8kTeoa874Acto1BaQvOB9FxgpIKAFzBkxi1FgoGOTghicw7v0zuaHG8DHk1MkW8G5oHgOCebSwBEnKGIpzCll45TkYIlEfs4KBjj52vrSB5n3b8LlGIxVlCgV6Sz4F56xl+dXnoTFMrWAp+nzY33yTln/+BXVDeKuECLTNjyfl3vspu+DmyGAxwllNU/N+tv7fV1iwrRfl4bUha0kB83/2C/QFBdNbuQlGkiRc1iEa9tbQXlnPYEcbjqEuAu5BpCOSKpwacgS5FnO8BW2UgYH2VvyesZFNMnk00alFFKxczqJLV6FSn53CtCRJ7HxxAx8+9whSKBxxFZ2ylGu+/gUsSdHTXLvJ59/f/j69TfvIWrCY6779o0m9V2TsfOq0Vw/x2p8OkrM4nvV3FqJQnh1C7kwgIhqNk4hoFGG2IIZC9LU0jXgiddZW4bYNjy0kCMSlZ4YztB3e0hYVferbUCId36kzPDxMeXk5+/buwzocTq8sIJAfn0Xhgkz8nbtRtHxAuusgcQwf9X6foKFNnsawIYdQbCG6lFISchYQn5qNIDvzTtTv87Lzv78no/FR0gkbHrslFW+L51AlKyYghL2ItKhJMcSTkZpGyYr5RGcmnPE9I0wdTquDnS+9Q93OzbitDRy5qq5NSGbllddSeM5qNNMg/PXWHWLgqX+jfHMLoeFhAPxKgY7VeZTe/y0yys6Z8jpFiDBTESWRl7c8xMBf/sLKQwFkEiCTEXXlFdSlKzCkZhCdnk9CZhFRlrn3fJYkicHOITpr2hjs6iLK7MPjtON12Olp6sMxOEzQ7yIU9CCFPByZve1IFCodyNJIyC6ldN05FK7Mi0xEj8A+OMxLv/wz/S07ABBkehZccjvrPnHFpG3Xmm4qPtjN2w/+CBC48fu/Jr1kcoXYyNj51Knd0c2mJ2oQRYmkHBOX3l+G1nB2CrtTTUQ0GicR0SjCbEWSJKzdXXTWVtJZXUVnbSXDPd1HlTPFJ4wISCmFJUQnpx53lT/S8Z0+IX+Q8rd3s2v/brrEoZHjSVFxLJ23mLTiHLoHO/A3f8gSoRY698FAHYR8x7yeR1IxoEomLacUYnLAlMb+Djv+oHh0YSmEJmAjOTSMd7AXt8uKKdhCohCux7Ck501xJfWyfCyxSaSnp5NiTCA9I53YzMQ5O2Cca/jcLpr27ebD5zYw3F0FBEfOqbTJZMw/h8VXns+eyv1T3nZDAT/7XnwI69NPk1Y1MHJckZyE5aabMNx4HZqY+CmrT4QIs40eVw/WykM4f/F3DJVVxyzjVoPDpMIbrceUlk1u/goUiQnI4uPoNYSIzyzCaJm7z3RRFHHbXdj7rdgHhlHrQvjdLkxx8ZgS01GolJF03ifh0KadvPfYgwR94a3ChphCbv3xNzHGxk1zzSYWSRT5y6cfwOfsIDp1GXf95vuTfs9TGTuHQkF6ehppDfTS5G7DG/JyYcaFZBjPPqPy9poh3vp7BX5PEGOclis/Px9zwvRYOJxNRESjcRIRjSLMJZzWIbpqq0bMtftbmpGksWKDNsoYFpAOZ2mLz8xBrginII2IRmeOFBJpfr+andu3Ux/oRBRGH7dKpZLo6GgsBjNGr5qYWDMo3EiBLiRrPSprHTHuRlJCXSiFY6+mng69kplN0jpUceeRnVdI7vIioiwRb6KP43X6sPYMYR8YxmkdJuD1IB0RuRObYkCuDE/CHEM+3PZjC30AMcl6FIcnLS6rD6ft+GWjk/Qo1eGybpsPh/U4AqLNirW7nLbyg4ihUaFIroompXA5y666mIyyXGDq225XUzkHHvst5rd2Y7GHv7Mi0FQYxZL7v0viBZcjnCX+IREijJeP2u/6lBR6XniahpoP0Qy5iRr2o/ee2tDdowKHSYk7Wk8w3kIoJ5WEBeewbNVNyA0RA9oI4Pd4efUPj9Cy/y1ARKnRsuqWO1lw8WXIxhHhPJPY9t/X2PH83wAlt/3sLyTlJE/6PY/sf0UpSGvVDixdToTWDnyNTfRU7UXW3o3msPWlTwEuTVgM9seZyLnpLvKvvROZ9uwxKx/qcvHaXw7iGPSi1im47P4ykvMs012tOU1ENBonEdEowlzG53bTXVdN52Ehqae+jmDAP6aMQq0mOa+A5IISEvMKONTYwhVXXx0Rjc4QKSTRv6uVPTt3UWdrxY4HUTpGlNBh1CgwKaKw6ExYMmJwh6yonZ2ke9vQ2jtQ+IfoDMqPiC054l4I+FGRoFEgk6LQGOJwx2STedEnMcVPbXasmUJf6wA9je1Yu3uwD/ThcTjwOh343U78XidR0eBzOfE4HEd5YcxkolPSSC1aTGrxEgrOmXdURMFUiEZSMIjzgw8of/i3mPY3hbfTAA6tQPd5xRTc9QUKy9ZOyr0jRJjLnKj9Om0D9LRUYm2rx9HZSoJTTrRdItDbg7OjFXdn24mFJUFAlZ6OoriQ+gwlmWsvJ3v+mjkblRTh5LRW1LPt6b/TXV8DQExaDitv/Az5y0umuWbj4+A7O3n34d8giW5Sii7llh9+blLv19S0j/o97+CorsRXVUdcv5uEPv9ohtDTQBYVhenKK1BffTmW+YsnvrIzELfdzxt/PURvsx2ZXODGby8lNjXiqTlZRESjcRIRjSKcTYSCAXqbGuisqRrJ0uZ1OccWEgTiM3NILQpnaEspLEZnMk9LfecCoVCI4eFhBgcH6W3opK+xG6vTyrDPgRPvyS9wBHo0xKrNxJtjSZ2fRVJuGjExMcjPsogOt91F88E6OmsaGOzowGD2Yevrxdbfi9dhP+3rCTINMoUWmVyDwOjWTVO8diTbmMfhx+sMHO8SGOO0yBWHyzr9eB0nKBurQX7Yc8Pr8uOxH7usIFOSs3gJy665gJiUtBN+hskSjaRQiNatbyFu3klo4wcE+/pGzrXlRKG65nKW3/ZldPpIJFuECGfKeNuv0zZIX1s1Q231ODpa8Le2omxsJ7HLi9A/dFR5u15gID8e5eIFpK+5lLzF5yOXKybio0SYJUiiyMGNb7L5yccIeD2AjJTi87n6a59Ba5hdES+SJPHK7x6lYeeLgIRCHc9dv/0DxtioCb9X0GrF/sYbND/9KLr6zmOW8SmBrDTiixejys4mlJGIK8lCav5C5P4QIbsd0W6nv7eF9g83Er+pnEDn6LW6UjSIl69n6Z1fxRybMuGfYSYR9Id457EqlCo56z9ZFEmSMYlERKNxEhGNIpzNSKLIYGd72Fi7poqO6kocg/1HlbMkpYz4IqUWlmBKSIw82CcAn8tLf0s3/e29DPYO4DGGkKuVKBQKBLeI1ONFZzGQkJZEUn4qhtiza2IuiiGs3V3U76ykpbwWW287blsPYnD4hO+TybUoNdGo9dGo9VFo9AZ0RiM6k4mUwkSios1oDEY0Oj0aY9ScCMufSNFI9Hqxbd1MwytPItu2F53riK1x0dEYr7ka9yUryZ63arzVjhAhApMbKRgcHMRbXUP9llfp2/YeiS32oyIhXBoBxcJ5pK25BN3SpWgKCxAUERFpIpAkCQkJmTAzI7sG2rt54Re/w9Ef9tOSK6NZc8dnWXTJymmu2anhdTl5+6+/p2F32OjblLiAW370TQzmiROMfF4Xu1/6B+ZNB5Bv3w+B8EKPCAzHafBkxDMQpSNnzXrSF60mLqcU2Wm0H0kUce/YQd8zT+F4552RjIp+BbQvSSXpljtZeNHtczY6UBIlRElCfniRzj7g4eC77Sy5PDNikj2BRESjcRIRjSJEGCUQCPDys/+lMCWRnvpaOmsqGWhvPaqc3hId9kQ6LCTFZWTOiYl3hOkhnEVnkOb91XTVNTDQ0YpSacXa1X7UdsqPEGR6NIYEomKTyFqYQ0JWGqa4BEzxiah1Z5+h4ngmnaLXi6+ujg/f+zehXftJONSJKjA6XHBqoGdhKmtv+wZRa9ciqCKDuAgRJpKp9CTzeZxUb3uV7q3vIhyoJKFxeMRr5SNErZqODB2ywjwSF55D9rIL0WVkIczRSet4CYWC9DZX0lN/AGtjDZ72VkK9fSj6h9FbPcQ75ciVKpTp6QSTYqjXOdBkZGLKLiApfwEJmSUolNP7XN36zJvsevlxpFA4+jwmfTnXffMLGGPN01qvE9Hb2MCrf/g5tt4e5AoFZeffxnmfumFCxBVJkijf/gotTz9GwtZajO7RPlFdVIT5mqsRLlqLJSlzQtvvUE8re574Dco3NpPYM+p32B+jRH7lhSy7+5so4+d2cokND1dSv7sXlUbO4kszmXde6ohnZIQzJyIajZO5IBp5KisRnS7kFjMKiwW52YwQ8aOJcAYcq+PzOB101YZ9kTqrK+lprB9jygug1GhR6VJJyC4kd/ECCs6Zh+osMvSLcOqIoRDW7k7q91TRtL8aW087HkfXyED14yjVGoxxKQjyOGLTMkjOyyFjXj7RyWenZ9PxONVBq2O4j4ZdG+k/sBNlYwcZ3SF8jY0QGmvAPmCEimI9Ueefz9or7ifNkjnJnyDCpOG1M9jTzM79hwhYOzD4+9AHrQhHDAkTjRoyYg2g0OBFzfvNTgIyNUGZGoPBSHR0DLHpRSRlFaNSa6bxw8xNpjMJhd/npnb7WyTWD+LZswfP3n2IzqOfx16NDGuaGX9uKgtXXosxrxBVVhZy09kRAev3uOlo2Edf3UHszfVk2NXoem3429vxtrchC5yBkc1hAnKwRitxxxtJLlhEWuFSlOlpCClJKFNTUGunxufFPmjjxV/8mYHW7QAIcgMXfeYBStetmZL7nyqiKLLxH89S8f6TIIUwxsVz5Ve+TWJO3riv3dFawcH//AnNhh0k94wuWtn1Mqxry1h334/QFBSMec9ktF9RFKnc8hKt/3mY5B1NaD+qilyOYe1a9NddTdSaNShVc+953F4zxIfPNzDQHn4OGSxqVlydTf6yRARZZJfDmRIRjcbJXBCNOr7wRRwbN445JjMawyKS2YLccuSPGUV0dPj/ZsuI0CQzGiMrSBFOqeML+H30NtSHM7TVVtFVW30MQ2EZGkMKsRkFZM4vo3j1YqKizZNe/wgzC2vPII37qumqrWegvRWBAWx9nYQCx/bwkSst6C3JRCenk7OkmIyyAszxiZFn0ylwrLYbstmo2PYy3fs/JFRTT1RzPzEDAY7125RHRzOYbmIwOxr9eeeRs/xC0qLSIttQZwFiSKS/r5O+5kpcnVVIA3Vo7c1kyfsw+fvB75iwewUlGcOaVGIzyyA2DzEmjzoxiaTs+ZiiI0LumTKTMpdKoRD1uzfStP1tXJUVaJu6SekNoTpOYk9flJq+GAW+lBhk6akYcgqIK5hHetEyDIboqa38OAk5nQQ6OvC3ttFTd4C6is0ougeI6nNitoVGzP+PRVAGVosCV3wUYlIcqqQkDCmZxGQUkJRdhkoU8Le10Vq9i5aq7ci7+tH1OYgeCqA4fq4MQgJ0JSlx5acQv+ZCFlx1F3rD5GaZ2r9hOx888VdCgbAfVv7ycznvrvswWKb/7+l2uHn6B7/A2rkXgNiMMm76/nfQGs58O5okSbi2fYj13//Gvvl9ZIf/HgE5dC5IJva6G1h4xV0ojiOYT3b7ddoG2PXk78n8oAHfgYMjxwcscmzXrmHVZ36A2Zww4fedTiRRom5XDztebsJ5OMNsXHoU516fS0pBJMvamRARjcbJXBCNen78E1w7dhCyWgkND8OZ/JnlcuRm8zGFJkX0Ea8Pn1NYzAg6XWRCMcc4k45PFEM076+lcvNuehprcA42IYmuo8rFpKaTWlRCfGYhiXmFxGfMbXO/s41QMEDj3koqNu9joLURl7UNMWg9ZlmlRoslKR2JGOIyMkkrzidrQQF608SbVp4teD1unn3ktyQFHRT3KvAcOIC/qemYZa1GOcOZFsjPYfm6WzCWLkCRkBB5ns9wHC43gcEWot0tMFCHu6ua5tqDpATbMQvHjtT7CEljpkuMxqtLwK1OwKWMRhIUfOT7nmjSkBWthYAHv9dFfWcfCtGHPOgl5Hcj91pJCnaiF45v3j+AmV5VGs6obARLFkqVmiSLgUSzHmRynAGJym4XEjIkIfwjIj/8fzlpsVFkxRlBJicgymgaDqI3xxFliSPKFHtaHiGzjZkkGn0cURJpHWqis3IXtkP7kDe0UjRswN/SQrCn5/jvA4bMctJLlqPJzUVTUkKwIBNzbvG0mW6LoshQTzNddfsZaqzC3dyE2NGFqmcIU78bo+vE42efEqwxatwJJuLzysgtXYUyLQ1FWiryxIQzivoIBvx0N5fTU3cQW2M1ua4oFN0D+Nvbcbc0IveOXWTxKaGzLJGoCy9k0bWfxmCOO+17ngpep4ftzz/N/rdeRBJFFCo1KUWrOf+u27EkTc49T0bLwXpe/s3PCPr6AYHsxVdx9VfvRqY4s61LwYCfHc/8gaQXtuOvqh453pVhQH75BSy67QsYY5NPep0p3V7a2Mjwc8/T8d9/jfgNOrUCPZcsZPnnf0h8yvijrWYSQX+Ig5va2fdWK35viKWXZ7LsyuzprtasJCIajZO5IBodiRQKu/KHrNaRn6DVSmjoiNfDVkLW4fDroSFE19ET/FNBUKtHhSWLeURQkkd/dGys2KSwmCNeGDOciej4xJBIW2UzNR/uo6uuCltfI2Lg6OwtcoURY0IOKQXF5C1bSOa8PGRnWRaw2YwYDFG3u5L+5ir6WqvpqK4g6PMdVU6mNKM3pxCdnE7WgiJyFhdjiouPRA+Nk4DfS+3Ot+jY/Dbi/griGwaPmXbbl2ChM0WNrDCXmPlLyV56PrHJOdNQ4wingiRJDAz00t1YjqOjCrGvDo29iVhvK6lSD0rh2OEeoiTQK4tjQJOBOyobIS6PuIxisrILwJgEKv346yaK9He3orI2YD4sXDk6qnB3VZPA4LivfyJEScAh6HAIRtzyKHTmeFKTkkEXjV9lZn8/YExGmb6EovxCtLPM/2Imi0YnQnS7aarYRk/NPpyNdYitHai7BrH0utH6jj3lcKsF+tONhAozMc1fRNbyC0nKmT9hJr+hUJCe5gp66g5ibaom0SphHvDhb2/D29KC4Pac8P1ysxllejpCahJVmkG0GVlYsotIKlhIXGr+lJoRi6JIf0s1nbvep3v7Jsw7a4kePuIZoFJiWLWaqIsuxLBuHQqzecLr0NfSxMaH/kxPQ93hIwosyfNYdNllzDt/2ZT9Pt7/92vsfe1hkAIIMj3rPvmlMzbr9nvcbH30f1E8+SpxA2FRTtBqMd94A+abb0aTc3p95HS0X5/Tzo6H/w/5M68TMxT+DH4FtK/JY94Xv0d64dIpqcdU4XH42b+xjSWXZaLShEXn/nYH/W0O9GY1epMKvUmNRq+MbGE7DhHRaJzMNdHoTBD9/rCINHyEsDQ0NCosWa2Ehq0EPxKehoaQjrO95GTIDIaxW+XMFuQfbZf7yJPpyB+TKTK5nEImo+OTJAmP3Rb2RKqppHrbPtzDHcDYx5EgaNCZ0ylZs5j00hISc/PPSkPjmYooinRUt1D5wU46qg/hGGhAEscOvrVGE0p1CvFZuaSVFJG7pBRj7NwKIw75fVgHOrANdOEc6sXttOJ22fC4bUg+H0mmNErmrUeVno48auIipyS/H09FJe7du2l4/2VUlUd4HBzGq4S+LDOZ51xE8op1aBcsQGGZW7//uUIoFKK7pZb+lnJMrmayhW4YqEfsr0PmPjqD5Uf4BA3qxAKIyYPYfKqDCUSllpCQWYxSM35h6Exx2q10N1Uw3FZJsLcGmb0dQkHSLGqSo5Qgibh9fhp7bcikEDJEZJKIjNH/W7QyzBo5iCF8gQBWm40o0XHC6KZj0SNZaFEX4ombT1TOCnIWrMYyw7fOzVbR6HhIksRwTyuDtYeIHfDjravDW16BreLAGJP9j7DrBezZ8cxfcRWqrCxUWZmos7KQH0cEEX0+Al1dBNrasDZWc/DABuRdfeh67ZiHAsfdSvcRVqMcR5yOQHIs8rQU9JnZxOSUkFawFGNs0gT8BiYHURSp/vBVWl5+muidtZj7RvtgUS7Qlm9GuX4186/7NHEpuRN63w/+vYHyTS8Q8HSNHJerYshasJbVt141aT6DoWCA5//vD7RXvA+AJiqTG7/3XeIzEk/7Wk7bINv+9kOinn8Piz38JXFpBOxXrWLNV35xxv3ldLbfUDDAzqf/iPuJJ0lpC1tFiDIB8xVXEHPvvWjy86e0PlOFJEo894s99LWO3X4tkwvojCpM8Tqu+crCkeNtVYO47cdOrKJQysldPGou3loxiMc5WtZgVpNaOP1bM8dLRDQaJxHR6PSRJAnJ7SY4IioNjUY0HSE0Ba1HCE/DwyCeYNP28ZDJkJtMY4UmS/THXh8pNEUj00e2zZ0pU9Xx2QdsVG89QPPBQwy21+N1tAEfM5EUBPSmJDSmNJLzCslbNp+MkpwzDkOOcPq4hq20HDzArlc2M9xThxi0jS0gKDHF57Hw4nNJL5tPbFrGrGt7kiTh6m6nct8GnC2NBIaHCNlsSHYHgsON3OEhJqDC4lcSstlOKzJTbrHQbvTjSTAjS01Gm5mFJaeI5PyFxKbkHXOFVhJFRKcTz/AAdZVb6f3wPYSD1aS3ehB8Ywc8Lo1AX14M8kVlJJ1zAQ2DIa686po5Memca0iiSEvtAXoOvo2qfRvZrgNYOL7XUJ8Qw4A6HY8xByEuH0NKEYk58zDGpcNZtpDi93mxW/twWftx2/vx2gZIVHlIUnrAM4RzuJ+65jZifO2kBluRM3asISEgxOZD+gr8KcsZiF5EUkbBjFqQmmui0fEI+L007n+fzt0f4CkvR9vQSXy397iePp4oFf6UOLzJ0ThDLhTdgxj6nETbxDFG7h8n7C+kxJVgICorn+J556FMS0eZnoaYFIdOP/vNuyVJwldXj2PDBuwb3sZf3zByThSgI8eIcN45lF13L0lZpRNyT1EUqdqynz2vvs5gx36QPlpAlpG/YiXzzr+E9NJ5E9K2nNZh6rZ/wMGNbzLU1QFAStEF3PCdz6FQnV4bCVqtbPztV4l9bScGT/h7Mxwlw3btWs594IdEmceXjWwmtF9RFDnw9r/p+/vfyKgZtQZQrlmJ+9ZLmHfejdNSr8nC4/CP+B25bD7cNh8ex2hAgyleyx0/Pmfk9X9/tpv+tmP3uTqTirt+sWrk9fO/3EtP0+h4N3NeLJc/MG8SPsXUEhGNxklENJoaJFFEtNsPC0sf2zp3HKFJdJyZeaegVB5t/n08oSk6GrnZjEytnuBPPDuZro7P7/VTv7uSjqoqAt5Ouutrsff3HlVOEDRozWnEpuWSWVZCelnYqFcQBFx2P0gCHH4tIIAs/K9cIUNnGv0bu4Z9iKKEIJNhSYhDb45kegNwDjkof28nXXXlOAcbGGhv/VgJGZqoNBKyS8hfvoTCVQtQqWfHllOvx0lL1Q56q/fhbKghvj9AbJ8Pf1PTGW3RdasFvFoZIZUCUaUAlZKQSoFJ0mAc8BAaPPF2Ha8SfIkWkpLyCDkdiDY7PtsQgsuDcJyeWm6xoFuyBNmiediLUshbcgFyRbidzoRBa4QjkCQYaoKWLUjNmxmq3ESMNNbjyycp6ZKn4DJmUzpvMcTmQ2weQXM2Ct3sn9ROB5LPSV/dLvpqPkTq2EucvYIkqe+ocn1E026YRyBlBbGl68gqXo5cPn0i0tncft0uGw27N0JNE/F9XnzNzXgbGxH7jh9xByDodKjS0lCmpVGu7keVnoYpu4CE/AUkZZehVJ5d47qm8q3Uvvg4qs17Se4YGwXcnxfL/E9/naiLL56w8a5zyM7WZ16nfud7+I+IPjLFJxKbvoyVN1110migoD9Ad0M7nXUt9Le0M9zThXOoB69rYMwilUZvYP3dX6Ro1eltRwv09jH02GMMP/MMojschTMQrcB3y2Ws+vR30WgnJhp4prVfT0Ulgw89hGPDhhGf27ZcI5b772PpZXfNusW9UyUUFHHb/bhsPsSgSHLeaOTYO49V4TlOpJFar+Sie0pGXm99th5r9+i4MCHLOCd8lCKi0TiJiEYzF8nvJ2SzjXoyDX9MaBoaGuvRNDSEdAxPlVNBptMdbf5t/rjwdMRWOpMJYQ7678ykjs9pHeLAhl20V1Ux1NmI19HJUdFIE4IcuSoOQ3QKsekZpBXlU7J2ARr91KS4nU58bh9VW/dRv3MPfc1V+FxHbxuMy8zGGJdHUl4pZectQ2ecvi0wp0JoeBhfUzOuhlp27ngBoa0LQ7eNmKEg8uP1gDIZg9FKnPEGgiY9GA3IjUbkZjMqczSx8ZnkpM9DbjIhM5lAr0OhOvHAO+R04Wipp+LAOzhb6gm2d6LoGiCq/+RZeCDsTeDQy7DlJaJavIC8864hY/6q4w72ZlLbPVvpaaunbe/bCC2byXLsI1YcO+n1SkoaNSU4k1diKTmf7HmrT/o9ijABOPuhYxe07aC/8n1Mw5WoPuYN1UYC9QmXUXjxp0nJLjnOhSaPSPsdiz/k50DrDportjFUW46514VRZUSXmY0lp4iUgsXEpuTO2cnveGmv3UPVC48g+2AXqS2jk1+52UzUtdfQd/EC5i+4eMLu19NYT8X771C95b0jsukKGGIKKV6zjpgUC65hPwMd3Wh1Lqy9XVi7uxju7kaSjr+XMCYti/kXXkzRuevQGE59TNbStJ9Dv/4BeVta4LCdhpSbycD1qznn9q9OeJr6mdp+fU3NbPr5F0jd2jgSzVdfYMDwhc+ydv2nkMvm3jwmwvGJiEbjJCIazS1Ej4fQ0NDo1rnhY5uBh4atI2UInWTz+7EQhPCk8siIpY+EJfMRQlP0aISTzGCY8QOcmdrxAfh9fhr3VNG0r5yexjq8zk4UiiAS4Ug2j9MPooQ0InpIIz+CIKA4whg16A8hSRJIInDsv78lKZmE7DzkqiSS8/PJXVqKLmp2eywF/D6662rpqC6n8oPd2Ptb+LgQJ1dasCQXsOTy1WQtXIDOOLMiHqRAAH9/Hz2tVfS0VDHcUk+guRlVRz9x/X40juMLxx4VWBN0+FJjMeQWsmDp5aizs1GmpyObQpN+n8dJR/1+lF0DmIIq5MYoemROflv7NxIScyjLWM6itBVkGE99u99MbrtzFbvdSv2ON/DVvkva0HbSpK4x5yWZEiF1KWStxhq/Al3OctTT6DsUIYzH5aDp4BbsdZvR9+wm11OOTjjiuZG6DObfAmU3gGZqnn+R9hthsuhuqST46tt4X3iNYHf3yPGGQiNRN9/Ayhu+gEo5MSJKwOdlx4tvc3Dj2/icbaf4LgUKdTRaYxzG2CSiU1JIyM4gpSCTmJS40xo3u53DbPrJ/aS+fgD14aGNdvFiYj/zafRr1kzaGHymt9+uxkMc+O0PSHuvZkQ8Kl9oZt63/4/CeeumtW4Rpo6IaDROIqLR2Y0kiogOx9Fb5YaPYQZutRIcHka02U5+4WOhUBzf/Nv8sSinj4QmzcSuhpyMmd7xTTSSKNLf3knz/ho6axoY7GjB5+7B5zrW1iIBhSYeU1wGCTl5ZC0oIXtR4YzenuW2u6jcspfmvQfob63B7+1CDI4ViQSZDmN8Pmkl8yhdu5yUgowJrYMoinhcw7jtQ7jtQ3jsQyi8QRJlZkSXC9HtZm/LVvxOO6LLjeTxgNuD4PEh8/gwBBWkKGKQXG5CDgcB69BJo3QUSUmos7JoMHkR05OILigjpWQ5cWlTm/lmKjnb2u50EAqFEHoOIWvaBA2bCLbtQCGNtqeQJNCgzMcavxxD4fnkLVmPWhcZV8x0Ql4njVueQVnxX7Lsuw4vJoBfUHPQeB5RK++hYOkFk+qBFGm/ESYbKRjE+cEHlP/j11gOtowcHzLJcVx5Lsvv+y6WuLQJu1/Tvlq2v/AK/a11iKEQcoWAWmcic34uiTmZWJKSiYpJxBwfNyFelfvefALnT39F3GD4mdyVZST2S19k/iW3j/vaJ2O2tN/++nIqfv5dEreFM+GJcoHEr36NmHvumeaaRZgKIqLROImIRhFOFykQIGSzHcOTaegoj6aPykieE6d4PR6CVnviLHNmC/JoyxGvzQgKxRl/ttnS8U02bruN3qYG2quqqd56ANdwO1LoaN8bQaYgPjMTndGESmvA2htCY4hCY4hCZ4xCbzaht5iJijZhTozGFDu52QCdVjsVH+yh5cBBBtpq8bm64GOmsHpLNKlFpcRlFBCXUUjm/GMbMh8Lr8+FtbMJW38njv4u3EO9eAcHCA4PYfLKyCaO0PAwweFh2joq0blD6LzSSUWe0yUog2GDgNusJhhrRshMRZeTT0LxIgrmrUOmP/uiOSJtd3Lo72qleeerCE2byHHsJhr7mPNdQgKdMStRFVxA9tJLiTLHTFNNI0wIjh4of5bQvn8jH6gZOdwqS6M750YKLvoMlriJz64Vab8RppLeuoMceOiXRL+7H4M73EF7ldC5Jp/1X/stuuzTSzl/IiRRQpSkSfMMGx7oZOt3PkPO5qbw6ygZ4lfuYeWtX5my6P7Z1n67939I1c++S3J5N2l//xuGtWsJikEUsjOfP0SY+UREo3ESEY0iTAWix0NoePhoT6bh45uBEzwz/x6Z0XiSLHPRY17LoqJGhIzZ1vFNFaIo0tfSTcOucjpqaxjqbMZj7wDp9Dy0BEGG2mBArdPjtstQqnUo1XrU+ig0BgNaoxGDxUJsWhyJ2YnoTOawt9Jxxj2OQTvd9dX0NFbTUVVBb3PDiOnhR8jkRozxOaQUllK2fhnJeZknHUiFQkHKNz0Lew8RPwyBzk78nZ34uruO7wt0CniV4FPLQKsmPjYTmU6HTKej1tuKTyUDnQZBq0Vm0CPX6VHoDRhMcRSnLQqXNRgY0ksoLdFYtNGRAc4RRNruOAj6wNYBtnZ8A610ttbi7G3GMFRFttgypqhfrkOVuw5y1iPlrEeImbjJVYSZgySK1O7dhH3bw5Ra3x3ZvuaXFJQbV2Nadhu551wNionxpIq03wjTgcdtZ/tjv0D47+sk9hwezwgChnXriP7kJ9EtXzajbRXsGzfS+L1voRkO+yg1npfL6p/+A1PMxAu7J2K2tl9PRSWakmIAHnj3AZL1yXxl8VcwqOa+p+fZSEQ0GicR0SjCTESSJESnc2yWuaEj/ZiOEJoOG4KHznTbnFyO3GxGEW1BMJnp93pJKSpCGRM9av59ePucwmJGbrEgaLUzeiAxFYjBEIPdXdh6OvE6HdgHh2mv7MTnduH3OPF7XQT9bkIBN2LIc0Rq2slFpjBjTsgltbiUolVLSSlIP6W/VcDv5cCGJ+l94yVidzVich47B3JIAJdOhlevwGdQE4rSIhkN6GISKM05B7nZjNxspkfuQmmxoItOQG+KQRtlQS6PiDyTxWwdtE4qkgTuQZzd9ZSX7wd7B4J7ELl3CLXPijZoxRIcJIZhBI49PBIlgUZlLoMJ52Iqu4TcRedNuIlqhJmNwzZE1YZHiK55irzQaFpz1EYouBR/wVXIctejUJ+5512k/UaYTkRRZO8bj6F9/h3k2/ePHO9J0cJNV7Dyzm+g1s4cISHY30/PT34azg4GDMVr0P6/r7Ho4jumpT6zvf0e6DvAJ978BADxuni+t+J7rEtbN72VijDhRESjcRIRjSLMFaRgkJDdPio0fdyTafhoQ/CPUpCeLoJaPWr+fVhQChq1VAU7UETHoI6JwxCXiCE2GVNCGpb4dFSa2W0iPV6CgQBepwOv04HTaqO7vgeXzY7Hbsdtt+N1OvG7nfi9DmRyH2LQhdfpOOl11YY48pcvIq2olNTiMqJiYk+5TpLfz57XH6X3jZdJ2NOCwTPaRbjVAr3zU1iy8nrUqWkoU5IJJkRjSEhFNgczB852ZvugdTwEA37a6/YzUL8LX3c18uEW8lUDxPi6wH/yNgSAQgvmNIJRqWzoVIIpnbjMInKXXTYp25EizE4aDm6jb8ujLPduRe4cNRV2S2qa1QXYYxeizVpB2rw1xCSknvJ1z+b2G2Fm4WtqZuhfTzD4/HPI/eGId5tBhnVtGfHnrqdg3TUYouOnpW6iKPL+X79H9MOvoHYHQaEg5p57iH3gfmTq6ctEORfa767uXfxw+w9pd7QDcHHmxXxr2beI1Z76mDLCzCYiGo2TOSEa7fgb9FWCLhb0caCPDf989FoXA4qZa9YbYfoQfb6RbXOhoSF8AwOUb9tGQXIy2OyHDcHH+jN9lL70dAnoVOhiEpBHRRHUa2gK9iAz6MPb4wx6QjoNkjIsRiTrk0k2JAPgCbqpHKw67nUTdQmkRoXNG30hH+UD5UeVkRRyJIWcBHMKObGFCEolglJJjbORxJwykrLLkCtmXkcfCgbxOlwM93vwOPx4XX68jiBeTwBLgo7k/BgsCebTuqbHZSO4fTf2DRtwvvc+omN0Uu3UCvQuyST20itYdOmnUGnPbqFvNjEXBq2nRNAPfVW4W/dSvmczFlsV6YFmNMLxn0tiVDJ1/lic2mSCmhjQxyI3xKI0xqOxJBGTnENcQgqc5dGTEU4DUYSO3VD1EsN7nsMc7DuqSKeQQHdUGfmLzsOYtxISy0B+7LZ51rTfCLMGa18bO/72E0yvfYjFPhp5LAJ9yVrS11xC3Mp16JYsQREdPen1aT2wlerv/Q8Z9eGoen9eGgW/+iOawsJJv/fJmCvt1xv08uDBB3mi8glCUgijysg3ln6Dq3KuOut3F8wFIqLROJkTotF/boT6DScuozEdFpGOEJJGBKaPXh/x/+MMbCLMbU7W8UmShOhyExoe3Rb30Va5oZ4Wapt3I7M5UdjdqJ1+tK4AerfETM9X5ZeDNVaFO9GMlJaIJiuHlMIl5M5bjTw2dlZ3llIwyHDVQSo/eAnb9q0kl/egOWJ+HYo20bIgnsTLr2X+hbdGtt7MUubKoPVIvB43bdW7sDbsRug5QKa/gXh3I4hHC0ROSUubOheXKR91Qh7xGUUkZhaBOQMmKJ10hAjHIhQK8f/bu/P4uOp6/+OvM3smycxk39M23elKqdQCbSkUWkAR8Ics9aoVVBQRFble7lVQUOHhVa7iVYp6BVRA5V5F0Bas0EKBUkpL94UuadMl+zYzyUxmMvP9/ZF2aJq0pNA2Sft+Ph7n0Tnf8z3nfE/ab8/JZ77fz9m7bQ11W16FfavIb13P0OTenhUdHuoyxrDTcxbOYedTOvWjFOb4gdOz/8rpIdbRzut//CktrywjsGU/BY2JHnXaS7KpHhEgY+qHqJhxBaWjznnfbyo1xmDau96WunvvRkIN1YT+8gy5r2zGBnQ4oHb+bC6648EB87xyuvXfzY2buef1e9jatJWhvqH835X/h8uuwQeDnYJGH9BpETTa8hzUbYW2emhv6PqzrfHg5wYwPf+Df0+eQO8BplTg6dC23INBJuUqOR2cjBtfojNOa8MBrNYQ7nAHiVCI5oZ9rN+9kniwhc5QEHt7FFd7J1ay67+owvRCCtILgK5vPrY1bTvq8fO8ealRSbFEjC2NW7ptt4zBljDYEkmy7T5ynQFMLE5HtI36xioCzXEcvafvAcCWno69vIxtGUGsshK8ZUPJGTqa3GFjyRk6ZsBNu+tsaCD09hrqV71K6O3VOLftxhnrfoEduT6KrriazLmXkjZ58kl9o5ucGqfDQ2sykWTzhlUcWP13sqqXMz6+kTQr1rOixw9Fk1mXGEq8YAIFoz9MybCzNG1SBoxgSwN71i8nsWclk60dXaOSoi3d6jQYH0vT5pI18/OcP2US/3jh+UHdf+X0Z4xh356NHHj1nwzZFaZ91So6tm/vUa/ZZ6N5VCGuKZMYMnkGxY4cEsEQyVCQt7a/TLy1BRMOY4XbsYcjONpjONvjZHRAWtQc9UUw70zMYfJ3fsyQs6ad7Es9LqfD/fdI8WSc3276LZPzJ3NOwTkANEeb2dK0helF0wf1l6lnKgWNPqDTImh0LMlk14NKW8NhAaWGnuvtjQeDTo1gjvEb9NGkZb0bRErPOexzXs91bzbY9HA/EJ2ON773Eo9FObBzPTVb19C6YwuxPXtw7KujqNnCVdfc421kR2rNsOHIz6OgfCyOvDwSOX622mrxFpbiLx5Clq8AtycdnE4slwu324vH0fXtWGcyQXtnW+pYJmkwkQjRcAvhYCMZCQe+hItkKESwqYaNu1eSDIUxoYMPW+0dONo7cLXH8Xe6cLfHMbGev2S3uWFfeRr28WOpuPwTjD3/o+/7W0AZmAZr3zWdHVi7l8OWv2G2/wMruL/b9mZ87PWMoi17HGlDzmHyubO6Rg/pgVUGE2OgcQdvv76E+J43qGhaTq5pArqSra+0n80W/yz+5ZZ/w+keGKMnRPqis7mZVf/4LU0rX8O1aReFe9uO+UVcnzkctHmgzWPRXJiO+7M3Mu+yLw/IYMVgvf8er19v+DU/XfNThvqGcsOYG7hy+JV609ogoqDRB3TaB42OVzIJkeYjAkz17waVDg8wHfp8lLfOHJ3VFTg6fNTS0fIxped2BaQUZDolzpQbX18lOzqI791L7ba1bF77Tzr37MWqa8Tb0EZWSyfu3r8M61+WhX34MF7y7aNpeC5F0y7kvOnXMip79IB82JITYzD13fZwK1tf/QvJzc8xJriCDN4NnMYtNzu9E0lWzCZ30mXkVWgknJyGEnFC65+j6eWHGdLyZqrY+EqwzlkAUxd0Pf+IDDJtoSa2vfoctStexlq/BX9DlIL8Ydgzfdh9PjbH9tCR5sCWmYnD78fpz8LjzyYtK5fM3CKGFJ+FPTNzUL2ldzDdfz+IX67/Jb/Z+Bva4l33bK/Dy8dGfIzrx1xPhb+in1sn70VBow9IQaMPKJnoCjL1CDA1HDZdruHd9Ugzxx1ksmyQln2UfEy5R+RqOhRk0i8Z78eZcuM7EToTnTTWVNK4ZxuZwU4ygjE66+pp3LedXTtX425uIy3YgT2exJEERye4+jBTNG6HDifEXDbcGX5yskuw+zJJeD1s6tgNGenYMjNw+Pw4/QHcviw8gRzy84d2PZhlZWHzDqwpc3LyDfS+21pTyfYVz2Hfvpixbau6Ja6Op+XhHPdRGHMFDDkfnGn92FKRUyta8w47Fj3EiH1/xpPs+mUsafewKutySi//BiUV4/q5hSJyLAP9/nsitcXbeHbnszy19SkqWytT5bNKZ/Gzi342aAJ9Z6LjiXko6YyceDb7u8Gbvkh0QqTpiOlxjfTIx3RoPdLcNV2u/WD9+q3vfQ7L3jWSqS/5mNJzu/I3Kcgkx8lhd1BQMpKCkpHdyvOAw9/lkTRJkgenfBpjsCUNNsuWWk8cnnPMsrDsduyWvdcb77ATfREiJ5hJJgi2NuGMNuMNVcLOpbRufAF/2y6mHqpkdb1ZqqrgYgJTrmHUOReB8hHJGcpTOIrR//JfPP+3WVw2JIZj1S+xVa9lWsOfSTz+F9ZkzsQ36zZGTJ6hpO4i0q/SnencMOYGrh99PW9Uv8FTW59i2d5l+N3+bs+t4VhYU9cGMQWNpP/ZHZCR37X0RSIO7U3Hzsd0+Hq0tSvxd1t919IXlv2woNJ75GNKz+kKMimSLn1ks2ypIBEAh/1ubAE2Tu9vpWRwa22P0dhQQ3tdJcVWE9lWCNqbaGqoZsfuKpyxFjzxVryJVjKTQXwmjN/qntDCDySMxTuOUbQUzyL/3P9HxbgPUaJgvUhK0ubCTLgKzr6RzSv+TvyVnzApuoop4Zfh7y+T/LuNcFoJ0cAI8ismQmbXCyDe2tNEIpk8LP3eu6O5M912xhV3vaENp5fXGjw4ssvJLR5OSUEeHqeCtSJy/CzLYnrxdKYXT2d/eD+HT2ba1rSNTy76JFcOv5JbJt1CnjevH1sq74eCRjL42J2QWdC19EUi3j3n0lEDTAeXjoNBpnBt19IXNsdR8jHldB/BdGikk8evIJOIDFzRILTsoX7vdjZuXo/VUkVa236yYtUUmToqrEiPXbKBc3s71sH/6mK2NFyBYhg2g2TFbA4EPsTYkpKTeRUipwfL4qzzPgLnfYRdG1fSuOTHjGp5Fb/Vhi+yF19kL1QvTVWfeoxDAbDh3Y/nH1bcYtLZa+XS6iog4i3GnVPOuZMngb8UfCWYzEIsu77UEJFjK8nofm9/qeolookof3rnTzy36zk+Pe7TLBi3AK9TqRMGCwWN5PRnd0JmYdfSF50dR+RgOjzgdGR+pkboCEKyE8I1XUtf2Jy9J/julpvpsKl07kwFmUTkhElGwzQe2Enjvndoq91Fomk3ztA+MiP7KbM14I63Al1TK2cfufPB/4oarCzwlZKbXwzebNodPtY3OrDSs3Fm5ODy5ZIeyCcjqwBfdj5uz7sPhzag7FRcqMhppmL8NCrG/4kDze38bvnbdNZtZbK7hguzmw6+iMRi7b4WEklDqrMC5uDnDLeDMUUHc1dEgxyo2k5mrJZM00bAaiNAG8T2QAxoAXa+e+4EdmqtXJpcxbR7y0gEynHmVpBRNJLioWPxZ2v0gIj0dMukW5haOJWfrvkp6+rXsXDdQp7e9jRfmvwlrhl5DQ6bQhIDnRJh90KJsOW4xKPvBpYOH7HUa36mBoiFj/8cdtdhQaQj8i/1tu7KOGFBpjMpmZ9IfwkHm6jesZ68jj0E4vUQC9PU1Mia7XtJMxG8pp00IrhNB9bBqSaBNCeBNCdgiCWSVLdGU9sAMAZjDJZl4fM4DtaFeHsrzmjDezfKm0Onr4zNkSwS/nJcOUPJLBxObtlIvHnDlJxa5CQ61fdeE20lWLuHxv07CdfvId5YRU6ijqGOZmjdiwkewErGj32Q9DzIHQW5o3g7ko8tbxR5wyZQWDYCm3KUyRlEz869M8awZM8SfrLmJ+wN7QXg7PyzeXze40qY3Q+UCFvkVHJ6wF/StfRFPNJzStyxEoDH2yARg+D+rqUv7O6jTI87YmTToXVXukYyyRktGmkjGWnG2xmGaAvtwQY27awi0d5KvKOdzliEREc7DhPD7XSQnVfM6IqhkJ6H8eZwIJ6BP7eI9Az/0V8HbwyRxioqt64lvH8LNLxDenAn+R1V5NHEyCOqZwNzjtXotoML4AKGHK2eASIHF0hlzAoaL7X2AlrdxcQySrGyhuLNH0bh0NEUlI0EdyYOYOIxf3IicjqwPH78QybiH3KUHp9MUFu9h8a979BWs4NEYyWOYBWZ7fvIiR8gl5Z3c0fueY2zD+33MkSMi/vZtAMAACz0SURBVAOOElq8Q4kFRhAoG8fY8ZMhPb/recThOjUXeQIlk4ZoZ4I0Z+8vqRCRnizL4tKhlzK7bDZ/3PZHFq5fyMXlF6sPDQIKGomcas40CJR1LX0Raz9KgOkoAafOCCQ6ILiva+kLR9oRQaV3P1uebApaK7EOFIKvsOsBz6U5yDJwdcQ6aKqvJli/n6xkC/m2ILTV0dZUzeYdu3DGWnF3BklLhEhPhsk04W6vewfwAh861klqgY1dHy3gUMg4apy0WH5C9gARZxYd7mzKszwUxKqgYTtpsTBnHeWQ9WQRCwynZOgY8PjosKVRHXGQcGYcXNJJONKwrK5v7PMy3eT7ut6cFIkn2FXflnrwsoDOZJLNmzYxbvw48nxeCjK76hpnGntNPkWFhYy0K/G0iLw3y2anoKSCgpKKXrcnIkHszTuhYTuR6i1s2fAWWe17KEnsJ82KMTxRCaFKCC2FvcDr7+4bJINWexZtjiyi7hzinhyS3jysjHxy8osYXl4OaVmYtABJTxZ216kZ5RhqbaKu6h380X3kxquheQ9ttTto3PcOaYkwHTjpwEXcctFpc5Owe8jy+SgvyAF3JnFnJmtqE9jSAti9fpzpAVzpAbyZ2Xh9WWT4s3GnZ3WlURA5gzjtTj551ie5csSVeOzvvgHylX2vsKhyEV85+ysUZxT3YwvlSJqe1gtNT5NBLdZ2lADTkfmZDm7vjB7/OZzeXqbHdc/HFHVl0UwmWXkleLx6xeaZrjMW7QrkNOynrbWJtmgUk+jEMglsJkGJ30mZ3w3JTiIdHWza24RFAiuZOFinq65lEuR57RRmOiHZSbitja07duHuaCCjswl/spmACWOzjv/WlsSGLc0PngAJt5/NzTbizkxwpmFzerC50kjY3HTE4pS52ylxtUFbPZ2heuLBOtKs2Huew1h2qiik0TOEaGAE9vzR+MrGUlwxCX927vv50R6VhseLDF6nS//tjMep2bONhj0biVZvwda4nQoOkNtZ0/U8kuw87mNGcBG2MmmzZdLu8BNz+ul0+fHn5DNySFlXgMkTYEurkzR/Ll5/Lr6sfDxp6V1TeI0Bk+x66UmohnDtTjZtWk+yqRJXsIrM6AHyOqvJInQSfiK9sLswlp1IPIkBDLaDf1oksTBYOOx2MnOKoGA8FI5nQ6KcwlEfIrewTKM0BqDTpf+eSsYYrnn2Gna07MBlczF/7HxunngzPpd+Fz9ZjifmoaBRLxQ0kjOGMQeDTEdL+F1PMlxPsHoXfkcMq72xaxTTcWo3blpsAcJ2P+3ObGLubIpLyigtKQNvLhHLQ027jczMTJyedHCmkbR7ME4PWHa8LgceR9eIiFgiSTBy6Jfzw/77SnZ99rrteA++MjjWmaAlEk9dqzlU37JjpfkJ+Py4HBpp8X4lEwnq9u+iaf8Ooq21xFvrSIbrsLXX44w2MTStnWzT0vXvKdp6StuWMBatlo+EN4+8wlJIzyeelsvqBgc2bxbO9CycmTl4MnPw+nPJCOSSkRn4QHk32sOttDTUEG6qJtJSQ0drHclQPZluG+Mmfqgr10fW0FM2FUMPrSKD1xnRf5NJoqFGtlfuItpcTay1jmS4FltbPY5IA+6ORordEfLt7RBpxkSasUzilDaxGR+R9DKKh46GrKEkA0PZHAmQm1+Mz5Uk0t5GpD1MtL2daKSNHE+SojQDHa20BZvZsLMKezyMKx7E1RnGk2zDa9rIMO2kW8f/PHWkRvzsd48gHBiDo3giOcOnUDZyEi63+wRcvbxfZ0T/PQk2N27mwbceZGXNSgC8Di/nl5zPjJIZzCidQW7aif1y7UynoNEHpKCRyLu63fgcDugIdZ8ul8rB1D0fU7S1Dlt7Ay7r1D7gHY+45caZngVpWcScPjY2WcRcfhIuPyYtq2tIeXo2roxsCgqKKC4sgrQs8PjB3v+ze6PxBHabhfMkTjGKx2PUVm2nae9WIrXbGWqrpSB+AJoqSTZVYku+9+iaQzqNjWbLT7stE2N3YCw7CewkLTtZGV7yfGlgcxBNWmyuaSd5aDt2kpaNpNX1uTg7k5GFAbA5SNgcvFVnw+EvwBMoIiOniEBeCb7sQqwB8HfUn/TQKjJ4qf/2lEwkaWpuorWpjkiwnmiwgXi4kWRbEybSTIm7g2HpMWhvIhZu5ED1ATJMGL8J4Tzas4jDgwmUs6EtQCS9DJM1BHfecPzFI8kvH0WGL+vkXEvS0BmP4Uq0QSxMIpmkIRTt+jLv4GgoiyRgsIwh3WXH234AajcQ3L2Glsq3KU0c6HVUbdxy4SwcC2Ufxoy+jETZeThcCiKdSuq/758xhuX7l/Nfq/+LHS07UuUfrfgoP5jxg1Qdg8Fm6YvfD0KJsEXk5LAs8Pi6luze8xoc4gFMMkkw2ExrwwHammqItNQSD9aRCNczOrODbILQ1kBLMEhNYxPOZAduYqTRQRoxPMSOOs0oaQ5/lfDhTbSwHRyqbYDOJD3qOUhiswxO0wHhGgjX4AKmAPRxtl7IpBG2Mmiz+4g6Mok5fHS6/STcfgryC6koK4W0AHGnnz0RF15fLhlZeWT6AlgmCZ1ROqLtmHgUj+mAaCttwUa27N5Loq2FZKQFokFssVYcsSDOeIhid4xcRwTi7cSNjerWGJ04SFh2sDkwNgfYnMQcGWRm5VFRWgyerjatqU1iT8/ClZGFJyMbrz+XdH8OmekZWG21WMEDENxPsG43W7ZsIi1cRXbHPgqS9ZRaCUp7+RnYgJixU2fLI+zIJuLK7spFkZaLlZlPYVEZQ8qHdCWLTs/D5vaT14eRPJ5Dfxd9YAem9bGuiIgMXja7jdzcXHJz33u0gQsYevCzSSYJh1sJtbXRGumkNZrAl+ZkbFEAXBlYNtspT/hvs1kHRwO5wZuNHSjIfq+9xsOoS/HNAB8QbQuyd9tqWirXYKo34m/dSml8F+kmCtXroHod1puP0I6XHb7p2M/6CKPOv5q0zJMTCBM5ESzLYmbpTC4ouYDNjZt5ed/LvLLvFWaWzUzV2dq0lS+9+CVmlMxgZulMphdPJ92ZftznSppkt8DTD1f9kE0Nm0j0MqIx3ZnOI5c8klpftGsRl1dcftznHKwUNBKRk8ay2fAFcvAFcoAJR60XOLj0GPhoDOZQYt8j5uz35bsFi3ffFNVNMkkyGqSzvQVXrAWiLYRa6tmxpytgYyLNWNEWHB0tOONB0jqDFLiipCdCEOvKcZBpRcgkAol6SAAdpN5kRTWwruujExhxlPYd+b1fOjD1WBd0WEDLCQw7/Idg6GpHAojT9aasA12b7LxHUufD+DgiCGN1JXeuthfT4ikhvXAUo8ZOhOwKTPYwbBkllPbhWzTr4CIiInIqWTYbGb4sMnxZFPV3Y04gT7qPkVNmw5TZqTKTTNDZuBtH3XrY8SLtG/+OL97ElOCL8MaLxFZ8g43eKcRHzKPigmvxFxz1vZsi/cpm2RifO57xueO5dfKt3bYt37+chkgDf9nxF/6y4y84bA6mFkxlZulMZpXOotxX3q1+W7yN3a272dW6i8rWSipbK9nVuoukSfLc1c+l6m1q2MSaujW9tufI3Er1kfoTdKWDg4JGIjJg9EjmeLKSO9ps2LwBXN5AqigTOLsvw1sSnXS0NVFbW017ayMdoUbi4SYSbU0kIy1Y0RaGeDsocnVApIWOUCOtzfVkmnCviZI7LRcOdxp4/HS6fGxrsRFzZtLp9JFw+cDjx0rzY0sLkJ9fwJDi4q6cT4kEkY6uZNLtkSjtHR1EIlGi0Qjx9laK3B2UpcUg2ko01MS2PXtxd4bxJEJ4k23d2hO3nDgDJeArxfiKWdnowZ4zHG/RKPKGjCW3sJxhvYwQstBNREREZKCwbHYcecMhbziMuxrX5f/FptVLaXn7r5TWLWUI+xkfWQUbVsGG+2jPnYh3wpUw5nLIP+vkPXedxjo6IgQb6wg11dAWbCIUS9IcSRJv2gP1W8FXABn5/d3M08pnxn2G8bnjWb5vOS/ve5m9ob28Uf0Gb1S/wQ9X/ZDH5z3OlIKuh/rbXryNZfuW9XocC4toZxSPo+sNbp8d/1mu77wet73ndE6HrfsT7+yy2T3qnM70vC8icjzsDty+fMp9fXsAcAOHakYjbYRamwjFwJXmxZeRQYbbBbauhzQHMK6PzbDRNTIJ4L3eTecBJvVS3hFtJxQKYU8LkJXRdYO0gA/3sQ0iIiIycDmcTsZ9+FL48KUYY9i+5W1qVv6Z7H3/ZGxiK96G9bB0PSz9HqG0EnZmz8Q1+hLcWSWkB3LJDOTiTfdh2c6c3DEmmaS1qY76vdtorakk3lpHor0JW6SR0b44OVYI2puItNaRCDeSYUXIA/J6O9gvu/6I5U9kddp5FE77fwwdc84Z9fM8GVx2F+cVn8d5xefxrx/6V3YHd/PKvldYvm8525q3MSH33dkNPnfXCKEcTw4VgQqG+YYxzN+1VPgrugWIZpXN6nMbjhzNdLpTIuxeKBG2yLuUzE9kcFLfFRm81H/lZGup20dg30uwdRFm11Kszt6TOsaMnTZbJlnZeV0vA0kLsKXFRisZGE8AKy2AzZuNKyMHZ1YJvsLhlBYVDujASEe0jdqq7TTv3065VU9Wx35o3k2oZgc07yHTihzX8RLGImhl0mZLx2EZnCRwESfDbcdqb+Lw7Jv7rCL2FlxE1pSrGXXORR/ora3SUywRw2V/9y219e31uOwu/G5/P7ZqYFIibBEREREREelVIL8U8j8FUz5FIhJixZL/xXpnMWXtm8hIhsg0YVxWomsxLdDYktp37Hsc23hzoXQqlExlX8Y4AiM+TIb/PTN9nzjxCLTug5YqGvfvZOeOLdiC+/C2HyAnXk2eaaLcMhw5ViQTUgkY68mi0VlIhyubuDuLhCeb0pISSopLwZtDhzNAQzKdzOxCMvw5ZNntHEox3i3o29HC1lf+RHTDs4xtX00p1ZTWPAGLnqBhUYCd2TMZPesGAuMuBofecvdBHR4wAsjz9joGTI6TgkYiIiIiI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-<div class="highlight-ipynb hl-python"><pre><span></span><span class="c1"># Function to calculate spectral angle</span>
-<span class="k">def</span> <span class="nf">spectral_angle_mapper</span><span class="p">(</span><span class="n">pixel</span><span class="p">,</span> <span class="n">reference</span><span class="p">):</span>
-    <span class="n">norm_pixel</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">norm</span><span class="p">(</span><span class="n">pixel</span><span class="p">)</span>
-    <span class="n">norm_reference</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">norm</span><span class="p">(</span><span class="n">reference</span><span class="p">)</span>
-    <span class="n">cos_theta</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">pixel</span><span class="p">,</span> <span class="n">reference</span><span class="p">)</span> <span class="o">/</span> <span class="p">(</span><span class="n">norm_pixel</span> <span class="o">*</span> <span class="n">norm_reference</span><span class="p">)</span>
-    <span class="n">angle</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arccos</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">clip</span><span class="p">(</span><span class="n">cos_theta</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">))</span>
-    <span class="k">return</span> <span class="n">angle</span>
-</pre></div>
-<div class="clipboard-copy-txt" id="cell-39"># Function to calculate spectral angle
-def spectral_angle_mapper(pixel, reference):
-    norm_pixel = np.linalg.norm(pixel)
-    norm_reference = np.linalg.norm(reference)
-    cos_theta = np.dot(pixel, reference) / (norm_pixel * norm_reference)
-    angle = np.arccos(np.clip(cos_theta, -1, 1))
-    return angle</div>
-</div>
-</div>
-</div>
-</div>
-</div>
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-<div class="highlight-ipynb hl-python"><pre><span></span><span class="c1"># Reshape data to (n_pixels, n_bands)</span>
-<span class="n">reshaped_data</span> <span class="o">=</span> <span class="n">da</span><span class="o">.</span><span class="n">values</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="n">da</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span>
-</pre></div>
-<div class="clipboard-copy-txt" id="cell-40"># Reshape data to (n_pixels, n_bands)
-reshaped_data = da.values.reshape(-1, da.shape[-1])</div>
-</div>
-</div>
-</div>
-</div>
-</div>
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-<div class="highlight-ipynb hl-python"><pre><span></span><span class="c1"># Apply SAM for each pixel and each endmember</span>
-<span class="n">angles</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="n">reshaped_data</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">endmembers</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]))</span>
-
-<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">reshaped_data</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]):</span>
-    <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">endmembers</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]):</span>
-        <span class="n">angles</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">]</span> <span class="o">=</span> <span class="n">spectral_angle_mapper</span><span class="p">(</span><span class="n">reshaped_data</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="p">:],</span> <span class="n">endmembers</span><span class="p">[</span><span class="n">j</span><span class="p">,</span> <span class="p">:])</span>
-</pre></div>
-<div class="clipboard-copy-txt" id="cell-41"># Apply SAM for each pixel and each endmember
-angles = np.zeros((reshaped_data.shape[0], endmembers.shape[0]))
-
-for i in range(reshaped_data.shape[0]):
-    for j in range(endmembers.shape[0]):
-        angles[i, j] = spectral_angle_mapper(reshaped_data[i, :], endmembers[j, :])</div>
-</div>
-</div>
-</div>
-</div>
-</div>
-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs">
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-<div class="highlight-ipynb hl-python"><pre><span></span><span class="c1"># Find the minimum angle (best match) for each pixel</span>
-<span class="n">best_match</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">argmin</span><span class="p">(</span><span class="n">angles</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
-</pre></div>
-<div class="clipboard-copy-txt" id="cell-42"># Find the minimum angle (best match) for each pixel
-best_match = np.argmin(angles, axis=1)</div>
-</div>
-</div>
-</div>
-</div>
-</div>
-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs">
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-<div class="highlight-ipynb hl-python"><pre><span></span><span class="c1"># Reshape best_match back to the original spatial dimensions</span>
-<span class="n">best_match</span> <span class="o">=</span> <span class="n">best_match</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">da</span><span class="o">.</span><span class="n">shape</span><span class="p">[:</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span>
-</pre></div>
-<div class="clipboard-copy-txt" id="cell-43"># Reshape best_match back to the original spatial dimensions
-best_match = best_match.reshape(da.shape[:-1])</div>
-</div>
-</div>
-</div>
-</div>
-</div>
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-<div class="highlight-ipynb hl-python"><pre><span></span><span class="c1"># Assume 'best_match' contains the SAM classification results reshaped to the original spatial dimensions</span>
-<span class="c1"># Also assume that 'da' has the original latitude and longitude data</span>
-<span class="n">latitudes</span> <span class="o">=</span> <span class="n">da</span><span class="o">.</span><span class="n">coords</span><span class="p">[</span><span class="s2">"latitude"</span><span class="p">]</span><span class="o">.</span><span class="n">values</span>
-<span class="n">longitudes</span> <span class="o">=</span> <span class="n">da</span><span class="o">.</span><span class="n">coords</span><span class="p">[</span><span class="s2">"longitude"</span><span class="p">]</span><span class="o">.</span><span class="n">values</span>
-
-<span class="c1"># Create a custom discrete color map</span>
-<span class="n">cmap</span> <span class="o">=</span> <span class="n">mcolors</span><span class="o">.</span><span class="n">ListedColormap</span><span class="p">(</span>
-    <span class="p">[</span><span class="s2">"#377eb8"</span><span class="p">,</span> <span class="s2">"#e41a1c"</span><span class="p">,</span> <span class="s2">"#4daf4a"</span><span class="p">,</span> <span class="s2">"#f781bf"</span><span class="p">,</span> <span class="s2">"#a65628"</span><span class="p">,</span> <span class="s2">"#984ea3"</span><span class="p">]</span>
-<span class="p">)</span>
-<span class="n">bounds</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="o">-</span><span class="mf">0.5</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">endmembers</span><span class="p">),</span> <span class="mi">1</span><span class="p">)</span>
-<span class="n">norm</span> <span class="o">=</span> <span class="n">mcolors</span><span class="o">.</span><span class="n">BoundaryNorm</span><span class="p">(</span><span class="n">bounds</span><span class="p">,</span> <span class="n">cmap</span><span class="o">.</span><span class="n">N</span><span class="p">)</span>
-
-<span class="c1"># Create a figure and axis with the correct map projection</span>
-<span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">12</span><span class="p">,</span> <span class="mi">10</span><span class="p">),</span> <span class="n">subplot_kw</span><span class="o">=</span><span class="p">{</span><span class="s2">"projection"</span><span class="p">:</span> <span class="n">ccrs</span><span class="o">.</span><span class="n">PlateCarree</span><span class="p">()})</span>
-
-<span class="c1"># Plot the SAM classification results</span>
-<span class="n">im</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">pcolormesh</span><span class="p">(</span>
-    <span class="n">longitudes</span><span class="p">,</span>
-    <span class="n">latitudes</span><span class="p">,</span>
-    <span class="n">best_match</span><span class="p">,</span>
-    <span class="n">cmap</span><span class="o">=</span><span class="n">cmap</span><span class="p">,</span>
-    <span class="n">norm</span><span class="o">=</span><span class="n">norm</span><span class="p">,</span>
-    <span class="n">transform</span><span class="o">=</span><span class="n">ccrs</span><span class="o">.</span><span class="n">PlateCarree</span><span class="p">(),</span>
-<span class="p">)</span>
-
-<span class="c1"># Add geographic features for context</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">add_feature</span><span class="p">(</span><span class="n">cfeature</span><span class="o">.</span><span class="n">COASTLINE</span><span class="p">)</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">add_feature</span><span class="p">(</span><span class="n">cfeature</span><span class="o">.</span><span class="n">BORDERS</span><span class="p">,</span> <span class="n">linestyle</span><span class="o">=</span><span class="s2">":"</span><span class="p">)</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">add_feature</span><span class="p">(</span><span class="n">cfeature</span><span class="o">.</span><span class="n">STATES</span><span class="p">,</span> <span class="n">linestyle</span><span class="o">=</span><span class="s2">"--"</span><span class="p">)</span>
-
-<span class="c1"># Adding axis labels</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s2">"Longitude"</span><span class="p">)</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s2">"Latitude"</span><span class="p">)</span>
-
-<span class="c1"># Adding a title</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s2">"Spectral Angle Mapper (SAM) Water Type Classification"</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">16</span><span class="p">)</span>
-
-<span class="c1"># Adding a color bar with discrete values</span>
-<span class="n">cbar</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">colorbar</span><span class="p">(</span>
-    <span class="n">im</span><span class="p">,</span>
-    <span class="n">ax</span><span class="o">=</span><span class="n">ax</span><span class="p">,</span>
-    <span class="n">orientation</span><span class="o">=</span><span class="s2">"vertical"</span><span class="p">,</span>
-    <span class="n">pad</span><span class="o">=</span><span class="mf">0.02</span><span class="p">,</span>
-    <span class="n">fraction</span><span class="o">=</span><span class="mf">0.05</span><span class="p">,</span>
-    <span class="n">ticks</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">endmembers</span><span class="p">)),</span>
-<span class="p">)</span>
-<span class="n">cbar</span><span class="o">.</span><span class="n">ax</span><span class="o">.</span><span class="n">set_yticklabels</span><span class="p">([</span><span class="sa">f</span><span class="s2">"Class </span><span class="si">{</span><span class="n">i</span><span class="o">+</span><span class="mi">1</span><span class="si">}</span><span class="s2">"</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">endmembers</span><span class="p">))])</span>
-<span class="n">cbar</span><span class="o">.</span><span class="n">set_label</span><span class="p">(</span><span class="s2">"Water Types"</span><span class="p">,</span> <span class="n">rotation</span><span class="o">=</span><span class="mi">270</span><span class="p">,</span> <span class="n">labelpad</span><span class="o">=</span><span class="mi">20</span><span class="p">)</span>
-
-<span class="c1"># Adding gridlines</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">gridlines</span><span class="p">(</span><span class="n">draw_labels</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">linestyle</span><span class="o">=</span><span class="s2">"--"</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mf">0.5</span><span class="p">)</span>
-
-<span class="c1"># Set the extent to zoom in to the specified region (adjust as needed)</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">set_extent</span><span class="p">([</span><span class="o">-</span><span class="mi">95</span><span class="p">,</span> <span class="o">-</span><span class="mi">85</span><span class="p">,</span> <span class="mi">27</span><span class="p">,</span> <span class="mi">33</span><span class="p">],</span> <span class="n">crs</span><span class="o">=</span><span class="n">ccrs</span><span class="o">.</span><span class="n">PlateCarree</span><span class="p">())</span>
-
-<span class="c1"># Show the plot</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
-</pre></div>
-<div class="clipboard-copy-txt" id="cell-44"># Assume 'best_match' contains the SAM classification results reshaped to the original spatial dimensions
-# Also assume that 'da' has the original latitude and longitude data
-latitudes = da.coords["latitude"].values
-longitudes = da.coords["longitude"].values
-
-# Create a custom discrete color map
-cmap = mcolors.ListedColormap(
-    ["#377eb8", "#e41a1c", "#4daf4a", "#f781bf", "#a65628", "#984ea3"]
-)
-bounds = np.arange(-0.5, len(endmembers), 1)
-norm = mcolors.BoundaryNorm(bounds, cmap.N)
-
-# Create a figure and axis with the correct map projection
-fig, ax = plt.subplots(figsize=(12, 10), subplot_kw={"projection": ccrs.PlateCarree()})
-
-# Plot the SAM classification results
-im = ax.pcolormesh(
-    longitudes,
-    latitudes,
-    best_match,
-    cmap=cmap,
-    norm=norm,
-    transform=ccrs.PlateCarree(),
-)
-
-# Add geographic features for context
-ax.add_feature(cfeature.COASTLINE)
-ax.add_feature(cfeature.BORDERS, linestyle=":")
-ax.add_feature(cfeature.STATES, linestyle="--")
-
-# Adding axis labels
-ax.set_xlabel("Longitude")
-ax.set_ylabel("Latitude")
-
-# Adding a title
-ax.set_title("Spectral Angle Mapper (SAM) Water Type Classification", fontsize=16)
-
-# Adding a color bar with discrete values
-cbar = plt.colorbar(
-    im,
-    ax=ax,
-    orientation="vertical",
-    pad=0.02,
-    fraction=0.05,
-    ticks=np.arange(len(endmembers)),
-)
-cbar.ax.set_yticklabels([f"Class {i+1}" for i in range(len(endmembers))])
-cbar.set_label("Water Types", rotation=270, labelpad=20)
-
-# Adding gridlines
-ax.gridlines(draw_labels=True, linestyle="--", linewidth=0.5)
-
-# Set the extent to zoom in to the specified region (adjust as needed)
-ax.set_extent([-95, -85, 27, 33], crs=ccrs.PlateCarree())
-
-# Show the plot
-plt.show()</div>
-</div>
-</div>
-</div>
-</div>
-<div class="jp-Cell-outputWrapper">
-<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
-</div>
-<div class="jp-OutputArea jp-Cell-outputArea">
-<div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedImage jp-OutputArea-output" tabindex="0">
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-<div class="highlight-ipynb hl-python"><pre><span></span><span class="c1"># Assume 'best_match' contains the SAM classification results reshaped to the original spatial dimensions</span>
-<span class="c1"># Also assume that 'da' has the original latitude and longitude data</span>
-<span class="n">latitudes</span> <span class="o">=</span> <span class="n">da</span><span class="o">.</span><span class="n">coords</span><span class="p">[</span><span class="s2">"latitude"</span><span class="p">]</span><span class="o">.</span><span class="n">values</span>
-<span class="n">longitudes</span> <span class="o">=</span> <span class="n">da</span><span class="o">.</span><span class="n">coords</span><span class="p">[</span><span class="s2">"longitude"</span><span class="p">]</span><span class="o">.</span><span class="n">values</span>
-
-<span class="c1"># Extract specific wavelengths for the conditions</span>
-<span class="n">wavelength_450</span> <span class="o">=</span> <span class="n">da</span><span class="o">.</span><span class="n">sel</span><span class="p">(</span><span class="n">wavelength</span><span class="o">=</span><span class="mi">450</span><span class="p">)</span><span class="o">.</span><span class="n">values</span>
-<span class="n">wavelength_620</span> <span class="o">=</span> <span class="n">da</span><span class="o">.</span><span class="n">sel</span><span class="p">(</span><span class="n">wavelength</span><span class="o">=</span><span class="mi">620</span><span class="p">)</span><span class="o">.</span><span class="n">values</span>
-<span class="n">wavelength_650</span> <span class="o">=</span> <span class="n">da</span><span class="o">.</span><span class="n">sel</span><span class="p">(</span><span class="n">wavelength</span><span class="o">=</span><span class="mi">650</span><span class="p">)</span><span class="o">.</span><span class="n">values</span>
-<span class="n">wavelength_681</span> <span class="o">=</span> <span class="n">da</span><span class="o">.</span><span class="n">sel</span><span class="p">(</span><span class="n">wavelength</span><span class="o">=</span><span class="mi">681</span><span class="p">)</span><span class="o">.</span><span class="n">values</span>
-<span class="n">wavelength_701</span> <span class="o">=</span> <span class="n">da</span><span class="o">.</span><span class="n">sel</span><span class="p">(</span><span class="n">wavelength</span><span class="o">=</span><span class="mi">701</span><span class="p">)</span><span class="o">.</span><span class="n">values</span>
-
-<span class="c1"># Apply the condition to filter the pixels</span>
-<span class="n">condition</span> <span class="o">=</span> <span class="p">(</span>
-    <span class="p">(</span><span class="n">wavelength_650</span> <span class="o">&gt;</span> <span class="n">wavelength_620</span><span class="p">)</span>
-    <span class="o">&amp;</span> <span class="p">(</span><span class="n">wavelength_701</span> <span class="o">&gt;</span> <span class="n">wavelength_681</span><span class="p">)</span>
-    <span class="o">&amp;</span> <span class="p">(</span><span class="n">wavelength_701</span> <span class="o">&gt;</span> <span class="n">wavelength_450</span><span class="p">)</span>
-<span class="p">)</span>
-
-<span class="c1"># Filter the best_match data based on the condition</span>
-<span class="n">filtered_best_match</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">condition</span><span class="p">,</span> <span class="n">best_match</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span><span class="p">)</span>
-
-<span class="c1"># Create a custom discrete color map</span>
-<span class="n">cmap</span> <span class="o">=</span> <span class="n">mcolors</span><span class="o">.</span><span class="n">ListedColormap</span><span class="p">(</span>
-    <span class="p">[</span><span class="s2">"#377eb8"</span><span class="p">,</span> <span class="s2">"#e41a1c"</span><span class="p">,</span> <span class="s2">"#4daf4a"</span><span class="p">,</span> <span class="s2">"#f781bf"</span><span class="p">,</span> <span class="s2">"#a65628"</span><span class="p">,</span> <span class="s2">"#984ea3"</span><span class="p">]</span>
-<span class="p">)</span>
-<span class="n">bounds</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="o">-</span><span class="mf">0.5</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">endmembers</span><span class="p">),</span> <span class="mi">1</span><span class="p">)</span>
-<span class="n">norm</span> <span class="o">=</span> <span class="n">mcolors</span><span class="o">.</span><span class="n">BoundaryNorm</span><span class="p">(</span><span class="n">bounds</span><span class="p">,</span> <span class="n">cmap</span><span class="o">.</span><span class="n">N</span><span class="p">)</span>
-
-<span class="c1"># Create a figure and axis with the correct map projection</span>
-<span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">12</span><span class="p">,</span> <span class="mi">10</span><span class="p">),</span> <span class="n">subplot_kw</span><span class="o">=</span><span class="p">{</span><span class="s2">"projection"</span><span class="p">:</span> <span class="n">ccrs</span><span class="o">.</span><span class="n">PlateCarree</span><span class="p">()})</span>
-
-<span class="c1"># Plot the SAM classification results for the filtered pixels</span>
-<span class="n">im</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">pcolormesh</span><span class="p">(</span>
-    <span class="n">longitudes</span><span class="p">,</span>
-    <span class="n">latitudes</span><span class="p">,</span>
-    <span class="n">filtered_best_match</span><span class="p">,</span>
-    <span class="n">cmap</span><span class="o">=</span><span class="n">cmap</span><span class="p">,</span>
-    <span class="n">norm</span><span class="o">=</span><span class="n">norm</span><span class="p">,</span>
-    <span class="n">transform</span><span class="o">=</span><span class="n">ccrs</span><span class="o">.</span><span class="n">PlateCarree</span><span class="p">(),</span>
-<span class="p">)</span>
-
-<span class="c1"># Add geographic features for context</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">add_feature</span><span class="p">(</span><span class="n">cfeature</span><span class="o">.</span><span class="n">COASTLINE</span><span class="p">)</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">add_feature</span><span class="p">(</span><span class="n">cfeature</span><span class="o">.</span><span class="n">BORDERS</span><span class="p">,</span> <span class="n">linestyle</span><span class="o">=</span><span class="s2">":"</span><span class="p">)</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">add_feature</span><span class="p">(</span><span class="n">cfeature</span><span class="o">.</span><span class="n">STATES</span><span class="p">,</span> <span class="n">linestyle</span><span class="o">=</span><span class="s2">"--"</span><span class="p">)</span>
-
-<span class="c1"># Adding axis labels</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s2">"Longitude"</span><span class="p">)</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s2">"Latitude"</span><span class="p">)</span>
-
-<span class="c1"># Adding a title</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s2">"SAM Water Type Classification"</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">16</span><span class="p">)</span>
-
-<span class="c1"># Adding a color bar with discrete values</span>
-<span class="n">cbar</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">colorbar</span><span class="p">(</span>
-    <span class="n">im</span><span class="p">,</span>
-    <span class="n">ax</span><span class="o">=</span><span class="n">ax</span><span class="p">,</span>
-    <span class="n">orientation</span><span class="o">=</span><span class="s2">"vertical"</span><span class="p">,</span>
-    <span class="n">pad</span><span class="o">=</span><span class="mf">0.02</span><span class="p">,</span>
-    <span class="n">fraction</span><span class="o">=</span><span class="mf">0.05</span><span class="p">,</span>
-    <span class="n">ticks</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">endmembers</span><span class="p">)),</span>
-<span class="p">)</span>
-<span class="n">cbar</span><span class="o">.</span><span class="n">ax</span><span class="o">.</span><span class="n">set_yticklabels</span><span class="p">([</span><span class="sa">f</span><span class="s2">"Class </span><span class="si">{</span><span class="n">i</span><span class="o">+</span><span class="mi">1</span><span class="si">}</span><span class="s2">"</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">endmembers</span><span class="p">))])</span>
-<span class="n">cbar</span><span class="o">.</span><span class="n">set_label</span><span class="p">(</span><span class="s2">"Water Types"</span><span class="p">,</span> <span class="n">rotation</span><span class="o">=</span><span class="mi">270</span><span class="p">,</span> <span class="n">labelpad</span><span class="o">=</span><span class="mi">20</span><span class="p">)</span>
-
-<span class="c1"># Adding gridlines</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">gridlines</span><span class="p">(</span><span class="n">draw_labels</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">linestyle</span><span class="o">=</span><span class="s2">"--"</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mf">0.5</span><span class="p">)</span>
-
-<span class="c1"># Set the extent to zoom in to the specified region (adjust as needed)</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">set_extent</span><span class="p">([</span><span class="o">-</span><span class="mi">95</span><span class="p">,</span> <span class="o">-</span><span class="mi">85</span><span class="p">,</span> <span class="mi">27</span><span class="p">,</span> <span class="mi">33</span><span class="p">],</span> <span class="n">crs</span><span class="o">=</span><span class="n">ccrs</span><span class="o">.</span><span class="n">PlateCarree</span><span class="p">())</span>
-
-<span class="c1"># Show the plot</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
-</pre></div>
-<div class="clipboard-copy-txt" id="cell-45"># Assume 'best_match' contains the SAM classification results reshaped to the original spatial dimensions
-# Also assume that 'da' has the original latitude and longitude data
-latitudes = da.coords["latitude"].values
-longitudes = da.coords["longitude"].values
-
-# Extract specific wavelengths for the conditions
-wavelength_450 = da.sel(wavelength=450).values
-wavelength_620 = da.sel(wavelength=620).values
-wavelength_650 = da.sel(wavelength=650).values
-wavelength_681 = da.sel(wavelength=681).values
-wavelength_701 = da.sel(wavelength=701).values
-
-# Apply the condition to filter the pixels
-condition = (
-    (wavelength_650 &gt; wavelength_620)
-    &amp; (wavelength_701 &gt; wavelength_681)
-    &amp; (wavelength_701 &gt; wavelength_450)
-)
-
-# Filter the best_match data based on the condition
-filtered_best_match = np.where(condition, best_match, np.nan)
-
-# Create a custom discrete color map
-cmap = mcolors.ListedColormap(
-    ["#377eb8", "#e41a1c", "#4daf4a", "#f781bf", "#a65628", "#984ea3"]
-)
-bounds = np.arange(-0.5, len(endmembers), 1)
-norm = mcolors.BoundaryNorm(bounds, cmap.N)
-
-# Create a figure and axis with the correct map projection
-fig, ax = plt.subplots(figsize=(12, 10), subplot_kw={"projection": ccrs.PlateCarree()})
-
-# Plot the SAM classification results for the filtered pixels
-im = ax.pcolormesh(
-    longitudes,
-    latitudes,
-    filtered_best_match,
-    cmap=cmap,
-    norm=norm,
-    transform=ccrs.PlateCarree(),
-)
-
-# Add geographic features for context
-ax.add_feature(cfeature.COASTLINE)
-ax.add_feature(cfeature.BORDERS, linestyle=":")
-ax.add_feature(cfeature.STATES, linestyle="--")
-
-# Adding axis labels
-ax.set_xlabel("Longitude")
-ax.set_ylabel("Latitude")
-
-# Adding a title
-ax.set_title("SAM Water Type Classification", fontsize=16)
-
-# Adding a color bar with discrete values
-cbar = plt.colorbar(
-    im,
-    ax=ax,
-    orientation="vertical",
-    pad=0.02,
-    fraction=0.05,
-    ticks=np.arange(len(endmembers)),
-)
-cbar.ax.set_yticklabels([f"Class {i+1}" for i in range(len(endmembers))])
-cbar.set_label("Water Types", rotation=270, labelpad=20)
-
-# Adding gridlines
-ax.gridlines(draw_labels=True, linestyle="--", linewidth=0.5)
-
-# Set the extent to zoom in to the specified region (adjust as needed)
-ax.set_extent([-95, -85, 27, 33], crs=ccrs.PlateCarree())
-
-# Show the plot
-plt.show()</div>
-</div>
-</div>
-</div>
-</div>
-<div class="jp-Cell-outputWrapper">
-<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
-</div>
-<div class="jp-OutputArea jp-Cell-outputArea">
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"/>
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         <style>
@@ -5240,7 +3003,7 @@ <h2 id="spectral-angle-mapper-sam">Spectral Angle Mapper (SAM)<a class="anchor-l
     <span class="md-icon" title="Last update">
       <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M21 13.1c-.1 0-.3.1-.4.2l-1 1 2.1 2.1 1-1c.2-.2.2-.6 0-.8l-1.3-1.3c-.1-.1-.2-.2-.4-.2m-1.9 1.8-6.1 6V23h2.1l6.1-6.1zM12.5 7v5.2l4 2.4-1 1L11 13V7zM11 21.9c-5.1-.5-9-4.8-9-9.9C2 6.5 6.5 2 12 2c5.3 0 9.6 4.1 10 9.3-.3-.1-.6-.2-1-.2s-.7.1-1 .2C19.6 7.2 16.2 4 12 4c-4.4 0-8 3.6-8 8 0 4.1 3.1 7.5 7.1 7.9l-.1.2z"/></svg>
     </span>
-    <span class="git-revision-date-localized-plugin git-revision-date-localized-plugin-timeago"><span class="timeago" datetime="2024-09-02T05:25:48+00:00" locale="en"></span></span><span class="git-revision-date-localized-plugin git-revision-date-localized-plugin-iso_date">2024-09-02</span>
+    <span class="git-revision-date-localized-plugin git-revision-date-localized-plugin-timeago"><span class="timeago" datetime="2024-10-13T04:02:00+00:00" locale="en"></span></span><span class="git-revision-date-localized-plugin git-revision-date-localized-plugin-iso_date">2024-10-13</span>
   </span>
 
     
diff --git a/examples/pace_cyano/pace_cyano.ipynb b/examples/pace_cyano/pace_cyano.ipynb
index 24658b12..31b6161d 100644
--- a/examples/pace_cyano/pace_cyano.ipynb
+++ b/examples/pace_cyano/pace_cyano.ipynb
@@ -49,7 +49,14 @@
    "outputs": [],
    "source": [
     "import earthaccess\n",
-    "import hypercoast"
+    "import hypercoast\n",
+    "from hypercoast.pace import (\n",
+    "    cyano_band_ratios,\n",
+    "    apply_kmeans,\n",
+    "    apply_pca,\n",
+    "    apply_sam,\n",
+    "    apply_sam_spectral,\n",
+    ")"
    ]
   },
   {
@@ -155,13 +162,7 @@
    },
    "outputs": [],
    "source": [
-    "da = dataset[\"Rrs\"]\n",
-    "data = (\n",
-    "    (da.sel(wavelength=650) > da.sel(wavelength=620))\n",
-    "    & (da.sel(wavelength=701) > da.sel(wavelength=681))\n",
-    "    & (da.sel(wavelength=701) > da.sel(wavelength=450))\n",
-    ")\n",
-    "# data"
+    "da = cyano_band_ratios(dataset, plot=True)"
    ]
   },
   {
@@ -175,65 +176,6 @@
     "![](https://i.imgur.com/pQP50bz.png)"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "EuKeNoNp-_JJ"
-   },
-   "source": [
-    "## Visualize the selected region based on band ratios"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 540
-    },
-    "id": "be6r9fkf-_JJ",
-    "outputId": "a1964c49-6587-475a-87bd-a95db40bb35e"
-   },
-   "outputs": [],
-   "source": [
-    "import xarray as xr\n",
-    "import matplotlib.pyplot as plt\n",
-    "import cartopy.crs as ccrs\n",
-    "import cartopy.feature as cfeature\n",
-    "\n",
-    "# Create a plot\n",
-    "fig, ax = plt.subplots(figsize=(12, 6), subplot_kw={\"projection\": ccrs.PlateCarree()})\n",
-    "\n",
-    "# ax.set_extent([-93, -87, 28, 32], crs=ccrs.PlateCarree())\n",
-    "\n",
-    "# Plot the data\n",
-    "data.plot(\n",
-    "    ax=ax,\n",
-    "    transform=ccrs.PlateCarree(),\n",
-    "    cmap=\"coolwarm\",\n",
-    "    cbar_kwargs={\"label\": \"Cyano\"},\n",
-    ")\n",
-    "\n",
-    "# Add coastlines\n",
-    "ax.coastlines()\n",
-    "\n",
-    "# Add state boundaries\n",
-    "states_provinces = cfeature.NaturalEarthFeature(\n",
-    "    category=\"cultural\",\n",
-    "    name=\"admin_1_states_provinces_lines\",\n",
-    "    scale=\"50m\",\n",
-    "    facecolor=\"none\",\n",
-    ")\n",
-    "\n",
-    "ax.add_feature(states_provinces, edgecolor=\"gray\")\n",
-    "\n",
-    "# Optionally, add gridlines, labels, etc.\n",
-    "ax.gridlines(draw_labels=True)\n",
-    "\n",
-    "plt.show()"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {
@@ -257,230 +199,20 @@
     "## K-means applied to the whole image"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "jDrdL13RcLFm"
-   },
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "from sklearn.cluster import KMeans"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "id": "8SYB8MKyZc0-",
-    "outputId": "02c93296-323b-41e9-e175-eaa18e0d19eb"
-   },
-   "outputs": [],
-   "source": [
-    "# Get the shape of the DataArray\n",
-    "print(\"Shape of da:\", da.shape)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "id": "eCH5c_uBZc28",
-    "outputId": "1301bab1-74d6-4507-ce27-b1f0ae81d943"
-   },
-   "outputs": [],
-   "source": [
-    "# Get the dimension names of the DataArray\n",
-    "print(\"Dimension names:\", da.dims)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "id": "0nzjK69bZc6d",
-    "outputId": "fd909e7d-dcc2-451c-f8aa-d67545ee7673"
-   },
-   "outputs": [],
-   "source": [
-    "# Get the size of each dimension\n",
-    "print(\"Size of each dimension:\", da.sizes)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "_O_obmMXZc9Z"
-   },
-   "outputs": [],
-   "source": [
-    "reshaped_data = da.values.reshape(-1, da.shape[-1])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "1V22cNgbZdAz"
-   },
-   "outputs": [],
-   "source": [
-    "reshaped_data_no_nan = reshaped_data[~np.isnan(reshaped_data).any(axis=1)]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 75
-    },
-    "id": "70hwmWwZelJn",
-    "outputId": "b0daf9fc-3b4a-400e-e512-c40de1ae885a"
-   },
-   "outputs": [],
-   "source": [
-    "# Apply K-means clustering to classify into 5-6 water types.\n",
-    "n_clusters = 6\n",
-    "kmeans = KMeans(n_clusters=n_clusters, random_state=0)\n",
-    "kmeans.fit(reshaped_data_no_nan)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "yF1Y40bhelMn"
-   },
-   "outputs": [],
-   "source": [
-    "# Initialize an array for cluster labels with NaN\n",
-    "labels = np.full(reshaped_data.shape[0], np.nan)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "nIrI-WyGelPj"
-   },
-   "outputs": [],
-   "source": [
-    "# Assign the computed cluster labels to the non-NaN positions\n",
-    "labels[~np.isnan(reshaped_data).any(axis=1)] = kmeans.labels_"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "6u86WCEnfnUU"
-   },
-   "outputs": [],
-   "source": [
-    "# Reshape the labels back to the original spatial dimensions\n",
-    "cluster_labels = labels.reshape(da.shape[:-1])"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
-    "import matplotlib.pyplot as plt\n",
-    "import matplotlib.colors as mcolors\n",
-    "import cartopy.crs as ccrs\n",
-    "import cartopy.feature as cfeature"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 704
-    },
-    "id": "K7mEYr4iny5f",
-    "outputId": "394a976d-d9d7-496c-9dec-0764dc4f1bf5"
-   },
-   "outputs": [],
-   "source": [
-    "# Assume 'cluster_labels' contains the K-means classification results reshaped to the original spatial dimensions\n",
-    "# Also assume that 'da' has the original latitude and longitude data\n",
-    "latitudes = da.coords[\"latitude\"].values\n",
-    "longitudes = da.coords[\"longitude\"].values\n",
-    "\n",
-    "# Create a custom discrete color map for K-means clusters\n",
-    "cmap = mcolors.ListedColormap(\n",
-    "    [\"#377eb8\", \"#ff7f00\", \"#4daf4a\", \"#f781bf\", \"#a65628\", \"#984ea3\"]\n",
-    ")\n",
-    "bounds = np.arange(-0.5, n_clusters, 1)\n",
-    "norm = mcolors.BoundaryNorm(bounds, cmap.N)\n",
-    "\n",
-    "# Create a figure and axis with the correct map projection\n",
-    "fig, ax = plt.subplots(\n",
-    "    figsize=(6, 4), dpi=200, subplot_kw={\"projection\": ccrs.PlateCarree()}\n",
-    ")\n",
-    "\n",
-    "# Plot the K-means classification results on the map\n",
-    "im = ax.pcolormesh(\n",
-    "    longitudes,\n",
-    "    latitudes,\n",
-    "    cluster_labels,\n",
-    "    cmap=cmap,\n",
-    "    norm=norm,\n",
-    "    transform=ccrs.PlateCarree(),\n",
-    ")\n",
-    "\n",
-    "# Add geographic features for context\n",
-    "ax.add_feature(cfeature.COASTLINE)\n",
-    "ax.add_feature(cfeature.BORDERS, linestyle=\":\")\n",
-    "ax.add_feature(cfeature.STATES, linestyle=\"--\")\n",
-    "\n",
-    "# Add gridlines\n",
-    "ax.gridlines(draw_labels=True)\n",
-    "\n",
-    "# Set the extent to zoom in to the specified region\n",
-    "ax.set_extent([-95, -85, 27, 33], crs=ccrs.PlateCarree())\n",
-    "\n",
-    "# Add color bar with labels\n",
-    "cbar = plt.colorbar(\n",
-    "    im,\n",
-    "    ax=ax,\n",
-    "    orientation=\"vertical\",\n",
-    "    pad=0.02,\n",
-    "    fraction=0.05,\n",
-    "    ticks=np.arange(n_clusters),\n",
-    ")\n",
-    "cbar.ax.set_yticklabels([f\"Class {i+1}\" for i in range(n_clusters)])\n",
-    "cbar.set_label(\"Water Types\", rotation=270, labelpad=20)\n",
-    "\n",
-    "# Add title\n",
-    "ax.set_title(\"Water Type Classification using K-means\", fontsize=16)\n",
-    "\n",
-    "# Show the plot\n",
-    "plt.show()"
+    "cluster_labels, latitudes, longitudes = apply_kmeans(dataset, n_clusters=6)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Keans applied to selected pixels"
+    "## K-means applied to selected pixels"
    ]
   },
   {
@@ -491,423 +223,26 @@
    },
    "outputs": [],
    "source": [
-    "# Assume 'cluster_labels' contains the K-means classification results reshaped to the original spatial dimensions\n",
-    "# Also assume that 'da' has the original latitude and longitude data\n",
-    "latitudes = da.coords[\"latitude\"].values\n",
-    "longitudes = da.coords[\"longitude\"].values\n",
-    "\n",
-    "# Filtering condition based on wavelength values\n",
-    "filter_condition = (\n",
-    "    (da.sel(wavelength=650) > da.sel(wavelength=620))\n",
-    "    & (da.sel(wavelength=701) > da.sel(wavelength=681))\n",
-    "    & (da.sel(wavelength=701) > da.sel(wavelength=450))\n",
-    ")\n",
-    "\n",
-    "# Apply the filtering condition to the K-means classification results\n",
-    "filtered_cluster_labels = np.where(filter_condition, cluster_labels, np.nan)\n",
-    "\n",
-    "# Create a custom discrete color map for K-means clusters\n",
-    "cmap = mcolors.ListedColormap(\n",
-    "    [\"#e41a1c\", \"#377eb8\", \"#4daf4a\", \"#f781bf\", \"#a65628\", \"#984ea3\"]\n",
-    ")\n",
-    "bounds = np.arange(-0.5, n_clusters, 1)\n",
-    "norm = mcolors.BoundaryNorm(bounds, cmap.N)\n",
-    "\n",
-    "# Create a figure and axis with the correct map projection\n",
-    "fig, ax = plt.subplots(\n",
-    "    figsize=(6, 4), dpi=200, subplot_kw={\"projection\": ccrs.PlateCarree()}\n",
-    ")\n",
-    "\n",
-    "# Plot the filtered K-means classification results on the map\n",
-    "im = ax.pcolormesh(\n",
-    "    longitudes,\n",
-    "    latitudes,\n",
-    "    filtered_cluster_labels,\n",
-    "    cmap=cmap,\n",
-    "    norm=norm,\n",
-    "    transform=ccrs.PlateCarree(),\n",
-    ")\n",
-    "\n",
-    "# Add geographic features for context\n",
-    "ax.add_feature(cfeature.COASTLINE)\n",
-    "ax.add_feature(cfeature.BORDERS, linestyle=\":\")\n",
-    "ax.add_feature(cfeature.STATES, linestyle=\"--\")\n",
-    "\n",
-    "# Add gridlines\n",
-    "ax.gridlines(draw_labels=True)\n",
-    "\n",
-    "# Set the extent to zoom in to the specified region\n",
-    "ax.set_extent([-95, -85, 27, 33], crs=ccrs.PlateCarree())\n",
-    "\n",
-    "# Add color bar with labels\n",
-    "cbar = plt.colorbar(\n",
-    "    im,\n",
-    "    ax=ax,\n",
-    "    orientation=\"vertical\",\n",
-    "    pad=0.02,\n",
-    "    fraction=0.05,\n",
-    "    ticks=np.arange(n_clusters),\n",
-    ")\n",
-    "cbar.ax.set_yticklabels([f\"Class {i+1}\" for i in range(n_clusters)])\n",
-    "cbar.set_label(\"Water Types\", rotation=270, labelpad=20)\n",
-    "\n",
-    "# Add title\n",
-    "ax.set_title(\"Water Type Classification using K-means\", fontsize=16)\n",
-    "\n",
-    "# Show the plot\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## PCA + SAM applied to whole image"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sklearn.decomposition import PCA"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "id": "svg0vrnxh6vp",
-    "outputId": "f7d2d17f-5035-4349-cd2d-32801f7feb17"
-   },
-   "outputs": [],
-   "source": [
-    "# Check the dimensions and coordinates\n",
-    "print(da.dims)\n",
-    "print(da.coords)  # This will show the available spectral bands"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "dksBIw9KjUfS"
-   },
-   "outputs": [],
-   "source": [
-    "# Reshape data to (n_pixels, n_bands)\n",
-    "reshaped_data = da.values.reshape(-1, da.shape[-1])\n",
-    "\n",
-    "# Handle NaNs by removing them\n",
-    "reshaped_data_no_nan = reshaped_data[~np.isnan(reshaped_data).any(axis=1)]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 718
-    },
-    "id": "f_R6a2nDjUjR",
-    "outputId": "2b8e4151-e9d3-492c-edcf-1db12e505322"
-   },
-   "outputs": [],
-   "source": [
-    "# Apply PCA to reduce dimensionality\n",
-    "pca = PCA(n_components=3)\n",
-    "pca_data = pca.fit_transform(reshaped_data_no_nan)\n",
-    "\n",
-    "# Visualize PCA components to manually identify endmembers\n",
-    "plt.figure(figsize=(10, 8))\n",
-    "plt.scatter(pca_data[:, 0], pca_data[:, 1], c=\"blue\", s=1)\n",
-    "plt.title(\"PCA of Spectral Data\")\n",
-    "plt.xlabel(\"Principal Component 1\")\n",
-    "plt.ylabel(\"Principal Component 2\")\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "sLCiBgBajpGC"
-   },
-   "outputs": [],
-   "source": [
-    "# Apply K-means to find clusters representing endmembers\n",
-    "n_clusters = 6  # Number of endmembers you want to find\n",
-    "kmeans = KMeans(n_clusters=n_clusters, random_state=0)\n",
-    "kmeans.fit(pca_data)\n",
-    "\n",
-    "# The cluster centers in the original spectral space are your endmembers\n",
-    "endmembers = pca.inverse_transform(kmeans.cluster_centers_)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "iPkI2yuRjpR9"
-   },
-   "outputs": [],
-   "source": [
-    "def spectral_angle_mapper(pixel, reference):\n",
-    "    norm_pixel = np.linalg.norm(pixel)\n",
-    "    norm_reference = np.linalg.norm(reference)\n",
-    "    cos_theta = np.dot(pixel, reference) / (norm_pixel * norm_reference)\n",
-    "    angle = np.arccos(np.clip(cos_theta, -1, 1))\n",
-    "    return angle\n",
-    "\n",
-    "\n",
-    "# Apply SAM for each pixel and each endmember\n",
-    "angles = np.zeros((reshaped_data_no_nan.shape[0], endmembers.shape[0]))\n",
-    "\n",
-    "for i in range(reshaped_data_no_nan.shape[0]):\n",
-    "    for j in range(endmembers.shape[0]):\n",
-    "        angles[i, j] = spectral_angle_mapper(\n",
-    "            reshaped_data_no_nan[i, :], endmembers[j, :]\n",
-    "        )\n",
-    "\n",
-    "# Find the minimum angle (best match) for each pixel\n",
-    "best_match = np.argmin(angles, axis=1)\n",
-    "\n",
-    "# Reshape best_match back to the original spatial dimensions\n",
-    "original_shape = da.shape[:-1]  # Get the spatial dimensions\n",
-    "best_match_full = np.full(reshaped_data.shape[0], np.nan)\n",
-    "best_match_full[~np.isnan(reshaped_data).any(axis=1)] = best_match\n",
-    "best_match_full = best_match_full.reshape(original_shape)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 704
-    },
-    "id": "3EuvKbPHjUq7",
-    "outputId": "41372b92-48e2-4c47-e732-5e597e4086a0"
-   },
-   "outputs": [],
-   "source": [
-    "# Assume 'best_match_full' contains the SAM classification results reshaped to the original spatial dimensions\n",
-    "# Also assume that 'da' has the original latitude and longitude data\n",
-    "latitudes = da.coords[\"latitude\"].values\n",
-    "longitudes = da.coords[\"longitude\"].values\n",
-    "\n",
-    "# Create a custom discrete color map\n",
-    "cmap = mcolors.ListedColormap(\n",
-    "    [\"#377eb8\", \"#ff7f00\", \"#4daf4a\", \"#f781bf\", \"#a65628\", \"#984ea3\"]\n",
-    ")\n",
-    "bounds = np.arange(-0.5, n_clusters, 1)\n",
-    "norm = mcolors.BoundaryNorm(bounds, cmap.N)\n",
-    "\n",
-    "# Create a figure and axis with the correct map projection\n",
-    "fig, ax = plt.subplots(figsize=(10, 8), subplot_kw={\"projection\": ccrs.PlateCarree()})\n",
-    "\n",
-    "# Plot the SAM classification results\n",
-    "im = ax.pcolormesh(\n",
-    "    longitudes,\n",
-    "    latitudes,\n",
-    "    best_match_full,\n",
-    "    cmap=cmap,\n",
-    "    norm=norm,\n",
-    "    transform=ccrs.PlateCarree(),\n",
-    ")\n",
-    "\n",
-    "# Add geographic features for context\n",
-    "ax.add_feature(cfeature.COASTLINE)\n",
-    "ax.add_feature(cfeature.BORDERS, linestyle=\":\")\n",
-    "ax.add_feature(cfeature.STATES, linestyle=\"--\")\n",
-    "\n",
-    "# Add gridlines\n",
-    "ax.gridlines(draw_labels=True)\n",
-    "\n",
-    "# Add color bar with labels\n",
-    "cbar = plt.colorbar(\n",
-    "    im,\n",
-    "    ax=ax,\n",
-    "    orientation=\"vertical\",\n",
-    "    pad=0.02,\n",
-    "    fraction=0.05,\n",
-    "    ticks=np.arange(n_clusters),\n",
-    ")\n",
-    "cbar.ax.set_yticklabels([f\"Class {i+1}\" for i in range(n_clusters)])\n",
-    "cbar.set_label(\"Water Types\", rotation=270, labelpad=20)\n",
-    "\n",
-    "# Add title\n",
-    "ax.set_title(\"Spectral Angle Mapper (SAM) Water Type Classification\", fontsize=16)\n",
-    "\n",
-    "# Show the plot\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 653
-    },
-    "id": "jnJ48tcxjUve",
-    "outputId": "70585cfb-d9e4-49e7-80a7-8393365a3094"
-   },
-   "outputs": [],
-   "source": [
-    "# Assume 'best_match_full' contains the SAM classification results reshaped to the original spatial dimensions\n",
-    "# Also assume that 'da' has the original latitude and longitude data\n",
-    "latitudes = da.coords[\"latitude\"].values\n",
-    "longitudes = da.coords[\"longitude\"].values\n",
-    "\n",
-    "# Create a custom discrete color map\n",
-    "cmap = mcolors.ListedColormap(\n",
-    "    [\"#377eb8\", \"#ff7f00\", \"#4daf4a\", \"#f781bf\", \"#a65628\", \"#984ea3\"]\n",
-    ")\n",
-    "bounds = np.arange(-0.5, n_clusters, 1)\n",
-    "norm = mcolors.BoundaryNorm(bounds, cmap.N)\n",
-    "\n",
-    "# Create a figure and axis with the correct map projection\n",
-    "fig, ax = plt.subplots(figsize=(10, 8), subplot_kw={\"projection\": ccrs.PlateCarree()})\n",
-    "\n",
-    "# Plot the SAM classification results\n",
-    "im = ax.pcolormesh(\n",
-    "    longitudes,\n",
-    "    latitudes,\n",
-    "    best_match_full,\n",
-    "    cmap=cmap,\n",
-    "    norm=norm,\n",
-    "    transform=ccrs.PlateCarree(),\n",
-    ")\n",
-    "\n",
-    "# Add geographic features for context\n",
-    "ax.add_feature(cfeature.COASTLINE)\n",
-    "ax.add_feature(cfeature.BORDERS, linestyle=\":\")\n",
-    "ax.add_feature(cfeature.STATES, linestyle=\"--\")\n",
-    "\n",
-    "# Add gridlines\n",
-    "ax.gridlines(draw_labels=True)\n",
-    "\n",
-    "# Set the extent to zoom in to the specified region\n",
-    "ax.set_extent([-95, -85, 27, 33], crs=ccrs.PlateCarree())\n",
-    "\n",
-    "# Add color bar with labels\n",
-    "cbar = plt.colorbar(\n",
-    "    im,\n",
-    "    ax=ax,\n",
-    "    orientation=\"vertical\",\n",
-    "    pad=0.02,\n",
-    "    fraction=0.05,\n",
-    "    ticks=np.arange(n_clusters),\n",
-    ")\n",
-    "cbar.ax.set_yticklabels([f\"Class {i+1}\" for i in range(n_clusters)])\n",
-    "cbar.set_label(\"Water Types\", rotation=270, labelpad=20)\n",
-    "\n",
-    "# Add title\n",
-    "ax.set_title(\"Spectral Angle Mapper (SAM) Water Type Classification\", fontsize=16)\n",
-    "\n",
-    "# Show the plot\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "HflML7NnjUym"
-   },
-   "outputs": [],
-   "source": [
-    "# Assume 'best_match_full' contains the SAM classification results reshaped to the original spatial dimensions\n",
-    "# Also assume that 'da' has the original latitude and longitude data\n",
-    "latitudes = da.coords[\"latitude\"].values\n",
-    "longitudes = da.coords[\"longitude\"].values\n",
+    "da = dataset[\"Rrs\"]\n",
     "\n",
-    "# Filtering condition based on wavelength values\n",
     "filter_condition = (\n",
     "    (da.sel(wavelength=650) > da.sel(wavelength=620))\n",
     "    & (da.sel(wavelength=701) > da.sel(wavelength=681))\n",
     "    & (da.sel(wavelength=701) > da.sel(wavelength=450))\n",
     ")\n",
+    "extent = [-95, -85, 27, 33]\n",
+    "colors = [\"#e41a1c\", \"#377eb8\", \"#4daf4a\", \"#f781bf\", \"#a65628\", \"#984ea3\"]\n",
     "\n",
-    "# Apply the filtering condition to the SAM classification results\n",
-    "filtered_best_match_full = np.where(filter_condition, best_match_full, np.nan)\n",
-    "\n",
-    "# Create a custom discrete color map\n",
-    "cmap = mcolors.ListedColormap(\n",
-    "    [\"#e41a1c\", \"#377eb8\", \"#4daf4a\", \"#f781bf\", \"#a65628\", \"#984ea3\"]\n",
-    ")\n",
-    "bounds = np.arange(-0.5, n_clusters, 1)\n",
-    "norm = mcolors.BoundaryNorm(bounds, cmap.N)\n",
-    "\n",
-    "# Create a figure and axis with the correct map projection\n",
-    "fig, ax = plt.subplots(figsize=(10, 8), subplot_kw={\"projection\": ccrs.PlateCarree()})\n",
-    "\n",
-    "# Plot the filtered SAM classification results\n",
-    "im = ax.pcolormesh(\n",
-    "    longitudes,\n",
-    "    latitudes,\n",
-    "    filtered_best_match_full,\n",
-    "    cmap=cmap,\n",
-    "    norm=norm,\n",
-    "    transform=ccrs.PlateCarree(),\n",
-    ")\n",
-    "\n",
-    "# Add geographic features for context\n",
-    "ax.add_feature(cfeature.COASTLINE)\n",
-    "ax.add_feature(cfeature.BORDERS, linestyle=\":\")\n",
-    "ax.add_feature(cfeature.STATES, linestyle=\"--\")\n",
-    "\n",
-    "# Add gridlines\n",
-    "ax.gridlines(draw_labels=True)\n",
-    "\n",
-    "# Set the extent to zoom in to the specified region\n",
-    "ax.set_extent([-95, -85, 27, 33], crs=ccrs.PlateCarree())\n",
-    "\n",
-    "# Add color bar with labels\n",
-    "cbar = plt.colorbar(\n",
-    "    im,\n",
-    "    ax=ax,\n",
-    "    orientation=\"vertical\",\n",
-    "    pad=0.02,\n",
-    "    fraction=0.05,\n",
-    "    ticks=np.arange(n_clusters),\n",
-    ")\n",
-    "cbar.ax.set_yticklabels([f\"Class {i+1}\" for i in range(n_clusters)])\n",
-    "cbar.set_label(\"Water Types\", rotation=270, labelpad=20)\n",
-    "\n",
-    "# Add title\n",
-    "ax.set_title(\"PCA + Spectral Angle Mapper (SAM) Water Type Classification\", fontsize=16)\n",
-    "\n",
-    "# Show the plot\n",
-    "plt.show()"
+    "cluster_labels, latitudes, longitudes = apply_kmeans(\n",
+    "    da, n_clusters=6, filter_condition=filter_condition, extent=extent, colors=colors\n",
+    ")"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Spectral Angle Mapper (SAM)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "import pandas as pd\n",
-    "import matplotlib.pyplot as plt\n",
-    "import matplotlib.colors as mcolors\n",
-    "from scipy.interpolate import interp1d"
+    "## Principal Component Analysis (PCA)"
    ]
   },
   {
@@ -916,10 +251,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Load the netCDF data\n",
-    "file_path = \"data/PACE_OCI.20240730T181157.L2.OC_AOP.V2_0.NRT.nc\"\n",
-    "dataset = hypercoast.read_pace(file_path)\n",
-    "da = dataset[\"Rrs\"]  # Assuming 'Rrs' contains the reflectance data"
+    "pca_data = apply_pca(dataset, n_components=3, x_component=0, y_component=1)"
    ]
   },
   {
@@ -928,73 +260,16 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Extract PACE wavelengths\n",
-    "pace_wavelengths = da[\"wavelength\"].values\n",
-    "print(pace_wavelengths)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Function to load and resample a single CSV spectral library file\n",
-    "def load_and_resample_spectral_library(csv_path, target_wavelengths):\n",
-    "    df = pd.read_csv(csv_path)\n",
-    "    original_wavelengths = df.iloc[:, 0].values  # First column is wavelength\n",
-    "    spectra_values = df.iloc[:, 1].values  # Second column is spectral values\n",
-    "\n",
-    "    # Interpolation function\n",
-    "    interp_func = interp1d(\n",
-    "        original_wavelengths, spectra_values, kind=\"linear\", fill_value=\"extrapolate\"\n",
-    "    )\n",
-    "\n",
-    "    # Resample to the target (PACE) wavelengths\n",
-    "    resampled_spectra = interp_func(target_wavelengths)\n",
-    "\n",
-    "    return resampled_spectra"
+    "pca_data = apply_pca(dataset, n_components=3, x_component=1, y_component=2)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Download the SAM spectral library:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "url = \"https://github.com/opengeos/datasets/releases/download/hypercoast/SAM_spectral_library.zip\"\n",
-    "hypercoast.download_file(url)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Load and resample all 6 endmembers\n",
-    "endmember_paths = [\n",
-    "    \"./SAM_spectral_library/Dataset_1.csv\",\n",
-    "    \"./SAM_spectral_library/Dataset_2.csv\",\n",
-    "    \"./SAM_spectral_library/Dataset_3.csv\",\n",
-    "    \"./SAM_spectral_library/Dataset_4.csv\",\n",
-    "    \"./SAM_spectral_library/Dataset_5.csv\",\n",
-    "    \"./SAM_spectral_library/Dataset_6.csv\",\n",
-    "]\n",
+    "## Spectral Angle Mapper (SAM)\n",
     "\n",
-    "endmembers = np.array(\n",
-    "    [\n",
-    "        load_and_resample_spectral_library(path, pace_wavelengths)\n",
-    "        for path in endmember_paths\n",
-    "    ]\n",
-    ")"
+    "### Apply SAM to the whole image"
    ]
   },
   {
@@ -1003,44 +278,18 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "print(endmembers)"
+    "data, latitudes, longitudes = apply_sam(\n",
+    "    dataset,\n",
+    "    n_components=3,\n",
+    "    n_clusters=6,\n",
+    ")"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [],
    "source": [
-    "# Plot sample spectra from the CSV files and their resampled versions\n",
-    "def plot_sample_spectra(csv_paths, pace_wavelengths):\n",
-    "    plt.figure(figsize=(14, 8))\n",
-    "\n",
-    "    for i, csv_path in enumerate(csv_paths):\n",
-    "        df = pd.read_csv(csv_path)\n",
-    "        original_wavelengths = df.iloc[:, 0].values\n",
-    "        spectra_values = df.iloc[:, 1].values\n",
-    "        resampled_spectra = load_and_resample_spectral_library(\n",
-    "            csv_path, pace_wavelengths\n",
-    "        )\n",
-    "\n",
-    "        plt.plot(\n",
-    "            original_wavelengths,\n",
-    "            spectra_values,\n",
-    "            label=f\"Original Spectra {i+1}\",\n",
-    "            linestyle=\"--\",\n",
-    "        )\n",
-    "        plt.plot(pace_wavelengths, resampled_spectra, label=f\"Resampled Spectra {i+1}\")\n",
-    "\n",
-    "    plt.xlabel(\"Wavelength (nm)\")\n",
-    "    plt.ylabel(\"Spectral Reflectance\")\n",
-    "    plt.title(\"Comparison of Original and Resampled Spectra\")\n",
-    "    plt.legend()\n",
-    "    plt.grid(True)\n",
-    "    plt.show()\n",
-    "\n",
-    "\n",
-    "plot_sample_spectra(endmember_paths, pace_wavelengths)"
+    "### Apply SAM to selected pixels"
    ]
   },
   {
@@ -1049,23 +298,22 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Function to calculate spectral angle\n",
-    "def spectral_angle_mapper(pixel, reference):\n",
-    "    norm_pixel = np.linalg.norm(pixel)\n",
-    "    norm_reference = np.linalg.norm(reference)\n",
-    "    cos_theta = np.dot(pixel, reference) / (norm_pixel * norm_reference)\n",
-    "    angle = np.arccos(np.clip(cos_theta, -1, 1))\n",
-    "    return angle"
+    "extent = [-95, -85, 27, 33]\n",
+    "colors = [\"#377eb8\", \"#ff7f00\", \"#4daf4a\", \"#f781bf\", \"#a65628\", \"#984ea3\"]\n",
+    "data, latitudes, longitudes = apply_sam(\n",
+    "    dataset,\n",
+    "    n_components=3,\n",
+    "    n_clusters=6,\n",
+    "    extent=extent,\n",
+    "    colors=colors,\n",
+    ")"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [],
    "source": [
-    "# Reshape data to (n_pixels, n_bands)\n",
-    "reshaped_data = da.values.reshape(-1, da.shape[-1])"
+    "### Apply SAM with a filtering condition"
    ]
   },
   {
@@ -1074,22 +322,31 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Apply SAM for each pixel and each endmember\n",
-    "angles = np.zeros((reshaped_data.shape[0], endmembers.shape[0]))\n",
+    "da = dataset[\"Rrs\"]\n",
     "\n",
-    "for i in range(reshaped_data.shape[0]):\n",
-    "    for j in range(endmembers.shape[0]):\n",
-    "        angles[i, j] = spectral_angle_mapper(reshaped_data[i, :], endmembers[j, :])"
+    "filter_condition = (\n",
+    "    (da.sel(wavelength=650) > da.sel(wavelength=620))\n",
+    "    & (da.sel(wavelength=701) > da.sel(wavelength=681))\n",
+    "    & (da.sel(wavelength=701) > da.sel(wavelength=450))\n",
+    ")\n",
+    "extent = [-95, -85, 27, 33]\n",
+    "colors = [\"#e41a1c\", \"#377eb8\", \"#4daf4a\", \"#f781bf\", \"#a65628\", \"#984ea3\"]\n",
+    "\n",
+    "data, latitudes, longitudes = apply_sam(\n",
+    "    dataset,\n",
+    "    n_components=3,\n",
+    "    n_clusters=6,\n",
+    "    filter_condition=filter_condition,\n",
+    "    extent=extent,\n",
+    "    colors=colors,\n",
+    ")"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [],
    "source": [
-    "# Find the minimum angle (best match) for each pixel\n",
-    "best_match = np.argmin(angles, axis=1)"
+    "### Use spectral library"
    ]
   },
   {
@@ -1098,8 +355,11 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Reshape best_match back to the original spatial dimensions\n",
-    "best_match = best_match.reshape(da.shape[:-1])"
+    "filepath = \"data/PACE_OCI.20240730T181157.L2.OC_AOP.V2_0.NRT.nc\"\n",
+    "dataset = hypercoast.read_pace(filepath)\n",
+    "url = \"https://github.com/opengeos/datasets/releases/download/hypercoast/SAM_spectral_library.zip\"\n",
+    "hypercoast.download_file(url)\n",
+    "spectral_library = \"./SAM_spectral_library/*.csv\""
    ]
   },
   {
@@ -1108,63 +368,12 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Assume 'best_match' contains the SAM classification results reshaped to the original spatial dimensions\n",
-    "# Also assume that 'da' has the original latitude and longitude data\n",
-    "latitudes = da.coords[\"latitude\"].values\n",
-    "longitudes = da.coords[\"longitude\"].values\n",
-    "\n",
-    "# Create a custom discrete color map\n",
-    "cmap = mcolors.ListedColormap(\n",
-    "    [\"#377eb8\", \"#e41a1c\", \"#4daf4a\", \"#f781bf\", \"#a65628\", \"#984ea3\"]\n",
-    ")\n",
-    "bounds = np.arange(-0.5, len(endmembers), 1)\n",
-    "norm = mcolors.BoundaryNorm(bounds, cmap.N)\n",
-    "\n",
-    "# Create a figure and axis with the correct map projection\n",
-    "fig, ax = plt.subplots(figsize=(12, 10), subplot_kw={\"projection\": ccrs.PlateCarree()})\n",
-    "\n",
-    "# Plot the SAM classification results\n",
-    "im = ax.pcolormesh(\n",
-    "    longitudes,\n",
-    "    latitudes,\n",
-    "    best_match,\n",
-    "    cmap=cmap,\n",
-    "    norm=norm,\n",
-    "    transform=ccrs.PlateCarree(),\n",
-    ")\n",
-    "\n",
-    "# Add geographic features for context\n",
-    "ax.add_feature(cfeature.COASTLINE)\n",
-    "ax.add_feature(cfeature.BORDERS, linestyle=\":\")\n",
-    "ax.add_feature(cfeature.STATES, linestyle=\"--\")\n",
-    "\n",
-    "# Adding axis labels\n",
-    "ax.set_xlabel(\"Longitude\")\n",
-    "ax.set_ylabel(\"Latitude\")\n",
-    "\n",
-    "# Adding a title\n",
-    "ax.set_title(\"Spectral Angle Mapper (SAM) Water Type Classification\", fontsize=16)\n",
-    "\n",
-    "# Adding a color bar with discrete values\n",
-    "cbar = plt.colorbar(\n",
-    "    im,\n",
-    "    ax=ax,\n",
-    "    orientation=\"vertical\",\n",
-    "    pad=0.02,\n",
-    "    fraction=0.05,\n",
-    "    ticks=np.arange(len(endmembers)),\n",
-    ")\n",
-    "cbar.ax.set_yticklabels([f\"Class {i+1}\" for i in range(len(endmembers))])\n",
-    "cbar.set_label(\"Water Types\", rotation=270, labelpad=20)\n",
-    "\n",
-    "# Adding gridlines\n",
-    "ax.gridlines(draw_labels=True, linestyle=\"--\", linewidth=0.5)\n",
-    "\n",
-    "# Set the extent to zoom in to the specified region (adjust as needed)\n",
-    "ax.set_extent([-95, -85, 27, 33], crs=ccrs.PlateCarree())\n",
-    "\n",
-    "# Show the plot\n",
-    "plt.show()"
+    "extent = [-95, -85, 27, 33]\n",
+    "data, latitudes, longitudes = apply_sam_spectral(\n",
+    "    dataset,\n",
+    "    spectral_library=spectral_library,\n",
+    "    extent=extent,\n",
+    ")"
    ]
   },
   {
@@ -1173,80 +382,19 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Assume 'best_match' contains the SAM classification results reshaped to the original spatial dimensions\n",
-    "# Also assume that 'da' has the original latitude and longitude data\n",
-    "latitudes = da.coords[\"latitude\"].values\n",
-    "longitudes = da.coords[\"longitude\"].values\n",
-    "\n",
-    "# Extract specific wavelengths for the conditions\n",
-    "wavelength_450 = da.sel(wavelength=450).values\n",
-    "wavelength_620 = da.sel(wavelength=620).values\n",
-    "wavelength_650 = da.sel(wavelength=650).values\n",
-    "wavelength_681 = da.sel(wavelength=681).values\n",
-    "wavelength_701 = da.sel(wavelength=701).values\n",
-    "\n",
-    "# Apply the condition to filter the pixels\n",
-    "condition = (\n",
-    "    (wavelength_650 > wavelength_620)\n",
-    "    & (wavelength_701 > wavelength_681)\n",
-    "    & (wavelength_701 > wavelength_450)\n",
-    ")\n",
-    "\n",
-    "# Filter the best_match data based on the condition\n",
-    "filtered_best_match = np.where(condition, best_match, np.nan)\n",
-    "\n",
-    "# Create a custom discrete color map\n",
-    "cmap = mcolors.ListedColormap(\n",
-    "    [\"#377eb8\", \"#e41a1c\", \"#4daf4a\", \"#f781bf\", \"#a65628\", \"#984ea3\"]\n",
-    ")\n",
-    "bounds = np.arange(-0.5, len(endmembers), 1)\n",
-    "norm = mcolors.BoundaryNorm(bounds, cmap.N)\n",
-    "\n",
-    "# Create a figure and axis with the correct map projection\n",
-    "fig, ax = plt.subplots(figsize=(12, 10), subplot_kw={\"projection\": ccrs.PlateCarree()})\n",
-    "\n",
-    "# Plot the SAM classification results for the filtered pixels\n",
-    "im = ax.pcolormesh(\n",
-    "    longitudes,\n",
-    "    latitudes,\n",
-    "    filtered_best_match,\n",
-    "    cmap=cmap,\n",
-    "    norm=norm,\n",
-    "    transform=ccrs.PlateCarree(),\n",
-    ")\n",
-    "\n",
-    "# Add geographic features for context\n",
-    "ax.add_feature(cfeature.COASTLINE)\n",
-    "ax.add_feature(cfeature.BORDERS, linestyle=\":\")\n",
-    "ax.add_feature(cfeature.STATES, linestyle=\"--\")\n",
-    "\n",
-    "# Adding axis labels\n",
-    "ax.set_xlabel(\"Longitude\")\n",
-    "ax.set_ylabel(\"Latitude\")\n",
-    "\n",
-    "# Adding a title\n",
-    "ax.set_title(\"SAM Water Type Classification\", fontsize=16)\n",
-    "\n",
-    "# Adding a color bar with discrete values\n",
-    "cbar = plt.colorbar(\n",
-    "    im,\n",
-    "    ax=ax,\n",
-    "    orientation=\"vertical\",\n",
-    "    pad=0.02,\n",
-    "    fraction=0.05,\n",
-    "    ticks=np.arange(len(endmembers)),\n",
+    "da = dataset[\"Rrs\"]\n",
+    "extent = [-95, -85, 27, 33]\n",
+    "filter_condition = (\n",
+    "    (da.sel(wavelength=650) > da.sel(wavelength=620))\n",
+    "    & (da.sel(wavelength=701) > da.sel(wavelength=681))\n",
+    "    & (da.sel(wavelength=701) > da.sel(wavelength=450))\n",
     ")\n",
-    "cbar.ax.set_yticklabels([f\"Class {i+1}\" for i in range(len(endmembers))])\n",
-    "cbar.set_label(\"Water Types\", rotation=270, labelpad=20)\n",
-    "\n",
-    "# Adding gridlines\n",
-    "ax.gridlines(draw_labels=True, linestyle=\"--\", linewidth=0.5)\n",
-    "\n",
-    "# Set the extent to zoom in to the specified region (adjust as needed)\n",
-    "ax.set_extent([-95, -85, 27, 33], crs=ccrs.PlateCarree())\n",
-    "\n",
-    "# Show the plot\n",
-    "plt.show()"
+    "data, latitudes, longitudes = apply_sam_spectral(\n",
+    "    da,\n",
+    "    spectral_library=spectral_library,\n",
+    "    filter_condition=filter_condition,\n",
+    "    extent=extent,\n",
+    ")"
    ]
   }
  ],
@@ -1255,7 +403,7 @@
    "provenance": []
   },
   "kernelspec": {
-   "display_name": "Hyper",
+   "display_name": "hyper",
    "language": "python",
    "name": "python3"
   },
diff --git a/examples/pace_oci_l1/index.html b/examples/pace_oci_l1/index.html
index 9e478d31..d44ef72e 100644
--- a/examples/pace_oci_l1/index.html
+++ b/examples/pace_oci_l1/index.html
@@ -22,7 +22,7 @@
       
       
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-      <meta name="generator" content="mkdocs-1.6.1, mkdocs-material-9.5.39">
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index eb9904d3..8b7e646a 100644
--- a/examples/pace_oci_l2/index.html
+++ b/examples/pace_oci_l2/index.html
@@ -22,7 +22,7 @@
       
       
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-      <meta name="generator" content="mkdocs-1.6.1, mkdocs-material-9.5.39">
+      <meta name="generator" content="mkdocs-1.6.1, mkdocs-material-9.5.40">
     
     
       
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+++ b/examples/search_data/index.html
@@ -22,7 +22,7 @@
       
       
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-      <meta name="generator" content="mkdocs-1.6.1, mkdocs-material-9.5.39">
+      <meta name="generator" content="mkdocs-1.6.1, mkdocs-material-9.5.40">
     
     
       
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index fb16f0ad..679ea7d0 100644
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@@ -22,7 +22,7 @@
       
       
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index da2269e6..02e2eff0 100644
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Binary files a/objects.inv and b/objects.inv differ
diff --git a/pace/index.html b/pace/index.html
index 9d32d364..bf3b3590 100644
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@@ -1147,6 +1147,51 @@
     </span>
   </a>
   
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#hypercoast.pace.apply_kmeans" class="md-nav__link">
+    <span class="md-ellipsis">
+      apply_kmeans()
+    </span>
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#hypercoast.pace.apply_pca" class="md-nav__link">
+    <span class="md-ellipsis">
+      apply_pca()
+    </span>
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#hypercoast.pace.apply_sam" class="md-nav__link">
+    <span class="md-ellipsis">
+      apply_sam()
+    </span>
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#hypercoast.pace.apply_sam_spectral" class="md-nav__link">
+    <span class="md-ellipsis">
+      apply_sam_spectral()
+    </span>
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#hypercoast.pace.cyano_band_ratios" class="md-nav__link">
+    <span class="md-ellipsis">
+      cyano_band_ratios()
+    </span>
+  </a>
+  
 </li>
       
         <li class="md-nav__item">
@@ -1336,6 +1381,51 @@
     </span>
   </a>
   
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#hypercoast.pace.apply_kmeans" class="md-nav__link">
+    <span class="md-ellipsis">
+      apply_kmeans()
+    </span>
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#hypercoast.pace.apply_pca" class="md-nav__link">
+    <span class="md-ellipsis">
+      apply_pca()
+    </span>
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#hypercoast.pace.apply_sam" class="md-nav__link">
+    <span class="md-ellipsis">
+      apply_sam()
+    </span>
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#hypercoast.pace.apply_sam_spectral" class="md-nav__link">
+    <span class="md-ellipsis">
+      apply_sam_spectral()
+    </span>
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#hypercoast.pace.cyano_band_ratios" class="md-nav__link">
+    <span class="md-ellipsis">
+      cyano_band_ratios()
+    </span>
+  </a>
+  
 </li>
       
         <li class="md-nav__item">
@@ -1497,6 +1587,1226 @@ <h1 id="pace-module">pace module<a class="headerlink" href="#pace-module" title=
 
 
 
+  <div class="doc doc-object doc-function">
+
+
+
+<h2 id="hypercoast.pace.apply_kmeans" class="doc doc-heading">
+<code class="highlight language-python"><span class="n">apply_kmeans</span><span class="p">(</span><span class="n">dataset</span><span class="p">,</span> <span class="n">n_clusters</span><span class="o">=</span><span class="mi">6</span><span class="p">,</span> <span class="n">filter_condition</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">plot</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">8</span><span class="p">,</span> <span class="mi">6</span><span class="p">),</span> <span class="n">colors</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">extent</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">title</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></code>
+
+
+<a href="#hypercoast.pace.apply_kmeans" class="headerlink" title="Permanent link">&para;</a></h2>
+
+    <div class="doc doc-contents ">
+
+      <p>Applies K-means clustering to the dataset and optionally plots the results.</p>
+
+<p><strong>Parameters:</strong></p>
+<table>
+  <thead>
+    <tr>
+      <th>Name</th>
+      <th>Type</th>
+      <th>Description</th>
+      <th>Default</th>
+    </tr>
+  </thead>
+  <tbody>
+      <tr>
+        <td><code>dataset</code></td>
+        <td><code>xr.Dataset | str</code></td>
+        <td><p>The dataset containing the PACE data or the file path to the dataset.</p></td>
+        <td><em>required</em></td>
+      </tr>
+      <tr>
+        <td><code>n_clusters</code></td>
+        <td><code>int</code></td>
+        <td><p>Number of clusters for K-means. Defaults to 6.</p></td>
+        <td><code>6</code></td>
+      </tr>
+      <tr>
+        <td><code>plot</code></td>
+        <td><code>bool</code></td>
+        <td><p>Whether to plot the data. Defaults to True.</p></td>
+        <td><code>True</code></td>
+      </tr>
+      <tr>
+        <td><code>figsize</code></td>
+        <td><code>tuple[int, int]</code></td>
+        <td><p>Figure size for the plot. Defaults to (8, 6).</p></td>
+        <td><code>(8, 6)</code></td>
+      </tr>
+      <tr>
+        <td><code>colors</code></td>
+        <td><code>list[str]</code></td>
+        <td><p>List of colors to use for the clusters. Defaults to None.</p></td>
+        <td><code>None</code></td>
+      </tr>
+      <tr>
+        <td><code>extent</code></td>
+        <td><code>list[float] | None</code></td>
+        <td><p>The extent to zoom in to the specified region. Defaults to None.</p></td>
+        <td><code>None</code></td>
+      </tr>
+      <tr>
+        <td><code>title</code></td>
+        <td><code>str | None</code></td>
+        <td><p>Title for the plot. Defaults to None.</p></td>
+        <td><code>None</code></td>
+      </tr>
+      <tr>
+        <td><code>**kwargs</code></td>
+        <td></td>
+        <td><p>Additional keyword arguments to pass to the <code>plt.subplots</code> function.</p></td>
+        <td><code>{}</code></td>
+      </tr>
+  </tbody>
+</table>
+<p><strong>Returns:</strong></p>
+<table>
+  <thead>
+    <tr>
+      <th>Type</th>
+      <th>Description</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <td><code>tuple[np.ndarray, np.ndarray, np.ndarray]</code></td>
+      <td><p>The cluster labels, latitudes, and longitudes.</p></td>
+    </tr>
+  </tbody>
+</table>
+        <details class="quote">
+          <summary>Source code in <code>hypercoast/pace.py</code></summary>
+          <div class="highlight"><pre><span></span><code><span class="k">def</span> <span class="nf">apply_kmeans</span><span class="p">(</span>
+    <span class="n">dataset</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="n">xr</span><span class="o">.</span><span class="n">Dataset</span><span class="p">,</span> <span class="nb">str</span><span class="p">],</span>
+    <span class="n">n_clusters</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">6</span><span class="p">,</span>
+    <span class="n">filter_condition</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Callable</span><span class="p">[[</span><span class="n">xr</span><span class="o">.</span><span class="n">DataArray</span><span class="p">],</span> <span class="n">xr</span><span class="o">.</span><span class="n">DataArray</span><span class="p">]]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+    <span class="n">plot</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span>
+    <span class="n">figsize</span><span class="p">:</span> <span class="nb">tuple</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="nb">int</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="mi">8</span><span class="p">,</span> <span class="mi">6</span><span class="p">),</span>
+    <span class="n">colors</span><span class="p">:</span> <span class="nb">list</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+    <span class="n">extent</span><span class="p">:</span> <span class="nb">list</span><span class="p">[</span><span class="nb">float</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+    <span class="n">title</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+    <span class="o">**</span><span class="n">kwargs</span><span class="p">,</span>
+<span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">tuple</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">]:</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Applies K-means clustering to the dataset and optionally plots the results.</span>
+
+<span class="sd">    Args:</span>
+<span class="sd">        dataset (xr.Dataset | str): The dataset containing the PACE data or the file path to the dataset.</span>
+<span class="sd">        n_clusters (int, optional): Number of clusters for K-means. Defaults to 6.</span>
+<span class="sd">        plot (bool, optional): Whether to plot the data. Defaults to True.</span>
+<span class="sd">        figsize (tuple[int, int], optional): Figure size for the plot. Defaults to (8, 6).</span>
+<span class="sd">        colors (list[str], optional): List of colors to use for the clusters. Defaults to None.</span>
+<span class="sd">        extent (list[float] | None, optional): The extent to zoom in to the specified region. Defaults to None.</span>
+<span class="sd">        title (str | None, optional): Title for the plot. Defaults to None.</span>
+<span class="sd">        **kwargs: Additional keyword arguments to pass to the `plt.subplots` function.</span>
+
+<span class="sd">    Returns:</span>
+<span class="sd">        tuple[np.ndarray, np.ndarray, np.ndarray]: The cluster labels, latitudes, and longitudes.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+    <span class="kn">from</span> <span class="nn">sklearn.cluster</span> <span class="kn">import</span> <span class="n">KMeans</span>
+
+    <span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+    <span class="kn">import</span> <span class="nn">matplotlib.colors</span> <span class="k">as</span> <span class="nn">mcolors</span>
+    <span class="kn">import</span> <span class="nn">cartopy.crs</span> <span class="k">as</span> <span class="nn">ccrs</span>
+    <span class="kn">import</span> <span class="nn">cartopy.feature</span> <span class="k">as</span> <span class="nn">cfeature</span>
+
+    <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">dataset</span><span class="p">,</span> <span class="nb">str</span><span class="p">):</span>
+        <span class="n">dataset</span> <span class="o">=</span> <span class="n">read_pace</span><span class="p">(</span><span class="n">dataset</span><span class="p">)</span>
+    <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">dataset</span><span class="p">,</span> <span class="n">xr</span><span class="o">.</span><span class="n">DataArray</span><span class="p">):</span>
+        <span class="n">dataset</span> <span class="o">=</span> <span class="n">dataset</span><span class="o">.</span><span class="n">to_dataset</span><span class="p">()</span>
+    <span class="k">elif</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">dataset</span><span class="p">,</span> <span class="n">xr</span><span class="o">.</span><span class="n">Dataset</span><span class="p">):</span>
+        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;dataset must be an xarray Dataset&quot;</span><span class="p">)</span>
+
+    <span class="k">if</span> <span class="n">title</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="n">title</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">&quot;K-means Clustering with </span><span class="si">{</span><span class="n">n_clusters</span><span class="si">}</span><span class="s2"> Clusters&quot;</span>
+
+    <span class="n">da</span> <span class="o">=</span> <span class="n">dataset</span><span class="p">[</span><span class="s2">&quot;Rrs&quot;</span><span class="p">]</span>
+
+    <span class="n">reshaped_data</span> <span class="o">=</span> <span class="n">da</span><span class="o">.</span><span class="n">values</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="n">da</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span>
+    <span class="n">reshaped_data_no_nan</span> <span class="o">=</span> <span class="n">reshaped_data</span><span class="p">[</span><span class="o">~</span><span class="n">np</span><span class="o">.</span><span class="n">isnan</span><span class="p">(</span><span class="n">reshaped_data</span><span class="p">)</span><span class="o">.</span><span class="n">any</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)]</span>
+
+    <span class="c1"># Apply K-means clustering to classify into 5-6 water types.</span>
+    <span class="n">kmeans</span> <span class="o">=</span> <span class="n">KMeans</span><span class="p">(</span><span class="n">n_clusters</span><span class="o">=</span><span class="n">n_clusters</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+    <span class="n">kmeans</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">reshaped_data_no_nan</span><span class="p">)</span>
+
+    <span class="c1"># Initialize an array for cluster labels with NaN</span>
+    <span class="n">labels</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">full</span><span class="p">(</span><span class="n">reshaped_data</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span><span class="p">)</span>
+
+    <span class="c1"># Assign the computed cluster labels to the non-NaN positions</span>
+    <span class="n">labels</span><span class="p">[</span><span class="o">~</span><span class="n">np</span><span class="o">.</span><span class="n">isnan</span><span class="p">(</span><span class="n">reshaped_data</span><span class="p">)</span><span class="o">.</span><span class="n">any</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)]</span> <span class="o">=</span> <span class="n">kmeans</span><span class="o">.</span><span class="n">labels_</span>
+
+    <span class="c1"># Reshape the labels back to the original spatial dimensions</span>
+    <span class="n">cluster_labels</span> <span class="o">=</span> <span class="n">labels</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">da</span><span class="o">.</span><span class="n">shape</span><span class="p">[:</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span>
+
+    <span class="k">if</span> <span class="n">filter_condition</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="n">cluster_labels</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">filter_condition</span><span class="p">,</span> <span class="n">cluster_labels</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span><span class="p">)</span>
+
+    <span class="n">latitudes</span> <span class="o">=</span> <span class="n">da</span><span class="o">.</span><span class="n">coords</span><span class="p">[</span><span class="s2">&quot;latitude&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">values</span>
+    <span class="n">longitudes</span> <span class="o">=</span> <span class="n">da</span><span class="o">.</span><span class="n">coords</span><span class="p">[</span><span class="s2">&quot;longitude&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">values</span>
+
+    <span class="k">if</span> <span class="n">plot</span><span class="p">:</span>
+
+        <span class="c1"># Create a custom discrete color map for K-means clusters</span>
+        <span class="k">if</span> <span class="n">colors</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">colors</span> <span class="o">=</span> <span class="p">[</span><span class="s2">&quot;#377eb8&quot;</span><span class="p">,</span> <span class="s2">&quot;#ff7f00&quot;</span><span class="p">,</span> <span class="s2">&quot;#4daf4a&quot;</span><span class="p">,</span> <span class="s2">&quot;#f781bf&quot;</span><span class="p">,</span> <span class="s2">&quot;#a65628&quot;</span><span class="p">,</span> <span class="s2">&quot;#984ea3&quot;</span><span class="p">]</span>
+        <span class="n">cmap</span> <span class="o">=</span> <span class="n">mcolors</span><span class="o">.</span><span class="n">ListedColormap</span><span class="p">(</span><span class="n">colors</span><span class="p">)</span>
+        <span class="n">bounds</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="o">-</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">n_clusters</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+        <span class="n">norm</span> <span class="o">=</span> <span class="n">mcolors</span><span class="o">.</span><span class="n">BoundaryNorm</span><span class="p">(</span><span class="n">bounds</span><span class="p">,</span> <span class="n">cmap</span><span class="o">.</span><span class="n">N</span><span class="p">)</span>
+
+        <span class="c1"># Create a figure and axis with the correct map projection</span>
+
+        <span class="k">if</span> <span class="s2">&quot;dpi&quot;</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">kwargs</span><span class="p">:</span>
+            <span class="n">kwargs</span><span class="p">[</span><span class="s2">&quot;dpi&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="mi">100</span>
+
+        <span class="k">if</span> <span class="s2">&quot;subplot_kw&quot;</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">kwargs</span><span class="p">:</span>
+            <span class="n">kwargs</span><span class="p">[</span><span class="s2">&quot;subplot_kw&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="p">{</span><span class="s2">&quot;projection&quot;</span><span class="p">:</span> <span class="n">ccrs</span><span class="o">.</span><span class="n">PlateCarree</span><span class="p">()}</span>
+
+        <span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span>
+            <span class="n">figsize</span><span class="o">=</span><span class="n">figsize</span><span class="p">,</span>
+            <span class="o">**</span><span class="n">kwargs</span><span class="p">,</span>
+        <span class="p">)</span>
+
+        <span class="c1"># Plot the K-means classification results on the map</span>
+        <span class="n">im</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">pcolormesh</span><span class="p">(</span>
+            <span class="n">longitudes</span><span class="p">,</span>
+            <span class="n">latitudes</span><span class="p">,</span>
+            <span class="n">cluster_labels</span><span class="p">,</span>
+            <span class="n">cmap</span><span class="o">=</span><span class="n">cmap</span><span class="p">,</span>
+            <span class="n">norm</span><span class="o">=</span><span class="n">norm</span><span class="p">,</span>
+            <span class="n">transform</span><span class="o">=</span><span class="n">ccrs</span><span class="o">.</span><span class="n">PlateCarree</span><span class="p">(),</span>
+        <span class="p">)</span>
+
+        <span class="c1"># Add geographic features for context</span>
+        <span class="n">ax</span><span class="o">.</span><span class="n">add_feature</span><span class="p">(</span><span class="n">cfeature</span><span class="o">.</span><span class="n">COASTLINE</span><span class="p">)</span>
+        <span class="n">ax</span><span class="o">.</span><span class="n">add_feature</span><span class="p">(</span><span class="n">cfeature</span><span class="o">.</span><span class="n">BORDERS</span><span class="p">,</span> <span class="n">linestyle</span><span class="o">=</span><span class="s2">&quot;:&quot;</span><span class="p">)</span>
+        <span class="n">ax</span><span class="o">.</span><span class="n">add_feature</span><span class="p">(</span><span class="n">cfeature</span><span class="o">.</span><span class="n">STATES</span><span class="p">,</span> <span class="n">linestyle</span><span class="o">=</span><span class="s2">&quot;--&quot;</span><span class="p">)</span>
+
+        <span class="c1"># Add gridlines</span>
+        <span class="n">ax</span><span class="o">.</span><span class="n">gridlines</span><span class="p">(</span><span class="n">draw_labels</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+
+        <span class="c1"># Set the extent to zoom in to the specified region</span>
+        <span class="k">if</span> <span class="n">extent</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">ax</span><span class="o">.</span><span class="n">set_extent</span><span class="p">(</span><span class="n">extent</span><span class="p">,</span> <span class="n">crs</span><span class="o">=</span><span class="n">ccrs</span><span class="o">.</span><span class="n">PlateCarree</span><span class="p">())</span>
+
+        <span class="c1"># Add color bar with labels</span>
+        <span class="n">cbar</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">colorbar</span><span class="p">(</span>
+            <span class="n">im</span><span class="p">,</span>
+            <span class="n">ax</span><span class="o">=</span><span class="n">ax</span><span class="p">,</span>
+            <span class="n">orientation</span><span class="o">=</span><span class="s2">&quot;vertical&quot;</span><span class="p">,</span>
+            <span class="c1"># pad=0.02,</span>
+            <span class="n">fraction</span><span class="o">=</span><span class="mf">0.05</span><span class="p">,</span>
+            <span class="n">ticks</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">n_clusters</span><span class="p">),</span>
+        <span class="p">)</span>
+        <span class="n">cbar</span><span class="o">.</span><span class="n">ax</span><span class="o">.</span><span class="n">set_yticklabels</span><span class="p">([</span><span class="sa">f</span><span class="s2">&quot;Class </span><span class="si">{</span><span class="n">i</span><span class="o">+</span><span class="mi">1</span><span class="si">}</span><span class="s2">&quot;</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">n_clusters</span><span class="p">)])</span>
+        <span class="n">cbar</span><span class="o">.</span><span class="n">set_label</span><span class="p">(</span><span class="s2">&quot;Water Types&quot;</span><span class="p">,</span> <span class="n">rotation</span><span class="o">=</span><span class="mi">270</span><span class="p">,</span> <span class="n">labelpad</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span>
+
+        <span class="c1"># Add title</span>
+        <span class="n">ax</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="n">title</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">14</span><span class="p">)</span>
+        <span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s2">&quot;Longitude&quot;</span><span class="p">)</span>
+        <span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s2">&quot;Latitude&quot;</span><span class="p">)</span>
+
+        <span class="c1"># Show the plot</span>
+        <span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
+
+    <span class="k">return</span> <span class="n">cluster_labels</span><span class="p">,</span> <span class="n">latitudes</span><span class="p">,</span> <span class="n">longitudes</span>
+</code></pre></div>
+        </details>
+    </div>
+
+  </div>
+
+
+
+  <div class="doc doc-object doc-function">
+
+
+
+<h2 id="hypercoast.pace.apply_pca" class="doc doc-heading">
+<code class="highlight language-python"><span class="n">apply_pca</span><span class="p">(</span><span class="n">dataset</span><span class="p">,</span> <span class="n">n_components</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">plot</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">8</span><span class="p">,</span> <span class="mi">6</span><span class="p">),</span> <span class="n">x_component</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">y_component</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;blue&#39;</span><span class="p">,</span> <span class="n">title</span><span class="o">=</span><span class="s1">&#39;PCA of Spectral Data&#39;</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></code>
+
+
+<a href="#hypercoast.pace.apply_pca" class="headerlink" title="Permanent link">&para;</a></h2>
+
+    <div class="doc doc-contents ">
+
+      <p>Applies Principal Component Analysis (PCA) to the dataset and optionally plots the results.</p>
+
+<p><strong>Parameters:</strong></p>
+<table>
+  <thead>
+    <tr>
+      <th>Name</th>
+      <th>Type</th>
+      <th>Description</th>
+      <th>Default</th>
+    </tr>
+  </thead>
+  <tbody>
+      <tr>
+        <td><code>dataset</code></td>
+        <td><code>xr.Dataset | str</code></td>
+        <td><p>The dataset containing the PACE data or the file path to the dataset.</p></td>
+        <td><em>required</em></td>
+      </tr>
+      <tr>
+        <td><code>n_components</code></td>
+        <td><code>int</code></td>
+        <td><p>Number of principal components to compute. Defaults to 3.</p></td>
+        <td><code>3</code></td>
+      </tr>
+      <tr>
+        <td><code>plot</code></td>
+        <td><code>bool</code></td>
+        <td><p>Whether to plot the data. Defaults to True.</p></td>
+        <td><code>True</code></td>
+      </tr>
+      <tr>
+        <td><code>figsize</code></td>
+        <td><code>tuple[int, int]</code></td>
+        <td><p>Figure size for the plot. Defaults to (8, 6).</p></td>
+        <td><code>(8, 6)</code></td>
+      </tr>
+      <tr>
+        <td><code>x_component</code></td>
+        <td><code>int</code></td>
+        <td><p>The principal component to plot on the x-axis. Defaults to 0.</p></td>
+        <td><code>0</code></td>
+      </tr>
+      <tr>
+        <td><code>y_component</code></td>
+        <td><code>int</code></td>
+        <td><p>The principal component to plot on the y-axis. Defaults to 1.</p></td>
+        <td><code>1</code></td>
+      </tr>
+      <tr>
+        <td><code>color</code></td>
+        <td><code>str</code></td>
+        <td><p>Color of the scatter plot points. Defaults to "blue".</p></td>
+        <td><code>&#39;blue&#39;</code></td>
+      </tr>
+      <tr>
+        <td><code>title</code></td>
+        <td><code>str</code></td>
+        <td><p>Title for the plot. Defaults to "PCA of Spectral Data".</p></td>
+        <td><code>&#39;PCA of Spectral Data&#39;</code></td>
+      </tr>
+      <tr>
+        <td><code>**kwargs</code></td>
+        <td></td>
+        <td><p>Additional keyword arguments to pass to the <code>plt.scatter</code> function.</p></td>
+        <td><code>{}</code></td>
+      </tr>
+  </tbody>
+</table>
+<p><strong>Returns:</strong></p>
+<table>
+  <thead>
+    <tr>
+      <th>Type</th>
+      <th>Description</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <td><code>np.ndarray</code></td>
+      <td><p>The PCA-transformed data.</p></td>
+    </tr>
+  </tbody>
+</table>
+        <details class="quote">
+          <summary>Source code in <code>hypercoast/pace.py</code></summary>
+          <div class="highlight"><pre><span></span><code><span class="k">def</span> <span class="nf">apply_pca</span><span class="p">(</span>
+    <span class="n">dataset</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="n">xr</span><span class="o">.</span><span class="n">Dataset</span><span class="p">,</span> <span class="nb">str</span><span class="p">],</span>
+    <span class="n">n_components</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">3</span><span class="p">,</span>
+    <span class="n">plot</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span>
+    <span class="n">figsize</span><span class="p">:</span> <span class="nb">tuple</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="nb">int</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="mi">8</span><span class="p">,</span> <span class="mi">6</span><span class="p">),</span>
+    <span class="n">x_component</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">0</span><span class="p">,</span>
+    <span class="n">y_component</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">1</span><span class="p">,</span>
+    <span class="n">color</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;blue&quot;</span><span class="p">,</span>
+    <span class="n">title</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;PCA of Spectral Data&quot;</span><span class="p">,</span>
+    <span class="o">**</span><span class="n">kwargs</span><span class="p">,</span>
+<span class="p">)</span> <span class="o">-&gt;</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">:</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Applies Principal Component Analysis (PCA) to the dataset and optionally plots the results.</span>
+
+<span class="sd">    Args:</span>
+<span class="sd">        dataset (xr.Dataset | str): The dataset containing the PACE data or the file path to the dataset.</span>
+<span class="sd">        n_components (int, optional): Number of principal components to compute. Defaults to 3.</span>
+<span class="sd">        plot (bool, optional): Whether to plot the data. Defaults to True.</span>
+<span class="sd">        figsize (tuple[int, int], optional): Figure size for the plot. Defaults to (8, 6).</span>
+<span class="sd">        x_component (int, optional): The principal component to plot on the x-axis. Defaults to 0.</span>
+<span class="sd">        y_component (int, optional): The principal component to plot on the y-axis. Defaults to 1.</span>
+<span class="sd">        color (str, optional): Color of the scatter plot points. Defaults to &quot;blue&quot;.</span>
+<span class="sd">        title (str, optional): Title for the plot. Defaults to &quot;PCA of Spectral Data&quot;.</span>
+<span class="sd">        **kwargs: Additional keyword arguments to pass to the `plt.scatter` function.</span>
+
+<span class="sd">    Returns:</span>
+<span class="sd">        np.ndarray: The PCA-transformed data.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="kn">from</span> <span class="nn">sklearn.decomposition</span> <span class="kn">import</span> <span class="n">PCA</span>
+
+    <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">dataset</span><span class="p">,</span> <span class="nb">str</span><span class="p">):</span>
+        <span class="n">dataset</span> <span class="o">=</span> <span class="n">read_pace</span><span class="p">(</span><span class="n">dataset</span><span class="p">)</span>
+    <span class="k">elif</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">dataset</span><span class="p">,</span> <span class="n">xr</span><span class="o">.</span><span class="n">Dataset</span><span class="p">):</span>
+        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;dataset must be an xarray Dataset&quot;</span><span class="p">)</span>
+
+    <span class="n">da</span> <span class="o">=</span> <span class="n">dataset</span><span class="p">[</span><span class="s2">&quot;Rrs&quot;</span><span class="p">]</span>
+
+    <span class="c1"># Reshape data to (n_pixels, n_bands)</span>
+    <span class="n">reshaped_data</span> <span class="o">=</span> <span class="n">da</span><span class="o">.</span><span class="n">values</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="n">da</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span>
+
+    <span class="c1"># Handle NaNs by removing them</span>
+    <span class="n">reshaped_data_no_nan</span> <span class="o">=</span> <span class="n">reshaped_data</span><span class="p">[</span><span class="o">~</span><span class="n">np</span><span class="o">.</span><span class="n">isnan</span><span class="p">(</span><span class="n">reshaped_data</span><span class="p">)</span><span class="o">.</span><span class="n">any</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)]</span>
+
+    <span class="c1"># Apply PCA to reduce dimensionality</span>
+    <span class="n">pca</span> <span class="o">=</span> <span class="n">PCA</span><span class="p">(</span><span class="n">n_components</span><span class="o">=</span><span class="n">n_components</span><span class="p">)</span>
+    <span class="n">pca_data</span> <span class="o">=</span> <span class="n">pca</span><span class="o">.</span><span class="n">fit_transform</span><span class="p">(</span><span class="n">reshaped_data_no_nan</span><span class="p">)</span>
+
+    <span class="k">if</span> <span class="n">plot</span><span class="p">:</span>
+        <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="n">figsize</span><span class="p">)</span>
+        <span class="k">if</span> <span class="s2">&quot;s&quot;</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">kwargs</span><span class="p">:</span>
+            <span class="n">kwargs</span><span class="p">[</span><span class="s2">&quot;s&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="mi">1</span>
+        <span class="n">plt</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span>
+            <span class="n">pca_data</span><span class="p">[:,</span> <span class="n">x_component</span><span class="p">],</span> <span class="n">pca_data</span><span class="p">[:,</span> <span class="n">y_component</span><span class="p">],</span> <span class="n">c</span><span class="o">=</span><span class="n">color</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span>
+        <span class="p">)</span>
+        <span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="n">title</span><span class="p">)</span>
+        <span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Principal Component </span><span class="si">{</span><span class="n">x_component</span><span class="w"> </span><span class="o">+</span><span class="w"> </span><span class="mi">1</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+        <span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Principal Component </span><span class="si">{</span><span class="n">y_component</span><span class="w"> </span><span class="o">+</span><span class="w"> </span><span class="mi">1</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+        <span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
+
+    <span class="k">return</span> <span class="n">pca_data</span>
+</code></pre></div>
+        </details>
+    </div>
+
+  </div>
+
+
+
+  <div class="doc doc-object doc-function">
+
+
+
+<h2 id="hypercoast.pace.apply_sam" class="doc doc-heading">
+<code class="highlight language-python"><span class="n">apply_sam</span><span class="p">(</span><span class="n">dataset</span><span class="p">,</span> <span class="n">n_components</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">n_clusters</span><span class="o">=</span><span class="mi">6</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">spectral_library</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">filter_condition</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">plot</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">8</span><span class="p">,</span> <span class="mi">6</span><span class="p">),</span> <span class="n">extent</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">colors</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">title</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></code>
+
+
+<a href="#hypercoast.pace.apply_sam" class="headerlink" title="Permanent link">&para;</a></h2>
+
+    <div class="doc doc-contents ">
+
+      <p>Applies Spectral Angle Mapper (SAM) to the dataset and optionally plots the results.</p>
+
+<p><strong>Parameters:</strong></p>
+<table>
+  <thead>
+    <tr>
+      <th>Name</th>
+      <th>Type</th>
+      <th>Description</th>
+      <th>Default</th>
+    </tr>
+  </thead>
+  <tbody>
+      <tr>
+        <td><code>dataset</code></td>
+        <td><code>Union[xr.Dataset, str]</code></td>
+        <td><p>The dataset containing the PACE data or the file path to the dataset.</p></td>
+        <td><em>required</em></td>
+      </tr>
+      <tr>
+        <td><code>n_components</code></td>
+        <td><code>int</code></td>
+        <td><p>Number of principal components to compute. Defaults to 3.</p></td>
+        <td><code>3</code></td>
+      </tr>
+      <tr>
+        <td><code>n_clusters</code></td>
+        <td><code>int</code></td>
+        <td><p>Number of clusters for K-means. Defaults to 6.</p></td>
+        <td><code>6</code></td>
+      </tr>
+      <tr>
+        <td><code>random_state</code></td>
+        <td><code>int</code></td>
+        <td><p>Random state for K-means. Defaults to 0.</p></td>
+        <td><code>0</code></td>
+      </tr>
+      <tr>
+        <td><code>spectral_library</code></td>
+        <td><code>Union[str, list[str]]</code></td>
+        <td><p>Path to the spectral library or a list of paths. Defaults to None.</p></td>
+        <td><code>None</code></td>
+      </tr>
+      <tr>
+        <td><code>filter_condition</code></td>
+        <td><code>Callable[[xr.DataArray], xr.DataArray]</code></td>
+        <td><p>A function to filter the data. Defaults to None.</p></td>
+        <td><code>None</code></td>
+      </tr>
+      <tr>
+        <td><code>plot</code></td>
+        <td><code>bool</code></td>
+        <td><p>Whether to plot the data. Defaults to True.</p></td>
+        <td><code>True</code></td>
+      </tr>
+      <tr>
+        <td><code>figsize</code></td>
+        <td><code>Tuple[int, int]</code></td>
+        <td><p>Figure size for the plot. Defaults to (8, 6).</p></td>
+        <td><code>(8, 6)</code></td>
+      </tr>
+      <tr>
+        <td><code>extent</code></td>
+        <td><code>List[float]</code></td>
+        <td><p>The extent to zoom in to the specified region. Defaults to None.</p></td>
+        <td><code>None</code></td>
+      </tr>
+      <tr>
+        <td><code>colors</code></td>
+        <td><code>List[str]</code></td>
+        <td><p>Colors for the clusters. Defaults to None.</p></td>
+        <td><code>None</code></td>
+      </tr>
+      <tr>
+        <td><code>title</code></td>
+        <td><code>str</code></td>
+        <td><p>Title for the plot. Defaults to "Spectral Angle Mapper".</p></td>
+        <td><code>None</code></td>
+      </tr>
+      <tr>
+        <td><code>**kwargs</code></td>
+        <td></td>
+        <td><p>Additional keyword arguments to pass to the <code>plt.subplots</code> function.</p></td>
+        <td><code>{}</code></td>
+      </tr>
+  </tbody>
+</table>
+<p><strong>Returns:</strong></p>
+<table>
+  <thead>
+    <tr>
+      <th>Type</th>
+      <th>Description</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <td><code>Tuple[np.ndarray, np.ndarray, np.ndarray]</code></td>
+      <td><p>The best match classification, latitudes, and longitudes.</p></td>
+    </tr>
+  </tbody>
+</table>
+        <details class="quote">
+          <summary>Source code in <code>hypercoast/pace.py</code></summary>
+          <div class="highlight"><pre><span></span><code><span class="k">def</span> <span class="nf">apply_sam</span><span class="p">(</span>
+    <span class="n">dataset</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="n">xr</span><span class="o">.</span><span class="n">Dataset</span><span class="p">,</span> <span class="nb">str</span><span class="p">],</span>
+    <span class="n">n_components</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">3</span><span class="p">,</span>
+    <span class="n">n_clusters</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">6</span><span class="p">,</span>
+    <span class="n">random_state</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">0</span><span class="p">,</span>
+    <span class="n">spectral_library</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="nb">list</span><span class="p">[</span><span class="nb">str</span><span class="p">]]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+    <span class="n">filter_condition</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Callable</span><span class="p">[[</span><span class="n">xr</span><span class="o">.</span><span class="n">DataArray</span><span class="p">],</span> <span class="n">xr</span><span class="o">.</span><span class="n">DataArray</span><span class="p">]]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+    <span class="n">plot</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span>
+    <span class="n">figsize</span><span class="p">:</span> <span class="nb">tuple</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="nb">int</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="mi">8</span><span class="p">,</span> <span class="mi">6</span><span class="p">),</span>
+    <span class="n">extent</span><span class="p">:</span> <span class="nb">list</span><span class="p">[</span><span class="nb">float</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+    <span class="n">colors</span><span class="p">:</span> <span class="nb">list</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+    <span class="n">title</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+    <span class="o">**</span><span class="n">kwargs</span><span class="p">,</span>
+<span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Tuple</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">]:</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Applies Spectral Angle Mapper (SAM) to the dataset and optionally plots the results.</span>
+
+<span class="sd">    Args:</span>
+<span class="sd">        dataset (Union[xr.Dataset, str]): The dataset containing the PACE data or the file path to the dataset.</span>
+<span class="sd">        n_components (int, optional): Number of principal components to compute. Defaults to 3.</span>
+<span class="sd">        n_clusters (int, optional): Number of clusters for K-means. Defaults to 6.</span>
+<span class="sd">        random_state (int, optional): Random state for K-means. Defaults to 0.</span>
+<span class="sd">        spectral_library (Union[str, list[str]], optional): Path to the spectral library or a list of paths. Defaults to None.</span>
+<span class="sd">        filter_condition (Callable[[xr.DataArray], xr.DataArray], optional): A function to filter the data. Defaults to None.</span>
+<span class="sd">        plot (bool, optional): Whether to plot the data. Defaults to True.</span>
+<span class="sd">        figsize (Tuple[int, int], optional): Figure size for the plot. Defaults to (8, 6).</span>
+<span class="sd">        extent (List[float], optional): The extent to zoom in to the specified region. Defaults to None.</span>
+<span class="sd">        colors (List[str], optional): Colors for the clusters. Defaults to None.</span>
+<span class="sd">        title (str, optional): Title for the plot. Defaults to &quot;Spectral Angle Mapper&quot;.</span>
+<span class="sd">        **kwargs: Additional keyword arguments to pass to the `plt.subplots` function.</span>
+
+<span class="sd">    Returns:</span>
+<span class="sd">        Tuple[np.ndarray, np.ndarray, np.ndarray]: The best match classification, latitudes, and longitudes.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="kn">import</span> <span class="nn">glob</span>
+    <span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
+    <span class="kn">from</span> <span class="nn">sklearn.cluster</span> <span class="kn">import</span> <span class="n">KMeans</span>
+    <span class="kn">import</span> <span class="nn">matplotlib.colors</span> <span class="k">as</span> <span class="nn">mcolors</span>
+    <span class="kn">import</span> <span class="nn">cartopy.crs</span> <span class="k">as</span> <span class="nn">ccrs</span>
+    <span class="kn">import</span> <span class="nn">cartopy.feature</span> <span class="k">as</span> <span class="nn">cfeature</span>
+    <span class="kn">from</span> <span class="nn">sklearn.decomposition</span> <span class="kn">import</span> <span class="n">PCA</span>
+    <span class="kn">from</span> <span class="nn">scipy.interpolate</span> <span class="kn">import</span> <span class="n">interp1d</span>
+
+    <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">dataset</span><span class="p">,</span> <span class="nb">str</span><span class="p">):</span>
+        <span class="n">dataset</span> <span class="o">=</span> <span class="n">read_pace</span><span class="p">(</span><span class="n">dataset</span><span class="p">)</span>
+    <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">dataset</span><span class="p">,</span> <span class="n">xr</span><span class="o">.</span><span class="n">DataArray</span><span class="p">):</span>
+        <span class="n">dataset</span> <span class="o">=</span> <span class="n">dataset</span><span class="o">.</span><span class="n">to_dataset</span><span class="p">()</span>
+    <span class="k">elif</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">dataset</span><span class="p">,</span> <span class="n">xr</span><span class="o">.</span><span class="n">Dataset</span><span class="p">):</span>
+        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;dataset must be an xarray Dataset&quot;</span><span class="p">)</span>
+
+    <span class="n">da</span> <span class="o">=</span> <span class="n">dataset</span><span class="p">[</span><span class="s2">&quot;Rrs&quot;</span><span class="p">]</span>
+    <span class="n">pace_wavelengths</span> <span class="o">=</span> <span class="n">da</span><span class="p">[</span><span class="s2">&quot;wavelength&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">values</span>
+
+    <span class="c1"># Reshape data to (n_pixels, n_bands)</span>
+    <span class="n">reshaped_data</span> <span class="o">=</span> <span class="n">da</span><span class="o">.</span><span class="n">values</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="n">da</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span>
+
+    <span class="c1"># Handle NaNs by removing them</span>
+    <span class="n">reshaped_data_no_nan</span> <span class="o">=</span> <span class="n">reshaped_data</span><span class="p">[</span><span class="o">~</span><span class="n">np</span><span class="o">.</span><span class="n">isnan</span><span class="p">(</span><span class="n">reshaped_data</span><span class="p">)</span><span class="o">.</span><span class="n">any</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)]</span>
+
+    <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">spectral_library</span><span class="p">,</span> <span class="nb">str</span><span class="p">):</span>
+        <span class="n">endmember_paths</span> <span class="o">=</span> <span class="nb">sorted</span><span class="p">(</span><span class="n">glob</span><span class="o">.</span><span class="n">glob</span><span class="p">(</span><span class="n">spectral_library</span><span class="p">))</span>
+    <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">spectral_library</span><span class="p">,</span> <span class="nb">list</span><span class="p">):</span>
+        <span class="n">endmember_paths</span> <span class="o">=</span> <span class="n">spectral_library</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="n">endmember_paths</span> <span class="o">=</span> <span class="kc">None</span>
+
+    <span class="c1"># Function to load and resample a single CSV spectral library file</span>
+    <span class="k">def</span> <span class="nf">load_and_resample_spectral_library</span><span class="p">(</span><span class="n">csv_path</span><span class="p">,</span> <span class="n">target_wavelengths</span><span class="p">):</span>
+        <span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="n">csv_path</span><span class="p">)</span>
+        <span class="n">original_wavelengths</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">iloc</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">values</span>  <span class="c1"># First column is wavelength</span>
+        <span class="n">spectra_values</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">iloc</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">values</span>  <span class="c1"># Second column is spectral values</span>
+
+        <span class="c1"># Interpolation function</span>
+        <span class="n">interp_func</span> <span class="o">=</span> <span class="n">interp1d</span><span class="p">(</span>
+            <span class="n">original_wavelengths</span><span class="p">,</span>
+            <span class="n">spectra_values</span><span class="p">,</span>
+            <span class="n">kind</span><span class="o">=</span><span class="s2">&quot;linear&quot;</span><span class="p">,</span>
+            <span class="n">fill_value</span><span class="o">=</span><span class="s2">&quot;extrapolate&quot;</span><span class="p">,</span>
+        <span class="p">)</span>
+
+        <span class="c1"># Resample to the target (PACE) wavelengths</span>
+        <span class="n">resampled_spectra</span> <span class="o">=</span> <span class="n">interp_func</span><span class="p">(</span><span class="n">target_wavelengths</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="n">resampled_spectra</span>
+
+    <span class="k">if</span> <span class="n">endmember_paths</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="n">endmembers</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span>
+            <span class="p">[</span>
+                <span class="n">load_and_resample_spectral_library</span><span class="p">(</span><span class="n">path</span><span class="p">,</span> <span class="n">pace_wavelengths</span><span class="p">)</span>
+                <span class="k">for</span> <span class="n">path</span> <span class="ow">in</span> <span class="n">endmember_paths</span>
+            <span class="p">]</span>
+        <span class="p">)</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="n">endmembers</span> <span class="o">=</span> <span class="kc">None</span>
+
+    <span class="k">if</span> <span class="n">endmembers</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="c1"># Apply PCA to reduce dimensionality</span>
+        <span class="n">pca</span> <span class="o">=</span> <span class="n">PCA</span><span class="p">(</span><span class="n">n_components</span><span class="o">=</span><span class="n">n_components</span><span class="p">)</span>
+        <span class="n">pca_data</span> <span class="o">=</span> <span class="n">pca</span><span class="o">.</span><span class="n">fit_transform</span><span class="p">(</span><span class="n">reshaped_data_no_nan</span><span class="p">)</span>
+
+        <span class="c1"># Apply K-means to find clusters representing endmembers</span>
+        <span class="n">kmeans</span> <span class="o">=</span> <span class="n">KMeans</span><span class="p">(</span><span class="n">n_clusters</span><span class="o">=</span><span class="n">n_clusters</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="n">random_state</span><span class="p">)</span>
+        <span class="n">kmeans</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">pca_data</span><span class="p">)</span>
+
+        <span class="c1"># The cluster centers in the original spectral space are your endmembers</span>
+        <span class="n">endmembers</span> <span class="o">=</span> <span class="n">pca</span><span class="o">.</span><span class="n">inverse_transform</span><span class="p">(</span><span class="n">kmeans</span><span class="o">.</span><span class="n">cluster_centers_</span><span class="p">)</span>
+
+    <span class="k">def</span> <span class="nf">spectral_angle_mapper</span><span class="p">(</span><span class="n">pixel</span><span class="p">,</span> <span class="n">reference</span><span class="p">):</span>
+        <span class="n">norm_pixel</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">norm</span><span class="p">(</span><span class="n">pixel</span><span class="p">)</span>
+        <span class="n">norm_reference</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">norm</span><span class="p">(</span><span class="n">reference</span><span class="p">)</span>
+        <span class="n">cos_theta</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">pixel</span><span class="p">,</span> <span class="n">reference</span><span class="p">)</span> <span class="o">/</span> <span class="p">(</span><span class="n">norm_pixel</span> <span class="o">*</span> <span class="n">norm_reference</span><span class="p">)</span>
+        <span class="n">angle</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arccos</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">clip</span><span class="p">(</span><span class="n">cos_theta</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">))</span>
+        <span class="k">return</span> <span class="n">angle</span>
+
+    <span class="c1"># Apply SAM for each pixel and each endmember</span>
+    <span class="n">angles</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="n">reshaped_data_no_nan</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">endmembers</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]))</span>
+
+    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">reshaped_data_no_nan</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]):</span>
+        <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">endmembers</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]):</span>
+            <span class="n">angles</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">]</span> <span class="o">=</span> <span class="n">spectral_angle_mapper</span><span class="p">(</span>
+                <span class="n">reshaped_data_no_nan</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="p">:],</span> <span class="n">endmembers</span><span class="p">[</span><span class="n">j</span><span class="p">,</span> <span class="p">:]</span>
+            <span class="p">)</span>
+
+    <span class="c1"># Find the minimum angle (best match) for each pixel</span>
+    <span class="n">best_match</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">argmin</span><span class="p">(</span><span class="n">angles</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+
+    <span class="c1"># Reshape best_match back to the original spatial dimensions</span>
+    <span class="n">original_shape</span> <span class="o">=</span> <span class="n">da</span><span class="o">.</span><span class="n">shape</span><span class="p">[:</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>  <span class="c1"># Get the spatial dimensions</span>
+    <span class="n">best_match_full</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">full</span><span class="p">(</span><span class="n">reshaped_data</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span><span class="p">)</span>
+    <span class="n">best_match_full</span><span class="p">[</span><span class="o">~</span><span class="n">np</span><span class="o">.</span><span class="n">isnan</span><span class="p">(</span><span class="n">reshaped_data</span><span class="p">)</span><span class="o">.</span><span class="n">any</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)]</span> <span class="o">=</span> <span class="n">best_match</span>
+    <span class="n">best_match_full</span> <span class="o">=</span> <span class="n">best_match_full</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">original_shape</span><span class="p">)</span>
+
+    <span class="k">if</span> <span class="n">filter_condition</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="n">best_match_full</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">filter_condition</span><span class="p">,</span> <span class="n">best_match_full</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span><span class="p">)</span>
+
+    <span class="n">latitudes</span> <span class="o">=</span> <span class="n">da</span><span class="o">.</span><span class="n">coords</span><span class="p">[</span><span class="s2">&quot;latitude&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">values</span>
+    <span class="n">longitudes</span> <span class="o">=</span> <span class="n">da</span><span class="o">.</span><span class="n">coords</span><span class="p">[</span><span class="s2">&quot;longitude&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">values</span>
+
+    <span class="c1"># Plot sample spectra from the CSV files and their resampled versions</span>
+    <span class="k">def</span> <span class="nf">plot_sample_spectra</span><span class="p">(</span><span class="n">csv_paths</span><span class="p">,</span> <span class="n">pace_wavelengths</span><span class="p">):</span>
+        <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="n">figsize</span><span class="p">)</span>
+
+        <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">csv_path</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">csv_paths</span><span class="p">):</span>
+            <span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="n">csv_path</span><span class="p">)</span>
+            <span class="n">original_wavelengths</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">iloc</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">values</span>
+            <span class="n">spectra_values</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">iloc</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">values</span>
+            <span class="n">resampled_spectra</span> <span class="o">=</span> <span class="n">load_and_resample_spectral_library</span><span class="p">(</span>
+                <span class="n">csv_path</span><span class="p">,</span> <span class="n">pace_wavelengths</span>
+            <span class="p">)</span>
+
+            <span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span>
+                <span class="n">original_wavelengths</span><span class="p">,</span>
+                <span class="n">spectra_values</span><span class="p">,</span>
+                <span class="n">label</span><span class="o">=</span><span class="sa">f</span><span class="s2">&quot;Original Spectra </span><span class="si">{</span><span class="n">i</span><span class="o">+</span><span class="mi">1</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">,</span>
+                <span class="n">linestyle</span><span class="o">=</span><span class="s2">&quot;--&quot;</span><span class="p">,</span>
+            <span class="p">)</span>
+            <span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span>
+                <span class="n">pace_wavelengths</span><span class="p">,</span> <span class="n">resampled_spectra</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="sa">f</span><span class="s2">&quot;Resampled Spectra </span><span class="si">{</span><span class="n">i</span><span class="o">+</span><span class="mi">1</span><span class="si">}</span><span class="s2">&quot;</span>
+            <span class="p">)</span>
+
+        <span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s2">&quot;Wavelength (nm)&quot;</span><span class="p">)</span>
+        <span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s2">&quot;Spectral Reflectance&quot;</span><span class="p">)</span>
+        <span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s2">&quot;Comparison of Original and Resampled Spectra&quot;</span><span class="p">)</span>
+        <span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>
+        <span class="n">plt</span><span class="o">.</span><span class="n">grid</span><span class="p">(</span><span class="kc">True</span><span class="p">)</span>
+        <span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
+
+    <span class="k">if</span> <span class="n">plot</span><span class="p">:</span>
+
+        <span class="k">if</span> <span class="n">endmember_paths</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+
+            <span class="n">plot_sample_spectra</span><span class="p">(</span><span class="n">endmember_paths</span><span class="p">,</span> <span class="n">pace_wavelengths</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="n">colors</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">colors</span> <span class="o">=</span> <span class="p">[</span><span class="s2">&quot;#377eb8&quot;</span><span class="p">,</span> <span class="s2">&quot;#ff7f00&quot;</span><span class="p">,</span> <span class="s2">&quot;#4daf4a&quot;</span><span class="p">,</span> <span class="s2">&quot;#f781bf&quot;</span><span class="p">,</span> <span class="s2">&quot;#a65628&quot;</span><span class="p">,</span> <span class="s2">&quot;#984ea3&quot;</span><span class="p">]</span>
+
+        <span class="k">if</span> <span class="n">title</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">title</span> <span class="o">=</span> <span class="s2">&quot;Spectral Angle Mapper Water Type Classification&quot;</span>
+        <span class="c1"># Create a custom discrete color map</span>
+        <span class="n">cmap</span> <span class="o">=</span> <span class="n">mcolors</span><span class="o">.</span><span class="n">ListedColormap</span><span class="p">(</span><span class="n">colors</span><span class="p">)</span>
+        <span class="k">if</span> <span class="n">spectral_library</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">n_clusters</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">endmember_paths</span><span class="p">)</span>
+        <span class="n">bounds</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="o">-</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">n_clusters</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+        <span class="n">norm</span> <span class="o">=</span> <span class="n">mcolors</span><span class="o">.</span><span class="n">BoundaryNorm</span><span class="p">(</span><span class="n">bounds</span><span class="p">,</span> <span class="n">cmap</span><span class="o">.</span><span class="n">N</span><span class="p">)</span>
+
+        <span class="c1"># Create a figure and axis with the correct map projection</span>
+        <span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span>
+            <span class="n">figsize</span><span class="o">=</span><span class="n">figsize</span><span class="p">,</span> <span class="n">subplot_kw</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;projection&quot;</span><span class="p">:</span> <span class="n">ccrs</span><span class="o">.</span><span class="n">PlateCarree</span><span class="p">()},</span> <span class="o">**</span><span class="n">kwargs</span>
+        <span class="p">)</span>
+
+        <span class="c1"># Plot the SAM classification results</span>
+        <span class="n">im</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">pcolormesh</span><span class="p">(</span>
+            <span class="n">longitudes</span><span class="p">,</span>
+            <span class="n">latitudes</span><span class="p">,</span>
+            <span class="n">best_match_full</span><span class="p">,</span>
+            <span class="n">cmap</span><span class="o">=</span><span class="n">cmap</span><span class="p">,</span>
+            <span class="n">norm</span><span class="o">=</span><span class="n">norm</span><span class="p">,</span>
+            <span class="n">transform</span><span class="o">=</span><span class="n">ccrs</span><span class="o">.</span><span class="n">PlateCarree</span><span class="p">(),</span>
+        <span class="p">)</span>
+
+        <span class="c1"># Add geographic features for context</span>
+        <span class="n">ax</span><span class="o">.</span><span class="n">add_feature</span><span class="p">(</span><span class="n">cfeature</span><span class="o">.</span><span class="n">COASTLINE</span><span class="p">)</span>
+        <span class="n">ax</span><span class="o">.</span><span class="n">add_feature</span><span class="p">(</span><span class="n">cfeature</span><span class="o">.</span><span class="n">BORDERS</span><span class="p">,</span> <span class="n">linestyle</span><span class="o">=</span><span class="s2">&quot;:&quot;</span><span class="p">)</span>
+        <span class="n">ax</span><span class="o">.</span><span class="n">add_feature</span><span class="p">(</span><span class="n">cfeature</span><span class="o">.</span><span class="n">STATES</span><span class="p">,</span> <span class="n">linestyle</span><span class="o">=</span><span class="s2">&quot;--&quot;</span><span class="p">)</span>
+
+        <span class="c1"># Add gridlines</span>
+        <span class="n">ax</span><span class="o">.</span><span class="n">gridlines</span><span class="p">(</span><span class="n">draw_labels</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+
+        <span class="c1"># Set the extent to zoom in to the specified region</span>
+        <span class="k">if</span> <span class="n">extent</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">ax</span><span class="o">.</span><span class="n">set_extent</span><span class="p">(</span><span class="n">extent</span><span class="p">,</span> <span class="n">crs</span><span class="o">=</span><span class="n">ccrs</span><span class="o">.</span><span class="n">PlateCarree</span><span class="p">())</span>
+
+        <span class="c1"># Add color bar with labels</span>
+        <span class="n">cbar</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">colorbar</span><span class="p">(</span>
+            <span class="n">im</span><span class="p">,</span>
+            <span class="n">ax</span><span class="o">=</span><span class="n">ax</span><span class="p">,</span>
+            <span class="n">orientation</span><span class="o">=</span><span class="s2">&quot;vertical&quot;</span><span class="p">,</span>
+            <span class="c1"># pad=0.02,</span>
+            <span class="n">fraction</span><span class="o">=</span><span class="mf">0.05</span><span class="p">,</span>
+            <span class="n">ticks</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">n_clusters</span><span class="p">),</span>
+        <span class="p">)</span>
+        <span class="n">cbar</span><span class="o">.</span><span class="n">ax</span><span class="o">.</span><span class="n">set_yticklabels</span><span class="p">([</span><span class="sa">f</span><span class="s2">&quot;Class </span><span class="si">{</span><span class="n">i</span><span class="o">+</span><span class="mi">1</span><span class="si">}</span><span class="s2">&quot;</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">n_clusters</span><span class="p">)])</span>
+        <span class="n">cbar</span><span class="o">.</span><span class="n">set_label</span><span class="p">(</span><span class="s2">&quot;Water Types&quot;</span><span class="p">,</span> <span class="n">rotation</span><span class="o">=</span><span class="mi">270</span><span class="p">,</span> <span class="n">labelpad</span><span class="o">=</span><span class="mi">20</span><span class="p">)</span>
+
+        <span class="c1"># Add title</span>
+        <span class="n">ax</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="n">title</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">14</span><span class="p">)</span>
+
+        <span class="c1"># Show the plot</span>
+        <span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
+
+    <span class="k">return</span> <span class="n">best_match_full</span><span class="p">,</span> <span class="n">latitudes</span><span class="p">,</span> <span class="n">longitudes</span>
+</code></pre></div>
+        </details>
+    </div>
+
+  </div>
+
+
+
+  <div class="doc doc-object doc-function">
+
+
+
+<h2 id="hypercoast.pace.apply_sam_spectral" class="doc doc-heading">
+<code class="highlight language-python"><span class="n">apply_sam_spectral</span><span class="p">(</span><span class="n">dataset</span><span class="p">,</span> <span class="n">spectral_library</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">filter_condition</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">plot</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">8</span><span class="p">,</span> <span class="mi">6</span><span class="p">),</span> <span class="n">extent</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">colors</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">title</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></code>
+
+
+<a href="#hypercoast.pace.apply_sam_spectral" class="headerlink" title="Permanent link">&para;</a></h2>
+
+    <div class="doc doc-contents ">
+
+      <p>Applies Spectral Angle Mapper (SAM) to the dataset and optionally plots the results.</p>
+
+<p><strong>Parameters:</strong></p>
+<table>
+  <thead>
+    <tr>
+      <th>Name</th>
+      <th>Type</th>
+      <th>Description</th>
+      <th>Default</th>
+    </tr>
+  </thead>
+  <tbody>
+      <tr>
+        <td><code>dataset</code></td>
+        <td><code>Union[xr.Dataset, str]</code></td>
+        <td><p>The dataset containing the PACE data or the file path to the dataset.</p></td>
+        <td><em>required</em></td>
+      </tr>
+      <tr>
+        <td><code>spectral_library</code></td>
+        <td><code>Union[str, list[str]]</code></td>
+        <td><p>The spectral library file path or list of file paths.</p></td>
+        <td><code>None</code></td>
+      </tr>
+      <tr>
+        <td><code>filter_condition</code></td>
+        <td><code>Callable[[xr.DataArray], xr.DataArray]</code></td>
+        <td><p>A function to filter the data. Defaults to None.</p></td>
+        <td><code>None</code></td>
+      </tr>
+      <tr>
+        <td><code>plot</code></td>
+        <td><code>bool</code></td>
+        <td><p>Whether to plot the data. Defaults to True.</p></td>
+        <td><code>True</code></td>
+      </tr>
+      <tr>
+        <td><code>figsize</code></td>
+        <td><code>Tuple[int, int]</code></td>
+        <td><p>Figure size for the plot. Defaults to (8, 6).</p></td>
+        <td><code>(8, 6)</code></td>
+      </tr>
+      <tr>
+        <td><code>extent</code></td>
+        <td><code>List[float]</code></td>
+        <td><p>The extent to zoom in to the specified region. Defaults to None.</p></td>
+        <td><code>None</code></td>
+      </tr>
+      <tr>
+        <td><code>colors</code></td>
+        <td><code>List[str]</code></td>
+        <td><p>Colors for the clusters. Defaults to None.</p></td>
+        <td><code>None</code></td>
+      </tr>
+      <tr>
+        <td><code>title</code></td>
+        <td><code>str</code></td>
+        <td><p>Title for the plot. Defaults to "Spectral Angle Mapper Water Type Classification".</p></td>
+        <td><code>None</code></td>
+      </tr>
+      <tr>
+        <td><code>**kwargs</code></td>
+        <td></td>
+        <td><p>Additional keyword arguments to pass to the <code>plt.subplots</code> function.</p></td>
+        <td><code>{}</code></td>
+      </tr>
+  </tbody>
+</table>
+<p><strong>Returns:</strong></p>
+<table>
+  <thead>
+    <tr>
+      <th>Type</th>
+      <th>Description</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <td><code>Tuple[np.ndarray, np.ndarray, np.ndarray]</code></td>
+      <td><p>The best match classification, latitudes, and longitudes.</p></td>
+    </tr>
+  </tbody>
+</table>
+        <details class="quote">
+          <summary>Source code in <code>hypercoast/pace.py</code></summary>
+          <div class="highlight"><pre><span></span><code><span class="k">def</span> <span class="nf">apply_sam_spectral</span><span class="p">(</span>
+    <span class="n">dataset</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="n">xr</span><span class="o">.</span><span class="n">Dataset</span><span class="p">,</span> <span class="nb">str</span><span class="p">],</span>
+    <span class="n">spectral_library</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="nb">list</span><span class="p">[</span><span class="nb">str</span><span class="p">]]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+    <span class="n">filter_condition</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Callable</span><span class="p">[[</span><span class="n">xr</span><span class="o">.</span><span class="n">DataArray</span><span class="p">],</span> <span class="n">xr</span><span class="o">.</span><span class="n">DataArray</span><span class="p">]]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+    <span class="n">plot</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span>
+    <span class="n">figsize</span><span class="p">:</span> <span class="nb">tuple</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="nb">int</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="mi">8</span><span class="p">,</span> <span class="mi">6</span><span class="p">),</span>
+    <span class="n">extent</span><span class="p">:</span> <span class="nb">list</span><span class="p">[</span><span class="nb">float</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+    <span class="n">colors</span><span class="p">:</span> <span class="nb">list</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+    <span class="n">title</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+    <span class="o">**</span><span class="n">kwargs</span><span class="p">,</span>
+<span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Tuple</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">]:</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Applies Spectral Angle Mapper (SAM) to the dataset and optionally plots the results.</span>
+
+<span class="sd">    Args:</span>
+<span class="sd">        dataset (Union[xr.Dataset, str]): The dataset containing the PACE data or the file path to the dataset.</span>
+<span class="sd">        spectral_library (Union[str, list[str]]): The spectral library file path or list of file paths.</span>
+<span class="sd">        filter_condition (Callable[[xr.DataArray], xr.DataArray], optional): A function to filter the data. Defaults to None.</span>
+<span class="sd">        plot (bool, optional): Whether to plot the data. Defaults to True.</span>
+<span class="sd">        figsize (Tuple[int, int], optional): Figure size for the plot. Defaults to (8, 6).</span>
+<span class="sd">        extent (List[float], optional): The extent to zoom in to the specified region. Defaults to None.</span>
+<span class="sd">        colors (List[str], optional): Colors for the clusters. Defaults to None.</span>
+<span class="sd">        title (str, optional): Title for the plot. Defaults to &quot;Spectral Angle Mapper Water Type Classification&quot;.</span>
+<span class="sd">        **kwargs: Additional keyword arguments to pass to the `plt.subplots` function.</span>
+
+<span class="sd">    Returns:</span>
+<span class="sd">        Tuple[np.ndarray, np.ndarray, np.ndarray]: The best match classification, latitudes, and longitudes.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="kn">import</span> <span class="nn">glob</span>
+    <span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
+    <span class="kn">import</span> <span class="nn">matplotlib.colors</span> <span class="k">as</span> <span class="nn">mcolors</span>
+    <span class="kn">import</span> <span class="nn">cartopy.crs</span> <span class="k">as</span> <span class="nn">ccrs</span>
+    <span class="kn">import</span> <span class="nn">cartopy.feature</span> <span class="k">as</span> <span class="nn">cfeature</span>
+    <span class="kn">from</span> <span class="nn">scipy.interpolate</span> <span class="kn">import</span> <span class="n">interp1d</span>
+
+    <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">dataset</span><span class="p">,</span> <span class="nb">str</span><span class="p">):</span>
+        <span class="n">dataset</span> <span class="o">=</span> <span class="n">read_pace</span><span class="p">(</span><span class="n">dataset</span><span class="p">)</span>
+    <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">dataset</span><span class="p">,</span> <span class="n">xr</span><span class="o">.</span><span class="n">DataArray</span><span class="p">):</span>
+        <span class="n">dataset</span> <span class="o">=</span> <span class="n">dataset</span><span class="o">.</span><span class="n">to_dataset</span><span class="p">()</span>
+    <span class="k">elif</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">dataset</span><span class="p">,</span> <span class="n">xr</span><span class="o">.</span><span class="n">Dataset</span><span class="p">):</span>
+        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;dataset must be an xarray Dataset&quot;</span><span class="p">)</span>
+
+    <span class="n">da</span> <span class="o">=</span> <span class="n">dataset</span><span class="p">[</span><span class="s2">&quot;Rrs&quot;</span><span class="p">]</span>
+    <span class="n">pace_wavelengths</span> <span class="o">=</span> <span class="n">da</span><span class="p">[</span><span class="s2">&quot;wavelength&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">values</span>
+
+    <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">spectral_library</span><span class="p">,</span> <span class="nb">str</span><span class="p">):</span>
+        <span class="n">endmember_paths</span> <span class="o">=</span> <span class="nb">sorted</span><span class="p">(</span><span class="n">glob</span><span class="o">.</span><span class="n">glob</span><span class="p">(</span><span class="n">spectral_library</span><span class="p">))</span>
+    <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">spectral_library</span><span class="p">,</span> <span class="nb">list</span><span class="p">):</span>
+        <span class="n">endmember_paths</span> <span class="o">=</span> <span class="n">spectral_library</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="n">endmember_paths</span> <span class="o">=</span> <span class="kc">None</span>
+
+    <span class="c1"># Function to load and resample a single CSV spectral library file</span>
+    <span class="k">def</span> <span class="nf">load_and_resample_spectral_library</span><span class="p">(</span><span class="n">csv_path</span><span class="p">,</span> <span class="n">target_wavelengths</span><span class="p">):</span>
+        <span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="n">csv_path</span><span class="p">)</span>
+        <span class="n">original_wavelengths</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">iloc</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">values</span>  <span class="c1"># First column is wavelength</span>
+        <span class="n">spectra_values</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">iloc</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">values</span>  <span class="c1"># Second column is spectral values</span>
+
+        <span class="c1"># Interpolation function</span>
+        <span class="n">interp_func</span> <span class="o">=</span> <span class="n">interp1d</span><span class="p">(</span>
+            <span class="n">original_wavelengths</span><span class="p">,</span>
+            <span class="n">spectra_values</span><span class="p">,</span>
+            <span class="n">kind</span><span class="o">=</span><span class="s2">&quot;linear&quot;</span><span class="p">,</span>
+            <span class="n">fill_value</span><span class="o">=</span><span class="s2">&quot;extrapolate&quot;</span><span class="p">,</span>
+        <span class="p">)</span>
+
+        <span class="c1"># Resample to the target (PACE) wavelengths</span>
+        <span class="n">resampled_spectra</span> <span class="o">=</span> <span class="n">interp_func</span><span class="p">(</span><span class="n">target_wavelengths</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="n">resampled_spectra</span>
+
+    <span class="k">if</span> <span class="n">endmember_paths</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="n">endmembers</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span>
+            <span class="p">[</span>
+                <span class="n">load_and_resample_spectral_library</span><span class="p">(</span><span class="n">path</span><span class="p">,</span> <span class="n">pace_wavelengths</span><span class="p">)</span>
+                <span class="k">for</span> <span class="n">path</span> <span class="ow">in</span> <span class="n">endmember_paths</span>
+            <span class="p">]</span>
+        <span class="p">)</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="n">endmembers</span> <span class="o">=</span> <span class="kc">None</span>
+
+    <span class="c1"># Function to calculate spectral angle</span>
+    <span class="k">def</span> <span class="nf">spectral_angle_mapper</span><span class="p">(</span><span class="n">pixel</span><span class="p">,</span> <span class="n">reference</span><span class="p">):</span>
+        <span class="n">norm_pixel</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">norm</span><span class="p">(</span><span class="n">pixel</span><span class="p">)</span>
+        <span class="n">norm_reference</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">norm</span><span class="p">(</span><span class="n">reference</span><span class="p">)</span>
+        <span class="n">cos_theta</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">pixel</span><span class="p">,</span> <span class="n">reference</span><span class="p">)</span> <span class="o">/</span> <span class="p">(</span><span class="n">norm_pixel</span> <span class="o">*</span> <span class="n">norm_reference</span><span class="p">)</span>
+        <span class="n">angle</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arccos</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">clip</span><span class="p">(</span><span class="n">cos_theta</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">))</span>
+        <span class="k">return</span> <span class="n">angle</span>
+
+    <span class="c1"># Reshape data to (n_pixels, n_bands)</span>
+    <span class="n">reshaped_data</span> <span class="o">=</span> <span class="n">da</span><span class="o">.</span><span class="n">values</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="n">da</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span>
+
+    <span class="c1"># Apply SAM for each pixel and each endmember</span>
+    <span class="n">angles</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="n">reshaped_data</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">endmembers</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]))</span>
+
+    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">reshaped_data</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]):</span>
+        <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">endmembers</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]):</span>
+            <span class="n">angles</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">]</span> <span class="o">=</span> <span class="n">spectral_angle_mapper</span><span class="p">(</span><span class="n">reshaped_data</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="p">:],</span> <span class="n">endmembers</span><span class="p">[</span><span class="n">j</span><span class="p">,</span> <span class="p">:])</span>
+
+    <span class="c1"># Find the minimum angle (best match) for each pixel</span>
+    <span class="n">best_match</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">argmin</span><span class="p">(</span><span class="n">angles</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+
+    <span class="c1"># Reshape best_match back to the original spatial dimensions</span>
+    <span class="n">best_match</span> <span class="o">=</span> <span class="n">best_match</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">da</span><span class="o">.</span><span class="n">shape</span><span class="p">[:</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span>
+
+    <span class="k">if</span> <span class="n">filter_condition</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="n">best_match</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">filter_condition</span><span class="p">,</span> <span class="n">best_match</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span><span class="p">)</span>
+
+    <span class="n">latitudes</span> <span class="o">=</span> <span class="n">da</span><span class="o">.</span><span class="n">coords</span><span class="p">[</span><span class="s2">&quot;latitude&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">values</span>
+    <span class="n">longitudes</span> <span class="o">=</span> <span class="n">da</span><span class="o">.</span><span class="n">coords</span><span class="p">[</span><span class="s2">&quot;longitude&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">values</span>
+
+    <span class="c1"># Plot sample spectra from the CSV files and their resampled versions</span>
+    <span class="k">def</span> <span class="nf">plot_sample_spectra</span><span class="p">(</span><span class="n">csv_paths</span><span class="p">,</span> <span class="n">pace_wavelengths</span><span class="p">):</span>
+        <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="n">figsize</span><span class="p">)</span>
+
+        <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">csv_path</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">csv_paths</span><span class="p">):</span>
+            <span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="n">csv_path</span><span class="p">)</span>
+            <span class="n">original_wavelengths</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">iloc</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">values</span>
+            <span class="n">spectra_values</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">iloc</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">values</span>
+            <span class="n">resampled_spectra</span> <span class="o">=</span> <span class="n">load_and_resample_spectral_library</span><span class="p">(</span>
+                <span class="n">csv_path</span><span class="p">,</span> <span class="n">pace_wavelengths</span>
+            <span class="p">)</span>
+
+            <span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span>
+                <span class="n">original_wavelengths</span><span class="p">,</span>
+                <span class="n">spectra_values</span><span class="p">,</span>
+                <span class="n">label</span><span class="o">=</span><span class="sa">f</span><span class="s2">&quot;Original Spectra </span><span class="si">{</span><span class="n">i</span><span class="o">+</span><span class="mi">1</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">,</span>
+                <span class="n">linestyle</span><span class="o">=</span><span class="s2">&quot;--&quot;</span><span class="p">,</span>
+            <span class="p">)</span>
+            <span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span>
+                <span class="n">pace_wavelengths</span><span class="p">,</span> <span class="n">resampled_spectra</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="sa">f</span><span class="s2">&quot;Resampled Spectra </span><span class="si">{</span><span class="n">i</span><span class="o">+</span><span class="mi">1</span><span class="si">}</span><span class="s2">&quot;</span>
+            <span class="p">)</span>
+
+        <span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s2">&quot;Wavelength (nm)&quot;</span><span class="p">)</span>
+        <span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s2">&quot;Spectral Reflectance&quot;</span><span class="p">)</span>
+        <span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s2">&quot;Comparison of Original and Resampled Spectra&quot;</span><span class="p">)</span>
+        <span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>
+        <span class="n">plt</span><span class="o">.</span><span class="n">grid</span><span class="p">(</span><span class="kc">True</span><span class="p">)</span>
+        <span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
+
+    <span class="k">if</span> <span class="n">plot</span><span class="p">:</span>
+
+        <span class="k">if</span> <span class="n">endmember_paths</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+
+            <span class="n">plot_sample_spectra</span><span class="p">(</span><span class="n">endmember_paths</span><span class="p">,</span> <span class="n">pace_wavelengths</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="n">colors</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">colors</span> <span class="o">=</span> <span class="p">[</span><span class="s2">&quot;#377eb8&quot;</span><span class="p">,</span> <span class="s2">&quot;#e41a1c&quot;</span><span class="p">,</span> <span class="s2">&quot;#4daf4a&quot;</span><span class="p">,</span> <span class="s2">&quot;#f781bf&quot;</span><span class="p">,</span> <span class="s2">&quot;#a65628&quot;</span><span class="p">,</span> <span class="s2">&quot;#984ea3&quot;</span><span class="p">]</span>
+
+        <span class="k">if</span> <span class="n">title</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">title</span> <span class="o">=</span> <span class="s2">&quot;Spectral Angle Mapper Water Type Classification&quot;</span>
+        <span class="c1"># Create a custom discrete color map</span>
+        <span class="n">cmap</span> <span class="o">=</span> <span class="n">mcolors</span><span class="o">.</span><span class="n">ListedColormap</span><span class="p">(</span><span class="n">colors</span><span class="p">)</span>
+        <span class="n">bounds</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="o">-</span><span class="mf">0.5</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">endmembers</span><span class="p">),</span> <span class="mi">1</span><span class="p">)</span>
+        <span class="n">norm</span> <span class="o">=</span> <span class="n">mcolors</span><span class="o">.</span><span class="n">BoundaryNorm</span><span class="p">(</span><span class="n">bounds</span><span class="p">,</span> <span class="n">cmap</span><span class="o">.</span><span class="n">N</span><span class="p">)</span>
+
+        <span class="c1"># Create a figure and axis with the correct map projection</span>
+        <span class="n">_</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span>
+            <span class="n">figsize</span><span class="o">=</span><span class="n">figsize</span><span class="p">,</span> <span class="n">subplot_kw</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;projection&quot;</span><span class="p">:</span> <span class="n">ccrs</span><span class="o">.</span><span class="n">PlateCarree</span><span class="p">()},</span> <span class="o">**</span><span class="n">kwargs</span>
+        <span class="p">)</span>
+
+        <span class="c1"># Plot the SAM classification results</span>
+        <span class="n">im</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">pcolormesh</span><span class="p">(</span>
+            <span class="n">longitudes</span><span class="p">,</span>
+            <span class="n">latitudes</span><span class="p">,</span>
+            <span class="n">best_match</span><span class="p">,</span>
+            <span class="n">cmap</span><span class="o">=</span><span class="n">cmap</span><span class="p">,</span>
+            <span class="n">norm</span><span class="o">=</span><span class="n">norm</span><span class="p">,</span>
+            <span class="n">transform</span><span class="o">=</span><span class="n">ccrs</span><span class="o">.</span><span class="n">PlateCarree</span><span class="p">(),</span>
+        <span class="p">)</span>
+
+        <span class="c1"># Add geographic features for context</span>
+        <span class="n">ax</span><span class="o">.</span><span class="n">add_feature</span><span class="p">(</span><span class="n">cfeature</span><span class="o">.</span><span class="n">COASTLINE</span><span class="p">)</span>
+        <span class="n">ax</span><span class="o">.</span><span class="n">add_feature</span><span class="p">(</span><span class="n">cfeature</span><span class="o">.</span><span class="n">BORDERS</span><span class="p">,</span> <span class="n">linestyle</span><span class="o">=</span><span class="s2">&quot;:&quot;</span><span class="p">)</span>
+        <span class="n">ax</span><span class="o">.</span><span class="n">add_feature</span><span class="p">(</span><span class="n">cfeature</span><span class="o">.</span><span class="n">STATES</span><span class="p">,</span> <span class="n">linestyle</span><span class="o">=</span><span class="s2">&quot;--&quot;</span><span class="p">)</span>
+
+        <span class="c1"># Adding axis labels</span>
+        <span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s2">&quot;Longitude&quot;</span><span class="p">)</span>
+        <span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s2">&quot;Latitude&quot;</span><span class="p">)</span>
+
+        <span class="c1"># Adding a title</span>
+        <span class="n">ax</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="n">title</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">14</span><span class="p">)</span>
+
+        <span class="c1"># Adding a color bar with discrete values</span>
+        <span class="n">cbar</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">colorbar</span><span class="p">(</span>
+            <span class="n">im</span><span class="p">,</span>
+            <span class="n">ax</span><span class="o">=</span><span class="n">ax</span><span class="p">,</span>
+            <span class="n">orientation</span><span class="o">=</span><span class="s2">&quot;vertical&quot;</span><span class="p">,</span>
+            <span class="c1"># pad=0.02,</span>
+            <span class="n">fraction</span><span class="o">=</span><span class="mf">0.05</span><span class="p">,</span>
+            <span class="n">ticks</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">endmembers</span><span class="p">)),</span>
+        <span class="p">)</span>
+        <span class="n">cbar</span><span class="o">.</span><span class="n">ax</span><span class="o">.</span><span class="n">set_yticklabels</span><span class="p">([</span><span class="sa">f</span><span class="s2">&quot;Class </span><span class="si">{</span><span class="n">i</span><span class="o">+</span><span class="mi">1</span><span class="si">}</span><span class="s2">&quot;</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">endmembers</span><span class="p">))])</span>
+        <span class="n">cbar</span><span class="o">.</span><span class="n">set_label</span><span class="p">(</span><span class="s2">&quot;Water Types&quot;</span><span class="p">,</span> <span class="n">rotation</span><span class="o">=</span><span class="mi">270</span><span class="p">,</span> <span class="n">labelpad</span><span class="o">=</span><span class="mi">20</span><span class="p">)</span>
+
+        <span class="c1"># Adding gridlines</span>
+        <span class="n">ax</span><span class="o">.</span><span class="n">gridlines</span><span class="p">(</span><span class="n">draw_labels</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">linestyle</span><span class="o">=</span><span class="s2">&quot;--&quot;</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mf">0.5</span><span class="p">)</span>
+
+        <span class="c1"># Set the extent to zoom in to the specified region (adjust as needed)</span>
+        <span class="k">if</span> <span class="n">extent</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">ax</span><span class="o">.</span><span class="n">set_extent</span><span class="p">(</span><span class="n">extent</span><span class="p">,</span> <span class="n">crs</span><span class="o">=</span><span class="n">ccrs</span><span class="o">.</span><span class="n">PlateCarree</span><span class="p">())</span>
+
+        <span class="c1"># Show the plot</span>
+        <span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
+
+    <span class="k">return</span> <span class="n">best_match</span><span class="p">,</span> <span class="n">latitudes</span><span class="p">,</span> <span class="n">longitudes</span>
+</code></pre></div>
+        </details>
+    </div>
+
+  </div>
+
+
+
+  <div class="doc doc-object doc-function">
+
+
+
+<h2 id="hypercoast.pace.cyano_band_ratios" class="doc doc-heading">
+<code class="highlight language-python"><span class="n">cyano_band_ratios</span><span class="p">(</span><span class="n">dataset</span><span class="p">,</span> <span class="n">plot</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">extent</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">12</span><span class="p">,</span> <span class="mi">6</span><span class="p">),</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></code>
+
+
+<a href="#hypercoast.pace.cyano_band_ratios" class="headerlink" title="Permanent link">&para;</a></h2>
+
+    <div class="doc doc-contents ">
+
+      <p>Calculates cyanobacteria band ratios from PACE data.</p>
+
+<p><strong>Parameters:</strong></p>
+<table>
+  <thead>
+    <tr>
+      <th>Name</th>
+      <th>Type</th>
+      <th>Description</th>
+      <th>Default</th>
+    </tr>
+  </thead>
+  <tbody>
+      <tr>
+        <td><code>dataset</code></td>
+        <td><code>xr.Dataset or str</code></td>
+        <td><p>The dataset containing the PACE data or the file path to the dataset.</p></td>
+        <td><em>required</em></td>
+      </tr>
+      <tr>
+        <td><code>plot</code></td>
+        <td><code>bool</code></td>
+        <td><p>Whether to plot the data. Defaults to True.</p></td>
+        <td><code>True</code></td>
+      </tr>
+      <tr>
+        <td><code>extent</code></td>
+        <td><code>list</code></td>
+        <td><p>The extent of the plot. Defaults to None.</p></td>
+        <td><code>None</code></td>
+      </tr>
+      <tr>
+        <td><code>figsize</code></td>
+        <td><code>tuple</code></td>
+        <td><p>Figure size. Defaults to (12, 6).</p></td>
+        <td><code>(12, 6)</code></td>
+      </tr>
+      <tr>
+        <td><code>**kwargs</code></td>
+        <td></td>
+        <td><p>Additional keyword arguments to pass to the <code>plt.subplots</code> function.</p></td>
+        <td><code>{}</code></td>
+      </tr>
+  </tbody>
+</table>
+<p><strong>Returns:</strong></p>
+<table>
+  <thead>
+    <tr>
+      <th>Type</th>
+      <th>Description</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <td><code>xr.DataArray</code></td>
+      <td><p>The cyanobacteria band ratios.</p></td>
+    </tr>
+  </tbody>
+</table>
+        <details class="quote">
+          <summary>Source code in <code>hypercoast/pace.py</code></summary>
+          <div class="highlight"><pre><span></span><code><span class="k">def</span> <span class="nf">cyano_band_ratios</span><span class="p">(</span>
+    <span class="n">dataset</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="n">xr</span><span class="o">.</span><span class="n">Dataset</span><span class="p">,</span> <span class="nb">str</span><span class="p">],</span>
+    <span class="n">plot</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span>
+    <span class="n">extent</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="nb">float</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+    <span class="n">figsize</span><span class="p">:</span> <span class="nb">tuple</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="nb">int</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="mi">12</span><span class="p">,</span> <span class="mi">6</span><span class="p">),</span>
+    <span class="o">**</span><span class="n">kwargs</span><span class="p">,</span>
+<span class="p">)</span> <span class="o">-&gt;</span> <span class="n">xr</span><span class="o">.</span><span class="n">DataArray</span><span class="p">:</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Calculates cyanobacteria band ratios from PACE data.</span>
+
+<span class="sd">    Args:</span>
+<span class="sd">        dataset (xr.Dataset or str): The dataset containing the PACE data or the file path to the dataset.</span>
+<span class="sd">        plot (bool, optional): Whether to plot the data. Defaults to True.</span>
+<span class="sd">        extent (list, optional): The extent of the plot. Defaults to None.</span>
+<span class="sd">        figsize (tuple, optional): Figure size. Defaults to (12, 6).</span>
+<span class="sd">        **kwargs: Additional keyword arguments to pass to the `plt.subplots` function.</span>
+
+<span class="sd">    Returns:</span>
+<span class="sd">        xr.DataArray: The cyanobacteria band ratios.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="kn">import</span> <span class="nn">cartopy.crs</span> <span class="k">as</span> <span class="nn">ccrs</span>
+    <span class="kn">import</span> <span class="nn">cartopy.feature</span> <span class="k">as</span> <span class="nn">cfeature</span>
+
+    <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">dataset</span><span class="p">,</span> <span class="nb">str</span><span class="p">):</span>
+        <span class="n">dataset</span> <span class="o">=</span> <span class="n">read_pace</span><span class="p">(</span><span class="n">dataset</span><span class="p">)</span>
+    <span class="k">elif</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">dataset</span><span class="p">,</span> <span class="n">xr</span><span class="o">.</span><span class="n">Dataset</span><span class="p">):</span>
+        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;dataset must be an xarray Dataset&quot;</span><span class="p">)</span>
+
+    <span class="n">da</span> <span class="o">=</span> <span class="n">dataset</span><span class="p">[</span><span class="s2">&quot;Rrs&quot;</span><span class="p">]</span>
+    <span class="n">data</span> <span class="o">=</span> <span class="p">(</span>
+        <span class="p">(</span><span class="n">da</span><span class="o">.</span><span class="n">sel</span><span class="p">(</span><span class="n">wavelength</span><span class="o">=</span><span class="mi">650</span><span class="p">)</span> <span class="o">&gt;</span> <span class="n">da</span><span class="o">.</span><span class="n">sel</span><span class="p">(</span><span class="n">wavelength</span><span class="o">=</span><span class="mi">620</span><span class="p">))</span>
+        <span class="o">&amp;</span> <span class="p">(</span><span class="n">da</span><span class="o">.</span><span class="n">sel</span><span class="p">(</span><span class="n">wavelength</span><span class="o">=</span><span class="mi">701</span><span class="p">)</span> <span class="o">&gt;</span> <span class="n">da</span><span class="o">.</span><span class="n">sel</span><span class="p">(</span><span class="n">wavelength</span><span class="o">=</span><span class="mi">681</span><span class="p">))</span>
+        <span class="o">&amp;</span> <span class="p">(</span><span class="n">da</span><span class="o">.</span><span class="n">sel</span><span class="p">(</span><span class="n">wavelength</span><span class="o">=</span><span class="mi">701</span><span class="p">)</span> <span class="o">&gt;</span> <span class="n">da</span><span class="o">.</span><span class="n">sel</span><span class="p">(</span><span class="n">wavelength</span><span class="o">=</span><span class="mi">450</span><span class="p">))</span>
+    <span class="p">)</span>
+
+    <span class="k">if</span> <span class="n">plot</span><span class="p">:</span>
+        <span class="c1"># Create a plot</span>
+        <span class="n">_</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span>
+            <span class="n">figsize</span><span class="o">=</span><span class="n">figsize</span><span class="p">,</span> <span class="n">subplot_kw</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;projection&quot;</span><span class="p">:</span> <span class="n">ccrs</span><span class="o">.</span><span class="n">PlateCarree</span><span class="p">()},</span> <span class="o">**</span><span class="n">kwargs</span>
+        <span class="p">)</span>
+
+        <span class="k">if</span> <span class="n">extent</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">ax</span><span class="o">.</span><span class="n">set_extent</span><span class="p">(</span><span class="n">extent</span><span class="p">,</span> <span class="n">crs</span><span class="o">=</span><span class="n">ccrs</span><span class="o">.</span><span class="n">PlateCarree</span><span class="p">())</span>
+
+        <span class="c1"># Plot the data</span>
+        <span class="n">data</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span>
+            <span class="n">ax</span><span class="o">=</span><span class="n">ax</span><span class="p">,</span>
+            <span class="n">transform</span><span class="o">=</span><span class="n">ccrs</span><span class="o">.</span><span class="n">PlateCarree</span><span class="p">(),</span>
+            <span class="n">cmap</span><span class="o">=</span><span class="s2">&quot;coolwarm&quot;</span><span class="p">,</span>
+            <span class="n">cbar_kwargs</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;label&quot;</span><span class="p">:</span> <span class="s2">&quot;Cyano&quot;</span><span class="p">},</span>
+        <span class="p">)</span>
+
+        <span class="c1"># Add coastlines</span>
+        <span class="n">ax</span><span class="o">.</span><span class="n">coastlines</span><span class="p">()</span>
+
+        <span class="c1"># Add state boundaries</span>
+        <span class="n">states_provinces</span> <span class="o">=</span> <span class="n">cfeature</span><span class="o">.</span><span class="n">NaturalEarthFeature</span><span class="p">(</span>
+            <span class="n">category</span><span class="o">=</span><span class="s2">&quot;cultural&quot;</span><span class="p">,</span>
+            <span class="n">name</span><span class="o">=</span><span class="s2">&quot;admin_1_states_provinces_lines&quot;</span><span class="p">,</span>
+            <span class="n">scale</span><span class="o">=</span><span class="s2">&quot;50m&quot;</span><span class="p">,</span>
+            <span class="n">facecolor</span><span class="o">=</span><span class="s2">&quot;none&quot;</span><span class="p">,</span>
+        <span class="p">)</span>
+
+        <span class="n">ax</span><span class="o">.</span><span class="n">add_feature</span><span class="p">(</span><span class="n">states_provinces</span><span class="p">,</span> <span class="n">edgecolor</span><span class="o">=</span><span class="s2">&quot;gray&quot;</span><span class="p">)</span>
+
+        <span class="c1"># Optionally, add gridlines, labels, etc.</span>
+        <span class="n">ax</span><span class="o">.</span><span class="n">gridlines</span><span class="p">(</span><span class="n">draw_labels</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+        <span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
+
+    <span class="k">return</span> <span class="n">data</span>
+</code></pre></div>
+        </details>
+    </div>
+
+  </div>
+
+
+
   <div class="doc doc-object doc-function">
 
