- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
Contribute
By the Project Pythia Community. - Last updated on 15 May 2024. + Last updated on 20 May 2024. diff --git a/appendix/template.html b/appendix/template.html index ea9adb6c5..ff2d9af86 100644 --- a/appendix/template.html +++ b/appendix/template.html @@ -80,7 +80,7 @@ - + @@ -612,8 +612,8 @@of further and further
header levels
Each section in your notebook can also contain \(\LaTeX\) equations, enabled through MathJax. In the following example, we illustrate some sample MathJax equations. (Rendering instructions, as well as detailed information about MathJax, can be found in this documentation.)
-
-(1)\[\begin{align}
+
+(1)\[\begin{align}
\dot{x} & = \sigma(y-x) \\
\dot{y} & = \rho x - y - xz \\
\dot{z} & = -\beta z + xy
@@ -998,7 +998,7 @@ Resources and references
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
@@ -1127,7 +1127,7 @@ Contribute
By the Project Pythia Community.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
diff --git a/core/cartopy.html b/core/cartopy.html
index fe1af21cc..e15746cb7 100644
--- a/core/cartopy.html
+++ b/core/cartopy.html
@@ -79,7 +79,7 @@
-
+
@@ -763,7 +763,7 @@ Cartopy
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
@@ -892,7 +892,7 @@ Contribute
By the Project Pythia Community.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
diff --git a/core/cartopy/cartopy.html b/core/cartopy/cartopy.html
index 2a9385aa4..dd539a1b8 100644
--- a/core/cartopy/cartopy.html
+++ b/core/cartopy/cartopy.html
@@ -79,7 +79,7 @@
-
+
@@ -571,7 +571,7 @@ Create a map with a specified projection
-<cartopy.mpl.feature_artist.FeatureArtist at 0x7f5f6cbc0500>
+<cartopy.mpl.feature_artist.FeatureArtist at 0x7f003cb648c0>
@@ -603,7 +603,7 @@ Add cartographic features to the map
-<cartopy.mpl.feature_artist.FeatureArtist at 0x7f5f6cb68fb0>
+<cartopy.mpl.feature_artist.FeatureArtist at 0x7f004cd08ad0>
@@ -1340,7 +1340,7 @@ Resources and References
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
@@ -1469,7 +1469,7 @@ Contribute
By the Project Pythia Community.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
diff --git a/core/data-formats.html b/core/data-formats.html
index ab2782359..af160f4d1 100644
--- a/core/data-formats.html
+++ b/core/data-formats.html
@@ -79,7 +79,7 @@
-
+
@@ -755,7 +755,7 @@ Data Formats
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
@@ -884,7 +884,7 @@ Contribute
By the Project Pythia Community.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
diff --git a/core/data-formats/netcdf-cf.html b/core/data-formats/netcdf-cf.html
index 5b25884fa..12d8c6e0e 100644
--- a/core/data-formats/netcdf-cf.html
+++ b/core/data-formats/netcdf-cf.html
@@ -79,7 +79,7 @@
-
+
@@ -564,7 +564,7 @@ Gridded Data
-/tmp/ipykernel_2523/2342439358.py:1: DeprecationWarning: datetime.datetime.utcnow() is deprecated and scheduled for removal in a future version. Use timezone-aware objects to represent datetimes in UTC: datetime.datetime.now(datetime.UTC).
+/tmp/ipykernel_2539/2342439358.py:1: DeprecationWarning: datetime.datetime.utcnow() is deprecated and scheduled for removal in a future version. Use timezone-aware objects to represent datetimes in UTC: datetime.datetime.now(datetime.UTC).
start = datetime.utcnow().replace(hour=22, minute=0, second=0, microsecond=0)
@@ -610,7 +610,7 @@ Creating the file and dimensions
-/tmp/ipykernel_2523/2008125275.py:5: DeprecationWarning: datetime.datetime.utcnow() is deprecated and scheduled for removal in a future version. Use timezone-aware objects to represent datetimes in UTC: datetime.datetime.now(datetime.UTC).
+/tmp/ipykernel_2539/2008125275.py:5: DeprecationWarning: datetime.datetime.utcnow() is deprecated and scheduled for removal in a future version. Use timezone-aware objects to represent datetimes in UTC: datetime.datetime.now(datetime.UTC).
nc.history = str(datetime.utcnow()) + ' Python'
@@ -651,7 +651,7 @@ Creating the file and dimensions
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
@@ -1693,7 +1693,7 @@ Contribute
By the Project Pythia Community.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
diff --git a/core/datetime.html b/core/datetime.html
index d7aeb1142..dfa7d6341 100644
--- a/core/datetime.html
+++ b/core/datetime.html
@@ -81,7 +81,7 @@
-
+
@@ -763,7 +763,7 @@ Datetime
@@ -892,7 +892,7 @@ Contribute
By the Project Pythia Community.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
diff --git a/core/datetime/datetime.html b/core/datetime/datetime.html
index 153198b8e..ce3241e6a 100644
--- a/core/datetime/datetime.html
+++ b/core/datetime/datetime.html
@@ -81,7 +81,7 @@
-
+
@@ -593,7 +593,7 @@ Some core terminology
-1715806495.8833458
+1716229655.9439774
@@ -614,7 +614,7 @@ time
-The benchmark took 1.0002374649047852 seconds
+The benchmark took 1.0002319812774658 seconds
@@ -743,7 +743,7 @@ See how many days have elapsed since the strike:
-6167
+6172
@@ -815,8 +815,8 @@ Time Zone Naive Versus Time Zone Aware
-I am time zone naive 2024-05-15 20:54:56.921356.
-I am time zone aware 2024-05-15 20:54:56.921392+00:00.
+I am time zone naive 2024-05-20 18:27:36.981418.
+I am time zone aware 2024-05-20 18:27:36.981452+00:00.
@@ -839,8 +839,8 @@ Full time zone support with the <
-I am time zone naive: 2024-05-15 20:54:56.925612.
-I am time zone aware: 2024-05-15 14:54:56.936190-06:00.
+I am time zone naive: 2024-05-20 18:27:36.985646.
+I am time zone aware: 2024-05-20 12:27:36.996878-06:00.
@@ -862,11 +862,11 @@ Print Time with a Different Time Zone
-The UTC time is May 15, 2024, 8:54PM.
-The 'US/Mountain' time is May 15, 2024, 2:54PM.
+The UTC time is May 20, 2024, 6:27PM.
+The 'US/Mountain' time is May 20, 2024, 12:27PM.
-/tmp/ipykernel_2546/3080495102.py:1: DeprecationWarning: datetime.datetime.utcnow() is deprecated and scheduled for removal in a future version. Use timezone-aware objects to represent datetimes in UTC: datetime.datetime.now(datetime.UTC).
+/tmp/ipykernel_2563/3080495102.py:1: DeprecationWarning: datetime.datetime.utcnow() is deprecated and scheduled for removal in a future version. Use timezone-aware objects to represent datetimes in UTC: datetime.datetime.now(datetime.UTC).
utc = dt.datetime.utcnow().replace(tzinfo=pytz.utc)
@@ -1243,7 +1243,7 @@ Resources and References
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
@@ -1372,7 +1372,7 @@ Contribute
By the Project Pythia Community.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
diff --git a/core/matplotlib.html b/core/matplotlib.html
index 36bdf5490..7ff675d8a 100644
--- a/core/matplotlib.html
+++ b/core/matplotlib.html
@@ -81,7 +81,7 @@
-
+
@@ -795,7 +795,7 @@ In this section
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
@@ -924,7 +924,7 @@ Contribute
By the Project Pythia Community.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
diff --git a/core/matplotlib/annotations-colorbars-layouts.html b/core/matplotlib/annotations-colorbars-layouts.html
index 97735445a..4c2ffca3e 100644
--- a/core/matplotlib/annotations-colorbars-layouts.html
+++ b/core/matplotlib/annotations-colorbars-layouts.html
@@ -81,7 +81,7 @@
-
+
@@ -730,7 +730,7 @@ Basic Colorbars
-
+
We can change which colormap to use by setting the keyword argument cmap = 'colormap_name' in the plotting function call. This sets the colormap not only for the plot, but for the colorbar as well. In this case, we use the magma colormap:
@@ -745,7 +745,7 @@ Basic Colorbars
-
+
@@ -764,10 +764,10 @@ Shared Colorbars
-<matplotlib.colorbar.Colorbar at 0x7f4e652e9af0>
+<matplotlib.colorbar.Colorbar at 0x7f07e48a40e0>
-
+
You may be wondering why the call to fig.colorbar uses the argument hist1[3]. The explanation is as follows: hist1 is a tuple returned by hist2d, and hist1[3] contains a matplotlib.collections.QuadMesh that points to the colormap for the first histogram. To make sure that both histograms are using the same colormap with the same range of values, vmax is set to 0.18 for both plots. This ensures that both histograms are using colormaps that represent values from 0 (the default for histograms) to 0.18. Because the same data values are used for both plots, it doesn’t matter whether we pass in hist1[3] or hist2[3] to fig.colorbar.
@@ -848,7 +848,7 @@
Custom Colorbars
-
+
@@ -865,7 +865,7 @@ Custom Colorbars
-
+
@@ -902,7 +902,7 @@ Mosaic Subplots
-
+
You’ll notice there is not a colorbar plotted by default. When constructing the colorbar, we need to specify the following:
@@ -930,7 +930,7 @@ Mosaic Subplots
-
+
@@ -1291,7 +1291,7 @@ Resources and references
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
@@ -1420,7 +1420,7 @@ Contribute
By the Project Pythia Community.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
diff --git a/core/matplotlib/histograms-piecharts-animation.html b/core/matplotlib/histograms-piecharts-animation.html
index ea6420ec0..9a591348d 100644
--- a/core/matplotlib/histograms-piecharts-animation.html
+++ b/core/matplotlib/histograms-piecharts-animation.html
@@ -79,7 +79,7 @@
-
+
@@ -3629,7 +3629,7 @@ Additional Resources
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
@@ -3758,7 +3758,7 @@ Contribute
By the Project Pythia Community.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
diff --git a/core/matplotlib/matplotlib-basics.html b/core/matplotlib/matplotlib-basics.html
index 323b54231..22fd1268a 100644
--- a/core/matplotlib/matplotlib-basics.html
+++ b/core/matplotlib/matplotlib-basics.html
@@ -79,7 +79,7 @@
-
+
@@ -1492,7 +1492,7 @@ Resources and References
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
@@ -1621,7 +1621,7 @@ Contribute
By the Project Pythia Community.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
diff --git a/core/numpy.html b/core/numpy.html
index 4fb55d7f5..5c535f0a9 100644
--- a/core/numpy.html
+++ b/core/numpy.html
@@ -81,7 +81,7 @@
-
+
@@ -760,7 +760,7 @@ NumPy
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
@@ -889,7 +889,7 @@ Contribute
By the Project Pythia Community.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
diff --git a/core/numpy/intermediate-numpy.html b/core/numpy/intermediate-numpy.html
index 59706e1a6..38a6afc63 100644
--- a/core/numpy/intermediate-numpy.html
+++ b/core/numpy/intermediate-numpy.html
@@ -81,7 +81,7 @@
-
+
@@ -1425,7 +1425,7 @@ Resources and references
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
@@ -1554,7 +1554,7 @@ Contribute
By the Project Pythia Community.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
diff --git a/core/numpy/numpy-basics.html b/core/numpy/numpy-basics.html
index cb11d7b3d..365a2f855 100644
--- a/core/numpy/numpy-basics.html
+++ b/core/numpy/numpy-basics.html
@@ -81,7 +81,7 @@
-
+
@@ -1168,7 +1168,7 @@ Indexing
Slices
-Slicing syntax is written as array[start:stop[:step]], where all numbers are optional.
+Slicing syntax is written as array[start:stop:step]. Note that all numbers are optional. Importantly, the step parameter is optional and can be omitted, in which case the slice uses a default step of 1.
defaults:
@@ -1775,7 +1775,7 @@ Resources and references
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
@@ -1904,7 +1904,7 @@ Contribute
By the Project Pythia Community.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
diff --git a/core/numpy/numpy-broadcasting.html b/core/numpy/numpy-broadcasting.html
index 78f882658..57e5dd9a6 100644
--- a/core/numpy/numpy-broadcasting.html
+++ b/core/numpy/numpy-broadcasting.html
@@ -81,7 +81,7 @@
-
+
@@ -1070,7 +1070,7 @@ Blocking
-
-
-
-
-array([[ 8.3, 11.6, 33.1],
- [11.6, 33.1, 18.3],
- [33.1, 18.3, 22.5],
- [18.3, 22.5, 24.5],
- [22.5, 24.5, 20.1],
- [24.5, 20.1, 20.1],
- [20.1, 20.1, 18. ],
- [20.1, 18. , 25. ]])
+array([[19.3, 18.8, 28.7],
+ [18.8, 28.7, 27.1],
+ [28.7, 27.1, 17.1],
+ [27.1, 17.1, 16.2],
+ [17.1, 16.2, 15. ],
+ [16.2, 15. , 15.1],
+ [15. , 15.1, 14. ],
+ [15.1, 14. , 19.2]])
@@ -1161,8 +1161,8 @@ Blocking
-
-array([[ 8.3, 11.6, 2000. ],
- [ 11.6, 2000. , 18.3],
- [2000. , 18.3, 22.5],
- [ 18.3, 22.5, 24.5],
- [ 22.5, 24.5, 20.1],
- [ 24.5, 20.1, 20.1],
- [ 20.1, 20.1, 18. ],
- [ 20.1, 18. , 25. ]])
+array([[ 19.3, 18.8, 2000. ],
+ [ 18.8, 2000. , 27.1],
+ [2000. , 27.1, 17.1],
+ [ 27.1, 17.1, 16.2],
+ [ 17.1, 16.2, 15. ],
+ [ 16.2, 15. , 15.1],
+ [ 15. , 15.1, 14. ],
+ [ 15.1, 14. , 19.2]])
@@ -1210,13 +1210,13 @@ Finding the difference between min and max
-array([[6, 6, 7, ..., 8, 7, 6],
- [6, 6, 6, ..., 7, 7, 7],
- [7, 7, 7, ..., 6, 7, 7],
+array([[7, 7, 7, ..., 7, 8, 7],
+ [6, 7, 6, ..., 7, 7, 6],
+ [7, 6, 7, ..., 7, 6, 6],
...,
- [7, 7, 7, ..., 7, 7, 6],
- [7, 6, 6, ..., 6, 7, 7],
- [7, 6, 7, ..., 7, 8, 7]])
+ [6, 7, 6, ..., 6, 6, 7],
+ [7, 7, 7, ..., 6, 6, 7],
+ [6, 7, 7, ..., 8, 8, 7]])
@@ -1241,13 +1241,13 @@ Finding the difference between min and max
-array([[775. , 775. , 737.5, ..., 700. , 737.5, 775. ],
- [775. , 775. , 775. , ..., 737.5, 737.5, 737.5],
- [737.5, 737.5, 737.5, ..., 775. , 737.5, 737.5],
+array([[737.5, 737.5, 737.5, ..., 737.5, 700. , 737.5],
+ [775. , 737.5, 775. , ..., 737.5, 737.5, 775. ],
+ [737.5, 775. , 737.5, ..., 737.5, 775. , 775. ],
...,
- [737.5, 737.5, 737.5, ..., 737.5, 737.5, 775. ],
- [737.5, 775. , 775. , ..., 775. , 737.5, 737.5],
- [737.5, 775. , 737.5, ..., 737.5, 700. , 737.5]])
+ [775. , 737.5, 775. , ..., 775. , 775. , 737.5],
+ [737.5, 737.5, 737.5, ..., 775. , 775. , 737.5],
+ [775. , 737.5, 737.5, ..., 700. , 700. , 737.5]])
@@ -1276,19 +1276,19 @@ Finding the difference between min and max
-array([[ -9.55417349, -9.78584972, -9.92661689, ..., -12.67789616,
- -10.49077377, -6.56515605],
- [-10.00010532, -10.70819797, -7.20985445, ..., -11.10789153,
- -11.73163442, -8.29531203],
- [-11.88696252, -12.29088724, -11.70346147, ..., -7.30859657,
- -8.97849533, -13.84614818],
+array([[-13.35480377, -11.37389473, -6.11490009, ..., -9.69409241,
+ -14.14853765, -11.04724036],
+ [ -6.50332158, -11.09958323, -5.95998607, ..., -13.2604716 ,
+ -13.19971009, -8.49574616],
+ [-12.28051357, -10.19815861, -7.42945122, ..., -11.5798199 ,
+ -8.91153322, -8.6079996 ],
...,
- [-11.90215938, -7.91187899, -13.08679868, ..., -10.41120932,
- -11.35468344, -11.06459146],
- [-11.50292233, -6.26889319, -7.38221861, ..., -10.24036433,
- -10.12594611, -5.63518882],
- [-10.32651953, -6.06759924, -9.54152312, ..., -10.45702773,
- -12.20574751, -8.01746694]])
+ [ -8.10255586, -12.16615351, -11.27253366, ..., -10.44432682,
+ -7.39154896, -10.99574973],
+ [-16.5968518 , -12.57487222, -8.39736923, ..., -4.15595587,
+ -8.86979291, -11.28873531],
+ [ -8.59386363, -9.38047716, -10.46838159, ..., -11.30765026,
+ -10.89295232, -9.24056526]])
@@ -1332,19 +1332,19 @@ Finding the difference between min and max
-array([[ -9.55417349, -9.78584972, -9.92661689, ..., -12.67789616,
- -10.49077377, -6.56515605],
- [-10.00010532, -10.70819797, -7.20985445, ..., -11.10789153,
- -11.73163442, -8.29531203],
- [-11.88696252, -12.29088724, -11.70346147, ..., -7.30859657,
- -8.97849533, -13.84614818],
+array([[-13.35480377, -11.37389473, -6.11490009, ..., -9.69409241,
+ -14.14853765, -11.04724036],
+ [ -6.50332158, -11.09958323, -5.95998607, ..., -13.2604716 ,
+ -13.19971009, -8.49574616],
+ [-12.28051357, -10.19815861, -7.42945122, ..., -11.5798199 ,
+ -8.91153322, -8.6079996 ],
...,
- [-11.90215938, -7.91187899, -13.08679868, ..., -10.41120932,
- -11.35468344, -11.06459146],
- [-11.50292233, -6.26889319, -7.38221861, ..., -10.24036433,
- -10.12594611, -5.63518882],
- [-10.32651953, -6.06759924, -9.54152312, ..., -10.45702773,
- -12.20574751, -8.01746694]])
+ [ -8.10255586, -12.16615351, -11.27253366, ..., -10.44432682,
+ -7.39154896, -10.99574973],
+ [-16.5968518 , -12.57487222, -8.39736923, ..., -4.15595587,
+ -8.86979291, -11.28873531],
+ [ -8.59386363, -9.38047716, -10.46838159, ..., -11.30765026,
+ -10.89295232, -9.24056526]])
@@ -1711,7 +1711,7 @@ Resources and references
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
@@ -1840,7 +1840,7 @@ Contribute
By the Project Pythia Community.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
diff --git a/core/overview.html b/core/overview.html
index 6acc0ae46..fd4752cc2 100644
--- a/core/overview.html
+++ b/core/overview.html
@@ -79,7 +79,7 @@
-
+
@@ -886,7 +886,7 @@ Tutorials
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
@@ -1015,7 +1015,7 @@ Contribute
By the Project Pythia Community.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
diff --git a/core/pandas.html b/core/pandas.html
index 2e4230efc..02509b347 100644
--- a/core/pandas.html
+++ b/core/pandas.html
@@ -79,7 +79,7 @@
-
+
@@ -759,7 +759,7 @@ Pandas
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
@@ -888,7 +888,7 @@ Contribute
By the Project Pythia Community.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
diff --git a/core/pandas/pandas.html b/core/pandas/pandas.html
index 734fecdb6..2e24138b7 100644
--- a/core/pandas/pandas.html
+++ b/core/pandas/pandas.html
@@ -79,7 +79,7 @@
-
+
@@ -1098,7 +1098,7 @@ Indexing a Seri
-/tmp/ipykernel_2735/737336773.py:1: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`
+/tmp/ipykernel_2746/737336773.py:1: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`
nino34_series[3]
@@ -1449,7 +1449,7 @@ Extending to the
-/tmp/ipykernel_2735/541596450.py:1: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`
+/tmp/ipykernel_2746/541596450.py:1: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`
df["Nino34"][3]
@@ -3645,7 +3645,7 @@ Resampling
-/tmp/ipykernel_2735/233158901.py:1: FutureWarning: 'Y' is deprecated and will be removed in a future version, please use 'YE' instead.
+/tmp/ipykernel_2746/233158901.py:1: FutureWarning: 'Y' is deprecated and will be removed in a future version, please use 'YE' instead.
df.Nino34.resample('1Y').mean().plot();
@@ -4434,7 +4434,7 @@ Resources and References
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
@@ -4563,7 +4563,7 @@ Contribute
By the Project Pythia Community.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
diff --git a/core/xarray.html b/core/xarray.html
index e4624108b..6bc787165 100644
--- a/core/xarray.html
+++ b/core/xarray.html
@@ -79,7 +79,7 @@
-
+
@@ -760,7 +760,7 @@ Xarray
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
@@ -889,7 +889,7 @@ Contribute
By the Project Pythia Community.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
diff --git a/core/xarray/computation-masking.html b/core/xarray/computation-masking.html
index b15ad248c..12f19902b 100644
--- a/core/xarray/computation-masking.html
+++ b/core/xarray/computation-masking.html
@@ -79,7 +79,7 @@
-
+
@@ -943,7 +943,7 @@ Data Setup
@@ -1576,7 +1576,7 @@ Arithmetic Operations
+ dtype='float64', name='lon', length=360))
In addition, there are many other arithmetic operations that can be performed on DataArrays. In this example, we demonstrate squaring the original Celsius values of our air temperature data:
@@ -2246,7 +2246,7 @@ Arithmetic Operations
+ dtype='float64', name='lon', length=360))
@@ -2876,7 +2876,7 @@ Aggregation Methodsdim keyword argument; this means that the function was applied over all of the dataset’s dimensions. In other words, the aggregation method computed the mean of every element of the temperature dataset across every temporal and spatial data point. However, if a dimension name is used with the dim keyword argument, the aggregation method computes an aggregation along the given dimension. In this next example, we use aggregation to calculate the temporal mean across all spatial data; this is performed by providing the dimension name 'time' to the dim keyword argument:
@@ -3276,7 +3276,7 @@ Aggregation Methods
@@ -3717,7 +3717,7 @@ Aggregation Methods
@@ -4325,7 +4325,7 @@ Aggregation Methods
@@ -4892,7 +4892,7 @@ Compute climatologyDataArray containing the climatology data, we can plot the data in different ways. In this example, we plot the climatology at a specific latitude-longitude point:
@@ -5412,7 +5412,7 @@ Compute anomaly
@@ -6095,7 +6095,7 @@ Compute anomaly.weighted() method and the newly loaded CESM2 dataset to weight the grid cell data as described above:
@@ -6598,7 +6598,7 @@ Other high level computation functionalityrolling method to compute averages in a moving window of 5 months of data:
@@ -7114,7 +7114,7 @@ Other high level computation functionality
@@ -7783,7 +7783,7 @@ Masking Data
Using where with one condition
@@ -8421,15 +8421,15 @@ Using where
+ dtype='float64', name='lon', length=360))
- cell_measures :
- area: areacello
- cell_methods :
- area: mean where sea time: mean
- comment :
- Model data on the 1x1 grid includes values in all cells for which ocean cells on the native grid cover more than 52.5 percent of the 1x1 grid cell. This 52.5 percent cutoff was chosen to produce ocean surface area on the 1x1 grid as close as possible to ocean surface area on the native grid, while not introducing fractional cell coverage.
- description :
- This may differ from "surface temperature" in regions of sea ice or floating ice shelves. For models using conservative temperature as the prognostic field, they should report the top ocean layer as surface potential temperature, which is the same as surface in situ temperature.
- frequency :
- mon
- id :
- tos
- long_name :
- Sea Surface Temperature
- mipTable :
- Omon
- out_name :
- tos
- prov :
- Omon ((isd.003))
- realm :
- ocean
- standard_name :
- sea_surface_temperature
- time :
- time
- time_label :
- time-mean
- time_title :
- Temporal mean
- title :
- Sea Surface Temperature
- type :
- real
- units :
- degC
- variable_id :
- tos
As shown in the previous example, methods like .isel() and .sel() return data of a different shape than the original data provided to them. However, .where() preserves the shape of the original data by masking the values with a Boolean condition. Data values for which the condition is True are returned identical to the values passed in. On the other hand, data values for which the condition is False are returned as a preset example value. (This example value defaults to nan, but can be set to other values as well.)
Before testing .where(), it is helpful to look at the official documentation. As stated above, the .where() method takes a Boolean condition. (Boolean conditions use operators such as less-than, greater-than, and equal-to, and return a value of True or False.) Most uses of .where() check whether or not specific data values are less than or greater than a constant value. As stated in the documentation, the data values specified in the Boolean condition of .where() can be any of the following:
@@ -8864,7 +8864,7 @@ Using where
+ dtype='float64', name='lon', length=360))- cell_measures :
- area: areacello
- cell_methods :
- area: mean where sea time: mean
- comment :
- Model data on the 1x1 grid includes values in all cells for which ocean cells on the native grid cover more than 52.5 percent of the 1x1 grid cell. This 52.5 percent cutoff was chosen to produce ocean surface area on the 1x1 grid as close as possible to ocean surface area on the native grid, while not introducing fractional cell coverage.
- description :
- This may differ from "surface temperature" in regions of sea ice or floating ice shelves. For models using conservative temperature as the prognostic field, they should report the top ocean layer as surface potential temperature, which is the same as surface in situ temperature.
- frequency :
- mon
- id :
- tos
- long_name :
- Sea Surface Temperature
- mipTable :
- Omon
- out_name :
- tos
- prov :
- Omon ((isd.003))
- realm :
- ocean
- standard_name :
- sea_surface_temperature
- time :
- time
- time_label :
- time-mean
- time_title :
- Temporal mean
- title :
- Sea Surface Temperature
- type :
- real
- units :
- degC
- variable_id :
- tos
In this example, we use Matplotlib to plot the original, unmasked data, as well as the masked data created in the previous example.
@@ -9321,7 +9321,7 @@ Resources and References
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
@@ -9450,7 +9450,7 @@ Contribute
By the Project Pythia Community.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
diff --git a/core/xarray/dask-arrays-xarray.html b/core/xarray/dask-arrays-xarray.html
index 0af9daaf3..65793b335 100644
--- a/core/xarray/dask-arrays-xarray.html
+++ b/core/xarray/dask-arrays-xarray.html
@@ -79,7 +79,7 @@
-
+
@@ -568,91 +568,91 @@ Create a dask.a
-array([[[0.08870167, 0.5536724 , 0.86869933, ..., 0.59745109,
- 0.66241166, 0.14712849],
- [0.79424186, 0.53985475, 0.83117213, ..., 0.64752434,
- 0.2068288 , 0.43561572],
- [0.93755297, 0.12641562, 0.55493093, ..., 0.44682309,
- 0.9108161 , 0.28494995],
+array([[[3.73479162e-01, 5.69354087e-01, 8.44121771e-01, ...,
+ 6.95300592e-01, 7.05791578e-01, 3.22757785e-01],
+ [7.34673387e-01, 3.22502093e-02, 2.98660716e-02, ...,
+ 4.48326876e-01, 1.28181189e-01, 6.17129937e-01],
+ [4.61524077e-01, 5.72981031e-01, 6.22455684e-02, ...,
+ 4.66387696e-01, 2.24365962e-02, 9.05528466e-01],
...,
- [0.36681494, 0.55104908, 0.67358062, ..., 0.93120538,
- 0.51164396, 0.2149192 ],
- [0.33220658, 0.18853655, 0.84133513, ..., 0.09990092,
- 0.45265087, 0.48841145],
- [0.54632195, 0.99301667, 0.40513953, ..., 0.5341915 ,
- 0.3220391 , 0.07544213]],
-
- [[0.8398981 , 0.14999579, 0.81431679, ..., 0.45814483,
- 0.81962377, 0.01308931],
- [0.52904343, 0.17038365, 0.49820524, ..., 0.71294698,
- 0.49906669, 0.32305634],
- [0.83597797, 0.12242548, 0.63334391, ..., 0.77669187,
- 0.44530452, 0.02031067],
+ [4.32840675e-01, 6.11791423e-01, 7.77157080e-01, ...,
+ 5.21898060e-01, 5.08370890e-01, 5.88153598e-01],
+ [8.04673850e-01, 7.80296541e-03, 5.00244166e-01, ...,
+ 9.45710973e-01, 5.08700949e-01, 8.89905823e-01],
+ [4.87536782e-01, 4.64576884e-01, 4.97751472e-01, ...,
+ 9.63294478e-01, 8.00438624e-01, 9.39160580e-01]],
+
+ [[9.59798128e-01, 9.67977225e-01, 5.67081374e-01, ...,
+ 1.15551677e-01, 5.93188567e-01, 8.21494821e-01],
+ [7.09798171e-01, 6.86764079e-01, 9.75639983e-01, ...,
+ 6.65980228e-01, 3.63136928e-01, 3.91634965e-01],
+ [2.45312843e-01, 7.92456108e-01, 7.52068086e-01, ...,
+ 8.31396290e-01, 7.34853345e-01, 1.59997513e-02],
...,
- [0.23487395, 0.54185136, 0.84738714, ..., 0.43194404,
- 0.96464114, 0.34661126],
- [0.06216953, 0.87402041, 0.89512514, ..., 0.38706893,
- 0.09187095, 0.88666451],
- [0.46384865, 0.02462827, 0.71575049, ..., 0.47421697,
- 0.30570578, 0.58421292]],
-
- [[0.05704318, 0.36232811, 0.03448708, ..., 0.86748361,
- 0.49546271, 0.39195962],
- [0.62907898, 0.52593284, 0.87418635, ..., 0.97638485,
- 0.63695351, 0.75326941],
- [0.11640535, 0.15534404, 0.80460691, ..., 0.26935451,
- 0.00686985, 0.42474694],
+ [6.29006234e-01, 5.66655665e-01, 9.59798385e-01, ...,
+ 4.64611557e-02, 8.47630616e-01, 9.96868460e-01],
+ [8.65409572e-01, 6.62829465e-01, 5.85935120e-01, ...,
+ 2.76645046e-01, 4.05531947e-01, 1.86970157e-01],
+ [5.88186192e-02, 4.80258125e-01, 7.50053106e-01, ...,
+ 5.82584007e-01, 7.16238302e-01, 3.09095729e-01]],
+
+ [[1.95867406e-01, 8.02997164e-01, 5.92335907e-02, ...,
+ 6.00495084e-02, 3.58568845e-02, 6.14435689e-01],
+ [5.40191024e-01, 7.45240262e-01, 5.83926237e-01, ...,
+ 5.42683512e-01, 7.15062271e-05, 8.15838308e-01],
+ [3.56121359e-01, 6.64103118e-01, 3.19197578e-01, ...,
+ 4.30885292e-01, 5.63274760e-01, 5.58304993e-01],
...,
- [0.53156325, 0.69190957, 0.69352384, ..., 0.77170882,
- 0.44011939, 0.39866217],
- [0.29442052, 0.74080675, 0.46131608, ..., 0.32366183,
- 0.73788577, 0.04341616],
- [0.94117032, 0.92630431, 0.79008536, ..., 0.02443356,
- 0.52042712, 0.83466538]],
+ [3.70429144e-01, 2.28949319e-01, 2.12465225e-01, ...,
+ 8.75347366e-01, 6.88876481e-01, 8.52985255e-01],
+ [8.43544201e-01, 3.50323808e-01, 9.94755069e-01, ...,
+ 2.31937385e-01, 6.46129523e-01, 7.56519847e-01],
+ [9.23292344e-01, 9.06543740e-01, 2.16039623e-01, ...,
+ 6.17841121e-01, 5.83778599e-02, 7.07627581e-01]],
...,
- [[0.1396787 , 0.60143505, 0.57058462, ..., 0.18893598,
- 0.82985107, 0.54242887],
- [0.68157985, 0.63122788, 0.1780818 , ..., 0.55248006,
- 0.63127177, 0.4663941 ],
- [0.55463676, 0.49022722, 0.28183346, ..., 0.64073606,
- 0.37788708, 0.05233016],
+ [[5.50076397e-01, 6.23247920e-01, 6.69863249e-01, ...,
+ 6.66141598e-01, 4.98073488e-01, 9.70545887e-01],
+ [7.38559992e-01, 3.21393458e-01, 4.05421647e-01, ...,
+ 2.52285356e-01, 8.08345653e-01, 7.28764634e-01],
+ [2.68191728e-01, 6.03169049e-02, 6.89258446e-01, ...,
+ 6.21042327e-01, 9.60250603e-01, 9.75257028e-01],
...,
- [0.39020736, 0.85735173, 0.00786958, ..., 0.66161596,
- 0.18152074, 0.17731408],
- [0.93613911, 0.96578828, 0.38973709, ..., 0.06864502,
- 0.5063675 , 0.73631955],
- [0.1671134 , 0.45039599, 0.38253178, ..., 0.74316064,
- 0.20253998, 0.93656703]],
-
- [[0.28018602, 0.84864745, 0.65231976, ..., 0.57238332,
- 0.37536731, 0.04826117],
- [0.47663448, 0.86778224, 0.95451621, ..., 0.76880871,
- 0.89875752, 0.06756555],
- [0.49609048, 0.21693506, 0.23207324, ..., 0.06636032,
- 0.95937476, 0.4827714 ],
+ [6.11978830e-01, 6.32479701e-01, 1.75826196e-01, ...,
+ 2.09161988e-01, 6.71446177e-01, 4.78915946e-01],
+ [6.45044934e-01, 1.15167985e-01, 9.05467373e-01, ...,
+ 7.79598426e-01, 8.76027493e-01, 5.70161686e-01],
+ [8.44173395e-01, 7.14987726e-01, 5.27098062e-01, ...,
+ 6.24560049e-01, 3.32587128e-02, 1.48219268e-01]],
+
+ [[9.16337354e-01, 1.38628117e-01, 2.76572964e-01, ...,
+ 5.68919305e-02, 3.07040211e-01, 6.70607173e-02],
+ [6.78200880e-01, 1.19012654e-01, 8.76844508e-01, ...,
+ 5.78903343e-01, 5.08716762e-01, 3.27430604e-01],
+ [5.19310927e-01, 1.62933592e-01, 6.94104791e-01, ...,
+ 8.28521396e-01, 5.63944660e-01, 9.23353464e-01],
...,
- [0.83197412, 0.22629066, 0.1589711 , ..., 0.55726833,
- 0.40921459, 0.94548168],
- [0.20373662, 0.76138868, 0.56265755, ..., 0.71025427,
- 0.19641998, 0.49627513],
- [0.20384921, 0.34173511, 0.44829 , ..., 0.3999668 ,
- 0.75240336, 0.21357396]],
-
- [[0.05024137, 0.77607526, 0.32452642, ..., 0.26169962,
- 0.2092348 , 0.79937281],
- [0.8834038 , 0.56909665, 0.84006875, ..., 0.09540328,
- 0.66257683, 0.78342945],
- [0.29906903, 0.08192958, 0.51913898, ..., 0.32946737,
- 0.91310302, 0.99198924],
+ [8.75833692e-01, 8.80034882e-01, 3.94957057e-01, ...,
+ 3.88766073e-01, 5.62146961e-01, 4.07401505e-01],
+ [8.83232515e-02, 7.93868945e-02, 7.09396623e-01, ...,
+ 4.00821090e-01, 9.67897242e-01, 3.30712526e-02],
+ [6.21839195e-01, 3.66731583e-01, 9.97239185e-01, ...,
+ 4.06892246e-01, 8.84142338e-01, 1.29206897e-01]],
+
+ [[3.13410458e-01, 7.92759677e-01, 8.24979480e-02, ...,
+ 5.38935776e-02, 4.52819725e-01, 1.48534750e-01],
+ [2.06595218e-01, 8.46746782e-01, 9.06689993e-01, ...,
+ 8.52571008e-01, 4.28351337e-01, 8.06352220e-01],
+ [3.77563013e-01, 9.99637726e-01, 3.37356897e-01, ...,
+ 8.18911136e-01, 8.32562303e-01, 8.45143802e-02],
...,
- [0.89344391, 0.04685752, 0.16870497, ..., 0.70133294,
- 0.2028986 , 0.16916596],
- [0.62721463, 0.12582174, 0.59300888, ..., 0.93023778,
- 0.50053284, 0.63544531],
- [0.89994404, 0.84669077, 0.65421957, ..., 0.19573779,
- 0.56024887, 0.73584493]]])
+ [5.51431397e-01, 8.77465751e-01, 9.63517992e-01, ...,
+ 3.34710544e-02, 9.70363045e-01, 5.42823121e-01],
+ [3.89247115e-01, 3.36500509e-01, 4.31638413e-02, ...,
+ 8.82214294e-02, 2.41024552e-01, 4.19862342e-01],
+ [5.65544128e-01, 6.09859197e-01, 2.77376901e-01, ...,
+ 5.51955058e-01, 6.67783854e-02, 4.08359511e-01]]])
@@ -856,7 +856,7 @@ Why the difference?
