diff --git a/docs/pangeo/chunking_introduction.ipynb b/docs/pangeo/chunking_introduction.ipynb old mode 100755 new mode 100644 index 635cd3b..94cb6e3 --- a/docs/pangeo/chunking_introduction.ipynb +++ b/docs/pangeo/chunking_introduction.ipynb @@ -3,7 +3,13 @@ { "cell_type": "markdown", "id": "1bfbae7a-12f1-4787-a520-c3de7529168d", - "metadata": {}, + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, "source": [ "# Data chunking" ] @@ -156,8 +162,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "CPU times: user 585 ms, sys: 81 ms, total: 666 ms\n", - "Wall time: 720 ms\n" + "CPU times: user 3.94 s, sys: 538 ms, total: 4.48 s\n", + "Wall time: 4.82 s\n" ] }, { @@ -424,6 +430,11 @@ " grid-column: 4;\n", "}\n", "\n", + ".xr-index-preview {\n", + " grid-column: 2 / 5;\n", + " color: var(--xr-font-color2);\n", + "}\n", + "\n", ".xr-var-name,\n", ".xr-var-dims,\n", ".xr-var-dtype,\n", @@ -445,14 +456,16 @@ "}\n", "\n", ".xr-var-attrs,\n", - ".xr-var-data {\n", + ".xr-var-data,\n", + ".xr-index-data {\n", " display: none;\n", " background-color: var(--xr-background-color) !important;\n", " padding-bottom: 5px !important;\n", "}\n", "\n", ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n", - ".xr-var-data-in:checked ~ .xr-var-data {\n", + ".xr-var-data-in:checked ~ .xr-var-data,\n", + ".xr-index-data-in:checked ~ .xr-index-data {\n", " display: block;\n", "}\n", "\n", @@ -462,13 +475,16 @@ "\n", ".xr-var-name span,\n", ".xr-var-data,\n", + ".xr-index-name div,\n", + ".xr-index-data,\n", ".xr-attrs {\n", " padding-left: 25px !important;\n", "}\n", "\n", ".xr-attrs,\n", ".xr-var-attrs,\n", - ".xr-var-data {\n", + ".xr-var-data,\n", + ".xr-index-data {\n", " grid-column: 1 / -1;\n", "}\n", "\n", @@ -506,7 +522,8 @@ "}\n", "\n", ".xr-icon-database,\n", - ".xr-icon-file-text2 {\n", + ".xr-icon-file-text2,\n", + ".xr-no-icon {\n", " display: inline-block;\n", " vertical-align: middle;\n", " width: 1em;\n", @@ -522,12 +539,12 @@ " * lat (lat) float64 80.0 79.99 79.98 79.97 ... -59.97 -59.98 -59.99\n", "Data variables:\n", " crs |S1 ...\n", - " min (lat, lon) float32 dask.array<chunksize=(15680, 40320), meta=np.ndarray>\n", - " median (lat, lon) float32 dask.array<chunksize=(15680, 40320), meta=np.ndarray>\n", - " max (lat, lon) float32 dask.array<chunksize=(15680, 40320), meta=np.ndarray>\n", - " mean (lat, lon) float32 dask.array<chunksize=(15680, 40320), meta=np.ndarray>\n", - " stdev (lat, lon) float32 dask.array<chunksize=(15680, 40320), meta=np.ndarray>\n", - " nobs (lat, lon) float32 dask.array<chunksize=(15680, 40320), meta=np.ndarray>\n", + " min (lat, lon) float32 dask.array<chunksize=(1207, 3102), meta=np.ndarray>\n", + " median (lat, lon) float32 dask.array<chunksize=(1207, 3102), meta=np.ndarray>\n", + " max (lat, lon) float32 dask.array<chunksize=(1207, 3102), meta=np.ndarray>\n", + " mean (lat, lon) float32 dask.array<chunksize=(1207, 3102), meta=np.ndarray>\n", + " stdev (lat, lon) float32 dask.array<chunksize=(1207, 3102), meta=np.ndarray>\n", + " nobs (lat, lon) float32 dask.array<chunksize=(1207, 3102), meta=np.ndarray>\n", "Attributes: (12/19)\n", " Conventions: CF-1.6\n", " parent_identifier: urn:cgls:global:ndvi_stats_all\n", @@ -541,12 +558,12 @@ " references: https://land.copernicus.eu/global/products/ndvi\n", " copyright: Copernicus Service information 2021\n", " archive_facility: VITO\n", - " history: 2021-03-01 - Processing line NDVI LTS" + "