 
diff --git a/search/search_index.json b/search/search_index.json
index 446d5e97..57f41813 100644
--- a/search/search_index.json
+++ b/search/search_index.json
@@ -1 +1 @@
-{"config":{"lang":["en"],"separator":"[\\s\\-]+","pipeline":["stopWordFilter"]},"docs":[{"location":"","title":"Welcome to HyperCoast","text":"<p>A Python Package for Visualizing and Analyzing Hyperspectral Data in Coastal Environments</p> <ul> <li>Free software: MIT License</li> <li>Documentation: https://hypercoast.org</li> </ul>"},{"location":"#introduction","title":"Introduction","text":"<p>HyperCoast is a Python package designed to provide an accessible and comprehensive set of tools for visualizing and analyzing hyperspectral data in coastal environments. Hyperspectral data refers to the information collected by sensors that capture light across a wide range of wavelengths, beyond what the human eye can see. This data allows scientists to detect and analyze various materials and conditions on the Earth's surface with great detail. Unlike multispectral data, which captures light in a limited number of broad wavelength bands (typically 3 to 10), hyperspectral data captures light in many narrow, contiguous wavelength bands, often numbering in the hundreds. This provides much more detailed spectral information. Leveraging the capabilities of popular packages like Leafmap and PyVista, HyperCoast streamlines the exploration and interpretation of complex hyperspectral remote sensing data from existing spaceborne and airborne missions. It is also poised to support future hyperspectral missions, such as NASA's SBG and GLIMR. It enables researchers and environmental managers to gain deeper insights into the dynamic processes occurring in aquatic environments.</p> <p>HyperCoast supports the reading and visualization of hyperspectral data from various missions, including AVIRIS, NEON, PACE, EMIT, and DESIS, along with other datasets like ECOSTRESS. Users can interactively explore hyperspectral data, extract spectral signatures, change band combinations and colormaps, visualize data in 3D, and perform interactive slicing and thresholding operations (see Figure 1). Additionally, by leveraging the earthaccess package, HyperCoast provides tools for interactively searching NASA's hyperspectral data. This makes HyperCoast a versatile and powerful tool for working with hyperspectral data globally, with a particular focus on coastal regions.</p> <p> Figure 1. An example of visualizing NASA EMIT hyperspectral data using HyperCoast.</p>"},{"location":"#citations","title":"Citations","text":"<p>If you find HyperCoast useful in your research, please consider citing the following papers to support us. Thank you!</p> <ul> <li>Liu, B., &amp; Wu, Q. (2024). HyperCoast: A Python Package for Visualizing and Analyzing Hyperspectral Data in Coastal Environments. Journal of Open Source Software, 9(100), 7025. https://doi.org/10.21105/joss.07025.</li> </ul>"},{"location":"#features","title":"Features","text":"<ul> <li>Searching for NASA hyperspectral data interactively</li> <li>Performing atmospheric correction using Acolite</li> <li>Interactive visualization and analysis of hyperspectral data, such as AVIRIS, DESIS, EMIT, PACE, NEON AOP</li> <li>Interactive visualization of NASA ECOSTRESS data</li> <li>Interactive visualization of PACE chlorophyll-a data</li> <li>Interactive extraction and visualization of spectral signatures</li> <li>Changing band combinations and colormaps interactively</li> <li>Visualizing hyperspectral data in 3D</li> <li>Visualizing ERA5 temperature data in 3D</li> <li>Interactive slicing and thresholding of hyperspectral data in 3D</li> <li>Saving spectral signatures as CSV files</li> </ul>"},{"location":"#demos","title":"Demos","text":"<ul> <li>Visualizing hyperspectral data in 3D (notebook)</li> </ul> <ul> <li>Interactive slicing of hyperspectral data in 3D (notebook)</li> </ul> <ul> <li>Interactive thresholding of hyperspectral data in 3D (notebook)</li> </ul> <ul> <li>Visualizing ERA5 temperature data in 3D (notebook)</li> </ul> <ul> <li>Changing band combinations and colormaps interactively (notebook)</li> </ul> <ul> <li>Visualizing NASA AVIRIS hyperspectral data interactively (notebook)</li> </ul> <ul> <li>Visualizing DESIS hyperspectral data interactively (notebook)</li> </ul> <ul> <li>Visualizing NASA EMIT hyperspectral data interactively (notebook)</li> </ul> <ul> <li>Visualizing NASA PACE hyperspectral data interactively (notebook)</li> </ul> <ul> <li>Visualizing NEON AOP hyperspectral data interactively (notebook)</li> </ul> <ul> <li>Interactive visualization of PACE chlorophyll-a data (notebook)</li> </ul>"},{"location":"#acknowledgement","title":"Acknowledgement","text":"<p>The HyperCoast project draws inspiration from the nasa/EMIT-Data-Resources repository. Credits to the original authors. We also acknowledge the NASA EMIT program support through grant no. 80NSSC24K0865.</p>"},{"location":"#license","title":"License","text":"<p>HyperCoast is released under the MIT License. However, some of the modules in HyperCoast adapt code from other open-source projects, which may have different licenses. Please refer to the license notice in each module for more information. Credits to the original authors.</p> <ul> <li>emit.py: Part of the code is adapted from the nasa/EMIT-Data-Resources repository, which is released under the Apache License 2.0.</li> <li>aviris.py: Part of the code is adapted from the jjmcnelis/aviris-ng-notebooks, which is released under the MIT License.</li> </ul>"},{"location":"aviris/","title":"aviris module","text":"<p>This module contains functions to read and process NASA AVIRIS hyperspectral data. More info about the data can be found at https://aviris.jpl.nasa.gov. A portion of the source code is adapted from the jjmcnelis/aviris-ng-notebooks repository available at https://bit.ly/4bRCgqs. It is licensed under the MIT License. Credit goes to the original author Jack McNelis.</p> <p>SPDX-FileCopyrightText = [     \"2024 Jack McNelis jjmcne@gmail.com\", ] SPDX-License-Identifier = \"MIT\"</p>"},{"location":"aviris/#hypercoast.aviris.aviris_to_image","title":"<code>aviris_to_image(dataset, wavelengths=None, method='nearest', output=None, **kwargs)</code>","text":"<p>Converts an AVIRIS dataset to an image.</p> <p>Parameters:</p> Name Type Description Default <code>dataset</code> <code>Union[xr.Dataset, str]</code> <p>The dataset containing the AVIRIS data or the file path to the dataset.</p> required <code>wavelengths</code> <code>np.ndarray</code> <p>The specific wavelengths to select. If None, all wavelengths are selected. Defaults to None.</p> <code>None</code> <code>method</code> <code>str</code> <p>The method to use for data interpolation. Defaults to \"nearest\".</p> <code>'nearest'</code> <code>output</code> <code>str</code> <p>The file path where the image will be saved. If None, the image will be returned as a PIL Image object. Defaults to None.</p> <code>None</code> <code>**kwargs</code> <code>Any</code> <p>Additional keyword arguments to be passed to <code>leafmap.array_to_image</code>.</p> <code>{}</code> <p>Returns:</p> Type Description <code>Optional[rasterio.Dataset]</code> <p>The image converted from the dataset. If     <code>output</code> is provided, the image will be saved to the specified file     and the function will return None.</p> Source code in <code>hypercoast/aviris.py</code> <pre><code>def aviris_to_image(\n    dataset: Union[xr.Dataset, str],\n    wavelengths: Optional[np.ndarray] = None,\n    method: str = \"nearest\",\n    output: Optional[str] = None,\n    **kwargs: Any,\n):\n    \"\"\"\n    Converts an AVIRIS dataset to an image.\n\n    Args:\n        dataset (Union[xr.Dataset, str]): The dataset containing the AVIRIS data\n            or the file path to the dataset.\n        wavelengths (np.ndarray, optional): The specific wavelengths to select. If None, all\n            wavelengths are selected. Defaults to None.\n        method (str, optional): The method to use for data interpolation.\n            Defaults to \"nearest\".\n        output (str, optional): The file path where the image will be saved. If\n            None, the image will be returned as a PIL Image object. Defaults to None.\n        **kwargs (Any): Additional keyword arguments to be passed to\n            `leafmap.array_to_image`.\n\n    Returns:\n        Optional[rasterio.Dataset]: The image converted from the dataset. If\n            `output` is provided, the image will be saved to the specified file\n            and the function will return None.\n    \"\"\"\n    from leafmap import array_to_image\n\n    if isinstance(dataset, str):\n        dataset = read_aviris(dataset, method=method)\n\n    if wavelengths is not None:\n        dataset = dataset.sel(wavelength=wavelengths, method=method)\n\n    return array_to_image(\n        dataset[\"reflectance\"],\n        output=output,\n        transpose=False,\n        dtype=np.float32,\n        **kwargs,\n    )\n</code></pre>"},{"location":"aviris/#hypercoast.aviris.extract_aviris","title":"<code>extract_aviris(dataset, lat, lon, offset=2.0)</code>","text":"<p>Extracts AVIRIS data from a given xarray Dataset.</p> <p>Parameters:</p> Name Type Description Default <code>dataset</code> <code>xarray.Dataset</code> <p>The dataset containing the AVIRIS data.</p> required <code>lat</code> <code>float</code> <p>The latitude of the point to extract.</p> required <code>lon</code> <code>float</code> <p>The longitude of the point to extract.</p> required <code>offset</code> <code>float</code> <p>The offset from the point to extract. Defaults to 2.0.</p> <code>2.0</code> <p>Returns:</p> Type Description <code>xarray.DataArray</code> <p>The extracted data.</p> Source code in <code>hypercoast/aviris.py</code> <pre><code>def extract_aviris(\n    dataset: xr.Dataset, lat: float, lon: float, offset: float = 2.0\n) -&gt; xr.DataArray:\n    \"\"\"\n    Extracts AVIRIS data from a given xarray Dataset.\n\n    Args:\n        dataset (xarray.Dataset): The dataset containing the AVIRIS data.\n        lat (float): The latitude of the point to extract.\n        lon (float): The longitude of the point to extract.\n        offset (float, optional): The offset from the point to extract. Defaults to 2.0.\n\n    Returns:\n        xarray.DataArray: The extracted data.\n    \"\"\"\n\n    crs = dataset.attrs[\"crs\"]\n\n    x, y = convert_coords([[lat, lon]], \"epsg:4326\", crs)[0]\n\n    da = dataset[\"reflectance\"]\n\n    x_con = (da[\"xc\"] &gt; x - offset) &amp; (da[\"xc\"] &lt; x + offset)\n    y_con = (da[\"yc\"] &gt; y - offset) &amp; (da[\"yc\"] &lt; y + offset)\n\n    try:\n        data = da.where(x_con &amp; y_con, drop=True)\n        data = data.mean(dim=[\"x\", \"y\"])\n    except ValueError:\n        data = np.nan * np.ones(da.sizes[\"wavelength\"])\n\n    da = xr.DataArray(\n        data, dims=[\"wavelength\"], coords={\"wavelength\": dataset.coords[\"wavelength\"]}\n    )\n\n    return da\n</code></pre>"},{"location":"aviris/#hypercoast.aviris.read_aviris","title":"<code>read_aviris(filepath, wavelengths=None, method='nearest', **kwargs)</code>","text":"<p>Reads NASA AVIRIS hyperspectral data and returns an xarray dataset.</p> <p>Parameters:</p> Name Type Description Default <code>filepath</code> <code>str</code> <p>The path to the AVIRIS data.</p> required <code>wavelengths</code> <code>List[float]</code> <p>The wavelengths to select. If None, all wavelengths are selected. Defaults to None.</p> <code>None</code> <code>method</code> <code>str</code> <p>The method to use for selection. Defaults to \"nearest\".</p> <code>'nearest'</code> <code>**kwargs</code> <code>Any</code> <p>Additional arguments to pass to the selection method.</p> <code>{}</code> <p>Returns:</p> Type Description <code>xr.Dataset</code> <p>The dataset containing the reflectance data.</p> Source code in <code>hypercoast/aviris.py</code> <pre><code>def read_aviris(\n    filepath: str,\n    wavelengths: Optional[List[float]] = None,\n    method: str = \"nearest\",\n    **kwargs: Any,\n) -&gt; xr.Dataset:\n    \"\"\"\n    Reads NASA AVIRIS hyperspectral data and returns an xarray dataset.\n\n    Args:\n        filepath (str): The path to the AVIRIS data.\n        wavelengths (List[float], optional): The wavelengths to select. If None,\n            all wavelengths are selected. Defaults to None.\n        method (str, optional): The method to use for selection. Defaults to\n            \"nearest\".\n        **kwargs (Any): Additional arguments to pass to the selection method.\n\n    Returns:\n        xr.Dataset: The dataset containing the reflectance data.\n    \"\"\"\n\n    if filepath.endswith(\".hdr\"):\n        filepath = filepath.replace(\".hdr\", \"\")\n\n    ds = xr.open_dataset(filepath, engine=\"rasterio\")\n\n    wavelength = ds[\"wavelength\"].values.tolist()\n    wavelength = [round(num, 2) for num in wavelength]\n\n    cols = ds.x.size\n    rows = ds.y.size\n\n    rio_transform = ds.rio.transform()\n    geo_transform = list(rio_transform)[:6]\n\n    # get the raster geotransform as its component parts\n    xres, _, xmin, _, yres, ymax = geo_transform\n\n    # generate coordinate arrays\n    xarr = np.array([xmin + i * xres for i in range(0, cols)])\n    yarr = np.array([ymax + i * yres for i in range(0, rows)])\n\n    ds[\"y\"] = xr.DataArray(\n        data=yarr,\n        dims=(\"y\"),\n        name=\"y\",\n        attrs=dict(\n            units=\"m\",\n            standard_name=\"projection_y_coordinate\",\n            long_name=\"y coordinate of projection\",\n        ),\n    )\n\n    ds[\"x\"] = xr.DataArray(\n        data=xarr,\n        dims=(\"x\"),\n        name=\"x\",\n        attrs=dict(\n            units=\"m\",\n            standard_name=\"projection_x_coordinate\",\n            long_name=\"x coordinate of projection\",\n        ),\n    )\n\n    global_atts = ds.attrs\n    global_atts[\"Conventions\"] = \"CF-1.6\"\n    ds.attrs = dict(\n        units=\"unitless\",\n        _FillValue=-9999,\n        grid_mapping=\"crs\",\n        standard_name=\"reflectance\",\n        long_name=\"atmospherically corrected surface reflectance\",\n    )\n    ds.attrs.update(global_atts)\n\n    ds = ds.transpose(\"y\", \"x\", \"band\")\n    ds = ds.drop_vars([\"wavelength\"])\n    ds = ds.rename({\"band\": \"wavelength\", \"band_data\": \"reflectance\"})\n    ds.coords[\"wavelength\"] = wavelength\n    ds.attrs[\"crs\"] = ds.rio.crs.to_string()\n    ds.rio.write_transform(rio_transform)\n\n    if wavelengths is not None:\n        ds = ds.sel(wavelength=wavelengths, method=method, **kwargs)\n    return ds\n</code></pre>"},{"location":"changelog/","title":"Changelog","text":""},{"location":"changelog/#v076-aug-07-2024","title":"v0.7.6 - Aug 07, 2024","text":"<p>What's Changed</p> <ul> <li>[pre-commit.ci] pre-commit autoupdate by @pre-commit-ci in #107</li> <li>Clean up file path in notebook by @giswqs in #108</li> <li>Fix read_pace bug for V2 by @giswqs in #109</li> </ul> <p>Full Changelog: v0.7.5...v0.7.6</p>"},{"location":"changelog/#v075-aug-05-2024","title":"v0.7.5 - Aug 05, 2024","text":"<p>What's Changed</p> <ul> <li>ci(Mergify): configuration update by @slowy07 in #99</li> <li>Add support for PCA by @giswqs in #100</li> <li>Update mergify by @giswqs in #101</li> <li>Add support for displaying field data by @giswqs in #106</li> </ul> <p>Full Changelog: v0.7.4...v0.7.5</p>"},{"location":"changelog/#v074-aug-01-2024","title":"v0.7.4 - Aug 01, 2024","text":"<p>What's Changed</p> <ul> <li>Remove AVIRIS image bounds by @giswqs in #96</li> <li>Fix read_pace bug for V2 by @giswqs in #97</li> </ul> <p>Full Changelog: v0.7.3...v0.7.4</p>"},{"location":"changelog/#v073-jul-30-2024","title":"v0.7.3 - Jul 30, 2024","text":"<p>What's Changed</p> <ul> <li>Add reuse license management framework by @giswqs in #83</li> <li>Add reuse dep and file header by @giswqs in #84</li> <li>Pin reuse version by @giswqs in #85</li> <li>Decapitalize Pringle et al title by @giswqs in #89</li> <li>Remove sentence and citations by @giswqs in #90</li> <li>Explain hyperspectral and other jargon by @giswqs in #91</li> <li>Add xlim parameter for the spectral widget by @giswqs in #93</li> </ul> <p>Full Changelog: v0.7.2...v0.7.3</p>"},{"location":"changelog/#v072-jul-23-2024","title":"v0.7.2 - Jul 23, 2024","text":"<p>What's Changed</p> <ul> <li>Update paper by @bingqing-liu in #74</li> <li>Update pyvista dependencies by @giswqs in #75</li> <li>chore: improving testing and typehinting by @slowy07 in #77</li> <li>Add support for generic dataset by @giswqs in #78</li> <li>Clarify license info by @giswqs in #79</li> <li>Add multispectral notebook example by @giswqs in #80</li> <li>Add support for acolite by @giswqs in #81</li> </ul> <p>New Contributors</p> <ul> <li>@slowy07 made their first contribution in #77</li> </ul> <p>Full Changelog: v0.7.0...v0.7.2</p>"},{"location":"changelog/#v071-jul-20-2024","title":"v0.7.1 - Jul 20, 2024","text":"<p>What's Changed</p> <ul> <li>Update paper by @bingqing-liu in #74</li> <li>Update pyvista dependencies by @giswqs in #75</li> <li>chore: improving testing and typehinting by @slowy07 in #77</li> <li>Add support for generic dataset by @giswqs in #78</li> <li>Clarify license info by @giswqs in #79</li> </ul> <p>New Contributors</p> <ul> <li>@slowy07 made their first contribution in #77</li> </ul> <p>Full Changelog: v0.7.0...v0.7.1</p>"},{"location":"changelog/#v070-jul-08-2024","title":"v0.7.0 - Jul 08, 2024","text":"<p>What's Changed</p> <ul> <li>Improve the pace chla function by @giswqs in #70</li> <li>Add JOSS paper draft by @giswqs in #71</li> <li>Add unittests by @giswqs in #72</li> <li>Add EMIT workshop notebook by @giswqs in #73</li> </ul> <p>Full Changelog: v0.6.3...v0.7.0</p>"},{"location":"changelog/#v063-jul-01-2024","title":"v0.6.3 - Jul 01, 2024","text":"<p>What's Changed</p> <ul> <li>Update pace_chla_to_image_function by @giswqs in #69</li> </ul> <p>Full Changelog: v0.6.2...v0.6.3</p>"},{"location":"changelog/#v062-jun-30-2024","title":"v0.6.2 - Jun 30, 2024","text":"<p>What's Changed</p> <ul> <li>Add search_datasets function and improve notebook example by @giswqs in #66</li> <li>Add PACE OCI Level-1 notebook example by @giswqs in #67</li> <li>Add PACE pixel location function by @giswqs in #68</li> </ul> <p>Full Changelog: v0.6.1...v0.6.2</p>"},{"location":"changelog/#v061-jun-30-2024","title":"v0.6.1 - Jun 30, 2024","text":"<p>What's Changed</p> <ul> <li>Add more demos to docs by @giswqs in #63</li> <li>Fix typos by @giswqs in #64</li> <li>Add support for reading PACE OCI L2 data by @giswqs in #65</li> </ul> <p>Full Changelog: v0.6.0...v0.6.1</p>"},{"location":"changelog/#v060-jun-26-2024","title":"v0.6.0 - Jun 26, 2024","text":"<p>What's Changed</p> <ul> <li>Add support for PACE Chlorophyll data by @giswqs in #62</li> </ul> <p>Full Changelog: v0.5.5...v0.6.0</p>"},{"location":"changelog/#v055-jun-25-2024","title":"v0.5.5 - Jun 25, 2024","text":"<p>What's Changed</p> <ul> <li>Add image slicing demos to docs by @giswqs in #56</li> <li>Add dependabot by @giswqs in #58</li> <li>Bump nwtgck/actions-netlify from 2.0 to 3.0 by @dependabot in #59</li> <li>Bump conda-incubator/setup-miniconda from 2 to 3 by @dependabot in #60</li> <li>Add ERA5 temperature data notebook by @giswqs in #61</li> </ul> <p>New Contributors</p> <ul> <li>@dependabot made their first contribution in #59</li> </ul> <p>Full Changelog: v0.5.4...v0.5.5</p>"},{"location":"changelog/#v054-jun-14-2024","title":"v0.5.4 - Jun 14, 2024","text":"<p>What's Changed</p> <ul> <li>Add support for interactive slicing by @giswqs in #54</li> <li>Add image slicing notebook example by @giswqs in #55</li> </ul> <p>Full Changelog: v0.5.3...v0.5.4</p>"},{"location":"changelog/#v053-jun-12-2024","title":"v0.5.3 - Jun 12, 2024","text":"<p>What's Changed</p> <ul> <li>Add EMIT image cube example by @giswqs in #51</li> <li>Refactor read_neon() function to support generalized NEON data reading by @gponce-ars in #52</li> </ul> <p>New Contributors</p> <ul> <li>@gponce-ars made their first contribution in #52</li> </ul> <p>Full Changelog: v0.5.2...v0.5.3</p>"},{"location":"changelog/#v052-jun-11-2024","title":"v0.5.2 - Jun 11, 2024","text":"<p>What's Changed</p> <ul> <li>Fix RGB image reshape bug for image cube by @giswqs in #49</li> </ul> <p>Full Changelog: v0.5.1...v0.5.2</p>"},{"location":"changelog/#v051-jun-11-2024","title":"v0.5.1 - Jun 11, 2024","text":"<p>What's Changed</p> <ul> <li>Add support for 3D visualization by @giswqs in #47</li> <li>Add image cube demo to docs by @giswqs in #48</li> </ul> <p>Full Changelog: v0.5.0...v0.5.1</p>"},{"location":"changelog/#v050-jun-10-2024","title":"v0.5.0 - Jun 10, 2024","text":"<p>What's Changed</p> <ul> <li>Add support for NEON AOP data by @giswqs in #43</li> <li>Add support for AVIRIS data by @giswqs in #44</li> <li>Add support for changing band combinations and colormaps interactively by @giswqs in #46</li> </ul> <p>Full Changelog: v0.4.0...v0.5.0</p>"},{"location":"changelog/#v040-jun-03-2024","title":"v0.4.0 - Jun 03, 2024","text":"<p>What's Changed</p> <ul> <li>Add support for searching and downloading ECOSTRESS data by @giswqs in #37</li> <li>Add support for visualizing DESIS hyperspectral data by @giswqs in #38</li> </ul> <p>Full Changelog: v0.3.3...v0.4.0</p>"},{"location":"changelog/#v033-may-19-2024","title":"v0.3.3 - May 19, 2024","text":"<p>What's Changed</p> <ul> <li>Add functions for searching PACE and EMIT data by @giswqs in #34</li> <li>Add stacking for spectral signatures by @giswqs in #35</li> </ul> <p>Full Changelog: v0.3.2...v0.3.3</p>"},{"location":"changelog/#v032-may-10-2024","title":"v0.3.2 - May 10, 2024","text":"<p>What's Changed</p> <ul> <li>Fix wavelength bug by @giswqs in #27</li> <li>Pin xarray version by @giswqs in #28</li> <li>Fix xr merge error by @giswqs in #29</li> </ul> <p>Full Changelog: v0.3.1...v0.3.2</p>"},{"location":"changelog/#v031-may-09-2024","title":"v0.3.1 - May 09, 2024","text":"<p>Full Changelog: v0.3.0...v0.3.1</p>"},{"location":"changelog/#v030-may-08-2024","title":"v0.3.0 - May 08, 2024","text":"<p>What's Changed</p> <ul> <li>Add filter_pace function by @giswqs in #21</li> <li>Add grid pace function by @giswqs in #22</li> <li>Add fetch depth for docs by @giswqs in #23</li> <li>Add support for multi-band visualization by @giswqs in #24</li> <li>Add extract_pace function by @giswqs in #25</li> <li>Add spectral signature viz for PACE data by @giswqs in #26</li> </ul> <p>Full Changelog: v0.2.0...v0.3.0</p>"},{"location":"changelog/#v020-may-05-2024","title":"v0.2.0 - May 05, 2024","text":"<p>What's Changed</p> <ul> <li>Add support for reading and visualizing PACE data by @giswqs in #20</li> </ul> <p>Full Changelog: v0.1.3...v0.2.0</p>"},{"location":"changelog/#v013-apr-30-2024","title":"v0.1.3 - Apr 30, 2024","text":"<p>What's Changed</p> <ul> <li>Move hvplot import into functions by @giswqs in #18</li> <li>[pre-commit.ci] pre-commit autoupdate by @pre-commit-ci in #17</li> <li>Add pace module by @giswqs in #19</li> </ul> <p>Full Changelog: v0.1.2...v0.1.3</p>"},{"location":"changelog/#v012-apr-25-2024","title":"v0.1.2 - Apr 25, 2024","text":"<p>What's Changed</p> <ul> <li>Add demo gif by @bingqing-liu in #15</li> <li>Fix typos and add method bug by @giswqs in #16</li> </ul> <p>Full Changelog: v0.1.1...v0.1.2</p>"},{"location":"changelog/#v011-apr-22-2024","title":"v0.1.1 - Apr 22, 2024","text":"<p>What's Changed</p> <ul> <li>Add an EMIT notebook by @giswqs in #13</li> <li>Fix dependency issue by @giswqs in #14</li> </ul> <p>Full Changelog: v0.1.0...v0.1.1</p>"},{"location":"changelog/#v010-apr-21-2024","title":"v0.1.0 - Apr 21, 2024","text":"<p>What's Changed</p> <ul> <li>Improve support for visualizing EMIT data by @giswqs in #10</li> <li>Add ui module by @giswqs in #11</li> <li>Add support for displaying spectral signature interactively by @giswqs in #12</li> </ul> <p>Full Changelog: v0.0.3...v0.1.0</p>"},{"location":"changelog/#v003-apr-20-2024","title":"v0.0.3 - Apr 20, 2024","text":"<p>What's Changed</p> <ul> <li>Fix typos by @giswqs in #7</li> <li>Add conda-forge installation instructions by @giswqs in #8</li> <li>Add support for visualizing EMIT data by @giswqs in #9</li> </ul> <p>Full Changelog: v0.0.2...v0.0.3</p>"},{"location":"changelog/#v002-apr-18-2024","title":"v0.0.2 - Apr 18, 2024","text":"<p>What's Changed</p> <ul> <li>[pre-commit.ci] pre-commit autoupdate by @pre-commit-ci in #4</li> <li>Update readme by @Bingqing9027 in #5</li> <li>Add Map class by @giswqs in #6</li> </ul> <p>New Contributors</p> <ul> <li>@pre-commit-ci made their first contribution in #4</li> <li>@Bingqing9027 made their first contribution in #5</li> <li>@giswqs made their first contribution in #6</li> </ul> <p>Full Changelog: v0.0.1...v0.0.2</p>"},{"location":"changelog/#v001-apr-08-2024","title":"v0.0.1 - Apr 08, 2024","text":"<p>What's Changed Full Changelog: v0.0.1</p>"},{"location":"changelog_update/","title":"Changelog update","text":"In\u00a0[1]: Copied! <pre>import re\n</pre> import re In\u00a0[2]: Copied! <pre># Copy the release notes from the GitHub release page\nmarkdown_text = \"\"\"\n## What's Changed\n* Update pace_chla_to_image_function by @giswqs in https://github.com/opengeos/HyperCoast/pull/69\n\n\n**Full Changelog**: https://github.com/opengeos/HyperCoast/compare/v0.6.2...v0.6.3\n\"\"\"\n</pre> # Copy the release notes from the GitHub release page markdown_text = \"\"\" ## What's Changed * Update pace_chla_to_image_function by @giswqs in https://github.com/opengeos/HyperCoast/pull/69   **Full Changelog**: https://github.com/opengeos/HyperCoast/compare/v0.6.2...v0.6.3 \"\"\" In\u00a0[3]: Copied! <pre># Regular expression pattern to match the Markdown hyperlinks\npattern = r\"https://github\\.com/opengeos/HyperCoast/pull/(\\d+)\"\n</pre> # Regular expression pattern to match the Markdown hyperlinks pattern = r\"https://github\\.com/opengeos/HyperCoast/pull/(\\d+)\" In\u00a0[4]: Copied! <pre># Function to replace matched URLs with the desired format\ndef replace_url(match):\n    pr_number = match.group(1)\n    return f\"[#{pr_number}](https://github.com/opengeos/HyperCoast/pull/{pr_number})\"\n</pre> # Function to replace matched URLs with the desired format def replace_url(match):     pr_number = match.group(1)     return f\"[#{pr_number}](https://github.com/opengeos/HyperCoast/pull/{pr_number})\" In\u00a0[5]: Copied! <pre># Use re.sub to replace URLs with the desired format\nformatted_text = re.sub(pattern, replace_url, markdown_text)\n</pre> # Use re.sub to replace URLs with the desired format formatted_text = re.sub(pattern, replace_url, markdown_text) In\u00a0[6]: Copied! <pre>for line in formatted_text.splitlines():\n    if \"Full Changelog\" in line:\n        prefix = line.split(\": \")[0]\n        link = line.split(\": \")[1]\n        version = line.split(\"/\")[-1]\n        formatted_text = (\n            formatted_text.replace(line, f\"{prefix}: [{version}]({link})\")\n            .replace(\"## What's Changed\", \"**What's Changed**\")\n            .replace(\"## New Contributors\", \"**New Contributors**\")\n        )\n</pre> for line in formatted_text.splitlines():     if \"Full Changelog\" in line:         prefix = line.split(\": \")[0]         link = line.split(\": \")[1]         version = line.split(\"/\")[-1]         formatted_text = (             formatted_text.replace(line, f\"{prefix}: [{version}]({link})\")             .replace(\"## What's Changed\", \"**What's Changed**\")             .replace(\"## New Contributors\", \"**New Contributors**\")         ) In\u00a0[7]: Copied! <pre>with open(\"docs/changelog_update.md\", \"w\") as f:\n    f.write(formatted_text)\n</pre> with open(\"docs/changelog_update.md\", \"w\") as f:     f.write(formatted_text) In\u00a0[8]: Copied! <pre># Print the formatted text\nprint(formatted_text)\n</pre> # Print the formatted text print(formatted_text) <pre>\n**What's Changed**\n* Update pace_chla_to_image_function by @giswqs in [#69](https://github.com/opengeos/HyperCoast/pull/69)\n\n\n**Full Changelog**: [v0.6.2...v0.6.3](https://github.com/opengeos/HyperCoast/compare/v0.6.2...v0.6.3)\n\n</pre> <p>Copy the formatted text and paste it to the CHANGELOG.md file</p>"},{"location":"cla/","title":"Fiduciary License Agreement 2.0","text":"<p>based on the</p>"},{"location":"cla/#individual-contributor-exclusive-license-agreement","title":"Individual Contributor Exclusive License Agreement","text":""},{"location":"cla/#including-the-traditional-patent-license-option","title":"(including the Traditional Patent License OPTION)","text":"<p>Thank you for your interest in contributing to Open Geospatial Solutions's HyperCoast (\"We\" or \"Us\").</p> <p>The purpose of this contributor agreement (\"Agreement\") is to clarify and document the rights granted by contributors to Us. To make this document effective, please follow the instructions at https://github.com/opengeos/HyperCoast.</p>"},{"location":"cla/#0-preamble","title":"0. Preamble","text":"<p>Software is deeply embedded in all aspects of our lives and it is important that it empower, rather than restrict us. Free Software gives everybody the rights to use, understand, adapt and share software. These rights help support other fundamental freedoms like freedom of speech, press and privacy.</p> <p>Development of Free Software can follow many patterns. In some cases whole development is handled by a sole programmer or a small group of people. But usually, the creation and maintenance of software is a complex process that requires the contribution of many individuals. This also affects who owns the rights to the software. In the latter case, rights in software are owned jointly by a great number of individuals.</p> <p>To tackle this issue some projects require a full copyright assignment to be signed by all contributors. The problem with such assignments is that they often lack checks and balances that would protect the contributors from potential abuse of power from the new copyright holder.</p> <p>FSFE\u2019s Fiduciary License Agreement (FLA) was created by the Free Software Foundation Europe e.V. with just that in mind \u2013 to concentrate all deciding power within one entity and prevent fragmentation of rights on one hand, while on the other preventing that single entity from abusing its power. The main aim is to ensure that the software covered under the FLA will forever remain Free Software.</p> <p>This process only serves for the transfer of economic rights. So-called moral rights (e.g. authors right to be identified as author) remain with the original author(s) and are inalienable.</p>"},{"location":"cla/#how-to-use-this-fla","title":"How to use this FLA","text":"<p>If You are an employee and have created the Contribution as part of your employment, You need to have Your employer approve this Agreement or sign the Entity version of this document. If You do not own the Copyright in the entire work of authorship, any other author of the Contribution should also sign this \u2013 in any event, please contact Us at opengeos@outlook.com</p>"},{"location":"cla/#1-definitions","title":"1. Definitions","text":"<p>\"You\" means the individual Copyright owner who Submits a Contribution to Us.</p> <p>\"Legal Entity\" means an entity that is not a natural person.</p> <p>\"Affiliate\" means any other Legal Entity that controls, is controlled by, or under common control with that Legal Entity. For the purposes of this definition, \"control\" means (i) the power, direct or indirect, to cause the direction or management of such Legal Entity, whether by contract or otherwise, (ii) ownership of fifty percent (50%) or more of the outstanding shares or securities that vote to elect the management or other persons who direct such Legal Entity or (iii) beneficial ownership of such entity.</p> <p>\"Contribution\" means any original work of authorship, including any original modifications or additions to an existing work of authorship, Submitted by You to Us, in which You own the Copyright.</p> <p>\"Copyright\" means all rights protecting works of authorship, including copyright, moral and neighboring rights, as appropriate, for the full term of their existence.</p> <p>\"Material\" means the software or documentation made available by Us to third parties. When this Agreement covers more than one software project, the Material means the software or documentation to which the Contribution was Submitted. After You Submit the Contribution, it may be included in the Material.</p> <p>\"Submit\" means any act by which a Contribution is transferred to Us by You by means of tangible or intangible media, including but not limited to electronic mailing lists, source code control systems, and issue tracking systems that are managed by, or on behalf of, Us, but excluding any transfer that is conspicuously marked or otherwise designated in writing by You as \"Not a Contribution.\"</p> <p>\"Documentation\" means any non-software portion of a Contribution.</p>"},{"location":"cla/#2-license-grant","title":"2. License grant","text":""},{"location":"cla/#21-copyright-license-to-us","title":"2.1 Copyright license to Us","text":"<p>Subject to the terms and conditions of this Agreement, You hereby grant to Us a worldwide, royalty-free, exclusive, perpetual and irrevocable (except as stated in Section 8.2) license, with the right to transfer an unlimited number of non-exclusive licenses or to grant sublicenses to third parties, under the Copyright covering the Contribution to use the Contribution by all means, including, but not limited to:</p> <ul> <li>publish the Contribution,</li> <li>modify the Contribution,</li> <li>prepare derivative works based upon or containing the Contribution and/or to combine the Contribution with other Materials,</li> <li>reproduce the Contribution in original or modified form,</li> <li>distribute, to make the Contribution available to the public, display and publicly perform the Contribution in original or modified form.</li> </ul>"},{"location":"cla/#22-moral-rights","title":"2.2 Moral rights","text":"<p>Moral Rights remain unaffected to the extent they are recognized and not waivable by applicable law. Notwithstanding, You may add your name to the attribution mechanism customary used in the Materials you Contribute to, such as the header of the source code files of Your Contribution, and We will respect this attribution when using Your Contribution.</p>"},{"location":"cla/#23-copyright-license-back-to-you","title":"2.3 Copyright license back to You","text":"<p>Upon such grant of rights to Us, We immediately grant to You a worldwide, royalty-free, non-exclusive, perpetual and irrevocable license, with the right to transfer an unlimited number of non-exclusive licenses or to grant sublicenses to third parties, under the Copyright covering the Contribution to use the Contribution by all means, including, but not limited to:</p> <ul> <li>publish the Contribution,</li> <li>modify the Contribution,</li> <li>prepare derivative works based upon or containing the Contribution and/or to combine the Contribution with other Materials,</li> <li>reproduce the Contribution in original or modified form,</li> <li>distribute, to make the Contribution available to the public, display and publicly perform the Contribution in original or modified form.</li> </ul> <p>This license back is limited to the Contribution and does not provide any rights to the Material.</p>"},{"location":"cla/#3-patents","title":"3. Patents","text":""},{"location":"cla/#31-patent-license","title":"3.1 Patent license","text":"<p>Subject to the terms and conditions of this Agreement You hereby grant to Us and to recipients of Materials distributed by Us a worldwide, royalty-free, non-exclusive, perpetual and irrevocable (except as stated in Section 3.2) patent license, with the right to transfer an unlimited number of non-exclusive licenses or to grant sublicenses to third parties, to make, have made, use, sell, offer for sale, import and otherwise transfer the Contribution and the Contribution in combination with any Material (and portions of such combination). This license applies to all patents owned or controlled by You, whether already acquired or hereafter acquired, that would be infringed by making, having made, using, selling, offering for sale, importing or otherwise transferring of Your Contribution(s) alone or by combination of Your Contribution(s) with any Material.</p>"},{"location":"cla/#32-revocation-of-patent-license","title":"3.2 Revocation of patent license","text":"<p>You reserve the right to revoke the patent license stated in section 3.1 if We make any infringement claim that is targeted at your Contribution and not asserted for a Defensive Purpose. An assertion of claims of the Patents shall be considered for a \"Defensive Purpose\" if the claims are asserted against an entity that has filed, maintained, threatened, or voluntarily participated in a patent infringement lawsuit against Us or any of Our licensees.</p>"},{"location":"cla/#4-license-obligations-by-us","title":"4. License obligations by Us","text":"<p>We agree to (sub)license the Contribution or any Materials containing, based on or derived from your Contribution under the terms of any licenses the Free Software Foundation classifies as Free Software License and which are approved by the Open Source Initiative as Open Source licenses.</p> <p>We agree to license patents owned or controlled by You only to the extent necessary to (sub)license Your Contribution(s) and the combination of Your Contribution(s) with the Material under the terms of any licenses the Free Software Foundation classifies as Free Software licenses and which are approved by the Open Source Initiative as Open Source licenses..</p>"},{"location":"cla/#5-disclaimer","title":"5. Disclaimer","text":"<p>THE CONTRIBUTION IS PROVIDED \"AS IS\". MORE PARTICULARLY, ALL EXPRESS OR IMPLIED WARRANTIES INCLUDING, WITHOUT LIMITATION, ANY IMPLIED WARRANTY OF SATISFACTORY QUALITY, FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT ARE EXPRESSLY DISCLAIMED BY YOU TO US AND BY US TO YOU. TO THE EXTENT THAT ANY SUCH WARRANTIES CANNOT BE DISCLAIMED, SUCH WARRANTY IS LIMITED IN DURATION AND EXTENT TO THE MINIMUM PERIOD AND EXTENT PERMITTED BY LAW.</p>"},{"location":"cla/#6-consequential-damage-waiver","title":"6. Consequential damage waiver","text":"<p>TO THE MAXIMUM EXTENT PERMITTED BY APPLICABLE LAW, IN NO EVENT WILL YOU OR WE BE LIABLE FOR ANY LOSS OF PROFITS, LOSS OF ANTICIPATED SAVINGS, LOSS OF DATA, INDIRECT, SPECIAL, INCIDENTAL, CONSEQUENTIAL AND EXEMPLARY DAMAGES ARISING OUT OF THIS AGREEMENT REGARDLESS OF THE LEGAL OR EQUITABLE THEORY (CONTRACT, TORT OR OTHERWISE) UPON WHICH THE CLAIM IS BASED.</p>"},{"location":"cla/#7-approximation-of-disclaimer-and-damage-waiver","title":"7. Approximation of disclaimer and damage waiver","text":"<p>IF THE DISCLAIMER AND DAMAGE WAIVER MENTIONED IN SECTION 5. AND SECTION 6. CANNOT BE GIVEN LEGAL EFFECT UNDER APPLICABLE LOCAL LAW, REVIEWING COURTS SHALL APPLY LOCAL LAW THAT MOST CLOSELY APPROXIMATES AN ABSOLUTE WAIVER OF ALL CIVIL OR CONTRACTUAL LIABILITY IN CONNECTION WITH THE CONTRIBUTION.</p>"},{"location":"cla/#8-term","title":"8. Term","text":"<p>8.1 This Agreement shall come into effect upon Your acceptance of the terms and conditions.</p> <p>8.2 This Agreement shall apply for the term of the copyright and patents licensed here. However, You shall have the right to terminate the Agreement if We do not fulfill the obligations as set forth in Section 4. Such termination must be made in writing.</p> <p>8.3 In the event of a termination of this Agreement Sections 5., 6., 7., 8., and 9. shall survive such termination and shall remain in full force thereafter. For the avoidance of doubt, Free and Open Source Software (sub)licenses that have already been granted for Contributions at the date of the termination shall remain in full force after the termination of this Agreement.</p>"},{"location":"cla/#9-miscellaneous","title":"9. Miscellaneous","text":"<p>9.1 This Agreement and all disputes, claims, actions, suits or other proceedings arising out of this agreement or relating in any way to it shall be governed by the laws of United States excluding its private international law provisions.</p> <p>9.2 This Agreement sets out the entire agreement between You and Us for Your Contributions to Us and overrides all other agreements or understandings.</p> <p>9.3 In case of Your death, this agreement shall continue with Your heirs. In case of more than one heir, all heirs must exercise their rights through a commonly authorized person.</p> <p>9.4 If any provision of this Agreement is found void and unenforceable, such provision will be replaced to the extent possible with a provision that comes closest to the meaning of the original provision and that is enforceable. The terms and conditions set forth in this Agreement shall apply notwithstanding any failure of essential purpose of this Agreement or any limited remedy to the maximum extent possible under law.</p> <p>9.5 You agree to notify Us of any facts or circumstances of which you become aware that would make this Agreement inaccurate in any respect.</p>"},{"location":"cla/#recreate-this-contributor-license-agreement","title":"Recreate this Contributor License Agreement","text":"<p>https://contributoragreements.org/u2s/2kw48zbt5d</p> <p></p> <p></p>"},{"location":"cla/#fiduciary-license-agreement-20_1","title":"Fiduciary License Agreement 2.0","text":"<p>based on the</p>"},{"location":"cla/#entity-contributor-exclusive-license-agreement","title":"Entity Contributor Exclusive License Agreement","text":""},{"location":"cla/#including-the-traditional-patent-license-option_1","title":"(including the Traditional Patent License OPTION)","text":"<p>Thank you for your interest in contributing to Open Geospatial Solutions's HyperCoast (\"We\" or \"Us\").</p> <p>The purpose of this contributor agreement (\"Agreement\") is to clarify and document the rights granted by contributors to Us. To make this document effective, please follow the instructions at https://github.com/opengeos/HyperCoast.</p>"},{"location":"cla/#0-preamble_1","title":"0. Preamble","text":"<p>Software is deeply embedded in all aspects of our lives and it is important that it empower, rather than restrict us. Free Software gives everybody the rights to use, understand, adapt and share software. These rights help support other fundamental freedoms like freedom of speech, press and privacy.</p> <p>Development of Free Software can follow many patterns. In some cases whole development is handled by a sole programmer or a small group of people. But usually, the creation and maintenance of software is a complex process that requires the contribution of many individuals. This also affects who owns the rights to the software. In the latter case, rights in software are owned jointly by a great number of individuals.</p> <p>To tackle this issue some projects require a full copyright assignment to be signed by all contributors. The problem with such assignments is that they often lack checks and balances that would protect the contributors from potential abuse of power from the new copyright holder.</p> <p>FSFE\u2019s Fiduciary License Agreement (FLA) was created by the Free Software Foundation Europe e.V. with just that in mind \u2013 to concentrate all deciding power within one entity and prevent fragmentation of rights on one hand, while on the other preventing that single entity from abusing its power. The main aim is to ensure that the software covered under the FLA will forever remain Free Software.</p> <p>This process only serves for the transfer of economic rights. So-called moral rights (e.g. authors right to be identified as author) remain with the original author(s) and are inalienable.</p>"},{"location":"cla/#how-to-use-this-fla_1","title":"How to use this FLA","text":"<p>If You are an employee and have created the Contribution as part of your employment, You need to have Your employer approve this Agreement or sign the Entity version of this document. If You do not own the Copyright in the entire work of authorship, any other author of the Contribution should also sign this \u2013 in any event, please contact Us at opengeos@outlook.com</p>"},{"location":"cla/#1-definitions_1","title":"1. Definitions","text":"<p>\"You\" means the individual Copyright owner who Submits a Contribution to Us.</p> <p>\"Legal Entity\" means an entity that is not a natural person.</p> <p>\"Affiliate\" means any other Legal Entity that controls, is controlled by, or under common control with that Legal Entity. For the purposes of this definition, \"control\" means (i) the power, direct or indirect, to cause the direction or management of such Legal Entity, whether by contract or otherwise, (ii) ownership of fifty percent (50%) or more of the outstanding shares or securities that vote to elect the management or other persons who direct such Legal Entity or (iii) beneficial ownership of such entity.</p> <p>\"Contribution\" means any original work of authorship, including any original modifications or additions to an existing work of authorship, Submitted by You to Us, in which You own the Copyright.</p> <p>\"Copyright\" means all rights protecting works of authorship, including copyright, moral and neighboring rights, as appropriate, for the full term of their existence.</p> <p>\"Material\" means the software or documentation made available by Us to third parties. When this Agreement covers more than one software project, the Material means the software or documentation to which the Contribution was Submitted. After You Submit the Contribution, it may be included in the Material.</p> <p>\"Submit\" means any act by which a Contribution is transferred to Us by You by means of tangible or intangible media, including but not limited to electronic mailing lists, source code control systems, and issue tracking systems that are managed by, or on behalf of, Us, but excluding any transfer that is conspicuously marked or otherwise designated in writing by You as \"Not a Contribution.\"</p> <p>\"Documentation\" means any non-software portion of a Contribution.</p>"},{"location":"cla/#2-license-grant_1","title":"2. License grant","text":""},{"location":"cla/#21-copyright-license-to-us_1","title":"2.1 Copyright license to Us","text":"<p>Subject to the terms and conditions of this Agreement, You hereby grant to Us a worldwide, royalty-free, exclusive, perpetual and irrevocable (except as stated in Section 8.2) license, with the right to transfer an unlimited number of non-exclusive licenses or to grant sublicenses to third parties, under the Copyright covering the Contribution to use the Contribution by all means, including, but not limited to:</p> <ul> <li>publish the Contribution,</li> <li>modify the Contribution,</li> <li>prepare derivative works based upon or containing the Contribution and/or to combine the Contribution with other Materials,</li> <li>reproduce the Contribution in original or modified form,</li> <li>distribute, to make the Contribution available to the public, display and publicly perform the Contribution in original or modified form.</li> </ul>"},{"location":"cla/#22-moral-rights_1","title":"2.2 Moral rights","text":"<p>Moral Rights remain unaffected to the extent they are recognized and not waivable by applicable law. Notwithstanding, You may add your name to the attribution mechanism customary used in the Materials you Contribute to, such as the header of the source code files of Your Contribution, and We will respect this attribution when using Your Contribution.</p>"},{"location":"cla/#23-copyright-license-back-to-you_1","title":"2.3 Copyright license back to You","text":"<p>Upon such grant of rights to Us, We immediately grant to You a worldwide, royalty-free, non-exclusive, perpetual and irrevocable license, with the right to transfer an unlimited number of non-exclusive licenses or to grant sublicenses to third parties, under the Copyright covering the Contribution to use the Contribution by all means, including, but not limited to:</p> <ul> <li>publish the Contribution,</li> <li>modify the Contribution,</li> <li>prepare derivative works based upon or containing the Contribution and/or to combine the Contribution with other Materials,</li> <li>reproduce the Contribution in original or modified form,</li> <li>distribute, to make the Contribution available to the public, display and publicly perform the Contribution in original or modified form.</li> </ul> <p>This license back is limited to the Contribution and does not provide any rights to the Material.</p>"},{"location":"cla/#3-patents_1","title":"3. Patents","text":""},{"location":"cla/#31-patent-license_1","title":"3.1 Patent license","text":"<p>Subject to the terms and conditions of this Agreement You hereby grant to Us and to recipients of Materials distributed by Us a worldwide, royalty-free, non-exclusive, perpetual and irrevocable (except as stated in Section 3.2) patent license, with the right to transfer an unlimited number of non-exclusive licenses or to grant sublicenses to third parties, to make, have made, use, sell, offer for sale, import and otherwise transfer the Contribution and the Contribution in combination with any Material (and portions of such combination). This license applies to all patents owned or controlled by You, whether already acquired or hereafter acquired, that would be infringed by making, having made, using, selling, offering for sale, importing or otherwise transferring of Your Contribution(s) alone or by combination of Your Contribution(s) with any Material.</p>"},{"location":"cla/#32-revocation-of-patent-license_1","title":"3.2 Revocation of patent license","text":"<p>You reserve the right to revoke the patent license stated in section 3.1 if We make any infringement claim that is targeted at your Contribution and not asserted for a Defensive Purpose. An assertion of claims of the Patents shall be considered for a \"Defensive Purpose\" if the claims are asserted against an entity that has filed, maintained, threatened, or voluntarily participated in a patent infringement lawsuit against Us or any of Our licensees.</p>"},{"location":"cla/#4-license-obligations-by-us_1","title":"4. License obligations by Us","text":"<p>We agree to (sub)license the Contribution or any Materials containing, based on or derived from your Contribution under the terms of any licenses the Free Software Foundation classifies as Free Software License and which are approved by the Open Source Initiative as Open Source licenses.</p> <p>We agree to license patents owned or controlled by You only to the extent necessary to (sub)license Your Contribution(s) and the combination of Your Contribution(s) with the Material under the terms of any licenses the Free Software Foundation classifies as Free Software licenses and which are approved by the Open Source Initiative as Open Source licenses..</p>"},{"location":"cla/#5-disclaimer_1","title":"5. Disclaimer","text":"<p>THE CONTRIBUTION IS PROVIDED \"AS IS\". MORE PARTICULARLY, ALL EXPRESS OR IMPLIED WARRANTIES INCLUDING, WITHOUT LIMITATION, ANY IMPLIED WARRANTY OF SATISFACTORY QUALITY, FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT ARE EXPRESSLY DISCLAIMED BY YOU TO US AND BY US TO YOU. TO THE EXTENT THAT ANY SUCH WARRANTIES CANNOT BE DISCLAIMED, SUCH WARRANTY IS LIMITED IN DURATION AND EXTENT TO THE MINIMUM PERIOD AND EXTENT PERMITTED BY LAW.</p>"},{"location":"cla/#6-consequential-damage-waiver_1","title":"6. Consequential damage waiver","text":"<p>TO THE MAXIMUM EXTENT PERMITTED BY APPLICABLE LAW, IN NO EVENT WILL YOU OR WE BE LIABLE FOR ANY LOSS OF PROFITS, LOSS OF ANTICIPATED SAVINGS, LOSS OF DATA, INDIRECT, SPECIAL, INCIDENTAL, CONSEQUENTIAL AND EXEMPLARY DAMAGES ARISING OUT OF THIS AGREEMENT REGARDLESS OF THE LEGAL OR EQUITABLE THEORY (CONTRACT, TORT OR OTHERWISE) UPON WHICH THE CLAIM IS BASED.</p>"},{"location":"cla/#7-approximation-of-disclaimer-and-damage-waiver_1","title":"7. Approximation of disclaimer and damage waiver","text":"<p>IF THE DISCLAIMER AND DAMAGE WAIVER MENTIONED IN SECTION 5. AND SECTION 6. CANNOT BE GIVEN LEGAL EFFECT UNDER APPLICABLE LOCAL LAW, REVIEWING COURTS SHALL APPLY LOCAL LAW THAT MOST CLOSELY APPROXIMATES AN ABSOLUTE WAIVER OF ALL CIVIL OR CONTRACTUAL LIABILITY IN CONNECTION WITH THE CONTRIBUTION.</p>"},{"location":"cla/#8-term_1","title":"8. Term","text":"<p>8.1 This Agreement shall come into effect upon Your acceptance of the terms and conditions.</p> <p>8.2 This Agreement shall apply for the term of the copyright and patents licensed here. However, You shall have the right to terminate the Agreement if We do not fulfill the obligations as set forth in Section 4. Such termination must be made in writing.</p> <p>8.3 In the event of a termination of this Agreement Sections 5., 6., 7., 8., and 9. shall survive such termination and shall remain in full force thereafter. For the avoidance of doubt, Free and Open Source Software (sub)licenses that have already been granted for Contributions at the date of the termination shall remain in full force after the termination of this Agreement.</p>"},{"location":"cla/#9-miscellaneous_1","title":"9. Miscellaneous","text":"<p>9.1 This Agreement and all disputes, claims, actions, suits or other proceedings arising out of this agreement or relating in any way to it shall be governed by the laws of United States excluding its private international law provisions.</p> <p>9.2 This Agreement sets out the entire agreement between You and Us for Your Contributions to Us and overrides all other agreements or understandings.</p> <p>9.3 In case of Your death, this agreement shall continue with Your heirs. In case of more than one heir, all heirs must exercise their rights through a commonly authorized person.</p> <p>9.4 If any provision of this Agreement is found void and unenforceable, such provision will be replaced to the extent possible with a provision that comes closest to the meaning of the original provision and that is enforceable. The terms and conditions set forth in this Agreement shall apply notwithstanding any failure of essential purpose of this Agreement or any limited remedy to the maximum extent possible under law.</p> <p>9.5 You agree to notify Us of any facts or circumstances of which you become aware that would make this Agreement inaccurate in any respect.</p>"},{"location":"cla/#recreate-this-contributor-license-agreement_1","title":"Recreate this Contributor License Agreement","text":"<p>https://contributoragreements.org/u2s/2kw48zbt5d</p> <p></p> <p></p>"},{"location":"common/","title":"common module","text":"<p>The common module contains common functions and classes used by the other modules.</p>"},{"location":"common/#hypercoast.common.convert_coords","title":"<code>convert_coords(coords, from_epsg, to_epsg)</code>","text":"<p>Convert a list of coordinates from one EPSG to another.</p> <p>Parameters:</p> Name Type Description Default <code>coords</code> <code>List[Tuple[float, float]]</code> <p>List of tuples containing coordinates in the format (latitude, longitude).</p> required <code>from_epsg</code> <code>str</code> <p>Source EPSG code (default is \"epsg:4326\").</p> required <code>to_epsg</code> <code>str</code> <p>Target EPSG code (default is \"epsg:32615\").</p> required <p>Returns:</p> Type Description <code>List[Tuple[float, float]]</code> <p>List of tuples containing converted coordinates in the format (x, y).</p> Source code in <code>hypercoast/common.py</code> <pre><code>def convert_coords(\n    coords: List[Tuple[float, float]], from_epsg: str, to_epsg: str\n) -&gt; List[Tuple[float, float]]:\n    \"\"\"\n    Convert a list of coordinates from one EPSG to another.\n\n    Args:\n        coords: List of tuples containing coordinates in the format (latitude, longitude).\n        from_epsg: Source EPSG code (default is \"epsg:4326\").\n        to_epsg: Target EPSG code (default is \"epsg:32615\").\n\n    Returns:\n        List of tuples containing converted coordinates in the format (x, y).\n    \"\"\"\n    import pyproj\n\n    # Define the coordinate transformation\n    transformer = pyproj.Transformer.from_crs(from_epsg, to_epsg, always_xy=True)\n\n    # Convert each coordinate\n    converted_coords = [transformer.transform(lon, lat) for lat, lon in coords]\n\n    return converted_coords\n</code></pre>"},{"location":"common/#hypercoast.common.download_acolite","title":"<code>download_acolite(outdir='.', platform=None)</code>","text":"<p>Downloads the Acolite release based on the OS platform and extracts it to the specified directory. For more information, see the Acolite manual https://github.com/acolite/acolite/releases.</p> <p>Parameters:</p> Name Type Description Default <code>outdir</code> <code>str</code> <p>The output directory where the file will be Acolite and extracted.</p> <code>'.'</code> <code>platform</code> <code>Optional[str]</code> <p>The platform for which to download acolite. If None, the current system platform is used.                       Valid values are 'linux', 'darwin', and 'windows'.</p> <code>None</code> <p>Returns:</p> Type Description <code>str</code> <p>The path to the extracted Acolite directory.</p> <p>Exceptions:</p> Type Description <code>Exception</code> <p>If the platform is unsupported or the download fails.</p> Source code in <code>hypercoast/common.py</code> <pre><code>def download_acolite(outdir: str = \".\", platform: Optional[str] = None) -&gt; str:\n    \"\"\"\n    Downloads the Acolite release based on the OS platform and extracts it to the specified directory.\n    For more information, see the Acolite manual https://github.com/acolite/acolite/releases.\n\n    Args:\n        outdir (str): The output directory where the file will be Acolite and extracted.\n        platform (Optional[str]): The platform for which to download acolite. If None, the current system platform is used.\n                                  Valid values are 'linux', 'darwin', and 'windows'.\n\n    Returns:\n        str: The path to the extracted Acolite directory.\n\n    Raises:\n        Exception: If the platform is unsupported or the download fails.\n    \"\"\"\n    import platform as pf\n    import requests\n    import tarfile\n    from tqdm import tqdm\n\n    base_url = \"https://github.com/acolite/acolite/releases/download/20231023.0/\"\n\n    if platform is None:\n        platform = pf.system().lower()\n    else:\n        platform = platform.lower()\n\n    if platform == \"linux\":\n        download_url = base_url + \"acolite_py_linux_20231023.0.tar.gz\"\n        root_dir = \"acolite_py_linux\"\n    elif platform == \"darwin\":\n        download_url = base_url + \"acolite_py_mac_20231023.0.tar.gz\"\n        root_dir = \"acolite_py_mac\"\n    elif platform == \"windows\":\n        download_url = base_url + \"acolite_py_win_20231023.0.tar.gz\"\n        root_dir = \"acolite_py_win\"\n    else:\n        print(f\"Unsupported OS platform: {platform}\")\n        return\n\n    if not os.path.exists(outdir):\n        os.makedirs(outdir)\n\n    extracted_path = os.path.join(outdir, root_dir)\n    file_name = os.path.join(outdir, download_url.split(\"/\")[-1])\n\n    if os.path.exists(file_name):\n        print(f\"{file_name} already exists. Skip downloading.\")\n        return extracted_path\n\n    response = requests.get(download_url, stream=True, timeout=60)\n    total_size = int(response.headers.get(\"content-length\", 0))\n    block_size = 8192\n\n    if response.status_code == 200:\n        with (\n            open(file_name, \"wb\") as file,\n            tqdm(\n                desc=file_name,\n                total=total_size,\n                unit=\"iB\",\n                unit_scale=True,\n                unit_divisor=1024,\n            ) as bar,\n        ):\n            for chunk in response.iter_content(chunk_size=block_size):\n                if chunk:\n                    file.write(chunk)\n                    bar.update(len(chunk))\n        print(f\"Downloaded {file_name}\")\n    else:\n        print(f\"Failed to download file from {download_url}\")\n        return\n\n    # Unzip the file\n    with tarfile.open(file_name, \"r:gz\") as tar:\n        tar.extractall(path=outdir)\n\n    print(f\"Extracted to {extracted_path}\")\n    return extracted_path\n</code></pre>"},{"location":"common/#hypercoast.common.download_ecostress","title":"<code>download_ecostress(granules, out_dir=None, threads=8)</code>","text":"<p>Downloads NASA ECOSTRESS granules.</p> <p>Parameters:</p> Name Type Description Default <code>granules</code> <code>List[dict]</code> <p>The granules to download.</p> required <code>out_dir</code> <code>str</code> <p>The output directory where the granules will be downloaded. Defaults to None (current directory).</p> <code>None</code> <code>threads</code> <code>int</code> <p>The number of threads to use for downloading. Defaults to 8.</p> <code>8</code> Source code in <code>hypercoast/common.py</code> <pre><code>def download_ecostress(\n    granules: List[dict],\n    out_dir: Optional[str] = None,\n    threads: int = 8,\n) -&gt; None:\n    \"\"\"Downloads NASA ECOSTRESS granules.\n\n    Args:\n        granules (List[dict]): The granules to download.\n        out_dir (str, optional): The output directory where the granules will be\n            downloaded. Defaults to None (current directory).\n        threads (int, optional): The number of threads to use for downloading.\n            Defaults to 8.\n    \"\"\"\n\n    download_nasa_data(granules=granules, out_dir=out_dir, threads=threads)\n</code></pre>"},{"location":"common/#hypercoast.common.download_emit","title":"<code>download_emit(granules, out_dir=None, threads=8)</code>","text":"<p>Downloads NASA EMIT granules.</p> <p>Parameters:</p> Name Type Description Default <code>granules</code> <code>List[dict]</code> <p>The granules to download.</p> required <code>out_dir</code> <code>str</code> <p>The output directory where the granules will be downloaded. Defaults to None (current directory).</p> <code>None</code> <code>threads</code> <code>int</code> <p>The number of threads to use for downloading. Defaults to 8.</p> <code>8</code> Source code in <code>hypercoast/common.py</code> <pre><code>def download_emit(\n    granules: List[dict],\n    out_dir: Optional[str] = None,\n    threads: int = 8,\n) -&gt; None:\n    \"\"\"Downloads NASA EMIT granules.\n\n    Args:\n        granules (List[dict]): The granules to download.\n        out_dir (str, optional): The output directory where the granules will be\n            downloaded. Defaults to None (current directory).\n        threads (int, optional): The number of threads to use for downloading.\n            Defaults to 8.\n    \"\"\"\n\n    download_nasa_data(granules=granules, out_dir=out_dir, threads=threads)\n</code></pre>"},{"location":"common/#hypercoast.common.download_file","title":"<code>download_file(url=None, output=None, quiet=True, proxy=None, speed=None, use_cookies=True, verify=True, uid=None, fuzzy=False, resume=False, unzip=True, overwrite=False, subfolder=False)</code>","text":"<p>Download a file from URL, including Google Drive shared URL.</p> <p>Parameters:</p> Name Type Description Default <code>url</code> <code>str</code> <p>Google Drive URL is also supported. Defaults to None.</p> <code>None</code> <code>output</code> <code>str</code> <p>Output filename. Default is basename of URL.</p> <code>None</code> <code>quiet</code> <code>bool</code> <p>Suppress terminal output. Default is True.</p> <code>True</code> <code>proxy</code> <code>str</code> <p>Proxy. Defaults to None.</p> <code>None</code> <code>speed</code> <code>float</code> <p>Download byte size per second (e.g., 256KB/s = 256 * 1024). Defaults to None.</p> <code>None</code> <code>use_cookies</code> <code>bool</code> <p>Flag to use cookies. Defaults to True.</p> <code>True</code> <code>verify</code> <code>bool | str</code> <p>Either a bool, in which case it controls whether the server's TLS certificate is verified, or a string, in which case it must be a path to a CA bundle to use. Default is True.. Defaults to True.</p> <code>True</code> <code>uid</code> <code>str</code> <p>Google Drive's file ID. Defaults to None.</p> <code>None</code> <code>fuzzy</code> <code>bool</code> <p>Fuzzy extraction of Google Drive's file Id. Defaults to False.</p> <code>False</code> <code>resume</code> <code>bool</code> <p>Resume the download from existing tmp file if possible. Defaults to False.</p> <code>False</code> <code>unzip</code> <code>bool</code> <p>Unzip the file. Defaults to True.</p> <code>True</code> <code>overwrite</code> <code>bool</code> <p>Overwrite the file if it already exists. Defaults to False.</p> <code>False</code> <code>subfolder</code> <code>bool</code> <p>Create a subfolder with the same name as the file. Defaults to False.</p> <code>False</code> <p>Returns:</p> Type Description <code>str</code> <p>The output file path.</p> Source code in <code>hypercoast/common.py</code> <pre><code>def download_file(\n    url: Optional[str] = None,\n    output: Optional[str] = None,\n    quiet: Optional[bool] = True,\n    proxy: Optional[str] = None,\n    speed: Optional[float] = None,\n    use_cookies: Optional[bool] = True,\n    verify: Optional[bool] = True,\n    uid: Optional[str] = None,\n    fuzzy: Optional[bool] = False,\n    resume: Optional[bool] = False,\n    unzip: Optional[bool] = True,\n    overwrite: Optional[bool] = False,\n    subfolder: Optional[bool] = False,\n) -&gt; str:\n    \"\"\"Download a file from URL, including Google Drive shared URL.\n\n    Args:\n        url (str, optional): Google Drive URL is also supported. Defaults to None.\n        output (str, optional): Output filename. Default is basename of URL.\n        quiet (bool, optional): Suppress terminal output. Default is True.\n        proxy (str, optional): Proxy. Defaults to None.\n        speed (float, optional): Download byte size per second (e.g., 256KB/s = 256 * 1024). Defaults to None.\n        use_cookies (bool, optional): Flag to use cookies. Defaults to True.\n        verify (bool | str, optional): Either a bool, in which case it controls whether the server's TLS certificate is verified, or a string,\n            in which case it must be a path to a CA bundle to use. Default is True.. Defaults to True.\n        uid (str, optional): Google Drive's file ID. Defaults to None.\n        fuzzy (bool, optional): Fuzzy extraction of Google Drive's file Id. Defaults to False.\n        resume (bool, optional): Resume the download from existing tmp file if possible. Defaults to False.\n        unzip (bool, optional): Unzip the file. Defaults to True.\n        overwrite (bool, optional): Overwrite the file if it already exists. Defaults to False.\n        subfolder (bool, optional): Create a subfolder with the same name as the file. Defaults to False.\n\n    Returns:\n        str: The output file path.\n    \"\"\"\n    import zipfile\n    import tarfile\n    import gdown\n\n    if output is None:\n        if isinstance(url, str) and url.startswith(\"http\"):\n            output = os.path.basename(url)\n\n    out_dir = os.path.abspath(os.path.dirname(output))\n    if not os.path.exists(out_dir):\n        os.makedirs(out_dir)\n\n    if isinstance(url, str):\n        if os.path.exists(os.path.abspath(output)) and (not overwrite):\n            print(\n                f\"{output} already exists. Skip downloading. Set overwrite=True to overwrite.\"\n            )\n            return os.path.abspath(output)\n        else:\n            url = github_raw_url(url)\n\n    if \"https://drive.google.com/file/d/\" in url:\n        fuzzy = True\n\n    output = gdown.download(\n        url, output, quiet, proxy, speed, use_cookies, verify, uid, fuzzy, resume\n    )\n\n    if unzip:\n        if output.endswith(\".zip\"):\n            with zipfile.ZipFile(output, \"r\") as zip_ref:\n                if not quiet:\n                    print(\"Extracting files...\")\n                if subfolder:\n                    basename = os.path.splitext(os.path.basename(output))[0]\n\n                    output = os.path.join(out_dir, basename)\n                    if not os.path.exists(output):\n                        os.makedirs(output)\n                    zip_ref.extractall(output)\n                else:\n                    zip_ref.extractall(os.path.dirname(output))\n        elif output.endswith(\".tar.gz\") or output.endswith(\".tar\"):\n            if output.endswith(\".tar.gz\"):\n                mode = \"r:gz\"\n            else:\n                mode = \"r\"\n\n            with tarfile.open(output, mode) as tar_ref:\n                if not quiet:\n                    print(\"Extracting files...\")\n                if subfolder:\n                    basename = os.path.splitext(os.path.basename(output))[0]\n                    output = os.path.join(out_dir, basename)\n                    if not os.path.exists(output):\n                        os.makedirs(output)\n                    tar_ref.extractall(output)\n                else:\n                    tar_ref.extractall(os.path.dirname(output))\n\n    return os.path.abspath(output)\n</code></pre>"},{"location":"common/#hypercoast.common.download_nasa_data","title":"<code>download_nasa_data(granules, out_dir=None, provider=None, threads=8)</code>","text":"<p>Downloads NASA Earthdata granules.</p> <p>Parameters:</p> Name Type Description Default <code>granules</code> <code>List[dict]</code> <p>The granules to download.</p> required <code>out_dir</code> <code>str</code> <p>The output directory where the granules will be downloaded. Defaults to None (current directory).</p> <code>None</code> <code>provider</code> <code>str</code> <p>The provider of the granules.</p> <code>None</code> <code>threads</code> <code>int</code> <p>The number of threads to use for downloading. Defaults to 8.</p> <code>8</code> Source code in <code>hypercoast/common.py</code> <pre><code>def download_nasa_data(\n    granules: List[dict],\n    out_dir: Optional[str] = None,\n    provider: Optional[str] = None,\n    threads: int = 8,\n) -&gt; None:\n    \"\"\"Downloads NASA Earthdata granules.\n\n    Args:\n        granules (List[dict]): The granules to download.\n        out_dir (str, optional): The output directory where the granules will be downloaded. Defaults to None (current directory).\n        provider (str, optional): The provider of the granules.\n        threads (int, optional): The number of threads to use for downloading. Defaults to 8.\n    \"\"\"\n\n    leafmap.nasa_data_download(\n        granules=granules, out_dir=out_dir, provider=provider, threads=threads\n    )\n</code></pre>"},{"location":"common/#hypercoast.common.download_pace","title":"<code>download_pace(granules, out_dir=None, threads=8)</code>","text":"<p>Downloads NASA PACE granules.</p> <p>Parameters:</p> Name Type Description Default <code>granules</code> <code>List[dict]</code> <p>The granules to download.</p> required <code>out_dir</code> <code>str</code> <p>The output directory where the granules will be downloaded. Defaults to None (current directory).</p> <code>None</code> <code>threads</code> <code>int</code> <p>The number of threads to use for downloading. Defaults to 8.</p> <code>8</code> Source code in <code>hypercoast/common.py</code> <pre><code>def download_pace(\n    granules: List[dict],\n    out_dir: Optional[str] = None,\n    threads: int = 8,\n) -&gt; None:\n    \"\"\"Downloads NASA PACE granules.\n\n    Args:\n        granules (List[dict]): The granules to download.\n        out_dir (str, optional): The output directory where the granules will be\n            downloaded. Defaults to None (current directory).\n        threads (int, optional): The number of threads to use for downloading.\n            Defaults to 8.\n    \"\"\"\n\n    download_nasa_data(granules=granules, out_dir=out_dir, threads=threads)\n</code></pre>"},{"location":"common/#hypercoast.common.extract_date_from_filename","title":"<code>extract_date_from_filename(filename)</code>","text":"<p>Extracts a date from a filename assuming the date is in 'YYYYMMDD' format.</p> <p>This function searches the filename for a sequence of 8 digits that represent a date in 'YYYYMMDD' format. If such a sequence is found, it converts the sequence into a pandas Timestamp object. If no such sequence is found, the function returns None.</p> <p>Parameters:</p> Name Type Description Default <code>filename</code> <code>str</code> <p>The filename from which to extract the date.</p> required <p>Returns:</p> Type Description <code>Optional[pd.Timestamp]</code> <p>A pandas Timestamp object representing the date found in the filename, or None if no date in 'YYYYMMDD' format is found.</p> <p>Examples:</p> <pre><code>&gt;&gt;&gt; extract_date_from_filename(\"example_20230101.txt\")\nTimestamp('2023-01-01 00:00:00')\n</code></pre> <pre><code>&gt;&gt;&gt; extract_date_from_filename(\"no_date_in_this_filename.txt\")\nNone\n</code></pre> Source code in <code>hypercoast/common.py</code> <pre><code>def extract_date_from_filename(filename: str):\n    \"\"\"\n    Extracts a date from a filename assuming the date is in 'YYYYMMDD' format.\n\n    This function searches the filename for a sequence of 8 digits that represent a date in\n    'YYYYMMDD' format. If such a sequence is found, it converts the sequence into a pandas\n    Timestamp object. If no such sequence is found, the function returns None.\n\n    Args:\n        filename (str): The filename from which to extract the date.\n\n    Returns:\n        Optional[pd.Timestamp]: A pandas Timestamp object representing the date found in the filename,\n        or None if no date in 'YYYYMMDD' format is found.\n\n    Examples:\n        &gt;&gt;&gt; extract_date_from_filename(\"example_20230101.txt\")\n        Timestamp('2023-01-01 00:00:00')\n\n        &gt;&gt;&gt; extract_date_from_filename(\"no_date_in_this_filename.txt\")\n        None\n    \"\"\"\n    import re\n    import pandas as pd\n\n    # Assuming the date format in filename is 'YYYYMMDD'\n    date_match = re.search(r\"\\d{8}\", filename)\n    if date_match:\n        return pd.to_datetime(date_match.group(), format=\"%Y%m%d\")\n    else:\n        return None\n</code></pre>"},{"location":"common/#hypercoast.common.extract_spectral","title":"<code>extract_spectral(ds, lat, lon, name='data')</code>","text":"<p>Extracts spectral signature from a given xarray Dataset.</p> <p>Parameters:</p> Name Type Description Default <code>ds</code> <code>xarray.Dataset</code> <p>The dataset containing the spectral data.</p> required <code>lat</code> <code>float</code> <p>The latitude of the point to extract.</p> required <code>lon</code> <code>float</code> <p>The longitude of the point to extract.</p> required <p>Returns:</p> Type Description <code>xarray.DataArray</code> <p>The extracted data.</p> Source code in <code>hypercoast/common.py</code> <pre><code>def extract_spectral(\n    ds: xr.Dataset, lat: float, lon: float, name: str = \"data\"\n) -&gt; xr.DataArray:\n    \"\"\"\n    Extracts spectral signature from a given xarray Dataset.\n\n    Args:\n        ds (xarray.Dataset): The dataset containing the spectral data.\n        lat (float): The latitude of the point to extract.\n        lon (float): The longitude of the point to extract.\n\n    Returns:\n        xarray.DataArray: The extracted data.\n    \"\"\"\n\n    crs = ds.rio.crs\n\n    x, y = convert_coords([[lat, lon]], \"epsg:4326\", crs)[0]\n\n    values = ds.sel(x=x, y=y, method=\"nearest\")[name].values\n\n    da = xr.DataArray(values, dims=[\"band\"], coords={\"band\": ds.coords[\"band\"]})\n\n    return da\n</code></pre>"},{"location":"common/#hypercoast.common.github_raw_url","title":"<code>github_raw_url(url)</code>","text":"<p>Get the raw URL for a GitHub file.</p> <p>Parameters:</p> Name Type Description Default <code>url</code> <code>str</code> <p>The GitHub URL.</p> required <p>Returns:</p> Type Description <code>str</code> <p>The raw URL.</p> Source code in <code>hypercoast/common.py</code> <pre><code>def github_raw_url(url: str) -&gt; str:\n    \"\"\"Get the raw URL for a GitHub file.\n\n    Args:\n        url (str): The GitHub URL.\n    Returns:\n        str: The raw URL.\n    \"\"\"\n    if isinstance(url, str) and url.startswith(\"https://github.com/\") and \"blob\" in url:\n        url = url.replace(\"github.com\", \"raw.githubusercontent.com\").replace(\n            \"blob/\", \"\"\n        )\n    return url\n</code></pre>"},{"location":"common/#hypercoast.common.image_cube","title":"<code>image_cube(dataset, variable='reflectance', cmap='jet', clim=(0, 0.5), title='Reflectance', rgb_bands=None, rgb_wavelengths=None, rgb_gamma=1.0, rgb_cmap=None, rgb_clim=None, rgb_args=None, widget=None, plotter_args=None, show_axes=True, grid_origin=(0, 0, 0), grid_spacing=(1, 1, 1), **kwargs)</code>","text":"<p>Creates an image cube from a dataset and plots it using PyVista.</p> <p>Parameters:</p> Name Type Description Default <code>dataset</code> <code>Union[str, xr.Dataset]</code> <p>The dataset to plot. Can be a path to a NetCDF file or an xarray Dataset.</p> required <code>variable</code> <code>str</code> <p>The variable to plot. Defaults to \"reflectance\".</p> <code>'reflectance'</code> <code>cmap</code> <code>str</code> <p>The colormap to use. Defaults to \"jet\".</p> <code>'jet'</code> <code>clim</code> <code>Tuple[float, float]</code> <p>The color limits. Defaults to (0, 0.5).</p> <code>(0, 0.5)</code> <code>title</code> <code>str</code> <p>The title for the scalar bar. Defaults to \"Reflectance\".</p> <code>'Reflectance'</code> <code>rgb_bands</code> <code>Optional[List[int]]</code> <p>The bands to use for the RGB image. Defaults to None.</p> <code>None</code> <code>rgb_wavelengths</code> <code>Optional[List[float]]</code> <p>The wavelengths to use for the RGB image. Defaults to None.</p> <code>None</code> <code>rgb_gamma</code> <code>float</code> <p>The gamma correction for the RGB image. Defaults to 1.</p> <code>1.0</code> <code>rgb_cmap</code> <code>Optional[str]</code> <p>The colormap to use for the RGB image. Defaults to None.</p> <code>None</code> <code>rgb_clim</code> <code>Optional[Tuple[float, float]]</code> <p>The color limits for the RGB image. Defaults to None.</p> <code>None</code> <code>rgb_args</code> <code>Dict[str, Any]</code> <p>Additional arguments for the <code>add_mesh</code> method for the RGB image. Defaults to {}.</p> <code>None</code> <code>widget</code> <code>Optional[str]</code> <p>The widget to use for the image cube. Can be one of the following: \"box\", \"plane\", \"slice\", \"orthogonal\", and \"threshold\". Defaults to None.</p> <code>None</code> <code>plotter_args</code> <code>Dict[str, Any]</code> <p>Additional arguments for the <code>pv.Plotter</code> constructor. Defaults to {}.</p> <code>None</code> <code>show_axes</code> <code>bool</code> <p>Whether to show the axes. Defaults to True.</p> <code>True</code> <code>grid_origin</code> <code>Tuple[float, float, float]</code> <p>The origin of the grid. Defaults to (0, 0, 0).</p> <code>(0, 0, 0)</code> <code>grid_spacing</code> <code>Tuple[float, float, float]</code> <p>The spacing of the grid.</p> <code>(1, 1, 1)</code> <code>**kwargs</code> <code>Dict[str, Any]</code> <p>Additional arguments for the <code>add_mesh</code> method. Defaults to {}.</p> <code>{}</code> <p>Returns:</p> Type Description <code>pv.Plotter</code> <p>The PyVista Plotter with the image cube added.</p> Source code in <code>hypercoast/common.py</code> <pre><code>def image_cube(\n    dataset,\n    variable: str = \"reflectance\",\n    cmap: str = \"jet\",\n    clim: Tuple[float, float] = (0, 0.5),\n    title: str = \"Reflectance\",\n    rgb_bands: Optional[List[int]] = None,\n    rgb_wavelengths: Optional[List[float]] = None,\n    rgb_gamma: float = 1.0,\n    rgb_cmap: Optional[str] = None,\n    rgb_clim: Optional[Tuple[float, float]] = None,\n    rgb_args: Dict[str, Any] = None,\n    widget=None,\n    plotter_args: Dict[str, Any] = None,\n    show_axes: bool = True,\n    grid_origin=(0, 0, 0),\n    grid_spacing=(1, 1, 1),\n    **kwargs: Any,\n):\n    \"\"\"\n    Creates an image cube from a dataset and plots it using PyVista.\n\n    Args:\n        dataset (Union[str, xr.Dataset]): The dataset to plot. Can be a path to\n            a NetCDF file or an xarray Dataset.\n        variable (str, optional): The variable to plot. Defaults to \"reflectance\".\n        cmap (str, optional): The colormap to use. Defaults to \"jet\".\n        clim (Tuple[float, float], optional): The color limits. Defaults to (0, 0.5).\n        title (str, optional): The title for the scalar bar. Defaults to \"Reflectance\".\n        rgb_bands (Optional[List[int]], optional): The bands to use for the RGB\n            image. Defaults to None.\n        rgb_wavelengths (Optional[List[float]], optional): The wavelengths to\n            use for the RGB image. Defaults to None.\n        rgb_gamma (float, optional): The gamma correction for the RGB image.\n            Defaults to 1.\n        rgb_cmap (Optional[str], optional): The colormap to use for the RGB image.\n            Defaults to None.\n        rgb_clim (Optional[Tuple[float, float]], optional): The color limits for\n            the RGB image. Defaults to None.\n        rgb_args (Dict[str, Any], optional): Additional arguments for the\n            `add_mesh` method for the RGB image. Defaults to {}.\n        widget (Optional[str], optional): The widget to use for the image cube.\n            Can be one of the following: \"box\", \"plane\", \"slice\", \"orthogonal\",\n            and \"threshold\". Defaults to None.\n        plotter_args (Dict[str, Any], optional): Additional arguments for the\n            `pv.Plotter` constructor. Defaults to {}.\n        show_axes (bool, optional): Whether to show the axes. Defaults to True.\n        grid_origin (Tuple[float, float, float], optional): The origin of the grid.\n            Defaults to (0, 0, 0).\n        grid_spacing (Tuple[float, float, float], optional): The spacing of the grid.\n        **kwargs (Dict[str, Any], optional): Additional arguments for the\n            `add_mesh` method. Defaults to {}.\n\n    Returns:\n        pv.Plotter: The PyVista Plotter with the image cube added.\n    \"\"\"\n\n    import pyvista as pv\n\n    if rgb_args is None:\n        rgb_args = {}\n\n    if plotter_args is None:\n        plotter_args = {}\n\n    allowed_widgets = [\"box\", \"plane\", \"slice\", \"orthogonal\", \"threshold\"]\n\n    if widget is not None:\n        if widget not in allowed_widgets:\n            raise ValueError(f\"widget must be one of the following: {allowed_widgets}\")\n\n    if isinstance(dataset, str):\n        dataset = xr.open_dataset(dataset)\n\n    da = dataset[variable]  # xarray DataArray\n    values = da.to_numpy()\n\n    # Create the spatial reference for the image cube\n    grid = pv.ImageData()\n\n    # Set the grid dimensions: shape because we want to inject our values on the POINT data\n    grid.dimensions = values.shape\n\n    # Edit the spatial reference\n    grid.origin = grid_origin  # The bottom left corner of the data set\n    grid.spacing = grid_spacing  # These are the cell sizes along each axis\n\n    # Add the data values to the cell data\n    grid.point_data[\"values\"] = values.flatten(order=\"F\")  # Flatten the array\n\n    # Plot the image cube with the RGB image overlay\n    p = pv.Plotter(**plotter_args)\n\n    if \"scalar_bar_args\" not in kwargs:\n        kwargs[\"scalar_bar_args\"] = {\"title\": title}\n    else:\n        kwargs[\"scalar_bar_args\"][\"title\"] = title\n\n    if \"show_edges\" not in kwargs:\n        kwargs[\"show_edges\"] = False\n\n    if widget == \"box\":\n        p.add_mesh_clip_box(grid, cmap=cmap, clim=clim, **kwargs)\n    elif widget == \"plane\":\n        if \"normal\" not in kwargs:\n            kwargs[\"normal\"] = (0, 0, 1)\n        if \"invert\" not in kwargs:\n            kwargs[\"invert\"] = True\n        if \"normal_rotation\" not in kwargs:\n            kwargs[\"normal_rotation\"] = False\n        p.add_mesh_clip_plane(grid, cmap=cmap, clim=clim, **kwargs)\n    elif widget == \"slice\":\n        if \"normal\" not in kwargs:\n            kwargs[\"normal\"] = (0, 0, 1)\n        if \"normal_rotation\" not in kwargs:\n            kwargs[\"normal_rotation\"] = False\n        p.add_mesh_slice(grid, cmap=cmap, clim=clim, **kwargs)\n    elif widget == \"orthogonal\":\n        p.add_mesh_slice_orthogonal(grid, cmap=cmap, clim=clim, **kwargs)\n    elif widget == \"threshold\":\n        p.add_mesh_threshold(grid, cmap=cmap, clim=clim, **kwargs)\n    else:\n        p.add_mesh(grid, cmap=cmap, clim=clim, **kwargs)\n\n    if rgb_bands is not None or rgb_wavelengths is not None:\n\n        if rgb_bands is not None:\n            rgb_image = dataset.isel(wavelength=rgb_bands, method=\"nearest\")[\n                variable\n            ].to_numpy()\n        elif rgb_wavelengths is not None:\n            rgb_image = dataset.sel(wavelength=rgb_wavelengths, method=\"nearest\")[\n                variable\n            ].to_numpy()\n\n        x_dim, y_dim = rgb_image.shape[0], rgb_image.shape[1]\n        z_dim = 1\n        im = pv.ImageData(dimensions=(x_dim, y_dim, z_dim))\n\n        # Add scalar data, you may also need to flatten this\n        im.point_data[\"rgb_image\"] = (\n            rgb_image.reshape(-1, rgb_image.shape[2], order=\"F\") * rgb_gamma\n        )\n\n        grid_z_max = grid.bounds[5]\n        im.origin = (0, 0, grid_z_max)\n\n        if rgb_image.shape[2] &lt; 3:\n            if rgb_cmap is None:\n                rgb_cmap = cmap\n            if rgb_clim is None:\n                rgb_clim = clim\n\n            if \"cmap\" not in rgb_args:\n                rgb_args[\"cmap\"] = rgb_cmap\n            if \"clim\" not in rgb_args:\n                rgb_args[\"clim\"] = rgb_clim\n        else:\n            if \"rgb\" not in rgb_args:\n                rgb_args[\"rgb\"] = True\n\n        if \"show_scalar_bar\" not in rgb_args:\n            rgb_args[\"show_scalar_bar\"] = False\n        if \"show_edges\" not in rgb_args:\n            rgb_args[\"show_edges\"] = False\n\n        p.add_mesh(im, **rgb_args)\n\n    if show_axes:\n        p.show_axes()\n\n    return p\n</code></pre>"},{"location":"common/#hypercoast.common.nasa_earth_login","title":"<code>nasa_earth_login(strategy='all', persist=True, **kwargs)</code>","text":"<p>Logs in to NASA Earthdata.</p> <p>Parameters:</p> Name Type Description Default <code>strategy</code> <code>str</code> <p>The login strategy. Defaults to \"all\".</p> <code>'all'</code> <code>persist</code> <code>bool</code> <p>Whether to persist the login. Defaults to True.</p> <code>True</code> Source code in <code>hypercoast/common.py</code> <pre><code>def nasa_earth_login(strategy: str = \"all\", persist: bool = True, **kwargs) -&gt; None:\n    \"\"\"Logs in to NASA Earthdata.\n\n    Args:\n        strategy (str, optional): The login strategy. Defaults to \"all\".\n        persist (bool, optional): Whether to persist the login. Defaults to True.\n    \"\"\"\n    from leafmap import get_api_key\n    import earthaccess\n\n    USERNAME = get_api_key(\"EARTHDATA_USERNAME\")\n    PASSWORD = get_api_key(\"EARTHDATA_PASSWORD\")\n    if (USERNAME is not None) and (PASSWORD is not None):\n        strategy = \"environment\"\n    earthaccess.login(strategy=strategy, persist=persist, **kwargs)\n</code></pre>"},{"location":"common/#hypercoast.common.netcdf_groups","title":"<code>netcdf_groups(filepath)</code>","text":"<p>Get the list of groups in a NetCDF file.</p> <p>Parameters:</p> Name Type Description Default <code>filepath</code> <code>str</code> <p>The path to the NetCDF file.</p> required <p>Returns:</p> Type Description <code>list</code> <p>A list of group names in the NetCDF file.</p> <p>Examples:</p> <pre><code>&gt;&gt;&gt; netcdf_groups('path/to/netcdf/file')\n['group1', 'group2', 'group3']\n</code></pre> Source code in <code>hypercoast/common.py</code> <pre><code>def netcdf_groups(filepath: str) -&gt; List[str]:\n    \"\"\"\n    Get the list of groups in a NetCDF file.\n\n    Args:\n        filepath (str): The path to the NetCDF file.\n\n    Returns:\n        list: A list of group names in the NetCDF file.\n\n    Example:\n        &gt;&gt;&gt; netcdf_groups('path/to/netcdf/file')\n        ['group1', 'group2', 'group3']\n    \"\"\"\n    import h5netcdf\n\n    with h5netcdf.File(filepath) as file:\n        groups = list(file)\n    return groups\n</code></pre>"},{"location":"common/#hypercoast.common.open_dataset","title":"<code>open_dataset(filename, engine=None, chunks=None, **kwargs)</code>","text":"<p>Opens and returns an xarray Dataset from a file.</p> <p>This function is a wrapper around <code>xarray.open_dataset</code> that allows for additional customization through keyword arguments.</p> <p>Parameters:</p> Name Type Description Default <code>filename</code> <code>str</code> <p>Path to the file to open.</p> required <code>engine</code> <code>Optional[str]</code> <p>Name of the engine to use for reading the file. If None, xarray's default engine is used. Examples include 'netcdf4', 'h5netcdf', 'zarr', etc.</p> <code>None</code> <code>chunks</code> <code>Optional[Dict[str, int]]</code> <p>Dictionary specifying how to chunk the dataset along each dimension. For example, <code>{'time': 1}</code> would load the dataset in single-time-step chunks. If None, the dataset is not chunked.</p> <code>None</code> <code>**kwargs</code> <code>Any</code> <p>Additional keyword arguments passed to <code>xarray.open_dataset</code>.</p> <code>{}</code> <p>Returns:</p> Type Description <code>xr.Dataset</code> <p>The opened dataset.</p> <p>Examples:</p> <p>Open a NetCDF file without chunking:</p> <pre><code>&gt;&gt;&gt; dataset = open_dataset('path/to/file.nc')\n</code></pre> <p>Open a Zarr dataset, chunking along the 'time' dimension:</p> <pre><code>&gt;&gt;&gt; dataset = open_dataset('path/to/dataset.zarr', engine='zarr', chunks={'time': 10})\n</code></pre> Source code in <code>hypercoast/common.py</code> <pre><code>def open_dataset(\n    filename: str,\n    engine: Optional[str] = None,\n    chunks: Optional[Dict[str, int]] = None,\n    **kwargs: Any,\n) -&gt; xr.Dataset:\n    \"\"\"\n    Opens and returns an xarray Dataset from a file.\n\n    This function is a wrapper around `xarray.open_dataset` that allows for additional\n    customization through keyword arguments.\n\n    Args:\n        filename (str): Path to the file to open.\n        engine (Optional[str]): Name of the engine to use for reading the file. If None, xarray's\n            default engine is used. Examples include 'netcdf4', 'h5netcdf', 'zarr', etc.\n        chunks (Optional[Dict[str, int]]): Dictionary specifying how to chunk the dataset along each dimension.\n            For example, `{'time': 1}` would load the dataset in single-time-step chunks. If None,\n            the dataset is not chunked.\n        **kwargs: Additional keyword arguments passed to `xarray.open_dataset`.\n\n    Returns:\n        xr.Dataset: The opened dataset.\n\n    Examples:\n        Open a NetCDF file without chunking:\n        &gt;&gt;&gt; dataset = open_dataset('path/to/file.nc')\n\n        Open a Zarr dataset, chunking along the 'time' dimension:\n        &gt;&gt;&gt; dataset = open_dataset('path/to/dataset.zarr', engine='zarr', chunks={'time': 10})\n    \"\"\"\n\n    try:\n        dataset = xr.open_dataset(filename, engine=engine, chunks=chunks, **kwargs)\n    except OSError:\n        dataset = xr.open_dataset(filename, engine=\"h5netcdf\", chunks=chunks, **kwargs)\n\n    return dataset\n</code></pre>"},{"location":"common/#hypercoast.common.pca","title":"<code>pca(input_file, output_file, n_components=3, **kwargs)</code>","text":"<p>Performs Principal Component Analysis (PCA) on a dataset.</p> <p>Parameters:</p> Name Type Description Default <code>input_file</code> <code>str</code> <p>The input file containing the data to analyze.</p> required <code>output_file</code> <code>str</code> <p>The output file to save the PCA results.</p> required <code>n_components</code> <code>int</code> <p>The number of principal components to compute. Defaults to 3.</p> <code>3</code> <code>**kwargs</code> <p>Additional keyword arguments to pass to the scikit-learn PCA function. For more info, see https://scikit-learn.org/stable/modules/generated/sklearn.decomposition.PCA.html.</p> <code>{}</code> <p>Returns:</p> Type Description <code>None</code> <p>This function does not return a value. It saves the PCA results to the output file.</p> Source code in <code>hypercoast/common.py</code> <pre><code>def pca(input_file, output_file, n_components=3, **kwargs):\n    \"\"\"\n    Performs Principal Component Analysis (PCA) on a dataset.\n\n    Args:\n        input_file (str): The input file containing the data to analyze.\n        output_file (str): The output file to save the PCA results.\n        n_components (int, optional): The number of principal components to compute. Defaults to 3.\n        **kwargs: Additional keyword arguments to pass to the scikit-learn PCA function.\n            For more info, see https://scikit-learn.org/stable/modules/generated/sklearn.decomposition.PCA.html.\n\n    Returns:\n        None: This function does not return a value. It saves the PCA results to the output file.\n    \"\"\"\n\n    import rasterio\n    from sklearn.decomposition import PCA\n\n    # Function to load the GeoTIFF image\n    def load_geotiff(file_path):\n        with rasterio.open(file_path) as src:\n            image = src.read()\n            profile = src.profile\n        return image, profile\n\n    # Function to perform PCA\n    def perform_pca(image, n_components=3, **kwargs):\n        # Reshape the image to [n_bands, n_pixels]\n        n_bands, width, height = image.shape\n        image_reshaped = image.reshape(n_bands, width * height).T\n\n        # Perform PCA\n        model = PCA(n_components=n_components, **kwargs)\n        principal_components = model.fit_transform(image_reshaped)\n\n        # Reshape the principal components back to image dimensions\n        pca_image = principal_components.T.reshape(n_components, width, height)\n        return pca_image\n\n    # Function to save the PCA-transformed image\n    def save_geotiff(file_path, image, profile):\n        profile.update(count=image.shape[0])\n        with rasterio.open(file_path, \"w\", **profile) as dst:\n            dst.write(image)\n\n    image, profile = load_geotiff(input_file)\n    pca_image = perform_pca(image, n_components, **kwargs)\n    save_geotiff(output_file, pca_image, profile)\n</code></pre>"},{"location":"common/#hypercoast.common.run_acolite","title":"<code>run_acolite(acolite_dir, settings_file=None, input_file=None, out_dir=None, polygon=None, l2w_parameters=None, rgb_rhot=True, rgb_rhos=True, map_l2w=True, verbose=True, **kwargs)</code>","text":"<p>Runs the Acolite software for atmospheric correction and water quality retrieval. For more information, see the Acolite manual https://github.com/acolite/acolite/releases</p> <p>This function constructs and executes a command to run the Acolite software with the specified parameters. It supports running Acolite with a settings file or with individual parameters specified directly. Additional parameters can be passed as keyword arguments.</p> <p>Parameters:</p> Name Type Description Default <code>acolite_dir</code> <code>str</code> <p>The directory where Acolite is installed.</p> required <code>settings_file</code> <code>Optional[str]</code> <p>The path to the Acolite settings file. If provided, other parameters except <code>verbose</code> are ignored. Defaults to None.</p> <code>None</code> <code>input_file</code> <code>Optional[str]</code> <p>The path to the input file for processing. Defaults to None.</p> <code>None</code> <code>out_dir</code> <code>Optional[str]</code> <p>The directory where output files will be saved. Defaults to None.</p> <code>None</code> <code>polygon</code> <code>Optional[str]</code> <p>The path to a polygon file for spatial subset. Defaults to None.</p> <code>None</code> <code>l2w_parameters</code> <code>Optional[str]</code> <p>Parameters for L2W processing. Defaults to None.</p> <code>None</code> <code>rgb_rhot</code> <code>bool</code> <p>Flag to generate RGB images using rhot. Defaults to True.</p> <code>True</code> <code>rgb_rhos</code> <code>bool</code> <p>Flag to generate RGB images using rhos. Defaults to True.</p> <code>True</code> <code>map_l2w</code> <code>bool</code> <p>Flag to map L2W products. Defaults to True.</p> <code>True</code> <code>verbose</code> <code>bool</code> <p>If True, prints the command output; otherwise, suppresses it. Defaults to True.</p> <code>True</code> <code>**kwargs</code> <code>Any</code> <p>Additional command line arguments to pass to acolite. Such as --l2w_export_geotiff, --merge_tiles, etc.</p> <code>{}</code> <p>Returns:</p> Type Description <code>None</code> <p>This function does not return a value. It executes the Acolite software.</p> <p>Examples:</p> <pre><code>&gt;&gt;&gt; run_acolite(\"/path/to/acolite\", input_file=\"/path/to/inputfile\", output=\"/path/to/output\")\n</code></pre> Source code in <code>hypercoast/common.py</code> <pre><code>def run_acolite(\n    acolite_dir: str,\n    settings_file: Optional[str] = None,\n    input_file: Optional[str] = None,\n    out_dir: Optional[str] = None,\n    polygon: Optional[str] = None,\n    l2w_parameters: Optional[str] = None,\n    rgb_rhot: bool = True,\n    rgb_rhos: bool = True,\n    map_l2w: bool = True,\n    verbose: bool = True,\n    **kwargs: Any,\n) -&gt; None:\n    \"\"\"\n    Runs the Acolite software for atmospheric correction and water quality retrieval.\n    For more information, see the Acolite manual https://github.com/acolite/acolite/releases\n\n    This function constructs and executes a command to run the Acolite software with the specified\n    parameters. It supports running Acolite with a settings file or with individual parameters\n    specified directly. Additional parameters can be passed as keyword arguments.\n\n    Args:\n        acolite_dir (str): The directory where Acolite is installed.\n        settings_file (Optional[str], optional): The path to the Acolite settings file. If provided,\n            other parameters except `verbose` are ignored. Defaults to None.\n        input_file (Optional[str], optional): The path to the input file for processing. Defaults to None.\n        out_dir (Optional[str], optional): The directory where output files will be saved. Defaults to None.\n        polygon (Optional[str], optional): The path to a polygon file for spatial subset. Defaults to None.\n        l2w_parameters (Optional[str], optional): Parameters for L2W processing. Defaults to None.\n        rgb_rhot (bool, optional): Flag to generate RGB images using rhot. Defaults to True.\n        rgb_rhos (bool, optional): Flag to generate RGB images using rhos. Defaults to True.\n        map_l2w (bool, optional): Flag to map L2W products. Defaults to True.\n        verbose (bool, optional): If True, prints the command output; otherwise, suppresses it. Defaults to True.\n        **kwargs (Any): Additional command line arguments to pass to acolite. Such as\n            --l2w_export_geotiff, --merge_tiles, etc.\n\n    Returns:\n        None: This function does not return a value. It executes the Acolite software.\n\n    Example:\n        &gt;&gt;&gt; run_acolite(\"/path/to/acolite\", input_file=\"/path/to/inputfile\", output=\"/path/to/output\")\n    \"\"\"\n\n    import subprocess\n    from datetime import datetime\n\n    def get_formatted_current_time(format_str=\"%Y-%m-%d %H:%M:%S\"):\n        current_time = datetime.now()\n        formatted_time = current_time.strftime(format_str)\n        return formatted_time\n\n    acolite_dir_name = os.path.split(acolite_dir)[-1]\n    acolite_exe = \"acolite\"\n    if acolite_dir_name.endswith(\"win\"):\n        acolite_exe += \".exe\"\n\n    if isinstance(input_file, list):\n        input_file = \",\".join(input_file)\n\n    if not os.path.exists(out_dir):\n        os.makedirs(out_dir)\n\n    acolite_exe_path = os.path.join(acolite_dir, \"dist\", \"acolite\", acolite_exe)\n    acolite_exe_path = acolite_exe_path.replace(\"\\\\\", \"/\")\n\n    acolite_cmd = [acolite_exe_path, \"--cli\"]\n\n    if settings_file is not None:\n        acolite_cmd.extend([\"--settings\", settings_file])\n    else:\n        lines = []\n        lines.append(\"## ACOLITE settings\")\n        lines.append(f\"## Written at {get_formatted_current_time()}\")\n        if input_file is not None:\n            input_file = input_file.replace(\"\\\\\", \"/\")\n            lines.append(f\"inputfile={input_file}\")\n        if out_dir is not None:\n            out_dir = out_dir.replace(\"\\\\\", \"/\")\n            lines.append(f\"output={out_dir}\")\n        if polygon is not None:\n            lines.append(f\"polygon={polygon}\")\n        else:\n            lines.append(\"polygon=None\")\n        if l2w_parameters is not None:\n            lines.append(f\"l2w_parameters={l2w_parameters}\")\n        if rgb_rhot:\n            lines.append(\"rgb_rhot=True\")\n        else:\n            lines.append(\"rgb_rhot=False\")\n        if rgb_rhos:\n            lines.append(\"rgb_rhos=True\")\n        else:\n            lines.append(\"rgb_rhos=False\")\n        if map_l2w:\n            lines.append(\"map_l2w=True\")\n        else:\n            lines.append(\"map_l2w=False\")\n\n        for key, value in kwargs.items():\n            lines.append(f\"{key}={value}\")\n\n        lines.append(f\"runid={get_formatted_current_time('%Y%m%d_%H%M%S')}\")\n        settings_filename = f\"acolite_run_{get_formatted_current_time('%Y%m%d_%H%M%S')}_settings_user.txt\"\n        settings_file = os.path.join(out_dir, settings_filename).replace(\"\\\\\", \"/\")\n        with open(settings_file, \"w\", encoding=\"utf-8\") as f:\n            f.write(\"\\n\".join(lines))\n        acolite_cmd.extend([\"--settings\", settings_file])\n\n    if acolite_dir_name.endswith(\"win\"):\n        acolite_cmd = \" \".join(acolite_cmd)\n\n    if verbose:\n        subprocess.run(acolite_cmd, check=True)\n    else:\n        subprocess.run(\n            acolite_cmd,\n            stdout=subprocess.DEVNULL,\n            stderr=subprocess.DEVNULL,\n            check=True,\n        )\n</code></pre>"},{"location":"common/#hypercoast.common.search_datasets","title":"<code>search_datasets(count=-1, **kwargs)</code>","text":"<p>Searches for datasets using the EarthAccess API with optional filters.</p> <p>This function wraps the <code>earthaccess.search_datasets</code> function, allowing for customized search queries based on a count limit and additional keyword arguments which serve as filters for the search.</p> <p>Parameters:</p> Name Type Description Default <code>count</code> <code>int</code> <p>The maximum number of datasets to return. A value of -1 indicates no limit. Defaults to -1.</p> <code>-1</code> <code>**kwargs</code> <code>Any</code> <p>Additional keyword arguments to pass as search filters to the EarthAccess API. keyword: case-insensitive and supports wildcards ? and * short_name: e.g. ATL08 doi: DOI for a dataset daac: e.g. NSIDC or PODAAC provider: particular to each DAAC, e.g. POCLOUD, LPDAAC etc. temporal: a tuple representing temporal bounds in the form (date_from, date_to) bounding_box: a tuple representing spatial bounds in the form (lower_left_lon, lower_left_lat, upper_right_lon, upper_right_lat)</p> <code>{}</code> <p>Returns:</p> Type Description <code>List[Dict[str, Any]]</code> <p>A list of dictionaries, where each dictionary contains     information about a dataset found in the search.</p> <p>Examples:</p> <pre><code>&gt;&gt;&gt; results = search_datasets(count=5, keyword='temperature')\n&gt;&gt;&gt; print(results)\n</code></pre> Source code in <code>hypercoast/common.py</code> <pre><code>def search_datasets(count: int = -1, **kwargs: Any) -&gt; List[Dict[str, Any]]:\n    \"\"\"\n    Searches for datasets using the EarthAccess API with optional filters.\n\n    This function wraps the `earthaccess.search_datasets` function, allowing for\n    customized search queries based on a count limit and additional keyword arguments\n    which serve as filters for the search.\n\n    Args:\n        count (int, optional): The maximum number of datasets to return. A value of -1\n            indicates no limit. Defaults to -1.\n        **kwargs (Any): Additional keyword arguments to pass as search filters to the\n            EarthAccess API.\n            keyword: case-insensitive and supports wildcards ? and *\n            short_name: e.g. ATL08\n            doi: DOI for a dataset\n            daac: e.g. NSIDC or PODAAC\n            provider: particular to each DAAC, e.g. POCLOUD, LPDAAC etc.\n            temporal: a tuple representing temporal bounds in the form (date_from, date_to)\n            bounding_box: a tuple representing spatial bounds in the form\n            (lower_left_lon, lower_left_lat, upper_right_lon, upper_right_lat)\n\n\n    Returns:\n        List[Dict[str, Any]]: A list of dictionaries, where each dictionary contains\n            information about a dataset found in the search.\n\n    Example:\n        &gt;&gt;&gt; results = search_datasets(count=5, keyword='temperature')\n        &gt;&gt;&gt; print(results)\n    \"\"\"\n\n    import earthaccess\n\n    return earthaccess.search_datasets(count=count, **kwargs)\n</code></pre>"},{"location":"common/#hypercoast.common.search_ecostress","title":"<code>search_ecostress(bbox=None, temporal=None, count=-1, short_name='ECO_L2T_LSTE', output=None, crs='EPSG:4326', return_gdf=False, **kwargs)</code>","text":"<p>Searches for NASA ECOSTRESS granules.</p> <p>Parameters:</p> Name Type Description Default <code>bbox</code> <code>List[float]</code> <p>The bounding box coordinates [xmin, ymin, xmax, ymax].</p> <code>None</code> <code>temporal</code> <code>str</code> <p>The temporal extent of the data.</p> <code>None</code> <code>count</code> <code>int</code> <p>The number of granules to retrieve. Defaults to -1 (retrieve all).</p> <code>-1</code> <code>short_name</code> <code>str</code> <p>The short name of the dataset. Defaults to \"ECO_L2T_LSTE\".</p> <code>'ECO_L2T_LSTE'</code> <code>output</code> <code>str</code> <p>The output file path to save the GeoDataFrame as a file.</p> <code>None</code> <code>crs</code> <code>str</code> <p>The coordinate reference system (CRS) of the GeoDataFrame. Defaults to \"EPSG:4326\".</p> <code>'EPSG:4326'</code> <code>return_gdf</code> <code>bool</code> <p>Whether to return the GeoDataFrame in addition to the granules. Defaults to False.</p> <code>False</code> <code>**kwargs</code> <p>Additional keyword arguments for the earthaccess.search_data() function.</p> <code>{}</code> <p>Returns:</p> Type Description <code>Union[List[dict], tuple]</code> <p>The retrieved granules. If return_gdf is True, also returns the resulting GeoDataFrame.</p> Source code in <code>hypercoast/common.py</code> <pre><code>def search_ecostress(\n    bbox: Optional[List[float]] = None,\n    temporal: Optional[str] = None,\n    count: int = -1,\n    short_name: Optional[str] = \"ECO_L2T_LSTE\",\n    output: Optional[str] = None,\n    crs: str = \"EPSG:4326\",\n    return_gdf: bool = False,\n    **kwargs,\n) -&gt; Union[List[dict], tuple]:\n    \"\"\"Searches for NASA ECOSTRESS granules.\n\n    Args:\n        bbox (List[float], optional): The bounding box coordinates [xmin, ymin, xmax, ymax].\n        temporal (str, optional): The temporal extent of the data.\n        count (int, optional): The number of granules to retrieve. Defaults to -1 (retrieve all).\n        short_name (str, optional): The short name of the dataset. Defaults to \"ECO_L2T_LSTE\".\n        output (str, optional): The output file path to save the GeoDataFrame as a file.\n        crs (str, optional): The coordinate reference system (CRS) of the GeoDataFrame. Defaults to \"EPSG:4326\".\n        return_gdf (bool, optional): Whether to return the GeoDataFrame in addition to the granules. Defaults to False.\n        **kwargs: Additional keyword arguments for the earthaccess.search_data() function.\n\n    Returns:\n        Union[List[dict], tuple]: The retrieved granules. If return_gdf is True, also returns the resulting GeoDataFrame.\n    \"\"\"\n\n    return search_nasa_data(\n        count=count,\n        short_name=short_name,\n        bbox=bbox,\n        temporal=temporal,\n        output=output,\n        crs=crs,\n        return_gdf=return_gdf,\n        **kwargs,\n    )\n</code></pre>"},{"location":"common/#hypercoast.common.search_emit","title":"<code>search_emit(bbox=None, temporal=None, count=-1, short_name='EMITL2ARFL', output=None, crs='EPSG:4326', return_gdf=False, **kwargs)</code>","text":"<p>Searches for NASA EMIT granules.</p> <p>Parameters:</p> Name Type Description Default <code>bbox</code> <code>List[float]</code> <p>The bounding box coordinates [xmin, ymin, xmax, ymax].</p> <code>None</code> <code>temporal</code> <code>str</code> <p>The temporal extent of the data.</p> <code>None</code> <code>count</code> <code>int</code> <p>The number of granules to retrieve. Defaults to -1 (retrieve all).</p> <code>-1</code> <code>short_name</code> <code>str</code> <p>The short name of the dataset. Defaults to \"EMITL2ARFL\".</p> <code>'EMITL2ARFL'</code> <code>output</code> <code>str</code> <p>The output file path to save the GeoDataFrame as a file.</p> <code>None</code> <code>crs</code> <code>str</code> <p>The coordinate reference system (CRS) of the GeoDataFrame. Defaults to \"EPSG:4326\".</p> <code>'EPSG:4326'</code> <code>return_gdf</code> <code>bool</code> <p>Whether to return the GeoDataFrame in addition to the granules. Defaults to False.</p> <code>False</code> <code>**kwargs</code> <p>Additional keyword arguments for the earthaccess.search_data() function.</p> <code>{}</code> <p>Returns:</p> Type Description <code>Union[List[dict], tuple]</code> <p>The retrieved granules. If return_gdf is True, also returns the resulting GeoDataFrame.</p> Source code in <code>hypercoast/common.py</code> <pre><code>def search_emit(\n    bbox: Optional[List[float]] = None,\n    temporal: Optional[str] = None,\n    count: int = -1,\n    short_name: Optional[str] = \"EMITL2ARFL\",\n    output: Optional[str] = None,\n    crs: str = \"EPSG:4326\",\n    return_gdf: bool = False,\n    **kwargs,\n) -&gt; Union[List[dict], tuple]:\n    \"\"\"Searches for NASA EMIT granules.\n\n    Args:\n        bbox (List[float], optional): The bounding box coordinates [xmin, ymin, xmax, ymax].\n        temporal (str, optional): The temporal extent of the data.\n        count (int, optional): The number of granules to retrieve. Defaults to -1 (retrieve all).\n        short_name (str, optional): The short name of the dataset. Defaults to \"EMITL2ARFL\".\n        output (str, optional): The output file path to save the GeoDataFrame as a file.\n        crs (str, optional): The coordinate reference system (CRS) of the GeoDataFrame. Defaults to \"EPSG:4326\".\n        return_gdf (bool, optional): Whether to return the GeoDataFrame in addition to the granules. Defaults to False.\n        **kwargs: Additional keyword arguments for the earthaccess.search_data() function.\n\n    Returns:\n        Union[List[dict], tuple]: The retrieved granules. If return_gdf is True, also returns the resulting GeoDataFrame.\n    \"\"\"\n\n    return search_nasa_data(\n        count=count,\n        short_name=short_name,\n        bbox=bbox,\n        temporal=temporal,\n        output=output,\n        crs=crs,\n        return_gdf=return_gdf,\n        **kwargs,\n    )\n</code></pre>"},{"location":"common/#hypercoast.common.search_nasa_data","title":"<code>search_nasa_data(count=-1, short_name=None, bbox=None, temporal=None, version=None, doi=None, daac=None, provider=None, output=None, crs='EPSG:4326', return_gdf=False, **kwargs)</code>","text":"<p>Searches for NASA Earthdata granules.</p> <p>Parameters:</p> Name Type Description Default <code>count</code> <code>int</code> <p>The number of granules to retrieve. Defaults to -1 (retrieve all).</p> <code>-1</code> <code>short_name</code> <code>str</code> <p>The short name of the dataset.</p> <code>None</code> <code>bbox</code> <code>List[float]</code> <p>The bounding box coordinates [xmin, ymin, xmax, ymax].</p> <code>None</code> <code>temporal</code> <code>str</code> <p>The temporal extent of the data.</p> <code>None</code> <code>version</code> <code>str</code> <p>The version of the dataset.</p> <code>None</code> <code>doi</code> <code>str</code> <p>The Digital Object Identifier (DOI) of the dataset.</p> <code>None</code> <code>daac</code> <code>str</code> <p>The Distributed Active Archive Center (DAAC) of the dataset.</p> <code>None</code> <code>provider</code> <code>str</code> <p>The provider of the dataset.</p> <code>None</code> <code>output</code> <code>str</code> <p>The output file path to save the GeoDataFrame as a file.</p> <code>None</code> <code>crs</code> <code>str</code> <p>The coordinate reference system (CRS) of the GeoDataFrame. Defaults to \"EPSG:4326\".</p> <code>'EPSG:4326'</code> <code>return_gdf</code> <code>bool</code> <p>Whether to return the GeoDataFrame in addition to the granules. Defaults to False.</p> <code>False</code> <code>**kwargs</code> <p>Additional keyword arguments for the earthaccess.search_data() function.</p> <code>{}</code> <p>Returns:</p> Type Description <code>Union[List[dict], tuple]</code> <p>The retrieved granules. If return_gdf is True, also returns the resulting GeoDataFrame.</p> Source code in <code>hypercoast/common.py</code> <pre><code>def search_nasa_data(\n    count: int = -1,\n    short_name: Optional[str] = None,\n    bbox: Optional[List[float]] = None,\n    temporal: Optional[str] = None,\n    version: Optional[str] = None,\n    doi: Optional[str] = None,\n    daac: Optional[str] = None,\n    provider: Optional[str] = None,\n    output: Optional[str] = None,\n    crs: str = \"EPSG:4326\",\n    return_gdf: bool = False,\n    **kwargs,\n) -&gt; Union[List[dict], tuple]:\n    \"\"\"Searches for NASA Earthdata granules.\n\n    Args:\n        count (int, optional): The number of granules to retrieve. Defaults to -1 (retrieve all).\n        short_name (str, optional): The short name of the dataset.\n        bbox (List[float], optional): The bounding box coordinates [xmin, ymin, xmax, ymax].\n        temporal (str, optional): The temporal extent of the data.\n        version (str, optional): The version of the dataset.\n        doi (str, optional): The Digital Object Identifier (DOI) of the dataset.\n        daac (str, optional): The Distributed Active Archive Center (DAAC) of the dataset.\n        provider (str, optional): The provider of the dataset.\n        output (str, optional): The output file path to save the GeoDataFrame as a file.\n        crs (str, optional): The coordinate reference system (CRS) of the GeoDataFrame. Defaults to \"EPSG:4326\".\n        return_gdf (bool, optional): Whether to return the GeoDataFrame in addition to the granules. Defaults to False.\n        **kwargs: Additional keyword arguments for the earthaccess.search_data() function.\n\n    Returns:\n        Union[List[dict], tuple]: The retrieved granules. If return_gdf is True, also returns the resulting GeoDataFrame.\n    \"\"\"\n\n    if isinstance(bbox, list):\n        bbox = tuple(bbox)\n\n    return leafmap.nasa_data_search(\n        count=count,\n        short_name=short_name,\n        bbox=bbox,\n        temporal=temporal,\n        version=version,\n        doi=doi,\n        daac=daac,\n        provider=provider,\n        output=output,\n        crs=crs,\n        return_gdf=return_gdf,\n        **kwargs,\n    )\n</code></pre>"},{"location":"common/#hypercoast.common.search_pace","title":"<code>search_pace(bbox=None, temporal=None, count=-1, short_name='PACE_OCI_L2_AOP_NRT', output=None, crs='EPSG:4326', return_gdf=False, **kwargs)</code>","text":"<p>Searches for NASA PACE granules.</p> <p>Parameters:</p> Name Type Description Default <code>bbox</code> <code>List[float]</code> <p>The bounding box coordinates [xmin, ymin, xmax, ymax].</p> <code>None</code> <code>temporal</code> <code>str</code> <p>The temporal extent of the data.</p> <code>None</code> <code>count</code> <code>int</code> <p>The number of granules to retrieve. Defaults to -1 (retrieve all).</p> <code>-1</code> <code>short_name</code> <code>str</code> <p>The short name of the dataset. Defaults to \"PACE_OCI_L2_AOP_NRT\".</p> <code>'PACE_OCI_L2_AOP_NRT'</code> <code>output</code> <code>str</code> <p>The output file path to save the GeoDataFrame as a file.</p> <code>None</code> <code>crs</code> <code>str</code> <p>The coordinate reference system (CRS) of the GeoDataFrame. Defaults to \"EPSG:4326\".</p> <code>'EPSG:4326'</code> <code>return_gdf</code> <code>bool</code> <p>Whether to return the GeoDataFrame in addition to the granules. Defaults to False.</p> <code>False</code> <code>**kwargs</code> <p>Additional keyword arguments for the earthaccess.search_data() function.</p> <code>{}</code> <p>Returns:</p> Type Description <code>Union[List[dict], tuple]</code> <p>The retrieved granules. If return_gdf is True, also returns the resulting GeoDataFrame.</p> Source code in <code>hypercoast/common.py</code> <pre><code>def search_pace(\n    bbox: Optional[List[float]] = None,\n    temporal: Optional[str] = None,\n    count: int = -1,\n    short_name: Optional[str] = \"PACE_OCI_L2_AOP_NRT\",\n    output: Optional[str] = None,\n    crs: str = \"EPSG:4326\",\n    return_gdf: bool = False,\n    **kwargs,\n) -&gt; Union[List[dict], tuple]:\n    \"\"\"Searches for NASA PACE granules.\n\n    Args:\n        bbox (List[float], optional): The bounding box coordinates [xmin, ymin, xmax, ymax].\n        temporal (str, optional): The temporal extent of the data.\n        count (int, optional): The number of granules to retrieve. Defaults to -1 (retrieve all).\n        short_name (str, optional): The short name of the dataset. Defaults to \"PACE_OCI_L2_AOP_NRT\".\n        output (str, optional): The output file path to save the GeoDataFrame as a file.\n        crs (str, optional): The coordinate reference system (CRS) of the GeoDataFrame. Defaults to \"EPSG:4326\".\n        return_gdf (bool, optional): Whether to return the GeoDataFrame in addition to the granules. Defaults to False.\n        **kwargs: Additional keyword arguments for the earthaccess.search_data() function.\n\n    Returns:\n        Union[List[dict], tuple]: The retrieved granules. If return_gdf is True, also returns the resulting GeoDataFrame.\n    \"\"\"\n\n    return search_nasa_data(\n        count=count,\n        short_name=short_name,\n        bbox=bbox,\n        temporal=temporal,\n        output=output,\n        crs=crs,\n        return_gdf=return_gdf,\n        **kwargs,\n    )\n</code></pre>"},{"location":"common/#hypercoast.common.search_pace_chla","title":"<code>search_pace_chla(bbox=None, temporal=None, count=-1, short_name='PACE_OCI_L3M_CHL_NRT', granule_name='*.DAY.*.0p1deg.*', output=None, crs='EPSG:4326', return_gdf=False, **kwargs)</code>","text":"<p>Searches for NASA PACE Chlorophyll granules.</p> <p>Parameters:</p> Name Type Description Default <code>bbox</code> <code>List[float]</code> <p>The bounding box coordinates [xmin, ymin, xmax, ymax].</p> <code>None</code> <code>temporal</code> <code>str</code> <p>The temporal extent of the data.</p> <code>None</code> <code>count</code> <code>int</code> <p>The number of granules to retrieve. Defaults to -1 (retrieve all).</p> <code>-1</code> <code>short_name</code> <code>str</code> <p>The short name of the dataset. Defaults to \"PACE_OCI_L3M_CHL_NRT\".</p> <code>'PACE_OCI_L3M_CHL_NRT'</code> <code>output</code> <code>str</code> <p>The output file path to save the GeoDataFrame as a file.</p> <code>None</code> <code>crs</code> <code>str</code> <p>The coordinate reference system (CRS) of the GeoDataFrame. Defaults to \"EPSG:4326\".</p> <code>'EPSG:4326'</code> <code>return_gdf</code> <code>bool</code> <p>Whether to return the GeoDataFrame in addition to the granules. Defaults to False.</p> <code>False</code> <code>**kwargs</code> <p>Additional keyword arguments for the earthaccess.search_data() function.</p> <code>{}</code> <p>Returns:</p> Type Description <code>Union[List[dict], tuple]</code> <p>The retrieved granules. If return_gdf is True, also returns the resulting GeoDataFrame.</p> Source code in <code>hypercoast/common.py</code> <pre><code>def search_pace_chla(\n    bbox: Optional[List[float]] = None,\n    temporal: Optional[str] = None,\n    count: int = -1,\n    short_name: Optional[str] = \"PACE_OCI_L3M_CHL_NRT\",\n    granule_name: Optional[str] = \"*.DAY.*.0p1deg.*\",\n    output: Optional[str] = None,\n    crs: str = \"EPSG:4326\",\n    return_gdf: bool = False,\n    **kwargs,\n) -&gt; Union[List[dict], tuple]:\n    \"\"\"Searches for NASA PACE Chlorophyll granules.\n\n    Args:\n        bbox (List[float], optional): The bounding box coordinates [xmin, ymin, xmax, ymax].\n        temporal (str, optional): The temporal extent of the data.\n        count (int, optional): The number of granules to retrieve. Defaults to -1 (retrieve all).\n        short_name (str, optional): The short name of the dataset. Defaults to \"PACE_OCI_L3M_CHL_NRT\".\n        output (str, optional): The output file path to save the GeoDataFrame as a file.\n        crs (str, optional): The coordinate reference system (CRS) of the GeoDataFrame. Defaults to \"EPSG:4326\".\n        return_gdf (bool, optional): Whether to return the GeoDataFrame in addition to the granules. Defaults to False.\n        **kwargs: Additional keyword arguments for the earthaccess.search_data() function.\n\n    Returns:\n        Union[List[dict], tuple]: The retrieved granules. If return_gdf is True, also returns the resulting GeoDataFrame.\n    \"\"\"\n\n    return search_nasa_data(\n        count=count,\n        short_name=short_name,\n        bbox=bbox,\n        temporal=temporal,\n        granule_name=granule_name,\n        output=output,\n        crs=crs,\n        return_gdf=return_gdf,\n        **kwargs,\n    )\n</code></pre>"},{"location":"common/#hypercoast.common.show_field_data","title":"<code>show_field_data(data, x_col='wavelength', y_col_prefix='(', x_label='Wavelengths (nm)', y_label='Reflectance', use_marker_cluster=True, min_width=400, max_width=600, min_height=200, max_height=250, layer_name='Marker Cluster', m=None, center=(20, 0), zoom=2)</code>","text":"<p>Displays field data on a map with interactive markers and popups showing time series data.</p> <p>Parameters:</p> Name Type Description Default <code>data</code> <code>Union[str, pd.DataFrame]</code> <p>Path to the CSV file or a pandas DataFrame containing the data.</p> required <code>x_col</code> <code>str</code> <p>Column name to use for the x-axis of the charts. Default is \"wavelength\".</p> <code>'wavelength'</code> <code>y_col_prefix</code> <code>str</code> <p>Prefix to identify the columns that contain the location-specific data. Default is \"(\".</p> <code>'('</code> <code>x_label</code> <code>str</code> <p>Label for the x-axis of the charts. Default is \"Wavelengths (nm)\".</p> <code>'Wavelengths (nm)'</code> <code>y_label</code> <code>str</code> <p>Label for the y-axis of the charts. Default is \"Reflectance\".</p> <code>'Reflectance'</code> <code>use_marker_cluster</code> <code>bool</code> <p>Whether to use marker clustering. Default is True.</p> <code>True</code> <code>min_width</code> <code>int</code> <p>Minimum width of the popup. Default is 400.</p> <code>400</code> <code>max_width</code> <code>int</code> <p>Maximum width of the popup. Default is 600.</p> <code>600</code> <code>min_height</code> <code>int</code> <p>Minimum height of the popup. Default is 200.</p> <code>200</code> <code>max_height</code> <code>int</code> <p>Maximum height of the popup. Default is 250.</p> <code>250</code> <code>layer_name</code> <code>str</code> <p>Name of the marker cluster layer. Default is \"Marker Cluster\".</p> <code>'Marker Cluster'</code> <code>m</code> <code>Map</code> <p>An ipyleaflet Map instance to add the markers to. Default is None.</p> <code>None</code> <code>center</code> <code>Tuple[float, float]</code> <p>Center of the map as a tuple of (latitude, longitude). Default is (20, 0).</p> <code>(20, 0)</code> <code>zoom</code> <code>int</code> <p>Zoom level of the map. Default is 2.</p> <code>2</code> <p>Returns:</p> Type Description <code>Map</code> <p>An ipyleaflet Map with the added markers and popups.</p> Source code in <code>hypercoast/common.py</code> <pre><code>def show_field_data(\n    data: Union[str],\n    x_col: str = \"wavelength\",\n    y_col_prefix: str = \"(\",\n    x_label: str = \"Wavelengths (nm)\",\n    y_label: str = \"Reflectance\",\n    use_marker_cluster: bool = True,\n    min_width: int = 400,\n    max_width: int = 600,\n    min_height: int = 200,\n    max_height: int = 250,\n    layer_name: str = \"Marker Cluster\",\n    m: object = None,\n    center: Tuple[float, float] = (20, 0),\n    zoom: int = 2,\n):\n    \"\"\"\n    Displays field data on a map with interactive markers and popups showing time series data.\n\n    Args:\n        data (Union[str, pd.DataFrame]): Path to the CSV file or a pandas DataFrame containing the data.\n        x_col (str): Column name to use for the x-axis of the charts. Default is \"wavelength\".\n        y_col_prefix (str): Prefix to identify the columns that contain the location-specific data. Default is \"(\".\n        x_label (str): Label for the x-axis of the charts. Default is \"Wavelengths (nm)\".\n        y_label (str): Label for the y-axis of the charts. Default is \"Reflectance\".\n        use_marker_cluster (bool): Whether to use marker clustering. Default is True.\n        min_width (int): Minimum width of the popup. Default is 400.\n        max_width (int): Maximum width of the popup. Default is 600.\n        min_height (int): Minimum height of the popup. Default is 200.\n        max_height (int): Maximum height of the popup. Default is 250.\n        layer_name (str): Name of the marker cluster layer. Default is \"Marker Cluster\".\n        m (Map, optional): An ipyleaflet Map instance to add the markers to. Default is None.\n        center (Tuple[float, float]): Center of the map as a tuple of (latitude, longitude). Default is (20, 0).\n        zoom (int): Zoom level of the map. Default is 2.\n\n    Returns:\n        Map: An ipyleaflet Map with the added markers and popups.\n    \"\"\"\n    import pandas as pd\n    import matplotlib.pyplot as plt\n    from ipyleaflet import Map, Marker, Popup, MarkerCluster\n    from ipywidgets import Output, VBox\n\n    # Read the CSV file\n    if isinstance(data, str):\n        data = pd.read_csv(data)\n    elif isinstance(data, pd.DataFrame):\n        pass\n    else:\n        raise ValueError(\"data must be a path to a CSV file or a pandas DataFrame\")\n\n    # Extract locations from columns\n    locations = [col for col in data.columns if col.startswith(y_col_prefix)]\n    coordinates = [tuple(map(float, loc.strip(\"()\").split())) for loc in locations]\n\n    # Create the map\n    if m is None:\n        m = Map(center=center, zoom=zoom)\n\n    # Function to create the chart\n    def create_chart(data, title):\n        _, ax = plt.subplots(figsize=(10, 6))  # Adjust the figure size here\n        ax.plot(data[x_col], data[\"values\"])\n        ax.set_title(title)\n        ax.set_xlabel(x_label)\n        ax.set_ylabel(y_label)\n        output = Output()  # Adjust the output widget size here\n        with output:\n            plt.show()\n        return output\n\n    # Define a callback function to create and show the popup\n    def callback_with_popup_creation(location, values):\n        def f(**kwargs):\n            marker_center = kwargs[\"coordinates\"]\n            output = create_chart(values, f\"Location: {location}\")\n            popup = Popup(\n                location=marker_center,\n                child=VBox([output]),\n                min_width=min_width,\n                max_width=max_width,\n                min_height=min_height,\n                max_height=max_height,\n            )\n            m.add_layer(popup)\n\n        return f\n\n    markers = []\n\n    # Add points to the map\n    for i, coord in enumerate(coordinates):\n        location = f\"{coord}\"\n        values = pd.DataFrame({x_col: data[x_col], \"values\": data[locations[i]]})\n        marker = Marker(location=coord, title=location, name=f\"Marker {i + 1}\")\n        marker.on_click(callback_with_popup_creation(location, values))\n        markers.append(marker)\n\n    if use_marker_cluster:\n        marker_cluster = MarkerCluster(markers=markers, name=layer_name)\n        m.add_layer(marker_cluster)\n    else:\n        for marker in markers:\n            m.add_layer(marker)\n\n    return m\n</code></pre>"},{"location":"contributing/","title":"Contributing","text":"<p>Contributions are welcome, and they are greatly appreciated! Every little bit helps, and credit will always be given.</p> <p>You can contribute in many ways:</p>"},{"location":"contributing/#types-of-contributions","title":"Types of Contributions","text":""},{"location":"contributing/#report-bugs","title":"Report Bugs","text":"<p>Report bugs at https://github.com/opengeos/HyperCoast/issues.</p> <p>If you are reporting a bug, please include:</p> <ul> <li>Your operating system name and version.</li> <li>Any details about your local setup that might be helpful in troubleshooting.</li> <li>Detailed steps to reproduce the bug.</li> </ul>"},{"location":"contributing/#fix-bugs","title":"Fix Bugs","text":"<p>Look through the GitHub issues for bugs. Anything tagged with <code>bug</code> and <code>help wanted</code> is open to whoever wants to implement it.</p>"},{"location":"contributing/#implement-features","title":"Implement Features","text":"<p>Look through the GitHub issues for features. Anything tagged with <code>enhancement</code> and <code>help wanted</code> is open to whoever wants to implement it.</p>"},{"location":"contributing/#write-documentation","title":"Write Documentation","text":"<p>HyperCoast could always use more documentation, whether as part of the official HyperCoast docs, in docstrings, or even on the web in blog posts, articles, and such.</p>"},{"location":"contributing/#submit-feedback","title":"Submit Feedback","text":"<p>The best way to send feedback is to file an issue at https://github.com/opengeos/HyperCoast/issues.</p> <p>If you are proposing a feature:</p> <ul> <li>Explain in detail how it would work.</li> <li>Keep the scope as narrow as possible, to make it easier to implement.</li> <li>Remember that this is a volunteer-driven project, and that contributions are welcome :)</li> </ul>"},{"location":"contributing/#get-started","title":"Get Started!","text":"<p>Ready to contribute? Here's how to set up HyperCoast for local development.</p> <ol> <li> <p>Fork the HyperCoast repo on GitHub.</p> </li> <li> <p>Clone your fork locally:</p> <pre><code>$ git clone https://github.com/&lt;YOUR-GITHUB-USERNAME&gt;/HyperCoast.git\n</code></pre> </li> <li> <p>Create a new conda environment to install HyperCoast and its dependencies. Assuming you have     Anaconda or     Miniconda installed,     this is how you set up your fork for local development:</p> <pre><code>$ conda install -n base mamba -c conda-forge\n$ conda create -n hyper python=3.11\n$ conda activate hyper\n$ mamba install -c conda-forge hypercoast cartopy earthaccess mapclassify pyvista\n$ cd HyperCoast/\n$ pip install -e .\n</code></pre> </li> <li> <p>Create a branch for local development:</p> <pre><code>$ git checkout -b name-of-your-bugfix-or-feature\n</code></pre> <p>Now you can make your changes locally.</p> </li> <li> <p>When you're done making changes, check that your changes pass     pre-commit and the tests:</p> <pre><code>$ pip install pre-commit\n$ pre-commit install\n$ pre-commit run --all-files\n$ python -m unittest discover tests/\n</code></pre> </li> <li> <p>Commit your changes and push your branch to GitHub:</p> <pre><code>$ git add .\n$ git commit -m \"Your detailed description of your changes.\"\n$ git push origin name-of-your-bugfix-or-feature\n</code></pre> </li> <li> <p>Submit a pull request through the GitHub website.</p> </li> <li>Check the status of your pull request on GitHub and make sure     that the tests for the pull request pass for all supported Python versions.</li> <li>Commit more changes to your branch to fix the text errors if necessary.</li> <li>Wait for the pull request to be reviewed by the maintainers.</li> <li>Congratulations! You've made your contribution to HyperCoast!</li> </ol>"},{"location":"contributing/#contributor-agreements","title":"Contributor Agreements","text":"<p>Before your contribution can be accepted, you will need to sign the appropriate contributor agreement. The Contributor License Agreement (CLA) assistant will walk you through the process of signing the CLA. Please follow the instructions provided by the assistant on the pull request.</p> <ul> <li>Individual Contributor Exclusive License Agreement</li> <li>Entity Contributor Exclusive License Agreement</li> </ul>"},{"location":"desis/","title":"desis module","text":"<p>This Module has the functions related to working with a DESIS dataset.</p>"},{"location":"desis/#hypercoast.desis.desis_to_image","title":"<code>desis_to_image(dataset, wavelengths=None, method='nearest', output=None, **kwargs)</code>","text":"<p>Converts an DESIS dataset to an image.</p> <p>Parameters:</p> Name Type Description Default <code>dataset</code> <code>xarray.Dataset or str</code> <p>The dataset containing the DESIS data or the file path to the dataset.</p> required <code>wavelengths</code> <code>array-like</code> <p>The specific wavelengths to select. If None, all wavelengths are selected. Defaults to None.</p> <code>None</code> <code>method</code> <code>str</code> <p>The method to use for data interpolation. Defaults to \"nearest\".</p> <code>'nearest'</code> <code>output</code> <code>str</code> <p>The file path where the image will be saved. If None, the image will be returned as a PIL Image object. Defaults to None.</p> <code>None</code> <code>**kwargs</code> <p>Additional keyword arguments to be passed to <code>leafmap.array_to_image</code>.</p> <code>{}</code> <p>Returns:</p> Type Description <code>rasterio.Dataset or None</code> <p>The image converted from the dataset. If     <code>output</code> is provided, the image will be saved to the specified file     and the function will return None.</p> Source code in <code>hypercoast/desis.py</code> <pre><code>def desis_to_image(\n    dataset: Union[xr.Dataset, str],\n    wavelengths: Union[list, tuple] = None,\n    method: Optional[str] = \"nearest\",\n    output: Optional[str] = None,\n    **kwargs,\n):\n    \"\"\"\n    Converts an DESIS dataset to an image.\n\n    Args:\n        dataset (xarray.Dataset or str): The dataset containing the DESIS data\n            or the file path to the dataset.\n        wavelengths (array-like, optional): The specific wavelengths to select.\n            If None, all wavelengths are selected. Defaults to None.\n        method (str, optional): The method to use for data interpolation.\n            Defaults to \"nearest\".\n        output (str, optional): The file path where the image will be saved. If\n            None, the image will be returned as a PIL Image object. Defaults to None.\n        **kwargs: Additional keyword arguments to be passed to\n            `leafmap.array_to_image`.\n\n    Returns:\n        rasterio.Dataset or None: The image converted from the dataset. If\n            `output` is provided, the image will be saved to the specified file\n            and the function will return None.\n    \"\"\"\n    from leafmap import array_to_image\n\n    if isinstance(dataset, str):\n        dataset = read_desis(dataset, method=method)\n\n    if wavelengths is not None:\n        dataset = dataset.sel(wavelength=wavelengths, method=method)\n\n    return array_to_image(\n        dataset[\"reflectance\"], output=output, transpose=False, **kwargs\n    )\n</code></pre>"},{"location":"desis/#hypercoast.desis.extract_desis","title":"<code>extract_desis(ds, lat, lon)</code>","text":"<p>Extracts DESIS data from a given xarray Dataset.</p> <p>Parameters:</p> Name Type Description Default <code>ds</code> <code>xarray.Dataset</code> <p>The dataset containing the DESIS data.</p> required <code>lat</code> <code>float</code> <p>The latitude of the point to extract.</p> required <code>lon</code> <code>float</code> <p>The longitude of the point to extract.</p> required <p>Returns:</p> Type Description <code>xarray.DataArray</code> <p>The extracted data.</p> Source code in <code>hypercoast/desis.py</code> <pre><code>def extract_desis(ds: xr.Dataset, lat: float, lon: float) -&gt; xr.DataArray:\n    \"\"\"\n    Extracts DESIS data from a given xarray Dataset.\n\n    Args:\n        ds (xarray.Dataset): The dataset containing the DESIS data.\n        lat (float): The latitude of the point to extract.\n        lon (float): The longitude of the point to extract.\n\n    Returns:\n        xarray.DataArray: The extracted data.\n    \"\"\"\n\n    crs = ds.attrs[\"crs\"]\n\n    x, y = convert_coords([[lat, lon]], \"epsg:4326\", crs)[0]\n\n    values = ds.sel(x=x, y=y, method=\"nearest\")[\"reflectance\"].values / 10000\n\n    da = xr.DataArray(\n        values, dims=[\"wavelength\"], coords={\"wavelength\": ds.coords[\"wavelength\"]}\n    )\n\n    return da\n</code></pre>"},{"location":"desis/#hypercoast.desis.filter_desis","title":"<code>filter_desis(dataset, lat, lon, return_plot=False, **kwargs)</code>","text":"<p>Filters a DESIS dataset based on latitude and longitude.</p> <p>Parameters:</p> Name Type Description Default <code>dataset</code> <code>xr.Dataset</code> <p>The DESIS dataset to filter.</p> required <code>lat</code> <code>float or tuple</code> <p>The latitude to filter by. If a tuple or list, it represents a range.</p> required <code>lon</code> <code>float or tuple</code> <p>The longitude to filter by. If a tuple or list, it represents a range.</p> required <p>Returns:</p> Type Description <code>xr.DataArray</code> <p>The filtered DESIS data.</p> Source code in <code>hypercoast/desis.py</code> <pre><code>def filter_desis(\n    dataset: xr.Dataset,\n    lat: Union[float, tuple],\n    lon: Union[float, tuple],\n    return_plot: Optional[bool] = False,\n    **kwargs,\n) -&gt; xr.Dataset:\n    \"\"\"\n    Filters a DESIS dataset based on latitude and longitude.\n\n    Args:\n        dataset (xr.Dataset): The DESIS dataset to filter.\n        lat (float or tuple): The latitude to filter by. If a tuple or list,\n            it represents a range.\n        lon (float or tuple): The longitude to filter by. If a tuple or\n            list, it represents a range.\n\n    Returns:\n        xr.DataArray: The filtered DESIS data.\n    \"\"\"\n\n    if isinstance(lat, list) or isinstance(lat, tuple):\n        min_lat = min(lat)\n        max_lat = max(lat)\n    else:\n        min_lat = lat\n        max_lat = lat\n\n    if isinstance(lon, list) or isinstance(lon, tuple):\n        min_lon = min(lon)\n        max_lon = max(lon)\n    else:\n        min_lon = lon\n        max_lon = lon\n\n    if min_lat == max_lat and min_lon == max_lon:\n        coords = [[min_lat, min_lon]]\n    else:\n        coords = [[min_lat, min_lon], [max_lat, max_lon]]\n    coords = convert_coords(coords, \"epsg:4326\", dataset.rio.crs.to_string())\n\n    if len(coords) == 1:\n        x, y = coords[0]\n        da = dataset.sel(x=x, y=y, method=\"nearest\")[\"reflectance\"]\n    else:\n        x_min, y_min = coords[0]\n        x_max, y_max = coords[1]\n        print(x_min, y_min, x_max, y_max)\n        da = dataset.sel(x=slice(x_min, x_max), y=slice(y_min, y_max))[\"reflectance\"]\n\n    if return_plot:\n        rrs_stack = da.stack(\n            {\"pixel\": [\"latitude\", \"longitude\"]},\n            create_index=False,\n        )\n        rrs_stack.plot.line(hue=\"pixel\", **kwargs)\n    else:\n        return da\n</code></pre>"},{"location":"desis/#hypercoast.desis.read_desis","title":"<code>read_desis(filepath, wavelengths=None, method='nearest', **kwargs)</code>","text":"<p>Reads DESIS data from a given file and returns an xarray Dataset.</p> <p>Parameters:</p> Name Type Description Default <code>filepath</code> <code>str</code> <p>Path to the file to read.</p> required <code>wavelengths</code> <code>array-like</code> <p>Specific wavelengths to select. If None, all wavelengths are selected.</p> <code>None</code> <code>method</code> <code>str</code> <p>Method to use for selection when wavelengths is not None. Defaults to \"nearest\".</p> <code>'nearest'</code> <code>**kwargs</code> <p>Additional keyword arguments to pass to the <code>sel</code> method when bands is not None.</p> <code>{}</code> <p>Returns:</p> Type Description <code>xr.Dataset</code> <p>An xarray Dataset containing the DESIS data.</p> Source code in <code>hypercoast/desis.py</code> <pre><code>def read_desis(\n    filepath: str,\n    wavelengths: Optional[Union[list, tuple]] = None,\n    method: Optional[str] = \"nearest\",\n    **kwargs,\n) -&gt; xr.Dataset:\n    \"\"\"\n    Reads DESIS data from a given file and returns an xarray Dataset.\n\n    Args:\n        filepath (str): Path to the file to read.\n        wavelengths (array-like, optional): Specific wavelengths to select. If\n            None, all wavelengths are selected.\n        method (str, optional): Method to use for selection when wavelengths is not\n            None. Defaults to \"nearest\".\n        **kwargs: Additional keyword arguments to pass to the `sel` method when\n            bands is not None.\n\n    Returns:\n        xr.Dataset: An xarray Dataset containing the DESIS data.\n    \"\"\"\n\n    url = \"https://github.com/opengeos/datasets/releases/download/hypercoast/desis_wavelengths.csv\"\n    df = pd.read_csv(url)\n    dataset = xr.open_dataset(filepath)\n    dataset = dataset.rename(\n        {\"band\": \"wavelength\", \"band_data\": \"reflectance\"}\n    ).transpose(\"y\", \"x\", \"wavelength\")\n    dataset[\"wavelength\"] = df[\"wavelength\"].tolist()\n\n    if wavelengths is not None:\n        dataset = dataset.sel(wavelength=wavelengths, method=method, **kwargs)\n\n    dataset.attrs[\"crs\"] = dataset.rio.crs.to_string()\n\n    return dataset\n</code></pre>"},{"location":"emit/","title":"emit module","text":"<p>This Module has the functions related to working with an EMIT dataset. This includes doing things like opening and flattening the data to work in xarray, orthorectification, and visualization.</p> <p>Some source code is adapted from https://github.com/nasa/EMIT-Data-Resources, which is licensed under the Apache License 2.0. Credits to the original authors, including Erik Bolch, Alex Leigh, and others.</p>"},{"location":"emit/#hypercoast.emit.apply_glt","title":"<code>apply_glt(ds_array, glt_array, fill_value=-9999, GLT_NODATA_VALUE=0)</code>","text":"<p>Applies the GLT array to a numpy array of either 2 or 3 dimensions to orthorectify the data.</p> <p>Parameters:</p> Name Type Description Default <code>ds_array</code> <code>numpy.ndarray</code> <p>A numpy array of the desired variable.</p> required <code>glt_array</code> <code>numpy.ndarray</code> <p>A GLT array constructed from EMIT GLT data.</p> required <code>fill_value</code> <code>int</code> <p>The value to fill in the output array where the GLT array has no data. Defaults to -9999.</p> <code>-9999</code> <code>GLT_NODATA_VALUE</code> <code>int</code> <p>The value in the GLT array that indicates no data. Defaults to 0.</p> <code>0</code> <p>Returns:</p> Type Description <code>numpy.ndarray</code> <p>A numpy array of orthorectified data.</p> Source code in <code>hypercoast/emit.py</code> <pre><code>def apply_glt(\n    ds_array: np.ndarray,\n    glt_array: np.ndarray,\n    fill_value: int = -9999,\n    GLT_NODATA_VALUE: Optional[int] = 0,\n) -&gt; np.ndarray:\n    \"\"\"\n    Applies the GLT array to a numpy array of either 2 or 3 dimensions to orthorectify the data.\n\n    Args:\n        ds_array (numpy.ndarray): A numpy array of the desired variable.\n        glt_array (numpy.ndarray): A GLT array constructed from EMIT GLT data.\n        fill_value (int, optional): The value to fill in the output array where the GLT array has no data. Defaults to -9999.\n        GLT_NODATA_VALUE (int, optional): The value in the GLT array that indicates no data. Defaults to 0.\n\n    Returns:\n        numpy.ndarray: A numpy array of orthorectified data.\n    \"\"\"\n\n    # Build Output Dataset\n    if ds_array.ndim == 2:\n        ds_array = ds_array[:, :, np.newaxis]\n    out_ds = np.full(\n        (glt_array.shape[0], glt_array.shape[1], ds_array.shape[-1]),\n        fill_value,\n        dtype=np.float32,\n    )\n    valid_glt = np.all(glt_array != GLT_NODATA_VALUE, axis=-1)\n\n    # Adjust for One based Index - make a copy to prevent decrementing multiple times inside ortho_xr when applying the glt to elev\n    glt_array_copy = glt_array.copy()\n    glt_array_copy[valid_glt] -= 1\n    out_ds[valid_glt, :] = ds_array[\n        glt_array_copy[valid_glt, 1], glt_array_copy[valid_glt, 0], :\n    ]\n    return out_ds\n</code></pre>"},{"location":"emit/#hypercoast.emit.band_mask","title":"<code>band_mask(filepath)</code>","text":"<p>Unpacks the packed band mask to apply to the dataset. Can be used manually or as an input in the emit_xarray() function.</p> <p>Parameters:</p> Name Type Description Default <code>filepath</code> <code>str</code> <p>An EMIT L2A Mask netCDF file.</p> required <p>Returns:</p> Type Description <code>numpy.ndarray</code> <p>A numpy array that can be used with the emit_xarray function to apply a band mask.</p> Source code in <code>hypercoast/emit.py</code> <pre><code>def band_mask(filepath: str) -&gt; np.ndarray:\n    \"\"\"\n    Unpacks the packed band mask to apply to the dataset. Can be used manually or as an input in the emit_xarray() function.\n\n    Args:\n        filepath (str): An EMIT L2A Mask netCDF file.\n\n    Returns:\n        numpy.ndarray: A numpy array that can be used with the emit_xarray function to apply a band mask.\n    \"\"\"\n    # Open Dataset\n    mask_ds = xr.open_dataset(filepath, engine=\"h5netcdf\")\n    # Open band_mask and convert to uint8\n    bmask = mask_ds.band_mask.data.astype(\"uint8\")\n    # Print Flags used\n    unpacked_bmask = np.unpackbits(bmask, axis=-1)\n    # Remove bands &gt; 285\n    unpacked_bmask = unpacked_bmask[:, :, 0:285]\n    # Check for data bands and build mask\n    return unpacked_bmask\n</code></pre>"},{"location":"emit/#hypercoast.emit.coord_vects","title":"<code>coord_vects(ds)</code>","text":"<p>This function calculates the Lat and Lon Coordinate Vectors using the GLT and Metadata from an EMIT dataset read into xarray.</p> <p>lon, lat (numpy.array): longitude and latitude array grid for the dataset</p> Source code in <code>hypercoast/emit.py</code> <pre><code>def coord_vects(ds: xr.Dataset) -&gt; np.ndarray:\n    \"\"\"\n    This function calculates the Lat and Lon Coordinate Vectors using the GLT and Metadata from an EMIT dataset read into xarray.\n\n    Parameters:\n    ds: an xarray.Dataset containing the root variable and metadata of an EMIT dataset\n    loc: an xarray.Dataset containing the 'location' group of an EMIT dataset\n\n    Returns:\n    lon, lat (numpy.array): longitude and latitude array grid for the dataset\n\n    \"\"\"\n    # Retrieve Geotransform from Metadata\n    GT = ds.geotransform\n    # Create Array for Lat and Lon and fill\n    dim_x = ds.glt_x.shape[1]\n    dim_y = ds.glt_x.shape[0]\n    lon = np.zeros(dim_x)\n    lat = np.zeros(dim_y)\n    # Note: no rotation for EMIT Data\n    for x in np.arange(dim_x):\n        x_geo = (GT[0] + 0.5 * GT[1]) + x * GT[1]  # Adjust coordinates to pixel-center\n        lon[x] = x_geo\n    for y in np.arange(dim_y):\n        y_geo = (GT[3] + 0.5 * GT[5]) + y * GT[5]\n        lat[y] = y_geo\n    return lon, lat\n</code></pre>"},{"location":"emit/#hypercoast.emit.emit_to_image","title":"<code>emit_to_image(data, wavelengths=None, method='nearest', output=None, **kwargs)</code>","text":"<p>Converts an EMIT dataset to an image.</p> <p>Parameters:</p> Name Type Description Default <code>data</code> <code>xarray.Dataset or str</code> <p>The dataset containing the EMIT data or the file path to the dataset.</p> required <code>wavelengths</code> <code>array-like</code> <p>The specific wavelengths to select. If None, all wavelengths are selected. Defaults to None.</p> <code>None</code> <code>method</code> <code>str</code> <p>The method to use for data selection. Defaults to \"nearest\".</p> <code>'nearest'</code> <code>output</code> <code>str</code> <p>The file path where the image will be saved. If None, the image will be returned as a PIL Image object. Defaults to None.</p> <code>None</code> <code>**kwargs</code> <p>Additional keyword arguments to be passed to <code>leafmap.array_to_image</code>.</p> <code>{}</code> <p>Returns:</p> Type Description <code>rasterio.Dataset or None</code> <p>The image converted from the dataset. If <code>output</code> is provided, the image will be saved to the specified file and the function will return None.</p> Source code in <code>hypercoast/emit.py</code> <pre><code>def emit_to_image(\n    data: Union[xr.Dataset, str],\n    wavelengths: Optional[Union[list, tuple]] = None,\n    method: Optional[str] = \"nearest\",\n    output: Optional[str] = None,\n    **kwargs,\n):\n    \"\"\"\n    Converts an EMIT dataset to an image.\n\n    Args:\n        data (xarray.Dataset or str): The dataset containing the EMIT data or the file path to the dataset.\n        wavelengths (array-like, optional): The specific wavelengths to select. If None, all wavelengths are selected. Defaults to None.\n        method (str, optional): The method to use for data selection. Defaults to \"nearest\".\n        output (str, optional): The file path where the image will be saved. If None, the image will be returned as a PIL Image object. Defaults to None.\n        **kwargs: Additional keyword arguments to be passed to `leafmap.array_to_image`.\n\n    Returns:\n        rasterio.Dataset or None: The image converted from the dataset. If `output` is provided, the image will be saved to the specified file and the function will return None.\n    \"\"\"\n    from leafmap import array_to_image\n\n    if isinstance(data, str):\n        data = read_emit(data, ortho=True)\n\n    ds = data[\"reflectance\"]\n\n    if wavelengths is not None:\n        ds = ds.sel(wavelength=wavelengths, method=method)\n    return array_to_image(ds, transpose=False, output=output, **kwargs)\n</code></pre>"},{"location":"emit/#hypercoast.emit.emit_to_netcdf","title":"<code>emit_to_netcdf(data, output, **kwargs)</code>","text":"<p>Transposes an EMIT dataset and saves it as a NetCDF file.</p> <p>Parameters:</p> Name Type Description Default <code>data</code> <code>xarray.Dataset or str</code> <p>The dataset containing the EMIT data or the file path to the dataset.</p> required <code>output</code> <code>str</code> <p>The file path where the NetCDF file will be saved.</p> required <code>**kwargs</code> <p>Additional keyword arguments to be passed to <code>xarray.Dataset.to_netcdf</code>.</p> <code>{}</code> Source code in <code>hypercoast/emit.py</code> <pre><code>def emit_to_netcdf(data: Union[xr.Dataset, str], output: str, **kwargs) -&gt; None:\n    \"\"\"\n    Transposes an EMIT dataset and saves it as a NetCDF file.\n\n    Args:\n        data (xarray.Dataset or str): The dataset containing the EMIT data or the file path to the dataset.\n        output (str): The file path where the NetCDF file will be saved.\n        **kwargs: Additional keyword arguments to be passed to `xarray.Dataset.to_netcdf`.\n\n    \"\"\"\n    if isinstance(data, str):\n        data = read_emit(data, ortho=True)\n\n    ds_geo = data.transpose(\"wavelengths\", \"latitude\", \"longitude\")\n    ds_geo.to_netcdf(output, **kwargs)\n</code></pre>"},{"location":"emit/#hypercoast.emit.emit_xarray","title":"<code>emit_xarray(filepath, ortho=False, qmask=None, unpacked_bmask=None, wavelengths=None, method='nearest')</code>","text":"<p>Streamlines opening an EMIT dataset as an xarray.Dataset.</p> <p>Parameters:</p> Name Type Description Default <code>filepath</code> <code>str</code> <p>A filepath to an EMIT netCDF file.</p> required <code>ortho</code> <code>bool</code> <p>Whether to orthorectify the dataset or leave in crosstrack/downtrack coordinates. Defaults to False.</p> <code>False</code> <code>qmask</code> <code>numpy.ndarray</code> <p>A numpy array output from the quality_mask function used to mask pixels based on quality flags selected in that function. Any non-orthorectified array with the proper crosstrack and downtrack dimensions can also be used. Defaults to None.</p> <code>None</code> <code>unpacked_bmask</code> <code>numpy.ndarray</code> <p>A numpy array from the band_mask function that can be used to mask band-specific pixels that have been interpolated. Defaults to None.</p> <code>None</code> <code>wavelengths</code> <code>array-like</code> <p>The specific wavelengths to select. If None, all wavelengths are selected. Defaults to None.</p> <code>None</code> <code>method</code> <code>str</code> <p>The method to use for data selection. Defaults to \"nearest\".</p> <code>'nearest'</code> <p>Returns:</p> Type Description <code>xarray.Dataset</code> <p>An xarray.Dataset constructed based on the parameters provided.</p> Source code in <code>hypercoast/emit.py</code> <pre><code>def emit_xarray(\n    filepath: str,\n    ortho: Optional[bool] = False,\n    qmask: Optional[np.ndarray] = None,\n    unpacked_bmask: Optional[np.ndarray] = None,\n    wavelengths: Optional[Union[tuple, list]] = None,\n    method: Optional[str] = \"nearest\",\n) -&gt; xr.Dataset:\n    \"\"\"\n    Streamlines opening an EMIT dataset as an xarray.Dataset.\n\n    Args:\n        filepath (str): A filepath to an EMIT netCDF file.\n        ortho (bool, optional): Whether to orthorectify the dataset or leave in crosstrack/downtrack coordinates. Defaults to False.\n        qmask (numpy.ndarray, optional): A numpy array output from the quality_mask function used to mask pixels based on quality flags selected in that function. Any non-orthorectified array with the proper crosstrack and downtrack dimensions can also be used. Defaults to None.\n        unpacked_bmask (numpy.ndarray, optional): A numpy array from the band_mask function that can be used to mask band-specific pixels that have been interpolated. Defaults to None.\n        wavelengths (array-like, optional): The specific wavelengths to select. If None, all wavelengths are selected. Defaults to None.\n        method (str, optional): The method to use for data selection. Defaults to \"nearest\".\n\n    Returns:\n        xarray.Dataset: An xarray.Dataset constructed based on the parameters provided.\n    \"\"\"\n    # Grab granule filename to check product\n    from s3fs.core import S3File\n    from fsspec.implementations.http import HTTPFile\n\n    if isinstance(filepath, S3File):\n        granule_id = filepath.info()[\"name\"].split(\"/\", -1)[-1].split(\".\", -1)[0]\n    elif isinstance(filepath, HTTPFile):\n        granule_id = filepath.path.split(\"/\", -1)[-1].split(\".\", -1)[0]\n    else:\n        granule_id = os.path.splitext(os.path.basename(filepath))[0]\n\n    # Read in Data as Xarray Datasets\n    engine, wvl_group = \"h5netcdf\", None\n\n    ds = xr.open_dataset(filepath, engine=engine)\n    loc = xr.open_dataset(filepath, engine=engine, group=\"location\")\n\n    # Check if mineral dataset and read in groups (only ds/loc for minunc)\n\n    if \"L2B_MIN_\" in granule_id:\n        wvl_group = \"mineral_metadata\"\n    elif \"L2B_MINUNC\" not in granule_id:\n        wvl_group = \"sensor_band_parameters\"\n\n    wvl = None\n\n    if wvl_group:\n        wvl = xr.open_dataset(filepath, engine=engine, group=wvl_group)\n\n    # Building Flat Dataset from Components\n    data_vars = {**ds.variables}\n\n    # Format xarray coordinates based upon emit product (no wvl for mineral uncertainty)\n    coords = {\n        \"downtrack\": ([\"downtrack\"], ds.downtrack.data),\n        \"crosstrack\": ([\"crosstrack\"], ds.crosstrack.data),\n        **loc.variables,\n    }\n\n    product_band_map = {\n        \"L2B_MIN_\": \"name\",\n        \"L2A_MASK_\": \"mask_bands\",\n        \"L1B_OBS_\": \"observation_bands\",\n        \"L2A_RFL_\": \"wavelengths\",\n        \"L1B_RAD_\": \"wavelengths\",\n        \"L2A_RFLUNCERT_\": \"wavelengths\",\n    }\n\n    # if band := product_band_map.get(next((k for k in product_band_map.keys() if k in granule_id), 'unknown'), None):\n    # coords['bands'] = wvl[band].data\n\n    if wvl:\n        coords = {**coords, **wvl.variables}\n\n    out_xr = xr.Dataset(data_vars=data_vars, coords=coords, attrs=ds.attrs)\n    out_xr.attrs[\"granule_id\"] = granule_id\n\n    if band := product_band_map.get(\n        next((k for k in product_band_map.keys() if k in granule_id), \"unknown\"), None\n    ):\n        if \"minerals\" in list(out_xr.dims):\n            out_xr = out_xr.swap_dims({\"minerals\": band})\n            out_xr = out_xr.rename({band: \"mineral_name\"})\n        else:\n            out_xr = out_xr.swap_dims({\"bands\": band})\n\n    # Apply Quality and Band Masks, set fill values to NaN\n    for var in list(ds.data_vars):\n        if qmask is not None:\n            out_xr[var].data[qmask == 1] = np.nan\n        if unpacked_bmask is not None:\n            out_xr[var].data[unpacked_bmask == 1] = np.nan\n        out_xr[var].data[out_xr[var].data == -9999] = np.nan\n\n    if ortho is True:\n        out_xr = ortho_xr(out_xr)\n        out_xr.attrs[\"Orthorectified\"] = \"True\"\n\n    if wavelengths is not None:\n        out_xr = out_xr.sel(wavelengths=wavelengths, method=method)\n\n    out_xr = out_xr.rename({\"wavelengths\": \"wavelength\"})\n    return out_xr\n</code></pre>"},{"location":"emit/#hypercoast.emit.envi_header","title":"<code>envi_header(inputpath)</code>","text":"<p>Convert a ENVI binary/header path to a header, handling extensions.</p> <p>Parameters:</p> Name Type Description Default <code>inputpath</code> <code>str</code> <p>Path to ENVI binary file.</p> required <p>Returns:</p> Type Description <code>str</code> <p>The header file associated with the input reference. If the header file does not exist, it returns the expected header file path.</p> Source code in <code>hypercoast/emit.py</code> <pre><code>def envi_header(inputpath: str) -&gt; str:\n    \"\"\"\n    Convert a ENVI binary/header path to a header, handling extensions.\n\n    Args:\n        inputpath (str): Path to ENVI binary file.\n\n    Returns:\n        str: The header file associated with the input reference. If the header file does not exist, it returns the expected header file path.\n    \"\"\"\n    if (\n        os.path.splitext(inputpath)[-1] == \".img\"\n        or os.path.splitext(inputpath)[-1] == \".dat\"\n        or os.path.splitext(inputpath)[-1] == \".raw\"\n    ):\n        # headers could be at either filename.img.hdr or filename.hdr.  Check both, return the one that exists if it\n        # does, if not return the latter (new file creation presumed).\n        hdrfile = os.path.splitext(inputpath)[0] + \".hdr\"\n        if os.path.isfile(hdrfile):\n            return hdrfile\n        elif os.path.isfile(inputpath + \".hdr\"):\n            return inputpath + \".hdr\"\n        return hdrfile\n    elif os.path.splitext(inputpath)[-1] == \".hdr\":\n        return inputpath\n    else:\n        return inputpath + \".hdr\"\n</code></pre>"},{"location":"emit/#hypercoast.emit.is_adjacent","title":"<code>is_adjacent(scene, same_orbit)</code>","text":"<p>Checks if the scene numbers from the same orbit are adjacent/sequential.</p> <p>Parameters:</p> Name Type Description Default <code>scene</code> <code>str</code> <p>The scene number to check.</p> required <code>same_orbit</code> <code>list</code> <p>A list of scene numbers from the same orbit.</p> required <p>Returns:</p> Type Description <code>bool</code> <p>True if the scene numbers are adjacent/sequential, False otherwise.</p> Source code in <code>hypercoast/emit.py</code> <pre><code>def is_adjacent(scene: str, same_orbit: list) -&gt; bool:\n    \"\"\"\n    Checks if the scene numbers from the same orbit are adjacent/sequential.\n\n    Args:\n        scene (str): The scene number to check.\n        same_orbit (list): A list of scene numbers from the same orbit.\n\n    Returns:\n        bool: True if the scene numbers are adjacent/sequential, False otherwise.\n    \"\"\"\n    scene_nums = [int(scene.split(\".\")[-2].split(\"_\")[-1]) for scene in same_orbit]\n    return all(b - a == 1 for a, b in zip(scene_nums[:-1], scene_nums[1:]))\n</code></pre>"},{"location":"emit/#hypercoast.emit.merge_emit","title":"<code>merge_emit(datasets, gdf)</code>","text":"<p>Merges xarray datasets formatted using emit_xarray. Note: GDF may only work with a single geometry.</p> <p>Parameters:</p> Name Type Description Default <code>datasets</code> <code>dict</code> <p>A dictionary of xarray datasets formatted using emit_xarray.</p> required <code>gdf</code> <code>gpd.GeoDataFrame</code> <p>A GeoDataFrame containing the geometry to be used for merging.</p> required <p>Returns:</p> Type Description <code>xarray.Dataset</code> <p>A merged xarray dataset.</p> <p>Exceptions:</p> Type Description <code>Exception</code> <p>If there are inconsistencies in the 1D variables across datasets.</p> Source code in <code>hypercoast/emit.py</code> <pre><code>def merge_emit(datasets: dict, gdf: gpd.GeoDataFrame) -&gt; xr.Dataset:\n    \"\"\"\n    Merges xarray datasets formatted using emit_xarray. Note: GDF may only work with a single geometry.\n\n    Args:\n        datasets (dict): A dictionary of xarray datasets formatted using emit_xarray.\n        gdf (gpd.GeoDataFrame): A GeoDataFrame containing the geometry to be used for merging.\n\n    Returns:\n        xarray.Dataset: A merged xarray dataset.\n\n    Raises:\n        Exception: If there are inconsistencies in the 1D variables across datasets.\n    \"\"\"\n    from rioxarray.merge import merge_arrays\n\n    nested_data_arrays = {}\n    # loop over datasets\n    for dataset in datasets:\n        # create dictionary of arrays for each dataset\n\n        # create dictionary of 1D variables, which should be consistent across datasets\n        one_d_arrays = {}\n\n        # Dictionary of variables to merge\n        data_arrays = {}\n        # Loop over variables in dataset including elevation\n        for var in list(datasets[dataset].data_vars) + [\"elev\"]:\n            # Get 1D for this variable and add to dictionary\n            if not one_d_arrays:\n                # These should be an array describing the others (wavelengths, mask_bands, etc.)\n                one_dim = [\n                    item\n                    for item in list(datasets[dataset].coords)\n                    if item not in [\"latitude\", \"longitude\", \"spatial_ref\"]\n                    and len(datasets[dataset][item].dims) == 1\n                ]\n                # print(one_dim)\n                for od in one_dim:\n                    one_d_arrays[od] = datasets[dataset].coords[od].data\n\n                # Update format for merging - This could probably be improved\n            da = datasets[dataset][var].reset_coords(\"elev\", drop=False)\n            da = da.rename({\"latitude\": \"y\", \"longitude\": \"x\"})\n            if len(da.dims) == 3:\n                if any(item in list(da.coords) for item in one_dim):\n                    da = da.drop_vars(one_dim)\n                da = da.drop_vars(\"elev\")\n                da = da.to_array(name=var).squeeze(\"variable\", drop=True)\n                da = da.transpose(da.dims[-1], da.dims[0], da.dims[1])\n                # print(da.dims)\n            if var == \"elev\":\n                da = da.to_array(name=var).squeeze(\"variable\", drop=True)\n            data_arrays[var] = da\n            nested_data_arrays[dataset] = data_arrays\n\n            # Transpose the nested arrays dict. This is horrible to read, but works to pair up variables (ie mask) from the different granules\n    transposed_dict = {\n        inner_key: {\n            outer_key: inner_dict[inner_key]\n            for outer_key, inner_dict in nested_data_arrays.items()\n        }\n        for inner_key in nested_data_arrays[next(iter(nested_data_arrays))]\n    }\n\n    # remove some unused data\n    del nested_data_arrays, data_arrays, da\n\n    # Merge the arrays using rioxarray.merge_arrays()\n    merged = {}\n    for _var in transposed_dict:\n        merged[_var] = merge_arrays(\n            list(transposed_dict[_var].values()),\n            bounds=gdf.unary_union.bounds,\n            nodata=np.nan,\n        )\n\n    # Create a new xarray dataset from the merged arrays\n    # Create Merged Dataset\n    merged_ds = xr.Dataset(data_vars=merged, coords=one_d_arrays)\n    # Rename x and y to longitude and latitude\n    merged_ds = merged_ds.rename({\"y\": \"latitude\", \"x\": \"longitude\"})\n    del transposed_dict, merged\n    return merged_ds\n</code></pre>"},{"location":"emit/#hypercoast.emit.ortho_browse","title":"<code>ortho_browse(url, glt, spatial_ref, geotransform, white_background=True)</code>","text":"<p>Use an EMIT GLT, geotransform, and spatial ref to orthorectify a browse image. (browse images are in native resolution)</p> <p>Parameters:</p> Name Type Description Default <code>url</code> <code>str</code> <p>URL of the browse image.</p> required <code>glt</code> <code>numpy.ndarray</code> <p>A GLT array constructed from EMIT GLT data.</p> required <code>spatial_ref</code> <code>str</code> <p>Spatial reference system.</p> required <code>geotransform</code> <code>list</code> <p>A list of six numbers that define the affine transform between pixel coordinates and map coordinates.</p> required <code>white_background</code> <code>bool</code> <p>If True, the fill value for the orthorectified image is white (255). If False, the fill value is black (0). Defaults to True.</p> <code>True</code> <p>Returns:</p> Type Description <code>xarray.DataArray</code> <p>An orthorectified browse image in the form of an xarray DataArray.</p> Source code in <code>hypercoast/emit.py</code> <pre><code>def ortho_browse(\n    url: str,\n    glt: np.ndarray,\n    spatial_ref: str,\n    geotransform: list,\n    white_background: Optional[bool] = True,\n) -&gt; xr.Dataset:\n    \"\"\"\n    Use an EMIT GLT, geotransform, and spatial ref to orthorectify a browse image. (browse images are in native resolution)\n\n    Args:\n        url (str): URL of the browse image.\n        glt (numpy.ndarray): A GLT array constructed from EMIT GLT data.\n        spatial_ref (str): Spatial reference system.\n        geotransform (list): A list of six numbers that define the affine transform between pixel coordinates and map coordinates.\n        white_background (bool, optional): If True, the fill value for the orthorectified image is white (255). If False, the fill value is black (0). Defaults to True.\n\n    Returns:\n        xarray.DataArray: An orthorectified browse image in the form of an xarray DataArray.\n    \"\"\"\n    from skimage import io\n\n    # Read Data\n    data = io.imread(url)\n    # Orthorectify using GLT and transpose so band is first dimension\n    if white_background is True:\n        fill = 255\n    else:\n        fill = 0\n    ortho_data = apply_glt(data, glt, fill_value=fill).transpose(2, 0, 1)\n    coords = {\n        \"y\": (\n            [\"y\"],\n            (geotransform[3] + 0.5 * geotransform[5])\n            + np.arange(glt.shape[0]) * geotransform[5],\n        ),\n        \"x\": (\n            [\"x\"],\n            (geotransform[0] + 0.5 * geotransform[1])\n            + np.arange(glt.shape[1]) * geotransform[1],\n        ),\n    }\n    ortho_data = ortho_data.astype(int)\n    ortho_data[ortho_data == -1] = 0\n    # Place in xarray.datarray\n    da = xr.DataArray(ortho_data, dims=[\"band\", \"y\", \"x\"], coords=coords)\n    da.rio.write_crs(spatial_ref, inplace=True)\n    return da\n</code></pre>"},{"location":"emit/#hypercoast.emit.ortho_xr","title":"<code>ortho_xr(ds, GLT_NODATA_VALUE=0, fill_value=-9999)</code>","text":"<p>Uses <code>apply_glt</code> to create an orthorectified xarray dataset.</p> <p>Parameters:</p> Name Type Description Default <code>ds</code> <code>xarray.Dataset</code> <p>An xarray dataset produced by emit_xarray.</p> required <code>GLT_NODATA_VALUE</code> <code>int</code> <p>No data value for the GLT tables. Defaults to 0.</p> <code>0</code> <code>fill_value</code> <code>int</code> <p>The fill value for EMIT datasets. Defaults to -9999.</p> <code>-9999</code> <p>Returns:</p> Type Description <code>xarray.Dataset</code> <p>An orthocorrected xarray dataset.</p> Source code in <code>hypercoast/emit.py</code> <pre><code>def ortho_xr(\n    ds: xr.Dataset,\n    GLT_NODATA_VALUE: Optional[int] = 0,\n    fill_value: Optional[int] = -9999,\n) -&gt; xr.Dataset:\n    \"\"\"\n    Uses `apply_glt` to create an orthorectified xarray dataset.\n\n    Args:\n        ds (xarray.Dataset): An xarray dataset produced by emit_xarray.\n        GLT_NODATA_VALUE (int, optional): No data value for the GLT tables. Defaults to 0.\n        fill_value (int, optional): The fill value for EMIT datasets. Defaults to -9999.\n\n    Returns:\n        xarray.Dataset: An orthocorrected xarray dataset.\n    \"\"\"\n    # Build glt_ds\n\n    glt_ds = np.nan_to_num(\n        np.stack([ds[\"glt_x\"].data, ds[\"glt_y\"].data], axis=-1), nan=GLT_NODATA_VALUE\n    ).astype(int)\n\n    # List Variables\n    var_list = list(ds.data_vars)\n\n    # Remove flat field from data vars - the flat field is only useful with additional information before orthorectification\n    if \"flat_field_update\" in var_list:\n        var_list.remove(\"flat_field_update\")\n\n    # Create empty dictionary for orthocorrected data vars\n    data_vars = {}\n\n    # Extract Rawspace Dataset Variable Values (Typically Reflectance)\n    for var in var_list:\n        raw_ds = ds[var].data\n        var_dims = ds[var].dims\n        # Apply GLT to dataset\n        out_ds = apply_glt(raw_ds, glt_ds, GLT_NODATA_VALUE=GLT_NODATA_VALUE)\n\n        # Mask fill values\n        out_ds[out_ds == fill_value] = np.nan\n\n        # Update variables - Only works for 2 or 3 dimensional arrays\n        if raw_ds.ndim == 2:\n            out_ds = out_ds.squeeze()\n            data_vars[var] = ([\"latitude\", \"longitude\"], out_ds)\n        else:\n            data_vars[var] = ([\"latitude\", \"longitude\", var_dims[-1]], out_ds)\n\n        del raw_ds\n\n    # Calculate Lat and Lon Vectors\n    lon, lat = coord_vects(\n        ds\n    )  # Reorder this function to make sense in case of multiple variables\n\n    # Apply GLT to elevation\n    elev_ds = apply_glt(ds[\"elev\"].data, glt_ds)\n    elev_ds[elev_ds == fill_value] = np.nan\n\n    # Delete glt_ds - no longer needed\n    del glt_ds\n\n    # Create Coordinate Dictionary\n    coords = {\n        \"latitude\": ([\"latitude\"], lat),\n        \"longitude\": ([\"longitude\"], lon),\n        **ds.coords,\n    }  # unpack to add appropriate coordinates\n\n    # Remove Unnecessary Coords\n    for key in [\"downtrack\", \"crosstrack\", \"lat\", \"lon\", \"glt_x\", \"glt_y\", \"elev\"]:\n        del coords[key]\n\n    # Add Orthocorrected Elevation\n    coords[\"elev\"] = ([\"latitude\", \"longitude\"], np.squeeze(elev_ds))\n\n    # Build Output xarray Dataset and assign data_vars array attributes\n    out_xr = xr.Dataset(data_vars=data_vars, coords=coords, attrs=ds.attrs)\n\n    del out_ds\n    # Assign Attributes from Original Datasets\n    for var in var_list:\n        out_xr[var].attrs = ds[var].attrs\n    out_xr.coords[\"latitude\"].attrs = ds[\"lat\"].attrs\n    out_xr.coords[\"longitude\"].attrs = ds[\"lon\"].attrs\n    out_xr.coords[\"elev\"].attrs = ds[\"elev\"].attrs\n\n    # Add Spatial Reference in recognizable format\n    out_xr.rio.write_crs(ds.spatial_ref, inplace=True)\n\n    return out_xr\n</code></pre>"},{"location":"emit/#hypercoast.emit.plot_emit","title":"<code>plot_emit(ds, longitude=None, latitude=None, downtrack=None, crosstrack=None, remove_nans=True, x='wavelengths', y='reflectance', color='black', frame_height=400, frame_width=600, title=None, method='nearest', ortho=True, options={}, **kwargs)</code>","text":"<p>Plots a line graph of the reflectance data from a given dataset.</p> <p>Parameters:</p> Name Type Description Default <code>ds</code> <code>xarray.Dataset or str</code> <p>The dataset containing the reflectance data or the file path to the dataset.</p> required <code>longitude</code> <code>float</code> <p>The longitude coordinate to select for orthorectified data. Defaults to None.</p> <code>None</code> <code>latitude</code> <code>float</code> <p>The latitude coordinate to select for orthorectified data. Defaults to None.</p> <code>None</code> <code>downtrack</code> <code>int</code> <p>The downtrack coordinate to select for non-orthorectified data. Defaults to None.</p> <code>None</code> <code>crosstrack</code> <code>int</code> <p>The crosstrack coordinate to select for non-orthorectified data. Defaults to None.</p> <code>None</code> <code>remove_nans</code> <code>bool</code> <p>If True, replace non-good wavelengths with NaN. Defaults to True.</p> <code>True</code> <code>x</code> <code>str</code> <p>The x-axis label. Defaults to \"wavelengths\".</p> <code>'wavelengths'</code> <code>y</code> <code>str</code> <p>The y-axis label. Defaults to \"reflectance\".</p> <code>'reflectance'</code> <code>color</code> <code>str</code> <p>The color of the line. Defaults to \"black\".</p> <code>'black'</code> <code>frame_height</code> <code>int</code> <p>The height of the frame. Defaults to 400.</p> <code>400</code> <code>frame_width</code> <code>int</code> <p>The width of the frame. Defaults to 600.</p> <code>600</code> <code>title</code> <code>str</code> <p>The title of the plot. If None, a default title will be generated. Defaults to None.</p> <code>None</code> <code>method</code> <code>str</code> <p>The method to use for data selection. Defaults to \"nearest\".</p> <code>'nearest'</code> <code>ortho</code> <code>bool</code> <p>If True, the function will use longitude and latitude for data selection. Defaults to True.</p> <code>True</code> <code>options</code> <code>dict</code> <p>Additional options to be passed to <code>hvplot.line</code>. Defaults to {}.</p> <code>{}</code> <code>**kwargs</code> <p>Additional keyword arguments to be passed to <code>hvplot.line</code>.</p> <code>{}</code> <p>Returns:</p> Type Description <code>hvplot.Plot</code> <p>The line plot of the reflectance data.</p> Source code in <code>hypercoast/emit.py</code> <pre><code>def plot_emit(\n    ds: Union[xr.Dataset, str],\n    longitude: Optional[float] = None,\n    latitude: Optional[float] = None,\n    downtrack: Optional[int] = None,\n    crosstrack: Optional[int] = None,\n    remove_nans: Optional[bool] = True,\n    x: str = \"wavelengths\",\n    y: str = \"reflectance\",\n    color: str = \"black\",\n    frame_height: Optional[int] = 400,\n    frame_width: Optional[int] = 600,\n    title: str = None,\n    method: Optional[str] = \"nearest\",\n    ortho: Optional[bool] = True,\n    options: Optional[dict] = {},\n    **kwargs,\n):\n    \"\"\"\n    Plots a line graph of the reflectance data from a given dataset.\n\n    Args:\n        ds (xarray.Dataset or str): The dataset containing the reflectance data or the file path to the dataset.\n        longitude (float, optional): The longitude coordinate to select for orthorectified data. Defaults to None.\n        latitude (float, optional): The latitude coordinate to select for orthorectified data. Defaults to None.\n        downtrack (int, optional): The downtrack coordinate to select for non-orthorectified data. Defaults to None.\n        crosstrack (int, optional): The crosstrack coordinate to select for non-orthorectified data. Defaults to None.\n        remove_nans (bool, optional): If True, replace non-good wavelengths with NaN. Defaults to True.\n        x (str, optional): The x-axis label. Defaults to \"wavelengths\".\n        y (str, optional): The y-axis label. Defaults to \"reflectance\".\n        color (str, optional): The color of the line. Defaults to \"black\".\n        frame_height (int, optional): The height of the frame. Defaults to 400.\n        frame_width (int, optional): The width of the frame. Defaults to 600.\n        title (str, optional): The title of the plot. If None, a default title will be generated. Defaults to None.\n        method (str, optional): The method to use for data selection. Defaults to \"nearest\".\n        ortho (bool, optional): If True, the function will use longitude and latitude for data selection. Defaults to True.\n        options (dict, optional): Additional options to be passed to `hvplot.line`. Defaults to {}.\n        **kwargs: Additional keyword arguments to be passed to `hvplot.line`.\n\n    Returns:\n        hvplot.Plot: The line plot of the reflectance data.\n    \"\"\"\n\n    import hvplot.xarray  # noqa F401\n\n    if ortho is True:\n        if longitude is None or latitude is None:\n            raise ValueError(\n                \"Longitude and Latitude must be provided for orthorectified data.\"\n            )\n    else:\n        if downtrack is None or crosstrack is None:\n            raise ValueError(\n                \"Downtrack and Crosstrack must be provided for non-orthorectified data.\"\n            )\n\n    if longitude is not None and latitude is not None:\n        ortho = True\n\n    if downtrack is not None and crosstrack is not None:\n        ortho = False\n\n    if isinstance(ds, str):\n        ds = read_emit(ds, ortho=ortho)\n\n    if remove_nans:\n        ds[\"reflectance\"].data[:, :, ds[\"good_wavelengths\"].data == 0] = np.nan\n\n    if ortho:\n        example = ds[\"reflectance\"].sel(\n            longitude=longitude, latitude=latitude, method=method\n        )\n        if title is None:\n            title = f\"Reflectance at longitude={longitude:.3f}, latitude={latitude:.3f}\"\n\n    else:\n        example = ds[\"reflectance\"].sel(\n            downtrack=downtrack, crosstrack=crosstrack, method=method\n        )\n        if title is None:\n            title = f\"Reflectance at downtrack={downtrack}, crosstrack={crosstrack}\"\n\n    line = example.hvplot.line(\n        y=y,\n        x=x,\n        color=color,\n        frame_height=frame_height,\n        frame_width=frame_width,\n        **kwargs,\n    ).opts(title=title, **options)\n    return line\n</code></pre>"},{"location":"emit/#hypercoast.emit.quality_mask","title":"<code>quality_mask(filepath, quality_bands)</code>","text":"<p>Builds a single layer mask to apply based on the bands selected from an EMIT L2A Mask file.</p> <p>Parameters:</p> Name Type Description Default <code>filepath</code> <code>str</code> <p>An EMIT L2A Mask netCDF file.</p> required <code>quality_bands</code> <code>list</code> <p>A list of bands (quality flags only) from the mask file that should be used in creation of mask.</p> required <p>Returns:</p> Type Description <code>numpy.ndarray</code> <p>A numpy array that can be used with the emit_xarray function to apply a quality mask.</p> <p>Exceptions:</p> Type Description <code>AttributeError</code> <p>If the selected flags include a data band (5 or 6) not just flag bands.</p> Source code in <code>hypercoast/emit.py</code> <pre><code>def quality_mask(filepath: str, quality_bands: list) -&gt; np.ndarray:\n    \"\"\"\n    Builds a single layer mask to apply based on the bands selected from an EMIT L2A Mask file.\n\n    Args:\n        filepath (str): An EMIT L2A Mask netCDF file.\n        quality_bands (list): A list of bands (quality flags only) from the mask file that should be used in creation of mask.\n\n    Returns:\n        numpy.ndarray: A numpy array that can be used with the emit_xarray function to apply a quality mask.\n\n    Raises:\n        AttributeError: If the selected flags include a data band (5 or 6) not just flag bands.\n    \"\"\"\n    # Open Dataset\n    mask_ds = xr.open_dataset(filepath, engine=\"h5netcdf\")\n    # Open Sensor band Group\n    mask_parameters_ds = xr.open_dataset(\n        filepath, engine=\"h5netcdf\", group=\"sensor_band_parameters\"\n    )\n    # Print Flags used\n    flags_used = mask_parameters_ds[\"mask_bands\"].data[quality_bands]\n    print(f\"Flags used: {flags_used}\")\n    # Check for data bands and build mask\n    if any(x in quality_bands for x in [5, 6]):\n        err_str = \"Selected flags include a data band (5 or 6) not just flag bands\"\n        raise AttributeError(err_str)\n    else:\n        qmask = np.sum(mask_ds[\"mask\"][:, :, quality_bands].values, axis=-1)\n        qmask[qmask &gt; 1] = 1\n    return qmask\n</code></pre>"},{"location":"emit/#hypercoast.emit.raw_spatial_crop","title":"<code>raw_spatial_crop(ds, shape)</code>","text":"<p>Use a polygon to clip the file GLT, then a bounding box to crop the spatially raw data. Regions clipped in the GLT are set to 0 so a mask will be applied when used to orthorectify the data at a later point in a workflow.</p> <p>Parameters:</p> Name Type Description Default <code>ds</code> <code>xarray.Dataset</code> <p>Raw spatial EMIT data (non-orthorectified) opened with the <code>emit_xarray</code> function.</p> required <code>shape</code> <code>geopandas.GeoDataFrame</code> <p>A polygon opened with geopandas.</p> required <p>Returns:</p> Type Description <code>xarray.Dataset</code> <p>A clipped GLT and raw spatial data clipped to a bounding box.</p> Source code in <code>hypercoast/emit.py</code> <pre><code>def raw_spatial_crop(ds: xr.Dataset, shape: gpd) -&gt; xr.Dataset:\n    \"\"\"\n    Use a polygon to clip the file GLT, then a bounding box to crop the spatially raw data. Regions clipped in the GLT are set to 0 so a mask will be applied when\n    used to orthorectify the data at a later point in a workflow.\n\n    Args:\n        ds (xarray.Dataset): Raw spatial EMIT data (non-orthorectified) opened with the `emit_xarray` function.\n        shape (geopandas.GeoDataFrame): A polygon opened with geopandas.\n\n    Returns:\n        xarray.Dataset: A clipped GLT and raw spatial data clipped to a bounding box.\n    \"\"\"\n    # Reformat the GLT\n    lon, lat = coord_vects(ds)\n    data_vars = {\n        \"glt_x\": ([\"latitude\", \"longitude\"], ds.glt_x.data),\n        \"glt_y\": ([\"latitude\", \"longitude\"], ds.glt_y.data),\n    }\n    coords = {\n        \"latitude\": ([\"latitude\"], lat),\n        \"longitude\": ([\"longitude\"], lon),\n        \"ortho_y\": ([\"latitude\"], ds.ortho_y.data),\n        \"ortho_x\": ([\"longitude\"], ds.ortho_x.data),\n    }\n    glt_ds = xr.Dataset(data_vars=data_vars, coords=coords, attrs=ds.attrs)\n    glt_ds.rio.write_crs(glt_ds.spatial_ref, inplace=True)\n\n    # Clip the emit glt\n    clipped = glt_ds.rio.clip(shape.geometry.values, shape.crs, all_touched=True)\n\n    # Pull new geotransform from clipped glt\n    clipped_gt = np.array(\n        [float(i) for i in clipped[\"spatial_ref\"].GeoTransform.split(\" \")]\n    )  # THIS GEOTRANSFORM IS OFF BY HALF A PIXEL\n\n    # Create Crosstrack and Downtrack masks for spatially raw dataset -1 is to account for 1 based index. May be a more robust way to do this exists\n    crosstrack_mask = (ds.crosstrack &gt;= np.nanmin(clipped.glt_x.data) - 1) &amp; (\n        ds.crosstrack &lt;= np.nanmax(clipped.glt_x.data) - 1\n    )\n    downtrack_mask = (ds.downtrack &gt;= np.nanmin(clipped.glt_y.data) - 1) &amp; (\n        ds.downtrack &lt;= np.nanmax(clipped.glt_y.data) - 1\n    )\n\n    # Mask Areas outside of crosstrack and downtrack covered by the shape\n    clipped_ds = ds.where((crosstrack_mask &amp; downtrack_mask), drop=True)\n    # Replace Full dataset geotransform with clipped geotransform\n    clipped_ds.attrs[\"geotransform\"] = clipped_gt\n\n    # Drop unnecessary vars from dataset\n    clipped_ds = clipped_ds.drop_vars([\"glt_x\", \"glt_y\", \"downtrack\", \"crosstrack\"])\n\n    # Re-index the GLT to the new array\n    glt_x_data = clipped.glt_x.data - np.nanmin(clipped.glt_x)\n    glt_y_data = clipped.glt_y.data - np.nanmin(clipped.glt_y)\n    clipped_ds = clipped_ds.assign_coords(\n        {\n            \"glt_x\": ([\"ortho_y\", \"ortho_x\"], np.nan_to_num(glt_x_data)),\n            \"glt_y\": ([\"ortho_y\", \"ortho_x\"], np.nan_to_num(glt_y_data)),\n        }\n    )\n    clipped_ds = clipped_ds.assign_coords(\n        {\n            \"downtrack\": (\n                [\"downtrack\"],\n                np.arange(0, clipped_ds[list(ds.data_vars.keys())[0]].shape[0]),\n            ),\n            \"crosstrack\": (\n                [\"crosstrack\"],\n                np.arange(0, clipped_ds[list(ds.data_vars.keys())[0]].shape[1]),\n            ),\n        }\n    )\n\n    return clipped_ds\n</code></pre>"},{"location":"emit/#hypercoast.emit.read_emit","title":"<code>read_emit(filepath, ortho=True, wavelengths=None, method='nearest', **kwargs)</code>","text":"<p>Opens an EMIT dataset from a file path and assigns new coordinates to it.</p> <p>Parameters:</p> Name Type Description Default <code>filepath</code> <code>str</code> <p>The file path to the EMIT dataset.</p> required <code>ortho</code> <code>bool</code> <p>If True, the function will return an orthorectified dataset. Defaults to True.</p> <code>True</code> <code>wavelengths</code> <code>array-like</code> <p>The specific wavelengths to select. If None, all wavelengths are selected. Defaults to None.</p> <code>None</code> <code>method</code> <code>str</code> <p>The method to use for data selection. Defaults to \"nearest\".</p> <code>'nearest'</code> <code>**kwargs</code> <p>Additional keyword arguments to be passed to <code>xr.open_dataset</code>.</p> <code>{}</code> <p>Returns:</p> Type Description <code>xarray.Dataset</code> <p>The dataset with new coordinates assigned.</p> Source code in <code>hypercoast/emit.py</code> <pre><code>def read_emit(\n    filepath: str,\n    ortho: Optional[bool] = True,\n    wavelengths: Union[tuple, list] = None,\n    method: Optional[str] = \"nearest\",\n    **kwargs,\n) -&gt; xr.Dataset:\n    \"\"\"\n    Opens an EMIT dataset from a file path and assigns new coordinates to it.\n\n    Args:\n        filepath (str): The file path to the EMIT dataset.\n        ortho (bool, optional): If True, the function will return an orthorectified dataset. Defaults to True.\n        wavelengths (array-like, optional): The specific wavelengths to select. If None, all wavelengths are selected. Defaults to None.\n        method (str, optional): The method to use for data selection. Defaults to \"nearest\".\n        **kwargs: Additional keyword arguments to be passed to `xr.open_dataset`.\n\n    Returns:\n        xarray.Dataset: The dataset with new coordinates assigned.\n\n    \"\"\"\n\n    if ortho is True:\n        return emit_xarray(\n            filepath, ortho=True, wavelengths=wavelengths, method=method, **kwargs\n        )\n    else:\n        ds = xr.open_dataset(filepath, **kwargs)\n        wvl = xr.open_dataset(filepath, group=\"sensor_band_parameters\")\n        loc = xr.open_dataset(filepath, group=\"location\")\n        ds = ds.assign_coords(\n            {\n                \"downtrack\": ([\"downtrack\"], ds.downtrack.data),\n                \"crosstrack\": ([\"crosstrack\"], ds.crosstrack.data),\n                **wvl.variables,\n                **loc.variables,\n            }\n        )\n        ds = ds.swap_dims({\"bands\": \"wavelengths\"})\n        del wvl\n        del loc\n\n        if wavelengths is not None:\n            ds = ds.sel(wavelengths=wavelengths, method=method)\n\n        ds = ds.rename({\"wavelengths\": \"wavelength\"})\n        return ds\n</code></pre>"},{"location":"emit/#hypercoast.emit.viz_emit","title":"<code>viz_emit(ds, wavelengths, cmap='viridis', frame_width=720, method='nearest', ortho=True, aspect='equal', tiles='ESRI', alpha=0.8, title=None, options={}, **kwargs)</code>","text":"<p>Visualizes the reflectance data from a given dataset at specific wavelengths.</p> <p>Parameters:</p> Name Type Description Default <code>ds</code> <code>xarray.Dataset or str</code> <p>The dataset containing the reflectance data or the file path to the dataset.</p> required <code>wavelengths</code> <code>array-like</code> <p>The specific wavelengths to visualize.</p> required <code>cmap</code> <code>str</code> <p>The colormap to use. Defaults to \"viridis\".</p> <code>'viridis'</code> <code>frame_width</code> <code>int</code> <p>The width of the frame. Defaults to 720.</p> <code>720</code> <code>method</code> <code>str</code> <p>The method to use for data selection. Defaults to \"nearest\".</p> <code>'nearest'</code> <code>ortho</code> <code>bool</code> <p>If True, the function will return an orthorectified image. Defaults to True.</p> <code>True</code> <code>aspect</code> <code>str</code> <p>The aspect ratio of the plot. Defaults to \"equal\".</p> <code>'equal'</code> <code>tiles</code> <code>str</code> <p>The tile source to use for the background map. Defaults to \"ESRI\".</p> <code>'ESRI'</code> <code>alpha</code> <code>float</code> <p>The alpha value for the image. Defaults to 0.8.</p> <code>0.8</code> <code>title</code> <code>str</code> <p>The title of the plot. If None, a default title will be generated. Defaults to None.</p> <code>None</code> <code>options</code> <code>dict</code> <p>Additional options to be passed to <code>hvplot.image</code>. Defaults to {}.</p> <code>{}</code> <code>**kwargs</code> <p>Additional keyword arguments to be passed to <code>hvplot.image</code>.</p> <code>{}</code> <p>Returns:</p> Type Description <code>hvplot.Plot</code> <p>The image plot of the reflectance data at the specified wavelengths.</p> Source code in <code>hypercoast/emit.py</code> <pre><code>def viz_emit(\n    ds: Union[xr.Dataset, str],\n    wavelengths: Union[tuple, list],\n    cmap: Optional[str] = \"viridis\",\n    frame_width: int = 720,\n    method: Optional[str] = \"nearest\",\n    ortho: Optional[bool] = True,\n    aspect: Optional[str] = \"equal\",\n    tiles: str = \"ESRI\",\n    alpha: Optional[float] = 0.8,\n    title: Optional[str] = None,\n    options: Optional[dict] = {},\n    **kwargs,\n):\n    \"\"\"\n    Visualizes the reflectance data from a given dataset at specific wavelengths.\n\n    Args:\n        ds (xarray.Dataset or str): The dataset containing the reflectance data or the file path to the dataset.\n        wavelengths (array-like): The specific wavelengths to visualize.\n        cmap (str, optional): The colormap to use. Defaults to \"viridis\".\n        frame_width (int, optional): The width of the frame. Defaults to 720.\n        method (str, optional): The method to use for data selection. Defaults to \"nearest\".\n        ortho (bool, optional): If True, the function will return an orthorectified image. Defaults to True.\n        aspect (str, optional): The aspect ratio of the plot. Defaults to \"equal\".\n        tiles (str, optional): The tile source to use for the background map. Defaults to \"ESRI\".\n        alpha (float, optional): The alpha value for the image. Defaults to 0.8.\n        title (str, optional): The title of the plot. If None, a default title will be generated. Defaults to None.\n        options (dict, optional): Additional options to be passed to `hvplot.image`. Defaults to {}.\n        **kwargs: Additional keyword arguments to be passed to `hvplot.image`.\n\n    Returns:\n        hvplot.Plot: The image plot of the reflectance data at the specified wavelengths.\n    \"\"\"\n    import hvplot.xarray  # noqa F401\n\n    if isinstance(ds, str):\n        ds = read_emit(ds, ortho=ortho)\n\n    if not isinstance(wavelengths, list):\n        wavelengths = [wavelengths]\n    example = ds.sel(wavelength=wavelengths, method=method)\n\n    wavelengths = \", \".join([f\"{w:.3f}\" for w in example[\"wavelength\"]])\n\n    if title is None:\n        title = f\"Reflectance at {wavelengths} {example.wavelength.units}\"\n\n    if ortho:\n        image = example.hvplot.image(\n            cmap=cmap,\n            geo=ortho,\n            tiles=tiles,\n            alpha=alpha,\n            frame_width=frame_width,\n            **kwargs,\n        ).opts(title=title, **options)\n    else:\n        image = example.hvplot.image(\n            cmap=cmap, aspect=aspect, alpha=alpha, frame_width=frame_width, **kwargs\n        ).opts(title=title, **options)\n\n    return image\n</code></pre>"},{"location":"hypercoast/","title":"hypercoast module","text":"<p>Main module.</p>"},{"location":"hypercoast/#hypercoast.hypercoast.Map","title":"<code> Map            (Map)         </code>","text":"<p>A class that extends leafmap.Map to provide additional functionality for     hypercoast.</p> <p>Methods</p> <p>Any methods inherited from leafmap.Map.</p> Source code in <code>hypercoast/hypercoast.py</code> <pre><code>class Map(leafmap.Map):\n    \"\"\"\n    A class that extends leafmap.Map to provide additional functionality for\n        hypercoast.\n\n    Attributes:\n        Any attributes inherited from leafmap.Map.\n\n    Methods:\n        Any methods inherited from leafmap.Map.\n    \"\"\"\n\n    def __init__(self, **kwargs):\n        \"\"\"\n        Initializes a new instance of the Map class.\n\n        Args:\n            **kwargs: Arbitrary keyword arguments that are passed to the parent\n                class's constructor.\n        \"\"\"\n        super().__init__(**kwargs)\n        self._spectral_data = {}\n        self._plot_options = None\n        self._plot_marker_cluster = ipyleaflet.MarkerCluster(name=\"Marker Cluster\")\n\n    def add(self, obj, position=\"topright\", xlim=None, ylim=None, **kwargs):\n        \"\"\"Add a layer to the map.\n\n        Args:\n            obj (str or object): The name of the layer or a layer object.\n            position (str, optional): The position of the layer widget. Can be\n                'topright', 'topleft', 'bottomright', or 'bottomleft'. Defaults\n                to 'topright'.\n            xlim (tuple, optional): The x-axis limits of the plot. Defaults to None.\n            ylim (tuple, optional): The y-axis limits of the plot. Defaults to None.\n            **kwargs: Arbitrary keyword arguments that are passed to the parent\n                class's add_layer method.\n        \"\"\"\n\n        if isinstance(obj, str):\n            if obj == \"spectral\":\n\n                SpectralWidget(self, position=position, xlim=xlim, ylim=ylim, **kwargs)\n                self.set_plot_options(add_marker_cluster=True)\n            else:\n                super().add(obj, **kwargs)\n\n        else:\n            super().add(obj, **kwargs)\n\n    def search_emit(self, default_dataset=\"EMITL2ARFL\"):\n        \"\"\"\n        Adds a NASA Earth Data search tool to the map with a default dataset for\n            EMIT.\n\n        Args:\n            default_dataset (str, optional): The default dataset to search for.\n                Defaults to \"EMITL2ARFL\".\n        \"\"\"\n        self.add(\"nasa_earth_data\", default_dataset=default_dataset)\n\n    def search_pace(self, default_dataset=\"PACE_OCI_L2_AOP_NRT\"):\n        \"\"\"\n        Adds a NASA Earth Data search tool to the map with a default dataset for\n            PACE.\n\n        Args:\n            default_dataset (str, optional): The default dataset to search for.\n                Defaults to \"PACE_OCI_L2_AOP_NRT\".\n        \"\"\"\n        self.add(\"nasa_earth_data\", default_dataset=default_dataset)\n\n    def search_ecostress(self, default_dataset=\"ECO_L2T_LSTE\"):\n        \"\"\"\n        Adds a NASA Earth Data search tool to the map with a default dataset for\n            ECOSTRESS.\n\n        Args:\n            default_dataset (str, optional): The default dataset to search for.\n                Defaults to \"ECO_L2T_LSTE\".\n        \"\"\"\n        self.add(\"nasa_earth_data\", default_dataset=default_dataset)\n\n    def add_raster(\n        self,\n        source,\n        indexes=None,\n        colormap=None,\n        vmin=None,\n        vmax=None,\n        nodata=None,\n        attribution=None,\n        layer_name=\"Raster\",\n        layer_index=None,\n        zoom_to_layer=True,\n        visible=True,\n        opacity=1.0,\n        array_args=None,\n        client_args={\"cors_all\": False},\n        open_args=None,\n        **kwargs,\n    ):\n        \"\"\"Add a local raster dataset to the map.\n            If you are using this function in JupyterHub on a remote server\n                (e.g., Binder, Microsoft Planetary Computer) and\n            if the raster does not render properly, try installing\n                jupyter-server-proxy using `pip install jupyter-server-proxy`,\n            then running the following code before calling this function. For\n                more info, see https://bit.ly/3JbmF93.\n\n            import os\n            os.environ['LOCALTILESERVER_CLIENT_PREFIX'] = 'proxy/{port}'\n\n        Args:\n            source (str): The path to the GeoTIFF file or the URL of the Cloud\n                Optimized GeoTIFF.\n            indexes (int, optional): The band(s) to use. Band indexing starts\n                at 1. Defaults to None.\n            colormap (str, optional): The name of the colormap from `matplotlib`\n                to use when plotting a single band. See\n                https://matplotlib.org/stable/gallery/color/colormap_reference.html.\n                Default is greyscale.\n            vmin (float, optional): The minimum value to use when colormapping\n                the palette when plotting a single band. Defaults to None.\n            vmax (float, optional): The maximum value to use when colormapping\n                the palette when plotting a single band. Defaults to None.\n            nodata (float, optional): The value from the band to use to interpret\n                as not valid data. Defaults to None.\n            attribution (str, optional): Attribution for the source raster. This\n                defaults to a message about it being a local file.. Defaults to None.\n            layer_name (str, optional): The layer name to use. Defaults to 'Raster'.\n            layer_index (int, optional): The index of the layer. Defaults to None.\n            zoom_to_layer (bool, optional): Whether to zoom to the extent of the\n                layer. Defaults to True.\n            visible (bool, optional): Whether the layer is visible. Defaults to\n                True.\n            opacity (float, optional): The opacity of the layer. Defaults to 1.0.\n            array_args (dict, optional): Additional arguments to pass to\n                `array_to_memory_file` when reading the raster. Defaults to {}.\n            client_args (dict, optional): Additional arguments to pass to\n                localtileserver.TileClient. Defaults to { \"cors_all\": False }.\n            open_args (dict, optional): Additional arguments to pass to\n                rioxarray.open_rasterio.\n\n        \"\"\"\n\n        import rioxarray as rxr\n\n        if array_args is None:\n            array_args = {}\n        if open_args is None:\n            open_args = {}\n\n        if nodata is None:\n            nodata = np.nan\n        super().add_raster(\n            source,\n            indexes=indexes,\n            colormap=colormap,\n            vmin=vmin,\n            vmax=vmax,\n            nodata=nodata,\n            attribution=attribution,\n            layer_name=layer_name,\n            layer_index=layer_index,\n            zoom_to_layer=zoom_to_layer,\n            visible=visible,\n            opacity=opacity,\n            array_args=array_args,\n            client_args=client_args,\n            **kwargs,\n        )\n\n        if isinstance(source, str):\n            da = rxr.open_rasterio(source, **open_args)\n            dims = da.dims\n            da = da.transpose(dims[1], dims[2], dims[0])\n\n            xds = da.to_dataset(name=\"data\")\n            self.cog_layer_dict[layer_name][\"xds\"] = xds\n            # if self.cog_layer_dict[layer_name].get(\"hyper\") is None:\n            #     self.cog_layer_dict[layer_name][\"hyper\"] = \"COG\"\n\n    def add_dataset(\n        self,\n        source,\n        indexes=None,\n        colormap=None,\n        vmin=None,\n        vmax=None,\n        nodata=None,\n        attribution=None,\n        layer_name=\"Raster\",\n        zoom_to_layer=True,\n        visible=True,\n        array_args=None,\n        open_args=None,\n        **kwargs,\n    ):\n        import rioxarray as rxr\n        from leafmap import array_to_image\n\n        if array_args is None:\n            array_args = {}\n\n        if open_args is None:\n            open_args = {}\n\n        if isinstance(source, str):\n            da = rxr.open_rasterio(source, **open_args)\n            dims = da.dims\n            da = da.transpose(dims[1], dims[2], dims[0])\n            xds = da.to_dataset(name=\"data\")\n\n        elif not isinstance(source, xr.Dataset):\n            raise ValueError(\n                \"source must be a path to a raster file or an xarray.Dataset object.\"\n            )\n        else:\n            xds = source\n\n        if indexes is None:\n            if xds.sizes[dims[2]] &lt; 3:\n                indexes = [1]\n            elif xds.sizes[dims[2]] &lt; 4:\n                indexes = [1, 2, 3]\n            else:\n                indexes = [3, 2, 1]\n\n        bands = [i - 1 for i in indexes]\n        da = xds.isel(band=bands)[\"data\"]\n        image = array_to_image(da, transpose=False)\n\n        self.add_raster(\n            image,\n            indexes=None,\n            colormap=colormap,\n            vmin=vmin,\n            vmax=vmax,\n            nodata=nodata,\n            attribution=attribution,\n            layer_name=layer_name,\n            zoom_to_layer=zoom_to_layer,\n            visible=visible,\n            array_args=array_args,\n            **kwargs,\n        )\n\n        self.cog_layer_dict[layer_name][\"xds\"] = xds\n        self.cog_layer_dict[layer_name][\"type\"] = \"XARRAY\"\n        self.cog_layer_dict[layer_name][\"hyper\"] = \"XARRAY\"\n        self.cog_layer_dict[layer_name][\"band_names\"] = [\n            \"b\" + str(i) for i in xds.coords[\"band\"].values.tolist()\n        ]\n        self.cog_layer_dict[layer_name][\"indexes\"] = indexes\n        self.cog_layer_dict[layer_name][\"vis_bands\"] = [\"b\" + str(i) for i in indexes]\n\n    def add_emit(\n        self,\n        source,\n        wavelengths=None,\n        indexes=None,\n        colormap=None,\n        vmin=None,\n        vmax=None,\n        nodata=np.nan,\n        attribution=None,\n        layer_name=\"EMIT\",\n        zoom_to_layer=True,\n        visible=True,\n        array_args=None,\n        **kwargs,\n    ):\n        \"\"\"Add an EMIT dataset to the map.\n            If you are using this function in JupyterHub on a remote server\n                (e.g., Binder, Microsoft Planetary Computer) and\n            if the raster does not render properly, try installing\n                jupyter-server-proxy using `pip install jupyter-server-proxy`,\n            then running the following code before calling this function. For\n                more info, see https://bit.ly/3JbmF93.\n\n            import os\n            os.environ['LOCALTILESERVER_CLIENT_PREFIX'] = 'proxy/{port}'\n\n        Args:\n            source (str): The path to the GeoTIFF file or the URL of the Cloud\n                Optimized GeoTIFF.\n            indexes (int, optional): The band(s) to use. Band indexing starts\n                at 1. Defaults to None.\n            colormap (str, optional): The name of the colormap from `matplotlib`\n                to use when plotting a single band.\n                    See https://matplotlib.org/stable/gallery/color/colormap_reference.html.\n                    Default is greyscale.\n            vmin (float, optional): The minimum value to use when colormapping\n                the palette when plotting a single band. Defaults to None.\n            vmax (float, optional): The maximum value to use when colormapping\n                the palette when plotting a single band. Defaults to None.\n            nodata (float, optional): The value from the band to use to\n                interpret as not valid data. Defaults to None.\n            attribution (str, optional): Attribution for the source raster. This\n                defaults to a message about it being a local file.. Defaults to None.\n            layer_name (str, optional): The layer name to use. Defaults to 'EMIT'.\n            zoom_to_layer (bool, optional): Whether to zoom to the extent of the\n                layer. Defaults to True.\n            visible (bool, optional): Whether the layer is visible. Defaults to\n                True.\n            array_args (dict, optional): Additional arguments to pass to\n                `array_to_memory_file` when reading the raster. Defaults to {}.\n        \"\"\"\n\n        if array_args is None:\n            array_args = {}\n\n        xds = None\n        if isinstance(source, str):\n\n            xds = read_emit(source)\n            source = emit_to_image(xds, wavelengths=wavelengths)\n        elif isinstance(source, xr.Dataset):\n            xds = source\n            source = emit_to_image(xds, wavelengths=wavelengths)\n\n        self.add_raster(\n            source,\n            indexes=indexes,\n            colormap=colormap,\n            vmin=vmin,\n            vmax=vmax,\n            nodata=nodata,\n            attribution=attribution,\n            layer_name=layer_name,\n            zoom_to_layer=zoom_to_layer,\n            visible=visible,\n            array_args=array_args,\n            **kwargs,\n        )\n\n        self.cog_layer_dict[layer_name][\"xds\"] = xds\n        self.cog_layer_dict[layer_name][\"hyper\"] = \"EMIT\"\n        self._update_band_names(layer_name, wavelengths)\n\n    def add_pace(\n        self,\n        source,\n        wavelengths=None,\n        indexes=None,\n        colormap=\"jet\",\n        vmin=None,\n        vmax=None,\n        nodata=np.nan,\n        attribution=None,\n        layer_name=\"PACE\",\n        zoom_to_layer=True,\n        visible=True,\n        method=\"nearest\",\n        gridded=False,\n        array_args=None,\n        **kwargs,\n    ):\n        \"\"\"Add a PACE dataset to the map.\n            If you are using this function in JupyterHub on a remote server\n                (e.g., Binder, Microsoft Planetary Computer) and\n            if the raster does not render properly, try installing\n                jupyter-server-proxy using `pip install jupyter-server-proxy`,\n            then running the following code before calling this function. For\n                more info, see https://bit.ly/3JbmF93.\n\n            import os\n            os.environ['LOCALTILESERVER_CLIENT_PREFIX'] = 'proxy/{port}'\n\n        Args:\n            source (str): The path to the GeoTIFF file or the URL of the Cloud\n                Optimized GeoTIFF.\n            indexes (int, optional): The band(s) to use. Band indexing starts\n                at 1. Defaults to None.\n            colormap (str, optional): The name of the colormap from `matplotlib`\n                to use when plotting a single band. See\n                    https://matplotlib.org/stable/gallery/color/colormap_reference.html.\n                    Default is greyscale.\n            vmin (float, optional): The minimum value to use when colormapping\n                the palette when plotting a single band. Defaults to None.\n            vmax (float, optional): The maximum value to use when colormapping\n                the palette when plotting a single band. Defaults to None.\n            nodata (float, optional): The value from the band to use to interpret\n                as not valid data. Defaults to None.\n            attribution (str, optional): Attribution for the source raster. This\n                defaults to a message about it being a local file.. Defaults to None.\n            layer_name (str, optional): The layer name to use. Defaults to 'EMIT'.\n            zoom_to_layer (bool, optional): Whether to zoom to the extent of the\n                layer. Defaults to True.\n            visible (bool, optional): Whether the layer is visible. Defaults to True.\n            array_args (dict, optional): Additional arguments to pass to\n                `array_to_memory_file` when reading the raster. Defaults to {}.\n        \"\"\"\n\n        if array_args is None:\n            array_args = {}\n\n        if isinstance(source, str):\n\n            source = read_pace(source)\n\n        try:\n            image = pace_to_image(\n                source, wavelengths=wavelengths, method=method, gridded=gridded\n            )\n\n            if isinstance(wavelengths, list) and len(wavelengths) &gt; 1:\n                colormap = None\n\n            self.add_raster(\n                image,\n                indexes=indexes,\n                colormap=colormap,\n                vmin=vmin,\n                vmax=vmax,\n                nodata=nodata,\n                attribution=attribution,\n                layer_name=layer_name,\n                zoom_to_layer=zoom_to_layer,\n                visible=visible,\n                array_args=array_args,\n                **kwargs,\n            )\n\n            self.cog_layer_dict[layer_name][\"xds\"] = source\n            self.cog_layer_dict[layer_name][\"hyper\"] = \"PACE\"\n            self._update_band_names(layer_name, wavelengths)\n        except Exception as e:\n            print(e)\n\n    def add_desis(\n        self,\n        source,\n        wavelengths=[900, 650, 525],\n        indexes=None,\n        colormap=\"jet\",\n        vmin=None,\n        vmax=None,\n        nodata=np.nan,\n        attribution=None,\n        layer_name=\"DESIS\",\n        zoom_to_layer=True,\n        visible=True,\n        method=\"nearest\",\n        array_args=None,\n        **kwargs,\n    ):\n        \"\"\"Add a DESIS dataset to the map.\n            If you are using this function in JupyterHub on a remote server\n                (e.g., Binder, Microsoft Planetary Computer) and\n            if the raster does not render properly, try installing\n                jupyter-server-proxy using `pip install jupyter-server-proxy`,\n            then running the following code before calling this function. For\n                more info, see https://bit.ly/3JbmF93.\n\n            import os\n            os.environ['LOCALTILESERVER_CLIENT_PREFIX'] = 'proxy/{port}'\n\n        Args:\n            source (str): The path to the GeoTIFF file or the URL of the Cloud\n                Optimized GeoTIFF.\n            indexes (int, optional): The band(s) to use. Band indexing starts\n                at 1. Defaults to None.\n            colormap (str, optional): The name of the colormap from `matplotlib`\n                to use when plotting a single band. See\n                https://matplotlib.org/stable/gallery/color/colormap_reference.html.\n                Default is 'jet'.\n            vmin (float, optional): The minimum value to use when colormapping\n                the palette when plotting a single band. Defaults to None.\n            vmax (float, optional): The maximum value to use when colormapping\n                the palette when plotting a single band. Defaults to None.\n            nodata (float, optional): The value from the band to use to interpret\n                as not valid data. Defaults to None.\n            attribution (str, optional): Attribution for the source raster. This\n                defaults to a message about it being a local file.. Defaults to None.\n            layer_name (str, optional): The layer name to use. Defaults to 'EMIT'.\n            zoom_to_layer (bool, optional): Whether to zoom to the extent of the\n                layer. Defaults to True.\n            visible (bool, optional): Whether the layer is visible. Defaults to True.\n            array_args (dict, optional): Additional arguments to pass to\n                `array_to_memory_file` when reading the raster. Defaults to {}.\n        \"\"\"\n        if array_args is None:\n            array_args = {}\n\n        if isinstance(source, str):\n\n            source = read_desis(source)\n\n        image = desis_to_image(source, wavelengths=wavelengths, method=method)\n\n        if isinstance(wavelengths, list) and len(wavelengths) &gt; 1:\n            colormap = None\n\n        if isinstance(wavelengths, int):\n            wavelengths = [wavelengths]\n\n        if indexes is None:\n            if isinstance(wavelengths, list) and len(wavelengths) == 1:\n                indexes = [1]\n            else:\n                indexes = [1, 2, 3]\n\n        self.add_raster(\n            image,\n            indexes=indexes,\n            colormap=colormap,\n            vmin=vmin,\n            vmax=vmax,\n            nodata=nodata,\n            attribution=attribution,\n            layer_name=layer_name,\n            zoom_to_layer=zoom_to_layer,\n            visible=visible,\n            array_args=array_args,\n            **kwargs,\n        )\n\n        self.cog_layer_dict[layer_name][\"xds\"] = source\n        self.cog_layer_dict[layer_name][\"hyper\"] = \"DESIS\"\n        self._update_band_names(layer_name, wavelengths)\n\n    def add_neon(\n        self,\n        source,\n        wavelengths=None,\n        indexes=None,\n        colormap=None,\n        vmin=0,\n        vmax=0.5,\n        nodata=np.nan,\n        attribution=None,\n        layer_name=\"NEON\",\n        zoom_to_layer=True,\n        visible=True,\n        array_args=None,\n        method=\"nearest\",\n        **kwargs,\n    ):\n        \"\"\"Add an NEON AOP dataset to the map.\n            If you are using this function in JupyterHub on a remote server\n                (e.g., Binder, Microsoft Planetary Computer) and\n            if the raster does not render properly, try installing\n                jupyter-server-proxy using `pip install jupyter-server-proxy`,\n            then running the following code before calling this function. For\n                more info, see https://bit.ly/3JbmF93.\n\n            import os\n            os.environ['LOCALTILESERVER_CLIENT_PREFIX'] = 'proxy/{port}'\n\n        Args:\n            source (str): The path to the NEON AOP HDF5 file.\n            indexes (int, optional): The band(s) to use. Band indexing starts\n                at 1. Defaults to None.\n            colormap (str, optional): The name of the colormap from `matplotlib`\n                to use when plotting a single band. See\n                    https://matplotlib.org/stable/gallery/color/colormap_reference.html.\n                    Default is greyscale.\n            vmin (float, optional): The minimum value to use when colormapping\n                the palette when plotting a single band. Defaults to 0.\n            vmax (float, optional): The maximum value to use when colormapping\n                the palette when plotting a single band. Defaults to 0.5.\n            nodata (float, optional): The value from the band to use to\n                interpret as not valid data. Defaults to np.nan.\n            attribution (str, optional): Attribution for the source raster. This\n                defaults to a message about it being a local file.. Defaults to None.\n            layer_name (str, optional): The layer name to use. Defaults to 'NEON'.\n            zoom_to_layer (bool, optional): Whether to zoom to the extent of the\n                layer. Defaults to True.\n            visible (bool, optional): Whether the layer is visible. Defaults\n                to True.\n            array_args (dict, optional): Additional arguments to pass to\n                `array_to_memory_file` when reading the raster. Defaults to {}.\n            method (str, optional): The method to use for data interpolation.\n                Defaults to \"nearest\".\n        \"\"\"\n\n        if array_args is None:\n            array_args = {}\n        xds = None\n        if isinstance(source, str):\n\n            xds = read_neon(source)\n            source = neon_to_image(xds, wavelengths=wavelengths, method=method)\n        elif isinstance(source, xr.Dataset):\n            xds = source\n            source = neon_to_image(xds, wavelengths=wavelengths, method=method)\n\n        self.add_raster(\n            source,\n            indexes=indexes,\n            colormap=colormap,\n            vmin=vmin,\n            vmax=vmax,\n            nodata=nodata,\n            attribution=attribution,\n            layer_name=layer_name,\n            zoom_to_layer=zoom_to_layer,\n            visible=visible,\n            array_args=array_args,\n            **kwargs,\n        )\n\n        self.cog_layer_dict[layer_name][\"xds\"] = xds\n        self.cog_layer_dict[layer_name][\"hyper\"] = \"NEON\"\n        self._update_band_names(layer_name, wavelengths)\n\n    def add_aviris(\n        self,\n        source,\n        wavelengths=None,\n        indexes=None,\n        colormap=None,\n        vmin=0,\n        vmax=0.5,\n        nodata=np.nan,\n        attribution=None,\n        layer_name=\"AVIRIS\",\n        zoom_to_layer=True,\n        visible=True,\n        array_args=None,\n        method=\"nearest\",\n        **kwargs,\n    ):\n        \"\"\"Add an AVIRIS dataset to the map.\n            If you are using this function in JupyterHub on a remote server\n                (e.g., Binder, Microsoft Planetary Computer) and\n            if the raster does not render properly, try installing\n                jupyter-server-proxy using `pip install jupyter-server-proxy`,\n            then running the following code before calling this function. For\n                more info, see https://bit.ly/3JbmF93.\n\n            import os\n            os.environ['LOCALTILESERVER_CLIENT_PREFIX'] = 'proxy/{port}'\n\n        Args:\n            source (str): The path to the AVIRIS file.\n            indexes (int, optional): The band(s) to use. Band indexing starts\n                at 1. Defaults to None.\n            colormap (str, optional): The name of the colormap from `matplotlib`\n                to use when plotting a single band. See\n                    https://matplotlib.org/stable/gallery/color/colormap_reference.html.\n                    Default is greyscale.\n            vmin (float, optional): The minimum value to use when colormapping\n                the palette when plotting a single band. Defaults to 0.\n            vmax (float, optional): The maximum value to use when colormapping\n                the palette when plotting a single band. Defaults to 0.5.\n            nodata (float, optional): The value from the band to use to\n                interpret as not valid data. Defaults to np.nan.\n            attribution (str, optional): Attribution for the source raster. This\n                defaults to a message about it being a local file.. Defaults to None.\n            layer_name (str, optional): The layer name to use. Defaults to 'NEON'.\n            zoom_to_layer (bool, optional): Whether to zoom to the extent of the\n                layer. Defaults to True.\n            visible (bool, optional): Whether the layer is visible. Defaults\n                to True.\n            array_args (dict, optional): Additional arguments to pass to\n                `array_to_memory_file` when reading the raster. Defaults to {}.\n            method (str, optional): The method to use for data interpolation.\n                Defaults to \"nearest\".\n        \"\"\"\n        if array_args is None:\n            array_args = {}\n\n        xds = None\n        if isinstance(source, str):\n\n            xds = read_aviris(source)\n            source = neon_to_image(xds, wavelengths=wavelengths, method=method)\n        elif isinstance(source, xr.Dataset):\n            xds = source\n            source = aviris_to_image(xds, wavelengths=wavelengths, method=method)\n\n        self.add_raster(\n            source,\n            indexes=indexes,\n            colormap=colormap,\n            vmin=vmin,\n            vmax=vmax,\n            nodata=nodata,\n            attribution=attribution,\n            layer_name=layer_name,\n            zoom_to_layer=zoom_to_layer,\n            visible=visible,\n            array_args=array_args,\n            **kwargs,\n        )\n\n        self.cog_layer_dict[layer_name][\"xds\"] = xds\n        self.cog_layer_dict[layer_name][\"hyper\"] = \"AVIRIS\"\n        self._update_band_names(layer_name, wavelengths)\n\n    def add_hyper(self, xds, dtype, wvl_indexes=None, **kwargs):\n        \"\"\"Add a hyperspectral dataset to the map.\n\n        Args:\n            xds (str): The Xarray dataset containing the hyperspectral data.\n            dtype (str): The type of the hyperspectral dataset. Can be one of\n                \"EMIT\", \"PACE\", \"DESIS\", \"NEON\", \"AVIRIS\".\n            **kwargs: Additional keyword arguments to pass to the corresponding\n                add function.\n        \"\"\"\n\n        if wvl_indexes is not None:\n            if dtype == \"XARRAY\":\n                kwargs[\"indexes\"] = [i + 1 for i in wvl_indexes]\n            else:\n\n                kwargs[\"wavelengths\"] = (\n                    xds.isel(wavelength=wvl_indexes)\n                    .coords[\"wavelength\"]\n                    .values.tolist()\n                )\n\n        if dtype == \"EMIT\":\n            self.add_emit(xds, **kwargs)\n        elif dtype == \"PACE\":\n            self.add_pace(xds, **kwargs)\n        elif dtype == \"DESIS\":\n            self.add_desis(xds, **kwargs)\n        elif dtype == \"NEON\":\n            self.add_neon(xds, **kwargs)\n        elif dtype == \"AVIRIS\":\n            self.add_aviris(xds, **kwargs)\n        elif dtype == \"XARRAY\":\n            kwargs.pop(\"wavelengths\", None)\n            self.add_dataset(xds, **kwargs)\n\n    def set_plot_options(\n        self,\n        add_marker_cluster=False,\n        plot_type=None,\n        overlay=False,\n        position=\"bottomright\",\n        min_width=None,\n        max_width=None,\n        min_height=None,\n        max_height=None,\n        **kwargs,\n    ):\n        \"\"\"Sets plotting options.\n\n        Args:\n            add_marker_cluster (bool, optional): Whether to add a marker cluster.\n                Defaults to False.\n            sample_scale (float, optional):  A nominal scale in meters of the\n                projection to sample in . Defaults to None.\n            plot_type (str, optional): The plot type can be one of \"None\", \"bar\",\n                \"scatter\" or \"hist\". Defaults to None.\n            overlay (bool, optional): Whether to overlay plotted lines on the\n                figure. Defaults to False.\n            position (str, optional): Position of the control, can be\n                \u2018bottomleft\u2019, \u2018bottomright\u2019, \u2018topleft\u2019, or \u2018topright\u2019. Defaults\n                to 'bottomright'.\n            min_width (int, optional): Min width of the widget (in pixels), if\n                None it will respect the content size. Defaults to None.\n            max_width (int, optional): Max width of the widget (in pixels), if\n                None it will respect the content size. Defaults to None.\n            min_height (int, optional): Min height of the widget (in pixels), if\n                None it will respect the content size. Defaults to None.\n            max_height (int, optional): Max height of the widget (in pixels), if\n                None it will respect the content size. Defaults to None.\n\n        \"\"\"\n        plot_options_dict = {}\n        plot_options_dict[\"add_marker_cluster\"] = add_marker_cluster\n        plot_options_dict[\"plot_type\"] = plot_type\n        plot_options_dict[\"overlay\"] = overlay\n        plot_options_dict[\"position\"] = position\n        plot_options_dict[\"min_width\"] = min_width\n        plot_options_dict[\"max_width\"] = max_width\n        plot_options_dict[\"min_height\"] = min_height\n        plot_options_dict[\"max_height\"] = max_height\n\n        for key in kwargs:\n            plot_options_dict[key] = kwargs[key]\n\n        self._plot_options = plot_options_dict\n\n        if not hasattr(self, \"_plot_marker_cluster\"):\n            self._plot_marker_cluster = ipyleaflet.MarkerCluster(name=\"Marker Cluster\")\n\n        if add_marker_cluster and (self._plot_marker_cluster not in self.layers):\n            self.add(self._plot_marker_cluster)\n\n    def spectral_to_df(self, **kwargs):\n        \"\"\"Converts the spectral data to a pandas DataFrame.\n\n        Returns:\n            pd.DataFrame: The spectral data as a pandas DataFrame.\n        \"\"\"\n        import pandas as pd\n\n        df = pd.DataFrame(self._spectral_data, **kwargs)\n        return df\n\n    def spectral_to_gdf(self, **kwargs):\n        \"\"\"Converts the spectral data to a GeoPandas GeoDataFrame.\n\n        Returns:\n            gpd.DataFrame: The spectral data as a pandas DataFrame.\n        \"\"\"\n        import geopandas as gpd\n        from shapely.geometry import Point\n\n        df = self.spectral_to_df()\n\n        if len(df) == 0:\n            return df\n\n        # Step 1: Extract the coordinates from the columns\n        if \"wavelength\" in df.columns:\n            df = df.rename(columns={\"wavelength\": \"latlon\"})\n        elif \"wavelengths\" in df.columns:\n            df = df.rename(columns={\"wavelengths\": \"latlon\"})\n        coordinates = [col.strip(\"()\").split() for col in df.columns[1:]]\n        coords = [(float(lat), float(lon)) for lat, lon in coordinates]\n\n        # Step 2: Create Point geometries for each coordinate\n        points = [Point(lon, lat) for lat, lon in coords]\n\n        # Step 3: Create a GeoDataFrame\n        df_transposed = df.set_index(\"latlon\").T\n\n        # Convert the column names to strings to ensure compatibility with GeoJSON\n        df_transposed.columns = df_transposed.columns.astype(str)\n\n        # Create the GeoDataFrame\n        gdf = gpd.GeoDataFrame(df_transposed, geometry=points, **kwargs)\n\n        # Set the coordinate reference system (CRS)\n        gdf = gdf.set_geometry(\"geometry\").set_crs(\"EPSG:4326\")\n\n        return gdf\n\n    def spectral_to_csv(self, filename, index=True, **kwargs):\n        \"\"\"Saves the spectral data to a CSV file.\n\n        Args:\n            filename (str): The output CSV file.\n            index (bool, optional): Whether to write the index. Defaults to True.\n        \"\"\"\n        df = self.spectral_to_df()\n        df = df.rename_axis(\"band\")\n        df.to_csv(filename, index=index, **kwargs)\n\n    def _update_band_names(self, layer_name, wavelengths):\n\n        # Function to find the nearest indices\n        def find_nearest_indices(\n            dataarray, selected_wavelengths, dim_name=\"wavelength\"\n        ):\n            indices = []\n            for wavelength in selected_wavelengths:\n                if dim_name == \"band\":\n                    nearest_wavelength = dataarray.sel(\n                        band=wavelength, method=\"nearest\"\n                    )\n                else:\n                    nearest_wavelength = dataarray.sel(\n                        wavelength=wavelength, method=\"nearest\"\n                    )\n                nearest_wavelength_index = nearest_wavelength[dim_name].item()\n                nearest_index = (\n                    dataarray[dim_name].values.tolist().index(nearest_wavelength_index)\n                )\n                indices.append(nearest_index + 1)\n            return indices\n\n        if \"xds\" in self.cog_layer_dict[layer_name]:\n            xds = self.cog_layer_dict[layer_name][\"xds\"]\n            dim_name = \"wavelength\"\n\n            if \"band\" in xds:\n                dim_name = \"band\"\n\n            band_count = xds.sizes[dim_name]\n            band_names = [\"b\" + str(band) for band in range(1, band_count + 1)]\n            self.cog_layer_dict[layer_name][\"band_names\"] = band_names\n\n            try:\n                indexes = find_nearest_indices(xds, wavelengths, dim_name=dim_name)\n                vis_bands = [\"b\" + str(index) for index in indexes]\n                self.cog_layer_dict[layer_name][\"indexes\"] = indexes\n                self.cog_layer_dict[layer_name][\"vis_bands\"] = vis_bands\n            except Exception as e:\n                print(e)\n\n    def add_field_data(\n        self,\n        data: Union[str],\n        x_col: str = \"wavelength\",\n        y_col_prefix: str = \"(\",\n        x_label: str = \"Wavelengths (nm)\",\n        y_label: str = \"Reflectance\",\n        use_marker_cluster: bool = True,\n        min_width: int = 400,\n        max_width: int = 600,\n        min_height: int = 200,\n        max_height: int = 250,\n        layer_name: str = \"Marker Cluster\",\n        **kwargs,\n    ):\n        \"\"\"\n        Displays field data on a map with interactive markers and popups showing time series data.\n\n        Args:\n            data (Union[str, pd.DataFrame]): Path to the CSV file or a pandas DataFrame containing the data.\n            x_col (str): Column name to use for the x-axis of the charts. Default is \"wavelength\".\n            y_col_prefix (str): Prefix to identify the columns that contain the location-specific data. Default is \"(\".\n            x_label (str): Label for the x-axis of the charts. Default is \"Wavelengths (nm)\".\n            y_label (str): Label for the y-axis of the charts. Default is \"Reflectance\".\n            use_marker_cluster (bool): Whether to use marker clustering. Default is True.\n            min_width (int): Minimum width of the popup. Default is 400.\n            max_width (int): Maximum width of the popup. Default is 600.\n            min_height (int): Minimum height of the popup. Default is 200.\n            max_height (int): Maximum height of the popup. Default is 250.\n            layer_name (str): Name of the marker cluster layer. Default is \"Marker Cluster\".\n\n        Returns:\n            Map: An ipyleaflet Map with the added markers and popups.\n        \"\"\"\n        show_field_data(\n            data,\n            x_col,\n            y_col_prefix,\n            x_label=x_label,\n            y_label=y_label,\n            use_marker_cluster=use_marker_cluster,\n            min_width=min_width,\n            max_width=max_width,\n            min_height=min_height,\n            max_height=max_height,\n            layer_name=layer_name,\n            m=self,\n            **kwargs,\n        )\n</code></pre>"},{"location":"hypercoast/#hypercoast.hypercoast.Map.__init__","title":"<code>__init__(self, **kwargs)</code>  <code>special</code>","text":"<p>Initializes a new instance of the Map class.</p> <p>Parameters:</p> Name Type Description Default <code>**kwargs</code> <p>Arbitrary keyword arguments that are passed to the parent class's constructor.</p> <code>{}</code> Source code in <code>hypercoast/hypercoast.py</code> <pre><code>def __init__(self, **kwargs):\n    \"\"\"\n    Initializes a new instance of the Map class.\n\n    Args:\n        **kwargs: Arbitrary keyword arguments that are passed to the parent\n            class's constructor.\n    \"\"\"\n    super().__init__(**kwargs)\n    self._spectral_data = {}\n    self._plot_options = None\n    self._plot_marker_cluster = ipyleaflet.MarkerCluster(name=\"Marker Cluster\")\n</code></pre>"},{"location":"hypercoast/#hypercoast.hypercoast.Map.add","title":"<code>add(self, obj, position='topright', xlim=None, ylim=None, **kwargs)</code>","text":"<p>Add a layer to the map.</p> <p>Parameters:</p> Name Type Description Default <code>obj</code> <code>str or object</code> <p>The name of the layer or a layer object.</p> required <code>position</code> <code>str</code> <p>The position of the layer widget. Can be 'topright', 'topleft', 'bottomright', or 'bottomleft'. Defaults to 'topright'.</p> <code>'topright'</code> <code>xlim</code> <code>tuple</code> <p>The x-axis limits of the plot. Defaults to None.</p> <code>None</code> <code>ylim</code> <code>tuple</code> <p>The y-axis limits of the plot. Defaults to None.</p> <code>None</code> <code>**kwargs</code> <p>Arbitrary keyword arguments that are passed to the parent class's add_layer method.</p> <code>{}</code> Source code in <code>hypercoast/hypercoast.py</code> <pre><code>def add(self, obj, position=\"topright\", xlim=None, ylim=None, **kwargs):\n    \"\"\"Add a layer to the map.\n\n    Args:\n        obj (str or object): The name of the layer or a layer object.\n        position (str, optional): The position of the layer widget. Can be\n            'topright', 'topleft', 'bottomright', or 'bottomleft'. Defaults\n            to 'topright'.\n        xlim (tuple, optional): The x-axis limits of the plot. Defaults to None.\n        ylim (tuple, optional): The y-axis limits of the plot. Defaults to None.\n        **kwargs: Arbitrary keyword arguments that are passed to the parent\n            class's add_layer method.\n    \"\"\"\n\n    if isinstance(obj, str):\n        if obj == \"spectral\":\n\n            SpectralWidget(self, position=position, xlim=xlim, ylim=ylim, **kwargs)\n            self.set_plot_options(add_marker_cluster=True)\n        else:\n            super().add(obj, **kwargs)\n\n    else:\n        super().add(obj, **kwargs)\n</code></pre>"},{"location":"hypercoast/#hypercoast.hypercoast.Map.add_aviris","title":"<code>add_aviris(self, source, wavelengths=None, indexes=None, colormap=None, vmin=0, vmax=0.5, nodata=nan, attribution=None, layer_name='AVIRIS', zoom_to_layer=True, visible=True, array_args=None, method='nearest', **kwargs)</code>","text":"<p>Add an AVIRIS dataset to the map.     If you are using this function in JupyterHub on a remote server         (e.g., Binder, Microsoft Planetary Computer) and     if the raster does not render properly, try installing         jupyter-server-proxy using <code>pip install jupyter-server-proxy</code>,     then running the following code before calling this function. For         more info, see https://bit.ly/3JbmF93.</p> <pre><code>import os\nos.environ['LOCALTILESERVER_CLIENT_PREFIX'] = 'proxy/{port}'\n</code></pre> <p>Parameters:</p> Name Type Description Default <code>source</code> <code>str</code> <p>The path to the AVIRIS file.</p> required <code>indexes</code> <code>int</code> <p>The band(s) to use. Band indexing starts at 1. Defaults to None.</p> <code>None</code> <code>colormap</code> <code>str</code> <p>The name of the colormap from <code>matplotlib</code> to use when plotting a single band. See     https://matplotlib.org/stable/gallery/color/colormap_reference.html.     Default is greyscale.</p> <code>None</code> <code>vmin</code> <code>float</code> <p>The minimum value to use when colormapping the palette when plotting a single band. Defaults to 0.</p> <code>0</code> <code>vmax</code> <code>float</code> <p>The maximum value to use when colormapping the palette when plotting a single band. Defaults to 0.5.</p> <code>0.5</code> <code>nodata</code> <code>float</code> <p>The value from the band to use to interpret as not valid data. Defaults to np.nan.</p> <code>nan</code> <code>attribution</code> <code>str</code> <p>Attribution for the source raster. This defaults to a message about it being a local file.. Defaults to None.</p> <code>None</code> <code>layer_name</code> <code>str</code> <p>The layer name to use. Defaults to 'NEON'.</p> <code>'AVIRIS'</code> <code>zoom_to_layer</code> <code>bool</code> <p>Whether to zoom to the extent of the layer. Defaults to True.</p> <code>True</code> <code>visible</code> <code>bool</code> <p>Whether the layer is visible. Defaults to True.</p> <code>True</code> <code>array_args</code> <code>dict</code> <p>Additional arguments to pass to <code>array_to_memory_file</code> when reading the raster. Defaults to {}.</p> <code>None</code> <code>method</code> <code>str</code> <p>The method to use for data interpolation. Defaults to \"nearest\".</p> <code>'nearest'</code> Source code in <code>hypercoast/hypercoast.py</code> <pre><code>def add_aviris(\n    self,\n    source,\n    wavelengths=None,\n    indexes=None,\n    colormap=None,\n    vmin=0,\n    vmax=0.5,\n    nodata=np.nan,\n    attribution=None,\n    layer_name=\"AVIRIS\",\n    zoom_to_layer=True,\n    visible=True,\n    array_args=None,\n    method=\"nearest\",\n    **kwargs,\n):\n    \"\"\"Add an AVIRIS dataset to the map.\n        If you are using this function in JupyterHub on a remote server\n            (e.g., Binder, Microsoft Planetary Computer) and\n        if the raster does not render properly, try installing\n            jupyter-server-proxy using `pip install jupyter-server-proxy`,\n        then running the following code before calling this function. For\n            more info, see https://bit.ly/3JbmF93.\n\n        import os\n        os.environ['LOCALTILESERVER_CLIENT_PREFIX'] = 'proxy/{port}'\n\n    Args:\n        source (str): The path to the AVIRIS file.\n        indexes (int, optional): The band(s) to use. Band indexing starts\n            at 1. Defaults to None.\n        colormap (str, optional): The name of the colormap from `matplotlib`\n            to use when plotting a single band. See\n                https://matplotlib.org/stable/gallery/color/colormap_reference.html.\n                Default is greyscale.\n        vmin (float, optional): The minimum value to use when colormapping\n            the palette when plotting a single band. Defaults to 0.\n        vmax (float, optional): The maximum value to use when colormapping\n            the palette when plotting a single band. Defaults to 0.5.\n        nodata (float, optional): The value from the band to use to\n            interpret as not valid data. Defaults to np.nan.\n        attribution (str, optional): Attribution for the source raster. This\n            defaults to a message about it being a local file.. Defaults to None.\n        layer_name (str, optional): The layer name to use. Defaults to 'NEON'.\n        zoom_to_layer (bool, optional): Whether to zoom to the extent of the\n            layer. Defaults to True.\n        visible (bool, optional): Whether the layer is visible. Defaults\n            to True.\n        array_args (dict, optional): Additional arguments to pass to\n            `array_to_memory_file` when reading the raster. Defaults to {}.\n        method (str, optional): The method to use for data interpolation.\n            Defaults to \"nearest\".\n    \"\"\"\n    if array_args is None:\n        array_args = {}\n\n    xds = None\n    if isinstance(source, str):\n\n        xds = read_aviris(source)\n        source = neon_to_image(xds, wavelengths=wavelengths, method=method)\n    elif isinstance(source, xr.Dataset):\n        xds = source\n        source = aviris_to_image(xds, wavelengths=wavelengths, method=method)\n\n    self.add_raster(\n        source,\n        indexes=indexes,\n        colormap=colormap,\n        vmin=vmin,\n        vmax=vmax,\n        nodata=nodata,\n        attribution=attribution,\n        layer_name=layer_name,\n        zoom_to_layer=zoom_to_layer,\n        visible=visible,\n        array_args=array_args,\n        **kwargs,\n    )\n\n    self.cog_layer_dict[layer_name][\"xds\"] = xds\n    self.cog_layer_dict[layer_name][\"hyper\"] = \"AVIRIS\"\n    self._update_band_names(layer_name, wavelengths)\n</code></pre>"},{"location":"hypercoast/#hypercoast.hypercoast.Map.add_desis","title":"<code>add_desis(self, source, wavelengths=[900, 650, 525], indexes=None, colormap='jet', vmin=None, vmax=None, nodata=nan, attribution=None, layer_name='DESIS', zoom_to_layer=True, visible=True, method='nearest', array_args=None, **kwargs)</code>","text":"<p>Add a DESIS dataset to the map.     If you are using this function in JupyterHub on a remote server         (e.g., Binder, Microsoft Planetary Computer) and     if the raster does not render properly, try installing         jupyter-server-proxy using <code>pip install jupyter-server-proxy</code>,     then running the following code before calling this function. For         more info, see https://bit.ly/3JbmF93.</p> <pre><code>import os\nos.environ['LOCALTILESERVER_CLIENT_PREFIX'] = 'proxy/{port}'\n</code></pre> <p>Parameters:</p> Name Type Description Default <code>source</code> <code>str</code> <p>The path to the GeoTIFF file or the URL of the Cloud Optimized GeoTIFF.</p> required <code>indexes</code> <code>int</code> <p>The band(s) to use. Band indexing starts at 1. Defaults to None.</p> <code>None</code> <code>colormap</code> <code>str</code> <p>The name of the colormap from <code>matplotlib</code> to use when plotting a single band. See https://matplotlib.org/stable/gallery/color/colormap_reference.html. Default is 'jet'.</p> <code>'jet'</code> <code>vmin</code> <code>float</code> <p>The minimum value to use when colormapping the palette when plotting a single band. Defaults to None.</p> <code>None</code> <code>vmax</code> <code>float</code> <p>The maximum value to use when colormapping the palette when plotting a single band. Defaults to None.</p> <code>None</code> <code>nodata</code> <code>float</code> <p>The value from the band to use to interpret as not valid data. Defaults to None.</p> <code>nan</code> <code>attribution</code> <code>str</code> <p>Attribution for the source raster. This defaults to a message about it being a local file.. Defaults to None.</p> <code>None</code> <code>layer_name</code> <code>str</code> <p>The layer name to use. Defaults to 'EMIT'.</p> <code>'DESIS'</code> <code>zoom_to_layer</code> <code>bool</code> <p>Whether to zoom to the extent of the layer. Defaults to True.</p> <code>True</code> <code>visible</code> <code>bool</code> <p>Whether the layer is visible. Defaults to True.</p> <code>True</code> <code>array_args</code> <code>dict</code> <p>Additional arguments to pass to <code>array_to_memory_file</code> when reading the raster. Defaults to {}.</p> <code>None</code> Source code in <code>hypercoast/hypercoast.py</code> <pre><code>def add_desis(\n    self,\n    source,\n    wavelengths=[900, 650, 525],\n    indexes=None,\n    colormap=\"jet\",\n    vmin=None,\n    vmax=None,\n    nodata=np.nan,\n    attribution=None,\n    layer_name=\"DESIS\",\n    zoom_to_layer=True,\n    visible=True,\n    method=\"nearest\",\n    array_args=None,\n    **kwargs,\n):\n    \"\"\"Add a DESIS dataset to the map.\n        If you are using this function in JupyterHub on a remote server\n            (e.g., Binder, Microsoft Planetary Computer) and\n        if the raster does not render properly, try installing\n            jupyter-server-proxy using `pip install jupyter-server-proxy`,\n        then running the following code before calling this function. For\n            more info, see https://bit.ly/3JbmF93.\n\n        import os\n        os.environ['LOCALTILESERVER_CLIENT_PREFIX'] = 'proxy/{port}'\n\n    Args:\n        source (str): The path to the GeoTIFF file or the URL of the Cloud\n            Optimized GeoTIFF.\n        indexes (int, optional): The band(s) to use. Band indexing starts\n            at 1. Defaults to None.\n        colormap (str, optional): The name of the colormap from `matplotlib`\n            to use when plotting a single band. See\n            https://matplotlib.org/stable/gallery/color/colormap_reference.html.\n            Default is 'jet'.\n        vmin (float, optional): The minimum value to use when colormapping\n            the palette when plotting a single band. Defaults to None.\n        vmax (float, optional): The maximum value to use when colormapping\n            the palette when plotting a single band. Defaults to None.\n        nodata (float, optional): The value from the band to use to interpret\n            as not valid data. Defaults to None.\n        attribution (str, optional): Attribution for the source raster. This\n            defaults to a message about it being a local file.. Defaults to None.\n        layer_name (str, optional): The layer name to use. Defaults to 'EMIT'.\n        zoom_to_layer (bool, optional): Whether to zoom to the extent of the\n            layer. Defaults to True.\n        visible (bool, optional): Whether the layer is visible. Defaults to True.\n        array_args (dict, optional): Additional arguments to pass to\n            `array_to_memory_file` when reading the raster. Defaults to {}.\n    \"\"\"\n    if array_args is None:\n        array_args = {}\n\n    if isinstance(source, str):\n\n        source = read_desis(source)\n\n    image = desis_to_image(source, wavelengths=wavelengths, method=method)\n\n    if isinstance(wavelengths, list) and len(wavelengths) &gt; 1:\n        colormap = None\n\n    if isinstance(wavelengths, int):\n        wavelengths = [wavelengths]\n\n    if indexes is None:\n        if isinstance(wavelengths, list) and len(wavelengths) == 1:\n            indexes = [1]\n        else:\n            indexes = [1, 2, 3]\n\n    self.add_raster(\n        image,\n        indexes=indexes,\n        colormap=colormap,\n        vmin=vmin,\n        vmax=vmax,\n        nodata=nodata,\n        attribution=attribution,\n        layer_name=layer_name,\n        zoom_to_layer=zoom_to_layer,\n        visible=visible,\n        array_args=array_args,\n        **kwargs,\n    )\n\n    self.cog_layer_dict[layer_name][\"xds\"] = source\n    self.cog_layer_dict[layer_name][\"hyper\"] = \"DESIS\"\n    self._update_band_names(layer_name, wavelengths)\n</code></pre>"},{"location":"hypercoast/#hypercoast.hypercoast.Map.add_emit","title":"<code>add_emit(self, source, wavelengths=None, indexes=None, colormap=None, vmin=None, vmax=None, nodata=nan, attribution=None, layer_name='EMIT', zoom_to_layer=True, visible=True, array_args=None, **kwargs)</code>","text":"<p>Add an EMIT dataset to the map.     If you are using this function in JupyterHub on a remote server         (e.g., Binder, Microsoft Planetary Computer) and     if the raster does not render properly, try installing         jupyter-server-proxy using <code>pip install jupyter-server-proxy</code>,     then running the following code before calling this function. For         more info, see https://bit.ly/3JbmF93.</p> <pre><code>import os\nos.environ['LOCALTILESERVER_CLIENT_PREFIX'] = 'proxy/{port}'\n</code></pre> <p>Parameters:</p> Name Type Description Default <code>source</code> <code>str</code> <p>The path to the GeoTIFF file or the URL of the Cloud Optimized GeoTIFF.</p> required <code>indexes</code> <code>int</code> <p>The band(s) to use. Band indexing starts at 1. Defaults to None.</p> <code>None</code> <code>colormap</code> <code>str</code> <p>The name of the colormap from <code>matplotlib</code> to use when plotting a single band.     See https://matplotlib.org/stable/gallery/color/colormap_reference.html.     Default is greyscale.</p> <code>None</code> <code>vmin</code> <code>float</code> <p>The minimum value to use when colormapping the palette when plotting a single band. Defaults to None.</p> <code>None</code> <code>vmax</code> <code>float</code> <p>The maximum value to use when colormapping the palette when plotting a single band. Defaults to None.</p> <code>None</code> <code>nodata</code> <code>float</code> <p>The value from the band to use to interpret as not valid data. Defaults to None.</p> <code>nan</code> <code>attribution</code> <code>str</code> <p>Attribution for the source raster. This defaults to a message about it being a local file.. Defaults to None.</p> <code>None</code> <code>layer_name</code> <code>str</code> <p>The layer name to use. Defaults to 'EMIT'.</p> <code>'EMIT'</code> <code>zoom_to_layer</code> <code>bool</code> <p>Whether to zoom to the extent of the layer. Defaults to True.</p> <code>True</code> <code>visible</code> <code>bool</code> <p>Whether the layer is visible. Defaults to True.</p> <code>True</code> <code>array_args</code> <code>dict</code> <p>Additional arguments to pass to <code>array_to_memory_file</code> when reading the raster. Defaults to {}.</p> <code>None</code> Source code in <code>hypercoast/hypercoast.py</code> <pre><code>def add_emit(\n    self,\n    source,\n    wavelengths=None,\n    indexes=None,\n    colormap=None,\n    vmin=None,\n    vmax=None,\n    nodata=np.nan,\n    attribution=None,\n    layer_name=\"EMIT\",\n    zoom_to_layer=True,\n    visible=True,\n    array_args=None,\n    **kwargs,\n):\n    \"\"\"Add an EMIT dataset to the map.\n        If you are using this function in JupyterHub on a remote server\n            (e.g., Binder, Microsoft Planetary Computer) and\n        if the raster does not render properly, try installing\n            jupyter-server-proxy using `pip install jupyter-server-proxy`,\n        then running the following code before calling this function. For\n            more info, see https://bit.ly/3JbmF93.\n\n        import os\n        os.environ['LOCALTILESERVER_CLIENT_PREFIX'] = 'proxy/{port}'\n\n    Args:\n        source (str): The path to the GeoTIFF file or the URL of the Cloud\n            Optimized GeoTIFF.\n        indexes (int, optional): The band(s) to use. Band indexing starts\n            at 1. Defaults to None.\n        colormap (str, optional): The name of the colormap from `matplotlib`\n            to use when plotting a single band.\n                See https://matplotlib.org/stable/gallery/color/colormap_reference.html.\n                Default is greyscale.\n        vmin (float, optional): The minimum value to use when colormapping\n            the palette when plotting a single band. Defaults to None.\n        vmax (float, optional): The maximum value to use when colormapping\n            the palette when plotting a single band. Defaults to None.\n        nodata (float, optional): The value from the band to use to\n            interpret as not valid data. Defaults to None.\n        attribution (str, optional): Attribution for the source raster. This\n            defaults to a message about it being a local file.. Defaults to None.\n        layer_name (str, optional): The layer name to use. Defaults to 'EMIT'.\n        zoom_to_layer (bool, optional): Whether to zoom to the extent of the\n            layer. Defaults to True.\n        visible (bool, optional): Whether the layer is visible. Defaults to\n            True.\n        array_args (dict, optional): Additional arguments to pass to\n            `array_to_memory_file` when reading the raster. Defaults to {}.\n    \"\"\"\n\n    if array_args is None:\n        array_args = {}\n\n    xds = None\n    if isinstance(source, str):\n\n        xds = read_emit(source)\n        source = emit_to_image(xds, wavelengths=wavelengths)\n    elif isinstance(source, xr.Dataset):\n        xds = source\n        source = emit_to_image(xds, wavelengths=wavelengths)\n\n    self.add_raster(\n        source,\n        indexes=indexes,\n        colormap=colormap,\n        vmin=vmin,\n        vmax=vmax,\n        nodata=nodata,\n        attribution=attribution,\n        layer_name=layer_name,\n        zoom_to_layer=zoom_to_layer,\n        visible=visible,\n        array_args=array_args,\n        **kwargs,\n    )\n\n    self.cog_layer_dict[layer_name][\"xds\"] = xds\n    self.cog_layer_dict[layer_name][\"hyper\"] = \"EMIT\"\n    self._update_band_names(layer_name, wavelengths)\n</code></pre>"},{"location":"hypercoast/#hypercoast.hypercoast.Map.add_field_data","title":"<code>add_field_data(self, data, x_col='wavelength', y_col_prefix='(', x_label='Wavelengths (nm)', y_label='Reflectance', use_marker_cluster=True, min_width=400, max_width=600, min_height=200, max_height=250, layer_name='Marker Cluster', **kwargs)</code>","text":"<p>Displays field data on a map with interactive markers and popups showing time series data.</p> <p>Parameters:</p> Name Type Description Default <code>data</code> <code>Union[str, pd.DataFrame]</code> <p>Path to the CSV file or a pandas DataFrame containing the data.</p> required <code>x_col</code> <code>str</code> <p>Column name to use for the x-axis of the charts. Default is \"wavelength\".</p> <code>'wavelength'</code> <code>y_col_prefix</code> <code>str</code> <p>Prefix to identify the columns that contain the location-specific data. Default is \"(\".</p> <code>'('</code> <code>x_label</code> <code>str</code> <p>Label for the x-axis of the charts. Default is \"Wavelengths (nm)\".</p> <code>'Wavelengths (nm)'</code> <code>y_label</code> <code>str</code> <p>Label for the y-axis of the charts. Default is \"Reflectance\".</p> <code>'Reflectance'</code> <code>use_marker_cluster</code> <code>bool</code> <p>Whether to use marker clustering. Default is True.</p> <code>True</code> <code>min_width</code> <code>int</code> <p>Minimum width of the popup. Default is 400.</p> <code>400</code> <code>max_width</code> <code>int</code> <p>Maximum width of the popup. Default is 600.</p> <code>600</code> <code>min_height</code> <code>int</code> <p>Minimum height of the popup. Default is 200.</p> <code>200</code> <code>max_height</code> <code>int</code> <p>Maximum height of the popup. Default is 250.</p> <code>250</code> <code>layer_name</code> <code>str</code> <p>Name of the marker cluster layer. Default is \"Marker Cluster\".</p> <code>'Marker Cluster'</code> <p>Returns:</p> Type Description <code>Map</code> <p>An ipyleaflet Map with the added markers and popups.</p> Source code in <code>hypercoast/hypercoast.py</code> <pre><code>def add_field_data(\n    self,\n    data: Union[str],\n    x_col: str = \"wavelength\",\n    y_col_prefix: str = \"(\",\n    x_label: str = \"Wavelengths (nm)\",\n    y_label: str = \"Reflectance\",\n    use_marker_cluster: bool = True,\n    min_width: int = 400,\n    max_width: int = 600,\n    min_height: int = 200,\n    max_height: int = 250,\n    layer_name: str = \"Marker Cluster\",\n    **kwargs,\n):\n    \"\"\"\n    Displays field data on a map with interactive markers and popups showing time series data.\n\n    Args:\n        data (Union[str, pd.DataFrame]): Path to the CSV file or a pandas DataFrame containing the data.\n        x_col (str): Column name to use for the x-axis of the charts. Default is \"wavelength\".\n        y_col_prefix (str): Prefix to identify the columns that contain the location-specific data. Default is \"(\".\n        x_label (str): Label for the x-axis of the charts. Default is \"Wavelengths (nm)\".\n        y_label (str): Label for the y-axis of the charts. Default is \"Reflectance\".\n        use_marker_cluster (bool): Whether to use marker clustering. Default is True.\n        min_width (int): Minimum width of the popup. Default is 400.\n        max_width (int): Maximum width of the popup. Default is 600.\n        min_height (int): Minimum height of the popup. Default is 200.\n        max_height (int): Maximum height of the popup. Default is 250.\n        layer_name (str): Name of the marker cluster layer. Default is \"Marker Cluster\".\n\n    Returns:\n        Map: An ipyleaflet Map with the added markers and popups.\n    \"\"\"\n    show_field_data(\n        data,\n        x_col,\n        y_col_prefix,\n        x_label=x_label,\n        y_label=y_label,\n        use_marker_cluster=use_marker_cluster,\n        min_width=min_width,\n        max_width=max_width,\n        min_height=min_height,\n        max_height=max_height,\n        layer_name=layer_name,\n        m=self,\n        **kwargs,\n    )\n</code></pre>"},{"location":"hypercoast/#hypercoast.hypercoast.Map.add_hyper","title":"<code>add_hyper(self, xds, dtype, wvl_indexes=None, **kwargs)</code>","text":"<p>Add a hyperspectral dataset to the map.</p> <p>Parameters:</p> Name Type Description Default <code>xds</code> <code>str</code> <p>The Xarray dataset containing the hyperspectral data.</p> required <code>dtype</code> <code>str</code> <p>The type of the hyperspectral dataset. Can be one of \"EMIT\", \"PACE\", \"DESIS\", \"NEON\", \"AVIRIS\".</p> required <code>**kwargs</code> <p>Additional keyword arguments to pass to the corresponding add function.</p> <code>{}</code> Source code in <code>hypercoast/hypercoast.py</code> <pre><code>def add_hyper(self, xds, dtype, wvl_indexes=None, **kwargs):\n    \"\"\"Add a hyperspectral dataset to the map.\n\n    Args:\n        xds (str): The Xarray dataset containing the hyperspectral data.\n        dtype (str): The type of the hyperspectral dataset. Can be one of\n            \"EMIT\", \"PACE\", \"DESIS\", \"NEON\", \"AVIRIS\".\n        **kwargs: Additional keyword arguments to pass to the corresponding\n            add function.\n    \"\"\"\n\n    if wvl_indexes is not None:\n        if dtype == \"XARRAY\":\n            kwargs[\"indexes\"] = [i + 1 for i in wvl_indexes]\n        else:\n\n            kwargs[\"wavelengths\"] = (\n                xds.isel(wavelength=wvl_indexes)\n                .coords[\"wavelength\"]\n                .values.tolist()\n            )\n\n    if dtype == \"EMIT\":\n        self.add_emit(xds, **kwargs)\n    elif dtype == \"PACE\":\n        self.add_pace(xds, **kwargs)\n    elif dtype == \"DESIS\":\n        self.add_desis(xds, **kwargs)\n    elif dtype == \"NEON\":\n        self.add_neon(xds, **kwargs)\n    elif dtype == \"AVIRIS\":\n        self.add_aviris(xds, **kwargs)\n    elif dtype == \"XARRAY\":\n        kwargs.pop(\"wavelengths\", None)\n        self.add_dataset(xds, **kwargs)\n</code></pre>"},{"location":"hypercoast/#hypercoast.hypercoast.Map.add_neon","title":"<code>add_neon(self, source, wavelengths=None, indexes=None, colormap=None, vmin=0, vmax=0.5, nodata=nan, attribution=None, layer_name='NEON', zoom_to_layer=True, visible=True, array_args=None, method='nearest', **kwargs)</code>","text":"<p>Add an NEON AOP dataset to the map.     If you are using this function in JupyterHub on a remote server         (e.g., Binder, Microsoft Planetary Computer) and     if the raster does not render properly, try installing         jupyter-server-proxy using <code>pip install jupyter-server-proxy</code>,     then running the following code before calling this function. For         more info, see https://bit.ly/3JbmF93.</p> <pre><code>import os\nos.environ['LOCALTILESERVER_CLIENT_PREFIX'] = 'proxy/{port}'\n</code></pre> <p>Parameters:</p> Name Type Description Default <code>source</code> <code>str</code> <p>The path to the NEON AOP HDF5 file.</p> required <code>indexes</code> <code>int</code> <p>The band(s) to use. Band indexing starts at 1. Defaults to None.</p> <code>None</code> <code>colormap</code> <code>str</code> <p>The name of the colormap from <code>matplotlib</code> to use when plotting a single band. See     https://matplotlib.org/stable/gallery/color/colormap_reference.html.     Default is greyscale.</p> <code>None</code> <code>vmin</code> <code>float</code> <p>The minimum value to use when colormapping the palette when plotting a single band. Defaults to 0.</p> <code>0</code> <code>vmax</code> <code>float</code> <p>The maximum value to use when colormapping the palette when plotting a single band. Defaults to 0.5.</p> <code>0.5</code> <code>nodata</code> <code>float</code> <p>The value from the band to use to interpret as not valid data. Defaults to np.nan.</p> <code>nan</code> <code>attribution</code> <code>str</code> <p>Attribution for the source raster. This defaults to a message about it being a local file.. Defaults to None.</p> <code>None</code> <code>layer_name</code> <code>str</code> <p>The layer name to use. Defaults to 'NEON'.</p> <code>'NEON'</code> <code>zoom_to_layer</code> <code>bool</code> <p>Whether to zoom to the extent of the layer. Defaults to True.</p> <code>True</code> <code>visible</code> <code>bool</code> <p>Whether the layer is visible. Defaults to True.</p> <code>True</code> <code>array_args</code> <code>dict</code> <p>Additional arguments to pass to <code>array_to_memory_file</code> when reading the raster. Defaults to {}.</p> <code>None</code> <code>method</code> <code>str</code> <p>The method to use for data interpolation. Defaults to \"nearest\".</p> <code>'nearest'</code> Source code in <code>hypercoast/hypercoast.py</code> <pre><code>def add_neon(\n    self,\n    source,\n    wavelengths=None,\n    indexes=None,\n    colormap=None,\n    vmin=0,\n    vmax=0.5,\n    nodata=np.nan,\n    attribution=None,\n    layer_name=\"NEON\",\n    zoom_to_layer=True,\n    visible=True,\n    array_args=None,\n    method=\"nearest\",\n    **kwargs,\n):\n    \"\"\"Add an NEON AOP dataset to the map.\n        If you are using this function in JupyterHub on a remote server\n            (e.g., Binder, Microsoft Planetary Computer) and\n        if the raster does not render properly, try installing\n            jupyter-server-proxy using `pip install jupyter-server-proxy`,\n        then running the following code before calling this function. For\n            more info, see https://bit.ly/3JbmF93.\n\n        import os\n        os.environ['LOCALTILESERVER_CLIENT_PREFIX'] = 'proxy/{port}'\n\n    Args:\n        source (str): The path to the NEON AOP HDF5 file.\n        indexes (int, optional): The band(s) to use. Band indexing starts\n            at 1. Defaults to None.\n        colormap (str, optional): The name of the colormap from `matplotlib`\n            to use when plotting a single band. See\n                https://matplotlib.org/stable/gallery/color/colormap_reference.html.\n                Default is greyscale.\n        vmin (float, optional): The minimum value to use when colormapping\n            the palette when plotting a single band. Defaults to 0.\n        vmax (float, optional): The maximum value to use when colormapping\n            the palette when plotting a single band. Defaults to 0.5.\n        nodata (float, optional): The value from the band to use to\n            interpret as not valid data. Defaults to np.nan.\n        attribution (str, optional): Attribution for the source raster. This\n            defaults to a message about it being a local file.. Defaults to None.\n        layer_name (str, optional): The layer name to use. Defaults to 'NEON'.\n        zoom_to_layer (bool, optional): Whether to zoom to the extent of the\n            layer. Defaults to True.\n        visible (bool, optional): Whether the layer is visible. Defaults\n            to True.\n        array_args (dict, optional): Additional arguments to pass to\n            `array_to_memory_file` when reading the raster. Defaults to {}.\n        method (str, optional): The method to use for data interpolation.\n            Defaults to \"nearest\".\n    \"\"\"\n\n    if array_args is None:\n        array_args = {}\n    xds = None\n    if isinstance(source, str):\n\n        xds = read_neon(source)\n        source = neon_to_image(xds, wavelengths=wavelengths, method=method)\n    elif isinstance(source, xr.Dataset):\n        xds = source\n        source = neon_to_image(xds, wavelengths=wavelengths, method=method)\n\n    self.add_raster(\n        source,\n        indexes=indexes,\n        colormap=colormap,\n        vmin=vmin,\n        vmax=vmax,\n        nodata=nodata,\n        attribution=attribution,\n        layer_name=layer_name,\n        zoom_to_layer=zoom_to_layer,\n        visible=visible,\n        array_args=array_args,\n        **kwargs,\n    )\n\n    self.cog_layer_dict[layer_name][\"xds\"] = xds\n    self.cog_layer_dict[layer_name][\"hyper\"] = \"NEON\"\n    self._update_band_names(layer_name, wavelengths)\n</code></pre>"},{"location":"hypercoast/#hypercoast.hypercoast.Map.add_pace","title":"<code>add_pace(self, source, wavelengths=None, indexes=None, colormap='jet', vmin=None, vmax=None, nodata=nan, attribution=None, layer_name='PACE', zoom_to_layer=True, visible=True, method='nearest', gridded=False, array_args=None, **kwargs)</code>","text":"<p>Add a PACE dataset to the map.     If you are using this function in JupyterHub on a remote server         (e.g., Binder, Microsoft Planetary Computer) and     if the raster does not render properly, try installing         jupyter-server-proxy using <code>pip install jupyter-server-proxy</code>,     then running the following code before calling this function. For         more info, see https://bit.ly/3JbmF93.</p> <pre><code>import os\nos.environ['LOCALTILESERVER_CLIENT_PREFIX'] = 'proxy/{port}'\n</code></pre> <p>Parameters:</p> Name Type Description Default <code>source</code> <code>str</code> <p>The path to the GeoTIFF file or the URL of the Cloud Optimized GeoTIFF.</p> required <code>indexes</code> <code>int</code> <p>The band(s) to use. Band indexing starts at 1. Defaults to None.</p> <code>None</code> <code>colormap</code> <code>str</code> <p>The name of the colormap from <code>matplotlib</code> to use when plotting a single band. See     https://matplotlib.org/stable/gallery/color/colormap_reference.html.     Default is greyscale.</p> <code>'jet'</code> <code>vmin</code> <code>float</code> <p>The minimum value to use when colormapping the palette when plotting a single band. Defaults to None.</p> <code>None</code> <code>vmax</code> <code>float</code> <p>The maximum value to use when colormapping the palette when plotting a single band. Defaults to None.</p> <code>None</code> <code>nodata</code> <code>float</code> <p>The value from the band to use to interpret as not valid data. Defaults to None.</p> <code>nan</code> <code>attribution</code> <code>str</code> <p>Attribution for the source raster. This defaults to a message about it being a local file.. Defaults to None.</p> <code>None</code> <code>layer_name</code> <code>str</code> <p>The layer name to use. Defaults to 'EMIT'.</p> <code>'PACE'</code> <code>zoom_to_layer</code> <code>bool</code> <p>Whether to zoom to the extent of the layer. Defaults to True.</p> <code>True</code> <code>visible</code> <code>bool</code> <p>Whether the layer is visible. Defaults to True.</p> <code>True</code> <code>array_args</code> <code>dict</code> <p>Additional arguments to pass to <code>array_to_memory_file</code> when reading the raster. Defaults to {}.</p> <code>None</code> Source code in <code>hypercoast/hypercoast.py</code> <pre><code>def add_pace(\n    self,\n    source,\n    wavelengths=None,\n    indexes=None,\n    colormap=\"jet\",\n    vmin=None,\n    vmax=None,\n    nodata=np.nan,\n    attribution=None,\n    layer_name=\"PACE\",\n    zoom_to_layer=True,\n    visible=True,\n    method=\"nearest\",\n    gridded=False,\n    array_args=None,\n    **kwargs,\n):\n    \"\"\"Add a PACE dataset to the map.\n        If you are using this function in JupyterHub on a remote server\n            (e.g., Binder, Microsoft Planetary Computer) and\n        if the raster does not render properly, try installing\n            jupyter-server-proxy using `pip install jupyter-server-proxy`,\n        then running the following code before calling this function. For\n            more info, see https://bit.ly/3JbmF93.\n\n        import os\n        os.environ['LOCALTILESERVER_CLIENT_PREFIX'] = 'proxy/{port}'\n\n    Args:\n        source (str): The path to the GeoTIFF file or the URL of the Cloud\n            Optimized GeoTIFF.\n        indexes (int, optional): The band(s) to use. Band indexing starts\n            at 1. Defaults to None.\n        colormap (str, optional): The name of the colormap from `matplotlib`\n            to use when plotting a single band. See\n                https://matplotlib.org/stable/gallery/color/colormap_reference.html.\n                Default is greyscale.\n        vmin (float, optional): The minimum value to use when colormapping\n            the palette when plotting a single band. Defaults to None.\n        vmax (float, optional): The maximum value to use when colormapping\n            the palette when plotting a single band. Defaults to None.\n        nodata (float, optional): The value from the band to use to interpret\n            as not valid data. Defaults to None.\n        attribution (str, optional): Attribution for the source raster. This\n            defaults to a message about it being a local file.. Defaults to None.\n        layer_name (str, optional): The layer name to use. Defaults to 'EMIT'.\n        zoom_to_layer (bool, optional): Whether to zoom to the extent of the\n            layer. Defaults to True.\n        visible (bool, optional): Whether the layer is visible. Defaults to True.\n        array_args (dict, optional): Additional arguments to pass to\n            `array_to_memory_file` when reading the raster. Defaults to {}.\n    \"\"\"\n\n    if array_args is None:\n        array_args = {}\n\n    if isinstance(source, str):\n\n        source = read_pace(source)\n\n    try:\n        image = pace_to_image(\n            source, wavelengths=wavelengths, method=method, gridded=gridded\n        )\n\n        if isinstance(wavelengths, list) and len(wavelengths) &gt; 1:\n            colormap = None\n\n        self.add_raster(\n            image,\n            indexes=indexes,\n            colormap=colormap,\n            vmin=vmin,\n            vmax=vmax,\n            nodata=nodata,\n            attribution=attribution,\n            layer_name=layer_name,\n            zoom_to_layer=zoom_to_layer,\n            visible=visible,\n            array_args=array_args,\n            **kwargs,\n        )\n\n        self.cog_layer_dict[layer_name][\"xds\"] = source\n        self.cog_layer_dict[layer_name][\"hyper\"] = \"PACE\"\n        self._update_band_names(layer_name, wavelengths)\n    except Exception as e:\n        print(e)\n</code></pre>"},{"location":"hypercoast/#hypercoast.hypercoast.Map.add_raster","title":"<code>add_raster(self, source, indexes=None, colormap=None, vmin=None, vmax=None, nodata=None, attribution=None, layer_name='Raster', layer_index=None, zoom_to_layer=True, visible=True, opacity=1.0, array_args=None, client_args={'cors_all': False}, open_args=None, **kwargs)</code>","text":"<p>Add a local raster dataset to the map.     If you are using this function in JupyterHub on a remote server         (e.g., Binder, Microsoft Planetary Computer) and     if the raster does not render properly, try installing         jupyter-server-proxy using <code>pip install jupyter-server-proxy</code>,     then running the following code before calling this function. For         more info, see https://bit.ly/3JbmF93.</p> <pre><code>import os\nos.environ['LOCALTILESERVER_CLIENT_PREFIX'] = 'proxy/{port}'\n</code></pre> <p>Parameters:</p> Name Type Description Default <code>source</code> <code>str</code> <p>The path to the GeoTIFF file or the URL of the Cloud Optimized GeoTIFF.</p> required <code>indexes</code> <code>int</code> <p>The band(s) to use. Band indexing starts at 1. Defaults to None.</p> <code>None</code> <code>colormap</code> <code>str</code> <p>The name of the colormap from <code>matplotlib</code> to use when plotting a single band. See https://matplotlib.org/stable/gallery/color/colormap_reference.html. Default is greyscale.</p> <code>None</code> <code>vmin</code> <code>float</code> <p>The minimum value to use when colormapping the palette when plotting a single band. Defaults to None.</p> <code>None</code> <code>vmax</code> <code>float</code> <p>The maximum value to use when colormapping the palette when plotting a single band. Defaults to None.</p> <code>None</code> <code>nodata</code> <code>float</code> <p>The value from the band to use to interpret as not valid data. Defaults to None.</p> <code>None</code> <code>attribution</code> <code>str</code> <p>Attribution for the source raster. This defaults to a message about it being a local file.. Defaults to None.</p> <code>None</code> <code>layer_name</code> <code>str</code> <p>The layer name to use. Defaults to 'Raster'.</p> <code>'Raster'</code> <code>layer_index</code> <code>int</code> <p>The index of the layer. Defaults to None.</p> <code>None</code> <code>zoom_to_layer</code> <code>bool</code> <p>Whether to zoom to the extent of the layer. Defaults to True.</p> <code>True</code> <code>visible</code> <code>bool</code> <p>Whether the layer is visible. Defaults to True.</p> <code>True</code> <code>opacity</code> <code>float</code> <p>The opacity of the layer. Defaults to 1.0.</p> <code>1.0</code> <code>array_args</code> <code>dict</code> <p>Additional arguments to pass to <code>array_to_memory_file</code> when reading the raster. Defaults to {}.</p> <code>None</code> <code>client_args</code> <code>dict</code> <p>Additional arguments to pass to localtileserver.TileClient. Defaults to { \"cors_all\": False }.</p> <code>{'cors_all': False}</code> <code>open_args</code> <code>dict</code> <p>Additional arguments to pass to rioxarray.open_rasterio.</p> <code>None</code> Source code in <code>hypercoast/hypercoast.py</code> <pre><code>def add_raster(\n    self,\n    source,\n    indexes=None,\n    colormap=None,\n    vmin=None,\n    vmax=None,\n    nodata=None,\n    attribution=None,\n    layer_name=\"Raster\",\n    layer_index=None,\n    zoom_to_layer=True,\n    visible=True,\n    opacity=1.0,\n    array_args=None,\n    client_args={\"cors_all\": False},\n    open_args=None,\n    **kwargs,\n):\n    \"\"\"Add a local raster dataset to the map.\n        If you are using this function in JupyterHub on a remote server\n            (e.g., Binder, Microsoft Planetary Computer) and\n        if the raster does not render properly, try installing\n            jupyter-server-proxy using `pip install jupyter-server-proxy`,\n        then running the following code before calling this function. For\n            more info, see https://bit.ly/3JbmF93.\n\n        import os\n        os.environ['LOCALTILESERVER_CLIENT_PREFIX'] = 'proxy/{port}'\n\n    Args:\n        source (str): The path to the GeoTIFF file or the URL of the Cloud\n            Optimized GeoTIFF.\n        indexes (int, optional): The band(s) to use. Band indexing starts\n            at 1. Defaults to None.\n        colormap (str, optional): The name of the colormap from `matplotlib`\n            to use when plotting a single band. See\n            https://matplotlib.org/stable/gallery/color/colormap_reference.html.\n            Default is greyscale.\n        vmin (float, optional): The minimum value to use when colormapping\n            the palette when plotting a single band. Defaults to None.\n        vmax (float, optional): The maximum value to use when colormapping\n            the palette when plotting a single band. Defaults to None.\n        nodata (float, optional): The value from the band to use to interpret\n            as not valid data. Defaults to None.\n        attribution (str, optional): Attribution for the source raster. This\n            defaults to a message about it being a local file.. Defaults to None.\n        layer_name (str, optional): The layer name to use. Defaults to 'Raster'.\n        layer_index (int, optional): The index of the layer. Defaults to None.\n        zoom_to_layer (bool, optional): Whether to zoom to the extent of the\n            layer. Defaults to True.\n        visible (bool, optional): Whether the layer is visible. Defaults to\n            True.\n        opacity (float, optional): The opacity of the layer. Defaults to 1.0.\n        array_args (dict, optional): Additional arguments to pass to\n            `array_to_memory_file` when reading the raster. Defaults to {}.\n        client_args (dict, optional): Additional arguments to pass to\n            localtileserver.TileClient. Defaults to { \"cors_all\": False }.\n        open_args (dict, optional): Additional arguments to pass to\n            rioxarray.open_rasterio.\n\n    \"\"\"\n\n    import rioxarray as rxr\n\n    if array_args is None:\n        array_args = {}\n    if open_args is None:\n        open_args = {}\n\n    if nodata is None:\n        nodata = np.nan\n    super().add_raster(\n        source,\n        indexes=indexes,\n        colormap=colormap,\n        vmin=vmin,\n        vmax=vmax,\n        nodata=nodata,\n        attribution=attribution,\n        layer_name=layer_name,\n        layer_index=layer_index,\n        zoom_to_layer=zoom_to_layer,\n        visible=visible,\n        opacity=opacity,\n        array_args=array_args,\n        client_args=client_args,\n        **kwargs,\n    )\n\n    if isinstance(source, str):\n        da = rxr.open_rasterio(source, **open_args)\n        dims = da.dims\n        da = da.transpose(dims[1], dims[2], dims[0])\n\n        xds = da.to_dataset(name=\"data\")\n        self.cog_layer_dict[layer_name][\"xds\"] = xds\n        # if self.cog_layer_dict[layer_name].get(\"hyper\") is None:\n        #     self.cog_layer_dict[layer_name][\"hyper\"] = \"COG\"\n</code></pre>"},{"location":"hypercoast/#hypercoast.hypercoast.Map.search_ecostress","title":"<code>search_ecostress(self, default_dataset='ECO_L2T_LSTE')</code>","text":"<p>Adds a NASA Earth Data search tool to the map with a default dataset for     ECOSTRESS.</p> <p>Parameters:</p> Name Type Description Default <code>default_dataset</code> <code>str</code> <p>The default dataset to search for. Defaults to \"ECO_L2T_LSTE\".</p> <code>'ECO_L2T_LSTE'</code> Source code in <code>hypercoast/hypercoast.py</code> <pre><code>def search_ecostress(self, default_dataset=\"ECO_L2T_LSTE\"):\n    \"\"\"\n    Adds a NASA Earth Data search tool to the map with a default dataset for\n        ECOSTRESS.\n\n    Args:\n        default_dataset (str, optional): The default dataset to search for.\n            Defaults to \"ECO_L2T_LSTE\".\n    \"\"\"\n    self.add(\"nasa_earth_data\", default_dataset=default_dataset)\n</code></pre>"},{"location":"hypercoast/#hypercoast.hypercoast.Map.search_emit","title":"<code>search_emit(self, default_dataset='EMITL2ARFL')</code>","text":"<p>Adds a NASA Earth Data search tool to the map with a default dataset for     EMIT.</p> <p>Parameters:</p> Name Type Description Default <code>default_dataset</code> <code>str</code> <p>The default dataset to search for. Defaults to \"EMITL2ARFL\".</p> <code>'EMITL2ARFL'</code> Source code in <code>hypercoast/hypercoast.py</code> <pre><code>def search_emit(self, default_dataset=\"EMITL2ARFL\"):\n    \"\"\"\n    Adds a NASA Earth Data search tool to the map with a default dataset for\n        EMIT.\n\n    Args:\n        default_dataset (str, optional): The default dataset to search for.\n            Defaults to \"EMITL2ARFL\".\n    \"\"\"\n    self.add(\"nasa_earth_data\", default_dataset=default_dataset)\n</code></pre>"},{"location":"hypercoast/#hypercoast.hypercoast.Map.search_pace","title":"<code>search_pace(self, default_dataset='PACE_OCI_L2_AOP_NRT')</code>","text":"<p>Adds a NASA Earth Data search tool to the map with a default dataset for     PACE.</p> <p>Parameters:</p> Name Type Description Default <code>default_dataset</code> <code>str</code> <p>The default dataset to search for. Defaults to \"PACE_OCI_L2_AOP_NRT\".</p> <code>'PACE_OCI_L2_AOP_NRT'</code> Source code in <code>hypercoast/hypercoast.py</code> <pre><code>def search_pace(self, default_dataset=\"PACE_OCI_L2_AOP_NRT\"):\n    \"\"\"\n    Adds a NASA Earth Data search tool to the map with a default dataset for\n        PACE.\n\n    Args:\n        default_dataset (str, optional): The default dataset to search for.\n            Defaults to \"PACE_OCI_L2_AOP_NRT\".\n    \"\"\"\n    self.add(\"nasa_earth_data\", default_dataset=default_dataset)\n</code></pre>"},{"location":"hypercoast/#hypercoast.hypercoast.Map.set_plot_options","title":"<code>set_plot_options(self, add_marker_cluster=False, plot_type=None, overlay=False, position='bottomright', min_width=None, max_width=None, min_height=None, max_height=None, **kwargs)</code>","text":"<p>Sets plotting options.</p> <p>Parameters:</p> Name Type Description Default <code>add_marker_cluster</code> <code>bool</code> <p>Whether to add a marker cluster. Defaults to False.</p> <code>False</code> <code>sample_scale</code> <code>float</code> <p>A nominal scale in meters of the projection to sample in . Defaults to None.</p> required <code>plot_type</code> <code>str</code> <p>The plot type can be one of \"None\", \"bar\", \"scatter\" or \"hist\". Defaults to None.</p> <code>None</code> <code>overlay</code> <code>bool</code> <p>Whether to overlay plotted lines on the figure. Defaults to False.</p> <code>False</code> <code>position</code> <code>str</code> <p>Position of the control, can be \u2018bottomleft\u2019, \u2018bottomright\u2019, \u2018topleft\u2019, or \u2018topright\u2019. Defaults to 'bottomright'.</p> <code>'bottomright'</code> <code>min_width</code> <code>int</code> <p>Min width of the widget (in pixels), if None it will respect the content size. Defaults to None.</p> <code>None</code> <code>max_width</code> <code>int</code> <p>Max width of the widget (in pixels), if None it will respect the content size. Defaults to None.</p> <code>None</code> <code>min_height</code> <code>int</code> <p>Min height of the widget (in pixels), if None it will respect the content size. Defaults to None.</p> <code>None</code> <code>max_height</code> <code>int</code> <p>Max height of the widget (in pixels), if None it will respect the content size. Defaults to None.</p> <code>None</code> Source code in <code>hypercoast/hypercoast.py</code> <pre><code>def set_plot_options(\n    self,\n    add_marker_cluster=False,\n    plot_type=None,\n    overlay=False,\n    position=\"bottomright\",\n    min_width=None,\n    max_width=None,\n    min_height=None,\n    max_height=None,\n    **kwargs,\n):\n    \"\"\"Sets plotting options.\n\n    Args:\n        add_marker_cluster (bool, optional): Whether to add a marker cluster.\n            Defaults to False.\n        sample_scale (float, optional):  A nominal scale in meters of the\n            projection to sample in . Defaults to None.\n        plot_type (str, optional): The plot type can be one of \"None\", \"bar\",\n            \"scatter\" or \"hist\". Defaults to None.\n        overlay (bool, optional): Whether to overlay plotted lines on the\n            figure. Defaults to False.\n        position (str, optional): Position of the control, can be\n            \u2018bottomleft\u2019, \u2018bottomright\u2019, \u2018topleft\u2019, or \u2018topright\u2019. Defaults\n            to 'bottomright'.\n        min_width (int, optional): Min width of the widget (in pixels), if\n            None it will respect the content size. Defaults to None.\n        max_width (int, optional): Max width of the widget (in pixels), if\n            None it will respect the content size. Defaults to None.\n        min_height (int, optional): Min height of the widget (in pixels), if\n            None it will respect the content size. Defaults to None.\n        max_height (int, optional): Max height of the widget (in pixels), if\n            None it will respect the content size. Defaults to None.\n\n    \"\"\"\n    plot_options_dict = {}\n    plot_options_dict[\"add_marker_cluster\"] = add_marker_cluster\n    plot_options_dict[\"plot_type\"] = plot_type\n    plot_options_dict[\"overlay\"] = overlay\n    plot_options_dict[\"position\"] = position\n    plot_options_dict[\"min_width\"] = min_width\n    plot_options_dict[\"max_width\"] = max_width\n    plot_options_dict[\"min_height\"] = min_height\n    plot_options_dict[\"max_height\"] = max_height\n\n    for key in kwargs:\n        plot_options_dict[key] = kwargs[key]\n\n    self._plot_options = plot_options_dict\n\n    if not hasattr(self, \"_plot_marker_cluster\"):\n        self._plot_marker_cluster = ipyleaflet.MarkerCluster(name=\"Marker Cluster\")\n\n    if add_marker_cluster and (self._plot_marker_cluster not in self.layers):\n        self.add(self._plot_marker_cluster)\n</code></pre>"},{"location":"hypercoast/#hypercoast.hypercoast.Map.spectral_to_csv","title":"<code>spectral_to_csv(self, filename, index=True, **kwargs)</code>","text":"<p>Saves the spectral data to a CSV file.</p> <p>Parameters:</p> Name Type Description Default <code>filename</code> <code>str</code> <p>The output CSV file.</p> required <code>index</code> <code>bool</code> <p>Whether to write the index. Defaults to True.</p> <code>True</code> Source code in <code>hypercoast/hypercoast.py</code> <pre><code>def spectral_to_csv(self, filename, index=True, **kwargs):\n    \"\"\"Saves the spectral data to a CSV file.\n\n    Args:\n        filename (str): The output CSV file.\n        index (bool, optional): Whether to write the index. Defaults to True.\n    \"\"\"\n    df = self.spectral_to_df()\n    df = df.rename_axis(\"band\")\n    df.to_csv(filename, index=index, **kwargs)\n</code></pre>"},{"location":"hypercoast/#hypercoast.hypercoast.Map.spectral_to_df","title":"<code>spectral_to_df(self, **kwargs)</code>","text":"<p>Converts the spectral data to a pandas DataFrame.</p> <p>Returns:</p> Type Description <code>pd.DataFrame</code> <p>The spectral data as a pandas DataFrame.</p> Source code in <code>hypercoast/hypercoast.py</code> <pre><code>def spectral_to_df(self, **kwargs):\n    \"\"\"Converts the spectral data to a pandas DataFrame.\n\n    Returns:\n        pd.DataFrame: The spectral data as a pandas DataFrame.\n    \"\"\"\n    import pandas as pd\n\n    df = pd.DataFrame(self._spectral_data, **kwargs)\n    return df\n</code></pre>"},{"location":"hypercoast/#hypercoast.hypercoast.Map.spectral_to_gdf","title":"<code>spectral_to_gdf(self, **kwargs)</code>","text":"<p>Converts the spectral data to a GeoPandas GeoDataFrame.</p> <p>Returns:</p> Type Description <code>gpd.DataFrame</code> <p>The spectral data as a pandas DataFrame.</p> Source code in <code>hypercoast/hypercoast.py</code> <pre><code>def spectral_to_gdf(self, **kwargs):\n    \"\"\"Converts the spectral data to a GeoPandas GeoDataFrame.\n\n    Returns:\n        gpd.DataFrame: The spectral data as a pandas DataFrame.\n    \"\"\"\n    import geopandas as gpd\n    from shapely.geometry import Point\n\n    df = self.spectral_to_df()\n\n    if len(df) == 0:\n        return df\n\n    # Step 1: Extract the coordinates from the columns\n    if \"wavelength\" in df.columns:\n        df = df.rename(columns={\"wavelength\": \"latlon\"})\n    elif \"wavelengths\" in df.columns:\n        df = df.rename(columns={\"wavelengths\": \"latlon\"})\n    coordinates = [col.strip(\"()\").split() for col in df.columns[1:]]\n    coords = [(float(lat), float(lon)) for lat, lon in coordinates]\n\n    # Step 2: Create Point geometries for each coordinate\n    points = [Point(lon, lat) for lat, lon in coords]\n\n    # Step 3: Create a GeoDataFrame\n    df_transposed = df.set_index(\"latlon\").T\n\n    # Convert the column names to strings to ensure compatibility with GeoJSON\n    df_transposed.columns = df_transposed.columns.astype(str)\n\n    # Create the GeoDataFrame\n    gdf = gpd.GeoDataFrame(df_transposed, geometry=points, **kwargs)\n\n    # Set the coordinate reference system (CRS)\n    gdf = gdf.set_geometry(\"geometry\").set_crs(\"EPSG:4326\")\n\n    return gdf\n</code></pre>"},{"location":"installation/","title":"Installation","text":""},{"location":"installation/#install-from-pypi","title":"Install from PyPI","text":"<p>hypercoast is available on PyPI. To install hypercoast, run this command in your terminal:</p> <pre><code>pip install hypercoast\n</code></pre> <p>HyperCoast has some optional dependencies that are not installed by default, such as cartopy, earthaccess, mapclassify, and pyvista. To install all optional dependencies all at once, run the following command:</p> <pre><code>pip install \"hypercoast[extra]\"\n</code></pre>"},{"location":"installation/#install-from-conda-forge","title":"Install from conda-forge","text":"<p>hypercoast is also available on conda-forge. If you have Anaconda or Miniconda installed on your computer, you can install hypercoast using the following command:</p> <pre><code>conda install -c conda-forge hypercoast\n</code></pre> <p>Alternatively, you can create a new conda environment and install hypercoast in the new environment. This is a good practice because it avoids potential conflicts with other packages installed in your base environment.</p> <pre><code>conda install -n base mamba -c conda-forge\nconda create -n hyper python=3.11\nconda activate hyper\nmamba install -c conda-forge hypercoast\n</code></pre> <p>To install the optional dependencies, run the following command:</p> <pre><code>mamba install -c conda-forge cartopy earthaccess mapclassify pyvista trame-vtk trame-vuetify\n</code></pre>"},{"location":"installation/#install-from-github","title":"Install from GitHub","text":"<p>To install the development version from GitHub using Git, run the following command in your terminal:</p> <pre><code>pip install git+https://github.com/opengeos/hypercoast\n</code></pre>"},{"location":"neon/","title":"neon module","text":"<p>This module contains functions to read and process NEON AOP hyperspectral data. More info about the data can be found at https://bit.ly/3Rfszdc. The source code is adapted from https://bit.ly/3KwyZkn. Credit goes to the original authors.</p>"},{"location":"neon/#hypercoast.neon.extract_neon","title":"<code>extract_neon(ds, lat, lon)</code>","text":"<p>Extracts NEON AOP data from a given xarray Dataset.</p> <p>Parameters:</p> Name Type Description Default <code>ds</code> <code>xarray.Dataset</code> <p>The dataset containing the NEON AOP data.</p> required <code>lat</code> <code>float</code> <p>The latitude of the point to extract.</p> required <code>lon</code> <code>float</code> <p>The longitude of the point to extract.</p> required <p>Returns:</p> Type Description <code>xarray.DataArray</code> <p>The extracted data.</p> Source code in <code>hypercoast/neon.py</code> <pre><code>def extract_neon(ds, lat, lon):\n    \"\"\"\n    Extracts NEON AOP data from a given xarray Dataset.\n\n    Args:\n        ds (xarray.Dataset): The dataset containing the NEON AOP data.\n        lat (float): The latitude of the point to extract.\n        lon (float): The longitude of the point to extract.\n\n    Returns:\n        xarray.DataArray: The extracted data.\n    \"\"\"\n\n    crs = ds.attrs[\"crs\"]\n\n    x, y = convert_coords([[lat, lon]], \"epsg:4326\", crs)[0]\n\n    values = ds.sel(x=x, y=y, method=\"nearest\")[\"reflectance\"].values\n\n    da = xr.DataArray(\n        values, dims=[\"wavelength\"], coords={\"wavelength\": ds.coords[\"wavelength\"]}\n    )\n\n    return da\n</code></pre>"},{"location":"neon/#hypercoast.neon.list_neon_datasets","title":"<code>list_neon_datasets(filepath, print_node=False)</code>","text":"<p>Lists all the datasets in an HDF5 file.</p> <p>Parameters:</p> Name Type Description Default <code>filepath</code> <code>str</code> <p>The path to the HDF5 file.</p> required <code>print_node</code> <code>bool</code> <p>If True, prints the node object of each dataset. If False, prints the name of each dataset. Defaults to False.</p> <code>False</code> Source code in <code>hypercoast/neon.py</code> <pre><code>def list_neon_datasets(filepath: str, print_node: bool = False) -&gt; None:\n    \"\"\"\n    Lists all the datasets in an HDF5 file.\n\n    Args:\n        filepath (str): The path to the HDF5 file.\n        print_node (bool, optional): If True, prints the node object of each dataset.\n            If False, prints the name of each dataset. Defaults to False.\n    \"\"\"\n\n    f = h5py.File(filepath, \"r\")\n\n    if print_node:\n\n        def list_dataset(_, node):\n            if isinstance(node, h5py.Dataset):\n                print(node)\n\n    else:\n\n        def list_dataset(name, node):\n            if isinstance(node, h5py.Dataset):\n                print(name)\n\n    f.visititems(list_dataset)\n</code></pre>"},{"location":"neon/#hypercoast.neon.neon_to_image","title":"<code>neon_to_image(dataset, wavelengths=None, method='nearest', output=None, **kwargs)</code>","text":"<p>Converts an NEON dataset to an image.</p> <p>Parameters:</p> Name Type Description Default <code>dataset</code> <code>Union[xr.Dataset, str]</code> <p>The dataset containing the NEON data or the file path to the dataset.</p> required <code>wavelengths</code> <code>np.ndarray</code> <p>The specific wavelengths to select. If None, all wavelengths are selected. Defaults to None.</p> <code>None</code> <code>method</code> <code>str</code> <p>The method to use for data interpolation. Defaults to \"nearest\".</p> <code>'nearest'</code> <code>output</code> <code>str</code> <p>The file path where the image will be saved. If None, the image will be returned as a PIL Image object. Defaults to None.</p> <code>None</code> <code>**kwargs</code> <code>Any</code> <p>Additional keyword arguments to be passed to <code>leafmap.array_to_image</code>.</p> <code>{}</code> <p>Returns:</p> Type Description <code>Optional[rasterio.Dataset]</code> <p>The image converted from the dataset. If     <code>output</code> is provided, the image will be saved to the specified file     and the function will return None.</p> Source code in <code>hypercoast/neon.py</code> <pre><code>def neon_to_image(\n    dataset: Union[xr.Dataset, str],\n    wavelengths: Optional[np.ndarray] = None,\n    method: str = \"nearest\",\n    output: Optional[str] = None,\n    **kwargs: Any,\n):\n    \"\"\"\n    Converts an NEON dataset to an image.\n\n    Args:\n        dataset (Union[xr.Dataset, str]): The dataset containing the NEON data\n            or the file path to the dataset.\n        wavelengths (np.ndarray, optional): The specific wavelengths to select. If None, all\n            wavelengths are selected. Defaults to None.\n        method (str, optional): The method to use for data interpolation.\n            Defaults to \"nearest\".\n        output (str, optional): The file path where the image will be saved. If\n            None, the image will be returned as a PIL Image object. Defaults to None.\n        **kwargs (Any): Additional keyword arguments to be passed to\n            `leafmap.array_to_image`.\n\n    Returns:\n        Optional[rasterio.Dataset]: The image converted from the dataset. If\n            `output` is provided, the image will be saved to the specified file\n            and the function will return None.\n    \"\"\"\n    from leafmap import array_to_image\n\n    if isinstance(dataset, str):\n        dataset = read_neon(dataset, method=method)\n\n    if wavelengths is not None:\n        dataset = dataset.sel(wavelength=wavelengths, method=method)\n\n    return array_to_image(\n        dataset[\"reflectance\"],\n        output=output,\n        transpose=False,\n        dtype=np.float32,\n        **kwargs,\n    )\n</code></pre>"},{"location":"neon/#hypercoast.neon.read_neon","title":"<code>read_neon(filepath, wavelengths=None, method='nearest', **kwargs)</code>","text":"<p>Reads NEON AOP hyperspectral hdf5 files and returns an xarray dataset.</p> <p>Parameters:</p> Name Type Description Default <code>filepath</code> <code>str</code> <p>The path to the hdf5 file.</p> required <code>wavelengths</code> <code>List[float]</code> <p>The wavelengths to select. If None, all wavelengths are selected. Defaults to None.</p> <code>None</code> <code>method</code> <code>str</code> <p>The method to use for selection. Defaults to \"nearest\".</p> <code>'nearest'</code> <code>**kwargs</code> <code>Any</code> <p>Additional arguments to pass to the selection method.</p> <code>{}</code> <p>Returns:</p> Type Description <code>xr.Dataset</code> <p>The dataset containing the reflectance data.</p> Source code in <code>hypercoast/neon.py</code> <pre><code>def read_neon(\n    filepath: str,\n    wavelengths: Optional[List[float]] = None,\n    method: str = \"nearest\",\n    **kwargs: Any,\n) -&gt; xr.Dataset:\n    \"\"\"\n    Reads NEON AOP hyperspectral hdf5 files and returns an xarray dataset.\n\n    Args:\n        filepath (str): The path to the hdf5 file.\n        wavelengths (List[float], optional): The wavelengths to select. If None,\n            all wavelengths are selected. Defaults to None.\n        method (str, optional): The method to use for selection. Defaults to\n            \"nearest\".\n        **kwargs (Any): Additional arguments to pass to the selection method.\n\n    Returns:\n        xr.Dataset: The dataset containing the reflectance data.\n    \"\"\"\n    with h5py.File(filepath, \"r\") as f:\n        # Extract site code dynamically from NEON HDF file metadata\n        # At the root of `keys` NEON stores the site code, which is the `root` folder at the [0] index of object `keys`\n        site_code = list(f.keys())[0]\n\n        # Access the reflectance data using the site code\n        site_refl = f[site_code][\"Reflectance\"]\n\n        # Extract wavelengths\n        wavelengths_list = site_refl[\"Metadata\"][\"Spectral_Data\"][\"Wavelength\"][\n            ()\n        ].tolist()\n        wavelengths_list = [round(num, 2) for num in wavelengths_list]\n\n        # Extract EPSG code\n        epsg_code = site_refl[\"Metadata\"][\"Coordinate_System\"][\"EPSG Code\"][()]\n        epsg_code_number = int(epsg_code.decode(\"utf-8\"))\n\n        # Extract map info\n        mapInfo_string = site_refl[\"Metadata\"][\"Coordinate_System\"][\"Map_Info\"][\n            ()\n        ].decode(\"utf-8\")\n        mapInfo_split = mapInfo_string.split(\",\")\n\n        res = float(mapInfo_split[5]), float(mapInfo_split[6])\n\n        # Extract reflectance array and shape\n        site_reflArray = site_refl[\"Reflectance_Data\"]\n        refl_shape = site_reflArray.shape\n\n        # Calculate coordinates\n        xMin = float(mapInfo_split[3])\n        yMax = float(mapInfo_split[4])\n\n        xMax = xMin + (refl_shape[1] * res[0])\n        yMin = yMax - (refl_shape[0] * res[1])\n\n        # Handle scale factor and no-data value\n        scaleFactor = site_reflArray.attrs[\"Scale_Factor\"]\n        noDataValue = site_reflArray.attrs[\"Data_Ignore_Value\"]\n\n        da = site_reflArray[:, :, :].astype(float)\n        da[da == int(noDataValue)] = np.nan\n        da[da &lt; 0] = np.nan\n        da[da &gt; 10000] = np.nan\n        da = da / scaleFactor\n\n        coords = {\n            \"y\": np.linspace(yMax, yMin, da.shape[0]),\n            \"x\": np.linspace(xMin, xMax, da.shape[1]),\n            \"wavelength\": wavelengths_list,\n        }\n\n        xda = xr.DataArray(\n            da,\n            coords=coords,\n            dims=[\"y\", \"x\", \"wavelength\"],\n            attrs={\n                \"scale_factor\": scaleFactor,\n                \"no_data_value\": noDataValue,\n                \"crs\": f\"EPSG:{epsg_code_number}\",\n                \"transform\": (res[0], 0.0, xMin, 0.0, -res[1], yMax),\n            },\n        )\n\n        if wavelengths is not None:\n            xda = xda.sel(wavelength=wavelengths, method=method, **kwargs)\n\n        dataset = xda.to_dataset(name=\"reflectance\")\n        dataset.attrs = dataset[\"reflectance\"].attrs\n\n    return dataset\n</code></pre>"},{"location":"pace/","title":"pace module","text":"<p>This module contains functions to read and process PACE data.</p>"},{"location":"pace/#hypercoast.pace.extract_pace","title":"<code>extract_pace(dataset, latitude, longitude, delta=0.01, return_plot=False, **kwargs)</code>","text":"<p>Extracts data from a PACE dataset for a given latitude and longitude range     and calculates the mean over these dimensions.</p> <p>Parameters:</p> Name Type Description Default <code>dataset</code> <code>Union[xr.Dataset, str]</code> <p>The PACE dataset or path to the dataset file.</p> required <code>latitude</code> <code>Union[float, Tuple[float, float]]</code> <p>The latitude or range of latitudes to extract data for.</p> required <code>longitude</code> <code>Union[float, Tuple[float, float]]</code> <p>The longitude or range of longitudes to extract data for.</p> required <code>delta</code> <code>float</code> <p>The range to add/subtract to the latitude and longitude if they are not ranges. Defaults to 0.01.</p> <code>0.01</code> <code>return_plot</code> <code>bool</code> <p>Whether to return a plot of the data. Defaults to False.</p> <code>False</code> <code>**kwargs</code> <p>Additional keyword arguments to pass to the plot function.</p> <code>{}</code> <p>Returns:</p> Type Description <code>Union[xr.DataArray, plt.figure.Figure]</code> <p>The mean data over the latitude     and longitude dimensions, or a plot of this data if return_plot is True.</p> Source code in <code>hypercoast/pace.py</code> <pre><code>def extract_pace(\n    dataset: Union[xr.Dataset, str],\n    latitude: Union[float, Tuple[float, float]],\n    longitude: Union[float, Tuple[float, float]],\n    delta: float = 0.01,\n    return_plot: bool = False,\n    **kwargs,\n) -&gt; Union[xr.DataArray, plt.Figure]:\n    \"\"\"\n    Extracts data from a PACE dataset for a given latitude and longitude range\n        and calculates the mean over these dimensions.\n\n    Args:\n        dataset (Union[xr.Dataset, str]): The PACE dataset or path to the dataset file.\n        latitude (Union[float, Tuple[float, float]]): The latitude or range of\n            latitudes to extract data for.\n        longitude (Union[float, Tuple[float, float]]): The longitude or range of\n            longitudes to extract data for.\n        delta (float, optional): The range to add/subtract to the latitude and\n            longitude if they are not ranges. Defaults to 0.01.\n        return_plot (bool, optional): Whether to return a plot of the data. Defaults to False.\n        **kwargs: Additional keyword arguments to pass to the plot function.\n\n    Returns:\n        Union[xr.DataArray, plt.figure.Figure]: The mean data over the latitude\n            and longitude dimensions, or a plot of this data if return_plot is True.\n    \"\"\"\n    if isinstance(latitude, list) or isinstance(latitude, tuple):\n        pass\n    else:\n        latitude = (latitude - delta, latitude + delta)\n\n    if isinstance(longitude, list) or isinstance(longitude, tuple):\n        pass\n    else:\n        longitude = (longitude - delta, longitude + delta)\n\n    ds = filter_pace(dataset, latitude, longitude, return_plot=False)\n    data = ds.mean(dim=[\"latitude\", \"longitude\"])\n    if return_plot:\n        return data.plot.line(**kwargs)\n    else:\n        return data\n</code></pre>"},{"location":"pace/#hypercoast.pace.filter_pace","title":"<code>filter_pace(dataset, latitude, longitude, drop=True, return_plot=False, **kwargs)</code>","text":"<p>Filters a PACE dataset based on latitude and longitude.</p> <p>Parameters:</p> Name Type Description Default <code>dataset</code> <code>xr.Dataset</code> <p>The PACE dataset to filter.</p> required <code>latitude</code> <code>float or tuple</code> <p>The latitude to filter by. If a tuple or list, it represents a range.</p> required <code>longitude</code> <code>float or tuple</code> <p>The longitude to filter by. If a tuple or list, it represents a range.</p> required <code>drop</code> <code>bool</code> <p>Whether to drop the filtered out data. Defaults to True.</p> <code>True</code> <p>Returns:</p> Type Description <code>xr.DataArray</code> <p>The filtered PACE data.</p> Source code in <code>hypercoast/pace.py</code> <pre><code>def filter_pace(dataset, latitude, longitude, drop=True, return_plot=False, **kwargs):\n    \"\"\"\n    Filters a PACE dataset based on latitude and longitude.\n\n    Args:\n        dataset (xr.Dataset): The PACE dataset to filter.\n        latitude (float or tuple): The latitude to filter by. If a tuple or list, it represents a range.\n        longitude (float or tuple): The longitude to filter by. If a tuple or list, it represents a range.\n        drop (bool, optional): Whether to drop the filtered out data. Defaults to True.\n\n    Returns:\n        xr.DataArray: The filtered PACE data.\n    \"\"\"\n    if isinstance(latitude, list) or isinstance(latitude, tuple):\n        lat_con = (dataset[\"latitude\"] &gt; latitude[0]) &amp; (\n            dataset[\"latitude\"] &lt; latitude[1]\n        )\n    else:\n        lat_con = dataset[\"latitude\"] == latitude\n\n    if isinstance(longitude, list) or isinstance(longitude, tuple):\n        lon_con = (dataset[\"longitude\"] &gt; longitude[0]) &amp; (\n            dataset[\"longitude\"] &lt; longitude[1]\n        )\n    else:\n        lon_con = dataset[\"longitude\"] == longitude\n\n    da = dataset[\"Rrs\"].where(lat_con &amp; lon_con, drop=drop, **kwargs)\n    da_filtered = da.dropna(dim=\"latitude\", how=\"all\")\n    da_filtered = da_filtered.dropna(dim=\"longitude\", how=\"all\")\n\n    if return_plot:\n        rrs_stack = da_filtered.stack(\n            {\"pixel\": [\"latitude\", \"longitude\"]},\n            create_index=False,\n        )\n        rrs_stack.plot.line(hue=\"pixel\")\n    else:\n        return da_filtered\n</code></pre>"},{"location":"pace/#hypercoast.pace.grid_pace","title":"<code>grid_pace(dataset, wavelengths=None, method='nearest', **kwargs)</code>","text":"<p>Grids a PACE dataset based on latitude and longitude.</p> <p>Parameters:</p> Name Type Description Default <code>dataset</code> <code>xr.Dataset</code> <p>The PACE dataset to grid.</p> required <code>wavelengths</code> <code>float or int</code> <p>The wavelength to select.</p> <code>None</code> <code>method</code> <code>str</code> <p>The method to use for griddata interpolation. Defaults to \"nearest\".</p> <code>'nearest'</code> <code>**kwargs</code> <p>Additional keyword arguments to pass to the xr.Dataset constructor.</p> <code>{}</code> <p>Returns:</p> Type Description <code>xr.DataArray</code> <p>The gridded PACE data.</p> Source code in <code>hypercoast/pace.py</code> <pre><code>def grid_pace(dataset, wavelengths=None, method=\"nearest\", **kwargs):\n    \"\"\"\n    Grids a PACE dataset based on latitude and longitude.\n\n    Args:\n        dataset (xr.Dataset): The PACE dataset to grid.\n        wavelengths (float or int): The wavelength to select.\n        method (str, optional): The method to use for griddata interpolation.\n            Defaults to \"nearest\".\n        **kwargs: Additional keyword arguments to pass to the xr.Dataset constructor.\n\n    Returns:\n        xr.DataArray: The gridded PACE data.\n    \"\"\"\n    from scipy.interpolate import griddata\n\n    if wavelengths is None:\n        wavelengths = dataset.coords[\"wavelength\"].values[0]\n\n    # Ensure wavelengths is a list\n    if not isinstance(wavelengths, list):\n        wavelengths = [wavelengths]\n\n    lat = dataset.latitude\n    lon = dataset.longitude\n\n    grid_lat = np.linspace(lat.min().values, lat.max().values, lat.shape[0])\n    grid_lon = np.linspace(lon.min().values, lon.max().values, lon.shape[1])\n    grid_lon_2d, grid_lat_2d = np.meshgrid(grid_lon, grid_lat)\n\n    gridded_data_dict = {}\n    for wavelength in wavelengths:\n        data = dataset.sel(wavelength=wavelength, method=\"nearest\")[\"Rrs\"]\n        gridded_data = griddata(\n            (lat.data.flatten(), lon.data.flatten()),\n            data.data.flatten(),\n            (grid_lat_2d, grid_lon_2d),\n            method=method,\n        )\n        gridded_data_dict[wavelength] = gridded_data\n\n    # Create a 3D array with dimensions latitude, longitude, and wavelength\n    gridded_data_3d = np.dstack(list(gridded_data_dict.values()))\n\n    dataset2 = xr.Dataset(\n        {\"Rrs\": ((\"latitude\", \"longitude\", \"wavelength\"), gridded_data_3d)},\n        coords={\n            \"latitude\": (\"latitude\", grid_lat),\n            \"longitude\": (\"longitude\", grid_lon),\n            \"wavelength\": (\"wavelength\", list(gridded_data_dict.keys())),\n        },\n        **kwargs,\n    )\n\n    dataset2[\"Rrs\"].rio.write_crs(\"EPSG:4326\", inplace=True)\n\n    return dataset2\n</code></pre>"},{"location":"pace/#hypercoast.pace.grid_pace_bgc","title":"<code>grid_pace_bgc(dataset, variable='chlor_a', method='nearest', **kwargs)</code>","text":"<p>Grids PACE BGC data using specified interpolation method.</p> <p>This function takes an xarray Dataset containing PACE BGC data, interpolates it onto a regular grid using the specified method, and returns the gridded data as an xarray DataArray with the specified variable.</p> <p>Parameters:</p> Name Type Description Default <code>dataset</code> <code>xr.Dataset</code> <p>The input dataset containing PACE BGC data with latitude and longitude coordinates.</p> required <code>variable</code> <code>str</code> <p>The variable within the dataset to grid. Can be one of chlor_a, carbon_phyto, poc, chlor_a_unc, carbon_phyto_unc, and l2_flags. Defaults to \"chlor_a\".</p> <code>'chlor_a'</code> <code>method</code> <code>str</code> <p>The interpolation method to use. Options include \"nearest\", \"linear\", and \"cubic\". Defaults to \"nearest\".</p> <code>'nearest'</code> <code>**kwargs</code> <code>Any</code> <p>Additional keyword arguments to pass to the xr.Dataset creation.</p> <code>{}</code> <p>Returns:</p> Type Description <code>xr.DataArray</code> <p>The gridded data as an xarray DataArray, with the specified variable and EPSG:4326 CRS.</p> <p>Examples:</p> <pre><code>&gt;&gt;&gt; dataset = hypercoast.read_pace_bgc(\"path_to_your_dataset.nc\")\n&gt;&gt;&gt; gridded_data = grid_pace_bgc(dataset, variable=\"chlor_a\", method=\"nearest\")\n&gt;&gt;&gt; print(gridded_data)\n</code></pre> Source code in <code>hypercoast/pace.py</code> <pre><code>def grid_pace_bgc(\n    dataset: xr.Dataset,\n    variable: str = \"chlor_a\",\n    method: str = \"nearest\",\n    **kwargs: Any,\n) -&gt; xr.DataArray:\n    \"\"\"\n    Grids PACE BGC data using specified interpolation method.\n\n    This function takes an xarray Dataset containing PACE BGC data, interpolates it onto a regular grid\n    using the specified method, and returns the gridded data as an xarray DataArray with the specified\n    variable.\n\n    Args:\n        dataset (xr.Dataset): The input dataset containing PACE BGC data with latitude and longitude coordinates.\n        variable (str, optional): The variable within the dataset to grid. Can be\n            one of chlor_a, carbon_phyto, poc, chlor_a_unc, carbon_phyto_unc, and l2_flags.\n            Defaults to \"chlor_a\".\n        method (str, optional): The interpolation method to use. Options include \"nearest\", \"linear\", and \"cubic\".\n            Defaults to \"nearest\".\n        **kwargs (Any): Additional keyword arguments to pass to the xr.Dataset creation.\n\n    Returns:\n        xr.DataArray: The gridded data as an xarray DataArray, with the specified variable and EPSG:4326 CRS.\n\n    Example:\n        &gt;&gt;&gt; dataset = hypercoast.read_pace_bgc(\"path_to_your_dataset.nc\")\n        &gt;&gt;&gt; gridded_data = grid_pace_bgc(dataset, variable=\"chlor_a\", method=\"nearest\")\n        &gt;&gt;&gt; print(gridded_data)\n    \"\"\"\n    import rioxarray\n    from scipy.interpolate import griddata\n\n    lat = dataset.latitude\n    lon = dataset.longitude\n\n    grid_lat = np.linspace(lat.min().values, lat.max().values, lat.shape[0])\n    grid_lon = np.linspace(lon.min().values, lon.max().values, lon.shape[1])\n    grid_lon_2d, grid_lat_2d = np.meshgrid(grid_lon, grid_lat)\n\n    data = dataset[variable]\n    gridded_data = griddata(\n        (lat.data.flatten(), lon.data.flatten()),\n        data.data.flatten(),\n        (grid_lat_2d, grid_lon_2d),\n        method=method,\n    )\n\n    dataset2 = xr.Dataset(\n        {variable: ((\"latitude\", \"longitude\"), gridded_data)},\n        coords={\n            \"latitude\": (\"latitude\", grid_lat),\n            \"longitude\": (\"longitude\", grid_lon),\n        },\n        **kwargs,\n    )\n\n    dataset2 = dataset2[variable].rio.write_crs(\"EPSG:4326\")\n\n    return dataset2\n</code></pre>"},{"location":"pace/#hypercoast.pace.pace_chla_to_image","title":"<code>pace_chla_to_image(data, output=None, **kwargs)</code>","text":"<p>Converts PACE chlorophyll-a data to an image.</p> <p>Parameters:</p> Name Type Description Default <code>data</code> <code>xr.DataArray or str</code> <p>The chlorophyll-a data or the file path to the data.</p> required <code>output</code> <code>str</code> <p>The file path where the image will be saved. If None, the image will be returned as a PIL Image object. Defaults to None.</p> <code>None</code> <code>**kwargs</code> <p>Additional keyword arguments to be passed to <code>leafmap.array_to_image</code>.</p> <code>{}</code> <p>Returns:</p> Type Description <code>rasterio.Dataset or None</code> <p>The image converted from the data. If <code>output</code> is provided, the image will be saved to the specified file and the function will return None.</p> Source code in <code>hypercoast/pace.py</code> <pre><code>def pace_chla_to_image(data, output=None, **kwargs):\n    \"\"\"\n    Converts PACE chlorophyll-a data to an image.\n\n    Args:\n        data (xr.DataArray or str): The chlorophyll-a data or the file path to the data.\n        output (str, optional): The file path where the image will be saved. If None, the image will be returned as a PIL Image object. Defaults to None.\n        **kwargs: Additional keyword arguments to be passed to `leafmap.array_to_image`.\n\n    Returns:\n        rasterio.Dataset or None: The image converted from the data. If `output` is provided, the image will be saved to the specified file and the function will return None.\n    \"\"\"\n    from leafmap import array_to_image, image_to_geotiff\n\n    if isinstance(data, str):\n        data = read_pace_chla(data)\n    elif not isinstance(data, xr.DataArray):\n        raise ValueError(\"data must be an xarray DataArray\")\n\n    image = array_to_image(data, transpose=False, output=None, **kwargs)\n\n    if output is not None:\n        image_to_geotiff(image, output, dtype=\"float32\")\n\n    return image\n</code></pre>"},{"location":"pace/#hypercoast.pace.pace_to_image","title":"<code>pace_to_image(dataset, wavelengths=None, method='nearest', gridded=False, output=None, **kwargs)</code>","text":"<p>Converts an PACE dataset to an image.</p> <p>Parameters:</p> Name Type Description Default <code>dataset</code> <code>xarray.Dataset or str</code> <p>The dataset containing the EMIT data or the file path to the dataset.</p> required <code>wavelengths</code> <code>array-like</code> <p>The specific wavelengths to select. If None, all wavelengths are selected. Defaults to None.</p> <code>None</code> <code>method</code> <code>str</code> <p>The method to use for data interpolation. Defaults to \"nearest\".</p> <code>'nearest'</code> <code>gridded</code> <code>bool</code> <p>Whether the dataset is a gridded dataset. Defaults to False,</p> <code>False</code> <code>output</code> <code>str</code> <p>The file path where the image will be saved. If None, the image will be returned as a PIL Image object. Defaults to None.</p> <code>None</code> <code>**kwargs</code> <p>Additional keyword arguments to be passed to <code>leafmap.array_to_image</code>.</p> <code>{}</code> <p>Returns:</p> Type Description <code>rasterio.Dataset or None</code> <p>The image converted from the dataset. If <code>output</code> is provided, the image will be saved to the specified file and the function will return None.</p> Source code in <code>hypercoast/pace.py</code> <pre><code>def pace_to_image(\n    dataset, wavelengths=None, method=\"nearest\", gridded=False, output=None, **kwargs\n):\n    \"\"\"\n    Converts an PACE dataset to an image.\n\n    Args:\n        dataset (xarray.Dataset or str): The dataset containing the EMIT data or the file path to the dataset.\n        wavelengths (array-like, optional): The specific wavelengths to select. If None, all wavelengths are selected. Defaults to None.\n        method (str, optional): The method to use for data interpolation. Defaults to \"nearest\".\n        gridded (bool, optional): Whether the dataset is a gridded dataset. Defaults to False,\n        output (str, optional): The file path where the image will be saved. If None, the image will be returned as a PIL Image object. Defaults to None.\n        **kwargs: Additional keyword arguments to be passed to `leafmap.array_to_image`.\n\n    Returns:\n        rasterio.Dataset or None: The image converted from the dataset. If `output` is provided, the image will be saved to the specified file and the function will return None.\n    \"\"\"\n    from leafmap import array_to_image\n\n    if isinstance(dataset, str):\n        dataset = read_pace(dataset, wavelengths=wavelengths, method=\"nearest\")\n\n    if wavelengths is not None:\n        dataset = dataset.sel(wavelength=wavelengths, method=\"nearest\")\n\n    if not gridded:\n        grid = grid_pace(dataset, wavelengths=wavelengths, method=method)\n    else:\n        grid = dataset\n    data = grid[\"Rrs\"]\n    data.rio.write_crs(\"EPSG:4326\", inplace=True)\n\n    return array_to_image(data, transpose=False, output=output, **kwargs)\n</code></pre>"},{"location":"pace/#hypercoast.pace.read_pace","title":"<code>read_pace(filepath, wavelengths=None, method='nearest', engine='h5netcdf', **kwargs)</code>","text":"<p>Reads PACE data from a given file and returns an xarray Dataset.</p> <p>Parameters:</p> Name Type Description Default <code>filepath</code> <code>str</code> <p>Path to the file to read.</p> required <code>wavelengths</code> <code>array-like</code> <p>Specific wavelengths to select. If None, all wavelengths are selected.</p> <code>None</code> <code>method</code> <code>str</code> <p>Method to use for selection when wavelengths is not None. Defaults to \"nearest\".</p> <code>'nearest'</code> <code>**kwargs</code> <p>Additional keyword arguments to pass to the <code>sel</code> method when wavelengths is not None.</p> <code>{}</code> <p>Returns:</p> Type Description <code>xr.Dataset</code> <p>An xarray Dataset containing the PACE data.</p> Source code in <code>hypercoast/pace.py</code> <pre><code>def read_pace(\n    filepath, wavelengths=None, method=\"nearest\", engine=\"h5netcdf\", **kwargs\n):\n    \"\"\"\n    Reads PACE data from a given file and returns an xarray Dataset.\n\n    Args:\n        filepath (str): Path to the file to read.\n        wavelengths (array-like, optional): Specific wavelengths to select. If None, all wavelengths are selected.\n        method (str, optional): Method to use for selection when wavelengths is not None. Defaults to \"nearest\".\n        **kwargs: Additional keyword arguments to pass to the `sel` method when wavelengths is not None.\n\n    Returns:\n        xr.Dataset: An xarray Dataset containing the PACE data.\n    \"\"\"\n\n    rrs = xr.open_dataset(filepath, engine=engine, group=\"geophysical_data\")[\"Rrs\"]\n    wvl = xr.open_dataset(filepath, engine=engine, group=\"sensor_band_parameters\")\n    dataset = xr.open_dataset(filepath, engine=engine, group=\"navigation_data\")\n    dataset = dataset.set_coords((\"longitude\", \"latitude\"))\n    if \"pixel_control_points\" in dataset.dims:\n        dataset = dataset.rename({\"pixel_control_points\": \"pixels_per_line\"})\n    dataset = xr.merge([rrs, dataset.coords.to_dataset()])\n    dataset.coords[\"wavelength_3d\"] = wvl.coords[\"wavelength_3d\"]\n    dataset = dataset.rename(\n        {\n            \"number_of_lines\": \"latitude\",\n            \"pixels_per_line\": \"longitude\",\n            \"wavelength_3d\": \"wavelength\",\n        }\n    )\n\n    if wavelengths is not None:\n        dataset = dataset.sel(wavelength=wavelengths, method=method, **kwargs)\n\n    return dataset\n</code></pre>"},{"location":"pace/#hypercoast.pace.read_pace_aop","title":"<code>read_pace_aop(filepath, engine='h5netcdf', **kwargs)</code>","text":"<p>Reads PACE data from a given file and returns an xarray Dataset.</p> <p>Parameters:</p> Name Type Description Default <code>filepath</code> <code>str</code> <p>Path to the file to read.</p> required <code>wavelengths</code> <code>array-like</code> <p>Specific wavelengths to select. If None, all wavelengths are selected.</p> required <code>method</code> <code>str</code> <p>Method to use for selection when wavelengths is not None. Defaults to \"nearest\".</p> required <code>**kwargs</code> <p>Additional keyword arguments to pass to the <code>sel</code> method when wavelengths is not None.</p> <code>{}</code> <p>Returns:</p> Type Description <code>xr.Dataset</code> <p>An xarray Dataset containing the PACE data.</p> Source code in <code>hypercoast/pace.py</code> <pre><code>def read_pace_aop(filepath, engine=\"h5netcdf\", **kwargs):\n    \"\"\"\n    Reads PACE data from a given file and returns an xarray Dataset.\n\n    Args:\n        filepath (str): Path to the file to read.\n        wavelengths (array-like, optional): Specific wavelengths to select. If None, all wavelengths are selected.\n        method (str, optional): Method to use for selection when wavelengths is not None. Defaults to \"nearest\".\n        **kwargs: Additional keyword arguments to pass to the `sel` method when wavelengths is not None.\n\n    Returns:\n        xr.Dataset: An xarray Dataset containing the PACE data.\n    \"\"\"\n\n    rrs = xr.open_dataset(filepath, engine=engine, group=\"geophysical_data\", **kwargs)[\n        \"Rrs\"\n    ]\n    wvl = xr.open_dataset(\n        filepath, engine=engine, group=\"sensor_band_parameters\", **kwargs\n    )\n    dataset = xr.open_dataset(\n        filepath, engine=engine, group=\"navigation_data\", **kwargs\n    )\n    dataset = dataset.set_coords((\"longitude\", \"latitude\"))\n    if \"pixel_control_points\" in dataset.dims:\n        dataset = dataset.rename({\"pixel_control_points\": \"pixels_per_line\"})\n    dataset = xr.merge([rrs, dataset.coords.to_dataset()])\n    dataset.coords[\"wavelength_3d\"] = wvl.coords[\"wavelength_3d\"]\n\n    return dataset\n</code></pre>"},{"location":"pace/#hypercoast.pace.read_pace_bgc","title":"<code>read_pace_bgc(filepath, variable=None, engine='h5netcdf', **kwargs)</code>","text":"<p>Reads PACE BGC data from a specified file and returns an xarray Dataset.</p> <p>This function opens a dataset from a file using the specified engine, optionally selects a single variable, merges geophysical and navigation data, sets appropriate coordinates, and renames dimensions for easier use.</p> <p>Parameters:</p> Name Type Description Default <code>filepath</code> <code>str</code> <p>The path to the file containing the PACE BGC data.</p> required <code>variable</code> <code>Optional[str]</code> <p>The specific variable to extract from the geophysical_data group. If None, all variables are read. Defaults to None.</p> <code>None</code> <code>engine</code> <code>str</code> <p>The engine to use for reading the file. Defaults to \"h5netcdf\".</p> <code>'h5netcdf'</code> <code>**kwargs</code> <code>Any</code> <p>Additional keyword arguments to pass to <code>xr.open_dataset</code>.</p> <code>{}</code> <p>Returns:</p> Type Description <code>xr.Dataset</code> <p>An xarray Dataset containing the requested PACE BGC data, with merged geophysical and navigation data, set coordinates, and renamed dimensions.</p> <p>Examples:</p> <pre><code>&gt;&gt;&gt; dataset = read_pace_bgc(\"path/to/your/datafile.h5\", variable=\"chlor_a\")\n&gt;&gt;&gt; print(dataset)\n</code></pre> Source code in <code>hypercoast/pace.py</code> <pre><code>def read_pace_bgc(\n    filepath: str,\n    variable: Optional[str] = None,\n    engine: str = \"h5netcdf\",\n    **kwargs: Any,\n) -&gt; xr.Dataset:\n    \"\"\"\n    Reads PACE BGC data from a specified file and returns an xarray Dataset.\n\n    This function opens a dataset from a file using the specified engine,\n    optionally selects a single variable, merges geophysical and navigation data,\n    sets appropriate coordinates, and renames dimensions for easier use.\n\n    Args:\n        filepath (str): The path to the file containing the PACE BGC data.\n        variable (Optional[str], optional): The specific variable to extract\n            from the geophysical_data group. If None, all variables are read. Defaults to None.\n        engine (str, optional): The engine to use for reading the file. Defaults to \"h5netcdf\".\n        **kwargs (Any): Additional keyword arguments to pass to `xr.open_dataset`.\n\n    Returns:\n        xr.Dataset: An xarray Dataset containing the requested PACE BGC data,\n        with merged geophysical and navigation data, set coordinates, and renamed dimensions.\n\n    Example:\n        &gt;&gt;&gt; dataset = read_pace_bgc(\"path/to/your/datafile.h5\", variable=\"chlor_a\")\n        &gt;&gt;&gt; print(dataset)\n    \"\"\"\n\n    ds = xr.open_dataset(filepath, engine=engine, group=\"geophysical_data\", **kwargs)\n    if variable is not None:\n        ds = ds[variable]\n    dataset = xr.open_dataset(\n        filepath, engine=engine, group=\"navigation_data\", **kwargs\n    )\n    dataset = dataset.set_coords((\"longitude\", \"latitude\"))\n    if \"pixel_control_points\" in dataset.dims:\n        dataset = dataset.rename({\"pixel_control_points\": \"pixels_per_line\"})\n    dataset = xr.merge([ds, dataset.coords.to_dataset()])\n    dataset = dataset.rename(\n        {\n            \"number_of_lines\": \"latitude\",\n            \"pixels_per_line\": \"longitude\",\n        }\n    )\n    attrs = xr.open_dataset(filepath, engine=engine, **kwargs).attrs\n    dataset.attrs.update(attrs)\n\n    return dataset\n</code></pre>"},{"location":"pace/#hypercoast.pace.read_pace_chla","title":"<code>read_pace_chla(filepaths, engine='h5netcdf', **kwargs)</code>","text":"<p>Reads chlorophyll-a data from PACE files and applies a logarithmic transformation.</p> <p>This function supports reading from a single file or multiple files. For multiple files, it combines them into a single dataset. It then extracts the chlorophyll-a variable, applies a logarithmic transformation, and sets the coordinate reference system to EPSG:4326.</p> <p>Parameters:</p> Name Type Description Default <code>filepaths</code> <code>Union[str, List[str]]</code> <p>A string or a list of strings containing the file path(s) to the PACE chlorophyll-a data files.</p> required <code>engine</code> <code>str</code> <p>The backend engine to use for reading files. Defaults to \"h5netcdf\".</p> <code>'h5netcdf'</code> <code>**kwargs</code> <p>Additional keyword arguments to pass to <code>xr.open_dataset</code> or <code>xr.open_mfdataset</code>.</p> <code>{}</code> <p>Returns:</p> Type Description <code>DataArray</code> <p>An xarray DataArray containing the logarithmically transformed chlorophyll-a data with updated attributes.</p> <p>Examples:</p> <p>Read chlorophyll-a data from a single file:</p> <pre><code>&gt;&gt;&gt; chla_data = read_pace_chla('path/to/single/file.nc')\n</code></pre> <p>Read and combine chlorophyll-a data from multiple files:</p> <pre><code>&gt;&gt;&gt; chla_data = read_pace_chla(['path/to/file1.nc', 'path/to/file2.nc'], combine='by_coords')\n</code></pre> Source code in <code>hypercoast/pace.py</code> <pre><code>def read_pace_chla(\n    filepaths: Union[str, List[str]], engine: str = \"h5netcdf\", **kwargs\n) -&gt; xr.DataArray:\n    \"\"\"\n    Reads chlorophyll-a data from PACE files and applies a logarithmic transformation.\n\n    This function supports reading from a single file or multiple files. For multiple files,\n    it combines them into a single dataset. It then extracts the chlorophyll-a variable,\n    applies a logarithmic transformation, and sets the coordinate reference system to EPSG:4326.\n\n    Args:\n        filepaths: A string or a list of strings containing the file path(s) to the PACE chlorophyll-a data files.\n        engine: The backend engine to use for reading files. Defaults to \"h5netcdf\".\n        **kwargs: Additional keyword arguments to pass to `xr.open_dataset` or `xr.open_mfdataset`.\n\n    Returns:\n        An xarray DataArray containing the logarithmically transformed chlorophyll-a data with updated attributes.\n\n    Examples:\n        Read chlorophyll-a data from a single file:\n        &gt;&gt;&gt; chla_data = read_pace_chla('path/to/single/file.nc')\n\n        Read and combine chlorophyll-a data from multiple files:\n        &gt;&gt;&gt; chla_data = read_pace_chla(['path/to/file1.nc', 'path/to/file2.nc'], combine='by_coords')\n    \"\"\"\n\n    import os\n    import glob\n    import rioxarray\n\n    date = None\n    if isinstance(filepaths, str) and os.path.isfile(filepaths):\n        filepaths = [filepaths]\n    if \"combine\" not in kwargs:\n        kwargs[\"combine\"] = \"nested\"\n    if \"concat_dim\" not in kwargs:\n        kwargs[\"concat_dim\"] = \"date\"\n    dataset = xr.open_mfdataset(filepaths, engine=engine, **kwargs)\n    if not isinstance(filepaths, list):\n        filepaths = glob.glob(filepaths)\n        filepaths.sort()\n\n    dates = [extract_date_from_filename(f) for f in filepaths]\n    date = [timestamp.strftime(\"%Y-%m-%d\") for timestamp in dates]\n    dataset = dataset.assign_coords(date=(\"date\", date))\n\n    chla = np.log10(dataset[\"chlor_a\"])\n    chla.attrs.update(\n        {\n            \"units\": f'lg({dataset[\"chlor_a\"].attrs[\"units\"]})',\n        }\n    )\n\n    if date is not None:\n        chla.attrs[\"date\"] = date\n\n    chla = chla.transpose(\"lat\", \"lon\", \"date\")\n\n    chla.rio.write_crs(\"EPSG:4326\", inplace=True)\n\n    return chla\n</code></pre>"},{"location":"pace/#hypercoast.pace.view_pace_pixel_locations","title":"<code>view_pace_pixel_locations(filepath, step=20, figsize=(8, 6), **kwargs)</code>","text":"<p>Visualizes a subset of PACE pixel locations on a scatter plot.</p> <p>This function reads PACE AOP data from a specified file, subsamples the data according to a step size, and plots the longitude and latitude of the selected pixels using a scatter plot.</p> <p>Parameters:</p> Name Type Description Default <code>filepath</code> <code>str</code> <p>The path to the file containing the PACE AOP data.</p> required <code>step</code> <code>int</code> <p>The step size for subsampling the data. A smaller step size results in more data points being plotted. Defaults to 20.</p> <code>20</code> <code>**kwargs</code> <code>Any</code> <p>Additional keyword arguments to pass to the <code>plot.scatter</code> method.</p> <code>{}</code> <p>Returns:</p> Type Description <code>plt.Figure</code> <p>A matplotlib figure object containing the scatter plot.</p> <p>Examples:</p> <pre><code>&gt;&gt;&gt; plot = view_pace_pixel_locations(\"path/to/your/datafile.h5\", step=10)\n&gt;&gt;&gt; plt.show()\n</code></pre> Source code in <code>hypercoast/pace.py</code> <pre><code>def view_pace_pixel_locations(\n    filepath: str, step: int = 20, figsize: Tuple[float, float] = (8, 6), **kwargs: Any\n) -&gt; plt.Figure:\n    \"\"\"\n    Visualizes a subset of PACE pixel locations on a scatter plot.\n\n    This function reads PACE AOP data from a specified file, subsamples the data according to a step size,\n    and plots the longitude and latitude of the selected pixels using a scatter plot.\n\n    Args:\n        filepath (str): The path to the file containing the PACE AOP data.\n        step (int, optional): The step size for subsampling the data. A smaller step size results in more\n            data points being plotted. Defaults to 20.\n        **kwargs (Any): Additional keyword arguments to pass to the `plot.scatter` method.\n\n    Returns:\n        plt.Figure: A matplotlib figure object containing the scatter plot.\n\n    Example:\n        &gt;&gt;&gt; plot = view_pace_pixel_locations(\"path/to/your/datafile.h5\", step=10)\n        &gt;&gt;&gt; plt.show()\n    \"\"\"\n\n    # Create a new figure\n    fig, ax = plt.subplots(figsize=figsize)\n\n    # Create the plot\n    dataset = read_pace_aop(filepath)\n    number_of_lines = dataset.sizes[\"number_of_lines\"]\n    pixels_per_line = dataset.sizes[\"pixels_per_line\"]\n\n    ax.scatter(\n        dataset.sel(\n            {\n                \"number_of_lines\": slice(None, None, number_of_lines // step),\n                \"pixels_per_line\": slice(None, None, pixels_per_line // step),\n            }\n        ).longitude,\n        dataset.sel(\n            {\n                \"number_of_lines\": slice(None, None, number_of_lines // step),\n                \"pixels_per_line\": slice(None, None, pixels_per_line // step),\n            }\n        ).latitude,\n        **kwargs,\n    )\n\n    # Set labels and title\n    ax.set_xlabel(\"Longitude\")\n    ax.set_ylabel(\"Latitude\")\n    ax.set_title(\"PACE Pixel Locations\")\n\n    return fig\n</code></pre>"},{"location":"pace/#hypercoast.pace.viz_pace","title":"<code>viz_pace(dataset, wavelengths=None, method='nearest', figsize=(6.4, 4.8), cmap='jet', vmin=0, vmax=0.02, ncols=1, crs=None, xlim=None, ylim=None, **kwargs)</code>","text":"<p>Plots PACE data from a given xarray Dataset.</p> <p>Parameters:</p> Name Type Description Default <code>dataset</code> <code>xr.Dataset</code> <p>An xarray Dataset containing the PACE data.</p> required <code>wavelengths</code> <code>array-like</code> <p>Specific wavelengths to select. If None, all wavelengths are selected.</p> <code>None</code> <code>method</code> <code>str</code> <p>Method to use for selection when wavelengths is not None. Defaults to \"nearest\".</p> <code>'nearest'</code> <code>figsize</code> <code>tuple</code> <p>Figure size. Defaults to (6.4, 4.8).</p> <code>(6.4, 4.8)</code> <code>cmap</code> <code>str</code> <p>Colormap to use. Defaults to \"jet\".</p> <code>'jet'</code> <code>vmin</code> <code>float</code> <p>Minimum value for the colormap. Defaults to 0.</p> <code>0</code> <code>vmax</code> <code>float</code> <p>Maximum value for the colormap. Defaults to 0.02.</p> <code>0.02</code> <code>ncols</code> <code>int</code> <p>Number of columns in the plot. Defaults to 1.</p> <code>1</code> <code>crs</code> <code>str or cartopy.crs.CRS</code> <p>Coordinate reference system to use. If None, a simple plot is created. Defaults to None. See https://scitools.org.uk/cartopy/docs/latest/reference/projections.html</p> <code>None</code> <code>xlim</code> <code>array-like</code> <p>Limits for the x-axis. Defaults to None.</p> <code>None</code> <code>ylim</code> <code>array-like</code> <p>Limits for the y-axis. Defaults to None.</p> <code>None</code> <code>**kwargs</code> <p>Additional keyword arguments to pass to the <code>plt.subplots</code> function.</p> <code>{}</code> Source code in <code>hypercoast/pace.py</code> <pre><code>def viz_pace(\n    dataset: Union[xr.Dataset, str],\n    wavelengths: Optional[Union[List[float], float]] = None,\n    method: str = \"nearest\",\n    figsize: Tuple[float, float] = (6.4, 4.8),\n    cmap: str = \"jet\",\n    vmin: float = 0,\n    vmax: float = 0.02,\n    ncols: int = 1,\n    crs: Optional[str] = None,\n    xlim: Optional[List[float]] = None,\n    ylim: Optional[List[float]] = None,\n    **kwargs,\n):\n    \"\"\"\n    Plots PACE data from a given xarray Dataset.\n\n    Args:\n        dataset (xr.Dataset): An xarray Dataset containing the PACE data.\n        wavelengths (array-like, optional): Specific wavelengths to select. If None, all wavelengths are selected.\n        method (str, optional): Method to use for selection when wavelengths is not None. Defaults to \"nearest\".\n        figsize (tuple, optional): Figure size. Defaults to (6.4, 4.8).\n        cmap (str, optional): Colormap to use. Defaults to \"jet\".\n        vmin (float, optional): Minimum value for the colormap. Defaults to 0.\n        vmax (float, optional): Maximum value for the colormap. Defaults to 0.02.\n        ncols (int, optional): Number of columns in the plot. Defaults to 1.\n        crs (str or cartopy.crs.CRS, optional): Coordinate reference system to use. If None, a simple plot is created. Defaults to None.\n            See https://scitools.org.uk/cartopy/docs/latest/reference/projections.html\n        xlim (array-like, optional): Limits for the x-axis. Defaults to None.\n        ylim (array-like, optional): Limits for the y-axis. Defaults to None.\n        **kwargs: Additional keyword arguments to pass to the `plt.subplots` function.\n    \"\"\"\n\n    import matplotlib.pyplot as plt\n    import numpy as np\n    import math\n\n    if isinstance(dataset, str):\n        dataset = read_pace(dataset, wavelengths, method)\n\n    if wavelengths is not None:\n        if not isinstance(wavelengths, list):\n            wavelengths = [wavelengths]\n        dataset = dataset.sel(wavelength=wavelengths, method=method)\n    else:\n        wavelengths = dataset.coords[\"wavelength\"][0].values.tolist()\n\n    lat = dataset.coords[\"latitude\"]\n    lon = dataset.coords[\"longitude\"]\n\n    nrows = math.ceil(len(wavelengths) / ncols)\n\n    if crs is None:\n\n        fig, axes = plt.subplots(\n            nrows=nrows,\n            ncols=ncols,\n            figsize=(figsize[0] * ncols, figsize[1] * nrows),\n            **kwargs,\n        )\n\n        for i in range(nrows):\n            for j in range(ncols):\n                index = i * ncols + j\n                if index &lt; len(wavelengths):\n                    wavelength = wavelengths[index]\n                    data = dataset.sel(wavelength=wavelength, method=method)[\"Rrs\"]\n\n                    if min(nrows, ncols) == 1:\n                        ax = axes[index]\n                    else:\n                        ax = axes[i, j]\n                    im = ax.pcolormesh(\n                        lon, lat, np.squeeze(data), cmap=cmap, vmin=vmin, vmax=vmax\n                    )\n                    ax.set_xlabel(\"Longitude\")\n                    ax.set_ylabel(\"Latitude\")\n                    ax.set_title(\n                        f\"wavelength = {dataset.coords['wavelength'].values[index]} [nm]\"\n                    )\n                    fig.colorbar(im, ax=ax, label=\"Reflectance\")\n\n        plt.tight_layout()\n        plt.show()\n\n    else:\n\n        import cartopy\n        from cartopy.mpl.ticker import LongitudeFormatter, LatitudeFormatter\n\n        if crs == \"default\":\n            crs = cartopy.crs.PlateCarree()\n\n        if xlim is None:\n            xlim = [math.floor(lon.min()), math.ceil(lon.max())]\n\n        if ylim is None:\n            ylim = [math.floor(lat.min()), math.ceil(lat.max())]\n\n        fig, axes = plt.subplots(\n            nrows=nrows,\n            ncols=ncols,\n            figsize=(figsize[0] * ncols, figsize[1] * nrows),\n            subplot_kw={\"projection\": cartopy.crs.PlateCarree()},\n            **kwargs,\n        )\n\n        for i in range(nrows):\n            for j in range(ncols):\n                index = i * ncols + j\n                if index &lt; len(wavelengths):\n                    wavelength = wavelengths[index]\n                    data = dataset.sel(wavelength=wavelength, method=method)[\"Rrs\"]\n\n                    if min(nrows, ncols) == 1:\n                        ax = axes[index]\n                    else:\n                        ax = axes[i, j]\n                    im = ax.pcolormesh(lon, lat, data, cmap=\"jet\", vmin=0, vmax=0.02)\n                    ax.coastlines()\n                    ax.add_feature(cartopy.feature.STATES, linewidth=0.5)\n                    ax.set_xticks(np.linspace(xlim[0], xlim[1], 5), crs=crs)\n                    ax.set_yticks(np.linspace(ylim[0], ylim[1], 5), crs=crs)\n                    lon_formatter = LongitudeFormatter(zero_direction_label=True)\n                    lat_formatter = LatitudeFormatter()\n                    ax.xaxis.set_major_formatter(lon_formatter)\n                    ax.yaxis.set_major_formatter(lat_formatter)\n                    ax.set_xlabel(\"Longitude\")\n                    ax.set_ylabel(\"Latitude\")\n                    ax.set_title(\n                        f\"wavelength = {dataset.coords['wavelength'].values[index]} [nm]\"\n                    )\n                    plt.colorbar(im, label=\"Reflectance\")\n\n        plt.tight_layout()\n        plt.show()\n</code></pre>"},{"location":"pace/#hypercoast.pace.viz_pace_chla","title":"<code>viz_pace_chla(data, date=None, aspect=2, cmap='jet', size=6, **kwargs)</code>","text":"<p>Visualizes PACE chlorophyll-a data using an xarray DataArray.</p> <p>This function supports loading data from a file path (str) or directly using an xarray DataArray. It allows for selection of a specific date for visualization or averages over all dates if none is specified.</p> <p>Parameters:</p> Name Type Description Default <code>data</code> <code>Union[str, xr.DataArray]</code> <p>The chlorophyll-a data to visualize. Can be a file path or an xarray DataArray.</p> required <code>date</code> <code>Optional[str]</code> <p>Specific date to visualize. If None, averages over all dates. Defaults to None.</p> <code>None</code> <code>aspect</code> <code>float</code> <p>Aspect ratio of the plot. Defaults to 2.</p> <code>2</code> <code>cmap</code> <code>str</code> <p>Colormap for the plot. Defaults to \"jet\".</p> <code>'jet'</code> <code>size</code> <code>int</code> <p>Size of the plot. Defaults to 6.</p> <code>6</code> <code>**kwargs</code> <code>Any</code> <p>Additional keyword arguments to pass to <code>xarray.plot</code>.</p> <code>{}</code> <p>Returns:</p> Type Description <code>xr.plot.facetgrid.FacetGrid</code> <p>The plot generated from the chlorophyll-a data.</p> <p>Exceptions:</p> Type Description <code>ValueError</code> <p>If <code>data</code> is not a file path (str) or an xarray DataArray.</p> Source code in <code>hypercoast/pace.py</code> <pre><code>def viz_pace_chla(\n    data: Union[str, xr.DataArray],\n    date: Optional[str] = None,\n    aspect: float = 2,\n    cmap: str = \"jet\",\n    size: int = 6,\n    **kwargs: Any,\n) -&gt; xr.plot.facetgrid.FacetGrid:\n    \"\"\"\n    Visualizes PACE chlorophyll-a data using an xarray DataArray.\n\n    This function supports loading data from a file path (str) or directly using an xarray DataArray.\n    It allows for selection of a specific date for visualization or averages over all dates if none is specified.\n\n    Args:\n        data (Union[str, xr.DataArray]): The chlorophyll-a data to visualize. Can be a file path or an xarray DataArray.\n        date (Optional[str], optional): Specific date to visualize. If None, averages over all dates. Defaults to None.\n        aspect (float, optional): Aspect ratio of the plot. Defaults to 2.\n        cmap (str, optional): Colormap for the plot. Defaults to \"jet\".\n        size (int, optional): Size of the plot. Defaults to 6.\n        **kwargs (Any): Additional keyword arguments to pass to `xarray.plot`.\n\n    Returns:\n        xr.plot.facetgrid.FacetGrid: The plot generated from the chlorophyll-a data.\n\n    Raises:\n        ValueError: If `data` is not a file path (str) or an xarray DataArray.\n    \"\"\"\n    if isinstance(data, str):\n        data = read_pace_chla(data)\n    elif not isinstance(data, xr.DataArray):\n        raise ValueError(\"data must be an xarray DataArray\")\n\n    if date is not None:\n        data = data.sel(date=date)\n    else:\n        if \"date\" in data.coords:\n            data = data.mean(dim=\"date\")\n\n    return data.plot(aspect=aspect, cmap=cmap, size=size, **kwargs)\n</code></pre>"},{"location":"ui/","title":"ui module","text":"<p>This module contains the user interface for the hypercoast package.</p>"},{"location":"ui/#hypercoast.ui.SpectralWidget","title":"<code> SpectralWidget            (HBox)         </code>","text":"<p>A widget for spectral data visualization on a map.</p> <p>Attributes:</p> Name Type Description <code>_host_map</code> <code>Map</code> <p>The map to host the widget.</p> <code>on_close</code> <code>function</code> <p>Function to be called when the widget is closed.</p> <code>_output_widget</code> <code>widgets.Output</code> <p>The output widget to display results.</p> <code>_output_control</code> <code>ipyleaflet.WidgetControl</code> <p>The control for the output widget.</p> <code>_on_map_interaction</code> <code>function</code> <p>Function to handle map interactions.</p> <code>_spectral_widget</code> <code>SpectralWidget</code> <p>The spectral widget itself.</p> <code>_spectral_control</code> <code>ipyleaflet.WidgetControl</code> <p>The control for the spectral widget.</p> Source code in <code>hypercoast/ui.py</code> <pre><code>class SpectralWidget(widgets.HBox):\n    \"\"\"\n    A widget for spectral data visualization on a map.\n\n    Attributes:\n        _host_map (Map): The map to host the widget.\n        on_close (function): Function to be called when the widget is closed.\n        _output_widget (widgets.Output): The output widget to display results.\n        _output_control (ipyleaflet.WidgetControl): The control for the output widget.\n        _on_map_interaction (function): Function to handle map interactions.\n        _spectral_widget (SpectralWidget): The spectral widget itself.\n        _spectral_control (ipyleaflet.WidgetControl): The control for the spectral widget.\n    \"\"\"\n\n    def __init__(\n        self, host_map, stack=True, position=\"topright\", xlim=None, ylim=None, **kwargs\n    ):\n        \"\"\"\n        Initializes a new instance of the SpectralWidget class.\n\n        Args:\n            host_map (Map): The map to host the widget.\n            stack (bool, optional): Whether to stack the plots. Defaults to True.\n            position (str, optional): The position of the widget on the map. Defaults to \"topright\".\n            xlim (tuple, optional): The x-axis limits. Defaults to None.\n            ylim (tuple, optional): The y-axis limits. Defaults to None.\n        \"\"\"\n        self._host_map = host_map\n        self.on_close = None\n        self._stack = stack\n        self._show_plot = False\n\n        fig_margin = {\"top\": 20, \"bottom\": 35, \"left\": 50, \"right\": 20}\n        fig = plt.figure(\n            # title=None,\n            fig_margin=fig_margin,\n            layout={\"width\": \"500px\", \"height\": \"300px\"},\n        )\n\n        self._fig = fig\n        self._host_map._fig = fig\n\n        layer_names = list(host_map.cog_layer_dict.keys())\n        layers_widget = widgets.Dropdown(options=layer_names)\n        layers_widget.layout.width = \"18ex\"\n\n        close_btn = widgets.Button(\n            icon=\"times\",\n            tooltip=\"Close the widget\",\n            button_style=\"primary\",\n            layout=widgets.Layout(width=\"32px\"),\n        )\n\n        reset_btn = widgets.Button(\n            icon=\"trash\",\n            tooltip=\"Remove all markers\",\n            button_style=\"primary\",\n            layout=widgets.Layout(width=\"32px\"),\n        )\n\n        settings_btn = widgets.Button(\n            icon=\"gear\",\n            tooltip=\"Change layer settings\",\n            button_style=\"primary\",\n            layout=widgets.Layout(width=\"32px\"),\n        )\n\n        stack_btn = widgets.ToggleButton(\n            value=stack,\n            icon=\"area-chart\",\n            tooltip=\"Stack spectral signatures\",\n            button_style=\"primary\",\n            layout=widgets.Layout(width=\"32px\"),\n        )\n\n        def settings_btn_click(_):\n            self._host_map._add_layer_editor(\n                position=\"topright\",\n                layer_dict=self._host_map.cog_layer_dict[layers_widget.value],\n            )\n\n        settings_btn.on_click(settings_btn_click)\n\n        def reset_btn_click(_):\n            if hasattr(self._host_map, \"_plot_marker_cluster\"):\n                self._host_map._plot_marker_cluster.markers = []\n                self._host_map._plot_markers = []\n\n            if hasattr(self._host_map, \"_spectral_data\"):\n                self._host_map._spectral_data = {}\n\n            self._output_widget.clear_output()\n            self._show_plot = False\n            plt.clear()\n\n        reset_btn.on_click(reset_btn_click)\n\n        save_btn = widgets.Button(\n            icon=\"floppy-o\",\n            tooltip=\"Save the data to a CSV\",\n            button_style=\"primary\",\n            layout=widgets.Layout(width=\"32px\"),\n        )\n\n        def chooser_callback(chooser):\n            if chooser.selected:\n                file_path = chooser.selected\n                self._host_map.spectral_to_csv(file_path)\n                if (\n                    hasattr(self._host_map, \"_file_chooser_control\")\n                    and self._host_map._file_chooser_control in self._host_map.controls\n                ):\n                    self._host_map.remove_control(self._host_map._file_chooser_control)\n                    self._host_map._file_chooser.close()\n\n        def save_btn_click(_):\n            if not hasattr(self._host_map, \"_spectral_data\"):\n                return\n\n            self._output_widget.clear_output()\n            file_chooser = FileChooser(\n                os.getcwd(), layout=widgets.Layout(width=\"454px\")\n            )\n            file_chooser.filter_pattern = \"*.csv\"\n            file_chooser.use_dir_icons = True\n            file_chooser.title = \"Save spectral data to a CSV file\"\n            file_chooser.default_filename = \"spectral_data.csv\"\n            file_chooser.show_hidden = False\n            file_chooser.register_callback(chooser_callback)\n            file_chooser_control = ipyleaflet.WidgetControl(\n                widget=file_chooser, position=\"topright\"\n            )\n            self._host_map.add(file_chooser_control)\n            setattr(self._host_map, \"_file_chooser\", file_chooser)\n            setattr(self._host_map, \"_file_chooser_control\", file_chooser_control)\n\n        save_btn.on_click(save_btn_click)\n\n        def close_widget(_):\n            self.cleanup()\n\n        close_btn.on_click(close_widget)\n\n        super().__init__(\n            [layers_widget, settings_btn, stack_btn, reset_btn, save_btn, close_btn]\n        )\n\n        output = widgets.Output()\n        output_control = ipyleaflet.WidgetControl(widget=output, position=\"bottomright\")\n        self._output_widget = output\n        self._output_control = output_control\n        self._host_map.add(output_control)\n\n        if not hasattr(self._host_map, \"_spectral_data\"):\n            self._host_map._spectral_data = {}\n\n        def handle_interaction(**kwargs):\n\n            latlon = kwargs.get(\"coordinates\")\n            lat = latlon[0]\n            lon = latlon[1]\n            if kwargs.get(\"type\") == \"click\" and self._host_map._layer_editor is None:\n                layer_name = layers_widget.value\n\n                if not hasattr(self._host_map, \"_plot_markers\"):\n                    self._host_map._plot_markers = []\n                markers = self._host_map._plot_markers\n                marker_cluster = self._host_map._plot_marker_cluster\n                markers.append(ipyleaflet.Marker(location=latlon, draggable=False))\n                marker_cluster.markers = markers\n                self._host_map._plot_marker_cluster = marker_cluster\n\n                xlabel = \"Wavelength (nm)\"\n                ylabel = \"Reflectance\"\n\n                ds = self._host_map.cog_layer_dict[layer_name][\"xds\"]\n\n                if self._host_map.cog_layer_dict[layer_name][\"hyper\"] == \"XARRAY\":\n                    da = extract_spectral(ds, lat, lon)\n                    xlabel = \"Band\"\n                    ylabel = \"Value\"\n\n                elif self._host_map.cog_layer_dict[layer_name][\"hyper\"] == \"EMIT\":\n                    da = ds.sel(latitude=lat, longitude=lon, method=\"nearest\")[\n                        \"reflectance\"\n                    ]\n\n                    if \"wavelength\" not in self._host_map._spectral_data:\n                        self._host_map._spectral_data[\"wavelength\"] = ds[\n                            \"wavelength\"\n                        ].values\n                elif self._host_map.cog_layer_dict[layer_name][\"hyper\"] == \"PACE\":\n                    try:\n                        da = extract_pace(ds, lat, lon)\n                    except:\n                        da = xr.DataArray(\n                            np.full(len(ds[\"wavelength\"]), np.nan),\n                            dims=[\"wavelength\"],\n                            coords={\"wavelength\": ds[\"wavelength\"]},\n                        )\n                    if \"wavelengths\" not in self._host_map._spectral_data:\n                        self._host_map._spectral_data[\"wavelengths\"] = ds[\n                            \"wavelength\"\n                        ].values\n\n                elif self._host_map.cog_layer_dict[layer_name][\"hyper\"] == \"DESIS\":\n                    da = extract_desis(ds, lat, lon)\n\n                elif self._host_map.cog_layer_dict[layer_name][\"hyper\"] == \"NEON\":\n                    da = extract_neon(ds, lat, lon)\n\n                elif self._host_map.cog_layer_dict[layer_name][\"hyper\"] == \"AVIRIS\":\n                    da = extract_aviris(ds, lat, lon)\n\n                self._host_map._spectral_data[f\"({lat:.4f} {lon:.4f})\"] = da.values\n\n                if self._host_map.cog_layer_dict[layer_name][\"hyper\"] != \"XARRAY\":\n                    da[da &lt; 0] = np.nan\n                    x_axis_options = {\"label_offset\": \"30px\"}\n                else:\n                    x_axis_options = {\n                        \"label_offset\": \"30px\",\n                        \"tick_format\": \"0d\",\n                        \"num_ticks\": da.sizes[\"band\"],\n                    }\n                axes_options = {\n                    \"x\": x_axis_options,\n                    \"y\": {\"label_offset\": \"35px\"},\n                }\n\n                if not stack_btn.value:\n                    plt.clear()\n                    plt.plot(\n                        da.coords[da.dims[0]].values,\n                        da.values,\n                        axes_options=axes_options,\n                    )\n                else:\n                    color = np.random.rand(\n                        3,\n                    )\n                    plt.plot(\n                        da.coords[da.dims[0]].values,\n                        da.values,\n                        color=color,\n                        axes_options=axes_options,\n                    )\n                    try:\n                        if isinstance(self._fig.axes[0], bqplot.ColorAxis):\n                            self._fig.axes = self._fig.axes[1:]\n                        elif isinstance(self._fig.axes[-1], bqplot.ColorAxis):\n                            self._fig.axes = self._fig.axes[:-1]\n                    except Exception:\n                        pass\n\n                plt.xlabel(xlabel)\n                plt.ylabel(ylabel)\n                if xlim:\n                    plt.xlim(xlim[0], xlim[1])\n                if ylim:\n                    plt.ylim(ylim[0], ylim[1])\n\n                if not self._show_plot:\n                    with self._output_widget:\n                        plt.show()\n                        self._show_plot = True\n\n                self._host_map.default_style = {\"cursor\": \"crosshair\"}\n\n        self._host_map.on_interaction(handle_interaction)\n        self._on_map_interaction = handle_interaction\n\n        self._spectral_widget = self\n        self._spectral_control = ipyleaflet.WidgetControl(\n            widget=self, position=position\n        )\n        self._host_map.add(self._spectral_control)\n\n    def cleanup(self):\n        \"\"\"Removes the widget from the map and performs cleanup.\"\"\"\n        if self._host_map:\n            self._host_map.default_style = {\"cursor\": \"default\"}\n            self._host_map.on_interaction(self._on_map_interaction, remove=True)\n\n            if self._output_control:\n                self._host_map.remove_control(self._output_control)\n\n                if self._output_widget:\n                    self._output_widget.close()\n                    self._output_widget = None\n\n            if self._spectral_control:\n                self._host_map.remove_control(self._spectral_control)\n                self._spectral_control = None\n\n                if self._spectral_widget:\n                    self._spectral_widget.close()\n                    self._spectral_widget = None\n\n            if hasattr(self._host_map, \"_plot_marker_cluster\"):\n                self._host_map._plot_marker_cluster.markers = []\n                self._host_map._plot_markers = []\n\n            if hasattr(self._host_map, \"_spectral_data\"):\n                self._host_map._spectral_data = {}\n\n            if hasattr(self, \"_output_widget\") and self._output_widget is not None:\n                self._output_widget.clear_output()\n\n        if self.on_close is not None:\n            self.on_close()\n</code></pre>"},{"location":"ui/#hypercoast.ui.SpectralWidget.__init__","title":"<code>__init__(self, host_map, stack=True, position='topright', xlim=None, ylim=None, **kwargs)</code>  <code>special</code>","text":"<p>Initializes a new instance of the SpectralWidget class.</p> <p>Parameters:</p> Name Type Description Default <code>host_map</code> <code>Map</code> <p>The map to host the widget.</p> required <code>stack</code> <code>bool</code> <p>Whether to stack the plots. Defaults to True.</p> <code>True</code> <code>position</code> <code>str</code> <p>The position of the widget on the map. Defaults to \"topright\".</p> <code>'topright'</code> <code>xlim</code> <code>tuple</code> <p>The x-axis limits. Defaults to None.</p> <code>None</code> <code>ylim</code> <code>tuple</code> <p>The y-axis limits. Defaults to None.</p> <code>None</code> Source code in <code>hypercoast/ui.py</code> <pre><code>def __init__(\n    self, host_map, stack=True, position=\"topright\", xlim=None, ylim=None, **kwargs\n):\n    \"\"\"\n    Initializes a new instance of the SpectralWidget class.\n\n    Args:\n        host_map (Map): The map to host the widget.\n        stack (bool, optional): Whether to stack the plots. Defaults to True.\n        position (str, optional): The position of the widget on the map. Defaults to \"topright\".\n        xlim (tuple, optional): The x-axis limits. Defaults to None.\n        ylim (tuple, optional): The y-axis limits. Defaults to None.\n    \"\"\"\n    self._host_map = host_map\n    self.on_close = None\n    self._stack = stack\n    self._show_plot = False\n\n    fig_margin = {\"top\": 20, \"bottom\": 35, \"left\": 50, \"right\": 20}\n    fig = plt.figure(\n        # title=None,\n        fig_margin=fig_margin,\n        layout={\"width\": \"500px\", \"height\": \"300px\"},\n    )\n\n    self._fig = fig\n    self._host_map._fig = fig\n\n    layer_names = list(host_map.cog_layer_dict.keys())\n    layers_widget = widgets.Dropdown(options=layer_names)\n    layers_widget.layout.width = \"18ex\"\n\n    close_btn = widgets.Button(\n        icon=\"times\",\n        tooltip=\"Close the widget\",\n        button_style=\"primary\",\n        layout=widgets.Layout(width=\"32px\"),\n    )\n\n    reset_btn = widgets.Button(\n        icon=\"trash\",\n        tooltip=\"Remove all markers\",\n        button_style=\"primary\",\n        layout=widgets.Layout(width=\"32px\"),\n    )\n\n    settings_btn = widgets.Button(\n        icon=\"gear\",\n        tooltip=\"Change layer settings\",\n        button_style=\"primary\",\n        layout=widgets.Layout(width=\"32px\"),\n    )\n\n    stack_btn = widgets.ToggleButton(\n        value=stack,\n        icon=\"area-chart\",\n        tooltip=\"Stack spectral signatures\",\n        button_style=\"primary\",\n        layout=widgets.Layout(width=\"32px\"),\n    )\n\n    def settings_btn_click(_):\n        self._host_map._add_layer_editor(\n            position=\"topright\",\n            layer_dict=self._host_map.cog_layer_dict[layers_widget.value],\n        )\n\n    settings_btn.on_click(settings_btn_click)\n\n    def reset_btn_click(_):\n        if hasattr(self._host_map, \"_plot_marker_cluster\"):\n            self._host_map._plot_marker_cluster.markers = []\n            self._host_map._plot_markers = []\n\n        if hasattr(self._host_map, \"_spectral_data\"):\n            self._host_map._spectral_data = {}\n\n        self._output_widget.clear_output()\n        self._show_plot = False\n        plt.clear()\n\n    reset_btn.on_click(reset_btn_click)\n\n    save_btn = widgets.Button(\n        icon=\"floppy-o\",\n        tooltip=\"Save the data to a CSV\",\n        button_style=\"primary\",\n        layout=widgets.Layout(width=\"32px\"),\n    )\n\n    def chooser_callback(chooser):\n        if chooser.selected:\n            file_path = chooser.selected\n            self._host_map.spectral_to_csv(file_path)\n            if (\n                hasattr(self._host_map, \"_file_chooser_control\")\n                and self._host_map._file_chooser_control in self._host_map.controls\n            ):\n                self._host_map.remove_control(self._host_map._file_chooser_control)\n                self._host_map._file_chooser.close()\n\n    def save_btn_click(_):\n        if not hasattr(self._host_map, \"_spectral_data\"):\n            return\n\n        self._output_widget.clear_output()\n        file_chooser = FileChooser(\n            os.getcwd(), layout=widgets.Layout(width=\"454px\")\n        )\n        file_chooser.filter_pattern = \"*.csv\"\n        file_chooser.use_dir_icons = True\n        file_chooser.title = \"Save spectral data to a CSV file\"\n        file_chooser.default_filename = \"spectral_data.csv\"\n        file_chooser.show_hidden = False\n        file_chooser.register_callback(chooser_callback)\n        file_chooser_control = ipyleaflet.WidgetControl(\n            widget=file_chooser, position=\"topright\"\n        )\n        self._host_map.add(file_chooser_control)\n        setattr(self._host_map, \"_file_chooser\", file_chooser)\n        setattr(self._host_map, \"_file_chooser_control\", file_chooser_control)\n\n    save_btn.on_click(save_btn_click)\n\n    def close_widget(_):\n        self.cleanup()\n\n    close_btn.on_click(close_widget)\n\n    super().__init__(\n        [layers_widget, settings_btn, stack_btn, reset_btn, save_btn, close_btn]\n    )\n\n    output = widgets.Output()\n    output_control = ipyleaflet.WidgetControl(widget=output, position=\"bottomright\")\n    self._output_widget = output\n    self._output_control = output_control\n    self._host_map.add(output_control)\n\n    if not hasattr(self._host_map, \"_spectral_data\"):\n        self._host_map._spectral_data = {}\n\n    def handle_interaction(**kwargs):\n\n        latlon = kwargs.get(\"coordinates\")\n        lat = latlon[0]\n        lon = latlon[1]\n        if kwargs.get(\"type\") == \"click\" and self._host_map._layer_editor is None:\n            layer_name = layers_widget.value\n\n            if not hasattr(self._host_map, \"_plot_markers\"):\n                self._host_map._plot_markers = []\n            markers = self._host_map._plot_markers\n            marker_cluster = self._host_map._plot_marker_cluster\n            markers.append(ipyleaflet.Marker(location=latlon, draggable=False))\n            marker_cluster.markers = markers\n            self._host_map._plot_marker_cluster = marker_cluster\n\n            xlabel = \"Wavelength (nm)\"\n            ylabel = \"Reflectance\"\n\n            ds = self._host_map.cog_layer_dict[layer_name][\"xds\"]\n\n            if self._host_map.cog_layer_dict[layer_name][\"hyper\"] == \"XARRAY\":\n                da = extract_spectral(ds, lat, lon)\n                xlabel = \"Band\"\n                ylabel = \"Value\"\n\n            elif self._host_map.cog_layer_dict[layer_name][\"hyper\"] == \"EMIT\":\n                da = ds.sel(latitude=lat, longitude=lon, method=\"nearest\")[\n                    \"reflectance\"\n                ]\n\n                if \"wavelength\" not in self._host_map._spectral_data:\n                    self._host_map._spectral_data[\"wavelength\"] = ds[\n                        \"wavelength\"\n                    ].values\n            elif self._host_map.cog_layer_dict[layer_name][\"hyper\"] == \"PACE\":\n                try:\n                    da = extract_pace(ds, lat, lon)\n                except:\n                    da = xr.DataArray(\n                        np.full(len(ds[\"wavelength\"]), np.nan),\n                        dims=[\"wavelength\"],\n                        coords={\"wavelength\": ds[\"wavelength\"]},\n                    )\n                if \"wavelengths\" not in self._host_map._spectral_data:\n                    self._host_map._spectral_data[\"wavelengths\"] = ds[\n                        \"wavelength\"\n                    ].values\n\n            elif self._host_map.cog_layer_dict[layer_name][\"hyper\"] == \"DESIS\":\n                da = extract_desis(ds, lat, lon)\n\n            elif self._host_map.cog_layer_dict[layer_name][\"hyper\"] == \"NEON\":\n                da = extract_neon(ds, lat, lon)\n\n            elif self._host_map.cog_layer_dict[layer_name][\"hyper\"] == \"AVIRIS\":\n                da = extract_aviris(ds, lat, lon)\n\n            self._host_map._spectral_data[f\"({lat:.4f} {lon:.4f})\"] = da.values\n\n            if self._host_map.cog_layer_dict[layer_name][\"hyper\"] != \"XARRAY\":\n                da[da &lt; 0] = np.nan\n                x_axis_options = {\"label_offset\": \"30px\"}\n            else:\n                x_axis_options = {\n                    \"label_offset\": \"30px\",\n                    \"tick_format\": \"0d\",\n                    \"num_ticks\": da.sizes[\"band\"],\n                }\n            axes_options = {\n                \"x\": x_axis_options,\n                \"y\": {\"label_offset\": \"35px\"},\n            }\n\n            if not stack_btn.value:\n                plt.clear()\n                plt.plot(\n                    da.coords[da.dims[0]].values,\n                    da.values,\n                    axes_options=axes_options,\n                )\n            else:\n                color = np.random.rand(\n                    3,\n                )\n                plt.plot(\n                    da.coords[da.dims[0]].values,\n                    da.values,\n                    color=color,\n                    axes_options=axes_options,\n                )\n                try:\n                    if isinstance(self._fig.axes[0], bqplot.ColorAxis):\n                        self._fig.axes = self._fig.axes[1:]\n                    elif isinstance(self._fig.axes[-1], bqplot.ColorAxis):\n                        self._fig.axes = self._fig.axes[:-1]\n                except Exception:\n                    pass\n\n            plt.xlabel(xlabel)\n            plt.ylabel(ylabel)\n            if xlim:\n                plt.xlim(xlim[0], xlim[1])\n            if ylim:\n                plt.ylim(ylim[0], ylim[1])\n\n            if not self._show_plot:\n                with self._output_widget:\n                    plt.show()\n                    self._show_plot = True\n\n            self._host_map.default_style = {\"cursor\": \"crosshair\"}\n\n    self._host_map.on_interaction(handle_interaction)\n    self._on_map_interaction = handle_interaction\n\n    self._spectral_widget = self\n    self._spectral_control = ipyleaflet.WidgetControl(\n        widget=self, position=position\n    )\n    self._host_map.add(self._spectral_control)\n</code></pre>"},{"location":"ui/#hypercoast.ui.SpectralWidget.cleanup","title":"<code>cleanup(self)</code>","text":"<p>Removes the widget from the map and performs cleanup.</p> Source code in <code>hypercoast/ui.py</code> <pre><code>def cleanup(self):\n    \"\"\"Removes the widget from the map and performs cleanup.\"\"\"\n    if self._host_map:\n        self._host_map.default_style = {\"cursor\": \"default\"}\n        self._host_map.on_interaction(self._on_map_interaction, remove=True)\n\n        if self._output_control:\n            self._host_map.remove_control(self._output_control)\n\n            if self._output_widget:\n                self._output_widget.close()\n                self._output_widget = None\n\n        if self._spectral_control:\n            self._host_map.remove_control(self._spectral_control)\n            self._spectral_control = None\n\n            if self._spectral_widget:\n                self._spectral_widget.close()\n                self._spectral_widget = None\n\n        if hasattr(self._host_map, \"_plot_marker_cluster\"):\n            self._host_map._plot_marker_cluster.markers = []\n            self._host_map._plot_markers = []\n\n        if hasattr(self._host_map, \"_spectral_data\"):\n            self._host_map._spectral_data = {}\n\n        if hasattr(self, \"_output_widget\") and self._output_widget is not None:\n            self._output_widget.clear_output()\n\n    if self.on_close is not None:\n        self.on_close()\n</code></pre>"},{"location":"usage/","title":"Usage","text":""},{"location":"usage/#import-library","title":"Import library","text":"<p>To use HyperCoast in a project:</p> <pre><code>import hypercoast\n</code></pre>"},{"location":"usage/#search-for-datasets","title":"Search for datasets","text":"<p>To download and access NASA hyperspectral data, you will need to create an Earthdata login. You can register for an account at urs.earthdata.nasa.gov. Once you have an account, run the following code to log in:</p> <pre><code>hypercoast.nasa_earth_login()\n</code></pre> <p>Collections on NASA Earthdata are discovered with the search_datasets function, which accepts an instrument filter as an easy way to get started. Each of the items in the list of collections returned has a \"short-name\". For example, to search for all datasets with the instrument \"oci\":</p> <pre><code>results = hypercoast.search_datasets(instrument=\"oci\")\ndatasets = set()\nfor item in results:\n    summary = item.summary()\n    short_name = summary[\"short-name\"]\n    if short_name not in datasets:\n        print(short_name)\n    datasets.add(short_name)\nprint(f\"\\nFound {len(datasets)} unique datasets\")\n</code></pre>"},{"location":"usage/#search-for-data-by-short-name","title":"Search for data by short name","text":"<p>Next, we use the <code>search_nasa_data</code> function to find granules within a collection. Let's use the short_name for the PACE/OCI Level-2 data product for bio-optical and biogeochemical properties.</p> <pre><code>results = hypercoast.search_nasa_data(\n    short_name=\"PACE_OCI_L2_BGC_NRT\",\n    count=1,\n)\n</code></pre> <p>We can refine our search by passing more parameters that describe the spatiotemporal domain of our use case. Here, we use the temporal parameter to request a date range and the bounding_box parameter to request granules that intersect with a bounding box. We can even provide a cloud_cover threshold to limit files that have a lower percentage of cloud cover. We do not provide a count, so we'll get all granules that satisfy the constraints.</p> <pre><code>tspan = (\"2024-04-01\", \"2024-04-16\")\nbbox = (-76.75, 36.97, -75.74, 39.01)\nclouds = (0, 50)\n\nresults, gdf = hypercoast.search_nasa_data(\n    short_name=\"PACE_OCI_L2_BGC_NRT\",\n    temporal=tspan,\n    bounding_box=bbox,\n    cloud_cover=clouds,\n    return_gdf=True,\n)\n</code></pre> <p>Display the footprints of the granules that match the search criteria.</p> <pre><code>gdf.explore()\n</code></pre> <p>We can also download all the results with one command.