-
+
@@ -872,11 +872,11 @@ Compute the result
-CPU times: user 406 ms, sys: 28 ms, total: 434 ms
-Wall time: 232 ms
+CPU times: user 399 ms, sys: 28.5 ms, total: 428 ms
+Wall time: 228 ms
-12002441.41340752
+12000189.444496717
@@ -894,7 +894,7 @@ Exercise with d
-
+
As is obvious from the above exercise, Dask quickly and easily determines a strategy for performing the operations, in this case a sum. This illustrates the appeal of Dask: automatic algorithm generation that scales from simple arithmetic problems to highly complex scientific equations with large datasets and multiple operations.
@@ -974,7 +974,7 @@ Exercise with d
-
+
@@ -1106,7 +1106,7 @@ Testing a bigger calculation
-
+
@@ -1117,34 +1117,34 @@ Testing a bigger calculation
-[ ] | 0% Completed | 169.21 us
+[ ] | 0% Completed | 255.13 us
-[ ] | 0% Completed | 105.92 ms
+[ ] | 0% Completed | 106.98 ms
-[ ] | 0% Completed | 206.66 ms
+[ ] | 0% Completed | 207.72 ms
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+[ ] | 0% Completed | 308.36 ms
-[ ] | 0% Completed | 408.13 ms
+[ ] | 0% Completed | 409.00 ms
-[ ] | 0% Completed | 508.89 ms
+[ ] | 0% Completed | 509.67 ms
-[ ] | 1% Completed | 609.51 ms
+[ ] | 0% Completed | 610.39 ms
-[# ] | 4% Completed | 710.49 ms
+[# ] | 3% Completed | 711.30 ms
-[### ] | 7% Completed | 811.54 ms
+[## ] | 6% Completed | 812.41 ms
-[#### ] | 10% Completed | 912.41 ms
+[#### ] | 10% Completed | 913.29 ms
[#### ] | 10% Completed | 1.01 s
@@ -1153,7 +1153,7 @@ Testing a bigger calculation[#### ] | 10% Completed | 1.11 s
-[#### ] | 10% Completed | 1.21 s
+[#### ] | 10% Completed | 1.22 s
[#### ] | 10% Completed | 1.32 s
@@ -1162,10 +1162,10 @@ Testing a bigger calculation[#### ] | 10% Completed | 1.42 s
-[##### ] | 13% Completed | 1.52 s
+[##### ] | 12% Completed | 1.52 s
-[###### ] | 16% Completed | 1.62 s
+[###### ] | 15% Completed | 1.62 s
[####### ] | 19% Completed | 1.72 s
@@ -1186,13 +1186,13 @@ Testing a bigger calculation[######## ] | 21% Completed | 2.22 s
-[######## ] | 22% Completed | 2.32 s
+[######## ] | 21% Completed | 2.32 s
-[########## ] | 25% Completed | 2.43 s
+[######### ] | 24% Completed | 2.43 s
-[########### ] | 27% Completed | 2.53 s
+[########### ] | 28% Completed | 2.53 s
[############ ] | 31% Completed | 2.63 s
@@ -1216,16 +1216,16 @@ Testing a bigger calculation[############## ] | 35% Completed | 3.23 s
-[############### ] | 38% Completed | 3.33 s
+[############## ] | 37% Completed | 3.33 s
-[################ ] | 40% Completed | 3.43 s
+[############### ] | 39% Completed | 3.43 s
-[################# ] | 44% Completed | 3.53 s
+[################# ] | 43% Completed | 3.53 s
-[################### ] | 48% Completed | 3.63 s
+[################## ] | 46% Completed | 3.64 s
[################### ] | 48% Completed | 3.74 s
@@ -1246,10 +1246,10 @@ Testing a bigger calculation[#################### ] | 50% Completed | 4.24 s
-[##################### ] | 53% Completed | 4.34 s
+[#################### ] | 52% Completed | 4.34 s
-[###################### ] | 57% Completed | 4.44 s
+[###################### ] | 55% Completed | 4.44 s
[####################### ] | 59% Completed | 4.54 s
@@ -1276,7 +1276,7 @@ Testing a bigger calculation[######################### ] | 64% Completed | 5.25 s
-[########################### ] | 67% Completed | 5.35 s
+[########################### ] | 68% Completed | 5.35 s
[############################ ] | 72% Completed | 5.45 s
@@ -1300,13 +1300,13 @@ Testing a bigger calculation[############################## ] | 75% Completed | 6.06 s
-[############################### ] | 77% Completed | 6.16 s
+[############################### ] | 78% Completed | 6.16 s
-[################################ ] | 80% Completed | 6.26 s
+[################################ ] | 82% Completed | 6.26 s
-[################################# ] | 83% Completed | 6.36 s
+[################################# ] | 84% Completed | 6.36 s
[################################## ] | 85% Completed | 6.46 s
@@ -1324,16 +1324,16 @@ Testing a bigger calculation[################################## ] | 85% Completed | 6.86 s
-[################################## ] | 86% Completed | 6.96 s
+[################################### ] | 89% Completed | 6.96 s
-[################################### ] | 89% Completed | 7.06 s
+[#################################### ] | 92% Completed | 7.07 s
-[##################################### ] | 92% Completed | 7.16 s
+[###################################### ] | 96% Completed | 7.17 s
-[###################################### ] | 96% Completed | 7.27 s
+[####################################### ] | 99% Completed | 7.27 s
[########################################] | 100% Completed | 7.37 s
@@ -1854,7 +1854,7 @@ Reading data with
- latPandasIndex
PandasIndex(Index([-89.5, -88.5, -87.5, -86.5, -85.5, -84.5, -83.5, -82.5, -81.5, -80.5,
+ 80.5, 81.5, 82.5, 83.5, 84.5, 85.5, 86.5, 87.5, 88.5, 89.5])
- lon(lon)float640.5 1.5 2.5 ... 357.5 358.5 359.5
array([ 0.5, 1.5, 2.5, ..., 357.5, 358.5, 359.5])
- latPandasIndex
PandasIndex(Index([-89.5, -88.5, -87.5, -86.5, -85.5, -84.5, -83.5, -82.5, -81.5, -80.5,
...
80.5, 81.5, 82.5, 83.5, 84.5, 85.5, 86.5, 87.5, 88.5, 89.5],
- dtype='float64', name='lat', length=180)) - lonPandasIndex
PandasIndex(Index([ 0.5, 1.5, 2.5, 3.5, 4.5, 5.5, 6.5, 7.5, 8.5, 9.5,
+ dtype='float64', name='lat', length=180))
- lonPandasIndex
PandasIndex(Index([ 0.5, 1.5, 2.5, 3.5, 4.5, 5.5, 6.5, 7.5, 8.5, 9.5,
...
350.5, 351.5, 352.5, 353.5, 354.5, 355.5, 356.5, 357.5, 358.5, 359.5],
- dtype='float64', name='lon', length=360))
+ dtype='float64', name='lon', length=360))
As shown in the above example, the result of the applied operations is an Xarray DataArray that contains a Dask Array, an identical object type to the object that the operations were performed on. This is true for any operations that can be applied to Xarray DataArrays, including subsetting operations; this next example illustrates this:
@@ -3739,7 +3739,7 @@ Parallel and lazy computation using
@@ -3794,10 +3794,10 @@ Parallel and lazy computation using dask.visualize() function, as shown in this example:
@@ -3819,10 +3819,10 @@ Parallel and lazy computation using
-[ ] | 0% Completed | 122.44 us
+[ ] | 0% Completed | 137.30 us
-[########################################] | 100% Completed | 101.18 ms
+[########################################] | 100% Completed | 101.19 ms
@@ -4236,7 +4236,7 @@ Resources and references
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
@@ -4365,7 +4365,7 @@ Contribute
By the Project Pythia Community.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
diff --git a/core/xarray/enso-xarray.html b/core/xarray/enso-xarray.html
index 316661573..6e86d4355 100644
--- a/core/xarray/enso-xarray.html
+++ b/core/xarray/enso-xarray.html
@@ -79,7 +79,7 @@
-
+
@@ -958,7 +958,7 @@ The Niño 3.4 Index
@@ -1590,7 +1590,7 @@ Select the Niño 3.4 regionwhere() method:
+array(1.8436452, dtype=float32)
The final step of the Niño 3.4 index calculation involves normalizing the data. In this example, we perform this normalization by dividing the smoothed anomaly data by the standard deviation calculated above:
@@ -3396,7 +3396,7 @@ Resources and References
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
@@ -3525,7 +3525,7 @@ Contribute
By the Project Pythia Community.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
diff --git a/core/xarray/xarray-intro.html b/core/xarray/xarray-intro.html
index e17e6628d..308dd9baa 100644
--- a/core/xarray/xarray-intro.html
+++ b/core/xarray/xarray-intro.html
@@ -79,7 +79,7 @@
-
+
@@ -583,25 +583,25 @@ Generate a random numpy array
-array([[[274.00119189, 291.26874805, 287.10212211, 281.93321028],
- [276.72439019, 281.86759901, 277.88521001, 291.15900492],
- [283.47892997, 281.69598295, 281.87670232, 280.04253353]],
+array([[[283.19514872, 279.93414516, 288.85955729, 280.30299831],
+ [282.03318795, 283.36237877, 288.28464039, 284.82856563],
+ [282.15998672, 276.06859236, 285.78192464, 280.47703387]],
- [[292.49183408, 286.75694127, 291.1430049 , 279.6708509 ],
- [283.19466798, 285.34236937, 289.30840199, 286.22515585],
- [289.80677567, 281.73710917, 294.57091617, 291.28505114]],
+ [[282.44435897, 282.262194 , 286.34635361, 285.32213341],
+ [283.03737484, 288.57815157, 290.02698113, 278.23328324],
+ [277.06837841, 286.4771544 , 284.58714744, 288.42673576]],
- [[278.59826878, 287.14196565, 284.68298455, 286.59720048],
- [287.51252429, 288.52590266, 273.69692215, 285.39338362],
- [292.66871999, 278.60078099, 280.78168263, 291.92156783]],
+ [[280.62831671, 275.374152 , 283.08613936, 292.56788174],
+ [286.26020096, 282.03531027, 280.16394984, 288.9366629 ],
+ [282.90971416, 278.40998651, 284.98850877, 283.14584888]],
- [[281.24772197, 286.52210588, 282.61809684, 288.82826349],
- [282.48419301, 286.34184212, 277.57835692, 279.66054122],
- [288.97073742, 287.33849395, 289.90083339, 285.95347237]],
+ [[284.34422355, 283.20830934, 278.27414095, 285.21976361],
+ [279.17521323, 292.21743424, 276.974416 , 284.06699408],
+ [274.74247997, 279.63780379, 275.87914515, 277.71758929]],
- [[282.699394 , 286.72856786, 281.89265707, 286.16350665],
- [278.02139036, 284.54696447, 283.16191651, 278.99615563],
- [284.94443941, 287.35039327, 289.38110818, 280.10681174]]])
+ [[282.71726594, 281.99808895, 279.66639367, 276.71709325],
+ [278.30599074, 277.08406243, 273.68999203, 279.75441922],
+ [271.39869986, 286.48138908, 285.22713979, 291.77285885]]])
@@ -982,44 +982,44 @@ Wrap the array: first attempt
@@ -1402,44 +1402,44 @@ Assign dimension names<xarray.DataArray (time: 5, lat: 3, lon: 4)> Size: 480B
-array([[[274.00119189, 291.26874805, 287.10212211, 281.93321028],
- [276.72439019, 281.86759901, 277.88521001, 291.15900492],
- [283.47892997, 281.69598295, 281.87670232, 280.04253353]],
-
- [[292.49183408, 286.75694127, 291.1430049 , 279.6708509 ],
- [283.19466798, 285.34236937, 289.30840199, 286.22515585],
- [289.80677567, 281.73710917, 294.57091617, 291.28505114]],
-
- [[278.59826878, 287.14196565, 284.68298455, 286.59720048],
- [287.51252429, 288.52590266, 273.69692215, 285.39338362],
- [292.66871999, 278.60078099, 280.78168263, 291.92156783]],
-
- [[281.24772197, 286.52210588, 282.61809684, 288.82826349],
- [282.48419301, 286.34184212, 277.57835692, 279.66054122],
- [288.97073742, 287.33849395, 289.90083339, 285.95347237]],
-
- [[282.699394 , 286.72856786, 281.89265707, 286.16350665],
- [278.02139036, 284.54696447, 283.16191651, 278.99615563],
- [284.94443941, 287.35039327, 289.38110818, 280.10681174]]])
-Dimensions without coordinates: time, lat, lon
+array([[[283.19514872, 279.93414516, 288.85955729, 280.30299831],
+ [282.03318795, 283.36237877, 288.28464039, 284.82856563],
+ [282.15998672, 276.06859236, 285.78192464, 280.47703387]],
+
+ [[282.44435897, 282.262194 , 286.34635361, 285.32213341],
+ [283.03737484, 288.57815157, 290.02698113, 278.23328324],
+ [277.06837841, 286.4771544 , 284.58714744, 288.42673576]],
+
+ [[280.62831671, 275.374152 , 283.08613936, 292.56788174],
+ [286.26020096, 282.03531027, 280.16394984, 288.9366629 ],
+ [282.90971416, 278.40998651, 284.98850877, 283.14584888]],
+
+ [[284.34422355, 283.20830934, 278.27414095, 285.21976361],
+ [279.17521323, 292.21743424, 276.974416 , 284.06699408],
+ [274.74247997, 279.63780379, 275.87914515, 277.71758929]],
+
+ [[282.71726594, 281.99808895, 279.66639367, 276.71709325],
+ [278.30599074, 277.08406243, 273.68999203, 279.75441922],
+ [271.39869986, 286.48138908, 285.22713979, 291.77285885]]])
+Dimensions without coordinates: time, lat, lon
This DataArray is already an improvement over a NumPy array; the DataArray contains names for each of the dimensions (or axes in NumPy parlance). An additional improvement is the association of coordinate-value arrays with data upon creation of a DataArray. In the next example, we illustrate the creation of NumPy arrays representing the coordinate values for each dimension of the DataArray, and how to associate these coordinate arrays with the data in our DataArray.
@@ -1849,51 +1849,51 @@ Initialize the
fill: currentColor;
}
<xarray.DataArray (time: 5, lat: 3, lon: 4)> Size: 480B
-array([[[274.00119189, 291.26874805, 287.10212211, 281.93321028],
- [276.72439019, 281.86759901, 277.88521001, 291.15900492],
- [283.47892997, 281.69598295, 281.87670232, 280.04253353]],
+array([[[283.19514872, 279.93414516, 288.85955729, 280.30299831],
+ [282.03318795, 283.36237877, 288.28464039, 284.82856563],
+ [282.15998672, 276.06859236, 285.78192464, 280.47703387]],
- [[292.49183408, 286.75694127, 291.1430049 , 279.6708509 ],
- [283.19466798, 285.34236937, 289.30840199, 286.22515585],
- [289.80677567, 281.73710917, 294.57091617, 291.28505114]],
+ [[282.44435897, 282.262194 , 286.34635361, 285.32213341],
+ [283.03737484, 288.57815157, 290.02698113, 278.23328324],
+ [277.06837841, 286.4771544 , 284.58714744, 288.42673576]],
- [[278.59826878, 287.14196565, 284.68298455, 286.59720048],
- [287.51252429, 288.52590266, 273.69692215, 285.39338362],
- [292.66871999, 278.60078099, 280.78168263, 291.92156783]],
+ [[280.62831671, 275.374152 , 283.08613936, 292.56788174],
+ [286.26020096, 282.03531027, 280.16394984, 288.9366629 ],
+ [282.90971416, 278.40998651, 284.98850877, 283.14584888]],
- [[281.24772197, 286.52210588, 282.61809684, 288.82826349],
- [282.48419301, 286.34184212, 277.57835692, 279.66054122],
- [288.97073742, 287.33849395, 289.90083339, 285.95347237]],
+ [[284.34422355, 283.20830934, 278.27414095, 285.21976361],
+ [279.17521323, 292.21743424, 276.974416 , 284.06699408],
+ [274.74247997, 279.63780379, 275.87914515, 277.71758929]],
- [[282.699394 , 286.72856786, 281.89265707, 286.16350665],
- [278.02139036, 284.54696447, 283.16191651, 278.99615563],
- [284.94443941, 287.35039327, 289.38110818, 280.10681174]]])
+ [[282.71726594, 281.99808895, 279.66639367, 276.71709325],
+ [278.30599074, 277.08406243, 273.68999203, 279.75441922],
+ [271.39869986, 286.48138908, 285.22713979, 291.77285885]]])
Coordinates:
* time (time) datetime64[ns] 40B 2018-01-01 2018-01-02 ... 2018-01-05
* lat (lat) float64 24B 25.0 40.0 55.0
- * lon (lon) float64 32B -120.0 -100.0 -80.0 -60.0
+ dtype='datetime64[ns]', name='time', freq='D'))
latPandasIndexPandasIndex(Index([25.0, 40.0, 55.0], dtype='float64', name='lat'))
lonPandasIndexPandasIndex(Index([-120.0, -100.0, -80.0, -60.0], dtype='float64', name='lon'))
@@ -2273,54 +2273,54 @@ Set useful attributes<xarray.DataArray (time: 5, lat: 3, lon: 4)> Size: 480B
-array([[[274.00119189, 291.26874805, 287.10212211, 281.93321028],
- [276.72439019, 281.86759901, 277.88521001, 291.15900492],
- [283.47892997, 281.69598295, 281.87670232, 280.04253353]],
+array([[[283.19514872, 279.93414516, 288.85955729, 280.30299831],
+ [282.03318795, 283.36237877, 288.28464039, 284.82856563],
+ [282.15998672, 276.06859236, 285.78192464, 280.47703387]],
- [[292.49183408, 286.75694127, 291.1430049 , 279.6708509 ],
- [283.19466798, 285.34236937, 289.30840199, 286.22515585],
- [289.80677567, 281.73710917, 294.57091617, 291.28505114]],
+ [[282.44435897, 282.262194 , 286.34635361, 285.32213341],
+ [283.03737484, 288.57815157, 290.02698113, 278.23328324],
+ [277.06837841, 286.4771544 , 284.58714744, 288.42673576]],
- [[278.59826878, 287.14196565, 284.68298455, 286.59720048],
- [287.51252429, 288.52590266, 273.69692215, 285.39338362],
- [292.66871999, 278.60078099, 280.78168263, 291.92156783]],
+ [[280.62831671, 275.374152 , 283.08613936, 292.56788174],
+ [286.26020096, 282.03531027, 280.16394984, 288.9366629 ],
+ [282.90971416, 278.40998651, 284.98850877, 283.14584888]],
- [[281.24772197, 286.52210588, 282.61809684, 288.82826349],
- [282.48419301, 286.34184212, 277.57835692, 279.66054122],
- [288.97073742, 287.33849395, 289.90083339, 285.95347237]],
+ [[284.34422355, 283.20830934, 278.27414095, 285.21976361],
+ [279.17521323, 292.21743424, 276.974416 , 284.06699408],
+ [274.74247997, 279.63780379, 275.87914515, 277.71758929]],
- [[282.699394 , 286.72856786, 281.89265707, 286.16350665],
- [278.02139036, 284.54696447, 283.16191651, 278.99615563],
- [284.94443941, 287.35039327, 289.38110818, 280.10681174]]])
+ [[282.71726594, 281.99808895, 279.66639367, 276.71709325],
+ [278.30599074, 277.08406243, 273.68999203, 279.75441922],
+ [271.39869986, 286.48138908, 285.22713979, 291.77285885]]])
Coordinates:
* time (time) datetime64[ns] 40B 2018-01-01 2018-01-02 ... 2018-01-05
* lat (lat) float64 24B 25.0 40.0 55.0
* lon (lon) float64 32B -120.0 -100.0 -80.0 -60.0
Attributes:
units: kelvin
- standard_name: air_temperature
+ dtype='datetime64[ns]', name='time', freq='D'))
latPandasIndexPandasIndex(Index([25.0, 40.0, 55.0], dtype='float64', name='lat'))
lonPandasIndexPandasIndex(Index([-120.0, -100.0, -80.0, -60.0], dtype='float64', name='lon'))
- units :
- kelvin
- standard_name :
- air_temperature
@@ -2699,51 +2699,51 @@ Issues with preservation of attributesdocumentation page.
@@ -3134,54 +3134,54 @@ Create a pressure
+ dtype='datetime64[ns]', name='time', freq='D'))latPandasIndexPandasIndex(Index([25.0, 40.0, 55.0], dtype='float64', name='lat'))
lonPandasIndexPandasIndex(Index([-120.0, -100.0, -80.0, -60.0], dtype='float64', name='lon'))
As listed in the rich display above, the new Dataset object is aware that both DataArrays share the same coordinate axes. (Please note that if this page is not run as a Jupyter Notebook, the rich display may be unavailable.)
@@ -3986,54 +3986,54 @@ Access Data variables and Coordinates in a DataArrays through a dictionary syntax, as shown in this example:
@@ -4407,54 +4407,54 @@ Access Data variables and Coordinates in a Dataset objects are mainly used for loading data from files, which will be covered later in this tutorial.
@@ -4838,18 +4838,18 @@ NumPy-like selection<xarray.DataArray (lat: 3, lon: 4)> Size: 96B
-array([[292.49183408, 286.75694127, 291.1430049 , 279.6708509 ],
- [283.19466798, 285.34236937, 289.30840199, 286.22515585],
- [289.80677567, 281.73710917, 294.57091617, 291.28505114]])
+array([[282.44435897, 282.262194 , 286.34635361, 285.32213341],
+ [283.03737484, 288.57815157, 290.02698113, 278.23328324],
+ [277.06837841, 286.4771544 , 284.58714744, 288.42673576]])
Coordinates:
time datetime64[ns] 8B 2018-01-02
* lat (lat) float64 24B 25.0 40.0 55.0
* lon (lon) float64 32B -120.0 -100.0 -80.0 -60.0
Attributes:
units: kelvin
- standard_name: air_temperature
+ standard_name: air_temperature
This example reveals one of the major shortcomings of index selection. In order to retrieve the correct data using index selection, anyone using a DataArray must have precise knowledge of the axes in the DataArray, including the order of the axes and the meaning of their indices.
By using named coordinates, as shown in the next set of examples, we can avoid this cumbersome burden.
@@ -5229,18 +5229,18 @@ Selecting with
fill: currentColor;
}
<xarray.DataArray (lat: 3, lon: 4)> Size: 96B
-array([[292.49183408, 286.75694127, 291.1430049 , 279.6708509 ],
- [283.19466798, 285.34236937, 289.30840199, 286.22515585],
- [289.80677567, 281.73710917, 294.57091617, 291.28505114]])
+array([[282.44435897, 282.262194 , 286.34635361, 285.32213341],
+ [283.03737484, 288.57815157, 290.02698113, 278.23328324],
+ [277.06837841, 286.4771544 , 284.58714744, 288.42673576]])
Coordinates:
time datetime64[ns] 8B 2018-01-02
* lat (lat) float64 24B 25.0 40.0 55.0
* lon (lon) float64 32B -120.0 -100.0 -80.0 -60.0
Attributes:
units: kelvin
- standard_name: air_temperature
+ standard_name: air_temperature
This method yields the same result as the index selection, however:
@@ -5627,18 +5627,18 @@ Nearest-neighbor sampling.sel indeed returned the data at the temporal value corresponding to the date 2018-01-05.
@@ -6017,20 +6017,20 @@ Interpolation
Info
@@ -6414,29 +6414,29 @@ Slicing along coordinates
Info
@@ -6818,18 +6818,18 @@ One more selection method: <xarray.DataArray (lat: 3, lon: 4)> Size: 96B
-array([[292.49183408, 286.75694127, 291.1430049 , 279.6708509 ],
- [283.19466798, 285.34236937, 289.30840199, 286.22515585],
- [289.80677567, 281.73710917, 294.57091617, 291.28505114]])
+array([[282.44435897, 282.262194 , 286.34635361, 285.32213341],
+ [283.03737484, 288.57815157, 290.02698113, 278.23328324],
+ [277.06837841, 286.4771544 , 284.58714744, 288.42673576]])
Coordinates:
time datetime64[ns] 8B 2018-01-02
* lat (lat) float64 24B 25.0 40.0 55.0
* lon (lon) float64 32B -120.0 -100.0 -80.0 -60.0
Attributes:
units: kelvin
- standard_name: air_temperature
+ standard_name: air_temperature
This selection technique is similar to NumPy’s index-based selection, as shown below:
temp[1,:,:]
@@ -7208,29 +7208,29 @@ One more selection method: <xarray.DataArray (time: 3, lat: 2, lon: 2)> Size: 96B
-array([[[291.26874805, 287.10212211],
- [281.86759901, 277.88521001]],
+array([[[279.93414516, 288.85955729],
+ [283.36237877, 288.28464039]],
- [[286.75694127, 291.1430049 ],
- [285.34236937, 289.30840199]],
+ [[282.262194 , 286.34635361],
+ [288.57815157, 290.02698113]],
- [[287.14196565, 284.68298455],
- [288.52590266, 273.69692215]]])
+ [[275.374152 , 283.08613936],
+ [282.03531027, 280.16394984]]])
Coordinates:
* time (time) datetime64[ns] 24B 2018-01-01 2018-01-02 2018-01-03
* lat (lat) float64 16B 25.0 40.0
* lon (lon) float64 16B -100.0 -80.0
Attributes:
units: kelvin
- standard_name: air_temperature
+ [[275.374152 , 283.08613936],
+ [282.03531027, 280.16394984]]])
- time(time)datetime64[ns]2018-01-01 2018-01-02 2018-01-03
array(['2018-01-01T00:00:00.000000000', '2018-01-02T00:00:00.000000000',
+ '2018-01-03T00:00:00.000000000'], dtype='datetime64[ns]')
- lat(lat)float6425.0 40.0
array([25., 40.])
- lon(lon)float64-100.0 -80.0
array([-100., -80.])
- timePandasIndex
PandasIndex(DatetimeIndex(['2018-01-01', '2018-01-02', '2018-01-03'], dtype='datetime64[ns]', name='time', freq='D'))
- latPandasIndex
PandasIndex(Index([25.0, 40.0], dtype='float64', name='lat'))
- lonPandasIndex
PandasIndex(Index([-100.0, -80.0], dtype='float64', name='lon'))
- units :
- kelvin
- standard_name :
- air_temperature
In contrast with the previous example, this example shows a useful advantage of using the .loc attribute. When using the .loc attribute, you can specify data slices using a syntax similar to NumPy in addition to, or instead of, using the slice function. Both of these slicing techniques are illustrated below:
@@ -7604,29 +7604,29 @@ One more selection method: <xarray.DataArray (time: 3, lat: 2, lon: 2)> Size: 96B
-array([[[291.26874805, 287.10212211],
- [281.86759901, 277.88521001]],
+array([[[279.93414516, 288.85955729],
+ [283.36237877, 288.28464039]],
- [[286.75694127, 291.1430049 ],
- [285.34236937, 289.30840199]],
+ [[282.262194 , 286.34635361],
+ [288.57815157, 290.02698113]],
- [[287.14196565, 284.68298455],
- [288.52590266, 273.69692215]]])
+ [[275.374152 , 283.08613936],
+ [282.03531027, 280.16394984]]])
Coordinates:
* time (time) datetime64[ns] 24B 2018-01-01 2018-01-02 2018-01-03
* lat (lat) float64 16B 25.0 40.0
* lon (lon) float64 16B -100.0 -80.0
Attributes:
units: kelvin
- standard_name: air_temperature
+ [[275.374152 , 283.08613936],
+ [282.03531027, 280.16394984]]])
- time(time)datetime64[ns]2018-01-01 2018-01-02 2018-01-03
array(['2018-01-01T00:00:00.000000000', '2018-01-02T00:00:00.000000000',
+ '2018-01-03T00:00:00.000000000'], dtype='datetime64[ns]')
- lat(lat)float6425.0 40.0
array([25., 40.])
- lon(lon)float64-100.0 -80.0
array([-100., -80.])
- timePandasIndex
PandasIndex(DatetimeIndex(['2018-01-01', '2018-01-02', '2018-01-03'], dtype='datetime64[ns]', name='time', freq='D'))
- latPandasIndex
PandasIndex(Index([25.0, 40.0], dtype='float64', name='lat'))
- lonPandasIndex
PandasIndex(Index([-100.0, -80.0], dtype='float64', name='lon'))
- units :
- kelvin
- standard_name :
- air_temperature
As described above, the arguments to .loc must be in the order of the DataArray’s dimensions. Attempting to slice data without ordering arguments properly can cause errors, as shown below:
@@ -8059,10 +8059,10 @@ Access netCDF data with
@@ -8504,17 +8504,17 @@ Subsetting the
positive: down
Grib_level_type: 100
_CoordinateAxisType: Pressure
- _CoordinateZisPositive: down
+ dtype='float32', name='isobaric1'))- units :
- hPa
- long_name :
- Isobaric surface
- positive :
- down
- Grib_level_type :
- 100
- _CoordinateAxisType :
- Pressure
- _CoordinateZisPositive :
- down
(As described earlier in this tutorial, we can also use dictionary syntax to select specific DataArrays; in this case, we would write ds['isobaric1'].)
Many of the subsetting operations usable on DataArrays can also be used on Datasets. However, when used on Datasets, these operations are performed on every DataArray in the Dataset, as shown below:
@@ -8916,7 +8916,7 @@ Subsetting the
geospatial_lat_min: 10.753308882144761
geospatial_lat_max: 46.8308828962289
geospatial_lon_min: -153.88242040519995
- geospatial_lon_max: -42.666108129242815
As shown above, the subsetting operation performed on the Dataset returned a new Dataset. If only a single DataArray is needed from this new Dataset, it can be retrieved using the familiar dot notation:
@@ -9356,7 +9356,7 @@ Subsetting the
Grib1_TableVersion: 131
Grib1_Parameter: 11
Grib1_Level_Type: 100
- Grib1_Level_Desc: Isobaric surface
+ dtype='float32', name='x', length=268))- long_name :
- Temperature @ Isobaric surface
- units :
- K
- description :
- Temperature
- grid_mapping :
- LambertConformal_Projection
- Grib_Variable_Id :
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- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
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By the Project Pythia Community.
- Last updated on 15 May 2024.
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- Last updated on 15 May 2024.
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By the Project Pythia Community.
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By the Project Pythia Community.
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index 1565427e6..94676c432 100644
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By the Project Pythia Community.
- Last updated on 15 May 2024.
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By the Project Pythia Community.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
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index c8dc2806e..344643277 100644
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By the Project Pythia Community.
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By the Project Pythia Community.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
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index 634495814..c4cb654ea 100644
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By the Project Pythia Community.
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By the Project Pythia Community.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
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index 81c713d97..f6f7addda 100644
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By the Project Pythia Community.
- Last updated on 15 May 2024.
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index ff147bc66..276e728be 100644
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By the Project Pythia Community.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
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- Last updated on 15 May 2024.
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By the Project Pythia Community.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
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- Last updated on 15 May 2024.