  • Conventions :
    CF-1.6
    parent_identifier :
    urn:cgls:global:ndvi_stats_all
    identifier :
    urn:cgls:global:ndvi_stats_all:NDVI-LTS_1999-2019-1221_GLOBE_V3.0.1
    long_name :
    Normalized Difference Vegetation Index
    title :
    Normalized Difference Vegetation Index: Long Term Statistics 1KM: GLOBE 1999-2019 1221
    product_version :
    V3.0.1
    time_coverage_start :
    1999-01-01T00:00:00Z
    time_coverage_end :
    2019-12-31T23:59:59Z
    platform :
    SPOT-4, SPOT-5, Proba-V
    sensor :
    VEGETATION-1, VEGETATION-2, VEGETATION
    orbit_type :
    LEO
    processing_level :
    L4
    institution :
    VITO NV
    source :
    Derived from EO satellite imagery
    processing_mode :
    Offline
    references :
    https://land.copernicus.eu/global/products/ndvi
    copyright :
    Copernicus Service information 2021
    archive_facility :
    VITO
    history :
    2021-03-01 - Processing line NDVI LTS
    • " ], "text/plain": [ "\n", @@ -895,12 +1062,12 @@ " * lat (lat) float64 80.0 79.99 79.98 79.97 ... -59.97 -59.98 -59.99\n", "Data variables:\n", " crs |S1 ...\n", - " min (lat, lon) float32 dask.array\n", - " median (lat, lon) float32 dask.array\n", - " max (lat, lon) float32 dask.array\n", - " mean (lat, lon) float32 dask.array\n", - " stdev (lat, lon) float32 dask.array\n", - " nobs (lat, lon) float32 dask.array\n", + " min (lat, lon) float32 dask.array\n", + " median (lat, lon) float32 dask.array\n", + " max (lat, lon) float32 dask.array\n", + " mean (lat, lon) float32 dask.array\n", + " stdev (lat, lon) float32 dask.array\n", + " nobs (lat, lon) float32 dask.array\n", "Attributes: (12/19)\n", " Conventions: CF-1.6\n", " parent_identifier: urn:cgls:global:ndvi_stats_all\n", @@ -1005,6 +1172,16 @@ "id": "3e554760-d78b-4d0f-a276-cf57f886080c", "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/srv/conda/envs/notebook/lib/python3.11/site-packages/xarray/core/dataset.py:282: UserWarning: The specified chunks separate the stored chunks along dimension \"lat\" starting at index 7840. This could degrade performance. Instead, consider rechunking after loading.\n", + " warnings.warn(\n", + "/srv/conda/envs/notebook/lib/python3.11/site-packages/xarray/core/dataset.py:282: UserWarning: The specified chunks separate the stored chunks along dimension \"lon\" starting at index 20160. This could degrade performance. Instead, consider rechunking after loading.\n", + " warnings.warn(\n" + ] + }, { "data": { "text/html": [ @@ -1269,6 +1446,11 @@ " grid-column: 4;\n", "}\n", "\n", + ".xr-index-preview {\n", + " grid-column: 2 / 5;\n", + " color: var(--xr-font-color2);\n", + "}\n", + "\n", ".xr-var-name,\n", ".xr-var-dims,\n", ".xr-var-dtype,\n", @@ -1290,14 +1472,16 @@ "}\n", "\n", ".xr-var-attrs,\n", - ".xr-var-data {\n", + ".xr-var-data,\n", + ".xr-index-data {\n", " display: none;\n", " background-color: var(--xr-background-color) !important;\n", " padding-bottom: 5px !important;\n", "}\n", "\n", ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n", - ".xr-var-data-in:checked ~ .xr-var-data {\n", + ".xr-var-data-in:checked ~ .xr-var-data,\n", + ".xr-index-data-in:checked ~ .xr-index-data {\n", " display: block;\n", "}\n", "\n", @@ -1307,13 +1491,16 @@ "\n", ".xr-var-name span,\n", ".xr-var-data,\n", + ".xr-index-name div,\n", + ".xr-index-data,\n", ".xr-attrs {\n", " padding-left: 25px !important;\n", "}\n", "\n", ".xr-attrs,\n", ".xr-var-attrs,\n", - ".xr-var-data {\n", + ".xr-var-data,\n", + ".xr-index-data {\n", " grid-column: 1 / -1;\n", "}\n", "\n", @@ -1351,7 +1538,8 @@ "}\n", "\n", ".xr-icon-database,\n", - ".xr-icon-file-text2 {\n", + ".xr-icon-file-text2,\n", + ".xr-no-icon {\n", " display: inline-block;\n", " vertical-align: middle;\n", " width: 1em;\n", @@ -1386,12 +1574,12 @@ " references: https://land.copernicus.eu/global/products/ndvi\n", " copyright: Copernicus Service information 2021\n", " archive_facility: VITO\n", - " history: 2021-03-01 - Processing line NDVI LTS" + "
    • lon
      PandasIndex
      PandasIndex(Index([             -180.0, -179.99107142857142, -179.98214285714283,\n",
+       "       -179.97321428571425, -179.96428571428567, -179.95535714285708,\n",
+       "        -179.9464285714285, -179.93749999999991, -179.92857142857133,\n",
+       "       -179.91964285714275,\n",
+       "       ...\n",
+       "         179.9107142862053,   179.9196428576339,  179.92857142906246,\n",
+       "          179.937500000491,  179.94642857191963,  179.95535714334824,\n",
+       "         179.9642857147768,  179.97321428620535,  179.98214285763396,\n",
+       "        179.99107142906257],\n",
+       "      dtype='float64', name='lon', length=40320))
    • lat
      PandasIndex
      PandasIndex(Index([               80.0,   79.99107142857143,   79.98214285714286,\n",
+       "         79.97321428571429,   79.96428571428572,   79.95535714285715,\n",
+       "         79.94642857142858,   79.93750000000001,   79.92857142857144,\n",
+       "         79.91964285714288,\n",
+       "       ...\n",
+       "       -59.910714285682474,  -59.91964285711106,  -59.92857142853961,\n",
+       "        -59.93749999996817,  -59.94642857139675, -59.955357142825335,\n",
+       "        -59.96428571425389, -59.973214285682445,  -59.98214285711103,\n",
+       "        -59.99107142853961],\n",
+       "      dtype='float64', name='lat', length=15680))
  • Conventions :
    CF-1.6
    parent_identifier :
    urn:cgls:global:ndvi_stats_all
    identifier :
    urn:cgls:global:ndvi_stats_all:NDVI-LTS_1999-2019-1221_GLOBE_V3.0.1
    long_name :
    Normalized Difference Vegetation Index
    title :
    Normalized Difference Vegetation Index: Long Term Statistics 1KM: GLOBE 1999-2019 1221
    product_version :
    V3.0.1
    time_coverage_start :
    1999-01-01T00:00:00Z
    time_coverage_end :
    2019-12-31T23:59:59Z
    platform :
    SPOT-4, SPOT-5, Proba-V
    sensor :
    VEGETATION-1, VEGETATION-2, VEGETATION
    orbit_type :
    LEO
    processing_level :
    L4
    institution :
    VITO NV
    source :
    Derived from EO satellite imagery
    processing_mode :
    Offline
    references :
    https://land.copernicus.eu/global/products/ndvi