</p> <pre><code>hypercoast.download_nasa_data(results, out_dir=\"data\")\n</code></pre>"},{"location":"usage/#search-for-pace-data","title":"Search for PACE data","text":"<p>To search for PACE data, we can use the <code>search_pace</code> function:</p> <pre><code>results, gdf = hypercoast.search_pace(\n    bounding_box=(-83, 25, -81, 28),\n    temporal=(\"2024-05-10\", \"2024-05-16\"),\n    count=10,  # use -1 to return all datasets\n    return_gdf=True,\n)\n</code></pre> <p>To download the PACE data, we can use the <code>download_pace</code> function:</p> <pre><code>hypercoast.download_pace(results, out_dir=\"data\")\n</code></pre>"},{"location":"usage/#search-for-emit-data","title":"Search for EMIT data","text":"<p>To search for EMIT data, we can use the <code>search_emit</code> function:</p> <pre><code>results, gdf = hypercoast.search_emit(\n    bounding_box=(-83, 25, -81, 28),\n    temporal=(\"2024-04-01\", \"2024-05-16\"),\n    count=10,  # use -1 to return all datasets\n    return_gdf=True,\n)\n</code></pre> <p>To download the EMIT data, we can use the <code>download_emit</code> function:</p> <pre><code>hypercoast.download_emit(results, out_dir=\"data\")\n</code></pre>"},{"location":"usage/#visualize-pace-data","title":"Visualize PACE data","text":"<p>Load the dataset as a <code>xarray.Dataset</code> object:</p> <pre><code>dataset = hypercoast.read_pace(filepath)\n</code></pre> <p>Visualize selected bands of the dataset:</p> <pre><code>hypercoast.viz_pace(dataset, wavelengths=[500, 510, 520, 530], ncols=2, crs=\"default\")\n</code></pre> <p>Visualize the dataset on an interactive map:</p> <pre><code>m = hypercoast.Map()\nm.add_basemap(\"Hybrid\")\nwavelengths = [450]\nm.add_pace(dataset, wavelengths, colormap=\"jet\", vmin=0, vmax=0.02, layer_name=\"PACE\")\nm.add_colormap(cmap=\"jet\", vmin=0, vmax=0.02, label=\"Reflectance\")\nm.add(\"spectral\")\nm\n</code></pre>"},{"location":"usage/#visualize-emit-data","title":"Visualize EMIT data","text":"<p>To visualize EMIT data, we can use the <code>read_emit</code> function:</p> <pre><code>dataset = hypercoast.read_emit(filepath)\n</code></pre> <p>Visualize the dataset on an interactive map:</p> <pre><code>m = hypercoast.Map()\nm.add_basemap(\"SATELLITE\")\nm.add_emit(dataset, wavelengths=[1000, 600, 500], vmin=0, vmax=0.3, layer_name=\"EMIT\")\nm.add(\"spectral\")\nm\n</code></pre>"},{"location":"usage/#create-an-image-cube","title":"Create an image cube","text":"<p>First , load the dataset as a <code>xarray.Dataset</code> object. Select a region of interest (ROI) using the <code>sel</code> method:</p> <pre><code>dataset = hypercoast.read_emit(filepath)\nds = dataset.sel(longitude=slice(-90.1482, -89.7321), latitude=slice(30.0225, 29.7451))\n</code></pre> <p>Create an image cube using the <code>image_cube</code> function:</p> <pre><code>cube = hypercoast.image_cube(\n    ds,\n    variable=\"reflectance\",\n    cmap=\"jet\",\n    clim=(0, 0.4),\n    rgb_wavelengths=[1000, 700, 500],\n    rgb_gamma=2,\n    title=\"EMIT Reflectance\",\n)\ncube.show()\n</code></pre>"},{"location":"usage/#interactive-slicing-and-thresholding","title":"Interactive slicing and thresholding","text":"<p>First , load the dataset as a <code>xarray.Dataset</code> object. Select a region of interest (ROI) using the <code>sel</code> method:</p> <pre><code>dataset = hypercoast.read_emit(filepath)\nds = dataset.sel(longitude=slice(-90.05, -89.99), latitude=slice(30.00, 29.93))\n</code></pre> <p>Interactive slicing along the z-axis (band):</p> <pre><code>p = hypercoast.image_cube(\n    ds,\n    variable=\"reflectance\",\n    cmap=\"jet\",\n    clim=(0, 0.5),\n    rgb_wavelengths=[1000, 700, 500],\n    rgb_gamma=2,\n    title=\"EMIT Reflectance\",\n    widget=\"plane\",\n)\np.add_text(\"Band slicing\", position=\"upper_right\", font_size=14)\np.show()\n</code></pre> <p>Interactive thresholding:</p> <pre><code>p = hypercoast.image_cube(\n    ds,\n    variable=\"reflectance\",\n    cmap=\"jet\",\n    clim=(0, 0.5),\n    rgb_wavelengths=[1000, 700, 500],\n    rgb_gamma=2,\n    title=\"EMIT Reflectance\",\n    widget=\"threshold\",\n)\np.add_text(\"Thresholding\", position=\"upper_right\", font_size=14)\np.show()\n</code></pre>"},{"location":"usage/#visualizing-pace-chlorophyll-a-concentration-data","title":"Visualizing PACE chlorophyll-a concentration data","text":"<p>Load all the data files in a directory as an <code>xarray.DataArray</code>:</p> <pre><code>files = \"data/*nc\"\narray = hypercoast.read_pace_chla(files)\n</code></pre> <p>Select a date and visualize the chlorophyll-a concentration data with Matplotlib.</p> <pre><code>hypercoast.viz_pace_chla(array, date=\"2024-06-01\", cmap=\"jet\", size=6)\n</code></pre> <p>If the date is not specified, the data are averaged over the entire time range.</p> <pre><code>hypercoast.viz_pace_chla(array, cmap=\"jet\", size=6)\n</code></pre> <p>Convert the data array to an image that can be displayed on an interactive map.</p> <pre><code>single_image = hypercoast.pace_chla_to_image(single_array)\n</code></pre> <p>Create an interactive map and display the image on the map.</p> <pre><code>m = hypercoast.Map(center=[40, -100], zoom=4)\nm.add_basemap(\"Hybrid\")\nm.add_raster(\n    single_image,\n    cmap=\"jet\",\n    vmin=-1,\n    vmax=2,\n    layer_name=\"Chlorophyll a\",\n    zoom_to_layer=False,\n)\nlabel = \"Chlorophyll Concentration [lg(lg(mg m^-3))]\"\nm.add_colormap(cmap=\"jet\", vmin=-1, vmax=2, label=label)\nm\n</code></pre>"},{"location":"examples/_earthaccess/","title":"earthaccess","text":"In\u00a0[1]: Copied! <pre># %pip install \"hypercoast[extra]\"\n</pre> # %pip install \"hypercoast[extra]\" In\u00a0[2]: Copied! <pre>import os\nimport earthaccess\nimport hypercoast\n</pre> import os import earthaccess import hypercoast In\u00a0[3]: Copied! <pre># os.environ[\"EARTHDATA_USERNAME\"] = \"your_username\"\n# os.environ[\"EARTHDATA_PASSWORD\"] = \"your_password\"\n</pre> # os.environ[\"EARTHDATA_USERNAME\"] = \"your_username\" # os.environ[\"EARTHDATA_PASSWORD\"] = \"your_password\" In\u00a0[4]: Copied! <pre>if os.environ.get(\"EARTHDATA_USERNAME\") is None:\n    raise ValueError(\"Please set the EARTHDATA_USERNAME environment variable\")\nif os.environ.get(\"EARTHDATA_PASSWORD\") is None:\n    raise ValueError(\"Please set the EARTHDATA_PASSWORD environment variable\")\n</pre> if os.environ.get(\"EARTHDATA_USERNAME\") is None:     raise ValueError(\"Please set the EARTHDATA_USERNAME environment variable\") if os.environ.get(\"EARTHDATA_PASSWORD\") is None:     raise ValueError(\"Please set the EARTHDATA_PASSWORD environment variable\") In\u00a0[5]: Copied! <pre>earthaccess.login(persist=True)\n</pre> earthaccess.login(persist=True) Out[5]: <pre>&lt;earthaccess.auth.Auth at 0x7f26e46a4cb0&gt;</pre> In\u00a0[6]: Copied! <pre>results, gdf = hypercoast.search_pace(\n    bounding_box=(-83, 25, -81, 28),\n    temporal=(\"2024-07-30\", \"2024-08-15\"),\n    short_name=\"PACE_OCI_L2_AOP_NRT\",\n    count=10,\n    return_gdf=True,\n)\n</pre> results, gdf = hypercoast.search_pace(     bounding_box=(-83, 25, -81, 28),     temporal=(\"2024-07-30\", \"2024-08-15\"),     short_name=\"PACE_OCI_L2_AOP_NRT\",     count=10,     return_gdf=True, ) In\u00a0[7]: Copied! <pre>hypercoast.download_pace(results[:1], out_dir=\"data\")\n</pre> hypercoast.download_pace(results[:1], out_dir=\"data\") In\u00a0[8]: Copied! <pre>filepath = \"data/PACE_OCI.20240730T181157.L2.OC_AOP.V2_0.NRT.nc\"\nif not os.path.exists(filepath):\n    raise FileNotFoundError(filepath)\n</pre> filepath = \"data/PACE_OCI.20240730T181157.L2.OC_AOP.V2_0.NRT.nc\" if not os.path.exists(filepath):     raise FileNotFoundError(filepath)"},{"location":"examples/_earthaccess/#testing-earthaccess-login","title":"Testing earthaccess login\u00b6","text":""},{"location":"examples/acolite/","title":"Acolite","text":"In\u00a0[\u00a0]: Copied! <pre># %pip install \"hypercoast[extra]\"\n</pre> # %pip install \"hypercoast[extra]\" In\u00a0[\u00a0]: Copied! <pre>import os\nimport hypercoast\n</pre> import os import hypercoast In\u00a0[\u00a0]: Copied! <pre>url = \"https://github.com/opengeos/datasets/releases/download/hypercoast/S2A_MSIL1C_20160920T164452_N0204_R083_T15RYN_20160920T164450.SAFE.zip\"\nwork_dir = os.path.expanduser(\"~/Downloads\")\nfilepath = os.path.join(work_dir, os.path.basename(url))\nhypercoast.download_file(url, filepath, quiet=True)\n</pre> url = \"https://github.com/opengeos/datasets/releases/download/hypercoast/S2A_MSIL1C_20160920T164452_N0204_R083_T15RYN_20160920T164450.SAFE.zip\" work_dir = os.path.expanduser(\"~/Downloads\") filepath = os.path.join(work_dir, os.path.basename(url)) hypercoast.download_file(url, filepath, quiet=True) In\u00a0[\u00a0]: Copied! <pre>input_dir = filepath.replace(\".zip\", \"\")\nif not os.path.exists(input_dir):\n    raise FileNotFoundError(f\"Directory {input_dir} not found\")\n</pre> input_dir = filepath.replace(\".zip\", \"\") if not os.path.exists(input_dir):     raise FileNotFoundError(f\"Directory {input_dir} not found\") In\u00a0[\u00a0]: Copied! <pre>acolite_dir = hypercoast.download_acolite(work_dir)\nprint(f\"Acolite directory: {acolite_dir}\")\n</pre> acolite_dir = hypercoast.download_acolite(work_dir) print(f\"Acolite directory: {acolite_dir}\") In\u00a0[\u00a0]: Copied! <pre>out_dir = os.path.join(work_dir, \"output\")\n</pre> out_dir = os.path.join(work_dir, \"output\") In\u00a0[\u00a0]: Copied! <pre>hypercoast.run_acolite(\n    acolite_dir=acolite_dir,\n    input_file=input_dir,\n    out_dir=out_dir,\n    l2w_parameters=\"Rrs_*,chl_oc3,chl_re_mishra,spm_nechad2016\",\n    rgb_rhot=True,\n    rgb_rhos=False,\n    map_l2w=True,\n)\n</pre> hypercoast.run_acolite(     acolite_dir=acolite_dir,     input_file=input_dir,     out_dir=out_dir,     l2w_parameters=\"Rrs_*,chl_oc3,chl_re_mishra,spm_nechad2016\",     rgb_rhot=True,     rgb_rhos=False,     map_l2w=True, ) In\u00a0[\u00a0]: Copied! <pre>input_dir = os.path.join(work_dir, \"data\")\ninput_files = [os.path.join(input_dir, f) for f in os.listdir(input_dir)]\ninput_files\n</pre> input_dir = os.path.join(work_dir, \"data\") input_files = [os.path.join(input_dir, f) for f in os.listdir(input_dir)] input_files <p>Run the following code to process all images in the <code>data</code> folder.</p> In\u00a0[\u00a0]: Copied! <pre>hypercoast.run_acolite(\n    acolite_dir=acolite_dir,\n    input_file=input_files,\n    out_dir=out_dir,\n    l2w_parameters=\"Rrs_*,chl_oc3,chl_re_mishra,spm_nechad2016\",\n    rgb_rhot=True,\n    rgb_rhos=False,\n    map_l2w=True,\n)\n</pre> hypercoast.run_acolite(     acolite_dir=acolite_dir,     input_file=input_files,     out_dir=out_dir,     l2w_parameters=\"Rrs_*,chl_oc3,chl_re_mishra,spm_nechad2016\",     rgb_rhot=True,     rgb_rhos=False,     map_l2w=True, )"},{"location":"examples/acolite/#atmospheric-correction-with-acolite","title":"Atmospheric Correction with Acolite\u00b6","text":"<p>Acolite can perform atmospheric correction on a variety of satellite sensors, including Landsat, Sentinel-2, PACE, EMIT, AVIRIS, among others. For more information on how to use Acolite, please refer to the Acolite manual</p> <p>In this example, we will use Acolite to perform atmospheric correction on a Sentinel-2 image.</p>"},{"location":"examples/acolite/#import-libraries","title":"Import libraries\u00b6","text":""},{"location":"examples/acolite/#download-sample-data","title":"Download sample data\u00b6","text":""},{"location":"examples/acolite/#download-acolite-software","title":"Download Acolite software\u00b6","text":""},{"location":"examples/acolite/#run-acolite","title":"Run Acolite\u00b6","text":""},{"location":"examples/acolite/#batch-processing","title":"Batch processing\u00b6","text":"<p>To process multiple images, put all the images in a folder. For example, unzip all the images in the <code>data</code> folder. Then, run the following code to make sure that all image folders are listed.</p>"},{"location":"examples/aviris/","title":"Aviris","text":"In\u00a0[\u00a0]: Copied! <pre># %pip install \"hypercoast[extra]\"\n</pre> # %pip install \"hypercoast[extra]\" In\u00a0[\u00a0]: Copied! <pre>import hypercoast\n</pre> import hypercoast <p>Download sample dataset from Access the AVIRIS-NG L2 Surface Reflectance product page via ORNL DAAC https://daac.ornl.gov/cgi-bin/dsviewer.pl?ds_id=1988</p> <p></p> <p>Download <code>ang20210401t150456_rfl_v2z1.zip</code> and unzip it.</p> <p></p> <p>The dataset contains 2 files: <code>ang20210401t150456_rfl_v2z1</code> and <code>ang20210401t150456_rfl_v2z1.hdr</code>. We will use the <code>ang20210401t150456_rfl_v2z1</code> file in this notebook.</p> In\u00a0[\u00a0]: Copied! <pre>filepath = \"ang20210401t150456_rfl_v2z1\"\n</pre> filepath = \"ang20210401t150456_rfl_v2z1\" <p>Read the AVIRIS data as an <code>xarray.Dataset</code> object.</p> In\u00a0[\u00a0]: Copied! <pre>ds = hypercoast.read_aviris(filepath)\nds\n</pre> ds = hypercoast.read_aviris(filepath) ds <p>Create an interactive map.</p> In\u00a0[\u00a0]: Copied! <pre>m = hypercoast.Map()\nm\n</pre> m = hypercoast.Map() m <p>Add the AVIRIS data to the map.</p> In\u00a0[\u00a0]: Copied! <pre>m.add_aviris(ds, wavelengths=[1000, 700, 400], vmin=0, vmax=0.2)\nm.add(\"spectral\")\n</pre> m.add_aviris(ds, wavelengths=[1000, 700, 400], vmin=0, vmax=0.2) m.add(\"spectral\") <p></p>"},{"location":"examples/aviris/#visualizing-aviris-data-interactively-with-hypercoast","title":"Visualizing AVIRIS data interactively with HyperCoast\u00b6","text":"<p>This notebook demonstrates how to visualize AVIRIS hyperspectral data interactively with HyperCoast. For more information about AVIRIS, please visit the links below:</p> <ul> <li>https://aviris.jpl.nasa.gov/</li> <li>https://aviris.jpl.nasa.gov/dataportal/</li> <li>https://popo.jpl.nasa.gov/mmgis-aviris/?s=ujooa</li> <li>https://daac.ornl.gov/cgi-bin/dsviewer.pl?ds_id=1988</li> <li>https://github.com/ornldaac/deltax_workshop_2022/tree/main</li> <li>https://github.com/jjmcnelis/aviris-ng-notebooks/tree/master</li> </ul>"},{"location":"examples/chlorophyll_a/","title":"Chlorophyll a","text":"In\u00a0[1]: Copied! <pre># %pip install \"hypercoast[extra]\"\n</pre> # %pip install \"hypercoast[extra]\" In\u00a0[2]: Copied! <pre>import hypercoast\n</pre> import hypercoast <p>To download PACE data, you will need to create an Earthdata login. You can register for an account at urs.earthdata.nasa.gov. Once you have an account, you can uncomment the code below to search and download the data.</p> In\u00a0[3]: Copied! <pre># hypercoast.nasa_earth_login()\n# temporal = (\"2024-06-01\", \"2024-07-01\")\n# results= hypercoast.search_pace_chla(temporal=temporal)\n# hypercoast.download_nasa_data(results, \"chla\")\n</pre> # hypercoast.nasa_earth_login() # temporal = (\"2024-06-01\", \"2024-07-01\") # results= hypercoast.search_pace_chla(temporal=temporal) # hypercoast.download_nasa_data(results, \"chla\") <p>Alternatively, you can download some sample data from here.</p> In\u00a0[4]: Copied! <pre>url = \"https://github.com/opengeos/datasets/releases/download/hypercoast/pace_chla.zip\"\nhypercoast.download_file(url)\n</pre> url = \"https://github.com/opengeos/datasets/releases/download/hypercoast/pace_chla.zip\" hypercoast.download_file(url) Out[4]: <pre>'/home/runner/work/HyperCoast/HyperCoast/docs/examples/pace_chla.zip'</pre> <p>The downloaded zip file is automatically extracted and saved in the <code>chla</code> directory, which contains 17 daily files of chlorophyll-a concentration data in the netCDF format. The date range of the data is from 2024-06-01 to 2024-06-17.</p> In\u00a0[5]: Copied! <pre>files = \"chla/*nc\"\n</pre> files = \"chla/*nc\" <p>Load all the data files in the <code>chla</code> directory as an xarray DataArray</p> In\u00a0[6]: Copied! <pre>array = hypercoast.read_pace_chla(files)\narray\n</pre> array = hypercoast.read_pace_chla(files) array Out[6]: <pre>&lt;xarray.DataArray 'chlor_a' (lat: 1800, lon: 3600, date: 17)&gt; Size: 441MB\ndask.array&lt;transpose, shape=(1800, 3600, 17), dtype=float32, chunksize=(512, 1024, 1), chunktype=numpy.ndarray&gt;\nCoordinates:\n  * lat          (lat) float32 7kB 89.95 89.85 89.75 ... -89.75 -89.85 -89.95\n  * lon          (lon) float32 14kB -179.9 -179.9 -179.8 ... 179.8 179.9 180.0\n  * date         (date) &lt;U10 680B '2024-06-01' '2024-06-02' ... '2024-06-17'\n    spatial_ref  int64 8B 0\nAttributes:\n    long_name:      Chlorophyll Concentration, OCI Algorithm\n    units:          lg(mg m^-3)\n    standard_name:  mass_concentration_of_chlorophyll_in_sea_water\n    valid_min:      0.001\n    valid_max:      100.0\n    reference:      Hu, C., Lee Z., and Franz, B.A. (2012). Chlorophyll-a alg...\n    display_scale:  log\n    display_min:    0.01\n    display_max:    20.0\n    date:           ['2024-06-01', '2024-06-02', '2024-06-03', '2024-06-04', ...</pre>xarray.DataArray'chlor_a'<ul><li>lat: 1800</li><li>lon: 3600</li><li>date: 17</li></ul><ul><li>dask.array&lt;chunksize=(512, 1024, 1), meta=np.ndarray&gt;  Array   Chunk   Bytes   420.23 MiB   2.00 MiB   Shape   (1800, 3600, 17)   (512, 1024, 1)   Dask graph   272 chunks in 54 graph layers   Data type   float32 numpy.ndarray  17 3600 1800 </li><li>Coordinates: (4)<ul><li>lat(lat)float3289.95 89.85 89.75 ... -89.85 -89.95long_name :Latitudeunits :degrees_northstandard_name :latitudevalid_min :-90.0valid_max :90.0<pre>array([ 89.95    ,  89.85    ,  89.75    , ..., -89.75    , -89.850006,\n       -89.950005], dtype=float32)</pre></li><li>lon(lon)float32-179.9 -179.9 ... 179.9 180.0long_name :Longitudeunits :degrees_eaststandard_name :longitudevalid_min :-180.0valid_max :180.0<pre>array([-179.95   , -179.85   , -179.75   , ...,  179.75   ,  179.85   ,\n        179.95001], dtype=float32)</pre></li><li>date(date)&lt;U10'2024-06-01' ... '2024-06-17'<pre>array(['2024-06-01', '2024-06-02', '2024-06-03', '2024-06-04', '2024-06-05',\n       '2024-06-06', '2024-06-07', '2024-06-08', '2024-06-09', '2024-06-10',\n       '2024-06-11', '2024-06-12', '2024-06-13', '2024-06-14', '2024-06-15',\n       '2024-06-16', '2024-06-17'], dtype='&lt;U10')</pre></li><li>spatial_ref()int640crs_wkt :GEOGCS[\"WGS 84\",DATUM[\"WGS_1984\",SPHEROID[\"WGS 84\",6378137,298.257223563,AUTHORITY[\"EPSG\",\"7030\"]],AUTHORITY[\"EPSG\",\"6326\"]],PRIMEM[\"Greenwich\",0,AUTHORITY[\"EPSG\",\"8901\"]],UNIT[\"degree\",0.0174532925199433,AUTHORITY[\"EPSG\",\"9122\"]],AXIS[\"Latitude\",NORTH],AXIS[\"Longitude\",EAST],AUTHORITY[\"EPSG\",\"4326\"]]semi_major_axis :6378137.0semi_minor_axis :6356752.314245179inverse_flattening :298.257223563reference_ellipsoid_name :WGS 84longitude_of_prime_meridian :0.0prime_meridian_name :Greenwichgeographic_crs_name :WGS 84horizontal_datum_name :World Geodetic System 1984grid_mapping_name :latitude_longitudespatial_ref :GEOGCS[\"WGS 84\",DATUM[\"WGS_1984\",SPHEROID[\"WGS 84\",6378137,298.257223563,AUTHORITY[\"EPSG\",\"7030\"]],AUTHORITY[\"EPSG\",\"6326\"]],PRIMEM[\"Greenwich\",0,AUTHORITY[\"EPSG\",\"8901\"]],UNIT[\"degree\",0.0174532925199433,AUTHORITY[\"EPSG\",\"9122\"]],AXIS[\"Latitude\",NORTH],AXIS[\"Longitude\",EAST],AUTHORITY[\"EPSG\",\"4326\"]]<pre>array(0)</pre></li></ul></li><li>Indexes: (3)<ul><li>latPandasIndex<pre>PandasIndex(Index([ 89.94999694824219,   89.8499984741211,              89.75,\n         89.6500015258789,  89.55000305175781,  89.44999694824219,\n         89.3499984741211,              89.25,   89.1500015258789,\n        89.05000305175781,\n       ...\n       -89.05000305175781,  -89.1500015258789,             -89.25,\n       -89.35000610351562, -89.45000457763672, -89.55000305175781,\n        -89.6500015258789,             -89.75, -89.85000610351562,\n       -89.95000457763672],\n      dtype='float32', name='lat', length=1800))</pre></li><li>lonPandasIndex<pre>PandasIndex(Index([ -179.9499969482422, -179.85000610351562,             -179.75,\n       -179.64999389648438,  -179.5500030517578,  -179.4499969482422,\n       -179.35000610351562,             -179.25, -179.14999389648438,\n        -179.0500030517578,\n       ...\n         179.0500030517578,  179.15000915527344,              179.25,\n        179.35000610351562,  179.45001220703125,   179.5500030517578,\n        179.65000915527344,              179.75,  179.85000610351562,\n        179.95001220703125],\n      dtype='float32', name='lon', length=3600))</pre></li><li>datePandasIndex<pre>PandasIndex(Index(['2024-06-01', '2024-06-02', '2024-06-03', '2024-06-04', '2024-06-05',\n       '2024-06-06', '2024-06-07', '2024-06-08', '2024-06-09', '2024-06-10',\n       '2024-06-11', '2024-06-12', '2024-06-13', '2024-06-14', '2024-06-15',\n       '2024-06-16', '2024-06-17'],\n      dtype='object', name='date'))</pre></li></ul></li><li>Attributes: (10)long_name :Chlorophyll Concentration, OCI Algorithmunits :lg(mg m^-3)standard_name :mass_concentration_of_chlorophyll_in_sea_watervalid_min :0.001valid_max :100.0reference :Hu, C., Lee Z., and Franz, B.A. (2012). Chlorophyll-a algorithms for oligotrophic oceans: A novel approach based on three-band reflectance difference, J. Geophys. Res., 117, C01011, doi:10.1029/2011JC007395.display_scale :logdisplay_min :0.01display_max :20.0date :['2024-06-01', '2024-06-02', '2024-06-03', '2024-06-04', '2024-06-05', '2024-06-06', '2024-06-07', '2024-06-08', '2024-06-09', '2024-06-10', '2024-06-11', '2024-06-12', '2024-06-13', '2024-06-14', '2024-06-15', '2024-06-16', '2024-06-17']</li></ul> <p>Select a date and visualize the chlorophyll-a concentration data with Matplotlib.</p> In\u00a0[7]: Copied! <pre>hypercoast.viz_pace_chla(array, date=\"2024-06-01\", cmap=\"jet\", size=6)\n</pre> hypercoast.viz_pace_chla(array, date=\"2024-06-01\", cmap=\"jet\", size=6) Out[7]: <pre>&lt;matplotlib.collections.QuadMesh at 0x7fc7244e82f0&gt;</pre> <p>If the date is not specified, the data are averaged over the entire time range.</p> In\u00a0[8]: Copied! <pre>hypercoast.viz_pace_chla(array, cmap=\"jet\", size=6)\n</pre> hypercoast.viz_pace_chla(array, cmap=\"jet\", size=6) Out[8]: <pre>&lt;matplotlib.collections.QuadMesh at 0x7fc723ebbfb0&gt;</pre> <p>To visualize the data interactively, we can select either a single date or aggregate the data over a time range.</p> <p>First, let's select a single date from the data array:</p> In\u00a0[9]: Copied! <pre>single_array = array.sel(date=\"2024-06-01\")\nsingle_array\n</pre> single_array = array.sel(date=\"2024-06-01\") single_array Out[9]: <pre>&lt;xarray.DataArray 'chlor_a' (lat: 1800, lon: 3600)&gt; Size: 26MB\ndask.array&lt;getitem, shape=(1800, 3600), dtype=float32, chunksize=(512, 1024), chunktype=numpy.ndarray&gt;\nCoordinates:\n  * lat          (lat) float32 7kB 89.95 89.85 89.75 ... -89.75 -89.85 -89.95\n  * lon          (lon) float32 14kB -179.9 -179.9 -179.8 ... 179.8 179.9 180.0\n    date         &lt;U10 40B '2024-06-01'\n    spatial_ref  int64 8B 0\nAttributes:\n    long_name:      Chlorophyll Concentration, OCI Algorithm\n    units:          lg(mg m^-3)\n    standard_name:  mass_concentration_of_chlorophyll_in_sea_water\n    valid_min:      0.001\n    valid_max:      100.0\n    reference:      Hu, C., Lee Z., and Franz, B.A. (2012). Chlorophyll-a alg...\n    display_scale:  log\n    display_min:    0.01\n    display_max:    20.0\n    date:           ['2024-06-01', '2024-06-02', '2024-06-03', '2024-06-04', ...</pre>xarray.DataArray'chlor_a'<ul><li>lat: 1800</li><li>lon: 3600</li></ul><ul><li>dask.array&lt;chunksize=(512, 1024), meta=np.ndarray&gt;  Array   Chunk   Bytes   24.72 MiB   2.00 MiB   Shape   (1800, 3600)   (512, 1024)   Dask graph   16 chunks in 55 graph layers   Data type   float32 numpy.ndarray  3600 1800 </li><li>Coordinates: (4)<ul><li>lat(lat)float3289.95 89.85 89.75 ... -89.85 -89.95long_name :Latitudeunits :degrees_northstandard_name :latitudevalid_min :-90.0valid_max :90.0<pre>array([ 89.95    ,  89.85    ,  89.75    , ..., -89.75    , -89.850006,\n       -89.950005], dtype=float32)</pre></li><li>lon(lon)float32-179.9 -179.9 ... 179.9 180.0long_name :Longitudeunits :degrees_eaststandard_name :longitudevalid_min :-180.0valid_max :180.0<pre>array([-179.95   , -179.85   , -179.75   , ...,  179.75   ,  179.85   ,\n        179.95001], dtype=float32)</pre></li><li>date()&lt;U10'2024-06-01'<pre>array('2024-06-01', dtype='&lt;U10')</pre></li><li>spatial_ref()int640crs_wkt :GEOGCS[\"WGS 84\",DATUM[\"WGS_1984\",SPHEROID[\"WGS 84\",6378137,298.257223563,AUTHORITY[\"EPSG\",\"7030\"]],AUTHORITY[\"EPSG\",\"6326\"]],PRIMEM[\"Greenwich\",0,AUTHORITY[\"EPSG\",\"8901\"]],UNIT[\"degree\",0.0174532925199433,AUTHORITY[\"EPSG\",\"9122\"]],AXIS[\"Latitude\",NORTH],AXIS[\"Longitude\",EAST],AUTHORITY[\"EPSG\",\"4326\"]]semi_major_axis :6378137.0semi_minor_axis :6356752.314245179inverse_flattening :298.257223563reference_ellipsoid_name :WGS 84longitude_of_prime_meridian :0.0prime_meridian_name :Greenwichgeographic_crs_name :WGS 84horizontal_datum_name :World Geodetic System 1984grid_mapping_name :latitude_longitudespatial_ref :GEOGCS[\"WGS 84\",DATUM[\"WGS_1984\",SPHEROID[\"WGS 84\",6378137,298.257223563,AUTHORITY[\"EPSG\",\"7030\"]],AUTHORITY[\"EPSG\",\"6326\"]],PRIMEM[\"Greenwich\",0,AUTHORITY[\"EPSG\",\"8901\"]],UNIT[\"degree\",0.0174532925199433,AUTHORITY[\"EPSG\",\"9122\"]],AXIS[\"Latitude\",NORTH],AXIS[\"Longitude\",EAST],AUTHORITY[\"EPSG\",\"4326\"]]<pre>array(0)</pre></li></ul></li><li>Indexes: (2)<ul><li>latPandasIndex<pre>PandasIndex(Index([ 89.94999694824219,   89.8499984741211,              89.75,\n         89.6500015258789,  89.55000305175781,  89.44999694824219,\n         89.3499984741211,              89.25,   89.1500015258789,\n        89.05000305175781,\n       ...\n       -89.05000305175781,  -89.1500015258789,             -89.25,\n       -89.35000610351562, -89.45000457763672, -89.55000305175781,\n        -89.6500015258789,             -89.75, -89.85000610351562,\n       -89.95000457763672],\n      dtype='float32', name='lat', length=1800))</pre></li><li>lonPandasIndex<pre>PandasIndex(Index([ -179.9499969482422, -179.85000610351562,             -179.75,\n       -179.64999389648438,  -179.5500030517578,  -179.4499969482422,\n       -179.35000610351562,             -179.25, -179.14999389648438,\n        -179.0500030517578,\n       ...\n         179.0500030517578,  179.15000915527344,              179.25,\n        179.35000610351562,  179.45001220703125,   179.5500030517578,\n        179.65000915527344,              179.75,  179.85000610351562,\n        179.95001220703125],\n      dtype='float32', name='lon', length=3600))</pre></li></ul></li><li>Attributes: (10)long_name :Chlorophyll Concentration, OCI Algorithmunits :lg(mg m^-3)standard_name :mass_concentration_of_chlorophyll_in_sea_watervalid_min :0.001valid_max :100.0reference :Hu, C., Lee Z., and Franz, B.A. (2012). Chlorophyll-a algorithms for oligotrophic oceans: A novel approach based on three-band reflectance difference, J. Geophys. Res., 117, C01011, doi:10.1029/2011JC007395.display_scale :logdisplay_min :0.01display_max :20.0date :['2024-06-01', '2024-06-02', '2024-06-03', '2024-06-04', '2024-06-05', '2024-06-06', '2024-06-07', '2024-06-08', '2024-06-09', '2024-06-10', '2024-06-11', '2024-06-12', '2024-06-13', '2024-06-14', '2024-06-15', '2024-06-16', '2024-06-17']</li></ul> <p>Convert the data array to an image that can be displayed on an interactive map.</p> In\u00a0[10]: Copied! <pre>single_image = hypercoast.pace_chla_to_image(single_array)\n</pre> single_image = hypercoast.pace_chla_to_image(single_array) <p>Create an interactive map and display the image on the map.</p> In\u00a0[11]: Copied! <pre>m = hypercoast.Map(center=[40, -100], zoom=4)\nm.add_basemap(\"Hybrid\")\nm.add_raster(\n    single_image,\n    cmap=\"jet\",\n    vmin=-1,\n    vmax=2,\n    layer_name=\"Chlorophyll a\",\n    zoom_to_layer=False,\n)\nlabel = \"Chlorophyll Concentration [lg(lg(mg m^-3))]\"\nm.add_colormap(cmap=\"jet\", vmin=-1, vmax=2, label=label)\nm\n</pre> m = hypercoast.Map(center=[40, -100], zoom=4) m.add_basemap(\"Hybrid\") m.add_raster(     single_image,     cmap=\"jet\",     vmin=-1,     vmax=2,     layer_name=\"Chlorophyll a\",     zoom_to_layer=False, ) label = \"Chlorophyll Concentration [lg(lg(mg m^-3))]\" m.add_colormap(cmap=\"jet\", vmin=-1, vmax=2, label=label) m Out[11]: <p></p> <p>The daily image does not have a global coverage. To visualize the data globally, we can aggregate the data over a time range.</p> In\u00a0[12]: Copied! <pre>mean_array = array.mean(dim=\"date\")\n</pre> mean_array = array.mean(dim=\"date\") <p>Convert the aggregated data array to an image that can be displayed on an interactive map.</p> In\u00a0[13]: Copied! <pre>image = hypercoast.pace_chla_to_image(mean_array)\n</pre> image = hypercoast.pace_chla_to_image(mean_array) <p>Create an interactive map and display the image on the map.</p> In\u00a0[14]: Copied! <pre>m = hypercoast.Map(center=[40, -100], zoom=4)\nm.add_basemap(\"Hybrid\")\nm.add_raster(\n    image, cmap=\"jet\", vmin=-1, vmax=2, layer_name=\"Chlorophyll a\", zoom_to_layer=False\n)\nlabel = \"Chlorophyll Concentration [lg(lg(mg m^-3))]\"\nm.add_colormap(cmap=\"jet\", vmin=-1, vmax=2, label=label)\nm\n</pre> m = hypercoast.Map(center=[40, -100], zoom=4) m.add_basemap(\"Hybrid\") m.add_raster(     image, cmap=\"jet\", vmin=-1, vmax=2, layer_name=\"Chlorophyll a\", zoom_to_layer=False ) label = \"Chlorophyll Concentration [lg(lg(mg m^-3))]\" m.add_colormap(cmap=\"jet\", vmin=-1, vmax=2, label=label) m Out[14]: <p></p>"},{"location":"examples/chlorophyll_a/#visualizing-pace-chlorophyll-a-data-interactively-with-hypercoast","title":"Visualizing PACE chlorophyll-a data interactively with HyperCoast\u00b6","text":"<p>This notebook demonstrates how to visualize Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) data interactively with HyperCoast.</p>"},{"location":"examples/desis/","title":"Desis","text":"In\u00a0[1]: Copied! <pre># %pip install \"hypercoast[extra]\"\n</pre> # %pip install \"hypercoast[extra]\" In\u00a0[2]: Copied! <pre>import hypercoast\n</pre> import hypercoast In\u00a0[3]: Copied! <pre>url = \"https://github.com/opengeos/datasets/releases/download/hypercoast/desis.tif\"\nfilepath = \"data/desis.tif\"\nhypercoast.download_file(url, filepath)\n</pre> url = \"https://github.com/opengeos/datasets/releases/download/hypercoast/desis.tif\" filepath = \"data/desis.tif\" hypercoast.download_file(url, filepath) Out[3]: <pre>'/home/runner/work/HyperCoast/HyperCoast/docs/examples/data/desis.tif'</pre> <p>Load the dataset as a xarray.Dataset object.</p> In\u00a0[4]: Copied! <pre>dataset = hypercoast.read_desis(filepath)\n</pre> dataset = hypercoast.read_desis(filepath) <p>Plot the spectral signature of a pixel.</p> In\u00a0[5]: Copied! <pre>hypercoast.filter_desis(dataset, lat=29.4315, lon=91.2927, return_plot=True)\n</pre> hypercoast.filter_desis(dataset, lat=29.4315, lon=91.2927, return_plot=True) <p>Visualize a single band of the hyperspectral image.</p> In\u00a0[6]: Copied! <pre>m = hypercoast.Map()\nm.add_basemap(\"Hybrid\")\nm.add_desis(filepath, wavelengths=[1000], vmin=0, vmax=5000, nodata=0, colormap=\"jet\")\nm.add_colormap(cmap=\"jet\", vmin=0, vmax=0.5, label=\"Reflectance\")\nm\n</pre> m = hypercoast.Map() m.add_basemap(\"Hybrid\") m.add_desis(filepath, wavelengths=[1000], vmin=0, vmax=5000, nodata=0, colormap=\"jet\") m.add_colormap(cmap=\"jet\", vmin=0, vmax=0.5, label=\"Reflectance\") m Out[6]: <p></p> <p>Plot the spectral signature of a pixel interactively.</p> In\u00a0[7]: Copied! <pre>m = hypercoast.Map()\nm.add_basemap(\"Hybrid\")\nm.add_desis(filepath, wavelengths=[900, 600, 525], vmin=0, vmax=1000, nodata=0)\nm.add(\"spectral\")\nm\n</pre> m = hypercoast.Map() m.add_basemap(\"Hybrid\") m.add_desis(filepath, wavelengths=[900, 600, 525], vmin=0, vmax=1000, nodata=0) m.add(\"spectral\") m Out[7]: <p></p>"},{"location":"examples/desis/#visualizing-desis-data-interactively-with-hypercoast","title":"Visualizing DESIS data interactively with HyperCoast\u00b6","text":"<p>This notebook demonstrates how to visualize DESIS hyperspectral data interactively with HyperCoast.</p>"},{"location":"examples/ecostress/","title":"Ecostress","text":"In\u00a0[\u00a0]: Copied! <pre>import hypercoast\n</pre> import hypercoast In\u00a0[\u00a0]: Copied! <pre>hypercoast.nasa_earth_login()\n</pre> hypercoast.nasa_earth_login() In\u00a0[\u00a0]: Copied! <pre>results, gdf = hypercoast.search_ecostress(\n    bbox=(-120.522, 34.4266, -120.2665, 34.5653),\n    temporal=(\"2023-04-01\", \"2023-04-02\"),\n    count=-1,  # use -1 to return all datasets\n    return_gdf=True,\n)\n</pre> results, gdf = hypercoast.search_ecostress(     bbox=(-120.522, 34.4266, -120.2665, 34.5653),     temporal=(\"2023-04-01\", \"2023-04-02\"),     count=-1,  # use -1 to return all datasets     return_gdf=True, ) In\u00a0[\u00a0]: Copied! <pre>gdf.explore()\n</pre> gdf.explore() In\u00a0[\u00a0]: Copied! <pre>hypercoast.download_ecostress(results[:5], out_dir=\"data\")\n</pre> hypercoast.download_ecostress(results[:5], out_dir=\"data\") In\u00a0[\u00a0]: Copied! <pre>m = hypercoast.Map(center=[34.5014, -120.4032], zoom=11)\nm.search_ecostress()\nm\n</pre> m = hypercoast.Map(center=[34.5014, -120.4032], zoom=11) m.search_ecostress() m In\u00a0[\u00a0]: Copied! <pre># m._NASA_DATA_GDF.head()\n</pre> # m._NASA_DATA_GDF.head() In\u00a0[\u00a0]: Copied! <pre># hypercoast.download_ecostress(m._NASA_DATA_RESULTS[:2], out_dir=\"data\")\n</pre> # hypercoast.download_ecostress(m._NASA_DATA_RESULTS[:2], out_dir=\"data\") In\u00a0[\u00a0]: Copied! <pre>url = \"https://github.com/opengeos/datasets/releases/download/raster/ECOv002_L2T_LSTE_26860_001_10SGD_20230401T203733_0710_01_LST.tif\"\n</pre> url = \"https://github.com/opengeos/datasets/releases/download/raster/ECOv002_L2T_LSTE_26860_001_10SGD_20230401T203733_0710_01_LST.tif\" In\u00a0[\u00a0]: Copied! <pre>filepath = \"data/ECOv002_L2T_LSTE_26860_001_10SGD_20230401T203733_0710_01_LST.tif\"\nhypercoast.download_file(url, filepath)\n</pre> filepath = \"data/ECOv002_L2T_LSTE_26860_001_10SGD_20230401T203733_0710_01_LST.tif\" hypercoast.download_file(url, filepath) <p>Visualize the data with HyperCoast.</p> In\u00a0[\u00a0]: Copied! <pre>m = hypercoast.Map()\nm.add_basemap(\"HYBRID\")\nm.add_raster(filepath, colormap=\"jet\", layer_name=\"LST\")\nm.add(\"spectral\")\nm\n</pre> m = hypercoast.Map() m.add_basemap(\"HYBRID\") m.add_raster(filepath, colormap=\"jet\", layer_name=\"LST\") m.add(\"spectral\") m <p></p>"},{"location":"examples/ecostress/#search-and-download-nasa-ecostress-data-with-hypercoast","title":"Search and download NASA ECOSTRESS data with HyperCoast\u00b6","text":"<p>This notebook demonstrates how to search and visualize NASA ECOSTRESS temperature data with HyperCoast.</p>"},{"location":"examples/ecostress/#search-for-ecostress-data-programmatically","title":"Search for ECOSTRESS data programmatically\u00b6","text":""},{"location":"examples/ecostress/#download-ecostress-data","title":"Download ECOSTRESS data\u00b6","text":""},{"location":"examples/ecostress/#search-for-ecostress-data-interactively","title":"Search for ECOSTRESS data interactively\u00b6","text":""},{"location":"examples/ecostress/#visualize-ecostress-data","title":"Visualize ECOSTRESS data\u00b6","text":"<p>Download a sample ECOSTRESS data file and visualize it with HyperCoast.</p>"},{"location":"examples/emit/","title":"Emit","text":"In\u00a0[1]: Copied! <pre># %pip install hypercoast\n</pre> # %pip install hypercoast In\u00a0[2]: Copied! <pre>import hypercoast\n</pre> import hypercoast <p>Download a sample EMIT data file from here.</p> In\u00a0[3]: Copied! <pre>url = \"https://github.com/opengeos/datasets/releases/download/netcdf/EMIT_L2A_RFL_001_20240404T161230_2409511_009.nc\"\n</pre> url = \"https://github.com/opengeos/datasets/releases/download/netcdf/EMIT_L2A_RFL_001_20240404T161230_2409511_009.nc\" In\u00a0[4]: Copied! <pre>filepath = \"data/EMIT_L2A_RFL_001_20240404T161230_2409511_009.nc\"\nhypercoast.download_file(url, filepath)\n</pre> filepath = \"data/EMIT_L2A_RFL_001_20240404T161230_2409511_009.nc\" hypercoast.download_file(url, filepath) Out[4]: <pre>'/home/runner/work/HyperCoast/HyperCoast/docs/examples/data/EMIT_L2A_RFL_001_20240404T161230_2409511_009.nc'</pre> <p>Load the dataset as a <code>xarray.Dataset</code> object.</p> In\u00a0[5]: Copied! <pre>dataset = hypercoast.read_emit(filepath)\n</pre> dataset = hypercoast.read_emit(filepath) <p>Visualize the data interactively with HyperCoast. By default, the plot will show all the bands in the dataset.</p> In\u00a0[6]: Copied! <pre>m = hypercoast.Map()\nm.add_basemap(\"SATELLITE\")\nm.add_emit(dataset, wavelengths=[1000, 600, 500], vmin=0, vmax=0.3, layer_name=\"EMIT\")\nm.add(\"spectral\")\nm\n</pre> m = hypercoast.Map() m.add_basemap(\"SATELLITE\") m.add_emit(dataset, wavelengths=[1000, 600, 500], vmin=0, vmax=0.3, layer_name=\"EMIT\") m.add(\"spectral\") m Out[6]: <p>To visualize a certain wavelength range, you can specify the <code>xlim</code> parameter as follows:</p> In\u00a0[7]: Copied! <pre>m = hypercoast.Map()\nm.add_basemap(\"SATELLITE\")\nm.add_emit(dataset, wavelengths=[1000, 600, 500], vmin=0, vmax=0.3, layer_name=\"EMIT\")\nm.add(\"spectral\", xlim=(400, 1200))\nm\n</pre> m = hypercoast.Map() m.add_basemap(\"SATELLITE\") m.add_emit(dataset, wavelengths=[1000, 600, 500], vmin=0, vmax=0.3, layer_name=\"EMIT\") m.add(\"spectral\", xlim=(400, 1200)) m Out[7]: <p>To access the selected spectral profiles from the mouse clicked location as a Pandas DataFrame, use the <code>Map.spectral_to_df()</code> method.</p> In\u00a0[8]: Copied! <pre>m.spectral_to_df()\n</pre> m.spectral_to_df() Out[8]: <p>To access the selected spectral profiles from the mouse clicked location as a GeoPandas GeoDataFrame, use the <code>Map.spectral_to_gdf()</code> method.</p> In\u00a0[9]: Copied! <pre>m.spectral_to_gdf()\n</pre> m.spectral_to_gdf() Out[9]: <p></p>"},{"location":"examples/emit/#visualizing-emit-data-interactively-with-hypercoast","title":"Visualizing EMIT data interactively with HyperCoast\u00b6","text":"<p>This notebook demonstrates how to visualize Earth Surface Mineral Dust Source Investigation (EMIT) data interactively with HyperCoast. This notebook is inspired by the EMIT data visualization tutorial - Exploring_EMIT_L2A_Reflectance.ipynb. We have made it much easier to visualize the data interactively with HyperCoast.</p>"},{"location":"examples/field_data/","title":"Field data","text":"In\u00a0[1]: Copied! <pre># %pip install \"hypercoast[extra]\"\n</pre> # %pip install \"hypercoast[extra]\" In\u00a0[2]: Copied! <pre>import hypercoast\nimport pandas as pd\n</pre> import hypercoast import pandas as pd <p>Download a sample dataset.</p> In\u00a0[3]: Copied! <pre>url = \"https://github.com/opengeos/datasets/releases/download/hypercoast/pace_sample_points.csv\"\ndata = pd.read_csv(url)\ndata.head()\n</pre> url = \"https://github.com/opengeos/datasets/releases/download/hypercoast/pace_sample_points.csv\" data = pd.read_csv(url) data.head() Out[3]: band wavelength (30.1926 -90.1318) (30.1594 -90.2856) (29.3295 -92.3071) (28.8783 -90.4559) (30.5481 -87.9840) (29.5305 -85.0671) (28.8254 -85.6659) (29.4587 -83.8477) (26.8878 -87.7643) (24.6570 -86.4954) (26.8045 -82.4854) 0 0 339.0 NaN NaN 0.000100 NaN NaN NaN NaN NaN 0.001718 0.000710 NaN 1 1 341.0 NaN NaN NaN NaN NaN NaN NaN NaN 0.001554 0.000846 NaN 2 2 344.0 NaN NaN 0.001816 NaN NaN NaN NaN NaN 0.004047 0.003598 NaN 3 3 346.0 0.000346 NaN 0.002393 NaN NaN NaN NaN NaN 0.005045 0.004646 NaN 4 4 348.0 NaN NaN 0.001578 NaN NaN NaN NaN NaN 0.004258 0.003984 NaN <p>Download PACE data.</p> In\u00a0[4]: Copied! <pre>url = \"https://github.com/opengeos/datasets/releases/download/hypercoast/PACE_OCI.20240730T181157.L2.OC_AOP.V2_0.NRT.nc\"\nfilepath = \"data/PACE_OCI.20240730T181157.L2.OC_AOP.V2_0.NRT.nc\"\nhypercoast.download_file(url, filepath)\n</pre> url = \"https://github.com/opengeos/datasets/releases/download/hypercoast/PACE_OCI.20240730T181157.L2.OC_AOP.V2_0.NRT.nc\" filepath = \"data/PACE_OCI.20240730T181157.L2.OC_AOP.V2_0.NRT.nc\" hypercoast.download_file(url, filepath) <pre>data/PACE_OCI.20240730T181157.L2.OC_AOP.V2_0.NRT.nc already exists. Skip downloading. Set overwrite=True to overwrite.\n</pre> Out[4]: <pre>'/home/runner/work/HyperCoast/HyperCoast/docs/examples/data/PACE_OCI.20240730T181157.L2.OC_AOP.V2_0.NRT.nc'</pre> <p>Read the PACE dataset.</p> In\u00a0[5]: Copied! <pre>dataset = hypercoast.read_pace(filepath)\n</pre> dataset = hypercoast.read_pace(filepath) <p>Run the following cell to show the map. Click on the markers to see the spectral data.</p> In\u00a0[6]: Copied! <pre>m = hypercoast.Map(center=[27.235094, -87.791748], zoom=6)\n\nm.add_basemap(\"Hybrid\")\nwavelengths = [450, 550, 650]\nm.add_pace(\n    dataset, wavelengths, indexes=[3, 2, 1], vmin=0, vmax=0.02, layer_name=\"PACE\"\n)\nm.add(\"spectral\")\n\nm.add_field_data(\n    data,\n    x_col=\"wavelength\",\n    y_col_prefix=\"(\",\n    x_label=\"Wavelength (nm)\",\n    y_label=\"Reflectance\",\n    use_marker_cluster=True,\n)\nm.set_center(-87.791748, 27.235094, zoom=6)\nm\n</pre> m = hypercoast.Map(center=[27.235094, -87.791748], zoom=6)  m.add_basemap(\"Hybrid\") wavelengths = [450, 550, 650] m.add_pace(     dataset, wavelengths, indexes=[3, 2, 1], vmin=0, vmax=0.02, layer_name=\"PACE\" ) m.add(\"spectral\")  m.add_field_data(     data,     x_col=\"wavelength\",     y_col_prefix=\"(\",     x_label=\"Wavelength (nm)\",     y_label=\"Reflectance\",     use_marker_cluster=True, ) m.set_center(-87.791748, 27.235094, zoom=6) m Out[6]: <p></p>"},{"location":"examples/field_data/#visualizing-spectral-data-from-field-measurements","title":"Visualizing Spectral Data from Field Measurements\u00b6","text":"<p>This notebook demonstrates how to visualize spectral data from field measurements.</p>"},{"location":"examples/hypoxia/","title":"Hypoxia","text":"In\u00a0[1]: Copied! <pre># %pip install -U openpyxl hypercoast\n</pre> # %pip install -U openpyxl hypercoast In\u00a0[2]: Copied! <pre>import pandas as pd\nimport hypercoast\n</pre> import pandas as pd import hypercoast In\u00a0[3]: Copied! <pre>url = \"https://github.com/opengeos/datasets/releases/download/hypercoast/Hypoxia_Data_Sheet.xlsx\"\nxls_path = \"data/Hypoxia_Data_Sheet.xlsx\"\nhypercoast.download_file(url, xls_path, overwrite=True)\n</pre> url = \"https://github.com/opengeos/datasets/releases/download/hypercoast/Hypoxia_Data_Sheet.xlsx\" xls_path = \"data/Hypoxia_Data_Sheet.xlsx\" hypercoast.download_file(url, xls_path, overwrite=True) Out[3]: <pre>'/home/runner/work/HyperCoast/HyperCoast/docs/examples/data/Hypoxia_Data_Sheet.xlsx'</pre> In\u00a0[4]: Copied! <pre>df = pd.read_excel(xls_path)\ndf.head()\n</pre> df = pd.read_excel(xls_path) df.head() Out[4]: Station Station.1 Time Date Lon Lat Depth (m) Secchi (m) Salinity Water Temp ... Absorption CDOM LISST Nano Surface \\npH Sufface \\nO2 Bottom \\nO2 FL-ECO FL-CDOM Notes 0 River stations NaN NaN NaT NaN NaN NaN NaN NaN NaN ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 1 R1 St1 09:39:00 2024-07-21 -89.45114 28.89887 NaN 2 28.71 30.74161 ... 125ml yes yes no NaN NaN NaN NaN NaN River Mouth 2 R2 St2 09:47:00 2024-07-21 -89.45306 28.90001 NaN 2 25.91325 30.91475 ... 150ml yes yes yes NaN NaN NaN NaN NaN River plume, seaside 3 R3 St3 09:59:00 2024-07-21 -89.43833 28.89487 NaN 0.75 24.44862 30.59565 ... 100ml yes yes yes NaN NaN NaN NaN NaN NaN 4 R4 St4 10:13:00 2024-07-21 -89.43162 28.90630 NaN 0.5 8.34838 30.13989 ... 100ml yes yes yes NaN NaN NaN NaN NaN NaN <p>5 rows \u00d7 22 columns</p> In\u00a0[5]: Copied! <pre>df_filtered = df.dropna(subset=[\"Lon\", \"Lat\"]).reset_index(drop=True)\ndf_filtered.head()\n</pre> df_filtered = df.dropna(subset=[\"Lon\", \"Lat\"]).reset_index(drop=True) df_filtered.head() Out[5]: Station Station.1 Time Date Lon Lat Depth (m) Secchi (m) Salinity Water Temp ... Absorption CDOM LISST Nano Surface \\npH Sufface \\nO2 Bottom \\nO2 FL-ECO FL-CDOM Notes 0 R1 St1 09:39:00 2024-07-21 -89.45114 28.89887 NaN 2 28.71 30.74161 ... 125ml yes yes no NaN NaN NaN NaN NaN River Mouth 1 R2 St2 09:47:00 2024-07-21 -89.45306 28.90001 NaN 2 25.91325 30.91475 ... 150ml yes yes yes NaN NaN NaN NaN NaN River plume, seaside 2 R3 St3 09:59:00 2024-07-21 -89.43833 28.89487 NaN 0.75 24.44862 30.59565 ... 100ml yes yes yes NaN NaN NaN NaN NaN NaN 3 R4 St4 10:13:00 2024-07-21 -89.43162 28.90630 NaN 0.5 8.34838 30.13989 ... 100ml yes yes yes NaN NaN NaN NaN NaN NaN 4 R5 St5 10:58:00 2024-07-21 -89.37324 28.98095 NaN 0.5 2.4625 30.3054 ... 100ml yes yes yes NaN NaN NaN NaN NaN NaN <p>5 rows \u00d7 22 columns</p> In\u00a0[6]: Copied! <pre>url = (\n    \"https://github.com/opengeos/datasets/releases/download/hypercoast/Hypoxia_Path.kml\"\n)\nkml_path = \"data/Hypoxia_Path.kml\"\nhypercoast.download_file(url, kml_path)\n</pre> url = (     \"https://github.com/opengeos/datasets/releases/download/hypercoast/Hypoxia_Path.kml\" ) kml_path = \"data/Hypoxia_Path.kml\" hypercoast.download_file(url, kml_path) Out[6]: <pre>'/home/runner/work/HyperCoast/HyperCoast/docs/examples/data/Hypoxia_Path.kml'</pre> In\u00a0[7]: Copied! <pre>url = \"https://github.com/opengeos/datasets/releases/download/hypercoast/PACE_OCI.20240730T181157.L2.OC_AOP.V2_0.NRT.nc\"\nfilepath = \"data/PACE_OCI.20240730T181157.L2.OC_AOP.V2_0.NRT.nc\"\nhypercoast.download_file(url, filepath)\n</pre> url = \"https://github.com/opengeos/datasets/releases/download/hypercoast/PACE_OCI.20240730T181157.L2.OC_AOP.V2_0.NRT.nc\" filepath = \"data/PACE_OCI.20240730T181157.L2.OC_AOP.V2_0.NRT.nc\" hypercoast.download_file(url, filepath) <pre>data/PACE_OCI.20240730T181157.L2.OC_AOP.V2_0.NRT.nc already exists. Skip downloading. Set overwrite=True to overwrite.\n</pre> Out[7]: <pre>'/home/runner/work/HyperCoast/HyperCoast/docs/examples/data/PACE_OCI.20240730T181157.L2.OC_AOP.V2_0.NRT.nc'</pre> In\u00a0[8]: Copied! <pre>dataset = hypercoast.read_pace(filepath)\n# dataset\n</pre> dataset = hypercoast.read_pace(filepath) # dataset In\u00a0[9]: Copied! <pre>m = hypercoast.Map()\nm.add_basemap(\"Hybrid\")\nwavelengths = [450, 550, 650]\nm.add_pace(\n    dataset, wavelengths, indexes=[3, 2, 1], vmin=0, vmax=0.02, layer_name=\"PACE\"\n)\nm.add(\"spectral\")\nstyle = {\"weight\": 2, \"color\": \"red\"}\nm.add_kml(kml_path, style=style, layer_name=\"Hypoxia Path\", info_mode=None)\nm.add_points_from_xy(\n    df_filtered,\n    x=\"Lon\",\n    y=\"Lat\",\n    max_cluster_radius=50,\n    layer_name=\"Hypoxia Data Points\",\n)\nm.set_center(-91.46118, 28.89758, zoom=8)\nm\n</pre> m = hypercoast.Map() m.add_basemap(\"Hybrid\") wavelengths = [450, 550, 650] m.add_pace(     dataset, wavelengths, indexes=[3, 2, 1], vmin=0, vmax=0.02, layer_name=\"PACE\" ) m.add(\"spectral\") style = {\"weight\": 2, \"color\": \"red\"} m.add_kml(kml_path, style=style, layer_name=\"Hypoxia Path\", info_mode=None) m.add_points_from_xy(     df_filtered,     x=\"Lon\",     y=\"Lat\",     max_cluster_radius=50,     layer_name=\"Hypoxia Data Points\", ) m.set_center(-91.46118, 28.89758, zoom=8) m Out[9]: <p></p>"},{"location":"examples/hypoxia/#visualizing-hypoxia-cruise-sampling-locations-in-the-gulf-of-mexico","title":"Visualizing Hypoxia Cruise Sampling Locations in the Gulf of Mexico\u00b6","text":""},{"location":"examples/image_cube/","title":"Image cube","text":"In\u00a0[\u00a0]: Copied! <pre># %pip install \"hypercoast[extra]\"\n</pre> # %pip install \"hypercoast[extra]\" In\u00a0[\u00a0]: Copied! <pre>import hypercoast\n</pre> import hypercoast In\u00a0[\u00a0]: Copied! <pre>url = \"https://github.com/opengeos/datasets/releases/download/hypercoast/NEON_D02_SERC_DP3_368000_4306000_reflectance.h5\"\nfilepath = \"data/neon.h5\"\nhypercoast.download_file(url, filepath)\n</pre> url = \"https://github.com/opengeos/datasets/releases/download/hypercoast/NEON_D02_SERC_DP3_368000_4306000_reflectance.h5\" filepath = \"data/neon.h5\" hypercoast.download_file(url, filepath) <p>Load the dataset as a <code>xarray.Dataset</code> object.</p> In\u00a0[\u00a0]: Copied! <pre>dataset = hypercoast.read_neon(filepath)\ndataset\n</pre> dataset = hypercoast.read_neon(filepath) dataset <p>Visualize the NEON AOP hyperspectral data in 3D with a selected band overlaid on top of the 3D plot.</p> In\u00a0[\u00a0]: Copied! <pre>cube = hypercoast.image_cube(\n    dataset,\n    variable=\"reflectance\",\n    cmap=\"jet\",\n    clim=(0, 0.5),\n    rgb_wavelengths=[700],\n    title=\"Reflectance\",\n)\ncube.show()\n</pre> cube = hypercoast.image_cube(     dataset,     variable=\"reflectance\",     cmap=\"jet\",     clim=(0, 0.5),     rgb_wavelengths=[700],     title=\"Reflectance\", ) cube.show() <p>Visualize the NEON AOP hyperspectral data in 3D with an RGB image overlaid on top of the 3D plot.</p> In\u00a0[\u00a0]: Copied! <pre>cube2 = hypercoast.image_cube(\n    dataset,\n    variable=\"reflectance\",\n    cmap=\"jet\",\n    clim=(0, 0.5),\n    rgb_wavelengths=[1000, 700, 500],\n    rgb_gamma=2,\n    title=\"Reflectance\",\n)\ncube2.show()\n</pre> cube2 = hypercoast.image_cube(     dataset,     variable=\"reflectance\",     cmap=\"jet\",     clim=(0, 0.5),     rgb_wavelengths=[1000, 700, 500],     rgb_gamma=2,     title=\"Reflectance\", ) cube2.show() <p></p> In\u00a0[\u00a0]: Copied! <pre>url = \"https://github.com/opengeos/datasets/releases/download/netcdf/EMIT_L2A_RFL_001_20240404T161230_2409511_009.nc\"\nfilepath = \"EMIT_L2A_RFL_001_20240404T161230_2409511_009.nc\"\nhypercoast.download_file(url)\n</pre> url = \"https://github.com/opengeos/datasets/releases/download/netcdf/EMIT_L2A_RFL_001_20240404T161230_2409511_009.nc\" filepath = \"EMIT_L2A_RFL_001_20240404T161230_2409511_009.nc\" hypercoast.download_file(url) In\u00a0[\u00a0]: Copied! <pre>dataset = hypercoast.read_emit(filepath)\ndataset\n</pre> dataset = hypercoast.read_emit(filepath) dataset <p>Select a subset of the data to avoid nodata areas.</p> In\u00a0[\u00a0]: Copied! <pre>ds = dataset.sel(longitude=slice(-90.1482, -89.7321), latitude=slice(30.0225, 29.7451))\nds\n</pre> ds = dataset.sel(longitude=slice(-90.1482, -89.7321), latitude=slice(30.0225, 29.7451)) ds <p>Visualize the EMIT data in 3D with an RGB image overlaid on top of the 3D plot.</p> In\u00a0[\u00a0]: Copied! <pre>cube = hypercoast.image_cube(\n    ds,\n    variable=\"reflectance\",\n    cmap=\"jet\",\n    clim=(0, 0.4),\n    rgb_wavelengths=[1000, 700, 500],\n    rgb_gamma=2,\n    title=\"EMIT Reflectance\",\n)\ncube.show()\n</pre> cube = hypercoast.image_cube(     ds,     variable=\"reflectance\",     cmap=\"jet\",     clim=(0, 0.4),     rgb_wavelengths=[1000, 700, 500],     rgb_gamma=2,     title=\"EMIT Reflectance\", ) cube.show() <p></p>"},{"location":"examples/image_cube/#visualizing-hyperspectral-data-in-3d","title":"Visualizing Hyperspectral Data in 3D\u00b6","text":"<p>This notebook demonstrates how to visualize hyperspectral data in 3D using the PyVista plotting backend.</p>"},{"location":"examples/image_cube/#visualize-neon-aop-hyperspectral-data","title":"Visualize NEON AOP Hyperspectral Data\u00b6","text":"<p>Download a sample NEON AOP hyperspectral data.</p>"},{"location":"examples/image_cube/#visualize-nasa-emit-hyperspectral-data","title":"Visualize NASA EMIT Hyperspectral Data\u00b6","text":"<p>Download a sample EMIT data file from here.</p>"},{"location":"examples/image_slicing/","title":"Image slicing","text":"In\u00a0[\u00a0]: Copied! <pre># %pip install \"hypercoast[extra]\"\n</pre> # %pip install \"hypercoast[extra]\" In\u00a0[\u00a0]: Copied! <pre>import hypercoast\n</pre> import hypercoast In\u00a0[\u00a0]: Copied! <pre>url = \"https://github.com/opengeos/datasets/releases/download/hypercoast/NEON_D02_SERC_DP3_368000_4306000_reflectance.h5\"\nfilepath = \"data/neon.h5\"\nhypercoast.download_file(url, filepath)\n</pre> url = \"https://github.com/opengeos/datasets/releases/download/hypercoast/NEON_D02_SERC_DP3_368000_4306000_reflectance.h5\" filepath = \"data/neon.h5\" hypercoast.download_file(url, filepath) <p>Load the dataset as a <code>xarray.Dataset</code> object.</p> In\u00a0[\u00a0]: Copied! <pre>dataset = hypercoast.read_neon(filepath)\ndataset\n</pre> dataset = hypercoast.read_neon(filepath) dataset <p>Extract a small subset of the dataset for demonstration purposes.</p> In\u00a0[\u00a0]: Copied! <pre>ds = dataset.isel(x=slice(100, 200), y=slice(100, 200))\nds\n</pre> ds = dataset.isel(x=slice(100, 200), y=slice(100, 200)) ds <p>Interactive slicing along the z-axis (band)</p> In\u00a0[\u00a0]: Copied! <pre>p = hypercoast.image_cube(\n    ds,\n    variable=\"reflectance\",\n    cmap=\"jet\",\n    clim=(0, 0.5),\n    rgb_wavelengths=[1000, 700, 500],\n    rgb_gamma=2,\n    widget=\"slice\",\n)\np.add_text(\"Band slicing \", position=\"upper_right\", font_size=14)\np.show()\n</pre> p = hypercoast.image_cube(     ds,     variable=\"reflectance\",     cmap=\"jet\",     clim=(0, 0.5),     rgb_wavelengths=[1000, 700, 500],     rgb_gamma=2,     widget=\"slice\", ) p.add_text(\"Band slicing \", position=\"upper_right\", font_size=14) p.show() <p></p> <p>Interactive slicing along the x-axis (longitude).</p> In\u00a0[\u00a0]: Copied! <pre>p = hypercoast.image_cube(\n    ds,\n    variable=\"reflectance\",\n    cmap=\"jet\",\n    clim=(0, 0.5),\n    rgb_wavelengths=[1000, 700, 500],\n    rgb_gamma=2,\n    widget=\"slice\",\n    normal=\"x\",\n)\np.add_text(\"X-axis slicing \", position=\"upper_right\", font_size=14)\np.show()\n</pre> p = hypercoast.image_cube(     ds,     variable=\"reflectance\",     cmap=\"jet\",     clim=(0, 0.5),     rgb_wavelengths=[1000, 700, 500],     rgb_gamma=2,     widget=\"slice\",     normal=\"x\", ) p.add_text(\"X-axis slicing \", position=\"upper_right\", font_size=14) p.show() <p></p> <p>Orthogonal slicing.</p> In\u00a0[\u00a0]: Copied! <pre>p = hypercoast.image_cube(\n    ds,\n    variable=\"reflectance\",\n    cmap=\"jet\",\n    clim=(0, 0.5),\n    rgb_wavelengths=[1000, 700, 500],\n    rgb_gamma=2,\n    widget=\"orthogonal\",\n)\np.add_text(\"Orthogonal slicing\", position=\"upper_right\", font_size=14)\np.show()\n</pre> p = hypercoast.image_cube(     ds,     variable=\"reflectance\",     cmap=\"jet\",     clim=(0, 0.5),     rgb_wavelengths=[1000, 700, 500],     rgb_gamma=2,     widget=\"orthogonal\", ) p.add_text(\"Orthogonal slicing\", position=\"upper_right\", font_size=14) p.show() <p></p> <p>Clip the image cube with a plane (band slicing).</p> In\u00a0[\u00a0]: Copied! <pre>p = hypercoast.image_cube(\n    ds,\n    variable=\"reflectance\",\n    cmap=\"jet\",\n    clim=(0, 0.5),\n    rgb_wavelengths=[1000, 700, 500],\n    rgb_gamma=2,\n    widget=\"plane\",\n)\np.add_text(\"Band slicing\", position=\"upper_right\", font_size=14)\np.show()\n</pre> p = hypercoast.image_cube(     ds,     variable=\"reflectance\",     cmap=\"jet\",     clim=(0, 0.5),     rgb_wavelengths=[1000, 700, 500],     rgb_gamma=2,     widget=\"plane\", ) p.add_text(\"Band slicing\", position=\"upper_right\", font_size=14) p.show() <p></p> <p>Interactive thresholding.</p> In\u00a0[\u00a0]: Copied! <pre>p = hypercoast.image_cube(\n    ds,\n    variable=\"reflectance\",\n    cmap=\"jet\",\n    clim=(0, 0.5),\n    rgb_wavelengths=[1000, 700, 500],\n    rgb_gamma=2,\n    widget=\"threshold\",\n)\np.add_text(\"Thresholding\", position=\"upper_right\", font_size=14)\np.show()\n</pre> p = hypercoast.image_cube(     ds,     variable=\"reflectance\",     cmap=\"jet\",     clim=(0, 0.5),     rgb_wavelengths=[1000, 700, 500],     rgb_gamma=2,     widget=\"threshold\", ) p.add_text(\"Thresholding\", position=\"upper_right\", font_size=14) p.show() <p></p> In\u00a0[\u00a0]: Copied! <pre>url = \"https://github.com/opengeos/datasets/releases/download/netcdf/EMIT_L2A_RFL_001_20240404T161230_2409511_009.nc\"\nfilepath = \"data/EMIT_L2A_RFL_001_20240404T161230_2409511_009.nc\"\nhypercoast.download_file(url, filepath)\n</pre> url = \"https://github.com/opengeos/datasets/releases/download/netcdf/EMIT_L2A_RFL_001_20240404T161230_2409511_009.nc\" filepath = \"data/EMIT_L2A_RFL_001_20240404T161230_2409511_009.nc\" hypercoast.download_file(url, filepath) <p>Load the dataset as a <code>xarray.Dataset</code> object.</p> In\u00a0[\u00a0]: Copied! <pre>dataset = hypercoast.read_emit(filepath)\ndataset\n</pre> dataset = hypercoast.read_emit(filepath) dataset <p>Select a subset of the data for demonstration purposes.</p> In\u00a0[\u00a0]: Copied! <pre>ds = dataset.sel(longitude=slice(-90.05, -89.99), latitude=slice(30.00, 29.93))\nds\n</pre> ds = dataset.sel(longitude=slice(-90.05, -89.99), latitude=slice(30.00, 29.93)) ds <p>Interactive slicing along the z-axis (band).</p> In\u00a0[\u00a0]: Copied! <pre>p = hypercoast.image_cube(\n    ds,\n    variable=\"reflectance\",\n    cmap=\"jet\",\n    clim=(0, 0.5),\n    rgb_wavelengths=[1000, 700, 500],\n    rgb_gamma=2,\n    title=\"EMIT Reflectance\",\n    widget=\"plane\",\n)\np.add_text(\"Band slicing\", position=\"upper_right\", font_size=14)\np.show()\n</pre> p = hypercoast.image_cube(     ds,     variable=\"reflectance\",     cmap=\"jet\",     clim=(0, 0.5),     rgb_wavelengths=[1000, 700, 500],     rgb_gamma=2,     title=\"EMIT Reflectance\",     widget=\"plane\", ) p.add_text(\"Band slicing\", position=\"upper_right\", font_size=14) p.show() <p></p> <p>Interactive thresholding.</p> In\u00a0[\u00a0]: Copied! <pre>p = hypercoast.image_cube(\n    ds,\n    variable=\"reflectance\",\n    cmap=\"jet\",\n    clim=(0, 0.5),\n    rgb_wavelengths=[1000, 700, 500],\n    rgb_gamma=2,\n    title=\"EMIT Reflectance\",\n    widget=\"threshold\",\n)\np.add_text(\"Thresholding\", position=\"upper_right\", font_size=14)\np.show()\n</pre> p = hypercoast.image_cube(     ds,     variable=\"reflectance\",     cmap=\"jet\",     clim=(0, 0.5),     rgb_wavelengths=[1000, 700, 500],     rgb_gamma=2,     title=\"EMIT Reflectance\",     widget=\"threshold\", ) p.add_text(\"Thresholding\", position=\"upper_right\", font_size=14) p.show() <p></p>"},{"location":"examples/image_slicing/#interactive-slicing-and-thresholding-of-hyperspectral-data-with-hypercoast","title":"Interactive slicing and thresholding of hyperspectral data with HyperCoast\u00b6","text":"<p>This notebook demonstrates how to perform interactive slicing and thresholding of hyperspectral data with HyperCoast using the PyVista plotting backend.</p>"},{"location":"examples/image_slicing/#neon-aop","title":"NEON AOP\u00b6","text":"<p>Download a sample NEON AOP hyperspectral dataset.</p>"},{"location":"examples/image_slicing/#nasa-emit","title":"NASA EMIT\u00b6","text":"<p>Download a sample NASA EMIT hyperspectral dataset from here.</p>"},{"location":"examples/multispectral/","title":"Multispectral","text":"In\u00a0[1]: Copied! <pre># %pip install \"hypercoast[extra]\n</pre> # %pip install \"hypercoast[extra] In\u00a0[2]: Copied! <pre>import hypercoast\n</pre> import hypercoast In\u00a0[3]: Copied! <pre>url = \"https://github.com/opengeos/datasets/releases/download/raster/cog.tif\"\nfilepath = \"data/cog.tif\"\nhypercoast.download_file(url, filepath, quiet=True)\n</pre> url = \"https://github.com/opengeos/datasets/releases/download/raster/cog.tif\" filepath = \"data/cog.tif\" hypercoast.download_file(url, filepath, quiet=True) Out[3]: <pre>'/home/runner/work/HyperCoast/HyperCoast/docs/examples/data/cog.tif'</pre> In\u00a0[4]: Copied! <pre>m = hypercoast.Map()\nfilepath = \"data/cog.tif\"  # replace it with your own raster data\nm.add_dataset(\n    filepath, indexes=[4, 1, 2], vmin=0, vmax=2500, layer_name=\"Landsat\", nodata=0\n)\nm.add(\"spectral\")\nm\n</pre> m = hypercoast.Map() filepath = \"data/cog.tif\"  # replace it with your own raster data m.add_dataset(     filepath, indexes=[4, 1, 2], vmin=0, vmax=2500, layer_name=\"Landsat\", nodata=0 ) m.add(\"spectral\") m <pre>Warning 1: TIFFReadDirectory:Sum of Photometric type-related color channels and ExtraSamples doesn't match SamplesPerPixel. Defining non-color channels as ExtraSamples.\n</pre> Out[4]:"},{"location":"examples/multispectral/#visualizing-multispectral-data-with-hypercoast","title":"Visualizing Multispectral Data with HyperCoast\u00b6","text":""},{"location":"examples/neon/","title":"Neon","text":"In\u00a0[1]: Copied! <pre># %pip install \"hypercoast[extra]\"\n</pre> # %pip install \"hypercoast[extra]\" In\u00a0[2]: Copied! <pre>import hypercoast\n</pre> import hypercoast In\u00a0[3]: Copied! <pre>url = \"https://github.com/opengeos/datasets/releases/download/hypercoast/NEON_D02_SERC_DP3_368000_4306000_reflectance.h5\"\nfilepath = \"data/neon.h5\"\nhypercoast.download_file(url, filepath)\n</pre> url = \"https://github.com/opengeos/datasets/releases/download/hypercoast/NEON_D02_SERC_DP3_368000_4306000_reflectance.h5\" filepath = \"data/neon.h5\" hypercoast.download_file(url, filepath) Out[3]: <pre>'/home/runner/work/HyperCoast/HyperCoast/docs/examples/data/neon.h5'</pre> <p>Load the dataset as a <code>xarray.Dataset</code> object.</p> In\u00a0[4]: Copied! <pre>dataset = hypercoast.read_neon(filepath)\ndataset\n</pre> dataset = hypercoast.read_neon(filepath) dataset Out[4]: <pre>&lt;xarray.Dataset&gt; Size: 3GB\nDimensions:      (y: 1000, x: 1000, wavelength: 426)\nCoordinates:\n  * y            (y) float64 8kB 4.307e+06 4.307e+06 ... 4.306e+06 4.306e+06\n  * x            (x) float64 8kB 3.68e+05 3.68e+05 ... 3.69e+05 3.69e+05\n  * wavelength   (wavelength) float64 3kB 383.9 388.9 ... 2.507e+03 2.512e+03\nData variables:\n    reflectance  (y, x, wavelength) float64 3GB 0.1569 0.1206 0.1034 ... nan nan\nAttributes:\n    scale_factor:   10000.0\n    no_data_value:  -9999.0\n    crs:            EPSG:32618\n    transform:      (1.0, 0.0, 368000.0, 0.0, -1.0, 4307000.0)</pre>xarray.Dataset<ul><li>Dimensions:<ul><li>y: 1000</li><li>x: 1000</li><li>wavelength: 426</li></ul></li><li>Coordinates: (3)<ul><li>y(y)float644.307e+06 4.307e+06 ... 4.306e+06<pre>array([4307000.      , 4306998.998999, 4306997.997998, ..., 4306002.002002,\n       4306001.001001, 4306000.      ])</pre></li><li>x(x)float643.68e+05 3.68e+05 ... 3.69e+05<pre>array([368000.      , 368001.001001, 368002.002002, ..., 368997.997998,\n       368998.998999, 369000.      ])</pre></li><li>wavelength(wavelength)float64383.9 388.9 ... 2.507e+03 2.512e+03<pre>array([ 383.88,  388.89,  393.9 , ..., 2502.17, 2507.17, 2512.18])</pre></li></ul></li><li>Data variables: (1)<ul><li>reflectance(y, x, wavelength)float640.1569 0.1206 0.1034 ... nan nanscale_factor :10000.0no_data_value :-9999.0crs :EPSG:32618transform :(1.0, 0.0, 368000.0, 0.0, -1.0, 4307000.0)<pre>array([[[0.1569, 0.1206, 0.1034, ..., 0.1512,    nan,    nan],\n        [0.1593, 0.118 , 0.105 , ...,    nan,    nan,    nan],\n        [0.1415, 0.1147, 0.1061, ..., 0.    , 0.    ,    nan],\n        ...,\n        [0.056 , 0.0319, 0.0292, ...,    nan,    nan,    nan],\n        [0.0604, 0.0412, 0.0257, ...,    nan,    nan,    nan],\n        [0.0723, 0.0444, 0.0366, ..., 0.7853,    nan,    nan]],\n\n       [[0.13  , 0.0988, 0.0842, ...,    nan,    nan,    nan],\n        [0.1515, 0.0961, 0.0826, ...,    nan,    nan,    nan],\n        [0.1431, 0.1253, 0.1069, ...,    nan,    nan,    nan],\n        ...,\n        [0.0586, 0.0411, 0.0224, ...,    nan,    nan,    nan],\n        [0.0467, 0.0424, 0.0289, ..., 0.4019,    nan,    nan],\n        [0.074 , 0.0453, 0.0301, ..., 0.7002,    nan,    nan]],\n\n       [[0.1026, 0.0716, 0.0623, ...,    nan,    nan,    nan],\n        [0.127 , 0.1103, 0.0986, ..., 0.7747,    nan,    nan],\n        [0.168 , 0.1335, 0.1359, ...,    nan,    nan,    nan],\n        ...,\n...\n        ...,\n        [0.0578, 0.0493, 0.0418, ...,    nan,    nan,    nan],\n        [0.1047, 0.0788, 0.057 , ...,    nan,    nan,    nan],\n        [0.0958, 0.0785, 0.0693, ...,    nan,    nan,    nan]],\n\n       [[0.0747, 0.0397, 0.0314, ...,    nan,    nan,    nan],\n        [0.065 , 0.041 , 0.0322, ..., 0.9747,    nan,    nan],\n        [0.0667, 0.0424, 0.0274, ...,    nan,    nan,    nan],\n        ...,\n        [0.1335, 0.1008, 0.0878, ..., 0.    ,    nan,    nan],\n        [0.1369, 0.0864, 0.0771, ...,    nan,    nan,    nan],\n        [0.1144, 0.0958, 0.0857, ...,    nan,    nan,    nan]],\n\n       [[0.0738, 0.0392, 0.031 , ...,    nan,    nan,    nan],\n        [0.0541, 0.0421, 0.0272, ...,    nan,    nan,    nan],\n        [0.0513, 0.0406, 0.0276, ...,    nan,    nan,    nan],\n        ...,\n        [0.082 , 0.0593, 0.0511, ...,    nan,    nan,    nan],\n        [0.0883, 0.0693, 0.0518, ...,    nan,    nan,    nan],\n        [0.1128, 0.1   , 0.0844, ..., 0.5833,    nan,    nan]]])</pre></li></ul></li><li>Indexes: (3)<ul><li>yPandasIndex<pre>PandasIndex(Index([        4307000.0, 4306998.998998999, 4306997.997997998,\n       4306996.996996997, 4306995.995995996, 4306994.994994995,\n       4306993.993993994, 4306992.992992993, 4306991.991991992,\n       4306990.990990991,\n       ...\n       4306009.009009009, 4306008.008008008, 4306007.007007007,\n       4306006.006006006, 4306005.005005005, 4306004.004004004,\n       4306003.003003003, 4306002.002002002, 4306001.001001001,\n               4306000.0],\n      dtype='float64', name='y', length=1000))</pre></li><li>xPandasIndex<pre>PandasIndex(Index([          368000.0,   368001.001001001, 368002.00200200203,\n         368003.003003003,   368004.004004004,   368005.005005005,\n       368006.00600600604,   368007.007007007,   368008.008008008,\n         368009.009009009,\n       ...\n         368990.990990991,   368991.991991992,   368992.992992993,\n       368993.99399399396,   368994.994994995,   368995.995995996,\n         368996.996996997,   368997.997997998,   368998.998998999,\n                 369000.0],\n      dtype='float64', name='x', length=1000))</pre></li><li>wavelengthPandasIndex<pre>PandasIndex(Index([ 383.88,  388.89,   393.9,  398.91,  403.92,  408.92,  413.93,  418.94,\n        423.95,  428.95,\n       ...\n       2467.11, 2472.12, 2477.13, 2482.13, 2487.14, 2492.15, 2497.16, 2502.17,\n       2507.17, 2512.18],\n      dtype='float64', name='wavelength', length=426))</pre></li></ul></li><li>Attributes: (4)scale_factor :10000.0no_data_value :-9999.0crs :EPSG:32618transform :(1.0, 0.0, 368000.0, 0.0, -1.0, 4307000.0)</li></ul> <p>Visualize the data interactively with HyperCoast.</p> In\u00a0[5]: Copied! <pre>m = hypercoast.Map()\nm.add_neon(filepath, wavelengths=[1000, 700, 500], vmin=0, vmax=0.5)\nm\n</pre> m = hypercoast.Map() m.add_neon(filepath, wavelengths=[1000, 700, 500], vmin=0, vmax=0.5) m Out[5]: In\u00a0[6]: Copied! <pre>m.set_center(-76.5134, 38.8973, 16)\n</pre> m.set_center(-76.5134, 38.8973, 16) In\u00a0[7]: Copied! <pre>m.add(\"spectral\")\n</pre> m.add(\"spectral\") <p></p>"},{"location":"examples/neon/#visualizing-neon-aop-hyperspectral-data-interactively-with-hypercoast","title":"Visualizing NEON AOP hyperspectral data interactively with HyperCoast\u00b6","text":"<p>This notebook demonstrates how to visualize NEON AOP hyperspectral data interactively with HyperCoast.</p>"},{"location":"examples/overview/","title":"Overview","text":"<ul> <li>Search and download NASA hyperspectral data</li> <li>Search and download NASA ECOSTRESS data</li> <li>Visualize Hyperspectral Data in 3D</li> <li>Interactive slicing and thresholding of hyperspectral data</li> <li>Visualize AVIRIS data interactively</li> <li>Visualize DESIS data interactively</li> <li>Visualize EMIT data interactively</li> <li>Visualize NEON AOP hyperspectral data interactively</li> <li>Visualize PACE data interactively</li> <li>Visualize PACE chlorophyll-a data interactively</li> <li>Visualize ERA5 temperature data interactively</li> <li>Visualize PACE OCI L1 data products</li> <li>Visualize PACE OCI L2 data products</li> <li>Visualize Multispectral data interactively</li> </ul>"},{"location":"examples/pace/","title":"Pace","text":"In\u00a0[1]: Copied! <pre># %pip install \"hypercoast[extra]\"\n</pre> # %pip install \"hypercoast[extra]\" In\u00a0[2]: Copied! <pre>import hypercoast\n</pre> import hypercoast <p>Download a sample PACE data file from here.</p> In\u00a0[3]: Copied! <pre>url = \"https://github.com/opengeos/datasets/releases/download/hypercoast/PACE_OCI.20240730T181157.L2.OC_AOP.V2_0.NRT.nc\"\n</pre> url = \"https://github.com/opengeos/datasets/releases/download/hypercoast/PACE_OCI.20240730T181157.L2.OC_AOP.V2_0.NRT.nc\" In\u00a0[4]: Copied! <pre>filepath = \"data/PACE_OCI.20240730T181157.L2.OC_AOP.V2_0.NRT.nc\"\nhypercoast.download_file(url, filepath)\n</pre> filepath = \"data/PACE_OCI.20240730T181157.L2.OC_AOP.V2_0.NRT.nc\" hypercoast.download_file(url, filepath) <pre>data/PACE_OCI.20240730T181157.L2.OC_AOP.V2_0.NRT.nc already exists. Skip downloading. Set overwrite=True to overwrite.\n</pre> Out[4]: <pre>'/home/runner/work/HyperCoast/HyperCoast/docs/examples/data/PACE_OCI.20240730T181157.L2.OC_AOP.V2_0.NRT.nc'</pre> <p>Let's make a scatter plot of the pixel locations so we can see the irregular spacing.</p> In\u00a0[5]: Copied! <pre>plot = hypercoast.view_pace_pixel_locations(filepath, step=20)\n</pre> plot = hypercoast.view_pace_pixel_locations(filepath, step=20) <p>Load the dataset as a <code>xarray.Dataset</code> object.</p> In\u00a0[6]: Copied! <pre>dataset = hypercoast.read_pace(filepath)\n</pre> dataset = hypercoast.read_pace(filepath) <p>Visualize selected bands of the dataset.</p> In\u00a0[7]: Copied! <pre>hypercoast.viz_pace(dataset, wavelengths=[500, 510, 520, 530], ncols=2)\n</pre> hypercoast.viz_pace(dataset, wavelengths=[500, 510, 520, 530], ncols=2) <p>Add projection.</p> In\u00a0[8]: Copied! <pre>hypercoast.viz_pace(dataset, wavelengths=[500, 510, 520, 530], ncols=2, crs=\"default\")\n</pre> hypercoast.viz_pace(dataset, wavelengths=[500, 510, 520, 530], ncols=2, crs=\"default\") <p>Plot a spectral signature.</p> In\u00a0[9]: Copied! <pre>latitude = 29.9307\nlongitude = -87.9106\nhypercoast.extract_pace(dataset, latitude, longitude, return_plot=True)\n</pre> latitude = 29.9307 longitude = -87.9106 hypercoast.extract_pace(dataset, latitude, longitude, return_plot=True) Out[9]: <pre>[&lt;matplotlib.lines.Line2D at 0x7f05d2707020&gt;]</pre> <p>Plot multiple spectral signatures.</p> In\u00a0[10]: Copied! <pre>latitude = (29.49, 29.50)\nlongitude = (-88.10, -88.00)\nhypercoast.filter_pace(dataset, latitude, longitude, return_plot=True)\n</pre> latitude = (29.49, 29.50) longitude = (-88.10, -88.00) hypercoast.filter_pace(dataset, latitude, longitude, return_plot=True) <p>Single-band visualization.</p> In\u00a0[11]: Copied! <pre>m = hypercoast.Map()\nm.add_basemap(\"Hybrid\")\nwavelengths = [450]\nm.add_pace(dataset, wavelengths, colormap=\"jet\", vmin=0, vmax=0.02, layer_name=\"PACE\")\nm.add_colormap(cmap=\"jet\", vmin=0, vmax=0.02, label=\"Reflectance\")\nm.add(\"spectral\")\nm\n</pre> m = hypercoast.Map() m.add_basemap(\"Hybrid\") wavelengths = [450] m.add_pace(dataset, wavelengths, colormap=\"jet\", vmin=0, vmax=0.02, layer_name=\"PACE\") m.add_colormap(cmap=\"jet\", vmin=0, vmax=0.02, label=\"Reflectance\") m.add(\"spectral\") m Out[11]: <p></p> <p>Multiple-band visualization.</p> In\u00a0[12]: Copied! <pre>m = hypercoast.Map()\nm.add_basemap(\"Hybrid\")\nwavelengths = [450, 550, 650]\nm.add_pace(\n    dataset, wavelengths, indexes=[3, 2, 1], vmin=0, vmax=0.02, layer_name=\"PACE\"\n)\nm.add(\"spectral\")\nm\n</pre> m = hypercoast.Map() m.add_basemap(\"Hybrid\") wavelengths = [450, 550, 650] m.add_pace(     dataset, wavelengths, indexes=[3, 2, 1], vmin=0, vmax=0.02, layer_name=\"PACE\" ) m.add(\"spectral\") m Out[12]: <p></p>"},{"location":"examples/pace/#visualizing-pace-data-interactively-with-hypercoast","title":"Visualizing PACE data interactively with HyperCoast\u00b6","text":"<p>This notebook demonstrates how to visualize Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) data interactively with HyperCoast.</p>"},{"location":"examples/pace_cyano/","title":"Pace cyano","text":"In\u00a0[1]: Copied! <pre># %pip install -U \"hypercoast[extra]\"\n</pre> # %pip install -U \"hypercoast[extra]\" In\u00a0[2]: Copied! <pre>import earthaccess\nimport hypercoast\n</pre> import earthaccess import hypercoast In\u00a0[3]: Copied! <pre>earthaccess.login(persist=True)\n</pre> earthaccess.login(persist=True) Out[3]: <pre>&lt;earthaccess.auth.Auth at 0x7f479064f260&gt;</pre> <p>Search for PACE AOP data:</p> In\u00a0[4]: Copied! <pre>results = hypercoast.search_pace(\n    bounding_box=(-83, 25, -81, 28),\n    temporal=(\"2024-07-30\", \"2024-08-15\"),\n    short_name=\"PACE_OCI_L2_AOP_NRT\",\n    count=1,\n)\n</pre> results = hypercoast.search_pace(     bounding_box=(-83, 25, -81, 28),     temporal=(\"2024-07-30\", \"2024-08-15\"),     short_name=\"PACE_OCI_L2_AOP_NRT\",     count=1, ) <p>Download PACE AOP data:</p> In\u00a0[5]: Copied! <pre>hypercoast.download_pace(results[:1], out_dir=\"data\")\n</pre> hypercoast.download_pace(results[:1], out_dir=\"data\") In\u00a0[6]: Copied! <pre>filepath = \"data/PACE_OCI.20240730T181157.L2.OC_AOP.V2_0.NRT.nc\"\ndataset = hypercoast.read_pace(filepath)\n# dataset\n</pre> filepath = \"data/PACE_OCI.20240730T181157.L2.OC_AOP.V2_0.NRT.nc\" dataset = hypercoast.read_pace(filepath) # dataset <p></p> In\u00a0[7]: Copied! <pre>da = dataset[\"Rrs\"]\ndata = (\n    (da.sel(wavelength=650) &gt; da.sel(wavelength=620))\n    &amp; (da.sel(wavelength=701) &gt; da.sel(wavelength=681))\n    &amp; (da.sel(wavelength=701) &gt; da.sel(wavelength=450))\n)\n# data\n</pre> da = dataset[\"Rrs\"] data = (     (da.sel(wavelength=650) &gt; da.sel(wavelength=620))     &amp; (da.sel(wavelength=701) &gt; da.sel(wavelength=681))     &amp; (da.sel(wavelength=701) &gt; da.sel(wavelength=450)) ) # data In\u00a0[8]: Copied! <pre>import xarray as xr\nimport matplotlib.pyplot as plt\nimport cartopy.crs as ccrs\nimport cartopy.feature as cfeature\n\n# Create a plot\nfig, ax = plt.subplots(figsize=(12, 6), subplot_kw={\"projection\": ccrs.PlateCarree()})\n\n# ax.set_extent([-93, -87, 28, 32], crs=ccrs.PlateCarree())\n\n# Plot the data\ndata.plot(\n    ax=ax,\n    transform=ccrs.PlateCarree(),\n    cmap=\"coolwarm\",\n    cbar_kwargs={\"label\": \"Cyano\"},\n)\n\n# Add coastlines\nax.coastlines()\n\n# Add state boundaries\nstates_provinces = cfeature.NaturalEarthFeature(\n    category=\"cultural\",\n    name=\"admin_1_states_provinces_lines\",\n    scale=\"50m\",\n    facecolor=\"none\",\n)\n\nax.add_feature(states_provinces, edgecolor=\"gray\")\n\n# Optionally, add gridlines, labels, etc.\nax.gridlines(draw_labels=True)\n\nplt.show()\n</pre> import xarray as xr import matplotlib.pyplot as plt import cartopy.crs as ccrs import cartopy.feature as cfeature  # Create a plot fig, ax = plt.subplots(figsize=(12, 6), subplot_kw={\"projection\": ccrs.PlateCarree()})  # ax.set_extent([-93, -87, 28, 32], crs=ccrs.PlateCarree())  # Plot the data data.plot(     ax=ax,     transform=ccrs.PlateCarree(),     cmap=\"coolwarm\",     cbar_kwargs={\"label\": \"Cyano\"}, )  # Add coastlines ax.coastlines()  # Add state boundaries states_provinces = cfeature.NaturalEarthFeature(     category=\"cultural\",     name=\"admin_1_states_provinces_lines\",     scale=\"50m\",     facecolor=\"none\", )  ax.add_feature(states_provinces, edgecolor=\"gray\")  # Optionally, add gridlines, labels, etc. ax.gridlines(draw_labels=True)  plt.show() In\u00a0[9]: Copied! <pre>import numpy as np\nfrom sklearn.cluster import KMeans\n</pre> import numpy as np from sklearn.cluster import KMeans In\u00a0[10]: Copied! <pre># Get the shape of the DataArray\nprint(\"Shape of da:\", da.shape)\n</pre> # Get the shape of the DataArray print(\"Shape of da:\", da.shape) <pre>Shape of da: (1710, 1272, 184)\n</pre> In\u00a0[11]: Copied! <pre># Get the dimension names of the DataArray\nprint(\"Dimension names:\", da.dims)\n</pre> # Get the dimension names of the DataArray print(\"Dimension names:\", da.dims) <pre>Dimension names: ('latitude', 'longitude', 'wavelength')\n</pre> In\u00a0[12]: Copied! <pre># Get the size of each dimension\nprint(\"Size of each dimension:\", da.sizes)\n</pre> # Get the size of each dimension print(\"Size of each dimension:\", da.sizes) <pre>Size of each dimension: Frozen({'latitude': 1710, 'longitude': 1272, 'wavelength': 184})\n</pre> In\u00a0[13]: Copied! <pre>reshaped_data = da.values.reshape(-1, da.shape[-1])\n</pre> reshaped_data = da.values.reshape(-1, da.shape[-1]) In\u00a0[14]: Copied! <pre>reshaped_data_no_nan = reshaped_data[~np.isnan(reshaped_data).any(axis=1)]\n</pre> reshaped_data_no_nan = reshaped_data[~np.isnan(reshaped_data).any(axis=1)] In\u00a0[15]: Copied! <pre># Apply K-means clustering to classify into 5-6 water types.\nn_clusters = 6\nkmeans = KMeans(n_clusters=n_clusters, random_state=0)\nkmeans.fit(reshaped_data_no_nan)\n</pre> # Apply K-means clustering to classify into 5-6 water types. n_clusters = 6 kmeans = KMeans(n_clusters=n_clusters, random_state=0) kmeans.fit(reshaped_data_no_nan) Out[15]: <pre>KMeans(n_clusters=6, random_state=0)</pre>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.\u00a0\u00a0KMeans?Documentation for KMeansiFitted<pre>KMeans(n_clusters=6, random_state=0)</pre> In\u00a0[16]: Copied! <pre># Initialize an array for cluster labels with NaN\nlabels = np.full(reshaped_data.shape[0], np.nan)\n</pre> # Initialize an array for cluster labels with NaN labels = np.full(reshaped_data.shape[0], np.nan) In\u00a0[17]: Copied! <pre># Assign the computed cluster labels to the non-NaN positions\nlabels[~np.isnan(reshaped_data).any(axis=1)] = kmeans.labels_\n</pre> # Assign the computed cluster labels to the non-NaN positions labels[~np.isnan(reshaped_data).any(axis=1)] = kmeans.labels_ In\u00a0[18]: Copied! <pre># Reshape the labels back to the original spatial dimensions\ncluster_labels = labels.reshape(da.shape[:-1])\n</pre> # Reshape the labels back to the original spatial dimensions cluster_labels = labels.reshape(da.shape[:-1]) In\u00a0[19]: Copied! <pre>import matplotlib.pyplot as plt\nimport matplotlib.colors as mcolors\nimport cartopy.crs as ccrs\nimport cartopy.feature as cfeature\n</pre> import matplotlib.pyplot as plt import matplotlib.colors as mcolors import cartopy.crs as ccrs import cartopy.feature as cfeature In\u00a0[20]: Copied! <pre># Assume 'cluster_labels' contains the K-means classification results reshaped to the original spatial dimensions\n# Also assume that 'da' has the original latitude and longitude data\nlatitudes = da.coords[\"latitude\"].values\nlongitudes = da.coords[\"longitude\"].values\n\n# Create a custom discrete color map for K-means clusters\ncmap = mcolors.ListedColormap(\n    [\"#377eb8\", \"#ff7f00\", \"#4daf4a\", \"#f781bf\", \"#a65628\", \"#984ea3\"]\n)\nbounds = np.arange(-0.5, n_clusters, 1)\nnorm = mcolors.BoundaryNorm(bounds, cmap.N)\n\n# Create a figure and axis with the correct map projection\nfig, ax = plt.subplots(\n    figsize=(6, 4), dpi=200, subplot_kw={\"projection\": ccrs.PlateCarree()}\n)\n\n# Plot the K-means classification results on the map\nim = ax.pcolormesh(\n    longitudes,\n    latitudes,\n    cluster_labels,\n    cmap=cmap,\n    norm=norm,\n    transform=ccrs.PlateCarree(),\n)\n\n# Add geographic features for context\nax.add_feature(cfeature.COASTLINE)\nax.add_feature(cfeature.BORDERS, linestyle=\":\")\nax.add_feature(cfeature.STATES, linestyle=\"--\")\n\n# Add gridlines\nax.gridlines(draw_labels=True)\n\n# Set the extent to zoom in to the specified region\nax.set_extent([-95, -85, 27, 33], crs=ccrs.PlateCarree())\n\n# Add color bar with labels\ncbar = plt.colorbar(\n    im,\n    ax=ax,\n    orientation=\"vertical\",\n    pad=0.02,\n    fraction=0.05,\n    ticks=np.arange(n_clusters),\n)\ncbar.ax.set_yticklabels([f\"Class {i+1}\" for i in range(n_clusters)])\ncbar.set_label(\"Water Types\", rotation=270, labelpad=20)\n\n# Add title\nax.set_title(\"Water Type Classification using K-means\", fontsize=16)\n\n# Show the plot\nplt.show()\n</pre> # Assume 'cluster_labels' contains the K-means classification results reshaped to the original spatial dimensions # Also assume that 'da' has the original latitude and longitude data latitudes = da.coords[\"latitude\"].values longitudes = da.coords[\"longitude\"].values  # Create a custom discrete color map for K-means clusters cmap = mcolors.ListedColormap(     [\"#377eb8\", \"#ff7f00\", \"#4daf4a\", \"#f781bf\", \"#a65628\", \"#984ea3\"] ) bounds = np.arange(-0.5, n_clusters, 1) norm = mcolors.BoundaryNorm(bounds, cmap.N)  # Create a figure and axis with the correct map projection fig, ax = plt.subplots(     figsize=(6, 4), dpi=200, subplot_kw={\"projection\": ccrs.PlateCarree()} )  # Plot the K-means classification results on the map im = ax.pcolormesh(     longitudes,     latitudes,     cluster_labels,     cmap=cmap,     norm=norm,     transform=ccrs.PlateCarree(), )  # Add geographic features for context ax.add_feature(cfeature.COASTLINE) ax.add_feature(cfeature.BORDERS, linestyle=\":\") ax.add_feature(cfeature.STATES, linestyle=\"--\")  # Add gridlines ax.gridlines(draw_labels=True)  # Set the extent to zoom in to the specified region ax.set_extent([-95, -85, 27, 33], crs=ccrs.PlateCarree())  # Add color bar with labels cbar = plt.colorbar(     im,     ax=ax,     orientation=\"vertical\",     pad=0.02,     fraction=0.05,     ticks=np.arange(n_clusters), ) cbar.ax.set_yticklabels([f\"Class {i+1}\" for i in range(n_clusters)]) cbar.set_label(\"Water Types\", rotation=270, labelpad=20)  # Add title ax.set_title(\"Water Type Classification using K-means\", fontsize=16)  # Show the plot plt.show() In\u00a0[21]: Copied! <pre># Assume 'cluster_labels' contains the K-means classification results reshaped to the original spatial dimensions\n# Also assume that 'da' has the original latitude and longitude data\nlatitudes = da.coords[\"latitude\"].values\nlongitudes = da.coords[\"longitude\"].values\n\n# Filtering condition based on wavelength values\nfilter_condition = (\n    (da.sel(wavelength=650) &gt; da.sel(wavelength=620))\n    &amp; (da.sel(wavelength=701) &gt; da.sel(wavelength=681))\n    &amp; (da.sel(wavelength=701) &gt; da.sel(wavelength=450))\n)\n\n# Apply the filtering condition to the K-means classification results\nfiltered_cluster_labels = np.where(filter_condition, cluster_labels, np.nan)\n\n# Create a custom discrete color map for K-means clusters\ncmap = mcolors.ListedColormap(\n    [\"#e41a1c\", \"#377eb8\", \"#4daf4a\", \"#f781bf\", \"#a65628\", \"#984ea3\"]\n)\nbounds = np.arange(-0.5, n_clusters, 1)\nnorm = mcolors.BoundaryNorm(bounds, cmap.N)\n\n# Create a figure and axis with the correct map projection\nfig, ax = plt.subplots(\n    figsize=(6, 4), dpi=200, subplot_kw={\"projection\": ccrs.PlateCarree()}\n)\n\n# Plot the filtered K-means classification results on the map\nim = ax.pcolormesh(\n    longitudes,\n    latitudes,\n    filtered_cluster_labels,\n    cmap=cmap,\n    norm=norm,\n    transform=ccrs.PlateCarree(),\n)\n\n# Add geographic features for context\nax.add_feature(cfeature.COASTLINE)\nax.add_feature(cfeature.BORDERS, linestyle=\":\")\nax.add_feature(cfeature.STATES, linestyle=\"--\")\n\n# Add gridlines\nax.gridlines(draw_labels=True)\n\n# Set the extent to zoom in to the specified region\nax.set_extent([-95, -85, 27, 33], crs=ccrs.PlateCarree())\n\n# Add color bar with labels\ncbar = plt.colorbar(\n    im,\n    ax=ax,\n    orientation=\"vertical\",\n    pad=0.02,\n    fraction=0.05,\n    ticks=np.arange(n_clusters),\n)\ncbar.ax.set_yticklabels([f\"Class {i+1}\" for i in range(n_clusters)])\ncbar.set_label(\"Water Types\", rotation=270, labelpad=20)\n\n# Add title\nax.set_title(\"Water Type Classification using K-means\", fontsize=16)\n\n# Show the plot\nplt.show()\n</pre> # Assume 'cluster_labels' contains the K-means classification results reshaped to the original spatial dimensions # Also assume that 'da' has the original latitude and longitude data latitudes = da.coords[\"latitude\"].values longitudes = da.coords[\"longitude\"].values  # Filtering condition based on wavelength values filter_condition = (     (da.sel(wavelength=650) &gt; da.sel(wavelength=620))     &amp; (da.sel(wavelength=701) &gt; da.sel(wavelength=681))     &amp; (da.sel(wavelength=701) &gt; da.sel(wavelength=450)) )  # Apply the filtering condition to the K-means classification results filtered_cluster_labels = np.where(filter_condition, cluster_labels, np.nan)  # Create a custom discrete color map for K-means clusters cmap = mcolors.ListedColormap(     [\"#e41a1c\", \"#377eb8\", \"#4daf4a\", \"#f781bf\", \"#a65628\", \"#984ea3\"] ) bounds = np.arange(-0.5, n_clusters, 1) norm = mcolors.BoundaryNorm(bounds, cmap.N)  # Create a figure and axis with the correct map projection fig, ax = plt.subplots(     figsize=(6, 4), dpi=200, subplot_kw={\"projection\": ccrs.PlateCarree()} )  # Plot the filtered K-means classification results on the map im = ax.pcolormesh(     longitudes,     latitudes,     filtered_cluster_labels,     cmap=cmap,     norm=norm,     transform=ccrs.PlateCarree(), )  # Add geographic features for context ax.add_feature(cfeature.COASTLINE) ax.add_feature(cfeature.BORDERS, linestyle=\":\") ax.add_feature(cfeature.STATES, linestyle=\"--\")  # Add gridlines ax.gridlines(draw_labels=True)  # Set the extent to zoom in to the specified region ax.set_extent([-95, -85, 27, 33], crs=ccrs.PlateCarree())  # Add color bar with labels cbar = plt.colorbar(     im,     ax=ax,     orientation=\"vertical\",     pad=0.02,     fraction=0.05,     ticks=np.arange(n_clusters), ) cbar.ax.set_yticklabels([f\"Class {i+1}\" for i in range(n_clusters)]) cbar.set_label(\"Water Types\", rotation=270, labelpad=20)  # Add title ax.set_title(\"Water Type Classification using K-means\", fontsize=16)  # Show the plot plt.show() In\u00a0[22]: Copied! <pre>from sklearn.decomposition import PCA\n</pre> from sklearn.decomposition import PCA In\u00a0[23]: Copied! <pre># Check the dimensions and coordinates\nprint(da.dims)\nprint(da.coords)  # This will show the available spectral bands\n</pre> # Check the dimensions and coordinates print(da.dims) print(da.coords)  # This will show the available spectral bands <pre>('latitude', 'longitude', 'wavelength')\nCoordinates:\n    longitude   (latitude, longitude) float32 9MB -92.88 -92.84 ... -70.54\n    latitude    (latitude, longitude) float32 9MB 15.71 15.72 ... 43.91 43.92\n  * wavelength  (wavelength) float64 1kB 339.0 341.0 344.0 ... 714.0 717.0 719.0\n</pre> In\u00a0[24]: Copied! <pre># Reshape data to (n_pixels, n_bands)\nreshaped_data = da.values.reshape(-1, da.shape[-1])\n\n# Handle NaNs by removing them\nreshaped_data_no_nan = reshaped_data[~np.isnan(reshaped_data).any(axis=1)]\n</pre> # Reshape data to (n_pixels, n_bands) reshaped_data = da.values.reshape(-1, da.shape[-1])  # Handle NaNs by removing them reshaped_data_no_nan = reshaped_data[~np.isnan(reshaped_data).any(axis=1)] In\u00a0[25]: Copied! <pre># Apply PCA to reduce dimensionality\npca = PCA(n_components=3)\npca_data = pca.fit_transform(reshaped_data_no_nan)\n\n# Visualize PCA components to manually identify endmembers\nplt.figure(figsize=(10, 8))\nplt.scatter(pca_data[:, 0], pca_data[:, 1], c=\"blue\", s=1)\nplt.title(\"PCA of Spectral Data\")\nplt.xlabel(\"Principal Component 1\")\nplt.ylabel(\"Principal Component 2\")\nplt.show()\n</pre> # Apply PCA to reduce dimensionality pca = PCA(n_components=3) pca_data = pca.fit_transform(reshaped_data_no_nan)  # Visualize PCA components to manually identify endmembers plt.figure(figsize=(10, 8)) plt.scatter(pca_data[:, 0], pca_data[:, 1], c=\"blue\", s=1) plt.title(\"PCA of Spectral Data\") plt.xlabel(\"Principal Component 1\") plt.ylabel(\"Principal Component 2\") plt.show() In\u00a0[26]: Copied! <pre># Apply K-means to find clusters representing endmembers\nn_clusters = 6  # Number of endmembers you want to find\nkmeans = KMeans(n_clusters=n_clusters, random_state=0)\nkmeans.fit(pca_data)\n\n# The cluster centers in the original spectral space are your endmembers\nendmembers = pca.inverse_transform(kmeans.cluster_centers_)\n</pre> # Apply K-means to find clusters representing endmembers n_clusters = 6  # Number of endmembers you want to find kmeans = KMeans(n_clusters=n_clusters, random_state=0) kmeans.fit(pca_data)  # The cluster centers in the original spectral space are your endmembers endmembers = pca.inverse_transform(kmeans.cluster_centers_) In\u00a0[27]: Copied! <pre>def spectral_angle_mapper(pixel, reference):\n    norm_pixel = np.linalg.norm(pixel)\n    norm_reference = np.linalg.norm(reference)\n    cos_theta = np.dot(pixel, reference) / (norm_pixel * norm_reference)\n    angle = np.arccos(np.clip(cos_theta, -1, 1))\n    return angle\n\n\n# Apply SAM for each pixel and each endmember\nangles = np.zeros((reshaped_data_no_nan.shape[0], endmembers.shape[0]))\n\nfor i in range(reshaped_data_no_nan.shape[0]):\n    for j in range(endmembers.shape[0]):\n        angles[i, j] = spectral_angle_mapper(\n            reshaped_data_no_nan[i, :], endmembers[j, :]\n        )\n\n# Find the minimum angle (best match) for each pixel\nbest_match = np.argmin(angles, axis=1)\n\n# Reshape best_match back to the original spatial dimensions\noriginal_shape = da.shape[:-1]  # Get the spatial dimensions\nbest_match_full = np.full(reshaped_data.shape[0], np.nan)\nbest_match_full[~np.isnan(reshaped_data).any(axis=1)] = best_match\nbest_match_full = best_match_full.reshape(original_shape)\n</pre> def spectral_angle_mapper(pixel, reference):     norm_pixel = np.linalg.norm(pixel)     norm_reference = np.linalg.norm(reference)     cos_theta = np.dot(pixel, reference) / (norm_pixel * norm_reference)     angle = np.arccos(np.clip(cos_theta, -1, 1))     return angle   # Apply SAM for each pixel and each endmember angles = np.zeros((reshaped_data_no_nan.shape[0], endmembers.shape[0]))  for i in range(reshaped_data_no_nan.shape[0]):     for j in range(endmembers.shape[0]):         angles[i, j] = spectral_angle_mapper(             reshaped_data_no_nan[i, :], endmembers[j, :]         )  # Find the minimum angle (best match) for each pixel best_match = np.argmin(angles, axis=1)  # Reshape best_match back to the original spatial dimensions original_shape = da.shape[:-1]  # Get the spatial dimensions best_match_full = np.full(reshaped_data.shape[0], np.nan) best_match_full[~np.isnan(reshaped_data).any(axis=1)] = best_match best_match_full = best_match_full.reshape(original_shape) In\u00a0[28]: Copied! <pre># Assume 'best_match_full' contains the SAM classification results reshaped to the original spatial dimensions\n# Also assume that 'da' has the original latitude and longitude data\nlatitudes = da.coords[\"latitude\"].values\nlongitudes = da.coords[\"longitude\"].values\n\n# Create a custom discrete color map\ncmap = mcolors.ListedColormap(\n    [\"#377eb8\", \"#ff7f00\", \"#4daf4a\", \"#f781bf\", \"#a65628\", \"#984ea3\"]\n)\nbounds = np.arange(-0.5, n_clusters, 1)\nnorm = mcolors.BoundaryNorm(bounds, cmap.N)\n\n# Create a figure and axis with the correct map projection\nfig, ax = plt.subplots(figsize=(10, 8), subplot_kw={\"projection\": ccrs.PlateCarree()})\n\n# Plot the SAM classification results\nim = ax.pcolormesh(\n    longitudes,\n    latitudes,\n    best_match_full,\n    cmap=cmap,\n    norm=norm,\n    transform=ccrs.PlateCarree(),\n)\n\n# Add geographic features for context\nax.add_feature(cfeature.COASTLINE)\nax.add_feature(cfeature.BORDERS, linestyle=\":\")\nax.add_feature(cfeature.STATES, linestyle=\"--\")\n\n# Add gridlines\nax.gridlines(draw_labels=True)\n\n# Add color bar with labels\ncbar = plt.colorbar(\n    im,\n    ax=ax,\n    orientation=\"vertical\",\n    pad=0.02,\n    fraction=0.05,\n    ticks=np.arange(n_clusters),\n)\ncbar.ax.set_yticklabels([f\"Class {i+1}\" for i in range(n_clusters)])\ncbar.set_label(\"Water Types\", rotation=270, labelpad=20)\n\n# Add title\nax.set_title(\"Spectral Angle Mapper (SAM) Water Type Classification\", fontsize=16)\n\n# Show the plot\nplt.show()\n</pre> # Assume 'best_match_full' contains the SAM classification results reshaped to the original spatial dimensions # Also assume that 'da' has the original latitude and longitude data latitudes = da.coords[\"latitude\"].values longitudes = da.coords[\"longitude\"].values  # Create a custom discrete color map cmap = mcolors.ListedColormap(     [\"#377eb8\", \"#ff7f00\", \"#4daf4a\", \"#f781bf\", \"#a65628\", \"#984ea3\"] ) bounds = np.arange(-0.5, n_clusters, 1) norm = mcolors.BoundaryNorm(bounds, cmap.N)  # Create a figure and axis with the correct map projection fig, ax = plt.subplots(figsize=(10, 8), subplot_kw={\"projection\": ccrs.PlateCarree()})  # Plot the SAM classification results im = ax.pcolormesh(     longitudes,     latitudes,     best_match_full,     cmap=cmap,     norm=norm,     transform=ccrs.PlateCarree(), )  # Add geographic features for context ax.add_feature(cfeature.COASTLINE) ax.add_feature(cfeature.BORDERS, linestyle=\":\") ax.add_feature(cfeature.STATES, linestyle=\"--\")  # Add gridlines ax.gridlines(draw_labels=True)  # Add color bar with labels cbar = plt.colorbar(     im,     ax=ax,     orientation=\"vertical\",     pad=0.02,     fraction=0.05,     ticks=np.arange(n_clusters), ) cbar.ax.set_yticklabels([f\"Class {i+1}\" for i in range(n_clusters)]) cbar.set_label(\"Water Types\", rotation=270, labelpad=20)  # Add title ax.set_title(\"Spectral Angle Mapper (SAM) Water Type Classification\", fontsize=16)  # Show the plot plt.show() In\u00a0[29]: Copied! <pre># Assume 'best_match_full' contains the SAM classification results reshaped to the original spatial dimensions\n# Also assume that 'da' has the original latitude and longitude data\nlatitudes = da.coords[\"latitude\"].values\nlongitudes = da.coords[\"longitude\"].values\n\n# Create a custom discrete color map\ncmap = mcolors.ListedColormap(\n    [\"#377eb8\", \"#ff7f00\", \"#4daf4a\", \"#f781bf\", \"#a65628\", \"#984ea3\"]\n)\nbounds = np.arange(-0.5, n_clusters, 1)\nnorm = mcolors.BoundaryNorm(bounds, cmap.N)\n\n# Create a figure and axis with the correct map projection\nfig, ax = plt.subplots(figsize=(10, 8), subplot_kw={\"projection\": ccrs.PlateCarree()})\n\n# Plot the SAM classification results\nim = ax.pcolormesh(\n    longitudes,\n    latitudes,\n    best_match_full,\n    cmap=cmap,\n    norm=norm,\n    transform=ccrs.PlateCarree(),\n)\n\n# Add geographic features for context\nax.add_feature(cfeature.COASTLINE)\nax.add_feature(cfeature.BORDERS, linestyle=\":\")\nax.add_feature(cfeature.STATES, linestyle=\"--\")\n\n# Add gridlines\nax.gridlines(draw_labels=True)\n\n# Set the extent to zoom in to the specified region\nax.set_extent([-95, -85, 27, 33], crs=ccrs.PlateCarree())\n\n# Add color bar with labels\ncbar = plt.colorbar(\n    im,\n    ax=ax,\n    orientation=\"vertical\",\n    pad=0.02,\n    fraction=0.05,\n    ticks=np.arange(n_clusters),\n)\ncbar.ax.set_yticklabels([f\"Class {i+1}\" for i in range(n_clusters)])\ncbar.set_label(\"Water Types\", rotation=270, labelpad=20)\n\n# Add title\nax.set_title(\"Spectral Angle Mapper (SAM) Water Type Classification\", fontsize=16)\n\n# Show the plot\nplt.show()\n</pre> # Assume 'best_match_full' contains the SAM classification results reshaped to the original spatial dimensions # Also assume that 'da' has the original latitude and longitude data latitudes = da.coords[\"latitude\"].values longitudes = da.coords[\"longitude\"].values  # Create a custom discrete color map cmap = mcolors.ListedColormap(     [\"#377eb8\", \"#ff7f00\", \"#4daf4a\", \"#f781bf\", \"#a65628\", \"#984ea3\"] ) bounds = np.arange(-0.5, n_clusters, 1) norm = mcolors.BoundaryNorm(bounds, cmap.N)  # Create a figure and axis with the correct map projection fig, ax = plt.subplots(figsize=(10, 8), subplot_kw={\"projection\": ccrs.PlateCarree()})  # Plot the SAM classification results im = ax.pcolormesh(     longitudes,     latitudes,     best_match_full,     cmap=cmap,     norm=norm,     transform=ccrs.PlateCarree(), )  # Add geographic features for context ax.add_feature(cfeature.COASTLINE) ax.add_feature(cfeature.BORDERS, linestyle=\":\") ax.add_feature(cfeature.STATES, linestyle=\"--\")  # Add gridlines ax.gridlines(draw_labels=True)  # Set the extent to zoom in to the specified region ax.set_extent([-95, -85, 27, 33], crs=ccrs.PlateCarree())  # Add color bar with labels cbar = plt.colorbar(     im,     ax=ax,     orientation=\"vertical\",     pad=0.02,     fraction=0.05,     ticks=np.arange(n_clusters), ) cbar.ax.set_yticklabels([f\"Class {i+1}\" for i in range(n_clusters)]) cbar.set_label(\"Water Types\", rotation=270, labelpad=20)  # Add title ax.set_title(\"Spectral Angle Mapper (SAM) Water Type Classification\", fontsize=16)  # Show the plot plt.show() In\u00a0[30]: Copied! <pre># Assume 'best_match_full' contains the SAM classification results reshaped to the original spatial dimensions\n# Also assume that 'da' has the original latitude and longitude data\nlatitudes = da.coords[\"latitude\"].values\nlongitudes = da.coords[\"longitude\"].values\n\n# Filtering condition based on wavelength values\nfilter_condition = (\n    (da.sel(wavelength=650) &gt; da.sel(wavelength=620))\n    &amp; (da.sel(wavelength=701) &gt; da.sel(wavelength=681))\n    &amp; (da.sel(wavelength=701) &gt; da.sel(wavelength=450))\n)\n\n# Apply the filtering condition to the SAM classification results\nfiltered_best_match_full = np.where(filter_condition, best_match_full, np.nan)\n\n# Create a custom discrete color map\ncmap = mcolors.ListedColormap(\n    [\"#e41a1c\", \"#377eb8\", \"#4daf4a\", \"#f781bf\", \"#a65628\", \"#984ea3\"]\n)\nbounds = np.arange(-0.5, n_clusters, 1)\nnorm = mcolors.BoundaryNorm(bounds, cmap.N)\n\n# Create a figure and axis with the correct map projection\nfig, ax = plt.subplots(figsize=(10, 8), subplot_kw={\"projection\": ccrs.PlateCarree()})\n\n# Plot the filtered SAM classification results\nim = ax.pcolormesh(\n    longitudes,\n    latitudes,\n    filtered_best_match_full,\n    cmap=cmap,\n    norm=norm,\n    transform=ccrs.PlateCarree(),\n)\n\n# Add geographic features for context\nax.add_feature(cfeature.COASTLINE)\nax.add_feature(cfeature.BORDERS, linestyle=\":\")\nax.add_feature(cfeature.STATES, linestyle=\"--\")\n\n# Add gridlines\nax.gridlines(draw_labels=True)\n\n# Set the extent to zoom in to the specified region\nax.set_extent([-95, -85, 27, 33], crs=ccrs.PlateCarree())\n\n# Add color bar with labels\ncbar = plt.colorbar(\n    im,\n    ax=ax,\n    orientation=\"vertical\",\n    pad=0.02,\n    fraction=0.05,\n    ticks=np.arange(n_clusters),\n)\ncbar.ax.set_yticklabels([f\"Class {i+1}\" for i in range(n_clusters)])\ncbar.set_label(\"Water Types\", rotation=270, labelpad=20)\n\n# Add title\nax.set_title(\"PCA + Spectral Angle Mapper (SAM) Water Type Classification\", fontsize=16)\n\n# Show the plot\nplt.show()\n</pre> # Assume 'best_match_full' contains the SAM classification results reshaped to the original spatial dimensions # Also assume that 'da' has the original latitude and longitude data latitudes = da.coords[\"latitude\"].values longitudes = da.coords[\"longitude\"].values  # Filtering condition based on wavelength values filter_condition = (     (da.sel(wavelength=650) &gt; da.sel(wavelength=620))     &amp; (da.sel(wavelength=701) &gt; da.sel(wavelength=681))     &amp; (da.sel(wavelength=701) &gt; da.sel(wavelength=450)) )  # Apply the filtering condition to the SAM classification results filtered_best_match_full = np.where(filter_condition, best_match_full, np.nan)  # Create a custom discrete color map cmap = mcolors.ListedColormap(     [\"#e41a1c\", \"#377eb8\", \"#4daf4a\", \"#f781bf\", \"#a65628\", \"#984ea3\"] ) bounds = np.arange(-0.5, n_clusters, 1) norm = mcolors.BoundaryNorm(bounds, cmap.N)  # Create a figure and axis with the correct map projection fig, ax = plt.subplots(figsize=(10, 8), subplot_kw={\"projection\": ccrs.PlateCarree()})  # Plot the filtered SAM classification results im = ax.pcolormesh(     longitudes,     latitudes,     filtered_best_match_full,     cmap=cmap,     norm=norm,     transform=ccrs.PlateCarree(), )  # Add geographic features for context ax.add_feature(cfeature.COASTLINE) ax.add_feature(cfeature.BORDERS, linestyle=\":\") ax.add_feature(cfeature.STATES, linestyle=\"--\")  # Add gridlines ax.gridlines(draw_labels=True)  # Set the extent to zoom in to the specified region ax.set_extent([-95, -85, 27, 33], crs=ccrs.PlateCarree())  # Add color bar with labels cbar = plt.colorbar(     im,     ax=ax,     orientation=\"vertical\",     pad=0.02,     fraction=0.05,     ticks=np.arange(n_clusters), ) cbar.ax.set_yticklabels([f\"Class {i+1}\" for i in range(n_clusters)]) cbar.set_label(\"Water Types\", rotation=270, labelpad=20)  # Add title ax.set_title(\"PCA + Spectral Angle Mapper (SAM) Water Type Classification\", fontsize=16)  # Show the plot plt.show() In\u00a0[31]: Copied! <pre>import numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport matplotlib.colors as mcolors\nfrom scipy.interpolate import interp1d\n</pre> import numpy as np import pandas as pd import matplotlib.pyplot as plt import matplotlib.colors as mcolors from scipy.interpolate import interp1d In\u00a0[32]: Copied! <pre># Load the netCDF data\nfile_path = \"data/PACE_OCI.20240730T181157.L2.OC_AOP.V2_0.NRT.nc\"\ndataset = hypercoast.read_pace(file_path)\nda = dataset[\"Rrs\"]  # Assuming 'Rrs' contains the reflectance data\n</pre> # Load the netCDF data file_path = \"data/PACE_OCI.20240730T181157.L2.OC_AOP.V2_0.NRT.nc\" dataset = hypercoast.read_pace(file_path) da = dataset[\"Rrs\"]  # Assuming 'Rrs' contains the reflectance data In\u00a0[33]: Copied! <pre># Extract PACE wavelengths\npace_wavelengths = da[\"wavelength\"].values\nprint(pace_wavelengths)\n</pre> # Extract PACE wavelengths pace_wavelengths = da[\"wavelength\"].values print(pace_wavelengths) <pre>[339. 341. 344. 346. 348. 351. 353. 356. 358. 361. 363. 366. 368. 371.\n 373. 375. 378. 380. 383. 385. 388. 390. 393. 395. 398. 400. 403. 405.\n 408. 410. 413. 415. 418. 420. 422. 425. 427. 430. 432. 435. 437. 440.\n 442. 445. 447. 450. 452. 455. 457. 460. 462. 465. 467. 470. 472. 475.\n 477. 480. 482. 485. 487. 490. 492. 495. 497. 500. 502. 505. 507. 510.\n 512. 515. 517. 520. 522. 525. 527. 530. 532. 535. 537. 540. 542. 545.\n 547. 550. 553. 555. 558. 560. 563. 565. 568. 570. 573. 575. 578. 580.\n 583. 586. 588. 591. 593. 596. 598. 601. 603. 605. 608. 610. 613. 615.\n 618. 620. 623. 625. 627. 630. 632. 635. 637. 640. 641. 642. 643. 645.\n 646. 647. 648. 650. 651. 652. 653. 655. 656. 657. 658. 660. 661. 662.\n 663. 665. 666. 667. 668. 670. 671. 672. 673. 675. 676. 677. 678. 679.\n 681. 682. 683. 684. 686. 687. 688. 689. 691. 692. 693. 694. 696. 697.\n 698. 699. 701. 702. 703. 704. 706. 707. 708. 709. 711. 712. 713. 714.\n 717. 719.]\n</pre> In\u00a0[34]: Copied! <pre># Function to load and resample a single CSV spectral library file\ndef load_and_resample_spectral_library(csv_path, target_wavelengths):\n    df = pd.read_csv(csv_path)\n    original_wavelengths = df.iloc[:, 0].values  # First column is wavelength\n    spectra_values = df.iloc[:, 1].values  # Second column is spectral values\n\n    # Interpolation function\n    interp_func = interp1d(\n        original_wavelengths, spectra_values, kind=\"linear\", fill_value=\"extrapolate\"\n    )\n\n    # Resample to the target (PACE) wavelengths\n    resampled_spectra = interp_func(target_wavelengths)\n\n    return resampled_spectra\n</pre> # Function to load and resample a single CSV spectral library file def load_and_resample_spectral_library(csv_path, target_wavelengths):     df = pd.read_csv(csv_path)     original_wavelengths = df.iloc[:, 0].values  # First column is wavelength     spectra_values = df.iloc[:, 1].values  # Second column is spectral values      # Interpolation function     interp_func = interp1d(         original_wavelengths, spectra_values, kind=\"linear\", fill_value=\"extrapolate\"     )      # Resample to the target (PACE) wavelengths     resampled_spectra = interp_func(target_wavelengths)      return resampled_spectra <p>Download the SAM spectral library:</p> In\u00a0[35]: Copied! <pre>url = \"https://github.com/opengeos/datasets/releases/download/hypercoast/SAM_spectral_library.zip\"\nhypercoast.download_file(url)\n</pre> url = \"https://github.com/opengeos/datasets/releases/download/hypercoast/SAM_spectral_library.zip\" hypercoast.download_file(url) Out[35]: <pre>'/home/runner/work/HyperCoast/HyperCoast/docs/examples/SAM_spectral_library.zip'</pre> In\u00a0[36]: Copied! <pre># Load and resample all 6 endmembers\nendmember_paths = [\n    \"./SAM_spectral_library/Dataset_1.csv\",\n    \"./SAM_spectral_library/Dataset_2.csv\",\n    \"./SAM_spectral_library/Dataset_3.csv\",\n    \"./SAM_spectral_library/Dataset_4.csv\",\n    \"./SAM_spectral_library/Dataset_5.csv\",\n    \"./SAM_spectral_library/Dataset_6.csv\",\n]\n\nendmembers = np.array(\n    [\n        load_and_resample_spectral_library(path, pace_wavelengths)\n        for path in endmember_paths\n    ]\n)\n</pre> # Load and resample all 6 endmembers endmember_paths = [     \"./SAM_spectral_library/Dataset_1.csv\",     \"./SAM_spectral_library/Dataset_2.csv\",     \"./SAM_spectral_library/Dataset_3.csv\",     \"./SAM_spectral_library/Dataset_4.csv\",     \"./SAM_spectral_library/Dataset_5.csv\",     \"./SAM_spectral_library/Dataset_6.csv\", ]  endmembers = np.array(     [         load_and_resample_spectral_library(path, pace_wavelengths)         for path in endmember_paths     ] ) In\u00a0[37]: Copied! <pre>print(endmembers)\n</pre> print(endmembers) <pre>[[0.00871842 0.00865469 0.00855908 ... 0.00442423 0.00389996 0.00375134]\n [0.01088283 0.01082291 0.01073303 ... 0.01052424 0.00998495 0.00957979]\n [0.0169974  0.0168173  0.01654716 ... 0.01991858 0.01881549 0.01798281]\n [0.0227546  0.022496   0.0221081  ... 0.0312176  0.03006733 0.02898674]\n [0.02859302 0.02826977 0.02778489 ... 0.05700807 0.05723728 0.05712533]\n [0.03042756 0.03000733 0.02937699 ... 0.07612622 0.08101502 0.08450761]]\n</pre> In\u00a0[38]: Copied! <pre># Plot sample spectra from the CSV files and their resampled versions\ndef plot_sample_spectra(csv_paths, pace_wavelengths):\n    plt.figure(figsize=(14, 8))\n\n    for i, csv_path in enumerate(csv_paths):\n        df = pd.read_csv(csv_path)\n        original_wavelengths = df.iloc[:, 0].values\n        spectra_values = df.iloc[:, 1].values\n        resampled_spectra = load_and_resample_spectral_library(\n            csv_path, pace_wavelengths\n        )\n\n        plt.plot(\n            original_wavelengths,\n            spectra_values,\n            label=f\"Original Spectra {i+1}\",\n            linestyle=\"--\",\n        )\n        plt.plot(pace_wavelengths, resampled_spectra, label=f\"Resampled Spectra {i+1}\")\n\n    plt.xlabel(\"Wavelength (nm)\")\n    plt.ylabel(\"Spectral Reflectance\")\n    plt.title(\"Comparison of Original and Resampled Spectra\")\n    plt.legend()\n    plt.grid(True)\n    plt.show()\n\n\nplot_sample_spectra(endmember_paths, pace_wavelengths)\n</pre> # Plot sample spectra from the CSV files and their resampled versions def plot_sample_spectra(csv_paths, pace_wavelengths):     plt.figure(figsize=(14, 8))      for i, csv_path in enumerate(csv_paths):         df = pd.read_csv(csv_path)         original_wavelengths = df.iloc[:, 0].values         spectra_values = df.iloc[:, 1].values         resampled_spectra = load_and_resample_spectral_library(             csv_path, pace_wavelengths         )          plt.plot(             original_wavelengths,             spectra_values,             label=f\"Original Spectra {i+1}\",             linestyle=\"--\",         )         plt.plot(pace_wavelengths, resampled_spectra, label=f\"Resampled Spectra {i+1}\")      plt.xlabel(\"Wavelength (nm)\")     plt.ylabel(\"Spectral Reflectance\")     plt.title(\"Comparison of Original and Resampled Spectra\")     plt.legend()     plt.grid(True)     plt.show()   plot_sample_spectra(endmember_paths, pace_wavelengths) In\u00a0[39]: Copied! <pre># Function to calculate spectral angle\ndef spectral_angle_mapper(pixel, reference):\n    norm_pixel = np.linalg.norm(pixel)\n    norm_reference = np.linalg.norm(reference)\n    cos_theta = np.dot(pixel, reference) / (norm_pixel * norm_reference)\n    angle = np.arccos(np.clip(cos_theta, -1, 1))\n    return angle\n</pre> # Function to calculate spectral angle def spectral_angle_mapper(pixel, reference):     norm_pixel = np.linalg.norm(pixel)     norm_reference = np.linalg.norm(reference)     cos_theta = np.dot(pixel, reference) / (norm_pixel * norm_reference)     angle = np.arccos(np.clip(cos_theta, -1, 1))     return angle In\u00a0[40]: Copied! <pre># Reshape data to (n_pixels, n_bands)\nreshaped_data = da.values.reshape(-1, da.shape[-1])\n</pre> # Reshape data to (n_pixels, n_bands) reshaped_data = da.values.reshape(-1, da.shape[-1]) In\u00a0[41]: Copied! <pre># Apply SAM for each pixel and each endmember\nangles = np.zeros((reshaped_data.shape[0], endmembers.shape[0]))\n\nfor i in range(reshaped_data.shape[0]):\n    for j in range(endmembers.shape[0]):\n        angles[i, j] = spectral_angle_mapper(reshaped_data[i, :], endmembers[j, :])\n</pre> # Apply SAM for each pixel and each endmember angles = np.zeros((reshaped_data.shape[0], endmembers.shape[0]))  for i in range(reshaped_data.shape[0]):     for j in range(endmembers.shape[0]):         angles[i, j] = spectral_angle_mapper(reshaped_data[i, :], endmembers[j, :]) In\u00a0[42]: Copied! <pre># Find the minimum angle (best match) for each pixel\nbest_match = np.argmin(angles, axis=1)\n</pre> # Find the minimum angle (best match) for each pixel best_match = np.argmin(angles, axis=1) In\u00a0[43]: Copied! <pre># Reshape best_match back to the original spatial dimensions\nbest_match = best_match.reshape(da.shape[:-1])\n</pre> # Reshape best_match back to the original spatial dimensions best_match = best_match.reshape(da.shape[:-1]) In\u00a0[44]: Copied! <pre># Assume 'best_match' contains the SAM classification results reshaped to the original spatial dimensions\n# Also assume that 'da' has the original latitude and longitude data\nlatitudes = da.coords[\"latitude\"].values\nlongitudes = da.coords[\"longitude\"].values\n\n# Create a custom discrete color map\ncmap = mcolors.ListedColormap(\n    [\"#377eb8\", \"#e41a1c\", \"#4daf4a\", \"#f781bf\", \"#a65628\", \"#984ea3\"]\n)\nbounds = np.arange(-0.5, len(endmembers), 1)\nnorm = mcolors.BoundaryNorm(bounds, cmap.N)\n\n# Create a figure and axis with the correct map projection\nfig, ax = plt.subplots(figsize=(12, 10), subplot_kw={\"projection\": ccrs.PlateCarree()})\n\n# Plot the SAM classification results\nim = ax.pcolormesh(\n    longitudes,\n    latitudes,\n    best_match,\n    cmap=cmap,\n    norm=norm,\n    transform=ccrs.PlateCarree(),\n)\n\n# Add geographic features for context\nax.add_feature(cfeature.COASTLINE)\nax.add_feature(cfeature.BORDERS, linestyle=\":\")\nax.add_feature(cfeature.STATES, linestyle=\"--\")\n\n# Adding axis labels\nax.set_xlabel(\"Longitude\")\nax.set_ylabel(\"Latitude\")\n\n# Adding a title\nax.set_title(\"Spectral Angle Mapper (SAM) Water Type Classification\", fontsize=16)\n\n# Adding a color bar with discrete values\ncbar = plt.colorbar(\n    im,\n    ax=ax,\n    orientation=\"vertical\",\n    pad=0.02,\n    fraction=0.05,\n    ticks=np.arange(len(endmembers)),\n)\ncbar.ax.set_yticklabels([f\"Class {i+1}\" for i in range(len(endmembers))])\ncbar.set_label(\"Water Types\", rotation=270, labelpad=20)\n\n# Adding gridlines\nax.gridlines(draw_labels=True, linestyle=\"--\", linewidth=0.5)\n\n# Set the extent to zoom in to the specified region (adjust as needed)\nax.set_extent([-95, -85, 27, 33], crs=ccrs.PlateCarree())\n\n# Show the plot\nplt.show()\n</pre> # Assume 'best_match' contains the SAM classification results reshaped to the original spatial dimensions # Also assume that 'da' has the original latitude and longitude data latitudes = da.coords[\"latitude\"].values longitudes = da.coords[\"longitude\"].values  # Create a custom discrete color map cmap = mcolors.ListedColormap(     [\"#377eb8\", \"#e41a1c\", \"#4daf4a\", \"#f781bf\", \"#a65628\", \"#984ea3\"] ) bounds = np.arange(-0.5, len(endmembers), 1) norm = mcolors.BoundaryNorm(bounds, cmap.N)  # Create a figure and axis with the correct map projection fig, ax = plt.subplots(figsize=(12, 10), subplot_kw={\"projection\": ccrs.PlateCarree()})  # Plot the SAM classification results im = ax.pcolormesh(     longitudes,     latitudes,     best_match,     cmap=cmap,     norm=norm,     transform=ccrs.PlateCarree(), )  # Add geographic features for context ax.add_feature(cfeature.COASTLINE) ax.add_feature(cfeature.BORDERS, linestyle=\":\") ax.add_feature(cfeature.STATES, linestyle=\"--\")  # Adding axis labels ax.set_xlabel(\"Longitude\") ax.set_ylabel(\"Latitude\")  # Adding a title ax.set_title(\"Spectral Angle Mapper (SAM) Water Type Classification\", fontsize=16)  # Adding a color bar with discrete values cbar = plt.colorbar(     im,     ax=ax,     orientation=\"vertical\",     pad=0.02,     fraction=0.05,     ticks=np.arange(len(endmembers)), ) cbar.ax.set_yticklabels([f\"Class {i+1}\" for i in range(len(endmembers))]) cbar.set_label(\"Water Types\", rotation=270, labelpad=20)  # Adding gridlines ax.gridlines(draw_labels=True, linestyle=\"--\", linewidth=0.5)  # Set the extent to zoom in to the specified region (adjust as needed) ax.set_extent([-95, -85, 27, 33], crs=ccrs.PlateCarree())  # Show the plot plt.show() In\u00a0[45]: Copied! <pre># Assume 'best_match' contains the SAM classification results reshaped to the original spatial dimensions\n# Also assume that 'da' has the original latitude and longitude data\nlatitudes = da.coords[\"latitude\"].values\nlongitudes = da.coords[\"longitude\"].values\n\n# Extract specific wavelengths for the conditions\nwavelength_450 = da.sel(wavelength=450).values\nwavelength_620 = da.sel(wavelength=620).values\nwavelength_650 = da.sel(wavelength=650).values\nwavelength_681 = da.sel(wavelength=681).values\nwavelength_701 = da.sel(wavelength=701).values\n\n# Apply the condition to filter the pixels\ncondition = (\n    (wavelength_650 &gt; wavelength_620)\n    &amp; (wavelength_701 &gt; wavelength_681)\n    &amp; (wavelength_701 &gt; wavelength_450)\n)\n\n# Filter the best_match data based on the condition\nfiltered_best_match = np.where(condition, best_match, np.nan)\n\n# Create a custom discrete color map\ncmap = mcolors.ListedColormap(\n    [\"#377eb8\", \"#e41a1c\", \"#4daf4a\", \"#f781bf\", \"#a65628\", \"#984ea3\"]\n)\nbounds = np.arange(-0.5, len(endmembers), 1)\nnorm = mcolors.BoundaryNorm(bounds, cmap.N)\n\n# Create a figure and axis with the correct map projection\nfig, ax = plt.subplots(figsize=(12, 10), subplot_kw={\"projection\": ccrs.PlateCarree()})\n\n# Plot the SAM classification results for the filtered pixels\nim = ax.pcolormesh(\n    longitudes,\n    latitudes,\n    filtered_best_match,\n    cmap=cmap,\n    norm=norm,\n    transform=ccrs.PlateCarree(),\n)\n\n# Add geographic features for context\nax.add_feature(cfeature.COASTLINE)\nax.add_feature(cfeature.BORDERS, linestyle=\":\")\nax.add_feature(cfeature.STATES, linestyle=\"--\")\n\n# Adding axis labels\nax.set_xlabel(\"Longitude\")\nax.set_ylabel(\"Latitude\")\n\n# Adding a title\nax.set_title(\"SAM Water Type Classification\", fontsize=16)\n\n# Adding a color bar with discrete values\ncbar = plt.colorbar(\n    im,\n    ax=ax,\n    orientation=\"vertical\",\n    pad=0.02,\n    fraction=0.05,\n    ticks=np.arange(len(endmembers)),\n)\ncbar.ax.set_yticklabels([f\"Class {i+1}\" for i in range(len(endmembers))])\ncbar.set_label(\"Water Types\", rotation=270, labelpad=20)\n\n# Adding gridlines\nax.gridlines(draw_labels=True, linestyle=\"--\", linewidth=0.5)\n\n# Set the extent to zoom in to the specified region (adjust as needed)\nax.set_extent([-95, -85, 27, 33], crs=ccrs.PlateCarree())\n\n# Show the plot\nplt.show()\n</pre> # Assume 'best_match' contains the SAM classification results reshaped to the original spatial dimensions # Also assume that 'da' has the original latitude and longitude data latitudes = da.coords[\"latitude\"].values longitudes = da.coords[\"longitude\"].values  # Extract specific wavelengths for the conditions wavelength_450 = da.sel(wavelength=450).values wavelength_620 = da.sel(wavelength=620).values wavelength_650 = da.sel(wavelength=650).values wavelength_681 = da.sel(wavelength=681).values wavelength_701 = da.sel(wavelength=701).values  # Apply the condition to filter the pixels condition = (     (wavelength_650 &gt; wavelength_620)     &amp; (wavelength_701 &gt; wavelength_681)     &amp; (wavelength_701 &gt; wavelength_450) )  # Filter the best_match data based on the condition filtered_best_match = np.where(condition, best_match, np.nan)  # Create a custom discrete color map cmap = mcolors.ListedColormap(     [\"#377eb8\", \"#e41a1c\", \"#4daf4a\", \"#f781bf\", \"#a65628\", \"#984ea3\"] ) bounds = np.arange(-0.5, len(endmembers), 1) norm = mcolors.BoundaryNorm(bounds, cmap.N)  # Create a figure and axis with the correct map projection fig, ax = plt.subplots(figsize=(12, 10), subplot_kw={\"projection\": ccrs.PlateCarree()})  # Plot the SAM classification results for the filtered pixels im = ax.pcolormesh(     longitudes,     latitudes,     filtered_best_match,     cmap=cmap,     norm=norm,     transform=ccrs.PlateCarree(), )  # Add geographic features for context ax.add_feature(cfeature.COASTLINE) ax.add_feature(cfeature.BORDERS, linestyle=\":\") ax.add_feature(cfeature.STATES, linestyle=\"--\")  # Adding axis labels ax.set_xlabel(\"Longitude\") ax.set_ylabel(\"Latitude\")  # Adding a title ax.set_title(\"SAM Water Type Classification\", fontsize=16)  # Adding a color bar with discrete values cbar = plt.colorbar(     im,     ax=ax,     orientation=\"vertical\",     pad=0.02,     fraction=0.05,     ticks=np.arange(len(endmembers)), ) cbar.ax.set_yticklabels([f\"Class {i+1}\" for i in range(len(endmembers))]) cbar.set_label(\"Water Types\", rotation=270, labelpad=20)  # Adding gridlines ax.gridlines(draw_labels=True, linestyle=\"--\", linewidth=0.5)  # Set the extent to zoom in to the specified region (adjust as needed) ax.set_extent([-95, -85, 27, 33], crs=ccrs.PlateCarree())  # Show the plot plt.show()"},{"location":"examples/pace_cyano/#mapping-cyanobacteria-with-pace-data","title":"Mapping Cyanobacteria with PACE data\u00b6","text":""},{"location":"examples/pace_cyano/#install-packages","title":"Install packages\u00b6","text":"<p>Uncomment the following cell to install the HyperCoast package.</p>"},{"location":"examples/pace_cyano/#import-libraries","title":"Import libraries\u00b6","text":""},{"location":"examples/pace_cyano/#download-pace-data","title":"Download PACE data\u00b6","text":"<p>To download and access the PACE AOP data, you will need to create an Earthdata login. You can register for an account at urs.earthdata.nasa.gov. Once you have an account, run the following cell and enter your NASA Earthdata login credentials.</p>"},{"location":"examples/pace_cyano/#read-pace-data","title":"Read PACE data\u00b6","text":"<p>Read PACE AOP data as an <code>xarray.Dataset</code>:</p>"},{"location":"examples/pace_cyano/#compute-band-ratios","title":"Compute band ratios\u00b6","text":""},{"location":"examples/pace_cyano/#the-spectra-of-cyanobacteria-bloom","title":"The spectra of cyanobacteria bloom:\u00b6","text":""},{"location":"examples/pace_cyano/#visualize-the-selected-region-based-on-band-ratios","title":"Visualize the selected region based on band ratios\u00b6","text":""},{"location":"examples/pace_cyano/#cyanobacteria-and-spectral-angle-mapper","title":"Cyanobacteria and Spectral Angle Mapper\u00b6","text":"<p>Spectral Angle Mapper: Spectral similarity Input: library of Cyanobacteria bloom Rrs spectra with Chla at different levels</p> <p>Spectral Mixture Analysis: unmix different cyanobacteria species based on spectral difference.</p> <p></p>"},{"location":"examples/pace_cyano/#k-means-applied-to-the-whole-image","title":"K-means applied to the whole image\u00b6","text":""},{"location":"examples/pace_cyano/#keans-applied-to-selected-pixels","title":"Keans applied to selected pixels\u00b6","text":""},{"location":"examples/pace_cyano/#pca-sam-applied-to-whole-image","title":"PCA + SAM applied to whole image\u00b6","text":""},{"location":"examples/pace_cyano/#spectral-angle-mapper-sam","title":"Spectral Angle Mapper (SAM)\u00b6","text":""},{"location":"examples/pace_oci_l1/","title":"Pace oci l1","text":"In\u00a0[\u00a0]: Copied! <pre># %pip install \"hypercoast[extra]\"\n</pre> # %pip install \"hypercoast[extra]\" In\u00a0[\u00a0]: Copied! <pre>import hypercoast\nimport xarray as xr\n</pre> import hypercoast import xarray as xr <p>To download and access the data, you will need to create an Earthdata login. You can register for an account at urs.earthdata.nasa.gov. Once you have an account, you can run the following cell to search and download PACE OCI L1 data products.</p> In\u00a0[\u00a0]: Copied! <pre>hypercoast.nasa_earth_login()\n</pre> hypercoast.nasa_earth_login() In\u00a0[\u00a0]: Copied! <pre>hypercoast.nasa_earth_login()\n\nshort_name = \"PACE_OCI_L1B_SCI\"\nresults, gdf = hypercoast.search_nasa_data(\n    short_name=short_name,\n    bbox=(-90.5642, 29.9749, -89.7143, 30.42),\n    temporal=(\"2024-06-15\", \"2024-06-16\"),\n    return_gdf=True,\n)\n</pre> hypercoast.nasa_earth_login()  short_name = \"PACE_OCI_L1B_SCI\" results, gdf = hypercoast.search_nasa_data(     short_name=short_name,     bbox=(-90.5642, 29.9749, -89.7143, 30.42),     temporal=(\"2024-06-15\", \"2024-06-16\"),     return_gdf=True, ) In\u00a0[\u00a0]: Copied! <pre>gdf.explore()\n</pre> gdf.explore() <p></p> In\u00a0[\u00a0]: Copied! <pre>hypercoast.download_nasa_data(results[0], out_dir=\"data\")\n</pre> hypercoast.download_nasa_data(results[0], out_dir=\"data\") <p>Alternatively, use the following code block to download a sample dataset from here.</p> In\u00a0[\u00a0]: Copied! <pre>url = \"https://github.com/opengeos/datasets/releases/download/hypercoast/PACE_OCI.20240615T182549.L1B.nc\"\nfilepath = \"data/PACE_OCI.20240615T182549.L1B.nc\"\nhypercoast.download_file(url, filepath)\n</pre> url = \"https://github.com/opengeos/datasets/releases/download/hypercoast/PACE_OCI.20240615T182549.L1B.nc\" filepath = \"data/PACE_OCI.20240615T182549.L1B.nc\" hypercoast.download_file(url, filepath) <p>Let's check the top-level groups in the sample dataset.</p> In\u00a0[\u00a0]: Copied! <pre>hypercoast.netcdf_groups(filepath)\n</pre> hypercoast.netcdf_groups(filepath) <p>The top-level groups in the sample dataset are:</p> <pre>['sensor_band_parameters',\n 'scan_line_attributes',\n 'geolocation_data',\n 'navigation_data',\n 'observation_data']\n</pre> <p>Let's open the <code>observation_data</code> group, which contains the core science variables.</p> In\u00a0[\u00a0]: Copied! <pre>dataset = xr.open_dataset(filepath, group=\"observation_data\")\nprint(list(dataset.variables))\n</pre> dataset = xr.open_dataset(filepath, group=\"observation_data\") print(list(dataset.variables)) <p>The data variables include:</p> <pre>['rhot_blue', 'qual_blue', 'rhot_red', 'qual_red', 'rhot_SWIR', 'qual_SWIR']\n</pre> <p>The dimensions of the <code>rhot_blue</code> variable are <code>(\"blue_bands\", \"number_of_scans\", \"ccd_pixels\")</code>, and it has shape <code>(119, 1710, 1272)</code>. The sizes attribute of a variable gives us that information as a dictionary.</p> In\u00a0[\u00a0]: Copied! <pre>dataset[\"rhot_blue\"].sizes\n</pre> dataset[\"rhot_blue\"].sizes <p>The dimensions of the <code>rhot_red</code> variable are <code>(\"red_bands\", \"number_of_scans\", \"ccd_pixels\")</code>, and it has shape <code>(163, 1710, 1272)</code></p> In\u00a0[\u00a0]: Copied! <pre>dataset[\"rhot_red\"].sizes\n</pre> dataset[\"rhot_red\"].sizes <p>The dimensions of the <code>rhot_SWIR</code> variable are <code>(\"SWIR_bands\", \"number_of_scans\", \"SWIR_pixels\")</code>, and it has shape <code>(9, 1710, 1272)</code></p> In\u00a0[\u00a0]: Copied! <pre>dataset[\"rhot_SWIR\"].sizes\n</pre> dataset[\"rhot_SWIR\"].sizes <p>Let's plot the reflectance at position <code>100</code> in the <code>blue_bands</code> dimension.</p> In\u00a0[\u00a0]: Copied! <pre>plot = dataset[\"rhot_blue\"].sel({\"blue_bands\": 100}).plot()\n</pre> plot = dataset[\"rhot_blue\"].sel({\"blue_bands\": 100}).plot() <p></p>"},{"location":"examples/pace_oci_l1/#visualizing-pace-oci-l1-data-products-with-hypercoast","title":"Visualizing PACE OCI L1 data products with HyperCoast\u00b6","text":"<p>This notebook demonstrates how to visualize Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) OCI L1 data products. Part of the notebook is adapted from the NASA OB.DAAC tutorial - File Structure at Three Processing Levels for the Ocean Color Instrument (OCI). Credits to the NASA OB.DAAC team for the tutorial.</p>"},{"location":"examples/pace_oci_l2/","title":"Pace oci l2","text":"In\u00a0[1]: Copied! <pre># %pip install \"hypercoast[extra]\"\n</pre> # %pip install \"hypercoast[extra]\" In\u00a0[2]: Copied! <pre>import hypercoast\n</pre> import hypercoast <p>To download and access the data, you will need to create an Earthdata login. You can register for an account at urs.earthdata.nasa.gov. Once you have an account, you can uncomment and run the following cell to search and download PACE OCI L2 data products.</p> In\u00a0[3]: Copied! <pre># hypercoast.nasa_earth_login()\n\n# short_name = \"PACE_OCI_L2_BGC_NRT\"\n# results, gdf = hypercoast.search_nasa_data(\n#     short_name=short_name,\n#     bbox=(-90.5642, 29.9749, -89.7143, 30.42),\n#     temporal=(\"2024-06-15\", \"2024-06-16\"),\n#     return_gdf=True\n#     )\n# hypercoast.download_nasa_data(results, out_dir=\"bgc\")\n</pre> # hypercoast.nasa_earth_login()  # short_name = \"PACE_OCI_L2_BGC_NRT\" # results, gdf = hypercoast.search_nasa_data( #     short_name=short_name, #     bbox=(-90.5642, 29.9749, -89.7143, 30.42), #     temporal=(\"2024-06-15\", \"2024-06-16\"), #     return_gdf=True #     ) # hypercoast.download_nasa_data(results, out_dir=\"bgc\") <p>Alternatively, use the following code block to download a sample dataset from here.</p> In\u00a0[4]: Copied! <pre>url = \"https://github.com/opengeos/datasets/releases/download/hypercoast/PACE_OCI.20240615T182549.L2.OC_BGC.V1_0_0.NRT.nc\"\nfilepath = \"data/PACE_OCI.20240615T182549.L2.OC_BGC.V1_0_0.NRT.nc\"\nhypercoast.download_file(url, filepath)\n</pre> url = \"https://github.com/opengeos/datasets/releases/download/hypercoast/PACE_OCI.20240615T182549.L2.OC_BGC.V1_0_0.NRT.nc\" filepath = \"data/PACE_OCI.20240615T182549.L2.OC_BGC.V1_0_0.NRT.nc\" hypercoast.download_file(url, filepath) Out[4]: <pre>'/home/runner/work/HyperCoast/HyperCoast/docs/examples/data/PACE_OCI.20240615T182549.L2.OC_BGC.V1_0_0.NRT.nc'</pre> <p>Load the downloaded dataset as an <code>xarray.Dataset</code>:</p> In\u00a0[5]: Copied! <pre>dataset = hypercoast.read_pace_bgc(filepath)\n</pre> dataset = hypercoast.read_pace_bgc(filepath) <p>Let's inspect the data variables contained in the dataset:</p> In\u00a0[6]: Copied! <pre>dataset.variables\n</pre> dataset.variables Out[6]: <pre>Frozen({'chlor_a': &lt;xarray.Variable (latitude: 1710, longitude: 1272)&gt; Size: 9MB\n[2175120 values with dtype=float32]\nAttributes:\n    long_name:      Chlorophyll Concentration, OCI Algorithm\n    units:          mg m^-3\n    standard_name:  mass_concentration_of_chlorophyll_in_sea_water\n    valid_min:      0.001\n    valid_max:      100.0\n    reference:      Hu, C., Lee Z., and Franz, B.A. (2012). Chlorophyll-a alg..., 'carbon_phyto': &lt;xarray.Variable (latitude: 1710, longitude: 1272)&gt; Size: 9MB\n[2175120 values with dtype=float32]\nAttributes:\n    long_name:  Phytoplankton Carbon\n    units:      mg m^-3\n    valid_min:  0.0\n    valid_max:  1000.0\n    reference:  Graff, J.R., Westberry, T.K., Milligan, A.J., Brown, M.B., Da..., 'poc': &lt;xarray.Variable (latitude: 1710, longitude: 1272)&gt; Size: 9MB\n[2175120 values with dtype=float32]\nAttributes:\n    long_name:  Particulate Organic Carbon, D. Stramski, 2022 (hybrid version)\n    units:      mg m^-3\n    valid_min:  -32000\n    valid_max:  -22000\n    reference:  Stramski, D., et al. \"Ocean color algorithms to estimate the ..., 'chlor_a_unc': &lt;xarray.Variable (latitude: 1710, longitude: 1272)&gt; Size: 9MB\n[2175120 values with dtype=float32]\nAttributes:\n    long_name:      Uncertainty in chlorophyll a concentration\n    units:          mg m^-3\n    standard_name:  chlorophyll_concentration_in_sea_water standard_error\n    valid_min:      0.001\n    valid_max:      100.0, 'carbon_phyto_unc': &lt;xarray.Variable (latitude: 1710, longitude: 1272)&gt; Size: 9MB\n[2175120 values with dtype=float32]\nAttributes:\n    long_name:  Phytoplankton Carbon standard uncertainty\n    units:      mg m^-3\n    valid_min:  0.0\n    valid_max:  1000.0\n    reference:  Graff, J.R., Westberry, T.K., Milligan, A.J., Brown, M.B., Da..., 'l2_flags': &lt;xarray.Variable (latitude: 1710, longitude: 1272)&gt; Size: 9MB\n[2175120 values with dtype=int32]\nAttributes:\n    long_name:      Level-2 Processing Flags\n    valid_min:      -2147483648\n    valid_max:      2147483647\n    flag_masks:     [          1           2           4           8         ...\n    flag_meanings:  ATMFAIL LAND PRODWARN HIGLINT HILT HISATZEN COASTZ SPARE ..., 'longitude': &lt;xarray.Variable (latitude: 1710, longitude: 1272)&gt; Size: 9MB\n[2175120 values with dtype=float32]\nAttributes:\n    long_name:      Longitude\n    units:          degrees_east\n    standard_name:  longitude\n    valid_min:      -180.0\n    valid_max:      180.0, 'latitude': &lt;xarray.Variable (latitude: 1710, longitude: 1272)&gt; Size: 9MB\n[2175120 values with dtype=float32]\nAttributes:\n    long_name:      Latitude\n    units:          degrees_north\n    standard_name:  latitude\n    valid_min:      -90.0\n    valid_max:      90.0})</pre> <p>We can see that the dataset contains the following variables:</p> <ul> <li>Chlorophyll Concentration</li> <li>Phytoplankton Carbon</li> <li>Particulate Organic Carbon</li> </ul> <p>Transform the xarray dataset into gridded data.</p> <p>Plot the Chlorophyll Concentration.</p> In\u00a0[7]: Copied! <pre>chlor_a = hypercoast.grid_pace_bgc(dataset, variable=\"chlor_a\", method=\"linear\")\nchlor_a.plot(vmin=0, vmax=20, cmap=\"jet\", size=6)\n</pre> chlor_a = hypercoast.grid_pace_bgc(dataset, variable=\"chlor_a\", method=\"linear\") chlor_a.plot(vmin=0, vmax=20, cmap=\"jet\", size=6) Out[7]: <pre>&lt;matplotlib.collections.QuadMesh at 0x7f9431aecad0&gt;</pre> <p>Plot the Phytoplankton Carbon.</p> In\u00a0[8]: Copied! <pre>carbon_phyto = hypercoast.grid_pace_bgc(\n    dataset, variable=\"carbon_phyto\", method=\"linear\"\n)\ncarbon_phyto.plot(vmin=0, vmax=120, cmap=\"jet\", size=6)\n</pre> carbon_phyto = hypercoast.grid_pace_bgc(     dataset, variable=\"carbon_phyto\", method=\"linear\" ) carbon_phyto.plot(vmin=0, vmax=120, cmap=\"jet\", size=6) Out[8]: <pre>&lt;matplotlib.collections.QuadMesh at 0x7f9430fecb00&gt;</pre> <p>Particulate Organic Carbon.</p> In\u00a0[9]: Copied! <pre>poc = hypercoast.grid_pace_bgc(dataset, variable=\"poc\", method=\"linear\")\npoc.plot(vmin=0, vmax=1000, cmap=\"jet\")\n</pre> poc = hypercoast.grid_pace_bgc(dataset, variable=\"poc\", method=\"linear\") poc.plot(vmin=0, vmax=1000, cmap=\"jet\") Out[9]: <pre>&lt;matplotlib.collections.QuadMesh at 0x7f9430b3ef90&gt;</pre> <p>Plot the data on an interactive map.</p> In\u00a0[10]: Copied! <pre>m = hypercoast.Map()\nm.add_basemap(\"Hybrid\")\nm.add_raster(chlor_a, layer_name=\"Chlorophyll-a\", colormap=\"jet\", vmin=0, vmax=20)\nm.add_raster(\n    carbon_phyto, layer_name=\"Phytoplankton Carbon\", colormap=\"plasma\", vmin=0, vmax=120\n)\nm.add_raster(\n    poc, layer_name=\"Particulate Organic Carbon\", colormap=\"coolwarm\", vmin=0, vmax=1000\n)\nm.add_layer_manager()\n\nm.add_colormap(cmap=\"jet\", vmin=0, vmax=20, label=\"Chlorophyll-a (mg/m3)\")\nm.add_colormap(cmap=\"plasma\", vmin=0, vmax=120, label=\"Phytoplankton Carbon (mg/m3)\")\nm.add_colormap(\n    cmap=\"coolwarm\", vmin=0, vmax=1000, label=\"Particulate Organic Carbon (mg/m3)\"\n)\nm\n</pre> m = hypercoast.Map() m.add_basemap(\"Hybrid\") m.add_raster(chlor_a, layer_name=\"Chlorophyll-a\", colormap=\"jet\", vmin=0, vmax=20) m.add_raster(     carbon_phyto, layer_name=\"Phytoplankton Carbon\", colormap=\"plasma\", vmin=0, vmax=120 ) m.add_raster(     poc, layer_name=\"Particulate Organic Carbon\", colormap=\"coolwarm\", vmin=0, vmax=1000 ) m.add_layer_manager()  m.add_colormap(cmap=\"jet\", vmin=0, vmax=20, label=\"Chlorophyll-a (mg/m3)\") m.add_colormap(cmap=\"plasma\", vmin=0, vmax=120, label=\"Phytoplankton Carbon (mg/m3)\") m.add_colormap(     cmap=\"coolwarm\", vmin=0, vmax=1000, label=\"Particulate Organic Carbon (mg/m3)\" ) m Out[10]: <p></p>"},{"location":"examples/pace_oci_l2/#visualizing-pace-oci-l2-data-products-with-hypercoast","title":"Visualizing PACE OCI L2 data products with HyperCoast\u00b6","text":"<p>This notebook demonstrates how to visualize Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) OCI L2 data products, including the concentration of chlorophyll-a, concentration of phytoplankton carbon, and concentration of particulate organic carbon.</p>"},{"location":"examples/pca/","title":"Pca","text":"In\u00a0[1]: Copied! <pre>import hypercoast\n</pre> import hypercoast In\u00a0[2]: Copied! <pre>url = \"https://github.com/opengeos/datasets/releases/download/hypercoast/Planet_2022-09-23.tif\"\nfilepath = \"data/Planet_2022-09-23.tif\"\nhypercoast.download_file(url, filepath)\n</pre> url = \"https://github.com/opengeos/datasets/releases/download/hypercoast/Planet_2022-09-23.tif\" filepath = \"data/Planet_2022-09-23.tif\" hypercoast.download_file(url, filepath) Out[2]: <pre>'/home/runner/work/HyperCoast/HyperCoast/docs/examples/data/Planet_2022-09-23.tif'</pre> In\u00a0[3]: Copied! <pre>output_file = filepath.replace(\".tif\", \"_pca.tif\")\n</pre> output_file = filepath.replace(\".tif\", \"_pca.tif\") In\u00a0[4]: Copied! <pre>hypercoast.pca(filepath, output_file, n_components=3)\n</pre> hypercoast.pca(filepath, output_file, n_components=3) In\u00a0[5]: Copied! <pre>m = hypercoast.Map()\nm.add_basemap(\"Hybrid\")\nm.add_dataset(\n    filepath, indexes=[6, 4, 2], vmin=0, vmax=2500, layer_name=\"Planet Image\", nodata=0\n)\nm.add_dataset(\n    output_file, indexes=[0, 1, 2], vmin=0, vmax=2500, layer_name=\"PCA Image\", nodata=0\n)\nm.add(\"spectral\")\nm\n</pre> m = hypercoast.Map() m.add_basemap(\"Hybrid\") m.add_dataset(     filepath, indexes=[6, 4, 2], vmin=0, vmax=2500, layer_name=\"Planet Image\", nodata=0 ) m.add_dataset(     output_file, indexes=[0, 1, 2], vmin=0, vmax=2500, layer_name=\"PCA Image\", nodata=0 ) m.add(\"spectral\") m Out[5]:"},{"location":"examples/pca/#principal-component-analysis-pca","title":"Principal Component Analysis (PCA)\u00b6","text":"<p>PCA is a technique used to emphasize variation and bring out strong patterns in a dataset. It's often used to make data easy to explore and visualize. In this notebook, we'll perform PCA on a multi-spectral imagery to reduce the dimensionality of the data and visualize the results.</p>"},{"location":"examples/search_data/","title":"Search data","text":"In\u00a0[\u00a0]: Copied! <pre># %pip install \"hypercoast[extra]\"\n</pre> # %pip install \"hypercoast[extra]\" In\u00a0[\u00a0]: Copied! <pre>import hypercoast\n</pre> import hypercoast In\u00a0[\u00a0]: Copied! <pre>hypercoast.nasa_earth_login()\n</pre> hypercoast.nasa_earth_login() In\u00a0[\u00a0]: Copied! <pre>results = hypercoast.search_datasets(instrument=\"oci\")\n</pre> results = hypercoast.search_datasets(instrument=\"oci\") In\u00a0[\u00a0]: Copied! <pre>datasets = set()\nfor item in results:\n    summary = item.summary()\n    short_name = summary[\"short-name\"]\n    if short_name not in datasets:\n        print(short_name)\n    datasets.add(short_name)\nprint(f\"\\nFound {len(datasets)} unique datasets\")\n</pre> datasets = set() for item in results:     summary = item.summary()     short_name = summary[\"short-name\"]     if short_name not in datasets:         print(short_name)     datasets.add(short_name) print(f\"\\nFound {len(datasets)} unique datasets\") In\u00a0[\u00a0]: Copied! <pre>results = hypercoast.search_nasa_data(\n    short_name=\"PACE_OCI_L2_BGC_NRT\",\n    count=1,\n)\n</pre> results = hypercoast.search_nasa_data(     short_name=\"PACE_OCI_L2_BGC_NRT\",     count=1, ) <p>We can refine our search by passing more parameters that describe the spatiotemporal domain of our use case. Here, we use the temporal parameter to request a date range and the bounding_box parameter to request granules that intersect with a bounding box. We can even provide a cloud_cover threshold to limit files that have a lower percentage of cloud cover. We do not provide a count, so we'll get all granules that satisfy the constraints.</p> In\u00a0[\u00a0]: Copied! <pre>tspan = (\"2024-04-01\", \"2024-04-16\")\nbbox = (-76.75, 36.97, -75.74, 39.01)\nclouds = (0, 50)\n\nresults, gdf = hypercoast.search_nasa_data(\n    short_name=\"PACE_OCI_L2_BGC_NRT\",\n    temporal=tspan,\n    bounding_box=bbox,\n    cloud_cover=clouds,\n    return_gdf=True,\n)\n</pre> tspan = (\"2024-04-01\", \"2024-04-16\") bbox = (-76.75, 36.97, -75.74, 39.01) clouds = (0, 50)  results, gdf = hypercoast.search_nasa_data(     short_name=\"PACE_OCI_L2_BGC_NRT\",     temporal=tspan,     bounding_box=bbox,     cloud_cover=clouds,     return_gdf=True, ) <p>Display the footprints of the granules that match the search criteria.</p> In\u00a0[\u00a0]: Copied! <pre>gdf.explore()\n</pre> gdf.explore() <p>Displaying a single result shows a direct download link: try it! The link will download the granule to your local machine, which may or may not be what you want to do. Even if you are running the notebook on a remote host, this download link will open a new browser tab or window and offer to save a file to your local machine. If you are running the notebook locally, this may be of use.</p> In\u00a0[\u00a0]: Copied! <pre>results[0]\n</pre> results[0] <p>We can also download all the results with one command.</p> In\u00a0[\u00a0]: Copied! <pre>hypercoast.download_nasa_data(results, out_dir=\"data\")\n</pre> hypercoast.download_nasa_data(results, out_dir=\"data\") In\u00a0[\u00a0]: Copied! <pre>hypercoast.nasa_earth_login()\n</pre> hypercoast.nasa_earth_login() In\u00a0[\u00a0]: Copied! <pre>results, gdf = hypercoast.search_pace(\n    bounding_box=(-83, 25, -81, 28),\n    temporal=(\"2024-05-10\", \"2024-05-16\"),\n    count=10,  # use -1 to return all datasets\n    return_gdf=True,\n)\n</pre> results, gdf = hypercoast.search_pace(     bounding_box=(-83, 25, -81, 28),     temporal=(\"2024-05-10\", \"2024-05-16\"),     count=10,  # use -1 to return all datasets     return_gdf=True, ) In\u00a0[\u00a0]: Copied! <pre>gdf.explore()\n</pre> gdf.explore() <p></p> In\u00a0[\u00a0]: Copied! <pre>hypercoast.download_pace(results[:2], out_dir=\"data\")\n</pre> hypercoast.download_pace(results[:2], out_dir=\"data\") In\u00a0[\u00a0]: Copied! <pre>results, gdf = hypercoast.search_emit(\n    bounding_box=(-83, 25, -81, 28),\n    temporal=(\"2024-04-01\", \"2024-05-16\"),\n    count=10,  # use -1 to return all datasets\n    return_gdf=True,\n)\n</pre> results, gdf = hypercoast.search_emit(     bounding_box=(-83, 25, -81, 28),     temporal=(\"2024-04-01\", \"2024-05-16\"),     count=10,  # use -1 to return all datasets     return_gdf=True, ) In\u00a0[\u00a0]: Copied! <pre>gdf.explore()\n</pre> gdf.explore() <p></p> In\u00a0[\u00a0]: Copied! <pre>hypercoast.download_emit(results[:2], out_dir=\"data\")\n</pre> hypercoast.download_emit(results[:2], out_dir=\"data\") In\u00a0[\u00a0]: Copied! <pre>results, gdf = hypercoast.search_ecostress(\n    bbox=(-120.522, 34.4266, -120.2665, 34.5653),\n    temporal=(\"2023-04-01\", \"2023-04-02\"),\n    count=-1,  # use -1 to return all datasets\n    return_gdf=True,\n)\n</pre> results, gdf = hypercoast.search_ecostress(     bbox=(-120.522, 34.4266, -120.2665, 34.5653),     temporal=(\"2023-04-01\", \"2023-04-02\"),     count=-1,  # use -1 to return all datasets     return_gdf=True, ) In\u00a0[\u00a0]: Copied! <pre>gdf.explore()\n</pre> gdf.explore() In\u00a0[\u00a0]: Copied! <pre>hypercoast.download_ecostress(results[:5], out_dir=\"data\")\n</pre> hypercoast.download_ecostress(results[:5], out_dir=\"data\") In\u00a0[\u00a0]: Copied! <pre>m = hypercoast.Map(center=[27.25, -83.05], zoom=6)\nm.search_pace()\nm\n</pre> m = hypercoast.Map(center=[27.25, -83.05], zoom=6) m.search_pace() m In\u00a0[\u00a0]: Copied! <pre># m._NASA_DATA_GDF.head()\n</pre> # m._NASA_DATA_GDF.head() In\u00a0[\u00a0]: Copied! <pre># hypercoast.download_pace(m._NASA_DATA_RESULTS[:2], out_dir=\"data\")\n</pre> # hypercoast.download_pace(m._NASA_DATA_RESULTS[:2], out_dir=\"data\") <p>Search for EMIT data interactively.</p> In\u00a0[\u00a0]: Copied! <pre>m = hypercoast.Map(center=[27.25, -83.05], zoom=6)\nm.search_emit()\nm\n</pre> m = hypercoast.Map(center=[27.25, -83.05], zoom=6) m.search_emit() m In\u00a0[\u00a0]: Copied! <pre># m._NASA_DATA_GDF.head()\n</pre> # m._NASA_DATA_GDF.head() In\u00a0[\u00a0]: Copied! <pre># hypercoast.download_emit(m._NASA_DATA_RESULTS[:2], out_dir=\"data\")\n</pre> # hypercoast.download_emit(m._NASA_DATA_RESULTS[:2], out_dir=\"data\") <p>Search for ECOSTRESS data interactively.</p> In\u00a0[\u00a0]: Copied! <pre>m = hypercoast.Map(center=[34.5014, -120.4032], zoom=11)\nm.search_ecostress()\nm\n</pre> m = hypercoast.Map(center=[34.5014, -120.4032], zoom=11) m.search_ecostress() m In\u00a0[\u00a0]: Copied! <pre># m._NASA_DATA_GDF.head()\n</pre> # m._NASA_DATA_GDF.head() In\u00a0[\u00a0]: Copied! <pre># hypercoast.download_ecostress(m._NASA_DATA_RESULTS[:2], out_dir=\"data\")\n</pre> # hypercoast.download_ecostress(m._NASA_DATA_RESULTS[:2], out_dir=\"data\")"},{"location":"examples/search_data/#search-and-download-nasa-hyperspectral-data-with-hypercoast","title":"Search and download NASA hyperspectral data with HyperCoast\u00b6","text":"<p>This notebook demonstrates how to search and download NASA hyperspectral data (e.g., EMIT, PACE) and ECOSTRESS temperature data with HyperCoast. Part of the source code is adapted from the NASA OB.DAAC tutorial - Access Data from the Ocean Color Instrument (OCI). Credits to the NASA OB.DAAC team.</p>"},{"location":"examples/search_data/#import-library","title":"Import library\u00b6","text":""},{"location":"examples/search_data/#login-to-earthdata","title":"Login to Earthdata\u00b6","text":"<p>To download and access the data, you will need to create an Earthdata login. You can register for an account at urs.earthdata.nasa.gov.</p>"},{"location":"examples/search_data/#search-for-datasets","title":"Search for datasets\u00b6","text":"<p>Collections on NASA Earthdata are discovered with the search_datasets function, which accepts an instrument filter as an easy way to get started. Each of the items in the list of collections returned has a \"short-name\".</p>"},{"location":"examples/search_data/#search-for-data-by-short-name","title":"Search for data by short name\u00b6","text":"<p>Next, we use the <code>search_nasa_data</code> function to find granules within a collection. Let's use the <code>short_name</code> for the PACE/OCI Level-2 data product for bio-optical and biogeochemical properties.</p>"},{"location":"examples/search_data/#search-for-pace-data","title":"Search for PACE data\u00b6","text":""},{"location":"examples/search_data/#download-pace-data","title":"Download PACE data\u00b6","text":"<p>Download the first 2 files</p>"},{"location":"examples/search_data/#search-for-emit-data","title":"Search for EMIT data\u00b6","text":""},{"location":"examples/search_data/#download-emit-data","title":"Download EMIT data\u00b6","text":"<p>Download the first 2 files</p>"},{"location":"examples/search_data/#download-ecostress-data","title":"Download ECOSTRESS data\u00b6","text":""},{"location":"examples/search_data/#interactive-search","title":"Interactive search\u00b6","text":"<p>Search for PACE data interactively.</p>"},{"location":"examples/temperature/","title":"Temperature","text":"In\u00a0[\u00a0]: Copied! <pre>import hypercoast\n</pre> import hypercoast In\u00a0[\u00a0]: Copied! <pre>url = \"https://github.com/opengeos/datasets/releases/download/hypercoast/ERA5_temperature_2023.nc\"\nfilepath = \"data/ERA5_temperature_2023.nc\"\nhypercoast.download_file(url, filepath)\n</pre> url = \"https://github.com/opengeos/datasets/releases/download/hypercoast/ERA5_temperature_2023.nc\" filepath = \"data/ERA5_temperature_2023.nc\" hypercoast.download_file(url, filepath) In\u00a0[\u00a0]: Copied! <pre>dataset = hypercoast.open_dataset(filepath)\ndataset\n</pre> dataset = hypercoast.open_dataset(filepath) dataset In\u00a0[\u00a0]: Copied! <pre>camera_position = [(-479.09, -82.89, -444.45), (89.5, 179.5, 16.5), (0.58, 0.14, -0.80)]\n</pre> camera_position = [(-479.09, -82.89, -444.45), (89.5, 179.5, 16.5), (0.58, 0.14, -0.80)] In\u00a0[\u00a0]: Copied! <pre>p = hypercoast.image_cube(\n    dataset,\n    variable=\"temperature_2m\",\n    clim=(270, 310),\n    title=\"Temperature\",\n    cmap=\"coolwarm\",\n    widget=\"plane\",\n    invert=False,\n    grid_spacing=(1, 1, 3),\n)\np.camera_position = camera_position\np.show()\n</pre> p = hypercoast.image_cube(     dataset,     variable=\"temperature_2m\",     clim=(270, 310),     title=\"Temperature\",     cmap=\"coolwarm\",     widget=\"plane\",     invert=False,     grid_spacing=(1, 1, 3), ) p.camera_position = camera_position p.show() In\u00a0[\u00a0]: Copied! <pre>p = hypercoast.image_cube(\n    dataset,\n    variable=\"temperature_2m\",\n    clim=(270, 310),\n    title=\"Temperature\",\n    cmap=\"coolwarm\",\n    widget=\"slice\",\n    grid_spacing=(1, 1, 3),\n)\np.camera_position = camera_position\np.show()\n</pre> p = hypercoast.image_cube(     dataset,     variable=\"temperature_2m\",     clim=(270, 310),     title=\"Temperature\",     cmap=\"coolwarm\",     widget=\"slice\",     grid_spacing=(1, 1, 3), ) p.camera_position = camera_position p.show() In\u00a0[\u00a0]: Copied! <pre>p = hypercoast.image_cube(\n    dataset,\n    variable=\"temperature_2m\",\n    clim=(270, 310),\n    title=\"Temperature\",\n    cmap=\"coolwarm\",\n    widget=\"threshold\",\n)\np.camera_position = camera_position\np.show()\n</pre> p = hypercoast.image_cube(     dataset,     variable=\"temperature_2m\",     clim=(270, 310),     title=\"Temperature\",     cmap=\"coolwarm\",     widget=\"threshold\", ) p.camera_position = camera_position p.show()"},{"location":"examples/temperature/#visualizing-era5-temperature-data-interactively-with-hypercoast","title":"Visualizing ERA5 temperature data interactively with HyperCoast\u00b6","text":"<p>This notebook demonstrates how to visualize ERA5 temperature data interactively with HyperCoast.</p>"},{"location":"workshops/emit/","title":"Emit","text":"In\u00a0[1]: Copied! <pre># %pip install \"hypercoast[extra]\"\n</pre> # %pip install \"hypercoast[extra]\" <p>Import library.</p> In\u00a0[2]: Copied! <pre>import hypercoast\n</pre> import hypercoast <p>To download and access the data, you will need to create an Earthdata login. You can register for an account at urs.earthdata.nasa.gov. Once you have an account, run the following cell and enter your NASA Earthdata login credentials.</p> In\u00a0[3]: Copied! <pre>hypercoast.nasa_earth_login()\n</pre> hypercoast.nasa_earth_login() In\u00a0[4]: Copied! <pre>results, gdf = hypercoast.search_emit(\n    bbox=(-83, 25, -81, 28),\n    temporal=(\"2024-04-01\", \"2024-05-16\"),\n    count=10,  # use -1 to return all datasets\n    return_gdf=True,\n)\n</pre> results, gdf = hypercoast.search_emit(     bbox=(-83, 25, -81, 28),     temporal=(\"2024-04-01\", \"2024-05-16\"),     count=10,  # use -1 to return all datasets     return_gdf=True, ) <p>Plot the footprints of the returned datasets on a map.</p> In\u00a0[5]: Copied! <pre>gdf.explore()\n</pre> gdf.explore() Out[5]: Make this Notebook Trusted to load map: File -&gt; Trust Notebook <p>Uncomment the following cell to download the first dataset from the search results. Note that the download may take some time.</p> In\u00a0[6]: Copied! <pre># hypercoast.download_emit(results[:1], out_dir=\"data\")\n</pre> # hypercoast.download_emit(results[:1], out_dir=\"data\") <p>Search for EMIT data interactively. Specify pan and zoom to the area of interest. Specify the time range of interest from the search dialog, then click on the Search button.</p> In\u00a0[7]: Copied! <pre>m = hypercoast.Map(center=[30.0262, -90.1345], zoom=8)\nm.search_emit()\nm\n</pre> m = hypercoast.Map(center=[30.0262, -90.1345], zoom=8) m.search_emit() m Out[7]: <p>Uncomment the following cell to display the GeoDataFrame of the search results.</p> In\u00a0[8]: Copied! <pre># m._NASA_DATA_GDF.head()\n</pre> # m._NASA_DATA_GDF.head() <p>Similarly, you can download the first dataset from the search results by uncommenting the following cell.</p> In\u00a0[9]: Copied! <pre># hypercoast.download_emit(results[:1], out_dir=\"data\")\n</pre> # hypercoast.download_emit(results[:1], out_dir=\"data\") In\u00a0[10]: Copied! <pre>url = \"https://github.com/opengeos/datasets/releases/download/hypercoast/EMIT_L2A_RFL_001_20240404T161230_2409511_009.nc\"\nfilepath = \"../examples/data/EMIT_L2A_RFL_001_20240404T161230_2409511_009.nc\"\nhypercoast.download_file(url, filepath, quiet=True)\n</pre> url = \"https://github.com/opengeos/datasets/releases/download/hypercoast/EMIT_L2A_RFL_001_20240404T161230_2409511_009.nc\" filepath = \"../examples/data/EMIT_L2A_RFL_001_20240404T161230_2409511_009.nc\" hypercoast.download_file(url, filepath, quiet=True) <pre>../examples/data/EMIT_L2A_RFL_001_20240404T161230_2409511_009.nc already exists. Skip downloading. Set overwrite=True to overwrite.\n</pre> Out[10]: <pre>'/home/runner/work/HyperCoast/HyperCoast/docs/examples/data/EMIT_L2A_RFL_001_20240404T161230_2409511_009.nc'</pre> In\u00a0[11]: Copied! <pre>dataset = hypercoast.read_emit(filepath)\n# dataset\n</pre> dataset = hypercoast.read_emit(filepath) # dataset <p></p> In\u00a0[12]: Copied! <pre>m = hypercoast.Map()\nm.add_basemap(\"SATELLITE\")\nm.add_emit(dataset, wavelengths=[1000, 600, 500], vmin=0, vmax=0.3, layer_name=\"EMIT\")\nm.add(\"spectral\")\nm\n</pre> m = hypercoast.Map() m.add_basemap(\"SATELLITE\") m.add_emit(dataset, wavelengths=[1000, 600, 500], vmin=0, vmax=0.3, layer_name=\"EMIT\") m.add(\"spectral\") m Out[12]: <p></p> In\u00a0[13]: Copied! <pre>ds = dataset.sel(longitude=slice(-90.1482, -89.7321), latitude=slice(30.0225, 29.7451))\n</pre> ds = dataset.sel(longitude=slice(-90.1482, -89.7321), latitude=slice(30.0225, 29.7451)) <p>Visualize the EMIT data in 3D with an RGB image overlaid on top of the 3D plot.</p> In\u00a0[14]: Copied! <pre>p = hypercoast.image_cube(\n    ds,\n    variable=\"reflectance\",\n    cmap=\"jet\",\n    clim=(0, 0.4),\n    rgb_wavelengths=[1000, 700, 500],\n    rgb_gamma=2,\n    title=\"EMIT Reflectance\",\n)\n</pre> p = hypercoast.image_cube(     ds,     variable=\"reflectance\",     cmap=\"jet\",     clim=(0, 0.4),     rgb_wavelengths=[1000, 700, 500],     rgb_gamma=2,     title=\"EMIT Reflectance\", ) <p>Uncomment the following cell to create an image cube. Note that this function does not work in the Google Colab environment.</p> In\u00a0[15]: Copied! <pre># p.show()\n</pre> # p.show() <p></p> In\u00a0[16]: Copied! <pre>ds = dataset.sel(longitude=slice(-90.05, -89.99), latitude=slice(30.00, 29.93))\n</pre> ds = dataset.sel(longitude=slice(-90.05, -89.99), latitude=slice(30.00, 29.93)) <p>Drag the plane up and down to slice the data in 3D.</p> In\u00a0[17]: Copied! <pre>p = hypercoast.image_cube(\n    ds,\n    variable=\"reflectance\",\n    cmap=\"jet\",\n    clim=(0, 0.5),\n    rgb_wavelengths=[1000, 700, 500],\n    rgb_gamma=2,\n    title=\"EMIT Reflectance\",\n    widget=\"plane\",\n)\np.add_text(\"Band slicing\", position=\"upper_right\", font_size=14)\n</pre> p = hypercoast.image_cube(     ds,     variable=\"reflectance\",     cmap=\"jet\",     clim=(0, 0.5),     rgb_wavelengths=[1000, 700, 500],     rgb_gamma=2,     title=\"EMIT Reflectance\",     widget=\"plane\", ) p.add_text(\"Band slicing\", position=\"upper_right\", font_size=14) Out[17]: <pre>&lt;CornerAnnotation(0xd89b00) at 0x7fcbd2d62fe0&gt;</pre> <p>Uncomment the following cell to display the interactive slicing widget. Note that this function does not work in the Google Colab environment.</p> In\u00a0[18]: Copied! <pre># p.show()\n</pre> # p.show() <p></p> In\u00a0[19]: Copied! <pre>p = hypercoast.image_cube(\n    ds,\n    variable=\"reflectance\",\n    cmap=\"jet\",\n    clim=(0, 0.5),\n    rgb_wavelengths=[1000, 700, 500],\n    rgb_gamma=2,\n    title=\"EMIT Reflectance\",\n    widget=\"threshold\",\n)\np.add_text(\"Thresholding\", position=\"upper_right\", font_size=14)\n</pre> p = hypercoast.image_cube(     ds,     variable=\"reflectance\",     cmap=\"jet\",     clim=(0, 0.5),     rgb_wavelengths=[1000, 700, 500],     rgb_gamma=2,     title=\"EMIT Reflectance\",     widget=\"threshold\", ) p.add_text(\"Thresholding\", position=\"upper_right\", font_size=14) Out[19]: <pre>&lt;CornerAnnotation(0x605f9b0) at 0x7fcbd2ba0ca0&gt;</pre> <p>Uncomment the following cell to display the thresholded data. Note that this function does not work in the Google Colab environment.</p> In\u00a0[20]: Copied! <pre># p.show()\n</pre> # p.show() <p></p>"},{"location":"workshops/emit/#working-with-nasa-emit-data-in-hypercoast","title":"Working with NASA EMIT data in HyperCoast\u00b6","text":"<p>This notebook demonstrates how to work with NASA Earth Surface Mineral Dust Source Investigation (EMIT) data in HyperCoast.</p>"},{"location":"workshops/emit/#environment-setup","title":"Environment setup\u00b6","text":"<p>Uncomment and run the following cell to install the required packages.</p>"},{"location":"workshops/emit/#search-for-emit-data","title":"Search for EMIT data\u00b6","text":"<p>Search for EMIT data programmatically. Specify the bounding box and time range of interest. Set <code>count=-1</code> to return all results or set <code>count=10</code> to return the first 10 results.</p>"},{"location":"workshops/emit/#download-a-sample-emit-dataset","title":"Download a sample EMIT dataset\u00b6","text":"<p>Let's download a sample EMIT dataset for the demonstration.</p>"},{"location":"workshops/emit/#read-emit-data","title":"Read EMIT data\u00b6","text":"<p>Read the downloaded EMIT data and process it as an <code>xarray.Dataset</code>. Note that the dataset has 285 bands.</p>"},{"location":"workshops/emit/#visualize-emit-data","title":"Visualize EMIT data\u00b6","text":"<p>Visualize the EMIT data on an interactive map. You can change the band combination and extract spectral profiles interactively. You can also export the spectral profiles as a CSV file.</p>"},{"location":"workshops/emit/#create-an-image-cube","title":"Create an image cube\u00b6","text":"<p>First, select a subset of the data to avoid nodata areas.</p>"},{"location":"workshops/emit/#interactive-slicing","title":"Interactive slicing\u00b6","text":"<p>First, select a subset of the data for demonstration purposes.</p>"},{"location":"workshops/emit/#interactive-thresholding","title":"Interactive thresholding\u00b6","text":"<p>Drag the threshold slider to threshold the data in 3D.</p>"},{"location":"workshops/pace/","title":"Pace","text":"In\u00a0[1]: Copied! <pre># %pip install \"hypercoast[extra]\"\n</pre> # %pip install \"hypercoast[extra]\" <p>Import libraries.</p> In\u00a0[2]: Copied! <pre>import earthaccess\nimport hypercoast\nimport pandas as pd\n</pre> import earthaccess import hypercoast import pandas as pd In\u00a0[3]: Copied! <pre>earthaccess.login(persist=True)\n</pre> earthaccess.login(persist=True) Out[3]: <pre>&lt;earthaccess.auth.Auth at 0x7f85cade0620&gt;</pre> In\u00a0[4]: Copied! <pre>results, gdf = hypercoast.search_pace(\n    bounding_box=(-83, 25, -81, 28),\n    temporal=(\"2024-07-30\", \"2024-08-15\"),\n    short_name=\"PACE_OCI_L2_AOP_NRT\",\n    count=10,\n    return_gdf=True,\n)\n</pre> results, gdf = hypercoast.search_pace(     bounding_box=(-83, 25, -81, 28),     temporal=(\"2024-07-30\", \"2024-08-15\"),     short_name=\"PACE_OCI_L2_AOP_NRT\",     count=10,     return_gdf=True, ) <p>Plot the footprints of the returned datasets on a map.</p> In\u00a0[5]: Copied! <pre>gdf.explore()\n</pre> gdf.explore() Out[5]: Make this Notebook Trusted to load map: File -&gt; Trust Notebook <p>Download the first dataset from the search results. Note that the download may take some time.</p> In\u00a0[6]: Copied! <pre>hypercoast.download_pace(results[:1], out_dir=\"data\")\n</pre> hypercoast.download_pace(results[:1], out_dir=\"data\") In\u00a0[7]: Copied! <pre>m = hypercoast.Map(center=[30.0262, -90.1345], zoom=8)\nm.search_pace(default_dataset=\"PACE_OCI_L2_AOP_NRT\")\nm\n</pre> m = hypercoast.Map(center=[30.0262, -90.1345], zoom=8) m.search_pace(default_dataset=\"PACE_OCI_L2_AOP_NRT\") m Out[7]: <p>By default, the <code>search_pace</code> method searches for the <code>PACE_OCI_L2_AOP_NRT</code> dataset, but you can specify the dataset name by setting the <code>default_dataset</code> parameter, such as <code>PACE_OCI_L2_BGC_NRT</code>. For more information about the available datasets, see the PACE Data Products page.</p> <p></p> <p>Uncomment the following cell to display the GeoDataFrame of the search results.</p> In\u00a0[8]: Copied! <pre># m._NASA_DATA_GDF.head()\n</pre> # m._NASA_DATA_GDF.head() <p>Similarly, you can download the first dataset from the search results by uncommenting the following cell.</p> In\u00a0[9]: Copied! <pre># hypercoast.download_pace(results[:1], out_dir=\"data\")\n</pre> # hypercoast.download_pace(results[:1], out_dir=\"data\") In\u00a0[10]: Copied! <pre>results = hypercoast.search_pace(\n    bounding_box=(-83, 25, -81, 28),\n    temporal=(\"2024-07-30\", \"2024-08-15\"),\n    short_name=\"PACE_OCI_L2_AOP_NRT\",\n    count=1,\n)\n</pre> results = hypercoast.search_pace(     bounding_box=(-83, 25, -81, 28),     temporal=(\"2024-07-30\", \"2024-08-15\"),     short_name=\"PACE_OCI_L2_AOP_NRT\",     count=1, ) In\u00a0[11]: Copied! <pre>hypercoast.download_pace(results[:1], out_dir=\"data\")\n</pre> hypercoast.download_pace(results[:1], out_dir=\"data\") <p>Let's make a scatter plot of the pixel locations so we can see the irregular spacing.</p> In\u00a0[12]: Copied! <pre>filepath = \"data/PACE_OCI.20240730T181157.L2.OC_AOP.V2_0.NRT.nc\"\nplot = hypercoast.view_pace_pixel_locations(filepath, step=20)\n</pre> filepath = \"data/PACE_OCI.20240730T181157.L2.OC_AOP.V2_0.NRT.nc\" plot = hypercoast.view_pace_pixel_locations(filepath, step=20) <p>Load the dataset as a <code>xarray.Dataset</code> object.</p> In\u00a0[13]: Copied! <pre>dataset = hypercoast.read_pace(filepath)\n# dataset\n</pre> dataset = hypercoast.read_pace(filepath) # dataset <p></p> In\u00a0[14]: Copied! <pre>hypercoast.viz_pace(dataset, wavelengths=[500, 510, 520, 530], ncols=2)\n</pre> hypercoast.viz_pace(dataset, wavelengths=[500, 510, 520, 530], ncols=2) <p>Add custom projection and administrative boundaries to the map. The default projection is <code>PlateCarree</code>. You can specify a custom projection by setting the <code>crs</code> parameter. For more information about the available projections, see the cartopy projection page.</p> In\u00a0[15]: Copied! <pre>hypercoast.viz_pace(dataset, wavelengths=[500, 510, 520, 530], ncols=2, crs=\"default\")\n</pre> hypercoast.viz_pace(dataset, wavelengths=[500, 510, 520, 530], ncols=2, crs=\"default\") In\u00a0[16]: Copied! <pre>latitude = 29.9307\nlongitude = -87.9106\nhypercoast.extract_pace(dataset, latitude, longitude, return_plot=True)\n</pre> latitude = 29.9307 longitude = -87.9106 hypercoast.extract_pace(dataset, latitude, longitude, return_plot=True) Out[16]: <pre>[&lt;matplotlib.lines.Line2D at 0x7f857ce72e40&gt;]</pre> <p>To return the extracted values as an xarray <code>DataArray</code>, set <code>return_plot=False</code>.</p> In\u00a0[17]: Copied! <pre>array = hypercoast.extract_pace(dataset, latitude, longitude, return_plot=False)\n# array\n</pre> array = hypercoast.extract_pace(dataset, latitude, longitude, return_plot=False) # array <p>To plot the spectral signatures of multiple pixels, you can specify the pixel locations as a list of tuples. All pixels within the specified latitude and longitude range will be extracted.</p> In\u00a0[18]: Copied! <pre>latitude = (29.49, 29.50)\nlongitude = (-88.10, -88.00)\nhypercoast.filter_pace(dataset, latitude, longitude, return_plot=True)\n</pre> latitude = (29.49, 29.50) longitude = (-88.10, -88.00) hypercoast.filter_pace(dataset, latitude, longitude, return_plot=True) <p>Visualize a selected band of the dataset interactively use the <code>add_pace</code> method and speccify the <code>wavelengths</code> parameter.</p> In\u00a0[19]: Copied! <pre>m = hypercoast.Map()\nm.add_basemap(\"Hybrid\")\nwavelengths = [450]\nm.add_pace(dataset, wavelengths, colormap=\"jet\", vmin=0, vmax=0.02, layer_name=\"PACE\")\nm.add_colormap(cmap=\"jet\", vmin=0, vmax=0.02, label=\"Reflectance\")\nm.add(\"spectral\")\nm.set_center(-80.7382, 26.5295, zoom=6)\nm\n</pre> m = hypercoast.Map() m.add_basemap(\"Hybrid\") wavelengths = [450] m.add_pace(dataset, wavelengths, colormap=\"jet\", vmin=0, vmax=0.02, layer_name=\"PACE\") m.add_colormap(cmap=\"jet\", vmin=0, vmax=0.02, label=\"Reflectance\") m.add(\"spectral\") m.set_center(-80.7382, 26.5295, zoom=6) m Out[19]: <p>Click on the map to display the spectral signature of the selected pixel.</p> <p></p> <p>Convert the spectral data of the selected pixels to a DataFrame.</p> In\u00a0[20]: Copied! <pre>df = m.spectral_to_df()\ndf.head()\n</pre> df = m.spectral_to_df() df.head() Out[20]: <p>Convert the spectral data of the selected pixels to a GeoDataFrame.</p> In\u00a0[21]: Copied! <pre>gdf = m.spectral_to_gdf()\ngdf.head()\n</pre> gdf = m.spectral_to_gdf() gdf.head() Out[21]: <p>Convert the spectral data of the selected pixels to a CSV file.</p> In\u00a0[22]: Copied! <pre>m.spectral_to_csv(\"data/spectral.csv\")\n</pre> m.spectral_to_csv(\"data/spectral.csv\") In\u00a0[23]: Copied! <pre>m = hypercoast.Map()\nm.add_basemap(\"Hybrid\")\nwavelengths = [450, 550, 650]\nm.add_pace(\n    dataset, wavelengths, indexes=[3, 2, 1], vmin=0, vmax=0.02, layer_name=\"PACE\"\n)\nm.add(\"spectral\")\nm.set_center(-80.7382, 26.5295, zoom=6)\nm\n</pre> m = hypercoast.Map() m.add_basemap(\"Hybrid\") wavelengths = [450, 550, 650] m.add_pace(     dataset, wavelengths, indexes=[3, 2, 1], vmin=0, vmax=0.02, layer_name=\"PACE\" ) m.add(\"spectral\") m.set_center(-80.7382, 26.5295, zoom=6) m Out[23]: <p></p> In\u00a0[24]: Copied! <pre>results, gdf = hypercoast.search_nasa_data(\n    short_name=\"PACE_OCI_L2_BGC_NRT\",\n    bbox=(-90.5642, 29.9749, -89.7143, 30.42),\n    temporal=(\"2024-07-30\", \"2024-08-15\"),\n    count=1,\n    return_gdf=True,\n)\nhypercoast.download_nasa_data(results, out_dir=\"data\")\n</pre> results, gdf = hypercoast.search_nasa_data(     short_name=\"PACE_OCI_L2_BGC_NRT\",     bbox=(-90.5642, 29.9749, -89.7143, 30.42),     temporal=(\"2024-07-30\", \"2024-08-15\"),     count=1,     return_gdf=True, ) hypercoast.download_nasa_data(results, out_dir=\"data\") <p>Load the downloaded dataset as an <code>xarray.Dataset</code>:</p> In\u00a0[25]: Copied! <pre>filepath = \"data/PACE_OCI.20240730T181157.L2.OC_BGC.V2_0.NRT.nc\"\ndataset = hypercoast.read_pace_bgc(filepath)\n</pre> filepath = \"data/PACE_OCI.20240730T181157.L2.OC_BGC.V2_0.NRT.nc\" dataset = hypercoast.read_pace_bgc(filepath) <p>Let's inspect the data variables contained in the dataset:</p> In\u00a0[26]: Copied! <pre>dataset.variables\n</pre> dataset.variables Out[26]: <pre>Frozen({'chlor_a': &lt;xarray.Variable (latitude: 1710, longitude: 1272)&gt; Size: 9MB\n[2175120 values with dtype=float32]\nAttributes:\n    long_name:      Chlorophyll Concentration, OCI Algorithm\n    units:          mg m^-3\n    standard_name:  mass_concentration_of_chlorophyll_in_sea_water\n    valid_min:      0.001\n    valid_max:      100.0\n    reference:      Hu, C., Lee Z., and Franz, B.A. (2012). Chlorophyll-a alg..., 'carbon_phyto': &lt;xarray.Variable (latitude: 1710, longitude: 1272)&gt; Size: 9MB\n[2175120 values with dtype=float32]\nAttributes:\n    long_name:  Phytoplankton Carbon\n    units:      mg m^-3\n    valid_min:  0.0\n    valid_max:  1000.0\n    reference:  Graff, J.R., Westberry, T.K., Milligan, A.J., Brown, M.B., Da..., 'poc': &lt;xarray.Variable (latitude: 1710, longitude: 1272)&gt; Size: 9MB\n[2175120 values with dtype=float32]\nAttributes:\n    long_name:  Particulate Organic Carbon, D. Stramski, 2022 (hybrid version)\n    units:      mg m^-3\n    valid_min:  -32000\n    valid_max:  -22000\n    reference:  Stramski, D., et al. \"Ocean color algorithms to estimate the ..., 'chlor_a_unc': &lt;xarray.Variable (latitude: 1710, longitude: 1272)&gt; Size: 9MB\n[2175120 values with dtype=float32]\nAttributes:\n    long_name:      Uncertainty in chlorophyll a concentration\n    units:          mg m^-3\n    standard_name:  chlorophyll_concentration_in_sea_water standard_error\n    valid_min:      0.001\n    valid_max:      100.0, 'carbon_phyto_unc': &lt;xarray.Variable (latitude: 1710, longitude: 1272)&gt; Size: 9MB\n[2175120 values with dtype=float32]\nAttributes:\n    long_name:  Phytoplankton Carbon standard uncertainty\n    units:      mg m^-3\n    valid_min:  0.0\n    valid_max:  1000.0\n    reference:  Graff, J.R., Westberry, T.K., Milligan, A.J., Brown, M.B., Da..., 'l2_flags': &lt;xarray.Variable (latitude: 1710, longitude: 1272)&gt; Size: 9MB\n[2175120 values with dtype=int32]\nAttributes:\n    long_name:      Level-2 Processing Flags\n    valid_min:      -2147483648\n    valid_max:      2147483647\n    flag_masks:     [          1           2           4           8         ...\n    flag_meanings:  ATMFAIL LAND PRODWARN HIGLINT HILT HISATZEN COASTZ SPARE ..., 'longitude': &lt;xarray.Variable (latitude: 1710, longitude: 1272)&gt; Size: 9MB\n[2175120 values with dtype=float32]\nAttributes:\n    long_name:      Longitude\n    units:          degrees_east\n    standard_name:  longitude\n    valid_min:      -180.0\n    valid_max:      180.0, 'latitude': &lt;xarray.Variable (latitude: 1710, longitude: 1272)&gt; Size: 9MB\n[2175120 values with dtype=float32]\nAttributes:\n    long_name:      Latitude\n    units:          degrees_north\n    standard_name:  latitude\n    valid_min:      -90.0\n    valid_max:      90.0})</pre> <p>We can see that the dataset contains the following variables:</p> <ul> <li>Chlorophyll Concentration</li> <li>Phytoplankton Carbon</li> <li>Particulate Organic Carbon</li> </ul> In\u00a0[27]: Copied! <pre>chlor_a = hypercoast.grid_pace_bgc(dataset, variable=\"chlor_a\", method=\"linear\")\n</pre> chlor_a = hypercoast.grid_pace_bgc(dataset, variable=\"chlor_a\", method=\"linear\") <p>Plot the gridded Chlorophyll Concentration data:</p> In\u00a0[28]: Copied! <pre>chlor_a.plot(vmin=0, vmax=20, cmap=\"jet\", size=6)\n</pre> chlor_a.plot(vmin=0, vmax=20, cmap=\"jet\", size=6) Out[28]: <pre>&lt;matplotlib.collections.QuadMesh at 0x7f8564c6e240&gt;</pre> <p>Plot the gridded Phytoplankton Carbon data:</p> In\u00a0[29]: Copied! <pre>carbon_phyto = hypercoast.grid_pace_bgc(\n    dataset, variable=\"carbon_phyto\", method=\"linear\"\n)\ncarbon_phyto.plot(vmin=0, vmax=120, cmap=\"jet\", size=6)\n</pre> carbon_phyto = hypercoast.grid_pace_bgc(     dataset, variable=\"carbon_phyto\", method=\"linear\" ) carbon_phyto.plot(vmin=0, vmax=120, cmap=\"jet\", size=6) Out[29]: <pre>&lt;matplotlib.collections.QuadMesh at 0x7f8564ba0050&gt;</pre> <p>Plot the gridded Particulate Organic Carbon data:</p> In\u00a0[30]: Copied! <pre>poc = hypercoast.grid_pace_bgc(dataset, variable=\"poc\", method=\"linear\")\npoc.plot(vmin=0, vmax=1000, cmap=\"jet\")\n</pre> poc = hypercoast.grid_pace_bgc(dataset, variable=\"poc\", method=\"linear\") poc.plot(vmin=0, vmax=1000, cmap=\"jet\") Out[30]: <pre>&lt;matplotlib.collections.QuadMesh at 0x7f8564cd0fb0&gt;</pre> <p>Plot the gridded BGC data on an interactive map.</p> In\u00a0[31]: Copied! <pre>m = hypercoast.Map()\nm.add_basemap(\"Hybrid\")\nm.add_raster(chlor_a, layer_name=\"Chlorophyll-a\", colormap=\"jet\", vmin=0, vmax=20)\nm.add_raster(\n    carbon_phyto, layer_name=\"Phytoplankton Carbon\", colormap=\"plasma\", vmin=0, vmax=120\n)\nm.add_raster(\n    poc, layer_name=\"Particulate Organic Carbon\", colormap=\"coolwarm\", vmin=0, vmax=1000\n)\nm.add_layer_manager()\n\nm.add_colormap(cmap=\"jet\", vmin=0, vmax=20, label=\"Chlorophyll-a (mg/m3)\")\nm.add_colormap(cmap=\"plasma\", vmin=0, vmax=120, label=\"Phytoplankton Carbon (mg/m3)\")\nm.add_colormap(\n    cmap=\"coolwarm\", vmin=0, vmax=1000, label=\"Particulate Organic Carbon (mg/m3)\"\n)\nm.set_center(-80.7382, 26.5295, zoom=6)\nm\n</pre> m = hypercoast.Map() m.add_basemap(\"Hybrid\") m.add_raster(chlor_a, layer_name=\"Chlorophyll-a\", colormap=\"jet\", vmin=0, vmax=20) m.add_raster(     carbon_phyto, layer_name=\"Phytoplankton Carbon\", colormap=\"plasma\", vmin=0, vmax=120 ) m.add_raster(     poc, layer_name=\"Particulate Organic Carbon\", colormap=\"coolwarm\", vmin=0, vmax=1000 ) m.add_layer_manager()  m.add_colormap(cmap=\"jet\", vmin=0, vmax=20, label=\"Chlorophyll-a (mg/m3)\") m.add_colormap(cmap=\"plasma\", vmin=0, vmax=120, label=\"Phytoplankton Carbon (mg/m3)\") m.add_colormap(     cmap=\"coolwarm\", vmin=0, vmax=1000, label=\"Particulate Organic Carbon (mg/m3)\" ) m.set_center(-80.7382, 26.5295, zoom=6) m Out[31]: <p></p> In\u00a0[32]: Copied! <pre>temporal = (\"2024-07-30\", \"2024-08-15\")\nresults = hypercoast.search_pace_chla(temporal=temporal)\nhypercoast.download_nasa_data(results, \"chla\")\n</pre> temporal = (\"2024-07-30\", \"2024-08-15\") results = hypercoast.search_pace_chla(temporal=temporal) hypercoast.download_nasa_data(results, \"chla\") <p>The downloaded datasets can be found in the <code>chla</code> directory, which contains 17 daily files of CCI data in the netCDF format. The date range of the data is from 2024-07-30 to 2024-08-15.</p> In\u00a0[33]: Copied! <pre>files = \"chla/*nc\"\n</pre> files = \"chla/*nc\" <p>Load all the data files in the <code>chla</code> directory as an xarray DataArray</p> In\u00a0[34]: Copied! <pre>array = hypercoast.read_pace_chla(files)\n# array\n</pre> array = hypercoast.read_pace_chla(files) # array <p></p> <p>Select a date and visualize the chlorophyll-a concentration data with Matplotlib.</p> In\u00a0[35]: Copied! <pre>hypercoast.viz_pace_chla(array, date=\"2024-07-30\", cmap=\"jet\", size=6)\n</pre> hypercoast.viz_pace_chla(array, date=\"2024-07-30\", cmap=\"jet\", size=6) Out[35]: <pre>&lt;matplotlib.collections.QuadMesh at 0x7f85647f6ab0&gt;</pre> <p>If the date is not specified, the data are averaged over the entire time range.</p> In\u00a0[36]: Copied! <pre>hypercoast.viz_pace_chla(array, cmap=\"jet\", size=6)\n</pre> hypercoast.viz_pace_chla(array, cmap=\"jet\", size=6) Out[36]: <pre>&lt;matplotlib.collections.QuadMesh at 0x7f8564e8b920&gt;</pre> <p>To visualize the data interactively, we can select either a single date or aggregate the data over a time range.</p> <p>First, let's select a single date from the data array:</p> In\u00a0[37]: Copied! <pre>single_array = array.sel(date=\"2024-07-30\")\n# single_array\n</pre> single_array = array.sel(date=\"2024-07-30\") # single_array <p>Convert the data array to an image that can be displayed on an interactive map.</p> In\u00a0[38]: Copied! <pre>single_image = hypercoast.pace_chla_to_image(single_array)\n</pre> single_image = hypercoast.pace_chla_to_image(single_array) <p>Create an interactive map and display the image on the map.</p> In\u00a0[39]: Copied! <pre>m = hypercoast.Map(center=[40, -100], zoom=4)\nm.add_basemap(\"Hybrid\")\nm.add_raster(\n    single_image,\n    cmap=\"jet\",\n    vmin=-1,\n    vmax=2,\n    layer_name=\"Chlorophyll a\",\n    zoom_to_layer=False,\n)\nlabel = \"Chlorophyll Concentration [lg(lg(mg m^-3))]\"\nm.add_colormap(cmap=\"jet\", vmin=-1, vmax=2, label=label)\nm\n</pre> m = hypercoast.Map(center=[40, -100], zoom=4) m.add_basemap(\"Hybrid\") m.add_raster(     single_image,     cmap=\"jet\",     vmin=-1,     vmax=2,     layer_name=\"Chlorophyll a\",     zoom_to_layer=False, ) label = \"Chlorophyll Concentration [lg(lg(mg m^-3))]\" m.add_colormap(cmap=\"jet\", vmin=-1, vmax=2, label=label) m Out[39]: <p></p> <p>The daily image does not have a global coverage. To visualize the data globally, we can aggregate the data over a time range.</p> In\u00a0[40]: Copied! <pre>mean_array = array.mean(dim=\"date\")\n</pre> mean_array = array.mean(dim=\"date\") <p>Convert the aggregated data array to an image that can be displayed on an interactive map.</p> In\u00a0[41]: Copied! <pre>image = hypercoast.pace_chla_to_image(mean_array)\n</pre> image = hypercoast.pace_chla_to_image(mean_array) <p>Create an interactive map and display the image on the map.</p> In\u00a0[42]: Copied! <pre>m = hypercoast.Map(center=[40, -100], zoom=4)\nm.add_basemap(\"Hybrid\")\nm.add_raster(\n    image, cmap=\"jet\", vmin=-1, vmax=2, layer_name=\"Chlorophyll a\", zoom_to_layer=False\n)\nlabel = \"Chlorophyll Concentration [lg(lg(mg m^-3))]\"\nm.add_colormap(cmap=\"jet\", vmin=-1, vmax=2, label=label)\nm\n</pre> m = hypercoast.Map(center=[40, -100], zoom=4) m.add_basemap(\"Hybrid\") m.add_raster(     image, cmap=\"jet\", vmin=-1, vmax=2, layer_name=\"Chlorophyll a\", zoom_to_layer=False ) label = \"Chlorophyll Concentration [lg(lg(mg m^-3))]\" m.add_colormap(cmap=\"jet\", vmin=-1, vmax=2, label=label) m Out[42]: <p></p> In\u00a0[43]: Copied! <pre>url = \"https://github.com/opengeos/datasets/releases/download/hypercoast/Hypoxia_Data_Sheet.xlsx\"\nxls_path = \"data/Hypoxia_Data_Sheet.xlsx\"\nhypercoast.download_file(url, xls_path, overwrite=True)\n</pre> url = \"https://github.com/opengeos/datasets/releases/download/hypercoast/Hypoxia_Data_Sheet.xlsx\" xls_path = \"data/Hypoxia_Data_Sheet.xlsx\" hypercoast.download_file(url, xls_path, overwrite=True) Out[43]: <pre>'/home/runner/work/HyperCoast/HyperCoast/docs/workshops/data/Hypoxia_Data_Sheet.xlsx'</pre> In\u00a0[44]: Copied! <pre>df = pd.read_excel(xls_path)\ndf.head()\n</pre> df = pd.read_excel(xls_path) df.head() Out[44]: Station Station.1 Time Date Lon Lat Depth (m) Secchi (m) Salinity Water Temp ... Absorption CDOM LISST Nano Surface \\npH Sufface \\nO2 Bottom \\nO2 FL-ECO FL-CDOM Notes 0 River stations NaN NaN NaT NaN NaN NaN NaN NaN NaN ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 1 R1 St1 09:39:00 2024-07-21 -89.45114 28.89887 NaN 2 28.71 30.74161 ... 125ml yes yes no NaN NaN NaN NaN NaN River Mouth 2 R2 St2 09:47:00 2024-07-21 -89.45306 28.90001 NaN 2 25.91325 30.91475 ... 150ml yes yes yes NaN NaN NaN NaN NaN River plume, seaside 3 R3 St3 09:59:00 2024-07-21 -89.43833 28.89487 NaN 0.75 24.44862 30.59565 ... 100ml yes yes yes NaN NaN NaN NaN NaN NaN 4 R4 St4 10:13:00 2024-07-21 -89.43162 28.90630 NaN 0.5 8.34838 30.13989 ... 100ml yes yes yes NaN NaN NaN NaN NaN NaN <p>5 rows \u00d7 22 columns</p> <p>Filter the data to select only the sampling locations with latitude and longitude coordinates.</p> In\u00a0[45]: Copied! <pre>df_filtered = df.dropna(subset=[\"Lon\", \"Lat\"]).reset_index(drop=True)\ndf_filtered.head()\n</pre> df_filtered = df.dropna(subset=[\"Lon\", \"Lat\"]).reset_index(drop=True) df_filtered.head() Out[45]: Station Station.1 Time Date Lon Lat Depth (m) Secchi (m) Salinity Water Temp ... Absorption CDOM LISST Nano Surface \\npH Sufface \\nO2 Bottom \\nO2 FL-ECO FL-CDOM Notes 0 R1 St1 09:39:00 2024-07-21 -89.45114 28.89887 NaN 2 28.71 30.74161 ... 125ml yes yes no NaN NaN NaN NaN NaN River Mouth 1 R2 St2 09:47:00 2024-07-21 -89.45306 28.90001 NaN 2 25.91325 30.91475 ... 150ml yes yes yes NaN NaN NaN NaN NaN River plume, seaside 2 R3 St3 09:59:00 2024-07-21 -89.43833 28.89487 NaN 0.75 24.44862 30.59565 ... 100ml yes yes yes NaN NaN NaN NaN NaN NaN 3 R4 St4 10:13:00 2024-07-21 -89.43162 28.90630 NaN 0.5 8.34838 30.13989 ... 100ml yes yes yes NaN NaN NaN NaN NaN NaN 4 R5 St5 10:58:00 2024-07-21 -89.37324 28.98095 NaN 0.5 2.4625 30.3054 ... 100ml yes yes yes NaN NaN NaN NaN NaN NaN <p>5 rows \u00d7 22 columns</p> <p>Download the KML file containing the cruise path.</p> In\u00a0[46]: Copied! <pre>url = (\n    \"https://github.com/opengeos/datasets/releases/download/hypercoast/Hypoxia_Path.kml\"\n)\nkml_path = \"data/Hypoxia_Path.kml\"\nhypercoast.download_file(url, kml_path)\n</pre> url = (     \"https://github.com/opengeos/datasets/releases/download/hypercoast/Hypoxia_Path.kml\" ) kml_path = \"data/Hypoxia_Path.kml\" hypercoast.download_file(url, kml_path) Out[46]: <pre>'/home/runner/work/HyperCoast/HyperCoast/docs/workshops/data/Hypoxia_Path.kml'</pre> <p>We will use the PACE AOP dataset acquired on July 30, 2024, to visualize the cruise sampling locations. The dataset should have been downloaded in the previous section.</p> In\u00a0[47]: Copied! <pre>filepath = \"data/PACE_OCI.20240730T181157.L2.OC_AOP.V2_0.NRT.nc\"\n</pre> filepath = \"data/PACE_OCI.20240730T181157.L2.OC_AOP.V2_0.NRT.nc\" <p>Read the PACE AOP dataset as an xarray Dataset.</p> In\u00a0[48]: Copied! <pre>dataset = hypercoast.read_pace(filepath)\n# dataset\n</pre> dataset = hypercoast.read_pace(filepath) # dataset <p>Visualize the cruise sampling locations and PACE data on the map.</p> In\u00a0[49]: Copied! <pre>m = hypercoast.Map()\nm.add_basemap(\"Hybrid\")\nwavelengths = [450, 550, 650]\nm.add_pace(\n    dataset, wavelengths, indexes=[3, 2, 1], vmin=0, vmax=0.02, layer_name=\"PACE\"\n)\nm.add(\"spectral\")\nstyle = {\"weight\": 2, \"color\": \"red\"}\nm.add_kml(kml_path, style=style, layer_name=\"Hypoxia Path\", info_mode=None)\nm.add_points_from_xy(\n    df_filtered,\n    x=\"Lon\",\n    y=\"Lat\",\n    max_cluster_radius=50,\n    layer_name=\"Hypoxia Data Points\",\n)\nm.set_center(-91.46118, 28.89758, zoom=8)\nm\n</pre> m = hypercoast.Map() m.add_basemap(\"Hybrid\") wavelengths = [450, 550, 650] m.add_pace(     dataset, wavelengths, indexes=[3, 2, 1], vmin=0, vmax=0.02, layer_name=\"PACE\" ) m.add(\"spectral\") style = {\"weight\": 2, \"color\": \"red\"} m.add_kml(kml_path, style=style, layer_name=\"Hypoxia Path\", info_mode=None) m.add_points_from_xy(     df_filtered,     x=\"Lon\",     y=\"Lat\",     max_cluster_radius=50,     layer_name=\"Hypoxia Data Points\", ) m.set_center(-91.46118, 28.89758, zoom=8) m Out[49]: <p></p> In\u00a0[50]: Copied! <pre>url = \"https://github.com/opengeos/datasets/releases/download/hypercoast/pace_sample_points.csv\"\ndata = pd.read_csv(url)\ndata.head()\n</pre> url = \"https://github.com/opengeos/datasets/releases/download/hypercoast/pace_sample_points.csv\" data = pd.read_csv(url) data.head() Out[50]: band wavelength (30.1926 -90.1318) (30.1594 -90.2856) (29.3295 -92.3071) (28.8783 -90.4559) (30.5481 -87.9840) (29.5305 -85.0671) (28.8254 -85.6659) (29.4587 -83.8477) (26.8878 -87.7643) (24.6570 -86.4954) (26.8045 -82.4854) 0 0 339.0 NaN NaN 0.000100 NaN NaN NaN NaN NaN 0.001718 0.000710 NaN 1 1 341.0 NaN NaN NaN NaN NaN NaN NaN NaN 0.001554 0.000846 NaN 2 2 344.0 NaN NaN 0.001816 NaN NaN NaN NaN NaN 0.004047 0.003598 NaN 3 3 346.0 0.000346 NaN 0.002393 NaN NaN NaN NaN NaN 0.005045 0.004646 NaN 4 4 348.0 NaN NaN 0.001578 NaN NaN NaN NaN NaN 0.004258 0.003984 NaN <p>Again, we will use the PACE AOP dataset acquired on July 30, 2024, to visualize the in-situ data. The dataset should have been downloaded in the previous section.</p> In\u00a0[51]: Copied! <pre>filepath = \"data/PACE_OCI.20240730T181157.L2.OC_AOP.V2_0.NRT.nc\"\n</pre> filepath = \"data/PACE_OCI.20240730T181157.L2.OC_AOP.V2_0.NRT.nc\" <p>Read the PACE dataset as an xarray Dataset.</p> In\u00a0[52]: Copied! <pre>dataset = hypercoast.read_pace(filepath)\n</pre> dataset = hypercoast.read_pace(filepath) <p>Visualize the in-situ data on the map.</p> In\u00a0[53]: Copied! <pre>m = hypercoast.Map(center=[27.235094, -87.791748], zoom=6)\n\nm.add_basemap(\"Hybrid\")\nwavelengths = [450]\nm.add_pace(dataset, wavelengths, colormap=\"jet\", vmin=0, vmax=0.02, layer_name=\"PACE\")\nm.add_colormap(cmap=\"jet\", vmin=0, vmax=0.02, label=\"Reflectance\")\nm.add(\"spectral\")\n\nm.add_field_data(\n    data,\n    x_col=\"wavelength\",\n    y_col_prefix=\"(\",\n    x_label=\"Wavelength (nm)\",\n    y_label=\"Reflectance\",\n    use_marker_cluster=True,\n)\nm.set_center(-87.791748, 27.235094, zoom=6)\nm\n</pre> m = hypercoast.Map(center=[27.235094, -87.791748], zoom=6)  m.add_basemap(\"Hybrid\") wavelengths = [450] m.add_pace(dataset, wavelengths, colormap=\"jet\", vmin=0, vmax=0.02, layer_name=\"PACE\") m.add_colormap(cmap=\"jet\", vmin=0, vmax=0.02, label=\"Reflectance\") m.add(\"spectral\")  m.add_field_data(     data,     x_col=\"wavelength\",     y_col_prefix=\"(\",     x_label=\"Wavelength (nm)\",     y_label=\"Reflectance\",     use_marker_cluster=True, ) m.set_center(-87.791748, 27.235094, zoom=6) m Out[53]: <p>Click on any marker to display the in-situ data.</p> <p></p>"},{"location":"workshops/pace/#working-with-nasa-pace-data-in-hypercoast","title":"Working with NASA PACE data in HyperCoast\u00b6","text":"<p>This notebook demonstrates how to visualize and analyze Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) data interactively with HyperCoast.</p>"},{"location":"workshops/pace/#environment-setup","title":"Environment setup\u00b6","text":"<p>Uncomment and run the following cell to install the required packages.</p>"},{"location":"workshops/pace/#search-for-pace-data","title":"Search for PACE data\u00b6","text":"<p>To download and access the data, you will need to create an Earthdata login. You can register for an account at urs.earthdata.nasa.gov. Once you have an account, run the following cell and enter your NASA Earthdata login credentials.</p>"},{"location":"workshops/pace/#search-data-programmatically","title":"Search data programmatically\u00b6","text":"<p>To search for PACE data programmatically, specify the bounding box and time range of interest. Set <code>count=-1</code> to return all results or set <code>count=10</code> to return the first 10 results.</p>"},{"location":"workshops/pace/#search-data-interactively","title":"Search data interactively\u00b6","text":"<p>To search for PACE data interactively, pan and zoom to the area of interest. Specify the time range of interest from the search dialog, then click on the Search button.</p>"},{"location":"workshops/pace/#read-pace-data","title":"Read PACE data\u00b6","text":"<p>Let's download a sample PACE Apparent Optical Properties (AOP) dataset for the demonstration.</p>"},{"location":"workshops/pace/#visualize-pace-aop-data","title":"Visualize PACE AOP data\u00b6","text":"<p>Visualize selected bands of the dataset.</p>"},{"location":"workshops/pace/#plot-spectral-signatures","title":"Plot spectral signatures\u00b6","text":"<p>Plot the spectral signature of a pixel using the <code>extract_pace</code> function. Set <code>return_plot=True</code> to return the plot object.</p>"},{"location":"workshops/pace/#interactive-visualization","title":"Interactive visualization\u00b6","text":""},{"location":"workshops/pace/#single-band-visualization","title":"Single-band visualization\u00b6","text":""},{"location":"workshops/pace/#multi-band-visualization","title":"Multi-band visualization\u00b6","text":"<p>Select three spectral bands to visualize as an RGB image.</p>"},{"location":"workshops/pace/#change-band-combination","title":"Change band combination\u00b6","text":"<p>Click on the gear icon on the toolbar to change the band combination.</p> <p></p>"},{"location":"workshops/pace/#pace-bgc-data","title":"PACE BGC data\u00b6","text":"<p>PACE has a variety of data products, including biogeochemical properties. For more information about the available datasets, see the PACE Data Products page.</p> <p>The PACE Biogeochemical (BGC) data products include chlorophyll-a concentration, particulate organic carbon, and particulate inorganic carbon.</p>"},{"location":"workshops/pace/#download-pace-bgc-data","title":"Download PACE BGC data\u00b6","text":"<p>Let's download a sample PACE BGC dataset for the demonstration.</p>"},{"location":"workshops/pace/#visualize-pace-bgc-data","title":"Visualize PACE BGC data\u00b6","text":"<p>Since the datasets are not gridded, we need to transform them into gridded data to visualize them. We can use the <code>grid_pace_bgc</code> function to transform the dataset into a gridded format.</p> <p>First, transform the <code>chlor_a</code> variable into a gridded format:</p>"},{"location":"workshops/pace/#pace-chlorophyll-level-3-data","title":"PACE Chlorophyll Level 3 data\u00b6","text":"<p>PACE Level 3 data products are gridded data products that are derived from Level 2 data. Once of the most common Level 3 data products is the Chlorophyll-Carotenoid Index (CCI) dataset.</p> <p>Let's download some daily PACE Chlorophyll Level 3 data for the demonstration.</p>"},{"location":"workshops/pace/#hypoxia-cruise-data","title":"Hypoxia Cruise data\u00b6","text":"<p>The Hypoxia Cruise collected water quality data in the Gulf of Mexico from July 21 to August 2, 2024. In this section, we will visualize the cruise sampling locations.</p> <p>First, let's download an Excel file containing the cruise sampling locations.</p>"},{"location":"workshops/pace/#visualize-in-situ-data","title":"Visualize in-situ data\u00b6","text":"<p>This section demonstrates how to visualize in-situ data on the map. First, let's download a hypothetical in-situ dataset.</p>"},{"location":"workshops/pace/#analyze-pace-data","title":"Analyze PACE data\u00b6","text":"<p>To anyalyze the PACE data with algorithms, such as K-means clustering, principal component analysis (PCA), or Spectral Angle Mapper (SAM), follow the notebook at https://hypercoast.org/examples/pace_cyano.</p>"}]}
\ No newline at end of file
+{"config":{"lang":["en"],"separator":"[\\s\\-]+","pipeline":["stopWordFilter"]},"docs":[{"location":"","title":"Welcome to HyperCoast","text":"<p>A Python Package for Visualizing and Analyzing Hyperspectral Data in Coastal Environments</p> <ul> <li>Free software: MIT License</li> <li>Documentation: https://hypercoast.org</li> </ul>"},{"location":"#introduction","title":"Introduction","text":"<p>HyperCoast is a Python package designed to provide an accessible and comprehensive set of tools for visualizing and analyzing hyperspectral data in coastal environments. Hyperspectral data refers to the information collected by sensors that capture light across a wide range of wavelengths, beyond what the human eye can see. This data allows scientists to detect and analyze various materials and conditions on the Earth's surface with great detail. Unlike multispectral data, which captures light in a limited number of broad wavelength bands (typically 3 to 10), hyperspectral data captures light in many narrow, contiguous wavelength bands, often numbering in the hundreds. This provides much more detailed spectral information. Leveraging the capabilities of popular packages like Leafmap and PyVista, HyperCoast streamlines the exploration and interpretation of complex hyperspectral remote sensing data from existing spaceborne and airborne missions. It is also poised to support future hyperspectral missions, such as NASA's SBG and GLIMR. It enables researchers and environmental managers to gain deeper insights into the dynamic processes occurring in aquatic environments.</p> <p>HyperCoast supports the reading and visualization of hyperspectral data from various missions, including AVIRIS, NEON, PACE, EMIT, and DESIS, along with other datasets like ECOSTRESS. Users can interactively explore hyperspectral data, extract spectral signatures, change band combinations and colormaps, visualize data in 3D, and perform interactive slicing and thresholding operations (see Figure 1). Additionally, by leveraging the earthaccess package, HyperCoast provides tools for interactively searching NASA's hyperspectral data. This makes HyperCoast a versatile and powerful tool for working with hyperspectral data globally, with a particular focus on coastal regions.</p> <p> Figure 1. An example of visualizing NASA EMIT hyperspectral data using HyperCoast.</p>"},{"location":"#citations","title":"Citations","text":"<p>If you find HyperCoast useful in your research, please consider citing the following papers to support us. Thank you!</p> <ul> <li>Liu, B., &amp; Wu, Q. (2024). HyperCoast: A Python Package for Visualizing and Analyzing Hyperspectral Data in Coastal Environments. Journal of Open Source Software, 9(100), 7025. https://doi.org/10.21105/joss.07025.</li> </ul>"},{"location":"#features","title":"Features","text":"<ul> <li>Searching for NASA hyperspectral data interactively</li> <li>Performing atmospheric correction using Acolite</li> <li>Interactive visualization and analysis of hyperspectral data, such as AVIRIS, DESIS, EMIT, PACE, NEON AOP</li> <li>Interactive visualization of NASA ECOSTRESS data</li> <li>Interactive visualization of PACE chlorophyll-a data</li> <li>Interactive extraction and visualization of spectral signatures</li> <li>Changing band combinations and colormaps interactively</li> <li>Visualizing hyperspectral data in 3D</li> <li>Visualizing ERA5 temperature data in 3D</li> <li>Interactive slicing and thresholding of hyperspectral data in 3D</li> <li>Saving spectral signatures as CSV files</li> </ul>"},{"location":"#demos","title":"Demos","text":"<ul> <li>Visualizing hyperspectral data in 3D (notebook)</li> </ul> <ul> <li>Interactive slicing of hyperspectral data in 3D (notebook)</li> </ul> <ul> <li>Interactive thresholding of hyperspectral data in 3D (notebook)</li> </ul> <ul> <li>Visualizing ERA5 temperature data in 3D (notebook)</li> </ul> <ul> <li>Changing band combinations and colormaps interactively (notebook)</li> </ul> <ul> <li>Visualizing NASA AVIRIS hyperspectral data interactively (notebook)</li> </ul> <ul> <li>Visualizing DESIS hyperspectral data interactively (notebook)</li> </ul> <ul> <li>Visualizing NASA EMIT hyperspectral data interactively (notebook)</li> </ul> <ul> <li>Visualizing NASA PACE hyperspectral data interactively (notebook)</li> </ul> <ul> <li>Visualizing NEON AOP hyperspectral data interactively (notebook)</li> </ul> <ul> <li>Interactive visualization of PACE chlorophyll-a data (notebook)</li> </ul>"},{"location":"#acknowledgement","title":"Acknowledgement","text":"<p>The HyperCoast project draws inspiration from the nasa/EMIT-Data-Resources repository. Credits to the original authors. We also acknowledge the NASA EMIT program support through grant no. 80NSSC24K0865.</p>"},{"location":"#license","title":"License","text":"<p>HyperCoast is released under the MIT License. However, some of the modules in HyperCoast adapt code from other open-source projects, which may have different licenses. Please refer to the license notice in each module for more information. Credits to the original authors.</p> <ul> <li>emit.py: Part of the code is adapted from the nasa/EMIT-Data-Resources repository, which is released under the Apache License 2.0.</li> <li>aviris.py: Part of the code is adapted from the jjmcnelis/aviris-ng-notebooks, which is released under the MIT License.</li> </ul>"},{"location":"aviris/","title":"aviris module","text":"<p>This module contains functions to read and process NASA AVIRIS hyperspectral data. More info about the data can be found at https://aviris.jpl.nasa.gov. A portion of the source code is adapted from the jjmcnelis/aviris-ng-notebooks repository available at https://bit.ly/4bRCgqs. It is licensed under the MIT License. Credit goes to the original author Jack McNelis.</p> <p>SPDX-FileCopyrightText = [     \"2024 Jack McNelis jjmcne@gmail.com\", ] SPDX-License-Identifier = \"MIT\"</p>"},{"location":"aviris/#hypercoast.aviris.aviris_to_image","title":"<code>aviris_to_image(dataset, wavelengths=None, method='nearest', output=None, **kwargs)</code>","text":"<p>Converts an AVIRIS dataset to an image.</p> <p>Parameters:</p> Name Type Description Default <code>dataset</code> <code>Union[xr.Dataset, str]</code> <p>The dataset containing the AVIRIS data or the file path to the dataset.</p> required <code>wavelengths</code> <code>np.ndarray</code> <p>The specific wavelengths to select. If None, all wavelengths are selected. Defaults to None.</p> <code>None</code> <code>method</code> <code>str</code> <p>The method to use for data interpolation. Defaults to \"nearest\".</p> <code>'nearest'</code> <code>output</code> <code>str</code> <p>The file path where the image will be saved. If None, the image will be returned as a PIL Image object. Defaults to None.</p> <code>None</code> <code>**kwargs</code> <code>Any</code> <p>Additional keyword arguments to be passed to <code>leafmap.array_to_image</code>.</p> <code>{}</code> <p>Returns:</p> Type Description <code>Optional[rasterio.Dataset]</code> <p>The image converted from the dataset. If     <code>output</code> is provided, the image will be saved to the specified file     and the function will return None.</p> Source code in <code>hypercoast/aviris.py</code> <pre><code>def aviris_to_image(\n    dataset: Union[xr.Dataset, str],\n    wavelengths: Optional[np.ndarray] = None,\n    method: str = \"nearest\",\n    output: Optional[str] = None,\n    **kwargs: Any,\n):\n    \"\"\"\n    Converts an AVIRIS dataset to an image.\n\n    Args:\n        dataset (Union[xr.Dataset, str]): The dataset containing the AVIRIS data\n            or the file path to the dataset.\n        wavelengths (np.ndarray, optional): The specific wavelengths to select. If None, all\n            wavelengths are selected. Defaults to None.\n        method (str, optional): The method to use for data interpolation.\n            Defaults to \"nearest\".\n        output (str, optional): The file path where the image will be saved. If\n            None, the image will be returned as a PIL Image object. Defaults to None.\n        **kwargs (Any): Additional keyword arguments to be passed to\n            `leafmap.array_to_image`.\n\n    Returns:\n        Optional[rasterio.Dataset]: The image converted from the dataset. If\n            `output` is provided, the image will be saved to the specified file\n            and the function will return None.\n    \"\"\"\n    from leafmap import array_to_image\n\n    if isinstance(dataset, str):\n        dataset = read_aviris(dataset, method=method)\n\n    if wavelengths is not None:\n        dataset = dataset.sel(wavelength=wavelengths, method=method)\n\n    return array_to_image(\n        dataset[\"reflectance\"],\n        output=output,\n        transpose=False,\n        dtype=np.float32,\n        **kwargs,\n    )\n</code></pre>"},{"location":"aviris/#hypercoast.aviris.extract_aviris","title":"<code>extract_aviris(dataset, lat, lon, offset=2.0)</code>","text":"<p>Extracts AVIRIS data from a given xarray Dataset.</p> <p>Parameters:</p> Name Type Description Default <code>dataset</code> <code>xarray.Dataset</code> <p>The dataset containing the AVIRIS data.</p> required <code>lat</code> <code>float</code> <p>The latitude of the point to extract.</p> required <code>lon</code> <code>float</code> <p>The longitude of the point to extract.</p> required <code>offset</code> <code>float</code> <p>The offset from the point to extract. Defaults to 2.0.</p> <code>2.0</code> <p>Returns:</p> Type Description <code>xarray.DataArray</code> <p>The extracted data.</p> Source code in <code>hypercoast/aviris.py</code> <pre><code>def extract_aviris(\n    dataset: xr.Dataset, lat: float, lon: float, offset: float = 2.0\n) -&gt; xr.DataArray:\n    \"\"\"\n    Extracts AVIRIS data from a given xarray Dataset.\n\n    Args:\n        dataset (xarray.Dataset): The dataset containing the AVIRIS data.\n        lat (float): The latitude of the point to extract.\n        lon (float): The longitude of the point to extract.\n        offset (float, optional): The offset from the point to extract. Defaults to 2.0.\n\n    Returns:\n        xarray.DataArray: The extracted data.\n    \"\"\"\n\n    crs = dataset.attrs[\"crs\"]\n\n    x, y = convert_coords([[lat, lon]], \"epsg:4326\", crs)[0]\n\n    da = dataset[\"reflectance\"]\n\n    x_con = (da[\"xc\"] &gt; x - offset) &amp; (da[\"xc\"] &lt; x + offset)\n    y_con = (da[\"yc\"] &gt; y - offset) &amp; (da[\"yc\"] &lt; y + offset)\n\n    try:\n        data = da.where(x_con &amp; y_con, drop=True)\n        data = data.mean(dim=[\"x\", \"y\"])\n    except ValueError:\n        data = np.nan * np.ones(da.sizes[\"wavelength\"])\n\n    da = xr.DataArray(\n        data, dims=[\"wavelength\"], coords={\"wavelength\": dataset.coords[\"wavelength\"]}\n    )\n\n    return da\n</code></pre>"},{"location":"aviris/#hypercoast.aviris.read_aviris","title":"<code>read_aviris(filepath, wavelengths=None, method='nearest', **kwargs)</code>","text":"<p>Reads NASA AVIRIS hyperspectral data and returns an xarray dataset.</p> <p>Parameters:</p> Name Type Description Default <code>filepath</code> <code>str</code> <p>The path to the AVIRIS data.</p> required <code>wavelengths</code> <code>List[float]</code> <p>The wavelengths to select. If None, all wavelengths are selected. Defaults to None.</p> <code>None</code> <code>method</code> <code>str</code> <p>The method to use for selection. Defaults to \"nearest\".</p> <code>'nearest'</code> <code>**kwargs</code> <code>Any</code> <p>Additional arguments to pass to the selection method.</p> <code>{}</code> <p>Returns:</p> Type Description <code>xr.Dataset</code> <p>The dataset containing the reflectance data.</p> Source code in <code>hypercoast/aviris.py</code> <pre><code>def read_aviris(\n    filepath: str,\n    wavelengths: Optional[List[float]] = None,\n    method: str = \"nearest\",\n    **kwargs: Any,\n) -&gt; xr.Dataset:\n    \"\"\"\n    Reads NASA AVIRIS hyperspectral data and returns an xarray dataset.\n\n    Args:\n        filepath (str): The path to the AVIRIS data.\n        wavelengths (List[float], optional): The wavelengths to select. If None,\n            all wavelengths are selected. Defaults to None.\n        method (str, optional): The method to use for selection. Defaults to\n            \"nearest\".\n        **kwargs (Any): Additional arguments to pass to the selection method.\n\n    Returns:\n        xr.Dataset: The dataset containing the reflectance data.\n    \"\"\"\n\n    if filepath.endswith(\".hdr\"):\n        filepath = filepath.replace(\".hdr\", \"\")\n\n    ds = xr.open_dataset(filepath, engine=\"rasterio\")\n\n    wavelength = ds[\"wavelength\"].values.tolist()\n    wavelength = [round(num, 2) for num in wavelength]\n\n    cols = ds.x.size\n    rows = ds.y.size\n\n    rio_transform = ds.rio.transform()\n    geo_transform = list(rio_transform)[:6]\n\n    # get the raster geotransform as its component parts\n    xres, _, xmin, _, yres, ymax = geo_transform\n\n    # generate coordinate arrays\n    xarr = np.array([xmin + i * xres for i in range(0, cols)])\n    yarr = np.array([ymax + i * yres for i in range(0, rows)])\n\n    ds[\"y\"] = xr.DataArray(\n        data=yarr,\n        dims=(\"y\"),\n        name=\"y\",\n        attrs=dict(\n            units=\"m\",\n            standard_name=\"projection_y_coordinate\",\n            long_name=\"y coordinate of projection\",\n        ),\n    )\n\n    ds[\"x\"] = xr.DataArray(\n        data=xarr,\n        dims=(\"x\"),\n        name=\"x\",\n        attrs=dict(\n            units=\"m\",\n            standard_name=\"projection_x_coordinate\",\n            long_name=\"x coordinate of projection\",\n        ),\n    )\n\n    global_atts = ds.attrs\n    global_atts[\"Conventions\"] = \"CF-1.6\"\n    ds.attrs = dict(\n        units=\"unitless\",\n        _FillValue=-9999,\n        grid_mapping=\"crs\",\n        standard_name=\"reflectance\",\n        long_name=\"atmospherically corrected surface reflectance\",\n    )\n    ds.attrs.update(global_atts)\n\n    ds = ds.transpose(\"y\", \"x\", \"band\")\n    ds = ds.drop_vars([\"wavelength\"])\n    ds = ds.rename({\"band\": \"wavelength\", \"band_data\": \"reflectance\"})\n    ds.coords[\"wavelength\"] = wavelength\n    ds.attrs[\"crs\"] = ds.rio.crs.to_string()\n    ds.rio.write_transform(rio_transform)\n\n    if wavelengths is not None:\n        ds = ds.sel(wavelength=wavelengths, method=method, **kwargs)\n    return ds\n</code></pre>"},{"location":"changelog/","title":"Changelog","text":""},{"location":"changelog/#v076-aug-07-2024","title":"v0.7.6 - Aug 07, 2024","text":"<p>What's Changed</p> <ul> <li>[pre-commit.ci] pre-commit autoupdate by @pre-commit-ci in #107</li> <li>Clean up file path in notebook by @giswqs in #108</li> <li>Fix read_pace bug for V2 by @giswqs in #109</li> </ul> <p>Full Changelog: v0.7.5...v0.7.6</p>"},{"location":"changelog/#v075-aug-05-2024","title":"v0.7.5 - Aug 05, 2024","text":"<p>What's Changed</p> <ul> <li>ci(Mergify): configuration update by @slowy07 in #99</li> <li>Add support for PCA by @giswqs in #100</li> <li>Update mergify by @giswqs in #101</li> <li>Add support for displaying field data by @giswqs in #106</li> </ul> <p>Full Changelog: v0.7.4...v0.7.5</p>"},{"location":"changelog/#v074-aug-01-2024","title":"v0.7.4 - Aug 01, 2024","text":"<p>What's Changed</p> <ul> <li>Remove AVIRIS image bounds by @giswqs in #96</li> <li>Fix read_pace bug for V2 by @giswqs in #97</li> </ul> <p>Full Changelog: v0.7.3...v0.7.4</p>"},{"location":"changelog/#v073-jul-30-2024","title":"v0.7.3 - Jul 30, 2024","text":"<p>What's Changed</p> <ul> <li>Add reuse license management framework by @giswqs in #83</li> <li>Add reuse dep and file header by @giswqs in #84</li> <li>Pin reuse version by @giswqs in #85</li> <li>Decapitalize Pringle et al title by @giswqs in #89</li> <li>Remove sentence and citations by @giswqs in #90</li> <li>Explain hyperspectral and other jargon by @giswqs in #91</li> <li>Add xlim parameter for the spectral widget by @giswqs in #93</li> </ul> <p>Full Changelog: v0.7.2...v0.7.3</p>"},{"location":"changelog/#v072-jul-23-2024","title":"v0.7.2 - Jul 23, 2024","text":"<p>What's Changed</p> <ul> <li>Update paper by @bingqing-liu in #74</li> <li>Update pyvista dependencies by @giswqs in #75</li> <li>chore: improving testing and typehinting by @slowy07 in #77</li> <li>Add support for generic dataset by @giswqs in #78</li> <li>Clarify license info by @giswqs in #79</li> <li>Add multispectral notebook example by @giswqs in #80</li> <li>Add support for acolite by @giswqs in #81</li> </ul> <p>New Contributors</p> <ul> <li>@slowy07 made their first contribution in #77</li> </ul> <p>Full Changelog: v0.7.0...v0.7.2</p>"},{"location":"changelog/#v071-jul-20-2024","title":"v0.7.1 - Jul 20, 2024","text":"<p>What's Changed</p> <ul> <li>Update paper by @bingqing-liu in #74</li> <li>Update pyvista dependencies by @giswqs in #75</li> <li>chore: improving testing and typehinting by @slowy07 in #77</li> <li>Add support for generic dataset by @giswqs in #78</li> <li>Clarify license info by @giswqs in #79</li> </ul> <p>New Contributors</p> <ul> <li>@slowy07 made their first contribution in #77</li> </ul> <p>Full Changelog: v0.7.0...v0.7.1</p>"},{"location":"changelog/#v070-jul-08-2024","title":"v0.7.0 - Jul 08, 2024","text":"<p>What's Changed</p> <ul> <li>Improve the pace chla function by @giswqs in #70</li> <li>Add JOSS paper draft by @giswqs in #71</li> <li>Add unittests by @giswqs in #72</li> <li>Add EMIT workshop notebook by @giswqs in #73</li> </ul> <p>Full Changelog: v0.6.3...v0.7.0</p>"},{"location":"changelog/#v063-jul-01-2024","title":"v0.6.3 - Jul 01, 2024","text":"<p>What's Changed</p> <ul> <li>Update pace_chla_to_image_function by @giswqs in #69</li> </ul> <p>Full Changelog: v0.6.2...v0.6.3</p>"},{"location":"changelog/#v062-jun-30-2024","title":"v0.6.2 - Jun 30, 2024","text":"<p>What's Changed</p> <ul> <li>Add search_datasets function and improve notebook example by @giswqs in #66</li> <li>Add PACE OCI Level-1 notebook example by @giswqs in #67</li> <li>Add PACE pixel location function by @giswqs in #68</li> </ul> <p>Full Changelog: v0.6.1...v0.6.2</p>"},{"location":"changelog/#v061-jun-30-2024","title":"v0.6.1 - Jun 30, 2024","text":"<p>What's Changed</p> <ul> <li>Add more demos to docs by @giswqs in #63</li> <li>Fix typos by @giswqs in #64</li> <li>Add support for reading PACE OCI L2 data by @giswqs in #65</li> </ul> <p>Full Changelog: v0.6.0...v0.6.1</p>"},{"location":"changelog/#v060-jun-26-2024","title":"v0.6.0 - Jun 26, 2024","text":"<p>What's Changed</p> <ul> <li>Add support for PACE Chlorophyll data by @giswqs in #62</li> </ul> <p>Full Changelog: v0.5.5...v0.6.0</p>"},{"location":"changelog/#v055-jun-25-2024","title":"v0.5.5 - Jun 25, 2024","text":"<p>What's Changed</p> <ul> <li>Add image slicing demos to docs by @giswqs in #56</li> <li>Add dependabot by @giswqs in #58</li> <li>Bump nwtgck/actions-netlify from 2.0 to 3.0 by @dependabot in #59</li> <li>Bump conda-incubator/setup-miniconda from 2 to 3 by @dependabot in #60</li> <li>Add ERA5 temperature data notebook by @giswqs in #61</li> </ul> <p>New Contributors</p> <ul> <li>@dependabot made their first contribution in #59</li> </ul> <p>Full Changelog: v0.5.4...v0.5.5</p>"},{"location":"changelog/#v054-jun-14-2024","title":"v0.5.4 - Jun 14, 2024","text":"<p>What's Changed</p> <ul> <li>Add support for interactive slicing by @giswqs in #54</li> <li>Add image slicing notebook example by @giswqs in #55</li> </ul> <p>Full Changelog: v0.5.3...v0.5.4</p>"},{"location":"changelog/#v053-jun-12-2024","title":"v0.5.3 - Jun 12, 2024","text":"<p>What's Changed</p> <ul> <li>Add EMIT image cube example by @giswqs in #51</li> <li>Refactor read_neon() function to support generalized NEON data reading by @gponce-ars in #52</li> </ul> <p>New Contributors</p> <ul> <li>@gponce-ars made their first contribution in #52</li> </ul> <p>Full Changelog: v0.5.2...v0.5.3</p>"},{"location":"changelog/#v052-jun-11-2024","title":"v0.5.2 - Jun 11, 2024","text":"<p>What's Changed</p> <ul> <li>Fix RGB image reshape bug for image cube by @giswqs in #49</li> </ul> <p>Full Changelog: v0.5.1...v0.5.2</p>"},{"location":"changelog/#v051-jun-11-2024","title":"v0.5.1 - Jun 11, 2024","text":"<p>What's Changed</p> <ul> <li>Add support for 3D visualization by @giswqs in #47</li> <li>Add image cube demo to docs by @giswqs in #48</li> </ul> <p>Full Changelog: v0.5.0...v0.5.1</p>"},{"location":"changelog/#v050-jun-10-2024","title":"v0.5.0 - Jun 10, 2024","text":"<p>What's Changed</p> <ul> <li>Add support for NEON AOP data by @giswqs in #43</li> <li>Add support for AVIRIS data by @giswqs in #44</li> <li>Add support for changing band combinations and colormaps interactively by @giswqs in #46</li> </ul> <p>Full Changelog: v0.4.0...v0.5.0</p>"},{"location":"changelog/#v040-jun-03-2024","title":"v0.4.0 - Jun 03, 2024","text":"<p>What's Changed</p> <ul> <li>Add support for searching and downloading ECOSTRESS data by @giswqs in #37</li> <li>Add support for visualizing DESIS hyperspectral data by @giswqs in #38</li> </ul> <p>Full Changelog: v0.3.3...v0.4.0</p>"},{"location":"changelog/#v033-may-19-2024","title":"v0.3.3 - May 19, 2024","text":"<p>What's Changed</p> <ul> <li>Add functions for searching PACE and EMIT data by @giswqs in #34</li> <li>Add stacking for spectral signatures by @giswqs in #35</li> </ul> <p>Full Changelog: v0.3.2...v0.3.3</p>"},{"location":"changelog/#v032-may-10-2024","title":"v0.3.2 - May 10, 2024","text":"<p>What's Changed</p> <ul> <li>Fix wavelength bug by @giswqs in #27</li> <li>Pin xarray version by @giswqs in #28</li> <li>Fix xr merge error by @giswqs in #29</li> </ul> <p>Full Changelog: v0.3.1...v0.3.2</p>"},{"location":"changelog/#v031-may-09-2024","title":"v0.3.1 - May 09, 2024","text":"<p>Full Changelog: v0.3.0...v0.3.1</p>"},{"location":"changelog/#v030-may-08-2024","title":"v0.3.0 - May 08, 2024","text":"<p>What's Changed</p> <ul> <li>Add filter_pace function by @giswqs in #21</li> <li>Add grid pace function by @giswqs in #22</li> <li>Add fetch depth for docs by @giswqs in #23</li> <li>Add support for multi-band visualization by @giswqs in #24</li> <li>Add extract_pace function by @giswqs in #25</li> <li>Add spectral signature viz for PACE data by @giswqs in #26</li> </ul> <p>Full Changelog: v0.2.0...v0.3.0</p>"},{"location":"changelog/#v020-may-05-2024","title":"v0.2.0 - May 05, 2024","text":"<p>What's Changed</p> <ul> <li>Add support for reading and visualizing PACE data by @giswqs in #20</li> </ul> <p>Full Changelog: v0.1.3...v0.2.0</p>"},{"location":"changelog/#v013-apr-30-2024","title":"v0.1.3 - Apr 30, 2024","text":"<p>What's Changed</p> <ul> <li>Move hvplot import into functions by @giswqs in #18</li> <li>[pre-commit.ci] pre-commit autoupdate by @pre-commit-ci in #17</li> <li>Add pace module by @giswqs in #19</li> </ul> <p>Full Changelog: v0.1.2...v0.1.3</p>"},{"location":"changelog/#v012-apr-25-2024","title":"v0.1.2 - Apr 25, 2024","text":"<p>What's Changed</p> <ul> <li>Add demo gif by @bingqing-liu in #15</li> <li>Fix typos and add method bug by @giswqs in #16</li> </ul> <p>Full Changelog: v0.1.1...v0.1.2</p>"},{"location":"changelog/#v011-apr-22-2024","title":"v0.1.1 - Apr 22, 2024","text":"<p>What's Changed</p> <ul> <li>Add an EMIT notebook by @giswqs in #13</li> <li>Fix dependency issue by @giswqs in #14</li> </ul> <p>Full Changelog: v0.1.0...v0.1.1</p>"},{"location":"changelog/#v010-apr-21-2024","title":"v0.1.0 - Apr 21, 2024","text":"<p>What's Changed</p> <ul> <li>Improve support for visualizing EMIT data by @giswqs in #10</li> <li>Add ui module by @giswqs in #11</li> <li>Add support for displaying spectral signature interactively by @giswqs in #12</li> </ul> <p>Full Changelog: v0.0.3...v0.1.0</p>"},{"location":"changelog/#v003-apr-20-2024","title":"v0.0.3 - Apr 20, 2024","text":"<p>What's Changed</p> <ul> <li>Fix typos by @giswqs in #7</li> <li>Add conda-forge installation instructions by @giswqs in #8</li> <li>Add support for visualizing EMIT data by @giswqs in #9</li> </ul> <p>Full Changelog: v0.0.2...v0.0.3</p>"},{"location":"changelog/#v002-apr-18-2024","title":"v0.0.2 - Apr 18, 2024","text":"<p>What's Changed</p> <ul> <li>[pre-commit.ci] pre-commit autoupdate by @pre-commit-ci in #4</li> <li>Update readme by @Bingqing9027 in #5</li> <li>Add Map class by @giswqs in #6</li> </ul> <p>New Contributors</p> <ul> <li>@pre-commit-ci made their first contribution in #4</li> <li>@Bingqing9027 made their first contribution in #5</li> <li>@giswqs made their first contribution in #6</li> </ul> <p>Full Changelog: v0.0.1...v0.0.2</p>"},{"location":"changelog/#v001-apr-08-2024","title":"v0.0.1 - Apr 08, 2024","text":"<p>What's Changed Full Changelog: v0.0.1</p>"},{"location":"changelog_update/","title":"Changelog update","text":"In\u00a0[1]: Copied! <pre>import re\n</pre> import re In\u00a0[2]: Copied! <pre># Copy the release notes from the GitHub release page\nmarkdown_text = \"\"\"\n## What's Changed\n* Update pace_chla_to_image_function by @giswqs in https://github.com/opengeos/HyperCoast/pull/69\n\n\n**Full Changelog**: https://github.com/opengeos/HyperCoast/compare/v0.6.2...v0.6.3\n\"\"\"\n</pre> # Copy the release notes from the GitHub release page markdown_text = \"\"\" ## What's Changed * Update pace_chla_to_image_function by @giswqs in https://github.com/opengeos/HyperCoast/pull/69   **Full Changelog**: https://github.com/opengeos/HyperCoast/compare/v0.6.2...v0.6.3 \"\"\" In\u00a0[3]: Copied! <pre># Regular expression pattern to match the Markdown hyperlinks\npattern = r\"https://github\\.com/opengeos/HyperCoast/pull/(\\d+)\"\n</pre> # Regular expression pattern to match the Markdown hyperlinks pattern = r\"https://github\\.com/opengeos/HyperCoast/pull/(\\d+)\" In\u00a0[4]: Copied! <pre># Function to replace matched URLs with the desired format\ndef replace_url(match):\n    pr_number = match.group(1)\n    return f\"[#{pr_number}](https://github.com/opengeos/HyperCoast/pull/{pr_number})\"\n</pre> # Function to replace matched URLs with the desired format def replace_url(match):     pr_number = match.group(1)     return f\"[#{pr_number}](https://github.com/opengeos/HyperCoast/pull/{pr_number})\" In\u00a0[5]: Copied! <pre># Use re.sub to replace URLs with the desired format\nformatted_text = re.sub(pattern, replace_url, markdown_text)\n</pre> # Use re.sub to replace URLs with the desired format formatted_text = re.sub(pattern, replace_url, markdown_text) In\u00a0[6]: Copied! <pre>for line in formatted_text.splitlines():\n    if \"Full Changelog\" in line:\n        prefix = line.split(\": \")[0]\n        link = line.split(\": \")[1]\n        version = line.split(\"/\")[-1]\n        formatted_text = (\n            formatted_text.replace(line, f\"{prefix}: [{version}]({link})\")\n            .replace(\"## What's Changed\", \"**What's Changed**\")\n            .replace(\"## New Contributors\", \"**New Contributors**\")\n        )\n</pre> for line in formatted_text.splitlines():     if \"Full Changelog\" in line:         prefix = line.split(\": \")[0]         link = line.split(\": \")[1]         version = line.split(\"/\")[-1]         formatted_text = (             formatted_text.replace(line, f\"{prefix}: [{version}]({link})\")             .replace(\"## What's Changed\", \"**What's Changed**\")             .replace(\"## New Contributors\", \"**New Contributors**\")         ) In\u00a0[7]: Copied! <pre>with open(\"docs/changelog_update.md\", \"w\") as f:\n    f.write(formatted_text)\n</pre> with open(\"docs/changelog_update.md\", \"w\") as f:     f.write(formatted_text) In\u00a0[8]: Copied! <pre># Print the formatted text\nprint(formatted_text)\n</pre> # Print the formatted text print(formatted_text) <pre>\n**What's Changed**\n* Update pace_chla_to_image_function by @giswqs in [#69](https://github.com/opengeos/HyperCoast/pull/69)\n\n\n**Full Changelog**: [v0.6.2...v0.6.3](https://github.com/opengeos/HyperCoast/compare/v0.6.2...v0.6.3)\n\n</pre> <p>Copy the formatted text and paste it to the CHANGELOG.md file</p>"},{"location":"cla/","title":"Fiduciary License Agreement 2.0","text":"<p>based on the</p>"},{"location":"cla/#individual-contributor-exclusive-license-agreement","title":"Individual Contributor Exclusive License Agreement","text":""},{"location":"cla/#including-the-traditional-patent-license-option","title":"(including the Traditional Patent License OPTION)","text":"<p>Thank you for your interest in contributing to Open Geospatial Solutions's HyperCoast (\"We\" or \"Us\").</p> <p>The purpose of this contributor agreement (\"Agreement\") is to clarify and document the rights granted by contributors to Us. To make this document effective, please follow the instructions at https://github.com/opengeos/HyperCoast.</p>"},{"location":"cla/#0-preamble","title":"0. Preamble","text":"<p>Software is deeply embedded in all aspects of our lives and it is important that it empower, rather than restrict us. Free Software gives everybody the rights to use, understand, adapt and share software. These rights help support other fundamental freedoms like freedom of speech, press and privacy.</p> <p>Development of Free Software can follow many patterns. In some cases whole development is handled by a sole programmer or a small group of people. But usually, the creation and maintenance of software is a complex process that requires the contribution of many individuals. This also affects who owns the rights to the software. In the latter case, rights in software are owned jointly by a great number of individuals.</p> <p>To tackle this issue some projects require a full copyright assignment to be signed by all contributors. The problem with such assignments is that they often lack checks and balances that would protect the contributors from potential abuse of power from the new copyright holder.</p> <p>FSFE\u2019s Fiduciary License Agreement (FLA) was created by the Free Software Foundation Europe e.V. with just that in mind \u2013 to concentrate all deciding power within one entity and prevent fragmentation of rights on one hand, while on the other preventing that single entity from abusing its power. The main aim is to ensure that the software covered under the FLA will forever remain Free Software.</p> <p>This process only serves for the transfer of economic rights. So-called moral rights (e.g. authors right to be identified as author) remain with the original author(s) and are inalienable.</p>"},{"location":"cla/#how-to-use-this-fla","title":"How to use this FLA","text":"<p>If You are an employee and have created the Contribution as part of your employment, You need to have Your employer approve this Agreement or sign the Entity version of this document. If You do not own the Copyright in the entire work of authorship, any other author of the Contribution should also sign this \u2013 in any event, please contact Us at opengeos@outlook.com</p>"},{"location":"cla/#1-definitions","title":"1. Definitions","text":"<p>\"You\" means the individual Copyright owner who Submits a Contribution to Us.</p> <p>\"Legal Entity\" means an entity that is not a natural person.</p> <p>\"Affiliate\" means any other Legal Entity that controls, is controlled by, or under common control with that Legal Entity. For the purposes of this definition, \"control\" means (i) the power, direct or indirect, to cause the direction or management of such Legal Entity, whether by contract or otherwise, (ii) ownership of fifty percent (50%) or more of the outstanding shares or securities that vote to elect the management or other persons who direct such Legal Entity or (iii) beneficial ownership of such entity.</p> <p>\"Contribution\" means any original work of authorship, including any original modifications or additions to an existing work of authorship, Submitted by You to Us, in which You own the Copyright.</p> <p>\"Copyright\" means all rights protecting works of authorship, including copyright, moral and neighboring rights, as appropriate, for the full term of their existence.</p> <p>\"Material\" means the software or documentation made available by Us to third parties. When this Agreement covers more than one software project, the Material means the software or documentation to which the Contribution was Submitted. After You Submit the Contribution, it may be included in the Material.</p> <p>\"Submit\" means any act by which a Contribution is transferred to Us by You by means of tangible or intangible media, including but not limited to electronic mailing lists, source code control systems, and issue tracking systems that are managed by, or on behalf of, Us, but excluding any transfer that is conspicuously marked or otherwise designated in writing by You as \"Not a Contribution.\"</p> <p>\"Documentation\" means any non-software portion of a Contribution.</p>"},{"location":"cla/#2-license-grant","title":"2. License grant","text":""},{"location":"cla/#21-copyright-license-to-us","title":"2.1 Copyright license to Us","text":"<p>Subject to the terms and conditions of this Agreement, You hereby grant to Us a worldwide, royalty-free, exclusive, perpetual and irrevocable (except as stated in Section 8.2) license, with the right to transfer an unlimited number of non-exclusive licenses or to grant sublicenses to third parties, under the Copyright covering the Contribution to use the Contribution by all means, including, but not limited to:</p> <ul> <li>publish the Contribution,</li> <li>modify the Contribution,</li> <li>prepare derivative works based upon or containing the Contribution and/or to combine the Contribution with other Materials,</li> <li>reproduce the Contribution in original or modified form,</li> <li>distribute, to make the Contribution available to the public, display and publicly perform the Contribution in original or modified form.</li> </ul>"},{"location":"cla/#22-moral-rights","title":"2.2 Moral rights","text":"<p>Moral Rights remain unaffected to the extent they are recognized and not waivable by applicable law. Notwithstanding, You may add your name to the attribution mechanism customary used in the Materials you Contribute to, such as the header of the source code files of Your Contribution, and We will respect this attribution when using Your Contribution.</p>"},{"location":"cla/#23-copyright-license-back-to-you","title":"2.3 Copyright license back to You","text":"<p>Upon such grant of rights to Us, We immediately grant to You a worldwide, royalty-free, non-exclusive, perpetual and irrevocable license, with the right to transfer an unlimited number of non-exclusive licenses or to grant sublicenses to third parties, under the Copyright covering the Contribution to use the Contribution by all means, including, but not limited to:</p> <ul> <li>publish the Contribution,</li> <li>modify the Contribution,</li> <li>prepare derivative works based upon or containing the Contribution and/or to combine the Contribution with other Materials,</li> <li>reproduce the Contribution in original or modified form,</li> <li>distribute, to make the Contribution available to the public, display and publicly perform the Contribution in original or modified form.</li> </ul> <p>This license back is limited to the Contribution and does not provide any rights to the Material.</p>"},{"location":"cla/#3-patents","title":"3. Patents","text":""},{"location":"cla/#31-patent-license","title":"3.1 Patent license","text":"<p>Subject to the terms and conditions of this Agreement You hereby grant to Us and to recipients of Materials distributed by Us a worldwide, royalty-free, non-exclusive, perpetual and irrevocable (except as stated in Section 3.2) patent license, with the right to transfer an unlimited number of non-exclusive licenses or to grant sublicenses to third parties, to make, have made, use, sell, offer for sale, import and otherwise transfer the Contribution and the Contribution in combination with any Material (and portions of such combination). This license applies to all patents owned or controlled by You, whether already acquired or hereafter acquired, that would be infringed by making, having made, using, selling, offering for sale, importing or otherwise transferring of Your Contribution(s) alone or by combination of Your Contribution(s) with any Material.</p>"},{"location":"cla/#32-revocation-of-patent-license","title":"3.2 Revocation of patent license","text":"<p>You reserve the right to revoke the patent license stated in section 3.1 if We make any infringement claim that is targeted at your Contribution and not asserted for a Defensive Purpose. An assertion of claims of the Patents shall be considered for a \"Defensive Purpose\" if the claims are asserted against an entity that has filed, maintained, threatened, or voluntarily participated in a patent infringement lawsuit against Us or any of Our licensees.</p>"},{"location":"cla/#4-license-obligations-by-us","title":"4. License obligations by Us","text":"<p>We agree to (sub)license the Contribution or any Materials containing, based on or derived from your Contribution under the terms of any licenses the Free Software Foundation classifies as Free Software License and which are approved by the Open Source Initiative as Open Source licenses.</p> <p>We agree to license patents owned or controlled by You only to the extent necessary to (sub)license Your Contribution(s) and the combination of Your Contribution(s) with the Material under the terms of any licenses the Free Software Foundation classifies as Free Software licenses and which are approved by the Open Source Initiative as Open Source licenses..</p>"},{"location":"cla/#5-disclaimer","title":"5. Disclaimer","text":"<p>THE CONTRIBUTION IS PROVIDED \"AS IS\". MORE PARTICULARLY, ALL EXPRESS OR IMPLIED WARRANTIES INCLUDING, WITHOUT LIMITATION, ANY IMPLIED WARRANTY OF SATISFACTORY QUALITY, FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT ARE EXPRESSLY DISCLAIMED BY YOU TO US AND BY US TO YOU. TO THE EXTENT THAT ANY SUCH WARRANTIES CANNOT BE DISCLAIMED, SUCH WARRANTY IS LIMITED IN DURATION AND EXTENT TO THE MINIMUM PERIOD AND EXTENT PERMITTED BY LAW.</p>"},{"location":"cla/#6-consequential-damage-waiver","title":"6. Consequential damage waiver","text":"<p>TO THE MAXIMUM EXTENT PERMITTED BY APPLICABLE LAW, IN NO EVENT WILL YOU OR WE BE LIABLE FOR ANY LOSS OF PROFITS, LOSS OF ANTICIPATED SAVINGS, LOSS OF DATA, INDIRECT, SPECIAL, INCIDENTAL, CONSEQUENTIAL AND EXEMPLARY DAMAGES ARISING OUT OF THIS AGREEMENT REGARDLESS OF THE LEGAL OR EQUITABLE THEORY (CONTRACT, TORT OR OTHERWISE) UPON WHICH THE CLAIM IS BASED.</p>"},{"location":"cla/#7-approximation-of-disclaimer-and-damage-waiver","title":"7. Approximation of disclaimer and damage waiver","text":"<p>IF THE DISCLAIMER AND DAMAGE WAIVER MENTIONED IN SECTION 5. AND SECTION 6. CANNOT BE GIVEN LEGAL EFFECT UNDER APPLICABLE LOCAL LAW, REVIEWING COURTS SHALL APPLY LOCAL LAW THAT MOST CLOSELY APPROXIMATES AN ABSOLUTE WAIVER OF ALL CIVIL OR CONTRACTUAL LIABILITY IN CONNECTION WITH THE CONTRIBUTION.</p>"},{"location":"cla/#8-term","title":"8. Term","text":"<p>8.1 This Agreement shall come into effect upon Your acceptance of the terms and conditions.</p> <p>8.2 This Agreement shall apply for the term of the copyright and patents licensed here. However, You shall have the right to terminate the Agreement if We do not fulfill the obligations as set forth in Section 4. Such termination must be made in writing.</p> <p>8.3 In the event of a termination of this Agreement Sections 5., 6., 7., 8., and 9. shall survive such termination and shall remain in full force thereafter. For the avoidance of doubt, Free and Open Source Software (sub)licenses that have already been granted for Contributions at the date of the termination shall remain in full force after the termination of this Agreement.</p>"},{"location":"cla/#9-miscellaneous","title":"9. Miscellaneous","text":"<p>9.1 This Agreement and all disputes, claims, actions, suits or other proceedings arising out of this agreement or relating in any way to it shall be governed by the laws of United States excluding its private international law provisions.</p> <p>9.2 This Agreement sets out the entire agreement between You and Us for Your Contributions to Us and overrides all other agreements or understandings.</p> <p>9.3 In case of Your death, this agreement shall continue with Your heirs. In case of more than one heir, all heirs must exercise their rights through a commonly authorized person.</p> <p>9.4 If any provision of this Agreement is found void and unenforceable, such provision will be replaced to the extent possible with a provision that comes closest to the meaning of the original provision and that is enforceable. The terms and conditions set forth in this Agreement shall apply notwithstanding any failure of essential purpose of this Agreement or any limited remedy to the maximum extent possible under law.</p> <p>9.5 You agree to notify Us of any facts or circumstances of which you become aware that would make this Agreement inaccurate in any respect.</p>"},{"location":"cla/#recreate-this-contributor-license-agreement","title":"Recreate this Contributor License Agreement","text":"<p>https://contributoragreements.org/u2s/2kw48zbt5d</p> <p></p> <p></p>"},{"location":"cla/#fiduciary-license-agreement-20_1","title":"Fiduciary License Agreement 2.0","text":"<p>based on the</p>"},{"location":"cla/#entity-contributor-exclusive-license-agreement","title":"Entity Contributor Exclusive License Agreement","text":""},{"location":"cla/#including-the-traditional-patent-license-option_1","title":"(including the Traditional Patent License OPTION)","text":"<p>Thank you for your interest in contributing to Open Geospatial Solutions's HyperCoast (\"We\" or \"Us\").</p> <p>The purpose of this contributor agreement (\"Agreement\") is to clarify and document the rights granted by contributors to Us. To make this document effective, please follow the instructions at https://github.com/opengeos/HyperCoast.</p>"},{"location":"cla/#0-preamble_1","title":"0. Preamble","text":"<p>Software is deeply embedded in all aspects of our lives and it is important that it empower, rather than restrict us. Free Software gives everybody the rights to use, understand, adapt and share software. These rights help support other fundamental freedoms like freedom of speech, press and privacy.</p> <p>Development of Free Software can follow many patterns. In some cases whole development is handled by a sole programmer or a small group of people. But usually, the creation and maintenance of software is a complex process that requires the contribution of many individuals. This also affects who owns the rights to the software. In the latter case, rights in software are owned jointly by a great number of individuals.</p> <p>To tackle this issue some projects require a full copyright assignment to be signed by all contributors. The problem with such assignments is that they often lack checks and balances that would protect the contributors from potential abuse of power from the new copyright holder.</p> <p>FSFE\u2019s Fiduciary License Agreement (FLA) was created by the Free Software Foundation Europe e.V. with just that in mind \u2013 to concentrate all deciding power within one entity and prevent fragmentation of rights on one hand, while on the other preventing that single entity from abusing its power. The main aim is to ensure that the software covered under the FLA will forever remain Free Software.</p> <p>This process only serves for the transfer of economic rights. So-called moral rights (e.g. authors right to be identified as author) remain with the original author(s) and are inalienable.</p>"},{"location":"cla/#how-to-use-this-fla_1","title":"How to use this FLA","text":"<p>If You are an employee and have created the Contribution as part of your employment, You need to have Your employer approve this Agreement or sign the Entity version of this document. If You do not own the Copyright in the entire work of authorship, any other author of the Contribution should also sign this \u2013 in any event, please contact Us at opengeos@outlook.com</p>"},{"location":"cla/#1-definitions_1","title":"1. Definitions","text":"<p>\"You\" means the individual Copyright owner who Submits a Contribution to Us.</p> <p>\"Legal Entity\" means an entity that is not a natural person.</p> <p>\"Affiliate\" means any other Legal Entity that controls, is controlled by, or under common control with that Legal Entity. For the purposes of this definition, \"control\" means (i) the power, direct or indirect, to cause the direction or management of such Legal Entity, whether by contract or otherwise, (ii) ownership of fifty percent (50%) or more of the outstanding shares or securities that vote to elect the management or other persons who direct such Legal Entity or (iii) beneficial ownership of such entity.</p> <p>\"Contribution\" means any original work of authorship, including any original modifications or additions to an existing work of authorship, Submitted by You to Us, in which You own the Copyright.</p> <p>\"Copyright\" means all rights protecting works of authorship, including copyright, moral and neighboring rights, as appropriate, for the full term of their existence.</p> <p>\"Material\" means the software or documentation made available by Us to third parties. When this Agreement covers more than one software project, the Material means the software or documentation to which the Contribution was Submitted. After You Submit the Contribution, it may be included in the Material.</p> <p>\"Submit\" means any act by which a Contribution is transferred to Us by You by means of tangible or intangible media, including but not limited to electronic mailing lists, source code control systems, and issue tracking systems that are managed by, or on behalf of, Us, but excluding any transfer that is conspicuously marked or otherwise designated in writing by You as \"Not a Contribution.\"</p> <p>\"Documentation\" means any non-software portion of a Contribution.</p>"},{"location":"cla/#2-license-grant_1","title":"2. License grant","text":""},{"location":"cla/#21-copyright-license-to-us_1","title":"2.1 Copyright license to Us","text":"<p>Subject to the terms and conditions of this Agreement, You hereby grant to Us a worldwide, royalty-free, exclusive, perpetual and irrevocable (except as stated in Section 8.2) license, with the right to transfer an unlimited number of non-exclusive licenses or to grant sublicenses to third parties, under the Copyright covering the Contribution to use the Contribution by all means, including, but not limited to:</p> <ul> <li>publish the Contribution,</li> <li>modify the Contribution,</li> <li>prepare derivative works based upon or containing the Contribution and/or to combine the Contribution with other Materials,</li> <li>reproduce the Contribution in original or modified form,</li> <li>distribute, to make the Contribution available to the public, display and publicly perform the Contribution in original or modified form.</li> </ul>"},{"location":"cla/#22-moral-rights_1","title":"2.2 Moral rights","text":"<p>Moral Rights remain unaffected to the extent they are recognized and not waivable by applicable law. Notwithstanding, You may add your name to the attribution mechanism customary used in the Materials you Contribute to, such as the header of the source code files of Your Contribution, and We will respect this attribution when using Your Contribution.</p>"},{"location":"cla/#23-copyright-license-back-to-you_1","title":"2.3 Copyright license back to You","text":"<p>Upon such grant of rights to Us, We immediately grant to You a worldwide, royalty-free, non-exclusive, perpetual and irrevocable license, with the right to transfer an unlimited number of non-exclusive licenses or to grant sublicenses to third parties, under the Copyright covering the Contribution to use the Contribution by all means, including, but not limited to:</p> <ul> <li>publish the Contribution,</li> <li>modify the Contribution,</li> <li>prepare derivative works based upon or containing the Contribution and/or to combine the Contribution with other Materials,</li> <li>reproduce the Contribution in original or modified form,</li> <li>distribute, to make the Contribution available to the public, display and publicly perform the Contribution in original or modified form.</li> </ul> <p>This license back is limited to the Contribution and does not provide any rights to the Material.</p>"},{"location":"cla/#3-patents_1","title":"3. Patents","text":""},{"location":"cla/#31-patent-license_1","title":"3.1 Patent license","text":"<p>Subject to the terms and conditions of this Agreement You hereby grant to Us and to recipients of Materials distributed by Us a worldwide, royalty-free, non-exclusive, perpetual and irrevocable (except as stated in Section 3.2) patent license, with the right to transfer an unlimited number of non-exclusive licenses or to grant sublicenses to third parties, to make, have made, use, sell, offer for sale, import and otherwise transfer the Contribution and the Contribution in combination with any Material (and portions of such combination). This license applies to all patents owned or controlled by You, whether already acquired or hereafter acquired, that would be infringed by making, having made, using, selling, offering for sale, importing or otherwise transferring of Your Contribution(s) alone or by combination of Your Contribution(s) with any Material.</p>"},{"location":"cla/#32-revocation-of-patent-license_1","title":"3.2 Revocation of patent license","text":"<p>You reserve the right to revoke the patent license stated in section 3.1 if We make any infringement claim that is targeted at your Contribution and not asserted for a Defensive Purpose. An assertion of claims of the Patents shall be considered for a \"Defensive Purpose\" if the claims are asserted against an entity that has filed, maintained, threatened, or voluntarily participated in a patent infringement lawsuit against Us or any of Our licensees.</p>"},{"location":"cla/#4-license-obligations-by-us_1","title":"4. License obligations by Us","text":"<p>We agree to (sub)license the Contribution or any Materials containing, based on or derived from your Contribution under the terms of any licenses the Free Software Foundation classifies as Free Software License and which are approved by the Open Source Initiative as Open Source licenses.</p> <p>We agree to license patents owned or controlled by You only to the extent necessary to (sub)license Your Contribution(s) and the combination of Your Contribution(s) with the Material under the terms of any licenses the Free Software Foundation classifies as Free Software licenses and which are approved by the Open Source Initiative as Open Source licenses..</p>"},{"location":"cla/#5-disclaimer_1","title":"5. Disclaimer","text":"<p>THE CONTRIBUTION IS PROVIDED \"AS IS\". MORE PARTICULARLY, ALL EXPRESS OR IMPLIED WARRANTIES INCLUDING, WITHOUT LIMITATION, ANY IMPLIED WARRANTY OF SATISFACTORY QUALITY, FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT ARE EXPRESSLY DISCLAIMED BY YOU TO US AND BY US TO YOU. TO THE EXTENT THAT ANY SUCH WARRANTIES CANNOT BE DISCLAIMED, SUCH WARRANTY IS LIMITED IN DURATION AND EXTENT TO THE MINIMUM PERIOD AND EXTENT PERMITTED BY LAW.</p>"},{"location":"cla/#6-consequential-damage-waiver_1","title":"6. Consequential damage waiver","text":"<p>TO THE MAXIMUM EXTENT PERMITTED BY APPLICABLE LAW, IN NO EVENT WILL YOU OR WE BE LIABLE FOR ANY LOSS OF PROFITS, LOSS OF ANTICIPATED SAVINGS, LOSS OF DATA, INDIRECT, SPECIAL, INCIDENTAL, CONSEQUENTIAL AND EXEMPLARY DAMAGES ARISING OUT OF THIS AGREEMENT REGARDLESS OF THE LEGAL OR EQUITABLE THEORY (CONTRACT, TORT OR OTHERWISE) UPON WHICH THE CLAIM IS BASED.</p>"},{"location":"cla/#7-approximation-of-disclaimer-and-damage-waiver_1","title":"7. Approximation of disclaimer and damage waiver","text":"<p>IF THE DISCLAIMER AND DAMAGE WAIVER MENTIONED IN SECTION 5. AND SECTION 6. CANNOT BE GIVEN LEGAL EFFECT UNDER APPLICABLE LOCAL LAW, REVIEWING COURTS SHALL APPLY LOCAL LAW THAT MOST CLOSELY APPROXIMATES AN ABSOLUTE WAIVER OF ALL CIVIL OR CONTRACTUAL LIABILITY IN CONNECTION WITH THE CONTRIBUTION.</p>"},{"location":"cla/#8-term_1","title":"8. Term","text":"<p>8.1 This Agreement shall come into effect upon Your acceptance of the terms and conditions.</p> <p>8.2 This Agreement shall apply for the term of the copyright and patents licensed here. However, You shall have the right to terminate the Agreement if We do not fulfill the obligations as set forth in Section 4. Such termination must be made in writing.</p> <p>8.3 In the event of a termination of this Agreement Sections 5., 6., 7., 8., and 9. shall survive such termination and shall remain in full force thereafter. For the avoidance of doubt, Free and Open Source Software (sub)licenses that have already been granted for Contributions at the date of the termination shall remain in full force after the termination of this Agreement.</p>"},{"location":"cla/#9-miscellaneous_1","title":"9. Miscellaneous","text":"<p>9.1 This Agreement and all disputes, claims, actions, suits or other proceedings arising out of this agreement or relating in any way to it shall be governed by the laws of United States excluding its private international law provisions.</p> <p>9.2 This Agreement sets out the entire agreement between You and Us for Your Contributions to Us and overrides all other agreements or understandings.</p> <p>9.3 In case of Your death, this agreement shall continue with Your heirs. In case of more than one heir, all heirs must exercise their rights through a commonly authorized person.</p> <p>9.4 If any provision of this Agreement is found void and unenforceable, such provision will be replaced to the extent possible with a provision that comes closest to the meaning of the original provision and that is enforceable. The terms and conditions set forth in this Agreement shall apply notwithstanding any failure of essential purpose of this Agreement or any limited remedy to the maximum extent possible under law.</p> <p>9.5 You agree to notify Us of any facts or circumstances of which you become aware that would make this Agreement inaccurate in any respect.</p>"},{"location":"cla/#recreate-this-contributor-license-agreement_1","title":"Recreate this Contributor License Agreement","text":"<p>https://contributoragreements.org/u2s/2kw48zbt5d</p> <p></p> <p></p>"},{"location":"common/","title":"common module","text":"<p>The common module contains common functions and classes used by the other modules.</p>"},{"location":"common/#hypercoast.common.convert_coords","title":"<code>convert_coords(coords, from_epsg, to_epsg)</code>","text":"<p>Convert a list of coordinates from one EPSG to another.</p> <p>Parameters:</p> Name Type Description Default <code>coords</code> <code>List[Tuple[float, float]]</code> <p>List of tuples containing coordinates in the format (latitude, longitude).</p> required <code>from_epsg</code> <code>str</code> <p>Source EPSG code (default is \"epsg:4326\").</p> required <code>to_epsg</code> <code>str</code> <p>Target EPSG code (default is \"epsg:32615\").</p> required <p>Returns:</p> Type Description <code>List[Tuple[float, float]]</code> <p>List of tuples containing converted coordinates in the format (x, y).</p> Source code in <code>hypercoast/common.py</code> <pre><code>def convert_coords(\n    coords: List[Tuple[float, float]], from_epsg: str, to_epsg: str\n) -&gt; List[Tuple[float, float]]:\n    \"\"\"\n    Convert a list of coordinates from one EPSG to another.\n\n    Args:\n        coords: List of tuples containing coordinates in the format (latitude, longitude).\n        from_epsg: Source EPSG code (default is \"epsg:4326\").\n        to_epsg: Target EPSG code (default is \"epsg:32615\").\n\n    Returns:\n        List of tuples containing converted coordinates in the format (x, y).\n    \"\"\"\n    import pyproj\n\n    # Define the coordinate transformation\n    transformer = pyproj.Transformer.from_crs(from_epsg, to_epsg, always_xy=True)\n\n    # Convert each coordinate\n    converted_coords = [transformer.transform(lon, lat) for lat, lon in coords]\n\n    return converted_coords\n</code></pre>"},{"location":"common/#hypercoast.common.download_acolite","title":"<code>download_acolite(outdir='.', platform=None)</code>","text":"<p>Downloads the Acolite release based on the OS platform and extracts it to the specified directory. For more information, see the Acolite manual https://github.com/acolite/acolite/releases.</p> <p>Parameters:</p> Name Type Description Default <code>outdir</code> <code>str</code> <p>The output directory where the file will be Acolite and extracted.</p> <code>'.'</code> <code>platform</code> <code>Optional[str]</code> <p>The platform for which to download acolite. If None, the current system platform is used.                       Valid values are 'linux', 'darwin', and 'windows'.</p> <code>None</code> <p>Returns:</p> Type Description <code>str</code> <p>The path to the extracted Acolite directory.</p> <p>Exceptions:</p> Type Description <code>Exception</code> <p>If the platform is unsupported or the download fails.</p> Source code in <code>hypercoast/common.py</code> <pre><code>def download_acolite(outdir: str = \".\", platform: Optional[str] = None) -&gt; str:\n    \"\"\"\n    Downloads the Acolite release based on the OS platform and extracts it to the specified directory.\n    For more information, see the Acolite manual https://github.com/acolite/acolite/releases.\n\n    Args:\n        outdir (str): The output directory where the file will be Acolite and extracted.\n        platform (Optional[str]): The platform for which to download acolite. If None, the current system platform is used.\n                                  Valid values are 'linux', 'darwin', and 'windows'.\n\n    Returns:\n        str: The path to the extracted Acolite directory.\n\n    Raises:\n        Exception: If the platform is unsupported or the download fails.\n    \"\"\"\n    import platform as pf\n    import requests\n    import tarfile\n    from tqdm import tqdm\n\n    base_url = \"https://github.com/acolite/acolite/releases/download/20231023.0/\"\n\n    if platform is None:\n        platform = pf.system().lower()\n    else:\n        platform = platform.lower()\n\n    if platform == \"linux\":\n        download_url = base_url + \"acolite_py_linux_20231023.0.tar.gz\"\n        root_dir = \"acolite_py_linux\"\n    elif platform == \"darwin\":\n        download_url = base_url + \"acolite_py_mac_20231023.0.tar.gz\"\n        root_dir = \"acolite_py_mac\"\n    elif platform == \"windows\":\n        download_url = base_url + \"acolite_py_win_20231023.0.tar.gz\"\n        root_dir = \"acolite_py_win\"\n    else:\n        print(f\"Unsupported OS platform: {platform}\")\n        return\n\n    if not os.path.exists(outdir):\n        os.makedirs(outdir)\n\n    extracted_path = os.path.join(outdir, root_dir)\n    file_name = os.path.join(outdir, download_url.split(\"/\")[-1])\n\n    if os.path.exists(file_name):\n        print(f\"{file_name} already exists. Skip downloading.\")\n        return extracted_path\n\n    response = requests.get(download_url, stream=True, timeout=60)\n    total_size = int(response.headers.get(\"content-length\", 0))\n    block_size = 8192\n\n    if response.status_code == 200:\n        with (\n            open(file_name, \"wb\") as file,\n            tqdm(\n                desc=file_name,\n                total=total_size,\n                unit=\"iB\",\n                unit_scale=True,\n                unit_divisor=1024,\n            ) as bar,\n        ):\n            for chunk in response.iter_content(chunk_size=block_size):\n                if chunk:\n                    file.write(chunk)\n                    bar.update(len(chunk))\n        print(f\"Downloaded {file_name}\")\n    else:\n        print(f\"Failed to download file from {download_url}\")\n        return\n\n    # Unzip the file\n    with tarfile.open(file_name, \"r:gz\") as tar:\n        tar.extractall(path=outdir)\n\n    print(f\"Extracted to {extracted_path}\")\n    return extracted_path\n</code></pre>"},{"location":"common/#hypercoast.common.download_ecostress","title":"<code>download_ecostress(granules, out_dir=None, threads=8)</code>","text":"<p>Downloads NASA ECOSTRESS granules.</p> <p>Parameters:</p> Name Type Description Default <code>granules</code> <code>List[dict]</code> <p>The granules to download.</p> required <code>out_dir</code> <code>str</code> <p>The output directory where the granules will be downloaded. Defaults to None (current directory).</p> <code>None</code> <code>threads</code> <code>int</code> <p>The number of threads to use for downloading. Defaults to 8.</p> <code>8</code> Source code in <code>hypercoast/common.py</code> <pre><code>def download_ecostress(\n    granules: List[dict],\n    out_dir: Optional[str] = None,\n    threads: int = 8,\n) -&gt; None:\n    \"\"\"Downloads NASA ECOSTRESS granules.\n\n    Args:\n        granules (List[dict]): The granules to download.\n        out_dir (str, optional): The output directory where the granules will be\n            downloaded. Defaults to None (current directory).\n        threads (int, optional): The number of threads to use for downloading.\n            Defaults to 8.\n    \"\"\"\n\n    download_nasa_data(granules=granules, out_dir=out_dir, threads=threads)\n</code></pre>"},{"location":"common/#hypercoast.common.download_emit","title":"<code>download_emit(granules, out_dir=None, threads=8)</code>","text":"<p>Downloads NASA EMIT granules.</p> <p>Parameters:</p> Name Type Description Default <code>granules</code> <code>List[dict]</code> <p>The granules to download.</p> required <code>out_dir</code> <code>str</code> <p>The output directory where the granules will be downloaded. Defaults to None (current directory).</p> <code>None</code> <code>threads</code> <code>int</code> <p>The number of threads to use for downloading. Defaults to 8.</p> <code>8</code> Source code in <code>hypercoast/common.py</code> <pre><code>def download_emit(\n    granules: List[dict],\n    out_dir: Optional[str] = None,\n    threads: int = 8,\n) -&gt; None:\n    \"\"\"Downloads NASA EMIT granules.\n\n    Args:\n        granules (List[dict]): The granules to download.\n        out_dir (str, optional): The output directory where the granules will be\n            downloaded. Defaults to None (current directory).\n        threads (int, optional): The number of threads to use for downloading.\n            Defaults to 8.\n    \"\"\"\n\n    download_nasa_data(granules=granules, out_dir=out_dir, threads=threads)\n</code></pre>"},{"location":"common/#hypercoast.common.download_file","title":"<code>download_file(url=None, output=None, quiet=True, proxy=None, speed=None, use_cookies=True, verify=True, uid=None, fuzzy=False, resume=False, unzip=True, overwrite=False, subfolder=False)</code>","text":"<p>Download a file from URL, including Google Drive shared URL.</p> <p>Parameters:</p> Name Type Description Default <code>url</code> <code>str</code> <p>Google Drive URL is also supported. Defaults to None.</p> <code>None</code> <code>output</code> <code>str</code> <p>Output filename. Default is basename of URL.</p> <code>None</code> <code>quiet</code> <code>bool</code> <p>Suppress terminal output. Default is True.</p> <code>True</code> <code>proxy</code> <code>str</code> <p>Proxy. Defaults to None.</p> <code>None</code> <code>speed</code> <code>float</code> <p>Download byte size per second (e.g., 256KB/s = 256 * 1024). Defaults to None.</p> <code>None</code> <code>use_cookies</code> <code>bool</code> <p>Flag to use cookies. Defaults to True.</p> <code>True</code> <code>verify</code> <code>bool | str</code> <p>Either a bool, in which case it controls whether the server's TLS certificate is verified, or a string, in which case it must be a path to a CA bundle to use. Default is True.. Defaults to True.</p> <code>True</code> <code>uid</code> <code>str</code> <p>Google Drive's file ID. Defaults to None.</p> <code>None</code> <code>fuzzy</code> <code>bool</code> <p>Fuzzy extraction of Google Drive's file Id. Defaults to False.</p> <code>False</code> <code>resume</code> <code>bool</code> <p>Resume the download from existing tmp file if possible. Defaults to False.</p> <code>False</code> <code>unzip</code> <code>bool</code> <p>Unzip the file. Defaults to True.</p> <code>True</code> <code>overwrite</code> <code>bool</code> <p>Overwrite the file if it already exists. Defaults to False.</p> <code>False</code> <code>subfolder</code> <code>bool</code> <p>Create a subfolder with the same name as the file. Defaults to False.</p> <code>False</code> <p>Returns:</p> Type Description <code>str</code> <p>The output file path.</p> Source code in <code>hypercoast/common.py</code> <pre><code>def download_file(\n    url: Optional[str] = None,\n    output: Optional[str] = None,\n    quiet: Optional[bool] = True,\n    proxy: Optional[str] = None,\n    speed: Optional[float] = None,\n    use_cookies: Optional[bool] = True,\n    verify: Optional[bool] = True,\n    uid: Optional[str] = None,\n    fuzzy: Optional[bool] = False,\n    resume: Optional[bool] = False,\n    unzip: Optional[bool] = True,\n    overwrite: Optional[bool] = False,\n    subfolder: Optional[bool] = False,\n) -&gt; str:\n    \"\"\"Download a file from URL, including Google Drive shared URL.\n\n    Args:\n        url (str, optional): Google Drive URL is also supported. Defaults to None.\n        output (str, optional): Output filename. Default is basename of URL.\n        quiet (bool, optional): Suppress terminal output. Default is True.\n        proxy (str, optional): Proxy. Defaults to None.\n        speed (float, optional): Download byte size per second (e.g., 256KB/s = 256 * 1024). Defaults to None.\n        use_cookies (bool, optional): Flag to use cookies. Defaults to True.\n        verify (bool | str, optional): Either a bool, in which case it controls whether the server's TLS certificate is verified, or a string,\n            in which case it must be a path to a CA bundle to use. Default is True.. Defaults to True.\n        uid (str, optional): Google Drive's file ID. Defaults to None.\n        fuzzy (bool, optional): Fuzzy extraction of Google Drive's file Id. Defaults to False.\n        resume (bool, optional): Resume the download from existing tmp file if possible. Defaults to False.\n        unzip (bool, optional): Unzip the file. Defaults to True.\n        overwrite (bool, optional): Overwrite the file if it already exists. Defaults to False.\n        subfolder (bool, optional): Create a subfolder with the same name as the file. Defaults to False.\n\n    Returns:\n        str: The output file path.\n    \"\"\"\n    import zipfile\n    import tarfile\n    import gdown\n\n    if output is None:\n        if isinstance(url, str) and url.startswith(\"http\"):\n            output = os.path.basename(url)\n\n    out_dir = os.path.abspath(os.path.dirname(output))\n    if not os.path.exists(out_dir):\n        os.makedirs(out_dir)\n\n    if isinstance(url, str):\n        if os.path.exists(os.path.abspath(output)) and (not overwrite):\n            print(\n                f\"{output} already exists. Skip downloading. Set overwrite=True to overwrite.\"\n            )\n            return os.path.abspath(output)\n        else:\n            url = github_raw_url(url)\n\n    if \"https://drive.google.com/file/d/\" in url:\n        fuzzy = True\n\n    output = gdown.download(\n        url, output, quiet, proxy, speed, use_cookies, verify, uid, fuzzy, resume\n    )\n\n    if unzip:\n        if output.endswith(\".zip\"):\n            with zipfile.ZipFile(output, \"r\") as zip_ref:\n                if not quiet:\n                    print(\"Extracting files...\")\n                if subfolder:\n                    basename = os.path.splitext(os.path.basename(output))[0]\n\n                    output = os.path.join(out_dir, basename)\n                    if not os.path.exists(output):\n                        os.makedirs(output)\n                    zip_ref.extractall(output)\n                else:\n                    zip_ref.extractall(os.path.dirname(output))\n        elif output.endswith(\".tar.gz\") or output.endswith(\".tar\"):\n            if output.endswith(\".tar.gz\"):\n                mode = \"r:gz\"\n            else:\n                mode = \"r\"\n\n            with tarfile.open(output, mode) as tar_ref:\n                if not quiet:\n                    print(\"Extracting files...\")\n                if subfolder:\n                    basename = os.path.splitext(os.path.basename(output))[0]\n                    output = os.path.join(out_dir, basename)\n                    if not os.path.exists(output):\n                        os.makedirs(output)\n                    tar_ref.extractall(output)\n                else:\n                    tar_ref.extractall(os.path.dirname(output))\n\n    return os.path.abspath(output)\n</code></pre>"},{"location":"common/#hypercoast.common.download_nasa_data","title":"<code>download_nasa_data(granules, out_dir=None, provider=None, threads=8)</code>","text":"<p>Downloads NASA Earthdata granules.</p> <p>Parameters:</p> Name Type Description Default <code>granules</code> <code>List[dict]</code> <p>The granules to download.</p> required <code>out_dir</code> <code>str</code> <p>The output directory where the granules will be downloaded. Defaults to None (current directory).</p> <code>None</code> <code>provider</code> <code>str</code> <p>The provider of the granules.</p> <code>None</code> <code>threads</code> <code>int</code> <p>The number of threads to use for downloading. Defaults to 8.</p> <code>8</code> Source code in <code>hypercoast/common.py</code> <pre><code>def download_nasa_data(\n    granules: List[dict],\n    out_dir: Optional[str] = None,\n    provider: Optional[str] = None,\n    threads: int = 8,\n) -&gt; None:\n    \"\"\"Downloads NASA Earthdata granules.\n\n    Args:\n        granules (List[dict]): The granules to download.\n        out_dir (str, optional): The output directory where the granules will be downloaded. Defaults to None (current directory).\n        provider (str, optional): The provider of the granules.\n        threads (int, optional): The number of threads to use for downloading. Defaults to 8.\n    \"\"\"\n\n    leafmap.nasa_data_download(\n        granules=granules, out_dir=out_dir, provider=provider, threads=threads\n    )\n</code></pre>"},{"location":"common/#hypercoast.common.download_pace","title":"<code>download_pace(granules, out_dir=None, threads=8)</code>","text":"<p>Downloads NASA PACE granules.</p> <p>Parameters:</p> Name Type Description Default <code>granules</code> <code>List[dict]</code> <p>The granules to download.</p> required <code>out_dir</code> <code>str</code> <p>The output directory where the granules will be downloaded. Defaults to None (current directory).</p> <code>None</code> <code>threads</code> <code>int</code> <p>The number of threads to use for downloading. Defaults to 8.</p> <code>8</code> Source code in <code>hypercoast/common.py</code> <pre><code>def download_pace(\n    granules: List[dict],\n    out_dir: Optional[str] = None,\n    threads: int = 8,\n) -&gt; None:\n    \"\"\"Downloads NASA PACE granules.\n\n    Args:\n        granules (List[dict]): The granules to download.\n        out_dir (str, optional): The output directory where the granules will be\n            downloaded. Defaults to None (current directory).\n        threads (int, optional): The number of threads to use for downloading.\n            Defaults to 8.\n    \"\"\"\n\n    download_nasa_data(granules=granules, out_dir=out_dir, threads=threads)\n</code></pre>"},{"location":"common/#hypercoast.common.extract_date_from_filename","title":"<code>extract_date_from_filename(filename)</code>","text":"<p>Extracts a date from a filename assuming the date is in 'YYYYMMDD' format.</p> <p>This function searches the filename for a sequence of 8 digits that represent a date in 'YYYYMMDD' format. If such a sequence is found, it converts the sequence into a pandas Timestamp object. If no such sequence is found, the function returns None.</p> <p>Parameters:</p> Name Type Description Default <code>filename</code> <code>str</code> <p>The filename from which to extract the date.</p> required <p>Returns:</p> Type Description <code>Optional[pd.Timestamp]</code> <p>A pandas Timestamp object representing the date found in the filename, or None if no date in 'YYYYMMDD' format is found.</p> <p>Examples:</p> <pre><code>&gt;&gt;&gt; extract_date_from_filename(\"example_20230101.txt\")\nTimestamp('2023-01-01 00:00:00')\n</code></pre> <pre><code>&gt;&gt;&gt; extract_date_from_filename(\"no_date_in_this_filename.txt\")\nNone\n</code></pre> Source code in <code>hypercoast/common.py</code> <pre><code>def extract_date_from_filename(filename: str):\n    \"\"\"\n    Extracts a date from a filename assuming the date is in 'YYYYMMDD' format.\n\n    This function searches the filename for a sequence of 8 digits that represent a date in\n    'YYYYMMDD' format. If such a sequence is found, it converts the sequence into a pandas\n    Timestamp object. If no such sequence is found, the function returns None.\n\n    Args:\n        filename (str): The filename from which to extract the date.\n\n    Returns:\n        Optional[pd.Timestamp]: A pandas Timestamp object representing the date found in the filename,\n        or None if no date in 'YYYYMMDD' format is found.\n\n    Examples:\n        &gt;&gt;&gt; extract_date_from_filename(\"example_20230101.txt\")\n        Timestamp('2023-01-01 00:00:00')\n\n        &gt;&gt;&gt; extract_date_from_filename(\"no_date_in_this_filename.txt\")\n        None\n    \"\"\"\n    import re\n    import pandas as pd\n\n    # Assuming the date format in filename is 'YYYYMMDD'\n    date_match = re.search(r\"\\d{8}\", filename)\n    if date_match:\n        return pd.to_datetime(date_match.group(), format=\"%Y%m%d\")\n    else:\n        return None\n</code></pre>"},{"location":"common/#hypercoast.common.extract_spectral","title":"<code>extract_spectral(ds, lat, lon, name='data')</code>","text":"<p>Extracts spectral signature from a given xarray Dataset.</p> <p>Parameters:</p> Name Type Description Default <code>ds</code> <code>xarray.Dataset</code> <p>The dataset containing the spectral data.</p> required <code>lat</code> <code>float</code> <p>The latitude of the point to extract.</p> required <code>lon</code> <code>float</code> <p>The longitude of the point to extract.</p> required <p>Returns:</p> Type Description <code>xarray.DataArray</code> <p>The extracted data.</p> Source code in <code>hypercoast/common.py</code> <pre><code>def extract_spectral(\n    ds: xr.Dataset, lat: float, lon: float, name: str = \"data\"\n) -&gt; xr.DataArray:\n    \"\"\"\n    Extracts spectral signature from a given xarray Dataset.\n\n    Args:\n        ds (xarray.Dataset): The dataset containing the spectral data.\n        lat (float): The latitude of the point to extract.\n        lon (float): The longitude of the point to extract.\n\n    Returns:\n        xarray.DataArray: The extracted data.\n    \"\"\"\n\n    crs = ds.rio.crs\n\n    x, y = convert_coords([[lat, lon]], \"epsg:4326\", crs)[0]\n\n    values = ds.sel(x=x, y=y, method=\"nearest\")[name].values\n\n    da = xr.DataArray(values, dims=[\"band\"], coords={\"band\": ds.coords[\"band\"]})\n\n    return da\n</code></pre>"},{"location":"common/#hypercoast.common.github_raw_url","title":"<code>github_raw_url(url)</code>","text":"<p>Get the raw URL for a GitHub file.</p> <p>Parameters:</p> Name Type Description Default <code>url</code> <code>str</code> <p>The GitHub URL.</p> required <p>Returns:</p> Type Description <code>str</code> <p>The raw URL.</p> Source code in <code>hypercoast/common.py</code> <pre><code>def github_raw_url(url: str) -&gt; str:\n    \"\"\"Get the raw URL for a GitHub file.\n\n    Args:\n        url (str): The GitHub URL.\n    Returns:\n        str: The raw URL.\n    \"\"\"\n    if isinstance(url, str) and url.startswith(\"https://github.com/\") and \"blob\" in url:\n        url = url.replace(\"github.com\", \"raw.githubusercontent.com\").replace(\n            \"blob/\", \"\"\n        )\n    return url\n</code></pre>"},{"location":"common/#hypercoast.common.image_cube","title":"<code>image_cube(dataset, variable='reflectance', cmap='jet', clim=(0, 0.5), title='Reflectance', rgb_bands=None, rgb_wavelengths=None, rgb_gamma=1.0, rgb_cmap=None, rgb_clim=None, rgb_args=None, widget=None, plotter_args=None, show_axes=True, grid_origin=(0, 0, 0), grid_spacing=(1, 1, 1), **kwargs)</code>","text":"<p>Creates an image cube from a dataset and plots it using PyVista.</p> <p>Parameters:</p> Name Type Description Default <code>dataset</code> <code>Union[str, xr.Dataset]</code> <p>The dataset to plot. Can be a path to a NetCDF file or an xarray Dataset.</p> required <code>variable</code> <code>str</code> <p>The variable to plot. Defaults to \"reflectance\".</p> <code>'reflectance'</code> <code>cmap</code> <code>str</code> <p>The colormap to use. Defaults to \"jet\".</p> <code>'jet'</code> <code>clim</code> <code>Tuple[float, float]</code> <p>The color limits. Defaults to (0, 0.5).</p> <code>(0, 0.5)</code> <code>title</code> <code>str</code> <p>The title for the scalar bar. Defaults to \"Reflectance\".</p> <code>'Reflectance'</code> <code>rgb_bands</code> <code>Optional[List[int]]</code> <p>The bands to use for the RGB image. Defaults to None.</p> <code>None</code> <code>rgb_wavelengths</code> <code>Optional[List[float]]</code> <p>The wavelengths to use for the RGB image. Defaults to None.</p> <code>None</code> <code>rgb_gamma</code> <code>float</code> <p>The gamma correction for the RGB image. Defaults to 1.</p> <code>1.0</code> <code>rgb_cmap</code> <code>Optional[str]</code> <p>The colormap to use for the RGB image. Defaults to None.</p> <code>None</code> <code>rgb_clim</code> <code>Optional[Tuple[float, float]]</code> <p>The color limits for the RGB image. Defaults to None.</p> <code>None</code> <code>rgb_args</code> <code>Dict[str, Any]</code> <p>Additional arguments for the <code>add_mesh</code> method for the RGB image. Defaults to {}.</p> <code>None</code> <code>widget</code> <code>Optional[str]</code> <p>The widget to use for the image cube. Can be one of the following: \"box\", \"plane\", \"slice\", \"orthogonal\", and \"threshold\". Defaults to None.</p> <code>None</code> <code>plotter_args</code> <code>Dict[str, Any]</code> <p>Additional arguments for the <code>pv.Plotter</code> constructor. Defaults to {}.</p> <code>None</code> <code>show_axes</code> <code>bool</code> <p>Whether to show the axes. Defaults to True.</p> <code>True</code> <code>grid_origin</code> <code>Tuple[float, float, float]</code> <p>The origin of the grid. Defaults to (0, 0, 0).</p> <code>(0, 0, 0)</code> <code>grid_spacing</code> <code>Tuple[float, float, float]</code> <p>The spacing of the grid.</p> <code>(1, 1, 1)</code> <code>**kwargs</code> <code>Dict[str, Any]</code> <p>Additional arguments for the <code>add_mesh</code> method. Defaults to {}.</p> <code>{}</code> <p>Returns:</p> Type Description <code>pv.Plotter</code> <p>The PyVista Plotter with the image cube added.</p> Source code in <code>hypercoast/common.py</code> <pre><code>def image_cube(\n    dataset,\n    variable: str = \"reflectance\",\n    cmap: str = \"jet\",\n    clim: Tuple[float, float] = (0, 0.5),\n    title: str = \"Reflectance\",\n    rgb_bands: Optional[List[int]] = None,\n    rgb_wavelengths: Optional[List[float]] = None,\n    rgb_gamma: float = 1.0,\n    rgb_cmap: Optional[str] = None,\n    rgb_clim: Optional[Tuple[float, float]] = None,\n    rgb_args: Dict[str, Any] = None,\n    widget=None,\n    plotter_args: Dict[str, Any] = None,\n    show_axes: bool = True,\n    grid_origin=(0, 0, 0),\n    grid_spacing=(1, 1, 1),\n    **kwargs: Any,\n):\n    \"\"\"\n    Creates an image cube from a dataset and plots it using PyVista.\n\n    Args:\n        dataset (Union[str, xr.Dataset]): The dataset to plot. Can be a path to\n            a NetCDF file or an xarray Dataset.\n        variable (str, optional): The variable to plot. Defaults to \"reflectance\".\n        cmap (str, optional): The colormap to use. Defaults to \"jet\".\n        clim (Tuple[float, float], optional): The color limits. Defaults to (0, 0.5).\n        title (str, optional): The title for the scalar bar. Defaults to \"Reflectance\".\n        rgb_bands (Optional[List[int]], optional): The bands to use for the RGB\n            image. Defaults to None.\n        rgb_wavelengths (Optional[List[float]], optional): The wavelengths to\n            use for the RGB image. Defaults to None.\n        rgb_gamma (float, optional): The gamma correction for the RGB image.\n            Defaults to 1.\n        rgb_cmap (Optional[str], optional): The colormap to use for the RGB image.\n            Defaults to None.\n        rgb_clim (Optional[Tuple[float, float]], optional): The color limits for\n            the RGB image. Defaults to None.\n        rgb_args (Dict[str, Any], optional): Additional arguments for the\n            `add_mesh` method for the RGB image. Defaults to {}.\n        widget (Optional[str], optional): The widget to use for the image cube.\n            Can be one of the following: \"box\", \"plane\", \"slice\", \"orthogonal\",\n            and \"threshold\". Defaults to None.\n        plotter_args (Dict[str, Any], optional): Additional arguments for the\n            `pv.Plotter` constructor. Defaults to {}.\n        show_axes (bool, optional): Whether to show the axes. Defaults to True.\n        grid_origin (Tuple[float, float, float], optional): The origin of the grid.\n            Defaults to (0, 0, 0).\n        grid_spacing (Tuple[float, float, float], optional): The spacing of the grid.\n        **kwargs (Dict[str, Any], optional): Additional arguments for the\n            `add_mesh` method. Defaults to {}.\n\n    Returns:\n        pv.Plotter: The PyVista Plotter with the image cube added.\n    \"\"\"\n\n    import pyvista as pv\n\n    if rgb_args is None:\n        rgb_args = {}\n\n    if plotter_args is None:\n        plotter_args = {}\n\n    allowed_widgets = [\"box\", \"plane\", \"slice\", \"orthogonal\", \"threshold\"]\n\n    if widget is not None:\n        if widget not in allowed_widgets:\n            raise ValueError(f\"widget must be one of the following: {allowed_widgets}\")\n\n    if isinstance(dataset, str):\n        dataset = xr.open_dataset(dataset)\n\n    da = dataset[variable]  # xarray DataArray\n    values = da.to_numpy()\n\n    # Create the spatial reference for the image cube\n    grid = pv.ImageData()\n\n    # Set the grid dimensions: shape because we want to inject our values on the POINT data\n    grid.dimensions = values.shape\n\n    # Edit the spatial reference\n    grid.origin = grid_origin  # The bottom left corner of the data set\n    grid.spacing = grid_spacing  # These are the cell sizes along each axis\n\n    # Add the data values to the cell data\n    grid.point_data[\"values\"] = values.flatten(order=\"F\")  # Flatten the array\n\n    # Plot the image cube with the RGB image overlay\n    p = pv.Plotter(**plotter_args)\n\n    if \"scalar_bar_args\" not in kwargs:\n        kwargs[\"scalar_bar_args\"] = {\"title\": title}\n    else:\n        kwargs[\"scalar_bar_args\"][\"title\"] = title\n\n    if \"show_edges\" not in kwargs:\n        kwargs[\"show_edges\"] = False\n\n    if widget == \"box\":\n        p.add_mesh_clip_box(grid, cmap=cmap, clim=clim, **kwargs)\n    elif widget == \"plane\":\n        if \"normal\" not in kwargs:\n            kwargs[\"normal\"] = (0, 0, 1)\n        if \"invert\" not in kwargs:\n            kwargs[\"invert\"] = True\n        if \"normal_rotation\" not in kwargs:\n            kwargs[\"normal_rotation\"] = False\n        p.add_mesh_clip_plane(grid, cmap=cmap, clim=clim, **kwargs)\n    elif widget == \"slice\":\n        if \"normal\" not in kwargs:\n            kwargs[\"normal\"] = (0, 0, 1)\n        if \"normal_rotation\" not in kwargs:\n            kwargs[\"normal_rotation\"] = False\n        p.add_mesh_slice(grid, cmap=cmap, clim=clim, **kwargs)\n    elif widget == \"orthogonal\":\n        p.add_mesh_slice_orthogonal(grid, cmap=cmap, clim=clim, **kwargs)\n    elif widget == \"threshold\":\n        p.add_mesh_threshold(grid, cmap=cmap, clim=clim, **kwargs)\n    else:\n        p.add_mesh(grid, cmap=cmap, clim=clim, **kwargs)\n\n    if rgb_bands is not None or rgb_wavelengths is not None:\n\n        if rgb_bands is not None:\n            rgb_image = dataset.isel(wavelength=rgb_bands, method=\"nearest\")[\n                variable\n            ].to_numpy()\n        elif rgb_wavelengths is not None:\n            rgb_image = dataset.sel(wavelength=rgb_wavelengths, method=\"nearest\")[\n                variable\n            ].to_numpy()\n\n        x_dim, y_dim = rgb_image.shape[0], rgb_image.shape[1]\n        z_dim = 1\n        im = pv.ImageData(dimensions=(x_dim, y_dim, z_dim))\n\n        # Add scalar data, you may also need to flatten this\n        im.point_data[\"rgb_image\"] = (\n            rgb_image.reshape(-1, rgb_image.shape[2], order=\"F\") * rgb_gamma\n        )\n\n        grid_z_max = grid.bounds[5]\n        im.origin = (0, 0, grid_z_max)\n\n        if rgb_image.shape[2] &lt; 3:\n            if rgb_cmap is None:\n                rgb_cmap = cmap\n            if rgb_clim is None:\n                rgb_clim = clim\n\n            if \"cmap\" not in rgb_args:\n                rgb_args[\"cmap\"] = rgb_cmap\n            if \"clim\" not in rgb_args:\n                rgb_args[\"clim\"] = rgb_clim\n        else:\n            if \"rgb\" not in rgb_args:\n                rgb_args[\"rgb\"] = True\n\n        if \"show_scalar_bar\" not in rgb_args:\n            rgb_args[\"show_scalar_bar\"] = False\n        if \"show_edges\" not in rgb_args:\n            rgb_args[\"show_edges\"] = False\n\n        p.add_mesh(im, **rgb_args)\n\n    if show_axes:\n        p.show_axes()\n\n    return p\n</code></pre>"},{"location":"common/#hypercoast.common.nasa_earth_login","title":"<code>nasa_earth_login(strategy='all', persist=True, **kwargs)</code>","text":"<p>Logs in to NASA Earthdata.</p> <p>Parameters:</p> Name Type Description Default <code>strategy</code> <code>str</code> <p>The login strategy. Defaults to \"all\".</p> <code>'all'</code> <code>persist</code> <code>bool</code> <p>Whether to persist the login. Defaults to True.</p> <code>True</code> Source code in <code>hypercoast/common.py</code> <pre><code>def nasa_earth_login(strategy: str = \"all\", persist: bool = True, **kwargs) -&gt; None:\n    \"\"\"Logs in to NASA Earthdata.\n\n    Args:\n        strategy (str, optional): The login strategy. Defaults to \"all\".\n        persist (bool, optional): Whether to persist the login. Defaults to True.\n    \"\"\"\n    from leafmap import get_api_key\n    import earthaccess\n\n    USERNAME = get_api_key(\"EARTHDATA_USERNAME\")\n    PASSWORD = get_api_key(\"EARTHDATA_PASSWORD\")\n    if (USERNAME is not None) and (PASSWORD is not None):\n        strategy = \"environment\"\n    earthaccess.login(strategy=strategy, persist=persist, **kwargs)\n</code></pre>"},{"location":"common/#hypercoast.common.netcdf_groups","title":"<code>netcdf_groups(filepath)</code>","text":"<p>Get the list of groups in a NetCDF file.</p> <p>Parameters:</p> Name Type Description Default <code>filepath</code> <code>str</code> <p>The path to the NetCDF file.</p> required <p>Returns:</p> Type Description <code>list</code> <p>A list of group names in the NetCDF file.</p> <p>Examples:</p> <pre><code>&gt;&gt;&gt; netcdf_groups('path/to/netcdf/file')\n['group1', 'group2', 'group3']\n</code></pre> Source code in <code>hypercoast/common.py</code> <pre><code>def netcdf_groups(filepath: str) -&gt; List[str]:\n    \"\"\"\n    Get the list of groups in a NetCDF file.\n\n    Args:\n        filepath (str): The path to the NetCDF file.\n\n    Returns:\n        list: A list of group names in the NetCDF file.\n\n    Example:\n        &gt;&gt;&gt; netcdf_groups('path/to/netcdf/file')\n        ['group1', 'group2', 'group3']\n    \"\"\"\n    import h5netcdf\n\n    with h5netcdf.File(filepath) as file:\n        groups = list(file)\n    return groups\n</code></pre>"},{"location":"common/#hypercoast.common.open_dataset","title":"<code>open_dataset(filename, engine=None, chunks=None, **kwargs)</code>","text":"<p>Opens and returns an xarray Dataset from a file.</p> <p>This function is a wrapper around <code>xarray.open_dataset</code> that allows for additional customization through keyword arguments.</p> <p>Parameters:</p> Name Type Description Default <code>filename</code> <code>str</code> <p>Path to the file to open.</p> required <code>engine</code> <code>Optional[str]</code> <p>Name of the engine to use for reading the file. If None, xarray's default engine is used. Examples include 'netcdf4', 'h5netcdf', 'zarr', etc.</p> <code>None</code> <code>chunks</code> <code>Optional[Dict[str, int]]</code> <p>Dictionary specifying how to chunk the dataset along each dimension. For example, <code>{'time': 1}</code> would load the dataset in single-time-step chunks. If None, the dataset is not chunked.</p> <code>None</code> <code>**kwargs</code> <code>Any</code> <p>Additional keyword arguments passed to <code>xarray.open_dataset</code>.</p> <code>{}</code> <p>Returns:</p> Type Description <code>xr.Dataset</code> <p>The opened dataset.</p> <p>Examples:</p> <p>Open a NetCDF file without chunking:</p> <pre><code>&gt;&gt;&gt; dataset = open_dataset('path/to/file.nc')\n</code></pre> <p>Open a Zarr dataset, chunking along the 'time' dimension:</p> <pre><code>&gt;&gt;&gt; dataset = open_dataset('path/to/dataset.zarr', engine='zarr', chunks={'time': 10})\n</code></pre> Source code in <code>hypercoast/common.py</code> <pre><code>def open_dataset(\n    filename: str,\n    engine: Optional[str] = None,\n    chunks: Optional[Dict[str, int]] = None,\n    **kwargs: Any,\n) -&gt; xr.Dataset:\n    \"\"\"\n    Opens and returns an xarray Dataset from a file.\n\n    This function is a wrapper around `xarray.open_dataset` that allows for additional\n    customization through keyword arguments.\n\n    Args:\n        filename (str): Path to the file to open.\n        engine (Optional[str]): Name of the engine to use for reading the file. If None, xarray's\n            default engine is used. Examples include 'netcdf4', 'h5netcdf', 'zarr', etc.\n        chunks (Optional[Dict[str, int]]): Dictionary specifying how to chunk the dataset along each dimension.\n            For example, `{'time': 1}` would load the dataset in single-time-step chunks. If None,\n            the dataset is not chunked.\n        **kwargs: Additional keyword arguments passed to `xarray.open_dataset`.\n\n    Returns:\n        xr.Dataset: The opened dataset.\n\n    Examples:\n        Open a NetCDF file without chunking:\n        &gt;&gt;&gt; dataset = open_dataset('path/to/file.nc')\n\n        Open a Zarr dataset, chunking along the 'time' dimension:\n        &gt;&gt;&gt; dataset = open_dataset('path/to/dataset.zarr', engine='zarr', chunks={'time': 10})\n    \"\"\"\n\n    try:\n        dataset = xr.open_dataset(filename, engine=engine, chunks=chunks, **kwargs)\n    except OSError:\n        dataset = xr.open_dataset(filename, engine=\"h5netcdf\", chunks=chunks, **kwargs)\n\n    return dataset\n</code></pre>"},{"location":"common/#hypercoast.common.pca","title":"<code>pca(input_file, output_file, n_components=3, **kwargs)</code>","text":"<p>Performs Principal Component Analysis (PCA) on a dataset.</p> <p>Parameters:</p> Name Type Description Default <code>input_file</code> <code>str</code> <p>The input file containing the data to analyze.</p> required <code>output_file</code> <code>str</code> <p>The output file to save the PCA results.</p> required <code>n_components</code> <code>int</code> <p>The number of principal components to compute. Defaults to 3.</p> <code>3</code> <code>**kwargs</code> <p>Additional keyword arguments to pass to the scikit-learn PCA function. For more info, see https://scikit-learn.org/stable/modules/generated/sklearn.decomposition.PCA.html.</p> <code>{}</code> <p>Returns:</p> Type Description <code>None</code> <p>This function does not return a value. It saves the PCA results to the output file.</p> Source code in <code>hypercoast/common.py</code> <pre><code>def pca(input_file, output_file, n_components=3, **kwargs):\n    \"\"\"\n    Performs Principal Component Analysis (PCA) on a dataset.\n\n    Args:\n        input_file (str): The input file containing the data to analyze.\n        output_file (str): The output file to save the PCA results.\n        n_components (int, optional): The number of principal components to compute. Defaults to 3.\n        **kwargs: Additional keyword arguments to pass to the scikit-learn PCA function.\n            For more info, see https://scikit-learn.org/stable/modules/generated/sklearn.decomposition.PCA.html.\n\n    Returns:\n        None: This function does not return a value. It saves the PCA results to the output file.\n    \"\"\"\n\n    import rasterio\n    from sklearn.decomposition import PCA\n\n    # Function to load the GeoTIFF image\n    def load_geotiff(file_path):\n        with rasterio.open(file_path) as src:\n            image = src.read()\n            profile = src.profile\n        return image, profile\n\n    # Function to perform PCA\n    def perform_pca(image, n_components=3, **kwargs):\n        # Reshape the image to [n_bands, n_pixels]\n        n_bands, width, height = image.shape\n        image_reshaped = image.reshape(n_bands, width * height).T\n\n        # Perform PCA\n        model = PCA(n_components=n_components, **kwargs)\n        principal_components = model.fit_transform(image_reshaped)\n\n        # Reshape the principal components back to image dimensions\n        pca_image = principal_components.T.reshape(n_components, width, height)\n        return pca_image\n\n    # Function to save the PCA-transformed image\n    def save_geotiff(file_path, image, profile):\n        profile.update(count=image.shape[0])\n        with rasterio.open(file_path, \"w\", **profile) as dst:\n            dst.write(image)\n\n    image, profile = load_geotiff(input_file)\n    pca_image = perform_pca(image, n_components, **kwargs)\n    save_geotiff(output_file, pca_image, profile)\n</code></pre>"},{"location":"common/#hypercoast.common.run_acolite","title":"<code>run_acolite(acolite_dir, settings_file=None, input_file=None, out_dir=None, polygon=None, l2w_parameters=None, rgb_rhot=True, rgb_rhos=True, map_l2w=True, verbose=True, **kwargs)</code>","text":"<p>Runs the Acolite software for atmospheric correction and water quality retrieval. For more information, see the Acolite manual https://github.com/acolite/acolite/releases</p> <p>This function constructs and executes a command to run the Acolite software with the specified parameters. It supports running Acolite with a settings file or with individual parameters specified directly. Additional parameters can be passed as keyword arguments.</p> <p>Parameters:</p> Name Type Description Default <code>acolite_dir</code> <code>str</code> <p>The directory where Acolite is installed.</p> required <code>settings_file</code> <code>Optional[str]</code> <p>The path to the Acolite settings file. If provided, other parameters except <code>verbose</code> are ignored. Defaults to None.</p> <code>None</code> <code>input_file</code> <code>Optional[str]</code> <p>The path to the input file for processing. Defaults to None.</p> <code>None</code> <code>out_dir</code> <code>Optional[str]</code> <p>The directory where output files will be saved. Defaults to None.</p> <code>None</code> <code>polygon</code> <code>Optional[str]</code> <p>The path to a polygon file for spatial subset. Defaults to None.</p> <code>None</code> <code>l2w_parameters</code> <code>Optional[str]</code> <p>Parameters for L2W processing. Defaults to None.</p> <code>None</code> <code>rgb_rhot</code> <code>bool</code> <p>Flag to generate RGB images using rhot. Defaults to True.</p> <code>True</code> <code>rgb_rhos</code> <code>bool</code> <p>Flag to generate RGB images using rhos. Defaults to True.</p> <code>True</code> <code>map_l2w</code> <code>bool</code> <p>Flag to map L2W products. Defaults to True.</p> <code>True</code> <code>verbose</code> <code>bool</code> <p>If True, prints the command output; otherwise, suppresses it. Defaults to True.</p> <code>True</code> <code>**kwargs</code> <code>Any</code> <p>Additional command line arguments to pass to acolite. Such as --l2w_export_geotiff, --merge_tiles, etc.</p> <code>{}</code> <p>Returns:</p> Type Description <code>None</code> <p>This function does not return a value. It executes the Acolite software.</p> <p>Examples:</p> <pre><code>&gt;&gt;&gt; run_acolite(\"/path/to/acolite\", input_file=\"/path/to/inputfile\", output=\"/path/to/output\")\n</code></pre> Source code in <code>hypercoast/common.py</code> <pre><code>def run_acolite(\n    acolite_dir: str,\n    settings_file: Optional[str] = None,\n    input_file: Optional[str] = None,\n    out_dir: Optional[str] = None,\n    polygon: Optional[str] = None,\n    l2w_parameters: Optional[str] = None,\n    rgb_rhot: bool = True,\n    rgb_rhos: bool = True,\n    map_l2w: bool = True,\n    verbose: bool = True,\n    **kwargs: Any,\n) -&gt; None:\n    \"\"\"\n    Runs the Acolite software for atmospheric correction and water quality retrieval.\n    For more information, see the Acolite manual https://github.com/acolite/acolite/releases\n\n    This function constructs and executes a command to run the Acolite software with the specified\n    parameters. It supports running Acolite with a settings file or with individual parameters\n    specified directly. Additional parameters can be passed as keyword arguments.\n\n    Args:\n        acolite_dir (str): The directory where Acolite is installed.\n        settings_file (Optional[str], optional): The path to the Acolite settings file. If provided,\n            other parameters except `verbose` are ignored. Defaults to None.\n        input_file (Optional[str], optional): The path to the input file for processing. Defaults to None.\n        out_dir (Optional[str], optional): The directory where output files will be saved. Defaults to None.\n        polygon (Optional[str], optional): The path to a polygon file for spatial subset. Defaults to None.\n        l2w_parameters (Optional[str], optional): Parameters for L2W processing. Defaults to None.\n        rgb_rhot (bool, optional): Flag to generate RGB images using rhot. Defaults to True.\n        rgb_rhos (bool, optional): Flag to generate RGB images using rhos. Defaults to True.\n        map_l2w (bool, optional): Flag to map L2W products. Defaults to True.\n        verbose (bool, optional): If True, prints the command output; otherwise, suppresses it. Defaults to True.\n        **kwargs (Any): Additional command line arguments to pass to acolite. Such as\n            --l2w_export_geotiff, --merge_tiles, etc.\n\n    Returns:\n        None: This function does not return a value. It executes the Acolite software.\n\n    Example:\n        &gt;&gt;&gt; run_acolite(\"/path/to/acolite\", input_file=\"/path/to/inputfile\", output=\"/path/to/output\")\n    \"\"\"\n\n    import subprocess\n    from datetime import datetime\n\n    def get_formatted_current_time(format_str=\"%Y-%m-%d %H:%M:%S\"):\n        current_time = datetime.now()\n        formatted_time = current_time.strftime(format_str)\n        return formatted_time\n\n    acolite_dir_name = os.path.split(acolite_dir)[-1]\n    acolite_exe = \"acolite\"\n    if acolite_dir_name.endswith(\"win\"):\n        acolite_exe += \".exe\"\n\n    if isinstance(input_file, list):\n        input_file = \",\".join(input_file)\n\n    if not os.path.exists(out_dir):\n        os.makedirs(out_dir)\n\n    acolite_exe_path = os.path.join(acolite_dir, \"dist\", \"acolite\", acolite_exe)\n    acolite_exe_path = acolite_exe_path.replace(\"\\\\\", \"/\")\n\n    acolite_cmd = [acolite_exe_path, \"--cli\"]\n\n    if settings_file is not None:\n        acolite_cmd.extend([\"--settings\", settings_file])\n    else:\n        lines = []\n        lines.append(\"## ACOLITE settings\")\n        lines.append(f\"## Written at {get_formatted_current_time()}\")\n        if input_file is not None:\n            input_file = input_file.replace(\"\\\\\", \"/\")\n            lines.append(f\"inputfile={input_file}\")\n        if out_dir is not None:\n            out_dir = out_dir.replace(\"\\\\\", \"/\")\n            lines.append(f\"output={out_dir}\")\n        if polygon is not None:\n            lines.append(f\"polygon={polygon}\")\n        else:\n            lines.append(\"polygon=None\")\n        if l2w_parameters is not None:\n            lines.append(f\"l2w_parameters={l2w_parameters}\")\n        if rgb_rhot:\n            lines.append(\"rgb_rhot=True\")\n        else:\n            lines.append(\"rgb_rhot=False\")\n        if rgb_rhos:\n            lines.append(\"rgb_rhos=True\")\n        else:\n            lines.append(\"rgb_rhos=False\")\n        if map_l2w:\n            lines.append(\"map_l2w=True\")\n        else:\n            lines.append(\"map_l2w=False\")\n\n        for key, value in kwargs.items():\n            lines.append(f\"{key}={value}\")\n\n        lines.append(f\"runid={get_formatted_current_time('%Y%m%d_%H%M%S')}\")\n        settings_filename = f\"acolite_run_{get_formatted_current_time('%Y%m%d_%H%M%S')}_settings_user.txt\"\n        settings_file = os.path.join(out_dir, settings_filename).replace(\"\\\\\", \"/\")\n        with open(settings_file, \"w\", encoding=\"utf-8\") as f:\n            f.write(\"\\n\".join(lines))\n        acolite_cmd.extend([\"--settings\", settings_file])\n\n    if acolite_dir_name.endswith(\"win\"):\n        acolite_cmd = \" \".join(acolite_cmd)\n\n    if verbose:\n        subprocess.run(acolite_cmd, check=True)\n    else:\n        subprocess.run(\n            acolite_cmd,\n            stdout=subprocess.DEVNULL,\n            stderr=subprocess.DEVNULL,\n            check=True,\n        )\n</code></pre>"},{"location":"common/#hypercoast.common.search_datasets","title":"<code>search_datasets(count=-1, **kwargs)</code>","text":"<p>Searches for datasets using the EarthAccess API with optional filters.</p> <p>This function wraps the <code>earthaccess.search_datasets</code> function, allowing for customized search queries based on a count limit and additional keyword arguments which serve as filters for the search.</p> <p>Parameters:</p> Name Type Description Default <code>count</code> <code>int</code> <p>The maximum number of datasets to return. A value of -1 indicates no limit. Defaults to -1.</p> <code>-1</code> <code>**kwargs</code> <code>Any</code> <p>Additional keyword arguments to pass as search filters to the EarthAccess API. keyword: case-insensitive and supports wildcards ? and * short_name: e.g. ATL08 doi: DOI for a dataset daac: e.g. NSIDC or PODAAC provider: particular to each DAAC, e.g. POCLOUD, LPDAAC etc. temporal: a tuple representing temporal bounds in the form (date_from, date_to) bounding_box: a tuple representing spatial bounds in the form (lower_left_lon, lower_left_lat, upper_right_lon, upper_right_lat)</p> <code>{}</code> <p>Returns:</p> Type Description <code>List[Dict[str, Any]]</code> <p>A list of dictionaries, where each dictionary contains     information about a dataset found in the search.</p> <p>Examples:</p> <pre><code>&gt;&gt;&gt; results = search_datasets(count=5, keyword='temperature')\n&gt;&gt;&gt; print(results)\n</code></pre> Source code in <code>hypercoast/common.py</code> <pre><code>def search_datasets(count: int = -1, **kwargs: Any) -&gt; List[Dict[str, Any]]:\n    \"\"\"\n    Searches for datasets using the EarthAccess API with optional filters.\n\n    This function wraps the `earthaccess.search_datasets` function, allowing for\n    customized search queries based on a count limit and additional keyword arguments\n    which serve as filters for the search.\n\n    Args:\n        count (int, optional): The maximum number of datasets to return. A value of -1\n            indicates no limit. Defaults to -1.\n        **kwargs (Any): Additional keyword arguments to pass as search filters to the\n            EarthAccess API.\n            keyword: case-insensitive and supports wildcards ? and *\n            short_name: e.g. ATL08\n            doi: DOI for a dataset\n            daac: e.g. NSIDC or PODAAC\n            provider: particular to each DAAC, e.g. POCLOUD, LPDAAC etc.\n            temporal: a tuple representing temporal bounds in the form (date_from, date_to)\n            bounding_box: a tuple representing spatial bounds in the form\n            (lower_left_lon, lower_left_lat, upper_right_lon, upper_right_lat)\n\n\n    Returns:\n        List[Dict[str, Any]]: A list of dictionaries, where each dictionary contains\n            information about a dataset found in the search.\n\n    Example:\n        &gt;&gt;&gt; results = search_datasets(count=5, keyword='temperature')\n        &gt;&gt;&gt; print(results)\n    \"\"\"\n\n    import earthaccess\n\n    return earthaccess.search_datasets(count=count, **kwargs)\n</code></pre>"},{"location":"common/#hypercoast.common.search_ecostress","title":"<code>search_ecostress(bbox=None, temporal=None, count=-1, short_name='ECO_L2T_LSTE', output=None, crs='EPSG:4326', return_gdf=False, **kwargs)</code>","text":"<p>Searches for NASA ECOSTRESS granules.</p> <p>Parameters:</p> Name Type Description Default <code>bbox</code> <code>List[float]</code> <p>The bounding box coordinates [xmin, ymin, xmax, ymax].</p> <code>None</code> <code>temporal</code> <code>str</code> <p>The temporal extent of the data.</p> <code>None</code> <code>count</code> <code>int</code> <p>The number of granules to retrieve. Defaults to -1 (retrieve all).</p> <code>-1</code> <code>short_name</code> <code>str</code> <p>The short name of the dataset. Defaults to \"ECO_L2T_LSTE\".</p> <code>'ECO_L2T_LSTE'</code> <code>output</code> <code>str</code> <p>The output file path to save the GeoDataFrame as a file.</p> <code>None</code> <code>crs</code> <code>str</code> <p>The coordinate reference system (CRS) of the GeoDataFrame. Defaults to \"EPSG:4326\".</p> <code>'EPSG:4326'</code> <code>return_gdf</code> <code>bool</code> <p>Whether to return the GeoDataFrame in addition to the granules. Defaults to False.</p> <code>False</code> <code>**kwargs</code> <p>Additional keyword arguments for the earthaccess.search_data() function.</p> <code>{}</code> <p>Returns:</p> Type Description <code>Union[List[dict], tuple]</code> <p>The retrieved granules. If return_gdf is True, also returns the resulting GeoDataFrame.</p> Source code in <code>hypercoast/common.py</code> <pre><code>def search_ecostress(\n    bbox: Optional[List[float]] = None,\n    temporal: Optional[str] = None,\n    count: int = -1,\n    short_name: Optional[str] = \"ECO_L2T_LSTE\",\n    output: Optional[str] = None,\n    crs: str = \"EPSG:4326\",\n    return_gdf: bool = False,\n    **kwargs,\n) -&gt; Union[List[dict], tuple]:\n    \"\"\"Searches for NASA ECOSTRESS granules.\n\n    Args:\n        bbox (List[float], optional): The bounding box coordinates [xmin, ymin, xmax, ymax].\n        temporal (str, optional): The temporal extent of the data.\n        count (int, optional): The number of granules to retrieve. Defaults to -1 (retrieve all).\n        short_name (str, optional): The short name of the dataset. Defaults to \"ECO_L2T_LSTE\".\n        output (str, optional): The output file path to save the GeoDataFrame as a file.\n        crs (str, optional): The coordinate reference system (CRS) of the GeoDataFrame. Defaults to \"EPSG:4326\".\n        return_gdf (bool, optional): Whether to return the GeoDataFrame in addition to the granules. Defaults to False.\n        **kwargs: Additional keyword arguments for the earthaccess.search_data() function.\n\n    Returns:\n        Union[List[dict], tuple]: The retrieved granules. If return_gdf is True, also returns the resulting GeoDataFrame.\n    \"\"\"\n\n    return search_nasa_data(\n        count=count,\n        short_name=short_name,\n        bbox=bbox,\n        temporal=temporal,\n        output=output,\n        crs=crs,\n        return_gdf=return_gdf,\n        **kwargs,\n    )\n</code></pre>"},{"location":"common/#hypercoast.common.search_emit","title":"<code>search_emit(bbox=None, temporal=None, count=-1, short_name='EMITL2ARFL', output=None, crs='EPSG:4326', return_gdf=False, **kwargs)</code>","text":"<p>Searches for NASA EMIT granules.</p> <p>Parameters:</p> Name Type Description Default <code>bbox</code> <code>List[float]</code> <p>The bounding box coordinates [xmin, ymin, xmax, ymax].</p> <code>None</code> <code>temporal</code> <code>str</code> <p>The temporal extent of the data.</p> <code>None</code> <code>count</code> <code>int</code> <p>The number of granules to retrieve. Defaults to -1 (retrieve all).</p> <code>-1</code> <code>short_name</code> <code>str</code> <p>The short name of the dataset. Defaults to \"EMITL2ARFL\".</p> <code>'EMITL2ARFL'</code> <code>output</code> <code>str</code> <p>The output file path to save the GeoDataFrame as a file.</p> <code>None</code> <code>crs</code> <code>str</code> <p>The coordinate reference system (CRS) of the GeoDataFrame. Defaults to \"EPSG:4326\".</p> <code>'EPSG:4326'</code> <code>return_gdf</code> <code>bool</code> <p>Whether to return the GeoDataFrame in addition to the granules. Defaults to False.</p> <code>False</code> <code>**kwargs</code> <p>Additional keyword arguments for the earthaccess.search_data() function.</p> <code>{}</code> <p>Returns:</p> Type Description <code>Union[List[dict], tuple]</code> <p>The retrieved granules. If return_gdf is True, also returns the resulting GeoDataFrame.</p> Source code in <code>hypercoast/common.py</code> <pre><code>def search_emit(\n    bbox: Optional[List[float]] = None,\n    temporal: Optional[str] = None,\n    count: int = -1,\n    short_name: Optional[str] = \"EMITL2ARFL\",\n    output: Optional[str] = None,\n    crs: str = \"EPSG:4326\",\n    return_gdf: bool = False,\n    **kwargs,\n) -&gt; Union[List[dict], tuple]:\n    \"\"\"Searches for NASA EMIT granules.\n\n    Args:\n        bbox (List[float], optional): The bounding box coordinates [xmin, ymin, xmax, ymax].\n        temporal (str, optional): The temporal extent of the data.\n        count (int, optional): The number of granules to retrieve. Defaults to -1 (retrieve all).\n        short_name (str, optional): The short name of the dataset. Defaults to \"EMITL2ARFL\".\n        output (str, optional): The output file path to save the GeoDataFrame as a file.\n        crs (str, optional): The coordinate reference system (CRS) of the GeoDataFrame. Defaults to \"EPSG:4326\".\n        return_gdf (bool, optional): Whether to return the GeoDataFrame in addition to the granules. Defaults to False.\n        **kwargs: Additional keyword arguments for the earthaccess.search_data() function.\n\n    Returns:\n        Union[List[dict], tuple]: The retrieved granules. If return_gdf is True, also returns the resulting GeoDataFrame.\n    \"\"\"\n\n    return search_nasa_data(\n        count=count,\n        short_name=short_name,\n        bbox=bbox,\n        temporal=temporal,\n        output=output,\n        crs=crs,\n        return_gdf=return_gdf,\n        **kwargs,\n    )\n</code></pre>"},{"location":"common/#hypercoast.common.search_nasa_data","title":"<code>search_nasa_data(count=-1, short_name=None, bbox=None, temporal=None, version=None, doi=None, daac=None, provider=None, output=None, crs='EPSG:4326', return_gdf=False, **kwargs)</code>","text":"<p>Searches for NASA Earthdata granules.</p> <p>Parameters:</p> Name Type Description Default <code>count</code> <code>int</code> <p>The number of granules to retrieve. Defaults to -1 (retrieve all).</p> <code>-1</code> <code>short_name</code> <code>str</code> <p>The short name of the dataset.</p> <code>None</code> <code>bbox</code> <code>List[float]</code> <p>The bounding box coordinates [xmin, ymin, xmax, ymax].</p> <code>None</code> <code>temporal</code> <code>str</code> <p>The temporal extent of the data.</p> <code>None</code> <code>version</code> <code>str</code> <p>The version of the dataset.</p> <code>None</code> <code>doi</code> <code>str</code> <p>The Digital Object Identifier (DOI) of the dataset.</p> <code>None</code> <code>daac</code> <code>str</code> <p>The Distributed Active Archive Center (DAAC) of the dataset.</p> <code>None</code> <code>provider</code> <code>str</code> <p>The provider of the dataset.</p> <code>None</code> <code>output</code> <code>str</code> <p>The output file path to save the GeoDataFrame as a file.</p> <code>None</code> <code>crs</code> <code>str</code> <p>The coordinate reference system (CRS) of the GeoDataFrame. Defaults to \"EPSG:4326\".</p> <code>'EPSG:4326'</code> <code>return_gdf</code> <code>bool</code> <p>Whether to return the GeoDataFrame in addition to the granules. Defaults to False.</p> <code>False</code> <code>**kwargs</code> <p>Additional keyword arguments for the earthaccess.search_data() function.</p> <code>{}</code> <p>Returns:</p> Type Description <code>Union[List[dict], tuple]</code> <p>The retrieved granules. If return_gdf is True, also returns the resulting GeoDataFrame.</p> Source code in <code>hypercoast/common.py</code> <pre><code>def search_nasa_data(\n    count: int = -1,\n    short_name: Optional[str] = None,\n    bbox: Optional[List[float]] = None,\n    temporal: Optional[str] = None,\n    version: Optional[str] = None,\n    doi: Optional[str] = None,\n    daac: Optional[str] = None,\n    provider: Optional[str] = None,\n    output: Optional[str] = None,\n    crs: str = \"EPSG:4326\",\n    return_gdf: bool = False,\n    **kwargs,\n) -&gt; Union[List[dict], tuple]:\n    \"\"\"Searches for NASA Earthdata granules.\n\n    Args:\n        count (int, optional): The number of granules to retrieve. Defaults to -1 (retrieve all).\n        short_name (str, optional): The short name of the dataset.\n        bbox (List[float], optional): The bounding box coordinates [xmin, ymin, xmax, ymax].\n        temporal (str, optional): The temporal extent of the data.\n        version (str, optional): The version of the dataset.\n        doi (str, optional): The Digital Object Identifier (DOI) of the dataset.\n        daac (str, optional): The Distributed Active Archive Center (DAAC) of the dataset.\n        provider (str, optional): The provider of the dataset.\n        output (str, optional): The output file path to save the GeoDataFrame as a file.\n        crs (str, optional): The coordinate reference system (CRS) of the GeoDataFrame. Defaults to \"EPSG:4326\".\n        return_gdf (bool, optional): Whether to return the GeoDataFrame in addition to the granules. Defaults to False.\n        **kwargs: Additional keyword arguments for the earthaccess.search_data() function.\n\n    Returns:\n        Union[List[dict], tuple]: The retrieved granules. If return_gdf is True, also returns the resulting GeoDataFrame.\n    \"\"\"\n\n    if isinstance(bbox, list):\n        bbox = tuple(bbox)\n\n    return leafmap.nasa_data_search(\n        count=count,\n        short_name=short_name,\n        bbox=bbox,\n        temporal=temporal,\n        version=version,\n        doi=doi,\n        daac=daac,\n        provider=provider,\n        output=output,\n        crs=crs,\n        return_gdf=return_gdf,\n        **kwargs,\n    )\n</code></pre>"},{"location":"common/#hypercoast.common.search_pace","title":"<code>search_pace(bbox=None, temporal=None, count=-1, short_name='PACE_OCI_L2_AOP_NRT', output=None, crs='EPSG:4326', return_gdf=False, **kwargs)</code>","text":"<p>Searches for NASA PACE granules.</p> <p>Parameters:</p> Name Type Description Default <code>bbox</code> <code>List[float]</code> <p>The bounding box coordinates [xmin, ymin, xmax, ymax].</p> <code>None</code> <code>temporal</code> <code>str</code> <p>The temporal extent of the data.</p> <code>None</code> <code>count</code> <code>int</code> <p>The number of granules to retrieve. Defaults to -1 (retrieve all).</p> <code>-1</code> <code>short_name</code> <code>str</code> <p>The short name of the dataset. Defaults to \"PACE_OCI_L2_AOP_NRT\".</p> <code>'PACE_OCI_L2_AOP_NRT'</code> <code>output</code> <code>str</code> <p>The output file path to save the GeoDataFrame as a file.</p> <code>None</code> <code>crs</code> <code>str</code> <p>The coordinate reference system (CRS) of the GeoDataFrame. Defaults to \"EPSG:4326\".</p> <code>'EPSG:4326'</code> <code>return_gdf</code> <code>bool</code> <p>Whether to return the GeoDataFrame in addition to the granules. Defaults to False.</p> <code>False</code> <code>**kwargs</code> <p>Additional keyword arguments for the earthaccess.search_data() function.</p> <code>{}</code> <p>Returns:</p> Type Description <code>Union[List[dict], tuple]</code> <p>The retrieved granules. If return_gdf is True, also returns the resulting GeoDataFrame.</p> Source code in <code>hypercoast/common.py</code> <pre><code>def search_pace(\n    bbox: Optional[List[float]] = None,\n    temporal: Optional[str] = None,\n    count: int = -1,\n    short_name: Optional[str] = \"PACE_OCI_L2_AOP_NRT\",\n    output: Optional[str] = None,\n    crs: str = \"EPSG:4326\",\n    return_gdf: bool = False,\n    **kwargs,\n) -&gt; Union[List[dict], tuple]:\n    \"\"\"Searches for NASA PACE granules.\n\n    Args:\n        bbox (List[float], optional): The bounding box coordinates [xmin, ymin, xmax, ymax].\n        temporal (str, optional): The temporal extent of the data.\n        count (int, optional): The number of granules to retrieve. Defaults to -1 (retrieve all).\n        short_name (str, optional): The short name of the dataset. Defaults to \"PACE_OCI_L2_AOP_NRT\".\n        output (str, optional): The output file path to save the GeoDataFrame as a file.\n        crs (str, optional): The coordinate reference system (CRS) of the GeoDataFrame. Defaults to \"EPSG:4326\".\n        return_gdf (bool, optional): Whether to return the GeoDataFrame in addition to the granules. Defaults to False.\n        **kwargs: Additional keyword arguments for the earthaccess.search_data() function.\n\n    Returns:\n        Union[List[dict], tuple]: The retrieved granules. If return_gdf is True, also returns the resulting GeoDataFrame.\n    \"\"\"\n\n    return search_nasa_data(\n        count=count,\n        short_name=short_name,\n        bbox=bbox,\n        temporal=temporal,\n        output=output,\n        crs=crs,\n        return_gdf=return_gdf,\n        **kwargs,\n    )\n</code></pre>"},{"location":"common/#hypercoast.common.search_pace_chla","title":"<code>search_pace_chla(bbox=None, temporal=None, count=-1, short_name='PACE_OCI_L3M_CHL_NRT', granule_name='*.DAY.*.0p1deg.*', output=None, crs='EPSG:4326', return_gdf=False, **kwargs)</code>","text":"<p>Searches for NASA PACE Chlorophyll granules.</p> <p>Parameters:</p> Name Type Description Default <code>bbox</code> <code>List[float]</code> <p>The bounding box coordinates [xmin, ymin, xmax, ymax].</p> <code>None</code> <code>temporal</code> <code>str</code> <p>The temporal extent of the data.</p> <code>None</code> <code>count</code> <code>int</code> <p>The number of granules to retrieve. Defaults to -1 (retrieve all).</p> <code>-1</code> <code>short_name</code> <code>str</code> <p>The short name of the dataset. Defaults to \"PACE_OCI_L3M_CHL_NRT\".</p> <code>'PACE_OCI_L3M_CHL_NRT'</code> <code>output</code> <code>str</code> <p>The output file path to save the GeoDataFrame as a file.</p> <code>None</code> <code>crs</code> <code>str</code> <p>The coordinate reference system (CRS) of the GeoDataFrame. Defaults to \"EPSG:4326\".</p> <code>'EPSG:4326'</code> <code>return_gdf</code> <code>bool</code> <p>Whether to return the GeoDataFrame in addition to the granules. Defaults to False.</p> <code>False</code> <code>**kwargs</code> <p>Additional keyword arguments for the earthaccess.search_data() function.</p> <code>{}</code> <p>Returns:</p> Type Description <code>Union[List[dict], tuple]</code> <p>The retrieved granules. If return_gdf is True, also returns the resulting GeoDataFrame.</p> Source code in <code>hypercoast/common.py</code> <pre><code>def search_pace_chla(\n    bbox: Optional[List[float]] = None,\n    temporal: Optional[str] = None,\n    count: int = -1,\n    short_name: Optional[str] = \"PACE_OCI_L3M_CHL_NRT\",\n    granule_name: Optional[str] = \"*.DAY.*.0p1deg.*\",\n    output: Optional[str] = None,\n    crs: str = \"EPSG:4326\",\n    return_gdf: bool = False,\n    **kwargs,\n) -&gt; Union[List[dict], tuple]:\n    \"\"\"Searches for NASA PACE Chlorophyll granules.\n\n    Args:\n        bbox (List[float], optional): The bounding box coordinates [xmin, ymin, xmax, ymax].\n        temporal (str, optional): The temporal extent of the data.\n        count (int, optional): The number of granules to retrieve. Defaults to -1 (retrieve all).\n        short_name (str, optional): The short name of the dataset. Defaults to \"PACE_OCI_L3M_CHL_NRT\".\n        output (str, optional): The output file path to save the GeoDataFrame as a file.\n        crs (str, optional): The coordinate reference system (CRS) of the GeoDataFrame. Defaults to \"EPSG:4326\".\n        return_gdf (bool, optional): Whether to return the GeoDataFrame in addition to the granules. Defaults to False.\n        **kwargs: Additional keyword arguments for the earthaccess.search_data() function.\n\n    Returns:\n        Union[List[dict], tuple]: The retrieved granules. If return_gdf is True, also returns the resulting GeoDataFrame.\n    \"\"\"\n\n    return search_nasa_data(\n        count=count,\n        short_name=short_name,\n        bbox=bbox,\n        temporal=temporal,\n        granule_name=granule_name,\n        output=output,\n        crs=crs,\n        return_gdf=return_gdf,\n        **kwargs,\n    )\n</code></pre>"},{"location":"common/#hypercoast.common.show_field_data","title":"<code>show_field_data(data, x_col='wavelength', y_col_prefix='(', x_label='Wavelengths (nm)', y_label='Reflectance', use_marker_cluster=True, min_width=400, max_width=600, min_height=200, max_height=250, layer_name='Marker Cluster', m=None, center=(20, 0), zoom=2)</code>","text":"<p>Displays field data on a map with interactive markers and popups showing time series data.</p> <p>Parameters:</p> Name Type Description Default <code>data</code> <code>Union[str, pd.DataFrame]</code> <p>Path to the CSV file or a pandas DataFrame containing the data.</p> required <code>x_col</code> <code>str</code> <p>Column name to use for the x-axis of the charts. Default is \"wavelength\".</p> <code>'wavelength'</code> <code>y_col_prefix</code> <code>str</code> <p>Prefix to identify the columns that contain the location-specific data. Default is \"(\".</p> <code>'('</code> <code>x_label</code> <code>str</code> <p>Label for the x-axis of the charts. Default is \"Wavelengths (nm)\".</p> <code>'Wavelengths (nm)'</code> <code>y_label</code> <code>str</code> <p>Label for the y-axis of the charts. Default is \"Reflectance\".</p> <code>'Reflectance'</code> <code>use_marker_cluster</code> <code>bool</code> <p>Whether to use marker clustering. Default is True.</p> <code>True</code> <code>min_width</code> <code>int</code> <p>Minimum width of the popup. Default is 400.</p> <code>400</code> <code>max_width</code> <code>int</code> <p>Maximum width of the popup. Default is 600.</p> <code>600</code> <code>min_height</code> <code>int</code> <p>Minimum height of the popup. Default is 200.</p> <code>200</code> <code>max_height</code> <code>int</code> <p>Maximum height of the popup. Default is 250.</p> <code>250</code> <code>layer_name</code> <code>str</code> <p>Name of the marker cluster layer. Default is \"Marker Cluster\".</p> <code>'Marker Cluster'</code> <code>m</code> <code>Map</code> <p>An ipyleaflet Map instance to add the markers to. Default is None.</p> <code>None</code> <code>center</code> <code>Tuple[float, float]</code> <p>Center of the map as a tuple of (latitude, longitude). Default is (20, 0).</p> <code>(20, 0)</code> <code>zoom</code> <code>int</code> <p>Zoom level of the map. Default is 2.</p> <code>2</code> <p>Returns:</p> Type Description <code>Map</code> <p>An ipyleaflet Map with the added markers and popups.</p> Source code in <code>hypercoast/common.py</code> <pre><code>def show_field_data(\n    data: Union[str],\n    x_col: str = \"wavelength\",\n    y_col_prefix: str = \"(\",\n    x_label: str = \"Wavelengths (nm)\",\n    y_label: str = \"Reflectance\",\n    use_marker_cluster: bool = True,\n    min_width: int = 400,\n    max_width: int = 600,\n    min_height: int = 200,\n    max_height: int = 250,\n    layer_name: str = \"Marker Cluster\",\n    m: object = None,\n    center: Tuple[float, float] = (20, 0),\n    zoom: int = 2,\n):\n    \"\"\"\n    Displays field data on a map with interactive markers and popups showing time series data.\n\n    Args:\n        data (Union[str, pd.DataFrame]): Path to the CSV file or a pandas DataFrame containing the data.\n        x_col (str): Column name to use for the x-axis of the charts. Default is \"wavelength\".\n        y_col_prefix (str): Prefix to identify the columns that contain the location-specific data. Default is \"(\".\n        x_label (str): Label for the x-axis of the charts. Default is \"Wavelengths (nm)\".\n        y_label (str): Label for the y-axis of the charts. Default is \"Reflectance\".\n        use_marker_cluster (bool): Whether to use marker clustering. Default is True.\n        min_width (int): Minimum width of the popup. Default is 400.\n        max_width (int): Maximum width of the popup. Default is 600.\n        min_height (int): Minimum height of the popup. Default is 200.\n        max_height (int): Maximum height of the popup. Default is 250.\n        layer_name (str): Name of the marker cluster layer. Default is \"Marker Cluster\".\n        m (Map, optional): An ipyleaflet Map instance to add the markers to. Default is None.\n        center (Tuple[float, float]): Center of the map as a tuple of (latitude, longitude). Default is (20, 0).\n        zoom (int): Zoom level of the map. Default is 2.\n\n    Returns:\n        Map: An ipyleaflet Map with the added markers and popups.\n    \"\"\"\n    import pandas as pd\n    import matplotlib.pyplot as plt\n    from ipyleaflet import Map, Marker, Popup, MarkerCluster\n    from ipywidgets import Output, VBox\n\n    # Read the CSV file\n    if isinstance(data, str):\n        data = pd.read_csv(data)\n    elif isinstance(data, pd.DataFrame):\n        pass\n    else:\n        raise ValueError(\"data must be a path to a CSV file or a pandas DataFrame\")\n\n    # Extract locations from columns\n    locations = [col for col in data.columns if col.startswith(y_col_prefix)]\n    coordinates = [tuple(map(float, loc.strip(\"()\").split())) for loc in locations]\n\n    # Create the map\n    if m is None:\n        m = Map(center=center, zoom=zoom)\n\n    # Function to create the chart\n    def create_chart(data, title):\n        _, ax = plt.subplots(figsize=(10, 6))  # Adjust the figure size here\n        ax.plot(data[x_col], data[\"values\"])\n        ax.set_title(title)\n        ax.set_xlabel(x_label)\n        ax.set_ylabel(y_label)\n        output = Output()  # Adjust the output widget size here\n        with output:\n            plt.show()\n        return output\n\n    # Define a callback function to create and show the popup\n    def callback_with_popup_creation(location, values):\n        def f(**kwargs):\n            marker_center = kwargs[\"coordinates\"]\n            output = create_chart(values, f\"Location: {location}\")\n            popup = Popup(\n                location=marker_center,\n                child=VBox([output]),\n                min_width=min_width,\n                max_width=max_width,\n                min_height=min_height,\n                max_height=max_height,\n            )\n            m.add_layer(popup)\n\n        return f\n\n    markers = []\n\n    # Add points to the map\n    for i, coord in enumerate(coordinates):\n        location = f\"{coord}\"\n        values = pd.DataFrame({x_col: data[x_col], \"values\": data[locations[i]]})\n        marker = Marker(location=coord, title=location, name=f\"Marker {i + 1}\")\n        marker.on_click(callback_with_popup_creation(location, values))\n        markers.append(marker)\n\n    if use_marker_cluster:\n        marker_cluster = MarkerCluster(markers=markers, name=layer_name)\n        m.add_layer(marker_cluster)\n    else:\n        for marker in markers:\n            m.add_layer(marker)\n\n    return m\n</code></pre>"},{"location":"contributing/","title":"Contributing","text":"<p>Contributions are welcome, and they are greatly appreciated! Every little bit helps, and credit will always be given.</p> <p>You can contribute in many ways:</p>"},{"location":"contributing/#types-of-contributions","title":"Types of Contributions","text":""},{"location":"contributing/#report-bugs","title":"Report Bugs","text":"<p>Report bugs at https://github.com/opengeos/HyperCoast/issues.</p> <p>If you are reporting a bug, please include:</p> <ul> <li>Your operating system name and version.</li> <li>Any details about your local setup that might be helpful in troubleshooting.</li> <li>Detailed steps to reproduce the bug.</li> </ul>"},{"location":"contributing/#fix-bugs","title":"Fix Bugs","text":"<p>Look through the GitHub issues for bugs. Anything tagged with <code>bug</code> and <code>help wanted</code> is open to whoever wants to implement it.</p>"},{"location":"contributing/#implement-features","title":"Implement Features","text":"<p>Look through the GitHub issues for features. Anything tagged with <code>enhancement</code> and <code>help wanted</code> is open to whoever wants to implement it.</p>"},{"location":"contributing/#write-documentation","title":"Write Documentation","text":"<p>HyperCoast could always use more documentation, whether as part of the official HyperCoast docs, in docstrings, or even on the web in blog posts, articles, and such.</p>"},{"location":"contributing/#submit-feedback","title":"Submit Feedback","text":"<p>The best way to send feedback is to file an issue at https://github.com/opengeos/HyperCoast/issues.</p> <p>If you are proposing a feature:</p> <ul> <li>Explain in detail how it would work.</li> <li>Keep the scope as narrow as possible, to make it easier to implement.</li> <li>Remember that this is a volunteer-driven project, and that contributions are welcome :)</li> </ul>"},{"location":"contributing/#get-started","title":"Get Started!","text":"<p>Ready to contribute? Here's how to set up HyperCoast for local development.</p> <ol> <li> <p>Fork the HyperCoast repo on GitHub.</p> </li> <li> <p>Clone your fork locally:</p> <pre><code>$ git clone https://github.com/&lt;YOUR-GITHUB-USERNAME&gt;/HyperCoast.git\n</code></pre> </li> <li> <p>Create a new conda environment to install HyperCoast and its dependencies. Assuming you have     Anaconda or     Miniconda installed,     this is how you set up your fork for local development:</p> <pre><code>$ conda install -n base mamba -c conda-forge\n$ conda create -n hyper python=3.11\n$ conda activate hyper\n$ mamba install -c conda-forge hypercoast cartopy earthaccess mapclassify pyvista\n$ cd HyperCoast/\n$ pip install -e .\n</code></pre> </li> <li> <p>Create a branch for local development:</p> <pre><code>$ git checkout -b name-of-your-bugfix-or-feature\n</code></pre> <p>Now you can make your changes locally.</p> </li> <li> <p>When you're done making changes, check that your changes pass     pre-commit and the tests:</p> <pre><code>$ pip install pre-commit\n$ pre-commit install\n$ pre-commit run --all-files\n$ python -m unittest discover tests/\n</code></pre> </li> <li> <p>Commit your changes and push your branch to GitHub:</p> <pre><code>$ git add .\n$ git commit -m \"Your detailed description of your changes.\"\n$ git push origin name-of-your-bugfix-or-feature\n</code></pre> </li> <li> <p>Submit a pull request through the GitHub website.</p> </li> <li>Check the status of your pull request on GitHub and make sure     that the tests for the pull request pass for all supported Python versions.</li> <li>Commit more changes to your branch to fix the text errors if necessary.</li> <li>Wait for the pull request to be reviewed by the maintainers.</li> <li>Congratulations! You've made your contribution to HyperCoast!</li> </ol>"},{"location":"contributing/#contributor-agreements","title":"Contributor Agreements","text":"<p>Before your contribution can be accepted, you will need to sign the appropriate contributor agreement. The Contributor License Agreement (CLA) assistant will walk you through the process of signing the CLA. Please follow the instructions provided by the assistant on the pull request.</p> <ul> <li>Individual Contributor Exclusive License Agreement</li> <li>Entity Contributor Exclusive License Agreement</li> </ul>"},{"location":"desis/","title":"desis module","text":"<p>This Module has the functions related to working with a DESIS dataset.</p>"},{"location":"desis/#hypercoast.desis.desis_to_image","title":"<code>desis_to_image(dataset, wavelengths=None, method='nearest', output=None, **kwargs)</code>","text":"<p>Converts an DESIS dataset to an image.</p> <p>Parameters:</p> Name Type Description Default <code>dataset</code> <code>xarray.Dataset or str</code> <p>The dataset containing the DESIS data or the file path to the dataset.</p> required <code>wavelengths</code> <code>array-like</code> <p>The specific wavelengths to select. If None, all wavelengths are selected. Defaults to None.</p> <code>None</code> <code>method</code> <code>str</code> <p>The method to use for data interpolation. Defaults to \"nearest\".</p> <code>'nearest'</code> <code>output</code> <code>str</code> <p>The file path where the image will be saved. If None, the image will be returned as a PIL Image object. Defaults to None.</p> <code>None</code> <code>**kwargs</code> <p>Additional keyword arguments to be passed to <code>leafmap.array_to_image</code>.</p> <code>{}</code> <p>Returns:</p> Type Description <code>rasterio.Dataset or None</code> <p>The image converted from the dataset. If     <code>output</code> is provided, the image will be saved to the specified file     and the function will return None.</p> Source code in <code>hypercoast/desis.py</code> <pre><code>def desis_to_image(\n    dataset: Union[xr.Dataset, str],\n    wavelengths: Union[list, tuple] = None,\n    method: Optional[str] = \"nearest\",\n    output: Optional[str] = None,\n    **kwargs,\n):\n    \"\"\"\n    Converts an DESIS dataset to an image.\n\n    Args:\n        dataset (xarray.Dataset or str): The dataset containing the DESIS data\n            or the file path to the dataset.\n        wavelengths (array-like, optional): The specific wavelengths to select.\n            If None, all wavelengths are selected. Defaults to None.\n        method (str, optional): The method to use for data interpolation.\n            Defaults to \"nearest\".\n        output (str, optional): The file path where the image will be saved. If\n            None, the image will be returned as a PIL Image object. Defaults to None.\n        **kwargs: Additional keyword arguments to be passed to\n            `leafmap.array_to_image`.\n\n    Returns:\n        rasterio.Dataset or None: The image converted from the dataset. If\n            `output` is provided, the image will be saved to the specified file\n            and the function will return None.\n    \"\"\"\n    from leafmap import array_to_image\n\n    if isinstance(dataset, str):\n        dataset = read_desis(dataset, method=method)\n\n    if wavelengths is not None:\n        dataset = dataset.sel(wavelength=wavelengths, method=method)\n\n    return array_to_image(\n        dataset[\"reflectance\"], output=output, transpose=False, **kwargs\n    )\n</code></pre>"},{"location":"desis/#hypercoast.desis.extract_desis","title":"<code>extract_desis(ds, lat, lon)</code>","text":"<p>Extracts DESIS data from a given xarray Dataset.</p> <p>Parameters:</p> Name Type Description Default <code>ds</code> <code>xarray.Dataset</code> <p>The dataset containing the DESIS data.</p> required <code>lat</code> <code>float</code> <p>The latitude of the point to extract.</p> required <code>lon</code> <code>float</code> <p>The longitude of the point to extract.</p> required <p>Returns:</p> Type Description <code>xarray.DataArray</code> <p>The extracted data.</p> Source code in <code>hypercoast/desis.py</code> <pre><code>def extract_desis(ds: xr.Dataset, lat: float, lon: float) -&gt; xr.DataArray:\n    \"\"\"\n    Extracts DESIS data from a given xarray Dataset.\n\n    Args:\n        ds (xarray.Dataset): The dataset containing the DESIS data.\n        lat (float): The latitude of the point to extract.\n        lon (float): The longitude of the point to extract.\n\n    Returns:\n        xarray.DataArray: The extracted data.\n    \"\"\"\n\n    crs = ds.attrs[\"crs\"]\n\n    x, y = convert_coords([[lat, lon]], \"epsg:4326\", crs)[0]\n\n    values = ds.sel(x=x, y=y, method=\"nearest\")[\"reflectance\"].values / 10000\n\n    da = xr.DataArray(\n        values, dims=[\"wavelength\"], coords={\"wavelength\": ds.coords[\"wavelength\"]}\n    )\n\n    return da\n</code></pre>"},{"location":"desis/#hypercoast.desis.filter_desis","title":"<code>filter_desis(dataset, lat, lon, return_plot=False, **kwargs)</code>","text":"<p>Filters a DESIS dataset based on latitude and longitude.</p> <p>Parameters:</p> Name Type Description Default <code>dataset</code> <code>xr.Dataset</code> <p>The DESIS dataset to filter.</p> required <code>lat</code> <code>float or tuple</code> <p>The latitude to filter by. If a tuple or list, it represents a range.</p> required <code>lon</code> <code>float or tuple</code> <p>The longitude to filter by. If a tuple or list, it represents a range.</p> required <p>Returns:</p> Type Description <code>xr.DataArray</code> <p>The filtered DESIS data.</p> Source code in <code>hypercoast/desis.py</code> <pre><code>def filter_desis(\n    dataset: xr.Dataset,\n    lat: Union[float, tuple],\n    lon: Union[float, tuple],\n    return_plot: Optional[bool] = False,\n    **kwargs,\n) -&gt; xr.Dataset:\n    \"\"\"\n    Filters a DESIS dataset based on latitude and longitude.\n\n    Args:\n        dataset (xr.Dataset): The DESIS dataset to filter.\n        lat (float or tuple): The latitude to filter by. If a tuple or list,\n            it represents a range.\n        lon (float or tuple): The longitude to filter by. If a tuple or\n            list, it represents a range.\n\n    Returns:\n        xr.DataArray: The filtered DESIS data.\n    \"\"\"\n\n    if isinstance(lat, list) or isinstance(lat, tuple):\n        min_lat = min(lat)\n        max_lat = max(lat)\n    else:\n        min_lat = lat\n        max_lat = lat\n\n    if isinstance(lon, list) or isinstance(lon, tuple):\n        min_lon = min(lon)\n        max_lon = max(lon)\n    else:\n        min_lon = lon\n        max_lon = lon\n\n    if min_lat == max_lat and min_lon == max_lon:\n        coords = [[min_lat, min_lon]]\n    else:\n        coords = [[min_lat, min_lon], [max_lat, max_lon]]\n    coords = convert_coords(coords, \"epsg:4326\", dataset.rio.crs.to_string())\n\n    if len(coords) == 1:\n        x, y = coords[0]\n        da = dataset.sel(x=x, y=y, method=\"nearest\")[\"reflectance\"]\n    else:\n        x_min, y_min = coords[0]\n        x_max, y_max = coords[1]\n        print(x_min, y_min, x_max, y_max)\n        da = dataset.sel(x=slice(x_min, x_max), y=slice(y_min, y_max))[\"reflectance\"]\n\n    if return_plot:\n        rrs_stack = da.stack(\n            {\"pixel\": [\"latitude\", \"longitude\"]},\n            create_index=False,\n        )\n        rrs_stack.plot.line(hue=\"pixel\", **kwargs)\n    else:\n        return da\n</code></pre>"},{"location":"desis/#hypercoast.desis.read_desis","title":"<code>read_desis(filepath, wavelengths=None, method='nearest', **kwargs)</code>","text":"<p>Reads DESIS data from a given file and returns an xarray Dataset.</p> <p>Parameters:</p> Name Type Description Default <code>filepath</code> <code>str</code> <p>Path to the file to read.</p> required <code>wavelengths</code> <code>array-like</code> <p>Specific wavelengths to select. If None, all wavelengths are selected.</p> <code>None</code> <code>method</code> <code>str</code> <p>Method to use for selection when wavelengths is not None. Defaults to \"nearest\".</p> <code>'nearest'</code> <code>**kwargs</code> <p>Additional keyword arguments to pass to the <code>sel</code> method when bands is not None.</p> <code>{}</code> <p>Returns:</p> Type Description <code>xr.Dataset</code> <p>An xarray Dataset containing the DESIS data.</p> Source code in <code>hypercoast/desis.py</code> <pre><code>def read_desis(\n    filepath: str,\n    wavelengths: Optional[Union[list, tuple]] = None,\n    method: Optional[str] = \"nearest\",\n    **kwargs,\n) -&gt; xr.Dataset:\n    \"\"\"\n    Reads DESIS data from a given file and returns an xarray Dataset.\n\n    Args:\n        filepath (str): Path to the file to read.\n        wavelengths (array-like, optional): Specific wavelengths to select. If\n            None, all wavelengths are selected.\n        method (str, optional): Method to use for selection when wavelengths is not\n            None. Defaults to \"nearest\".\n        **kwargs: Additional keyword arguments to pass to the `sel` method when\n            bands is not None.\n\n    Returns:\n        xr.Dataset: An xarray Dataset containing the DESIS data.\n    \"\"\"\n\n    url = \"https://github.com/opengeos/datasets/releases/download/hypercoast/desis_wavelengths.csv\"\n    df = pd.read_csv(url)\n    dataset = xr.open_dataset(filepath)\n    dataset = dataset.rename(\n        {\"band\": \"wavelength\", \"band_data\": \"reflectance\"}\n    ).transpose(\"y\", \"x\", \"wavelength\")\n    dataset[\"wavelength\"] = df[\"wavelength\"].tolist()\n\n    if wavelengths is not None:\n        dataset = dataset.sel(wavelength=wavelengths, method=method, **kwargs)\n\n    dataset.attrs[\"crs\"] = dataset.rio.crs.to_string()\n\n    return dataset\n</code></pre>"},{"location":"emit/","title":"emit module","text":"<p>This Module has the functions related to working with an EMIT dataset. This includes doing things like opening and flattening the data to work in xarray, orthorectification, and visualization.</p> <p>Some source code is adapted from https://github.com/nasa/EMIT-Data-Resources, which is licensed under the Apache License 2.0. Credits to the original authors, including Erik Bolch, Alex Leigh, and others.</p>"},{"location":"emit/#hypercoast.emit.apply_glt","title":"<code>apply_glt(ds_array, glt_array, fill_value=-9999, GLT_NODATA_VALUE=0)</code>","text":"<p>Applies the GLT array to a numpy array of either 2 or 3 dimensions to orthorectify the data.</p> <p>Parameters:</p> Name Type Description Default <code>ds_array</code> <code>numpy.ndarray</code> <p>A numpy array of the desired variable.</p> required <code>glt_array</code> <code>numpy.ndarray</code> <p>A GLT array constructed from EMIT GLT data.</p> required <code>fill_value</code> <code>int</code> <p>The value to fill in the output array where the GLT array has no data. Defaults to -9999.</p> <code>-9999</code> <code>GLT_NODATA_VALUE</code> <code>int</code> <p>The value in the GLT array that indicates no data. Defaults to 0.</p> <code>0</code> <p>Returns:</p> Type Description <code>numpy.ndarray</code> <p>A numpy array of orthorectified data.</p> Source code in <code>hypercoast/emit.py</code> <pre><code>def apply_glt(\n    ds_array: np.ndarray,\n    glt_array: np.ndarray,\n    fill_value: int = -9999,\n    GLT_NODATA_VALUE: Optional[int] = 0,\n) -&gt; np.ndarray:\n    \"\"\"\n    Applies the GLT array to a numpy array of either 2 or 3 dimensions to orthorectify the data.\n\n    Args:\n        ds_array (numpy.ndarray): A numpy array of the desired variable.\n        glt_array (numpy.ndarray): A GLT array constructed from EMIT GLT data.\n        fill_value (int, optional): The value to fill in the output array where the GLT array has no data. Defaults to -9999.\n        GLT_NODATA_VALUE (int, optional): The value in the GLT array that indicates no data. Defaults to 0.\n\n    Returns:\n        numpy.ndarray: A numpy array of orthorectified data.\n    \"\"\"\n\n    # Build Output Dataset\n    if ds_array.ndim == 2:\n        ds_array = ds_array[:, :, np.newaxis]\n    out_ds = np.full(\n        (glt_array.shape[0], glt_array.shape[1], ds_array.shape[-1]),\n        fill_value,\n        dtype=np.float32,\n    )\n    valid_glt = np.all(glt_array != GLT_NODATA_VALUE, axis=-1)\n\n    # Adjust for One based Index - make a copy to prevent decrementing multiple times inside ortho_xr when applying the glt to elev\n    glt_array_copy = glt_array.copy()\n    glt_array_copy[valid_glt] -= 1\n    out_ds[valid_glt, :] = ds_array[\n        glt_array_copy[valid_glt, 1], glt_array_copy[valid_glt, 0], :\n    ]\n    return out_ds\n</code></pre>"},{"location":"emit/#hypercoast.emit.band_mask","title":"<code>band_mask(filepath)</code>","text":"<p>Unpacks the packed band mask to apply to the dataset. Can be used manually or as an input in the emit_xarray() function.</p> <p>Parameters:</p> Name Type Description Default <code>filepath</code> <code>str</code> <p>An EMIT L2A Mask netCDF file.</p> required <p>Returns:</p> Type Description <code>numpy.ndarray</code> <p>A numpy array that can be used with the emit_xarray function to apply a band mask.</p> Source code in <code>hypercoast/emit.py</code> <pre><code>def band_mask(filepath: str) -&gt; np.ndarray:\n    \"\"\"\n    Unpacks the packed band mask to apply to the dataset. Can be used manually or as an input in the emit_xarray() function.\n\n    Args:\n        filepath (str): An EMIT L2A Mask netCDF file.\n\n    Returns:\n        numpy.ndarray: A numpy array that can be used with the emit_xarray function to apply a band mask.\n    \"\"\"\n    # Open Dataset\n    mask_ds = xr.open_dataset(filepath, engine=\"h5netcdf\")\n    # Open band_mask and convert to uint8\n    bmask = mask_ds.band_mask.data.astype(\"uint8\")\n    # Print Flags used\n    unpacked_bmask = np.unpackbits(bmask, axis=-1)\n    # Remove bands &gt; 285\n    unpacked_bmask = unpacked_bmask[:, :, 0:285]\n    # Check for data bands and build mask\n    return unpacked_bmask\n</code></pre>"},{"location":"emit/#hypercoast.emit.coord_vects","title":"<code>coord_vects(ds)</code>","text":"<p>This function calculates the Lat and Lon Coordinate Vectors using the GLT and Metadata from an EMIT dataset read into xarray.</p> <p>lon, lat (numpy.array): longitude and latitude array grid for the dataset</p> Source code in <code>hypercoast/emit.py</code> <pre><code>def coord_vects(ds: xr.Dataset) -&gt; np.ndarray:\n    \"\"\"\n    This function calculates the Lat and Lon Coordinate Vectors using the GLT and Metadata from an EMIT dataset read into xarray.\n\n    Parameters:\n    ds: an xarray.Dataset containing the root variable and metadata of an EMIT dataset\n    loc: an xarray.Dataset containing the 'location' group of an EMIT dataset\n\n    Returns:\n    lon, lat (numpy.array): longitude and latitude array grid for the dataset\n\n    \"\"\"\n    # Retrieve Geotransform from Metadata\n    GT = ds.geotransform\n    # Create Array for Lat and Lon and fill\n    dim_x = ds.glt_x.shape[1]\n    dim_y = ds.glt_x.shape[0]\n    lon = np.zeros(dim_x)\n    lat = np.zeros(dim_y)\n    # Note: no rotation for EMIT Data\n    for x in np.arange(dim_x):\n        x_geo = (GT[0] + 0.5 * GT[1]) + x * GT[1]  # Adjust coordinates to pixel-center\n        lon[x] = x_geo\n    for y in np.arange(dim_y):\n        y_geo = (GT[3] + 0.5 * GT[5]) + y * GT[5]\n        lat[y] = y_geo\n    return lon, lat\n</code></pre>"},{"location":"emit/#hypercoast.emit.emit_to_image","title":"<code>emit_to_image(data, wavelengths=None, method='nearest', output=None, **kwargs)</code>","text":"<p>Converts an EMIT dataset to an image.</p> <p>Parameters:</p> Name Type Description Default <code>data</code> <code>xarray.Dataset or str</code> <p>The dataset containing the EMIT data or the file path to the dataset.</p> required <code>wavelengths</code> <code>array-like</code> <p>The specific wavelengths to select. If None, all wavelengths are selected. Defaults to None.</p> <code>None</code> <code>method</code> <code>str</code> <p>The method to use for data selection. Defaults to \"nearest\".</p> <code>'nearest'</code> <code>output</code> <code>str</code> <p>The file path where the image will be saved. If None, the image will be returned as a PIL Image object. Defaults to None.</p> <code>None</code> <code>**kwargs</code> <p>Additional keyword arguments to be passed to <code>leafmap.array_to_image</code>.</p> <code>{}</code> <p>Returns:</p> Type Description <code>rasterio.Dataset or None</code> <p>The image converted from the dataset. If <code>output</code> is provided, the image will be saved to the specified file and the function will return None.</p> Source code in <code>hypercoast/emit.py</code> <pre><code>def emit_to_image(\n    data: Union[xr.Dataset, str],\n    wavelengths: Optional[Union[list, tuple]] = None,\n    method: Optional[str] = \"nearest\",\n    output: Optional[str] = None,\n    **kwargs,\n):\n    \"\"\"\n    Converts an EMIT dataset to an image.\n\n    Args:\n        data (xarray.Dataset or str): The dataset containing the EMIT data or the file path to the dataset.\n        wavelengths (array-like, optional): The specific wavelengths to select. If None, all wavelengths are selected. Defaults to None.\n        method (str, optional): The method to use for data selection. Defaults to \"nearest\".\n        output (str, optional): The file path where the image will be saved. If None, the image will be returned as a PIL Image object. Defaults to None.\n        **kwargs: Additional keyword arguments to be passed to `leafmap.array_to_image`.\n\n    Returns:\n        rasterio.Dataset or None: The image converted from the dataset. If `output` is provided, the image will be saved to the specified file and the function will return None.\n    \"\"\"\n    from leafmap import array_to_image\n\n    if isinstance(data, str):\n        data = read_emit(data, ortho=True)\n\n    ds = data[\"reflectance\"]\n\n    if wavelengths is not None:\n        ds = ds.sel(wavelength=wavelengths, method=method)\n    return array_to_image(ds, transpose=False, output=output, **kwargs)\n</code></pre>"},{"location":"emit/#hypercoast.emit.emit_to_netcdf","title":"<code>emit_to_netcdf(data, output, **kwargs)</code>","text":"<p>Transposes an EMIT dataset and saves it as a NetCDF file.</p> <p>Parameters:</p> Name Type Description Default <code>data</code> <code>xarray.Dataset or str</code> <p>The dataset containing the EMIT data or the file path to the dataset.</p> required <code>output</code> <code>str</code> <p>The file path where the NetCDF file will be saved.</p> required <code>**kwargs</code> <p>Additional keyword arguments to be passed to <code>xarray.Dataset.to_netcdf</code>.</p> <code>{}</code> Source code in <code>hypercoast/emit.py</code> <pre><code>def emit_to_netcdf(data: Union[xr.Dataset, str], output: str, **kwargs) -&gt; None:\n    \"\"\"\n    Transposes an EMIT dataset and saves it as a NetCDF file.\n\n    Args:\n        data (xarray.Dataset or str): The dataset containing the EMIT data or the file path to the dataset.\n        output (str): The file path where the NetCDF file will be saved.\n        **kwargs: Additional keyword arguments to be passed to `xarray.Dataset.to_netcdf`.\n\n    \"\"\"\n    if isinstance(data, str):\n        data = read_emit(data, ortho=True)\n\n    ds_geo = data.transpose(\"wavelengths\", \"latitude\", \"longitude\")\n    ds_geo.to_netcdf(output, **kwargs)\n</code></pre>"},{"location":"emit/#hypercoast.emit.emit_xarray","title":"<code>emit_xarray(filepath, ortho=False, qmask=None, unpacked_bmask=None, wavelengths=None, method='nearest')</code>","text":"<p>Streamlines opening an EMIT dataset as an xarray.Dataset.</p> <p>Parameters:</p> Name Type Description Default <code>filepath</code> <code>str</code> <p>A filepath to an EMIT netCDF file.</p> required <code>ortho</code> <code>bool</code> <p>Whether to orthorectify the dataset or leave in crosstrack/downtrack coordinates. Defaults to False.</p> <code>False</code> <code>qmask</code> <code>numpy.ndarray</code> <p>A numpy array output from the quality_mask function used to mask pixels based on quality flags selected in that function. Any non-orthorectified array with the proper crosstrack and downtrack dimensions can also be used. Defaults to None.</p> <code>None</code> <code>unpacked_bmask</code> <code>numpy.ndarray</code> <p>A numpy array from the band_mask function that can be used to mask band-specific pixels that have been interpolated. Defaults to None.</p> <code>None</code> <code>wavelengths</code> <code>array-like</code> <p>The specific wavelengths to select. If None, all wavelengths are selected. Defaults to None.</p> <code>None</code> <code>method</code> <code>str</code> <p>The method to use for data selection. Defaults to \"nearest\".</p> <code>'nearest'</code> <p>Returns:</p> Type Description <code>xarray.Dataset</code> <p>An xarray.Dataset constructed based on the parameters provided.</p> Source code in <code>hypercoast/emit.py</code> <pre><code>def emit_xarray(\n    filepath: str,\n    ortho: Optional[bool] = False,\n    qmask: Optional[np.ndarray] = None,\n    unpacked_bmask: Optional[np.ndarray] = None,\n    wavelengths: Optional[Union[tuple, list]] = None,\n    method: Optional[str] = \"nearest\",\n) -&gt; xr.Dataset:\n    \"\"\"\n    Streamlines opening an EMIT dataset as an xarray.Dataset.\n\n    Args:\n        filepath (str): A filepath to an EMIT netCDF file.\n        ortho (bool, optional): Whether to orthorectify the dataset or leave in crosstrack/downtrack coordinates. Defaults to False.\n        qmask (numpy.ndarray, optional): A numpy array output from the quality_mask function used to mask pixels based on quality flags selected in that function. Any non-orthorectified array with the proper crosstrack and downtrack dimensions can also be used. Defaults to None.\n        unpacked_bmask (numpy.ndarray, optional): A numpy array from the band_mask function that can be used to mask band-specific pixels that have been interpolated. Defaults to None.\n        wavelengths (array-like, optional): The specific wavelengths to select. If None, all wavelengths are selected. Defaults to None.\n        method (str, optional): The method to use for data selection. Defaults to \"nearest\".\n\n    Returns:\n        xarray.Dataset: An xarray.Dataset constructed based on the parameters provided.\n    \"\"\"\n    # Grab granule filename to check product\n    from s3fs.core import S3File\n    from fsspec.implementations.http import HTTPFile\n\n    if isinstance(filepath, S3File):\n        granule_id = filepath.info()[\"name\"].split(\"/\", -1)[-1].split(\".\", -1)[0]\n    elif isinstance(filepath, HTTPFile):\n        granule_id = filepath.path.split(\"/\", -1)[-1].split(\".\", -1)[0]\n    else:\n        granule_id = os.path.splitext(os.path.basename(filepath))[0]\n\n    # Read in Data as Xarray Datasets\n    engine, wvl_group = \"h5netcdf\", None\n\n    ds = xr.open_dataset(filepath, engine=engine)\n    loc = xr.open_dataset(filepath, engine=engine, group=\"location\")\n\n    # Check if mineral dataset and read in groups (only ds/loc for minunc)\n\n    if \"L2B_MIN_\" in granule_id:\n        wvl_group = \"mineral_metadata\"\n    elif \"L2B_MINUNC\" not in granule_id:\n        wvl_group = \"sensor_band_parameters\"\n\n    wvl = None\n\n    if wvl_group:\n        wvl = xr.open_dataset(filepath, engine=engine, group=wvl_group)\n\n    # Building Flat Dataset from Components\n    data_vars = {**ds.variables}\n\n    # Format xarray coordinates based upon emit product (no wvl for mineral uncertainty)\n    coords = {\n        \"downtrack\": ([\"downtrack\"], ds.downtrack.data),\n        \"crosstrack\": ([\"crosstrack\"], ds.crosstrack.data),\n        **loc.variables,\n    }\n\n    product_band_map = {\n        \"L2B_MIN_\": \"name\",\n        \"L2A_MASK_\": \"mask_bands\",\n        \"L1B_OBS_\": \"observation_bands\",\n        \"L2A_RFL_\": \"wavelengths\",\n        \"L1B_RAD_\": \"wavelengths\",\n        \"L2A_RFLUNCERT_\": \"wavelengths\",\n    }\n\n    # if band := product_band_map.get(next((k for k in product_band_map.keys() if k in granule_id), 'unknown'), None):\n    # coords['bands'] = wvl[band].data\n\n    if wvl:\n        coords = {**coords, **wvl.variables}\n\n    out_xr = xr.Dataset(data_vars=data_vars, coords=coords, attrs=ds.attrs)\n    out_xr.attrs[\"granule_id\"] = granule_id\n\n    if band := product_band_map.get(\n        next((k for k in product_band_map.keys() if k in granule_id), \"unknown\"), None\n    ):\n        if \"minerals\" in list(out_xr.dims):\n            out_xr = out_xr.swap_dims({\"minerals\": band})\n            out_xr = out_xr.rename({band: \"mineral_name\"})\n        else:\n            out_xr = out_xr.swap_dims({\"bands\": band})\n\n    # Apply Quality and Band Masks, set fill values to NaN\n    for var in list(ds.data_vars):\n        if qmask is not None:\n            out_xr[var].data[qmask == 1] = np.nan\n        if unpacked_bmask is not None:\n            out_xr[var].data[unpacked_bmask == 1] = np.nan\n        out_xr[var].data[out_xr[var].data == -9999] = np.nan\n\n    if ortho is True:\n        out_xr = ortho_xr(out_xr)\n        out_xr.attrs[\"Orthorectified\"] = \"True\"\n\n    if wavelengths is not None:\n        out_xr = out_xr.sel(wavelengths=wavelengths, method=method)\n\n    out_xr = out_xr.rename({\"wavelengths\": \"wavelength\"})\n    return out_xr\n</code></pre>"},{"location":"emit/#hypercoast.emit.envi_header","title":"<code>envi_header(inputpath)</code>","text":"<p>Convert a ENVI binary/header path to a header, handling extensions.</p> <p>Parameters:</p> Name Type Description Default <code>inputpath</code> <code>str</code> <p>Path to ENVI binary file.</p> required <p>Returns:</p> Type Description <code>str</code> <p>The header file associated with the input reference. If the header file does not exist, it returns the expected header file path.</p> Source code in <code>hypercoast/emit.py</code> <pre><code>def envi_header(inputpath: str) -&gt; str:\n    \"\"\"\n    Convert a ENVI binary/header path to a header, handling extensions.\n\n    Args:\n        inputpath (str): Path to ENVI binary file.\n\n    Returns:\n        str: The header file associated with the input reference. If the header file does not exist, it returns the expected header file path.\n    \"\"\"\n    if (\n        os.path.splitext(inputpath)[-1] == \".img\"\n        or os.path.splitext(inputpath)[-1] == \".dat\"\n        or os.path.splitext(inputpath)[-1] == \".raw\"\n    ):\n        # headers could be at either filename.img.hdr or filename.hdr.  Check both, return the one that exists if it\n        # does, if not return the latter (new file creation presumed).\n        hdrfile = os.path.splitext(inputpath)[0] + \".hdr\"\n        if os.path.isfile(hdrfile):\n            return hdrfile\n        elif os.path.isfile(inputpath + \".hdr\"):\n            return inputpath + \".hdr\"\n        return hdrfile\n    elif os.path.splitext(inputpath)[-1] == \".hdr\":\n        return inputpath\n    else:\n        return inputpath + \".hdr\"\n</code></pre>"},{"location":"emit/#hypercoast.emit.is_adjacent","title":"<code>is_adjacent(scene, same_orbit)</code>","text":"<p>Checks if the scene numbers from the same orbit are adjacent/sequential.</p> <p>Parameters:</p> Name Type Description Default <code>scene</code> <code>str</code> <p>The scene number to check.</p> required <code>same_orbit</code> <code>list</code> <p>A list of scene numbers from the same orbit.</p> required <p>Returns:</p> Type Description <code>bool</code> <p>True if the scene numbers are adjacent/sequential, False otherwise.</p> Source code in <code>hypercoast/emit.py</code> <pre><code>def is_adjacent(scene: str, same_orbit: list) -&gt; bool:\n    \"\"\"\n    Checks if the scene numbers from the same orbit are adjacent/sequential.\n\n    Args:\n        scene (str): The scene number to check.\n        same_orbit (list): A list of scene numbers from the same orbit.\n\n    Returns:\n        bool: True if the scene numbers are adjacent/sequential, False otherwise.\n    \"\"\"\n    scene_nums = [int(scene.split(\".\")[-2].split(\"_\")[-1]) for scene in same_orbit]\n    return all(b - a == 1 for a, b in zip(scene_nums[:-1], scene_nums[1:]))\n</code></pre>"},{"location":"emit/#hypercoast.emit.merge_emit","title":"<code>merge_emit(datasets, gdf)</code>","text":"<p>Merges xarray datasets formatted using emit_xarray. Note: GDF may only work with a single geometry.</p> <p>Parameters:</p> Name Type Description Default <code>datasets</code> <code>dict</code> <p>A dictionary of xarray datasets formatted using emit_xarray.</p> required <code>gdf</code> <code>gpd.GeoDataFrame</code> <p>A GeoDataFrame containing the geometry to be used for merging.</p> required <p>Returns:</p> Type Description <code>xarray.Dataset</code> <p>A merged xarray dataset.</p> <p>Exceptions:</p> Type Description <code>Exception</code> <p>If there are inconsistencies in the 1D variables across datasets.</p> Source code in <code>hypercoast/emit.py</code> <pre><code>def merge_emit(datasets: dict, gdf: gpd.GeoDataFrame) -&gt; xr.Dataset:\n    \"\"\"\n    Merges xarray datasets formatted using emit_xarray. Note: GDF may only work with a single geometry.\n\n    Args:\n        datasets (dict): A dictionary of xarray datasets formatted using emit_xarray.\n        gdf (gpd.GeoDataFrame): A GeoDataFrame containing the geometry to be used for merging.\n\n    Returns:\n        xarray.Dataset: A merged xarray dataset.\n\n    Raises:\n        Exception: If there are inconsistencies in the 1D variables across datasets.\n    \"\"\"\n    from rioxarray.merge import merge_arrays\n\n    nested_data_arrays = {}\n    # loop over datasets\n    for dataset in datasets:\n        # create dictionary of arrays for each dataset\n\n        # create dictionary of 1D variables, which should be consistent across datasets\n        one_d_arrays = {}\n\n        # Dictionary of variables to merge\n        data_arrays = {}\n        # Loop over variables in dataset including elevation\n        for var in list(datasets[dataset].data_vars) + [\"elev\"]:\n            # Get 1D for this variable and add to dictionary\n            if not one_d_arrays:\n                # These should be an array describing the others (wavelengths, mask_bands, etc.)\n                one_dim = [\n                    item\n                    for item in list(datasets[dataset].coords)\n                    if item not in [\"latitude\", \"longitude\", \"spatial_ref\"]\n                    and len(datasets[dataset][item].dims) == 1\n                ]\n                # print(one_dim)\n                for od in one_dim:\n                    one_d_arrays[od] = datasets[dataset].coords[od].data\n\n                # Update format for merging - This could probably be improved\n            da = datasets[dataset][var].reset_coords(\"elev\", drop=False)\n            da = da.rename({\"latitude\": \"y\", \"longitude\": \"x\"})\n            if len(da.dims) == 3:\n                if any(item in list(da.coords) for item in one_dim):\n                    da = da.drop_vars(one_dim)\n                da = da.drop_vars(\"elev\")\n                da = da.to_array(name=var).squeeze(\"variable\", drop=True)\n                da = da.transpose(da.dims[-1], da.dims[0], da.dims[1])\n                # print(da.dims)\n            if var == \"elev\":\n                da = da.to_array(name=var).squeeze(\"variable\", drop=True)\n            data_arrays[var] = da\n            nested_data_arrays[dataset] = data_arrays\n\n            # Transpose the nested arrays dict. This is horrible to read, but works to pair up variables (ie mask) from the different granules\n    transposed_dict = {\n        inner_key: {\n            outer_key: inner_dict[inner_key]\n            for outer_key, inner_dict in nested_data_arrays.items()\n        }\n        for inner_key in nested_data_arrays[next(iter(nested_data_arrays))]\n    }\n\n    # remove some unused data\n    del nested_data_arrays, data_arrays, da\n\n    # Merge the arrays using rioxarray.merge_arrays()\n    merged = {}\n    for _var in transposed_dict:\n        merged[_var] = merge_arrays(\n            list(transposed_dict[_var].values()),\n            bounds=gdf.unary_union.bounds,\n            nodata=np.nan,\n        )\n\n    # Create a new xarray dataset from the merged arrays\n    # Create Merged Dataset\n    merged_ds = xr.Dataset(data_vars=merged, coords=one_d_arrays)\n    # Rename x and y to longitude and latitude\n    merged_ds = merged_ds.rename({\"y\": \"latitude\", \"x\": \"longitude\"})\n    del transposed_dict, merged\n    return merged_ds\n</code></pre>"},{"location":"emit/#hypercoast.emit.ortho_browse","title":"<code>ortho_browse(url, glt, spatial_ref, geotransform, white_background=True)</code>","text":"<p>Use an EMIT GLT, geotransform, and spatial ref to orthorectify a browse image. (browse images are in native resolution)</p> <p>Parameters:</p> Name Type Description Default <code>url</code> <code>str</code> <p>URL of the browse image.</p> required <code>glt</code> <code>numpy.ndarray</code> <p>A GLT array constructed from EMIT GLT data.</p> required <code>spatial_ref</code> <code>str</code> <p>Spatial reference system.</p> required <code>geotransform</code> <code>list</code> <p>A list of six numbers that define the affine transform between pixel coordinates and map coordinates.</p> required <code>white_background</code> <code>bool</code> <p>If True, the fill value for the orthorectified image is white (255). If False, the fill value is black (0). Defaults to True.</p> <code>True</code> <p>Returns:</p> Type Description <code>xarray.DataArray</code> <p>An orthorectified browse image in the form of an xarray DataArray.</p> Source code in <code>hypercoast/emit.py</code> <pre><code>def ortho_browse(\n    url: str,\n    glt: np.ndarray,\n    spatial_ref: str,\n    geotransform: list,\n    white_background: Optional[bool] = True,\n) -&gt; xr.Dataset:\n    \"\"\"\n    Use an EMIT GLT, geotransform, and spatial ref to orthorectify a browse image. (browse images are in native resolution)\n\n    Args:\n        url (str): URL of the browse image.\n        glt (numpy.ndarray): A GLT array constructed from EMIT GLT data.\n        spatial_ref (str): Spatial reference system.\n        geotransform (list): A list of six numbers that define the affine transform between pixel coordinates and map coordinates.\n        white_background (bool, optional): If True, the fill value for the orthorectified image is white (255). If False, the fill value is black (0). Defaults to True.\n\n    Returns:\n        xarray.DataArray: An orthorectified browse image in the form of an xarray DataArray.\n    \"\"\"\n    from skimage import io\n\n    # Read Data\n    data = io.imread(url)\n    # Orthorectify using GLT and transpose so band is first dimension\n    if white_background is True:\n        fill = 255\n    else:\n        fill = 0\n    ortho_data = apply_glt(data, glt, fill_value=fill).transpose(2, 0, 1)\n    coords = {\n        \"y\": (\n            [\"y\"],\n            (geotransform[3] + 0.5 * geotransform[5])\n            + np.arange(glt.shape[0]) * geotransform[5],\n        ),\n        \"x\": (\n            [\"x\"],\n            (geotransform[0] + 0.5 * geotransform[1])\n            + np.arange(glt.shape[1]) * geotransform[1],\n        ),\n    }\n    ortho_data = ortho_data.astype(int)\n    ortho_data[ortho_data == -1] = 0\n    # Place in xarray.datarray\n    da = xr.DataArray(ortho_data, dims=[\"band\", \"y\", \"x\"], coords=coords)\n    da.rio.write_crs(spatial_ref, inplace=True)\n    return da\n</code></pre>"},{"location":"emit/#hypercoast.emit.ortho_xr","title":"<code>ortho_xr(ds, GLT_NODATA_VALUE=0, fill_value=-9999)</code>","text":"<p>Uses <code>apply_glt</code> to create an orthorectified xarray dataset.</p> <p>Parameters:</p> Name Type Description Default <code>ds</code> <code>xarray.Dataset</code> <p>An xarray dataset produced by emit_xarray.</p> required <code>GLT_NODATA_VALUE</code> <code>int</code> <p>No data value for the GLT tables. Defaults to 0.</p> <code>0</code> <code>fill_value</code> <code>int</code> <p>The fill value for EMIT datasets. Defaults to -9999.</p> <code>-9999</code> <p>Returns:</p> Type Description <code>xarray.Dataset</code> <p>An orthocorrected xarray dataset.</p> Source code in <code>hypercoast/emit.py</code> <pre><code>def ortho_xr(\n    ds: xr.Dataset,\n    GLT_NODATA_VALUE: Optional[int] = 0,\n    fill_value: Optional[int] = -9999,\n) -&gt; xr.Dataset:\n    \"\"\"\n    Uses `apply_glt` to create an orthorectified xarray dataset.\n\n    Args:\n        ds (xarray.Dataset): An xarray dataset produced by emit_xarray.\n        GLT_NODATA_VALUE (int, optional): No data value for the GLT tables. Defaults to 0.\n        fill_value (int, optional): The fill value for EMIT datasets. Defaults to -9999.\n\n    Returns:\n        xarray.Dataset: An orthocorrected xarray dataset.\n    \"\"\"\n    # Build glt_ds\n\n    glt_ds = np.nan_to_num(\n        np.stack([ds[\"glt_x\"].data, ds[\"glt_y\"].data], axis=-1), nan=GLT_NODATA_VALUE\n    ).astype(int)\n\n    # List Variables\n    var_list = list(ds.data_vars)\n\n    # Remove flat field from data vars - the flat field is only useful with additional information before orthorectification\n    if \"flat_field_update\" in var_list:\n        var_list.remove(\"flat_field_update\")\n\n    # Create empty dictionary for orthocorrected data vars\n    data_vars = {}\n\n    # Extract Rawspace Dataset Variable Values (Typically Reflectance)\n    for var in var_list:\n        raw_ds = ds[var].data\n        var_dims = ds[var].dims\n        # Apply GLT to dataset\n        out_ds = apply_glt(raw_ds, glt_ds, GLT_NODATA_VALUE=GLT_NODATA_VALUE)\n\n        # Mask fill values\n        out_ds[out_ds == fill_value] = np.nan\n\n        # Update variables - Only works for 2 or 3 dimensional arrays\n        if raw_ds.ndim == 2:\n            out_ds = out_ds.squeeze()\n            data_vars[var] = ([\"latitude\", \"longitude\"], out_ds)\n        else:\n            data_vars[var] = ([\"latitude\", \"longitude\", var_dims[-1]], out_ds)\n\n        del raw_ds\n\n    # Calculate Lat and Lon Vectors\n    lon, lat = coord_vects(\n        ds\n    )  # Reorder this function to make sense in case of multiple variables\n\n    # Apply GLT to elevation\n    elev_ds = apply_glt(ds[\"elev\"].data, glt_ds)\n    elev_ds[elev_ds == fill_value] = np.nan\n\n    # Delete glt_ds - no longer needed\n    del glt_ds\n\n    # Create Coordinate Dictionary\n    coords = {\n        \"latitude\": ([\"latitude\"], lat),\n        \"longitude\": ([\"longitude\"], lon),\n        **ds.coords,\n    }  # unpack to add appropriate coordinates\n\n    # Remove Unnecessary Coords\n    for key in [\"downtrack\", \"crosstrack\", \"lat\", \"lon\", \"glt_x\", \"glt_y\", \"elev\"]:\n        del coords[key]\n\n    # Add Orthocorrected Elevation\n    coords[\"elev\"] = ([\"latitude\", \"longitude\"], np.squeeze(elev_ds))\n\n    # Build Output xarray Dataset and assign data_vars array attributes\n    out_xr = xr.Dataset(data_vars=data_vars, coords=coords, attrs=ds.attrs)\n\n    del out_ds\n    # Assign Attributes from Original Datasets\n    for var in var_list:\n        out_xr[var].attrs = ds[var].attrs\n    out_xr.coords[\"latitude\"].attrs = ds[\"lat\"].attrs\n    out_xr.coords[\"longitude\"].attrs = ds[\"lon\"].attrs\n    out_xr.coords[\"elev\"].attrs = ds[\"elev\"].attrs\n\n    # Add Spatial Reference in recognizable format\n    out_xr.rio.write_crs(ds.spatial_ref, inplace=True)\n\n    return out_xr\n</code></pre>"},{"location":"emit/#hypercoast.emit.plot_emit","title":"<code>plot_emit(ds, longitude=None, latitude=None, downtrack=None, crosstrack=None, remove_nans=True, x='wavelengths', y='reflectance', color='black', frame_height=400, frame_width=600, title=None, method='nearest', ortho=True, options={}, **kwargs)</code>","text":"<p>Plots a line graph of the reflectance data from a given dataset.</p> <p>Parameters:</p> Name Type Description Default <code>ds</code> <code>xarray.Dataset or str</code> <p>The dataset containing the reflectance data or the file path to the dataset.</p> required <code>longitude</code> <code>float</code> <p>The longitude coordinate to select for orthorectified data. Defaults to None.</p> <code>None</code> <code>latitude</code> <code>float</code> <p>The latitude coordinate to select for orthorectified data. Defaults to None.</p> <code>None</code> <code>downtrack</code> <code>int</code> <p>The downtrack coordinate to select for non-orthorectified data. Defaults to None.</p> <code>None</code> <code>crosstrack</code> <code>int</code> <p>The crosstrack coordinate to select for non-orthorectified data. Defaults to None.</p> <code>None</code> <code>remove_nans</code> <code>bool</code> <p>If True, replace non-good wavelengths with NaN. Defaults to True.</p> <code>True</code> <code>x</code> <code>str</code> <p>The x-axis label. Defaults to \"wavelengths\".</p> <code>'wavelengths'</code> <code>y</code> <code>str</code> <p>The y-axis label. Defaults to \"reflectance\".</p> <code>'reflectance'</code> <code>color</code> <code>str</code> <p>The color of the line. Defaults to \"black\".</p> <code>'black'</code> <code>frame_height</code> <code>int</code> <p>The height of the frame. Defaults to 400.</p> <code>400</code> <code>frame_width</code> <code>int</code> <p>The width of the frame. Defaults to 600.</p> <code>600</code> <code>title</code> <code>str</code> <p>The title of the plot. If None, a default title will be generated. Defaults to None.</p> <code>None</code> <code>method</code> <code>str</code> <p>The method to use for data selection. Defaults to \"nearest\".</p> <code>'nearest'</code> <code>ortho</code> <code>bool</code> <p>If True, the function will use longitude and latitude for data selection. Defaults to True.</p> <code>True</code> <code>options</code> <code>dict</code> <p>Additional options to be passed to <code>hvplot.line</code>. Defaults to {}.</p> <code>{}</code> <code>**kwargs</code> <p>Additional keyword arguments to be passed to <code>hvplot.line</code>.</p> <code>{}</code> <p>Returns:</p> Type Description <code>hvplot.Plot</code> <p>The line plot of the reflectance data.</p> Source code in <code>hypercoast/emit.py</code> <pre><code>def plot_emit(\n    ds: Union[xr.Dataset, str],\n    longitude: Optional[float] = None,\n    latitude: Optional[float] = None,\n    downtrack: Optional[int] = None,\n    crosstrack: Optional[int] = None,\n    remove_nans: Optional[bool] = True,\n    x: str = \"wavelengths\",\n    y: str = \"reflectance\",\n    color: str = \"black\",\n    frame_height: Optional[int] = 400,\n    frame_width: Optional[int] = 600,\n    title: str = None,\n    method: Optional[str] = \"nearest\",\n    ortho: Optional[bool] = True,\n    options: Optional[dict] = {},\n    **kwargs,\n):\n    \"\"\"\n    Plots a line graph of the reflectance data from a given dataset.\n\n    Args:\n        ds (xarray.Dataset or str): The dataset containing the reflectance data or the file path to the dataset.\n        longitude (float, optional): The longitude coordinate to select for orthorectified data. Defaults to None.\n        latitude (float, optional): The latitude coordinate to select for orthorectified data. Defaults to None.\n        downtrack (int, optional): The downtrack coordinate to select for non-orthorectified data. Defaults to None.\n        crosstrack (int, optional): The crosstrack coordinate to select for non-orthorectified data. Defaults to None.\n        remove_nans (bool, optional): If True, replace non-good wavelengths with NaN. Defaults to True.\n        x (str, optional): The x-axis label. Defaults to \"wavelengths\".\n        y (str, optional): The y-axis label. Defaults to \"reflectance\".\n        color (str, optional): The color of the line. Defaults to \"black\".\n        frame_height (int, optional): The height of the frame. Defaults to 400.\n        frame_width (int, optional): The width of the frame. Defaults to 600.\n        title (str, optional): The title of the plot. If None, a default title will be generated. Defaults to None.\n        method (str, optional): The method to use for data selection. Defaults to \"nearest\".\n        ortho (bool, optional): If True, the function will use longitude and latitude for data selection. Defaults to True.\n        options (dict, optional): Additional options to be passed to `hvplot.line`. Defaults to {}.\n        **kwargs: Additional keyword arguments to be passed to `hvplot.line`.\n\n    Returns:\n        hvplot.Plot: The line plot of the reflectance data.\n    \"\"\"\n\n    import hvplot.xarray  # noqa F401\n\n    if ortho is True:\n        if longitude is None or latitude is None:\n            raise ValueError(\n                \"Longitude and Latitude must be provided for orthorectified data.\"\n            )\n    else:\n        if downtrack is None or crosstrack is None:\n            raise ValueError(\n                \"Downtrack and Crosstrack must be provided for non-orthorectified data.\"\n            )\n\n    if longitude is not None and latitude is not None:\n        ortho = True\n\n    if downtrack is not None and crosstrack is not None:\n        ortho = False\n\n    if isinstance(ds, str):\n        ds = read_emit(ds, ortho=ortho)\n\n    if remove_nans:\n        ds[\"reflectance\"].data[:, :, ds[\"good_wavelengths\"].data == 0] = np.nan\n\n    if ortho:\n        example = ds[\"reflectance\"].sel(\n            longitude=longitude, latitude=latitude, method=method\n        )\n        if title is None:\n            title = f\"Reflectance at longitude={longitude:.3f}, latitude={latitude:.3f}\"\n\n    else:\n        example = ds[\"reflectance\"].sel(\n            downtrack=downtrack, crosstrack=crosstrack, method=method\n        )\n        if title is None:\n            title = f\"Reflectance at downtrack={downtrack}, crosstrack={crosstrack}\"\n\n    line = example.hvplot.line(\n        y=y,\n        x=x,\n        color=color,\n        frame_height=frame_height,\n        frame_width=frame_width,\n        **kwargs,\n    ).opts(title=title, **options)\n    return line\n</code></pre>"},{"location":"emit/#hypercoast.emit.quality_mask","title":"<code>quality_mask(filepath, quality_bands)</code>","text":"<p>Builds a single layer mask to apply based on the bands selected from an EMIT L2A Mask file.</p> <p>Parameters:</p> Name Type Description Default <code>filepath</code> <code>str</code> <p>An EMIT L2A Mask netCDF file.</p> required <code>quality_bands</code> <code>list</code> <p>A list of bands (quality flags only) from the mask file that should be used in creation of mask.</p> required <p>Returns:</p> Type Description <code>numpy.ndarray</code> <p>A numpy array that can be used with the emit_xarray function to apply a quality mask.</p> <p>Exceptions:</p> Type Description <code>AttributeError</code> <p>If the selected flags include a data band (5 or 6) not just flag bands.</p> Source code in <code>hypercoast/emit.py</code> <pre><code>def quality_mask(filepath: str, quality_bands: list) -&gt; np.ndarray:\n    \"\"\"\n    Builds a single layer mask to apply based on the bands selected from an EMIT L2A Mask file.\n\n    Args:\n        filepath (str): An EMIT L2A Mask netCDF file.\n        quality_bands (list): A list of bands (quality flags only) from the mask file that should be used in creation of mask.\n\n    Returns:\n        numpy.ndarray: A numpy array that can be used with the emit_xarray function to apply a quality mask.\n\n    Raises:\n        AttributeError: If the selected flags include a data band (5 or 6) not just flag bands.\n    \"\"\"\n    # Open Dataset\n    mask_ds = xr.open_dataset(filepath, engine=\"h5netcdf\")\n    # Open Sensor band Group\n    mask_parameters_ds = xr.open_dataset(\n        filepath, engine=\"h5netcdf\", group=\"sensor_band_parameters\"\n    )\n    # Print Flags used\n    flags_used = mask_parameters_ds[\"mask_bands\"].data[quality_bands]\n    print(f\"Flags used: {flags_used}\")\n    # Check for data bands and build mask\n    if any(x in quality_bands for x in [5, 6]):\n        err_str = \"Selected flags include a data band (5 or 6) not just flag bands\"\n        raise AttributeError(err_str)\n    else:\n        qmask = np.sum(mask_ds[\"mask\"][:, :, quality_bands].values, axis=-1)\n        qmask[qmask &gt; 1] = 1\n    return qmask\n</code></pre>"},{"location":"emit/#hypercoast.emit.raw_spatial_crop","title":"<code>raw_spatial_crop(ds, shape)</code>","text":"<p>Use a polygon to clip the file GLT, then a bounding box to crop the spatially raw data. Regions clipped in the GLT are set to 0 so a mask will be applied when used to orthorectify the data at a later point in a workflow.</p> <p>Parameters:</p> Name Type Description Default <code>ds</code> <code>xarray.Dataset</code> <p>Raw spatial EMIT data (non-orthorectified) opened with the <code>emit_xarray</code> function.</p> required <code>shape</code> <code>geopandas.GeoDataFrame</code> <p>A polygon opened with geopandas.</p> required <p>Returns:</p> Type Description <code>xarray.Dataset</code> <p>A clipped GLT and raw spatial data clipped to a bounding box.</p> Source code in <code>hypercoast/emit.py</code> <pre><code>def raw_spatial_crop(ds: xr.Dataset, shape: gpd) -&gt; xr.Dataset:\n    \"\"\"\n    Use a polygon to clip the file GLT, then a bounding box to crop the spatially raw data. Regions clipped in the GLT are set to 0 so a mask will be applied when\n    used to orthorectify the data at a later point in a workflow.\n\n    Args:\n        ds (xarray.Dataset): Raw spatial EMIT data (non-orthorectified) opened with the `emit_xarray` function.\n        shape (geopandas.GeoDataFrame): A polygon opened with geopandas.\n\n    Returns:\n        xarray.Dataset: A clipped GLT and raw spatial data clipped to a bounding box.\n    \"\"\"\n    # Reformat the GLT\n    lon, lat = coord_vects(ds)\n    data_vars = {\n        \"glt_x\": ([\"latitude\", \"longitude\"], ds.glt_x.data),\n        \"glt_y\": ([\"latitude\", \"longitude\"], ds.glt_y.data),\n    }\n    coords = {\n        \"latitude\": ([\"latitude\"], lat),\n        \"longitude\": ([\"longitude\"], lon),\n        \"ortho_y\": ([\"latitude\"], ds.ortho_y.data),\n        \"ortho_x\": ([\"longitude\"], ds.ortho_x.data),\n    }\n    glt_ds = xr.Dataset(data_vars=data_vars, coords=coords, attrs=ds.attrs)\n    glt_ds.rio.write_crs(glt_ds.spatial_ref, inplace=True)\n\n    # Clip the emit glt\n    clipped = glt_ds.rio.clip(shape.geometry.values, shape.crs, all_touched=True)\n\n    # Pull new geotransform from clipped glt\n    clipped_gt = np.array(\n        [float(i) for i in clipped[\"spatial_ref\"].GeoTransform.split(\" \")]\n    )  # THIS GEOTRANSFORM IS OFF BY HALF A PIXEL\n\n    # Create Crosstrack and Downtrack masks for spatially raw dataset -1 is to account for 1 based index. May be a more robust way to do this exists\n    crosstrack_mask = (ds.crosstrack &gt;= np.nanmin(clipped.glt_x.data) - 1) &amp; (\n        ds.crosstrack &lt;= np.nanmax(clipped.glt_x.data) - 1\n    )\n    downtrack_mask = (ds.downtrack &gt;= np.nanmin(clipped.glt_y.data) - 1) &amp; (\n        ds.downtrack &lt;= np.nanmax(clipped.glt_y.data) - 1\n    )\n\n    # Mask Areas outside of crosstrack and downtrack covered by the shape\n    clipped_ds = ds.where((crosstrack_mask &amp; downtrack_mask), drop=True)\n    # Replace Full dataset geotransform with clipped geotransform\n    clipped_ds.attrs[\"geotransform\"] = clipped_gt\n\n    # Drop unnecessary vars from dataset\n    clipped_ds = clipped_ds.drop_vars([\"glt_x\", \"glt_y\", \"downtrack\", \"crosstrack\"])\n\n    # Re-index the GLT to the new array\n    glt_x_data = clipped.glt_x.data - np.nanmin(clipped.glt_x)\n    glt_y_data = clipped.glt_y.data - np.nanmin(clipped.glt_y)\n    clipped_ds = clipped_ds.assign_coords(\n        {\n            \"glt_x\": ([\"ortho_y\", \"ortho_x\"], np.nan_to_num(glt_x_data)),\n            \"glt_y\": ([\"ortho_y\", \"ortho_x\"], np.nan_to_num(glt_y_data)),\n        }\n    )\n    clipped_ds = clipped_ds.assign_coords(\n        {\n            \"downtrack\": (\n                [\"downtrack\"],\n                np.arange(0, clipped_ds[list(ds.data_vars.keys())[0]].shape[0]),\n            ),\n            \"crosstrack\": (\n                [\"crosstrack\"],\n                np.arange(0, clipped_ds[list(ds.data_vars.keys())[0]].shape[1]),\n            ),\n        }\n    )\n\n    return clipped_ds\n</code></pre>"},{"location":"emit/#hypercoast.emit.read_emit","title":"<code>read_emit(filepath, ortho=True, wavelengths=None, method='nearest', **kwargs)</code>","text":"<p>Opens an EMIT dataset from a file path and assigns new coordinates to it.</p> <p>Parameters:</p> Name Type Description Default <code>filepath</code> <code>str</code> <p>The file path to the EMIT dataset.</p> required <code>ortho</code> <code>bool</code> <p>If True, the function will return an orthorectified dataset. Defaults to True.</p> <code>True</code> <code>wavelengths</code> <code>array-like</code> <p>The specific wavelengths to select. If None, all wavelengths are selected. Defaults to None.</p> <code>None</code> <code>method</code> <code>str</code> <p>The method to use for data selection. Defaults to \"nearest\".</p> <code>'nearest'</code> <code>**kwargs</code> <p>Additional keyword arguments to be passed to <code>xr.open_dataset</code>.</p> <code>{}</code> <p>Returns:</p> Type Description <code>xarray.Dataset</code> <p>The dataset with new coordinates assigned.</p> Source code in <code>hypercoast/emit.py</code> <pre><code>def read_emit(\n    filepath: str,\n    ortho: Optional[bool] = True,\n    wavelengths: Union[tuple, list] = None,\n    method: Optional[str] = \"nearest\",\n    **kwargs,\n) -&gt; xr.Dataset:\n    \"\"\"\n    Opens an EMIT dataset from a file path and assigns new coordinates to it.\n\n    Args:\n        filepath (str): The file path to the EMIT dataset.\n        ortho (bool, optional): If True, the function will return an orthorectified dataset. Defaults to True.\n        wavelengths (array-like, optional): The specific wavelengths to select. If None, all wavelengths are selected. Defaults to None.\n        method (str, optional): The method to use for data selection. Defaults to \"nearest\".\n        **kwargs: Additional keyword arguments to be passed to `xr.open_dataset`.\n\n    Returns:\n        xarray.Dataset: The dataset with new coordinates assigned.