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By the Project Pythia Community.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
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index 57210f52a..705201479 100644
--- a/search.html
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By the Project Pythia Community.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
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index 18d9c718b..f531c0445 100644
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\ No newline at end of file
+Search.setIndex({"alltitles": {"2nd Derivative": [[15, "nd-derivative"]], "A community learning resource for Python-based computing in the geosciences": [[48, "a-community-learning-resource-for-python-based-computing-in-the-geosciences"]], "A content subsection": [[1, "a-content-subsection"]], "A figure with two different regional maps": [[3, "a-figure-with-two-different-regional-maps"]], "A little history and nomenclature": [[28, "a-little-history-and-nomenclature"]], "A simple query of just the year:": [[7, "a-simple-query-of-just-the-year"]], "A very first Python program": [[45, "a-very-first-python-program"]], "About version control and git": [[28, "about-version-control-and-git"]], "Access Data variables and Coordinates in a Dataset": [[23, "access-data-variables-and-coordinates-in-a-dataset"]], "Access netCDF data with xr.open_dataset": [[23, "access-netcdf-data-with-xr-open-dataset"]], "Add a Box Around the Text": [[9, "add-a-box-around-the-text"]], "Add a fancy background image to the map.": [[3, "add-a-fancy-background-image-to-the-map"]], "Add cartographic features to the map": [[3, "add-cartographic-features-to-the-map"]], "Add in the cartographic shapefiles": [[3, "add-in-the-cartographic-shapefiles"]], "Add some predefined features": [[3, "add-some-predefined-features"]], "Adding Annotations": [[9, "adding-annotations"]], "Adding labels and a grid": [[11, "adding-labels-and-a-grid"]], "Adding labels to an Axes object": [[11, "adding-labels-to-an-axes-object"]], "Adding titles to each subplot": [[11, "adding-titles-to-each-subplot"]], "Additional Resources": [[10, "additional-resources"]], "Advanced GitHub Topics": [[31, "advanced-github-topics"]], "Aggregation Methods": [[20, "aggregation-methods"]], "Aggregation operations": [[23, "aggregation-operations"]], "An example of plotting data": [[3, "an-example-of-plotting-data"]], "Animations": [[10, "animations"]], "Annotations, Colorbars, and Advanced Layouts": [[9, "annotations-colorbars-and-advanced-layouts"]], "Another content subsection": [[1, "another-content-subsection"]], "Apply & Combine": [[20, "apply-combine"]], "Applying operations to a DataFrame": [[18, "applying-operations-to-a-dataframe"]], "Approximate selection and interpolation": [[23, "approximate-selection-and-interpolation"]], "Arithmetic": [[14, "arithmetic"]], "Arithmetic Operations": [[20, "arithmetic-operations"]], "Array comparisons": [[13, "array-comparisons"]], "Array math functions": [[14, "array-math-functions"]], "Arrays of numbers with NumPy": [[45, "arrays-of-numbers-with-numpy"]], "Assign dimension names": [[23, "assign-dimension-names"]], "Auxiliary Coordinates": [[5, "auxiliary-coordinates"]], "Basic Colorbars": [[9, "basic-colorbars"]], "Basic Line Plots": [[11, "basic-line-plots"]], "Basic Python data types": [[45, "basic-python-data-types"]], "Basic Statistics": [[18, "basic-statistics"]], "Basic Version Control with git": [[28, "basic-version-control-with-git"]], "Basic concepts: map projections and GeoAxes": [[3, "basic-concepts-map-projections-and-geoaxes"]], "Basic flow control": [[45, "basic-flow-control"]], "Blocked algorithms": [[21, "blocked-algorithms"]], "Blocking": [[15, "blocking"]], "Building the book locally": [[0, "building-the-book-locally"]], "Building the site": [[0, "building-the-site"]], "Calculating ENSO with Xarray": [[22, "calculating-enso-with-xarray"]], "Calculating coastal tides with the timedelta class": [[7, "calculating-coastal-tides-with-the-timedelta-class"]], "Cartopy": [[2, "cartopy"]], "Cartopy\u2019s set_extent method": [[3, "cartopys-set-extent-method"]], "Cell Bounds": [[5, "cell-bounds"]], "Character strings (str)": [[45, "character-strings-str"]], "Checking out a previous commit": [[28, "checking-out-a-previous-commit"]], "Choosing a Python Platform": [[40, "choosing-a-python-platform"]], "Classes of colormaps": [[9, "classes-of-colormaps"]], "Clone, branch, change, commit, push": [[37, "clone-branch-change-commit-push"]], "Cloning a repository": [[32, "cloning-a-repository"]], "Cloning and Forking a Repository": [[32, "cloning-and-forking-a-repository"]], "Cloning and forking": [[32, "cloning-and-forking"]], "Cloning the remote repository": [[37, "cloning-the-remote-repository"]], "Code Cells": [[42, "code-cells"]], "Colormap Overview": [[9, "colormap-overview"]], "Command Mode Shortcuts": [[42, "command-mode-shortcuts"]], "Commands we used in this tutorial": [[28, "commands-we-used-in-this-tutorial"]], "Committing": [[28, "committing"]], "Compare across branches": [[28, "compare-across-branches"]], "Compare across commits": [[28, "compare-across-commits"]], "Comparing versions": [[28, "comparing-versions"]], "Complete Workflow": [[30, "complete-workflow"]], "Computations and Masks with Xarray": [[20, "computations-and-masks-with-xarray"]], "Compute anomaly": [[20, "compute-anomaly"]], "Compute climatology": [[20, "compute-climatology"]], "Compute the anomalies": [[22, "compute-the-anomalies"]], "Compute the result": [[21, "compute-the-result"]], "Configuring Your GitHub Account": [[36, "configuring-your-github-account"]], "Consoles": [[42, "consoles"]], "Constants": [[14, "constants"]], "Content section": [[31, "content-section"]], "Contour and Filled Contour Plots": [[11, "contour-and-filled-contour-plots"]], "Contribute to Project Pythia via GitHub": [[29, "contribute-to-project-pythia-via-github"]], "Contributing a new Jupyter Notebook": [[0, "contributing-a-new-jupyter-notebook"]], "Controlling the output format with strftime()": [[7, "controlling-the-output-format-with-strftime"]], "Coordinate System Information": [[5, "coordinate-system-information"]], "Coordinate variables": [[5, "coordinate-variables"]], "Core libraries": [[16, "core-libraries"]], "Create Some Sample Data": [[9, "create-some-sample-data"]], "Create a DataArray with named Coordinates": [[23, "create-a-dataarray-with-named-coordinates"]], "Create a Dataset object": [[23, "create-a-dataset-object"]], "Create a Pull Request": [[29, "create-a-pull-request"]], "Create a conda environment": [[0, "create-a-conda-environment"]], "Create a dask.array object": [[21, "create-a-dask-array-object"]], "Create a map with a specified projection": [[3, "create-a-map-with-a-specified-projection"]], "Create a new branch": [[37, "create-a-new-branch"]], "Create a new feature branch": [[28, "create-a-new-feature-branch"]], "Create a pressure DataArray using the same coordinates": [[23, "create-a-pressure-dataarray-using-the-same-coordinates"]], "Create a regional map centered over New York State": [[3, "create-a-regional-map-centered-over-new-york-state"]], "Create an array of \u2018data\u2019": [[14, "create-an-array-of-data"]], "Create regional maps": [[3, "create-regional-maps"]], "Creating a Conda Environment": [[24, "creating-a-conda-environment"]], "Creating a New Branch": [[30, "creating-a-new-branch"]], "Creating and filling a variable": [[5, "creating-and-filling-a-variable"]], "Creating the file and dimensions": [[5, "creating-the-file-and-dimensions"]], "Creation and basic setup": [[5, "creation-and-basic-setup"]], "Creation of a DataArray object": [[23, "creation-of-a-dataarray-object"]], "Custom Colorbars": [[9, "custom-colorbars"]], "Customize your Plot": [[18, "customize-your-plot"]], "Customizing colors": [[11, "customizing-colors"]], "Customizing the plot": [[23, "customizing-the-plot"]], "Danger": [[1, null], [5, null], [18, null], [42, null]], "Dask Arrays with Xarray": [[21, "dask-arrays-with-xarray"], [21, "id1"]], "Dask Shortcomings": [[21, "dask-shortcomings"]], "Data Formats": [[4, "data-formats"]], "Data Setup": [[20, "data-setup"]], "Datetime": [[6, "datetime"]], "Dealing with Time Zones": [[7, "dealing-with-time-zones"]], "Deleting Branches": [[30, "deleting-branches"]], "Dictionaries (dict)": [[45, "dictionaries-dict"]], "Did you know?": [[38, null], [39, null], [39, null]], "Discussions": [[33, "discussions"]], "Displaying Images": [[11, "displaying-images"]], "Domain-specific libraries": [[16, "domain-specific-libraries"]], "Email notifications": [[36, "email-notifications"]], "Example usage of the datetime object": [[7, "example-usage-of-the-datetime-object"]], "Executing Jupyter": [[26, "executing-jupyter"]], "Exercise with dask.arrays": [[21, "exercise-with-dask-arrays"]], "Exploratory Data Analysis": [[18, "exploratory-data-analysis"]], "Explore some of Cartopy\u2019s map projections": [[3, "explore-some-of-cartopys-map-projections"]], "Extend Matplotlib\u2019s axes into georeferenced GeoAxes": [[3, "extend-matplotlibs-axes-into-georeferenced-geoaxes"]], "Extending to higher dimensions": [[15, "extending-to-higher-dimensions"]], "Extending to the DataFrame": [[18, "extending-to-the-dataframe"]], "FOSS + VCS + Web = GitHub": [[39, "foss-vcs-web-github"]], "FOSS and VCS on the Internet": [[39, "foss-and-vcs-on-the-internet"]], "Figure and Axes": [[11, "figure-and-axes"]], "Finding the difference between min and max": [[15, "finding-the-difference-between-min-and-max"]], "Floating point numbers (float)": [[45, "floating-point-numbers-float"]], "Forking Workflow": [[37, "forking-workflow"]], "Forking a repository": [[32, "forking-a-repository"]], "Forking the upstream repository": [[37, "forking-the-upstream-repository"]], "Formatted Text in the Notebook with Markdown": [[43, "formatted-text-in-the-notebook-with-markdown"]], "Free \\neq open source!": [[39, null]], "Free and open-source software (FOSS)": [[39, "free-and-open-source-software-foss"]], "Full time zone support with the pytz module": [[7, "full-time-zone-support-with-the-pytz-module"]], "Generate a random numpy array": [[23, "generate-a-random-numpy-array"]], "Generate a secure personal access token for https": [[36, "generate-a-secure-personal-access-token-for-https"]], "Generate an SSH public/private keypair": [[36, "generate-an-ssh-public-private-keypair"]], "Generate test data using 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32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 45, 46], "when": 38, "where": 20, "why": [8, 21, 28, 47], "window": 32, "within": 11, "work": 37, "workflow": [30, 34, 37], "would": 21, "wrap": 23, "write": [5, 7], "xarrai": [19, 20, 21, 22, 23], "xr": 23, "year": 7, "york": 3, "you": [21, 28, 30, 38, 39], "your": [0, 1, 18, 30, 34, 36, 41, 45], "zero": 45, "zone": 7}})
\ No newline at end of file
+(1)\[\begin{align}
\dot{x} & = \sigma(y-x) \\
\dot{y} & = \rho x - y - xz \\
\dot{z} & = -\beta z + xy
@@ -998,7 +998,7 @@ Resources and references
@@ -1127,7 +1127,7 @@ Resources and references
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
Contribute
By the Project Pythia Community. - Last updated on 15 May 2024. + Last updated on 20 May 2024.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
Contribute
By the Project Pythia Community. - Last updated on 15 May 2024. + Last updated on 20 May 2024. diff --git a/core/cartopy/cartopy.html b/core/cartopy/cartopy.html index 2a9385aa4..dd539a1b8 100644 --- a/core/cartopy/cartopy.html +++ b/core/cartopy/cartopy.html @@ -79,7 +79,7 @@ - + @@ -571,7 +571,7 @@Create a map with a specified projection
-<cartopy.mpl.feature_artist.FeatureArtist at 0x7f5f6cbc0500>
+<cartopy.mpl.feature_artist.FeatureArtist at 0x7f003cb648c0>
@@ -603,7 +603,7 @@ Add cartographic features to the map
-<cartopy.mpl.feature_artist.FeatureArtist at 0x7f5f6cb68fb0>
+<cartopy.mpl.feature_artist.FeatureArtist at 0x7f004cd08ad0>
@@ -1340,7 +1340,7 @@ Resources and References
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
@@ -1469,7 +1469,7 @@ Contribute
By the Project Pythia Community.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
diff --git a/core/data-formats.html b/core/data-formats.html
index ab2782359..af160f4d1 100644
--- a/core/data-formats.html
+++ b/core/data-formats.html
@@ -79,7 +79,7 @@
-
+
@@ -755,7 +755,7 @@ Data Formats
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
@@ -884,7 +884,7 @@ Contribute
By the Project Pythia Community.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
diff --git a/core/data-formats/netcdf-cf.html b/core/data-formats/netcdf-cf.html
index 5b25884fa..12d8c6e0e 100644
--- a/core/data-formats/netcdf-cf.html
+++ b/core/data-formats/netcdf-cf.html
@@ -79,7 +79,7 @@
-
+
@@ -564,7 +564,7 @@ Gridded Data
-/tmp/ipykernel_2523/2342439358.py:1: DeprecationWarning: datetime.datetime.utcnow() is deprecated and scheduled for removal in a future version. Use timezone-aware objects to represent datetimes in UTC: datetime.datetime.now(datetime.UTC).
+/tmp/ipykernel_2539/2342439358.py:1: DeprecationWarning: datetime.datetime.utcnow() is deprecated and scheduled for removal in a future version. Use timezone-aware objects to represent datetimes in UTC: datetime.datetime.now(datetime.UTC).
start = datetime.utcnow().replace(hour=22, minute=0, second=0, microsecond=0)
@@ -610,7 +610,7 @@ Creating the file and dimensions
-/tmp/ipykernel_2523/2008125275.py:5: DeprecationWarning: datetime.datetime.utcnow() is deprecated and scheduled for removal in a future version. Use timezone-aware objects to represent datetimes in UTC: datetime.datetime.now(datetime.UTC).
+/tmp/ipykernel_2539/2008125275.py:5: DeprecationWarning: datetime.datetime.utcnow() is deprecated and scheduled for removal in a future version. Use timezone-aware objects to represent datetimes in UTC: datetime.datetime.now(datetime.UTC).
nc.history = str(datetime.utcnow()) + ' Python'
@@ -651,7 +651,7 @@ Creating the file and dimensions
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
@@ -1693,7 +1693,7 @@ Contribute
By the Project Pythia Community.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
diff --git a/core/datetime.html b/core/datetime.html
index d7aeb1142..dfa7d6341 100644
--- a/core/datetime.html
+++ b/core/datetime.html
@@ -81,7 +81,7 @@
-
+
@@ -763,7 +763,7 @@ Datetime
@@ -892,7 +892,7 @@ Contribute
By the Project Pythia Community.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
diff --git a/core/datetime/datetime.html b/core/datetime/datetime.html
index 153198b8e..ce3241e6a 100644
--- a/core/datetime/datetime.html
+++ b/core/datetime/datetime.html
@@ -81,7 +81,7 @@
-
+
@@ -593,7 +593,7 @@ Some core terminology
-1715806495.8833458
+1716229655.9439774
@@ -614,7 +614,7 @@ time
-The benchmark took 1.0002374649047852 seconds
+The benchmark took 1.0002319812774658 seconds
@@ -743,7 +743,7 @@ See how many days have elapsed since the strike:
-6167
+6172
@@ -815,8 +815,8 @@ Time Zone Naive Versus Time Zone Aware
-I am time zone naive 2024-05-15 20:54:56.921356.
-I am time zone aware 2024-05-15 20:54:56.921392+00:00.
+I am time zone naive 2024-05-20 18:27:36.981418.
+I am time zone aware 2024-05-20 18:27:36.981452+00:00.
@@ -839,8 +839,8 @@ Full time zone support with the <
-I am time zone naive: 2024-05-15 20:54:56.925612.
-I am time zone aware: 2024-05-15 14:54:56.936190-06:00.
+I am time zone naive: 2024-05-20 18:27:36.985646.
+I am time zone aware: 2024-05-20 12:27:36.996878-06:00.
@@ -862,11 +862,11 @@ Print Time with a Different Time Zone
-The UTC time is May 15, 2024, 8:54PM.
-The 'US/Mountain' time is May 15, 2024, 2:54PM.
+The UTC time is May 20, 2024, 6:27PM.
+The 'US/Mountain' time is May 20, 2024, 12:27PM.
-/tmp/ipykernel_2546/3080495102.py:1: DeprecationWarning: datetime.datetime.utcnow() is deprecated and scheduled for removal in a future version. Use timezone-aware objects to represent datetimes in UTC: datetime.datetime.now(datetime.UTC).
+/tmp/ipykernel_2563/3080495102.py:1: DeprecationWarning: datetime.datetime.utcnow() is deprecated and scheduled for removal in a future version. Use timezone-aware objects to represent datetimes in UTC: datetime.datetime.now(datetime.UTC).
utc = dt.datetime.utcnow().replace(tzinfo=pytz.utc)
@@ -1243,7 +1243,7 @@ Resources and References
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
@@ -1372,7 +1372,7 @@ Contribute
By the Project Pythia Community.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
diff --git a/core/matplotlib.html b/core/matplotlib.html
index 36bdf5490..7ff675d8a 100644
--- a/core/matplotlib.html
+++ b/core/matplotlib.html
@@ -81,7 +81,7 @@
-
+
@@ -795,7 +795,7 @@ In this section
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
@@ -924,7 +924,7 @@ Contribute
By the Project Pythia Community.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
diff --git a/core/matplotlib/annotations-colorbars-layouts.html b/core/matplotlib/annotations-colorbars-layouts.html
index 97735445a..4c2ffca3e 100644
--- a/core/matplotlib/annotations-colorbars-layouts.html
+++ b/core/matplotlib/annotations-colorbars-layouts.html
@@ -81,7 +81,7 @@
-
+
@@ -730,7 +730,7 @@ Basic Colorbars
-
+
We can change which colormap to use by setting the keyword argument cmap = 'colormap_name' in the plotting function call. This sets the colormap not only for the plot, but for the colorbar as well. In this case, we use the magma colormap:
@@ -745,7 +745,7 @@ Basic Colorbars
-
+
@@ -764,10 +764,10 @@ Shared Colorbars
-<matplotlib.colorbar.Colorbar at 0x7f4e652e9af0>
+<matplotlib.colorbar.Colorbar at 0x7f07e48a40e0>
-
+
You may be wondering why the call to fig.colorbar uses the argument hist1[3]. The explanation is as follows: hist1 is a tuple returned by hist2d, and hist1[3] contains a matplotlib.collections.QuadMesh that points to the colormap for the first histogram. To make sure that both histograms are using the same colormap with the same range of values, vmax is set to 0.18 for both plots. This ensures that both histograms are using colormaps that represent values from 0 (the default for histograms) to 0.18. Because the same data values are used for both plots, it doesn’t matter whether we pass in hist1[3] or hist2[3] to fig.colorbar.
@@ -848,7 +848,7 @@
Custom Colorbars
-
+
@@ -865,7 +865,7 @@ Custom Colorbars
-
+
@@ -902,7 +902,7 @@ Mosaic Subplots
-
+
You’ll notice there is not a colorbar plotted by default. When constructing the colorbar, we need to specify the following:
@@ -930,7 +930,7 @@ Mosaic Subplots
-
+
@@ -1291,7 +1291,7 @@ Resources and references
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
@@ -1420,7 +1420,7 @@ Contribute
By the Project Pythia Community.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
diff --git a/core/matplotlib/histograms-piecharts-animation.html b/core/matplotlib/histograms-piecharts-animation.html
index ea6420ec0..9a591348d 100644
--- a/core/matplotlib/histograms-piecharts-animation.html
+++ b/core/matplotlib/histograms-piecharts-animation.html
@@ -79,7 +79,7 @@
-
+
@@ -3629,7 +3629,7 @@ Additional Resources
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
@@ -3758,7 +3758,7 @@ Contribute
By the Project Pythia Community.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
diff --git a/core/matplotlib/matplotlib-basics.html b/core/matplotlib/matplotlib-basics.html
index 323b54231..22fd1268a 100644
--- a/core/matplotlib/matplotlib-basics.html
+++ b/core/matplotlib/matplotlib-basics.html
@@ -79,7 +79,7 @@
-
+
@@ -1492,7 +1492,7 @@ Resources and References
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
@@ -1621,7 +1621,7 @@ Contribute
By the Project Pythia Community.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
diff --git a/core/numpy.html b/core/numpy.html
index 4fb55d7f5..5c535f0a9 100644
--- a/core/numpy.html
+++ b/core/numpy.html
@@ -81,7 +81,7 @@
-
+
@@ -760,7 +760,7 @@ NumPy
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
@@ -889,7 +889,7 @@ Contribute
By the Project Pythia Community.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
diff --git a/core/numpy/intermediate-numpy.html b/core/numpy/intermediate-numpy.html
index 59706e1a6..38a6afc63 100644
--- a/core/numpy/intermediate-numpy.html
+++ b/core/numpy/intermediate-numpy.html
@@ -81,7 +81,7 @@
-
+
@@ -1425,7 +1425,7 @@ Resources and references
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
@@ -1554,7 +1554,7 @@ Contribute
By the Project Pythia Community.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
diff --git a/core/numpy/numpy-basics.html b/core/numpy/numpy-basics.html
index cb11d7b3d..365a2f855 100644
--- a/core/numpy/numpy-basics.html
+++ b/core/numpy/numpy-basics.html
@@ -81,7 +81,7 @@
-
+
@@ -1168,7 +1168,7 @@ Indexing
Slices
-Slicing syntax is written as array[start:stop[:step]], where all numbers are optional.
+Slicing syntax is written as array[start:stop:step]. Note that all numbers are optional. Importantly, the step parameter is optional and can be omitted, in which case the slice uses a default step of 1.
defaults:
@@ -1775,7 +1775,7 @@ Resources and references
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
@@ -1904,7 +1904,7 @@ Contribute
By the Project Pythia Community.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
diff --git a/core/numpy/numpy-broadcasting.html b/core/numpy/numpy-broadcasting.html
index 78f882658..57e5dd9a6 100644
--- a/core/numpy/numpy-broadcasting.html
+++ b/core/numpy/numpy-broadcasting.html
@@ -81,7 +81,7 @@
-
+
@@ -1070,7 +1070,7 @@ Blocking
-
-
-
-
-array([[ 8.3, 11.6, 33.1],
- [11.6, 33.1, 18.3],
- [33.1, 18.3, 22.5],
- [18.3, 22.5, 24.5],
- [22.5, 24.5, 20.1],
- [24.5, 20.1, 20.1],
- [20.1, 20.1, 18. ],
- [20.1, 18. , 25. ]])
+array([[19.3, 18.8, 28.7],
+ [18.8, 28.7, 27.1],
+ [28.7, 27.1, 17.1],
+ [27.1, 17.1, 16.2],
+ [17.1, 16.2, 15. ],
+ [16.2, 15. , 15.1],
+ [15. , 15.1, 14. ],
+ [15.1, 14. , 19.2]])
@@ -1161,8 +1161,8 @@ Blocking
-
-array([[ 8.3, 11.6, 2000. ],
- [ 11.6, 2000. , 18.3],
- [2000. , 18.3, 22.5],
- [ 18.3, 22.5, 24.5],
- [ 22.5, 24.5, 20.1],
- [ 24.5, 20.1, 20.1],
- [ 20.1, 20.1, 18. ],
- [ 20.1, 18. , 25. ]])
+array([[ 19.3, 18.8, 2000. ],
+ [ 18.8, 2000. , 27.1],
+ [2000. , 27.1, 17.1],
+ [ 27.1, 17.1, 16.2],
+ [ 17.1, 16.2, 15. ],
+ [ 16.2, 15. , 15.1],
+ [ 15. , 15.1, 14. ],
+ [ 15.1, 14. , 19.2]])
@@ -1210,13 +1210,13 @@ Finding the difference between min and max
-array([[6, 6, 7, ..., 8, 7, 6],
- [6, 6, 6, ..., 7, 7, 7],
- [7, 7, 7, ..., 6, 7, 7],
+array([[7, 7, 7, ..., 7, 8, 7],
+ [6, 7, 6, ..., 7, 7, 6],
+ [7, 6, 7, ..., 7, 6, 6],
...,
- [7, 7, 7, ..., 7, 7, 6],
- [7, 6, 6, ..., 6, 7, 7],
- [7, 6, 7, ..., 7, 8, 7]])
+ [6, 7, 6, ..., 6, 6, 7],
+ [7, 7, 7, ..., 6, 6, 7],
+ [6, 7, 7, ..., 8, 8, 7]])
@@ -1241,13 +1241,13 @@ Finding the difference between min and max
-array([[775. , 775. , 737.5, ..., 700. , 737.5, 775. ],
- [775. , 775. , 775. , ..., 737.5, 737.5, 737.5],
- [737.5, 737.5, 737.5, ..., 775. , 737.5, 737.5],
+array([[737.5, 737.5, 737.5, ..., 737.5, 700. , 737.5],
+ [775. , 737.5, 775. , ..., 737.5, 737.5, 775. ],
+ [737.5, 775. , 737.5, ..., 737.5, 775. , 775. ],
...,
- [737.5, 737.5, 737.5, ..., 737.5, 737.5, 775. ],
- [737.5, 775. , 775. , ..., 775. , 737.5, 737.5],
- [737.5, 775. , 737.5, ..., 737.5, 700. , 737.5]])
+ [775. , 737.5, 775. , ..., 775. , 775. , 737.5],
+ [737.5, 737.5, 737.5, ..., 775. , 775. , 737.5],
+ [775. , 737.5, 737.5, ..., 700. , 700. , 737.5]])
@@ -1276,19 +1276,19 @@ Finding the difference between min and max
-array([[ -9.55417349, -9.78584972, -9.92661689, ..., -12.67789616,
- -10.49077377, -6.56515605],
- [-10.00010532, -10.70819797, -7.20985445, ..., -11.10789153,
- -11.73163442, -8.29531203],
- [-11.88696252, -12.29088724, -11.70346147, ..., -7.30859657,
- -8.97849533, -13.84614818],
+array([[-13.35480377, -11.37389473, -6.11490009, ..., -9.69409241,
+ -14.14853765, -11.04724036],
+ [ -6.50332158, -11.09958323, -5.95998607, ..., -13.2604716 ,
+ -13.19971009, -8.49574616],
+ [-12.28051357, -10.19815861, -7.42945122, ..., -11.5798199 ,
+ -8.91153322, -8.6079996 ],
...,
- [-11.90215938, -7.91187899, -13.08679868, ..., -10.41120932,
- -11.35468344, -11.06459146],
- [-11.50292233, -6.26889319, -7.38221861, ..., -10.24036433,
- -10.12594611, -5.63518882],
- [-10.32651953, -6.06759924, -9.54152312, ..., -10.45702773,
- -12.20574751, -8.01746694]])
+ [ -8.10255586, -12.16615351, -11.27253366, ..., -10.44432682,
+ -7.39154896, -10.99574973],
+ [-16.5968518 , -12.57487222, -8.39736923, ..., -4.15595587,
+ -8.86979291, -11.28873531],
+ [ -8.59386363, -9.38047716, -10.46838159, ..., -11.30765026,
+ -10.89295232, -9.24056526]])
@@ -1332,19 +1332,19 @@ Finding the difference between min and max
-array([[ -9.55417349, -9.78584972, -9.92661689, ..., -12.67789616,
- -10.49077377, -6.56515605],
- [-10.00010532, -10.70819797, -7.20985445, ..., -11.10789153,
- -11.73163442, -8.29531203],
- [-11.88696252, -12.29088724, -11.70346147, ..., -7.30859657,
- -8.97849533, -13.84614818],
+array([[-13.35480377, -11.37389473, -6.11490009, ..., -9.69409241,
+ -14.14853765, -11.04724036],
+ [ -6.50332158, -11.09958323, -5.95998607, ..., -13.2604716 ,
+ -13.19971009, -8.49574616],
+ [-12.28051357, -10.19815861, -7.42945122, ..., -11.5798199 ,
+ -8.91153322, -8.6079996 ],
...,
- [-11.90215938, -7.91187899, -13.08679868, ..., -10.41120932,
- -11.35468344, -11.06459146],
- [-11.50292233, -6.26889319, -7.38221861, ..., -10.24036433,
- -10.12594611, -5.63518882],
- [-10.32651953, -6.06759924, -9.54152312, ..., -10.45702773,
- -12.20574751, -8.01746694]])
+ [ -8.10255586, -12.16615351, -11.27253366, ..., -10.44432682,
+ -7.39154896, -10.99574973],
+ [-16.5968518 , -12.57487222, -8.39736923, ..., -4.15595587,
+ -8.86979291, -11.28873531],
+ [ -8.59386363, -9.38047716, -10.46838159, ..., -11.30765026,
+ -10.89295232, -9.24056526]])
@@ -1711,7 +1711,7 @@ Resources and references
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
@@ -1840,7 +1840,7 @@ Contribute
By the Project Pythia Community.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
diff --git a/core/overview.html b/core/overview.html
index 6acc0ae46..fd4752cc2 100644
--- a/core/overview.html
+++ b/core/overview.html
@@ -79,7 +79,7 @@
-
+
@@ -886,7 +886,7 @@ Tutorials
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
@@ -1015,7 +1015,7 @@ Contribute
By the Project Pythia Community.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
diff --git a/core/pandas.html b/core/pandas.html
index 2e4230efc..02509b347 100644
--- a/core/pandas.html
+++ b/core/pandas.html
@@ -79,7 +79,7 @@
-
+
@@ -759,7 +759,7 @@ Pandas
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
@@ -888,7 +888,7 @@ Contribute
By the Project Pythia Community.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
diff --git a/core/pandas/pandas.html b/core/pandas/pandas.html
index 734fecdb6..2e24138b7 100644
--- a/core/pandas/pandas.html
+++ b/core/pandas/pandas.html
@@ -79,7 +79,7 @@
-
+
@@ -1098,7 +1098,7 @@ Indexing a Seri
-/tmp/ipykernel_2735/737336773.py:1: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`
+/tmp/ipykernel_2746/737336773.py:1: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`
nino34_series[3]
@@ -1449,7 +1449,7 @@ Extending to the
-/tmp/ipykernel_2735/541596450.py:1: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`
+/tmp/ipykernel_2746/541596450.py:1: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`
df["Nino34"][3]
@@ -3645,7 +3645,7 @@ Resampling
-/tmp/ipykernel_2735/233158901.py:1: FutureWarning: 'Y' is deprecated and will be removed in a future version, please use 'YE' instead.
+/tmp/ipykernel_2746/233158901.py:1: FutureWarning: 'Y' is deprecated and will be removed in a future version, please use 'YE' instead.
df.Nino34.resample('1Y').mean().plot();
@@ -4434,7 +4434,7 @@ Resources and References
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
@@ -4563,7 +4563,7 @@ Contribute
By the Project Pythia Community.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
diff --git a/core/xarray.html b/core/xarray.html
index e4624108b..6bc787165 100644
--- a/core/xarray.html
+++ b/core/xarray.html
@@ -79,7 +79,7 @@
-
+
@@ -760,7 +760,7 @@ Xarray
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
@@ -889,7 +889,7 @@ Contribute
By the Project Pythia Community.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
diff --git a/core/xarray/computation-masking.html b/core/xarray/computation-masking.html
index b15ad248c..12f19902b 100644
--- a/core/xarray/computation-masking.html
+++ b/core/xarray/computation-masking.html
@@ -79,7 +79,7 @@
-
+
@@ -943,7 +943,7 @@ Data Setup
@@ -1576,7 +1576,7 @@ Arithmetic Operations
+ dtype='float64', name='lon', length=360))
In addition, there are many other arithmetic operations that can be performed on DataArrays. In this example, we demonstrate squaring the original Celsius values of our air temperature data:
@@ -2246,7 +2246,7 @@ Arithmetic Operations
+ dtype='float64', name='lon', length=360))
@@ -2876,7 +2876,7 @@ Aggregation Methodsdim keyword argument; this means that the function was applied over all of the dataset’s dimensions. In other words, the aggregation method computed the mean of every element of the temperature dataset across every temporal and spatial data point. However, if a dimension name is used with the dim keyword argument, the aggregation method computes an aggregation along the given dimension. In this next example, we use aggregation to calculate the temporal mean across all spatial data; this is performed by providing the dimension name 'time' to the dim keyword argument:
@@ -3276,7 +3276,7 @@ Aggregation Methods
@@ -3717,7 +3717,7 @@ Aggregation Methods
@@ -4325,7 +4325,7 @@ Aggregation Methods
@@ -4892,7 +4892,7 @@ Compute climatologyDataArray containing the climatology data, we can plot the data in different ways. In this example, we plot the climatology at a specific latitude-longitude point:
@@ -5412,7 +5412,7 @@ Compute anomaly
@@ -6095,7 +6095,7 @@ Compute anomaly.weighted() method and the newly loaded CESM2 dataset to weight the grid cell data as described above:
@@ -6598,7 +6598,7 @@ Other high level computation functionalityrolling method to compute averages in a moving window of 5 months of data:
@@ -7114,7 +7114,7 @@ Other high level computation functionality
@@ -7783,7 +7783,7 @@ Masking Data
Using where with one condition
@@ -8421,15 +8421,15 @@ Using where
+ dtype='float64', name='lon', length=360))
- cell_measures :
- area: areacello
- cell_methods :
- area: mean where sea time: mean
- comment :
- Model data on the 1x1 grid includes values in all cells for which ocean cells on the native grid cover more than 52.5 percent of the 1x1 grid cell. This 52.5 percent cutoff was chosen to produce ocean surface area on the 1x1 grid as close as possible to ocean surface area on the native grid, while not introducing fractional cell coverage.
- description :
- This may differ from "surface temperature" in regions of sea ice or floating ice shelves. For models using conservative temperature as the prognostic field, they should report the top ocean layer as surface potential temperature, which is the same as surface in situ temperature.
- frequency :
- mon
- id :
- tos
- long_name :
- Sea Surface Temperature
- mipTable :
- Omon
- out_name :
- tos
- prov :
- Omon ((isd.003))
- realm :
- ocean
- standard_name :
- sea_surface_temperature
- time :
- time
- time_label :
- time-mean
- time_title :
- Temporal mean
- title :
- Sea Surface Temperature
- type :
- real
- units :
- degC
- variable_id :
- tos
As shown in the previous example, methods like .isel() and .sel() return data of a different shape than the original data provided to them. However, .where() preserves the shape of the original data by masking the values with a Boolean condition. Data values for which the condition is True are returned identical to the values passed in. On the other hand, data values for which the condition is False are returned as a preset example value. (This example value defaults to nan, but can be set to other values as well.)
Before testing .where(), it is helpful to look at the official documentation. As stated above, the .where() method takes a Boolean condition. (Boolean conditions use operators such as less-than, greater-than, and equal-to, and return a value of True or False.) Most uses of .where() check whether or not specific data values are less than or greater than a constant value. As stated in the documentation, the data values specified in the Boolean condition of .where() can be any of the following:
@@ -8864,7 +8864,7 @@ Using where
+ dtype='float64', name='lon', length=360))- cell_measures :
- area: areacello
- cell_methods :
- area: mean where sea time: mean
- comment :
- Model data on the 1x1 grid includes values in all cells for which ocean cells on the native grid cover more than 52.5 percent of the 1x1 grid cell. This 52.5 percent cutoff was chosen to produce ocean surface area on the 1x1 grid as close as possible to ocean surface area on the native grid, while not introducing fractional cell coverage.
- description :
- This may differ from "surface temperature" in regions of sea ice or floating ice shelves. For models using conservative temperature as the prognostic field, they should report the top ocean layer as surface potential temperature, which is the same as surface in situ temperature.
- frequency :
- mon
- id :
- tos
- long_name :
- Sea Surface Temperature
- mipTable :
- Omon
- out_name :
- tos
- prov :
- Omon ((isd.003))
- realm :
- ocean
- standard_name :
- sea_surface_temperature
- time :
- time
- time_label :
- time-mean
- time_title :
- Temporal mean
- title :
- Sea Surface Temperature
- type :
- real
- units :
- degC
- variable_id :
- tos
In this example, we use Matplotlib to plot the original, unmasked data, as well as the masked data created in the previous example.