    copyright :
    Copernicus Service information 2021
    archive_facility :
    VITO
    history :
    2021-03-01 - Processing line NDVI LTS
    • " ], "text/plain": [ "\n", @@ -2104,6 +2298,11 @@ " grid-column: 4;\n", "}\n", "\n", + ".xr-index-preview {\n", + " grid-column: 2 / 5;\n", + " color: var(--xr-font-color2);\n", + "}\n", + "\n", ".xr-var-name,\n", ".xr-var-dims,\n", ".xr-var-dtype,\n", @@ -2125,14 +2324,16 @@ "}\n", "\n", ".xr-var-attrs,\n", - ".xr-var-data {\n", + ".xr-var-data,\n", + ".xr-index-data {\n", " display: none;\n", " background-color: var(--xr-background-color) !important;\n", " padding-bottom: 5px !important;\n", "}\n", "\n", ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n", - ".xr-var-data-in:checked ~ .xr-var-data {\n", + ".xr-var-data-in:checked ~ .xr-var-data,\n", + ".xr-index-data-in:checked ~ .xr-index-data {\n", " display: block;\n", "}\n", "\n", @@ -2142,13 +2343,16 @@ "\n", ".xr-var-name span,\n", ".xr-var-data,\n", + ".xr-index-name div,\n", + ".xr-index-data,\n", ".xr-attrs {\n", " padding-left: 25px !important;\n", "}\n", "\n", ".xr-attrs,\n", ".xr-var-attrs,\n", - ".xr-var-data {\n", + ".xr-var-data,\n", + ".xr-index-data {\n", " grid-column: 1 / -1;\n", "}\n", "\n", @@ -2186,7 +2390,8 @@ "}\n", "\n", ".xr-icon-database,\n", - ".xr-icon-file-text2 {\n", + ".xr-icon-file-text2,\n", + ".xr-no-icon {\n", " display: inline-block;\n", " vertical-align: middle;\n", " width: 1em;\n", @@ -2205,7 +2410,25 @@ " grid_mapping: crs\n", " units: \n", " valid_range: [ 1 250]\n", - " cell_methods: area: mean" + " cell_methods: area: mean" ], "text/plain": [ "\n", @@ -2511,6 +2734,11 @@ " grid-column: 4;\n", "}\n", "\n", + ".xr-index-preview {\n", + " grid-column: 2 / 5;\n", + " color: var(--xr-font-color2);\n", + "}\n", + "\n", ".xr-var-name,\n", ".xr-var-dims,\n", ".xr-var-dtype,\n", @@ -2532,14 +2760,16 @@ "}\n", "\n", ".xr-var-attrs,\n", - ".xr-var-data {\n", + ".xr-var-data,\n", + ".xr-index-data {\n", " display: none;\n", " background-color: var(--xr-background-color) !important;\n", " padding-bottom: 5px !important;\n", "}\n", "\n", ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n", - ".xr-var-data-in:checked ~ .xr-var-data {\n", + ".xr-var-data-in:checked ~ .xr-var-data,\n", + ".xr-index-data-in:checked ~ .xr-index-data {\n", " display: block;\n", "}\n", "\n", @@ -2549,13 +2779,16 @@ "\n", ".xr-var-name span,\n", ".xr-var-data,\n", + ".xr-index-name div,\n", + ".xr-index-data,\n", ".xr-attrs {\n", " padding-left: 25px !important;\n", "}\n", "\n", ".xr-attrs,\n", ".xr-var-attrs,\n", - ".xr-var-data {\n", + ".xr-var-data,\n", + ".xr-index-data {\n", " grid-column: 1 / -1;\n", "}\n", "\n", @@ -2593,7 +2826,8 @@ "}\n", "\n", ".xr-icon-database,\n", - ".xr-icon-file-text2 {\n", + ".xr-icon-file-text2,\n", + ".xr-no-icon {\n", " display: inline-block;\n", " vertical-align: middle;\n", " width: 1em;\n", @@ -2612,10 +2846,10 @@ " grid_mapping: crs\n", " units: \n", " valid_range: [ 1 250]\n", - " cell_methods: area: mean" + "
    • lon
      (lon)
      float64
      70.0 70.01 70.02 ... 89.98 89.99
      _CoordinateAxisType :
      Lon
      axis :
      X
      DIMENSION_LABELS :
      lon