\n\n    \"\"\"\n\n    if ortho is True:\n        return emit_xarray(\n            filepath, ortho=True, wavelengths=wavelengths, method=method, **kwargs\n        )\n    else:\n        ds = xr.open_dataset(filepath, **kwargs)\n        wvl = xr.open_dataset(filepath, group=\"sensor_band_parameters\")\n        loc = xr.open_dataset(filepath, group=\"location\")\n        ds = ds.assign_coords(\n            {\n                \"downtrack\": ([\"downtrack\"], ds.downtrack.data),\n                \"crosstrack\": ([\"crosstrack\"], ds.crosstrack.data),\n                **wvl.variables,\n                **loc.variables,\n            }\n        )\n        ds = ds.swap_dims({\"bands\": \"wavelengths\"})\n        del wvl\n        del loc\n\n        if wavelengths is not None:\n            ds = ds.sel(wavelengths=wavelengths, method=method)\n\n        ds = ds.rename({\"wavelengths\": \"wavelength\"})\n        return ds\n</code></pre>"},{"location":"emit/#hypercoast.emit.viz_emit","title":"<code>viz_emit(ds, wavelengths, cmap='viridis', frame_width=720, method='nearest', ortho=True, aspect='equal', tiles='ESRI', alpha=0.8, title=None, options={}, **kwargs)</code>","text":"<p>Visualizes the reflectance data from a given dataset at specific wavelengths.</p> <p>Parameters:</p> Name Type Description Default <code>ds</code> <code>xarray.Dataset or str</code> <p>The dataset containing the reflectance data or the file path to the dataset.</p> required <code>wavelengths</code> <code>array-like</code> <p>The specific wavelengths to visualize.</p> required <code>cmap</code> <code>str</code> <p>The colormap to use. Defaults to \"viridis\".</p> <code>'viridis'</code> <code>frame_width</code> <code>int</code> <p>The width of the frame. Defaults to 720.</p> <code>720</code> <code>method</code> <code>str</code> <p>The method to use for data selection. Defaults to \"nearest\".</p> <code>'nearest'</code> <code>ortho</code> <code>bool</code> <p>If True, the function will return an orthorectified image. Defaults to True.</p> <code>True</code> <code>aspect</code> <code>str</code> <p>The aspect ratio of the plot. Defaults to \"equal\".</p> <code>'equal'</code> <code>tiles</code> <code>str</code> <p>The tile source to use for the background map. Defaults to \"ESRI\".</p> <code>'ESRI'</code> <code>alpha</code> <code>float</code> <p>The alpha value for the image. Defaults to 0.8.</p> <code>0.8</code> <code>title</code> <code>str</code> <p>The title of the plot. If None, a default title will be generated. Defaults to None.</p> <code>None</code> <code>options</code> <code>dict</code> <p>Additional options to be passed to <code>hvplot.image</code>. Defaults to {}.</p> <code>{}</code> <code>**kwargs</code> <p>Additional keyword arguments to be passed to <code>hvplot.image</code>.</p> <code>{}</code> <p>Returns:</p> Type Description <code>hvplot.Plot</code> <p>The image plot of the reflectance data at the specified wavelengths.</p> Source code in <code>hypercoast/emit.py</code> <pre><code>def viz_emit(\n    ds: Union[xr.Dataset, str],\n    wavelengths: Union[tuple, list],\n    cmap: Optional[str] = \"viridis\",\n    frame_width: int = 720,\n    method: Optional[str] = \"nearest\",\n    ortho: Optional[bool] = True,\n    aspect: Optional[str] = \"equal\",\n    tiles: str = \"ESRI\",\n    alpha: Optional[float] = 0.8,\n    title: Optional[str] = None,\n    options: Optional[dict] = {},\n    **kwargs,\n):\n    \"\"\"\n    Visualizes the reflectance data from a given dataset at specific wavelengths.\n\n    Args:\n        ds (xarray.Dataset or str): The dataset containing the reflectance data or the file path to the dataset.\n        wavelengths (array-like): The specific wavelengths to visualize.\n        cmap (str, optional): The colormap to use. Defaults to \"viridis\".\n        frame_width (int, optional): The width of the frame. Defaults to 720.\n        method (str, optional): The method to use for data selection. Defaults to \"nearest\".\n        ortho (bool, optional): If True, the function will return an orthorectified image. Defaults to True.\n        aspect (str, optional): The aspect ratio of the plot. Defaults to \"equal\".\n        tiles (str, optional): The tile source to use for the background map. Defaults to \"ESRI\".\n        alpha (float, optional): The alpha value for the image. Defaults to 0.8.\n        title (str, optional): The title of the plot. If None, a default title will be generated. Defaults to None.\n        options (dict, optional): Additional options to be passed to `hvplot.image`. Defaults to {}.\n        **kwargs: Additional keyword arguments to be passed to `hvplot.image`.\n\n    Returns:\n        hvplot.Plot: The image plot of the reflectance data at the specified wavelengths.\n    \"\"\"\n    import hvplot.xarray  # noqa F401\n\n    if isinstance(ds, str):\n        ds = read_emit(ds, ortho=ortho)\n\n    if not isinstance(wavelengths, list):\n        wavelengths = [wavelengths]\n    example = ds.sel(wavelength=wavelengths, method=method)\n\n    wavelengths = \", \".join([f\"{w:.3f}\" for w in example[\"wavelength\"]])\n\n    if title is None:\n        title = f\"Reflectance at {wavelengths} {example.wavelength.units}\"\n\n    if ortho:\n        image = example.hvplot.image(\n            cmap=cmap,\n            geo=ortho,\n            tiles=tiles,\n            alpha=alpha,\n            frame_width=frame_width,\n            **kwargs,\n        ).opts(title=title, **options)\n    else:\n        image = example.hvplot.image(\n            cmap=cmap, aspect=aspect, alpha=alpha, frame_width=frame_width, **kwargs\n        ).opts(title=title, **options)\n\n    return image\n</code></pre>"},{"location":"hypercoast/","title":"hypercoast module","text":"<p>Main module.</p>"},{"location":"hypercoast/#hypercoast.hypercoast.Map","title":"<code> Map            (Map)         </code>","text":"<p>A class that extends leafmap.Map to provide additional functionality for     hypercoast.</p> <p>Methods</p> <p>Any methods inherited from leafmap.Map.</p> Source code in <code>hypercoast/hypercoast.py</code> <pre><code>class Map(leafmap.Map):\n    \"\"\"\n    A class that extends leafmap.Map to provide additional functionality for\n        hypercoast.\n\n    Attributes:\n        Any attributes inherited from leafmap.Map.\n\n    Methods:\n        Any methods inherited from leafmap.Map.\n    \"\"\"\n\n    def __init__(self, **kwargs):\n        \"\"\"\n        Initializes a new instance of the Map class.\n\n        Args:\n            **kwargs: Arbitrary keyword arguments that are passed to the parent\n                class's constructor.\n        \"\"\"\n        super().__init__(**kwargs)\n        self._spectral_data = {}\n        self._plot_options = None\n        self._plot_marker_cluster = ipyleaflet.MarkerCluster(name=\"Marker Cluster\")\n\n    def add(self, obj, position=\"topright\", xlim=None, ylim=None, **kwargs):\n        \"\"\"Add a layer to the map.\n\n        Args:\n            obj (str or object): The name of the layer or a layer object.\n            position (str, optional): The position of the layer widget. Can be\n                'topright', 'topleft', 'bottomright', or 'bottomleft'. Defaults\n                to 'topright'.\n            xlim (tuple, optional): The x-axis limits of the plot. Defaults to None.\n            ylim (tuple, optional): The y-axis limits of the plot. Defaults to None.\n            **kwargs: Arbitrary keyword arguments that are passed to the parent\n                class's add_layer method.\n        \"\"\"\n\n        if isinstance(obj, str):\n            if obj == \"spectral\":\n\n                SpectralWidget(self, position=position, xlim=xlim, ylim=ylim, **kwargs)\n                self.set_plot_options(add_marker_cluster=True)\n            else:\n                super().add(obj, **kwargs)\n\n        else:\n            super().add(obj, **kwargs)\n\n    def search_emit(self, default_dataset=\"EMITL2ARFL\"):\n        \"\"\"\n        Adds a NASA Earth Data search tool to the map with a default dataset for\n            EMIT.\n\n        Args:\n            default_dataset (str, optional): The default dataset to search for.\n                Defaults to \"EMITL2ARFL\".\n        \"\"\"\n        self.add(\"nasa_earth_data\", default_dataset=default_dataset)\n\n    def search_pace(self, default_dataset=\"PACE_OCI_L2_AOP_NRT\"):\n        \"\"\"\n        Adds a NASA Earth Data search tool to the map with a default dataset for\n            PACE.\n\n        Args:\n            default_dataset (str, optional): The default dataset to search for.\n                Defaults to \"PACE_OCI_L2_AOP_NRT\".\n        \"\"\"\n        self.add(\"nasa_earth_data\", default_dataset=default_dataset)\n\n    def search_ecostress(self, default_dataset=\"ECO_L2T_LSTE\"):\n        \"\"\"\n        Adds a NASA Earth Data search tool to the map with a default dataset for\n            ECOSTRESS.\n\n        Args:\n            default_dataset (str, optional): The default dataset to search for.\n                Defaults to \"ECO_L2T_LSTE\".\n        \"\"\"\n        self.add(\"nasa_earth_data\", default_dataset=default_dataset)\n\n    def add_raster(\n        self,\n        source,\n        indexes=None,\n        colormap=None,\n        vmin=None,\n        vmax=None,\n        nodata=None,\n        attribution=None,\n        layer_name=\"Raster\",\n        layer_index=None,\n        zoom_to_layer=True,\n        visible=True,\n        opacity=1.0,\n        array_args=None,\n        client_args={\"cors_all\": False},\n        open_args=None,\n        **kwargs,\n    ):\n        \"\"\"Add a local raster dataset to the map.\n            If you are using this function in JupyterHub on a remote server\n                (e.g., Binder, Microsoft Planetary Computer) and\n            if the raster does not render properly, try installing\n                jupyter-server-proxy using `pip install jupyter-server-proxy`,\n            then running the following code before calling this function. For\n                more info, see https://bit.ly/3JbmF93.\n\n            import os\n            os.environ['LOCALTILESERVER_CLIENT_PREFIX'] = 'proxy/{port}'\n\n        Args:\n            source (str): The path to the GeoTIFF file or the URL of the Cloud\n                Optimized GeoTIFF.\n            indexes (int, optional): The band(s) to use. Band indexing starts\n                at 1. Defaults to None.\n            colormap (str, optional): The name of the colormap from `matplotlib`\n                to use when plotting a single band. See\n                https://matplotlib.org/stable/gallery/color/colormap_reference.html.\n                Default is greyscale.\n            vmin (float, optional): The minimum value to use when colormapping\n                the palette when plotting a single band. Defaults to None.\n            vmax (float, optional): The maximum value to use when colormapping\n                the palette when plotting a single band. Defaults to None.\n            nodata (float, optional): The value from the band to use to interpret\n                as not valid data. Defaults to None.\n            attribution (str, optional): Attribution for the source raster. This\n                defaults to a message about it being a local file.. Defaults to None.\n            layer_name (str, optional): The layer name to use. Defaults to 'Raster'.\n            layer_index (int, optional): The index of the layer. Defaults to None.\n            zoom_to_layer (bool, optional): Whether to zoom to the extent of the\n                layer. Defaults to True.\n            visible (bool, optional): Whether the layer is visible. Defaults to\n                True.\n            opacity (float, optional): The opacity of the layer. Defaults to 1.0.\n            array_args (dict, optional): Additional arguments to pass to\n                `array_to_memory_file` when reading the raster. Defaults to {}.\n            client_args (dict, optional): Additional arguments to pass to\n                localtileserver.TileClient. Defaults to { \"cors_all\": False }.\n            open_args (dict, optional): Additional arguments to pass to\n                rioxarray.open_rasterio.\n\n        \"\"\"\n\n        import rioxarray as rxr\n\n        if array_args is None:\n            array_args = {}\n        if open_args is None:\n            open_args = {}\n\n        if nodata is None:\n            nodata = np.nan\n        super().add_raster(\n            source,\n            indexes=indexes,\n            colormap=colormap,\n            vmin=vmin,\n            vmax=vmax,\n            nodata=nodata,\n            attribution=attribution,\n            layer_name=layer_name,\n            layer_index=layer_index,\n            zoom_to_layer=zoom_to_layer,\n            visible=visible,\n            opacity=opacity,\n            array_args=array_args,\n            client_args=client_args,\n            **kwargs,\n        )\n\n        if isinstance(source, str):\n            da = rxr.open_rasterio(source, **open_args)\n            dims = da.dims\n            da = da.transpose(dims[1], dims[2], dims[0])\n\n            xds = da.to_dataset(name=\"data\")\n            self.cog_layer_dict[layer_name][\"xds\"] = xds\n            # if self.cog_layer_dict[layer_name].get(\"hyper\") is None:\n            #     self.cog_layer_dict[layer_name][\"hyper\"] = \"COG\"\n\n    def add_dataset(\n        self,\n        source,\n        indexes=None,\n        colormap=None,\n        vmin=None,\n        vmax=None,\n        nodata=None,\n        attribution=None,\n        layer_name=\"Raster\",\n        zoom_to_layer=True,\n        visible=True,\n        array_args=None,\n        open_args=None,\n        **kwargs,\n    ):\n        import rioxarray as rxr\n        from leafmap import array_to_image\n\n        if array_args is None:\n            array_args = {}\n\n        if open_args is None:\n            open_args = {}\n\n        if isinstance(source, str):\n            da = rxr.open_rasterio(source, **open_args)\n            dims = da.dims\n            da = da.transpose(dims[1], dims[2], dims[0])\n            xds = da.to_dataset(name=\"data\")\n\n        elif not isinstance(source, xr.Dataset):\n            raise ValueError(\n                \"source must be a path to a raster file or an xarray.Dataset object.\"\n            )\n        else:\n            xds = source\n\n        if indexes is None:\n            if xds.sizes[dims[2]] &lt; 3:\n                indexes = [1]\n            elif xds.sizes[dims[2]] &lt; 4:\n                indexes = [1, 2, 3]\n            else:\n                indexes = [3, 2, 1]\n\n        bands = [i - 1 for i in indexes]\n        da = xds.isel(band=bands)[\"data\"]\n        image = array_to_image(da, transpose=False)\n\n        self.add_raster(\n            image,\n            indexes=None,\n            colormap=colormap,\n            vmin=vmin,\n            vmax=vmax,\n            nodata=nodata,\n            attribution=attribution,\n            layer_name=layer_name,\n            zoom_to_layer=zoom_to_layer,\n            visible=visible,\n            array_args=array_args,\n            **kwargs,\n        )\n\n        self.cog_layer_dict[layer_name][\"xds\"] = xds\n        self.cog_layer_dict[layer_name][\"type\"] = \"XARRAY\"\n        self.cog_layer_dict[layer_name][\"hyper\"] = \"XARRAY\"\n        self.cog_layer_dict[layer_name][\"band_names\"] = [\n            \"b\" + str(i) for i in xds.coords[\"band\"].values.tolist()\n        ]\n        self.cog_layer_dict[layer_name][\"indexes\"] = indexes\n        self.cog_layer_dict[layer_name][\"vis_bands\"] = [\"b\" + str(i) for i in indexes]\n\n    def add_emit(\n        self,\n        source,\n        wavelengths=None,\n        indexes=None,\n        colormap=None,\n        vmin=None,\n        vmax=None,\n        nodata=np.nan,\n        attribution=None,\n        layer_name=\"EMIT\",\n        zoom_to_layer=True,\n        visible=True,\n        array_args=None,\n        **kwargs,\n    ):\n        \"\"\"Add an EMIT dataset to the map.\n            If you are using this function in JupyterHub on a remote server\n                (e.g., Binder, Microsoft Planetary Computer) and\n            if the raster does not render properly, try installing\n                jupyter-server-proxy using `pip install jupyter-server-proxy`,\n            then running the following code before calling this function. For\n                more info, see https://bit.ly/3JbmF93.\n\n            import os\n            os.environ['LOCALTILESERVER_CLIENT_PREFIX'] = 'proxy/{port}'\n\n        Args:\n            source (str): The path to the GeoTIFF file or the URL of the Cloud\n                Optimized GeoTIFF.\n            indexes (int, optional): The band(s) to use. Band indexing starts\n                at 1. Defaults to None.\n            colormap (str, optional): The name of the colormap from `matplotlib`\n                to use when plotting a single band.\n                    See https://matplotlib.org/stable/gallery/color/colormap_reference.html.\n                    Default is greyscale.\n            vmin (float, optional): The minimum value to use when colormapping\n                the palette when plotting a single band. Defaults to None.\n            vmax (float, optional): The maximum value to use when colormapping\n                the palette when plotting a single band. Defaults to None.\n            nodata (float, optional): The value from the band to use to\n                interpret as not valid data. Defaults to None.\n            attribution (str, optional): Attribution for the source raster. This\n                defaults to a message about it being a local file.. Defaults to None.\n            layer_name (str, optional): The layer name to use. Defaults to 'EMIT'.\n            zoom_to_layer (bool, optional): Whether to zoom to the extent of the\n                layer. Defaults to True.\n            visible (bool, optional): Whether the layer is visible. Defaults to\n                True.\n            array_args (dict, optional): Additional arguments to pass to\n                `array_to_memory_file` when reading the raster. Defaults to {}.\n        \"\"\"\n\n        if array_args is None:\n            array_args = {}\n\n        xds = None\n        if isinstance(source, str):\n\n            xds = read_emit(source)\n            source = emit_to_image(xds, wavelengths=wavelengths)\n        elif isinstance(source, xr.Dataset):\n            xds = source\n            source = emit_to_image(xds, wavelengths=wavelengths)\n\n        self.add_raster(\n            source,\n            indexes=indexes,\n            colormap=colormap,\n            vmin=vmin,\n            vmax=vmax,\n            nodata=nodata,\n            attribution=attribution,\n            layer_name=layer_name,\n            zoom_to_layer=zoom_to_layer,\n            visible=visible,\n            array_args=array_args,\n            **kwargs,\n        )\n\n        self.cog_layer_dict[layer_name][\"xds\"] = xds\n        self.cog_layer_dict[layer_name][\"hyper\"] = \"EMIT\"\n        self._update_band_names(layer_name, wavelengths)\n\n    def add_pace(\n        self,\n        source,\n        wavelengths=None,\n        indexes=None,\n        colormap=\"jet\",\n        vmin=None,\n        vmax=None,\n        nodata=np.nan,\n        attribution=None,\n        layer_name=\"PACE\",\n        zoom_to_layer=True,\n        visible=True,\n        method=\"nearest\",\n        gridded=False,\n        array_args=None,\n        **kwargs,\n    ):\n        \"\"\"Add a PACE dataset to the map.\n            If you are using this function in JupyterHub on a remote server\n                (e.g., Binder, Microsoft Planetary Computer) and\n            if the raster does not render properly, try installing\n                jupyter-server-proxy using `pip install jupyter-server-proxy`,\n            then running the following code before calling this function. For\n                more info, see https://bit.ly/3JbmF93.\n\n            import os\n            os.environ['LOCALTILESERVER_CLIENT_PREFIX'] = 'proxy/{port}'\n\n        Args:\n            source (str): The path to the GeoTIFF file or the URL of the Cloud\n                Optimized GeoTIFF.\n            indexes (int, optional): The band(s) to use. Band indexing starts\n                at 1. Defaults to None.\n            colormap (str, optional): The name of the colormap from `matplotlib`\n                to use when plotting a single band. See\n                    https://matplotlib.org/stable/gallery/color/colormap_reference.html.\n                    Default is greyscale.\n            vmin (float, optional): The minimum value to use when colormapping\n                the palette when plotting a single band. Defaults to None.\n            vmax (float, optional): The maximum value to use when colormapping\n                the palette when plotting a single band. Defaults to None.\n            nodata (float, optional): The value from the band to use to interpret\n                as not valid data. Defaults to None.\n            attribution (str, optional): Attribution for the source raster. This\n                defaults to a message about it being a local file.. Defaults to None.\n            layer_name (str, optional): The layer name to use. Defaults to 'EMIT'.\n            zoom_to_layer (bool, optional): Whether to zoom to the extent of the\n                layer. Defaults to True.\n            visible (bool, optional): Whether the layer is visible. Defaults to True.\n            array_args (dict, optional): Additional arguments to pass to\n                `array_to_memory_file` when reading the raster. Defaults to {}.\n        \"\"\"\n\n        if array_args is None:\n            array_args = {}\n\n        if isinstance(source, str):\n\n            source = read_pace(source)\n\n        try:\n            image = pace_to_image(\n                source, wavelengths=wavelengths, method=method, gridded=gridded\n            )\n\n            if isinstance(wavelengths, list) and len(wavelengths) &gt; 1:\n                colormap = None\n\n            self.add_raster(\n                image,\n                indexes=indexes,\n                colormap=colormap,\n                vmin=vmin,\n                vmax=vmax,\n                nodata=nodata,\n                attribution=attribution,\n                layer_name=layer_name,\n                zoom_to_layer=zoom_to_layer,\n                visible=visible,\n                array_args=array_args,\n                **kwargs,\n            )\n\n            self.cog_layer_dict[layer_name][\"xds\"] = source\n            self.cog_layer_dict[layer_name][\"hyper\"] = \"PACE\"\n            self._update_band_names(layer_name, wavelengths)\n        except Exception as e:\n            print(e)\n\n    def add_desis(\n        self,\n        source,\n        wavelengths=[900, 650, 525],\n        indexes=None,\n        colormap=\"jet\",\n        vmin=None,\n        vmax=None,\n        nodata=np.nan,\n        attribution=None,\n        layer_name=\"DESIS\",\n        zoom_to_layer=True,\n        visible=True,\n        method=\"nearest\",\n        array_args=None,\n        **kwargs,\n    ):\n        \"\"\"Add a DESIS dataset to the map.\n            If you are using this function in JupyterHub on a remote server\n                (e.g., Binder, Microsoft Planetary Computer) and\n            if the raster does not render properly, try installing\n                jupyter-server-proxy using `pip install jupyter-server-proxy`,\n            then running the following code before calling this function. For\n                more info, see https://bit.ly/3JbmF93.\n\n            import os\n            os.environ['LOCALTILESERVER_CLIENT_PREFIX'] = 'proxy/{port}'\n\n        Args:\n            source (str): The path to the GeoTIFF file or the URL of the Cloud\n                Optimized GeoTIFF.\n            indexes (int, optional): The band(s) to use. Band indexing starts\n                at 1. Defaults to None.\n            colormap (str, optional): The name of the colormap from `matplotlib`\n                to use when plotting a single band. See\n                https://matplotlib.org/stable/gallery/color/colormap_reference.html.\n                Default is 'jet'.\n            vmin (float, optional): The minimum value to use when colormapping\n                the palette when plotting a single band. Defaults to None.\n            vmax (float, optional): The maximum value to use when colormapping\n                the palette when plotting a single band. Defaults to None.\n            nodata (float, optional): The value from the band to use to interpret\n                as not valid data. Defaults to None.\n            attribution (str, optional): Attribution for the source raster. This\n                defaults to a message about it being a local file.. Defaults to None.\n            layer_name (str, optional): The layer name to use. Defaults to 'EMIT'.\n            zoom_to_layer (bool, optional): Whether to zoom to the extent of the\n                layer. Defaults to True.\n            visible (bool, optional): Whether the layer is visible. Defaults to True.\n            array_args (dict, optional): Additional arguments to pass to\n                `array_to_memory_file` when reading the raster. Defaults to {}.\n        \"\"\"\n        if array_args is None:\n            array_args = {}\n\n        if isinstance(source, str):\n\n            source = read_desis(source)\n\n        image = desis_to_image(source, wavelengths=wavelengths, method=method)\n\n        if isinstance(wavelengths, list) and len(wavelengths) &gt; 1:\n            colormap = None\n\n        if isinstance(wavelengths, int):\n            wavelengths = [wavelengths]\n\n        if indexes is None:\n            if isinstance(wavelengths, list) and len(wavelengths) == 1:\n                indexes = [1]\n            else:\n                indexes = [1, 2, 3]\n\n        self.add_raster(\n            image,\n            indexes=indexes,\n            colormap=colormap,\n            vmin=vmin,\n            vmax=vmax,\n            nodata=nodata,\n            attribution=attribution,\n            layer_name=layer_name,\n            zoom_to_layer=zoom_to_layer,\n            visible=visible,\n            array_args=array_args,\n            **kwargs,\n        )\n\n        self.cog_layer_dict[layer_name][\"xds\"] = source\n        self.cog_layer_dict[layer_name][\"hyper\"] = \"DESIS\"\n        self._update_band_names(layer_name, wavelengths)\n\n    def add_neon(\n        self,\n        source,\n        wavelengths=None,\n        indexes=None,\n        colormap=None,\n        vmin=0,\n        vmax=0.5,\n        nodata=np.nan,\n        attribution=None,\n        layer_name=\"NEON\",\n        zoom_to_layer=True,\n        visible=True,\n        array_args=None,\n        method=\"nearest\",\n        **kwargs,\n    ):\n        \"\"\"Add an NEON AOP dataset to the map.\n            If you are using this function in JupyterHub on a remote server\n                (e.g., Binder, Microsoft Planetary Computer) and\n            if the raster does not render properly, try installing\n                jupyter-server-proxy using `pip install jupyter-server-proxy`,\n            then running the following code before calling this function. For\n                more info, see https://bit.ly/3JbmF93.\n\n            import os\n            os.environ['LOCALTILESERVER_CLIENT_PREFIX'] = 'proxy/{port}'\n\n        Args:\n            source (str): The path to the NEON AOP HDF5 file.\n            indexes (int, optional): The band(s) to use. Band indexing starts\n                at 1. Defaults to None.\n            colormap (str, optional): The name of the colormap from `matplotlib`\n                to use when plotting a single band. See\n                    https://matplotlib.org/stable/gallery/color/colormap_reference.html.\n                    Default is greyscale.\n            vmin (float, optional): The minimum value to use when colormapping\n                the palette when plotting a single band. Defaults to 0.\n            vmax (float, optional): The maximum value to use when colormapping\n                the palette when plotting a single band. Defaults to 0.5.\n            nodata (float, optional): The value from the band to use to\n                interpret as not valid data. Defaults to np.nan.\n            attribution (str, optional): Attribution for the source raster. This\n                defaults to a message about it being a local file.. Defaults to None.\n            layer_name (str, optional): The layer name to use. Defaults to 'NEON'.\n            zoom_to_layer (bool, optional): Whether to zoom to the extent of the\n                layer. Defaults to True.\n            visible (bool, optional): Whether the layer is visible. Defaults\n                to True.\n            array_args (dict, optional): Additional arguments to pass to\n                `array_to_memory_file` when reading the raster. Defaults to {}.\n            method (str, optional): The method to use for data interpolation.\n                Defaults to \"nearest\".\n        \"\"\"\n\n        if array_args is None:\n            array_args = {}\n        xds = None\n        if isinstance(source, str):\n\n            xds = read_neon(source)\n            source = neon_to_image(xds, wavelengths=wavelengths, method=method)\n        elif isinstance(source, xr.Dataset):\n            xds = source\n            source = neon_to_image(xds, wavelengths=wavelengths, method=method)\n\n        self.add_raster(\n            source,\n            indexes=indexes,\n            colormap=colormap,\n            vmin=vmin,\n            vmax=vmax,\n            nodata=nodata,\n            attribution=attribution,\n            layer_name=layer_name,\n            zoom_to_layer=zoom_to_layer,\n            visible=visible,\n            array_args=array_args,\n            **kwargs,\n        )\n\n        self.cog_layer_dict[layer_name][\"xds\"] = xds\n        self.cog_layer_dict[layer_name][\"hyper\"] = \"NEON\"\n        self._update_band_names(layer_name, wavelengths)\n\n    def add_aviris(\n        self,\n        source,\n        wavelengths=None,\n        indexes=None,\n        colormap=None,\n        vmin=0,\n        vmax=0.5,\n        nodata=np.nan,\n        attribution=None,\n        layer_name=\"AVIRIS\",\n        zoom_to_layer=True,\n        visible=True,\n        array_args=None,\n        method=\"nearest\",\n        **kwargs,\n    ):\n        \"\"\"Add an AVIRIS dataset to the map.\n            If you are using this function in JupyterHub on a remote server\n                (e.g., Binder, Microsoft Planetary Computer) and\n            if the raster does not render properly, try installing\n                jupyter-server-proxy using `pip install jupyter-server-proxy`,\n            then running the following code before calling this function. For\n                more info, see https://bit.ly/3JbmF93.\n\n            import os\n            os.environ['LOCALTILESERVER_CLIENT_PREFIX'] = 'proxy/{port}'\n\n        Args:\n            source (str): The path to the AVIRIS file.\n            indexes (int, optional): The band(s) to use. Band indexing starts\n                at 1. Defaults to None.\n            colormap (str, optional): The name of the colormap from `matplotlib`\n                to use when plotting a single band. See\n                    https://matplotlib.org/stable/gallery/color/colormap_reference.html.\n                    Default is greyscale.\n            vmin (float, optional): The minimum value to use when colormapping\n                the palette when plotting a single band. Defaults to 0.\n            vmax (float, optional): The maximum value to use when colormapping\n                the palette when plotting a single band. Defaults to 0.5.\n            nodata (float, optional): The value from the band to use to\n                interpret as not valid data. Defaults to np.nan.\n            attribution (str, optional): Attribution for the source raster. This\n                defaults to a message about it being a local file.. Defaults to None.\n            layer_name (str, optional): The layer name to use. Defaults to 'NEON'.\n            zoom_to_layer (bool, optional): Whether to zoom to the extent of the\n                layer. Defaults to True.\n            visible (bool, optional): Whether the layer is visible. Defaults\n                to True.\n            array_args (dict, optional): Additional arguments to pass to\n                `array_to_memory_file` when reading the raster. Defaults to {}.\n            method (str, optional): The method to use for data interpolation.\n                Defaults to \"nearest\".\n        \"\"\"\n        if array_args is None:\n            array_args = {}\n\n        xds = None\n        if isinstance(source, str):\n\n            xds = read_aviris(source)\n            source = neon_to_image(xds, wavelengths=wavelengths, method=method)\n        elif isinstance(source, xr.Dataset):\n            xds = source\n            source = aviris_to_image(xds, wavelengths=wavelengths, method=method)\n\n        self.add_raster(\n            source,\n            indexes=indexes,\n            colormap=colormap,\n            vmin=vmin,\n            vmax=vmax,\n            nodata=nodata,\n            attribution=attribution,\n            layer_name=layer_name,\n            zoom_to_layer=zoom_to_layer,\n            visible=visible,\n            array_args=array_args,\n            **kwargs,\n        )\n\n        self.cog_layer_dict[layer_name][\"xds\"] = xds\n        self.cog_layer_dict[layer_name][\"hyper\"] = \"AVIRIS\"\n        self._update_band_names(layer_name, wavelengths)\n\n    def add_hyper(self, xds, dtype, wvl_indexes=None, **kwargs):\n        \"\"\"Add a hyperspectral dataset to the map.\n\n        Args:\n            xds (str): The Xarray dataset containing the hyperspectral data.\n            dtype (str): The type of the hyperspectral dataset. Can be one of\n                \"EMIT\", \"PACE\", \"DESIS\", \"NEON\", \"AVIRIS\".\n            **kwargs: Additional keyword arguments to pass to the corresponding\n                add function.\n        \"\"\"\n\n        if wvl_indexes is not None:\n            if dtype == \"XARRAY\":\n                kwargs[\"indexes\"] = [i + 1 for i in wvl_indexes]\n            else:\n\n                kwargs[\"wavelengths\"] = (\n                    xds.isel(wavelength=wvl_indexes)\n                    .coords[\"wavelength\"]\n                    .values.tolist()\n                )\n\n        if dtype == \"EMIT\":\n            self.add_emit(xds, **kwargs)\n        elif dtype == \"PACE\":\n            self.add_pace(xds, **kwargs)\n        elif dtype == \"DESIS\":\n            self.add_desis(xds, **kwargs)\n        elif dtype == \"NEON\":\n            self.add_neon(xds, **kwargs)\n        elif dtype == \"AVIRIS\":\n            self.add_aviris(xds, **kwargs)\n        elif dtype == \"XARRAY\":\n            kwargs.pop(\"wavelengths\", None)\n            self.add_dataset(xds, **kwargs)\n\n    def set_plot_options(\n        self,\n        add_marker_cluster=False,\n        plot_type=None,\n        overlay=False,\n        position=\"bottomright\",\n        min_width=None,\n        max_width=None,\n        min_height=None,\n        max_height=None,\n        **kwargs,\n    ):\n        \"\"\"Sets plotting options.\n\n        Args:\n            add_marker_cluster (bool, optional): Whether to add a marker cluster.\n                Defaults to False.\n            sample_scale (float, optional):  A nominal scale in meters of the\n                projection to sample in . Defaults to None.\n            plot_type (str, optional): The plot type can be one of \"None\", \"bar\",\n                \"scatter\" or \"hist\". Defaults to None.\n            overlay (bool, optional): Whether to overlay plotted lines on the\n                figure. Defaults to False.\n            position (str, optional): Position of the control, can be\n                \u2018bottomleft\u2019, \u2018bottomright\u2019, \u2018topleft\u2019, or \u2018topright\u2019. Defaults\n                to 'bottomright'.\n            min_width (int, optional): Min width of the widget (in pixels), if\n                None it will respect the content size. Defaults to None.\n            max_width (int, optional): Max width of the widget (in pixels), if\n                None it will respect the content size. Defaults to None.\n            min_height (int, optional): Min height of the widget (in pixels), if\n                None it will respect the content size. Defaults to None.\n            max_height (int, optional): Max height of the widget (in pixels), if\n                None it will respect the content size. Defaults to None.\n\n        \"\"\"\n        plot_options_dict = {}\n        plot_options_dict[\"add_marker_cluster\"] = add_marker_cluster\n        plot_options_dict[\"plot_type\"] = plot_type\n        plot_options_dict[\"overlay\"] = overlay\n        plot_options_dict[\"position\"] = position\n        plot_options_dict[\"min_width\"] = min_width\n        plot_options_dict[\"max_width\"] = max_width\n        plot_options_dict[\"min_height\"] = min_height\n        plot_options_dict[\"max_height\"] = max_height\n\n        for key in kwargs:\n            plot_options_dict[key] = kwargs[key]\n\n        self._plot_options = plot_options_dict\n\n        if not hasattr(self, \"_plot_marker_cluster\"):\n            self._plot_marker_cluster = ipyleaflet.MarkerCluster(name=\"Marker Cluster\")\n\n        if add_marker_cluster and (self._plot_marker_cluster not in self.layers):\n            self.add(self._plot_marker_cluster)\n\n    def spectral_to_df(self, **kwargs):\n        \"\"\"Converts the spectral data to a pandas DataFrame.\n\n        Returns:\n            pd.DataFrame: The spectral data as a pandas DataFrame.\n        \"\"\"\n        import pandas as pd\n\n        df = pd.DataFrame(self._spectral_data, **kwargs)\n        return df\n\n    def spectral_to_gdf(self, **kwargs):\n        \"\"\"Converts the spectral data to a GeoPandas GeoDataFrame.\n\n        Returns:\n            gpd.DataFrame: The spectral data as a pandas DataFrame.\n        \"\"\"\n        import geopandas as gpd\n        from shapely.geometry import Point\n\n        df = self.spectral_to_df()\n\n        if len(df) == 0:\n            return df\n\n        # Step 1: Extract the coordinates from the columns\n        if \"wavelength\" in df.columns:\n            df = df.rename(columns={\"wavelength\": \"latlon\"})\n        elif \"wavelengths\" in df.columns:\n            df = df.rename(columns={\"wavelengths\": \"latlon\"})\n        coordinates = [col.strip(\"()\").split() for col in df.columns[1:]]\n        coords = [(float(lat), float(lon)) for lat, lon in coordinates]\n\n        # Step 2: Create Point geometries for each coordinate\n        points = [Point(lon, lat) for lat, lon in coords]\n\n        # Step 3: Create a GeoDataFrame\n        df_transposed = df.set_index(\"latlon\").T\n\n        # Convert the column names to strings to ensure compatibility with GeoJSON\n        df_transposed.columns = df_transposed.columns.astype(str)\n\n        # Create the GeoDataFrame\n        gdf = gpd.GeoDataFrame(df_transposed, geometry=points, **kwargs)\n\n        # Set the coordinate reference system (CRS)\n        gdf = gdf.set_geometry(\"geometry\").set_crs(\"EPSG:4326\")\n\n        return gdf\n\n    def spectral_to_csv(self, filename, index=True, **kwargs):\n        \"\"\"Saves the spectral data to a CSV file.\n\n        Args:\n            filename (str): The output CSV file.\n            index (bool, optional): Whether to write the index. Defaults to True.\n        \"\"\"\n        df = self.spectral_to_df()\n        df = df.rename_axis(\"band\")\n        df.to_csv(filename, index=index, **kwargs)\n\n    def _update_band_names(self, layer_name, wavelengths):\n\n        # Function to find the nearest indices\n        def find_nearest_indices(\n            dataarray, selected_wavelengths, dim_name=\"wavelength\"\n        ):\n            indices = []\n            for wavelength in selected_wavelengths:\n                if dim_name == \"band\":\n                    nearest_wavelength = dataarray.sel(\n                        band=wavelength, method=\"nearest\"\n                    )\n                else:\n                    nearest_wavelength = dataarray.sel(\n                        wavelength=wavelength, method=\"nearest\"\n                    )\n                nearest_wavelength_index = nearest_wavelength[dim_name].item()\n                nearest_index = (\n                    dataarray[dim_name].values.tolist().index(nearest_wavelength_index)\n                )\n                indices.append(nearest_index + 1)\n            return indices\n\n        if \"xds\" in self.cog_layer_dict[layer_name]:\n            xds = self.cog_layer_dict[layer_name][\"xds\"]\n            dim_name = \"wavelength\"\n\n            if \"band\" in xds:\n                dim_name = \"band\"\n\n            band_count = xds.sizes[dim_name]\n            band_names = [\"b\" + str(band) for band in range(1, band_count + 1)]\n            self.cog_layer_dict[layer_name][\"band_names\"] = band_names\n\n            try:\n                indexes = find_nearest_indices(xds, wavelengths, dim_name=dim_name)\n                vis_bands = [\"b\" + str(index) for index in indexes]\n                self.cog_layer_dict[layer_name][\"indexes\"] = indexes\n                self.cog_layer_dict[layer_name][\"vis_bands\"] = vis_bands\n            except Exception as e:\n                print(e)\n\n    def add_field_data(\n        self,\n        data: Union[str],\n        x_col: str = \"wavelength\",\n        y_col_prefix: str = \"(\",\n        x_label: str = \"Wavelengths (nm)\",\n        y_label: str = \"Reflectance\",\n        use_marker_cluster: bool = True,\n        min_width: int = 400,\n        max_width: int = 600,\n        min_height: int = 200,\n        max_height: int = 250,\n        layer_name: str = \"Marker Cluster\",\n        **kwargs,\n    ):\n        \"\"\"\n        Displays field data on a map with interactive markers and popups showing time series data.\n\n        Args:\n            data (Union[str, pd.DataFrame]): Path to the CSV file or a pandas DataFrame containing the data.\n            x_col (str): Column name to use for the x-axis of the charts. Default is \"wavelength\".\n            y_col_prefix (str): Prefix to identify the columns that contain the location-specific data. Default is \"(\".\n            x_label (str): Label for the x-axis of the charts. Default is \"Wavelengths (nm)\".\n            y_label (str): Label for the y-axis of the charts. Default is \"Reflectance\".\n            use_marker_cluster (bool): Whether to use marker clustering. Default is True.\n            min_width (int): Minimum width of the popup. Default is 400.\n            max_width (int): Maximum width of the popup. Default is 600.\n            min_height (int): Minimum height of the popup. Default is 200.\n            max_height (int): Maximum height of the popup. Default is 250.\n            layer_name (str): Name of the marker cluster layer. Default is \"Marker Cluster\".\n\n        Returns:\n            Map: An ipyleaflet Map with the added markers and popups.\n        \"\"\"\n        show_field_data(\n            data,\n            x_col,\n            y_col_prefix,\n            x_label=x_label,\n            y_label=y_label,\n            use_marker_cluster=use_marker_cluster,\n            min_width=min_width,\n            max_width=max_width,\n            min_height=min_height,\n            max_height=max_height,\n            layer_name=layer_name,\n            m=self,\n            **kwargs,\n        )\n</code></pre>"},{"location":"hypercoast/#hypercoast.hypercoast.Map.__init__","title":"<code>__init__(self, **kwargs)</code>  <code>special</code>","text":"<p>Initializes a new instance of the Map class.</p> <p>Parameters:</p> Name Type Description Default <code>**kwargs</code> <p>Arbitrary keyword arguments that are passed to the parent class's constructor.</p> <code>{}</code> Source code in <code>hypercoast/hypercoast.py</code> <pre><code>def __init__(self, **kwargs):\n    \"\"\"\n    Initializes a new instance of the Map class.\n\n    Args:\n        **kwargs: Arbitrary keyword arguments that are passed to the parent\n            class's constructor.\n    \"\"\"\n    super().__init__(**kwargs)\n    self._spectral_data = {}\n    self._plot_options = None\n    self._plot_marker_cluster = ipyleaflet.MarkerCluster(name=\"Marker Cluster\")\n</code></pre>"},{"location":"hypercoast/#hypercoast.hypercoast.Map.add","title":"<code>add(self, obj, position='topright', xlim=None, ylim=None, **kwargs)</code>","text":"<p>Add a layer to the map.</p> <p>Parameters:</p> Name Type Description Default <code>obj</code> <code>str or object</code> <p>The name of the layer or a layer object.</p> required <code>position</code> <code>str</code> <p>The position of the layer widget. Can be 'topright', 'topleft', 'bottomright', or 'bottomleft'. Defaults to 'topright'.</p> <code>'topright'</code> <code>xlim</code> <code>tuple</code> <p>The x-axis limits of the plot. Defaults to None.</p> <code>None</code> <code>ylim</code> <code>tuple</code> <p>The y-axis limits of the plot. Defaults to None.</p> <code>None</code> <code>**kwargs</code> <p>Arbitrary keyword arguments that are passed to the parent class's add_layer method.</p> <code>{}</code> Source code in <code>hypercoast/hypercoast.py</code> <pre><code>def add(self, obj, position=\"topright\", xlim=None, ylim=None, **kwargs):\n    \"\"\"Add a layer to the map.\n\n    Args:\n        obj (str or object): The name of the layer or a layer object.\n        position (str, optional): The position of the layer widget. Can be\n            'topright', 'topleft', 'bottomright', or 'bottomleft'. Defaults\n            to 'topright'.\n        xlim (tuple, optional): The x-axis limits of the plot. Defaults to None.\n        ylim (tuple, optional): The y-axis limits of the plot. Defaults to None.\n        **kwargs: Arbitrary keyword arguments that are passed to the parent\n            class's add_layer method.\n    \"\"\"\n\n    if isinstance(obj, str):\n        if obj == \"spectral\":\n\n            SpectralWidget(self, position=position, xlim=xlim, ylim=ylim, **kwargs)\n            self.set_plot_options(add_marker_cluster=True)\n        else:\n            super().add(obj, **kwargs)\n\n    else:\n        super().add(obj, **kwargs)\n</code></pre>"},{"location":"hypercoast/#hypercoast.hypercoast.Map.add_aviris","title":"<code>add_aviris(self, source, wavelengths=None, indexes=None, colormap=None, vmin=0, vmax=0.5, nodata=nan, attribution=None, layer_name='AVIRIS', zoom_to_layer=True, visible=True, array_args=None, method='nearest', **kwargs)</code>","text":"<p>Add an AVIRIS dataset to the map.     If you are using this function in JupyterHub on a remote server         (e.g., Binder, Microsoft Planetary Computer) and     if the raster does not render properly, try installing         jupyter-server-proxy using <code>pip install jupyter-server-proxy</code>,     then running the following code before calling this function. For         more info, see https://bit.ly/3JbmF93.</p> <pre><code>import os\nos.environ['LOCALTILESERVER_CLIENT_PREFIX'] = 'proxy/{port}'\n</code></pre> <p>Parameters:</p> Name Type Description Default <code>source</code> <code>str</code> <p>The path to the AVIRIS file.</p> required <code>indexes</code> <code>int</code> <p>The band(s) to use. Band indexing starts at 1. Defaults to None.</p> <code>None</code> <code>colormap</code> <code>str</code> <p>The name of the colormap from <code>matplotlib</code> to use when plotting a single band. See     https://matplotlib.org/stable/gallery/color/colormap_reference.html.     Default is greyscale.</p> <code>None</code> <code>vmin</code> <code>float</code> <p>The minimum value to use when colormapping the palette when plotting a single band. Defaults to 0.</p> <code>0</code> <code>vmax</code> <code>float</code> <p>The maximum value to use when colormapping the palette when plotting a single band. Defaults to 0.5.</p> <code>0.5</code> <code>nodata</code> <code>float</code> <p>The value from the band to use to interpret as not valid data. Defaults to np.nan.</p> <code>nan</code> <code>attribution</code> <code>str</code> <p>Attribution for the source raster. This defaults to a message about it being a local file.. Defaults to None.</p> <code>None</code> <code>layer_name</code> <code>str</code> <p>The layer name to use. Defaults to 'NEON'.</p> <code>'AVIRIS'</code> <code>zoom_to_layer</code> <code>bool</code> <p>Whether to zoom to the extent of the layer. Defaults to True.</p> <code>True</code> <code>visible</code> <code>bool</code> <p>Whether the layer is visible. Defaults to True.</p> <code>True</code> <code>array_args</code> <code>dict</code> <p>Additional arguments to pass to <code>array_to_memory_file</code> when reading the raster. Defaults to {}.</p> <code>None</code> <code>method</code> <code>str</code> <p>The method to use for data interpolation. Defaults to \"nearest\".</p> <code>'nearest'</code> Source code in <code>hypercoast/hypercoast.py</code> <pre><code>def add_aviris(\n    self,\n    source,\n    wavelengths=None,\n    indexes=None,\n    colormap=None,\n    vmin=0,\n    vmax=0.5,\n    nodata=np.nan,\n    attribution=None,\n    layer_name=\"AVIRIS\",\n    zoom_to_layer=True,\n    visible=True,\n    array_args=None,\n    method=\"nearest\",\n    **kwargs,\n):\n    \"\"\"Add an AVIRIS dataset to the map.\n        If you are using this function in JupyterHub on a remote server\n            (e.g., Binder, Microsoft Planetary Computer) and\n        if the raster does not render properly, try installing\n            jupyter-server-proxy using `pip install jupyter-server-proxy`,\n        then running the following code before calling this function. For\n            more info, see https://bit.ly/3JbmF93.\n\n        import os\n        os.environ['LOCALTILESERVER_CLIENT_PREFIX'] = 'proxy/{port}'\n\n    Args:\n        source (str): The path to the AVIRIS file.\n        indexes (int, optional): The band(s) to use. Band indexing starts\n            at 1. Defaults to None.\n        colormap (str, optional): The name of the colormap from `matplotlib`\n            to use when plotting a single band. See\n                https://matplotlib.org/stable/gallery/color/colormap_reference.html.\n                Default is greyscale.\n        vmin (float, optional): The minimum value to use when colormapping\n            the palette when plotting a single band. Defaults to 0.\n        vmax (float, optional): The maximum value to use when colormapping\n            the palette when plotting a single band. Defaults to 0.5.\n        nodata (float, optional): The value from the band to use to\n            interpret as not valid data. Defaults to np.nan.\n        attribution (str, optional): Attribution for the source raster. This\n            defaults to a message about it being a local file.. Defaults to None.\n        layer_name (str, optional): The layer name to use. Defaults to 'NEON'.\n        zoom_to_layer (bool, optional): Whether to zoom to the extent of the\n            layer. Defaults to True.\n        visible (bool, optional): Whether the layer is visible. Defaults\n            to True.\n        array_args (dict, optional): Additional arguments to pass to\n            `array_to_memory_file` when reading the raster. Defaults to {}.\n        method (str, optional): The method to use for data interpolation.\n            Defaults to \"nearest\".\n    \"\"\"\n    if array_args is None:\n        array_args = {}\n\n    xds = None\n    if isinstance(source, str):\n\n        xds = read_aviris(source)\n        source = neon_to_image(xds, wavelengths=wavelengths, method=method)\n    elif isinstance(source, xr.Dataset):\n        xds = source\n        source = aviris_to_image(xds, wavelengths=wavelengths, method=method)\n\n    self.add_raster(\n        source,\n        indexes=indexes,\n        colormap=colormap,\n        vmin=vmin,\n        vmax=vmax,\n        nodata=nodata,\n        attribution=attribution,\n        layer_name=layer_name,\n        zoom_to_layer=zoom_to_layer,\n        visible=visible,\n        array_args=array_args,\n        **kwargs,\n    )\n\n    self.cog_layer_dict[layer_name][\"xds\"] = xds\n    self.cog_layer_dict[layer_name][\"hyper\"] = \"AVIRIS\"\n    self._update_band_names(layer_name, wavelengths)\n</code></pre>"},{"location":"hypercoast/#hypercoast.hypercoast.Map.add_desis","title":"<code>add_desis(self, source, wavelengths=[900, 650, 525], indexes=None, colormap='jet', vmin=None, vmax=None, nodata=nan, attribution=None, layer_name='DESIS', zoom_to_layer=True, visible=True, method='nearest', array_args=None, **kwargs)</code>","text":"<p>Add a DESIS dataset to the map.     If you are using this function in JupyterHub on a remote server         (e.g., Binder, Microsoft Planetary Computer) and     if the raster does not render properly, try installing         jupyter-server-proxy using <code>pip install jupyter-server-proxy</code>,     then running the following code before calling this function. For         more info, see https://bit.ly/3JbmF93.</p> <pre><code>import os\nos.environ['LOCALTILESERVER_CLIENT_PREFIX'] = 'proxy/{port}'\n</code></pre> <p>Parameters:</p> Name Type Description Default <code>source</code> <code>str</code> <p>The path to the GeoTIFF file or the URL of the Cloud Optimized GeoTIFF.</p> required <code>indexes</code> <code>int</code> <p>The band(s) to use. Band indexing starts at 1. Defaults to None.</p> <code>None</code> <code>colormap</code> <code>str</code> <p>The name of the colormap from <code>matplotlib</code> to use when plotting a single band. See https://matplotlib.org/stable/gallery/color/colormap_reference.html. Default is 'jet'.</p> <code>'jet'</code> <code>vmin</code> <code>float</code> <p>The minimum value to use when colormapping the palette when plotting a single band. Defaults to None.</p> <code>None</code> <code>vmax</code> <code>float</code> <p>The maximum value to use when colormapping the palette when plotting a single band. Defaults to None.</p> <code>None</code> <code>nodata</code> <code>float</code> <p>The value from the band to use to interpret as not valid data. Defaults to None.</p> <code>nan</code> <code>attribution</code> <code>str</code> <p>Attribution for the source raster. This defaults to a message about it being a local file.. Defaults to None.</p> <code>None</code> <code>layer_name</code> <code>str</code> <p>The layer name to use. Defaults to 'EMIT'.</p> <code>'DESIS'</code> <code>zoom_to_layer</code> <code>bool</code> <p>Whether to zoom to the extent of the layer. Defaults to True.</p> <code>True</code> <code>visible</code> <code>bool</code> <p>Whether the layer is visible. Defaults to True.</p> <code>True</code> <code>array_args</code> <code>dict</code> <p>Additional arguments to pass to <code>array_to_memory_file</code> when reading the raster. Defaults to {}.</p> <code>None</code> Source code in <code>hypercoast/hypercoast.py</code> <pre><code>def add_desis(\n    self,\n    source,\n    wavelengths=[900, 650, 525],\n    indexes=None,\n    colormap=\"jet\",\n    vmin=None,\n    vmax=None,\n    nodata=np.nan,\n    attribution=None,\n    layer_name=\"DESIS\",\n    zoom_to_layer=True,\n    visible=True,\n    method=\"nearest\",\n    array_args=None,\n    **kwargs,\n):\n    \"\"\"Add a DESIS dataset to the map.\n        If you are using this function in JupyterHub on a remote server\n            (e.g., Binder, Microsoft Planetary Computer) and\n        if the raster does not render properly, try installing\n            jupyter-server-proxy using `pip install jupyter-server-proxy`,\n        then running the following code before calling this function. For\n            more info, see https://bit.ly/3JbmF93.\n\n        import os\n        os.environ['LOCALTILESERVER_CLIENT_PREFIX'] = 'proxy/{port}'\n\n    Args:\n        source (str): The path to the GeoTIFF file or the URL of the Cloud\n            Optimized GeoTIFF.\n        indexes (int, optional): The band(s) to use. Band indexing starts\n            at 1. Defaults to None.\n        colormap (str, optional): The name of the colormap from `matplotlib`\n            to use when plotting a single band. See\n            https://matplotlib.org/stable/gallery/color/colormap_reference.html.\n            Default is 'jet'.\n        vmin (float, optional): The minimum value to use when colormapping\n            the palette when plotting a single band. Defaults to None.\n        vmax (float, optional): The maximum value to use when colormapping\n            the palette when plotting a single band. Defaults to None.\n        nodata (float, optional): The value from the band to use to interpret\n            as not valid data. Defaults to None.\n        attribution (str, optional): Attribution for the source raster. This\n            defaults to a message about it being a local file.. Defaults to None.\n        layer_name (str, optional): The layer name to use. Defaults to 'EMIT'.\n        zoom_to_layer (bool, optional): Whether to zoom to the extent of the\n            layer. Defaults to True.\n        visible (bool, optional): Whether the layer is visible. Defaults to True.\n        array_args (dict, optional): Additional arguments to pass to\n            `array_to_memory_file` when reading the raster. Defaults to {}.\n    \"\"\"\n    if array_args is None:\n        array_args = {}\n\n    if isinstance(source, str):\n\n        source = read_desis(source)\n\n    image = desis_to_image(source, wavelengths=wavelengths, method=method)\n\n    if isinstance(wavelengths, list) and len(wavelengths) &gt; 1:\n        colormap = None\n\n    if isinstance(wavelengths, int):\n        wavelengths = [wavelengths]\n\n    if indexes is None:\n        if isinstance(wavelengths, list) and len(wavelengths) == 1:\n            indexes = [1]\n        else:\n            indexes = [1, 2, 3]\n\n    self.add_raster(\n        image,\n        indexes=indexes,\n        colormap=colormap,\n        vmin=vmin,\n        vmax=vmax,\n        nodata=nodata,\n        attribution=attribution,\n        layer_name=layer_name,\n        zoom_to_layer=zoom_to_layer,\n        visible=visible,\n        array_args=array_args,\n        **kwargs,\n    )\n\n    self.cog_layer_dict[layer_name][\"xds\"] = source\n    self.cog_layer_dict[layer_name][\"hyper\"] = \"DESIS\"\n    self._update_band_names(layer_name, wavelengths)\n</code></pre>"},{"location":"hypercoast/#hypercoast.hypercoast.Map.add_emit","title":"<code>add_emit(self, source, wavelengths=None, indexes=None, colormap=None, vmin=None, vmax=None, nodata=nan, attribution=None, layer_name='EMIT', zoom_to_layer=True, visible=True, array_args=None, **kwargs)</code>","text":"<p>Add an EMIT dataset to the map.     If you are using this function in JupyterHub on a remote server         (e.g., Binder, Microsoft Planetary Computer) and     if the raster does not render properly, try installing         jupyter-server-proxy using <code>pip install jupyter-server-proxy</code>,     then running the following code before calling this function. For         more info, see https://bit.ly/3JbmF93.</p> <pre><code>import os\nos.environ['LOCALTILESERVER_CLIENT_PREFIX'] = 'proxy/{port}'\n</code></pre> <p>Parameters:</p> Name Type Description Default <code>source</code> <code>str</code> <p>The path to the GeoTIFF file or the URL of the Cloud Optimized GeoTIFF.</p> required <code>indexes</code> <code>int</code> <p>The band(s) to use. Band indexing starts at 1. Defaults to None.</p> <code>None</code> <code>colormap</code> <code>str</code> <p>The name of the colormap from <code>matplotlib</code> to use when plotting a single band.     See https://matplotlib.org/stable/gallery/color/colormap_reference.html.     Default is greyscale.</p> <code>None</code> <code>vmin</code> <code>float</code> <p>The minimum value to use when colormapping the palette when plotting a single band. Defaults to None.</p> <code>None</code> <code>vmax</code> <code>float</code> <p>The maximum value to use when colormapping the palette when plotting a single band. Defaults to None.</p> <code>None</code> <code>nodata</code> <code>float</code> <p>The value from the band to use to interpret as not valid data. Defaults to None.</p> <code>nan</code> <code>attribution</code> <code>str</code> <p>Attribution for the source raster. This defaults to a message about it being a local file.. Defaults to None.</p> <code>None</code> <code>layer_name</code> <code>str</code> <p>The layer name to use. Defaults to 'EMIT'.</p> <code>'EMIT'</code> <code>zoom_to_layer</code> <code>bool</code> <p>Whether to zoom to the extent of the layer. Defaults to True.</p> <code>True</code> <code>visible</code> <code>bool</code> <p>Whether the layer is visible. Defaults to True.</p> <code>True</code> <code>array_args</code> <code>dict</code> <p>Additional arguments to pass to <code>array_to_memory_file</code> when reading the raster. Defaults to {}.</p> <code>None</code> Source code in <code>hypercoast/hypercoast.py</code> <pre><code>def add_emit(\n    self,\n    source,\n    wavelengths=None,\n    indexes=None,\n    colormap=None,\n    vmin=None,\n    vmax=None,\n    nodata=np.nan,\n    attribution=None,\n    layer_name=\"EMIT\",\n    zoom_to_layer=True,\n    visible=True,\n    array_args=None,\n    **kwargs,\n):\n    \"\"\"Add an EMIT dataset to the map.\n        If you are using this function in JupyterHub on a remote server\n            (e.g., Binder, Microsoft Planetary Computer) and\n        if the raster does not render properly, try installing\n            jupyter-server-proxy using `pip install jupyter-server-proxy`,\n        then running the following code before calling this function. For\n            more info, see https://bit.ly/3JbmF93.\n\n        import os\n        os.environ['LOCALTILESERVER_CLIENT_PREFIX'] = 'proxy/{port}'\n\n    Args:\n        source (str): The path to the GeoTIFF file or the URL of the Cloud\n            Optimized GeoTIFF.\n        indexes (int, optional): The band(s) to use. Band indexing starts\n            at 1. Defaults to None.\n        colormap (str, optional): The name of the colormap from `matplotlib`\n            to use when plotting a single band.\n                See https://matplotlib.org/stable/gallery/color/colormap_reference.html.\n                Default is greyscale.\n        vmin (float, optional): The minimum value to use when colormapping\n            the palette when plotting a single band. Defaults to None.\n        vmax (float, optional): The maximum value to use when colormapping\n            the palette when plotting a single band. Defaults to None.\n        nodata (float, optional): The value from the band to use to\n            interpret as not valid data. Defaults to None.\n        attribution (str, optional): Attribution for the source raster. This\n            defaults to a message about it being a local file.. Defaults to None.\n        layer_name (str, optional): The layer name to use. Defaults to 'EMIT'.\n        zoom_to_layer (bool, optional): Whether to zoom to the extent of the\n            layer. Defaults to True.\n        visible (bool, optional): Whether the layer is visible. Defaults to\n            True.\n        array_args (dict, optional): Additional arguments to pass to\n            `array_to_memory_file` when reading the raster. Defaults to {}.\n    \"\"\"\n\n    if array_args is None:\n        array_args = {}\n\n    xds = None\n    if isinstance(source, str):\n\n        xds = read_emit(source)\n        source = emit_to_image(xds, wavelengths=wavelengths)\n    elif isinstance(source, xr.Dataset):\n        xds = source\n        source = emit_to_image(xds, wavelengths=wavelengths)\n\n    self.add_raster(\n        source,\n        indexes=indexes,\n        colormap=colormap,\n        vmin=vmin,\n        vmax=vmax,\n        nodata=nodata,\n        attribution=attribution,\n        layer_name=layer_name,\n        zoom_to_layer=zoom_to_layer,\n        visible=visible,\n        array_args=array_args,\n        **kwargs,\n    )\n\n    self.cog_layer_dict[layer_name][\"xds\"] = xds\n    self.cog_layer_dict[layer_name][\"hyper\"] = \"EMIT\"\n    self._update_band_names(layer_name, wavelengths)\n</code></pre>"},{"location":"hypercoast/#hypercoast.hypercoast.Map.add_field_data","title":"<code>add_field_data(self, data, x_col='wavelength', y_col_prefix='(', x_label='Wavelengths (nm)', y_label='Reflectance', use_marker_cluster=True, min_width=400, max_width=600, min_height=200, max_height=250, layer_name='Marker Cluster', **kwargs)</code>","text":"<p>Displays field data on a map with interactive markers and popups showing time series data.</p> <p>Parameters:</p> Name Type Description Default <code>data</code> <code>Union[str, pd.DataFrame]</code> <p>Path to the CSV file or a pandas DataFrame containing the data.</p> required <code>x_col</code> <code>str</code> <p>Column name to use for the x-axis of the charts. Default is \"wavelength\".</p> <code>'wavelength'</code> <code>y_col_prefix</code> <code>str</code> <p>Prefix to identify the columns that contain the location-specific data. Default is \"(\".</p> <code>'('</code> <code>x_label</code> <code>str</code> <p>Label for the x-axis of the charts. Default is \"Wavelengths (nm)\".</p> <code>'Wavelengths (nm)'</code> <code>y_label</code> <code>str</code> <p>Label for the y-axis of the charts. Default is \"Reflectance\".</p> <code>'Reflectance'</code> <code>use_marker_cluster</code> <code>bool</code> <p>Whether to use marker clustering. Default is True.</p> <code>True</code> <code>min_width</code> <code>int</code> <p>Minimum width of the popup. Default is 400.</p> <code>400</code> <code>max_width</code> <code>int</code> <p>Maximum width of the popup. Default is 600.</p> <code>600</code> <code>min_height</code> <code>int</code> <p>Minimum height of the popup. Default is 200.</p> <code>200</code> <code>max_height</code> <code>int</code> <p>Maximum height of the popup. Default is 250.</p> <code>250</code> <code>layer_name</code> <code>str</code> <p>Name of the marker cluster layer. Default is \"Marker Cluster\".</p> <code>'Marker Cluster'</code> <p>Returns:</p> Type Description <code>Map</code> <p>An ipyleaflet Map with the added markers and popups.</p> Source code in <code>hypercoast/hypercoast.py</code> <pre><code>def add_field_data(\n    self,\n    data: Union[str],\n    x_col: str = \"wavelength\",\n    y_col_prefix: str = \"(\",\n    x_label: str = \"Wavelengths (nm)\",\n    y_label: str = \"Reflectance\",\n    use_marker_cluster: bool = True,\n    min_width: int = 400,\n    max_width: int = 600,\n    min_height: int = 200,\n    max_height: int = 250,\n    layer_name: str = \"Marker Cluster\",\n    **kwargs,\n):\n    \"\"\"\n    Displays field data on a map with interactive markers and popups showing time series data.\n\n    Args:\n        data (Union[str, pd.DataFrame]): Path to the CSV file or a pandas DataFrame containing the data.\n        x_col (str): Column name to use for the x-axis of the charts. Default is \"wavelength\".\n        y_col_prefix (str): Prefix to identify the columns that contain the location-specific data. Default is \"(\".\n        x_label (str): Label for the x-axis of the charts. Default is \"Wavelengths (nm)\".\n        y_label (str): Label for the y-axis of the charts. Default is \"Reflectance\".\n        use_marker_cluster (bool): Whether to use marker clustering. Default is True.\n        min_width (int): Minimum width of the popup. Default is 400.\n        max_width (int): Maximum width of the popup. Default is 600.\n        min_height (int): Minimum height of the popup. Default is 200.\n        max_height (int): Maximum height of the popup. Default is 250.\n        layer_name (str): Name of the marker cluster layer. Default is \"Marker Cluster\".\n\n    Returns:\n        Map: An ipyleaflet Map with the added markers and popups.\n    \"\"\"\n    show_field_data(\n        data,\n        x_col,\n        y_col_prefix,\n        x_label=x_label,\n        y_label=y_label,\n        use_marker_cluster=use_marker_cluster,\n        min_width=min_width,\n        max_width=max_width,\n        min_height=min_height,\n        max_height=max_height,\n        layer_name=layer_name,\n        m=self,\n        **kwargs,\n    )\n</code></pre>"},{"location":"hypercoast/#hypercoast.hypercoast.Map.add_hyper","title":"<code>add_hyper(self, xds, dtype, wvl_indexes=None, **kwargs)</code>","text":"<p>Add a hyperspectral dataset to the map.</p> <p>Parameters:</p> Name Type Description Default <code>xds</code> <code>str</code> <p>The Xarray dataset containing the hyperspectral data.</p> required <code>dtype</code> <code>str</code> <p>The type of the hyperspectral dataset. Can be one of \"EMIT\", \"PACE\", \"DESIS\", \"NEON\", \"AVIRIS\".</p> required <code>**kwargs</code> <p>Additional keyword arguments to pass to the corresponding add function.</p> <code>{}</code> Source code in <code>hypercoast/hypercoast.py</code> <pre><code>def add_hyper(self, xds, dtype, wvl_indexes=None, **kwargs):\n    \"\"\"Add a hyperspectral dataset to the map.\n\n    Args:\n        xds (str): The Xarray dataset containing the hyperspectral data.\n        dtype (str): The type of the hyperspectral dataset. Can be one of\n            \"EMIT\", \"PACE\", \"DESIS\", \"NEON\", \"AVIRIS\".\n        **kwargs: Additional keyword arguments to pass to the corresponding\n            add function.\n    \"\"\"\n\n    if wvl_indexes is not None:\n        if dtype == \"XARRAY\":\n            kwargs[\"indexes\"] = [i + 1 for i in wvl_indexes]\n        else:\n\n            kwargs[\"wavelengths\"] = (\n                xds.isel(wavelength=wvl_indexes)\n                .coords[\"wavelength\"]\n                .values.tolist()\n            )\n\n    if dtype == \"EMIT\":\n        self.add_emit(xds, **kwargs)\n    elif dtype == \"PACE\":\n        self.add_pace(xds, **kwargs)\n    elif dtype == \"DESIS\":\n        self.add_desis(xds, **kwargs)\n    elif dtype == \"NEON\":\n        self.add_neon(xds, **kwargs)\n    elif dtype == \"AVIRIS\":\n        self.add_aviris(xds, **kwargs)\n    elif dtype == \"XARRAY\":\n        kwargs.pop(\"wavelengths\", None)\n        self.add_dataset(xds, **kwargs)\n</code></pre>"},{"location":"hypercoast/#hypercoast.hypercoast.Map.add_neon","title":"<code>add_neon(self, source, wavelengths=None, indexes=None, colormap=None, vmin=0, vmax=0.5, nodata=nan, attribution=None, layer_name='NEON', zoom_to_layer=True, visible=True, array_args=None, method='nearest', **kwargs)</code>","text":"<p>Add an NEON AOP dataset to the map.     If you are using this function in JupyterHub on a remote server         (e.g., Binder, Microsoft Planetary Computer) and     if the raster does not render properly, try installing         jupyter-server-proxy using <code>pip install jupyter-server-proxy</code>,     then running the following code before calling this function. For         more info, see https://bit.ly/3JbmF93.</p> <pre><code>import os\nos.environ['LOCALTILESERVER_CLIENT_PREFIX'] = 'proxy/{port}'\n</code></pre> <p>Parameters:</p> Name Type Description Default <code>source</code> <code>str</code> <p>The path to the NEON AOP HDF5 file.</p> required <code>indexes</code> <code>int</code> <p>The band(s) to use. Band indexing starts at 1. Defaults to None.</p> <code>None</code> <code>colormap</code> <code>str</code> <p>The name of the colormap from <code>matplotlib</code> to use when plotting a single band. See     https://matplotlib.org/stable/gallery/color/colormap_reference.html.     Default is greyscale.</p> <code>None</code> <code>vmin</code> <code>float</code> <p>The minimum value to use when colormapping the palette when plotting a single band. Defaults to 0.</p> <code>0</code> <code>vmax</code> <code>float</code> <p>The maximum value to use when colormapping the palette when plotting a single band. Defaults to 0.5.</p> <code>0.5</code> <code>nodata</code> <code>float</code> <p>The value from the band to use to interpret as not valid data. Defaults to np.nan.</p> <code>nan</code> <code>attribution</code> <code>str</code> <p>Attribution for the source raster. This defaults to a message about it being a local file.. Defaults to None.</p> <code>None</code> <code>layer_name</code> <code>str</code> <p>The layer name to use. Defaults to 'NEON'.</p> <code>'NEON'</code> <code>zoom_to_layer</code> <code>bool</code> <p>Whether to zoom to the extent of the layer. Defaults to True.</p> <code>True</code> <code>visible</code> <code>bool</code> <p>Whether the layer is visible. Defaults to True.</p> <code>True</code> <code>array_args</code> <code>dict</code> <p>Additional arguments to pass to <code>array_to_memory_file</code> when reading the raster. Defaults to {}.</p> <code>None</code> <code>method</code> <code>str</code> <p>The method to use for data interpolation. Defaults to \"nearest\".</p> <code>'nearest'</code> Source code in <code>hypercoast/hypercoast.py</code> <pre><code>def add_neon(\n    self,\n    source,\n    wavelengths=None,\n    indexes=None,\n    colormap=None,\n    vmin=0,\n    vmax=0.5,\n    nodata=np.nan,\n    attribution=None,\n    layer_name=\"NEON\",\n    zoom_to_layer=True,\n    visible=True,\n    array_args=None,\n    method=\"nearest\",\n    **kwargs,\n):\n    \"\"\"Add an NEON AOP dataset to the map.\n        If you are using this function in JupyterHub on a remote server\n            (e.g., Binder, Microsoft Planetary Computer) and\n        if the raster does not render properly, try installing\n            jupyter-server-proxy using `pip install jupyter-server-proxy`,\n        then running the following code before calling this function. For\n            more info, see https://bit.ly/3JbmF93.\n\n        import os\n        os.environ['LOCALTILESERVER_CLIENT_PREFIX'] = 'proxy/{port}'\n\n    Args:\n        source (str): The path to the NEON AOP HDF5 file.\n        indexes (int, optional): The band(s) to use. Band indexing starts\n            at 1. Defaults to None.\n        colormap (str, optional): The name of the colormap from `matplotlib`\n            to use when plotting a single band. See\n                https://matplotlib.org/stable/gallery/color/colormap_reference.html.\n                Default is greyscale.\n        vmin (float, optional): The minimum value to use when colormapping\n            the palette when plotting a single band. Defaults to 0.\n        vmax (float, optional): The maximum value to use when colormapping\n            the palette when plotting a single band. Defaults to 0.5.\n        nodata (float, optional): The value from the band to use to\n            interpret as not valid data. Defaults to np.nan.\n        attribution (str, optional): Attribution for the source raster. This\n            defaults to a message about it being a local file.. Defaults to None.\n        layer_name (str, optional): The layer name to use. Defaults to 'NEON'.\n        zoom_to_layer (bool, optional): Whether to zoom to the extent of the\n            layer. Defaults to True.\n        visible (bool, optional): Whether the layer is visible. Defaults\n            to True.\n        array_args (dict, optional): Additional arguments to pass to\n            `array_to_memory_file` when reading the raster. Defaults to {}.\n        method (str, optional): The method to use for data interpolation.\n            Defaults to \"nearest\".\n    \"\"\"\n\n    if array_args is None:\n        array_args = {}\n    xds = None\n    if isinstance(source, str):\n\n        xds = read_neon(source)\n        source = neon_to_image(xds, wavelengths=wavelengths, method=method)\n    elif isinstance(source, xr.Dataset):\n        xds = source\n        source = neon_to_image(xds, wavelengths=wavelengths, method=method)\n\n    self.add_raster(\n        source,\n        indexes=indexes,\n        colormap=colormap,\n        vmin=vmin,\n        vmax=vmax,\n        nodata=nodata,\n        attribution=attribution,\n        layer_name=layer_name,\n        zoom_to_layer=zoom_to_layer,\n        visible=visible,\n        array_args=array_args,\n        **kwargs,\n    )\n\n    self.cog_layer_dict[layer_name][\"xds\"] = xds\n    self.cog_layer_dict[layer_name][\"hyper\"] = \"NEON\"\n    self._update_band_names(layer_name, wavelengths)\n</code></pre>"},{"location":"hypercoast/#hypercoast.hypercoast.Map.add_pace","title":"<code>add_pace(self, source, wavelengths=None, indexes=None, colormap='jet', vmin=None, vmax=None, nodata=nan, attribution=None, layer_name='PACE', zoom_to_layer=True, visible=True, method='nearest', gridded=False, array_args=None, **kwargs)</code>","text":"<p>Add a PACE dataset to the map.     If you are using this function in JupyterHub on a remote server         (e.g., Binder, Microsoft Planetary Computer) and     if the raster does not render properly, try installing         jupyter-server-proxy using <code>pip install jupyter-server-proxy</code>,     then running the following code before calling this function. For         more info, see https://bit.ly/3JbmF93.</p> <pre><code>import os\nos.environ['LOCALTILESERVER_CLIENT_PREFIX'] = 'proxy/{port}'\n</code></pre> <p>Parameters:</p> Name Type Description Default <code>source</code> <code>str</code> <p>The path to the GeoTIFF file or the URL of the Cloud Optimized GeoTIFF.</p> required <code>indexes</code> <code>int</code> <p>The band(s) to use. Band indexing starts at 1. Defaults to None.</p> <code>None</code> <code>colormap</code> <code>str</code> <p>The name of the colormap from <code>matplotlib</code> to use when plotting a single band. See     https://matplotlib.org/stable/gallery/color/colormap_reference.html.     Default is greyscale.</p> <code>'jet'</code> <code>vmin</code> <code>float</code> <p>The minimum value to use when colormapping the palette when plotting a single band. Defaults to None.</p> <code>None</code> <code>vmax</code> <code>float</code> <p>The maximum value to use when colormapping the palette when plotting a single band. Defaults to None.</p> <code>None</code> <code>nodata</code> <code>float</code> <p>The value from the band to use to interpret as not valid data. Defaults to None.</p> <code>nan</code> <code>attribution</code> <code>str</code> <p>Attribution for the source raster. This defaults to a message about it being a local file.. Defaults to None.</p> <code>None</code> <code>layer_name</code> <code>str</code> <p>The layer name to use. Defaults to 'EMIT'.</p> <code>'PACE'</code> <code>zoom_to_layer</code> <code>bool</code> <p>Whether to zoom to the extent of the layer. Defaults to True.</p> <code>True</code> <code>visible</code> <code>bool</code> <p>Whether the layer is visible. Defaults to True.</p> <code>True</code> <code>array_args</code> <code>dict</code> <p>Additional arguments to pass to <code>array_to_memory_file</code> when reading the raster. Defaults to {}.</p> <code>None</code> Source code in <code>hypercoast/hypercoast.py</code> <pre><code>def add_pace(\n    self,\n    source,\n    wavelengths=None,\n    indexes=None,\n    colormap=\"jet\",\n    vmin=None,\n    vmax=None,\n    nodata=np.nan,\n    attribution=None,\n    layer_name=\"PACE\",\n    zoom_to_layer=True,\n    visible=True,\n    method=\"nearest\",\n    gridded=False,\n    array_args=None,\n    **kwargs,\n):\n    \"\"\"Add a PACE dataset to the map.\n        If you are using this function in JupyterHub on a remote server\n            (e.g., Binder, Microsoft Planetary Computer) and\n        if the raster does not render properly, try installing\n            jupyter-server-proxy using `pip install jupyter-server-proxy`,\n        then running the following code before calling this function. For\n            more info, see https://bit.ly/3JbmF93.\n\n        import os\n        os.environ['LOCALTILESERVER_CLIENT_PREFIX'] = 'proxy/{port}'\n\n    Args:\n        source (str): The path to the GeoTIFF file or the URL of the Cloud\n            Optimized GeoTIFF.\n        indexes (int, optional): The band(s) to use. Band indexing starts\n            at 1. Defaults to None.\n        colormap (str, optional): The name of the colormap from `matplotlib`\n            to use when plotting a single band. See\n                https://matplotlib.org/stable/gallery/color/colormap_reference.html.\n                Default is greyscale.\n        vmin (float, optional): The minimum value to use when colormapping\n            the palette when plotting a single band. Defaults to None.\n        vmax (float, optional): The maximum value to use when colormapping\n            the palette when plotting a single band. Defaults to None.\n        nodata (float, optional): The value from the band to use to interpret\n            as not valid data. Defaults to None.\n        attribution (str, optional): Attribution for the source raster. This\n            defaults to a message about it being a local file.. Defaults to None.\n        layer_name (str, optional): The layer name to use. Defaults to 'EMIT'.\n        zoom_to_layer (bool, optional): Whether to zoom to the extent of the\n            layer. Defaults to True.\n        visible (bool, optional): Whether the layer is visible. Defaults to True.\n        array_args (dict, optional): Additional arguments to pass to\n            `array_to_memory_file` when reading the raster. Defaults to {}.\n    \"\"\"\n\n    if array_args is None:\n        array_args = {}\n\n    if isinstance(source, str):\n\n        source = read_pace(source)\n\n    try:\n        image = pace_to_image(\n            source, wavelengths=wavelengths, method=method, gridded=gridded\n        )\n\n        if isinstance(wavelengths, list) and len(wavelengths) &gt; 1:\n            colormap = None\n\n        self.add_raster(\n            image,\n            indexes=indexes,\n            colormap=colormap,\n            vmin=vmin,\n            vmax=vmax,\n            nodata=nodata,\n            attribution=attribution,\n            layer_name=layer_name,\n            zoom_to_layer=zoom_to_layer,\n            visible=visible,\n            array_args=array_args,\n            **kwargs,\n        )\n\n        self.cog_layer_dict[layer_name][\"xds\"] = source\n        self.cog_layer_dict[layer_name][\"hyper\"] = \"PACE\"\n        self._update_band_names(layer_name, wavelengths)\n    except Exception as e:\n        print(e)\n</code></pre>"},{"location":"hypercoast/#hypercoast.hypercoast.Map.add_raster","title":"<code>add_raster(self, source, indexes=None, colormap=None, vmin=None, vmax=None, nodata=None, attribution=None, layer_name='Raster', layer_index=None, zoom_to_layer=True, visible=True, opacity=1.0, array_args=None, client_args={'cors_all': False}, open_args=None, **kwargs)</code>","text":"<p>Add a local raster dataset to the map.     If you are using this function in JupyterHub on a remote server         (e.g., Binder, Microsoft Planetary Computer) and     if the raster does not render properly, try installing         jupyter-server-proxy using <code>pip install jupyter-server-proxy</code>,     then running the following code before calling this function. For         more info, see https://bit.ly/3JbmF93.</p> <pre><code>import os\nos.environ['LOCALTILESERVER_CLIENT_PREFIX'] = 'proxy/{port}'\n</code></pre> <p>Parameters:</p> Name Type Description Default <code>source</code> <code>str</code> <p>The path to the GeoTIFF file or the URL of the Cloud Optimized GeoTIFF.</p> required <code>indexes</code> <code>int</code> <p>The band(s) to use. Band indexing starts at 1. Defaults to None.</p> <code>None</code> <code>colormap</code> <code>str</code> <p>The name of the colormap from <code>matplotlib</code> to use when plotting a single band. See https://matplotlib.org/stable/gallery/color/colormap_reference.html. Default is greyscale.</p> <code>None</code> <code>vmin</code> <code>float</code> <p>The minimum value to use when colormapping the palette when plotting a single band. Defaults to None.</p> <code>None</code> <code>vmax</code> <code>float</code> <p>The maximum value to use when colormapping the palette when plotting a single band. Defaults to None.</p> <code>None</code> <code>nodata</code> <code>float</code> <p>The value from the band to use to interpret as not valid data. Defaults to None.</p> <code>None</code> <code>attribution</code> <code>str</code> <p>Attribution for the source raster. This defaults to a message about it being a local file.. Defaults to None.</p> <code>None</code> <code>layer_name</code> <code>str</code> <p>The layer name to use. Defaults to 'Raster'.</p> <code>'Raster'</code> <code>layer_index</code> <code>int</code> <p>The index of the layer. Defaults to None.</p> <code>None</code> <code>zoom_to_layer</code> <code>bool</code> <p>Whether to zoom to the extent of the layer. Defaults to True.</p> <code>True</code> <code>visible</code> <code>bool</code> <p>Whether the layer is visible. Defaults to True.</p> <code>True</code> <code>opacity</code> <code>float</code> <p>The opacity of the layer. Defaults to 1.0.</p> <code>1.0</code> <code>array_args</code> <code>dict</code> <p>Additional arguments to pass to <code>array_to_memory_file</code> when reading the raster. Defaults to {}.</p> <code>None</code> <code>client_args</code> <code>dict</code> <p>Additional arguments to pass to localtileserver.TileClient. Defaults to { \"cors_all\": False }.</p> <code>{'cors_all': False}</code> <code>open_args</code> <code>dict</code> <p>Additional arguments to pass to rioxarray.open_rasterio.</p> <code>None</code> Source code in <code>hypercoast/hypercoast.py</code> <pre><code>def add_raster(\n    self,\n    source,\n    indexes=None,\n    colormap=None,\n    vmin=None,\n    vmax=None,\n    nodata=None,\n    attribution=None,\n    layer_name=\"Raster\",\n    layer_index=None,\n    zoom_to_layer=True,\n    visible=True,\n    opacity=1.0,\n    array_args=None,\n    client_args={\"cors_all\": False},\n    open_args=None,\n    **kwargs,\n):\n    \"\"\"Add a local raster dataset to the map.\n        If you are using this function in JupyterHub on a remote server\n            (e.g., Binder, Microsoft Planetary Computer) and\n        if the raster does not render properly, try installing\n            jupyter-server-proxy using `pip install jupyter-server-proxy`,\n        then running the following code before calling this function. For\n            more info, see https://bit.ly/3JbmF93.\n\n        import os\n        os.environ['LOCALTILESERVER_CLIENT_PREFIX'] = 'proxy/{port}'\n\n    Args:\n        source (str): The path to the GeoTIFF file or the URL of the Cloud\n            Optimized GeoTIFF.\n        indexes (int, optional): The band(s) to use. Band indexing starts\n            at 1. Defaults to None.\n        colormap (str, optional): The name of the colormap from `matplotlib`\n            to use when plotting a single band. See\n            https://matplotlib.org/stable/gallery/color/colormap_reference.html.\n            Default is greyscale.\n        vmin (float, optional): The minimum value to use when colormapping\n            the palette when plotting a single band. Defaults to None.\n        vmax (float, optional): The maximum value to use when colormapping\n            the palette when plotting a single band. Defaults to None.\n        nodata (float, optional): The value from the band to use to interpret\n            as not valid data. Defaults to None.\n        attribution (str, optional): Attribution for the source raster. This\n            defaults to a message about it being a local file.. Defaults to None.\n        layer_name (str, optional): The layer name to use. Defaults to 'Raster'.\n        layer_index (int, optional): The index of the layer. Defaults to None.\n        zoom_to_layer (bool, optional): Whether to zoom to the extent of the\n            layer. Defaults to True.\n        visible (bool, optional): Whether the layer is visible. Defaults to\n            True.\n        opacity (float, optional): The opacity of the layer. Defaults to 1.0.\n        array_args (dict, optional): Additional arguments to pass to\n            `array_to_memory_file` when reading the raster. Defaults to {}.\n        client_args (dict, optional): Additional arguments to pass to\n            localtileserver.TileClient. Defaults to { \"cors_all\": False }.\n        open_args (dict, optional): Additional arguments to pass to\n            rioxarray.open_rasterio.\n\n    \"\"\"\n\n    import rioxarray as rxr\n\n    if array_args is None:\n        array_args = {}\n    if open_args is None:\n        open_args = {}\n\n    if nodata is None:\n        nodata = np.nan\n    super().add_raster(\n        source,\n        indexes=indexes,\n        colormap=colormap,\n        vmin=vmin,\n        vmax=vmax,\n        nodata=nodata,\n        attribution=attribution,\n        layer_name=layer_name,\n        layer_index=layer_index,\n        zoom_to_layer=zoom_to_layer,\n        visible=visible,\n        opacity=opacity,\n        array_args=array_args,\n        client_args=client_args,\n        **kwargs,\n    )\n\n    if isinstance(source, str):\n        da = rxr.open_rasterio(source, **open_args)\n        dims = da.dims\n        da = da.transpose(dims[1], dims[2], dims[0])\n\n        xds = da.to_dataset(name=\"data\")\n        self.cog_layer_dict[layer_name][\"xds\"] = xds\n        # if self.cog_layer_dict[layer_name].get(\"hyper\") is None:\n        #     self.cog_layer_dict[layer_name][\"hyper\"] = \"COG\"\n</code></pre>"},{"location":"hypercoast/#hypercoast.hypercoast.Map.search_ecostress","title":"<code>search_ecostress(self, default_dataset='ECO_L2T_LSTE')</code>","text":"<p>Adds a NASA Earth Data search tool to the map with a default dataset for     ECOSTRESS.</p> <p>Parameters:</p> Name Type Description Default <code>default_dataset</code> <code>str</code> <p>The default dataset to search for. Defaults to \"ECO_L2T_LSTE\".</p> <code>'ECO_L2T_LSTE'</code> Source code in <code>hypercoast/hypercoast.py</code> <pre><code>def search_ecostress(self, default_dataset=\"ECO_L2T_LSTE\"):\n    \"\"\"\n    Adds a NASA Earth Data search tool to the map with a default dataset for\n        ECOSTRESS.\n\n    Args:\n        default_dataset (str, optional): The default dataset to search for.\n            Defaults to \"ECO_L2T_LSTE\".\n    \"\"\"\n    self.add(\"nasa_earth_data\", default_dataset=default_dataset)\n</code></pre>"},{"location":"hypercoast/#hypercoast.hypercoast.Map.search_emit","title":"<code>search_emit(self, default_dataset='EMITL2ARFL')</code>","text":"<p>Adds a NASA Earth Data search tool to the map with a default dataset for     EMIT.</p> <p>Parameters:</p> Name Type Description Default <code>default_dataset</code> <code>str</code> <p>The default dataset to search for. Defaults to \"EMITL2ARFL\".</p> <code>'EMITL2ARFL'</code> Source code in <code>hypercoast/hypercoast.py</code> <pre><code>def search_emit(self, default_dataset=\"EMITL2ARFL\"):\n    \"\"\"\n    Adds a NASA Earth Data search tool to the map with a default dataset for\n        EMIT.\n\n    Args:\n        default_dataset (str, optional): The default dataset to search for.\n            Defaults to \"EMITL2ARFL\".\n    \"\"\"\n    self.add(\"nasa_earth_data\", default_dataset=default_dataset)\n</code></pre>"},{"location":"hypercoast/#hypercoast.hypercoast.Map.search_pace","title":"<code>search_pace(self, default_dataset='PACE_OCI_L2_AOP_NRT')</code>","text":"<p>Adds a NASA Earth Data search tool to the map with a default dataset for     PACE.</p> <p>Parameters:</p> Name Type Description Default <code>default_dataset</code> <code>str</code> <p>The default dataset to search for. Defaults to \"PACE_OCI_L2_AOP_NRT\".</p> <code>'PACE_OCI_L2_AOP_NRT'</code> Source code in <code>hypercoast/hypercoast.py</code> <pre><code>def search_pace(self, default_dataset=\"PACE_OCI_L2_AOP_NRT\"):\n    \"\"\"\n    Adds a NASA Earth Data search tool to the map with a default dataset for\n        PACE.\n\n    Args:\n        default_dataset (str, optional): The default dataset to search for.\n            Defaults to \"PACE_OCI_L2_AOP_NRT\".\n    \"\"\"\n    self.add(\"nasa_earth_data\", default_dataset=default_dataset)\n</code></pre>"},{"location":"hypercoast/#hypercoast.hypercoast.Map.set_plot_options","title":"<code>set_plot_options(self, add_marker_cluster=False, plot_type=None, overlay=False, position='bottomright', min_width=None, max_width=None, min_height=None, max_height=None, **kwargs)</code>","text":"<p>Sets plotting options.</p> <p>Parameters:</p> Name Type Description Default <code>add_marker_cluster</code> <code>bool</code> <p>Whether to add a marker cluster. Defaults to False.</p> <code>False</code> <code>sample_scale</code> <code>float</code> <p>A nominal scale in meters of the projection to sample in . Defaults to None.</p> required <code>plot_type</code> <code>str</code> <p>The plot type can be one of \"None\", \"bar\", \"scatter\" or \"hist\". Defaults to None.</p> <code>None</code> <code>overlay</code> <code>bool</code> <p>Whether to overlay plotted lines on the figure. Defaults to False.</p> <code>False</code> <code>position</code> <code>str</code> <p>Position of the control, can be \u2018bottomleft\u2019, \u2018bottomright\u2019, \u2018topleft\u2019, or \u2018topright\u2019. Defaults to 'bottomright'.</p> <code>'bottomright'</code> <code>min_width</code> <code>int</code> <p>Min width of the widget (in pixels), if None it will respect the content size. Defaults to None.</p> <code>None</code> <code>max_width</code> <code>int</code> <p>Max width of the widget (in pixels), if None it will respect the content size. Defaults to None.</p> <code>None</code> <code>min_height</code> <code>int</code> <p>Min height of the widget (in pixels), if None it will respect the content size. Defaults to None.</p> <code>None</code> <code>max_height</code> <code>int</code> <p>Max height of the widget (in pixels), if None it will respect the content size. Defaults to None.</p> <code>None</code> Source code in <code>hypercoast/hypercoast.py</code> <pre><code>def set_plot_options(\n    self,\n    add_marker_cluster=False,\n    plot_type=None,\n    overlay=False,\n    position=\"bottomright\",\n    min_width=None,\n    max_width=None,\n    min_height=None,\n    max_height=None,\n    **kwargs,\n):\n    \"\"\"Sets plotting options.\n\n    Args:\n        add_marker_cluster (bool, optional): Whether to add a marker cluster.\n            Defaults to False.\n        sample_scale (float, optional):  A nominal scale in meters of the\n            projection to sample in . Defaults to None.\n        plot_type (str, optional): The plot type can be one of \"None\", \"bar\",\n            \"scatter\" or \"hist\". Defaults to None.\n        overlay (bool, optional): Whether to overlay plotted lines on the\n            figure. Defaults to False.\n        position (str, optional): Position of the control, can be\n            \u2018bottomleft\u2019, \u2018bottomright\u2019, \u2018topleft\u2019, or \u2018topright\u2019. Defaults\n            to 'bottomright'.\n        min_width (int, optional): Min width of the widget (in pixels), if\n            None it will respect the content size. Defaults to None.\n        max_width (int, optional): Max width of the widget (in pixels), if\n            None it will respect the content size. Defaults to None.\n        min_height (int, optional): Min height of the widget (in pixels), if\n            None it will respect the content size. Defaults to None.\n        max_height (int, optional): Max height of the widget (in pixels), if\n            None it will respect the content size. Defaults to None.\n\n    \"\"\"\n    plot_options_dict = {}\n    plot_options_dict[\"add_marker_cluster\"] = add_marker_cluster\n    plot_options_dict[\"plot_type\"] = plot_type\n    plot_options_dict[\"overlay\"] = overlay\n    plot_options_dict[\"position\"] = position\n    plot_options_dict[\"min_width\"] = min_width\n    plot_options_dict[\"max_width\"] = max_width\n    plot_options_dict[\"min_height\"] = min_height\n    plot_options_dict[\"max_height\"] = max_height\n\n    for key in kwargs:\n        plot_options_dict[key] = kwargs[key]\n\n    self._plot_options = plot_options_dict\n\n    if not hasattr(self, \"_plot_marker_cluster\"):\n        self._plot_marker_cluster = ipyleaflet.MarkerCluster(name=\"Marker Cluster\")\n\n    if add_marker_cluster and (self._plot_marker_cluster not in self.layers):\n        self.add(self._plot_marker_cluster)\n</code></pre>"},{"location":"hypercoast/#hypercoast.hypercoast.Map.spectral_to_csv","title":"<code>spectral_to_csv(self, filename, index=True, **kwargs)</code>","text":"<p>Saves the spectral data to a CSV file.</p> <p>Parameters:</p> Name Type Description Default <code>filename</code> <code>str</code> <p>The output CSV file.</p> required <code>index</code> <code>bool</code> <p>Whether to write the index. Defaults to True.</p> <code>True</code> Source code in <code>hypercoast/hypercoast.py</code> <pre><code>def spectral_to_csv(self, filename, index=True, **kwargs):\n    \"\"\"Saves the spectral data to a CSV file.\n\n    Args:\n        filename (str): The output CSV file.\n        index (bool, optional): Whether to write the index. Defaults to True.\n    \"\"\"\n    df = self.spectral_to_df()\n    df = df.rename_axis(\"band\")\n    df.to_csv(filename, index=index, **kwargs)\n</code></pre>"},{"location":"hypercoast/#hypercoast.hypercoast.Map.spectral_to_df","title":"<code>spectral_to_df(self, **kwargs)</code>","text":"<p>Converts the spectral data to a pandas DataFrame.</p> <p>Returns:</p> Type Description <code>pd.DataFrame</code> <p>The spectral data as a pandas DataFrame.</p> Source code in <code>hypercoast/hypercoast.py</code> <pre><code>def spectral_to_df(self, **kwargs):\n    \"\"\"Converts the spectral data to a pandas DataFrame.\n\n    Returns:\n        pd.DataFrame: The spectral data as a pandas DataFrame.\n    \"\"\"\n    import pandas as pd\n\n    df = pd.DataFrame(self._spectral_data, **kwargs)\n    return df\n</code></pre>"},{"location":"hypercoast/#hypercoast.hypercoast.Map.spectral_to_gdf","title":"<code>spectral_to_gdf(self, **kwargs)</code>","text":"<p>Converts the spectral data to a GeoPandas GeoDataFrame.</p> <p>Returns:</p> Type Description <code>gpd.DataFrame</code> <p>The spectral data as a pandas DataFrame.</p> Source code in <code>hypercoast/hypercoast.py</code> <pre><code>def spectral_to_gdf(self, **kwargs):\n    \"\"\"Converts the spectral data to a GeoPandas GeoDataFrame.\n\n    Returns:\n        gpd.DataFrame: The spectral data as a pandas DataFrame.\n    \"\"\"\n    import geopandas as gpd\n    from shapely.geometry import Point\n\n    df = self.spectral_to_df()\n\n    if len(df) == 0:\n        return df\n\n    # Step 1: Extract the coordinates from the columns\n    if \"wavelength\" in df.columns:\n        df = df.rename(columns={\"wavelength\": \"latlon\"})\n    elif \"wavelengths\" in df.columns:\n        df = df.rename(columns={\"wavelengths\": \"latlon\"})\n    coordinates = [col.strip(\"()\").split() for col in df.columns[1:]]\n    coords = [(float(lat), float(lon)) for lat, lon in coordinates]\n\n    # Step 2: Create Point geometries for each coordinate\n    points = [Point(lon, lat) for lat, lon in coords]\n\n    # Step 3: Create a GeoDataFrame\n    df_transposed = df.set_index(\"latlon\").T\n\n    # Convert the column names to strings to ensure compatibility with GeoJSON\n    df_transposed.columns = df_transposed.columns.astype(str)\n\n    # Create the GeoDataFrame\n    gdf = gpd.GeoDataFrame(df_transposed, geometry=points, **kwargs)\n\n    # Set the coordinate reference system (CRS)\n    gdf = gdf.set_geometry(\"geometry\").set_crs(\"EPSG:4326\")\n\n    return gdf\n</code></pre>"},{"location":"installation/","title":"Installation","text":""},{"location":"installation/#install-from-pypi","title":"Install from PyPI","text":"<p>hypercoast is available on PyPI. To install hypercoast, run this command in your terminal:</p> <pre><code>pip install hypercoast\n</code></pre> <p>HyperCoast has some optional dependencies that are not installed by default, such as cartopy, earthaccess, mapclassify, and pyvista. To install all optional dependencies all at once, run the following command:</p> <pre><code>pip install \"hypercoast[extra]\"\n</code></pre>"},{"location":"installation/#install-from-conda-forge","title":"Install from conda-forge","text":"<p>hypercoast is also available on conda-forge. If you have Anaconda or Miniconda installed on your computer, you can install hypercoast using the following command:</p> <pre><code>conda install -c conda-forge hypercoast\n</code></pre> <p>Alternatively, you can create a new conda environment and install hypercoast in the new environment. This is a good practice because it avoids potential conflicts with other packages installed in your base environment.</p> <pre><code>conda install -n base mamba -c conda-forge\nconda create -n hyper python=3.11\nconda activate hyper\nmamba install -c conda-forge hypercoast\n</code></pre> <p>To install the optional dependencies, run the following command:</p> <pre><code>mamba install -c conda-forge cartopy earthaccess mapclassify pyvista trame-vtk trame-vuetify\n</code></pre>"},{"location":"installation/#install-from-github","title":"Install from GitHub","text":"<p>To install the development version from GitHub using Git, run the following command in your terminal:</p> <pre><code>pip install git+https://github.com/opengeos/hypercoast\n</code></pre>"},{"location":"neon/","title":"neon module","text":"<p>This module contains functions to read and process NEON AOP hyperspectral data. More info about the data can be found at https://bit.ly/3Rfszdc. The source code is adapted from https://bit.ly/3KwyZkn. Credit goes to the original authors.</p>"},{"location":"neon/#hypercoast.neon.extract_neon","title":"<code>extract_neon(ds, lat, lon)</code>","text":"<p>Extracts NEON AOP data from a given xarray Dataset.</p> <p>Parameters:</p> Name Type Description Default <code>ds</code> <code>xarray.Dataset</code> <p>The dataset containing the NEON AOP data.</p> required <code>lat</code> <code>float</code> <p>The latitude of the point to extract.</p> required <code>lon</code> <code>float</code> <p>The longitude of the point to extract.</p> required <p>Returns:</p> Type Description <code>xarray.DataArray</code> <p>The extracted data.</p> Source code in <code>hypercoast/neon.py</code> <pre><code>def extract_neon(ds, lat, lon):\n    \"\"\"\n    Extracts NEON AOP data from a given xarray Dataset.\n\n    Args:\n        ds (xarray.Dataset): The dataset containing the NEON AOP data.\n        lat (float): The latitude of the point to extract.\n        lon (float): The longitude of the point to extract.\n\n    Returns:\n        xarray.DataArray: The extracted data.\n    \"\"\"\n\n    crs = ds.attrs[\"crs\"]\n\n    x, y = convert_coords([[lat, lon]], \"epsg:4326\", crs)[0]\n\n    values = ds.sel(x=x, y=y, method=\"nearest\")[\"reflectance\"].values\n\n    da = xr.DataArray(\n        values, dims=[\"wavelength\"], coords={\"wavelength\": ds.coords[\"wavelength\"]}\n    )\n\n    return da\n</code></pre>"},{"location":"neon/#hypercoast.neon.list_neon_datasets","title":"<code>list_neon_datasets(filepath, print_node=False)</code>","text":"<p>Lists all the datasets in an HDF5 file.</p> <p>Parameters:</p> Name Type Description Default <code>filepath</code> <code>str</code> <p>The path to the HDF5 file.</p> required <code>print_node</code> <code>bool</code> <p>If True, prints the node object of each dataset. If False, prints the name of each dataset. Defaults to False.</p> <code>False</code> Source code in <code>hypercoast/neon.py</code> <pre><code>def list_neon_datasets(filepath: str, print_node: bool = False) -&gt; None:\n    \"\"\"\n    Lists all the datasets in an HDF5 file.\n\n    Args:\n        filepath (str): The path to the HDF5 file.\n        print_node (bool, optional): If True, prints the node object of each dataset.\n            If False, prints the name of each dataset. Defaults to False.\n    \"\"\"\n\n    f = h5py.File(filepath, \"r\")\n\n    if print_node:\n\n        def list_dataset(_, node):\n            if isinstance(node, h5py.Dataset):\n                print(node)\n\n    else:\n\n        def list_dataset(name, node):\n            if isinstance(node, h5py.Dataset):\n                print(name)\n\n    f.visititems(list_dataset)\n</code></pre>"},{"location":"neon/#hypercoast.neon.neon_to_image","title":"<code>neon_to_image(dataset, wavelengths=None, method='nearest', output=None, **kwargs)</code>","text":"<p>Converts an NEON dataset to an image.</p> <p>Parameters:</p> Name Type Description Default <code>dataset</code> <code>Union[xr.Dataset, str]</code> <p>The dataset containing the NEON data or the file path to the dataset.</p> required <code>wavelengths</code> <code>np.ndarray</code> <p>The specific wavelengths to select. If None, all wavelengths are selected. Defaults to None.</p> <code>None</code> <code>method</code> <code>str</code> <p>The method to use for data interpolation. Defaults to \"nearest\".</p> <code>'nearest'</code> <code>output</code> <code>str</code> <p>The file path where the image will be saved. If None, the image will be returned as a PIL Image object. Defaults to None.</p> <code>None</code> <code>**kwargs</code> <code>Any</code> <p>Additional keyword arguments to be passed to <code>leafmap.array_to_image</code>.</p> <code>{}</code> <p>Returns:</p> Type Description <code>Optional[rasterio.Dataset]</code> <p>The image converted from the dataset. If     <code>output</code> is provided, the image will be saved to the specified file     and the function will return None.</p> Source code in <code>hypercoast/neon.py</code> <pre><code>def neon_to_image(\n    dataset: Union[xr.Dataset, str],\n    wavelengths: Optional[np.ndarray] = None,\n    method: str = \"nearest\",\n    output: Optional[str] = None,\n    **kwargs: Any,\n):\n    \"\"\"\n    Converts an NEON dataset to an image.\n\n    Args:\n        dataset (Union[xr.Dataset, str]): The dataset containing the NEON data\n            or the file path to the dataset.\n        wavelengths (np.ndarray, optional): The specific wavelengths to select. If None, all\n            wavelengths are selected. Defaults to None.\n        method (str, optional): The method to use for data interpolation.\n            Defaults to \"nearest\".\n        output (str, optional): The file path where the image will be saved. If\n            None, the image will be returned as a PIL Image object. Defaults to None.\n        **kwargs (Any): Additional keyword arguments to be passed to\n            `leafmap.array_to_image`.\n\n    Returns:\n        Optional[rasterio.Dataset]: The image converted from the dataset. If\n            `output` is provided, the image will be saved to the specified file\n            and the function will return None.\n    \"\"\"\n    from leafmap import array_to_image\n\n    if isinstance(dataset, str):\n        dataset = read_neon(dataset, method=method)\n\n    if wavelengths is not None:\n        dataset = dataset.sel(wavelength=wavelengths, method=method)\n\n    return array_to_image(\n        dataset[\"reflectance\"],\n        output=output,\n        transpose=False,\n        dtype=np.float32,\n        **kwargs,\n    )\n</code></pre>"},{"location":"neon/#hypercoast.neon.read_neon","title":"<code>read_neon(filepath, wavelengths=None, method='nearest', **kwargs)</code>","text":"<p>Reads NEON AOP hyperspectral hdf5 files and returns an xarray dataset.</p> <p>Parameters:</p> Name Type Description Default <code>filepath</code> <code>str</code> <p>The path to the hdf5 file.</p> required <code>wavelengths</code> <code>List[float]</code> <p>The wavelengths to select. If None, all wavelengths are selected. Defaults to None.</p> <code>None</code> <code>method</code> <code>str</code> <p>The method to use for selection. Defaults to \"nearest\".</p> <code>'nearest'</code> <code>**kwargs</code> <code>Any</code> <p>Additional arguments to pass to the selection method.</p> <code>{}</code> <p>Returns:</p> Type Description <code>xr.Dataset</code> <p>The dataset containing the reflectance data.</p> Source code in <code>hypercoast/neon.py</code> <pre><code>def read_neon(\n    filepath: str,\n    wavelengths: Optional[List[float]] = None,\n    method: str = \"nearest\",\n    **kwargs: Any,\n) -&gt; xr.Dataset:\n    \"\"\"\n    Reads NEON AOP hyperspectral hdf5 files and returns an xarray dataset.\n\n    Args:\n        filepath (str): The path to the hdf5 file.\n        wavelengths (List[float], optional): The wavelengths to select. If None,\n            all wavelengths are selected. Defaults to None.\n        method (str, optional): The method to use for selection. Defaults to\n            \"nearest\".\n        **kwargs (Any): Additional arguments to pass to the selection method.\n\n    Returns:\n        xr.Dataset: The dataset containing the reflectance data.\n    \"\"\"\n    with h5py.File(filepath, \"r\") as f:\n        # Extract site code dynamically from NEON HDF file metadata\n        # At the root of `keys` NEON stores the site code, which is the `root` folder at the [0] index of object `keys`\n        site_code = list(f.keys())[0]\n\n        # Access the reflectance data using the site code\n        site_refl = f[site_code][\"Reflectance\"]\n\n        # Extract wavelengths\n        wavelengths_list = site_refl[\"Metadata\"][\"Spectral_Data\"][\"Wavelength\"][\n            ()\n        ].tolist()\n        wavelengths_list = [round(num, 2) for num in wavelengths_list]\n\n        # Extract EPSG code\n        epsg_code = site_refl[\"Metadata\"][\"Coordinate_System\"][\"EPSG Code\"][()]\n        epsg_code_number = int(epsg_code.decode(\"utf-8\"))\n\n        # Extract map info\n        mapInfo_string = site_refl[\"Metadata\"][\"Coordinate_System\"][\"Map_Info\"][\n            ()\n        ].decode(\"utf-8\")\n        mapInfo_split = mapInfo_string.split(\",\")\n\n        res = float(mapInfo_split[5]), float(mapInfo_split[6])\n\n        # Extract reflectance array and shape\n        site_reflArray = site_refl[\"Reflectance_Data\"]\n        refl_shape = site_reflArray.shape\n\n        # Calculate coordinates\n        xMin = float(mapInfo_split[3])\n        yMax = float(mapInfo_split[4])\n\n        xMax = xMin + (refl_shape[1] * res[0])\n        yMin = yMax - (refl_shape[0] * res[1])\n\n        # Handle scale factor and no-data value\n        scaleFactor = site_reflArray.attrs[\"Scale_Factor\"]\n        noDataValue = site_reflArray.attrs[\"Data_Ignore_Value\"]\n\n        da = site_reflArray[:, :, :].astype(float)\n        da[da == int(noDataValue)] = np.nan\n        da[da &lt; 0] = np.nan\n        da[da &gt; 10000] = np.nan\n        da = da / scaleFactor\n\n        coords = {\n            \"y\": np.linspace(yMax, yMin, da.shape[0]),\n            \"x\": np.linspace(xMin, xMax, da.shape[1]),\n            \"wavelength\": wavelengths_list,\n        }\n\n        xda = xr.DataArray(\n            da,\n            coords=coords,\n            dims=[\"y\", \"x\", \"wavelength\"],\n            attrs={\n                \"scale_factor\": scaleFactor,\n                \"no_data_value\": noDataValue,\n                \"crs\": f\"EPSG:{epsg_code_number}\",\n                \"transform\": (res[0], 0.0, xMin, 0.0, -res[1], yMax),\n            },\n        )\n\n        if wavelengths is not None:\n            xda = xda.sel(wavelength=wavelengths, method=method, **kwargs)\n\n        dataset = xda.to_dataset(name=\"reflectance\")\n        dataset.attrs = dataset[\"reflectance\"].attrs\n\n    return dataset\n</code></pre>"},{"location":"pace/","title":"pace module","text":"<p>This module contains functions to read and process PACE data.</p>"},{"location":"pace/#hypercoast.pace.apply_kmeans","title":"<code>apply_kmeans(dataset, n_clusters=6, filter_condition=None, plot=True, figsize=(8, 6), colors=None, extent=None, title=None, **kwargs)</code>","text":"<p>Applies K-means clustering to the dataset and optionally plots the results.</p> <p>Parameters:</p> Name Type Description Default <code>dataset</code> <code>xr.Dataset | str</code> <p>The dataset containing the PACE data or the file path to the dataset.</p> required <code>n_clusters</code> <code>int</code> <p>Number of clusters for K-means. Defaults to 6.</p> <code>6</code> <code>plot</code> <code>bool</code> <p>Whether to plot the data. Defaults to True.</p> <code>True</code> <code>figsize</code> <code>tuple[int, int]</code> <p>Figure size for the plot. Defaults to (8, 6).</p> <code>(8, 6)</code> <code>colors</code> <code>list[str]</code> <p>List of colors to use for the clusters. Defaults to None.</p> <code>None</code> <code>extent</code> <code>list[float] | None</code> <p>The extent to zoom in to the specified region. Defaults to None.</p> <code>None</code> <code>title</code> <code>str | None</code> <p>Title for the plot. Defaults to None.</p> <code>None</code> <code>**kwargs</code> <p>Additional keyword arguments to pass to the <code>plt.subplots</code> function.</p> <code>{}</code> <p>Returns:</p> Type Description <code>tuple[np.ndarray, np.ndarray, np.ndarray]</code> <p>The cluster labels, latitudes, and longitudes.</p> Source code in <code>hypercoast/pace.py</code> <pre><code>def apply_kmeans(\n    dataset: Union[xr.Dataset, str],\n    n_clusters: int = 6,\n    filter_condition: Optional[Callable[[xr.DataArray], xr.DataArray]] = None,\n    plot: bool = True,\n    figsize: tuple[int, int] = (8, 6),\n    colors: list[str] = None,\n    extent: list[float] = None,\n    title: str = None,\n    **kwargs,\n) -&gt; tuple[np.ndarray, np.ndarray, np.ndarray]:\n    \"\"\"\n    Applies K-means clustering to the dataset and optionally plots the results.\n\n    Args:\n        dataset (xr.Dataset | str): The dataset containing the PACE data or the file path to the dataset.\n        n_clusters (int, optional): Number of clusters for K-means. Defaults to 6.\n        plot (bool, optional): Whether to plot the data. Defaults to True.\n        figsize (tuple[int, int], optional): Figure size for the plot. Defaults to (8, 6).\n        colors (list[str], optional): List of colors to use for the clusters. Defaults to None.\n        extent (list[float] | None, optional): The extent to zoom in to the specified region. Defaults to None.\n        title (str | None, optional): Title for the plot. Defaults to None.\n        **kwargs: Additional keyword arguments to pass to the `plt.subplots` function.\n\n    Returns:\n        tuple[np.ndarray, np.ndarray, np.ndarray]: The cluster labels, latitudes, and longitudes.\n    \"\"\"\n\n    import numpy as np\n    from sklearn.cluster import KMeans\n\n    import matplotlib.pyplot as plt\n    import matplotlib.colors as mcolors\n    import cartopy.crs as ccrs\n    import cartopy.feature as cfeature\n\n    if isinstance(dataset, str):\n        dataset = read_pace(dataset)\n    elif isinstance(dataset, xr.DataArray):\n        dataset = dataset.to_dataset()\n    elif not isinstance(dataset, xr.Dataset):\n        raise ValueError(\"dataset must be an xarray Dataset\")\n\n    if title is None:\n        title = f\"K-means Clustering with {n_clusters} Clusters\"\n\n    da = dataset[\"Rrs\"]\n\n    reshaped_data = da.values.reshape(-1, da.shape[-1])\n    reshaped_data_no_nan = reshaped_data[~np.isnan(reshaped_data).any(axis=1)]\n\n    # Apply K-means clustering to classify into 5-6 water types.\n    kmeans = KMeans(n_clusters=n_clusters, random_state=0)\n    kmeans.fit(reshaped_data_no_nan)\n\n    # Initialize an array for cluster labels with NaN\n    labels = np.full(reshaped_data.shape[0], np.nan)\n\n    # Assign the computed cluster labels to the non-NaN positions\n    labels[~np.isnan(reshaped_data).any(axis=1)] = kmeans.labels_\n\n    # Reshape the labels back to the original spatial dimensions\n    cluster_labels = labels.reshape(da.shape[:-1])\n\n    if filter_condition is not None:\n        cluster_labels = np.where(filter_condition, cluster_labels, np.nan)\n\n    latitudes = da.coords[\"latitude\"].values\n    longitudes = da.coords[\"longitude\"].values\n\n    if plot:\n\n        # Create a custom discrete color map for K-means clusters\n        if colors is None:\n            colors = [\"#377eb8\", \"#ff7f00\", \"#4daf4a\", \"#f781bf\", \"#a65628\", \"#984ea3\"]\n        cmap = mcolors.ListedColormap(colors)\n        bounds = np.arange(-0.5, n_clusters, 1)\n        norm = mcolors.BoundaryNorm(bounds, cmap.N)\n\n        # Create a figure and axis with the correct map projection\n\n        if \"dpi\" not in kwargs:\n            kwargs[\"dpi\"] = 100\n\n        if \"subplot_kw\" not in kwargs:\n            kwargs[\"subplot_kw\"] = {\"projection\": ccrs.PlateCarree()}\n\n        fig, ax = plt.subplots(\n            figsize=figsize,\n            **kwargs,\n        )\n\n        # Plot the K-means classification results on the map\n        im = ax.pcolormesh(\n            longitudes,\n            latitudes,\n            cluster_labels,\n            cmap=cmap,\n            norm=norm,\n            transform=ccrs.PlateCarree(),\n        )\n\n        # Add geographic features for context\n        ax.add_feature(cfeature.COASTLINE)\n        ax.add_feature(cfeature.BORDERS, linestyle=\":\")\n        ax.add_feature(cfeature.STATES, linestyle=\"--\")\n\n        # Add gridlines\n        ax.gridlines(draw_labels=True)\n\n        # Set the extent to zoom in to the specified region\n        if extent is not None:\n            ax.set_extent(extent, crs=ccrs.PlateCarree())\n\n        # Add color bar with labels\n        cbar = plt.colorbar(\n            im,\n            ax=ax,\n            orientation=\"vertical\",\n            # pad=0.02,\n            fraction=0.05,\n            ticks=np.arange(n_clusters),\n        )\n        cbar.ax.set_yticklabels([f\"Class {i+1}\" for i in range(n_clusters)])\n        cbar.set_label(\"Water Types\", rotation=270, labelpad=10)\n\n        # Add title\n        ax.set_title(title, fontsize=14)\n        ax.set_xlabel(\"Longitude\")\n        ax.set_ylabel(\"Latitude\")\n\n        # Show the plot\n        plt.show()\n\n    return cluster_labels, latitudes, longitudes\n</code></pre>"},{"location":"pace/#hypercoast.pace.apply_pca","title":"<code>apply_pca(dataset, n_components=3, plot=True, figsize=(8, 6), x_component=0, y_component=1, color='blue', title='PCA of Spectral Data', **kwargs)</code>","text":"<p>Applies Principal Component Analysis (PCA) to the dataset and optionally plots the results.</p> <p>Parameters:</p> Name Type Description Default <code>dataset</code> <code>xr.Dataset | str</code> <p>The dataset containing the PACE data or the file path to the dataset.</p> required <code>n_components</code> <code>int</code> <p>Number of principal components to compute. Defaults to 3.</p> <code>3</code> <code>plot</code> <code>bool</code> <p>Whether to plot the data. Defaults to True.</p> <code>True</code> <code>figsize</code> <code>tuple[int, int]</code> <p>Figure size for the plot. Defaults to (8, 6).</p> <code>(8, 6)</code> <code>x_component</code> <code>int</code> <p>The principal component to plot on the x-axis. Defaults to 0.</p> <code>0</code> <code>y_component</code> <code>int</code> <p>The principal component to plot on the y-axis. Defaults to 1.</p> <code>1</code> <code>color</code> <code>str</code> <p>Color of the scatter plot points. Defaults to \"blue\".</p> <code>'blue'</code> <code>title</code> <code>str</code> <p>Title for the plot. Defaults to \"PCA of Spectral Data\".</p> <code>'PCA of Spectral Data'</code> <code>**kwargs</code> <p>Additional keyword arguments to pass to the <code>plt.scatter</code> function.</p> <code>{}</code> <p>Returns:</p> Type Description <code>np.ndarray</code> <p>The PCA-transformed data.</p> Source code in <code>hypercoast/pace.py</code> <pre><code>def apply_pca(\n    dataset: Union[xr.Dataset, str],\n    n_components: int = 3,\n    plot: bool = True,\n    figsize: tuple[int, int] = (8, 6),\n    x_component: int = 0,\n    y_component: int = 1,\n    color: str = \"blue\",\n    title: str = \"PCA of Spectral Data\",\n    **kwargs,\n) -&gt; np.ndarray:\n    \"\"\"\n    Applies Principal Component Analysis (PCA) to the dataset and optionally plots the results.\n\n    Args:\n        dataset (xr.Dataset | str): The dataset containing the PACE data or the file path to the dataset.\n        n_components (int, optional): Number of principal components to compute. Defaults to 3.\n        plot (bool, optional): Whether to plot the data. Defaults to True.\n        figsize (tuple[int, int], optional): Figure size for the plot. Defaults to (8, 6).\n        x_component (int, optional): The principal component to plot on the x-axis. Defaults to 0.\n        y_component (int, optional): The principal component to plot on the y-axis. Defaults to 1.\n        color (str, optional): Color of the scatter plot points. Defaults to \"blue\".\n        title (str, optional): Title for the plot. Defaults to \"PCA of Spectral Data\".\n        **kwargs: Additional keyword arguments to pass to the `plt.scatter` function.\n\n    Returns:\n        np.ndarray: The PCA-transformed data.\n    \"\"\"\n    from sklearn.decomposition import PCA\n\n    if isinstance(dataset, str):\n        dataset = read_pace(dataset)\n    elif not isinstance(dataset, xr.Dataset):\n        raise ValueError(\"dataset must be an xarray Dataset\")\n\n    da = dataset[\"Rrs\"]\n\n    # Reshape data to (n_pixels, n_bands)\n    reshaped_data = da.values.reshape(-1, da.shape[-1])\n\n    # Handle NaNs by removing them\n    reshaped_data_no_nan = reshaped_data[~np.isnan(reshaped_data).any(axis=1)]\n\n    # Apply PCA to reduce dimensionality\n    pca = PCA(n_components=n_components)\n    pca_data = pca.fit_transform(reshaped_data_no_nan)\n\n    if plot:\n        plt.figure(figsize=figsize)\n        if \"s\" not in kwargs:\n            kwargs[\"s\"] = 1\n        plt.scatter(\n            pca_data[:, x_component], pca_data[:, y_component], c=color, **kwargs\n        )\n        plt.title(title)\n        plt.xlabel(f\"Principal Component {x_component + 1}\")\n        plt.ylabel(f\"Principal Component {y_component + 1}\")\n        plt.show()\n\n    return pca_data\n</code></pre>"},{"location":"pace/#hypercoast.pace.apply_sam","title":"<code>apply_sam(dataset, n_components=3, n_clusters=6, random_state=0, spectral_library=None, filter_condition=None, plot=True, figsize=(8, 6), extent=None, colors=None, title=None, **kwargs)</code>","text":"<p>Applies Spectral Angle Mapper (SAM) to the dataset and optionally plots the results.</p> <p>Parameters:</p> Name Type Description Default <code>dataset</code> <code>Union[xr.Dataset, str]</code> <p>The dataset containing the PACE data or the file path to the dataset.</p> required <code>n_components</code> <code>int</code> <p>Number of principal components to compute. Defaults to 3.</p> <code>3</code> <code>n_clusters</code> <code>int</code> <p>Number of clusters for K-means. Defaults to 6.</p> <code>6</code> <code>random_state</code> <code>int</code> <p>Random state for K-means. Defaults to 0.</p> <code>0</code> <code>spectral_library</code> <code>Union[str, list[str]]</code> <p>Path to the spectral library or a list of paths. Defaults to None.</p> <code>None</code> <code>filter_condition</code> <code>Callable[[xr.DataArray], xr.DataArray]</code> <p>A function to filter the data. Defaults to None.</p> <code>None</code> <code>plot</code> <code>bool</code> <p>Whether to plot the data. Defaults to True.</p> <code>True</code> <code>figsize</code> <code>Tuple[int, int]</code> <p>Figure size for the plot. Defaults to (8, 6).</p> <code>(8, 6)</code> <code>extent</code> <code>List[float]</code> <p>The extent to zoom in to the specified region. Defaults to None.</p> <code>None</code> <code>colors</code> <code>List[str]</code> <p>Colors for the clusters. Defaults to None.</p> <code>None</code> <code>title</code> <code>str</code> <p>Title for the plot. Defaults to \"Spectral Angle Mapper\".</p> <code>None</code> <code>**kwargs</code> <p>Additional keyword arguments to pass to the <code>plt.subplots</code> function.</p> <code>{}</code> <p>Returns:</p> Type Description <code>Tuple[np.ndarray, np.ndarray, np.ndarray]</code> <p>The best match classification, latitudes, and longitudes.</p> Source code in <code>hypercoast/pace.py</code> <pre><code>def apply_sam(\n    dataset: Union[xr.Dataset, str],\n    n_components: int = 3,\n    n_clusters: int = 6,\n    random_state: int = 0,\n    spectral_library: Union[str, list[str]] = None,\n    filter_condition: Optional[Callable[[xr.DataArray], xr.DataArray]] = None,\n    plot: bool = True,\n    figsize: tuple[int, int] = (8, 6),\n    extent: list[float] = None,\n    colors: list[str] = None,\n    title: str = None,\n    **kwargs,\n) -&gt; Tuple[np.ndarray, np.ndarray, np.ndarray]:\n    \"\"\"\n    Applies Spectral Angle Mapper (SAM) to the dataset and optionally plots the results.\n\n    Args:\n        dataset (Union[xr.Dataset, str]): The dataset containing the PACE data or the file path to the dataset.\n        n_components (int, optional): Number of principal components to compute. Defaults to 3.\n        n_clusters (int, optional): Number of clusters for K-means. Defaults to 6.\n        random_state (int, optional): Random state for K-means. Defaults to 0.\n        spectral_library (Union[str, list[str]], optional): Path to the spectral library or a list of paths. Defaults to None.\n        filter_condition (Callable[[xr.DataArray], xr.DataArray], optional): A function to filter the data. Defaults to None.\n        plot (bool, optional): Whether to plot the data. Defaults to True.\n        figsize (Tuple[int, int], optional): Figure size for the plot. Defaults to (8, 6).\n        extent (List[float], optional): The extent to zoom in to the specified region. Defaults to None.\n        colors (List[str], optional): Colors for the clusters. Defaults to None.\n        title (str, optional): Title for the plot. Defaults to \"Spectral Angle Mapper\".\n        **kwargs: Additional keyword arguments to pass to the `plt.subplots` function.\n\n    Returns:\n        Tuple[np.ndarray, np.ndarray, np.ndarray]: The best match classification, latitudes, and longitudes.\n    \"\"\"\n    import glob\n    import pandas as pd\n    from sklearn.cluster import KMeans\n    import matplotlib.colors as mcolors\n    import cartopy.crs as ccrs\n    import cartopy.feature as cfeature\n    from sklearn.decomposition import PCA\n    from scipy.interpolate import interp1d\n\n    if isinstance(dataset, str):\n        dataset = read_pace(dataset)\n    elif isinstance(dataset, xr.DataArray):\n        dataset = dataset.to_dataset()\n    elif not isinstance(dataset, xr.Dataset):\n        raise ValueError(\"dataset must be an xarray Dataset\")\n\n    da = dataset[\"Rrs\"]\n    pace_wavelengths = da[\"wavelength\"].values\n\n    # Reshape data to (n_pixels, n_bands)\n    reshaped_data = da.values.reshape(-1, da.shape[-1])\n\n    # Handle NaNs by removing them\n    reshaped_data_no_nan = reshaped_data[~np.isnan(reshaped_data).any(axis=1)]\n\n    if isinstance(spectral_library, str):\n        endmember_paths = sorted(glob.glob(spectral_library))\n    elif isinstance(spectral_library, list):\n        endmember_paths = spectral_library\n    else:\n        endmember_paths = None\n\n    # Function to load and resample a single CSV spectral library file\n    def load_and_resample_spectral_library(csv_path, target_wavelengths):\n        df = pd.read_csv(csv_path)\n        original_wavelengths = df.iloc[:, 0].values  # First column is wavelength\n        spectra_values = df.iloc[:, 1].values  # Second column is spectral values\n\n        # Interpolation function\n        interp_func = interp1d(\n            original_wavelengths,\n            spectra_values,\n            kind=\"linear\",\n            fill_value=\"extrapolate\",\n        )\n\n        # Resample to the target (PACE) wavelengths\n        resampled_spectra = interp_func(target_wavelengths)\n\n        return resampled_spectra\n\n    if endmember_paths is not None:\n        endmembers = np.array(\n            [\n                load_and_resample_spectral_library(path, pace_wavelengths)\n                for path in endmember_paths\n            ]\n        )\n    else:\n        endmembers = None\n\n    if endmembers is None:\n        # Apply PCA to reduce dimensionality\n        pca = PCA(n_components=n_components)\n        pca_data = pca.fit_transform(reshaped_data_no_nan)\n\n        # Apply K-means to find clusters representing endmembers\n        kmeans = KMeans(n_clusters=n_clusters, random_state=random_state)\n        kmeans.fit(pca_data)\n\n        # The cluster centers in the original spectral space are your endmembers\n        endmembers = pca.inverse_transform(kmeans.cluster_centers_)\n\n    def spectral_angle_mapper(pixel, reference):\n        norm_pixel = np.linalg.norm(pixel)\n        norm_reference = np.linalg.norm(reference)\n        cos_theta = np.dot(pixel, reference) / (norm_pixel * norm_reference)\n        angle = np.arccos(np.clip(cos_theta, -1, 1))\n        return angle\n\n    # Apply SAM for each pixel and each endmember\n    angles = np.zeros((reshaped_data_no_nan.shape[0], endmembers.shape[0]))\n\n    for i in range(reshaped_data_no_nan.shape[0]):\n        for j in range(endmembers.shape[0]):\n            angles[i, j] = spectral_angle_mapper(\n                reshaped_data_no_nan[i, :], endmembers[j, :]\n            )\n\n    # Find the minimum angle (best match) for each pixel\n    best_match = np.argmin(angles, axis=1)\n\n    # Reshape best_match back to the original spatial dimensions\n    original_shape = da.shape[:-1]  # Get the spatial dimensions\n    best_match_full = np.full(reshaped_data.shape[0], np.nan)\n    best_match_full[~np.isnan(reshaped_data).any(axis=1)] = best_match\n    best_match_full = best_match_full.reshape(original_shape)\n\n    if filter_condition is not None:\n        best_match_full = np.where(filter_condition, best_match_full, np.nan)\n\n    latitudes = da.coords[\"latitude\"].values\n    longitudes = da.coords[\"longitude\"].values\n\n    # Plot sample spectra from the CSV files and their resampled versions\n    def plot_sample_spectra(csv_paths, pace_wavelengths):\n        plt.figure(figsize=figsize)\n\n        for i, csv_path in enumerate(csv_paths):\n            df = pd.read_csv(csv_path)\n            original_wavelengths = df.iloc[:, 0].values\n            spectra_values = df.iloc[:, 1].values\n            resampled_spectra = load_and_resample_spectral_library(\n                csv_path, pace_wavelengths\n            )\n\n            plt.plot(\n                original_wavelengths,\n                spectra_values,\n                label=f\"Original Spectra {i+1}\",\n                linestyle=\"--\",\n            )\n            plt.plot(\n                pace_wavelengths, resampled_spectra, label=f\"Resampled Spectra {i+1}\"\n            )\n\n        plt.xlabel(\"Wavelength (nm)\")\n        plt.ylabel(\"Spectral Reflectance\")\n        plt.title(\"Comparison of Original and Resampled Spectra\")\n        plt.legend()\n        plt.grid(True)\n        plt.show()\n\n    if plot:\n\n        if endmember_paths is not None:\n\n            plot_sample_spectra(endmember_paths, pace_wavelengths)\n\n        if colors is None:\n            colors = [\"#377eb8\", \"#ff7f00\", \"#4daf4a\", \"#f781bf\", \"#a65628\", \"#984ea3\"]\n\n        if title is None:\n            title = \"Spectral Angle Mapper Water Type Classification\"\n        # Create a custom discrete color map\n        cmap = mcolors.ListedColormap(colors)\n        if spectral_library is not None:\n            n_clusters = len(endmember_paths)\n        bounds = np.arange(-0.5, n_clusters, 1)\n        norm = mcolors.BoundaryNorm(bounds, cmap.N)\n\n        # Create a figure and axis with the correct map projection\n        fig, ax = plt.subplots(\n            figsize=figsize, subplot_kw={\"projection\": ccrs.PlateCarree()}, **kwargs\n        )\n\n        # Plot the SAM classification results\n        im = ax.pcolormesh(\n            longitudes,\n            latitudes,\n            best_match_full,\n            cmap=cmap,\n            norm=norm,\n            transform=ccrs.PlateCarree(),\n        )\n\n        # Add geographic features for context\n        ax.add_feature(cfeature.COASTLINE)\n        ax.add_feature(cfeature.BORDERS, linestyle=\":\")\n        ax.add_feature(cfeature.STATES, linestyle=\"--\")\n\n        # Add gridlines\n        ax.gridlines(draw_labels=True)\n\n        # Set the extent to zoom in to the specified region\n        if extent is not None:\n            ax.set_extent(extent, crs=ccrs.PlateCarree())\n\n        # Add color bar with labels\n        cbar = plt.colorbar(\n            im,\n            ax=ax,\n            orientation=\"vertical\",\n            # pad=0.02,\n            fraction=0.05,\n            ticks=np.arange(n_clusters),\n        )\n        cbar.ax.set_yticklabels([f\"Class {i+1}\" for i in range(n_clusters)])\n        cbar.set_label(\"Water Types\", rotation=270, labelpad=20)\n\n        # Add title\n        ax.set_title(title, fontsize=14)\n\n        # Show the plot\n        plt.show()\n\n    return best_match_full, latitudes, longitudes\n</code></pre>"},{"location":"pace/#hypercoast.pace.apply_sam_spectral","title":"<code>apply_sam_spectral(dataset, spectral_library=None, filter_condition=None, plot=True, figsize=(8, 6), extent=None, colors=None, title=None, **kwargs)</code>","text":"<p>Applies Spectral Angle Mapper (SAM) to the dataset and optionally plots the results.</p> <p>Parameters:</p> Name Type Description Default <code>dataset</code> <code>Union[xr.Dataset, str]</code> <p>The dataset containing the PACE data or the file path to the dataset.</p> required <code>spectral_library</code> <code>Union[str, list[str]]</code> <p>The spectral library file path or list of file paths.</p> <code>None</code> <code>filter_condition</code> <code>Callable[[xr.DataArray], xr.DataArray]</code> <p>A function to filter the data. Defaults to None.</p> <code>None</code> <code>plot</code> <code>bool</code> <p>Whether to plot the data. Defaults to True.</p> <code>True</code> <code>figsize</code> <code>Tuple[int, int]</code> <p>Figure size for the plot. Defaults to (8, 6).</p> <code>(8, 6)</code> <code>extent</code> <code>List[float]</code> <p>The extent to zoom in to the specified region. Defaults to None.</p> <code>None</code> <code>colors</code> <code>List[str]</code> <p>Colors for the clusters. Defaults to None.</p> <code>None</code> <code>title</code> <code>str</code> <p>Title for the plot. Defaults to \"Spectral Angle Mapper Water Type Classification\".</p> <code>None</code> <code>**kwargs</code> <p>Additional keyword arguments to pass to the <code>plt.subplots</code> function.</p> <code>{}</code> <p>Returns:</p> Type Description <code>Tuple[np.ndarray, np.ndarray, np.ndarray]</code> <p>The best match classification, latitudes, and longitudes.</p> Source code in <code>hypercoast/pace.py</code> <pre><code>def apply_sam_spectral(\n    dataset: Union[xr.Dataset, str],\n    spectral_library: Union[str, list[str]] = None,\n    filter_condition: Optional[Callable[[xr.DataArray], xr.DataArray]] = None,\n    plot: bool = True,\n    figsize: tuple[int, int] = (8, 6),\n    extent: list[float] = None,\n    colors: list[str] = None,\n    title: str = None,\n    **kwargs,\n) -&gt; Tuple[np.ndarray, np.ndarray, np.ndarray]:\n    \"\"\"\n    Applies Spectral Angle Mapper (SAM) to the dataset and optionally plots the results.\n\n    Args:\n        dataset (Union[xr.Dataset, str]): The dataset containing the PACE data or the file path to the dataset.\n        spectral_library (Union[str, list[str]]): The spectral library file path or list of file paths.\n        filter_condition (Callable[[xr.DataArray], xr.DataArray], optional): A function to filter the data. Defaults to None.\n        plot (bool, optional): Whether to plot the data. Defaults to True.\n        figsize (Tuple[int, int], optional): Figure size for the plot. Defaults to (8, 6).\n        extent (List[float], optional): The extent to zoom in to the specified region. Defaults to None.\n        colors (List[str], optional): Colors for the clusters. Defaults to None.\n        title (str, optional): Title for the plot. Defaults to \"Spectral Angle Mapper Water Type Classification\".\n        **kwargs: Additional keyword arguments to pass to the `plt.subplots` function.\n\n    Returns:\n        Tuple[np.ndarray, np.ndarray, np.ndarray]: The best match classification, latitudes, and longitudes.\n    \"\"\"\n    import glob\n    import pandas as pd\n    import matplotlib.colors as mcolors\n    import cartopy.crs as ccrs\n    import cartopy.feature as cfeature\n    from scipy.interpolate import interp1d\n\n    if isinstance(dataset, str):\n        dataset = read_pace(dataset)\n    elif isinstance(dataset, xr.DataArray):\n        dataset = dataset.to_dataset()\n    elif not isinstance(dataset, xr.Dataset):\n        raise ValueError(\"dataset must be an xarray Dataset\")\n\n    da = dataset[\"Rrs\"]\n    pace_wavelengths = da[\"wavelength\"].values\n\n    if isinstance(spectral_library, str):\n        endmember_paths = sorted(glob.glob(spectral_library))\n    elif isinstance(spectral_library, list):\n        endmember_paths = spectral_library\n    else:\n        endmember_paths = None\n\n    # Function to load and resample a single CSV spectral library file\n    def load_and_resample_spectral_library(csv_path, target_wavelengths):\n        df = pd.read_csv(csv_path)\n        original_wavelengths = df.iloc[:, 0].values  # First column is wavelength\n        spectra_values = df.iloc[:, 1].values  # Second column is spectral values\n\n        # Interpolation function\n        interp_func = interp1d(\n            original_wavelengths,\n            spectra_values,\n            kind=\"linear\",\n            fill_value=\"extrapolate\",\n        )\n\n        # Resample to the target (PACE) wavelengths\n        resampled_spectra = interp_func(target_wavelengths)\n\n        return resampled_spectra\n\n    if endmember_paths is not None:\n        endmembers = np.array(\n            [\n                load_and_resample_spectral_library(path, pace_wavelengths)\n                for path in endmember_paths\n            ]\n        )\n    else:\n        endmembers = None\n\n    # Function to calculate spectral angle\n    def spectral_angle_mapper(pixel, reference):\n        norm_pixel = np.linalg.norm(pixel)\n        norm_reference = np.linalg.norm(reference)\n        cos_theta = np.dot(pixel, reference) / (norm_pixel * norm_reference)\n        angle = np.arccos(np.clip(cos_theta, -1, 1))\n        return angle\n\n    # Reshape data to (n_pixels, n_bands)\n    reshaped_data = da.values.reshape(-1, da.shape[-1])\n\n    # Apply SAM for each pixel and each endmember\n    angles = np.zeros((reshaped_data.shape[0], endmembers.shape[0]))\n\n    for i in range(reshaped_data.shape[0]):\n        for j in range(endmembers.shape[0]):\n            angles[i, j] = spectral_angle_mapper(reshaped_data[i, :], endmembers[j, :])\n\n    # Find the minimum angle (best match) for each pixel\n    best_match = np.argmin(angles, axis=1)\n\n    # Reshape best_match back to the original spatial dimensions\n    best_match = best_match.reshape(da.shape[:-1])\n\n    if filter_condition is not None:\n        best_match = np.where(filter_condition, best_match, np.nan)\n\n    latitudes = da.coords[\"latitude\"].values\n    longitudes = da.coords[\"longitude\"].values\n\n    # Plot sample spectra from the CSV files and their resampled versions\n    def plot_sample_spectra(csv_paths, pace_wavelengths):\n        plt.figure(figsize=figsize)\n\n        for i, csv_path in enumerate(csv_paths):\n            df = pd.read_csv(csv_path)\n            original_wavelengths = df.iloc[:, 0].values\n            spectra_values = df.iloc[:, 1].values\n            resampled_spectra = load_and_resample_spectral_library(\n                csv_path, pace_wavelengths\n            )\n\n            plt.plot(\n                original_wavelengths,\n                spectra_values,\n                label=f\"Original Spectra {i+1}\",\n                linestyle=\"--\",\n            )\n            plt.plot(\n                pace_wavelengths, resampled_spectra, label=f\"Resampled Spectra {i+1}\"\n            )\n\n        plt.xlabel(\"Wavelength (nm)\")\n        plt.ylabel(\"Spectral Reflectance\")\n        plt.title(\"Comparison of Original and Resampled Spectra\")\n        plt.legend()\n        plt.grid(True)\n        plt.show()\n\n    if plot:\n\n        if endmember_paths is not None:\n\n            plot_sample_spectra(endmember_paths, pace_wavelengths)\n\n        if colors is None:\n            colors = [\"#377eb8\", \"#e41a1c\", \"#4daf4a\", \"#f781bf\", \"#a65628\", \"#984ea3\"]\n\n        if title is None:\n            title = \"Spectral Angle Mapper Water Type Classification\"\n        # Create a custom discrete color map\n        cmap = mcolors.ListedColormap(colors)\n        bounds = np.arange(-0.5, len(endmembers), 1)\n        norm = mcolors.BoundaryNorm(bounds, cmap.N)\n\n        # Create a figure and axis with the correct map projection\n        _, ax = plt.subplots(\n            figsize=figsize, subplot_kw={\"projection\": ccrs.PlateCarree()}, **kwargs\n        )\n\n        # Plot the SAM classification results\n        im = ax.pcolormesh(\n            longitudes,\n            latitudes,\n            best_match,\n            cmap=cmap,\n            norm=norm,\n            transform=ccrs.PlateCarree(),\n        )\n\n        # Add geographic features for context\n        ax.add_feature(cfeature.COASTLINE)\n        ax.add_feature(cfeature.BORDERS, linestyle=\":\")\n        ax.add_feature(cfeature.STATES, linestyle=\"--\")\n\n        # Adding axis labels\n        ax.set_xlabel(\"Longitude\")\n        ax.set_ylabel(\"Latitude\")\n\n        # Adding a title\n        ax.set_title(title, fontsize=14)\n\n        # Adding a color bar with discrete values\n        cbar = plt.colorbar(\n            im,\n            ax=ax,\n            orientation=\"vertical\",\n            # pad=0.02,\n            fraction=0.05,\n            ticks=np.arange(len(endmembers)),\n        )\n        cbar.ax.set_yticklabels([f\"Class {i+1}\" for i in range(len(endmembers))])\n        cbar.set_label(\"Water Types\", rotation=270, labelpad=20)\n\n        # Adding gridlines\n        ax.gridlines(draw_labels=True, linestyle=\"--\", linewidth=0.5)\n\n        # Set the extent to zoom in to the specified region (adjust as needed)\n        if extent is not None:\n            ax.set_extent(extent, crs=ccrs.PlateCarree())\n\n        # Show the plot\n        plt.show()\n\n    return best_match, latitudes, longitudes\n</code></pre>"},{"location":"pace/#hypercoast.pace.cyano_band_ratios","title":"<code>cyano_band_ratios(dataset, plot=True, extent=None, figsize=(12, 6), **kwargs)</code>","text":"<p>Calculates cyanobacteria band ratios from PACE data.</p> <p>Parameters:</p> Name Type Description Default <code>dataset</code> <code>xr.Dataset or str</code> <p>The dataset containing the PACE data or the file path to the dataset.</p> required <code>plot</code> <code>bool</code> <p>Whether to plot the data. Defaults to True.</p> <code>True</code> <code>extent</code> <code>list</code> <p>The extent of the plot. Defaults to None.</p> <code>None</code> <code>figsize</code> <code>tuple</code> <p>Figure size. Defaults to (12, 6).</p> <code>(12, 6)</code> <code>**kwargs</code> <p>Additional keyword arguments to pass to the <code>plt.subplots</code> function.</p> <code>{}</code> <p>Returns:</p> Type Description <code>xr.DataArray</code> <p>The cyanobacteria band ratios.</p> Source code in <code>hypercoast/pace.py</code> <pre><code>def cyano_band_ratios(\n    dataset: Union[xr.Dataset, str],\n    plot: bool = True,\n    extent: List[float] = None,\n    figsize: tuple[int, int] = (12, 6),\n    **kwargs,\n) -&gt; xr.DataArray:\n    \"\"\"\n    Calculates cyanobacteria band ratios from PACE data.\n\n    Args:\n        dataset (xr.Dataset or str): The dataset containing the PACE data or the file path to the dataset.\n        plot (bool, optional): Whether to plot the data. Defaults to True.\n        extent (list, optional): The extent of the plot. Defaults to None.\n        figsize (tuple, optional): Figure size. Defaults to (12, 6).\n        **kwargs: Additional keyword arguments to pass to the `plt.subplots` function.\n\n    Returns:\n        xr.DataArray: The cyanobacteria band ratios.\n    \"\"\"\n    import cartopy.crs as ccrs\n    import cartopy.feature as cfeature\n\n    if isinstance(dataset, str):\n        dataset = read_pace(dataset)\n    elif not isinstance(dataset, xr.Dataset):\n        raise ValueError(\"dataset must be an xarray Dataset\")\n\n    da = dataset[\"Rrs\"]\n    data = (\n        (da.sel(wavelength=650) &gt; da.sel(wavelength=620))\n        &amp; (da.sel(wavelength=701) &gt; da.sel(wavelength=681))\n        &amp; (da.sel(wavelength=701) &gt; da.sel(wavelength=450))\n    )\n\n    if plot:\n        # Create a plot\n        _, ax = plt.subplots(\n            figsize=figsize, subplot_kw={\"projection\": ccrs.PlateCarree()}, **kwargs\n        )\n\n        if extent is not None:\n            ax.set_extent(extent, crs=ccrs.PlateCarree())\n\n        # Plot the data\n        data.plot(\n            ax=ax,\n            transform=ccrs.PlateCarree(),\n            cmap=\"coolwarm\",\n            cbar_kwargs={\"label\": \"Cyano\"},\n        )\n\n        # Add coastlines\n        ax.coastlines()\n\n        # Add state boundaries\n        states_provinces = cfeature.NaturalEarthFeature(\n            category=\"cultural\",\n            name=\"admin_1_states_provinces_lines\",\n            scale=\"50m\",\n            facecolor=\"none\",\n        )\n\n        ax.add_feature(states_provinces, edgecolor=\"gray\")\n\n        # Optionally, add gridlines, labels, etc.\n        ax.gridlines(draw_labels=True)\n        plt.show()\n\n    return data\n</code></pre>"},{"location":"pace/#hypercoast.pace.extract_pace","title":"<code>extract_pace(dataset, latitude, longitude, delta=0.01, return_plot=False, **kwargs)</code>","text":"<p>Extracts data from a PACE dataset for a given latitude and longitude range     and calculates the mean over these dimensions.</p> <p>Parameters:</p> Name Type Description Default <code>dataset</code> <code>Union[xr.Dataset, str]</code> <p>The PACE dataset or path to the dataset file.</p> required <code>latitude</code> <code>Union[float, Tuple[float, float]]</code> <p>The latitude or range of latitudes to extract data for.</p> required <code>longitude</code> <code>Union[float, Tuple[float, float]]</code> <p>The longitude or range of longitudes to extract data for.</p> required <code>delta</code> <code>float</code> <p>The range to add/subtract to the latitude and longitude if they are not ranges. Defaults to 0.01.</p> <code>0.01</code> <code>return_plot</code> <code>bool</code> <p>Whether to return a plot of the data. Defaults to False.</p> <code>False</code> <code>**kwargs</code> <p>Additional keyword arguments to pass to the plot function.</p> <code>{}</code> <p>Returns:</p> Type Description <code>Union[xr.DataArray, plt.figure.Figure]</code> <p>The mean data over the latitude     and longitude dimensions, or a plot of this data if return_plot is True.</p> Source code in <code>hypercoast/pace.py</code> <pre><code>def extract_pace(\n    dataset: Union[xr.Dataset, str],\n    latitude: Union[float, Tuple[float, float]],\n    longitude: Union[float, Tuple[float, float]],\n    delta: float = 0.01,\n    return_plot: bool = False,\n    **kwargs,\n) -&gt; Union[xr.DataArray, plt.Figure]:\n    \"\"\"\n    Extracts data from a PACE dataset for a given latitude and longitude range\n        and calculates the mean over these dimensions.\n\n    Args:\n        dataset (Union[xr.Dataset, str]): The PACE dataset or path to the dataset file.\n        latitude (Union[float, Tuple[float, float]]): The latitude or range of\n            latitudes to extract data for.\n        longitude (Union[float, Tuple[float, float]]): The longitude or range of\n            longitudes to extract data for.\n        delta (float, optional): The range to add/subtract to the latitude and\n            longitude if they are not ranges. Defaults to 0.01.\n        return_plot (bool, optional): Whether to return a plot of the data. Defaults to False.\n        **kwargs: Additional keyword arguments to pass to the plot function.\n\n    Returns:\n        Union[xr.DataArray, plt.figure.Figure]: The mean data over the latitude\n            and longitude dimensions, or a plot of this data if return_plot is True.\n    \"\"\"\n    if isinstance(latitude, list) or isinstance(latitude, tuple):\n        pass\n    else:\n        latitude = (latitude - delta, latitude + delta)\n\n    if isinstance(longitude, list) or isinstance(longitude, tuple):\n        pass\n    else:\n        longitude = (longitude - delta, longitude + delta)\n\n    ds = filter_pace(dataset, latitude, longitude, return_plot=False)\n    data = ds.mean(dim=[\"latitude\", \"longitude\"])\n    if return_plot:\n        return data.plot.line(**kwargs)\n    else:\n        return data\n</code></pre>"},{"location":"pace/#hypercoast.pace.filter_pace","title":"<code>filter_pace(dataset, latitude, longitude, drop=True, return_plot=False, **kwargs)</code>","text":"<p>Filters a PACE dataset based on latitude and longitude.</p> <p>Parameters:</p> Name Type Description Default <code>dataset</code> <code>xr.Dataset</code> <p>The PACE dataset to filter.</p> required <code>latitude</code> <code>float or tuple</code> <p>The latitude to filter by. If a tuple or list, it represents a range.</p> required <code>longitude</code> <code>float or tuple</code> <p>The longitude to filter by. If a tuple or list, it represents a range.</p> required <code>drop</code> <code>bool</code> <p>Whether to drop the filtered out data. Defaults to True.</p> <code>True</code> <p>Returns:</p> Type Description <code>xr.DataArray</code> <p>The filtered PACE data.</p> Source code in <code>hypercoast/pace.py</code> <pre><code>def filter_pace(dataset, latitude, longitude, drop=True, return_plot=False, **kwargs):\n    \"\"\"\n    Filters a PACE dataset based on latitude and longitude.\n\n    Args:\n        dataset (xr.Dataset): The PACE dataset to filter.\n        latitude (float or tuple): The latitude to filter by. If a tuple or list, it represents a range.\n        longitude (float or tuple): The longitude to filter by. If a tuple or list, it represents a range.\n        drop (bool, optional): Whether to drop the filtered out data. Defaults to True.\n\n    Returns:\n        xr.DataArray: The filtered PACE data.\n    \"\"\"\n    if isinstance(latitude, list) or isinstance(latitude, tuple):\n        lat_con = (dataset[\"latitude\"] &gt; latitude[0]) &amp; (\n            dataset[\"latitude\"] &lt; latitude[1]\n        )\n    else:\n        lat_con = dataset[\"latitude\"] == latitude\n\n    if isinstance(longitude, list) or isinstance(longitude, tuple):\n        lon_con = (dataset[\"longitude\"] &gt; longitude[0]) &amp; (\n            dataset[\"longitude\"] &lt; longitude[1]\n        )\n    else:\n        lon_con = dataset[\"longitude\"] == longitude\n\n    da = dataset[\"Rrs\"].where(lat_con &amp; lon_con, drop=drop, **kwargs)\n    da_filtered = da.dropna(dim=\"latitude\", how=\"all\")\n    da_filtered = da_filtered.dropna(dim=\"longitude\", how=\"all\")\n\n    if return_plot:\n        rrs_stack = da_filtered.stack(\n            {\"pixel\": [\"latitude\", \"longitude\"]},\n            create_index=False,\n        )\n        rrs_stack.plot.line(hue=\"pixel\")\n    else:\n        return da_filtered\n</code></pre>"},{"location":"pace/#hypercoast.pace.grid_pace","title":"<code>grid_pace(dataset, wavelengths=None, method='nearest', **kwargs)</code>","text":"<p>Grids a PACE dataset based on latitude and longitude.</p> <p>Parameters:</p> Name Type Description Default <code>dataset</code> <code>xr.Dataset</code> <p>The PACE dataset to grid.</p> required <code>wavelengths</code> <code>float or int</code> <p>The wavelength to select.</p> <code>None</code> <code>method</code> <code>str</code> <p>The method to use for griddata interpolation. Defaults to \"nearest\".</p> <code>'nearest'</code> <code>**kwargs</code> <p>Additional keyword arguments to pass to the xr.Dataset constructor.</p> <code>{}</code> <p>Returns:</p> Type Description <code>xr.DataArray</code> <p>The gridded PACE data.</p> Source code in <code>hypercoast/pace.py</code> <pre><code>def grid_pace(dataset, wavelengths=None, method=\"nearest\", **kwargs):\n    \"\"\"\n    Grids a PACE dataset based on latitude and longitude.\n\n    Args:\n        dataset (xr.Dataset): The PACE dataset to grid.\n        wavelengths (float or int): The wavelength to select.\n        method (str, optional): The method to use for griddata interpolation.\n            Defaults to \"nearest\".\n        **kwargs: Additional keyword arguments to pass to the xr.Dataset constructor.\n\n    Returns:\n        xr.DataArray: The gridded PACE data.\n    \"\"\"\n    from scipy.interpolate import griddata\n\n    if wavelengths is None:\n        wavelengths = dataset.coords[\"wavelength\"].values[0]\n\n    # Ensure wavelengths is a list\n    if not isinstance(wavelengths, list):\n        wavelengths = [wavelengths]\n\n    lat = dataset.latitude\n    lon = dataset.longitude\n\n    grid_lat = np.linspace(lat.min().values, lat.max().values, lat.shape[0])\n    grid_lon = np.linspace(lon.min().values, lon.max().values, lon.shape[1])\n    grid_lon_2d, grid_lat_2d = np.meshgrid(grid_lon, grid_lat)\n\n    gridded_data_dict = {}\n    for wavelength in wavelengths:\n        data = dataset.sel(wavelength=wavelength, method=\"nearest\")[\"Rrs\"]\n        gridded_data = griddata(\n            (lat.data.flatten(), lon.data.flatten()),\n            data.data.flatten(),\n            (grid_lat_2d, grid_lon_2d),\n            method=method,\n        )\n        gridded_data_dict[wavelength] = gridded_data\n\n    # Create a 3D array with dimensions latitude, longitude, and wavelength\n    gridded_data_3d = np.dstack(list(gridded_data_dict.values()))\n\n    dataset2 = xr.Dataset(\n        {\"Rrs\": ((\"latitude\", \"longitude\", \"wavelength\"), gridded_data_3d)},\n        coords={\n            \"latitude\": (\"latitude\", grid_lat),\n            \"longitude\": (\"longitude\", grid_lon),\n            \"wavelength\": (\"wavelength\", list(gridded_data_dict.keys())),\n        },\n        **kwargs,\n    )\n\n    dataset2[\"Rrs\"].rio.write_crs(\"EPSG:4326\", inplace=True)\n\n    return dataset2\n</code></pre>"},{"location":"pace/#hypercoast.pace.grid_pace_bgc","title":"<code>grid_pace_bgc(dataset, variable='chlor_a', method='nearest', **kwargs)</code>","text":"<p>Grids PACE BGC data using specified interpolation method.</p> <p>This function takes an xarray Dataset containing PACE BGC data, interpolates it onto a regular grid using the specified method, and returns the gridded data as an xarray DataArray with the specified variable.</p> <p>Parameters:</p> Name Type Description Default <code>dataset</code> <code>xr.Dataset</code> <p>The input dataset containing PACE BGC data with latitude and longitude coordinates.</p> required <code>variable</code> <code>str</code> <p>The variable within the dataset to grid. Can be one of chlor_a, carbon_phyto, poc, chlor_a_unc, carbon_phyto_unc, and l2_flags. Defaults to \"chlor_a\".</p> <code>'chlor_a'</code> <code>method</code> <code>str</code> <p>The interpolation method to use. Options include \"nearest\", \"linear\", and \"cubic\". Defaults to \"nearest\".</p> <code>'nearest'</code> <code>**kwargs</code> <code>Any</code> <p>Additional keyword arguments to pass to the xr.Dataset creation.</p> <code>{}</code> <p>Returns:</p> Type Description <code>xr.DataArray</code> <p>The gridded data as an xarray DataArray, with the specified variable and EPSG:4326 CRS.</p> <p>Examples:</p> <pre><code>&gt;&gt;&gt; dataset = hypercoast.read_pace_bgc(\"path_to_your_dataset.nc\")\n&gt;&gt;&gt; gridded_data = grid_pace_bgc(dataset, variable=\"chlor_a\", method=\"nearest\")\n&gt;&gt;&gt; print(gridded_data)\n</code></pre> Source code in <code>hypercoast/pace.py</code> <pre><code>def grid_pace_bgc(\n    dataset: xr.Dataset,\n    variable: str = \"chlor_a\",\n    method: str = \"nearest\",\n    **kwargs: Any,\n) -&gt; xr.DataArray:\n    \"\"\"\n    Grids PACE BGC data using specified interpolation method.\n\n    This function takes an xarray Dataset containing PACE BGC data, interpolates it onto a regular grid\n    using the specified method, and returns the gridded data as an xarray DataArray with the specified\n    variable.\n\n    Args:\n        dataset (xr.Dataset): The input dataset containing PACE BGC data with latitude and longitude coordinates.\n        variable (str, optional): The variable within the dataset to grid. Can be\n            one of chlor_a, carbon_phyto, poc, chlor_a_unc, carbon_phyto_unc, and l2_flags.\n            Defaults to \"chlor_a\".\n        method (str, optional): The interpolation method to use. Options include \"nearest\", \"linear\", and \"cubic\".\n            Defaults to \"nearest\".\n        **kwargs (Any): Additional keyword arguments to pass to the xr.Dataset creation.\n\n    Returns:\n        xr.DataArray: The gridded data as an xarray DataArray, with the specified variable and EPSG:4326 CRS.\n\n    Example:\n        &gt;&gt;&gt; dataset = hypercoast.read_pace_bgc(\"path_to_your_dataset.nc\")\n        &gt;&gt;&gt; gridded_data = grid_pace_bgc(dataset, variable=\"chlor_a\", method=\"nearest\")\n        &gt;&gt;&gt; print(gridded_data)\n    \"\"\"\n    import rioxarray\n    from scipy.interpolate import griddata\n\n    lat = dataset.latitude\n    lon = dataset.longitude\n\n    grid_lat = np.linspace(lat.min().values, lat.max().values, lat.shape[0])\n    grid_lon = np.linspace(lon.min().values, lon.max().values, lon.shape[1])\n    grid_lon_2d, grid_lat_2d = np.meshgrid(grid_lon, grid_lat)\n\n    data = dataset[variable]\n    gridded_data = griddata(\n        (lat.data.flatten(), lon.data.flatten()),\n        data.data.flatten(),\n        (grid_lat_2d, grid_lon_2d),\n        method=method,\n    )\n\n    dataset2 = xr.Dataset(\n        {variable: ((\"latitude\", \"longitude\"), gridded_data)},\n        coords={\n            \"latitude\": (\"latitude\", grid_lat),\n            \"longitude\": (\"longitude\", grid_lon),\n        },\n        **kwargs,\n    )\n\n    dataset2 = dataset2[variable].rio.write_crs(\"EPSG:4326\")\n\n    return dataset2\n</code></pre>"},{"location":"pace/#hypercoast.pace.pace_chla_to_image","title":"<code>pace_chla_to_image(data, output=None, **kwargs)</code>","text":"<p>Converts PACE chlorophyll-a data to an image.</p> <p>Parameters:</p> Name Type Description Default <code>data</code> <code>xr.DataArray or str</code> <p>The chlorophyll-a data or the file path to the data.</p> required <code>output</code> <code>str</code> <p>The file path where the image will be saved. If None, the image will be returned as a PIL Image object. Defaults to None.</p> <code>None</code> <code>**kwargs</code> <p>Additional keyword arguments to be passed to <code>leafmap.array_to_image</code>.</p> <code>{}</code> <p>Returns:</p> Type Description <code>rasterio.Dataset or None</code> <p>The image converted from the data. If <code>output</code> is provided, the image will be saved to the specified file and the function will return None.</p> Source code in <code>hypercoast/pace.py</code> <pre><code>def pace_chla_to_image(data, output=None, **kwargs):\n    \"\"\"\n    Converts PACE chlorophyll-a data to an image.\n\n    Args:\n        data (xr.DataArray or str): The chlorophyll-a data or the file path to the data.\n        output (str, optional): The file path where the image will be saved. If None, the image will be returned as a PIL Image object. Defaults to None.\n        **kwargs: Additional keyword arguments to be passed to `leafmap.array_to_image`.\n\n    Returns:\n        rasterio.Dataset or None: The image converted from the data. If `output` is provided, the image will be saved to the specified file and the function will return None.\n    \"\"\"\n    from leafmap import array_to_image, image_to_geotiff\n\n    if isinstance(data, str):\n        data = read_pace_chla(data)\n    elif not isinstance(data, xr.DataArray):\n        raise ValueError(\"data must be an xarray DataArray\")\n\n    image = array_to_image(data, transpose=False, output=None, **kwargs)\n\n    if output is not None:\n        image_to_geotiff(image, output, dtype=\"float32\")\n\n    return image\n</code></pre>"},{"location":"pace/#hypercoast.pace.pace_to_image","title":"<code>pace_to_image(dataset, wavelengths=None, method='nearest', gridded=False, output=None, **kwargs)</code>","text":"<p>Converts an PACE dataset to an image.</p> <p>Parameters:</p> Name Type Description Default <code>dataset</code> <code>xarray.Dataset or str</code> <p>The dataset containing the EMIT data or the file path to the dataset.</p> required <code>wavelengths</code> <code>array-like</code> <p>The specific wavelengths to select. If None, all wavelengths are selected. Defaults to None.</p> <code>None</code> <code>method</code> <code>str</code> <p>The method to use for data interpolation. Defaults to \"nearest\".</p> <code>'nearest'</code> <code>gridded</code> <code>bool</code> <p>Whether the dataset is a gridded dataset. Defaults to False,</p> <code>False</code> <code>output</code> <code>str</code> <p>The file path where the image will be saved. If None, the image will be returned as a PIL Image object. Defaults to None.</p> <code>None</code> <code>**kwargs</code> <p>Additional keyword arguments to be passed to <code>leafmap.array_to_image</code>.</p> <code>{}</code> <p>Returns:</p> Type Description <code>rasterio.Dataset or None</code> <p>The image converted from the dataset. If <code>output</code> is provided, the image will be saved to the specified file and the function will return None.</p> Source code in <code>hypercoast/pace.py</code> <pre><code>def pace_to_image(\n    dataset, wavelengths=None, method=\"nearest\", gridded=False, output=None, **kwargs\n):\n    \"\"\"\n    Converts an PACE dataset to an image.\n\n    Args:\n        dataset (xarray.Dataset or str): The dataset containing the EMIT data or the file path to the dataset.\n        wavelengths (array-like, optional): The specific wavelengths to select. If None, all wavelengths are selected. Defaults to None.\n        method (str, optional): The method to use for data interpolation. Defaults to \"nearest\".\n        gridded (bool, optional): Whether the dataset is a gridded dataset. Defaults to False,\n        output (str, optional): The file path where the image will be saved. If None, the image will be returned as a PIL Image object. Defaults to None.\n        **kwargs: Additional keyword arguments to be passed to `leafmap.array_to_image`.\n\n    Returns:\n        rasterio.Dataset or None: The image converted from the dataset. If `output` is provided, the image will be saved to the specified file and the function will return None.\n    \"\"\"\n    from leafmap import array_to_image\n\n    if isinstance(dataset, str):\n        dataset = read_pace(dataset, wavelengths=wavelengths, method=\"nearest\")\n\n    if wavelengths is not None:\n        dataset = dataset.sel(wavelength=wavelengths, method=\"nearest\")\n\n    if not gridded:\n        grid = grid_pace(dataset, wavelengths=wavelengths, method=method)\n    else:\n        grid = dataset\n    data = grid[\"Rrs\"]\n    data.rio.write_crs(\"EPSG:4326\", inplace=True)\n\n    return array_to_image(data, transpose=False, output=output, **kwargs)\n</code></pre>"},{"location":"pace/#hypercoast.pace.read_pace","title":"<code>read_pace(filepath, wavelengths=None, method='nearest', engine='h5netcdf', **kwargs)</code>","text":"<p>Reads PACE data from a given file and returns an xarray Dataset.</p> <p>Parameters:</p> Name Type Description Default <code>filepath</code> <code>str</code> <p>Path to the file to read.</p> required <code>wavelengths</code> <code>array-like</code> <p>Specific wavelengths to select. If None, all wavelengths are selected.</p> <code>None</code> <code>method</code> <code>str</code> <p>Method to use for selection when wavelengths is not None. Defaults to \"nearest\".</p> <code>'nearest'</code> <code>**kwargs</code> <p>Additional keyword arguments to pass to the <code>sel</code> method when wavelengths is not None.</p> <code>{}</code> <p>Returns:</p> Type Description <code>xr.Dataset</code> <p>An xarray Dataset containing the PACE data.</p> Source code in <code>hypercoast/pace.py</code> <pre><code>def read_pace(\n    filepath, wavelengths=None, method=\"nearest\", engine=\"h5netcdf\", **kwargs\n):\n    \"\"\"\n    Reads PACE data from a given file and returns an xarray Dataset.\n\n    Args:\n        filepath (str): Path to the file to read.\n        wavelengths (array-like, optional): Specific wavelengths to select. If None, all wavelengths are selected.\n        method (str, optional): Method to use for selection when wavelengths is not None. Defaults to \"nearest\".\n        **kwargs: Additional keyword arguments to pass to the `sel` method when wavelengths is not None.\n\n    Returns:\n        xr.Dataset: An xarray Dataset containing the PACE data.\n    \"\"\"\n\n    rrs = xr.open_dataset(filepath, engine=engine, group=\"geophysical_data\")[\"Rrs\"]\n    wvl = xr.open_dataset(filepath, engine=engine, group=\"sensor_band_parameters\")\n    dataset = xr.open_dataset(filepath, engine=engine, group=\"navigation_data\")\n    dataset = dataset.set_coords((\"longitude\", \"latitude\"))\n    if \"pixel_control_points\" in dataset.dims:\n        dataset = dataset.rename({\"pixel_control_points\": \"pixels_per_line\"})\n    dataset = xr.merge([rrs, dataset.coords.to_dataset()])\n    dataset.coords[\"wavelength_3d\"] = wvl.coords[\"wavelength_3d\"]\n    dataset = dataset.rename(\n        {\n            \"number_of_lines\": \"latitude\",\n            \"pixels_per_line\": \"longitude\",\n            \"wavelength_3d\": \"wavelength\",\n        }\n    )\n\n    if wavelengths is not None:\n        dataset = dataset.sel(wavelength=wavelengths, method=method, **kwargs)\n\n    return dataset\n</code></pre>"},{"location":"pace/#hypercoast.pace.read_pace_aop","title":"<code>read_pace_aop(filepath, engine='h5netcdf', **kwargs)</code>","text":"<p>Reads PACE data from a given file and returns an xarray Dataset.</p> <p>Parameters:</p> Name Type Description Default <code>filepath</code> <code>str</code> <p>Path to the file to read.</p> required <code>wavelengths</code> <code>array-like</code> <p>Specific wavelengths to select. If None, all wavelengths are selected.</p> required <code>method</code> <code>str</code> <p>Method to use for selection when wavelengths is not None. Defaults to \"nearest\".</p> required <code>**kwargs</code> <p>Additional keyword arguments to pass to the <code>sel</code> method when wavelengths is not None.</p> <code>{}</code> <p>Returns:</p> Type Description <code>xr.Dataset</code> <p>An xarray Dataset containing the PACE data.</p> Source code in <code>hypercoast/pace.py</code> <pre><code>def read_pace_aop(filepath, engine=\"h5netcdf\", **kwargs):\n    \"\"\"\n    Reads PACE data from a given file and returns an xarray Dataset.\n\n    Args:\n        filepath (str): Path to the file to read.\n        wavelengths (array-like, optional): Specific wavelengths to select. If None, all wavelengths are selected.\n        method (str, optional): Method to use for selection when wavelengths is not None. Defaults to \"nearest\".\n        **kwargs: Additional keyword arguments to pass to the `sel` method when wavelengths is not None.\n\n    Returns:\n        xr.Dataset: An xarray Dataset containing the PACE data.\n    \"\"\"\n\n    rrs = xr.open_dataset(filepath, engine=engine, group=\"geophysical_data\", **kwargs)[\n        \"Rrs\"\n    ]\n    wvl = xr.open_dataset(\n        filepath, engine=engine, group=\"sensor_band_parameters\", **kwargs\n    )\n    dataset = xr.open_dataset(\n        filepath, engine=engine, group=\"navigation_data\", **kwargs\n    )\n    dataset = dataset.set_coords((\"longitude\", \"latitude\"))\n    if \"pixel_control_points\" in dataset.dims:\n        dataset = dataset.rename({\"pixel_control_points\": \"pixels_per_line\"})\n    dataset = xr.merge([rrs, dataset.coords.to_dataset()])\n    dataset.coords[\"wavelength_3d\"] = wvl.coords[\"wavelength_3d\"]\n\n    return dataset\n</code></pre>"},{"location":"pace/#hypercoast.pace.read_pace_bgc","title":"<code>read_pace_bgc(filepath, variable=None, engine='h5netcdf', **kwargs)</code>","text":"<p>Reads PACE BGC data from a specified file and returns an xarray Dataset.</p> <p>This function opens a dataset from a file using the specified engine, optionally selects a single variable, merges geophysical and navigation data, sets appropriate coordinates, and renames dimensions for easier use.</p> <p>Parameters:</p> Name Type Description Default <code>filepath</code> <code>str</code> <p>The path to the file containing the PACE BGC data.</p> required <code>variable</code> <code>Optional[str]</code> <p>The specific variable to extract from the geophysical_data group. If None, all variables are read. Defaults to None.</p> <code>None</code> <code>engine</code> <code>str</code> <p>The engine to use for reading the file. Defaults to \"h5netcdf\".</p> <code>'h5netcdf'</code> <code>**kwargs</code> <code>Any</code> <p>Additional keyword arguments to pass to <code>xr.open_dataset</code>.</p> <code>{}</code> <p>Returns:</p> Type Description <code>xr.Dataset</code> <p>An xarray Dataset containing the requested PACE BGC data, with merged geophysical and navigation data, set coordinates, and renamed dimensions.</p> <p>Examples:</p> <pre><code>&gt;&gt;&gt; dataset = read_pace_bgc(\"path/to/your/datafile.h5\", variable=\"chlor_a\")\n&gt;&gt;&gt; print(dataset)\n</code></pre> Source code in <code>hypercoast/pace.py</code> <pre><code>def read_pace_bgc(\n    filepath: str,\n    variable: Optional[str] = None,\n    engine: str = \"h5netcdf\",\n    **kwargs: Any,\n) -&gt; xr.Dataset:\n    \"\"\"\n    Reads PACE BGC data from a specified file and returns an xarray Dataset.\n\n    This function opens a dataset from a file using the specified engine,\n    optionally selects a single variable, merges geophysical and navigation data,\n    sets appropriate coordinates, and renames dimensions for easier use.\n\n    Args:\n        filepath (str): The path to the file containing the PACE BGC data.\n        variable (Optional[str], optional): The specific variable to extract\n            from the geophysical_data group. If None, all variables are read. Defaults to None.\n        engine (str, optional): The engine to use for reading the file. Defaults to \"h5netcdf\".\n        **kwargs (Any): Additional keyword arguments to pass to `xr.open_dataset`.\n\n    Returns:\n        xr.Dataset: An xarray Dataset containing the requested PACE BGC data,\n        with merged geophysical and navigation data, set coordinates, and renamed dimensions.\n\n    Example:\n        &gt;&gt;&gt; dataset = read_pace_bgc(\"path/to/your/datafile.h5\", variable=\"chlor_a\")\n        &gt;&gt;&gt; print(dataset)\n    \"\"\"\n\n    ds = xr.open_dataset(filepath, engine=engine, group=\"geophysical_data\", **kwargs)\n    if variable is not None:\n        ds = ds[variable]\n    dataset = xr.open_dataset(\n        filepath, engine=engine, group=\"navigation_data\", **kwargs\n    )\n    dataset = dataset.set_coords((\"longitude\", \"latitude\"))\n    if \"pixel_control_points\" in dataset.dims:\n        dataset = dataset.rename({\"pixel_control_points\": \"pixels_per_line\"})\n    dataset = xr.merge([ds, dataset.coords.to_dataset()])\n    dataset = dataset.rename(\n        {\n            \"number_of_lines\": \"latitude\",\n            \"pixels_per_line\": \"longitude\",\n        }\n    )\n    attrs = xr.open_dataset(filepath, engine=engine, **kwargs).attrs\n    dataset.attrs.update(attrs)\n\n    return dataset\n</code></pre>"},{"location":"pace/#hypercoast.pace.read_pace_chla","title":"<code>read_pace_chla(filepaths, engine='h5netcdf', **kwargs)</code>","text":"<p>Reads chlorophyll-a data from PACE files and applies a logarithmic transformation.</p> <p>This function supports reading from a single file or multiple files. For multiple files, it combines them into a single dataset. It then extracts the chlorophyll-a variable, applies a logarithmic transformation, and sets the coordinate reference system to EPSG:4326.</p> <p>Parameters:</p> Name Type Description Default <code>filepaths</code> <code>Union[str, List[str]]</code> <p>A string or a list of strings containing the file path(s) to the PACE chlorophyll-a data files.</p> required <code>engine</code> <code>str</code> <p>The backend engine to use for reading files. Defaults to \"h5netcdf\".</p> <code>'h5netcdf'</code> <code>**kwargs</code> <p>Additional keyword arguments to pass to <code>xr.open_dataset</code> or <code>xr.open_mfdataset</code>.</p> <code>{}</code> <p>Returns:</p> Type Description <code>DataArray</code> <p>An xarray DataArray containing the logarithmically transformed chlorophyll-a data with updated attributes.</p> <p>Examples:</p> <p>Read chlorophyll-a data from a single file:</p> <pre><code>&gt;&gt;&gt; chla_data = read_pace_chla('path/to/single/file.nc')\n</code></pre> <p>Read and combine chlorophyll-a data from multiple files:</p> <pre><code>&gt;&gt;&gt; chla_data = read_pace_chla(['path/to/file1.nc', 'path/to/file2.nc'], combine='by_coords')\n</code></pre> Source code in <code>hypercoast/pace.py</code> <pre><code>def read_pace_chla(\n    filepaths: Union[str, List[str]], engine: str = \"h5netcdf\", **kwargs\n) -&gt; xr.DataArray:\n    \"\"\"\n    Reads chlorophyll-a data from PACE files and applies a logarithmic transformation.\n\n    This function supports reading from a single file or multiple files. For multiple files,\n    it combines them into a single dataset. It then extracts the chlorophyll-a variable,\n    applies a logarithmic transformation, and sets the coordinate reference system to EPSG:4326.\n\n    Args:\n        filepaths: A string or a list of strings containing the file path(s) to the PACE chlorophyll-a data files.\n        engine: The backend engine to use for reading files. Defaults to \"h5netcdf\".\n        **kwargs: Additional keyword arguments to pass to `xr.open_dataset` or `xr.open_mfdataset`.\n\n    Returns:\n        An xarray DataArray containing the logarithmically transformed chlorophyll-a data with updated attributes.\n\n    Examples:\n        Read chlorophyll-a data from a single file:\n        &gt;&gt;&gt; chla_data = read_pace_chla('path/to/single/file.nc')\n\n        Read and combine chlorophyll-a data from multiple files:\n        &gt;&gt;&gt; chla_data = read_pace_chla(['path/to/file1.nc', 'path/to/file2.nc'], combine='by_coords')\n    \"\"\"\n\n    import os\n    import glob\n    import rioxarray\n\n    date = None\n    if isinstance(filepaths, str) and os.path.isfile(filepaths):\n        filepaths = [filepaths]\n    if \"combine\" not in kwargs:\n        kwargs[\"combine\"] = \"nested\"\n    if \"concat_dim\" not in kwargs:\n        kwargs[\"concat_dim\"] = \"date\"\n    dataset = xr.open_mfdataset(filepaths, engine=engine, **kwargs)\n    if not isinstance(filepaths, list):\n        filepaths = glob.glob(filepaths)\n        filepaths.sort()\n\n    dates = [extract_date_from_filename(f) for f in filepaths]\n    date = [timestamp.strftime(\"%Y-%m-%d\") for timestamp in dates]\n    dataset = dataset.assign_coords(date=(\"date\", date))\n\n    chla = np.log10(dataset[\"chlor_a\"])\n    chla.attrs.update(\n        {\n            \"units\": f'lg({dataset[\"chlor_a\"].attrs[\"units\"]})',\n        }\n    )\n\n    if date is not None:\n        chla.attrs[\"date\"] = date\n\n    chla = chla.transpose(\"lat\", \"lon\", \"date\")\n\n    chla.rio.write_crs(\"EPSG:4326\", inplace=True)\n\n    return chla\n</code></pre>"},{"location":"pace/#hypercoast.pace.view_pace_pixel_locations","title":"<code>view_pace_pixel_locations(filepath, step=20, figsize=(8, 6), **kwargs)</code>","text":"<p>Visualizes a subset of PACE pixel locations on a scatter plot.</p> <p>This function reads PACE AOP data from a specified file, subsamples the data according to a step size, and plots the longitude and latitude of the selected pixels using a scatter plot.</p> <p>Parameters:</p> Name Type Description Default <code>filepath</code> <code>str</code> <p>The path to the file containing the PACE AOP data.</p> required <code>step</code> <code>int</code> <p>The step size for subsampling the data. A smaller step size results in more data points being plotted. Defaults to 20.</p> <code>20</code> <code>**kwargs</code> <code>Any</code> <p>Additional keyword arguments to pass to the <code>plot.scatter</code> method.</p> <code>{}</code> <p>Returns:</p> Type Description <code>plt.Figure</code> <p>A matplotlib figure object containing the scatter plot.</p> <p>Examples:</p> <pre><code>&gt;&gt;&gt; plot = view_pace_pixel_locations(\"path/to/your/datafile.h5\", step=10)\n&gt;&gt;&gt; plt.show()\n</code></pre> Source code in <code>hypercoast/pace.py</code> <pre><code>def view_pace_pixel_locations(\n    filepath: str, step: int = 20, figsize: Tuple[float, float] = (8, 6), **kwargs: Any\n) -&gt; plt.Figure:\n    \"\"\"\n    Visualizes a subset of PACE pixel locations on a scatter plot.\n\n    This function reads PACE AOP data from a specified file, subsamples the data according to a step size,\n    and plots the longitude and latitude of the selected pixels using a scatter plot.\n\n    Args:\n        filepath (str): The path to the file containing the PACE AOP data.\n        step (int, optional): The step size for subsampling the data. A smaller step size results in more\n            data points being plotted. Defaults to 20.\n        **kwargs (Any): Additional keyword arguments to pass to the `plot.scatter` method.\n\n    Returns:\n        plt.Figure: A matplotlib figure object containing the scatter plot.\n\n    Example:\n        &gt;&gt;&gt; plot = view_pace_pixel_locations(\"path/to/your/datafile.h5\", step=10)\n        &gt;&gt;&gt; plt.show()\n    \"\"\"\n\n    # Create a new figure\n    fig, ax = plt.subplots(figsize=figsize)\n\n    # Create the plot\n    dataset = read_pace_aop(filepath)\n    number_of_lines = dataset.sizes[\"number_of_lines\"]\n    pixels_per_line = dataset.sizes[\"pixels_per_line\"]\n\n    ax.scatter(\n        dataset.sel(\n            {\n                \"number_of_lines\": slice(None, None, number_of_lines // step),\n                \"pixels_per_line\": slice(None, None, pixels_per_line // step),\n            }\n        ).longitude,\n        dataset.sel(\n            {\n                \"number_of_lines\": slice(None, None, number_of_lines // step),\n                \"pixels_per_line\": slice(None, None, pixels_per_line // step),\n            }\n        ).latitude,\n        **kwargs,\n    )\n\n    # Set labels and title\n    ax.set_xlabel(\"Longitude\")\n    ax.set_ylabel(\"Latitude\")\n    ax.set_title(\"PACE Pixel Locations\")\n\n    return fig\n</code></pre>"},{"location":"pace/#hypercoast.pace.viz_pace","title":"<code>viz_pace(dataset, wavelengths=None, method='nearest', figsize=(6.4, 4.8), cmap='jet', vmin=0, vmax=0.02, ncols=1, crs=None, xlim=None, ylim=None, **kwargs)</code>","text":"<p>Plots PACE data from a given xarray Dataset.</p> <p>Parameters:</p> Name Type Description Default <code>dataset</code> <code>xr.Dataset</code> <p>An xarray Dataset containing the PACE data.</p> required <code>wavelengths</code> <code>array-like</code> <p>Specific wavelengths to select. If None, all wavelengths are selected.</p> <code>None</code> <code>method</code> <code>str</code> <p>Method to use for selection when wavelengths is not None. Defaults to \"nearest\".</p> <code>'nearest'</code> <code>figsize</code> <code>tuple</code> <p>Figure size. Defaults to (6.4, 4.8).</p> <code>(6.4, 4.8)</code> <code>cmap</code> <code>str</code> <p>Colormap to use. Defaults to \"jet\".</p> <code>'jet'</code> <code>vmin</code> <code>float</code> <p>Minimum value for the colormap. Defaults to 0.</p> <code>0</code> <code>vmax</code> <code>float</code> <p>Maximum value for the colormap. Defaults to 0.02.</p> <code>0.02</code> <code>ncols</code> <code>int</code> <p>Number of columns in the plot. Defaults to 1.</p> <code>1</code> <code>crs</code> <code>str or cartopy.crs.CRS</code> <p>Coordinate reference system to use. If None, a simple plot is created. Defaults to None. See https://scitools.org.uk/cartopy/docs/latest/reference/projections.html</p> <code>None</code> <code>xlim</code> <code>array-like</code> <p>Limits for the x-axis. Defaults to None.</p> <code>None</code> <code>ylim</code> <code>array-like</code> <p>Limits for the y-axis. Defaults to None.</p> <code>None</code> <code>**kwargs</code> <p>Additional keyword arguments to pass to the <code>plt.subplots</code> function.</p> <code>{}</code> Source code in <code>hypercoast/pace.py</code> <pre><code>def viz_pace(\n    dataset: Union[xr.Dataset, str],\n    wavelengths: Optional[Union[List[float], float]] = None,\n    method: str = \"nearest\",\n    figsize: Tuple[float, float] = (6.4, 4.8),\n    cmap: str = \"jet\",\n    vmin: float = 0,\n    vmax: float = 0.02,\n    ncols: int = 1,\n    crs: Optional[str] = None,\n    xlim: Optional[List[float]] = None,\n    ylim: Optional[List[float]] = None,\n    **kwargs,\n):\n    \"\"\"\n    Plots PACE data from a given xarray Dataset.\n\n    Args:\n        dataset (xr.Dataset): An xarray Dataset containing the PACE data.\n        wavelengths (array-like, optional): Specific wavelengths to select. If None, all wavelengths are selected.\n        method (str, optional): Method to use for selection when wavelengths is not None. Defaults to \"nearest\".\n        figsize (tuple, optional): Figure size. Defaults to (6.4, 4.8).\n        cmap (str, optional): Colormap to use. Defaults to \"jet\".\n        vmin (float, optional): Minimum value for the colormap. Defaults to 0.\n        vmax (float, optional): Maximum value for the colormap. Defaults to 0.02.\n        ncols (int, optional): Number of columns in the plot. Defaults to 1.\n        crs (str or cartopy.crs.CRS, optional): Coordinate reference system to use. If None, a simple plot is created. Defaults to None.\n            See https://scitools.org.uk/cartopy/docs/latest/reference/projections.html\n        xlim (array-like, optional): Limits for the x-axis. Defaults to None.\n        ylim (array-like, optional): Limits for the y-axis. Defaults to None.\n        **kwargs: Additional keyword arguments to pass to the `plt.subplots` function.\n    \"\"\"\n\n    import matplotlib.pyplot as plt\n    import numpy as np\n    import math\n\n    if isinstance(dataset, str):\n        dataset = read_pace(dataset, wavelengths, method)\n\n    if wavelengths is not None:\n        if not isinstance(wavelengths, list):\n            wavelengths = [wavelengths]\n        dataset = dataset.sel(wavelength=wavelengths, method=method)\n    else:\n        wavelengths = dataset.coords[\"wavelength\"][0].values.tolist()\n\n    lat = dataset.coords[\"latitude\"]\n    lon = dataset.coords[\"longitude\"]\n\n    nrows = math.ceil(len(wavelengths) / ncols)\n\n    if crs is None:\n\n        fig, axes = plt.subplots(\n            nrows=nrows,\n            ncols=ncols,\n            figsize=(figsize[0] * ncols, figsize[1] * nrows),\n            **kwargs,\n        )\n\n        for i in range(nrows):\n            for j in range(ncols):\n                index = i * ncols + j\n                if index &lt; len(wavelengths):\n                    wavelength = wavelengths[index]\n                    data = dataset.sel(wavelength=wavelength, method=method)[\"Rrs\"]\n\n                    if min(nrows, ncols) == 1:\n                        ax = axes[index]\n                    else:\n                        ax = axes[i, j]\n                    im = ax.pcolormesh(\n                        lon, lat, np.squeeze(data), cmap=cmap, vmin=vmin, vmax=vmax\n                    )\n                    ax.set_xlabel(\"Longitude\")\n                    ax.set_ylabel(\"Latitude\")\n                    ax.set_title(\n                        f\"wavelength = {dataset.coords['wavelength'].values[index]} [nm]\"\n                    )\n                    fig.colorbar(im, ax=ax, label=\"Reflectance\")\n\n        plt.tight_layout()\n        plt.show()\n\n    else:\n\n        import cartopy\n        from cartopy.mpl.ticker import LongitudeFormatter, LatitudeFormatter\n\n        if crs == \"default\":\n            crs = cartopy.crs.PlateCarree()\n\n        if xlim is None:\n            xlim = [math.floor(lon.min()), math.ceil(lon.max())]\n\n        if ylim is None:\n            ylim = [math.floor(lat.min()), math.ceil(lat.max())]\n\n        fig, axes = plt.subplots(\n            nrows=nrows,\n            ncols=ncols,\n            figsize=(figsize[0] * ncols, figsize[1] * nrows),\n            subplot_kw={\"projection\": cartopy.crs.PlateCarree()},\n            **kwargs,\n        )\n\n        for i in range(nrows):\n            for j in range(ncols):\n                index = i * ncols + j\n                if index &lt; len(wavelengths):\n                    wavelength = wavelengths[index]\n                    data = dataset.sel(wavelength=wavelength, method=method)[\"Rrs\"]\n\n                    if min(nrows, ncols) == 1:\n                        ax = axes[index]\n                    else:\n                        ax = axes[i, j]\n                    im = ax.pcolormesh(lon, lat, data, cmap=\"jet\", vmin=0, vmax=0.02)\n                    ax.coastlines()\n                    ax.add_feature(cartopy.feature.STATES, linewidth=0.5)\n                    ax.set_xticks(np.linspace(xlim[0], xlim[1], 5), crs=crs)\n                    ax.set_yticks(np.linspace(ylim[0], ylim[1], 5), crs=crs)\n                    lon_formatter = LongitudeFormatter(zero_direction_label=True)\n                    lat_formatter = LatitudeFormatter()\n                    ax.xaxis.set_major_formatter(lon_formatter)\n                    ax.yaxis.set_major_formatter(lat_formatter)\n                    ax.set_xlabel(\"Longitude\")\n                    ax.set_ylabel(\"Latitude\")\n                    ax.set_title(\n                        f\"wavelength = {dataset.coords['wavelength'].values[index]} [nm]\"\n                    )\n                    plt.colorbar(im, label=\"Reflectance\")\n\n        plt.tight_layout()\n        plt.show()\n</code></pre>"},{"location":"pace/#hypercoast.pace.viz_pace_chla","title":"<code>viz_pace_chla(data, date=None, aspect=2, cmap='jet', size=6, **kwargs)</code>","text":"<p>Visualizes PACE chlorophyll-a data using an xarray DataArray.</p> <p>This function supports loading data from a file path (str) or directly using an xarray DataArray. It allows for selection of a specific date for visualization or averages over all dates if none is specified.</p> <p>Parameters:</p> Name Type Description Default <code>data</code> <code>Union[str, xr.DataArray]</code> <p>The chlorophyll-a data to visualize. Can be a file path or an xarray DataArray.</p> required <code>date</code> <code>Optional[str]</code> <p>Specific date to visualize. If None, averages over all dates. Defaults to None.</p> <code>None</code> <code>aspect</code> <code>float</code> <p>Aspect ratio of the plot. Defaults to 2.</p> <code>2</code> <code>cmap</code> <code>str</code> <p>Colormap for the plot. Defaults to \"jet\".</p> <code>'jet'</code> <code>size</code> <code>int</code> <p>Size of the plot. Defaults to 6.</p> <code>6</code> <code>**kwargs</code> <code>Any</code> <p>Additional keyword arguments to pass to <code>xarray.plot</code>.</p> <code>{}</code> <p>Returns:</p> Type Description <code>xr.plot.facetgrid.FacetGrid</code> <p>The plot generated from the chlorophyll-a data.</p> <p>Exceptions:</p> Type Description <code>ValueError</code> <p>If <code>data</code> is not a file path (str) or an xarray DataArray.</p> Source code in <code>hypercoast/pace.py</code> <pre><code>def viz_pace_chla(\n    data: Union[str, xr.DataArray],\n    date: Optional[str] = None,\n    aspect: float = 2,\n    cmap: str = \"jet\",\n    size: int = 6,\n    **kwargs: Any,\n) -&gt; xr.plot.facetgrid.FacetGrid:\n    \"\"\"\n    Visualizes PACE chlorophyll-a data using an xarray DataArray.\n\n    This function supports loading data from a file path (str) or directly using an xarray DataArray.\n    It allows for selection of a specific date for visualization or averages over all dates if none is specified.\n\n    Args:\n        data (Union[str, xr.DataArray]): The chlorophyll-a data to visualize. Can be a file path or an xarray DataArray.\n        date (Optional[str], optional): Specific date to visualize. If None, averages over all dates. Defaults to None.\n        aspect (float, optional): Aspect ratio of the plot. Defaults to 2.\n        cmap (str, optional): Colormap for the plot. Defaults to \"jet\".\n        size (int, optional): Size of the plot. Defaults to 6.\n        **kwargs (Any): Additional keyword arguments to pass to `xarray.plot`.\n\n    Returns:\n        xr.plot.facetgrid.FacetGrid: The plot generated from the chlorophyll-a data.\n\n    Raises:\n        ValueError: If `data` is not a file path (str) or an xarray DataArray.\n    \"\"\"\n    if isinstance(data, str):\n        data = read_pace_chla(data)\n    elif not isinstance(data, xr.DataArray):\n        raise ValueError(\"data must be an xarray DataArray\")\n\n    if date is not None:\n        data = data.sel(date=date)\n    else:\n        if \"date\" in data.coords:\n            data = data.mean(dim=\"date\")\n\n    return data.plot(aspect=aspect, cmap=cmap, size=size, **kwargs)\n</code></pre>"},{"location":"ui/","title":"ui module","text":"<p>This module contains the user interface for the hypercoast package.</p>"},{"location":"ui/#hypercoast.ui.SpectralWidget","title":"<code> SpectralWidget            (HBox)         </code>","text":"<p>A widget for spectral data visualization on a map.</p> <p>Attributes:</p> Name Type Description <code>_host_map</code> <code>Map</code> <p>The map to host the widget.</p> <code>on_close</code> <code>function</code> <p>Function to be called when the widget is closed.</p> <code>_output_widget</code> <code>widgets.Output</code> <p>The output widget to display results.</p> <code>_output_control</code> <code>ipyleaflet.WidgetControl</code> <p>The control for the output widget.</p> <code>_on_map_interaction</code> <code>function</code> <p>Function to handle map interactions.</p> <code>_spectral_widget</code> <code>SpectralWidget</code> <p>The spectral widget itself.</p> <code>_spectral_control</code> <code>ipyleaflet.WidgetControl</code> <p>The control for the spectral widget.</p> Source code in <code>hypercoast/ui.py</code> <pre><code>class SpectralWidget(widgets.HBox):\n    \"\"\"\n    A widget for spectral data visualization on a map.\n\n    Attributes:\n        _host_map (Map): The map to host the widget.\n        on_close (function): Function to be called when the widget is closed.\n        _output_widget (widgets.Output): The output widget to display results.\n        _output_control (ipyleaflet.WidgetControl): The control for the output widget.\n        _on_map_interaction (function): Function to handle map interactions.\n        _spectral_widget (SpectralWidget): The spectral widget itself.\n        _spectral_control (ipyleaflet.WidgetControl): The control for the spectral widget.\n    \"\"\"\n\n    def __init__(\n        self, host_map, stack=True, position=\"topright\", xlim=None, ylim=None, **kwargs\n    ):\n        \"\"\"\n        Initializes a new instance of the SpectralWidget class.\n\n        Args:\n            host_map (Map): The map to host the widget.\n            stack (bool, optional): Whether to stack the plots. Defaults to True.\n            position (str, optional): The position of the widget on the map. Defaults to \"topright\".\n            xlim (tuple, optional): The x-axis limits. Defaults to None.\n            ylim (tuple, optional): The y-axis limits. Defaults to None.\n        \"\"\"\n        self._host_map = host_map\n        self.on_close = None\n        self._stack = stack\n        self._show_plot = False\n\n        fig_margin = {\"top\": 20, \"bottom\": 35, \"left\": 50, \"right\": 20}\n        fig = plt.figure(\n            # title=None,\n            fig_margin=fig_margin,\n            layout={\"width\": \"500px\", \"height\": \"300px\"},\n        )\n\n        self._fig = fig\n        self._host_map._fig = fig\n\n        layer_names = list(host_map.cog_layer_dict.keys())\n        layers_widget = widgets.Dropdown(options=layer_names)\n        layers_widget.layout.width = \"18ex\"\n\n        close_btn = widgets.Button(\n            icon=\"times\",\n            tooltip=\"Close the widget\",\n            button_style=\"primary\",\n            layout=widgets.Layout(width=\"32px\"),\n        )\n\n        reset_btn = widgets.Button(\n            icon=\"trash\",\n            tooltip=\"Remove all markers\",\n            button_style=\"primary\",\n            layout=widgets.Layout(width=\"32px\"),\n        )\n\n        settings_btn = widgets.Button(\n            icon=\"gear\",\n            tooltip=\"Change layer settings\",\n            button_style=\"primary\",\n            layout=widgets.Layout(width=\"32px\"),\n        )\n\n        stack_btn = widgets.ToggleButton(\n            value=stack,\n            icon=\"area-chart\",\n            tooltip=\"Stack spectral signatures\",\n            button_style=\"primary\",\n            layout=widgets.Layout(width=\"32px\"),\n        )\n\n        def settings_btn_click(_):\n            self._host_map._add_layer_editor(\n                position=\"topright\",\n                layer_dict=self._host_map.cog_layer_dict[layers_widget.value],\n            )\n\n        settings_btn.on_click(settings_btn_click)\n\n        def reset_btn_click(_):\n            if hasattr(self._host_map, \"_plot_marker_cluster\"):\n                self._host_map._plot_marker_cluster.markers = []\n                self._host_map._plot_markers = []\n\n            if hasattr(self._host_map, \"_spectral_data\"):\n                self._host_map._spectral_data = {}\n\n            self._output_widget.clear_output()\n            self._show_plot = False\n            plt.clear()\n\n        reset_btn.on_click(reset_btn_click)\n\n        save_btn = widgets.Button(\n            icon=\"floppy-o\",\n            tooltip=\"Save the data to a CSV\",\n            button_style=\"primary\",\n            layout=widgets.Layout(width=\"32px\"),\n        )\n\n        def chooser_callback(chooser):\n            if chooser.selected:\n                file_path = chooser.selected\n                self._host_map.spectral_to_csv(file_path)\n                if (\n                    hasattr(self._host_map, \"_file_chooser_control\")\n                    and self._host_map._file_chooser_control in self._host_map.controls\n                ):\n                    self._host_map.remove_control(self._host_map._file_chooser_control)\n                    self._host_map._file_chooser.close()\n\n        def save_btn_click(_):\n            if not hasattr(self._host_map, \"_spectral_data\"):\n                return\n\n            self._output_widget.clear_output()\n            file_chooser = FileChooser(\n                os.getcwd(), layout=widgets.Layout(width=\"454px\")\n            )\n            file_chooser.filter_pattern = \"*.csv\"\n            file_chooser.use_dir_icons = True\n            file_chooser.title = \"Save spectral data to a CSV file\"\n            file_chooser.default_filename = \"spectral_data.csv\"\n            file_chooser.show_hidden = False\n            file_chooser.register_callback(chooser_callback)\n            file_chooser_control = ipyleaflet.WidgetControl(\n                widget=file_chooser, position=\"topright\"\n            )\n            self._host_map.add(file_chooser_control)\n            setattr(self._host_map, \"_file_chooser\", file_chooser)\n            setattr(self._host_map, \"_file_chooser_control\", file_chooser_control)\n\n        save_btn.on_click(save_btn_click)\n\n        def close_widget(_):\n            self.cleanup()\n\n        close_btn.on_click(close_widget)\n\n        super().__init__(\n            [layers_widget, settings_btn, stack_btn, reset_btn, save_btn, close_btn]\n        )\n\n        output = widgets.Output()\n        output_control = ipyleaflet.WidgetControl(widget=output, position=\"bottomright\")\n        self._output_widget = output\n        self._output_control = output_control\n        self._host_map.add(output_control)\n\n        if not hasattr(self._host_map, \"_spectral_data\"):\n            self._host_map._spectral_data = {}\n\n        def handle_interaction(**kwargs):\n\n            latlon = kwargs.get(\"coordinates\")\n            lat = latlon[0]\n            lon = latlon[1]\n            if kwargs.get(\"type\") == \"click\" and self._host_map._layer_editor is None:\n                layer_name = layers_widget.value\n\n                if not hasattr(self._host_map, \"_plot_markers\"):\n                    self._host_map._plot_markers = []\n                markers = self._host_map._plot_markers\n                marker_cluster = self._host_map._plot_marker_cluster\n                markers.append(ipyleaflet.Marker(location=latlon, draggable=False))\n                marker_cluster.markers = markers\n                self._host_map._plot_marker_cluster = marker_cluster\n\n                xlabel = \"Wavelength (nm)\"\n                ylabel = \"Reflectance\"\n\n                ds = self._host_map.cog_layer_dict[layer_name][\"xds\"]\n\n                if self._host_map.cog_layer_dict[layer_name][\"hyper\"] == \"XARRAY\":\n                    da = extract_spectral(ds, lat, lon)\n                    xlabel = \"Band\"\n                    ylabel = \"Value\"\n\n                elif self._host_map.cog_layer_dict[layer_name][\"hyper\"] == \"EMIT\":\n                    da = ds.sel(latitude=lat, longitude=lon, method=\"nearest\")[\n                        \"reflectance\"\n                    ]\n\n                    if \"wavelength\" not in self._host_map._spectral_data:\n                        self._host_map._spectral_data[\"wavelength\"] = ds[\n                            \"wavelength\"\n                        ].values\n                elif self._host_map.cog_layer_dict[layer_name][\"hyper\"] == \"PACE\":\n                    try:\n                        da = extract_pace(ds, lat, lon)\n                    except:\n                        da = xr.DataArray(\n                            np.full(len(ds[\"wavelength\"]), np.nan),\n                            dims=[\"wavelength\"],\n                            coords={\"wavelength\": ds[\"wavelength\"]},\n                        )\n                    if \"wavelengths\" not in self._host_map._spectral_data:\n                        self._host_map._spectral_data[\"wavelengths\"] = ds[\n                            \"wavelength\"\n                        ].values\n\n                elif self._host_map.cog_layer_dict[layer_name][\"hyper\"] == \"DESIS\":\n                    da = extract_desis(ds, lat, lon)\n\n                elif self._host_map.cog_layer_dict[layer_name][\"hyper\"] == \"NEON\":\n                    da = extract_neon(ds, lat, lon)\n\n                elif self._host_map.cog_layer_dict[layer_name][\"hyper\"] == \"AVIRIS\":\n                    da = extract_aviris(ds, lat, lon)\n\n                self._host_map._spectral_data[f\"({lat:.4f} {lon:.4f})\"] = da.values\n\n                if self._host_map.cog_layer_dict[layer_name][\"hyper\"] != \"XARRAY\":\n                    da[da &lt; 0] = np.nan\n                    x_axis_options = {\"label_offset\": \"30px\"}\n                else:\n                    x_axis_options = {\n                        \"label_offset\": \"30px\",\n                        \"tick_format\": \"0d\",\n                        \"num_ticks\": da.sizes[\"band\"],\n                    }\n                axes_options = {\n                    \"x\": x_axis_options,\n                    \"y\": {\"label_offset\": \"35px\"},\n                }\n\n                if not stack_btn.value:\n                    plt.clear()\n                    plt.plot(\n                        da.coords[da.dims[0]].values,\n                        da.values,\n                        axes_options=axes_options,\n                    )\n                else:\n                    color = np.random.rand(\n                        3,\n                    )\n                    plt.plot(\n                        da.coords[da.dims[0]].values,\n                        da.values,\n                        color=color,\n                        axes_options=axes_options,\n                    )\n                    try:\n                        if isinstance(self._fig.axes[0], bqplot.ColorAxis):\n                            self._fig.axes = self._fig.axes[1:]\n                        elif isinstance(self._fig.axes[-1], bqplot.ColorAxis):\n                            self._fig.axes = self._fig.axes[:-1]\n                    except Exception:\n                        pass\n\n                plt.xlabel(xlabel)\n                plt.ylabel(ylabel)\n                if xlim:\n                    plt.xlim(xlim[0], xlim[1])\n                if ylim:\n                    plt.ylim(ylim[0], ylim[1])\n\n                if not self._show_plot:\n                    with self._output_widget:\n                        plt.show()\n                        self._show_plot = True\n\n                self._host_map.default_style = {\"cursor\": \"crosshair\"}\n\n        self._host_map.on_interaction(handle_interaction)\n        self._on_map_interaction = handle_interaction\n\n        self._spectral_widget = self\n        self._spectral_control = ipyleaflet.WidgetControl(\n            widget=self, position=position\n        )\n        self._host_map.add(self._spectral_control)\n\n    def cleanup(self):\n        \"\"\"Removes the widget from the map and performs cleanup.\"\"\"\n        if self._host_map:\n            self._host_map.default_style = {\"cursor\": \"default\"}\n            self._host_map.on_interaction(self._on_map_interaction, remove=True)\n\n            if self._output_control:\n                self._host_map.remove_control(self._output_control)\n\n                if self._output_widget:\n                    self._output_widget.close()\n                    self._output_widget = None\n\n            if self._spectral_control:\n                self._host_map.remove_control(self._spectral_control)\n                self._spectral_control = None\n\n                if self._spectral_widget:\n                    self._spectral_widget.close()\n                    self._spectral_widget = None\n\n            if hasattr(self._host_map, \"_plot_marker_cluster\"):\n                self._host_map._plot_marker_cluster.markers = []\n                self._host_map._plot_markers = []\n\n            if hasattr(self._host_map, \"_spectral_data\"):\n                self._host_map._spectral_data = {}\n\n            if hasattr(self, \"_output_widget\") and self._output_widget is not None:\n                self._output_widget.clear_output()\n\n        if self.on_close is not None:\n            self.on_close()\n</code></pre>"},{"location":"ui/#hypercoast.ui.SpectralWidget.__init__","title":"<code>__init__(self, host_map, stack=True, position='topright', xlim=None, ylim=None, **kwargs)</code>  <code>special</code>","text":"<p>Initializes a new instance of the SpectralWidget class.</p> <p>Parameters:</p> Name Type Description Default <code>host_map</code> <code>Map</code> <p>The map to host the widget.</p> required <code>stack</code> <code>bool</code> <p>Whether to stack the plots. Defaults to True.</p> <code>True</code> <code>position</code> <code>str</code> <p>The position of the widget on the map. Defaults to \"topright\".</p> <code>'topright'</code> <code>xlim</code> <code>tuple</code> <p>The x-axis limits. Defaults to None.</p> <code>None</code> <code>ylim</code> <code>tuple</code> <p>The y-axis limits. Defaults to None.</p> <code>None</code> Source code in <code>hypercoast/ui.py</code> <pre><code>def __init__(\n    self, host_map, stack=True, position=\"topright\", xlim=None, ylim=None, **kwargs\n):\n    \"\"\"\n    Initializes a new instance of the SpectralWidget class.\n\n    Args:\n        host_map (Map): The map to host the widget.\n        stack (bool, optional): Whether to stack the plots. Defaults to True.\n        position (str, optional): The position of the widget on the map. Defaults to \"topright\".\n        xlim (tuple, optional): The x-axis limits. Defaults to None.\n        ylim (tuple, optional): The y-axis limits. Defaults to None.\n    \"\"\"\n    self._host_map = host_map\n    self.on_close = None\n    self._stack = stack\n    self._show_plot = False\n\n    fig_margin = {\"top\": 20, \"bottom\": 35, \"left\": 50, \"right\": 20}\n    fig = plt.figure(\n        # title=None,\n        fig_margin=fig_margin,\n        layout={\"width\": \"500px\", \"height\": \"300px\"},\n    )\n\n    self._fig = fig\n    self._host_map._fig = fig\n\n    layer_names = list(host_map.cog_layer_dict.keys())\n    layers_widget = widgets.Dropdown(options=layer_names)\n    layers_widget.layout.width = \"18ex\"\n\n    close_btn = widgets.Button(\n        icon=\"times\",\n        tooltip=\"Close the widget\",\n        button_style=\"primary\",\n        layout=widgets.Layout(width=\"32px\"),\n    )\n\n    reset_btn = widgets.Button(\n        icon=\"trash\",\n        tooltip=\"Remove all markers\",\n        button_style=\"primary\",\n        layout=widgets.Layout(width=\"32px\"),\n    )\n\n    settings_btn = widgets.Button(\n        icon=\"gear\",\n        tooltip=\"Change layer settings\",\n        button_style=\"primary\",\n        layout=widgets.Layout(width=\"32px\"),\n    )\n\n    stack_btn = widgets.ToggleButton(\n        value=stack,\n        icon=\"area-chart\",\n        tooltip=\"Stack spectral signatures\",\n        button_style=\"primary\",\n        layout=widgets.Layout(width=\"32px\"),\n    )\n\n    def settings_btn_click(_):\n        self._host_map._add_layer_editor(\n            position=\"topright\",\n            layer_dict=self._host_map.cog_layer_dict[layers_widget.value],\n        )\n\n    settings_btn.on_click(settings_btn_click)\n\n    def reset_btn_click(_):\n        if hasattr(self._host_map, \"_plot_marker_cluster\"):\n            self._host_map._plot_marker_cluster.markers = []\n            self._host_map._plot_markers = []\n\n        if hasattr(self._host_map, \"_spectral_data\"):\n            self._host_map._spectral_data = {}\n\n        self._output_widget.clear_output()\n        self._show_plot = False\n        plt.clear()\n\n    reset_btn.on_click(reset_btn_click)\n\n    save_btn = widgets.Button(\n        icon=\"floppy-o\",\n        tooltip=\"Save the data to a CSV\",\n        button_style=\"primary\",\n        layout=widgets.Layout(width=\"32px\"),\n    )\n\n    def chooser_callback(chooser):\n        if chooser.selected:\n            file_path = chooser.selected\n            self._host_map.spectral_to_csv(file_path)\n            if (\n                hasattr(self._host_map, \"_file_chooser_control\")\n                and self._host_map._file_chooser_control in self._host_map.controls\n            ):\n                self._host_map.remove_control(self._host_map._file_chooser_control)\n                self._host_map._file_chooser.close()\n\n    def save_btn_click(_):\n        if not hasattr(self._host_map, \"_spectral_data\"):\n            return\n\n        self._output_widget.clear_output()\n        file_chooser = FileChooser(\n            os.getcwd(), layout=widgets.Layout(width=\"454px\")\n        )\n        file_chooser.filter_pattern = \"*.csv\"\n        file_chooser.use_dir_icons = True\n        file_chooser.title = \"Save spectral data to a CSV file\"\n        file_chooser.default_filename = \"spectral_data.csv\"\n        file_chooser.show_hidden = False\n        file_chooser.register_callback(chooser_callback)\n        file_chooser_control = ipyleaflet.WidgetControl(\n            widget=file_chooser, position=\"topright\"\n        )\n        self._host_map.add(file_chooser_control)\n        setattr(self._host_map, \"_file_chooser\", file_chooser)\n        setattr(self._host_map, \"_file_chooser_control\", file_chooser_control)\n\n    save_btn.on_click(save_btn_click)\n\n    def close_widget(_):\n        self.cleanup()\n\n    close_btn.on_click(close_widget)\n\n    super().__init__(\n        [layers_widget, settings_btn, stack_btn, reset_btn, save_btn, close_btn]\n    )\n\n    output = widgets.Output()\n    output_control = ipyleaflet.WidgetControl(widget=output, position=\"bottomright\")\n    self._output_widget = output\n    self._output_control = output_control\n    self._host_map.add(output_control)\n\n    if not hasattr(self._host_map, \"_spectral_data\"):\n        self._host_map._spectral_data = {}\n\n    def handle_interaction(**kwargs):\n\n        latlon = kwargs.get(\"coordinates\")\n        lat = latlon[0]\n        lon = latlon[1]\n        if kwargs.get(\"type\") == \"click\" and self._host_map._layer_editor is None:\n            layer_name = layers_widget.value\n\n            if not hasattr(self._host_map, \"_plot_markers\"):\n                self._host_map._plot_markers = []\n            markers = self._host_map._plot_markers\n            marker_cluster = self._host_map._plot_marker_cluster\n            markers.append(ipyleaflet.Marker(location=latlon, draggable=False))\n            marker_cluster.markers = markers\n            self._host_map._plot_marker_cluster = marker_cluster\n\n            xlabel = \"Wavelength (nm)\"\n            ylabel = \"Reflectance\"\n\n            ds = self._host_map.cog_layer_dict[layer_name][\"xds\"]\n\n            if self._host_map.cog_layer_dict[layer_name][\"hyper\"] == \"XARRAY\":\n                da = extract_spectral(ds, lat, lon)\n                xlabel = \"Band\"\n                ylabel = \"Value\"\n\n            elif self._host_map.cog_layer_dict[layer_name][\"hyper\"] == \"EMIT\":\n                da = ds.sel(latitude=lat, longitude=lon, method=\"nearest\")[\n                    \"reflectance\"\n                ]\n\n                if \"wavelength\" not in self._host_map._spectral_data:\n                    self._host_map._spectral_data[\"wavelength\"] = ds[\n                        \"wavelength\"\n                    ].values\n            elif self._host_map.cog_layer_dict[layer_name][\"hyper\"] == \"PACE\":\n                try:\n                    da = extract_pace(ds, lat, lon)\n                except:\n                    da = xr.DataArray(\n                        np.full(len(ds[\"wavelength\"]), np.nan),\n                        dims=[\"wavelength\"],\n                        coords={\"wavelength\": ds[\"wavelength\"]},\n                    )\n                if \"wavelengths\" not in self._host_map._spectral_data:\n                    self._host_map._spectral_data[\"wavelengths\"] = ds[\n                        \"wavelength\"\n                    ].values\n\n            elif self._host_map.cog_layer_dict[layer_name][\"hyper\"] == \"DESIS\":\n                da = extract_desis(ds, lat, lon)\n\n            elif self._host_map.cog_layer_dict[layer_name][\"hyper\"] == \"NEON\":\n                da = extract_neon(ds, lat, lon)\n\n            elif self._host_map.cog_layer_dict[layer_name][\"hyper\"] == \"AVIRIS\":\n                da = extract_aviris(ds, lat, lon)\n\n            self._host_map._spectral_data[f\"({lat:.4f} {lon:.4f})\"] = da.values\n\n            if self._host_map.cog_layer_dict[layer_name][\"hyper\"] != \"XARRAY\":\n                da[da &lt; 0] = np.nan\n                x_axis_options = {\"label_offset\": \"30px\"}\n            else:\n                x_axis_options = {\n                    \"label_offset\": \"30px\",\n                    \"tick_format\": \"0d\",\n                    \"num_ticks\": da.sizes[\"band\"],\n                }\n            axes_options = {\n                \"x\": x_axis_options,\n                \"y\": {\"label_offset\": \"35px\"},\n            }\n\n            if not stack_btn.value:\n                plt.clear()\n                plt.plot(\n                    da.coords[da.dims[0]].values,\n                    da.values,\n                    axes_options=axes_options,\n                )\n            else:\n                color = np.random.rand(\n                    3,\n                )\n                plt.plot(\n                    da.coords[da.dims[0]].values,\n                    da.values,\n                    color=color,\n                    axes_options=axes_options,\n                )\n                try:\n                    if isinstance(self._fig.axes[0], bqplot.ColorAxis):\n                        self._fig.axes = self._fig.axes[1:]\n                    elif isinstance(self._fig.axes[-1], bqplot.ColorAxis):\n                        self._fig.axes = self._fig.axes[:-1]\n                except Exception:\n                    pass\n\n            plt.xlabel(xlabel)\n            plt.ylabel(ylabel)\n            if xlim:\n                plt.xlim(xlim[0], xlim[1])\n            if ylim:\n                plt.ylim(ylim[0], ylim[1])\n\n            if not self._show_plot:\n                with self._output_widget:\n                    plt.show()\n                    self._show_plot = True\n\n            self._host_map.default_style = {\"cursor\": \"crosshair\"}\n\n    self._host_map.on_interaction(handle_interaction)\n    self._on_map_interaction = handle_interaction\n\n    self._spectral_widget = self\n    self._spectral_control = ipyleaflet.WidgetControl(\n        widget=self, position=position\n    )\n    self._host_map.add(self._spectral_control)\n</code></pre>"},{"location":"ui/#hypercoast.ui.SpectralWidget.cleanup","title":"<code>cleanup(self)</code>","text":"<p>Removes the widget from the map and performs cleanup.</p> Source code in <code>hypercoast/ui.py</code> <pre><code>def cleanup(self):\n    \"\"\"Removes the widget from the map and performs cleanup.\"\"\"\n    if self._host_map:\n        self._host_map.default_style = {\"cursor\": \"default\"}\n        self._host_map.on_interaction(self._on_map_interaction, remove=True)\n\n        if self._output_control:\n            self._host_map.remove_control(self._output_control)\n\n            if self._output_widget:\n                self._output_widget.close()\n                self._output_widget = None\n\n        if self._spectral_control:\n            self._host_map.remove_control(self._spectral_control)\n            self._spectral_control = None\n\n            if self._spectral_widget:\n                self._spectral_widget.close()\n                self._spectral_widget = None\n\n        if hasattr(self._host_map, \"_plot_marker_cluster\"):\n            self._host_map._plot_marker_cluster.markers = []\n            self._host_map._plot_markers = []\n\n        if hasattr(self._host_map, \"_spectral_data\"):\n            self._host_map._spectral_data = {}\n\n        if hasattr(self, \"_output_widget\") and self._output_widget is not None:\n            self._output_widget.clear_output()\n\n    if self.on_close is not None:\n        self.on_close()\n</code></pre>"},{"location":"usage/","title":"Usage","text":""},{"location":"usage/#import-library","title":"Import library","text":"<p>To use HyperCoast in a project:</p> <pre><code>import hypercoast\n</code></pre>"},{"location":"usage/#search-for-datasets","title":"Search for datasets","text":"<p>To download and access NASA hyperspectral data, you will need to create an Earthdata login. You can register for an account at urs.earthdata.nasa.gov. Once you have an account, run the following code to log in:</p> <pre><code>hypercoast.nasa_earth_login()\n</code></pre> <p>Collections on NASA Earthdata are discovered with the search_datasets function, which accepts an instrument filter as an easy way to get started. Each of the items in the list of collections returned has a \"short-name\". For example, to search for all datasets with the instrument \"oci\":</p> <pre><code>results = hypercoast.search_datasets(instrument=\"oci\")\ndatasets = set()\nfor item in results:\n    summary = item.summary()\n    short_name = summary[\"short-name\"]\n    if short_name not in datasets:\n        print(short_name)\n    datasets.add(short_name)\nprint(f\"\\nFound {len(datasets)} unique datasets\")\n</code></pre>"},{"location":"usage/#search-for-data-by-short-name","title":"Search for data by short name","text":"<p>Next, we use the <code>search_nasa_data</code> function to find granules within a collection. Let's use the short_name for the PACE/OCI Level-2 data product for bio-optical and biogeochemical properties.</p> <pre><code>results = hypercoast.search_nasa_data(\n    short_name=\"PACE_OCI_L2_BGC_NRT\",\n    count=1,\n)\n</code></pre> <p>We can refine our search by passing more parameters that describe the spatiotemporal domain of our use case. Here, we use the temporal parameter to request a date range and the bounding_box parameter to request granules that intersect with a bounding box. We can even provide a cloud_cover threshold to limit files that have a lower percentage of cloud cover. We do not provide a count, so we'll get all granules that satisfy the constraints.</p> <pre><code>tspan = (\"2024-04-01\", \"2024-04-16\")\nbbox = (-76.75, 36.97, -75.74, 39.01)\nclouds = (0, 50)\n\nresults, gdf = hypercoast.search_nasa_data(\n    short_name=\"PACE_OCI_L2_BGC_NRT\",\n    temporal=tspan,\n    bounding_box=bbox,\n    cloud_cover=clouds,\n    return_gdf=True,\n)\n</code></pre> <p>Display the footprints of the granules that match the search criteria.</p> <pre><code>gdf.explore()\n</code></pre> <p>We can also download all the results with one command.</p> <pre><code>hypercoast.download_nasa_data(results, out_dir=\"data\")\n</code></pre>"},{"location":"usage/#search-for-pace-data","title":"Search for PACE data","text":"<p>To search for PACE data, we can use the <code>search_pace</code> function:</p> <pre><code>results, gdf = hypercoast.search_pace(\n    bounding_box=(-83, 25, -81, 28),\n    temporal=(\"2024-05-10\", \"2024-05-16\"),\n    count=10,  # use -1 to return all datasets\n    return_gdf=True,\n)\n</code></pre> <p>To download the PACE data, we can use the <code>download_pace</code> function:</p> <pre><code>hypercoast.download_pace(results, out_dir=\"data\")\n</code></pre>"},{"location":"usage/#search-for-emit-data","title":"Search for EMIT data","text":"<p>To search for EMIT data, we can use the <code>search_emit</code> function:</p> <pre><code>results, gdf = hypercoast.search_emit(\n    bounding_box=(-83, 25, -81, 28),\n    temporal=(\"2024-04-01\", \"2024-05-16\"),\n    count=10,  # use -1 to return all datasets\n    return_gdf=True,\n)\n</code></pre> <p>To download the EMIT data, we can use the <code>download_emit</code> function:</p> <pre><code>hypercoast.download_emit(results, out_dir=\"data\")\n</code></pre>"},{"location":"usage/#visualize-pace-data","title":"Visualize PACE data","text":"<p>Load the dataset as a <code>xarray.Dataset</code> object:</p> <pre><code>dataset = hypercoast.read_pace(filepath)\n</code></pre> <p>Visualize selected bands of the dataset:</p> <pre><code>hypercoast.viz_pace(dataset, wavelengths=[500, 510, 520, 530], ncols=2, crs=\"default\")\n</code></pre> <p>Visualize the dataset on an interactive map:</p> <pre><code>m = hypercoast.Map()\nm.add_basemap(\"Hybrid\")\nwavelengths = [450]\nm.add_pace(dataset, wavelengths, colormap=\"jet\", vmin=0, vmax=0.02, layer_name=\"PACE\")\nm.add_colormap(cmap=\"jet\", vmin=0, vmax=0.02, label=\"Reflectance\")\nm.add(\"spectral\")\nm\n</code></pre>"},{"location":"usage/#visualize-emit-data","title":"Visualize EMIT data","text":"<p>To visualize EMIT data, we can use the <code>read_emit</code> function:</p> <pre><code>dataset = hypercoast.read_emit(filepath)\n</code></pre> <p>Visualize the dataset on an interactive map:</p> <pre><code>m = hypercoast.Map()\nm.add_basemap(\"SATELLITE\")\nm.add_emit(dataset, wavelengths=[1000, 600, 500], vmin=0, vmax=0.3, layer_name=\"EMIT\")\nm.add(\"spectral\")\nm\n</code></pre>"},{"location":"usage/#create-an-image-cube","title":"Create an image cube","text":"<p>First , load the dataset as a <code>xarray.Dataset</code> object. Select a region of interest (ROI) using the <code>sel</code> method:</p> <pre><code>dataset = hypercoast.read_emit(filepath)\nds = dataset.sel(longitude=slice(-90.1482, -89.7321), latitude=slice(30.0225, 29.7451))\n</code></pre> <p>Create an image cube using the <code>image_cube</code> function:</p> <pre><code>cube = hypercoast.image_cube(\n    ds,\n    variable=\"reflectance\",\n    cmap=\"jet\",\n    clim=(0, 0.4),\n    rgb_wavelengths=[1000, 700, 500],\n    rgb_gamma=2,\n    title=\"EMIT Reflectance\",\n)\ncube.show()\n</code></pre>"},{"location":"usage/#interactive-slicing-and-thresholding","title":"Interactive slicing and thresholding","text":"<p>First , load the dataset as a <code>xarray.Dataset</code> object. Select a region of interest (ROI) using the <code>sel</code> method:</p> <pre><code>dataset = hypercoast.read_emit(filepath)\nds = dataset.sel(longitude=slice(-90.05, -89.99), latitude=slice(30.00, 29.93))\n</code></pre> <p>Interactive slicing along the z-axis (band):</p> <pre><code>p = hypercoast.image_cube(\n    ds,\n    variable=\"reflectance\",\n    cmap=\"jet\",\n    clim=(0, 0.5),\n    rgb_wavelengths=[1000, 700, 500],\n    rgb_gamma=2,\n    title=\"EMIT Reflectance\",\n    widget=\"plane\",\n)\np.add_text(\"Band slicing\", position=\"upper_right\", font_size=14)\np.show()\n</code></pre> <p>Interactive thresholding:</p> <pre><code>p = hypercoast.image_cube(\n    ds,\n    variable=\"reflectance\",\n    cmap=\"jet\",\n    clim=(0, 0.5),\n    rgb_wavelengths=[1000, 700, 500],\n    rgb_gamma=2,\n    title=\"EMIT Reflectance\",\n    widget=\"threshold\",\n)\np.add_text(\"Thresholding\", position=\"upper_right\", font_size=14)\np.show()\n</code></pre>"},{"location":"usage/#visualizing-pace-chlorophyll-a-concentration-data","title":"Visualizing PACE chlorophyll-a concentration data","text":"<p>Load all the data files in a directory as an <code>xarray.DataArray</code>:</p> <pre><code>files = \"data/*nc\"\narray = hypercoast.read_pace_chla(files)\n</code></pre> <p>Select a date and visualize the chlorophyll-a concentration data with Matplotlib.</p> <pre><code>hypercoast.viz_pace_chla(array, date=\"2024-06-01\", cmap=\"jet\", size=6)\n</code></pre> <p>If the date is not specified, the data are averaged over the entire time range.</p> <pre><code>hypercoast.viz_pace_chla(array, cmap=\"jet\", size=6)\n</code></pre> <p>Convert the data array to an image that can be displayed on an interactive map.</p> <pre><code>single_image = hypercoast.pace_chla_to_image(single_array)\n</code></pre> <p>Create an interactive map and display the image on the map.</p> <pre><code>m = hypercoast.Map(center=[40, -100], zoom=4)\nm.add_basemap(\"Hybrid\")\nm.add_raster(\n    single_image,\n    cmap=\"jet\",\n    vmin=-1,\n    vmax=2,\n    layer_name=\"Chlorophyll a\",\n    zoom_to_layer=False,\n)\nlabel = \"Chlorophyll Concentration [lg(lg(mg m^-3))]\"\nm.add_colormap(cmap=\"jet\", vmin=-1, vmax=2, label=label)\nm\n</code></pre>"},{"location":"examples/_earthaccess/","title":"earthaccess","text":"In\u00a0[1]: Copied! <pre># %pip install \"hypercoast[extra]\"\n</pre> # %pip install \"hypercoast[extra]\" In\u00a0[2]: Copied! <pre>import os\nimport earthaccess\nimport hypercoast\n</pre> import os import earthaccess import hypercoast In\u00a0[3]: Copied! <pre># os.environ[\"EARTHDATA_USERNAME\"] = \"your_username\"\n# os.environ[\"EARTHDATA_PASSWORD\"] = \"your_password\"\n</pre> # os.environ[\"EARTHDATA_USERNAME\"] = \"your_username\" # os.environ[\"EARTHDATA_PASSWORD\"] = \"your_password\" In\u00a0[4]: Copied! <pre>if os.environ.get(\"EARTHDATA_USERNAME\") is None:\n    raise ValueError(\"Please set the EARTHDATA_USERNAME environment variable\")\nif os.environ.get(\"EARTHDATA_PASSWORD\") is None:\n    raise ValueError(\"Please set the EARTHDATA_PASSWORD environment variable\")\n</pre> if os.environ.get(\"EARTHDATA_USERNAME\") is None:     raise ValueError(\"Please set the EARTHDATA_USERNAME environment variable\") if os.environ.get(\"EARTHDATA_PASSWORD\") is None:     raise ValueError(\"Please set the EARTHDATA_PASSWORD environment variable\") In\u00a0[5]: Copied! <pre>earthaccess.login(persist=True)\n</pre> earthaccess.login(persist=True) Out[5]: <pre>&lt;earthaccess.auth.Auth at 0x7fa84805b500&gt;</pre> In\u00a0[6]: Copied! <pre>results, gdf = hypercoast.search_pace(\n    bounding_box=(-83, 25, -81, 28),\n    temporal=(\"2024-07-30\", \"2024-08-15\"),\n    short_name=\"PACE_OCI_L2_AOP_NRT\",\n    count=10,\n    return_gdf=True,\n)\n</pre> results, gdf = hypercoast.search_pace(     bounding_box=(-83, 25, -81, 28),     temporal=(\"2024-07-30\", \"2024-08-15\"),     short_name=\"PACE_OCI_L2_AOP_NRT\",     count=10,     return_gdf=True, ) In\u00a0[7]: Copied! <pre>hypercoast.download_pace(results[:1], out_dir=\"data\")\n</pre> hypercoast.download_pace(results[:1], out_dir=\"data\") In\u00a0[8]: Copied! <pre>filepath = \"data/PACE_OCI.20240730T181157.L2.OC_AOP.V2_0.NRT.nc\"\nif not os.path.exists(filepath):\n    raise FileNotFoundError(filepath)\n</pre> filepath = \"data/PACE_OCI.20240730T181157.L2.OC_AOP.V2_0.NRT.nc\" if not os.path.exists(filepath):     raise FileNotFoundError(filepath)"},{"location":"examples/_earthaccess/#testing-earthaccess-login","title":"Testing earthaccess login\u00b6","text":""},{"location":"examples/acolite/","title":"Acolite","text":"In\u00a0[\u00a0]: Copied! <pre># %pip install \"hypercoast[extra]\"\n</pre> # %pip install \"hypercoast[extra]\" In\u00a0[\u00a0]: Copied! <pre>import os\nimport hypercoast\n</pre> import os import hypercoast In\u00a0[\u00a0]: Copied! <pre>url = \"https://github.com/opengeos/datasets/releases/download/hypercoast/S2A_MSIL1C_20160920T164452_N0204_R083_T15RYN_20160920T164450.SAFE.zip\"\nwork_dir = os.path.expanduser(\"~/Downloads\")\nfilepath = os.path.join(work_dir, os.path.basename(url))\nhypercoast.download_file(url, filepath, quiet=True)\n</pre> url = \"https://github.com/opengeos/datasets/releases/download/hypercoast/S2A_MSIL1C_20160920T164452_N0204_R083_T15RYN_20160920T164450.SAFE.zip\" work_dir = os.path.expanduser(\"~/Downloads\") filepath = os.path.join(work_dir, os.path.basename(url)) hypercoast.download_file(url, filepath, quiet=True) In\u00a0[\u00a0]: Copied! <pre>input_dir = filepath.replace(\".zip\", \"\")\nif not os.path.exists(input_dir):\n    raise FileNotFoundError(f\"Directory {input_dir} not found\")\n</pre> input_dir = filepath.replace(\".zip\", \"\") if not os.path.exists(input_dir):     raise FileNotFoundError(f\"Directory {input_dir} not found\") In\u00a0[\u00a0]: Copied! <pre>acolite_dir = hypercoast.download_acolite(work_dir)\nprint(f\"Acolite directory: {acolite_dir}\")\n</pre> acolite_dir = hypercoast.download_acolite(work_dir) print(f\"Acolite directory: {acolite_dir}\") In\u00a0[\u00a0]: Copied! <pre>out_dir = os.path.join(work_dir, \"output\")\n</pre> out_dir = os.path.join(work_dir, \"output\") In\u00a0[\u00a0]: Copied! <pre>hypercoast.run_acolite(\n    acolite_dir=acolite_dir,\n    input_file=input_dir,\n    out_dir=out_dir,\n    l2w_parameters=\"Rrs_*,chl_oc3,chl_re_mishra,spm_nechad2016\",\n    rgb_rhot=True,\n    rgb_rhos=False,\n    map_l2w=True,\n)\n</pre> hypercoast.run_acolite(     acolite_dir=acolite_dir,     input_file=input_dir,     out_dir=out_dir,     l2w_parameters=\"Rrs_*,chl_oc3,chl_re_mishra,spm_nechad2016\",     rgb_rhot=True,     rgb_rhos=False,     map_l2w=True, ) In\u00a0[\u00a0]: Copied! <pre>input_dir = os.path.join(work_dir, \"data\")\ninput_files = [os.path.join(input_dir, f) for f in os.listdir(input_dir)]\ninput_files\n</pre> input_dir = os.path.join(work_dir, \"data\") input_files = [os.path.join(input_dir, f) for f in os.listdir(input_dir)] input_files <p>Run the following code to process all images in the <code>data</code> folder.</p> In\u00a0[\u00a0]: Copied! <pre>hypercoast.run_acolite(\n    acolite_dir=acolite_dir,\n    input_file=input_files,\n    out_dir=out_dir,\n    l2w_parameters=\"Rrs_*,chl_oc3,chl_re_mishra,spm_nechad2016\",\n    rgb_rhot=True,\n    rgb_rhos=False,\n    map_l2w=True,\n)\n</pre> hypercoast.run_acolite(     acolite_dir=acolite_dir,     input_file=input_files,     out_dir=out_dir,     l2w_parameters=\"Rrs_*,chl_oc3,chl_re_mishra,spm_nechad2016\",     rgb_rhot=True,     rgb_rhos=False,     map_l2w=True, )"},{"location":"examples/acolite/#atmospheric-correction-with-acolite","title":"Atmospheric Correction with Acolite\u00b6","text":"<p>Acolite can perform atmospheric correction on a variety of satellite sensors, including Landsat, Sentinel-2, PACE, EMIT, AVIRIS, among others. For more information on how to use Acolite, please refer to the Acolite manual</p> <p>In this example, we will use Acolite to perform atmospheric correction on a Sentinel-2 image.</p>"},{"location":"examples/acolite/#import-libraries","title":"Import libraries\u00b6","text":""},{"location":"examples/acolite/#download-sample-data","title":"Download sample data\u00b6","text":""},{"location":"examples/acolite/#download-acolite-software","title":"Download Acolite software\u00b6","text":""},{"location":"examples/acolite/#run-acolite","title":"Run Acolite\u00b6","text":""},{"location":"examples/acolite/#batch-processing","title":"Batch processing\u00b6","text":"<p>To process multiple images, put all the images in a folder. For example, unzip all the images in the <code>data</code> folder. Then, run the following code to make sure that all image folders are listed.</p>"},{"location":"examples/aviris/","title":"Aviris","text":"In\u00a0[\u00a0]: Copied! <pre># %pip install \"hypercoast[extra]\"\n</pre> # %pip install \"hypercoast[extra]\" In\u00a0[\u00a0]: Copied! <pre>import hypercoast\n</pre> import hypercoast <p>Download sample dataset from Access the AVIRIS-NG L2 Surface Reflectance product page via ORNL DAAC https://daac.ornl.gov/cgi-bin/dsviewer.pl?ds_id=1988</p> <p></p> <p>Download <code>ang20210401t150456_rfl_v2z1.zip</code> and unzip it.</p> <p></p> <p>The dataset contains 2 files: <code>ang20210401t150456_rfl_v2z1</code> and <code>ang20210401t150456_rfl_v2z1.hdr</code>. We will use the <code>ang20210401t150456_rfl_v2z1</code> file in this notebook.</p> In\u00a0[\u00a0]: Copied! <pre>filepath = \"ang20210401t150456_rfl_v2z1\"\n</pre> filepath = \"ang20210401t150456_rfl_v2z1\" <p>Read the AVIRIS data as an <code>xarray.Dataset</code> object.</p> In\u00a0[\u00a0]: Copied! <pre>ds = hypercoast.read_aviris(filepath)\nds\n</pre> ds = hypercoast.read_aviris(filepath) ds <p>Create an interactive map.</p> In\u00a0[\u00a0]: Copied! <pre>m = hypercoast.Map()\nm\n</pre> m = hypercoast.Map() m <p>Add the AVIRIS data to the map.</p> In\u00a0[\u00a0]: Copied! <pre>m.add_aviris(ds, wavelengths=[1000, 700, 400], vmin=0, vmax=0.2)\nm.add(\"spectral\")\n</pre> m.add_aviris(ds, wavelengths=[1000, 700, 400], vmin=0, vmax=0.2) m.add(\"spectral\") <p></p>"},{"location":"examples/aviris/#visualizing-aviris-data-interactively-with-hypercoast","title":"Visualizing AVIRIS data interactively with HyperCoast\u00b6","text":"<p>This notebook demonstrates how to visualize AVIRIS hyperspectral data interactively with HyperCoast. For more information about AVIRIS, please visit the links below:</p> <ul> <li>https://aviris.jpl.nasa.gov/</li> <li>https://aviris.jpl.nasa.gov/dataportal/</li> <li>https://popo.jpl.nasa.gov/mmgis-aviris/?s=ujooa</li> <li>https://daac.ornl.gov/cgi-bin/dsviewer.pl?ds_id=1988</li> <li>https://github.com/ornldaac/deltax_workshop_2022/tree/main</li> <li>https://github.com/jjmcnelis/aviris-ng-notebooks/tree/master</li> </ul>"},{"location":"examples/chlorophyll_a/","title":"Chlorophyll a","text":"In\u00a0[1]: Copied! <pre># %pip install \"hypercoast[extra]\"\n</pre> # %pip install \"hypercoast[extra]\" In\u00a0[2]: Copied! <pre>import hypercoast\n</pre> import hypercoast <p>To download PACE data, you will need to create an Earthdata login. You can register for an account at urs.earthdata.nasa.gov. Once you have an account, you can uncomment the code below to search and download the data.</p> In\u00a0[3]: Copied! <pre># hypercoast.nasa_earth_login()\n# temporal = (\"2024-06-01\", \"2024-07-01\")\n# results= hypercoast.search_pace_chla(temporal=temporal)\n# hypercoast.download_nasa_data(results, \"chla\")\n</pre> # hypercoast.nasa_earth_login() # temporal = (\"2024-06-01\", \"2024-07-01\") # results= hypercoast.search_pace_chla(temporal=temporal) # hypercoast.download_nasa_data(results, \"chla\") <p>Alternatively, you can download some sample data from here.</p> In\u00a0[4]: Copied! <pre>url = \"https://github.com/opengeos/datasets/releases/download/hypercoast/pace_chla.zip\"\nhypercoast.download_file(url)\n</pre> url = \"https://github.com/opengeos/datasets/releases/download/hypercoast/pace_chla.zip\" hypercoast.download_file(url) Out[4]: <pre>'/home/runner/work/HyperCoast/HyperCoast/docs/examples/pace_chla.zip'</pre> <p>The downloaded zip file is automatically extracted and saved in the <code>chla</code> directory, which contains 17 daily files of chlorophyll-a concentration data in the netCDF format. The date range of the data is from 2024-06-01 to 2024-06-17.</p> In\u00a0[5]: Copied! <pre>files = \"chla/*nc\"\n</pre> files = \"chla/*nc\" <p>Load all the data files in the <code>chla</code> directory as an xarray DataArray</p> In\u00a0[6]: Copied! <pre>array = hypercoast.read_pace_chla(files)\narray\n</pre> array = hypercoast.read_pace_chla(files) array Out[6]: <pre>&lt;xarray.DataArray 'chlor_a' (lat: 1800, lon: 3600, date: 17)&gt; Size: 441MB\ndask.array&lt;transpose, shape=(1800, 3600, 17), dtype=float32, chunksize=(512, 1024, 1), chunktype=numpy.ndarray&gt;\nCoordinates:\n  * lat          (lat) float32 7kB 89.95 89.85 89.75 ... -89.75 -89.85 -89.95\n  * lon          (lon) float32 14kB -179.9 -179.9 -179.8 ... 179.8 179.9 180.0\n  * date         (date) &lt;U10 680B '2024-06-01' '2024-06-02' ... '2024-06-17'\n    spatial_ref  int64 8B 0\nAttributes:\n    long_name:      Chlorophyll Concentration, OCI Algorithm\n    units:          lg(mg m^-3)\n    standard_name:  mass_concentration_of_chlorophyll_in_sea_water\n    valid_min:      0.001\n    valid_max:      100.0\n    reference:      Hu, C., Lee Z., and Franz, B.A. (2012). Chlorophyll-a alg...\n    display_scale:  log\n    display_min:    0.01\n    display_max:    20.0\n    date:           ['2024-06-01', '2024-06-02', '2024-06-03', '2024-06-04', ...</pre>xarray.DataArray'chlor_a'<ul><li>lat: 1800</li><li>lon: 3600</li><li>date: 17</li></ul><ul><li>dask.array&lt;chunksize=(512, 1024, 1), meta=np.ndarray&gt;  Array   Chunk   Bytes   420.23 MiB   2.00 MiB   Shape   (1800, 3600, 17)   (512, 1024, 1)   Dask graph   272 chunks in 54 graph layers   Data type   float32 numpy.ndarray  17 3600 1800 </li><li>Coordinates: (4)<ul><li>lat(lat)float3289.95 89.85 89.75 ... -89.85 -89.95long_name :Latitudeunits :degrees_northstandard_name :latitudevalid_min :-90.0valid_max :90.0<pre>array([ 89.95    ,  89.85    ,  89.75    , ..., -89.75    , -89.850006,\n       -89.950005], dtype=float32)</pre></li><li>lon(lon)float32-179.9 -179.9 ... 179.9 180.0long_name :Longitudeunits :degrees_eaststandard_name :longitudevalid_min :-180.0valid_max :180.0<pre>array([-179.95   , -179.85   , -179.75   , ...,  179.75   ,  179.85   ,\n        179.95001], dtype=float32)</pre></li><li>date(date)&lt;U10'2024-06-01' ... '2024-06-17'<pre>array(['2024-06-01', '2024-06-02', '2024-06-03', '2024-06-04', '2024-06-05',\n       '2024-06-06', '2024-06-07', '2024-06-08', '2024-06-09', '2024-06-10',\n       '2024-06-11', '2024-06-12', '2024-06-13', '2024-06-14', '2024-06-15',\n       '2024-06-16', '2024-06-17'], dtype='&lt;U10')</pre></li><li>spatial_ref()int640crs_wkt :GEOGCS[\"WGS 84\",DATUM[\"WGS_1984\",SPHEROID[\"WGS 84\",6378137,298.257223563,AUTHORITY[\"EPSG\",\"7030\"]],AUTHORITY[\"EPSG\",\"6326\"]],PRIMEM[\"Greenwich\",0,AUTHORITY[\"EPSG\",\"8901\"]],UNIT[\"degree\",0.0174532925199433,AUTHORITY[\"EPSG\",\"9122\"]],AXIS[\"Latitude\",NORTH],AXIS[\"Longitude\",EAST],AUTHORITY[\"EPSG\",\"4326\"]]semi_major_axis :6378137.0semi_minor_axis :6356752.314245179inverse_flattening :298.257223563reference_ellipsoid_name :WGS 84longitude_of_prime_meridian :0.0prime_meridian_name :Greenwichgeographic_crs_name :WGS 84horizontal_datum_name :World Geodetic System 1984grid_mapping_name :latitude_longitudespatial_ref :GEOGCS[\"WGS 84\",DATUM[\"WGS_1984\",SPHEROID[\"WGS 84\",6378137,298.257223563,AUTHORITY[\"EPSG\",\"7030\"]],AUTHORITY[\"EPSG\",\"6326\"]],PRIMEM[\"Greenwich\",0,AUTHORITY[\"EPSG\",\"8901\"]],UNIT[\"degree\",0.0174532925199433,AUTHORITY[\"EPSG\",\"9122\"]],AXIS[\"Latitude\",NORTH],AXIS[\"Longitude\",EAST],AUTHORITY[\"EPSG\",\"4326\"]]<pre>array(0)</pre></li></ul></li><li>Indexes: (3)<ul><li>latPandasIndex<pre>PandasIndex(Index([ 89.94999694824219,   89.8499984741211,              89.75,\n         89.6500015258789,  89.55000305175781,  89.44999694824219,\n         89.3499984741211,              89.25,   89.1500015258789,\n        89.05000305175781,\n       ...\n       -89.05000305175781,  -89.1500015258789,             -89.25,\n       -89.35000610351562, -89.45000457763672, -89.55000305175781,\n        -89.6500015258789,             -89.75, -89.85000610351562,\n       -89.95000457763672],\n      dtype='float32', name='lat', length=1800))</pre></li><li>lonPandasIndex<pre>PandasIndex(Index([ -179.9499969482422, -179.85000610351562,             -179.75,\n       -179.64999389648438,  -179.5500030517578,  -179.4499969482422,\n       -179.35000610351562,             -179.25, -179.14999389648438,\n        -179.0500030517578,\n       ...\n         179.0500030517578,  179.15000915527344,              179.25,\n        179.35000610351562,  179.45001220703125,   179.5500030517578,\n        179.65000915527344,              179.75,  179.85000610351562,\n        179.95001220703125],\n      dtype='float32', name='lon', length=3600))</pre></li><li>datePandasIndex<pre>PandasIndex(Index(['2024-06-01', '2024-06-02', '2024-06-03', '2024-06-04', '2024-06-05',\n       '2024-06-06', '2024-06-07', '2024-06-08', '2024-06-09', '2024-06-10',\n       '2024-06-11', '2024-06-12', '2024-06-13', '2024-06-14', '2024-06-15',\n       '2024-06-16', '2024-06-17'],\n      dtype='object', name='date'))</pre></li></ul></li><li>Attributes: (10)long_name :Chlorophyll Concentration, OCI Algorithmunits :lg(mg m^-3)standard_name :mass_concentration_of_chlorophyll_in_sea_watervalid_min :0.001valid_max :100.0reference :Hu, C., Lee Z., and Franz, B.A. (2012). Chlorophyll-a algorithms for oligotrophic oceans: A novel approach based on three-band reflectance difference, J. Geophys. Res., 117, C01011, doi:10.1029/2011JC007395.display_scale :logdisplay_min :0.01display_max :20.0date :['2024-06-01', '2024-06-02', '2024-06-03', '2024-06-04', '2024-06-05', '2024-06-06', '2024-06-07', '2024-06-08', '2024-06-09', '2024-06-10', '2024-06-11', '2024-06-12', '2024-06-13', '2024-06-14', '2024-06-15', '2024-06-16', '2024-06-17']</li></ul> <p>Select a date and visualize the chlorophyll-a concentration data with Matplotlib.</p> In\u00a0[7]: Copied! <pre>hypercoast.viz_pace_chla(array, date=\"2024-06-01\", cmap=\"jet\", size=6)\n</pre> hypercoast.viz_pace_chla(array, date=\"2024-06-01\", cmap=\"jet\", size=6) Out[7]: <pre>&lt;matplotlib.collections.QuadMesh at 0x7fc64a6f7ef0&gt;</pre> <p>If the date is not specified, the data are averaged over the entire time range.</p> In\u00a0[8]: Copied! <pre>hypercoast.viz_pace_chla(array, cmap=\"jet\", size=6)\n</pre> hypercoast.viz_pace_chla(array, cmap=\"jet\", size=6) Out[8]: <pre>&lt;matplotlib.collections.QuadMesh at 0x7fc64a5f9ee0&gt;</pre> <p>To visualize the data interactively, we can select either a single date or aggregate the data over a time range.</p> <p>First, let's select a single date from the data array:</p> In\u00a0[9]: Copied! <pre>single_array = array.sel(date=\"2024-06-01\")\nsingle_array\n</pre> single_array = array.sel(date=\"2024-06-01\") single_array Out[9]: <pre>&lt;xarray.DataArray 'chlor_a' (lat: 1800, lon: 3600)&gt; Size: 26MB\ndask.array&lt;getitem, shape=(1800, 3600), dtype=float32, chunksize=(512, 1024), chunktype=numpy.ndarray&gt;\nCoordinates:\n  * lat          (lat) float32 7kB 89.95 89.85 89.75 ... -89.75 -89.85 -89.95\n  * lon          (lon) float32 14kB -179.9 -179.9 -179.8 ... 179.8 179.9 180.0\n    date         &lt;U10 40B '2024-06-01'\n    spatial_ref  int64 8B 0\nAttributes:\n    long_name:      Chlorophyll Concentration, OCI Algorithm\n    units:          lg(mg m^-3)\n    standard_name:  mass_concentration_of_chlorophyll_in_sea_water\n    valid_min:      0.001\n    valid_max:      100.0\n    reference:      Hu, C., Lee Z., and Franz, B.A. (2012). Chlorophyll-a alg...\n    display_scale:  log\n    display_min:    0.01\n    display_max:    20.0\n    date:           ['2024-06-01', '2024-06-02', '2024-06-03', '2024-06-04', ...</pre>xarray.DataArray'chlor_a'<ul><li>lat: 1800</li><li>lon: 3600</li></ul><ul><li>dask.array&lt;chunksize=(512, 1024), meta=np.ndarray&gt;  Array   Chunk   Bytes   24.72 MiB   2.00 MiB   Shape   (1800, 3600)   (512, 1024)   Dask graph   16 chunks in 55 graph layers   Data type   float32 numpy.ndarray  3600 1800 </li><li>Coordinates: (4)<ul><li>lat(lat)float3289.95 89.85 89.75 ... -89.85 -89.95long_name :Latitudeunits :degrees_northstandard_name :latitudevalid_min :-90.0valid_max :90.0<pre>array([ 89.95    ,  89.85    ,  89.75    , ..., -89.75    , -89.850006,\n       -89.950005], dtype=float32)</pre></li><li>lon(lon)float32-179.9 -179.9 ... 179.9 180.0long_name :Longitudeunits :degrees_eaststandard_name :longitudevalid_min :-180.0valid_max :180.0<pre>array([-179.95   , -179.85   , -179.75   , ...,  179.75   ,  179.85   ,\n        179.95001], dtype=float32)</pre></li><li>date()&lt;U10'2024-06-01'<pre>array('2024-06-01', dtype='&lt;U10')</pre></li><li>spatial_ref()int640crs_wkt :GEOGCS[\"WGS 84\",DATUM[\"WGS_1984\",SPHEROID[\"WGS 84\",6378137,298.257223563,AUTHORITY[\"EPSG\",\"7030\"]],AUTHORITY[\"EPSG\",\"6326\"]],PRIMEM[\"Greenwich\",0,AUTHORITY[\"EPSG\",\"8901\"]],UNIT[\"degree\",0.0174532925199433,AUTHORITY[\"EPSG\",\"9122\"]],AXIS[\"Latitude\",NORTH],AXIS[\"Longitude\",EAST],AUTHORITY[\"EPSG\",\"4326\"]]semi_major_axis :6378137.0semi_minor_axis :6356752.314245179inverse_flattening :298.257223563reference_ellipsoid_name :WGS 84longitude_of_prime_meridian :0.0prime_meridian_name :Greenwichgeographic_crs_name :WGS 84horizontal_datum_name :World Geodetic System 1984grid_mapping_name :latitude_longitudespatial_ref :GEOGCS[\"WGS 84\",DATUM[\"WGS_1984\",SPHEROID[\"WGS 84\",6378137,298.257223563,AUTHORITY[\"EPSG\",\"7030\"]],AUTHORITY[\"EPSG\",\"6326\"]],PRIMEM[\"Greenwich\",0,AUTHORITY[\"EPSG\",\"8901\"]],UNIT[\"degree\",0.0174532925199433,AUTHORITY[\"EPSG\",\"9122\"]],AXIS[\"Latitude\",NORTH],AXIS[\"Longitude\",EAST],AUTHORITY[\"EPSG\",\"4326\"]]<pre>array(0)</pre></li></ul></li><li>Indexes: (2)<ul><li>latPandasIndex<pre>PandasIndex(Index([ 89.94999694824219,   89.8499984741211,              89.75,\n         89.6500015258789,  89.55000305175781,  89.44999694824219,\n         89.3499984741211,              89.25,   89.1500015258789,\n        89.05000305175781,\n       ...\n       -89.05000305175781,  -89.1500015258789,             -89.25,\n       -89.35000610351562, -89.45000457763672, -89.55000305175781,\n        -89.6500015258789,             -89.75, -89.85000610351562,\n       -89.95000457763672],\n      dtype='float32', name='lat', length=1800))</pre></li><li>lonPandasIndex<pre>PandasIndex(Index([ -179.9499969482422, -179.85000610351562,             -179.75,\n       -179.64999389648438,  -179.5500030517578,  -179.4499969482422,\n       -179.35000610351562,             -179.25, -179.14999389648438,\n        -179.0500030517578,\n       ...\n         179.0500030517578,  179.15000915527344,              179.25,\n        179.35000610351562,  179.45001220703125,   179.5500030517578,\n        179.65000915527344,              179.75,  179.85000610351562,\n        179.95001220703125],\n      dtype='float32', name='lon', length=3600))</pre></li></ul></li><li>Attributes: (10)long_name :Chlorophyll Concentration, OCI Algorithmunits :lg(mg m^-3)standard_name :mass_concentration_of_chlorophyll_in_sea_watervalid_min :0.001valid_max :100.0reference :Hu, C., Lee Z., and Franz, B.A. (2012). Chlorophyll-a algorithms for oligotrophic oceans: A novel approach based on three-band reflectance difference, J. Geophys. Res., 117, C01011, doi:10.1029/2011JC007395.display_scale :logdisplay_min :0.01display_max :20.0date :['2024-06-01', '2024-06-02', '2024-06-03', '2024-06-04', '2024-06-05', '2024-06-06', '2024-06-07', '2024-06-08', '2024-06-09', '2024-06-10', '2024-06-11', '2024-06-12', '2024-06-13', '2024-06-14', '2024-06-15', '2024-06-16', '2024-06-17']</li></ul> <p>Convert the data array to an image that can be displayed on an interactive map.</p> In\u00a0[10]: Copied! <pre>single_image = hypercoast.pace_chla_to_image(single_array)\n</pre> single_image = hypercoast.pace_chla_to_image(single_array) <p>Create an interactive map and display the image on the map.</p> In\u00a0[11]: Copied! <pre>m = hypercoast.Map(center=[40, -100], zoom=4)\nm.add_basemap(\"Hybrid\")\nm.add_raster(\n    single_image,\n    cmap=\"jet\",\n    vmin=-1,\n    vmax=2,\n    layer_name=\"Chlorophyll a\",\n    zoom_to_layer=False,\n)\nlabel = \"Chlorophyll Concentration [lg(lg(mg m^-3))]\"\nm.add_colormap(cmap=\"jet\", vmin=-1, vmax=2, label=label)\nm\n</pre> m = hypercoast.Map(center=[40, -100], zoom=4) m.add_basemap(\"Hybrid\") m.add_raster(     single_image,     cmap=\"jet\",     vmin=-1,     vmax=2,     layer_name=\"Chlorophyll a\",     zoom_to_layer=False, ) label = \"Chlorophyll Concentration [lg(lg(mg m^-3))]\" m.add_colormap(cmap=\"jet\", vmin=-1, vmax=2, label=label) m Out[11]: <p></p> <p>The daily image does not have a global coverage. To visualize the data globally, we can aggregate the data over a time range.</p> In\u00a0[12]: Copied! <pre>mean_array = array.mean(dim=\"date\")\n</pre> mean_array = array.mean(dim=\"date\") <p>Convert the aggregated data array to an image that can be displayed on an interactive map.</p> In\u00a0[13]: Copied! <pre>image = hypercoast.pace_chla_to_image(mean_array)\n</pre> image = hypercoast.pace_chla_to_image(mean_array) <p>Create an interactive map and display the image on the map.</p> In\u00a0[14]: Copied! <pre>m = hypercoast.Map(center=[40, -100], zoom=4)\nm.add_basemap(\"Hybrid\")\nm.add_raster(\n    image, cmap=\"jet\", vmin=-1, vmax=2, layer_name=\"Chlorophyll a\", zoom_to_layer=False\n)\nlabel = \"Chlorophyll Concentration [lg(lg(mg m^-3))]\"\nm.add_colormap(cmap=\"jet\", vmin=-1, vmax=2, label=label)\nm\n</pre> m = hypercoast.Map(center=[40, -100], zoom=4) m.add_basemap(\"Hybrid\") m.add_raster(     image, cmap=\"jet\", vmin=-1, vmax=2, layer_name=\"Chlorophyll a\", zoom_to_layer=False ) label = \"Chlorophyll Concentration [lg(lg(mg m^-3))]\" m.add_colormap(cmap=\"jet\", vmin=-1, vmax=2, label=label) m Out[14]: <p></p>"},{"location":"examples/chlorophyll_a/#visualizing-pace-chlorophyll-a-data-interactively-with-hypercoast","title":"Visualizing PACE chlorophyll-a data interactively with HyperCoast\u00b6","text":"<p>This notebook demonstrates how to visualize Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) data interactively with HyperCoast.</p>"},{"location":"examples/desis/","title":"Desis","text":"In\u00a0[1]: Copied! <pre># %pip install \"hypercoast[extra]\"\n</pre> # %pip install \"hypercoast[extra]\" In\u00a0[2]: Copied! <pre>import hypercoast\n</pre> import hypercoast In\u00a0[3]: Copied! <pre>url = \"https://github.com/opengeos/datasets/releases/download/hypercoast/desis.tif\"\nfilepath = \"data/desis.tif\"\nhypercoast.download_file(url, filepath)\n</pre> url = \"https://github.com/opengeos/datasets/releases/download/hypercoast/desis.tif\" filepath = \"data/desis.tif\" hypercoast.download_file(url, filepath) Out[3]: <pre>'/home/runner/work/HyperCoast/HyperCoast/docs/examples/data/desis.tif'</pre> <p>Load the dataset as a xarray.Dataset object.</p> In\u00a0[4]: Copied! <pre>dataset = hypercoast.read_desis(filepath)\n</pre> dataset = hypercoast.read_desis(filepath) <p>Plot the spectral signature of a pixel.</p> In\u00a0[5]: Copied! <pre>hypercoast.filter_desis(dataset, lat=29.4315, lon=91.2927, return_plot=True)\n</pre> hypercoast.filter_desis(dataset, lat=29.4315, lon=91.2927, return_plot=True) <p>Visualize a single band of the hyperspectral image.</p> In\u00a0[6]: Copied! <pre>m = hypercoast.Map()\nm.add_basemap(\"Hybrid\")\nm.add_desis(filepath, wavelengths=[1000], vmin=0, vmax=5000, nodata=0, colormap=\"jet\")\nm.add_colormap(cmap=\"jet\", vmin=0, vmax=0.5, label=\"Reflectance\")\nm\n</pre> m = hypercoast.Map() m.add_basemap(\"Hybrid\") m.add_desis(filepath, wavelengths=[1000], vmin=0, vmax=5000, nodata=0, colormap=\"jet\") m.add_colormap(cmap=\"jet\", vmin=0, vmax=0.5, label=\"Reflectance\") m Out[6]: <p></p> <p>Plot the spectral signature of a pixel interactively.</p> In\u00a0[7]: Copied! <pre>m = hypercoast.Map()\nm.add_basemap(\"Hybrid\")\nm.add_desis(filepath, wavelengths=[900, 600, 525], vmin=0, vmax=1000, nodata=0)\nm.add(\"spectral\")\nm\n</pre> m = hypercoast.Map() m.add_basemap(\"Hybrid\") m.add_desis(filepath, wavelengths=[900, 600, 525], vmin=0, vmax=1000, nodata=0) m.add(\"spectral\") m Out[7]: <p></p>"},{"location":"examples/desis/#visualizing-desis-data-interactively-with-hypercoast","title":"Visualizing DESIS data interactively with HyperCoast\u00b6","text":"<p>This notebook demonstrates how to visualize DESIS hyperspectral data interactively with HyperCoast.</p>"},{"location":"examples/ecostress/","title":"Ecostress","text":"In\u00a0[\u00a0]: Copied! <pre>import hypercoast\n</pre> import hypercoast In\u00a0[\u00a0]: Copied! <pre>hypercoast.nasa_earth_login()\n</pre> hypercoast.nasa_earth_login() In\u00a0[\u00a0]: Copied! <pre>results, gdf = hypercoast.search_ecostress(\n    bbox=(-120.522, 34.4266, -120.2665, 34.5653),\n    temporal=(\"2023-04-01\", \"2023-04-02\"),\n    count=-1,  # use -1 to return all datasets\n    return_gdf=True,\n)\n</pre> results, gdf = hypercoast.search_ecostress(     bbox=(-120.522, 34.4266, -120.2665, 34.5653),     temporal=(\"2023-04-01\", \"2023-04-02\"),     count=-1,  # use -1 to return all datasets     return_gdf=True, ) In\u00a0[\u00a0]: Copied! <pre>gdf.explore()\n</pre> gdf.explore() In\u00a0[\u00a0]: Copied! <pre>hypercoast.download_ecostress(results[:5], out_dir=\"data\")\n</pre> hypercoast.download_ecostress(results[:5], out_dir=\"data\") In\u00a0[\u00a0]: Copied! <pre>m = hypercoast.Map(center=[34.5014, -120.4032], zoom=11)\nm.search_ecostress()\nm\n</pre> m = hypercoast.Map(center=[34.5014, -120.4032], zoom=11) m.search_ecostress() m In\u00a0[\u00a0]: Copied! <pre># m._NASA_DATA_GDF.head()\n</pre> # m._NASA_DATA_GDF.head() In\u00a0[\u00a0]: Copied! <pre># hypercoast.download_ecostress(m._NASA_DATA_RESULTS[:2], out_dir=\"data\")\n</pre> # hypercoast.download_ecostress(m._NASA_DATA_RESULTS[:2], out_dir=\"data\") In\u00a0[\u00a0]: Copied! <pre>url = \"https://github.com/opengeos/datasets/releases/download/raster/ECOv002_L2T_LSTE_26860_001_10SGD_20230401T203733_0710_01_LST.tif\"\n</pre> url = \"https://github.com/opengeos/datasets/releases/download/raster/ECOv002_L2T_LSTE_26860_001_10SGD_20230401T203733_0710_01_LST.tif\" In\u00a0[\u00a0]: Copied! <pre>filepath = \"data/ECOv002_L2T_LSTE_26860_001_10SGD_20230401T203733_0710_01_LST.tif\"\nhypercoast.download_file(url, filepath)\n</pre> filepath = \"data/ECOv002_L2T_LSTE_26860_001_10SGD_20230401T203733_0710_01_LST.tif\" hypercoast.download_file(url, filepath) <p>Visualize the data with HyperCoast.</p> In\u00a0[\u00a0]: Copied! <pre>m = hypercoast.Map()\nm.add_basemap(\"HYBRID\")\nm.add_raster(filepath, colormap=\"jet\", layer_name=\"LST\")\nm.add(\"spectral\")\nm\n</pre> m = hypercoast.Map() m.add_basemap(\"HYBRID\") m.add_raster(filepath, colormap=\"jet\", layer_name=\"LST\") m.add(\"spectral\") m <p></p>"},{"location":"examples/ecostress/#search-and-download-nasa-ecostress-data-with-hypercoast","title":"Search and download NASA ECOSTRESS data with HyperCoast\u00b6","text":"<p>This notebook demonstrates how to search and visualize NASA ECOSTRESS temperature data with HyperCoast.</p>"},{"location":"examples/ecostress/#search-for-ecostress-data-programmatically","title":"Search for ECOSTRESS data programmatically\u00b6","text":""},{"location":"examples/ecostress/#download-ecostress-data","title":"Download ECOSTRESS data\u00b6","text":""},{"location":"examples/ecostress/#search-for-ecostress-data-interactively","title":"Search for ECOSTRESS data interactively\u00b6","text":""},{"location":"examples/ecostress/#visualize-ecostress-data","title":"Visualize ECOSTRESS data\u00b6","text":"<p>Download a sample ECOSTRESS data file and visualize it with HyperCoast.</p>"},{"location":"examples/emit/","title":"Emit","text":"In\u00a0[1]: Copied! <pre># %pip install hypercoast\n</pre> # %pip install hypercoast In\u00a0[2]: Copied! <pre>import hypercoast\n</pre> import hypercoast <p>Download a sample EMIT data file from here.</p> In\u00a0[3]: Copied! <pre>url = \"https://github.com/opengeos/datasets/releases/download/netcdf/EMIT_L2A_RFL_001_20240404T161230_2409511_009.nc\"\n</pre> url = \"https://github.com/opengeos/datasets/releases/download/netcdf/EMIT_L2A_RFL_001_20240404T161230_2409511_009.nc\" In\u00a0[4]: Copied! <pre>filepath = \"data/EMIT_L2A_RFL_001_20240404T161230_2409511_009.nc\"\nhypercoast.download_file(url, filepath)\n</pre> filepath = \"data/EMIT_L2A_RFL_001_20240404T161230_2409511_009.nc\" hypercoast.download_file(url, filepath) Out[4]: <pre>'/home/runner/work/HyperCoast/HyperCoast/docs/examples/data/EMIT_L2A_RFL_001_20240404T161230_2409511_009.nc'</pre> <p>Load the dataset as a <code>xarray.Dataset</code> object.</p> In\u00a0[5]: Copied! <pre>dataset = hypercoast.read_emit(filepath)\n</pre> dataset = hypercoast.read_emit(filepath) <p>Visualize the data interactively with HyperCoast. By default, the plot will show all the bands in the dataset.</p> In\u00a0[6]: Copied! <pre>m = hypercoast.Map()\nm.add_basemap(\"SATELLITE\")\nm.add_emit(dataset, wavelengths=[1000, 600, 500], vmin=0, vmax=0.3, layer_name=\"EMIT\")\nm.add(\"spectral\")\nm\n</pre> m = hypercoast.Map() m.add_basemap(\"SATELLITE\") m.add_emit(dataset, wavelengths=[1000, 600, 500], vmin=0, vmax=0.3, layer_name=\"EMIT\") m.add(\"spectral\") m Out[6]: <p>To visualize a certain wavelength range, you can specify the <code>xlim</code> parameter as follows:</p> In\u00a0[7]: Copied! <pre>m = hypercoast.Map()\nm.add_basemap(\"SATELLITE\")\nm.add_emit(dataset, wavelengths=[1000, 600, 500], vmin=0, vmax=0.3, layer_name=\"EMIT\")\nm.add(\"spectral\", xlim=(400, 1200))\nm\n</pre> m = hypercoast.Map() m.add_basemap(\"SATELLITE\") m.add_emit(dataset, wavelengths=[1000, 600, 500], vmin=0, vmax=0.3, layer_name=\"EMIT\") m.add(\"spectral\", xlim=(400, 1200)) m Out[7]: <p>To access the selected spectral profiles from the mouse clicked location as a Pandas DataFrame, use the <code>Map.spectral_to_df()</code> method.</p> In\u00a0[8]: Copied! <pre>m.spectral_to_df()\n</pre> m.spectral_to_df() Out[8]: <p>To access the selected spectral profiles from the mouse clicked location as a GeoPandas GeoDataFrame, use the <code>Map.spectral_to_gdf()</code> method.</p> In\u00a0[9]: Copied! <pre>m.spectral_to_gdf()\n</pre> m.spectral_to_gdf() Out[9]: <p></p>"},{"location":"examples/emit/#visualizing-emit-data-interactively-with-hypercoast","title":"Visualizing EMIT data interactively with HyperCoast\u00b6","text":"<p>This notebook demonstrates how to visualize Earth Surface Mineral Dust Source Investigation (EMIT) data interactively with HyperCoast. This notebook is inspired by the EMIT data visualization tutorial - Exploring_EMIT_L2A_Reflectance.ipynb. We have made it much easier to visualize the data interactively with HyperCoast.</p>"},{"location":"examples/field_data/","title":"Field data","text":"In\u00a0[1]: Copied! <pre># %pip install \"hypercoast[extra]\"\n</pre> # %pip install \"hypercoast[extra]\" In\u00a0[2]: Copied! <pre>import hypercoast\nimport pandas as pd\n</pre> import hypercoast import pandas as pd <p>Download a sample dataset.</p> In\u00a0[3]: Copied! <pre>url = \"https://github.com/opengeos/datasets/releases/download/hypercoast/pace_sample_points.csv\"\ndata = pd.read_csv(url)\ndata.head()\n</pre> url = \"https://github.com/opengeos/datasets/releases/download/hypercoast/pace_sample_points.csv\" data = pd.read_csv(url) data.head() Out[3]: band wavelength (30.1926 -90.1318) (30.1594 -90.2856) (29.3295 -92.3071) (28.8783 -90.4559) (30.5481 -87.9840) (29.5305 -85.0671) (28.8254 -85.6659) (29.4587 -83.8477) (26.8878 -87.7643) (24.6570 -86.4954) (26.8045 -82.4854) 0 0 339.0 NaN NaN 0.000100 NaN NaN NaN NaN NaN 0.001718 0.000710 NaN 1 1 341.0 NaN NaN NaN NaN NaN NaN NaN NaN 0.001554 0.000846 NaN 2 2 344.0 NaN NaN 0.001816 NaN NaN NaN NaN NaN 0.004047 0.003598 NaN 3 3 346.0 0.000346 NaN 0.002393 NaN NaN NaN NaN NaN 0.005045 0.004646 NaN 4 4 348.0 NaN NaN 0.001578 NaN NaN NaN NaN NaN 0.004258 0.003984 NaN <p>Download PACE data.</p> In\u00a0[4]: Copied! <pre>url = \"https://github.com/opengeos/datasets/releases/download/hypercoast/PACE_OCI.20240730T181157.L2.OC_AOP.V2_0.NRT.nc\"\nfilepath = \"data/PACE_OCI.20240730T181157.L2.OC_AOP.V2_0.NRT.nc\"\nhypercoast.download_file(url, filepath)\n</pre> url = \"https://github.com/opengeos/datasets/releases/download/hypercoast/PACE_OCI.20240730T181157.L2.OC_AOP.V2_0.NRT.nc\" filepath = \"data/PACE_OCI.20240730T181157.L2.OC_AOP.V2_0.NRT.nc\" hypercoast.download_file(url, filepath) <pre>data/PACE_OCI.20240730T181157.L2.OC_AOP.V2_0.NRT.nc already exists. Skip downloading. Set overwrite=True to overwrite.\n</pre> Out[4]: <pre>'/home/runner/work/HyperCoast/HyperCoast/docs/examples/data/PACE_OCI.20240730T181157.L2.OC_AOP.V2_0.NRT.nc'</pre> <p>Read the PACE dataset.</p> In\u00a0[5]: Copied! <pre>dataset = hypercoast.read_pace(filepath)\n</pre> dataset = hypercoast.read_pace(filepath) <p>Run the following cell to show the map. Click on the markers to see the spectral data.</p> In\u00a0[6]: Copied! <pre>m = hypercoast.Map(center=[27.235094, -87.791748], zoom=6)\n\nm.add_basemap(\"Hybrid\")\nwavelengths = [450, 550, 650]\nm.add_pace(\n    dataset, wavelengths, indexes=[3, 2, 1], vmin=0, vmax=0.02, layer_name=\"PACE\"\n)\nm.add(\"spectral\")\n\nm.add_field_data(\n    data,\n    x_col=\"wavelength\",\n    y_col_prefix=\"(\",\n    x_label=\"Wavelength (nm)\",\n    y_label=\"Reflectance\",\n    use_marker_cluster=True,\n)\nm.set_center(-87.791748, 27.235094, zoom=6)\nm\n</pre> m = hypercoast.Map(center=[27.235094, -87.791748], zoom=6)  m.add_basemap(\"Hybrid\") wavelengths = [450, 550, 650] m.add_pace(     dataset, wavelengths, indexes=[3, 2, 1], vmin=0, vmax=0.02, layer_name=\"PACE\" ) m.add(\"spectral\")  m.add_field_data(     data,     x_col=\"wavelength\",     y_col_prefix=\"(\",     x_label=\"Wavelength (nm)\",     y_label=\"Reflectance\",     use_marker_cluster=True, ) m.set_center(-87.791748, 27.235094, zoom=6) m Out[6]: <p></p>"},{"location":"examples/field_data/#visualizing-spectral-data-from-field-measurements","title":"Visualizing Spectral Data from Field Measurements\u00b6","text":"<p>This notebook demonstrates how to visualize spectral data from field measurements.</p>"},{"location":"examples/hypoxia/","title":"Hypoxia","text":"In\u00a0[1]: Copied! <pre># %pip install -U openpyxl hypercoast\n</pre> # %pip install -U openpyxl hypercoast In\u00a0[2]: Copied! <pre>import pandas as pd\nimport hypercoast\n</pre> import pandas as pd import hypercoast In\u00a0[3]: Copied! <pre>url = \"https://github.com/opengeos/datasets/releases/download/hypercoast/Hypoxia_Data_Sheet.xlsx\"\nxls_path = \"data/Hypoxia_Data_Sheet.xlsx\"\nhypercoast.download_file(url, xls_path, overwrite=True)\n</pre> url = \"https://github.com/opengeos/datasets/releases/download/hypercoast/Hypoxia_Data_Sheet.xlsx\" xls_path = \"data/Hypoxia_Data_Sheet.xlsx\" hypercoast.download_file(url, xls_path, overwrite=True) Out[3]: <pre>'/home/runner/work/HyperCoast/HyperCoast/docs/examples/data/Hypoxia_Data_Sheet.xlsx'</pre> In\u00a0[4]: Copied! <pre>df = pd.read_excel(xls_path)\ndf.head()\n</pre> df = pd.read_excel(xls_path) df.head() Out[4]: Station Station.1 Time Date Lon Lat Depth (m) Secchi (m) Salinity Water Temp ... Absorption CDOM LISST Nano Surface \\npH Sufface \\nO2 Bottom \\nO2 FL-ECO FL-CDOM Notes 0 River stations NaN NaN NaT NaN NaN NaN NaN NaN NaN ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 1 R1 St1 09:39:00 2024-07-21 -89.45114 28.89887 NaN 2 28.71 30.74161 ... 125ml yes yes no NaN NaN NaN NaN NaN River Mouth 2 R2 St2 09:47:00 2024-07-21 -89.45306 28.90001 NaN 2 25.91325 30.91475 ... 150ml yes yes yes NaN NaN NaN NaN NaN River plume, seaside 3 R3 St3 09:59:00 2024-07-21 -89.43833 28.89487 NaN 0.75 24.44862 30.59565 ... 100ml yes yes yes NaN NaN NaN NaN NaN NaN 4 R4 St4 10:13:00 2024-07-21 -89.43162 28.90630 NaN 0.5 8.34838 30.13989 ... 100ml yes yes yes NaN NaN NaN NaN NaN NaN <p>5 rows \u00d7 22 columns</p> In\u00a0[5]: Copied! <pre>df_filtered = df.dropna(subset=[\"Lon\", \"Lat\"]).reset_index(drop=True)\ndf_filtered.head()\n</pre> df_filtered = df.dropna(subset=[\"Lon\", \"Lat\"]).reset_index(drop=True) df_filtered.head() Out[5]: Station Station.1 Time Date Lon Lat Depth (m) Secchi (m) Salinity Water Temp ... Absorption CDOM LISST Nano Surface \\npH Sufface \\nO2 Bottom \\nO2 FL-ECO FL-CDOM Notes 0 R1 St1 09:39:00 2024-07-21 -89.45114 28.89887 NaN 2 28.71 30.74161 ... 125ml yes yes no NaN NaN NaN NaN NaN River Mouth 1 R2 St2 09:47:00 2024-07-21 -89.45306 28.90001 NaN 2 25.91325 30.91475 ... 150ml yes yes yes NaN NaN NaN NaN NaN River plume, seaside 2 R3 St3 09:59:00 2024-07-21 -89.43833 28.89487 NaN 0.75 24.44862 30.59565 ... 100ml yes yes yes NaN NaN NaN NaN NaN NaN 3 R4 St4 10:13:00 2024-07-21 -89.43162 28.90630 NaN 0.5 8.34838 30.13989 ... 100ml yes yes yes NaN NaN NaN NaN NaN NaN 4 R5 St5 10:58:00 2024-07-21 -89.37324 28.98095 NaN 0.5 2.4625 30.3054 ... 100ml yes yes yes NaN NaN NaN NaN NaN NaN <p>5 rows \u00d7 22 columns</p> In\u00a0[6]: Copied! <pre>url = (\n    \"https://github.com/opengeos/datasets/releases/download/hypercoast/Hypoxia_Path.kml\"\n)\nkml_path = \"data/Hypoxia_Path.kml\"\nhypercoast.download_file(url, kml_path)\n</pre> url = (     \"https://github.com/opengeos/datasets/releases/download/hypercoast/Hypoxia_Path.kml\" ) kml_path = \"data/Hypoxia_Path.kml\" hypercoast.download_file(url, kml_path) Out[6]: <pre>'/home/runner/work/HyperCoast/HyperCoast/docs/examples/data/Hypoxia_Path.kml'</pre> In\u00a0[7]: Copied! <pre>url = \"https://github.com/opengeos/datasets/releases/download/hypercoast/PACE_OCI.20240730T181157.L2.OC_AOP.V2_0.NRT.nc\"\nfilepath = \"data/PACE_OCI.20240730T181157.L2.OC_AOP.V2_0.NRT.nc\"\nhypercoast.download_file(url, filepath)\n</pre> url = \"https://github.com/opengeos/datasets/releases/download/hypercoast/PACE_OCI.20240730T181157.L2.OC_AOP.V2_0.NRT.nc\" filepath = \"data/PACE_OCI.20240730T181157.L2.OC_AOP.V2_0.NRT.nc\" hypercoast.download_file(url, filepath) <pre>data/PACE_OCI.20240730T181157.L2.OC_AOP.V2_0.NRT.nc already exists. Skip downloading. Set overwrite=True to overwrite.\n</pre> Out[7]: <pre>'/home/runner/work/HyperCoast/HyperCoast/docs/examples/data/PACE_OCI.20240730T181157.L2.OC_AOP.V2_0.NRT.nc'</pre> In\u00a0[8]: Copied! <pre>dataset = hypercoast.read_pace(filepath)\n# dataset\n</pre> dataset = hypercoast.read_pace(filepath) # dataset In\u00a0[9]: Copied! <pre>m = hypercoast.Map()\nm.add_basemap(\"Hybrid\")\nwavelengths = [450, 550, 650]\nm.add_pace(\n    dataset, wavelengths, indexes=[3, 2, 1], vmin=0, vmax=0.02, layer_name=\"PACE\"\n)\nm.add(\"spectral\")\nstyle = {\"weight\": 2, \"color\": \"red\"}\nm.add_kml(kml_path, style=style, layer_name=\"Hypoxia Path\", info_mode=None)\nm.add_points_from_xy(\n    df_filtered,\n    x=\"Lon\",\n    y=\"Lat\",\n    max_cluster_radius=50,\n    layer_name=\"Hypoxia Data Points\",\n)\nm.set_center(-91.46118, 28.89758, zoom=8)\nm\n</pre> m = hypercoast.Map() m.add_basemap(\"Hybrid\") wavelengths = [450, 550, 650] m.add_pace(     dataset, wavelengths, indexes=[3, 2, 1], vmin=0, vmax=0.02, layer_name=\"PACE\" ) m.add(\"spectral\") style = {\"weight\": 2, \"color\": \"red\"} m.add_kml(kml_path, style=style, layer_name=\"Hypoxia Path\", info_mode=None) m.add_points_from_xy(     df_filtered,     x=\"Lon\",     y=\"Lat\",     max_cluster_radius=50,     layer_name=\"Hypoxia Data Points\", ) m.set_center(-91.46118, 28.89758, zoom=8) m Out[9]: <p></p>"},{"location":"examples/hypoxia/#visualizing-hypoxia-cruise-sampling-locations-in-the-gulf-of-mexico","title":"Visualizing Hypoxia Cruise Sampling Locations in the Gulf of Mexico\u00b6","text":""},{"location":"examples/image_cube/","title":"Image cube","text":"In\u00a0[\u00a0]: Copied! <pre># %pip install \"hypercoast[extra]\"\n</pre> # %pip install \"hypercoast[extra]\" In\u00a0[\u00a0]: Copied! <pre>import hypercoast\n</pre> import hypercoast In\u00a0[\u00a0]: Copied! <pre>url = \"https://github.com/opengeos/datasets/releases/download/hypercoast/NEON_D02_SERC_DP3_368000_4306000_reflectance.h5\"\nfilepath = \"data/neon.h5\"\nhypercoast.download_file(url, filepath)\n</pre> url = \"https://github.com/opengeos/datasets/releases/download/hypercoast/NEON_D02_SERC_DP3_368000_4306000_reflectance.h5\" filepath = \"data/neon.h5\" hypercoast.download_file(url, filepath) <p>Load the dataset as a <code>xarray.Dataset</code> object.</p> In\u00a0[\u00a0]: Copied! <pre>dataset = hypercoast.read_neon(filepath)\ndataset\n</pre> dataset = hypercoast.read_neon(filepath) dataset <p>Visualize the NEON AOP hyperspectral data in 3D with a selected band overlaid on top of the 3D plot.</p> In\u00a0[\u00a0]: Copied! <pre>cube = hypercoast.image_cube(\n    dataset,\n    variable=\"reflectance\",\n    cmap=\"jet\",\n    clim=(0, 0.5),\n    rgb_wavelengths=[700],\n    title=\"Reflectance\",\n)\ncube.show()\n</pre> cube = hypercoast.image_cube(     dataset,     variable=\"reflectance\",     cmap=\"jet\",     clim=(0, 0.5),     rgb_wavelengths=[700],     title=\"Reflectance\", ) cube.show() <p>Visualize the NEON AOP hyperspectral data in 3D with an RGB image overlaid on top of the 3D plot.</p> In\u00a0[\u00a0]: Copied! <pre>cube2 = hypercoast.image_cube(\n    dataset,\n    variable=\"reflectance\",\n    cmap=\"jet\",\n    clim=(0, 0.5),\n    rgb_wavelengths=[1000, 700, 500],\n    rgb_gamma=2,\n    title=\"Reflectance\",\n)\ncube2.show()\n</pre> cube2 = hypercoast.image_cube(     dataset,     variable=\"reflectance\",     cmap=\"jet\",     clim=(0, 0.5),     rgb_wavelengths=[1000, 700, 500],     rgb_gamma=2,     title=\"Reflectance\", ) cube2.show() <p></p> In\u00a0[\u00a0]: Copied! <pre>url = \"https://github.com/opengeos/datasets/releases/download/netcdf/EMIT_L2A_RFL_001_20240404T161230_2409511_009.nc\"\nfilepath = \"EMIT_L2A_RFL_001_20240404T161230_2409511_009.nc\"\nhypercoast.download_file(url)\n</pre> url = \"https://github.com/opengeos/datasets/releases/download/netcdf/EMIT_L2A_RFL_001_20240404T161230_2409511_009.nc\" filepath = \"EMIT_L2A_RFL_001_20240404T161230_2409511_009.nc\" hypercoast.download_file(url) In\u00a0[\u00a0]: Copied! <pre>dataset = hypercoast.read_emit(filepath)\ndataset\n</pre> dataset = hypercoast.read_emit(filepath) dataset <p>Select a subset of the data to avoid nodata areas.</p> In\u00a0[\u00a0]: Copied! <pre>ds = dataset.sel(longitude=slice(-90.1482, -89.7321), latitude=slice(30.0225, 29.7451))\nds\n</pre> ds = dataset.sel(longitude=slice(-90.1482, -89.7321), latitude=slice(30.0225, 29.7451)) ds <p>Visualize the EMIT data in 3D with an RGB image overlaid on top of the 3D plot.</p> In\u00a0[\u00a0]: Copied! <pre>cube = hypercoast.image_cube(\n    ds,\n    variable=\"reflectance\",\n    cmap=\"jet\",\n    clim=(0, 0.4),\n    rgb_wavelengths=[1000, 700, 500],\n    rgb_gamma=2,\n    title=\"EMIT Reflectance\",\n)\ncube.show()\n</pre> cube = hypercoast.image_cube(     ds,     variable=\"reflectance\",     cmap=\"jet\",     clim=(0, 0.4),     rgb_wavelengths=[1000, 700, 500],     rgb_gamma=2,     title=\"EMIT Reflectance\", ) cube.show() <p></p>"},{"location":"examples/image_cube/#visualizing-hyperspectral-data-in-3d","title":"Visualizing Hyperspectral Data in 3D\u00b6","text":"<p>This notebook demonstrates how to visualize hyperspectral data in 3D using the PyVista plotting backend.</p>"},{"location":"examples/image_cube/#visualize-neon-aop-hyperspectral-data","title":"Visualize NEON AOP Hyperspectral Data\u00b6","text":"<p>Download a sample NEON AOP hyperspectral data.</p>"},{"location":"examples/image_cube/#visualize-nasa-emit-hyperspectral-data","title":"Visualize NASA EMIT Hyperspectral Data\u00b6","text":"<p>Download a sample EMIT data file from here.</p>"},{"location":"examples/image_slicing/","title":"Image slicing","text":"In\u00a0[\u00a0]: Copied! <pre># %pip install \"hypercoast[extra]\"\n</pre> # %pip install \"hypercoast[extra]\" In\u00a0[\u00a0]: Copied! <pre>import hypercoast\n</pre> import hypercoast In\u00a0[\u00a0]: Copied! <pre>url = \"https://github.com/opengeos/datasets/releases/download/hypercoast/NEON_D02_SERC_DP3_368000_4306000_reflectance.h5\"\nfilepath = \"data/neon.h5\"\nhypercoast.download_file(url, filepath)\n</pre> url = \"https://github.com/opengeos/datasets/releases/download/hypercoast/NEON_D02_SERC_DP3_368000_4306000_reflectance.h5\" filepath = \"data/neon.h5\" hypercoast.download_file(url, filepath) <p>Load the dataset as a <code>xarray.Dataset</code> object.</p> In\u00a0[\u00a0]: Copied! <pre>dataset = hypercoast.read_neon(filepath)\ndataset\n</pre> dataset = hypercoast.read_neon(filepath) dataset <p>Extract a small subset of the dataset for demonstration purposes.</p> In\u00a0[\u00a0]: Copied! <pre>ds = dataset.isel(x=slice(100, 200), y=slice(100, 200))\nds\n</pre> ds = dataset.isel(x=slice(100, 200), y=slice(100, 200)) ds <p>Interactive slicing along the z-axis (band)</p> In\u00a0[\u00a0]: Copied! <pre>p = hypercoast.image_cube(\n    ds,\n    variable=\"reflectance\",\n    cmap=\"jet\",\n    clim=(0, 0.5),\n    rgb_wavelengths=[1000, 700, 500],\n    rgb_gamma=2,\n    widget=\"slice\",\n)\np.add_text(\"Band slicing \", position=\"upper_right\", font_size=14)\np.show()\n</pre> p = hypercoast.image_cube(     ds,     variable=\"reflectance\",     cmap=\"jet\",     clim=(0, 0.5),     rgb_wavelengths=[1000, 700, 500],     rgb_gamma=2,     widget=\"slice\", ) p.add_text(\"Band slicing \", position=\"upper_right\", font_size=14) p.show() <p></p> <p>Interactive slicing along the x-axis (longitude).</p> In\u00a0[\u00a0]: Copied! <pre>p = hypercoast.image_cube(\n    ds,\n    variable=\"reflectance\",\n    cmap=\"jet\",\n    clim=(0, 0.5),\n    rgb_wavelengths=[1000, 700, 500],\n    rgb_gamma=2,\n    widget=\"slice\",\n    normal=\"x\",\n)\np.add_text(\"X-axis slicing \", position=\"upper_right\", font_size=14)\np.show()\n</pre> p = hypercoast.image_cube(     ds,     variable=\"reflectance\",     cmap=\"jet\",     clim=(0, 0.5),     rgb_wavelengths=[1000, 700, 500],     rgb_gamma=2,     widget=\"slice\",     normal=\"x\", ) p.add_text(\"X-axis slicing \", position=\"upper_right\", font_size=14) p.show() <p></p> <p>Orthogonal slicing.</p> In\u00a0[\u00a0]: Copied! <pre>p = hypercoast.image_cube(\n    ds,\n    variable=\"reflectance\",\n    cmap=\"jet\",\n    clim=(0, 0.5),\n    rgb_wavelengths=[1000, 700, 500],\n    rgb_gamma=2,\n    widget=\"orthogonal\",\n)\np.add_text(\"Orthogonal slicing\", position=\"upper_right\", font_size=14)\np.show()\n</pre> p = hypercoast.image_cube(     ds,     variable=\"reflectance\",     cmap=\"jet\",     clim=(0, 0.5),     rgb_wavelengths=[1000, 700, 500],     rgb_gamma=2,     widget=\"orthogonal\", ) p.add_text(\"Orthogonal slicing\", position=\"upper_right\", font_size=14) p.show() <p></p> <p>Clip the image cube with a plane (band slicing).</p> In\u00a0[\u00a0]: Copied! <pre>p = hypercoast.image_cube(\n    ds,\n    variable=\"reflectance\",\n    cmap=\"jet\",\n    clim=(0, 0.5),\n    rgb_wavelengths=[1000, 700, 500],\n    rgb_gamma=2,\n    widget=\"plane\",\n)\np.add_text(\"Band slicing\", position=\"upper_right\", font_size=14)\np.show()\n</pre> p = hypercoast.image_cube(     ds,     variable=\"reflectance\",     cmap=\"jet\",     clim=(0, 0.5),     rgb_wavelengths=[1000, 700, 500],     rgb_gamma=2,     widget=\"plane\", ) p.add_text(\"Band slicing\", position=\"upper_right\", font_size=14) p.show() <p></p> <p>Interactive thresholding.</p> In\u00a0[\u00a0]: Copied! <pre>p = hypercoast.image_cube(\n    ds,\n    variable=\"reflectance\",\n    cmap=\"jet\",\n    clim=(0, 0.5),\n    rgb_wavelengths=[1000, 700, 500],\n    rgb_gamma=2,\n    widget=\"threshold\",\n)\np.add_text(\"Thresholding\", position=\"upper_right\", font_size=14)\np.show()\n</pre> p = hypercoast.image_cube(     ds,     variable=\"reflectance\",     cmap=\"jet\",     clim=(0, 0.5),     rgb_wavelengths=[1000, 700, 500],     rgb_gamma=2,     widget=\"threshold\", ) p.add_text(\"Thresholding\", position=\"upper_right\", font_size=14) p.show() <p></p> In\u00a0[\u00a0]: Copied! <pre>url = \"https://github.com/opengeos/datasets/releases/download/netcdf/EMIT_L2A_RFL_001_20240404T161230_2409511_009.nc\"\nfilepath = \"data/EMIT_L2A_RFL_001_20240404T161230_2409511_009.nc\"\nhypercoast.download_file(url, filepath)\n</pre> url = \"https://github.com/opengeos/datasets/releases/download/netcdf/EMIT_L2A_RFL_001_20240404T161230_2409511_009.nc\" filepath = \"data/EMIT_L2A_RFL_001_20240404T161230_2409511_009.nc\" hypercoast.download_file(url, filepath) <p>Load the dataset as a <code>xarray.Dataset</code> object.</p> In\u00a0[\u00a0]: Copied! <pre>dataset = hypercoast.read_emit(filepath)\ndataset\n</pre> dataset = hypercoast.read_emit(filepath) dataset <p>Select a subset of the data for demonstration purposes.</p> In\u00a0[\u00a0]: Copied! <pre>ds = dataset.sel(longitude=slice(-90.05, -89.99), latitude=slice(30.00, 29.93))\nds\n</pre> ds = dataset.sel(longitude=slice(-90.05, -89.99), latitude=slice(30.00, 29.93)) ds <p>Interactive slicing along the z-axis (band).</p> In\u00a0[\u00a0]: Copied! <pre>p = hypercoast.image_cube(\n    ds,\n    variable=\"reflectance\",\n    cmap=\"jet\",\n    clim=(0, 0.5),\n    rgb_wavelengths=[1000, 700, 500],\n    rgb_gamma=2,\n    title=\"EMIT Reflectance\",\n    widget=\"plane\",\n)\np.add_text(\"Band slicing\", position=\"upper_right\", font_size=14)\np.show()\n</pre> p = hypercoast.image_cube(     ds,     variable=\"reflectance\",     cmap=\"jet\",     clim=(0, 0.5),     rgb_wavelengths=[1000, 700, 500],     rgb_gamma=2,     title=\"EMIT Reflectance\",     widget=\"plane\", ) p.add_text(\"Band slicing\", position=\"upper_right\", font_size=14) p.show() <p></p> <p>Interactive thresholding.</p> In\u00a0[\u00a0]: Copied! <pre>p = hypercoast.image_cube(\n    ds,\n    variable=\"reflectance\",\n    cmap=\"jet\",\n    clim=(0, 0.5),\n    rgb_wavelengths=[1000, 700, 500],\n    rgb_gamma=2,\n    title=\"EMIT Reflectance\",\n    widget=\"threshold\",\n)\np.add_text(\"Thresholding\", position=\"upper_right\", font_size=14)\np.show()\n</pre> p = hypercoast.image_cube(     ds,     variable=\"reflectance\",     cmap=\"jet\",     clim=(0, 0.5),     rgb_wavelengths=[1000, 700, 500],     rgb_gamma=2,     title=\"EMIT Reflectance\",     widget=\"threshold\", ) p.add_text(\"Thresholding\", position=\"upper_right\", font_size=14) p.show() <p></p>"},{"location":"examples/image_slicing/#interactive-slicing-and-thresholding-of-hyperspectral-data-with-hypercoast","title":"Interactive slicing and thresholding of hyperspectral data with HyperCoast\u00b6","text":"<p>This notebook demonstrates how to perform interactive slicing and thresholding of hyperspectral data with HyperCoast using the PyVista plotting backend.</p>"},{"location":"examples/image_slicing/#neon-aop","title":"NEON AOP\u00b6","text":"<p>Download a sample NEON AOP hyperspectral dataset.</p>"},{"location":"examples/image_slicing/#nasa-emit","title":"NASA EMIT\u00b6","text":"<p>Download a sample NASA EMIT hyperspectral dataset from here.</p>"},{"location":"examples/multispectral/","title":"Multispectral","text":"In\u00a0[1]: Copied! <pre># %pip install \"hypercoast[extra]\n</pre> # %pip install \"hypercoast[extra] In\u00a0[2]: Copied! <pre>import hypercoast\n</pre> import hypercoast In\u00a0[3]: Copied! <pre>url = \"https://github.com/opengeos/datasets/releases/download/raster/cog.tif\"\nfilepath = \"data/cog.tif\"\nhypercoast.download_file(url, filepath, quiet=True)\n</pre> url = \"https://github.com/opengeos/datasets/releases/download/raster/cog.tif\" filepath = \"data/cog.tif\" hypercoast.download_file(url, filepath, quiet=True) Out[3]: <pre>'/home/runner/work/HyperCoast/HyperCoast/docs/examples/data/cog.tif'</pre> In\u00a0[4]: Copied! <pre>m = hypercoast.Map()\nfilepath = \"data/cog.tif\"  # replace it with your own raster data\nm.add_dataset(\n    filepath, indexes=[4, 1, 2], vmin=0, vmax=2500, layer_name=\"Landsat\", nodata=0\n)\nm.add(\"spectral\")\nm\n</pre> m = hypercoast.Map() filepath = \"data/cog.tif\"  # replace it with your own raster data m.add_dataset(     filepath, indexes=[4, 1, 2], vmin=0, vmax=2500, layer_name=\"Landsat\", nodata=0 ) m.add(\"spectral\") m <pre>Warning 1: TIFFReadDirectory:Sum of Photometric type-related color channels and ExtraSamples doesn't match SamplesPerPixel. Defining non-color channels as ExtraSamples.\n</pre> Out[4]:"},{"location":"examples/multispectral/#visualizing-multispectral-data-with-hypercoast","title":"Visualizing Multispectral Data with HyperCoast\u00b6","text":""},{"location":"examples/neon/","title":"Neon","text":"In\u00a0[1]: Copied! <pre># %pip install \"hypercoast[extra]\"\n</pre> # %pip install \"hypercoast[extra]\" In\u00a0[2]: Copied! <pre>import hypercoast\n</pre> import hypercoast In\u00a0[3]: Copied! <pre>url = \"https://github.com/opengeos/datasets/releases/download/hypercoast/NEON_D02_SERC_DP3_368000_4306000_reflectance.h5\"\nfilepath = \"data/neon.h5\"\nhypercoast.download_file(url, filepath)\n</pre> url = \"https://github.com/opengeos/datasets/releases/download/hypercoast/NEON_D02_SERC_DP3_368000_4306000_reflectance.h5\" filepath = \"data/neon.h5\" hypercoast.download_file(url, filepath) Out[3]: <pre>'/home/runner/work/HyperCoast/HyperCoast/docs/examples/data/neon.h5'</pre> <p>Load the dataset as a <code>xarray.Dataset</code> object.</p> In\u00a0[4]: Copied! <pre>dataset = hypercoast.read_neon(filepath)\ndataset\n</pre> dataset = hypercoast.read_neon(filepath) dataset Out[4]: <pre>&lt;xarray.Dataset&gt; Size: 3GB\nDimensions:      (y: 1000, x: 1000, wavelength: 426)\nCoordinates:\n  * y            (y) float64 8kB 4.307e+06 4.307e+06 ... 4.306e+06 4.306e+06\n  * x            (x) float64 8kB 3.68e+05 3.68e+05 ... 3.69e+05 3.69e+05\n  * wavelength   (wavelength) float64 3kB 383.9 388.9 ... 2.507e+03 2.512e+03\nData variables:\n    reflectance  (y, x, wavelength) float64 3GB 0.1569 0.1206 0.1034 ... nan nan\nAttributes:\n    scale_factor:   10000.0\n    no_data_value:  -9999.0\n    crs:            EPSG:32618\n    transform:      (1.0, 0.0, 368000.0, 0.0, -1.0, 4307000.0)</pre>xarray.Dataset<ul><li>Dimensions:<ul><li>y: 1000</li><li>x: 1000</li><li>wavelength: 426</li></ul></li><li>Coordinates: (3)<ul><li>y(y)float644.307e+06 4.307e+06 ... 4.306e+06<pre>array([4307000.      , 4306998.998999, 4306997.997998, ..., 4306002.002002,\n       4306001.001001, 4306000.      ])</pre></li><li>x(x)float643.68e+05 3.68e+05 ... 3.69e+05<pre>array([368000.      , 368001.001001, 368002.002002, ..., 368997.997998,\n       368998.998999, 369000.      ])</pre></li><li>wavelength(wavelength)float64383.9 388.9 ... 2.507e+03 2.512e+03<pre>array([ 383.88,  388.89,  393.9 , ..., 2502.17, 2507.17, 2512.18])</pre></li></ul></li><li>Data variables: (1)<ul><li>reflectance(y, x, wavelength)float640.1569 0.1206 0.1034 ... nan nanscale_factor :10000.0no_data_value :-9999.0crs :EPSG:32618transform :(1.0, 0.0, 368000.0, 0.0, -1.0, 4307000.0)<pre>array([[[0.1569, 0.1206, 0.1034, ..., 0.1512,    nan,    nan],\n        [0.1593, 0.118 , 0.105 , ...,    nan,    nan,    nan],\n        [0.1415, 0.1147, 0.1061, ..., 0.    , 0.    ,    nan],\n        ...,\n        [0.056 , 0.0319, 0.0292, ...,    nan,    nan,    nan],\n        [0.0604, 0.0412, 0.0257, ...,    nan,    nan,    nan],\n        [0.0723, 0.0444, 0.0366, ..., 0.7853,    nan,    nan]],\n\n       [[0.13  , 0.0988, 0.0842, ...,    nan,    nan,    nan],\n        [0.1515, 0.0961, 0.0826, ...,    nan,    nan,    nan],\n        [0.1431, 0.1253, 0.1069, ...,    nan,    nan,    nan],\n        ...,\n        [0.0586, 0.0411, 0.0224, ...,    nan,    nan,    nan],\n        [0.0467, 0.0424, 0.0289, ..., 0.4019,    nan,    nan],\n        [0.074 , 0.0453, 0.0301, ..., 0.7002,    nan,    nan]],\n\n       [[0.1026, 0.0716, 0.0623, ...,    nan,    nan,    nan],\n        [0.127 , 0.1103, 0.0986, ..., 0.7747,    nan,    nan],\n        [0.168 , 0.1335, 0.1359, ...,    nan,    nan,    nan],\n        ...,\n...\n        ...,\n        [0.0578, 0.0493, 0.0418, ...,    nan,    nan,    nan],\n        [0.1047, 0.0788, 0.057 , ...,    nan,    nan,    nan],\n        [0.0958, 0.0785, 0.0693, ...,    nan,    nan,    nan]],\n\n       [[0.0747, 0.0397, 0.0314, ...,    nan,    nan,    nan],\n        [0.065 , 0.041 , 0.0322, ..., 0.9747,    nan,    nan],\n        [0.0667, 0.0424, 0.0274, ...,    nan,    nan,    nan],\n        ...,\n        [0.1335, 0.1008, 0.0878, ..., 0.    ,    nan,    nan],\n        [0.1369, 0.0864, 0.0771, ...,    nan,    nan,    nan],\n        [0.1144, 0.0958, 0.0857, ...,    nan,    nan,    nan]],\n\n       [[0.0738, 0.0392, 0.031 , ...,    nan,    nan,    nan],\n        [0.0541, 0.0421, 0.0272, ...,    nan,    nan,    nan],\n        [0.0513, 0.0406, 0.0276, ...,    nan,    nan,    nan],\n        ...,\n        [0.082 , 0.0593, 0.0511, ...,    nan,    nan,    nan],\n        [0.0883, 0.0693, 0.0518, ...,    nan,    nan,    nan],\n        [0.1128, 0.1   , 0.0844, ..., 0.5833,    nan,    nan]]])</pre></li></ul></li><li>Indexes: (3)<ul><li>yPandasIndex<pre>PandasIndex(Index([        4307000.0, 4306998.998998999, 4306997.997997998,\n       4306996.996996997, 4306995.995995996, 4306994.994994995,\n       4306993.993993994, 4306992.992992993, 4306991.991991992,\n       4306990.990990991,\n       ...\n       4306009.009009009, 4306008.008008008, 4306007.007007007,\n       4306006.006006006, 4306005.005005005, 4306004.004004004,\n       4306003.003003003, 4306002.002002002, 4306001.001001001,\n               4306000.0],\n      dtype='float64', name='y', length=1000))</pre></li><li>xPandasIndex<pre>PandasIndex(Index([          368000.0,   368001.001001001, 368002.00200200203,\n         368003.003003003,   368004.004004004,   368005.005005005,\n       368006.00600600604,   368007.007007007,   368008.008008008,\n         368009.009009009,\n       ...\n         368990.990990991,   368991.991991992,   368992.992992993,\n       368993.99399399396,   368994.994994995,   368995.995995996,\n         368996.996996997,   368997.997997998,   368998.998998999,\n                 369000.0],\n      dtype='float64', name='x', length=1000))</pre></li><li>wavelengthPandasIndex<pre>PandasIndex(Index([ 383.88,  388.89,   393.9,  398.91,  403.92,  408.92,  413.93,  418.94,\n        423.95,  428.95,\n       ...\n       2467.11, 2472.12, 2477.13, 2482.13, 2487.14, 2492.15, 2497.16, 2502.17,\n       2507.17, 2512.18],\n      dtype='float64', name='wavelength', length=426))</pre></li></ul></li><li>Attributes: (4)scale_factor :10000.0no_data_value :-9999.0crs :EPSG:32618transform :(1.0, 0.0, 368000.0, 0.0, -1.0, 4307000.0)</li></ul> <p>Visualize the data interactively with HyperCoast.</p> In\u00a0[5]: Copied! <pre>m = hypercoast.Map()\nm.add_neon(filepath, wavelengths=[1000, 700, 500], vmin=0, vmax=0.5)\nm\n</pre> m = hypercoast.Map() m.add_neon(filepath, wavelengths=[1000, 700, 500], vmin=0, vmax=0.5) m Out[5]: In\u00a0[6]: Copied! <pre>m.set_center(-76.5134, 38.8973, 16)\n</pre> m.set_center(-76.5134, 38.8973, 16) In\u00a0[7]: Copied! <pre>m.add(\"spectral\")\n</pre> m.add(\"spectral\") <p></p>"},{"location":"examples/neon/#visualizing-neon-aop-hyperspectral-data-interactively-with-hypercoast","title":"Visualizing NEON AOP hyperspectral data interactively with HyperCoast\u00b6","text":"<p>This notebook demonstrates how to visualize NEON AOP hyperspectral data interactively with HyperCoast.</p>"},{"location":"examples/overview/","title":"Overview","text":"<ul> <li>Search and download NASA hyperspectral data</li> <li>Search and download NASA ECOSTRESS data</li> <li>Visualize Hyperspectral Data in 3D</li> <li>Interactive slicing and thresholding of hyperspectral data</li> <li>Visualize AVIRIS data interactively</li> <li>Visualize DESIS data interactively</li> <li>Visualize EMIT data interactively</li> <li>Visualize NEON AOP hyperspectral data interactively</li> <li>Visualize PACE data interactively</li> <li>Visualize PACE chlorophyll-a data interactively</li> <li>Visualize ERA5 temperature data interactively</li> <li>Visualize PACE OCI L1 data products</li> <li>Visualize PACE OCI L2 data products</li> <li>Visualize Multispectral data interactively</li> </ul>"},{"location":"examples/pace/","title":"Pace","text":"In\u00a0[1]: Copied! <pre># %pip install \"hypercoast[extra]\"\n</pre> # %pip install \"hypercoast[extra]\" In\u00a0[2]: Copied! <pre>import hypercoast\n</pre> import hypercoast <p>Download a sample PACE data file from here.</p> In\u00a0[3]: Copied! <pre>url = \"https://github.com/opengeos/datasets/releases/download/hypercoast/PACE_OCI.20240730T181157.L2.OC_AOP.V2_0.NRT.nc\"\n</pre> url = \"https://github.com/opengeos/datasets/releases/download/hypercoast/PACE_OCI.20240730T181157.L2.OC_AOP.V2_0.NRT.nc\" In\u00a0[4]: Copied! <pre>filepath = \"data/PACE_OCI.20240730T181157.L2.OC_AOP.V2_0.NRT.nc\"\nhypercoast.download_file(url, filepath)\n</pre> filepath = \"data/PACE_OCI.20240730T181157.L2.OC_AOP.V2_0.NRT.nc\" hypercoast.download_file(url, filepath) <pre>data/PACE_OCI.20240730T181157.L2.OC_AOP.V2_0.NRT.nc already exists. Skip downloading. Set overwrite=True to overwrite.\n</pre> Out[4]: <pre>'/home/runner/work/HyperCoast/HyperCoast/docs/examples/data/PACE_OCI.20240730T181157.L2.OC_AOP.V2_0.NRT.nc'</pre> <p>Let's make a scatter plot of the pixel locations so we can see the irregular spacing.</p> In\u00a0[5]: Copied! <pre>plot = hypercoast.view_pace_pixel_locations(filepath, step=20)\n</pre> plot = hypercoast.view_pace_pixel_locations(filepath, step=20) <p>Load the dataset as a <code>xarray.Dataset</code> object.</p> In\u00a0[6]: Copied! <pre>dataset = hypercoast.read_pace(filepath)\n</pre> dataset = hypercoast.read_pace(filepath) <p>Visualize selected bands of the dataset.</p> In\u00a0[7]: Copied! <pre>hypercoast.viz_pace(dataset, wavelengths=[500, 510, 520, 530], ncols=2)\n</pre> hypercoast.viz_pace(dataset, wavelengths=[500, 510, 520, 530], ncols=2) <p>Add projection.</p> In\u00a0[8]: Copied! <pre>hypercoast.viz_pace(dataset, wavelengths=[500, 510, 520, 530], ncols=2, crs=\"default\")\n</pre> hypercoast.viz_pace(dataset, wavelengths=[500, 510, 520, 530], ncols=2, crs=\"default\") <p>Plot a spectral signature.</p> In\u00a0[9]: Copied! <pre>latitude = 29.9307\nlongitude = -87.9106\nhypercoast.extract_pace(dataset, latitude, longitude, return_plot=True)\n</pre> latitude = 29.9307 longitude = -87.9106 hypercoast.extract_pace(dataset, latitude, longitude, return_plot=True) Out[9]: <pre>[&lt;matplotlib.lines.Line2D at 0x7f0198e76960&gt;]</pre> <p>Plot multiple spectral signatures.</p> In\u00a0[10]: Copied! <pre>latitude = (29.49, 29.50)\nlongitude = (-88.10, -88.00)\nhypercoast.filter_pace(dataset, latitude, longitude, return_plot=True)\n</pre> latitude = (29.49, 29.50) longitude = (-88.10, -88.00) hypercoast.filter_pace(dataset, latitude, longitude, return_plot=True) <p>Single-band visualization.</p> In\u00a0[11]: Copied! <pre>m = hypercoast.Map()\nm.add_basemap(\"Hybrid\")\nwavelengths = [450]\nm.add_pace(dataset, wavelengths, colormap=\"jet\", vmin=0, vmax=0.02, layer_name=\"PACE\")\nm.add_colormap(cmap=\"jet\", vmin=0, vmax=0.02, label=\"Reflectance\")\nm.add(\"spectral\")\nm\n</pre> m = hypercoast.Map() m.add_basemap(\"Hybrid\") wavelengths = [450] m.add_pace(dataset, wavelengths, colormap=\"jet\", vmin=0, vmax=0.02, layer_name=\"PACE\") m.add_colormap(cmap=\"jet\", vmin=0, vmax=0.02, label=\"Reflectance\") m.add(\"spectral\") m Out[11]: <p></p> <p>Multiple-band visualization.</p> In\u00a0[12]: Copied! <pre>m = hypercoast.Map()\nm.add_basemap(\"Hybrid\")\nwavelengths = [450, 550, 650]\nm.add_pace(\n    dataset, wavelengths, indexes=[3, 2, 1], vmin=0, vmax=0.02, layer_name=\"PACE\"\n)\nm.add(\"spectral\")\nm\n</pre> m = hypercoast.Map() m.add_basemap(\"Hybrid\") wavelengths = [450, 550, 650] m.add_pace(     dataset, wavelengths, indexes=[3, 2, 1], vmin=0, vmax=0.02, layer_name=\"PACE\" ) m.add(\"spectral\") m Out[12]: <p></p>"},{"location":"examples/pace/#visualizing-pace-data-interactively-with-hypercoast","title":"Visualizing PACE data interactively with HyperCoast\u00b6","text":"<p>This notebook demonstrates how to visualize Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) data interactively with HyperCoast.</p>"},{"location":"examples/pace_cyano/","title":"Pace cyano","text":"In\u00a0[1]: Copied! <pre># %pip install -U \"hypercoast[extra]\"\n</pre> # %pip install -U \"hypercoast[extra]\" In\u00a0[2]: Copied! <pre>import earthaccess\nimport hypercoast\nfrom hypercoast.pace import (\n    cyano_band_ratios,\n    apply_kmeans,\n    apply_pca,\n    apply_sam,\n    apply_sam_spectral,\n)\n</pre> import earthaccess import hypercoast from hypercoast.pace import (     cyano_band_ratios,     apply_kmeans,     apply_pca,     apply_sam,     apply_sam_spectral, ) In\u00a0[3]: Copied! <pre>earthaccess.login(persist=True)\n</pre> earthaccess.login(persist=True) Out[3]: <pre>&lt;earthaccess.auth.Auth at 0x7fb3f8723bc0&gt;</pre> <p>Search for PACE AOP data:</p> In\u00a0[4]: Copied! <pre>results = hypercoast.search_pace(\n    bounding_box=(-83, 25, -81, 28),\n    temporal=(\"2024-07-30\", \"2024-08-15\"),\n    short_name=\"PACE_OCI_L2_AOP_NRT\",\n    count=1,\n)\n</pre> results = hypercoast.search_pace(     bounding_box=(-83, 25, -81, 28),     temporal=(\"2024-07-30\", \"2024-08-15\"),     short_name=\"PACE_OCI_L2_AOP_NRT\",     count=1, ) <p>Download PACE AOP data:</p> In\u00a0[5]: Copied! <pre>hypercoast.download_pace(results[:1], out_dir=\"data\")\n</pre> hypercoast.download_pace(results[:1], out_dir=\"data\") In\u00a0[6]: Copied! <pre>filepath = \"data/PACE_OCI.20240730T181157.L2.OC_AOP.V2_0.NRT.nc\"\ndataset = hypercoast.read_pace(filepath)\n# dataset\n</pre> filepath = \"data/PACE_OCI.20240730T181157.L2.OC_AOP.V2_0.NRT.nc\" dataset = hypercoast.read_pace(filepath) # dataset <p></p> In\u00a0[7]: Copied! <pre>da = cyano_band_ratios(dataset, plot=True)\n</pre> da = cyano_band_ratios(dataset, plot=True) In\u00a0[8]: Copied! <pre>cluster_labels, latitudes, longitudes = apply_kmeans(dataset, n_clusters=6)\n</pre> cluster_labels, latitudes, longitudes = apply_kmeans(dataset, n_clusters=6) In\u00a0[9]: Copied! <pre>da = dataset[\"Rrs\"]\n\nfilter_condition = (\n    (da.sel(wavelength=650) &gt; da.sel(wavelength=620))\n    &amp; (da.sel(wavelength=701) &gt; da.sel(wavelength=681))\n    &amp; (da.sel(wavelength=701) &gt; da.sel(wavelength=450))\n)\nextent = [-95, -85, 27, 33]\ncolors = [\"#e41a1c\", \"#377eb8\", \"#4daf4a\", \"#f781bf\", \"#a65628\", \"#984ea3\"]\n\ncluster_labels, latitudes, longitudes = apply_kmeans(\n    da, n_clusters=6, filter_condition=filter_condition, extent=extent, colors=colors\n)\n</pre> da = dataset[\"Rrs\"]  filter_condition = (     (da.sel(wavelength=650) &gt; da.sel(wavelength=620))     &amp; (da.sel(wavelength=701) &gt; da.sel(wavelength=681))     &amp; (da.sel(wavelength=701) &gt; da.sel(wavelength=450)) ) extent = [-95, -85, 27, 33] colors = [\"#e41a1c\", \"#377eb8\", \"#4daf4a\", \"#f781bf\", \"#a65628\", \"#984ea3\"]  cluster_labels, latitudes, longitudes = apply_kmeans(     da, n_clusters=6, filter_condition=filter_condition, extent=extent, colors=colors ) In\u00a0[10]: Copied! <pre>pca_data = apply_pca(dataset, n_components=3, x_component=0, y_component=1)\n</pre> pca_data = apply_pca(dataset, n_components=3, x_component=0, y_component=1) In\u00a0[11]: Copied! <pre>pca_data = apply_pca(dataset, n_components=3, x_component=1, y_component=2)\n</pre> pca_data = apply_pca(dataset, n_components=3, x_component=1, y_component=2) In\u00a0[12]: Copied! <pre>data, latitudes, longitudes = apply_sam(\n    dataset,\n    n_components=3,\n    n_clusters=6,\n)\n</pre> data, latitudes, longitudes = apply_sam(     dataset,     n_components=3,     n_clusters=6, ) In\u00a0[13]: Copied! <pre>extent = [-95, -85, 27, 33]\ncolors = [\"#377eb8\", \"#ff7f00\", \"#4daf4a\", \"#f781bf\", \"#a65628\", \"#984ea3\"]\ndata, latitudes, longitudes = apply_sam(\n    dataset,\n    n_components=3,\n    n_clusters=6,\n    extent=extent,\n    colors=colors,\n)\n</pre> extent = [-95, -85, 27, 33] colors = [\"#377eb8\", \"#ff7f00\", \"#4daf4a\", \"#f781bf\", \"#a65628\", \"#984ea3\"] data, latitudes, longitudes = apply_sam(     dataset,     n_components=3,     n_clusters=6,     extent=extent,     colors=colors, ) In\u00a0[14]: Copied! <pre>da = dataset[\"Rrs\"]\n\nfilter_condition = (\n    (da.sel(wavelength=650) &gt; da.sel(wavelength=620))\n    &amp; (da.sel(wavelength=701) &gt; da.sel(wavelength=681))\n    &amp; (da.sel(wavelength=701) &gt; da.sel(wavelength=450))\n)\nextent = [-95, -85, 27, 33]\ncolors = [\"#e41a1c\", \"#377eb8\", \"#4daf4a\", \"#f781bf\", \"#a65628\", \"#984ea3\"]\n\ndata, latitudes, longitudes = apply_sam(\n    dataset,\n    n_components=3,\n    n_clusters=6,\n    filter_condition=filter_condition,\n    extent=extent,\n    colors=colors,\n)\n</pre> da = dataset[\"Rrs\"]  filter_condition = (     (da.sel(wavelength=650) &gt; da.sel(wavelength=620))     &amp; (da.sel(wavelength=701) &gt; da.sel(wavelength=681))     &amp; (da.sel(wavelength=701) &gt; da.sel(wavelength=450)) ) extent = [-95, -85, 27, 33] colors = [\"#e41a1c\", \"#377eb8\", \"#4daf4a\", \"#f781bf\", \"#a65628\", \"#984ea3\"]  data, latitudes, longitudes = apply_sam(     dataset,     n_components=3,     n_clusters=6,     filter_condition=filter_condition,     extent=extent,     colors=colors, ) In\u00a0[15]: Copied! <pre>filepath = \"data/PACE_OCI.20240730T181157.L2.OC_AOP.V2_0.NRT.nc\"\ndataset = hypercoast.read_pace(filepath)\nurl = \"https://github.com/opengeos/datasets/releases/download/hypercoast/SAM_spectral_library.zip\"\nhypercoast.download_file(url)\nspectral_library = \"./SAM_spectral_library/*.csv\"\n</pre> filepath = \"data/PACE_OCI.20240730T181157.L2.OC_AOP.V2_0.NRT.nc\" dataset = hypercoast.read_pace(filepath) url = \"https://github.com/opengeos/datasets/releases/download/hypercoast/SAM_spectral_library.zip\" hypercoast.download_file(url) spectral_library = \"./SAM_spectral_library/*.csv\" In\u00a0[16]: Copied! <pre>extent = [-95, -85, 27, 33]\ndata, latitudes, longitudes = apply_sam_spectral(\n    dataset,\n    spectral_library=spectral_library,\n    extent=extent,\n)\n</pre> extent = [-95, -85, 27, 33] data, latitudes, longitudes = apply_sam_spectral(     dataset,     spectral_library=spectral_library,     extent=extent, ) In\u00a0[17]: Copied! <pre>da = dataset[\"Rrs\"]\nextent = [-95, -85, 27, 33]\nfilter_condition = (\n    (da.sel(wavelength=650) &gt; da.sel(wavelength=620))\n    &amp; (da.sel(wavelength=701) &gt; da.sel(wavelength=681))\n    &amp; (da.sel(wavelength=701) &gt; da.sel(wavelength=450))\n)\ndata, latitudes, longitudes = apply_sam_spectral(\n    da,\n    spectral_library=spectral_library,\n    filter_condition=filter_condition,\n    extent=extent,\n)\n</pre> da = dataset[\"Rrs\"] extent = [-95, -85, 27, 33] filter_condition = (     (da.sel(wavelength=650) &gt; da.sel(wavelength=620))     &amp; (da.sel(wavelength=701) &gt; da.sel(wavelength=681))     &amp; (da.sel(wavelength=701) &gt; da.sel(wavelength=450)) ) data, latitudes, longitudes = apply_sam_spectral(     da,     spectral_library=spectral_library,     filter_condition=filter_condition,     extent=extent, )"},{"location":"examples/pace_cyano/#mapping-cyanobacteria-with-pace-data","title":"Mapping Cyanobacteria with PACE data\u00b6","text":""},{"location":"examples/pace_cyano/#install-packages","title":"Install packages\u00b6","text":"<p>Uncomment the following cell to install the HyperCoast package.</p>"},{"location":"examples/pace_cyano/#import-libraries","title":"Import libraries\u00b6","text":""},{"location":"examples/pace_cyano/#download-pace-data","title":"Download PACE data\u00b6","text":"<p>To download and access the PACE AOP data, you will need to create an Earthdata login. You can register for an account at urs.earthdata.nasa.gov. Once you have an account, run the following cell and enter your NASA Earthdata login credentials.</p>"},{"location":"examples/pace_cyano/#read-pace-data","title":"Read PACE data\u00b6","text":"<p>Read PACE AOP data as an <code>xarray.Dataset</code>:</p>"},{"location":"examples/pace_cyano/#compute-band-ratios","title":"Compute band ratios\u00b6","text":""},{"location":"examples/pace_cyano/#the-spectra-of-cyanobacteria-bloom","title":"The spectra of cyanobacteria bloom:\u00b6","text":""},{"location":"examples/pace_cyano/#cyanobacteria-and-spectral-angle-mapper","title":"Cyanobacteria and Spectral Angle Mapper\u00b6","text":"<p>Spectral Angle Mapper: Spectral similarity Input: library of Cyanobacteria bloom Rrs spectra with Chla at different levels</p> <p>Spectral Mixture Analysis: unmix different cyanobacteria species based on spectral difference.</p> <p></p>"},{"location":"examples/pace_cyano/#k-means-applied-to-the-whole-image","title":"K-means applied to the whole image\u00b6","text":""},{"location":"examples/pace_cyano/#k-means-applied-to-selected-pixels","title":"K-means applied to selected pixels\u00b6","text":""},{"location":"examples/pace_cyano/#principal-component-analysis-pca","title":"Principal Component Analysis (PCA)\u00b6","text":""},{"location":"examples/pace_cyano/#spectral-angle-mapper-sam","title":"Spectral Angle Mapper (SAM)\u00b6","text":""},{"location":"examples/pace_cyano/#apply-sam-to-the-whole-image","title":"Apply SAM to the whole image\u00b6","text":""},{"location":"examples/pace_cyano/#apply-sam-to-selected-pixels","title":"Apply SAM to selected pixels\u00b6","text":""},{"location":"examples/pace_cyano/#apply-sam-with-a-filtering-condition","title":"Apply SAM with a filtering condition\u00b6","text":""},{"location":"examples/pace_cyano/#use-spectral-library","title":"Use spectral library\u00b6","text":""},{"location":"examples/pace_oci_l1/","title":"Pace oci l1","text":"In\u00a0[\u00a0]: Copied! <pre># %pip install \"hypercoast[extra]\"\n</pre> # %pip install \"hypercoast[extra]\" In\u00a0[\u00a0]: Copied! <pre>import hypercoast\nimport xarray as xr\n</pre> import hypercoast import xarray as xr <p>To download and access the data, you will need to create an Earthdata login. You can register for an account at urs.earthdata.nasa.gov. Once you have an account, you can run the following cell to search and download PACE OCI L1 data products.</p> In\u00a0[\u00a0]: Copied! <pre>hypercoast.nasa_earth_login()\n</pre> hypercoast.nasa_earth_login() In\u00a0[\u00a0]: Copied! <pre>hypercoast.nasa_earth_login()\n\nshort_name = \"PACE_OCI_L1B_SCI\"\nresults, gdf = hypercoast.search_nasa_data(\n    short_name=short_name,\n    bbox=(-90.5642, 29.9749, -89.7143, 30.42),\n    temporal=(\"2024-06-15\", \"2024-06-16\"),\n    return_gdf=True,\n)\n</pre> hypercoast.nasa_earth_login()  short_name = \"PACE_OCI_L1B_SCI\" results, gdf = hypercoast.search_nasa_data(     short_name=short_name,     bbox=(-90.5642, 29.9749, -89.7143, 30.42),     temporal=(\"2024-06-15\", \"2024-06-16\"),     return_gdf=True, ) In\u00a0[\u00a0]: Copied! <pre>gdf.explore()\n</pre> gdf.explore() <p></p> In\u00a0[\u00a0]: Copied! <pre>hypercoast.download_nasa_data(results[0], out_dir=\"data\")\n</pre> hypercoast.download_nasa_data(results[0], out_dir=\"data\") <p>Alternatively, use the following code block to download a sample dataset from here.</p> In\u00a0[\u00a0]: Copied! <pre>url = \"https://github.com/opengeos/datasets/releases/download/hypercoast/PACE_OCI.20240615T182549.L1B.nc\"\nfilepath = \"data/PACE_OCI.20240615T182549.L1B.nc\"\nhypercoast.download_file(url, filepath)\n</pre> url = \"https://github.com/opengeos/datasets/releases/download/hypercoast/PACE_OCI.20240615T182549.L1B.nc\" filepath = \"data/PACE_OCI.20240615T182549.L1B.nc\" hypercoast.download_file(url, filepath) <p>Let's check the top-level groups in the sample dataset.</p> In\u00a0[\u00a0]: Copied! <pre>hypercoast.netcdf_groups(filepath)\n</pre> hypercoast.netcdf_groups(filepath) <p>The top-level groups in the sample dataset are:</p> <pre>['sensor_band_parameters',\n 'scan_line_attributes',\n 'geolocation_data',\n 'navigation_data',\n 'observation_data']\n</pre> <p>Let's open the <code>observation_data</code> group, which contains the core science variables.</p> In\u00a0[\u00a0]: Copied! <pre>dataset = xr.open_dataset(filepath, group=\"observation_data\")\nprint(list(dataset.variables))\n</pre> dataset = xr.open_dataset(filepath, group=\"observation_data\") print(list(dataset.variables)) <p>The data variables include:</p> <pre>['rhot_blue', 'qual_blue', 'rhot_red', 'qual_red', 'rhot_SWIR', 'qual_SWIR']\n</pre> <p>The dimensions of the <code>rhot_blue</code> variable are <code>(\"blue_bands\", \"number_of_scans\", \"ccd_pixels\")</code>, and it has shape <code>(119, 1710, 1272)</code>. The sizes attribute of a variable gives us that information as a dictionary.</p> In\u00a0[\u00a0]: Copied! <pre>dataset[\"rhot_blue\"].sizes\n</pre> dataset[\"rhot_blue\"].sizes <p>The dimensions of the <code>rhot_red</code> variable are <code>(\"red_bands\", \"number_of_scans\", \"ccd_pixels\")</code>, and it has shape <code>(163, 1710, 1272)</code></p> In\u00a0[\u00a0]: Copied! <pre>dataset[\"rhot_red\"].sizes\n</pre> dataset[\"rhot_red\"].sizes <p>The dimensions of the <code>rhot_SWIR</code> variable are <code>(\"SWIR_bands\", \"number_of_scans\", \"SWIR_pixels\")</code>, and it has shape <code>(9, 1710, 1272)</code></p> In\u00a0[\u00a0]: Copied! <pre>dataset[\"rhot_SWIR\"].sizes\n</pre> dataset[\"rhot_SWIR\"].sizes <p>Let's plot the reflectance at position <code>100</code> in the <code>blue_bands</code> dimension.</p> In\u00a0[\u00a0]: Copied! <pre>plot = dataset[\"rhot_blue\"].sel({\"blue_bands\": 100}).plot()\n</pre> plot = dataset[\"rhot_blue\"].sel({\"blue_bands\": 100}).plot() <p></p>"},{"location":"examples/pace_oci_l1/#visualizing-pace-oci-l1-data-products-with-hypercoast","title":"Visualizing PACE OCI L1 data products with HyperCoast\u00b6","text":"<p>This notebook demonstrates how to visualize Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) OCI L1 data products. Part of the notebook is adapted from the NASA OB.DAAC tutorial - File Structure at Three Processing Levels for the Ocean Color Instrument (OCI). Credits to the NASA OB.DAAC team for the tutorial.</p>"},{"location":"examples/pace_oci_l2/","title":"Pace oci l2","text":"In\u00a0[1]: Copied! <pre># %pip install \"hypercoast[extra]\"\n</pre> # %pip install \"hypercoast[extra]\" In\u00a0[2]: Copied! <pre>import hypercoast\n</pre> import hypercoast <p>To download and access the data, you will need to create an Earthdata login. You can register for an account at urs.earthdata.nasa.gov. Once you have an account, you can uncomment and run the following cell to search and download PACE OCI L2 data products.</p> In\u00a0[3]: Copied! <pre># hypercoast.nasa_earth_login()\n\n# short_name = \"PACE_OCI_L2_BGC_NRT\"\n# results, gdf = hypercoast.search_nasa_data(\n#     short_name=short_name,\n#     bbox=(-90.5642, 29.9749, -89.7143, 30.42),\n#     temporal=(\"2024-06-15\", \"2024-06-16\"),\n#     return_gdf=True\n#     )\n# hypercoast.download_nasa_data(results, out_dir=\"bgc\")\n</pre> # hypercoast.nasa_earth_login()  # short_name = \"PACE_OCI_L2_BGC_NRT\" # results, gdf = hypercoast.search_nasa_data( #     short_name=short_name, #     bbox=(-90.5642, 29.9749, -89.7143, 30.42), #     temporal=(\"2024-06-15\", \"2024-06-16\"), #     return_gdf=True #     ) # hypercoast.download_nasa_data(results, out_dir=\"bgc\") <p>Alternatively, use the following code block to download a sample dataset from here.</p> In\u00a0[4]: Copied! <pre>url = \"https://github.com/opengeos/datasets/releases/download/hypercoast/PACE_OCI.20240615T182549.L2.OC_BGC.V1_0_0.NRT.nc\"\nfilepath = \"data/PACE_OCI.20240615T182549.L2.OC_BGC.V1_0_0.NRT.nc\"\nhypercoast.download_file(url, filepath)\n</pre> url = \"https://github.com/opengeos/datasets/releases/download/hypercoast/PACE_OCI.20240615T182549.L2.OC_BGC.V1_0_0.NRT.nc\" filepath = \"data/PACE_OCI.20240615T182549.L2.OC_BGC.V1_0_0.NRT.nc\" hypercoast.download_file(url, filepath) Out[4]: <pre>'/home/runner/work/HyperCoast/HyperCoast/docs/examples/data/PACE_OCI.20240615T182549.L2.OC_BGC.V1_0_0.NRT.nc'</pre> <p>Load the downloaded dataset as an <code>xarray.Dataset</code>:</p> In\u00a0[5]: Copied! <pre>dataset = hypercoast.read_pace_bgc(filepath)\n</pre> dataset = hypercoast.read_pace_bgc(filepath) <p>Let's inspect the data variables contained in the dataset:</p> In\u00a0[6]: Copied! <pre>dataset.variables\n</pre> dataset.variables Out[6]: <pre>Frozen({'chlor_a': &lt;xarray.Variable (latitude: 1710, longitude: 1272)&gt; Size: 9MB\n[2175120 values with dtype=float32]\nAttributes:\n    long_name:      Chlorophyll Concentration, OCI Algorithm\n    units:          mg m^-3\n    standard_name:  mass_concentration_of_chlorophyll_in_sea_water\n    valid_min:      0.001\n    valid_max:      100.0\n    reference:      Hu, C., Lee Z., and Franz, B.A. (2012). Chlorophyll-a alg..., 'carbon_phyto': &lt;xarray.Variable (latitude: 1710, longitude: 1272)&gt; Size: 9MB\n[2175120 values with dtype=float32]\nAttributes:\n    long_name:  Phytoplankton Carbon\n    units:      mg m^-3\n    valid_min:  0.0\n    valid_max:  1000.0\n    reference:  Graff, J.R., Westberry, T.K., Milligan, A.J., Brown, M.B., Da..., 'poc': &lt;xarray.Variable (latitude: 1710, longitude: 1272)&gt; Size: 9MB\n[2175120 values with dtype=float32]\nAttributes:\n    long_name:  Particulate Organic Carbon, D. Stramski, 2022 (hybrid version)\n    units:      mg m^-3\n    valid_min:  -32000\n    valid_max:  -22000\n    reference:  Stramski, D., et al. \"Ocean color algorithms to estimate the ..., 'chlor_a_unc': &lt;xarray.Variable (latitude: 1710, longitude: 1272)&gt; Size: 9MB\n[2175120 values with dtype=float32]\nAttributes:\n    long_name:      Uncertainty in chlorophyll a concentration\n    units:          mg m^-3\n    standard_name:  chlorophyll_concentration_in_sea_water standard_error\n    valid_min:      0.001\n    valid_max:      100.0, 'carbon_phyto_unc': &lt;xarray.Variable (latitude: 1710, longitude: 1272)&gt; Size: 9MB\n[2175120 values with dtype=float32]\nAttributes:\n    long_name:  Phytoplankton Carbon standard uncertainty\n    units:      mg m^-3\n    valid_min:  0.0\n    valid_max:  1000.0\n    reference:  Graff, J.R., Westberry, T.K., Milligan, A.J., Brown, M.B., Da..., 'l2_flags': &lt;xarray.Variable (latitude: 1710, longitude: 1272)&gt; Size: 9MB\n[2175120 values with dtype=int32]\nAttributes:\n    long_name:      Level-2 Processing Flags\n    valid_min:      -2147483648\n    valid_max:      2147483647\n    flag_masks:     [          1           2           4           8         ...\n    flag_meanings:  ATMFAIL LAND PRODWARN HIGLINT HILT HISATZEN COASTZ SPARE ..., 'longitude': &lt;xarray.Variable (latitude: 1710, longitude: 1272)&gt; Size: 9MB\n[2175120 values with dtype=float32]\nAttributes:\n    long_name:      Longitude\n    units:          degrees_east\n    standard_name:  longitude\n    valid_min:      -180.0\n    valid_max:      180.0, 'latitude': &lt;xarray.Variable (latitude: 1710, longitude: 1272)&gt; Size: 9MB\n[2175120 values with dtype=float32]\nAttributes:\n    long_name:      Latitude\n    units:          degrees_north\n    standard_name:  latitude\n    valid_min:      -90.0\n    valid_max:      90.0})</pre> <p>We can see that the dataset contains the following variables:</p> <ul> <li>Chlorophyll Concentration</li> <li>Phytoplankton Carbon</li> <li>Particulate Organic Carbon</li> </ul> <p>Transform the xarray dataset into gridded data.</p> <p>Plot the Chlorophyll Concentration.</p> In\u00a0[7]: Copied! <pre>chlor_a = hypercoast.grid_pace_bgc(dataset, variable=\"chlor_a\", method=\"linear\")\nchlor_a.plot(vmin=0, vmax=20, cmap=\"jet\", size=6)\n</pre> chlor_a = hypercoast.grid_pace_bgc(dataset, variable=\"chlor_a\", method=\"linear\") chlor_a.plot(vmin=0, vmax=20, cmap=\"jet\", size=6) Out[7]: <pre>&lt;matplotlib.collections.QuadMesh at 0x7f46a44918e0&gt;</pre> <p>Plot the Phytoplankton Carbon.</p> In\u00a0[8]: Copied! <pre>carbon_phyto = hypercoast.grid_pace_bgc(\n    dataset, variable=\"carbon_phyto\", method=\"linear\"\n)\ncarbon_phyto.plot(vmin=0, vmax=120, cmap=\"jet\", size=6)\n</pre> carbon_phyto = hypercoast.grid_pace_bgc(     dataset, variable=\"carbon_phyto\", method=\"linear\" ) carbon_phyto.plot(vmin=0, vmax=120, cmap=\"jet\", size=6) Out[8]: <pre>&lt;matplotlib.collections.QuadMesh at 0x7f46a3fdda90&gt;</pre> <p>Particulate Organic Carbon.</p> In\u00a0[9]: Copied! <pre>poc = hypercoast.grid_pace_bgc(dataset, variable=\"poc\", method=\"linear\")\npoc.plot(vmin=0, vmax=1000, cmap=\"jet\")\n</pre> poc = hypercoast.grid_pace_bgc(dataset, variable=\"poc\", method=\"linear\") poc.plot(vmin=0, vmax=1000, cmap=\"jet\") Out[9]: <pre>&lt;matplotlib.collections.QuadMesh at 0x7f46a3fdd8b0&gt;</pre> <p>Plot the data on an interactive map.</p> In\u00a0[10]: Copied! <pre>m = hypercoast.Map()\nm.add_basemap(\"Hybrid\")\nm.add_raster(chlor_a, layer_name=\"Chlorophyll-a\", colormap=\"jet\", vmin=0, vmax=20)\nm.add_raster(\n    carbon_phyto, layer_name=\"Phytoplankton Carbon\", colormap=\"plasma\", vmin=0, vmax=120\n)\nm.add_raster(\n    poc, layer_name=\"Particulate Organic Carbon\", colormap=\"coolwarm\", vmin=0, vmax=1000\n)\nm.add_layer_manager()\n\nm.add_colormap(cmap=\"jet\", vmin=0, vmax=20, label=\"Chlorophyll-a (mg/m3)\")\nm.add_colormap(cmap=\"plasma\", vmin=0, vmax=120, label=\"Phytoplankton Carbon (mg/m3)\")\nm.add_colormap(\n    cmap=\"coolwarm\", vmin=0, vmax=1000, label=\"Particulate Organic Carbon (mg/m3)\"\n)\nm\n</pre> m = hypercoast.Map() m.add_basemap(\"Hybrid\") m.add_raster(chlor_a, layer_name=\"Chlorophyll-a\", colormap=\"jet\", vmin=0, vmax=20) m.add_raster(     carbon_phyto, layer_name=\"Phytoplankton Carbon\", colormap=\"plasma\", vmin=0, vmax=120 ) m.add_raster(     poc, layer_name=\"Particulate Organic Carbon\", colormap=\"coolwarm\", vmin=0, vmax=1000 ) m.add_layer_manager()  m.add_colormap(cmap=\"jet\", vmin=0, vmax=20, label=\"Chlorophyll-a (mg/m3)\") m.add_colormap(cmap=\"plasma\", vmin=0, vmax=120, label=\"Phytoplankton Carbon (mg/m3)\") m.add_colormap(     cmap=\"coolwarm\", vmin=0, vmax=1000, label=\"Particulate Organic Carbon (mg/m3)\" ) m Out[10]: <p></p>"},{"location":"examples/pace_oci_l2/#visualizing-pace-oci-l2-data-products-with-hypercoast","title":"Visualizing PACE OCI L2 data products with HyperCoast\u00b6","text":"<p>This notebook demonstrates how to visualize Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) OCI L2 data products, including the concentration of chlorophyll-a, concentration of phytoplankton carbon, and concentration of particulate organic carbon.</p>"},{"location":"examples/pca/","title":"Pca","text":"In\u00a0[1]: Copied! <pre>import hypercoast\n</pre> import hypercoast In\u00a0[2]: Copied! <pre>url = \"https://github.com/opengeos/datasets/releases/download/hypercoast/Planet_2022-09-23.tif\"\nfilepath = \"data/Planet_2022-09-23.tif\"\nhypercoast.download_file(url, filepath)\n</pre> url = \"https://github.com/opengeos/datasets/releases/download/hypercoast/Planet_2022-09-23.tif\" filepath = \"data/Planet_2022-09-23.tif\" hypercoast.download_file(url, filepath) Out[2]: <pre>'/home/runner/work/HyperCoast/HyperCoast/docs/examples/data/Planet_2022-09-23.tif'</pre> In\u00a0[3]: Copied! <pre>output_file = filepath.replace(\".tif\", \"_pca.tif\")\n</pre> output_file = filepath.replace(\".tif\", \"_pca.tif\") In\u00a0[4]: Copied! <pre>hypercoast.pca(filepath, output_file, n_components=3)\n</pre> hypercoast.pca(filepath, output_file, n_components=3) In\u00a0[5]: Copied! <pre>m = hypercoast.Map()\nm.add_basemap(\"Hybrid\")\nm.add_dataset(\n    filepath, indexes=[6, 4, 2], vmin=0, vmax=2500, layer_name=\"Planet Image\", nodata=0\n)\nm.add_dataset(\n    output_file, indexes=[0, 1, 2], vmin=0, vmax=2500, layer_name=\"PCA Image\", nodata=0\n)\nm.add(\"spectral\")\nm\n</pre> m = hypercoast.Map() m.add_basemap(\"Hybrid\") m.add_dataset(     filepath, indexes=[6, 4, 2], vmin=0, vmax=2500, layer_name=\"Planet Image\", nodata=0 ) m.add_dataset(     output_file, indexes=[0, 1, 2], vmin=0, vmax=2500, layer_name=\"PCA Image\", nodata=0 ) m.add(\"spectral\") m Out[5]:"},{"location":"examples/pca/#principal-component-analysis-pca","title":"Principal Component Analysis (PCA)\u00b6","text":"<p>PCA is a technique used to emphasize variation and bring out strong patterns in a dataset. It's often used to make data easy to explore and visualize. In this notebook, we'll perform PCA on a multi-spectral imagery to reduce the dimensionality of the data and visualize the results.</p>"},{"location":"examples/search_data/","title":"Search data","text":"In\u00a0[\u00a0]: Copied! <pre># %pip install \"hypercoast[extra]\"\n</pre> # %pip install \"hypercoast[extra]\" In\u00a0[\u00a0]: Copied! <pre>import hypercoast\n</pre> import hypercoast In\u00a0[\u00a0]: Copied! <pre>hypercoast.nasa_earth_login()\n</pre> hypercoast.nasa_earth_login() In\u00a0[\u00a0]: Copied! <pre>results = hypercoast.search_datasets(instrument=\"oci\")\n</pre> results = hypercoast.search_datasets(instrument=\"oci\") In\u00a0[\u00a0]: Copied! <pre>datasets = set()\nfor item in results:\n    summary = item.summary()\n    short_name = summary[\"short-name\"]\n    if short_name not in datasets:\n        print(short_name)\n    datasets.add(short_name)\nprint(f\"\\nFound {len(datasets)} unique datasets\")\n</pre> datasets = set() for item in results:     summary = item.summary()     short_name = summary[\"short-name\"]     if short_name not in datasets:         print(short_name)     datasets.add(short_name) print(f\"\\nFound {len(datasets)} unique datasets\") In\u00a0[\u00a0]: Copied! <pre>results = hypercoast.search_nasa_data(\n    short_name=\"PACE_OCI_L2_BGC_NRT\",\n    count=1,\n)\n</pre> results = hypercoast.search_nasa_data(     short_name=\"PACE_OCI_L2_BGC_NRT\",     count=1, ) <p>We can refine our search by passing more parameters that describe the spatiotemporal domain of our use case. Here, we use the temporal parameter to request a date range and the bounding_box parameter to request granules that intersect with a bounding box. We can even provide a cloud_cover threshold to limit files that have a lower percentage of cloud cover. We do not provide a count, so we'll get all granules that satisfy the constraints.</p> In\u00a0[\u00a0]: Copied! <pre>tspan = (\"2024-04-01\", \"2024-04-16\")\nbbox = (-76.75, 36.97, -75.74, 39.01)\nclouds = (0, 50)\n\nresults, gdf = hypercoast.search_nasa_data(\n    short_name=\"PACE_OCI_L2_BGC_NRT\",\n    temporal=tspan,\n    bounding_box=bbox,\n    cloud_cover=clouds,\n    return_gdf=True,\n)\n</pre> tspan = (\"2024-04-01\", \"2024-04-16\") bbox = (-76.75, 36.97, -75.74, 39.01) clouds = (0, 50)  results, gdf = hypercoast.search_nasa_data(     short_name=\"PACE_OCI_L2_BGC_NRT\",     temporal=tspan,     bounding_box=bbox,     cloud_cover=clouds,     return_gdf=True, ) <p>Display the footprints of the granules that match the search criteria.</p> In\u00a0[\u00a0]: Copied! <pre>gdf.explore()\n</pre> gdf.explore() <p>Displaying a single result shows a direct download link: try it! The link will download the granule to your local machine, which may or may not be what you want to do. Even if you are running the notebook on a remote host, this download link will open a new browser tab or window and offer to save a file to your local machine. If you are running the notebook locally, this may be of use.</p> In\u00a0[\u00a0]: Copied! <pre>results[0]\n</pre> results[0] <p>We can also download all the results with one command.</p> In\u00a0[\u00a0]: Copied! <pre>hypercoast.download_nasa_data(results, out_dir=\"data\")\n</pre> hypercoast.download_nasa_data(results, out_dir=\"data\") In\u00a0[\u00a0]: Copied! <pre>hypercoast.nasa_earth_login()\n</pre> hypercoast.nasa_earth_login() In\u00a0[\u00a0]: Copied! <pre>results, gdf = hypercoast.search_pace(\n    bounding_box=(-83, 25, -81, 28),\n    temporal=(\"2024-05-10\", \"2024-05-16\"),\n    count=10,  # use -1 to return all datasets\n    return_gdf=True,\n)\n</pre> results, gdf = hypercoast.search_pace(     bounding_box=(-83, 25, -81, 28),     temporal=(\"2024-05-10\", \"2024-05-16\"),     count=10,  # use -1 to return all datasets     return_gdf=True, ) In\u00a0[\u00a0]: Copied! <pre>gdf.explore()\n</pre> gdf.explore() <p></p> In\u00a0[\u00a0]: Copied! <pre>hypercoast.download_pace(results[:2], out_dir=\"data\")\n</pre> hypercoast.download_pace(results[:2], out_dir=\"data\") In\u00a0[\u00a0]: Copied! <pre>results, gdf = hypercoast.search_emit(\n    bounding_box=(-83, 25, -81, 28),\n    temporal=(\"2024-04-01\", \"2024-05-16\"),\n    count=10,  # use -1 to return all datasets\n    return_gdf=True,\n)\n</pre> results, gdf = hypercoast.search_emit(     bounding_box=(-83, 25, -81, 28),     temporal=(\"2024-04-01\", \"2024-05-16\"),     count=10,  # use -1 to return all datasets     return_gdf=True, ) In\u00a0[\u00a0]: Copied! <pre>gdf.explore()\n</pre> gdf.explore() <p></p> In\u00a0[\u00a0]: Copied! <pre>hypercoast.download_emit(results[:2], out_dir=\"data\")\n</pre> hypercoast.download_emit(results[:2], out_dir=\"data\") In\u00a0[\u00a0]: Copied! <pre>results, gdf = hypercoast.search_ecostress(\n    bbox=(-120.522, 34.4266, -120.2665, 34.5653),\n    temporal=(\"2023-04-01\", \"2023-04-02\"),\n    count=-1,  # use -1 to return all datasets\n    return_gdf=True,\n)\n</pre> results, gdf = hypercoast.search_ecostress(     bbox=(-120.522, 34.4266, -120.2665, 34.5653),     temporal=(\"2023-04-01\", \"2023-04-02\"),     count=-1,  # use -1 to return all datasets     return_gdf=True, ) In\u00a0[\u00a0]: Copied! <pre>gdf.explore()\n</pre> gdf.explore() In\u00a0[\u00a0]: Copied! <pre>hypercoast.download_ecostress(results[:5], out_dir=\"data\")\n</pre> hypercoast.download_ecostress(results[:5], out_dir=\"data\") In\u00a0[\u00a0]: Copied! <pre>m = hypercoast.Map(center=[27.25, -83.05], zoom=6)\nm.search_pace()\nm\n</pre> m = hypercoast.Map(center=[27.25, -83.05], zoom=6) m.search_pace() m In\u00a0[\u00a0]: Copied! <pre># m._NASA_DATA_GDF.head()\n</pre> # m._NASA_DATA_GDF.head() In\u00a0[\u00a0]: Copied! <pre># hypercoast.download_pace(m._NASA_DATA_RESULTS[:2], out_dir=\"data\")\n</pre> # hypercoast.download_pace(m._NASA_DATA_RESULTS[:2], out_dir=\"data\") <p>Search for EMIT data interactively.</p> In\u00a0[\u00a0]: Copied! <pre>m = hypercoast.Map(center=[27.25, -83.05], zoom=6)\nm.search_emit()\nm\n</pre> m = hypercoast.Map(center=[27.25, -83.05], zoom=6) m.search_emit() m In\u00a0[\u00a0]: Copied! <pre># m._NASA_DATA_GDF.head()\n</pre> # m._NASA_DATA_GDF.head() In\u00a0[\u00a0]: Copied! <pre># hypercoast.download_emit(m._NASA_DATA_RESULTS[:2], out_dir=\"data\")\n</pre> # hypercoast.download_emit(m._NASA_DATA_RESULTS[:2], out_dir=\"data\") <p>Search for ECOSTRESS data interactively.</p> In\u00a0[\u00a0]: Copied! <pre>m = hypercoast.Map(center=[34.5014, -120.4032], zoom=11)\nm.search_ecostress()\nm\n</pre> m = hypercoast.Map(center=[34.5014, -120.4032], zoom=11) m.search_ecostress() m In\u00a0[\u00a0]: Copied! <pre># m._NASA_DATA_GDF.head()\n</pre> # m._NASA_DATA_GDF.head() In\u00a0[\u00a0]: Copied! <pre># hypercoast.download_ecostress(m._NASA_DATA_RESULTS[:2], out_dir=\"data\")\n</pre> # hypercoast.download_ecostress(m._NASA_DATA_RESULTS[:2], out_dir=\"data\")"},{"location":"examples/search_data/#search-and-download-nasa-hyperspectral-data-with-hypercoast","title":"Search and download NASA hyperspectral data with HyperCoast\u00b6","text":"<p>This notebook demonstrates how to search and download NASA hyperspectral data (e.g., EMIT, PACE) and ECOSTRESS temperature data with HyperCoast. Part of the source code is adapted from the NASA OB.DAAC tutorial - Access Data from the Ocean Color Instrument (OCI). Credits to the NASA OB.DAAC team.</p>"},{"location":"examples/search_data/#import-library","title":"Import library\u00b6","text":""},{"location":"examples/search_data/#login-to-earthdata","title":"Login to Earthdata\u00b6","text":"<p>To download and access the data, you will need to create an Earthdata login. You can register for an account at urs.earthdata.nasa.gov.</p>"},{"location":"examples/search_data/#search-for-datasets","title":"Search for datasets\u00b6","text":"<p>Collections on NASA Earthdata are discovered with the search_datasets function, which accepts an instrument filter as an easy way to get started. Each of the items in the list of collections returned has a \"short-name\".</p>"},{"location":"examples/search_data/#search-for-data-by-short-name","title":"Search for data by short name\u00b6","text":"<p>Next, we use the <code>search_nasa_data</code> function to find granules within a collection. Let's use the <code>short_name</code> for the PACE/OCI Level-2 data product for bio-optical and biogeochemical properties.</p>"},{"location":"examples/search_data/#search-for-pace-data","title":"Search for PACE data\u00b6","text":""},{"location":"examples/search_data/#download-pace-data","title":"Download PACE data\u00b6","text":"<p>Download the first 2 files</p>"},{"location":"examples/search_data/#search-for-emit-data","title":"Search for EMIT data\u00b6","text":""},{"location":"examples/search_data/#download-emit-data","title":"Download EMIT data\u00b6","text":"<p>Download the first 2 files</p>"},{"location":"examples/search_data/#download-ecostress-data","title":"Download ECOSTRESS data\u00b6","text":""},{"location":"examples/search_data/#interactive-search","title":"Interactive search\u00b6","text":"<p>Search for PACE data interactively.</p>"},{"location":"examples/temperature/","title":"Temperature","text":"In\u00a0[\u00a0]: Copied! <pre>import hypercoast\n</pre> import hypercoast In\u00a0[\u00a0]: Copied! <pre>url = \"https://github.com/opengeos/datasets/releases/download/hypercoast/ERA5_temperature_2023.nc\"\nfilepath = \"data/ERA5_temperature_2023.nc\"\nhypercoast.download_file(url, filepath)\n</pre> url = \"https://github.com/opengeos/datasets/releases/download/hypercoast/ERA5_temperature_2023.nc\" filepath = \"data/ERA5_temperature_2023.nc\" hypercoast.download_file(url, filepath) In\u00a0[\u00a0]: Copied! <pre>dataset = hypercoast.open_dataset(filepath)\ndataset\n</pre> dataset = hypercoast.open_dataset(filepath) dataset In\u00a0[\u00a0]: Copied! <pre>camera_position = [(-479.09, -82.89, -444.45), (89.5, 179.5, 16.5), (0.58, 0.14, -0.80)]\n</pre> camera_position = [(-479.09, -82.89, -444.45), (89.5, 179.5, 16.5), (0.58, 0.14, -0.80)] In\u00a0[\u00a0]: Copied! <pre>p = hypercoast.image_cube(\n    dataset,\n    variable=\"temperature_2m\",\n    clim=(270, 310),\n    title=\"Temperature\",\n    cmap=\"coolwarm\",\n    widget=\"plane\",\n    invert=False,\n    grid_spacing=(1, 1, 3),\n)\np.camera_position = camera_position\np.show()\n</pre> p = hypercoast.image_cube(     dataset,     variable=\"temperature_2m\",     clim=(270, 310),     title=\"Temperature\",     cmap=\"coolwarm\",     widget=\"plane\",     invert=False,     grid_spacing=(1, 1, 3), ) p.camera_position = camera_position p.show() In\u00a0[\u00a0]: Copied! <pre>p = hypercoast.image_cube(\n    dataset,\n    variable=\"temperature_2m\",\n    clim=(270, 310),\n    title=\"Temperature\",\n    cmap=\"coolwarm\",\n    widget=\"slice\",\n    grid_spacing=(1, 1, 3),\n)\np.camera_position = camera_position\np.show()\n</pre> p = hypercoast.image_cube(     dataset,     variable=\"temperature_2m\",     clim=(270, 310),     title=\"Temperature\",     cmap=\"coolwarm\",     widget=\"slice\",     grid_spacing=(1, 1, 3), ) p.camera_position = camera_position p.show() In\u00a0[\u00a0]: Copied! <pre>p = hypercoast.image_cube(\n    dataset,\n    variable=\"temperature_2m\",\n    clim=(270, 310),\n    title=\"Temperature\",\n    cmap=\"coolwarm\",\n    widget=\"threshold\",\n)\np.camera_position = camera_position\np.show()\n</pre> p = hypercoast.image_cube(     dataset,     variable=\"temperature_2m\",     clim=(270, 310),     title=\"Temperature\",     cmap=\"coolwarm\",     widget=\"threshold\", ) p.camera_position = camera_position p.show()"},{"location":"examples/temperature/#visualizing-era5-temperature-data-interactively-with-hypercoast","title":"Visualizing ERA5 temperature data interactively with HyperCoast\u00b6","text":"<p>This notebook demonstrates how to visualize ERA5 temperature data interactively with HyperCoast.</p>"},{"location":"workshops/emit/","title":"Emit","text":"In\u00a0[1]: Copied! <pre># %pip install \"hypercoast[extra]\"\n</pre> # %pip install \"hypercoast[extra]\" <p>Import library.</p> In\u00a0[2]: Copied! <pre>import hypercoast\n</pre> import hypercoast <p>To download and access the data, you will need to create an Earthdata login. You can register for an account at urs.earthdata.nasa.gov. Once you have an account, run the following cell and enter your NASA Earthdata login credentials.</p> In\u00a0[3]: Copied! <pre>hypercoast.nasa_earth_login()\n</pre> hypercoast.nasa_earth_login() In\u00a0[4]: Copied! <pre>results, gdf = hypercoast.search_emit(\n    bbox=(-83, 25, -81, 28),\n    temporal=(\"2024-04-01\", \"2024-05-16\"),\n    count=10,  # use -1 to return all datasets\n    return_gdf=True,\n)\n</pre> results, gdf = hypercoast.search_emit(     bbox=(-83, 25, -81, 28),     temporal=(\"2024-04-01\", \"2024-05-16\"),     count=10,  # use -1 to return all datasets     return_gdf=True, ) <p>Plot the footprints of the returned datasets on a map.</p> In\u00a0[5]: Copied! <pre>gdf.explore()\n</pre> gdf.explore() Out[5]: Make this Notebook Trusted to load map: File -&gt; Trust Notebook <p>Uncomment the following cell to download the first dataset from the search results. Note that the download may take some time.</p> In\u00a0[6]: Copied! <pre># hypercoast.download_emit(results[:1], out_dir=\"data\")\n</pre> # hypercoast.download_emit(results[:1], out_dir=\"data\") <p>Search for EMIT data interactively. Specify pan and zoom to the area of interest. Specify the time range of interest from the search dialog, then click on the Search button.</p> In\u00a0[7]: Copied! <pre>m = hypercoast.Map(center=[30.0262, -90.1345], zoom=8)\nm.search_emit()\nm\n</pre> m = hypercoast.Map(center=[30.0262, -90.1345], zoom=8) m.search_emit() m Out[7]: <p>Uncomment the following cell to display the GeoDataFrame of the search results.</p> In\u00a0[8]: Copied! <pre># m._NASA_DATA_GDF.head()\n</pre> # m._NASA_DATA_GDF.head() <p>Similarly, you can download the first dataset from the search results by uncommenting the following cell.</p> In\u00a0[9]: Copied! <pre># hypercoast.download_emit(results[:1], out_dir=\"data\")\n</pre> # hypercoast.download_emit(results[:1], out_dir=\"data\") In\u00a0[10]: Copied! <pre>url = \"https://github.com/opengeos/datasets/releases/download/hypercoast/EMIT_L2A_RFL_001_20240404T161230_2409511_009.nc\"\nfilepath = \"../examples/data/EMIT_L2A_RFL_001_20240404T161230_2409511_009.nc\"\nhypercoast.download_file(url, filepath, quiet=True)\n</pre> url = \"https://github.com/opengeos/datasets/releases/download/hypercoast/EMIT_L2A_RFL_001_20240404T161230_2409511_009.nc\" filepath = \"../examples/data/EMIT_L2A_RFL_001_20240404T161230_2409511_009.nc\" hypercoast.download_file(url, filepath, quiet=True) <pre>../examples/data/EMIT_L2A_RFL_001_20240404T161230_2409511_009.nc already exists. Skip downloading. Set overwrite=True to overwrite.\n</pre> Out[10]: <pre>'/home/runner/work/HyperCoast/HyperCoast/docs/examples/data/EMIT_L2A_RFL_001_20240404T161230_2409511_009.nc'</pre> In\u00a0[11]: Copied! <pre>dataset = hypercoast.read_emit(filepath)\n# dataset\n</pre> dataset = hypercoast.read_emit(filepath) # dataset <p></p> In\u00a0[12]: Copied! <pre>m = hypercoast.Map()\nm.add_basemap(\"SATELLITE\")\nm.add_emit(dataset, wavelengths=[1000, 600, 500], vmin=0, vmax=0.3, layer_name=\"EMIT\")\nm.add(\"spectral\")\nm\n</pre> m = hypercoast.Map() m.add_basemap(\"SATELLITE\") m.add_emit(dataset, wavelengths=[1000, 600, 500], vmin=0, vmax=0.3, layer_name=\"EMIT\") m.add(\"spectral\") m Out[12]: <p></p> In\u00a0[13]: Copied! <pre>ds = dataset.sel(longitude=slice(-90.1482, -89.7321), latitude=slice(30.0225, 29.7451))\n</pre> ds = dataset.sel(longitude=slice(-90.1482, -89.7321), latitude=slice(30.0225, 29.7451)) <p>Visualize the EMIT data in 3D with an RGB image overlaid on top of the 3D plot.</p> In\u00a0[14]: Copied! <pre>p = hypercoast.image_cube(\n    ds,\n    variable=\"reflectance\",\n    cmap=\"jet\",\n    clim=(0, 0.4),\n    rgb_wavelengths=[1000, 700, 500],\n    rgb_gamma=2,\n    title=\"EMIT Reflectance\",\n)\n</pre> p = hypercoast.image_cube(     ds,     variable=\"reflectance\",     cmap=\"jet\",     clim=(0, 0.4),     rgb_wavelengths=[1000, 700, 500],     rgb_gamma=2,     title=\"EMIT Reflectance\", ) <p>Uncomment the following cell to create an image cube. Note that this function does not work in the Google Colab environment.</p> In\u00a0[15]: Copied! <pre># p.show()\n</pre> # p.show() <p></p> In\u00a0[16]: Copied! <pre>ds = dataset.sel(longitude=slice(-90.05, -89.99), latitude=slice(30.00, 29.93))\n</pre> ds = dataset.sel(longitude=slice(-90.05, -89.99), latitude=slice(30.00, 29.93)) <p>Drag the plane up and down to slice the data in 3D.</p> In\u00a0[17]: Copied! <pre>p = hypercoast.image_cube(\n    ds,\n    variable=\"reflectance\",\n    cmap=\"jet\",\n    clim=(0, 0.5),\n    rgb_wavelengths=[1000, 700, 500],\n    rgb_gamma=2,\n    title=\"EMIT Reflectance\",\n    widget=\"plane\",\n)\np.add_text(\"Band slicing\", position=\"upper_right\", font_size=14)\n</pre> p = hypercoast.image_cube(     ds,     variable=\"reflectance\",     cmap=\"jet\",     clim=(0, 0.5),     rgb_wavelengths=[1000, 700, 500],     rgb_gamma=2,     title=\"EMIT Reflectance\",     widget=\"plane\", ) p.add_text(\"Band slicing\", position=\"upper_right\", font_size=14) Out[17]: <pre>&lt;CornerAnnotation(0x1c4db00) at 0x7f0c3825ee60&gt;</pre> <p>Uncomment the following cell to display the interactive slicing widget. Note that this function does not work in the Google Colab environment.</p> In\u00a0[18]: Copied! <pre># p.show()\n</pre> # p.show() <p></p> In\u00a0[19]: Copied! <pre>p = hypercoast.image_cube(\n    ds,\n    variable=\"reflectance\",\n    cmap=\"jet\",\n    clim=(0, 0.5),\n    rgb_wavelengths=[1000, 700, 500],\n    rgb_gamma=2,\n    title=\"EMIT Reflectance\",\n    widget=\"threshold\",\n)\np.add_text(\"Thresholding\", position=\"upper_right\", font_size=14)\n</pre> p = hypercoast.image_cube(     ds,     variable=\"reflectance\",     cmap=\"jet\",     clim=(0, 0.5),     rgb_wavelengths=[1000, 700, 500],     rgb_gamma=2,     title=\"EMIT Reflectance\",     widget=\"threshold\", ) p.add_text(\"Thresholding\", position=\"upper_right\", font_size=14) Out[19]: <pre>&lt;CornerAnnotation(0x6ffc5b0) at 0x7f0c16d2c760&gt;</pre> <p>Uncomment the following cell to display the thresholded data. Note that this function does not work in the Google Colab environment.</p> In\u00a0[20]: Copied! <pre># p.show()\n</pre> # p.show() <p></p>"},{"location":"workshops/emit/#working-with-nasa-emit-data-in-hypercoast","title":"Working with NASA EMIT data in HyperCoast\u00b6","text":"<p>This notebook demonstrates how to work with NASA Earth Surface Mineral Dust Source Investigation (EMIT) data in HyperCoast.</p>"},{"location":"workshops/emit/#environment-setup","title":"Environment setup\u00b6","text":"<p>Uncomment and run the following cell to install the required packages.</p>"},{"location":"workshops/emit/#search-for-emit-data","title":"Search for EMIT data\u00b6","text":"<p>Search for EMIT data programmatically. Specify the bounding box and time range of interest. Set <code>count=-1</code> to return all results or set <code>count=10</code> to return the first 10 results.</p>"},{"location":"workshops/emit/#download-a-sample-emit-dataset","title":"Download a sample EMIT dataset\u00b6","text":"<p>Let's download a sample EMIT dataset for the demonstration.</p>"},{"location":"workshops/emit/#read-emit-data","title":"Read EMIT data\u00b6","text":"<p>Read the downloaded EMIT data and process it as an <code>xarray.Dataset</code>. Note that the dataset has 285 bands.</p>"},{"location":"workshops/emit/#visualize-emit-data","title":"Visualize EMIT data\u00b6","text":"<p>Visualize the EMIT data on an interactive map. You can change the band combination and extract spectral profiles interactively. You can also export the spectral profiles as a CSV file.</p>"},{"location":"workshops/emit/#create-an-image-cube","title":"Create an image cube\u00b6","text":"<p>First, select a subset of the data to avoid nodata areas.</p>"},{"location":"workshops/emit/#interactive-slicing","title":"Interactive slicing\u00b6","text":"<p>First, select a subset of the data for demonstration purposes.</p>"},{"location":"workshops/emit/#interactive-thresholding","title":"Interactive thresholding\u00b6","text":"<p>Drag the threshold slider to threshold the data in 3D.</p>"},{"location":"workshops/pace/","title":"Pace","text":"In\u00a0[1]: Copied! <pre># %pip install \"hypercoast[extra]\"\n</pre> # %pip install \"hypercoast[extra]\" <p>Import libraries.</p> In\u00a0[2]: Copied! <pre>import earthaccess\nimport hypercoast\nimport pandas as pd\n</pre> import earthaccess import hypercoast import pandas as pd In\u00a0[3]: Copied! <pre>earthaccess.login(persist=True)\n</pre> earthaccess.login(persist=True) Out[3]: <pre>&lt;earthaccess.auth.Auth at 0x7f2f7861ed20&gt;</pre> In\u00a0[4]: Copied! <pre>results, gdf = hypercoast.search_pace(\n    bounding_box=(-83, 25, -81, 28),\n    temporal=(\"2024-07-30\", \"2024-08-15\"),\n    short_name=\"PACE_OCI_L2_AOP_NRT\",\n    count=10,\n    return_gdf=True,\n)\n</pre> results, gdf = hypercoast.search_pace(     bounding_box=(-83, 25, -81, 28),     temporal=(\"2024-07-30\", \"2024-08-15\"),     short_name=\"PACE_OCI_L2_AOP_NRT\",     count=10,     return_gdf=True, ) <p>Plot the footprints of the returned datasets on a map.</p> In\u00a0[5]: Copied! <pre>gdf.explore()\n</pre> gdf.explore() Out[5]: Make this Notebook Trusted to load map: File -&gt; Trust Notebook <p>Download the first dataset from the search results. Note that the download may take some time.</p> In\u00a0[6]: Copied! <pre>hypercoast.download_pace(results[:1], out_dir=\"data\")\n</pre> hypercoast.download_pace(results[:1], out_dir=\"data\") In\u00a0[7]: Copied! <pre>m = hypercoast.Map(center=[30.0262, -90.1345], zoom=8)\nm.search_pace(default_dataset=\"PACE_OCI_L2_AOP_NRT\")\nm\n</pre> m = hypercoast.Map(center=[30.0262, -90.1345], zoom=8) m.search_pace(default_dataset=\"PACE_OCI_L2_AOP_NRT\") m Out[7]: <p>By default, the <code>search_pace</code> method searches for the <code>PACE_OCI_L2_AOP_NRT</code> dataset, but you can specify the dataset name by setting the <code>default_dataset</code> parameter, such as <code>PACE_OCI_L2_BGC_NRT</code>. For more information about the available datasets, see the PACE Data Products page.</p> <p></p> <p>Uncomment the following cell to display the GeoDataFrame of the search results.</p> In\u00a0[8]: Copied! <pre># m._NASA_DATA_GDF.head()\n</pre> # m._NASA_DATA_GDF.head() <p>Similarly, you can download the first dataset from the search results by uncommenting the following cell.</p> In\u00a0[9]: Copied! <pre># hypercoast.download_pace(results[:1], out_dir=\"data\")\n</pre> # hypercoast.download_pace(results[:1], out_dir=\"data\") In\u00a0[10]: Copied! <pre>results = hypercoast.search_pace(\n    bounding_box=(-83, 25, -81, 28),\n    temporal=(\"2024-07-30\", \"2024-08-15\"),\n    short_name=\"PACE_OCI_L2_AOP_NRT\",\n    count=1,\n)\n</pre> results = hypercoast.search_pace(     bounding_box=(-83, 25, -81, 28),     temporal=(\"2024-07-30\", \"2024-08-15\"),     short_name=\"PACE_OCI_L2_AOP_NRT\",     count=1, ) In\u00a0[11]: Copied! <pre>hypercoast.download_pace(results[:1], out_dir=\"data\")\n</pre> hypercoast.download_pace(results[:1], out_dir=\"data\") <p>Let's make a scatter plot of the pixel locations so we can see the irregular spacing.</p> In\u00a0[12]: Copied! <pre>filepath = \"data/PACE_OCI.20240730T181157.L2.OC_AOP.V2_0.NRT.nc\"\nplot = hypercoast.view_pace_pixel_locations(filepath, step=20)\n</pre> filepath = \"data/PACE_OCI.20240730T181157.L2.OC_AOP.V2_0.NRT.nc\" plot = hypercoast.view_pace_pixel_locations(filepath, step=20) <p>Load the dataset as a <code>xarray.Dataset</code> object.</p> In\u00a0[13]: Copied! <pre>dataset = hypercoast.read_pace(filepath)\n# dataset\n</pre> dataset = hypercoast.read_pace(filepath) # dataset <p></p> In\u00a0[14]: Copied! <pre>hypercoast.viz_pace(dataset, wavelengths=[500, 510, 520, 530], ncols=2)\n</pre> hypercoast.viz_pace(dataset, wavelengths=[500, 510, 520, 530], ncols=2) <p>Add custom projection and administrative boundaries to the map. The default projection is <code>PlateCarree</code>. You can specify a custom projection by setting the <code>crs</code> parameter. For more information about the available projections, see the cartopy projection page.</p> In\u00a0[15]: Copied! <pre>hypercoast.viz_pace(dataset, wavelengths=[500, 510, 520, 530], ncols=2, crs=\"default\")\n</pre> hypercoast.viz_pace(dataset, wavelengths=[500, 510, 520, 530], ncols=2, crs=\"default\") In\u00a0[16]: Copied! <pre>latitude = 29.9307\nlongitude = -87.9106\nhypercoast.extract_pace(dataset, latitude, longitude, return_plot=True)\n</pre> latitude = 29.9307 longitude = -87.9106 hypercoast.extract_pace(dataset, latitude, longitude, return_plot=True) Out[16]: <pre>[&lt;matplotlib.lines.Line2D at 0x7f2f18f4ce30&gt;]</pre> <p>To return the extracted values as an xarray <code>DataArray</code>, set <code>return_plot=False</code>.</p> In\u00a0[17]: Copied! <pre>array = hypercoast.extract_pace(dataset, latitude, longitude, return_plot=False)\n# array\n</pre> array = hypercoast.extract_pace(dataset, latitude, longitude, return_plot=False) # array <p>To plot the spectral signatures of multiple pixels, you can specify the pixel locations as a list of tuples. All pixels within the specified latitude and longitude range will be extracted.</p> In\u00a0[18]: Copied! <pre>latitude = (29.49, 29.50)\nlongitude = (-88.10, -88.00)\nhypercoast.filter_pace(dataset, latitude, longitude, return_plot=True)\n</pre> latitude = (29.49, 29.50) longitude = (-88.10, -88.00) hypercoast.filter_pace(dataset, latitude, longitude, return_plot=True) <p>Visualize a selected band of the dataset interactively use the <code>add_pace</code> method and speccify the <code>wavelengths</code> parameter.</p> In\u00a0[19]: Copied! <pre>m = hypercoast.Map()\nm.add_basemap(\"Hybrid\")\nwavelengths = [450]\nm.add_pace(dataset, wavelengths, colormap=\"jet\", vmin=0, vmax=0.02, layer_name=\"PACE\")\nm.add_colormap(cmap=\"jet\", vmin=0, vmax=0.02, label=\"Reflectance\")\nm.add(\"spectral\")\nm.set_center(-80.7382, 26.5295, zoom=6)\nm\n</pre> m = hypercoast.Map() m.add_basemap(\"Hybrid\") wavelengths = [450] m.add_pace(dataset, wavelengths, colormap=\"jet\", vmin=0, vmax=0.02, layer_name=\"PACE\") m.add_colormap(cmap=\"jet\", vmin=0, vmax=0.02, label=\"Reflectance\") m.add(\"spectral\") m.set_center(-80.7382, 26.5295, zoom=6) m Out[19]: <p>Click on the map to display the spectral signature of the selected pixel.</p> <p></p> <p>Convert the spectral data of the selected pixels to a DataFrame.</p> In\u00a0[20]: Copied! <pre>df = m.spectral_to_df()\ndf.head()\n</pre> df = m.spectral_to_df() df.head() Out[20]: <p>Convert the spectral data of the selected pixels to a GeoDataFrame.</p> In\u00a0[21]: Copied! <pre>gdf = m.spectral_to_gdf()\ngdf.head()\n</pre> gdf = m.spectral_to_gdf() gdf.head() Out[21]: <p>Convert the spectral data of the selected pixels to a CSV file.</p> In\u00a0[22]: Copied! <pre>m.spectral_to_csv(\"data/spectral.csv\")\n</pre> m.spectral_to_csv(\"data/spectral.csv\") In\u00a0[23]: Copied! <pre>m = hypercoast.Map()\nm.add_basemap(\"Hybrid\")\nwavelengths = [450, 550, 650]\nm.add_pace(\n    dataset, wavelengths, indexes=[3, 2, 1], vmin=0, vmax=0.02, layer_name=\"PACE\"\n)\nm.add(\"spectral\")\nm.set_center(-80.7382, 26.5295, zoom=6)\nm\n</pre> m = hypercoast.Map() m.add_basemap(\"Hybrid\") wavelengths = [450, 550, 650] m.add_pace(     dataset, wavelengths, indexes=[3, 2, 1], vmin=0, vmax=0.02, layer_name=\"PACE\" ) m.add(\"spectral\") m.set_center(-80.7382, 26.5295, zoom=6) m Out[23]: <p></p> In\u00a0[24]: Copied! <pre>results, gdf = hypercoast.search_nasa_data(\n    short_name=\"PACE_OCI_L2_BGC_NRT\",\n    bbox=(-90.5642, 29.9749, -89.7143, 30.42),\n    temporal=(\"2024-07-30\", \"2024-08-15\"),\n    count=1,\n    return_gdf=True,\n)\nhypercoast.download_nasa_data(results, out_dir=\"data\")\n</pre> results, gdf = hypercoast.search_nasa_data(     short_name=\"PACE_OCI_L2_BGC_NRT\",     bbox=(-90.5642, 29.9749, -89.7143, 30.42),     temporal=(\"2024-07-30\", \"2024-08-15\"),     count=1,     return_gdf=True, ) hypercoast.download_nasa_data(results, out_dir=\"data\") <p>Load the downloaded dataset as an <code>xarray.Dataset</code>:</p> In\u00a0[25]: Copied! <pre>filepath = \"data/PACE_OCI.20240730T181157.L2.OC_BGC.V2_0.NRT.nc\"\ndataset = hypercoast.read_pace_bgc(filepath)\n</pre> filepath = \"data/PACE_OCI.20240730T181157.L2.OC_BGC.V2_0.NRT.nc\" dataset = hypercoast.read_pace_bgc(filepath) <p>Let's inspect the data variables contained in the dataset:</p> In\u00a0[26]: Copied! <pre>dataset.variables\n</pre> dataset.variables Out[26]: <pre>Frozen({'chlor_a': &lt;xarray.Variable (latitude: 1710, longitude: 1272)&gt; Size: 9MB\n[2175120 values with dtype=float32]\nAttributes:\n    long_name:      Chlorophyll Concentration, OCI Algorithm\n    units:          mg m^-3\n    standard_name:  mass_concentration_of_chlorophyll_in_sea_water\n    valid_min:      0.001\n    valid_max:      100.0\n    reference:      Hu, C., Lee Z., and Franz, B.A. (2012). Chlorophyll-a alg..., 'carbon_phyto': &lt;xarray.Variable (latitude: 1710, longitude: 1272)&gt; Size: 9MB\n[2175120 values with dtype=float32]\nAttributes:\n    long_name:  Phytoplankton Carbon\n    units:      mg m^-3\n    valid_min:  0.0\n    valid_max:  1000.0\n    reference:  Graff, J.R., Westberry, T.K., Milligan, A.J., Brown, M.B., Da..., 'poc': &lt;xarray.Variable (latitude: 1710, longitude: 1272)&gt; Size: 9MB\n[2175120 values with dtype=float32]\nAttributes:\n    long_name:  Particulate Organic Carbon, D. Stramski, 2022 (hybrid version)\n    units:      mg m^-3\n    valid_min:  -32000\n    valid_max:  -22000\n    reference:  Stramski, D., et al. \"Ocean color algorithms to estimate the ..., 'chlor_a_unc': &lt;xarray.Variable (latitude: 1710, longitude: 1272)&gt; Size: 9MB\n[2175120 values with dtype=float32]\nAttributes:\n    long_name:      Uncertainty in chlorophyll a concentration\n    units:          mg m^-3\n    standard_name:  chlorophyll_concentration_in_sea_water standard_error\n    valid_min:      0.001\n    valid_max:      100.0, 'carbon_phyto_unc': &lt;xarray.Variable (latitude: 1710, longitude: 1272)&gt; Size: 9MB\n[2175120 values with dtype=float32]\nAttributes:\n    long_name:  Phytoplankton Carbon standard uncertainty\n    units:      mg m^-3\n    valid_min:  0.0\n    valid_max:  1000.0\n    reference:  Graff, J.R., Westberry, T.K., Milligan, A.J., Brown, M.B., Da..., 'l2_flags': &lt;xarray.Variable (latitude: 1710, longitude: 1272)&gt; Size: 9MB\n[2175120 values with dtype=int32]\nAttributes:\n    long_name:      Level-2 Processing Flags\n    valid_min:      -2147483648\n    valid_max:      2147483647\n    flag_masks:     [          1           2           4           8         ...\n    flag_meanings:  ATMFAIL LAND PRODWARN HIGLINT HILT HISATZEN COASTZ SPARE ..., 'longitude': &lt;xarray.Variable (latitude: 1710, longitude: 1272)&gt; Size: 9MB\n[2175120 values with dtype=float32]\nAttributes:\n    long_name:      Longitude\n    units:          degrees_east\n    standard_name:  longitude\n    valid_min:      -180.0\n    valid_max:      180.0, 'latitude': &lt;xarray.Variable (latitude: 1710, longitude: 1272)&gt; Size: 9MB\n[2175120 values with dtype=float32]\nAttributes:\n    long_name:      Latitude\n    units:          degrees_north\n    standard_name:  latitude\n    valid_min:      -90.0\n    valid_max:      90.0})</pre> <p>We can see that the dataset contains the following variables:</p> <ul> <li>Chlorophyll Concentration</li> <li>Phytoplankton Carbon</li> <li>Particulate Organic Carbon</li> </ul> In\u00a0[27]: Copied! <pre>chlor_a = hypercoast.grid_pace_bgc(dataset, variable=\"chlor_a\", method=\"linear\")\n</pre> chlor_a = hypercoast.grid_pace_bgc(dataset, variable=\"chlor_a\", method=\"linear\") <p>Plot the gridded Chlorophyll Concentration data:</p> In\u00a0[28]: Copied! <pre>chlor_a.plot(vmin=0, vmax=20, cmap=\"jet\", size=6)\n</pre> chlor_a.plot(vmin=0, vmax=20, cmap=\"jet\", size=6) Out[28]: <pre>&lt;matplotlib.collections.QuadMesh at 0x7f2f0ab35430&gt;</pre> <p>Plot the gridded Phytoplankton Carbon data:</p> In\u00a0[29]: Copied! <pre>carbon_phyto = hypercoast.grid_pace_bgc(\n    dataset, variable=\"carbon_phyto\", method=\"linear\"\n)\ncarbon_phyto.plot(vmin=0, vmax=120, cmap=\"jet\", size=6)\n</pre> carbon_phyto = hypercoast.grid_pace_bgc(     dataset, variable=\"carbon_phyto\", method=\"linear\" ) carbon_phyto.plot(vmin=0, vmax=120, cmap=\"jet\", size=6) Out[29]: <pre>&lt;matplotlib.collections.QuadMesh at 0x7f2f0a9d28d0&gt;</pre> <p>Plot the gridded Particulate Organic Carbon data:</p> In\u00a0[30]: Copied! <pre>poc = hypercoast.grid_pace_bgc(dataset, variable=\"poc\", method=\"linear\")\npoc.plot(vmin=0, vmax=1000, cmap=\"jet\")\n</pre> poc = hypercoast.grid_pace_bgc(dataset, variable=\"poc\", method=\"linear\") poc.plot(vmin=0, vmax=1000, cmap=\"jet\") Out[30]: <pre>&lt;matplotlib.collections.QuadMesh at 0x7f2f0a8b0590&gt;</pre> <p>Plot the gridded BGC data on an interactive map.</p> In\u00a0[31]: Copied! <pre>m = hypercoast.Map()\nm.add_basemap(\"Hybrid\")\nm.add_raster(chlor_a, layer_name=\"Chlorophyll-a\", colormap=\"jet\", vmin=0, vmax=20)\nm.add_raster(\n    carbon_phyto, layer_name=\"Phytoplankton Carbon\", colormap=\"plasma\", vmin=0, vmax=120\n)\nm.add_raster(\n    poc, layer_name=\"Particulate Organic Carbon\", colormap=\"coolwarm\", vmin=0, vmax=1000\n)\nm.add_layer_manager()\n\nm.add_colormap(cmap=\"jet\", vmin=0, vmax=20, label=\"Chlorophyll-a (mg/m3)\")\nm.add_colormap(cmap=\"plasma\", vmin=0, vmax=120, label=\"Phytoplankton Carbon (mg/m3)\")\nm.add_colormap(\n    cmap=\"coolwarm\", vmin=0, vmax=1000, label=\"Particulate Organic Carbon (mg/m3)\"\n)\nm.set_center(-80.7382, 26.5295, zoom=6)\nm\n</pre> m = hypercoast.Map() m.add_basemap(\"Hybrid\") m.add_raster(chlor_a, layer_name=\"Chlorophyll-a\", colormap=\"jet\", vmin=0, vmax=20) m.add_raster(     carbon_phyto, layer_name=\"Phytoplankton Carbon\", colormap=\"plasma\", vmin=0, vmax=120 ) m.add_raster(     poc, layer_name=\"Particulate Organic Carbon\", colormap=\"coolwarm\", vmin=0, vmax=1000 ) m.add_layer_manager()  m.add_colormap(cmap=\"jet\", vmin=0, vmax=20, label=\"Chlorophyll-a (mg/m3)\") m.add_colormap(cmap=\"plasma\", vmin=0, vmax=120, label=\"Phytoplankton Carbon (mg/m3)\") m.add_colormap(     cmap=\"coolwarm\", vmin=0, vmax=1000, label=\"Particulate Organic Carbon (mg/m3)\" ) m.set_center(-80.7382, 26.5295, zoom=6) m Out[31]: <p></p> In\u00a0[32]: Copied! <pre>temporal = (\"2024-07-30\", \"2024-08-15\")\nresults = hypercoast.search_pace_chla(temporal=temporal)\nhypercoast.download_nasa_data(results, \"chla\")\n</pre> temporal = (\"2024-07-30\", \"2024-08-15\") results = hypercoast.search_pace_chla(temporal=temporal) hypercoast.download_nasa_data(results, \"chla\") <p>The downloaded datasets can be found in the <code>chla</code> directory, which contains 17 daily files of CCI data in the netCDF format. The date range of the data is from 2024-07-30 to 2024-08-15.</p> In\u00a0[33]: Copied! <pre>files = \"chla/*nc\"\n</pre> files = \"chla/*nc\" <p>Load all the data files in the <code>chla</code> directory as an xarray DataArray</p> In\u00a0[34]: Copied! <pre>array = hypercoast.read_pace_chla(files)\n# array\n</pre> array = hypercoast.read_pace_chla(files) # array <p></p> <p>Select a date and visualize the chlorophyll-a concentration data with Matplotlib.</p> In\u00a0[35]: Copied! <pre>hypercoast.viz_pace_chla(array, date=\"2024-07-30\", cmap=\"jet\", size=6)\n</pre> hypercoast.viz_pace_chla(array, date=\"2024-07-30\", cmap=\"jet\", size=6) Out[35]: <pre>&lt;matplotlib.collections.QuadMesh at 0x7f2f0a768b00&gt;</pre> <p>If the date is not specified, the data are averaged over the entire time range.</p> In\u00a0[36]: Copied! <pre>hypercoast.viz_pace_chla(array, cmap=\"jet\", size=6)\n</pre> hypercoast.viz_pace_chla(array, cmap=\"jet\", size=6) Out[36]: <pre>&lt;matplotlib.collections.QuadMesh at 0x7f2f0a4b9ee0&gt;</pre> <p>To visualize the data interactively, we can select either a single date or aggregate the data over a time range.</p> <p>First, let's select a single date from the data array:</p> In\u00a0[37]: Copied! <pre>single_array = array.sel(date=\"2024-07-30\")\n# single_array\n</pre> single_array = array.sel(date=\"2024-07-30\") # single_array <p>Convert the data array to an image that can be displayed on an interactive map.</p> In\u00a0[38]: Copied! <pre>single_image = hypercoast.pace_chla_to_image(single_array)\n</pre> single_image = hypercoast.pace_chla_to_image(single_array) <p>Create an interactive map and display the image on the map.</p> In\u00a0[39]: Copied! <pre>m = hypercoast.Map(center=[40, -100], zoom=4)\nm.add_basemap(\"Hybrid\")\nm.add_raster(\n    single_image,\n    cmap=\"jet\",\n    vmin=-1,\n    vmax=2,\n    layer_name=\"Chlorophyll a\",\n    zoom_to_layer=False,\n)\nlabel = \"Chlorophyll Concentration [lg(lg(mg m^-3))]\"\nm.add_colormap(cmap=\"jet\", vmin=-1, vmax=2, label=label)\nm\n</pre> m = hypercoast.Map(center=[40, -100], zoom=4) m.add_basemap(\"Hybrid\") m.add_raster(     single_image,     cmap=\"jet\",     vmin=-1,     vmax=2,     layer_name=\"Chlorophyll a\",     zoom_to_layer=False, ) label = \"Chlorophyll Concentration [lg(lg(mg m^-3))]\" m.add_colormap(cmap=\"jet\", vmin=-1, vmax=2, label=label) m Out[39]: <p></p> <p>The daily image does not have a global coverage. To visualize the data globally, we can aggregate the data over a time range.</p> In\u00a0[40]: Copied! <pre>mean_array = array.mean(dim=\"date\")\n</pre> mean_array = array.mean(dim=\"date\") <p>Convert the aggregated data array to an image that can be displayed on an interactive map.</p> In\u00a0[41]: Copied! <pre>image = hypercoast.pace_chla_to_image(mean_array)\n</pre> image = hypercoast.pace_chla_to_image(mean_array) <p>Create an interactive map and display the image on the map.</p> In\u00a0[42]: Copied! <pre>m = hypercoast.Map(center=[40, -100], zoom=4)\nm.add_basemap(\"Hybrid\")\nm.add_raster(\n    image, cmap=\"jet\", vmin=-1, vmax=2, layer_name=\"Chlorophyll a\", zoom_to_layer=False\n)\nlabel = \"Chlorophyll Concentration [lg(lg(mg m^-3))]\"\nm.add_colormap(cmap=\"jet\", vmin=-1, vmax=2, label=label)\nm\n</pre> m = hypercoast.Map(center=[40, -100], zoom=4) m.add_basemap(\"Hybrid\") m.add_raster(     image, cmap=\"jet\", vmin=-1, vmax=2, layer_name=\"Chlorophyll a\", zoom_to_layer=False ) label = \"Chlorophyll Concentration [lg(lg(mg m^-3))]\" m.add_colormap(cmap=\"jet\", vmin=-1, vmax=2, label=label) m Out[42]: <p></p> In\u00a0[43]: Copied! <pre>url = \"https://github.com/opengeos/datasets/releases/download/hypercoast/Hypoxia_Data_Sheet.xlsx\"\nxls_path = \"data/Hypoxia_Data_Sheet.xlsx\"\nhypercoast.download_file(url, xls_path, overwrite=True)\n</pre> url = \"https://github.com/opengeos/datasets/releases/download/hypercoast/Hypoxia_Data_Sheet.xlsx\" xls_path = \"data/Hypoxia_Data_Sheet.xlsx\" hypercoast.download_file(url, xls_path, overwrite=True) Out[43]: <pre>'/home/runner/work/HyperCoast/HyperCoast/docs/workshops/data/Hypoxia_Data_Sheet.xlsx'</pre> In\u00a0[44]: Copied! <pre>df = pd.read_excel(xls_path)\ndf.head()\n</pre> df = pd.read_excel(xls_path) df.head() Out[44]: Station Station.1 Time Date Lon Lat Depth (m) Secchi (m) Salinity Water Temp ... Absorption CDOM LISST Nano Surface \\npH Sufface \\nO2 Bottom \\nO2 FL-ECO FL-CDOM Notes 0 River stations NaN NaN NaT NaN NaN NaN NaN NaN NaN ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 1 R1 St1 09:39:00 2024-07-21 -89.45114 28.89887 NaN 2 28.71 30.74161 ... 125ml yes yes no NaN NaN NaN NaN NaN River Mouth 2 R2 St2 09:47:00 2024-07-21 -89.45306 28.90001 NaN 2 25.91325 30.91475 ... 150ml yes yes yes NaN NaN NaN NaN NaN River plume, seaside 3 R3 St3 09:59:00 2024-07-21 -89.43833 28.89487 NaN 0.75 24.44862 30.59565 ... 100ml yes yes yes NaN NaN NaN NaN NaN NaN 4 R4 St4 10:13:00 2024-07-21 -89.43162 28.90630 NaN 0.5 8.34838 30.13989 ... 100ml yes yes yes NaN NaN NaN NaN NaN NaN <p>5 rows \u00d7 22 columns</p> <p>Filter the data to select only the sampling locations with latitude and longitude coordinates.</p> In\u00a0[45]: Copied! <pre>df_filtered = df.dropna(subset=[\"Lon\", \"Lat\"]).reset_index(drop=True)\ndf_filtered.head()\n</pre> df_filtered = df.dropna(subset=[\"Lon\", \"Lat\"]).reset_index(drop=True) df_filtered.head() Out[45]: Station Station.1 Time Date Lon Lat Depth (m) Secchi (m) Salinity Water Temp ... Absorption CDOM LISST Nano Surface \\npH Sufface \\nO2 Bottom \\nO2 FL-ECO FL-CDOM Notes 0 R1 St1 09:39:00 2024-07-21 -89.45114 28.89887 NaN 2 28.71 30.74161 ... 125ml yes yes no NaN NaN NaN NaN NaN River Mouth 1 R2 St2 09:47:00 2024-07-21 -89.45306 28.90001 NaN 2 25.91325 30.91475 ... 150ml yes yes yes NaN NaN NaN NaN NaN River plume, seaside 2 R3 St3 09:59:00 2024-07-21 -89.43833 28.89487 NaN 0.75 24.44862 30.59565 ... 100ml yes yes yes NaN NaN NaN NaN NaN NaN 3 R4 St4 10:13:00 2024-07-21 -89.43162 28.90630 NaN 0.5 8.34838 30.13989 ... 100ml yes yes yes NaN NaN NaN NaN NaN NaN 4 R5 St5 10:58:00 2024-07-21 -89.37324 28.98095 NaN 0.5 2.4625 30.3054 ... 100ml yes yes yes NaN NaN NaN NaN NaN NaN <p>5 rows \u00d7 22 columns</p> <p>Download the KML file containing the cruise path.</p> In\u00a0[46]: Copied! <pre>url = (\n    \"https://github.com/opengeos/datasets/releases/download/hypercoast/Hypoxia_Path.kml\"\n)\nkml_path = \"data/Hypoxia_Path.kml\"\nhypercoast.download_file(url, kml_path)\n</pre> url = (     \"https://github.com/opengeos/datasets/releases/download/hypercoast/Hypoxia_Path.kml\" ) kml_path = \"data/Hypoxia_Path.kml\" hypercoast.download_file(url, kml_path) Out[46]: <pre>'/home/runner/work/HyperCoast/HyperCoast/docs/workshops/data/Hypoxia_Path.kml'</pre> <p>We will use the PACE AOP dataset acquired on July 30, 2024, to visualize the cruise sampling locations. The dataset should have been downloaded in the previous section.</p> In\u00a0[47]: Copied! <pre>filepath = \"data/PACE_OCI.20240730T181157.L2.OC_AOP.V2_0.NRT.nc\"\n</pre> filepath = \"data/PACE_OCI.20240730T181157.L2.OC_AOP.V2_0.NRT.nc\" <p>Read the PACE AOP dataset as an xarray Dataset.</p> In\u00a0[48]: Copied! <pre>dataset = hypercoast.read_pace(filepath)\n# dataset\n</pre> dataset = hypercoast.read_pace(filepath) # dataset <p>Visualize the cruise sampling locations and PACE data on the map.</p> In\u00a0[49]: Copied! <pre>m = hypercoast.Map()\nm.add_basemap(\"Hybrid\")\nwavelengths = [450, 550, 650]\nm.add_pace(\n    dataset, wavelengths, indexes=[3, 2, 1], vmin=0, vmax=0.02, layer_name=\"PACE\"\n)\nm.add(\"spectral\")\nstyle = {\"weight\": 2, \"color\": \"red\"}\nm.add_kml(kml_path, style=style, layer_name=\"Hypoxia Path\", info_mode=None)\nm.add_points_from_xy(\n    df_filtered,\n    x=\"Lon\",\n    y=\"Lat\",\n    max_cluster_radius=50,\n    layer_name=\"Hypoxia Data Points\",\n)\nm.set_center(-91.46118, 28.89758, zoom=8)\nm\n</pre> m = hypercoast.Map() m.add_basemap(\"Hybrid\") wavelengths = [450, 550, 650] m.add_pace(     dataset, wavelengths, indexes=[3, 2, 1], vmin=0, vmax=0.02, layer_name=\"PACE\" ) m.add(\"spectral\") style = {\"weight\": 2, \"color\": \"red\"} m.add_kml(kml_path, style=style, layer_name=\"Hypoxia Path\", info_mode=None) m.add_points_from_xy(     df_filtered,     x=\"Lon\",     y=\"Lat\",     max_cluster_radius=50,     layer_name=\"Hypoxia Data Points\", ) m.set_center(-91.46118, 28.89758, zoom=8) m Out[49]: <p></p> In\u00a0[50]: Copied! <pre>url = \"https://github.com/opengeos/datasets/releases/download/hypercoast/pace_sample_points.csv\"\ndata = pd.read_csv(url)\ndata.head()\n</pre> url = \"https://github.com/opengeos/datasets/releases/download/hypercoast/pace_sample_points.csv\" data = pd.read_csv(url) data.head() Out[50]: band wavelength (30.1926 -90.1318) (30.1594 -90.2856) (29.3295 -92.3071) (28.8783 -90.4559) (30.5481 -87.9840) (29.5305 -85.0671) (28.8254 -85.6659) (29.4587 -83.8477) (26.8878 -87.7643) (24.6570 -86.4954) (26.8045 -82.4854) 0 0 339.0 NaN NaN 0.000100 NaN NaN NaN NaN NaN 0.001718 0.000710 NaN 1 1 341.0 NaN NaN NaN NaN NaN NaN NaN NaN 0.001554 0.000846 NaN 2 2 344.0 NaN NaN 0.001816 NaN NaN NaN NaN NaN 0.004047 0.003598 NaN 3 3 346.0 0.000346 NaN 0.002393 NaN NaN NaN NaN NaN 0.005045 0.004646 NaN 4 4 348.0 NaN NaN 0.001578 NaN NaN NaN NaN NaN 0.004258 0.003984 NaN <p>Again, we will use the PACE AOP dataset acquired on July 30, 2024, to visualize the in-situ data. The dataset should have been downloaded in the previous section.</p> In\u00a0[51]: Copied! <pre>filepath = \"data/PACE_OCI.20240730T181157.L2.OC_AOP.V2_0.NRT.nc\"\n</pre> filepath = \"data/PACE_OCI.20240730T181157.L2.OC_AOP.V2_0.NRT.nc\" <p>Read the PACE dataset as an xarray Dataset.</p> In\u00a0[52]: Copied! <pre>dataset = hypercoast.read_pace(filepath)\n</pre> dataset = hypercoast.read_pace(filepath) <p>Visualize the in-situ data on the map.</p> In\u00a0[53]: Copied! <pre>m = hypercoast.Map(center=[27.235094, -87.791748], zoom=6)\n\nm.add_basemap(\"Hybrid\")\nwavelengths = [450]\nm.add_pace(dataset, wavelengths, colormap=\"jet\", vmin=0, vmax=0.02, layer_name=\"PACE\")\nm.add_colormap(cmap=\"jet\", vmin=0, vmax=0.02, label=\"Reflectance\")\nm.add(\"spectral\")\n\nm.add_field_data(\n    data,\n    x_col=\"wavelength\",\n    y_col_prefix=\"(\",\n    x_label=\"Wavelength (nm)\",\n    y_label=\"Reflectance\",\n    use_marker_cluster=True,\n)\nm.set_center(-87.791748, 27.235094, zoom=6)\nm\n</pre> m = hypercoast.Map(center=[27.235094, -87.791748], zoom=6)  m.add_basemap(\"Hybrid\") wavelengths = [450] m.add_pace(dataset, wavelengths, colormap=\"jet\", vmin=0, vmax=0.02, layer_name=\"PACE\") m.add_colormap(cmap=\"jet\", vmin=0, vmax=0.02, label=\"Reflectance\") m.add(\"spectral\")  m.add_field_data(     data,     x_col=\"wavelength\",     y_col_prefix=\"(\",     x_label=\"Wavelength (nm)\",     y_label=\"Reflectance\",     use_marker_cluster=True, ) m.set_center(-87.791748, 27.235094, zoom=6) m Out[53]: <p>Click on any marker to display the in-situ data.</p> <p></p>"},{"location":"workshops/pace/#working-with-nasa-pace-data-in-hypercoast","title":"Working with NASA PACE data in HyperCoast\u00b6","text":"<p>This notebook demonstrates how to visualize and analyze Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) data interactively with HyperCoast.</p>"},{"location":"workshops/pace/#environment-setup","title":"Environment setup\u00b6","text":"<p>Uncomment and run the following cell to install the required packages.</p>"},{"location":"workshops/pace/#search-for-pace-data","title":"Search for PACE data\u00b6","text":"<p>To download and access the data, you will need to create an Earthdata login. You can register for an account at urs.earthdata.nasa.gov. Once you have an account, run the following cell and enter your NASA Earthdata login credentials.</p>"},{"location":"workshops/pace/#search-data-programmatically","title":"Search data programmatically\u00b6","text":"<p>To search for PACE data programmatically, specify the bounding box and time range of interest. Set <code>count=-1</code> to return all results or set <code>count=10</code> to return the first 10 results.</p>"},{"location":"workshops/pace/#search-data-interactively","title":"Search data interactively\u00b6","text":"<p>To search for PACE data interactively, pan and zoom to the area of interest. Specify the time range of interest from the search dialog, then click on the Search button.</p>"},{"location":"workshops/pace/#read-pace-data","title":"Read PACE data\u00b6","text":"<p>Let's download a sample PACE Apparent Optical Properties (AOP) dataset for the demonstration.</p>"},{"location":"workshops/pace/#visualize-pace-aop-data","title":"Visualize PACE AOP data\u00b6","text":"<p>Visualize selected bands of the dataset.</p>"},{"location":"workshops/pace/#plot-spectral-signatures","title":"Plot spectral signatures\u00b6","text":"<p>Plot the spectral signature of a pixel using the <code>extract_pace</code> function. Set <code>return_plot=True</code> to return the plot object.</p>"},{"location":"workshops/pace/#interactive-visualization","title":"Interactive visualization\u00b6","text":""},{"location":"workshops/pace/#single-band-visualization","title":"Single-band visualization\u00b6","text":""},{"location":"workshops/pace/#multi-band-visualization","title":"Multi-band visualization\u00b6","text":"<p>Select three spectral bands to visualize as an RGB image.</p>"},{"location":"workshops/pace/#change-band-combination","title":"Change band combination\u00b6","text":"<p>Click on the gear icon on the toolbar to change the band combination.</p> <p></p>"},{"location":"workshops/pace/#pace-bgc-data","title":"PACE BGC data\u00b6","text":"<p>PACE has a variety of data products, including biogeochemical properties. For more information about the available datasets, see the PACE Data Products page.</p> <p>The PACE Biogeochemical (BGC) data products include chlorophyll-a concentration, particulate organic carbon, and particulate inorganic carbon.</p>"},{"location":"workshops/pace/#download-pace-bgc-data","title":"Download PACE BGC data\u00b6","text":"<p>Let's download a sample PACE BGC dataset for the demonstration.</p>"},{"location":"workshops/pace/#visualize-pace-bgc-data","title":"Visualize PACE BGC data\u00b6","text":"<p>Since the datasets are not gridded, we need to transform them into gridded data to visualize them. We can use the <code>grid_pace_bgc</code> function to transform the dataset into a gridded format.</p> <p>First, transform the <code>chlor_a</code> variable into a gridded format:</p>"},{"location":"workshops/pace/#pace-chlorophyll-level-3-data","title":"PACE Chlorophyll Level 3 data\u00b6","text":"<p>PACE Level 3 data products are gridded data products that are derived from Level 2 data. Once of the most common Level 3 data products is the Chlorophyll-Carotenoid Index (CCI) dataset.</p> <p>Let's download some daily PACE Chlorophyll Level 3 data for the demonstration.</p>"},{"location":"workshops/pace/#hypoxia-cruise-data","title":"Hypoxia Cruise data\u00b6","text":"<p>The Hypoxia Cruise collected water quality data in the Gulf of Mexico from July 21 to August 2, 2024. In this section, we will visualize the cruise sampling locations.</p> <p>First, let's download an Excel file containing the cruise sampling locations.</p>"},{"location":"workshops/pace/#visualize-in-situ-data","title":"Visualize in-situ data\u00b6","text":"<p>This section demonstrates how to visualize in-situ data on the map. First, let's download a hypothetical in-situ dataset.</p>"},{"location":"workshops/pace/#analyze-pace-data","title":"Analyze PACE data\u00b6","text":"<p>To anyalyze the PACE data with algorithms, such as K-means clustering, principal component analysis (PCA), or Spectral Angle Mapper (SAM), follow the notebook at https://hypercoast.org/examples/pace_cyano.</p>"}]}
\ No newline at end of file
diff --git a/sitemap.xml b/sitemap.xml
index ee6089b4..ad41fdf1 100644
--- a/sitemap.xml
+++ b/sitemap.xml
@@ -2,154 +2,154 @@
 <urlset xmlns="http://www.sitemaps.org/schemas/sitemap/0.9">
     <url>
          <loc>https://hypercoast.org/</loc>
-         <lastmod>2024-10-08</lastmod>
+         <lastmod>2024-10-13</lastmod>
     </url>
     <url>
          <loc>https://hypercoast.org/aviris/</loc>
-         <lastmod>2024-10-08</lastmod>
+         <lastmod>2024-10-13</lastmod>
     </url>
     <url>
          <loc>https://hypercoast.org/changelog/</loc>
-         <lastmod>2024-10-08</lastmod>
+         <lastmod>2024-10-13</lastmod>
     </url>
     <url>
          <loc>https://hypercoast.org/changelog_update/</loc>
-         <lastmod>2024-10-08</lastmod>
+         <lastmod>2024-10-13</lastmod>
     </url>
     <url>
          <loc>https://hypercoast.org/cla/</loc>
-         <lastmod>2024-10-08</lastmod>
+         <lastmod>2024-10-13</lastmod>
     </url>
     <url>
          <loc>https://hypercoast.org/common/</loc>
-         <lastmod>2024-10-08</lastmod>
+         <lastmod>2024-10-13</lastmod>
     </url>
     <url>
          <loc>https://hypercoast.org/contributing/</loc>
-         <lastmod>2024-10-08</lastmod>
+         <lastmod>2024-10-13</lastmod>
     </url>
     <url>
          <loc>https://hypercoast.org/desis/</loc>
-         <lastmod>2024-10-08</lastmod>
+         <lastmod>2024-10-13</lastmod>
     </url>
     <url>
          <loc>https://hypercoast.org/emit/</loc>
-         <lastmod>2024-10-08</lastmod>
+         <lastmod>2024-10-13</lastmod>
     </url>
     <url>
          <loc>https://hypercoast.org/hypercoast/</loc>
-         <lastmod>2024-10-08</lastmod>
+         <lastmod>2024-10-13</lastmod>
     </url>
     <url>
          <loc>https://hypercoast.org/installation/</loc>
-         <lastmod>2024-10-08</lastmod>
+         <lastmod>2024-10-13</lastmod>
     </url>
     <url>
          <loc>https://hypercoast.org/neon/</loc>
-         <lastmod>2024-10-08</lastmod>
+         <lastmod>2024-10-13</lastmod>
     </url>
     <url>
          <loc>https://hypercoast.org/pace/</loc>
-         <lastmod>2024-10-08</lastmod>
+         <lastmod>2024-10-13</lastmod>
     </url>
     <url>
          <loc>https://hypercoast.org/ui/</loc>
-         <lastmod>2024-10-08</lastmod>
+         <lastmod>2024-10-13</lastmod>
     </url>
     <url>
          <loc>https://hypercoast.org/usage/</loc>
-         <lastmod>2024-10-08</lastmod>
+         <lastmod>2024-10-13</lastmod>
     </url>
     <url>
          <loc>https://hypercoast.org/examples/_earthaccess/</loc>
-         <lastmod>2024-10-08</lastmod>
+         <lastmod>2024-10-13</lastmod>
     </url>
     <url>
          <loc>https://hypercoast.org/examples/acolite/</loc>
-         <lastmod>2024-10-08</lastmod>
+         <lastmod>2024-10-13</lastmod>
     </url>
     <url>
          <loc>https://hypercoast.org/examples/aviris/</loc>
-         <lastmod>2024-10-08</lastmod>
+         <lastmod>2024-10-13</lastmod>
     </url>
     <url>
          <loc>https://hypercoast.org/examples/chlorophyll_a/</loc>
-         <lastmod>2024-10-08</lastmod>
+         <lastmod>2024-10-13</lastmod>
     </url>
     <url>
          <loc>https://hypercoast.org/examples/desis/</loc>
-         <lastmod>2024-10-08</lastmod>
+         <lastmod>2024-10-13</lastmod>
     </url>
     <url>
          <loc>https://hypercoast.org/examples/ecostress/</loc>
-         <lastmod>2024-10-08</lastmod>
+         <lastmod>2024-10-13</lastmod>
     </url>
     <url>
          <loc>https://hypercoast.org/examples/emit/</loc>
-         <lastmod>2024-10-08</lastmod>
+         <lastmod>2024-10-13</lastmod>
     </url>
     <url>
          <loc>https://hypercoast.org/examples/field_data/</loc>
-         <lastmod>2024-10-08</lastmod>
+         <lastmod>2024-10-13</lastmod>
     </url>
     <url>
          <loc>https://hypercoast.org/examples/hypoxia/</loc>
-         <lastmod>2024-10-08</lastmod>
+         <lastmod>2024-10-13</lastmod>
     </url>
     <url>
          <loc>https://hypercoast.org/examples/image_cube/</loc>
-         <lastmod>2024-10-08</lastmod>
+         <lastmod>2024-10-13</lastmod>
     </url>
     <url>
          <loc>https://hypercoast.org/examples/image_slicing/</loc>
-         <lastmod>2024-10-08</lastmod>
+         <lastmod>2024-10-13</lastmod>
     </url>
     <url>
          <loc>https://hypercoast.org/examples/multispectral/</loc>
-         <lastmod>2024-10-08</lastmod>
+         <lastmod>2024-10-13</lastmod>
     </url>
     <url>
          <loc>https://hypercoast.org/examples/neon/</loc>
-         <lastmod>2024-10-08</lastmod>
+         <lastmod>2024-10-13</lastmod>
     </url>
     <url>
          <loc>https://hypercoast.org/examples/overview/</loc>
-         <lastmod>2024-10-08</lastmod>
+         <lastmod>2024-10-13</lastmod>
     </url>
     <url>
          <loc>https://hypercoast.org/examples/pace/</loc>
-         <lastmod>2024-10-08</lastmod>
+         <lastmod>2024-10-13</lastmod>
     </url>
     <url>
          <loc>https://hypercoast.org/examples/pace_cyano/</loc>
-         <lastmod>2024-10-08</lastmod>
+         <lastmod>2024-10-13</lastmod>
     </url>
     <url>
          <loc>https://hypercoast.org/examples/pace_oci_l1/</loc>
-         <lastmod>2024-10-08</lastmod>
+         <lastmod>2024-10-13</lastmod>
     </url>
     <url>
          <loc>https://hypercoast.org/examples/pace_oci_l2/</loc>
-         <lastmod>2024-10-08</lastmod>
+         <lastmod>2024-10-13</lastmod>
     </url>
     <url>
          <loc>https://hypercoast.org/examples/pca/</loc>
-         <lastmod>2024-10-08</lastmod>
+         <lastmod>2024-10-13</lastmod>
     </url>
     <url>
          <loc>https://hypercoast.org/examples/search_data/</loc>
-         <lastmod>2024-10-08</lastmod>
+         <lastmod>2024-10-13</lastmod>
     </url>
     <url>
          <loc>https://hypercoast.org/examples/temperature/</loc>
-         <lastmod>2024-10-08</lastmod>
+         <lastmod>2024-10-13</lastmod>
     </url>
     <url>
          <loc>https://hypercoast.org/workshops/emit/</loc>
-         <lastmod>2024-10-08</lastmod>
+         <lastmod>2024-10-13</lastmod>
     </url>
     <url>
          <loc>https://hypercoast.org/workshops/pace/</loc>
-         <lastmod>2024-10-08</lastmod>
+         <lastmod>2024-10-13</lastmod>
     </url>
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index faf3656e..c858f231 100644
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index 36084db0..aad280c9 100644
--- a/ui/index.html
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index 4c617922..f152bd06 100644
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index aae62e83..1073da55 100644
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+                #map_ded0b96b4952f4f91ab4fc8daa44b482 {
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-            &lt;div class=&quot;folium-map&quot; id=&quot;map_b4e7ecfbdbcec7af83525fcd407332a8&quot; &gt;&lt;/div&gt;
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-                &quot;map_b4e7ecfbdbcec7af83525fcd407332a8&quot;,
+            var map_ded0b96b4952f4f91ab4fc8daa44b482 = L.map(
+                &quot;map_ded0b96b4952f4f91ab4fc8daa44b482&quot;,
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                     center: [26.378920555114746, -82.14947509765625],
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@@ -2115,79 +2115,79 @@ <h2 id="search-for-emit-data">Search for EMIT data<a class="anchor-link" href="#
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                 }
             );
-            L.control.scale().addTo(map_b4e7ecfbdbcec7af83525fcd407332a8);
+            L.control.scale().addTo(map_ded0b96b4952f4f91ab4fc8daa44b482);
 