@@ -9321,7 +9321,7 @@ Resources and References
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
@@ -9450,7 +9450,7 @@ Contribute
By the Project Pythia Community.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
diff --git a/core/xarray/dask-arrays-xarray.html b/core/xarray/dask-arrays-xarray.html
index 0af9daaf3..65793b335 100644
--- a/core/xarray/dask-arrays-xarray.html
+++ b/core/xarray/dask-arrays-xarray.html
@@ -79,7 +79,7 @@
-
+
@@ -568,91 +568,91 @@ Create a dask.a
-array([[[0.08870167, 0.5536724 , 0.86869933, ..., 0.59745109,
- 0.66241166, 0.14712849],
- [0.79424186, 0.53985475, 0.83117213, ..., 0.64752434,
- 0.2068288 , 0.43561572],
- [0.93755297, 0.12641562, 0.55493093, ..., 0.44682309,
- 0.9108161 , 0.28494995],
+array([[[3.73479162e-01, 5.69354087e-01, 8.44121771e-01, ...,
+ 6.95300592e-01, 7.05791578e-01, 3.22757785e-01],
+ [7.34673387e-01, 3.22502093e-02, 2.98660716e-02, ...,
+ 4.48326876e-01, 1.28181189e-01, 6.17129937e-01],
+ [4.61524077e-01, 5.72981031e-01, 6.22455684e-02, ...,
+ 4.66387696e-01, 2.24365962e-02, 9.05528466e-01],
...,
- [0.36681494, 0.55104908, 0.67358062, ..., 0.93120538,
- 0.51164396, 0.2149192 ],
- [0.33220658, 0.18853655, 0.84133513, ..., 0.09990092,
- 0.45265087, 0.48841145],
- [0.54632195, 0.99301667, 0.40513953, ..., 0.5341915 ,
- 0.3220391 , 0.07544213]],
-
- [[0.8398981 , 0.14999579, 0.81431679, ..., 0.45814483,
- 0.81962377, 0.01308931],
- [0.52904343, 0.17038365, 0.49820524, ..., 0.71294698,
- 0.49906669, 0.32305634],
- [0.83597797, 0.12242548, 0.63334391, ..., 0.77669187,
- 0.44530452, 0.02031067],
+ [4.32840675e-01, 6.11791423e-01, 7.77157080e-01, ...,
+ 5.21898060e-01, 5.08370890e-01, 5.88153598e-01],
+ [8.04673850e-01, 7.80296541e-03, 5.00244166e-01, ...,
+ 9.45710973e-01, 5.08700949e-01, 8.89905823e-01],
+ [4.87536782e-01, 4.64576884e-01, 4.97751472e-01, ...,
+ 9.63294478e-01, 8.00438624e-01, 9.39160580e-01]],
+
+ [[9.59798128e-01, 9.67977225e-01, 5.67081374e-01, ...,
+ 1.15551677e-01, 5.93188567e-01, 8.21494821e-01],
+ [7.09798171e-01, 6.86764079e-01, 9.75639983e-01, ...,
+ 6.65980228e-01, 3.63136928e-01, 3.91634965e-01],
+ [2.45312843e-01, 7.92456108e-01, 7.52068086e-01, ...,
+ 8.31396290e-01, 7.34853345e-01, 1.59997513e-02],
...,
- [0.23487395, 0.54185136, 0.84738714, ..., 0.43194404,
- 0.96464114, 0.34661126],
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- 0.09187095, 0.88666451],
- [0.46384865, 0.02462827, 0.71575049, ..., 0.47421697,
- 0.30570578, 0.58421292]],
-
- [[0.05704318, 0.36232811, 0.03448708, ..., 0.86748361,
- 0.49546271, 0.39195962],
- [0.62907898, 0.52593284, 0.87418635, ..., 0.97638485,
- 0.63695351, 0.75326941],
- [0.11640535, 0.15534404, 0.80460691, ..., 0.26935451,
- 0.00686985, 0.42474694],
+ [6.29006234e-01, 5.66655665e-01, 9.59798385e-01, ...,
+ 4.64611557e-02, 8.47630616e-01, 9.96868460e-01],
+ [8.65409572e-01, 6.62829465e-01, 5.85935120e-01, ...,
+ 2.76645046e-01, 4.05531947e-01, 1.86970157e-01],
+ [5.88186192e-02, 4.80258125e-01, 7.50053106e-01, ...,
+ 5.82584007e-01, 7.16238302e-01, 3.09095729e-01]],
+
+ [[1.95867406e-01, 8.02997164e-01, 5.92335907e-02, ...,
+ 6.00495084e-02, 3.58568845e-02, 6.14435689e-01],
+ [5.40191024e-01, 7.45240262e-01, 5.83926237e-01, ...,
+ 5.42683512e-01, 7.15062271e-05, 8.15838308e-01],
+ [3.56121359e-01, 6.64103118e-01, 3.19197578e-01, ...,
+ 4.30885292e-01, 5.63274760e-01, 5.58304993e-01],
...,
- [0.53156325, 0.69190957, 0.69352384, ..., 0.77170882,
- 0.44011939, 0.39866217],
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- 0.73788577, 0.04341616],
- [0.94117032, 0.92630431, 0.79008536, ..., 0.02443356,
- 0.52042712, 0.83466538]],
+ [3.70429144e-01, 2.28949319e-01, 2.12465225e-01, ...,
+ 8.75347366e-01, 6.88876481e-01, 8.52985255e-01],
+ [8.43544201e-01, 3.50323808e-01, 9.94755069e-01, ...,
+ 2.31937385e-01, 6.46129523e-01, 7.56519847e-01],
+ [9.23292344e-01, 9.06543740e-01, 2.16039623e-01, ...,
+ 6.17841121e-01, 5.83778599e-02, 7.07627581e-01]],
...,
- [[0.1396787 , 0.60143505, 0.57058462, ..., 0.18893598,
- 0.82985107, 0.54242887],
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- 0.63127177, 0.4663941 ],
- [0.55463676, 0.49022722, 0.28183346, ..., 0.64073606,
- 0.37788708, 0.05233016],
+ [[5.50076397e-01, 6.23247920e-01, 6.69863249e-01, ...,
+ 6.66141598e-01, 4.98073488e-01, 9.70545887e-01],
+ [7.38559992e-01, 3.21393458e-01, 4.05421647e-01, ...,
+ 2.52285356e-01, 8.08345653e-01, 7.28764634e-01],
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...,
- [0.39020736, 0.85735173, 0.00786958, ..., 0.66161596,
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- 0.5063675 , 0.73631955],
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- 0.20253998, 0.93656703]],
-
- [[0.28018602, 0.84864745, 0.65231976, ..., 0.57238332,
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- 0.89875752, 0.06756555],
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- 0.95937476, 0.4827714 ],
+ [6.11978830e-01, 6.32479701e-01, 1.75826196e-01, ...,
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+ 6.24560049e-01, 3.32587128e-02, 1.48219268e-01]],
+
+ [[9.16337354e-01, 1.38628117e-01, 2.76572964e-01, ...,
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...,
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- 0.75240336, 0.21357396]],
-
- [[0.05024137, 0.77607526, 0.32452642, ..., 0.26169962,
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- 0.66257683, 0.78342945],
- [0.29906903, 0.08192958, 0.51913898, ..., 0.32946737,
- 0.91310302, 0.99198924],
+ [8.75833692e-01, 8.80034882e-01, 3.94957057e-01, ...,
+ 3.88766073e-01, 5.62146961e-01, 4.07401505e-01],
+ [8.83232515e-02, 7.93868945e-02, 7.09396623e-01, ...,
+ 4.00821090e-01, 9.67897242e-01, 3.30712526e-02],
+ [6.21839195e-01, 3.66731583e-01, 9.97239185e-01, ...,
+ 4.06892246e-01, 8.84142338e-01, 1.29206897e-01]],
+
+ [[3.13410458e-01, 7.92759677e-01, 8.24979480e-02, ...,
+ 5.38935776e-02, 4.52819725e-01, 1.48534750e-01],
+ [2.06595218e-01, 8.46746782e-01, 9.06689993e-01, ...,
+ 8.52571008e-01, 4.28351337e-01, 8.06352220e-01],
+ [3.77563013e-01, 9.99637726e-01, 3.37356897e-01, ...,
+ 8.18911136e-01, 8.32562303e-01, 8.45143802e-02],
...,
- [0.89344391, 0.04685752, 0.16870497, ..., 0.70133294,
- 0.2028986 , 0.16916596],
- [0.62721463, 0.12582174, 0.59300888, ..., 0.93023778,
- 0.50053284, 0.63544531],
- [0.89994404, 0.84669077, 0.65421957, ..., 0.19573779,
- 0.56024887, 0.73584493]]])
+ [5.51431397e-01, 8.77465751e-01, 9.63517992e-01, ...,
+ 3.34710544e-02, 9.70363045e-01, 5.42823121e-01],
+ [3.89247115e-01, 3.36500509e-01, 4.31638413e-02, ...,
+ 8.82214294e-02, 2.41024552e-01, 4.19862342e-01],
+ [5.65544128e-01, 6.09859197e-01, 2.77376901e-01, ...,
+ 5.51955058e-01, 6.67783854e-02, 4.08359511e-01]]])
@@ -856,7 +856,7 @@ Why the difference?
-
+
@@ -872,11 +872,11 @@ Compute the result
-CPU times: user 406 ms, sys: 28 ms, total: 434 ms
-Wall time: 232 ms
+CPU times: user 399 ms, sys: 28.5 ms, total: 428 ms
+Wall time: 228 ms
-12002441.41340752
+12000189.444496717
@@ -894,7 +894,7 @@ Exercise with d
-
+
As is obvious from the above exercise, Dask quickly and easily determines a strategy for performing the operations, in this case a sum. This illustrates the appeal of Dask: automatic algorithm generation that scales from simple arithmetic problems to highly complex scientific equations with large datasets and multiple operations.
@@ -974,7 +974,7 @@ Exercise with d
-
+
@@ -1106,7 +1106,7 @@ Testing a bigger calculation
-
+
@@ -1117,34 +1117,34 @@ Testing a bigger calculation
-[ ] | 0% Completed | 169.21 us
+[ ] | 0% Completed | 255.13 us
-[ ] | 0% Completed | 105.92 ms
+[ ] | 0% Completed | 106.98 ms
-[ ] | 0% Completed | 206.66 ms
+[ ] | 0% Completed | 207.72 ms
-[ ] | 0% Completed | 307.49 ms
+[ ] | 0% Completed | 308.36 ms
-[ ] | 0% Completed | 408.13 ms
+[ ] | 0% Completed | 409.00 ms
-[ ] | 0% Completed | 508.89 ms
+[ ] | 0% Completed | 509.67 ms
-[ ] | 1% Completed | 609.51 ms
+[ ] | 0% Completed | 610.39 ms
-[# ] | 4% Completed | 710.49 ms
+[# ] | 3% Completed | 711.30 ms
-[### ] | 7% Completed | 811.54 ms
+[## ] | 6% Completed | 812.41 ms
-[#### ] | 10% Completed | 912.41 ms
+[#### ] | 10% Completed | 913.29 ms
[#### ] | 10% Completed | 1.01 s
@@ -1153,7 +1153,7 @@ Testing a bigger calculation[#### ] | 10% Completed | 1.11 s
-[#### ] | 10% Completed | 1.21 s
+[#### ] | 10% Completed | 1.22 s
[#### ] | 10% Completed | 1.32 s
@@ -1162,10 +1162,10 @@ Testing a bigger calculation[#### ] | 10% Completed | 1.42 s
-[##### ] | 13% Completed | 1.52 s
+[##### ] | 12% Completed | 1.52 s
-[###### ] | 16% Completed | 1.62 s
+[###### ] | 15% Completed | 1.62 s
[####### ] | 19% Completed | 1.72 s
@@ -1186,13 +1186,13 @@ Testing a bigger calculation[######## ] | 21% Completed | 2.22 s
-[######## ] | 22% Completed | 2.32 s
+[######## ] | 21% Completed | 2.32 s
-[########## ] | 25% Completed | 2.43 s
+[######### ] | 24% Completed | 2.43 s
-[########### ] | 27% Completed | 2.53 s
+[########### ] | 28% Completed | 2.53 s
[############ ] | 31% Completed | 2.63 s
@@ -1216,16 +1216,16 @@ Testing a bigger calculation[############## ] | 35% Completed | 3.23 s
-[############### ] | 38% Completed | 3.33 s
+[############## ] | 37% Completed | 3.33 s
-[################ ] | 40% Completed | 3.43 s
+[############### ] | 39% Completed | 3.43 s
-[################# ] | 44% Completed | 3.53 s
+[################# ] | 43% Completed | 3.53 s
-[################### ] | 48% Completed | 3.63 s
+[################## ] | 46% Completed | 3.64 s
[################### ] | 48% Completed | 3.74 s
@@ -1246,10 +1246,10 @@ Testing a bigger calculation[#################### ] | 50% Completed | 4.24 s
-[##################### ] | 53% Completed | 4.34 s
+[#################### ] | 52% Completed | 4.34 s
-[###################### ] | 57% Completed | 4.44 s
+[###################### ] | 55% Completed | 4.44 s
[####################### ] | 59% Completed | 4.54 s
@@ -1276,7 +1276,7 @@ Testing a bigger calculation[######################### ] | 64% Completed | 5.25 s
-[########################### ] | 67% Completed | 5.35 s
+[########################### ] | 68% Completed | 5.35 s
[############################ ] | 72% Completed | 5.45 s
@@ -1300,13 +1300,13 @@ Testing a bigger calculation[############################## ] | 75% Completed | 6.06 s
-[############################### ] | 77% Completed | 6.16 s
+[############################### ] | 78% Completed | 6.16 s
-[################################ ] | 80% Completed | 6.26 s
+[################################ ] | 82% Completed | 6.26 s
-[################################# ] | 83% Completed | 6.36 s
+[################################# ] | 84% Completed | 6.36 s
[################################## ] | 85% Completed | 6.46 s
@@ -1324,16 +1324,16 @@ Testing a bigger calculation[################################## ] | 85% Completed | 6.86 s
-[################################## ] | 86% Completed | 6.96 s
+[################################### ] | 89% Completed | 6.96 s
-[################################### ] | 89% Completed | 7.06 s
+[#################################### ] | 92% Completed | 7.07 s
-[##################################### ] | 92% Completed | 7.16 s
+[###################################### ] | 96% Completed | 7.17 s
-[###################################### ] | 96% Completed | 7.27 s
+[####################################### ] | 99% Completed | 7.27 s
[########################################] | 100% Completed | 7.37 s
@@ -1854,7 +1854,7 @@ Reading data with
- latPandasIndex
PandasIndex(Index([-89.5, -88.5, -87.5, -86.5, -85.5, -84.5, -83.5, -82.5, -81.5, -80.5,
+ 80.5, 81.5, 82.5, 83.5, 84.5, 85.5, 86.5, 87.5, 88.5, 89.5])
- lon(lon)float640.5 1.5 2.5 ... 357.5 358.5 359.5
array([ 0.5, 1.5, 2.5, ..., 357.5, 358.5, 359.5])
- latPandasIndex
PandasIndex(Index([-89.5, -88.5, -87.5, -86.5, -85.5, -84.5, -83.5, -82.5, -81.5, -80.5,
...
80.5, 81.5, 82.5, 83.5, 84.5, 85.5, 86.5, 87.5, 88.5, 89.5],
- dtype='float64', name='lat', length=180)) - lonPandasIndex
PandasIndex(Index([ 0.5, 1.5, 2.5, 3.5, 4.5, 5.5, 6.5, 7.5, 8.5, 9.5,
+ dtype='float64', name='lat', length=180))
- lonPandasIndex
PandasIndex(Index([ 0.5, 1.5, 2.5, 3.5, 4.5, 5.5, 6.5, 7.5, 8.5, 9.5,
...
350.5, 351.5, 352.5, 353.5, 354.5, 355.5, 356.5, 357.5, 358.5, 359.5],
- dtype='float64', name='lon', length=360))
+ dtype='float64', name='lon', length=360))
As shown in the above example, the result of the applied operations is an Xarray DataArray that contains a Dask Array, an identical object type to the object that the operations were performed on. This is true for any operations that can be applied to Xarray DataArrays, including subsetting operations; this next example illustrates this:
@@ -3739,7 +3739,7 @@ Parallel and lazy computation using
@@ -3794,10 +3794,10 @@ Parallel and lazy computation using dask.visualize() function, as shown in this example:
@@ -3819,10 +3819,10 @@ Parallel and lazy computation using
-[ ] | 0% Completed | 122.44 us
+[ ] | 0% Completed | 137.30 us
-[########################################] | 100% Completed | 101.18 ms
+[########################################] | 100% Completed | 101.19 ms
@@ -4236,7 +4236,7 @@ Resources and references
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
@@ -4365,7 +4365,7 @@ Contribute
By the Project Pythia Community.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
diff --git a/core/xarray/enso-xarray.html b/core/xarray/enso-xarray.html
index 316661573..6e86d4355 100644
--- a/core/xarray/enso-xarray.html
+++ b/core/xarray/enso-xarray.html
@@ -79,7 +79,7 @@
-
+
@@ -958,7 +958,7 @@ The Niño 3.4 Index
@@ -1590,7 +1590,7 @@ Select the Niño 3.4 regionwhere() method:
+array(1.8436452, dtype=float32)
The final step of the Niño 3.4 index calculation involves normalizing the data. In this example, we perform this normalization by dividing the smoothed anomaly data by the standard deviation calculated above:
@@ -3396,7 +3396,7 @@ Resources and References
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
@@ -3525,7 +3525,7 @@ Contribute
By the Project Pythia Community.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
diff --git a/core/xarray/xarray-intro.html b/core/xarray/xarray-intro.html
index e17e6628d..308dd9baa 100644
--- a/core/xarray/xarray-intro.html
+++ b/core/xarray/xarray-intro.html
@@ -79,7 +79,7 @@
-
+
@@ -583,25 +583,25 @@ Generate a random numpy array
-array([[[274.00119189, 291.26874805, 287.10212211, 281.93321028],
- [276.72439019, 281.86759901, 277.88521001, 291.15900492],
- [283.47892997, 281.69598295, 281.87670232, 280.04253353]],
+array([[[283.19514872, 279.93414516, 288.85955729, 280.30299831],
+ [282.03318795, 283.36237877, 288.28464039, 284.82856563],
+ [282.15998672, 276.06859236, 285.78192464, 280.47703387]],
- [[292.49183408, 286.75694127, 291.1430049 , 279.6708509 ],
- [283.19466798, 285.34236937, 289.30840199, 286.22515585],
- [289.80677567, 281.73710917, 294.57091617, 291.28505114]],
+ [[282.44435897, 282.262194 , 286.34635361, 285.32213341],
+ [283.03737484, 288.57815157, 290.02698113, 278.23328324],
+ [277.06837841, 286.4771544 , 284.58714744, 288.42673576]],
- [[278.59826878, 287.14196565, 284.68298455, 286.59720048],
- [287.51252429, 288.52590266, 273.69692215, 285.39338362],
- [292.66871999, 278.60078099, 280.78168263, 291.92156783]],
+ [[280.62831671, 275.374152 , 283.08613936, 292.56788174],
+ [286.26020096, 282.03531027, 280.16394984, 288.9366629 ],
+ [282.90971416, 278.40998651, 284.98850877, 283.14584888]],
- [[281.24772197, 286.52210588, 282.61809684, 288.82826349],
- [282.48419301, 286.34184212, 277.57835692, 279.66054122],
- [288.97073742, 287.33849395, 289.90083339, 285.95347237]],
+ [[284.34422355, 283.20830934, 278.27414095, 285.21976361],
+ [279.17521323, 292.21743424, 276.974416 , 284.06699408],
+ [274.74247997, 279.63780379, 275.87914515, 277.71758929]],
- [[282.699394 , 286.72856786, 281.89265707, 286.16350665],
- [278.02139036, 284.54696447, 283.16191651, 278.99615563],
- [284.94443941, 287.35039327, 289.38110818, 280.10681174]]])
+ [[282.71726594, 281.99808895, 279.66639367, 276.71709325],
+ [278.30599074, 277.08406243, 273.68999203, 279.75441922],
+ [271.39869986, 286.48138908, 285.22713979, 291.77285885]]])
@@ -982,44 +982,44 @@ Wrap the array: first attempt
@@ -1402,44 +1402,44 @@ Assign dimension names<xarray.DataArray (time: 5, lat: 3, lon: 4)> Size: 480B
-array([[[274.00119189, 291.26874805, 287.10212211, 281.93321028],
- [276.72439019, 281.86759901, 277.88521001, 291.15900492],
- [283.47892997, 281.69598295, 281.87670232, 280.04253353]],
-
- [[292.49183408, 286.75694127, 291.1430049 , 279.6708509 ],
- [283.19466798, 285.34236937, 289.30840199, 286.22515585],
- [289.80677567, 281.73710917, 294.57091617, 291.28505114]],
-
- [[278.59826878, 287.14196565, 284.68298455, 286.59720048],
- [287.51252429, 288.52590266, 273.69692215, 285.39338362],
- [292.66871999, 278.60078099, 280.78168263, 291.92156783]],
-
- [[281.24772197, 286.52210588, 282.61809684, 288.82826349],
- [282.48419301, 286.34184212, 277.57835692, 279.66054122],
- [288.97073742, 287.33849395, 289.90083339, 285.95347237]],
-
- [[282.699394 , 286.72856786, 281.89265707, 286.16350665],
- [278.02139036, 284.54696447, 283.16191651, 278.99615563],
- [284.94443941, 287.35039327, 289.38110818, 280.10681174]]])
-Dimensions without coordinates: time, lat, lon
+array([[[283.19514872, 279.93414516, 288.85955729, 280.30299831],
+ [282.03318795, 283.36237877, 288.28464039, 284.82856563],
+ [282.15998672, 276.06859236, 285.78192464, 280.47703387]],
+
+ [[282.44435897, 282.262194 , 286.34635361, 285.32213341],
+ [283.03737484, 288.57815157, 290.02698113, 278.23328324],
+ [277.06837841, 286.4771544 , 284.58714744, 288.42673576]],
+
+ [[280.62831671, 275.374152 , 283.08613936, 292.56788174],
+ [286.26020096, 282.03531027, 280.16394984, 288.9366629 ],
+ [282.90971416, 278.40998651, 284.98850877, 283.14584888]],
+
+ [[284.34422355, 283.20830934, 278.27414095, 285.21976361],
+ [279.17521323, 292.21743424, 276.974416 , 284.06699408],
+ [274.74247997, 279.63780379, 275.87914515, 277.71758929]],
+
+ [[282.71726594, 281.99808895, 279.66639367, 276.71709325],
+ [278.30599074, 277.08406243, 273.68999203, 279.75441922],
+ [271.39869986, 286.48138908, 285.22713979, 291.77285885]]])
+Dimensions without coordinates: time, lat, lon
This DataArray is already an improvement over a NumPy array; the DataArray contains names for each of the dimensions (or axes in NumPy parlance). An additional improvement is the association of coordinate-value arrays with data upon creation of a DataArray. In the next example, we illustrate the creation of NumPy arrays representing the coordinate values for each dimension of the DataArray, and how to associate these coordinate arrays with the data in our DataArray.
@@ -1849,51 +1849,51 @@ Initialize the
fill: currentColor;
}
<xarray.DataArray (time: 5, lat: 3, lon: 4)> Size: 480B
-array([[[274.00119189, 291.26874805, 287.10212211, 281.93321028],
- [276.72439019, 281.86759901, 277.88521001, 291.15900492],
- [283.47892997, 281.69598295, 281.87670232, 280.04253353]],
+array([[[283.19514872, 279.93414516, 288.85955729, 280.30299831],
+ [282.03318795, 283.36237877, 288.28464039, 284.82856563],
+ [282.15998672, 276.06859236, 285.78192464, 280.47703387]],
- [[292.49183408, 286.75694127, 291.1430049 , 279.6708509 ],
- [283.19466798, 285.34236937, 289.30840199, 286.22515585],
- [289.80677567, 281.73710917, 294.57091617, 291.28505114]],
+ [[282.44435897, 282.262194 , 286.34635361, 285.32213341],
+ [283.03737484, 288.57815157, 290.02698113, 278.23328324],
+ [277.06837841, 286.4771544 , 284.58714744, 288.42673576]],
- [[278.59826878, 287.14196565, 284.68298455, 286.59720048],
- [287.51252429, 288.52590266, 273.69692215, 285.39338362],
- [292.66871999, 278.60078099, 280.78168263, 291.92156783]],
+ [[280.62831671, 275.374152 , 283.08613936, 292.56788174],
+ [286.26020096, 282.03531027, 280.16394984, 288.9366629 ],
+ [282.90971416, 278.40998651, 284.98850877, 283.14584888]],
- [[281.24772197, 286.52210588, 282.61809684, 288.82826349],
- [282.48419301, 286.34184212, 277.57835692, 279.66054122],
- [288.97073742, 287.33849395, 289.90083339, 285.95347237]],
+ [[284.34422355, 283.20830934, 278.27414095, 285.21976361],
+ [279.17521323, 292.21743424, 276.974416 , 284.06699408],
+ [274.74247997, 279.63780379, 275.87914515, 277.71758929]],
- [[282.699394 , 286.72856786, 281.89265707, 286.16350665],
- [278.02139036, 284.54696447, 283.16191651, 278.99615563],
- [284.94443941, 287.35039327, 289.38110818, 280.10681174]]])
+ [[282.71726594, 281.99808895, 279.66639367, 276.71709325],
+ [278.30599074, 277.08406243, 273.68999203, 279.75441922],
+ [271.39869986, 286.48138908, 285.22713979, 291.77285885]]])
Coordinates:
* time (time) datetime64[ns] 40B 2018-01-01 2018-01-02 ... 2018-01-05
* lat (lat) float64 24B 25.0 40.0 55.0
- * lon (lon) float64 32B -120.0 -100.0 -80.0 -60.0
+ dtype='datetime64[ns]', name='time', freq='D'))
latPandasIndexPandasIndex(Index([25.0, 40.0, 55.0], dtype='float64', name='lat'))
lonPandasIndexPandasIndex(Index([-120.0, -100.0, -80.0, -60.0], dtype='float64', name='lon'))
@@ -2273,54 +2273,54 @@ Set useful attributes<xarray.DataArray (time: 5, lat: 3, lon: 4)> Size: 480B
-array([[[274.00119189, 291.26874805, 287.10212211, 281.93321028],
- [276.72439019, 281.86759901, 277.88521001, 291.15900492],
- [283.47892997, 281.69598295, 281.87670232, 280.04253353]],
+array([[[283.19514872, 279.93414516, 288.85955729, 280.30299831],
+ [282.03318795, 283.36237877, 288.28464039, 284.82856563],
+ [282.15998672, 276.06859236, 285.78192464, 280.47703387]],
- [[292.49183408, 286.75694127, 291.1430049 , 279.6708509 ],
- [283.19466798, 285.34236937, 289.30840199, 286.22515585],
- [289.80677567, 281.73710917, 294.57091617, 291.28505114]],
+ [[282.44435897, 282.262194 , 286.34635361, 285.32213341],
+ [283.03737484, 288.57815157, 290.02698113, 278.23328324],
+ [277.06837841, 286.4771544 , 284.58714744, 288.42673576]],
- [[278.59826878, 287.14196565, 284.68298455, 286.59720048],
- [287.51252429, 288.52590266, 273.69692215, 285.39338362],
- [292.66871999, 278.60078099, 280.78168263, 291.92156783]],
+ [[280.62831671, 275.374152 , 283.08613936, 292.56788174],
+ [286.26020096, 282.03531027, 280.16394984, 288.9366629 ],
+ [282.90971416, 278.40998651, 284.98850877, 283.14584888]],
- [[281.24772197, 286.52210588, 282.61809684, 288.82826349],
- [282.48419301, 286.34184212, 277.57835692, 279.66054122],
- [288.97073742, 287.33849395, 289.90083339, 285.95347237]],
+ [[284.34422355, 283.20830934, 278.27414095, 285.21976361],
+ [279.17521323, 292.21743424, 276.974416 , 284.06699408],
+ [274.74247997, 279.63780379, 275.87914515, 277.71758929]],
- [[282.699394 , 286.72856786, 281.89265707, 286.16350665],
- [278.02139036, 284.54696447, 283.16191651, 278.99615563],
- [284.94443941, 287.35039327, 289.38110818, 280.10681174]]])
+ [[282.71726594, 281.99808895, 279.66639367, 276.71709325],
+ [278.30599074, 277.08406243, 273.68999203, 279.75441922],
+ [271.39869986, 286.48138908, 285.22713979, 291.77285885]]])
Coordinates:
* time (time) datetime64[ns] 40B 2018-01-01 2018-01-02 ... 2018-01-05
* lat (lat) float64 24B 25.0 40.0 55.0
* lon (lon) float64 32B -120.0 -100.0 -80.0 -60.0
Attributes:
units: kelvin
- standard_name: air_temperature
+ dtype='datetime64[ns]', name='time', freq='D'))
latPandasIndexPandasIndex(Index([25.0, 40.0, 55.0], dtype='float64', name='lat'))
lonPandasIndexPandasIndex(Index([-120.0, -100.0, -80.0, -60.0], dtype='float64', name='lon'))
- units :
- kelvin
- standard_name :
- air_temperature
@@ -2699,51 +2699,51 @@ Issues with preservation of attributesdocumentation page.
@@ -3134,54 +3134,54 @@ Create a pressure
+ dtype='datetime64[ns]', name='time', freq='D'))latPandasIndexPandasIndex(Index([25.0, 40.0, 55.0], dtype='float64', name='lat'))
lonPandasIndexPandasIndex(Index([-120.0, -100.0, -80.0, -60.0], dtype='float64', name='lon'))
As listed in the rich display above, the new Dataset object is aware that both DataArrays share the same coordinate axes. (Please note that if this page is not run as a Jupyter Notebook, the rich display may be unavailable.)
@@ -3986,54 +3986,54 @@ Access Data variables and Coordinates in a DataArrays through a dictionary syntax, as shown in this example:
@@ -4407,54 +4407,54 @@ Access Data variables and Coordinates in a Dataset objects are mainly used for loading data from files, which will be covered later in this tutorial.
@@ -4838,18 +4838,18 @@ NumPy-like selection<xarray.DataArray (lat: 3, lon: 4)> Size: 96B
-array([[292.49183408, 286.75694127, 291.1430049 , 279.6708509 ],
- [283.19466798, 285.34236937, 289.30840199, 286.22515585],
- [289.80677567, 281.73710917, 294.57091617, 291.28505114]])
+array([[282.44435897, 282.262194 , 286.34635361, 285.32213341],
+ [283.03737484, 288.57815157, 290.02698113, 278.23328324],
+ [277.06837841, 286.4771544 , 284.58714744, 288.42673576]])
Coordinates:
time datetime64[ns] 8B 2018-01-02
* lat (lat) float64 24B 25.0 40.0 55.0
* lon (lon) float64 32B -120.0 -100.0 -80.0 -60.0
Attributes:
units: kelvin
- standard_name: air_temperature
+ standard_name: air_temperature
This example reveals one of the major shortcomings of index selection. In order to retrieve the correct data using index selection, anyone using a DataArray must have precise knowledge of the axes in the DataArray, including the order of the axes and the meaning of their indices.
By using named coordinates, as shown in the next set of examples, we can avoid this cumbersome burden.
@@ -5229,18 +5229,18 @@ Selecting with
fill: currentColor;
}
<xarray.DataArray (lat: 3, lon: 4)> Size: 96B
-array([[292.49183408, 286.75694127, 291.1430049 , 279.6708509 ],
- [283.19466798, 285.34236937, 289.30840199, 286.22515585],
- [289.80677567, 281.73710917, 294.57091617, 291.28505114]])
+array([[282.44435897, 282.262194 , 286.34635361, 285.32213341],
+ [283.03737484, 288.57815157, 290.02698113, 278.23328324],
+ [277.06837841, 286.4771544 , 284.58714744, 288.42673576]])
Coordinates:
time datetime64[ns] 8B 2018-01-02
* lat (lat) float64 24B 25.0 40.0 55.0
* lon (lon) float64 32B -120.0 -100.0 -80.0 -60.0
Attributes:
units: kelvin
- standard_name: air_temperature
+ standard_name: air_temperature
This method yields the same result as the index selection, however:
@@ -5627,18 +5627,18 @@ Nearest-neighbor sampling.sel indeed returned the data at the temporal value corresponding to the date 2018-01-05.
@@ -6017,20 +6017,20 @@ Interpolation
Info
@@ -6414,29 +6414,29 @@ Slicing along coordinates
Info
@@ -6818,18 +6818,18 @@ One more selection method: <xarray.DataArray (lat: 3, lon: 4)> Size: 96B
-array([[292.49183408, 286.75694127, 291.1430049 , 279.6708509 ],
- [283.19466798, 285.34236937, 289.30840199, 286.22515585],
- [289.80677567, 281.73710917, 294.57091617, 291.28505114]])
+array([[282.44435897, 282.262194 , 286.34635361, 285.32213341],
+ [283.03737484, 288.57815157, 290.02698113, 278.23328324],
+ [277.06837841, 286.4771544 , 284.58714744, 288.42673576]])
Coordinates:
time datetime64[ns] 8B 2018-01-02
* lat (lat) float64 24B 25.0 40.0 55.0
* lon (lon) float64 32B -120.0 -100.0 -80.0 -60.0
Attributes:
units: kelvin
- standard_name: air_temperature
+ standard_name: air_temperature
This selection technique is similar to NumPy’s index-based selection, as shown below:
temp[1,:,:]
@@ -7208,29 +7208,29 @@ One more selection method: <xarray.DataArray (time: 3, lat: 2, lon: 2)> Size: 96B
-array([[[291.26874805, 287.10212211],
- [281.86759901, 277.88521001]],
+array([[[279.93414516, 288.85955729],
+ [283.36237877, 288.28464039]],
- [[286.75694127, 291.1430049 ],
- [285.34236937, 289.30840199]],
+ [[282.262194 , 286.34635361],
+ [288.57815157, 290.02698113]],
- [[287.14196565, 284.68298455],
- [288.52590266, 273.69692215]]])
+ [[275.374152 , 283.08613936],
+ [282.03531027, 280.16394984]]])
Coordinates:
* time (time) datetime64[ns] 24B 2018-01-01 2018-01-02 2018-01-03
* lat (lat) float64 16B 25.0 40.0
* lon (lon) float64 16B -100.0 -80.0
Attributes:
units: kelvin
- standard_name: air_temperature
+ [[275.374152 , 283.08613936],
+ [282.03531027, 280.16394984]]])
- time(time)datetime64[ns]2018-01-01 2018-01-02 2018-01-03
array(['2018-01-01T00:00:00.000000000', '2018-01-02T00:00:00.000000000',
+ '2018-01-03T00:00:00.000000000'], dtype='datetime64[ns]')
- lat(lat)float6425.0 40.0
array([25., 40.])
- lon(lon)float64-100.0 -80.0
array([-100., -80.])
- timePandasIndex
PandasIndex(DatetimeIndex(['2018-01-01', '2018-01-02', '2018-01-03'], dtype='datetime64[ns]', name='time', freq='D'))
- latPandasIndex
PandasIndex(Index([25.0, 40.0], dtype='float64', name='lat'))
- lonPandasIndex
PandasIndex(Index([-100.0, -80.0], dtype='float64', name='lon'))
- units :
- kelvin
- standard_name :
- air_temperature
In contrast with the previous example, this example shows a useful advantage of using the .loc attribute. When using the .loc attribute, you can specify data slices using a syntax similar to NumPy in addition to, or instead of, using the slice function. Both of these slicing techniques are illustrated below:
@@ -7604,29 +7604,29 @@ One more selection method: <xarray.DataArray (time: 3, lat: 2, lon: 2)> Size: 96B
-array([[[291.26874805, 287.10212211],
- [281.86759901, 277.88521001]],
+array([[[279.93414516, 288.85955729],
+ [283.36237877, 288.28464039]],
- [[286.75694127, 291.1430049 ],
- [285.34236937, 289.30840199]],
+ [[282.262194 , 286.34635361],
+ [288.57815157, 290.02698113]],
- [[287.14196565, 284.68298455],
- [288.52590266, 273.69692215]]])
+ [[275.374152 , 283.08613936],
+ [282.03531027, 280.16394984]]])
Coordinates:
* time (time) datetime64[ns] 24B 2018-01-01 2018-01-02 2018-01-03
* lat (lat) float64 16B 25.0 40.0
* lon (lon) float64 16B -100.0 -80.0
Attributes:
units: kelvin
- standard_name: air_temperature
+ [[275.374152 , 283.08613936],
+ [282.03531027, 280.16394984]]])
- time(time)datetime64[ns]2018-01-01 2018-01-02 2018-01-03
array(['2018-01-01T00:00:00.000000000', '2018-01-02T00:00:00.000000000',
+ '2018-01-03T00:00:00.000000000'], dtype='datetime64[ns]')
- lat(lat)float6425.0 40.0
array([25., 40.])
- lon(lon)float64-100.0 -80.0
array([-100., -80.])
- timePandasIndex
PandasIndex(DatetimeIndex(['2018-01-01', '2018-01-02', '2018-01-03'], dtype='datetime64[ns]', name='time', freq='D'))
- latPandasIndex
PandasIndex(Index([25.0, 40.0], dtype='float64', name='lat'))
- lonPandasIndex
PandasIndex(Index([-100.0, -80.0], dtype='float64', name='lon'))
- units :
- kelvin
- standard_name :
- air_temperature
As described above, the arguments to .loc must be in the order of the DataArray’s dimensions. Attempting to slice data without ordering arguments properly can cause errors, as shown below:
@@ -8059,10 +8059,10 @@ Access netCDF data with
@@ -8504,17 +8504,17 @@ Subsetting the
positive: down
Grib_level_type: 100
_CoordinateAxisType: Pressure
- _CoordinateZisPositive: down
+ dtype='float32', name='isobaric1'))- units :
- hPa
- long_name :
- Isobaric surface
- positive :
- down
- Grib_level_type :
- 100
- _CoordinateAxisType :
- Pressure
- _CoordinateZisPositive :
- down
(As described earlier in this tutorial, we can also use dictionary syntax to select specific DataArrays; in this case, we would write ds['isobaric1'].)
Many of the subsetting operations usable on DataArrays can also be used on Datasets. However, when used on Datasets, these operations are performed on every DataArray in the Dataset, as shown below:
@@ -8916,7 +8916,7 @@ Subsetting the
geospatial_lat_min: 10.753308882144761
geospatial_lat_max: 46.8308828962289
geospatial_lon_min: -153.88242040519995
- geospatial_lon_max: -42.666108129242815
As shown above, the subsetting operation performed on the Dataset returned a new Dataset. If only a single DataArray is needed from this new Dataset, it can be retrieved using the familiar dot notation:
@@ -9356,7 +9356,7 @@ Subsetting the
Grib1_TableVersion: 131
Grib1_Parameter: 11
Grib1_Level_Type: 100
- Grib1_Level_Desc: Isobaric surface
+ dtype='float32', name='x', length=268))- long_name :
- Temperature @ Isobaric surface
- units :
- K
- description :
- Temperature
- grid_mapping :
- LambertConformal_Projection
- Grib_Variable_Id :
- VAR_7-15-131-11_L100
- Grib1_Center :
- 7
- Grib1_Subcenter :
- 15
- Grib1_TableVersion :
- 131
- Grib1_Parameter :
- 11
- Grib1_Level_Type :
- 100
- Grib1_Level_Desc :
- Isobaric surface
@@ -9790,19 +9790,19 @@ Aggregation operations
+ dtype='float32', name='isobaric1'))
Info
@@ -10196,19 +10196,19 @@ Aggregation operations
+ dtype='float32', name='isobaric1'))
@@ -10226,7 +10226,7 @@ Simple visualization with
-[<matplotlib.lines.Line2D at 0x7f2e0f58d130>]
+[<matplotlib.lines.Line2D at 0x7f505db3d9d0>]
@@ -10250,7 +10250,7 @@ Customizing the plot
-[<matplotlib.lines.Line2D at 0x7f2e0f58eff0>]
+[<matplotlib.lines.Line2D at 0x7f5055a98d40>]
@@ -10268,7 +10268,7 @@ Plotting 2-D data
-<matplotlib.collections.QuadMesh at 0x7f2df9c43b60>
+<matplotlib.collections.QuadMesh at 0x7f5055ad1be0>
@@ -10667,7 +10667,7 @@ Resources and references
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
@@ -10796,7 +10796,7 @@ Contribute
By the Project Pythia Community.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
diff --git a/foundations/conda.html b/foundations/conda.html
index c07684127..6f809cfd0 100644
--- a/foundations/conda.html
+++ b/foundations/conda.html
@@ -79,7 +79,7 @@
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+
@@ -913,7 +913,7 @@ Resources and References
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+ Last updated on 20 May 2024.