      long_name :
      longitude
      standard_name :
      longitude
      units :
      degrees_east
      array([70.      , 70.008929, 70.017857, ..., 89.973214, 89.982143, 89.991071])
    • lat
      (lat)
      float64
      80.0 79.99 79.98 ... 70.01 70.0
      _CoordinateAxisType :
      Lat
      axis :
      Y
      DIMENSION_LABELS :
      lat
      long_name :
      latitude
      standard_name :
      latitude
      units :
      degrees_north
      array([80.      , 79.991071, 79.982143, ..., 70.017857, 70.008929, 70.      ])
    • lon
      PandasIndex
      PandasIndex(Index([70.00000000034106, 70.00892857176964, 70.01785714319823,\n",
+       "       70.02678571462681,  70.0357142860554, 70.04464285748398,\n",
+       "       70.05357142891256, 70.06250000034115, 70.07142857176973,\n",
+       "       70.08035714319831,\n",
+       "       ...\n",
+       "       89.91071428608251, 89.91964285751112, 89.92857142893968,\n",
+       "       89.93750000036823, 89.94642857179684, 89.95535714322546,\n",
+       "       89.96428571465401, 89.97321428608257, 89.98214285751118,\n",
+       "       89.99107142893979],\n",
+       "      dtype='float64', name='lon', length=2240))
    • lat
      PandasIndex
      PandasIndex(Index([             80.0, 79.99107142857143, 79.98214285714286,\n",
+       "       79.97321428571429, 79.96428571428572, 79.95535714285715,\n",
+       "       79.94642857142858, 79.93750000000001, 79.92857142857144,\n",
+       "       79.91964285714288,\n",
+       "       ...\n",
+       "        70.0803571428594, 70.07142857143083, 70.06250000000226,\n",
+       "       70.05357142857369, 70.04464285714512, 70.03571428571655,\n",
+       "       70.02678571428798, 70.01785714285941, 70.00892857143084,\n",
+       "       70.00000000000227],\n",
+       "      dtype='float64', name='lat', length=1121))
  • standard_name :
    normalized_difference_vegetation_index number_of_observations
    grid_mapping :
    crs
    units :
    valid_range :
    [ 1 250]
    cell_methods :
    area: mean
    • " ], "text/plain": [ "\n", @@ -2730,7 +2980,7 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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", "text/plain": [ "" ] @@ -2840,7 +3090,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 10, @@ -2892,7 +3142,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "92K\ttest.zarr/\n" + "12K\ttest.zarr/\n" ] } ], @@ -2915,19 +3165,19 @@ "name": "stdout", "output_type": "stream", "text": [ - "total 48\n", - "drwxr-xr-x 2 jovyan jovyan 4096 Oct 11 21:49 .\n", - "drwxr-xr-x 5 jovyan jovyan 4096 Oct 11 21:49 ..\n", - "-rw-r--r-- 1 jovyan jovyan 341 Oct 11 21:49 .zarray\n", - "-rw-r--r-- 1 jovyan jovyan 304 Oct 11 21:49 .zattrs\n", - "-rw-r--r-- 1 jovyan jovyan 1482 Oct 11 21:49 0.0\n", - "-rw-r--r-- 1 jovyan jovyan 1482 Oct 11 21:49 0.1\n", - "-rw-r--r-- 1 jovyan jovyan 1482 Oct 11 21:49 0.2\n", - "-rw-r--r-- 1 jovyan jovyan 1482 Oct 11 21:49 0.3\n", - "-rw-r--r-- 1 jovyan jovyan 1482 Oct 11 21:49 1.0\n", - "-rw-r--r-- 1 jovyan jovyan 1482 Oct 11 21:49 1.1\n", - "-rw-r--r-- 1 jovyan jovyan 1482 Oct 11 21:49 1.2\n", - "-rw-r--r-- 1 jovyan jovyan 1482 Oct 11 21:49 1.3\n" + "total 6\n", + "drwxr-xr-x 2 jovyan jovyan 4096 Jun 18 12:23 .