             
 
         
     
-            var tile_layer_f7fe9dec0395d9634bef417a4da99e13 = L.tileLayer(
+            var tile_layer_7684e33564179a516f01374f1f700a39 = L.tileLayer(
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             );
         
     
-            tile_layer_f7fe9dec0395d9634bef417a4da99e13.addTo(map_b4e7ecfbdbcec7af83525fcd407332a8);
+            tile_layer_7684e33564179a516f01374f1f700a39.addTo(map_ded0b96b4952f4f91ab4fc8daa44b482);
         
     
-            map_b4e7ecfbdbcec7af83525fcd407332a8.fitBounds(
+            map_ded0b96b4952f4f91ab4fc8daa44b482.fitBounds(
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             );
         
     
-        function geo_json_141d8922ee17674e3dc5bf85991a21bc_styler(feature) {
+        function geo_json_717fabaa9c5019838e13cda6cb792012_styler(feature) {
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-        function geo_json_141d8922ee17674e3dc5bf85991a21bc_highlighter(feature) {
+        function geo_json_717fabaa9c5019838e13cda6cb792012_highlighter(feature) {
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             }
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-        function geo_json_141d8922ee17674e3dc5bf85991a21bc_pointToLayer(feature, latlng) {
+        function geo_json_717fabaa9c5019838e13cda6cb792012_pointToLayer(feature, latlng) {
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-            let style = geo_json_141d8922ee17674e3dc5bf85991a21bc_styler(feature)
+            let style = geo_json_717fabaa9c5019838e13cda6cb792012_styler(feature)
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             return new L.CircleMarker(latlng, opts)
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-        function geo_json_141d8922ee17674e3dc5bf85991a21bc_onEachFeature(feature, layer) {
+        function geo_json_717fabaa9c5019838e13cda6cb792012_onEachFeature(feature, layer) {
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                 mouseout: function(e) {
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-                            geo_json_141d8922ee17674e3dc5bf85991a21bc.resetStyle(e.target);
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                 mouseover: function(e) {
                     if(typeof e.target.setStyle === &quot;function&quot;){
-                        const highlightStyle = geo_json_141d8922ee17674e3dc5bf85991a21bc_highlighter(e.target.feature)
+                        const highlightStyle = geo_json_717fabaa9c5019838e13cda6cb792012_highlighter(e.target.feature)
                         e.target.setStyle(highlightStyle);
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-        var geo_json_141d8922ee17674e3dc5bf85991a21bc = L.geoJson(null, {
-                onEachFeature: geo_json_141d8922ee17674e3dc5bf85991a21bc_onEachFeature,
+        var geo_json_717fabaa9c5019838e13cda6cb792012 = L.geoJson(null, {
+                onEachFeature: geo_json_717fabaa9c5019838e13cda6cb792012_onEachFeature,
             
-                style: geo_json_141d8922ee17674e3dc5bf85991a21bc_styler,
-                pointToLayer: geo_json_141d8922ee17674e3dc5bf85991a21bc_pointToLayer,
+                style: geo_json_717fabaa9c5019838e13cda6cb792012_styler,
+                pointToLayer: geo_json_717fabaa9c5019838e13cda6cb792012_pointToLayer,
         });
 