@@ -1042,7 +1042,7 @@ Contribute
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diff --git a/foundations/getting-started-github.html b/foundations/getting-started-github.html
index 4a2af1d14..ec22e00df 100644
--- a/foundations/getting-started-github.html
+++ b/foundations/getting-started-github.html
@@ -81,7 +81,7 @@
-
+
@@ -768,7 +768,7 @@ Getting Started with GitHub
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
@@ -897,7 +897,7 @@ Contribute
By the Project Pythia Community.
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+ Last updated on 20 May 2024.
diff --git a/foundations/getting-started-jupyter.html b/foundations/getting-started-jupyter.html
index b1d2107c0..b95652e74 100644
--- a/foundations/getting-started-jupyter.html
+++ b/foundations/getting-started-jupyter.html
@@ -79,7 +79,7 @@
-
+
@@ -905,7 +905,7 @@ Resources and references
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+ Last updated on 20 May 2024.
@@ -1034,7 +1034,7 @@ Contribute
By the Project Pythia Community.
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+ Last updated on 20 May 2024.
diff --git a/foundations/getting-started-python.html b/foundations/getting-started-python.html
index 286ad4516..19ac49850 100644
--- a/foundations/getting-started-python.html
+++ b/foundations/getting-started-python.html
@@ -81,7 +81,7 @@
-
+
@@ -779,7 +779,7 @@ Topics
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+ Last updated on 20 May 2024.
@@ -908,7 +908,7 @@ Contribute
By the Project Pythia Community.
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+ Last updated on 20 May 2024.
diff --git a/foundations/github/basic-git.html b/foundations/github/basic-git.html
index 441aa5056..32e0b6b24 100644
--- a/foundations/github/basic-git.html
+++ b/foundations/github/basic-git.html
@@ -79,7 +79,7 @@
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+
@@ -1222,7 +1222,7 @@ References
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+ Last updated on 20 May 2024.
@@ -1351,7 +1351,7 @@ Contribute
By the Project Pythia Community.
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+ Last updated on 20 May 2024.
diff --git a/foundations/github/contribute-to-pythia.html b/foundations/github/contribute-to-pythia.html
index d1e1bad12..11ebc0cc2 100644
--- a/foundations/github/contribute-to-pythia.html
+++ b/foundations/github/contribute-to-pythia.html
@@ -79,7 +79,7 @@
-
+
@@ -885,7 +885,7 @@ Summary
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+ Last updated on 20 May 2024.
@@ -1014,7 +1014,7 @@ Contribute
By the Project Pythia Community.
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+ Last updated on 20 May 2024.
diff --git a/foundations/github/git-branches.html b/foundations/github/git-branches.html
index 6dac4b319..4e316269f 100644
--- a/foundations/github/git-branches.html
+++ b/foundations/github/git-branches.html
@@ -79,7 +79,7 @@
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+
@@ -1036,7 +1036,7 @@ Resources and references
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+ Last updated on 20 May 2024.
@@ -1165,7 +1165,7 @@ Contribute
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+ Last updated on 20 May 2024.
diff --git a/foundations/github/github-advanced.html b/foundations/github/github-advanced.html
index 9e93b611f..def634b01 100644
--- a/foundations/github/github-advanced.html
+++ b/foundations/github/github-advanced.html
@@ -77,7 +77,7 @@
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+
@@ -810,7 +810,7 @@ References
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@@ -939,7 +939,7 @@ Contribute
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+ Last updated on 20 May 2024.
diff --git a/foundations/github/github-cloning-forking.html b/foundations/github/github-cloning-forking.html
index c03c6f72a..18b821529 100644
--- a/foundations/github/github-cloning-forking.html
+++ b/foundations/github/github-cloning-forking.html
@@ -79,7 +79,7 @@
-
+
@@ -988,7 +988,7 @@ References
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+ Last updated on 20 May 2024.
@@ -1117,7 +1117,7 @@ Contribute
By the Project Pythia Community.
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+ Last updated on 20 May 2024.
diff --git a/foundations/github/github-issues.html b/foundations/github/github-issues.html
index fe56bf8f1..cf5e4670a 100644
--- a/foundations/github/github-issues.html
+++ b/foundations/github/github-issues.html
@@ -79,7 +79,7 @@
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+
@@ -875,7 +875,7 @@ References
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@@ -1004,7 +1004,7 @@ Contribute
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diff --git a/foundations/github/github-pull-request.html b/foundations/github/github-pull-request.html
index 566d2d767..a2f449208 100644
--- a/foundations/github/github-pull-request.html
+++ b/foundations/github/github-pull-request.html
@@ -79,7 +79,7 @@
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+
@@ -946,7 +946,7 @@ References
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+ Last updated on 20 May 2024.
@@ -1075,7 +1075,7 @@ Contribute
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diff --git a/foundations/github/github-repos.html b/foundations/github/github-repos.html
index 7d26de757..389418ea0 100644
--- a/foundations/github/github-repos.html
+++ b/foundations/github/github-repos.html
@@ -79,7 +79,7 @@
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+
@@ -885,7 +885,7 @@ References
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@@ -1014,7 +1014,7 @@ Contribute
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diff --git a/foundations/github/github-setup-advanced.html b/foundations/github/github-setup-advanced.html
index f2123372f..71344fd0b 100644
--- a/foundations/github/github-setup-advanced.html
+++ b/foundations/github/github-setup-advanced.html
@@ -79,7 +79,7 @@
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+
@@ -924,7 +924,7 @@ References
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@@ -1053,7 +1053,7 @@ Contribute
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+ Last updated on 20 May 2024.
diff --git a/foundations/github/github-workflows.html b/foundations/github/github-workflows.html
index a5c6b6dfc..17e8fd580 100644
--- a/foundations/github/github-workflows.html
+++ b/foundations/github/github-workflows.html
@@ -79,7 +79,7 @@
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+
@@ -1253,7 +1253,7 @@ References
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+ Last updated on 20 May 2024.
@@ -1382,7 +1382,7 @@ Contribute
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+ Last updated on 20 May 2024.
diff --git a/foundations/github/review-pr.html b/foundations/github/review-pr.html
index f5b0820fb..fe5674aa9 100644
--- a/foundations/github/review-pr.html
+++ b/foundations/github/review-pr.html
@@ -79,7 +79,7 @@
-
+
@@ -918,7 +918,7 @@ Resources and References
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
@@ -1047,7 +1047,7 @@ Contribute
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diff --git a/foundations/github/what-is-github.html b/foundations/github/what-is-github.html
index 27ef2dcd4..d0e70d102 100644
--- a/foundations/github/what-is-github.html
+++ b/foundations/github/what-is-github.html
@@ -81,7 +81,7 @@
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+
@@ -916,7 +916,7 @@ References
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@@ -1045,7 +1045,7 @@ Contribute
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diff --git a/foundations/how-to-run-python.html b/foundations/how-to-run-python.html
index 8b1aab94d..d7e56e4bc 100644
--- a/foundations/how-to-run-python.html
+++ b/foundations/how-to-run-python.html
@@ -79,7 +79,7 @@
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+
@@ -871,7 +871,7 @@ Resources and References
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@@ -1000,7 +1000,7 @@ Contribute
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diff --git a/foundations/jupyter.html b/foundations/jupyter.html
index 0d2537ca8..901251796 100644
--- a/foundations/jupyter.html
+++ b/foundations/jupyter.html
@@ -79,7 +79,7 @@
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+
@@ -885,7 +885,7 @@ Resources and References
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@@ -1014,7 +1014,7 @@ Contribute
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diff --git a/foundations/jupyterlab.html b/foundations/jupyterlab.html
index 1565427e6..94676c432 100644
--- a/foundations/jupyterlab.html
+++ b/foundations/jupyterlab.html
@@ -79,7 +79,7 @@
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+
@@ -1161,7 +1161,7 @@ Resources and references
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@@ -1290,7 +1290,7 @@ Contribute
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diff --git a/foundations/markdown.html b/foundations/markdown.html
index 80c1a4850..364430a39 100644
--- a/foundations/markdown.html
+++ b/foundations/markdown.html
@@ -79,7 +79,7 @@
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+
@@ -754,7 +754,7 @@ Formatted Text in the Notebook with Markdown
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@@ -883,7 +883,7 @@ Contribute
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diff --git a/foundations/overview.html b/foundations/overview.html
index c8dc2806e..344643277 100644
--- a/foundations/overview.html
+++ b/foundations/overview.html
@@ -79,7 +79,7 @@
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@@ -778,7 +778,7 @@ Topics
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@@ -907,7 +907,7 @@ Contribute
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diff --git a/foundations/quickstart.html b/foundations/quickstart.html
index c1a4cabac..8e51f8ba9 100644
--- a/foundations/quickstart.html
+++ b/foundations/quickstart.html
@@ -81,7 +81,7 @@
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+
@@ -1267,7 +1267,7 @@ Resources and references
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diff --git a/foundations/terminal.html b/foundations/terminal.html
index 634495814..c4cb654ea 100644
--- a/foundations/terminal.html
+++ b/foundations/terminal.html
@@ -79,7 +79,7 @@
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@@ -881,7 +881,7 @@ Resources and References
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diff --git a/foundations/why-python.html b/foundations/why-python.html
index abbbd234f..b955e39c2 100644
--- a/foundations/why-python.html
+++ b/foundations/why-python.html
@@ -79,7 +79,7 @@
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@@ -791,7 +791,7 @@ Open source
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diff --git a/genindex.html b/genindex.html
index 81c713d97..f6f7addda 100644
--- a/genindex.html
+++ b/genindex.html
@@ -76,7 +76,7 @@
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+
@@ -648,7 +648,7 @@ Index
@@ -777,7 +777,7 @@ Contribute
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index ff147bc66..276e728be 100644
--- a/landing-page.html
+++ b/landing-page.html
@@ -80,7 +80,7 @@
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+
@@ -776,7 +776,7 @@ A community learning resource for Python-based computing in the geosciences<
@@ -905,7 +905,7 @@ Contribute
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+ Last updated on 20 May 2024.
diff --git a/preamble/how-to-cite.html b/preamble/how-to-cite.html
index 6055d6478..8dfbb5b1d 100644
--- a/preamble/how-to-cite.html
+++ b/preamble/how-to-cite.html
@@ -79,7 +79,7 @@
-
+
@@ -755,7 +755,7 @@ How to Cite This Book
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
@@ -884,7 +884,7 @@ Contribute
By the Project Pythia Community.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
diff --git a/preamble/how-to-use.html b/preamble/how-to-use.html
index 626d2a29a..a79c9377d 100644
--- a/preamble/how-to-use.html
+++ b/preamble/how-to-use.html
@@ -79,7 +79,7 @@
-
+
@@ -895,7 +895,7 @@ Interacting with Jupyter Books locally
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
@@ -1024,7 +1024,7 @@ Contribute
By the Project Pythia Community.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
diff --git a/search.html b/search.html
index 57210f52a..705201479 100644
--- a/search.html
+++ b/search.html
@@ -78,7 +78,7 @@
-
+
@@ -673,7 +673,7 @@ Search
@@ -802,7 +802,7 @@ Contribute
By the Project Pythia Community.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
diff --git a/searchindex.js b/searchindex.js
index 18d9c718b..f531c0445 100644
--- a/searchindex.js
+++ b/searchindex.js
@@ -1 +1 @@
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\ No newline at end of file
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commit, push": [[37, "clone-branch-change-commit-push"]], "Cloning a repository": [[32, "cloning-a-repository"]], "Cloning and Forking a Repository": [[32, "cloning-and-forking-a-repository"]], "Cloning and forking": [[32, "cloning-and-forking"]], "Cloning the remote repository": [[37, "cloning-the-remote-repository"]], "Code Cells": [[42, "code-cells"]], "Colormap Overview": [[9, "colormap-overview"]], "Command Mode Shortcuts": [[42, "command-mode-shortcuts"]], "Commands we used in this tutorial": [[28, "commands-we-used-in-this-tutorial"]], "Committing": [[28, "committing"]], "Compare across branches": [[28, "compare-across-branches"]], "Compare across commits": [[28, "compare-across-commits"]], "Comparing versions": [[28, "comparing-versions"]], "Complete Workflow": [[30, "complete-workflow"]], "Computations and Masks with Xarray": [[20, "computations-and-masks-with-xarray"]], "Compute anomaly": [[20, "compute-anomaly"]], "Compute climatology": [[20, "compute-climatology"]], 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\ No newline at end of file
<cartopy.mpl.feature_artist.FeatureArtist at 0x7f5f6cbc0500>
+<cartopy.mpl.feature_artist.FeatureArtist at 0x7f003cb648c0>
Add cartographic features to the map
-<cartopy.mpl.feature_artist.FeatureArtist at 0x7f5f6cb68fb0>
+<cartopy.mpl.feature_artist.FeatureArtist at 0x7f004cd08ad0>
@@ -1340,7 +1340,7 @@ Resources and References
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
@@ -1469,7 +1469,7 @@ Contribute
By the Project Pythia Community.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
<cartopy.mpl.feature_artist.FeatureArtist at 0x7f5f6cb68fb0>
+<cartopy.mpl.feature_artist.FeatureArtist at 0x7f004cd08ad0>
Resources and References
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
Contribute
By the Project Pythia Community. - Last updated on 15 May 2024. + Last updated on 20 May 2024. diff --git a/core/data-formats/netcdf-cf.html b/core/data-formats/netcdf-cf.html index 5b25884fa..12d8c6e0e 100644 --- a/core/data-formats/netcdf-cf.html +++ b/core/data-formats/netcdf-cf.html @@ -79,7 +79,7 @@ - + @@ -564,7 +564,7 @@Gridded Data
-/tmp/ipykernel_2523/2342439358.py:1: DeprecationWarning: datetime.datetime.utcnow() is deprecated and scheduled for removal in a future version. Use timezone-aware objects to represent datetimes in UTC: datetime.datetime.now(datetime.UTC).
+/tmp/ipykernel_2539/2342439358.py:1: DeprecationWarning: datetime.datetime.utcnow() is deprecated and scheduled for removal in a future version. Use timezone-aware objects to represent datetimes in UTC: datetime.datetime.now(datetime.UTC).
start = datetime.utcnow().replace(hour=22, minute=0, second=0, microsecond=0)
@@ -610,7 +610,7 @@ Creating the file and dimensions
-/tmp/ipykernel_2523/2008125275.py:5: DeprecationWarning: datetime.datetime.utcnow() is deprecated and scheduled for removal in a future version. Use timezone-aware objects to represent datetimes in UTC: datetime.datetime.now(datetime.UTC).
+/tmp/ipykernel_2539/2008125275.py:5: DeprecationWarning: datetime.datetime.utcnow() is deprecated and scheduled for removal in a future version. Use timezone-aware objects to represent datetimes in UTC: datetime.datetime.now(datetime.UTC).
nc.history = str(datetime.utcnow()) + ' Python'
@@ -651,7 +651,7 @@ Creating the file and dimensions
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
@@ -1693,7 +1693,7 @@ Contribute
By the Project Pythia Community.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
diff --git a/core/datetime.html b/core/datetime.html
index d7aeb1142..dfa7d6341 100644
--- a/core/datetime.html
+++ b/core/datetime.html
@@ -81,7 +81,7 @@
-
+
@@ -763,7 +763,7 @@ Datetime
@@ -892,7 +892,7 @@ Contribute
By the Project Pythia Community.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
diff --git a/core/datetime/datetime.html b/core/datetime/datetime.html
index 153198b8e..ce3241e6a 100644
--- a/core/datetime/datetime.html
+++ b/core/datetime/datetime.html
@@ -81,7 +81,7 @@
-
+
@@ -593,7 +593,7 @@ Some core terminology
-1715806495.8833458
+1716229655.9439774
@@ -614,7 +614,7 @@ time
-The benchmark took 1.0002374649047852 seconds
+The benchmark took 1.0002319812774658 seconds
@@ -743,7 +743,7 @@ See how many days have elapsed since the strike:
-6167
+6172
@@ -815,8 +815,8 @@ Time Zone Naive Versus Time Zone Aware
-I am time zone naive 2024-05-15 20:54:56.921356.
-I am time zone aware 2024-05-15 20:54:56.921392+00:00.
+I am time zone naive 2024-05-20 18:27:36.981418.
+I am time zone aware 2024-05-20 18:27:36.981452+00:00.
@@ -839,8 +839,8 @@ Full time zone support with the <
-I am time zone naive: 2024-05-15 20:54:56.925612.
-I am time zone aware: 2024-05-15 14:54:56.936190-06:00.
+I am time zone naive: 2024-05-20 18:27:36.985646.
+I am time zone aware: 2024-05-20 12:27:36.996878-06:00.
@@ -862,11 +862,11 @@ Print Time with a Different Time Zone
-The UTC time is May 15, 2024, 8:54PM.
-The 'US/Mountain' time is May 15, 2024, 2:54PM.
+The UTC time is May 20, 2024, 6:27PM.
+The 'US/Mountain' time is May 20, 2024, 12:27PM.
-/tmp/ipykernel_2546/3080495102.py:1: DeprecationWarning: datetime.datetime.utcnow() is deprecated and scheduled for removal in a future version. Use timezone-aware objects to represent datetimes in UTC: datetime.datetime.now(datetime.UTC).
+/tmp/ipykernel_2563/3080495102.py:1: DeprecationWarning: datetime.datetime.utcnow() is deprecated and scheduled for removal in a future version. Use timezone-aware objects to represent datetimes in UTC: datetime.datetime.now(datetime.UTC).
utc = dt.datetime.utcnow().replace(tzinfo=pytz.utc)
@@ -1243,7 +1243,7 @@ Resources and References
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
@@ -1372,7 +1372,7 @@ Contribute
By the Project Pythia Community.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
diff --git a/core/matplotlib.html b/core/matplotlib.html
index 36bdf5490..7ff675d8a 100644
--- a/core/matplotlib.html
+++ b/core/matplotlib.html
@@ -81,7 +81,7 @@
-
+
@@ -795,7 +795,7 @@ In this section
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
@@ -924,7 +924,7 @@ Contribute
By the Project Pythia Community.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
diff --git a/core/matplotlib/annotations-colorbars-layouts.html b/core/matplotlib/annotations-colorbars-layouts.html
index 97735445a..4c2ffca3e 100644
--- a/core/matplotlib/annotations-colorbars-layouts.html
+++ b/core/matplotlib/annotations-colorbars-layouts.html
@@ -81,7 +81,7 @@
-
+
@@ -730,7 +730,7 @@ Basic Colorbars
-
+
We can change which colormap to use by setting the keyword argument cmap = 'colormap_name' in the plotting function call. This sets the colormap not only for the plot, but for the colorbar as well. In this case, we use the magma colormap:
@@ -745,7 +745,7 @@ Basic Colorbars
-
+
@@ -764,10 +764,10 @@ Shared Colorbars
-<matplotlib.colorbar.Colorbar at 0x7f4e652e9af0>
+<matplotlib.colorbar.Colorbar at 0x7f07e48a40e0>
-
+
You may be wondering why the call to fig.colorbar uses the argument hist1[3]. The explanation is as follows: hist1 is a tuple returned by hist2d, and hist1[3] contains a matplotlib.collections.QuadMesh that points to the colormap for the first histogram. To make sure that both histograms are using the same colormap with the same range of values, vmax is set to 0.18 for both plots. This ensures that both histograms are using colormaps that represent values from 0 (the default for histograms) to 0.18. Because the same data values are used for both plots, it doesn’t matter whether we pass in hist1[3] or hist2[3] to fig.colorbar.
@@ -848,7 +848,7 @@
Custom Colorbars
-
+
@@ -865,7 +865,7 @@ Custom Colorbars
-
+
@@ -902,7 +902,7 @@ Mosaic Subplots
-
+
You’ll notice there is not a colorbar plotted by default. When constructing the colorbar, we need to specify the following:
@@ -930,7 +930,7 @@ Mosaic Subplots
-
+
@@ -1291,7 +1291,7 @@ Resources and references
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
@@ -1420,7 +1420,7 @@ Contribute
By the Project Pythia Community.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
diff --git a/core/matplotlib/histograms-piecharts-animation.html b/core/matplotlib/histograms-piecharts-animation.html
index ea6420ec0..9a591348d 100644
--- a/core/matplotlib/histograms-piecharts-animation.html
+++ b/core/matplotlib/histograms-piecharts-animation.html
@@ -79,7 +79,7 @@
-
+
@@ -3629,7 +3629,7 @@ Additional Resources
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
@@ -3758,7 +3758,7 @@ Contribute
By the Project Pythia Community.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
diff --git a/core/matplotlib/matplotlib-basics.html b/core/matplotlib/matplotlib-basics.html
index 323b54231..22fd1268a 100644
--- a/core/matplotlib/matplotlib-basics.html
+++ b/core/matplotlib/matplotlib-basics.html
@@ -79,7 +79,7 @@
-
+
@@ -1492,7 +1492,7 @@ Resources and References
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
@@ -1621,7 +1621,7 @@ Contribute
By the Project Pythia Community.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
diff --git a/core/numpy.html b/core/numpy.html
index 4fb55d7f5..5c535f0a9 100644
--- a/core/numpy.html
+++ b/core/numpy.html
@@ -81,7 +81,7 @@
-
+
@@ -760,7 +760,7 @@ NumPy
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
@@ -889,7 +889,7 @@ Contribute
By the Project Pythia Community.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
diff --git a/core/numpy/intermediate-numpy.html b/core/numpy/intermediate-numpy.html
index 59706e1a6..38a6afc63 100644
--- a/core/numpy/intermediate-numpy.html
+++ b/core/numpy/intermediate-numpy.html
@@ -81,7 +81,7 @@
-
+
@@ -1425,7 +1425,7 @@ Resources and references
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
@@ -1554,7 +1554,7 @@ Contribute
By the Project Pythia Community.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
diff --git a/core/numpy/numpy-basics.html b/core/numpy/numpy-basics.html
index cb11d7b3d..365a2f855 100644
--- a/core/numpy/numpy-basics.html
+++ b/core/numpy/numpy-basics.html
@@ -81,7 +81,7 @@
-
+
@@ -1168,7 +1168,7 @@ Indexing
Slices
-Slicing syntax is written as array[start:stop[:step]], where all numbers are optional.
+Slicing syntax is written as array[start:stop:step]. Note that all numbers are optional. Importantly, the step parameter is optional and can be omitted, in which case the slice uses a default step of 1.
defaults:
@@ -1775,7 +1775,7 @@ Resources and references
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
@@ -1904,7 +1904,7 @@ Contribute
By the Project Pythia Community.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
diff --git a/core/numpy/numpy-broadcasting.html b/core/numpy/numpy-broadcasting.html
index 78f882658..57e5dd9a6 100644
--- a/core/numpy/numpy-broadcasting.html
+++ b/core/numpy/numpy-broadcasting.html
@@ -81,7 +81,7 @@
-
+
@@ -1070,7 +1070,7 @@ Blocking
-
-
-
-
-array([[ 8.3, 11.6, 33.1],
- [11.6, 33.1, 18.3],
- [33.1, 18.3, 22.5],
- [18.3, 22.5, 24.5],
- [22.5, 24.5, 20.1],
- [24.5, 20.1, 20.1],
- [20.1, 20.1, 18. ],
- [20.1, 18. , 25. ]])
+array([[19.3, 18.8, 28.7],
+ [18.8, 28.7, 27.1],
+ [28.7, 27.1, 17.1],
+ [27.1, 17.1, 16.2],
+ [17.1, 16.2, 15. ],
+ [16.2, 15. , 15.1],
+ [15. , 15.1, 14. ],
+ [15.1, 14. , 19.2]])
@@ -1161,8 +1161,8 @@ Blocking
-
-array([[ 8.3, 11.6, 2000. ],
- [ 11.6, 2000. , 18.3],
- [2000. , 18.3, 22.5],
- [ 18.3, 22.5, 24.5],
- [ 22.5, 24.5, 20.1],
- [ 24.5, 20.1, 20.1],
- [ 20.1, 20.1, 18. ],
- [ 20.1, 18. , 25. ]])
+array([[ 19.3, 18.8, 2000. ],
+ [ 18.8, 2000. , 27.1],
+ [2000. , 27.1, 17.1],
+ [ 27.1, 17.1, 16.2],
+ [ 17.1, 16.2, 15. ],
+ [ 16.2, 15. , 15.1],
+ [ 15. , 15.1, 14. ],
+ [ 15.1, 14. , 19.2]])
@@ -1210,13 +1210,13 @@ Finding the difference between min and max
-array([[6, 6, 7, ..., 8, 7, 6],
- [6, 6, 6, ..., 7, 7, 7],
- [7, 7, 7, ..., 6, 7, 7],
+array([[7, 7, 7, ..., 7, 8, 7],
+ [6, 7, 6, ..., 7, 7, 6],
+ [7, 6, 7, ..., 7, 6, 6],
...,
- [7, 7, 7, ..., 7, 7, 6],
- [7, 6, 6, ..., 6, 7, 7],
- [7, 6, 7, ..., 7, 8, 7]])
+ [6, 7, 6, ..., 6, 6, 7],
+ [7, 7, 7, ..., 6, 6, 7],
+ [6, 7, 7, ..., 8, 8, 7]])
@@ -1241,13 +1241,13 @@ Finding the difference between min and max
-array([[775. , 775. , 737.5, ..., 700. , 737.5, 775. ],
- [775. , 775. , 775. , ..., 737.5, 737.5, 737.5],
- [737.5, 737.5, 737.5, ..., 775. , 737.5, 737.5],
+array([[737.5, 737.5, 737.5, ..., 737.5, 700. , 737.5],
+ [775. , 737.5, 775. , ..., 737.5, 737.5, 775. ],
+ [737.5, 775. , 737.5, ..., 737.5, 775. , 775. ],
...,
- [737.5, 737.5, 737.5, ..., 737.5, 737.5, 775. ],
- [737.5, 775. , 775. , ..., 775. , 737.5, 737.5],
- [737.5, 775. , 737.5, ..., 737.5, 700. , 737.5]])
+ [775. , 737.5, 775. , ..., 775. , 775. , 737.5],
+ [737.5, 737.5, 737.5, ..., 775. , 775. , 737.5],
+ [775. , 737.5, 737.5, ..., 700. , 700. , 737.5]])
@@ -1276,19 +1276,19 @@ Finding the difference between min and max
-array([[ -9.55417349, -9.78584972, -9.92661689, ..., -12.67789616,
- -10.49077377, -6.56515605],
- [-10.00010532, -10.70819797, -7.20985445, ..., -11.10789153,
- -11.73163442, -8.29531203],
- [-11.88696252, -12.29088724, -11.70346147, ..., -7.30859657,
- -8.97849533, -13.84614818],
+array([[-13.35480377, -11.37389473, -6.11490009, ..., -9.69409241,
+ -14.14853765, -11.04724036],
+ [ -6.50332158, -11.09958323, -5.95998607, ..., -13.2604716 ,
+ -13.19971009, -8.49574616],
+ [-12.28051357, -10.19815861, -7.42945122, ..., -11.5798199 ,
+ -8.91153322, -8.6079996 ],
...,
- [-11.90215938, -7.91187899, -13.08679868, ..., -10.41120932,
- -11.35468344, -11.06459146],
- [-11.50292233, -6.26889319, -7.38221861, ..., -10.24036433,
- -10.12594611, -5.63518882],
- [-10.32651953, -6.06759924, -9.54152312, ..., -10.45702773,
- -12.20574751, -8.01746694]])
+ [ -8.10255586, -12.16615351, -11.27253366, ..., -10.44432682,
+ -7.39154896, -10.99574973],
+ [-16.5968518 , -12.57487222, -8.39736923, ..., -4.15595587,
+ -8.86979291, -11.28873531],
+ [ -8.59386363, -9.38047716, -10.46838159, ..., -11.30765026,
+ -10.89295232, -9.24056526]])
@@ -1332,19 +1332,19 @@ Finding the difference between min and max
-array([[ -9.55417349, -9.78584972, -9.92661689, ..., -12.67789616,
- -10.49077377, -6.56515605],
- [-10.00010532, -10.70819797, -7.20985445, ..., -11.10789153,
- -11.73163442, -8.29531203],
- [-11.88696252, -12.29088724, -11.70346147, ..., -7.30859657,
- -8.97849533, -13.84614818],
+array([[-13.35480377, -11.37389473, -6.11490009, ..., -9.69409241,
+ -14.14853765, -11.04724036],
+ [ -6.50332158, -11.09958323, -5.95998607, ..., -13.2604716 ,
+ -13.19971009, -8.49574616],
+ [-12.28051357, -10.19815861, -7.42945122, ..., -11.5798199 ,
+ -8.91153322, -8.6079996 ],
...,
- [-11.90215938, -7.91187899, -13.08679868, ..., -10.41120932,
- -11.35468344, -11.06459146],
- [-11.50292233, -6.26889319, -7.38221861, ..., -10.24036433,
- -10.12594611, -5.63518882],
- [-10.32651953, -6.06759924, -9.54152312, ..., -10.45702773,
- -12.20574751, -8.01746694]])
+ [ -8.10255586, -12.16615351, -11.27253366, ..., -10.44432682,
+ -7.39154896, -10.99574973],
+ [-16.5968518 , -12.57487222, -8.39736923, ..., -4.15595587,
+ -8.86979291, -11.28873531],
+ [ -8.59386363, -9.38047716, -10.46838159, ..., -11.30765026,
+ -10.89295232, -9.24056526]])
@@ -1711,7 +1711,7 @@ Resources and references
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
@@ -1840,7 +1840,7 @@ Contribute
By the Project Pythia Community.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
diff --git a/core/overview.html b/core/overview.html
index 6acc0ae46..fd4752cc2 100644
--- a/core/overview.html
+++ b/core/overview.html
@@ -79,7 +79,7 @@
-
+
@@ -886,7 +886,7 @@ Tutorials
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
@@ -1015,7 +1015,7 @@ Contribute
By the Project Pythia Community.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
diff --git a/core/pandas.html b/core/pandas.html
index 2e4230efc..02509b347 100644
--- a/core/pandas.html
+++ b/core/pandas.html
@@ -79,7 +79,7 @@
-
+
@@ -759,7 +759,7 @@ Pandas
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
@@ -888,7 +888,7 @@ Contribute
By the Project Pythia Community.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
diff --git a/core/pandas/pandas.html b/core/pandas/pandas.html
index 734fecdb6..2e24138b7 100644
--- a/core/pandas/pandas.html
+++ b/core/pandas/pandas.html
@@ -79,7 +79,7 @@
-
+
@@ -1098,7 +1098,7 @@ Indexing a Seri
-/tmp/ipykernel_2735/737336773.py:1: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`
+/tmp/ipykernel_2746/737336773.py:1: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`
nino34_series[3]
@@ -1449,7 +1449,7 @@ Extending to the
-/tmp/ipykernel_2735/541596450.py:1: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`
+/tmp/ipykernel_2746/541596450.py:1: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`
df["Nino34"][3]
@@ -3645,7 +3645,7 @@ Resampling
-/tmp/ipykernel_2735/233158901.py:1: FutureWarning: 'Y' is deprecated and will be removed in a future version, please use 'YE' instead.
+/tmp/ipykernel_2746/233158901.py:1: FutureWarning: 'Y' is deprecated and will be removed in a future version, please use 'YE' instead.
df.Nino34.resample('1Y').mean().plot();
@@ -4434,7 +4434,7 @@ Resources and References
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
@@ -4563,7 +4563,7 @@ Contribute
By the Project Pythia Community.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
diff --git a/core/xarray.html b/core/xarray.html
index e4624108b..6bc787165 100644
--- a/core/xarray.html
+++ b/core/xarray.html
@@ -79,7 +79,7 @@
-
+
@@ -760,7 +760,7 @@ Xarray
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
@@ -889,7 +889,7 @@ Contribute
By the Project Pythia Community.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
diff --git a/core/xarray/computation-masking.html b/core/xarray/computation-masking.html
index b15ad248c..12f19902b 100644
--- a/core/xarray/computation-masking.html
+++ b/core/xarray/computation-masking.html
@@ -79,7 +79,7 @@
-
+
@@ -943,7 +943,7 @@ Data Setup
@@ -1576,7 +1576,7 @@ Arithmetic Operations
+ dtype='float64', name='lon', length=360))
In addition, there are many other arithmetic operations that can be performed on DataArrays. In this example, we demonstrate squaring the original Celsius values of our air temperature data:
@@ -2246,7 +2246,7 @@ Arithmetic Operations
+ dtype='float64', name='lon', length=360))
@@ -2876,7 +2876,7 @@ Aggregation Methodsdim keyword argument; this means that the function was applied over all of the dataset’s dimensions. In other words, the aggregation method computed the mean of every element of the temperature dataset across every temporal and spatial data point. However, if a dimension name is used with the dim keyword argument, the aggregation method computes an aggregation along the given dimension. In this next example, we use aggregation to calculate the temporal mean across all spatial data; this is performed by providing the dimension name 'time' to the dim keyword argument:
@@ -3276,7 +3276,7 @@ Aggregation Methods
@@ -3717,7 +3717,7 @@ Aggregation Methods
@@ -4325,7 +4325,7 @@ Aggregation Methods
@@ -4892,7 +4892,7 @@ Compute climatologyDataArray containing the climatology data, we can plot the data in different ways. In this example, we plot the climatology at a specific latitude-longitude point:
@@ -5412,7 +5412,7 @@ Compute anomaly
@@ -6095,7 +6095,7 @@ Compute anomaly.weighted() method and the newly loaded CESM2 dataset to weight the grid cell data as described above:
@@ -6598,7 +6598,7 @@ Other high level computation functionalityrolling method to compute averages in a moving window of 5 months of data:
@@ -7114,7 +7114,7 @@ Other high level computation functionality
@@ -7783,7 +7783,7 @@ Masking Data
Using where with one condition
@@ -8421,15 +8421,15 @@ Using where
+ dtype='float64', name='lon', length=360))
- cell_measures :
- area: areacello
- cell_methods :
- area: mean where sea time: mean
- comment :
- Model data on the 1x1 grid includes values in all cells for which ocean cells on the native grid cover more than 52.5 percent of the 1x1 grid cell. This 52.5 percent cutoff was chosen to produce ocean surface area on the 1x1 grid as close as possible to ocean surface area on the native grid, while not introducing fractional cell coverage.
- description :
- This may differ from "surface temperature" in regions of sea ice or floating ice shelves. For models using conservative temperature as the prognostic field, they should report the top ocean layer as surface potential temperature, which is the same as surface in situ temperature.
- frequency :
- mon
- id :
- tos
- long_name :
- Sea Surface Temperature
- mipTable :
- Omon
- out_name :
- tos
- prov :
- Omon ((isd.003))
- realm :
- ocean
- standard_name :
- sea_surface_temperature
- time :
- time
- time_label :
- time-mean
- time_title :
- Temporal mean
- title :
- Sea Surface Temperature
- type :
- real
- units :
- degC
- variable_id :
- tos
As shown in the previous example, methods like .isel() and .sel() return data of a different shape than the original data provided to them. However, .where() preserves the shape of the original data by masking the values with a Boolean condition. Data values for which the condition is True are returned identical to the values passed in. On the other hand, data values for which the condition is False are returned as a preset example value. (This example value defaults to nan, but can be set to other values as well.)
Before testing .where(), it is helpful to look at the official documentation. As stated above, the .where() method takes a Boolean condition. (Boolean conditions use operators such as less-than, greater-than, and equal-to, and return a value of True or False.) Most uses of .where() check whether or not specific data values are less than or greater than a constant value. As stated in the documentation, the data values specified in the Boolean condition of .where() can be any of the following:
@@ -8864,7 +8864,7 @@ Using where
+ dtype='float64', name='lon', length=360))- cell_measures :
- area: areacello
- cell_methods :
- area: mean where sea time: mean
- comment :
- Model data on the 1x1 grid includes values in all cells for which ocean cells on the native grid cover more than 52.5 percent of the 1x1 grid cell. This 52.5 percent cutoff was chosen to produce ocean surface area on the 1x1 grid as close as possible to ocean surface area on the native grid, while not introducing fractional cell coverage.