\n", + "drwxr-xr-x 5 jovyan jovyan 4096 Jun 18 12:23 ..\n", + "-rw-r--r-- 1 jovyan jovyan 341 Jun 18 12:23 .zarray\n", + "-rw-r--r-- 1 jovyan jovyan 304 Jun 18 12:23 .zattrs\n", + "-rw-r--r-- 1 jovyan jovyan 1482 Jun 18 12:23 0.0\n", + "-rw-r--r-- 1 jovyan jovyan 1482 Jun 18 12:23 0.1\n", + "-rw-r--r-- 1 jovyan jovyan 1482 Jun 18 12:23 0.2\n", + "-rw-r--r-- 1 jovyan jovyan 1482 Jun 18 12:23 0.3\n", + "-rw-r--r-- 1 jovyan jovyan 1482 Jun 18 12:23 1.0\n", + "-rw-r--r-- 1 jovyan jovyan 1482 Jun 18 12:23 1.1\n", + "-rw-r--r-- 1 jovyan jovyan 1482 Jun 18 12:23 1.2\n", + "-rw-r--r-- 1 jovyan jovyan 1482 Jun 18 12:23 1.3\n" ] } ], @@ -3030,7 +3280,13 @@ { "cell_type": "markdown", "id": "3b2c231f", - "metadata": {}, + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, "source": [ "### Extract chunk information\n", "\n", @@ -3045,9 +3301,14 @@ "id": "f2798bd8", "metadata": { "collapsed": false, + "editable": true, "jupyter": { "outputs_hidden": false - } + }, + "slideshow": { + "slide_type": "" + }, + "tags": [] }, "outputs": [], "source": [ @@ -3068,9 +3329,14 @@ "id": "335e1fab", "metadata": { "collapsed": false, + "editable": true, "jupyter": { "outputs_hidden": false - } + }, + "slideshow": { + "slide_type": "" + }, + "tags": [] }, "outputs": [], "source": [ @@ -3096,9 +3362,13 @@ "id": "33c5ce25", "metadata": { "collapsed": true, + "editable": true, "jupyter": { "outputs_hidden": true }, + "slideshow": { + "slide_type": "" + }, "tags": [ "hide-output" ] @@ -3108,1411 +3378,3123 @@ "name": "stdout", "output_type": "stream", "text": [ - "{'refs': {'.zattrs': '{\\n'\n", - " ' \"Conventions\": \"CF-1.6\",\\n'\n", - " ' \"archive_facility\": \"VITO\",\\n'\n", - " ' \"copyright\": \"Copernicus Service information 2021\",\\n'\n", - " ' \"history\": \"2021-03-01 - Processing line NDVI LTS\",\\n'\n", - " ' \"identifier\": '\n", - " '\"urn:cgls:global:ndvi_stats_all:NDVI-LTS_1999-2019-1221_GLOBE_V3.0.1\",\\n'\n", - " ' \"institution\": \"VITO NV\",\\n'\n", - " ' \"long_name\": \"Normalized Difference Vegetation '\n", - " 'Index\",\\n'\n", - " ' \"orbit_type\": \"LEO\",\\n'\n", - " ' \"parent_identifier\": '\n", - " '\"urn:cgls:global:ndvi_stats_all\",\\n'\n", - " ' \"platform\": \"SPOT-4, SPOT-5, Proba-V\",\\n'\n", - " ' \"processing_level\": \"L4\",\\n'\n", - " ' \"processing_mode\": \"Offline\",\\n'\n", - " ' \"product_version\": \"V3.0.1\",\\n'\n", - " ' \"references\": '\n", - " '\"https://land.copernicus.eu/global/products/ndvi\",\\n'\n", - " ' \"sensor\": \"VEGETATION-1, VEGETATION-2, VEGETATION\",\\n'\n", - " ' \"source\": \"Derived from EO satellite imagery\",\\n'\n", - " ' \"time_coverage_end\": \"2019-12-31T23:59:59Z\",\\n'\n", - " ' \"time_coverage_start\": \"1999-01-01T00:00:00Z\",\\n'\n", - " ' \"title\": \"Normalized Difference Vegetation Index: '\n", - " 'Long Term Statistics 1KM: GLOBE 1999-2019 1221\"\\n'\n", - " '}',\n", - " '.zgroup': '{\\n \"zarr_format\": 2\\n}',\n", - " 'crs/.zarray': '{\\n'\n", - " ' \"chunks\": [],\\n'\n", - " ' \"compressor\": null,\\n'\n", - " ' \"dtype\": \"|S1\",\\n'\n", - " ' \"fill_value\": \"IA==\",\\n'\n", - " ' \"filters\": null,\\n'\n", - " ' \"order\": \"C\",\\n'\n", - " ' \"shape\": [],\\n'\n", - " ' \"zarr_format\": 