-        function geo_json_141d8922ee17674e3dc5bf85991a21bc_add (data) {
-            geo_json_141d8922ee17674e3dc5bf85991a21bc
+        function geo_json_717fabaa9c5019838e13cda6cb792012_add (data) {
+            geo_json_717fabaa9c5019838e13cda6cb792012
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@@ -2211,7 +2211,7 @@ <h2 id="search-for-emit-data">Search for EMIT data<a class="anchor-link" href="#
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@@ -2305,12 +2305,12 @@ <h2 id="search-for-emit-data">Search for EMIT data<a class="anchor-link" href="#
 <div class="jp-OutputArea jp-Cell-outputArea">
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 <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[7]:</div>
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@@ -2556,12 +2556,12 @@ <h2 id="visualize-emit-data">Visualize EMIT data<a class="anchor-link" href="#vi
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@@ -2822,7 +2822,7 @@ <h2 id="interactive-slicing">Interactive slicing<a class="anchor-link" href="#in
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+<pre>&lt;CornerAnnotation(0x1c4db00) at 0x7f0c3825ee60&gt;</pre>
 </div>
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@@ -2941,7 +2941,7 @@ <h2 id="interactive-thresholding">Interactive thresholding<a class="anchor-link"
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-<pre>&lt;CornerAnnotation(0x605f9b0) at 0x7fcbd2ba0ca0&gt;</pre>
+<pre>&lt;CornerAnnotation(0x6ffc5b0) at 0x7f0c16d2c760&gt;</pre>
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"AIRS3SPM", "AIRS3SPM", "AIRS3ST8", "AIRS3STD", "AIRS3STD", "AIRS3STM", "AIRS3STM", "AIRSAC3MNH3", "AIRSAR_INT_JPG", "AIRSAR_POL_3FP", "AIRSAR_POL_SYN_3FP", "AIRSAR_TOP_C-DEM_STOKES", "AIRSAR_TOP_DEM", "AIRSAR_TOP_DEM_C", "AIRSAR_TOP_DEM_L", "AIRSAR_TOP_DEM_P", "AIRSAR_TOP_L-STOKES", "AIRSAR_TOP_P-STOKES", "AIRSIL3MSOLR", "AIRSM_CPR_MAT", "AIRS_CPR_IND", "AIRS_CPR_MAT", "AIRS_MDS_IND", "AIRS_MLS_IND", "AIRVBQAP", "AIRVBQAP_NRT", "AIRVBRAD", "AIRVBRAD_NRT", "AIRX2RET", "AIRX2RET", "AIRX2SPC", "AIRX2STC", "AIRX2SUP", "AIRX2SUP", "AIRX3C28", "AIRX3C2D", "AIRX3C2M", "AIRX3QP5", "AIRX3QPM", "AIRX3SP8", "AIRX3SPD", "AIRX3SPD", "AIRX3SPM", "AIRX3SPM", "AIRX3ST8", "AIRX3STD", "AIRX3STD", "AIRX3STM", "AIRX3STM", "AIRXAMAP", "AIRXBCAL", "AKFED_V1_1282", "AKWANAVT", "AK_North_Slope_NEE_CH4_Flux_1562", "AK_Regional_CO2_Flux_1389", "AK_Tundra_PFT_FractionalCover_1830", "AK_Yukon_PFT_TopCover_2032", "ALAN_VIIRS_CONUS", "ALOS_AVNIR_OBS_ORI", "ALOS_PSR_KMZ", "ALOS_PSR_L1.0", "ALOS_PSR_L1.1", "ALOS_PSR_L1.5", "ALOS_PSR_L2.2", "ALOS_PSR_RTC_HIGH", "ALOS_PSR_RTC_LOW", "ALTIKA_SARAL_L2_OST_XOGDR", "ALT_GPR_Barrow_1355", "ALT_Maps_AK_CA_2332", "ALT_TIDE_GAUGE_L4_OST_SLA_US_WEST_COAST", "ALT_TIDE_GAUGE_L4_OST_SLA_US_WEST_COAST_DAILY", "AMAZE-08_1308", "AMDBLWV", "AMDBLWV", "AMLR", "AMMA", "AMMBLWV", "AMMBLWV", "AMSR-L1A", "AMSR2-REMSS-L2P-v8.2", "AMSR2-REMSS-L2P_RT-v8.2", "AMSR2-REMSS-L3U-v8.2", "AMSR2-REMSS-L3U-v8a", "AMSR2-REMSS-L3U_RT-v8.2", "AMSR2-REMSS-L3U_RT-v8a", "AMSRE-REMSS-L2P-v7a", "AMSRE-REMSS-L3U-v7a", "AMSREL1A", "AMSRERR_CPR", "AMSRE_AVRMO", "AMSRE_STDMO", "AMT", "ANACONDAS", "ANT", "ANTARCTICA_MASS_TELLUS_MASCON_CRI_TIME_SERIES_RL06.1_V3", "ANTARES_Baja_California_Station", "ANTARES_Ubatuba_Station", "AOL", "AOPEX", "AOSNII", "APPSS", "AQ2_SM", "AQ3_ANSM", "AQ3_DYSM", "AQ3_FT", "AQ3_MCSM", "AQ3_MOSM", "AQ3_NRCS", "AQ3_SCSM", "AQ3_SNSM", "AQ3_SSS", "AQ3_TB", "AQ3_WKSM", "AQUALOOKS", "AQUARIUS_ANCILLARY_CELESTIALSKY_V1", "AQUARIUS_ANCILLARY_RFI_V1", "AQUARIUS_L2_SSS_CAP_V5", "AQUARIUS_L2_SSS_V5", "AQUARIUS_L3_ANCILLARY_SST_SMIA_28DAY-RUNNINGMEAN_V5", "AQUARIUS_L3_ANCILLARY_SST_SMIA_3MONTH_V5", "AQUARIUS_L3_ANCILLARY_SST_SMIA_7DAY_V5", "AQUARIUS_L3_ANCILLARY_SST_SMIA_ANNUAL_V5", "AQUARIUS_L3_ANCILLARY_SST_SMIA_CUMULATIVE_V5", "AQUARIUS_L3_ANCILLARY_SST_SMIA_DAILY_V5", "AQUARIUS_L3_ANCILLARY_SST_SMIA_MONTHLY-CLIMATOLOGY_V5", "AQUARIUS_L3_ANCILLARY_SST_SMIA_MONTHLY_V5", "AQUARIUS_L3_ANCILLARY_SST_SMIA_SEASONAL-CLIMATOLOGY_V5", "AQUARIUS_L3_ANCILLARY_SST_SMID_28DAY-RUNNINGMEAN_V5", "AQUARIUS_L3_ANCILLARY_SST_SMID_3MONTH_V5", "AQUARIUS_L3_ANCILLARY_SST_SMID_7DAY_V5", "AQUARIUS_L3_ANCILLARY_SST_SMID_ANNUAL_V5", "AQUARIUS_L3_ANCILLARY_SST_SMID_CUMULATIVE_V5", "AQUARIUS_L3_ANCILLARY_SST_SMID_DAILY_V5", "AQUARIUS_L3_ANCILLARY_SST_SMID_MONTHLY-CLIMATOLOGY_V5", "AQUARIUS_L3_ANCILLARY_SST_SMID_MONTHLY_V5", "AQUARIUS_L3_ANCILLARY_SST_SMID_SEASONAL-CLIMATOLOGY_V5", "AQUARIUS_L3_ANCILLARY_SST_SMI_28DAY-RUNNINGMEAN_V5", "AQUARIUS_L3_ANCILLARY_SST_SMI_3MONTH_V5", "AQUARIUS_L3_ANCILLARY_SST_SMI_7DAY-RUNNINGMEAN_V5", "AQUARIUS_L3_ANCILLARY_SST_SMI_7DAY_V5", "AQUARIUS_L3_ANCILLARY_SST_SMI_ANNUAL_V5", "AQUARIUS_L3_ANCILLARY_SST_SMI_CUMULATIVE_V5", "AQUARIUS_L3_ANCILLARY_SST_SMI_DAILY_V5", "AQUARIUS_L3_ANCILLARY_SST_SMI_MONTHLY-CLIMATOLOGY_V5", "AQUARIUS_L3_ANCILLARY_SST_SMI_MONTHLY_V5", "AQUARIUS_L3_ANCILLARY_SST_SMI_SEASONAL-CLIMATOLOGY_V5", "AQUARIUS_L3_DENSITY_SMIA_28DAY-RUNNINGMEAN_V5", "AQUARIUS_L3_DENSITY_SMIA_3MONTH_V5", "AQUARIUS_L3_DENSITY_SMIA_7DAY_V5", "AQUARIUS_L3_DENSITY_SMIA_ANNUAL_V5", "AQUARIUS_L3_DENSITY_SMIA_CUMULATIVE_V5", "AQUARIUS_L3_DENSITY_SMIA_DAILY_V5", "AQUARIUS_L3_DENSITY_SMIA_MONTHLY-CLIMATOLOGY_V5", "AQUARIUS_L3_DENSITY_SMIA_MONTHLY_V5", "AQUARIUS_L3_DENSITY_SMIA_SEASONAL-CLIMATOLOGY_V5", "AQUARIUS_L3_DENSITY_SMID_28DAY-RUNNINGMEAN_V5", "AQUARIUS_L3_DENSITY_SMID_3MONTH_V5", "AQUARIUS_L3_DENSITY_SMID_7DAY_V5", "AQUARIUS_L3_DENSITY_SMID_ANNUAL_V5", "AQUARIUS_L3_DENSITY_SMID_CUMULATIVE_V5", "AQUARIUS_L3_DENSITY_SMID_DAILY_V5", "AQUARIUS_L3_DENSITY_SMID_MONTHLY-CLIMATOLOGY_V5", "AQUARIUS_L3_DENSITY_SMID_MONTHLY_V5", "AQUARIUS_L3_DENSITY_SMID_SEASONAL-CLIMATOLOGY_V5", "AQUARIUS_L3_DENSITY_SMI_28DAY-RUNNINGMEAN_V5", "AQUARIUS_L3_DENSITY_SMI_3MONTH_V5", "AQUARIUS_L3_DENSITY_SMI_7DAY-RUNNINGMEAN_V5", "AQUARIUS_L3_DENSITY_SMI_7DAY_V5", "AQUARIUS_L3_DENSITY_SMI_ANNUAL_V5", "AQUARIUS_L3_DENSITY_SMI_CUMULATIVE_V5", "AQUARIUS_L3_DENSITY_SMI_DAILY_V5", "AQUARIUS_L3_DENSITY_SMI_MONTHLY-CLIMATOLOGY_V5", "AQUARIUS_L3_DENSITY_SMI_MONTHLY_V5", "AQUARIUS_L3_DENSITY_SMI_SEASONAL-CLIMATOLOGY_V5", "AQUARIUS_L3_SPICINESS_SMIA_28DAY-RUNNINGMEAN_V5", "AQUARIUS_L3_SPICINESS_SMIA_3MONTH_V5", "AQUARIUS_L3_SPICINESS_SMIA_7DAY_V5", "AQUARIUS_L3_SPICINESS_SMIA_ANNUAL_V5", "AQUARIUS_L3_SPICINESS_SMIA_CUMULATIVE_V5", "AQUARIUS_L3_SPICINESS_SMIA_DAILY_V5", "AQUARIUS_L3_SPICINESS_SMIA_MONTHLY-CLIMATOLOGY_V5", "AQUARIUS_L3_SPICINESS_SMIA_MONTHLY_V5", "AQUARIUS_L3_SPICINESS_SMIA_SEASONAL-CLIMATOLOGY_V5", "AQUARIUS_L3_SPICINESS_SMID_28DAY-RUNNINGMEAN_V5", "AQUARIUS_L3_SPICINESS_SMID_3MONTH_V5", "AQUARIUS_L3_SPICINESS_SMID_7DAY_V5", "AQUARIUS_L3_SPICINESS_SMID_ANNUAL_V5", "AQUARIUS_L3_SPICINESS_SMID_CUMULATIVE_V5", "AQUARIUS_L3_SPICINESS_SMID_DAILY_V5", "AQUARIUS_L3_SPICINESS_SMID_MONTHLY-CLIMATOLOGY_V5", "AQUARIUS_L3_SPICINESS_SMID_MONTHLY_V5", "AQUARIUS_L3_SPICINESS_SMID_SEASONAL-CLIMATOLOGY_V5", "AQUARIUS_L3_SPICINESS_SMI_28DAY-RUNNINGMEAN_V5", "AQUARIUS_L3_SPICINESS_SMI_3MONTH_V5", "AQUARIUS_L3_SPICINESS_SMI_7DAY-RUNNINGMEAN_V5", "AQUARIUS_L3_SPICINESS_SMI_7DAY_V5", "AQUARIUS_L3_SPICINESS_SMI_ANNUAL_V5", "AQUARIUS_L3_SPICINESS_SMI_CUMULATIVE_V5", "AQUARIUS_L3_SPICINESS_SMI_DAILY_V5", "AQUARIUS_L3_SPICINESS_SMI_MONTHLY-CLIMATOLOGY_V5", "AQUARIUS_L3_SPICINESS_SMI_MONTHLY_V5", "AQUARIUS_L3_SPICINESS_SMI_SEASONAL-CLIMATOLOGY_V5", "AQUARIUS_L3_SSS-RainFlagged_SMIA_28DAY-RUNNINGMEAN_V5", "AQUARIUS_L3_SSS-RainFlagged_SMIA_3MONTH_V5", "AQUARIUS_L3_SSS-RainFlagged_SMIA_7DAY_V5", "AQUARIUS_L3_SSS-RainFlagged_SMIA_ANNUAL_V5", "AQUARIUS_L3_SSS-RainFlagged_SMIA_CUMULATIVE_V5", "AQUARIUS_L3_SSS-RainFlagged_SMIA_DAILY_V5", "AQUARIUS_L3_SSS-RainFlagged_SMIA_MONTHLY-CLIMATOLOGY_V5", "AQUARIUS_L3_SSS-RainFlagged_SMIA_MONTHLY_V5", "AQUARIUS_L3_SSS-RainFlagged_SMIA_SEASONAL-CLIMATOLOGY_V5", "AQUARIUS_L3_SSS-RainFlagged_SMID_28DAY-RUNNINGMEAN_V5", "AQUARIUS_L3_SSS-RainFlagged_SMID_3MONTH_V5", "AQUARIUS_L3_SSS-RainFlagged_SMID_7DAY_V5", "AQUARIUS_L3_SSS-RainFlagged_SMID_ANNUAL_V5", "AQUARIUS_L3_SSS-RainFlagged_SMID_CUMULATIVE_V5", "AQUARIUS_L3_SSS-RainFlagged_SMID_DAILY_V5", "AQUARIUS_L3_SSS-RainFlagged_SMID_MONTHLY-CLIMATOLOGY_V5", "AQUARIUS_L3_SSS-RainFlagged_SMID_MONTHLY_V5", "AQUARIUS_L3_SSS-RainFlagged_SMID_SEASONAL-CLIMATOLOGY_V5", "AQUARIUS_L3_SSS-RainFlagged_SMI_28DAY-RUNNINGMEAN_V5", "AQUARIUS_L3_SSS-RainFlagged_SMI_3MONTH_V5", "AQUARIUS_L3_SSS-RainFlagged_SMI_7DAY-RUNNINGMEAN_V5", "AQUARIUS_L3_SSS-RainFlagged_SMI_7DAY_V5", "AQUARIUS_L3_SSS-RainFlagged_SMI_ANNUAL_V5", "AQUARIUS_L3_SSS-RainFlagged_SMI_CUMULATIVE_V5", "AQUARIUS_L3_SSS-RainFlagged_SMI_DAILY_V5", "AQUARIUS_L3_SSS-RainFlagged_SMI_MONTHLY-CLIMATOLOGY_V5", "AQUARIUS_L3_SSS-RainFlagged_SMI_MONTHLY_V5", "AQUARIUS_L3_SSS-RainFlagged_SMI_SEASONAL-CLIMATOLOGY_V5", "AQUARIUS_L3_SSS-RainFlagged_SM_SMIA_MONTHLY_V5", "AQUARIUS_L3_SSS-RainFlagged_SM_SMID_MONTHLY_V5", "AQUARIUS_L3_SSS-RainFlagged_SM_SMI_MONTHLY_V5", "AQUARIUS_L3_SSS_CAP_7DAY_V5", "AQUARIUS_L3_SSS_CAP_MONTHLY_V5", "AQUARIUS_L3_SSS_RAINCORRECTED_CAP_7DAY_V5", "AQUARIUS_L3_SSS_RAINCORRECTED_CAP_MONTHLY_V5", "AQUARIUS_L3_SSS_SMIA_28DAY-RUNNINGMEAN_V5", "AQUARIUS_L3_SSS_SMIA_3MONTH_V5", "AQUARIUS_L3_SSS_SMIA_7DAY_V5", "AQUARIUS_L3_SSS_SMIA_ANNUAL_V5", "AQUARIUS_L3_SSS_SMIA_CUMULATIVE_V5", "AQUARIUS_L3_SSS_SMIA_DAILY_V5", "AQUARIUS_L3_SSS_SMIA_MONTHLY-CLIMATOLOGY_V5", "AQUARIUS_L3_SSS_SMIA_MONTHLY_V5", "AQUARIUS_L3_SSS_SMIA_SEASONAL-CLIMATOLOGY_V5", "AQUARIUS_L3_SSS_SMID_28DAY-RUNNINGMEAN_V5", "AQUARIUS_L3_SSS_SMID_3MONTH_V5", "AQUARIUS_L3_SSS_SMID_7DAY_V5", "AQUARIUS_L3_SSS_SMID_ANNUAL_V5", "AQUARIUS_L3_SSS_SMID_CUMULATIVE_V5", "AQUARIUS_L3_SSS_SMID_DAILY_V5", "AQUARIUS_L3_SSS_SMID_MONTHLY-CLIMATOLOGY_V5", "AQUARIUS_L3_SSS_SMID_MONTHLY_V5", "AQUARIUS_L3_SSS_SMID_SEASONAL-CLIMATOLOGY_V5", "AQUARIUS_L3_SSS_SMI_28DAY-RUNNINGMEAN_V5", "AQUARIUS_L3_SSS_SMI_3MONTH_V5", "AQUARIUS_L3_SSS_SMI_7DAY-RUNNINGMEAN_V5", "AQUARIUS_L3_SSS_SMI_7DAY_V5", "AQUARIUS_L3_SSS_SMI_ANNUAL_V5", "AQUARIUS_L3_SSS_SMI_CUMULATIVE_V5", "AQUARIUS_L3_SSS_SMI_DAILY_V5", "AQUARIUS_L3_SSS_SMI_MONTHLY-CLIMATOLOGY_V5", "AQUARIUS_L3_SSS_SMI_MONTHLY_V5", "AQUARIUS_L3_SSS_SMI_SEASONAL-CLIMATOLOGY_V5", "AQUARIUS_L3_SSS_SM_SMIA_MONTHLY_V5", "AQUARIUS_L3_SSS_SM_SMID_MONTHLY_V5", "AQUARIUS_L3_SSS_SM_SMI_MONTHLY_V5", "AQUARIUS_L3_WIND_SPEED_CAP_7DAY_V5", "AQUARIUS_L3_WIND_SPEED_CAP_MONTHLY_V5", "AQUARIUS_L3_WIND_SPEED_SMIA_28DAY-RUNNINGMEAN_V5", "AQUARIUS_L3_WIND_SPEED_SMIA_3MONTH_V5", "AQUARIUS_L3_WIND_SPEED_SMIA_7DAY_V5", "AQUARIUS_L3_WIND_SPEED_SMIA_ANNUAL_V5", "AQUARIUS_L3_WIND_SPEED_SMIA_CUMULATIVE_V5", "AQUARIUS_L3_WIND_SPEED_SMIA_DAILY_V5", "AQUARIUS_L3_WIND_SPEED_SMIA_MONTHLY-CLIMATOLOGY_V5", "AQUARIUS_L3_WIND_SPEED_SMIA_MONTHLY_V5", "AQUARIUS_L3_WIND_SPEED_SMIA_SEASONAL-CLIMATOLOGY_V5", "AQUARIUS_L3_WIND_SPEED_SMID_28DAY-RUNNINGMEAN_V5", "AQUARIUS_L3_WIND_SPEED_SMID_3MONTH_V5", "AQUARIUS_L3_WIND_SPEED_SMID_7DAY_V5", "AQUARIUS_L3_WIND_SPEED_SMID_ANNUAL_V5", "AQUARIUS_L3_WIND_SPEED_SMID_CUMULATIVE_V5", "AQUARIUS_L3_WIND_SPEED_SMID_DAILY_V5", "AQUARIUS_L3_WIND_SPEED_SMID_MONTHLY-CLIMATOLOGY_V5", "AQUARIUS_L3_WIND_SPEED_SMID_MONTHLY_V5", "AQUARIUS_L3_WIND_SPEED_SMID_SEASONAL-CLIMATOLOGY_V5", "AQUARIUS_L3_WIND_SPEED_SMI_28DAY-RUNNINGMEAN_V5", "AQUARIUS_L3_WIND_SPEED_SMI_3MONTH_V5", "AQUARIUS_L3_WIND_SPEED_SMI_7DAY-RUNNINGMEAN_V5", "AQUARIUS_L3_WIND_SPEED_SMI_7DAY_V5", "AQUARIUS_L3_WIND_SPEED_SMI_ANNUAL_V5", "AQUARIUS_L3_WIND_SPEED_SMI_CUMULATIVE_V5", "AQUARIUS_L3_WIND_SPEED_SMI_DAILY_V5", "AQUARIUS_L3_WIND_SPEED_SMI_MONTHLY-CLIMATOLOGY_V5", "AQUARIUS_L3_WIND_SPEED_SMI_MONTHLY_V5", "AQUARIUS_L3_WIND_SPEED_SMI_SEASONAL-CLIMATOLOGY_V5", "AQUARIUS_L4_OISSS_IPRC_7DAY_V5", "ARB_48_IN_LIDAR", "ARC02", "ARCTAS_AerosolTraceGas_AircraftInSitu_DC8_Data", "ARCTAS_Aerosol_AircraftInSitu_DC8_Data", "ARCTAS_Aerosol_AircraftInSitu_P3B_Data", "ARCTAS_AircraftRemoteSensing_BE200_HSRL_Data", "ARCTAS_AircraftRemoteSensing_DC8_DIAL_Data", "ARCTAS_AircraftRemoteSensing_P3B_AATS14_Data", "ARCTAS_AircraftRemoteSensing_P3B_CAR_Data", "ARCTAS_Cloud_AircraftInSitu_DC8_Data", "ARCTAS_Ground_Data", "ARCTAS_JValue_AircraftInSitu_DC8_Data", "ARCTAS_Merge_DC8-Aircraft_Data", "ARCTAS_Merge_P3B-Aircraft_Data", "ARCTAS_MetNav_AircraftInSitu_DC8_Data", "ARCTAS_MetNav_AircraftInSitu_P3B_Data", "ARCTAS_Model_Data", "ARCTAS_Ozonesondes_Data", "ARCTAS_Radiation_AircraftInSitu_P3B_Data", "ARCTAS_Satellite_Data", "ARCTAS_TraceGas_AircraftInSitu_DC8_Data", "ARCTAS_TraceGas_AircraftInSitu_P3B_Data", "ARCTAS_Trajectory_Data", "ARCTICCC", "ARESE_ER2_MAS", "ARIA_S1_GUNW", "ARISE_Aerosol-TraceGas_AircraftRemoteSensing_C130_Data", "ARISE_Cloud_AircraftInSitu_C130_Data", "ARISE_Merge_Data", "ARISE_MetNav_AircraftInSitu_C130_Data", "ARISE_Radiation_AircraftInSitu_C130_Data", "ARK", "ARME_898", "ASCATA-L2-25km", "ASCATA-L2-Coastal", "ASCATA_ESDR_ANCILLARY_L2_V1.1", "ASCATA_ESDR_L2_WIND_STRESS_V1.1", "ASCATA_L2_25KM_CDR", "ASCATA_L2_COASTAL_CDR", "ASCATB-L2-25km", "ASCATB-L2-Coastal", "ASCATB_ESDR_ANCILLARY_L2_V1.1", "ASCATB_ESDR_L2_WIND_STRESS_V1.1", "ASCATC-L2-25km", "ASCATC-L2-Coastal", "ASCENDS_AVOCET_CA_NV_Feb_2016_2115", "ASCENDS_LAS_IN_Sept_2014_2116", "ASIRI", "ASO_3M_PCDTM", "ASO_3M_SD", "ASO_50M_SD", "ASO_50M_SWE", "AST14DEM", "AST14DMO", "AST14OTH", "ASTGTM", "ASTGTM_NC", "ASTGTM_NUMNC", "ASTWBD", "ASTWBD_ATTNC", "ASTWBD_NC", "AST_05", "AST_07", "AST_07XT", "AST_08", "AST_09", "AST_09T", "AST_09XT", "AST_L1A", "AST_L1AE", "AST_L1B", "AST_L1BE", "AST_L1T", "AST_L1T", "ATCS", "ATL03", "ATL03", "ATL06", "ATL06", "ATL07", "ATL07", "ATL07QL", "ATL08", "ATL08", "ATL08QL", "ATL09", "ATL09", "ATL09QL", "ATL10", "ATL10", "ATL10QL", "ATL11", "ATL11", "ATL12", "ATL12", "ATL13", "ATL13", "ATL13QL", "ATL14", "ATL14", "ATL14", "ATL14", "ATL15", "ATL15", "ATL15", "ATL15", "ATL16", "ATL16", "ATL17", "ATL17", "ATL19", "ATL19", "ATL20", "ATL20", "ATL21", "ATL21", "ATL22", "ATL22", "ATL23", "ATL23", "ATLAS_DEALIASED_SASS_L2", "ATLAS_Veg_Plots_1541", "ATMOSL1", "ATMOSL2AF", "ATMOSL2AT", "ATMOSL2PF", "ATMOSL2PT", "ATMOSL2TF", "ATMOSL2TT", "ATSM2AEF", "ATSM2LSF", "ATSM2STF", "ATSMIB2E", "ATSMIB2T", "ATSMIGEO", "ATTREX-Aircraft_Radiation_Measurements", "ATTREX-Aircraft_RemoteSensing_Temperature_Measurements", "ATTREX-Aircraft_insitu_Cloud_property_Measurements", "ATTREX-Aircraft_insitu_TraceGas_Measurements", "ATTREX-Aircraft_navigational_and_meteorological_Measurements", "ATom_AMP_Instrument_Data_1671", "ATom_AO2_Instrument_Data_V2_1880", "ATom_ATHOS_Instrument_Data_V2_1930", "ATom_Aerosol_Properties_V2_2111", "ATom_Aerosols_Meteorology_1684", "ATom_CAFS_Instrument_Data_V2_1933", "ATom_CAPSVienna_Data_1981", "ATom_CESM2_1878", "ATom_CIT_Instrument_Data_V2_1927", "ATom_CO_GEOS_1604", "ATom_Carbon_Aerosol_Loadings_1618", "ATom_Clouds_Aerosols_2250", "ATom_DLH_Instrument_Data_V2_1937", "ATom_FlightTrack_Influences_1889", "ATom_Forward_Flight_Videos_1938", "ATom_FullModel_DataStream_1877", "ATom_GT_CIMS_Instrument_Data_1715", "ATom_GlobalModelInitiative_CTM_1897", "ATom_HIPPO_ORCAS_1788", "ATom_HR-AMS_Instrument_Data_1716", "ATom_ISAF_Instrument_Data_1730", "ATom_MMS_Instrument_Data_1731", "ATom_Mapping_OH_Troposphere_1669", "ATom_Medusa_Instrument_Data_V2_1881", "ATom_Mineral_Dust_Cirrus_Cloud_2006", "ATom_Modeled_Observed_Data_1857", "ATom_NMASS_Data_1607", "ATom_NOyO3_Instrument_Data_1734", "ATom_Organic_Aerossols_1795", "ATom_Ozonesonde_InstrumentData_1910", "ATom_PALMS_Instrument_Data_1733", "ATom_PANTHER_Instrument_Data_1914", "ATom_PFP_Instrument_Data_1746", "ATom_Particulate_Iodine_1773", "ATom_Photolysis_Rates_1651", "ATom_Picarro_Instrument_Data_1732", "ATom_QCLS_Instrument_Data_V2_1932", "ATom_Rad_Measurements_ARMAS_1906", "ATom_SAGA_Instrument_Data_1748", "ATom_SO2_LIF_Instrument_Data_1890", "ATom_SOAP_Instrument_Data_1898", "ATom_SP2_Instrument_Data_1672", "ATom_SP2_LAM_FeOx_MMR_1828", "ATom_Simulated_Data_1597", "ATom_TOGA_Instrument_Data_V2_1936", "ATom_UCATS_Instrument_Data_1750", "ATom_UHSAS_Data_1619", "ATom_WAS_Instrument_Data_1751", "ATom_merge_1581", "ATom_merge_V2_1925", "ATom_nav_1613", "AU_5DSno", "AU_DyOcn", "AU_DySno", "AU_DySno_NRT_R02", "AU_Land_NRT_R02", "AU_MoSno", "AU_Ocean_NRT_R01", "AU_Rain_NRT_R02", "AU_SI12", "AU_SI12_NRT_R04", "AU_SI25", "AU_SI25_NRT_R04", "AU_SI6", "AU_SI6_NRT_R04", "AVHRR18_G-NAVO-L2P-v1.0", "AVHRR19_G-NAVO-L2P-v1.0", "AVHRR19_L-NAVO-L2P-v1.0", "AVHRRF_MA-STAR-L2P-v2.80", "AVHRRF_MA-STAR-L3U-v2.80", "AVHRRF_MB-STAR-L2P-v2.80", "AVHRRF_MB-STAR-L3U-v2.80", "AVHRRF_MC-STAR-L2P-v2.80", "AVHRRF_MC-STAR-L3U-v2.80", "AVHRRMTA_G-NAVO-L2P-v1.0", "AVHRRMTA_G-NAVO-L2P-v2.0", "AVHRRMTB_G-NAVO-L2P-v1.0", "AVHRRMTB_G-NAVO-L2P-v2.0", "AVHRRMTC_G-NAVO-L2P-v2.0", "AVHRR_Fire_Products_1545", "AVHRR_LST_826", "AVHRR_OI-NCEI-L4-GLOB-v2.0", "AVHRR_OI-NCEI-L4-GLOB-v2.1", "AVHRR_SST_METOP_A-OSISAF-L2P-v1.0", "AVHRR_SST_METOP_A_GLB-OSISAF-L3C-v1.0", "AVHRR_SST_METOP_A_NAR-OSISAF-L3C-v1.0", "AVHRR_SST_METOP_B-OSISAF-L2P-v1.0", "AVHRR_SST_METOP_B_GLB-OSISAF-L3C-v1.0", "AVHRR_SST_METOP_B_NAR-OSISAF-L3C-v1.0", "AVHRR_SST_NOAA19_NAR-OSISAF-L3C-v1.0", "AVIRIS-Classic_L1B_Radiance_2155", "AVIRIS-Classic_L2_Reflectance_2154", "AVIRIS-NG_Data_Idaho_1533", "AVIRIS-NG_L1B_radiance_2095", "AVIRIS-NG_L2_Reflectance_2110", "AVIRIS_FlightLine_Locator_2140", "Academ_Kurchatov", "Active_Fluorescence_2001", "Active_Layer_Thaw_Depths_1701", "Aeolus-CalVal-DAWN_DC8", "Aeolus-CalVal-Dropsondes_DC8", "Aeolus-CalVal-HALO_DC8", "Aeolus-CalVal-MetNav_DC8", "Aerosol_Sulfate_LowermostStrat_1868", "AfriSAR_AGB_Maps_1681", "AfriSAR_LVIS_Footprint_Cover_1591", "AfriSAR_Mondah_Field_Data_1580", "African_Rainfall_Patterns_1263", "Afrisar_LVIS_Biomass_VProfiles_1775", "AirMOSS_Field_Data_Harvard_1677", "AirMOSS_L1_Sigma0_BERMS_1406", "AirMOSS_L1_Sigma0_Chamel_1407", "AirMOSS_L1_Sigma0_DukeFr_1408", "AirMOSS_L1_Sigma0_Harvrd_1409", "AirMOSS_L1_Sigma0_Howlnd_1410", "AirMOSS_L1_Sigma0_LaSelv_1411", "AirMOSS_L1_Sigma0_Metoli_1412", "AirMOSS_L1_Sigma0_Moisst_1413", "AirMOSS_L1_Sigma0_TonziR_1414", "AirMOSS_L1_Sigma0_Walnut_1415", "AirMOSS_L2_3_RZ_Soil_Moisture_1418", "AirMOSS_L2_Carbon_Flux_1420", "AirMOSS_L2_Inground_Soil_Moist_1416", "AirMOSS_L2_Precipitation_1417", "AirMOSS_L4_Daily_NEE_1422", "AirMOSS_L4_RZ_Soil_Moisture_1421", "AirMOSS_L4_Regional_NEE_1423", "AirMSPI_ACEPOL_Ellipsoid-projected_Georegistered_Radiance_Data", "AirMSPI_ACEPOL_Terrain-projected_Georegistered_Radiance_Data", "AirMSPI_CalWater-2_Ellipsoid-projected_Georegistered_Radiance_Data", "AirMSPI_CalWater-2_Terrain-projected_Georegistered_Radiance_Data", "AirMSPI_FIREX-AQ_Terrain-projected_Georegistered_Radiance_Data", "AirMSPI_ImPACT-PM_Ellipsoid-projected_Georegistered_Radiance_Data", "AirMSPI_ImPACT-PM_Terrain-projected_Georegistered_Radiance_Data", "AirMSPI_ORACLES_Ellipsoid-projected_Georegistered_Radiance_Data", "AirMSPI_ORACLES_Terrain-projected_Georegistered_Radiance_Data", "AirMSPI_PODEX_Ellipsoid-projected_Georegistered_Radiance_Data", "AirMSPI_PODEX_Terrain-projected_Georegistered_Radiance_Data", "AirMSPI_RADEX_Ellipsoid-projected_Georegistered_Radiance_Data", "AirMSPI_RADEX_Terrain-projected_Georegistered_Radiance_Data", "AirMSPI_SEAC4RS_Ellipsoid-projected_Georegistered_Radiance_Data", "AirMSPI_SEAC4RS_Terrain-projected_Georegistered_Radiance_Data", "AirMSPI_SPEX-PR_Ellipsoid-projected_Georegistered_Radiance_Data", "AirMSPI_SPEX-PR_Terrain-projected_Georegistered_Radiance_Data", "AirSWOT_Orthomosaic_WaterMask_1655", "Airborne_Insitu_Measurements_1784", "Akademik_Sergey_Vavilov", "Alaska_Arctic_Tundra_Veg_Map_1353", "Alaska_L4_WRF_STILT_Footprints_1544", "Alaska_L4_WRF_STILT_Particle_1571", "Alaska_Lake_Pond_Maps_2134", "Alaska_Yukon_NDVI_1614", "Alaskan_CH4_CO2_Fluxes_1316", "Alaskan_CO2_Flux_1325", "Albedo_Boreal_North_America_1605", "Alder_Shrub_Soil_Alaska_V2_2300", "Annual_30m_AGB_1808", "Annual_Burned_Area_Maps_1708", "Annual_Landcover_ABoVE_1691", "Annual_Seasonality_Greenness_1698", "Annual_Thaw_Slump_1724", "Apero", "Aqua_AIRS_MODIS1km_IND", "ArabianSea_2011", "Arc00", "ArcticNET", "ArcticTreeLine_Dendrometry_Env_2185", "ArcticTreeLine_Spruce_CO2_WV_1948", "Arctic_Boreal_Burned_Area_V2_2328", "Arctic_Boreal_CO2_Flux_1934", "Arctic_Flux_629", "Arctic_Network_Veg_plots_1542", "Arctic_RSWQ", "Arctic_Soil_Properties_2149", "Arctic_Vegetation_Maps_1323", "Arctic_Wildlife_Refuge_Veg_Map_1384", "Arctic_Winter_Respiration_v2_1762", "Arrigetch_Peaks_Veg_Plots_1358", "Atmospheric_CO2_California_1641", "Atqasuk_Veg_Plots_1371", "B01", "B02", "B03", "B04", "B05", "B06", "B07", "B08", "B09", "BAHAMAS2004", "BAROCLINIC_HRET14", "BASIN_TCP_963", "BBOP", "BCFLEXPART", "BC_Aerosol_Dynamics_Alaska_1340", "BDSNP_CMAQ_Model_1351", "BENEFIT", "BEST", "BFO_dsp01_ccrs_avhrr_landcover_589", "BFO_dsp01_ccrs_tm_landcover_588", "BFO_dsp04_ers_freeze-thaw_maps_590", "BFO_dsp05_ccrs_avhrr_npp_591", "BFO_dsp06_casi_lai_cc_592", "BFO_dsp08_polder_surface_params_594", "BFO_dsp09_moss_map_595", "BFO_dsp09_sask_fire_map_596", "BFO_dsp10_fpar_lai_1994_585", "BFO_dsp10_landcover_598", "BFO_dsp10_landcover_tm_mosaic_602", "BFO_dsp10_landcover_tm_reclassed_597", "BFO_dsp10_moss_cover_599", "BFO_dsp10_ndvi_600", "BFO_dsp10_peatlands_601", "BFO_flx01_derived_data_603", "BFO_flx01_flux_met_temp_604", "BFO_flx03_area_avg_flux_586", "BFO_flx04_nsa_burn_flux_587", "BFO_hmet01_goes8_level2_srb_605", "BFO_hmet01_ssmi_precip_606", "BFO_hmet02_gridded_met_phase2-3_607", "BFO_hmet03_hourly_met_p1_608", "BFO_hmet04_src_nsa_96-98_609", "BFO_mod01_gridded_met_p3_daily_610", "BGC_glider_GNATS", "BILIM", "BIO-GO-SHIP", "BIOCOMPLEXITY", "BIODIVERSITY", "BIOME", "BIOME_BGC_4_1_1_805", "BIOME_BGC_m2_4_1_2_809", "BIOME_BGC_m_4_1_1_806", "BIOSCAPE_COASTAL_CARBON", "BIOSOPE", "BIO_BURN_5X5_HAO_NAT", "BLATM1B", "BLATM2", "BLVIS2", "BNL", "BOA", "BOREAS_CDS_1350", "BOREAS_RSS-03_Snapshots_289", "BOREAS_SLICER_508", "BOUSSOLE", "BRAZIL", "BRMCR2", "BUVN04L2", "BUVN04L3zm", "BUVN4L1DCM", "BUVN4L1DCW", "BUVN4L1PDB", "BUVN4L1RUT", "BUVN4L2CPOZ", "BUVN4L2HDBUV", "BUVN4L3ZMT", "Barrow_NGEE_Arctic_Veg_Plots_1505", "Barrow_Tundra_Veg_Plots_1535", "Barter_Barrow_Veg_Plots_1534", "BenthicEcology_fromSpace", "Ber95", "Ber96", "BeringSea_LaserFluoMon", "Bermuda_Testbed_Mooring", "Big_Bend", "Bio_optics_Chl_polarization", "Biogenic_CO2flux_SIF_SMUrF_1899", "BlueFlux_AirborneObs_Florida_2327", "BlueFlux_Tidal_River_Water_2333", "Blue_Carbon_Tidal_Wetland_Maps_2091", "Bold_EPAHypoxia", "BorealForest_Greenness_Trends_2023", "Boreal_AGB_Density_ICESat2_2186", "Boreal_Arctic_Wetland_CH4_2351", "Boreal_CanopyCover_StandAge_2012", "Boreal_Fire_Severity_Metrics_1520", "BowdoinBuoy", "BurnedArea_Emissions_AK_YT_NWT_1812", "Burned_Area_Depth_AK_CA_2063", "C-HARRIER", "C5", "CABARE_918", "CALIPSO-NVF_HSRL2_KingAir_Data", "CAL_IIR_L1-Standard-V2-00", "CAL_IIR_L2_Swath-Standard-V4-20", "CAL_IIR_L2_Swath-Standard-V4-21", "CAL_IIR_L2_Swath-Standard-V4-51", "CAL_IIR_L2_Track-Standard-V4-20", "CAL_IIR_L2_Track-Standard-V4-21", "CAL_IIR_L2_Track-Standard-V4-51", "CAL_IIR_L3_GEWEX_Cloud-Standard-V1-00", "CAL_LID_L1-Standard-V4-10", "CAL_LID_L1-Standard-V4-11", "CAL_LID_L1-Standard-V4-51", "CAL_LID_L15-Standard-V1-00", "CAL_LID_L15-Standard-V1-01", "CAL_LID_L2_01kmCLay-Standard-V4-20", "CAL_LID_L2_01kmCLay-Standard-V4-21", "CAL_LID_L2_01kmCLay-Standard-V4-51", "CAL_LID_L2_05kmALay-Prov-V3-02", "CAL_LID_L2_05kmALay-Prov-V3-30", "CAL_LID_L2_05kmALay-Standard-V4-20", "CAL_LID_L2_05kmALay-Standard-V4-21", "CAL_LID_L2_05kmALay-Standard-V4-51", "CAL_LID_L2_05kmAPro-Prov-V3-02", "CAL_LID_L2_05kmAPro-Standard-V4-20", "CAL_LID_L2_05kmAPro-Standard-V4-21", "CAL_LID_L2_05kmAPro-Standard-V4-51", "CAL_LID_L2_05kmCLay-Standard-V4-20", "CAL_LID_L2_05kmCLay-Standard-V4-21", "CAL_LID_L2_05kmCLay-Standard-V4-51", "CAL_LID_L2_05kmCPro-Standard-V4-20", "CAL_LID_L2_05kmCPro-Standard-V4-21", "CAL_LID_L2_05kmCPro-Standard-V4-51", "CAL_LID_L2_05kmMLay-Standard-V4-20", "CAL_LID_L2_05kmMLay-Standard-V4-21", "CAL_LID_L2_05kmMLay-Standard-V4-51", "CAL_LID_L2_333mCLay-ValStage1-V3-30", "CAL_LID_L2_333mMLay-Standard-V4-20", "CAL_LID_L2_333mMLay-Standard-V4-21", "CAL_LID_L2_333mMLay-Standard-V4-51", "CAL_LID_L2_BlowingSnow_Antarctica-Standard-V1-00", "CAL_LID_L2_BlowingSnow_Antarctica-Standard-V1-01", "CAL_LID_L2_BlowingSnow_Antarctica-Standard-V2-00", "CAL_LID_L2_BlowingSnow_Greenland-Standard-V1-00", "CAL_LID_L2_PSCMask-Standard-V2-00", "CAL_LID_L2_VFM-Standard-V4-20", "CAL_LID_L2_VFM-Standard-V4-21", "CAL_LID_L2_VFM-Standard-V4-51", "CAL_LID_L3_Cloud_Occurrence-Standard-V1-00", "CAL_LID_L3_GEWEX_Cloud-Standard-V1-00", "CAL_LID_L3_Stratospheric_APro-Standard-V1-00", "CAL_LID_L3_Stratospheric_APro-Standard-V1-01", "CAL_LID_L3_Tropospheric_APro_AllSky-Standard-V4-20", "CAL_LID_L3_Tropospheric_APro_AllSky-Standard-V4-21", "CAL_LID_L3_Tropospheric_APro_CloudFree-Standard-V4-20", "CAL_LID_L3_Tropospheric_APro_CloudFree-Standard-V4-21", "CAL_LID_L3_Tropospheric_APro_CloudySkyOpaque-Standard-V4-20", "CAL_LID_L3_Tropospheric_APro_CloudySkyOpaque-Standard-V4-21", "CAL_LID_L3_Tropospheric_APro_CloudySkyTransparent-Standard-V4-20", "CAL_LID_L3_Tropospheric_APro_CloudySkyTransparent-Standard-V4-21", "CAL_WFC_L1_125m-ValStage1-V3-01", "CAL_WFC_L1_125m-ValStage1-V3-02", "CAL_WFC_L1_1Km-ValStage1-V3-01", "CAL_WFC_L1_1Km-ValStage1-V3-02", "CAL_WFC_L1_IIR-ValStage1-V3-01", "CAM5K30CF", "CAM5K30CF", "CAM5K30EM", "CAM5K30EM", "CAM5K30UC", "CAM5K30UC", "CAMEX4_ER2_MAS", "CAML", "CAMP2Ex-Cloud-Precip-Retrieval", "CAMP2Ex_Aerosol_AircraftInSitu_Learjet_Data", "CAMP2Ex_Aerosol_AircraftInSitu_P3_Data", "CAMP2Ex_Aerosol_AircraftRemoteSensing_P3_Data", "CAMP2Ex_Cloud_AircraftInSitu_Learjet_Data", "CAMP2Ex_Cloud_AircraftInSitu_P3_Data", "CAMP2Ex_Merge_Data", "CAMP2Ex_MetNav_AircraftInSitu_Learjet_Data", "CAMP2Ex_MetNav_AircraftInSitu_P3_Data", "CAMP2Ex_Miscellaneous_Data", "CAMP2Ex_Radiation_AircraftInSitu_P3_Data", "CAMP2Ex_TraceGas_AircraftInSitu_P3_Data", "CAMREX_904", "CARAFE_2016_2017_v2_2002", "CARIACO", "CARMIAAE", "CARMITST", "CARVE_Ecosystem_CH4_Flux_1558", "CARVE_L1_FTS_Spectra_1426", "CARVE_L1_FlightPath_1425", "CARVE_L1_FlightPath_Winds_1427", "CARVE_L1_Ground_Flux_1424", "CARVE_L1_Infrared_1428", "CARVE_L2_AtmosGas_Ground_1419", "CARVE_L2_AtmosGas_Harvard_1403", "CARVE_L2_AtmosGas_Merge_1402", "CARVE_L2_AtmosGas_NOAA_1401", "CARVE_L2_FTS_ColumnGas_1429", "CARVE_L2_Flask_1404", "CARVE_L2_Flask_Ground_1405", "CARVE_L4_WRF-STILT_Footprint_1431", "CARVE_L4_WRF-STILT_Particle_1430", "CARVE_Land_Thaw_State_1383", "CARVE_Photos_1435", "CARVE_Reports_1434", "CARVE_Videos_1433", "CAR_ARCTAS_BRDF", "CAR_ARCTAS_L1C", "CAR_ARMCAS_BRDF", "CAR_ARMCAS_L1C", "CAR_BRDF_709", "CAR_CLAMS_BRDF", "CAR_CLAMS_L1C", "CAR_CLASIC_BRDF", "CAR_CLASIC_L1C", "CAR_DISCOVERAQ_BRDF", "CAR_DISCOVERAQ_L1C", "CAR_ECO3D_BRDF", "CAR_ECO3D_L1C", "CAR_FIREACE_BRDF", "CAR_FIREACE_L1C", "CAR_INTEXB_BRDF", "CAR_INTEXB_L1C", "CAR_KUWAITOILFIRE_BRDF", "CAR_KUWAITOILFIRE_L1C", "CAR_LEADEX_BRDF", "CAR_LEADEX_L1C", "CAR_SAFARI_BRDF", "CAR_SAFARI_L1C", "CAR_SCARA_BRDF", "CAR_SCARA_L1C", "CAR_SCARB_BRDF", "CAR_SCARB_L1C", "CAR_SKUKUZA_BRDF", "CAR_SKUKUZA_L1C", "CAR_SNOWEX17_BRDF", "CAR_SNOWEX17_L1C", "CAR_TARFOX_BRDF", "CAR_TARFOX_L1C", "CASES", "CATS-ISS_L1B_D-M7.1-V3-00", "CATS-ISS_L1B_D-M7.2-V3-00", "CATS-ISS_L1B_N-M7.1-V3-00", "CATS-ISS_L1B_N-M7.2-V3-00", "CATS-ISS_L2O_D-M7.1-V3-01_05kmLay", "CATS-ISS_L2O_D-M7.1-V3-01_05kmPro", "CATS-ISS_L2O_D-M7.2-V3-00_05kmLay", "CATS-ISS_L2O_D-M7.2-V3-00_05kmPro", "CATS-ISS_L2O_D-M7.2-V3-01_05kmLay", "CATS-ISS_L2O_D-M7.2-V3-01_05kmPro", "CATS-ISS_L2O_N-M7.1-V3-01_05kmLay", "CATS-ISS_L2O_N-M7.1-V3-01_05kmPro", "CATS-ISS_L2O_N-M7.2-V3-00_05kmLay", "CATS-ISS_L2O_N-M7.2-V3-00_05kmPro", "CATS-ISS_L2O_N-M7.2-V3-01_05kmLay", "CATS-ISS_L2O_N-M7.2-V3-01_05kmPro", "CCE-LTER", "CCMP_WINDS_10M6HR_L4_V3.1", "CCMP_WINDS_10MMONTHLY_L4_V3.1", "CD01_BRAMS_907", "CD01_CIRSAN_Meteorology_2001_1114", "CD02_Atmosphere_CO2_Isotopes_1011", "CD02_C_N_Isotopes_1097", "CD02_C_N_O_Organic_983", "CD02_Forest_Canopy_Structure_1009", "CD02_Leaf_Level_Gas_Exchange_1010", "CD02_Leaf_Water_Potential_1100", "CD02_O_H_Isotopes_1008", "CD03_Ceilometer_Km67_942", "CD03_Mesoscale_Meteorology_944", "CD03_Pasture_Flux_962", "CD03_Tethered_Balloon_1108", "CD04_Biomass_990", "CD04_CO2_Profiles_947", "CD04_Dendrometry_989", "CD04_LAI_992", "CD04_LAI_Estimates_1103", "CD04_Leaf_Level_Gas_Exchange_1060", "CD04_Leaf_Litter_991", "CD04_Logging_Damage_1038", "CD04_Meteorology_Fluxes_946", "CD04_Soil_Moisture_Km83_979", "CD04_Soil_Respiration_1039", "CD04_Tower_Flux_Gap_978", "CD05_Fuel_Loads_1233", "CD05_Micromet_1169", "CD05_REE_Fuel_Sticks_Moisture_1232", "CD06_BGC_JiParana_1227", "CD06_C02_Exchange_1136", "CD06_C_Isotopes_1120", "CD06_Camrex_1086", "CD06_Carbon_respiration_1125", "CD06_LULC_Map_JiParana_1087", "CD06_Landuse_Timeseries_JiParana_844", "CD06_Outgassing_1151", "CD06_Physical_Template_JiParana_1090", "CD06_Soils_JiParana_1088", "CD06_Water_Balance_JiParana_1132", "CD07_GOES_L3_Gridded_SRB_831", "CD08_CWD_Res_and_Decomp_Manaus_911", "CD08_C_Isotopes_Belowground_1025", "CD08_Ecosystem_Resp_Manaus_912", "CD08_Leaf_Isotopes_Manaus_1245", "CD08_Radiocarbon_Dates_997", "CD08_Radiocarbon_Dates_Manaus_996", "CD08_Tree_Growth_Manaus_1194", "CD08_Tree_Inventory_Ducke_910", "CD09_Soils_Veg_Tapajos_1104", "CD10_Biometry_Tapajos_854", "CD10_CO2_Profiles_Tapajos_855", "CD10_CO_CO2_Maxaranguape_1012", "CD10_CO_Tapajos_856", "CD10_CWD_Tapajos_858", "CD10_DBH_Tapajos_859", "CD10_EddyFlux_Tapajos_860", "CD10_H2O_Profiles_Tapajos_861", "CD10_Litter_Tapajos_862", "CD10_Temperature_Profiles_Tapajos_863", "CD11_Forest_Degradation_1118", "CD15_Productivity_1167", "CD17_Forest_Survey_1254", "CD32_Fluxes_Brazil_1842", "CD32_LBA_MIP_Drivers_1177", "CD34_Amazon_Hyperion_1064", "CD34_Amazon_Landsat_1176", "CD36_SALDAS_1162", "CD37_Biomass_Landsat_Glas_1145", "CDDIS_DORIS_data_cycle", "CDDIS_DORIS_data_rinex", "CDDIS_DORIS_information", "CDDIS_DORIS_products_geocenter", "CDDIS_DORIS_products_ionosphere", "CDDIS_DORIS_products_orbit", "CDDIS_DORIS_products_positions", "CDDIS_DORIS_products_quaternions", "CDDIS_DORIS_products_stcd", "CDDIS_GNSS_highrate_data", "CDDIS_GNSS_products_IGS20", "CDDIS_GNSS_products_clocks_final", "CDDIS_GNSS_products_clocks_rapid", "CDDIS_GNSS_products_clocks_realtime", "CDDIS_GNSS_products_erp", "CDDIS_GNSS_products_ionosphere_final", "CDDIS_GNSS_products_ionosphere_predicted", "CDDIS_GNSS_products_ionosphere_rapid", "CDDIS_GNSS_products_orbit_final", "CDDIS_GNSS_products_orbit_rapid", "CDDIS_GNSS_products_orbit_realtime", "CDDIS_GNSS_products_orbit_ultrarapid", "CDDIS_GNSS_products_positions", "CDDIS_GNSS_products_troposphere", "CDDIS_MISC_information", "CDDIS_SLR_data", "CDDIS_SLR_predictions", "CDDIS_SLR_products_poseop", "CDDIS_VLBI_data", "CDDIS_VLBI_products_eop", "CDDIS_VLBI_products_positions", "CDDIS_VLBI_products_troposphere", "CDIAC_NDP41_220", "CER-NEWS_CCCM_Aqua-FM3-MODIS-CAL-CS", "CERES-MISR-MODIS_SSF-SSFM_Terra-FM1", "CERES-MISR-MODIS_SSF-SSFM_Terra-FM2", "CERES_EBAF", "CERES_EBAF", "CERES_EBAF-TOA", "CERES_EBAF-TOA", "CER_BDS_Aqua-FM4", "CER_BDS_Aqua-FM4", "CER_BDS_TRMM-PFM", "CER_CCCM_Aqua-FM3-MODIS-CAL-CS", "CER_CCCM_Aqua-FM3-MODIS-CAL-CS", "CER_CRS1deg-Hour_Aqua-MODIS", "CER_CRS1deg-Hour_Terra-MODIS", "CER_CRS_Aqua-FM3-MODIS", "CER_CRS_Aqua-FM3-MODIS", "CER_CRS_Aqua-FM4-MODIS", "CER_CRS_TRMM-PFM-VIRS", "CER_CRS_Terra-FM1-MODIS", "CER_CRS_Terra-FM1-MODIS", "CER_CRS_Terra-FM1-MODIS", "CER_CRS_Terra-FM2-MODIS", "CER_CldTypHist_GEO-MODIS", "CER_CldTypHist_GEO-MODIS-VIIRS", "CER_ES4_Aqua-Xtrk", "CER_ES4_NOAA20-FM6", "CER_ES4_NPP-FM5", "CER_ES4_TRMM-PFM", "CER_ES4_Terra+Aqua", "CER_ES4_Terra-Xtrk", "CER_ES8_Aqua-FM4", "CER_ES8_TRMM-PFM", "CER_ES9_Aqua-Xtrk", "CER_ES9_NOAA20-FM6", "CER_ES9_NPP-FM5", "CER_ES9_TRMM-PFM", "CER_ES9_Terra+Aqua", "CER_ES9_Terra-Xtrk", "CER_FSW_Aqua-FM4-MODIS", "CER_FSW_TRMM-PFM-VIRS", "CER_FSW_Terra-FM1-MODIS", "CER_FSW_Terra-FM1-MODIS", "CER_FSW_Terra-FM1-MODIS", "CER_FSW_Terra-FM2-MODIS", "CER_FluxByCldTyp-Day_NOAA20-VIIRS", "CER_FluxByCldTyp-Day_Terra-Aqua-MODIS", "CER_FluxByCldTyp-Month_NOAA20-VIIRS", "CER_FluxByCldTyp-Month_Terra-Aqua-MODIS", "CER_GEO_Ed4_GMS05_FD", "CER_GEO_Ed4_GOE08_NH", "CER_GEO_Ed4_GOE08_SH", "CER_GEO_Ed4_GOE09_NH", "CER_GEO_Ed4_GOE09_SH", "CER_GEO_Ed4_GOE10_NH", "CER_GEO_Ed4_GOE10_SH", "CER_GEO_Ed4_GOE11_NH", "CER_GEO_Ed4_GOE11_SH", "CER_GEO_Ed4_GOE12_NH", "CER_GEO_Ed4_GOE12_SH", "CER_GEO_Ed4_GOE13_NH", "CER_GEO_Ed4_GOE13_NH", "CER_GEO_Ed4_GOE13_SH", "CER_GEO_Ed4_GOE13_SH", "CER_GEO_Ed4_GOE14_NH", "CER_GEO_Ed4_GOE14_SH", "CER_GEO_Ed4_GOE15_NH", "CER_GEO_Ed4_GOE15_NH", "CER_GEO_Ed4_GOE15_SH", "CER_GEO_Ed4_GOE15_SH", "CER_GEO_Ed4_MET05_FD", "CER_GEO_Ed4_MET07_FD", "CER_GEO_Ed4_MET08_NH", "CER_GEO_Ed4_MET08_NH", "CER_GEO_Ed4_MET08_SH", "CER_GEO_Ed4_MET09_NH", "CER_GEO_Ed4_MET09_SH", "CER_GEO_Ed4_MET10_NH", "CER_GEO_Ed4_MET10_SH", "CER_GEO_Ed4_MTS01_NH", "CER_GEO_Ed4_MTS01_SH", "CER_GEO_Ed4_MTS02_NH", "CER_GEO_Ed4_MTS02_SH", "CER_ISCCP-D2like-Day_Aqua-FM3-MODIS", "CER_ISCCP-D2like-Day_Aqua-FM4-MODIS", "CER_ISCCP-D2like-Day_Terra-FM1-MODIS", "CER_ISCCP-D2like-Day_Terra-FM2-MODIS", "CER_ISCCP-D2like-GEO_DAY", "CER_ISCCP-D2like-Mrg_GEO-MODIS-DAY", "CER_ISCCP-D2like-Nit_Aqua-FM3-MODIS", "CER_ISCCP-D2like-Nit_Aqua-FM4-MODIS", "CER_ISCCP-D2like-Nit_Terra-FM1-MODIS", "CER_ISCCP-D2like-Nit_Terra-FM2-MODIS", "CER_SSF1deg-Day_Aqua-MODIS", "CER_SSF1deg-Day_NOAA20-VIIRS", "CER_SSF1deg-Day_NPP-VIIRS", "CER_SSF1deg-Day_Terra-MODIS", "CER_SSF1deg-Hour_Aqua-MODIS", "CER_SSF1deg-Hour_NOAA20-VIIRS", "CER_SSF1deg-Hour_NPP-VIIRS", "CER_SSF1deg-Hour_Terra-MODIS", "CER_SSF1deg-Month_Aqua-MODIS", "CER_SSF1deg-Month_NOAA20-VIIRS", "CER_SSF1deg-Month_NPP-VIIRS", "CER_SSF1deg-Month_Terra-MODIS", "CER_SSF_Aqua-FM4-MODIS", "CER_SSF_TRMM-PFM-VIRS", "CER_SYN1deg-1Hour_Terra-Aqua-MODIS", "CER_SYN1deg-1Hour_Terra-MODIS", "CER_SYN1deg-1Hour_Terra-NOAA20", "CER_SYN1deg-1Hour_Terra-NPP", "CER_SYN1deg-3Hour_Terra-Aqua-MODIS", "CER_SYN1deg-3Hour_Terra-MODIS", "CER_SYN1deg-3Hour_Terra-NPP", "CER_SYN1deg-Day_Terra-Aqua-MODIS", "CER_SYN1deg-Day_Terra-MODIS", "CER_SYN1deg-Day_Terra-NOAA20", "CER_SYN1deg-Day_Terra-NPP", "CER_SYN1deg-MHour_Terra-Aqua-MODIS", "CER_SYN1deg-MHour_Terra-MODIS", "CER_SYN1deg-MHour_Terra-NOAA20", "CER_SYN1deg-MHour_Terra-NPP", "CER_SYN1deg-Month_Terra-Aqua-MODIS", "CER_SYN1deg-Month_Terra-MODIS", "CER_SYN1deg-Month_Terra-NOAA20", "CER_SYN1deg-Month_Terra-NPP", "CFL", "CH4_Aircraft_STILT_footprints_1300", "CH4_CO2_WaterBodies_YK_Delta_2178", "CH4_Flux_BigTrail_Goldstream_1778", "CH4_Fluxes_ThermokarstLakes_AK_1870", "CH4_Plume_AVIRIS-NG_1727", "CHELTON_SEASAT_SASS_L3", "CIESIN_CHRR_NDH_CYCLONE_HFD", "CIESIN_CHRR_NDH_CYCLONE_MRD", "CIESIN_CHRR_NDH_CYCLONE_PELRD", "CIESIN_CHRR_NDH_CYCLONE_TELRD", "CIESIN_CHRR_NDH_DROUGHT_HFD", "CIESIN_CHRR_NDH_DROUGHT_MRD", "CIESIN_CHRR_NDH_DROUGHT_PELRD", "CIESIN_CHRR_NDH_DROUGHT_TELRD", "CIESIN_CHRR_NDH_EQUAKE_HFD", "CIESIN_CHRR_NDH_EQUAKE_MRD", "CIESIN_CHRR_NDH_EQUAKE_PELRD", "CIESIN_CHRR_NDH_EQUAKE_PGA", "CIESIN_CHRR_NDH_EQUAKE_TELRD", "CIESIN_CHRR_NDH_FLOOD_HFD", "CIESIN_CHRR_NDH_FLOOD_MRD", "CIESIN_CHRR_NDH_FLOOD_PELRD", "CIESIN_CHRR_NDH_FLOOD_TELRD", "CIESIN_CHRR_NDH_LSLIDE_HD", "CIESIN_CHRR_NDH_LSLIDE_MRD", "CIESIN_CHRR_NDH_LSLIDE_PELRD", "CIESIN_CHRR_NDH_LSLIDE_TELRD", "CIESIN_CHRR_NDH_MULTI_HFD", "CIESIN_CHRR_NDH_MULTI_MRD", "CIESIN_CHRR_NDH_MULTI_PELRD", "CIESIN_CHRR_NDH_MULTI_TELRD", "CIESIN_CHRR_NDH_VOLCANO_HFD", "CIESIN_CHRR_NDH_VOLCANO_MRD", "CIESIN_CHRR_NDH_VOLCANO_PELRD", "CIESIN_CHRR_NDH_VOLCANO_TELRD", "CIESIN_SEDAC_ACRP_1980_SAS", "CIESIN_SEDAC_ACRP_1990_CBS", "CIESIN_SEDAC_ACRP_1990_EMF", "CIESIN_SEDAC_ACRP_1990_PBF", "CIESIN_SEDAC_ACRP_1990_SAS", "CIESIN_SEDAC_ACRP_1990_SEF", "CIESIN_SEDAC_ACRP_1990_SI", "CIESIN_SEDAC_ACRP_1990_STF1B", "CIESIN_SEDAC_ACRP_1990_ZIPEF", "CIESIN_SEDAC_ACRP_1992_BF", "CIESIN_SEDAC_ACRP_PUMS", "CIESIN_SEDAC_AGLANDS_CROP2000", "CIESIN_SEDAC_AGLANDS_PAS2000", "CIESIN_SEDAC_ANTHROMES_v1", "CIESIN_SEDAC_ANTHROMES_v2_1700", "CIESIN_SEDAC_ANTHROMES_v2_1900", "CIESIN_SEDAC_AQDH_CTMAP_2003_2018", "CIESIN_SEDAC_AQDH_DANO2_US_1KM_1.10", "CIESIN_SEDAC_AQDH_DAO3_US_1KM", "CIESIN_SEDAC_AQDH_DAO3_US_1KM_1.10", "CIESIN_SEDAC_AQDH_DAPM25_US_1KM", "CIESIN_SEDAC_AQDH_DAPM25_US_1KM_1.10", "CIESIN_SEDAC_AQDH_NO2_US_1KM", "CIESIN_SEDAC_AQDH_PM25COM_US_1KM", "CIESIN_SEDAC_AQDH_PM25O3NO2_ZIPCODE", "CIESIN_SEDAC_AQDH_TRACE_US_1KM", "CIESIN_SEDAC_CD_ ADM_GIS_1990", "CIESIN_SEDAC_CD_AGENDA21", "CIESIN_SEDAC_CD_BIB_ADM_GEOG", "CIESIN_SEDAC_CD_CTY_ECON_YRBK", "CIESIN_SEDAC_CD_CTY_POPAG_GIS", "CIESIN_SEDAC_CD_FUNDGIS", "CIESIN_SEDAC_CD_GBCODES", "CIESIN_SEDAC_CD_HOS_EPI_50-85", "CIESIN_SEDAC_CD_MAPS_BIBDB", "CIESIN_SEDAC_CD_STAT_1949-1990", "CIESIN_SEDAC_CESIC_2004EVI", "CIESIN_SEDAC_CESIC_2006NFA", "CIESIN_SEDAC_CESIC_ANC", "CIESIN_SEDAC_CESIC_COMPLETE_V11", "CIESIN_SEDAC_CESIC_RIOJO", "CIESIN_SEDAC_CESIC_WELLBEING", "CIESIN_SEDAC_CLIMMIG_GASPMP18SR", "CIESIN_SEDAC_CLIMMIG_GSPMP18SR", "CIESIN_SEDAC_CROPCLIM_EFCCGPSRES", "CIESIN_SEDAC_CROPCLIM_GISS_DB", "CIESIN_SEDAC_CRV_USCRPC_2040-2049", "CIESIN_SEDAC_DEDC_ACE_V2", "CIESIN_SEDAC_ENERGY_NPPCLA", "CIESIN_SEDAC_ENERGY_NPPL", "CIESIN_SEDAC_EPI_2006", "CIESIN_SEDAC_EPI_2008", "CIESIN_SEDAC_EPI_2010", "CIESIN_SEDAC_EPI_2012", "CIESIN_SEDAC_EPI_2014", "CIESIN_SEDAC_EPI_2016", "CIESIN_SEDAC_EPI_2018", "CIESIN_SEDAC_EPI_2020", "CIESIN_SEDAC_EPI_2022", "CIESIN_SEDAC_ESI_2000", "CIESIN_SEDAC_ESI_2001", "CIESIN_SEDAC_ESI_2002", "CIESIN_SEDAC_ESI_2005", "CIESIN_SEDAC_FERMANv1_NAPP", "CIESIN_SEDAC_FERMANv1_NMAN", "CIESIN_SEDAC_FERMANv1_PAPP", "CIESIN_SEDAC_FERMANv1_PESTG_V1.01", "CIESIN_SEDAC_FERMANv1_PMAN", "CIESIN_SEDAC_FOOD_CROPSTAT_1900-2017", "CIESIN_SEDAC_FOOD_FIH_V1", "CIESIN_SEDAC_GEO-MEX_GIS_SMI", "CIESIN_SEDAC_GEO-MEX_POPDBMEX", "CIESIN_SEDAC_GEO-MEX_RASTERGIS", "CIESIN_SEDAC_GEO-MEX_URBANGIS", "CIESIN_SEDAC_GEO-MEX_URB_TSER", "CIESIN_SEDAC_GHSL_PBSMOD", "CIESIN_SEDAC_GPWv3_CENTROIDS", "CIESIN_SEDAC_GPWv3_COASTLINES", "CIESIN_SEDAC_GPWv3_LANDGEOG", "CIESIN_SEDAC_GPWv3_NADMINBND", "CIESIN_SEDAC_GPWv3_NATIDEN", "CIESIN_SEDAC_GPWv3_POPCOUNT", "CIESIN_SEDAC_GPWv3_POPCOUNTFE", "CIESIN_SEDAC_GPWv3_POPDENS", "CIESIN_SEDAC_GPWv3_POPDENSFE", "CIESIN_SEDAC_GPWv3_SUBADBND", "CIESIN_SEDAC_GPWv4_ADUCPPE_R11", "CIESIN_SEDAC_GPWv4_APCT_WPP_2015_R11", "CIESIN_SEDAC_GPWv4_APDENS_WPP_2015_R11", "CIESIN_SEDAC_GPWv4_BDC_R11", "CIESIN_SEDAC_GPWv4_DQI_R11", "CIESIN_SEDAC_GPWv4_LANDH2OAREA_R11", "CIESIN_SEDAC_GPWv4_NATIDEN_R11", "CIESIN_SEDAC_GPWv4_POPCOUNT_R11", "CIESIN_SEDAC_GPWv4_POPDENS_R11", "CIESIN_SEDAC_GRANDv1_DAMS", "CIESIN_SEDAC_GRANDv1_RESERVOIRS", "CIESIN_SEDAC_GROADS_CCRDSv1", "CIESIN_SEDAC_GROADS_V1", "CIESIN_SEDAC_GRUMPv1_COASTLINES", "CIESIN_SEDAC_GRUMPv1_EXT", "CIESIN_SEDAC_GRUMPv1_EXT02", "CIESIN_SEDAC_GRUMPv1_LGUAG", "CIESIN_SEDAC_GRUMPv1_NADMINBND", "CIESIN_SEDAC_GRUMPv1_NATIDEN", "CIESIN_SEDAC_GRUMPv1_POPCOUNT", "CIESIN_SEDAC_GRUMPv1_POPDENS", "CIESIN_SEDAC_GRUMPv1_STLMNT", "CIESIN_SEDAC_GRUMPv1_STLMNT01", "CIESIN_SEDAC_HANPP", "CIESIN_SEDAC_HANPP_CPROD", "CIESIN_SEDAC_HANPP_NPP", "CIESIN_SEDAC_HANPP_PERCENT", "CIESIN_SEDAC_HASO2_SO2_1850-2005", "CIESIN_SEDAC_ICWQ_ACHLOA", "CIESIN_SEDAC_ICWQ_ANC", "CIESIN_SEDAC_ICWQ_CCHLOA", "CIESIN_SEDAC_INDIA_AWCA", "CIESIN_SEDAC_INDIA_CENSUS_2011", "CIESIN_SEDAC_INDIA_VILSOCIOECON", "CIESIN_SEDAC_IPCC_BASELINE", "CIESIN_SEDAC_IPCC_GRI_MIDYR_0.3.7", "CIESIN_SEDAC_IPCC_IS92_V11", "CIESIN_SEDAC_IPCC_OBSERVED", "CIESIN_SEDAC_IPCC_OBSERVED_AR5", "CIESIN_SEDAC_IPCC_SAGDVCC", "CIESIN_SEDAC_IPCC_SRES_1X1EM", "CIESIN_SEDAC_IPCC_SRES_EMSC_V11", "CIESIN_SEDAC_IPCC_SRES_FLGASES", "CIESIN_SEDAC_LECZ_DELTA_V1", "CIESIN_SEDAC_LECZ_SLRIRAMSARWII", "CIESIN_SEDAC_LECZ_URPEV1", "CIESIN_SEDAC_LECZ_URPLAEV2", "CIESIN_SEDAC_LECZ_URPLAEV3", "CIESIN_SEDAC_LULC_CAMVEGLCCCS", "CIESIN_SEDAC_LULC_DPI", "CIESIN_SEDAC_LULC_DT1_V1", "CIESIN_SEDAC_LULC_HMTS", "CIESIN_SEDAC_LULC_MANGROVES_2000", "CIESIN_SEDAC_LULC_PUEXPANS_2030", "CIESIN_SEDAC_LWP2_HF_GEOG", "CIESIN_SEDAC_LWP2_HF_IGHP", "CIESIN_SEDAC_LWP2_HII_GEOG", "CIESIN_SEDAC_LWP2_HII_IGHP", "CIESIN_SEDAC_LWP2_LW_GEOG", "CIESIN_SEDAC_LWP2_LW_IGHP", "CIESIN_SEDAC_LWP3_HF_1993", "CIESIN_SEDAC_LWP3_HF_2009", "CIESIN_SEDAC_LWP_HFP_GEOG", "CIESIN_SEDAC_LWP_HFP_IGHP", "CIESIN_SEDAC_LWP_LW_GEOG", "CIESIN_SEDAC_LWP_LW_IGHP", "CIESIN_SEDAC_LWP_TOPWA_GEOG", "CIESIN_SEDAC_LWP_TOPWA_IGHP", "CIESIN_SEDAC_MA_BIODIVERSITY", "CIESIN_SEDAC_MA_CLIMLC", "CIESIN_SEDAC_MA_ECOSYSTEMS", "CIESIN_SEDAC_MA_POPULATION", "CIESIN_SEDAC_MA_RLCC", "CIESIN_SEDAC_MA_SCENARIOS", "CIESIN_SEDAC_NAGDC_PLACE", "CIESIN_SEDAC_NAGDC_PLACEII", "CIESIN_SEDAC_NAGDC_PLACEIII", "CIESIN_SEDAC_NAGDC_PLACEIV", "CIESIN_SEDAC_NRMI_2010", "CIESIN_SEDAC_NRMI_2011", "CIESIN_SEDAC_NRMI_NRPCHI12", "CIESIN_SEDAC_NRMI_NRPCHI13", "CIESIN_SEDAC_NRMI_NRPCHI14", "CIESIN_SEDAC_NRMI_NRPCHI15", "CIESIN_SEDAC_NRMI_NRPCHI16", "CIESIN_SEDAC_NRMI_NRPCHI17", "CIESIN_SEDAC_NRMI_NRPCHI18", "CIESIN_SEDAC_NRMI_NRPCHI19", "CIESIN_SEDAC_NRMI_NRPCHI20", "CIESIN_SEDAC_NRMI_NRPCHI21", "CIESIN_SEDAC_NRMI_NRPCHI22", "CIESIN_SEDAC_NRMI_NRPCHI23", "CIESIN_SEDAC_PD_LACPOP", "CIESIN_SEDAC_PD_NETMIG_1970_2000", "CIESIN_SEDAC_PD_POPCGTSE", "CIESIN_SEDAC_PD_POPDGTSE", "CIESIN_SEDAC_PD_PPUSCASRCSSP", "CIESIN_SEDAC_PD_SSPBSYR_1_8th", "CIESIN_SEDAC_PD_SSPBSYR_1km", "CIESIN_SEDAC_PEND_ALLOMSSF3", "CIESIN_SEDAC_PEND_ALLOMSTXSF1", "CIESIN_SEDAC_PEND_GDIS", "CIESIN_SEDAC_PEND_HSF1", "CIESIN_SEDAC_PEND_NOSF1", "CIESIN_SEDAC_PEND_NOSF3", "CIESIN_SEDAC_PMP_GRDI_2010_2020", "CIESIN_SEDAC_PMP_GSPCM", "CIESIN_SEDAC_PMP_IMR", "CIESIN_SEDAC_PMP_IMR_V2.01", "CIESIN_SEDAC_PMP_PFSCS", "CIESIN_SEDAC_PMP_SAEPI", "CIESIN_SEDAC_PMP_UBN", "CIESIN_SEDAC_SDEI_APM25_URBAN", "CIESIN_SEDAC_SDEI_GEHE", "CIESIN_SEDAC_SDEI_GFA_GRACE", "CIESIN_SEDAC_SDEI_GFED4_CLTAB", "CIESIN_SEDAC_SDEI_GFED4_GRIDS", "CIESIN_SEDAC_SDEI_GWRPM25_MMSAOD_4GL03", "CIESIN_SEDAC_SDEI_LST2013", "CIESIN_SEDAC_SDEI_NO2", "CIESIN_SEDAC_SDEI_UHE", "CIESIN_SEDAC_SDEI_UHI2013", "CIESIN_SEDAC_SDEI_VDL", "CIESIN_SEDAC_SDEI_YCEOUHI_V4", "CIESIN_SEDAC_SDGI_ACCELEC_2023", "CIESIN_SEDAC_SDGI_RAI_2023", "CIESIN_SEDAC_SDGI_UAPT_2023", "CIESIN_SEDAC_SDGI_UPSAE_2023", "CIESIN_SEDAC_SDP_CGDP_A1A2B1B2", "CIESIN_SEDAC_SDP_CPOP_A1B1A2", "CIESIN_SEDAC_SDP_CPOP_B2", "CIESIN_SEDAC_SDP_GGDP_B2", "CIESIN_SEDAC_SDP_GPOP_B2", "CIESIN_SEDAC_SPATIALECON_GECON4", "CIESIN_SEDAC_SPATIALECON_LGII_V1", "CIESIN_SEDAC_SPECIES_AMP_RICH15", "CIESIN_SEDAC_SPECIES_MAM_RICH15", "CIESIN_SEDAC_SSF_EPANPLSPDCMv2", "CIESIN_SEDAC_SSF_HWSPDv2", "CIESIN_SEDAC_SSP_1-8thDULEPBYGSSP", "CIESIN_SEDAC_SSP_1-kmDULEPBYGSSP", "CIESIN_SEDAC_SSP_GSNLITDB_V1", "CIESIN_SEDAC_SSP_LITDB_V1", "CIESIN_SEDAC_ULANDSAT_GMIS_V1", "CIESIN_SEDAC_ULANDSAT_HBASE_V1", "CIESIN_SEDAC_ULANDSAT_URBAN_LANDSAT", "CIESIN_SEDAC_USCG_90SF1", "CIESIN_SEDAC_USCG_90SF1MSA", "CIESIN_SEDAC_USCG_90SF3", "CIESIN_SEDAC_USCG_90SF3MSA", "CIESIN_SEDAC_USCG_SF1", "CIESIN_SEDAC_USCG_SF12010", "CIESIN_SEDAC_USCG_SF1MSA", "CIESIN_SEDAC_USCG_SF3", "CIESIN_SEDAC_USCG_SF3MSA", "CIESIN_SEDAC_USCG_USSVIG01", "CIESIN_SEDAC_USPAT_BSCATTER_1993_2020", "CIESIN_SEDAC_USPAT_DESLUIS", "CIESIN_SEDAC_USPAT_GUPPD_V1", "CIESIN_SEDAC_USPAT_HUP", "CIESIN_SEDAC_USPAT_USUEXT2015", "CIESIN_SEDAC_WACVM_CCPROD", "CIESIN_SEDAC_WACVM_DEFOR200012", "CIESIN_SEDAC_WACVM_DHS", "CIESIN_SEDAC_WACVM_ECONSI", "CIESIN_SEDAC_WACVM_GPWv4PR_PD", "CIESIN_SEDAC_WACVM_GPWv4PR_PG", "CIESIN_SEDAC_WACVM_GSSPEPP", "CIESIN_SEDAC_WACVM_JRCMA", "CIESIN_SEDAC_WACVM_MFD2000_POL", "CIESIN_SEDAC_WACVM_OSM_ROADS", "CIESIN_SEDAC_WACVM_PGLF_200813", "CIESIN_SEDAC_WACVM_POPPROJ_203050", "CIESIN_SEDAC_WACVM_SDMSPOLSNLEA_2010", "CIESIN_SEDAC_WACVM_SMAMAMP_RICH", "CIESIN_SEDAC_WACVM_SUBSETACE2", "CIESIN_SEDAC_WACVM_SVI", "CIESIN_SEDAC_WATER_WSIM_GLDAS_V1", "CL07LC", "CL07PLBK", "CL07PLTB", "CL07SM", "CL07ST", "CL07V", "CL07VWC", "CLAMS_CERES_CHESLIGHT_SONDE", "CLAMS_CV580_CAR", "CLAMS_ER2_MAS", "CLAMS_MODIS_L2_AEROSOL_PRODUCTS", "CLAMS_UWASH_CONVAIR_DATA", "CLDMSK_L2_MODIS_Aqua", "CLDMSK_L2_VIIRS_NOAA20", "CLDMSK_L2_VIIRS_NOAA20_NRT", "CLDMSK_L2_VIIRS_SNPP", "CLDMSK_L2_VIIRS_SNPP_NRT", "CLDPROPCOSP_D3_MODIS_Aqua", "CLDPROPCOSP_D3_VIIRS_NOAA20", "CLDPROPCOSP_D3_VIIRS_SNPP", "CLDPROPCOSP_M3_MODIS_Aqua", "CLDPROPCOSP_M3_VIIRS_NOAA20", "CLDPROPCOSP_M3_VIIRS_SNPP", "CLDPROP_D3_MODIS_Aqua", "CLDPROP_D3_VIIRS_NOAA20", "CLDPROP_D3_VIIRS_SNPP", "CLDPROP_M3_MODIS_Aqua", "CLDPROP_M3_VIIRS_NOAA20", "CLDPROP_M3_VIIRS_SNPP", "CLIVAR", "CMAQ-N_Module_1661", "CMC0.1deg-CMC-L4-GLOB-v3.0", "CMC0.2deg-CMC-L4-GLOB-v2.0", "CMRMIAAE", "CMRMIALS", "CMRMIGEO", "CMRMITST", "CMSFluxFire", "CMSFluxFire", "CMSFluxFirepost", "CMSFluxFossilFuelPrior", "CMSFluxFossilFuelPrior", "CMSFluxFossilfuel", "CMSFluxLandPrior", "CMSFluxMISC", "CMSFluxNBE", "CMSFluxNBE", "CMSFluxNBEPrior", "CMSFluxNEE", "CMSFluxOcean", "CMSFluxOcean", "CMSFluxOceanPrior", "CMSFluxOceanPrior", "CMSFluxTotal", "CMSFluxTotal", "CMSFluxTotalPrior", "CMSFluxTotalpost", "CMSFluxTotalprior", "CMSGCH4F", "CMSLakeHuronPPM", "CMSLakeHuronPPY", "CMSLakeMichiganPPM", "CMSLakeMichiganPPY", "CMSLakeSuperiorPPM", "CMSLakeSuperiorPPY", "CMS_AGB_Landcover_Indonesia_1645", "CMS_AGB_NW_USA_1719", "CMS_CH4_FLX_CA", "CMS_CH4_FLX_MX", "CMS_CH4_FLX_NAD", "CMS_CO2_Fluxes_TBMO_1315", "CMS_CONUS_Biomass_1752", "CMS_CTL_NA_GOSAT_FOOTPRINTS", "CMS_CTL_NA_OCO2_FOOTPRINTS", "CMS_CTL_NA_TCCON_FOOTPRINTS", "CMS_CTL_SA_OCO2_FOOTPRINTS", "CMS_Coastal_Wetland_Resilience_1839", "CMS_DARTE_V2_1735", "CMS_Daily_ET_MexFlux_1309", "CMS_EFT_CONUS_1659", "CMS_Fire_Weather_Data_AK_1509", "CMS_FluxEstimates_Aircraft_CO2_2336", "CMS_Forest_Carbon_Fluxes_1313", "CMS_Forest_Carbon_Maryland_1660", "CMS_Forest_Productivity_1221", "CMS_GO_CH4_SEC_TDYC_NA", "CMS_Global_Cropland_Carbon_1279", "CMS_Global_Fire_Atlas_1642", "CMS_Global_Forest_AGC_2180", "CMS_Global_Forest_Age_2345", "CMS_Global_Livestock_CH4_CO2_1329", "CMS_Global_Mangrove_Forest_Ht_2251", "CMS_Global_Mangrove_Loss_1768", "CMS_Global_Map_Mangrove_Canopy_1665", "CMS_Global_Monthly_Wetland_CH4_1502", "CMS_Global_Soil_Respiration_1736", "CMS_Great_Basin_Biomass_1755", "CMS_HR_MNA_CH4_FLUX", "CMS_Landcover_Indonesia_1838", "CMS_Landscapes_Brazil_Forests_1301", "CMS_Landscapes_Brazil_LiDAR_1302", "CMS_LiDAR_AGB_California_1537", "CMS_LiDAR_AGB_PEF_2012_1318", "CMS_LiDAR_Biomass_CanHt_Sonoma_1523", "CMS_LiDAR_Biomass_MD_PA_DE_1538", "CMS_LiDAR_Indonesia_1518", "CMS_LiDAR_Point_Cloud_Zambezi_1521", "CMS_LiDAR_Products_Indonesia_1540", "CMS_Mangrove_Biomass_Zambezi_1522", "CMS_Mangrove_CanHt_Stand_Age_1377", "CMS_Mangrove_Canopy_Height_1327", "CMS_Mangrove_Canopy_Ht_Zambezi_1357", "CMS_Mangrove_Cover_1670", "CMS_Maryland_AGB_Canopy_1320", "CMS_Methane_Emissions_Boston_1291", "CMS_Monthly_CO2_Gulf_of_Mexico_1668", "CMS_OCE_BGC_CCS", "CMS_Pantropical_Forest_Biomass_1337", "CMS_Pennsylvania_Tree_Cover_1334", "CMS_Pilot_Biomass_1257", "CMS_SABGOM_Model_Simulations_1510", "CMS_SOC_Mexico_1754", "CMS_SOC_Mexico_CONUS_1737", "CMS_SST_GPP_Mexico_1310", "CMS_Simulated_SIF_NiwotRidge_1720", "CMS_Soil_CO2_Efflux_1298", "CMS_WFEIS_CONUS-AK_1306", "CMS_WRF_Footprints_CO2_Signals_1381", "CMS_WRF_Model_Products_1338", "COASTAL", "COMEX_AJAX_CO2_CH4_2347", "COMEX_AVIRIS_Classic_Flights_2343", "COMEX_AVIRIS_NG_Flights_2342", "COMEX_LongwaveInfrared_Imagery_2331", "COOA", "CORAL", "COWVR_STPH8_L1_TSDR_V10.0", "CPEXAW-ADM-Aeolus", "CPEXAW-DAWN_DC8", "CPEXAW-Dropsondes", "CPEXAW-HALO_DC8", "CPEXCV-DAWN_DC8", "CPEXCV-Dropsondes", "CPEXCV-HALO_DC8", "CPEXCV_Cloud_AircraftInSitu_DC8_Data", "CPEXCV_Merge_Data", "CPL_ABL_Top_Height_1825", "CRP", "CStocks_Greenness_Mangroves_MX_1853", "CUSTARD", "CV-580_710", "CYGNSS_L1_CAL_RAW_IF_V1.0", "CYGNSS_L1_CDR_V1.0", "CYGNSS_L1_CDR_V1.1", "CYGNSS_L1_CDR_V1.2", "CYGNSS_L1_FULL_DDM", "CYGNSS_L1_FULL_DDM_V3.0", "CYGNSS_L1_RAW_IF", "CYGNSS_L1_V2.1", "CYGNSS_L1_V3.0", "CYGNSS_L1_V3.2", "CYGNSS_L2_CDR_V1.0", "CYGNSS_L2_CDR_V1.1", "CYGNSS_L2_CDR_V1.2", "CYGNSS_L2_SURFACE_FLUX_CDR_V1.0", "CYGNSS_L2_SURFACE_FLUX_CDR_V1.1", "CYGNSS_L2_SURFACE_FLUX_CDR_V1.2", "CYGNSS_L2_SURFACE_FLUX_V1.0", "CYGNSS_L2_SURFACE_FLUX_V2.0", "CYGNSS_L2_SURFACE_FLUX_V3.2", "CYGNSS_L2_V2.1", "CYGNSS_L2_V3.0", "CYGNSS_L2_V3.2", "CYGNSS_L3_CDR_V1.0", "CYGNSS_L3_CDR_V1.1", "CYGNSS_L3_CDR_V1.2", "CYGNSS_L3_MICROPLASTIC_V1.0", "CYGNSS_L3_MICROPLASTIC_V3.2", "CYGNSS_L3_MRG_NRT_V3.2", "CYGNSS_L3_MRG_NRT_V3.2.1", "CYGNSS_L3_MRG_V3.2", "CYGNSS_L3_MRG_V3.2.1", "CYGNSS_L3_S1.0", "CYGNSS_L3_SOIL_MOISTURE_V1.0", "CYGNSS_L3_SOIL_MOISTURE_V3.2", "CYGNSS_L3_UC_BERKELEY_WATERMASK_DAILY_V3.2", "CYGNSS_L3_UC_BERKELEY_WATERMASK_V3.1", "CYGNSS_L3_V2.1", "CYGNSS_L3_V3.0", "CYGNSS_L3_V3.1", "CYGNSS_L3_V3.2", "CYGNSS_NOAA_L2_SWSP_25KM_V1.1", "CYGNSS_NOAA_L2_SWSP_25KM_V1.2", "CZCS_L1", "CZCS_L2_OC", "CZCS_L3b_CHL", "CZCS_L3b_KD", "CZCS_L3b_RRS", "CZCS_L3m_CHL", "CZCS_L3m_KD", "CZCS_L3m_RRS", "C_FluxStocks_CLM5_DART_WestUS_1856", "C_Pools_Fluxes_CONUS_1837", "CaTS", "CalCOFI", "California_2002", "CanSIS_Regional_Soils_1347", "Canada_Boreal_Forest_Greenness_1587", "Canadian_West_Arctic_Veg_Plots_1543", "CapeHatteras2010", "Carbon_Estuaries", "Carbon_Transport_MS_River", "Carbon_UAV", "Cartagena_Station", "Catlin_Arctic_Survey", "ChesBay", "Chesapeake_Bay_Helicopter", "Chesapeake_Bay_Plume", "Chesapeake_Bay_Water_Quality", "Chesapeake_Light_Tower", "Chlorophyll_Fluorescence_Data_1785", "CircumArctic_Trends_Hotspots_2322", "CliVEC", "Climate_Normals_Modern_LGM_AK_1663", "CoJet", "Coastal_Carbon_Budget_NE_US_1594", "Coastal_US_Elevation_Data_1844", "Continuous_Lifeform_Maps_CONUS_1809", "Country_SOC_Latin_America_1615", "CramerLeemans_416", "Cropland_Carbon_Fluxes_2125", "Crops_SIF_VegIndices_IL_NE_2136", "CubeSat_Arctic_Boreal_LakeArea_1667", "Cyanate", "DC3_Aerosol_AircraftInSitu_DC8_Data", "DC3_Aerosol_AircraftInSitu_DLR-Falcon_Data", "DC3_Aerosol_AircraftInSitu_NSF-GV-HIAPER_Data", "DC3_AircraftRemoteSensing_DIAL_DC8_Data", "DC3_Cloud_AircraftInSitu_DC8_Data", "DC3_Cloud_AircraftInSitu_NSF-GV-HIAPER_Data", "DC3_Merge_Data", "DC3_MetNav_AircraftInSitu_DC8_Data", "DC3_MetNav_AircraftInSitu_DLR-Falcon_Data", "DC3_MetNav_AircraftInSitu_NSF-GV-HIAPER_Data", "DC3_Miscellaneous_DC8_Data", "DC3_Miscellaneous_DLR-Falcon_Data", "DC3_Miscellaneous_NSF-GV-HIAPER_Data", "DC3_Radiation_AircraftInSitu_DC8_Data", "DC3_TraceGas_AircraftInSitu_DC8_Data", "DC3_TraceGas_AircraftInSitu_DLR-Falcon_Data", "DC3_TraceGas_AircraftInSitu_NSF-GV-HIAPER_Data", "DC3_jValue_AircraftInSitu_DC8_Data", "DC3_jValue_AircraftInSitu_NSF-GV-HIAPER_Data", "DCOTSS-Aircraft-Data", "DCOTSS-Balloon-Data", "DCOTSS-Model-Output", "DCOTSS-Radar-Satellite-Data", "DCOTSS-Reports", "DECKLAB", "DEVOTE_Aerosol_AircraftInSitu_B200_Data", "DEVOTE_AircraftRemoteSensing_UC12_HSRL_Data", "DEVOTE_AircraftRemoteSensing_UC12_RSP_Data", "DEVOTE_MetNav_AircraftInSitu_B200_Data", "DEVOTE_MetNav_AircraftInSitu_UC12_Data", "DFO_Canada_Time_Series", "DISCOVERAQ_California_Aerosol_AircraftInSitu_P3B_Data", "DISCOVERAQ_California_AircraftRemoteSensing_B200_ACAM_Data", "DISCOVERAQ_California_AircraftRemoteSensing_B200_HSRL2_Data", "DISCOVERAQ_California_Ground_Analysis_Ancillary_Data", "DISCOVERAQ_California_Ground_Bakersfield_Data", "DISCOVERAQ_California_Ground_CARB_Data", "DISCOVERAQ_California_Ground_Fresno_Data", "DISCOVERAQ_California_Ground_Huron_Data", "DISCOVERAQ_California_Ground_Oildale_Data", "DISCOVERAQ_California_Ground_Pandora_Data", "DISCOVERAQ_California_Ground_Porterville_Data", "DISCOVERAQ_California_Ground_SJV_Data", "DISCOVERAQ_California_Merge_Data", "DISCOVERAQ_California_MetNav_AircraftInSitu_B200_Data", "DISCOVERAQ_California_MetNav_AircraftInSitu_P3B_Data", "DISCOVERAQ_California_Ozonesondes_Data", "DISCOVERAQ_California_TraceGas_AircraftInSitu_P3B_Data", "DISCOVERAQ_Colorado_Aerosol_AircraftInSitu_P3B_Data", "DISCOVERAQ_Colorado_AircraftRemoteSensing_B200_ACAM_Data", "DISCOVERAQ_Colorado_AircraftRemoteSensing_B200_GCAS_Data", "DISCOVERAQ_Colorado_AircraftRemoteSensing_B200_HSRL2_Data", "DISCOVERAQ_Colorado_AircraftRemoteSensing_Falcon_GeoTASO_Data", "DISCOVERAQ_Colorado_Ground_BAOTower_Data", "DISCOVERAQ_Colorado_Ground_CDPHE_Data", "DISCOVERAQ_Colorado_Ground_EPA_Data", "DISCOVERAQ_Colorado_Ground_FortCollins_Data", "DISCOVERAQ_Colorado_Ground_Mobile_Data", "DISCOVERAQ_Colorado_Ground_NREL-Golden_Data", "DISCOVERAQ_Colorado_Ground_Pandora_Data", "DISCOVERAQ_Colorado_Ground_Platteville_Data", "DISCOVERAQ_Colorado_Ground_TableMountain_Data", "DISCOVERAQ_Colorado_Merge_Data", "DISCOVERAQ_Colorado_MetNav_AircraftInSitu_B200_Data", "DISCOVERAQ_Colorado_MetNav_AircraftInSitu_P3B_Data", "DISCOVERAQ_Colorado_Model_Data", "DISCOVERAQ_Colorado_Ozonesondes_Data", "DISCOVERAQ_Colorado_Radiation_AircraftRemoteSensing_CAR_Data", "DISCOVERAQ_Colorado_TraceGas_AircraftInSitu_P3B_Data", "DISCOVERAQ_Maryland_Aerosol_AircraftInSitu_P3B_Data", "DISCOVERAQ_Maryland_AircraftRemoteSensing_UC12_ACAM_Data", "DISCOVERAQ_Maryland_AircraftRemoteSensing_UC12_HSRL_Data", "DISCOVERAQ_Maryland_Ground_Aldino_Data", "DISCOVERAQ_Maryland_Ground_Analysis_Ancillary_Data", "DISCOVERAQ_Maryland_Ground_Beltsville_Data", "DISCOVERAQ_Maryland_Ground_Edgewood_Data", "DISCOVERAQ_Maryland_Ground_Essex_Data", "DISCOVERAQ_Maryland_Ground_Fairhill_Data", "DISCOVERAQ_Maryland_Ground_Oldtown_Data", "DISCOVERAQ_Maryland_Ground_Padonia_Data", "DISCOVERAQ_Maryland_Ground_Pandora_Data", "DISCOVERAQ_Maryland_Ground_UMBC_Data", "DISCOVERAQ_Maryland_Merge_Data", "DISCOVERAQ_Maryland_MetNav_AircraftInSitu_P3B_Data", "DISCOVERAQ_Maryland_MetNav_AircraftInSitu_UC12_Data", "DISCOVERAQ_Maryland_Ozonesondes_Data", "DISCOVERAQ_Maryland_Ship_NOAA-DelawareII_Data", "DISCOVERAQ_Maryland_TraceGas_AircraftInSitu_P3B_Data", "DISCOVERAQ_Maryland_UMDAircraft_Data", "DISCOVERAQ_Texas_Aerosol_AircraftInSitu_P3B_Data", "DISCOVERAQ_Texas_AircraftRemoteSensing_B200_ACAM_Data", "DISCOVERAQ_Texas_AircraftRemoteSensing_B200_GCAS_Data", "DISCOVERAQ_Texas_AircraftRemoteSensing_B200_HSRL2_Data", "DISCOVERAQ_Texas_AircraftRemoteSensing_Falcon_GeoTASO_Data", "DISCOVERAQ_Texas_Ground_Ancillary_Analysis_Data", "DISCOVERAQ_Texas_Ground_EPA_Data", "DISCOVERAQ_Texas_Ground_Galveston_Data", "DISCOVERAQ_Texas_Ground_LaPorteAirport_Data", "DISCOVERAQ_Texas_Ground_ManvelCroix_Data", "DISCOVERAQ_Texas_Ground_MoodyTower_Data", "DISCOVERAQ_Texas_Ground_Pandora_Data", "DISCOVERAQ_Texas_Ground_SmithPoint_Data", "DISCOVERAQ_Texas_Ground_TCEQ_Data", "DISCOVERAQ_Texas_Merge_Data", "DISCOVERAQ_Texas_MetNav_AircraftInSitu_B200_Data", "DISCOVERAQ_Texas_MetNav_AircraftInSitu_P3B_Data", "DISCOVERAQ_Texas_Ozonesondes_Data", "DISCOVERAQ_Texas_TraceGas_AircraftInSitu_P3B_Data", "DISCover_land_cover_679", "DLEM_C_N_Export_1699", "DMI_OI-DMI-L4-GLOB-v1.0", "DSCOVR_EPIC_L2_COMPOSITE", "DSCOVR_EPIC_L2_COMPOSITE", "DSCOVR_EPIC_L3_PAR", "DSCOVR_EPIC_L3_PAR-IMAGE", "DSCOVR_EPIC_L4_TrO3", "DSCOVR_NISTAR_L2_FLX", "DUSTFLEXPART", "Daily_FineParticulateMatter_AK_2157", "Dairy_Methane_CA_V1-2_1902", "Dall_Sheep_Population_Dynamics_1640", "Dall_Sheep_Snowpack_1602", "Daymet_Annual_V4R1_2130", "Daymet_Daily_V4R1_2129", "Daymet_Monthly_V4R1_2131", "Daymet_SubDaily_Puerto_Rico_1977", "Daymet_V4_Daily_MonthlyLatency_1904", "Daymet_xval_V4R1_2132", "Decadal_LULC_India_1336", "Decadal_Water_Maps_1324", "DeciduousFractionl_CanopyCover_2296", "Del_Ches_Bay_Fluorescence", "DeltaX_ADCP_Measurements_V2_2081", "DeltaX_AGB_AGN_V2_2237", "DeltaX_ANUGA_AtchafalayaBasin_2306", "DeltaX_ANUGA_Hydrodynamics_MRD_2310", "DeltaX_BGB_BGN_V2_2238", "DeltaX_BG_Root_Stable_Isotopes_2193", "DeltaX_DEM_MRD_LA_2181", "DeltaX_Delft3D_294_Terrebonne_2303", "DeltaX_Delft3D_322_Terrebonne_2312", "DeltaX_Delft3D_396_Terrebonne_2314", "DeltaX_Delft3D_399_Terrebonne_2313", "DeltaX_Delft3D_421_Terrebonne_2304", "DeltaX_Delft3D_Atchafalaya_MRD_2302", "DeltaX_Delft3D_Terrebonne_MRD_2301", "DeltaX_Ecogeomorphic_Products_2108", "DeltaX_Feldspar_Sediment_V3_2290", "DeltaX_Foliar_Stable_Isotopes_2194", "DeltaX_H2O_Surface_Elevation_2086", "DeltaX_Herb_WetlandSoil_V3_2239", "DeltaX_Insitu_POC_2073", "DeltaX_Insitu_Reflectance_V3_2153", "DeltaX_Insitu_WQ_Indicators_V2_2080", "DeltaX_Island_Channel_Model_2106", "DeltaX_L1B_UAVSAR_WaterLevels_1979", "DeltaX_L1_AVIRIS_Radiance_1987", "DeltaX_L1_UAVSAR_SLC_Stack_1984", "DeltaX_L1b_AirSWOT_1996", "DeltaX_L2A_AVIRIS-NG_BRDF_V2_2139", "DeltaX_L2_AVIRIS_Reflectance_1988", "DeltaX_L2_AirSWOT_WaterElev_V3_2350", "DeltaX_L2_UAVSAR_WaterLevels_2057", "DeltaX_L3_AVIRIS-NG_AGB_V2_2138", "DeltaX_L3_AVIRIS-NG_Veg_Types_2352", "DeltaX_L3_AVIRIS-NG_Water_V3_2152", "DeltaX_L3_AirSWOT_WaterElev_V2_2349", "DeltaX_L3_UAVSAR_WaterLevels_2058", "DeltaX_LandAccretionMap_WLD_2308", "DeltaX_MarshAccretion_NUMAR_2354", "DeltaX_Particle_Size_LISST_V2_2077", "DeltaX_RTK_Elevation_2071", "DeltaX_Sediment_Grain_Size_V2_2135", "DeltaX_Sonar_Bathymetry_2085", "DeltaX_TSS_Concentration_V2_2075", "DeltaX_TotalSubsidenceRate_MRD_2307", "DeltaX_Turbidity_Data_V4_2241", "DeltaX_UAVSAR_L3_ChannelMap_V2_2344", "DeltaX_Vegetation_Structure_V2_2240", "Dendrophenology_Eastern_US_1369", "Dissolved_Gases_Alaska_Rivers_2360", "Disturbance_Biomass_Maps_1679", "Dolche-Vita", "Dunne_545", "ECCO_L4_ANCILLARY_DATA_V4R4", "ECCO_L4_ATM_STATE_05DEG_DAILY_V4R4", "ECCO_L4_ATM_STATE_05DEG_MONTHLY_V4R4", "ECCO_L4_ATM_STATE_LLC0090GRID_DAILY_V4R4", "ECCO_L4_ATM_STATE_LLC0090GRID_MONTHLY_V4R4", "ECCO_L4_BOLUS_05DEG_DAILY_V4R4", "ECCO_L4_BOLUS_05DEG_MONTHLY_V4R4", "ECCO_L4_BOLUS_LLC0090GRID_DAILY_V4R4", "ECCO_L4_BOLUS_LLC0090GRID_MONTHLY_V4R4", "ECCO_L4_DENS_STRAT_PRESS_05DEG_DAILY_V4R4", "ECCO_L4_DENS_STRAT_PRESS_05DEG_MONTHLY_V4R4", "ECCO_L4_DENS_STRAT_PRESS_LLC0090GRID_DAILY_V4R4", "ECCO_L4_DENS_STRAT_PRESS_LLC0090GRID_MONTHLY_V4R4", "ECCO_L4_FRESH_FLUX_05DEG_DAILY_V4R4", "ECCO_L4_FRESH_FLUX_05DEG_MONTHLY_V4R4", "ECCO_L4_FRESH_FLUX_LLC0090GRID_DAILY_V4R4", "ECCO_L4_FRESH_FLUX_LLC0090GRID_MONTHLY_V4R4", "ECCO_L4_GEOMETRY_05DEG_V4R4", "ECCO_L4_GEOMETRY_LLC0090GRID_V4R4", "ECCO_L4_GMAP_TIME_SERIES_SNAPSHOT_V4R4", "ECCO_L4_GMAP_TIME_SERIES_SNAPSHOT_V4R4B", "ECCO_L4_GMSL_TIME_SERIES_DAILY_V4R4", "ECCO_L4_GMSL_TIME_SERIES_MONTHLY_V4R4", "ECCO_L4_HEAT_FLUX_05DEG_DAILY_V4R4", "ECCO_L4_HEAT_FLUX_05DEG_MONTHLY_V4R4", "ECCO_L4_HEAT_FLUX_LLC0090GRID_DAILY_V4R4", "ECCO_L4_HEAT_FLUX_LLC0090GRID_MONTHLY_V4R4", "ECCO_L4_MIXED_LAYER_DEPTH_05DEG_DAILY_V4R4", "ECCO_L4_MIXED_LAYER_DEPTH_05DEG_MONTHLY_V4R4", "ECCO_L4_MIXED_LAYER_DEPTH_LLC0090GRID_DAILY_V4R4", "ECCO_L4_MIXED_LAYER_DEPTH_LLC0090GRID_MONTHLY_V4R4", "ECCO_L4_OBP_05DEG_DAILY_V4R4", "ECCO_L4_OBP_05DEG_DAILY_V4R4B", "ECCO_L4_OBP_05DEG_MONTHLY_V4R4", "ECCO_L4_OBP_05DEG_MONTHLY_V4R4B", "ECCO_L4_OBP_LLC0090GRID_DAILY_V4R4", "ECCO_L4_OBP_LLC0090GRID_DAILY_V4R4B", "ECCO_L4_OBP_LLC0090GRID_MONTHLY_V4R4", "ECCO_L4_OBP_LLC0090GRID_MONTHLY_V4R4B", "ECCO_L4_OBP_LLC0090GRID_SNAPSHOT_V4R4", "ECCO_L4_OCEAN_3D_MIX_COEFFS_05DEG_V4R4", "ECCO_L4_OCEAN_3D_MIX_COEFFS_LLC0090GRID_V4R4", "ECCO_L4_OCEAN_3D_MOMENTUM_TEND_LLC0090GRID_DAILY_V4R4", "ECCO_L4_OCEAN_3D_MOMENTUM_TEND_LLC0090GRID_MONTHLY_V4R4", "ECCO_L4_OCEAN_3D_SALINITY_FLUX_LLC0090GRID_DAILY_V4R4", "ECCO_L4_OCEAN_3D_SALINITY_FLUX_LLC0090GRID_MONTHLY_V4R4", "ECCO_L4_OCEAN_3D_TEMPERATURE_FLUX_LLC0090GRID_DAILY_V4R4", "ECCO_L4_OCEAN_3D_TEMPERATURE_FLUX_LLC0090GRID_MONTHLY_V4R4", "ECCO_L4_OCEAN_3D_VOLUME_FLUX_LLC0090GRID_DAILY_V4R4", "ECCO_L4_OCEAN_3D_VOLUME_FLUX_LLC0090GRID_MONTHLY_V4R4", "ECCO_L4_OCEAN_BOLUS_STREAMFUNCTION_LLC0090GRID_DAILY_V4R4", "ECCO_L4_OCEAN_BOLUS_STREAMFUNCTION_LLC0090GRID_MONTHLY_V4R4", "ECCO_L4_OCEAN_VEL_05DEG_DAILY_V4R4", "ECCO_L4_OCEAN_VEL_05DEG_MONTHLY_V4R4", "ECCO_L4_OCEAN_VEL_LLC0090GRID_DAILY_V4R4", "ECCO_L4_OCEAN_VEL_LLC0090GRID_MONTHLY_V4R4", "ECCO_L4_SBO_CORE_TIME_SERIES_SNAPSHOT_V4R4", "ECCO_L4_SBO_CORE_TIME_SERIES_SNAPSHOT_V4R4B", "ECCO_L4_SEA_ICE_CONC_THICKNESS_05DEG_DAILY_V4R4", "ECCO_L4_SEA_ICE_CONC_THICKNESS_05DEG_MONTHLY_V4R4", "ECCO_L4_SEA_ICE_CONC_THICKNESS_LLC0090GRID_DAILY_V4R4", "ECCO_L4_SEA_ICE_CONC_THICKNESS_LLC0090GRID_MONTHLY_V4R4", "ECCO_L4_SEA_ICE_CONC_THICKNESS_LLC0090GRID_SNAPSHOT_V4R4", "ECCO_L4_SEA_ICE_HORIZ_VOLUME_FLUX_LLC0090GRID_DAILY_V4R4", "ECCO_L4_SEA_ICE_HORIZ_VOLUME_FLUX_LLC0090GRID_MONTHLY_V4R4", "ECCO_L4_SEA_ICE_SALT_PLUME_FLUX_LLC0090GRID_DAILY_V4R4", "ECCO_L4_SEA_ICE_SALT_PLUME_FLUX_LLC0090GRID_MONTHLY_V4R4", "ECCO_L4_SEA_ICE_VELOCITY_05DEG_DAILY_V4R4", "ECCO_L4_SEA_ICE_VELOCITY_05DEG_MONTHLY_V4R4", "ECCO_L4_SEA_ICE_VELOCITY_LLC0090GRID_DAILY_V4R4", "ECCO_L4_SEA_ICE_VELOCITY_LLC0090GRID_MONTHLY_V4R4", "ECCO_L4_SEA_ICE_VELOCITY_LLC0090GRID_SNAPSHOT_V4R4", "ECCO_L4_SSH_05DEG_DAILY_V4R4", "ECCO_L4_SSH_05DEG_DAILY_V4R4B", "ECCO_L4_SSH_05DEG_MONTHLY_V4R4", "ECCO_L4_SSH_05DEG_MONTHLY_V4R4B", "ECCO_L4_SSH_LLC0090GRID_DAILY_V4R4", "ECCO_L4_SSH_LLC0090GRID_DAILY_V4R4B", "ECCO_L4_SSH_LLC0090GRID_MONTHLY_V4R4", "ECCO_L4_SSH_LLC0090GRID_MONTHLY_V4R4B", "ECCO_L4_SSH_LLC0090GRID_SNAPSHOT_V4R4", "ECCO_L4_STRESS_05DEG_DAILY_V4R4", "ECCO_L4_STRESS_05DEG_MONTHLY_V4R4", "ECCO_L4_STRESS_LLC0090GRID_DAILY_V4R4", "ECCO_L4_STRESS_LLC0090GRID_MONTHLY_V4R4", "ECCO_L4_TEMP_SALINITY_05DEG_DAILY_V4R4", "ECCO_L4_TEMP_SALINITY_05DEG_MONTHLY_V4R4", "ECCO_L4_TEMP_SALINITY_LLC0090GRID_DAILY_V4R4", "ECCO_L4_TEMP_SALINITY_LLC0090GRID_MONTHLY_V4R4", "ECCO_L4_TEMP_SALINITY_LLC0090GRID_SNAPSHOT_V4R4", "ECO1BATT", "ECO1BGEO", "ECO1BMAPRAD", "ECO1BRAD", "ECO2CLD", "ECO2LSTE", "ECO3ANCQA", "ECO3ETALEXI", "ECO3ETALEXIU", "ECO3ETPTJPL", "ECO4ESIALEXI", "ECO4ESIALEXIU", "ECO4ESIPTJPL", "ECO4WUE", "ECOA", "ECOHAB", "ECOMON", "ECO_L1B_ATT", "ECO_L1B_GEO", "ECO_L1B_RAD", "ECO_L1CG_RAD", "ECO_L1CT_RAD", "ECO_L2G_CLOUD", "ECO_L2G_LSTE", "ECO_L2T_LSTE", "ECO_L2T_STARS", "ECO_L2_CLOUD", "ECO_L2_LSTE", "ECO_L3G_JET", "ECO_L3G_MET", "ECO_L3G_SEB", "ECO_L3G_SM", "ECO_L3T_JET", "ECO_L3T_MET", "ECO_L3T_SEB", "ECO_L3T_SM", "ECO_L4G_ESI", "ECO_L4G_WUE", "ECO_L4T_ESI", "ECO_L4T_WUE", "ECS", "EDDIES", "EDM_SA_Vegetation_1149", "EDM_US_Carbon_1160", "EF_Data_Mexico_1693", "EGEE3", "EGEE5", "EKAMSAT_Pilot_ASTRAL", "EM27_XCO2_XCH4_XCO_AK_1831", "EMITL1BATT", "EMITL1BRAD", "EMITL2ARFL", "EMITL2BCH4ENH", "EMITL2BCH4PLM", "EMITL2BCO2ENH", "EMITL2BCO2PLM", "EMITL2BMIN", "EMITL3ASA", "EMITL4ESM", "EN1_MDSI_MER_FRS_1P", "EN1_MDSI_MER_FRS_2P", "EN1_MDSI_MER_RR__2P", "ENVISAT_SCIAMACHY_SIF_1871", "EOLE1", "EPEA", "ERBE_S10N_WFOV_NF", "ERBE_S10N_WFOV_NF", "ERBE_S10N_WFOV_SF", "ERBE_S10N_WFOV_SF", "ERBE_S10N_WFOV_SF_ERBS_AreaAverageTimeSeries", "ERBE_S10N_WFOV_SF_ERBS_AreaAverageTimeSeries_Edition4", "ERBE_S10N_WFOV_SF_ERBS_Regional", "ERBE_S10N_WFOV_SF_ERBS_Regional", "ERBE_S10N_WFV_NF_NAT", "ERBE_S10N_WFV_SF_NAT", "ERBE_S10_MFOV_NF_NAT", "ERBE_S10_MFOV_SF_NAT", "ERBE_S10_WFOV_NF_NAT", "ERBE_S10_WFOV_SF_NAT", "ERBE_S4GN_WFOV_NF", "ERBE_S4GN_WFOV_NF_ZG", "ERBE_S4GN_WFOV_SF", "ERBE_S4GN_WFOV_SF_ZG", "ERBE_S4GN_WFV_NF_N10", "ERBE_S4G_MFOV_NF", "ERBE_S4G_MFOV_NF_N10", "ERBE_S4G_MFOV_NF_ZG", "ERBE_S4G_MFOV_SF", "ERBE_S4G_MFOV_SF_ZG", "ERBE_S4G_SC_2.5", "ERBE_S4G_SC_NEST10", "ERBE_S4G_SC_NEST5", "ERBE_S4G_SC_ZG", "ERBE_S4G_WFOV_NF", "ERBE_S4G_WFOV_NF_N10", "ERBE_S4G_WFOV_NF_ZG", "ERBE_S4G_WFOV_SF", "ERBE_S4G_WFOV_SF_ZG", "ERBE_S4N_NAT", "ERBE_S4_NAT", "ERBE_S7_NAT", "ERBE_S8_NAT", "ERBE_S9_NAT", "ERBE_TSI_ERBS_NAT", "ERS-1_BYU_L3_OW_SIGMA0_ENHANCED", "ERS-1_L0", "ERS-1_L1", "ERS-2_BYU_L3_OW_SIGMA0_ENHANCED", "ERS-2_L0", "ERS-2_L1", "ERS2_GOME_SIF_1758", "ESCAPE", "ESMRN5IM", "ESMRN5L1", "ESMRN6IM", "EUCFe", "EUMELI", "EWSG1-NAVO-L2P-v01", "EXP7L1TRTALL", "EXP7L1TRTWHT", "EXPORTS", "EastAnglia10YearMean_549", "EastAnglia30YearMean_550", "EastAngliaClimate_542", "EastAngliaPrecip_417", "Ecosystem_Map_SRD_PAD_1947", "Effect_Environment_Moose_1739", "End_of_Season_Snow_Depth_1702", "Environmental_Disturbances_AK_1705", "Erosion_Vegetation_Yukon_1616", "Estimated_Biomass_Stock_Amazon_1648", "Eurasia_Biomass_1278", "FEWS_precip_711", "FIA_Forest_Biomass_Estimates_1873", "FIFE_CD_V3_130", "FIREXAQ_AerosolCloud_AircraftRemoteSensing_ER2_CPL_Data", "FIREXAQ_Aerosol_AircraftInSitu_DC8_Data", "FIREXAQ_Aerosol_AircraftInSitu_N48_Data", "FIREXAQ_Analysis_Data", "FIREXAQ_Analysis_N48_Data", "FIREXAQ_Cloud_AircraftInSitu_DC8_Data", "FIREXAQ_Ground_InSitu_Data", "FIREXAQ_HSRL_AircraftRemoteSensing_DC8_Data", "FIREXAQ_Merge_Data", "FIREXAQ_MetNav_AircraftInSitu_DC8_Data", "FIREXAQ_MetNav_AircraftInSitu_ER2_Data", "FIREXAQ_MetNav_AircraftInSitu_N48_Data", "FIREXAQ_N46_Data", "FIREXAQ_Radiation_AircraftInSitu_DC8_Data", "FIREXAQ_Satellite_Data", "FIREXAQ_SurfaceMobile_Aerodyne_InSitu_Data", "FIREXAQ_SurfaceMobile_CARB_InSitu_Data", "FIREXAQ_SurfaceMobile_MACH2_InSitu_Data", "FIREXAQ_TraceGas_AircraftInSitu_DC8_Data", "FIREXAQ_TraceGas_AircraftInSitu_N48_Data", "FIREXAQ_TraceGas_AircraftRemoteSensing_DC8_Data", "FIREXAQ_TraceGas_AircraftRemoteSensing_ER2_GCAS_Data", "FIREXAQ_TraceGas_AircraftRemoteSensing_ER2_NASTI_Data", "FIREXAQ_TraceGas_AircraftRemoteSensing_ER2_SHIS_Data", "FIREXAQ_jValue_AircraftInSitu_DC8_Data", "FIREXAQ_jValue_AircraftInSitu_N48_Data", "FIRE_ACE_AMPR", "FIRE_ACE_C130_CFD", "FIRE_ACE_C130_RAMS", "FIRE_ACE_ER2_MAS", "FIRE_ACE_ER2_MIR", "FIRE_ACE_SHIP_SSFR", "FIRE_ACE_UTRECHT_BALLOON", "FIRE_ACE_UTRECHT_TOWER", "FIRE_ACE_UWCV580_GMETER", "FIRE_ACE_UWCV580_UWA", "FIRE_AX_CMS_LWFLUX", "FIRE_AX_CMS_SOLAR_DY", "FIRE_AX_CMS_SOLAR_HR", "FIRE_AX_CMS_SOLAR_MN", "FIRE_AX_CMS_SOLAR_WK", "FIRE_AX_CMS_SST_DAY", "FIRE_AX_CMS_SST_FNTS", "FIRE_AX_CMS_SST_WEEK", "FIRE_AX_CSU_CEILOM", "FIRE_AX_CSU_MET_SFC", "FIRE_AX_CSU_PRT6", "FIRE_AX_CSU_WNDPRFS", "FIRE_AX_ECMWF_BASIC", "FIRE_AX_ECMWF_CLOUDS", "FIRE_AX_ECMWF_DIAG", "FIRE_AX_ECMWF_MEANW", "FIRE_AX_ECMWF_SFDIAG", "FIRE_AX_ECMWF_SURFCE", "FIRE_AX_ER2_LIDAR", "FIRE_AX_ER2_MAS", "FIRE_AX_ERS1_ALTIMTR", "FIRE_AX_ERS1_MCRWRAD", "FIRE_AX_ERS1_SCTRMTR", "FIRE_AX_ISCCP_DX", "FIRE_AX_MAGE_OCN_AIR", "FIRE_AX_MAGE_OCN_MET", "FIRE_AX_MAGE_OCN_SEA", "FIRE_AX_MAGE_TETROON", "FIRE_AX_MALBAL_SONDE", "FIRE_AX_METEOSAT", "FIRE_AX_OCEANS_SONDE", "FIRE_AX_PORSAN_SONDE", "FIRE_AX_PSU_CEIL_MAL", "FIRE_AX_PSU_CEIL_SAN", "FIRE_AX_PSU_CEIL_VAL", "FIRE_AX_PSU_H2OVAP", "FIRE_AX_PSU_MALBAL", "FIRE_AX_PSU_WND_MAL", "FIRE_AX_RWSONDE_LVL1", "FIRE_AX_RWSONDE_LVL2", "FIRE_AX_SANMAR_SONDE", "FIRE_AX_SFC_FUNCHAL", "FIRE_AX_SFC_IMAU", "FIRE_AX_SOF_ARAT_FLT", "FIRE_AX_SOF_ARAT_TRB", "FIRE_AX_SOF_BUOY_DFT", "FIRE_AX_SOF_BUOY_SPR", "FIRE_AX_SOF_PTU", "FIRE_AX_SOF_SUR_BUCK", "FIRE_AX_SOF_SUR_DRAK", "FIRE_AX_SOF_SUR_HYD", "FIRE_AX_SOF_SUR_MET", "FIRE_AX_SOF_SUR_RAD", "FIRE_AX_SOF_SUR_TBAL", "FIRE_AX_SUROIT_SONDE", "FIRE_AX_UKMO_C130", "FIRE_AX_UW_C131A", "FIRE_AX_UW_DSCRT", "FIRE_AX_UW_GERB_10HZ", "FIRE_AX_UW_GERB_1HZ", "FIRE_AX_VALDIV_SONDE", "FIRE_CI1_CSU_SABRE", "FIRE_CI1_ER2_LIDAR", "FIRE_CI1_ER2_RAD", "FIRE_CI1_ISCCP_DX", "FIRE_CI1_KINGAIR", "FIRE_CI1_LARC8_LIDAR", "FIRE_CI1_RAWINSONDES", "FIRE_CI1_SABRELINER", "FIRE_CI1_SRB_ALASKA", "FIRE_CI1_SRB_CANADA", "FIRE_CI1_SRB_LW", "FIRE_CI1_SRB_SO_POLE", "FIRE_CI1_SRB_SW", "FIRE_CI1_SRB_SWITZ", "FIRE_CI1_TOVS", "FIRE_CI2_CITATN_1HZ", "FIRE_CI2_CITATN_24HZ", "FIRE_CI2_CITATN_5SEC", "FIRE_CI2_CITATN_IWC", "FIRE_CI2_CITATN_PMS", "FIRE_CI2_CLASS_SONDE", "FIRE_CI2_CSU_PRT6", "FIRE_CI2_CSU_SONDES", "FIRE_CI2_CSU_STN1", "FIRE_CI2_CSU_STN2", "FIRE_CI2_CSU_WNDPRFS", "FIRE_CI2_DOPLR_LIDAR", "FIRE_CI2_ER2_LIDAR", "FIRE_CI2_ER2_MAS", "FIRE_CI2_ETL_RADAR", "FIRE_CI2_HIS", "FIRE_CI2_HSRL", "FIRE_CI2_ISCCP_DX", "FIRE_CI2_KINGAIR", "FIRE_CI2_KINGAIR_2D", "FIRE_CI2_KINGAIR_IWC", "FIRE_CI2_LARC8_LIDAR", "FIRE_CI2_MAPS", "FIRE_CI2_NOAA_WNDPFS", "FIRE_CI2_NWS_IN_SND", "FIRE_CI2_NWS_OUT_SND", "FIRE_CI2_PAMS", "FIRE_CI2_RAMAN_LIDAR", "FIRE_CI2_SABRELINER", "FIRE_CI2_SABRLNR_2D", "FIRE_CI2_SABRLNR_IWC", "FIRE_CI2_SPECT_SIRIS", "FIRE_CI2_UTAH_PDL", "FIRE_CI2_VIL_RTI", "FIRE_CI2_VIL_SCAN", "FIRE_ETO_UTAH_PDL", "FIRE_MS_CEILOM_CLASS", "FIRE_MS_CSU_RADSURF", "FIRE_MS_CSU_TBALLOON", "FIRE_MS_ELECTRA", "FIRE_MS_ER2_LIDAR", "FIRE_MS_ISCCP_DX", "FIRE_MS_MCRW_RAD", "FIRE_MS_NOAAWNDS", "FIRE_MS_UKMO_C130", "FLASH_TISA_Terra-Aqua", "FLASH_TISA_Terra-NOAA20", "FLASH_TISA_Terra-NOAA20", "FLDAS_NOAH01_CP_GL_M", "FLDAS_NOAH01_C_GL_M", "FLDAS_NOAH01_C_GL_MA", "FLDAS_NOAH01_C_GL_MC", "FLDAS_NOAHMP001_G_CA_D", "FLEXPART_Influence_Functions_2018", "FLOODAMA_903", "FLUAMAZON_896", "FLUXNET_Canada_1335", "FRONT", "FieldData_Alaska_Tundra_2177", "Field_Measurements_868", "Fire_Emissions_Indonesia_2118", "Fire_Emissions_NWT_1561", "Fire_Ignitions_Locations_AK_CA_2316", "Flight_Environment_Parameters_1909", "FluxSat_GPP_FPAR_1835", "Flux_Tower_Zona_Veg_Plots_1546", "Fluxnet_site_DB_1530", "Fluxnet_website_archive_copy_1549", "ForestHt_Biomass_GEDI_TDX_2298", "Forest_AGB_NW_USA_1766", "Forest_Carbon_Priority_1803", "Forest_Disturbance_Intensity_2059", "Forest_Inventory_Acre_Brazil_1654", "Forest_Inventory_Brazil_2007", "Forest_Inventory_Data_Brazil_1563", "Forest_Inventory_Tapajos_1552", "Forested_Areas_Amazonas_Brazil_1515", "Forested_Areas_Para_Brazil_1514", "Frac_FuelComponent_Maps_Tundra_1761", "Freeze_Thaw_NorthernHemisphere_2323", "Frost_Boils_Veg_Plots_1361", "Ft_Lauderdale", "G18-ABI-L2P-ACSPO-v2.90", "G18-ABI-L3C-ACSPO-v2.90", "GAMSSA_28km-ABOM-L4-GLOB-v01", "GCAM_Land_Cover_2005-2095_1216", "GCRW_DEM_2016_1793", "GEDI01_B", "GEDI02_A", "GEDI02_B", "GEDI_Fusion_Structure_2236", "GEDI_HighQuality_Shots_Rasters_2339", "GEDI_ICESAT2_Global_Veg_Height_2294", "GEDI_L3_LandSurface_Metrics_V2_1952", "GEDI_L4A_AGB_Density_GW_2028", "GEDI_L4A_AGB_Density_V2_1_2056", "GEDI_L4B_Country_Biomass_2321", "GEDI_L4B_Gridded_Biomass_V2_1_2299", "GEDI_L4C_WSCI_2338", "GEO-CAPE", "GEO-CAPE_GOCI", "GEOS-3_ALT_GDR", "GEOS2OBSINPUTINTL", "GEOS3STST", "GEOS_CASAGFED_3H_NEE", "GEOS_CASAGFED_3H_NEE", "GEOS_CASAGFED_D_FIRE", "GEOS_CASAGFED_D_FIRE", "GEOS_CASAGFED_M_FLUX", "GEOS_CASAGFED_M_FLUX", "GEOTRACES", "GEWEXSRB_Rel4-IP_Ancillary_3hrly", "GEWEXSRB_Rel4-IP_Ancillary_3hrly_landonly", "GEWEXSRB_Rel4-IP_Ancillary_3hrly_oceanonly", "GEWEXSRB_Rel4-IP_Longwave_3hrly", "GEWEXSRB_Rel4-IP_Longwave_3hrlymonthly_landonly_utc", "GEWEXSRB_Rel4-IP_Longwave_3hrlymonthly_oceanonly_utc", "GEWEXSRB_Rel4-IP_Longwave_3hrlymonthly_utc", "GEWEXSRB_Rel4-IP_Longwave_daily_landonly_utc", "GEWEXSRB_Rel4-IP_Longwave_daily_oceanonly_utc", "GEWEXSRB_Rel4-IP_Longwave_daily_utc", "GEWEXSRB_Rel4-IP_Longwave_monthly_landonly_utc", "GEWEXSRB_Rel4-IP_Longwave_monthly_local", "GEWEXSRB_Rel4-IP_Longwave_monthly_oceanonly_utc", "GEWEXSRB_Rel4-IP_Longwave_monthly_utc", "GEWEXSRB_Rel4-IP_Shortwave_3hrly", "GEWEXSRB_Rel4-IP_Shortwave_3hrlymonthly_utc", "GEWEXSRB_Rel4-IP_Shortwave_daily_local", "GEWEXSRB_Rel4-IP_Shortwave_daily_utc", "GEWEXSRB_Rel4-IP_Shortwave_monthly_local", "GEWEXSRB_Rel4-IP_Shortwave_monthly_utc", "GEWEXSRB_Rel4_1-IP_Longwave_3hrly_landonly", "GEWEXSRB_Rel4_1-IP_Longwave_3hrly_oceanonly", "GEWEXSRB_Rel4_1-IP_Longwave_daily_landonly_local", "GEWEXSRB_Rel4_1-IP_Longwave_daily_local", "GEWEXSRB_Rel4_1-IP_Longwave_daily_oceanonly_local", "GEWEXSRB_Rel4_1-IP_Longwave_monthly_landonly_local", "GEWEXSRB_Rel4_1-IP_Longwave_monthly_oceanonly_local", "GFCC30FCC", "GFCC30SR", "GFCC30TC", "GFCC30WC", "GFEI_CH4", "GFSAD1KCD", "GFSAD1KCM", "GFSAD30AFCE", "GFSAD30AUNZCNMOCE", "GFSAD30EUCEARUMECE", "GFSAD30NACE", "GFSAD30SAAFGIRCE", "GFSAD30SACE", "GFSAD30SEACE", "GFSAD30VAL", "GHISACASIA", "GHISACONUS", "GIMMS3g_NDVI_Trends_1275", "GIS_EastAngliaClimateMonthly_551", "GLAH01", "GLAH01", "GLAH02", "GLAH02", "GLAH03", "GLAH03", "GLAH04", "GLAH04", "GLAH05", "GLAH05", "GLAH06", "GLAH06", "GLAH07", "GLAH07", "GLAH08", "GLAH08", "GLAH09", "GLAH09", "GLAH10", "GLAH10", "GLAH11", "GLAH11", "GLAH12", "GLAH12", "GLAH13", "GLAH13", "GLAH14", "GLAH14", "GLAH15", "GLAH15", "GLCHMK", "GLCHMT", "GLDAS_CLSM025_D", "GLDAS_CLSM025_DA1_D", "GLDAS_CLSM025_DA1_D_EP", "GLDAS_CLSM10_3H", "GLDAS_CLSM10_3H", "GLDAS_CLSM10_3H_EP", "GLDAS_CLSM10_M", "GLDAS_CLSM10_M", "GLDAS_CLSM10_M_EP", "GLDAS_NOAH025_3H", "GLDAS_NOAH025_3H", "GLDAS_NOAH025_3H_EP", "GLDAS_NOAH025_M", "GLDAS_NOAH025_M", "GLDAS_NOAH025_M_EP", "GLDAS_NOAH10_3H", "GLDAS_NOAH10_3H", "GLDAS_NOAH10_3H_EP", "GLDAS_NOAH10_M", "GLDAS_NOAH10_M", "GLDAS_NOAH10_M_EP", "GLDAS_VIC10_3H", "GLDAS_VIC10_3H", "GLDAS_VIC10_3H_EP", "GLDAS_VIC10_M", "GLDAS_VIC10_M", "GLDAS_VIC10_M_EP", "GLDSMT", "GLDTMK", "GLDTMT", "GLHYANC", "GLHYVI", "GLLIDARPC", "GLMETRICS", "GLOBEC", "GLORTHO", "GLRADS", "GLREFL", "GLTRAJECTORY", "GLanCE30", "GMAO_M2SCREAM_INST3_CHEM", "GMAO_M2SCREAM_MONTH_UNCERT", "GMI-REMSS-L3U-v8.2a", "GNATS", "GO-BGC", "GO-SHIP", "GOA97", "GOC", "GOCAL", "GOCI_2013", "GOES13-OSISAF-L3C-v1.0", "GOES13-OSPO-L2P-v1.0", "GOES15-OSPO-L2P-v1.0", "GOES16-SST-OSISAF-L3C-v1.0", "GOM", "GOMECC", "GOMEX", "GOME_MINDS_NO2", "GOMI2AE", "GOMI2LS", "GOMI2ST", "GOMIB2E", "GOMIB2T", "GOMIGEO", "GOM_Oil_Spill", "GPCPDAY", "GPCPDAY", "GPCPMON", "GPCPMON", "GPCP_precip_712", "GPM_1AGMI", "GPM_1ATMI", "GPM_1AVIRS", "GPM_1BGMI", "GPM_1BPR", "GPM_1BTMI", "GPM_1BVIRS", "GPM_1CAQUAAMSRE", "GPM_1CF08SSMI", "GPM_1CF10SSMI", "GPM_1CF11SSMI", "GPM_1CF13SSMI", "GPM_1CF14SSMI", "GPM_1CF15SSMI", "GPM_1CF16SSMIS", "GPM_1CF17SSMIS", "GPM_1CF18SSMIS", "GPM_1CF19SSMIS", "GPM_1CGCOMW1AMSR2", "GPM_1CGPMGMI", "GPM_1CGPMGMI_R", "GPM_1CMETOPAMHS", "GPM_1CMETOPBMHS", "GPM_1CMETOPCMHS", "GPM_1CMT1SAPHIR", "GPM_1CNOAA15AMSUB", "GPM_1CNOAA16AMSUB", "GPM_1CNOAA17AMSUB", "GPM_1CNOAA18MHS", "GPM_1CNOAA19MHS", "GPM_1CNOAA20ATMS", "GPM_1CNOAA21ATMS", "GPM_1CNPPATMS", "GPM_1CTRMMTMI", "GPM_2ADPR", "GPM_2ADPRENV", "GPM_2AGPROFAQUAAMSRE_CLIM", "GPM_2AGPROFF08SSMI_CLIM", "GPM_2AGPROFF10SSMI_CLIM", "GPM_2AGPROFF11SSMI_CLIM", "GPM_2AGPROFF13SSMI_CLIM", "GPM_2AGPROFF14SSMI_CLIM", "GPM_2AGPROFF15SSMI_CLIM", "GPM_2AGPROFF16SSMIS", "GPM_2AGPROFF16SSMIS_CLIM", "GPM_2AGPROFF17SSMIS", "GPM_2AGPROFF17SSMIS_CLIM", "GPM_2AGPROFF18SSMIS", "GPM_2AGPROFF18SSMIS_CLIM", "GPM_2AGPROFF19SSMIS_CLIM", "GPM_2AGPROFGCOMW1AMSR2", "GPM_2AGPROFGCOMW1AMSR2_CLIM", "GPM_2AGPROFGPMGMI", "GPM_2AGPROFGPMGMI_CLIM", "GPM_2AGPROFMETOPAMHS_CLIM", "GPM_2AGPROFMETOPBMHS", "GPM_2AGPROFMETOPBMHS_CLIM", "GPM_2AGPROFMETOPCMHS", "GPM_2AGPROFMETOPCMHS_CLIM", "GPM_2AGPROFNOAA15AMSUB_CLIM", "GPM_2AGPROFNOAA16AMSUB_CLIM", "GPM_2AGPROFNOAA17AMSUB_CLIM", "GPM_2AGPROFNOAA18MHS_CLIM", "GPM_2AGPROFNOAA19MHS", "GPM_2AGPROFNOAA19MHS_CLIM", "GPM_2AGPROFNOAA20ATMS", "GPM_2AGPROFNOAA20ATMS_CLIM", "GPM_2AGPROFNOAA21ATMS", "GPM_2AGPROFNOAA21ATMS_CLIM", "GPM_2AGPROFNPPATMS", "GPM_2AGPROFNPPATMS_CLIM", "GPM_2AGPROFTRMMTMI_CLIM", "GPM_2AKa", "GPM_2AKaENV", "GPM_2AKu", "GPM_2AKuENV", "GPM_2APR", "GPM_2APRPSMT1SAPHIR", "GPM_2APRPSMT1SAPHIR_CLIM", "GPM_2BCMB", "GPM_2BCMB_TRMM", "GPM_2HCSH", "GPM_2HCSH_TRMM", "GPM_2HSLH", "GPM_2HSLH_TRMM", "GPM_3CMB", "GPM_3CMB_DAY", "GPM_3CMB_TRMM", "GPM_3CMB_TRMM_DAY", "GPM_3DPR", "GPM_3DPRD", "GPM_3DPR_ASC", "GPM_3DPR_DES", "GPM_3GCSH", "GPM_3GCSH_TRMM", "GPM_3GPROFAQUAAMSRE_CLIM", "GPM_3GPROFAQUAAMSRE_DAY_CLIM", "GPM_3GPROFF08SSMI_CLIM", "GPM_3GPROFF08SSMI_DAY_CLIM", "GPM_3GPROFF10SSMI_CLIM", "GPM_3GPROFF10SSMI_DAY_CLIM", "GPM_3GPROFF11SSMI_CLIM", "GPM_3GPROFF11SSMI_DAY_CLIM", "GPM_3GPROFF13SSMI_CLIM", "GPM_3GPROFF13SSMI_DAY_CLIM", "GPM_3GPROFF14SSMI_CLIM", "GPM_3GPROFF14SSMI_DAY_CLIM", "GPM_3GPROFF15SSMI_CLIM", "GPM_3GPROFF15SSMI_DAY_CLIM", "GPM_3GPROFF16SSMIS", "GPM_3GPROFF16SSMIS_CLIM", "GPM_3GPROFF16SSMIS_DAY", "GPM_3GPROFF16SSMIS_DAY_CLIM", "GPM_3GPROFF17SSMIS", "GPM_3GPROFF17SSMIS_CLIM", "GPM_3GPROFF17SSMIS_DAY", "GPM_3GPROFF17SSMIS_DAY_CLIM", "GPM_3GPROFF18SSMIS", "GPM_3GPROFF18SSMIS_CLIM", "GPM_3GPROFF18SSMIS_DAY", "GPM_3GPROFF18SSMIS_DAY_CLIM", "GPM_3GPROFF19SSMIS_CLIM", "GPM_3GPROFF19SSMIS_DAY_CLIM", "GPM_3GPROFGCOMW1AMSR2", "GPM_3GPROFGCOMW1AMSR2_CLIM", "GPM_3GPROFGCOMW1AMSR2_DAY", "GPM_3GPROFGCOMW1AMSR2_DAY_CLIM", "GPM_3GPROFGPMGMI", "GPM_3GPROFGPMGMI_CLIM", "GPM_3GPROFGPMGMI_DAY", "GPM_3GPROFGPMGMI_DAY_CLIM", "GPM_3GPROFMETOPAMHS_CLIM", "GPM_3GPROFMETOPAMHS_DAY_CLIM", "GPM_3GPROFMETOPBMHS", "GPM_3GPROFMETOPBMHS_CLIM", "GPM_3GPROFMETOPBMHS_DAY", "GPM_3GPROFMETOPBMHS_DAY_CLIM", "GPM_3GPROFMETOPCMHS", "GPM_3GPROFMETOPCMHS_CLIM", "GPM_3GPROFMETOPCMHS_DAY", "GPM_3GPROFMETOPCMHS_DAY_CLIM", "GPM_3GPROFNOAA15AMSUB_CLIM", "GPM_3GPROFNOAA15AMSUB_DAY_CLIM", "GPM_3GPROFNOAA16AMSUB_CLIM", "GPM_3GPROFNOAA16AMSUB_DAY_CLIM", "GPM_3GPROFNOAA17AMSUB_CLIM", "GPM_3GPROFNOAA17AMSUB_DAY_CLIM", "GPM_3GPROFNOAA18MHS_CLIM", "GPM_3GPROFNOAA18MHS_DAY_CLIM", "GPM_3GPROFNOAA19MHS", "GPM_3GPROFNOAA19MHS_CLIM", "GPM_3GPROFNOAA19MHS_DAY", "GPM_3GPROFNOAA19MHS_DAY_CLIM", "GPM_3GPROFNOAA20ATMS", "GPM_3GPROFNOAA20ATMS_CLIM", "GPM_3GPROFNOAA20ATMS_DAY", "GPM_3GPROFNOAA20ATMS_DAY_CLIM", "GPM_3GPROFNOAA21ATMS", "GPM_3GPROFNOAA21ATMS_CLIM", "GPM_3GPROFNOAA21ATMS_DAY", "GPM_3GPROFNOAA21ATMS_DAY_CLIM", "GPM_3GPROFNPPATMS", "GPM_3GPROFNPPATMS_CLIM", "GPM_3GPROFNPPATMS_DAY", "GPM_3GPROFNPPATMS_DAY_CLIM", "GPM_3GPROFTRMMTMI_CLIM", "GPM_3GPROFTRMMTMI_DAY_CLIM", "GPM_3GSLH", "GPM_3GSLH_TRMM", "GPM_3HCSH", "GPM_3HCSH_TRMM", "GPM_3HSLH", "GPM_3HSLH_DAY", "GPM_3HSLH_TRMM", "GPM_3HSLH_TRMM_DAY", "GPM_3IMERGDE", "GPM_3IMERGDE", "GPM_3IMERGDF", "GPM_3IMERGDL", "GPM_3IMERGDL", "GPM_3IMERGHH", "GPM_3IMERGHHE", "GPM_3IMERGHHE", "GPM_3IMERGHHL", "GPM_3IMERGHHL", "GPM_3IMERGM", "GPM_3PR", "GPM_3PRD", "GPM_3PRPSMT1SAPHIR", "GPM_3PRPSMT1SAPHIR_CLIM", "GPM_3PRPSMT1SAPHIR_DAY", "GPM_3PRPSMT1SAPHIR_DAY_CLIM", "GPM_3PR_ASC", "GPM_3PR_DES", "GPM_BASEGPMGMI", "GPM_BASEGPMGMI_RSS", "GPM_BASEGPMGMI_XCAL", "GPM_BASETRMMTMI", "GPM_IMERG_LandSeaMask", "GPM_MERGIR", "GPM_PRL1KA", "GPM_PRL1KU", "GPP_CONUS_TROPOMI_1875", "GPP_MODIS_Alaska_Canada_2024", "GPP_surfaces_749", "GPROF_precip_716", "GPSROZPBLA", "GPSROZPBLA", "GPSROZPBLS", "GPSROZPBLS", "GRACEDADM_CLSM0125US_7D", "GRACEDADM_CLSM025GL_7D", "GRACEFO_L1A_ASCII_GRAV_JPL_RL04", "GRACEFO_L2_CSR_MONTHLY_0062", "GRACEFO_L2_GFZ_MONTHLY_0061", "GRACEFO_L2_JPL_MONTHLY_0061", "GRACEFO_L2_JPL_MONTHLY_0063", "GRACE_AOD1B_GRAV_GFZ_RL06", "GRACE_GAA_L2_GRAV_GFZ_RL06", "GRACE_GAA_L2_GRAV_JPL_RL06", "GRACE_GAB_L2_GRAV_GFZ_RL06", "GRACE_GAB_L2_GRAV_JPL_RL06", "GRACE_GAC_L2_GRAV_CSR_RL06", "GRACE_GAC_L2_GRAV_GFZ_RL06", "GRACE_GAC_L2_GRAV_JPL_RL06", "GRACE_GAD_L2_GRAV_CSR_RL06", "GRACE_GAD_L2_GRAV_GFZ_RL06", "GRACE_GAD_L2_GRAV_JPL_RL06", "GRACE_GSM_L2_GRAV_CSR_RL06", "GRACE_GSM_L2_GRAV_GFZ_RL06", "GRACE_GSM_L2_GRAV_JPL_RL06", "GRACE_L1B_GRAV_JPL_RL02", "GRACE_L1B_GRAV_JPL_RL03", "GRC-GFO_GRIDDED_AOD1B_JPL_1-DEG_RL06", "GRC-GFO_GRIDDED_AOD1B_JPL_MASCON_RL06", "GREENLAND_MASS_TELLUS_MASCON_CRI_TIME_SERIES_RL06.1_V3", "GSMNP_Vegetation_Structure_R1_1286", "GSSTF", "GSSTF", "GSSTFM", "GSSTFM", "GSSTFMC", "GSSTFMC", "GSSTFM_NCEP", "GSSTFM_NCEP", "GSSTFSC", "GSSTFSC", "GSSTFYC", "GSSTFYC", "GSSTF_F08", "GSSTF_F08", "GSSTF_F10", "GSSTF_F10", "GSSTF_F11", "GSSTF_F11", "GSSTF_F13", "GSSTF_F13", "GSSTF_F14", "GSSTF_F14", "GSSTF_F15", "GSSTF_F15", "GSSTF_NCEP", "GSSTF_NCEP", "GWELDMO", "GWELDMO", "GWELDMO", "GWELDYR", "GWELDYR", "GWELDYR", "GasEx", "GePCO", "Geo_Polar_Blended-OSPO-L4-GLOB-v1.0", "Geo_Polar_Blended_Night-OSPO-L4-GLOB-v1.0", "GloSSAC", "GloSSAC", "GloSSAC", "GloSSAC", "GloSSAC", "GloSSAC", "Global_Biomass_1950-2010_1296", "Global_CDOM", "Global_Clumping_Index_1531", "Global_Freshwater_CH4Emissions_2253", "Global_Hydrologic_Soil_Group_1566", "Global_Lakes_Methane_2008", "Global_Landslide_Exposure_Maps", "Global_Landslide_Nowcast", "Global_Landslide_Nowcast", "Global_Litter_Carbon_Nutrients_1244", "Global_Maps_C_Density_2010_1763", "Global_Microbial_Biomass_C_N_P_1264", "Global_Monthly_GPP_1789", "Global_Phosphorus_Dist_Map_1223", "Global_Phosphorus_Hedley_Fract_1230", "Global_RTSG_Flux_1078", "Global_Reservoirs_Methane_1918", "Global_Riverine_N2O_Emissions_1791", "Global_SIF_OCO2_MODIS_1863", "Global_Salt_Marsh_Change_2122", "Global_Soil_Regolith_Sediment_1304", "Global_Veg_Greenness_GIMMS_3G_2187", "GozMmlpH2O", "GozMmlpHCl", "GozMmlpHNO3", "GozMmlpN2O", "GozMmlpO3", "GozSmlpH2O", "GozSmlpHCl", "GozSmlpHNO3", "GozSmlpN2O", "GozSmlpO3", "GozSmlpT", "Great_Belt", "Great_Lakes", "Great_Slave_Lake_Ecosystem_Map_1695", "GreenBay", "Gridded_Biomass_Africa_1777", "GroundMSPI_ACEPOL_Radiance_Data", "GulfCarbon", "GulfOfMaine", "Gulf_Of_Maine", "Gulf_of_Maine_Nutrients", "Gulf_of_Maine_Optics", "H09-AHI-L2P-ACSPO-v2.90", "H09-AHI-L3C-ACSPO-v2.90", "H3ZFC12MEXT", "H3ZFC8MEXT", "H3ZFCCFC11", "H3ZFCCFC12", "H3ZFCCLONO2", "H3ZFCGPH", "H3ZFCH2O", "H3ZFCHNO3", "H3ZFCN2O", "H3ZFCN2O5", "H3ZFCNO2", "H3ZFCO3", "H3ZFCT", "HAB", "HALO_LiDAR_AOP_ML_Heights_1833", "HAQES_NA_PM25_BC", "HAQES_NA_PM25_BC_CENSUS", "HAQES_NA_PM25_BC_COUNTY", "HAQES_NA_PM25_OC", "HAQES_NA_PM25_OC_CENSUS", "HAQES_NA_PM25_OC_COUNTY", "HAQES_NA_PM25_TOT", "HAQES_NA_PM25_TOT_CENSUS", "HAQES_NA_PM25_TOT_COUNTY", "HAQ_TROPOMI_NO2_CONUS_A_L3", "HAQ_TROPOMI_NO2_CONUS_M_L3", "HAQ_TROPOMI_NO2_CONUS_S_L3", "HAQ_TROPOMI_NO2_GLOBAL_A_L3", "HAQ_TROPOMI_NO2_GLOBAL_M_L3", "HAWKEYE_L1", "HAWKEYE_L1", "HAWKEYE_L2_OC", "HAWKEYE_L2_OC", "HCDN_810", "HICO_L1", "HIR3SCOL", "HIRDLS2", "HIRMLS3IWC", "HIRSN6IM", "HIRSN6L1GARP", "HIVE", "HLSL30", "HLSS30", "HMA2_DDSMET", "HMA2_DSPAT", "HMA2_FGP", "HMA2_GFTP", "HMA2_GGP", "HMA2_HFD", "HMA2_LHI", "HMA2_MATCHA", "HMA2_MTLI", "HMA2_NLSMR", "HMA2_WBP", "HMA_AWS", "HMA_DEM8m_AT", "HMA_DEM8m_CT", "HMA_DEM8m_MOS", "HMA_DM_6H", "HMA_DTE", "HMA_EAPrecip_FLOR", "HMA_FreezeThawMelt_ASCAT", "HMA_GLI", "HMA_GL_RCP", "HMA_GL_RCPR", "HMA_GSM", "HMA_GlacierAvg_dH", "HMA_Glacier_dH", "HMA_Glacier_dH_Mosaics", "HMA_LIS_LandSurfaceHydro", "HMA_LS_Cat", "HMA_MAR3_5", "HMA_MTLI", "HMA_OptDepth", "HMA_Precip_3B42", "HMA_Precip_FLOR", "HMA_RCMO_1H", "HMA_RCMO_6H", "HMA_RCMO_D", "HMA_RCMO_M", "HMA_SBRF", "HMA_SDI", "HMA_SR_D", "HMA_STParams", "HMA_Snowfield", "HOMAGE_GGFO_L4_GOMA_Monthly_v01", "HOMAGE_STERIC_OHC_TIME_SERIES_v01", "HOT", "HRAC_Precip", "HRIRN1IM", "HRIRN1L1", "HRIRN2IM", "HRIRN2L1", "HRIRN3IM", "HRIRN3L1", "HWSD_1247", "HYCODE_LEO-15", "Happy_Valley_Veg_Plots_1354", "HighRes_ClimateData_Western_US_1682", "High_Res_Tidal_Marsh_Veg_1609", "Historic_S2K_Website_1765", "HistoricalLai_584", "Historical_Lake_Shorelines_AK_1859", "Horn_Island", "HourlyUrban_GreenhouseGases_US_1916", "Hudson_River_Fluorescence", "Hydroprofiler", "IAKST1B", "IAKST1B", "IAPRS1B", "IASI_SST_METOP_A-OSISAF-L2P-v1.0", "IASI_SST_METOP_B-OSISAF-L2P-v1.0", "ICESCAPE", "ICEVOLC_FlowerKahn2020", "IDBMG4", "IDCSI4", "IDHDT4", "IDS_LIS", "IGBGM1B", "IGBGM2", "IGBP-DIS_565", "IGBP-SurfaceProducts_569", "IGBTH4", "IGCMG1B", "IGCMG2", "IGGRV1B", "IGLGS1B", "IGZLS1B", "ILAKP1B", "ILAKS1B", "ILATM1B", "ILATM1B", "ILATM2", "ILATMGR", "ILATMW1B", "ILNIRW1B", "ILNSA1B", "ILNSA1B", "ILNSAW1B", "ILSIG1B", "ILSNP1B", "ILSNP4", "ILUTP1B", "ILUTP2", "ILVGH1B", "ILVGH2", "ILVIS0", "ILVIS1B", "ILVIS2", "ILVIS2", "IMARPE-Callao_Station", "IMCS31B", "IMECOCAL", "IMERG_Precip_Canada_Alaska_2097", "IMFGM1B", "INDOEX", "INTEXA_AIRMAP", "INTEXA_DC8_AIRCRAFT", "INTEXA_J31_AIRCRAFT", "INTEXA_MERGES", "INTEXA_MODEL", "INTEXA_O3SONDES", "INTEXA_PROTEUS_AIRCRAFT", "INTEXA_SATELLITE", "INTEXA_TRAJECTORY", "INTEXB_Be200_AIRCRAFT", "INTEXB_C130_AIRCRAFT", "INTEXB_Cessna_AIRCRAFT", "INTEXB_DC8_AIRCRAFT", "INTEXB_Duchess_AIRCRAFT", "INTEXB_GROUND", "INTEXB_J31_AIRCRAFT", "INTEXB_MERGES", "INTEXB_MODEL", "INTEXB_O3SONDES", "INTEXB_SATELLITE", "INTEXB_TRAJECTORY", "INTRO", "IOCAM0", "IOCAM1B", "IOCAM1B", "IODCC0", "IODEM3", "IODIM3", "IODMS1B", "IODMS3", "IOFFE", "IOLVIS1A", "IPFLR1B", "IPFLT1B", "IR1HI1B", "IR1HI2", "IR2HI1B", "IR2HI2", "IRACC1B", "IRARES1B", "IRISN4RAD", "IRKUB1B", "IRMCR1B", "IRMCR2", "IRMCR3", "IRPAR2", "IRSNO1B", "IRTIT3", "IRUAFHF1B", "IRWIS2", "IS2ATBABD", "IS2GZANT", "IS2MPDDA", "IS2OLVIS1BCV", "IS2SITDAT4", "IS2SITMOGR4", "ISCCP_B3_NAT", "ISCCP_D1", "ISCCP_D2", "ISCCP_DX", "ISCCP_ICESNOW_NAT", "ISCCP_TOVS_NAT", "ISERV", "ISLSCP_919", "ISSITGR4", "ITPRN5L1", "IXBMI2AE", "IXBMI2LS", "IXBMI2ST", "IXBMIB2E", "IXBMIB2T", "IXBMIGEO", "Idaho_field_shrub_data_1503", "Imnavait_Creek_Veg_Maps_1385", "Imnavait_Creek_Veg_Plots_1356", "InSAR_Prudhoe_Bay_1267", "Insitu_Tower_Greenhouse_Gas_1798", "Interior_Alaska_Subsistence_1725", "Interpolated_Met_Products_1876", "InundationMap_YkFlats_PeaceAth_1901", "IronEx", "IsricWiseGrids_546", "IsricWise_547", "J1_CrIS_VIIRS750m_IND", "JASON-1_JMR_ENH", "JASON-1_L2_OST_GPN_E", "JASON-1_L2_OST_GPN_E_GEODETIC", "JASON-1_L2_OST_GPR_E", "JASON-1_L2_OST_GPR_E_GEODETIC", "JASON-1_L2_OST_GPS_E", "JASON-1_L2_OST_GPS_E_GEODETIC", "JASON_3_L2_OST_OGDR_GPS", "JASON_3_PD_CORRECTION", "JASON_CS_S6A_L1A_ALT_HR_NTC_F08", "JASON_CS_S6A_L1A_ALT_HR_STC_F", "JASON_CS_S6A_L1B_ALT_HR_NTC_F08", "JASON_CS_S6A_L1B_ALT_HR_STC_F", "JASON_CS_S6A_L1B_ALT_LR_NTC_F08", "JASON_CS_S6A_L1B_ALT_LR_STC_F", "JASON_CS_S6A_L1B_GNSS_POD_DAILY", "JASON_CS_S6A_L1B_GNSS_POD_HOURLY", "JASON_CS_S6A_L1B_GNSS_RO_POD_HOURLY", "JASON_CS_S6A_L2P_ALT_HR_OST_NTC_F08", "JASON_CS_S6A_L2P_ALT_LR_OST_NTC_F08", "JASON_CS_S6A_L2_ALT_HR_RED_OST_NRT_F", "JASON_CS_S6A_L2_ALT_HR_RED_OST_NTC_F08", "JASON_CS_S6A_L2_ALT_HR_RED_OST_NTC_F08_UNVALIDATED", "JASON_CS_S6A_L2_ALT_HR_RED_OST_STC_F", "JASON_CS_S6A_L2_ALT_HR_STD_OST_NRT_F", "JASON_CS_S6A_L2_ALT_HR_STD_OST_NTC_F08", "JASON_CS_S6A_L2_ALT_HR_STD_OST_NTC_F08_UNVALIDATED", "JASON_CS_S6A_L2_ALT_HR_STD_OST_STC_F", "JASON_CS_S6A_L2_ALT_LR_RED_OST_NRT_F", "JASON_CS_S6A_L2_ALT_LR_RED_OST_NTC_F08", "JASON_CS_S6A_L2_ALT_LR_RED_OST_NTC_F08_UNVALIDATED", "JASON_CS_S6A_L2_ALT_LR_RED_OST_STC_F", "JASON_CS_S6A_L2_ALT_LR_STD_OST_NRT_F", "JASON_CS_S6A_L2_ALT_LR_STD_OST_NTC_F08", "JASON_CS_S6A_L2_ALT_LR_STD_OST_NTC_F08_UNVALIDATED", "JASON_CS_S6A_L2_ALT_LR_STD_OST_STC_F", "JASON_CS_S6A_L2_AMR_RAD_NRT", "JASON_CS_S6A_L2_AMR_RAD_NTC_F08", "JASON_CS_S6A_L2_AMR_RAD_NTC_F08_UNVALIDATED", "JASON_CS_S6A_L2_AMR_RAD_STC", "JASON_CS_S6A_L3_ALT_HR_OST_NTC_F08", "JASON_CS_S6A_L3_ALT_LR_OST_NTC_F08", "JERS-1_L0", "JERS-1_L1", "JERS-1_SAR_GRFM_Amazon_Mosaics_1280", "JGOFS", "JGOFS_Arabian_Sea", "JGOFS_BOFS", "JGOFS_EQPAC", "JGOFS_WOCE", "JHUAPL_SRI_Kauai", "JPL_RECON_GMSL", "K10_SST-NAVO-L4-GLOB-v01", "KAIMIMOANA", "KILVOLC_FlowerKahn2021", "KORUS", "KORUSAQ_Aerosol_AircraftInSitu_DC8_Data", "KORUSAQ_AircraftRemoteSensing_DIAL_DC8_Data", "KORUSAQ_AircraftRemoteSensing_GeoTASO_B200_Data", "KORUSAQ_Analysis_Data", "KORUSAQ_Cloud_AircraftInSitu_DC8_Data", "KORUSAQ_Ground_EPA_Data", "KORUSAQ_Ground_NASA_Data", "KORUSAQ_Merge_Data", "KORUSAQ_MetNav_AircraftInSitu_B200_Data", "KORUSAQ_MetNav_AircraftInSitu_DC8_Data", "KORUSAQ_Miscellaneous_Data", "KORUSAQ_Model_Data", "KORUSAQ_Pandora_Data", "KORUSAQ_RVJangMokShip_Data", "KORUSAQ_RVOnnuriShip_Data", "KORUSAQ_Sondes_Data", "KORUSAQ_TraceGas_AircraftInSitu_DC8_Data", "KORUSAQ_TraceGas_AircraftInSitu_HanseoKingAir_Data", "KORUSAQ_Trajectory_Data", "KORUSAQ_jValue_AircraftInSitu_DC8_Data", "Kennebec", "Kieber_Photochemistry", "KingSejong", "King_Rim_Fire_Analysis_1288", "Kuparuk_Veg_Maps_1378", "Kuroshio_Area", "L3S_LEO_AM-STAR-v2.80", "L3S_LEO_DY-STAR-v2.81", "L3S_LEO_PM-STAR-v2.81", "LAB97", "LAI_Africa_2325", "LAI_Canada_816", "LAI_VALERI_Canada_829", "LAI_Woody_Plants_1231", "LAI_surfaces_747", "LAMONT_ATL", "LAMONT_GOM", "LAMONT_SAB", "LAMONT_SCS", "LANDMET", "LANDMET_ANC_SM", "LANDMET_ANC_ST", "LANDMET_ANC_TEIME", "LANDMET_ANC_TESA", "LASE_AFWEX", "LASE_CAMEX3", "LASE_SGP97", "LASE_SOLVE", "LASE_TARFOX", "LASE_VALIDATION", "LATTE", "LB02", "LC01_Boundaries_Ecuador_1057", "LC01_Cities_Communities_Roads_1058", "LC01_Households_NEC_1052", "LC01_Hydrography_Edaphology_NEC_1059", "LC01_LULC_Classes_Ecuador_ISA_1084", "LC01_Landsat_1187", "LC01_SRTM_DEM_90m_NEC_1083", "LC01_Topography_Ecuador_ISA_1082", "LC02_Forest_Flammability_Acre_1089", "LC02_GOES8_Hotpixel_Acre_1092", "LC02_MAP_Fire_Indicators_1044", "LC02_Meteorology_Acre_1091", "LC02_PermPlot_Acre_1237", "LC02_Streams_Acre_1243", "LC02_Water_Table_Acre_1062", "LC03_Hypsography_DEM_1094", "LC03_SAR_LC_Biomass_1093", "LC04_IBIS_Model_1139", "LC04_Land_Use_5min_906", "LC04_Macrohydrology_1048", "LC04_THMB-HYDRA_Model_1138", "LC05_BDFF_Biomass_Soils_1040", "LC07_Airborne_Rasters_1274", "LC07_Airborne_Videography_1272", "LC07_Amazon_Wetlands_1284", "LC07_Bathymetry_Curuai_999", "LC07_Biomass_LGrande_1127", "LC07_Curuai_chl_1134", "LC07_Lake_Chlorophyll_MODIS_1000", "LC07_Lake_Nutrient_Sediments_1050", "LC07_Monthly_Inundated_Areas_1049", "LC07_Reservoir_GHG_1143", "LC07_Reservoir_Methane_Emissions_1047", "LC07_SMMR_Inundated_Area_1051", "LC07_Spectroradiometry_1144", "LC07_Wetlands_fluxes_1209", "LC08_EOS_Maps_1155", "LC08_Ecosystem_Demography_Model_1102", "LC08_Fire_Observations_1095", "LC09_GIS_Study_Areas_986", "LC09_Landsat_987", "LC09_Precipitation_940", "LC09_Soil_Composition_938", "LC09_Transition_Matricies_1098", "LC09_Vegetation_Composition_939", "LC10_Landsat_ETM_846", "LC10_Landsat_TM_852", "LC13_GIS_Cauaxi_890", "LC13_GIS_Juruena_888", "LC13_GIS_Tapajos_893", "LC14_Aboveground_Prod_1196", "LC14_Amazon_Scenarios_1153", "LC14_REE_SLA_1211", "LC14_RISQUE_1147", "LC14_Surface_Roots_Phenology_1268", "LC15_AGLB_Distribution_Map_908", "LC15_GRFM_JERS1_Mosaic_1024", "LC15_MODIS_TreeCover_1035", "LC15_Roughness_Map_1182", "LC15_SPOT_Metrics_1239", "LC15_SRTM_Topography_1181", "LC18_Hyperion_889", "LC19_Field_2002_1261", "LC21_Foliar_Nutrients_1234", "LC21_Fractional_Cover_1152", "LC21_Selective_Logging_1172", "LC21_Soil_Characteristics_1236", "LC22_MODIS_Field_Val_2004_1262", "LC22_MODIS_Field_Val_2005_1260", "LC22_MODIS_Phenology_Mato_Grosso_1185", "LC22_MODIS_VCF_Tree_Cover_1112", "LC22_Post_Deforestation_LULC_1099", "LC23_MODIS_ASTER_Fire_Comparisons_839", "LC23_Vegetation_Fire_Dynamics_843", "LC23_Vegetation_Fires_2003_887", "LC24_Basin_Scale_Hot_Pixels_2001_882", "LC24_Cadastral_Property_Map_Para_1042", "LC24_ETM_Deforestation_Map_Para_1999_1054", "LC24_Historical_Roads_Amazon_1043", "LC24_Land_Cover_Southern_Para_1055", "LC24_Land_Cover_Uruara_Para_1053", "LC24_MODIS_Forest_Cover_500-m_1056", "LC31_AMZ_Historical_LU_1170", "LC31_SITE_1173", "LC35_GOES_WF_ABBA_1180", "LC35_Landsat7_Fire_Masks_1071", "LC39_DECAF_Model_1190", "LC39_MODIS_Fire_SA_1186", "LEO", "LEOLSTCMG30", "LEOLSTCMG30", "LGRIP30", "LIDAR", "LIDAR_FOREST_CANOPY_HEIGHTS_1271", "LIMSN7L1PROFILER", "LIMSN7L1RAT", "LIMSN7L2", "LIMSN7L3", "LINKAGES_1166", "LIS", "LISCO_mooring", "LISTOS_AircraftInSitu_StonyBrookAircraft_Data", "LISTOS_AircraftInSitu_UMDAircraft_Data", "LISTOS_AircraftRemoteSensing_NASAAircraft_Data", "LISTOS_Ground_Bayonne_Data", "LISTOS_Ground_BronxPfizer_Data", "LISTOS_Ground_CCNY_Data", "LISTOS_Ground_FlaxPond_Data", "LISTOS_Ground_Hammonasset_Data", "LISTOS_Ground_NewHaven_Data", "LISTOS_Ground_Other_Data", "LISTOS_Ground_OuterIsland_Data", "LISTOS_Ground_QueensCollege_Data", "LISTOS_Ground_Rutgers_Data", "LISTOS_Ground_Westport_Data", "LISTOS_Ground_YaleCoastal_Data", "LISTOS_MetNav_AircraftInSitu_NASAAircraft_Data", "LISTOS_SurfaceMobile_InSitu_Data", "LITE_L1", "LMER-TIES", "LMOS_AircraftInSitu_ScientificAviation_Data", "LMOS_AircraftRemoteSensing_UC12_Data", "LMOS_Ground_Grafton_Data", "LMOS_Ground_IEPA_Data", "LMOS_Ground_Milwaukee_Data", "LMOS_Ground_SchillerPark_Data", "LMOS_Ground_Sheboygan_Data", "LMOS_Ground_WDNRRoutine_Data", "LMOS_Ground_Zion_Data", "LMOS_MetNav_AircraftInSitu_UC12_Data", "LMOS_Miscellaneous_Data", "LMOS_TraceGas_ShipInSitu_Data", "LMOS_TraceGas_SurfaceMobile_EPA-GMAP_Data", "LMOS_TraceGas_SurfaceMobile_UWEC-Auto_Data", "LOBO_timeseries", "LOCSS_L1_V1", "LPJ-WHyMe_v1-3-1_1150", "LPJ_EOSIM_L2_DCH4E", "LPJ_EOSIM_L2_DCH4E_LL", "LPJ_EOSIM_L2_MCH4E", "LPJ_EOSIM_L2_MCH4E_LL", "LPJ_L2_SSREF", "LPJ_L2_SSREF", "LPRM_AMSR2_A_SOILM3", "LPRM_AMSR2_DS_A_SOILM3", "LPRM_AMSR2_DS_D_SOILM3", "LPRM_AMSR2_DS_SOILM2", "LPRM_AMSR2_D_SOILM3", "LPRM_AMSR2_SOILM2", "LPRM_AMSRE_A_SOILM3", "LPRM_AMSRE_D_SOILM3", "LPRM_AMSRE_SOILM2", "LPRM_TMI_DY_SOILM3", "LPRM_TMI_NT_SOILM3", "LPRM_TMI_SOILM2", "LPRM_WINDSAT_DY_SOILM3", "LPRM_WINDSAT_NT_SOILM3", "LPRM_WINDSAT_SOILM2", "LRIRN6L2IPAT", "LSM_807", "LTER", "LUH2_GCB2019_1851", "LULC_Nigeria_Ethiopia_SAfrica_2367", "LWAD01-2", "LaSelva_Biomass_1215", "LaSelva_Land_Use_1312", "Lab2000", "Lab96", "LakeBathymetry_Model_NSlope_AK_2243", "LakeSuperior", "Lake_MI_2012_WaterQual", "Lake_Wetland_Classes_UAVSAR_1883", "Land_Cover_surfaces_748", "Land_Use_Harmonization_V1_1248", "Land_Use_Harmonization_V2_1721", "Landcover_Colombian_Amazon_1783", "Landsat8_Sentinel2_Phenocam_2248", "Large_River_DOC_Export", "Last_Day_Spring_Snow_1528", "Leaf_Carbon_Nutrients_1106", "Leaf_Photosynthesis_Traits_1224", "LiDAR_Forest_Inventory_Brazil_1644", "LiDAR_Tundra_Forest_AK_1782", "LiDAR_Veg_Ht_Idaho_1532", "Line_P", "M1_AVH02C1", "M1_AVH09C1", "M1_AVH13C1", "M2C0NXASM", "M2C0NXCTM", "M2C0NXLND", "M2I1NXASM", "M2I1NXINT", "M2I1NXLFO", "M2I3NPASM", "M2I3NVAER", "M2I3NVASM", "M2I3NVCHM", "M2I3NVGAS", "M2I3NXGAS", "M2I6NPANA", "M2I6NVANA", "M2IMNPANA", "M2IMNPASM", "M2IMNXASM", "M2IMNXGAS", "M2IMNXINT", "M2IMNXLFO", "M2IUNPANA", "M2IUNPASM", "M2IUNXASM", "M2IUNXGAS", "M2IUNXINT", "M2IUNXLFO", "M2SDNXSLV", "M2SMNXEDI", "M2SMNXEDI", "M2SMNXPCT", "M2SMNXPCT", "M2SMNXSLV", "M2T1NXADG", "M2T1NXAER", "M2T1NXCHM", "M2T1NXCSP", "M2T1NXFLX", "M2T1NXINT", "M2T1NXLFO", "M2T1NXLND", "M2T1NXOCN", "M2T1NXRAD", "M2T1NXSLV", "M2T3NEMST", "M2T3NENAV", "M2T3NETRB", "M2T3NPCLD", "M2T3NPMST", "M2T3NPODT", "M2T3NPQDT", "M2T3NPRAD", "M2T3NPTDT", "M2T3NPTRB", "M2T3NPUDT", "M2T3NVASM", "M2T3NVCLD", "M2T3NVMST", "M2T3NVRAD", "M2T3NXGLC", "M2TCNPLTM", "M2TCNPLTM", "M2TCNXLTM", "M2TCNXLTM", "M2TMNPCLD", "M2TMNPMST", "M2TMNPODT", "M2TMNPQDT", "M2TMNPRAD", "M2TMNPTDT", "M2TMNPTRB", "M2TMNPUDT", "M2TMNXADG", "M2TMNXAER", "M2TMNXCHM", "M2TMNXCSP", "M2TMNXFLX", "M2TMNXGLC", "M2TMNXINT", "M2TMNXLFO", "M2TMNXLND", "M2TMNXOCN", "M2TMNXRAD", "M2TMNXSLV", "M2TUNPCLD", "M2TUNPMST", "M2TUNPODT", "M2TUNPQDT", "M2TUNPRAD", "M2TUNPTDT", "M2TUNPTRB", "M2TUNPUDT", "M2TUNXADG", "M2TUNXAER", "M2TUNXCHM", "M2TUNXCSP", "M2TUNXFLX", "M2TUNXGLC", "M2TUNXINT", "M2TUNXLFO", "M2TUNXLND", "M2TUNXOCN", "M2TUNXRAD", "M2TUNXSLV", "M2_TMAX_PM25", "MAA", "MAC021S0", "MAC021S1", "MAC02QS0", "MAC02QS1", "MAC03S0", "MAC03S1", "MAC04S0", "MAC04S1", "MAC05S0", "MAC05S1", "MAC06S0", "MAC06S1", "MAC07S0", "MAC07S1", "MAC35S0", "MAC35S1", "MACLWP_diurnal", "MACLWP_mean", "MACPEX_Aerosol_AircraftInSitu_WB57_Data", "MACPEX_Cloud_AircraftInSitu_WB57_Data", "MACPEX_MetNav_AircraftInSitu_WB57_Data", "MACPEX_Satellite_Data", "MACPEX_Sondes_Data", "MACPEX_TraceGas_AircraftInSitu_WB57_Data", "MACPEX_Water_AircraftInSitu_WB57_Data", "MACTWP_mean", "MALINA", "MAM03S0", "MAM04S0", "MAM05S0", "MAM06S0", "MAM07S0", "MAM35S0", "MANTRA_PIRANA", "MAPSS_853", "MAPS_OSTA3_CO5X5_HDF", "MAPS_OSTA3_COSEC_HDF", "MAPS_SRL1_CO5X5_HDF", "MAPS_SRL1_COSEC_HDF", "MAPS_SRL2_CO5X5_HDF", "MAPS_SRL2_COSEC_HDF", "MASL1B", "MASL2CLD", "MASS_BAY", "MASTER_Ames_August_2003_2045", "MASTER_B200_Fall_1999_2099", "MASTER_Baja_Mexico_1999_2102", "MASTER_CARTA_2003_2054", "MASTER_CARTA_2005_2034", "MASTER_CLASIC_2007_2019", "MASTER_DFRC_April_2004_2043", "MASTER_DFRC_August_2003_2046", "MASTER_DFRC_Fall_2005_2030", "MASTER_DFRC_July-Aug_2001_V2_2144", "MASTER_DFRC_July_2004_2039", "MASTER_DFRC_June_1999_2100", "MASTER_DFRC_June_2011_1976", "MASTER_DFRC_May-June_2008_2011", "MASTER_DFRC_May_2011_1985", "MASTER_DFRC_November_2011_1973", "MASTER_DFRC_October_2003_2044", "MASTER_DFRC_RSL_August_2002_2066", "MASTER_DFRC_September_2002_2064", "MASTER_DFRC_September_2006_2022", "MASTER_FIREX_AQ_JulySept_2019_1941", "MASTER_FireSense_2023_2330", "MASTER_Flightline_Locator_2151", "MASTER_GEMx_Spring_2024_2370", "MASTER_GEMx_Summer_2023_2319", "MASTER_Hawaii_October_2001_V2_2142", "MASTER_Houston_2010_2126", "MASTER_Houston_2011_1972", "MASTER_HyspIRI_EarlyS2013_V2_2146", "MASTER_HyspIRI_EarlySpring2014_1965", "MASTER_HyspIRI_Fall_2013_1966", "MASTER_HyspIRI_Fall_2014_1962", "MASTER_HyspIRI_Fall_2015_1956", "MASTER_HyspIRI_Hawaii_2017_1951", "MASTER_HyspIRI_Hawaii_2018_1945", "MASTER_HyspIRI_LateSpring_2013_1968", "MASTER_HyspIRI_LateSpring_2014_1963", "MASTER_HyspIRI_Spring_2015_1957", "MASTER_HyspIRI_Summer_2014_1964", "MASTER_HyspIRI_Summer_2015_1959", "MASTER_HyspIRI_Summer_2016_1913", "MASTER_HyspIRI_Summer_2017_1950", "MASTER_HyspIRI_Summer_2018_1942", "MASTER_PacificRim_2000_2093", "MASTER_RSL-DFRC_October_2008_2014", "MASTER_RSLPhoenix_2011_1975", "MASTER_RSL_April_2003_2053", "MASTER_RSL_April_2008_2016", "MASTER_RSL_August_2001_V2_2143", "MASTER_RSL_August_2004_2036", "MASTER_RSL_August_2006_2027", "MASTER_RSL_August_2007_2020", "MASTER_RSL_August_2008_2010", "MASTER_RSL_December_1998_2104", "MASTER_RSL_January_1999_2103", "MASTER_RSL_July_2003_2047", "MASTER_RSL_July_2004_2038", "MASTER_RSL_June_1999_2098", "MASTER_RSL_June_2000_2096", "MASTER_RSL_June_2001_2090", "MASTER_RSL_June_2002_2067", "MASTER_RSL_June_2004_2042", "MASTER_RSL_March_2000_2094", "MASTER_RSL_March_2002_2068", "MASTER_RSL_May_1999_2101", "MASTER_RSL_May_2001_V2_2145", "MASTER_RSL_May_2002_V2_2148", "MASTER_RSL_May_2004_2041", "MASTER_RSL_May_2006_2029", "MASTER_RSL_November_2007_2015", "MASTER_RSL_Oct_2002_V2_2147", "MASTER_RSL_October_2003_2048", "MASTER_RSL_October_2005_2031", "MASTER_RSL_October_2007_2013", "MASTER_RSL_October_2010_2127", "MASTER_RSL_September_2005_2037", "MASTER_SARP_2009_1990", "MASTER_SARP_2010_1989", "MASTER_SARP_2011_1974", "MASTER_SARP_2012_1983", "MASTER_SARP_2013_1967", "MASTER_SARP_2014_1961", "MASTER_SARP_2015_1958", "MASTER_SARP_2016_1912", "MASTER_Sky_2003_2052", "MASTER_Sky_2004_2035", "MASTER_Sky_2006_2026", "MASTER_SuomiNPP_2013_1969", "MASTER_SuomiNPP_2015_1970", "MASTER_TC4_2007_2021", "MASTER_WDTS_Fall_2022_2141", "MASTER_WDTS_SeptOct_2020_1940", "MASTER_WDTS_Spring_2021_1953", "MASTER_WDTS_Spring_2023_2252", "MAS_832", "MB2LME", "MCD06COSP_D3_MODIS", "MCD06COSP_M3_MODIS", "MCD12C1", "MCD12Q1", "MCD12Q2", "MCD15A2H", "MCD15A3H", "MCD18A1", "MCD18A2", "MCD18C1", "MCD18C2", "MCD19A1", "MCD19A1CMGL", "MCD19A1CMGO", "MCD19A1N", "MCD19A2", "MCD19A2CMG", "MCD19A2N", "MCD19A3CMG", "MCD19A3D", "MCD19A3DN", "MCD43A1", "MCD43A1N", "MCD43A2", "MCD43A2N", "MCD43A3", "MCD43A3N", "MCD43A4", "MCD43A4N", "MCD43C1", "MCD43C2", "MCD43C3", "MCD43C4", "MCD43D01", "MCD43D02", "MCD43D03", "MCD43D04", "MCD43D05", "MCD43D06", "MCD43D07", "MCD43D08", "MCD43D09", "MCD43D10", "MCD43D11", "MCD43D12", "MCD43D13", "MCD43D14", "MCD43D15", "MCD43D16", "MCD43D17", "MCD43D18", "MCD43D19", "MCD43D20", "MCD43D21", "MCD43D22", "MCD43D23", "MCD43D24", "MCD43D25", "MCD43D26", "MCD43D27", "MCD43D28", "MCD43D29", "MCD43D30", "MCD43D31", "MCD43D32", "MCD43D33", "MCD43D34", "MCD43D35", "MCD43D36", "MCD43D37", "MCD43D38", "MCD43D39", "MCD43D40", "MCD43D41", "MCD43D42", "MCD43D43", "MCD43D44", "MCD43D45", "MCD43D46", "MCD43D47", "MCD43D48", "MCD43D49", "MCD43D50", "MCD43D51", "MCD43D52", "MCD43D53", "MCD43D54", "MCD43D55", "MCD43D56", "MCD43D57", "MCD43D58", "MCD43D59", "MCD43D60", "MCD43D61", "MCD43D62", "MCD43D63", "MCD43D64", "MCD43D65", "MCD43D66", "MCD43D67", "MCD43D68", "MCD43GF", "MCD64A1", "MCDAODHD", "MCDWD_L3_F1C_NRT", "MCDWD_L3_F1_NRT", "MCDWD_L3_F2_NRT", "MCDWD_L3_F3_NRT", "MCDWD_L3_NRT", "MCR_LTER", "MELVILLE", "MERCHANT_SHIP", "MERGED_S3_OLCI_L3b_CYANTC", "MERGED_S3_OLCI_L3b_ILW", "MERGED_S3_OLCI_L3m_CYAN", "MERGED_S3_OLCI_L3m_CYANTC", "MERGED_S3_OLCI_L3m_ILW", "MERGED_TP_J1_OSTM_OST_CYCLES_V52", "MERGED_TP_J1_OSTM_OST_GMSL_ASCII_V51", "MERIS_L1_FRS", "MERIS_L1_RR", "MERIS_L2_FRS_IOP", "MERIS_L2_FRS_OC", "MERIS_L2_RR_IOP", "MERIS_L2_RR_OC", "MERIS_L3b_CHL", "MERIS_L3b_CYAN", "MERIS_L3b_CYANTC", "MERIS_L3b_IOP", "MERIS_L3b_KD", "MERIS_L3b_PAR", "MERIS_L3b_PIC", "MERIS_L3b_POC", "MERIS_L3b_RRS", "MERIS_L3m_CHL", "MERIS_L3m_CYAN", "MERIS_L3m_CYANTC", "MERIS_L3m_IOP", "MERIS_L3m_KD", "MERIS_L3m_PAR", "MERIS_L3m_PIC", "MERIS_L3m_POC", "MERIS_L3m_RRS", "MERIS_L4b_GSM", "MERIS_L4m_GSM", "MERRA2_CNN_HAQAST_PM25", "MFLL_CO2_Weighting_Functions_1891", "MFLL_XCO2_Range_10Hz_1892", "MI1B1", "MI1B2_ELLIPSOID_NRT", "MI1B2_TERRAIN_NRT", "MI2AS_AEROSOL_NRT", "MI2TC_CMV_BFR_NRT", "MI2TC_CMV_HDF_NRT", "MI3DAEF", "MI3DAENF", "MI3DAER", "MI3DALF", "MI3DALNF", "MI3DCDF", "MI3DCDNF", "MI3DCLDN", "MI3DLSF", "MI3DLSNF", "MI3DLSR", "MI3DRDF", "MI3DRDR", "MI3MAEF", "MI3MAENF", "MI3MAER", "MI3MALF", "MI3MALNF", "MI3MCDF", "MI3MCDNF", "MI3MCLDN", "MI3MCMVN", "MI3MLSF", "MI3MLSNF", "MI3MLSR", "MI3MRDF", "MI3MRDR", "MI3QCLDN", "MI3QCMVN", "MI3YCLDN", "MI3YCMVN", "MIANACP", "MIANCAGP", "MIANCARP", "MIANTASC", "MIB1LM", "MICRONESIAN", "MIL2ASAE", "MIL2ASLS", "MIL2TCAF", "MIL2TCAL", "MIL2TCCF", "MIL2TCCF", "MIL2TCCL", "MIL2TCSF", "MIL2TCSP", "MIL2TCSP", "MIL2TCST", "MIL3DAE", "MIL3DAEN", "MIL3DAL", "MIL3DALN", "MIL3DCFA", "MIL3DCLD", "MIL3DCOD", "MIL3DLS", "MIL3DLSN", "MIL3DRD", "MIL3MAE", "MIL3MAEN", "MIL3MAL", "MIL3MALN", "MIL3MCFA", "MIL3MCLD", "MIL3MCOD", "MIL3MJTA", "MIL3MLS", "MIL3MLSN", "MIL3MRD", "MIL3QAE", "MIL3QAEN", "MIL3QAL", "MIL3QALN", "MIL3QCFA", "MIL3QCLD", "MIL3QCOD", "MIL3QLS", "MIL3QLSN", "MIL3QRD", "MIL3YAE", "MIL3YAEN", "MIL3YAL", "MIL3YALN", "MIL3YCFA", "MIL3YCLD", "MIL3YCOD", "MIL3YLS", "MIL3YLSN", "MIL3YRD", "MIPOT", "MIRAI", "MIRC", "MIRCCM", "MIRCCMF", "MIRCCMF", "MISBR", "MISC_Apex_Floats", "MISR_885", "MISR_AEROSOL_CLIM", "MISR_Forest_AGB_SW_US_1978", "MITgcm_LLC4320_Pre-SWOT_JPL_L4_ACC_SMST_v1.0", "MITgcm_LLC4320_Pre-SWOT_JPL_L4_BassStrait_v1.0", "MITgcm_LLC4320_Pre-SWOT_JPL_L4_Boknis_v1.0", "MITgcm_LLC4320_Pre-SWOT_JPL_L4_CapeBasin_v1.0", "MITgcm_LLC4320_Pre-SWOT_JPL_L4_GotlandBasin_v1.0", "MITgcm_LLC4320_Pre-SWOT_JPL_L4_LabradorSea_v1.0", "MITgcm_LLC4320_Pre-SWOT_JPL_L4_MarmaraSea_v1.0", "MITgcm_LLC4320_Pre-SWOT_JPL_L4_NWAustralia_v1.0", "MITgcm_LLC4320_Pre-SWOT_JPL_L4_NWPacific_v1.0", "MITgcm_LLC4320_Pre-SWOT_JPL_L4_NewCaledonia_v1.0", "MITgcm_LLC4320_Pre-SWOT_JPL_L4_ROAM_MIZ_v1.0", "MITgcm_LLC4320_Pre-SWOT_JPL_L4_RockallTrough_v1.0", "MITgcm_LLC4320_Pre-SWOT_JPL_L4_WestAtlantic_v1.0", "MITgcm_LLC4320_Pre-SWOT_JPL_L4_WesternMed_v1.0", "MITgcm_LLC4320_Pre-SWOT_JPL_L4_Yongala_v1.0", "ML1OA", "ML1OA", "ML1RADD", "ML1RADD", "ML1RADG", "ML1RADG", "ML1RADT", "ML1RADT", "ML2BRO", "ML2BRO", "ML2CH3CL", "ML2CH3CL", "ML2CH3CN", "ML2CH3CN", "ML2CH3OH", "ML2CH3OH", "ML2CLO", "ML2CLO", "ML2CO", "ML2CO", "ML2CO_NRT", "ML2DGG", "ML2DGG", "ML2DGM", "ML2DGM", "ML2GPH", "ML2GPH", "ML2H2O", "ML2H2O", "ML2H2O_NRT", "ML2HCL", "ML2HCL", "ML2HCN", "ML2HCN", "ML2HNO3", "ML2HNO3", "ML2HNO3_NRT", "ML2HO2", "ML2HO2", "ML2HOCL", "ML2HOCL", "ML2IWC", "ML2IWC", "ML2N2O", "ML2N2O", "ML2N2O_NRT", "ML2O3", "ML2O3", "ML2O3_NRT", "ML2OH", "ML2OH", "ML2RHI", "ML2RHI", "ML2SO2", "ML2SO2", "ML2SO2_NRT", "ML2T", "ML2T", "ML2T_NRT", "ML3DBCH3CL", "ML3DBCH3CL", "ML3DBCLO", "ML3DBCLO", "ML3DBCO", "ML3DBCO", "ML3DBGPH", "ML3DBGPH", "ML3DBH2O", "ML3DBH2O", "ML3DBHCL", "ML3DBHCL", "ML3DBHCN", "ML3DBHCN", "ML3DBHNO3", "ML3DBHNO3", "ML3DBHOCL", "ML3DBHOCL", "ML3DBIWC", "ML3DBIWC", "ML3DBN2O", "ML3DBN2O", "ML3DBO3", "ML3DBO3", "ML3DBOH", "ML3DBOH", "ML3DBRHI", "ML3DBRHI", "ML3DBSO2", "ML3DBSO2", "ML3DBT", "ML3DBT", "ML3DZCH3CL", "ML3DZCH3CL", "ML3DZCLO", "ML3DZCLO", "ML3DZCO", "ML3DZCO", "ML3DZGPH", "ML3DZGPH", "ML3DZH2O", "ML3DZH2O", "ML3DZHCL", "ML3DZHCL", "ML3DZHCN", "ML3DZHCN", "ML3DZHNO3", "ML3DZHNO3", "ML3DZHOCL", "ML3DZHOCL", "ML3DZIWC", "ML3DZIWC", "ML3DZMBRO", "ML3DZMBRO", "ML3DZMHO2", "ML3DZMHO2", "ML3DZN2O", "ML3DZN2O", "ML3DZO3", "ML3DZO3", "ML3DZOH", "ML3DZOH", "ML3DZRHI", "ML3DZRHI", "ML3DZSO2", "ML3DZSO2", "ML3DZT", "ML3DZT", "ML3MBCH3CL", "ML3MBCH3CL", "ML3MBCLO", "ML3MBCLO", "ML3MBCO", "ML3MBCO", "ML3MBGPH", "ML3MBGPH", "ML3MBH2O", "ML3MBH2O", "ML3MBHCL", "ML3MBHCL", "ML3MBHCN", "ML3MBHCN", "ML3MBHNO3", "ML3MBHNO3", "ML3MBHOCL", "ML3MBHOCL", "ML3MBIWC", "ML3MBIWC", "ML3MBN2O", "ML3MBN2O", "ML3MBO3", "ML3MBO3", "ML3MBOH", "ML3MBOH", "ML3MBRHI", "ML3MBRHI", "ML3MBSO2", "ML3MBSO2", "ML3MBT", "ML3MBT", "MOCE", "MOD00F", "MOD00S", "MOD08_D3", "MOD08_E3", "MOD08_M3", "MOD09A1", "MOD09A1N", "MOD09CMA", "MOD09CMG", "MOD09CMG", "MOD09GA", "MOD09GA", "MOD09GQ", "MOD09GQ", "MOD09Q1", "MOD09Q1N", "MOD10A1", "MOD10A1F", "MOD10A2", "MOD10C1", "MOD10C2", "MOD10CM", "MOD11A1", "MOD11A2", "MOD11B1", "MOD11B2", "MOD11B3", "MOD11C1", "MOD11C2", "MOD11C3", "MOD11CM1D", "MOD11CM1N", "MOD11_L2", "MOD13A1", "MOD13A2", "MOD13A3", "MOD13A4N", "MOD13C1", "MOD13C2", "MOD13Q1", "MOD13Q4N", "MOD14", "MOD14A1", "MOD14A2", "MOD14CM1", "MOD15A2H", "MOD16A2", "MOD16A2GF", "MOD16A3GF", "MOD17A2H", "MOD17A2HGF", "MOD17A3HGF", "MOD21", "MOD21A1D", "MOD21A1N", "MOD21A2", "MOD21C1", "MOD21C2", "MOD21C3", "MOD28C2", "MOD28C3", "MOD29E1D", "MOD29P1D", "MOD29P1N", "MOD44B", "MOD44W", "MODAODHD", "MODCSR_8", "MODCSR_B", "MODGRNLD", "MODISA_L1", "MODISA_L1_GEO", "MODISA_L2_IOP", "MODISA_L2_IOP_NRT", "MODISA_L2_OC", "MODISA_L2_OC_NRT", "MODISA_L2_SST", "MODISA_L2_SST4", "MODISA_L2_SST4_NRT", "MODISA_L2_SST_NRT", "MODISA_L3b_CHL", "MODISA_L3b_CHL_NRT", "MODISA_L3b_FLH", "MODISA_L3b_FLH_NRT", "MODISA_L3b_IOP", "MODISA_L3b_IOP_NRT", "MODISA_L3b_KD", "MODISA_L3b_KD_NRT", "MODISA_L3b_LAND", "MODISA_L3b_NSST", "MODISA_L3b_NSST_NRT", "MODISA_L3b_PAR", "MODISA_L3b_PAR_NRT", "MODISA_L3b_PIC", "MODISA_L3b_PIC_NRT", "MODISA_L3b_POC", "MODISA_L3b_POC_NRT", "MODISA_L3b_RRS", "MODISA_L3b_RRS_NRT", "MODISA_L3b_SST", "MODISA_L3b_SST4", "MODISA_L3b_SST4_NRT", "MODISA_L3b_SST_NRT", "MODISA_L3m_CHL", "MODISA_L3m_CHL_NRT", "MODISA_L3m_FLH", "MODISA_L3m_FLH_NRT", "MODISA_L3m_IOP", "MODISA_L3m_IOP_NRT", "MODISA_L3m_KD", "MODISA_L3m_KD_NRT", "MODISA_L3m_LAND", "MODISA_L3m_NSST", "MODISA_L3m_NSST_NRT", "MODISA_L3m_PAR", "MODISA_L3m_PAR_NRT", "MODISA_L3m_PIC", "MODISA_L3m_PIC_NRT", "MODISA_L3m_POC", "MODISA_L3m_POC_NRT", "MODISA_L3m_RRS", "MODISA_L3m_RRS_NRT", "MODISA_L3m_SST", "MODISA_L3m_SST4", "MODISA_L3m_SST4_NRT", "MODISA_L3m_SST_NRT", "MODISA_L4b_GSM", "MODISA_L4m_GSM", "MODIST_L1", "MODIST_L1_GEO", "MODIST_L2_IOP", "MODIST_L2_IOP_NRT", "MODIST_L2_OC", "MODIST_L2_OC_NRT", "MODIST_L2_SST", "MODIST_L2_SST4", "MODIST_L2_SST4_NRT", "MODIST_L2_SST_NRT", "MODIST_L3b_CHL", "MODIST_L3b_CHL_NRT", "MODIST_L3b_FLH", "MODIST_L3b_FLH_NRT", "MODIST_L3b_IOP", "MODIST_L3b_IOP_NRT", "MODIST_L3b_KD", "MODIST_L3b_KD_NRT", "MODIST_L3b_NSST", "MODIST_L3b_NSST_NRT", "MODIST_L3b_PAR", "MODIST_L3b_PAR_NRT", "MODIST_L3b_PIC", "MODIST_L3b_PIC_NRT", "MODIST_L3b_POC", "MODIST_L3b_POC_NRT", "MODIST_L3b_RRS", "MODIST_L3b_RRS_NRT", "MODIST_L3b_SST", "MODIST_L3b_SST4", "MODIST_L3b_SST4_NRT", "MODIST_L3b_SST_NRT", "MODIST_L3m_CHL", "MODIST_L3m_CHL_NRT", "MODIST_L3m_FLH", "MODIST_L3m_FLH_NRT", "MODIST_L3m_IOP", "MODIST_L3m_IOP_NRT", "MODIST_L3m_KD", "MODIST_L3m_KD_NRT", "MODIST_L3m_NSST", "MODIST_L3m_NSST_NRT", "MODIST_L3m_PAR", "MODIST_L3m_PAR_NRT", "MODIST_L3m_PIC", "MODIST_L3m_PIC_NRT", "MODIST_L3m_POC", "MODIST_L3m_POC_NRT", "MODIST_L3m_RRS", "MODIST_L3m_RRS_NRT", "MODIST_L3m_SST", "MODIST_L3m_SST4", "MODIST_L3m_SST4_NRT", "MODIST_L3m_SST_NRT", "MODIST_L4b_GSM", "MODIST_L4m_GSM", "MODIS_A-JPL-L2P-v2019.0", "MODIS_AQUA_L3_SST_MID-IR_8DAY_4KM_NIGHTTIME_V2019.0", "MODIS_AQUA_L3_SST_MID-IR_8DAY_9KM_NIGHTTIME_V2019.0", "MODIS_AQUA_L3_SST_MID-IR_ANNUAL_4KM_NIGHTTIME_V2019.0", "MODIS_AQUA_L3_SST_MID-IR_ANNUAL_9KM_NIGHTTIME_V2019.0", "MODIS_AQUA_L3_SST_MID-IR_DAILY_4KM_NIGHTTIME_V2019.0", "MODIS_AQUA_L3_SST_MID-IR_DAILY_9KM_NIGHTTIME_V2019.0", "MODIS_AQUA_L3_SST_MID-IR_MONTHLY_4KM_NIGHTTIME_V2019.0", "MODIS_AQUA_L3_SST_MID-IR_MONTHLY_9KM_NIGHTTIME_V2019.0", "MODIS_AQUA_L3_SST_THERMAL_8DAY_4KM_DAYTIME_V2019.0", "MODIS_AQUA_L3_SST_THERMAL_8DAY_4KM_NIGHTTIME_V2019.0", "MODIS_AQUA_L3_SST_THERMAL_8DAY_9KM_DAYTIME_V2019.0", "MODIS_AQUA_L3_SST_THERMAL_8DAY_9KM_NIGHTTIME_V2019.0", "MODIS_AQUA_L3_SST_THERMAL_ANNUAL_4KM_DAYTIME_V2019.0", "MODIS_AQUA_L3_SST_THERMAL_ANNUAL_4KM_NIGHTTIME_V2019.0", "MODIS_AQUA_L3_SST_THERMAL_ANNUAL_9KM_DAYTIME_V2019.0", "MODIS_AQUA_L3_SST_THERMAL_ANNUAL_9KM_NIGHTTIME_V2019.0", "MODIS_AQUA_L3_SST_THERMAL_DAILY_4KM_DAYTIME_V2019.0", "MODIS_AQUA_L3_SST_THERMAL_DAILY_4KM_NIGHTTIME_V2019.0", "MODIS_AQUA_L3_SST_THERMAL_DAILY_9KM_DAYTIME_V2019.0", "MODIS_AQUA_L3_SST_THERMAL_DAILY_9KM_NIGHTTIME_V2019.0", "MODIS_AQUA_L3_SST_THERMAL_MONTHLY_4KM_DAYTIME_V2019.0", "MODIS_AQUA_L3_SST_THERMAL_MONTHLY_4KM_NIGHTTIME_V2019.0", "MODIS_AQUA_L3_SST_THERMAL_MONTHLY_9KM_DAYTIME_V2019.0", "MODIS_AQUA_L3_SST_THERMAL_MONTHLY_9KM_NIGHTTIME_V2019.0", "MODIS_CCaN_NDVI_Trends_Alaska_1666", "MODIS_CR_Equal_Angle_3h", "MODIS_CR_Equal_Angle_Daily", "MODIS_CR_Equal_Area_3h", "MODIS_MAIAC_Reflectance_1700", "MODIS_PAR_1140", "MODIS_T-JPL-L2P-v2019.0", "MODIS_TERRA_L3_SST_MID-IR_8DAY_4KM_NIGHTTIME_V2019.0", "MODIS_TERRA_L3_SST_MID-IR_8DAY_9KM_NIGHTTIME_V2019.0", "MODIS_TERRA_L3_SST_MID-IR_ANNUAL_4KM_NIGHTTIME_V2019.0", "MODIS_TERRA_L3_SST_MID-IR_ANNUAL_9KM_NIGHTTIME_V2019.0", "MODIS_TERRA_L3_SST_MID-IR_DAILY_4KM_NIGHTTIME_V2019.0", "MODIS_TERRA_L3_SST_MID-IR_DAILY_9KM_NIGHTTIME_V2019.0", "MODIS_TERRA_L3_SST_MID-IR_MONTHLY_4KM_NIGHTTIME_V2019.0", "MODIS_TERRA_L3_SST_MID-IR_MONTHLY_9KM_NIGHTTIME_V2019.0", "MODIS_TERRA_L3_SST_THERMAL_8DAY_4KM_DAYTIME_V2019.0", "MODIS_TERRA_L3_SST_THERMAL_8DAY_4KM_NIGHTTIME_V2019.0", "MODIS_TERRA_L3_SST_THERMAL_8DAY_9KM_DAYTIME_V2019.0", "MODIS_TERRA_L3_SST_THERMAL_8DAY_9KM_NIGHTTIME_V2019.0", "MODIS_TERRA_L3_SST_THERMAL_ANNUAL_4KM_DAYTIME_V2019.0", "MODIS_TERRA_L3_SST_THERMAL_ANNUAL_4KM_NIGHTTIME_V2019.0", "MODIS_TERRA_L3_SST_THERMAL_ANNUAL_9KM_DAYTIME_V2019.0", "MODIS_TERRA_L3_SST_THERMAL_ANNUAL_9KM_NIGHTTIME_V2019.0", "MODIS_TERRA_L3_SST_THERMAL_DAILY_4KM_DAYTIME_V2019.0", "MODIS_TERRA_L3_SST_THERMAL_DAILY_4KM_NIGHTTIME_V2019.0", "MODIS_TERRA_L3_SST_THERMAL_DAILY_9KM_DAYTIME_V2019.0", "MODIS_TERRA_L3_SST_THERMAL_DAILY_9KM_NIGHTTIME_V2019.0", "MODIS_TERRA_L3_SST_THERMAL_MONTHLY_4KM_DAYTIME_V2019.0", "MODIS_TERRA_L3_SST_THERMAL_MONTHLY_4KM_NIGHTTIME_V2019.0", "MODIS_TERRA_L3_SST_THERMAL_MONTHLY_9KM_DAYTIME_V2019.0", "MODIS_TERRA_L3_SST_THERMAL_MONTHLY_9KM_NIGHTTIME_V2019.0", "MODIS_emissions_758", "MODVI", "MOD_L2_DC", "MOKIHANA", "MONTEREY_BAY", "MOOSE_Aerodyne-Mobile-Laboratory", "MOOSE_AircraftRemoteSensing_NASA-G3_GCAS_Data", "MOP02J", "MOP02J", "MOP02J", "MOP02N", "MOP02N", "MOP02N", "MOP02T", "MOP02T", "MOP02T", "MOP03J", "MOP03J", "MOP03J", "MOP03JM", "MOP03JM", "MOP03JM", "MOP03N", "MOP03N", "MOP03N", "MOP03NM", "MOP03NM", "MOP03NM", "MOP03T", "MOP03T", "MOP03T", "MOP03TM", "MOP03TM", "MOP03TM", "MOPCH", "MOPITT_co_835", "MRIRN2IM", "MRIRN2L1", "MRIRN3IM", "MRIRN3L1", "MSAQSO2L4", "MSG01-OSPO-L2P-v1.0", "MSG02-OSPO-L2P-v1.0", "MSG03-OSPO-L2P-v1.0", "MSG04-OSPO-L2P-v1.0", "MSLERLSTL3d10", "MSLERLSTL3zm", "MSLERNNL3d10", "MSLERNNL3zm", "MSLSP30NA", "MSO3L3zm5", "MSULST", "MSULTT", "MSUOP", "MSUUTT", "MSVOLSO2L4", "MS_Sound", "MTSAT2-OSPO-L2P-v1.0", "MUR-JPL-L4-GLOB-v4.1", "MUR25-JPL-L4-GLOB-v04.2", "MURI_Camouflage", "MURI_HI", "MUSE", "MVCO", "MW_IR_OI-REMSS-L4-GLOB-v5.0", "MW_IR_OI-REMSS-L4-GLOB-v5.1", "MW_OI-REMSS-L4-GLOB-v5.0", "MW_OI-REMSS-L4-GLOB-v5.1", "MYD00F", "MYD00S", "MYD08_D3", "MYD08_E3", "MYD08_M3", "MYD09A1", "MYD09CMA", "MYD09CMG", "MYD09CMG", "MYD09GA", "MYD09GA", "MYD09GQ", "MYD09GQ", "MYD09Q1", "MYD10A1", "MYD10A1F", "MYD10A2", "MYD10C1", "MYD10C2", "MYD10CM", "MYD11A1", "MYD11A2", "MYD11B1", "MYD11B2", "MYD11B3", "MYD11C1", "MYD11C2", "MYD11C3", "MYD11CM1D", "MYD11CM1N", "MYD11_L2", "MYD13A1", "MYD13A2", "MYD13A3", "MYD13C1", "MYD13C2", "MYD13Q1", "MYD14", "MYD14A1", "MYD14A2", "MYD14CM1", "MYD15A2H", "MYD16A2", "MYD16A2GF", "MYD16A3GF", "MYD17A2H", "MYD17A2HGF", "MYD17A3HGF", "MYD21", "MYD21A1D", "MYD21A1N", "MYD21A2", "MYD21C1", "MYD21C2", "MYD21C3", "MYD28C2", "MYD28C3", "MYD29E1D", "MYD29P1D", "MYD29P1N", "MYDAODHD", "MYDGB0", "MYDVI", "MYD_L2_CB", "MYD_L2_DC", "MYD_L2_MPLCT", "MYD_L3_OFLCT", "MagMix", "Main_Melt_Onset_Dates_1841", "Maps_AGB_North_Slope_AK_1565", "Marlon_Lewis_92", "Maryland_Temperature_Humidity_1319", "MatthewsVegetation_419", "Mean_Seasonal_LAI_1653", "Medit_Ligurian", "MetOpA_GOME2_SIF_V2_2292", "MetOpB_GOME2_SIF_2182", "Meteorological_1065", "Methane_Ebullition_Lakes_AK_1861", "Methane_Ethane_MA_NH_1982", "Methane_Flaring_Sites_VIIRS_1874", "Microbiome", "Mid-latitude_soils_705", "Missouri_Reservoirs_RSWQ", "MonthlyWetland_CH4_WetCHARTsV2_2346", "Monthly_Hydrological_Fluxes_1647", "MultiInstrumentFusedXCO2", "MumfordCove", "N01", "N07_AVH02C1", "N07_AVH09C1", "N07_AVH13C1", "N09_AVH02C1", "N09_AVH09C1", "N09_AVH13C1", "N11_AVH02C1", "N11_AVH09C1", "N11_AVH13C1", "N14_AVH02C1", "N14_AVH09C1", "N14_AVH13C1", "N16_AVH02C1", "N16_AVH09C1", "N16_AVH13C1", "N18_AVH02C1", "N18_AVH09C1", "N18_AVH13C1", "N19_AVH02C1", "N19_AVH09C1", "N19_AVH13C1", "N21-VIIRS-L2P-ACSPO-v2.80", "N21-VIIRS-L3U-ACSPO-v2.80", "NAAMES", "NAAMES_AerosolCloud_AircraftRemoteSensing_Data", "NAAMES_Aerosol_AircraftInSitu_Data", "NAAMES_Aerosol_ShipInSitu_Data", "NAAMES_Cloud_AircraftInSitu_Data", "NAAMES_Merge_Data", "NAAMES_MetNav_AircraftInSitu_Data", "NAAMES_MetNav_ShipInSitu_Data", "NAAMES_Met_SondeInSitu_Data", "NAAMES_Misc_Ship_Data", "NAAMES_Ocean_AircraftRemoteSensing_Data", "NAAMES_Radiation_AircraftInSitu_Data", "NAAMES_TraceGas_AircraftInSitu_Data", "NAAMES_TraceGas_ShipInSitu_Data", "NAB08", "NABE", "NACP_ACES_V2_1943", "NACP_BlackSpruce_Burn-Severity_1331", "NACP_Boreal_Biome_Biomass_1273", "NACP_Forest_Biophysical_1046", "NACP_Forest_Conservation_1662", "NACP_GHG_Data_Compilation_1206", "NACP_MCI_CO2_Inventory_1205", "NACP_MCI_CO2_Inversions_1204", "NACP_MCI_CO2_Measurements_1202", "NACP_MCI_Crop_GPP_1217", "NACP_Modeled_NEE_NEP_Fluxes_1203", "NACP_MsTMIP_Model_Driver_1220", "NACP_MsTMIP_Model_Structure_1228", "NACP_MsTMIP_TBMO_1225", "NACP_MsTMIP_Unified_NA_SoilMap_1242", "NACP_NAM_HYSPLIT_Footprints_1586", "NACP_PNW_Carbon_Balance_1317", "NACP_PalEON_MIP_1779", "NACP_Peatland_Burn-Severity_1283", "NACP_Peatland_Land_Cover_MI_1513", "NACP_Regional_Model_GHG_Aggr_1179", "NACP_Regional_Obs_Model_Grid_1157", "NACP_Regional_Obs_Model_Orig_1193", "NACP_Regional_Obs_Model_Suppl_1158", "NACP_Site_Model_Data_Orig_Fmt_1192", "NACP_Site_Model_Flux_Std_Fmt_1183", "NACP_Site_Tower_Met_and_Flux_1178", "NACP_TERRA-PNW_1292", "NACP_Vista_CA_CH4_Inventory_1726", "NACP_Vista_LA_CH4_Inventory_1525", "NACP_Wild_Cropland_Fuel_Map_1163", "NACP_Woody_Veg_N_Slope_AK_V2_1365", "NAFD-NEX_Attribution_1799", "NAFD-NEX_Forest_Disturbance_1290", "NAFD_Disturbance_1077", "NARSTO_EPA_SS_ATLANTA_1999_CHEM_PM_MET_DATA", "NARSTO_EPA_SS_ATLANTA_1999_UAH_MIPS_DATA", "NARSTO_EPA_SS_ATLANTA_RAPID_SPMS_DATA", "NARSTO_EPA_SS_BALTIMORE_JHU_LIDAR_DATA", "NARSTO_EPA_SS_BALTIMORE_JHU_MET_DATA", "NARSTO_EPA_SS_BALTIMORE_RAPID_SPMS_DATA", "NARSTO_EPA_SS_BALTIMORE_SEAS_PM25_METAL_CYTOKINES", "NARSTO_EPA_SS_FRESNO_AETHALOM_MULTI_WL_CARBON", "NARSTO_EPA_SS_FRESNO_BAM_PM_MASS", "NARSTO_EPA_SS_FRESNO_EC_PM25_FRACTION", "NARSTO_EPA_SS_FRESNO_MET_DATA", "NARSTO_EPA_SS_FRESNO_PARTICLE_PAC_DATA", "NARSTO_EPA_SS_FRESNO_PM25_NO3_SO4", "NARSTO_EPA_SS_FRESNO_PM25_OC_EC", "NARSTO_EPA_SS_FRESNO_TEOM_PM_MASS", "NARSTO_EPA_SS_HOUSTON_DMA_DATA", "NARSTO_EPA_SS_HOUSTON_NH3_HNO3_DATA", "NARSTO_EPA_SS_HOUSTON_NO3_SO4_C_DATA", "NARSTO_EPA_SS_HOUSTON_RAPID_SPMS_DATA", "NARSTO_EPA_SS_HOUSTON_TEXAQS2000_CAMS_DATA", "NARSTO_EPA_SS_HOUSTON_TEXAQS2000_DOE_G-1_DATA", "NARSTO_EPA_SS_HOUSTON_TEXAQS2000_HCHO_H2O2_DATA", "NARSTO_EPA_SS_HOUSTON_TEXAQS2000_PM25_ORG_DATA", "NARSTO_EPA_SS_HOUSTON_TEXAQS2000_PM_FTIR", "NARSTO_EPA_SS_HOUSTON_TEXAQS2000_PM_SIZE_MASS", "NARSTO_EPA_SS_HOUSTON_TEXAQS2000_WB_TUNNEL", "NARSTO_EPA_SS_LOS_ANGELES_AETHALOMETER_EC_DATA", "NARSTO_EPA_SS_LOS_ANGELES_APS_DATA", "NARSTO_EPA_SS_LOS_ANGELES_HEADS_PART_IONS_MASS", "NARSTO_EPA_SS_LOS_ANGELES_MOUDI_DATA", "NARSTO_EPA_SS_LOS_ANGELES_PARTISOL_DATA", "NARSTO_EPA_SS_LOS_ANGELES_PM25_CARBON", "NARSTO_EPA_SS_LOS_ANGELES_PM25_NITRATE", "NARSTO_EPA_SS_LOS_ANGELES_SMPS_DATA", "NARSTO_EPA_SS_LOS_ANGELES_TEOM_PM25_DATA", "NARSTO_EPA_SS_NEW_YORK_AIR_CHEM_PM_MET_DATA", "NARSTO_EPA_SS_PITTSBURGH_GAS_PM_PROPERTY_DATA", "NARSTO_EPA_SS_PITTSBURGH_MET_DATA", "NARSTO_EPA_SS_PITTSBURGH_PM_COMPOSITION_DATA", "NARSTO_EPA_SS_PITTSBURGH_RAPID_SPMS_DATA", "NARSTO_EPA_SS_ST_LOUIS_AIR_CHEM_PM_MET_DATA", "NARSTO_ICARTT_NEAX_2004_DOE_G-1_DATA", "NARSTO_NE_MODEL", "NARSTO_NE_NEXRAD_IMAGES", "NARSTO_PAC2001_CASSIAR_TUNNEL_GAS_PM_DATA", "NARSTO_PAC2001_CESSNA_VOC_PM_OZONE_MET_DATA", "NARSTO_PAC2001_CONVAIR_PM_OZONE_MET_DATA", "NARSTO_PAC2001_GOLDEN_EARS_GAS_PM_DATA", "NARSTO_PAC2001_GVRD_CAPMON_AIR_QUAL_DATA", "NARSTO_PAC2001_LANGLEY_GAS_PM_MET_DATA", "NARSTO_PAC2001_SLOCAN_PARK_GAS_PM_MET_DATA", "NARSTO_PAC2001_SUMAS_MTN_GAS_PM_MET_DATA", "NARSTO_SHEMP_CANADA_PM_COMPOSITION_DATA", "NARSTO_SOS99NASH_G-1_AIR_CHEMISTRY_DATA", "NARSTO_SOS99NASH_SURFACE_MET_CHEM_DATA", "NARSTO_SOS99NASH_WIND_PROFILER_DATA", "NARSTO_SOS99NASH_WP3D_CHEMISTRY_DATA", "NARSTO_SOS_SC_UPSTATE_PM25_COMPOSITION", "NARSTO_STIFS_CANADA_PM_INORG_VOC_DATA", "NARSTO_Texas_PM2.5_Sampling_and_Analysis_Study_1997-1998", "NASADEM_HGT", "NASADEM_NC", "NASADEM_NUMNC", "NASADEM_SC", "NASADEM_SHHP", "NASADEM_SIM", "NASADEM_SSP", "NASASatellite_Dev_Applications_2293", "NASA_Airborne_Lidar_Flights", "NASMo_TiAM_250m_2326", "NA_MODIS_Surface_Biophysics_1210", "NA_TreeAge_1096", "NBCD2000_V2_1161", "NCALDAS_NOAH0125_D", "NCALDAS_NOAH0125_Trends", "ND01_Age_Maps_1184", "ND01_Georectified_Products_1165", "ND01_Land_Cover_Maps_1259", "ND01_Pasture_Nutrients_1135", "ND01_Pasture_Spectra_1154", "ND01_Registered_TM_MSS_1197", "ND01_Spectral_Mixture_Models_1188", "ND01_Stream_Chemistry_1119", "ND01_Watershed_Defor_1159", "ND02_Fertilization_Experiment_954", "ND02_Landsat_TM_MSS_Para_1156", "ND02_Mulching_Experiment_950", "ND02_Non_Woody_Biomass_1115", "ND02_REE_Soil_VWC_1061", "ND02_REE_Trace_Gas_Tapajos_955", "ND02_Soil_CO2_Extracts_1074", "ND02_Soil_CO2_Flux_1066", "ND02_Soil_Gas_Flux_Apeu_953", "ND02_Soil_Gases_REE_1117", "ND02_Soil_Hydraulic_Conductivity_1075", "ND02_Tree_Heights_DBH_951", "ND02_Water_Chemistry_Paragominas_1067", "ND03_Flowpath_Chemistry_1076", "ND03_Streams_Soilwater_1113", "ND04_C_Nutrient_Stocks_1069", "ND04_Secondary_Forest_Recovery_1068", "ND04_Soil_H2O_Manaus_1246", "ND04_Termite_Mounds_1072", "ND06_LandUse_Studies_1130", "ND07_15N_Leaves_Soils_1121", "ND07_NO_Flux_Cerrado_1124", "ND07_PLFA_Soils_Microbial_Biomass_1017", "ND07_Stream_Chemistry_Brasilia_1018", "ND07_Trace_Gas_Land_Use_1016", "ND08_Biomass_Jari_1148", "ND08_Soil_Respiration_1250", "ND10_Soil_Chemistry_1171", "ND11_Carbon_Export_CPOM_913", "ND11_Logging_Damage_MT_977", "ND11_Nitrogen_Transfer_Leaf_Litter_915", "ND11_Regeneration_Succession_965", "ND11_Soil_Nitrate_Moisture_MT_976", "ND11_Soil_Spatial_Variability_914", "ND11_Soil_Water_Pressure_851", "ND11_Stream_Nutrients_921", "ND11_Tree_Vine_Biomass_MT_922", "ND11_Veg_Biomass_MT_964", "ND30_Litter_Para_1129", "ND30_Pasture_Degradation_1164", "ND30_REE_Water_Chemistry_1131", "NDVI_Forest_Structure_1797", "NEMSN5L2", "NES-LTER", "NEUROST_SSH-SST_L4_V2024.0", "NEWS_WEB_ACLIM", "NEWS_WEB_MCLIM", "NFRDI", "NGLI_Lake_Bourne", "NHICEM", "NHSNOWM", "NIH-NSF_Lake_Erie", "NIMBUS7_ERB_Ch10C_TSI_NAT", "NIMBUS7_ERB_SEFDT", "NIMBUS7_NFOV_MLCE", "NISE", "NISE", "NLDAS_FORA0125_H", "NLDAS_FORA0125_H", "NLDAS_FORA0125_M", "NLDAS_FORA0125_M", "NLDAS_FORA0125_MC", "NLDAS_FORA0125_MC", "NLDAS_FORB0125_H", "NLDAS_FORB0125_H", "NLDAS_FORB0125_M", "NLDAS_FORB0125_M", "NLDAS_FORB0125_MC", "NLDAS_FORB0125_MC", "NLDAS_MOS0125_H", "NLDAS_MOS0125_H", "NLDAS_MOS0125_M", "NLDAS_MOS0125_M", "NLDAS_MOS0125_MC", "NLDAS_MOS0125_MC", "NLDAS_NOAH0125_H", "NLDAS_NOAH0125_H", "NLDAS_NOAH0125_M", "NLDAS_NOAH0125_M", "NLDAS_NOAH0125_MC", "NLDAS_NOAH0125_MC", "NLDAS_VIC0125_H", "NLDAS_VIC0125_H", "NLDAS_VIC0125_M", "NLDAS_VIC0125_M", "NLDAS_VIC0125_MC", "NLDAS_VIC0125_MC", "NMMIEAI-L2-NRT", "NMSO2-PCA-L2-NRT", "NMTO3NRT", "NOAA", "NOAA_ToF_CIMS_Instrument_Data_1921", "NOBM_DAY", "NOBM_MON", "NOPP_float", "NORBAL", "NPBUVO3-L2-NRT", "NPP_ATH_577", "NPP_BCN_204", "NPP_BDK_203", "NPP_BOREAL_611", "NPP_BRD_205", "NPP_BRR_157", "NPP_BRW_580", "NPP_CBAY_473", "NPP_CHM_578", "NPP_CHR_468", "NPP_CLB_206", "NPP_CNL_465", "NPP_CNS_191", "NPP_CPR_145", "NPP_DCK_207", "NPP_DRN_215", "NPP_DZH_192", "NPP_EMDI_615", "NPP_FLK_201", "NPP_GNN_474", "NPP_GPPDI_617", "NPP_GRIDDED_GPPDI_614", "NPP_GSM_804", "NPP_Grassland_31_654", "NPP_Grassland_613", "NPP_HYS_208", "NPP_IBP_198", "NPP_JDR_202", "NPP_JHN_475", "NPP_JRN_209", "NPP_KDE_216", "NPP_KHC_217", "NPP_KHM_146", "NPP_KLN_147", "NPP_KNZ_148", "NPP_KRK_193", "NPP_KRS_149", "NPP_KSM_466", "NPP_LMT_150", "NPP_LQL_476", "NPP_MDL_210", "NPP_MGD_477", "NPP_MMG_802", "NPP_MNS_579", "NPP_MNT_413", "NPP_MRF_478", "NPP_MSS_572", "NPP_MTD_469", "NPP_MULTIBIOME_653", "NPP_Multi-Biome_125_Sites_1352", "NPP_NLS_194", "NPP_NRB_151", "NPP_ODS_214", "NPP_OLK_195", "NPP_OSG_211", "NPP_OTR_152", "NPP_OTTER_472", "NPP_PIK_575", "NPP_PMP_212", "NPP_PSH_219", "NPP_REDWOOD_803", "NPP_RMY_574", "NPP_SCH_573", "NPP_SCR_479", "NPP_SES_480", "NPP_SHR_153", "NPP_SLV_218", "NPP_SNF_190", "NPP_SSP_467", "NPP_TEM_471", "NPP_TLK_581", "NPP_TLL_196", "NPP_TMG_470", "NPP_TMN_154", "NPP_TROPICAL_616", "NPP_TVA_155", "NPP_TWM_213", "NPP_VAST_576", "NPP_VND_197", "NPP_WBW_819", "NPP_WOODY_655", "NPP_XLN_156", "NPP_surfaces_750", "NSCAT_25KM_MGDR_V2", "NSCAT_AER_HOFFMAN_L2_OW_WIND_VECTOR_AMBIGUITY_REMOVAL", "NSCAT_BYU_L3_OW_SIGMA0_ENHANCED", "NSCAT_LEVEL_1.7_V2", "NSCAT_LEVEL_2_V2", "NSCAT_LEVEL_3_BROWSE_IMAGES", "NSCAT_LEVEL_3_V2", "NSCAT_W25_RMGDR_V2", "NSF-BWZ", "NSF_Gulf_Rapid", "NSIDC-0051", "NSIDC-0071", "NSIDC-0077", "NSIDC-0081", "NSIDC-0477", "NSIDC-0478", "NSIDC-0481", "NSIDC-0484", "NSIDC-0498", "NSIDC-0525", "NSIDC-0530", "NSIDC-0531", "NSIDC-0532", "NSIDC-0533", "NSIDC-0534", "NSIDC-0535", "NSIDC-0545", "NSIDC-0547", "NSIDC-0642", "NSIDC-0645", "NSIDC-0646", "NSIDC-0666", "NSIDC-0670", "NSIDC-0709", "NSIDC-0712", "NSIDC-0713", "NSIDC-0714", "NSIDC-0715", "NSIDC-0720", "NSIDC-0723", "NSIDC-0724", "NSIDC-0725", "NSIDC-0727", "NSIDC-0731", "NSIDC-0738", "NSIDC-0754", "NSIDC-0756", "NSIDC-0761", "NSIDC-0764", "NSIDC-0766", "NSIDC-0766", "NSIDC-0774", "NSIDC-0775", "NSIDC-0776", "NSIDC-0777", "NSIDC-0778", "NSIDC-0779", "NSIDC-0781", "NSIDC-0782", "NSIDC-0786", "NSIDC-0787", "NSIDC-0791", "NSIDC-0792", "NSIDC-0793", "NSIDC-0794", "NSIDC-0796", "NVAP_CLIMATE_Layered-Precipitable-Water", "NVAP_CLIMATE_Total-Precipitable-Water", "NVAP_OCEAN_Total-Precipitable-Water", "NVAP_WEATHER_Layered-Precipitable-Water", "NVAP_WEATHER_Total-Precipitable-Water", "NWT_Burn_Severity_Maps_1694", "New_England_CH4_1311", "New_Hampshire_Landcover_1305", "Niwot_Ridge_CNPAM_Fluorescence_1722", "Niwot_Ridge_Pigment_1723", "NmAVCS1H", "NmAVCS3G", "NmAVCS3H", "NmHRIR1H", "NmHRIR1T", "NmHRIR3G", "NmHRIR3H", "NmIDCS1H", "NmIDCS3G", "NmIDCS3H", "NmIcEdg2", "NmTHIR115-1H", "NmTHIR115-1T", "NmTHIR115-3G", "NmTHIR115-3H", "NmTHIR67-1H", "NmTHIR67-1T", "NmTHIR67-3G", "NmTHIR67-3H", "NmTHIRmtg-1T", "Nome_Veg_Plots_1372", "Non-Forest_Trees_Sahara_Sahel_1832", "Nongrowing_Season_CO2_Flux_1692", "NorthSlope_NEE_TVPRM_1920", "North_Carolina_Coast", "North_Carolina_Sabrina", "North_Sea", "North_Slope_Transect_Veg_Maps_1386", "North_Slope_Veg_Plots_1536", "Northern_Alaska_Veg_Maps_1359", "OAHU", "OASIS_Moorings", "OCEANSAT-2", "OCEAN_LIDAR", "OCEAN_MASS_TELLUS_MASCON_CRI_TIME_SERIES_RL06.1_V3", "OCFLEXPART", "OCO2GriddedXCO2", "OCO2_Att", "OCO2_Att", "OCO2_Att", "OCO2_Att", "OCO2_Att", "OCO2_Eph", "OCO2_Eph", "OCO2_Eph", "OCO2_Eph", "OCO2_Eph", "OCO2_GEOS_L3CO2_DAY", "OCO2_GEOS_L3CO2_MONTH", "OCO2_L1B_Calibration", "OCO2_L1B_Calibration", "OCO2_L1B_Calibration", "OCO2_L1B_Calibration", "OCO2_L1B_Science", "OCO2_L1B_Science", "OCO2_L1B_Science", "OCO2_L1B_Science", "OCO2_L1aIn_Pixel", "OCO2_L1aIn_Pixel", "OCO2_L1aIn_Pixel", "OCO2_L1aIn_Pixel", "OCO2_L1aIn_Pixel", "OCO2_L1aIn_Sample", "OCO2_L1aIn_Sample", "OCO2_L1aIn_Sample", "OCO2_L1aIn_Sample", "OCO2_L1aIn_Sample", "OCO2_L2_ABand", "OCO2_L2_ABand", "OCO2_L2_ABand", "OCO2_L2_ABand", "OCO2_L2_CO2Prior", "OCO2_L2_CO2Prior", "OCO2_L2_CO2Prior", "OCO2_L2_CO2Prior", "OCO2_L2_Diagnostic", "OCO2_L2_Diagnostic", "OCO2_L2_Diagnostic", "OCO2_L2_Diagnostic", "OCO2_L2_IMAPDOAS", "OCO2_L2_IMAPDOAS", "OCO2_L2_IMAPDOAS", "OCO2_L2_IMAPDOAS", "OCO2_L2_Lite_FP", "OCO2_L2_Lite_FP", "OCO2_L2_Lite_FP", "OCO2_L2_Lite_SIF", "OCO2_L2_Lite_SIF", "OCO2_L2_Met", "OCO2_L2_Met", "OCO2_L2_Met", "OCO2_L2_Met", "OCO2_L2_Met", "OCO2_L2_Standard", "OCO2_L2_Standard", "OCO2_L2_Standard", "OCO2_L2_Standard", "OCO3_L1B_Calibration", "OCO3_L1B_Calibration", "OCO3_L1B_Calibration", "OCO3_L1B_Science", "OCO3_L1B_Science", "OCO3_L1B_Science", "OCO3_L2_ABand", "OCO3_L2_ABand", "OCO3_L2_ABand", "OCO3_L2_CO2Prior", "OCO3_L2_CO2Prior", "OCO3_L2_CO2Prior", "OCO3_L2_Diagnostic", "OCO3_L2_Diagnostic", "OCO3_L2_Diagnostic", "OCO3_L2_IMAPDOAS", "OCO3_L2_IMAPDOAS", "OCO3_L2_IMAPDOAS", "OCO3_L2_Lite_FP", "OCO3_L2_Lite_FP", "OCO3_L2_Lite_SIF", "OCO3_L2_Met", "OCO3_L2_Met", "OCO3_L2_Met", "OCO3_L2_Standard", "OCO3_L2_Standard", "OCO3_L2_Standard", "OCTS_L1", "OCTS_L2_IOP", "OCTS_L2_OC", "OCTS_L3b_CHL", "OCTS_L3b_IOP", "OCTS_L3b_KD", "OCTS_L3b_PAR", "OCTS_L3b_PIC", "OCTS_L3b_POC", "OCTS_L3b_RRS", "OCTS_L3m_CHL", "OCTS_L3m_IOP", "OCTS_L3m_KD", "OCTS_L3m_PAR", "OCTS_L3m_PIC", "OCTS_L3m_POC", "OCTS_L3m_RRS", "ODU_CBM", "OISSS_L4_multimission_7day_v1", "OISSS_L4_multimission_7day_v2", "OISSS_L4_multimission_monthly_v1", "OISSS_L4_multimission_monthly_v2", "OISST_HR_NRT-GOS-L4-BLK-v2.0", "OISST_HR_NRT-GOS-L4-MED-v2.0", "OISST_UHR_NRT-GOS-L4-BLK-v2.0", "OISST_UHR_NRT-GOS-L4-MED-v2.0", "OLCIS3A_L1_EFR", "OLCIS3A_L1_ERR", "OLCIS3A_L2_EFR_IOP", "OLCIS3A_L2_EFR_OC", "OLCIS3A_L2_ERR_IOP", "OLCIS3A_L2_ERR_IOP_NRT", "OLCIS3A_L2_ERR_OC", "OLCIS3A_L2_ERR_OC_NRT", "OLCIS3A_L2_ILW", "OLCIS3B_L1_EFR", "OLCIS3B_L1_ERR", "OLCIS3B_L2_EFR_IOP", "OLCIS3B_L2_EFR_OC", "OLCIS3B_L2_ERR_IOP", "OLCIS3B_L2_ERR_IOP_NRT", "OLCIS3B_L2_ERR_OC", "OLCIS3B_L2_ERR_OC_NRT", "OLCIS3B_L2_ILW", "OLIPAC", "OMAERO", "OMAEROG", "OMAEROZ", "OMAEROe", "OMAERUV", "OMAERUV", "OMAERUV", "OMAERUVG", "OMAERUV_CPR", "OMAERUVd", "OMBRO", "OMCLDO2", "OMCLDO2G", "OMCLDO2Z", "OMCLDO2_CPR", "OMCLDRR", "OMCLDRR", "OMCLDRR", "OMCLDRRG", "OMCLDRR_CPR", "OMDOAO3", "OMDOAO3G", "OMDOAO3Z", "OMDOAO3e", "OMEXII", "OMGLER", "OMG_L1B_AIRGRAV", "OMG_L1_FLOAT_ALAMO", "OMG_L1_FLOAT_APEX", "OMG_L2_AXBT", "OMG_L2_AXCTD", "OMG_L2_Bathy_MBES_Gridded", "OMG_L2_Bathy_SBES_Gridded", "OMG_L2_CTD", "OMG_L3_ICE_ELEV_GLISTINA", "OMG_NARWHALS_MOORING_TEMP_CTD_1.0", "OMG_NARWHALS_SHIPBOARD_CTD_1.0", "OMHCHO", "OMHCHOG", "OMHCHOd", "OMIAuraAER", "OMI_MINDS_NO2", "OMI_MINDS_NO2G", "OMI_MINDS_NO2d", "OML1BIRR", "OML1BIRR", "OML1BRUG", "OML1BRUG", "OML1BRUZ", "OML1BRUZ", "OML1BRVG", "OML1BRVG", "OML1BRVZ", "OML1BRVZ", "OMLER", "OMMYDAGEO", "OMMYDCLD", "OMNO2", "OMNO2G", "OMNO2_CPR", "OMNO2d", "OMO3PR", "OMOCLO", "OMPIXCOR", "OMPIXCORZ", "OMPS_N20_NMHCHO_L2", "OMPS_N20_NMSO2_PCA_L2_Step1", "OMPS_N21_LP_L1G_EV", "OMPS_N21_LP_L2_O3_DAILY", "OMPS_NPP_LP_L1G_EV", "OMPS_NPP_LP_L1G_EV", "OMPS_NPP_LP_L2_AER_DAILY", "OMPS_NPP_LP_L2_O3_DAILY", "OMPS_NPP_LP_L2_O3_DAILY", "OMPS_NPP_LP_L2_Temp_DAILY", "OMPS_NPP_LP_L3_AER_MONTHLY", "OMPS_NPP_NMCLDRR_L2", "OMPS_NPP_NMEV_L1B", "OMPS_NPP_NMHCHO_L2", "OMPS_NPP_NMMIEAI_L2", "OMPS_NPP_NMNO2_L2", "OMPS_NPP_NMSO2_L2", "OMPS_NPP_NMSO2_PCA_L2", "OMPS_NPP_NMSO2_PCA_L3_DAILY", "OMPS_NPP_NMTO3_L2", "OMPS_NPP_NMTO3_L3_DAILY", "OMPS_NPP_NPBUVO3_L2", "OMPS_NPP_NPBUVO3_L2", "OMPS_NPP_NPEV_L1B", "OMSO2", "OMSO2", "OMSO2G", "OMSO2_CPR", "OMSO2e", "OMTO3", "OMTO3G", "OMTO3_CPR", "OMTO3d", "OMTO3e", "OMTO3e", "OMUANC", "OMUFPITMET", "OMUFPMET", "OMUFPSLV", "OMUVB", "OMUVBG", "OMUVBd", "OMVANC", "OMVFPITMET", "OMVFPMET", "OMVFPSLV", "ONDEQUE", "ONR-MAB", "ONR_PenobscotRiverSystem", "OPERA_L2_CSLC-S1-STATIC_V1", "OPERA_L2_CSLC-S1_V1", "OPERA_L2_RTC-S1-STATIC_V1", "OPERA_L2_RTC-S1_V1", "OPERA_L3_DIST-ALERT-HLS_V1", "OPERA_L3_DIST-ANN-HLS_V1", "OPERA_L3_DSWX-HLS_V1", "ORACLES_AerosolCloud_AircraftRemoteSensing_Data", "ORACLES_Aerosol_AircraftInSitu_Data", "ORACLES_Cloud_AircraftInSitu_Data", "ORACLES_Merge_Data", "ORACLES_MetNav_AircraftInSitu_Data", "ORACLES_Model_Data", "ORACLES_Radiation_AircraftInSitu_Data", "ORACLES_TraceGas_AircraftInSitu_Data", "ORINOCO", "OS2_OSCAT_LEVEL_2B_OWV_COMP_12_V2", "OSCAR_L4_OC_1deg", "OSCAR_L4_OC_FINAL_V2.0", "OSCAR_L4_OC_INTERIM_V2.0", "OSCAR_L4_OC_NRT_V2.0", "OSCAR_L4_OC_third-deg", "OSCAR_L4_OC_third-deg_YEARLY", "OSTIA-UKMO-L4-GLOB-REP-v2.0", "OSTIA-UKMO-L4-GLOB-v2.0", "OTZ_WHOI", "OWLETS1_Model_Data", "OWLETS1_Pandora_Data", "OWLETS1_SherpaAircraft_Data", "OWLETS1_Sondes_Data", "OWLETS1_SurfaceLidar_Data", "OWLETS2_Aerosol_Data", "OWLETS2_Pandora_Data", "OWLETS2_Ship_Data", "OWLETS2_Sondes_Data", "OWLETS2_SurfaceLidar_Data", "OWLETS2_Surface_Data", "OWLETS2_UMDAircraft_Data", "Ocean_Color_Cal_Val", "Oceania", "Odyssee_Saint_Laurent", "Okeechobee", "Opt_Sed_Trap_Cal", "Optical_Layers", "Optique_St_Laurent", "Oumalik_Veg_plots_1506", "P380108", "PACE_ABSclosure", "PACE_EPH_DEF", "PACE_HARP2_L0_D1", "PACE_HARP2_L0_D2", "PACE_HARP2_L0_D3", "PACE_HARP2_L1A_SCI", "PACE_HARP2_L1B_SCI", "PACE_HARP2_L1C_SCI", "PACE_HKT", "PACE_HSK", "PACE_OCI_L0_SCI", "PACE_OCI_L1A_SCI", "PACE_OCI_L1B_SCI", "PACE_OCI_L1C_SCI", "PACE_OCI_L2_AOP_NRT", "PACE_OCI_L2_BGC_NRT", "PACE_OCI_L2_IOP_NRT", "PACE_OCI_L2_PAR_NRT", "PACE_OCI_L3M_CHL_NRT", "PACE_SPEXONE_L0", "PACE_SPEXONE_L1A_SCI", "PACE_SPEXONE_L1B_SCI", "PACE_SPEXONE_L1C_SCI", "PAC_DFO_PAR_BUOY", "PAD_2011_1133", "PAD_935", "PAL-LTER", "PARASOLRB_CPR", "PATEX", "PC06_ECMWF_LBA_1141", "PEACETIME", "PERU", "PET_PU_3H025", "PET_PU_3H025", "PICOLO", "PISTON-ONR-NOAA-Autonomous-Uncrewed_2018-2019", "PISTON-ONR-NOAA_Islands_2018-2019", "PISTON-ONR-NOAA_RVMirai_2018", "PISTON-ONR-NOAA_RVSallyRide_2019", "PISTON-ONR-NOAA_RVThompson_2018", "PMNS", "PMRN6L1RAD_CDROM", "POAM2_VER6", "POAM3", "POLARIS_Aerosol_AircraftInSitu_ER2_Data", "POLARIS_Analysis_ER2_Data", "POLARIS_Ground_Data", "POLARIS_MetNav_AircraftInSitu_ER2_Data", "POLARIS_Model_Data", "POLARIS_Satellite_Data", "POLARIS_Sondes_Data", "POLARIS_TraceGas_AircraftInSitu_ER2_Data", "POLARIS_jValue_AircraftInSitu_ER2_Data", "POMME", "PRECIP_AMSR2_GCOMW1", "PRECIP_AMSRE_AQUA", "PRECIP_GMI_GPM", "PRECIP_SMMR_NIMBUS7", "PRECIP_SSMIS_F16", "PRECIP_SSMIS_F17", "PRECIP_SSMIS_F18", "PRECIP_SSMIS_F19", "PRECIP_SSMI_F08", "PRECIP_SSMI_F10", "PRECIP_SSMI_F11", "PRECIP_SSMI_F13", "PRECIP_SSMI_F14", "PRECIP_SSMI_F15", "PRECIP_TMI_TRMM", "PREFIRE_SAT2_0-BUS-TLM", "PREFIRE_SAT2_0-PAYLOAD", "PREFIRE_SAT2_0-PAYLOAD-TLM", "PREPP", "PRESWOT_HYDRO_GRRATS_L2_DAILY_VIRTUAL_STATION_HEIGHTS_V2", "PRESWOT_HYDRO_GRRATS_L2_VIRTUAL_STATION_HEIGHTS_V2", "PRESWOT_HYDRO_L2_GREALM_LAKE_HEIGHT_V2", "PRESWOT_HYDRO_L3_LAKE_RESEVOIR_AREA_V2", "PRESWOT_HYDRO_L4_LAKE_STORAGE_TIME_SERIES_V2", "PRIM_SMAP_L2_V1", "PRISM_CORAL_L1", "PRISM_CORAL_L2", "PRISM_Elkhorn_Slough", "PRONEX", "PSCNH_ARCHIVED_DATA", "PTLO", "PVST_HOTPACE", "PVST_NORTHERN_INDIAN_OCEAN", "PVST_PRINGLS", "PVST_SBCR", "PVST_VDIUP", "PanamaCity", "Panhandle_OWQ", "Passive_Microwave_Snowoff_Data_1711", "Patagonian_Coastal", "Peatland_carbon_balance_1382", "Pelican_PCO2", "PenBaySurvey", "PermafrostThaw_CarbonEmissions_1872", "Permafrost_ActiveLayer_NSlope_1759", "Permafrost_Thaw_Depth_YK_1598", "PhenoCam_V2_1674", "Phenocam_Images_V2_1689", "Phenology_AmeriFlux_Neon_Sites_2033", "Phenology_Deciduous_Forest_1570", "Photos_ThermokarstLakes_AK_1845", "Pingo_Veg_Plots_1507", "Plot_Data_Noatak_Seward_AK_1919", "Plumes_and_Blooms", "PolInSAR_Canopy_Height_1589", "Polar-VPRM_Alaskan-NEE_1314", "PolarWindsII_DAWN_DC8", "PolarWindsI_DAWN_KingAirUC-12B", "Polarimetric_CT_1601", "Polarimetric_height_profile_1577", "Poplar_Veg_Plots_1376", "PostFire_Tree_Regeneration_1955", "Post_Fire_C_Emissions_1787", "Post_Fire_SOC_NWT_2235", "PreABoVE_AirMOSS_L1_Alaska_1678", "PreDeltaX_ADCP_Measurements_1806", "PreDeltaX_ASO_LiDAR_WaterLevel_1820", "PreDeltaX_Insitu_Reflectance_1804", "PreDeltaX_L2_AVIRIS_SR_1826", "PreDeltaX_L2_AirSWOT_1818", "PreDeltaX_L3_AVIRIS_Biomass_1821", "PreDeltaX_L3_AVIRIS_Sediment_1822", "PreDeltaX_L3_AirSWOT_WL_1819", "PreDeltaX_Sonar_Bathymetry_1807", "PreDeltaX_TSS_Concentration_1802", "PreDeltaX_UAVSAR_Channel_Maps_1954", "PreDeltaX_UAVSAR_SLC_1816", "PreDeltaX_UAVSAR_WaterLevel_1823", "PreDeltaX_Vegetation_Structure_1805", "PreDeltaX_Water_Level_Data_1801", "Pre_LBA_ABRACOS_899", "Proantar", "Profile_based_PBL_heights_1706", "Prudhoe_Bay_ArcSEES_Veg_Plots_1555", "Prudhoe_Bay_Veg_Maps_1387", "Prudhoe_Bay_Veg_Plots_1360", "Prudhoe_Freshets", "QSCAT_ESDR_MODELED_L2_AUX_V1.0", "QSCAT_L1C_NONSPINNING_SIGMA0_WINDS_V2", "QSCAT_LEVEL_1B_V2", "QSCAT_LEVEL_2B_OWV_COMP_12", "QSCAT_LEVEL_2B_OWV_COMP_12_KUSST_LCRES_4.1", "QSCAT_LEVEL_2B_OWV_COMP_12_LCR_3.1", "QUIKSCAT_ESDR_L2_WIND_STRESS_V1.0", "RADAMBrasil_941", "RAIN_JAEGER", "RAIN_LEGATES", "RAMSSA_09km-ABOM-L4-AUS-v01", "RBLE_917", "RDBTS4", "RDEFT4", "RDGBV4", "RDSISC4", "RDSISCO4", "RECON_SEA_LEVEL_OST_L4_V1", "RED_TIDE", "REMO_OI_SST_5km-UFRJ-L4-SAMERICA-v1.0", "RESOLUTE", "REYNOLDS_NCDC_L4_MONTHLY_V5", "RGGRV1B", "RICMIAAE", "RICMIB2E", "RICMIRCM", "RICMITAL", "RICMITCL", "RICMITST", "RIO-SFE", "ROAVERRS", "RON_BROWN", "RRRAG4", "RSAT-1_L0", "RSAT-1_L1", "RSCAT_COLOCATED_RSS_RADIOMETER_LEVEL_2B_V1", "RSCAT_L1B_V2.0", "RSCAT_L2A_12KM_V2.0", "RSCAT_L2A_25KM_V2.0", "RSCAT_LEVEL_2B_OWV_CLIM_12_V1", "RSCAT_LEVEL_2B_OWV_CLIM_12_V2", "RSCAT_LEVEL_2B_OWV_COMP_12_V1.1", "RSCAT_LEVEL_2B_OWV_COMP_12_V1.2", "RSCAT_LEVEL_2B_OWV_COMP_12_V1.3", "RSS18_AVIRIS_L1B_449", "RSS_WindSat_L1C_TB_V08.0", "Radial_Growth_PRI_1781", "Rain-on-Snow_Data_1611", "RdlP_PT", "ReSALT_ALT_GPR_1265", "ReSALT_InSAR_Barrow_1266", "Reflectance_Spectra_Alaska_1685", "RemSensPOC", "Respiration_622", "River_Ice_Breakup_Freezeup_1697", "Russian_Forest_Disturbance_1294", "S2K_EACM_Subset_623", "S3A_OL_1_EFR", "S5P_L1B_IR_SIR", "S5P_L1B_IR_SIR", "S5P_L1B_IR_UVN", "S5P_L1B_IR_UVN", "S5P_L1B_RA_BD1", "S5P_L1B_RA_BD1_HiR", "S5P_L1B_RA_BD1_HiR", "S5P_L1B_RA_BD2", "S5P_L1B_RA_BD2_HiR", "S5P_L1B_RA_BD2_HiR", "S5P_L1B_RA_BD3", "S5P_L1B_RA_BD3_HiR", "S5P_L1B_RA_BD3_HiR", "S5P_L1B_RA_BD4", "S5P_L1B_RA_BD4_HiR", "S5P_L1B_RA_BD4_HiR", "S5P_L1B_RA_BD5", "S5P_L1B_RA_BD5_HiR", "S5P_L1B_RA_BD5_HiR", "S5P_L1B_RA_BD6", "S5P_L1B_RA_BD6_HiR", "S5P_L1B_RA_BD6_HiR", "S5P_L1B_RA_BD7", "S5P_L1B_RA_BD7_HiR", "S5P_L1B_RA_BD7_HiR", "S5P_L1B_RA_BD8", "S5P_L1B_RA_BD8_HiR", "S5P_L1B_RA_BD8_HiR", "S5P_L2__AER_AI", "S5P_L2__AER_AI_HiR", "S5P_L2__AER_AI_HiR", "S5P_L2__AER_LH", "S5P_L2__AER_LH_HiR", "S5P_L2__AER_LH_HiR", "S5P_L2__CH4___", "S5P_L2__CH4____HiR", "S5P_L2__CH4____HiR", "S5P_L2__CLOUD_", "S5P_L2__CLOUD__HiR", "S5P_L2__CLOUD__HiR", "S5P_L2__CO____", "S5P_L2__CO_____HiR", "S5P_L2__CO_____HiR", "S5P_L2__HCHO__", "S5P_L2__HCHO___HiR", "S5P_L2__HCHO___HiR", "S5P_L2__NO2___", "S5P_L2__NO2____HiR", "S5P_L2__NO2____HiR", "S5P_L2__NP_BD3", "S5P_L2__NP_BD3_HiR", "S5P_L2__NP_BD3_HiR", "S5P_L2__NP_BD6", "S5P_L2__NP_BD6_HiR", "S5P_L2__NP_BD6_HiR", "S5P_L2__NP_BD7", "S5P_L2__NP_BD7_HiR", "S5P_L2__NP_BD7_HiR", "S5P_L2__O3_TCL", "S5P_L2__O3_TCL", "S5P_L2__O3_TOT", "S5P_L2__O3_TOT_HiR", "S5P_L2__O3_TOT_HiR", "S5P_L2__O3__PR_HiR", "S5P_L2__SO2___", "S5P_L2__SO2____HiR", "S5P_L2__SO2____HiR", "SAB_Mapping", "SAGE1_AERO_PRF", "SAGE2_AEROSOL_O3_NO2_H2O_BINARY", "SAILDRONE_ARCTIC", "SAILDRONE_ARCTIC_2021", "SAILDRONE_ARCTIC_2022", "SAILDRONE_ATOMIC", "SAILDRONE_BAJA_SURFACE", "SAM2_AERO_PRF_NAT", "SAMMI2AE", "SAMMI2LS", "SAMMI2ST", "SAMMIB2E", "SAMMIB2T", "SAMMIGEO", "SAMSN7L1RAD_CDROM", "SAMSN7L1RAT", "SAMSN7L3GRIDT", "SAMSN7L3ZMTG", "SAR_Methane_Ebullition_AK_1790", "SASSIE_L1_SWIFT_V1", "SASSIE_L1_WAVEGLIDER_V1", "SASSIE_L2_ALTO_ALAMO_FLOATS_V1", "SASSIE_L2_DRIFTER_UPTEMPO_V1", "SASSIE_L2_DRIFTER_UPTEMPO_V2p", "SASSIE_L2_JET_SSP_V1", "SASSIE_L2_PALS_V1", "SASSIE_L2_SBAND_ML_V1", "SASSIE_L2_SHIPBOARD_ADCP_V1", "SASSIE_L2_SHIPBOARD_CASTAWAY_CTD_V1", "SASSIE_L2_SHIPBOARD_DELTA_18O_V1", "SASSIE_L2_SHIPBOARD_METEOROLOGY_V1", "SASSIE_L2_SHIPBOARD_SALINITY_SNAKE_V1", "SASSIE_L2_SHIPBOARD_TSG_V1", "SASSIE_L2_SHIPBOARD_UCTD_V1", "SASSIE_L2_SWIFT_V1", "SASSIE_L2_UNDER_ICE_FLOAT_V1", "SASSIE_L2_WAVEGLIDERS_V1", "SASSIE_L3_SHIPBOARD_SBAND_V1", "SASSIE_L4_SHIPBOARD_SBAND_V1", "SAV_Plymouth_Bay", "SAWB_JRA_713", "SAWB_JRB_714", "SBI", "SBUV2N09L2", "SBUV2N09L3zm", "SBUV2N09O3", "SBUV2N11L2", "SBUV2N11L3zm", "SBUV2N11O3", "SBUV2N14L2", "SBUV2N14L3zm", "SBUV2N16L2", "SBUV2N16L3zm", "SBUV2N16O3", "SBUV2N17L2", "SBUV2N17L3zm", "SBUV2N18L2", "SBUV2N18L3zm", "SBUV2N19L2", "SBUV2N19L3zm", "SBUVN07L2", "SBUVN07L3zm", "SBUVN7O3", "SCAMSN6IM", "SCAMSN6L2", "SCAR-B_916", "SCARB_ER2_MAS", "SCAR_A_ER2_MAS", "SCAR_B_AERONET", "SCAR_B_G8_FIRE", "SCAR_B_UWC131A", "SCATSAT1_ESDR_ANCILLARY_L2_V1.1", "SCATSAT1_ESDR_L2_WIND_STRESS_V1.1", "SCMRN5L1RAD", "SCOAPE_Ground_Data", "SCOAPE_Pandora_Data", "SCOAPE_RVPointSur_Data", "SCOAPE_Sondes_Data", "SCRN4L1RAD", "SCRN4L1RAD_CDROM", "SCRN5L1RAD", "SCRN5L1RAD_CDROM", "SEA", "SEAC4RS_Aerosol_AircraftInSitu_DC8_Data", "SEAC4RS_Aerosol_AircraftInSitu_Learjet_Data", "SEAC4RS_AircraftRemoteSensing_DIAL_DC8_Data", "SEAC4RS_AircraftRemoteSensing_RSP_ER2_Data", "SEAC4RS_Analysis_Data", "SEAC4RS_Cloud_AircraftInSitu_DC8_Data", "SEAC4RS_Cloud_AircraftInSitu_ER2_Data", "SEAC4RS_Cloud_AircraftInSitu_Learjet_Data", "SEAC4RS_Merge_Data", "SEAC4RS_MetNav_AircraftInSitu_DC8_Data", "SEAC4RS_MetNav_AircraftInSitu_ER2_Data", "SEAC4RS_MetNav_AircraftInSitu_Learjet_Data", "SEAC4RS_Miscellaneous_DC8_Data", "SEAC4RS_Miscellaneous_ER2_Data", "SEAC4RS_Radiation_AircraftInSitu_DC8_Data", "SEAC4RS_Radiation_AircraftInSitu_ER2_Data", "SEAC4RS_Sondes_Data", "SEAC4RS_TraceGas_AircraftInSitu_DC8_Data", "SEAC4RS_TraceGas_AircraftInSitu_ER2_Data", "SEAC4RS_jValue_AircraftInSitu_DC8_Data", "SEAGLIDER_GUAM_2019", "SEAHAWK_VALIDATION", "SEASAT_SAR_L1_HDF5", "SEASAT_SAR_L1_TIFF", "SEA_SURFACE_HEIGHT_ALT_GRIDS_L4_2SATS_5DAY_6THDEG_V_JPL2205", "SEED", "SENTINEL-1A_DP_GRD_FULL", "SENTINEL-1A_DP_GRD_HIGH", "SENTINEL-1A_DP_GRD_MEDIUM", "SENTINEL-1A_DP_META_GRD_FULL", "SENTINEL-1A_DP_META_GRD_HIGH", "SENTINEL-1A_DP_META_GRD_MEDIUM", "SENTINEL-1A_META_OCN", "SENTINEL-1A_META_RAW", "SENTINEL-1A_META_SLC", "SENTINEL-1A_OCN", "SENTINEL-1A_RAW", "SENTINEL-1A_SLC", "SENTINEL-1A_SP_GRD_HIGH", "SENTINEL-1A_SP_GRD_MEDIUM", "SENTINEL-1A_SP_META_GRD_HIGH", "SENTINEL-1A_SP_META_GRD_MEDIUM", "SENTINEL-1B_DP_GRD_HIGH", "SENTINEL-1B_DP_GRD_MEDIUM", "SENTINEL-1B_DP_META_GRD_HIGH", "SENTINEL-1B_DP_META_GRD_MEDIUM", "SENTINEL-1B_META_OCN", "SENTINEL-1B_META_RAW", "SENTINEL-1B_META_SLC", "SENTINEL-1B_OCN", "SENTINEL-1B_RAW", "SENTINEL-1B_SLC", "SENTINEL-1B_SP_GRD_HIGH", "SENTINEL-1B_SP_GRD_MEDIUM", "SENTINEL-1B_SP_META_GRD_HIGH", "SENTINEL-1B_SP_META_GRD_MEDIUM", "SENTINEL-1_BURSTS", "SENTINEL-1_INTERFEROGRAMS", "SENTINEL-1_INTERFEROGRAMS_AMPLITUDE", "SENTINEL-1_INTERFEROGRAMS_COHERENCE", "SENTINEL-1_INTERFEROGRAMS_CONNECTED_COMPONENTS", "SENTINEL-1_INTERFEROGRAMS_UNWRAPPED_PHASE", "SEVIRI_IO_SST-OSISAF-L3C-v1.0", "SEVIRI_SST-OSISAF-L3C-v1.0", "SEVIRI_SST_DR-OSISAF-L3C-v1.0", "SFC_NITROGEN_DIOXIDE_CONC", "SFMBON", "SFP", "SGER", "SHIFT_AVNG_Canopy_WaterContent_2242", "SHIFT_AVNG_FullRes_QkLook_2189", "SHIFT_AVNG_L1A_RDN_unrec_2184", "SHIFT_DriedGround_Leaf_Reflec_2244", "SHIFT_Field_Survey_Metadata_2295", "SHIFT_Foliar_Chemical_Analysis_2337", "SHIFT_HyTES_L1_2022_2245", "SHIFT_HyTES_L2_2022_2246", "SHIFT_Leaf_Traits_Chl_SB_CA_2233", "SHIFT_Plot_Level_Photos_2334", "SHIFT_RamsesTriOS_Rrs_SB_CA_2234", "SIF_PAR_fPAR_US_Midwest_2018_1813", "SIF_SCIAMACHY_GOME2_Harmonized_2317", "SIMBAD_DESCHAMPS_LOA", "SIO-Pier", "SIRSN3L1", "SIRSN4L1", "SISTER_Workflow_V004_2335", "SLOPE_GPP_CONUS_1786", "SMAP_JPL_L2B_NRT2_SSS_CAP_V5", "SMAP_JPL_L2B_NRT_SSS_CAP_V5", "SMAP_JPL_L2B_SSS_CAP_V5", "SMAP_JPL_L3_SSS_CAP_8DAY-RUNNINGMEAN_V5", "SMAP_JPL_L3_SSS_CAP_MONTHLY_V5", "SMAP_RSS_L2_SSS_NRT_V5", "SMAP_RSS_L2_SSS_NRT_V6", "SMAP_RSS_L2_SSS_V4", "SMAP_RSS_L2_SSS_V5", "SMAP_RSS_L2_SSS_V5.3", "SMAP_RSS_L2_SSS_V6", "SMAP_RSS_L3_SSS_SMI_8DAY-RUNNINGMEAN_V4", "SMAP_RSS_L3_SSS_SMI_8DAY-RUNNINGMEAN_V5", "SMAP_RSS_L3_SSS_SMI_8DAY-RUNNINGMEAN_V5.3", "SMAP_RSS_L3_SSS_SMI_8DAY-RUNNINGMEAN_V6", "SMAP_RSS_L3_SSS_SMI_MONTHLY_V4", "SMAP_RSS_L3_SSS_SMI_MONTHLY_V5", "SMAP_RSS_L3_SSS_SMI_MONTHLY_V5.3", "SMAP_RSS_L3_SSS_SMI_MONTHLY_V6", "SMERGE_RZSM0_40CM", "SMMRN7IM", "SMMR_ALW_PRABHAKARA", "SMMR_IWV_PRABHAKARA", "SMODE_L1_ASIT_KABODS_V1", "SMODE_L1_ASIT_SLOPEFIELDS_V1", "SMODE_L1_DOPPLERSCATT_V1", "SMODE_L1_MASS_DOPPVIS_V1", "SMODE_L1_MASS_DOPVISIBLE_V1", "SMODE_L1_MASS_HYPERSPECTRAL_V1", "SMODE_L1_MASS_LIDAR_V1", "SMODE_L1_MASS_LWIR_V1", "SMODE_L1_MASS_VISIBLE_V1", "SMODE_L1_PRISM_V1", "SMODE_L1_SAILDRONES_V1", "SMODE_L2_APEX_FLOAT_V1", "SMODE_L2_DOPPLERSCATT_WINDS_CURRENT_V1", "SMODE_L2_DOPPLERSCATT_WINDS_CURRENT_V2", "SMODE_L2_DRIFTER_POSITIONS_V1", "SMODE_L2_LAGRANGIAN_FLOATS_V1", "SMODE_L2_MOSES_LWIR_SST_V1", "SMODE_L2_PRISM_CHLA_V1", "SMODE_L2_SAILDRONES_V1", "SMODE_L2_SEAGLIDERS_V1", "SMODE_L2_SHIPBOARD_ADCP_V1", "SMODE_L2_SHIPBOARD_BIO_V1", "SMODE_L2_SHIPBOARD_BOTTLES_V1", "SMODE_L2_SHIPBOARD_CTD_V1", "SMODE_L2_SHIPBOARD_RADIOSONDES_METEOROLOGY_V1", "SMODE_L2_SHIPBOARD_SUNA_V1", "SMODE_L2_SHIPBOARD_TSG_V1", "SMODE_L2_SHIPBOARD_UCTD_ECOCTD_V1", "SMODE_L2_SLOCUM_GLIDERS_V1", "SMODE_L2_WAVEGLIDERS_TEMP_SALINITY_V1", "SMODE_L2a_PRISM_REFL_V1", "SMODE_L3_SEAGLIDERS_TEMP_SALINITY_V1", "SMODE_L3_SHIPBOARD_UCTD_ECOCTD_V1", "SMODE_L4_NCOM_V1", "SNDR13CHRP1", "SNDR13CHRP1AQCal", "SNDR13CHRP1J1Cal", "SNDR13CHRP1SNCal", "SNDR13IML3SSDFCNSAT", "SNDR13IML3SSDFCVPD", "SNDRAQIL2CCPCCR", "SNDRAQIL2CCPRET", "SNDRAQIL2CPS", "SNDRAQIL2JSFRET", "SNDRAQIL3CDCCP", "SNDRAQIL3CMCCP", "SNDRAQIL3SDCCP", "SNDRAQIL3SMCCP", "SNDRAQIL3SSDFCNSAT", "SNDRAQIL3SSDFCVPD", "SNDRAQIML1BCALSUBSUM", "SNDRAQIML1CCALSUBRND", "SNDRAQIML2CCPCCR", "SNDRAQIML2CCPRET", "SNDRAQIML2CPS", "SNDRAQIML3CDCCP", "SNDRAQIML3CMCCP", "SNDRAQIML3SDCCP", "SNDRAQIML3SMCCP", "SNDRJ1ATMSL1B", "SNDRJ1ATMSL1B", "SNDRJ1ATMSMAP", "SNDRJ1CrISL1B", "SNDRJ1CrISL1B", "SNDRJ1CrISL1BIMG", "SNDRJ1CrISL1BIMGC", "SNDRJ1CrISL1BPCARED", "SNDRJ1IML2CCPCCR", "SNDRJ1IML2CCPRET", "SNDRJ1IML2CPS", "SNDRJ1IML3CDCCP", "SNDRJ1IML3CMCCP", "SNDRJ1IML3SDCCP", "SNDRJ1IML3SMCCP", "SNDRJ1ML2RMS", "SNDRJ1ML2RMSSUP", "SNDRJ1ML3DRMS", "SNDRJ1ML3MRMS", "SNDRJ2CrISL1B", "SNDRJ2CrISL1BIMG", "SNDRJ2CrISL1BIMGC", "SNDRSNATMSMAP", "SNDRSNCrISL1BIMG", "SNDRSNCrISL1BIMGC", "SNDRSNIL1BCALSUBRNDN", "SNDRSNIL1BCALSUBSUMN", "SNDRSNIL2ESPNH3", "SNDRSNIML2CCPCCR", "SNDRSNIML2CCPCCRN", "SNDRSNIML2CCPRET", "SNDRSNIML2CCPRETN", "SNDRSNIML2CHTCCRN", "SNDRSNIML2CHTRETN", "SNDRSNIML2CPS", "SNDRSNIML2CPSN", "SNDRSNIML2SFSPRET", "SNDRSNIML2SFSPSUP", "SNDRSNIML3CDCCP", "SNDRSNIML3CDCCPN", "SNDRSNIML3CDCHTN", "SNDRSNIML3CMCCP", "SNDRSNIML3CMCCPN", "SNDRSNIML3CMCHTN", "SNDRSNIML3SDCCP", "SNDRSNIML3SDCCPN", "SNDRSNIML3SDCHTN", "SNDRSNIML3SDSFSP", "SNDRSNIML3SMCCP", "SNDRSNIML3SMCCPN", "SNDRSNIML3SMCHTN", "SNDRSNIML3SMSFSP", "SNDRSNML2RMS", "SNDRSNML2RMS", "SNDRSNML2RMSSUP", "SNDRSNML3DRMS", "SNDRSNML3MRMS", "SNEX17_CAR", "SNEX17_DGNSS1", "SNEX17_GPR", "SNEX17_GPR_Raw", "SNEX17_KT15_TB", "SNEX17_P3V", "SNEX17_QWIP_ST", "SNEX17_SBR", "SNEX17_SD", "SNEX17_SD_Perm", "SNEX17_SMP", "SNEX17_SMP2", "SNEX17_SSA", "SNEX17_SSD", "SNEX17_SnowPits", "SNEX17_SnowSAR", "SNEX17_SnowSAR_Raw", "SNEX17_TLI", "SNEX17_TLS_PC_BSU", "SNEX17_TLS_PC_BSU_Raw", "SNEX17_TLS_PC_CRREL", "SNEX17_UWScat", "SNEX20_A19_GSM", "SNEX20_A19_SD", "SNEX20_A19_SP", "SNEX20_A19_SWE", "SNEX20_BR_BSU_SMP", "SNEX20_BSU_CMP_Raw", "SNEX20_BSU_CMP_SWE", "SNEX20_BSU_CMP_TT", "SNEX20_BSU_GPR", "SNEX20_BSU_GPR_Raw", "SNEX20_COCP_GPR", "SNEX20_COCP_GPR_Raw", "SNEX20_COCP_SPD", "SNEX20_CRSM", "SNEX20_CSSM", "SNEX20_DSM", "SNEX20_GIS_REF", "SNEX20_GM_CSU_GPR", "SNEX20_GM_CSU_GPR_RAW", "SNEX20_GM_CTSM", "SNEX20_GM_Lidar", "SNEX20_GM_SP", "SNEX20_GM_SWE_SD", "SNEX20_J_UNM_GPR", "SNEX20_QSI_DEM", "SNEX20_QSI_DEM_3m", "SNEX20_QSI_SD", "SNEX20_QSI_SD_3m", "SNEX20_QSI_VH", "SNEX20_QSI_VH_3m", "SNEX20_SB_GST", "SNEX20_SD", "SNEX20_SD_TLI", "SNEX20_SMP", "SNEX20_SSA", "SNEX20_SWESARR_TB", "SNEX20_TLI", "SNEX20_TLS_PC_BSU", "SNEX20_TLS_PC_BSU_RAW", "SNEX20_TLS_PC_CRREL", "SNEX20_TS_SP", "SNEX20_UNM_GPR", "SNEX20_VPTS_Raw", "SNEX21_COCP_GPR", "SNEX21_COCP_GPR_Raw", "SNEX21_DSM", "SNEX21_PS_DSM", "SNEX21_PS_MET", "SNEX21_SSR", "SNEX21_TS_SP", "SNEX23_BCEF_TLS", "SNEX23_BCEF_TLS_Raw", "SNEX23_CRREL_GPR", "SNEX23_CRREL_GPR_Raw", "SNEX23_CSU_GPR_Raw", "SNEX23_Lidar", "SNEX23_Lidar_Raw", "SNEX23_MAR22_SD", "SNEX23_MAR23_SD", "SNEX23_SSA", "SNEX23_SSA_SO", "SNEX23_SWE", "SNEX23_UW_GPR", "SNEX_HRSI_SD_DEM_CO", "SNEX_Met", "SNEX_Met_Raw", "SNF_ASP_CVR_140", "SNF_BIOMASS_141", "SNF_BIOPHYS_142", "SNF_CAN_COMP_143", "SNF_CJ_SITES_187", "SNF_CJ_VEG_189", "SNF_HELO_MMR_144", "SNF_LEAFCARY_183", "SNF_LEAF_EXP_180", "SNF_LEAF_TMS_184", "SNF_MET_1972_158", "SNF_MET_1972_1990_159", "SNF_MET_1973_160", "SNF_MET_1974_161", "SNF_MET_1975_162", "SNF_MET_1976_163", "SNF_MET_1977_164", "SNF_MET_1978_165", "SNF_MET_1979_166", "SNF_MET_1980_167", "SNF_MET_1981_168", "SNF_MET_1982_169", "SNF_MET_1983_170", "SNF_MET_1984_171", "SNF_MET_1985_172", "SNF_MET_1986_173", "SNF_MET_1987_174", "SNF_MET_1988_175", "SNF_MET_1989_176", "SNF_MET_1990_177", "SNF_MET_SUMM_178", "SNF_NS001_185", "SNF_SAT_INV_186", "SNF_SITECOMP_179", "SNF_SITE_86_188", "SNF_TAB3_3T_182", "SNF_UND_CVR_181", "SNPPATMSL1B", "SNPPATMSL1B", "SNPPCrISL1B", "SNPPCrISL1B", "SNPPCrISL1BNSR", "SNPPCrISL1BNSR", "SNPP_CrIS_VIIRS750m_IND", "SOCCOM", "SOC_3M_Maps_NE_TidalWetlands_1905", "SOC_Stocks_Great_Plains_1603", "SOIL_CO2_Flux_Costa_Rica_1373", "SOLVE1_Aerosol_AircraftInSitu_DC8_Data", "SOLVE1_Aerosol_AircraftInSitu_ER2_Data", "SOLVE1_AircraftRemoteSensing_DC8_HSRL_Data", "SOLVE1_AircraftRemoteSensing_DC8_LASE_Data", "SOLVE1_AircraftRemoteSensing_DC8_Lidar_Data", "SOLVE1_Analysis_DC8_Data", "SOLVE1_Analysis_ER2_Data", "SOLVE1_Ground_Data", "SOLVE1_MetNav_AircraftInSitu_DC8_Data", "SOLVE1_MetNav_AircraftInSitu_ER2_Data", "SOLVE1_Miscellaneous_DC8_Data", "SOLVE1_Miscellaneous_ER2_Data", "SOLVE1_Satellite_Data", "SOLVE1_Sondes_Data", "SOLVE1_TraceGas_AircraftInSitu_DC8_Data", "SOLVE1_TraceGas_AircraftInSitu_ER2_Data", "SOLVE1_TraceGas_AircraftRemoteSensing_DC8_Data", "SOLVE1_jValue_AircraftInSitu_DC8_Data", "SOLVE1_jValue_AircraftInSitu_ER2_Data", "SOLVE2_Aerosol_AircraftInSitu_DC8_Data", "SOLVE2_AircraftRemoteSensing_DC8_AATS14_Data", "SOLVE2_AircraftRemoteSensing_DC8_HSRL_Data", "SOLVE2_AircraftRemoteSensing_DC8_Lidar_Data", "SOLVE2_Analysis_DC8_Data", "SOLVE2_MetNav_AircraftInSitu_DC8_Data", "SOLVE2_Miscellaneous_DC8_Data", "SOLVE2_Radiation_AircraftInSitu_DC8_Data", "SOLVE2_Rocket_Data", "SOLVE2_Satellite_Data", "SOLVE2_Sondes_Data", "SOLVE2_TraceGas_AircraftInSitu_DC8_Data", "SOLVE2_TraceGas_AircraftRemoteSensing_DC8_Data", "SONEX_Aerosol_AircraftInSitu_DC8_Data", "SONEX_AircraftRemoteSensing_DC8_DIAL_Data", "SONEX_Merge_DC8_Data", "SONEX_MetNav_AircraftInSitu_DC8_Data", "SONEX_Miscellaneous_DC8_Data", "SONEX_Model_Data", "SONEX_Other_Data", "SONEX_Satellite_Data", "SONEX_TraceGas_AircraftInSitu_DC8_Data", "SONEX_jValue_AircraftInSitu_DC8_Data", "SOR3D_COMBINED", "SOR3SIMD", "SOR3SIMD_TAV", "SOR3SOLD_HIGH", "SOR3SOLD_MGII", "SOR3SOLFUVD", "SOR3SOLMUVD", "SOR3SOLS_LA", "SOR3SOLS_MGII", "SOR3TSI6", "SOR3TSID", "SOR3XPS6", "SOR3XPSD", "SOR4XPS5", "SOR4XPSD_HIGH", "SOR4XPSD_LOW", "SORTIE", "SPL1A_001", "SPL1A_002", "SPL1A_METADATA_001", "SPL1A_METADATA_002", "SPL1A_QA_001", "SPL1A_QA_002", "SPL1A_RO_001", "SPL1A_RO_002", "SPL1A_RO_003", "SPL1A_RO_METADATA_001", "SPL1A_RO_METADATA_002", "SPL1A_RO_METADATA_003", "SPL1A_RO_QA_001", "SPL1A_RO_QA_002", "SPL1A_RO_QA_003", "SPL1B_SO_LoRes_001", "SPL1B_SO_LoRes_002", "SPL1B_SO_LoRes_003", "SPL1B_SO_LoRes_METADATA_001", "SPL1B_SO_LoRes_METADATA_002", "SPL1B_SO_LoRes_METADATA_003", "SPL1B_SO_LoRes_QA_001", "SPL1B_SO_LoRes_QA_002", "SPL1B_SO_LoRes_QA_003", "SPL1C_S0_HiRes_001", "SPL1C_S0_HiRes_002", "SPL1C_S0_HiRes_003", "SPL1C_S0_HiRes_METADATA_001", "SPL1C_S0_HiRes_METADATA_002", "SPL1C_S0_HiRes_METADATA_003", "SPL1C_S0_HiRes_QA_001", "SPL1C_S0_HiRes_QA_002", "SPL1C_S0_HiRes_QA_003", "SPL2SMA", "SPL2SMAP", "SPL2SMAP_S", "SPL3FTA", "SPL3SMA", "SPL3SMAP", "SPL3SMP", "SPL3SMP_E", "SPL4CMDL", "SPL4SMAU", "SPL4SMGP", "SPL4SMLM", "SPURS1_ADCP", "SPURS1_ARGO", "SPURS1_CTD", "SPURS1_DRIFTER", "SPURS1_ECOMAPPER", "SPURS1_FLOAT_NEUTRALLYBUOYANT", "SPURS1_METEO", "SPURS1_MOORING_PICO", "SPURS1_MOORING_WHOI", "SPURS1_SEAGLIDER", "SPURS1_SEASOAR", "SPURS1_TENUSEGLIDER", "SPURS1_TSG", "SPURS1_UCTD", "SPURS1_WAVEGLIDER", "SPURS2_ADCP", "SPURS2_ARGO", "SPURS2_CFT", "SPURS2_CTD", "SPURS2_DISDR", "SPURS2_DRIFTER", "SPURS2_FLOAT_NEUTRALLYBUOYANT", "SPURS2_LADYAMBER", "SPURS2_METEO", "SPURS2_MOORING_CENTRAL", "SPURS2_MOORING_PICO", "SPURS2_PALS", "SPURS2_RAINRADAR", "SPURS2_RAWINSONDE", "SPURS2_SAILDRONE", "SPURS2_SALINITYSNAKE", "SPURS2_SEAGLIDER", "SPURS2_SSP", "SPURS2_UCTD", "SPURS2_UNDERWAY_pCO2_DIC_pH", "SPURS2_USPS", "SPURS2_WAMOS", "SPURS2_WAVEGLIDER", "SPURS2_XBAND", "SPURS2_XBAND_IMG", "SPURS2_XBT", "SRB_REL3.0_SW_3HRLY_MONTHLY_UTC_NC", "SRDB_V5_1827", "SRTMGL1", "SRTMGL1N", "SRTMGL3", "SRTMGL30", "SRTMGL3N", "SRTMGL3S", "SRTMGL3_NC", "SRTMGL3_NUMNC", "SRTMIMGM", "SRTMIMGR", "SRTMSWBD", "SSBUVIRR", "SSBUVO3", "SSFR_irradiance_841", "STAQS_AircraftRemoteSensing_JSC-GV_GCAS_Data", "STAQS_AircraftRemoteSensing_JSC-GV_HSRL2_Data", "STAQS_AircraftRemoteSensing_NASA-G3_GCAS_Data", "STAQS_AircraftRemoteSensing_NASA-G3_HALO_Data", "STAQS_Chiwaukee-Prairie_Data", "STAQS_Drone_Data", "STAQS_Ground_Data", "STAQS_INSTEP_Data", "STAQS_SeaRey_Data", "STAQS_Sondes_Data", "STRAT_Aerosol_AircraftInSitu_ER2_Data", "STRAT_Analysis_ER2_Data", "STRAT_Ground_Data", "STRAT_MetNav_AircraftInSitu_ER2_Data", "STRAT_Model_Data", "STRAT_Satellite_Data", "STRAT_Sondes_Data", "STRAT_TraceGas_AircraftInSitu_ER2_Data", "STRAT_jValue_AircraftInSitu_ER2_Data", "STS-59_GRD", "STS-59_META_GRD", "STS-59_META_SLC", "STS-59_SLC", "STS-68_GRD", "STS-68_META_GRD", "STS-68_META_SLC", "STS-68_SLC", "SUCCESS_UTAH_PDL", "SUPERYACHT_SCIENCE", "SV08LC", "SV08PLBK", "SV08PLTB", "SV08SM", "SV08SR", "SV08ST", "SV08V", "SV08VWC", "SV12CSMA", "SV12CST", "SV12LC", "SV12PLBK", "SV12PLSM", "SV12PLTB", "SV12PSMA", "SV12PSMF", "SV12SRA", "SV12SRF", "SV12STM", "SV12UBK", "SV12VA", "SV12VF", "SV12VWC", "SV15PLSM", "SV15PLTB", "SV15PSM", "SV16I_PLTBSM", "SV16I_PNET", "SV16I_TNET", "SV16I_TPSM", "SV16M_CRS", "SV16M_CSM", "SV16M_LAI", "SV16M_LC", "SV16M_MET", "SV16M_PLTBSM", "SV16M_PSM", "SV16M_SDB", "SV16M_SR", "SV16M_SSM", "SV16M_ST", "SV16M_TB", "SV16M_V", "SV19MA_DEM", "SV19MA_DSM", "SV19MA_LID", "SV19MA_TNET", "SV19MA_VOD", "SV19MB_DEM", "SV19MB_DSM", "SV19MB_LID", "SV19MB_TNET", "SWDB_L2", "SWDB_L305", "SWDB_L310", "SWDB_L3M05", "SWDB_L3M10", "SWDB_L3MC05", "SWDB_L3MC10", "SWFL", "SWOT_L1B_LR_INTF_1.0", "SWOT_L1B_LR_INTF_1.1", "SWOT_L2_HR_LakeSP_2.0", "SWOT_L2_HR_PIXC_2.0", "SWOT_L2_HR_Raster_2.0", "SWOT_L2_HR_RiverSP_2.0", "SWOT_L2_LR_SSH_1.0", "SWOT_L2_LR_SSH_1.1", "SWOT_L2_LR_SSH_2.0", "SWOT_L2_NALT_GDR_2.0", "SWOT_L2_NALT_IGDR_1.0", "SWOT_L2_NALT_IGDR_2.0", "SWOT_L2_NALT_OGDR_1.0", "SWOT_L2_NALT_OGDR_2.0", "SWOT_L4_DAWG_SOS_DISCHARGE", "SWOT_PRELAUNCH_L2_BPR_V1", "SWOT_PRELAUNCH_L2_GLIDER_V1", "SWOT_PRELAUNCH_L2_GPS_V1", "SWOT_PRELAUNCH_L2_PIES_V1", "SWOT_PRELAUNCH_L2_PRAWLER_V1", "SWOT_PRELAUNCH_L2_SIOCTD_V1", "SWOT_PRELAUNCH_L2_WHOICTD_V1", "SWOT_PRELAUNCH_L2_WW_V1", "SWOT_SIMULATED_L2_KARIN_SSH_ECCO_LLC4320_CALVAL_V1", "SWOT_SIMULATED_L2_KARIN_SSH_ECCO_LLC4320_SCIENCE_V1", "SWOT_SIMULATED_L2_KARIN_SSH_GLORYS_CALVAL_V1", "SWOT_SIMULATED_L2_KARIN_SSH_GLORYS_SCIENCE_V1", "SWOT_SIMULATED_L2_NADIR_SSH_ECCO_LLC4320_CALVAL_V1", "SWOT_SIMULATED_L2_NADIR_SSH_ECCO_LLC4320_SCIENCE_V1", "SWOT_SIMULATED_L2_NADIR_SSH_GLORYS_CALVAL_V1", "SWOT_SIMULATED_L2_NADIR_SSH_GLORYS_SCIENCE_V1", "SWOT_SIMULATED_NA_CONTINENT_L2_HR_LAKESP_V1", "SWOT_SIMULATED_NA_CONTINENT_L2_HR_PIXCVEC_V1", "SWOT_SIMULATED_NA_CONTINENT_L2_HR_PIXC_V1", "SWOT_SIMULATED_NA_CONTINENT_L2_HR_RASTER_V1", "SWOT_SIMULATED_NA_CONTINENT_L2_HR_RIVERSP_V1", "Sahel_Water_Bodies_1269", "Salt_Marsh_Biomass_CONUS_2348", "San_Diego_Coastal_Project", "Sargassum_GOM", "Saskatchewan_Soils_125m_SSA_1346", "Sat_ActiveLayer_Thickness_Maps_1760", "SatelliteDerived_Forest_Mexico_2320", "Scotia_Prince_ferry", "Sea2Space", "SeaWiFS_L1_GAC", "SeaWiFS_L1_GAC", "SeaWiFS_L1_MLAC", "SeaWiFS_L1_MLAC", "SeaWiFS_L2_GAC_IOP", "SeaWiFS_L2_GAC_OC", "SeaWiFS_L2_MLAC_IOP", "SeaWiFS_L2_MLAC_OC", "SeaWiFS_L3b_CHL", "SeaWiFS_L3b_IOP", "SeaWiFS_L3b_KD", "SeaWiFS_L3b_PAR", "SeaWiFS_L3b_PIC", "SeaWiFS_L3b_POC", "SeaWiFS_L3b_RRS", "SeaWiFS_L3m_CHL", "SeaWiFS_L3m_IOP", "SeaWiFS_L3m_KD", "SeaWiFS_L3m_PAR", "SeaWiFS_L3m_PIC", "SeaWiFS_L3m_POC", "SeaWiFS_L3m_RRS", "SeaWiFS_L4b_GSM", "SeaWiFS_L4m_GSM", "Sea_of_Japan", "Seagrass_Mapping_Florida", "Searcher", "Seasonality_Tundra_Vegetation_1606", "Secret", "Semi-Arid_Tree_Carbon_50cm_2117", "Sentinel-1_Burst_Map", "Seward_Peninsula_Veg_Maps_1363", "Shrub_Biomass_Toolik_Lake_AK_1573", "SiB3_Carbon_Flux_909", "SiB4_Global_HalfDegree_Daily_1849", "SiB4_Global_HalfDegree_Hourly_1847", "SiB4_Global_HalfDegree_Monthly_1848", "Siberian_Biomass_Wildfire_1321", "Siberian_Larch_Stand_Age_1364", "SnowMeltDuration_PMicrowave_1843", "Snow_Cover_Extent_and_Depth_1757", "Snow_Depth_Data_Images_1656", "Snow_Wildlife_Tracks_AK_WA_2188", "Snowmelt_timing_maps_V2_1712", "Snowpack_Dall_Sheep_Track_1583", "SoilResp_HeterotrophicResp_1928", "SoilSCAPE_1339", "SoilSCAPE_V2_2049", "Soil_ActiveLayer_Properties_AK_2315", "Soil_Carbon_Flux_Maps_1683", "Soil_Moisture_Alaska_Alberta_2123", "Soil_Temp_Moisture_Alaska_1869", "Soil_Temperature_Profiles_AK_1767", "Sonoma_County_Forest_AGB_1764", "Southern_Boreal_Plot_Attribute_1740", "Southern_Ocean_Drifter", "Surface_Oligo_Med_Sea", "SwAO", "TAHOE", "TAO", "TAO_Moorings", "TARA-EUROPA", "TARA_PACIFIC_expedition", "TARFOX_UWC131A", "TARFOX_UWC131A_SUNP", "TARFOX_WALLOPS_MET", "TARFOX_WALLOPS_SMPS", "TARFOX_WALLOPS_SONDE", "TCTE3TSI6", "TCTE3TSID", "TELLUS_GIA_L3_0.5-DEG_V1.0", "TELLUS_GIA_L3_1-DEG_V1.0", "TELLUS_GLDAS-NOAH-3.3_TWS-ANOMALY_MONTHLY", "TELLUS_GRAC-GRFO_MASCON_CRI_GRID_RL06.1_V3", "TELLUS_GRAC-GRFO_MASCON_CRI_GRID_RL06.3_V4", "TELLUS_GRAC-GRFO_MASCON_GRID_RL06.1_V3", "TELLUS_GRAC-GRFO_MASCON_GRID_RL06.3_V4", "TELLUS_GRAC_L3_CSR_RL06_LND_v04", "TELLUS_GRAC_L3_CSR_RL06_OCN_v04", "TELLUS_GRAC_L3_GFZ_RL06_LND_v04", "TELLUS_GRAC_L3_GFZ_RL06_OCN_v04", "TELLUS_GRAC_L3_JPL_RL06_LND_v04", "TELLUS_GRAC_L3_JPL_RL06_OCN_v04", "TELLUS_GRFO_L3_CSR_RL06.1_LND_v04", "TELLUS_GRFO_L3_CSR_RL06.1_OCN_v04", "TELLUS_GRFO_L3_CSR_RL06.2_LND_v04", "TELLUS_GRFO_L3_CSR_RL06.2_OCN_v04", "TELLUS_GRFO_L3_GFZ_RL06.1_LND_v04", "TELLUS_GRFO_L3_GFZ_RL06.1_OCN_v04", "TELLUS_GRFO_L3_JPL_RL06.1_LND_v04", "TELLUS_GRFO_L3_JPL_RL06.1_OCN_v04", "TELLUS_GRFO_L3_JPL_RL06.3_LND_v04", "TELLUS_GRFO_L3_JPL_RL06.3_OCN_v04", "TEMPEST_STPH8_L1_TSDR_V10.0", "TEMPO_HCHO_L2", "TEMPO_NO2_L2", "TG02_Balloon_VOC_1110", "TG03_AERONET_AOT_1128", "TG03_Aeronet_Solar_Flux_1137", "TG05_CASA_1199", "TG06_Vertical_Profiles_1175", "TG07_Autochamber_Soil_CO2_Flux_Km67_927", "TG07_DBH_Cauaxi_1063", "TG07_FFT_Survey_Km83_923", "TG07_Fallen_Standing_Necromass_998", "TG07_Litter_Decomposition_925", "TG07_Manual_Flux_Km67_1026", "TG07_Root_Mortality_Experiment_924", "TG07_Root_Mortality_Longterm_1116", "TG07_STM_GLAS_836", "TG07_Soil-Atmosphere_Flux_Km83_926", "TG07_Soil_Nutrients_1085", "TG07_Trace_Gas_Profiles_1107", "TG08_Soil_Gas_Fertilization_1105", "TG08_Soil_Gas_Wetting_1101", "TG09_N2O_Soils_1013", "TG10_TROFFEE_1195", "THAILAND", "THIRN4IMCH67", "THIRN4L1CH115", "THIRN4L1CH67", "THIRN5L1CH115", "THIRN5L1CH67", "THIRN6IM", "THIRN6L1CH115", "THIRN6L1CH67", "THIRN7IM", "THIRN7L1BCLT", "THIRN7L1CLDT", "THORPEX_ER2_MAS", "TIROS2L1FMRT", "TIROS3L1FMRT", "TIROS3L1ORT", "TIROS4L1FMRT", "TIROS4L1ORR", "TIROS4L1ORT", "TIROS7L1FMRT", "TL1BL", "TL1BN", "TL1BSOL", "TL2ANC", "TL2ANC", "TL2ANCS", "TL2ANCS", "TL2ATMLN", "TL2ATMLN", "TL2ATMTL", "TL2ATMTN", "TL2ATMTN", "TL2CH4LN", "TL2CH4LN", "TL2CH4N", "TL2CH4N", "TL2CH4NS", "TL2CH4NS", "TL2CO2LN", "TL2CO2LN", "TL2CO2N", "TL2CO2N", "TL2CO2NS", "TL2CO2NS", "TL2COLN", "TL2COLN", "TL2CON", "TL2CON", "TL2CONS", "TL2CONS", "TL2FORLN", "TL2FORLN", "TL2FORN", "TL2FORN", "TL2FORNS", "TL2FORNS", "TL2H2OL", "TL2H2OLN", "TL2H2OLN", "TL2H2OLS", "TL2H2ON", "TL2H2ON", "TL2H2ONS", "TL2H2ONS", "TL2HCNN", "TL2HCNNS", "TL2HDOL", "TL2HDOLN", "TL2HDOLN", "TL2HDOLS", "TL2HDON", "TL2HDON", "TL2HDONS", "TL2HDONS", "TL2HNO3L", "TL2HNO3S", "TL2IRKN", "TL2IRKN", "TL2IRKNS", "TL2IRKNS", "TL2MTLLN", "TL2MTLN", "TL2MTLN", "TL2MTLNS", "TL2MTLNS", "TL2N2ON", "TL2N2ON", "TL2N2ONS", "TL2N2ONS", "TL2NH3LN", "TL2NH3LN", "TL2NH3N", "TL2NH3N", "TL2NH3NS", "TL2NH3NS", "TL2O3L", "TL2O3LN", "TL2O3LN", "TL2O3LS", "TL2O3N", "TL2O3N", "TL2O3NS", "TL2O3NS", "TL2OCSLN", "TL2OCSN", "TL2OCSN", "TL2OCSNS", "TL2OCSNS", "TL2PANLN", "TL2PANN", "TL2PANN", "TL2PANNS", "TL2PANNS", "TL2RHLN", "TL2SUM", "TL2SUM", "TL2SUMS", "TL2SUMS", "TL2SUP", "TL2SUP", "TL2SUPS", "TL2SUPS", "TL2TLS", "TL2TNS", "TL2TNS", "TL3ATD", "TL3ATD", "TL3ATM", "TL3ATM", "TL3ATM", "TL3CH4D", "TL3CH4D", "TL3CH4M", "TL3CH4M", "TL3CH4M", "TL3COD", "TL3COD", "TL3COM", "TL3COM", "TL3H2OD", "TL3H2OD", "TL3H2OM", "TL3H2OM", "TL3H2OM", "TL3HDOD", "TL3HDOD", "TL3HDOM", "TL3HDOM", "TL3HDOM", "TL3HNOD", "TL3HNOM", "TL3O3D", "TL3O3D", "TL3O3M", "TL3O3M", "TL3O3M", "TLS_Lidar_BlueFlux_Mangroves_2311", "TMI-REMSS-L2P-v4", "TMI-REMSS-L3U-v7.1a", "TML2CO", "TML2COS", "TOL2O3", "TOL2O3", "TOL2O3S", "TOL2O3S", "TOMSEPAER", "TOMSEPL2", "TOMSEPL3", "TOMSEPL3daer", "TOMSEPL3dery", "TOMSEPL3dref", "TOMSEPL3dtoz", "TOMSEPL3maer", "TOMSEPL3mery", "TOMSEPL3mref", "TOMSEPL3mtoz", "TOMSEPL3zaer", "TOMSEPL3zref", "TOMSEPL3ztoz", "TOMSEPOVP", "TOMSM3L3", "TOMSM3L3dref", "TOMSM3L3dtoz", "TOMSM3OVP", "TOMSN7AER", "TOMSN7L2", "TOMSN7L3", "TOMSN7L3daer", "TOMSN7L3dery", "TOMSN7L3dref", "TOMSN7L3dtoz", "TOMSN7L3maer", "TOMSN7L3mery", "TOMSN7L3mref", "TOMSN7L3mtoz", "TOMSN7L3zaer", "TOMSN7L3zref", "TOMSN7L3ztoz", "TOMSN7OVP", "TOMSN7SO2", "TOMS_aerosol_823", "TOMS_ozone_824", "TOPEX_ALTSDR", "TOPEX_POSEIDON_GDR_F", "TOTE-VOTE_Aerosol_AircraftInSitu_DC8_Data", "TOTE-VOTE_AircraftRemoteSensing_DC8_DIAL_Data", "TOTE-VOTE_AircraftRemoteSensing_DC8_Lidar_Data", "TOTE-VOTE_Analysis_DC8_Data", "TOTE-VOTE_Ground_Data", "TOTE-VOTE_MetNav_AircraftInSitu_DC8_Data", "TOTE-VOTE_Miscellaneous_DC8_Data", "TOTE-VOTE_Satellite_Data", "TOTE-VOTE_Sondes_Data", "TOTE-VOTE_TraceGas_AircraftInSitu_DC8_Data", "TOTO", "TOVSA5ND", "TOVSA5NF", "TOVSA5NG", "TOVSA5NH", "TOVSA5TN", "TOVSADND", "TOVSADNF", "TOVSADNG", "TOVSADNH", "TOVSADTN", "TOVSAMNA", "TOVSAMNC", "TOVSAMND", "TOVSAMND", "TOVSAMNE", "TOVSAMNF", "TOVSAMNF", "TOVSAMNG", "TOVSAMNG", "TOVSAMNH", "TOVSAMNH", "TOVSAMNJ", "TOVSAMTN", "TOVSAMTN", "TOVSB5ND", "TOVSB5NG", "TOVSBDND", "TOVSBDNG", "TOVSBMND", "TOVSBMNG", "TOVS_717", "TRACE-A_920", "TRACE-A_AircraftRemoteSensing_DC8_DIAL_Data", "TRACE-A_Brazil_Data", "TRACE-A_Merge_Data", "TRACE-A_MetNav_AircraftInSitu_DC8_Data", "TRACE-A_Satellite_Data", "TRACE-A_Sondes_Data", "TRACE-A_TraceGas_AircraftInSitu_DC8_Data", "TRACE-A_Trajectory_Data", "TRACE-P_Aerosol_AircraftInSitu_DC8_Data", "TRACE-P_Aerosol_AircraftInSitu_P3B_Data", "TRACE-P_AircraftRemoteSensing_DC8_DIAL_Data", "TRACE-P_Cloud_AircraftInSitu_DC8_Data", "TRACE-P_Ground_Data", "TRACE-P_Merge_Data", "TRACE-P_MetNav_AircraftInSitu_DC8_Data", "TRACE-P_MetNav_Aircraft_InSitu_P3B_Data", "TRACE-P_Model_Data", "TRACE-P_Satellite_Data", "TRACE-P_Sondes_Data", "TRACE-P_TraceGas_AircraftInSitu_DC8_Data", "TRACE-P_TraceGas_InSitu_P3B_Data", "TRACE-P_Trajectory_DC8_Data", "TRACE-P_Trajectory_P3B_Data", "TRACE-P_jValue_AircraftInSitu_DC8_Data", "TRACE-P_jValue_AircraftInSitu_P3B_Data", "TRACERAQ_AircraftRemoteSensing_GV_GCAS_Data", "TRACERAQ_AircraftRemoteSensing_GV_HSRL2_Data", "TRACERAQ_Ground_LaPorte_Data", "TRACERAQ_Pandora_Data", "TRACERAQ_Sondes_Data", "TRATLEQ1", "TRMM_1A01", "TRMM_1A11", "TRMM_1B01", "TRMM_1B11", "TRMM_1B21", "TRMM_1B51", "TRMM_1C21", "TRMM_1C51", "TRMM_1C51UW", "TRMM_2A12", "TRMM_2A21", "TRMM_2A23", "TRMM_2A25", "TRMM_2A52", "TRMM_2A53", "TRMM_2A53UW", "TRMM_2A54", "TRMM_2A54UW", "TRMM_2A55", "TRMM_2A55UW", "TRMM_2A56", "TRMM_2B31", "TRMM_3A11", "TRMM_3A12", "TRMM_3A25", "TRMM_3A26", "TRMM_3A53", "TRMM_3A54", "TRMM_3A54UW", "TRMM_3A55", "TRMM_3B31", "TRMM_3B40RT", "TRMM_3B41RT", "TRMM_3B42", "TRMM_3B42RT", "TRMM_3B42RT_Daily", "TRMM_3B42_Daily", "TRMM_3B43", "TRMM_3G25", "TRMM_3G31", "TRMM_3H25", "TRMM_3H31", "TRMM_CSH", "TRMM_TMPA_LandSeaMask", "TRMM_baseUW", "TRMM_precip_718", "TROPICS01ANTTL1A", "TROPICS01BRTTL1B", "TROPICS01MIRSL2B", "TROPICS01PRPSL2B", "TROPICS01URADL2A", "TROPICS03ANTTL1A", "TROPICS03BRTTL1B", "TROPICS03MIRSL2B", "TROPICS03PRPSL2B", "TROPICS03URADL2A", "TROPICS05ANTTL1A", "TROPICS05BRTTL1B", "TROPICS05URADL2A", "TROPICS06ANTTL1A", "TROPICS06BRTTL1B", "TROPICS06MIRSL2B", "TROPICS06PRPSL2B", "TROPICS06URADL2A", "TROPICS07ANTTL1A", "TROPICS07BRTTL1B", "TROPICS07URADL2A", "TROPOMAER", "TROPOMI_MINDS_NO2", "TRPSCRAERNH42H2D", "TRPSCRAERNH46H3D", "TRPSCRAERNH4M3D", "TRPSCRAERNO32H2D", "TRPSCRAERNO36H3D", "TRPSCRAERNO3M3D", "TRPSCRAERSO42H2D", "TRPSCRAERSO46H3D", "TRPSCRAERSO4M3D", "TRPSCRCH2O2H2D", "TRPSCRCH2O6H3D", "TRPSCRCH2OM3D", "TRPSCRCO2H2D", "TRPSCRCO6H3D", "TRPSCRCOM3D", "TRPSCRCOS6H3D", "TRPSCRCOSM3D", "TRPSCRECOAM2D", "TRPSCRECOBM2D", "TRPSCRECOTM2D", "TRPSCRENOXAM2D", "TRPSCRENOXBM2D", "TRPSCRENOXLM2D", "TRPSCRENOXSM2D", "TRPSCRENOXTM2D", "TRPSCRESO2TM2D", "TRPSCRHNO32H2D", "TRPSCRHNO36H3D", "TRPSCRHNO3M3D", "TRPSCRNO22H2D", "TRPSCRNO26H3D", "TRPSCRNO2H2D", "TRPSCRNO2M3D", "TRPSCRNO2S6H3D", "TRPSCRNO2SM3D", "TRPSCRNO6H3D", "TRPSCRNOM3D", "TRPSCRO32H2D", "TRPSCRO36H3D", "TRPSCRO3I6H3D", "TRPSCRO3IM3D", "TRPSCRO3M3D", "TRPSCRO3S6H3D", "TRPSCRO3SM3D", "TRPSCROH2H2D", "TRPSCROH6H3D", "TRPSCROHM3D", "TRPSCRPAN2H2D", "TRPSCRPAN6H3D", "TRPSCRPANM3D", "TRPSCRPS2H2D", "TRPSCRPS6H2D", "TRPSCRPSM2D", "TRPSCRQ2H2D", "TRPSCRQ6H3D", "TRPSCRQM3D", "TRPSCRSO22H2D", "TRPSCRSO26H3D", "TRPSCRSO2M3D", "TRPSCRT2H2D", "TRPSCRT6H3D", "TRPSCRTM3D", "TRPSCRU2H2D", "TRPSCRU6H3D", "TRPSCRUM3D", "TRPSCRV2H2D", "TRPSCRV6H3D", "TRPSCRVM3D", "TRPSDL2ALLCRSMGBEI", "TRPSDL2ALLCRSMGDEL", "TRPSDL2ALLCRSMGKAR", "TRPSDL2ALLCRSMGLAG", "TRPSDL2ALLCRSMGLOS", "TRPSDL2ALLCRSMGMEX", "TRPSDL2ALLCRSMGSAO", "TRPSDL2ALLCRSMGTOK", "TRPSDL2CH4AIRSFS", "TRPSDL2CH4CRS1FS", "TRPSDL2CH4CRSAUS", "TRPSDL2CH4CRSFS", "TRPSDL2COAIRSFS", "TRPSDL2COCRS1FS", "TRPSDL2COCRSAUS", "TRPSDL2COCRSFS", "TRPSDL2COCRSTS1", "TRPSDL2COCRSTS2", "TRPSDL2COCRSWCF", "TRPSDL2COCRSWCFHI", "TRPSDL2H2OAIRSFS", "TRPSDL2H2OCRS1FS", "TRPSDL2H2OCRSAUS", "TRPSDL2H2OCRSFS", "TRPSDL2HDOAIRSFS", "TRPSDL2HDOCRS1FS", "TRPSDL2HDOCRSAUS", "TRPSDL2HDOCRSFS", "TRPSDL2NH3AIRSFS", "TRPSDL2NH3CRS1FS", "TRPSDL2NH3CRSAUS", "TRPSDL2NH3CRSFS", "TRPSDL2NH3CRSWCF", "TRPSDL2NH3CRSWCFHI", "TRPSDL2O3AIRSFS", "TRPSDL2O3AIRSOMIFS", "TRPSDL2O3CRS1FS", "TRPSDL2O3CRSAUS", "TRPSDL2O3CRSFS", "TRPSDL2O3CRSWCF", "TRPSDL2O3CRSWCFHI", "TRPSDL2O3OMIFS", "TRPSDL2PANCRS1FS", "TRPSDL2PANCRSAUS", "TRPSDL2PANCRSFS", "TRPSDL2PANCRSWCF", "TRPSDL2PANCRSWCFHI", "TRPSDL2TATMAIRSFS", "TRPSDL2TATMCRS1FS", "TRPSDL2TATMCRSAUS", "TRPSDL2TATMCRSFS", "TRPSYL2ALLCRSMGBEI", "TRPSYL2ALLCRSMGDEL", "TRPSYL2ALLCRSMGKAR", "TRPSYL2ALLCRSMGLAG", "TRPSYL2ALLCRSMGLOS", "TRPSYL2ALLCRSMGMEX", "TRPSYL2ALLCRSMGSAO", "TRPSYL2ALLCRSMGTOK", "TRPSYL2CH4AIRSFS", "TRPSYL2CH4AIRSORS", "TRPSYL2CH4CRS1FS", "TRPSYL2CH4CRSFS", "TRPSYL2CH4CRSRS", "TRPSYL2COAIRSFS", "TRPSYL2COAIRSORS", "TRPSYL2COCRS1FS", "TRPSYL2COCRSFS", "TRPSYL2COCRSRS", "TRPSYL2HDOAIRSFS", "TRPSYL2HDOAIRSORS", "TRPSYL2HDOCRS1FS", "TRPSYL2HDOCRSFS", "TRPSYL2HDOCRSRS", "TRPSYL2NH3AIRSFS", "TRPSYL2NH3CRS1FS", "TRPSYL2NH3CRSFS", "TRPSYL2NH3CRSRS", "TRPSYL2O3AIRSFS", "TRPSYL2O3AIRSOMIFS", "TRPSYL2O3AIRSORS", "TRPSYL2O3CRS1FS", "TRPSYL2O3CRSFS", "TRPSYL2O3CRSRS", "TRPSYL2O3OMIFS", "TRPSYL2PANCRS1FS", "TRPSYL2PANCRSFS", "TRPSYL2PANCRSRS", "TSIS_SSI_L3_12HR", "TSIS_SSI_L3_24HR", "TSIS_TSI_L3_06HR", "TSIS_TSI_L3_24HR", "Taiga_Tundra_Tree_Cover_1218", "Tampa_Bay", "Tara_Mediterranean", "Tara_Oceans_Polar_Circle", "Tara_Oceans_expedition", "Thermokarst_Circumpolar_Map_1332", "ThreeRivers", "Tidal_Marsh_Biomass_US_V1-1_1879", "Tidal_Marsh_Vegetation_US_1608", "Tidal_Wetland_Estuaries_Data_1742", "Tidal_Wetland_GPP_CONUS_1792", "Tidal_Wetland_Soil_Carbon_1612", "TillageErosion_SOCRedistribute_1944", "Tokyo_Bay", "Toolik_Lake_Area_Veg_Maps_1380", "Toolik_Lake_Veg_Plots_1333", "TopSoil_Erosion_MidWest_US_1774", "TowerBased_PhotoSpec_SIF_SK_CA_1887", "Tree_Canopy_Cover_Mexico_2137", "Tree_Mortality_Western_US_1512", "TundraTransect_VegRefl_Soil_2232", "TundraVeg_Reflectance_Soil_CO2_1960", "Tundra_Fire_Veg_Plots_1547", "Tundra_Greeness_Temp_Trends_1893", "Tundra_Leaf_Spectra_2005", "Turbid9", "Turkish_Seas", "UAEM1LME", "UAEM1LMT", "UAEMIAAE", "UAEMIALS", "UAEMIB2E", "UAEMIB2E", "UAEMIB2T", "UAEMIB2T", "UAEMIRCM", "UAEMITAL", "UAEMITCL", "UAEMITST", "UAEMRDAE", "UAEMRDLS", "UAEMRDRD", "UAEMRMLS", "UAEMRMRD", "UARCL3AL", "UARCL3AT", "UARHA2FN", "UARHA3AT", "UARHR3AL", "UARHR3AT", "UARIS3AL", "UARIS3AT", "UARML3AL", "UARML3AT", "UARPE2AXIS1", "UARPE2AXIS2", "UARPE2HEPSA", "UARPE2HEPSB", "UARPE2MEPS", "UARPE2VMAGAC", "UARPE2VMAGDC", "UARPE3AT", "UARPE3TP", "UARSO3BS", "UARSU3BS", "UARWI3AL", "UARWI3AT", "UARZCNMC", "UARZCUKM", "UAVSAR_INSAR_AMP", "UAVSAR_INSAR_AMP_GRD", "UAVSAR_INSAR_DEM", "UAVSAR_INSAR_INT", "UAVSAR_INSAR_INT_GRD", "UAVSAR_INSAR_KMZ", "UAVSAR_INSAR_META", "UAVSAR_POL_DEM", "UAVSAR_POL_INC", "UAVSAR_POL_KMZ", "UAVSAR_POL_META", "UAVSAR_POL_ML_CMPLX_GRD", "UAVSAR_POL_ML_CMPLX_GRD_3X3", "UAVSAR_POL_ML_CMPLX_GRD_5X5", "UAVSAR_POL_ML_CMPLX_SLANT", "UAVSAR_POL_PAULI", "UAVSAR_POL_SLOPE", "UAVSAR_POL_STOKES", "UAV_Imagery_BigLakeTrail_1834", "UCLA_DEALIASED_SASS_L3", "UMD_GEOL388", "USArray_Ground_Temperature_1680", "USM_pCO2", "US_MODIS_NDVI_1299", "US_MODIS_Veg_Parameters_1539", "Umiat_Veg_Plots_1370", "Unalaska_Veg_Plots_1375", "Uncertainty_US_Coastal_GHG_1650", "Understory_Veg_Biomass_Alaska_2340", "VBEMI2AE", "VBEMI2LS", "VBEMI2ST", "VBEMIB2E", "VBEMIB2T", "VBEMIGEO", "VCF5KYR", "VIIRSJ1_L1", "VIIRSJ1_L1_GEO", "VIIRSJ1_L2_IOP", "VIIRSJ1_L2_IOP_NRT", "VIIRSJ1_L2_OC", "VIIRSJ1_L2_OC_NRT", "VIIRSJ1_L2_SST", "VIIRSJ1_L2_SST3", "VIIRSJ1_L2_SST3_NRT", "VIIRSJ1_L2_SST_NRT", "VIIRSJ2_L1", "VIIRSJ2_L1_GEO", "VIIRSJ2_L2_IOP", "VIIRSJ2_L2_IOP_NRT", "VIIRSJ2_L2_OC", "VIIRSJ2_L2_OC_NRT", "VIIRSN_L1", "VIIRSN_L1_GEO", "VIIRSN_L2_IOP", "VIIRSN_L2_IOP_NRT", "VIIRSN_L2_OC", "VIIRSN_L2_OC_NRT", "VIIRSN_L2_SST", "VIIRSN_L2_SST3", "VIIRSN_L2_SST3_NRT", "VIIRSN_L2_SST_NRT", "VIIRSN_L3b_NSST", "VIIRSN_L3b_NSST_NRT", "VIIRSN_L3b_SST", "VIIRSN_L3b_SST3", "VIIRSN_L3b_SST3_NRT", "VIIRSN_L3b_SST_NRT", "VIIRSN_L3m_NSST", "VIIRSN_L3m_NSST_NRT", "VIIRSN_L3m_SST", "VIIRSN_L3m_SST3", "VIIRSN_L3m_SST3_NRT", "VIIRSN_L3m_SST_NRT", "VIIRS_N20-NAVO-L2P-v3.0", "VIIRS_N20-STAR-L2P-v2.80", "VIIRS_N20-STAR-L3U-v2.80", "VIIRS_N21-NAVO-L2P-v3.0", "VIIRS_NPP-JPL-L2P-v2016.2", "VIIRS_NPP-NAVO-L2P-v1.0", "VIIRS_NPP-NAVO-L2P-v2.0", "VIIRS_NPP-NAVO-L2P-v3.0", "VIIRS_NPP-STAR-L2P-v2.80", "VIIRS_NPP-STAR-L3U-v2.80", "VIIRS_SST_NPP_NAR-OSISAF-L3C-v1.0", "VIIRS_Val_FLKeys", "VIIRS_Validation", "VIMS_2005", "VIP01", "VIP07", "VIP15", "VIP30", "VIPPHEN_EVI2", "VIPPHEN_NDVI", "VIRGAS_MetNav_AircraftInSitu_Data", "VIRGAS_TraceGas_AircraftInSitu_Data", "VISSRGOES1IMIR", "VISSRGOES1IMVIS", "VISSRGOES2IMIR", "VISSRGOES2IMVIS", "VISSRGOES3IMIR", "VISSRGOES3IMVIS", "VISSRSMS1IMIR", "VISSRSMS1IMVIS", "VISSRSMS1L1AOIPS", "VISSRSMS1L1EHT", "VISSRSMS2IMIR", "VISSRSMS2IMVIS", "VISSRSMS2L1AOIPS", "VISSRSMS2L1EHT", "VJ103MODLL", "VJ109", "VJ109A1", "VJ109CMG", "VJ109GA", "VJ109H1", "VJ113A1", "VJ113A2", "VJ113A3", "VJ113C1", "VJ113C2", "VJ114", "VJ114A1", "VJ114IMG", "VJ115A2H", "VJ121", "VJ121A1D", "VJ121A1N", "VJ121A2", "VJ121C1", "VJ121C2", "VJ121C3", "VJ128C2", "VJ128C3", "VJ146A1G_NRT", "VJ146A1_NRT", "VNP02DNB", "VNP02IMG", "VNP02MOD", "VNP03DNB", "VNP03IMG", "VNP03MOD", "VNP03MODLL", "VNP03MODLL", "VNP09", "VNP09A1", "VNP09A1", "VNP09CMG", "VNP09CMG", "VNP09GA", "VNP09GA", "VNP09H1", "VNP09H1", "VNP10", "VNP10A1", "VNP10A1", "VNP10A1F", "VNP13A1", "VNP13A1", "VNP13A2", "VNP13A2", "VNP13A3", "VNP13A3", "VNP13C1", "VNP13C1", "VNP13C2", "VNP13C2", "VNP14", "VNP14", "VNP14A1", "VNP14A1", "VNP14IMG", "VNP14IMGTDL_NRT", "VNP15A2H", "VNP15A2H", "VNP21", "VNP21", "VNP21A1D", "VNP21A1D", "VNP21A1N", "VNP21A1N", "VNP21A2", "VNP21A2", "VNP21C1", "VNP21C2", "VNP21C3", "VNP22C2", "VNP22Q2", "VNP28C2", "VNP28C3", "VNP29", "VNP30", "VNP43C1", "VNP43C2", "VNP43C3", "VNP43C4", "VNP43DNBA1", "VNP43DNBA2", "VNP43DNBA3", "VNP43DNBA4", "VNP43IA1", "VNP43IA2", "VNP43IA3", "VNP43IA4", "VNP43MA1", "VNP43MA2", "VNP43MA3", "VNP43MA4", "VNP46A1", "VNP46A1", "VNP46A1G_NRT", "VNP46A1_NRT", "VNP46A2", "VNP46A3", "VNP46A4", "VNP64A1", "VOLPE", "VPRM_North_America_Parameters_1349", "Veg_Soil_Tundra_Burned_Area_2119", "Vegetation_Maps_Toolik_Lake_1690", "Vegetation_Photos_Toolik_Lake_1718", "Vegetation_greenness_trend_1576", "Vermont_HighRes_LandCover_2072", "Vulcan_V3_Annual_Emissions_1741", "Vulcan_V3_Hourly_Emissions_1810", "WACS2", "WAF_DEALIASED_SASS_L2", "WAVeTrends_1738", "WC_LSMEM_SOILM_025", "WC_MULTISEN_PREC_025", "WC_PM_ET_050", "WENTZ_NIMBUS-7_SMMR_L2", "WENTZ_SASS_SIGMA0_L2", "WHITECAPS", "WLDAS_NOAHMP001_DA1", "WRF_STILT_Footprints_Boston_1572", "WRF_STILT_Particles_Boston_1596", "WUS_UCLA_SR", "WV04_MSI_L1B", "WV04_Pan_L1B", "WV_LCC_SC_FSCA", "WaterBalance_Daily_Historical_GRIDMET", "WaterBalance_Monthly_Historical_GRIDMET", "WebbRosenzweig_548", "West_Florida_Shelf", "West_Soil_Carbon_1238", "Western_Gulf_of_Maine", "Wetland_Soil_CarbonStocks_WA_2249", "Wetland_VegClassification_PAD_2069", "WhitePhenoregions_799", "WhiteSpruce_Leaf_Traits_Alaska_2124", "Wildfire_Effects_Spruce_Field_1595", "Wildfire_Impacts_Boreal_Ecosys_2359", "Wildfires_2014_NWT_Canada_1307", "Wildfires_Date_of_Burning_1559", "Wildfires_NWT_Canada_1548", "Wildfires_NWT_Canada_2018_1703", "Wildfires_NWT_Canada_2019_1900", "Willow_Veg_Plots_1368", "WindSat-REMSS-L3U-v7.0.1a", "Wolves_Denning_Pups_Climate_1846", "XAERDT_L2_ABI_G16", "XAERDT_L2_ABI_G17", "XAERDT_L2_AHI_H08", "XAERDT_L2_AHI_H09", "XAERDT_L2_MODIS_Aqua", "XAERDT_L2_MODIS_Terra", "XAERDT_L2_VIIRS_NOAA20", "XAERDT_L2_VIIRS_SNPP", "Xingu_Albedo_Radiation_1622", "YKDelta_EnvChange_InfoExchange_1894", "Young_Russian_Forest_Map_1330", "ZinkeSoil_221", "Zinke_soil_683", "ZoblerSoilDerived_540", "ZoblerSoil_418", "Zobler_Soil_649", "ZonalFlux", "aamhcpex", "aces1am", "aces1cont", "aces1efm", "aces1log", "aces1time", "aces1trig", "aerosol_properties_725", "aes5davg_236", "aes_upl1_238", "aes_upl2_239", "afm06ihd_240", "afm06ptd_241", "afm06pwd_242", "afm06smd_243", "afm11afr_244", "afm13afr_245", "afm2as94_494", "afm3as94_496", "afm3mw94_495", "afm4toas_498", "afm4tofx_497", "afm6gifs_433", "african_woody_savanna_850", "air_sea_gas_exchange_xdeg_1208", "airscm3b_448", "airscpex", "airssy3b_507", "airsunp_61", "ali_etm_tandem_821", "amazon_precip_228", "ames_sunphotometer_643", "amprimpacts", "amprtbcx1", "amprtbcx2", "amprtbcx3", "amprtbf3a", "amprtbjax", "amprtbkwj", "amprtblba", "amprtbta", "amprtbtc", "ams_cs93_403", "ams_cs94_404", "ams_cs95_405", "ams_cs96_406", "amsua15sp", "amsua16sp", "amsua17sp", "aoci0bil_281", "apr3cpex", "apr3cpexaw", "apr3cpexcv", "apuimpacts", "asas_l1b_562", "asasrefl_287", "ascatcpex", "asosimpacts", "atmos_co2_by_erosion_xdeg_1019", "avapsimpacts", "avhrr_822", "avhrr_albedo_1995_xdeg_928", "avhrrl3b_481", "avhrrl4b_438", "avhrrl4c_439", "avhrrlc1_434", "basin_border_670", "billmark_828", "biofuel_emissions_753", "biomass_allocation_703", "biomdens_450", "biomebg2_296", "biomebgc_295", "boreas_aeshrday_235", "burning_emissions_752", "bvoc_flux_759", "c4_percent_1deg_932", "c4aaero", "c4dcm", "c4dcstar", "c4dcvi", "c4dd8d", "c4dicats", "c4djlh", "c4dlase", "c4dlip", "c4dmms", "c4dmtp", "c4dnevzor", "c4dpr2", "c4eampr", "c4eedop", "c4eehad", "c4ehamsr", "c4elip", "c4emas", "c4emtp", "c4enav", "c4enlh", "c4eo3p", "c4gandros", "c4gmipclo", "c4gmipfm", "c4gmipsod", "c4gmipss1", "c4gmipss2", "c4gmipwp", "c4gmisrep", "c4gnpol", "c4gsmart", "c4gtoga", "c4gxpow", "c4sg8", "calibgas_500", "canopychem_422", "canopyspec_423", "century_vemap_m4_820", "chem_26", "chesapeake_val_2013", "cmimpacts", "cmx3aeri", "cmx3andros", "cmx3g8", "cmx3misrep", "cmx3srl", "co2_emissions_1deg_1021", "combined_ancillary_xdeg_1200", "comp_runoff_monthly_xdeg_994", "cosmirimpacts", "cossirimpacts", "cp_lidar_images_721", "cplimpacts", "cramer_leemans_637", "cropland_612", "crsimpacts", "cru_monthly_climate_xdeg_1014", "cru_monthly_mean_xdeg_1015", "csgcpex01", "daily_precip_est_793", "dc8ammr", "dc8avaps", "dc8capac", "dc8dads", "dc8jplsaw", "dc8lase", "dc8laserh", "dc8macaws", "dc8mms", "dc8psr", "diffuse_irradiance_791", "dlhimpacts", "dtms0bil_247", "eMASL1B", "eMASL2AER", "eMASL2CLD", "echidna_1045", "ecmwf2_523", "ecmwf_met_1deg_1222", "ecosystem_roots_1deg_929", "edc_landcover_xdeg_930", "edgar_atmos_emissions_1deg_1022", "elev_arc_250", "er2_aerial_photos_722", "er2edop", "er2flog_501", "er2lip", "er2mams", "er2mir", "er2mts", "er2nasti", "er2nav", "er2navimpacts", "erbe_albedo_monthly_xdeg_957", "eta_model_723", "ewing", "explorer", "exrad3dimpacts", "exradepoch", "exradimpacts", "faamwdat_237", "fasir_biophys_monthly_xdeg_970", "fasir_ndvi_monthly_xdeg_972", "ffo_Betts_1987-1989_afd_93", "ffo_Betts_1987-1989_ams_89", "ffo_Betts_1987-1989_gsm_97", "ffo_Betts_1987-1989_nsm_101", "ffo_Betts_1987_afd_92", "ffo_Betts_1987_ams_88", "ffo_Betts_1987_gsm_96", "ffo_Betts_1987_nsm_100", "ffo_Betts_1988_afd_94", "ffo_Betts_1988_ams_90", "ffo_Betts_1988_gsm_98", "ffo_Betts_1988_nsm_102", "ffo_Betts_1989_afd_95", "ffo_Betts_1989_ams_91", "ffo_Betts_1989_gsm_99", "ffo_Betts_1989_nsm_103", "fhstmanr_386", "fhstmanv_387", "fieldsunp_65", "fife_AF_dtrnd_nae_3", "fife_AF_dtrnd_ncar_5", "fife_AF_dtrnd_wyo_4", "fife_AF_filtr_nae_6", "fife_AF_filtr_ncar_8", "fife_AF_filtr_wyo_7", "fife_AF_raw_nae_9", "fife_AF_raw_ncar_11", "fife_AF_raw_wyo_10", "fife_atmos_brut_drv_14", "fife_atmos_brut_son_15", "fife_atmos_lidar_ht_17", "fife_atmos_ncdc_son_13", "fife_atmos_nmc_upr_57", "fife_atmos_noaa_son_73", "fife_atmos_noaa_tov_16", "fife_atmos_sodar_18", "fife_atmos_tempprof_124", "fife_atmos_wind_lid_138", "fife_atmos_wind_son_139", "fife_biology_biomass_118", "fife_biology_leaf_ang_44", "fife_biology_leaf_h2o_126", "fife_biology_mow_biop_55", "fife_biology_mow_exo_56", "fife_biology_pho_box_27", "fife_biology_pho_leaf_46", "fife_biology_plantpro_69", "fife_biology_root_bio_75", "fife_biology_soil_co2_105", "fife_biology_soil_gas_106", "fife_biology_veg_biop_135", "fife_biology_veg_ref_137", "fife_biology_veg_spec_136", "fife_hydrology_strm_15m_1", "fife_hydrology_strm_day_119", "fife_hydrology_strm_st_120", "fife_optical_ot_brug_62", "fife_optical_ot_c130_63", "fife_optical_ot_calib_60", "fife_optical_ot_frasr_64", "fife_optical_ot_staff_66", "fife_sat_obs_ns001tms_59", "fife_sat_obs_sat_avhr_77", "fife_sat_obs_sat_coef_76", "fife_sat_obs_sat_ltm_78", "fife_sat_obs_sat_spot_79", "fife_soilmstr_peck_gam_37", "fife_soilmstr_peck_sm_109", "fife_soilmstr_sm_grav_110", "fife_soilmstr_sm_neut_111", "fife_soilmstr_sm_tran_113", "fife_soilmstr_soil_imp_108", "fife_soilprop_soil_h2o_117", "fife_soilprop_soil_rel_112", "fife_soilprop_soildens_104", "fife_soilprop_soilhydc_107", "fife_soilprop_soilsurv_115", "fife_soilprop_soilther_116", "fife_sur_flux_30_min_brg_20", "fife_sur_flux_30_min_brk_21", "fife_sur_flux_30_min_brl_19", "fife_sur_flux_30_min_brs_22", "fife_sur_flux_30_min_brv_23", "fife_sur_flux_30_min_brw_24", "fife_sur_flux_30_min_eca_30", "fife_sur_flux_30_min_ecb_32", "fife_sur_flux_30_min_ecg_31", "fife_sur_flux_30_min_ecv_33", "fife_sur_flux_30_min_ecw_34", "fife_sur_flux_basel_92_121", "fife_sur_met_ams_12", "fife_sur_met_cld_cam_28", "fife_sur_met_hday_met_39", "fife_sur_met_hmon_met_40", "fife_sur_met_ncdc_sur_122", "fife_sur_met_noaa_sur_58", "fife_sur_met_rain_30m_2", "fife_sur_met_rain_day_29", "fife_sur_refl_gem_helo_38", "fife_sur_refl_irt_grnd_72", "fife_sur_refl_irt_helo_70", "fife_sur_refl_irt_mult_71", "fife_sur_refl_lightwnd_43", "fife_sur_refl_ltbr_ksu_41", "fife_sur_refl_ltbr_unl_42", "fife_sur_refl_mmr_calb_51", "fife_sur_refl_mmr_grnd_52", "fife_sur_refl_mmr_helo_53", "fife_sur_refl_mmr_leaf_54", "fife_sur_refl_parabola_68", "fife_sur_refl_se5_gsfc_81", "fife_sur_refl_se5_helo_87", "fife_sur_refl_se5_leaf_85", "fife_sur_refl_se5_unl_82", "fife_sur_refl_soilrefl_114", "fife_sur_refl_unl_long_49", "fife_sur_refl_unl_surf_123", "fire_emissions_724", "fire_emissions_v4_R1_1293", "fltrepepoch", "fluxnet_point_1029", "forest_carbon_flux_949", "fuel_load_755", "g3acld", "g3acldb", "g3alsp", "g3alspb", "g3assp", "g3asspb", "g3at", "g3atb", "g3blmnc", "g3blsp", "g3blsp", "g3blspb", "g3bsmnc", "g3bssp", "g3bsspb", "g3bsspb", "g3bt", "g3bt", "g3btb", "g3btmnc", "gap_filled_marconi_811", "gdp_xdeg_974", "geocoord_556", "geoecology_R1_656", "gfscpex", "gghydro_676", "ghcn_631", "gimms_ndvi_monthly_xdeg_973", "giss_wetlands_632", "glider", "glmcierra", "glmgoesL3", "global_N_cycle_797", "global_N_deposition_maps_830", "global_population_xdeg_975", "globalir", "globalview_ch4_point_1109", "globalview_co2_point_1111", "globe_dem_630", "goes71_444", "goes71a_300", "goes72_554", "goes81_445", "goes81a_446", "goescpex", "goescpexcv", "goesimpacts", "goesrpltaviris", "goesrpltavirisng", "goesrpltcolma", "goesrpltcpl", "goesrpltcrs", "goesrpltdclma", "goesrpltfegs", "goesrpltgcas", "goesrpltivanpah", "goesrpltksclma", "goesrpltlip", "goesrpltmisrep", "goesrpltnalma", "goesrpltnaver2", "goesrpltoklma", "goesrpltredlake", "goesrpltshis", "goesrpltsolma", "goesrpltwtlma", "gpcc_precip_monthly_xdeg_995", "gpcp_precip_monthly_xdeg_1003", "gpcp_precip_pentad_xdeg_1002", "gpm2dc3vp", "gpm2dgcpex", "gpm2dhymex", "gpm2difld", "gpm2diphx", "gpm2dlpvex", "gpm2dmc3e", "gpm2dnsstc", "gpm2dvdolyx", "gpm2dwff2", "gpmadmirgcpex", "gpmahdmetolyx", "gpmampriphx2", "gpmamprmc3e", "gpmamprolyx", "gpmapr2gcpex", "gpmapr3olyx2", "gpmapuicepop", "gpmapuolyx", "gpmapuwff", "gpmarsifld", "gpmasinaiphx", "gpmasoolyx", "gpmavapsolyx", "gpmcax1cfolyx", "gpmcax1odolyx", "gpmcax1rbolyx", "gpmceilgcpex", "gpmceiliphx", "gpmchillmc3e", "gpmcilpvex", "gpmcitvidiphx", "gpmcmgcpex2", "gpmcmiphx", "gpmcmmc3e", "gpmcmolyx", "gpmcmorphnifld", "gpmcmorphniphx", "gpmcosmirgcpex", "gpmcosmiriphx", "gpmcosmirmc3e", "gpmcosmirolyx", "gpmcplolyx", "gpmcrsiphx", "gpmcrsolyx", "gpmcxsiolyx", "gpmd3rgcpex", "gpmd3ricepop", "gpmd3rifld", "gpmd3riphx", "gpmd3rolyx", "gpmdowolyx2", "gpmepfl", "gpmer2navmc3e", "gpmexradiphx", "gpmfltsummc3e", "gpmgaugewff", "gpmgoes13gcpexB", "gpmgoes13iphxB", "gpmgoes13mc3eB", "gpmgoes14iphxB", "gpmgoes15olyxB", "gpmgprof2014ifld", "gpmgsmapjifld", "gpmheifld", "gpmheiphx", "gpmhiwrapiphx", "gpmhiwrapmc3e", "gpmhiwrapolyx", "gpmikalpvex", "gpmjwlpvex", "gpmjwnsstc", "gpmkapxgcpex", "gpmkarx2ifld", "gpmkarx3ifld", "gpmkatx2olyx", "gpmkboxgcpex", "gpmkbufgcpex", "gpmkcae2iphx", "gpmkcbwgcpex", "gpmkcradgcpex", "gpmkcxxgcpex", "gpmkdmx2ifld", "gpmkdmx3ifld", "gpmkdvn2ifld", "gpmkdvn3ifld", "gpmkerlpvex", "gpmkgldmc3e", "gpmkgsp2iphx", "gpmkgyxgcpex", "gpmkhtx2iphx", "gpmkictmc3e", "gpmkinxmc3e", "gpmklgx2olyx", "gpmkltx2iphx", "gpmkmhx2iphx", "gpmkmpx2ifld", "gpmkmpx3ifld", "gpmkmrx2iphx", "gpmkorlpvex", "gpmkrax2iphx", "gpmkrtx2olyx", "gpmktlxmc3e", "gpmktwxmc3e", "gpmktyxgcpex", "gpmkumlpvex", "gpmkvnxmc3e", "gpmlidargcpex", "gpmlipiphx", "gpmmascolyx", "gpmmastmetlpvex", "gpmmettecgcpex", "gpmmisrepgcpex", "gpmmisrepifld", "gpmmisrepiphx", "gpmmisrepmc3e", "gpmmisrepolyx", "gpmmrms", "gpmmrrdukeiphx", "gpmmrrecgcpex2", "gpmmrrhymex", "gpmmrricepop", "gpmmrrlpvex", "gpmmrrnaachiphx", "gpmmrrnagcpex2", "gpmmrrnaifld", "gpmmrrnaiphx", "gpmmrrnamc3e", "gpmmrrolyx", "gpmmwrdukeiphx", "gpmnavcitgcpex2", "gpmnavcitiphx", "gpmnavcitmc3e", "gpmnavcitolyx", "gpmnavdc8gcpex", "gpmnavdc8olyx", "gpmnaver2iphx", "gpmnaver2olyx", "gpmncamdc8gcpex", "gpmnmqifld", "gpmnmqiphx", "gpmnoxpiphx", "gpmnpolifld2", "gpmnpoliphx", "gpmnpolmc3e", "gpmnpololyx2", "gpmnpolwff", "gpmnrlrtifld", "gpmodmlpvex", "gpmopasscgcpex", "gpmopassolyx", "gpmoumesmc3e", "gpmpadukeiphx", "gpmpagcpex", "gpmpahymex", "gpmpaifld", "gpmpaiphx", "gpmpal", "gpmpalpvex", "gpmpamc3e", "gpmpanoaamc3e", "gpmpansstc", "gpmparawifld", "gpmparprbgcpex", "gpmparprbiphx", "gpmparprbmc3e", "gpmparprbolyx", "gpmpawneemc3e", "gpmpersucifld", "gpmpipicepop", "gpmplgcpex", "gpmpllpvex", "gpmplolyx", "gpmpmastmetlpvex", "gpmpossgcpex", "gpmposslpvex", "gpmprecipmgcpex", "gpmprecipolyx", "gpmpvigcpex", "gpmpvilpvex", "gpmraddpgcpex", "gpmradioiphx", "gpmradmecgcpex", "gpmradpmgcpex", "gpmradsecgcpex", "gpmrefifld", "gpmrefpreiphx", "gpmrfcmpifld", "gpmrgachiphx", "gpmrgdukeiphx", "gpmrgifcifld", "gpmrgnaifld2", "gpmrgnaiphx2", "gpmrgnamc3e2", "gpmrgnaolyx", "gpmrgsaiphx", "gpmsatpaifld", "gpmsbdminmc3e", "gpmsbdorgmc3e", "gpmsbdrwncmc3e", "gpmsbdrwspcmc3e", "gpmscampriphx", "gpmseafluxicepop", "gpmsgifcifld", "gpmsgusgsifld", "gpmsimorbc3vp", "gpmsimorblpvex", "gpmsimorbmc3e", "gpmsimorbtwpice", "gpmsmdukeiphx", "gpmsnowgcpex", "gpmsnowolyx", "gpmsogcpex", "gpmsomc3e", "gpmsondelpvex", "gpmsondeolyx", "gpmsslpvex", "gpmsurmetc3vp", "gpmsurmetmc3e", "gpmtfmifld", "gpmtmpaifld", "gpmtmpaiphx", "gpmtpshpgcpex", "gpmtrm2A25iphx", "gpmuhfnoaamc3e", "gpmvanlpvex", "gpmvertixgcpex", "gpmvisecc3vp", "gpmvisecgcpex", "gpmvn", "gpmwacrc3vp", "gpmwbandgcpex", "gpmwbandiphx", "gpmwcrlpvex", "gpmwebecgcpex", "gpmwkacmlpvex", "gpmwpecgcpex", "gpmwrflpvex", "gpmwrfmc3e", "gpmxetc3vp", "gpmxpolifld", "gppdi_npp_gridded_xdeg_1023", "gppdi_npp_point_1033", "gripapr2", "gripcaps", "gripdawn", "gripdropdc83", "gripflt", "gripghis", "gripgoes11B", "gripgoes13B", "gripgoesot", "griphamsr", "griphirad", "griphirad1", "griphiwrap", "griplarge", "griplase", "griplip", "gripmisrep", "gripmms", "gripmsg", "gripnavdc8", "gripnavgh", "gripnavwb57", "gripradio", "gripstorm", "gts_precip_daily_xdeg_1001", "h01_shd_253", "h01smpvd_255", "h01uncpd_254", "h02swed_256", "h03candd_258", "h03dbhd_264", "h03scrdd_266", "h03sd96d_259", "h03sntmd_261", "h03sp96d_260", "h03stdnd_257", "h03swed_262", "h04assd_267", "h04stsnd_268", "h06grmsd_272", "h06grsmd_271", "h08gm94_273", "h08gm96_274", "h08gp96_275", "h09rradi_278", "h09stmgd_279", "h3scmet_265", "h3swe96d_263", "h5flxd_269", "h6acgsmd_270", "h8thrfld_277", "h8utmdem_276", "h9rgbl94_229", "h9rgbl95_231", "h9rgbl96_233", "h9rgtb94_230", "h9rgtb95_232", "h9rgtb96_234", "hamsrcpex", "hamsrcpexaw", "hamsrcpexcv", "hamsrepoch", "handheld_haze_708", "historic_cropland_xdeg_966", "historic_landcover_xdeg_967", "historical_croplands_675", "hiwat", "hiwrapimpacts", "hs3avaps2", "hs3cimssbt", "hs3cimsscth", "hs3cimsstot", "hs3cpl", "hs3fltrep", "hs3gmaodustaot", "hs3hamsr", "hs3hirad", "hs3hiwrap", "hs3navgh", "hs3nrltrop", "hs3ships", "hs3shis", "hs3wwlln", "hydro1k_elevation_xdeg_1007", "hydrographic_gghydro_636", "ibis_2_5_808", "imergcpex", "islscp2_soils_1deg_1004", "isslis_v2_fin", "isslisg_v2_fin", "jornada_albedo_667", "jornada_canopy_brf_668", "jornada_landcover_lai_665", "jornada_mquals_666", "kakqimpacts", "kalahari_aot_h2o_vapor_719", "kalahari_co2_heat_flux_765", "kbgmimpacts", "kboximpacts", "kbufimpacts", "kccximpacts", "kcleimpacts", "kcxximpacts", "kdiximpacts", "kdoximpacts", "kdtximpacts", "kdvnimpacts", "kenximpacts", "kfcximpacts", "kgrbimpacts", "kgrrimpacts", "kgyximpacts", "kilnimpacts", "kilximpacts", "kindimpacts", "kiwximpacts", "kjklimpacts", "klotimpacts", "klwximpacts", "kmhximpacts", "kmkximpacts", "knp_fire_maps_756", "knp_fire_residue_751", "knp_met_761", "kokximpacts", "kpbzimpacts", "kraximpacts", "krlximpacts", "kscmill", "kt_canopy_structure_768", "kt_gps_photos_769", "kt_lai_770", "kt_lai_trac_771", "kt_leaf_meas_772", "kt_leaf_spectra_773", "kt_pai_estimates_774", "kt_par_794", "kt_stem_map_775", "kt_veg_inventory_776", "kt_woody_veg_777", "ktyximpacts", "kvwximpacts", "lai_45", "lake_erie_aug_2014", "land_cover_data-1km_627", "land_cover_data_1deg_677", "land_cover_data_1km_678", "land_cover_data_8km_680", "landcover_bvoc_est_764", "lba_ghcn_702", "lba_gisswetlands_688", "lba_isric_wise_701", "lba_tree_cover-1km_686", "ldarraw", "leaf_voc_emissions_763", "leafchem_421", "leafspec_424", "leemans_cramer_681", "legal_amazon_mask_671", "level_1_annual_co2_895", "level_2_seasonal_co2_1198", "lipimpacts", "lislip", "lislipG", "lisvhrac", "lisvhrdc", "lisvhrfc", "lisvhrmc", "lisvhrsc", "litter_decomp_651", "lohrac", "lohrfc", "lohrmc", "lolrac", "lolracts", "lolradc", "lolrdc", "lolrfc", "lolrmts", "lolrts", "lsatmssd_435", "ltm_ii3a_280", "ltm_ii3b_425", "ltm_ii3p_426", "ltm_ii3s_427", "ltmmaxln_429", "ltmmaxls_430", "ltmphysn_431", "ltmphyss_432", "mapss_modis_aerosol_814", "mapss_modis_watervapor_815", "marsii94_407", "mas_lv2_561", "masccpex", "maslv1b_560", "maun_met_flux_760", "met_profile_SA_729", "met_profile_skukuza_728", "meteo_50", "metnavcpexaw", "metnavcpexcv", "micro_pulse_lidar_715", "misrepcpexaw", "misrepimpacts", "model_36", "model_npp_xdeg_1027", "modis_MOD04_aerosol_813", "modis_MOD05_watervapor_812", "modis_albedo_2002_filled_xdeg_960", "modis_albedo_2002_xdeg_958", "modis_aster_fire_707", "modis_burned_area_796", "modis_h2o_heat_flux_762", "modis_l3_albedo_840", "modis_landcover_xdeg_968", "modiscpex", "mongu_daily_rainfall_785", "mongu_fpar_trac_784", "mongu_irradiance_782", "mongu_lai-2000_781", "mongu_skukuza_albedo_786", "mongu_skukuza_soil_prop_789", "mongu_tree_rings_788", "mongu_veg_structure_795", "mrmsimpacts", "msutls", "msutlt", "msutmt", "msuttp", "musondeimpacts", "mwlezflx_493", "n_s_dem_248", "nalma", "nalmaraw", "nam2ds", "namapr2", "namcaps", "namcobalt", "namcvi", "namdblue", "namdc8nav", "namdlh", "namdrop", "namdrop_raw", "namhamsr", "namlarge", "namlargen", "namlase", "nammms", "namnpol", "namradio", "namsenegal", "namsenrg", "namsmart", "namtoga", "namukatd", "namzeus", "nauru99", "navdc8cpex", "navghepoch", "nbi_veg_maps_787", "ncep_met_1deg_1226", "ncsusndimpacts", "net_carbon_flux_662", 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"AQUARIUS_L3_SSS-RainFlagged_SMI_7DAY-RUNNINGMEAN_V5", "AQUARIUS_L3_SSS-RainFlagged_SMI_7DAY_V5", "AQUARIUS_L3_SSS-RainFlagged_SMI_ANNUAL_V5", "AQUARIUS_L3_SSS-RainFlagged_SMI_CUMULATIVE_V5", "AQUARIUS_L3_SSS-RainFlagged_SMI_DAILY_V5", "AQUARIUS_L3_SSS-RainFlagged_SMI_MONTHLY-CLIMATOLOGY_V5", "AQUARIUS_L3_SSS-RainFlagged_SMI_MONTHLY_V5", "AQUARIUS_L3_SSS-RainFlagged_SMI_SEASONAL-CLIMATOLOGY_V5", "AQUARIUS_L3_SSS-RainFlagged_SM_SMIA_MONTHLY_V5", "AQUARIUS_L3_SSS-RainFlagged_SM_SMID_MONTHLY_V5", "AQUARIUS_L3_SSS-RainFlagged_SM_SMI_MONTHLY_V5", "AQUARIUS_L3_SSS_CAP_7DAY_V5", "AQUARIUS_L3_SSS_CAP_MONTHLY_V5", "AQUARIUS_L3_SSS_RAINCORRECTED_CAP_7DAY_V5", "AQUARIUS_L3_SSS_RAINCORRECTED_CAP_MONTHLY_V5", "AQUARIUS_L3_SSS_SMIA_28DAY-RUNNINGMEAN_V5", "AQUARIUS_L3_SSS_SMIA_3MONTH_V5", "AQUARIUS_L3_SSS_SMIA_7DAY_V5", "AQUARIUS_L3_SSS_SMIA_ANNUAL_V5", "AQUARIUS_L3_SSS_SMIA_CUMULATIVE_V5", "AQUARIUS_L3_SSS_SMIA_DAILY_V5", "AQUARIUS_L3_SSS_SMIA_MONTHLY-CLIMATOLOGY_V5", "AQUARIUS_L3_SSS_SMIA_MONTHLY_V5", "AQUARIUS_L3_SSS_SMIA_SEASONAL-CLIMATOLOGY_V5", "AQUARIUS_L3_SSS_SMID_28DAY-RUNNINGMEAN_V5", "AQUARIUS_L3_SSS_SMID_3MONTH_V5", "AQUARIUS_L3_SSS_SMID_7DAY_V5", "AQUARIUS_L3_SSS_SMID_ANNUAL_V5", "AQUARIUS_L3_SSS_SMID_CUMULATIVE_V5", "AQUARIUS_L3_SSS_SMID_DAILY_V5", "AQUARIUS_L3_SSS_SMID_MONTHLY-CLIMATOLOGY_V5", "AQUARIUS_L3_SSS_SMID_MONTHLY_V5", "AQUARIUS_L3_SSS_SMID_SEASONAL-CLIMATOLOGY_V5", "AQUARIUS_L3_SSS_SMI_28DAY-RUNNINGMEAN_V5", "AQUARIUS_L3_SSS_SMI_3MONTH_V5", "AQUARIUS_L3_SSS_SMI_7DAY-RUNNINGMEAN_V5", "AQUARIUS_L3_SSS_SMI_7DAY_V5", "AQUARIUS_L3_SSS_SMI_ANNUAL_V5", "AQUARIUS_L3_SSS_SMI_CUMULATIVE_V5", "AQUARIUS_L3_SSS_SMI_DAILY_V5", "AQUARIUS_L3_SSS_SMI_MONTHLY-CLIMATOLOGY_V5", "AQUARIUS_L3_SSS_SMI_MONTHLY_V5", "AQUARIUS_L3_SSS_SMI_SEASONAL-CLIMATOLOGY_V5", "AQUARIUS_L3_SSS_SM_SMIA_MONTHLY_V5", "AQUARIUS_L3_SSS_SM_SMID_MONTHLY_V5", "AQUARIUS_L3_SSS_SM_SMI_MONTHLY_V5", "AQUARIUS_L3_WIND_SPEED_CAP_7DAY_V5", "AQUARIUS_L3_WIND_SPEED_CAP_MONTHLY_V5", "AQUARIUS_L3_WIND_SPEED_SMIA_28DAY-RUNNINGMEAN_V5", "AQUARIUS_L3_WIND_SPEED_SMIA_3MONTH_V5", "AQUARIUS_L3_WIND_SPEED_SMIA_7DAY_V5", "AQUARIUS_L3_WIND_SPEED_SMIA_ANNUAL_V5", "AQUARIUS_L3_WIND_SPEED_SMIA_CUMULATIVE_V5", "AQUARIUS_L3_WIND_SPEED_SMIA_DAILY_V5", "AQUARIUS_L3_WIND_SPEED_SMIA_MONTHLY-CLIMATOLOGY_V5", "AQUARIUS_L3_WIND_SPEED_SMIA_MONTHLY_V5", "AQUARIUS_L3_WIND_SPEED_SMIA_SEASONAL-CLIMATOLOGY_V5", "AQUARIUS_L3_WIND_SPEED_SMID_28DAY-RUNNINGMEAN_V5", "AQUARIUS_L3_WIND_SPEED_SMID_3MONTH_V5", "AQUARIUS_L3_WIND_SPEED_SMID_7DAY_V5", "AQUARIUS_L3_WIND_SPEED_SMID_ANNUAL_V5", "AQUARIUS_L3_WIND_SPEED_SMID_CUMULATIVE_V5", "AQUARIUS_L3_WIND_SPEED_SMID_DAILY_V5", "AQUARIUS_L3_WIND_SPEED_SMID_MONTHLY-CLIMATOLOGY_V5", "AQUARIUS_L3_WIND_SPEED_SMID_MONTHLY_V5", "AQUARIUS_L3_WIND_SPEED_SMID_SEASONAL-CLIMATOLOGY_V5", "AQUARIUS_L3_WIND_SPEED_SMI_28DAY-RUNNINGMEAN_V5", "AQUARIUS_L3_WIND_SPEED_SMI_3MONTH_V5", "AQUARIUS_L3_WIND_SPEED_SMI_7DAY-RUNNINGMEAN_V5", "AQUARIUS_L3_WIND_SPEED_SMI_7DAY_V5", "AQUARIUS_L3_WIND_SPEED_SMI_ANNUAL_V5", "AQUARIUS_L3_WIND_SPEED_SMI_CUMULATIVE_V5", "AQUARIUS_L3_WIND_SPEED_SMI_DAILY_V5", "AQUARIUS_L3_WIND_SPEED_SMI_MONTHLY-CLIMATOLOGY_V5", "AQUARIUS_L3_WIND_SPEED_SMI_MONTHLY_V5", "AQUARIUS_L3_WIND_SPEED_SMI_SEASONAL-CLIMATOLOGY_V5", "AQUARIUS_L4_OISSS_IPRC_7DAY_V5", "ARB_48_IN_LIDAR", "ARC02", "ARCTAS_AerosolTraceGas_AircraftInSitu_DC8_Data", "ARCTAS_Aerosol_AircraftInSitu_DC8_Data", "ARCTAS_Aerosol_AircraftInSitu_P3B_Data", "ARCTAS_AircraftRemoteSensing_BE200_HSRL_Data", "ARCTAS_AircraftRemoteSensing_DC8_DIAL_Data", "ARCTAS_AircraftRemoteSensing_P3B_AATS14_Data", "ARCTAS_AircraftRemoteSensing_P3B_CAR_Data", "ARCTAS_Cloud_AircraftInSitu_DC8_Data", "ARCTAS_Ground_Data", "ARCTAS_JValue_AircraftInSitu_DC8_Data", "ARCTAS_Merge_DC8-Aircraft_Data", "ARCTAS_Merge_P3B-Aircraft_Data", "ARCTAS_MetNav_AircraftInSitu_DC8_Data", "ARCTAS_MetNav_AircraftInSitu_P3B_Data", "ARCTAS_Model_Data", "ARCTAS_Ozonesondes_Data", "ARCTAS_Radiation_AircraftInSitu_P3B_Data", "ARCTAS_Satellite_Data", "ARCTAS_TraceGas_AircraftInSitu_DC8_Data", "ARCTAS_TraceGas_AircraftInSitu_P3B_Data", "ARCTAS_Trajectory_Data", "ARCTICCC", "ARESE_ER2_MAS", "ARIA_S1_GUNW", "ARISE_Aerosol-TraceGas_AircraftRemoteSensing_C130_Data", "ARISE_Cloud_AircraftInSitu_C130_Data", "ARISE_Merge_Data", "ARISE_MetNav_AircraftInSitu_C130_Data", "ARISE_Radiation_AircraftInSitu_C130_Data", "ARK", "ARME_898", "ASCATA-L2-25km", "ASCATA-L2-Coastal", "ASCATA_ESDR_ANCILLARY_L2_V1.1", "ASCATA_ESDR_L2_WIND_STRESS_V1.1", "ASCATA_L2_25KM_CDR", "ASCATA_L2_COASTAL_CDR", "ASCATB-L2-25km", "ASCATB-L2-Coastal", "ASCATB_ESDR_ANCILLARY_L2_V1.1", "ASCATB_ESDR_L2_WIND_STRESS_V1.1", "ASCATC-L2-25km", "ASCATC-L2-Coastal", "ASCENDS_AVOCET_CA_NV_Feb_2016_2115", "ASCENDS_LAS_IN_Sept_2014_2116", "ASIRI", "ASO_3M_PCDTM", "ASO_3M_SD", "ASO_50M_SD", "ASO_50M_SWE", "AST14DEM", "AST14DMO", "AST14OTH", "ASTGTM", "ASTGTM_NC", "ASTGTM_NUMNC", "ASTWBD", "ASTWBD_ATTNC", "ASTWBD_NC", "AST_05", "AST_07", "AST_07XT", "AST_08", "AST_09", "AST_09T", "AST_09XT", "AST_L1A", "AST_L1AE", "AST_L1B", "AST_L1BE", "AST_L1T", "AST_L1T", "ATCS", "ATL03", "ATL03", "ATL06", "ATL06", "ATL07", "ATL07", "ATL07QL", "ATL08", "ATL08", "ATL08QL", "ATL09", "ATL09", "ATL09QL", "ATL10", "ATL10", "ATL10QL", "ATL11", "ATL11", "ATL12", "ATL12", "ATL13", "ATL13", "ATL13QL", "ATL14", "ATL14", "ATL14", "ATL14", "ATL15", "ATL15", "ATL15", "ATL15", "ATL16", "ATL16", "ATL17", "ATL17", "ATL19", "ATL19", "ATL20", "ATL20", "ATL21", "ATL21", "ATL22", "ATL22", "ATL23", "ATL23", "ATLAS_DEALIASED_SASS_L2", "ATLAS_Veg_Plots_1541", "ATMOSL1", "ATMOSL2AF", "ATMOSL2AT", "ATMOSL2PF", "ATMOSL2PT", "ATMOSL2TF", "ATMOSL2TT", "ATSM2AEF", "ATSM2LSF", "ATSM2STF", "ATSMIB2E", "ATSMIB2T", "ATSMIGEO", "ATTREX-Aircraft_Radiation_Measurements", "ATTREX-Aircraft_RemoteSensing_Temperature_Measurements", "ATTREX-Aircraft_insitu_Cloud_property_Measurements", "ATTREX-Aircraft_insitu_TraceGas_Measurements", "ATTREX-Aircraft_navigational_and_meteorological_Measurements", "ATom_AMP_Instrument_Data_1671", "ATom_AO2_Instrument_Data_V2_1880", "ATom_ATHOS_Instrument_Data_V2_1930", "ATom_Aerosol_Properties_V2_2111", "ATom_Aerosols_Meteorology_1684", "ATom_CAFS_Instrument_Data_V2_1933", "ATom_CAPSVienna_Data_1981", "ATom_CESM2_1878", "ATom_CIT_Instrument_Data_V2_1927", "ATom_CO_GEOS_1604", "ATom_Carbon_Aerosol_Loadings_1618", "ATom_Clouds_Aerosols_2250", "ATom_DLH_Instrument_Data_V2_1937", "ATom_FlightTrack_Influences_1889", "ATom_Forward_Flight_Videos_1938", "ATom_FullModel_DataStream_1877", "ATom_GT_CIMS_Instrument_Data_1715", "ATom_GlobalModelInitiative_CTM_1897", "ATom_HIPPO_ORCAS_1788", "ATom_HR-AMS_Instrument_Data_1716", "ATom_ISAF_Instrument_Data_1730", "ATom_MMS_Instrument_Data_1731", "ATom_Mapping_OH_Troposphere_1669", "ATom_Medusa_Instrument_Data_V2_1881", "ATom_Mineral_Dust_Cirrus_Cloud_2006", "ATom_Modeled_Observed_Data_1857", "ATom_NMASS_Data_1607", "ATom_NOyO3_Instrument_Data_1734", "ATom_Organic_Aerossols_1795", "ATom_Ozonesonde_InstrumentData_1910", "ATom_PALMS_Instrument_Data_1733", "ATom_PANTHER_Instrument_Data_1914", "ATom_PFP_Instrument_Data_1746", "ATom_Particulate_Iodine_1773", "ATom_Photolysis_Rates_1651", "ATom_Picarro_Instrument_Data_1732", "ATom_QCLS_Instrument_Data_V2_1932", "ATom_Rad_Measurements_ARMAS_1906", "ATom_SAGA_Instrument_Data_1748", "ATom_SO2_LIF_Instrument_Data_1890", "ATom_SOAP_Instrument_Data_1898", "ATom_SP2_Instrument_Data_1672", "ATom_SP2_LAM_FeOx_MMR_1828", "ATom_Simulated_Data_1597", "ATom_TOGA_Instrument_Data_V2_1936", "ATom_UCATS_Instrument_Data_1750", "ATom_UHSAS_Data_1619", "ATom_WAS_Instrument_Data_1751", "ATom_merge_1581", "ATom_merge_V2_1925", "ATom_nav_1613", "AU_5DSno", "AU_DyOcn", "AU_DySno", "AU_DySno_NRT_R02", "AU_Land_NRT_R02", "AU_MoSno", "AU_Ocean_NRT_R01", "AU_Rain_NRT_R02", "AU_SI12", "AU_SI12_NRT_R04", "AU_SI25", "AU_SI25_NRT_R04", "AU_SI6", "AU_SI6_NRT_R04", "AVHRR18_G-NAVO-L2P-v1.0", "AVHRR19_G-NAVO-L2P-v1.0", "AVHRR19_L-NAVO-L2P-v1.0", "AVHRRF_MA-STAR-L2P-v2.80", "AVHRRF_MA-STAR-L3U-v2.80", "AVHRRF_MB-STAR-L2P-v2.80", "AVHRRF_MB-STAR-L3U-v2.80", "AVHRRF_MC-STAR-L2P-v2.80", "AVHRRF_MC-STAR-L3U-v2.80", "AVHRRMTA_G-NAVO-L2P-v1.0", "AVHRRMTA_G-NAVO-L2P-v2.0", "AVHRRMTB_G-NAVO-L2P-v1.0", "AVHRRMTB_G-NAVO-L2P-v2.0", "AVHRRMTC_G-NAVO-L2P-v2.0", "AVHRR_Fire_Products_1545", "AVHRR_LST_826", "AVHRR_OI-NCEI-L4-GLOB-v2.0", "AVHRR_OI-NCEI-L4-GLOB-v2.1", "AVHRR_SST_METOP_A-OSISAF-L2P-v1.0", "AVHRR_SST_METOP_A_GLB-OSISAF-L3C-v1.0", "AVHRR_SST_METOP_A_NAR-OSISAF-L3C-v1.0", "AVHRR_SST_METOP_B-OSISAF-L2P-v1.0", "AVHRR_SST_METOP_B_GLB-OSISAF-L3C-v1.0", "AVHRR_SST_METOP_B_NAR-OSISAF-L3C-v1.0", "AVHRR_SST_NOAA19_NAR-OSISAF-L3C-v1.0", "AVIRIS-Classic_L1B_Radiance_2155", "AVIRIS-Classic_L2_Reflectance_2154", "AVIRIS-NG_Data_Idaho_1533", "AVIRIS-NG_L1B_radiance_2095", "AVIRIS-NG_L2_Reflectance_2110", "AVIRIS_FlightLine_Locator_2140", "Academ_Kurchatov", "Active_Fluorescence_2001", "Active_Layer_Thaw_Depths_1701", "Aeolus-CalVal-DAWN_DC8", "Aeolus-CalVal-Dropsondes_DC8", "Aeolus-CalVal-HALO_DC8", "Aeolus-CalVal-MetNav_DC8", "Aerosol_Sulfate_LowermostStrat_1868", "AfriSAR_AGB_Maps_1681", "AfriSAR_LVIS_Footprint_Cover_1591", "AfriSAR_Mondah_Field_Data_1580", "African_Rainfall_Patterns_1263", "Afrisar_LVIS_Biomass_VProfiles_1775", "AirMOSS_Field_Data_Harvard_1677", "AirMOSS_L1_Sigma0_BERMS_1406", "AirMOSS_L1_Sigma0_Chamel_1407", "AirMOSS_L1_Sigma0_DukeFr_1408", "AirMOSS_L1_Sigma0_Harvrd_1409", "AirMOSS_L1_Sigma0_Howlnd_1410", "AirMOSS_L1_Sigma0_LaSelv_1411", "AirMOSS_L1_Sigma0_Metoli_1412", "AirMOSS_L1_Sigma0_Moisst_1413", "AirMOSS_L1_Sigma0_TonziR_1414", "AirMOSS_L1_Sigma0_Walnut_1415", "AirMOSS_L2_3_RZ_Soil_Moisture_1418", "AirMOSS_L2_Carbon_Flux_1420", "AirMOSS_L2_Inground_Soil_Moist_1416", "AirMOSS_L2_Precipitation_1417", "AirMOSS_L4_Daily_NEE_1422", "AirMOSS_L4_RZ_Soil_Moisture_1421", "AirMOSS_L4_Regional_NEE_1423", "AirMSPI_ACEPOL_Ellipsoid-projected_Georegistered_Radiance_Data", "AirMSPI_ACEPOL_Terrain-projected_Georegistered_Radiance_Data", "AirMSPI_CalWater-2_Ellipsoid-projected_Georegistered_Radiance_Data", "AirMSPI_CalWater-2_Terrain-projected_Georegistered_Radiance_Data", "AirMSPI_FIREX-AQ_Terrain-projected_Georegistered_Radiance_Data", "AirMSPI_ImPACT-PM_Ellipsoid-projected_Georegistered_Radiance_Data", "AirMSPI_ImPACT-PM_Terrain-projected_Georegistered_Radiance_Data", "AirMSPI_ORACLES_Ellipsoid-projected_Georegistered_Radiance_Data", "AirMSPI_ORACLES_Terrain-projected_Georegistered_Radiance_Data", "AirMSPI_PODEX_Ellipsoid-projected_Georegistered_Radiance_Data", "AirMSPI_PODEX_Terrain-projected_Georegistered_Radiance_Data", "AirMSPI_RADEX_Ellipsoid-projected_Georegistered_Radiance_Data", "AirMSPI_RADEX_Terrain-projected_Georegistered_Radiance_Data", "AirMSPI_SEAC4RS_Ellipsoid-projected_Georegistered_Radiance_Data", "AirMSPI_SEAC4RS_Terrain-projected_Georegistered_Radiance_Data", "AirMSPI_SPEX-PR_Ellipsoid-projected_Georegistered_Radiance_Data", "AirMSPI_SPEX-PR_Terrain-projected_Georegistered_Radiance_Data", "AirSWOT_Orthomosaic_WaterMask_1655", "Airborne_Insitu_Measurements_1784", "Akademik_Sergey_Vavilov", "Alaska_Arctic_Tundra_Veg_Map_1353", "Alaska_L4_WRF_STILT_Footprints_1544", "Alaska_L4_WRF_STILT_Particle_1571", "Alaska_Lake_Pond_Maps_2134", "Alaska_Yukon_NDVI_1614", "Alaskan_CH4_CO2_Fluxes_1316", "Alaskan_CO2_Flux_1325", "Albedo_Boreal_North_America_1605", "Alder_Shrub_Soil_Alaska_V2_2300", "Annual_30m_AGB_1808", "Annual_Burned_Area_Maps_1708", "Annual_Landcover_ABoVE_1691", "Annual_Seasonality_Greenness_1698", "Annual_Thaw_Slump_1724", "Apero", "Aqua_AIRS_MODIS1km_IND", "ArabianSea_2011", "Arc00", "ArcticNET", "ArcticTreeLine_Dendrometry_Env_2185", "ArcticTreeLine_Spruce_CO2_WV_1948", "Arctic_Boreal_Burned_Area_V2_2328", "Arctic_Boreal_CO2_Flux_1934", "Arctic_Flux_629", "Arctic_Network_Veg_plots_1542", "Arctic_RSWQ", "Arctic_Soil_Properties_2149", "Arctic_Vegetation_Maps_1323", "Arctic_Wildlife_Refuge_Veg_Map_1384", "Arctic_Winter_Respiration_v2_1762", "Arrigetch_Peaks_Veg_Plots_1358", "Atmospheric_CO2_California_1641", "Atqasuk_Veg_Plots_1371", "B01", "B02", "B03", "B04", "B05", "B06", "B07", "B08", "B09", "BAHAMAS2004", "BAROCLINIC_HRET14", "BASIN_TCP_963", "BBOP", "BCFLEXPART", "BC_Aerosol_Dynamics_Alaska_1340", "BDSNP_CMAQ_Model_1351", "BENEFIT", "BEST", "BFO_dsp01_ccrs_avhrr_landcover_589", "BFO_dsp01_ccrs_tm_landcover_588", "BFO_dsp04_ers_freeze-thaw_maps_590", "BFO_dsp05_ccrs_avhrr_npp_591", "BFO_dsp06_casi_lai_cc_592", "BFO_dsp08_polder_surface_params_594", "BFO_dsp09_moss_map_595", "BFO_dsp09_sask_fire_map_596", "BFO_dsp10_fpar_lai_1994_585", "BFO_dsp10_landcover_598", "BFO_dsp10_landcover_tm_mosaic_602", "BFO_dsp10_landcover_tm_reclassed_597", "BFO_dsp10_moss_cover_599", "BFO_dsp10_ndvi_600", "BFO_dsp10_peatlands_601", "BFO_flx01_derived_data_603", "BFO_flx01_flux_met_temp_604", "BFO_flx03_area_avg_flux_586", "BFO_flx04_nsa_burn_flux_587", "BFO_hmet01_goes8_level2_srb_605", "BFO_hmet01_ssmi_precip_606", "BFO_hmet02_gridded_met_phase2-3_607", "BFO_hmet03_hourly_met_p1_608", "BFO_hmet04_src_nsa_96-98_609", "BFO_mod01_gridded_met_p3_daily_610", "BGC_glider_GNATS", "BILIM", "BIO-GO-SHIP", "BIOCOMPLEXITY", "BIODIVERSITY", "BIOME", "BIOME_BGC_4_1_1_805", "BIOME_BGC_m2_4_1_2_809", "BIOME_BGC_m_4_1_1_806", "BIOSCAPE_COASTAL_CARBON", "BIOSOPE", "BIO_BURN_5X5_HAO_NAT", "BLATM1B", "BLATM2", "BLVIS2", "BNL", "BOA", "BOREAS_CDS_1350", "BOREAS_RSS-03_Snapshots_289", "BOREAS_SLICER_508", "BOUSSOLE", "BRAZIL", "BRMCR2", "BUVN04L2", "BUVN04L3zm", "BUVN4L1DCM", "BUVN4L1DCW", "BUVN4L1PDB", "BUVN4L1RUT", "BUVN4L2CPOZ", "BUVN4L2HDBUV", "BUVN4L3ZMT", "Barrow_NGEE_Arctic_Veg_Plots_1505", "Barrow_Tundra_Veg_Plots_1535", "Barter_Barrow_Veg_Plots_1534", "BenthicEcology_fromSpace", "Ber95", "Ber96", "BeringSea_LaserFluoMon", "Bermuda_Testbed_Mooring", "Big_Bend", "Bio_optics_Chl_polarization", "Biogenic_CO2flux_SIF_SMUrF_1899", "BlueFlux_AirborneObs_Florida_2327", "BlueFlux_Tidal_River_Water_2333", "Blue_Carbon_Tidal_Wetland_Maps_2091", "Bold_EPAHypoxia", "BorealForest_Greenness_Trends_2023", "Boreal_AGB_Density_ICESat2_2186", "Boreal_Arctic_Wetland_CH4_2351", "Boreal_CanopyCover_StandAge_2012", "Boreal_Fire_Severity_Metrics_1520", "BowdoinBuoy", "BurnedArea_Emissions_AK_YT_NWT_1812", "Burned_Area_Depth_AK_CA_2063", "C-HARRIER", "C5", "CABARE_918", "CALIPSO-NVF_HSRL2_KingAir_Data", "CAL_IIR_L1-Standard-V2-00", "CAL_IIR_L2_Swath-Standard-V4-20", "CAL_IIR_L2_Swath-Standard-V4-21", "CAL_IIR_L2_Swath-Standard-V4-51", "CAL_IIR_L2_Track-Standard-V4-20", "CAL_IIR_L2_Track-Standard-V4-21", "CAL_IIR_L2_Track-Standard-V4-51", "CAL_IIR_L3_GEWEX_Cloud-Standard-V1-00", "CAL_LID_L1-Standard-V4-10", "CAL_LID_L1-Standard-V4-11", "CAL_LID_L1-Standard-V4-51", "CAL_LID_L15-Standard-V1-00", "CAL_LID_L15-Standard-V1-01", "CAL_LID_L2_01kmCLay-Standard-V4-20", "CAL_LID_L2_01kmCLay-Standard-V4-21", "CAL_LID_L2_01kmCLay-Standard-V4-51", "CAL_LID_L2_05kmALay-Prov-V3-02", "CAL_LID_L2_05kmALay-Prov-V3-30", "CAL_LID_L2_05kmALay-Standard-V4-20", "CAL_LID_L2_05kmALay-Standard-V4-21", "CAL_LID_L2_05kmALay-Standard-V4-51", "CAL_LID_L2_05kmAPro-Prov-V3-02", "CAL_LID_L2_05kmAPro-Standard-V4-20", "CAL_LID_L2_05kmAPro-Standard-V4-21", "CAL_LID_L2_05kmAPro-Standard-V4-51", "CAL_LID_L2_05kmCLay-Standard-V4-20", "CAL_LID_L2_05kmCLay-Standard-V4-21", "CAL_LID_L2_05kmCLay-Standard-V4-51", "CAL_LID_L2_05kmCPro-Standard-V4-20", "CAL_LID_L2_05kmCPro-Standard-V4-21", "CAL_LID_L2_05kmCPro-Standard-V4-51", "CAL_LID_L2_05kmMLay-Standard-V4-20", "CAL_LID_L2_05kmMLay-Standard-V4-21", "CAL_LID_L2_05kmMLay-Standard-V4-51", "CAL_LID_L2_333mCLay-ValStage1-V3-30", "CAL_LID_L2_333mMLay-Standard-V4-20", "CAL_LID_L2_333mMLay-Standard-V4-21", "CAL_LID_L2_333mMLay-Standard-V4-51", "CAL_LID_L2_BlowingSnow_Antarctica-Standard-V1-00", "CAL_LID_L2_BlowingSnow_Antarctica-Standard-V1-01", "CAL_LID_L2_BlowingSnow_Antarctica-Standard-V2-00", "CAL_LID_L2_BlowingSnow_Greenland-Standard-V1-00", "CAL_LID_L2_PSCMask-Standard-V2-00", "CAL_LID_L2_VFM-Standard-V4-20", "CAL_LID_L2_VFM-Standard-V4-21", "CAL_LID_L2_VFM-Standard-V4-51", "CAL_LID_L3_Cloud_Occurrence-Standard-V1-00", "CAL_LID_L3_GEWEX_Cloud-Standard-V1-00", "CAL_LID_L3_Stratospheric_APro-Standard-V1-00", "CAL_LID_L3_Stratospheric_APro-Standard-V1-01", "CAL_LID_L3_Tropospheric_APro_AllSky-Standard-V4-20", "CAL_LID_L3_Tropospheric_APro_AllSky-Standard-V4-21", "CAL_LID_L3_Tropospheric_APro_CloudFree-Standard-V4-20", "CAL_LID_L3_Tropospheric_APro_CloudFree-Standard-V4-21", "CAL_LID_L3_Tropospheric_APro_CloudySkyOpaque-Standard-V4-20", "CAL_LID_L3_Tropospheric_APro_CloudySkyOpaque-Standard-V4-21", "CAL_LID_L3_Tropospheric_APro_CloudySkyTransparent-Standard-V4-20", "CAL_LID_L3_Tropospheric_APro_CloudySkyTransparent-Standard-V4-21", "CAL_WFC_L1_125m-ValStage1-V3-01", "CAL_WFC_L1_125m-ValStage1-V3-02", "CAL_WFC_L1_1Km-ValStage1-V3-01", "CAL_WFC_L1_1Km-ValStage1-V3-02", "CAL_WFC_L1_IIR-ValStage1-V3-01", "CAM5K30CF", "CAM5K30CF", "CAM5K30EM", "CAM5K30EM", "CAM5K30UC", "CAM5K30UC", "CAMEX4_ER2_MAS", "CAML", "CAMP2Ex-Cloud-Precip-Retrieval", "CAMP2Ex_Aerosol_AircraftInSitu_Learjet_Data", "CAMP2Ex_Aerosol_AircraftInSitu_P3_Data", "CAMP2Ex_Aerosol_AircraftRemoteSensing_P3_Data", "CAMP2Ex_Cloud_AircraftInSitu_Learjet_Data", "CAMP2Ex_Cloud_AircraftInSitu_P3_Data", "CAMP2Ex_Merge_Data", "CAMP2Ex_MetNav_AircraftInSitu_Learjet_Data", "CAMP2Ex_MetNav_AircraftInSitu_P3_Data", "CAMP2Ex_Miscellaneous_Data", "CAMP2Ex_Radiation_AircraftInSitu_P3_Data", "CAMP2Ex_TraceGas_AircraftInSitu_P3_Data", "CAMREX_904", "CARAFE_2016_2017_v2_2002", "CARIACO", "CARMIAAE", "CARMITST", "CARVE_Ecosystem_CH4_Flux_1558", "CARVE_L1_FTS_Spectra_1426", "CARVE_L1_FlightPath_1425", "CARVE_L1_FlightPath_Winds_1427", "CARVE_L1_Ground_Flux_1424", "CARVE_L1_Infrared_1428", "CARVE_L2_AtmosGas_Ground_1419", "CARVE_L2_AtmosGas_Harvard_1403", "CARVE_L2_AtmosGas_Merge_1402", "CARVE_L2_AtmosGas_NOAA_1401", "CARVE_L2_FTS_ColumnGas_1429", "CARVE_L2_Flask_1404", "CARVE_L2_Flask_Ground_1405", "CARVE_L4_WRF-STILT_Footprint_1431", "CARVE_L4_WRF-STILT_Particle_1430", "CARVE_Land_Thaw_State_1383", "CARVE_Photos_1435", "CARVE_Reports_1434", "CARVE_Videos_1433", "CAR_ARCTAS_BRDF", "CAR_ARCTAS_L1C", "CAR_ARMCAS_BRDF", "CAR_ARMCAS_L1C", "CAR_BRDF_709", "CAR_CLAMS_BRDF", "CAR_CLAMS_L1C", "CAR_CLASIC_BRDF", "CAR_CLASIC_L1C", "CAR_DISCOVERAQ_BRDF", "CAR_DISCOVERAQ_L1C", "CAR_ECO3D_BRDF", "CAR_ECO3D_L1C", "CAR_FIREACE_BRDF", "CAR_FIREACE_L1C", "CAR_INTEXB_BRDF", "CAR_INTEXB_L1C", "CAR_KUWAITOILFIRE_BRDF", "CAR_KUWAITOILFIRE_L1C", "CAR_LEADEX_BRDF", "CAR_LEADEX_L1C", "CAR_SAFARI_BRDF", "CAR_SAFARI_L1C", "CAR_SCARA_BRDF", "CAR_SCARA_L1C", "CAR_SCARB_BRDF", "CAR_SCARB_L1C", "CAR_SKUKUZA_BRDF", "CAR_SKUKUZA_L1C", "CAR_SNOWEX17_BRDF", "CAR_SNOWEX17_L1C", "CAR_TARFOX_BRDF", "CAR_TARFOX_L1C", "CASES", "CATS-ISS_L1B_D-M7.1-V3-00", "CATS-ISS_L1B_D-M7.2-V3-00", "CATS-ISS_L1B_N-M7.1-V3-00", "CATS-ISS_L1B_N-M7.2-V3-00", "CATS-ISS_L2O_D-M7.1-V3-01_05kmLay", "CATS-ISS_L2O_D-M7.1-V3-01_05kmPro", "CATS-ISS_L2O_D-M7.2-V3-00_05kmLay", "CATS-ISS_L2O_D-M7.2-V3-00_05kmPro", "CATS-ISS_L2O_D-M7.2-V3-01_05kmLay", "CATS-ISS_L2O_D-M7.2-V3-01_05kmPro", "CATS-ISS_L2O_N-M7.1-V3-01_05kmLay", "CATS-ISS_L2O_N-M7.1-V3-01_05kmPro", "CATS-ISS_L2O_N-M7.2-V3-00_05kmLay", "CATS-ISS_L2O_N-M7.2-V3-00_05kmPro", "CATS-ISS_L2O_N-M7.2-V3-01_05kmLay", "CATS-ISS_L2O_N-M7.2-V3-01_05kmPro", "CCE-LTER", "CCMP_WINDS_10M6HR_L4_V3.1", "CCMP_WINDS_10MMONTHLY_L4_V3.1", "CD01_BRAMS_907", "CD01_CIRSAN_Meteorology_2001_1114", "CD02_Atmosphere_CO2_Isotopes_1011", "CD02_C_N_Isotopes_1097", "CD02_C_N_O_Organic_983", "CD02_Forest_Canopy_Structure_1009", "CD02_Leaf_Level_Gas_Exchange_1010", "CD02_Leaf_Water_Potential_1100", "CD02_O_H_Isotopes_1008", "CD03_Ceilometer_Km67_942", "CD03_Mesoscale_Meteorology_944", "CD03_Pasture_Flux_962", "CD03_Tethered_Balloon_1108", "CD04_Biomass_990", "CD04_CO2_Profiles_947", "CD04_Dendrometry_989", "CD04_LAI_992", "CD04_LAI_Estimates_1103", "CD04_Leaf_Level_Gas_Exchange_1060", "CD04_Leaf_Litter_991", "CD04_Logging_Damage_1038", "CD04_Meteorology_Fluxes_946", "CD04_Soil_Moisture_Km83_979", "CD04_Soil_Respiration_1039", "CD04_Tower_Flux_Gap_978", "CD05_Fuel_Loads_1233", "CD05_Micromet_1169", "CD05_REE_Fuel_Sticks_Moisture_1232", "CD06_BGC_JiParana_1227", "CD06_C02_Exchange_1136", "CD06_C_Isotopes_1120", "CD06_Camrex_1086", "CD06_Carbon_respiration_1125", "CD06_LULC_Map_JiParana_1087", "CD06_Landuse_Timeseries_JiParana_844", "CD06_Outgassing_1151", "CD06_Physical_Template_JiParana_1090", "CD06_Soils_JiParana_1088", "CD06_Water_Balance_JiParana_1132", "CD07_GOES_L3_Gridded_SRB_831", "CD08_CWD_Res_and_Decomp_Manaus_911", "CD08_C_Isotopes_Belowground_1025", "CD08_Ecosystem_Resp_Manaus_912", "CD08_Leaf_Isotopes_Manaus_1245", "CD08_Radiocarbon_Dates_997", "CD08_Radiocarbon_Dates_Manaus_996", "CD08_Tree_Growth_Manaus_1194", "CD08_Tree_Inventory_Ducke_910", "CD09_Soils_Veg_Tapajos_1104", "CD10_Biometry_Tapajos_854", "CD10_CO2_Profiles_Tapajos_855", "CD10_CO_CO2_Maxaranguape_1012", "CD10_CO_Tapajos_856", "CD10_CWD_Tapajos_858", "CD10_DBH_Tapajos_859", "CD10_EddyFlux_Tapajos_860", "CD10_H2O_Profiles_Tapajos_861", "CD10_Litter_Tapajos_862", "CD10_Temperature_Profiles_Tapajos_863", "CD11_Forest_Degradation_1118", "CD15_Productivity_1167", "CD17_Forest_Survey_1254", "CD32_Fluxes_Brazil_1842", "CD32_LBA_MIP_Drivers_1177", "CD34_Amazon_Hyperion_1064", "CD34_Amazon_Landsat_1176", "CD36_SALDAS_1162", "CD37_Biomass_Landsat_Glas_1145", "CDDIS_DORIS_IDSEOP_product", "CDDIS_DORIS_IDScumulativePositions_product", "CDDIS_DORIS_IDSdpod_product", "CDDIS_DORIS_IDSgeocenter_product", "CDDIS_DORIS_IDSionosphere_product", "CDDIS_DORIS_data_cycle", "CDDIS_DORIS_data_rinex", "CDDIS_DORIS_information", "CDDIS_DORIS_products_geocenter", "CDDIS_DORIS_products_ionosphere", "CDDIS_DORIS_products_orbit", "CDDIS_DORIS_products_positions", "CDDIS_DORIS_products_quaternions", "CDDIS_DORIS_products_stcd", "CDDIS_GNSS_highrate_data", "CDDIS_GNSS_products_IGS20", "CDDIS_GNSS_products_clocks_final", "CDDIS_GNSS_products_clocks_rapid", "CDDIS_GNSS_products_clocks_realtime", "CDDIS_GNSS_products_erp", "CDDIS_GNSS_products_ionosphere_final", "CDDIS_GNSS_products_ionosphere_predicted", "CDDIS_GNSS_products_ionosphere_rapid", "CDDIS_GNSS_products_orbit_final", "CDDIS_GNSS_products_orbit_rapid", "CDDIS_GNSS_products_orbit_realtime", "CDDIS_GNSS_products_orbit_ultrarapid", "CDDIS_GNSS_products_positions", "CDDIS_GNSS_products_troposphere", "CDDIS_MISC_information", "CDDIS_SLR_data", "CDDIS_SLR_predictions", "CDDIS_SLR_products_poseop", "CDDIS_VLBI_data", "CDDIS_VLBI_products_eop", "CDDIS_VLBI_products_positions", "CDDIS_VLBI_products_troposphere", "CDIAC_NDP41_220", "CER-NEWS_CCCM_Aqua-FM3-MODIS-CAL-CS", "CERES-MISR-MODIS_SSF-SSFM_Terra-FM1", "CERES-MISR-MODIS_SSF-SSFM_Terra-FM2", "CERES_EBAF", "CERES_EBAF", "CERES_EBAF-TOA", "CERES_EBAF-TOA", "CER_BDS_Aqua-FM4", "CER_BDS_Aqua-FM4", "CER_BDS_TRMM-PFM", "CER_CCCM_Aqua-FM3-MODIS-CAL-CS", "CER_CCCM_Aqua-FM3-MODIS-CAL-CS", "CER_CRS1deg-Hour_Aqua-MODIS", "CER_CRS1deg-Hour_Terra-MODIS", "CER_CRS_Aqua-FM3-MODIS", "CER_CRS_Aqua-FM3-MODIS", "CER_CRS_Aqua-FM4-MODIS", "CER_CRS_TRMM-PFM-VIRS", "CER_CRS_Terra-FM1-MODIS", "CER_CRS_Terra-FM1-MODIS", "CER_CRS_Terra-FM1-MODIS", "CER_CRS_Terra-FM2-MODIS", "CER_CldTypHist_GEO-MODIS", "CER_CldTypHist_GEO-MODIS-VIIRS", "CER_ES4_Aqua-Xtrk", "CER_ES4_NOAA20-FM6", "CER_ES4_NPP-FM5", "CER_ES4_TRMM-PFM", "CER_ES4_Terra+Aqua", "CER_ES4_Terra-Xtrk", "CER_ES8_Aqua-FM4", "CER_ES8_TRMM-PFM", "CER_ES9_Aqua-Xtrk", "CER_ES9_NOAA20-FM6", "CER_ES9_NPP-FM5", "CER_ES9_TRMM-PFM", "CER_ES9_Terra+Aqua", "CER_ES9_Terra-Xtrk", "CER_FSW_Aqua-FM4-MODIS", "CER_FSW_TRMM-PFM-VIRS", "CER_FSW_Terra-FM1-MODIS", "CER_FSW_Terra-FM1-MODIS", "CER_FSW_Terra-FM1-MODIS", "CER_FSW_Terra-FM2-MODIS", "CER_FluxByCldTyp-Day_NOAA20-VIIRS", "CER_FluxByCldTyp-Day_Terra-Aqua-MODIS", "CER_FluxByCldTyp-Month_NOAA20-VIIRS", "CER_FluxByCldTyp-Month_Terra-Aqua-MODIS", "CER_GEO_Ed4_GMS05_FD", "CER_GEO_Ed4_GOE08_NH", "CER_GEO_Ed4_GOE08_SH", "CER_GEO_Ed4_GOE09_NH", "CER_GEO_Ed4_GOE09_SH", "CER_GEO_Ed4_GOE10_NH", "CER_GEO_Ed4_GOE10_SH", "CER_GEO_Ed4_GOE11_NH", "CER_GEO_Ed4_GOE11_SH", "CER_GEO_Ed4_GOE12_NH", "CER_GEO_Ed4_GOE12_SH", "CER_GEO_Ed4_GOE13_NH", "CER_GEO_Ed4_GOE13_NH", "CER_GEO_Ed4_GOE13_SH", "CER_GEO_Ed4_GOE13_SH", "CER_GEO_Ed4_GOE14_NH", "CER_GEO_Ed4_GOE14_SH", "CER_GEO_Ed4_GOE15_NH", "CER_GEO_Ed4_GOE15_NH", "CER_GEO_Ed4_GOE15_SH", "CER_GEO_Ed4_GOE15_SH", "CER_GEO_Ed4_MET05_FD", "CER_GEO_Ed4_MET07_FD", "CER_GEO_Ed4_MET08_NH", "CER_GEO_Ed4_MET08_NH", "CER_GEO_Ed4_MET08_SH", "CER_GEO_Ed4_MET09_NH", "CER_GEO_Ed4_MET09_SH", "CER_GEO_Ed4_MET10_NH", "CER_GEO_Ed4_MET10_SH", "CER_GEO_Ed4_MTS01_NH", "CER_GEO_Ed4_MTS01_SH", "CER_GEO_Ed4_MTS02_NH", "CER_GEO_Ed4_MTS02_SH", "CER_ISCCP-D2like-Day_Aqua-FM3-MODIS", "CER_ISCCP-D2like-Day_Aqua-FM4-MODIS", "CER_ISCCP-D2like-Day_Terra-FM1-MODIS", "CER_ISCCP-D2like-Day_Terra-FM2-MODIS", "CER_ISCCP-D2like-GEO_DAY", "CER_ISCCP-D2like-Mrg_GEO-MODIS-DAY", "CER_ISCCP-D2like-Nit_Aqua-FM3-MODIS", "CER_ISCCP-D2like-Nit_Aqua-FM4-MODIS", "CER_ISCCP-D2like-Nit_Terra-FM1-MODIS", "CER_ISCCP-D2like-Nit_Terra-FM2-MODIS", "CER_SSF1deg-Day_Aqua-MODIS", "CER_SSF1deg-Day_NOAA20-VIIRS", "CER_SSF1deg-Day_NPP-VIIRS", "CER_SSF1deg-Day_Terra-MODIS", "CER_SSF1deg-Hour_Aqua-MODIS", "CER_SSF1deg-Hour_NOAA20-VIIRS", "CER_SSF1deg-Hour_NPP-VIIRS", "CER_SSF1deg-Hour_Terra-MODIS", "CER_SSF1deg-Month_Aqua-MODIS", "CER_SSF1deg-Month_NOAA20-VIIRS", "CER_SSF1deg-Month_NPP-VIIRS", "CER_SSF1deg-Month_Terra-MODIS", "CER_SSF_Aqua-FM4-MODIS", "CER_SSF_TRMM-PFM-VIRS", "CER_SYN1deg-1Hour_Terra-Aqua-MODIS", "CER_SYN1deg-1Hour_Terra-MODIS", "CER_SYN1deg-1Hour_Terra-NOAA20", "CER_SYN1deg-1Hour_Terra-NOAA20", "CER_SYN1deg-1Hour_Terra-NPP", "CER_SYN1deg-3Hour_Terra-Aqua-MODIS", "CER_SYN1deg-3Hour_Terra-MODIS", "CER_SYN1deg-3Hour_Terra-NPP", "CER_SYN1deg-Day_Terra-Aqua-MODIS", "CER_SYN1deg-Day_Terra-MODIS", "CER_SYN1deg-Day_Terra-NOAA20", "CER_SYN1deg-Day_Terra-NOAA20", "CER_SYN1deg-Day_Terra-NPP", "CER_SYN1deg-MHour_Terra-Aqua-MODIS", "CER_SYN1deg-MHour_Terra-MODIS", "CER_SYN1deg-MHour_Terra-NOAA20", "CER_SYN1deg-MHour_Terra-NOAA20", "CER_SYN1deg-MHour_Terra-NPP", "CER_SYN1deg-Month_Terra-Aqua-MODIS", "CER_SYN1deg-Month_Terra-MODIS", "CER_SYN1deg-Month_Terra-NOAA20", "CER_SYN1deg-Month_Terra-NOAA20", "CER_SYN1deg-Month_Terra-NPP", "CFL", "CH4_Aircraft_STILT_footprints_1300", "CH4_CO2_WaterBodies_YK_Delta_2178", "CH4_Flux_BigTrail_Goldstream_1778", "CH4_Fluxes_ThermokarstLakes_AK_1870", "CH4_Plume_AVIRIS-NG_1727", "CHELTON_SEASAT_SASS_L3", "CIESIN_CHRR_NDH_CYCLONE_HFD", "CIESIN_CHRR_NDH_CYCLONE_MRD", "CIESIN_CHRR_NDH_CYCLONE_PELRD", "CIESIN_CHRR_NDH_CYCLONE_TELRD", "CIESIN_CHRR_NDH_DROUGHT_HFD", "CIESIN_CHRR_NDH_DROUGHT_MRD", "CIESIN_CHRR_NDH_DROUGHT_PELRD", "CIESIN_CHRR_NDH_DROUGHT_TELRD", "CIESIN_CHRR_NDH_EQUAKE_HFD", "CIESIN_CHRR_NDH_EQUAKE_MRD", "CIESIN_CHRR_NDH_EQUAKE_PELRD", "CIESIN_CHRR_NDH_EQUAKE_PGA", "CIESIN_CHRR_NDH_EQUAKE_TELRD", "CIESIN_CHRR_NDH_FLOOD_HFD", "CIESIN_CHRR_NDH_FLOOD_MRD", "CIESIN_CHRR_NDH_FLOOD_PELRD", "CIESIN_CHRR_NDH_FLOOD_TELRD", "CIESIN_CHRR_NDH_LSLIDE_HD", "CIESIN_CHRR_NDH_LSLIDE_MRD", "CIESIN_CHRR_NDH_LSLIDE_PELRD", "CIESIN_CHRR_NDH_LSLIDE_TELRD", "CIESIN_CHRR_NDH_MULTI_HFD", "CIESIN_CHRR_NDH_MULTI_MRD", "CIESIN_CHRR_NDH_MULTI_PELRD", "CIESIN_CHRR_NDH_MULTI_TELRD", "CIESIN_CHRR_NDH_VOLCANO_HFD", "CIESIN_CHRR_NDH_VOLCANO_MRD", "CIESIN_CHRR_NDH_VOLCANO_PELRD", "CIESIN_CHRR_NDH_VOLCANO_TELRD", "CIESIN_SEDAC_ACRP_1980_SAS", "CIESIN_SEDAC_ACRP_1990_CBS", "CIESIN_SEDAC_ACRP_1990_EMF", "CIESIN_SEDAC_ACRP_1990_PBF", "CIESIN_SEDAC_ACRP_1990_SAS", "CIESIN_SEDAC_ACRP_1990_SEF", "CIESIN_SEDAC_ACRP_1990_SI", "CIESIN_SEDAC_ACRP_1990_STF1B", "CIESIN_SEDAC_ACRP_1990_ZIPEF", "CIESIN_SEDAC_ACRP_1992_BF", "CIESIN_SEDAC_ACRP_PUMS", "CIESIN_SEDAC_AGLANDS_CROP2000", "CIESIN_SEDAC_AGLANDS_PAS2000", "CIESIN_SEDAC_ANTHROMES_v1", "CIESIN_SEDAC_ANTHROMES_v2_1700", "CIESIN_SEDAC_ANTHROMES_v2_1900", "CIESIN_SEDAC_AQDH_CTMAP_2003_2018", "CIESIN_SEDAC_AQDH_DANO2_US_1KM_1.10", "CIESIN_SEDAC_AQDH_DAO3_US_1KM", "CIESIN_SEDAC_AQDH_DAO3_US_1KM_1.10", "CIESIN_SEDAC_AQDH_DAPM25_US_1KM", "CIESIN_SEDAC_AQDH_DAPM25_US_1KM_1.10", "CIESIN_SEDAC_AQDH_NO2_US_1KM", "CIESIN_SEDAC_AQDH_PM25COM_US_1KM", "CIESIN_SEDAC_AQDH_PM25O3NO2_ZIPCODE", "CIESIN_SEDAC_AQDH_TRACE_US_1KM", "CIESIN_SEDAC_CD_ADM_GIS_1990", "CIESIN_SEDAC_CD_AGENDA21", "CIESIN_SEDAC_CD_BIB_ADM_GEOG", "CIESIN_SEDAC_CD_CTY_ECON_YRBK", "CIESIN_SEDAC_CD_CTY_POPAG_GIS", "CIESIN_SEDAC_CD_FUNDGIS", "CIESIN_SEDAC_CD_GBCODES", "CIESIN_SEDAC_CD_HOS_EPI_50-85", "CIESIN_SEDAC_CD_MAPS_BIBDB", "CIESIN_SEDAC_CD_STAT_1949-1990", "CIESIN_SEDAC_CESIC_2004EVI", "CIESIN_SEDAC_CESIC_2006NFA", "CIESIN_SEDAC_CESIC_ANC", "CIESIN_SEDAC_CESIC_COMPLETE_V11", "CIESIN_SEDAC_CESIC_RIOJO", "CIESIN_SEDAC_CESIC_WELLBEING", "CIESIN_SEDAC_CLIMMIG_GASPMP18SR", "CIESIN_SEDAC_CLIMMIG_GSPMP18SR", "CIESIN_SEDAC_CROPCLIM_EFCCGPSRES", "CIESIN_SEDAC_CROPCLIM_GISS_DB", "CIESIN_SEDAC_CRV_USCRPC_2040-2049", "CIESIN_SEDAC_DEDC_ACE_V2", "CIESIN_SEDAC_ENERGY_NPPCLA", "CIESIN_SEDAC_ENERGY_NPPL", "CIESIN_SEDAC_EPI_2006", "CIESIN_SEDAC_EPI_2008", "CIESIN_SEDAC_EPI_2010", "CIESIN_SEDAC_EPI_2012", "CIESIN_SEDAC_EPI_2014", "CIESIN_SEDAC_EPI_2016", "CIESIN_SEDAC_EPI_2018", "CIESIN_SEDAC_EPI_2020", "CIESIN_SEDAC_EPI_2022", "CIESIN_SEDAC_ESI_2000", "CIESIN_SEDAC_ESI_2001", "CIESIN_SEDAC_ESI_2002", "CIESIN_SEDAC_ESI_2005", "CIESIN_SEDAC_FERMANv1_NAPP", "CIESIN_SEDAC_FERMANv1_NMAN", "CIESIN_SEDAC_FERMANv1_PAPP", "CIESIN_SEDAC_FERMANv1_PESTG_V1.01", "CIESIN_SEDAC_FERMANv1_PMAN", "CIESIN_SEDAC_FOOD_CROPSTAT_1900-2017", "CIESIN_SEDAC_FOOD_FIH_V1", "CIESIN_SEDAC_GEO-MEX_GIS_SMI", "CIESIN_SEDAC_GEO-MEX_POPDBMEX", "CIESIN_SEDAC_GEO-MEX_RASTERGIS", "CIESIN_SEDAC_GEO-MEX_URBANGIS", "CIESIN_SEDAC_GEO-MEX_URB_TSER", "CIESIN_SEDAC_GHSL_PBSMOD", "CIESIN_SEDAC_GPWv3_CENTROIDS", "CIESIN_SEDAC_GPWv3_COASTLINES", "CIESIN_SEDAC_GPWv3_LANDGEOG", "CIESIN_SEDAC_GPWv3_NADMINBND", "CIESIN_SEDAC_GPWv3_NATIDEN", "CIESIN_SEDAC_GPWv3_POPCOUNT", "CIESIN_SEDAC_GPWv3_POPCOUNTFE", "CIESIN_SEDAC_GPWv3_POPDENS", "CIESIN_SEDAC_GPWv3_POPDENSFE", "CIESIN_SEDAC_GPWv3_SUBADBND", "CIESIN_SEDAC_GPWv4_ADUCPPE_R11", "CIESIN_SEDAC_GPWv4_APCT_WPP_2015_R11", "CIESIN_SEDAC_GPWv4_APDENS_WPP_2015_R11", "CIESIN_SEDAC_GPWv4_BDC_R11", "CIESIN_SEDAC_GPWv4_DQI_R11", "CIESIN_SEDAC_GPWv4_LANDH2OAREA_R11", "CIESIN_SEDAC_GPWv4_NATIDEN_R11", "CIESIN_SEDAC_GPWv4_POPCOUNT_R11", "CIESIN_SEDAC_GPWv4_POPDENS_R11", "CIESIN_SEDAC_GRANDv1_DAMS", "CIESIN_SEDAC_GRANDv1_RESERVOIRS", "CIESIN_SEDAC_GROADS_CCRDSv1", "CIESIN_SEDAC_GROADS_V1", "CIESIN_SEDAC_GRUMPv1_COASTLINES", "CIESIN_SEDAC_GRUMPv1_EXT", "CIESIN_SEDAC_GRUMPv1_EXT02", "CIESIN_SEDAC_GRUMPv1_LGUAG", "CIESIN_SEDAC_GRUMPv1_NADMINBND", "CIESIN_SEDAC_GRUMPv1_NATIDEN", "CIESIN_SEDAC_GRUMPv1_POPCOUNT", "CIESIN_SEDAC_GRUMPv1_POPDENS", "CIESIN_SEDAC_GRUMPv1_STLMNT", "CIESIN_SEDAC_GRUMPv1_STLMNT01", "CIESIN_SEDAC_HANPP", "CIESIN_SEDAC_HANPP_CPROD", "CIESIN_SEDAC_HANPP_NPP", "CIESIN_SEDAC_HANPP_PERCENT", "CIESIN_SEDAC_HASO2_SO2_1850-2005", "CIESIN_SEDAC_ICWQ_ACHLOA", "CIESIN_SEDAC_ICWQ_ANC", "CIESIN_SEDAC_ICWQ_CCHLOA", "CIESIN_SEDAC_INDIA_AWCA", "CIESIN_SEDAC_INDIA_CENSUS_2011", "CIESIN_SEDAC_INDIA_VILSOCIOECON", "CIESIN_SEDAC_IPCC_BASELINE", "CIESIN_SEDAC_IPCC_GRI_MIDYR_0.3.7", "CIESIN_SEDAC_IPCC_IS92_V11", "CIESIN_SEDAC_IPCC_OBSERVED", "CIESIN_SEDAC_IPCC_OBSERVED_AR5", "CIESIN_SEDAC_IPCC_SAGDVCC", "CIESIN_SEDAC_IPCC_SRES_1X1EM", "CIESIN_SEDAC_IPCC_SRES_EMSC_V11", "CIESIN_SEDAC_IPCC_SRES_FLGASES", "CIESIN_SEDAC_LECZ_DELTA_V1", "CIESIN_SEDAC_LECZ_SLRIRAMSARWII", "CIESIN_SEDAC_LECZ_URPEV1", "CIESIN_SEDAC_LECZ_URPLAEV2", "CIESIN_SEDAC_LECZ_URPLAEV3", "CIESIN_SEDAC_LULC_CAMVEGLCCCS", "CIESIN_SEDAC_LULC_DPI", "CIESIN_SEDAC_LULC_DT1_V1", "CIESIN_SEDAC_LULC_HMTS", "CIESIN_SEDAC_LULC_MANGROVES_2000", "CIESIN_SEDAC_LULC_PUEXPANS_2030", "CIESIN_SEDAC_LWP2_HF_GEOG", "CIESIN_SEDAC_LWP2_HF_IGHP", "CIESIN_SEDAC_LWP2_HII_GEOG", "CIESIN_SEDAC_LWP2_HII_IGHP", "CIESIN_SEDAC_LWP2_LW_GEOG", "CIESIN_SEDAC_LWP2_LW_IGHP", "CIESIN_SEDAC_LWP3_HF_1993", "CIESIN_SEDAC_LWP3_HF_2009", "CIESIN_SEDAC_LWP_HFP_GEOG", "CIESIN_SEDAC_LWP_HFP_IGHP", "CIESIN_SEDAC_LWP_LW_GEOG", "CIESIN_SEDAC_LWP_LW_IGHP", "CIESIN_SEDAC_LWP_TOPWA_GEOG", "CIESIN_SEDAC_LWP_TOPWA_IGHP", "CIESIN_SEDAC_MA_BIODIVERSITY", "CIESIN_SEDAC_MA_CLIMLC", "CIESIN_SEDAC_MA_ECOSYSTEMS", "CIESIN_SEDAC_MA_POPULATION", "CIESIN_SEDAC_MA_RLCC", "CIESIN_SEDAC_MA_SCENARIOS", "CIESIN_SEDAC_NAGDC_PLACE", "CIESIN_SEDAC_NAGDC_PLACEII", "CIESIN_SEDAC_NAGDC_PLACEIII", "CIESIN_SEDAC_NAGDC_PLACEIV", "CIESIN_SEDAC_NRMI_2010", "CIESIN_SEDAC_NRMI_2011", "CIESIN_SEDAC_NRMI_NRPCHI12", "CIESIN_SEDAC_NRMI_NRPCHI13", "CIESIN_SEDAC_NRMI_NRPCHI14", "CIESIN_SEDAC_NRMI_NRPCHI15", "CIESIN_SEDAC_NRMI_NRPCHI16", "CIESIN_SEDAC_NRMI_NRPCHI17", "CIESIN_SEDAC_NRMI_NRPCHI18", "CIESIN_SEDAC_NRMI_NRPCHI19", "CIESIN_SEDAC_NRMI_NRPCHI20", "CIESIN_SEDAC_NRMI_NRPCHI21", "CIESIN_SEDAC_NRMI_NRPCHI22", "CIESIN_SEDAC_NRMI_NRPCHI23", "CIESIN_SEDAC_PD_LACPOP", "CIESIN_SEDAC_PD_NETMIG_1970_2000", "CIESIN_SEDAC_PD_POPCGTSE", "CIESIN_SEDAC_PD_POPDGTSE", "CIESIN_SEDAC_PD_PPUSCASRCSSP", "CIESIN_SEDAC_PD_SSPBSYR_1_8th", "CIESIN_SEDAC_PD_SSPBSYR_1km", "CIESIN_SEDAC_PEND_ALLOMSSF3", "CIESIN_SEDAC_PEND_ALLOMSTXSF1", "CIESIN_SEDAC_PEND_GDIS", "CIESIN_SEDAC_PEND_HSF1", "CIESIN_SEDAC_PEND_NOSF1", "CIESIN_SEDAC_PEND_NOSF3", "CIESIN_SEDAC_PMP_GRDI_2010_2020", "CIESIN_SEDAC_PMP_GSPCM", "CIESIN_SEDAC_PMP_IMR", "CIESIN_SEDAC_PMP_IMR_V2.01", "CIESIN_SEDAC_PMP_PFSCS", "CIESIN_SEDAC_PMP_SAEPI", "CIESIN_SEDAC_PMP_UBN", "CIESIN_SEDAC_SDEI_APM25_URBAN", "CIESIN_SEDAC_SDEI_GEHE", "CIESIN_SEDAC_SDEI_GFA_GRACE", "CIESIN_SEDAC_SDEI_GFED4_CLTAB", "CIESIN_SEDAC_SDEI_GFED4_GRIDS", "CIESIN_SEDAC_SDEI_GWRPM25_MMSAOD_4GL03", "CIESIN_SEDAC_SDEI_LST2013", "CIESIN_SEDAC_SDEI_NO2", "CIESIN_SEDAC_SDEI_UHE", "CIESIN_SEDAC_SDEI_UHI2013", "CIESIN_SEDAC_SDEI_VDL", "CIESIN_SEDAC_SDEI_YCEOUHI_V4", "CIESIN_SEDAC_SDGI_ACCELEC_2023", "CIESIN_SEDAC_SDGI_RAI_2023", "CIESIN_SEDAC_SDGI_UAPT_2023", "CIESIN_SEDAC_SDGI_UPSAE_2023", "CIESIN_SEDAC_SDP_CGDP_A1A2B1B2", "CIESIN_SEDAC_SDP_CPOP_A1B1A2", "CIESIN_SEDAC_SDP_CPOP_B2", "CIESIN_SEDAC_SDP_GGDP_B2", "CIESIN_SEDAC_SDP_GPOP_B2", "CIESIN_SEDAC_SPATIALECON_GECON4", "CIESIN_SEDAC_SPATIALECON_LGII_V1", "CIESIN_SEDAC_SPECIES_AMP_RICH15", "CIESIN_SEDAC_SPECIES_MAM_RICH15", "CIESIN_SEDAC_SSF_EPANPLSPDCMv2", "CIESIN_SEDAC_SSF_HWSPDv2", "CIESIN_SEDAC_SSP_1-8thDULEPBYGSSP", "CIESIN_SEDAC_SSP_1-kmDULEPBYGSSP", "CIESIN_SEDAC_SSP_GSNLITDB_V1", "CIESIN_SEDAC_SSP_LITDB_V1", "CIESIN_SEDAC_ULANDSAT_GMIS_V1", "CIESIN_SEDAC_ULANDSAT_HBASE_V1", "CIESIN_SEDAC_ULANDSAT_URBAN_LANDSAT", "CIESIN_SEDAC_USCG_90SF1", "CIESIN_SEDAC_USCG_90SF1MSA", "CIESIN_SEDAC_USCG_90SF3", "CIESIN_SEDAC_USCG_90SF3MSA", "CIESIN_SEDAC_USCG_SF1", "CIESIN_SEDAC_USCG_SF12010", "CIESIN_SEDAC_USCG_SF1MSA", "CIESIN_SEDAC_USCG_SF3", "CIESIN_SEDAC_USCG_SF3MSA", "CIESIN_SEDAC_USCG_USSVIG01", "CIESIN_SEDAC_USPAT_BSCATTER_1993_2020", "CIESIN_SEDAC_USPAT_DESLUIS", "CIESIN_SEDAC_USPAT_GUPPD_V1", "CIESIN_SEDAC_USPAT_HUP", "CIESIN_SEDAC_USPAT_USUEXT2015", "CIESIN_SEDAC_WACVM_CCPROD", "CIESIN_SEDAC_WACVM_DEFOR200012", "CIESIN_SEDAC_WACVM_DHS", "CIESIN_SEDAC_WACVM_ECONSI", "CIESIN_SEDAC_WACVM_GPWv4PR_PD", "CIESIN_SEDAC_WACVM_GPWv4PR_PG", "CIESIN_SEDAC_WACVM_GSSPEPP", "CIESIN_SEDAC_WACVM_JRCMA", "CIESIN_SEDAC_WACVM_MFD2000_POL", "CIESIN_SEDAC_WACVM_OSM_ROADS", "CIESIN_SEDAC_WACVM_PGLF_200813", "CIESIN_SEDAC_WACVM_POPPROJ_203050", "CIESIN_SEDAC_WACVM_SDMSPOLSNLEA_2010", "CIESIN_SEDAC_WACVM_SMAMAMP_RICH", "CIESIN_SEDAC_WACVM_SUBSETACE2", "CIESIN_SEDAC_WACVM_SVI", "CIESIN_SEDAC_WATER_WSIM_GLDAS_V1", "CL07LC", "CL07PLBK", "CL07PLTB", "CL07SM", "CL07ST", "CL07V", "CL07VWC", "CLAMS_CERES_CHESLIGHT_SONDE", "CLAMS_CV580_CAR", "CLAMS_ER2_MAS", "CLAMS_MODIS_L2_AEROSOL_PRODUCTS", "CLAMS_UWASH_CONVAIR_DATA", "CLDMSK_L2_MODIS_Aqua", "CLDMSK_L2_VIIRS_NOAA20", "CLDMSK_L2_VIIRS_NOAA20_NRT", "CLDMSK_L2_VIIRS_SNPP", "CLDMSK_L2_VIIRS_SNPP_NRT", "CLDPROPCOSP_D3_MODIS_Aqua", "CLDPROPCOSP_D3_VIIRS_NOAA20", "CLDPROPCOSP_D3_VIIRS_SNPP", "CLDPROPCOSP_M3_MODIS_Aqua", "CLDPROPCOSP_M3_VIIRS_NOAA20", "CLDPROPCOSP_M3_VIIRS_SNPP", "CLDPROP_D3_MODIS_Aqua", "CLDPROP_D3_VIIRS_NOAA20", "CLDPROP_D3_VIIRS_SNPP", "CLDPROP_M3_MODIS_Aqua", "CLDPROP_M3_VIIRS_NOAA20", "CLDPROP_M3_VIIRS_SNPP", "CLIVAR", "CMAQ-N_Module_1661", "CMC0.1deg-CMC-L4-GLOB-v3.0", "CMC0.2deg-CMC-L4-GLOB-v2.0", "CMRMIAAE", "CMRMIALS", "CMRMIGEO", "CMRMITST", "CMSFluxFire", "CMSFluxFire", "CMSFluxFirepost", "CMSFluxFossilFuelPrior", "CMSFluxFossilFuelPrior", "CMSFluxFossilfuel", "CMSFluxLandPrior", "CMSFluxMISC", "CMSFluxNBE", "CMSFluxNBE", "CMSFluxNBEPrior", "CMSFluxNEE", "CMSFluxOcean", "CMSFluxOcean", "CMSFluxOceanPrior", "CMSFluxOceanPrior", "CMSFluxTotal", "CMSFluxTotal", "CMSFluxTotalPrior", "CMSFluxTotalpost", "CMSFluxTotalprior", "CMSGCH4F", "CMSLakeHuronPPM", "CMSLakeHuronPPY", "CMSLakeMichiganPPM", "CMSLakeMichiganPPY", "CMSLakeSuperiorPPM", "CMSLakeSuperiorPPY", "CMS_AGB_Landcover_Indonesia_1645", "CMS_AGB_NW_USA_1719", "CMS_CH4_FLX_CA", "CMS_CH4_FLX_MX", "CMS_CH4_FLX_NAD", "CMS_CO2_Fluxes_TBMO_1315", "CMS_CONUS_Biomass_1752", "CMS_CTL_NA_GOSAT_FOOTPRINTS", "CMS_CTL_NA_OCO2_FOOTPRINTS", "CMS_CTL_NA_TCCON_FOOTPRINTS", "CMS_CTL_SA_OCO2_FOOTPRINTS", "CMS_Coastal_Wetland_Resilience_1839", "CMS_DARTE_V2_1735", "CMS_Daily_ET_MexFlux_1309", "CMS_EFT_CONUS_1659", "CMS_Fire_Weather_Data_AK_1509", "CMS_FluxEstimates_Aircraft_CO2_2336", "CMS_Forest_Carbon_Fluxes_1313", "CMS_Forest_Carbon_Maryland_1660", "CMS_Forest_Productivity_1221", "CMS_GO_CH4_SEC_TDYC_NA", "CMS_Global_Cropland_Carbon_1279", "CMS_Global_Fire_Atlas_1642", "CMS_Global_Forest_AGC_2180", "CMS_Global_Forest_Age_2345", "CMS_Global_Livestock_CH4_CO2_1329", "CMS_Global_Mangrove_Forest_Ht_2251", "CMS_Global_Mangrove_Loss_1768", "CMS_Global_Map_Mangrove_Canopy_1665", "CMS_Global_Monthly_Wetland_CH4_1502", "CMS_Global_Soil_Respiration_1736", "CMS_Great_Basin_Biomass_1755", "CMS_HR_MNA_CH4_FLUX", "CMS_Landcover_Indonesia_1838", "CMS_Landscapes_Brazil_Forests_1301", "CMS_Landscapes_Brazil_LiDAR_1302", "CMS_LiDAR_AGB_California_1537", "CMS_LiDAR_AGB_PEF_2012_1318", "CMS_LiDAR_Biomass_CanHt_Sonoma_1523", "CMS_LiDAR_Biomass_MD_PA_DE_1538", "CMS_LiDAR_Indonesia_1518", "CMS_LiDAR_Point_Cloud_Zambezi_1521", "CMS_LiDAR_Products_Indonesia_1540", "CMS_Mangrove_Biomass_Zambezi_1522", "CMS_Mangrove_CanHt_Stand_Age_1377", "CMS_Mangrove_Canopy_Height_1327", "CMS_Mangrove_Canopy_Ht_Zambezi_1357", "CMS_Mangrove_Cover_1670", "CMS_Maryland_AGB_Canopy_1320", "CMS_Methane_Emissions_Boston_1291", "CMS_Monthly_CO2_Gulf_of_Mexico_1668", "CMS_OCE_BGC_CCS", "CMS_Pantropical_Forest_Biomass_1337", "CMS_Pennsylvania_Tree_Cover_1334", "CMS_Pilot_Biomass_1257", "CMS_SABGOM_Model_Simulations_1510", "CMS_SOC_Mexico_1754", "CMS_SOC_Mexico_CONUS_1737", "CMS_SST_GPP_Mexico_1310", "CMS_Simulated_SIF_NiwotRidge_1720", "CMS_Soil_CO2_Efflux_1298", "CMS_WFEIS_CONUS-AK_1306", "CMS_WRF_Footprints_CO2_Signals_1381", "CMS_WRF_Model_Products_1338", "CO2Fluxes_Arctic_Boreal_Domain_2377", "COASTAL", "COMEX_AJAX_CO2_CH4_2347", "COMEX_AVIRIS_Classic_Flights_2343", "COMEX_AVIRIS_NG_Flights_2342", "COMEX_LongwaveInfrared_Imagery_2331", "COOA", "CORAL", "CPEXAW-ADM-Aeolus", "CPEXAW-DAWN_DC8", "CPEXAW-Dropsondes", "CPEXAW-HALO_DC8", "CPEXCV-DAWN_DC8", "CPEXCV-Dropsondes", "CPEXCV-HALO_DC8", "CPEXCV_Cloud_AircraftInSitu_DC8_Data", "CPEXCV_Merge_Data", "CPL_ABL_Top_Height_1825", "CRP", "CStocks_Greenness_Mangroves_MX_1853", "CUSTARD", "CV-580_710", "CYGNSS_L1_CAL_RAW_IF_V1.0", "CYGNSS_L1_CDR_V1.0", "CYGNSS_L1_CDR_V1.1", "CYGNSS_L1_CDR_V1.2", "CYGNSS_L1_FULL_DDM", "CYGNSS_L1_FULL_DDM_V3.0", "CYGNSS_L1_RAW_IF", "CYGNSS_L1_V2.1", "CYGNSS_L1_V3.0", "CYGNSS_L1_V3.2", "CYGNSS_L2_CDR_V1.0", "CYGNSS_L2_CDR_V1.1", "CYGNSS_L2_CDR_V1.2", "CYGNSS_L2_SURFACE_FLUX_CDR_V1.0", "CYGNSS_L2_SURFACE_FLUX_CDR_V1.1", "CYGNSS_L2_SURFACE_FLUX_CDR_V1.2", "CYGNSS_L2_SURFACE_FLUX_V1.0", "CYGNSS_L2_SURFACE_FLUX_V2.0", "CYGNSS_L2_SURFACE_FLUX_V3.2", "CYGNSS_L2_V2.1", "CYGNSS_L2_V3.0", "CYGNSS_L2_V3.2", "CYGNSS_L3_CDR_V1.0", "CYGNSS_L3_CDR_V1.1", "CYGNSS_L3_CDR_V1.2", "CYGNSS_L3_MICROPLASTIC_V1.0", "CYGNSS_L3_MICROPLASTIC_V3.2", "CYGNSS_L3_MRG_NRT_V3.2", "CYGNSS_L3_MRG_NRT_V3.2.1", "CYGNSS_L3_MRG_V3.2", "CYGNSS_L3_MRG_V3.2.1", "CYGNSS_L3_S1.0", "CYGNSS_L3_SOIL_MOISTURE_V1.0", "CYGNSS_L3_SOIL_MOISTURE_V3.2", "CYGNSS_L3_UC_BERKELEY_WATERMASK_DAILY_V3.2", "CYGNSS_L3_UC_BERKELEY_WATERMASK_V3.1", "CYGNSS_L3_V2.1", "CYGNSS_L3_V3.0", "CYGNSS_L3_V3.1", "CYGNSS_L3_V3.2", "CYGNSS_NOAA_L2_SWSP_25KM_V1.1", "CYGNSS_NOAA_L2_SWSP_25KM_V1.2", "CZCS_L1", "CZCS_L2_OC", "CZCS_L3b_CHL", "CZCS_L3b_KD", "CZCS_L3b_RRS", "CZCS_L3m_CHL", "CZCS_L3m_KD", "CZCS_L3m_RRS", "C_FluxStocks_CLM5_DART_WestUS_1856", "C_Pools_Fluxes_CONUS_1837", "CaTS", "CalCOFI", "California_2002", "CanSIS_Regional_Soils_1347", "Canada_Boreal_Forest_Greenness_1587", "Canadian_West_Arctic_Veg_Plots_1543", "CapeHatteras2010", "Carbon_Estuaries", "Carbon_Transport_MS_River", "Carbon_UAV", "Cartagena_Station", "Catlin_Arctic_Survey", "ChesBay", "Chesapeake_Bay_Helicopter", "Chesapeake_Bay_Plume", "Chesapeake_Bay_Water_Quality", "Chesapeake_Light_Tower", "Chlorophyll_Fluorescence_Data_1785", "CircumArctic_Trends_Hotspots_2322", "CliVEC", "Climate_Normals_Modern_LGM_AK_1663", "CoJet", "Coastal_Carbon_Budget_NE_US_1594", "Coastal_US_Elevation_Data_1844", "Continuous_Lifeform_Maps_CONUS_1809", "Country_SOC_Latin_America_1615", "CramerLeemans_416", "Cropland_Carbon_Fluxes_2125", "Crops_SIF_VegIndices_IL_NE_2136", "CubeSat_Arctic_Boreal_LakeArea_1667", "Cyanate", "DC3_Aerosol_AircraftInSitu_DC8_Data", "DC3_Aerosol_AircraftInSitu_DLR-Falcon_Data", "DC3_Aerosol_AircraftInSitu_NSF-GV-HIAPER_Data", "DC3_AircraftRemoteSensing_DIAL_DC8_Data", "DC3_Cloud_AircraftInSitu_DC8_Data", "DC3_Cloud_AircraftInSitu_NSF-GV-HIAPER_Data", "DC3_Merge_Data", "DC3_MetNav_AircraftInSitu_DC8_Data", "DC3_MetNav_AircraftInSitu_DLR-Falcon_Data", "DC3_MetNav_AircraftInSitu_NSF-GV-HIAPER_Data", "DC3_Miscellaneous_DC8_Data", "DC3_Miscellaneous_DLR-Falcon_Data", "DC3_Miscellaneous_NSF-GV-HIAPER_Data", "DC3_Radiation_AircraftInSitu_DC8_Data", "DC3_TraceGas_AircraftInSitu_DC8_Data", "DC3_TraceGas_AircraftInSitu_DLR-Falcon_Data", "DC3_TraceGas_AircraftInSitu_NSF-GV-HIAPER_Data", "DC3_jValue_AircraftInSitu_DC8_Data", "DC3_jValue_AircraftInSitu_NSF-GV-HIAPER_Data", "DCOTSS-Aircraft-Data", "DCOTSS-Balloon-Data", "DCOTSS-Model-Output", "DCOTSS-Radar-Satellite-Data", "DCOTSS-Reports", "DECKLAB", "DEVOTE_Aerosol_AircraftInSitu_B200_Data", "DEVOTE_AircraftRemoteSensing_UC12_HSRL_Data", "DEVOTE_AircraftRemoteSensing_UC12_RSP_Data", "DEVOTE_MetNav_AircraftInSitu_B200_Data", "DEVOTE_MetNav_AircraftInSitu_UC12_Data", "DFO_Canada_Time_Series", "DISCOVERAQ_California_Aerosol_AircraftInSitu_P3B_Data", "DISCOVERAQ_California_AircraftRemoteSensing_B200_ACAM_Data", "DISCOVERAQ_California_AircraftRemoteSensing_B200_HSRL2_Data", "DISCOVERAQ_California_Ground_Analysis_Ancillary_Data", "DISCOVERAQ_California_Ground_Bakersfield_Data", "DISCOVERAQ_California_Ground_CARB_Data", "DISCOVERAQ_California_Ground_Fresno_Data", "DISCOVERAQ_California_Ground_Huron_Data", "DISCOVERAQ_California_Ground_Oildale_Data", "DISCOVERAQ_California_Ground_Pandora_Data", "DISCOVERAQ_California_Ground_Porterville_Data", "DISCOVERAQ_California_Ground_SJV_Data", "DISCOVERAQ_California_Merge_Data", "DISCOVERAQ_California_MetNav_AircraftInSitu_B200_Data", "DISCOVERAQ_California_MetNav_AircraftInSitu_P3B_Data", "DISCOVERAQ_California_Ozonesondes_Data", "DISCOVERAQ_California_TraceGas_AircraftInSitu_P3B_Data", "DISCOVERAQ_Colorado_Aerosol_AircraftInSitu_P3B_Data", "DISCOVERAQ_Colorado_AircraftRemoteSensing_B200_ACAM_Data", "DISCOVERAQ_Colorado_AircraftRemoteSensing_B200_GCAS_Data", "DISCOVERAQ_Colorado_AircraftRemoteSensing_B200_HSRL2_Data", "DISCOVERAQ_Colorado_AircraftRemoteSensing_Falcon_GeoTASO_Data", "DISCOVERAQ_Colorado_Ground_BAOTower_Data", "DISCOVERAQ_Colorado_Ground_CDPHE_Data", "DISCOVERAQ_Colorado_Ground_EPA_Data", "DISCOVERAQ_Colorado_Ground_FortCollins_Data", "DISCOVERAQ_Colorado_Ground_Mobile_Data", "DISCOVERAQ_Colorado_Ground_NREL-Golden_Data", "DISCOVERAQ_Colorado_Ground_Pandora_Data", "DISCOVERAQ_Colorado_Ground_Platteville_Data", "DISCOVERAQ_Colorado_Ground_TableMountain_Data", "DISCOVERAQ_Colorado_Merge_Data", "DISCOVERAQ_Colorado_MetNav_AircraftInSitu_B200_Data", "DISCOVERAQ_Colorado_MetNav_AircraftInSitu_P3B_Data", "DISCOVERAQ_Colorado_Model_Data", "DISCOVERAQ_Colorado_Ozonesondes_Data", "DISCOVERAQ_Colorado_Radiation_AircraftRemoteSensing_CAR_Data", "DISCOVERAQ_Colorado_TraceGas_AircraftInSitu_P3B_Data", "DISCOVERAQ_Maryland_Aerosol_AircraftInSitu_P3B_Data", "DISCOVERAQ_Maryland_AircraftRemoteSensing_UC12_ACAM_Data", "DISCOVERAQ_Maryland_AircraftRemoteSensing_UC12_HSRL_Data", "DISCOVERAQ_Maryland_Ground_Aldino_Data", "DISCOVERAQ_Maryland_Ground_Analysis_Ancillary_Data", "DISCOVERAQ_Maryland_Ground_Beltsville_Data", "DISCOVERAQ_Maryland_Ground_Edgewood_Data", "DISCOVERAQ_Maryland_Ground_Essex_Data", "DISCOVERAQ_Maryland_Ground_Fairhill_Data", "DISCOVERAQ_Maryland_Ground_Oldtown_Data", "DISCOVERAQ_Maryland_Ground_Padonia_Data", "DISCOVERAQ_Maryland_Ground_Pandora_Data", "DISCOVERAQ_Maryland_Ground_UMBC_Data", "DISCOVERAQ_Maryland_Merge_Data", "DISCOVERAQ_Maryland_MetNav_AircraftInSitu_P3B_Data", "DISCOVERAQ_Maryland_MetNav_AircraftInSitu_UC12_Data", "DISCOVERAQ_Maryland_Ozonesondes_Data", "DISCOVERAQ_Maryland_Ship_NOAA-DelawareII_Data", "DISCOVERAQ_Maryland_TraceGas_AircraftInSitu_P3B_Data", "DISCOVERAQ_Maryland_UMDAircraft_Data", "DISCOVERAQ_Texas_Aerosol_AircraftInSitu_P3B_Data", "DISCOVERAQ_Texas_AircraftRemoteSensing_B200_ACAM_Data", "DISCOVERAQ_Texas_AircraftRemoteSensing_B200_GCAS_Data", "DISCOVERAQ_Texas_AircraftRemoteSensing_B200_HSRL2_Data", "DISCOVERAQ_Texas_AircraftRemoteSensing_Falcon_GeoTASO_Data", "DISCOVERAQ_Texas_Ground_Ancillary_Analysis_Data", "DISCOVERAQ_Texas_Ground_EPA_Data", "DISCOVERAQ_Texas_Ground_Galveston_Data", "DISCOVERAQ_Texas_Ground_LaPorteAirport_Data", "DISCOVERAQ_Texas_Ground_ManvelCroix_Data", "DISCOVERAQ_Texas_Ground_MoodyTower_Data", "DISCOVERAQ_Texas_Ground_Pandora_Data", "DISCOVERAQ_Texas_Ground_SmithPoint_Data", "DISCOVERAQ_Texas_Ground_TCEQ_Data", "DISCOVERAQ_Texas_Merge_Data", "DISCOVERAQ_Texas_MetNav_AircraftInSitu_B200_Data", "DISCOVERAQ_Texas_MetNav_AircraftInSitu_P3B_Data", "DISCOVERAQ_Texas_Ozonesondes_Data", "DISCOVERAQ_Texas_TraceGas_AircraftInSitu_P3B_Data", "DISCover_land_cover_679", "DLEM_C_N_Export_1699", "DMI_OI-DMI-L4-GLOB-v1.0", "DSCOVR_EPIC_L2_COMPOSITE", "DSCOVR_EPIC_L2_COMPOSITE", "DSCOVR_EPIC_L3_PAR", "DSCOVR_EPIC_L3_PAR-IMAGE", "DSCOVR_EPIC_L4_TrO3", "DSCOVR_NISTAR_L2_FLX", "DUSTFLEXPART", "Daily_FineParticulateMatter_AK_2157", "Dairy_Methane_CA_V1-2_1902", "Dall_Sheep_Population_Dynamics_1640", "Dall_Sheep_Snowpack_1602", "Daymet_Annual_V4R1_2130", "Daymet_Daily_V4R1_2129", "Daymet_Monthly_V4R1_2131", "Daymet_SubDaily_Puerto_Rico_1977", "Daymet_V4_Daily_MonthlyLatency_1904", "Daymet_xval_V4R1_2132", "Decadal_LULC_India_1336", "Decadal_Water_Maps_1324", "DeciduousFractionl_CanopyCover_2296", "Del_Ches_Bay_Fluorescence", "DeltaX_ADCP_Measurements_V2_2081", "DeltaX_AGB_AGN_V2_2237", "DeltaX_ANUGA_AtchafalayaBasin_2306", "DeltaX_ANUGA_Hydrodynamics_MRD_2310", "DeltaX_BGB_BGN_V2_2238", "DeltaX_BG_Root_Stable_Isotopes_2193", "DeltaX_DEM_MRD_LA_2181", "DeltaX_Delft3D_294_Terrebonne_2303", "DeltaX_Delft3D_322_Terrebonne_2312", "DeltaX_Delft3D_396_Terrebonne_2314", "DeltaX_Delft3D_399_Terrebonne_2313", "DeltaX_Delft3D_421_Terrebonne_2304", "DeltaX_Delft3D_Atchafalaya_MRD_2302", "DeltaX_Delft3D_Terrebonne_MRD_2301", "DeltaX_Ecogeomorphic_Products_2108", "DeltaX_Feldspar_Sediment_V3_2290", "DeltaX_Foliar_Stable_Isotopes_2194", "DeltaX_H2O_Surface_Elevation_2086", "DeltaX_Herb_WetlandSoil_V3_2239", "DeltaX_Insitu_POC_2073", "DeltaX_Insitu_Reflectance_V3_2153", "DeltaX_Insitu_WQ_Indicators_V2_2080", "DeltaX_Island_Channel_Model_2106", "DeltaX_L1B_UAVSAR_WaterLevels_1979", "DeltaX_L1_AVIRIS_Radiance_1987", "DeltaX_L1_UAVSAR_SLC_Stack_1984", "DeltaX_L1b_AirSWOT_1996", "DeltaX_L2A_AVIRIS-NG_BRDF_V2_2139", "DeltaX_L2_AVIRIS_Reflectance_1988", "DeltaX_L2_AirSWOT_WaterElev_V3_2350", "DeltaX_L2_UAVSAR_WaterLevels_2057", "DeltaX_L3_AVIRIS-NG_AGB_V2_2138", "DeltaX_L3_AVIRIS-NG_Veg_Types_2352", "DeltaX_L3_AVIRIS-NG_Water_V3_2152", "DeltaX_L3_AirSWOT_WaterElev_V2_2349", "DeltaX_L3_UAVSAR_WaterLevels_2058", "DeltaX_LandAccretionMap_WLD_2308", "DeltaX_MarshAccretion_NUMAR_2354", "DeltaX_Particle_Size_LISST_V2_2077", "DeltaX_RTK_Elevation_2071", "DeltaX_Sediment_Grain_Size_V2_2135", "DeltaX_Sonar_Bathymetry_2085", "DeltaX_TSS_Concentration_V2_2075", "DeltaX_TotalSubsidenceRate_MRD_2307", "DeltaX_Turbidity_Data_V4_2241", "DeltaX_UAVSAR_L3_ChannelMap_V2_2344", "DeltaX_Vegetation_Structure_V2_2240", "Dendrophenology_Eastern_US_1369", "Dissolved_Gases_Alaska_Rivers_2360", "Disturbance_Biomass_Maps_1679", "Dolche-Vita", "Dunne_545", "ECCO_L4_ANCILLARY_DATA_V4R4", "ECCO_L4_ATM_STATE_05DEG_DAILY_V4R4", "ECCO_L4_ATM_STATE_05DEG_MONTHLY_V4R4", "ECCO_L4_ATM_STATE_LLC0090GRID_DAILY_V4R4", "ECCO_L4_ATM_STATE_LLC0090GRID_MONTHLY_V4R4", "ECCO_L4_BOLUS_05DEG_DAILY_V4R4", "ECCO_L4_BOLUS_05DEG_MONTHLY_V4R4", "ECCO_L4_BOLUS_LLC0090GRID_DAILY_V4R4", "ECCO_L4_BOLUS_LLC0090GRID_MONTHLY_V4R4", "ECCO_L4_DENS_STRAT_PRESS_05DEG_DAILY_V4R4", "ECCO_L4_DENS_STRAT_PRESS_05DEG_MONTHLY_V4R4", "ECCO_L4_DENS_STRAT_PRESS_LLC0090GRID_DAILY_V4R4", "ECCO_L4_DENS_STRAT_PRESS_LLC0090GRID_MONTHLY_V4R4", "ECCO_L4_FRESH_FLUX_05DEG_DAILY_V4R4", "ECCO_L4_FRESH_FLUX_05DEG_MONTHLY_V4R4", "ECCO_L4_FRESH_FLUX_LLC0090GRID_DAILY_V4R4", "ECCO_L4_FRESH_FLUX_LLC0090GRID_MONTHLY_V4R4", "ECCO_L4_GEOMETRY_05DEG_V4R4", "ECCO_L4_GEOMETRY_LLC0090GRID_V4R4", "ECCO_L4_GMAP_TIME_SERIES_SNAPSHOT_V4R4", "ECCO_L4_GMAP_TIME_SERIES_SNAPSHOT_V4R4B", "ECCO_L4_GMSL_TIME_SERIES_DAILY_V4R4", "ECCO_L4_GMSL_TIME_SERIES_MONTHLY_V4R4", "ECCO_L4_HEAT_FLUX_05DEG_DAILY_V4R4", "ECCO_L4_HEAT_FLUX_05DEG_MONTHLY_V4R4", "ECCO_L4_HEAT_FLUX_LLC0090GRID_DAILY_V4R4", "ECCO_L4_HEAT_FLUX_LLC0090GRID_MONTHLY_V4R4", "ECCO_L4_MIXED_LAYER_DEPTH_05DEG_DAILY_V4R4", "ECCO_L4_MIXED_LAYER_DEPTH_05DEG_MONTHLY_V4R4", "ECCO_L4_MIXED_LAYER_DEPTH_LLC0090GRID_DAILY_V4R4", "ECCO_L4_MIXED_LAYER_DEPTH_LLC0090GRID_MONTHLY_V4R4", "ECCO_L4_OBP_05DEG_DAILY_V4R4", "ECCO_L4_OBP_05DEG_DAILY_V4R4B", "ECCO_L4_OBP_05DEG_MONTHLY_V4R4", "ECCO_L4_OBP_05DEG_MONTHLY_V4R4B", "ECCO_L4_OBP_LLC0090GRID_DAILY_V4R4", "ECCO_L4_OBP_LLC0090GRID_DAILY_V4R4B", "ECCO_L4_OBP_LLC0090GRID_MONTHLY_V4R4", "ECCO_L4_OBP_LLC0090GRID_MONTHLY_V4R4B", "ECCO_L4_OBP_LLC0090GRID_SNAPSHOT_V4R4", "ECCO_L4_OCEAN_3D_MIX_COEFFS_05DEG_V4R4", "ECCO_L4_OCEAN_3D_MIX_COEFFS_LLC0090GRID_V4R4", "ECCO_L4_OCEAN_3D_MOMENTUM_TEND_LLC0090GRID_DAILY_V4R4", "ECCO_L4_OCEAN_3D_MOMENTUM_TEND_LLC0090GRID_MONTHLY_V4R4", "ECCO_L4_OCEAN_3D_SALINITY_FLUX_LLC0090GRID_DAILY_V4R4", "ECCO_L4_OCEAN_3D_SALINITY_FLUX_LLC0090GRID_MONTHLY_V4R4", "ECCO_L4_OCEAN_3D_TEMPERATURE_FLUX_LLC0090GRID_DAILY_V4R4", "ECCO_L4_OCEAN_3D_TEMPERATURE_FLUX_LLC0090GRID_MONTHLY_V4R4", "ECCO_L4_OCEAN_3D_VOLUME_FLUX_LLC0090GRID_DAILY_V4R4", "ECCO_L4_OCEAN_3D_VOLUME_FLUX_LLC0090GRID_MONTHLY_V4R4", "ECCO_L4_OCEAN_BOLUS_STREAMFUNCTION_LLC0090GRID_DAILY_V4R4", "ECCO_L4_OCEAN_BOLUS_STREAMFUNCTION_LLC0090GRID_MONTHLY_V4R4", "ECCO_L4_OCEAN_VEL_05DEG_DAILY_V4R4", "ECCO_L4_OCEAN_VEL_05DEG_MONTHLY_V4R4", "ECCO_L4_OCEAN_VEL_LLC0090GRID_DAILY_V4R4", "ECCO_L4_OCEAN_VEL_LLC0090GRID_MONTHLY_V4R4", "ECCO_L4_SBO_CORE_TIME_SERIES_SNAPSHOT_V4R4", "ECCO_L4_SBO_CORE_TIME_SERIES_SNAPSHOT_V4R4B", "ECCO_L4_SEA_ICE_CONC_THICKNESS_05DEG_DAILY_V4R4", "ECCO_L4_SEA_ICE_CONC_THICKNESS_05DEG_MONTHLY_V4R4", "ECCO_L4_SEA_ICE_CONC_THICKNESS_LLC0090GRID_DAILY_V4R4", "ECCO_L4_SEA_ICE_CONC_THICKNESS_LLC0090GRID_MONTHLY_V4R4", "ECCO_L4_SEA_ICE_CONC_THICKNESS_LLC0090GRID_SNAPSHOT_V4R4", "ECCO_L4_SEA_ICE_HORIZ_VOLUME_FLUX_LLC0090GRID_DAILY_V4R4", "ECCO_L4_SEA_ICE_HORIZ_VOLUME_FLUX_LLC0090GRID_MONTHLY_V4R4", "ECCO_L4_SEA_ICE_SALT_PLUME_FLUX_LLC0090GRID_DAILY_V4R4", "ECCO_L4_SEA_ICE_SALT_PLUME_FLUX_LLC0090GRID_MONTHLY_V4R4", "ECCO_L4_SEA_ICE_VELOCITY_05DEG_DAILY_V4R4", "ECCO_L4_SEA_ICE_VELOCITY_05DEG_MONTHLY_V4R4", "ECCO_L4_SEA_ICE_VELOCITY_LLC0090GRID_DAILY_V4R4", "ECCO_L4_SEA_ICE_VELOCITY_LLC0090GRID_MONTHLY_V4R4", "ECCO_L4_SEA_ICE_VELOCITY_LLC0090GRID_SNAPSHOT_V4R4", "ECCO_L4_SSH_05DEG_DAILY_V4R4", "ECCO_L4_SSH_05DEG_DAILY_V4R4B", "ECCO_L4_SSH_05DEG_MONTHLY_V4R4", "ECCO_L4_SSH_05DEG_MONTHLY_V4R4B", "ECCO_L4_SSH_LLC0090GRID_DAILY_V4R4", "ECCO_L4_SSH_LLC0090GRID_DAILY_V4R4B", "ECCO_L4_SSH_LLC0090GRID_MONTHLY_V4R4", "ECCO_L4_SSH_LLC0090GRID_MONTHLY_V4R4B", "ECCO_L4_SSH_LLC0090GRID_SNAPSHOT_V4R4", "ECCO_L4_STRESS_05DEG_DAILY_V4R4", "ECCO_L4_STRESS_05DEG_MONTHLY_V4R4", "ECCO_L4_STRESS_LLC0090GRID_DAILY_V4R4", "ECCO_L4_STRESS_LLC0090GRID_MONTHLY_V4R4", "ECCO_L4_TEMP_SALINITY_05DEG_DAILY_V4R4", "ECCO_L4_TEMP_SALINITY_05DEG_MONTHLY_V4R4", "ECCO_L4_TEMP_SALINITY_LLC0090GRID_DAILY_V4R4", "ECCO_L4_TEMP_SALINITY_LLC0090GRID_MONTHLY_V4R4", "ECCO_L4_TEMP_SALINITY_LLC0090GRID_SNAPSHOT_V4R4", "ECO1BATT", "ECO1BGEO", "ECO1BMAPRAD", "ECO1BRAD", "ECO2CLD", "ECO2LSTE", "ECO3ANCQA", "ECO3ETALEXI", "ECO3ETALEXIU", "ECO3ETPTJPL", "ECO4ESIALEXI", "ECO4ESIALEXIU", "ECO4ESIPTJPL", "ECO4WUE", "ECOA", "ECOHAB", "ECOMON", "ECO_L1B_ATT", "ECO_L1B_GEO", "ECO_L1B_RAD", "ECO_L1CG_RAD", "ECO_L1CT_RAD", "ECO_L2G_CLOUD", "ECO_L2G_LSTE", "ECO_L2T_LSTE", "ECO_L2T_STARS", "ECO_L2_CLOUD", "ECO_L2_LSTE", "ECO_L3G_JET", "ECO_L3G_MET", "ECO_L3G_SEB", "ECO_L3G_SM", "ECO_L3T_JET", "ECO_L3T_MET", "ECO_L3T_SEB", "ECO_L3T_SM", "ECO_L4G_ESI", "ECO_L4G_WUE", "ECO_L4T_ESI", "ECO_L4T_WUE", "ECS", "EDDIES", "EDM_SA_Vegetation_1149", "EDM_US_Carbon_1160", "EF_Data_Mexico_1693", "EGEE3", "EGEE5", "EKAMSAT_Pilot_ASTRAL", "EM27_XCO2_XCH4_XCO_AK_1831", "EMITL1BATT", "EMITL1BRAD", "EMITL2ARFL", "EMITL2BCH4ENH", "EMITL2BCH4PLM", "EMITL2BCO2ENH", "EMITL2BCO2PLM", "EMITL2BMIN", "EMITL3ASA", "EMITL4ESM", "EN1_MDSI_MER_FRS_1P", "EN1_MDSI_MER_FRS_2P", "EN1_MDSI_MER_RR__2P", "ENVISAT_SCIAMACHY_SIF_1871", "EOLE1", "EPEA", "ERBE_S10N_WFOV_NF", "ERBE_S10N_WFOV_NF", "ERBE_S10N_WFOV_SF", "ERBE_S10N_WFOV_SF", "ERBE_S10N_WFOV_SF_ERBS_AreaAverageTimeSeries", "ERBE_S10N_WFOV_SF_ERBS_AreaAverageTimeSeries_Edition4", "ERBE_S10N_WFOV_SF_ERBS_Regional", "ERBE_S10N_WFOV_SF_ERBS_Regional", "ERBE_S10N_WFV_NF_NAT", "ERBE_S10N_WFV_SF_NAT", "ERBE_S10_MFOV_NF_NAT", "ERBE_S10_MFOV_SF_NAT", "ERBE_S10_WFOV_NF_NAT", "ERBE_S10_WFOV_SF_NAT", "ERBE_S4GN_WFOV_NF", "ERBE_S4GN_WFOV_NF_ZG", "ERBE_S4GN_WFOV_SF", "ERBE_S4GN_WFOV_SF_ZG", "ERBE_S4GN_WFV_NF_N10", "ERBE_S4G_MFOV_NF", "ERBE_S4G_MFOV_NF_N10", "ERBE_S4G_MFOV_NF_ZG", "ERBE_S4G_MFOV_SF", "ERBE_S4G_MFOV_SF_ZG", "ERBE_S4G_SC_2.5", "ERBE_S4G_SC_NEST10", "ERBE_S4G_SC_NEST5", "ERBE_S4G_SC_ZG", "ERBE_S4G_WFOV_NF", "ERBE_S4G_WFOV_NF_N10", "ERBE_S4G_WFOV_NF_ZG", "ERBE_S4G_WFOV_SF", "ERBE_S4G_WFOV_SF_ZG", "ERBE_S4N_NAT", "ERBE_S4_NAT", "ERBE_S7_NAT", "ERBE_S8_NAT", "ERBE_S9_NAT", "ERBE_TSI_ERBS_NAT", "ERS-1_BYU_L3_OW_SIGMA0_ENHANCED", "ERS-1_L0", "ERS-1_L1", "ERS-2_BYU_L3_OW_SIGMA0_ENHANCED", "ERS-2_L0", "ERS-2_L1", "ERS2_GOME_SIF_1758", "ESCAPE", "ESMRN5IM", "ESMRN5L1", "ESMRN6IM", "EUCFe", "EUMELI", "EWSG1-NAVO-L2P-v01", "EXP7L1TRTALL", "EXP7L1TRTWHT", "EXPORTS", "EastAnglia10YearMean_549", "EastAnglia30YearMean_550", "EastAngliaClimate_542", "EastAngliaPrecip_417", "Ecosystem_Map_SRD_PAD_1947", "Effect_Environment_Moose_1739", "End_of_Season_Snow_Depth_1702", "Environmental_Disturbances_AK_1705", "Erosion_Vegetation_Yukon_1616", "Estimated_Biomass_Stock_Amazon_1648", "Eurasia_Biomass_1278", "FEWS_precip_711", "FIA_Forest_Biomass_Estimates_1873", "FIFE_CD_V3_130", "FIREXAQ_AerosolCloud_AircraftRemoteSensing_ER2_CPL_Data", "FIREXAQ_Aerosol_AircraftInSitu_DC8_Data", "FIREXAQ_Aerosol_AircraftInSitu_N48_Data", "FIREXAQ_Analysis_Data", "FIREXAQ_Analysis_N48_Data", "FIREXAQ_Cloud_AircraftInSitu_DC8_Data", "FIREXAQ_Ground_InSitu_Data", "FIREXAQ_HSRL_AircraftRemoteSensing_DC8_Data", "FIREXAQ_Merge_Data", "FIREXAQ_MetNav_AircraftInSitu_DC8_Data", "FIREXAQ_MetNav_AircraftInSitu_ER2_Data", "FIREXAQ_MetNav_AircraftInSitu_N48_Data", "FIREXAQ_N46_Data", "FIREXAQ_Radiation_AircraftInSitu_DC8_Data", "FIREXAQ_Satellite_Data", "FIREXAQ_SurfaceMobile_Aerodyne_InSitu_Data", "FIREXAQ_SurfaceMobile_CARB_InSitu_Data", "FIREXAQ_SurfaceMobile_MACH2_InSitu_Data", "FIREXAQ_TraceGas_AircraftInSitu_DC8_Data", "FIREXAQ_TraceGas_AircraftInSitu_N48_Data", "FIREXAQ_TraceGas_AircraftRemoteSensing_DC8_Data", "FIREXAQ_TraceGas_AircraftRemoteSensing_ER2_GCAS_Data", "FIREXAQ_TraceGas_AircraftRemoteSensing_ER2_NASTI_Data", "FIREXAQ_TraceGas_AircraftRemoteSensing_ER2_SHIS_Data", "FIREXAQ_jValue_AircraftInSitu_DC8_Data", "FIREXAQ_jValue_AircraftInSitu_N48_Data", "FIRE_ACE_AMPR", "FIRE_ACE_C130_CFD", "FIRE_ACE_C130_RAMS", "FIRE_ACE_ER2_MAS", "FIRE_ACE_ER2_MIR", "FIRE_ACE_SHIP_SSFR", "FIRE_ACE_UTRECHT_BALLOON", "FIRE_ACE_UTRECHT_TOWER", "FIRE_ACE_UWCV580_GMETER", "FIRE_ACE_UWCV580_UWA", "FIRE_AX_CMS_LWFLUX", "FIRE_AX_CMS_SOLAR_DY", "FIRE_AX_CMS_SOLAR_HR", "FIRE_AX_CMS_SOLAR_MN", "FIRE_AX_CMS_SOLAR_WK", "FIRE_AX_CMS_SST_DAY", "FIRE_AX_CMS_SST_FNTS", "FIRE_AX_CMS_SST_WEEK", "FIRE_AX_CSU_CEILOM", "FIRE_AX_CSU_MET_SFC", "FIRE_AX_CSU_PRT6", "FIRE_AX_CSU_WNDPRFS", "FIRE_AX_ECMWF_BASIC", "FIRE_AX_ECMWF_CLOUDS", "FIRE_AX_ECMWF_DIAG", "FIRE_AX_ECMWF_MEANW", "FIRE_AX_ECMWF_SFDIAG", "FIRE_AX_ECMWF_SURFCE", "FIRE_AX_ER2_LIDAR", "FIRE_AX_ER2_MAS", "FIRE_AX_ERS1_ALTIMTR", "FIRE_AX_ERS1_MCRWRAD", "FIRE_AX_ERS1_SCTRMTR", "FIRE_AX_ISCCP_DX", "FIRE_AX_MAGE_OCN_AIR", "FIRE_AX_MAGE_OCN_MET", "FIRE_AX_MAGE_OCN_SEA", "FIRE_AX_MAGE_TETROON", "FIRE_AX_MALBAL_SONDE", "FIRE_AX_METEOSAT", "FIRE_AX_OCEANS_SONDE", "FIRE_AX_PORSAN_SONDE", "FIRE_AX_PSU_CEIL_MAL", "FIRE_AX_PSU_CEIL_SAN", "FIRE_AX_PSU_CEIL_VAL", "FIRE_AX_PSU_H2OVAP", "FIRE_AX_PSU_MALBAL", "FIRE_AX_PSU_WND_MAL", "FIRE_AX_RWSONDE_LVL1", "FIRE_AX_RWSONDE_LVL2", "FIRE_AX_SANMAR_SONDE", "FIRE_AX_SFC_FUNCHAL", "FIRE_AX_SFC_IMAU", "FIRE_AX_SOF_ARAT_FLT", "FIRE_AX_SOF_ARAT_TRB", "FIRE_AX_SOF_BUOY_DFT", "FIRE_AX_SOF_BUOY_SPR", "FIRE_AX_SOF_PTU", "FIRE_AX_SOF_SUR_BUCK", "FIRE_AX_SOF_SUR_DRAK", "FIRE_AX_SOF_SUR_HYD", "FIRE_AX_SOF_SUR_MET", "FIRE_AX_SOF_SUR_RAD", "FIRE_AX_SOF_SUR_TBAL", "FIRE_AX_SUROIT_SONDE", "FIRE_AX_UKMO_C130", "FIRE_AX_UW_C131A", "FIRE_AX_UW_DSCRT", "FIRE_AX_UW_GERB_10HZ", "FIRE_AX_UW_GERB_1HZ", "FIRE_AX_VALDIV_SONDE", "FIRE_CI1_CSU_SABRE", "FIRE_CI1_ER2_LIDAR", "FIRE_CI1_ER2_RAD", "FIRE_CI1_ISCCP_DX", "FIRE_CI1_KINGAIR", "FIRE_CI1_LARC8_LIDAR", "FIRE_CI1_RAWINSONDES", "FIRE_CI1_SABRELINER", "FIRE_CI1_SRB_ALASKA", "FIRE_CI1_SRB_CANADA", "FIRE_CI1_SRB_LW", "FIRE_CI1_SRB_SO_POLE", "FIRE_CI1_SRB_SW", "FIRE_CI1_SRB_SWITZ", "FIRE_CI1_TOVS", "FIRE_CI2_CITATN_1HZ", "FIRE_CI2_CITATN_24HZ", "FIRE_CI2_CITATN_5SEC", "FIRE_CI2_CITATN_IWC", "FIRE_CI2_CITATN_PMS", "FIRE_CI2_CLASS_SONDE", "FIRE_CI2_CSU_PRT6", "FIRE_CI2_CSU_SONDES", "FIRE_CI2_CSU_STN1", "FIRE_CI2_CSU_STN2", "FIRE_CI2_CSU_WNDPRFS", "FIRE_CI2_DOPLR_LIDAR", "FIRE_CI2_ER2_LIDAR", "FIRE_CI2_ER2_MAS", "FIRE_CI2_ETL_RADAR", "FIRE_CI2_HIS", "FIRE_CI2_HSRL", "FIRE_CI2_ISCCP_DX", "FIRE_CI2_KINGAIR", "FIRE_CI2_KINGAIR_2D", "FIRE_CI2_KINGAIR_IWC", "FIRE_CI2_LARC8_LIDAR", "FIRE_CI2_MAPS", "FIRE_CI2_NOAA_WNDPFS", "FIRE_CI2_NWS_IN_SND", "FIRE_CI2_NWS_OUT_SND", "FIRE_CI2_PAMS", "FIRE_CI2_RAMAN_LIDAR", "FIRE_CI2_SABRELINER", "FIRE_CI2_SABRLNR_2D", "FIRE_CI2_SABRLNR_IWC", "FIRE_CI2_SPECT_SIRIS", "FIRE_CI2_UTAH_PDL", "FIRE_CI2_VIL_RTI", "FIRE_CI2_VIL_SCAN", "FIRE_ETO_UTAH_PDL", "FIRE_MS_CEILOM_CLASS", "FIRE_MS_CSU_RADSURF", "FIRE_MS_CSU_TBALLOON", "FIRE_MS_ELECTRA", "FIRE_MS_ER2_LIDAR", "FIRE_MS_ISCCP_DX", "FIRE_MS_MCRW_RAD", "FIRE_MS_NOAAWNDS", "FIRE_MS_UKMO_C130", "FLASH_TISA_Terra-Aqua", "FLASH_TISA_Terra-NOAA20", "FLASH_TISA_Terra-NOAA20", "FLDAS_NOAH01_CP_GL_M", "FLDAS_NOAH01_C_GL_M", "FLDAS_NOAH01_C_GL_MA", "FLDAS_NOAH01_C_GL_MC", "FLDAS_NOAHMP001_G_CA_D", "FLEXPART_Influence_Functions_2018", "FLOODAMA_903", "FLUAMAZON_896", "FLUXNET_Canada_1335", "FRONT", "FieldData_Alaska_Tundra_2177", "Field_Measurements_868", "Fire_Emissions_Indonesia_2118", "Fire_Emissions_NWT_1561", "Fire_Ignitions_Locations_AK_CA_2316", "Flight_Environment_Parameters_1909", "FluxSat_GPP_FPAR_1835", "Flux_Tower_Zona_Veg_Plots_1546", "Fluxnet_site_DB_1530", "Fluxnet_website_archive_copy_1549", "ForestHt_Biomass_GEDI_TDX_2298", "Forest_AGB_NW_USA_1766", "Forest_Carbon_Priority_1803", "Forest_Disturbance_Intensity_2059", "Forest_Inventory_Acre_Brazil_1654", "Forest_Inventory_Brazil_2007", "Forest_Inventory_Data_Brazil_1563", "Forest_Inventory_Tapajos_1552", "Forested_Areas_Amazonas_Brazil_1515", "Forested_Areas_Para_Brazil_1514", "Frac_FuelComponent_Maps_Tundra_1761", "Freeze_Thaw_NorthernHemisphere_2323", "Frost_Boils_Veg_Plots_1361", "Ft_Lauderdale", "G18-ABI-L2P-ACSPO-v2.90", "G18-ABI-L3C-ACSPO-v2.90", "GAMSSA_28km-ABOM-L4-GLOB-v01", "GCAM_Land_Cover_2005-2095_1216", "GCRW_DEM_2016_1793", "GEDI01_B", "GEDI02_A", "GEDI02_B", "GEDI_Fusion_Structure_2236", "GEDI_HighQuality_Shots_Rasters_2339", "GEDI_ICESAT2_Global_Veg_Height_2294", "GEDI_L3_LandSurface_Metrics_V2_1952", "GEDI_L4A_AGB_Density_GW_2028", "GEDI_L4A_AGB_Density_V2_1_2056", "GEDI_L4B_Country_Biomass_2321", "GEDI_L4B_Gridded_Biomass_V2_1_2299", "GEDI_L4C_WSCI_2338", "GEO-CAPE", "GEO-CAPE_GOCI", "GEOS-3_ALT_GDR", "GEOS2OBSINPUTINTL", "GEOS3STST", "GEOS_CASAGFED_3H_NEE", "GEOS_CASAGFED_3H_NEE", "GEOS_CASAGFED_D_FIRE", "GEOS_CASAGFED_D_FIRE", "GEOS_CASAGFED_M_FLUX", "GEOS_CASAGFED_M_FLUX", "GEOTRACES", "GEWEXSRB_Rel4-IP_Ancillary_3hrly", "GEWEXSRB_Rel4-IP_Ancillary_3hrly_landonly", "GEWEXSRB_Rel4-IP_Ancillary_3hrly_oceanonly", "GEWEXSRB_Rel4-IP_Longwave_3hrly", "GEWEXSRB_Rel4-IP_Longwave_3hrlymonthly_landonly_utc", "GEWEXSRB_Rel4-IP_Longwave_3hrlymonthly_oceanonly_utc", "GEWEXSRB_Rel4-IP_Longwave_3hrlymonthly_utc", "GEWEXSRB_Rel4-IP_Longwave_daily_landonly_utc", "GEWEXSRB_Rel4-IP_Longwave_daily_oceanonly_utc", "GEWEXSRB_Rel4-IP_Longwave_daily_utc", "GEWEXSRB_Rel4-IP_Longwave_monthly_landonly_utc", "GEWEXSRB_Rel4-IP_Longwave_monthly_local", "GEWEXSRB_Rel4-IP_Longwave_monthly_oceanonly_utc", "GEWEXSRB_Rel4-IP_Longwave_monthly_utc", "GEWEXSRB_Rel4-IP_Shortwave_3hrly", "GEWEXSRB_Rel4-IP_Shortwave_3hrlymonthly_utc", "GEWEXSRB_Rel4-IP_Shortwave_daily_local", "GEWEXSRB_Rel4-IP_Shortwave_daily_utc", "GEWEXSRB_Rel4-IP_Shortwave_monthly_local", "GEWEXSRB_Rel4-IP_Shortwave_monthly_utc", "GEWEXSRB_Rel4_1-IP_Longwave_3hrly_landonly", "GEWEXSRB_Rel4_1-IP_Longwave_3hrly_oceanonly", "GEWEXSRB_Rel4_1-IP_Longwave_daily_landonly_local", "GEWEXSRB_Rel4_1-IP_Longwave_daily_local", "GEWEXSRB_Rel4_1-IP_Longwave_daily_oceanonly_local", "GEWEXSRB_Rel4_1-IP_Longwave_monthly_landonly_local", "GEWEXSRB_Rel4_1-IP_Longwave_monthly_oceanonly_local", "GFCC30FCC", "GFCC30SR", "GFCC30TC", "GFCC30WC", "GFEI_CH4", "GFSAD1KCD", "GFSAD1KCM", "GFSAD30AFCE", "GFSAD30AUNZCNMOCE", "GFSAD30EUCEARUMECE", "GFSAD30NACE", "GFSAD30SAAFGIRCE", "GFSAD30SACE", "GFSAD30SEACE", "GFSAD30VAL", "GHISACASIA", "GHISACONUS", "GIMMS3g_NDVI_Trends_1275", "GIS_EastAngliaClimateMonthly_551", "GLAH01", "GLAH01", "GLAH02", "GLAH02", "GLAH03", "GLAH03", "GLAH04", "GLAH04", "GLAH05", "GLAH05", "GLAH06", "GLAH06", "GLAH07", "GLAH07", "GLAH08", "GLAH08", "GLAH09", "GLAH09", "GLAH10", "GLAH10", "GLAH11", "GLAH11", "GLAH12", "GLAH12", "GLAH13", "GLAH13", "GLAH14", "GLAH14", "GLAH15", "GLAH15", "GLCHMK", "GLCHMT", "GLDAS_CLSM025_D", "GLDAS_CLSM025_DA1_D", "GLDAS_CLSM025_DA1_D_EP", "GLDAS_CLSM10_3H", "GLDAS_CLSM10_3H", "GLDAS_CLSM10_3H_EP", "GLDAS_CLSM10_M", "GLDAS_CLSM10_M", "GLDAS_CLSM10_M_EP", "GLDAS_NOAH025_3H", "GLDAS_NOAH025_3H", "GLDAS_NOAH025_3H_EP", "GLDAS_NOAH025_M", "GLDAS_NOAH025_M", "GLDAS_NOAH025_M_EP", "GLDAS_NOAH10_3H", "GLDAS_NOAH10_3H", "GLDAS_NOAH10_3H_EP", "GLDAS_NOAH10_M", "GLDAS_NOAH10_M", "GLDAS_NOAH10_M_EP", "GLDAS_VIC10_3H", "GLDAS_VIC10_3H", "GLDAS_VIC10_3H_EP", "GLDAS_VIC10_M", "GLDAS_VIC10_M", "GLDAS_VIC10_M_EP", "GLDSMT", "GLDTMK", "GLDTMT", "GLHYANC", "GLHYVI", "GLLIDARPC", "GLMETRICS", "GLOBEC", "GLORTHO", "GLRADS", "GLREFL", "GLTRAJECTORY", "GLanCE30", "GMAO_M2SCREAM_INST3_CHEM", "GMAO_M2SCREAM_MONTH_UNCERT", "GMI-REMSS-L3U-v8.2a", "GNATS", "GO-BGC", "GO-SHIP", "GOA97", "GOC", "GOCAL", "GOCI_2013", "GOES13-OSISAF-L3C-v1.0", "GOES13-OSPO-L2P-v1.0", "GOES15-OSPO-L2P-v1.0", "GOES16-SST-OSISAF-L3C-v1.0", "GOM", "GOMECC", "GOMEX", "GOME_MINDS_NO2", "GOMI2AE", "GOMI2LS", "GOMI2ST", "GOMIB2E", "GOMIB2T", "GOMIGEO", "GOM_Oil_Spill", "GPCPDAY", "GPCPDAY", "GPCPMON", "GPCPMON", "GPCP_precip_712", "GPM_1AGMI", "GPM_1ATMI", "GPM_1AVIRS", "GPM_1BGMI", "GPM_1BPR", "GPM_1BTMI", "GPM_1BVIRS", "GPM_1CAQUAAMSRE", "GPM_1CF08SSMI", "GPM_1CF10SSMI", "GPM_1CF11SSMI", "GPM_1CF13SSMI", "GPM_1CF14SSMI", "GPM_1CF15SSMI", "GPM_1CF16SSMIS", "GPM_1CF17SSMIS", "GPM_1CF18SSMIS", "GPM_1CF19SSMIS", "GPM_1CGCOMW1AMSR2", "GPM_1CGPMGMI", "GPM_1CGPMGMI_R", "GPM_1CMETOPAMHS", "GPM_1CMETOPBMHS", "GPM_1CMETOPCMHS", "GPM_1CMT1SAPHIR", "GPM_1CNOAA15AMSUB", "GPM_1CNOAA16AMSUB", "GPM_1CNOAA17AMSUB", "GPM_1CNOAA18MHS", "GPM_1CNOAA19MHS", "GPM_1CNOAA20ATMS", "GPM_1CNOAA21ATMS", "GPM_1CNPPATMS", "GPM_1CTRMMTMI", "GPM_2ADPR", "GPM_2ADPRENV", "GPM_2AGPROFAQUAAMSRE_CLIM", "GPM_2AGPROFF08SSMI_CLIM", "GPM_2AGPROFF10SSMI_CLIM", "GPM_2AGPROFF11SSMI_CLIM", "GPM_2AGPROFF13SSMI_CLIM", "GPM_2AGPROFF14SSMI_CLIM", "GPM_2AGPROFF15SSMI_CLIM", "GPM_2AGPROFF16SSMIS", "GPM_2AGPROFF16SSMIS_CLIM", "GPM_2AGPROFF17SSMIS", "GPM_2AGPROFF17SSMIS_CLIM", "GPM_2AGPROFF18SSMIS", "GPM_2AGPROFF18SSMIS_CLIM", "GPM_2AGPROFF19SSMIS_CLIM", "GPM_2AGPROFGCOMW1AMSR2", "GPM_2AGPROFGCOMW1AMSR2_CLIM", "GPM_2AGPROFGPMGMI", "GPM_2AGPROFGPMGMI_CLIM", "GPM_2AGPROFMETOPAMHS_CLIM", "GPM_2AGPROFMETOPBMHS", "GPM_2AGPROFMETOPBMHS_CLIM", "GPM_2AGPROFMETOPCMHS", "GPM_2AGPROFMETOPCMHS_CLIM", "GPM_2AGPROFNOAA15AMSUB_CLIM", "GPM_2AGPROFNOAA16AMSUB_CLIM", "GPM_2AGPROFNOAA17AMSUB_CLIM", "GPM_2AGPROFNOAA18MHS_CLIM", "GPM_2AGPROFNOAA19MHS", "GPM_2AGPROFNOAA19MHS_CLIM", "GPM_2AGPROFNOAA20ATMS", "GPM_2AGPROFNOAA20ATMS_CLIM", "GPM_2AGPROFNOAA21ATMS", "GPM_2AGPROFNOAA21ATMS_CLIM", "GPM_2AGPROFNPPATMS", "GPM_2AGPROFNPPATMS_CLIM", "GPM_2AGPROFTRMMTMI_CLIM", "GPM_2AKa", "GPM_2AKaENV", "GPM_2AKu", "GPM_2AKuENV", "GPM_2APR", "GPM_2APRPSMT1SAPHIR", "GPM_2APRPSMT1SAPHIR_CLIM", "GPM_2BCMB", "GPM_2BCMB_TRMM", "GPM_2HCSH", "GPM_2HCSH_TRMM", "GPM_2HSLH", "GPM_2HSLH_TRMM", "GPM_3CMB", "GPM_3CMB_DAY", "GPM_3CMB_TRMM", "GPM_3CMB_TRMM_DAY", "GPM_3DPR", "GPM_3DPRD", "GPM_3DPR_ASC", "GPM_3DPR_DES", "GPM_3GCSH", "GPM_3GCSH_TRMM", "GPM_3GPROFAQUAAMSRE_CLIM", "GPM_3GPROFAQUAAMSRE_DAY_CLIM", "GPM_3GPROFF08SSMI_CLIM", "GPM_3GPROFF08SSMI_DAY_CLIM", "GPM_3GPROFF10SSMI_CLIM", "GPM_3GPROFF10SSMI_DAY_CLIM", "GPM_3GPROFF11SSMI_CLIM", "GPM_3GPROFF11SSMI_DAY_CLIM", "GPM_3GPROFF13SSMI_CLIM", "GPM_3GPROFF13SSMI_DAY_CLIM", "GPM_3GPROFF14SSMI_CLIM", "GPM_3GPROFF14SSMI_DAY_CLIM", "GPM_3GPROFF15SSMI_CLIM", "GPM_3GPROFF15SSMI_DAY_CLIM", "GPM_3GPROFF16SSMIS", "GPM_3GPROFF16SSMIS_CLIM", "GPM_3GPROFF16SSMIS_DAY", "GPM_3GPROFF16SSMIS_DAY_CLIM", "GPM_3GPROFF17SSMIS", "GPM_3GPROFF17SSMIS_CLIM", "GPM_3GPROFF17SSMIS_DAY", "GPM_3GPROFF17SSMIS_DAY_CLIM", "GPM_3GPROFF18SSMIS", "GPM_3GPROFF18SSMIS_CLIM", "GPM_3GPROFF18SSMIS_DAY", "GPM_3GPROFF18SSMIS_DAY_CLIM", "GPM_3GPROFF19SSMIS_CLIM", "GPM_3GPROFF19SSMIS_DAY_CLIM", "GPM_3GPROFGCOMW1AMSR2", "GPM_3GPROFGCOMW1AMSR2_CLIM", "GPM_3GPROFGCOMW1AMSR2_DAY", "GPM_3GPROFGCOMW1AMSR2_DAY_CLIM", "GPM_3GPROFGPMGMI", "GPM_3GPROFGPMGMI_CLIM", "GPM_3GPROFGPMGMI_DAY", "GPM_3GPROFGPMGMI_DAY_CLIM", "GPM_3GPROFMETOPAMHS_CLIM", "GPM_3GPROFMETOPAMHS_DAY_CLIM", "GPM_3GPROFMETOPBMHS", "GPM_3GPROFMETOPBMHS_CLIM", "GPM_3GPROFMETOPBMHS_DAY", "GPM_3GPROFMETOPBMHS_DAY_CLIM", "GPM_3GPROFMETOPCMHS", "GPM_3GPROFMETOPCMHS_CLIM", "GPM_3GPROFMETOPCMHS_DAY", "GPM_3GPROFMETOPCMHS_DAY_CLIM", "GPM_3GPROFNOAA15AMSUB_CLIM", "GPM_3GPROFNOAA15AMSUB_DAY_CLIM", "GPM_3GPROFNOAA16AMSUB_CLIM", "GPM_3GPROFNOAA16AMSUB_DAY_CLIM", "GPM_3GPROFNOAA17AMSUB_CLIM", "GPM_3GPROFNOAA17AMSUB_DAY_CLIM", "GPM_3GPROFNOAA18MHS_CLIM", "GPM_3GPROFNOAA18MHS_DAY_CLIM", "GPM_3GPROFNOAA19MHS", "GPM_3GPROFNOAA19MHS_CLIM", "GPM_3GPROFNOAA19MHS_DAY", "GPM_3GPROFNOAA19MHS_DAY_CLIM", "GPM_3GPROFNOAA20ATMS", "GPM_3GPROFNOAA20ATMS_CLIM", "GPM_3GPROFNOAA20ATMS_DAY", "GPM_3GPROFNOAA20ATMS_DAY_CLIM", "GPM_3GPROFNOAA21ATMS", "GPM_3GPROFNOAA21ATMS_CLIM", "GPM_3GPROFNOAA21ATMS_DAY", "GPM_3GPROFNOAA21ATMS_DAY_CLIM", "GPM_3GPROFNPPATMS", "GPM_3GPROFNPPATMS_CLIM", "GPM_3GPROFNPPATMS_DAY", "GPM_3GPROFNPPATMS_DAY_CLIM", "GPM_3GPROFTRMMTMI_CLIM", "GPM_3GPROFTRMMTMI_DAY_CLIM", "GPM_3GSLH", "GPM_3GSLH_TRMM", "GPM_3HCSH", "GPM_3HCSH_TRMM", "GPM_3HSLH", "GPM_3HSLH_DAY", "GPM_3HSLH_TRMM", "GPM_3HSLH_TRMM_DAY", "GPM_3IMERGDE", "GPM_3IMERGDE", "GPM_3IMERGDF", "GPM_3IMERGDL", "GPM_3IMERGDL", "GPM_3IMERGHH", "GPM_3IMERGHHE", "GPM_3IMERGHHE", "GPM_3IMERGHHL", "GPM_3IMERGHHL", "GPM_3IMERGM", "GPM_3PR", "GPM_3PRD", "GPM_3PRPSMT1SAPHIR", "GPM_3PRPSMT1SAPHIR_CLIM", "GPM_3PRPSMT1SAPHIR_DAY", "GPM_3PRPSMT1SAPHIR_DAY_CLIM", "GPM_3PR_ASC", "GPM_3PR_DES", "GPM_BASEGPMGMI", "GPM_BASEGPMGMI_RSS", "GPM_BASEGPMGMI_XCAL", "GPM_BASETRMMTMI", "GPM_IMERG_LandSeaMask", "GPM_MERGIR", "GPM_PRL1KA", "GPM_PRL1KU", "GPP_CONUS_TROPOMI_1875", "GPP_MODIS_Alaska_Canada_2024", "GPP_surfaces_749", "GPROF_precip_716", "GPSROZPBLA", "GPSROZPBLA", "GPSROZPBLS", "GPSROZPBLS", "GRACEDADM_CLSM0125US_7D", "GRACEDADM_CLSM025GL_7D", "GRACEFO_L1A_ASCII_GRAV_JPL_RL04", "GRACEFO_L2_CSR_MONTHLY_0062", "GRACEFO_L2_CSR_MONTHLY_0063", "GRACEFO_L2_GFZ_MONTHLY_0061", "GRACEFO_L2_JPL_MONTHLY_0063", "GRACE_ABPR_FO_L2_V1.0", "GRACE_AOD1B_GRAV_GFZ_RL06", "GRACE_GAA_L2_GRAV_GFZ_RL06", "GRACE_GAA_L2_GRAV_JPL_RL06", "GRACE_GAB_L2_GRAV_GFZ_RL06", "GRACE_GAB_L2_GRAV_JPL_RL06", "GRACE_GAC_L2_GRAV_CSR_RL06", "GRACE_GAC_L2_GRAV_GFZ_RL06", "GRACE_GAC_L2_GRAV_JPL_RL06", "GRACE_GAD_L2_GRAV_CSR_RL06", "GRACE_GAD_L2_GRAV_GFZ_RL06", "GRACE_GAD_L2_GRAV_JPL_RL06", "GRACE_GSM_L2_GRAV_CSR_RL06", "GRACE_GSM_L2_GRAV_GFZ_RL06", "GRACE_GSM_L2_GRAV_JPL_RL06", "GRACE_L1B_GRAV_JPL_RL02", "GRACE_L1B_GRAV_JPL_RL03", "GRC-GFO_GRIDDED_AOD1B_JPL_1-DEG_RL06", "GRC-GFO_GRIDDED_AOD1B_JPL_MASCON_RL06", "GREENLAND_MASS_TELLUS_MASCON_CRI_TIME_SERIES_RL06.1_V3", "GSMNP_Vegetation_Structure_R1_1286", "GSSTF", "GSSTF", "GSSTFM", "GSSTFM", "GSSTFMC", "GSSTFMC", "GSSTFM_NCEP", "GSSTFM_NCEP", "GSSTFSC", "GSSTFSC", "GSSTFYC", "GSSTFYC", "GSSTF_F08", "GSSTF_F08", "GSSTF_F10", "GSSTF_F10", "GSSTF_F11", "GSSTF_F11", "GSSTF_F13", "GSSTF_F13", "GSSTF_F14", "GSSTF_F14", "GSSTF_F15", "GSSTF_F15", "GSSTF_NCEP", "GSSTF_NCEP", "GWELDMO", "GWELDMO", "GWELDMO", "GWELDYR", "GWELDYR", "GWELDYR", "GasEx", "GePCO", "Geo_Polar_Blended-OSPO-L4-GLOB-v1.0", "Geo_Polar_Blended_Night-OSPO-L4-GLOB-v1.0", "GloSSAC", "GloSSAC", "GloSSAC", "GloSSAC", "GloSSAC", "GloSSAC", "Global_Biomass_1950-2010_1296", "Global_CDOM", "Global_Clumping_Index_1531", "Global_Freshwater_CH4Emissions_2253", "Global_Hydrologic_Soil_Group_1566", "Global_Lakes_Methane_2008", "Global_Landslide_Exposure_Maps", "Global_Landslide_Nowcast", "Global_Landslide_Nowcast", "Global_Litter_Carbon_Nutrients_1244", "Global_Maps_C_Density_2010_1763", "Global_Microbial_Biomass_C_N_P_1264", "Global_Monthly_GPP_1789", "Global_Phosphorus_Dist_Map_1223", "Global_Phosphorus_Hedley_Fract_1230", "Global_RTSG_Flux_1078", "Global_Reservoirs_Methane_1918", "Global_Riverine_N2O_Emissions_1791", "Global_SIF_OCO2_MODIS_1863", "Global_Salt_Marsh_Change_2122", "Global_Soil_Regolith_Sediment_1304", "Global_Veg_Greenness_GIMMS_3G_2187", "GozMmlpH2O", "GozMmlpHCl", "GozMmlpHNO3", "GozMmlpN2O", "GozMmlpO3", "GozSmlpH2O", "GozSmlpHCl", "GozSmlpHNO3", "GozSmlpN2O", "GozSmlpO3", "GozSmlpT", "Great_Belt", "Great_Lakes", "Great_Slave_Lake_Ecosystem_Map_1695", "GreenBay", "Gridded_Biomass_Africa_1777", "GroundMSPI_ACEPOL_Radiance_Data", "GulfCarbon", "GulfOfMaine", "Gulf_Of_Maine", "Gulf_of_Maine_Nutrients", "Gulf_of_Maine_Optics", "H09-AHI-L2P-ACSPO-v2.90", "H09-AHI-L3C-ACSPO-v2.90", "H3ZFC12MEXT", "H3ZFC8MEXT", "H3ZFCCFC11", "H3ZFCCFC12", "H3ZFCCLONO2", "H3ZFCGPH", "H3ZFCH2O", "H3ZFCHNO3", "H3ZFCN2O", "H3ZFCN2O5", "H3ZFCNO2", "H3ZFCO3", "H3ZFCT", "HAB", "HALO_LiDAR_AOP_ML_Heights_1833", "HAQES_NA_PM25_BC", "HAQES_NA_PM25_BC_CENSUS", "HAQES_NA_PM25_BC_COUNTY", "HAQES_NA_PM25_OC", "HAQES_NA_PM25_OC_CENSUS", "HAQES_NA_PM25_OC_COUNTY", "HAQES_NA_PM25_TOT", "HAQES_NA_PM25_TOT_CENSUS", "HAQES_NA_PM25_TOT_COUNTY", "HAQ_TROPOMI_NO2_CONUS_A_L3", "HAQ_TROPOMI_NO2_CONUS_M_L3", "HAQ_TROPOMI_NO2_CONUS_S_L3", "HAQ_TROPOMI_NO2_GLOBAL_A_L3", "HAQ_TROPOMI_NO2_GLOBAL_M_L3", "HAWKEYE_L1", "HAWKEYE_L1", "HAWKEYE_L2_OC", "HAWKEYE_L2_OC", "HCDN_810", "HICO_L1", "HIR3SCOL", "HIRDLS2", "HIRMLS3IWC", "HIRSN6IM", "HIRSN6L1GARP", "HIVE", "HLSL30", "HLSS30", "HMA2_DCG_SMB", "HMA2_DDSMET", "HMA2_DSPAT", "HMA2_FGP", "HMA2_GFTP", "HMA2_GGP", "HMA2_HFD", "HMA2_LHI", "HMA2_MATCHA", "HMA2_MTLI", "HMA2_NLSMR", "HMA2_WBP", "HMA_AWS", "HMA_DEM8m_AT", "HMA_DEM8m_CT", "HMA_DEM8m_MOS", "HMA_DM_6H", "HMA_DTE", "HMA_EAPrecip_FLOR", "HMA_FreezeThawMelt_ASCAT", "HMA_GLI", "HMA_GL_RCP", "HMA_GL_RCPR", "HMA_GSM", "HMA_GlacierAvg_dH", "HMA_Glacier_dH", "HMA_Glacier_dH_Mosaics", "HMA_LIS_LandSurfaceHydro", "HMA_LS_Cat", "HMA_MAR3_5", "HMA_MTLI", "HMA_OptDepth", "HMA_Precip_3B42", "HMA_Precip_FLOR", "HMA_RCMO_1H", "HMA_RCMO_6H", "HMA_RCMO_D", "HMA_RCMO_M", "HMA_SBRF", "HMA_SDI", "HMA_SR_D", "HMA_STParams", "HMA_Snowfield", "HOMAGE_GGFO_L4_GOMA_Monthly_v01", "HOMAGE_STERIC_OHC_TIME_SERIES_v01", "HOT", "HRAC_Precip", "HRIRN1IM", "HRIRN1L1", "HRIRN2IM", "HRIRN2L1", "HRIRN3IM", "HRIRN3L1", "HWSD_1247", "HYCODE_LEO-15", "Happy_Valley_Veg_Plots_1354", "HighRes_ClimateData_Western_US_1682", "High_Res_Tidal_Marsh_Veg_1609", "Historic_S2K_Website_1765", "HistoricalLai_584", "Historical_Lake_Shorelines_AK_1859", "Horn_Island", "HourlyUrban_GreenhouseGases_US_1916", "Hudson_River_Fluorescence", "Hydroprofiler", "IAKST1B", "IAKST1B", "IAPRS1B", "IASI_SST_METOP_A-OSISAF-L2P-v1.0", "IASI_SST_METOP_B-OSISAF-L2P-v1.0", "ICESCAPE", "ICEVOLC_FlowerKahn2020", "IDBMG4", "IDCSI4", "IDHDT4", "IDS_LIS", "IGBGM1B", "IGBGM2", "IGBP-DIS_565", "IGBP-SurfaceProducts_569", "IGBTH4", "IGCMG1B", "IGCMG2", "IGGRV1B", "IGLGS1B", "IGZLS1B", "ILAKP1B", "ILAKS1B", "ILATM1B", "ILATM1B", "ILATM2", "ILATMGR", "ILATMW1B", "ILNIRW1B", "ILNSA1B", "ILNSA1B", "ILNSAW1B", "ILSIG1B", "ILSNP1B", "ILSNP4", "ILUTP1B", "ILUTP2", "ILVGH1B", "ILVGH2", "ILVIS0", "ILVIS1B", "ILVIS2", "ILVIS2", "IMARPE-Callao_Station", "IMCS31B", "IMECOCAL", "IMERG_Precip_Canada_Alaska_2097", "IMFGM1B", "INDOEX", "INTEXA_AIRMAP", "INTEXA_DC8_AIRCRAFT", "INTEXA_J31_AIRCRAFT", "INTEXA_MERGES", "INTEXA_MODEL", "INTEXA_O3SONDES", "INTEXA_PROTEUS_AIRCRAFT", "INTEXA_SATELLITE", "INTEXA_TRAJECTORY", "INTEXB_Be200_AIRCRAFT", "INTEXB_C130_AIRCRAFT", "INTEXB_Cessna_AIRCRAFT", "INTEXB_DC8_AIRCRAFT", "INTEXB_Duchess_AIRCRAFT", "INTEXB_GROUND", "INTEXB_J31_AIRCRAFT", "INTEXB_MERGES", "INTEXB_MODEL", "INTEXB_O3SONDES", "INTEXB_SATELLITE", "INTEXB_TRAJECTORY", "INTRO", "IOCAM0", "IOCAM1B", "IOCAM1B", "IODCC0", "IODEM3", "IODIM3", "IODMS1B", "IODMS3", "IOFFE", "IOLVIS1A", "IPFLR1B", "IPFLT1B", "IR1HI1B", "IR1HI2", "IR2HI1B", "IR2HI2", "IRACC1B", "IRARES1B", "IRISN4RAD", "IRKUB1B", "IRMCR1B", "IRMCR2", "IRMCR3", "IRPAR2", "IRSNO1B", "IRTIT3", "IRUAFHF1B", "IRWIS2", "IS2ATBABD", "IS2GZANT", "IS2MPDDA", "IS2OLVIS1BCV", "IS2SITDAT4", "IS2SITMOGR4", "ISCCP_B3_NAT", "ISCCP_D1", "ISCCP_D2", "ISCCP_DX", "ISCCP_ICESNOW_NAT", "ISCCP_TOVS_NAT", "ISERV", "ISLSCP_919", "ISSITGR4", "ITPRN5L1", "IXBMI2AE", "IXBMI2LS", "IXBMI2ST", "IXBMIB2E", "IXBMIB2T", "IXBMIGEO", "Idaho_field_shrub_data_1503", "Imnavait_Creek_Veg_Maps_1385", "Imnavait_Creek_Veg_Plots_1356", "InSAR_Prudhoe_Bay_1267", "Insitu_Tower_Greenhouse_Gas_1798", "Interior_Alaska_Subsistence_1725", "Interpolated_Met_Products_1876", "InundationMap_YkFlats_PeaceAth_1901", "IronEx", "IsricWiseGrids_546", "IsricWise_547", "J1_CrIS_VIIRS750m_IND", "JASON-1_JMR_ENH", "JASON-1_L2_OST_GPN_E", "JASON-1_L2_OST_GPN_E_GEODETIC", "JASON-1_L2_OST_GPR_E", "JASON-1_L2_OST_GPR_E_GEODETIC", "JASON-1_L2_OST_GPS_E", "JASON-1_L2_OST_GPS_E_GEODETIC", "JASON_3_L2_OST_OGDR_GPS", "JASON_3_PD_CORRECTION", "JASON_CS_S6A_L1A_ALT_HR_NTC_F08", "JASON_CS_S6A_L1A_ALT_HR_STC_F", "JASON_CS_S6A_L1B_ALT_HR_NTC_F08", "JASON_CS_S6A_L1B_ALT_HR_STC_F", "JASON_CS_S6A_L1B_ALT_LR_NTC_F08", "JASON_CS_S6A_L1B_ALT_LR_STC_F", "JASON_CS_S6A_L1B_GNSS_POD_DAILY", "JASON_CS_S6A_L1B_GNSS_POD_HOURLY", "JASON_CS_S6A_L1B_GNSS_RO_POD_HOURLY", "JASON_CS_S6A_L2P_ALT_HR_OST_NTC_F08", "JASON_CS_S6A_L2P_ALT_LR_OST_NTC_F08", "JASON_CS_S6A_L2_ALT_HR_RED_OST_NRT_F", "JASON_CS_S6A_L2_ALT_HR_RED_OST_NTC_F08", "JASON_CS_S6A_L2_ALT_HR_RED_OST_NTC_F08_UNVALIDATED", "JASON_CS_S6A_L2_ALT_HR_RED_OST_STC_F", "JASON_CS_S6A_L2_ALT_HR_STD_OST_NRT_F", "JASON_CS_S6A_L2_ALT_HR_STD_OST_NTC_F08", "JASON_CS_S6A_L2_ALT_HR_STD_OST_NTC_F08_UNVALIDATED", "JASON_CS_S6A_L2_ALT_HR_STD_OST_STC_F", "JASON_CS_S6A_L2_ALT_LR_RED_OST_NRT_F", "JASON_CS_S6A_L2_ALT_LR_RED_OST_NTC_F08", "JASON_CS_S6A_L2_ALT_LR_RED_OST_NTC_F08_UNVALIDATED", "JASON_CS_S6A_L2_ALT_LR_RED_OST_STC_F", "JASON_CS_S6A_L2_ALT_LR_STD_OST_NRT_F", "JASON_CS_S6A_L2_ALT_LR_STD_OST_NTC_F08", "JASON_CS_S6A_L2_ALT_LR_STD_OST_NTC_F08_UNVALIDATED", "JASON_CS_S6A_L2_ALT_LR_STD_OST_STC_F", "JASON_CS_S6A_L2_AMR_RAD_NRT", "JASON_CS_S6A_L2_AMR_RAD_NTC_F08", "JASON_CS_S6A_L2_AMR_RAD_NTC_F08_UNVALIDATED", "JASON_CS_S6A_L2_AMR_RAD_STC", "JASON_CS_S6A_L3_ALT_HR_OST_NTC_F08", "JASON_CS_S6A_L3_ALT_LR_OST_NTC_F08", "JERS-1_L0", "JERS-1_L1", "JERS-1_SAR_GRFM_Amazon_Mosaics_1280", "JGOFS", "JGOFS_Arabian_Sea", "JGOFS_BOFS", "JGOFS_EQPAC", "JGOFS_WOCE", "JHUAPL_SRI_Kauai", "JPL_RECON_GMSL", "K10_SST-NAVO-L4-GLOB-v01", "KAIMIMOANA", "KILVOLC_FlowerKahn2021", "KORUS", "KORUSAQ_Aerosol_AircraftInSitu_DC8_Data", "KORUSAQ_AircraftRemoteSensing_DIAL_DC8_Data", "KORUSAQ_AircraftRemoteSensing_GeoTASO_B200_Data", "KORUSAQ_Analysis_Data", "KORUSAQ_Cloud_AircraftInSitu_DC8_Data", "KORUSAQ_Ground_EPA_Data", "KORUSAQ_Ground_NASA_Data", "KORUSAQ_Merge_Data", "KORUSAQ_MetNav_AircraftInSitu_B200_Data", "KORUSAQ_MetNav_AircraftInSitu_DC8_Data", "KORUSAQ_Miscellaneous_Data", "KORUSAQ_Model_Data", "KORUSAQ_Pandora_Data", "KORUSAQ_RVJangMokShip_Data", "KORUSAQ_RVOnnuriShip_Data", "KORUSAQ_Sondes_Data", "KORUSAQ_TraceGas_AircraftInSitu_DC8_Data", "KORUSAQ_TraceGas_AircraftInSitu_HanseoKingAir_Data", "KORUSAQ_Trajectory_Data", "KORUSAQ_jValue_AircraftInSitu_DC8_Data", "Kennebec", "Kieber_Photochemistry", "KingSejong", "King_Rim_Fire_Analysis_1288", "Kuparuk_Veg_Maps_1378", "Kuroshio_Area", "L3S_LEO_AM-STAR-v2.80", "L3S_LEO_DY-STAR-v2.81", "L3S_LEO_PM-STAR-v2.81", "LAB97", "LAI_Africa_2325", "LAI_Canada_816", "LAI_VALERI_Canada_829", "LAI_Woody_Plants_1231", "LAI_surfaces_747", "LAMONT_ATL", "LAMONT_GOM", "LAMONT_SAB", "LAMONT_SCS", "LANDMET", "LANDMET_ANC_SM", "LANDMET_ANC_ST", "LANDMET_ANC_TEIME", "LANDMET_ANC_TESA", "LASE_AFWEX", "LASE_CAMEX3", "LASE_SGP97", "LASE_SOLVE", "LASE_TARFOX", "LASE_VALIDATION", "LATTE", "LB02", "LC01_Boundaries_Ecuador_1057", "LC01_Cities_Communities_Roads_1058", "LC01_Households_NEC_1052", "LC01_Hydrography_Edaphology_NEC_1059", "LC01_LULC_Classes_Ecuador_ISA_1084", "LC01_Landsat_1187", "LC01_SRTM_DEM_90m_NEC_1083", "LC01_Topography_Ecuador_ISA_1082", "LC02_Forest_Flammability_Acre_1089", "LC02_GOES8_Hotpixel_Acre_1092", "LC02_MAP_Fire_Indicators_1044", "LC02_Meteorology_Acre_1091", "LC02_PermPlot_Acre_1237", "LC02_Streams_Acre_1243", "LC02_Water_Table_Acre_1062", "LC03_Hypsography_DEM_1094", "LC03_SAR_LC_Biomass_1093", "LC04_IBIS_Model_1139", "LC04_Land_Use_5min_906", "LC04_Macrohydrology_1048", "LC04_THMB-HYDRA_Model_1138", "LC05_BDFF_Biomass_Soils_1040", "LC07_Airborne_Rasters_1274", "LC07_Airborne_Videography_1272", "LC07_Amazon_Wetlands_1284", "LC07_Bathymetry_Curuai_999", "LC07_Biomass_LGrande_1127", "LC07_Curuai_chl_1134", "LC07_Lake_Chlorophyll_MODIS_1000", "LC07_Lake_Nutrient_Sediments_1050", "LC07_Monthly_Inundated_Areas_1049", "LC07_Reservoir_GHG_1143", "LC07_Reservoir_Methane_Emissions_1047", "LC07_SMMR_Inundated_Area_1051", "LC07_Spectroradiometry_1144", "LC07_Wetlands_fluxes_1209", "LC08_EOS_Maps_1155", "LC08_Ecosystem_Demography_Model_1102", "LC08_Fire_Observations_1095", "LC09_GIS_Study_Areas_986", "LC09_Landsat_987", "LC09_Precipitation_940", "LC09_Soil_Composition_938", "LC09_Transition_Matricies_1098", "LC09_Vegetation_Composition_939", "LC10_Landsat_ETM_846", "LC10_Landsat_TM_852", "LC13_GIS_Cauaxi_890", "LC13_GIS_Juruena_888", "LC13_GIS_Tapajos_893", "LC14_Aboveground_Prod_1196", "LC14_Amazon_Scenarios_1153", "LC14_REE_SLA_1211", "LC14_RISQUE_1147", "LC14_Surface_Roots_Phenology_1268", "LC15_AGLB_Distribution_Map_908", "LC15_GRFM_JERS1_Mosaic_1024", "LC15_MODIS_TreeCover_1035", "LC15_Roughness_Map_1182", "LC15_SPOT_Metrics_1239", "LC15_SRTM_Topography_1181", "LC18_Hyperion_889", "LC19_Field_2002_1261", "LC21_Foliar_Nutrients_1234", "LC21_Fractional_Cover_1152", "LC21_Selective_Logging_1172", "LC21_Soil_Characteristics_1236", "LC22_MODIS_Field_Val_2004_1262", "LC22_MODIS_Field_Val_2005_1260", "LC22_MODIS_Phenology_Mato_Grosso_1185", "LC22_MODIS_VCF_Tree_Cover_1112", "LC22_Post_Deforestation_LULC_1099", "LC23_MODIS_ASTER_Fire_Comparisons_839", "LC23_Vegetation_Fire_Dynamics_843", "LC23_Vegetation_Fires_2003_887", "LC24_Basin_Scale_Hot_Pixels_2001_882", "LC24_Cadastral_Property_Map_Para_1042", "LC24_ETM_Deforestation_Map_Para_1999_1054", "LC24_Historical_Roads_Amazon_1043", "LC24_Land_Cover_Southern_Para_1055", "LC24_Land_Cover_Uruara_Para_1053", "LC24_MODIS_Forest_Cover_500-m_1056", "LC31_AMZ_Historical_LU_1170", "LC31_SITE_1173", "LC35_GOES_WF_ABBA_1180", "LC35_Landsat7_Fire_Masks_1071", "LC39_DECAF_Model_1190", "LC39_MODIS_Fire_SA_1186", "LEO", "LEOLSTCMG30", "LEOLSTCMG30", "LGRIP30", "LIDAR", "LIDAR_FOREST_CANOPY_HEIGHTS_1271", "LIMSN7L1PROFILER", "LIMSN7L1RAT", "LIMSN7L2", "LIMSN7L3", "LINKAGES_1166", "LIS", "LISCO_mooring", "LISTOS_AircraftInSitu_StonyBrookAircraft_Data", "LISTOS_AircraftInSitu_UMDAircraft_Data", "LISTOS_AircraftRemoteSensing_NASAAircraft_Data", "LISTOS_Ground_Bayonne_Data", "LISTOS_Ground_BronxPfizer_Data", "LISTOS_Ground_CCNY_Data", "LISTOS_Ground_FlaxPond_Data", "LISTOS_Ground_Hammonasset_Data", "LISTOS_Ground_NewHaven_Data", "LISTOS_Ground_Other_Data", "LISTOS_Ground_OuterIsland_Data", "LISTOS_Ground_QueensCollege_Data", "LISTOS_Ground_Rutgers_Data", "LISTOS_Ground_Westport_Data", "LISTOS_Ground_YaleCoastal_Data", "LISTOS_MetNav_AircraftInSitu_NASAAircraft_Data", "LISTOS_SurfaceMobile_InSitu_Data", "LITE_L1", "LMER-TIES", "LMOS_AircraftInSitu_ScientificAviation_Data", "LMOS_AircraftRemoteSensing_UC12_Data", "LMOS_Ground_Grafton_Data", "LMOS_Ground_IEPA_Data", "LMOS_Ground_Milwaukee_Data", "LMOS_Ground_SchillerPark_Data", "LMOS_Ground_Sheboygan_Data", "LMOS_Ground_WDNRRoutine_Data", "LMOS_Ground_Zion_Data", "LMOS_MetNav_AircraftInSitu_UC12_Data", "LMOS_Miscellaneous_Data", "LMOS_TraceGas_ShipInSitu_Data", "LMOS_TraceGas_SurfaceMobile_EPA-GMAP_Data", "LMOS_TraceGas_SurfaceMobile_UWEC-Auto_Data", "LOBO_timeseries", "LOCSS_L1_V1", "LPJ-WHyMe_v1-3-1_1150", "LPJ_EOSIM_L2_DCH4E", "LPJ_EOSIM_L2_DCH4E_LL", "LPJ_EOSIM_L2_MCH4E", "LPJ_EOSIM_L2_MCH4E_LL", "LPJ_L2_SSREF", "LPJ_L2_SSREF", "LPRM_AMSR2_A_SOILM3", "LPRM_AMSR2_DS_A_SOILM3", "LPRM_AMSR2_DS_D_SOILM3", "LPRM_AMSR2_DS_SOILM2", "LPRM_AMSR2_D_SOILM3", "LPRM_AMSR2_SOILM2", "LPRM_AMSRE_A_SOILM3", "LPRM_AMSRE_D_SOILM3", "LPRM_AMSRE_SOILM2", "LPRM_TMI_DY_SOILM3", "LPRM_TMI_NT_SOILM3", "LPRM_TMI_SOILM2", "LPRM_WINDSAT_DY_SOILM3", "LPRM_WINDSAT_NT_SOILM3", "LPRM_WINDSAT_SOILM2", "LRIRN6L2IPAT", "LSM_807", "LTER", "LUH2_GCB2019_1851", "LULC_Nigeria_Ethiopia_SAfrica_2367", "LWAD01-2", "LaSelva_Biomass_1215", "LaSelva_Land_Use_1312", "Lab2000", "Lab96", "LakeBathymetry_Model_NSlope_AK_2243", "LakeSuperior", "Lake_MI_2012_WaterQual", "Lake_Wetland_Classes_UAVSAR_1883", "Land_Cover_surfaces_748", "Land_Use_Harmonization_V1_1248", "Land_Use_Harmonization_V2_1721", "Landcover_Colombian_Amazon_1783", "Landsat8_Sentinel2_Phenocam_2248", "Large_River_DOC_Export", "Last_Day_Spring_Snow_1528", "Leaf_Carbon_Nutrients_1106", "Leaf_Photosynthesis_Traits_1224", "LiDAR_Forest_Inventory_Brazil_1644", "LiDAR_Tundra_Forest_AK_1782", "LiDAR_Veg_Ht_Idaho_1532", "Line_P", "M1_AVH02C1", "M1_AVH09C1", "M1_AVH13C1", "M2C0NXASM", "M2C0NXCTM", "M2C0NXLND", "M2I1NXASM", "M2I1NXINT", "M2I1NXLFO", "M2I3NPASM", "M2I3NVAER", "M2I3NVASM", "M2I3NVCHM", "M2I3NVGAS", "M2I3NXGAS", "M2I6NPANA", "M2I6NVANA", "M2IMNPANA", "M2IMNPASM", "M2IMNXASM", "M2IMNXGAS", "M2IMNXINT", "M2IMNXLFO", "M2IUNPANA", "M2IUNPASM", "M2IUNXASM", "M2IUNXGAS", "M2IUNXINT", "M2IUNXLFO", "M2SDNXSLV", "M2SMNXEDI", "M2SMNXEDI", "M2SMNXPCT", "M2SMNXPCT", "M2SMNXSLV", "M2T1NXADG", "M2T1NXAER", "M2T1NXCHM", "M2T1NXCSP", "M2T1NXFLX", "M2T1NXINT", "M2T1NXLFO", "M2T1NXLND", "M2T1NXOCN", "M2T1NXRAD", "M2T1NXSLV", "M2T3NEMST", "M2T3NENAV", "M2T3NETRB", "M2T3NPCLD", "M2T3NPMST", "M2T3NPODT", "M2T3NPQDT", "M2T3NPRAD", "M2T3NPTDT", "M2T3NPTRB", "M2T3NPUDT", "M2T3NVASM", "M2T3NVCLD", "M2T3NVMST", "M2T3NVRAD", "M2T3NXGLC", "M2TCNPLTM", "M2TCNPLTM", "M2TCNXLTM", "M2TCNXLTM", "M2TMNPCLD", "M2TMNPMST", "M2TMNPODT", "M2TMNPQDT", "M2TMNPRAD", "M2TMNPTDT", "M2TMNPTRB", "M2TMNPUDT", "M2TMNXADG", "M2TMNXAER", "M2TMNXCHM", "M2TMNXCSP", "M2TMNXFLX", "M2TMNXGLC", "M2TMNXINT", "M2TMNXLFO", "M2TMNXLND", "M2TMNXOCN", "M2TMNXRAD", "M2TMNXSLV", "M2TUNPCLD", "M2TUNPMST", "M2TUNPODT", "M2TUNPQDT", "M2TUNPRAD", "M2TUNPTDT", "M2TUNPTRB", "M2TUNPUDT", "M2TUNXADG", "M2TUNXAER", "M2TUNXCHM", "M2TUNXCSP", "M2TUNXFLX", "M2TUNXGLC", "M2TUNXINT", "M2TUNXLFO", "M2TUNXLND", "M2TUNXOCN", "M2TUNXRAD", "M2TUNXSLV", "M2_TMAX_PM25", "MAA", "MAC021S0", "MAC021S1", "MAC02QS0", "MAC02QS1", "MAC03S0", "MAC03S1", "MAC04S0", "MAC04S1", "MAC05S0", "MAC05S1", "MAC06S0", "MAC06S1", "MAC07S0", "MAC07S1", "MAC35S0", "MAC35S1", "MACLWP_diurnal", "MACLWP_mean", "MACPEX_Aerosol_AircraftInSitu_WB57_Data", "MACPEX_Cloud_AircraftInSitu_WB57_Data", "MACPEX_MetNav_AircraftInSitu_WB57_Data", "MACPEX_Satellite_Data", "MACPEX_Sondes_Data", "MACPEX_TraceGas_AircraftInSitu_WB57_Data", "MACPEX_Water_AircraftInSitu_WB57_Data", "MACTWP_mean", "MALINA", "MAM03S0", "MAM04S0", "MAM05S0", "MAM06S0", "MAM07S0", "MAM35S0", "MANTRA_PIRANA", "MAPSS_853", "MAPS_OSTA3_CO5X5_HDF", "MAPS_OSTA3_COSEC_HDF", "MAPS_SRL1_CO5X5_HDF", "MAPS_SRL1_COSEC_HDF", "MAPS_SRL2_CO5X5_HDF", "MAPS_SRL2_COSEC_HDF", "MASL1B", "MASL2CLD", "MASS_BAY", "MASTER_Ames_August_2003_2045", "MASTER_B200_Fall_1999_2099", "MASTER_Baja_Mexico_1999_2102", "MASTER_CARTA_2003_2054", "MASTER_CARTA_2005_2034", "MASTER_CLASIC_2007_2019", "MASTER_DFRC_April_2004_2043", "MASTER_DFRC_August_2003_2046", "MASTER_DFRC_Fall_2005_2030", "MASTER_DFRC_July-Aug_2001_V2_2144", "MASTER_DFRC_July_2004_2039", "MASTER_DFRC_June_1999_2100", "MASTER_DFRC_June_2011_1976", "MASTER_DFRC_May-June_2008_2011", "MASTER_DFRC_May_2011_1985", "MASTER_DFRC_November_2011_1973", "MASTER_DFRC_October_2003_2044", "MASTER_DFRC_RSL_August_2002_2066", "MASTER_DFRC_September_2002_2064", "MASTER_DFRC_September_2006_2022", "MASTER_FIREX_AQ_JulySept_2019_1941", "MASTER_FireSense_2023_2330", "MASTER_Flightline_Locator_2151", "MASTER_GEMx_Spring_2024_2370", "MASTER_GEMx_Summer_2023_2319", "MASTER_Hawaii_October_2001_V2_2142", "MASTER_Houston_2010_2126", "MASTER_Houston_2011_1972", "MASTER_HyspIRI_EarlyS2013_V2_2146", "MASTER_HyspIRI_EarlySpring2014_1965", "MASTER_HyspIRI_Fall_2013_1966", "MASTER_HyspIRI_Fall_2014_1962", "MASTER_HyspIRI_Fall_2015_1956", "MASTER_HyspIRI_Hawaii_2017_1951", "MASTER_HyspIRI_Hawaii_2018_1945", "MASTER_HyspIRI_LateSpring_2013_1968", "MASTER_HyspIRI_LateSpring_2014_1963", "MASTER_HyspIRI_Spring_2015_1957", "MASTER_HyspIRI_Summer_2014_1964", "MASTER_HyspIRI_Summer_2015_1959", "MASTER_HyspIRI_Summer_2016_1913", "MASTER_HyspIRI_Summer_2017_1950", "MASTER_HyspIRI_Summer_2018_1942", "MASTER_PacificRim_2000_2093", "MASTER_RSL-DFRC_October_2008_2014", "MASTER_RSLPhoenix_2011_1975", "MASTER_RSL_April_2003_2053", "MASTER_RSL_April_2008_2016", "MASTER_RSL_August_2001_V2_2143", "MASTER_RSL_August_2004_2036", "MASTER_RSL_August_2006_2027", "MASTER_RSL_August_2007_2020", "MASTER_RSL_August_2008_2010", "MASTER_RSL_December_1998_2104", "MASTER_RSL_January_1999_2103", "MASTER_RSL_July_2003_2047", "MASTER_RSL_July_2004_2038", "MASTER_RSL_June_1999_2098", "MASTER_RSL_June_2000_2096", "MASTER_RSL_June_2001_2090", "MASTER_RSL_June_2002_2067", "MASTER_RSL_June_2004_2042", "MASTER_RSL_March_2000_2094", "MASTER_RSL_March_2002_2068", "MASTER_RSL_May_1999_2101", "MASTER_RSL_May_2001_V2_2145", "MASTER_RSL_May_2002_V2_2148", "MASTER_RSL_May_2004_2041", "MASTER_RSL_May_2006_2029", "MASTER_RSL_November_2007_2015", "MASTER_RSL_Oct_2002_V2_2147", "MASTER_RSL_October_2003_2048", "MASTER_RSL_October_2005_2031", "MASTER_RSL_October_2007_2013", "MASTER_RSL_October_2010_2127", "MASTER_RSL_September_2005_2037", "MASTER_SARP_2009_1990", "MASTER_SARP_2010_1989", "MASTER_SARP_2011_1974", "MASTER_SARP_2012_1983", "MASTER_SARP_2013_1967", "MASTER_SARP_2014_1961", "MASTER_SARP_2015_1958", "MASTER_SARP_2016_1912", "MASTER_Sky_2003_2052", "MASTER_Sky_2004_2035", "MASTER_Sky_2006_2026", "MASTER_SuomiNPP_2013_1969", "MASTER_SuomiNPP_2015_1970", "MASTER_TC4_2007_2021", "MASTER_WDTS_Fall_2022_2141", "MASTER_WDTS_SeptOct_2020_1940", "MASTER_WDTS_Spring_2021_1953", "MASTER_WDTS_Spring_2023_2252", "MAS_832", "MCD06COSP_D3_MODIS", "MCD06COSP_M3_MODIS", "MCD12C1", "MCD12Q1", "MCD12Q2", "MCD15A2H", "MCD15A3H", "MCD18A1", "MCD18A2", "MCD18C1", "MCD18C2", "MCD19A1", "MCD19A1CMGL", "MCD19A1CMGO", "MCD19A1N", "MCD19A2", "MCD19A2CMG", "MCD19A2N", "MCD19A3CMG", "MCD19A3D", "MCD19A3DN", "MCD43A1", "MCD43A1N", "MCD43A2", "MCD43A2N", "MCD43A3", "MCD43A3N", "MCD43A4", "MCD43A4N", "MCD43C1", "MCD43C2", "MCD43C3", "MCD43C4", "MCD43D01", "MCD43D02", "MCD43D03", "MCD43D04", "MCD43D05", "MCD43D06", "MCD43D07", "MCD43D08", "MCD43D09", "MCD43D10", "MCD43D11", "MCD43D12", "MCD43D13", "MCD43D14", "MCD43D15", "MCD43D16", "MCD43D17", "MCD43D18", "MCD43D19", "MCD43D20", "MCD43D21", "MCD43D22", "MCD43D23", "MCD43D24", "MCD43D25", "MCD43D26", "MCD43D27", "MCD43D28", "MCD43D29", "MCD43D30", "MCD43D31", "MCD43D32", "MCD43D33", "MCD43D34", "MCD43D35", "MCD43D36", "MCD43D37", "MCD43D38", "MCD43D39", "MCD43D40", "MCD43D41", "MCD43D42", "MCD43D43", "MCD43D44", "MCD43D45", "MCD43D46", "MCD43D47", "MCD43D48", "MCD43D49", "MCD43D50", "MCD43D51", "MCD43D52", "MCD43D53", "MCD43D54", "MCD43D55", "MCD43D56", "MCD43D57", "MCD43D58", "MCD43D59", "MCD43D60", "MCD43D61", "MCD43D62", "MCD43D63", "MCD43D64", "MCD43D65", "MCD43D66", "MCD43D67", "MCD43D68", "MCD43GF", "MCD64A1", "MCDAODHD", "MCDWD_L3_F1C_NRT", "MCDWD_L3_F1_NRT", "MCDWD_L3_F2_NRT", "MCDWD_L3_F3_NRT", "MCDWD_L3_NRT", "MCR_LTER", "MELVILLE", "MERCHANT_SHIP", "MERGED_S3_OLCI_L3b_CYANTC", "MERGED_S3_OLCI_L3b_ILW", "MERGED_S3_OLCI_L3m_CYAN", "MERGED_S3_OLCI_L3m_CYANTC", "MERGED_S3_OLCI_L3m_ILW", "MERGED_TP_J1_OSTM_OST_CYCLES_V52", "MERGED_TP_J1_OSTM_OST_GMSL_ASCII_V51", "MERIS_L1_FRS", "MERIS_L1_RR", "MERIS_L2_FRS_IOP", "MERIS_L2_FRS_OC", "MERIS_L2_RR_IOP", "MERIS_L2_RR_OC", "MERIS_L3b_CHL", "MERIS_L3b_CYAN", "MERIS_L3b_CYANTC", "MERIS_L3b_IOP", "MERIS_L3b_KD", "MERIS_L3b_PAR", "MERIS_L3b_PIC", "MERIS_L3b_POC", "MERIS_L3b_RRS", "MERIS_L3m_CHL", "MERIS_L3m_CYAN", "MERIS_L3m_CYANTC", "MERIS_L3m_IOP", "MERIS_L3m_KD", "MERIS_L3m_PAR", "MERIS_L3m_PIC", "MERIS_L3m_POC", "MERIS_L3m_RRS", "MERIS_L4b_GSM", "MERIS_L4m_GSM", "MERRA2_CNN_HAQAST_PM25", "MFLL_CO2_Weighting_Functions_1891", "MFLL_XCO2_Range_10Hz_1892", "MI1B2_ELLIPSOID_NRT", "MI1B2_TERRAIN_NRT", "MI2AS_AEROSOL_NRT", "MI2TC_CMV_BFR_NRT", "MI2TC_CMV_HDF_NRT", "MI3DAEF", "MI3DAENF", "MI3DAER", "MI3DALF", "MI3DALNF", "MI3DCDF", "MI3DCDNF", "MI3DCLDN", "MI3DLSF", "MI3DLSNF", "MI3DLSR", "MI3DRDF", "MI3DRDR", "MI3MAEF", "MI3MAENF", "MI3MAER", "MI3MALF", "MI3MALNF", "MI3MCDF", "MI3MCDNF", "MI3MCLDN", "MI3MCMVN", "MI3MLSF", "MI3MLSNF", "MI3MLSR", "MI3MRDF", "MI3MRDR", "MI3QCLDN", "MI3QCMVN", "MI3YCLDN", "MI3YCMVN", "MIANACP", "MIANCAGP", "MIANCARP", "MIANTASC", "MIB1LM", "MICRONESIAN", "MIL2ASAE", "MIL2ASLS", "MIL2TCAF", "MIL2TCAL", "MIL2TCCF", "MIL2TCCF", "MIL2TCCL", "MIL2TCSF", "MIL2TCSP", "MIL2TCSP", "MIL3DAE", "MIL3DAEN", "MIL3DAL", "MIL3DALN", "MIL3DCFA", "MIL3DCLD", "MIL3DCOD", "MIL3DLS", "MIL3DLSN", "MIL3DRD", "MIL3MAE", "MIL3MAEN", "MIL3MAL", "MIL3MALN", "MIL3MCFA", "MIL3MCLD", "MIL3MCOD", "MIL3MJTA", "MIL3MLS", "MIL3MLSN", "MIL3MRD", "MIL3QAE", "MIL3QAEN", "MIL3QAL", "MIL3QALN", "MIL3QCFA", "MIL3QCLD", "MIL3QCOD", "MIL3QLS", "MIL3QLSN", "MIL3QRD", "MIL3YAE", "MIL3YAEN", "MIL3YAL", "MIL3YALN", "MIL3YCFA", "MIL3YCLD", "MIL3YCOD", "MIL3YLS", "MIL3YLSN", "MIL3YRD", "MIPOT", "MIRAI", "MIRC", "MIRCCM", "MIRCCMF", "MISC_Apex_Floats", "MISR_885", "MISR_AEROSOL_CLIM", "MISR_Forest_AGB_SW_US_1978", "MITgcm_LLC4320_Pre-SWOT_JPL_L4_ACC_SMST_v1.0", "MITgcm_LLC4320_Pre-SWOT_JPL_L4_BassStrait_v1.0", "MITgcm_LLC4320_Pre-SWOT_JPL_L4_Boknis_v1.0", "MITgcm_LLC4320_Pre-SWOT_JPL_L4_CapeBasin_v1.0", "MITgcm_LLC4320_Pre-SWOT_JPL_L4_GotlandBasin_v1.0", "MITgcm_LLC4320_Pre-SWOT_JPL_L4_LabradorSea_v1.0", "MITgcm_LLC4320_Pre-SWOT_JPL_L4_MarmaraSea_v1.0", "MITgcm_LLC4320_Pre-SWOT_JPL_L4_NWAustralia_v1.0", "MITgcm_LLC4320_Pre-SWOT_JPL_L4_NWPacific_v1.0", "MITgcm_LLC4320_Pre-SWOT_JPL_L4_NewCaledonia_v1.0", "MITgcm_LLC4320_Pre-SWOT_JPL_L4_ROAM_MIZ_v1.0", "MITgcm_LLC4320_Pre-SWOT_JPL_L4_RockallTrough_v1.0", "MITgcm_LLC4320_Pre-SWOT_JPL_L4_WestAtlantic_v1.0", "MITgcm_LLC4320_Pre-SWOT_JPL_L4_WesternMed_v1.0", "MITgcm_LLC4320_Pre-SWOT_JPL_L4_Yongala_v1.0", "ML1OA", "ML1OA", "ML1RADD", "ML1RADD", "ML1RADG", "ML1RADG", "ML1RADT", "ML1RADT", "ML2BRO", "ML2BRO", "ML2CH3CL", "ML2CH3CL", "ML2CH3CN", "ML2CH3CN", "ML2CH3OH", "ML2CH3OH", "ML2CLO", "ML2CLO", "ML2CO", "ML2CO", "ML2CO_NRT", "ML2DGG", "ML2DGG", "ML2DGM", "ML2DGM", "ML2GPH", "ML2GPH", "ML2H2O", "ML2H2O", "ML2H2O_NRT", "ML2HCL", "ML2HCL", "ML2HCN", "ML2HCN", "ML2HNO3", "ML2HNO3", "ML2HNO3_NRT", "ML2HO2", "ML2HO2", "ML2HOCL", "ML2HOCL", "ML2IWC", "ML2IWC", "ML2N2O", "ML2N2O", "ML2N2O_NRT", "ML2O3", "ML2O3", "ML2O3_NRT", "ML2OH", "ML2OH", "ML2RHI", "ML2RHI", "ML2SO2", "ML2SO2", "ML2SO2_NRT", "ML2T", "ML2T", "ML2T_NRT", "ML3DBCH3CL", "ML3DBCH3CL", "ML3DBCLO", "ML3DBCLO", "ML3DBCO", "ML3DBCO", "ML3DBGPH", "ML3DBGPH", "ML3DBH2O", "ML3DBH2O", "ML3DBHCL", "ML3DBHCL", "ML3DBHCN", "ML3DBHCN", "ML3DBHNO3", "ML3DBHNO3", "ML3DBHOCL", "ML3DBHOCL", "ML3DBIWC", "ML3DBIWC", "ML3DBN2O", "ML3DBN2O", "ML3DBO3", "ML3DBO3", "ML3DBOH", "ML3DBOH", "ML3DBRHI", "ML3DBRHI", "ML3DBSO2", "ML3DBSO2", "ML3DBT", "ML3DBT", "ML3DZCH3CL", "ML3DZCH3CL", "ML3DZCLO", "ML3DZCLO", "ML3DZCO", "ML3DZCO", "ML3DZGPH", "ML3DZGPH", "ML3DZH2O", "ML3DZH2O", "ML3DZHCL", "ML3DZHCL", "ML3DZHCN", "ML3DZHCN", "ML3DZHNO3", "ML3DZHNO3", "ML3DZHOCL", "ML3DZHOCL", "ML3DZIWC", "ML3DZIWC", "ML3DZMBRO", "ML3DZMBRO", "ML3DZMHO2", "ML3DZMHO2", "ML3DZN2O", "ML3DZN2O", "ML3DZO3", "ML3DZO3", "ML3DZOH", "ML3DZOH", "ML3DZRHI", "ML3DZRHI", "ML3DZSO2", "ML3DZSO2", "ML3DZT", "ML3DZT", "ML3MBCH3CL", "ML3MBCH3CL", "ML3MBCLO", "ML3MBCLO", "ML3MBCO", "ML3MBCO", "ML3MBGPH", "ML3MBGPH", "ML3MBH2O", "ML3MBH2O", "ML3MBHCL", "ML3MBHCL", "ML3MBHCN", "ML3MBHCN", "ML3MBHNO3", "ML3MBHNO3", "ML3MBHOCL", "ML3MBHOCL", "ML3MBIWC", "ML3MBIWC", "ML3MBN2O", "ML3MBN2O", "ML3MBO3", "ML3MBO3", "ML3MBOH", "ML3MBOH", "ML3MBRHI", "ML3MBRHI", "ML3MBSO2", "ML3MBSO2", "ML3MBT", "ML3MBT", "MOCE", "MOD00F", "MOD00S", "MOD08_D3", "MOD08_E3", "MOD08_M3", "MOD09A1", "MOD09A1N", "MOD09CMA", "MOD09CMG", "MOD09CMG", "MOD09GA", "MOD09GA", "MOD09GQ", "MOD09GQ", "MOD09Q1", "MOD09Q1N", "MOD10A1", "MOD10A1F", "MOD10A2", "MOD10C1", "MOD10C2", "MOD10CM", "MOD11A1", "MOD11A2", "MOD11B1", "MOD11B2", "MOD11B3", "MOD11C1", "MOD11C2", "MOD11C3", "MOD11CM1D", "MOD11CM1N", "MOD11_L2", "MOD13A1", "MOD13A2", "MOD13A3", "MOD13A4N", "MOD13C1", "MOD13C2", "MOD13Q1", "MOD13Q4N", "MOD14", "MOD14A1", "MOD14A2", "MOD14CM1", "MOD15A2H", "MOD16A2", "MOD16A2GF", "MOD16A3GF", "MOD17A2H", "MOD17A2HGF", "MOD17A3HGF", "MOD21", "MOD21A1D", "MOD21A1N", "MOD21A2", "MOD21C1", "MOD21C2", "MOD21C3", "MOD28C2", "MOD28C3", "MOD29E1D", "MOD29P1D", "MOD29P1N", "MOD44B", "MOD44W", "MODAODHD", "MODCSR_8", "MODCSR_B", "MODGRNLD", "MODISA_L1", "MODISA_L1_GEO", "MODISA_L2_IOP", "MODISA_L2_IOP_NRT", "MODISA_L2_OC", "MODISA_L2_OC_NRT", "MODISA_L2_SST", "MODISA_L2_SST4", "MODISA_L2_SST4_NRT", "MODISA_L2_SST_NRT", "MODISA_L3b_CHL", "MODISA_L3b_CHL_NRT", "MODISA_L3b_FLH", "MODISA_L3b_FLH_NRT", "MODISA_L3b_IOP", "MODISA_L3b_IOP_NRT", "MODISA_L3b_KD", "MODISA_L3b_KD_NRT", "MODISA_L3b_LAND", "MODISA_L3b_NSST", "MODISA_L3b_NSST_NRT", "MODISA_L3b_PAR", "MODISA_L3b_PAR_NRT", "MODISA_L3b_PIC", "MODISA_L3b_PIC_NRT", "MODISA_L3b_POC", "MODISA_L3b_POC_NRT", "MODISA_L3b_RRS", "MODISA_L3b_RRS_NRT", "MODISA_L3b_SST", "MODISA_L3b_SST4", "MODISA_L3b_SST4_NRT", "MODISA_L3b_SST_NRT", "MODISA_L3m_CHL", "MODISA_L3m_CHL_NRT", "MODISA_L3m_FLH", "MODISA_L3m_FLH_NRT", "MODISA_L3m_IOP", "MODISA_L3m_IOP_NRT", "MODISA_L3m_KD", "MODISA_L3m_KD_NRT", "MODISA_L3m_LAND", "MODISA_L3m_NSST", "MODISA_L3m_NSST_NRT", "MODISA_L3m_PAR", "MODISA_L3m_PAR_NRT", "MODISA_L3m_PIC", "MODISA_L3m_PIC_NRT", "MODISA_L3m_POC", "MODISA_L3m_POC_NRT", "MODISA_L3m_RRS", "MODISA_L3m_RRS_NRT", "MODISA_L3m_SST", "MODISA_L3m_SST4", "MODISA_L3m_SST4_NRT", "MODISA_L3m_SST_NRT", "MODISA_L4b_GSM", "MODISA_L4m_GSM", "MODIST_L1", "MODIST_L1_GEO", "MODIST_L2_IOP", "MODIST_L2_IOP_NRT", "MODIST_L2_OC", "MODIST_L2_OC_NRT", "MODIST_L2_SST", "MODIST_L2_SST4", "MODIST_L2_SST4_NRT", "MODIST_L2_SST_NRT", "MODIST_L3b_CHL", "MODIST_L3b_CHL_NRT", "MODIST_L3b_FLH", "MODIST_L3b_FLH_NRT", "MODIST_L3b_IOP", "MODIST_L3b_IOP_NRT", "MODIST_L3b_KD", "MODIST_L3b_KD_NRT", "MODIST_L3b_NSST", "MODIST_L3b_NSST_NRT", "MODIST_L3b_PAR", "MODIST_L3b_PAR_NRT", "MODIST_L3b_PIC", "MODIST_L3b_PIC_NRT", "MODIST_L3b_POC", "MODIST_L3b_POC_NRT", "MODIST_L3b_RRS", "MODIST_L3b_RRS_NRT", "MODIST_L3b_SST", "MODIST_L3b_SST4", "MODIST_L3b_SST4_NRT", "MODIST_L3b_SST_NRT", "MODIST_L3m_CHL", "MODIST_L3m_CHL_NRT", "MODIST_L3m_FLH", "MODIST_L3m_FLH_NRT", "MODIST_L3m_IOP", "MODIST_L3m_IOP_NRT", "MODIST_L3m_KD", "MODIST_L3m_KD_NRT", "MODIST_L3m_NSST", "MODIST_L3m_NSST_NRT", "MODIST_L3m_PAR", "MODIST_L3m_PAR_NRT", "MODIST_L3m_PIC", "MODIST_L3m_PIC_NRT", "MODIST_L3m_POC", "MODIST_L3m_POC_NRT", "MODIST_L3m_RRS", "MODIST_L3m_RRS_NRT", "MODIST_L3m_SST", "MODIST_L3m_SST4", "MODIST_L3m_SST4_NRT", "MODIST_L3m_SST_NRT", "MODIST_L4b_GSM", "MODIST_L4m_GSM", "MODIS_A-JPL-L2P-v2019.0", "MODIS_AQUA_L3_SST_MID-IR_8DAY_4KM_NIGHTTIME_V2019.0", "MODIS_AQUA_L3_SST_MID-IR_8DAY_9KM_NIGHTTIME_V2019.0", "MODIS_AQUA_L3_SST_MID-IR_ANNUAL_4KM_NIGHTTIME_V2019.0", "MODIS_AQUA_L3_SST_MID-IR_ANNUAL_9KM_NIGHTTIME_V2019.0", "MODIS_AQUA_L3_SST_MID-IR_DAILY_4KM_NIGHTTIME_V2019.0", "MODIS_AQUA_L3_SST_MID-IR_DAILY_9KM_NIGHTTIME_V2019.0", "MODIS_AQUA_L3_SST_MID-IR_MONTHLY_4KM_NIGHTTIME_V2019.0", "MODIS_AQUA_L3_SST_MID-IR_MONTHLY_9KM_NIGHTTIME_V2019.0", "MODIS_AQUA_L3_SST_THERMAL_8DAY_4KM_DAYTIME_V2019.0", "MODIS_AQUA_L3_SST_THERMAL_8DAY_4KM_NIGHTTIME_V2019.0", "MODIS_AQUA_L3_SST_THERMAL_8DAY_9KM_DAYTIME_V2019.0", "MODIS_AQUA_L3_SST_THERMAL_8DAY_9KM_NIGHTTIME_V2019.0", "MODIS_AQUA_L3_SST_THERMAL_ANNUAL_4KM_DAYTIME_V2019.0", "MODIS_AQUA_L3_SST_THERMAL_ANNUAL_4KM_NIGHTTIME_V2019.0", "MODIS_AQUA_L3_SST_THERMAL_ANNUAL_9KM_DAYTIME_V2019.0", "MODIS_AQUA_L3_SST_THERMAL_ANNUAL_9KM_NIGHTTIME_V2019.0", "MODIS_AQUA_L3_SST_THERMAL_DAILY_4KM_DAYTIME_V2019.0", "MODIS_AQUA_L3_SST_THERMAL_DAILY_4KM_NIGHTTIME_V2019.0", "MODIS_AQUA_L3_SST_THERMAL_DAILY_9KM_DAYTIME_V2019.0", "MODIS_AQUA_L3_SST_THERMAL_DAILY_9KM_NIGHTTIME_V2019.0", "MODIS_AQUA_L3_SST_THERMAL_MONTHLY_4KM_DAYTIME_V2019.0", "MODIS_AQUA_L3_SST_THERMAL_MONTHLY_4KM_NIGHTTIME_V2019.0", "MODIS_AQUA_L3_SST_THERMAL_MONTHLY_9KM_DAYTIME_V2019.0", "MODIS_AQUA_L3_SST_THERMAL_MONTHLY_9KM_NIGHTTIME_V2019.0", "MODIS_CCaN_NDVI_Trends_Alaska_1666", "MODIS_CR_Equal_Angle_3h", "MODIS_CR_Equal_Angle_Daily", "MODIS_CR_Equal_Area_3h", "MODIS_MAIAC_Reflectance_1700", "MODIS_PAR_1140", "MODIS_T-JPL-L2P-v2019.0", "MODIS_TERRA_L3_SST_MID-IR_8DAY_4KM_NIGHTTIME_V2019.0", "MODIS_TERRA_L3_SST_MID-IR_8DAY_9KM_NIGHTTIME_V2019.0", "MODIS_TERRA_L3_SST_MID-IR_ANNUAL_4KM_NIGHTTIME_V2019.0", "MODIS_TERRA_L3_SST_MID-IR_ANNUAL_9KM_NIGHTTIME_V2019.0", "MODIS_TERRA_L3_SST_MID-IR_DAILY_4KM_NIGHTTIME_V2019.0", "MODIS_TERRA_L3_SST_MID-IR_DAILY_9KM_NIGHTTIME_V2019.0", "MODIS_TERRA_L3_SST_MID-IR_MONTHLY_4KM_NIGHTTIME_V2019.0", "MODIS_TERRA_L3_SST_MID-IR_MONTHLY_9KM_NIGHTTIME_V2019.0", "MODIS_TERRA_L3_SST_THERMAL_8DAY_4KM_DAYTIME_V2019.0", "MODIS_TERRA_L3_SST_THERMAL_8DAY_4KM_NIGHTTIME_V2019.0", "MODIS_TERRA_L3_SST_THERMAL_8DAY_9KM_DAYTIME_V2019.0", "MODIS_TERRA_L3_SST_THERMAL_8DAY_9KM_NIGHTTIME_V2019.0", "MODIS_TERRA_L3_SST_THERMAL_ANNUAL_4KM_DAYTIME_V2019.0", "MODIS_TERRA_L3_SST_THERMAL_ANNUAL_4KM_NIGHTTIME_V2019.0", "MODIS_TERRA_L3_SST_THERMAL_ANNUAL_9KM_DAYTIME_V2019.0", "MODIS_TERRA_L3_SST_THERMAL_ANNUAL_9KM_NIGHTTIME_V2019.0", "MODIS_TERRA_L3_SST_THERMAL_DAILY_4KM_DAYTIME_V2019.0", "MODIS_TERRA_L3_SST_THERMAL_DAILY_4KM_NIGHTTIME_V2019.0", "MODIS_TERRA_L3_SST_THERMAL_DAILY_9KM_DAYTIME_V2019.0", "MODIS_TERRA_L3_SST_THERMAL_DAILY_9KM_NIGHTTIME_V2019.0", "MODIS_TERRA_L3_SST_THERMAL_MONTHLY_4KM_DAYTIME_V2019.0", "MODIS_TERRA_L3_SST_THERMAL_MONTHLY_4KM_NIGHTTIME_V2019.0", "MODIS_TERRA_L3_SST_THERMAL_MONTHLY_9KM_DAYTIME_V2019.0", "MODIS_TERRA_L3_SST_THERMAL_MONTHLY_9KM_NIGHTTIME_V2019.0", "MODIS_emissions_758", "MODVI", "MOD_L2_DC", "MOKIHANA", "MONTEREY_BAY", "MOOSE_Aerodyne-Mobile-Laboratory", "MOOSE_AircraftRemoteSensing_NASA-G3_GCAS_Data", "MOP02J", "MOP02J", "MOP02J", "MOP02N", "MOP02N", "MOP02N", "MOP02T", "MOP02T", "MOP02T", "MOP03J", "MOP03J", "MOP03J", "MOP03JM", "MOP03JM", "MOP03JM", "MOP03N", "MOP03N", "MOP03N", "MOP03NM", "MOP03NM", "MOP03NM", "MOP03T", "MOP03T", "MOP03T", "MOP03TM", "MOP03TM", "MOP03TM", "MOPCH", "MOPITT_co_835", "MRIRN2IM", "MRIRN2L1", "MRIRN3IM", "MRIRN3L1", "MSAQSO2L4", "MSG01-OSPO-L2P-v1.0", "MSG02-OSPO-L2P-v1.0", "MSG03-OSPO-L2P-v1.0", "MSG04-OSPO-L2P-v1.0", "MSLERLSTL3d10", "MSLERLSTL3zm", "MSLERNNL3d10", "MSLERNNL3zm", "MSLSP30NA", "MSO3L3zm5", "MSULST", "MSULTT", "MSUOP", "MSUUTT", "MSVOLSO2L4", "MS_Sound", "MTSAT2-OSPO-L2P-v1.0", "MUR-JPL-L4-GLOB-v4.1", "MUR25-JPL-L4-GLOB-v04.2", "MURI_Camouflage", "MURI_HI", "MUSE", "MVCO", "MW_IR_OI-REMSS-L4-GLOB-v5.0", "MW_IR_OI-REMSS-L4-GLOB-v5.1", "MW_OI-REMSS-L4-GLOB-v5.0", "MW_OI-REMSS-L4-GLOB-v5.1", "MYD00F", "MYD00S", "MYD08_D3", "MYD09A1", "MYD09CMG", "MYD09GA", "MYD09GQ", "MYD09Q1", "MYD10A1", "MYD11A1", "MYD11A2", "MYD11B1", "MYD11B2", "MYD11B3", "MYD11C1", "MYD11C2", "MYD11C3", "MYD11CM1D", "MYD11CM1N", "MYD11_L2", "MYD13A1", "MYD13A2", "MYD13A3", "MYD13C1", "MYD13C2", "MYD13Q1", "MYD14", "MYD14A1", "MYD14A2", "MYD14CM1", "MYD15A2H", "MYD16A2", "MYD16A2GF", "MYD16A3GF", "MYD17A2H", "MYD17A2HGF", "MYD17A3HGF", "MYD21", "MYD21A1D", "MYD21A1N", "MYD21A2", "MYD21C1", "MYD21C2", "MYD21C3", "MYD28C2", "MYD28C3", "MYDGB0", "MYDVI", "MYD_L2_CB", "MYD_L2_DC", "MYD_L2_MPLCT", "MYD_L3_OFLCT", "MagMix", "Main_Melt_Onset_Dates_1841", "Maps_AGB_North_Slope_AK_1565", "Marlon_Lewis_92", "Maryland_Temperature_Humidity_1319", "MatthewsVegetation_419", "Mean_Seasonal_LAI_1653", "Medit_Ligurian", "MetOpA_GOME2_SIF_V2_2292", "MetOpB_GOME2_SIF_2182", "Meteorological_1065", "Methane_Ebullition_Lakes_AK_1861", "Methane_Ethane_MA_NH_1982", "Methane_Flaring_Sites_VIIRS_1874", "Microbiome", "Mid-latitude_soils_705", "Missouri_Reservoirs_RSWQ", "MonthlyWetland_CH4_WetCHARTsV2_2346", "Monthly_Hydrological_Fluxes_1647", "MultiInstrumentFusedXCO2", "MumfordCove", "N01", "N07_AVH02C1", "N07_AVH09C1", "N07_AVH13C1", "N09_AVH02C1", "N09_AVH09C1", "N09_AVH13C1", "N11_AVH02C1", "N11_AVH09C1", "N11_AVH13C1", "N14_AVH02C1", "N14_AVH09C1", "N14_AVH13C1", "N16_AVH02C1", "N16_AVH09C1", "N16_AVH13C1", "N18_AVH02C1", "N18_AVH09C1", "N18_AVH13C1", "N19_AVH02C1", "N19_AVH09C1", "N19_AVH13C1", "N21-VIIRS-L2P-ACSPO-v2.80", "N21-VIIRS-L3U-ACSPO-v2.80", "NAAMES", "NAAMES_AerosolCloud_AircraftRemoteSensing_Data", "NAAMES_Aerosol_AircraftInSitu_Data", "NAAMES_Aerosol_ShipInSitu_Data", "NAAMES_Cloud_AircraftInSitu_Data", "NAAMES_Merge_Data", "NAAMES_MetNav_AircraftInSitu_Data", "NAAMES_MetNav_ShipInSitu_Data", "NAAMES_Met_SondeInSitu_Data", "NAAMES_Misc_Ship_Data", "NAAMES_Ocean_AircraftRemoteSensing_Data", "NAAMES_Radiation_AircraftInSitu_Data", "NAAMES_TraceGas_AircraftInSitu_Data", "NAAMES_TraceGas_ShipInSitu_Data", "NAB08", "NABE", "NACP_ACES_V2_1943", "NACP_BlackSpruce_Burn-Severity_1331", "NACP_Boreal_Biome_Biomass_1273", "NACP_Forest_Biophysical_1046", "NACP_Forest_Conservation_1662", "NACP_GHG_Data_Compilation_1206", "NACP_MCI_CO2_Inventory_1205", "NACP_MCI_CO2_Inversions_1204", "NACP_MCI_CO2_Measurements_1202", "NACP_MCI_Crop_GPP_1217", "NACP_Modeled_NEE_NEP_Fluxes_1203", "NACP_MsTMIP_Model_Driver_1220", "NACP_MsTMIP_Model_Structure_1228", "NACP_MsTMIP_TBMO_1225", "NACP_MsTMIP_Unified_NA_SoilMap_1242", "NACP_NAM_HYSPLIT_Footprints_1586", "NACP_PNW_Carbon_Balance_1317", "NACP_PalEON_MIP_1779", "NACP_Peatland_Burn-Severity_1283", "NACP_Peatland_Land_Cover_MI_1513", "NACP_Regional_Model_GHG_Aggr_1179", "NACP_Regional_Obs_Model_Grid_1157", "NACP_Regional_Obs_Model_Orig_1193", "NACP_Regional_Obs_Model_Suppl_1158", "NACP_Site_Model_Data_Orig_Fmt_1192", "NACP_Site_Model_Flux_Std_Fmt_1183", "NACP_Site_Tower_Met_and_Flux_1178", "NACP_TERRA-PNW_1292", "NACP_Vista_CA_CH4_Inventory_1726", "NACP_Vista_LA_CH4_Inventory_1525", "NACP_Wild_Cropland_Fuel_Map_1163", "NACP_Woody_Veg_N_Slope_AK_V2_1365", "NAFD-NEX_Attribution_1799", "NAFD-NEX_Forest_Disturbance_1290", "NAFD_Disturbance_1077", "NARSTO_EPA_SS_ATLANTA_1999_CHEM_PM_MET_DATA", "NARSTO_EPA_SS_ATLANTA_1999_UAH_MIPS_DATA", "NARSTO_EPA_SS_ATLANTA_RAPID_SPMS_DATA", "NARSTO_EPA_SS_BALTIMORE_JHU_LIDAR_DATA", "NARSTO_EPA_SS_BALTIMORE_JHU_MET_DATA", "NARSTO_EPA_SS_BALTIMORE_RAPID_SPMS_DATA", "NARSTO_EPA_SS_BALTIMORE_SEAS_PM25_METAL_CYTOKINES", "NARSTO_EPA_SS_FRESNO_AETHALOM_MULTI_WL_CARBON", "NARSTO_EPA_SS_FRESNO_BAM_PM_MASS", "NARSTO_EPA_SS_FRESNO_EC_PM25_FRACTION", "NARSTO_EPA_SS_FRESNO_MET_DATA", "NARSTO_EPA_SS_FRESNO_PARTICLE_PAC_DATA", "NARSTO_EPA_SS_FRESNO_PM25_NO3_SO4", "NARSTO_EPA_SS_FRESNO_PM25_OC_EC", "NARSTO_EPA_SS_FRESNO_TEOM_PM_MASS", "NARSTO_EPA_SS_HOUSTON_DMA_DATA", "NARSTO_EPA_SS_HOUSTON_NH3_HNO3_DATA", "NARSTO_EPA_SS_HOUSTON_NO3_SO4_C_DATA", "NARSTO_EPA_SS_HOUSTON_RAPID_SPMS_DATA", "NARSTO_EPA_SS_HOUSTON_TEXAQS2000_CAMS_DATA", "NARSTO_EPA_SS_HOUSTON_TEXAQS2000_DOE_G-1_DATA", "NARSTO_EPA_SS_HOUSTON_TEXAQS2000_HCHO_H2O2_DATA", "NARSTO_EPA_SS_HOUSTON_TEXAQS2000_PM25_ORG_DATA", "NARSTO_EPA_SS_HOUSTON_TEXAQS2000_PM_FTIR", "NARSTO_EPA_SS_HOUSTON_TEXAQS2000_PM_SIZE_MASS", "NARSTO_EPA_SS_HOUSTON_TEXAQS2000_WB_TUNNEL", "NARSTO_EPA_SS_LOS_ANGELES_AETHALOMETER_EC_DATA", "NARSTO_EPA_SS_LOS_ANGELES_APS_DATA", "NARSTO_EPA_SS_LOS_ANGELES_HEADS_PART_IONS_MASS", "NARSTO_EPA_SS_LOS_ANGELES_MOUDI_DATA", "NARSTO_EPA_SS_LOS_ANGELES_PARTISOL_DATA", "NARSTO_EPA_SS_LOS_ANGELES_PM25_CARBON", "NARSTO_EPA_SS_LOS_ANGELES_PM25_NITRATE", "NARSTO_EPA_SS_LOS_ANGELES_SMPS_DATA", "NARSTO_EPA_SS_LOS_ANGELES_TEOM_PM25_DATA", "NARSTO_EPA_SS_NEW_YORK_AIR_CHEM_PM_MET_DATA", "NARSTO_EPA_SS_PITTSBURGH_GAS_PM_PROPERTY_DATA", "NARSTO_EPA_SS_PITTSBURGH_MET_DATA", "NARSTO_EPA_SS_PITTSBURGH_PM_COMPOSITION_DATA", "NARSTO_EPA_SS_PITTSBURGH_RAPID_SPMS_DATA", "NARSTO_EPA_SS_ST_LOUIS_AIR_CHEM_PM_MET_DATA", "NARSTO_ICARTT_NEAX_2004_DOE_G-1_DATA", "NARSTO_NE_MODEL", "NARSTO_NE_NEXRAD_IMAGES", "NARSTO_PAC2001_CASSIAR_TUNNEL_GAS_PM_DATA", "NARSTO_PAC2001_CESSNA_VOC_PM_OZONE_MET_DATA", "NARSTO_PAC2001_CONVAIR_PM_OZONE_MET_DATA", "NARSTO_PAC2001_GOLDEN_EARS_GAS_PM_DATA", "NARSTO_PAC2001_GVRD_CAPMON_AIR_QUAL_DATA", "NARSTO_PAC2001_LANGLEY_GAS_PM_MET_DATA", "NARSTO_PAC2001_SLOCAN_PARK_GAS_PM_MET_DATA", "NARSTO_PAC2001_SUMAS_MTN_GAS_PM_MET_DATA", "NARSTO_SHEMP_CANADA_PM_COMPOSITION_DATA", "NARSTO_SOS99NASH_G-1_AIR_CHEMISTRY_DATA", "NARSTO_SOS99NASH_SURFACE_MET_CHEM_DATA", "NARSTO_SOS99NASH_WIND_PROFILER_DATA", "NARSTO_SOS99NASH_WP3D_CHEMISTRY_DATA", "NARSTO_SOS_SC_UPSTATE_PM25_COMPOSITION", "NARSTO_STIFS_CANADA_PM_INORG_VOC_DATA", "NARSTO_Texas_PM2.5_Sampling_and_Analysis_Study_1997-1998", "NASADEM_HGT", "NASADEM_NC", "NASADEM_NUMNC", "NASADEM_SC", "NASADEM_SHHP", "NASADEM_SIM", "NASADEM_SSP", "NASASatellite_Dev_Applications_2293", "NASA_Airborne_Lidar_Flights", "NASMo_TiAM_250m_2326", "NA_MODIS_Surface_Biophysics_1210", "NA_TreeAge_1096", "NBCD2000_V2_1161", "NCALDAS_NOAH0125_D", "NCALDAS_NOAH0125_Trends", "ND01_Age_Maps_1184", "ND01_Georectified_Products_1165", "ND01_Land_Cover_Maps_1259", "ND01_Pasture_Nutrients_1135", "ND01_Pasture_Spectra_1154", "ND01_Registered_TM_MSS_1197", "ND01_Spectral_Mixture_Models_1188", "ND01_Stream_Chemistry_1119", "ND01_Watershed_Defor_1159", "ND02_Fertilization_Experiment_954", "ND02_Landsat_TM_MSS_Para_1156", "ND02_Mulching_Experiment_950", "ND02_Non_Woody_Biomass_1115", "ND02_REE_Soil_VWC_1061", "ND02_REE_Trace_Gas_Tapajos_955", "ND02_Soil_CO2_Extracts_1074", "ND02_Soil_CO2_Flux_1066", "ND02_Soil_Gas_Flux_Apeu_953", "ND02_Soil_Gases_REE_1117", "ND02_Soil_Hydraulic_Conductivity_1075", "ND02_Tree_Heights_DBH_951", "ND02_Water_Chemistry_Paragominas_1067", "ND03_Flowpath_Chemistry_1076", "ND03_Streams_Soilwater_1113", "ND04_C_Nutrient_Stocks_1069", "ND04_Secondary_Forest_Recovery_1068", "ND04_Soil_H2O_Manaus_1246", "ND04_Termite_Mounds_1072", "ND06_LandUse_Studies_1130", "ND07_15N_Leaves_Soils_1121", "ND07_NO_Flux_Cerrado_1124", "ND07_PLFA_Soils_Microbial_Biomass_1017", "ND07_Stream_Chemistry_Brasilia_1018", "ND07_Trace_Gas_Land_Use_1016", "ND08_Biomass_Jari_1148", "ND08_Soil_Respiration_1250", "ND10_Soil_Chemistry_1171", "ND11_Carbon_Export_CPOM_913", "ND11_Logging_Damage_MT_977", "ND11_Nitrogen_Transfer_Leaf_Litter_915", "ND11_Regeneration_Succession_965", "ND11_Soil_Nitrate_Moisture_MT_976", "ND11_Soil_Spatial_Variability_914", "ND11_Soil_Water_Pressure_851", "ND11_Stream_Nutrients_921", "ND11_Tree_Vine_Biomass_MT_922", "ND11_Veg_Biomass_MT_964", "ND30_Litter_Para_1129", "ND30_Pasture_Degradation_1164", "ND30_REE_Water_Chemistry_1131", "NDVI_Forest_Structure_1797", "NEMSN5L2", "NES-LTER", "NEUROST_SSH-SST_L4_V2024.0", "NEWS_WEB_ACLIM", "NEWS_WEB_MCLIM", "NFRDI", "NGLI_Lake_Bourne", "NHICEM", "NHSNOWM", "NIH-NSF_Lake_Erie", "NIMBUS7_ERB_Ch10C_TSI_NAT", "NIMBUS7_ERB_SEFDT", "NIMBUS7_NFOV_MLCE", "NISE", "NISE", "NLDAS_FORA0125_H", "NLDAS_FORA0125_H", "NLDAS_FORA0125_M", "NLDAS_FORA0125_M", "NLDAS_FORA0125_MC", "NLDAS_FORA0125_MC", "NLDAS_FORB0125_H", "NLDAS_FORB0125_H", "NLDAS_FORB0125_M", "NLDAS_FORB0125_M", "NLDAS_FORB0125_MC", "NLDAS_FORB0125_MC", "NLDAS_MOS0125_H", "NLDAS_MOS0125_H", "NLDAS_MOS0125_M", "NLDAS_MOS0125_M", "NLDAS_MOS0125_MC", "NLDAS_MOS0125_MC", "NLDAS_NOAH0125_H", "NLDAS_NOAH0125_H", "NLDAS_NOAH0125_M", "NLDAS_NOAH0125_M", "NLDAS_NOAH0125_MC", "NLDAS_NOAH0125_MC", "NLDAS_VIC0125_H", "NLDAS_VIC0125_H", "NLDAS_VIC0125_M", "NLDAS_VIC0125_M", "NLDAS_VIC0125_MC", "NLDAS_VIC0125_MC", "NMMIEAI-L2-NRT", "NMSO2-PCA-L2-NRT", "NMTO3NRT", "NOAA", "NOAA_ToF_CIMS_Instrument_Data_1921", "NOBM_DAY", "NOBM_MON", "NOPP_float", "NORBAL", "NPBUVO3-L2-NRT", "NPP_ATH_577", "NPP_BCN_204", "NPP_BDK_203", "NPP_BOREAL_611", "NPP_BRD_205", "NPP_BRR_157", "NPP_BRW_580", "NPP_CBAY_473", "NPP_CHM_578", "NPP_CHR_468", "NPP_CLB_206", "NPP_CNL_465", "NPP_CNS_191", "NPP_CPR_145", "NPP_DCK_207", "NPP_DRN_215", "NPP_DZH_192", "NPP_EMDI_615", "NPP_FLK_201", "NPP_GNN_474", "NPP_GPPDI_617", "NPP_GRIDDED_GPPDI_614", "NPP_GSM_804", "NPP_Grassland_31_654", "NPP_Grassland_613", "NPP_HYS_208", "NPP_IBP_198", "NPP_JDR_202", "NPP_JHN_475", "NPP_JRN_209", "NPP_KDE_216", "NPP_KHC_217", "NPP_KHM_146", "NPP_KLN_147", "NPP_KNZ_148", "NPP_KRK_193", "NPP_KRS_149", "NPP_KSM_466", "NPP_LMT_150", "NPP_LQL_476", "NPP_MDL_210", "NPP_MGD_477", "NPP_MMG_802", "NPP_MNS_579", "NPP_MNT_413", "NPP_MRF_478", "NPP_MSS_572", "NPP_MTD_469", "NPP_MULTIBIOME_653", "NPP_Multi-Biome_125_Sites_1352", "NPP_NLS_194", "NPP_NRB_151", "NPP_ODS_214", "NPP_OLK_195", "NPP_OSG_211", "NPP_OTR_152", "NPP_OTTER_472", "NPP_PIK_575", "NPP_PMP_212", "NPP_PSH_219", "NPP_REDWOOD_803", "NPP_RMY_574", "NPP_SCH_573", "NPP_SCR_479", "NPP_SES_480", "NPP_SHR_153", "NPP_SLV_218", "NPP_SNF_190", "NPP_SSP_467", "NPP_TEM_471", "NPP_TLK_581", "NPP_TLL_196", "NPP_TMG_470", "NPP_TMN_154", "NPP_TROPICAL_616", "NPP_TVA_155", "NPP_TWM_213", "NPP_VAST_576", "NPP_VND_197", "NPP_WBW_819", "NPP_WOODY_655", "NPP_XLN_156", "NPP_surfaces_750", "NSCAT_25KM_MGDR_V2", "NSCAT_AER_HOFFMAN_L2_OW_WIND_VECTOR_AMBIGUITY_REMOVAL", "NSCAT_BYU_L3_OW_SIGMA0_ENHANCED", "NSCAT_LEVEL_1.7_V2", "NSCAT_LEVEL_2_V2", "NSCAT_LEVEL_3_BROWSE_IMAGES", "NSCAT_LEVEL_3_V2", "NSCAT_W25_RMGDR_V2", "NSF-BWZ", "NSF_Gulf_Rapid", "NSIDC-0051", "NSIDC-0071", "NSIDC-0077", "NSIDC-0081", "NSIDC-0477", "NSIDC-0478", "NSIDC-0481", "NSIDC-0484", "NSIDC-0498", "NSIDC-0525", "NSIDC-0530", "NSIDC-0531", "NSIDC-0532", "NSIDC-0533", "NSIDC-0534", "NSIDC-0535", "NSIDC-0545", "NSIDC-0547", "NSIDC-0642", "NSIDC-0645", "NSIDC-0646", "NSIDC-0666", "NSIDC-0670", "NSIDC-0709", "NSIDC-0712", "NSIDC-0713", "NSIDC-0714", "NSIDC-0715", "NSIDC-0720", "NSIDC-0723", "NSIDC-0724", "NSIDC-0725", "NSIDC-0727", "NSIDC-0731", "NSIDC-0738", "NSIDC-0754", "NSIDC-0756", "NSIDC-0761", "NSIDC-0764", "NSIDC-0766", "NSIDC-0766", "NSIDC-0774", "NSIDC-0775", "NSIDC-0776", "NSIDC-0777", "NSIDC-0778", "NSIDC-0779", "NSIDC-0781", "NSIDC-0782", "NSIDC-0786", "NSIDC-0787", "NSIDC-0791", "NSIDC-0792", "NSIDC-0793", "NSIDC-0794", "NSIDC-0796", "NVAP_CLIMATE_Layered-Precipitable-Water", "NVAP_CLIMATE_Total-Precipitable-Water", "NVAP_OCEAN_Total-Precipitable-Water", "NVAP_WEATHER_Layered-Precipitable-Water", "NVAP_WEATHER_Total-Precipitable-Water", "NWT_Burn_Severity_Maps_1694", "New_England_CH4_1311", "New_Hampshire_Landcover_1305", "Niwot_Ridge_CNPAM_Fluorescence_1722", "Niwot_Ridge_Pigment_1723", "NmAVCS1H", "NmAVCS3G", "NmAVCS3H", "NmHRIR1H", "NmHRIR1T", "NmHRIR3G", "NmHRIR3H", "NmIDCS1H", "NmIDCS3G", "NmIDCS3H", "NmIcEdg2", "NmTHIR115-1H", "NmTHIR115-1T", "NmTHIR115-3G", "NmTHIR115-3H", "NmTHIR67-1H", "NmTHIR67-1T", "NmTHIR67-3G", "NmTHIR67-3H", "NmTHIRmtg-1T", "Nome_Veg_Plots_1372", "Non-Forest_Trees_Sahara_Sahel_1832", "Nongrowing_Season_CO2_Flux_1692", "NorthSlope_NEE_TVPRM_1920", "North_Carolina_Coast", "North_Carolina_Sabrina", "North_Sea", "North_Slope_Transect_Veg_Maps_1386", "North_Slope_Veg_Plots_1536", "Northern_Alaska_Veg_Maps_1359", "OAHU", "OASIS_Moorings", "OCEANSAT-2", "OCEAN_LIDAR", "OCEAN_MASS_TELLUS_MASCON_CRI_TIME_SERIES_RL06.1_V3", "OCFLEXPART", "OCO2GriddedXCO2", "OCO2_Att", "OCO2_Att", "OCO2_Att", "OCO2_Att", "OCO2_Att", "OCO2_Eph", "OCO2_Eph", "OCO2_Eph", "OCO2_Eph", "OCO2_Eph", "OCO2_GEOS_L3CO2_DAY", "OCO2_GEOS_L3CO2_MONTH", "OCO2_L1B_Calibration", "OCO2_L1B_Calibration", "OCO2_L1B_Calibration", "OCO2_L1B_Calibration", "OCO2_L1B_Science", "OCO2_L1B_Science", "OCO2_L1B_Science", "OCO2_L1B_Science", "OCO2_L1aIn_Pixel", "OCO2_L1aIn_Pixel", "OCO2_L1aIn_Pixel", "OCO2_L1aIn_Pixel", "OCO2_L1aIn_Pixel", "OCO2_L1aIn_Sample", "OCO2_L1aIn_Sample", "OCO2_L1aIn_Sample", "OCO2_L1aIn_Sample", "OCO2_L1aIn_Sample", "OCO2_L2_ABand", "OCO2_L2_ABand", "OCO2_L2_ABand", "OCO2_L2_ABand", "OCO2_L2_CO2Prior", "OCO2_L2_CO2Prior", "OCO2_L2_CO2Prior", "OCO2_L2_CO2Prior", "OCO2_L2_Diagnostic", "OCO2_L2_Diagnostic", "OCO2_L2_Diagnostic", "OCO2_L2_Diagnostic", "OCO2_L2_IMAPDOAS", "OCO2_L2_IMAPDOAS", "OCO2_L2_IMAPDOAS", "OCO2_L2_IMAPDOAS", "OCO2_L2_Lite_FP", "OCO2_L2_Lite_FP", "OCO2_L2_Lite_FP", "OCO2_L2_Lite_SIF", "OCO2_L2_Lite_SIF", "OCO2_L2_Met", "OCO2_L2_Met", "OCO2_L2_Met", "OCO2_L2_Met", "OCO2_L2_Met", "OCO2_L2_Standard", "OCO2_L2_Standard", "OCO2_L2_Standard", "OCO2_L2_Standard", "OCO3_L1B_Calibration", "OCO3_L1B_Calibration", "OCO3_L1B_Calibration", "OCO3_L1B_Science", "OCO3_L1B_Science", "OCO3_L1B_Science", "OCO3_L2_ABand", "OCO3_L2_ABand", "OCO3_L2_ABand", "OCO3_L2_CO2Prior", "OCO3_L2_CO2Prior", "OCO3_L2_CO2Prior", "OCO3_L2_Diagnostic", "OCO3_L2_Diagnostic", "OCO3_L2_Diagnostic", "OCO3_L2_IMAPDOAS", "OCO3_L2_IMAPDOAS", "OCO3_L2_IMAPDOAS", "OCO3_L2_Lite_FP", "OCO3_L2_Lite_FP", "OCO3_L2_Lite_SIF", "OCO3_L2_Met", "OCO3_L2_Met", "OCO3_L2_Met", "OCO3_L2_Standard", "OCO3_L2_Standard", "OCO3_L2_Standard", "OCTS_L1", "OCTS_L2_IOP", "OCTS_L2_OC", "OCTS_L3b_CHL", "OCTS_L3b_IOP", "OCTS_L3b_KD", "OCTS_L3b_PAR", "OCTS_L3b_PIC", "OCTS_L3b_POC", "OCTS_L3b_RRS", "OCTS_L3m_CHL", "OCTS_L3m_IOP", "OCTS_L3m_KD", "OCTS_L3m_PAR", "OCTS_L3m_PIC", "OCTS_L3m_POC", "OCTS_L3m_RRS", "ODU_CBM", "OISSS_L4_multimission_7day_v1", "OISSS_L4_multimission_7day_v2", "OISSS_L4_multimission_monthly_v1", "OISSS_L4_multimission_monthly_v2", "OISST_HR_NRT-GOS-L4-BLK-v2.0", "OISST_HR_NRT-GOS-L4-MED-v2.0", "OISST_UHR_NRT-GOS-L4-BLK-v2.0", "OISST_UHR_NRT-GOS-L4-MED-v2.0", "OLCIS3A_L1_EFR", "OLCIS3A_L1_ERR", "OLCIS3A_L2_EFR_IOP", "OLCIS3A_L2_EFR_OC", "OLCIS3A_L2_ERR_IOP", "OLCIS3A_L2_ERR_IOP_NRT", "OLCIS3A_L2_ERR_OC", "OLCIS3A_L2_ERR_OC_NRT", "OLCIS3A_L2_ILW", "OLCIS3B_L1_EFR", "OLCIS3B_L1_ERR", "OLCIS3B_L2_EFR_IOP", "OLCIS3B_L2_EFR_OC", "OLCIS3B_L2_ERR_IOP", "OLCIS3B_L2_ERR_IOP_NRT", "OLCIS3B_L2_ERR_OC", "OLCIS3B_L2_ERR_OC_NRT", "OLCIS3B_L2_ILW", "OLIPAC", "OMAERO", "OMAEROG", "OMAEROZ", "OMAEROe", "OMAERUV", "OMAERUV", "OMAERUV", "OMAERUVG", "OMAERUV_CPR", "OMAERUVd", "OMBRO", "OMCLDO2", "OMCLDO2G", "OMCLDO2Z", "OMCLDO2_CPR", "OMCLDRR", "OMCLDRR", "OMCLDRR", "OMCLDRRG", "OMCLDRR_CPR", "OMDOAO3", "OMDOAO3G", "OMDOAO3Z", "OMDOAO3e", "OMEXII", "OMGLER", "OMG_L1B_AIRGRAV", "OMG_L1_FLOAT_ALAMO", "OMG_L1_FLOAT_APEX", "OMG_L2_AXBT", "OMG_L2_AXCTD", "OMG_L2_Bathy_MBES_Gridded", "OMG_L2_Bathy_SBES_Gridded", "OMG_L2_CTD", "OMG_L3_ICE_ELEV_GLISTINA", "OMG_NARWHALS_MOORING_TEMP_CTD_1.0", "OMG_NARWHALS_SHIPBOARD_CTD_1.0", "OMHCHO", "OMHCHOG", "OMHCHOd", "OMIAuraAER", "OMI_MINDS_NO2", "OMI_MINDS_NO2G", "OMI_MINDS_NO2d", "OML1BIRR", "OML1BIRR", "OML1BRUG", "OML1BRUG", "OML1BRUZ", "OML1BRUZ", "OML1BRVG", "OML1BRVG", "OML1BRVZ", "OML1BRVZ", "OMLER", "OMMYDAGEO", "OMMYDCLD", "OMNO2", "OMNO2G", "OMNO2_CPR", "OMNO2d", "OMO3PR", "OMOCLO", "OMPIXCOR", "OMPIXCORZ", "OMPS_N20_NMHCHO_L2", "OMPS_N20_NMSO2_PCA_L2_Step1", "OMPS_N21_LP_L1G_EV", "OMPS_N21_LP_L2_O3_DAILY", "OMPS_NPP_LP_L1G_EV", "OMPS_NPP_LP_L1G_EV", "OMPS_NPP_LP_L2_AER_DAILY", "OMPS_NPP_LP_L2_O3_DAILY", "OMPS_NPP_LP_L2_O3_DAILY", "OMPS_NPP_LP_L2_Temp_DAILY", "OMPS_NPP_LP_L3_AER_MONTHLY", "OMPS_NPP_NMCLDRR_L2", "OMPS_NPP_NMEV_L1B", "OMPS_NPP_NMHCHO_L2", "OMPS_NPP_NMMIEAI_L2", "OMPS_NPP_NMNO2_L2", "OMPS_NPP_NMSO2_L2", "OMPS_NPP_NMSO2_PCA_L2", "OMPS_NPP_NMSO2_PCA_L3_DAILY", "OMPS_NPP_NMTO3_L2", "OMPS_NPP_NMTO3_L3_DAILY", "OMPS_NPP_NPBUVO3_L2", "OMPS_NPP_NPBUVO3_L2", "OMPS_NPP_NPEV_L1B", "OMSO2", "OMSO2", "OMSO2G", "OMSO2_CPR", "OMSO2e", "OMTO3", "OMTO3G", "OMTO3_CPR", "OMTO3d", "OMTO3e", "OMTO3e", "OMUANC", "OMUFPITMET", "OMUFPMET", "OMUFPSLV", "OMUVB", "OMUVBG", "OMUVBd", "OMVANC", "OMVFPITMET", "OMVFPMET", "OMVFPSLV", "ONDEQUE", "ONR-MAB", "ONR_PenobscotRiverSystem", "OPERA_L2_CSLC-S1-STATIC_V1", "OPERA_L2_CSLC-S1_V1", "OPERA_L2_RTC-S1-STATIC_V1", "OPERA_L2_RTC-S1_V1", "OPERA_L3_DIST-ALERT-HLS_V1", "OPERA_L3_DIST-ANN-HLS_V1", "OPERA_L3_DSWX-HLS_V1", "ORACLES_AerosolCloud_AircraftRemoteSensing_Data", "ORACLES_Aerosol_AircraftInSitu_Data", "ORACLES_Cloud_AircraftInSitu_Data", "ORACLES_Merge_Data", "ORACLES_MetNav_AircraftInSitu_Data", "ORACLES_Model_Data", "ORACLES_Radiation_AircraftInSitu_Data", "ORACLES_TraceGas_AircraftInSitu_Data", "ORINOCO", "OS2_OSCAT_LEVEL_2B_OWV_COMP_12_V2", "OSCAR_L4_OC_1deg", "OSCAR_L4_OC_FINAL_V2.0", "OSCAR_L4_OC_INTERIM_V2.0", "OSCAR_L4_OC_NRT_V2.0", "OSCAR_L4_OC_third-deg", "OSCAR_L4_OC_third-deg_YEARLY", "OSTIA-UKMO-L4-GLOB-REP-v2.0", "OSTIA-UKMO-L4-GLOB-v2.0", "OTZ_WHOI", "OWLETS1_Model_Data", "OWLETS1_Pandora_Data", "OWLETS1_SherpaAircraft_Data", "OWLETS1_Sondes_Data", "OWLETS1_SurfaceLidar_Data", "OWLETS2_Aerosol_Data", "OWLETS2_Pandora_Data", "OWLETS2_Ship_Data", "OWLETS2_Sondes_Data", "OWLETS2_SurfaceLidar_Data", "OWLETS2_Surface_Data", "OWLETS2_UMDAircraft_Data", "Ocean_Color_Cal_Val", "Oceania", "Odyssee_Saint_Laurent", "Okeechobee", "Opt_Sed_Trap_Cal", "Optical_Layers", "Optique_St_Laurent", "Oumalik_Veg_plots_1506", "P380108", "PACE_ABSclosure", "PACE_EPH_DEF", "PACE_HARP2_L0_D1", "PACE_HARP2_L0_D2", "PACE_HARP2_L0_D3", "PACE_HARP2_L1A_SCI", "PACE_HARP2_L1B_SCI", "PACE_HARP2_L1C_SCI", "PACE_HKT", "PACE_HSK", "PACE_OCI_L0_SCI", "PACE_OCI_L1A_SCI", "PACE_OCI_L1B_SCI", "PACE_OCI_L1C_SCI", "PACE_OCI_L2_AOP_NRT", "PACE_OCI_L2_BGC_NRT", "PACE_OCI_L2_IOP_NRT", "PACE_OCI_L2_PAR_NRT", "PACE_OCI_L3M_CHL_NRT", "PACE_SPEXONE_L0", "PACE_SPEXONE_L1A_SCI", "PACE_SPEXONE_L1B_SCI", "PACE_SPEXONE_L1C_SCI", "PAC_DFO_PAR_BUOY", "PAD_2011_1133", "PAD_935", "PAL-LTER", "PARASOLRB_CPR", "PATEX", "PC06_ECMWF_LBA_1141", "PEACETIME", "PERU", "PET_PU_3H025", "PET_PU_3H025", "PICOLO", "PISTON-ONR-NOAA-Autonomous-Uncrewed_2018-2019", "PISTON-ONR-NOAA_Islands_2018-2019", "PISTON-ONR-NOAA_RVMirai_2018", "PISTON-ONR-NOAA_RVSallyRide_2019", "PISTON-ONR-NOAA_RVThompson_2018", "PMNS", "PMRN6L1RAD_CDROM", "POAM2_VER6", "POAM3", "POLARIS_Aerosol_AircraftInSitu_ER2_Data", "POLARIS_Analysis_ER2_Data", "POLARIS_Ground_Data", "POLARIS_MetNav_AircraftInSitu_ER2_Data", "POLARIS_Model_Data", "POLARIS_Satellite_Data", "POLARIS_Sondes_Data", "POLARIS_TraceGas_AircraftInSitu_ER2_Data", "POLARIS_jValue_AircraftInSitu_ER2_Data", "POMME", "PRECIP_AMSR2_GCOMW1", "PRECIP_AMSRE_AQUA", "PRECIP_GMI_GPM", "PRECIP_SMMR_NIMBUS7", "PRECIP_SSMIS_F16", "PRECIP_SSMIS_F17", "PRECIP_SSMIS_F18", "PRECIP_SSMIS_F19", "PRECIP_SSMI_F08", "PRECIP_SSMI_F10", "PRECIP_SSMI_F11", "PRECIP_SSMI_F13", "PRECIP_SSMI_F14", "PRECIP_SSMI_F15", "PRECIP_TMI_TRMM", "PREFIRE_SAT1_0-BUS-TLM", "PREFIRE_SAT1_0-PAYLOAD", "PREFIRE_SAT1_0-PAYLOAD-TLM", "PREFIRE_SAT2_0-BUS-TLM", "PREFIRE_SAT2_0-PAYLOAD", "PREFIRE_SAT2_0-PAYLOAD-TLM", "PREPP", "PRESWOT_HYDRO_GRRATS_L2_DAILY_VIRTUAL_STATION_HEIGHTS_V2", "PRESWOT_HYDRO_GRRATS_L2_VIRTUAL_STATION_HEIGHTS_V2", "PRESWOT_HYDRO_L2_GREALM_LAKE_HEIGHT_V2", "PRESWOT_HYDRO_L3_LAKE_RESEVOIR_AREA_V2", "PRESWOT_HYDRO_L4_LAKE_STORAGE_TIME_SERIES_V2", "PRIM_SMAP_L2_V1", "PRISM_CORAL_L1", "PRISM_CORAL_L2", "PRISM_Elkhorn_Slough", "PRONEX", "PSCNH_ARCHIVED_DATA", "PTLO", "PVST_HOTPACE", "PVST_NORTHERN_INDIAN_OCEAN", "PVST_PRINGLS", "PVST_SBCR", "PVST_VDIUP", "PanamaCity", "Panhandle_OWQ", "Passive_Microwave_Snowoff_Data_1711", "Patagonian_Coastal", "Peatland_carbon_balance_1382", "Pelican_PCO2", "PenBaySurvey", "PermafrostThaw_CarbonEmissions_1872", "Permafrost_ActiveLayer_NSlope_1759", "Permafrost_Thaw_Depth_YK_1598", "PhenoCam_V2_1674", "Phenocam_Images_V2_1689", "Phenology_AmeriFlux_Neon_Sites_2033", "Phenology_Deciduous_Forest_1570", "Photos_ThermokarstLakes_AK_1845", "Pingo_Veg_Plots_1507", "Plot_Data_Noatak_Seward_AK_1919", "Plumes_and_Blooms", "PolInSAR_Canopy_Height_1589", "Polar-VPRM_Alaskan-NEE_1314", "PolarWindsII_DAWN_DC8", "PolarWindsI_DAWN_KingAirUC-12B", "Polarimetric_CT_1601", "Polarimetric_height_profile_1577", "Poplar_Veg_Plots_1376", "PostFire_Tree_Regeneration_1955", "Post_Fire_C_Emissions_1787", "Post_Fire_SOC_NWT_2235", "PreABoVE_AirMOSS_L1_Alaska_1678", "PreDeltaX_ADCP_Measurements_1806", "PreDeltaX_ASO_LiDAR_WaterLevel_1820", "PreDeltaX_Insitu_Reflectance_1804", "PreDeltaX_L2_AVIRIS_SR_1826", "PreDeltaX_L2_AirSWOT_1818", "PreDeltaX_L3_AVIRIS_Biomass_1821", "PreDeltaX_L3_AVIRIS_Sediment_1822", "PreDeltaX_L3_AirSWOT_WL_1819", "PreDeltaX_Sonar_Bathymetry_1807", "PreDeltaX_TSS_Concentration_1802", "PreDeltaX_UAVSAR_Channel_Maps_1954", "PreDeltaX_UAVSAR_SLC_1816", "PreDeltaX_UAVSAR_WaterLevel_1823", "PreDeltaX_Vegetation_Structure_1805", "PreDeltaX_Water_Level_Data_1801", "Pre_LBA_ABRACOS_899", "Proantar", "Profile_based_PBL_heights_1706", "Prudhoe_Bay_ArcSEES_Veg_Plots_1555", "Prudhoe_Bay_Veg_Maps_1387", "Prudhoe_Bay_Veg_Plots_1360", "Prudhoe_Freshets", "QSCAT_ESDR_MODELED_L2_AUX_V1.0", "QSCAT_L1C_NONSPINNING_SIGMA0_WINDS_V2", "QSCAT_LEVEL_1B_V2", "QSCAT_LEVEL_2B_OWV_COMP_12", "QSCAT_LEVEL_2B_OWV_COMP_12_KUSST_LCRES_4.1", "QSCAT_LEVEL_2B_OWV_COMP_12_LCR_3.1", "QUIKSCAT_ESDR_L2_WIND_STRESS_V1.0", "RADAMBrasil_941", "RAIN_JAEGER", "RAIN_LEGATES", "RAMSSA_09km-ABOM-L4-AUS-v01", "RBLE_917", "RDBTS4", "RDEFT4", "RDGBV4", "RDSISC4", "RDSISCO4", "RECON_SEA_LEVEL_OST_L4_V1", "RED_TIDE", "REMO_OI_SST_5km-UFRJ-L4-SAMERICA-v1.0", "RESOLUTE", "REYNOLDS_NCDC_L4_MONTHLY_V5", "RGGRV1B", "RICMIAAE", "RICMIB2E", "RICMIRCM", "RICMITAL", "RICMITCL", "RICMITST", "RIO-SFE", "ROAVERRS", "RON_BROWN", "RRRAG4", "RSAT-1_L0", "RSAT-1_L1", "RSCAT_COLOCATED_RSS_RADIOMETER_LEVEL_2B_V1", "RSCAT_L1B_V2.0", "RSCAT_L2A_12KM_V2.0", "RSCAT_L2A_25KM_V2.0", "RSCAT_LEVEL_2B_OWV_CLIM_12_V1", "RSCAT_LEVEL_2B_OWV_CLIM_12_V2", "RSCAT_LEVEL_2B_OWV_COMP_12_V1.1", "RSCAT_LEVEL_2B_OWV_COMP_12_V1.2", "RSCAT_LEVEL_2B_OWV_COMP_12_V1.3", "RSS18_AVIRIS_L1B_449", "RSS_WindSat_L1C_TB_V08.0", "Radial_Growth_PRI_1781", "Rain-on-Snow_Data_1611", "RdlP_PT", "ReSALT_ALT_GPR_1265", "ReSALT_InSAR_Barrow_1266", "Reflectance_Spectra_Alaska_1685", "RemSensPOC", "Respiration_622", "River_Ice_Breakup_Freezeup_1697", "Russian_Forest_Disturbance_1294", "S2K_EACM_Subset_623", "S3A_OL_1_EFR", "S5P_L1B_IR_SIR", "S5P_L1B_IR_SIR", "S5P_L1B_IR_UVN", "S5P_L1B_IR_UVN", "S5P_L1B_RA_BD1", "S5P_L1B_RA_BD1_HiR", "S5P_L1B_RA_BD1_HiR", "S5P_L1B_RA_BD2", "S5P_L1B_RA_BD2_HiR", "S5P_L1B_RA_BD2_HiR", "S5P_L1B_RA_BD3", "S5P_L1B_RA_BD3_HiR", "S5P_L1B_RA_BD3_HiR", "S5P_L1B_RA_BD4", "S5P_L1B_RA_BD4_HiR", "S5P_L1B_RA_BD4_HiR", "S5P_L1B_RA_BD5", "S5P_L1B_RA_BD5_HiR", "S5P_L1B_RA_BD5_HiR", "S5P_L1B_RA_BD6", "S5P_L1B_RA_BD6_HiR", "S5P_L1B_RA_BD6_HiR", "S5P_L1B_RA_BD7", "S5P_L1B_RA_BD7_HiR", "S5P_L1B_RA_BD7_HiR", "S5P_L1B_RA_BD8", "S5P_L1B_RA_BD8_HiR", "S5P_L1B_RA_BD8_HiR", "S5P_L2__AER_AI", "S5P_L2__AER_AI_HiR", "S5P_L2__AER_AI_HiR", "S5P_L2__AER_LH", "S5P_L2__AER_LH_HiR", "S5P_L2__AER_LH_HiR", "S5P_L2__CH4___", "S5P_L2__CH4____HiR", "S5P_L2__CH4____HiR", "S5P_L2__CLOUD_", "S5P_L2__CLOUD__HiR", "S5P_L2__CLOUD__HiR", "S5P_L2__CO____", "S5P_L2__CO_____HiR", "S5P_L2__CO_____HiR", "S5P_L2__HCHO__", "S5P_L2__HCHO___HiR", "S5P_L2__HCHO___HiR", "S5P_L2__NO2___", "S5P_L2__NO2____HiR", "S5P_L2__NO2____HiR", "S5P_L2__NP_BD3", "S5P_L2__NP_BD3_HiR", "S5P_L2__NP_BD3_HiR", "S5P_L2__NP_BD6", "S5P_L2__NP_BD6_HiR", "S5P_L2__NP_BD6_HiR", "S5P_L2__NP_BD7", "S5P_L2__NP_BD7_HiR", "S5P_L2__NP_BD7_HiR", "S5P_L2__O3_TCL", "S5P_L2__O3_TCL", "S5P_L2__O3_TOT", "S5P_L2__O3_TOT_HiR", "S5P_L2__O3_TOT_HiR", "S5P_L2__O3__PR_HiR", "S5P_L2__SO2___", "S5P_L2__SO2____HiR", "S5P_L2__SO2____HiR", "SAB_Mapping", "SAGE1_AERO_PRF", "SAGE2_AEROSOL_O3_NO2_H2O_BINARY", "SAILDRONE_ARCTIC", "SAILDRONE_ARCTIC_2021", "SAILDRONE_ARCTIC_2022", "SAILDRONE_ATOMIC", "SAILDRONE_BAJA_SURFACE", "SAM2_AERO_PRF_NAT", "SAMMI2AE", "SAMMI2LS", "SAMMI2ST", "SAMMIB2E", "SAMMIB2T", "SAMMIGEO", "SAMSN7L1RAD_CDROM", "SAMSN7L1RAT", "SAMSN7L3GRIDT", "SAMSN7L3ZMTG", "SAR_Methane_Ebullition_AK_1790", "SASSIE_L1_SWIFT_V1", "SASSIE_L1_WAVEGLIDER_V1", "SASSIE_L2_ALTO_ALAMO_FLOATS_V1", "SASSIE_L2_DRIFTER_UPTEMPO_V1", "SASSIE_L2_DRIFTER_UPTEMPO_V2p", "SASSIE_L2_JET_SSP_V1", "SASSIE_L2_PALS_V1", "SASSIE_L2_SBAND_ML_V1", "SASSIE_L2_SHIPBOARD_ADCP_V1", "SASSIE_L2_SHIPBOARD_CASTAWAY_CTD_V1", "SASSIE_L2_SHIPBOARD_DELTA_18O_V1", "SASSIE_L2_SHIPBOARD_METEOROLOGY_V1", "SASSIE_L2_SHIPBOARD_SALINITY_SNAKE_V1", "SASSIE_L2_SHIPBOARD_TSG_V1", "SASSIE_L2_SHIPBOARD_UCTD_V1", "SASSIE_L2_SWIFT_V1", "SASSIE_L2_UNDER_ICE_FLOAT_V1", "SASSIE_L2_WAVEGLIDERS_V1", "SASSIE_L3_SHIPBOARD_SBAND_V1", "SASSIE_L4_SHIPBOARD_SBAND_V1", "SAV_Plymouth_Bay", "SAWB_JRA_713", "SAWB_JRB_714", "SBI", "SBUV2N09L2", "SBUV2N09L3zm", "SBUV2N09O3", "SBUV2N11L2", "SBUV2N11L3zm", "SBUV2N11O3", "SBUV2N14L2", "SBUV2N14L3zm", "SBUV2N16L2", "SBUV2N16L3zm", "SBUV2N16O3", "SBUV2N17L2", "SBUV2N17L3zm", "SBUV2N18L2", "SBUV2N18L3zm", "SBUV2N19L2", "SBUV2N19L3zm", "SBUVN07L2", "SBUVN07L3zm", "SBUVN7O3", "SCAMSN6IM", "SCAMSN6L2", "SCAR-B_916", "SCARB_ER2_MAS", "SCAR_A_ER2_MAS", "SCAR_B_AERONET", "SCAR_B_G8_FIRE", "SCAR_B_UWC131A", "SCATSAT1_ESDR_ANCILLARY_L2_V1.1", "SCATSAT1_ESDR_L2_WIND_STRESS_V1.1", "SCMRN5L1RAD", "SCOAPE_Ground_Data", "SCOAPE_Pandora_Data", "SCOAPE_RVPointSur_Data", "SCOAPE_Sondes_Data", "SCRN4L1RAD", "SCRN4L1RAD_CDROM", "SCRN5L1RAD", "SCRN5L1RAD_CDROM", "SEA", "SEAC4RS_Aerosol_AircraftInSitu_DC8_Data", "SEAC4RS_Aerosol_AircraftInSitu_Learjet_Data", "SEAC4RS_AircraftRemoteSensing_DIAL_DC8_Data", "SEAC4RS_AircraftRemoteSensing_RSP_ER2_Data", "SEAC4RS_Analysis_Data", "SEAC4RS_Cloud_AircraftInSitu_DC8_Data", "SEAC4RS_Cloud_AircraftInSitu_ER2_Data", "SEAC4RS_Cloud_AircraftInSitu_Learjet_Data", "SEAC4RS_Merge_Data", "SEAC4RS_MetNav_AircraftInSitu_DC8_Data", "SEAC4RS_MetNav_AircraftInSitu_ER2_Data", "SEAC4RS_MetNav_AircraftInSitu_Learjet_Data", "SEAC4RS_Miscellaneous_DC8_Data", "SEAC4RS_Miscellaneous_ER2_Data", "SEAC4RS_Radiation_AircraftInSitu_DC8_Data", "SEAC4RS_Radiation_AircraftInSitu_ER2_Data", "SEAC4RS_Sondes_Data", "SEAC4RS_TraceGas_AircraftInSitu_DC8_Data", "SEAC4RS_TraceGas_AircraftInSitu_ER2_Data", "SEAC4RS_jValue_AircraftInSitu_DC8_Data", "SEAGLIDER_GUAM_2019", "SEAHAWK_VALIDATION", "SEASAT_SAR_L1_HDF5", "SEASAT_SAR_L1_TIFF", "SEA_SURFACE_HEIGHT_ALT_GRIDS_L4_2SATS_5DAY_6THDEG_V_JPL2205", "SEED", "SENTINEL-1A_DP_GRD_FULL", "SENTINEL-1A_DP_GRD_HIGH", "SENTINEL-1A_DP_GRD_MEDIUM", "SENTINEL-1A_DP_META_GRD_FULL", "SENTINEL-1A_DP_META_GRD_HIGH", "SENTINEL-1A_DP_META_GRD_MEDIUM", "SENTINEL-1A_META_OCN", "SENTINEL-1A_META_RAW", "SENTINEL-1A_META_SLC", "SENTINEL-1A_OCN", "SENTINEL-1A_RAW", "SENTINEL-1A_SLC", "SENTINEL-1A_SP_GRD_HIGH", "SENTINEL-1A_SP_GRD_MEDIUM", "SENTINEL-1A_SP_META_GRD_HIGH", "SENTINEL-1A_SP_META_GRD_MEDIUM", "SENTINEL-1B_DP_GRD_HIGH", "SENTINEL-1B_DP_GRD_MEDIUM", "SENTINEL-1B_DP_META_GRD_HIGH", "SENTINEL-1B_DP_META_GRD_MEDIUM", "SENTINEL-1B_META_OCN", "SENTINEL-1B_META_RAW", "SENTINEL-1B_META_SLC", "SENTINEL-1B_OCN", "SENTINEL-1B_RAW", "SENTINEL-1B_SLC", "SENTINEL-1B_SP_GRD_HIGH", "SENTINEL-1B_SP_GRD_MEDIUM", "SENTINEL-1B_SP_META_GRD_HIGH", "SENTINEL-1B_SP_META_GRD_MEDIUM", "SENTINEL-1_BURSTS", "SENTINEL-1_INTERFEROGRAMS", "SENTINEL-1_INTERFEROGRAMS_AMPLITUDE", "SENTINEL-1_INTERFEROGRAMS_COHERENCE", "SENTINEL-1_INTERFEROGRAMS_CONNECTED_COMPONENTS", "SENTINEL-1_INTERFEROGRAMS_UNWRAPPED_PHASE", "SEVIRI_IO_SST-OSISAF-L3C-v1.0", "SEVIRI_SST-OSISAF-L3C-v1.0", "SEVIRI_SST_DR-OSISAF-L3C-v1.0", "SFC_NITROGEN_DIOXIDE_CONC", "SFMBON", "SFP", "SGER", "SHIFT_AVIRISNG_L2A_refl_2376", "SHIFT_AVNG_Canopy_WaterContent_2242", "SHIFT_AVNG_FullRes_QkLook_2189", "SHIFT_AVNG_L1A_RDN_unrec_2184", "SHIFT_DriedGround_Leaf_Reflec_2244", "SHIFT_Field_Survey_Metadata_2295", "SHIFT_Foliar_Chemical_Analysis_2337", "SHIFT_HyTES_L1_2022_2245", "SHIFT_HyTES_L2_2022_2246", "SHIFT_Leaf_Traits_Chl_SB_CA_2233", "SHIFT_Plot_Level_Photos_2334", "SHIFT_RamsesTriOS_Rrs_SB_CA_2234", "SIF_PAR_fPAR_US_Midwest_2018_1813", "SIF_SCIAMACHY_GOME2_Harmonized_2317", "SIMBAD_DESCHAMPS_LOA", "SIO-Pier", "SIRSN3L1", "SIRSN4L1", "SISTER_Workflow_V004_2335", "SLOPE_GPP_CONUS_1786", "SMAP_JPL_L2B_NRT2_SSS_CAP_V5", "SMAP_JPL_L2B_NRT_SSS_CAP_V5", "SMAP_JPL_L2B_SSS_CAP_V5", "SMAP_JPL_L3_SSS_CAP_8DAY-RUNNINGMEAN_V5", "SMAP_JPL_L3_SSS_CAP_MONTHLY_V5", "SMAP_RSS_L2_SSS_NRT_V5", "SMAP_RSS_L2_SSS_NRT_V6", "SMAP_RSS_L2_SSS_V4", "SMAP_RSS_L2_SSS_V5", "SMAP_RSS_L2_SSS_V5.3", "SMAP_RSS_L2_SSS_V6", "SMAP_RSS_L3_SSS_SMI_8DAY-RUNNINGMEAN_V4", "SMAP_RSS_L3_SSS_SMI_8DAY-RUNNINGMEAN_V5", "SMAP_RSS_L3_SSS_SMI_8DAY-RUNNINGMEAN_V5.3", "SMAP_RSS_L3_SSS_SMI_8DAY-RUNNINGMEAN_V6", "SMAP_RSS_L3_SSS_SMI_MONTHLY_V4", "SMAP_RSS_L3_SSS_SMI_MONTHLY_V5", "SMAP_RSS_L3_SSS_SMI_MONTHLY_V5.3", "SMAP_RSS_L3_SSS_SMI_MONTHLY_V6", "SMERGE_RZSM0_40CM", "SMMRN7IM", "SMMR_ALW_PRABHAKARA", "SMMR_IWV_PRABHAKARA", "SMODE_L1_ASIT_KABODS_V1", "SMODE_L1_ASIT_SLOPEFIELDS_V1", "SMODE_L1_DOPPLERSCATT_V1", "SMODE_L1_MASS_DOPPVIS_V1", "SMODE_L1_MASS_DOPVISIBLE_V1", "SMODE_L1_MASS_HYPERSPECTRAL_V1", "SMODE_L1_MASS_LIDAR_V1", "SMODE_L1_MASS_LWIR_V1", "SMODE_L1_MASS_VISIBLE_V1", "SMODE_L1_PRISM_V1", "SMODE_L1_SAILDRONES_V1", "SMODE_L2_APEX_FLOAT_V1", "SMODE_L2_DOPPLERSCATT_WINDS_CURRENT_V1", "SMODE_L2_DOPPLERSCATT_WINDS_CURRENT_V2", "SMODE_L2_DRIFTER_POSITIONS_V1", "SMODE_L2_LAGRANGIAN_FLOATS_V1", "SMODE_L2_MOSES_LWIR_SST_V1", "SMODE_L2_PRISM_CHLA_V1", "SMODE_L2_SAILDRONES_V1", "SMODE_L2_SEAGLIDERS_V1", "SMODE_L2_SHIPBOARD_ADCP_V1", "SMODE_L2_SHIPBOARD_BIO_V1", "SMODE_L2_SHIPBOARD_BOTTLES_V1", "SMODE_L2_SHIPBOARD_CTD_V1", "SMODE_L2_SHIPBOARD_RADIOSONDES_METEOROLOGY_V1", "SMODE_L2_SHIPBOARD_SUNA_V1", "SMODE_L2_SHIPBOARD_TSG_V1", "SMODE_L2_SHIPBOARD_UCTD_ECOCTD_V1", "SMODE_L2_SLOCUM_GLIDERS_V1", "SMODE_L2_WAVEGLIDERS_TEMP_SALINITY_V1", "SMODE_L2a_PRISM_REFL_V1", "SMODE_L3_SEAGLIDERS_TEMP_SALINITY_V1", "SMODE_L3_SHIPBOARD_UCTD_ECOCTD_V1", "SMODE_L4_NCOM_V1", "SNDR13CHRP1", "SNDR13CHRP1AQCal", "SNDR13CHRP1J1Cal", "SNDR13CHRP1SNCal", "SNDR13IML3SSDFCNSAT", "SNDR13IML3SSDFCVPD", "SNDRAQIL2CCPCCR", "SNDRAQIL2CCPRET", "SNDRAQIL2CPS", "SNDRAQIL2JSFRET", "SNDRAQIL3CDCCP", "SNDRAQIL3CMCCP", "SNDRAQIL3SDCCP", "SNDRAQIL3SMCCP", "SNDRAQIL3SSDFCNSAT", "SNDRAQIL3SSDFCVPD", "SNDRAQIML1BCALSUBSUM", "SNDRAQIML1CCALSUBRND", "SNDRAQIML2CCPCCR", "SNDRAQIML2CCPRET", "SNDRAQIML2CPS", "SNDRAQIML3CDCCP", "SNDRAQIML3CMCCP", "SNDRAQIML3SDCCP", "SNDRAQIML3SMCCP", "SNDRJ1ATMSL1B", "SNDRJ1ATMSL1B", "SNDRJ1ATMSMAP", "SNDRJ1CrISL1B", "SNDRJ1CrISL1B", "SNDRJ1CrISL1BIMG", "SNDRJ1CrISL1BIMGC", "SNDRJ1CrISL1BPCARED", "SNDRJ1IML2CCPCCR", "SNDRJ1IML2CCPRET", "SNDRJ1IML2CPS", "SNDRJ1IML3CDCCP", "SNDRJ1IML3CMCCP", "SNDRJ1IML3SDCCP", "SNDRJ1IML3SMCCP", "SNDRJ1ML2RMS", "SNDRJ1ML2RMSSUP", "SNDRJ1ML3DRMS", "SNDRJ1ML3MRMS", "SNDRJ2CrISL1B", "SNDRJ2CrISL1BIMG", "SNDRJ2CrISL1BIMGC", "SNDRSNATMSMAP", "SNDRSNCrISL1BIMG", "SNDRSNCrISL1BIMGC", "SNDRSNIL1BCALSUBRNDN", "SNDRSNIL1BCALSUBSUMN", "SNDRSNIL2ESPNH3", "SNDRSNIML2CCPCCR", "SNDRSNIML2CCPCCRN", "SNDRSNIML2CCPRET", "SNDRSNIML2CCPRETN", "SNDRSNIML2CHTCCRN", "SNDRSNIML2CHTRETN", "SNDRSNIML2CPS", "SNDRSNIML2CPSN", "SNDRSNIML2SFSPRET", "SNDRSNIML2SFSPSUP", "SNDRSNIML3CDCCP", "SNDRSNIML3CDCCPN", "SNDRSNIML3CDCHTN", "SNDRSNIML3CMCCP", "SNDRSNIML3CMCCPN", "SNDRSNIML3CMCHTN", "SNDRSNIML3SDCCP", "SNDRSNIML3SDCCPN", "SNDRSNIML3SDCHTN", "SNDRSNIML3SDSFSP", "SNDRSNIML3SMCCP", "SNDRSNIML3SMCCPN", "SNDRSNIML3SMCHTN", "SNDRSNIML3SMSFSP", "SNDRSNML2RMS", "SNDRSNML2RMS", "SNDRSNML2RMSSUP", "SNDRSNML3DRMS", "SNDRSNML3MRMS", "SNEX17_CAR", "SNEX17_DGNSS1", "SNEX17_GPR", "SNEX17_GPR_Raw", "SNEX17_KT15_TB", "SNEX17_P3V", "SNEX17_QWIP_ST", "SNEX17_SBR", "SNEX17_SD", "SNEX17_SD_Perm", "SNEX17_SMP", "SNEX17_SMP2", "SNEX17_SSA", "SNEX17_SSD", "SNEX17_SnowPits", "SNEX17_SnowSAR", "SNEX17_SnowSAR_Raw", "SNEX17_TLI", "SNEX17_TLS_PC_BSU", "SNEX17_TLS_PC_BSU_Raw", "SNEX17_TLS_PC_CRREL", "SNEX17_UWScat", "SNEX20_A19_GSM", "SNEX20_A19_SD", "SNEX20_A19_SP", "SNEX20_A19_SWE", "SNEX20_BR_BSU_SMP", "SNEX20_BSU_CMP_Raw", "SNEX20_BSU_CMP_SWE", "SNEX20_BSU_CMP_TT", "SNEX20_BSU_GPR", "SNEX20_BSU_GPR_Raw", "SNEX20_COCP_GPR", "SNEX20_COCP_GPR_Raw", "SNEX20_COCP_SPD", "SNEX20_CRSM", "SNEX20_CSSM", "SNEX20_DSM", "SNEX20_GIS_REF", "SNEX20_GM_CSU_GPR", "SNEX20_GM_CSU_GPR_RAW", "SNEX20_GM_CTSM", "SNEX20_GM_Lidar", "SNEX20_GM_SP", "SNEX20_GM_SWE_SD", "SNEX20_J_UNM_GPR", "SNEX20_QSI_DEM", "SNEX20_QSI_DEM_3m", "SNEX20_QSI_SD", "SNEX20_QSI_SD_3m", "SNEX20_QSI_VH", "SNEX20_QSI_VH_3m", "SNEX20_SB_GST", "SNEX20_SD", "SNEX20_SD_TLI", "SNEX20_SMP", "SNEX20_SSA", "SNEX20_SWESARR_TB", "SNEX20_TLI", "SNEX20_TLS_PC_BSU", "SNEX20_TLS_PC_BSU_RAW", "SNEX20_TLS_PC_CRREL", "SNEX20_TS_SP", "SNEX20_UNM_GPR", "SNEX20_VPTS_Raw", "SNEX21_COCP_GPR", "SNEX21_COCP_GPR_Raw", "SNEX21_DSM", "SNEX21_PS_DSM", "SNEX21_PS_MET", "SNEX21_SSR", "SNEX21_TS_SP", "SNEX23_BCEF_TLS", "SNEX23_BCEF_TLS_Raw", "SNEX23_CRREL_GPR", "SNEX23_CRREL_GPR_Raw", "SNEX23_CSU_GPR_Raw", "SNEX23_Lidar", "SNEX23_Lidar_Raw", "SNEX23_MAR22_SD", "SNEX23_MAR23_SD", "SNEX23_SSA", "SNEX23_SSA_SO", "SNEX23_SWE", "SNEX23_UW_GPR", "SNEX_HRSI_SD_DEM_CO", "SNEX_Met", "SNEX_Met_Raw", "SNF_ASP_CVR_140", "SNF_BIOMASS_141", "SNF_BIOPHYS_142", "SNF_CAN_COMP_143", "SNF_CJ_SITES_187", "SNF_CJ_VEG_189", "SNF_HELO_MMR_144", "SNF_LEAFCARY_183", "SNF_LEAF_EXP_180", "SNF_LEAF_TMS_184", "SNF_MET_1972_158", "SNF_MET_1972_1990_159", "SNF_MET_1973_160", "SNF_MET_1974_161", "SNF_MET_1975_162", "SNF_MET_1976_163", "SNF_MET_1977_164", "SNF_MET_1978_165", "SNF_MET_1979_166", "SNF_MET_1980_167", "SNF_MET_1981_168", "SNF_MET_1982_169", "SNF_MET_1983_170", "SNF_MET_1984_171", "SNF_MET_1985_172", "SNF_MET_1986_173", "SNF_MET_1987_174", "SNF_MET_1988_175", "SNF_MET_1989_176", "SNF_MET_1990_177", "SNF_MET_SUMM_178", "SNF_NS001_185", "SNF_SAT_INV_186", "SNF_SITECOMP_179", "SNF_SITE_86_188", "SNF_TAB3_3T_182", "SNF_UND_CVR_181", "SNPPATMSL1B", "SNPPATMSL1B", "SNPPCrISL1B", "SNPPCrISL1B", "SNPPCrISL1BNSR", "SNPPCrISL1BNSR", "SNPP_CrIS_VIIRS750m_IND", "SOCCOM", "SOC_3M_Maps_NE_TidalWetlands_1905", "SOC_Stocks_Great_Plains_1603", "SOIL_CO2_Flux_Costa_Rica_1373", "SOLVE1_Aerosol_AircraftInSitu_DC8_Data", "SOLVE1_Aerosol_AircraftInSitu_ER2_Data", "SOLVE1_AircraftRemoteSensing_DC8_HSRL_Data", "SOLVE1_AircraftRemoteSensing_DC8_LASE_Data", "SOLVE1_AircraftRemoteSensing_DC8_Lidar_Data", "SOLVE1_Analysis_DC8_Data", "SOLVE1_Analysis_ER2_Data", "SOLVE1_Ground_Data", "SOLVE1_MetNav_AircraftInSitu_DC8_Data", "SOLVE1_MetNav_AircraftInSitu_ER2_Data", "SOLVE1_Miscellaneous_DC8_Data", "SOLVE1_Miscellaneous_ER2_Data", "SOLVE1_Satellite_Data", "SOLVE1_Sondes_Data", "SOLVE1_TraceGas_AircraftInSitu_DC8_Data", "SOLVE1_TraceGas_AircraftInSitu_ER2_Data", "SOLVE1_TraceGas_AircraftRemoteSensing_DC8_Data", "SOLVE1_jValue_AircraftInSitu_DC8_Data", "SOLVE1_jValue_AircraftInSitu_ER2_Data", "SOLVE2_Aerosol_AircraftInSitu_DC8_Data", "SOLVE2_AircraftRemoteSensing_DC8_AATS14_Data", "SOLVE2_AircraftRemoteSensing_DC8_HSRL_Data", "SOLVE2_AircraftRemoteSensing_DC8_Lidar_Data", "SOLVE2_Analysis_DC8_Data", "SOLVE2_MetNav_AircraftInSitu_DC8_Data", "SOLVE2_Miscellaneous_DC8_Data", "SOLVE2_Radiation_AircraftInSitu_DC8_Data", "SOLVE2_Rocket_Data", "SOLVE2_Satellite_Data", "SOLVE2_Sondes_Data", "SOLVE2_TraceGas_AircraftInSitu_DC8_Data", "SOLVE2_TraceGas_AircraftRemoteSensing_DC8_Data", "SONEX_Aerosol_AircraftInSitu_DC8_Data", "SONEX_AircraftRemoteSensing_DC8_DIAL_Data", "SONEX_Merge_DC8_Data", "SONEX_MetNav_AircraftInSitu_DC8_Data", "SONEX_Miscellaneous_DC8_Data", "SONEX_Model_Data", "SONEX_Other_Data", "SONEX_Satellite_Data", "SONEX_TraceGas_AircraftInSitu_DC8_Data", "SONEX_jValue_AircraftInSitu_DC8_Data", "SOR3D_COMBINED", "SOR3SIMD", "SOR3SIMD_TAV", "SOR3SOLD_HIGH", "SOR3SOLD_MGII", "SOR3SOLFUVD", "SOR3SOLMUVD", "SOR3SOLS_LA", "SOR3SOLS_MGII", "SOR3TSI6", "SOR3TSID", "SOR3XPS6", "SOR3XPSD", "SOR4XPS5", "SOR4XPSD_HIGH", "SOR4XPSD_LOW", "SORTIE", "SPL1A_001", "SPL1A_002", "SPL1A_METADATA_001", "SPL1A_METADATA_002", "SPL1A_QA_001", "SPL1A_QA_002", "SPL1A_RO_001", "SPL1A_RO_002", "SPL1A_RO_003", "SPL1A_RO_METADATA_001", "SPL1A_RO_METADATA_002", "SPL1A_RO_METADATA_003", "SPL1A_RO_QA_001", "SPL1A_RO_QA_002", "SPL1A_RO_QA_003", "SPL1B_SO_LoRes_001", "SPL1B_SO_LoRes_002", "SPL1B_SO_LoRes_003", "SPL1B_SO_LoRes_METADATA_001", "SPL1B_SO_LoRes_METADATA_002", "SPL1B_SO_LoRes_METADATA_003", "SPL1B_SO_LoRes_QA_001", "SPL1B_SO_LoRes_QA_002", "SPL1B_SO_LoRes_QA_003", "SPL1C_S0_HiRes_001", "SPL1C_S0_HiRes_002", "SPL1C_S0_HiRes_003", "SPL1C_S0_HiRes_METADATA_001", "SPL1C_S0_HiRes_METADATA_002", "SPL1C_S0_HiRes_METADATA_003", "SPL1C_S0_HiRes_QA_001", "SPL1C_S0_HiRes_QA_002", "SPL1C_S0_HiRes_QA_003", "SPL2SMA", "SPL2SMAP", "SPL2SMAP_S", "SPL3FTA", "SPL3SMA", "SPL3SMAP", "SPL3SMP", "SPL3SMP_E", "SPL4CMDL", "SPL4SMAU", "SPL4SMGP", "SPL4SMLM", "SPURS1_ADCP", "SPURS1_ARGO", "SPURS1_CTD", "SPURS1_DRIFTER", "SPURS1_ECOMAPPER", "SPURS1_FLOAT_NEUTRALLYBUOYANT", "SPURS1_METEO", "SPURS1_MOORING_PICO", "SPURS1_MOORING_WHOI", "SPURS1_SEAGLIDER", "SPURS1_SEASOAR", "SPURS1_TENUSEGLIDER", "SPURS1_TSG", "SPURS1_UCTD", "SPURS1_WAVEGLIDER", "SPURS2_ADCP", "SPURS2_ARGO", "SPURS2_CFT", "SPURS2_CTD", "SPURS2_DISDR", "SPURS2_DRIFTER", "SPURS2_FLOAT_NEUTRALLYBUOYANT", "SPURS2_LADYAMBER", "SPURS2_METEO", "SPURS2_MOORING_CENTRAL", "SPURS2_MOORING_PICO", "SPURS2_PALS", "SPURS2_RAINRADAR", "SPURS2_RAWINSONDE", "SPURS2_SAILDRONE", "SPURS2_SALINITYSNAKE", "SPURS2_SEAGLIDER", "SPURS2_SSP", "SPURS2_UCTD", "SPURS2_UNDERWAY_pCO2_DIC_pH", "SPURS2_USPS", "SPURS2_WAMOS", "SPURS2_WAVEGLIDER", "SPURS2_XBAND", "SPURS2_XBAND_IMG", "SPURS2_XBT", "SRB_REL3.0_SW_3HRLY_MONTHLY_UTC_NC", "SRDB_V5_1827", "SRTMGL1", "SRTMGL1N", "SRTMGL3", "SRTMGL30", "SRTMGL3N", "SRTMGL3S", "SRTMGL3_NC", "SRTMGL3_NUMNC", "SRTMIMGM", "SRTMIMGR", "SRTMSWBD", "SSBUVIRR", "SSBUVO3", "SSFR_irradiance_841", "STAQS_AircraftRemoteSensing_JSC-GV_GCAS_Data", "STAQS_AircraftRemoteSensing_JSC-GV_HSRL2_Data", "STAQS_AircraftRemoteSensing_NASA-G3_GCAS_Data", "STAQS_AircraftRemoteSensing_NASA-G3_HALO_Data", "STAQS_Chiwaukee-Prairie_Data", "STAQS_Drone_Data", "STAQS_Ground_Data", "STAQS_INSTEP_Data", "STAQS_SeaRey_Data", "STAQS_Sondes_Data", "STRAT_Aerosol_AircraftInSitu_ER2_Data", "STRAT_Analysis_ER2_Data", "STRAT_Ground_Data", "STRAT_MetNav_AircraftInSitu_ER2_Data", "STRAT_Model_Data", "STRAT_Satellite_Data", "STRAT_Sondes_Data", "STRAT_TraceGas_AircraftInSitu_ER2_Data", "STRAT_jValue_AircraftInSitu_ER2_Data", "STS-59_GRD", "STS-59_META_GRD", "STS-59_META_SLC", "STS-59_SLC", "STS-68_GRD", "STS-68_META_GRD", "STS-68_META_SLC", "STS-68_SLC", "SUCCESS_UTAH_PDL", "SUPERYACHT_SCIENCE", "SV08LC", "SV08PLBK", "SV08PLTB", "SV08SM", "SV08SR", "SV08ST", "SV08V", "SV08VWC", "SV12CSMA", "SV12CST", "SV12LC", "SV12PLBK", "SV12PLSM", "SV12PLTB", "SV12PSMA", "SV12PSMF", "SV12SRA", "SV12SRF", "SV12STM", "SV12UBK", "SV12VA", "SV12VF", "SV12VWC", "SV15PLSM", "SV15PLTB", "SV15PSM", "SV16I_PLTBSM", "SV16I_PNET", "SV16I_TNET", "SV16I_TPSM", "SV16M_CRS", "SV16M_CSM", "SV16M_LAI", "SV16M_LC", "SV16M_MET", "SV16M_PLTBSM", "SV16M_PSM", "SV16M_SDB", "SV16M_SR", "SV16M_SSM", "SV16M_ST", "SV16M_TB", "SV16M_V", "SV19MA_DEM", "SV19MA_DSM", "SV19MA_LID", "SV19MA_TNET", "SV19MA_VOD", "SV19MB_DEM", "SV19MB_DSM", "SV19MB_LID", "SV19MB_TNET", "SWDB_L2", "SWDB_L305", "SWDB_L310", "SWDB_L3M05", "SWDB_L3M10", "SWDB_L3MC05", "SWDB_L3MC10", "SWFL", "SWOT_L1B_LR_INTF_1.0", "SWOT_L1B_LR_INTF_1.1", "SWOT_L2_HR_LakeSP_2.0", "SWOT_L2_HR_PIXC_2.0", "SWOT_L2_HR_Raster_2.0", "SWOT_L2_HR_RiverSP_2.0", "SWOT_L2_LR_SSH_1.0", "SWOT_L2_LR_SSH_1.1", "SWOT_L2_LR_SSH_2.0", "SWOT_L2_NALT_GDR_2.0", "SWOT_L2_NALT_IGDR_1.0", "SWOT_L2_NALT_IGDR_2.0", "SWOT_L2_NALT_OGDR_1.0", "SWOT_L2_NALT_OGDR_2.0", "SWOT_L4_DAWG_SOS_DISCHARGE", "SWOT_PRELAUNCH_L2_BPR_V1", "SWOT_PRELAUNCH_L2_GLIDER_V1", "SWOT_PRELAUNCH_L2_GPS_V1", "SWOT_PRELAUNCH_L2_PIES_V1", "SWOT_PRELAUNCH_L2_PRAWLER_V1", "SWOT_PRELAUNCH_L2_SIOCTD_V1", "SWOT_PRELAUNCH_L2_WHOICTD_V1", "SWOT_PRELAUNCH_L2_WW_V1", "SWOT_SIMULATED_L2_KARIN_SSH_ECCO_LLC4320_CALVAL_V1", "SWOT_SIMULATED_L2_KARIN_SSH_ECCO_LLC4320_SCIENCE_V1", "SWOT_SIMULATED_L2_KARIN_SSH_GLORYS_CALVAL_V1", "SWOT_SIMULATED_L2_KARIN_SSH_GLORYS_SCIENCE_V1", "SWOT_SIMULATED_L2_NADIR_SSH_ECCO_LLC4320_CALVAL_V1", "SWOT_SIMULATED_L2_NADIR_SSH_ECCO_LLC4320_SCIENCE_V1", "SWOT_SIMULATED_L2_NADIR_SSH_GLORYS_CALVAL_V1", "SWOT_SIMULATED_L2_NADIR_SSH_GLORYS_SCIENCE_V1", "SWOT_SIMULATED_NA_CONTINENT_L2_HR_LAKESP_V1", "SWOT_SIMULATED_NA_CONTINENT_L2_HR_PIXCVEC_V1", "SWOT_SIMULATED_NA_CONTINENT_L2_HR_PIXC_V1", "SWOT_SIMULATED_NA_CONTINENT_L2_HR_RASTER_V1", "SWOT_SIMULATED_NA_CONTINENT_L2_HR_RIVERSP_V1", "Sahel_Water_Bodies_1269", "Salt_Marsh_Biomass_CONUS_2348", "San_Diego_Coastal_Project", "Sargassum_GOM", "Saskatchewan_Soils_125m_SSA_1346", "Sat_ActiveLayer_Thickness_Maps_1760", "SatelliteDerived_Forest_Mexico_2320", "Scotia_Prince_ferry", "Sea2Space", "SeaWiFS_L1_GAC", "SeaWiFS_L1_GAC", "SeaWiFS_L1_MLAC", "SeaWiFS_L1_MLAC", "SeaWiFS_L2_GAC_IOP", "SeaWiFS_L2_GAC_OC", "SeaWiFS_L2_MLAC_IOP", "SeaWiFS_L2_MLAC_OC", "SeaWiFS_L3b_CHL", "SeaWiFS_L3b_IOP", "SeaWiFS_L3b_KD", "SeaWiFS_L3b_PAR", "SeaWiFS_L3b_PIC", "SeaWiFS_L3b_POC", "SeaWiFS_L3b_RRS", "SeaWiFS_L3m_CHL", "SeaWiFS_L3m_IOP", "SeaWiFS_L3m_KD", "SeaWiFS_L3m_PAR", "SeaWiFS_L3m_PIC", "SeaWiFS_L3m_POC", "SeaWiFS_L3m_RRS", "SeaWiFS_L4b_GSM", "SeaWiFS_L4m_GSM", "Sea_of_Japan", "Seagrass_Mapping_Florida", "Searcher", "Seasonality_Tundra_Vegetation_1606", "Secret", "Semi-Arid_Tree_Carbon_50cm_2117", "Sentinel-1_Burst_Map", "Seward_Peninsula_Veg_Maps_1363", "Shrub_Biomass_Toolik_Lake_AK_1573", "SiB3_Carbon_Flux_909", "SiB4_Global_HalfDegree_Daily_1849", "SiB4_Global_HalfDegree_Hourly_1847", "SiB4_Global_HalfDegree_Monthly_1848", "Siberian_Biomass_Wildfire_1321", "Siberian_Larch_Stand_Age_1364", "SnowMeltDuration_PMicrowave_1843", "Snow_Cover_Extent_and_Depth_1757", "Snow_Depth_Data_Images_1656", "Snow_Wildlife_Tracks_AK_WA_2188", "Snowmelt_timing_maps_V2_1712", "Snowpack_Dall_Sheep_Track_1583", "SoilResp_HeterotrophicResp_1928", "SoilSCAPE_1339", "SoilSCAPE_V2_2049", "Soil_ActiveLayer_Properties_AK_2315", "Soil_Carbon_Flux_Maps_1683", "Soil_Moisture_Alaska_Alberta_2123", "Soil_Temp_Moisture_Alaska_1869", "Soil_Temperature_Profiles_AK_1767", "Sonoma_County_Forest_AGB_1764", "Southern_Boreal_Plot_Attribute_1740", "Southern_Ocean_Drifter", "Surface_Oligo_Med_Sea", "SwAO", "TAHOE", "TAO", "TAO_Moorings", "TARA-EUROPA", "TARA_PACIFIC_expedition", "TARFOX_UWC131A", "TARFOX_UWC131A_SUNP", "TARFOX_WALLOPS_MET", "TARFOX_WALLOPS_SMPS", "TARFOX_WALLOPS_SONDE", "TCTE3TSI6", "TCTE3TSID", "TELLUS_GIA_L3_0.5-DEG_V1.0", "TELLUS_GIA_L3_1-DEG_V1.0", "TELLUS_GLDAS-NOAH-3.3_TWS-ANOMALY_MONTHLY", "TELLUS_GRAC-GRFO_MASCON_CRI_GRID_RL06.1_V3", "TELLUS_GRAC-GRFO_MASCON_CRI_GRID_RL06.3_V4", "TELLUS_GRAC-GRFO_MASCON_GRID_RL06.1_V3", "TELLUS_GRAC-GRFO_MASCON_GRID_RL06.3_V4", "TELLUS_GRAC_L3_CSR_RL06_LND_v04", "TELLUS_GRAC_L3_CSR_RL06_OCN_v04", "TELLUS_GRAC_L3_GFZ_RL06_LND_v04", "TELLUS_GRAC_L3_GFZ_RL06_OCN_v04", "TELLUS_GRAC_L3_JPL_RL06_LND_v04", "TELLUS_GRAC_L3_JPL_RL06_OCN_v04", "TELLUS_GRFO_L3_CSR_RL06.1_LND_v04", "TELLUS_GRFO_L3_CSR_RL06.1_OCN_v04", "TELLUS_GRFO_L3_CSR_RL06.2_LND_v04", "TELLUS_GRFO_L3_CSR_RL06.2_OCN_v04", "TELLUS_GRFO_L3_CSR_RL06.3_LND_v04", "TELLUS_GRFO_L3_CSR_RL06.3_OCN_v04", "TELLUS_GRFO_L3_GFZ_RL06.1_LND_v04", "TELLUS_GRFO_L3_GFZ_RL06.1_OCN_v04", "TELLUS_GRFO_L3_JPL_RL06.3_LND_v04", "TELLUS_GRFO_L3_JPL_RL06.3_OCN_v04", "TEMPO_HCHO-PROXY_L2", "TEMPO_NO2_L2", "TEMPO_NO2_L3", "TG02_Balloon_VOC_1110", "TG03_AERONET_AOT_1128", "TG03_Aeronet_Solar_Flux_1137", "TG05_CASA_1199", "TG06_Vertical_Profiles_1175", "TG07_Autochamber_Soil_CO2_Flux_Km67_927", "TG07_DBH_Cauaxi_1063", "TG07_FFT_Survey_Km83_923", "TG07_Fallen_Standing_Necromass_998", "TG07_Litter_Decomposition_925", "TG07_Manual_Flux_Km67_1026", "TG07_Root_Mortality_Experiment_924", "TG07_Root_Mortality_Longterm_1116", "TG07_STM_GLAS_836", "TG07_Soil-Atmosphere_Flux_Km83_926", "TG07_Soil_Nutrients_1085", "TG07_Trace_Gas_Profiles_1107", "TG08_Soil_Gas_Fertilization_1105", "TG08_Soil_Gas_Wetting_1101", "TG09_N2O_Soils_1013", "TG10_TROFFEE_1195", "THAILAND", "THIRN4IMCH67", "THIRN4L1CH115", "THIRN4L1CH67", "THIRN5L1CH115", "THIRN5L1CH67", "THIRN6IM", "THIRN6L1CH115", "THIRN6L1CH67", "THIRN7IM", "THIRN7L1BCLT", "THIRN7L1CLDT", "THORPEX_ER2_MAS", "TIROS2L1FMRT", "TIROS3L1FMRT", "TIROS3L1ORT", "TIROS4L1FMRT", "TIROS4L1ORR", "TIROS4L1ORT", "TIROS7L1FMRT", "TL1BL", "TL1BN", "TL1BSOL", "TL2ANC", "TL2ANC", "TL2ANCS", "TL2ANCS", "TL2ATMLN", "TL2ATMLN", "TL2ATMTL", "TL2ATMTN", "TL2ATMTN", "TL2CH4LN", "TL2CH4LN", "TL2CH4N", "TL2CH4N", "TL2CH4NS", "TL2CH4NS", "TL2CO2LN", "TL2CO2LN", "TL2CO2N", "TL2CO2N", "TL2CO2NS", "TL2CO2NS", "TL2COLN", "TL2COLN", "TL2CON", "TL2CON", "TL2CONS", "TL2CONS", "TL2FORLN", "TL2FORLN", "TL2FORN", "TL2FORN", "TL2FORNS", "TL2FORNS", "TL2H2OL", "TL2H2OLN", "TL2H2OLN", "TL2H2OLS", "TL2H2ON", "TL2H2ON", "TL2H2ONS", "TL2H2ONS", "TL2HCNN", "TL2HCNNS", "TL2HDOL", "TL2HDOLN", "TL2HDOLN", "TL2HDOLS", "TL2HDON", "TL2HDON", "TL2HDONS", "TL2HDONS", "TL2HNO3L", "TL2HNO3S", "TL2IRKN", "TL2IRKN", "TL2IRKNS", "TL2IRKNS", "TL2MTLLN", "TL2MTLN", "TL2MTLN", "TL2MTLNS", "TL2MTLNS", "TL2N2ON", "TL2N2ON", "TL2N2ONS", "TL2N2ONS", "TL2NH3LN", "TL2NH3LN", "TL2NH3N", "TL2NH3N", "TL2NH3NS", "TL2NH3NS", "TL2O3L", "TL2O3LN", "TL2O3LN", "TL2O3LS", "TL2O3N", "TL2O3N", "TL2O3NS", "TL2O3NS", "TL2OCSLN", "TL2OCSN", "TL2OCSN", "TL2OCSNS", "TL2OCSNS", "TL2PANLN", "TL2PANN", "TL2PANN", "TL2PANNS", "TL2PANNS", "TL2RHLN", "TL2SUM", "TL2SUM", "TL2SUMS", "TL2SUMS", "TL2SUP", "TL2SUP", "TL2SUPS", "TL2SUPS", "TL2TLS", "TL2TNS", "TL2TNS", "TL3ATD", "TL3ATD", "TL3ATM", "TL3ATM", "TL3ATM", "TL3CH4D", "TL3CH4D", "TL3CH4M", "TL3CH4M", "TL3CH4M", "TL3COD", "TL3COD", "TL3COM", "TL3COM", "TL3H2OD", "TL3H2OD", "TL3H2OM", "TL3H2OM", "TL3H2OM", "TL3HDOD", "TL3HDOD", "TL3HDOM", "TL3HDOM", "TL3HDOM", "TL3HNOD", "TL3HNOM", "TL3O3D", "TL3O3D", "TL3O3M", "TL3O3M", "TL3O3M", "TLS_Lidar_BlueFlux_Mangroves_2311", "TMI-REMSS-L2P-v4", "TMI-REMSS-L3U-v7.1a", "TML2CO", "TML2COS", "TOL2O3", "TOL2O3", "TOL2O3S", "TOL2O3S", "TOMSEPAER", "TOMSEPL2", "TOMSEPL3", "TOMSEPL3daer", "TOMSEPL3dery", "TOMSEPL3dref", "TOMSEPL3dtoz", "TOMSEPL3maer", "TOMSEPL3mery", "TOMSEPL3mref", "TOMSEPL3mtoz", "TOMSEPL3zaer", "TOMSEPL3zref", "TOMSEPL3ztoz", "TOMSEPOVP", "TOMSM3L3", "TOMSM3L3dref", "TOMSM3L3dtoz", "TOMSM3OVP", "TOMSN7AER", "TOMSN7L2", "TOMSN7L3", "TOMSN7L3daer", "TOMSN7L3dery", "TOMSN7L3dref", "TOMSN7L3dtoz", "TOMSN7L3maer", "TOMSN7L3mery", "TOMSN7L3mref", "TOMSN7L3mtoz", "TOMSN7L3zaer", "TOMSN7L3zref", "TOMSN7L3ztoz", "TOMSN7OVP", "TOMSN7SO2", "TOMS_aerosol_823", "TOMS_ozone_824", "TOPEX_ALTSDR", "TOPEX_POSEIDON_GDR_F", "TOTE-VOTE_Aerosol_AircraftInSitu_DC8_Data", "TOTE-VOTE_AircraftRemoteSensing_DC8_DIAL_Data", "TOTE-VOTE_AircraftRemoteSensing_DC8_Lidar_Data", "TOTE-VOTE_Analysis_DC8_Data", "TOTE-VOTE_Ground_Data", "TOTE-VOTE_MetNav_AircraftInSitu_DC8_Data", "TOTE-VOTE_Miscellaneous_DC8_Data", "TOTE-VOTE_Satellite_Data", "TOTE-VOTE_Sondes_Data", "TOTE-VOTE_TraceGas_AircraftInSitu_DC8_Data", "TOTO", "TOVSA5ND", "TOVSA5NF", "TOVSA5NG", "TOVSA5NH", "TOVSA5TN", "TOVSADND", "TOVSADNF", "TOVSADNG", "TOVSADNH", "TOVSADTN", "TOVSAMNA", "TOVSAMNC", "TOVSAMND", "TOVSAMND", "TOVSAMNE", "TOVSAMNF", "TOVSAMNF", "TOVSAMNG", "TOVSAMNG", "TOVSAMNH", "TOVSAMNH", "TOVSAMNJ", "TOVSAMTN", "TOVSAMTN", "TOVSB5ND", "TOVSB5NG", "TOVSBDND", "TOVSBDNG", "TOVSBMND", "TOVSBMNG", "TOVS_717", "TRACE-A_920", "TRACE-A_AircraftRemoteSensing_DC8_DIAL_Data", "TRACE-A_Brazil_Data", "TRACE-A_Merge_Data", "TRACE-A_MetNav_AircraftInSitu_DC8_Data", "TRACE-A_Satellite_Data", "TRACE-A_Sondes_Data", "TRACE-A_TraceGas_AircraftInSitu_DC8_Data", "TRACE-A_Trajectory_Data", "TRACE-P_Aerosol_AircraftInSitu_DC8_Data", "TRACE-P_Aerosol_AircraftInSitu_P3B_Data", "TRACE-P_AircraftRemoteSensing_DC8_DIAL_Data", "TRACE-P_Cloud_AircraftInSitu_DC8_Data", "TRACE-P_Ground_Data", "TRACE-P_Merge_Data", "TRACE-P_MetNav_AircraftInSitu_DC8_Data", "TRACE-P_MetNav_Aircraft_InSitu_P3B_Data", "TRACE-P_Model_Data", "TRACE-P_Satellite_Data", "TRACE-P_Sondes_Data", "TRACE-P_TraceGas_AircraftInSitu_DC8_Data", "TRACE-P_TraceGas_InSitu_P3B_Data", "TRACE-P_Trajectory_DC8_Data", "TRACE-P_Trajectory_P3B_Data", "TRACE-P_jValue_AircraftInSitu_DC8_Data", "TRACE-P_jValue_AircraftInSitu_P3B_Data", "TRACERAQ_AircraftRemoteSensing_GV_GCAS_Data", "TRACERAQ_AircraftRemoteSensing_GV_HSRL2_Data", "TRACERAQ_Ground_LaPorte_Data", "TRACERAQ_Pandora_Data", "TRACERAQ_Sondes_Data", "TRATLEQ1", "TRMM_1A01", "TRMM_1A11", "TRMM_1B01", "TRMM_1B11", "TRMM_1B21", "TRMM_1B51", "TRMM_1C21", "TRMM_1C51", "TRMM_1C51UW", "TRMM_2A12", "TRMM_2A21", "TRMM_2A23", "TRMM_2A25", "TRMM_2A52", "TRMM_2A53", "TRMM_2A53UW", "TRMM_2A54", "TRMM_2A54UW", "TRMM_2A55", "TRMM_2A55UW", "TRMM_2A56", "TRMM_2B31", "TRMM_3A11", "TRMM_3A12", "TRMM_3A25", "TRMM_3A26", "TRMM_3A53", "TRMM_3A54", "TRMM_3A54UW", "TRMM_3A55", "TRMM_3B31", "TRMM_3B40RT", "TRMM_3B41RT", "TRMM_3B42", "TRMM_3B42RT", "TRMM_3B42RT_Daily", "TRMM_3B42_Daily", "TRMM_3B43", "TRMM_3G25", "TRMM_3G31", "TRMM_3H25", "TRMM_3H31", "TRMM_CSH", "TRMM_TMPA_LandSeaMask", "TRMM_baseUW", "TRMM_precip_718", "TROPICS01ANTTL1A", "TROPICS01BRTTL1B", "TROPICS01MIRSL2B", "TROPICS01PRPSL2B", "TROPICS01URADL2A", "TROPICS03ANTTL1A", "TROPICS03BRTTL1B", "TROPICS03MIRSL2B", "TROPICS03PRPSL2B", "TROPICS03URADL2A", "TROPICS05ANTTL1A", "TROPICS05BRTTL1B", "TROPICS05URADL2A", "TROPICS06ANTTL1A", "TROPICS06BRTTL1B", "TROPICS06MIRSL2B", "TROPICS06PRPSL2B", "TROPICS06URADL2A", "TROPICS07ANTTL1A", "TROPICS07BRTTL1B", "TROPICS07URADL2A", "TROPOMAER", "TROPOMI_MINDS_NO2", "TRPSCRAERNH42H2D", "TRPSCRAERNH46H3D", "TRPSCRAERNH4M3D", "TRPSCRAERNO32H2D", "TRPSCRAERNO36H3D", "TRPSCRAERNO3M3D", "TRPSCRAERSO42H2D", "TRPSCRAERSO46H3D", "TRPSCRAERSO4M3D", "TRPSCRCH2O2H2D", "TRPSCRCH2O6H3D", "TRPSCRCH2OM3D", "TRPSCRCO2H2D", "TRPSCRCO6H3D", "TRPSCRCOM3D", "TRPSCRCOS6H3D", "TRPSCRCOSM3D", "TRPSCRECOAM2D", "TRPSCRECOBM2D", "TRPSCRECOTM2D", "TRPSCRENOXAM2D", "TRPSCRENOXBM2D", "TRPSCRENOXLM2D", "TRPSCRENOXSM2D", "TRPSCRENOXTM2D", "TRPSCRESO2TM2D", "TRPSCRHNO32H2D", "TRPSCRHNO36H3D", "TRPSCRHNO3M3D", "TRPSCRNO22H2D", "TRPSCRNO26H3D", "TRPSCRNO2H2D", "TRPSCRNO2M3D", "TRPSCRNO2S6H3D", "TRPSCRNO2SM3D", "TRPSCRNO6H3D", "TRPSCRNOM3D", "TRPSCRO32H2D", "TRPSCRO36H3D", "TRPSCRO3I6H3D", "TRPSCRO3IM3D", "TRPSCRO3M3D", "TRPSCRO3S6H3D", "TRPSCRO3SM3D", "TRPSCROH2H2D", "TRPSCROH6H3D", "TRPSCROHM3D", "TRPSCRPAN2H2D", "TRPSCRPAN6H3D", "TRPSCRPANM3D", "TRPSCRPS2H2D", "TRPSCRPS6H2D", "TRPSCRPSM2D", "TRPSCRQ2H2D", "TRPSCRQ6H3D", "TRPSCRQM3D", "TRPSCRSO22H2D", "TRPSCRSO26H3D", "TRPSCRSO2M3D", "TRPSCRT2H2D", "TRPSCRT6H3D", "TRPSCRTM3D", "TRPSCRU2H2D", "TRPSCRU6H3D", "TRPSCRUM3D", "TRPSCRV2H2D", "TRPSCRV6H3D", "TRPSCRVM3D", "TRPSDL2ALLCRSMGBEI", "TRPSDL2ALLCRSMGDEL", "TRPSDL2ALLCRSMGKAR", "TRPSDL2ALLCRSMGLAG", "TRPSDL2ALLCRSMGLOS", "TRPSDL2ALLCRSMGMEX", "TRPSDL2ALLCRSMGSAO", "TRPSDL2ALLCRSMGTOK", "TRPSDL2CH4AIRSFS", "TRPSDL2CH4CRS1FS", "TRPSDL2CH4CRSAUS", "TRPSDL2CH4CRSFS", "TRPSDL2COAIRSFS", "TRPSDL2COCRS1FS", "TRPSDL2COCRSAUS", "TRPSDL2COCRSFS", "TRPSDL2COCRSTS1", "TRPSDL2COCRSTS2", "TRPSDL2COCRSWCF", "TRPSDL2COCRSWCFHI", "TRPSDL2H2OAIRSFS", "TRPSDL2H2OCRS1FS", "TRPSDL2H2OCRSAUS", "TRPSDL2H2OCRSFS", "TRPSDL2HDOAIRSFS", "TRPSDL2HDOCRS1FS", "TRPSDL2HDOCRSAUS", "TRPSDL2HDOCRSFS", "TRPSDL2NH3AIRSFS", "TRPSDL2NH3CRS1FS", "TRPSDL2NH3CRSAUS", "TRPSDL2NH3CRSFS", "TRPSDL2NH3CRSWCF", "TRPSDL2NH3CRSWCFHI", "TRPSDL2O3AIRSFS", "TRPSDL2O3AIRSOMIFS", "TRPSDL2O3CRS1FS", "TRPSDL2O3CRSAUS", "TRPSDL2O3CRSFS", "TRPSDL2O3CRSWCF", "TRPSDL2O3CRSWCFHI", "TRPSDL2O3OMIFS", "TRPSDL2PANCRS1FS", "TRPSDL2PANCRSAUS", "TRPSDL2PANCRSFS", "TRPSDL2PANCRSWCF", "TRPSDL2PANCRSWCFHI", "TRPSDL2TATMAIRSFS", "TRPSDL2TATMCRS1FS", "TRPSDL2TATMCRSAUS", "TRPSDL2TATMCRSFS", "TRPSYL2ALLCRSMGBEI", "TRPSYL2ALLCRSMGDEL", "TRPSYL2ALLCRSMGKAR", "TRPSYL2ALLCRSMGLAG", "TRPSYL2ALLCRSMGLOS", "TRPSYL2ALLCRSMGMEX", "TRPSYL2ALLCRSMGSAO", "TRPSYL2ALLCRSMGTOK", "TRPSYL2CH4AIRSFS", "TRPSYL2CH4AIRSORS", "TRPSYL2CH4CRS1FS", "TRPSYL2CH4CRSFS", "TRPSYL2CH4CRSRS", "TRPSYL2COAIRSFS", "TRPSYL2COAIRSORS", "TRPSYL2COCRS1FS", "TRPSYL2COCRSFS", "TRPSYL2COCRSRS", "TRPSYL2HDOAIRSFS", "TRPSYL2HDOAIRSORS", "TRPSYL2HDOCRS1FS", "TRPSYL2HDOCRSFS", "TRPSYL2HDOCRSRS", "TRPSYL2NH3AIRSFS", "TRPSYL2NH3CRS1FS", "TRPSYL2NH3CRSFS", "TRPSYL2NH3CRSRS", "TRPSYL2O3AIRSFS", "TRPSYL2O3AIRSOMIFS", "TRPSYL2O3AIRSORS", "TRPSYL2O3CRS1FS", "TRPSYL2O3CRSFS", "TRPSYL2O3CRSRS", "TRPSYL2O3OMIFS", "TRPSYL2PANCRS1FS", "TRPSYL2PANCRSFS", "TRPSYL2PANCRSRS", "TSIS_SSI_L3_12HR", "TSIS_SSI_L3_24HR", "TSIS_TSI_L3_06HR", "TSIS_TSI_L3_24HR", "Taiga_Tundra_Tree_Cover_1218", "Tampa_Bay", "Tara_Mediterranean", "Tara_Oceans_Polar_Circle", "Tara_Oceans_expedition", "Thermokarst_Circumpolar_Map_1332", "ThreeRivers", "Tidal_Marsh_Biomass_US_V1-1_1879", "Tidal_Marsh_Vegetation_US_1608", "Tidal_Wetland_Estuaries_Data_1742", "Tidal_Wetland_GPP_CONUS_1792", "Tidal_Wetland_Soil_Carbon_1612", "TillageErosion_SOCRedistribute_1944", "Tokyo_Bay", "Toolik_Lake_Area_Veg_Maps_1380", "Toolik_Lake_Veg_Plots_1333", "TopSoil_Erosion_MidWest_US_1774", "TowerBased_PhotoSpec_SIF_SK_CA_1887", "Tree_Canopy_Cover_Mexico_2137", "Tree_Mortality_Western_US_1512", "TundraTransect_VegRefl_Soil_2232", "TundraVeg_Reflectance_Soil_CO2_1960", "Tundra_Fire_Veg_Plots_1547", "Tundra_Greeness_Temp_Trends_1893", "Tundra_Leaf_Spectra_2005", "Turbid9", "Turkish_Seas", "UAEM1LME", "UAEM1LMT", "UAEMIAAE", "UAEMIALS", "UAEMIB2E", "UAEMIB2E", "UAEMIB2T", "UAEMIB2T", "UAEMIRCM", "UAEMITAL", "UAEMITCL", "UAEMITST", "UAEMRDAE", "UAEMRDLS", "UAEMRDRD", "UAEMRMLS", "UAEMRMRD", "UARCL3AL", "UARCL3AT", "UARHA2FN", "UARHA3AT", "UARHR3AL", "UARHR3AT", "UARIS3AL", "UARIS3AT", "UARML3AL", "UARML3AT", "UARPE2AXIS1", "UARPE2AXIS2", "UARPE2HEPSA", "UARPE2HEPSB", "UARPE2MEPS", "UARPE2VMAGAC", "UARPE2VMAGDC", "UARPE3AT", "UARPE3TP", "UARSO3BS", "UARSU3BS", "UARWI3AL", "UARWI3AT", "UARZCNMC", "UARZCUKM", "UAVSAR_INSAR_AMP", "UAVSAR_INSAR_AMP_GRD", "UAVSAR_INSAR_DEM", "UAVSAR_INSAR_INT", "UAVSAR_INSAR_INT_GRD", "UAVSAR_INSAR_KMZ", "UAVSAR_INSAR_META", "UAVSAR_POL_DEM", "UAVSAR_POL_INC", "UAVSAR_POL_KMZ", "UAVSAR_POL_META", "UAVSAR_POL_ML_CMPLX_GRD", "UAVSAR_POL_ML_CMPLX_GRD_3X3", "UAVSAR_POL_ML_CMPLX_GRD_5X5", "UAVSAR_POL_ML_CMPLX_SLANT", "UAVSAR_POL_PAULI", "UAVSAR_POL_SLOPE", "UAVSAR_POL_STOKES", "UAV_Imagery_BigLakeTrail_1834", "UCLA_DEALIASED_SASS_L3", "UMD_GEOL388", "USArray_Ground_Temperature_1680", "USM_pCO2", "US_MODIS_NDVI_1299", "US_MODIS_Veg_Parameters_1539", "Umiat_Veg_Plots_1370", "Unalaska_Veg_Plots_1375", "Uncertainty_US_Coastal_GHG_1650", "Understory_Veg_Biomass_Alaska_2340", "VBEMI2AE", "VBEMI2LS", "VBEMI2ST", "VBEMIB2E", "VBEMIB2T", "VBEMIGEO", "VCF5KYR", "VIIRSJ1_L1", "VIIRSJ1_L1_GEO", "VIIRSJ1_L2_IOP", "VIIRSJ1_L2_IOP_NRT", "VIIRSJ1_L2_OC", "VIIRSJ1_L2_OC_NRT", "VIIRSJ1_L2_SST", "VIIRSJ1_L2_SST3", "VIIRSJ1_L2_SST3_NRT", "VIIRSJ1_L2_SST_NRT", "VIIRSJ2_L1", "VIIRSJ2_L1_GEO", "VIIRSJ2_L2_IOP", "VIIRSJ2_L2_IOP_NRT", "VIIRSJ2_L2_OC", "VIIRSJ2_L2_OC_NRT", "VIIRSN_L1", "VIIRSN_L1_GEO", "VIIRSN_L2_IOP", "VIIRSN_L2_IOP_NRT", "VIIRSN_L2_OC", "VIIRSN_L2_OC_NRT", "VIIRSN_L2_SST", "VIIRSN_L2_SST3", "VIIRSN_L2_SST3_NRT", "VIIRSN_L2_SST_NRT", "VIIRSN_L3b_NSST", "VIIRSN_L3b_NSST_NRT", "VIIRSN_L3b_SST", "VIIRSN_L3b_SST3", "VIIRSN_L3b_SST3_NRT", "VIIRSN_L3b_SST_NRT", "VIIRSN_L3m_NSST", "VIIRSN_L3m_NSST_NRT", "VIIRSN_L3m_SST", "VIIRSN_L3m_SST3", "VIIRSN_L3m_SST3_NRT", "VIIRSN_L3m_SST_NRT", "VIIRS_N20-NAVO-L2P-v3.0", "VIIRS_N20-STAR-L2P-v2.80", "VIIRS_N20-STAR-L3U-v2.80", "VIIRS_N21-NAVO-L2P-v3.0", "VIIRS_NPP-JPL-L2P-v2016.2", "VIIRS_NPP-NAVO-L2P-v1.0", "VIIRS_NPP-NAVO-L2P-v2.0", "VIIRS_NPP-NAVO-L2P-v3.0", "VIIRS_NPP-STAR-L2P-v2.80", "VIIRS_NPP-STAR-L3U-v2.80", "VIIRS_SST_NPP_NAR-OSISAF-L3C-v1.0", "VIIRS_Val_FLKeys", "VIIRS_Validation", "VIMS_2005", "VIP01", "VIP07", "VIP15", "VIP30", "VIPPHEN_EVI2", "VIPPHEN_NDVI", "VIRGAS_MetNav_AircraftInSitu_Data", "VIRGAS_TraceGas_AircraftInSitu_Data", "VISSRGOES1IMIR", "VISSRGOES1IMVIS", "VISSRGOES2IMIR", "VISSRGOES2IMVIS", "VISSRGOES3IMIR", "VISSRGOES3IMVIS", "VISSRSMS1IMIR", "VISSRSMS1IMVIS", "VISSRSMS1L1AOIPS", "VISSRSMS1L1EHT", "VISSRSMS2IMIR", "VISSRSMS2IMVIS", "VISSRSMS2L1AOIPS", "VISSRSMS2L1EHT", "VJ103MODLL", "VJ109", "VJ109A1", "VJ109CMG", "VJ109GA", "VJ109H1", "VJ113A1", "VJ113A2", "VJ113A3", "VJ113C1", "VJ113C2", "VJ114", "VJ114A1", "VJ114IMG", "VJ115A2H", "VJ121", "VJ121A1D", "VJ121A1N", "VJ121A2", "VJ121C1", "VJ121C2", "VJ121C3", "VJ128C2", "VJ128C3", "VJ146A1G_NRT", "VJ146A1_NRT", "VNP02DNB", "VNP02IMG", "VNP02MOD", "VNP03DNB", "VNP03IMG", "VNP03MOD", "VNP03MODLL", "VNP03MODLL", "VNP09", "VNP09A1", "VNP09A1", "VNP09CMG", "VNP09CMG", "VNP09GA", "VNP09GA", "VNP09H1", "VNP09H1", "VNP10", "VNP10A1", "VNP10A1", "VNP10A1F", "VNP13A1", "VNP13A1", "VNP13A2", "VNP13A2", "VNP13A3", "VNP13A3", "VNP13C1", "VNP13C1", "VNP13C2", "VNP13C2", "VNP14", "VNP14", "VNP14A1", "VNP14A1", "VNP14IMG", "VNP14IMGTDL_NRT", "VNP15A2H", "VNP15A2H", "VNP21", "VNP21", "VNP21A1D", "VNP21A1D", "VNP21A1N", "VNP21A1N", "VNP21A2", "VNP21A2", "VNP21C1", "VNP21C2", "VNP21C3", "VNP22C2", "VNP22Q2", "VNP28C2", "VNP28C3", "VNP29", "VNP30", "VNP43C1", "VNP43C2", "VNP43C3", "VNP43C4", "VNP43DNBA1", "VNP43DNBA2", "VNP43DNBA3", "VNP43DNBA4", "VNP43IA1", "VNP43IA2", "VNP43IA3", "VNP43IA4", "VNP43MA1", "VNP43MA2", "VNP43MA3", "VNP43MA4", "VNP46A1", "VNP46A1", "VNP46A1G_NRT", "VNP46A1_NRT", "VNP46A2", "VNP46A3", "VNP46A4", "VNP64A1", "VOLPE", "VPRM_North_America_Parameters_1349", "Veg_Soil_Tundra_Burned_Area_2119", "Vegetation_Maps_Toolik_Lake_1690", "Vegetation_Photos_Toolik_Lake_1718", "Vegetation_greenness_trend_1576", "Vermont_HighRes_LandCover_2072", "Vulcan_V3_Annual_Emissions_1741", "Vulcan_V3_Hourly_Emissions_1810", "WACS2", "WAF_DEALIASED_SASS_L2", "WAVeTrends_1738", "WC_LSMEM_SOILM_025", "WC_MULTISEN_PREC_025", "WC_PM_ET_050", "WENTZ_NIMBUS-7_SMMR_L2", "WENTZ_SASS_SIGMA0_L2", "WHITECAPS", "WLDAS_NOAHMP001_DA1", "WRF_STILT_Footprints_Boston_1572", "WRF_STILT_Particles_Boston_1596", "WUS_UCLA_SR", "WV04_MSI_L1B", "WV04_Pan_L1B", "WV_LCC_SC_FSCA", "WaterBalance_Daily_Historical_GRIDMET", "WaterBalance_Monthly_Historical_GRIDMET", "WebbRosenzweig_548", "West_Florida_Shelf", "West_Soil_Carbon_1238", "Western_Gulf_of_Maine", "Wetland_Soil_CarbonStocks_WA_2249", "Wetland_VegClassification_PAD_2069", "WhitePhenoregions_799", "WhiteSpruce_Leaf_Traits_Alaska_2124", "Wildfire_Effects_Spruce_Field_1595", "Wildfire_Impacts_Boreal_Ecosys_2359", "Wildfires_2014_NWT_Canada_1307", "Wildfires_Date_of_Burning_1559", "Wildfires_NWT_Canada_1548", "Wildfires_NWT_Canada_2018_1703", "Wildfires_NWT_Canada_2019_1900", "Willow_Veg_Plots_1368", "WindSat-REMSS-L3U-v7.0.1a", "Wolves_Denning_Pups_Climate_1846", "XAERDT_L2_ABI_G16", "XAERDT_L2_ABI_G17", "XAERDT_L2_AHI_H08", "XAERDT_L2_AHI_H09", "XAERDT_L2_MODIS_Aqua", "XAERDT_L2_MODIS_Terra", "XAERDT_L2_VIIRS_NOAA20", "XAERDT_L2_VIIRS_SNPP", "Xingu_Albedo_Radiation_1622", "YKDelta_EnvChange_InfoExchange_1894", "Young_Russian_Forest_Map_1330", "ZinkeSoil_221", "Zinke_soil_683", "ZoblerSoilDerived_540", "ZoblerSoil_418", "Zobler_Soil_649", "ZonalFlux", "aamhcpex", "aces1am", "aces1cont", "aces1efm", "aces1log", "aces1time", "aces1trig", "aerosol_properties_725", "aes5davg_236", "aes_upl1_238", "aes_upl2_239", "afm06ihd_240", "afm06ptd_241", "afm06pwd_242", "afm06smd_243", "afm11afr_244", "afm13afr_245", "afm2as94_494", "afm3as94_496", "afm3mw94_495", "afm4toas_498", "afm4tofx_497", "afm6gifs_433", "african_woody_savanna_850", "air_sea_gas_exchange_xdeg_1208", "airscm3b_448", "airscpex", "airssy3b_507", "airsunp_61", "ali_etm_tandem_821", "amazon_precip_228", "ames_sunphotometer_643", "amprimpacts", "amprtbcx1", "amprtbcx2", "amprtbcx3", "amprtbf3a", "amprtbjax", "amprtbkwj", "amprtblba", "amprtbta", "amprtbtc", "ams_cs93_403", "ams_cs94_404", "ams_cs95_405", "ams_cs96_406", "amsua15sp", "amsua16sp", "amsua17sp", "aoci0bil_281", "apr3cpex", "apr3cpexaw", "apr3cpexcv", "apuimpacts", "asas_l1b_562", "asasrefl_287", "ascatcpex", "asosimpacts", "atmos_co2_by_erosion_xdeg_1019", "avapsimpacts", "avhrr_822", "avhrr_albedo_1995_xdeg_928", "avhrrl3b_481", "avhrrl4b_438", "avhrrl4c_439", "avhrrlc1_434", "basin_border_670", "billmark_828", "biofuel_emissions_753", "biomass_allocation_703", "biomdens_450", "biomebg2_296", "biomebgc_295", "boreas_aeshrday_235", "burning_emissions_752", "bvoc_flux_759", "c4_percent_1deg_932", "c4aaero", "c4dcm", "c4dcstar", "c4dcvi", "c4dd8d", "c4dicats", "c4djlh", "c4dlase", "c4dlip", "c4dmms", "c4dmtp", "c4dnevzor", "c4dpr2", "c4eampr", "c4eedop", "c4eehad", "c4ehamsr", "c4elip", "c4emas", "c4emtp", "c4enav", "c4enlh", "c4eo3p", "c4gandros", "c4gmipclo", "c4gmipfm", "c4gmipsod", "c4gmipss1", "c4gmipss2", "c4gmipwp", "c4gmisrep", "c4gnpol", "c4gsmart", "c4gtoga", "c4gxpow", "c4sg8", "calibgas_500", "canopychem_422", "canopyspec_423", "century_vemap_m4_820", "chem_26", "chesapeake_val_2013", "cmimpacts", "cmx3aeri", "cmx3andros", "cmx3g8", "cmx3misrep", "cmx3srl", "co2_emissions_1deg_1021", "combined_ancillary_xdeg_1200", "comp_runoff_monthly_xdeg_994", "cosmirimpacts", "cossirimpacts", "cp_lidar_images_721", "cplimpacts", "cramer_leemans_637", "cropland_612", "crsimpacts", "cru_monthly_climate_xdeg_1014", "cru_monthly_mean_xdeg_1015", "csgcpex01", "daily_precip_est_793", "dc8ammr", "dc8avaps", "dc8capac", "dc8dads", "dc8jplsaw", "dc8lase", "dc8laserh", "dc8macaws", "dc8mms", "dc8psr", "diffuse_irradiance_791", "dlhimpacts", "dtms0bil_247", "eMASL1B", "eMASL2AER", "eMASL2CLD", "echidna_1045", "ecmwf2_523", "ecmwf_met_1deg_1222", "ecosystem_roots_1deg_929", "edc_landcover_xdeg_930", "edgar_atmos_emissions_1deg_1022", "elev_arc_250", "er2_aerial_photos_722", "er2edop", "er2flog_501", "er2lip", "er2mams", "er2mir", "er2mts", "er2nasti", "er2nav", "er2navimpacts", "erbe_albedo_monthly_xdeg_957", "eta_model_723", "ewing", "explorer", "exrad3dimpacts", "exradepoch", "exradimpacts", "faamwdat_237", "fasir_biophys_monthly_xdeg_970", "fasir_ndvi_monthly_xdeg_972", "ffo_Betts_1987-1989_afd_93", "ffo_Betts_1987-1989_ams_89", "ffo_Betts_1987-1989_gsm_97", "ffo_Betts_1987-1989_nsm_101", "ffo_Betts_1987_afd_92", "ffo_Betts_1987_ams_88", "ffo_Betts_1987_gsm_96", "ffo_Betts_1987_nsm_100", "ffo_Betts_1988_afd_94", "ffo_Betts_1988_ams_90", "ffo_Betts_1988_gsm_98", "ffo_Betts_1988_nsm_102", "ffo_Betts_1989_afd_95", "ffo_Betts_1989_ams_91", "ffo_Betts_1989_gsm_99", "ffo_Betts_1989_nsm_103", "fhstmanr_386", "fhstmanv_387", "fieldsunp_65", "fife_AF_dtrnd_nae_3", "fife_AF_dtrnd_ncar_5", "fife_AF_dtrnd_wyo_4", "fife_AF_filtr_nae_6", "fife_AF_filtr_ncar_8", "fife_AF_filtr_wyo_7", "fife_AF_raw_nae_9", "fife_AF_raw_ncar_11", "fife_AF_raw_wyo_10", "fife_atmos_brut_drv_14", "fife_atmos_brut_son_15", "fife_atmos_lidar_ht_17", "fife_atmos_ncdc_son_13", "fife_atmos_nmc_upr_57", "fife_atmos_noaa_son_73", "fife_atmos_noaa_tov_16", "fife_atmos_sodar_18", "fife_atmos_tempprof_124", "fife_atmos_wind_lid_138", "fife_atmos_wind_son_139", "fife_biology_biomass_118", "fife_biology_leaf_ang_44", "fife_biology_leaf_h2o_126", "fife_biology_mow_biop_55", "fife_biology_mow_exo_56", "fife_biology_pho_box_27", "fife_biology_pho_leaf_46", "fife_biology_plantpro_69", "fife_biology_root_bio_75", "fife_biology_soil_co2_105", "fife_biology_soil_gas_106", "fife_biology_veg_biop_135", "fife_biology_veg_ref_137", "fife_biology_veg_spec_136", "fife_hydrology_strm_15m_1", "fife_hydrology_strm_day_119", "fife_hydrology_strm_st_120", "fife_optical_ot_brug_62", "fife_optical_ot_c130_63", "fife_optical_ot_calib_60", "fife_optical_ot_frasr_64", "fife_optical_ot_staff_66", "fife_sat_obs_ns001tms_59", "fife_sat_obs_sat_avhr_77", "fife_sat_obs_sat_coef_76", "fife_sat_obs_sat_ltm_78", "fife_sat_obs_sat_spot_79", "fife_soilmstr_peck_gam_37", "fife_soilmstr_peck_sm_109", "fife_soilmstr_sm_grav_110", "fife_soilmstr_sm_neut_111", "fife_soilmstr_sm_tran_113", "fife_soilmstr_soil_imp_108", "fife_soilprop_soil_h2o_117", "fife_soilprop_soil_rel_112", "fife_soilprop_soildens_104", "fife_soilprop_soilhydc_107", "fife_soilprop_soilsurv_115", "fife_soilprop_soilther_116", "fife_sur_flux_30_min_brg_20", "fife_sur_flux_30_min_brk_21", "fife_sur_flux_30_min_brl_19", "fife_sur_flux_30_min_brs_22", "fife_sur_flux_30_min_brv_23", "fife_sur_flux_30_min_brw_24", "fife_sur_flux_30_min_eca_30", "fife_sur_flux_30_min_ecb_32", "fife_sur_flux_30_min_ecg_31", "fife_sur_flux_30_min_ecv_33", "fife_sur_flux_30_min_ecw_34", "fife_sur_flux_basel_92_121", "fife_sur_met_ams_12", "fife_sur_met_cld_cam_28", "fife_sur_met_hday_met_39", "fife_sur_met_hmon_met_40", "fife_sur_met_ncdc_sur_122", "fife_sur_met_noaa_sur_58", "fife_sur_met_rain_30m_2", "fife_sur_met_rain_day_29", "fife_sur_refl_gem_helo_38", "fife_sur_refl_irt_grnd_72", "fife_sur_refl_irt_helo_70", "fife_sur_refl_irt_mult_71", "fife_sur_refl_lightwnd_43", "fife_sur_refl_ltbr_ksu_41", "fife_sur_refl_ltbr_unl_42", "fife_sur_refl_mmr_calb_51", "fife_sur_refl_mmr_grnd_52", "fife_sur_refl_mmr_helo_53", "fife_sur_refl_mmr_leaf_54", "fife_sur_refl_parabola_68", "fife_sur_refl_se5_gsfc_81", "fife_sur_refl_se5_helo_87", "fife_sur_refl_se5_leaf_85", "fife_sur_refl_se5_unl_82", "fife_sur_refl_soilrefl_114", "fife_sur_refl_unl_long_49", "fife_sur_refl_unl_surf_123", "fire_emissions_724", "fire_emissions_v4_R1_1293", "fltrepepoch", "fluxnet_point_1029", "forest_carbon_flux_949", "fuel_load_755", "g3acld", "g3acldb", "g3alsp", "g3alspb", "g3assp", "g3asspb", "g3at", "g3atb", "g3blmnc", "g3blsp", "g3blsp", "g3blspb", "g3bsmnc", "g3bssp", "g3bsspb", "g3bsspb", "g3bt", "g3bt", "g3btb", "g3btmnc", "gap_filled_marconi_811", "gdp_xdeg_974", "geocoord_556", "geoecology_R1_656", "gfscpex", "gghydro_676", "ghcn_631", "gimms_ndvi_monthly_xdeg_973", "giss_wetlands_632", "glider", "glmcierra", "glmgoesL3", "global_N_cycle_797", "global_N_deposition_maps_830", "global_population_xdeg_975", "globalir", "globalview_ch4_point_1109", "globalview_co2_point_1111", "globe_dem_630", "goes71_444", "goes71a_300", "goes72_554", "goes81_445", "goes81a_446", "goescpex", "goescpexcv", "goesimpacts", "goesrpltaviris", "goesrpltavirisng", "goesrpltcolma", "goesrpltcpl", "goesrpltcrs", "goesrpltdclma", "goesrpltfegs", "goesrpltgcas", "goesrpltivanpah", "goesrpltksclma", "goesrpltlip", "goesrpltmisrep", "goesrpltnalma", "goesrpltnaver2", "goesrpltoklma", "goesrpltredlake", "goesrpltshis", "goesrpltsolma", "goesrpltwtlma", "gpcc_precip_monthly_xdeg_995", "gpcp_precip_monthly_xdeg_1003", "gpcp_precip_pentad_xdeg_1002", "gpm2dc3vp", "gpm2dgcpex", "gpm2dhymex", "gpm2difld", "gpm2diphx", "gpm2dlpvex", "gpm2dmc3e", "gpm2dnsstc", "gpm2dvdolyx", "gpm2dwff2", "gpmadmirgcpex", "gpmahdmetolyx", "gpmampriphx2", "gpmamprmc3e", "gpmamprolyx", "gpmapr2gcpex", "gpmapr3olyx2", "gpmapuicepop", "gpmapuolyx", "gpmapuwff", "gpmarsifld", "gpmasinaiphx", "gpmasoolyx", "gpmavapsolyx", "gpmcax1cfolyx", "gpmcax1odolyx", "gpmcax1rbolyx", "gpmceilgcpex", "gpmceiliphx", "gpmchillmc3e", "gpmcilpvex", "gpmcitvidiphx", "gpmcmgcpex2", "gpmcmiphx", "gpmcmmc3e", "gpmcmolyx", "gpmcmorphnifld", "gpmcmorphniphx", "gpmcosmirgcpex", "gpmcosmiriphx", "gpmcosmirmc3e", "gpmcosmirolyx", "gpmcplolyx", "gpmcrsiphx", "gpmcrsolyx", "gpmcxsiolyx", "gpmd3rgcpex", "gpmd3ricepop", "gpmd3rifld", "gpmd3riphx", "gpmd3rolyx", "gpmdowolyx2", "gpmepfl", "gpmer2navmc3e", "gpmexradiphx", "gpmfltsummc3e", "gpmgaugewff", "gpmgoes13gcpexB", "gpmgoes13iphxB", "gpmgoes13mc3eB", "gpmgoes14iphxB", "gpmgoes15olyxB", "gpmgprof2014ifld", "gpmgsmapjifld", "gpmheifld", "gpmheiphx", "gpmhiwrapiphx", "gpmhiwrapmc3e", "gpmhiwrapolyx", "gpmikalpvex", "gpmjwlpvex", "gpmjwnsstc", "gpmkapxgcpex", "gpmkarx2ifld", "gpmkarx3ifld", "gpmkatx2olyx", "gpmkboxgcpex", "gpmkbufgcpex", "gpmkcae2iphx", "gpmkcbwgcpex", "gpmkcradgcpex", "gpmkcxxgcpex", "gpmkdmx2ifld", "gpmkdmx3ifld", "gpmkdvn2ifld", "gpmkdvn3ifld", "gpmkerlpvex", "gpmkgldmc3e", 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         <style>
diff --git a/workshops/pace/index.html b/workshops/pace/index.html
index 59dd1e2d..274bbf66 100644
--- a/workshops/pace/index.html
+++ b/workshops/pace/index.html
@@ -22,7 +22,7 @@
       