- description :
- This may differ from "surface temperature" in regions of sea ice or floating ice shelves. For models using conservative temperature as the prognostic field, they should report the top ocean layer as surface potential temperature, which is the same as surface in situ temperature.
- frequency :
- mon
- id :
- tos
- long_name :
- Sea Surface Temperature
- mipTable :
- Omon
- out_name :
- tos
- prov :
- Omon ((isd.003))
- realm :
- ocean
- standard_name :
- sea_surface_temperature
- time :
- time
- time_label :
- time-mean
- time_title :
- Temporal mean
- title :
- Sea Surface Temperature
- type :
- real
- units :
- degC
- variable_id :
- tos
In this example, we use Matplotlib to plot the original, unmasked data, as well as the masked data created in the previous example.
@@ -9321,7 +9321,7 @@ Resources and References
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
@@ -9450,7 +9450,7 @@ Contribute
By the Project Pythia Community.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
diff --git a/core/xarray/dask-arrays-xarray.html b/core/xarray/dask-arrays-xarray.html
index 0af9daaf3..65793b335 100644
--- a/core/xarray/dask-arrays-xarray.html
+++ b/core/xarray/dask-arrays-xarray.html
@@ -79,7 +79,7 @@
-
+
@@ -568,91 +568,91 @@ Create a dask.a
-array([[[0.08870167, 0.5536724 , 0.86869933, ..., 0.59745109,
- 0.66241166, 0.14712849],
- [0.79424186, 0.53985475, 0.83117213, ..., 0.64752434,
- 0.2068288 , 0.43561572],
- [0.93755297, 0.12641562, 0.55493093, ..., 0.44682309,
- 0.9108161 , 0.28494995],
+array([[[3.73479162e-01, 5.69354087e-01, 8.44121771e-01, ...,
+ 6.95300592e-01, 7.05791578e-01, 3.22757785e-01],
+ [7.34673387e-01, 3.22502093e-02, 2.98660716e-02, ...,
+ 4.48326876e-01, 1.28181189e-01, 6.17129937e-01],
+ [4.61524077e-01, 5.72981031e-01, 6.22455684e-02, ...,
+ 4.66387696e-01, 2.24365962e-02, 9.05528466e-01],
...,
- [0.36681494, 0.55104908, 0.67358062, ..., 0.93120538,
- 0.51164396, 0.2149192 ],
- [0.33220658, 0.18853655, 0.84133513, ..., 0.09990092,
- 0.45265087, 0.48841145],
- [0.54632195, 0.99301667, 0.40513953, ..., 0.5341915 ,
- 0.3220391 , 0.07544213]],
-
- [[0.8398981 , 0.14999579, 0.81431679, ..., 0.45814483,
- 0.81962377, 0.01308931],
- [0.52904343, 0.17038365, 0.49820524, ..., 0.71294698,
- 0.49906669, 0.32305634],
- [0.83597797, 0.12242548, 0.63334391, ..., 0.77669187,
- 0.44530452, 0.02031067],
+ [4.32840675e-01, 6.11791423e-01, 7.77157080e-01, ...,
+ 5.21898060e-01, 5.08370890e-01, 5.88153598e-01],
+ [8.04673850e-01, 7.80296541e-03, 5.00244166e-01, ...,
+ 9.45710973e-01, 5.08700949e-01, 8.89905823e-01],
+ [4.87536782e-01, 4.64576884e-01, 4.97751472e-01, ...,
+ 9.63294478e-01, 8.00438624e-01, 9.39160580e-01]],
+
+ [[9.59798128e-01, 9.67977225e-01, 5.67081374e-01, ...,
+ 1.15551677e-01, 5.93188567e-01, 8.21494821e-01],
+ [7.09798171e-01, 6.86764079e-01, 9.75639983e-01, ...,
+ 6.65980228e-01, 3.63136928e-01, 3.91634965e-01],
+ [2.45312843e-01, 7.92456108e-01, 7.52068086e-01, ...,
+ 8.31396290e-01, 7.34853345e-01, 1.59997513e-02],
...,
- [0.23487395, 0.54185136, 0.84738714, ..., 0.43194404,
- 0.96464114, 0.34661126],
- [0.06216953, 0.87402041, 0.89512514, ..., 0.38706893,
- 0.09187095, 0.88666451],
- [0.46384865, 0.02462827, 0.71575049, ..., 0.47421697,
- 0.30570578, 0.58421292]],
-
- [[0.05704318, 0.36232811, 0.03448708, ..., 0.86748361,
- 0.49546271, 0.39195962],
- [0.62907898, 0.52593284, 0.87418635, ..., 0.97638485,
- 0.63695351, 0.75326941],
- [0.11640535, 0.15534404, 0.80460691, ..., 0.26935451,
- 0.00686985, 0.42474694],
+ [6.29006234e-01, 5.66655665e-01, 9.59798385e-01, ...,
+ 4.64611557e-02, 8.47630616e-01, 9.96868460e-01],
+ [8.65409572e-01, 6.62829465e-01, 5.85935120e-01, ...,
+ 2.76645046e-01, 4.05531947e-01, 1.86970157e-01],
+ [5.88186192e-02, 4.80258125e-01, 7.50053106e-01, ...,
+ 5.82584007e-01, 7.16238302e-01, 3.09095729e-01]],
+
+ [[1.95867406e-01, 8.02997164e-01, 5.92335907e-02, ...,
+ 6.00495084e-02, 3.58568845e-02, 6.14435689e-01],
+ [5.40191024e-01, 7.45240262e-01, 5.83926237e-01, ...,
+ 5.42683512e-01, 7.15062271e-05, 8.15838308e-01],
+ [3.56121359e-01, 6.64103118e-01, 3.19197578e-01, ...,
+ 4.30885292e-01, 5.63274760e-01, 5.58304993e-01],
...,
- [0.53156325, 0.69190957, 0.69352384, ..., 0.77170882,
- 0.44011939, 0.39866217],
- [0.29442052, 0.74080675, 0.46131608, ..., 0.32366183,
- 0.73788577, 0.04341616],
- [0.94117032, 0.92630431, 0.79008536, ..., 0.02443356,
- 0.52042712, 0.83466538]],
+ [3.70429144e-01, 2.28949319e-01, 2.12465225e-01, ...,
+ 8.75347366e-01, 6.88876481e-01, 8.52985255e-01],
+ [8.43544201e-01, 3.50323808e-01, 9.94755069e-01, ...,
+ 2.31937385e-01, 6.46129523e-01, 7.56519847e-01],
+ [9.23292344e-01, 9.06543740e-01, 2.16039623e-01, ...,
+ 6.17841121e-01, 5.83778599e-02, 7.07627581e-01]],
...,
- [[0.1396787 , 0.60143505, 0.57058462, ..., 0.18893598,
- 0.82985107, 0.54242887],
- [0.68157985, 0.63122788, 0.1780818 , ..., 0.55248006,
- 0.63127177, 0.4663941 ],
- [0.55463676, 0.49022722, 0.28183346, ..., 0.64073606,
- 0.37788708, 0.05233016],
+ [[5.50076397e-01, 6.23247920e-01, 6.69863249e-01, ...,
+ 6.66141598e-01, 4.98073488e-01, 9.70545887e-01],
+ [7.38559992e-01, 3.21393458e-01, 4.05421647e-01, ...,
+ 2.52285356e-01, 8.08345653e-01, 7.28764634e-01],
+ [2.68191728e-01, 6.03169049e-02, 6.89258446e-01, ...,
+ 6.21042327e-01, 9.60250603e-01, 9.75257028e-01],
...,
- [0.39020736, 0.85735173, 0.00786958, ..., 0.66161596,
- 0.18152074, 0.17731408],
- [0.93613911, 0.96578828, 0.38973709, ..., 0.06864502,
- 0.5063675 , 0.73631955],
- [0.1671134 , 0.45039599, 0.38253178, ..., 0.74316064,
- 0.20253998, 0.93656703]],
-
- [[0.28018602, 0.84864745, 0.65231976, ..., 0.57238332,
- 0.37536731, 0.04826117],
- [0.47663448, 0.86778224, 0.95451621, ..., 0.76880871,
- 0.89875752, 0.06756555],
- [0.49609048, 0.21693506, 0.23207324, ..., 0.06636032,
- 0.95937476, 0.4827714 ],
+ [6.11978830e-01, 6.32479701e-01, 1.75826196e-01, ...,
+ 2.09161988e-01, 6.71446177e-01, 4.78915946e-01],
+ [6.45044934e-01, 1.15167985e-01, 9.05467373e-01, ...,
+ 7.79598426e-01, 8.76027493e-01, 5.70161686e-01],
+ [8.44173395e-01, 7.14987726e-01, 5.27098062e-01, ...,
+ 6.24560049e-01, 3.32587128e-02, 1.48219268e-01]],
+
+ [[9.16337354e-01, 1.38628117e-01, 2.76572964e-01, ...,
+ 5.68919305e-02, 3.07040211e-01, 6.70607173e-02],
+ [6.78200880e-01, 1.19012654e-01, 8.76844508e-01, ...,
+ 5.78903343e-01, 5.08716762e-01, 3.27430604e-01],
+ [5.19310927e-01, 1.62933592e-01, 6.94104791e-01, ...,
+ 8.28521396e-01, 5.63944660e-01, 9.23353464e-01],
...,
- [0.83197412, 0.22629066, 0.1589711 , ..., 0.55726833,
- 0.40921459, 0.94548168],
- [0.20373662, 0.76138868, 0.56265755, ..., 0.71025427,
- 0.19641998, 0.49627513],
- [0.20384921, 0.34173511, 0.44829 , ..., 0.3999668 ,
- 0.75240336, 0.21357396]],
-
- [[0.05024137, 0.77607526, 0.32452642, ..., 0.26169962,
- 0.2092348 , 0.79937281],
- [0.8834038 , 0.56909665, 0.84006875, ..., 0.09540328,
- 0.66257683, 0.78342945],
- [0.29906903, 0.08192958, 0.51913898, ..., 0.32946737,
- 0.91310302, 0.99198924],
+ [8.75833692e-01, 8.80034882e-01, 3.94957057e-01, ...,
+ 3.88766073e-01, 5.62146961e-01, 4.07401505e-01],
+ [8.83232515e-02, 7.93868945e-02, 7.09396623e-01, ...,
+ 4.00821090e-01, 9.67897242e-01, 3.30712526e-02],
+ [6.21839195e-01, 3.66731583e-01, 9.97239185e-01, ...,
+ 4.06892246e-01, 8.84142338e-01, 1.29206897e-01]],
+
+ [[3.13410458e-01, 7.92759677e-01, 8.24979480e-02, ...,
+ 5.38935776e-02, 4.52819725e-01, 1.48534750e-01],
+ [2.06595218e-01, 8.46746782e-01, 9.06689993e-01, ...,
+ 8.52571008e-01, 4.28351337e-01, 8.06352220e-01],
+ [3.77563013e-01, 9.99637726e-01, 3.37356897e-01, ...,
+ 8.18911136e-01, 8.32562303e-01, 8.45143802e-02],
...,
- [0.89344391, 0.04685752, 0.16870497, ..., 0.70133294,
- 0.2028986 , 0.16916596],
- [0.62721463, 0.12582174, 0.59300888, ..., 0.93023778,
- 0.50053284, 0.63544531],
- [0.89994404, 0.84669077, 0.65421957, ..., 0.19573779,
- 0.56024887, 0.73584493]]])
+ [5.51431397e-01, 8.77465751e-01, 9.63517992e-01, ...,
+ 3.34710544e-02, 9.70363045e-01, 5.42823121e-01],
+ [3.89247115e-01, 3.36500509e-01, 4.31638413e-02, ...,
+ 8.82214294e-02, 2.41024552e-01, 4.19862342e-01],
+ [5.65544128e-01, 6.09859197e-01, 2.77376901e-01, ...,
+ 5.51955058e-01, 6.67783854e-02, 4.08359511e-01]]])
@@ -856,7 +856,7 @@ Why the difference?
-
+
@@ -872,11 +872,11 @@ Compute the result
-CPU times: user 406 ms, sys: 28 ms, total: 434 ms
-Wall time: 232 ms
+CPU times: user 399 ms, sys: 28.5 ms, total: 428 ms
+Wall time: 228 ms
-12002441.41340752
+12000189.444496717
@@ -894,7 +894,7 @@ Exercise with d
-
+
As is obvious from the above exercise, Dask quickly and easily determines a strategy for performing the operations, in this case a sum. This illustrates the appeal of Dask: automatic algorithm generation that scales from simple arithmetic problems to highly complex scientific equations with large datasets and multiple operations.
@@ -974,7 +974,7 @@ Exercise with d
-
+
@@ -1106,7 +1106,7 @@ Testing a bigger calculation
-
+
@@ -1117,34 +1117,34 @@ Testing a bigger calculation
-[ ] | 0% Completed | 169.21 us
+[ ] | 0% Completed | 255.13 us
-[ ] | 0% Completed | 105.92 ms
+[ ] | 0% Completed | 106.98 ms
-[ ] | 0% Completed | 206.66 ms
+[ ] | 0% Completed | 207.72 ms
-[ ] | 0% Completed | 307.49 ms
+[ ] | 0% Completed | 308.36 ms
-[ ] | 0% Completed | 408.13 ms
+[ ] | 0% Completed | 409.00 ms
-[ ] | 0% Completed | 508.89 ms
+[ ] | 0% Completed | 509.67 ms
-[ ] | 1% Completed | 609.51 ms
+[ ] | 0% Completed | 610.39 ms
-[# ] | 4% Completed | 710.49 ms
+[# ] | 3% Completed | 711.30 ms
-[### ] | 7% Completed | 811.54 ms
+[## ] | 6% Completed | 812.41 ms
-[#### ] | 10% Completed | 912.41 ms
+[#### ] | 10% Completed | 913.29 ms
[#### ] | 10% Completed | 1.01 s
@@ -1153,7 +1153,7 @@ Testing a bigger calculation[#### ] | 10% Completed | 1.11 s
-[#### ] | 10% Completed | 1.21 s
+[#### ] | 10% Completed | 1.22 s
[#### ] | 10% Completed | 1.32 s
@@ -1162,10 +1162,10 @@ Testing a bigger calculation[#### ] | 10% Completed | 1.42 s
-[##### ] | 13% Completed | 1.52 s
+[##### ] | 12% Completed | 1.52 s
-[###### ] | 16% Completed | 1.62 s
+[###### ] | 15% Completed | 1.62 s
[####### ] | 19% Completed | 1.72 s
@@ -1186,13 +1186,13 @@ Testing a bigger calculation[######## ] | 21% Completed | 2.22 s
-[######## ] | 22% Completed | 2.32 s
+[######## ] | 21% Completed | 2.32 s
-[########## ] | 25% Completed | 2.43 s
+[######### ] | 24% Completed | 2.43 s
-[########### ] | 27% Completed | 2.53 s
+[########### ] | 28% Completed | 2.53 s
[############ ] | 31% Completed | 2.63 s
@@ -1216,16 +1216,16 @@ Testing a bigger calculation[############## ] | 35% Completed | 3.23 s
-[############### ] | 38% Completed | 3.33 s
+[############## ] | 37% Completed | 3.33 s
-[################ ] | 40% Completed | 3.43 s
+[############### ] | 39% Completed | 3.43 s
-[################# ] | 44% Completed | 3.53 s
+[################# ] | 43% Completed | 3.53 s
-[################### ] | 48% Completed | 3.63 s
+[################## ] | 46% Completed | 3.64 s
[################### ] | 48% Completed | 3.74 s
@@ -1246,10 +1246,10 @@ Testing a bigger calculation[#################### ] | 50% Completed | 4.24 s
-[##################### ] | 53% Completed | 4.34 s
+[#################### ] | 52% Completed | 4.34 s
-[###################### ] | 57% Completed | 4.44 s
+[###################### ] | 55% Completed | 4.44 s
[####################### ] | 59% Completed | 4.54 s
@@ -1276,7 +1276,7 @@ Testing a bigger calculation[######################### ] | 64% Completed | 5.25 s
-[########################### ] | 67% Completed | 5.35 s
+[########################### ] | 68% Completed | 5.35 s
[############################ ] | 72% Completed | 5.45 s
@@ -1300,13 +1300,13 @@ Testing a bigger calculation[############################## ] | 75% Completed | 6.06 s
-[############################### ] | 77% Completed | 6.16 s
+[############################### ] | 78% Completed | 6.16 s
-[################################ ] | 80% Completed | 6.26 s
+[################################ ] | 82% Completed | 6.26 s
-[################################# ] | 83% Completed | 6.36 s
+[################################# ] | 84% Completed | 6.36 s
[################################## ] | 85% Completed | 6.46 s
@@ -1324,16 +1324,16 @@ Testing a bigger calculation[################################## ] | 85% Completed | 6.86 s
-[################################## ] | 86% Completed | 6.96 s
+[################################### ] | 89% Completed | 6.96 s
-[################################### ] | 89% Completed | 7.06 s
+[#################################### ] | 92% Completed | 7.07 s
-[##################################### ] | 92% Completed | 7.16 s
+[###################################### ] | 96% Completed | 7.17 s
-[###################################### ] | 96% Completed | 7.27 s
+[####################################### ] | 99% Completed | 7.27 s
[########################################] | 100% Completed | 7.37 s
@@ -1854,7 +1854,7 @@ Reading data with
- latPandasIndex
PandasIndex(Index([-89.5, -88.5, -87.5, -86.5, -85.5, -84.5, -83.5, -82.5, -81.5, -80.5,
+ 80.5, 81.5, 82.5, 83.5, 84.5, 85.5, 86.5, 87.5, 88.5, 89.5])
- lon(lon)float640.5 1.5 2.5 ... 357.5 358.5 359.5
array([ 0.5, 1.5, 2.5, ..., 357.5, 358.5, 359.5])
- latPandasIndex
PandasIndex(Index([-89.5, -88.5, -87.5, -86.5, -85.5, -84.5, -83.5, -82.5, -81.5, -80.5,
...
80.5, 81.5, 82.5, 83.5, 84.5, 85.5, 86.5, 87.5, 88.5, 89.5],
- dtype='float64', name='lat', length=180)) - lonPandasIndex
PandasIndex(Index([ 0.5, 1.5, 2.5, 3.5, 4.5, 5.5, 6.5, 7.5, 8.5, 9.5,
+ dtype='float64', name='lat', length=180))
- lonPandasIndex
PandasIndex(Index([ 0.5, 1.5, 2.5, 3.5, 4.5, 5.5, 6.5, 7.5, 8.5, 9.5,
...
350.5, 351.5, 352.5, 353.5, 354.5, 355.5, 356.5, 357.5, 358.5, 359.5],
- dtype='float64', name='lon', length=360))
+ dtype='float64', name='lon', length=360))
As shown in the above example, the result of the applied operations is an Xarray DataArray that contains a Dask Array, an identical object type to the object that the operations were performed on. This is true for any operations that can be applied to Xarray DataArrays, including subsetting operations; this next example illustrates this:
@@ -3739,7 +3739,7 @@ Parallel and lazy computation using
@@ -3794,10 +3794,10 @@ Parallel and lazy computation using dask.visualize() function, as shown in this example:
@@ -3819,10 +3819,10 @@ Parallel and lazy computation using
-[ ] | 0% Completed | 122.44 us
+[ ] | 0% Completed | 137.30 us
-[########################################] | 100% Completed | 101.18 ms
+[########################################] | 100% Completed | 101.19 ms
@@ -4236,7 +4236,7 @@ Resources and references
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
@@ -4365,7 +4365,7 @@ Contribute
By the Project Pythia Community.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
diff --git a/core/xarray/enso-xarray.html b/core/xarray/enso-xarray.html
index 316661573..6e86d4355 100644
--- a/core/xarray/enso-xarray.html
+++ b/core/xarray/enso-xarray.html
@@ -79,7 +79,7 @@
-
+
@@ -958,7 +958,7 @@ The Niño 3.4 Index
@@ -1590,7 +1590,7 @@ Select the Niño 3.4 regionwhere() method:
+array(1.8436452, dtype=float32)
The final step of the Niño 3.4 index calculation involves normalizing the data. In this example, we perform this normalization by dividing the smoothed anomaly data by the standard deviation calculated above:
@@ -3396,7 +3396,7 @@ Resources and References
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
@@ -3525,7 +3525,7 @@ Contribute
By the Project Pythia Community.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
diff --git a/core/xarray/xarray-intro.html b/core/xarray/xarray-intro.html
index e17e6628d..308dd9baa 100644
--- a/core/xarray/xarray-intro.html
+++ b/core/xarray/xarray-intro.html
@@ -79,7 +79,7 @@
-
+
@@ -583,25 +583,25 @@ Generate a random numpy array
-array([[[274.00119189, 291.26874805, 287.10212211, 281.93321028],
- [276.72439019, 281.86759901, 277.88521001, 291.15900492],
- [283.47892997, 281.69598295, 281.87670232, 280.04253353]],
+array([[[283.19514872, 279.93414516, 288.85955729, 280.30299831],
+ [282.03318795, 283.36237877, 288.28464039, 284.82856563],
+ [282.15998672, 276.06859236, 285.78192464, 280.47703387]],
- [[292.49183408, 286.75694127, 291.1430049 , 279.6708509 ],
- [283.19466798, 285.34236937, 289.30840199, 286.22515585],
- [289.80677567, 281.73710917, 294.57091617, 291.28505114]],
+ [[282.44435897, 282.262194 , 286.34635361, 285.32213341],
+ [283.03737484, 288.57815157, 290.02698113, 278.23328324],
+ [277.06837841, 286.4771544 , 284.58714744, 288.42673576]],
- [[278.59826878, 287.14196565, 284.68298455, 286.59720048],
- [287.51252429, 288.52590266, 273.69692215, 285.39338362],
- [292.66871999, 278.60078099, 280.78168263, 291.92156783]],
+ [[280.62831671, 275.374152 , 283.08613936, 292.56788174],
+ [286.26020096, 282.03531027, 280.16394984, 288.9366629 ],
+ [282.90971416, 278.40998651, 284.98850877, 283.14584888]],
- [[281.24772197, 286.52210588, 282.61809684, 288.82826349],
- [282.48419301, 286.34184212, 277.57835692, 279.66054122],
- [288.97073742, 287.33849395, 289.90083339, 285.95347237]],
+ [[284.34422355, 283.20830934, 278.27414095, 285.21976361],
+ [279.17521323, 292.21743424, 276.974416 , 284.06699408],
+ [274.74247997, 279.63780379, 275.87914515, 277.71758929]],
- [[282.699394 , 286.72856786, 281.89265707, 286.16350665],
- [278.02139036, 284.54696447, 283.16191651, 278.99615563],
- [284.94443941, 287.35039327, 289.38110818, 280.10681174]]])
+ [[282.71726594, 281.99808895, 279.66639367, 276.71709325],
+ [278.30599074, 277.08406243, 273.68999203, 279.75441922],
+ [271.39869986, 286.48138908, 285.22713979, 291.77285885]]])
@@ -982,44 +982,44 @@ Wrap the array: first attempt
@@ -1402,44 +1402,44 @@ Assign dimension names<xarray.DataArray (time: 5, lat: 3, lon: 4)> Size: 480B
-array([[[274.00119189, 291.26874805, 287.10212211, 281.93321028],
- [276.72439019, 281.86759901, 277.88521001, 291.15900492],
- [283.47892997, 281.69598295, 281.87670232, 280.04253353]],
-
- [[292.49183408, 286.75694127, 291.1430049 , 279.6708509 ],
- [283.19466798, 285.34236937, 289.30840199, 286.22515585],
- [289.80677567, 281.73710917, 294.57091617, 291.28505114]],
-
- [[278.59826878, 287.14196565, 284.68298455, 286.59720048],
- [287.51252429, 288.52590266, 273.69692215, 285.39338362],
- [292.66871999, 278.60078099, 280.78168263, 291.92156783]],
-
- [[281.24772197, 286.52210588, 282.61809684, 288.82826349],
- [282.48419301, 286.34184212, 277.57835692, 279.66054122],
- [288.97073742, 287.33849395, 289.90083339, 285.95347237]],
-
- [[282.699394 , 286.72856786, 281.89265707, 286.16350665],
- [278.02139036, 284.54696447, 283.16191651, 278.99615563],
- [284.94443941, 287.35039327, 289.38110818, 280.10681174]]])
-Dimensions without coordinates: time, lat, lon
+array([[[283.19514872, 279.93414516, 288.85955729, 280.30299831],
+ [282.03318795, 283.36237877, 288.28464039, 284.82856563],
+ [282.15998672, 276.06859236, 285.78192464, 280.47703387]],
+
+ [[282.44435897, 282.262194 , 286.34635361, 285.32213341],
+ [283.03737484, 288.57815157, 290.02698113, 278.23328324],
+ [277.06837841, 286.4771544 , 284.58714744, 288.42673576]],
+
+ [[280.62831671, 275.374152 , 283.08613936, 292.56788174],
+ [286.26020096, 282.03531027, 280.16394984, 288.9366629 ],
+ [282.90971416, 278.40998651, 284.98850877, 283.14584888]],
+
+ [[284.34422355, 283.20830934, 278.27414095, 285.21976361],
+ [279.17521323, 292.21743424, 276.974416 , 284.06699408],
+ [274.74247997, 279.63780379, 275.87914515, 277.71758929]],
+
+ [[282.71726594, 281.99808895, 279.66639367, 276.71709325],
+ [278.30599074, 277.08406243, 273.68999203, 279.75441922],
+ [271.39869986, 286.48138908, 285.22713979, 291.77285885]]])
+Dimensions without coordinates: time, lat, lon
This DataArray is already an improvement over a NumPy array; the DataArray contains names for each of the dimensions (or axes in NumPy parlance). An additional improvement is the association of coordinate-value arrays with data upon creation of a DataArray. In the next example, we illustrate the creation of NumPy arrays representing the coordinate values for each dimension of the DataArray, and how to associate these coordinate arrays with the data in our DataArray.
@@ -1849,51 +1849,51 @@ Initialize the
fill: currentColor;
}
<xarray.DataArray (time: 5, lat: 3, lon: 4)> Size: 480B
-array([[[274.00119189, 291.26874805, 287.10212211, 281.93321028],
- [276.72439019, 281.86759901, 277.88521001, 291.15900492],
- [283.47892997, 281.69598295, 281.87670232, 280.04253353]],
+array([[[283.19514872, 279.93414516, 288.85955729, 280.30299831],
+ [282.03318795, 283.36237877, 288.28464039, 284.82856563],
+ [282.15998672, 276.06859236, 285.78192464, 280.47703387]],
- [[292.49183408, 286.75694127, 291.1430049 , 279.6708509 ],
- [283.19466798, 285.34236937, 289.30840199, 286.22515585],
- [289.80677567, 281.73710917, 294.57091617, 291.28505114]],
+ [[282.44435897, 282.262194 , 286.34635361, 285.32213341],
+ [283.03737484, 288.57815157, 290.02698113, 278.23328324],
+ [277.06837841, 286.4771544 , 284.58714744, 288.42673576]],
- [[278.59826878, 287.14196565, 284.68298455, 286.59720048],
- [287.51252429, 288.52590266, 273.69692215, 285.39338362],
- [292.66871999, 278.60078099, 280.78168263, 291.92156783]],
+ [[280.62831671, 275.374152 , 283.08613936, 292.56788174],
+ [286.26020096, 282.03531027, 280.16394984, 288.9366629 ],
+ [282.90971416, 278.40998651, 284.98850877, 283.14584888]],
- [[281.24772197, 286.52210588, 282.61809684, 288.82826349],
- [282.48419301, 286.34184212, 277.57835692, 279.66054122],
- [288.97073742, 287.33849395, 289.90083339, 285.95347237]],
+ [[284.34422355, 283.20830934, 278.27414095, 285.21976361],
+ [279.17521323, 292.21743424, 276.974416 , 284.06699408],
+ [274.74247997, 279.63780379, 275.87914515, 277.71758929]],
- [[282.699394 , 286.72856786, 281.89265707, 286.16350665],
- [278.02139036, 284.54696447, 283.16191651, 278.99615563],
- [284.94443941, 287.35039327, 289.38110818, 280.10681174]]])
+ [[282.71726594, 281.99808895, 279.66639367, 276.71709325],
+ [278.30599074, 277.08406243, 273.68999203, 279.75441922],
+ [271.39869986, 286.48138908, 285.22713979, 291.77285885]]])
Coordinates:
* time (time) datetime64[ns] 40B 2018-01-01 2018-01-02 ... 2018-01-05
* lat (lat) float64 24B 25.0 40.0 55.0
- * lon (lon) float64 32B -120.0 -100.0 -80.0 -60.0
+ dtype='datetime64[ns]', name='time', freq='D'))
latPandasIndexPandasIndex(Index([25.0, 40.0, 55.0], dtype='float64', name='lat'))
lonPandasIndexPandasIndex(Index([-120.0, -100.0, -80.0, -60.0], dtype='float64', name='lon'))
@@ -2273,54 +2273,54 @@ Set useful attributes<xarray.DataArray (time: 5, lat: 3, lon: 4)> Size: 480B
-array([[[274.00119189, 291.26874805, 287.10212211, 281.93321028],
- [276.72439019, 281.86759901, 277.88521001, 291.15900492],
- [283.47892997, 281.69598295, 281.87670232, 280.04253353]],
+array([[[283.19514872, 279.93414516, 288.85955729, 280.30299831],
+ [282.03318795, 283.36237877, 288.28464039, 284.82856563],
+ [282.15998672, 276.06859236, 285.78192464, 280.47703387]],
- [[292.49183408, 286.75694127, 291.1430049 , 279.6708509 ],
- [283.19466798, 285.34236937, 289.30840199, 286.22515585],
- [289.80677567, 281.73710917, 294.57091617, 291.28505114]],
+ [[282.44435897, 282.262194 , 286.34635361, 285.32213341],
+ [283.03737484, 288.57815157, 290.02698113, 278.23328324],
+ [277.06837841, 286.4771544 , 284.58714744, 288.42673576]],
- [[278.59826878, 287.14196565, 284.68298455, 286.59720048],
- [287.51252429, 288.52590266, 273.69692215, 285.39338362],
- [292.66871999, 278.60078099, 280.78168263, 291.92156783]],
+ [[280.62831671, 275.374152 , 283.08613936, 292.56788174],
+ [286.26020096, 282.03531027, 280.16394984, 288.9366629 ],
+ [282.90971416, 278.40998651, 284.98850877, 283.14584888]],
- [[281.24772197, 286.52210588, 282.61809684, 288.82826349],
- [282.48419301, 286.34184212, 277.57835692, 279.66054122],
- [288.97073742, 287.33849395, 289.90083339, 285.95347237]],
+ [[284.34422355, 283.20830934, 278.27414095, 285.21976361],
+ [279.17521323, 292.21743424, 276.974416 , 284.06699408],
+ [274.74247997, 279.63780379, 275.87914515, 277.71758929]],
- [[282.699394 , 286.72856786, 281.89265707, 286.16350665],
- [278.02139036, 284.54696447, 283.16191651, 278.99615563],
- [284.94443941, 287.35039327, 289.38110818, 280.10681174]]])
+ [[282.71726594, 281.99808895, 279.66639367, 276.71709325],
+ [278.30599074, 277.08406243, 273.68999203, 279.75441922],
+ [271.39869986, 286.48138908, 285.22713979, 291.77285885]]])
Coordinates:
* time (time) datetime64[ns] 40B 2018-01-01 2018-01-02 ... 2018-01-05
* lat (lat) float64 24B 25.0 40.0 55.0
* lon (lon) float64 32B -120.0 -100.0 -80.0 -60.0
Attributes:
units: kelvin
- standard_name: air_temperature
+ dtype='datetime64[ns]', name='time', freq='D'))
latPandasIndexPandasIndex(Index([25.0, 40.0, 55.0], dtype='float64', name='lat'))
lonPandasIndexPandasIndex(Index([-120.0, -100.0, -80.0, -60.0], dtype='float64', name='lon'))
- units :
- kelvin
- standard_name :
- air_temperature
@@ -2699,51 +2699,51 @@ Issues with preservation of attributesdocumentation page.
@@ -3134,54 +3134,54 @@ Create a pressure
+ dtype='datetime64[ns]', name='time', freq='D'))latPandasIndexPandasIndex(Index([25.0, 40.0, 55.0], dtype='float64', name='lat'))
lonPandasIndexPandasIndex(Index([-120.0, -100.0, -80.0, -60.0], dtype='float64', name='lon'))
As listed in the rich display above, the new Dataset object is aware that both DataArrays share the same coordinate axes. (Please note that if this page is not run as a Jupyter Notebook, the rich display may be unavailable.)
@@ -3986,54 +3986,54 @@ Access Data variables and Coordinates in a DataArrays through a dictionary syntax, as shown in this example:
@@ -4407,54 +4407,54 @@ Access Data variables and Coordinates in a Dataset objects are mainly used for loading data from files, which will be covered later in this tutorial.
@@ -4838,18 +4838,18 @@ NumPy-like selection<xarray.DataArray (lat: 3, lon: 4)> Size: 96B
-array([[292.49183408, 286.75694127, 291.1430049 , 279.6708509 ],
- [283.19466798, 285.34236937, 289.30840199, 286.22515585],
- [289.80677567, 281.73710917, 294.57091617, 291.28505114]])
+array([[282.44435897, 282.262194 , 286.34635361, 285.32213341],
+ [283.03737484, 288.57815157, 290.02698113, 278.23328324],
+ [277.06837841, 286.4771544 , 284.58714744, 288.42673576]])
Coordinates:
time datetime64[ns] 8B 2018-01-02
* lat (lat) float64 24B 25.0 40.0 55.0
* lon (lon) float64 32B -120.0 -100.0 -80.0 -60.0
Attributes:
units: kelvin
- standard_name: air_temperature
+ standard_name: air_temperature
This example reveals one of the major shortcomings of index selection. In order to retrieve the correct data using index selection, anyone using a DataArray must have precise knowledge of the axes in the DataArray, including the order of the axes and the meaning of their indices.
By using named coordinates, as shown in the next set of examples, we can avoid this cumbersome burden.
@@ -5229,18 +5229,18 @@ Selecting with
fill: currentColor;
}
<xarray.DataArray (lat: 3, lon: 4)> Size: 96B
-array([[292.49183408, 286.75694127, 291.1430049 , 279.6708509 ],
- [283.19466798, 285.34236937, 289.30840199, 286.22515585],
- [289.80677567, 281.73710917, 294.57091617, 291.28505114]])
+array([[282.44435897, 282.262194 , 286.34635361, 285.32213341],
+ [283.03737484, 288.57815157, 290.02698113, 278.23328324],
+ [277.06837841, 286.4771544 , 284.58714744, 288.42673576]])
Coordinates:
time datetime64[ns] 8B 2018-01-02
* lat (lat) float64 24B 25.0 40.0 55.0
* lon (lon) float64 32B -120.0 -100.0 -80.0 -60.0
Attributes:
units: kelvin
- standard_name: air_temperature
+ standard_name: air_temperature
This method yields the same result as the index selection, however:
@@ -5627,18 +5627,18 @@ Nearest-neighbor sampling.sel indeed returned the data at the temporal value corresponding to the date 2018-01-05.