2\\n'\n", - " '}',\n", - " 'crs/.zattrs': '{\\n'\n", - " ' \"GeoTransform\": \"-180.0000000000 0.0089285714 '\n", - " '0.0 80.0000000000 0.0 -0.0089285714\",\\n'\n", - " ' \"_ARRAY_DIMENSIONS\": [],\\n'\n", - " ' \"_CoordinateAxisTypes\": \"GeoX GeoY\",\\n'\n", - " ' \"_CoordinateTransformType\": \"Projection\",\\n'\n", - " ' \"grid_mapping_name\": \"latitude_longitude\",\\n'\n", - " ' \"inverse_flattening\": 298.257223563,\\n'\n", - " ' \"long_name\": \"coordinate reference system\",\\n'\n", - " ' \"longitude_of_prime_meridian\": 0.0,\\n'\n", - " ' \"semi_major_axis\": 6378137.0,\\n'\n", - " ' \"spatial_ref\": \"GEOGCS[\\\\\"WGS '\n", + "{'refs': {'.zattrs': '{\"Conventions\":\"CF-1.6\",\"archive_facility\":\"VITO\",\"copyright\":\"Copernicus '\n", + " 'Service information 2021\",\"history\":\"2021-03-01 - '\n", + " 'Processing line NDVI '\n", + " 'LTS\",\"identifier\":\"urn:cgls:global:ndvi_stats_all:NDVI-LTS_1999-2019-1221_GLOBE_V3.0.1\",\"institution\":\"VITO '\n", + " 'NV\",\"long_name\":\"Normalized Difference Vegetation '\n", + " 'Index\",\"orbit_type\":\"LEO\",\"parent_identifier\":\"urn:cgls:global:ndvi_stats_all\",\"platform\":\"SPOT-4, '\n", + " 'SPOT-5, '\n", + " 'Proba-V\",\"processing_level\":\"L4\",\"processing_mode\":\"Offline\",\"product_version\":\"V3.0.1\",\"references\":\"https:\\\\/\\\\/land.copernicus.eu\\\\/global\\\\/products\\\\/ndvi\",\"sensor\":\"VEGETATION-1, '\n", + " 'VEGETATION-2, VEGETATION\",\"source\":\"Derived from EO '\n", + " 'satellite '\n", + " 'imagery\",\"time_coverage_end\":\"2019-12-31T23:59:59Z\",\"time_coverage_start\":\"1999-01-01T00:00:00Z\",\"title\":\"Normalized '\n", + " 'Difference Vegetation Index: Long Term Statistics 1KM: '\n", + " 'GLOBE 1999-2019 1221\"}',\n", + " '.zgroup': '{\"zarr_format\":2}',\n", + " 'crs/.zarray': '{\"chunks\":[],\"compressor\":null,\"dtype\":\"|S1\",\"fill_value\":\"IA==\",\"filters\":null,\"order\":\"C\",\"shape\":[],\"zarr_format\":2}',\n", + " 'crs/.zattrs': '{\"GeoTransform\":\"-180.0000000000 0.0089285714 0.0 '\n", + " '80.0000000000 0.0 '\n", + " '-0.0089285714\",\"_ARRAY_DIMENSIONS\":[],\"_CoordinateAxisTypes\":\"GeoX '\n", + " 'GeoY\",\"_CoordinateTransformType\":\"Projection\",\"grid_mapping_name\":\"latitude_longitude\",\"inverse_flattening\":298.257223563,\"long_name\":\"coordinate '\n", + " 'reference '\n", + " 'system\",\"longitude_of_prime_meridian\":0.0,\"semi_major_axis\":6378137.0,\"spatial_ref\":\"GEOGCS[\\\\\"WGS '\n", " '84\\\\\",DATUM[\\\\\"WGS_1984\\\\\",SPHEROID[\\\\\"WGS '\n", - " '84\\\\\",6378137,298.257223563,AUTHORITY[\\\\\"EPSG\\\\\",\\\\\"7030\\\\\"]],TOWGS84[0,0,0,0,0,0,0],AUTHORITY[\\\\\"EPSG\\\\\",\\\\\"6326\\\\\"]],PRIMEM[\\\\\"Greenwich\\\\\",0,AUTHORITY[\\\\\"EPSG\\\\\",\\\\\"8901\\\\\"]],UNIT[\\\\\"degree\\\\\",0.0174532925199433,AUTHORITY[\\\\\"EPSG\\\\\",\\\\\"9108\\\\\"]],AUTHORITY[\\\\\"EPSG\\\\\",\\\\\"4326\\\\\"]]\"\\n'\n", - " '}',\n", - " 'lat/.zarray': '{\\n'\n", - " ' \"chunks\": [\\n'\n", - " ' 15680\\n'\n", - " ' ],\\n'\n", - " ' \"compressor\": null,\\n'\n", - " ' \"dtype\": \"\n", " Exercise\n", @@ -4538,9 +6526,17 @@ }, { "cell_type": "markdown", - "id": "804dffbd", - "metadata": {}, + "id": "e7940f5e-d8eb-458b-b8de-6ef1386c67ef", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, "source": [ + "### Open dataset using chunks\n", + "\n", "After we have collected information on the native file chunks in the original data file and consolidated our Zarr metadata, we can open the files using `zarr` and pass this chunk information into a storage option. We also need to pass `\"consolidated\": False` because the original dataset does not contain any `zarr` consolidating metadata." ] }, @@ -4550,9 +6546,14 @@ "id": "b35a2a88", "metadata": { "collapsed": false, + "editable": true, "jupyter": { "outputs_hidden": false - } + }, + "slideshow": { + "slide_type": "" + }, + "tags": [] }, "outputs": [ { @@ -4819,6 +6820,11 @@ " grid-column: 4;\n", "}\n", "\n", + ".xr-index-preview {\n", + " grid-column: 2 / 5;\n", + " color: var(--xr-font-color2);\n", + "}\n", + "\n", ".xr-var-name,\n", ".xr-var-dims,\n", ".xr-var-dtype,\n", @@ -4840,14 +6846,16 @@ "}\n", "\n", ".xr-var-attrs,\n", - ".xr-var-data {\n", + ".xr-var-data,\n", + ".xr-index-data {\n", " display: none;\n", " background-color: var(--xr-background-color) !important;\n", " padding-bottom: 5px !important;\n", "}\n", "\n", ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n", - ".xr-var-data-in:checked ~ .xr-var-data {\n", + ".xr-var-data-in:checked ~ .xr-var-data,\n", + ".xr-index-data-in:checked ~ .xr-index-data {\n", " display: block;\n", "}\n", "\n", @@ -4857,13 +6865,16 @@ "\n", ".xr-var-name span,\n", ".xr-var-data,\n", + ".xr-index-name div,\n", + ".xr-index-data,\n", ".xr-attrs {\n", " padding-left: 25px !important;\n", "}\n", "\n", ".xr-attrs,\n", ".xr-var-attrs,\n", - ".xr-var-data {\n", + ".xr-var-data,\n", + ".xr-index-data {\n", " grid-column: 1 / -1;\n", "}\n", "\n", @@ -4901,7 +6912,8 @@ "}\n", "\n", ".xr-icon-database,\n", - ".xr-icon-file-text2 {\n", + ".xr-icon-file-text2,\n", + ".xr-no-icon {\n", " display: inline-block;\n", " vertical-align: middle;\n", " width: 1em;\n", @@ -4917,12 +6929,12 @@ " * lon (lon) float64 -180.0 -180.0 -180.0 -180.0 ... 180.0 180.0 180.0\n", "Data variables:\n", " crs object ...\n", - " max (lat, lon) float32 dask.array<chunksize=(1207, 3102), meta=np.ndarray>\n", - " mean (lat, lon) float32 dask.array<chunksize=(1207, 3102), meta=np.ndarray>\n", - " median (lat, lon) float32 dask.array<chunksize=(1207, 3102), meta=np.ndarray>\n", - " min (lat, lon) float32 dask.array<chunksize=(1207, 3102), meta=np.ndarray>\n", - " nobs (lat, lon) float32 dask.array<chunksize=(1207, 3102), meta=np.ndarray>\n", - " stdev (lat, lon) float32 dask.array<chunksize=(1207, 3102), meta=np.ndarray>\n", + " max (lat, lon) float32 ...\n", + " mean (lat, lon) float32 ...\n", + " median (lat, lon) float32 ...\n", + " min (lat, lon) float32 ...\n", + " nobs (lat, lon) float32 ...\n", + " stdev (lat, lon) float32 ...\n", "Attributes: (12/19)\n", " Conventions: CF-1.6\n", " archive_facility: VITO\n", @@ -4936,856 +6948,2493 @@ " source: Derived from EO satellite imagery\n", " time_coverage_end: 2019-12-31T23:59:59Z\n", " time_coverage_start: 1999-01-01T00:00:00Z\n", - " title: Normalized Difference Vegetation Index: Long Term S...