       
       <link rel="icon" href="../../assets/images/favicon.png">
-      <meta name="generator" content="mkdocs-1.6.1, mkdocs-material-9.5.39">
+      <meta name="generator" content="mkdocs-1.6.1, mkdocs-material-9.5.40">
     
     
       
@@ -2176,7 +2176,7 @@ <h2 id="search-for-pace-data">Search for PACE data<a class="anchor-link" href="#
 <div class="jp-OutputArea-child jp-OutputArea-executeResult">
 <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[3]:</div>
 <div class="jp-RenderedText jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/plain" tabindex="0">
-<pre>&lt;earthaccess.auth.Auth at 0x7f85cade0620&gt;</pre>
+<pre>&lt;earthaccess.auth.Auth at 0x7f2f7861ed20&gt;</pre>
 </div>
 </div>
 </div>
@@ -2303,7 +2303,7 @@ <h3 id="search-data-programmatically">Search data programmatically<a class="anch
             &lt;meta name=&quot;viewport&quot; content=&quot;width=device-width,
                 initial-scale=1.0, maximum-scale=1.0, user-scalable=no&quot; /&gt;
             &lt;style&gt;
-                #map_0cc024b08549069e0592bba38229dbe8 {
+                #map_9d20135b84efb2cf56ae99f7204a0bb4 {
                     position: relative;
                     width: 100.0%;
                     height: 100.0%;
@@ -2333,14 +2333,14 @@ <h3 id="search-data-programmatically">Search data programmatically<a class="anch
 &lt;body&gt;
     
     
-            &lt;div class=&quot;folium-map&quot; id=&quot;map_0cc024b08549069e0592bba38229dbe8&quot; &gt;&lt;/div&gt;
+            &lt;div class=&quot;folium-map&quot; id=&quot;map_9d20135b84efb2cf56ae99f7204a0bb4&quot; &gt;&lt;/div&gt;
         
 &lt;/body&gt;
 &lt;script&gt;
     
     
-            var map_0cc024b08549069e0592bba38229dbe8 = L.map(
-                &quot;map_0cc024b08549069e0592bba38229dbe8&quot;,
+            var map_9d20135b84efb2cf56ae99f7204a0bb4 = L.map(
+                &quot;map_9d20135b84efb2cf56ae99f7204a0bb4&quot;,
                 {
                     center: [20.61947, -82.39408],
                     crs: L.CRS.EPSG3857,
@@ -2349,79 +2349,79 @@ <h3 id="search-data-programmatically">Search data programmatically<a class="anch
                     preferCanvas: false,
                 }
             );
-            L.control.scale().addTo(map_0cc024b08549069e0592bba38229dbe8);
+            L.control.scale().addTo(map_9d20135b84efb2cf56ae99f7204a0bb4);
 
             
 
         
     
-            var tile_layer_fad99948545a90b3f0b9535fd41031c9 = L.tileLayer(
+            var tile_layer_ac9441186bf59ff2fa5590132dbdf637 = L.tileLayer(
                 &quot;https://tile.openstreetmap.org/{z}/{x}/{y}.png&quot;,
                 {&quot;attribution&quot;: &quot;\u0026copy; \u003ca href=\&quot;https://www.openstreetmap.org/copyright\&quot;\u003eOpenStreetMap\u003c/a\u003e contributors&quot;, &quot;detectRetina&quot;: false, &quot;maxNativeZoom&quot;: 19, &quot;maxZoom&quot;: 19, &quot;minZoom&quot;: 0, &quot;noWrap&quot;: false, &quot;opacity&quot;: 1, &quot;subdomains&quot;: &quot;abc&quot;, &quot;tms&quot;: false}
             );
         
     
-            tile_layer_fad99948545a90b3f0b9535fd41031c9.addTo(map_0cc024b08549069e0592bba38229dbe8);
+            tile_layer_ac9441186bf59ff2fa5590132dbdf637.addTo(map_9d20135b84efb2cf56ae99f7204a0bb4);
         
     
-            map_0cc024b08549069e0592bba38229dbe8.fitBounds(
+            map_9d20135b84efb2cf56ae99f7204a0bb4.fitBounds(
                 [[-2.67701, -111.36832], [43.91595, -53.41984]],
                 {}
             );
         
     
-        function geo_json_1930cdbee66525996e8264d003925e42_styler(feature) {
+        function geo_json_a8d97d41361be8e2c479543d1ee57c8c_styler(feature) {
             switch(feature.id) {
                 default:
                     return {&quot;fillOpacity&quot;: 0.5, &quot;weight&quot;: 2};
             }
         }
-        function geo_json_1930cdbee66525996e8264d003925e42_highlighter(feature) {
+        function geo_json_a8d97d41361be8e2c479543d1ee57c8c_highlighter(feature) {
             switch(feature.id) {
                 default:
                     return {&quot;fillOpacity&quot;: 0.75};
             }
         }
-        function geo_json_1930cdbee66525996e8264d003925e42_pointToLayer(feature, latlng) {
+        function geo_json_a8d97d41361be8e2c479543d1ee57c8c_pointToLayer(feature, latlng) {
             var opts = {&quot;bubblingMouseEvents&quot;: true, &quot;color&quot;: &quot;#3388ff&quot;, &quot;dashArray&quot;: null, &quot;dashOffset&quot;: null, &quot;fill&quot;: true, &quot;fillColor&quot;: &quot;#3388ff&quot;, &quot;fillOpacity&quot;: 0.2, &quot;fillRule&quot;: &quot;evenodd&quot;, &quot;lineCap&quot;: &quot;round&quot;, &quot;lineJoin&quot;: &quot;round&quot;, &quot;opacity&quot;: 1.0, &quot;radius&quot;: 2, &quot;stroke&quot;: true, &quot;weight&quot;: 3};
             
-            let style = geo_json_1930cdbee66525996e8264d003925e42_styler(feature)
+            let style = geo_json_a8d97d41361be8e2c479543d1ee57c8c_styler(feature)
             Object.assign(opts, style)
             
             return new L.CircleMarker(latlng, opts)
         }
 
-        function geo_json_1930cdbee66525996e8264d003925e42_onEachFeature(feature, layer) {
+        function geo_json_a8d97d41361be8e2c479543d1ee57c8c_onEachFeature(feature, layer) {
             layer.on({
                 mouseout: function(e) {
                     if(typeof e.target.setStyle === &quot;function&quot;){
-                            geo_json_1930cdbee66525996e8264d003925e42.resetStyle(e.target);
+                            geo_json_a8d97d41361be8e2c479543d1ee57c8c.resetStyle(e.target);
                     }
                 },
                 mouseover: function(e) {
                     if(typeof e.target.setStyle === &quot;function&quot;){
-                        const highlightStyle = geo_json_1930cdbee66525996e8264d003925e42_highlighter(e.target.feature)
+                        const highlightStyle = geo_json_a8d97d41361be8e2c479543d1ee57c8c_highlighter(e.target.feature)
                         e.target.setStyle(highlightStyle);
                     }
                 },
             });
         };
-        var geo_json_1930cdbee66525996e8264d003925e42 = L.geoJson(null, {
-                onEachFeature: geo_json_1930cdbee66525996e8264d003925e42_onEachFeature,
+        var geo_json_a8d97d41361be8e2c479543d1ee57c8c = L.geoJson(null, {
+                onEachFeature: geo_json_a8d97d41361be8e2c479543d1ee57c8c_onEachFeature,
             
-                style: geo_json_1930cdbee66525996e8264d003925e42_styler,
-                pointToLayer: geo_json_1930cdbee66525996e8264d003925e42_pointToLayer,
+                style: geo_json_a8d97d41361be8e2c479543d1ee57c8c_styler,
+                pointToLayer: geo_json_a8d97d41361be8e2c479543d1ee57c8c_pointToLayer,
         });
 