@@ -6017,20 +6017,20 @@ Interpolation
Info
@@ -6414,29 +6414,29 @@ Slicing along coordinates
Info
@@ -6818,18 +6818,18 @@ One more selection method: <xarray.DataArray (lat: 3, lon: 4)> Size: 96B
-array([[292.49183408, 286.75694127, 291.1430049 , 279.6708509 ],
- [283.19466798, 285.34236937, 289.30840199, 286.22515585],
- [289.80677567, 281.73710917, 294.57091617, 291.28505114]])
+array([[282.44435897, 282.262194 , 286.34635361, 285.32213341],
+ [283.03737484, 288.57815157, 290.02698113, 278.23328324],
+ [277.06837841, 286.4771544 , 284.58714744, 288.42673576]])
Coordinates:
time datetime64[ns] 8B 2018-01-02
* lat (lat) float64 24B 25.0 40.0 55.0
* lon (lon) float64 32B -120.0 -100.0 -80.0 -60.0
Attributes:
units: kelvin
- standard_name: air_temperature
+ standard_name: air_temperature
This selection technique is similar to NumPy’s index-based selection, as shown below:
temp[1,:,:]
@@ -7208,29 +7208,29 @@ One more selection method: <xarray.DataArray (time: 3, lat: 2, lon: 2)> Size: 96B
-array([[[291.26874805, 287.10212211],
- [281.86759901, 277.88521001]],
+array([[[279.93414516, 288.85955729],
+ [283.36237877, 288.28464039]],
- [[286.75694127, 291.1430049 ],
- [285.34236937, 289.30840199]],
+ [[282.262194 , 286.34635361],
+ [288.57815157, 290.02698113]],
- [[287.14196565, 284.68298455],
- [288.52590266, 273.69692215]]])
+ [[275.374152 , 283.08613936],
+ [282.03531027, 280.16394984]]])
Coordinates:
* time (time) datetime64[ns] 24B 2018-01-01 2018-01-02 2018-01-03
* lat (lat) float64 16B 25.0 40.0
* lon (lon) float64 16B -100.0 -80.0
Attributes:
units: kelvin
- standard_name: air_temperature
+ [[275.374152 , 283.08613936],
+ [282.03531027, 280.16394984]]])
- time(time)datetime64[ns]2018-01-01 2018-01-02 2018-01-03
array(['2018-01-01T00:00:00.000000000', '2018-01-02T00:00:00.000000000',
+ '2018-01-03T00:00:00.000000000'], dtype='datetime64[ns]')
- lat(lat)float6425.0 40.0
array([25., 40.])
- lon(lon)float64-100.0 -80.0
array([-100., -80.])
- timePandasIndex
PandasIndex(DatetimeIndex(['2018-01-01', '2018-01-02', '2018-01-03'], dtype='datetime64[ns]', name='time', freq='D'))
- latPandasIndex
PandasIndex(Index([25.0, 40.0], dtype='float64', name='lat'))
- lonPandasIndex
PandasIndex(Index([-100.0, -80.0], dtype='float64', name='lon'))
- units :
- kelvin
- standard_name :
- air_temperature
In contrast with the previous example, this example shows a useful advantage of using the .loc attribute. When using the .loc attribute, you can specify data slices using a syntax similar to NumPy in addition to, or instead of, using the slice function. Both of these slicing techniques are illustrated below:
@@ -7604,29 +7604,29 @@ One more selection method: <xarray.DataArray (time: 3, lat: 2, lon: 2)> Size: 96B
-array([[[291.26874805, 287.10212211],
- [281.86759901, 277.88521001]],
+array([[[279.93414516, 288.85955729],
+ [283.36237877, 288.28464039]],
- [[286.75694127, 291.1430049 ],
- [285.34236937, 289.30840199]],
+ [[282.262194 , 286.34635361],
+ [288.57815157, 290.02698113]],
- [[287.14196565, 284.68298455],
- [288.52590266, 273.69692215]]])
+ [[275.374152 , 283.08613936],
+ [282.03531027, 280.16394984]]])
Coordinates:
* time (time) datetime64[ns] 24B 2018-01-01 2018-01-02 2018-01-03
* lat (lat) float64 16B 25.0 40.0
* lon (lon) float64 16B -100.0 -80.0
Attributes:
units: kelvin
- standard_name: air_temperature
+ [[275.374152 , 283.08613936],
+ [282.03531027, 280.16394984]]])
- time(time)datetime64[ns]2018-01-01 2018-01-02 2018-01-03
array(['2018-01-01T00:00:00.000000000', '2018-01-02T00:00:00.000000000',
+ '2018-01-03T00:00:00.000000000'], dtype='datetime64[ns]')
- lat(lat)float6425.0 40.0
array([25., 40.])
- lon(lon)float64-100.0 -80.0
array([-100., -80.])
- timePandasIndex
PandasIndex(DatetimeIndex(['2018-01-01', '2018-01-02', '2018-01-03'], dtype='datetime64[ns]', name='time', freq='D'))
- latPandasIndex
PandasIndex(Index([25.0, 40.0], dtype='float64', name='lat'))
- lonPandasIndex
PandasIndex(Index([-100.0, -80.0], dtype='float64', name='lon'))
- units :
- kelvin
- standard_name :
- air_temperature
As described above, the arguments to .loc must be in the order of the DataArray’s dimensions. Attempting to slice data without ordering arguments properly can cause errors, as shown below:
@@ -8059,10 +8059,10 @@ Access netCDF data with
@@ -8504,17 +8504,17 @@ Subsetting the
positive: down
Grib_level_type: 100
_CoordinateAxisType: Pressure
- _CoordinateZisPositive: down
+ dtype='float32', name='isobaric1'))- units :
- hPa
- long_name :
- Isobaric surface
- positive :
- down
- Grib_level_type :
- 100
- _CoordinateAxisType :
- Pressure
- _CoordinateZisPositive :
- down
(As described earlier in this tutorial, we can also use dictionary syntax to select specific DataArrays; in this case, we would write ds['isobaric1'].)
Many of the subsetting operations usable on DataArrays can also be used on Datasets. However, when used on Datasets, these operations are performed on every DataArray in the Dataset, as shown below:
@@ -8916,7 +8916,7 @@ Subsetting the
geospatial_lat_min: 10.753308882144761
geospatial_lat_max: 46.8308828962289
geospatial_lon_min: -153.88242040519995
- geospatial_lon_max: -42.666108129242815
As shown above, the subsetting operation performed on the Dataset returned a new Dataset. If only a single DataArray is needed from this new Dataset, it can be retrieved using the familiar dot notation:
@@ -9356,7 +9356,7 @@ Subsetting the
Grib1_TableVersion: 131
Grib1_Parameter: 11
Grib1_Level_Type: 100
- Grib1_Level_Desc: Isobaric surface
+ dtype='float32', name='x', length=268))- long_name :
- Temperature @ Isobaric surface
- units :
- K
- description :
- Temperature
- grid_mapping :
- LambertConformal_Projection
- Grib_Variable_Id :
- VAR_7-15-131-11_L100
- Grib1_Center :
- 7
- Grib1_Subcenter :
- 15
- Grib1_TableVersion :
- 131
- Grib1_Parameter :
- 11
- Grib1_Level_Type :
- 100
- Grib1_Level_Desc :
- Isobaric surface
@@ -9790,19 +9790,19 @@ Aggregation operations
+ dtype='float32', name='isobaric1'))
Info
@@ -10196,19 +10196,19 @@ Aggregation operations
+ dtype='float32', name='isobaric1'))
@@ -10226,7 +10226,7 @@ Simple visualization with
-[<matplotlib.lines.Line2D at 0x7f2e0f58d130>]
+[<matplotlib.lines.Line2D at 0x7f505db3d9d0>]
@@ -10250,7 +10250,7 @@ Customizing the plot
-[<matplotlib.lines.Line2D at 0x7f2e0f58eff0>]
+[<matplotlib.lines.Line2D at 0x7f5055a98d40>]
@@ -10268,7 +10268,7 @@ Plotting 2-D data
-<matplotlib.collections.QuadMesh at 0x7f2df9c43b60>
+<matplotlib.collections.QuadMesh at 0x7f5055ad1be0>
@@ -10667,7 +10667,7 @@ Resources and references
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
@@ -10796,7 +10796,7 @@ Contribute
By the Project Pythia Community.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
diff --git a/foundations/conda.html b/foundations/conda.html
index c07684127..6f809cfd0 100644
--- a/foundations/conda.html
+++ b/foundations/conda.html
@@ -79,7 +79,7 @@
-
+
@@ -913,7 +913,7 @@ Resources and References
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
@@ -1042,7 +1042,7 @@ Contribute
By the Project Pythia Community.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
diff --git a/foundations/getting-started-github.html b/foundations/getting-started-github.html
index 4a2af1d14..ec22e00df 100644
--- a/foundations/getting-started-github.html
+++ b/foundations/getting-started-github.html
@@ -81,7 +81,7 @@
-
+
@@ -768,7 +768,7 @@ Getting Started with GitHub
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
@@ -897,7 +897,7 @@ Contribute
By the Project Pythia Community.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
diff --git a/foundations/getting-started-jupyter.html b/foundations/getting-started-jupyter.html
index b1d2107c0..b95652e74 100644
--- a/foundations/getting-started-jupyter.html
+++ b/foundations/getting-started-jupyter.html
@@ -79,7 +79,7 @@
-
+
@@ -905,7 +905,7 @@ Resources and references
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
@@ -1034,7 +1034,7 @@ Contribute
By the Project Pythia Community.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
diff --git a/foundations/getting-started-python.html b/foundations/getting-started-python.html
index 286ad4516..19ac49850 100644
--- a/foundations/getting-started-python.html
+++ b/foundations/getting-started-python.html
@@ -81,7 +81,7 @@
-
+
@@ -779,7 +779,7 @@ Topics
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
@@ -908,7 +908,7 @@ Contribute
By the Project Pythia Community.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
diff --git a/foundations/github/basic-git.html b/foundations/github/basic-git.html
index 441aa5056..32e0b6b24 100644
--- a/foundations/github/basic-git.html
+++ b/foundations/github/basic-git.html
@@ -79,7 +79,7 @@
-
+
@@ -1222,7 +1222,7 @@ References
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
@@ -1351,7 +1351,7 @@ Contribute
By the Project Pythia Community.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
diff --git a/foundations/github/contribute-to-pythia.html b/foundations/github/contribute-to-pythia.html
index d1e1bad12..11ebc0cc2 100644
--- a/foundations/github/contribute-to-pythia.html
+++ b/foundations/github/contribute-to-pythia.html
@@ -79,7 +79,7 @@
-
+
@@ -885,7 +885,7 @@ Summary
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
@@ -1014,7 +1014,7 @@ Contribute
By the Project Pythia Community.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
diff --git a/foundations/github/git-branches.html b/foundations/github/git-branches.html
index 6dac4b319..4e316269f 100644
--- a/foundations/github/git-branches.html
+++ b/foundations/github/git-branches.html
@@ -79,7 +79,7 @@
-
+
@@ -1036,7 +1036,7 @@ Resources and references
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
@@ -1165,7 +1165,7 @@ Contribute
By the Project Pythia Community.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
diff --git a/foundations/github/github-advanced.html b/foundations/github/github-advanced.html
index 9e93b611f..def634b01 100644
--- a/foundations/github/github-advanced.html
+++ b/foundations/github/github-advanced.html
@@ -77,7 +77,7 @@
-
+
@@ -810,7 +810,7 @@ References
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
@@ -939,7 +939,7 @@ Contribute
By the Project Pythia Community.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
diff --git a/foundations/github/github-cloning-forking.html b/foundations/github/github-cloning-forking.html
index c03c6f72a..18b821529 100644
--- a/foundations/github/github-cloning-forking.html
+++ b/foundations/github/github-cloning-forking.html
@@ -79,7 +79,7 @@
-
+
@@ -988,7 +988,7 @@ References
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
@@ -1117,7 +1117,7 @@ Contribute
By the Project Pythia Community.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
diff --git a/foundations/github/github-issues.html b/foundations/github/github-issues.html
index fe56bf8f1..cf5e4670a 100644
--- a/foundations/github/github-issues.html
+++ b/foundations/github/github-issues.html
@@ -79,7 +79,7 @@
-
+
@@ -875,7 +875,7 @@ References
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
@@ -1004,7 +1004,7 @@ Contribute
By the Project Pythia Community.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
diff --git a/foundations/github/github-pull-request.html b/foundations/github/github-pull-request.html
index 566d2d767..a2f449208 100644
--- a/foundations/github/github-pull-request.html
+++ b/foundations/github/github-pull-request.html
@@ -79,7 +79,7 @@
-
+
@@ -946,7 +946,7 @@ References
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
@@ -1075,7 +1075,7 @@ Contribute
By the Project Pythia Community.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
diff --git a/foundations/github/github-repos.html b/foundations/github/github-repos.html
index 7d26de757..389418ea0 100644
--- a/foundations/github/github-repos.html
+++ b/foundations/github/github-repos.html
@@ -79,7 +79,7 @@
-
+
@@ -885,7 +885,7 @@ References
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
@@ -1014,7 +1014,7 @@ Contribute
By the Project Pythia Community.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
diff --git a/foundations/github/github-setup-advanced.html b/foundations/github/github-setup-advanced.html
index f2123372f..71344fd0b 100644
--- a/foundations/github/github-setup-advanced.html
+++ b/foundations/github/github-setup-advanced.html
@@ -79,7 +79,7 @@
-
+
@@ -924,7 +924,7 @@ References
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
@@ -1053,7 +1053,7 @@ Contribute
By the Project Pythia Community.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
diff --git a/foundations/github/github-workflows.html b/foundations/github/github-workflows.html
index a5c6b6dfc..17e8fd580 100644
--- a/foundations/github/github-workflows.html
+++ b/foundations/github/github-workflows.html
@@ -79,7 +79,7 @@
-
+
@@ -1253,7 +1253,7 @@ References
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
@@ -1382,7 +1382,7 @@ Contribute
By the Project Pythia Community.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
diff --git a/foundations/github/review-pr.html b/foundations/github/review-pr.html
index f5b0820fb..fe5674aa9 100644
--- a/foundations/github/review-pr.html
+++ b/foundations/github/review-pr.html
@@ -79,7 +79,7 @@
-
+
@@ -918,7 +918,7 @@ Resources and References
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
@@ -1047,7 +1047,7 @@ Contribute
By the Project Pythia Community.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
diff --git a/foundations/github/what-is-github.html b/foundations/github/what-is-github.html
index 27ef2dcd4..d0e70d102 100644
--- a/foundations/github/what-is-github.html
+++ b/foundations/github/what-is-github.html
@@ -81,7 +81,7 @@
-
+
@@ -916,7 +916,7 @@ References
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
@@ -1045,7 +1045,7 @@ Contribute
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diff --git a/foundations/how-to-run-python.html b/foundations/how-to-run-python.html
index 8b1aab94d..d7e56e4bc 100644
--- a/foundations/how-to-run-python.html
+++ b/foundations/how-to-run-python.html
@@ -79,7 +79,7 @@
-
+
@@ -871,7 +871,7 @@ Resources and References
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+ Last updated on 20 May 2024.
@@ -1000,7 +1000,7 @@ Contribute
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diff --git a/foundations/jupyter.html b/foundations/jupyter.html
index 0d2537ca8..901251796 100644
--- a/foundations/jupyter.html
+++ b/foundations/jupyter.html
@@ -79,7 +79,7 @@
-
+
@@ -885,7 +885,7 @@ Resources and References
- Last updated on 15 May 2024.
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@@ -1014,7 +1014,7 @@ Contribute
By the Project Pythia Community.
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+ Last updated on 20 May 2024.
diff --git a/foundations/jupyterlab.html b/foundations/jupyterlab.html
index 1565427e6..94676c432 100644
--- a/foundations/jupyterlab.html
+++ b/foundations/jupyterlab.html
@@ -79,7 +79,7 @@
-
+
@@ -1161,7 +1161,7 @@ Resources and references
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@@ -1290,7 +1290,7 @@ Contribute
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diff --git a/foundations/markdown.html b/foundations/markdown.html
index 80c1a4850..364430a39 100644
--- a/foundations/markdown.html
+++ b/foundations/markdown.html
@@ -79,7 +79,7 @@
-
+
@@ -754,7 +754,7 @@ Formatted Text in the Notebook with Markdown
- Last updated on 15 May 2024.
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@@ -883,7 +883,7 @@ Contribute
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diff --git a/foundations/overview.html b/foundations/overview.html
index c8dc2806e..344643277 100644
--- a/foundations/overview.html
+++ b/foundations/overview.html
@@ -79,7 +79,7 @@
-
+
@@ -778,7 +778,7 @@ Topics
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
@@ -907,7 +907,7 @@ Contribute
By the Project Pythia Community.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
diff --git a/foundations/quickstart.html b/foundations/quickstart.html
index c1a4cabac..8e51f8ba9 100644
--- a/foundations/quickstart.html
+++ b/foundations/quickstart.html
@@ -81,7 +81,7 @@
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+
@@ -1267,7 +1267,7 @@ Resources and references
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
@@ -1396,7 +1396,7 @@ Contribute
By the Project Pythia Community.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
diff --git a/foundations/terminal.html b/foundations/terminal.html
index 634495814..c4cb654ea 100644
--- a/foundations/terminal.html
+++ b/foundations/terminal.html
@@ -79,7 +79,7 @@
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+
@@ -881,7 +881,7 @@ Resources and References
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
@@ -1010,7 +1010,7 @@ Contribute
By the Project Pythia Community.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
diff --git a/foundations/why-python.html b/foundations/why-python.html
index abbbd234f..b955e39c2 100644
--- a/foundations/why-python.html
+++ b/foundations/why-python.html
@@ -79,7 +79,7 @@
-
+
@@ -791,7 +791,7 @@ Open source
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
@@ -920,7 +920,7 @@ Contribute
By the Project Pythia Community.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
diff --git a/genindex.html b/genindex.html
index 81c713d97..f6f7addda 100644
--- a/genindex.html
+++ b/genindex.html
@@ -76,7 +76,7 @@
-
+
@@ -648,7 +648,7 @@ Index
@@ -777,7 +777,7 @@ Contribute
By the Project Pythia Community.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
diff --git a/landing-page.html b/landing-page.html
index ff147bc66..276e728be 100644
--- a/landing-page.html
+++ b/landing-page.html
@@ -80,7 +80,7 @@
-
+
@@ -776,7 +776,7 @@ A community learning resource for Python-based computing in the geosciences<
@@ -905,7 +905,7 @@ Contribute
By the Project Pythia Community.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
diff --git a/preamble/how-to-cite.html b/preamble/how-to-cite.html
index 6055d6478..8dfbb5b1d 100644
--- a/preamble/how-to-cite.html
+++ b/preamble/how-to-cite.html
@@ -79,7 +79,7 @@
-
+
@@ -755,7 +755,7 @@ How to Cite This Book
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
@@ -884,7 +884,7 @@ Contribute
By the Project Pythia Community.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
diff --git a/preamble/how-to-use.html b/preamble/how-to-use.html
index 626d2a29a..a79c9377d 100644
--- a/preamble/how-to-use.html
+++ b/preamble/how-to-use.html
@@ -79,7 +79,7 @@
-
+
@@ -895,7 +895,7 @@ Interacting with Jupyter Books locally
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
@@ -1024,7 +1024,7 @@ Contribute
By the Project Pythia Community.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
diff --git a/search.html b/search.html
index 57210f52a..705201479 100644
--- a/search.html
+++ b/search.html
@@ -78,7 +78,7 @@
-
+
@@ -673,7 +673,7 @@ Search
@@ -802,7 +802,7 @@ Contribute
By the Project Pythia Community.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
diff --git a/searchindex.js b/searchindex.js
index 18d9c718b..f531c0445 100644
--- a/searchindex.js
+++ b/searchindex.js
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"annotations-colorbars-and-advanced-layouts"]], "Another content subsection": [[1, "another-content-subsection"]], "Apply & Combine": [[20, "apply-combine"]], "Applying operations to a DataFrame": [[18, "applying-operations-to-a-dataframe"]], "Approximate selection and interpolation": [[23, "approximate-selection-and-interpolation"]], "Arithmetic": [[14, "arithmetic"]], "Arithmetic Operations": [[20, "arithmetic-operations"]], "Array comparisons": [[13, "array-comparisons"]], "Array math functions": [[14, "array-math-functions"]], "Arrays of numbers with NumPy": [[45, "arrays-of-numbers-with-numpy"]], "Assign dimension names": [[23, "assign-dimension-names"]], "Auxiliary Coordinates": [[5, "auxiliary-coordinates"]], "Basic Colorbars": [[9, "basic-colorbars"]], "Basic Line Plots": [[11, "basic-line-plots"]], "Basic Python data types": [[45, "basic-python-data-types"]], "Basic Statistics": [[18, "basic-statistics"]], "Basic Version Control with git": [[28, "basic-version-control-with-git"]], "Basic concepts: map projections and GeoAxes": [[3, "basic-concepts-map-projections-and-geoaxes"]], "Basic flow control": [[45, "basic-flow-control"]], "Blocked algorithms": [[21, "blocked-algorithms"]], "Blocking": [[15, "blocking"]], "Building the book locally": [[0, "building-the-book-locally"]], "Building the site": [[0, "building-the-site"]], "Calculating ENSO with Xarray": [[22, "calculating-enso-with-xarray"]], "Calculating coastal tides with the timedelta class": [[7, "calculating-coastal-tides-with-the-timedelta-class"]], "Cartopy": [[2, "cartopy"]], "Cartopy\u2019s set_extent method": [[3, "cartopys-set-extent-method"]], "Cell Bounds": [[5, "cell-bounds"]], "Character strings (str)": [[45, "character-strings-str"]], "Checking out a previous commit": [[28, "checking-out-a-previous-commit"]], "Choosing a Python Platform": [[40, "choosing-a-python-platform"]], "Classes of colormaps": [[9, "classes-of-colormaps"]], "Clone, branch, change, 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"Compute the anomalies": [[22, "compute-the-anomalies"]], "Compute the result": [[21, "compute-the-result"]], "Configuring Your GitHub Account": [[36, "configuring-your-github-account"]], "Consoles": [[42, "consoles"]], "Constants": [[14, "constants"]], "Content section": [[31, "content-section"]], "Contour and Filled Contour Plots": [[11, "contour-and-filled-contour-plots"]], "Contribute to Project Pythia via GitHub": [[29, "contribute-to-project-pythia-via-github"]], "Contributing a new Jupyter Notebook": [[0, "contributing-a-new-jupyter-notebook"]], "Controlling the output format with strftime()": [[7, "controlling-the-output-format-with-strftime"]], "Coordinate System Information": [[5, "coordinate-system-information"]], "Coordinate variables": [[5, "coordinate-variables"]], "Core libraries": [[16, "core-libraries"]], "Create Some Sample Data": [[9, "create-some-sample-data"]], "Create a DataArray with named Coordinates": [[23, "create-a-dataarray-with-named-coordinates"]], "Create 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"discussions"]], "Displaying Images": [[11, "displaying-images"]], "Domain-specific libraries": [[16, "domain-specific-libraries"]], "Email notifications": [[36, "email-notifications"]], "Example usage of the datetime object": [[7, "example-usage-of-the-datetime-object"]], "Executing Jupyter": [[26, "executing-jupyter"]], "Exercise with dask.arrays": [[21, "exercise-with-dask-arrays"]], "Exploratory Data Analysis": [[18, "exploratory-data-analysis"]], "Explore some of Cartopy\u2019s map projections": [[3, "explore-some-of-cartopys-map-projections"]], "Extend Matplotlib\u2019s axes into georeferenced GeoAxes": [[3, "extend-matplotlibs-axes-into-georeferenced-geoaxes"]], "Extending to higher dimensions": [[15, "extending-to-higher-dimensions"]], "Extending to the DataFrame": [[18, "extending-to-the-dataframe"]], "FOSS + VCS + Web = GitHub": [[39, "foss-vcs-web-github"]], "FOSS and VCS on the Internet": [[39, "foss-and-vcs-on-the-internet"]], "Figure and Axes": [[11, "figure-and-axes"]], 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NumPy": [[11, "generate-test-data-using-numpy"]], "Generation": [[14, "generation"]], "Get a Quick Look at the Beginning/End of your DataFrame": [[18, "get-a-quick-look-at-the-beginning-end-of-your-dataframe"]], "Getting Started with GitHub": [[25, "getting-started-with-github"]], "Getting Started with Jupyter": [[26, "getting-started-with-jupyter"]], "Getting Started with Python": [[27, "getting-started-with-python"]], "Git Branches": [[30, "git-branches"]], "Git Feature Branch Workflow": [[37, "git-feature-branch-workflow"]], "Git commands mini-reference": [[28, "git-commands-mini-reference"]], "Git is a distributed VCS": [[28, "git-is-a-distributed-vcs"]], "Git is not GitHub": [[28, "git-is-not-github"]], "GitHub Repositories": [[35, "github-repositories"]], "GitHub Workflows": [[34, "github-workflows"], [37, "github-workflows"]], "GitHub notifications": [[36, "github-notifications"]], "GitHub secure key generation": [[36, "github-secure-key-generation"]], "GitHub workflows": [[37, "id1"]], "GitHub\u2019s distributed repositories": [[35, "githubs-distributed-repositories"]], "Giving NumPy room for broadcasting": [[15, "giving-numpy-room-for-broadcasting"]], "Going back in time": [[28, "going-back-in-time"]], "Gridded Data": [[5, "gridded-data"]], "GroupBy: Split, Apply, Combine": [[20, "groupby-split-apply-combine"]], "HTTPS vs SSH: Either is fine!": [[36, null]], "Have you forked the repository?": [[30, null]], "High level languages": [[47, "high-level-languages"]], "High-level libraries": [[16, "high-level-libraries"]], "Histograms": [[10, "histograms"]], "Histograms, Pie Charts, and Animations": [[10, "histograms-pie-charts-and-animations"]], "How to Cite This Book": [[49, "how-to-cite-this-book"]], "How to Use This Book": [[50, "how-to-use-this-book"]], "How to Use This Page": [[1, "how-to-use-this-page"]], "Imports": [[1, "imports"], [3, "imports"], [5, "imports"], [7, "imports"], [9, "imports"], [10, "imports"], [11, "imports"], [13, "imports"], [14, 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"Introducing the DataArray and Dataset": [[23, "introducing-the-dataarray-and-dataset"]], "Introduction to Cartopy": [[3, "introduction-to-cartopy"]], "Introduction to Pandas": [[18, "introduction-to-pandas"]], "Introduction to Xarray": [[23, "introduction-to-xarray"]], "Investigating Extreme Values": [[18, "investigating-extreme-values"]], "Issues": [[33, "issues"]], "Issues and Discussions": [[33, "issues-and-discussions"]], "Issues with preservation of attributes": [[23, "issues-with-preservation-of-attributes"]], "Jupyter Hub": [[26, "jupyter-hub"]], "Jupyter Kernels": [[26, "jupyter-kernels"]], "Jupyter Lab": [[26, "jupyter-lab"]], "Jupyter Notebooks": [[26, "jupyter-notebooks"], [40, "jupyter-notebooks"]], "JupyterLab": [[42, "jupyterlab"]], "Keeping your dev environment up to date": [[0, "keeping-your-dev-environment-up-to-date"]], "Labels and Grid Lines": [[11, "labels-and-grid-lines"]], "Lambert Azimuthal Equal Area Projection": [[3, 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\ No newline at end of file
/tmp/ipykernel_2523/2342439358.py:1: DeprecationWarning: datetime.datetime.utcnow() is deprecated and scheduled for removal in a future version. Use timezone-aware objects to represent datetimes in UTC: datetime.datetime.now(datetime.UTC).
+/tmp/ipykernel_2539/2342439358.py:1: DeprecationWarning: datetime.datetime.utcnow() is deprecated and scheduled for removal in a future version. Use timezone-aware objects to represent datetimes in UTC: datetime.datetime.now(datetime.UTC).
start = datetime.utcnow().replace(hour=22, minute=0, second=0, microsecond=0)
@@ -610,7 +610,7 @@ Creating the file and dimensions
-/tmp/ipykernel_2523/2008125275.py:5: DeprecationWarning: datetime.datetime.utcnow() is deprecated and scheduled for removal in a future version. Use timezone-aware objects to represent datetimes in UTC: datetime.datetime.now(datetime.UTC).
+/tmp/ipykernel_2539/2008125275.py:5: DeprecationWarning: datetime.datetime.utcnow() is deprecated and scheduled for removal in a future version. Use timezone-aware objects to represent datetimes in UTC: datetime.datetime.now(datetime.UTC).
nc.history = str(datetime.utcnow()) + ' Python'
@@ -651,7 +651,7 @@ Creating the file and dimensions
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
@@ -1693,7 +1693,7 @@ Contribute
By the Project Pythia Community.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
Datetime
@@ -892,7 +892,7 @@ Contribute
By the Project Pythia Community.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
-
@@ -614,7 +614,7 @@
Basic Colorbars
-
@@ -764,10 +764,10 @@ 1715806495.8833458
+1716229655.9439774
time
-
@@ -743,7 +743,7 @@ See how many days have elapsed since the strike:
-
The benchmark took 1.0002374649047852 seconds
+The benchmark took 1.0002319812774658 seconds
See how many days have elapsed since the strike:
-6167
+6172
@@ -815,8 +815,8 @@ Time Zone Naive Versus Time Zone Aware
-I am time zone naive 2024-05-15 20:54:56.921356.
-I am time zone aware 2024-05-15 20:54:56.921392+00:00.
+I am time zone naive 2024-05-20 18:27:36.981418.
+I am time zone aware 2024-05-20 18:27:36.981452+00:00.
@@ -839,8 +839,8 @@ Full time zone support with the <
-I am time zone naive: 2024-05-15 20:54:56.925612.
-I am time zone aware: 2024-05-15 14:54:56.936190-06:00.
+I am time zone naive: 2024-05-20 18:27:36.985646.
+I am time zone aware: 2024-05-20 12:27:36.996878-06:00.
@@ -862,11 +862,11 @@ Print Time with a Different Time Zone
-The UTC time is May 15, 2024, 8:54PM.
-The 'US/Mountain' time is May 15, 2024, 2:54PM.
+The UTC time is May 20, 2024, 6:27PM.
+The 'US/Mountain' time is May 20, 2024, 12:27PM.
-/tmp/ipykernel_2546/3080495102.py:1: DeprecationWarning: datetime.datetime.utcnow() is deprecated and scheduled for removal in a future version. Use timezone-aware objects to represent datetimes in UTC: datetime.datetime.now(datetime.UTC).
+/tmp/ipykernel_2563/3080495102.py:1: DeprecationWarning: datetime.datetime.utcnow() is deprecated and scheduled for removal in a future version. Use timezone-aware objects to represent datetimes in UTC: datetime.datetime.now(datetime.UTC).
utc = dt.datetime.utcnow().replace(tzinfo=pytz.utc)
@@ -1243,7 +1243,7 @@ Resources and References
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
@@ -1372,7 +1372,7 @@ Contribute
By the Project Pythia Community.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
diff --git a/core/matplotlib.html b/core/matplotlib.html
index 36bdf5490..7ff675d8a 100644
--- a/core/matplotlib.html
+++ b/core/matplotlib.html
@@ -81,7 +81,7 @@
-
+
@@ -795,7 +795,7 @@ In this section
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
@@ -924,7 +924,7 @@ Contribute
By the Project Pythia Community.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
diff --git a/core/matplotlib/annotations-colorbars-layouts.html b/core/matplotlib/annotations-colorbars-layouts.html
index 97735445a..4c2ffca3e 100644
--- a/core/matplotlib/annotations-colorbars-layouts.html
+++ b/core/matplotlib/annotations-colorbars-layouts.html
@@ -81,7 +81,7 @@
-
+
@@ -730,7 +730,7 @@ Basic Colorbars
-
+
6167
+6172
Time Zone Naive Versus Time Zone Aware
-I am time zone naive 2024-05-15 20:54:56.921356.
-I am time zone aware 2024-05-15 20:54:56.921392+00:00.
+I am time zone naive 2024-05-20 18:27:36.981418.
+I am time zone aware 2024-05-20 18:27:36.981452+00:00.
@@ -839,8 +839,8 @@ Full time zone support with the <
I am time zone naive 2024-05-15 20:54:56.921356.
-I am time zone aware 2024-05-15 20:54:56.921392+00:00.
+I am time zone naive 2024-05-20 18:27:36.981418.
+I am time zone aware 2024-05-20 18:27:36.981452+00:00.
Full time zone support with the <
I am time zone naive: 2024-05-15 20:54:56.925612.
-I am time zone aware: 2024-05-15 14:54:56.936190-06:00.
+I am time zone naive: 2024-05-20 18:27:36.985646.
+I am time zone aware: 2024-05-20 12:27:36.996878-06:00.
Print Time with a Different Time Zone
-The UTC time is May 15, 2024, 8:54PM.
-The 'US/Mountain' time is May 15, 2024, 2:54PM.
+The UTC time is May 20, 2024, 6:27PM.
+The 'US/Mountain' time is May 20, 2024, 12:27PM.
-/tmp/ipykernel_2546/3080495102.py:1: DeprecationWarning: datetime.datetime.utcnow() is deprecated and scheduled for removal in a future version. Use timezone-aware objects to represent datetimes in UTC: datetime.datetime.now(datetime.UTC).
+/tmp/ipykernel_2563/3080495102.py:1: DeprecationWarning: datetime.datetime.utcnow() is deprecated and scheduled for removal in a future version. Use timezone-aware objects to represent datetimes in UTC: datetime.datetime.now(datetime.UTC).
utc = dt.datetime.utcnow().replace(tzinfo=pytz.utc)
@@ -1243,7 +1243,7 @@ Resources and References
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
@@ -1372,7 +1372,7 @@ Contribute
By the Project Pythia Community.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
diff --git a/core/matplotlib.html b/core/matplotlib.html
index 36bdf5490..7ff675d8a 100644
--- a/core/matplotlib.html
+++ b/core/matplotlib.html
@@ -81,7 +81,7 @@
-
+
@@ -795,7 +795,7 @@ In this section
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
@@ -924,7 +924,7 @@ Contribute
By the Project Pythia Community.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
The UTC time is May 15, 2024, 8:54PM.
-The 'US/Mountain' time is May 15, 2024, 2:54PM.
+The UTC time is May 20, 2024, 6:27PM.
+The 'US/Mountain' time is May 20, 2024, 12:27PM.
-/tmp/ipykernel_2546/3080495102.py:1: DeprecationWarning: datetime.datetime.utcnow() is deprecated and scheduled for removal in a future version. Use timezone-aware objects to represent datetimes in UTC: datetime.datetime.now(datetime.UTC).
+/tmp/ipykernel_2563/3080495102.py:1: DeprecationWarning: datetime.datetime.utcnow() is deprecated and scheduled for removal in a future version. Use timezone-aware objects to represent datetimes in UTC: datetime.datetime.now(datetime.UTC).
utc = dt.datetime.utcnow().replace(tzinfo=pytz.utc)
@@ -1243,7 +1243,7 @@ Resources and References
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
@@ -1372,7 +1372,7 @@ Contribute
By the Project Pythia Community.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
In this section
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
+
We can change which colormap to use by setting the keyword argument cmap = 'colormap_name' in the plotting function call. This sets the colormap not only for the plot, but for the colorbar as well. In this case, we use the magma colormap:
Basic Colorbars
-
+
Shared Colorbars
-<matplotlib.colorbar.Colorbar at 0x7f4e652e9af0>
+<matplotlib.colorbar.Colorbar at 0x7f07e48a40e0>
-
+
You may be wondering why the call to fig.colorbar uses the argument hist1[3]. The explanation is as follows: hist1 is a tuple returned by hist2d, and hist1[3] contains a matplotlib.collections.QuadMesh that points to the colormap for the first histogram. To make sure that both histograms are using the same colormap with the same range of values, vmax is set to 0.18 for both plots. This ensures that both histograms are using colormaps that represent values from 0 (the default for histograms) to 0.18. Because the same data values are used for both plots, it doesn’t matter whether we pass in hist1[3] or hist2[3] to fig.colorbar.