-        function geo_json_1930cdbee66525996e8264d003925e42_add (data) {
-            geo_json_1930cdbee66525996e8264d003925e42
+        function geo_json_a8d97d41361be8e2c479543d1ee57c8c_add (data) {
+            geo_json_a8d97d41361be8e2c479543d1ee57c8c
                 .addData(data);
         }
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&quot;application/vnd.nasa.cmr.umm+json&quot;, &quot;native-id&quot;: &quot;PACE_OCI_L2_AOP_NRT_PACE_OCI.20240731T170812.L2.OC_AOP.V2_0.NRT.nc_2.0&quot;, &quot;provider-id&quot;: &quot;OB_CLOUD&quot;, &quot;revision-date&quot;: &quot;2024-07-31T21:26:20.264Z&quot;, &quot;revision-id&quot;: 1, &quot;size&quot;: 280.602294921875}, &quot;type&quot;: &quot;Feature&quot;}, {&quot;bbox&quot;: [-103.84962, -2.67701, -74.28098, 25.69035], &quot;geometry&quot;: {&quot;coordinates&quot;: [[[-78.13645, 25.69035], [-103.84962, 20.43232], [-97.85031, -2.67701], [-74.28098, 2.63037], [-78.13645, 25.69035]]], &quot;type&quot;: &quot;Polygon&quot;}, &quot;id&quot;: &quot;2&quot;, &quot;properties&quot;: {&quot;ArchiveAndDistributionInformation&quot;: &quot;[{\u0027MimeType\u0027: \u0027application/x-netcdf\u0027, \u0027Format\u0027: \u0027netCDF-4\u0027, \u0027SizeInBytes\u0027: 156610707, \u0027Name\u0027: \u0027PACE_OCI.20240731T184130.L2.OC_AOP.V2_0.NRT.nc\u0027}]&quot;, 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&quot;[{\u0027Instruments\u0027: [{\u0027ShortName\u0027: \u0027OCI\u0027}], \u0027ShortName\u0027: \u0027PACE\u0027}]&quot;, &quot;ProductionDateTime&quot;: &quot;2024-07-31T20:52:40.020Z&quot;, &quot;ProviderDates&quot;: &quot;[{\u0027Date\u0027: \u00272024-07-31T20:58:55Z\u0027, \u0027Type\u0027: \u0027Insert\u0027}, {\u0027Type\u0027: \u0027Update\u0027, \u0027Date\u0027: \u00272024-07-31T20:52:40.020Z\u0027}]&quot;, &quot;RelatedUrls&quot;: &quot;[{\u0027URL\u0027: \u0027https://obdaac-tea.earthdatacloud.nasa.gov/ob-cumulus-prod-public/PACE_OCI.20240731T184130.L2.OC_AOP.V2_0.NRT.nc\u0027, \u0027Description\u0027: \u0027Download PACE_OCI.20240731T184130.L2.OC_AOP.V2_0.NRT.nc\u0027, \u0027Type\u0027: \u0027GET DATA\u0027}, {\u0027URL\u0027: \u0027s3://ob-cumulus-prod-public/PACE_OCI.20240731T184130.L2.OC_AOP.V2_0.NRT.nc\u0027, \u0027Description\u0027: \u0027This link provides direct download access via S3 to the granule\u0027, \u0027Type\u0027: \u0027GET DATA VIA DIRECT ACCESS\u0027}, {\u0027URL\u0027: \u0027https://obdaac-tea.earthdatacloud.nasa.gov/s3credentials\u0027, \u0027Description\u0027: \u0027api endpoint to retrieve temporary credentials valid for same-region direct s3 access\u0027, \u0027Type\u0027: \u0027VIEW RELATED INFORMATION\u0027}]&quot;, &quot;ShortName&quot;: &quot;PACE_OCI_L2_AOP_NRT&quot;, &quot;URL&quot;: &quot;https://cdn.earthdata.nasa.gov/umm/granule/v1.6.6&quot;, &quot;collection-concept-id&quot;: &quot;C3020920190-OB_CLOUD&quot;, &quot;concept-id&quot;: &quot;G3180202382-OB_CLOUD&quot;, &quot;concept-type&quot;: &quot;granule&quot;, &quot;format&quot;: &quot;application/vnd.nasa.cmr.umm+json&quot;, &quot;native-id&quot;: &quot;PACE_OCI_L2_AOP_NRT_PACE_OCI.20240731T184130.L2.OC_AOP.V2_0.NRT.nc_2.0&quot;, &quot;provider-id&quot;: &quot;OB_CLOUD&quot;, &quot;revision-date&quot;: &quot;2024-07-31T21:26:18.041Z&quot;, &quot;revision-id&quot;: 1, &quot;size&quot;: 149.3556089401245}, &quot;type&quot;: &quot;Feature&quot;}, 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"AERDB_M3_VIIRS_NOAA20", "AERDB_M3_VIIRS_SNPP", "AERDB_M3_VIIRS_SNPP", "AERDT_L2_VIIRS_NOAA20_NRT", "AERDT_L2_VIIRS_SNPP", "AERDT_L2_VIIRS_SNPP_NRT", "AERDT_L2_VIIRS_SNPP_NRT", "AERONET_aerosol_706", "AEROSE", "AE_5DSno", "AE_DyOcn", "AE_DySno", "AE_L2A", "AE_Land", "AE_Land", "AE_Land3", "AE_MoOcn", "AE_MoSno", "AE_Ocean", "AE_Rain", "AE_Rain", "AE_RnGd", "AE_SI12", "AE_SI25", "AE_SI6", "AE_SID", "AE_WkOcn", "AFLVIS1B", "AFLVIS2", "AFOLVIS1A", "AG100", "AG1km", "AG5KMMOH", "AGB_CanopyHt_Cover_NewEngland_1854", "AGB_Carbon_Sequestration_RGGI_1922", "AGB_Great_Slave_Lake_NWT_2365", "AGB_NEP_Disturbance_US_Forests_1829", "AGB_Pantropics_Amazon_Mexico_1824", "AHI_H08-STAR-L2P-v2.70", "AHI_H08-STAR-L3C-v2.70", "AIRABRAD", "AIRABRAD_NRT", "AIRG2SSD", "AIRG2SSD_IRonly", "AIRG2SSD_IRonly", "AIRH2CCF", "AIRH2CCF", "AIRH2RET", "AIRH2RET", "AIRH2SUP", "AIRH2SUP", "AIRH3QP5", "AIRH3QPM", "AIRH3SP8", "AIRH3SPD", "AIRH3SPD", "AIRH3SPM", "AIRH3SPM", "AIRH3ST8", "AIRH3STD", "AIRH3STD", "AIRH3STM", "AIRH3STM", "AIRHBRAD", "AIRI2CCF", "AIRI2CCF", "AIRIBQAP", "AIRIBQAP_NRT", "AIRIBRAD", "AIRIBRAD_NRT", "AIRIBRAD_NRT_BUFR", "AIRICRAD", "AIRICRAD_NRT", "AIRMISR_BARC_2001", "AIRMISR_BARTLETT_2003", "AIRMISR_CLAMS_2001", "AIRMISR_HARVARD_2003", "AIRMISR_HOWLAND_2003", "AIRMISR_KONVEX", "AIRMISR_LUNAR_LAKE_2000", "AIRMISR_LUNAR_LAKE_2001", "AIRMISR_MONTEREY_1999", "AIRMISR_MORGAN_MONROE_2003", "AIRMISR_ROGERS_LAKE_2001", "AIRMISR_SAFARI", "AIRMISR_SERC_2003", "AIRMISR_SNOW_ICE_2001", "AIRMISR_WISCONSIN_2000", "AIRS2CCF", "AIRS2CCF", "AIRS2CCF_NRT", "AIRS2CCF_NRT", "AIRS2RET", "AIRS2RET", "AIRS2RET_NRT", "AIRS2RET_NRT", "AIRS2SPC", "AIRS2STC", "AIRS2SUP", "AIRS2SUP", "AIRS2SUP_NRT", "AIRS2SUP_NRT", "AIRS3C28", "AIRS3C2D", "AIRS3C2M", "AIRS3QP5", "AIRS3QPM", "AIRS3SP8", "AIRS3SPD", "AIRS3SPD", "AIRS3SPM", "AIRS3SPM", "AIRS3ST8", "AIRS3STD", "AIRS3STD", "AIRS3STM", "AIRS3STM", "AIRSAC3MNH3", "AIRSAR_INT_JPG", "AIRSAR_POL_3FP", "AIRSAR_POL_SYN_3FP", "AIRSAR_TOP_C-DEM_STOKES", "AIRSAR_TOP_DEM", "AIRSAR_TOP_DEM_C", "AIRSAR_TOP_DEM_L", "AIRSAR_TOP_DEM_P", "AIRSAR_TOP_L-STOKES", "AIRSAR_TOP_P-STOKES", "AIRSIL3MSOLR", "AIRSM_CPR_MAT", "AIRS_CPR_IND", "AIRS_CPR_MAT", "AIRS_MDS_IND", "AIRS_MLS_IND", "AIRVBQAP", "AIRVBQAP_NRT", "AIRVBRAD", "AIRVBRAD_NRT", "AIRX2RET", "AIRX2RET", "AIRX2SPC", "AIRX2STC", "AIRX2SUP", "AIRX2SUP", "AIRX3C28", "AIRX3C2D", "AIRX3C2M", "AIRX3QP5", "AIRX3QPM", "AIRX3SP8", "AIRX3SPD", "AIRX3SPD", "AIRX3SPM", "AIRX3SPM", "AIRX3ST8", "AIRX3STD", "AIRX3STD", "AIRX3STM", "AIRX3STM", "AIRXAMAP", "AIRXBCAL", "AKFED_V1_1282", "AKWANAVT", "AK_North_Slope_NEE_CH4_Flux_1562", "AK_Regional_CO2_Flux_1389", "AK_Tundra_PFT_FractionalCover_1830", "AK_Yukon_PFT_TopCover_2032", "ALAN_VIIRS_CONUS", "ALOS_AVNIR_OBS_ORI", "ALOS_PSR_KMZ", "ALOS_PSR_L1.0", "ALOS_PSR_L1.1", "ALOS_PSR_L1.5", "ALOS_PSR_L2.2", "ALOS_PSR_RTC_HIGH", "ALOS_PSR_RTC_LOW", "ALTIKA_SARAL_L2_OST_XOGDR", "ALT_GPR_Barrow_1355", "ALT_Maps_AK_CA_2332", "ALT_TIDE_GAUGE_L4_OST_SLA_US_WEST_COAST", "ALT_TIDE_GAUGE_L4_OST_SLA_US_WEST_COAST_DAILY", "AMAZE-08_1308", "AMDBLWV", "AMDBLWV", "AMLR", "AMMA", "AMMBLWV", "AMMBLWV", "AMSR-L1A", "AMSR2-REMSS-L2P-v8.2", "AMSR2-REMSS-L2P_RT-v8.2", "AMSR2-REMSS-L3U-v8.2", "AMSR2-REMSS-L3U-v8a", "AMSR2-REMSS-L3U_RT-v8.2", "AMSR2-REMSS-L3U_RT-v8a", "AMSRE-REMSS-L2P-v7a", "AMSRE-REMSS-L3U-v7a", "AMSREL1A", "AMSRERR_CPR", "AMSRE_AVRMO", "AMSRE_STDMO", "AMT", "ANACONDAS", "ANT", "ANTARCTICA_MASS_TELLUS_MASCON_CRI_TIME_SERIES_RL06.1_V3", "ANTARES_Baja_California_Station", "ANTARES_Ubatuba_Station", "AOL", "AOPEX", "AOSNII", "APPSS", "AQ2_SM", "AQ3_ANSM", "AQ3_DYSM", "AQ3_FT", "AQ3_MCSM", "AQ3_MOSM", "AQ3_NRCS", "AQ3_SCSM", "AQ3_SNSM", "AQ3_SSS", "AQ3_TB", "AQ3_WKSM", "AQUALOOKS", "AQUARIUS_ANCILLARY_CELESTIALSKY_V1", "AQUARIUS_ANCILLARY_RFI_V1", "AQUARIUS_L2_SSS_CAP_V5", "AQUARIUS_L2_SSS_V5", "AQUARIUS_L3_ANCILLARY_SST_SMIA_28DAY-RUNNINGMEAN_V5", "AQUARIUS_L3_ANCILLARY_SST_SMIA_3MONTH_V5", "AQUARIUS_L3_ANCILLARY_SST_SMIA_7DAY_V5", "AQUARIUS_L3_ANCILLARY_SST_SMIA_ANNUAL_V5", "AQUARIUS_L3_ANCILLARY_SST_SMIA_CUMULATIVE_V5", "AQUARIUS_L3_ANCILLARY_SST_SMIA_DAILY_V5", "AQUARIUS_L3_ANCILLARY_SST_SMIA_MONTHLY-CLIMATOLOGY_V5", "AQUARIUS_L3_ANCILLARY_SST_SMIA_MONTHLY_V5", "AQUARIUS_L3_ANCILLARY_SST_SMIA_SEASONAL-CLIMATOLOGY_V5", "AQUARIUS_L3_ANCILLARY_SST_SMID_28DAY-RUNNINGMEAN_V5", "AQUARIUS_L3_ANCILLARY_SST_SMID_3MONTH_V5", "AQUARIUS_L3_ANCILLARY_SST_SMID_7DAY_V5", "AQUARIUS_L3_ANCILLARY_SST_SMID_ANNUAL_V5", "AQUARIUS_L3_ANCILLARY_SST_SMID_CUMULATIVE_V5", "AQUARIUS_L3_ANCILLARY_SST_SMID_DAILY_V5", "AQUARIUS_L3_ANCILLARY_SST_SMID_MONTHLY-CLIMATOLOGY_V5", "AQUARIUS_L3_ANCILLARY_SST_SMID_MONTHLY_V5", "AQUARIUS_L3_ANCILLARY_SST_SMID_SEASONAL-CLIMATOLOGY_V5", "AQUARIUS_L3_ANCILLARY_SST_SMI_28DAY-RUNNINGMEAN_V5", "AQUARIUS_L3_ANCILLARY_SST_SMI_3MONTH_V5", "AQUARIUS_L3_ANCILLARY_SST_SMI_7DAY-RUNNINGMEAN_V5", "AQUARIUS_L3_ANCILLARY_SST_SMI_7DAY_V5", "AQUARIUS_L3_ANCILLARY_SST_SMI_ANNUAL_V5", "AQUARIUS_L3_ANCILLARY_SST_SMI_CUMULATIVE_V5", "AQUARIUS_L3_ANCILLARY_SST_SMI_DAILY_V5", "AQUARIUS_L3_ANCILLARY_SST_SMI_MONTHLY-CLIMATOLOGY_V5", "AQUARIUS_L3_ANCILLARY_SST_SMI_MONTHLY_V5", "AQUARIUS_L3_ANCILLARY_SST_SMI_SEASONAL-CLIMATOLOGY_V5", "AQUARIUS_L3_DENSITY_SMIA_28DAY-RUNNINGMEAN_V5", "AQUARIUS_L3_DENSITY_SMIA_3MONTH_V5", "AQUARIUS_L3_DENSITY_SMIA_7DAY_V5", "AQUARIUS_L3_DENSITY_SMIA_ANNUAL_V5", "AQUARIUS_L3_DENSITY_SMIA_CUMULATIVE_V5", "AQUARIUS_L3_DENSITY_SMIA_DAILY_V5", "AQUARIUS_L3_DENSITY_SMIA_MONTHLY-CLIMATOLOGY_V5", "AQUARIUS_L3_DENSITY_SMIA_MONTHLY_V5", "AQUARIUS_L3_DENSITY_SMIA_SEASONAL-CLIMATOLOGY_V5", "AQUARIUS_L3_DENSITY_SMID_28DAY-RUNNINGMEAN_V5", "AQUARIUS_L3_DENSITY_SMID_3MONTH_V5", "AQUARIUS_L3_DENSITY_SMID_7DAY_V5", "AQUARIUS_L3_DENSITY_SMID_ANNUAL_V5", "AQUARIUS_L3_DENSITY_SMID_CUMULATIVE_V5", "AQUARIUS_L3_DENSITY_SMID_DAILY_V5", "AQUARIUS_L3_DENSITY_SMID_MONTHLY-CLIMATOLOGY_V5", "AQUARIUS_L3_DENSITY_SMID_MONTHLY_V5", "AQUARIUS_L3_DENSITY_SMID_SEASONAL-CLIMATOLOGY_V5", "AQUARIUS_L3_DENSITY_SMI_28DAY-RUNNINGMEAN_V5", "AQUARIUS_L3_DENSITY_SMI_3MONTH_V5", "AQUARIUS_L3_DENSITY_SMI_7DAY-RUNNINGMEAN_V5", "AQUARIUS_L3_DENSITY_SMI_7DAY_V5", "AQUARIUS_L3_DENSITY_SMI_ANNUAL_V5", "AQUARIUS_L3_DENSITY_SMI_CUMULATIVE_V5", "AQUARIUS_L3_DENSITY_SMI_DAILY_V5", "AQUARIUS_L3_DENSITY_SMI_MONTHLY-CLIMATOLOGY_V5", "AQUARIUS_L3_DENSITY_SMI_MONTHLY_V5", "AQUARIUS_L3_DENSITY_SMI_SEASONAL-CLIMATOLOGY_V5", "AQUARIUS_L3_SPICINESS_SMIA_28DAY-RUNNINGMEAN_V5", "AQUARIUS_L3_SPICINESS_SMIA_3MONTH_V5", "AQUARIUS_L3_SPICINESS_SMIA_7DAY_V5", "AQUARIUS_L3_SPICINESS_SMIA_ANNUAL_V5", "AQUARIUS_L3_SPICINESS_SMIA_CUMULATIVE_V5", "AQUARIUS_L3_SPICINESS_SMIA_DAILY_V5", "AQUARIUS_L3_SPICINESS_SMIA_MONTHLY-CLIMATOLOGY_V5", "AQUARIUS_L3_SPICINESS_SMIA_MONTHLY_V5", "AQUARIUS_L3_SPICINESS_SMIA_SEASONAL-CLIMATOLOGY_V5", "AQUARIUS_L3_SPICINESS_SMID_28DAY-RUNNINGMEAN_V5", "AQUARIUS_L3_SPICINESS_SMID_3MONTH_V5", "AQUARIUS_L3_SPICINESS_SMID_7DAY_V5", "AQUARIUS_L3_SPICINESS_SMID_ANNUAL_V5", "AQUARIUS_L3_SPICINESS_SMID_CUMULATIVE_V5", "AQUARIUS_L3_SPICINESS_SMID_DAILY_V5", "AQUARIUS_L3_SPICINESS_SMID_MONTHLY-CLIMATOLOGY_V5", "AQUARIUS_L3_SPICINESS_SMID_MONTHLY_V5", "AQUARIUS_L3_SPICINESS_SMID_SEASONAL-CLIMATOLOGY_V5", "AQUARIUS_L3_SPICINESS_SMI_28DAY-RUNNINGMEAN_V5", "AQUARIUS_L3_SPICINESS_SMI_3MONTH_V5", "AQUARIUS_L3_SPICINESS_SMI_7DAY-RUNNINGMEAN_V5", "AQUARIUS_L3_SPICINESS_SMI_7DAY_V5", "AQUARIUS_L3_SPICINESS_SMI_ANNUAL_V5", "AQUARIUS_L3_SPICINESS_SMI_CUMULATIVE_V5", "AQUARIUS_L3_SPICINESS_SMI_DAILY_V5", "AQUARIUS_L3_SPICINESS_SMI_MONTHLY-CLIMATOLOGY_V5", "AQUARIUS_L3_SPICINESS_SMI_MONTHLY_V5", "AQUARIUS_L3_SPICINESS_SMI_SEASONAL-CLIMATOLOGY_V5", "AQUARIUS_L3_SSS-RainFlagged_SMIA_28DAY-RUNNINGMEAN_V5", "AQUARIUS_L3_SSS-RainFlagged_SMIA_3MONTH_V5", "AQUARIUS_L3_SSS-RainFlagged_SMIA_7DAY_V5", "AQUARIUS_L3_SSS-RainFlagged_SMIA_ANNUAL_V5", "AQUARIUS_L3_SSS-RainFlagged_SMIA_CUMULATIVE_V5", "AQUARIUS_L3_SSS-RainFlagged_SMIA_DAILY_V5", "AQUARIUS_L3_SSS-RainFlagged_SMIA_MONTHLY-CLIMATOLOGY_V5", "AQUARIUS_L3_SSS-RainFlagged_SMIA_MONTHLY_V5", "AQUARIUS_L3_SSS-RainFlagged_SMIA_SEASONAL-CLIMATOLOGY_V5", "AQUARIUS_L3_SSS-RainFlagged_SMID_28DAY-RUNNINGMEAN_V5", "AQUARIUS_L3_SSS-RainFlagged_SMID_3MONTH_V5", "AQUARIUS_L3_SSS-RainFlagged_SMID_7DAY_V5", "AQUARIUS_L3_SSS-RainFlagged_SMID_ANNUAL_V5", "AQUARIUS_L3_SSS-RainFlagged_SMID_CUMULATIVE_V5", "AQUARIUS_L3_SSS-RainFlagged_SMID_DAILY_V5", "AQUARIUS_L3_SSS-RainFlagged_SMID_MONTHLY-CLIMATOLOGY_V5", "AQUARIUS_L3_SSS-RainFlagged_SMID_MONTHLY_V5", "AQUARIUS_L3_SSS-RainFlagged_SMID_SEASONAL-CLIMATOLOGY_V5", "AQUARIUS_L3_SSS-RainFlagged_SMI_28DAY-RUNNINGMEAN_V5", "AQUARIUS_L3_SSS-RainFlagged_SMI_3MONTH_V5", "AQUARIUS_L3_SSS-RainFlagged_SMI_7DAY-RUNNINGMEAN_V5", "AQUARIUS_L3_SSS-RainFlagged_SMI_7DAY_V5", "AQUARIUS_L3_SSS-RainFlagged_SMI_ANNUAL_V5", "AQUARIUS_L3_SSS-RainFlagged_SMI_CUMULATIVE_V5", "AQUARIUS_L3_SSS-RainFlagged_SMI_DAILY_V5", "AQUARIUS_L3_SSS-RainFlagged_SMI_MONTHLY-CLIMATOLOGY_V5", "AQUARIUS_L3_SSS-RainFlagged_SMI_MONTHLY_V5", "AQUARIUS_L3_SSS-RainFlagged_SMI_SEASONAL-CLIMATOLOGY_V5", "AQUARIUS_L3_SSS-RainFlagged_SM_SMIA_MONTHLY_V5", "AQUARIUS_L3_SSS-RainFlagged_SM_SMID_MONTHLY_V5", "AQUARIUS_L3_SSS-RainFlagged_SM_SMI_MONTHLY_V5", "AQUARIUS_L3_SSS_CAP_7DAY_V5", "AQUARIUS_L3_SSS_CAP_MONTHLY_V5", "AQUARIUS_L3_SSS_RAINCORRECTED_CAP_7DAY_V5", "AQUARIUS_L3_SSS_RAINCORRECTED_CAP_MONTHLY_V5", "AQUARIUS_L3_SSS_SMIA_28DAY-RUNNINGMEAN_V5", "AQUARIUS_L3_SSS_SMIA_3MONTH_V5", "AQUARIUS_L3_SSS_SMIA_7DAY_V5", "AQUARIUS_L3_SSS_SMIA_ANNUAL_V5", "AQUARIUS_L3_SSS_SMIA_CUMULATIVE_V5", "AQUARIUS_L3_SSS_SMIA_DAILY_V5", "AQUARIUS_L3_SSS_SMIA_MONTHLY-CLIMATOLOGY_V5", "AQUARIUS_L3_SSS_SMIA_MONTHLY_V5", "AQUARIUS_L3_SSS_SMIA_SEASONAL-CLIMATOLOGY_V5", "AQUARIUS_L3_SSS_SMID_28DAY-RUNNINGMEAN_V5", "AQUARIUS_L3_SSS_SMID_3MONTH_V5", "AQUARIUS_L3_SSS_SMID_7DAY_V5", "AQUARIUS_L3_SSS_SMID_ANNUAL_V5", "AQUARIUS_L3_SSS_SMID_CUMULATIVE_V5", "AQUARIUS_L3_SSS_SMID_DAILY_V5", "AQUARIUS_L3_SSS_SMID_MONTHLY-CLIMATOLOGY_V5", "AQUARIUS_L3_SSS_SMID_MONTHLY_V5", "AQUARIUS_L3_SSS_SMID_SEASONAL-CLIMATOLOGY_V5", "AQUARIUS_L3_SSS_SMI_28DAY-RUNNINGMEAN_V5", "AQUARIUS_L3_SSS_SMI_3MONTH_V5", "AQUARIUS_L3_SSS_SMI_7DAY-RUNNINGMEAN_V5", "AQUARIUS_L3_SSS_SMI_7DAY_V5", "AQUARIUS_L3_SSS_SMI_ANNUAL_V5", "AQUARIUS_L3_SSS_SMI_CUMULATIVE_V5", "AQUARIUS_L3_SSS_SMI_DAILY_V5", "AQUARIUS_L3_SSS_SMI_MONTHLY-CLIMATOLOGY_V5", "AQUARIUS_L3_SSS_SMI_MONTHLY_V5", "AQUARIUS_L3_SSS_SMI_SEASONAL-CLIMATOLOGY_V5", "AQUARIUS_L3_SSS_SM_SMIA_MONTHLY_V5", "AQUARIUS_L3_SSS_SM_SMID_MONTHLY_V5", "AQUARIUS_L3_SSS_SM_SMI_MONTHLY_V5", "AQUARIUS_L3_WIND_SPEED_CAP_7DAY_V5", "AQUARIUS_L3_WIND_SPEED_CAP_MONTHLY_V5", "AQUARIUS_L3_WIND_SPEED_SMIA_28DAY-RUNNINGMEAN_V5", "AQUARIUS_L3_WIND_SPEED_SMIA_3MONTH_V5", "AQUARIUS_L3_WIND_SPEED_SMIA_7DAY_V5", "AQUARIUS_L3_WIND_SPEED_SMIA_ANNUAL_V5", "AQUARIUS_L3_WIND_SPEED_SMIA_CUMULATIVE_V5", "AQUARIUS_L3_WIND_SPEED_SMIA_DAILY_V5", "AQUARIUS_L3_WIND_SPEED_SMIA_MONTHLY-CLIMATOLOGY_V5", "AQUARIUS_L3_WIND_SPEED_SMIA_MONTHLY_V5", "AQUARIUS_L3_WIND_SPEED_SMIA_SEASONAL-CLIMATOLOGY_V5", "AQUARIUS_L3_WIND_SPEED_SMID_28DAY-RUNNINGMEAN_V5", "AQUARIUS_L3_WIND_SPEED_SMID_3MONTH_V5", "AQUARIUS_L3_WIND_SPEED_SMID_7DAY_V5", "AQUARIUS_L3_WIND_SPEED_SMID_ANNUAL_V5", "AQUARIUS_L3_WIND_SPEED_SMID_CUMULATIVE_V5", "AQUARIUS_L3_WIND_SPEED_SMID_DAILY_V5", "AQUARIUS_L3_WIND_SPEED_SMID_MONTHLY-CLIMATOLOGY_V5", "AQUARIUS_L3_WIND_SPEED_SMID_MONTHLY_V5", "AQUARIUS_L3_WIND_SPEED_SMID_SEASONAL-CLIMATOLOGY_V5", "AQUARIUS_L3_WIND_SPEED_SMI_28DAY-RUNNINGMEAN_V5", "AQUARIUS_L3_WIND_SPEED_SMI_3MONTH_V5", "AQUARIUS_L3_WIND_SPEED_SMI_7DAY-RUNNINGMEAN_V5", "AQUARIUS_L3_WIND_SPEED_SMI_7DAY_V5", "AQUARIUS_L3_WIND_SPEED_SMI_ANNUAL_V5", "AQUARIUS_L3_WIND_SPEED_SMI_CUMULATIVE_V5", "AQUARIUS_L3_WIND_SPEED_SMI_DAILY_V5", "AQUARIUS_L3_WIND_SPEED_SMI_MONTHLY-CLIMATOLOGY_V5", "AQUARIUS_L3_WIND_SPEED_SMI_MONTHLY_V5", "AQUARIUS_L3_WIND_SPEED_SMI_SEASONAL-CLIMATOLOGY_V5", "AQUARIUS_L4_OISSS_IPRC_7DAY_V5", "ARB_48_IN_LIDAR", "ARC02", "ARCTAS_AerosolTraceGas_AircraftInSitu_DC8_Data", "ARCTAS_Aerosol_AircraftInSitu_DC8_Data", "ARCTAS_Aerosol_AircraftInSitu_P3B_Data", "ARCTAS_AircraftRemoteSensing_BE200_HSRL_Data", "ARCTAS_AircraftRemoteSensing_DC8_DIAL_Data", "ARCTAS_AircraftRemoteSensing_P3B_AATS14_Data", "ARCTAS_AircraftRemoteSensing_P3B_CAR_Data", "ARCTAS_Cloud_AircraftInSitu_DC8_Data", "ARCTAS_Ground_Data", "ARCTAS_JValue_AircraftInSitu_DC8_Data", "ARCTAS_Merge_DC8-Aircraft_Data", "ARCTAS_Merge_P3B-Aircraft_Data", "ARCTAS_MetNav_AircraftInSitu_DC8_Data", "ARCTAS_MetNav_AircraftInSitu_P3B_Data", "ARCTAS_Model_Data", "ARCTAS_Ozonesondes_Data", "ARCTAS_Radiation_AircraftInSitu_P3B_Data", "ARCTAS_Satellite_Data", "ARCTAS_TraceGas_AircraftInSitu_DC8_Data", "ARCTAS_TraceGas_AircraftInSitu_P3B_Data", "ARCTAS_Trajectory_Data", "ARCTICCC", "ARESE_ER2_MAS", "ARIA_S1_GUNW", "ARISE_Aerosol-TraceGas_AircraftRemoteSensing_C130_Data", "ARISE_Cloud_AircraftInSitu_C130_Data", "ARISE_Merge_Data", "ARISE_MetNav_AircraftInSitu_C130_Data", "ARISE_Radiation_AircraftInSitu_C130_Data", "ARK", "ARME_898", "ASCATA-L2-25km", "ASCATA-L2-Coastal", "ASCATA_ESDR_ANCILLARY_L2_V1.1", "ASCATA_ESDR_L2_WIND_STRESS_V1.1", "ASCATA_L2_25KM_CDR", "ASCATA_L2_COASTAL_CDR", "ASCATB-L2-25km", "ASCATB-L2-Coastal", "ASCATB_ESDR_ANCILLARY_L2_V1.1", "ASCATB_ESDR_L2_WIND_STRESS_V1.1", "ASCATC-L2-25km", "ASCATC-L2-Coastal", "ASCENDS_AVOCET_CA_NV_Feb_2016_2115", "ASCENDS_LAS_IN_Sept_2014_2116", "ASIRI", "ASO_3M_PCDTM", "ASO_3M_SD", "ASO_50M_SD", "ASO_50M_SWE", "AST14DEM", "AST14DMO", "AST14OTH", "ASTGTM", "ASTGTM_NC", "ASTGTM_NUMNC", "ASTWBD", "ASTWBD_ATTNC", "ASTWBD_NC", "AST_05", "AST_07", "AST_07XT", "AST_08", "AST_09", "AST_09T", "AST_09XT", "AST_L1A", "AST_L1AE", "AST_L1B", "AST_L1BE", "AST_L1T", "AST_L1T", "ATCS", "ATL03", "ATL03", "ATL06", "ATL06", "ATL07", "ATL07", "ATL07QL", "ATL08", "ATL08", "ATL08QL", "ATL09", "ATL09", "ATL09QL", "ATL10", "ATL10", "ATL10QL", "ATL11", "ATL11", "ATL12", "ATL12", "ATL13", "ATL13", "ATL13QL", "ATL14", "ATL14", "ATL14", "ATL14", "ATL15", "ATL15", "ATL15", "ATL15", "ATL16", "ATL16", "ATL17", "ATL17", "ATL19", "ATL19", "ATL20", "ATL20", "ATL21", "ATL21", "ATL22", "ATL22", "ATL23", "ATL23", "ATLAS_DEALIASED_SASS_L2", "ATLAS_Veg_Plots_1541", "ATMOSL1", "ATMOSL2AF", "ATMOSL2AT", "ATMOSL2PF", "ATMOSL2PT", "ATMOSL2TF", "ATMOSL2TT", "ATSM2AEF", "ATSM2LSF", "ATSM2STF", "ATSMIB2E", "ATSMIB2T", "ATSMIGEO", "ATTREX-Aircraft_Radiation_Measurements", "ATTREX-Aircraft_RemoteSensing_Temperature_Measurements", "ATTREX-Aircraft_insitu_Cloud_property_Measurements", "ATTREX-Aircraft_insitu_TraceGas_Measurements", "ATTREX-Aircraft_navigational_and_meteorological_Measurements", "ATom_AMP_Instrument_Data_1671", "ATom_AO2_Instrument_Data_V2_1880", "ATom_ATHOS_Instrument_Data_V2_1930", "ATom_Aerosol_Properties_V2_2111", "ATom_Aerosols_Meteorology_1684", "ATom_CAFS_Instrument_Data_V2_1933", "ATom_CAPSVienna_Data_1981", "ATom_CESM2_1878", "ATom_CIT_Instrument_Data_V2_1927", "ATom_CO_GEOS_1604", "ATom_Carbon_Aerosol_Loadings_1618", "ATom_Clouds_Aerosols_2250", "ATom_DLH_Instrument_Data_V2_1937", "ATom_FlightTrack_Influences_1889", "ATom_Forward_Flight_Videos_1938", "ATom_FullModel_DataStream_1877", "ATom_GT_CIMS_Instrument_Data_1715", "ATom_GlobalModelInitiative_CTM_1897", "ATom_HIPPO_ORCAS_1788", "ATom_HR-AMS_Instrument_Data_1716", "ATom_ISAF_Instrument_Data_1730", "ATom_MMS_Instrument_Data_1731", "ATom_Mapping_OH_Troposphere_1669", "ATom_Medusa_Instrument_Data_V2_1881", "ATom_Mineral_Dust_Cirrus_Cloud_2006", "ATom_Modeled_Observed_Data_1857", "ATom_NMASS_Data_1607", "ATom_NOyO3_Instrument_Data_1734", "ATom_Organic_Aerossols_1795", "ATom_Ozonesonde_InstrumentData_1910", "ATom_PALMS_Instrument_Data_1733", "ATom_PANTHER_Instrument_Data_1914", "ATom_PFP_Instrument_Data_1746", "ATom_Particulate_Iodine_1773", "ATom_Photolysis_Rates_1651", "ATom_Picarro_Instrument_Data_1732", "ATom_QCLS_Instrument_Data_V2_1932", "ATom_Rad_Measurements_ARMAS_1906", "ATom_SAGA_Instrument_Data_1748", "ATom_SO2_LIF_Instrument_Data_1890", "ATom_SOAP_Instrument_Data_1898", "ATom_SP2_Instrument_Data_1672", "ATom_SP2_LAM_FeOx_MMR_1828", "ATom_Simulated_Data_1597", "ATom_TOGA_Instrument_Data_V2_1936", "ATom_UCATS_Instrument_Data_1750", "ATom_UHSAS_Data_1619", "ATom_WAS_Instrument_Data_1751", "ATom_merge_1581", "ATom_merge_V2_1925", "ATom_nav_1613", "AU_5DSno", "AU_DyOcn", "AU_DySno", "AU_DySno_NRT_R02", "AU_Land_NRT_R02", "AU_MoSno", "AU_Ocean_NRT_R01", "AU_Rain_NRT_R02", "AU_SI12", "AU_SI12_NRT_R04", "AU_SI25", "AU_SI25_NRT_R04", "AU_SI6", "AU_SI6_NRT_R04", "AVHRR18_G-NAVO-L2P-v1.0", "AVHRR19_G-NAVO-L2P-v1.0", "AVHRR19_L-NAVO-L2P-v1.0", "AVHRRF_MA-STAR-L2P-v2.80", "AVHRRF_MA-STAR-L3U-v2.80", "AVHRRF_MB-STAR-L2P-v2.80", "AVHRRF_MB-STAR-L3U-v2.80", "AVHRRF_MC-STAR-L2P-v2.80", "AVHRRF_MC-STAR-L3U-v2.80", "AVHRRMTA_G-NAVO-L2P-v1.0", "AVHRRMTA_G-NAVO-L2P-v2.0", "AVHRRMTB_G-NAVO-L2P-v1.0", "AVHRRMTB_G-NAVO-L2P-v2.0", "AVHRRMTC_G-NAVO-L2P-v2.0", "AVHRR_Fire_Products_1545", "AVHRR_LST_826", "AVHRR_OI-NCEI-L4-GLOB-v2.0", "AVHRR_OI-NCEI-L4-GLOB-v2.1", "AVHRR_SST_METOP_A-OSISAF-L2P-v1.0", "AVHRR_SST_METOP_A_GLB-OSISAF-L3C-v1.0", "AVHRR_SST_METOP_A_NAR-OSISAF-L3C-v1.0", "AVHRR_SST_METOP_B-OSISAF-L2P-v1.0", "AVHRR_SST_METOP_B_GLB-OSISAF-L3C-v1.0", "AVHRR_SST_METOP_B_NAR-OSISAF-L3C-v1.0", "AVHRR_SST_NOAA19_NAR-OSISAF-L3C-v1.0", "AVIRIS-Classic_L1B_Radiance_2155", "AVIRIS-Classic_L2_Reflectance_2154", "AVIRIS-NG_Data_Idaho_1533", "AVIRIS-NG_L1B_radiance_2095", "AVIRIS-NG_L2_Reflectance_2110", "AVIRIS_FlightLine_Locator_2140", "Academ_Kurchatov", "Active_Fluorescence_2001", "Active_Layer_Thaw_Depths_1701", "Aeolus-CalVal-DAWN_DC8", "Aeolus-CalVal-Dropsondes_DC8", "Aeolus-CalVal-HALO_DC8", "Aeolus-CalVal-MetNav_DC8", "Aerosol_Sulfate_LowermostStrat_1868", "AfriSAR_AGB_Maps_1681", "AfriSAR_LVIS_Footprint_Cover_1591", "AfriSAR_Mondah_Field_Data_1580", "African_Rainfall_Patterns_1263", "Afrisar_LVIS_Biomass_VProfiles_1775", "AirMOSS_Field_Data_Harvard_1677", "AirMOSS_L1_Sigma0_BERMS_1406", "AirMOSS_L1_Sigma0_Chamel_1407", "AirMOSS_L1_Sigma0_DukeFr_1408", "AirMOSS_L1_Sigma0_Harvrd_1409", "AirMOSS_L1_Sigma0_Howlnd_1410", "AirMOSS_L1_Sigma0_LaSelv_1411", "AirMOSS_L1_Sigma0_Metoli_1412", "AirMOSS_L1_Sigma0_Moisst_1413", "AirMOSS_L1_Sigma0_TonziR_1414", "AirMOSS_L1_Sigma0_Walnut_1415", "AirMOSS_L2_3_RZ_Soil_Moisture_1418", "AirMOSS_L2_Carbon_Flux_1420", "AirMOSS_L2_Inground_Soil_Moist_1416", "AirMOSS_L2_Precipitation_1417", "AirMOSS_L4_Daily_NEE_1422", "AirMOSS_L4_RZ_Soil_Moisture_1421", "AirMOSS_L4_Regional_NEE_1423", "AirMSPI_ACEPOL_Ellipsoid-projected_Georegistered_Radiance_Data", "AirMSPI_ACEPOL_Terrain-projected_Georegistered_Radiance_Data", "AirMSPI_CalWater-2_Ellipsoid-projected_Georegistered_Radiance_Data", "AirMSPI_CalWater-2_Terrain-projected_Georegistered_Radiance_Data", "AirMSPI_FIREX-AQ_Terrain-projected_Georegistered_Radiance_Data", "AirMSPI_ImPACT-PM_Ellipsoid-projected_Georegistered_Radiance_Data", "AirMSPI_ImPACT-PM_Terrain-projected_Georegistered_Radiance_Data", "AirMSPI_ORACLES_Ellipsoid-projected_Georegistered_Radiance_Data", "AirMSPI_ORACLES_Terrain-projected_Georegistered_Radiance_Data", "AirMSPI_PODEX_Ellipsoid-projected_Georegistered_Radiance_Data", "AirMSPI_PODEX_Terrain-projected_Georegistered_Radiance_Data", "AirMSPI_RADEX_Ellipsoid-projected_Georegistered_Radiance_Data", "AirMSPI_RADEX_Terrain-projected_Georegistered_Radiance_Data", "AirMSPI_SEAC4RS_Ellipsoid-projected_Georegistered_Radiance_Data", "AirMSPI_SEAC4RS_Terrain-projected_Georegistered_Radiance_Data", "AirMSPI_SPEX-PR_Ellipsoid-projected_Georegistered_Radiance_Data", "AirMSPI_SPEX-PR_Terrain-projected_Georegistered_Radiance_Data", "AirSWOT_Orthomosaic_WaterMask_1655", "Airborne_Insitu_Measurements_1784", "Akademik_Sergey_Vavilov", "Alaska_Arctic_Tundra_Veg_Map_1353", "Alaska_L4_WRF_STILT_Footprints_1544", "Alaska_L4_WRF_STILT_Particle_1571", "Alaska_Lake_Pond_Maps_2134", "Alaska_Yukon_NDVI_1614", "Alaskan_CH4_CO2_Fluxes_1316", "Alaskan_CO2_Flux_1325", "Albedo_Boreal_North_America_1605", "Alder_Shrub_Soil_Alaska_V2_2300", "Annual_30m_AGB_1808", "Annual_Burned_Area_Maps_1708", "Annual_Landcover_ABoVE_1691", "Annual_Seasonality_Greenness_1698", "Annual_Thaw_Slump_1724", "Apero", "Aqua_AIRS_MODIS1km_IND", "ArabianSea_2011", "Arc00", "ArcticNET", "ArcticTreeLine_Dendrometry_Env_2185", "ArcticTreeLine_Spruce_CO2_WV_1948", "Arctic_Boreal_Burned_Area_V2_2328", "Arctic_Boreal_CO2_Flux_1934", "Arctic_Flux_629", "Arctic_Network_Veg_plots_1542", "Arctic_RSWQ", "Arctic_Soil_Properties_2149", "Arctic_Vegetation_Maps_1323", "Arctic_Wildlife_Refuge_Veg_Map_1384", "Arctic_Winter_Respiration_v2_1762", "Arrigetch_Peaks_Veg_Plots_1358", "Atmospheric_CO2_California_1641", "Atqasuk_Veg_Plots_1371", "B01", "B02", "B03", "B04", "B05", "B06", "B07", "B08", "B09", "BAHAMAS2004", "BAROCLINIC_HRET14", "BASIN_TCP_963", "BBOP", "BCFLEXPART", "BC_Aerosol_Dynamics_Alaska_1340", "BDSNP_CMAQ_Model_1351", "BENEFIT", "BEST", "BFO_dsp01_ccrs_avhrr_landcover_589", "BFO_dsp01_ccrs_tm_landcover_588", "BFO_dsp04_ers_freeze-thaw_maps_590", "BFO_dsp05_ccrs_avhrr_npp_591", "BFO_dsp06_casi_lai_cc_592", "BFO_dsp08_polder_surface_params_594", "BFO_dsp09_moss_map_595", "BFO_dsp09_sask_fire_map_596", "BFO_dsp10_fpar_lai_1994_585", "BFO_dsp10_landcover_598", "BFO_dsp10_landcover_tm_mosaic_602", "BFO_dsp10_landcover_tm_reclassed_597", "BFO_dsp10_moss_cover_599", "BFO_dsp10_ndvi_600", "BFO_dsp10_peatlands_601", "BFO_flx01_derived_data_603", "BFO_flx01_flux_met_temp_604", "BFO_flx03_area_avg_flux_586", "BFO_flx04_nsa_burn_flux_587", "BFO_hmet01_goes8_level2_srb_605", "BFO_hmet01_ssmi_precip_606", "BFO_hmet02_gridded_met_phase2-3_607", "BFO_hmet03_hourly_met_p1_608", "BFO_hmet04_src_nsa_96-98_609", "BFO_mod01_gridded_met_p3_daily_610", "BGC_glider_GNATS", "BILIM", "BIO-GO-SHIP", "BIOCOMPLEXITY", "BIODIVERSITY", "BIOME", "BIOME_BGC_4_1_1_805", "BIOME_BGC_m2_4_1_2_809", "BIOME_BGC_m_4_1_1_806", "BIOSCAPE_COASTAL_CARBON", "BIOSOPE", "BIO_BURN_5X5_HAO_NAT", "BLATM1B", "BLATM2", "BLVIS2", "BNL", "BOA", "BOREAS_CDS_1350", "BOREAS_RSS-03_Snapshots_289", "BOREAS_SLICER_508", "BOUSSOLE", "BRAZIL", "BRMCR2", "BUVN04L2", "BUVN04L3zm", "BUVN4L1DCM", "BUVN4L1DCW", "BUVN4L1PDB", "BUVN4L1RUT", "BUVN4L2CPOZ", "BUVN4L2HDBUV", "BUVN4L3ZMT", "Barrow_NGEE_Arctic_Veg_Plots_1505", "Barrow_Tundra_Veg_Plots_1535", "Barter_Barrow_Veg_Plots_1534", "BenthicEcology_fromSpace", "Ber95", "Ber96", "BeringSea_LaserFluoMon", "Bermuda_Testbed_Mooring", "Big_Bend", "Bio_optics_Chl_polarization", "Biogenic_CO2flux_SIF_SMUrF_1899", "BlueFlux_AirborneObs_Florida_2327", "BlueFlux_Tidal_River_Water_2333", "Blue_Carbon_Tidal_Wetland_Maps_2091", "Bold_EPAHypoxia", "BorealForest_Greenness_Trends_2023", "Boreal_AGB_Density_ICESat2_2186", "Boreal_Arctic_Wetland_CH4_2351", "Boreal_CanopyCover_StandAge_2012", "Boreal_Fire_Severity_Metrics_1520", "BowdoinBuoy", "BurnedArea_Emissions_AK_YT_NWT_1812", "Burned_Area_Depth_AK_CA_2063", "C-HARRIER", "C5", "CABARE_918", "CALIPSO-NVF_HSRL2_KingAir_Data", "CAL_IIR_L1-Standard-V2-00", "CAL_IIR_L2_Swath-Standard-V4-20", "CAL_IIR_L2_Swath-Standard-V4-21", "CAL_IIR_L2_Swath-Standard-V4-51", "CAL_IIR_L2_Track-Standard-V4-20", "CAL_IIR_L2_Track-Standard-V4-21", "CAL_IIR_L2_Track-Standard-V4-51", "CAL_IIR_L3_GEWEX_Cloud-Standard-V1-00", "CAL_LID_L1-Standard-V4-10", "CAL_LID_L1-Standard-V4-11", "CAL_LID_L1-Standard-V4-51", "CAL_LID_L15-Standard-V1-00", "CAL_LID_L15-Standard-V1-01", "CAL_LID_L2_01kmCLay-Standard-V4-20", "CAL_LID_L2_01kmCLay-Standard-V4-21", "CAL_LID_L2_01kmCLay-Standard-V4-51", "CAL_LID_L2_05kmALay-Prov-V3-02", "CAL_LID_L2_05kmALay-Prov-V3-30", "CAL_LID_L2_05kmALay-Standard-V4-20", "CAL_LID_L2_05kmALay-Standard-V4-21", "CAL_LID_L2_05kmALay-Standard-V4-51", "CAL_LID_L2_05kmAPro-Prov-V3-02", "CAL_LID_L2_05kmAPro-Standard-V4-20", "CAL_LID_L2_05kmAPro-Standard-V4-21", "CAL_LID_L2_05kmAPro-Standard-V4-51", "CAL_LID_L2_05kmCLay-Standard-V4-20", "CAL_LID_L2_05kmCLay-Standard-V4-21", "CAL_LID_L2_05kmCLay-Standard-V4-51", "CAL_LID_L2_05kmCPro-Standard-V4-20", "CAL_LID_L2_05kmCPro-Standard-V4-21", "CAL_LID_L2_05kmCPro-Standard-V4-51", "CAL_LID_L2_05kmMLay-Standard-V4-20", "CAL_LID_L2_05kmMLay-Standard-V4-21", "CAL_LID_L2_05kmMLay-Standard-V4-51", "CAL_LID_L2_333mCLay-ValStage1-V3-30", "CAL_LID_L2_333mMLay-Standard-V4-20", "CAL_LID_L2_333mMLay-Standard-V4-21", "CAL_LID_L2_333mMLay-Standard-V4-51", "CAL_LID_L2_BlowingSnow_Antarctica-Standard-V1-00", "CAL_LID_L2_BlowingSnow_Antarctica-Standard-V1-01", "CAL_LID_L2_BlowingSnow_Antarctica-Standard-V2-00", "CAL_LID_L2_BlowingSnow_Greenland-Standard-V1-00", "CAL_LID_L2_PSCMask-Standard-V2-00", "CAL_LID_L2_VFM-Standard-V4-20", "CAL_LID_L2_VFM-Standard-V4-21", "CAL_LID_L2_VFM-Standard-V4-51", "CAL_LID_L3_Cloud_Occurrence-Standard-V1-00", "CAL_LID_L3_GEWEX_Cloud-Standard-V1-00", "CAL_LID_L3_Stratospheric_APro-Standard-V1-00", "CAL_LID_L3_Stratospheric_APro-Standard-V1-01", "CAL_LID_L3_Tropospheric_APro_AllSky-Standard-V4-20", "CAL_LID_L3_Tropospheric_APro_AllSky-Standard-V4-21", "CAL_LID_L3_Tropospheric_APro_CloudFree-Standard-V4-20", "CAL_LID_L3_Tropospheric_APro_CloudFree-Standard-V4-21", "CAL_LID_L3_Tropospheric_APro_CloudySkyOpaque-Standard-V4-20", "CAL_LID_L3_Tropospheric_APro_CloudySkyOpaque-Standard-V4-21", "CAL_LID_L3_Tropospheric_APro_CloudySkyTransparent-Standard-V4-20", "CAL_LID_L3_Tropospheric_APro_CloudySkyTransparent-Standard-V4-21", "CAL_WFC_L1_125m-ValStage1-V3-01", "CAL_WFC_L1_125m-ValStage1-V3-02", "CAL_WFC_L1_1Km-ValStage1-V3-01", "CAL_WFC_L1_1Km-ValStage1-V3-02", "CAL_WFC_L1_IIR-ValStage1-V3-01", "CAM5K30CF", "CAM5K30CF", "CAM5K30EM", "CAM5K30EM", "CAM5K30UC", "CAM5K30UC", "CAMEX4_ER2_MAS", "CAML", "CAMP2Ex-Cloud-Precip-Retrieval", "CAMP2Ex_Aerosol_AircraftInSitu_Learjet_Data", "CAMP2Ex_Aerosol_AircraftInSitu_P3_Data", "CAMP2Ex_Aerosol_AircraftRemoteSensing_P3_Data", "CAMP2Ex_Cloud_AircraftInSitu_Learjet_Data", "CAMP2Ex_Cloud_AircraftInSitu_P3_Data", "CAMP2Ex_Merge_Data", "CAMP2Ex_MetNav_AircraftInSitu_Learjet_Data", "CAMP2Ex_MetNav_AircraftInSitu_P3_Data", "CAMP2Ex_Miscellaneous_Data", "CAMP2Ex_Radiation_AircraftInSitu_P3_Data", "CAMP2Ex_TraceGas_AircraftInSitu_P3_Data", "CAMREX_904", "CARAFE_2016_2017_v2_2002", "CARIACO", "CARMIAAE", "CARMITST", "CARVE_Ecosystem_CH4_Flux_1558", "CARVE_L1_FTS_Spectra_1426", "CARVE_L1_FlightPath_1425", "CARVE_L1_FlightPath_Winds_1427", "CARVE_L1_Ground_Flux_1424", "CARVE_L1_Infrared_1428", "CARVE_L2_AtmosGas_Ground_1419", "CARVE_L2_AtmosGas_Harvard_1403", "CARVE_L2_AtmosGas_Merge_1402", "CARVE_L2_AtmosGas_NOAA_1401", "CARVE_L2_FTS_ColumnGas_1429", "CARVE_L2_Flask_1404", "CARVE_L2_Flask_Ground_1405", "CARVE_L4_WRF-STILT_Footprint_1431", "CARVE_L4_WRF-STILT_Particle_1430", "CARVE_Land_Thaw_State_1383", "CARVE_Photos_1435", "CARVE_Reports_1434", "CARVE_Videos_1433", "CAR_ARCTAS_BRDF", "CAR_ARCTAS_L1C", "CAR_ARMCAS_BRDF", "CAR_ARMCAS_L1C", "CAR_BRDF_709", "CAR_CLAMS_BRDF", "CAR_CLAMS_L1C", "CAR_CLASIC_BRDF", "CAR_CLASIC_L1C", "CAR_DISCOVERAQ_BRDF", "CAR_DISCOVERAQ_L1C", "CAR_ECO3D_BRDF", "CAR_ECO3D_L1C", "CAR_FIREACE_BRDF", "CAR_FIREACE_L1C", "CAR_INTEXB_BRDF", "CAR_INTEXB_L1C", "CAR_KUWAITOILFIRE_BRDF", "CAR_KUWAITOILFIRE_L1C", "CAR_LEADEX_BRDF", "CAR_LEADEX_L1C", "CAR_SAFARI_BRDF", "CAR_SAFARI_L1C", "CAR_SCARA_BRDF", "CAR_SCARA_L1C", "CAR_SCARB_BRDF", "CAR_SCARB_L1C", "CAR_SKUKUZA_BRDF", "CAR_SKUKUZA_L1C", "CAR_SNOWEX17_BRDF", "CAR_SNOWEX17_L1C", "CAR_TARFOX_BRDF", "CAR_TARFOX_L1C", "CASES", "CATS-ISS_L1B_D-M7.1-V3-00", "CATS-ISS_L1B_D-M7.2-V3-00", "CATS-ISS_L1B_N-M7.1-V3-00", "CATS-ISS_L1B_N-M7.2-V3-00", "CATS-ISS_L2O_D-M7.1-V3-01_05kmLay", "CATS-ISS_L2O_D-M7.1-V3-01_05kmPro", "CATS-ISS_L2O_D-M7.2-V3-00_05kmLay", "CATS-ISS_L2O_D-M7.2-V3-00_05kmPro", "CATS-ISS_L2O_D-M7.2-V3-01_05kmLay", "CATS-ISS_L2O_D-M7.2-V3-01_05kmPro", "CATS-ISS_L2O_N-M7.1-V3-01_05kmLay", "CATS-ISS_L2O_N-M7.1-V3-01_05kmPro", "CATS-ISS_L2O_N-M7.2-V3-00_05kmLay", "CATS-ISS_L2O_N-M7.2-V3-00_05kmPro", "CATS-ISS_L2O_N-M7.2-V3-01_05kmLay", "CATS-ISS_L2O_N-M7.2-V3-01_05kmPro", "CCE-LTER", "CCMP_WINDS_10M6HR_L4_V3.1", "CCMP_WINDS_10MMONTHLY_L4_V3.1", "CD01_BRAMS_907", "CD01_CIRSAN_Meteorology_2001_1114", "CD02_Atmosphere_CO2_Isotopes_1011", "CD02_C_N_Isotopes_1097", "CD02_C_N_O_Organic_983", "CD02_Forest_Canopy_Structure_1009", "CD02_Leaf_Level_Gas_Exchange_1010", "CD02_Leaf_Water_Potential_1100", "CD02_O_H_Isotopes_1008", "CD03_Ceilometer_Km67_942", "CD03_Mesoscale_Meteorology_944", "CD03_Pasture_Flux_962", "CD03_Tethered_Balloon_1108", "CD04_Biomass_990", "CD04_CO2_Profiles_947", "CD04_Dendrometry_989", "CD04_LAI_992", "CD04_LAI_Estimates_1103", "CD04_Leaf_Level_Gas_Exchange_1060", "CD04_Leaf_Litter_991", "CD04_Logging_Damage_1038", "CD04_Meteorology_Fluxes_946", "CD04_Soil_Moisture_Km83_979", "CD04_Soil_Respiration_1039", "CD04_Tower_Flux_Gap_978", "CD05_Fuel_Loads_1233", "CD05_Micromet_1169", "CD05_REE_Fuel_Sticks_Moisture_1232", "CD06_BGC_JiParana_1227", "CD06_C02_Exchange_1136", "CD06_C_Isotopes_1120", "CD06_Camrex_1086", "CD06_Carbon_respiration_1125", "CD06_LULC_Map_JiParana_1087", "CD06_Landuse_Timeseries_JiParana_844", "CD06_Outgassing_1151", "CD06_Physical_Template_JiParana_1090", "CD06_Soils_JiParana_1088", "CD06_Water_Balance_JiParana_1132", "CD07_GOES_L3_Gridded_SRB_831", "CD08_CWD_Res_and_Decomp_Manaus_911", "CD08_C_Isotopes_Belowground_1025", "CD08_Ecosystem_Resp_Manaus_912", "CD08_Leaf_Isotopes_Manaus_1245", "CD08_Radiocarbon_Dates_997", "CD08_Radiocarbon_Dates_Manaus_996", "CD08_Tree_Growth_Manaus_1194", "CD08_Tree_Inventory_Ducke_910", "CD09_Soils_Veg_Tapajos_1104", "CD10_Biometry_Tapajos_854", "CD10_CO2_Profiles_Tapajos_855", "CD10_CO_CO2_Maxaranguape_1012", "CD10_CO_Tapajos_856", "CD10_CWD_Tapajos_858", "CD10_DBH_Tapajos_859", "CD10_EddyFlux_Tapajos_860", "CD10_H2O_Profiles_Tapajos_861", "CD10_Litter_Tapajos_862", "CD10_Temperature_Profiles_Tapajos_863", "CD11_Forest_Degradation_1118", "CD15_Productivity_1167", "CD17_Forest_Survey_1254", "CD32_Fluxes_Brazil_1842", "CD32_LBA_MIP_Drivers_1177", "CD34_Amazon_Hyperion_1064", "CD34_Amazon_Landsat_1176", "CD36_SALDAS_1162", "CD37_Biomass_Landsat_Glas_1145", "CDDIS_DORIS_data_cycle", "CDDIS_DORIS_data_rinex", "CDDIS_DORIS_information", "CDDIS_DORIS_products_geocenter", "CDDIS_DORIS_products_ionosphere", "CDDIS_DORIS_products_orbit", "CDDIS_DORIS_products_positions", "CDDIS_DORIS_products_quaternions", "CDDIS_DORIS_products_stcd", "CDDIS_GNSS_highrate_data", "CDDIS_GNSS_products_IGS20", "CDDIS_GNSS_products_clocks_final", "CDDIS_GNSS_products_clocks_rapid", "CDDIS_GNSS_products_clocks_realtime", "CDDIS_GNSS_products_erp", "CDDIS_GNSS_products_ionosphere_final", "CDDIS_GNSS_products_ionosphere_predicted", "CDDIS_GNSS_products_ionosphere_rapid", "CDDIS_GNSS_products_orbit_final", "CDDIS_GNSS_products_orbit_rapid", "CDDIS_GNSS_products_orbit_realtime", "CDDIS_GNSS_products_orbit_ultrarapid", "CDDIS_GNSS_products_positions", "CDDIS_GNSS_products_troposphere", "CDDIS_MISC_information", "CDDIS_SLR_data", "CDDIS_SLR_predictions", "CDDIS_SLR_products_poseop", "CDDIS_VLBI_data", "CDDIS_VLBI_products_eop", "CDDIS_VLBI_products_positions", "CDDIS_VLBI_products_troposphere", "CDIAC_NDP41_220", "CER-NEWS_CCCM_Aqua-FM3-MODIS-CAL-CS", "CERES-MISR-MODIS_SSF-SSFM_Terra-FM1", "CERES-MISR-MODIS_SSF-SSFM_Terra-FM2", "CERES_EBAF", "CERES_EBAF", "CERES_EBAF-TOA", "CERES_EBAF-TOA", "CER_BDS_Aqua-FM4", "CER_BDS_Aqua-FM4", "CER_BDS_TRMM-PFM", "CER_CCCM_Aqua-FM3-MODIS-CAL-CS", "CER_CCCM_Aqua-FM3-MODIS-CAL-CS", "CER_CRS1deg-Hour_Aqua-MODIS", "CER_CRS1deg-Hour_Terra-MODIS", "CER_CRS_Aqua-FM3-MODIS", "CER_CRS_Aqua-FM3-MODIS", "CER_CRS_Aqua-FM4-MODIS", "CER_CRS_TRMM-PFM-VIRS", "CER_CRS_Terra-FM1-MODIS", "CER_CRS_Terra-FM1-MODIS", "CER_CRS_Terra-FM1-MODIS", "CER_CRS_Terra-FM2-MODIS", "CER_CldTypHist_GEO-MODIS", "CER_CldTypHist_GEO-MODIS-VIIRS", "CER_ES4_Aqua-Xtrk", "CER_ES4_NOAA20-FM6", "CER_ES4_NPP-FM5", "CER_ES4_TRMM-PFM", "CER_ES4_Terra+Aqua", "CER_ES4_Terra-Xtrk", "CER_ES8_Aqua-FM4", "CER_ES8_TRMM-PFM", "CER_ES9_Aqua-Xtrk", "CER_ES9_NOAA20-FM6", "CER_ES9_NPP-FM5", "CER_ES9_TRMM-PFM", "CER_ES9_Terra+Aqua", "CER_ES9_Terra-Xtrk", "CER_FSW_Aqua-FM4-MODIS", "CER_FSW_TRMM-PFM-VIRS", "CER_FSW_Terra-FM1-MODIS", "CER_FSW_Terra-FM1-MODIS", "CER_FSW_Terra-FM1-MODIS", "CER_FSW_Terra-FM2-MODIS", "CER_FluxByCldTyp-Day_NOAA20-VIIRS", "CER_FluxByCldTyp-Day_Terra-Aqua-MODIS", "CER_FluxByCldTyp-Month_NOAA20-VIIRS", "CER_FluxByCldTyp-Month_Terra-Aqua-MODIS", "CER_GEO_Ed4_GMS05_FD", "CER_GEO_Ed4_GOE08_NH", "CER_GEO_Ed4_GOE08_SH", "CER_GEO_Ed4_GOE09_NH", "CER_GEO_Ed4_GOE09_SH", "CER_GEO_Ed4_GOE10_NH", "CER_GEO_Ed4_GOE10_SH", "CER_GEO_Ed4_GOE11_NH", "CER_GEO_Ed4_GOE11_SH", "CER_GEO_Ed4_GOE12_NH", "CER_GEO_Ed4_GOE12_SH", "CER_GEO_Ed4_GOE13_NH", "CER_GEO_Ed4_GOE13_NH", "CER_GEO_Ed4_GOE13_SH", "CER_GEO_Ed4_GOE13_SH", "CER_GEO_Ed4_GOE14_NH", "CER_GEO_Ed4_GOE14_SH", "CER_GEO_Ed4_GOE15_NH", "CER_GEO_Ed4_GOE15_NH", "CER_GEO_Ed4_GOE15_SH", "CER_GEO_Ed4_GOE15_SH", "CER_GEO_Ed4_MET05_FD", "CER_GEO_Ed4_MET07_FD", "CER_GEO_Ed4_MET08_NH", "CER_GEO_Ed4_MET08_NH", "CER_GEO_Ed4_MET08_SH", "CER_GEO_Ed4_MET09_NH", "CER_GEO_Ed4_MET09_SH", "CER_GEO_Ed4_MET10_NH", "CER_GEO_Ed4_MET10_SH", "CER_GEO_Ed4_MTS01_NH", "CER_GEO_Ed4_MTS01_SH", "CER_GEO_Ed4_MTS02_NH", "CER_GEO_Ed4_MTS02_SH", "CER_ISCCP-D2like-Day_Aqua-FM3-MODIS", "CER_ISCCP-D2like-Day_Aqua-FM4-MODIS", "CER_ISCCP-D2like-Day_Terra-FM1-MODIS", "CER_ISCCP-D2like-Day_Terra-FM2-MODIS", "CER_ISCCP-D2like-GEO_DAY", "CER_ISCCP-D2like-Mrg_GEO-MODIS-DAY", "CER_ISCCP-D2like-Nit_Aqua-FM3-MODIS", "CER_ISCCP-D2like-Nit_Aqua-FM4-MODIS", "CER_ISCCP-D2like-Nit_Terra-FM1-MODIS", "CER_ISCCP-D2like-Nit_Terra-FM2-MODIS", "CER_SSF1deg-Day_Aqua-MODIS", "CER_SSF1deg-Day_NOAA20-VIIRS", "CER_SSF1deg-Day_NPP-VIIRS", "CER_SSF1deg-Day_Terra-MODIS", "CER_SSF1deg-Hour_Aqua-MODIS", "CER_SSF1deg-Hour_NOAA20-VIIRS", "CER_SSF1deg-Hour_NPP-VIIRS", "CER_SSF1deg-Hour_Terra-MODIS", "CER_SSF1deg-Month_Aqua-MODIS", "CER_SSF1deg-Month_NOAA20-VIIRS", "CER_SSF1deg-Month_NPP-VIIRS", "CER_SSF1deg-Month_Terra-MODIS", "CER_SSF_Aqua-FM4-MODIS", "CER_SSF_TRMM-PFM-VIRS", "CER_SYN1deg-1Hour_Terra-Aqua-MODIS", "CER_SYN1deg-1Hour_Terra-MODIS", "CER_SYN1deg-1Hour_Terra-NOAA20", "CER_SYN1deg-1Hour_Terra-NPP", "CER_SYN1deg-3Hour_Terra-Aqua-MODIS", "CER_SYN1deg-3Hour_Terra-MODIS", "CER_SYN1deg-3Hour_Terra-NPP", "CER_SYN1deg-Day_Terra-Aqua-MODIS", "CER_SYN1deg-Day_Terra-MODIS", "CER_SYN1deg-Day_Terra-NOAA20", "CER_SYN1deg-Day_Terra-NPP", "CER_SYN1deg-MHour_Terra-Aqua-MODIS", "CER_SYN1deg-MHour_Terra-MODIS", "CER_SYN1deg-MHour_Terra-NOAA20", "CER_SYN1deg-MHour_Terra-NPP", "CER_SYN1deg-Month_Terra-Aqua-MODIS", "CER_SYN1deg-Month_Terra-MODIS", "CER_SYN1deg-Month_Terra-NOAA20", "CER_SYN1deg-Month_Terra-NPP", "CFL", "CH4_Aircraft_STILT_footprints_1300", "CH4_CO2_WaterBodies_YK_Delta_2178", "CH4_Flux_BigTrail_Goldstream_1778", "CH4_Fluxes_ThermokarstLakes_AK_1870", "CH4_Plume_AVIRIS-NG_1727", "CHELTON_SEASAT_SASS_L3", "CIESIN_CHRR_NDH_CYCLONE_HFD", "CIESIN_CHRR_NDH_CYCLONE_MRD", "CIESIN_CHRR_NDH_CYCLONE_PELRD", "CIESIN_CHRR_NDH_CYCLONE_TELRD", "CIESIN_CHRR_NDH_DROUGHT_HFD", "CIESIN_CHRR_NDH_DROUGHT_MRD", "CIESIN_CHRR_NDH_DROUGHT_PELRD", "CIESIN_CHRR_NDH_DROUGHT_TELRD", "CIESIN_CHRR_NDH_EQUAKE_HFD", "CIESIN_CHRR_NDH_EQUAKE_MRD", "CIESIN_CHRR_NDH_EQUAKE_PELRD", "CIESIN_CHRR_NDH_EQUAKE_PGA", "CIESIN_CHRR_NDH_EQUAKE_TELRD", "CIESIN_CHRR_NDH_FLOOD_HFD", "CIESIN_CHRR_NDH_FLOOD_MRD", "CIESIN_CHRR_NDH_FLOOD_PELRD", "CIESIN_CHRR_NDH_FLOOD_TELRD", "CIESIN_CHRR_NDH_LSLIDE_HD", "CIESIN_CHRR_NDH_LSLIDE_MRD", "CIESIN_CHRR_NDH_LSLIDE_PELRD", "CIESIN_CHRR_NDH_LSLIDE_TELRD", "CIESIN_CHRR_NDH_MULTI_HFD", "CIESIN_CHRR_NDH_MULTI_MRD", "CIESIN_CHRR_NDH_MULTI_PELRD", "CIESIN_CHRR_NDH_MULTI_TELRD", "CIESIN_CHRR_NDH_VOLCANO_HFD", "CIESIN_CHRR_NDH_VOLCANO_MRD", "CIESIN_CHRR_NDH_VOLCANO_PELRD", "CIESIN_CHRR_NDH_VOLCANO_TELRD", "CIESIN_SEDAC_ACRP_1980_SAS", "CIESIN_SEDAC_ACRP_1990_CBS", "CIESIN_SEDAC_ACRP_1990_EMF", "CIESIN_SEDAC_ACRP_1990_PBF", "CIESIN_SEDAC_ACRP_1990_SAS", "CIESIN_SEDAC_ACRP_1990_SEF", "CIESIN_SEDAC_ACRP_1990_SI", "CIESIN_SEDAC_ACRP_1990_STF1B", "CIESIN_SEDAC_ACRP_1990_ZIPEF", "CIESIN_SEDAC_ACRP_1992_BF", "CIESIN_SEDAC_ACRP_PUMS", "CIESIN_SEDAC_AGLANDS_CROP2000", "CIESIN_SEDAC_AGLANDS_PAS2000", "CIESIN_SEDAC_ANTHROMES_v1", "CIESIN_SEDAC_ANTHROMES_v2_1700", "CIESIN_SEDAC_ANTHROMES_v2_1900", "CIESIN_SEDAC_AQDH_CTMAP_2003_2018", "CIESIN_SEDAC_AQDH_DANO2_US_1KM_1.10", "CIESIN_SEDAC_AQDH_DAO3_US_1KM", "CIESIN_SEDAC_AQDH_DAO3_US_1KM_1.10", "CIESIN_SEDAC_AQDH_DAPM25_US_1KM", "CIESIN_SEDAC_AQDH_DAPM25_US_1KM_1.10", "CIESIN_SEDAC_AQDH_NO2_US_1KM", "CIESIN_SEDAC_AQDH_PM25COM_US_1KM", "CIESIN_SEDAC_AQDH_PM25O3NO2_ZIPCODE", "CIESIN_SEDAC_AQDH_TRACE_US_1KM", "CIESIN_SEDAC_CD_ ADM_GIS_1990", "CIESIN_SEDAC_CD_AGENDA21", "CIESIN_SEDAC_CD_BIB_ADM_GEOG", "CIESIN_SEDAC_CD_CTY_ECON_YRBK", "CIESIN_SEDAC_CD_CTY_POPAG_GIS", "CIESIN_SEDAC_CD_FUNDGIS", "CIESIN_SEDAC_CD_GBCODES", "CIESIN_SEDAC_CD_HOS_EPI_50-85", "CIESIN_SEDAC_CD_MAPS_BIBDB", "CIESIN_SEDAC_CD_STAT_1949-1990", "CIESIN_SEDAC_CESIC_2004EVI", "CIESIN_SEDAC_CESIC_2006NFA", "CIESIN_SEDAC_CESIC_ANC", "CIESIN_SEDAC_CESIC_COMPLETE_V11", "CIESIN_SEDAC_CESIC_RIOJO", "CIESIN_SEDAC_CESIC_WELLBEING", "CIESIN_SEDAC_CLIMMIG_GASPMP18SR", "CIESIN_SEDAC_CLIMMIG_GSPMP18SR", "CIESIN_SEDAC_CROPCLIM_EFCCGPSRES", "CIESIN_SEDAC_CROPCLIM_GISS_DB", "CIESIN_SEDAC_CRV_USCRPC_2040-2049", "CIESIN_SEDAC_DEDC_ACE_V2", "CIESIN_SEDAC_ENERGY_NPPCLA", "CIESIN_SEDAC_ENERGY_NPPL", "CIESIN_SEDAC_EPI_2006", "CIESIN_SEDAC_EPI_2008", "CIESIN_SEDAC_EPI_2010", "CIESIN_SEDAC_EPI_2012", "CIESIN_SEDAC_EPI_2014", "CIESIN_SEDAC_EPI_2016", "CIESIN_SEDAC_EPI_2018", "CIESIN_SEDAC_EPI_2020", "CIESIN_SEDAC_EPI_2022", "CIESIN_SEDAC_ESI_2000", "CIESIN_SEDAC_ESI_2001", "CIESIN_SEDAC_ESI_2002", "CIESIN_SEDAC_ESI_2005", "CIESIN_SEDAC_FERMANv1_NAPP", "CIESIN_SEDAC_FERMANv1_NMAN", "CIESIN_SEDAC_FERMANv1_PAPP", "CIESIN_SEDAC_FERMANv1_PESTG_V1.01", "CIESIN_SEDAC_FERMANv1_PMAN", "CIESIN_SEDAC_FOOD_CROPSTAT_1900-2017", "CIESIN_SEDAC_FOOD_FIH_V1", "CIESIN_SEDAC_GEO-MEX_GIS_SMI", "CIESIN_SEDAC_GEO-MEX_POPDBMEX", "CIESIN_SEDAC_GEO-MEX_RASTERGIS", "CIESIN_SEDAC_GEO-MEX_URBANGIS", "CIESIN_SEDAC_GEO-MEX_URB_TSER", "CIESIN_SEDAC_GHSL_PBSMOD", "CIESIN_SEDAC_GPWv3_CENTROIDS", "CIESIN_SEDAC_GPWv3_COASTLINES", "CIESIN_SEDAC_GPWv3_LANDGEOG", "CIESIN_SEDAC_GPWv3_NADMINBND", "CIESIN_SEDAC_GPWv3_NATIDEN", "CIESIN_SEDAC_GPWv3_POPCOUNT", "CIESIN_SEDAC_GPWv3_POPCOUNTFE", "CIESIN_SEDAC_GPWv3_POPDENS", "CIESIN_SEDAC_GPWv3_POPDENSFE", "CIESIN_SEDAC_GPWv3_SUBADBND", "CIESIN_SEDAC_GPWv4_ADUCPPE_R11", "CIESIN_SEDAC_GPWv4_APCT_WPP_2015_R11", "CIESIN_SEDAC_GPWv4_APDENS_WPP_2015_R11", "CIESIN_SEDAC_GPWv4_BDC_R11", "CIESIN_SEDAC_GPWv4_DQI_R11", "CIESIN_SEDAC_GPWv4_LANDH2OAREA_R11", "CIESIN_SEDAC_GPWv4_NATIDEN_R11", "CIESIN_SEDAC_GPWv4_POPCOUNT_R11", "CIESIN_SEDAC_GPWv4_POPDENS_R11", "CIESIN_SEDAC_GRANDv1_DAMS", "CIESIN_SEDAC_GRANDv1_RESERVOIRS", "CIESIN_SEDAC_GROADS_CCRDSv1", "CIESIN_SEDAC_GROADS_V1", "CIESIN_SEDAC_GRUMPv1_COASTLINES", "CIESIN_SEDAC_GRUMPv1_EXT", "CIESIN_SEDAC_GRUMPv1_EXT02", "CIESIN_SEDAC_GRUMPv1_LGUAG", "CIESIN_SEDAC_GRUMPv1_NADMINBND", "CIESIN_SEDAC_GRUMPv1_NATIDEN", "CIESIN_SEDAC_GRUMPv1_POPCOUNT", "CIESIN_SEDAC_GRUMPv1_POPDENS", "CIESIN_SEDAC_GRUMPv1_STLMNT", "CIESIN_SEDAC_GRUMPv1_STLMNT01", "CIESIN_SEDAC_HANPP", "CIESIN_SEDAC_HANPP_CPROD", "CIESIN_SEDAC_HANPP_NPP", "CIESIN_SEDAC_HANPP_PERCENT", "CIESIN_SEDAC_HASO2_SO2_1850-2005", "CIESIN_SEDAC_ICWQ_ACHLOA", "CIESIN_SEDAC_ICWQ_ANC", "CIESIN_SEDAC_ICWQ_CCHLOA", "CIESIN_SEDAC_INDIA_AWCA", "CIESIN_SEDAC_INDIA_CENSUS_2011", "CIESIN_SEDAC_INDIA_VILSOCIOECON", "CIESIN_SEDAC_IPCC_BASELINE", "CIESIN_SEDAC_IPCC_GRI_MIDYR_0.3.7", "CIESIN_SEDAC_IPCC_IS92_V11", "CIESIN_SEDAC_IPCC_OBSERVED", "CIESIN_SEDAC_IPCC_OBSERVED_AR5", "CIESIN_SEDAC_IPCC_SAGDVCC", "CIESIN_SEDAC_IPCC_SRES_1X1EM", "CIESIN_SEDAC_IPCC_SRES_EMSC_V11", "CIESIN_SEDAC_IPCC_SRES_FLGASES", "CIESIN_SEDAC_LECZ_DELTA_V1", "CIESIN_SEDAC_LECZ_SLRIRAMSARWII", "CIESIN_SEDAC_LECZ_URPEV1", "CIESIN_SEDAC_LECZ_URPLAEV2", "CIESIN_SEDAC_LECZ_URPLAEV3", "CIESIN_SEDAC_LULC_CAMVEGLCCCS", "CIESIN_SEDAC_LULC_DPI", "CIESIN_SEDAC_LULC_DT1_V1", "CIESIN_SEDAC_LULC_HMTS", "CIESIN_SEDAC_LULC_MANGROVES_2000", "CIESIN_SEDAC_LULC_PUEXPANS_2030", "CIESIN_SEDAC_LWP2_HF_GEOG", "CIESIN_SEDAC_LWP2_HF_IGHP", "CIESIN_SEDAC_LWP2_HII_GEOG", "CIESIN_SEDAC_LWP2_HII_IGHP", "CIESIN_SEDAC_LWP2_LW_GEOG", "CIESIN_SEDAC_LWP2_LW_IGHP", "CIESIN_SEDAC_LWP3_HF_1993", "CIESIN_SEDAC_LWP3_HF_2009", "CIESIN_SEDAC_LWP_HFP_GEOG", "CIESIN_SEDAC_LWP_HFP_IGHP", "CIESIN_SEDAC_LWP_LW_GEOG", "CIESIN_SEDAC_LWP_LW_IGHP", "CIESIN_SEDAC_LWP_TOPWA_GEOG", "CIESIN_SEDAC_LWP_TOPWA_IGHP", "CIESIN_SEDAC_MA_BIODIVERSITY", "CIESIN_SEDAC_MA_CLIMLC", "CIESIN_SEDAC_MA_ECOSYSTEMS", "CIESIN_SEDAC_MA_POPULATION", "CIESIN_SEDAC_MA_RLCC", "CIESIN_SEDAC_MA_SCENARIOS", "CIESIN_SEDAC_NAGDC_PLACE", "CIESIN_SEDAC_NAGDC_PLACEII", "CIESIN_SEDAC_NAGDC_PLACEIII", "CIESIN_SEDAC_NAGDC_PLACEIV", "CIESIN_SEDAC_NRMI_2010", "CIESIN_SEDAC_NRMI_2011", "CIESIN_SEDAC_NRMI_NRPCHI12", "CIESIN_SEDAC_NRMI_NRPCHI13", "CIESIN_SEDAC_NRMI_NRPCHI14", "CIESIN_SEDAC_NRMI_NRPCHI15", "CIESIN_SEDAC_NRMI_NRPCHI16", "CIESIN_SEDAC_NRMI_NRPCHI17", "CIESIN_SEDAC_NRMI_NRPCHI18", "CIESIN_SEDAC_NRMI_NRPCHI19", "CIESIN_SEDAC_NRMI_NRPCHI20", "CIESIN_SEDAC_NRMI_NRPCHI21", "CIESIN_SEDAC_NRMI_NRPCHI22", "CIESIN_SEDAC_NRMI_NRPCHI23", "CIESIN_SEDAC_PD_LACPOP", "CIESIN_SEDAC_PD_NETMIG_1970_2000", "CIESIN_SEDAC_PD_POPCGTSE", "CIESIN_SEDAC_PD_POPDGTSE", "CIESIN_SEDAC_PD_PPUSCASRCSSP", "CIESIN_SEDAC_PD_SSPBSYR_1_8th", "CIESIN_SEDAC_PD_SSPBSYR_1km", "CIESIN_SEDAC_PEND_ALLOMSSF3", "CIESIN_SEDAC_PEND_ALLOMSTXSF1", "CIESIN_SEDAC_PEND_GDIS", "CIESIN_SEDAC_PEND_HSF1", "CIESIN_SEDAC_PEND_NOSF1", "CIESIN_SEDAC_PEND_NOSF3", "CIESIN_SEDAC_PMP_GRDI_2010_2020", "CIESIN_SEDAC_PMP_GSPCM", "CIESIN_SEDAC_PMP_IMR", "CIESIN_SEDAC_PMP_IMR_V2.01", "CIESIN_SEDAC_PMP_PFSCS", "CIESIN_SEDAC_PMP_SAEPI", "CIESIN_SEDAC_PMP_UBN", "CIESIN_SEDAC_SDEI_APM25_URBAN", "CIESIN_SEDAC_SDEI_GEHE", "CIESIN_SEDAC_SDEI_GFA_GRACE", "CIESIN_SEDAC_SDEI_GFED4_CLTAB", "CIESIN_SEDAC_SDEI_GFED4_GRIDS", "CIESIN_SEDAC_SDEI_GWRPM25_MMSAOD_4GL03", "CIESIN_SEDAC_SDEI_LST2013", "CIESIN_SEDAC_SDEI_NO2", "CIESIN_SEDAC_SDEI_UHE", "CIESIN_SEDAC_SDEI_UHI2013", "CIESIN_SEDAC_SDEI_VDL", "CIESIN_SEDAC_SDEI_YCEOUHI_V4", "CIESIN_SEDAC_SDGI_ACCELEC_2023", "CIESIN_SEDAC_SDGI_RAI_2023", "CIESIN_SEDAC_SDGI_UAPT_2023", "CIESIN_SEDAC_SDGI_UPSAE_2023", "CIESIN_SEDAC_SDP_CGDP_A1A2B1B2", "CIESIN_SEDAC_SDP_CPOP_A1B1A2", "CIESIN_SEDAC_SDP_CPOP_B2", "CIESIN_SEDAC_SDP_GGDP_B2", "CIESIN_SEDAC_SDP_GPOP_B2", "CIESIN_SEDAC_SPATIALECON_GECON4", "CIESIN_SEDAC_SPATIALECON_LGII_V1", "CIESIN_SEDAC_SPECIES_AMP_RICH15", "CIESIN_SEDAC_SPECIES_MAM_RICH15", "CIESIN_SEDAC_SSF_EPANPLSPDCMv2", "CIESIN_SEDAC_SSF_HWSPDv2", "CIESIN_SEDAC_SSP_1-8thDULEPBYGSSP", "CIESIN_SEDAC_SSP_1-kmDULEPBYGSSP", "CIESIN_SEDAC_SSP_GSNLITDB_V1", "CIESIN_SEDAC_SSP_LITDB_V1", "CIESIN_SEDAC_ULANDSAT_GMIS_V1", "CIESIN_SEDAC_ULANDSAT_HBASE_V1", "CIESIN_SEDAC_ULANDSAT_URBAN_LANDSAT", "CIESIN_SEDAC_USCG_90SF1", "CIESIN_SEDAC_USCG_90SF1MSA", "CIESIN_SEDAC_USCG_90SF3", "CIESIN_SEDAC_USCG_90SF3MSA", "CIESIN_SEDAC_USCG_SF1", "CIESIN_SEDAC_USCG_SF12010", "CIESIN_SEDAC_USCG_SF1MSA", "CIESIN_SEDAC_USCG_SF3", "CIESIN_SEDAC_USCG_SF3MSA", "CIESIN_SEDAC_USCG_USSVIG01", "CIESIN_SEDAC_USPAT_BSCATTER_1993_2020", "CIESIN_SEDAC_USPAT_DESLUIS", "CIESIN_SEDAC_USPAT_GUPPD_V1", "CIESIN_SEDAC_USPAT_HUP", "CIESIN_SEDAC_USPAT_USUEXT2015", "CIESIN_SEDAC_WACVM_CCPROD", "CIESIN_SEDAC_WACVM_DEFOR200012", "CIESIN_SEDAC_WACVM_DHS", "CIESIN_SEDAC_WACVM_ECONSI", "CIESIN_SEDAC_WACVM_GPWv4PR_PD", "CIESIN_SEDAC_WACVM_GPWv4PR_PG", "CIESIN_SEDAC_WACVM_GSSPEPP", "CIESIN_SEDAC_WACVM_JRCMA", "CIESIN_SEDAC_WACVM_MFD2000_POL", "CIESIN_SEDAC_WACVM_OSM_ROADS", "CIESIN_SEDAC_WACVM_PGLF_200813", "CIESIN_SEDAC_WACVM_POPPROJ_203050", "CIESIN_SEDAC_WACVM_SDMSPOLSNLEA_2010", "CIESIN_SEDAC_WACVM_SMAMAMP_RICH", "CIESIN_SEDAC_WACVM_SUBSETACE2", "CIESIN_SEDAC_WACVM_SVI", "CIESIN_SEDAC_WATER_WSIM_GLDAS_V1", "CL07LC", "CL07PLBK", "CL07PLTB", "CL07SM", "CL07ST", "CL07V", "CL07VWC", "CLAMS_CERES_CHESLIGHT_SONDE", "CLAMS_CV580_CAR", "CLAMS_ER2_MAS", "CLAMS_MODIS_L2_AEROSOL_PRODUCTS", "CLAMS_UWASH_CONVAIR_DATA", "CLDMSK_L2_MODIS_Aqua", "CLDMSK_L2_VIIRS_NOAA20", "CLDMSK_L2_VIIRS_NOAA20_NRT", "CLDMSK_L2_VIIRS_SNPP", "CLDMSK_L2_VIIRS_SNPP_NRT", "CLDPROPCOSP_D3_MODIS_Aqua", "CLDPROPCOSP_D3_VIIRS_NOAA20", "CLDPROPCOSP_D3_VIIRS_SNPP", "CLDPROPCOSP_M3_MODIS_Aqua", "CLDPROPCOSP_M3_VIIRS_NOAA20", "CLDPROPCOSP_M3_VIIRS_SNPP", "CLDPROP_D3_MODIS_Aqua", "CLDPROP_D3_VIIRS_NOAA20", "CLDPROP_D3_VIIRS_SNPP", "CLDPROP_M3_MODIS_Aqua", "CLDPROP_M3_VIIRS_NOAA20", "CLDPROP_M3_VIIRS_SNPP", "CLIVAR", "CMAQ-N_Module_1661", "CMC0.1deg-CMC-L4-GLOB-v3.0", "CMC0.2deg-CMC-L4-GLOB-v2.0", "CMRMIAAE", "CMRMIALS", "CMRMIGEO", "CMRMITST", "CMSFluxFire", "CMSFluxFire", "CMSFluxFirepost", "CMSFluxFossilFuelPrior", "CMSFluxFossilFuelPrior", "CMSFluxFossilfuel", "CMSFluxLandPrior", "CMSFluxMISC", "CMSFluxNBE", "CMSFluxNBE", "CMSFluxNBEPrior", "CMSFluxNEE", "CMSFluxOcean", "CMSFluxOcean", "CMSFluxOceanPrior", "CMSFluxOceanPrior", "CMSFluxTotal", "CMSFluxTotal", "CMSFluxTotalPrior", "CMSFluxTotalpost", "CMSFluxTotalprior", "CMSGCH4F", "CMSLakeHuronPPM", "CMSLakeHuronPPY", "CMSLakeMichiganPPM", "CMSLakeMichiganPPY", "CMSLakeSuperiorPPM", "CMSLakeSuperiorPPY", "CMS_AGB_Landcover_Indonesia_1645", "CMS_AGB_NW_USA_1719", "CMS_CH4_FLX_CA", "CMS_CH4_FLX_MX", "CMS_CH4_FLX_NAD", "CMS_CO2_Fluxes_TBMO_1315", "CMS_CONUS_Biomass_1752", "CMS_CTL_NA_GOSAT_FOOTPRINTS", "CMS_CTL_NA_OCO2_FOOTPRINTS", "CMS_CTL_NA_TCCON_FOOTPRINTS", "CMS_CTL_SA_OCO2_FOOTPRINTS", "CMS_Coastal_Wetland_Resilience_1839", "CMS_DARTE_V2_1735", "CMS_Daily_ET_MexFlux_1309", "CMS_EFT_CONUS_1659", "CMS_Fire_Weather_Data_AK_1509", "CMS_FluxEstimates_Aircraft_CO2_2336", "CMS_Forest_Carbon_Fluxes_1313", "CMS_Forest_Carbon_Maryland_1660", "CMS_Forest_Productivity_1221", "CMS_GO_CH4_SEC_TDYC_NA", "CMS_Global_Cropland_Carbon_1279", "CMS_Global_Fire_Atlas_1642", "CMS_Global_Forest_AGC_2180", "CMS_Global_Forest_Age_2345", "CMS_Global_Livestock_CH4_CO2_1329", "CMS_Global_Mangrove_Forest_Ht_2251", "CMS_Global_Mangrove_Loss_1768", "CMS_Global_Map_Mangrove_Canopy_1665", "CMS_Global_Monthly_Wetland_CH4_1502", "CMS_Global_Soil_Respiration_1736", "CMS_Great_Basin_Biomass_1755", "CMS_HR_MNA_CH4_FLUX", "CMS_Landcover_Indonesia_1838", "CMS_Landscapes_Brazil_Forests_1301", "CMS_Landscapes_Brazil_LiDAR_1302", "CMS_LiDAR_AGB_California_1537", "CMS_LiDAR_AGB_PEF_2012_1318", "CMS_LiDAR_Biomass_CanHt_Sonoma_1523", "CMS_LiDAR_Biomass_MD_PA_DE_1538", "CMS_LiDAR_Indonesia_1518", "CMS_LiDAR_Point_Cloud_Zambezi_1521", "CMS_LiDAR_Products_Indonesia_1540", "CMS_Mangrove_Biomass_Zambezi_1522", "CMS_Mangrove_CanHt_Stand_Age_1377", "CMS_Mangrove_Canopy_Height_1327", "CMS_Mangrove_Canopy_Ht_Zambezi_1357", "CMS_Mangrove_Cover_1670", "CMS_Maryland_AGB_Canopy_1320", "CMS_Methane_Emissions_Boston_1291", "CMS_Monthly_CO2_Gulf_of_Mexico_1668", "CMS_OCE_BGC_CCS", "CMS_Pantropical_Forest_Biomass_1337", "CMS_Pennsylvania_Tree_Cover_1334", "CMS_Pilot_Biomass_1257", "CMS_SABGOM_Model_Simulations_1510", "CMS_SOC_Mexico_1754", "CMS_SOC_Mexico_CONUS_1737", "CMS_SST_GPP_Mexico_1310", "CMS_Simulated_SIF_NiwotRidge_1720", "CMS_Soil_CO2_Efflux_1298", "CMS_WFEIS_CONUS-AK_1306", "CMS_WRF_Footprints_CO2_Signals_1381", "CMS_WRF_Model_Products_1338", "COASTAL", "COMEX_AJAX_CO2_CH4_2347", "COMEX_AVIRIS_Classic_Flights_2343", "COMEX_AVIRIS_NG_Flights_2342", "COMEX_LongwaveInfrared_Imagery_2331", "COOA", "CORAL", "COWVR_STPH8_L1_TSDR_V10.0", "CPEXAW-ADM-Aeolus", "CPEXAW-DAWN_DC8", "CPEXAW-Dropsondes", "CPEXAW-HALO_DC8", "CPEXCV-DAWN_DC8", "CPEXCV-Dropsondes", "CPEXCV-HALO_DC8", "CPEXCV_Cloud_AircraftInSitu_DC8_Data", "CPEXCV_Merge_Data", "CPL_ABL_Top_Height_1825", "CRP", "CStocks_Greenness_Mangroves_MX_1853", "CUSTARD", "CV-580_710", "CYGNSS_L1_CAL_RAW_IF_V1.0", "CYGNSS_L1_CDR_V1.0", "CYGNSS_L1_CDR_V1.1", "CYGNSS_L1_CDR_V1.2", "CYGNSS_L1_FULL_DDM", "CYGNSS_L1_FULL_DDM_V3.0", "CYGNSS_L1_RAW_IF", "CYGNSS_L1_V2.1", "CYGNSS_L1_V3.0", "CYGNSS_L1_V3.2", "CYGNSS_L2_CDR_V1.0", "CYGNSS_L2_CDR_V1.1", "CYGNSS_L2_CDR_V1.2", "CYGNSS_L2_SURFACE_FLUX_CDR_V1.0", "CYGNSS_L2_SURFACE_FLUX_CDR_V1.1", "CYGNSS_L2_SURFACE_FLUX_CDR_V1.2", "CYGNSS_L2_SURFACE_FLUX_V1.0", "CYGNSS_L2_SURFACE_FLUX_V2.0", "CYGNSS_L2_SURFACE_FLUX_V3.2", "CYGNSS_L2_V2.1", "CYGNSS_L2_V3.0", "CYGNSS_L2_V3.2", "CYGNSS_L3_CDR_V1.0", "CYGNSS_L3_CDR_V1.1", "CYGNSS_L3_CDR_V1.2", "CYGNSS_L3_MICROPLASTIC_V1.0", "CYGNSS_L3_MICROPLASTIC_V3.2", "CYGNSS_L3_MRG_NRT_V3.2", "CYGNSS_L3_MRG_NRT_V3.2.1", "CYGNSS_L3_MRG_V3.2", "CYGNSS_L3_MRG_V3.2.1", "CYGNSS_L3_S1.0", "CYGNSS_L3_SOIL_MOISTURE_V1.0", "CYGNSS_L3_SOIL_MOISTURE_V3.2", "CYGNSS_L3_UC_BERKELEY_WATERMASK_DAILY_V3.2", "CYGNSS_L3_UC_BERKELEY_WATERMASK_V3.1", "CYGNSS_L3_V2.1", "CYGNSS_L3_V3.0", "CYGNSS_L3_V3.1", "CYGNSS_L3_V3.2", "CYGNSS_NOAA_L2_SWSP_25KM_V1.1", "CYGNSS_NOAA_L2_SWSP_25KM_V1.2", "CZCS_L1", "CZCS_L2_OC", "CZCS_L3b_CHL", "CZCS_L3b_KD", "CZCS_L3b_RRS", "CZCS_L3m_CHL", "CZCS_L3m_KD", "CZCS_L3m_RRS", "C_FluxStocks_CLM5_DART_WestUS_1856", "C_Pools_Fluxes_CONUS_1837", "CaTS", "CalCOFI", "California_2002", "CanSIS_Regional_Soils_1347", "Canada_Boreal_Forest_Greenness_1587", "Canadian_West_Arctic_Veg_Plots_1543", "CapeHatteras2010", "Carbon_Estuaries", "Carbon_Transport_MS_River", "Carbon_UAV", "Cartagena_Station", "Catlin_Arctic_Survey", "ChesBay", "Chesapeake_Bay_Helicopter", "Chesapeake_Bay_Plume", "Chesapeake_Bay_Water_Quality", "Chesapeake_Light_Tower", "Chlorophyll_Fluorescence_Data_1785", "CircumArctic_Trends_Hotspots_2322", "CliVEC", "Climate_Normals_Modern_LGM_AK_1663", "CoJet", "Coastal_Carbon_Budget_NE_US_1594", "Coastal_US_Elevation_Data_1844", "Continuous_Lifeform_Maps_CONUS_1809", "Country_SOC_Latin_America_1615", "CramerLeemans_416", "Cropland_Carbon_Fluxes_2125", "Crops_SIF_VegIndices_IL_NE_2136", "CubeSat_Arctic_Boreal_LakeArea_1667", "Cyanate", "DC3_Aerosol_AircraftInSitu_DC8_Data", "DC3_Aerosol_AircraftInSitu_DLR-Falcon_Data", "DC3_Aerosol_AircraftInSitu_NSF-GV-HIAPER_Data", "DC3_AircraftRemoteSensing_DIAL_DC8_Data", "DC3_Cloud_AircraftInSitu_DC8_Data", "DC3_Cloud_AircraftInSitu_NSF-GV-HIAPER_Data", "DC3_Merge_Data", "DC3_MetNav_AircraftInSitu_DC8_Data", "DC3_MetNav_AircraftInSitu_DLR-Falcon_Data", "DC3_MetNav_AircraftInSitu_NSF-GV-HIAPER_Data", "DC3_Miscellaneous_DC8_Data", "DC3_Miscellaneous_DLR-Falcon_Data", "DC3_Miscellaneous_NSF-GV-HIAPER_Data", "DC3_Radiation_AircraftInSitu_DC8_Data", "DC3_TraceGas_AircraftInSitu_DC8_Data", "DC3_TraceGas_AircraftInSitu_DLR-Falcon_Data", "DC3_TraceGas_AircraftInSitu_NSF-GV-HIAPER_Data", "DC3_jValue_AircraftInSitu_DC8_Data", "DC3_jValue_AircraftInSitu_NSF-GV-HIAPER_Data", "DCOTSS-Aircraft-Data", "DCOTSS-Balloon-Data", "DCOTSS-Model-Output", "DCOTSS-Radar-Satellite-Data", "DCOTSS-Reports", "DECKLAB", "DEVOTE_Aerosol_AircraftInSitu_B200_Data", "DEVOTE_AircraftRemoteSensing_UC12_HSRL_Data", "DEVOTE_AircraftRemoteSensing_UC12_RSP_Data", "DEVOTE_MetNav_AircraftInSitu_B200_Data", "DEVOTE_MetNav_AircraftInSitu_UC12_Data", "DFO_Canada_Time_Series", "DISCOVERAQ_California_Aerosol_AircraftInSitu_P3B_Data", "DISCOVERAQ_California_AircraftRemoteSensing_B200_ACAM_Data", "DISCOVERAQ_California_AircraftRemoteSensing_B200_HSRL2_Data", "DISCOVERAQ_California_Ground_Analysis_Ancillary_Data", "DISCOVERAQ_California_Ground_Bakersfield_Data", "DISCOVERAQ_California_Ground_CARB_Data", "DISCOVERAQ_California_Ground_Fresno_Data", "DISCOVERAQ_California_Ground_Huron_Data", "DISCOVERAQ_California_Ground_Oildale_Data", "DISCOVERAQ_California_Ground_Pandora_Data", "DISCOVERAQ_California_Ground_Porterville_Data", "DISCOVERAQ_California_Ground_SJV_Data", "DISCOVERAQ_California_Merge_Data", "DISCOVERAQ_California_MetNav_AircraftInSitu_B200_Data", "DISCOVERAQ_California_MetNav_AircraftInSitu_P3B_Data", "DISCOVERAQ_California_Ozonesondes_Data", "DISCOVERAQ_California_TraceGas_AircraftInSitu_P3B_Data", "DISCOVERAQ_Colorado_Aerosol_AircraftInSitu_P3B_Data", "DISCOVERAQ_Colorado_AircraftRemoteSensing_B200_ACAM_Data", "DISCOVERAQ_Colorado_AircraftRemoteSensing_B200_GCAS_Data", "DISCOVERAQ_Colorado_AircraftRemoteSensing_B200_HSRL2_Data", "DISCOVERAQ_Colorado_AircraftRemoteSensing_Falcon_GeoTASO_Data", "DISCOVERAQ_Colorado_Ground_BAOTower_Data", "DISCOVERAQ_Colorado_Ground_CDPHE_Data", "DISCOVERAQ_Colorado_Ground_EPA_Data", "DISCOVERAQ_Colorado_Ground_FortCollins_Data", "DISCOVERAQ_Colorado_Ground_Mobile_Data", "DISCOVERAQ_Colorado_Ground_NREL-Golden_Data", "DISCOVERAQ_Colorado_Ground_Pandora_Data", "DISCOVERAQ_Colorado_Ground_Platteville_Data", "DISCOVERAQ_Colorado_Ground_TableMountain_Data", "DISCOVERAQ_Colorado_Merge_Data", "DISCOVERAQ_Colorado_MetNav_AircraftInSitu_B200_Data", "DISCOVERAQ_Colorado_MetNav_AircraftInSitu_P3B_Data", "DISCOVERAQ_Colorado_Model_Data", "DISCOVERAQ_Colorado_Ozonesondes_Data", "DISCOVERAQ_Colorado_Radiation_AircraftRemoteSensing_CAR_Data", "DISCOVERAQ_Colorado_TraceGas_AircraftInSitu_P3B_Data", "DISCOVERAQ_Maryland_Aerosol_AircraftInSitu_P3B_Data", "DISCOVERAQ_Maryland_AircraftRemoteSensing_UC12_ACAM_Data", "DISCOVERAQ_Maryland_AircraftRemoteSensing_UC12_HSRL_Data", "DISCOVERAQ_Maryland_Ground_Aldino_Data", "DISCOVERAQ_Maryland_Ground_Analysis_Ancillary_Data", "DISCOVERAQ_Maryland_Ground_Beltsville_Data", "DISCOVERAQ_Maryland_Ground_Edgewood_Data", "DISCOVERAQ_Maryland_Ground_Essex_Data", "DISCOVERAQ_Maryland_Ground_Fairhill_Data", "DISCOVERAQ_Maryland_Ground_Oldtown_Data", "DISCOVERAQ_Maryland_Ground_Padonia_Data", "DISCOVERAQ_Maryland_Ground_Pandora_Data", "DISCOVERAQ_Maryland_Ground_UMBC_Data", "DISCOVERAQ_Maryland_Merge_Data", "DISCOVERAQ_Maryland_MetNav_AircraftInSitu_P3B_Data", "DISCOVERAQ_Maryland_MetNav_AircraftInSitu_UC12_Data", "DISCOVERAQ_Maryland_Ozonesondes_Data", "DISCOVERAQ_Maryland_Ship_NOAA-DelawareII_Data", "DISCOVERAQ_Maryland_TraceGas_AircraftInSitu_P3B_Data", "DISCOVERAQ_Maryland_UMDAircraft_Data", "DISCOVERAQ_Texas_Aerosol_AircraftInSitu_P3B_Data", "DISCOVERAQ_Texas_AircraftRemoteSensing_B200_ACAM_Data", "DISCOVERAQ_Texas_AircraftRemoteSensing_B200_GCAS_Data", "DISCOVERAQ_Texas_AircraftRemoteSensing_B200_HSRL2_Data", "DISCOVERAQ_Texas_AircraftRemoteSensing_Falcon_GeoTASO_Data", "DISCOVERAQ_Texas_Ground_Ancillary_Analysis_Data", "DISCOVERAQ_Texas_Ground_EPA_Data", "DISCOVERAQ_Texas_Ground_Galveston_Data", "DISCOVERAQ_Texas_Ground_LaPorteAirport_Data", "DISCOVERAQ_Texas_Ground_ManvelCroix_Data", "DISCOVERAQ_Texas_Ground_MoodyTower_Data", "DISCOVERAQ_Texas_Ground_Pandora_Data", "DISCOVERAQ_Texas_Ground_SmithPoint_Data", "DISCOVERAQ_Texas_Ground_TCEQ_Data", "DISCOVERAQ_Texas_Merge_Data", "DISCOVERAQ_Texas_MetNav_AircraftInSitu_B200_Data", "DISCOVERAQ_Texas_MetNav_AircraftInSitu_P3B_Data", "DISCOVERAQ_Texas_Ozonesondes_Data", "DISCOVERAQ_Texas_TraceGas_AircraftInSitu_P3B_Data", "DISCover_land_cover_679", "DLEM_C_N_Export_1699", "DMI_OI-DMI-L4-GLOB-v1.0", "DSCOVR_EPIC_L2_COMPOSITE", "DSCOVR_EPIC_L2_COMPOSITE", "DSCOVR_EPIC_L3_PAR", "DSCOVR_EPIC_L3_PAR-IMAGE", "DSCOVR_EPIC_L4_TrO3", "DSCOVR_NISTAR_L2_FLX", "DUSTFLEXPART", "Daily_FineParticulateMatter_AK_2157", "Dairy_Methane_CA_V1-2_1902", "Dall_Sheep_Population_Dynamics_1640", "Dall_Sheep_Snowpack_1602", "Daymet_Annual_V4R1_2130", "Daymet_Daily_V4R1_2129", "Daymet_Monthly_V4R1_2131", "Daymet_SubDaily_Puerto_Rico_1977", "Daymet_V4_Daily_MonthlyLatency_1904", "Daymet_xval_V4R1_2132", "Decadal_LULC_India_1336", "Decadal_Water_Maps_1324", "DeciduousFractionl_CanopyCover_2296", "Del_Ches_Bay_Fluorescence", "DeltaX_ADCP_Measurements_V2_2081", "DeltaX_AGB_AGN_V2_2237", "DeltaX_ANUGA_AtchafalayaBasin_2306", "DeltaX_ANUGA_Hydrodynamics_MRD_2310", "DeltaX_BGB_BGN_V2_2238", "DeltaX_BG_Root_Stable_Isotopes_2193", "DeltaX_DEM_MRD_LA_2181", "DeltaX_Delft3D_294_Terrebonne_2303", "DeltaX_Delft3D_322_Terrebonne_2312", "DeltaX_Delft3D_396_Terrebonne_2314", "DeltaX_Delft3D_399_Terrebonne_2313", "DeltaX_Delft3D_421_Terrebonne_2304", "DeltaX_Delft3D_Atchafalaya_MRD_2302", "DeltaX_Delft3D_Terrebonne_MRD_2301", "DeltaX_Ecogeomorphic_Products_2108", "DeltaX_Feldspar_Sediment_V3_2290", "DeltaX_Foliar_Stable_Isotopes_2194", "DeltaX_H2O_Surface_Elevation_2086", "DeltaX_Herb_WetlandSoil_V3_2239", "DeltaX_Insitu_POC_2073", "DeltaX_Insitu_Reflectance_V3_2153", "DeltaX_Insitu_WQ_Indicators_V2_2080", "DeltaX_Island_Channel_Model_2106", "DeltaX_L1B_UAVSAR_WaterLevels_1979", "DeltaX_L1_AVIRIS_Radiance_1987", "DeltaX_L1_UAVSAR_SLC_Stack_1984", "DeltaX_L1b_AirSWOT_1996", "DeltaX_L2A_AVIRIS-NG_BRDF_V2_2139", "DeltaX_L2_AVIRIS_Reflectance_1988", "DeltaX_L2_AirSWOT_WaterElev_V3_2350", "DeltaX_L2_UAVSAR_WaterLevels_2057", "DeltaX_L3_AVIRIS-NG_AGB_V2_2138", "DeltaX_L3_AVIRIS-NG_Veg_Types_2352", "DeltaX_L3_AVIRIS-NG_Water_V3_2152", "DeltaX_L3_AirSWOT_WaterElev_V2_2349", "DeltaX_L3_UAVSAR_WaterLevels_2058", "DeltaX_LandAccretionMap_WLD_2308", "DeltaX_MarshAccretion_NUMAR_2354", "DeltaX_Particle_Size_LISST_V2_2077", "DeltaX_RTK_Elevation_2071", "DeltaX_Sediment_Grain_Size_V2_2135", "DeltaX_Sonar_Bathymetry_2085", "DeltaX_TSS_Concentration_V2_2075", "DeltaX_TotalSubsidenceRate_MRD_2307", "DeltaX_Turbidity_Data_V4_2241", "DeltaX_UAVSAR_L3_ChannelMap_V2_2344", "DeltaX_Vegetation_Structure_V2_2240", "Dendrophenology_Eastern_US_1369", "Dissolved_Gases_Alaska_Rivers_2360", "Disturbance_Biomass_Maps_1679", "Dolche-Vita", "Dunne_545", "ECCO_L4_ANCILLARY_DATA_V4R4", "ECCO_L4_ATM_STATE_05DEG_DAILY_V4R4", "ECCO_L4_ATM_STATE_05DEG_MONTHLY_V4R4", "ECCO_L4_ATM_STATE_LLC0090GRID_DAILY_V4R4", "ECCO_L4_ATM_STATE_LLC0090GRID_MONTHLY_V4R4", "ECCO_L4_BOLUS_05DEG_DAILY_V4R4", "ECCO_L4_BOLUS_05DEG_MONTHLY_V4R4", "ECCO_L4_BOLUS_LLC0090GRID_DAILY_V4R4", "ECCO_L4_BOLUS_LLC0090GRID_MONTHLY_V4R4", "ECCO_L4_DENS_STRAT_PRESS_05DEG_DAILY_V4R4", "ECCO_L4_DENS_STRAT_PRESS_05DEG_MONTHLY_V4R4", "ECCO_L4_DENS_STRAT_PRESS_LLC0090GRID_DAILY_V4R4", "ECCO_L4_DENS_STRAT_PRESS_LLC0090GRID_MONTHLY_V4R4", "ECCO_L4_FRESH_FLUX_05DEG_DAILY_V4R4", "ECCO_L4_FRESH_FLUX_05DEG_MONTHLY_V4R4", "ECCO_L4_FRESH_FLUX_LLC0090GRID_DAILY_V4R4", "ECCO_L4_FRESH_FLUX_LLC0090GRID_MONTHLY_V4R4", "ECCO_L4_GEOMETRY_05DEG_V4R4", "ECCO_L4_GEOMETRY_LLC0090GRID_V4R4", "ECCO_L4_GMAP_TIME_SERIES_SNAPSHOT_V4R4", "ECCO_L4_GMAP_TIME_SERIES_SNAPSHOT_V4R4B", "ECCO_L4_GMSL_TIME_SERIES_DAILY_V4R4", "ECCO_L4_GMSL_TIME_SERIES_MONTHLY_V4R4", "ECCO_L4_HEAT_FLUX_05DEG_DAILY_V4R4", "ECCO_L4_HEAT_FLUX_05DEG_MONTHLY_V4R4", "ECCO_L4_HEAT_FLUX_LLC0090GRID_DAILY_V4R4", "ECCO_L4_HEAT_FLUX_LLC0090GRID_MONTHLY_V4R4", "ECCO_L4_MIXED_LAYER_DEPTH_05DEG_DAILY_V4R4", "ECCO_L4_MIXED_LAYER_DEPTH_05DEG_MONTHLY_V4R4", "ECCO_L4_MIXED_LAYER_DEPTH_LLC0090GRID_DAILY_V4R4", "ECCO_L4_MIXED_LAYER_DEPTH_LLC0090GRID_MONTHLY_V4R4", "ECCO_L4_OBP_05DEG_DAILY_V4R4", "ECCO_L4_OBP_05DEG_DAILY_V4R4B", "ECCO_L4_OBP_05DEG_MONTHLY_V4R4", "ECCO_L4_OBP_05DEG_MONTHLY_V4R4B", "ECCO_L4_OBP_LLC0090GRID_DAILY_V4R4", "ECCO_L4_OBP_LLC0090GRID_DAILY_V4R4B", "ECCO_L4_OBP_LLC0090GRID_MONTHLY_V4R4", "ECCO_L4_OBP_LLC0090GRID_MONTHLY_V4R4B", "ECCO_L4_OBP_LLC0090GRID_SNAPSHOT_V4R4", "ECCO_L4_OCEAN_3D_MIX_COEFFS_05DEG_V4R4", "ECCO_L4_OCEAN_3D_MIX_COEFFS_LLC0090GRID_V4R4", "ECCO_L4_OCEAN_3D_MOMENTUM_TEND_LLC0090GRID_DAILY_V4R4", "ECCO_L4_OCEAN_3D_MOMENTUM_TEND_LLC0090GRID_MONTHLY_V4R4", "ECCO_L4_OCEAN_3D_SALINITY_FLUX_LLC0090GRID_DAILY_V4R4", "ECCO_L4_OCEAN_3D_SALINITY_FLUX_LLC0090GRID_MONTHLY_V4R4", "ECCO_L4_OCEAN_3D_TEMPERATURE_FLUX_LLC0090GRID_DAILY_V4R4", "ECCO_L4_OCEAN_3D_TEMPERATURE_FLUX_LLC0090GRID_MONTHLY_V4R4", "ECCO_L4_OCEAN_3D_VOLUME_FLUX_LLC0090GRID_DAILY_V4R4", "ECCO_L4_OCEAN_3D_VOLUME_FLUX_LLC0090GRID_MONTHLY_V4R4", "ECCO_L4_OCEAN_BOLUS_STREAMFUNCTION_LLC0090GRID_DAILY_V4R4", "ECCO_L4_OCEAN_BOLUS_STREAMFUNCTION_LLC0090GRID_MONTHLY_V4R4", "ECCO_L4_OCEAN_VEL_05DEG_DAILY_V4R4", "ECCO_L4_OCEAN_VEL_05DEG_MONTHLY_V4R4", "ECCO_L4_OCEAN_VEL_LLC0090GRID_DAILY_V4R4", "ECCO_L4_OCEAN_VEL_LLC0090GRID_MONTHLY_V4R4", "ECCO_L4_SBO_CORE_TIME_SERIES_SNAPSHOT_V4R4", "ECCO_L4_SBO_CORE_TIME_SERIES_SNAPSHOT_V4R4B", "ECCO_L4_SEA_ICE_CONC_THICKNESS_05DEG_DAILY_V4R4", "ECCO_L4_SEA_ICE_CONC_THICKNESS_05DEG_MONTHLY_V4R4", "ECCO_L4_SEA_ICE_CONC_THICKNESS_LLC0090GRID_DAILY_V4R4", "ECCO_L4_SEA_ICE_CONC_THICKNESS_LLC0090GRID_MONTHLY_V4R4", "ECCO_L4_SEA_ICE_CONC_THICKNESS_LLC0090GRID_SNAPSHOT_V4R4", "ECCO_L4_SEA_ICE_HORIZ_VOLUME_FLUX_LLC0090GRID_DAILY_V4R4", "ECCO_L4_SEA_ICE_HORIZ_VOLUME_FLUX_LLC0090GRID_MONTHLY_V4R4", "ECCO_L4_SEA_ICE_SALT_PLUME_FLUX_LLC0090GRID_DAILY_V4R4", "ECCO_L4_SEA_ICE_SALT_PLUME_FLUX_LLC0090GRID_MONTHLY_V4R4", "ECCO_L4_SEA_ICE_VELOCITY_05DEG_DAILY_V4R4", "ECCO_L4_SEA_ICE_VELOCITY_05DEG_MONTHLY_V4R4", "ECCO_L4_SEA_ICE_VELOCITY_LLC0090GRID_DAILY_V4R4", "ECCO_L4_SEA_ICE_VELOCITY_LLC0090GRID_MONTHLY_V4R4", "ECCO_L4_SEA_ICE_VELOCITY_LLC0090GRID_SNAPSHOT_V4R4", "ECCO_L4_SSH_05DEG_DAILY_V4R4", "ECCO_L4_SSH_05DEG_DAILY_V4R4B", "ECCO_L4_SSH_05DEG_MONTHLY_V4R4", "ECCO_L4_SSH_05DEG_MONTHLY_V4R4B", "ECCO_L4_SSH_LLC0090GRID_DAILY_V4R4", "ECCO_L4_SSH_LLC0090GRID_DAILY_V4R4B", "ECCO_L4_SSH_LLC0090GRID_MONTHLY_V4R4", "ECCO_L4_SSH_LLC0090GRID_MONTHLY_V4R4B", "ECCO_L4_SSH_LLC0090GRID_SNAPSHOT_V4R4", "ECCO_L4_STRESS_05DEG_DAILY_V4R4", "ECCO_L4_STRESS_05DEG_MONTHLY_V4R4", "ECCO_L4_STRESS_LLC0090GRID_DAILY_V4R4", "ECCO_L4_STRESS_LLC0090GRID_MONTHLY_V4R4", "ECCO_L4_TEMP_SALINITY_05DEG_DAILY_V4R4", "ECCO_L4_TEMP_SALINITY_05DEG_MONTHLY_V4R4", "ECCO_L4_TEMP_SALINITY_LLC0090GRID_DAILY_V4R4", "ECCO_L4_TEMP_SALINITY_LLC0090GRID_MONTHLY_V4R4", "ECCO_L4_TEMP_SALINITY_LLC0090GRID_SNAPSHOT_V4R4", "ECO1BATT", "ECO1BGEO", "ECO1BMAPRAD", "ECO1BRAD", "ECO2CLD", "ECO2LSTE", "ECO3ANCQA", "ECO3ETALEXI", "ECO3ETALEXIU", "ECO3ETPTJPL", "ECO4ESIALEXI", "ECO4ESIALEXIU", "ECO4ESIPTJPL", "ECO4WUE", "ECOA", "ECOHAB", "ECOMON", "ECO_L1B_ATT", "ECO_L1B_GEO", "ECO_L1B_RAD", "ECO_L1CG_RAD", "ECO_L1CT_RAD", "ECO_L2G_CLOUD", "ECO_L2G_LSTE", "ECO_L2T_LSTE", "ECO_L2T_STARS", "ECO_L2_CLOUD", "ECO_L2_LSTE", "ECO_L3G_JET", "ECO_L3G_MET", "ECO_L3G_SEB", "ECO_L3G_SM", "ECO_L3T_JET", "ECO_L3T_MET", "ECO_L3T_SEB", "ECO_L3T_SM", "ECO_L4G_ESI", "ECO_L4G_WUE", "ECO_L4T_ESI", "ECO_L4T_WUE", "ECS", "EDDIES", "EDM_SA_Vegetation_1149", "EDM_US_Carbon_1160", "EF_Data_Mexico_1693", "EGEE3", "EGEE5", "EKAMSAT_Pilot_ASTRAL", "EM27_XCO2_XCH4_XCO_AK_1831", "EMITL1BATT", "EMITL1BRAD", "EMITL2ARFL", "EMITL2BCH4ENH", "EMITL2BCH4PLM", "EMITL2BCO2ENH", "EMITL2BCO2PLM", "EMITL2BMIN", "EMITL3ASA", "EMITL4ESM", "EN1_MDSI_MER_FRS_1P", "EN1_MDSI_MER_FRS_2P", "EN1_MDSI_MER_RR__2P", "ENVISAT_SCIAMACHY_SIF_1871", "EOLE1", "EPEA", "ERBE_S10N_WFOV_NF", "ERBE_S10N_WFOV_NF", "ERBE_S10N_WFOV_SF", "ERBE_S10N_WFOV_SF", "ERBE_S10N_WFOV_SF_ERBS_AreaAverageTimeSeries", "ERBE_S10N_WFOV_SF_ERBS_AreaAverageTimeSeries_Edition4", "ERBE_S10N_WFOV_SF_ERBS_Regional", "ERBE_S10N_WFOV_SF_ERBS_Regional", "ERBE_S10N_WFV_NF_NAT", "ERBE_S10N_WFV_SF_NAT", "ERBE_S10_MFOV_NF_NAT", "ERBE_S10_MFOV_SF_NAT", "ERBE_S10_WFOV_NF_NAT", "ERBE_S10_WFOV_SF_NAT", "ERBE_S4GN_WFOV_NF", "ERBE_S4GN_WFOV_NF_ZG", "ERBE_S4GN_WFOV_SF", "ERBE_S4GN_WFOV_SF_ZG", "ERBE_S4GN_WFV_NF_N10", "ERBE_S4G_MFOV_NF", "ERBE_S4G_MFOV_NF_N10", "ERBE_S4G_MFOV_NF_ZG", "ERBE_S4G_MFOV_SF", "ERBE_S4G_MFOV_SF_ZG", "ERBE_S4G_SC_2.5", "ERBE_S4G_SC_NEST10", "ERBE_S4G_SC_NEST5", "ERBE_S4G_SC_ZG", "ERBE_S4G_WFOV_NF", "ERBE_S4G_WFOV_NF_N10", "ERBE_S4G_WFOV_NF_ZG", "ERBE_S4G_WFOV_SF", "ERBE_S4G_WFOV_SF_ZG", "ERBE_S4N_NAT", "ERBE_S4_NAT", "ERBE_S7_NAT", "ERBE_S8_NAT", "ERBE_S9_NAT", "ERBE_TSI_ERBS_NAT", "ERS-1_BYU_L3_OW_SIGMA0_ENHANCED", "ERS-1_L0", "ERS-1_L1", "ERS-2_BYU_L3_OW_SIGMA0_ENHANCED", "ERS-2_L0", "ERS-2_L1", "ERS2_GOME_SIF_1758", "ESCAPE", "ESMRN5IM", "ESMRN5L1", "ESMRN6IM", "EUCFe", "EUMELI", "EWSG1-NAVO-L2P-v01", "EXP7L1TRTALL", "EXP7L1TRTWHT", "EXPORTS", "EastAnglia10YearMean_549", "EastAnglia30YearMean_550", "EastAngliaClimate_542", "EastAngliaPrecip_417", "Ecosystem_Map_SRD_PAD_1947", "Effect_Environment_Moose_1739", "End_of_Season_Snow_Depth_1702", "Environmental_Disturbances_AK_1705", "Erosion_Vegetation_Yukon_1616", "Estimated_Biomass_Stock_Amazon_1648", "Eurasia_Biomass_1278", "FEWS_precip_711", "FIA_Forest_Biomass_Estimates_1873", "FIFE_CD_V3_130", "FIREXAQ_AerosolCloud_AircraftRemoteSensing_ER2_CPL_Data", "FIREXAQ_Aerosol_AircraftInSitu_DC8_Data", "FIREXAQ_Aerosol_AircraftInSitu_N48_Data", "FIREXAQ_Analysis_Data", "FIREXAQ_Analysis_N48_Data", "FIREXAQ_Cloud_AircraftInSitu_DC8_Data", "FIREXAQ_Ground_InSitu_Data", "FIREXAQ_HSRL_AircraftRemoteSensing_DC8_Data", "FIREXAQ_Merge_Data", "FIREXAQ_MetNav_AircraftInSitu_DC8_Data", "FIREXAQ_MetNav_AircraftInSitu_ER2_Data", "FIREXAQ_MetNav_AircraftInSitu_N48_Data", "FIREXAQ_N46_Data", "FIREXAQ_Radiation_AircraftInSitu_DC8_Data", "FIREXAQ_Satellite_Data", "FIREXAQ_SurfaceMobile_Aerodyne_InSitu_Data", "FIREXAQ_SurfaceMobile_CARB_InSitu_Data", "FIREXAQ_SurfaceMobile_MACH2_InSitu_Data", "FIREXAQ_TraceGas_AircraftInSitu_DC8_Data", "FIREXAQ_TraceGas_AircraftInSitu_N48_Data", "FIREXAQ_TraceGas_AircraftRemoteSensing_DC8_Data", "FIREXAQ_TraceGas_AircraftRemoteSensing_ER2_GCAS_Data", "FIREXAQ_TraceGas_AircraftRemoteSensing_ER2_NASTI_Data", "FIREXAQ_TraceGas_AircraftRemoteSensing_ER2_SHIS_Data", "FIREXAQ_jValue_AircraftInSitu_DC8_Data", "FIREXAQ_jValue_AircraftInSitu_N48_Data", "FIRE_ACE_AMPR", "FIRE_ACE_C130_CFD", "FIRE_ACE_C130_RAMS", "FIRE_ACE_ER2_MAS", "FIRE_ACE_ER2_MIR", "FIRE_ACE_SHIP_SSFR", "FIRE_ACE_UTRECHT_BALLOON", "FIRE_ACE_UTRECHT_TOWER", "FIRE_ACE_UWCV580_GMETER", "FIRE_ACE_UWCV580_UWA", "FIRE_AX_CMS_LWFLUX", "FIRE_AX_CMS_SOLAR_DY", "FIRE_AX_CMS_SOLAR_HR", "FIRE_AX_CMS_SOLAR_MN", "FIRE_AX_CMS_SOLAR_WK", "FIRE_AX_CMS_SST_DAY", "FIRE_AX_CMS_SST_FNTS", "FIRE_AX_CMS_SST_WEEK", "FIRE_AX_CSU_CEILOM", "FIRE_AX_CSU_MET_SFC", "FIRE_AX_CSU_PRT6", "FIRE_AX_CSU_WNDPRFS", "FIRE_AX_ECMWF_BASIC", "FIRE_AX_ECMWF_CLOUDS", "FIRE_AX_ECMWF_DIAG", "FIRE_AX_ECMWF_MEANW", "FIRE_AX_ECMWF_SFDIAG", "FIRE_AX_ECMWF_SURFCE", "FIRE_AX_ER2_LIDAR", "FIRE_AX_ER2_MAS", "FIRE_AX_ERS1_ALTIMTR", "FIRE_AX_ERS1_MCRWRAD", "FIRE_AX_ERS1_SCTRMTR", "FIRE_AX_ISCCP_DX", "FIRE_AX_MAGE_OCN_AIR", "FIRE_AX_MAGE_OCN_MET", "FIRE_AX_MAGE_OCN_SEA", "FIRE_AX_MAGE_TETROON", "FIRE_AX_MALBAL_SONDE", "FIRE_AX_METEOSAT", "FIRE_AX_OCEANS_SONDE", "FIRE_AX_PORSAN_SONDE", "FIRE_AX_PSU_CEIL_MAL", "FIRE_AX_PSU_CEIL_SAN", "FIRE_AX_PSU_CEIL_VAL", "FIRE_AX_PSU_H2OVAP", "FIRE_AX_PSU_MALBAL", "FIRE_AX_PSU_WND_MAL", "FIRE_AX_RWSONDE_LVL1", "FIRE_AX_RWSONDE_LVL2", "FIRE_AX_SANMAR_SONDE", "FIRE_AX_SFC_FUNCHAL", "FIRE_AX_SFC_IMAU", "FIRE_AX_SOF_ARAT_FLT", "FIRE_AX_SOF_ARAT_TRB", "FIRE_AX_SOF_BUOY_DFT", "FIRE_AX_SOF_BUOY_SPR", "FIRE_AX_SOF_PTU", "FIRE_AX_SOF_SUR_BUCK", "FIRE_AX_SOF_SUR_DRAK", "FIRE_AX_SOF_SUR_HYD", "FIRE_AX_SOF_SUR_MET", "FIRE_AX_SOF_SUR_RAD", "FIRE_AX_SOF_SUR_TBAL", "FIRE_AX_SUROIT_SONDE", "FIRE_AX_UKMO_C130", "FIRE_AX_UW_C131A", "FIRE_AX_UW_DSCRT", "FIRE_AX_UW_GERB_10HZ", "FIRE_AX_UW_GERB_1HZ", "FIRE_AX_VALDIV_SONDE", "FIRE_CI1_CSU_SABRE", "FIRE_CI1_ER2_LIDAR", "FIRE_CI1_ER2_RAD", "FIRE_CI1_ISCCP_DX", "FIRE_CI1_KINGAIR", "FIRE_CI1_LARC8_LIDAR", "FIRE_CI1_RAWINSONDES", "FIRE_CI1_SABRELINER", "FIRE_CI1_SRB_ALASKA", "FIRE_CI1_SRB_CANADA", "FIRE_CI1_SRB_LW", "FIRE_CI1_SRB_SO_POLE", "FIRE_CI1_SRB_SW", "FIRE_CI1_SRB_SWITZ", "FIRE_CI1_TOVS", "FIRE_CI2_CITATN_1HZ", "FIRE_CI2_CITATN_24HZ", "FIRE_CI2_CITATN_5SEC", "FIRE_CI2_CITATN_IWC", "FIRE_CI2_CITATN_PMS", "FIRE_CI2_CLASS_SONDE", "FIRE_CI2_CSU_PRT6", "FIRE_CI2_CSU_SONDES", "FIRE_CI2_CSU_STN1", "FIRE_CI2_CSU_STN2", "FIRE_CI2_CSU_WNDPRFS", "FIRE_CI2_DOPLR_LIDAR", "FIRE_CI2_ER2_LIDAR", "FIRE_CI2_ER2_MAS", "FIRE_CI2_ETL_RADAR", "FIRE_CI2_HIS", "FIRE_CI2_HSRL", "FIRE_CI2_ISCCP_DX", "FIRE_CI2_KINGAIR", "FIRE_CI2_KINGAIR_2D", "FIRE_CI2_KINGAIR_IWC", "FIRE_CI2_LARC8_LIDAR", "FIRE_CI2_MAPS", "FIRE_CI2_NOAA_WNDPFS", "FIRE_CI2_NWS_IN_SND", "FIRE_CI2_NWS_OUT_SND", "FIRE_CI2_PAMS", "FIRE_CI2_RAMAN_LIDAR", "FIRE_CI2_SABRELINER", "FIRE_CI2_SABRLNR_2D", "FIRE_CI2_SABRLNR_IWC", "FIRE_CI2_SPECT_SIRIS", "FIRE_CI2_UTAH_PDL", "FIRE_CI2_VIL_RTI", "FIRE_CI2_VIL_SCAN", "FIRE_ETO_UTAH_PDL", "FIRE_MS_CEILOM_CLASS", "FIRE_MS_CSU_RADSURF", "FIRE_MS_CSU_TBALLOON", "FIRE_MS_ELECTRA", "FIRE_MS_ER2_LIDAR", "FIRE_MS_ISCCP_DX", "FIRE_MS_MCRW_RAD", "FIRE_MS_NOAAWNDS", "FIRE_MS_UKMO_C130", "FLASH_TISA_Terra-Aqua", "FLASH_TISA_Terra-NOAA20", "FLASH_TISA_Terra-NOAA20", "FLDAS_NOAH01_CP_GL_M", "FLDAS_NOAH01_C_GL_M", "FLDAS_NOAH01_C_GL_MA", "FLDAS_NOAH01_C_GL_MC", "FLDAS_NOAHMP001_G_CA_D", "FLEXPART_Influence_Functions_2018", "FLOODAMA_903", "FLUAMAZON_896", "FLUXNET_Canada_1335", "FRONT", "FieldData_Alaska_Tundra_2177", "Field_Measurements_868", "Fire_Emissions_Indonesia_2118", "Fire_Emissions_NWT_1561", "Fire_Ignitions_Locations_AK_CA_2316", "Flight_Environment_Parameters_1909", "FluxSat_GPP_FPAR_1835", "Flux_Tower_Zona_Veg_Plots_1546", "Fluxnet_site_DB_1530", "Fluxnet_website_archive_copy_1549", "ForestHt_Biomass_GEDI_TDX_2298", "Forest_AGB_NW_USA_1766", "Forest_Carbon_Priority_1803", "Forest_Disturbance_Intensity_2059", "Forest_Inventory_Acre_Brazil_1654", "Forest_Inventory_Brazil_2007", "Forest_Inventory_Data_Brazil_1563", "Forest_Inventory_Tapajos_1552", "Forested_Areas_Amazonas_Brazil_1515", "Forested_Areas_Para_Brazil_1514", "Frac_FuelComponent_Maps_Tundra_1761", "Freeze_Thaw_NorthernHemisphere_2323", "Frost_Boils_Veg_Plots_1361", "Ft_Lauderdale", "G18-ABI-L2P-ACSPO-v2.90", "G18-ABI-L3C-ACSPO-v2.90", "GAMSSA_28km-ABOM-L4-GLOB-v01", "GCAM_Land_Cover_2005-2095_1216", "GCRW_DEM_2016_1793", "GEDI01_B", "GEDI02_A", "GEDI02_B", "GEDI_Fusion_Structure_2236", "GEDI_HighQuality_Shots_Rasters_2339", "GEDI_ICESAT2_Global_Veg_Height_2294", "GEDI_L3_LandSurface_Metrics_V2_1952", "GEDI_L4A_AGB_Density_GW_2028", "GEDI_L4A_AGB_Density_V2_1_2056", "GEDI_L4B_Country_Biomass_2321", "GEDI_L4B_Gridded_Biomass_V2_1_2299", "GEDI_L4C_WSCI_2338", "GEO-CAPE", "GEO-CAPE_GOCI", "GEOS-3_ALT_GDR", "GEOS2OBSINPUTINTL", "GEOS3STST", "GEOS_CASAGFED_3H_NEE", "GEOS_CASAGFED_3H_NEE", "GEOS_CASAGFED_D_FIRE", "GEOS_CASAGFED_D_FIRE", "GEOS_CASAGFED_M_FLUX", "GEOS_CASAGFED_M_FLUX", "GEOTRACES", "GEWEXSRB_Rel4-IP_Ancillary_3hrly", "GEWEXSRB_Rel4-IP_Ancillary_3hrly_landonly", "GEWEXSRB_Rel4-IP_Ancillary_3hrly_oceanonly", "GEWEXSRB_Rel4-IP_Longwave_3hrly", "GEWEXSRB_Rel4-IP_Longwave_3hrlymonthly_landonly_utc", "GEWEXSRB_Rel4-IP_Longwave_3hrlymonthly_oceanonly_utc", "GEWEXSRB_Rel4-IP_Longwave_3hrlymonthly_utc", "GEWEXSRB_Rel4-IP_Longwave_daily_landonly_utc", "GEWEXSRB_Rel4-IP_Longwave_daily_oceanonly_utc", "GEWEXSRB_Rel4-IP_Longwave_daily_utc", "GEWEXSRB_Rel4-IP_Longwave_monthly_landonly_utc", "GEWEXSRB_Rel4-IP_Longwave_monthly_local", "GEWEXSRB_Rel4-IP_Longwave_monthly_oceanonly_utc", "GEWEXSRB_Rel4-IP_Longwave_monthly_utc", "GEWEXSRB_Rel4-IP_Shortwave_3hrly", "GEWEXSRB_Rel4-IP_Shortwave_3hrlymonthly_utc", "GEWEXSRB_Rel4-IP_Shortwave_daily_local", "GEWEXSRB_Rel4-IP_Shortwave_daily_utc", "GEWEXSRB_Rel4-IP_Shortwave_monthly_local", "GEWEXSRB_Rel4-IP_Shortwave_monthly_utc", "GEWEXSRB_Rel4_1-IP_Longwave_3hrly_landonly", "GEWEXSRB_Rel4_1-IP_Longwave_3hrly_oceanonly", "GEWEXSRB_Rel4_1-IP_Longwave_daily_landonly_local", "GEWEXSRB_Rel4_1-IP_Longwave_daily_local", "GEWEXSRB_Rel4_1-IP_Longwave_daily_oceanonly_local", "GEWEXSRB_Rel4_1-IP_Longwave_monthly_landonly_local", "GEWEXSRB_Rel4_1-IP_Longwave_monthly_oceanonly_local", "GFCC30FCC", "GFCC30SR", "GFCC30TC", "GFCC30WC", "GFEI_CH4", "GFSAD1KCD", "GFSAD1KCM", "GFSAD30AFCE", "GFSAD30AUNZCNMOCE", "GFSAD30EUCEARUMECE", "GFSAD30NACE", "GFSAD30SAAFGIRCE", "GFSAD30SACE", "GFSAD30SEACE", "GFSAD30VAL", "GHISACASIA", "GHISACONUS", "GIMMS3g_NDVI_Trends_1275", "GIS_EastAngliaClimateMonthly_551", "GLAH01", "GLAH01", "GLAH02", "GLAH02", "GLAH03", "GLAH03", "GLAH04", "GLAH04", "GLAH05", "GLAH05", "GLAH06", "GLAH06", "GLAH07", "GLAH07", "GLAH08", "GLAH08", "GLAH09", "GLAH09", "GLAH10", "GLAH10", "GLAH11", "GLAH11", "GLAH12", "GLAH12", "GLAH13", "GLAH13", "GLAH14", "GLAH14", "GLAH15", "GLAH15", "GLCHMK", "GLCHMT", "GLDAS_CLSM025_D", "GLDAS_CLSM025_DA1_D", "GLDAS_CLSM025_DA1_D_EP", "GLDAS_CLSM10_3H", "GLDAS_CLSM10_3H", "GLDAS_CLSM10_3H_EP", "GLDAS_CLSM10_M", "GLDAS_CLSM10_M", "GLDAS_CLSM10_M_EP", "GLDAS_NOAH025_3H", "GLDAS_NOAH025_3H", "GLDAS_NOAH025_3H_EP", "GLDAS_NOAH025_M", "GLDAS_NOAH025_M", "GLDAS_NOAH025_M_EP", "GLDAS_NOAH10_3H", "GLDAS_NOAH10_3H", "GLDAS_NOAH10_3H_EP", "GLDAS_NOAH10_M", "GLDAS_NOAH10_M", "GLDAS_NOAH10_M_EP", "GLDAS_VIC10_3H", "GLDAS_VIC10_3H", "GLDAS_VIC10_3H_EP", "GLDAS_VIC10_M", "GLDAS_VIC10_M", "GLDAS_VIC10_M_EP", "GLDSMT", "GLDTMK", "GLDTMT", "GLHYANC", "GLHYVI", "GLLIDARPC", "GLMETRICS", "GLOBEC", "GLORTHO", "GLRADS", "GLREFL", "GLTRAJECTORY", "GLanCE30", "GMAO_M2SCREAM_INST3_CHEM", "GMAO_M2SCREAM_MONTH_UNCERT", "GMI-REMSS-L3U-v8.2a", "GNATS", "GO-BGC", "GO-SHIP", "GOA97", "GOC", "GOCAL", "GOCI_2013", "GOES13-OSISAF-L3C-v1.0", "GOES13-OSPO-L2P-v1.0", "GOES15-OSPO-L2P-v1.0", "GOES16-SST-OSISAF-L3C-v1.0", "GOM", "GOMECC", "GOMEX", "GOME_MINDS_NO2", "GOMI2AE", "GOMI2LS", "GOMI2ST", "GOMIB2E", "GOMIB2T", "GOMIGEO", "GOM_Oil_Spill", "GPCPDAY", "GPCPDAY", "GPCPMON", "GPCPMON", "GPCP_precip_712", "GPM_1AGMI", "GPM_1ATMI", "GPM_1AVIRS", "GPM_1BGMI", "GPM_1BPR", "GPM_1BTMI", "GPM_1BVIRS", "GPM_1CAQUAAMSRE", "GPM_1CF08SSMI", "GPM_1CF10SSMI", "GPM_1CF11SSMI", "GPM_1CF13SSMI", "GPM_1CF14SSMI", "GPM_1CF15SSMI", "GPM_1CF16SSMIS", "GPM_1CF17SSMIS", "GPM_1CF18SSMIS", "GPM_1CF19SSMIS", "GPM_1CGCOMW1AMSR2", "GPM_1CGPMGMI", "GPM_1CGPMGMI_R", "GPM_1CMETOPAMHS", "GPM_1CMETOPBMHS", "GPM_1CMETOPCMHS", "GPM_1CMT1SAPHIR", "GPM_1CNOAA15AMSUB", "GPM_1CNOAA16AMSUB", "GPM_1CNOAA17AMSUB", "GPM_1CNOAA18MHS", "GPM_1CNOAA19MHS", "GPM_1CNOAA20ATMS", "GPM_1CNOAA21ATMS", "GPM_1CNPPATMS", "GPM_1CTRMMTMI", "GPM_2ADPR", "GPM_2ADPRENV", "GPM_2AGPROFAQUAAMSRE_CLIM", "GPM_2AGPROFF08SSMI_CLIM", "GPM_2AGPROFF10SSMI_CLIM", "GPM_2AGPROFF11SSMI_CLIM", "GPM_2AGPROFF13SSMI_CLIM", "GPM_2AGPROFF14SSMI_CLIM", "GPM_2AGPROFF15SSMI_CLIM", "GPM_2AGPROFF16SSMIS", "GPM_2AGPROFF16SSMIS_CLIM", "GPM_2AGPROFF17SSMIS", "GPM_2AGPROFF17SSMIS_CLIM", "GPM_2AGPROFF18SSMIS", "GPM_2AGPROFF18SSMIS_CLIM", "GPM_2AGPROFF19SSMIS_CLIM", "GPM_2AGPROFGCOMW1AMSR2", "GPM_2AGPROFGCOMW1AMSR2_CLIM", "GPM_2AGPROFGPMGMI", "GPM_2AGPROFGPMGMI_CLIM", "GPM_2AGPROFMETOPAMHS_CLIM", "GPM_2AGPROFMETOPBMHS", "GPM_2AGPROFMETOPBMHS_CLIM", "GPM_2AGPROFMETOPCMHS", "GPM_2AGPROFMETOPCMHS_CLIM", "GPM_2AGPROFNOAA15AMSUB_CLIM", "GPM_2AGPROFNOAA16AMSUB_CLIM", "GPM_2AGPROFNOAA17AMSUB_CLIM", "GPM_2AGPROFNOAA18MHS_CLIM", "GPM_2AGPROFNOAA19MHS", "GPM_2AGPROFNOAA19MHS_CLIM", "GPM_2AGPROFNOAA20ATMS", "GPM_2AGPROFNOAA20ATMS_CLIM", "GPM_2AGPROFNOAA21ATMS", "GPM_2AGPROFNOAA21ATMS_CLIM", "GPM_2AGPROFNPPATMS", "GPM_2AGPROFNPPATMS_CLIM", "GPM_2AGPROFTRMMTMI_CLIM", "GPM_2AKa", "GPM_2AKaENV", "GPM_2AKu", "GPM_2AKuENV", "GPM_2APR", "GPM_2APRPSMT1SAPHIR", "GPM_2APRPSMT1SAPHIR_CLIM", "GPM_2BCMB", "GPM_2BCMB_TRMM", "GPM_2HCSH", "GPM_2HCSH_TRMM", "GPM_2HSLH", "GPM_2HSLH_TRMM", "GPM_3CMB", "GPM_3CMB_DAY", "GPM_3CMB_TRMM", "GPM_3CMB_TRMM_DAY", "GPM_3DPR", "GPM_3DPRD", "GPM_3DPR_ASC", "GPM_3DPR_DES", "GPM_3GCSH", "GPM_3GCSH_TRMM", "GPM_3GPROFAQUAAMSRE_CLIM", "GPM_3GPROFAQUAAMSRE_DAY_CLIM", "GPM_3GPROFF08SSMI_CLIM", "GPM_3GPROFF08SSMI_DAY_CLIM", "GPM_3GPROFF10SSMI_CLIM", "GPM_3GPROFF10SSMI_DAY_CLIM", "GPM_3GPROFF11SSMI_CLIM", "GPM_3GPROFF11SSMI_DAY_CLIM", "GPM_3GPROFF13SSMI_CLIM", "GPM_3GPROFF13SSMI_DAY_CLIM", "GPM_3GPROFF14SSMI_CLIM", "GPM_3GPROFF14SSMI_DAY_CLIM", "GPM_3GPROFF15SSMI_CLIM", "GPM_3GPROFF15SSMI_DAY_CLIM", "GPM_3GPROFF16SSMIS", "GPM_3GPROFF16SSMIS_CLIM", "GPM_3GPROFF16SSMIS_DAY", "GPM_3GPROFF16SSMIS_DAY_CLIM", "GPM_3GPROFF17SSMIS", "GPM_3GPROFF17SSMIS_CLIM", "GPM_3GPROFF17SSMIS_DAY", "GPM_3GPROFF17SSMIS_DAY_CLIM", "GPM_3GPROFF18SSMIS", "GPM_3GPROFF18SSMIS_CLIM", "GPM_3GPROFF18SSMIS_DAY", "GPM_3GPROFF18SSMIS_DAY_CLIM", "GPM_3GPROFF19SSMIS_CLIM", "GPM_3GPROFF19SSMIS_DAY_CLIM", "GPM_3GPROFGCOMW1AMSR2", "GPM_3GPROFGCOMW1AMSR2_CLIM", "GPM_3GPROFGCOMW1AMSR2_DAY", "GPM_3GPROFGCOMW1AMSR2_DAY_CLIM", "GPM_3GPROFGPMGMI", "GPM_3GPROFGPMGMI_CLIM", "GPM_3GPROFGPMGMI_DAY", "GPM_3GPROFGPMGMI_DAY_CLIM", "GPM_3GPROFMETOPAMHS_CLIM", "GPM_3GPROFMETOPAMHS_DAY_CLIM", "GPM_3GPROFMETOPBMHS", "GPM_3GPROFMETOPBMHS_CLIM", "GPM_3GPROFMETOPBMHS_DAY", "GPM_3GPROFMETOPBMHS_DAY_CLIM", "GPM_3GPROFMETOPCMHS", "GPM_3GPROFMETOPCMHS_CLIM", "GPM_3GPROFMETOPCMHS_DAY", "GPM_3GPROFMETOPCMHS_DAY_CLIM", "GPM_3GPROFNOAA15AMSUB_CLIM", "GPM_3GPROFNOAA15AMSUB_DAY_CLIM", "GPM_3GPROFNOAA16AMSUB_CLIM", "GPM_3GPROFNOAA16AMSUB_DAY_CLIM", "GPM_3GPROFNOAA17AMSUB_CLIM", "GPM_3GPROFNOAA17AMSUB_DAY_CLIM", "GPM_3GPROFNOAA18MHS_CLIM", "GPM_3GPROFNOAA18MHS_DAY_CLIM", "GPM_3GPROFNOAA19MHS", "GPM_3GPROFNOAA19MHS_CLIM", "GPM_3GPROFNOAA19MHS_DAY", "GPM_3GPROFNOAA19MHS_DAY_CLIM", "GPM_3GPROFNOAA20ATMS", "GPM_3GPROFNOAA20ATMS_CLIM", "GPM_3GPROFNOAA20ATMS_DAY", "GPM_3GPROFNOAA20ATMS_DAY_CLIM", "GPM_3GPROFNOAA21ATMS", "GPM_3GPROFNOAA21ATMS_CLIM", "GPM_3GPROFNOAA21ATMS_DAY", "GPM_3GPROFNOAA21ATMS_DAY_CLIM", "GPM_3GPROFNPPATMS", "GPM_3GPROFNPPATMS_CLIM", "GPM_3GPROFNPPATMS_DAY", "GPM_3GPROFNPPATMS_DAY_CLIM", "GPM_3GPROFTRMMTMI_CLIM", "GPM_3GPROFTRMMTMI_DAY_CLIM", "GPM_3GSLH", "GPM_3GSLH_TRMM", "GPM_3HCSH", "GPM_3HCSH_TRMM", "GPM_3HSLH", "GPM_3HSLH_DAY", "GPM_3HSLH_TRMM", "GPM_3HSLH_TRMM_DAY", "GPM_3IMERGDE", "GPM_3IMERGDE", "GPM_3IMERGDF", "GPM_3IMERGDL", "GPM_3IMERGDL", "GPM_3IMERGHH", "GPM_3IMERGHHE", "GPM_3IMERGHHE", "GPM_3IMERGHHL", "GPM_3IMERGHHL", "GPM_3IMERGM", "GPM_3PR", "GPM_3PRD", "GPM_3PRPSMT1SAPHIR", "GPM_3PRPSMT1SAPHIR_CLIM", "GPM_3PRPSMT1SAPHIR_DAY", "GPM_3PRPSMT1SAPHIR_DAY_CLIM", "GPM_3PR_ASC", "GPM_3PR_DES", "GPM_BASEGPMGMI", "GPM_BASEGPMGMI_RSS", "GPM_BASEGPMGMI_XCAL", "GPM_BASETRMMTMI", "GPM_IMERG_LandSeaMask", "GPM_MERGIR", "GPM_PRL1KA", "GPM_PRL1KU", "GPP_CONUS_TROPOMI_1875", "GPP_MODIS_Alaska_Canada_2024", "GPP_surfaces_749", "GPROF_precip_716", "GPSROZPBLA", "GPSROZPBLA", "GPSROZPBLS", "GPSROZPBLS", "GRACEDADM_CLSM0125US_7D", "GRACEDADM_CLSM025GL_7D", "GRACEFO_L1A_ASCII_GRAV_JPL_RL04", "GRACEFO_L2_CSR_MONTHLY_0062", "GRACEFO_L2_GFZ_MONTHLY_0061", "GRACEFO_L2_JPL_MONTHLY_0061", "GRACEFO_L2_JPL_MONTHLY_0063", "GRACE_AOD1B_GRAV_GFZ_RL06", "GRACE_GAA_L2_GRAV_GFZ_RL06", "GRACE_GAA_L2_GRAV_JPL_RL06", "GRACE_GAB_L2_GRAV_GFZ_RL06", "GRACE_GAB_L2_GRAV_JPL_RL06", "GRACE_GAC_L2_GRAV_CSR_RL06", "GRACE_GAC_L2_GRAV_GFZ_RL06", "GRACE_GAC_L2_GRAV_JPL_RL06", "GRACE_GAD_L2_GRAV_CSR_RL06", "GRACE_GAD_L2_GRAV_GFZ_RL06", "GRACE_GAD_L2_GRAV_JPL_RL06", "GRACE_GSM_L2_GRAV_CSR_RL06", "GRACE_GSM_L2_GRAV_GFZ_RL06", "GRACE_GSM_L2_GRAV_JPL_RL06", "GRACE_L1B_GRAV_JPL_RL02", "GRACE_L1B_GRAV_JPL_RL03", "GRC-GFO_GRIDDED_AOD1B_JPL_1-DEG_RL06", "GRC-GFO_GRIDDED_AOD1B_JPL_MASCON_RL06", "GREENLAND_MASS_TELLUS_MASCON_CRI_TIME_SERIES_RL06.1_V3", "GSMNP_Vegetation_Structure_R1_1286", "GSSTF", "GSSTF", "GSSTFM", "GSSTFM", "GSSTFMC", "GSSTFMC", "GSSTFM_NCEP", "GSSTFM_NCEP", "GSSTFSC", "GSSTFSC", "GSSTFYC", "GSSTFYC", "GSSTF_F08", "GSSTF_F08", "GSSTF_F10", "GSSTF_F10", "GSSTF_F11", "GSSTF_F11", "GSSTF_F13", "GSSTF_F13", "GSSTF_F14", "GSSTF_F14", "GSSTF_F15", "GSSTF_F15", "GSSTF_NCEP", "GSSTF_NCEP", "GWELDMO", "GWELDMO", "GWELDMO", "GWELDYR", "GWELDYR", "GWELDYR", "GasEx", "GePCO", "Geo_Polar_Blended-OSPO-L4-GLOB-v1.0", "Geo_Polar_Blended_Night-OSPO-L4-GLOB-v1.0", "GloSSAC", "GloSSAC", "GloSSAC", "GloSSAC", "GloSSAC", "GloSSAC", "Global_Biomass_1950-2010_1296", "Global_CDOM", "Global_Clumping_Index_1531", "Global_Freshwater_CH4Emissions_2253", "Global_Hydrologic_Soil_Group_1566", "Global_Lakes_Methane_2008", "Global_Landslide_Exposure_Maps", "Global_Landslide_Nowcast", "Global_Landslide_Nowcast", "Global_Litter_Carbon_Nutrients_1244", "Global_Maps_C_Density_2010_1763", "Global_Microbial_Biomass_C_N_P_1264", "Global_Monthly_GPP_1789", "Global_Phosphorus_Dist_Map_1223", "Global_Phosphorus_Hedley_Fract_1230", "Global_RTSG_Flux_1078", "Global_Reservoirs_Methane_1918", "Global_Riverine_N2O_Emissions_1791", "Global_SIF_OCO2_MODIS_1863", "Global_Salt_Marsh_Change_2122", "Global_Soil_Regolith_Sediment_1304", "Global_Veg_Greenness_GIMMS_3G_2187", "GozMmlpH2O", "GozMmlpHCl", "GozMmlpHNO3", "GozMmlpN2O", "GozMmlpO3", "GozSmlpH2O", "GozSmlpHCl", "GozSmlpHNO3", "GozSmlpN2O", "GozSmlpO3", "GozSmlpT", "Great_Belt", "Great_Lakes", "Great_Slave_Lake_Ecosystem_Map_1695", "GreenBay", "Gridded_Biomass_Africa_1777", "GroundMSPI_ACEPOL_Radiance_Data", "GulfCarbon", "GulfOfMaine", "Gulf_Of_Maine", "Gulf_of_Maine_Nutrients", "Gulf_of_Maine_Optics", "H09-AHI-L2P-ACSPO-v2.90", "H09-AHI-L3C-ACSPO-v2.90", "H3ZFC12MEXT", "H3ZFC8MEXT", "H3ZFCCFC11", "H3ZFCCFC12", "H3ZFCCLONO2", "H3ZFCGPH", "H3ZFCH2O", "H3ZFCHNO3", "H3ZFCN2O", "H3ZFCN2O5", "H3ZFCNO2", "H3ZFCO3", "H3ZFCT", "HAB", "HALO_LiDAR_AOP_ML_Heights_1833", "HAQES_NA_PM25_BC", "HAQES_NA_PM25_BC_CENSUS", "HAQES_NA_PM25_BC_COUNTY", "HAQES_NA_PM25_OC", "HAQES_NA_PM25_OC_CENSUS", "HAQES_NA_PM25_OC_COUNTY", "HAQES_NA_PM25_TOT", "HAQES_NA_PM25_TOT_CENSUS", "HAQES_NA_PM25_TOT_COUNTY", "HAQ_TROPOMI_NO2_CONUS_A_L3", "HAQ_TROPOMI_NO2_CONUS_M_L3", "HAQ_TROPOMI_NO2_CONUS_S_L3", "HAQ_TROPOMI_NO2_GLOBAL_A_L3", "HAQ_TROPOMI_NO2_GLOBAL_M_L3", "HAWKEYE_L1", "HAWKEYE_L1", "HAWKEYE_L2_OC", "HAWKEYE_L2_OC", "HCDN_810", "HICO_L1", "HIR3SCOL", "HIRDLS2", "HIRMLS3IWC", "HIRSN6IM", "HIRSN6L1GARP", "HIVE", "HLSL30", "HLSS30", "HMA2_DDSMET", "HMA2_DSPAT", "HMA2_FGP", "HMA2_GFTP", "HMA2_GGP", "HMA2_HFD", "HMA2_LHI", "HMA2_MATCHA", "HMA2_MTLI", "HMA2_NLSMR", "HMA2_WBP", "HMA_AWS", "HMA_DEM8m_AT", "HMA_DEM8m_CT", "HMA_DEM8m_MOS", "HMA_DM_6H", "HMA_DTE", "HMA_EAPrecip_FLOR", "HMA_FreezeThawMelt_ASCAT", "HMA_GLI", "HMA_GL_RCP", "HMA_GL_RCPR", "HMA_GSM", "HMA_GlacierAvg_dH", "HMA_Glacier_dH", "HMA_Glacier_dH_Mosaics", "HMA_LIS_LandSurfaceHydro", "HMA_LS_Cat", "HMA_MAR3_5", "HMA_MTLI", "HMA_OptDepth", "HMA_Precip_3B42", "HMA_Precip_FLOR", "HMA_RCMO_1H", "HMA_RCMO_6H", "HMA_RCMO_D", "HMA_RCMO_M", "HMA_SBRF", "HMA_SDI", "HMA_SR_D", "HMA_STParams", "HMA_Snowfield", "HOMAGE_GGFO_L4_GOMA_Monthly_v01", "HOMAGE_STERIC_OHC_TIME_SERIES_v01", "HOT", "HRAC_Precip", "HRIRN1IM", "HRIRN1L1", "HRIRN2IM", "HRIRN2L1", "HRIRN3IM", "HRIRN3L1", "HWSD_1247", "HYCODE_LEO-15", "Happy_Valley_Veg_Plots_1354", "HighRes_ClimateData_Western_US_1682", "High_Res_Tidal_Marsh_Veg_1609", "Historic_S2K_Website_1765", "HistoricalLai_584", "Historical_Lake_Shorelines_AK_1859", "Horn_Island", "HourlyUrban_GreenhouseGases_US_1916", "Hudson_River_Fluorescence", "Hydroprofiler", "IAKST1B", "IAKST1B", "IAPRS1B", "IASI_SST_METOP_A-OSISAF-L2P-v1.0", "IASI_SST_METOP_B-OSISAF-L2P-v1.0", "ICESCAPE", "ICEVOLC_FlowerKahn2020", "IDBMG4", "IDCSI4", "IDHDT4", "IDS_LIS", "IGBGM1B", "IGBGM2", "IGBP-DIS_565", "IGBP-SurfaceProducts_569", "IGBTH4", "IGCMG1B", "IGCMG2", "IGGRV1B", "IGLGS1B", "IGZLS1B", "ILAKP1B", "ILAKS1B", "ILATM1B", "ILATM1B", "ILATM2", "ILATMGR", "ILATMW1B", "ILNIRW1B", "ILNSA1B", "ILNSA1B", "ILNSAW1B", "ILSIG1B", "ILSNP1B", "ILSNP4", "ILUTP1B", "ILUTP2", "ILVGH1B", "ILVGH2", "ILVIS0", "ILVIS1B", "ILVIS2", "ILVIS2", "IMARPE-Callao_Station", "IMCS31B", "IMECOCAL", "IMERG_Precip_Canada_Alaska_2097", "IMFGM1B", "INDOEX", "INTEXA_AIRMAP", "INTEXA_DC8_AIRCRAFT", "INTEXA_J31_AIRCRAFT", "INTEXA_MERGES", "INTEXA_MODEL", "INTEXA_O3SONDES", "INTEXA_PROTEUS_AIRCRAFT", "INTEXA_SATELLITE", "INTEXA_TRAJECTORY", "INTEXB_Be200_AIRCRAFT", "INTEXB_C130_AIRCRAFT", "INTEXB_Cessna_AIRCRAFT", "INTEXB_DC8_AIRCRAFT", "INTEXB_Duchess_AIRCRAFT", "INTEXB_GROUND", "INTEXB_J31_AIRCRAFT", "INTEXB_MERGES", "INTEXB_MODEL", "INTEXB_O3SONDES", "INTEXB_SATELLITE", "INTEXB_TRAJECTORY", "INTRO", "IOCAM0", "IOCAM1B", "IOCAM1B", "IODCC0", "IODEM3", "IODIM3", "IODMS1B", "IODMS3", "IOFFE", "IOLVIS1A", "IPFLR1B", "IPFLT1B", "IR1HI1B", "IR1HI2", "IR2HI1B", "IR2HI2", "IRACC1B", "IRARES1B", "IRISN4RAD", "IRKUB1B", "IRMCR1B", "IRMCR2", "IRMCR3", "IRPAR2", "IRSNO1B", "IRTIT3", "IRUAFHF1B", "IRWIS2", "IS2ATBABD", "IS2GZANT", "IS2MPDDA", "IS2OLVIS1BCV", "IS2SITDAT4", "IS2SITMOGR4", "ISCCP_B3_NAT", "ISCCP_D1", "ISCCP_D2", "ISCCP_DX", "ISCCP_ICESNOW_NAT", "ISCCP_TOVS_NAT", "ISERV", "ISLSCP_919", "ISSITGR4", "ITPRN5L1", "IXBMI2AE", "IXBMI2LS", "IXBMI2ST", "IXBMIB2E", "IXBMIB2T", "IXBMIGEO", "Idaho_field_shrub_data_1503", "Imnavait_Creek_Veg_Maps_1385", "Imnavait_Creek_Veg_Plots_1356", "InSAR_Prudhoe_Bay_1267", "Insitu_Tower_Greenhouse_Gas_1798", "Interior_Alaska_Subsistence_1725", "Interpolated_Met_Products_1876", "InundationMap_YkFlats_PeaceAth_1901", "IronEx", "IsricWiseGrids_546", "IsricWise_547", "J1_CrIS_VIIRS750m_IND", "JASON-1_JMR_ENH", "JASON-1_L2_OST_GPN_E", "JASON-1_L2_OST_GPN_E_GEODETIC", "JASON-1_L2_OST_GPR_E", "JASON-1_L2_OST_GPR_E_GEODETIC", "JASON-1_L2_OST_GPS_E", "JASON-1_L2_OST_GPS_E_GEODETIC", "JASON_3_L2_OST_OGDR_GPS", "JASON_3_PD_CORRECTION", "JASON_CS_S6A_L1A_ALT_HR_NTC_F08", "JASON_CS_S6A_L1A_ALT_HR_STC_F", "JASON_CS_S6A_L1B_ALT_HR_NTC_F08", "JASON_CS_S6A_L1B_ALT_HR_STC_F", "JASON_CS_S6A_L1B_ALT_LR_NTC_F08", "JASON_CS_S6A_L1B_ALT_LR_STC_F", "JASON_CS_S6A_L1B_GNSS_POD_DAILY", "JASON_CS_S6A_L1B_GNSS_POD_HOURLY", "JASON_CS_S6A_L1B_GNSS_RO_POD_HOURLY", "JASON_CS_S6A_L2P_ALT_HR_OST_NTC_F08", "JASON_CS_S6A_L2P_ALT_LR_OST_NTC_F08", "JASON_CS_S6A_L2_ALT_HR_RED_OST_NRT_F", "JASON_CS_S6A_L2_ALT_HR_RED_OST_NTC_F08", "JASON_CS_S6A_L2_ALT_HR_RED_OST_NTC_F08_UNVALIDATED", "JASON_CS_S6A_L2_ALT_HR_RED_OST_STC_F", "JASON_CS_S6A_L2_ALT_HR_STD_OST_NRT_F", "JASON_CS_S6A_L2_ALT_HR_STD_OST_NTC_F08", "JASON_CS_S6A_L2_ALT_HR_STD_OST_NTC_F08_UNVALIDATED", "JASON_CS_S6A_L2_ALT_HR_STD_OST_STC_F", "JASON_CS_S6A_L2_ALT_LR_RED_OST_NRT_F", "JASON_CS_S6A_L2_ALT_LR_RED_OST_NTC_F08", "JASON_CS_S6A_L2_ALT_LR_RED_OST_NTC_F08_UNVALIDATED", "JASON_CS_S6A_L2_ALT_LR_RED_OST_STC_F", "JASON_CS_S6A_L2_ALT_LR_STD_OST_NRT_F", "JASON_CS_S6A_L2_ALT_LR_STD_OST_NTC_F08", "JASON_CS_S6A_L2_ALT_LR_STD_OST_NTC_F08_UNVALIDATED", "JASON_CS_S6A_L2_ALT_LR_STD_OST_STC_F", "JASON_CS_S6A_L2_AMR_RAD_NRT", "JASON_CS_S6A_L2_AMR_RAD_NTC_F08", "JASON_CS_S6A_L2_AMR_RAD_NTC_F08_UNVALIDATED", "JASON_CS_S6A_L2_AMR_RAD_STC", "JASON_CS_S6A_L3_ALT_HR_OST_NTC_F08", "JASON_CS_S6A_L3_ALT_LR_OST_NTC_F08", "JERS-1_L0", "JERS-1_L1", "JERS-1_SAR_GRFM_Amazon_Mosaics_1280", "JGOFS", "JGOFS_Arabian_Sea", "JGOFS_BOFS", "JGOFS_EQPAC", "JGOFS_WOCE", "JHUAPL_SRI_Kauai", "JPL_RECON_GMSL", "K10_SST-NAVO-L4-GLOB-v01", "KAIMIMOANA", "KILVOLC_FlowerKahn2021", "KORUS", "KORUSAQ_Aerosol_AircraftInSitu_DC8_Data", "KORUSAQ_AircraftRemoteSensing_DIAL_DC8_Data", "KORUSAQ_AircraftRemoteSensing_GeoTASO_B200_Data", "KORUSAQ_Analysis_Data", "KORUSAQ_Cloud_AircraftInSitu_DC8_Data", "KORUSAQ_Ground_EPA_Data", "KORUSAQ_Ground_NASA_Data", "KORUSAQ_Merge_Data", "KORUSAQ_MetNav_AircraftInSitu_B200_Data", "KORUSAQ_MetNav_AircraftInSitu_DC8_Data", "KORUSAQ_Miscellaneous_Data", "KORUSAQ_Model_Data", "KORUSAQ_Pandora_Data", "KORUSAQ_RVJangMokShip_Data", "KORUSAQ_RVOnnuriShip_Data", "KORUSAQ_Sondes_Data", "KORUSAQ_TraceGas_AircraftInSitu_DC8_Data", "KORUSAQ_TraceGas_AircraftInSitu_HanseoKingAir_Data", "KORUSAQ_Trajectory_Data", "KORUSAQ_jValue_AircraftInSitu_DC8_Data", "Kennebec", "Kieber_Photochemistry", "KingSejong", "King_Rim_Fire_Analysis_1288", "Kuparuk_Veg_Maps_1378", "Kuroshio_Area", "L3S_LEO_AM-STAR-v2.80", "L3S_LEO_DY-STAR-v2.81", "L3S_LEO_PM-STAR-v2.81", "LAB97", "LAI_Africa_2325", "LAI_Canada_816", "LAI_VALERI_Canada_829", "LAI_Woody_Plants_1231", "LAI_surfaces_747", "LAMONT_ATL", "LAMONT_GOM", "LAMONT_SAB", "LAMONT_SCS", "LANDMET", "LANDMET_ANC_SM", "LANDMET_ANC_ST", "LANDMET_ANC_TEIME", "LANDMET_ANC_TESA", "LASE_AFWEX", "LASE_CAMEX3", "LASE_SGP97", "LASE_SOLVE", "LASE_TARFOX", "LASE_VALIDATION", "LATTE", "LB02", "LC01_Boundaries_Ecuador_1057", "LC01_Cities_Communities_Roads_1058", "LC01_Households_NEC_1052", "LC01_Hydrography_Edaphology_NEC_1059", "LC01_LULC_Classes_Ecuador_ISA_1084", "LC01_Landsat_1187", "LC01_SRTM_DEM_90m_NEC_1083", "LC01_Topography_Ecuador_ISA_1082", "LC02_Forest_Flammability_Acre_1089", "LC02_GOES8_Hotpixel_Acre_1092", "LC02_MAP_Fire_Indicators_1044", "LC02_Meteorology_Acre_1091", "LC02_PermPlot_Acre_1237", "LC02_Streams_Acre_1243", "LC02_Water_Table_Acre_1062", "LC03_Hypsography_DEM_1094", "LC03_SAR_LC_Biomass_1093", "LC04_IBIS_Model_1139", "LC04_Land_Use_5min_906", "LC04_Macrohydrology_1048", "LC04_THMB-HYDRA_Model_1138", "LC05_BDFF_Biomass_Soils_1040", "LC07_Airborne_Rasters_1274", "LC07_Airborne_Videography_1272", "LC07_Amazon_Wetlands_1284", "LC07_Bathymetry_Curuai_999", "LC07_Biomass_LGrande_1127", "LC07_Curuai_chl_1134", "LC07_Lake_Chlorophyll_MODIS_1000", "LC07_Lake_Nutrient_Sediments_1050", "LC07_Monthly_Inundated_Areas_1049", "LC07_Reservoir_GHG_1143", "LC07_Reservoir_Methane_Emissions_1047", "LC07_SMMR_Inundated_Area_1051", "LC07_Spectroradiometry_1144", "LC07_Wetlands_fluxes_1209", "LC08_EOS_Maps_1155", "LC08_Ecosystem_Demography_Model_1102", "LC08_Fire_Observations_1095", "LC09_GIS_Study_Areas_986", "LC09_Landsat_987", "LC09_Precipitation_940", "LC09_Soil_Composition_938", "LC09_Transition_Matricies_1098", "LC09_Vegetation_Composition_939", "LC10_Landsat_ETM_846", "LC10_Landsat_TM_852", "LC13_GIS_Cauaxi_890", "LC13_GIS_Juruena_888", "LC13_GIS_Tapajos_893", "LC14_Aboveground_Prod_1196", "LC14_Amazon_Scenarios_1153", "LC14_REE_SLA_1211", "LC14_RISQUE_1147", "LC14_Surface_Roots_Phenology_1268", "LC15_AGLB_Distribution_Map_908", "LC15_GRFM_JERS1_Mosaic_1024", "LC15_MODIS_TreeCover_1035", "LC15_Roughness_Map_1182", "LC15_SPOT_Metrics_1239", "LC15_SRTM_Topography_1181", "LC18_Hyperion_889", "LC19_Field_2002_1261", "LC21_Foliar_Nutrients_1234", "LC21_Fractional_Cover_1152", "LC21_Selective_Logging_1172", "LC21_Soil_Characteristics_1236", "LC22_MODIS_Field_Val_2004_1262", "LC22_MODIS_Field_Val_2005_1260", "LC22_MODIS_Phenology_Mato_Grosso_1185", "LC22_MODIS_VCF_Tree_Cover_1112", "LC22_Post_Deforestation_LULC_1099", "LC23_MODIS_ASTER_Fire_Comparisons_839", "LC23_Vegetation_Fire_Dynamics_843", "LC23_Vegetation_Fires_2003_887", "LC24_Basin_Scale_Hot_Pixels_2001_882", "LC24_Cadastral_Property_Map_Para_1042", "LC24_ETM_Deforestation_Map_Para_1999_1054", "LC24_Historical_Roads_Amazon_1043", "LC24_Land_Cover_Southern_Para_1055", "LC24_Land_Cover_Uruara_Para_1053", "LC24_MODIS_Forest_Cover_500-m_1056", "LC31_AMZ_Historical_LU_1170", "LC31_SITE_1173", "LC35_GOES_WF_ABBA_1180", "LC35_Landsat7_Fire_Masks_1071", "LC39_DECAF_Model_1190", "LC39_MODIS_Fire_SA_1186", "LEO", "LEOLSTCMG30", "LEOLSTCMG30", "LGRIP30", "LIDAR", "LIDAR_FOREST_CANOPY_HEIGHTS_1271", "LIMSN7L1PROFILER", "LIMSN7L1RAT", "LIMSN7L2", "LIMSN7L3", "LINKAGES_1166", "LIS", "LISCO_mooring", "LISTOS_AircraftInSitu_StonyBrookAircraft_Data", "LISTOS_AircraftInSitu_UMDAircraft_Data", "LISTOS_AircraftRemoteSensing_NASAAircraft_Data", "LISTOS_Ground_Bayonne_Data", "LISTOS_Ground_BronxPfizer_Data", "LISTOS_Ground_CCNY_Data", "LISTOS_Ground_FlaxPond_Data", "LISTOS_Ground_Hammonasset_Data", "LISTOS_Ground_NewHaven_Data", "LISTOS_Ground_Other_Data", "LISTOS_Ground_OuterIsland_Data", "LISTOS_Ground_QueensCollege_Data", "LISTOS_Ground_Rutgers_Data", "LISTOS_Ground_Westport_Data", "LISTOS_Ground_YaleCoastal_Data", "LISTOS_MetNav_AircraftInSitu_NASAAircraft_Data", "LISTOS_SurfaceMobile_InSitu_Data", "LITE_L1", "LMER-TIES", "LMOS_AircraftInSitu_ScientificAviation_Data", "LMOS_AircraftRemoteSensing_UC12_Data", "LMOS_Ground_Grafton_Data", "LMOS_Ground_IEPA_Data", "LMOS_Ground_Milwaukee_Data", "LMOS_Ground_SchillerPark_Data", "LMOS_Ground_Sheboygan_Data", "LMOS_Ground_WDNRRoutine_Data", "LMOS_Ground_Zion_Data", "LMOS_MetNav_AircraftInSitu_UC12_Data", "LMOS_Miscellaneous_Data", "LMOS_TraceGas_ShipInSitu_Data", "LMOS_TraceGas_SurfaceMobile_EPA-GMAP_Data", "LMOS_TraceGas_SurfaceMobile_UWEC-Auto_Data", "LOBO_timeseries", "LOCSS_L1_V1", "LPJ-WHyMe_v1-3-1_1150", "LPJ_EOSIM_L2_DCH4E", "LPJ_EOSIM_L2_DCH4E_LL", "LPJ_EOSIM_L2_MCH4E", "LPJ_EOSIM_L2_MCH4E_LL", "LPJ_L2_SSREF", "LPJ_L2_SSREF", "LPRM_AMSR2_A_SOILM3", "LPRM_AMSR2_DS_A_SOILM3", "LPRM_AMSR2_DS_D_SOILM3", "LPRM_AMSR2_DS_SOILM2", "LPRM_AMSR2_D_SOILM3", "LPRM_AMSR2_SOILM2", "LPRM_AMSRE_A_SOILM3", "LPRM_AMSRE_D_SOILM3", "LPRM_AMSRE_SOILM2", "LPRM_TMI_DY_SOILM3", "LPRM_TMI_NT_SOILM3", "LPRM_TMI_SOILM2", "LPRM_WINDSAT_DY_SOILM3", "LPRM_WINDSAT_NT_SOILM3", "LPRM_WINDSAT_SOILM2", "LRIRN6L2IPAT", "LSM_807", "LTER", "LUH2_GCB2019_1851", "LULC_Nigeria_Ethiopia_SAfrica_2367", "LWAD01-2", "LaSelva_Biomass_1215", "LaSelva_Land_Use_1312", "Lab2000", "Lab96", "LakeBathymetry_Model_NSlope_AK_2243", "LakeSuperior", "Lake_MI_2012_WaterQual", "Lake_Wetland_Classes_UAVSAR_1883", "Land_Cover_surfaces_748", "Land_Use_Harmonization_V1_1248", "Land_Use_Harmonization_V2_1721", "Landcover_Colombian_Amazon_1783", "Landsat8_Sentinel2_Phenocam_2248", "Large_River_DOC_Export", "Last_Day_Spring_Snow_1528", "Leaf_Carbon_Nutrients_1106", "Leaf_Photosynthesis_Traits_1224", "LiDAR_Forest_Inventory_Brazil_1644", "LiDAR_Tundra_Forest_AK_1782", "LiDAR_Veg_Ht_Idaho_1532", "Line_P", "M1_AVH02C1", "M1_AVH09C1", "M1_AVH13C1", "M2C0NXASM", "M2C0NXCTM", "M2C0NXLND", "M2I1NXASM", "M2I1NXINT", "M2I1NXLFO", "M2I3NPASM", "M2I3NVAER", "M2I3NVASM", "M2I3NVCHM", "M2I3NVGAS", "M2I3NXGAS", "M2I6NPANA", "M2I6NVANA", "M2IMNPANA", "M2IMNPASM", "M2IMNXASM", "M2IMNXGAS", "M2IMNXINT", "M2IMNXLFO", "M2IUNPANA", "M2IUNPASM", "M2IUNXASM", "M2IUNXGAS", "M2IUNXINT", "M2IUNXLFO", "M2SDNXSLV", "M2SMNXEDI", "M2SMNXEDI", "M2SMNXPCT", "M2SMNXPCT", "M2SMNXSLV", "M2T1NXADG", "M2T1NXAER", "M2T1NXCHM", "M2T1NXCSP", "M2T1NXFLX", "M2T1NXINT", "M2T1NXLFO", "M2T1NXLND", "M2T1NXOCN", "M2T1NXRAD", "M2T1NXSLV", "M2T3NEMST", "M2T3NENAV", "M2T3NETRB", "M2T3NPCLD", "M2T3NPMST", "M2T3NPODT", "M2T3NPQDT", "M2T3NPRAD", "M2T3NPTDT", "M2T3NPTRB", "M2T3NPUDT", "M2T3NVASM", "M2T3NVCLD", "M2T3NVMST", "M2T3NVRAD", "M2T3NXGLC", "M2TCNPLTM", "M2TCNPLTM", "M2TCNXLTM", "M2TCNXLTM", "M2TMNPCLD", "M2TMNPMST", "M2TMNPODT", "M2TMNPQDT", "M2TMNPRAD", "M2TMNPTDT", "M2TMNPTRB", "M2TMNPUDT", "M2TMNXADG", "M2TMNXAER", "M2TMNXCHM", "M2TMNXCSP", "M2TMNXFLX", "M2TMNXGLC", "M2TMNXINT", "M2TMNXLFO", "M2TMNXLND", "M2TMNXOCN", "M2TMNXRAD", "M2TMNXSLV", "M2TUNPCLD", "M2TUNPMST", "M2TUNPODT", "M2TUNPQDT", "M2TUNPRAD", "M2TUNPTDT", "M2TUNPTRB", "M2TUNPUDT", "M2TUNXADG", "M2TUNXAER", "M2TUNXCHM", "M2TUNXCSP", "M2TUNXFLX", "M2TUNXGLC", "M2TUNXINT", "M2TUNXLFO", "M2TUNXLND", "M2TUNXOCN", "M2TUNXRAD", "M2TUNXSLV", "M2_TMAX_PM25", "MAA", "MAC021S0", "MAC021S1", "MAC02QS0", "MAC02QS1", "MAC03S0", "MAC03S1", "MAC04S0", "MAC04S1", "MAC05S0", "MAC05S1", "MAC06S0", "MAC06S1", "MAC07S0", "MAC07S1", "MAC35S0", "MAC35S1", "MACLWP_diurnal", "MACLWP_mean", "MACPEX_Aerosol_AircraftInSitu_WB57_Data", "MACPEX_Cloud_AircraftInSitu_WB57_Data", "MACPEX_MetNav_AircraftInSitu_WB57_Data", "MACPEX_Satellite_Data", "MACPEX_Sondes_Data", "MACPEX_TraceGas_AircraftInSitu_WB57_Data", "MACPEX_Water_AircraftInSitu_WB57_Data", "MACTWP_mean", "MALINA", "MAM03S0", "MAM04S0", "MAM05S0", "MAM06S0", "MAM07S0", "MAM35S0", "MANTRA_PIRANA", "MAPSS_853", "MAPS_OSTA3_CO5X5_HDF", "MAPS_OSTA3_COSEC_HDF", "MAPS_SRL1_CO5X5_HDF", "MAPS_SRL1_COSEC_HDF", "MAPS_SRL2_CO5X5_HDF", "MAPS_SRL2_COSEC_HDF", "MASL1B", "MASL2CLD", "MASS_BAY", "MASTER_Ames_August_2003_2045", "MASTER_B200_Fall_1999_2099", "MASTER_Baja_Mexico_1999_2102", "MASTER_CARTA_2003_2054", "MASTER_CARTA_2005_2034", "MASTER_CLASIC_2007_2019", "MASTER_DFRC_April_2004_2043", "MASTER_DFRC_August_2003_2046", "MASTER_DFRC_Fall_2005_2030", "MASTER_DFRC_July-Aug_2001_V2_2144", "MASTER_DFRC_July_2004_2039", "MASTER_DFRC_June_1999_2100", "MASTER_DFRC_June_2011_1976", "MASTER_DFRC_May-June_2008_2011", "MASTER_DFRC_May_2011_1985", "MASTER_DFRC_November_2011_1973", "MASTER_DFRC_October_2003_2044", "MASTER_DFRC_RSL_August_2002_2066", "MASTER_DFRC_September_2002_2064", "MASTER_DFRC_September_2006_2022", "MASTER_FIREX_AQ_JulySept_2019_1941", "MASTER_FireSense_2023_2330", "MASTER_Flightline_Locator_2151", "MASTER_GEMx_Spring_2024_2370", "MASTER_GEMx_Summer_2023_2319", "MASTER_Hawaii_October_2001_V2_2142", "MASTER_Houston_2010_2126", "MASTER_Houston_2011_1972", "MASTER_HyspIRI_EarlyS2013_V2_2146", "MASTER_HyspIRI_EarlySpring2014_1965", "MASTER_HyspIRI_Fall_2013_1966", "MASTER_HyspIRI_Fall_2014_1962", "MASTER_HyspIRI_Fall_2015_1956", "MASTER_HyspIRI_Hawaii_2017_1951", "MASTER_HyspIRI_Hawaii_2018_1945", "MASTER_HyspIRI_LateSpring_2013_1968", "MASTER_HyspIRI_LateSpring_2014_1963", "MASTER_HyspIRI_Spring_2015_1957", "MASTER_HyspIRI_Summer_2014_1964", "MASTER_HyspIRI_Summer_2015_1959", "MASTER_HyspIRI_Summer_2016_1913", "MASTER_HyspIRI_Summer_2017_1950", "MASTER_HyspIRI_Summer_2018_1942", "MASTER_PacificRim_2000_2093", "MASTER_RSL-DFRC_October_2008_2014", "MASTER_RSLPhoenix_2011_1975", "MASTER_RSL_April_2003_2053", "MASTER_RSL_April_2008_2016", "MASTER_RSL_August_2001_V2_2143", "MASTER_RSL_August_2004_2036", "MASTER_RSL_August_2006_2027", "MASTER_RSL_August_2007_2020", "MASTER_RSL_August_2008_2010", "MASTER_RSL_December_1998_2104", "MASTER_RSL_January_1999_2103", "MASTER_RSL_July_2003_2047", "MASTER_RSL_July_2004_2038", "MASTER_RSL_June_1999_2098", "MASTER_RSL_June_2000_2096", "MASTER_RSL_June_2001_2090", "MASTER_RSL_June_2002_2067", "MASTER_RSL_June_2004_2042", "MASTER_RSL_March_2000_2094", "MASTER_RSL_March_2002_2068", "MASTER_RSL_May_1999_2101", "MASTER_RSL_May_2001_V2_2145", "MASTER_RSL_May_2002_V2_2148", "MASTER_RSL_May_2004_2041", "MASTER_RSL_May_2006_2029", "MASTER_RSL_November_2007_2015", "MASTER_RSL_Oct_2002_V2_2147", "MASTER_RSL_October_2003_2048", "MASTER_RSL_October_2005_2031", "MASTER_RSL_October_2007_2013", "MASTER_RSL_October_2010_2127", "MASTER_RSL_September_2005_2037", "MASTER_SARP_2009_1990", "MASTER_SARP_2010_1989", "MASTER_SARP_2011_1974", "MASTER_SARP_2012_1983", "MASTER_SARP_2013_1967", "MASTER_SARP_2014_1961", "MASTER_SARP_2015_1958", "MASTER_SARP_2016_1912", "MASTER_Sky_2003_2052", "MASTER_Sky_2004_2035", "MASTER_Sky_2006_2026", "MASTER_SuomiNPP_2013_1969", "MASTER_SuomiNPP_2015_1970", "MASTER_TC4_2007_2021", "MASTER_WDTS_Fall_2022_2141", "MASTER_WDTS_SeptOct_2020_1940", "MASTER_WDTS_Spring_2021_1953", "MASTER_WDTS_Spring_2023_2252", "MAS_832", "MB2LME", "MCD06COSP_D3_MODIS", "MCD06COSP_M3_MODIS", "MCD12C1", "MCD12Q1", "MCD12Q2", "MCD15A2H", "MCD15A3H", "MCD18A1", "MCD18A2", "MCD18C1", "MCD18C2", "MCD19A1", "MCD19A1CMGL", "MCD19A1CMGO", "MCD19A1N", "MCD19A2", "MCD19A2CMG", "MCD19A2N", "MCD19A3CMG", "MCD19A3D", "MCD19A3DN", "MCD43A1", "MCD43A1N", "MCD43A2", "MCD43A2N", "MCD43A3", "MCD43A3N", "MCD43A4", "MCD43A4N", "MCD43C1", "MCD43C2", "MCD43C3", "MCD43C4", "MCD43D01", "MCD43D02", "MCD43D03", "MCD43D04", "MCD43D05", "MCD43D06", "MCD43D07", "MCD43D08", "MCD43D09", "MCD43D10", "MCD43D11", "MCD43D12", "MCD43D13", "MCD43D14", "MCD43D15", "MCD43D16", "MCD43D17", "MCD43D18", "MCD43D19", "MCD43D20", "MCD43D21", "MCD43D22", "MCD43D23", "MCD43D24", "MCD43D25", "MCD43D26", "MCD43D27", "MCD43D28", "MCD43D29", "MCD43D30", "MCD43D31", "MCD43D32", "MCD43D33", "MCD43D34", "MCD43D35", "MCD43D36", "MCD43D37", "MCD43D38", "MCD43D39", "MCD43D40", "MCD43D41", "MCD43D42", "MCD43D43", "MCD43D44", "MCD43D45", "MCD43D46", "MCD43D47", "MCD43D48", "MCD43D49", "MCD43D50", "MCD43D51", "MCD43D52", "MCD43D53", "MCD43D54", "MCD43D55", "MCD43D56", "MCD43D57", "MCD43D58", "MCD43D59", "MCD43D60", "MCD43D61", "MCD43D62", "MCD43D63", "MCD43D64", "MCD43D65", "MCD43D66", "MCD43D67", "MCD43D68", "MCD43GF", "MCD64A1", "MCDAODHD", "MCDWD_L3_F1C_NRT", "MCDWD_L3_F1_NRT", "MCDWD_L3_F2_NRT", "MCDWD_L3_F3_NRT", "MCDWD_L3_NRT", "MCR_LTER", "MELVILLE", "MERCHANT_SHIP", "MERGED_S3_OLCI_L3b_CYANTC", "MERGED_S3_OLCI_L3b_ILW", "MERGED_S3_OLCI_L3m_CYAN", "MERGED_S3_OLCI_L3m_CYANTC", "MERGED_S3_OLCI_L3m_ILW", "MERGED_TP_J1_OSTM_OST_CYCLES_V52", "MERGED_TP_J1_OSTM_OST_GMSL_ASCII_V51", "MERIS_L1_FRS", "MERIS_L1_RR", "MERIS_L2_FRS_IOP", "MERIS_L2_FRS_OC", "MERIS_L2_RR_IOP", "MERIS_L2_RR_OC", "MERIS_L3b_CHL", "MERIS_L3b_CYAN", "MERIS_L3b_CYANTC", "MERIS_L3b_IOP", "MERIS_L3b_KD", "MERIS_L3b_PAR", "MERIS_L3b_PIC", "MERIS_L3b_POC", "MERIS_L3b_RRS", "MERIS_L3m_CHL", "MERIS_L3m_CYAN", "MERIS_L3m_CYANTC", "MERIS_L3m_IOP", "MERIS_L3m_KD", "MERIS_L3m_PAR", "MERIS_L3m_PIC", "MERIS_L3m_POC", "MERIS_L3m_RRS", "MERIS_L4b_GSM", "MERIS_L4m_GSM", "MERRA2_CNN_HAQAST_PM25", "MFLL_CO2_Weighting_Functions_1891", "MFLL_XCO2_Range_10Hz_1892", "MI1B1", "MI1B2_ELLIPSOID_NRT", "MI1B2_TERRAIN_NRT", "MI2AS_AEROSOL_NRT", "MI2TC_CMV_BFR_NRT", "MI2TC_CMV_HDF_NRT", "MI3DAEF", "MI3DAENF", "MI3DAER", "MI3DALF", "MI3DALNF", "MI3DCDF", "MI3DCDNF", "MI3DCLDN", "MI3DLSF", "MI3DLSNF", "MI3DLSR", "MI3DRDF", "MI3DRDR", "MI3MAEF", "MI3MAENF", "MI3MAER", "MI3MALF", "MI3MALNF", "MI3MCDF", "MI3MCDNF", "MI3MCLDN", "MI3MCMVN", "MI3MLSF", "MI3MLSNF", "MI3MLSR", "MI3MRDF", "MI3MRDR", "MI3QCLDN", "MI3QCMVN", "MI3YCLDN", "MI3YCMVN", "MIANACP", "MIANCAGP", "MIANCARP", "MIANTASC", "MIB1LM", "MICRONESIAN", "MIL2ASAE", "MIL2ASLS", "MIL2TCAF", "MIL2TCAL", "MIL2TCCF", "MIL2TCCF", "MIL2TCCL", "MIL2TCSF", "MIL2TCSP", "MIL2TCSP", "MIL2TCST", "MIL3DAE", "MIL3DAEN", "MIL3DAL", "MIL3DALN", "MIL3DCFA", "MIL3DCLD", "MIL3DCOD", "MIL3DLS", "MIL3DLSN", "MIL3DRD", "MIL3MAE", "MIL3MAEN", "MIL3MAL", "MIL3MALN", "MIL3MCFA", "MIL3MCLD", "MIL3MCOD", "MIL3MJTA", "MIL3MLS", "MIL3MLSN", "MIL3MRD", "MIL3QAE", "MIL3QAEN", "MIL3QAL", "MIL3QALN", "MIL3QCFA", "MIL3QCLD", "MIL3QCOD", "MIL3QLS", "MIL3QLSN", "MIL3QRD", "MIL3YAE", "MIL3YAEN", "MIL3YAL", "MIL3YALN", "MIL3YCFA", "MIL3YCLD", "MIL3YCOD", "MIL3YLS", "MIL3YLSN", "MIL3YRD", "MIPOT", "MIRAI", "MIRC", "MIRCCM", "MIRCCMF", "MIRCCMF", "MISBR", "MISC_Apex_Floats", "MISR_885", "MISR_AEROSOL_CLIM", "MISR_Forest_AGB_SW_US_1978", "MITgcm_LLC4320_Pre-SWOT_JPL_L4_ACC_SMST_v1.0", "MITgcm_LLC4320_Pre-SWOT_JPL_L4_BassStrait_v1.0", "MITgcm_LLC4320_Pre-SWOT_JPL_L4_Boknis_v1.0", "MITgcm_LLC4320_Pre-SWOT_JPL_L4_CapeBasin_v1.0", "MITgcm_LLC4320_Pre-SWOT_JPL_L4_GotlandBasin_v1.0", "MITgcm_LLC4320_Pre-SWOT_JPL_L4_LabradorSea_v1.0", "MITgcm_LLC4320_Pre-SWOT_JPL_L4_MarmaraSea_v1.0", "MITgcm_LLC4320_Pre-SWOT_JPL_L4_NWAustralia_v1.0", "MITgcm_LLC4320_Pre-SWOT_JPL_L4_NWPacific_v1.0", "MITgcm_LLC4320_Pre-SWOT_JPL_L4_NewCaledonia_v1.0", "MITgcm_LLC4320_Pre-SWOT_JPL_L4_ROAM_MIZ_v1.0", "MITgcm_LLC4320_Pre-SWOT_JPL_L4_RockallTrough_v1.0", "MITgcm_LLC4320_Pre-SWOT_JPL_L4_WestAtlantic_v1.0", "MITgcm_LLC4320_Pre-SWOT_JPL_L4_WesternMed_v1.0", "MITgcm_LLC4320_Pre-SWOT_JPL_L4_Yongala_v1.0", "ML1OA", "ML1OA", "ML1RADD", "ML1RADD", "ML1RADG", "ML1RADG", "ML1RADT", "ML1RADT", "ML2BRO", "ML2BRO", "ML2CH3CL", "ML2CH3CL", "ML2CH3CN", "ML2CH3CN", "ML2CH3OH", "ML2CH3OH", "ML2CLO", "ML2CLO", "ML2CO", "ML2CO", "ML2CO_NRT", "ML2DGG", "ML2DGG", "ML2DGM", "ML2DGM", "ML2GPH", "ML2GPH", "ML2H2O", "ML2H2O", "ML2H2O_NRT", "ML2HCL", "ML2HCL", "ML2HCN", "ML2HCN", "ML2HNO3", "ML2HNO3", "ML2HNO3_NRT", "ML2HO2", "ML2HO2", "ML2HOCL", "ML2HOCL", "ML2IWC", "ML2IWC", "ML2N2O", "ML2N2O", "ML2N2O_NRT", "ML2O3", "ML2O3", "ML2O3_NRT", "ML2OH", "ML2OH", "ML2RHI", "ML2RHI", "ML2SO2", "ML2SO2", "ML2SO2_NRT", "ML2T", "ML2T", "ML2T_NRT", "ML3DBCH3CL", "ML3DBCH3CL", "ML3DBCLO", "ML3DBCLO", "ML3DBCO", "ML3DBCO", "ML3DBGPH", "ML3DBGPH", "ML3DBH2O", "ML3DBH2O", "ML3DBHCL", "ML3DBHCL", "ML3DBHCN", "ML3DBHCN", "ML3DBHNO3", "ML3DBHNO3", "ML3DBHOCL", "ML3DBHOCL", "ML3DBIWC", "ML3DBIWC", "ML3DBN2O", "ML3DBN2O", "ML3DBO3", "ML3DBO3", "ML3DBOH", "ML3DBOH", "ML3DBRHI", "ML3DBRHI", "ML3DBSO2", "ML3DBSO2", "ML3DBT", "ML3DBT", "ML3DZCH3CL", "ML3DZCH3CL", "ML3DZCLO", "ML3DZCLO", "ML3DZCO", "ML3DZCO", "ML3DZGPH", "ML3DZGPH", "ML3DZH2O", "ML3DZH2O", "ML3DZHCL", "ML3DZHCL", "ML3DZHCN", "ML3DZHCN", "ML3DZHNO3", "ML3DZHNO3", "ML3DZHOCL", "ML3DZHOCL", "ML3DZIWC", "ML3DZIWC", "ML3DZMBRO", "ML3DZMBRO", "ML3DZMHO2", "ML3DZMHO2", "ML3DZN2O", "ML3DZN2O", "ML3DZO3", "ML3DZO3", "ML3DZOH", "ML3DZOH", "ML3DZRHI", "ML3DZRHI", "ML3DZSO2", "ML3DZSO2", "ML3DZT", "ML3DZT", "ML3MBCH3CL", "ML3MBCH3CL", "ML3MBCLO", "ML3MBCLO", "ML3MBCO", "ML3MBCO", "ML3MBGPH", "ML3MBGPH", "ML3MBH2O", "ML3MBH2O", "ML3MBHCL", "ML3MBHCL", "ML3MBHCN", "ML3MBHCN", "ML3MBHNO3", "ML3MBHNO3", "ML3MBHOCL", "ML3MBHOCL", "ML3MBIWC", "ML3MBIWC", "ML3MBN2O", "ML3MBN2O", "ML3MBO3", "ML3MBO3", "ML3MBOH", "ML3MBOH", "ML3MBRHI", "ML3MBRHI", "ML3MBSO2", "ML3MBSO2", "ML3MBT", "ML3MBT", "MOCE", "MOD00F", "MOD00S", "MOD08_D3", "MOD08_E3", "MOD08_M3", "MOD09A1", "MOD09A1N", "MOD09CMA", "MOD09CMG", "MOD09CMG", "MOD09GA", "MOD09GA", "MOD09GQ", "MOD09GQ", "MOD09Q1", "MOD09Q1N", "MOD10A1", "MOD10A1F", "MOD10A2", "MOD10C1", "MOD10C2", "MOD10CM", "MOD11A1", "MOD11A2", "MOD11B1", "MOD11B2", "MOD11B3", "MOD11C1", "MOD11C2", "MOD11C3", "MOD11CM1D", "MOD11CM1N", "MOD11_L2", "MOD13A1", "MOD13A2", "MOD13A3", "MOD13A4N", "MOD13C1", "MOD13C2", "MOD13Q1", "MOD13Q4N", "MOD14", "MOD14A1", "MOD14A2", "MOD14CM1", "MOD15A2H", "MOD16A2", "MOD16A2GF", "MOD16A3GF", "MOD17A2H", "MOD17A2HGF", "MOD17A3HGF", "MOD21", "MOD21A1D", "MOD21A1N", "MOD21A2", "MOD21C1", "MOD21C2", "MOD21C3", "MOD28C2", "MOD28C3", "MOD29E1D", "MOD29P1D", "MOD29P1N", "MOD44B", "MOD44W", "MODAODHD", "MODCSR_8", "MODCSR_B", "MODGRNLD", "MODISA_L1", "MODISA_L1_GEO", "MODISA_L2_IOP", "MODISA_L2_IOP_NRT", "MODISA_L2_OC", "MODISA_L2_OC_NRT", "MODISA_L2_SST", "MODISA_L2_SST4", "MODISA_L2_SST4_NRT", "MODISA_L2_SST_NRT", "MODISA_L3b_CHL", "MODISA_L3b_CHL_NRT", "MODISA_L3b_FLH", "MODISA_L3b_FLH_NRT", "MODISA_L3b_IOP", "MODISA_L3b_IOP_NRT", "MODISA_L3b_KD", "MODISA_L3b_KD_NRT", "MODISA_L3b_LAND", "MODISA_L3b_NSST", "MODISA_L3b_NSST_NRT", "MODISA_L3b_PAR", "MODISA_L3b_PAR_NRT", "MODISA_L3b_PIC", "MODISA_L3b_PIC_NRT", "MODISA_L3b_POC", "MODISA_L3b_POC_NRT", "MODISA_L3b_RRS", "MODISA_L3b_RRS_NRT", "MODISA_L3b_SST", "MODISA_L3b_SST4", "MODISA_L3b_SST4_NRT", "MODISA_L3b_SST_NRT", "MODISA_L3m_CHL", "MODISA_L3m_CHL_NRT", "MODISA_L3m_FLH", "MODISA_L3m_FLH_NRT", "MODISA_L3m_IOP", "MODISA_L3m_IOP_NRT", "MODISA_L3m_KD", "MODISA_L3m_KD_NRT", "MODISA_L3m_LAND", "MODISA_L3m_NSST", "MODISA_L3m_NSST_NRT", "MODISA_L3m_PAR", "MODISA_L3m_PAR_NRT", "MODISA_L3m_PIC", "MODISA_L3m_PIC_NRT", "MODISA_L3m_POC", "MODISA_L3m_POC_NRT", "MODISA_L3m_RRS", "MODISA_L3m_RRS_NRT", "MODISA_L3m_SST", "MODISA_L3m_SST4", "MODISA_L3m_SST4_NRT", "MODISA_L3m_SST_NRT", "MODISA_L4b_GSM", "MODISA_L4m_GSM", "MODIST_L1", "MODIST_L1_GEO", "MODIST_L2_IOP", "MODIST_L2_IOP_NRT", "MODIST_L2_OC", "MODIST_L2_OC_NRT", "MODIST_L2_SST", "MODIST_L2_SST4", "MODIST_L2_SST4_NRT", "MODIST_L2_SST_NRT", "MODIST_L3b_CHL", "MODIST_L3b_CHL_NRT", "MODIST_L3b_FLH", "MODIST_L3b_FLH_NRT", "MODIST_L3b_IOP", "MODIST_L3b_IOP_NRT", "MODIST_L3b_KD", "MODIST_L3b_KD_NRT", "MODIST_L3b_NSST", "MODIST_L3b_NSST_NRT", "MODIST_L3b_PAR", "MODIST_L3b_PAR_NRT", "MODIST_L3b_PIC", "MODIST_L3b_PIC_NRT", "MODIST_L3b_POC", "MODIST_L3b_POC_NRT", "MODIST_L3b_RRS", "MODIST_L3b_RRS_NRT", "MODIST_L3b_SST", "MODIST_L3b_SST4", "MODIST_L3b_SST4_NRT", "MODIST_L3b_SST_NRT", "MODIST_L3m_CHL", "MODIST_L3m_CHL_NRT", "MODIST_L3m_FLH", "MODIST_L3m_FLH_NRT", "MODIST_L3m_IOP", "MODIST_L3m_IOP_NRT", "MODIST_L3m_KD", "MODIST_L3m_KD_NRT", "MODIST_L3m_NSST", "MODIST_L3m_NSST_NRT", "MODIST_L3m_PAR", "MODIST_L3m_PAR_NRT", "MODIST_L3m_PIC", "MODIST_L3m_PIC_NRT", "MODIST_L3m_POC", "MODIST_L3m_POC_NRT", "MODIST_L3m_RRS", "MODIST_L3m_RRS_NRT", "MODIST_L3m_SST", "MODIST_L3m_SST4", "MODIST_L3m_SST4_NRT", "MODIST_L3m_SST_NRT", "MODIST_L4b_GSM", "MODIST_L4m_GSM", "MODIS_A-JPL-L2P-v2019.0", "MODIS_AQUA_L3_SST_MID-IR_8DAY_4KM_NIGHTTIME_V2019.0", "MODIS_AQUA_L3_SST_MID-IR_8DAY_9KM_NIGHTTIME_V2019.0", "MODIS_AQUA_L3_SST_MID-IR_ANNUAL_4KM_NIGHTTIME_V2019.0", "MODIS_AQUA_L3_SST_MID-IR_ANNUAL_9KM_NIGHTTIME_V2019.0", "MODIS_AQUA_L3_SST_MID-IR_DAILY_4KM_NIGHTTIME_V2019.0", "MODIS_AQUA_L3_SST_MID-IR_DAILY_9KM_NIGHTTIME_V2019.0", "MODIS_AQUA_L3_SST_MID-IR_MONTHLY_4KM_NIGHTTIME_V2019.0", "MODIS_AQUA_L3_SST_MID-IR_MONTHLY_9KM_NIGHTTIME_V2019.0", "MODIS_AQUA_L3_SST_THERMAL_8DAY_4KM_DAYTIME_V2019.0", "MODIS_AQUA_L3_SST_THERMAL_8DAY_4KM_NIGHTTIME_V2019.0", "MODIS_AQUA_L3_SST_THERMAL_8DAY_9KM_DAYTIME_V2019.0", "MODIS_AQUA_L3_SST_THERMAL_8DAY_9KM_NIGHTTIME_V2019.0", "MODIS_AQUA_L3_SST_THERMAL_ANNUAL_4KM_DAYTIME_V2019.0", "MODIS_AQUA_L3_SST_THERMAL_ANNUAL_4KM_NIGHTTIME_V2019.0", "MODIS_AQUA_L3_SST_THERMAL_ANNUAL_9KM_DAYTIME_V2019.0", "MODIS_AQUA_L3_SST_THERMAL_ANNUAL_9KM_NIGHTTIME_V2019.0", "MODIS_AQUA_L3_SST_THERMAL_DAILY_4KM_DAYTIME_V2019.0", "MODIS_AQUA_L3_SST_THERMAL_DAILY_4KM_NIGHTTIME_V2019.0", "MODIS_AQUA_L3_SST_THERMAL_DAILY_9KM_DAYTIME_V2019.0", "MODIS_AQUA_L3_SST_THERMAL_DAILY_9KM_NIGHTTIME_V2019.0", "MODIS_AQUA_L3_SST_THERMAL_MONTHLY_4KM_DAYTIME_V2019.0", "MODIS_AQUA_L3_SST_THERMAL_MONTHLY_4KM_NIGHTTIME_V2019.0", "MODIS_AQUA_L3_SST_THERMAL_MONTHLY_9KM_DAYTIME_V2019.0", "MODIS_AQUA_L3_SST_THERMAL_MONTHLY_9KM_NIGHTTIME_V2019.0", "MODIS_CCaN_NDVI_Trends_Alaska_1666", "MODIS_CR_Equal_Angle_3h", "MODIS_CR_Equal_Angle_Daily", "MODIS_CR_Equal_Area_3h", "MODIS_MAIAC_Reflectance_1700", "MODIS_PAR_1140", "MODIS_T-JPL-L2P-v2019.0", "MODIS_TERRA_L3_SST_MID-IR_8DAY_4KM_NIGHTTIME_V2019.0", "MODIS_TERRA_L3_SST_MID-IR_8DAY_9KM_NIGHTTIME_V2019.0", "MODIS_TERRA_L3_SST_MID-IR_ANNUAL_4KM_NIGHTTIME_V2019.0", "MODIS_TERRA_L3_SST_MID-IR_ANNUAL_9KM_NIGHTTIME_V2019.0", "MODIS_TERRA_L3_SST_MID-IR_DAILY_4KM_NIGHTTIME_V2019.0", "MODIS_TERRA_L3_SST_MID-IR_DAILY_9KM_NIGHTTIME_V2019.0", "MODIS_TERRA_L3_SST_MID-IR_MONTHLY_4KM_NIGHTTIME_V2019.0", "MODIS_TERRA_L3_SST_MID-IR_MONTHLY_9KM_NIGHTTIME_V2019.0", "MODIS_TERRA_L3_SST_THERMAL_8DAY_4KM_DAYTIME_V2019.0", "MODIS_TERRA_L3_SST_THERMAL_8DAY_4KM_NIGHTTIME_V2019.0", "MODIS_TERRA_L3_SST_THERMAL_8DAY_9KM_DAYTIME_V2019.0", "MODIS_TERRA_L3_SST_THERMAL_8DAY_9KM_NIGHTTIME_V2019.0", "MODIS_TERRA_L3_SST_THERMAL_ANNUAL_4KM_DAYTIME_V2019.0", "MODIS_TERRA_L3_SST_THERMAL_ANNUAL_4KM_NIGHTTIME_V2019.0", "MODIS_TERRA_L3_SST_THERMAL_ANNUAL_9KM_DAYTIME_V2019.0", "MODIS_TERRA_L3_SST_THERMAL_ANNUAL_9KM_NIGHTTIME_V2019.0", "MODIS_TERRA_L3_SST_THERMAL_DAILY_4KM_DAYTIME_V2019.0", "MODIS_TERRA_L3_SST_THERMAL_DAILY_4KM_NIGHTTIME_V2019.0", "MODIS_TERRA_L3_SST_THERMAL_DAILY_9KM_DAYTIME_V2019.0", "MODIS_TERRA_L3_SST_THERMAL_DAILY_9KM_NIGHTTIME_V2019.0", "MODIS_TERRA_L3_SST_THERMAL_MONTHLY_4KM_DAYTIME_V2019.0", "MODIS_TERRA_L3_SST_THERMAL_MONTHLY_4KM_NIGHTTIME_V2019.0", "MODIS_TERRA_L3_SST_THERMAL_MONTHLY_9KM_DAYTIME_V2019.0", "MODIS_TERRA_L3_SST_THERMAL_MONTHLY_9KM_NIGHTTIME_V2019.0", "MODIS_emissions_758", "MODVI", "MOD_L2_DC", "MOKIHANA", "MONTEREY_BAY", "MOOSE_Aerodyne-Mobile-Laboratory", "MOOSE_AircraftRemoteSensing_NASA-G3_GCAS_Data", "MOP02J", "MOP02J", "MOP02J", "MOP02N", "MOP02N", "MOP02N", "MOP02T", "MOP02T", "MOP02T", "MOP03J", "MOP03J", "MOP03J", "MOP03JM", "MOP03JM", "MOP03JM", "MOP03N", "MOP03N", "MOP03N", "MOP03NM", "MOP03NM", "MOP03NM", "MOP03T", "MOP03T", "MOP03T", "MOP03TM", "MOP03TM", "MOP03TM", "MOPCH", "MOPITT_co_835", "MRIRN2IM", "MRIRN2L1", "MRIRN3IM", "MRIRN3L1", "MSAQSO2L4", "MSG01-OSPO-L2P-v1.0", "MSG02-OSPO-L2P-v1.0", "MSG03-OSPO-L2P-v1.0", "MSG04-OSPO-L2P-v1.0", "MSLERLSTL3d10", "MSLERLSTL3zm", "MSLERNNL3d10", "MSLERNNL3zm", "MSLSP30NA", "MSO3L3zm5", "MSULST", "MSULTT", "MSUOP", "MSUUTT", "MSVOLSO2L4", "MS_Sound", "MTSAT2-OSPO-L2P-v1.0", "MUR-JPL-L4-GLOB-v4.1", "MUR25-JPL-L4-GLOB-v04.2", "MURI_Camouflage", "MURI_HI", "MUSE", "MVCO", "MW_IR_OI-REMSS-L4-GLOB-v5.0", "MW_IR_OI-REMSS-L4-GLOB-v5.1", "MW_OI-REMSS-L4-GLOB-v5.0", "MW_OI-REMSS-L4-GLOB-v5.1", "MYD00F", "MYD00S", "MYD08_D3", "MYD08_E3", "MYD08_M3", "MYD09A1", "MYD09CMA", "MYD09CMG", "MYD09CMG", "MYD09GA", "MYD09GA", "MYD09GQ", "MYD09GQ", "MYD09Q1", "MYD10A1", "MYD10A1F", "MYD10A2", "MYD10C1", "MYD10C2", "MYD10CM", "MYD11A1", "MYD11A2", "MYD11B1", "MYD11B2", "MYD11B3", "MYD11C1", "MYD11C2", "MYD11C3", "MYD11CM1D", "MYD11CM1N", "MYD11_L2", "MYD13A1", "MYD13A2", "MYD13A3", "MYD13C1", "MYD13C2", "MYD13Q1", "MYD14", "MYD14A1", "MYD14A2", "MYD14CM1", "MYD15A2H", "MYD16A2", "MYD16A2GF", "MYD16A3GF", "MYD17A2H", "MYD17A2HGF", "MYD17A3HGF", "MYD21", "MYD21A1D", "MYD21A1N", "MYD21A2", "MYD21C1", "MYD21C2", "MYD21C3", "MYD28C2", "MYD28C3", "MYD29E1D", "MYD29P1D", "MYD29P1N", "MYDAODHD", "MYDGB0", "MYDVI", "MYD_L2_CB", "MYD_L2_DC", "MYD_L2_MPLCT", "MYD_L3_OFLCT", "MagMix", "Main_Melt_Onset_Dates_1841", "Maps_AGB_North_Slope_AK_1565", "Marlon_Lewis_92", "Maryland_Temperature_Humidity_1319", "MatthewsVegetation_419", "Mean_Seasonal_LAI_1653", "Medit_Ligurian", "MetOpA_GOME2_SIF_V2_2292", "MetOpB_GOME2_SIF_2182", "Meteorological_1065", "Methane_Ebullition_Lakes_AK_1861", "Methane_Ethane_MA_NH_1982", "Methane_Flaring_Sites_VIIRS_1874", "Microbiome", "Mid-latitude_soils_705", "Missouri_Reservoirs_RSWQ", "MonthlyWetland_CH4_WetCHARTsV2_2346", "Monthly_Hydrological_Fluxes_1647", "MultiInstrumentFusedXCO2", "MumfordCove", "N01", "N07_AVH02C1", "N07_AVH09C1", "N07_AVH13C1", "N09_AVH02C1", "N09_AVH09C1", "N09_AVH13C1", "N11_AVH02C1", "N11_AVH09C1", "N11_AVH13C1", "N14_AVH02C1", "N14_AVH09C1", "N14_AVH13C1", "N16_AVH02C1", "N16_AVH09C1", "N16_AVH13C1", "N18_AVH02C1", "N18_AVH09C1", "N18_AVH13C1", "N19_AVH02C1", "N19_AVH09C1", "N19_AVH13C1", "N21-VIIRS-L2P-ACSPO-v2.80", "N21-VIIRS-L3U-ACSPO-v2.80", "NAAMES", "NAAMES_AerosolCloud_AircraftRemoteSensing_Data", "NAAMES_Aerosol_AircraftInSitu_Data", "NAAMES_Aerosol_ShipInSitu_Data", "NAAMES_Cloud_AircraftInSitu_Data", "NAAMES_Merge_Data", "NAAMES_MetNav_AircraftInSitu_Data", "NAAMES_MetNav_ShipInSitu_Data", "NAAMES_Met_SondeInSitu_Data", "NAAMES_Misc_Ship_Data", "NAAMES_Ocean_AircraftRemoteSensing_Data", "NAAMES_Radiation_AircraftInSitu_Data", "NAAMES_TraceGas_AircraftInSitu_Data", "NAAMES_TraceGas_ShipInSitu_Data", "NAB08", "NABE", "NACP_ACES_V2_1943", "NACP_BlackSpruce_Burn-Severity_1331", "NACP_Boreal_Biome_Biomass_1273", "NACP_Forest_Biophysical_1046", "NACP_Forest_Conservation_1662", "NACP_GHG_Data_Compilation_1206", "NACP_MCI_CO2_Inventory_1205", "NACP_MCI_CO2_Inversions_1204", "NACP_MCI_CO2_Measurements_1202", "NACP_MCI_Crop_GPP_1217", "NACP_Modeled_NEE_NEP_Fluxes_1203", "NACP_MsTMIP_Model_Driver_1220", "NACP_MsTMIP_Model_Structure_1228", "NACP_MsTMIP_TBMO_1225", "NACP_MsTMIP_Unified_NA_SoilMap_1242", "NACP_NAM_HYSPLIT_Footprints_1586", "NACP_PNW_Carbon_Balance_1317", "NACP_PalEON_MIP_1779", "NACP_Peatland_Burn-Severity_1283", "NACP_Peatland_Land_Cover_MI_1513", "NACP_Regional_Model_GHG_Aggr_1179", "NACP_Regional_Obs_Model_Grid_1157", "NACP_Regional_Obs_Model_Orig_1193", "NACP_Regional_Obs_Model_Suppl_1158", "NACP_Site_Model_Data_Orig_Fmt_1192", "NACP_Site_Model_Flux_Std_Fmt_1183", "NACP_Site_Tower_Met_and_Flux_1178", "NACP_TERRA-PNW_1292", "NACP_Vista_CA_CH4_Inventory_1726", "NACP_Vista_LA_CH4_Inventory_1525", "NACP_Wild_Cropland_Fuel_Map_1163", "NACP_Woody_Veg_N_Slope_AK_V2_1365", "NAFD-NEX_Attribution_1799", "NAFD-NEX_Forest_Disturbance_1290", "NAFD_Disturbance_1077", "NARSTO_EPA_SS_ATLANTA_1999_CHEM_PM_MET_DATA", "NARSTO_EPA_SS_ATLANTA_1999_UAH_MIPS_DATA", "NARSTO_EPA_SS_ATLANTA_RAPID_SPMS_DATA", "NARSTO_EPA_SS_BALTIMORE_JHU_LIDAR_DATA", "NARSTO_EPA_SS_BALTIMORE_JHU_MET_DATA", "NARSTO_EPA_SS_BALTIMORE_RAPID_SPMS_DATA", "NARSTO_EPA_SS_BALTIMORE_SEAS_PM25_METAL_CYTOKINES", "NARSTO_EPA_SS_FRESNO_AETHALOM_MULTI_WL_CARBON", "NARSTO_EPA_SS_FRESNO_BAM_PM_MASS", "NARSTO_EPA_SS_FRESNO_EC_PM25_FRACTION", "NARSTO_EPA_SS_FRESNO_MET_DATA", "NARSTO_EPA_SS_FRESNO_PARTICLE_PAC_DATA", "NARSTO_EPA_SS_FRESNO_PM25_NO3_SO4", "NARSTO_EPA_SS_FRESNO_PM25_OC_EC", "NARSTO_EPA_SS_FRESNO_TEOM_PM_MASS", "NARSTO_EPA_SS_HOUSTON_DMA_DATA", "NARSTO_EPA_SS_HOUSTON_NH3_HNO3_DATA", "NARSTO_EPA_SS_HOUSTON_NO3_SO4_C_DATA", "NARSTO_EPA_SS_HOUSTON_RAPID_SPMS_DATA", "NARSTO_EPA_SS_HOUSTON_TEXAQS2000_CAMS_DATA", "NARSTO_EPA_SS_HOUSTON_TEXAQS2000_DOE_G-1_DATA", "NARSTO_EPA_SS_HOUSTON_TEXAQS2000_HCHO_H2O2_DATA", "NARSTO_EPA_SS_HOUSTON_TEXAQS2000_PM25_ORG_DATA", "NARSTO_EPA_SS_HOUSTON_TEXAQS2000_PM_FTIR", "NARSTO_EPA_SS_HOUSTON_TEXAQS2000_PM_SIZE_MASS", "NARSTO_EPA_SS_HOUSTON_TEXAQS2000_WB_TUNNEL", "NARSTO_EPA_SS_LOS_ANGELES_AETHALOMETER_EC_DATA", "NARSTO_EPA_SS_LOS_ANGELES_APS_DATA", "NARSTO_EPA_SS_LOS_ANGELES_HEADS_PART_IONS_MASS", "NARSTO_EPA_SS_LOS_ANGELES_MOUDI_DATA", "NARSTO_EPA_SS_LOS_ANGELES_PARTISOL_DATA", "NARSTO_EPA_SS_LOS_ANGELES_PM25_CARBON", "NARSTO_EPA_SS_LOS_ANGELES_PM25_NITRATE", "NARSTO_EPA_SS_LOS_ANGELES_SMPS_DATA", "NARSTO_EPA_SS_LOS_ANGELES_TEOM_PM25_DATA", "NARSTO_EPA_SS_NEW_YORK_AIR_CHEM_PM_MET_DATA", "NARSTO_EPA_SS_PITTSBURGH_GAS_PM_PROPERTY_DATA", "NARSTO_EPA_SS_PITTSBURGH_MET_DATA", "NARSTO_EPA_SS_PITTSBURGH_PM_COMPOSITION_DATA", "NARSTO_EPA_SS_PITTSBURGH_RAPID_SPMS_DATA", "NARSTO_EPA_SS_ST_LOUIS_AIR_CHEM_PM_MET_DATA", "NARSTO_ICARTT_NEAX_2004_DOE_G-1_DATA", "NARSTO_NE_MODEL", "NARSTO_NE_NEXRAD_IMAGES", "NARSTO_PAC2001_CASSIAR_TUNNEL_GAS_PM_DATA", "NARSTO_PAC2001_CESSNA_VOC_PM_OZONE_MET_DATA", "NARSTO_PAC2001_CONVAIR_PM_OZONE_MET_DATA", "NARSTO_PAC2001_GOLDEN_EARS_GAS_PM_DATA", "NARSTO_PAC2001_GVRD_CAPMON_AIR_QUAL_DATA", "NARSTO_PAC2001_LANGLEY_GAS_PM_MET_DATA", "NARSTO_PAC2001_SLOCAN_PARK_GAS_PM_MET_DATA", "NARSTO_PAC2001_SUMAS_MTN_GAS_PM_MET_DATA", "NARSTO_SHEMP_CANADA_PM_COMPOSITION_DATA", "NARSTO_SOS99NASH_G-1_AIR_CHEMISTRY_DATA", "NARSTO_SOS99NASH_SURFACE_MET_CHEM_DATA", "NARSTO_SOS99NASH_WIND_PROFILER_DATA", "NARSTO_SOS99NASH_WP3D_CHEMISTRY_DATA", "NARSTO_SOS_SC_UPSTATE_PM25_COMPOSITION", "NARSTO_STIFS_CANADA_PM_INORG_VOC_DATA", "NARSTO_Texas_PM2.5_Sampling_and_Analysis_Study_1997-1998", "NASADEM_HGT", "NASADEM_NC", "NASADEM_NUMNC", "NASADEM_SC", "NASADEM_SHHP", "NASADEM_SIM", "NASADEM_SSP", "NASASatellite_Dev_Applications_2293", "NASA_Airborne_Lidar_Flights", "NASMo_TiAM_250m_2326", "NA_MODIS_Surface_Biophysics_1210", "NA_TreeAge_1096", "NBCD2000_V2_1161", "NCALDAS_NOAH0125_D", "NCALDAS_NOAH0125_Trends", "ND01_Age_Maps_1184", "ND01_Georectified_Products_1165", "ND01_Land_Cover_Maps_1259", "ND01_Pasture_Nutrients_1135", "ND01_Pasture_Spectra_1154", "ND01_Registered_TM_MSS_1197", "ND01_Spectral_Mixture_Models_1188", "ND01_Stream_Chemistry_1119", "ND01_Watershed_Defor_1159", "ND02_Fertilization_Experiment_954", "ND02_Landsat_TM_MSS_Para_1156", "ND02_Mulching_Experiment_950", "ND02_Non_Woody_Biomass_1115", "ND02_REE_Soil_VWC_1061", "ND02_REE_Trace_Gas_Tapajos_955", "ND02_Soil_CO2_Extracts_1074", "ND02_Soil_CO2_Flux_1066", "ND02_Soil_Gas_Flux_Apeu_953", "ND02_Soil_Gases_REE_1117", "ND02_Soil_Hydraulic_Conductivity_1075", "ND02_Tree_Heights_DBH_951", "ND02_Water_Chemistry_Paragominas_1067", "ND03_Flowpath_Chemistry_1076", "ND03_Streams_Soilwater_1113", "ND04_C_Nutrient_Stocks_1069", "ND04_Secondary_Forest_Recovery_1068", "ND04_Soil_H2O_Manaus_1246", "ND04_Termite_Mounds_1072", "ND06_LandUse_Studies_1130", "ND07_15N_Leaves_Soils_1121", "ND07_NO_Flux_Cerrado_1124", "ND07_PLFA_Soils_Microbial_Biomass_1017", "ND07_Stream_Chemistry_Brasilia_1018", "ND07_Trace_Gas_Land_Use_1016", "ND08_Biomass_Jari_1148", "ND08_Soil_Respiration_1250", "ND10_Soil_Chemistry_1171", "ND11_Carbon_Export_CPOM_913", "ND11_Logging_Damage_MT_977", "ND11_Nitrogen_Transfer_Leaf_Litter_915", "ND11_Regeneration_Succession_965", "ND11_Soil_Nitrate_Moisture_MT_976", "ND11_Soil_Spatial_Variability_914", "ND11_Soil_Water_Pressure_851", "ND11_Stream_Nutrients_921", "ND11_Tree_Vine_Biomass_MT_922", "ND11_Veg_Biomass_MT_964", "ND30_Litter_Para_1129", "ND30_Pasture_Degradation_1164", "ND30_REE_Water_Chemistry_1131", "NDVI_Forest_Structure_1797", "NEMSN5L2", "NES-LTER", "NEUROST_SSH-SST_L4_V2024.0", "NEWS_WEB_ACLIM", "NEWS_WEB_MCLIM", "NFRDI", "NGLI_Lake_Bourne", "NHICEM", "NHSNOWM", "NIH-NSF_Lake_Erie", "NIMBUS7_ERB_Ch10C_TSI_NAT", "NIMBUS7_ERB_SEFDT", "NIMBUS7_NFOV_MLCE", "NISE", "NISE", "NLDAS_FORA0125_H", "NLDAS_FORA0125_H", "NLDAS_FORA0125_M", "NLDAS_FORA0125_M", "NLDAS_FORA0125_MC", "NLDAS_FORA0125_MC", "NLDAS_FORB0125_H", "NLDAS_FORB0125_H", "NLDAS_FORB0125_M", "NLDAS_FORB0125_M", "NLDAS_FORB0125_MC", "NLDAS_FORB0125_MC", "NLDAS_MOS0125_H", "NLDAS_MOS0125_H", "NLDAS_MOS0125_M", "NLDAS_MOS0125_M", "NLDAS_MOS0125_MC", "NLDAS_MOS0125_MC", "NLDAS_NOAH0125_H", "NLDAS_NOAH0125_H", "NLDAS_NOAH0125_M", "NLDAS_NOAH0125_M", "NLDAS_NOAH0125_MC", "NLDAS_NOAH0125_MC", "NLDAS_VIC0125_H", "NLDAS_VIC0125_H", "NLDAS_VIC0125_M", "NLDAS_VIC0125_M", "NLDAS_VIC0125_MC", "NLDAS_VIC0125_MC", "NMMIEAI-L2-NRT", "NMSO2-PCA-L2-NRT", "NMTO3NRT", "NOAA", "NOAA_ToF_CIMS_Instrument_Data_1921", "NOBM_DAY", "NOBM_MON", "NOPP_float", "NORBAL", "NPBUVO3-L2-NRT", "NPP_ATH_577", "NPP_BCN_204", "NPP_BDK_203", "NPP_BOREAL_611", "NPP_BRD_205", "NPP_BRR_157", "NPP_BRW_580", "NPP_CBAY_473", "NPP_CHM_578", "NPP_CHR_468", "NPP_CLB_206", "NPP_CNL_465", "NPP_CNS_191", "NPP_CPR_145", "NPP_DCK_207", "NPP_DRN_215", "NPP_DZH_192", "NPP_EMDI_615", "NPP_FLK_201", "NPP_GNN_474", "NPP_GPPDI_617", "NPP_GRIDDED_GPPDI_614", "NPP_GSM_804", "NPP_Grassland_31_654", "NPP_Grassland_613", "NPP_HYS_208", "NPP_IBP_198", "NPP_JDR_202", "NPP_JHN_475", "NPP_JRN_209", "NPP_KDE_216", "NPP_KHC_217", "NPP_KHM_146", "NPP_KLN_147", "NPP_KNZ_148", "NPP_KRK_193", "NPP_KRS_149", "NPP_KSM_466", "NPP_LMT_150", "NPP_LQL_476", "NPP_MDL_210", "NPP_MGD_477", "NPP_MMG_802", "NPP_MNS_579", "NPP_MNT_413", "NPP_MRF_478", "NPP_MSS_572", "NPP_MTD_469", "NPP_MULTIBIOME_653", "NPP_Multi-Biome_125_Sites_1352", "NPP_NLS_194", "NPP_NRB_151", "NPP_ODS_214", "NPP_OLK_195", "NPP_OSG_211", "NPP_OTR_152", "NPP_OTTER_472", "NPP_PIK_575", "NPP_PMP_212", "NPP_PSH_219", "NPP_REDWOOD_803", "NPP_RMY_574", "NPP_SCH_573", "NPP_SCR_479", "NPP_SES_480", "NPP_SHR_153", "NPP_SLV_218", "NPP_SNF_190", "NPP_SSP_467", "NPP_TEM_471", "NPP_TLK_581", "NPP_TLL_196", "NPP_TMG_470", "NPP_TMN_154", "NPP_TROPICAL_616", "NPP_TVA_155", "NPP_TWM_213", "NPP_VAST_576", "NPP_VND_197", "NPP_WBW_819", "NPP_WOODY_655", "NPP_XLN_156", "NPP_surfaces_750", "NSCAT_25KM_MGDR_V2", "NSCAT_AER_HOFFMAN_L2_OW_WIND_VECTOR_AMBIGUITY_REMOVAL", "NSCAT_BYU_L3_OW_SIGMA0_ENHANCED", "NSCAT_LEVEL_1.7_V2", "NSCAT_LEVEL_2_V2", "NSCAT_LEVEL_3_BROWSE_IMAGES", "NSCAT_LEVEL_3_V2", "NSCAT_W25_RMGDR_V2", "NSF-BWZ", "NSF_Gulf_Rapid", "NSIDC-0051", "NSIDC-0071", "NSIDC-0077", "NSIDC-0081", "NSIDC-0477", "NSIDC-0478", "NSIDC-0481", "NSIDC-0484", "NSIDC-0498", "NSIDC-0525", "NSIDC-0530", "NSIDC-0531", "NSIDC-0532", "NSIDC-0533", "NSIDC-0534", "NSIDC-0535", "NSIDC-0545", "NSIDC-0547", "NSIDC-0642", "NSIDC-0645", "NSIDC-0646", "NSIDC-0666", "NSIDC-0670", "NSIDC-0709", "NSIDC-0712", "NSIDC-0713", "NSIDC-0714", "NSIDC-0715", "NSIDC-0720", "NSIDC-0723", "NSIDC-0724", "NSIDC-0725", "NSIDC-0727", "NSIDC-0731", "NSIDC-0738", "NSIDC-0754", "NSIDC-0756", "NSIDC-0761", "NSIDC-0764", "NSIDC-0766", "NSIDC-0766", "NSIDC-0774", "NSIDC-0775", "NSIDC-0776", "NSIDC-0777", "NSIDC-0778", "NSIDC-0779", "NSIDC-0781", "NSIDC-0782", "NSIDC-0786", "NSIDC-0787", "NSIDC-0791", "NSIDC-0792", "NSIDC-0793", "NSIDC-0794", "NSIDC-0796", "NVAP_CLIMATE_Layered-Precipitable-Water", "NVAP_CLIMATE_Total-Precipitable-Water", "NVAP_OCEAN_Total-Precipitable-Water", "NVAP_WEATHER_Layered-Precipitable-Water", "NVAP_WEATHER_Total-Precipitable-Water", "NWT_Burn_Severity_Maps_1694", "New_England_CH4_1311", "New_Hampshire_Landcover_1305", "Niwot_Ridge_CNPAM_Fluorescence_1722", "Niwot_Ridge_Pigment_1723", "NmAVCS1H", "NmAVCS3G", "NmAVCS3H", "NmHRIR1H", "NmHRIR1T", "NmHRIR3G", "NmHRIR3H", "NmIDCS1H", "NmIDCS3G", "NmIDCS3H", "NmIcEdg2", "NmTHIR115-1H", "NmTHIR115-1T", "NmTHIR115-3G", "NmTHIR115-3H", "NmTHIR67-1H", "NmTHIR67-1T", "NmTHIR67-3G", "NmTHIR67-3H", "NmTHIRmtg-1T", "Nome_Veg_Plots_1372", "Non-Forest_Trees_Sahara_Sahel_1832", "Nongrowing_Season_CO2_Flux_1692", "NorthSlope_NEE_TVPRM_1920", "North_Carolina_Coast", "North_Carolina_Sabrina", "North_Sea", "North_Slope_Transect_Veg_Maps_1386", "North_Slope_Veg_Plots_1536", "Northern_Alaska_Veg_Maps_1359", "OAHU", "OASIS_Moorings", "OCEANSAT-2", "OCEAN_LIDAR", "OCEAN_MASS_TELLUS_MASCON_CRI_TIME_SERIES_RL06.1_V3", "OCFLEXPART", "OCO2GriddedXCO2", "OCO2_Att", "OCO2_Att", "OCO2_Att", "OCO2_Att", "OCO2_Att", "OCO2_Eph", "OCO2_Eph", "OCO2_Eph", "OCO2_Eph", "OCO2_Eph", "OCO2_GEOS_L3CO2_DAY", "OCO2_GEOS_L3CO2_MONTH", "OCO2_L1B_Calibration", "OCO2_L1B_Calibration", "OCO2_L1B_Calibration", "OCO2_L1B_Calibration", "OCO2_L1B_Science", "OCO2_L1B_Science", "OCO2_L1B_Science", "OCO2_L1B_Science", "OCO2_L1aIn_Pixel", "OCO2_L1aIn_Pixel", "OCO2_L1aIn_Pixel", "OCO2_L1aIn_Pixel", "OCO2_L1aIn_Pixel", "OCO2_L1aIn_Sample", "OCO2_L1aIn_Sample", "OCO2_L1aIn_Sample", "OCO2_L1aIn_Sample", "OCO2_L1aIn_Sample", "OCO2_L2_ABand", "OCO2_L2_ABand", "OCO2_L2_ABand", "OCO2_L2_ABand", "OCO2_L2_CO2Prior", "OCO2_L2_CO2Prior", "OCO2_L2_CO2Prior", "OCO2_L2_CO2Prior", "OCO2_L2_Diagnostic", "OCO2_L2_Diagnostic", "OCO2_L2_Diagnostic", "OCO2_L2_Diagnostic", "OCO2_L2_IMAPDOAS", "OCO2_L2_IMAPDOAS", "OCO2_L2_IMAPDOAS", "OCO2_L2_IMAPDOAS", "OCO2_L2_Lite_FP", "OCO2_L2_Lite_FP", "OCO2_L2_Lite_FP", "OCO2_L2_Lite_SIF", "OCO2_L2_Lite_SIF", "OCO2_L2_Met", "OCO2_L2_Met", "OCO2_L2_Met", "OCO2_L2_Met", "OCO2_L2_Met", "OCO2_L2_Standard", "OCO2_L2_Standard", "OCO2_L2_Standard", "OCO2_L2_Standard", "OCO3_L1B_Calibration", "OCO3_L1B_Calibration", "OCO3_L1B_Calibration", "OCO3_L1B_Science", "OCO3_L1B_Science", "OCO3_L1B_Science", "OCO3_L2_ABand", "OCO3_L2_ABand", "OCO3_L2_ABand", "OCO3_L2_CO2Prior", "OCO3_L2_CO2Prior", "OCO3_L2_CO2Prior", "OCO3_L2_Diagnostic", "OCO3_L2_Diagnostic", "OCO3_L2_Diagnostic", "OCO3_L2_IMAPDOAS", "OCO3_L2_IMAPDOAS", "OCO3_L2_IMAPDOAS", "OCO3_L2_Lite_FP", "OCO3_L2_Lite_FP", "OCO3_L2_Lite_SIF", "OCO3_L2_Met", "OCO3_L2_Met", "OCO3_L2_Met", "OCO3_L2_Standard", "OCO3_L2_Standard", "OCO3_L2_Standard", "OCTS_L1", "OCTS_L2_IOP", "OCTS_L2_OC", "OCTS_L3b_CHL", "OCTS_L3b_IOP", "OCTS_L3b_KD", "OCTS_L3b_PAR", "OCTS_L3b_PIC", "OCTS_L3b_POC", "OCTS_L3b_RRS", "OCTS_L3m_CHL", "OCTS_L3m_IOP", "OCTS_L3m_KD", "OCTS_L3m_PAR", "OCTS_L3m_PIC", "OCTS_L3m_POC", "OCTS_L3m_RRS", "ODU_CBM", "OISSS_L4_multimission_7day_v1", "OISSS_L4_multimission_7day_v2", "OISSS_L4_multimission_monthly_v1", "OISSS_L4_multimission_monthly_v2", "OISST_HR_NRT-GOS-L4-BLK-v2.0", "OISST_HR_NRT-GOS-L4-MED-v2.0", "OISST_UHR_NRT-GOS-L4-BLK-v2.0", "OISST_UHR_NRT-GOS-L4-MED-v2.0", "OLCIS3A_L1_EFR", "OLCIS3A_L1_ERR", "OLCIS3A_L2_EFR_IOP", "OLCIS3A_L2_EFR_OC", "OLCIS3A_L2_ERR_IOP", "OLCIS3A_L2_ERR_IOP_NRT", "OLCIS3A_L2_ERR_OC", "OLCIS3A_L2_ERR_OC_NRT", "OLCIS3A_L2_ILW", "OLCIS3B_L1_EFR", "OLCIS3B_L1_ERR", "OLCIS3B_L2_EFR_IOP", "OLCIS3B_L2_EFR_OC", "OLCIS3B_L2_ERR_IOP", "OLCIS3B_L2_ERR_IOP_NRT", "OLCIS3B_L2_ERR_OC", "OLCIS3B_L2_ERR_OC_NRT", "OLCIS3B_L2_ILW", "OLIPAC", "OMAERO", "OMAEROG", "OMAEROZ", "OMAEROe", "OMAERUV", "OMAERUV", "OMAERUV", "OMAERUVG", "OMAERUV_CPR", "OMAERUVd", "OMBRO", "OMCLDO2", "OMCLDO2G", "OMCLDO2Z", "OMCLDO2_CPR", "OMCLDRR", "OMCLDRR", "OMCLDRR", "OMCLDRRG", "OMCLDRR_CPR", "OMDOAO3", "OMDOAO3G", "OMDOAO3Z", "OMDOAO3e", "OMEXII", "OMGLER", "OMG_L1B_AIRGRAV", "OMG_L1_FLOAT_ALAMO", "OMG_L1_FLOAT_APEX", "OMG_L2_AXBT", "OMG_L2_AXCTD", "OMG_L2_Bathy_MBES_Gridded", "OMG_L2_Bathy_SBES_Gridded", "OMG_L2_CTD", "OMG_L3_ICE_ELEV_GLISTINA", "OMG_NARWHALS_MOORING_TEMP_CTD_1.0", "OMG_NARWHALS_SHIPBOARD_CTD_1.0", "OMHCHO", "OMHCHOG", "OMHCHOd", "OMIAuraAER", "OMI_MINDS_NO2", "OMI_MINDS_NO2G", "OMI_MINDS_NO2d", "OML1BIRR", "OML1BIRR", "OML1BRUG", "OML1BRUG", "OML1BRUZ", "OML1BRUZ", "OML1BRVG", "OML1BRVG", "OML1BRVZ", "OML1BRVZ", "OMLER", "OMMYDAGEO", "OMMYDCLD", "OMNO2", "OMNO2G", "OMNO2_CPR", "OMNO2d", "OMO3PR", "OMOCLO", "OMPIXCOR", "OMPIXCORZ", "OMPS_N20_NMHCHO_L2", "OMPS_N20_NMSO2_PCA_L2_Step1", "OMPS_N21_LP_L1G_EV", "OMPS_N21_LP_L2_O3_DAILY", "OMPS_NPP_LP_L1G_EV", "OMPS_NPP_LP_L1G_EV", "OMPS_NPP_LP_L2_AER_DAILY", "OMPS_NPP_LP_L2_O3_DAILY", "OMPS_NPP_LP_L2_O3_DAILY", "OMPS_NPP_LP_L2_Temp_DAILY", "OMPS_NPP_LP_L3_AER_MONTHLY", "OMPS_NPP_NMCLDRR_L2", "OMPS_NPP_NMEV_L1B", "OMPS_NPP_NMHCHO_L2", "OMPS_NPP_NMMIEAI_L2", "OMPS_NPP_NMNO2_L2", "OMPS_NPP_NMSO2_L2", "OMPS_NPP_NMSO2_PCA_L2", "OMPS_NPP_NMSO2_PCA_L3_DAILY", "OMPS_NPP_NMTO3_L2", "OMPS_NPP_NMTO3_L3_DAILY", "OMPS_NPP_NPBUVO3_L2", "OMPS_NPP_NPBUVO3_L2", "OMPS_NPP_NPEV_L1B", "OMSO2", "OMSO2", "OMSO2G", "OMSO2_CPR", "OMSO2e", "OMTO3", "OMTO3G", "OMTO3_CPR", "OMTO3d", "OMTO3e", "OMTO3e", "OMUANC", "OMUFPITMET", "OMUFPMET", "OMUFPSLV", "OMUVB", "OMUVBG", "OMUVBd", "OMVANC", "OMVFPITMET", "OMVFPMET", "OMVFPSLV", "ONDEQUE", "ONR-MAB", "ONR_PenobscotRiverSystem", "OPERA_L2_CSLC-S1-STATIC_V1", "OPERA_L2_CSLC-S1_V1", "OPERA_L2_RTC-S1-STATIC_V1", "OPERA_L2_RTC-S1_V1", "OPERA_L3_DIST-ALERT-HLS_V1", "OPERA_L3_DIST-ANN-HLS_V1", "OPERA_L3_DSWX-HLS_V1", "ORACLES_AerosolCloud_AircraftRemoteSensing_Data", "ORACLES_Aerosol_AircraftInSitu_Data", "ORACLES_Cloud_AircraftInSitu_Data", "ORACLES_Merge_Data", "ORACLES_MetNav_AircraftInSitu_Data", "ORACLES_Model_Data", "ORACLES_Radiation_AircraftInSitu_Data", "ORACLES_TraceGas_AircraftInSitu_Data", "ORINOCO", "OS2_OSCAT_LEVEL_2B_OWV_COMP_12_V2", "OSCAR_L4_OC_1deg", "OSCAR_L4_OC_FINAL_V2.0", "OSCAR_L4_OC_INTERIM_V2.0", "OSCAR_L4_OC_NRT_V2.0", "OSCAR_L4_OC_third-deg", "OSCAR_L4_OC_third-deg_YEARLY", "OSTIA-UKMO-L4-GLOB-REP-v2.0", "OSTIA-UKMO-L4-GLOB-v2.0", "OTZ_WHOI", "OWLETS1_Model_Data", "OWLETS1_Pandora_Data", "OWLETS1_SherpaAircraft_Data", "OWLETS1_Sondes_Data", "OWLETS1_SurfaceLidar_Data", "OWLETS2_Aerosol_Data", "OWLETS2_Pandora_Data", "OWLETS2_Ship_Data", "OWLETS2_Sondes_Data", "OWLETS2_SurfaceLidar_Data", "OWLETS2_Surface_Data", "OWLETS2_UMDAircraft_Data", "Ocean_Color_Cal_Val", "Oceania", "Odyssee_Saint_Laurent", "Okeechobee", "Opt_Sed_Trap_Cal", "Optical_Layers", "Optique_St_Laurent", "Oumalik_Veg_plots_1506", "P380108", "PACE_ABSclosure", "PACE_EPH_DEF", "PACE_HARP2_L0_D1", "PACE_HARP2_L0_D2", "PACE_HARP2_L0_D3", "PACE_HARP2_L1A_SCI", "PACE_HARP2_L1B_SCI", "PACE_HARP2_L1C_SCI", "PACE_HKT", "PACE_HSK", "PACE_OCI_L0_SCI", "PACE_OCI_L1A_SCI", "PACE_OCI_L1B_SCI", "PACE_OCI_L1C_SCI", "PACE_OCI_L2_AOP_NRT", "PACE_OCI_L2_BGC_NRT", "PACE_OCI_L2_IOP_NRT", "PACE_OCI_L2_PAR_NRT", "PACE_OCI_L3M_CHL_NRT", "PACE_SPEXONE_L0", "PACE_SPEXONE_L1A_SCI", "PACE_SPEXONE_L1B_SCI", "PACE_SPEXONE_L1C_SCI", "PAC_DFO_PAR_BUOY", "PAD_2011_1133", "PAD_935", "PAL-LTER", "PARASOLRB_CPR", "PATEX", "PC06_ECMWF_LBA_1141", "PEACETIME", "PERU", "PET_PU_3H025", "PET_PU_3H025", "PICOLO", "PISTON-ONR-NOAA-Autonomous-Uncrewed_2018-2019", "PISTON-ONR-NOAA_Islands_2018-2019", "PISTON-ONR-NOAA_RVMirai_2018", "PISTON-ONR-NOAA_RVSallyRide_2019", "PISTON-ONR-NOAA_RVThompson_2018", "PMNS", "PMRN6L1RAD_CDROM", "POAM2_VER6", "POAM3", "POLARIS_Aerosol_AircraftInSitu_ER2_Data", "POLARIS_Analysis_ER2_Data", "POLARIS_Ground_Data", "POLARIS_MetNav_AircraftInSitu_ER2_Data", "POLARIS_Model_Data", "POLARIS_Satellite_Data", "POLARIS_Sondes_Data", "POLARIS_TraceGas_AircraftInSitu_ER2_Data", "POLARIS_jValue_AircraftInSitu_ER2_Data", "POMME", "PRECIP_AMSR2_GCOMW1", "PRECIP_AMSRE_AQUA", "PRECIP_GMI_GPM", "PRECIP_SMMR_NIMBUS7", "PRECIP_SSMIS_F16", "PRECIP_SSMIS_F17", "PRECIP_SSMIS_F18", "PRECIP_SSMIS_F19", "PRECIP_SSMI_F08", "PRECIP_SSMI_F10", "PRECIP_SSMI_F11", "PRECIP_SSMI_F13", "PRECIP_SSMI_F14", "PRECIP_SSMI_F15", "PRECIP_TMI_TRMM", "PREFIRE_SAT2_0-BUS-TLM", "PREFIRE_SAT2_0-PAYLOAD", "PREFIRE_SAT2_0-PAYLOAD-TLM", "PREPP", "PRESWOT_HYDRO_GRRATS_L2_DAILY_VIRTUAL_STATION_HEIGHTS_V2", "PRESWOT_HYDRO_GRRATS_L2_VIRTUAL_STATION_HEIGHTS_V2", "PRESWOT_HYDRO_L2_GREALM_LAKE_HEIGHT_V2", "PRESWOT_HYDRO_L3_LAKE_RESEVOIR_AREA_V2", "PRESWOT_HYDRO_L4_LAKE_STORAGE_TIME_SERIES_V2", "PRIM_SMAP_L2_V1", "PRISM_CORAL_L1", "PRISM_CORAL_L2", "PRISM_Elkhorn_Slough", "PRONEX", "PSCNH_ARCHIVED_DATA", "PTLO", "PVST_HOTPACE", "PVST_NORTHERN_INDIAN_OCEAN", "PVST_PRINGLS", "PVST_SBCR", "PVST_VDIUP", "PanamaCity", "Panhandle_OWQ", "Passive_Microwave_Snowoff_Data_1711", "Patagonian_Coastal", "Peatland_carbon_balance_1382", "Pelican_PCO2", "PenBaySurvey", "PermafrostThaw_CarbonEmissions_1872", "Permafrost_ActiveLayer_NSlope_1759", "Permafrost_Thaw_Depth_YK_1598", "PhenoCam_V2_1674", "Phenocam_Images_V2_1689", "Phenology_AmeriFlux_Neon_Sites_2033", "Phenology_Deciduous_Forest_1570", "Photos_ThermokarstLakes_AK_1845", "Pingo_Veg_Plots_1507", "Plot_Data_Noatak_Seward_AK_1919", "Plumes_and_Blooms", "PolInSAR_Canopy_Height_1589", "Polar-VPRM_Alaskan-NEE_1314", "PolarWindsII_DAWN_DC8", "PolarWindsI_DAWN_KingAirUC-12B", "Polarimetric_CT_1601", "Polarimetric_height_profile_1577", "Poplar_Veg_Plots_1376", "PostFire_Tree_Regeneration_1955", "Post_Fire_C_Emissions_1787", "Post_Fire_SOC_NWT_2235", "PreABoVE_AirMOSS_L1_Alaska_1678", "PreDeltaX_ADCP_Measurements_1806", "PreDeltaX_ASO_LiDAR_WaterLevel_1820", "PreDeltaX_Insitu_Reflectance_1804", "PreDeltaX_L2_AVIRIS_SR_1826", "PreDeltaX_L2_AirSWOT_1818", "PreDeltaX_L3_AVIRIS_Biomass_1821", "PreDeltaX_L3_AVIRIS_Sediment_1822", "PreDeltaX_L3_AirSWOT_WL_1819", "PreDeltaX_Sonar_Bathymetry_1807", "PreDeltaX_TSS_Concentration_1802", "PreDeltaX_UAVSAR_Channel_Maps_1954", "PreDeltaX_UAVSAR_SLC_1816", "PreDeltaX_UAVSAR_WaterLevel_1823", "PreDeltaX_Vegetation_Structure_1805", "PreDeltaX_Water_Level_Data_1801", "Pre_LBA_ABRACOS_899", "Proantar", "Profile_based_PBL_heights_1706", "Prudhoe_Bay_ArcSEES_Veg_Plots_1555", "Prudhoe_Bay_Veg_Maps_1387", "Prudhoe_Bay_Veg_Plots_1360", "Prudhoe_Freshets", "QSCAT_ESDR_MODELED_L2_AUX_V1.0", "QSCAT_L1C_NONSPINNING_SIGMA0_WINDS_V2", "QSCAT_LEVEL_1B_V2", "QSCAT_LEVEL_2B_OWV_COMP_12", "QSCAT_LEVEL_2B_OWV_COMP_12_KUSST_LCRES_4.1", "QSCAT_LEVEL_2B_OWV_COMP_12_LCR_3.1", "QUIKSCAT_ESDR_L2_WIND_STRESS_V1.0", "RADAMBrasil_941", "RAIN_JAEGER", "RAIN_LEGATES", "RAMSSA_09km-ABOM-L4-AUS-v01", "RBLE_917", "RDBTS4", "RDEFT4", "RDGBV4", "RDSISC4", "RDSISCO4", "RECON_SEA_LEVEL_OST_L4_V1", "RED_TIDE", "REMO_OI_SST_5km-UFRJ-L4-SAMERICA-v1.0", "RESOLUTE", "REYNOLDS_NCDC_L4_MONTHLY_V5", "RGGRV1B", "RICMIAAE", "RICMIB2E", "RICMIRCM", "RICMITAL", "RICMITCL", "RICMITST", "RIO-SFE", "ROAVERRS", "RON_BROWN", "RRRAG4", "RSAT-1_L0", "RSAT-1_L1", "RSCAT_COLOCATED_RSS_RADIOMETER_LEVEL_2B_V1", "RSCAT_L1B_V2.0", "RSCAT_L2A_12KM_V2.0", "RSCAT_L2A_25KM_V2.0", "RSCAT_LEVEL_2B_OWV_CLIM_12_V1", "RSCAT_LEVEL_2B_OWV_CLIM_12_V2", "RSCAT_LEVEL_2B_OWV_COMP_12_V1.1", "RSCAT_LEVEL_2B_OWV_COMP_12_V1.2", "RSCAT_LEVEL_2B_OWV_COMP_12_V1.3", "RSS18_AVIRIS_L1B_449", "RSS_WindSat_L1C_TB_V08.0", "Radial_Growth_PRI_1781", "Rain-on-Snow_Data_1611", "RdlP_PT", "ReSALT_ALT_GPR_1265", "ReSALT_InSAR_Barrow_1266", "Reflectance_Spectra_Alaska_1685", "RemSensPOC", "Respiration_622", "River_Ice_Breakup_Freezeup_1697", "Russian_Forest_Disturbance_1294", "S2K_EACM_Subset_623", "S3A_OL_1_EFR", "S5P_L1B_IR_SIR", "S5P_L1B_IR_SIR", "S5P_L1B_IR_UVN", "S5P_L1B_IR_UVN", "S5P_L1B_RA_BD1", "S5P_L1B_RA_BD1_HiR", "S5P_L1B_RA_BD1_HiR", "S5P_L1B_RA_BD2", "S5P_L1B_RA_BD2_HiR", "S5P_L1B_RA_BD2_HiR", "S5P_L1B_RA_BD3", "S5P_L1B_RA_BD3_HiR", "S5P_L1B_RA_BD3_HiR", "S5P_L1B_RA_BD4", "S5P_L1B_RA_BD4_HiR", "S5P_L1B_RA_BD4_HiR", "S5P_L1B_RA_BD5", "S5P_L1B_RA_BD5_HiR", "S5P_L1B_RA_BD5_HiR", "S5P_L1B_RA_BD6", "S5P_L1B_RA_BD6_HiR", "S5P_L1B_RA_BD6_HiR", "S5P_L1B_RA_BD7", "S5P_L1B_RA_BD7_HiR", "S5P_L1B_RA_BD7_HiR", "S5P_L1B_RA_BD8", "S5P_L1B_RA_BD8_HiR", "S5P_L1B_RA_BD8_HiR", "S5P_L2__AER_AI", "S5P_L2__AER_AI_HiR", "S5P_L2__AER_AI_HiR", "S5P_L2__AER_LH", "S5P_L2__AER_LH_HiR", "S5P_L2__AER_LH_HiR", "S5P_L2__CH4___", "S5P_L2__CH4____HiR", "S5P_L2__CH4____HiR", "S5P_L2__CLOUD_", "S5P_L2__CLOUD__HiR", "S5P_L2__CLOUD__HiR", "S5P_L2__CO____", "S5P_L2__CO_____HiR", "S5P_L2__CO_____HiR", "S5P_L2__HCHO__", "S5P_L2__HCHO___HiR", "S5P_L2__HCHO___HiR", "S5P_L2__NO2___", "S5P_L2__NO2____HiR", "S5P_L2__NO2____HiR", "S5P_L2__NP_BD3", "S5P_L2__NP_BD3_HiR", "S5P_L2__NP_BD3_HiR", "S5P_L2__NP_BD6", "S5P_L2__NP_BD6_HiR", "S5P_L2__NP_BD6_HiR", "S5P_L2__NP_BD7", "S5P_L2__NP_BD7_HiR", "S5P_L2__NP_BD7_HiR", "S5P_L2__O3_TCL", "S5P_L2__O3_TCL", "S5P_L2__O3_TOT", "S5P_L2__O3_TOT_HiR", "S5P_L2__O3_TOT_HiR", "S5P_L2__O3__PR_HiR", "S5P_L2__SO2___", "S5P_L2__SO2____HiR", "S5P_L2__SO2____HiR", "SAB_Mapping", "SAGE1_AERO_PRF", "SAGE2_AEROSOL_O3_NO2_H2O_BINARY", "SAILDRONE_ARCTIC", "SAILDRONE_ARCTIC_2021", "SAILDRONE_ARCTIC_2022", "SAILDRONE_ATOMIC", "SAILDRONE_BAJA_SURFACE", "SAM2_AERO_PRF_NAT", "SAMMI2AE", "SAMMI2LS", "SAMMI2ST", "SAMMIB2E", "SAMMIB2T", "SAMMIGEO", "SAMSN7L1RAD_CDROM", "SAMSN7L1RAT", "SAMSN7L3GRIDT", "SAMSN7L3ZMTG", "SAR_Methane_Ebullition_AK_1790", "SASSIE_L1_SWIFT_V1", "SASSIE_L1_WAVEGLIDER_V1", "SASSIE_L2_ALTO_ALAMO_FLOATS_V1", "SASSIE_L2_DRIFTER_UPTEMPO_V1", "SASSIE_L2_DRIFTER_UPTEMPO_V2p", "SASSIE_L2_JET_SSP_V1", "SASSIE_L2_PALS_V1", "SASSIE_L2_SBAND_ML_V1", "SASSIE_L2_SHIPBOARD_ADCP_V1", "SASSIE_L2_SHIPBOARD_CASTAWAY_CTD_V1", "SASSIE_L2_SHIPBOARD_DELTA_18O_V1", "SASSIE_L2_SHIPBOARD_METEOROLOGY_V1", "SASSIE_L2_SHIPBOARD_SALINITY_SNAKE_V1", "SASSIE_L2_SHIPBOARD_TSG_V1", "SASSIE_L2_SHIPBOARD_UCTD_V1", "SASSIE_L2_SWIFT_V1", "SASSIE_L2_UNDER_ICE_FLOAT_V1", "SASSIE_L2_WAVEGLIDERS_V1", "SASSIE_L3_SHIPBOARD_SBAND_V1", "SASSIE_L4_SHIPBOARD_SBAND_V1", "SAV_Plymouth_Bay", "SAWB_JRA_713", "SAWB_JRB_714", "SBI", "SBUV2N09L2", "SBUV2N09L3zm", "SBUV2N09O3", "SBUV2N11L2", "SBUV2N11L3zm", "SBUV2N11O3", "SBUV2N14L2", "SBUV2N14L3zm", "SBUV2N16L2", "SBUV2N16L3zm", "SBUV2N16O3", "SBUV2N17L2", "SBUV2N17L3zm", "SBUV2N18L2", "SBUV2N18L3zm", "SBUV2N19L2", "SBUV2N19L3zm", "SBUVN07L2", "SBUVN07L3zm", "SBUVN7O3", "SCAMSN6IM", "SCAMSN6L2", "SCAR-B_916", "SCARB_ER2_MAS", "SCAR_A_ER2_MAS", "SCAR_B_AERONET", "SCAR_B_G8_FIRE", "SCAR_B_UWC131A", "SCATSAT1_ESDR_ANCILLARY_L2_V1.1", "SCATSAT1_ESDR_L2_WIND_STRESS_V1.1", "SCMRN5L1RAD", "SCOAPE_Ground_Data", "SCOAPE_Pandora_Data", "SCOAPE_RVPointSur_Data", "SCOAPE_Sondes_Data", "SCRN4L1RAD", "SCRN4L1RAD_CDROM", "SCRN5L1RAD", "SCRN5L1RAD_CDROM", "SEA", "SEAC4RS_Aerosol_AircraftInSitu_DC8_Data", "SEAC4RS_Aerosol_AircraftInSitu_Learjet_Data", "SEAC4RS_AircraftRemoteSensing_DIAL_DC8_Data", "SEAC4RS_AircraftRemoteSensing_RSP_ER2_Data", "SEAC4RS_Analysis_Data", "SEAC4RS_Cloud_AircraftInSitu_DC8_Data", "SEAC4RS_Cloud_AircraftInSitu_ER2_Data", "SEAC4RS_Cloud_AircraftInSitu_Learjet_Data", "SEAC4RS_Merge_Data", "SEAC4RS_MetNav_AircraftInSitu_DC8_Data", "SEAC4RS_MetNav_AircraftInSitu_ER2_Data", "SEAC4RS_MetNav_AircraftInSitu_Learjet_Data", "SEAC4RS_Miscellaneous_DC8_Data", "SEAC4RS_Miscellaneous_ER2_Data", "SEAC4RS_Radiation_AircraftInSitu_DC8_Data", "SEAC4RS_Radiation_AircraftInSitu_ER2_Data", "SEAC4RS_Sondes_Data", "SEAC4RS_TraceGas_AircraftInSitu_DC8_Data", "SEAC4RS_TraceGas_AircraftInSitu_ER2_Data", "SEAC4RS_jValue_AircraftInSitu_DC8_Data", "SEAGLIDER_GUAM_2019", "SEAHAWK_VALIDATION", "SEASAT_SAR_L1_HDF5", "SEASAT_SAR_L1_TIFF", "SEA_SURFACE_HEIGHT_ALT_GRIDS_L4_2SATS_5DAY_6THDEG_V_JPL2205", "SEED", "SENTINEL-1A_DP_GRD_FULL", "SENTINEL-1A_DP_GRD_HIGH", "SENTINEL-1A_DP_GRD_MEDIUM", "SENTINEL-1A_DP_META_GRD_FULL", "SENTINEL-1A_DP_META_GRD_HIGH", "SENTINEL-1A_DP_META_GRD_MEDIUM", "SENTINEL-1A_META_OCN", "SENTINEL-1A_META_RAW", "SENTINEL-1A_META_SLC", "SENTINEL-1A_OCN", "SENTINEL-1A_RAW", "SENTINEL-1A_SLC", "SENTINEL-1A_SP_GRD_HIGH", "SENTINEL-1A_SP_GRD_MEDIUM", "SENTINEL-1A_SP_META_GRD_HIGH", "SENTINEL-1A_SP_META_GRD_MEDIUM", "SENTINEL-1B_DP_GRD_HIGH", "SENTINEL-1B_DP_GRD_MEDIUM", "SENTINEL-1B_DP_META_GRD_HIGH", "SENTINEL-1B_DP_META_GRD_MEDIUM", "SENTINEL-1B_META_OCN", "SENTINEL-1B_META_RAW", "SENTINEL-1B_META_SLC", "SENTINEL-1B_OCN", "SENTINEL-1B_RAW", "SENTINEL-1B_SLC", "SENTINEL-1B_SP_GRD_HIGH", "SENTINEL-1B_SP_GRD_MEDIUM", "SENTINEL-1B_SP_META_GRD_HIGH", "SENTINEL-1B_SP_META_GRD_MEDIUM", "SENTINEL-1_BURSTS", "SENTINEL-1_INTERFEROGRAMS", "SENTINEL-1_INTERFEROGRAMS_AMPLITUDE", "SENTINEL-1_INTERFEROGRAMS_COHERENCE", "SENTINEL-1_INTERFEROGRAMS_CONNECTED_COMPONENTS", "SENTINEL-1_INTERFEROGRAMS_UNWRAPPED_PHASE", "SEVIRI_IO_SST-OSISAF-L3C-v1.0", "SEVIRI_SST-OSISAF-L3C-v1.0", "SEVIRI_SST_DR-OSISAF-L3C-v1.0", "SFC_NITROGEN_DIOXIDE_CONC", "SFMBON", "SFP", "SGER", "SHIFT_AVNG_Canopy_WaterContent_2242", "SHIFT_AVNG_FullRes_QkLook_2189", "SHIFT_AVNG_L1A_RDN_unrec_2184", "SHIFT_DriedGround_Leaf_Reflec_2244", "SHIFT_Field_Survey_Metadata_2295", "SHIFT_Foliar_Chemical_Analysis_2337", "SHIFT_HyTES_L1_2022_2245", "SHIFT_HyTES_L2_2022_2246", "SHIFT_Leaf_Traits_Chl_SB_CA_2233", "SHIFT_Plot_Level_Photos_2334", "SHIFT_RamsesTriOS_Rrs_SB_CA_2234", "SIF_PAR_fPAR_US_Midwest_2018_1813", "SIF_SCIAMACHY_GOME2_Harmonized_2317", "SIMBAD_DESCHAMPS_LOA", "SIO-Pier", "SIRSN3L1", "SIRSN4L1", "SISTER_Workflow_V004_2335", "SLOPE_GPP_CONUS_1786", "SMAP_JPL_L2B_NRT2_SSS_CAP_V5", "SMAP_JPL_L2B_NRT_SSS_CAP_V5", "SMAP_JPL_L2B_SSS_CAP_V5", "SMAP_JPL_L3_SSS_CAP_8DAY-RUNNINGMEAN_V5", "SMAP_JPL_L3_SSS_CAP_MONTHLY_V5", "SMAP_RSS_L2_SSS_NRT_V5", "SMAP_RSS_L2_SSS_NRT_V6", "SMAP_RSS_L2_SSS_V4", "SMAP_RSS_L2_SSS_V5", "SMAP_RSS_L2_SSS_V5.3", "SMAP_RSS_L2_SSS_V6", "SMAP_RSS_L3_SSS_SMI_8DAY-RUNNINGMEAN_V4", "SMAP_RSS_L3_SSS_SMI_8DAY-RUNNINGMEAN_V5", "SMAP_RSS_L3_SSS_SMI_8DAY-RUNNINGMEAN_V5.3", "SMAP_RSS_L3_SSS_SMI_8DAY-RUNNINGMEAN_V6", "SMAP_RSS_L3_SSS_SMI_MONTHLY_V4", "SMAP_RSS_L3_SSS_SMI_MONTHLY_V5", "SMAP_RSS_L3_SSS_SMI_MONTHLY_V5.3", "SMAP_RSS_L3_SSS_SMI_MONTHLY_V6", "SMERGE_RZSM0_40CM", "SMMRN7IM", "SMMR_ALW_PRABHAKARA", "SMMR_IWV_PRABHAKARA", "SMODE_L1_ASIT_KABODS_V1", "SMODE_L1_ASIT_SLOPEFIELDS_V1", "SMODE_L1_DOPPLERSCATT_V1", "SMODE_L1_MASS_DOPPVIS_V1", "SMODE_L1_MASS_DOPVISIBLE_V1", "SMODE_L1_MASS_HYPERSPECTRAL_V1", "SMODE_L1_MASS_LIDAR_V1", "SMODE_L1_MASS_LWIR_V1", "SMODE_L1_MASS_VISIBLE_V1", "SMODE_L1_PRISM_V1", "SMODE_L1_SAILDRONES_V1", "SMODE_L2_APEX_FLOAT_V1", "SMODE_L2_DOPPLERSCATT_WINDS_CURRENT_V1", "SMODE_L2_DOPPLERSCATT_WINDS_CURRENT_V2", "SMODE_L2_DRIFTER_POSITIONS_V1", "SMODE_L2_LAGRANGIAN_FLOATS_V1", "SMODE_L2_MOSES_LWIR_SST_V1", "SMODE_L2_PRISM_CHLA_V1", "SMODE_L2_SAILDRONES_V1", "SMODE_L2_SEAGLIDERS_V1", "SMODE_L2_SHIPBOARD_ADCP_V1", "SMODE_L2_SHIPBOARD_BIO_V1", "SMODE_L2_SHIPBOARD_BOTTLES_V1", "SMODE_L2_SHIPBOARD_CTD_V1", "SMODE_L2_SHIPBOARD_RADIOSONDES_METEOROLOGY_V1", "SMODE_L2_SHIPBOARD_SUNA_V1", "SMODE_L2_SHIPBOARD_TSG_V1", "SMODE_L2_SHIPBOARD_UCTD_ECOCTD_V1", "SMODE_L2_SLOCUM_GLIDERS_V1", "SMODE_L2_WAVEGLIDERS_TEMP_SALINITY_V1", "SMODE_L2a_PRISM_REFL_V1", "SMODE_L3_SEAGLIDERS_TEMP_SALINITY_V1", "SMODE_L3_SHIPBOARD_UCTD_ECOCTD_V1", "SMODE_L4_NCOM_V1", "SNDR13CHRP1", "SNDR13CHRP1AQCal", "SNDR13CHRP1J1Cal", "SNDR13CHRP1SNCal", "SNDR13IML3SSDFCNSAT", "SNDR13IML3SSDFCVPD", "SNDRAQIL2CCPCCR", "SNDRAQIL2CCPRET", "SNDRAQIL2CPS", "SNDRAQIL2JSFRET", "SNDRAQIL3CDCCP", "SNDRAQIL3CMCCP", "SNDRAQIL3SDCCP", "SNDRAQIL3SMCCP", "SNDRAQIL3SSDFCNSAT", "SNDRAQIL3SSDFCVPD", "SNDRAQIML1BCALSUBSUM", "SNDRAQIML1CCALSUBRND", "SNDRAQIML2CCPCCR", "SNDRAQIML2CCPRET", "SNDRAQIML2CPS", "SNDRAQIML3CDCCP", "SNDRAQIML3CMCCP", "SNDRAQIML3SDCCP", "SNDRAQIML3SMCCP", "SNDRJ1ATMSL1B", "SNDRJ1ATMSL1B", "SNDRJ1ATMSMAP", "SNDRJ1CrISL1B", "SNDRJ1CrISL1B", "SNDRJ1CrISL1BIMG", "SNDRJ1CrISL1BIMGC", "SNDRJ1CrISL1BPCARED", "SNDRJ1IML2CCPCCR", "SNDRJ1IML2CCPRET", "SNDRJ1IML2CPS", "SNDRJ1IML3CDCCP", "SNDRJ1IML3CMCCP", "SNDRJ1IML3SDCCP", "SNDRJ1IML3SMCCP", "SNDRJ1ML2RMS", "SNDRJ1ML2RMSSUP", "SNDRJ1ML3DRMS", "SNDRJ1ML3MRMS", "SNDRJ2CrISL1B", "SNDRJ2CrISL1BIMG", "SNDRJ2CrISL1BIMGC", "SNDRSNATMSMAP", "SNDRSNCrISL1BIMG", "SNDRSNCrISL1BIMGC", "SNDRSNIL1BCALSUBRNDN", "SNDRSNIL1BCALSUBSUMN", "SNDRSNIL2ESPNH3", "SNDRSNIML2CCPCCR", "SNDRSNIML2CCPCCRN", "SNDRSNIML2CCPRET", "SNDRSNIML2CCPRETN", "SNDRSNIML2CHTCCRN", "SNDRSNIML2CHTRETN", "SNDRSNIML2CPS", "SNDRSNIML2CPSN", "SNDRSNIML2SFSPRET", "SNDRSNIML2SFSPSUP", "SNDRSNIML3CDCCP", "SNDRSNIML3CDCCPN", "SNDRSNIML3CDCHTN", "SNDRSNIML3CMCCP", "SNDRSNIML3CMCCPN", "SNDRSNIML3CMCHTN", "SNDRSNIML3SDCCP", "SNDRSNIML3SDCCPN", "SNDRSNIML3SDCHTN", "SNDRSNIML3SDSFSP", "SNDRSNIML3SMCCP", "SNDRSNIML3SMCCPN", "SNDRSNIML3SMCHTN", "SNDRSNIML3SMSFSP", "SNDRSNML2RMS", "SNDRSNML2RMS", "SNDRSNML2RMSSUP", "SNDRSNML3DRMS", "SNDRSNML3MRMS", "SNEX17_CAR", "SNEX17_DGNSS1", "SNEX17_GPR", "SNEX17_GPR_Raw", "SNEX17_KT15_TB", "SNEX17_P3V", "SNEX17_QWIP_ST", "SNEX17_SBR", "SNEX17_SD", "SNEX17_SD_Perm", "SNEX17_SMP", "SNEX17_SMP2", "SNEX17_SSA", "SNEX17_SSD", "SNEX17_SnowPits", "SNEX17_SnowSAR", "SNEX17_SnowSAR_Raw", "SNEX17_TLI", "SNEX17_TLS_PC_BSU", "SNEX17_TLS_PC_BSU_Raw", "SNEX17_TLS_PC_CRREL", "SNEX17_UWScat", "SNEX20_A19_GSM", "SNEX20_A19_SD", "SNEX20_A19_SP", "SNEX20_A19_SWE", "SNEX20_BR_BSU_SMP", "SNEX20_BSU_CMP_Raw", "SNEX20_BSU_CMP_SWE", "SNEX20_BSU_CMP_TT", "SNEX20_BSU_GPR", "SNEX20_BSU_GPR_Raw", "SNEX20_COCP_GPR", "SNEX20_COCP_GPR_Raw", "SNEX20_COCP_SPD", "SNEX20_CRSM", "SNEX20_CSSM", "SNEX20_DSM", "SNEX20_GIS_REF", "SNEX20_GM_CSU_GPR", "SNEX20_GM_CSU_GPR_RAW", "SNEX20_GM_CTSM", "SNEX20_GM_Lidar", "SNEX20_GM_SP", "SNEX20_GM_SWE_SD", "SNEX20_J_UNM_GPR", "SNEX20_QSI_DEM", "SNEX20_QSI_DEM_3m", "SNEX20_QSI_SD", "SNEX20_QSI_SD_3m", "SNEX20_QSI_VH", "SNEX20_QSI_VH_3m", "SNEX20_SB_GST", "SNEX20_SD", "SNEX20_SD_TLI", "SNEX20_SMP", "SNEX20_SSA", "SNEX20_SWESARR_TB", "SNEX20_TLI", "SNEX20_TLS_PC_BSU", "SNEX20_TLS_PC_BSU_RAW", "SNEX20_TLS_PC_CRREL", "SNEX20_TS_SP", "SNEX20_UNM_GPR", "SNEX20_VPTS_Raw", "SNEX21_COCP_GPR", "SNEX21_COCP_GPR_Raw", "SNEX21_DSM", "SNEX21_PS_DSM", "SNEX21_PS_MET", "SNEX21_SSR", "SNEX21_TS_SP", "SNEX23_BCEF_TLS", "SNEX23_BCEF_TLS_Raw", "SNEX23_CRREL_GPR", "SNEX23_CRREL_GPR_Raw", "SNEX23_CSU_GPR_Raw", "SNEX23_Lidar", "SNEX23_Lidar_Raw", "SNEX23_MAR22_SD", "SNEX23_MAR23_SD", "SNEX23_SSA", "SNEX23_SSA_SO", "SNEX23_SWE", "SNEX23_UW_GPR", "SNEX_HRSI_SD_DEM_CO", "SNEX_Met", "SNEX_Met_Raw", "SNF_ASP_CVR_140", "SNF_BIOMASS_141", "SNF_BIOPHYS_142", "SNF_CAN_COMP_143", "SNF_CJ_SITES_187", "SNF_CJ_VEG_189", "SNF_HELO_MMR_144", "SNF_LEAFCARY_183", "SNF_LEAF_EXP_180", "SNF_LEAF_TMS_184", "SNF_MET_1972_158", "SNF_MET_1972_1990_159", "SNF_MET_1973_160", "SNF_MET_1974_161", "SNF_MET_1975_162", "SNF_MET_1976_163", "SNF_MET_1977_164", "SNF_MET_1978_165", "SNF_MET_1979_166", "SNF_MET_1980_167", "SNF_MET_1981_168", "SNF_MET_1982_169", "SNF_MET_1983_170", "SNF_MET_1984_171", "SNF_MET_1985_172", "SNF_MET_1986_173", "SNF_MET_1987_174", "SNF_MET_1988_175", "SNF_MET_1989_176", "SNF_MET_1990_177", "SNF_MET_SUMM_178", "SNF_NS001_185", "SNF_SAT_INV_186", "SNF_SITECOMP_179", "SNF_SITE_86_188", "SNF_TAB3_3T_182", "SNF_UND_CVR_181", "SNPPATMSL1B", "SNPPATMSL1B", "SNPPCrISL1B", "SNPPCrISL1B", "SNPPCrISL1BNSR", "SNPPCrISL1BNSR", "SNPP_CrIS_VIIRS750m_IND", "SOCCOM", "SOC_3M_Maps_NE_TidalWetlands_1905", "SOC_Stocks_Great_Plains_1603", "SOIL_CO2_Flux_Costa_Rica_1373", "SOLVE1_Aerosol_AircraftInSitu_DC8_Data", "SOLVE1_Aerosol_AircraftInSitu_ER2_Data", "SOLVE1_AircraftRemoteSensing_DC8_HSRL_Data", "SOLVE1_AircraftRemoteSensing_DC8_LASE_Data", "SOLVE1_AircraftRemoteSensing_DC8_Lidar_Data", "SOLVE1_Analysis_DC8_Data", "SOLVE1_Analysis_ER2_Data", "SOLVE1_Ground_Data", "SOLVE1_MetNav_AircraftInSitu_DC8_Data", "SOLVE1_MetNav_AircraftInSitu_ER2_Data", "SOLVE1_Miscellaneous_DC8_Data", "SOLVE1_Miscellaneous_ER2_Data", "SOLVE1_Satellite_Data", "SOLVE1_Sondes_Data", "SOLVE1_TraceGas_AircraftInSitu_DC8_Data", "SOLVE1_TraceGas_AircraftInSitu_ER2_Data", "SOLVE1_TraceGas_AircraftRemoteSensing_DC8_Data", "SOLVE1_jValue_AircraftInSitu_DC8_Data", "SOLVE1_jValue_AircraftInSitu_ER2_Data", "SOLVE2_Aerosol_AircraftInSitu_DC8_Data", "SOLVE2_AircraftRemoteSensing_DC8_AATS14_Data", "SOLVE2_AircraftRemoteSensing_DC8_HSRL_Data", "SOLVE2_AircraftRemoteSensing_DC8_Lidar_Data", "SOLVE2_Analysis_DC8_Data", "SOLVE2_MetNav_AircraftInSitu_DC8_Data", "SOLVE2_Miscellaneous_DC8_Data", "SOLVE2_Radiation_AircraftInSitu_DC8_Data", "SOLVE2_Rocket_Data", "SOLVE2_Satellite_Data", "SOLVE2_Sondes_Data", "SOLVE2_TraceGas_AircraftInSitu_DC8_Data", "SOLVE2_TraceGas_AircraftRemoteSensing_DC8_Data", "SONEX_Aerosol_AircraftInSitu_DC8_Data", "SONEX_AircraftRemoteSensing_DC8_DIAL_Data", "SONEX_Merge_DC8_Data", "SONEX_MetNav_AircraftInSitu_DC8_Data", "SONEX_Miscellaneous_DC8_Data", "SONEX_Model_Data", "SONEX_Other_Data", "SONEX_Satellite_Data", "SONEX_TraceGas_AircraftInSitu_DC8_Data", "SONEX_jValue_AircraftInSitu_DC8_Data", "SOR3D_COMBINED", "SOR3SIMD", "SOR3SIMD_TAV", "SOR3SOLD_HIGH", "SOR3SOLD_MGII", "SOR3SOLFUVD", "SOR3SOLMUVD", "SOR3SOLS_LA", "SOR3SOLS_MGII", "SOR3TSI6", "SOR3TSID", "SOR3XPS6", "SOR3XPSD", "SOR4XPS5", "SOR4XPSD_HIGH", "SOR4XPSD_LOW", "SORTIE", "SPL1A_001", "SPL1A_002", "SPL1A_METADATA_001", "SPL1A_METADATA_002", "SPL1A_QA_001", "SPL1A_QA_002", "SPL1A_RO_001", "SPL1A_RO_002", "SPL1A_RO_003", "SPL1A_RO_METADATA_001", "SPL1A_RO_METADATA_002", "SPL1A_RO_METADATA_003", "SPL1A_RO_QA_001", "SPL1A_RO_QA_002", "SPL1A_RO_QA_003", "SPL1B_SO_LoRes_001", "SPL1B_SO_LoRes_002", "SPL1B_SO_LoRes_003", "SPL1B_SO_LoRes_METADATA_001", "SPL1B_SO_LoRes_METADATA_002", "SPL1B_SO_LoRes_METADATA_003", "SPL1B_SO_LoRes_QA_001", "SPL1B_SO_LoRes_QA_002", "SPL1B_SO_LoRes_QA_003", "SPL1C_S0_HiRes_001", "SPL1C_S0_HiRes_002", "SPL1C_S0_HiRes_003", "SPL1C_S0_HiRes_METADATA_001", "SPL1C_S0_HiRes_METADATA_002", "SPL1C_S0_HiRes_METADATA_003", "SPL1C_S0_HiRes_QA_001", "SPL1C_S0_HiRes_QA_002", "SPL1C_S0_HiRes_QA_003", "SPL2SMA", "SPL2SMAP", "SPL2SMAP_S", "SPL3FTA", "SPL3SMA", "SPL3SMAP", "SPL3SMP", "SPL3SMP_E", "SPL4CMDL", "SPL4SMAU", "SPL4SMGP", "SPL4SMLM", "SPURS1_ADCP", "SPURS1_ARGO", "SPURS1_CTD", "SPURS1_DRIFTER", "SPURS1_ECOMAPPER", "SPURS1_FLOAT_NEUTRALLYBUOYANT", "SPURS1_METEO", "SPURS1_MOORING_PICO", "SPURS1_MOORING_WHOI", "SPURS1_SEAGLIDER", "SPURS1_SEASOAR", "SPURS1_TENUSEGLIDER", "SPURS1_TSG", "SPURS1_UCTD", "SPURS1_WAVEGLIDER", "SPURS2_ADCP", "SPURS2_ARGO", "SPURS2_CFT", "SPURS2_CTD", "SPURS2_DISDR", "SPURS2_DRIFTER", "SPURS2_FLOAT_NEUTRALLYBUOYANT", "SPURS2_LADYAMBER", "SPURS2_METEO", "SPURS2_MOORING_CENTRAL", "SPURS2_MOORING_PICO", "SPURS2_PALS", "SPURS2_RAINRADAR", "SPURS2_RAWINSONDE", "SPURS2_SAILDRONE", "SPURS2_SALINITYSNAKE", "SPURS2_SEAGLIDER", "SPURS2_SSP", "SPURS2_UCTD", "SPURS2_UNDERWAY_pCO2_DIC_pH", "SPURS2_USPS", "SPURS2_WAMOS", "SPURS2_WAVEGLIDER", "SPURS2_XBAND", "SPURS2_XBAND_IMG", "SPURS2_XBT", "SRB_REL3.0_SW_3HRLY_MONTHLY_UTC_NC", "SRDB_V5_1827", "SRTMGL1", "SRTMGL1N", "SRTMGL3", "SRTMGL30", "SRTMGL3N", "SRTMGL3S", "SRTMGL3_NC", "SRTMGL3_NUMNC", "SRTMIMGM", "SRTMIMGR", "SRTMSWBD", "SSBUVIRR", "SSBUVO3", "SSFR_irradiance_841", "STAQS_AircraftRemoteSensing_JSC-GV_GCAS_Data", "STAQS_AircraftRemoteSensing_JSC-GV_HSRL2_Data", "STAQS_AircraftRemoteSensing_NASA-G3_GCAS_Data", "STAQS_AircraftRemoteSensing_NASA-G3_HALO_Data", "STAQS_Chiwaukee-Prairie_Data", "STAQS_Drone_Data", "STAQS_Ground_Data", "STAQS_INSTEP_Data", "STAQS_SeaRey_Data", "STAQS_Sondes_Data", "STRAT_Aerosol_AircraftInSitu_ER2_Data", "STRAT_Analysis_ER2_Data", "STRAT_Ground_Data", "STRAT_MetNav_AircraftInSitu_ER2_Data", "STRAT_Model_Data", "STRAT_Satellite_Data", "STRAT_Sondes_Data", "STRAT_TraceGas_AircraftInSitu_ER2_Data", "STRAT_jValue_AircraftInSitu_ER2_Data", "STS-59_GRD", "STS-59_META_GRD", "STS-59_META_SLC", "STS-59_SLC", "STS-68_GRD", "STS-68_META_GRD", "STS-68_META_SLC", "STS-68_SLC", "SUCCESS_UTAH_PDL", "SUPERYACHT_SCIENCE", "SV08LC", "SV08PLBK", "SV08PLTB", "SV08SM", "SV08SR", "SV08ST", "SV08V", "SV08VWC", "SV12CSMA", "SV12CST", "SV12LC", "SV12PLBK", "SV12PLSM", "SV12PLTB", "SV12PSMA", "SV12PSMF", "SV12SRA", "SV12SRF", "SV12STM", "SV12UBK", "SV12VA", "SV12VF", "SV12VWC", "SV15PLSM", "SV15PLTB", "SV15PSM", "SV16I_PLTBSM", "SV16I_PNET", "SV16I_TNET", "SV16I_TPSM", "SV16M_CRS", "SV16M_CSM", "SV16M_LAI", "SV16M_LC", "SV16M_MET", "SV16M_PLTBSM", "SV16M_PSM", "SV16M_SDB", "SV16M_SR", "SV16M_SSM", "SV16M_ST", "SV16M_TB", "SV16M_V", "SV19MA_DEM", "SV19MA_DSM", "SV19MA_LID", "SV19MA_TNET", "SV19MA_VOD", "SV19MB_DEM", "SV19MB_DSM", "SV19MB_LID", "SV19MB_TNET", "SWDB_L2", "SWDB_L305", "SWDB_L310", "SWDB_L3M05", "SWDB_L3M10", "SWDB_L3MC05", "SWDB_L3MC10", "SWFL", "SWOT_L1B_LR_INTF_1.0", "SWOT_L1B_LR_INTF_1.1", "SWOT_L2_HR_LakeSP_2.0", "SWOT_L2_HR_PIXC_2.0", "SWOT_L2_HR_Raster_2.0", "SWOT_L2_HR_RiverSP_2.0", "SWOT_L2_LR_SSH_1.0", "SWOT_L2_LR_SSH_1.1", "SWOT_L2_LR_SSH_2.0", "SWOT_L2_NALT_GDR_2.0", "SWOT_L2_NALT_IGDR_1.0", "SWOT_L2_NALT_IGDR_2.0", "SWOT_L2_NALT_OGDR_1.0", "SWOT_L2_NALT_OGDR_2.0", "SWOT_L4_DAWG_SOS_DISCHARGE", "SWOT_PRELAUNCH_L2_BPR_V1", "SWOT_PRELAUNCH_L2_GLIDER_V1", "SWOT_PRELAUNCH_L2_GPS_V1", "SWOT_PRELAUNCH_L2_PIES_V1", "SWOT_PRELAUNCH_L2_PRAWLER_V1", "SWOT_PRELAUNCH_L2_SIOCTD_V1", "SWOT_PRELAUNCH_L2_WHOICTD_V1", "SWOT_PRELAUNCH_L2_WW_V1", "SWOT_SIMULATED_L2_KARIN_SSH_ECCO_LLC4320_CALVAL_V1", "SWOT_SIMULATED_L2_KARIN_SSH_ECCO_LLC4320_SCIENCE_V1", "SWOT_SIMULATED_L2_KARIN_SSH_GLORYS_CALVAL_V1", "SWOT_SIMULATED_L2_KARIN_SSH_GLORYS_SCIENCE_V1", "SWOT_SIMULATED_L2_NADIR_SSH_ECCO_LLC4320_CALVAL_V1", "SWOT_SIMULATED_L2_NADIR_SSH_ECCO_LLC4320_SCIENCE_V1", "SWOT_SIMULATED_L2_NADIR_SSH_GLORYS_CALVAL_V1", "SWOT_SIMULATED_L2_NADIR_SSH_GLORYS_SCIENCE_V1", "SWOT_SIMULATED_NA_CONTINENT_L2_HR_LAKESP_V1", "SWOT_SIMULATED_NA_CONTINENT_L2_HR_PIXCVEC_V1", "SWOT_SIMULATED_NA_CONTINENT_L2_HR_PIXC_V1", "SWOT_SIMULATED_NA_CONTINENT_L2_HR_RASTER_V1", "SWOT_SIMULATED_NA_CONTINENT_L2_HR_RIVERSP_V1", "Sahel_Water_Bodies_1269", "Salt_Marsh_Biomass_CONUS_2348", "San_Diego_Coastal_Project", "Sargassum_GOM", "Saskatchewan_Soils_125m_SSA_1346", "Sat_ActiveLayer_Thickness_Maps_1760", "SatelliteDerived_Forest_Mexico_2320", "Scotia_Prince_ferry", "Sea2Space", "SeaWiFS_L1_GAC", "SeaWiFS_L1_GAC", "SeaWiFS_L1_MLAC", "SeaWiFS_L1_MLAC", "SeaWiFS_L2_GAC_IOP", "SeaWiFS_L2_GAC_OC", "SeaWiFS_L2_MLAC_IOP", "SeaWiFS_L2_MLAC_OC", "SeaWiFS_L3b_CHL", "SeaWiFS_L3b_IOP", "SeaWiFS_L3b_KD", "SeaWiFS_L3b_PAR", "SeaWiFS_L3b_PIC", "SeaWiFS_L3b_POC", "SeaWiFS_L3b_RRS", "SeaWiFS_L3m_CHL", "SeaWiFS_L3m_IOP", "SeaWiFS_L3m_KD", "SeaWiFS_L3m_PAR", "SeaWiFS_L3m_PIC", "SeaWiFS_L3m_POC", "SeaWiFS_L3m_RRS", "SeaWiFS_L4b_GSM", "SeaWiFS_L4m_GSM", "Sea_of_Japan", "Seagrass_Mapping_Florida", "Searcher", "Seasonality_Tundra_Vegetation_1606", "Secret", "Semi-Arid_Tree_Carbon_50cm_2117", "Sentinel-1_Burst_Map", "Seward_Peninsula_Veg_Maps_1363", "Shrub_Biomass_Toolik_Lake_AK_1573", "SiB3_Carbon_Flux_909", "SiB4_Global_HalfDegree_Daily_1849", "SiB4_Global_HalfDegree_Hourly_1847", "SiB4_Global_HalfDegree_Monthly_1848", "Siberian_Biomass_Wildfire_1321", "Siberian_Larch_Stand_Age_1364", "SnowMeltDuration_PMicrowave_1843", "Snow_Cover_Extent_and_Depth_1757", "Snow_Depth_Data_Images_1656", "Snow_Wildlife_Tracks_AK_WA_2188", "Snowmelt_timing_maps_V2_1712", "Snowpack_Dall_Sheep_Track_1583", "SoilResp_HeterotrophicResp_1928", "SoilSCAPE_1339", "SoilSCAPE_V2_2049", "Soil_ActiveLayer_Properties_AK_2315", "Soil_Carbon_Flux_Maps_1683", "Soil_Moisture_Alaska_Alberta_2123", "Soil_Temp_Moisture_Alaska_1869", "Soil_Temperature_Profiles_AK_1767", "Sonoma_County_Forest_AGB_1764", "Southern_Boreal_Plot_Attribute_1740", "Southern_Ocean_Drifter", "Surface_Oligo_Med_Sea", "SwAO", "TAHOE", "TAO", "TAO_Moorings", "TARA-EUROPA", "TARA_PACIFIC_expedition", "TARFOX_UWC131A", "TARFOX_UWC131A_SUNP", "TARFOX_WALLOPS_MET", "TARFOX_WALLOPS_SMPS", "TARFOX_WALLOPS_SONDE", "TCTE3TSI6", "TCTE3TSID", "TELLUS_GIA_L3_0.5-DEG_V1.0", "TELLUS_GIA_L3_1-DEG_V1.0", "TELLUS_GLDAS-NOAH-3.3_TWS-ANOMALY_MONTHLY", "TELLUS_GRAC-GRFO_MASCON_CRI_GRID_RL06.1_V3", "TELLUS_GRAC-GRFO_MASCON_CRI_GRID_RL06.3_V4", "TELLUS_GRAC-GRFO_MASCON_GRID_RL06.1_V3", "TELLUS_GRAC-GRFO_MASCON_GRID_RL06.3_V4", "TELLUS_GRAC_L3_CSR_RL06_LND_v04", "TELLUS_GRAC_L3_CSR_RL06_OCN_v04", "TELLUS_GRAC_L3_GFZ_RL06_LND_v04", "TELLUS_GRAC_L3_GFZ_RL06_OCN_v04", "TELLUS_GRAC_L3_JPL_RL06_LND_v04", "TELLUS_GRAC_L3_JPL_RL06_OCN_v04", "TELLUS_GRFO_L3_CSR_RL06.1_LND_v04", "TELLUS_GRFO_L3_CSR_RL06.1_OCN_v04", "TELLUS_GRFO_L3_CSR_RL06.2_LND_v04", "TELLUS_GRFO_L3_CSR_RL06.2_OCN_v04", "TELLUS_GRFO_L3_GFZ_RL06.1_LND_v04", "TELLUS_GRFO_L3_GFZ_RL06.1_OCN_v04", "TELLUS_GRFO_L3_JPL_RL06.1_LND_v04", "TELLUS_GRFO_L3_JPL_RL06.1_OCN_v04", "TELLUS_GRFO_L3_JPL_RL06.3_LND_v04", "TELLUS_GRFO_L3_JPL_RL06.3_OCN_v04", "TEMPEST_STPH8_L1_TSDR_V10.0", "TEMPO_HCHO_L2", "TEMPO_NO2_L2", "TG02_Balloon_VOC_1110", "TG03_AERONET_AOT_1128", "TG03_Aeronet_Solar_Flux_1137", "TG05_CASA_1199", "TG06_Vertical_Profiles_1175", "TG07_Autochamber_Soil_CO2_Flux_Km67_927", "TG07_DBH_Cauaxi_1063", "TG07_FFT_Survey_Km83_923", "TG07_Fallen_Standing_Necromass_998", "TG07_Litter_Decomposition_925", "TG07_Manual_Flux_Km67_1026", "TG07_Root_Mortality_Experiment_924", "TG07_Root_Mortality_Longterm_1116", "TG07_STM_GLAS_836", "TG07_Soil-Atmosphere_Flux_Km83_926", "TG07_Soil_Nutrients_1085", "TG07_Trace_Gas_Profiles_1107", "TG08_Soil_Gas_Fertilization_1105", "TG08_Soil_Gas_Wetting_1101", "TG09_N2O_Soils_1013", "TG10_TROFFEE_1195", "THAILAND", "THIRN4IMCH67", "THIRN4L1CH115", "THIRN4L1CH67", "THIRN5L1CH115", "THIRN5L1CH67", "THIRN6IM", "THIRN6L1CH115", "THIRN6L1CH67", "THIRN7IM", "THIRN7L1BCLT", "THIRN7L1CLDT", "THORPEX_ER2_MAS", "TIROS2L1FMRT", "TIROS3L1FMRT", "TIROS3L1ORT", "TIROS4L1FMRT", "TIROS4L1ORR", "TIROS4L1ORT", "TIROS7L1FMRT", "TL1BL", "TL1BN", "TL1BSOL", "TL2ANC", "TL2ANC", "TL2ANCS", "TL2ANCS", "TL2ATMLN", "TL2ATMLN", "TL2ATMTL", "TL2ATMTN", "TL2ATMTN", "TL2CH4LN", "TL2CH4LN", "TL2CH4N", "TL2CH4N", "TL2CH4NS", "TL2CH4NS", "TL2CO2LN", "TL2CO2LN", "TL2CO2N", "TL2CO2N", "TL2CO2NS", "TL2CO2NS", "TL2COLN", "TL2COLN", "TL2CON", "TL2CON", "TL2CONS", "TL2CONS", "TL2FORLN", "TL2FORLN", "TL2FORN", "TL2FORN", "TL2FORNS", "TL2FORNS", "TL2H2OL", "TL2H2OLN", "TL2H2OLN", "TL2H2OLS", "TL2H2ON", "TL2H2ON", "TL2H2ONS", "TL2H2ONS", "TL2HCNN", "TL2HCNNS", "TL2HDOL", "TL2HDOLN", "TL2HDOLN", "TL2HDOLS", "TL2HDON", "TL2HDON", "TL2HDONS", "TL2HDONS", "TL2HNO3L", "TL2HNO3S", "TL2IRKN", "TL2IRKN", "TL2IRKNS", "TL2IRKNS", "TL2MTLLN", "TL2MTLN", "TL2MTLN", "TL2MTLNS", "TL2MTLNS", "TL2N2ON", "TL2N2ON", "TL2N2ONS", "TL2N2ONS", "TL2NH3LN", "TL2NH3LN", "TL2NH3N", "TL2NH3N", "TL2NH3NS", "TL2NH3NS", "TL2O3L", "TL2O3LN", "TL2O3LN", "TL2O3LS", "TL2O3N", "TL2O3N", "TL2O3NS", "TL2O3NS", "TL2OCSLN", "TL2OCSN", "TL2OCSN", "TL2OCSNS", "TL2OCSNS", "TL2PANLN", "TL2PANN", "TL2PANN", "TL2PANNS", "TL2PANNS", "TL2RHLN", "TL2SUM", "TL2SUM", "TL2SUMS", "TL2SUMS", "TL2SUP", "TL2SUP", "TL2SUPS", "TL2SUPS", "TL2TLS", "TL2TNS", "TL2TNS", "TL3ATD", "TL3ATD", "TL3ATM", "TL3ATM", "TL3ATM", "TL3CH4D", "TL3CH4D", "TL3CH4M", "TL3CH4M", "TL3CH4M", "TL3COD", "TL3COD", "TL3COM", "TL3COM", "TL3H2OD", "TL3H2OD", "TL3H2OM", "TL3H2OM", "TL3H2OM", "TL3HDOD", "TL3HDOD", "TL3HDOM", "TL3HDOM", "TL3HDOM", "TL3HNOD", "TL3HNOM", "TL3O3D", "TL3O3D", "TL3O3M", "TL3O3M", "TL3O3M", "TLS_Lidar_BlueFlux_Mangroves_2311", "TMI-REMSS-L2P-v4", "TMI-REMSS-L3U-v7.1a", "TML2CO", "TML2COS", "TOL2O3", "TOL2O3", "TOL2O3S", "TOL2O3S", "TOMSEPAER", "TOMSEPL2", "TOMSEPL3", "TOMSEPL3daer", "TOMSEPL3dery", "TOMSEPL3dref", "TOMSEPL3dtoz", "TOMSEPL3maer", "TOMSEPL3mery", "TOMSEPL3mref", "TOMSEPL3mtoz", "TOMSEPL3zaer", "TOMSEPL3zref", "TOMSEPL3ztoz", "TOMSEPOVP", "TOMSM3L3", "TOMSM3L3dref", "TOMSM3L3dtoz", "TOMSM3OVP", "TOMSN7AER", "TOMSN7L2", "TOMSN7L3", "TOMSN7L3daer", "TOMSN7L3dery", "TOMSN7L3dref", "TOMSN7L3dtoz", "TOMSN7L3maer", "TOMSN7L3mery", "TOMSN7L3mref", "TOMSN7L3mtoz", "TOMSN7L3zaer", "TOMSN7L3zref", "TOMSN7L3ztoz", "TOMSN7OVP", "TOMSN7SO2", "TOMS_aerosol_823", "TOMS_ozone_824", "TOPEX_ALTSDR", "TOPEX_POSEIDON_GDR_F", "TOTE-VOTE_Aerosol_AircraftInSitu_DC8_Data", "TOTE-VOTE_AircraftRemoteSensing_DC8_DIAL_Data", "TOTE-VOTE_AircraftRemoteSensing_DC8_Lidar_Data", "TOTE-VOTE_Analysis_DC8_Data", "TOTE-VOTE_Ground_Data", "TOTE-VOTE_MetNav_AircraftInSitu_DC8_Data", "TOTE-VOTE_Miscellaneous_DC8_Data", "TOTE-VOTE_Satellite_Data", "TOTE-VOTE_Sondes_Data", "TOTE-VOTE_TraceGas_AircraftInSitu_DC8_Data", "TOTO", "TOVSA5ND", "TOVSA5NF", "TOVSA5NG", "TOVSA5NH", "TOVSA5TN", "TOVSADND", "TOVSADNF", "TOVSADNG", "TOVSADNH", "TOVSADTN", "TOVSAMNA", "TOVSAMNC", "TOVSAMND", "TOVSAMND", "TOVSAMNE", "TOVSAMNF", "TOVSAMNF", "TOVSAMNG", "TOVSAMNG", "TOVSAMNH", "TOVSAMNH", "TOVSAMNJ", "TOVSAMTN", "TOVSAMTN", "TOVSB5ND", "TOVSB5NG", "TOVSBDND", "TOVSBDNG", "TOVSBMND", "TOVSBMNG", "TOVS_717", "TRACE-A_920", "TRACE-A_AircraftRemoteSensing_DC8_DIAL_Data", "TRACE-A_Brazil_Data", "TRACE-A_Merge_Data", "TRACE-A_MetNav_AircraftInSitu_DC8_Data", "TRACE-A_Satellite_Data", "TRACE-A_Sondes_Data", "TRACE-A_TraceGas_AircraftInSitu_DC8_Data", "TRACE-A_Trajectory_Data", "TRACE-P_Aerosol_AircraftInSitu_DC8_Data", "TRACE-P_Aerosol_AircraftInSitu_P3B_Data", "TRACE-P_AircraftRemoteSensing_DC8_DIAL_Data", "TRACE-P_Cloud_AircraftInSitu_DC8_Data", "TRACE-P_Ground_Data", "TRACE-P_Merge_Data", "TRACE-P_MetNav_AircraftInSitu_DC8_Data", "TRACE-P_MetNav_Aircraft_InSitu_P3B_Data", "TRACE-P_Model_Data", "TRACE-P_Satellite_Data", "TRACE-P_Sondes_Data", "TRACE-P_TraceGas_AircraftInSitu_DC8_Data", "TRACE-P_TraceGas_InSitu_P3B_Data", "TRACE-P_Trajectory_DC8_Data", "TRACE-P_Trajectory_P3B_Data", "TRACE-P_jValue_AircraftInSitu_DC8_Data", "TRACE-P_jValue_AircraftInSitu_P3B_Data", "TRACERAQ_AircraftRemoteSensing_GV_GCAS_Data", "TRACERAQ_AircraftRemoteSensing_GV_HSRL2_Data", "TRACERAQ_Ground_LaPorte_Data", "TRACERAQ_Pandora_Data", "TRACERAQ_Sondes_Data", "TRATLEQ1", "TRMM_1A01", "TRMM_1A11", "TRMM_1B01", "TRMM_1B11", "TRMM_1B21", "TRMM_1B51", "TRMM_1C21", "TRMM_1C51", "TRMM_1C51UW", "TRMM_2A12", "TRMM_2A21", "TRMM_2A23", "TRMM_2A25", "TRMM_2A52", "TRMM_2A53", "TRMM_2A53UW", "TRMM_2A54", "TRMM_2A54UW", "TRMM_2A55", "TRMM_2A55UW", "TRMM_2A56", "TRMM_2B31", "TRMM_3A11", "TRMM_3A12", "TRMM_3A25", "TRMM_3A26", "TRMM_3A53", "TRMM_3A54", "TRMM_3A54UW", "TRMM_3A55", "TRMM_3B31", "TRMM_3B40RT", "TRMM_3B41RT", "TRMM_3B42", "TRMM_3B42RT", "TRMM_3B42RT_Daily", "TRMM_3B42_Daily", "TRMM_3B43", "TRMM_3G25", "TRMM_3G31", "TRMM_3H25", "TRMM_3H31", "TRMM_CSH", "TRMM_TMPA_LandSeaMask", "TRMM_baseUW", "TRMM_precip_718", "TROPICS01ANTTL1A", "TROPICS01BRTTL1B", "TROPICS01MIRSL2B", "TROPICS01PRPSL2B", "TROPICS01URADL2A", "TROPICS03ANTTL1A", "TROPICS03BRTTL1B", "TROPICS03MIRSL2B", "TROPICS03PRPSL2B", "TROPICS03URADL2A", "TROPICS05ANTTL1A", "TROPICS05BRTTL1B", "TROPICS05URADL2A", "TROPICS06ANTTL1A", "TROPICS06BRTTL1B", "TROPICS06MIRSL2B", "TROPICS06PRPSL2B", "TROPICS06URADL2A", "TROPICS07ANTTL1A", "TROPICS07BRTTL1B", "TROPICS07URADL2A", "TROPOMAER", "TROPOMI_MINDS_NO2", "TRPSCRAERNH42H2D", "TRPSCRAERNH46H3D", "TRPSCRAERNH4M3D", "TRPSCRAERNO32H2D", "TRPSCRAERNO36H3D", "TRPSCRAERNO3M3D", "TRPSCRAERSO42H2D", "TRPSCRAERSO46H3D", "TRPSCRAERSO4M3D", "TRPSCRCH2O2H2D", "TRPSCRCH2O6H3D", "TRPSCRCH2OM3D", "TRPSCRCO2H2D", "TRPSCRCO6H3D", "TRPSCRCOM3D", "TRPSCRCOS6H3D", "TRPSCRCOSM3D", "TRPSCRECOAM2D", "TRPSCRECOBM2D", "TRPSCRECOTM2D", "TRPSCRENOXAM2D", "TRPSCRENOXBM2D", "TRPSCRENOXLM2D", "TRPSCRENOXSM2D", "TRPSCRENOXTM2D", "TRPSCRESO2TM2D", "TRPSCRHNO32H2D", "TRPSCRHNO36H3D", "TRPSCRHNO3M3D", "TRPSCRNO22H2D", "TRPSCRNO26H3D", "TRPSCRNO2H2D", "TRPSCRNO2M3D", "TRPSCRNO2S6H3D", "TRPSCRNO2SM3D", "TRPSCRNO6H3D", "TRPSCRNOM3D", "TRPSCRO32H2D", "TRPSCRO36H3D", "TRPSCRO3I6H3D", "TRPSCRO3IM3D", "TRPSCRO3M3D", "TRPSCRO3S6H3D", "TRPSCRO3SM3D", "TRPSCROH2H2D", "TRPSCROH6H3D", "TRPSCROHM3D", "TRPSCRPAN2H2D", "TRPSCRPAN6H3D", "TRPSCRPANM3D", "TRPSCRPS2H2D", "TRPSCRPS6H2D", "TRPSCRPSM2D", "TRPSCRQ2H2D", "TRPSCRQ6H3D", "TRPSCRQM3D", "TRPSCRSO22H2D", "TRPSCRSO26H3D", "TRPSCRSO2M3D", "TRPSCRT2H2D", "TRPSCRT6H3D", "TRPSCRTM3D", "TRPSCRU2H2D", "TRPSCRU6H3D", "TRPSCRUM3D", "TRPSCRV2H2D", "TRPSCRV6H3D", "TRPSCRVM3D", "TRPSDL2ALLCRSMGBEI", "TRPSDL2ALLCRSMGDEL", "TRPSDL2ALLCRSMGKAR", "TRPSDL2ALLCRSMGLAG", "TRPSDL2ALLCRSMGLOS", "TRPSDL2ALLCRSMGMEX", "TRPSDL2ALLCRSMGSAO", "TRPSDL2ALLCRSMGTOK", "TRPSDL2CH4AIRSFS", "TRPSDL2CH4CRS1FS", "TRPSDL2CH4CRSAUS", "TRPSDL2CH4CRSFS", "TRPSDL2COAIRSFS", "TRPSDL2COCRS1FS", "TRPSDL2COCRSAUS", "TRPSDL2COCRSFS", "TRPSDL2COCRSTS1", "TRPSDL2COCRSTS2", "TRPSDL2COCRSWCF", "TRPSDL2COCRSWCFHI", "TRPSDL2H2OAIRSFS", "TRPSDL2H2OCRS1FS", "TRPSDL2H2OCRSAUS", "TRPSDL2H2OCRSFS", "TRPSDL2HDOAIRSFS", "TRPSDL2HDOCRS1FS", "TRPSDL2HDOCRSAUS", "TRPSDL2HDOCRSFS", "TRPSDL2NH3AIRSFS", "TRPSDL2NH3CRS1FS", "TRPSDL2NH3CRSAUS", "TRPSDL2NH3CRSFS", "TRPSDL2NH3CRSWCF", "TRPSDL2NH3CRSWCFHI", "TRPSDL2O3AIRSFS", "TRPSDL2O3AIRSOMIFS", "TRPSDL2O3CRS1FS", "TRPSDL2O3CRSAUS", "TRPSDL2O3CRSFS", "TRPSDL2O3CRSWCF", "TRPSDL2O3CRSWCFHI", "TRPSDL2O3OMIFS", "TRPSDL2PANCRS1FS", "TRPSDL2PANCRSAUS", "TRPSDL2PANCRSFS", "TRPSDL2PANCRSWCF", "TRPSDL2PANCRSWCFHI", "TRPSDL2TATMAIRSFS", "TRPSDL2TATMCRS1FS", "TRPSDL2TATMCRSAUS", "TRPSDL2TATMCRSFS", "TRPSYL2ALLCRSMGBEI", "TRPSYL2ALLCRSMGDEL", "TRPSYL2ALLCRSMGKAR", "TRPSYL2ALLCRSMGLAG", "TRPSYL2ALLCRSMGLOS", "TRPSYL2ALLCRSMGMEX", "TRPSYL2ALLCRSMGSAO", "TRPSYL2ALLCRSMGTOK", "TRPSYL2CH4AIRSFS", "TRPSYL2CH4AIRSORS", "TRPSYL2CH4CRS1FS", "TRPSYL2CH4CRSFS", "TRPSYL2CH4CRSRS", "TRPSYL2COAIRSFS", "TRPSYL2COAIRSORS", "TRPSYL2COCRS1FS", "TRPSYL2COCRSFS", "TRPSYL2COCRSRS", "TRPSYL2HDOAIRSFS", "TRPSYL2HDOAIRSORS", "TRPSYL2HDOCRS1FS", "TRPSYL2HDOCRSFS", "TRPSYL2HDOCRSRS", "TRPSYL2NH3AIRSFS", "TRPSYL2NH3CRS1FS", "TRPSYL2NH3CRSFS", "TRPSYL2NH3CRSRS", "TRPSYL2O3AIRSFS", "TRPSYL2O3AIRSOMIFS", "TRPSYL2O3AIRSORS", "TRPSYL2O3CRS1FS", "TRPSYL2O3CRSFS", "TRPSYL2O3CRSRS", "TRPSYL2O3OMIFS", "TRPSYL2PANCRS1FS", "TRPSYL2PANCRSFS", "TRPSYL2PANCRSRS", "TSIS_SSI_L3_12HR", "TSIS_SSI_L3_24HR", "TSIS_TSI_L3_06HR", "TSIS_TSI_L3_24HR", "Taiga_Tundra_Tree_Cover_1218", "Tampa_Bay", "Tara_Mediterranean", "Tara_Oceans_Polar_Circle", "Tara_Oceans_expedition", "Thermokarst_Circumpolar_Map_1332", "ThreeRivers", "Tidal_Marsh_Biomass_US_V1-1_1879", "Tidal_Marsh_Vegetation_US_1608", "Tidal_Wetland_Estuaries_Data_1742", "Tidal_Wetland_GPP_CONUS_1792", "Tidal_Wetland_Soil_Carbon_1612", "TillageErosion_SOCRedistribute_1944", "Tokyo_Bay", "Toolik_Lake_Area_Veg_Maps_1380", "Toolik_Lake_Veg_Plots_1333", "TopSoil_Erosion_MidWest_US_1774", "TowerBased_PhotoSpec_SIF_SK_CA_1887", "Tree_Canopy_Cover_Mexico_2137", "Tree_Mortality_Western_US_1512", "TundraTransect_VegRefl_Soil_2232", "TundraVeg_Reflectance_Soil_CO2_1960", "Tundra_Fire_Veg_Plots_1547", "Tundra_Greeness_Temp_Trends_1893", "Tundra_Leaf_Spectra_2005", "Turbid9", "Turkish_Seas", "UAEM1LME", "UAEM1LMT", "UAEMIAAE", "UAEMIALS", "UAEMIB2E", "UAEMIB2E", "UAEMIB2T", "UAEMIB2T", "UAEMIRCM", "UAEMITAL", "UAEMITCL", "UAEMITST", "UAEMRDAE", "UAEMRDLS", "UAEMRDRD", "UAEMRMLS", "UAEMRMRD", "UARCL3AL", "UARCL3AT", "UARHA2FN", "UARHA3AT", "UARHR3AL", "UARHR3AT", "UARIS3AL", "UARIS3AT", "UARML3AL", "UARML3AT", "UARPE2AXIS1", "UARPE2AXIS2", "UARPE2HEPSA", "UARPE2HEPSB", "UARPE2MEPS", "UARPE2VMAGAC", "UARPE2VMAGDC", "UARPE3AT", "UARPE3TP", "UARSO3BS", "UARSU3BS", "UARWI3AL", "UARWI3AT", "UARZCNMC", "UARZCUKM", "UAVSAR_INSAR_AMP", "UAVSAR_INSAR_AMP_GRD", "UAVSAR_INSAR_DEM", "UAVSAR_INSAR_INT", "UAVSAR_INSAR_INT_GRD", "UAVSAR_INSAR_KMZ", "UAVSAR_INSAR_META", "UAVSAR_POL_DEM", "UAVSAR_POL_INC", "UAVSAR_POL_KMZ", "UAVSAR_POL_META", "UAVSAR_POL_ML_CMPLX_GRD", "UAVSAR_POL_ML_CMPLX_GRD_3X3", "UAVSAR_POL_ML_CMPLX_GRD_5X5", "UAVSAR_POL_ML_CMPLX_SLANT", "UAVSAR_POL_PAULI", "UAVSAR_POL_SLOPE", "UAVSAR_POL_STOKES", "UAV_Imagery_BigLakeTrail_1834", "UCLA_DEALIASED_SASS_L3", "UMD_GEOL388", "USArray_Ground_Temperature_1680", "USM_pCO2", "US_MODIS_NDVI_1299", "US_MODIS_Veg_Parameters_1539", "Umiat_Veg_Plots_1370", "Unalaska_Veg_Plots_1375", "Uncertainty_US_Coastal_GHG_1650", "Understory_Veg_Biomass_Alaska_2340", "VBEMI2AE", "VBEMI2LS", "VBEMI2ST", "VBEMIB2E", "VBEMIB2T", "VBEMIGEO", "VCF5KYR", "VIIRSJ1_L1", "VIIRSJ1_L1_GEO", "VIIRSJ1_L2_IOP", "VIIRSJ1_L2_IOP_NRT", "VIIRSJ1_L2_OC", "VIIRSJ1_L2_OC_NRT", "VIIRSJ1_L2_SST", "VIIRSJ1_L2_SST3", "VIIRSJ1_L2_SST3_NRT", "VIIRSJ1_L2_SST_NRT", "VIIRSJ2_L1", "VIIRSJ2_L1_GEO", "VIIRSJ2_L2_IOP", "VIIRSJ2_L2_IOP_NRT", "VIIRSJ2_L2_OC", "VIIRSJ2_L2_OC_NRT", "VIIRSN_L1", "VIIRSN_L1_GEO", "VIIRSN_L2_IOP", "VIIRSN_L2_IOP_NRT", "VIIRSN_L2_OC", "VIIRSN_L2_OC_NRT", "VIIRSN_L2_SST", "VIIRSN_L2_SST3", "VIIRSN_L2_SST3_NRT", "VIIRSN_L2_SST_NRT", "VIIRSN_L3b_NSST", "VIIRSN_L3b_NSST_NRT", "VIIRSN_L3b_SST", "VIIRSN_L3b_SST3", "VIIRSN_L3b_SST3_NRT", "VIIRSN_L3b_SST_NRT", "VIIRSN_L3m_NSST", "VIIRSN_L3m_NSST_NRT", "VIIRSN_L3m_SST", "VIIRSN_L3m_SST3", "VIIRSN_L3m_SST3_NRT", "VIIRSN_L3m_SST_NRT", "VIIRS_N20-NAVO-L2P-v3.0", "VIIRS_N20-STAR-L2P-v2.80", "VIIRS_N20-STAR-L3U-v2.80", "VIIRS_N21-NAVO-L2P-v3.0", "VIIRS_NPP-JPL-L2P-v2016.2", "VIIRS_NPP-NAVO-L2P-v1.0", "VIIRS_NPP-NAVO-L2P-v2.0", "VIIRS_NPP-NAVO-L2P-v3.0", "VIIRS_NPP-STAR-L2P-v2.80", "VIIRS_NPP-STAR-L3U-v2.80", "VIIRS_SST_NPP_NAR-OSISAF-L3C-v1.0", "VIIRS_Val_FLKeys", "VIIRS_Validation", "VIMS_2005", "VIP01", "VIP07", "VIP15", "VIP30", "VIPPHEN_EVI2", "VIPPHEN_NDVI", "VIRGAS_MetNav_AircraftInSitu_Data", "VIRGAS_TraceGas_AircraftInSitu_Data", "VISSRGOES1IMIR", "VISSRGOES1IMVIS", "VISSRGOES2IMIR", "VISSRGOES2IMVIS", "VISSRGOES3IMIR", "VISSRGOES3IMVIS", "VISSRSMS1IMIR", "VISSRSMS1IMVIS", "VISSRSMS1L1AOIPS", "VISSRSMS1L1EHT", "VISSRSMS2IMIR", "VISSRSMS2IMVIS", "VISSRSMS2L1AOIPS", "VISSRSMS2L1EHT", "VJ103MODLL", "VJ109", "VJ109A1", "VJ109CMG", "VJ109GA", "VJ109H1", "VJ113A1", "VJ113A2", "VJ113A3", "VJ113C1", "VJ113C2", "VJ114", "VJ114A1", "VJ114IMG", "VJ115A2H", "VJ121", "VJ121A1D", "VJ121A1N", "VJ121A2", "VJ121C1", "VJ121C2", "VJ121C3", "VJ128C2", "VJ128C3", "VJ146A1G_NRT", "VJ146A1_NRT", "VNP02DNB", "VNP02IMG", "VNP02MOD", "VNP03DNB", "VNP03IMG", "VNP03MOD", "VNP03MODLL", "VNP03MODLL", "VNP09", "VNP09A1", "VNP09A1", "VNP09CMG", "VNP09CMG", "VNP09GA", "VNP09GA", "VNP09H1", "VNP09H1", "VNP10", "VNP10A1", "VNP10A1", "VNP10A1F", "VNP13A1", "VNP13A1", "VNP13A2", "VNP13A2", "VNP13A3", "VNP13A3", "VNP13C1", "VNP13C1", "VNP13C2", "VNP13C2", "VNP14", "VNP14", "VNP14A1", "VNP14A1", "VNP14IMG", "VNP14IMGTDL_NRT", "VNP15A2H", "VNP15A2H", "VNP21", "VNP21", "VNP21A1D", "VNP21A1D", "VNP21A1N", "VNP21A1N", "VNP21A2", "VNP21A2", "VNP21C1", "VNP21C2", "VNP21C3", "VNP22C2", "VNP22Q2", "VNP28C2", "VNP28C3", "VNP29", "VNP30", "VNP43C1", "VNP43C2", "VNP43C3", "VNP43C4", "VNP43DNBA1", "VNP43DNBA2", "VNP43DNBA3", "VNP43DNBA4", "VNP43IA1", "VNP43IA2", "VNP43IA3", "VNP43IA4", "VNP43MA1", "VNP43MA2", "VNP43MA3", "VNP43MA4", "VNP46A1", "VNP46A1", "VNP46A1G_NRT", "VNP46A1_NRT", "VNP46A2", "VNP46A3", "VNP46A4", "VNP64A1", "VOLPE", "VPRM_North_America_Parameters_1349", "Veg_Soil_Tundra_Burned_Area_2119", "Vegetation_Maps_Toolik_Lake_1690", "Vegetation_Photos_Toolik_Lake_1718", "Vegetation_greenness_trend_1576", "Vermont_HighRes_LandCover_2072", "Vulcan_V3_Annual_Emissions_1741", "Vulcan_V3_Hourly_Emissions_1810", "WACS2", "WAF_DEALIASED_SASS_L2", "WAVeTrends_1738", "WC_LSMEM_SOILM_025", "WC_MULTISEN_PREC_025", "WC_PM_ET_050", "WENTZ_NIMBUS-7_SMMR_L2", "WENTZ_SASS_SIGMA0_L2", "WHITECAPS", "WLDAS_NOAHMP001_DA1", "WRF_STILT_Footprints_Boston_1572", "WRF_STILT_Particles_Boston_1596", "WUS_UCLA_SR", "WV04_MSI_L1B", "WV04_Pan_L1B", "WV_LCC_SC_FSCA", "WaterBalance_Daily_Historical_GRIDMET", "WaterBalance_Monthly_Historical_GRIDMET", "WebbRosenzweig_548", "West_Florida_Shelf", "West_Soil_Carbon_1238", "Western_Gulf_of_Maine", "Wetland_Soil_CarbonStocks_WA_2249", "Wetland_VegClassification_PAD_2069", "WhitePhenoregions_799", "WhiteSpruce_Leaf_Traits_Alaska_2124", "Wildfire_Effects_Spruce_Field_1595", "Wildfire_Impacts_Boreal_Ecosys_2359", "Wildfires_2014_NWT_Canada_1307", "Wildfires_Date_of_Burning_1559", "Wildfires_NWT_Canada_1548", "Wildfires_NWT_Canada_2018_1703", "Wildfires_NWT_Canada_2019_1900", "Willow_Veg_Plots_1368", "WindSat-REMSS-L3U-v7.0.1a", "Wolves_Denning_Pups_Climate_1846", "XAERDT_L2_ABI_G16", "XAERDT_L2_ABI_G17", "XAERDT_L2_AHI_H08", "XAERDT_L2_AHI_H09", "XAERDT_L2_MODIS_Aqua", "XAERDT_L2_MODIS_Terra", "XAERDT_L2_VIIRS_NOAA20", "XAERDT_L2_VIIRS_SNPP", "Xingu_Albedo_Radiation_1622", "YKDelta_EnvChange_InfoExchange_1894", "Young_Russian_Forest_Map_1330", "ZinkeSoil_221", "Zinke_soil_683", "ZoblerSoilDerived_540", "ZoblerSoil_418", "Zobler_Soil_649", "ZonalFlux", "aamhcpex", "aces1am", "aces1cont", "aces1efm", "aces1log", "aces1time", "aces1trig", "aerosol_properties_725", "aes5davg_236", "aes_upl1_238", "aes_upl2_239", "afm06ihd_240", "afm06ptd_241", "afm06pwd_242", "afm06smd_243", "afm11afr_244", "afm13afr_245", "afm2as94_494", "afm3as94_496", "afm3mw94_495", "afm4toas_498", "afm4tofx_497", "afm6gifs_433", "african_woody_savanna_850", "air_sea_gas_exchange_xdeg_1208", "airscm3b_448", "airscpex", "airssy3b_507", "airsunp_61", "ali_etm_tandem_821", "amazon_precip_228", "ames_sunphotometer_643", "amprimpacts", "amprtbcx1", "amprtbcx2", "amprtbcx3", "amprtbf3a", "amprtbjax", "amprtbkwj", "amprtblba", "amprtbta", "amprtbtc", "ams_cs93_403", "ams_cs94_404", "ams_cs95_405", "ams_cs96_406", "amsua15sp", "amsua16sp", "amsua17sp", "aoci0bil_281", "apr3cpex", "apr3cpexaw", "apr3cpexcv", "apuimpacts", "asas_l1b_562", "asasrefl_287", "ascatcpex", "asosimpacts", "atmos_co2_by_erosion_xdeg_1019", "avapsimpacts", "avhrr_822", "avhrr_albedo_1995_xdeg_928", "avhrrl3b_481", "avhrrl4b_438", "avhrrl4c_439", "avhrrlc1_434", "basin_border_670", "billmark_828", "biofuel_emissions_753", "biomass_allocation_703", "biomdens_450", "biomebg2_296", "biomebgc_295", "boreas_aeshrday_235", "burning_emissions_752", "bvoc_flux_759", "c4_percent_1deg_932", "c4aaero", "c4dcm", "c4dcstar", "c4dcvi", "c4dd8d", "c4dicats", "c4djlh", "c4dlase", "c4dlip", "c4dmms", "c4dmtp", "c4dnevzor", "c4dpr2", "c4eampr", "c4eedop", "c4eehad", "c4ehamsr", "c4elip", "c4emas", "c4emtp", "c4enav", "c4enlh", "c4eo3p", "c4gandros", "c4gmipclo", "c4gmipfm", "c4gmipsod", "c4gmipss1", "c4gmipss2", "c4gmipwp", "c4gmisrep", "c4gnpol", "c4gsmart", "c4gtoga", "c4gxpow", "c4sg8", "calibgas_500", "canopychem_422", "canopyspec_423", "century_vemap_m4_820", "chem_26", "chesapeake_val_2013", "cmimpacts", "cmx3aeri", "cmx3andros", "cmx3g8", "cmx3misrep", "cmx3srl", "co2_emissions_1deg_1021", "combined_ancillary_xdeg_1200", "comp_runoff_monthly_xdeg_994", "cosmirimpacts", "cossirimpacts", "cp_lidar_images_721", "cplimpacts", "cramer_leemans_637", "cropland_612", "crsimpacts", "cru_monthly_climate_xdeg_1014", "cru_monthly_mean_xdeg_1015", "csgcpex01", "daily_precip_est_793", "dc8ammr", "dc8avaps", "dc8capac", "dc8dads", "dc8jplsaw", "dc8lase", "dc8laserh", "dc8macaws", "dc8mms", "dc8psr", "diffuse_irradiance_791", "dlhimpacts", "dtms0bil_247", "eMASL1B", "eMASL2AER", "eMASL2CLD", "echidna_1045", "ecmwf2_523", "ecmwf_met_1deg_1222", "ecosystem_roots_1deg_929", "edc_landcover_xdeg_930", "edgar_atmos_emissions_1deg_1022", "elev_arc_250", "er2_aerial_photos_722", "er2edop", "er2flog_501", "er2lip", "er2mams", "er2mir", "er2mts", "er2nasti", "er2nav", "er2navimpacts", "erbe_albedo_monthly_xdeg_957", "eta_model_723", "ewing", "explorer", "exrad3dimpacts", "exradepoch", "exradimpacts", "faamwdat_237", "fasir_biophys_monthly_xdeg_970", "fasir_ndvi_monthly_xdeg_972", "ffo_Betts_1987-1989_afd_93", "ffo_Betts_1987-1989_ams_89", "ffo_Betts_1987-1989_gsm_97", "ffo_Betts_1987-1989_nsm_101", "ffo_Betts_1987_afd_92", "ffo_Betts_1987_ams_88", "ffo_Betts_1987_gsm_96", "ffo_Betts_1987_nsm_100", "ffo_Betts_1988_afd_94", "ffo_Betts_1988_ams_90", "ffo_Betts_1988_gsm_98", "ffo_Betts_1988_nsm_102", "ffo_Betts_1989_afd_95", "ffo_Betts_1989_ams_91", "ffo_Betts_1989_gsm_99", "ffo_Betts_1989_nsm_103", "fhstmanr_386", "fhstmanv_387", "fieldsunp_65", "fife_AF_dtrnd_nae_3", "fife_AF_dtrnd_ncar_5", "fife_AF_dtrnd_wyo_4", "fife_AF_filtr_nae_6", "fife_AF_filtr_ncar_8", "fife_AF_filtr_wyo_7", "fife_AF_raw_nae_9", "fife_AF_raw_ncar_11", "fife_AF_raw_wyo_10", "fife_atmos_brut_drv_14", "fife_atmos_brut_son_15", "fife_atmos_lidar_ht_17", "fife_atmos_ncdc_son_13", "fife_atmos_nmc_upr_57", "fife_atmos_noaa_son_73", "fife_atmos_noaa_tov_16", "fife_atmos_sodar_18", "fife_atmos_tempprof_124", "fife_atmos_wind_lid_138", "fife_atmos_wind_son_139", "fife_biology_biomass_118", "fife_biology_leaf_ang_44", "fife_biology_leaf_h2o_126", "fife_biology_mow_biop_55", "fife_biology_mow_exo_56", "fife_biology_pho_box_27", "fife_biology_pho_leaf_46", "fife_biology_plantpro_69", "fife_biology_root_bio_75", "fife_biology_soil_co2_105", "fife_biology_soil_gas_106", "fife_biology_veg_biop_135", "fife_biology_veg_ref_137", "fife_biology_veg_spec_136", "fife_hydrology_strm_15m_1", "fife_hydrology_strm_day_119", "fife_hydrology_strm_st_120", "fife_optical_ot_brug_62", "fife_optical_ot_c130_63", "fife_optical_ot_calib_60", "fife_optical_ot_frasr_64", "fife_optical_ot_staff_66", "fife_sat_obs_ns001tms_59", "fife_sat_obs_sat_avhr_77", "fife_sat_obs_sat_coef_76", "fife_sat_obs_sat_ltm_78", "fife_sat_obs_sat_spot_79", "fife_soilmstr_peck_gam_37", "fife_soilmstr_peck_sm_109", "fife_soilmstr_sm_grav_110", "fife_soilmstr_sm_neut_111", "fife_soilmstr_sm_tran_113", "fife_soilmstr_soil_imp_108", "fife_soilprop_soil_h2o_117", "fife_soilprop_soil_rel_112", "fife_soilprop_soildens_104", "fife_soilprop_soilhydc_107", "fife_soilprop_soilsurv_115", "fife_soilprop_soilther_116", "fife_sur_flux_30_min_brg_20", "fife_sur_flux_30_min_brk_21", "fife_sur_flux_30_min_brl_19", "fife_sur_flux_30_min_brs_22", "fife_sur_flux_30_min_brv_23", "fife_sur_flux_30_min_brw_24", "fife_sur_flux_30_min_eca_30", "fife_sur_flux_30_min_ecb_32", "fife_sur_flux_30_min_ecg_31", "fife_sur_flux_30_min_ecv_33", "fife_sur_flux_30_min_ecw_34", "fife_sur_flux_basel_92_121", "fife_sur_met_ams_12", "fife_sur_met_cld_cam_28", "fife_sur_met_hday_met_39", "fife_sur_met_hmon_met_40", "fife_sur_met_ncdc_sur_122", "fife_sur_met_noaa_sur_58", "fife_sur_met_rain_30m_2", "fife_sur_met_rain_day_29", "fife_sur_refl_gem_helo_38", "fife_sur_refl_irt_grnd_72", "fife_sur_refl_irt_helo_70", "fife_sur_refl_irt_mult_71", "fife_sur_refl_lightwnd_43", "fife_sur_refl_ltbr_ksu_41", "fife_sur_refl_ltbr_unl_42", "fife_sur_refl_mmr_calb_51", "fife_sur_refl_mmr_grnd_52", "fife_sur_refl_mmr_helo_53", "fife_sur_refl_mmr_leaf_54", "fife_sur_refl_parabola_68", "fife_sur_refl_se5_gsfc_81", "fife_sur_refl_se5_helo_87", "fife_sur_refl_se5_leaf_85", "fife_sur_refl_se5_unl_82", "fife_sur_refl_soilrefl_114", "fife_sur_refl_unl_long_49", "fife_sur_refl_unl_surf_123", "fire_emissions_724", "fire_emissions_v4_R1_1293", "fltrepepoch", "fluxnet_point_1029", "forest_carbon_flux_949", "fuel_load_755", "g3acld", "g3acldb", "g3alsp", "g3alspb", "g3assp", "g3asspb", "g3at", "g3atb", "g3blmnc", "g3blsp", "g3blsp", "g3blspb", "g3bsmnc", "g3bssp", "g3bsspb", "g3bsspb", "g3bt", "g3bt", "g3btb", "g3btmnc", "gap_filled_marconi_811", "gdp_xdeg_974", "geocoord_556", "geoecology_R1_656", "gfscpex", "gghydro_676", "ghcn_631", "gimms_ndvi_monthly_xdeg_973", "giss_wetlands_632", "glider", "glmcierra", "glmgoesL3", "global_N_cycle_797", "global_N_deposition_maps_830", "global_population_xdeg_975", "globalir", "globalview_ch4_point_1109", "globalview_co2_point_1111", "globe_dem_630", "goes71_444", "goes71a_300", "goes72_554", "goes81_445", "goes81a_446", "goescpex", "goescpexcv", "goesimpacts", "goesrpltaviris", "goesrpltavirisng", "goesrpltcolma", "goesrpltcpl", "goesrpltcrs", "goesrpltdclma", "goesrpltfegs", "goesrpltgcas", "goesrpltivanpah", "goesrpltksclma", "goesrpltlip", "goesrpltmisrep", "goesrpltnalma", "goesrpltnaver2", "goesrpltoklma", "goesrpltredlake", "goesrpltshis", "goesrpltsolma", "goesrpltwtlma", "gpcc_precip_monthly_xdeg_995", "gpcp_precip_monthly_xdeg_1003", "gpcp_precip_pentad_xdeg_1002", "gpm2dc3vp", "gpm2dgcpex", "gpm2dhymex", "gpm2difld", "gpm2diphx", "gpm2dlpvex", "gpm2dmc3e", "gpm2dnsstc", "gpm2dvdolyx", "gpm2dwff2", "gpmadmirgcpex", "gpmahdmetolyx", "gpmampriphx2", "gpmamprmc3e", "gpmamprolyx", "gpmapr2gcpex", "gpmapr3olyx2", "gpmapuicepop", "gpmapuolyx", "gpmapuwff", "gpmarsifld", "gpmasinaiphx", "gpmasoolyx", "gpmavapsolyx", "gpmcax1cfolyx", "gpmcax1odolyx", "gpmcax1rbolyx", "gpmceilgcpex", "gpmceiliphx", "gpmchillmc3e", "gpmcilpvex", "gpmcitvidiphx", "gpmcmgcpex2", "gpmcmiphx", "gpmcmmc3e", "gpmcmolyx", "gpmcmorphnifld", "gpmcmorphniphx", "gpmcosmirgcpex", "gpmcosmiriphx", "gpmcosmirmc3e", "gpmcosmirolyx", "gpmcplolyx", "gpmcrsiphx", "gpmcrsolyx", "gpmcxsiolyx", "gpmd3rgcpex", "gpmd3ricepop", "gpmd3rifld", "gpmd3riphx", "gpmd3rolyx", "gpmdowolyx2", "gpmepfl", "gpmer2navmc3e", "gpmexradiphx", "gpmfltsummc3e", "gpmgaugewff", "gpmgoes13gcpexB", "gpmgoes13iphxB", "gpmgoes13mc3eB", "gpmgoes14iphxB", "gpmgoes15olyxB", "gpmgprof2014ifld", "gpmgsmapjifld", "gpmheifld", "gpmheiphx", "gpmhiwrapiphx", "gpmhiwrapmc3e", "gpmhiwrapolyx", "gpmikalpvex", "gpmjwlpvex", "gpmjwnsstc", "gpmkapxgcpex", "gpmkarx2ifld", "gpmkarx3ifld", "gpmkatx2olyx", "gpmkboxgcpex", "gpmkbufgcpex", "gpmkcae2iphx", "gpmkcbwgcpex", "gpmkcradgcpex", "gpmkcxxgcpex", "gpmkdmx2ifld", "gpmkdmx3ifld", "gpmkdvn2ifld", "gpmkdvn3ifld", "gpmkerlpvex", "gpmkgldmc3e", "gpmkgsp2iphx", "gpmkgyxgcpex", "gpmkhtx2iphx", "gpmkictmc3e", "gpmkinxmc3e", "gpmklgx2olyx", "gpmkltx2iphx", "gpmkmhx2iphx", "gpmkmpx2ifld", "gpmkmpx3ifld", "gpmkmrx2iphx", "gpmkorlpvex", "gpmkrax2iphx", "gpmkrtx2olyx", "gpmktlxmc3e", "gpmktwxmc3e", "gpmktyxgcpex", "gpmkumlpvex", "gpmkvnxmc3e", "gpmlidargcpex", "gpmlipiphx", "gpmmascolyx", "gpmmastmetlpvex", "gpmmettecgcpex", "gpmmisrepgcpex", "gpmmisrepifld", "gpmmisrepiphx", "gpmmisrepmc3e", "gpmmisrepolyx", "gpmmrms", "gpmmrrdukeiphx", "gpmmrrecgcpex2", "gpmmrrhymex", "gpmmrricepop", "gpmmrrlpvex", "gpmmrrnaachiphx", "gpmmrrnagcpex2", "gpmmrrnaifld", "gpmmrrnaiphx", "gpmmrrnamc3e", "gpmmrrolyx", "gpmmwrdukeiphx", "gpmnavcitgcpex2", "gpmnavcitiphx", "gpmnavcitmc3e", "gpmnavcitolyx", "gpmnavdc8gcpex", "gpmnavdc8olyx", "gpmnaver2iphx", "gpmnaver2olyx", "gpmncamdc8gcpex", "gpmnmqifld", "gpmnmqiphx", "gpmnoxpiphx", "gpmnpolifld2", "gpmnpoliphx", "gpmnpolmc3e", "gpmnpololyx2", "gpmnpolwff", "gpmnrlrtifld", "gpmodmlpvex", "gpmopasscgcpex", "gpmopassolyx", "gpmoumesmc3e", "gpmpadukeiphx", "gpmpagcpex", "gpmpahymex", "gpmpaifld", "gpmpaiphx", "gpmpal", "gpmpalpvex", "gpmpamc3e", "gpmpanoaamc3e", "gpmpansstc", "gpmparawifld", "gpmparprbgcpex", "gpmparprbiphx", "gpmparprbmc3e", "gpmparprbolyx", "gpmpawneemc3e", "gpmpersucifld", "gpmpipicepop", "gpmplgcpex", "gpmpllpvex", "gpmplolyx", "gpmpmastmetlpvex", "gpmpossgcpex", "gpmposslpvex", "gpmprecipmgcpex", "gpmprecipolyx", "gpmpvigcpex", "gpmpvilpvex", "gpmraddpgcpex", "gpmradioiphx", 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"AERDB_L2_VIIRS_SNPP_NRT", "AERDB_M3_VIIRS_NOAA20", "AERDB_M3_VIIRS_SNPP", "AERDB_M3_VIIRS_SNPP", "AERDT_L2_VIIRS_NOAA20_NRT", "AERDT_L2_VIIRS_SNPP", "AERDT_L2_VIIRS_SNPP_NRT", "AERDT_L2_VIIRS_SNPP_NRT", "AERONET_aerosol_706", "AEROSE", "AE_5DSno", "AE_DyOcn", "AE_DySno", "AE_L2A", "AE_Land", "AE_Land", "AE_Land3", "AE_MoOcn", "AE_MoSno", "AE_Ocean", "AE_Rain", "AE_Rain", "AE_RnGd", "AE_SI12", "AE_SI25", "AE_SI6", "AE_SID", "AE_WkOcn", "AFLVIS1B", "AFLVIS2", "AFOLVIS1A", "AG100", "AG1km", "AG5KMMOH", "AGB_CanopyHt_Cover_NewEngland_1854", "AGB_Carbon_Sequestration_RGGI_1922", "AGB_Great_Slave_Lake_NWT_2365", "AGB_NEP_Disturbance_US_Forests_1829", "AGB_Pantropics_Amazon_Mexico_1824", "AHI_H08-STAR-L2P-v2.70", "AHI_H08-STAR-L3C-v2.70", "AIRABRAD", "AIRABRAD_NRT", "AIRG2SSD", "AIRG2SSD_IRonly", "AIRG2SSD_IRonly", "AIRH2CCF", "AIRH2CCF", "AIRH2RET", "AIRH2RET", "AIRH2SUP", "AIRH2SUP", "AIRH3QP5", "AIRH3QPM", "AIRH3SP8", "AIRH3SPD", "AIRH3SPD", "AIRH3SPM", "AIRH3SPM", "AIRH3ST8", "AIRH3STD", "AIRH3STD", "AIRH3STM", "AIRH3STM", "AIRHBRAD", "AIRI2CCF", "AIRI2CCF", "AIRIBQAP", "AIRIBQAP_NRT", "AIRIBRAD", "AIRIBRAD_NRT", "AIRIBRAD_NRT_BUFR", "AIRICRAD", "AIRICRAD_NRT", "AIRMISR_BARC_2001", "AIRMISR_BARTLETT_2003", "AIRMISR_CLAMS_2001", "AIRMISR_HARVARD_2003", "AIRMISR_HOWLAND_2003", "AIRMISR_KONVEX", "AIRMISR_LUNAR_LAKE_2000", "AIRMISR_LUNAR_LAKE_2001", "AIRMISR_MONTEREY_1999", "AIRMISR_MORGAN_MONROE_2003", "AIRMISR_ROGERS_LAKE_2001", "AIRMISR_SAFARI", "AIRMISR_SERC_2003", "AIRMISR_SNOW_ICE_2001", "AIRMISR_WISCONSIN_2000", "AIRS2CCF", "AIRS2CCF", "AIRS2CCF_NRT", "AIRS2CCF_NRT", "AIRS2RET", "AIRS2RET", "AIRS2RET_NRT", "AIRS2RET_NRT", "AIRS2SPC", "AIRS2STC", "AIRS2SUP", "AIRS2SUP", "AIRS2SUP_NRT", "AIRS2SUP_NRT", "AIRS3C28", "AIRS3C2D", "AIRS3C2M", "AIRS3QP5", "AIRS3QPM", "AIRS3SP8", "AIRS3SPD", "AIRS3SPD", "AIRS3SPM", "AIRS3SPM", "AIRS3ST8", "AIRS3STD", "AIRS3STD", "AIRS3STM", "AIRS3STM", "AIRSAC3MNH3", "AIRSAR_INT_JPG", "AIRSAR_POL_3FP", "AIRSAR_POL_SYN_3FP", "AIRSAR_TOP_C-DEM_STOKES", "AIRSAR_TOP_DEM", "AIRSAR_TOP_DEM_C", "AIRSAR_TOP_DEM_L", "AIRSAR_TOP_DEM_P", "AIRSAR_TOP_L-STOKES", "AIRSAR_TOP_P-STOKES", "AIRSIL3MSOLR", "AIRSM_CPR_MAT", "AIRS_CPR_IND", "AIRS_CPR_MAT", "AIRS_MDS_IND", "AIRS_MLS_IND", "AIRVBQAP", "AIRVBQAP_NRT", "AIRVBRAD", "AIRVBRAD_NRT", "AIRX2RET", "AIRX2RET", "AIRX2SPC", "AIRX2STC", "AIRX2SUP", "AIRX2SUP", "AIRX3C28", "AIRX3C2D", "AIRX3C2M", "AIRX3QP5", "AIRX3QPM", "AIRX3SP8", "AIRX3SPD", "AIRX3SPD", "AIRX3SPM", "AIRX3SPM", "AIRX3ST8", "AIRX3STD", "AIRX3STD", "AIRX3STM", "AIRX3STM", "AIRXAMAP", "AIRXBCAL", "AKFED_V1_1282", "AKWANAVT", "AK_North_Slope_NEE_CH4_Flux_1562", "AK_Regional_CO2_Flux_1389", "AK_Tundra_PFT_FractionalCover_1830", "AK_Yukon_PFT_TopCover_2032", "ALAN_VIIRS_CONUS", "ALOS_AVNIR_OBS_ORI", "ALOS_PSR_KMZ", "ALOS_PSR_L1.0", "ALOS_PSR_L1.1", "ALOS_PSR_L1.5", "ALOS_PSR_L2.2", "ALOS_PSR_RTC_HIGH", "ALOS_PSR_RTC_LOW", "ALTIKA_SARAL_L2_OST_XOGDR", "ALT_GPR_Barrow_1355", "ALT_Maps_AK_CA_2332", "ALT_TIDE_GAUGE_L4_OST_SLA_US_WEST_COAST", "ALT_TIDE_GAUGE_L4_OST_SLA_US_WEST_COAST_DAILY", "AMAZE-08_1308", "AMDBLWV", "AMDBLWV", "AMLR", "AMMA", "AMMBLWV", "AMMBLWV", "AMSR-L1A", "AMSR2-REMSS-L2P-v8.2", "AMSR2-REMSS-L2P_RT-v8.2", "AMSR2-REMSS-L3U-v8.2", "AMSR2-REMSS-L3U-v8a", "AMSR2-REMSS-L3U_RT-v8.2", "AMSR2-REMSS-L3U_RT-v8a", "AMSRE-REMSS-L2P-v7a", "AMSRE-REMSS-L3U-v7a", "AMSREL1A", "AMSRERR_CPR", "AMSRE_AVRMO", "AMSRE_STDMO", "AMT", "ANACONDAS", "ANT", "ANTARCTICA_MASS_TELLUS_MASCON_CRI_TIME_SERIES_RL06.1_V3", "ANTARES_Baja_California_Station", "ANTARES_Ubatuba_Station", "AOL", "AOPEX", "AOSNII", "APPSS", "AQ2_SM", "AQ3_ANSM", "AQ3_DYSM", "AQ3_FT", "AQ3_MCSM", "AQ3_MOSM", "AQ3_NRCS", "AQ3_SCSM", "AQ3_SNSM", "AQ3_SSS", "AQ3_TB", "AQ3_WKSM", "AQUALOOKS", "AQUARIUS_ANCILLARY_CELESTIALSKY_V1", "AQUARIUS_ANCILLARY_RFI_V1", "AQUARIUS_L2_SSS_CAP_V5", "AQUARIUS_L2_SSS_V5", "AQUARIUS_L3_ANCILLARY_SST_SMIA_28DAY-RUNNINGMEAN_V5", "AQUARIUS_L3_ANCILLARY_SST_SMIA_3MONTH_V5", "AQUARIUS_L3_ANCILLARY_SST_SMIA_7DAY_V5", "AQUARIUS_L3_ANCILLARY_SST_SMIA_ANNUAL_V5", "AQUARIUS_L3_ANCILLARY_SST_SMIA_CUMULATIVE_V5", "AQUARIUS_L3_ANCILLARY_SST_SMIA_DAILY_V5", "AQUARIUS_L3_ANCILLARY_SST_SMIA_MONTHLY-CLIMATOLOGY_V5", "AQUARIUS_L3_ANCILLARY_SST_SMIA_MONTHLY_V5", "AQUARIUS_L3_ANCILLARY_SST_SMIA_SEASONAL-CLIMATOLOGY_V5", "AQUARIUS_L3_ANCILLARY_SST_SMID_28DAY-RUNNINGMEAN_V5", "AQUARIUS_L3_ANCILLARY_SST_SMID_3MONTH_V5", "AQUARIUS_L3_ANCILLARY_SST_SMID_7DAY_V5", "AQUARIUS_L3_ANCILLARY_SST_SMID_ANNUAL_V5", "AQUARIUS_L3_ANCILLARY_SST_SMID_CUMULATIVE_V5", "AQUARIUS_L3_ANCILLARY_SST_SMID_DAILY_V5", "AQUARIUS_L3_ANCILLARY_SST_SMID_MONTHLY-CLIMATOLOGY_V5", "AQUARIUS_L3_ANCILLARY_SST_SMID_MONTHLY_V5", "AQUARIUS_L3_ANCILLARY_SST_SMID_SEASONAL-CLIMATOLOGY_V5", "AQUARIUS_L3_ANCILLARY_SST_SMI_28DAY-RUNNINGMEAN_V5", "AQUARIUS_L3_ANCILLARY_SST_SMI_3MONTH_V5", "AQUARIUS_L3_ANCILLARY_SST_SMI_7DAY-RUNNINGMEAN_V5", "AQUARIUS_L3_ANCILLARY_SST_SMI_7DAY_V5", "AQUARIUS_L3_ANCILLARY_SST_SMI_ANNUAL_V5", "AQUARIUS_L3_ANCILLARY_SST_SMI_CUMULATIVE_V5", "AQUARIUS_L3_ANCILLARY_SST_SMI_DAILY_V5", "AQUARIUS_L3_ANCILLARY_SST_SMI_MONTHLY-CLIMATOLOGY_V5", "AQUARIUS_L3_ANCILLARY_SST_SMI_MONTHLY_V5", "AQUARIUS_L3_ANCILLARY_SST_SMI_SEASONAL-CLIMATOLOGY_V5", "AQUARIUS_L3_DENSITY_SMIA_28DAY-RUNNINGMEAN_V5", "AQUARIUS_L3_DENSITY_SMIA_3MONTH_V5", "AQUARIUS_L3_DENSITY_SMIA_7DAY_V5", "AQUARIUS_L3_DENSITY_SMIA_ANNUAL_V5", "AQUARIUS_L3_DENSITY_SMIA_CUMULATIVE_V5", "AQUARIUS_L3_DENSITY_SMIA_DAILY_V5", "AQUARIUS_L3_DENSITY_SMIA_MONTHLY-CLIMATOLOGY_V5", "AQUARIUS_L3_DENSITY_SMIA_MONTHLY_V5", "AQUARIUS_L3_DENSITY_SMIA_SEASONAL-CLIMATOLOGY_V5", "AQUARIUS_L3_DENSITY_SMID_28DAY-RUNNINGMEAN_V5", "AQUARIUS_L3_DENSITY_SMID_3MONTH_V5", "AQUARIUS_L3_DENSITY_SMID_7DAY_V5", "AQUARIUS_L3_DENSITY_SMID_ANNUAL_V5", "AQUARIUS_L3_DENSITY_SMID_CUMULATIVE_V5", "AQUARIUS_L3_DENSITY_SMID_DAILY_V5", "AQUARIUS_L3_DENSITY_SMID_MONTHLY-CLIMATOLOGY_V5", "AQUARIUS_L3_DENSITY_SMID_MONTHLY_V5", "AQUARIUS_L3_DENSITY_SMID_SEASONAL-CLIMATOLOGY_V5", "AQUARIUS_L3_DENSITY_SMI_28DAY-RUNNINGMEAN_V5", "AQUARIUS_L3_DENSITY_SMI_3MONTH_V5", "AQUARIUS_L3_DENSITY_SMI_7DAY-RUNNINGMEAN_V5", "AQUARIUS_L3_DENSITY_SMI_7DAY_V5", "AQUARIUS_L3_DENSITY_SMI_ANNUAL_V5", "AQUARIUS_L3_DENSITY_SMI_CUMULATIVE_V5", "AQUARIUS_L3_DENSITY_SMI_DAILY_V5", "AQUARIUS_L3_DENSITY_SMI_MONTHLY-CLIMATOLOGY_V5", "AQUARIUS_L3_DENSITY_SMI_MONTHLY_V5", "AQUARIUS_L3_DENSITY_SMI_SEASONAL-CLIMATOLOGY_V5", "AQUARIUS_L3_SPICINESS_SMIA_28DAY-RUNNINGMEAN_V5", "AQUARIUS_L3_SPICINESS_SMIA_3MONTH_V5", "AQUARIUS_L3_SPICINESS_SMIA_7DAY_V5", "AQUARIUS_L3_SPICINESS_SMIA_ANNUAL_V5", "AQUARIUS_L3_SPICINESS_SMIA_CUMULATIVE_V5", "AQUARIUS_L3_SPICINESS_SMIA_DAILY_V5", "AQUARIUS_L3_SPICINESS_SMIA_MONTHLY-CLIMATOLOGY_V5", "AQUARIUS_L3_SPICINESS_SMIA_MONTHLY_V5", "AQUARIUS_L3_SPICINESS_SMIA_SEASONAL-CLIMATOLOGY_V5", "AQUARIUS_L3_SPICINESS_SMID_28DAY-RUNNINGMEAN_V5", "AQUARIUS_L3_SPICINESS_SMID_3MONTH_V5", "AQUARIUS_L3_SPICINESS_SMID_7DAY_V5", "AQUARIUS_L3_SPICINESS_SMID_ANNUAL_V5", "AQUARIUS_L3_SPICINESS_SMID_CUMULATIVE_V5", "AQUARIUS_L3_SPICINESS_SMID_DAILY_V5", "AQUARIUS_L3_SPICINESS_SMID_MONTHLY-CLIMATOLOGY_V5", "AQUARIUS_L3_SPICINESS_SMID_MONTHLY_V5", "AQUARIUS_L3_SPICINESS_SMID_SEASONAL-CLIMATOLOGY_V5", "AQUARIUS_L3_SPICINESS_SMI_28DAY-RUNNINGMEAN_V5", "AQUARIUS_L3_SPICINESS_SMI_3MONTH_V5", "AQUARIUS_L3_SPICINESS_SMI_7DAY-RUNNINGMEAN_V5", "AQUARIUS_L3_SPICINESS_SMI_7DAY_V5", "AQUARIUS_L3_SPICINESS_SMI_ANNUAL_V5", "AQUARIUS_L3_SPICINESS_SMI_CUMULATIVE_V5", "AQUARIUS_L3_SPICINESS_SMI_DAILY_V5", "AQUARIUS_L3_SPICINESS_SMI_MONTHLY-CLIMATOLOGY_V5", "AQUARIUS_L3_SPICINESS_SMI_MONTHLY_V5", "AQUARIUS_L3_SPICINESS_SMI_SEASONAL-CLIMATOLOGY_V5", "AQUARIUS_L3_SSS-RainFlagged_SMIA_28DAY-RUNNINGMEAN_V5", "AQUARIUS_L3_SSS-RainFlagged_SMIA_3MONTH_V5", "AQUARIUS_L3_SSS-RainFlagged_SMIA_7DAY_V5", "AQUARIUS_L3_SSS-RainFlagged_SMIA_ANNUAL_V5", "AQUARIUS_L3_SSS-RainFlagged_SMIA_CUMULATIVE_V5", "AQUARIUS_L3_SSS-RainFlagged_SMIA_DAILY_V5", "AQUARIUS_L3_SSS-RainFlagged_SMIA_MONTHLY-CLIMATOLOGY_V5", "AQUARIUS_L3_SSS-RainFlagged_SMIA_MONTHLY_V5", "AQUARIUS_L3_SSS-RainFlagged_SMIA_SEASONAL-CLIMATOLOGY_V5", "AQUARIUS_L3_SSS-RainFlagged_SMID_28DAY-RUNNINGMEAN_V5", "AQUARIUS_L3_SSS-RainFlagged_SMID_3MONTH_V5", "AQUARIUS_L3_SSS-RainFlagged_SMID_7DAY_V5", "AQUARIUS_L3_SSS-RainFlagged_SMID_ANNUAL_V5", "AQUARIUS_L3_SSS-RainFlagged_SMID_CUMULATIVE_V5", "AQUARIUS_L3_SSS-RainFlagged_SMID_DAILY_V5", "AQUARIUS_L3_SSS-RainFlagged_SMID_MONTHLY-CLIMATOLOGY_V5", "AQUARIUS_L3_SSS-RainFlagged_SMID_MONTHLY_V5", "AQUARIUS_L3_SSS-RainFlagged_SMID_SEASONAL-CLIMATOLOGY_V5", "AQUARIUS_L3_SSS-RainFlagged_SMI_28DAY-RUNNINGMEAN_V5", "AQUARIUS_L3_SSS-RainFlagged_SMI_3MONTH_V5", "AQUARIUS_L3_SSS-RainFlagged_SMI_7DAY-RUNNINGMEAN_V5", "AQUARIUS_L3_SSS-RainFlagged_SMI_7DAY_V5", "AQUARIUS_L3_SSS-RainFlagged_SMI_ANNUAL_V5", "AQUARIUS_L3_SSS-RainFlagged_SMI_CUMULATIVE_V5", "AQUARIUS_L3_SSS-RainFlagged_SMI_DAILY_V5", "AQUARIUS_L3_SSS-RainFlagged_SMI_MONTHLY-CLIMATOLOGY_V5", "AQUARIUS_L3_SSS-RainFlagged_SMI_MONTHLY_V5", "AQUARIUS_L3_SSS-RainFlagged_SMI_SEASONAL-CLIMATOLOGY_V5", "AQUARIUS_L3_SSS-RainFlagged_SM_SMIA_MONTHLY_V5", "AQUARIUS_L3_SSS-RainFlagged_SM_SMID_MONTHLY_V5", "AQUARIUS_L3_SSS-RainFlagged_SM_SMI_MONTHLY_V5", "AQUARIUS_L3_SSS_CAP_7DAY_V5", "AQUARIUS_L3_SSS_CAP_MONTHLY_V5", "AQUARIUS_L3_SSS_RAINCORRECTED_CAP_7DAY_V5", "AQUARIUS_L3_SSS_RAINCORRECTED_CAP_MONTHLY_V5", "AQUARIUS_L3_SSS_SMIA_28DAY-RUNNINGMEAN_V5", "AQUARIUS_L3_SSS_SMIA_3MONTH_V5", "AQUARIUS_L3_SSS_SMIA_7DAY_V5", "AQUARIUS_L3_SSS_SMIA_ANNUAL_V5", "AQUARIUS_L3_SSS_SMIA_CUMULATIVE_V5", "AQUARIUS_L3_SSS_SMIA_DAILY_V5", "AQUARIUS_L3_SSS_SMIA_MONTHLY-CLIMATOLOGY_V5", "AQUARIUS_L3_SSS_SMIA_MONTHLY_V5", "AQUARIUS_L3_SSS_SMIA_SEASONAL-CLIMATOLOGY_V5", "AQUARIUS_L3_SSS_SMID_28DAY-RUNNINGMEAN_V5", "AQUARIUS_L3_SSS_SMID_3MONTH_V5", "AQUARIUS_L3_SSS_SMID_7DAY_V5", "AQUARIUS_L3_SSS_SMID_ANNUAL_V5", "AQUARIUS_L3_SSS_SMID_CUMULATIVE_V5", "AQUARIUS_L3_SSS_SMID_DAILY_V5", "AQUARIUS_L3_SSS_SMID_MONTHLY-CLIMATOLOGY_V5", "AQUARIUS_L3_SSS_SMID_MONTHLY_V5", "AQUARIUS_L3_SSS_SMID_SEASONAL-CLIMATOLOGY_V5", "AQUARIUS_L3_SSS_SMI_28DAY-RUNNINGMEAN_V5", "AQUARIUS_L3_SSS_SMI_3MONTH_V5", "AQUARIUS_L3_SSS_SMI_7DAY-RUNNINGMEAN_V5", "AQUARIUS_L3_SSS_SMI_7DAY_V5", "AQUARIUS_L3_SSS_SMI_ANNUAL_V5", "AQUARIUS_L3_SSS_SMI_CUMULATIVE_V5", "AQUARIUS_L3_SSS_SMI_DAILY_V5", "AQUARIUS_L3_SSS_SMI_MONTHLY-CLIMATOLOGY_V5", "AQUARIUS_L3_SSS_SMI_MONTHLY_V5", "AQUARIUS_L3_SSS_SMI_SEASONAL-CLIMATOLOGY_V5", "AQUARIUS_L3_SSS_SM_SMIA_MONTHLY_V5", "AQUARIUS_L3_SSS_SM_SMID_MONTHLY_V5", "AQUARIUS_L3_SSS_SM_SMI_MONTHLY_V5", "AQUARIUS_L3_WIND_SPEED_CAP_7DAY_V5", "AQUARIUS_L3_WIND_SPEED_CAP_MONTHLY_V5", "AQUARIUS_L3_WIND_SPEED_SMIA_28DAY-RUNNINGMEAN_V5", "AQUARIUS_L3_WIND_SPEED_SMIA_3MONTH_V5", "AQUARIUS_L3_WIND_SPEED_SMIA_7DAY_V5", "AQUARIUS_L3_WIND_SPEED_SMIA_ANNUAL_V5", "AQUARIUS_L3_WIND_SPEED_SMIA_CUMULATIVE_V5", "AQUARIUS_L3_WIND_SPEED_SMIA_DAILY_V5", "AQUARIUS_L3_WIND_SPEED_SMIA_MONTHLY-CLIMATOLOGY_V5", "AQUARIUS_L3_WIND_SPEED_SMIA_MONTHLY_V5", "AQUARIUS_L3_WIND_SPEED_SMIA_SEASONAL-CLIMATOLOGY_V5", "AQUARIUS_L3_WIND_SPEED_SMID_28DAY-RUNNINGMEAN_V5", "AQUARIUS_L3_WIND_SPEED_SMID_3MONTH_V5", "AQUARIUS_L3_WIND_SPEED_SMID_7DAY_V5", "AQUARIUS_L3_WIND_SPEED_SMID_ANNUAL_V5", "AQUARIUS_L3_WIND_SPEED_SMID_CUMULATIVE_V5", "AQUARIUS_L3_WIND_SPEED_SMID_DAILY_V5", "AQUARIUS_L3_WIND_SPEED_SMID_MONTHLY-CLIMATOLOGY_V5", "AQUARIUS_L3_WIND_SPEED_SMID_MONTHLY_V5", "AQUARIUS_L3_WIND_SPEED_SMID_SEASONAL-CLIMATOLOGY_V5", "AQUARIUS_L3_WIND_SPEED_SMI_28DAY-RUNNINGMEAN_V5", "AQUARIUS_L3_WIND_SPEED_SMI_3MONTH_V5", "AQUARIUS_L3_WIND_SPEED_SMI_7DAY-RUNNINGMEAN_V5", "AQUARIUS_L3_WIND_SPEED_SMI_7DAY_V5", "AQUARIUS_L3_WIND_SPEED_SMI_ANNUAL_V5", "AQUARIUS_L3_WIND_SPEED_SMI_CUMULATIVE_V5", "AQUARIUS_L3_WIND_SPEED_SMI_DAILY_V5", "AQUARIUS_L3_WIND_SPEED_SMI_MONTHLY-CLIMATOLOGY_V5", "AQUARIUS_L3_WIND_SPEED_SMI_MONTHLY_V5", "AQUARIUS_L3_WIND_SPEED_SMI_SEASONAL-CLIMATOLOGY_V5", "AQUARIUS_L4_OISSS_IPRC_7DAY_V5", "ARB_48_IN_LIDAR", "ARC02", "ARCTAS_AerosolTraceGas_AircraftInSitu_DC8_Data", "ARCTAS_Aerosol_AircraftInSitu_DC8_Data", "ARCTAS_Aerosol_AircraftInSitu_P3B_Data", "ARCTAS_AircraftRemoteSensing_BE200_HSRL_Data", "ARCTAS_AircraftRemoteSensing_DC8_DIAL_Data", "ARCTAS_AircraftRemoteSensing_P3B_AATS14_Data", "ARCTAS_AircraftRemoteSensing_P3B_CAR_Data", "ARCTAS_Cloud_AircraftInSitu_DC8_Data", "ARCTAS_Ground_Data", "ARCTAS_JValue_AircraftInSitu_DC8_Data", "ARCTAS_Merge_DC8-Aircraft_Data", "ARCTAS_Merge_P3B-Aircraft_Data", "ARCTAS_MetNav_AircraftInSitu_DC8_Data", "ARCTAS_MetNav_AircraftInSitu_P3B_Data", "ARCTAS_Model_Data", "ARCTAS_Ozonesondes_Data", "ARCTAS_Radiation_AircraftInSitu_P3B_Data", "ARCTAS_Satellite_Data", "ARCTAS_TraceGas_AircraftInSitu_DC8_Data", "ARCTAS_TraceGas_AircraftInSitu_P3B_Data", "ARCTAS_Trajectory_Data", "ARCTICCC", "ARESE_ER2_MAS", "ARIA_S1_GUNW", "ARISE_Aerosol-TraceGas_AircraftRemoteSensing_C130_Data", "ARISE_Cloud_AircraftInSitu_C130_Data", "ARISE_Merge_Data", "ARISE_MetNav_AircraftInSitu_C130_Data", "ARISE_Radiation_AircraftInSitu_C130_Data", "ARK", "ARME_898", "ASCATA-L2-25km", "ASCATA-L2-Coastal", "ASCATA_ESDR_ANCILLARY_L2_V1.1", "ASCATA_ESDR_L2_WIND_STRESS_V1.1", "ASCATA_L2_25KM_CDR", "ASCATA_L2_COASTAL_CDR", "ASCATB-L2-25km", "ASCATB-L2-Coastal", "ASCATB_ESDR_ANCILLARY_L2_V1.1", "ASCATB_ESDR_L2_WIND_STRESS_V1.1", "ASCATC-L2-25km", "ASCATC-L2-Coastal", "ASCENDS_AVOCET_CA_NV_Feb_2016_2115", "ASCENDS_LAS_IN_Sept_2014_2116", "ASIRI", "ASO_3M_PCDTM", "ASO_3M_SD", "ASO_50M_SD", "ASO_50M_SWE", "AST14DEM", "AST14DMO", "AST14OTH", "ASTGTM", "ASTGTM_NC", "ASTGTM_NUMNC", "ASTWBD", "ASTWBD_ATTNC", "ASTWBD_NC", "AST_05", "AST_07", "AST_07XT", "AST_08", "AST_09", "AST_09T", "AST_09XT", "AST_L1A", "AST_L1AE", "AST_L1B", "AST_L1BE", "AST_L1T", "AST_L1T", "ATCS", "ATL03", "ATL03", "ATL06", "ATL06", "ATL07", "ATL07", "ATL07QL", "ATL08", "ATL08", "ATL08QL", "ATL09", "ATL09", "ATL09QL", "ATL10", "ATL10", "ATL10QL", "ATL11", "ATL11", "ATL12", "ATL12", "ATL13", "ATL13", "ATL13QL", "ATL14", "ATL14", "ATL14", "ATL14", "ATL15", "ATL15", "ATL15", "ATL15", "ATL16", "ATL16", "ATL17", "ATL17", "ATL19", "ATL19", "ATL20", "ATL20", "ATL21", "ATL21", "ATL22", "ATL22", "ATL23", "ATL23", "ATLAS_DEALIASED_SASS_L2", "ATLAS_Veg_Plots_1541", "ATMOSL1", "ATMOSL2AF", "ATMOSL2AT", "ATMOSL2PF", "ATMOSL2PT", "ATMOSL2TF", "ATMOSL2TT", "ATSM2AEF", "ATSM2LSF", "ATSM2STF", "ATSMIB2E", "ATSMIB2T", "ATSMIGEO", "ATTREX-Aircraft_Radiation_Measurements", "ATTREX-Aircraft_RemoteSensing_Temperature_Measurements", "ATTREX-Aircraft_insitu_Cloud_property_Measurements", "ATTREX-Aircraft_insitu_TraceGas_Measurements", "ATTREX-Aircraft_navigational_and_meteorological_Measurements", "ATom_AMP_Instrument_Data_1671", "ATom_AO2_Instrument_Data_V2_1880", "ATom_ATHOS_Instrument_Data_V2_1930", "ATom_Aerosol_Properties_V2_2111", "ATom_Aerosols_Meteorology_1684", "ATom_CAFS_Instrument_Data_V2_1933", "ATom_CAPSVienna_Data_1981", "ATom_CESM2_1878", "ATom_CIT_Instrument_Data_V2_1927", "ATom_CO_GEOS_1604", "ATom_Carbon_Aerosol_Loadings_1618", "ATom_Clouds_Aerosols_2250", "ATom_DLH_Instrument_Data_V2_1937", "ATom_FlightTrack_Influences_1889", "ATom_Forward_Flight_Videos_1938", "ATom_FullModel_DataStream_1877", "ATom_GT_CIMS_Instrument_Data_1715", "ATom_GlobalModelInitiative_CTM_1897", "ATom_HIPPO_ORCAS_1788", "ATom_HR-AMS_Instrument_Data_1716", "ATom_ISAF_Instrument_Data_1730", "ATom_MMS_Instrument_Data_1731", "ATom_Mapping_OH_Troposphere_1669", "ATom_Medusa_Instrument_Data_V2_1881", "ATom_Mineral_Dust_Cirrus_Cloud_2006", "ATom_Modeled_Observed_Data_1857", "ATom_NMASS_Data_1607", "ATom_NOyO3_Instrument_Data_1734", "ATom_Organic_Aerossols_1795", "ATom_Ozonesonde_InstrumentData_1910", "ATom_PALMS_Instrument_Data_1733", "ATom_PANTHER_Instrument_Data_1914", "ATom_PFP_Instrument_Data_1746", "ATom_Particulate_Iodine_1773", "ATom_Photolysis_Rates_1651", "ATom_Picarro_Instrument_Data_1732", "ATom_QCLS_Instrument_Data_V2_1932", "ATom_Rad_Measurements_ARMAS_1906", "ATom_SAGA_Instrument_Data_1748", "ATom_SO2_LIF_Instrument_Data_1890", "ATom_SOAP_Instrument_Data_1898", "ATom_SP2_Instrument_Data_1672", "ATom_SP2_LAM_FeOx_MMR_1828", "ATom_Simulated_Data_1597", "ATom_TOGA_Instrument_Data_V2_1936", "ATom_UCATS_Instrument_Data_1750", "ATom_UHSAS_Data_1619", "ATom_WAS_Instrument_Data_1751", "ATom_merge_1581", "ATom_merge_V2_1925", "ATom_nav_1613", "AU_5DSno", "AU_DyOcn", "AU_DySno", "AU_DySno_NRT_R02", "AU_Land_NRT_R02", "AU_MoSno", "AU_Ocean_NRT_R01", "AU_Rain_NRT_R02", "AU_SI12", "AU_SI12_NRT_R04", "AU_SI25", "AU_SI25_NRT_R04", "AU_SI6", "AU_SI6_NRT_R04", "AVHRR18_G-NAVO-L2P-v1.0", "AVHRR19_G-NAVO-L2P-v1.0", "AVHRR19_L-NAVO-L2P-v1.0", "AVHRRF_MA-STAR-L2P-v2.80", "AVHRRF_MA-STAR-L3U-v2.80", "AVHRRF_MB-STAR-L2P-v2.80", "AVHRRF_MB-STAR-L3U-v2.80", "AVHRRF_MC-STAR-L2P-v2.80", "AVHRRF_MC-STAR-L3U-v2.80", "AVHRRMTA_G-NAVO-L2P-v1.0", "AVHRRMTA_G-NAVO-L2P-v2.0", "AVHRRMTB_G-NAVO-L2P-v1.0", "AVHRRMTB_G-NAVO-L2P-v2.0", "AVHRRMTC_G-NAVO-L2P-v2.0", "AVHRR_Fire_Products_1545", "AVHRR_LST_826", "AVHRR_OI-NCEI-L4-GLOB-v2.0", "AVHRR_OI-NCEI-L4-GLOB-v2.1", "AVHRR_SST_METOP_A-OSISAF-L2P-v1.0", "AVHRR_SST_METOP_A_GLB-OSISAF-L3C-v1.0", "AVHRR_SST_METOP_A_NAR-OSISAF-L3C-v1.0", "AVHRR_SST_METOP_B-OSISAF-L2P-v1.0", "AVHRR_SST_METOP_B_GLB-OSISAF-L3C-v1.0", "AVHRR_SST_METOP_B_NAR-OSISAF-L3C-v1.0", "AVHRR_SST_NOAA19_NAR-OSISAF-L3C-v1.0", "AVIRIS-Classic_L1B_Radiance_2155", "AVIRIS-Classic_L2_Reflectance_2154", "AVIRIS-NG_Data_Idaho_1533", "AVIRIS-NG_L1B_radiance_2095", "AVIRIS-NG_L2_Reflectance_2110", "AVIRIS_FlightLine_Locator_2140", "Academ_Kurchatov", "Active_Fluorescence_2001", "Active_Layer_Thaw_Depths_1701", "Aeolus-CalVal-DAWN_DC8", "Aeolus-CalVal-Dropsondes_DC8", "Aeolus-CalVal-HALO_DC8", "Aeolus-CalVal-MetNav_DC8", "Aerosol_Sulfate_LowermostStrat_1868", "AfriSAR_AGB_Maps_1681", "AfriSAR_LVIS_Footprint_Cover_1591", "AfriSAR_Mondah_Field_Data_1580", "African_Rainfall_Patterns_1263", "Afrisar_LVIS_Biomass_VProfiles_1775", "AirMOSS_Field_Data_Harvard_1677", "AirMOSS_L1_Sigma0_BERMS_1406", "AirMOSS_L1_Sigma0_Chamel_1407", "AirMOSS_L1_Sigma0_DukeFr_1408", "AirMOSS_L1_Sigma0_Harvrd_1409", "AirMOSS_L1_Sigma0_Howlnd_1410", "AirMOSS_L1_Sigma0_LaSelv_1411", "AirMOSS_L1_Sigma0_Metoli_1412", "AirMOSS_L1_Sigma0_Moisst_1413", "AirMOSS_L1_Sigma0_TonziR_1414", "AirMOSS_L1_Sigma0_Walnut_1415", "AirMOSS_L2_3_RZ_Soil_Moisture_1418", "AirMOSS_L2_Carbon_Flux_1420", "AirMOSS_L2_Inground_Soil_Moist_1416", "AirMOSS_L2_Precipitation_1417", "AirMOSS_L4_Daily_NEE_1422", "AirMOSS_L4_RZ_Soil_Moisture_1421", "AirMOSS_L4_Regional_NEE_1423", "AirMSPI_ACEPOL_Ellipsoid-projected_Georegistered_Radiance_Data", "AirMSPI_ACEPOL_Terrain-projected_Georegistered_Radiance_Data", "AirMSPI_CalWater-2_Ellipsoid-projected_Georegistered_Radiance_Data", "AirMSPI_CalWater-2_Terrain-projected_Georegistered_Radiance_Data", "AirMSPI_FIREX-AQ_Terrain-projected_Georegistered_Radiance_Data", "AirMSPI_ImPACT-PM_Ellipsoid-projected_Georegistered_Radiance_Data", "AirMSPI_ImPACT-PM_Terrain-projected_Georegistered_Radiance_Data", "AirMSPI_ORACLES_Ellipsoid-projected_Georegistered_Radiance_Data", "AirMSPI_ORACLES_Terrain-projected_Georegistered_Radiance_Data", "AirMSPI_PODEX_Ellipsoid-projected_Georegistered_Radiance_Data", "AirMSPI_PODEX_Terrain-projected_Georegistered_Radiance_Data", "AirMSPI_RADEX_Ellipsoid-projected_Georegistered_Radiance_Data", "AirMSPI_RADEX_Terrain-projected_Georegistered_Radiance_Data", "AirMSPI_SEAC4RS_Ellipsoid-projected_Georegistered_Radiance_Data", "AirMSPI_SEAC4RS_Terrain-projected_Georegistered_Radiance_Data", "AirMSPI_SPEX-PR_Ellipsoid-projected_Georegistered_Radiance_Data", "AirMSPI_SPEX-PR_Terrain-projected_Georegistered_Radiance_Data", "AirSWOT_Orthomosaic_WaterMask_1655", "Airborne_Insitu_Measurements_1784", "Akademik_Sergey_Vavilov", "Alaska_Arctic_Tundra_Veg_Map_1353", "Alaska_L4_WRF_STILT_Footprints_1544", "Alaska_L4_WRF_STILT_Particle_1571", "Alaska_Lake_Pond_Maps_2134", "Alaska_Yukon_NDVI_1614", "Alaskan_CH4_CO2_Fluxes_1316", "Alaskan_CO2_Flux_1325", "Albedo_Boreal_North_America_1605", "Alder_Shrub_Soil_Alaska_V2_2300", "Annual_30m_AGB_1808", "Annual_Burned_Area_Maps_1708", "Annual_Landcover_ABoVE_1691", "Annual_Seasonality_Greenness_1698", "Annual_Thaw_Slump_1724", "Apero", "Aqua_AIRS_MODIS1km_IND", "ArabianSea_2011", "Arc00", "ArcticNET", "ArcticTreeLine_Dendrometry_Env_2185", "ArcticTreeLine_Spruce_CO2_WV_1948", "Arctic_Boreal_Burned_Area_V2_2328", "Arctic_Boreal_CO2_Flux_1934", "Arctic_Flux_629", "Arctic_Network_Veg_plots_1542", "Arctic_RSWQ", "Arctic_Soil_Properties_2149", "Arctic_Vegetation_Maps_1323", "Arctic_Wildlife_Refuge_Veg_Map_1384", "Arctic_Winter_Respiration_v2_1762", "Arrigetch_Peaks_Veg_Plots_1358", "Atmospheric_CO2_California_1641", "Atqasuk_Veg_Plots_1371", "B01", "B02", "B03", "B04", "B05", "B06", "B07", "B08", "B09", "BAHAMAS2004", "BAROCLINIC_HRET14", "BASIN_TCP_963", "BBOP", "BCFLEXPART", "BC_Aerosol_Dynamics_Alaska_1340", "BDSNP_CMAQ_Model_1351", "BENEFIT", "BEST", "BFO_dsp01_ccrs_avhrr_landcover_589", "BFO_dsp01_ccrs_tm_landcover_588", "BFO_dsp04_ers_freeze-thaw_maps_590", "BFO_dsp05_ccrs_avhrr_npp_591", "BFO_dsp06_casi_lai_cc_592", "BFO_dsp08_polder_surface_params_594", "BFO_dsp09_moss_map_595", "BFO_dsp09_sask_fire_map_596", "BFO_dsp10_fpar_lai_1994_585", "BFO_dsp10_landcover_598", "BFO_dsp10_landcover_tm_mosaic_602", "BFO_dsp10_landcover_tm_reclassed_597", "BFO_dsp10_moss_cover_599", "BFO_dsp10_ndvi_600", "BFO_dsp10_peatlands_601", "BFO_flx01_derived_data_603", "BFO_flx01_flux_met_temp_604", "BFO_flx03_area_avg_flux_586", "BFO_flx04_nsa_burn_flux_587", "BFO_hmet01_goes8_level2_srb_605", "BFO_hmet01_ssmi_precip_606", "BFO_hmet02_gridded_met_phase2-3_607", "BFO_hmet03_hourly_met_p1_608", "BFO_hmet04_src_nsa_96-98_609", "BFO_mod01_gridded_met_p3_daily_610", "BGC_glider_GNATS", "BILIM", "BIO-GO-SHIP", "BIOCOMPLEXITY", "BIODIVERSITY", "BIOME", "BIOME_BGC_4_1_1_805", "BIOME_BGC_m2_4_1_2_809", "BIOME_BGC_m_4_1_1_806", "BIOSCAPE_COASTAL_CARBON", "BIOSOPE", "BIO_BURN_5X5_HAO_NAT", "BLATM1B", "BLATM2", "BLVIS2", "BNL", "BOA", "BOREAS_CDS_1350", "BOREAS_RSS-03_Snapshots_289", "BOREAS_SLICER_508", "BOUSSOLE", "BRAZIL", "BRMCR2", "BUVN04L2", "BUVN04L3zm", "BUVN4L1DCM", "BUVN4L1DCW", "BUVN4L1PDB", "BUVN4L1RUT", "BUVN4L2CPOZ", "BUVN4L2HDBUV", "BUVN4L3ZMT", "Barrow_NGEE_Arctic_Veg_Plots_1505", "Barrow_Tundra_Veg_Plots_1535", "Barter_Barrow_Veg_Plots_1534", "BenthicEcology_fromSpace", "Ber95", "Ber96", "BeringSea_LaserFluoMon", "Bermuda_Testbed_Mooring", "Big_Bend", "Bio_optics_Chl_polarization", "Biogenic_CO2flux_SIF_SMUrF_1899", "BlueFlux_AirborneObs_Florida_2327", "BlueFlux_Tidal_River_Water_2333", "Blue_Carbon_Tidal_Wetland_Maps_2091", "Bold_EPAHypoxia", "BorealForest_Greenness_Trends_2023", "Boreal_AGB_Density_ICESat2_2186", "Boreal_Arctic_Wetland_CH4_2351", "Boreal_CanopyCover_StandAge_2012", "Boreal_Fire_Severity_Metrics_1520", "BowdoinBuoy", "BurnedArea_Emissions_AK_YT_NWT_1812", "Burned_Area_Depth_AK_CA_2063", "C-HARRIER", "C5", "CABARE_918", "CALIPSO-NVF_HSRL2_KingAir_Data", "CAL_IIR_L1-Standard-V2-00", "CAL_IIR_L2_Swath-Standard-V4-20", "CAL_IIR_L2_Swath-Standard-V4-21", "CAL_IIR_L2_Swath-Standard-V4-51", "CAL_IIR_L2_Track-Standard-V4-20", "CAL_IIR_L2_Track-Standard-V4-21", "CAL_IIR_L2_Track-Standard-V4-51", "CAL_IIR_L3_GEWEX_Cloud-Standard-V1-00", "CAL_LID_L1-Standard-V4-10", "CAL_LID_L1-Standard-V4-11", "CAL_LID_L1-Standard-V4-51", "CAL_LID_L15-Standard-V1-00", "CAL_LID_L15-Standard-V1-01", "CAL_LID_L2_01kmCLay-Standard-V4-20", "CAL_LID_L2_01kmCLay-Standard-V4-21", "CAL_LID_L2_01kmCLay-Standard-V4-51", "CAL_LID_L2_05kmALay-Prov-V3-02", "CAL_LID_L2_05kmALay-Prov-V3-30", "CAL_LID_L2_05kmALay-Standard-V4-20", "CAL_LID_L2_05kmALay-Standard-V4-21", "CAL_LID_L2_05kmALay-Standard-V4-51", "CAL_LID_L2_05kmAPro-Prov-V3-02", "CAL_LID_L2_05kmAPro-Standard-V4-20", "CAL_LID_L2_05kmAPro-Standard-V4-21", "CAL_LID_L2_05kmAPro-Standard-V4-51", "CAL_LID_L2_05kmCLay-Standard-V4-20", "CAL_LID_L2_05kmCLay-Standard-V4-21", "CAL_LID_L2_05kmCLay-Standard-V4-51", "CAL_LID_L2_05kmCPro-Standard-V4-20", "CAL_LID_L2_05kmCPro-Standard-V4-21", "CAL_LID_L2_05kmCPro-Standard-V4-51", "CAL_LID_L2_05kmMLay-Standard-V4-20", "CAL_LID_L2_05kmMLay-Standard-V4-21", "CAL_LID_L2_05kmMLay-Standard-V4-51", "CAL_LID_L2_333mCLay-ValStage1-V3-30", "CAL_LID_L2_333mMLay-Standard-V4-20", "CAL_LID_L2_333mMLay-Standard-V4-21", "CAL_LID_L2_333mMLay-Standard-V4-51", "CAL_LID_L2_BlowingSnow_Antarctica-Standard-V1-00", "CAL_LID_L2_BlowingSnow_Antarctica-Standard-V1-01", "CAL_LID_L2_BlowingSnow_Antarctica-Standard-V2-00", "CAL_LID_L2_BlowingSnow_Greenland-Standard-V1-00", "CAL_LID_L2_PSCMask-Standard-V2-00", "CAL_LID_L2_VFM-Standard-V4-20", "CAL_LID_L2_VFM-Standard-V4-21", "CAL_LID_L2_VFM-Standard-V4-51", "CAL_LID_L3_Cloud_Occurrence-Standard-V1-00", "CAL_LID_L3_GEWEX_Cloud-Standard-V1-00", "CAL_LID_L3_Stratospheric_APro-Standard-V1-00", "CAL_LID_L3_Stratospheric_APro-Standard-V1-01", "CAL_LID_L3_Tropospheric_APro_AllSky-Standard-V4-20", "CAL_LID_L3_Tropospheric_APro_AllSky-Standard-V4-21", "CAL_LID_L3_Tropospheric_APro_CloudFree-Standard-V4-20", "CAL_LID_L3_Tropospheric_APro_CloudFree-Standard-V4-21", "CAL_LID_L3_Tropospheric_APro_CloudySkyOpaque-Standard-V4-20", "CAL_LID_L3_Tropospheric_APro_CloudySkyOpaque-Standard-V4-21", "CAL_LID_L3_Tropospheric_APro_CloudySkyTransparent-Standard-V4-20", "CAL_LID_L3_Tropospheric_APro_CloudySkyTransparent-Standard-V4-21", "CAL_WFC_L1_125m-ValStage1-V3-01", "CAL_WFC_L1_125m-ValStage1-V3-02", "CAL_WFC_L1_1Km-ValStage1-V3-01", "CAL_WFC_L1_1Km-ValStage1-V3-02", "CAL_WFC_L1_IIR-ValStage1-V3-01", "CAM5K30CF", "CAM5K30CF", "CAM5K30EM", "CAM5K30EM", "CAM5K30UC", "CAM5K30UC", "CAMEX4_ER2_MAS", "CAML", "CAMP2Ex-Cloud-Precip-Retrieval", "CAMP2Ex_Aerosol_AircraftInSitu_Learjet_Data", "CAMP2Ex_Aerosol_AircraftInSitu_P3_Data", "CAMP2Ex_Aerosol_AircraftRemoteSensing_P3_Data", "CAMP2Ex_Cloud_AircraftInSitu_Learjet_Data", "CAMP2Ex_Cloud_AircraftInSitu_P3_Data", "CAMP2Ex_Merge_Data", "CAMP2Ex_MetNav_AircraftInSitu_Learjet_Data", "CAMP2Ex_MetNav_AircraftInSitu_P3_Data", "CAMP2Ex_Miscellaneous_Data", "CAMP2Ex_Radiation_AircraftInSitu_P3_Data", "CAMP2Ex_TraceGas_AircraftInSitu_P3_Data", "CAMREX_904", "CARAFE_2016_2017_v2_2002", "CARIACO", "CARMIAAE", "CARMITST", "CARVE_Ecosystem_CH4_Flux_1558", "CARVE_L1_FTS_Spectra_1426", "CARVE_L1_FlightPath_1425", "CARVE_L1_FlightPath_Winds_1427", "CARVE_L1_Ground_Flux_1424", "CARVE_L1_Infrared_1428", "CARVE_L2_AtmosGas_Ground_1419", "CARVE_L2_AtmosGas_Harvard_1403", "CARVE_L2_AtmosGas_Merge_1402", "CARVE_L2_AtmosGas_NOAA_1401", "CARVE_L2_FTS_ColumnGas_1429", "CARVE_L2_Flask_1404", "CARVE_L2_Flask_Ground_1405", "CARVE_L4_WRF-STILT_Footprint_1431", "CARVE_L4_WRF-STILT_Particle_1430", "CARVE_Land_Thaw_State_1383", "CARVE_Photos_1435", "CARVE_Reports_1434", "CARVE_Videos_1433", "CAR_ARCTAS_BRDF", "CAR_ARCTAS_L1C", "CAR_ARMCAS_BRDF", "CAR_ARMCAS_L1C", "CAR_BRDF_709", "CAR_CLAMS_BRDF", "CAR_CLAMS_L1C", "CAR_CLASIC_BRDF", "CAR_CLASIC_L1C", "CAR_DISCOVERAQ_BRDF", "CAR_DISCOVERAQ_L1C", "CAR_ECO3D_BRDF", "CAR_ECO3D_L1C", "CAR_FIREACE_BRDF", "CAR_FIREACE_L1C", "CAR_INTEXB_BRDF", "CAR_INTEXB_L1C", "CAR_KUWAITOILFIRE_BRDF", "CAR_KUWAITOILFIRE_L1C", "CAR_LEADEX_BRDF", "CAR_LEADEX_L1C", "CAR_SAFARI_BRDF", "CAR_SAFARI_L1C", "CAR_SCARA_BRDF", "CAR_SCARA_L1C", "CAR_SCARB_BRDF", "CAR_SCARB_L1C", "CAR_SKUKUZA_BRDF", "CAR_SKUKUZA_L1C", "CAR_SNOWEX17_BRDF", "CAR_SNOWEX17_L1C", "CAR_TARFOX_BRDF", "CAR_TARFOX_L1C", "CASES", "CATS-ISS_L1B_D-M7.1-V3-00", "CATS-ISS_L1B_D-M7.2-V3-00", "CATS-ISS_L1B_N-M7.1-V3-00", "CATS-ISS_L1B_N-M7.2-V3-00", "CATS-ISS_L2O_D-M7.1-V3-01_05kmLay", "CATS-ISS_L2O_D-M7.1-V3-01_05kmPro", "CATS-ISS_L2O_D-M7.2-V3-00_05kmLay", "CATS-ISS_L2O_D-M7.2-V3-00_05kmPro", "CATS-ISS_L2O_D-M7.2-V3-01_05kmLay", "CATS-ISS_L2O_D-M7.2-V3-01_05kmPro", "CATS-ISS_L2O_N-M7.1-V3-01_05kmLay", "CATS-ISS_L2O_N-M7.1-V3-01_05kmPro", "CATS-ISS_L2O_N-M7.2-V3-00_05kmLay", "CATS-ISS_L2O_N-M7.2-V3-00_05kmPro", "CATS-ISS_L2O_N-M7.2-V3-01_05kmLay", "CATS-ISS_L2O_N-M7.2-V3-01_05kmPro", "CCE-LTER", "CCMP_WINDS_10M6HR_L4_V3.1", "CCMP_WINDS_10MMONTHLY_L4_V3.1", "CD01_BRAMS_907", "CD01_CIRSAN_Meteorology_2001_1114", "CD02_Atmosphere_CO2_Isotopes_1011", "CD02_C_N_Isotopes_1097", "CD02_C_N_O_Organic_983", "CD02_Forest_Canopy_Structure_1009", "CD02_Leaf_Level_Gas_Exchange_1010", "CD02_Leaf_Water_Potential_1100", "CD02_O_H_Isotopes_1008", "CD03_Ceilometer_Km67_942", "CD03_Mesoscale_Meteorology_944", "CD03_Pasture_Flux_962", "CD03_Tethered_Balloon_1108", "CD04_Biomass_990", "CD04_CO2_Profiles_947", "CD04_Dendrometry_989", "CD04_LAI_992", "CD04_LAI_Estimates_1103", "CD04_Leaf_Level_Gas_Exchange_1060", "CD04_Leaf_Litter_991", "CD04_Logging_Damage_1038", "CD04_Meteorology_Fluxes_946", "CD04_Soil_Moisture_Km83_979", "CD04_Soil_Respiration_1039", "CD04_Tower_Flux_Gap_978", "CD05_Fuel_Loads_1233", "CD05_Micromet_1169", "CD05_REE_Fuel_Sticks_Moisture_1232", "CD06_BGC_JiParana_1227", "CD06_C02_Exchange_1136", "CD06_C_Isotopes_1120", "CD06_Camrex_1086", "CD06_Carbon_respiration_1125", "CD06_LULC_Map_JiParana_1087", "CD06_Landuse_Timeseries_JiParana_844", "CD06_Outgassing_1151", "CD06_Physical_Template_JiParana_1090", "CD06_Soils_JiParana_1088", "CD06_Water_Balance_JiParana_1132", "CD07_GOES_L3_Gridded_SRB_831", "CD08_CWD_Res_and_Decomp_Manaus_911", "CD08_C_Isotopes_Belowground_1025", "CD08_Ecosystem_Resp_Manaus_912", "CD08_Leaf_Isotopes_Manaus_1245", "CD08_Radiocarbon_Dates_997", "CD08_Radiocarbon_Dates_Manaus_996", "CD08_Tree_Growth_Manaus_1194", "CD08_Tree_Inventory_Ducke_910", "CD09_Soils_Veg_Tapajos_1104", "CD10_Biometry_Tapajos_854", "CD10_CO2_Profiles_Tapajos_855", "CD10_CO_CO2_Maxaranguape_1012", "CD10_CO_Tapajos_856", "CD10_CWD_Tapajos_858", "CD10_DBH_Tapajos_859", "CD10_EddyFlux_Tapajos_860", "CD10_H2O_Profiles_Tapajos_861", "CD10_Litter_Tapajos_862", "CD10_Temperature_Profiles_Tapajos_863", "CD11_Forest_Degradation_1118", "CD15_Productivity_1167", "CD17_Forest_Survey_1254", "CD32_Fluxes_Brazil_1842", "CD32_LBA_MIP_Drivers_1177", "CD34_Amazon_Hyperion_1064", "CD34_Amazon_Landsat_1176", "CD36_SALDAS_1162", "CD37_Biomass_Landsat_Glas_1145", "CDDIS_DORIS_IDSEOP_product", "CDDIS_DORIS_IDScumulativePositions_product", "CDDIS_DORIS_IDSdpod_product", "CDDIS_DORIS_IDSgeocenter_product", "CDDIS_DORIS_IDSionosphere_product", "CDDIS_DORIS_data_cycle", "CDDIS_DORIS_data_rinex", "CDDIS_DORIS_information", "CDDIS_DORIS_products_geocenter", "CDDIS_DORIS_products_ionosphere", "CDDIS_DORIS_products_orbit", "CDDIS_DORIS_products_positions", "CDDIS_DORIS_products_quaternions", "CDDIS_DORIS_products_stcd", "CDDIS_GNSS_highrate_data", "CDDIS_GNSS_products_IGS20", "CDDIS_GNSS_products_clocks_final", "CDDIS_GNSS_products_clocks_rapid", "CDDIS_GNSS_products_clocks_realtime", "CDDIS_GNSS_products_erp", "CDDIS_GNSS_products_ionosphere_final", "CDDIS_GNSS_products_ionosphere_predicted", "CDDIS_GNSS_products_ionosphere_rapid", "CDDIS_GNSS_products_orbit_final", "CDDIS_GNSS_products_orbit_rapid", "CDDIS_GNSS_products_orbit_realtime", "CDDIS_GNSS_products_orbit_ultrarapid", "CDDIS_GNSS_products_positions", "CDDIS_GNSS_products_troposphere", "CDDIS_MISC_information", "CDDIS_SLR_data", "CDDIS_SLR_predictions", "CDDIS_SLR_products_poseop", "CDDIS_VLBI_data", "CDDIS_VLBI_products_eop", "CDDIS_VLBI_products_positions", "CDDIS_VLBI_products_troposphere", "CDIAC_NDP41_220", "CER-NEWS_CCCM_Aqua-FM3-MODIS-CAL-CS", "CERES-MISR-MODIS_SSF-SSFM_Terra-FM1", "CERES-MISR-MODIS_SSF-SSFM_Terra-FM2", "CERES_EBAF", "CERES_EBAF", "CERES_EBAF-TOA", "CERES_EBAF-TOA", "CER_BDS_Aqua-FM4", "CER_BDS_Aqua-FM4", "CER_BDS_TRMM-PFM", "CER_CCCM_Aqua-FM3-MODIS-CAL-CS", "CER_CCCM_Aqua-FM3-MODIS-CAL-CS", "CER_CRS1deg-Hour_Aqua-MODIS", "CER_CRS1deg-Hour_Terra-MODIS", "CER_CRS_Aqua-FM3-MODIS", "CER_CRS_Aqua-FM3-MODIS", "CER_CRS_Aqua-FM4-MODIS", "CER_CRS_TRMM-PFM-VIRS", "CER_CRS_Terra-FM1-MODIS", "CER_CRS_Terra-FM1-MODIS", "CER_CRS_Terra-FM1-MODIS", "CER_CRS_Terra-FM2-MODIS", "CER_CldTypHist_GEO-MODIS", "CER_CldTypHist_GEO-MODIS-VIIRS", "CER_ES4_Aqua-Xtrk", "CER_ES4_NOAA20-FM6", "CER_ES4_NPP-FM5", "CER_ES4_TRMM-PFM", "CER_ES4_Terra+Aqua", "CER_ES4_Terra-Xtrk", "CER_ES8_Aqua-FM4", "CER_ES8_TRMM-PFM", "CER_ES9_Aqua-Xtrk", "CER_ES9_NOAA20-FM6", "CER_ES9_NPP-FM5", "CER_ES9_TRMM-PFM", "CER_ES9_Terra+Aqua", "CER_ES9_Terra-Xtrk", "CER_FSW_Aqua-FM4-MODIS", "CER_FSW_TRMM-PFM-VIRS", "CER_FSW_Terra-FM1-MODIS", "CER_FSW_Terra-FM1-MODIS", "CER_FSW_Terra-FM1-MODIS", "CER_FSW_Terra-FM2-MODIS", "CER_FluxByCldTyp-Day_NOAA20-VIIRS", "CER_FluxByCldTyp-Day_Terra-Aqua-MODIS", "CER_FluxByCldTyp-Month_NOAA20-VIIRS", "CER_FluxByCldTyp-Month_Terra-Aqua-MODIS", "CER_GEO_Ed4_GMS05_FD", "CER_GEO_Ed4_GOE08_NH", "CER_GEO_Ed4_GOE08_SH", "CER_GEO_Ed4_GOE09_NH", "CER_GEO_Ed4_GOE09_SH", "CER_GEO_Ed4_GOE10_NH", "CER_GEO_Ed4_GOE10_SH", "CER_GEO_Ed4_GOE11_NH", "CER_GEO_Ed4_GOE11_SH", "CER_GEO_Ed4_GOE12_NH", "CER_GEO_Ed4_GOE12_SH", "CER_GEO_Ed4_GOE13_NH", "CER_GEO_Ed4_GOE13_NH", "CER_GEO_Ed4_GOE13_SH", "CER_GEO_Ed4_GOE13_SH", "CER_GEO_Ed4_GOE14_NH", "CER_GEO_Ed4_GOE14_SH", "CER_GEO_Ed4_GOE15_NH", "CER_GEO_Ed4_GOE15_NH", "CER_GEO_Ed4_GOE15_SH", "CER_GEO_Ed4_GOE15_SH", "CER_GEO_Ed4_MET05_FD", "CER_GEO_Ed4_MET07_FD", "CER_GEO_Ed4_MET08_NH", "CER_GEO_Ed4_MET08_NH", "CER_GEO_Ed4_MET08_SH", "CER_GEO_Ed4_MET09_NH", "CER_GEO_Ed4_MET09_SH", "CER_GEO_Ed4_MET10_NH", "CER_GEO_Ed4_MET10_SH", "CER_GEO_Ed4_MTS01_NH", "CER_GEO_Ed4_MTS01_SH", "CER_GEO_Ed4_MTS02_NH", "CER_GEO_Ed4_MTS02_SH", "CER_ISCCP-D2like-Day_Aqua-FM3-MODIS", "CER_ISCCP-D2like-Day_Aqua-FM4-MODIS", "CER_ISCCP-D2like-Day_Terra-FM1-MODIS", "CER_ISCCP-D2like-Day_Terra-FM2-MODIS", "CER_ISCCP-D2like-GEO_DAY", "CER_ISCCP-D2like-Mrg_GEO-MODIS-DAY", "CER_ISCCP-D2like-Nit_Aqua-FM3-MODIS", "CER_ISCCP-D2like-Nit_Aqua-FM4-MODIS", "CER_ISCCP-D2like-Nit_Terra-FM1-MODIS", "CER_ISCCP-D2like-Nit_Terra-FM2-MODIS", "CER_SSF1deg-Day_Aqua-MODIS", "CER_SSF1deg-Day_NOAA20-VIIRS", "CER_SSF1deg-Day_NPP-VIIRS", "CER_SSF1deg-Day_Terra-MODIS", "CER_SSF1deg-Hour_Aqua-MODIS", "CER_SSF1deg-Hour_NOAA20-VIIRS", "CER_SSF1deg-Hour_NPP-VIIRS", "CER_SSF1deg-Hour_Terra-MODIS", "CER_SSF1deg-Month_Aqua-MODIS", "CER_SSF1deg-Month_NOAA20-VIIRS", "CER_SSF1deg-Month_NPP-VIIRS", "CER_SSF1deg-Month_Terra-MODIS", "CER_SSF_Aqua-FM4-MODIS", "CER_SSF_TRMM-PFM-VIRS", "CER_SYN1deg-1Hour_Terra-Aqua-MODIS", "CER_SYN1deg-1Hour_Terra-MODIS", "CER_SYN1deg-1Hour_Terra-NOAA20", "CER_SYN1deg-1Hour_Terra-NOAA20", "CER_SYN1deg-1Hour_Terra-NPP", "CER_SYN1deg-3Hour_Terra-Aqua-MODIS", "CER_SYN1deg-3Hour_Terra-MODIS", "CER_SYN1deg-3Hour_Terra-NPP", "CER_SYN1deg-Day_Terra-Aqua-MODIS", "CER_SYN1deg-Day_Terra-MODIS", "CER_SYN1deg-Day_Terra-NOAA20", "CER_SYN1deg-Day_Terra-NOAA20", "CER_SYN1deg-Day_Terra-NPP", "CER_SYN1deg-MHour_Terra-Aqua-MODIS", "CER_SYN1deg-MHour_Terra-MODIS", "CER_SYN1deg-MHour_Terra-NOAA20", "CER_SYN1deg-MHour_Terra-NOAA20", "CER_SYN1deg-MHour_Terra-NPP", "CER_SYN1deg-Month_Terra-Aqua-MODIS", "CER_SYN1deg-Month_Terra-MODIS", "CER_SYN1deg-Month_Terra-NOAA20", "CER_SYN1deg-Month_Terra-NOAA20", "CER_SYN1deg-Month_Terra-NPP", "CFL", "CH4_Aircraft_STILT_footprints_1300", "CH4_CO2_WaterBodies_YK_Delta_2178", "CH4_Flux_BigTrail_Goldstream_1778", "CH4_Fluxes_ThermokarstLakes_AK_1870", "CH4_Plume_AVIRIS-NG_1727", "CHELTON_SEASAT_SASS_L3", "CIESIN_CHRR_NDH_CYCLONE_HFD", "CIESIN_CHRR_NDH_CYCLONE_MRD", "CIESIN_CHRR_NDH_CYCLONE_PELRD", "CIESIN_CHRR_NDH_CYCLONE_TELRD", "CIESIN_CHRR_NDH_DROUGHT_HFD", "CIESIN_CHRR_NDH_DROUGHT_MRD", "CIESIN_CHRR_NDH_DROUGHT_PELRD", "CIESIN_CHRR_NDH_DROUGHT_TELRD", "CIESIN_CHRR_NDH_EQUAKE_HFD", "CIESIN_CHRR_NDH_EQUAKE_MRD", "CIESIN_CHRR_NDH_EQUAKE_PELRD", "CIESIN_CHRR_NDH_EQUAKE_PGA", "CIESIN_CHRR_NDH_EQUAKE_TELRD", "CIESIN_CHRR_NDH_FLOOD_HFD", "CIESIN_CHRR_NDH_FLOOD_MRD", "CIESIN_CHRR_NDH_FLOOD_PELRD", "CIESIN_CHRR_NDH_FLOOD_TELRD", "CIESIN_CHRR_NDH_LSLIDE_HD", "CIESIN_CHRR_NDH_LSLIDE_MRD", "CIESIN_CHRR_NDH_LSLIDE_PELRD", "CIESIN_CHRR_NDH_LSLIDE_TELRD", "CIESIN_CHRR_NDH_MULTI_HFD", "CIESIN_CHRR_NDH_MULTI_MRD", "CIESIN_CHRR_NDH_MULTI_PELRD", "CIESIN_CHRR_NDH_MULTI_TELRD", "CIESIN_CHRR_NDH_VOLCANO_HFD", "CIESIN_CHRR_NDH_VOLCANO_MRD", "CIESIN_CHRR_NDH_VOLCANO_PELRD", "CIESIN_CHRR_NDH_VOLCANO_TELRD", "CIESIN_SEDAC_ACRP_1980_SAS", "CIESIN_SEDAC_ACRP_1990_CBS", "CIESIN_SEDAC_ACRP_1990_EMF", "CIESIN_SEDAC_ACRP_1990_PBF", "CIESIN_SEDAC_ACRP_1990_SAS", "CIESIN_SEDAC_ACRP_1990_SEF", "CIESIN_SEDAC_ACRP_1990_SI", "CIESIN_SEDAC_ACRP_1990_STF1B", "CIESIN_SEDAC_ACRP_1990_ZIPEF", "CIESIN_SEDAC_ACRP_1992_BF", "CIESIN_SEDAC_ACRP_PUMS", "CIESIN_SEDAC_AGLANDS_CROP2000", "CIESIN_SEDAC_AGLANDS_PAS2000", "CIESIN_SEDAC_ANTHROMES_v1", "CIESIN_SEDAC_ANTHROMES_v2_1700", "CIESIN_SEDAC_ANTHROMES_v2_1900", "CIESIN_SEDAC_AQDH_CTMAP_2003_2018", "CIESIN_SEDAC_AQDH_DANO2_US_1KM_1.10", "CIESIN_SEDAC_AQDH_DAO3_US_1KM", "CIESIN_SEDAC_AQDH_DAO3_US_1KM_1.10", "CIESIN_SEDAC_AQDH_DAPM25_US_1KM", "CIESIN_SEDAC_AQDH_DAPM25_US_1KM_1.10", "CIESIN_SEDAC_AQDH_NO2_US_1KM", "CIESIN_SEDAC_AQDH_PM25COM_US_1KM", "CIESIN_SEDAC_AQDH_PM25O3NO2_ZIPCODE", "CIESIN_SEDAC_AQDH_TRACE_US_1KM", "CIESIN_SEDAC_CD_ADM_GIS_1990", "CIESIN_SEDAC_CD_AGENDA21", "CIESIN_SEDAC_CD_BIB_ADM_GEOG", "CIESIN_SEDAC_CD_CTY_ECON_YRBK", "CIESIN_SEDAC_CD_CTY_POPAG_GIS", "CIESIN_SEDAC_CD_FUNDGIS", "CIESIN_SEDAC_CD_GBCODES", "CIESIN_SEDAC_CD_HOS_EPI_50-85", "CIESIN_SEDAC_CD_MAPS_BIBDB", "CIESIN_SEDAC_CD_STAT_1949-1990", "CIESIN_SEDAC_CESIC_2004EVI", "CIESIN_SEDAC_CESIC_2006NFA", "CIESIN_SEDAC_CESIC_ANC", "CIESIN_SEDAC_CESIC_COMPLETE_V11", "CIESIN_SEDAC_CESIC_RIOJO", "CIESIN_SEDAC_CESIC_WELLBEING", "CIESIN_SEDAC_CLIMMIG_GASPMP18SR", "CIESIN_SEDAC_CLIMMIG_GSPMP18SR", "CIESIN_SEDAC_CROPCLIM_EFCCGPSRES", "CIESIN_SEDAC_CROPCLIM_GISS_DB", "CIESIN_SEDAC_CRV_USCRPC_2040-2049", "CIESIN_SEDAC_DEDC_ACE_V2", "CIESIN_SEDAC_ENERGY_NPPCLA", "CIESIN_SEDAC_ENERGY_NPPL", "CIESIN_SEDAC_EPI_2006", "CIESIN_SEDAC_EPI_2008", "CIESIN_SEDAC_EPI_2010", "CIESIN_SEDAC_EPI_2012", "CIESIN_SEDAC_EPI_2014", "CIESIN_SEDAC_EPI_2016", "CIESIN_SEDAC_EPI_2018", "CIESIN_SEDAC_EPI_2020", "CIESIN_SEDAC_EPI_2022", "CIESIN_SEDAC_ESI_2000", "CIESIN_SEDAC_ESI_2001", "CIESIN_SEDAC_ESI_2002", "CIESIN_SEDAC_ESI_2005", "CIESIN_SEDAC_FERMANv1_NAPP", "CIESIN_SEDAC_FERMANv1_NMAN", "CIESIN_SEDAC_FERMANv1_PAPP", "CIESIN_SEDAC_FERMANv1_PESTG_V1.01", "CIESIN_SEDAC_FERMANv1_PMAN", "CIESIN_SEDAC_FOOD_CROPSTAT_1900-2017", "CIESIN_SEDAC_FOOD_FIH_V1", "CIESIN_SEDAC_GEO-MEX_GIS_SMI", "CIESIN_SEDAC_GEO-MEX_POPDBMEX", "CIESIN_SEDAC_GEO-MEX_RASTERGIS", "CIESIN_SEDAC_GEO-MEX_URBANGIS", "CIESIN_SEDAC_GEO-MEX_URB_TSER", "CIESIN_SEDAC_GHSL_PBSMOD", "CIESIN_SEDAC_GPWv3_CENTROIDS", "CIESIN_SEDAC_GPWv3_COASTLINES", "CIESIN_SEDAC_GPWv3_LANDGEOG", "CIESIN_SEDAC_GPWv3_NADMINBND", "CIESIN_SEDAC_GPWv3_NATIDEN", "CIESIN_SEDAC_GPWv3_POPCOUNT", "CIESIN_SEDAC_GPWv3_POPCOUNTFE", "CIESIN_SEDAC_GPWv3_POPDENS", "CIESIN_SEDAC_GPWv3_POPDENSFE", "CIESIN_SEDAC_GPWv3_SUBADBND", "CIESIN_SEDAC_GPWv4_ADUCPPE_R11", "CIESIN_SEDAC_GPWv4_APCT_WPP_2015_R11", "CIESIN_SEDAC_GPWv4_APDENS_WPP_2015_R11", "CIESIN_SEDAC_GPWv4_BDC_R11", "CIESIN_SEDAC_GPWv4_DQI_R11", "CIESIN_SEDAC_GPWv4_LANDH2OAREA_R11", "CIESIN_SEDAC_GPWv4_NATIDEN_R11", "CIESIN_SEDAC_GPWv4_POPCOUNT_R11", "CIESIN_SEDAC_GPWv4_POPDENS_R11", "CIESIN_SEDAC_GRANDv1_DAMS", "CIESIN_SEDAC_GRANDv1_RESERVOIRS", "CIESIN_SEDAC_GROADS_CCRDSv1", "CIESIN_SEDAC_GROADS_V1", "CIESIN_SEDAC_GRUMPv1_COASTLINES", "CIESIN_SEDAC_GRUMPv1_EXT", "CIESIN_SEDAC_GRUMPv1_EXT02", "CIESIN_SEDAC_GRUMPv1_LGUAG", "CIESIN_SEDAC_GRUMPv1_NADMINBND", "CIESIN_SEDAC_GRUMPv1_NATIDEN", "CIESIN_SEDAC_GRUMPv1_POPCOUNT", "CIESIN_SEDAC_GRUMPv1_POPDENS", "CIESIN_SEDAC_GRUMPv1_STLMNT", "CIESIN_SEDAC_GRUMPv1_STLMNT01", "CIESIN_SEDAC_HANPP", "CIESIN_SEDAC_HANPP_CPROD", "CIESIN_SEDAC_HANPP_NPP", "CIESIN_SEDAC_HANPP_PERCENT", "CIESIN_SEDAC_HASO2_SO2_1850-2005", "CIESIN_SEDAC_ICWQ_ACHLOA", "CIESIN_SEDAC_ICWQ_ANC", "CIESIN_SEDAC_ICWQ_CCHLOA", "CIESIN_SEDAC_INDIA_AWCA", "CIESIN_SEDAC_INDIA_CENSUS_2011", "CIESIN_SEDAC_INDIA_VILSOCIOECON", "CIESIN_SEDAC_IPCC_BASELINE", "CIESIN_SEDAC_IPCC_GRI_MIDYR_0.3.7", "CIESIN_SEDAC_IPCC_IS92_V11", "CIESIN_SEDAC_IPCC_OBSERVED", "CIESIN_SEDAC_IPCC_OBSERVED_AR5", "CIESIN_SEDAC_IPCC_SAGDVCC", "CIESIN_SEDAC_IPCC_SRES_1X1EM", "CIESIN_SEDAC_IPCC_SRES_EMSC_V11", "CIESIN_SEDAC_IPCC_SRES_FLGASES", "CIESIN_SEDAC_LECZ_DELTA_V1", "CIESIN_SEDAC_LECZ_SLRIRAMSARWII", "CIESIN_SEDAC_LECZ_URPEV1", "CIESIN_SEDAC_LECZ_URPLAEV2", "CIESIN_SEDAC_LECZ_URPLAEV3", "CIESIN_SEDAC_LULC_CAMVEGLCCCS", "CIESIN_SEDAC_LULC_DPI", "CIESIN_SEDAC_LULC_DT1_V1", "CIESIN_SEDAC_LULC_HMTS", "CIESIN_SEDAC_LULC_MANGROVES_2000", "CIESIN_SEDAC_LULC_PUEXPANS_2030", "CIESIN_SEDAC_LWP2_HF_GEOG", "CIESIN_SEDAC_LWP2_HF_IGHP", "CIESIN_SEDAC_LWP2_HII_GEOG", "CIESIN_SEDAC_LWP2_HII_IGHP", "CIESIN_SEDAC_LWP2_LW_GEOG", "CIESIN_SEDAC_LWP2_LW_IGHP", "CIESIN_SEDAC_LWP3_HF_1993", "CIESIN_SEDAC_LWP3_HF_2009", "CIESIN_SEDAC_LWP_HFP_GEOG", "CIESIN_SEDAC_LWP_HFP_IGHP", "CIESIN_SEDAC_LWP_LW_GEOG", "CIESIN_SEDAC_LWP_LW_IGHP", "CIESIN_SEDAC_LWP_TOPWA_GEOG", "CIESIN_SEDAC_LWP_TOPWA_IGHP", "CIESIN_SEDAC_MA_BIODIVERSITY", "CIESIN_SEDAC_MA_CLIMLC", "CIESIN_SEDAC_MA_ECOSYSTEMS", "CIESIN_SEDAC_MA_POPULATION", "CIESIN_SEDAC_MA_RLCC", "CIESIN_SEDAC_MA_SCENARIOS", "CIESIN_SEDAC_NAGDC_PLACE", "CIESIN_SEDAC_NAGDC_PLACEII", "CIESIN_SEDAC_NAGDC_PLACEIII", "CIESIN_SEDAC_NAGDC_PLACEIV", "CIESIN_SEDAC_NRMI_2010", "CIESIN_SEDAC_NRMI_2011", "CIESIN_SEDAC_NRMI_NRPCHI12", "CIESIN_SEDAC_NRMI_NRPCHI13", "CIESIN_SEDAC_NRMI_NRPCHI14", "CIESIN_SEDAC_NRMI_NRPCHI15", "CIESIN_SEDAC_NRMI_NRPCHI16", "CIESIN_SEDAC_NRMI_NRPCHI17", "CIESIN_SEDAC_NRMI_NRPCHI18", "CIESIN_SEDAC_NRMI_NRPCHI19", "CIESIN_SEDAC_NRMI_NRPCHI20", "CIESIN_SEDAC_NRMI_NRPCHI21", "CIESIN_SEDAC_NRMI_NRPCHI22", "CIESIN_SEDAC_NRMI_NRPCHI23", "CIESIN_SEDAC_PD_LACPOP", "CIESIN_SEDAC_PD_NETMIG_1970_2000", "CIESIN_SEDAC_PD_POPCGTSE", "CIESIN_SEDAC_PD_POPDGTSE", "CIESIN_SEDAC_PD_PPUSCASRCSSP", "CIESIN_SEDAC_PD_SSPBSYR_1_8th", "CIESIN_SEDAC_PD_SSPBSYR_1km", "CIESIN_SEDAC_PEND_ALLOMSSF3", "CIESIN_SEDAC_PEND_ALLOMSTXSF1", "CIESIN_SEDAC_PEND_GDIS", "CIESIN_SEDAC_PEND_HSF1", "CIESIN_SEDAC_PEND_NOSF1", "CIESIN_SEDAC_PEND_NOSF3", "CIESIN_SEDAC_PMP_GRDI_2010_2020", "CIESIN_SEDAC_PMP_GSPCM", "CIESIN_SEDAC_PMP_IMR", "CIESIN_SEDAC_PMP_IMR_V2.01", "CIESIN_SEDAC_PMP_PFSCS", "CIESIN_SEDAC_PMP_SAEPI", "CIESIN_SEDAC_PMP_UBN", "CIESIN_SEDAC_SDEI_APM25_URBAN", "CIESIN_SEDAC_SDEI_GEHE", "CIESIN_SEDAC_SDEI_GFA_GRACE", "CIESIN_SEDAC_SDEI_GFED4_CLTAB", "CIESIN_SEDAC_SDEI_GFED4_GRIDS", "CIESIN_SEDAC_SDEI_GWRPM25_MMSAOD_4GL03", "CIESIN_SEDAC_SDEI_LST2013", "CIESIN_SEDAC_SDEI_NO2", "CIESIN_SEDAC_SDEI_UHE", "CIESIN_SEDAC_SDEI_UHI2013", "CIESIN_SEDAC_SDEI_VDL", "CIESIN_SEDAC_SDEI_YCEOUHI_V4", "CIESIN_SEDAC_SDGI_ACCELEC_2023", "CIESIN_SEDAC_SDGI_RAI_2023", "CIESIN_SEDAC_SDGI_UAPT_2023", "CIESIN_SEDAC_SDGI_UPSAE_2023", "CIESIN_SEDAC_SDP_CGDP_A1A2B1B2", "CIESIN_SEDAC_SDP_CPOP_A1B1A2", "CIESIN_SEDAC_SDP_CPOP_B2", "CIESIN_SEDAC_SDP_GGDP_B2", "CIESIN_SEDAC_SDP_GPOP_B2", "CIESIN_SEDAC_SPATIALECON_GECON4", "CIESIN_SEDAC_SPATIALECON_LGII_V1", "CIESIN_SEDAC_SPECIES_AMP_RICH15", "CIESIN_SEDAC_SPECIES_MAM_RICH15", "CIESIN_SEDAC_SSF_EPANPLSPDCMv2", "CIESIN_SEDAC_SSF_HWSPDv2", "CIESIN_SEDAC_SSP_1-8thDULEPBYGSSP", "CIESIN_SEDAC_SSP_1-kmDULEPBYGSSP", "CIESIN_SEDAC_SSP_GSNLITDB_V1", "CIESIN_SEDAC_SSP_LITDB_V1", "CIESIN_SEDAC_ULANDSAT_GMIS_V1", "CIESIN_SEDAC_ULANDSAT_HBASE_V1", "CIESIN_SEDAC_ULANDSAT_URBAN_LANDSAT", "CIESIN_SEDAC_USCG_90SF1", "CIESIN_SEDAC_USCG_90SF1MSA", "CIESIN_SEDAC_USCG_90SF3", "CIESIN_SEDAC_USCG_90SF3MSA", "CIESIN_SEDAC_USCG_SF1", "CIESIN_SEDAC_USCG_SF12010", "CIESIN_SEDAC_USCG_SF1MSA", "CIESIN_SEDAC_USCG_SF3", "CIESIN_SEDAC_USCG_SF3MSA", "CIESIN_SEDAC_USCG_USSVIG01", "CIESIN_SEDAC_USPAT_BSCATTER_1993_2020", "CIESIN_SEDAC_USPAT_DESLUIS", "CIESIN_SEDAC_USPAT_GUPPD_V1", "CIESIN_SEDAC_USPAT_HUP", "CIESIN_SEDAC_USPAT_USUEXT2015", "CIESIN_SEDAC_WACVM_CCPROD", "CIESIN_SEDAC_WACVM_DEFOR200012", "CIESIN_SEDAC_WACVM_DHS", "CIESIN_SEDAC_WACVM_ECONSI", "CIESIN_SEDAC_WACVM_GPWv4PR_PD", "CIESIN_SEDAC_WACVM_GPWv4PR_PG", "CIESIN_SEDAC_WACVM_GSSPEPP", "CIESIN_SEDAC_WACVM_JRCMA", "CIESIN_SEDAC_WACVM_MFD2000_POL", "CIESIN_SEDAC_WACVM_OSM_ROADS", "CIESIN_SEDAC_WACVM_PGLF_200813", "CIESIN_SEDAC_WACVM_POPPROJ_203050", "CIESIN_SEDAC_WACVM_SDMSPOLSNLEA_2010", "CIESIN_SEDAC_WACVM_SMAMAMP_RICH", "CIESIN_SEDAC_WACVM_SUBSETACE2", "CIESIN_SEDAC_WACVM_SVI", "CIESIN_SEDAC_WATER_WSIM_GLDAS_V1", "CL07LC", "CL07PLBK", "CL07PLTB", "CL07SM", "CL07ST", "CL07V", "CL07VWC", "CLAMS_CERES_CHESLIGHT_SONDE", "CLAMS_CV580_CAR", "CLAMS_ER2_MAS", "CLAMS_MODIS_L2_AEROSOL_PRODUCTS", "CLAMS_UWASH_CONVAIR_DATA", "CLDMSK_L2_MODIS_Aqua", "CLDMSK_L2_VIIRS_NOAA20", "CLDMSK_L2_VIIRS_NOAA20_NRT", "CLDMSK_L2_VIIRS_SNPP", "CLDMSK_L2_VIIRS_SNPP_NRT", "CLDPROPCOSP_D3_MODIS_Aqua", "CLDPROPCOSP_D3_VIIRS_NOAA20", "CLDPROPCOSP_D3_VIIRS_SNPP", "CLDPROPCOSP_M3_MODIS_Aqua", "CLDPROPCOSP_M3_VIIRS_NOAA20", "CLDPROPCOSP_M3_VIIRS_SNPP", "CLDPROP_D3_MODIS_Aqua", "CLDPROP_D3_VIIRS_NOAA20", "CLDPROP_D3_VIIRS_SNPP", "CLDPROP_M3_MODIS_Aqua", "CLDPROP_M3_VIIRS_NOAA20", "CLDPROP_M3_VIIRS_SNPP", "CLIVAR", "CMAQ-N_Module_1661", "CMC0.1deg-CMC-L4-GLOB-v3.0", "CMC0.2deg-CMC-L4-GLOB-v2.0", "CMRMIAAE", "CMRMIALS", "CMRMIGEO", "CMRMITST", "CMSFluxFire", "CMSFluxFire", "CMSFluxFirepost", "CMSFluxFossilFuelPrior", "CMSFluxFossilFuelPrior", "CMSFluxFossilfuel", "CMSFluxLandPrior", "CMSFluxMISC", "CMSFluxNBE", "CMSFluxNBE", "CMSFluxNBEPrior", "CMSFluxNEE", "CMSFluxOcean", "CMSFluxOcean", "CMSFluxOceanPrior", "CMSFluxOceanPrior", "CMSFluxTotal", "CMSFluxTotal", "CMSFluxTotalPrior", "CMSFluxTotalpost", "CMSFluxTotalprior", "CMSGCH4F", "CMSLakeHuronPPM", "CMSLakeHuronPPY", "CMSLakeMichiganPPM", "CMSLakeMichiganPPY", "CMSLakeSuperiorPPM", "CMSLakeSuperiorPPY", "CMS_AGB_Landcover_Indonesia_1645", "CMS_AGB_NW_USA_1719", "CMS_CH4_FLX_CA", "CMS_CH4_FLX_MX", "CMS_CH4_FLX_NAD", "CMS_CO2_Fluxes_TBMO_1315", "CMS_CONUS_Biomass_1752", "CMS_CTL_NA_GOSAT_FOOTPRINTS", "CMS_CTL_NA_OCO2_FOOTPRINTS", "CMS_CTL_NA_TCCON_FOOTPRINTS", "CMS_CTL_SA_OCO2_FOOTPRINTS", "CMS_Coastal_Wetland_Resilience_1839", "CMS_DARTE_V2_1735", "CMS_Daily_ET_MexFlux_1309", "CMS_EFT_CONUS_1659", "CMS_Fire_Weather_Data_AK_1509", "CMS_FluxEstimates_Aircraft_CO2_2336", "CMS_Forest_Carbon_Fluxes_1313", "CMS_Forest_Carbon_Maryland_1660", "CMS_Forest_Productivity_1221", "CMS_GO_CH4_SEC_TDYC_NA", "CMS_Global_Cropland_Carbon_1279", "CMS_Global_Fire_Atlas_1642", "CMS_Global_Forest_AGC_2180", "CMS_Global_Forest_Age_2345", "CMS_Global_Livestock_CH4_CO2_1329", "CMS_Global_Mangrove_Forest_Ht_2251", "CMS_Global_Mangrove_Loss_1768", "CMS_Global_Map_Mangrove_Canopy_1665", "CMS_Global_Monthly_Wetland_CH4_1502", "CMS_Global_Soil_Respiration_1736", "CMS_Great_Basin_Biomass_1755", "CMS_HR_MNA_CH4_FLUX", "CMS_Landcover_Indonesia_1838", "CMS_Landscapes_Brazil_Forests_1301", "CMS_Landscapes_Brazil_LiDAR_1302", "CMS_LiDAR_AGB_California_1537", "CMS_LiDAR_AGB_PEF_2012_1318", "CMS_LiDAR_Biomass_CanHt_Sonoma_1523", "CMS_LiDAR_Biomass_MD_PA_DE_1538", "CMS_LiDAR_Indonesia_1518", "CMS_LiDAR_Point_Cloud_Zambezi_1521", "CMS_LiDAR_Products_Indonesia_1540", "CMS_Mangrove_Biomass_Zambezi_1522", "CMS_Mangrove_CanHt_Stand_Age_1377", "CMS_Mangrove_Canopy_Height_1327", "CMS_Mangrove_Canopy_Ht_Zambezi_1357", "CMS_Mangrove_Cover_1670", "CMS_Maryland_AGB_Canopy_1320", "CMS_Methane_Emissions_Boston_1291", "CMS_Monthly_CO2_Gulf_of_Mexico_1668", "CMS_OCE_BGC_CCS", "CMS_Pantropical_Forest_Biomass_1337", "CMS_Pennsylvania_Tree_Cover_1334", "CMS_Pilot_Biomass_1257", "CMS_SABGOM_Model_Simulations_1510", "CMS_SOC_Mexico_1754", "CMS_SOC_Mexico_CONUS_1737", "CMS_SST_GPP_Mexico_1310", "CMS_Simulated_SIF_NiwotRidge_1720", "CMS_Soil_CO2_Efflux_1298", "CMS_WFEIS_CONUS-AK_1306", "CMS_WRF_Footprints_CO2_Signals_1381", "CMS_WRF_Model_Products_1338", "CO2Fluxes_Arctic_Boreal_Domain_2377", "COASTAL", "COMEX_AJAX_CO2_CH4_2347", "COMEX_AVIRIS_Classic_Flights_2343", "COMEX_AVIRIS_NG_Flights_2342", "COMEX_LongwaveInfrared_Imagery_2331", "COOA", "CORAL", "CPEXAW-ADM-Aeolus", "CPEXAW-DAWN_DC8", "CPEXAW-Dropsondes", "CPEXAW-HALO_DC8", "CPEXCV-DAWN_DC8", "CPEXCV-Dropsondes", "CPEXCV-HALO_DC8", "CPEXCV_Cloud_AircraftInSitu_DC8_Data", "CPEXCV_Merge_Data", "CPL_ABL_Top_Height_1825", "CRP", "CStocks_Greenness_Mangroves_MX_1853", "CUSTARD", "CV-580_710", "CYGNSS_L1_CAL_RAW_IF_V1.0", "CYGNSS_L1_CDR_V1.0", "CYGNSS_L1_CDR_V1.1", "CYGNSS_L1_CDR_V1.2", "CYGNSS_L1_FULL_DDM", "CYGNSS_L1_FULL_DDM_V3.0", "CYGNSS_L1_RAW_IF", "CYGNSS_L1_V2.1", "CYGNSS_L1_V3.0", "CYGNSS_L1_V3.2", "CYGNSS_L2_CDR_V1.0", "CYGNSS_L2_CDR_V1.1", "CYGNSS_L2_CDR_V1.2", "CYGNSS_L2_SURFACE_FLUX_CDR_V1.0", "CYGNSS_L2_SURFACE_FLUX_CDR_V1.1", "CYGNSS_L2_SURFACE_FLUX_CDR_V1.2", "CYGNSS_L2_SURFACE_FLUX_V1.0", "CYGNSS_L2_SURFACE_FLUX_V2.0", "CYGNSS_L2_SURFACE_FLUX_V3.2", "CYGNSS_L2_V2.1", "CYGNSS_L2_V3.0", "CYGNSS_L2_V3.2", "CYGNSS_L3_CDR_V1.0", "CYGNSS_L3_CDR_V1.1", "CYGNSS_L3_CDR_V1.2", "CYGNSS_L3_MICROPLASTIC_V1.0", "CYGNSS_L3_MICROPLASTIC_V3.2", "CYGNSS_L3_MRG_NRT_V3.2", "CYGNSS_L3_MRG_NRT_V3.2.1", "CYGNSS_L3_MRG_V3.2", "CYGNSS_L3_MRG_V3.2.1", "CYGNSS_L3_S1.0", "CYGNSS_L3_SOIL_MOISTURE_V1.0", "CYGNSS_L3_SOIL_MOISTURE_V3.2", "CYGNSS_L3_UC_BERKELEY_WATERMASK_DAILY_V3.2", "CYGNSS_L3_UC_BERKELEY_WATERMASK_V3.1", "CYGNSS_L3_V2.1", "CYGNSS_L3_V3.0", "CYGNSS_L3_V3.1", "CYGNSS_L3_V3.2", "CYGNSS_NOAA_L2_SWSP_25KM_V1.1", "CYGNSS_NOAA_L2_SWSP_25KM_V1.2", "CZCS_L1", "CZCS_L2_OC", "CZCS_L3b_CHL", "CZCS_L3b_KD", "CZCS_L3b_RRS", "CZCS_L3m_CHL", "CZCS_L3m_KD", "CZCS_L3m_RRS", "C_FluxStocks_CLM5_DART_WestUS_1856", "C_Pools_Fluxes_CONUS_1837", "CaTS", "CalCOFI", "California_2002", "CanSIS_Regional_Soils_1347", "Canada_Boreal_Forest_Greenness_1587", "Canadian_West_Arctic_Veg_Plots_1543", "CapeHatteras2010", "Carbon_Estuaries", "Carbon_Transport_MS_River", "Carbon_UAV", "Cartagena_Station", "Catlin_Arctic_Survey", "ChesBay", "Chesapeake_Bay_Helicopter", "Chesapeake_Bay_Plume", "Chesapeake_Bay_Water_Quality", "Chesapeake_Light_Tower", "Chlorophyll_Fluorescence_Data_1785", "CircumArctic_Trends_Hotspots_2322", "CliVEC", "Climate_Normals_Modern_LGM_AK_1663", "CoJet", "Coastal_Carbon_Budget_NE_US_1594", "Coastal_US_Elevation_Data_1844", "Continuous_Lifeform_Maps_CONUS_1809", "Country_SOC_Latin_America_1615", "CramerLeemans_416", "Cropland_Carbon_Fluxes_2125", "Crops_SIF_VegIndices_IL_NE_2136", "CubeSat_Arctic_Boreal_LakeArea_1667", "Cyanate", "DC3_Aerosol_AircraftInSitu_DC8_Data", "DC3_Aerosol_AircraftInSitu_DLR-Falcon_Data", "DC3_Aerosol_AircraftInSitu_NSF-GV-HIAPER_Data", "DC3_AircraftRemoteSensing_DIAL_DC8_Data", "DC3_Cloud_AircraftInSitu_DC8_Data", "DC3_Cloud_AircraftInSitu_NSF-GV-HIAPER_Data", "DC3_Merge_Data", "DC3_MetNav_AircraftInSitu_DC8_Data", "DC3_MetNav_AircraftInSitu_DLR-Falcon_Data", "DC3_MetNav_AircraftInSitu_NSF-GV-HIAPER_Data", "DC3_Miscellaneous_DC8_Data", "DC3_Miscellaneous_DLR-Falcon_Data", "DC3_Miscellaneous_NSF-GV-HIAPER_Data", "DC3_Radiation_AircraftInSitu_DC8_Data", "DC3_TraceGas_AircraftInSitu_DC8_Data", "DC3_TraceGas_AircraftInSitu_DLR-Falcon_Data", "DC3_TraceGas_AircraftInSitu_NSF-GV-HIAPER_Data", "DC3_jValue_AircraftInSitu_DC8_Data", "DC3_jValue_AircraftInSitu_NSF-GV-HIAPER_Data", "DCOTSS-Aircraft-Data", "DCOTSS-Balloon-Data", "DCOTSS-Model-Output", "DCOTSS-Radar-Satellite-Data", "DCOTSS-Reports", "DECKLAB", "DEVOTE_Aerosol_AircraftInSitu_B200_Data", "DEVOTE_AircraftRemoteSensing_UC12_HSRL_Data", "DEVOTE_AircraftRemoteSensing_UC12_RSP_Data", "DEVOTE_MetNav_AircraftInSitu_B200_Data", "DEVOTE_MetNav_AircraftInSitu_UC12_Data", "DFO_Canada_Time_Series", "DISCOVERAQ_California_Aerosol_AircraftInSitu_P3B_Data", "DISCOVERAQ_California_AircraftRemoteSensing_B200_ACAM_Data", "DISCOVERAQ_California_AircraftRemoteSensing_B200_HSRL2_Data", "DISCOVERAQ_California_Ground_Analysis_Ancillary_Data", "DISCOVERAQ_California_Ground_Bakersfield_Data", "DISCOVERAQ_California_Ground_CARB_Data", "DISCOVERAQ_California_Ground_Fresno_Data", "DISCOVERAQ_California_Ground_Huron_Data", "DISCOVERAQ_California_Ground_Oildale_Data", "DISCOVERAQ_California_Ground_Pandora_Data", "DISCOVERAQ_California_Ground_Porterville_Data", "DISCOVERAQ_California_Ground_SJV_Data", "DISCOVERAQ_California_Merge_Data", "DISCOVERAQ_California_MetNav_AircraftInSitu_B200_Data", "DISCOVERAQ_California_MetNav_AircraftInSitu_P3B_Data", "DISCOVERAQ_California_Ozonesondes_Data", "DISCOVERAQ_California_TraceGas_AircraftInSitu_P3B_Data", "DISCOVERAQ_Colorado_Aerosol_AircraftInSitu_P3B_Data", "DISCOVERAQ_Colorado_AircraftRemoteSensing_B200_ACAM_Data", "DISCOVERAQ_Colorado_AircraftRemoteSensing_B200_GCAS_Data", "DISCOVERAQ_Colorado_AircraftRemoteSensing_B200_HSRL2_Data", "DISCOVERAQ_Colorado_AircraftRemoteSensing_Falcon_GeoTASO_Data", "DISCOVERAQ_Colorado_Ground_BAOTower_Data", "DISCOVERAQ_Colorado_Ground_CDPHE_Data", "DISCOVERAQ_Colorado_Ground_EPA_Data", "DISCOVERAQ_Colorado_Ground_FortCollins_Data", "DISCOVERAQ_Colorado_Ground_Mobile_Data", "DISCOVERAQ_Colorado_Ground_NREL-Golden_Data", "DISCOVERAQ_Colorado_Ground_Pandora_Data", "DISCOVERAQ_Colorado_Ground_Platteville_Data", "DISCOVERAQ_Colorado_Ground_TableMountain_Data", "DISCOVERAQ_Colorado_Merge_Data", "DISCOVERAQ_Colorado_MetNav_AircraftInSitu_B200_Data", "DISCOVERAQ_Colorado_MetNav_AircraftInSitu_P3B_Data", "DISCOVERAQ_Colorado_Model_Data", "DISCOVERAQ_Colorado_Ozonesondes_Data", "DISCOVERAQ_Colorado_Radiation_AircraftRemoteSensing_CAR_Data", "DISCOVERAQ_Colorado_TraceGas_AircraftInSitu_P3B_Data", "DISCOVERAQ_Maryland_Aerosol_AircraftInSitu_P3B_Data", "DISCOVERAQ_Maryland_AircraftRemoteSensing_UC12_ACAM_Data", "DISCOVERAQ_Maryland_AircraftRemoteSensing_UC12_HSRL_Data", "DISCOVERAQ_Maryland_Ground_Aldino_Data", "DISCOVERAQ_Maryland_Ground_Analysis_Ancillary_Data", "DISCOVERAQ_Maryland_Ground_Beltsville_Data", "DISCOVERAQ_Maryland_Ground_Edgewood_Data", "DISCOVERAQ_Maryland_Ground_Essex_Data", "DISCOVERAQ_Maryland_Ground_Fairhill_Data", "DISCOVERAQ_Maryland_Ground_Oldtown_Data", "DISCOVERAQ_Maryland_Ground_Padonia_Data", "DISCOVERAQ_Maryland_Ground_Pandora_Data", "DISCOVERAQ_Maryland_Ground_UMBC_Data", "DISCOVERAQ_Maryland_Merge_Data", "DISCOVERAQ_Maryland_MetNav_AircraftInSitu_P3B_Data", "DISCOVERAQ_Maryland_MetNav_AircraftInSitu_UC12_Data", "DISCOVERAQ_Maryland_Ozonesondes_Data", "DISCOVERAQ_Maryland_Ship_NOAA-DelawareII_Data", "DISCOVERAQ_Maryland_TraceGas_AircraftInSitu_P3B_Data", "DISCOVERAQ_Maryland_UMDAircraft_Data", "DISCOVERAQ_Texas_Aerosol_AircraftInSitu_P3B_Data", "DISCOVERAQ_Texas_AircraftRemoteSensing_B200_ACAM_Data", "DISCOVERAQ_Texas_AircraftRemoteSensing_B200_GCAS_Data", "DISCOVERAQ_Texas_AircraftRemoteSensing_B200_HSRL2_Data", "DISCOVERAQ_Texas_AircraftRemoteSensing_Falcon_GeoTASO_Data", "DISCOVERAQ_Texas_Ground_Ancillary_Analysis_Data", "DISCOVERAQ_Texas_Ground_EPA_Data", "DISCOVERAQ_Texas_Ground_Galveston_Data", "DISCOVERAQ_Texas_Ground_LaPorteAirport_Data", "DISCOVERAQ_Texas_Ground_ManvelCroix_Data", "DISCOVERAQ_Texas_Ground_MoodyTower_Data", "DISCOVERAQ_Texas_Ground_Pandora_Data", "DISCOVERAQ_Texas_Ground_SmithPoint_Data", "DISCOVERAQ_Texas_Ground_TCEQ_Data", "DISCOVERAQ_Texas_Merge_Data", "DISCOVERAQ_Texas_MetNav_AircraftInSitu_B200_Data", "DISCOVERAQ_Texas_MetNav_AircraftInSitu_P3B_Data", "DISCOVERAQ_Texas_Ozonesondes_Data", "DISCOVERAQ_Texas_TraceGas_AircraftInSitu_P3B_Data", "DISCover_land_cover_679", "DLEM_C_N_Export_1699", "DMI_OI-DMI-L4-GLOB-v1.0", "DSCOVR_EPIC_L2_COMPOSITE", "DSCOVR_EPIC_L2_COMPOSITE", "DSCOVR_EPIC_L3_PAR", "DSCOVR_EPIC_L3_PAR-IMAGE", "DSCOVR_EPIC_L4_TrO3", "DSCOVR_NISTAR_L2_FLX", "DUSTFLEXPART", "Daily_FineParticulateMatter_AK_2157", "Dairy_Methane_CA_V1-2_1902", "Dall_Sheep_Population_Dynamics_1640", "Dall_Sheep_Snowpack_1602", "Daymet_Annual_V4R1_2130", "Daymet_Daily_V4R1_2129", "Daymet_Monthly_V4R1_2131", "Daymet_SubDaily_Puerto_Rico_1977", "Daymet_V4_Daily_MonthlyLatency_1904", "Daymet_xval_V4R1_2132", "Decadal_LULC_India_1336", "Decadal_Water_Maps_1324", "DeciduousFractionl_CanopyCover_2296", "Del_Ches_Bay_Fluorescence", "DeltaX_ADCP_Measurements_V2_2081", "DeltaX_AGB_AGN_V2_2237", "DeltaX_ANUGA_AtchafalayaBasin_2306", "DeltaX_ANUGA_Hydrodynamics_MRD_2310", "DeltaX_BGB_BGN_V2_2238", "DeltaX_BG_Root_Stable_Isotopes_2193", "DeltaX_DEM_MRD_LA_2181", "DeltaX_Delft3D_294_Terrebonne_2303", "DeltaX_Delft3D_322_Terrebonne_2312", "DeltaX_Delft3D_396_Terrebonne_2314", "DeltaX_Delft3D_399_Terrebonne_2313", "DeltaX_Delft3D_421_Terrebonne_2304", "DeltaX_Delft3D_Atchafalaya_MRD_2302", "DeltaX_Delft3D_Terrebonne_MRD_2301", "DeltaX_Ecogeomorphic_Products_2108", "DeltaX_Feldspar_Sediment_V3_2290", "DeltaX_Foliar_Stable_Isotopes_2194", "DeltaX_H2O_Surface_Elevation_2086", "DeltaX_Herb_WetlandSoil_V3_2239", "DeltaX_Insitu_POC_2073", "DeltaX_Insitu_Reflectance_V3_2153", "DeltaX_Insitu_WQ_Indicators_V2_2080", "DeltaX_Island_Channel_Model_2106", "DeltaX_L1B_UAVSAR_WaterLevels_1979", "DeltaX_L1_AVIRIS_Radiance_1987", "DeltaX_L1_UAVSAR_SLC_Stack_1984", "DeltaX_L1b_AirSWOT_1996", "DeltaX_L2A_AVIRIS-NG_BRDF_V2_2139", "DeltaX_L2_AVIRIS_Reflectance_1988", "DeltaX_L2_AirSWOT_WaterElev_V3_2350", "DeltaX_L2_UAVSAR_WaterLevels_2057", "DeltaX_L3_AVIRIS-NG_AGB_V2_2138", "DeltaX_L3_AVIRIS-NG_Veg_Types_2352", "DeltaX_L3_AVIRIS-NG_Water_V3_2152", "DeltaX_L3_AirSWOT_WaterElev_V2_2349", "DeltaX_L3_UAVSAR_WaterLevels_2058", "DeltaX_LandAccretionMap_WLD_2308", "DeltaX_MarshAccretion_NUMAR_2354", "DeltaX_Particle_Size_LISST_V2_2077", "DeltaX_RTK_Elevation_2071", "DeltaX_Sediment_Grain_Size_V2_2135", "DeltaX_Sonar_Bathymetry_2085", "DeltaX_TSS_Concentration_V2_2075", "DeltaX_TotalSubsidenceRate_MRD_2307", "DeltaX_Turbidity_Data_V4_2241", "DeltaX_UAVSAR_L3_ChannelMap_V2_2344", "DeltaX_Vegetation_Structure_V2_2240", "Dendrophenology_Eastern_US_1369", "Dissolved_Gases_Alaska_Rivers_2360", "Disturbance_Biomass_Maps_1679", "Dolche-Vita", "Dunne_545", "ECCO_L4_ANCILLARY_DATA_V4R4", "ECCO_L4_ATM_STATE_05DEG_DAILY_V4R4", "ECCO_L4_ATM_STATE_05DEG_MONTHLY_V4R4", "ECCO_L4_ATM_STATE_LLC0090GRID_DAILY_V4R4", "ECCO_L4_ATM_STATE_LLC0090GRID_MONTHLY_V4R4", "ECCO_L4_BOLUS_05DEG_DAILY_V4R4", "ECCO_L4_BOLUS_05DEG_MONTHLY_V4R4", "ECCO_L4_BOLUS_LLC0090GRID_DAILY_V4R4", "ECCO_L4_BOLUS_LLC0090GRID_MONTHLY_V4R4", "ECCO_L4_DENS_STRAT_PRESS_05DEG_DAILY_V4R4", "ECCO_L4_DENS_STRAT_PRESS_05DEG_MONTHLY_V4R4", "ECCO_L4_DENS_STRAT_PRESS_LLC0090GRID_DAILY_V4R4", "ECCO_L4_DENS_STRAT_PRESS_LLC0090GRID_MONTHLY_V4R4", "ECCO_L4_FRESH_FLUX_05DEG_DAILY_V4R4", "ECCO_L4_FRESH_FLUX_05DEG_MONTHLY_V4R4", "ECCO_L4_FRESH_FLUX_LLC0090GRID_DAILY_V4R4", "ECCO_L4_FRESH_FLUX_LLC0090GRID_MONTHLY_V4R4", "ECCO_L4_GEOMETRY_05DEG_V4R4", "ECCO_L4_GEOMETRY_LLC0090GRID_V4R4", "ECCO_L4_GMAP_TIME_SERIES_SNAPSHOT_V4R4", "ECCO_L4_GMAP_TIME_SERIES_SNAPSHOT_V4R4B", "ECCO_L4_GMSL_TIME_SERIES_DAILY_V4R4", "ECCO_L4_GMSL_TIME_SERIES_MONTHLY_V4R4", "ECCO_L4_HEAT_FLUX_05DEG_DAILY_V4R4", "ECCO_L4_HEAT_FLUX_05DEG_MONTHLY_V4R4", "ECCO_L4_HEAT_FLUX_LLC0090GRID_DAILY_V4R4", "ECCO_L4_HEAT_FLUX_LLC0090GRID_MONTHLY_V4R4", "ECCO_L4_MIXED_LAYER_DEPTH_05DEG_DAILY_V4R4", "ECCO_L4_MIXED_LAYER_DEPTH_05DEG_MONTHLY_V4R4", "ECCO_L4_MIXED_LAYER_DEPTH_LLC0090GRID_DAILY_V4R4", "ECCO_L4_MIXED_LAYER_DEPTH_LLC0090GRID_MONTHLY_V4R4", "ECCO_L4_OBP_05DEG_DAILY_V4R4", "ECCO_L4_OBP_05DEG_DAILY_V4R4B", "ECCO_L4_OBP_05DEG_MONTHLY_V4R4", "ECCO_L4_OBP_05DEG_MONTHLY_V4R4B", "ECCO_L4_OBP_LLC0090GRID_DAILY_V4R4", "ECCO_L4_OBP_LLC0090GRID_DAILY_V4R4B", "ECCO_L4_OBP_LLC0090GRID_MONTHLY_V4R4", "ECCO_L4_OBP_LLC0090GRID_MONTHLY_V4R4B", "ECCO_L4_OBP_LLC0090GRID_SNAPSHOT_V4R4", "ECCO_L4_OCEAN_3D_MIX_COEFFS_05DEG_V4R4", "ECCO_L4_OCEAN_3D_MIX_COEFFS_LLC0090GRID_V4R4", "ECCO_L4_OCEAN_3D_MOMENTUM_TEND_LLC0090GRID_DAILY_V4R4", "ECCO_L4_OCEAN_3D_MOMENTUM_TEND_LLC0090GRID_MONTHLY_V4R4", "ECCO_L4_OCEAN_3D_SALINITY_FLUX_LLC0090GRID_DAILY_V4R4", "ECCO_L4_OCEAN_3D_SALINITY_FLUX_LLC0090GRID_MONTHLY_V4R4", "ECCO_L4_OCEAN_3D_TEMPERATURE_FLUX_LLC0090GRID_DAILY_V4R4", "ECCO_L4_OCEAN_3D_TEMPERATURE_FLUX_LLC0090GRID_MONTHLY_V4R4", "ECCO_L4_OCEAN_3D_VOLUME_FLUX_LLC0090GRID_DAILY_V4R4", "ECCO_L4_OCEAN_3D_VOLUME_FLUX_LLC0090GRID_MONTHLY_V4R4", "ECCO_L4_OCEAN_BOLUS_STREAMFUNCTION_LLC0090GRID_DAILY_V4R4", "ECCO_L4_OCEAN_BOLUS_STREAMFUNCTION_LLC0090GRID_MONTHLY_V4R4", "ECCO_L4_OCEAN_VEL_05DEG_DAILY_V4R4", "ECCO_L4_OCEAN_VEL_05DEG_MONTHLY_V4R4", "ECCO_L4_OCEAN_VEL_LLC0090GRID_DAILY_V4R4", "ECCO_L4_OCEAN_VEL_LLC0090GRID_MONTHLY_V4R4", "ECCO_L4_SBO_CORE_TIME_SERIES_SNAPSHOT_V4R4", "ECCO_L4_SBO_CORE_TIME_SERIES_SNAPSHOT_V4R4B", "ECCO_L4_SEA_ICE_CONC_THICKNESS_05DEG_DAILY_V4R4", "ECCO_L4_SEA_ICE_CONC_THICKNESS_05DEG_MONTHLY_V4R4", "ECCO_L4_SEA_ICE_CONC_THICKNESS_LLC0090GRID_DAILY_V4R4", "ECCO_L4_SEA_ICE_CONC_THICKNESS_LLC0090GRID_MONTHLY_V4R4", "ECCO_L4_SEA_ICE_CONC_THICKNESS_LLC0090GRID_SNAPSHOT_V4R4", "ECCO_L4_SEA_ICE_HORIZ_VOLUME_FLUX_LLC0090GRID_DAILY_V4R4", "ECCO_L4_SEA_ICE_HORIZ_VOLUME_FLUX_LLC0090GRID_MONTHLY_V4R4", "ECCO_L4_SEA_ICE_SALT_PLUME_FLUX_LLC0090GRID_DAILY_V4R4", "ECCO_L4_SEA_ICE_SALT_PLUME_FLUX_LLC0090GRID_MONTHLY_V4R4", "ECCO_L4_SEA_ICE_VELOCITY_05DEG_DAILY_V4R4", "ECCO_L4_SEA_ICE_VELOCITY_05DEG_MONTHLY_V4R4", "ECCO_L4_SEA_ICE_VELOCITY_LLC0090GRID_DAILY_V4R4", "ECCO_L4_SEA_ICE_VELOCITY_LLC0090GRID_MONTHLY_V4R4", "ECCO_L4_SEA_ICE_VELOCITY_LLC0090GRID_SNAPSHOT_V4R4", "ECCO_L4_SSH_05DEG_DAILY_V4R4", "ECCO_L4_SSH_05DEG_DAILY_V4R4B", "ECCO_L4_SSH_05DEG_MONTHLY_V4R4", "ECCO_L4_SSH_05DEG_MONTHLY_V4R4B", "ECCO_L4_SSH_LLC0090GRID_DAILY_V4R4", "ECCO_L4_SSH_LLC0090GRID_DAILY_V4R4B", "ECCO_L4_SSH_LLC0090GRID_MONTHLY_V4R4", "ECCO_L4_SSH_LLC0090GRID_MONTHLY_V4R4B", "ECCO_L4_SSH_LLC0090GRID_SNAPSHOT_V4R4", "ECCO_L4_STRESS_05DEG_DAILY_V4R4", "ECCO_L4_STRESS_05DEG_MONTHLY_V4R4", "ECCO_L4_STRESS_LLC0090GRID_DAILY_V4R4", "ECCO_L4_STRESS_LLC0090GRID_MONTHLY_V4R4", "ECCO_L4_TEMP_SALINITY_05DEG_DAILY_V4R4", "ECCO_L4_TEMP_SALINITY_05DEG_MONTHLY_V4R4", "ECCO_L4_TEMP_SALINITY_LLC0090GRID_DAILY_V4R4", "ECCO_L4_TEMP_SALINITY_LLC0090GRID_MONTHLY_V4R4", "ECCO_L4_TEMP_SALINITY_LLC0090GRID_SNAPSHOT_V4R4", "ECO1BATT", "ECO1BGEO", "ECO1BMAPRAD", "ECO1BRAD", "ECO2CLD", "ECO2LSTE", "ECO3ANCQA", "ECO3ETALEXI", "ECO3ETALEXIU", "ECO3ETPTJPL", "ECO4ESIALEXI", "ECO4ESIALEXIU", "ECO4ESIPTJPL", "ECO4WUE", "ECOA", "ECOHAB", "ECOMON", "ECO_L1B_ATT", "ECO_L1B_GEO", "ECO_L1B_RAD", "ECO_L1CG_RAD", "ECO_L1CT_RAD", "ECO_L2G_CLOUD", "ECO_L2G_LSTE", "ECO_L2T_LSTE", "ECO_L2T_STARS", "ECO_L2_CLOUD", "ECO_L2_LSTE", "ECO_L3G_JET", "ECO_L3G_MET", "ECO_L3G_SEB", "ECO_L3G_SM", "ECO_L3T_JET", "ECO_L3T_MET", "ECO_L3T_SEB", "ECO_L3T_SM", "ECO_L4G_ESI", "ECO_L4G_WUE", "ECO_L4T_ESI", "ECO_L4T_WUE", "ECS", "EDDIES", "EDM_SA_Vegetation_1149", "EDM_US_Carbon_1160", "EF_Data_Mexico_1693", "EGEE3", "EGEE5", "EKAMSAT_Pilot_ASTRAL", "EM27_XCO2_XCH4_XCO_AK_1831", "EMITL1BATT", "EMITL1BRAD", "EMITL2ARFL", "EMITL2BCH4ENH", "EMITL2BCH4PLM", "EMITL2BCO2ENH", "EMITL2BCO2PLM", "EMITL2BMIN", "EMITL3ASA", "EMITL4ESM", "EN1_MDSI_MER_FRS_1P", "EN1_MDSI_MER_FRS_2P", "EN1_MDSI_MER_RR__2P", "ENVISAT_SCIAMACHY_SIF_1871", "EOLE1", "EPEA", "ERBE_S10N_WFOV_NF", "ERBE_S10N_WFOV_NF", "ERBE_S10N_WFOV_SF", "ERBE_S10N_WFOV_SF", "ERBE_S10N_WFOV_SF_ERBS_AreaAverageTimeSeries", "ERBE_S10N_WFOV_SF_ERBS_AreaAverageTimeSeries_Edition4", "ERBE_S10N_WFOV_SF_ERBS_Regional", "ERBE_S10N_WFOV_SF_ERBS_Regional", "ERBE_S10N_WFV_NF_NAT", "ERBE_S10N_WFV_SF_NAT", "ERBE_S10_MFOV_NF_NAT", "ERBE_S10_MFOV_SF_NAT", "ERBE_S10_WFOV_NF_NAT", "ERBE_S10_WFOV_SF_NAT", "ERBE_S4GN_WFOV_NF", "ERBE_S4GN_WFOV_NF_ZG", "ERBE_S4GN_WFOV_SF", "ERBE_S4GN_WFOV_SF_ZG", "ERBE_S4GN_WFV_NF_N10", "ERBE_S4G_MFOV_NF", "ERBE_S4G_MFOV_NF_N10", "ERBE_S4G_MFOV_NF_ZG", "ERBE_S4G_MFOV_SF", "ERBE_S4G_MFOV_SF_ZG", "ERBE_S4G_SC_2.5", "ERBE_S4G_SC_NEST10", "ERBE_S4G_SC_NEST5", "ERBE_S4G_SC_ZG", "ERBE_S4G_WFOV_NF", "ERBE_S4G_WFOV_NF_N10", "ERBE_S4G_WFOV_NF_ZG", "ERBE_S4G_WFOV_SF", "ERBE_S4G_WFOV_SF_ZG", "ERBE_S4N_NAT", "ERBE_S4_NAT", "ERBE_S7_NAT", "ERBE_S8_NAT", "ERBE_S9_NAT", "ERBE_TSI_ERBS_NAT", "ERS-1_BYU_L3_OW_SIGMA0_ENHANCED", "ERS-1_L0", "ERS-1_L1", "ERS-2_BYU_L3_OW_SIGMA0_ENHANCED", "ERS-2_L0", "ERS-2_L1", "ERS2_GOME_SIF_1758", "ESCAPE", "ESMRN5IM", "ESMRN5L1", "ESMRN6IM", "EUCFe", "EUMELI", "EWSG1-NAVO-L2P-v01", "EXP7L1TRTALL", "EXP7L1TRTWHT", "EXPORTS", "EastAnglia10YearMean_549", "EastAnglia30YearMean_550", "EastAngliaClimate_542", "EastAngliaPrecip_417", "Ecosystem_Map_SRD_PAD_1947", "Effect_Environment_Moose_1739", "End_of_Season_Snow_Depth_1702", "Environmental_Disturbances_AK_1705", "Erosion_Vegetation_Yukon_1616", "Estimated_Biomass_Stock_Amazon_1648", "Eurasia_Biomass_1278", "FEWS_precip_711", "FIA_Forest_Biomass_Estimates_1873", "FIFE_CD_V3_130", "FIREXAQ_AerosolCloud_AircraftRemoteSensing_ER2_CPL_Data", "FIREXAQ_Aerosol_AircraftInSitu_DC8_Data", "FIREXAQ_Aerosol_AircraftInSitu_N48_Data", "FIREXAQ_Analysis_Data", "FIREXAQ_Analysis_N48_Data", "FIREXAQ_Cloud_AircraftInSitu_DC8_Data", "FIREXAQ_Ground_InSitu_Data", "FIREXAQ_HSRL_AircraftRemoteSensing_DC8_Data", "FIREXAQ_Merge_Data", "FIREXAQ_MetNav_AircraftInSitu_DC8_Data", "FIREXAQ_MetNav_AircraftInSitu_ER2_Data", "FIREXAQ_MetNav_AircraftInSitu_N48_Data", "FIREXAQ_N46_Data", "FIREXAQ_Radiation_AircraftInSitu_DC8_Data", "FIREXAQ_Satellite_Data", "FIREXAQ_SurfaceMobile_Aerodyne_InSitu_Data", "FIREXAQ_SurfaceMobile_CARB_InSitu_Data", "FIREXAQ_SurfaceMobile_MACH2_InSitu_Data", "FIREXAQ_TraceGas_AircraftInSitu_DC8_Data", "FIREXAQ_TraceGas_AircraftInSitu_N48_Data", "FIREXAQ_TraceGas_AircraftRemoteSensing_DC8_Data", "FIREXAQ_TraceGas_AircraftRemoteSensing_ER2_GCAS_Data", "FIREXAQ_TraceGas_AircraftRemoteSensing_ER2_NASTI_Data", "FIREXAQ_TraceGas_AircraftRemoteSensing_ER2_SHIS_Data", "FIREXAQ_jValue_AircraftInSitu_DC8_Data", "FIREXAQ_jValue_AircraftInSitu_N48_Data", "FIRE_ACE_AMPR", "FIRE_ACE_C130_CFD", "FIRE_ACE_C130_RAMS", "FIRE_ACE_ER2_MAS", "FIRE_ACE_ER2_MIR", "FIRE_ACE_SHIP_SSFR", "FIRE_ACE_UTRECHT_BALLOON", "FIRE_ACE_UTRECHT_TOWER", "FIRE_ACE_UWCV580_GMETER", "FIRE_ACE_UWCV580_UWA", "FIRE_AX_CMS_LWFLUX", "FIRE_AX_CMS_SOLAR_DY", "FIRE_AX_CMS_SOLAR_HR", "FIRE_AX_CMS_SOLAR_MN", "FIRE_AX_CMS_SOLAR_WK", "FIRE_AX_CMS_SST_DAY", "FIRE_AX_CMS_SST_FNTS", "FIRE_AX_CMS_SST_WEEK", "FIRE_AX_CSU_CEILOM", "FIRE_AX_CSU_MET_SFC", "FIRE_AX_CSU_PRT6", "FIRE_AX_CSU_WNDPRFS", "FIRE_AX_ECMWF_BASIC", "FIRE_AX_ECMWF_CLOUDS", "FIRE_AX_ECMWF_DIAG", "FIRE_AX_ECMWF_MEANW", "FIRE_AX_ECMWF_SFDIAG", "FIRE_AX_ECMWF_SURFCE", "FIRE_AX_ER2_LIDAR", "FIRE_AX_ER2_MAS", "FIRE_AX_ERS1_ALTIMTR", "FIRE_AX_ERS1_MCRWRAD", "FIRE_AX_ERS1_SCTRMTR", "FIRE_AX_ISCCP_DX", "FIRE_AX_MAGE_OCN_AIR", "FIRE_AX_MAGE_OCN_MET", "FIRE_AX_MAGE_OCN_SEA", "FIRE_AX_MAGE_TETROON", "FIRE_AX_MALBAL_SONDE", "FIRE_AX_METEOSAT", "FIRE_AX_OCEANS_SONDE", "FIRE_AX_PORSAN_SONDE", "FIRE_AX_PSU_CEIL_MAL", "FIRE_AX_PSU_CEIL_SAN", "FIRE_AX_PSU_CEIL_VAL", "FIRE_AX_PSU_H2OVAP", "FIRE_AX_PSU_MALBAL", "FIRE_AX_PSU_WND_MAL", "FIRE_AX_RWSONDE_LVL1", "FIRE_AX_RWSONDE_LVL2", "FIRE_AX_SANMAR_SONDE", "FIRE_AX_SFC_FUNCHAL", "FIRE_AX_SFC_IMAU", "FIRE_AX_SOF_ARAT_FLT", "FIRE_AX_SOF_ARAT_TRB", "FIRE_AX_SOF_BUOY_DFT", "FIRE_AX_SOF_BUOY_SPR", "FIRE_AX_SOF_PTU", "FIRE_AX_SOF_SUR_BUCK", "FIRE_AX_SOF_SUR_DRAK", "FIRE_AX_SOF_SUR_HYD", "FIRE_AX_SOF_SUR_MET", "FIRE_AX_SOF_SUR_RAD", "FIRE_AX_SOF_SUR_TBAL", "FIRE_AX_SUROIT_SONDE", "FIRE_AX_UKMO_C130", "FIRE_AX_UW_C131A", "FIRE_AX_UW_DSCRT", "FIRE_AX_UW_GERB_10HZ", "FIRE_AX_UW_GERB_1HZ", "FIRE_AX_VALDIV_SONDE", "FIRE_CI1_CSU_SABRE", "FIRE_CI1_ER2_LIDAR", "FIRE_CI1_ER2_RAD", "FIRE_CI1_ISCCP_DX", "FIRE_CI1_KINGAIR", "FIRE_CI1_LARC8_LIDAR", "FIRE_CI1_RAWINSONDES", "FIRE_CI1_SABRELINER", "FIRE_CI1_SRB_ALASKA", "FIRE_CI1_SRB_CANADA", "FIRE_CI1_SRB_LW", "FIRE_CI1_SRB_SO_POLE", "FIRE_CI1_SRB_SW", "FIRE_CI1_SRB_SWITZ", "FIRE_CI1_TOVS", "FIRE_CI2_CITATN_1HZ", "FIRE_CI2_CITATN_24HZ", "FIRE_CI2_CITATN_5SEC", "FIRE_CI2_CITATN_IWC", "FIRE_CI2_CITATN_PMS", "FIRE_CI2_CLASS_SONDE", "FIRE_CI2_CSU_PRT6", "FIRE_CI2_CSU_SONDES", "FIRE_CI2_CSU_STN1", "FIRE_CI2_CSU_STN2", "FIRE_CI2_CSU_WNDPRFS", "FIRE_CI2_DOPLR_LIDAR", "FIRE_CI2_ER2_LIDAR", "FIRE_CI2_ER2_MAS", "FIRE_CI2_ETL_RADAR", "FIRE_CI2_HIS", "FIRE_CI2_HSRL", "FIRE_CI2_ISCCP_DX", "FIRE_CI2_KINGAIR", "FIRE_CI2_KINGAIR_2D", "FIRE_CI2_KINGAIR_IWC", "FIRE_CI2_LARC8_LIDAR", "FIRE_CI2_MAPS", "FIRE_CI2_NOAA_WNDPFS", "FIRE_CI2_NWS_IN_SND", "FIRE_CI2_NWS_OUT_SND", "FIRE_CI2_PAMS", "FIRE_CI2_RAMAN_LIDAR", "FIRE_CI2_SABRELINER", "FIRE_CI2_SABRLNR_2D", "FIRE_CI2_SABRLNR_IWC", "FIRE_CI2_SPECT_SIRIS", "FIRE_CI2_UTAH_PDL", "FIRE_CI2_VIL_RTI", "FIRE_CI2_VIL_SCAN", "FIRE_ETO_UTAH_PDL", "FIRE_MS_CEILOM_CLASS", "FIRE_MS_CSU_RADSURF", "FIRE_MS_CSU_TBALLOON", "FIRE_MS_ELECTRA", "FIRE_MS_ER2_LIDAR", "FIRE_MS_ISCCP_DX", "FIRE_MS_MCRW_RAD", "FIRE_MS_NOAAWNDS", "FIRE_MS_UKMO_C130", "FLASH_TISA_Terra-Aqua", "FLASH_TISA_Terra-NOAA20", "FLASH_TISA_Terra-NOAA20", "FLDAS_NOAH01_CP_GL_M", "FLDAS_NOAH01_C_GL_M", "FLDAS_NOAH01_C_GL_MA", "FLDAS_NOAH01_C_GL_MC", "FLDAS_NOAHMP001_G_CA_D", "FLEXPART_Influence_Functions_2018", "FLOODAMA_903", "FLUAMAZON_896", "FLUXNET_Canada_1335", "FRONT", "FieldData_Alaska_Tundra_2177", "Field_Measurements_868", "Fire_Emissions_Indonesia_2118", "Fire_Emissions_NWT_1561", "Fire_Ignitions_Locations_AK_CA_2316", "Flight_Environment_Parameters_1909", "FluxSat_GPP_FPAR_1835", "Flux_Tower_Zona_Veg_Plots_1546", "Fluxnet_site_DB_1530", "Fluxnet_website_archive_copy_1549", "ForestHt_Biomass_GEDI_TDX_2298", "Forest_AGB_NW_USA_1766", "Forest_Carbon_Priority_1803", "Forest_Disturbance_Intensity_2059", "Forest_Inventory_Acre_Brazil_1654", "Forest_Inventory_Brazil_2007", "Forest_Inventory_Data_Brazil_1563", "Forest_Inventory_Tapajos_1552", "Forested_Areas_Amazonas_Brazil_1515", "Forested_Areas_Para_Brazil_1514", "Frac_FuelComponent_Maps_Tundra_1761", "Freeze_Thaw_NorthernHemisphere_2323", "Frost_Boils_Veg_Plots_1361", "Ft_Lauderdale", "G18-ABI-L2P-ACSPO-v2.90", "G18-ABI-L3C-ACSPO-v2.90", "GAMSSA_28km-ABOM-L4-GLOB-v01", "GCAM_Land_Cover_2005-2095_1216", "GCRW_DEM_2016_1793", "GEDI01_B", "GEDI02_A", "GEDI02_B", "GEDI_Fusion_Structure_2236", "GEDI_HighQuality_Shots_Rasters_2339", "GEDI_ICESAT2_Global_Veg_Height_2294", "GEDI_L3_LandSurface_Metrics_V2_1952", "GEDI_L4A_AGB_Density_GW_2028", "GEDI_L4A_AGB_Density_V2_1_2056", "GEDI_L4B_Country_Biomass_2321", "GEDI_L4B_Gridded_Biomass_V2_1_2299", "GEDI_L4C_WSCI_2338", "GEO-CAPE", "GEO-CAPE_GOCI", "GEOS-3_ALT_GDR", "GEOS2OBSINPUTINTL", "GEOS3STST", "GEOS_CASAGFED_3H_NEE", "GEOS_CASAGFED_3H_NEE", "GEOS_CASAGFED_D_FIRE", "GEOS_CASAGFED_D_FIRE", "GEOS_CASAGFED_M_FLUX", "GEOS_CASAGFED_M_FLUX", "GEOTRACES", "GEWEXSRB_Rel4-IP_Ancillary_3hrly", "GEWEXSRB_Rel4-IP_Ancillary_3hrly_landonly", "GEWEXSRB_Rel4-IP_Ancillary_3hrly_oceanonly", "GEWEXSRB_Rel4-IP_Longwave_3hrly", "GEWEXSRB_Rel4-IP_Longwave_3hrlymonthly_landonly_utc", "GEWEXSRB_Rel4-IP_Longwave_3hrlymonthly_oceanonly_utc", "GEWEXSRB_Rel4-IP_Longwave_3hrlymonthly_utc", "GEWEXSRB_Rel4-IP_Longwave_daily_landonly_utc", "GEWEXSRB_Rel4-IP_Longwave_daily_oceanonly_utc", "GEWEXSRB_Rel4-IP_Longwave_daily_utc", "GEWEXSRB_Rel4-IP_Longwave_monthly_landonly_utc", "GEWEXSRB_Rel4-IP_Longwave_monthly_local", "GEWEXSRB_Rel4-IP_Longwave_monthly_oceanonly_utc", "GEWEXSRB_Rel4-IP_Longwave_monthly_utc", "GEWEXSRB_Rel4-IP_Shortwave_3hrly", "GEWEXSRB_Rel4-IP_Shortwave_3hrlymonthly_utc", "GEWEXSRB_Rel4-IP_Shortwave_daily_local", "GEWEXSRB_Rel4-IP_Shortwave_daily_utc", "GEWEXSRB_Rel4-IP_Shortwave_monthly_local", "GEWEXSRB_Rel4-IP_Shortwave_monthly_utc", "GEWEXSRB_Rel4_1-IP_Longwave_3hrly_landonly", "GEWEXSRB_Rel4_1-IP_Longwave_3hrly_oceanonly", "GEWEXSRB_Rel4_1-IP_Longwave_daily_landonly_local", "GEWEXSRB_Rel4_1-IP_Longwave_daily_local", "GEWEXSRB_Rel4_1-IP_Longwave_daily_oceanonly_local", "GEWEXSRB_Rel4_1-IP_Longwave_monthly_landonly_local", "GEWEXSRB_Rel4_1-IP_Longwave_monthly_oceanonly_local", "GFCC30FCC", "GFCC30SR", "GFCC30TC", "GFCC30WC", "GFEI_CH4", "GFSAD1KCD", "GFSAD1KCM", "GFSAD30AFCE", "GFSAD30AUNZCNMOCE", "GFSAD30EUCEARUMECE", "GFSAD30NACE", "GFSAD30SAAFGIRCE", "GFSAD30SACE", "GFSAD30SEACE", "GFSAD30VAL", "GHISACASIA", "GHISACONUS", "GIMMS3g_NDVI_Trends_1275", "GIS_EastAngliaClimateMonthly_551", "GLAH01", "GLAH01", "GLAH02", "GLAH02", "GLAH03", "GLAH03", "GLAH04", "GLAH04", "GLAH05", "GLAH05", "GLAH06", "GLAH06", "GLAH07", "GLAH07", "GLAH08", "GLAH08", "GLAH09", "GLAH09", "GLAH10", "GLAH10", "GLAH11", "GLAH11", "GLAH12", "GLAH12", "GLAH13", "GLAH13", "GLAH14", "GLAH14", "GLAH15", "GLAH15", "GLCHMK", "GLCHMT", "GLDAS_CLSM025_D", "GLDAS_CLSM025_DA1_D", "GLDAS_CLSM025_DA1_D_EP", "GLDAS_CLSM10_3H", "GLDAS_CLSM10_3H", "GLDAS_CLSM10_3H_EP", "GLDAS_CLSM10_M", "GLDAS_CLSM10_M", "GLDAS_CLSM10_M_EP", "GLDAS_NOAH025_3H", "GLDAS_NOAH025_3H", "GLDAS_NOAH025_3H_EP", "GLDAS_NOAH025_M", "GLDAS_NOAH025_M", "GLDAS_NOAH025_M_EP", "GLDAS_NOAH10_3H", "GLDAS_NOAH10_3H", "GLDAS_NOAH10_3H_EP", "GLDAS_NOAH10_M", "GLDAS_NOAH10_M", "GLDAS_NOAH10_M_EP", "GLDAS_VIC10_3H", "GLDAS_VIC10_3H", "GLDAS_VIC10_3H_EP", "GLDAS_VIC10_M", "GLDAS_VIC10_M", "GLDAS_VIC10_M_EP", "GLDSMT", "GLDTMK", "GLDTMT", "GLHYANC", "GLHYVI", "GLLIDARPC", "GLMETRICS", "GLOBEC", "GLORTHO", "GLRADS", "GLREFL", "GLTRAJECTORY", "GLanCE30", "GMAO_M2SCREAM_INST3_CHEM", "GMAO_M2SCREAM_MONTH_UNCERT", "GMI-REMSS-L3U-v8.2a", "GNATS", "GO-BGC", "GO-SHIP", "GOA97", "GOC", "GOCAL", "GOCI_2013", "GOES13-OSISAF-L3C-v1.0", "GOES13-OSPO-L2P-v1.0", "GOES15-OSPO-L2P-v1.0", "GOES16-SST-OSISAF-L3C-v1.0", "GOM", "GOMECC", "GOMEX", "GOME_MINDS_NO2", "GOMI2AE", "GOMI2LS", "GOMI2ST", "GOMIB2E", "GOMIB2T", "GOMIGEO", "GOM_Oil_Spill", "GPCPDAY", "GPCPDAY", "GPCPMON", "GPCPMON", "GPCP_precip_712", "GPM_1AGMI", "GPM_1ATMI", "GPM_1AVIRS", "GPM_1BGMI", "GPM_1BPR", "GPM_1BTMI", "GPM_1BVIRS", "GPM_1CAQUAAMSRE", "GPM_1CF08SSMI", "GPM_1CF10SSMI", "GPM_1CF11SSMI", "GPM_1CF13SSMI", "GPM_1CF14SSMI", "GPM_1CF15SSMI", "GPM_1CF16SSMIS", "GPM_1CF17SSMIS", "GPM_1CF18SSMIS", "GPM_1CF19SSMIS", "GPM_1CGCOMW1AMSR2", "GPM_1CGPMGMI", "GPM_1CGPMGMI_R", "GPM_1CMETOPAMHS", "GPM_1CMETOPBMHS", "GPM_1CMETOPCMHS", "GPM_1CMT1SAPHIR", "GPM_1CNOAA15AMSUB", "GPM_1CNOAA16AMSUB", "GPM_1CNOAA17AMSUB", "GPM_1CNOAA18MHS", "GPM_1CNOAA19MHS", "GPM_1CNOAA20ATMS", "GPM_1CNOAA21ATMS", "GPM_1CNPPATMS", "GPM_1CTRMMTMI", "GPM_2ADPR", "GPM_2ADPRENV", "GPM_2AGPROFAQUAAMSRE_CLIM", "GPM_2AGPROFF08SSMI_CLIM", "GPM_2AGPROFF10SSMI_CLIM", "GPM_2AGPROFF11SSMI_CLIM", "GPM_2AGPROFF13SSMI_CLIM", "GPM_2AGPROFF14SSMI_CLIM", "GPM_2AGPROFF15SSMI_CLIM", "GPM_2AGPROFF16SSMIS", "GPM_2AGPROFF16SSMIS_CLIM", "GPM_2AGPROFF17SSMIS", "GPM_2AGPROFF17SSMIS_CLIM", "GPM_2AGPROFF18SSMIS", "GPM_2AGPROFF18SSMIS_CLIM", "GPM_2AGPROFF19SSMIS_CLIM", "GPM_2AGPROFGCOMW1AMSR2", "GPM_2AGPROFGCOMW1AMSR2_CLIM", "GPM_2AGPROFGPMGMI", "GPM_2AGPROFGPMGMI_CLIM", "GPM_2AGPROFMETOPAMHS_CLIM", "GPM_2AGPROFMETOPBMHS", "GPM_2AGPROFMETOPBMHS_CLIM", "GPM_2AGPROFMETOPCMHS", "GPM_2AGPROFMETOPCMHS_CLIM", "GPM_2AGPROFNOAA15AMSUB_CLIM", "GPM_2AGPROFNOAA16AMSUB_CLIM", "GPM_2AGPROFNOAA17AMSUB_CLIM", "GPM_2AGPROFNOAA18MHS_CLIM", "GPM_2AGPROFNOAA19MHS", "GPM_2AGPROFNOAA19MHS_CLIM", "GPM_2AGPROFNOAA20ATMS", "GPM_2AGPROFNOAA20ATMS_CLIM", "GPM_2AGPROFNOAA21ATMS", "GPM_2AGPROFNOAA21ATMS_CLIM", "GPM_2AGPROFNPPATMS", "GPM_2AGPROFNPPATMS_CLIM", "GPM_2AGPROFTRMMTMI_CLIM", "GPM_2AKa", "GPM_2AKaENV", "GPM_2AKu", "GPM_2AKuENV", "GPM_2APR", "GPM_2APRPSMT1SAPHIR", "GPM_2APRPSMT1SAPHIR_CLIM", "GPM_2BCMB", "GPM_2BCMB_TRMM", "GPM_2HCSH", "GPM_2HCSH_TRMM", "GPM_2HSLH", "GPM_2HSLH_TRMM", "GPM_3CMB", "GPM_3CMB_DAY", "GPM_3CMB_TRMM", "GPM_3CMB_TRMM_DAY", "GPM_3DPR", "GPM_3DPRD", "GPM_3DPR_ASC", "GPM_3DPR_DES", "GPM_3GCSH", "GPM_3GCSH_TRMM", "GPM_3GPROFAQUAAMSRE_CLIM", "GPM_3GPROFAQUAAMSRE_DAY_CLIM", "GPM_3GPROFF08SSMI_CLIM", "GPM_3GPROFF08SSMI_DAY_CLIM", "GPM_3GPROFF10SSMI_CLIM", "GPM_3GPROFF10SSMI_DAY_CLIM", "GPM_3GPROFF11SSMI_CLIM", "GPM_3GPROFF11SSMI_DAY_CLIM", "GPM_3GPROFF13SSMI_CLIM", "GPM_3GPROFF13SSMI_DAY_CLIM", "GPM_3GPROFF14SSMI_CLIM", "GPM_3GPROFF14SSMI_DAY_CLIM", "GPM_3GPROFF15SSMI_CLIM", "GPM_3GPROFF15SSMI_DAY_CLIM", "GPM_3GPROFF16SSMIS", "GPM_3GPROFF16SSMIS_CLIM", "GPM_3GPROFF16SSMIS_DAY", "GPM_3GPROFF16SSMIS_DAY_CLIM", "GPM_3GPROFF17SSMIS", "GPM_3GPROFF17SSMIS_CLIM", "GPM_3GPROFF17SSMIS_DAY", "GPM_3GPROFF17SSMIS_DAY_CLIM", "GPM_3GPROFF18SSMIS", "GPM_3GPROFF18SSMIS_CLIM", "GPM_3GPROFF18SSMIS_DAY", "GPM_3GPROFF18SSMIS_DAY_CLIM", "GPM_3GPROFF19SSMIS_CLIM", "GPM_3GPROFF19SSMIS_DAY_CLIM", "GPM_3GPROFGCOMW1AMSR2", "GPM_3GPROFGCOMW1AMSR2_CLIM", "GPM_3GPROFGCOMW1AMSR2_DAY", "GPM_3GPROFGCOMW1AMSR2_DAY_CLIM", "GPM_3GPROFGPMGMI", "GPM_3GPROFGPMGMI_CLIM", "GPM_3GPROFGPMGMI_DAY", "GPM_3GPROFGPMGMI_DAY_CLIM", "GPM_3GPROFMETOPAMHS_CLIM", "GPM_3GPROFMETOPAMHS_DAY_CLIM", "GPM_3GPROFMETOPBMHS", "GPM_3GPROFMETOPBMHS_CLIM", "GPM_3GPROFMETOPBMHS_DAY", "GPM_3GPROFMETOPBMHS_DAY_CLIM", "GPM_3GPROFMETOPCMHS", "GPM_3GPROFMETOPCMHS_CLIM", "GPM_3GPROFMETOPCMHS_DAY", "GPM_3GPROFMETOPCMHS_DAY_CLIM", "GPM_3GPROFNOAA15AMSUB_CLIM", "GPM_3GPROFNOAA15AMSUB_DAY_CLIM", "GPM_3GPROFNOAA16AMSUB_CLIM", "GPM_3GPROFNOAA16AMSUB_DAY_CLIM", "GPM_3GPROFNOAA17AMSUB_CLIM", "GPM_3GPROFNOAA17AMSUB_DAY_CLIM", "GPM_3GPROFNOAA18MHS_CLIM", "GPM_3GPROFNOAA18MHS_DAY_CLIM", "GPM_3GPROFNOAA19MHS", "GPM_3GPROFNOAA19MHS_CLIM", "GPM_3GPROFNOAA19MHS_DAY", "GPM_3GPROFNOAA19MHS_DAY_CLIM", "GPM_3GPROFNOAA20ATMS", "GPM_3GPROFNOAA20ATMS_CLIM", "GPM_3GPROFNOAA20ATMS_DAY", "GPM_3GPROFNOAA20ATMS_DAY_CLIM", "GPM_3GPROFNOAA21ATMS", "GPM_3GPROFNOAA21ATMS_CLIM", "GPM_3GPROFNOAA21ATMS_DAY", "GPM_3GPROFNOAA21ATMS_DAY_CLIM", "GPM_3GPROFNPPATMS", "GPM_3GPROFNPPATMS_CLIM", "GPM_3GPROFNPPATMS_DAY", "GPM_3GPROFNPPATMS_DAY_CLIM", "GPM_3GPROFTRMMTMI_CLIM", "GPM_3GPROFTRMMTMI_DAY_CLIM", "GPM_3GSLH", "GPM_3GSLH_TRMM", "GPM_3HCSH", "GPM_3HCSH_TRMM", "GPM_3HSLH", "GPM_3HSLH_DAY", "GPM_3HSLH_TRMM", "GPM_3HSLH_TRMM_DAY", "GPM_3IMERGDE", "GPM_3IMERGDE", "GPM_3IMERGDF", "GPM_3IMERGDL", "GPM_3IMERGDL", "GPM_3IMERGHH", "GPM_3IMERGHHE", "GPM_3IMERGHHE", "GPM_3IMERGHHL", "GPM_3IMERGHHL", "GPM_3IMERGM", "GPM_3PR", "GPM_3PRD", "GPM_3PRPSMT1SAPHIR", "GPM_3PRPSMT1SAPHIR_CLIM", "GPM_3PRPSMT1SAPHIR_DAY", "GPM_3PRPSMT1SAPHIR_DAY_CLIM", "GPM_3PR_ASC", "GPM_3PR_DES", "GPM_BASEGPMGMI", "GPM_BASEGPMGMI_RSS", "GPM_BASEGPMGMI_XCAL", "GPM_BASETRMMTMI", "GPM_IMERG_LandSeaMask", "GPM_MERGIR", "GPM_PRL1KA", "GPM_PRL1KU", "GPP_CONUS_TROPOMI_1875", "GPP_MODIS_Alaska_Canada_2024", "GPP_surfaces_749", "GPROF_precip_716", "GPSROZPBLA", "GPSROZPBLA", "GPSROZPBLS", "GPSROZPBLS", "GRACEDADM_CLSM0125US_7D", "GRACEDADM_CLSM025GL_7D", "GRACEFO_L1A_ASCII_GRAV_JPL_RL04", "GRACEFO_L2_CSR_MONTHLY_0062", "GRACEFO_L2_CSR_MONTHLY_0063", "GRACEFO_L2_GFZ_MONTHLY_0061", "GRACEFO_L2_JPL_MONTHLY_0063", "GRACE_ABPR_FO_L2_V1.0", "GRACE_AOD1B_GRAV_GFZ_RL06", "GRACE_GAA_L2_GRAV_GFZ_RL06", "GRACE_GAA_L2_GRAV_JPL_RL06", "GRACE_GAB_L2_GRAV_GFZ_RL06", "GRACE_GAB_L2_GRAV_JPL_RL06", "GRACE_GAC_L2_GRAV_CSR_RL06", "GRACE_GAC_L2_GRAV_GFZ_RL06", "GRACE_GAC_L2_GRAV_JPL_RL06", "GRACE_GAD_L2_GRAV_CSR_RL06", "GRACE_GAD_L2_GRAV_GFZ_RL06", "GRACE_GAD_L2_GRAV_JPL_RL06", "GRACE_GSM_L2_GRAV_CSR_RL06", "GRACE_GSM_L2_GRAV_GFZ_RL06", "GRACE_GSM_L2_GRAV_JPL_RL06", "GRACE_L1B_GRAV_JPL_RL02", "GRACE_L1B_GRAV_JPL_RL03", "GRC-GFO_GRIDDED_AOD1B_JPL_1-DEG_RL06", "GRC-GFO_GRIDDED_AOD1B_JPL_MASCON_RL06", "GREENLAND_MASS_TELLUS_MASCON_CRI_TIME_SERIES_RL06.1_V3", "GSMNP_Vegetation_Structure_R1_1286", "GSSTF", "GSSTF", "GSSTFM", "GSSTFM", "GSSTFMC", "GSSTFMC", "GSSTFM_NCEP", "GSSTFM_NCEP", "GSSTFSC", "GSSTFSC", "GSSTFYC", "GSSTFYC", "GSSTF_F08", "GSSTF_F08", "GSSTF_F10", "GSSTF_F10", "GSSTF_F11", "GSSTF_F11", "GSSTF_F13", "GSSTF_F13", "GSSTF_F14", "GSSTF_F14", "GSSTF_F15", "GSSTF_F15", "GSSTF_NCEP", "GSSTF_NCEP", "GWELDMO", "GWELDMO", "GWELDMO", "GWELDYR", "GWELDYR", "GWELDYR", "GasEx", "GePCO", "Geo_Polar_Blended-OSPO-L4-GLOB-v1.0", "Geo_Polar_Blended_Night-OSPO-L4-GLOB-v1.0", "GloSSAC", "GloSSAC", "GloSSAC", "GloSSAC", "GloSSAC", "GloSSAC", "Global_Biomass_1950-2010_1296", "Global_CDOM", "Global_Clumping_Index_1531", "Global_Freshwater_CH4Emissions_2253", "Global_Hydrologic_Soil_Group_1566", "Global_Lakes_Methane_2008", "Global_Landslide_Exposure_Maps", "Global_Landslide_Nowcast", "Global_Landslide_Nowcast", "Global_Litter_Carbon_Nutrients_1244", "Global_Maps_C_Density_2010_1763", "Global_Microbial_Biomass_C_N_P_1264", "Global_Monthly_GPP_1789", "Global_Phosphorus_Dist_Map_1223", "Global_Phosphorus_Hedley_Fract_1230", "Global_RTSG_Flux_1078", "Global_Reservoirs_Methane_1918", "Global_Riverine_N2O_Emissions_1791", "Global_SIF_OCO2_MODIS_1863", "Global_Salt_Marsh_Change_2122", "Global_Soil_Regolith_Sediment_1304", "Global_Veg_Greenness_GIMMS_3G_2187", "GozMmlpH2O", "GozMmlpHCl", "GozMmlpHNO3", "GozMmlpN2O", "GozMmlpO3", "GozSmlpH2O", "GozSmlpHCl", "GozSmlpHNO3", "GozSmlpN2O", "GozSmlpO3", "GozSmlpT", "Great_Belt", "Great_Lakes", "Great_Slave_Lake_Ecosystem_Map_1695", "GreenBay", "Gridded_Biomass_Africa_1777", "GroundMSPI_ACEPOL_Radiance_Data", "GulfCarbon", "GulfOfMaine", "Gulf_Of_Maine", "Gulf_of_Maine_Nutrients", "Gulf_of_Maine_Optics", "H09-AHI-L2P-ACSPO-v2.90", "H09-AHI-L3C-ACSPO-v2.90", "H3ZFC12MEXT", "H3ZFC8MEXT", "H3ZFCCFC11", "H3ZFCCFC12", "H3ZFCCLONO2", "H3ZFCGPH", "H3ZFCH2O", "H3ZFCHNO3", "H3ZFCN2O", "H3ZFCN2O5", "H3ZFCNO2", "H3ZFCO3", "H3ZFCT", "HAB", "HALO_LiDAR_AOP_ML_Heights_1833", "HAQES_NA_PM25_BC", "HAQES_NA_PM25_BC_CENSUS", "HAQES_NA_PM25_BC_COUNTY", "HAQES_NA_PM25_OC", "HAQES_NA_PM25_OC_CENSUS", "HAQES_NA_PM25_OC_COUNTY", "HAQES_NA_PM25_TOT", "HAQES_NA_PM25_TOT_CENSUS", "HAQES_NA_PM25_TOT_COUNTY", "HAQ_TROPOMI_NO2_CONUS_A_L3", "HAQ_TROPOMI_NO2_CONUS_M_L3", "HAQ_TROPOMI_NO2_CONUS_S_L3", "HAQ_TROPOMI_NO2_GLOBAL_A_L3", "HAQ_TROPOMI_NO2_GLOBAL_M_L3", "HAWKEYE_L1", "HAWKEYE_L1", "HAWKEYE_L2_OC", "HAWKEYE_L2_OC", "HCDN_810", "HICO_L1", "HIR3SCOL", "HIRDLS2", "HIRMLS3IWC", "HIRSN6IM", "HIRSN6L1GARP", "HIVE", "HLSL30", "HLSS30", "HMA2_DCG_SMB", "HMA2_DDSMET", "HMA2_DSPAT", "HMA2_FGP", "HMA2_GFTP", "HMA2_GGP", "HMA2_HFD", "HMA2_LHI", "HMA2_MATCHA", "HMA2_MTLI", "HMA2_NLSMR", "HMA2_WBP", "HMA_AWS", "HMA_DEM8m_AT", "HMA_DEM8m_CT", "HMA_DEM8m_MOS", "HMA_DM_6H", "HMA_DTE", "HMA_EAPrecip_FLOR", "HMA_FreezeThawMelt_ASCAT", "HMA_GLI", "HMA_GL_RCP", "HMA_GL_RCPR", "HMA_GSM", "HMA_GlacierAvg_dH", "HMA_Glacier_dH", "HMA_Glacier_dH_Mosaics", "HMA_LIS_LandSurfaceHydro", "HMA_LS_Cat", "HMA_MAR3_5", "HMA_MTLI", "HMA_OptDepth", "HMA_Precip_3B42", "HMA_Precip_FLOR", "HMA_RCMO_1H", "HMA_RCMO_6H", "HMA_RCMO_D", "HMA_RCMO_M", "HMA_SBRF", "HMA_SDI", "HMA_SR_D", "HMA_STParams", "HMA_Snowfield", "HOMAGE_GGFO_L4_GOMA_Monthly_v01", "HOMAGE_STERIC_OHC_TIME_SERIES_v01", "HOT", "HRAC_Precip", "HRIRN1IM", "HRIRN1L1", "HRIRN2IM", "HRIRN2L1", "HRIRN3IM", "HRIRN3L1", "HWSD_1247", "HYCODE_LEO-15", "Happy_Valley_Veg_Plots_1354", "HighRes_ClimateData_Western_US_1682", "High_Res_Tidal_Marsh_Veg_1609", "Historic_S2K_Website_1765", "HistoricalLai_584", "Historical_Lake_Shorelines_AK_1859", "Horn_Island", "HourlyUrban_GreenhouseGases_US_1916", "Hudson_River_Fluorescence", "Hydroprofiler", "IAKST1B", "IAKST1B", "IAPRS1B", "IASI_SST_METOP_A-OSISAF-L2P-v1.0", "IASI_SST_METOP_B-OSISAF-L2P-v1.0", "ICESCAPE", "ICEVOLC_FlowerKahn2020", "IDBMG4", "IDCSI4", "IDHDT4", "IDS_LIS", "IGBGM1B", "IGBGM2", "IGBP-DIS_565", "IGBP-SurfaceProducts_569", "IGBTH4", "IGCMG1B", "IGCMG2", "IGGRV1B", "IGLGS1B", "IGZLS1B", "ILAKP1B", "ILAKS1B", "ILATM1B", "ILATM1B", "ILATM2", "ILATMGR", "ILATMW1B", "ILNIRW1B", "ILNSA1B", "ILNSA1B", "ILNSAW1B", "ILSIG1B", "ILSNP1B", "ILSNP4", "ILUTP1B", "ILUTP2", "ILVGH1B", "ILVGH2", "ILVIS0", "ILVIS1B", "ILVIS2", "ILVIS2", "IMARPE-Callao_Station", "IMCS31B", "IMECOCAL", "IMERG_Precip_Canada_Alaska_2097", "IMFGM1B", "INDOEX", "INTEXA_AIRMAP", "INTEXA_DC8_AIRCRAFT", "INTEXA_J31_AIRCRAFT", "INTEXA_MERGES", "INTEXA_MODEL", "INTEXA_O3SONDES", "INTEXA_PROTEUS_AIRCRAFT", "INTEXA_SATELLITE", "INTEXA_TRAJECTORY", "INTEXB_Be200_AIRCRAFT", "INTEXB_C130_AIRCRAFT", "INTEXB_Cessna_AIRCRAFT", "INTEXB_DC8_AIRCRAFT", "INTEXB_Duchess_AIRCRAFT", "INTEXB_GROUND", "INTEXB_J31_AIRCRAFT", "INTEXB_MERGES", "INTEXB_MODEL", "INTEXB_O3SONDES", "INTEXB_SATELLITE", "INTEXB_TRAJECTORY", "INTRO", "IOCAM0", "IOCAM1B", "IOCAM1B", "IODCC0", "IODEM3", "IODIM3", "IODMS1B", "IODMS3", "IOFFE", "IOLVIS1A", "IPFLR1B", "IPFLT1B", "IR1HI1B", "IR1HI2", "IR2HI1B", "IR2HI2", "IRACC1B", "IRARES1B", "IRISN4RAD", "IRKUB1B", "IRMCR1B", "IRMCR2", "IRMCR3", "IRPAR2", "IRSNO1B", "IRTIT3", "IRUAFHF1B", "IRWIS2", "IS2ATBABD", "IS2GZANT", "IS2MPDDA", "IS2OLVIS1BCV", "IS2SITDAT4", "IS2SITMOGR4", "ISCCP_B3_NAT", "ISCCP_D1", "ISCCP_D2", "ISCCP_DX", "ISCCP_ICESNOW_NAT", "ISCCP_TOVS_NAT", "ISERV", "ISLSCP_919", "ISSITGR4", "ITPRN5L1", "IXBMI2AE", "IXBMI2LS", "IXBMI2ST", "IXBMIB2E", "IXBMIB2T", "IXBMIGEO", "Idaho_field_shrub_data_1503", "Imnavait_Creek_Veg_Maps_1385", "Imnavait_Creek_Veg_Plots_1356", "InSAR_Prudhoe_Bay_1267", "Insitu_Tower_Greenhouse_Gas_1798", "Interior_Alaska_Subsistence_1725", "Interpolated_Met_Products_1876", "InundationMap_YkFlats_PeaceAth_1901", "IronEx", "IsricWiseGrids_546", "IsricWise_547", "J1_CrIS_VIIRS750m_IND", "JASON-1_JMR_ENH", "JASON-1_L2_OST_GPN_E", "JASON-1_L2_OST_GPN_E_GEODETIC", "JASON-1_L2_OST_GPR_E", "JASON-1_L2_OST_GPR_E_GEODETIC", "JASON-1_L2_OST_GPS_E", "JASON-1_L2_OST_GPS_E_GEODETIC", "JASON_3_L2_OST_OGDR_GPS", "JASON_3_PD_CORRECTION", "JASON_CS_S6A_L1A_ALT_HR_NTC_F08", "JASON_CS_S6A_L1A_ALT_HR_STC_F", "JASON_CS_S6A_L1B_ALT_HR_NTC_F08", "JASON_CS_S6A_L1B_ALT_HR_STC_F", "JASON_CS_S6A_L1B_ALT_LR_NTC_F08", "JASON_CS_S6A_L1B_ALT_LR_STC_F", "JASON_CS_S6A_L1B_GNSS_POD_DAILY", "JASON_CS_S6A_L1B_GNSS_POD_HOURLY", "JASON_CS_S6A_L1B_GNSS_RO_POD_HOURLY", "JASON_CS_S6A_L2P_ALT_HR_OST_NTC_F08", "JASON_CS_S6A_L2P_ALT_LR_OST_NTC_F08", "JASON_CS_S6A_L2_ALT_HR_RED_OST_NRT_F", "JASON_CS_S6A_L2_ALT_HR_RED_OST_NTC_F08", "JASON_CS_S6A_L2_ALT_HR_RED_OST_NTC_F08_UNVALIDATED", "JASON_CS_S6A_L2_ALT_HR_RED_OST_STC_F", "JASON_CS_S6A_L2_ALT_HR_STD_OST_NRT_F", "JASON_CS_S6A_L2_ALT_HR_STD_OST_NTC_F08", "JASON_CS_S6A_L2_ALT_HR_STD_OST_NTC_F08_UNVALIDATED", "JASON_CS_S6A_L2_ALT_HR_STD_OST_STC_F", "JASON_CS_S6A_L2_ALT_LR_RED_OST_NRT_F", "JASON_CS_S6A_L2_ALT_LR_RED_OST_NTC_F08", "JASON_CS_S6A_L2_ALT_LR_RED_OST_NTC_F08_UNVALIDATED", "JASON_CS_S6A_L2_ALT_LR_RED_OST_STC_F", "JASON_CS_S6A_L2_ALT_LR_STD_OST_NRT_F", "JASON_CS_S6A_L2_ALT_LR_STD_OST_NTC_F08", "JASON_CS_S6A_L2_ALT_LR_STD_OST_NTC_F08_UNVALIDATED", "JASON_CS_S6A_L2_ALT_LR_STD_OST_STC_F", "JASON_CS_S6A_L2_AMR_RAD_NRT", "JASON_CS_S6A_L2_AMR_RAD_NTC_F08", "JASON_CS_S6A_L2_AMR_RAD_NTC_F08_UNVALIDATED", "JASON_CS_S6A_L2_AMR_RAD_STC", "JASON_CS_S6A_L3_ALT_HR_OST_NTC_F08", "JASON_CS_S6A_L3_ALT_LR_OST_NTC_F08", "JERS-1_L0", "JERS-1_L1", "JERS-1_SAR_GRFM_Amazon_Mosaics_1280", "JGOFS", "JGOFS_Arabian_Sea", "JGOFS_BOFS", "JGOFS_EQPAC", "JGOFS_WOCE", "JHUAPL_SRI_Kauai", "JPL_RECON_GMSL", "K10_SST-NAVO-L4-GLOB-v01", "KAIMIMOANA", "KILVOLC_FlowerKahn2021", "KORUS", "KORUSAQ_Aerosol_AircraftInSitu_DC8_Data", "KORUSAQ_AircraftRemoteSensing_DIAL_DC8_Data", "KORUSAQ_AircraftRemoteSensing_GeoTASO_B200_Data", "KORUSAQ_Analysis_Data", "KORUSAQ_Cloud_AircraftInSitu_DC8_Data", "KORUSAQ_Ground_EPA_Data", "KORUSAQ_Ground_NASA_Data", "KORUSAQ_Merge_Data", "KORUSAQ_MetNav_AircraftInSitu_B200_Data", "KORUSAQ_MetNav_AircraftInSitu_DC8_Data", "KORUSAQ_Miscellaneous_Data", "KORUSAQ_Model_Data", "KORUSAQ_Pandora_Data", "KORUSAQ_RVJangMokShip_Data", "KORUSAQ_RVOnnuriShip_Data", "KORUSAQ_Sondes_Data", "KORUSAQ_TraceGas_AircraftInSitu_DC8_Data", "KORUSAQ_TraceGas_AircraftInSitu_HanseoKingAir_Data", "KORUSAQ_Trajectory_Data", "KORUSAQ_jValue_AircraftInSitu_DC8_Data", "Kennebec", "Kieber_Photochemistry", "KingSejong", "King_Rim_Fire_Analysis_1288", "Kuparuk_Veg_Maps_1378", "Kuroshio_Area", "L3S_LEO_AM-STAR-v2.80", "L3S_LEO_DY-STAR-v2.81", "L3S_LEO_PM-STAR-v2.81", "LAB97", "LAI_Africa_2325", "LAI_Canada_816", "LAI_VALERI_Canada_829", "LAI_Woody_Plants_1231", "LAI_surfaces_747", "LAMONT_ATL", "LAMONT_GOM", "LAMONT_SAB", "LAMONT_SCS", "LANDMET", "LANDMET_ANC_SM", "LANDMET_ANC_ST", "LANDMET_ANC_TEIME", "LANDMET_ANC_TESA", "LASE_AFWEX", "LASE_CAMEX3", "LASE_SGP97", "LASE_SOLVE", "LASE_TARFOX", "LASE_VALIDATION", "LATTE", "LB02", "LC01_Boundaries_Ecuador_1057", "LC01_Cities_Communities_Roads_1058", "LC01_Households_NEC_1052", "LC01_Hydrography_Edaphology_NEC_1059", "LC01_LULC_Classes_Ecuador_ISA_1084", "LC01_Landsat_1187", "LC01_SRTM_DEM_90m_NEC_1083", "LC01_Topography_Ecuador_ISA_1082", "LC02_Forest_Flammability_Acre_1089", "LC02_GOES8_Hotpixel_Acre_1092", "LC02_MAP_Fire_Indicators_1044", "LC02_Meteorology_Acre_1091", "LC02_PermPlot_Acre_1237", "LC02_Streams_Acre_1243", "LC02_Water_Table_Acre_1062", "LC03_Hypsography_DEM_1094", "LC03_SAR_LC_Biomass_1093", "LC04_IBIS_Model_1139", "LC04_Land_Use_5min_906", "LC04_Macrohydrology_1048", "LC04_THMB-HYDRA_Model_1138", "LC05_BDFF_Biomass_Soils_1040", "LC07_Airborne_Rasters_1274", "LC07_Airborne_Videography_1272", "LC07_Amazon_Wetlands_1284", "LC07_Bathymetry_Curuai_999", "LC07_Biomass_LGrande_1127", "LC07_Curuai_chl_1134", "LC07_Lake_Chlorophyll_MODIS_1000", "LC07_Lake_Nutrient_Sediments_1050", "LC07_Monthly_Inundated_Areas_1049", "LC07_Reservoir_GHG_1143", "LC07_Reservoir_Methane_Emissions_1047", "LC07_SMMR_Inundated_Area_1051", "LC07_Spectroradiometry_1144", "LC07_Wetlands_fluxes_1209", "LC08_EOS_Maps_1155", "LC08_Ecosystem_Demography_Model_1102", "LC08_Fire_Observations_1095", "LC09_GIS_Study_Areas_986", "LC09_Landsat_987", "LC09_Precipitation_940", "LC09_Soil_Composition_938", "LC09_Transition_Matricies_1098", "LC09_Vegetation_Composition_939", "LC10_Landsat_ETM_846", "LC10_Landsat_TM_852", "LC13_GIS_Cauaxi_890", "LC13_GIS_Juruena_888", "LC13_GIS_Tapajos_893", "LC14_Aboveground_Prod_1196", "LC14_Amazon_Scenarios_1153", "LC14_REE_SLA_1211", "LC14_RISQUE_1147", "LC14_Surface_Roots_Phenology_1268", "LC15_AGLB_Distribution_Map_908", "LC15_GRFM_JERS1_Mosaic_1024", "LC15_MODIS_TreeCover_1035", "LC15_Roughness_Map_1182", "LC15_SPOT_Metrics_1239", "LC15_SRTM_Topography_1181", "LC18_Hyperion_889", "LC19_Field_2002_1261", "LC21_Foliar_Nutrients_1234", "LC21_Fractional_Cover_1152", "LC21_Selective_Logging_1172", "LC21_Soil_Characteristics_1236", "LC22_MODIS_Field_Val_2004_1262", "LC22_MODIS_Field_Val_2005_1260", "LC22_MODIS_Phenology_Mato_Grosso_1185", "LC22_MODIS_VCF_Tree_Cover_1112", "LC22_Post_Deforestation_LULC_1099", "LC23_MODIS_ASTER_Fire_Comparisons_839", "LC23_Vegetation_Fire_Dynamics_843", "LC23_Vegetation_Fires_2003_887", "LC24_Basin_Scale_Hot_Pixels_2001_882", "LC24_Cadastral_Property_Map_Para_1042", "LC24_ETM_Deforestation_Map_Para_1999_1054", "LC24_Historical_Roads_Amazon_1043", "LC24_Land_Cover_Southern_Para_1055", "LC24_Land_Cover_Uruara_Para_1053", "LC24_MODIS_Forest_Cover_500-m_1056", "LC31_AMZ_Historical_LU_1170", "LC31_SITE_1173", "LC35_GOES_WF_ABBA_1180", "LC35_Landsat7_Fire_Masks_1071", "LC39_DECAF_Model_1190", "LC39_MODIS_Fire_SA_1186", "LEO", "LEOLSTCMG30", "LEOLSTCMG30", "LGRIP30", "LIDAR", "LIDAR_FOREST_CANOPY_HEIGHTS_1271", "LIMSN7L1PROFILER", "LIMSN7L1RAT", "LIMSN7L2", "LIMSN7L3", "LINKAGES_1166", "LIS", "LISCO_mooring", "LISTOS_AircraftInSitu_StonyBrookAircraft_Data", "LISTOS_AircraftInSitu_UMDAircraft_Data", "LISTOS_AircraftRemoteSensing_NASAAircraft_Data", "LISTOS_Ground_Bayonne_Data", "LISTOS_Ground_BronxPfizer_Data", "LISTOS_Ground_CCNY_Data", "LISTOS_Ground_FlaxPond_Data", "LISTOS_Ground_Hammonasset_Data", "LISTOS_Ground_NewHaven_Data", "LISTOS_Ground_Other_Data", "LISTOS_Ground_OuterIsland_Data", "LISTOS_Ground_QueensCollege_Data", "LISTOS_Ground_Rutgers_Data", "LISTOS_Ground_Westport_Data", "LISTOS_Ground_YaleCoastal_Data", "LISTOS_MetNav_AircraftInSitu_NASAAircraft_Data", "LISTOS_SurfaceMobile_InSitu_Data", "LITE_L1", "LMER-TIES", "LMOS_AircraftInSitu_ScientificAviation_Data", "LMOS_AircraftRemoteSensing_UC12_Data", "LMOS_Ground_Grafton_Data", "LMOS_Ground_IEPA_Data", "LMOS_Ground_Milwaukee_Data", "LMOS_Ground_SchillerPark_Data", "LMOS_Ground_Sheboygan_Data", "LMOS_Ground_WDNRRoutine_Data", "LMOS_Ground_Zion_Data", "LMOS_MetNav_AircraftInSitu_UC12_Data", "LMOS_Miscellaneous_Data", "LMOS_TraceGas_ShipInSitu_Data", "LMOS_TraceGas_SurfaceMobile_EPA-GMAP_Data", "LMOS_TraceGas_SurfaceMobile_UWEC-Auto_Data", "LOBO_timeseries", "LOCSS_L1_V1", "LPJ-WHyMe_v1-3-1_1150", "LPJ_EOSIM_L2_DCH4E", "LPJ_EOSIM_L2_DCH4E_LL", "LPJ_EOSIM_L2_MCH4E", "LPJ_EOSIM_L2_MCH4E_LL", "LPJ_L2_SSREF", "LPJ_L2_SSREF", "LPRM_AMSR2_A_SOILM3", "LPRM_AMSR2_DS_A_SOILM3", "LPRM_AMSR2_DS_D_SOILM3", "LPRM_AMSR2_DS_SOILM2", "LPRM_AMSR2_D_SOILM3", "LPRM_AMSR2_SOILM2", "LPRM_AMSRE_A_SOILM3", "LPRM_AMSRE_D_SOILM3", "LPRM_AMSRE_SOILM2", "LPRM_TMI_DY_SOILM3", "LPRM_TMI_NT_SOILM3", "LPRM_TMI_SOILM2", "LPRM_WINDSAT_DY_SOILM3", "LPRM_WINDSAT_NT_SOILM3", "LPRM_WINDSAT_SOILM2", "LRIRN6L2IPAT", "LSM_807", "LTER", "LUH2_GCB2019_1851", "LULC_Nigeria_Ethiopia_SAfrica_2367", "LWAD01-2", "LaSelva_Biomass_1215", "LaSelva_Land_Use_1312", "Lab2000", "Lab96", "LakeBathymetry_Model_NSlope_AK_2243", "LakeSuperior", "Lake_MI_2012_WaterQual", "Lake_Wetland_Classes_UAVSAR_1883", "Land_Cover_surfaces_748", "Land_Use_Harmonization_V1_1248", "Land_Use_Harmonization_V2_1721", "Landcover_Colombian_Amazon_1783", "Landsat8_Sentinel2_Phenocam_2248", "Large_River_DOC_Export", "Last_Day_Spring_Snow_1528", "Leaf_Carbon_Nutrients_1106", "Leaf_Photosynthesis_Traits_1224", "LiDAR_Forest_Inventory_Brazil_1644", "LiDAR_Tundra_Forest_AK_1782", "LiDAR_Veg_Ht_Idaho_1532", "Line_P", "M1_AVH02C1", "M1_AVH09C1", "M1_AVH13C1", "M2C0NXASM", "M2C0NXCTM", "M2C0NXLND", "M2I1NXASM", "M2I1NXINT", "M2I1NXLFO", "M2I3NPASM", "M2I3NVAER", "M2I3NVASM", "M2I3NVCHM", "M2I3NVGAS", "M2I3NXGAS", "M2I6NPANA", "M2I6NVANA", "M2IMNPANA", "M2IMNPASM", "M2IMNXASM", "M2IMNXGAS", "M2IMNXINT", "M2IMNXLFO", "M2IUNPANA", "M2IUNPASM", "M2IUNXASM", "M2IUNXGAS", "M2IUNXINT", "M2IUNXLFO", "M2SDNXSLV", "M2SMNXEDI", "M2SMNXEDI", "M2SMNXPCT", "M2SMNXPCT", "M2SMNXSLV", "M2T1NXADG", "M2T1NXAER", "M2T1NXCHM", "M2T1NXCSP", "M2T1NXFLX", "M2T1NXINT", "M2T1NXLFO", "M2T1NXLND", "M2T1NXOCN", "M2T1NXRAD", "M2T1NXSLV", "M2T3NEMST", "M2T3NENAV", "M2T3NETRB", "M2T3NPCLD", "M2T3NPMST", "M2T3NPODT", "M2T3NPQDT", "M2T3NPRAD", "M2T3NPTDT", "M2T3NPTRB", "M2T3NPUDT", "M2T3NVASM", "M2T3NVCLD", "M2T3NVMST", "M2T3NVRAD", "M2T3NXGLC", "M2TCNPLTM", "M2TCNPLTM", "M2TCNXLTM", "M2TCNXLTM", "M2TMNPCLD", "M2TMNPMST", "M2TMNPODT", "M2TMNPQDT", "M2TMNPRAD", "M2TMNPTDT", "M2TMNPTRB", "M2TMNPUDT", "M2TMNXADG", "M2TMNXAER", "M2TMNXCHM", "M2TMNXCSP", "M2TMNXFLX", "M2TMNXGLC", "M2TMNXINT", "M2TMNXLFO", "M2TMNXLND", "M2TMNXOCN", "M2TMNXRAD", "M2TMNXSLV", "M2TUNPCLD", "M2TUNPMST", "M2TUNPODT", "M2TUNPQDT", "M2TUNPRAD", "M2TUNPTDT", "M2TUNPTRB", "M2TUNPUDT", "M2TUNXADG", "M2TUNXAER", "M2TUNXCHM", "M2TUNXCSP", "M2TUNXFLX", "M2TUNXGLC", "M2TUNXINT", "M2TUNXLFO", "M2TUNXLND", "M2TUNXOCN", "M2TUNXRAD", "M2TUNXSLV", "M2_TMAX_PM25", "MAA", "MAC021S0", "MAC021S1", "MAC02QS0", "MAC02QS1", "MAC03S0", "MAC03S1", "MAC04S0", "MAC04S1", "MAC05S0", "MAC05S1", "MAC06S0", "MAC06S1", "MAC07S0", "MAC07S1", "MAC35S0", "MAC35S1", "MACLWP_diurnal", "MACLWP_mean", "MACPEX_Aerosol_AircraftInSitu_WB57_Data", "MACPEX_Cloud_AircraftInSitu_WB57_Data", "MACPEX_MetNav_AircraftInSitu_WB57_Data", "MACPEX_Satellite_Data", "MACPEX_Sondes_Data", "MACPEX_TraceGas_AircraftInSitu_WB57_Data", "MACPEX_Water_AircraftInSitu_WB57_Data", "MACTWP_mean", "MALINA", "MAM03S0", "MAM04S0", "MAM05S0", "MAM06S0", "MAM07S0", "MAM35S0", "MANTRA_PIRANA", "MAPSS_853", "MAPS_OSTA3_CO5X5_HDF", "MAPS_OSTA3_COSEC_HDF", "MAPS_SRL1_CO5X5_HDF", "MAPS_SRL1_COSEC_HDF", "MAPS_SRL2_CO5X5_HDF", "MAPS_SRL2_COSEC_HDF", "MASL1B", "MASL2CLD", "MASS_BAY", "MASTER_Ames_August_2003_2045", "MASTER_B200_Fall_1999_2099", "MASTER_Baja_Mexico_1999_2102", "MASTER_CARTA_2003_2054", "MASTER_CARTA_2005_2034", "MASTER_CLASIC_2007_2019", "MASTER_DFRC_April_2004_2043", "MASTER_DFRC_August_2003_2046", "MASTER_DFRC_Fall_2005_2030", "MASTER_DFRC_July-Aug_2001_V2_2144", "MASTER_DFRC_July_2004_2039", "MASTER_DFRC_June_1999_2100", "MASTER_DFRC_June_2011_1976", "MASTER_DFRC_May-June_2008_2011", "MASTER_DFRC_May_2011_1985", "MASTER_DFRC_November_2011_1973", "MASTER_DFRC_October_2003_2044", "MASTER_DFRC_RSL_August_2002_2066", "MASTER_DFRC_September_2002_2064", "MASTER_DFRC_September_2006_2022", "MASTER_FIREX_AQ_JulySept_2019_1941", "MASTER_FireSense_2023_2330", "MASTER_Flightline_Locator_2151", "MASTER_GEMx_Spring_2024_2370", "MASTER_GEMx_Summer_2023_2319", "MASTER_Hawaii_October_2001_V2_2142", "MASTER_Houston_2010_2126", "MASTER_Houston_2011_1972", "MASTER_HyspIRI_EarlyS2013_V2_2146", "MASTER_HyspIRI_EarlySpring2014_1965", "MASTER_HyspIRI_Fall_2013_1966", "MASTER_HyspIRI_Fall_2014_1962", "MASTER_HyspIRI_Fall_2015_1956", "MASTER_HyspIRI_Hawaii_2017_1951", "MASTER_HyspIRI_Hawaii_2018_1945", "MASTER_HyspIRI_LateSpring_2013_1968", "MASTER_HyspIRI_LateSpring_2014_1963", "MASTER_HyspIRI_Spring_2015_1957", "MASTER_HyspIRI_Summer_2014_1964", "MASTER_HyspIRI_Summer_2015_1959", "MASTER_HyspIRI_Summer_2016_1913", "MASTER_HyspIRI_Summer_2017_1950", "MASTER_HyspIRI_Summer_2018_1942", "MASTER_PacificRim_2000_2093", "MASTER_RSL-DFRC_October_2008_2014", "MASTER_RSLPhoenix_2011_1975", "MASTER_RSL_April_2003_2053", "MASTER_RSL_April_2008_2016", "MASTER_RSL_August_2001_V2_2143", "MASTER_RSL_August_2004_2036", "MASTER_RSL_August_2006_2027", "MASTER_RSL_August_2007_2020", "MASTER_RSL_August_2008_2010", "MASTER_RSL_December_1998_2104", "MASTER_RSL_January_1999_2103", "MASTER_RSL_July_2003_2047", "MASTER_RSL_July_2004_2038", "MASTER_RSL_June_1999_2098", "MASTER_RSL_June_2000_2096", "MASTER_RSL_June_2001_2090", "MASTER_RSL_June_2002_2067", "MASTER_RSL_June_2004_2042", "MASTER_RSL_March_2000_2094", "MASTER_RSL_March_2002_2068", "MASTER_RSL_May_1999_2101", "MASTER_RSL_May_2001_V2_2145", "MASTER_RSL_May_2002_V2_2148", "MASTER_RSL_May_2004_2041", "MASTER_RSL_May_2006_2029", "MASTER_RSL_November_2007_2015", "MASTER_RSL_Oct_2002_V2_2147", "MASTER_RSL_October_2003_2048", "MASTER_RSL_October_2005_2031", "MASTER_RSL_October_2007_2013", "MASTER_RSL_October_2010_2127", "MASTER_RSL_September_2005_2037", "MASTER_SARP_2009_1990", "MASTER_SARP_2010_1989", "MASTER_SARP_2011_1974", "MASTER_SARP_2012_1983", "MASTER_SARP_2013_1967", "MASTER_SARP_2014_1961", "MASTER_SARP_2015_1958", "MASTER_SARP_2016_1912", "MASTER_Sky_2003_2052", "MASTER_Sky_2004_2035", "MASTER_Sky_2006_2026", "MASTER_SuomiNPP_2013_1969", "MASTER_SuomiNPP_2015_1970", "MASTER_TC4_2007_2021", "MASTER_WDTS_Fall_2022_2141", "MASTER_WDTS_SeptOct_2020_1940", "MASTER_WDTS_Spring_2021_1953", "MASTER_WDTS_Spring_2023_2252", "MAS_832", "MCD06COSP_D3_MODIS", "MCD06COSP_M3_MODIS", "MCD12C1", "MCD12Q1", "MCD12Q2", "MCD15A2H", "MCD15A3H", "MCD18A1", "MCD18A2", "MCD18C1", "MCD18C2", "MCD19A1", "MCD19A1CMGL", "MCD19A1CMGO", "MCD19A1N", "MCD19A2", "MCD19A2CMG", "MCD19A2N", "MCD19A3CMG", "MCD19A3D", "MCD19A3DN", "MCD43A1", "MCD43A1N", "MCD43A2", "MCD43A2N", "MCD43A3", "MCD43A3N", "MCD43A4", "MCD43A4N", "MCD43C1", "MCD43C2", "MCD43C3", "MCD43C4", "MCD43D01", "MCD43D02", "MCD43D03", "MCD43D04", "MCD43D05", "MCD43D06", "MCD43D07", "MCD43D08", "MCD43D09", "MCD43D10", "MCD43D11", "MCD43D12", "MCD43D13", "MCD43D14", "MCD43D15", "MCD43D16", "MCD43D17", "MCD43D18", "MCD43D19", "MCD43D20", "MCD43D21", "MCD43D22", "MCD43D23", "MCD43D24", "MCD43D25", "MCD43D26", "MCD43D27", "MCD43D28", "MCD43D29", "MCD43D30", "MCD43D31", "MCD43D32", "MCD43D33", "MCD43D34", "MCD43D35", "MCD43D36", "MCD43D37", "MCD43D38", "MCD43D39", "MCD43D40", "MCD43D41", "MCD43D42", "MCD43D43", "MCD43D44", "MCD43D45", "MCD43D46", "MCD43D47", "MCD43D48", "MCD43D49", "MCD43D50", "MCD43D51", "MCD43D52", "MCD43D53", "MCD43D54", "MCD43D55", "MCD43D56", "MCD43D57", "MCD43D58", "MCD43D59", "MCD43D60", "MCD43D61", "MCD43D62", "MCD43D63", "MCD43D64", "MCD43D65", "MCD43D66", "MCD43D67", "MCD43D68", "MCD43GF", "MCD64A1", "MCDAODHD", "MCDWD_L3_F1C_NRT", "MCDWD_L3_F1_NRT", "MCDWD_L3_F2_NRT", "MCDWD_L3_F3_NRT", "MCDWD_L3_NRT", "MCR_LTER", "MELVILLE", "MERCHANT_SHIP", "MERGED_S3_OLCI_L3b_CYANTC", "MERGED_S3_OLCI_L3b_ILW", "MERGED_S3_OLCI_L3m_CYAN", "MERGED_S3_OLCI_L3m_CYANTC", "MERGED_S3_OLCI_L3m_ILW", "MERGED_TP_J1_OSTM_OST_CYCLES_V52", "MERGED_TP_J1_OSTM_OST_GMSL_ASCII_V51", "MERIS_L1_FRS", "MERIS_L1_RR", "MERIS_L2_FRS_IOP", "MERIS_L2_FRS_OC", "MERIS_L2_RR_IOP", "MERIS_L2_RR_OC", "MERIS_L3b_CHL", "MERIS_L3b_CYAN", "MERIS_L3b_CYANTC", "MERIS_L3b_IOP", "MERIS_L3b_KD", "MERIS_L3b_PAR", "MERIS_L3b_PIC", "MERIS_L3b_POC", "MERIS_L3b_RRS", "MERIS_L3m_CHL", "MERIS_L3m_CYAN", "MERIS_L3m_CYANTC", "MERIS_L3m_IOP", "MERIS_L3m_KD", "MERIS_L3m_PAR", "MERIS_L3m_PIC", "MERIS_L3m_POC", "MERIS_L3m_RRS", "MERIS_L4b_GSM", "MERIS_L4m_GSM", "MERRA2_CNN_HAQAST_PM25", "MFLL_CO2_Weighting_Functions_1891", "MFLL_XCO2_Range_10Hz_1892", "MI1B2_ELLIPSOID_NRT", "MI1B2_TERRAIN_NRT", "MI2AS_AEROSOL_NRT", "MI2TC_CMV_BFR_NRT", "MI2TC_CMV_HDF_NRT", "MI3DAEF", "MI3DAENF", "MI3DAER", "MI3DALF", "MI3DALNF", "MI3DCDF", "MI3DCDNF", "MI3DCLDN", "MI3DLSF", "MI3DLSNF", "MI3DLSR", "MI3DRDF", "MI3DRDR", "MI3MAEF", "MI3MAENF", "MI3MAER", "MI3MALF", "MI3MALNF", "MI3MCDF", "MI3MCDNF", "MI3MCLDN", "MI3MCMVN", "MI3MLSF", "MI3MLSNF", "MI3MLSR", "MI3MRDF", "MI3MRDR", "MI3QCLDN", "MI3QCMVN", "MI3YCLDN", "MI3YCMVN", "MIANACP", "MIANCAGP", "MIANCARP", "MIANTASC", "MIB1LM", "MICRONESIAN", "MIL2ASAE", "MIL2ASLS", "MIL2TCAF", "MIL2TCAL", "MIL2TCCF", "MIL2TCCF", "MIL2TCCL", "MIL2TCSF", "MIL2TCSP", "MIL2TCSP", "MIL3DAE", "MIL3DAEN", "MIL3DAL", "MIL3DALN", "MIL3DCFA", "MIL3DCLD", "MIL3DCOD", "MIL3DLS", "MIL3DLSN", "MIL3DRD", "MIL3MAE", "MIL3MAEN", "MIL3MAL", "MIL3MALN", "MIL3MCFA", "MIL3MCLD", "MIL3MCOD", "MIL3MJTA", "MIL3MLS", "MIL3MLSN", "MIL3MRD", "MIL3QAE", "MIL3QAEN", "MIL3QAL", "MIL3QALN", "MIL3QCFA", "MIL3QCLD", "MIL3QCOD", "MIL3QLS", "MIL3QLSN", "MIL3QRD", "MIL3YAE", "MIL3YAEN", "MIL3YAL", "MIL3YALN", "MIL3YCFA", "MIL3YCLD", "MIL3YCOD", "MIL3YLS", "MIL3YLSN", "MIL3YRD", "MIPOT", "MIRAI", "MIRC", "MIRCCM", "MIRCCMF", "MISC_Apex_Floats", "MISR_885", "MISR_AEROSOL_CLIM", "MISR_Forest_AGB_SW_US_1978", "MITgcm_LLC4320_Pre-SWOT_JPL_L4_ACC_SMST_v1.0", "MITgcm_LLC4320_Pre-SWOT_JPL_L4_BassStrait_v1.0", "MITgcm_LLC4320_Pre-SWOT_JPL_L4_Boknis_v1.0", "MITgcm_LLC4320_Pre-SWOT_JPL_L4_CapeBasin_v1.0", "MITgcm_LLC4320_Pre-SWOT_JPL_L4_GotlandBasin_v1.0", "MITgcm_LLC4320_Pre-SWOT_JPL_L4_LabradorSea_v1.0", "MITgcm_LLC4320_Pre-SWOT_JPL_L4_MarmaraSea_v1.0", "MITgcm_LLC4320_Pre-SWOT_JPL_L4_NWAustralia_v1.0", "MITgcm_LLC4320_Pre-SWOT_JPL_L4_NWPacific_v1.0", "MITgcm_LLC4320_Pre-SWOT_JPL_L4_NewCaledonia_v1.0", "MITgcm_LLC4320_Pre-SWOT_JPL_L4_ROAM_MIZ_v1.0", "MITgcm_LLC4320_Pre-SWOT_JPL_L4_RockallTrough_v1.0", "MITgcm_LLC4320_Pre-SWOT_JPL_L4_WestAtlantic_v1.0", "MITgcm_LLC4320_Pre-SWOT_JPL_L4_WesternMed_v1.0", "MITgcm_LLC4320_Pre-SWOT_JPL_L4_Yongala_v1.0", "ML1OA", "ML1OA", "ML1RADD", "ML1RADD", "ML1RADG", "ML1RADG", "ML1RADT", "ML1RADT", "ML2BRO", "ML2BRO", "ML2CH3CL", "ML2CH3CL", "ML2CH3CN", "ML2CH3CN", "ML2CH3OH", "ML2CH3OH", "ML2CLO", "ML2CLO", "ML2CO", "ML2CO", "ML2CO_NRT", "ML2DGG", "ML2DGG", "ML2DGM", "ML2DGM", "ML2GPH", "ML2GPH", "ML2H2O", "ML2H2O", "ML2H2O_NRT", "ML2HCL", "ML2HCL", "ML2HCN", "ML2HCN", "ML2HNO3", "ML2HNO3", "ML2HNO3_NRT", "ML2HO2", "ML2HO2", "ML2HOCL", "ML2HOCL", "ML2IWC", "ML2IWC", "ML2N2O", "ML2N2O", "ML2N2O_NRT", "ML2O3", "ML2O3", "ML2O3_NRT", "ML2OH", "ML2OH", "ML2RHI", "ML2RHI", "ML2SO2", "ML2SO2", "ML2SO2_NRT", "ML2T", "ML2T", "ML2T_NRT", "ML3DBCH3CL", "ML3DBCH3CL", "ML3DBCLO", "ML3DBCLO", "ML3DBCO", "ML3DBCO", "ML3DBGPH", "ML3DBGPH", "ML3DBH2O", "ML3DBH2O", "ML3DBHCL", "ML3DBHCL", "ML3DBHCN", "ML3DBHCN", "ML3DBHNO3", "ML3DBHNO3", "ML3DBHOCL", "ML3DBHOCL", "ML3DBIWC", "ML3DBIWC", "ML3DBN2O", "ML3DBN2O", "ML3DBO3", "ML3DBO3", "ML3DBOH", "ML3DBOH", "ML3DBRHI", "ML3DBRHI", "ML3DBSO2", "ML3DBSO2", "ML3DBT", "ML3DBT", "ML3DZCH3CL", "ML3DZCH3CL", "ML3DZCLO", "ML3DZCLO", "ML3DZCO", "ML3DZCO", "ML3DZGPH", "ML3DZGPH", "ML3DZH2O", "ML3DZH2O", "ML3DZHCL", "ML3DZHCL", "ML3DZHCN", "ML3DZHCN", "ML3DZHNO3", "ML3DZHNO3", "ML3DZHOCL", "ML3DZHOCL", "ML3DZIWC", "ML3DZIWC", "ML3DZMBRO", "ML3DZMBRO", "ML3DZMHO2", "ML3DZMHO2", "ML3DZN2O", "ML3DZN2O", "ML3DZO3", "ML3DZO3", "ML3DZOH", "ML3DZOH", "ML3DZRHI", "ML3DZRHI", "ML3DZSO2", "ML3DZSO2", "ML3DZT", "ML3DZT", "ML3MBCH3CL", "ML3MBCH3CL", "ML3MBCLO", "ML3MBCLO", "ML3MBCO", "ML3MBCO", "ML3MBGPH", "ML3MBGPH", "ML3MBH2O", "ML3MBH2O", "ML3MBHCL", "ML3MBHCL", "ML3MBHCN", "ML3MBHCN", "ML3MBHNO3", "ML3MBHNO3", "ML3MBHOCL", "ML3MBHOCL", "ML3MBIWC", "ML3MBIWC", "ML3MBN2O", "ML3MBN2O", "ML3MBO3", "ML3MBO3", "ML3MBOH", "ML3MBOH", "ML3MBRHI", "ML3MBRHI", "ML3MBSO2", "ML3MBSO2", "ML3MBT", "ML3MBT", "MOCE", "MOD00F", "MOD00S", "MOD08_D3", "MOD08_E3", "MOD08_M3", "MOD09A1", "MOD09A1N", "MOD09CMA", "MOD09CMG", "MOD09CMG", "MOD09GA", "MOD09GA", "MOD09GQ", "MOD09GQ", "MOD09Q1", "MOD09Q1N", "MOD10A1", "MOD10A1F", "MOD10A2", "MOD10C1", "MOD10C2", "MOD10CM", "MOD11A1", "MOD11A2", "MOD11B1", "MOD11B2", "MOD11B3", "MOD11C1", "MOD11C2", "MOD11C3", "MOD11CM1D", "MOD11CM1N", "MOD11_L2", "MOD13A1", "MOD13A2", "MOD13A3", "MOD13A4N", "MOD13C1", "MOD13C2", "MOD13Q1", "MOD13Q4N", "MOD14", "MOD14A1", "MOD14A2", "MOD14CM1", "MOD15A2H", "MOD16A2", "MOD16A2GF", "MOD16A3GF", "MOD17A2H", "MOD17A2HGF", "MOD17A3HGF", "MOD21", "MOD21A1D", "MOD21A1N", "MOD21A2", "MOD21C1", "MOD21C2", "MOD21C3", "MOD28C2", "MOD28C3", "MOD29E1D", "MOD29P1D", "MOD29P1N", "MOD44B", "MOD44W", "MODAODHD", "MODCSR_8", "MODCSR_B", "MODGRNLD", "MODISA_L1", "MODISA_L1_GEO", "MODISA_L2_IOP", "MODISA_L2_IOP_NRT", "MODISA_L2_OC", "MODISA_L2_OC_NRT", "MODISA_L2_SST", "MODISA_L2_SST4", "MODISA_L2_SST4_NRT", "MODISA_L2_SST_NRT", "MODISA_L3b_CHL", "MODISA_L3b_CHL_NRT", "MODISA_L3b_FLH", "MODISA_L3b_FLH_NRT", "MODISA_L3b_IOP", "MODISA_L3b_IOP_NRT", "MODISA_L3b_KD", "MODISA_L3b_KD_NRT", "MODISA_L3b_LAND", "MODISA_L3b_NSST", "MODISA_L3b_NSST_NRT", "MODISA_L3b_PAR", "MODISA_L3b_PAR_NRT", "MODISA_L3b_PIC", "MODISA_L3b_PIC_NRT", "MODISA_L3b_POC", "MODISA_L3b_POC_NRT", "MODISA_L3b_RRS", "MODISA_L3b_RRS_NRT", "MODISA_L3b_SST", "MODISA_L3b_SST4", "MODISA_L3b_SST4_NRT", "MODISA_L3b_SST_NRT", "MODISA_L3m_CHL", "MODISA_L3m_CHL_NRT", "MODISA_L3m_FLH", "MODISA_L3m_FLH_NRT", "MODISA_L3m_IOP", "MODISA_L3m_IOP_NRT", "MODISA_L3m_KD", "MODISA_L3m_KD_NRT", "MODISA_L3m_LAND", "MODISA_L3m_NSST", "MODISA_L3m_NSST_NRT", "MODISA_L3m_PAR", "MODISA_L3m_PAR_NRT", "MODISA_L3m_PIC", "MODISA_L3m_PIC_NRT", "MODISA_L3m_POC", "MODISA_L3m_POC_NRT", "MODISA_L3m_RRS", "MODISA_L3m_RRS_NRT", "MODISA_L3m_SST", "MODISA_L3m_SST4", "MODISA_L3m_SST4_NRT", "MODISA_L3m_SST_NRT", "MODISA_L4b_GSM", "MODISA_L4m_GSM", "MODIST_L1", "MODIST_L1_GEO", "MODIST_L2_IOP", "MODIST_L2_IOP_NRT", "MODIST_L2_OC", "MODIST_L2_OC_NRT", "MODIST_L2_SST", "MODIST_L2_SST4", "MODIST_L2_SST4_NRT", "MODIST_L2_SST_NRT", "MODIST_L3b_CHL", "MODIST_L3b_CHL_NRT", "MODIST_L3b_FLH", "MODIST_L3b_FLH_NRT", "MODIST_L3b_IOP", "MODIST_L3b_IOP_NRT", "MODIST_L3b_KD", "MODIST_L3b_KD_NRT", "MODIST_L3b_NSST", "MODIST_L3b_NSST_NRT", "MODIST_L3b_PAR", "MODIST_L3b_PAR_NRT", "MODIST_L3b_PIC", "MODIST_L3b_PIC_NRT", "MODIST_L3b_POC", "MODIST_L3b_POC_NRT", "MODIST_L3b_RRS", "MODIST_L3b_RRS_NRT", "MODIST_L3b_SST", "MODIST_L3b_SST4", "MODIST_L3b_SST4_NRT", "MODIST_L3b_SST_NRT", "MODIST_L3m_CHL", "MODIST_L3m_CHL_NRT", "MODIST_L3m_FLH", "MODIST_L3m_FLH_NRT", "MODIST_L3m_IOP", "MODIST_L3m_IOP_NRT", "MODIST_L3m_KD", "MODIST_L3m_KD_NRT", "MODIST_L3m_NSST", "MODIST_L3m_NSST_NRT", "MODIST_L3m_PAR", "MODIST_L3m_PAR_NRT", "MODIST_L3m_PIC", "MODIST_L3m_PIC_NRT", "MODIST_L3m_POC", "MODIST_L3m_POC_NRT", "MODIST_L3m_RRS", "MODIST_L3m_RRS_NRT", "MODIST_L3m_SST", "MODIST_L3m_SST4", "MODIST_L3m_SST4_NRT", "MODIST_L3m_SST_NRT", "MODIST_L4b_GSM", "MODIST_L4m_GSM", "MODIS_A-JPL-L2P-v2019.0", "MODIS_AQUA_L3_SST_MID-IR_8DAY_4KM_NIGHTTIME_V2019.0", "MODIS_AQUA_L3_SST_MID-IR_8DAY_9KM_NIGHTTIME_V2019.0", "MODIS_AQUA_L3_SST_MID-IR_ANNUAL_4KM_NIGHTTIME_V2019.0", "MODIS_AQUA_L3_SST_MID-IR_ANNUAL_9KM_NIGHTTIME_V2019.0", "MODIS_AQUA_L3_SST_MID-IR_DAILY_4KM_NIGHTTIME_V2019.0", "MODIS_AQUA_L3_SST_MID-IR_DAILY_9KM_NIGHTTIME_V2019.0", "MODIS_AQUA_L3_SST_MID-IR_MONTHLY_4KM_NIGHTTIME_V2019.0", "MODIS_AQUA_L3_SST_MID-IR_MONTHLY_9KM_NIGHTTIME_V2019.0", "MODIS_AQUA_L3_SST_THERMAL_8DAY_4KM_DAYTIME_V2019.0", "MODIS_AQUA_L3_SST_THERMAL_8DAY_4KM_NIGHTTIME_V2019.0", "MODIS_AQUA_L3_SST_THERMAL_8DAY_9KM_DAYTIME_V2019.0", "MODIS_AQUA_L3_SST_THERMAL_8DAY_9KM_NIGHTTIME_V2019.0", "MODIS_AQUA_L3_SST_THERMAL_ANNUAL_4KM_DAYTIME_V2019.0", "MODIS_AQUA_L3_SST_THERMAL_ANNUAL_4KM_NIGHTTIME_V2019.0", "MODIS_AQUA_L3_SST_THERMAL_ANNUAL_9KM_DAYTIME_V2019.0", "MODIS_AQUA_L3_SST_THERMAL_ANNUAL_9KM_NIGHTTIME_V2019.0", "MODIS_AQUA_L3_SST_THERMAL_DAILY_4KM_DAYTIME_V2019.0", "MODIS_AQUA_L3_SST_THERMAL_DAILY_4KM_NIGHTTIME_V2019.0", "MODIS_AQUA_L3_SST_THERMAL_DAILY_9KM_DAYTIME_V2019.0", "MODIS_AQUA_L3_SST_THERMAL_DAILY_9KM_NIGHTTIME_V2019.0", "MODIS_AQUA_L3_SST_THERMAL_MONTHLY_4KM_DAYTIME_V2019.0", "MODIS_AQUA_L3_SST_THERMAL_MONTHLY_4KM_NIGHTTIME_V2019.0", "MODIS_AQUA_L3_SST_THERMAL_MONTHLY_9KM_DAYTIME_V2019.0", "MODIS_AQUA_L3_SST_THERMAL_MONTHLY_9KM_NIGHTTIME_V2019.0", "MODIS_CCaN_NDVI_Trends_Alaska_1666", "MODIS_CR_Equal_Angle_3h", "MODIS_CR_Equal_Angle_Daily", "MODIS_CR_Equal_Area_3h", "MODIS_MAIAC_Reflectance_1700", "MODIS_PAR_1140", "MODIS_T-JPL-L2P-v2019.0", "MODIS_TERRA_L3_SST_MID-IR_8DAY_4KM_NIGHTTIME_V2019.0", "MODIS_TERRA_L3_SST_MID-IR_8DAY_9KM_NIGHTTIME_V2019.0", "MODIS_TERRA_L3_SST_MID-IR_ANNUAL_4KM_NIGHTTIME_V2019.0", "MODIS_TERRA_L3_SST_MID-IR_ANNUAL_9KM_NIGHTTIME_V2019.0", "MODIS_TERRA_L3_SST_MID-IR_DAILY_4KM_NIGHTTIME_V2019.0", "MODIS_TERRA_L3_SST_MID-IR_DAILY_9KM_NIGHTTIME_V2019.0", "MODIS_TERRA_L3_SST_MID-IR_MONTHLY_4KM_NIGHTTIME_V2019.0", "MODIS_TERRA_L3_SST_MID-IR_MONTHLY_9KM_NIGHTTIME_V2019.0", "MODIS_TERRA_L3_SST_THERMAL_8DAY_4KM_DAYTIME_V2019.0", "MODIS_TERRA_L3_SST_THERMAL_8DAY_4KM_NIGHTTIME_V2019.0", "MODIS_TERRA_L3_SST_THERMAL_8DAY_9KM_DAYTIME_V2019.0", "MODIS_TERRA_L3_SST_THERMAL_8DAY_9KM_NIGHTTIME_V2019.0", "MODIS_TERRA_L3_SST_THERMAL_ANNUAL_4KM_DAYTIME_V2019.0", "MODIS_TERRA_L3_SST_THERMAL_ANNUAL_4KM_NIGHTTIME_V2019.0", "MODIS_TERRA_L3_SST_THERMAL_ANNUAL_9KM_DAYTIME_V2019.0", "MODIS_TERRA_L3_SST_THERMAL_ANNUAL_9KM_NIGHTTIME_V2019.0", "MODIS_TERRA_L3_SST_THERMAL_DAILY_4KM_DAYTIME_V2019.0", "MODIS_TERRA_L3_SST_THERMAL_DAILY_4KM_NIGHTTIME_V2019.0", "MODIS_TERRA_L3_SST_THERMAL_DAILY_9KM_DAYTIME_V2019.0", "MODIS_TERRA_L3_SST_THERMAL_DAILY_9KM_NIGHTTIME_V2019.0", "MODIS_TERRA_L3_SST_THERMAL_MONTHLY_4KM_DAYTIME_V2019.0", "MODIS_TERRA_L3_SST_THERMAL_MONTHLY_4KM_NIGHTTIME_V2019.0", "MODIS_TERRA_L3_SST_THERMAL_MONTHLY_9KM_DAYTIME_V2019.0", "MODIS_TERRA_L3_SST_THERMAL_MONTHLY_9KM_NIGHTTIME_V2019.0", "MODIS_emissions_758", "MODVI", "MOD_L2_DC", "MOKIHANA", "MONTEREY_BAY", "MOOSE_Aerodyne-Mobile-Laboratory", "MOOSE_AircraftRemoteSensing_NASA-G3_GCAS_Data", "MOP02J", "MOP02J", "MOP02J", "MOP02N", "MOP02N", "MOP02N", "MOP02T", "MOP02T", "MOP02T", "MOP03J", "MOP03J", "MOP03J", "MOP03JM", "MOP03JM", "MOP03JM", "MOP03N", "MOP03N", "MOP03N", "MOP03NM", "MOP03NM", "MOP03NM", "MOP03T", "MOP03T", "MOP03T", "MOP03TM", "MOP03TM", "MOP03TM", "MOPCH", "MOPITT_co_835", "MRIRN2IM", "MRIRN2L1", "MRIRN3IM", "MRIRN3L1", "MSAQSO2L4", "MSG01-OSPO-L2P-v1.0", "MSG02-OSPO-L2P-v1.0", "MSG03-OSPO-L2P-v1.0", "MSG04-OSPO-L2P-v1.0", "MSLERLSTL3d10", "MSLERLSTL3zm", "MSLERNNL3d10", "MSLERNNL3zm", "MSLSP30NA", "MSO3L3zm5", "MSULST", "MSULTT", "MSUOP", "MSUUTT", "MSVOLSO2L4", "MS_Sound", "MTSAT2-OSPO-L2P-v1.0", "MUR-JPL-L4-GLOB-v4.1", "MUR25-JPL-L4-GLOB-v04.2", "MURI_Camouflage", "MURI_HI", "MUSE", "MVCO", "MW_IR_OI-REMSS-L4-GLOB-v5.0", "MW_IR_OI-REMSS-L4-GLOB-v5.1", "MW_OI-REMSS-L4-GLOB-v5.0", "MW_OI-REMSS-L4-GLOB-v5.1", "MYD00F", "MYD00S", "MYD08_D3", "MYD09A1", "MYD09CMG", "MYD09GA", "MYD09GQ", "MYD09Q1", "MYD10A1", "MYD11A1", "MYD11A2", "MYD11B1", "MYD11B2", "MYD11B3", "MYD11C1", "MYD11C2", "MYD11C3", "MYD11CM1D", "MYD11CM1N", "MYD11_L2", "MYD13A1", "MYD13A2", "MYD13A3", "MYD13C1", "MYD13C2", "MYD13Q1", "MYD14", "MYD14A1", "MYD14A2", "MYD14CM1", "MYD15A2H", "MYD16A2", "MYD16A2GF", "MYD16A3GF", "MYD17A2H", "MYD17A2HGF", "MYD17A3HGF", "MYD21", "MYD21A1D", "MYD21A1N", "MYD21A2", "MYD21C1", "MYD21C2", "MYD21C3", "MYD28C2", "MYD28C3", "MYDGB0", "MYDVI", "MYD_L2_CB", "MYD_L2_DC", "MYD_L2_MPLCT", "MYD_L3_OFLCT", "MagMix", "Main_Melt_Onset_Dates_1841", "Maps_AGB_North_Slope_AK_1565", "Marlon_Lewis_92", "Maryland_Temperature_Humidity_1319", "MatthewsVegetation_419", "Mean_Seasonal_LAI_1653", "Medit_Ligurian", "MetOpA_GOME2_SIF_V2_2292", "MetOpB_GOME2_SIF_2182", "Meteorological_1065", "Methane_Ebullition_Lakes_AK_1861", "Methane_Ethane_MA_NH_1982", "Methane_Flaring_Sites_VIIRS_1874", "Microbiome", "Mid-latitude_soils_705", "Missouri_Reservoirs_RSWQ", "MonthlyWetland_CH4_WetCHARTsV2_2346", "Monthly_Hydrological_Fluxes_1647", "MultiInstrumentFusedXCO2", "MumfordCove", "N01", "N07_AVH02C1", "N07_AVH09C1", "N07_AVH13C1", "N09_AVH02C1", "N09_AVH09C1", "N09_AVH13C1", "N11_AVH02C1", "N11_AVH09C1", "N11_AVH13C1", "N14_AVH02C1", "N14_AVH09C1", "N14_AVH13C1", "N16_AVH02C1", "N16_AVH09C1", "N16_AVH13C1", "N18_AVH02C1", "N18_AVH09C1", "N18_AVH13C1", "N19_AVH02C1", "N19_AVH09C1", "N19_AVH13C1", "N21-VIIRS-L2P-ACSPO-v2.80", "N21-VIIRS-L3U-ACSPO-v2.80", "NAAMES", "NAAMES_AerosolCloud_AircraftRemoteSensing_Data", "NAAMES_Aerosol_AircraftInSitu_Data", "NAAMES_Aerosol_ShipInSitu_Data", "NAAMES_Cloud_AircraftInSitu_Data", "NAAMES_Merge_Data", "NAAMES_MetNav_AircraftInSitu_Data", "NAAMES_MetNav_ShipInSitu_Data", "NAAMES_Met_SondeInSitu_Data", "NAAMES_Misc_Ship_Data", "NAAMES_Ocean_AircraftRemoteSensing_Data", "NAAMES_Radiation_AircraftInSitu_Data", "NAAMES_TraceGas_AircraftInSitu_Data", "NAAMES_TraceGas_ShipInSitu_Data", "NAB08", "NABE", "NACP_ACES_V2_1943", "NACP_BlackSpruce_Burn-Severity_1331", "NACP_Boreal_Biome_Biomass_1273", "NACP_Forest_Biophysical_1046", "NACP_Forest_Conservation_1662", "NACP_GHG_Data_Compilation_1206", "NACP_MCI_CO2_Inventory_1205", "NACP_MCI_CO2_Inversions_1204", "NACP_MCI_CO2_Measurements_1202", "NACP_MCI_Crop_GPP_1217", "NACP_Modeled_NEE_NEP_Fluxes_1203", "NACP_MsTMIP_Model_Driver_1220", "NACP_MsTMIP_Model_Structure_1228", "NACP_MsTMIP_TBMO_1225", "NACP_MsTMIP_Unified_NA_SoilMap_1242", "NACP_NAM_HYSPLIT_Footprints_1586", "NACP_PNW_Carbon_Balance_1317", "NACP_PalEON_MIP_1779", "NACP_Peatland_Burn-Severity_1283", "NACP_Peatland_Land_Cover_MI_1513", "NACP_Regional_Model_GHG_Aggr_1179", "NACP_Regional_Obs_Model_Grid_1157", "NACP_Regional_Obs_Model_Orig_1193", "NACP_Regional_Obs_Model_Suppl_1158", "NACP_Site_Model_Data_Orig_Fmt_1192", "NACP_Site_Model_Flux_Std_Fmt_1183", "NACP_Site_Tower_Met_and_Flux_1178", "NACP_TERRA-PNW_1292", "NACP_Vista_CA_CH4_Inventory_1726", "NACP_Vista_LA_CH4_Inventory_1525", "NACP_Wild_Cropland_Fuel_Map_1163", "NACP_Woody_Veg_N_Slope_AK_V2_1365", "NAFD-NEX_Attribution_1799", "NAFD-NEX_Forest_Disturbance_1290", "NAFD_Disturbance_1077", "NARSTO_EPA_SS_ATLANTA_1999_CHEM_PM_MET_DATA", "NARSTO_EPA_SS_ATLANTA_1999_UAH_MIPS_DATA", "NARSTO_EPA_SS_ATLANTA_RAPID_SPMS_DATA", "NARSTO_EPA_SS_BALTIMORE_JHU_LIDAR_DATA", "NARSTO_EPA_SS_BALTIMORE_JHU_MET_DATA", "NARSTO_EPA_SS_BALTIMORE_RAPID_SPMS_DATA", "NARSTO_EPA_SS_BALTIMORE_SEAS_PM25_METAL_CYTOKINES", "NARSTO_EPA_SS_FRESNO_AETHALOM_MULTI_WL_CARBON", "NARSTO_EPA_SS_FRESNO_BAM_PM_MASS", "NARSTO_EPA_SS_FRESNO_EC_PM25_FRACTION", "NARSTO_EPA_SS_FRESNO_MET_DATA", "NARSTO_EPA_SS_FRESNO_PARTICLE_PAC_DATA", "NARSTO_EPA_SS_FRESNO_PM25_NO3_SO4", "NARSTO_EPA_SS_FRESNO_PM25_OC_EC", "NARSTO_EPA_SS_FRESNO_TEOM_PM_MASS", "NARSTO_EPA_SS_HOUSTON_DMA_DATA", "NARSTO_EPA_SS_HOUSTON_NH3_HNO3_DATA", "NARSTO_EPA_SS_HOUSTON_NO3_SO4_C_DATA", "NARSTO_EPA_SS_HOUSTON_RAPID_SPMS_DATA", "NARSTO_EPA_SS_HOUSTON_TEXAQS2000_CAMS_DATA", "NARSTO_EPA_SS_HOUSTON_TEXAQS2000_DOE_G-1_DATA", "NARSTO_EPA_SS_HOUSTON_TEXAQS2000_HCHO_H2O2_DATA", "NARSTO_EPA_SS_HOUSTON_TEXAQS2000_PM25_ORG_DATA", "NARSTO_EPA_SS_HOUSTON_TEXAQS2000_PM_FTIR", "NARSTO_EPA_SS_HOUSTON_TEXAQS2000_PM_SIZE_MASS", "NARSTO_EPA_SS_HOUSTON_TEXAQS2000_WB_TUNNEL", "NARSTO_EPA_SS_LOS_ANGELES_AETHALOMETER_EC_DATA", "NARSTO_EPA_SS_LOS_ANGELES_APS_DATA", "NARSTO_EPA_SS_LOS_ANGELES_HEADS_PART_IONS_MASS", "NARSTO_EPA_SS_LOS_ANGELES_MOUDI_DATA", "NARSTO_EPA_SS_LOS_ANGELES_PARTISOL_DATA", "NARSTO_EPA_SS_LOS_ANGELES_PM25_CARBON", "NARSTO_EPA_SS_LOS_ANGELES_PM25_NITRATE", "NARSTO_EPA_SS_LOS_ANGELES_SMPS_DATA", "NARSTO_EPA_SS_LOS_ANGELES_TEOM_PM25_DATA", "NARSTO_EPA_SS_NEW_YORK_AIR_CHEM_PM_MET_DATA", "NARSTO_EPA_SS_PITTSBURGH_GAS_PM_PROPERTY_DATA", "NARSTO_EPA_SS_PITTSBURGH_MET_DATA", "NARSTO_EPA_SS_PITTSBURGH_PM_COMPOSITION_DATA", "NARSTO_EPA_SS_PITTSBURGH_RAPID_SPMS_DATA", "NARSTO_EPA_SS_ST_LOUIS_AIR_CHEM_PM_MET_DATA", "NARSTO_ICARTT_NEAX_2004_DOE_G-1_DATA", "NARSTO_NE_MODEL", "NARSTO_NE_NEXRAD_IMAGES", "NARSTO_PAC2001_CASSIAR_TUNNEL_GAS_PM_DATA", "NARSTO_PAC2001_CESSNA_VOC_PM_OZONE_MET_DATA", "NARSTO_PAC2001_CONVAIR_PM_OZONE_MET_DATA", "NARSTO_PAC2001_GOLDEN_EARS_GAS_PM_DATA", "NARSTO_PAC2001_GVRD_CAPMON_AIR_QUAL_DATA", "NARSTO_PAC2001_LANGLEY_GAS_PM_MET_DATA", "NARSTO_PAC2001_SLOCAN_PARK_GAS_PM_MET_DATA", "NARSTO_PAC2001_SUMAS_MTN_GAS_PM_MET_DATA", "NARSTO_SHEMP_CANADA_PM_COMPOSITION_DATA", "NARSTO_SOS99NASH_G-1_AIR_CHEMISTRY_DATA", "NARSTO_SOS99NASH_SURFACE_MET_CHEM_DATA", "NARSTO_SOS99NASH_WIND_PROFILER_DATA", "NARSTO_SOS99NASH_WP3D_CHEMISTRY_DATA", "NARSTO_SOS_SC_UPSTATE_PM25_COMPOSITION", "NARSTO_STIFS_CANADA_PM_INORG_VOC_DATA", "NARSTO_Texas_PM2.5_Sampling_and_Analysis_Study_1997-1998", "NASADEM_HGT", "NASADEM_NC", "NASADEM_NUMNC", "NASADEM_SC", "NASADEM_SHHP", "NASADEM_SIM", "NASADEM_SSP", "NASASatellite_Dev_Applications_2293", "NASA_Airborne_Lidar_Flights", "NASMo_TiAM_250m_2326", "NA_MODIS_Surface_Biophysics_1210", "NA_TreeAge_1096", "NBCD2000_V2_1161", "NCALDAS_NOAH0125_D", "NCALDAS_NOAH0125_Trends", "ND01_Age_Maps_1184", "ND01_Georectified_Products_1165", "ND01_Land_Cover_Maps_1259", "ND01_Pasture_Nutrients_1135", "ND01_Pasture_Spectra_1154", "ND01_Registered_TM_MSS_1197", "ND01_Spectral_Mixture_Models_1188", "ND01_Stream_Chemistry_1119", "ND01_Watershed_Defor_1159", "ND02_Fertilization_Experiment_954", "ND02_Landsat_TM_MSS_Para_1156", "ND02_Mulching_Experiment_950", "ND02_Non_Woody_Biomass_1115", "ND02_REE_Soil_VWC_1061", "ND02_REE_Trace_Gas_Tapajos_955", "ND02_Soil_CO2_Extracts_1074", "ND02_Soil_CO2_Flux_1066", "ND02_Soil_Gas_Flux_Apeu_953", "ND02_Soil_Gases_REE_1117", "ND02_Soil_Hydraulic_Conductivity_1075", "ND02_Tree_Heights_DBH_951", "ND02_Water_Chemistry_Paragominas_1067", "ND03_Flowpath_Chemistry_1076", "ND03_Streams_Soilwater_1113", "ND04_C_Nutrient_Stocks_1069", "ND04_Secondary_Forest_Recovery_1068", "ND04_Soil_H2O_Manaus_1246", "ND04_Termite_Mounds_1072", "ND06_LandUse_Studies_1130", "ND07_15N_Leaves_Soils_1121", "ND07_NO_Flux_Cerrado_1124", "ND07_PLFA_Soils_Microbial_Biomass_1017", "ND07_Stream_Chemistry_Brasilia_1018", "ND07_Trace_Gas_Land_Use_1016", "ND08_Biomass_Jari_1148", "ND08_Soil_Respiration_1250", "ND10_Soil_Chemistry_1171", "ND11_Carbon_Export_CPOM_913", "ND11_Logging_Damage_MT_977", "ND11_Nitrogen_Transfer_Leaf_Litter_915", "ND11_Regeneration_Succession_965", "ND11_Soil_Nitrate_Moisture_MT_976", "ND11_Soil_Spatial_Variability_914", "ND11_Soil_Water_Pressure_851", "ND11_Stream_Nutrients_921", "ND11_Tree_Vine_Biomass_MT_922", "ND11_Veg_Biomass_MT_964", "ND30_Litter_Para_1129", "ND30_Pasture_Degradation_1164", "ND30_REE_Water_Chemistry_1131", "NDVI_Forest_Structure_1797", "NEMSN5L2", "NES-LTER", "NEUROST_SSH-SST_L4_V2024.0", "NEWS_WEB_ACLIM", "NEWS_WEB_MCLIM", "NFRDI", "NGLI_Lake_Bourne", "NHICEM", "NHSNOWM", "NIH-NSF_Lake_Erie", "NIMBUS7_ERB_Ch10C_TSI_NAT", "NIMBUS7_ERB_SEFDT", "NIMBUS7_NFOV_MLCE", "NISE", "NISE", "NLDAS_FORA0125_H", "NLDAS_FORA0125_H", "NLDAS_FORA0125_M", "NLDAS_FORA0125_M", "NLDAS_FORA0125_MC", "NLDAS_FORA0125_MC", "NLDAS_FORB0125_H", "NLDAS_FORB0125_H", "NLDAS_FORB0125_M", "NLDAS_FORB0125_M", "NLDAS_FORB0125_MC", "NLDAS_FORB0125_MC", "NLDAS_MOS0125_H", "NLDAS_MOS0125_H", "NLDAS_MOS0125_M", "NLDAS_MOS0125_M", "NLDAS_MOS0125_MC", "NLDAS_MOS0125_MC", "NLDAS_NOAH0125_H", "NLDAS_NOAH0125_H", "NLDAS_NOAH0125_M", "NLDAS_NOAH0125_M", "NLDAS_NOAH0125_MC", "NLDAS_NOAH0125_MC", "NLDAS_VIC0125_H", "NLDAS_VIC0125_H", "NLDAS_VIC0125_M", "NLDAS_VIC0125_M", "NLDAS_VIC0125_MC", "NLDAS_VIC0125_MC", "NMMIEAI-L2-NRT", "NMSO2-PCA-L2-NRT", "NMTO3NRT", "NOAA", "NOAA_ToF_CIMS_Instrument_Data_1921", "NOBM_DAY", "NOBM_MON", "NOPP_float", "NORBAL", "NPBUVO3-L2-NRT", "NPP_ATH_577", "NPP_BCN_204", "NPP_BDK_203", "NPP_BOREAL_611", "NPP_BRD_205", "NPP_BRR_157", "NPP_BRW_580", "NPP_CBAY_473", "NPP_CHM_578", "NPP_CHR_468", "NPP_CLB_206", "NPP_CNL_465", "NPP_CNS_191", "NPP_CPR_145", "NPP_DCK_207", "NPP_DRN_215", "NPP_DZH_192", "NPP_EMDI_615", "NPP_FLK_201", "NPP_GNN_474", "NPP_GPPDI_617", "NPP_GRIDDED_GPPDI_614", "NPP_GSM_804", "NPP_Grassland_31_654", "NPP_Grassland_613", "NPP_HYS_208", "NPP_IBP_198", "NPP_JDR_202", "NPP_JHN_475", "NPP_JRN_209", "NPP_KDE_216", "NPP_KHC_217", "NPP_KHM_146", "NPP_KLN_147", "NPP_KNZ_148", "NPP_KRK_193", "NPP_KRS_149", "NPP_KSM_466", "NPP_LMT_150", "NPP_LQL_476", "NPP_MDL_210", "NPP_MGD_477", "NPP_MMG_802", "NPP_MNS_579", "NPP_MNT_413", "NPP_MRF_478", "NPP_MSS_572", "NPP_MTD_469", "NPP_MULTIBIOME_653", "NPP_Multi-Biome_125_Sites_1352", "NPP_NLS_194", "NPP_NRB_151", "NPP_ODS_214", "NPP_OLK_195", "NPP_OSG_211", "NPP_OTR_152", "NPP_OTTER_472", "NPP_PIK_575", "NPP_PMP_212", "NPP_PSH_219", "NPP_REDWOOD_803", "NPP_RMY_574", "NPP_SCH_573", "NPP_SCR_479", "NPP_SES_480", "NPP_SHR_153", "NPP_SLV_218", "NPP_SNF_190", "NPP_SSP_467", "NPP_TEM_471", "NPP_TLK_581", "NPP_TLL_196", "NPP_TMG_470", "NPP_TMN_154", "NPP_TROPICAL_616", "NPP_TVA_155", "NPP_TWM_213", "NPP_VAST_576", "NPP_VND_197", "NPP_WBW_819", "NPP_WOODY_655", "NPP_XLN_156", "NPP_surfaces_750", "NSCAT_25KM_MGDR_V2", "NSCAT_AER_HOFFMAN_L2_OW_WIND_VECTOR_AMBIGUITY_REMOVAL", "NSCAT_BYU_L3_OW_SIGMA0_ENHANCED", "NSCAT_LEVEL_1.7_V2", "NSCAT_LEVEL_2_V2", "NSCAT_LEVEL_3_BROWSE_IMAGES", "NSCAT_LEVEL_3_V2", "NSCAT_W25_RMGDR_V2", "NSF-BWZ", "NSF_Gulf_Rapid", "NSIDC-0051", "NSIDC-0071", "NSIDC-0077", "NSIDC-0081", "NSIDC-0477", "NSIDC-0478", "NSIDC-0481", "NSIDC-0484", "NSIDC-0498", "NSIDC-0525", "NSIDC-0530", "NSIDC-0531", "NSIDC-0532", "NSIDC-0533", "NSIDC-0534", "NSIDC-0535", "NSIDC-0545", "NSIDC-0547", "NSIDC-0642", "NSIDC-0645", "NSIDC-0646", "NSIDC-0666", "NSIDC-0670", "NSIDC-0709", "NSIDC-0712", "NSIDC-0713", "NSIDC-0714", "NSIDC-0715", "NSIDC-0720", "NSIDC-0723", "NSIDC-0724", "NSIDC-0725", "NSIDC-0727", "NSIDC-0731", "NSIDC-0738", "NSIDC-0754", "NSIDC-0756", "NSIDC-0761", "NSIDC-0764", "NSIDC-0766", "NSIDC-0766", "NSIDC-0774", "NSIDC-0775", "NSIDC-0776", "NSIDC-0777", "NSIDC-0778", "NSIDC-0779", "NSIDC-0781", "NSIDC-0782", "NSIDC-0786", "NSIDC-0787", "NSIDC-0791", "NSIDC-0792", "NSIDC-0793", "NSIDC-0794", "NSIDC-0796", "NVAP_CLIMATE_Layered-Precipitable-Water", "NVAP_CLIMATE_Total-Precipitable-Water", "NVAP_OCEAN_Total-Precipitable-Water", "NVAP_WEATHER_Layered-Precipitable-Water", "NVAP_WEATHER_Total-Precipitable-Water", "NWT_Burn_Severity_Maps_1694", "New_England_CH4_1311", "New_Hampshire_Landcover_1305", "Niwot_Ridge_CNPAM_Fluorescence_1722", "Niwot_Ridge_Pigment_1723", "NmAVCS1H", "NmAVCS3G", "NmAVCS3H", "NmHRIR1H", "NmHRIR1T", "NmHRIR3G", "NmHRIR3H", "NmIDCS1H", "NmIDCS3G", "NmIDCS3H", "NmIcEdg2", "NmTHIR115-1H", "NmTHIR115-1T", "NmTHIR115-3G", "NmTHIR115-3H", "NmTHIR67-1H", "NmTHIR67-1T", "NmTHIR67-3G", "NmTHIR67-3H", "NmTHIRmtg-1T", "Nome_Veg_Plots_1372", "Non-Forest_Trees_Sahara_Sahel_1832", "Nongrowing_Season_CO2_Flux_1692", "NorthSlope_NEE_TVPRM_1920", "North_Carolina_Coast", "North_Carolina_Sabrina", "North_Sea", "North_Slope_Transect_Veg_Maps_1386", "North_Slope_Veg_Plots_1536", "Northern_Alaska_Veg_Maps_1359", "OAHU", "OASIS_Moorings", "OCEANSAT-2", "OCEAN_LIDAR", "OCEAN_MASS_TELLUS_MASCON_CRI_TIME_SERIES_RL06.1_V3", "OCFLEXPART", "OCO2GriddedXCO2", "OCO2_Att", "OCO2_Att", "OCO2_Att", "OCO2_Att", "OCO2_Att", "OCO2_Eph", "OCO2_Eph", "OCO2_Eph", "OCO2_Eph", "OCO2_Eph", "OCO2_GEOS_L3CO2_DAY", "OCO2_GEOS_L3CO2_MONTH", "OCO2_L1B_Calibration", "OCO2_L1B_Calibration", "OCO2_L1B_Calibration", "OCO2_L1B_Calibration", "OCO2_L1B_Science", "OCO2_L1B_Science", "OCO2_L1B_Science", "OCO2_L1B_Science", "OCO2_L1aIn_Pixel", "OCO2_L1aIn_Pixel", "OCO2_L1aIn_Pixel", "OCO2_L1aIn_Pixel", "OCO2_L1aIn_Pixel", "OCO2_L1aIn_Sample", "OCO2_L1aIn_Sample", "OCO2_L1aIn_Sample", "OCO2_L1aIn_Sample", "OCO2_L1aIn_Sample", "OCO2_L2_ABand", "OCO2_L2_ABand", "OCO2_L2_ABand", "OCO2_L2_ABand", "OCO2_L2_CO2Prior", "OCO2_L2_CO2Prior", "OCO2_L2_CO2Prior", "OCO2_L2_CO2Prior", "OCO2_L2_Diagnostic", "OCO2_L2_Diagnostic", "OCO2_L2_Diagnostic", "OCO2_L2_Diagnostic", "OCO2_L2_IMAPDOAS", "OCO2_L2_IMAPDOAS", "OCO2_L2_IMAPDOAS", "OCO2_L2_IMAPDOAS", "OCO2_L2_Lite_FP", "OCO2_L2_Lite_FP", "OCO2_L2_Lite_FP", "OCO2_L2_Lite_SIF", "OCO2_L2_Lite_SIF", "OCO2_L2_Met", "OCO2_L2_Met", "OCO2_L2_Met", "OCO2_L2_Met", "OCO2_L2_Met", "OCO2_L2_Standard", "OCO2_L2_Standard", "OCO2_L2_Standard", "OCO2_L2_Standard", "OCO3_L1B_Calibration", "OCO3_L1B_Calibration", "OCO3_L1B_Calibration", "OCO3_L1B_Science", "OCO3_L1B_Science", "OCO3_L1B_Science", "OCO3_L2_ABand", "OCO3_L2_ABand", "OCO3_L2_ABand", "OCO3_L2_CO2Prior", "OCO3_L2_CO2Prior", "OCO3_L2_CO2Prior", "OCO3_L2_Diagnostic", "OCO3_L2_Diagnostic", "OCO3_L2_Diagnostic", "OCO3_L2_IMAPDOAS", "OCO3_L2_IMAPDOAS", "OCO3_L2_IMAPDOAS", "OCO3_L2_Lite_FP", "OCO3_L2_Lite_FP", "OCO3_L2_Lite_SIF", "OCO3_L2_Met", "OCO3_L2_Met", "OCO3_L2_Met", "OCO3_L2_Standard", "OCO3_L2_Standard", "OCO3_L2_Standard", "OCTS_L1", "OCTS_L2_IOP", "OCTS_L2_OC", "OCTS_L3b_CHL", "OCTS_L3b_IOP", "OCTS_L3b_KD", "OCTS_L3b_PAR", "OCTS_L3b_PIC", "OCTS_L3b_POC", "OCTS_L3b_RRS", "OCTS_L3m_CHL", "OCTS_L3m_IOP", "OCTS_L3m_KD", "OCTS_L3m_PAR", "OCTS_L3m_PIC", "OCTS_L3m_POC", "OCTS_L3m_RRS", "ODU_CBM", "OISSS_L4_multimission_7day_v1", "OISSS_L4_multimission_7day_v2", "OISSS_L4_multimission_monthly_v1", "OISSS_L4_multimission_monthly_v2", "OISST_HR_NRT-GOS-L4-BLK-v2.0", "OISST_HR_NRT-GOS-L4-MED-v2.0", "OISST_UHR_NRT-GOS-L4-BLK-v2.0", "OISST_UHR_NRT-GOS-L4-MED-v2.0", "OLCIS3A_L1_EFR", "OLCIS3A_L1_ERR", "OLCIS3A_L2_EFR_IOP", "OLCIS3A_L2_EFR_OC", "OLCIS3A_L2_ERR_IOP", "OLCIS3A_L2_ERR_IOP_NRT", "OLCIS3A_L2_ERR_OC", "OLCIS3A_L2_ERR_OC_NRT", "OLCIS3A_L2_ILW", "OLCIS3B_L1_EFR", "OLCIS3B_L1_ERR", "OLCIS3B_L2_EFR_IOP", "OLCIS3B_L2_EFR_OC", "OLCIS3B_L2_ERR_IOP", "OLCIS3B_L2_ERR_IOP_NRT", "OLCIS3B_L2_ERR_OC", "OLCIS3B_L2_ERR_OC_NRT", "OLCIS3B_L2_ILW", "OLIPAC", "OMAERO", "OMAEROG", "OMAEROZ", "OMAEROe", "OMAERUV", "OMAERUV", "OMAERUV", "OMAERUVG", "OMAERUV_CPR", "OMAERUVd", "OMBRO", "OMCLDO2", "OMCLDO2G", "OMCLDO2Z", "OMCLDO2_CPR", "OMCLDRR", "OMCLDRR", "OMCLDRR", "OMCLDRRG", "OMCLDRR_CPR", "OMDOAO3", "OMDOAO3G", "OMDOAO3Z", "OMDOAO3e", "OMEXII", "OMGLER", "OMG_L1B_AIRGRAV", "OMG_L1_FLOAT_ALAMO", "OMG_L1_FLOAT_APEX", "OMG_L2_AXBT", "OMG_L2_AXCTD", "OMG_L2_Bathy_MBES_Gridded", "OMG_L2_Bathy_SBES_Gridded", "OMG_L2_CTD", "OMG_L3_ICE_ELEV_GLISTINA", "OMG_NARWHALS_MOORING_TEMP_CTD_1.0", "OMG_NARWHALS_SHIPBOARD_CTD_1.0", "OMHCHO", "OMHCHOG", "OMHCHOd", "OMIAuraAER", "OMI_MINDS_NO2", "OMI_MINDS_NO2G", "OMI_MINDS_NO2d", "OML1BIRR", "OML1BIRR", "OML1BRUG", "OML1BRUG", "OML1BRUZ", "OML1BRUZ", "OML1BRVG", "OML1BRVG", "OML1BRVZ", "OML1BRVZ", "OMLER", "OMMYDAGEO", "OMMYDCLD", "OMNO2", "OMNO2G", "OMNO2_CPR", "OMNO2d", "OMO3PR", "OMOCLO", "OMPIXCOR", "OMPIXCORZ", "OMPS_N20_NMHCHO_L2", "OMPS_N20_NMSO2_PCA_L2_Step1", "OMPS_N21_LP_L1G_EV", "OMPS_N21_LP_L2_O3_DAILY", "OMPS_NPP_LP_L1G_EV", "OMPS_NPP_LP_L1G_EV", "OMPS_NPP_LP_L2_AER_DAILY", "OMPS_NPP_LP_L2_O3_DAILY", "OMPS_NPP_LP_L2_O3_DAILY", "OMPS_NPP_LP_L2_Temp_DAILY", "OMPS_NPP_LP_L3_AER_MONTHLY", "OMPS_NPP_NMCLDRR_L2", "OMPS_NPP_NMEV_L1B", "OMPS_NPP_NMHCHO_L2", "OMPS_NPP_NMMIEAI_L2", "OMPS_NPP_NMNO2_L2", "OMPS_NPP_NMSO2_L2", "OMPS_NPP_NMSO2_PCA_L2", "OMPS_NPP_NMSO2_PCA_L3_DAILY", "OMPS_NPP_NMTO3_L2", "OMPS_NPP_NMTO3_L3_DAILY", "OMPS_NPP_NPBUVO3_L2", "OMPS_NPP_NPBUVO3_L2", "OMPS_NPP_NPEV_L1B", "OMSO2", "OMSO2", "OMSO2G", "OMSO2_CPR", "OMSO2e", "OMTO3", "OMTO3G", "OMTO3_CPR", "OMTO3d", "OMTO3e", "OMTO3e", "OMUANC", "OMUFPITMET", "OMUFPMET", "OMUFPSLV", "OMUVB", "OMUVBG", "OMUVBd", "OMVANC", "OMVFPITMET", "OMVFPMET", "OMVFPSLV", "ONDEQUE", "ONR-MAB", "ONR_PenobscotRiverSystem", "OPERA_L2_CSLC-S1-STATIC_V1", "OPERA_L2_CSLC-S1_V1", "OPERA_L2_RTC-S1-STATIC_V1", "OPERA_L2_RTC-S1_V1", "OPERA_L3_DIST-ALERT-HLS_V1", "OPERA_L3_DIST-ANN-HLS_V1", "OPERA_L3_DSWX-HLS_V1", "ORACLES_AerosolCloud_AircraftRemoteSensing_Data", "ORACLES_Aerosol_AircraftInSitu_Data", "ORACLES_Cloud_AircraftInSitu_Data", "ORACLES_Merge_Data", "ORACLES_MetNav_AircraftInSitu_Data", "ORACLES_Model_Data", "ORACLES_Radiation_AircraftInSitu_Data", "ORACLES_TraceGas_AircraftInSitu_Data", "ORINOCO", "OS2_OSCAT_LEVEL_2B_OWV_COMP_12_V2", "OSCAR_L4_OC_1deg", "OSCAR_L4_OC_FINAL_V2.0", "OSCAR_L4_OC_INTERIM_V2.0", "OSCAR_L4_OC_NRT_V2.0", "OSCAR_L4_OC_third-deg", "OSCAR_L4_OC_third-deg_YEARLY", "OSTIA-UKMO-L4-GLOB-REP-v2.0", "OSTIA-UKMO-L4-GLOB-v2.0", "OTZ_WHOI", "OWLETS1_Model_Data", "OWLETS1_Pandora_Data", "OWLETS1_SherpaAircraft_Data", "OWLETS1_Sondes_Data", "OWLETS1_SurfaceLidar_Data", "OWLETS2_Aerosol_Data", "OWLETS2_Pandora_Data", "OWLETS2_Ship_Data", "OWLETS2_Sondes_Data", "OWLETS2_SurfaceLidar_Data", "OWLETS2_Surface_Data", "OWLETS2_UMDAircraft_Data", "Ocean_Color_Cal_Val", "Oceania", "Odyssee_Saint_Laurent", "Okeechobee", "Opt_Sed_Trap_Cal", "Optical_Layers", "Optique_St_Laurent", "Oumalik_Veg_plots_1506", "P380108", "PACE_ABSclosure", "PACE_EPH_DEF", "PACE_HARP2_L0_D1", "PACE_HARP2_L0_D2", "PACE_HARP2_L0_D3", "PACE_HARP2_L1A_SCI", "PACE_HARP2_L1B_SCI", "PACE_HARP2_L1C_SCI", "PACE_HKT", "PACE_HSK", "PACE_OCI_L0_SCI", "PACE_OCI_L1A_SCI", "PACE_OCI_L1B_SCI", "PACE_OCI_L1C_SCI", "PACE_OCI_L2_AOP_NRT", "PACE_OCI_L2_BGC_NRT", "PACE_OCI_L2_IOP_NRT", "PACE_OCI_L2_PAR_NRT", "PACE_OCI_L3M_CHL_NRT", "PACE_SPEXONE_L0", "PACE_SPEXONE_L1A_SCI", "PACE_SPEXONE_L1B_SCI", "PACE_SPEXONE_L1C_SCI", "PAC_DFO_PAR_BUOY", "PAD_2011_1133", "PAD_935", "PAL-LTER", "PARASOLRB_CPR", "PATEX", "PC06_ECMWF_LBA_1141", "PEACETIME", "PERU", "PET_PU_3H025", "PET_PU_3H025", "PICOLO", "PISTON-ONR-NOAA-Autonomous-Uncrewed_2018-2019", "PISTON-ONR-NOAA_Islands_2018-2019", "PISTON-ONR-NOAA_RVMirai_2018", "PISTON-ONR-NOAA_RVSallyRide_2019", "PISTON-ONR-NOAA_RVThompson_2018", "PMNS", "PMRN6L1RAD_CDROM", "POAM2_VER6", "POAM3", "POLARIS_Aerosol_AircraftInSitu_ER2_Data", "POLARIS_Analysis_ER2_Data", "POLARIS_Ground_Data", "POLARIS_MetNav_AircraftInSitu_ER2_Data", "POLARIS_Model_Data", "POLARIS_Satellite_Data", "POLARIS_Sondes_Data", "POLARIS_TraceGas_AircraftInSitu_ER2_Data", "POLARIS_jValue_AircraftInSitu_ER2_Data", "POMME", "PRECIP_AMSR2_GCOMW1", "PRECIP_AMSRE_AQUA", "PRECIP_GMI_GPM", "PRECIP_SMMR_NIMBUS7", "PRECIP_SSMIS_F16", "PRECIP_SSMIS_F17", "PRECIP_SSMIS_F18", "PRECIP_SSMIS_F19", "PRECIP_SSMI_F08", "PRECIP_SSMI_F10", "PRECIP_SSMI_F11", "PRECIP_SSMI_F13", "PRECIP_SSMI_F14", "PRECIP_SSMI_F15", "PRECIP_TMI_TRMM", "PREFIRE_SAT1_0-BUS-TLM", "PREFIRE_SAT1_0-PAYLOAD", "PREFIRE_SAT1_0-PAYLOAD-TLM", "PREFIRE_SAT2_0-BUS-TLM", "PREFIRE_SAT2_0-PAYLOAD", "PREFIRE_SAT2_0-PAYLOAD-TLM", "PREPP", "PRESWOT_HYDRO_GRRATS_L2_DAILY_VIRTUAL_STATION_HEIGHTS_V2", "PRESWOT_HYDRO_GRRATS_L2_VIRTUAL_STATION_HEIGHTS_V2", "PRESWOT_HYDRO_L2_GREALM_LAKE_HEIGHT_V2", "PRESWOT_HYDRO_L3_LAKE_RESEVOIR_AREA_V2", "PRESWOT_HYDRO_L4_LAKE_STORAGE_TIME_SERIES_V2", "PRIM_SMAP_L2_V1", "PRISM_CORAL_L1", "PRISM_CORAL_L2", "PRISM_Elkhorn_Slough", "PRONEX", "PSCNH_ARCHIVED_DATA", "PTLO", "PVST_HOTPACE", "PVST_NORTHERN_INDIAN_OCEAN", "PVST_PRINGLS", "PVST_SBCR", "PVST_VDIUP", "PanamaCity", "Panhandle_OWQ", "Passive_Microwave_Snowoff_Data_1711", "Patagonian_Coastal", "Peatland_carbon_balance_1382", "Pelican_PCO2", "PenBaySurvey", "PermafrostThaw_CarbonEmissions_1872", "Permafrost_ActiveLayer_NSlope_1759", "Permafrost_Thaw_Depth_YK_1598", "PhenoCam_V2_1674", "Phenocam_Images_V2_1689", "Phenology_AmeriFlux_Neon_Sites_2033", "Phenology_Deciduous_Forest_1570", "Photos_ThermokarstLakes_AK_1845", "Pingo_Veg_Plots_1507", "Plot_Data_Noatak_Seward_AK_1919", "Plumes_and_Blooms", "PolInSAR_Canopy_Height_1589", "Polar-VPRM_Alaskan-NEE_1314", "PolarWindsII_DAWN_DC8", "PolarWindsI_DAWN_KingAirUC-12B", "Polarimetric_CT_1601", "Polarimetric_height_profile_1577", "Poplar_Veg_Plots_1376", "PostFire_Tree_Regeneration_1955", "Post_Fire_C_Emissions_1787", "Post_Fire_SOC_NWT_2235", "PreABoVE_AirMOSS_L1_Alaska_1678", "PreDeltaX_ADCP_Measurements_1806", "PreDeltaX_ASO_LiDAR_WaterLevel_1820", "PreDeltaX_Insitu_Reflectance_1804", "PreDeltaX_L2_AVIRIS_SR_1826", "PreDeltaX_L2_AirSWOT_1818", "PreDeltaX_L3_AVIRIS_Biomass_1821", "PreDeltaX_L3_AVIRIS_Sediment_1822", "PreDeltaX_L3_AirSWOT_WL_1819", "PreDeltaX_Sonar_Bathymetry_1807", "PreDeltaX_TSS_Concentration_1802", "PreDeltaX_UAVSAR_Channel_Maps_1954", "PreDeltaX_UAVSAR_SLC_1816", "PreDeltaX_UAVSAR_WaterLevel_1823", "PreDeltaX_Vegetation_Structure_1805", "PreDeltaX_Water_Level_Data_1801", "Pre_LBA_ABRACOS_899", "Proantar", "Profile_based_PBL_heights_1706", "Prudhoe_Bay_ArcSEES_Veg_Plots_1555", "Prudhoe_Bay_Veg_Maps_1387", "Prudhoe_Bay_Veg_Plots_1360", "Prudhoe_Freshets", "QSCAT_ESDR_MODELED_L2_AUX_V1.0", "QSCAT_L1C_NONSPINNING_SIGMA0_WINDS_V2", "QSCAT_LEVEL_1B_V2", "QSCAT_LEVEL_2B_OWV_COMP_12", "QSCAT_LEVEL_2B_OWV_COMP_12_KUSST_LCRES_4.1", "QSCAT_LEVEL_2B_OWV_COMP_12_LCR_3.1", "QUIKSCAT_ESDR_L2_WIND_STRESS_V1.0", "RADAMBrasil_941", "RAIN_JAEGER", "RAIN_LEGATES", "RAMSSA_09km-ABOM-L4-AUS-v01", "RBLE_917", "RDBTS4", "RDEFT4", "RDGBV4", "RDSISC4", "RDSISCO4", "RECON_SEA_LEVEL_OST_L4_V1", "RED_TIDE", "REMO_OI_SST_5km-UFRJ-L4-SAMERICA-v1.0", "RESOLUTE", "REYNOLDS_NCDC_L4_MONTHLY_V5", "RGGRV1B", "RICMIAAE", "RICMIB2E", "RICMIRCM", "RICMITAL", "RICMITCL", "RICMITST", "RIO-SFE", "ROAVERRS", "RON_BROWN", "RRRAG4", "RSAT-1_L0", "RSAT-1_L1", "RSCAT_COLOCATED_RSS_RADIOMETER_LEVEL_2B_V1", "RSCAT_L1B_V2.0", "RSCAT_L2A_12KM_V2.0", "RSCAT_L2A_25KM_V2.0", "RSCAT_LEVEL_2B_OWV_CLIM_12_V1", "RSCAT_LEVEL_2B_OWV_CLIM_12_V2", "RSCAT_LEVEL_2B_OWV_COMP_12_V1.1", "RSCAT_LEVEL_2B_OWV_COMP_12_V1.2", "RSCAT_LEVEL_2B_OWV_COMP_12_V1.3", "RSS18_AVIRIS_L1B_449", "RSS_WindSat_L1C_TB_V08.0", "Radial_Growth_PRI_1781", "Rain-on-Snow_Data_1611", "RdlP_PT", "ReSALT_ALT_GPR_1265", "ReSALT_InSAR_Barrow_1266", "Reflectance_Spectra_Alaska_1685", "RemSensPOC", "Respiration_622", "River_Ice_Breakup_Freezeup_1697", "Russian_Forest_Disturbance_1294", "S2K_EACM_Subset_623", "S3A_OL_1_EFR", "S5P_L1B_IR_SIR", "S5P_L1B_IR_SIR", "S5P_L1B_IR_UVN", "S5P_L1B_IR_UVN", "S5P_L1B_RA_BD1", "S5P_L1B_RA_BD1_HiR", "S5P_L1B_RA_BD1_HiR", "S5P_L1B_RA_BD2", "S5P_L1B_RA_BD2_HiR", "S5P_L1B_RA_BD2_HiR", "S5P_L1B_RA_BD3", "S5P_L1B_RA_BD3_HiR", "S5P_L1B_RA_BD3_HiR", "S5P_L1B_RA_BD4", "S5P_L1B_RA_BD4_HiR", "S5P_L1B_RA_BD4_HiR", "S5P_L1B_RA_BD5", "S5P_L1B_RA_BD5_HiR", "S5P_L1B_RA_BD5_HiR", "S5P_L1B_RA_BD6", "S5P_L1B_RA_BD6_HiR", "S5P_L1B_RA_BD6_HiR", "S5P_L1B_RA_BD7", "S5P_L1B_RA_BD7_HiR", "S5P_L1B_RA_BD7_HiR", "S5P_L1B_RA_BD8", "S5P_L1B_RA_BD8_HiR", "S5P_L1B_RA_BD8_HiR", "S5P_L2__AER_AI", "S5P_L2__AER_AI_HiR", "S5P_L2__AER_AI_HiR", "S5P_L2__AER_LH", "S5P_L2__AER_LH_HiR", "S5P_L2__AER_LH_HiR", "S5P_L2__CH4___", "S5P_L2__CH4____HiR", "S5P_L2__CH4____HiR", "S5P_L2__CLOUD_", "S5P_L2__CLOUD__HiR", "S5P_L2__CLOUD__HiR", "S5P_L2__CO____", "S5P_L2__CO_____HiR", "S5P_L2__CO_____HiR", "S5P_L2__HCHO__", "S5P_L2__HCHO___HiR", "S5P_L2__HCHO___HiR", "S5P_L2__NO2___", "S5P_L2__NO2____HiR", "S5P_L2__NO2____HiR", "S5P_L2__NP_BD3", "S5P_L2__NP_BD3_HiR", "S5P_L2__NP_BD3_HiR", "S5P_L2__NP_BD6", "S5P_L2__NP_BD6_HiR", "S5P_L2__NP_BD6_HiR", "S5P_L2__NP_BD7", "S5P_L2__NP_BD7_HiR", "S5P_L2__NP_BD7_HiR", "S5P_L2__O3_TCL", "S5P_L2__O3_TCL", "S5P_L2__O3_TOT", "S5P_L2__O3_TOT_HiR", "S5P_L2__O3_TOT_HiR", "S5P_L2__O3__PR_HiR", "S5P_L2__SO2___", "S5P_L2__SO2____HiR", "S5P_L2__SO2____HiR", "SAB_Mapping", "SAGE1_AERO_PRF", "SAGE2_AEROSOL_O3_NO2_H2O_BINARY", "SAILDRONE_ARCTIC", "SAILDRONE_ARCTIC_2021", "SAILDRONE_ARCTIC_2022", "SAILDRONE_ATOMIC", "SAILDRONE_BAJA_SURFACE", "SAM2_AERO_PRF_NAT", "SAMMI2AE", "SAMMI2LS", "SAMMI2ST", "SAMMIB2E", "SAMMIB2T", "SAMMIGEO", "SAMSN7L1RAD_CDROM", "SAMSN7L1RAT", "SAMSN7L3GRIDT", "SAMSN7L3ZMTG", "SAR_Methane_Ebullition_AK_1790", "SASSIE_L1_SWIFT_V1", "SASSIE_L1_WAVEGLIDER_V1", "SASSIE_L2_ALTO_ALAMO_FLOATS_V1", "SASSIE_L2_DRIFTER_UPTEMPO_V1", "SASSIE_L2_DRIFTER_UPTEMPO_V2p", "SASSIE_L2_JET_SSP_V1", "SASSIE_L2_PALS_V1", "SASSIE_L2_SBAND_ML_V1", "SASSIE_L2_SHIPBOARD_ADCP_V1", "SASSIE_L2_SHIPBOARD_CASTAWAY_CTD_V1", "SASSIE_L2_SHIPBOARD_DELTA_18O_V1", "SASSIE_L2_SHIPBOARD_METEOROLOGY_V1", "SASSIE_L2_SHIPBOARD_SALINITY_SNAKE_V1", "SASSIE_L2_SHIPBOARD_TSG_V1", "SASSIE_L2_SHIPBOARD_UCTD_V1", "SASSIE_L2_SWIFT_V1", "SASSIE_L2_UNDER_ICE_FLOAT_V1", "SASSIE_L2_WAVEGLIDERS_V1", "SASSIE_L3_SHIPBOARD_SBAND_V1", "SASSIE_L4_SHIPBOARD_SBAND_V1", "SAV_Plymouth_Bay", "SAWB_JRA_713", "SAWB_JRB_714", "SBI", "SBUV2N09L2", "SBUV2N09L3zm", "SBUV2N09O3", "SBUV2N11L2", "SBUV2N11L3zm", "SBUV2N11O3", "SBUV2N14L2", "SBUV2N14L3zm", "SBUV2N16L2", "SBUV2N16L3zm", "SBUV2N16O3", "SBUV2N17L2", "SBUV2N17L3zm", "SBUV2N18L2", "SBUV2N18L3zm", "SBUV2N19L2", "SBUV2N19L3zm", "SBUVN07L2", "SBUVN07L3zm", "SBUVN7O3", "SCAMSN6IM", "SCAMSN6L2", "SCAR-B_916", "SCARB_ER2_MAS", "SCAR_A_ER2_MAS", "SCAR_B_AERONET", "SCAR_B_G8_FIRE", "SCAR_B_UWC131A", "SCATSAT1_ESDR_ANCILLARY_L2_V1.1", "SCATSAT1_ESDR_L2_WIND_STRESS_V1.1", "SCMRN5L1RAD", "SCOAPE_Ground_Data", "SCOAPE_Pandora_Data", "SCOAPE_RVPointSur_Data", "SCOAPE_Sondes_Data", "SCRN4L1RAD", "SCRN4L1RAD_CDROM", "SCRN5L1RAD", "SCRN5L1RAD_CDROM", "SEA", "SEAC4RS_Aerosol_AircraftInSitu_DC8_Data", "SEAC4RS_Aerosol_AircraftInSitu_Learjet_Data", "SEAC4RS_AircraftRemoteSensing_DIAL_DC8_Data", "SEAC4RS_AircraftRemoteSensing_RSP_ER2_Data", "SEAC4RS_Analysis_Data", "SEAC4RS_Cloud_AircraftInSitu_DC8_Data", "SEAC4RS_Cloud_AircraftInSitu_ER2_Data", "SEAC4RS_Cloud_AircraftInSitu_Learjet_Data", "SEAC4RS_Merge_Data", "SEAC4RS_MetNav_AircraftInSitu_DC8_Data", "SEAC4RS_MetNav_AircraftInSitu_ER2_Data", "SEAC4RS_MetNav_AircraftInSitu_Learjet_Data", "SEAC4RS_Miscellaneous_DC8_Data", "SEAC4RS_Miscellaneous_ER2_Data", "SEAC4RS_Radiation_AircraftInSitu_DC8_Data", "SEAC4RS_Radiation_AircraftInSitu_ER2_Data", "SEAC4RS_Sondes_Data", "SEAC4RS_TraceGas_AircraftInSitu_DC8_Data", "SEAC4RS_TraceGas_AircraftInSitu_ER2_Data", "SEAC4RS_jValue_AircraftInSitu_DC8_Data", "SEAGLIDER_GUAM_2019", "SEAHAWK_VALIDATION", "SEASAT_SAR_L1_HDF5", "SEASAT_SAR_L1_TIFF", "SEA_SURFACE_HEIGHT_ALT_GRIDS_L4_2SATS_5DAY_6THDEG_V_JPL2205", "SEED", "SENTINEL-1A_DP_GRD_FULL", "SENTINEL-1A_DP_GRD_HIGH", "SENTINEL-1A_DP_GRD_MEDIUM", "SENTINEL-1A_DP_META_GRD_FULL", "SENTINEL-1A_DP_META_GRD_HIGH", "SENTINEL-1A_DP_META_GRD_MEDIUM", "SENTINEL-1A_META_OCN", "SENTINEL-1A_META_RAW", "SENTINEL-1A_META_SLC", "SENTINEL-1A_OCN", "SENTINEL-1A_RAW", "SENTINEL-1A_SLC", "SENTINEL-1A_SP_GRD_HIGH", "SENTINEL-1A_SP_GRD_MEDIUM", "SENTINEL-1A_SP_META_GRD_HIGH", "SENTINEL-1A_SP_META_GRD_MEDIUM", "SENTINEL-1B_DP_GRD_HIGH", "SENTINEL-1B_DP_GRD_MEDIUM", "SENTINEL-1B_DP_META_GRD_HIGH", "SENTINEL-1B_DP_META_GRD_MEDIUM", "SENTINEL-1B_META_OCN", "SENTINEL-1B_META_RAW", "SENTINEL-1B_META_SLC", "SENTINEL-1B_OCN", "SENTINEL-1B_RAW", "SENTINEL-1B_SLC", "SENTINEL-1B_SP_GRD_HIGH", "SENTINEL-1B_SP_GRD_MEDIUM", "SENTINEL-1B_SP_META_GRD_HIGH", "SENTINEL-1B_SP_META_GRD_MEDIUM", "SENTINEL-1_BURSTS", "SENTINEL-1_INTERFEROGRAMS", "SENTINEL-1_INTERFEROGRAMS_AMPLITUDE", "SENTINEL-1_INTERFEROGRAMS_COHERENCE", "SENTINEL-1_INTERFEROGRAMS_CONNECTED_COMPONENTS", "SENTINEL-1_INTERFEROGRAMS_UNWRAPPED_PHASE", "SEVIRI_IO_SST-OSISAF-L3C-v1.0", "SEVIRI_SST-OSISAF-L3C-v1.0", "SEVIRI_SST_DR-OSISAF-L3C-v1.0", "SFC_NITROGEN_DIOXIDE_CONC", "SFMBON", "SFP", "SGER", "SHIFT_AVIRISNG_L2A_refl_2376", "SHIFT_AVNG_Canopy_WaterContent_2242", "SHIFT_AVNG_FullRes_QkLook_2189", "SHIFT_AVNG_L1A_RDN_unrec_2184", "SHIFT_DriedGround_Leaf_Reflec_2244", "SHIFT_Field_Survey_Metadata_2295", "SHIFT_Foliar_Chemical_Analysis_2337", "SHIFT_HyTES_L1_2022_2245", "SHIFT_HyTES_L2_2022_2246", "SHIFT_Leaf_Traits_Chl_SB_CA_2233", "SHIFT_Plot_Level_Photos_2334", "SHIFT_RamsesTriOS_Rrs_SB_CA_2234", "SIF_PAR_fPAR_US_Midwest_2018_1813", "SIF_SCIAMACHY_GOME2_Harmonized_2317", "SIMBAD_DESCHAMPS_LOA", "SIO-Pier", "SIRSN3L1", "SIRSN4L1", "SISTER_Workflow_V004_2335", "SLOPE_GPP_CONUS_1786", "SMAP_JPL_L2B_NRT2_SSS_CAP_V5", "SMAP_JPL_L2B_NRT_SSS_CAP_V5", "SMAP_JPL_L2B_SSS_CAP_V5", "SMAP_JPL_L3_SSS_CAP_8DAY-RUNNINGMEAN_V5", "SMAP_JPL_L3_SSS_CAP_MONTHLY_V5", "SMAP_RSS_L2_SSS_NRT_V5", "SMAP_RSS_L2_SSS_NRT_V6", "SMAP_RSS_L2_SSS_V4", "SMAP_RSS_L2_SSS_V5", "SMAP_RSS_L2_SSS_V5.3", "SMAP_RSS_L2_SSS_V6", "SMAP_RSS_L3_SSS_SMI_8DAY-RUNNINGMEAN_V4", "SMAP_RSS_L3_SSS_SMI_8DAY-RUNNINGMEAN_V5", "SMAP_RSS_L3_SSS_SMI_8DAY-RUNNINGMEAN_V5.3", "SMAP_RSS_L3_SSS_SMI_8DAY-RUNNINGMEAN_V6", "SMAP_RSS_L3_SSS_SMI_MONTHLY_V4", "SMAP_RSS_L3_SSS_SMI_MONTHLY_V5", "SMAP_RSS_L3_SSS_SMI_MONTHLY_V5.3", "SMAP_RSS_L3_SSS_SMI_MONTHLY_V6", "SMERGE_RZSM0_40CM", "SMMRN7IM", "SMMR_ALW_PRABHAKARA", "SMMR_IWV_PRABHAKARA", "SMODE_L1_ASIT_KABODS_V1", "SMODE_L1_ASIT_SLOPEFIELDS_V1", "SMODE_L1_DOPPLERSCATT_V1", "SMODE_L1_MASS_DOPPVIS_V1", "SMODE_L1_MASS_DOPVISIBLE_V1", "SMODE_L1_MASS_HYPERSPECTRAL_V1", "SMODE_L1_MASS_LIDAR_V1", "SMODE_L1_MASS_LWIR_V1", "SMODE_L1_MASS_VISIBLE_V1", "SMODE_L1_PRISM_V1", "SMODE_L1_SAILDRONES_V1", "SMODE_L2_APEX_FLOAT_V1", "SMODE_L2_DOPPLERSCATT_WINDS_CURRENT_V1", "SMODE_L2_DOPPLERSCATT_WINDS_CURRENT_V2", "SMODE_L2_DRIFTER_POSITIONS_V1", "SMODE_L2_LAGRANGIAN_FLOATS_V1", "SMODE_L2_MOSES_LWIR_SST_V1", "SMODE_L2_PRISM_CHLA_V1", "SMODE_L2_SAILDRONES_V1", "SMODE_L2_SEAGLIDERS_V1", "SMODE_L2_SHIPBOARD_ADCP_V1", "SMODE_L2_SHIPBOARD_BIO_V1", "SMODE_L2_SHIPBOARD_BOTTLES_V1", "SMODE_L2_SHIPBOARD_CTD_V1", "SMODE_L2_SHIPBOARD_RADIOSONDES_METEOROLOGY_V1", "SMODE_L2_SHIPBOARD_SUNA_V1", "SMODE_L2_SHIPBOARD_TSG_V1", "SMODE_L2_SHIPBOARD_UCTD_ECOCTD_V1", "SMODE_L2_SLOCUM_GLIDERS_V1", "SMODE_L2_WAVEGLIDERS_TEMP_SALINITY_V1", "SMODE_L2a_PRISM_REFL_V1", "SMODE_L3_SEAGLIDERS_TEMP_SALINITY_V1", "SMODE_L3_SHIPBOARD_UCTD_ECOCTD_V1", "SMODE_L4_NCOM_V1", "SNDR13CHRP1", "SNDR13CHRP1AQCal", "SNDR13CHRP1J1Cal", "SNDR13CHRP1SNCal", "SNDR13IML3SSDFCNSAT", "SNDR13IML3SSDFCVPD", "SNDRAQIL2CCPCCR", "SNDRAQIL2CCPRET", "SNDRAQIL2CPS", "SNDRAQIL2JSFRET", "SNDRAQIL3CDCCP", "SNDRAQIL3CMCCP", "SNDRAQIL3SDCCP", "SNDRAQIL3SMCCP", "SNDRAQIL3SSDFCNSAT", "SNDRAQIL3SSDFCVPD", "SNDRAQIML1BCALSUBSUM", "SNDRAQIML1CCALSUBRND", "SNDRAQIML2CCPCCR", "SNDRAQIML2CCPRET", "SNDRAQIML2CPS", "SNDRAQIML3CDCCP", "SNDRAQIML3CMCCP", "SNDRAQIML3SDCCP", "SNDRAQIML3SMCCP", "SNDRJ1ATMSL1B", "SNDRJ1ATMSL1B", "SNDRJ1ATMSMAP", "SNDRJ1CrISL1B", "SNDRJ1CrISL1B", "SNDRJ1CrISL1BIMG", "SNDRJ1CrISL1BIMGC", "SNDRJ1CrISL1BPCARED", "SNDRJ1IML2CCPCCR", "SNDRJ1IML2CCPRET", "SNDRJ1IML2CPS", "SNDRJ1IML3CDCCP", "SNDRJ1IML3CMCCP", "SNDRJ1IML3SDCCP", "SNDRJ1IML3SMCCP", "SNDRJ1ML2RMS", "SNDRJ1ML2RMSSUP", "SNDRJ1ML3DRMS", "SNDRJ1ML3MRMS", "SNDRJ2CrISL1B", "SNDRJ2CrISL1BIMG", "SNDRJ2CrISL1BIMGC", "SNDRSNATMSMAP", "SNDRSNCrISL1BIMG", "SNDRSNCrISL1BIMGC", "SNDRSNIL1BCALSUBRNDN", "SNDRSNIL1BCALSUBSUMN", "SNDRSNIL2ESPNH3", "SNDRSNIML2CCPCCR", "SNDRSNIML2CCPCCRN", "SNDRSNIML2CCPRET", "SNDRSNIML2CCPRETN", "SNDRSNIML2CHTCCRN", "SNDRSNIML2CHTRETN", "SNDRSNIML2CPS", "SNDRSNIML2CPSN", "SNDRSNIML2SFSPRET", "SNDRSNIML2SFSPSUP", "SNDRSNIML3CDCCP", "SNDRSNIML3CDCCPN", "SNDRSNIML3CDCHTN", "SNDRSNIML3CMCCP", "SNDRSNIML3CMCCPN", "SNDRSNIML3CMCHTN", "SNDRSNIML3SDCCP", "SNDRSNIML3SDCCPN", "SNDRSNIML3SDCHTN", "SNDRSNIML3SDSFSP", "SNDRSNIML3SMCCP", "SNDRSNIML3SMCCPN", "SNDRSNIML3SMCHTN", "SNDRSNIML3SMSFSP", "SNDRSNML2RMS", "SNDRSNML2RMS", "SNDRSNML2RMSSUP", "SNDRSNML3DRMS", "SNDRSNML3MRMS", "SNEX17_CAR", "SNEX17_DGNSS1", "SNEX17_GPR", "SNEX17_GPR_Raw", "SNEX17_KT15_TB", "SNEX17_P3V", "SNEX17_QWIP_ST", "SNEX17_SBR", "SNEX17_SD", "SNEX17_SD_Perm", "SNEX17_SMP", "SNEX17_SMP2", "SNEX17_SSA", "SNEX17_SSD", "SNEX17_SnowPits", "SNEX17_SnowSAR", "SNEX17_SnowSAR_Raw", "SNEX17_TLI", "SNEX17_TLS_PC_BSU", "SNEX17_TLS_PC_BSU_Raw", "SNEX17_TLS_PC_CRREL", "SNEX17_UWScat", "SNEX20_A19_GSM", "SNEX20_A19_SD", "SNEX20_A19_SP", "SNEX20_A19_SWE", "SNEX20_BR_BSU_SMP", "SNEX20_BSU_CMP_Raw", "SNEX20_BSU_CMP_SWE", "SNEX20_BSU_CMP_TT", "SNEX20_BSU_GPR", "SNEX20_BSU_GPR_Raw", "SNEX20_COCP_GPR", "SNEX20_COCP_GPR_Raw", "SNEX20_COCP_SPD", "SNEX20_CRSM", "SNEX20_CSSM", "SNEX20_DSM", "SNEX20_GIS_REF", "SNEX20_GM_CSU_GPR", "SNEX20_GM_CSU_GPR_RAW", "SNEX20_GM_CTSM", "SNEX20_GM_Lidar", "SNEX20_GM_SP", "SNEX20_GM_SWE_SD", "SNEX20_J_UNM_GPR", "SNEX20_QSI_DEM", "SNEX20_QSI_DEM_3m", "SNEX20_QSI_SD", "SNEX20_QSI_SD_3m", "SNEX20_QSI_VH", "SNEX20_QSI_VH_3m", "SNEX20_SB_GST", "SNEX20_SD", "SNEX20_SD_TLI", "SNEX20_SMP", "SNEX20_SSA", "SNEX20_SWESARR_TB", "SNEX20_TLI", "SNEX20_TLS_PC_BSU", "SNEX20_TLS_PC_BSU_RAW", "SNEX20_TLS_PC_CRREL", "SNEX20_TS_SP", "SNEX20_UNM_GPR", "SNEX20_VPTS_Raw", "SNEX21_COCP_GPR", "SNEX21_COCP_GPR_Raw", "SNEX21_DSM", "SNEX21_PS_DSM", "SNEX21_PS_MET", "SNEX21_SSR", "SNEX21_TS_SP", "SNEX23_BCEF_TLS", "SNEX23_BCEF_TLS_Raw", "SNEX23_CRREL_GPR", "SNEX23_CRREL_GPR_Raw", "SNEX23_CSU_GPR_Raw", "SNEX23_Lidar", "SNEX23_Lidar_Raw", "SNEX23_MAR22_SD", "SNEX23_MAR23_SD", "SNEX23_SSA", "SNEX23_SSA_SO", "SNEX23_SWE", "SNEX23_UW_GPR", "SNEX_HRSI_SD_DEM_CO", "SNEX_Met", "SNEX_Met_Raw", "SNF_ASP_CVR_140", "SNF_BIOMASS_141", "SNF_BIOPHYS_142", "SNF_CAN_COMP_143", "SNF_CJ_SITES_187", "SNF_CJ_VEG_189", "SNF_HELO_MMR_144", "SNF_LEAFCARY_183", "SNF_LEAF_EXP_180", "SNF_LEAF_TMS_184", "SNF_MET_1972_158", "SNF_MET_1972_1990_159", "SNF_MET_1973_160", "SNF_MET_1974_161", "SNF_MET_1975_162", "SNF_MET_1976_163", "SNF_MET_1977_164", "SNF_MET_1978_165", "SNF_MET_1979_166", "SNF_MET_1980_167", "SNF_MET_1981_168", "SNF_MET_1982_169", "SNF_MET_1983_170", "SNF_MET_1984_171", "SNF_MET_1985_172", "SNF_MET_1986_173", "SNF_MET_1987_174", "SNF_MET_1988_175", "SNF_MET_1989_176", "SNF_MET_1990_177", "SNF_MET_SUMM_178", "SNF_NS001_185", "SNF_SAT_INV_186", "SNF_SITECOMP_179", "SNF_SITE_86_188", "SNF_TAB3_3T_182", "SNF_UND_CVR_181", "SNPPATMSL1B", "SNPPATMSL1B", "SNPPCrISL1B", "SNPPCrISL1B", "SNPPCrISL1BNSR", "SNPPCrISL1BNSR", "SNPP_CrIS_VIIRS750m_IND", "SOCCOM", "SOC_3M_Maps_NE_TidalWetlands_1905", "SOC_Stocks_Great_Plains_1603", "SOIL_CO2_Flux_Costa_Rica_1373", "SOLVE1_Aerosol_AircraftInSitu_DC8_Data", "SOLVE1_Aerosol_AircraftInSitu_ER2_Data", "SOLVE1_AircraftRemoteSensing_DC8_HSRL_Data", "SOLVE1_AircraftRemoteSensing_DC8_LASE_Data", "SOLVE1_AircraftRemoteSensing_DC8_Lidar_Data", "SOLVE1_Analysis_DC8_Data", "SOLVE1_Analysis_ER2_Data", "SOLVE1_Ground_Data", "SOLVE1_MetNav_AircraftInSitu_DC8_Data", "SOLVE1_MetNav_AircraftInSitu_ER2_Data", "SOLVE1_Miscellaneous_DC8_Data", "SOLVE1_Miscellaneous_ER2_Data", "SOLVE1_Satellite_Data", "SOLVE1_Sondes_Data", "SOLVE1_TraceGas_AircraftInSitu_DC8_Data", "SOLVE1_TraceGas_AircraftInSitu_ER2_Data", "SOLVE1_TraceGas_AircraftRemoteSensing_DC8_Data", "SOLVE1_jValue_AircraftInSitu_DC8_Data", "SOLVE1_jValue_AircraftInSitu_ER2_Data", "SOLVE2_Aerosol_AircraftInSitu_DC8_Data", "SOLVE2_AircraftRemoteSensing_DC8_AATS14_Data", "SOLVE2_AircraftRemoteSensing_DC8_HSRL_Data", "SOLVE2_AircraftRemoteSensing_DC8_Lidar_Data", "SOLVE2_Analysis_DC8_Data", "SOLVE2_MetNav_AircraftInSitu_DC8_Data", "SOLVE2_Miscellaneous_DC8_Data", "SOLVE2_Radiation_AircraftInSitu_DC8_Data", "SOLVE2_Rocket_Data", "SOLVE2_Satellite_Data", "SOLVE2_Sondes_Data", "SOLVE2_TraceGas_AircraftInSitu_DC8_Data", "SOLVE2_TraceGas_AircraftRemoteSensing_DC8_Data", "SONEX_Aerosol_AircraftInSitu_DC8_Data", "SONEX_AircraftRemoteSensing_DC8_DIAL_Data", "SONEX_Merge_DC8_Data", "SONEX_MetNav_AircraftInSitu_DC8_Data", "SONEX_Miscellaneous_DC8_Data", "SONEX_Model_Data", "SONEX_Other_Data", "SONEX_Satellite_Data", "SONEX_TraceGas_AircraftInSitu_DC8_Data", "SONEX_jValue_AircraftInSitu_DC8_Data", "SOR3D_COMBINED", "SOR3SIMD", "SOR3SIMD_TAV", "SOR3SOLD_HIGH", "SOR3SOLD_MGII", "SOR3SOLFUVD", "SOR3SOLMUVD", "SOR3SOLS_LA", "SOR3SOLS_MGII", "SOR3TSI6", "SOR3TSID", "SOR3XPS6", "SOR3XPSD", "SOR4XPS5", "SOR4XPSD_HIGH", "SOR4XPSD_LOW", "SORTIE", "SPL1A_001", "SPL1A_002", "SPL1A_METADATA_001", "SPL1A_METADATA_002", "SPL1A_QA_001", "SPL1A_QA_002", "SPL1A_RO_001", "SPL1A_RO_002", "SPL1A_RO_003", "SPL1A_RO_METADATA_001", "SPL1A_RO_METADATA_002", "SPL1A_RO_METADATA_003", "SPL1A_RO_QA_001", "SPL1A_RO_QA_002", "SPL1A_RO_QA_003", "SPL1B_SO_LoRes_001", "SPL1B_SO_LoRes_002", "SPL1B_SO_LoRes_003", "SPL1B_SO_LoRes_METADATA_001", "SPL1B_SO_LoRes_METADATA_002", "SPL1B_SO_LoRes_METADATA_003", "SPL1B_SO_LoRes_QA_001", "SPL1B_SO_LoRes_QA_002", "SPL1B_SO_LoRes_QA_003", "SPL1C_S0_HiRes_001", "SPL1C_S0_HiRes_002", "SPL1C_S0_HiRes_003", "SPL1C_S0_HiRes_METADATA_001", "SPL1C_S0_HiRes_METADATA_002", "SPL1C_S0_HiRes_METADATA_003", "SPL1C_S0_HiRes_QA_001", "SPL1C_S0_HiRes_QA_002", "SPL1C_S0_HiRes_QA_003", "SPL2SMA", "SPL2SMAP", "SPL2SMAP_S", "SPL3FTA", "SPL3SMA", "SPL3SMAP", "SPL3SMP", "SPL3SMP_E", "SPL4CMDL", "SPL4SMAU", "SPL4SMGP", "SPL4SMLM", "SPURS1_ADCP", "SPURS1_ARGO", "SPURS1_CTD", "SPURS1_DRIFTER", "SPURS1_ECOMAPPER", "SPURS1_FLOAT_NEUTRALLYBUOYANT", "SPURS1_METEO", "SPURS1_MOORING_PICO", "SPURS1_MOORING_WHOI", "SPURS1_SEAGLIDER", "SPURS1_SEASOAR", "SPURS1_TENUSEGLIDER", "SPURS1_TSG", "SPURS1_UCTD", "SPURS1_WAVEGLIDER", "SPURS2_ADCP", "SPURS2_ARGO", "SPURS2_CFT", "SPURS2_CTD", "SPURS2_DISDR", "SPURS2_DRIFTER", "SPURS2_FLOAT_NEUTRALLYBUOYANT", "SPURS2_LADYAMBER", "SPURS2_METEO", "SPURS2_MOORING_CENTRAL", "SPURS2_MOORING_PICO", "SPURS2_PALS", "SPURS2_RAINRADAR", "SPURS2_RAWINSONDE", "SPURS2_SAILDRONE", "SPURS2_SALINITYSNAKE", "SPURS2_SEAGLIDER", "SPURS2_SSP", "SPURS2_UCTD", "SPURS2_UNDERWAY_pCO2_DIC_pH", "SPURS2_USPS", "SPURS2_WAMOS", "SPURS2_WAVEGLIDER", "SPURS2_XBAND", "SPURS2_XBAND_IMG", "SPURS2_XBT", "SRB_REL3.0_SW_3HRLY_MONTHLY_UTC_NC", "SRDB_V5_1827", "SRTMGL1", "SRTMGL1N", "SRTMGL3", "SRTMGL30", "SRTMGL3N", "SRTMGL3S", "SRTMGL3_NC", "SRTMGL3_NUMNC", "SRTMIMGM", "SRTMIMGR", "SRTMSWBD", "SSBUVIRR", "SSBUVO3", "SSFR_irradiance_841", "STAQS_AircraftRemoteSensing_JSC-GV_GCAS_Data", "STAQS_AircraftRemoteSensing_JSC-GV_HSRL2_Data", "STAQS_AircraftRemoteSensing_NASA-G3_GCAS_Data", "STAQS_AircraftRemoteSensing_NASA-G3_HALO_Data", "STAQS_Chiwaukee-Prairie_Data", "STAQS_Drone_Data", "STAQS_Ground_Data", "STAQS_INSTEP_Data", "STAQS_SeaRey_Data", "STAQS_Sondes_Data", "STRAT_Aerosol_AircraftInSitu_ER2_Data", "STRAT_Analysis_ER2_Data", "STRAT_Ground_Data", "STRAT_MetNav_AircraftInSitu_ER2_Data", "STRAT_Model_Data", "STRAT_Satellite_Data", "STRAT_Sondes_Data", "STRAT_TraceGas_AircraftInSitu_ER2_Data", "STRAT_jValue_AircraftInSitu_ER2_Data", "STS-59_GRD", "STS-59_META_GRD", "STS-59_META_SLC", "STS-59_SLC", "STS-68_GRD", "STS-68_META_GRD", "STS-68_META_SLC", "STS-68_SLC", "SUCCESS_UTAH_PDL", "SUPERYACHT_SCIENCE", "SV08LC", "SV08PLBK", "SV08PLTB", "SV08SM", "SV08SR", "SV08ST", "SV08V", "SV08VWC", "SV12CSMA", "SV12CST", "SV12LC", "SV12PLBK", "SV12PLSM", "SV12PLTB", "SV12PSMA", "SV12PSMF", "SV12SRA", "SV12SRF", "SV12STM", "SV12UBK", "SV12VA", "SV12VF", "SV12VWC", "SV15PLSM", "SV15PLTB", "SV15PSM", "SV16I_PLTBSM", "SV16I_PNET", "SV16I_TNET", "SV16I_TPSM", "SV16M_CRS", "SV16M_CSM", "SV16M_LAI", "SV16M_LC", "SV16M_MET", "SV16M_PLTBSM", "SV16M_PSM", "SV16M_SDB", "SV16M_SR", "SV16M_SSM", "SV16M_ST", "SV16M_TB", "SV16M_V", "SV19MA_DEM", "SV19MA_DSM", "SV19MA_LID", "SV19MA_TNET", "SV19MA_VOD", "SV19MB_DEM", "SV19MB_DSM", "SV19MB_LID", "SV19MB_TNET", "SWDB_L2", "SWDB_L305", "SWDB_L310", "SWDB_L3M05", "SWDB_L3M10", "SWDB_L3MC05", "SWDB_L3MC10", "SWFL", "SWOT_L1B_LR_INTF_1.0", "SWOT_L1B_LR_INTF_1.1", "SWOT_L2_HR_LakeSP_2.0", "SWOT_L2_HR_PIXC_2.0", "SWOT_L2_HR_Raster_2.0", "SWOT_L2_HR_RiverSP_2.0", "SWOT_L2_LR_SSH_1.0", "SWOT_L2_LR_SSH_1.1", "SWOT_L2_LR_SSH_2.0", "SWOT_L2_NALT_GDR_2.0", "SWOT_L2_NALT_IGDR_1.0", "SWOT_L2_NALT_IGDR_2.0", "SWOT_L2_NALT_OGDR_1.0", "SWOT_L2_NALT_OGDR_2.0", "SWOT_L4_DAWG_SOS_DISCHARGE", "SWOT_PRELAUNCH_L2_BPR_V1", "SWOT_PRELAUNCH_L2_GLIDER_V1", "SWOT_PRELAUNCH_L2_GPS_V1", "SWOT_PRELAUNCH_L2_PIES_V1", "SWOT_PRELAUNCH_L2_PRAWLER_V1", "SWOT_PRELAUNCH_L2_SIOCTD_V1", "SWOT_PRELAUNCH_L2_WHOICTD_V1", "SWOT_PRELAUNCH_L2_WW_V1", "SWOT_SIMULATED_L2_KARIN_SSH_ECCO_LLC4320_CALVAL_V1", "SWOT_SIMULATED_L2_KARIN_SSH_ECCO_LLC4320_SCIENCE_V1", "SWOT_SIMULATED_L2_KARIN_SSH_GLORYS_CALVAL_V1", "SWOT_SIMULATED_L2_KARIN_SSH_GLORYS_SCIENCE_V1", "SWOT_SIMULATED_L2_NADIR_SSH_ECCO_LLC4320_CALVAL_V1", "SWOT_SIMULATED_L2_NADIR_SSH_ECCO_LLC4320_SCIENCE_V1", "SWOT_SIMULATED_L2_NADIR_SSH_GLORYS_CALVAL_V1", "SWOT_SIMULATED_L2_NADIR_SSH_GLORYS_SCIENCE_V1", "SWOT_SIMULATED_NA_CONTINENT_L2_HR_LAKESP_V1", "SWOT_SIMULATED_NA_CONTINENT_L2_HR_PIXCVEC_V1", "SWOT_SIMULATED_NA_CONTINENT_L2_HR_PIXC_V1", "SWOT_SIMULATED_NA_CONTINENT_L2_HR_RASTER_V1", "SWOT_SIMULATED_NA_CONTINENT_L2_HR_RIVERSP_V1", "Sahel_Water_Bodies_1269", "Salt_Marsh_Biomass_CONUS_2348", "San_Diego_Coastal_Project", "Sargassum_GOM", "Saskatchewan_Soils_125m_SSA_1346", "Sat_ActiveLayer_Thickness_Maps_1760", "SatelliteDerived_Forest_Mexico_2320", "Scotia_Prince_ferry", "Sea2Space", "SeaWiFS_L1_GAC", "SeaWiFS_L1_GAC", "SeaWiFS_L1_MLAC", "SeaWiFS_L1_MLAC", "SeaWiFS_L2_GAC_IOP", "SeaWiFS_L2_GAC_OC", "SeaWiFS_L2_MLAC_IOP", "SeaWiFS_L2_MLAC_OC", "SeaWiFS_L3b_CHL", "SeaWiFS_L3b_IOP", "SeaWiFS_L3b_KD", "SeaWiFS_L3b_PAR", "SeaWiFS_L3b_PIC", "SeaWiFS_L3b_POC", "SeaWiFS_L3b_RRS", "SeaWiFS_L3m_CHL", "SeaWiFS_L3m_IOP", "SeaWiFS_L3m_KD", "SeaWiFS_L3m_PAR", "SeaWiFS_L3m_PIC", "SeaWiFS_L3m_POC", "SeaWiFS_L3m_RRS", "SeaWiFS_L4b_GSM", "SeaWiFS_L4m_GSM", "Sea_of_Japan", "Seagrass_Mapping_Florida", "Searcher", "Seasonality_Tundra_Vegetation_1606", "Secret", "Semi-Arid_Tree_Carbon_50cm_2117", "Sentinel-1_Burst_Map", "Seward_Peninsula_Veg_Maps_1363", "Shrub_Biomass_Toolik_Lake_AK_1573", "SiB3_Carbon_Flux_909", "SiB4_Global_HalfDegree_Daily_1849", "SiB4_Global_HalfDegree_Hourly_1847", "SiB4_Global_HalfDegree_Monthly_1848", "Siberian_Biomass_Wildfire_1321", "Siberian_Larch_Stand_Age_1364", "SnowMeltDuration_PMicrowave_1843", "Snow_Cover_Extent_and_Depth_1757", "Snow_Depth_Data_Images_1656", "Snow_Wildlife_Tracks_AK_WA_2188", "Snowmelt_timing_maps_V2_1712", "Snowpack_Dall_Sheep_Track_1583", "SoilResp_HeterotrophicResp_1928", "SoilSCAPE_1339", "SoilSCAPE_V2_2049", "Soil_ActiveLayer_Properties_AK_2315", "Soil_Carbon_Flux_Maps_1683", "Soil_Moisture_Alaska_Alberta_2123", "Soil_Temp_Moisture_Alaska_1869", "Soil_Temperature_Profiles_AK_1767", "Sonoma_County_Forest_AGB_1764", "Southern_Boreal_Plot_Attribute_1740", "Southern_Ocean_Drifter", "Surface_Oligo_Med_Sea", "SwAO", "TAHOE", "TAO", "TAO_Moorings", "TARA-EUROPA", "TARA_PACIFIC_expedition", "TARFOX_UWC131A", "TARFOX_UWC131A_SUNP", "TARFOX_WALLOPS_MET", "TARFOX_WALLOPS_SMPS", "TARFOX_WALLOPS_SONDE", "TCTE3TSI6", "TCTE3TSID", "TELLUS_GIA_L3_0.5-DEG_V1.0", "TELLUS_GIA_L3_1-DEG_V1.0", "TELLUS_GLDAS-NOAH-3.3_TWS-ANOMALY_MONTHLY", "TELLUS_GRAC-GRFO_MASCON_CRI_GRID_RL06.1_V3", "TELLUS_GRAC-GRFO_MASCON_CRI_GRID_RL06.3_V4", "TELLUS_GRAC-GRFO_MASCON_GRID_RL06.1_V3", "TELLUS_GRAC-GRFO_MASCON_GRID_RL06.3_V4", "TELLUS_GRAC_L3_CSR_RL06_LND_v04", "TELLUS_GRAC_L3_CSR_RL06_OCN_v04", "TELLUS_GRAC_L3_GFZ_RL06_LND_v04", "TELLUS_GRAC_L3_GFZ_RL06_OCN_v04", "TELLUS_GRAC_L3_JPL_RL06_LND_v04", "TELLUS_GRAC_L3_JPL_RL06_OCN_v04", "TELLUS_GRFO_L3_CSR_RL06.1_LND_v04", "TELLUS_GRFO_L3_CSR_RL06.1_OCN_v04", "TELLUS_GRFO_L3_CSR_RL06.2_LND_v04", "TELLUS_GRFO_L3_CSR_RL06.2_OCN_v04", "TELLUS_GRFO_L3_CSR_RL06.3_LND_v04", "TELLUS_GRFO_L3_CSR_RL06.3_OCN_v04", "TELLUS_GRFO_L3_GFZ_RL06.1_LND_v04", "TELLUS_GRFO_L3_GFZ_RL06.1_OCN_v04", "TELLUS_GRFO_L3_JPL_RL06.3_LND_v04", "TELLUS_GRFO_L3_JPL_RL06.3_OCN_v04", "TEMPO_HCHO-PROXY_L2", "TEMPO_NO2_L2", "TEMPO_NO2_L3", "TG02_Balloon_VOC_1110", "TG03_AERONET_AOT_1128", "TG03_Aeronet_Solar_Flux_1137", "TG05_CASA_1199", "TG06_Vertical_Profiles_1175", "TG07_Autochamber_Soil_CO2_Flux_Km67_927", "TG07_DBH_Cauaxi_1063", "TG07_FFT_Survey_Km83_923", "TG07_Fallen_Standing_Necromass_998", "TG07_Litter_Decomposition_925", "TG07_Manual_Flux_Km67_1026", "TG07_Root_Mortality_Experiment_924", "TG07_Root_Mortality_Longterm_1116", "TG07_STM_GLAS_836", "TG07_Soil-Atmosphere_Flux_Km83_926", "TG07_Soil_Nutrients_1085", "TG07_Trace_Gas_Profiles_1107", "TG08_Soil_Gas_Fertilization_1105", "TG08_Soil_Gas_Wetting_1101", "TG09_N2O_Soils_1013", "TG10_TROFFEE_1195", "THAILAND", "THIRN4IMCH67", "THIRN4L1CH115", "THIRN4L1CH67", "THIRN5L1CH115", "THIRN5L1CH67", "THIRN6IM", "THIRN6L1CH115", "THIRN6L1CH67", "THIRN7IM", "THIRN7L1BCLT", "THIRN7L1CLDT", "THORPEX_ER2_MAS", "TIROS2L1FMRT", "TIROS3L1FMRT", "TIROS3L1ORT", "TIROS4L1FMRT", "TIROS4L1ORR", "TIROS4L1ORT", "TIROS7L1FMRT", "TL1BL", "TL1BN", "TL1BSOL", "TL2ANC", "TL2ANC", "TL2ANCS", "TL2ANCS", "TL2ATMLN", "TL2ATMLN", "TL2ATMTL", "TL2ATMTN", "TL2ATMTN", "TL2CH4LN", "TL2CH4LN", "TL2CH4N", "TL2CH4N", "TL2CH4NS", "TL2CH4NS", "TL2CO2LN", "TL2CO2LN", "TL2CO2N", "TL2CO2N", "TL2CO2NS", "TL2CO2NS", "TL2COLN", "TL2COLN", "TL2CON", "TL2CON", "TL2CONS", "TL2CONS", "TL2FORLN", "TL2FORLN", "TL2FORN", "TL2FORN", "TL2FORNS", "TL2FORNS", "TL2H2OL", "TL2H2OLN", "TL2H2OLN", "TL2H2OLS", "TL2H2ON", "TL2H2ON", "TL2H2ONS", "TL2H2ONS", "TL2HCNN", "TL2HCNNS", "TL2HDOL", "TL2HDOLN", "TL2HDOLN", "TL2HDOLS", "TL2HDON", "TL2HDON", "TL2HDONS", "TL2HDONS", "TL2HNO3L", "TL2HNO3S", "TL2IRKN", "TL2IRKN", "TL2IRKNS", "TL2IRKNS", "TL2MTLLN", "TL2MTLN", "TL2MTLN", "TL2MTLNS", "TL2MTLNS", "TL2N2ON", "TL2N2ON", "TL2N2ONS", "TL2N2ONS", "TL2NH3LN", "TL2NH3LN", "TL2NH3N", "TL2NH3N", "TL2NH3NS", "TL2NH3NS", "TL2O3L", "TL2O3LN", "TL2O3LN", "TL2O3LS", "TL2O3N", "TL2O3N", "TL2O3NS", "TL2O3NS", "TL2OCSLN", "TL2OCSN", "TL2OCSN", "TL2OCSNS", "TL2OCSNS", "TL2PANLN", "TL2PANN", "TL2PANN", "TL2PANNS", "TL2PANNS", "TL2RHLN", "TL2SUM", "TL2SUM", "TL2SUMS", "TL2SUMS", "TL2SUP", "TL2SUP", "TL2SUPS", "TL2SUPS", "TL2TLS", "TL2TNS", "TL2TNS", "TL3ATD", "TL3ATD", "TL3ATM", "TL3ATM", "TL3ATM", "TL3CH4D", "TL3CH4D", "TL3CH4M", "TL3CH4M", "TL3CH4M", "TL3COD", "TL3COD", "TL3COM", "TL3COM", "TL3H2OD", "TL3H2OD", "TL3H2OM", "TL3H2OM", "TL3H2OM", "TL3HDOD", "TL3HDOD", "TL3HDOM", "TL3HDOM", "TL3HDOM", "TL3HNOD", "TL3HNOM", "TL3O3D", "TL3O3D", "TL3O3M", "TL3O3M", "TL3O3M", "TLS_Lidar_BlueFlux_Mangroves_2311", "TMI-REMSS-L2P-v4", "TMI-REMSS-L3U-v7.1a", "TML2CO", "TML2COS", "TOL2O3", "TOL2O3", "TOL2O3S", "TOL2O3S", "TOMSEPAER", "TOMSEPL2", "TOMSEPL3", "TOMSEPL3daer", "TOMSEPL3dery", "TOMSEPL3dref", "TOMSEPL3dtoz", "TOMSEPL3maer", "TOMSEPL3mery", "TOMSEPL3mref", "TOMSEPL3mtoz", "TOMSEPL3zaer", "TOMSEPL3zref", "TOMSEPL3ztoz", "TOMSEPOVP", "TOMSM3L3", "TOMSM3L3dref", "TOMSM3L3dtoz", "TOMSM3OVP", "TOMSN7AER", "TOMSN7L2", "TOMSN7L3", "TOMSN7L3daer", "TOMSN7L3dery", "TOMSN7L3dref", "TOMSN7L3dtoz", "TOMSN7L3maer", "TOMSN7L3mery", "TOMSN7L3mref", "TOMSN7L3mtoz", "TOMSN7L3zaer", "TOMSN7L3zref", "TOMSN7L3ztoz", "TOMSN7OVP", "TOMSN7SO2", "TOMS_aerosol_823", "TOMS_ozone_824", "TOPEX_ALTSDR", "TOPEX_POSEIDON_GDR_F", "TOTE-VOTE_Aerosol_AircraftInSitu_DC8_Data", "TOTE-VOTE_AircraftRemoteSensing_DC8_DIAL_Data", "TOTE-VOTE_AircraftRemoteSensing_DC8_Lidar_Data", "TOTE-VOTE_Analysis_DC8_Data", "TOTE-VOTE_Ground_Data", "TOTE-VOTE_MetNav_AircraftInSitu_DC8_Data", "TOTE-VOTE_Miscellaneous_DC8_Data", "TOTE-VOTE_Satellite_Data", "TOTE-VOTE_Sondes_Data", "TOTE-VOTE_TraceGas_AircraftInSitu_DC8_Data", "TOTO", "TOVSA5ND", "TOVSA5NF", "TOVSA5NG", "TOVSA5NH", "TOVSA5TN", "TOVSADND", "TOVSADNF", "TOVSADNG", "TOVSADNH", "TOVSADTN", "TOVSAMNA", "TOVSAMNC", "TOVSAMND", "TOVSAMND", "TOVSAMNE", "TOVSAMNF", "TOVSAMNF", "TOVSAMNG", "TOVSAMNG", "TOVSAMNH", "TOVSAMNH", "TOVSAMNJ", "TOVSAMTN", "TOVSAMTN", "TOVSB5ND", "TOVSB5NG", "TOVSBDND", "TOVSBDNG", "TOVSBMND", "TOVSBMNG", "TOVS_717", "TRACE-A_920", "TRACE-A_AircraftRemoteSensing_DC8_DIAL_Data", "TRACE-A_Brazil_Data", "TRACE-A_Merge_Data", "TRACE-A_MetNav_AircraftInSitu_DC8_Data", "TRACE-A_Satellite_Data", "TRACE-A_Sondes_Data", "TRACE-A_TraceGas_AircraftInSitu_DC8_Data", "TRACE-A_Trajectory_Data", "TRACE-P_Aerosol_AircraftInSitu_DC8_Data", "TRACE-P_Aerosol_AircraftInSitu_P3B_Data", "TRACE-P_AircraftRemoteSensing_DC8_DIAL_Data", "TRACE-P_Cloud_AircraftInSitu_DC8_Data", "TRACE-P_Ground_Data", "TRACE-P_Merge_Data", "TRACE-P_MetNav_AircraftInSitu_DC8_Data", "TRACE-P_MetNav_Aircraft_InSitu_P3B_Data", "TRACE-P_Model_Data", "TRACE-P_Satellite_Data", "TRACE-P_Sondes_Data", "TRACE-P_TraceGas_AircraftInSitu_DC8_Data", "TRACE-P_TraceGas_InSitu_P3B_Data", "TRACE-P_Trajectory_DC8_Data", "TRACE-P_Trajectory_P3B_Data", "TRACE-P_jValue_AircraftInSitu_DC8_Data", "TRACE-P_jValue_AircraftInSitu_P3B_Data", "TRACERAQ_AircraftRemoteSensing_GV_GCAS_Data", "TRACERAQ_AircraftRemoteSensing_GV_HSRL2_Data", "TRACERAQ_Ground_LaPorte_Data", "TRACERAQ_Pandora_Data", "TRACERAQ_Sondes_Data", "TRATLEQ1", "TRMM_1A01", "TRMM_1A11", "TRMM_1B01", "TRMM_1B11", "TRMM_1B21", "TRMM_1B51", "TRMM_1C21", "TRMM_1C51", "TRMM_1C51UW", "TRMM_2A12", "TRMM_2A21", "TRMM_2A23", "TRMM_2A25", "TRMM_2A52", "TRMM_2A53", "TRMM_2A53UW", "TRMM_2A54", "TRMM_2A54UW", "TRMM_2A55", "TRMM_2A55UW", "TRMM_2A56", "TRMM_2B31", "TRMM_3A11", "TRMM_3A12", "TRMM_3A25", "TRMM_3A26", "TRMM_3A53", "TRMM_3A54", "TRMM_3A54UW", "TRMM_3A55", "TRMM_3B31", "TRMM_3B40RT", "TRMM_3B41RT", "TRMM_3B42", "TRMM_3B42RT", "TRMM_3B42RT_Daily", "TRMM_3B42_Daily", "TRMM_3B43", "TRMM_3G25", "TRMM_3G31", "TRMM_3H25", "TRMM_3H31", "TRMM_CSH", "TRMM_TMPA_LandSeaMask", "TRMM_baseUW", "TRMM_precip_718", "TROPICS01ANTTL1A", "TROPICS01BRTTL1B", "TROPICS01MIRSL2B", "TROPICS01PRPSL2B", "TROPICS01URADL2A", "TROPICS03ANTTL1A", "TROPICS03BRTTL1B", "TROPICS03MIRSL2B", "TROPICS03PRPSL2B", "TROPICS03URADL2A", "TROPICS05ANTTL1A", "TROPICS05BRTTL1B", "TROPICS05URADL2A", "TROPICS06ANTTL1A", "TROPICS06BRTTL1B", "TROPICS06MIRSL2B", "TROPICS06PRPSL2B", "TROPICS06URADL2A", "TROPICS07ANTTL1A", "TROPICS07BRTTL1B", "TROPICS07URADL2A", "TROPOMAER", "TROPOMI_MINDS_NO2", "TRPSCRAERNH42H2D", "TRPSCRAERNH46H3D", "TRPSCRAERNH4M3D", "TRPSCRAERNO32H2D", "TRPSCRAERNO36H3D", "TRPSCRAERNO3M3D", "TRPSCRAERSO42H2D", "TRPSCRAERSO46H3D", "TRPSCRAERSO4M3D", "TRPSCRCH2O2H2D", "TRPSCRCH2O6H3D", "TRPSCRCH2OM3D", "TRPSCRCO2H2D", "TRPSCRCO6H3D", "TRPSCRCOM3D", "TRPSCRCOS6H3D", "TRPSCRCOSM3D", "TRPSCRECOAM2D", "TRPSCRECOBM2D", "TRPSCRECOTM2D", "TRPSCRENOXAM2D", "TRPSCRENOXBM2D", "TRPSCRENOXLM2D", "TRPSCRENOXSM2D", "TRPSCRENOXTM2D", "TRPSCRESO2TM2D", "TRPSCRHNO32H2D", "TRPSCRHNO36H3D", "TRPSCRHNO3M3D", "TRPSCRNO22H2D", "TRPSCRNO26H3D", "TRPSCRNO2H2D", "TRPSCRNO2M3D", "TRPSCRNO2S6H3D", "TRPSCRNO2SM3D", "TRPSCRNO6H3D", "TRPSCRNOM3D", "TRPSCRO32H2D", "TRPSCRO36H3D", "TRPSCRO3I6H3D", "TRPSCRO3IM3D", "TRPSCRO3M3D", "TRPSCRO3S6H3D", "TRPSCRO3SM3D", "TRPSCROH2H2D", "TRPSCROH6H3D", "TRPSCROHM3D", "TRPSCRPAN2H2D", "TRPSCRPAN6H3D", "TRPSCRPANM3D", "TRPSCRPS2H2D", "TRPSCRPS6H2D", "TRPSCRPSM2D", "TRPSCRQ2H2D", "TRPSCRQ6H3D", "TRPSCRQM3D", "TRPSCRSO22H2D", "TRPSCRSO26H3D", "TRPSCRSO2M3D", "TRPSCRT2H2D", "TRPSCRT6H3D", "TRPSCRTM3D", "TRPSCRU2H2D", "TRPSCRU6H3D", "TRPSCRUM3D", "TRPSCRV2H2D", "TRPSCRV6H3D", "TRPSCRVM3D", "TRPSDL2ALLCRSMGBEI", "TRPSDL2ALLCRSMGDEL", "TRPSDL2ALLCRSMGKAR", "TRPSDL2ALLCRSMGLAG", "TRPSDL2ALLCRSMGLOS", "TRPSDL2ALLCRSMGMEX", "TRPSDL2ALLCRSMGSAO", "TRPSDL2ALLCRSMGTOK", "TRPSDL2CH4AIRSFS", "TRPSDL2CH4CRS1FS", "TRPSDL2CH4CRSAUS", "TRPSDL2CH4CRSFS", "TRPSDL2COAIRSFS", "TRPSDL2COCRS1FS", "TRPSDL2COCRSAUS", "TRPSDL2COCRSFS", "TRPSDL2COCRSTS1", "TRPSDL2COCRSTS2", "TRPSDL2COCRSWCF", "TRPSDL2COCRSWCFHI", "TRPSDL2H2OAIRSFS", "TRPSDL2H2OCRS1FS", "TRPSDL2H2OCRSAUS", "TRPSDL2H2OCRSFS", "TRPSDL2HDOAIRSFS", "TRPSDL2HDOCRS1FS", "TRPSDL2HDOCRSAUS", "TRPSDL2HDOCRSFS", "TRPSDL2NH3AIRSFS", "TRPSDL2NH3CRS1FS", "TRPSDL2NH3CRSAUS", "TRPSDL2NH3CRSFS", "TRPSDL2NH3CRSWCF", "TRPSDL2NH3CRSWCFHI", "TRPSDL2O3AIRSFS", "TRPSDL2O3AIRSOMIFS", "TRPSDL2O3CRS1FS", "TRPSDL2O3CRSAUS", "TRPSDL2O3CRSFS", "TRPSDL2O3CRSWCF", "TRPSDL2O3CRSWCFHI", "TRPSDL2O3OMIFS", "TRPSDL2PANCRS1FS", "TRPSDL2PANCRSAUS", "TRPSDL2PANCRSFS", "TRPSDL2PANCRSWCF", "TRPSDL2PANCRSWCFHI", "TRPSDL2TATMAIRSFS", "TRPSDL2TATMCRS1FS", "TRPSDL2TATMCRSAUS", "TRPSDL2TATMCRSFS", "TRPSYL2ALLCRSMGBEI", "TRPSYL2ALLCRSMGDEL", "TRPSYL2ALLCRSMGKAR", "TRPSYL2ALLCRSMGLAG", "TRPSYL2ALLCRSMGLOS", "TRPSYL2ALLCRSMGMEX", "TRPSYL2ALLCRSMGSAO", "TRPSYL2ALLCRSMGTOK", "TRPSYL2CH4AIRSFS", "TRPSYL2CH4AIRSORS", "TRPSYL2CH4CRS1FS", "TRPSYL2CH4CRSFS", "TRPSYL2CH4CRSRS", "TRPSYL2COAIRSFS", "TRPSYL2COAIRSORS", "TRPSYL2COCRS1FS", "TRPSYL2COCRSFS", "TRPSYL2COCRSRS", "TRPSYL2HDOAIRSFS", "TRPSYL2HDOAIRSORS", "TRPSYL2HDOCRS1FS", "TRPSYL2HDOCRSFS", "TRPSYL2HDOCRSRS", "TRPSYL2NH3AIRSFS", "TRPSYL2NH3CRS1FS", "TRPSYL2NH3CRSFS", "TRPSYL2NH3CRSRS", "TRPSYL2O3AIRSFS", "TRPSYL2O3AIRSOMIFS", "TRPSYL2O3AIRSORS", "TRPSYL2O3CRS1FS", "TRPSYL2O3CRSFS", "TRPSYL2O3CRSRS", "TRPSYL2O3OMIFS", "TRPSYL2PANCRS1FS", "TRPSYL2PANCRSFS", "TRPSYL2PANCRSRS", "TSIS_SSI_L3_12HR", "TSIS_SSI_L3_24HR", "TSIS_TSI_L3_06HR", "TSIS_TSI_L3_24HR", "Taiga_Tundra_Tree_Cover_1218", "Tampa_Bay", "Tara_Mediterranean", "Tara_Oceans_Polar_Circle", "Tara_Oceans_expedition", "Thermokarst_Circumpolar_Map_1332", "ThreeRivers", "Tidal_Marsh_Biomass_US_V1-1_1879", "Tidal_Marsh_Vegetation_US_1608", "Tidal_Wetland_Estuaries_Data_1742", "Tidal_Wetland_GPP_CONUS_1792", "Tidal_Wetland_Soil_Carbon_1612", "TillageErosion_SOCRedistribute_1944", "Tokyo_Bay", "Toolik_Lake_Area_Veg_Maps_1380", "Toolik_Lake_Veg_Plots_1333", "TopSoil_Erosion_MidWest_US_1774", "TowerBased_PhotoSpec_SIF_SK_CA_1887", "Tree_Canopy_Cover_Mexico_2137", "Tree_Mortality_Western_US_1512", "TundraTransect_VegRefl_Soil_2232", "TundraVeg_Reflectance_Soil_CO2_1960", "Tundra_Fire_Veg_Plots_1547", "Tundra_Greeness_Temp_Trends_1893", "Tundra_Leaf_Spectra_2005", "Turbid9", "Turkish_Seas", "UAEM1LME", "UAEM1LMT", "UAEMIAAE", "UAEMIALS", "UAEMIB2E", "UAEMIB2E", "UAEMIB2T", "UAEMIB2T", "UAEMIRCM", "UAEMITAL", "UAEMITCL", "UAEMITST", "UAEMRDAE", "UAEMRDLS", "UAEMRDRD", "UAEMRMLS", "UAEMRMRD", "UARCL3AL", "UARCL3AT", "UARHA2FN", "UARHA3AT", "UARHR3AL", "UARHR3AT", "UARIS3AL", "UARIS3AT", "UARML3AL", "UARML3AT", "UARPE2AXIS1", "UARPE2AXIS2", "UARPE2HEPSA", "UARPE2HEPSB", "UARPE2MEPS", "UARPE2VMAGAC", "UARPE2VMAGDC", "UARPE3AT", "UARPE3TP", "UARSO3BS", "UARSU3BS", "UARWI3AL", "UARWI3AT", "UARZCNMC", "UARZCUKM", "UAVSAR_INSAR_AMP", "UAVSAR_INSAR_AMP_GRD", "UAVSAR_INSAR_DEM", "UAVSAR_INSAR_INT", "UAVSAR_INSAR_INT_GRD", "UAVSAR_INSAR_KMZ", "UAVSAR_INSAR_META", "UAVSAR_POL_DEM", "UAVSAR_POL_INC", "UAVSAR_POL_KMZ", "UAVSAR_POL_META", "UAVSAR_POL_ML_CMPLX_GRD", "UAVSAR_POL_ML_CMPLX_GRD_3X3", "UAVSAR_POL_ML_CMPLX_GRD_5X5", "UAVSAR_POL_ML_CMPLX_SLANT", "UAVSAR_POL_PAULI", "UAVSAR_POL_SLOPE", "UAVSAR_POL_STOKES", "UAV_Imagery_BigLakeTrail_1834", "UCLA_DEALIASED_SASS_L3", "UMD_GEOL388", "USArray_Ground_Temperature_1680", "USM_pCO2", "US_MODIS_NDVI_1299", "US_MODIS_Veg_Parameters_1539", "Umiat_Veg_Plots_1370", "Unalaska_Veg_Plots_1375", "Uncertainty_US_Coastal_GHG_1650", "Understory_Veg_Biomass_Alaska_2340", "VBEMI2AE", "VBEMI2LS", "VBEMI2ST", "VBEMIB2E", "VBEMIB2T", "VBEMIGEO", "VCF5KYR", "VIIRSJ1_L1", "VIIRSJ1_L1_GEO", "VIIRSJ1_L2_IOP", "VIIRSJ1_L2_IOP_NRT", "VIIRSJ1_L2_OC", "VIIRSJ1_L2_OC_NRT", "VIIRSJ1_L2_SST", "VIIRSJ1_L2_SST3", "VIIRSJ1_L2_SST3_NRT", "VIIRSJ1_L2_SST_NRT", "VIIRSJ2_L1", "VIIRSJ2_L1_GEO", "VIIRSJ2_L2_IOP", "VIIRSJ2_L2_IOP_NRT", "VIIRSJ2_L2_OC", "VIIRSJ2_L2_OC_NRT", "VIIRSN_L1", "VIIRSN_L1_GEO", "VIIRSN_L2_IOP", "VIIRSN_L2_IOP_NRT", "VIIRSN_L2_OC", "VIIRSN_L2_OC_NRT", "VIIRSN_L2_SST", "VIIRSN_L2_SST3", "VIIRSN_L2_SST3_NRT", "VIIRSN_L2_SST_NRT", "VIIRSN_L3b_NSST", "VIIRSN_L3b_NSST_NRT", "VIIRSN_L3b_SST", "VIIRSN_L3b_SST3", "VIIRSN_L3b_SST3_NRT", "VIIRSN_L3b_SST_NRT", "VIIRSN_L3m_NSST", "VIIRSN_L3m_NSST_NRT", "VIIRSN_L3m_SST", "VIIRSN_L3m_SST3", "VIIRSN_L3m_SST3_NRT", "VIIRSN_L3m_SST_NRT", "VIIRS_N20-NAVO-L2P-v3.0", "VIIRS_N20-STAR-L2P-v2.80", "VIIRS_N20-STAR-L3U-v2.80", "VIIRS_N21-NAVO-L2P-v3.0", "VIIRS_NPP-JPL-L2P-v2016.2", "VIIRS_NPP-NAVO-L2P-v1.0", "VIIRS_NPP-NAVO-L2P-v2.0", "VIIRS_NPP-NAVO-L2P-v3.0", "VIIRS_NPP-STAR-L2P-v2.80", "VIIRS_NPP-STAR-L3U-v2.80", "VIIRS_SST_NPP_NAR-OSISAF-L3C-v1.0", "VIIRS_Val_FLKeys", "VIIRS_Validation", "VIMS_2005", "VIP01", "VIP07", "VIP15", "VIP30", "VIPPHEN_EVI2", "VIPPHEN_NDVI", "VIRGAS_MetNav_AircraftInSitu_Data", "VIRGAS_TraceGas_AircraftInSitu_Data", "VISSRGOES1IMIR", "VISSRGOES1IMVIS", "VISSRGOES2IMIR", "VISSRGOES2IMVIS", "VISSRGOES3IMIR", "VISSRGOES3IMVIS", "VISSRSMS1IMIR", "VISSRSMS1IMVIS", "VISSRSMS1L1AOIPS", "VISSRSMS1L1EHT", "VISSRSMS2IMIR", "VISSRSMS2IMVIS", "VISSRSMS2L1AOIPS", "VISSRSMS2L1EHT", "VJ103MODLL", "VJ109", "VJ109A1", "VJ109CMG", "VJ109GA", "VJ109H1", "VJ113A1", "VJ113A2", "VJ113A3", "VJ113C1", "VJ113C2", "VJ114", "VJ114A1", "VJ114IMG", "VJ115A2H", "VJ121", "VJ121A1D", "VJ121A1N", "VJ121A2", "VJ121C1", "VJ121C2", "VJ121C3", "VJ128C2", "VJ128C3", "VJ146A1G_NRT", "VJ146A1_NRT", "VNP02DNB", "VNP02IMG", "VNP02MOD", "VNP03DNB", "VNP03IMG", "VNP03MOD", "VNP03MODLL", "VNP03MODLL", "VNP09", "VNP09A1", "VNP09A1", "VNP09CMG", "VNP09CMG", "VNP09GA", "VNP09GA", "VNP09H1", "VNP09H1", "VNP10", "VNP10A1", "VNP10A1", "VNP10A1F", "VNP13A1", "VNP13A1", "VNP13A2", "VNP13A2", "VNP13A3", "VNP13A3", "VNP13C1", "VNP13C1", "VNP13C2", "VNP13C2", "VNP14", "VNP14", "VNP14A1", "VNP14A1", "VNP14IMG", "VNP14IMGTDL_NRT", "VNP15A2H", "VNP15A2H", "VNP21", "VNP21", "VNP21A1D", "VNP21A1D", "VNP21A1N", "VNP21A1N", "VNP21A2", "VNP21A2", "VNP21C1", "VNP21C2", "VNP21C3", "VNP22C2", "VNP22Q2", "VNP28C2", "VNP28C3", "VNP29", "VNP30", "VNP43C1", "VNP43C2", "VNP43C3", "VNP43C4", "VNP43DNBA1", "VNP43DNBA2", "VNP43DNBA3", "VNP43DNBA4", "VNP43IA1", "VNP43IA2", "VNP43IA3", "VNP43IA4", "VNP43MA1", "VNP43MA2", "VNP43MA3", "VNP43MA4", "VNP46A1", "VNP46A1", "VNP46A1G_NRT", "VNP46A1_NRT", "VNP46A2", "VNP46A3", "VNP46A4", "VNP64A1", "VOLPE", "VPRM_North_America_Parameters_1349", "Veg_Soil_Tundra_Burned_Area_2119", "Vegetation_Maps_Toolik_Lake_1690", "Vegetation_Photos_Toolik_Lake_1718", "Vegetation_greenness_trend_1576", "Vermont_HighRes_LandCover_2072", "Vulcan_V3_Annual_Emissions_1741", "Vulcan_V3_Hourly_Emissions_1810", "WACS2", "WAF_DEALIASED_SASS_L2", "WAVeTrends_1738", "WC_LSMEM_SOILM_025", "WC_MULTISEN_PREC_025", "WC_PM_ET_050", "WENTZ_NIMBUS-7_SMMR_L2", "WENTZ_SASS_SIGMA0_L2", "WHITECAPS", "WLDAS_NOAHMP001_DA1", "WRF_STILT_Footprints_Boston_1572", "WRF_STILT_Particles_Boston_1596", "WUS_UCLA_SR", "WV04_MSI_L1B", "WV04_Pan_L1B", "WV_LCC_SC_FSCA", "WaterBalance_Daily_Historical_GRIDMET", "WaterBalance_Monthly_Historical_GRIDMET", "WebbRosenzweig_548", "West_Florida_Shelf", "West_Soil_Carbon_1238", "Western_Gulf_of_Maine", "Wetland_Soil_CarbonStocks_WA_2249", "Wetland_VegClassification_PAD_2069", "WhitePhenoregions_799", "WhiteSpruce_Leaf_Traits_Alaska_2124", "Wildfire_Effects_Spruce_Field_1595", "Wildfire_Impacts_Boreal_Ecosys_2359", "Wildfires_2014_NWT_Canada_1307", "Wildfires_Date_of_Burning_1559", "Wildfires_NWT_Canada_1548", "Wildfires_NWT_Canada_2018_1703", "Wildfires_NWT_Canada_2019_1900", "Willow_Veg_Plots_1368", "WindSat-REMSS-L3U-v7.0.1a", "Wolves_Denning_Pups_Climate_1846", "XAERDT_L2_ABI_G16", "XAERDT_L2_ABI_G17", "XAERDT_L2_AHI_H08", "XAERDT_L2_AHI_H09", "XAERDT_L2_MODIS_Aqua", "XAERDT_L2_MODIS_Terra", "XAERDT_L2_VIIRS_NOAA20", "XAERDT_L2_VIIRS_SNPP", "Xingu_Albedo_Radiation_1622", "YKDelta_EnvChange_InfoExchange_1894", "Young_Russian_Forest_Map_1330", "ZinkeSoil_221", "Zinke_soil_683", "ZoblerSoilDerived_540", "ZoblerSoil_418", "Zobler_Soil_649", "ZonalFlux", "aamhcpex", "aces1am", "aces1cont", "aces1efm", "aces1log", "aces1time", "aces1trig", "aerosol_properties_725", "aes5davg_236", "aes_upl1_238", "aes_upl2_239", "afm06ihd_240", "afm06ptd_241", "afm06pwd_242", "afm06smd_243", "afm11afr_244", "afm13afr_245", "afm2as94_494", "afm3as94_496", "afm3mw94_495", "afm4toas_498", "afm4tofx_497", "afm6gifs_433", "african_woody_savanna_850", "air_sea_gas_exchange_xdeg_1208", "airscm3b_448", "airscpex", "airssy3b_507", "airsunp_61", "ali_etm_tandem_821", "amazon_precip_228", "ames_sunphotometer_643", "amprimpacts", "amprtbcx1", "amprtbcx2", "amprtbcx3", "amprtbf3a", "amprtbjax", "amprtbkwj", "amprtblba", "amprtbta", "amprtbtc", "ams_cs93_403", "ams_cs94_404", "ams_cs95_405", "ams_cs96_406", "amsua15sp", "amsua16sp", "amsua17sp", "aoci0bil_281", "apr3cpex", "apr3cpexaw", "apr3cpexcv", "apuimpacts", "asas_l1b_562", "asasrefl_287", "ascatcpex", "asosimpacts", "atmos_co2_by_erosion_xdeg_1019", "avapsimpacts", "avhrr_822", "avhrr_albedo_1995_xdeg_928", "avhrrl3b_481", "avhrrl4b_438", "avhrrl4c_439", "avhrrlc1_434", "basin_border_670", "billmark_828", "biofuel_emissions_753", "biomass_allocation_703", "biomdens_450", "biomebg2_296", "biomebgc_295", "boreas_aeshrday_235", "burning_emissions_752", "bvoc_flux_759", "c4_percent_1deg_932", "c4aaero", "c4dcm", "c4dcstar", "c4dcvi", "c4dd8d", "c4dicats", "c4djlh", "c4dlase", "c4dlip", "c4dmms", "c4dmtp", "c4dnevzor", "c4dpr2", "c4eampr", "c4eedop", "c4eehad", "c4ehamsr", "c4elip", "c4emas", "c4emtp", "c4enav", "c4enlh", "c4eo3p", "c4gandros", "c4gmipclo", "c4gmipfm", "c4gmipsod", "c4gmipss1", "c4gmipss2", "c4gmipwp", "c4gmisrep", "c4gnpol", "c4gsmart", "c4gtoga", "c4gxpow", "c4sg8", "calibgas_500", "canopychem_422", "canopyspec_423", "century_vemap_m4_820", "chem_26", "chesapeake_val_2013", "cmimpacts", "cmx3aeri", "cmx3andros", "cmx3g8", "cmx3misrep", "cmx3srl", "co2_emissions_1deg_1021", "combined_ancillary_xdeg_1200", "comp_runoff_monthly_xdeg_994", "cosmirimpacts", "cossirimpacts", "cp_lidar_images_721", "cplimpacts", "cramer_leemans_637", "cropland_612", "crsimpacts", "cru_monthly_climate_xdeg_1014", "cru_monthly_mean_xdeg_1015", "csgcpex01", "daily_precip_est_793", "dc8ammr", "dc8avaps", "dc8capac", "dc8dads", "dc8jplsaw", "dc8lase", "dc8laserh", "dc8macaws", "dc8mms", "dc8psr", "diffuse_irradiance_791", "dlhimpacts", "dtms0bil_247", "eMASL1B", "eMASL2AER", "eMASL2CLD", "echidna_1045", "ecmwf2_523", "ecmwf_met_1deg_1222", "ecosystem_roots_1deg_929", "edc_landcover_xdeg_930", "edgar_atmos_emissions_1deg_1022", "elev_arc_250", "er2_aerial_photos_722", "er2edop", "er2flog_501", "er2lip", "er2mams", "er2mir", "er2mts", "er2nasti", "er2nav", "er2navimpacts", "erbe_albedo_monthly_xdeg_957", "eta_model_723", "ewing", "explorer", "exrad3dimpacts", "exradepoch", "exradimpacts", "faamwdat_237", "fasir_biophys_monthly_xdeg_970", "fasir_ndvi_monthly_xdeg_972", "ffo_Betts_1987-1989_afd_93", "ffo_Betts_1987-1989_ams_89", "ffo_Betts_1987-1989_gsm_97", "ffo_Betts_1987-1989_nsm_101", "ffo_Betts_1987_afd_92", "ffo_Betts_1987_ams_88", "ffo_Betts_1987_gsm_96", "ffo_Betts_1987_nsm_100", "ffo_Betts_1988_afd_94", "ffo_Betts_1988_ams_90", "ffo_Betts_1988_gsm_98", "ffo_Betts_1988_nsm_102", "ffo_Betts_1989_afd_95", "ffo_Betts_1989_ams_91", "ffo_Betts_1989_gsm_99", "ffo_Betts_1989_nsm_103", "fhstmanr_386", "fhstmanv_387", "fieldsunp_65", "fife_AF_dtrnd_nae_3", "fife_AF_dtrnd_ncar_5", "fife_AF_dtrnd_wyo_4", "fife_AF_filtr_nae_6", "fife_AF_filtr_ncar_8", "fife_AF_filtr_wyo_7", "fife_AF_raw_nae_9", "fife_AF_raw_ncar_11", "fife_AF_raw_wyo_10", "fife_atmos_brut_drv_14", "fife_atmos_brut_son_15", "fife_atmos_lidar_ht_17", "fife_atmos_ncdc_son_13", "fife_atmos_nmc_upr_57", "fife_atmos_noaa_son_73", "fife_atmos_noaa_tov_16", "fife_atmos_sodar_18", "fife_atmos_tempprof_124", "fife_atmos_wind_lid_138", "fife_atmos_wind_son_139", "fife_biology_biomass_118", "fife_biology_leaf_ang_44", "fife_biology_leaf_h2o_126", "fife_biology_mow_biop_55", "fife_biology_mow_exo_56", "fife_biology_pho_box_27", "fife_biology_pho_leaf_46", "fife_biology_plantpro_69", "fife_biology_root_bio_75", "fife_biology_soil_co2_105", "fife_biology_soil_gas_106", "fife_biology_veg_biop_135", "fife_biology_veg_ref_137", "fife_biology_veg_spec_136", "fife_hydrology_strm_15m_1", "fife_hydrology_strm_day_119", "fife_hydrology_strm_st_120", "fife_optical_ot_brug_62", "fife_optical_ot_c130_63", "fife_optical_ot_calib_60", "fife_optical_ot_frasr_64", "fife_optical_ot_staff_66", "fife_sat_obs_ns001tms_59", "fife_sat_obs_sat_avhr_77", "fife_sat_obs_sat_coef_76", "fife_sat_obs_sat_ltm_78", "fife_sat_obs_sat_spot_79", "fife_soilmstr_peck_gam_37", "fife_soilmstr_peck_sm_109", "fife_soilmstr_sm_grav_110", "fife_soilmstr_sm_neut_111", "fife_soilmstr_sm_tran_113", "fife_soilmstr_soil_imp_108", "fife_soilprop_soil_h2o_117", "fife_soilprop_soil_rel_112", "fife_soilprop_soildens_104", "fife_soilprop_soilhydc_107", "fife_soilprop_soilsurv_115", "fife_soilprop_soilther_116", "fife_sur_flux_30_min_brg_20", "fife_sur_flux_30_min_brk_21", "fife_sur_flux_30_min_brl_19", "fife_sur_flux_30_min_brs_22", "fife_sur_flux_30_min_brv_23", "fife_sur_flux_30_min_brw_24", "fife_sur_flux_30_min_eca_30", "fife_sur_flux_30_min_ecb_32", "fife_sur_flux_30_min_ecg_31", "fife_sur_flux_30_min_ecv_33", "fife_sur_flux_30_min_ecw_34", "fife_sur_flux_basel_92_121", "fife_sur_met_ams_12", "fife_sur_met_cld_cam_28", "fife_sur_met_hday_met_39", "fife_sur_met_hmon_met_40", "fife_sur_met_ncdc_sur_122", "fife_sur_met_noaa_sur_58", "fife_sur_met_rain_30m_2", "fife_sur_met_rain_day_29", "fife_sur_refl_gem_helo_38", "fife_sur_refl_irt_grnd_72", "fife_sur_refl_irt_helo_70", "fife_sur_refl_irt_mult_71", "fife_sur_refl_lightwnd_43", "fife_sur_refl_ltbr_ksu_41", "fife_sur_refl_ltbr_unl_42", "fife_sur_refl_mmr_calb_51", "fife_sur_refl_mmr_grnd_52", "fife_sur_refl_mmr_helo_53", "fife_sur_refl_mmr_leaf_54", "fife_sur_refl_parabola_68", "fife_sur_refl_se5_gsfc_81", "fife_sur_refl_se5_helo_87", "fife_sur_refl_se5_leaf_85", "fife_sur_refl_se5_unl_82", "fife_sur_refl_soilrefl_114", "fife_sur_refl_unl_long_49", "fife_sur_refl_unl_surf_123", "fire_emissions_724", "fire_emissions_v4_R1_1293", "fltrepepoch", "fluxnet_point_1029", "forest_carbon_flux_949", "fuel_load_755", "g3acld", "g3acldb", "g3alsp", "g3alspb", "g3assp", "g3asspb", "g3at", "g3atb", "g3blmnc", "g3blsp", "g3blsp", "g3blspb", "g3bsmnc", "g3bssp", "g3bsspb", "g3bsspb", "g3bt", "g3bt", "g3btb", "g3btmnc", "gap_filled_marconi_811", "gdp_xdeg_974", "geocoord_556", "geoecology_R1_656", "gfscpex", "gghydro_676", "ghcn_631", "gimms_ndvi_monthly_xdeg_973", "giss_wetlands_632", "glider", "glmcierra", "glmgoesL3", "global_N_cycle_797", "global_N_deposition_maps_830", "global_population_xdeg_975", "globalir", "globalview_ch4_point_1109", "globalview_co2_point_1111", "globe_dem_630", "goes71_444", "goes71a_300", "goes72_554", "goes81_445", "goes81a_446", "goescpex", "goescpexcv", "goesimpacts", "goesrpltaviris", "goesrpltavirisng", "goesrpltcolma", "goesrpltcpl", "goesrpltcrs", "goesrpltdclma", "goesrpltfegs", "goesrpltgcas", "goesrpltivanpah", "goesrpltksclma", "goesrpltlip", "goesrpltmisrep", "goesrpltnalma", "goesrpltnaver2", "goesrpltoklma", "goesrpltredlake", "goesrpltshis", "goesrpltsolma", "goesrpltwtlma", "gpcc_precip_monthly_xdeg_995", "gpcp_precip_monthly_xdeg_1003", "gpcp_precip_pentad_xdeg_1002", "gpm2dc3vp", "gpm2dgcpex", "gpm2dhymex", "gpm2difld", "gpm2diphx", "gpm2dlpvex", "gpm2dmc3e", "gpm2dnsstc", "gpm2dvdolyx", "gpm2dwff2", "gpmadmirgcpex", "gpmahdmetolyx", "gpmampriphx2", "gpmamprmc3e", "gpmamprolyx", "gpmapr2gcpex", "gpmapr3olyx2", "gpmapuicepop", "gpmapuolyx", "gpmapuwff", "gpmarsifld", "gpmasinaiphx", "gpmasoolyx", "gpmavapsolyx", "gpmcax1cfolyx", "gpmcax1odolyx", "gpmcax1rbolyx", "gpmceilgcpex", "gpmceiliphx", "gpmchillmc3e", "gpmcilpvex", "gpmcitvidiphx", "gpmcmgcpex2", "gpmcmiphx", "gpmcmmc3e", "gpmcmolyx", "gpmcmorphnifld", "gpmcmorphniphx", "gpmcosmirgcpex", "gpmcosmiriphx", "gpmcosmirmc3e", "gpmcosmirolyx", "gpmcplolyx", "gpmcrsiphx", "gpmcrsolyx", "gpmcxsiolyx", "gpmd3rgcpex", "gpmd3ricepop", "gpmd3rifld", "gpmd3riphx", "gpmd3rolyx", "gpmdowolyx2", "gpmepfl", "gpmer2navmc3e", "gpmexradiphx", "gpmfltsummc3e", "gpmgaugewff", "gpmgoes13gcpexB", "gpmgoes13iphxB", "gpmgoes13mc3eB", "gpmgoes14iphxB", "gpmgoes15olyxB", "gpmgprof2014ifld", "gpmgsmapjifld", "gpmheifld", "gpmheiphx", "gpmhiwrapiphx", "gpmhiwrapmc3e", "gpmhiwrapolyx", "gpmikalpvex", "gpmjwlpvex", "gpmjwnsstc", "gpmkapxgcpex", "gpmkarx2ifld", "gpmkarx3ifld", "gpmkatx2olyx", "gpmkboxgcpex", "gpmkbufgcpex", "gpmkcae2iphx", "gpmkcbwgcpex", "gpmkcradgcpex", "gpmkcxxgcpex", "gpmkdmx2ifld", "gpmkdmx3ifld", "gpmkdvn2ifld", "gpmkdvn3ifld", "gpmkerlpvex", "gpmkgldmc3e", "gpmkgsp2iphx", "gpmkgyxgcpex", "gpmkhtx2iphx", "gpmkictmc3e", "gpmkinxmc3e", "gpmklgx2olyx", "gpmkltx2iphx", "gpmkmhx2iphx", "gpmkmpx2ifld", "gpmkmpx3ifld", "gpmkmrx2iphx", "gpmkorlpvex", "gpmkrax2iphx", "gpmkrtx2olyx", "gpmktlxmc3e", "gpmktwxmc3e", "gpmktyxgcpex", "gpmkumlpvex", "gpmkvnxmc3e", "gpmlidargcpex", "gpmlipiphx", "gpmmascolyx", "gpmmastmetlpvex", "gpmmettecgcpex", "gpmmisrepgcpex", "gpmmisrepifld", "gpmmisrepiphx", "gpmmisrepmc3e", "gpmmisrepolyx", "gpmmrms", "gpmmrrdukeiphx", "gpmmrrecgcpex2", "gpmmrrhymex", "gpmmrricepop", "gpmmrrlpvex", "gpmmrrnaachiphx", "gpmmrrnagcpex2", "gpmmrrnaifld", "gpmmrrnaiphx", "gpmmrrnamc3e", "gpmmrrolyx", "gpmmwrdukeiphx", "gpmnavcitgcpex2", "gpmnavcitiphx", "gpmnavcitmc3e", "gpmnavcitolyx", "gpmnavdc8gcpex", "gpmnavdc8olyx", "gpmnaver2iphx", "gpmnaver2olyx", "gpmncamdc8gcpex", "gpmnmqifld", "gpmnmqiphx", "gpmnoxpiphx", "gpmnpolifld2", "gpmnpoliphx", "gpmnpolmc3e", "gpmnpololyx2", "gpmnpolwff", "gpmnrlrtifld", "gpmodmlpvex", "gpmopasscgcpex", "gpmopassolyx", "gpmoumesmc3e", "gpmpadukeiphx", "gpmpagcpex", "gpmpahymex", "gpmpaifld", "gpmpaiphx", "gpmpal", "gpmpalpvex", "gpmpamc3e", "gpmpanoaamc3e", "gpmpansstc", "gpmparawifld", "gpmparprbgcpex", "gpmparprbiphx", "gpmparprbmc3e", "gpmparprbolyx", "gpmpawneemc3e", "gpmpersucifld", "gpmpipicepop", "gpmplgcpex", "gpmpllpvex", "gpmplolyx", "gpmpmastmetlpvex", "gpmpossgcpex", "gpmposslpvex", "gpmprecipmgcpex", "gpmprecipolyx", "gpmpvigcpex", "gpmpvilpvex", "gpmraddpgcpex", "gpmradioiphx", "gpmradmecgcpex", "gpmradpmgcpex", "gpmradsecgcpex", "gpmrefifld", "gpmrefpreiphx", "gpmrfcmpifld", "gpmrgachiphx", "gpmrgdukeiphx", "gpmrgifcifld", "gpmrgnaifld2", "gpmrgnaiphx2", "gpmrgnamc3e2", "gpmrgnaolyx", "gpmrgsaiphx", "gpmsatpaifld", "gpmsbdminmc3e", "gpmsbdorgmc3e", "gpmsbdrwncmc3e", "gpmsbdrwspcmc3e", "gpmscampriphx", "gpmseafluxicepop", "gpmsgifcifld", "gpmsgusgsifld", "gpmsimorbc3vp", "gpmsimorblpvex", "gpmsimorbmc3e", "gpmsimorbtwpice", "gpmsmdukeiphx", "gpmsnowgcpex", "gpmsnowolyx", "gpmsogcpex", "gpmsomc3e", "gpmsondelpvex", "gpmsondeolyx", "gpmsslpvex", "gpmsurmetc3vp", "gpmsurmetmc3e", "gpmtfmifld", "gpmtmpaifld", "gpmtmpaiphx", "gpmtpshpgcpex", "gpmtrm2A25iphx", "gpmuhfnoaamc3e", "gpmvanlpvex", "gpmvertixgcpex", "gpmvisecc3vp", "gpmvisecgcpex", "gpmvn", "gpmwacrc3vp", "gpmwbandgcpex", "gpmwbandiphx", "gpmwcrlpvex", "gpmwebecgcpex", "gpmwkacmlpvex", "gpmwpecgcpex", "gpmwrflpvex", "gpmwrfmc3e", "gpmxetc3vp", "gpmxpolifld", "gppdi_npp_gridded_xdeg_1023", "gppdi_npp_point_1033", "gripapr2", "gripcaps", "gripdawn", "gripdropdc83", "gripflt", "gripghis", "gripgoes11B", "gripgoes13B", "gripgoesot", "griphamsr", "griphirad", "griphirad1", "griphiwrap", "griplarge", "griplase", "griplip", "gripmisrep", "gripmms", "gripmsg", "gripnavdc8", "gripnavgh", "gripnavwb57", "gripradio", "gripstorm", "gts_precip_daily_xdeg_1001", "h01_shd_253", "h01smpvd_255", "h01uncpd_254", "h02swed_256", "h03candd_258", "h03dbhd_264", "h03scrdd_266", "h03sd96d_259", "h03sntmd_261", "h03sp96d_260", "h03stdnd_257", "h03swed_262", "h04assd_267", "h04stsnd_268", "h06grmsd_272", "h06grsmd_271", "h08gm94_273", "h08gm96_274", "h08gp96_275", "h09rradi_278", "h09stmgd_279", "h3scmet_265", "h3swe96d_263", "h5flxd_269", "h6acgsmd_270", "h8thrfld_277", "h8utmdem_276", "h9rgbl94_229", "h9rgbl95_231", "h9rgbl96_233", "h9rgtb94_230", "h9rgtb95_232", "h9rgtb96_234", "hamsrcpex", "hamsrcpexaw", "hamsrcpexcv", "hamsrepoch", "handheld_haze_708", "historic_cropland_xdeg_966", "historic_landcover_xdeg_967", "historical_croplands_675", "hiwat", "hiwrapimpacts", "hs3avaps2", "hs3cimssbt", "hs3cimsscth", "hs3cimsstot", "hs3cpl", "hs3fltrep", "hs3gmaodustaot", "hs3hamsr", "hs3hirad", "hs3hiwrap", "hs3navgh", "hs3nrltrop", "hs3ships", "hs3shis", "hs3wwlln", "hydro1k_elevation_xdeg_1007", "hydrographic_gghydro_636", "ibis_2_5_808", "imergcpex", "islscp2_soils_1deg_1004", "isslis_v2_fin", "isslisg_v2_fin", "jornada_albedo_667", "jornada_canopy_brf_668", "jornada_landcover_lai_665", "jornada_mquals_666", "kakqimpacts", "kalahari_aot_h2o_vapor_719", "kalahari_co2_heat_flux_765", "kbgmimpacts", "kboximpacts", "kbufimpacts", "kccximpacts", "kcleimpacts", "kcxximpacts", "kdiximpacts", "kdoximpacts", "kdtximpacts", "kdvnimpacts", "kenximpacts", "kfcximpacts", "kgrbimpacts", "kgrrimpacts", "kgyximpacts", "kilnimpacts", "kilximpacts", "kindimpacts", "kiwximpacts", "kjklimpacts", "klotimpacts", "klwximpacts", "kmhximpacts", "kmkximpacts", "knp_fire_maps_756", "knp_fire_residue_751", "knp_met_761", "kokximpacts", "kpbzimpacts", "kraximpacts", "krlximpacts", "kscmill", "kt_canopy_structure_768", "kt_gps_photos_769", "kt_lai_770", "kt_lai_trac_771", "kt_leaf_meas_772", "kt_leaf_spectra_773", "kt_pai_estimates_774", "kt_par_794", "kt_stem_map_775", "kt_veg_inventory_776", "kt_woody_veg_777", "ktyximpacts", "kvwximpacts", "lai_45", "lake_erie_aug_2014", "land_cover_data-1km_627", "land_cover_data_1deg_677", "land_cover_data_1km_678", "land_cover_data_8km_680", "landcover_bvoc_est_764", "lba_ghcn_702", "lba_gisswetlands_688", "lba_isric_wise_701", "lba_tree_cover-1km_686", "ldarraw", "leaf_voc_emissions_763", "leafchem_421", 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