@@ -848,7 +848,7 @@
Custom Colorbars
-
+
@@ -865,7 +865,7 @@ Custom Colorbars
-
+
@@ -902,7 +902,7 @@ Mosaic Subplots
-
+
You’ll notice there is not a colorbar plotted by default. When constructing the colorbar, we need to specify the following:
@@ -930,7 +930,7 @@ Mosaic Subplots
-
+
@@ -1291,7 +1291,7 @@ Resources and references
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
@@ -1420,7 +1420,7 @@ Contribute
By the Project Pythia Community.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
diff --git a/core/matplotlib/histograms-piecharts-animation.html b/core/matplotlib/histograms-piecharts-animation.html
index ea6420ec0..9a591348d 100644
--- a/core/matplotlib/histograms-piecharts-animation.html
+++ b/core/matplotlib/histograms-piecharts-animation.html
@@ -79,7 +79,7 @@
-
+
@@ -3629,7 +3629,7 @@ Additional Resources
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
@@ -3758,7 +3758,7 @@ Contribute
By the Project Pythia Community.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
diff --git a/core/matplotlib/matplotlib-basics.html b/core/matplotlib/matplotlib-basics.html
index 323b54231..22fd1268a 100644
--- a/core/matplotlib/matplotlib-basics.html
+++ b/core/matplotlib/matplotlib-basics.html
@@ -79,7 +79,7 @@
-
+
@@ -1492,7 +1492,7 @@ Resources and References
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
@@ -1621,7 +1621,7 @@ Contribute
By the Project Pythia Community.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
diff --git a/core/numpy.html b/core/numpy.html
index 4fb55d7f5..5c535f0a9 100644
--- a/core/numpy.html
+++ b/core/numpy.html
@@ -81,7 +81,7 @@
-
+
@@ -760,7 +760,7 @@ NumPy
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
@@ -889,7 +889,7 @@ Contribute
By the Project Pythia Community.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
diff --git a/core/numpy/intermediate-numpy.html b/core/numpy/intermediate-numpy.html
index 59706e1a6..38a6afc63 100644
--- a/core/numpy/intermediate-numpy.html
+++ b/core/numpy/intermediate-numpy.html
@@ -81,7 +81,7 @@
-
+
@@ -1425,7 +1425,7 @@ Resources and references
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
@@ -1554,7 +1554,7 @@ Contribute
By the Project Pythia Community.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
diff --git a/core/numpy/numpy-basics.html b/core/numpy/numpy-basics.html
index cb11d7b3d..365a2f855 100644
--- a/core/numpy/numpy-basics.html
+++ b/core/numpy/numpy-basics.html
@@ -81,7 +81,7 @@
-
+
@@ -1168,7 +1168,7 @@ Indexing
Slices
-Slicing syntax is written as array[start:stop[:step]], where all numbers are optional.
+Slicing syntax is written as array[start:stop:step]. Note that all numbers are optional. Importantly, the step parameter is optional and can be omitted, in which case the slice uses a default step of 1.
defaults:
@@ -1775,7 +1775,7 @@ Resources and references
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
@@ -1904,7 +1904,7 @@ Contribute
By the Project Pythia Community.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
diff --git a/core/numpy/numpy-broadcasting.html b/core/numpy/numpy-broadcasting.html
index 78f882658..57e5dd9a6 100644
--- a/core/numpy/numpy-broadcasting.html
+++ b/core/numpy/numpy-broadcasting.html
@@ -81,7 +81,7 @@
-
+
@@ -1070,7 +1070,7 @@ Blocking
-
-
-
-
-array([[ 8.3, 11.6, 33.1],
- [11.6, 33.1, 18.3],
- [33.1, 18.3, 22.5],
- [18.3, 22.5, 24.5],
- [22.5, 24.5, 20.1],
- [24.5, 20.1, 20.1],
- [20.1, 20.1, 18. ],
- [20.1, 18. , 25. ]])
+array([[19.3, 18.8, 28.7],
+ [18.8, 28.7, 27.1],
+ [28.7, 27.1, 17.1],
+ [27.1, 17.1, 16.2],
+ [17.1, 16.2, 15. ],
+ [16.2, 15. , 15.1],
+ [15. , 15.1, 14. ],
+ [15.1, 14. , 19.2]])
@@ -1161,8 +1161,8 @@ Blocking
-
-array([[ 8.3, 11.6, 2000. ],
- [ 11.6, 2000. , 18.3],
- [2000. , 18.3, 22.5],
- [ 18.3, 22.5, 24.5],
- [ 22.5, 24.5, 20.1],
- [ 24.5, 20.1, 20.1],
- [ 20.1, 20.1, 18. ],
- [ 20.1, 18. , 25. ]])
+array([[ 19.3, 18.8, 2000. ],
+ [ 18.8, 2000. , 27.1],
+ [2000. , 27.1, 17.1],
+ [ 27.1, 17.1, 16.2],
+ [ 17.1, 16.2, 15. ],
+ [ 16.2, 15. , 15.1],
+ [ 15. , 15.1, 14. ],
+ [ 15.1, 14. , 19.2]])
@@ -1210,13 +1210,13 @@ Finding the difference between min and max
-array([[6, 6, 7, ..., 8, 7, 6],
- [6, 6, 6, ..., 7, 7, 7],
- [7, 7, 7, ..., 6, 7, 7],
+array([[7, 7, 7, ..., 7, 8, 7],
+ [6, 7, 6, ..., 7, 7, 6],
+ [7, 6, 7, ..., 7, 6, 6],
...,
- [7, 7, 7, ..., 7, 7, 6],
- [7, 6, 6, ..., 6, 7, 7],
- [7, 6, 7, ..., 7, 8, 7]])
+ [6, 7, 6, ..., 6, 6, 7],
+ [7, 7, 7, ..., 6, 6, 7],
+ [6, 7, 7, ..., 8, 8, 7]])
@@ -1241,13 +1241,13 @@ Finding the difference between min and max
-array([[775. , 775. , 737.5, ..., 700. , 737.5, 775. ],
- [775. , 775. , 775. , ..., 737.5, 737.5, 737.5],
- [737.5, 737.5, 737.5, ..., 775. , 737.5, 737.5],
+array([[737.5, 737.5, 737.5, ..., 737.5, 700. , 737.5],
+ [775. , 737.5, 775. , ..., 737.5, 737.5, 775. ],
+ [737.5, 775. , 737.5, ..., 737.5, 775. , 775. ],
...,
- [737.5, 737.5, 737.5, ..., 737.5, 737.5, 775. ],
- [737.5, 775. , 775. , ..., 775. , 737.5, 737.5],
- [737.5, 775. , 737.5, ..., 737.5, 700. , 737.5]])
+ [775. , 737.5, 775. , ..., 775. , 775. , 737.5],
+ [737.5, 737.5, 737.5, ..., 775. , 775. , 737.5],
+ [775. , 737.5, 737.5, ..., 700. , 700. , 737.5]])
@@ -1276,19 +1276,19 @@ Finding the difference between min and max
-array([[ -9.55417349, -9.78584972, -9.92661689, ..., -12.67789616,
- -10.49077377, -6.56515605],
- [-10.00010532, -10.70819797, -7.20985445, ..., -11.10789153,
- -11.73163442, -8.29531203],
- [-11.88696252, -12.29088724, -11.70346147, ..., -7.30859657,
- -8.97849533, -13.84614818],
+array([[-13.35480377, -11.37389473, -6.11490009, ..., -9.69409241,
+ -14.14853765, -11.04724036],
+ [ -6.50332158, -11.09958323, -5.95998607, ..., -13.2604716 ,
+ -13.19971009, -8.49574616],
+ [-12.28051357, -10.19815861, -7.42945122, ..., -11.5798199 ,
+ -8.91153322, -8.6079996 ],
...,
- [-11.90215938, -7.91187899, -13.08679868, ..., -10.41120932,
- -11.35468344, -11.06459146],
- [-11.50292233, -6.26889319, -7.38221861, ..., -10.24036433,
- -10.12594611, -5.63518882],
- [-10.32651953, -6.06759924, -9.54152312, ..., -10.45702773,
- -12.20574751, -8.01746694]])
+ [ -8.10255586, -12.16615351, -11.27253366, ..., -10.44432682,
+ -7.39154896, -10.99574973],
+ [-16.5968518 , -12.57487222, -8.39736923, ..., -4.15595587,
+ -8.86979291, -11.28873531],
+ [ -8.59386363, -9.38047716, -10.46838159, ..., -11.30765026,
+ -10.89295232, -9.24056526]])
@@ -1332,19 +1332,19 @@ Finding the difference between min and max
-array([[ -9.55417349, -9.78584972, -9.92661689, ..., -12.67789616,
- -10.49077377, -6.56515605],
- [-10.00010532, -10.70819797, -7.20985445, ..., -11.10789153,
- -11.73163442, -8.29531203],
- [-11.88696252, -12.29088724, -11.70346147, ..., -7.30859657,
- -8.97849533, -13.84614818],
+array([[-13.35480377, -11.37389473, -6.11490009, ..., -9.69409241,
+ -14.14853765, -11.04724036],
+ [ -6.50332158, -11.09958323, -5.95998607, ..., -13.2604716 ,
+ -13.19971009, -8.49574616],
+ [-12.28051357, -10.19815861, -7.42945122, ..., -11.5798199 ,
+ -8.91153322, -8.6079996 ],
...,
- [-11.90215938, -7.91187899, -13.08679868, ..., -10.41120932,
- -11.35468344, -11.06459146],
- [-11.50292233, -6.26889319, -7.38221861, ..., -10.24036433,
- -10.12594611, -5.63518882],
- [-10.32651953, -6.06759924, -9.54152312, ..., -10.45702773,
- -12.20574751, -8.01746694]])
+ [ -8.10255586, -12.16615351, -11.27253366, ..., -10.44432682,
+ -7.39154896, -10.99574973],
+ [-16.5968518 , -12.57487222, -8.39736923, ..., -4.15595587,
+ -8.86979291, -11.28873531],
+ [ -8.59386363, -9.38047716, -10.46838159, ..., -11.30765026,
+ -10.89295232, -9.24056526]])
@@ -1711,7 +1711,7 @@ Resources and references
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
@@ -1840,7 +1840,7 @@ Contribute
By the Project Pythia Community.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
diff --git a/core/overview.html b/core/overview.html
index 6acc0ae46..fd4752cc2 100644
--- a/core/overview.html
+++ b/core/overview.html
@@ -79,7 +79,7 @@
-
+
@@ -886,7 +886,7 @@ Tutorials
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
@@ -1015,7 +1015,7 @@ Contribute
By the Project Pythia Community.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
diff --git a/core/pandas.html b/core/pandas.html
index 2e4230efc..02509b347 100644
--- a/core/pandas.html
+++ b/core/pandas.html
@@ -79,7 +79,7 @@
-
+
@@ -759,7 +759,7 @@ Pandas
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
@@ -888,7 +888,7 @@ Contribute
By the Project Pythia Community.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
diff --git a/core/pandas/pandas.html b/core/pandas/pandas.html
index 734fecdb6..2e24138b7 100644
--- a/core/pandas/pandas.html
+++ b/core/pandas/pandas.html
@@ -79,7 +79,7 @@
-
+
@@ -1098,7 +1098,7 @@ Indexing a Seri
-/tmp/ipykernel_2735/737336773.py:1: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`
+/tmp/ipykernel_2746/737336773.py:1: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`
nino34_series[3]
@@ -1449,7 +1449,7 @@ Extending to the
-/tmp/ipykernel_2735/541596450.py:1: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`
+/tmp/ipykernel_2746/541596450.py:1: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`
df["Nino34"][3]
@@ -3645,7 +3645,7 @@ Resampling
-/tmp/ipykernel_2735/233158901.py:1: FutureWarning: 'Y' is deprecated and will be removed in a future version, please use 'YE' instead.
+/tmp/ipykernel_2746/233158901.py:1: FutureWarning: 'Y' is deprecated and will be removed in a future version, please use 'YE' instead.
df.Nino34.resample('1Y').mean().plot();
@@ -4434,7 +4434,7 @@ Resources and References
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
@@ -4563,7 +4563,7 @@ Contribute
By the Project Pythia Community.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
diff --git a/core/xarray.html b/core/xarray.html
index e4624108b..6bc787165 100644
--- a/core/xarray.html
+++ b/core/xarray.html
@@ -79,7 +79,7 @@
-
+
@@ -760,7 +760,7 @@ Xarray
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
@@ -889,7 +889,7 @@ Contribute
By the Project Pythia Community.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
diff --git a/core/xarray/computation-masking.html b/core/xarray/computation-masking.html
index b15ad248c..12f19902b 100644
--- a/core/xarray/computation-masking.html
+++ b/core/xarray/computation-masking.html
@@ -79,7 +79,7 @@
-
+
@@ -943,7 +943,7 @@ Data Setup
@@ -1576,7 +1576,7 @@ Arithmetic Operations
+ dtype='float64', name='lon', length=360))
In addition, there are many other arithmetic operations that can be performed on DataArrays. In this example, we demonstrate squaring the original Celsius values of our air temperature data:
@@ -2246,7 +2246,7 @@ Arithmetic Operations
+ dtype='float64', name='lon', length=360))
@@ -2876,7 +2876,7 @@ Aggregation Methodsdim keyword argument; this means that the function was applied over all of the dataset’s dimensions. In other words, the aggregation method computed the mean of every element of the temperature dataset across every temporal and spatial data point. However, if a dimension name is used with the dim keyword argument, the aggregation method computes an aggregation along the given dimension. In this next example, we use aggregation to calculate the temporal mean across all spatial data; this is performed by providing the dimension name 'time' to the dim keyword argument:
@@ -3276,7 +3276,7 @@ Aggregation Methods
@@ -3717,7 +3717,7 @@ Aggregation Methods
@@ -4325,7 +4325,7 @@ Aggregation Methods
@@ -4892,7 +4892,7 @@ Compute climatologyDataArray containing the climatology data, we can plot the data in different ways. In this example, we plot the climatology at a specific latitude-longitude point:
@@ -5412,7 +5412,7 @@ Compute anomaly
@@ -6095,7 +6095,7 @@ Compute anomaly.weighted() method and the newly loaded CESM2 dataset to weight the grid cell data as described above:
@@ -6598,7 +6598,7 @@ Other high level computation functionalityrolling method to compute averages in a moving window of 5 months of data:
@@ -7114,7 +7114,7 @@ Other high level computation functionality
@@ -7783,7 +7783,7 @@ Masking Data
Using where with one condition
@@ -8421,15 +8421,15 @@ Using where
+ dtype='float64', name='lon', length=360))
- cell_measures :
- area: areacello
- cell_methods :
- area: mean where sea time: mean
- comment :
- Model data on the 1x1 grid includes values in all cells for which ocean cells on the native grid cover more than 52.5 percent of the 1x1 grid cell. This 52.5 percent cutoff was chosen to produce ocean surface area on the 1x1 grid as close as possible to ocean surface area on the native grid, while not introducing fractional cell coverage.
- description :
- This may differ from "surface temperature" in regions of sea ice or floating ice shelves. For models using conservative temperature as the prognostic field, they should report the top ocean layer as surface potential temperature, which is the same as surface in situ temperature.
- frequency :
- mon
- id :
- tos
- long_name :
- Sea Surface Temperature
- mipTable :
- Omon
- out_name :
- tos
- prov :
- Omon ((isd.003))
- realm :
- ocean
- standard_name :
- sea_surface_temperature
- time :
- time
- time_label :
- time-mean
- time_title :
- Temporal mean
- title :
- Sea Surface Temperature
- type :
- real
- units :
- degC
- variable_id :
- tos
As shown in the previous example, methods like .isel() and .sel() return data of a different shape than the original data provided to them. However, .where() preserves the shape of the original data by masking the values with a Boolean condition. Data values for which the condition is True are returned identical to the values passed in. On the other hand, data values for which the condition is False are returned as a preset example value. (This example value defaults to nan, but can be set to other values as well.)
Before testing .where(), it is helpful to look at the official documentation. As stated above, the .where() method takes a Boolean condition. (Boolean conditions use operators such as less-than, greater-than, and equal-to, and return a value of True or False.) Most uses of .where() check whether or not specific data values are less than or greater than a constant value. As stated in the documentation, the data values specified in the Boolean condition of .where() can be any of the following:
@@ -8864,7 +8864,7 @@ Using where
+ dtype='float64', name='lon', length=360))- cell_measures :
- area: areacello
- cell_methods :
- area: mean where sea time: mean
- comment :
- Model data on the 1x1 grid includes values in all cells for which ocean cells on the native grid cover more than 52.5 percent of the 1x1 grid cell. This 52.5 percent cutoff was chosen to produce ocean surface area on the 1x1 grid as close as possible to ocean surface area on the native grid, while not introducing fractional cell coverage.
- description :
- This may differ from "surface temperature" in regions of sea ice or floating ice shelves. For models using conservative temperature as the prognostic field, they should report the top ocean layer as surface potential temperature, which is the same as surface in situ temperature.
- frequency :
- mon
- id :
- tos
- long_name :
- Sea Surface Temperature
- mipTable :
- Omon
- out_name :
- tos
- prov :
- Omon ((isd.003))
- realm :
- ocean
- standard_name :
- sea_surface_temperature
- time :
- time
- time_label :
- time-mean
- time_title :
- Temporal mean
- title :
- Sea Surface Temperature
- type :
- real
- units :
- degC
- variable_id :
- tos
In this example, we use Matplotlib to plot the original, unmasked data, as well as the masked data created in the previous example.
@@ -9321,7 +9321,7 @@ Resources and References
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
@@ -9450,7 +9450,7 @@ Contribute
By the Project Pythia Community.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
diff --git a/core/xarray/dask-arrays-xarray.html b/core/xarray/dask-arrays-xarray.html
index 0af9daaf3..65793b335 100644
--- a/core/xarray/dask-arrays-xarray.html
+++ b/core/xarray/dask-arrays-xarray.html
@@ -79,7 +79,7 @@
-
+
@@ -568,91 +568,91 @@ Create a dask.a
-array([[[0.08870167, 0.5536724 , 0.86869933, ..., 0.59745109,
- 0.66241166, 0.14712849],
- [0.79424186, 0.53985475, 0.83117213, ..., 0.64752434,
- 0.2068288 , 0.43561572],
- [0.93755297, 0.12641562, 0.55493093, ..., 0.44682309,
- 0.9108161 , 0.28494995],
+array([[[3.73479162e-01, 5.69354087e-01, 8.44121771e-01, ...,
+ 6.95300592e-01, 7.05791578e-01, 3.22757785e-01],
+ [7.34673387e-01, 3.22502093e-02, 2.98660716e-02, ...,
+ 4.48326876e-01, 1.28181189e-01, 6.17129937e-01],
+ [4.61524077e-01, 5.72981031e-01, 6.22455684e-02, ...,
+ 4.66387696e-01, 2.24365962e-02, 9.05528466e-01],
...,
- [0.36681494, 0.55104908, 0.67358062, ..., 0.93120538,
- 0.51164396, 0.2149192 ],
- [0.33220658, 0.18853655, 0.84133513, ..., 0.09990092,
- 0.45265087, 0.48841145],
- [0.54632195, 0.99301667, 0.40513953, ..., 0.5341915 ,
- 0.3220391 , 0.07544213]],
-
- [[0.8398981 , 0.14999579, 0.81431679, ..., 0.45814483,
- 0.81962377, 0.01308931],
- [0.52904343, 0.17038365, 0.49820524, ..., 0.71294698,
- 0.49906669, 0.32305634],
- [0.83597797, 0.12242548, 0.63334391, ..., 0.77669187,
- 0.44530452, 0.02031067],
+ [4.32840675e-01, 6.11791423e-01, 7.77157080e-01, ...,
+ 5.21898060e-01, 5.08370890e-01, 5.88153598e-01],
+ [8.04673850e-01, 7.80296541e-03, 5.00244166e-01, ...,
+ 9.45710973e-01, 5.08700949e-01, 8.89905823e-01],
+ [4.87536782e-01, 4.64576884e-01, 4.97751472e-01, ...,
+ 9.63294478e-01, 8.00438624e-01, 9.39160580e-01]],
+
+ [[9.59798128e-01, 9.67977225e-01, 5.67081374e-01, ...,
+ 1.15551677e-01, 5.93188567e-01, 8.21494821e-01],
+ [7.09798171e-01, 6.86764079e-01, 9.75639983e-01, ...,
+ 6.65980228e-01, 3.63136928e-01, 3.91634965e-01],
+ [2.45312843e-01, 7.92456108e-01, 7.52068086e-01, ...,
+ 8.31396290e-01, 7.34853345e-01, 1.59997513e-02],
...,
- [0.23487395, 0.54185136, 0.84738714, ..., 0.43194404,
- 0.96464114, 0.34661126],
- [0.06216953, 0.87402041, 0.89512514, ..., 0.38706893,
- 0.09187095, 0.88666451],
- [0.46384865, 0.02462827, 0.71575049, ..., 0.47421697,
- 0.30570578, 0.58421292]],
-
- [[0.05704318, 0.36232811, 0.03448708, ..., 0.86748361,
- 0.49546271, 0.39195962],
- [0.62907898, 0.52593284, 0.87418635, ..., 0.97638485,
- 0.63695351, 0.75326941],
- [0.11640535, 0.15534404, 0.80460691, ..., 0.26935451,
- 0.00686985, 0.42474694],
+ [6.29006234e-01, 5.66655665e-01, 9.59798385e-01, ...,
+ 4.64611557e-02, 8.47630616e-01, 9.96868460e-01],
+ [8.65409572e-01, 6.62829465e-01, 5.85935120e-01, ...,
+ 2.76645046e-01, 4.05531947e-01, 1.86970157e-01],
+ [5.88186192e-02, 4.80258125e-01, 7.50053106e-01, ...,
+ 5.82584007e-01, 7.16238302e-01, 3.09095729e-01]],
+
+ [[1.95867406e-01, 8.02997164e-01, 5.92335907e-02, ...,
+ 6.00495084e-02, 3.58568845e-02, 6.14435689e-01],
+ [5.40191024e-01, 7.45240262e-01, 5.83926237e-01, ...,
+ 5.42683512e-01, 7.15062271e-05, 8.15838308e-01],
+ [3.56121359e-01, 6.64103118e-01, 3.19197578e-01, ...,
+ 4.30885292e-01, 5.63274760e-01, 5.58304993e-01],
...,
- [0.53156325, 0.69190957, 0.69352384, ..., 0.77170882,
- 0.44011939, 0.39866217],
- [0.29442052, 0.74080675, 0.46131608, ..., 0.32366183,
- 0.73788577, 0.04341616],
- [0.94117032, 0.92630431, 0.79008536, ..., 0.02443356,
- 0.52042712, 0.83466538]],
+ [3.70429144e-01, 2.28949319e-01, 2.12465225e-01, ...,
+ 8.75347366e-01, 6.88876481e-01, 8.52985255e-01],
+ [8.43544201e-01, 3.50323808e-01, 9.94755069e-01, ...,
+ 2.31937385e-01, 6.46129523e-01, 7.56519847e-01],
+ [9.23292344e-01, 9.06543740e-01, 2.16039623e-01, ...,
+ 6.17841121e-01, 5.83778599e-02, 7.07627581e-01]],
...,
- [[0.1396787 , 0.60143505, 0.57058462, ..., 0.18893598,
- 0.82985107, 0.54242887],
- [0.68157985, 0.63122788, 0.1780818 , ..., 0.55248006,
- 0.63127177, 0.4663941 ],
- [0.55463676, 0.49022722, 0.28183346, ..., 0.64073606,
- 0.37788708, 0.05233016],
+ [[5.50076397e-01, 6.23247920e-01, 6.69863249e-01, ...,
+ 6.66141598e-01, 4.98073488e-01, 9.70545887e-01],
+ [7.38559992e-01, 3.21393458e-01, 4.05421647e-01, ...,
+ 2.52285356e-01, 8.08345653e-01, 7.28764634e-01],
+ [2.68191728e-01, 6.03169049e-02, 6.89258446e-01, ...,
+ 6.21042327e-01, 9.60250603e-01, 9.75257028e-01],
...,
- [0.39020736, 0.85735173, 0.00786958, ..., 0.66161596,
- 0.18152074, 0.17731408],
- [0.93613911, 0.96578828, 0.38973709, ..., 0.06864502,
- 0.5063675 , 0.73631955],
- [0.1671134 , 0.45039599, 0.38253178, ..., 0.74316064,
- 0.20253998, 0.93656703]],
-
- [[0.28018602, 0.84864745, 0.65231976, ..., 0.57238332,
- 0.37536731, 0.04826117],
- [0.47663448, 0.86778224, 0.95451621, ..., 0.76880871,
- 0.89875752, 0.06756555],
- [0.49609048, 0.21693506, 0.23207324, ..., 0.06636032,
- 0.95937476, 0.4827714 ],
+ [6.11978830e-01, 6.32479701e-01, 1.75826196e-01, ...,
+ 2.09161988e-01, 6.71446177e-01, 4.78915946e-01],
+ [6.45044934e-01, 1.15167985e-01, 9.05467373e-01, ...,
+ 7.79598426e-01, 8.76027493e-01, 5.70161686e-01],
+ [8.44173395e-01, 7.14987726e-01, 5.27098062e-01, ...,
+ 6.24560049e-01, 3.32587128e-02, 1.48219268e-01]],
+
+ [[9.16337354e-01, 1.38628117e-01, 2.76572964e-01, ...,
+ 5.68919305e-02, 3.07040211e-01, 6.70607173e-02],
+ [6.78200880e-01, 1.19012654e-01, 8.76844508e-01, ...,
+ 5.78903343e-01, 5.08716762e-01, 3.27430604e-01],
+ [5.19310927e-01, 1.62933592e-01, 6.94104791e-01, ...,
+ 8.28521396e-01, 5.63944660e-01, 9.23353464e-01],
...,
- [0.83197412, 0.22629066, 0.1589711 , ..., 0.55726833,
- 0.40921459, 0.94548168],
- [0.20373662, 0.76138868, 0.56265755, ..., 0.71025427,
- 0.19641998, 0.49627513],
- [0.20384921, 0.34173511, 0.44829 , ..., 0.3999668 ,
- 0.75240336, 0.21357396]],
-
- [[0.05024137, 0.77607526, 0.32452642, ..., 0.26169962,
- 0.2092348 , 0.79937281],
- [0.8834038 , 0.56909665, 0.84006875, ..., 0.09540328,
- 0.66257683, 0.78342945],
- [0.29906903, 0.08192958, 0.51913898, ..., 0.32946737,
- 0.91310302, 0.99198924],
+ [8.75833692e-01, 8.80034882e-01, 3.94957057e-01, ...,
+ 3.88766073e-01, 5.62146961e-01, 4.07401505e-01],
+ [8.83232515e-02, 7.93868945e-02, 7.09396623e-01, ...,
+ 4.00821090e-01, 9.67897242e-01, 3.30712526e-02],
+ [6.21839195e-01, 3.66731583e-01, 9.97239185e-01, ...,
+ 4.06892246e-01, 8.84142338e-01, 1.29206897e-01]],
+
+ [[3.13410458e-01, 7.92759677e-01, 8.24979480e-02, ...,
+ 5.38935776e-02, 4.52819725e-01, 1.48534750e-01],
+ [2.06595218e-01, 8.46746782e-01, 9.06689993e-01, ...,
+ 8.52571008e-01, 4.28351337e-01, 8.06352220e-01],
+ [3.77563013e-01, 9.99637726e-01, 3.37356897e-01, ...,
+ 8.18911136e-01, 8.32562303e-01, 8.45143802e-02],
...,
- [0.89344391, 0.04685752, 0.16870497, ..., 0.70133294,
- 0.2028986 , 0.16916596],
- [0.62721463, 0.12582174, 0.59300888, ..., 0.93023778,
- 0.50053284, 0.63544531],
- [0.89994404, 0.84669077, 0.65421957, ..., 0.19573779,
- 0.56024887, 0.73584493]]])
+ [5.51431397e-01, 8.77465751e-01, 9.63517992e-01, ...,
+ 3.34710544e-02, 9.70363045e-01, 5.42823121e-01],
+ [3.89247115e-01, 3.36500509e-01, 4.31638413e-02, ...,
+ 8.82214294e-02, 2.41024552e-01, 4.19862342e-01],
+ [5.65544128e-01, 6.09859197e-01, 2.77376901e-01, ...,
+ 5.51955058e-01, 6.67783854e-02, 4.08359511e-01]]])
@@ -856,7 +856,7 @@ Why the difference?
-
+
@@ -872,11 +872,11 @@ Compute the result
-CPU times: user 406 ms, sys: 28 ms, total: 434 ms
-Wall time: 232 ms
+CPU times: user 399 ms, sys: 28.5 ms, total: 428 ms
+Wall time: 228 ms
-12002441.41340752
+12000189.444496717
@@ -894,7 +894,7 @@ Exercise with d
-
+
As is obvious from the above exercise, Dask quickly and easily determines a strategy for performing the operations, in this case a sum. This illustrates the appeal of Dask: automatic algorithm generation that scales from simple arithmetic problems to highly complex scientific equations with large datasets and multiple operations.
@@ -974,7 +974,7 @@ Exercise with d
-
+
@@ -1106,7 +1106,7 @@ Testing a bigger calculation
-
+
@@ -1117,34 +1117,34 @@ Testing a bigger calculation
-[ ] | 0% Completed | 169.21 us
+[ ] | 0% Completed | 255.13 us
-[ ] | 0% Completed | 105.92 ms
+[ ] | 0% Completed | 106.98 ms
-[ ] | 0% Completed | 206.66 ms
+[ ] | 0% Completed | 207.72 ms
-[ ] | 0% Completed | 307.49 ms
+[ ] | 0% Completed | 308.36 ms
-[ ] | 0% Completed | 408.13 ms
+[ ] | 0% Completed | 409.00 ms
-[ ] | 0% Completed | 508.89 ms
+[ ] | 0% Completed | 509.67 ms
-[ ] | 1% Completed | 609.51 ms
+[ ] | 0% Completed | 610.39 ms
-[# ] | 4% Completed | 710.49 ms
+[# ] | 3% Completed | 711.30 ms
-[### ] | 7% Completed | 811.54 ms
+[## ] | 6% Completed | 812.41 ms
-[#### ] | 10% Completed | 912.41 ms
+[#### ] | 10% Completed | 913.29 ms
[#### ] | 10% Completed | 1.01 s
@@ -1153,7 +1153,7 @@ Testing a bigger calculation[#### ] | 10% Completed | 1.11 s
-[#### ] | 10% Completed | 1.21 s
+[#### ] | 10% Completed | 1.22 s
[#### ] | 10% Completed | 1.32 s
@@ -1162,10 +1162,10 @@ Testing a bigger calculation[#### ] | 10% Completed | 1.42 s
-[##### ] | 13% Completed | 1.52 s
+[##### ] | 12% Completed | 1.52 s
-[###### ] | 16% Completed | 1.62 s
+[###### ] | 15% Completed | 1.62 s
[####### ] | 19% Completed | 1.72 s
@@ -1186,13 +1186,13 @@ Testing a bigger calculation[######## ] | 21% Completed | 2.22 s
-[######## ] | 22% Completed | 2.32 s
+[######## ] | 21% Completed | 2.32 s
-[########## ] | 25% Completed | 2.43 s
+[######### ] | 24% Completed | 2.43 s
-[########### ] | 27% Completed | 2.53 s
+[########### ] | 28% Completed | 2.53 s
[############ ] | 31% Completed | 2.63 s
@@ -1216,16 +1216,16 @@ Testing a bigger calculation[############## ] | 35% Completed | 3.23 s
-[############### ] | 38% Completed | 3.33 s
+[############## ] | 37% Completed | 3.33 s
-[################ ] | 40% Completed | 3.43 s
+[############### ] | 39% Completed | 3.43 s
-[################# ] | 44% Completed | 3.53 s
+[################# ] | 43% Completed | 3.53 s
-[################### ] | 48% Completed | 3.63 s
+[################## ] | 46% Completed | 3.64 s
[################### ] | 48% Completed | 3.74 s
@@ -1246,10 +1246,10 @@ Testing a bigger calculation[#################### ] | 50% Completed | 4.24 s
-[##################### ] | 53% Completed | 4.34 s
+[#################### ] | 52% Completed | 4.34 s
-[###################### ] | 57% Completed | 4.44 s
+[###################### ] | 55% Completed | 4.44 s
[####################### ] | 59% Completed | 4.54 s
@@ -1276,7 +1276,7 @@ Testing a bigger calculation[######################### ] | 64% Completed | 5.25 s
-[########################### ] | 67% Completed | 5.35 s
+[########################### ] | 68% Completed | 5.35 s
[############################ ] | 72% Completed | 5.45 s
@@ -1300,13 +1300,13 @@ Testing a bigger calculation[############################## ] | 75% Completed | 6.06 s
-[############################### ] | 77% Completed | 6.16 s
+[############################### ] | 78% Completed | 6.16 s
-[################################ ] | 80% Completed | 6.26 s
+[################################ ] | 82% Completed | 6.26 s
-[################################# ] | 83% Completed | 6.36 s
+[################################# ] | 84% Completed | 6.36 s
[################################## ] | 85% Completed | 6.46 s
@@ -1324,16 +1324,16 @@ Testing a bigger calculation[################################## ] | 85% Completed | 6.86 s
-[################################## ] | 86% Completed | 6.96 s
+[################################### ] | 89% Completed | 6.96 s
-[################################### ] | 89% Completed | 7.06 s
+[#################################### ] | 92% Completed | 7.07 s
-[##################################### ] | 92% Completed | 7.16 s
+[###################################### ] | 96% Completed | 7.17 s
-[###################################### ] | 96% Completed | 7.27 s
+[####################################### ] | 99% Completed | 7.27 s
[########################################] | 100% Completed | 7.37 s
@@ -1854,7 +1854,7 @@ Reading data with
- latPandasIndex
PandasIndex(Index([-89.5, -88.5, -87.5, -86.5, -85.5, -84.5, -83.5, -82.5, -81.5, -80.5,
+ 80.5, 81.5, 82.5, 83.5, 84.5, 85.5, 86.5, 87.5, 88.5, 89.5])
- lon(lon)float640.5 1.5 2.5 ... 357.5 358.5 359.5
array([ 0.5, 1.5, 2.5, ..., 357.5, 358.5, 359.5])
- latPandasIndex
PandasIndex(Index([-89.5, -88.5, -87.5, -86.5, -85.5, -84.5, -83.5, -82.5, -81.5, -80.5,
...
80.5, 81.5, 82.5, 83.5, 84.5, 85.5, 86.5, 87.5, 88.5, 89.5],
- dtype='float64', name='lat', length=180)) - lonPandasIndex
PandasIndex(Index([ 0.5, 1.5, 2.5, 3.5, 4.5, 5.5, 6.5, 7.5, 8.5, 9.5,
+ dtype='float64', name='lat', length=180))
- lonPandasIndex
PandasIndex(Index([ 0.5, 1.5, 2.5, 3.5, 4.5, 5.5, 6.5, 7.5, 8.5, 9.5,
...
350.5, 351.5, 352.5, 353.5, 354.5, 355.5, 356.5, 357.5, 358.5, 359.5],
- dtype='float64', name='lon', length=360))
+ dtype='float64', name='lon', length=360))
As shown in the above example, the result of the applied operations is an Xarray DataArray that contains a Dask Array, an identical object type to the object that the operations were performed on. This is true for any operations that can be applied to Xarray DataArrays, including subsetting operations; this next example illustrates this:
@@ -3739,7 +3739,7 @@ Parallel and lazy computation using
@@ -3794,10 +3794,10 @@ Parallel and lazy computation using dask.visualize() function, as shown in this example:
@@ -3819,10 +3819,10 @@ Parallel and lazy computation using
-[ ] | 0% Completed | 122.44 us
+[ ] | 0% Completed | 137.30 us
-[########################################] | 100% Completed | 101.18 ms
+[########################################] | 100% Completed | 101.19 ms
@@ -4236,7 +4236,7 @@ Resources and references
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
@@ -4365,7 +4365,7 @@ Contribute
By the Project Pythia Community.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
diff --git a/core/xarray/enso-xarray.html b/core/xarray/enso-xarray.html
index 316661573..6e86d4355 100644
--- a/core/xarray/enso-xarray.html
+++ b/core/xarray/enso-xarray.html
@@ -79,7 +79,7 @@
-
+
@@ -958,7 +958,7 @@ The Niño 3.4 Index
@@ -1590,7 +1590,7 @@ Select the Niño 3.4 regionwhere() method:
+array(1.8436452, dtype=float32)
The final step of the Niño 3.4 index calculation involves normalizing the data. In this example, we perform this normalization by dividing the smoothed anomaly data by the standard deviation calculated above:
@@ -3396,7 +3396,7 @@ Resources and References
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
@@ -3525,7 +3525,7 @@ Contribute
By the Project Pythia Community.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
diff --git a/core/xarray/xarray-intro.html b/core/xarray/xarray-intro.html
index e17e6628d..308dd9baa 100644
--- a/core/xarray/xarray-intro.html
+++ b/core/xarray/xarray-intro.html
@@ -79,7 +79,7 @@
-
+
@@ -583,25 +583,25 @@ Generate a random numpy array
-array([[[274.00119189, 291.26874805, 287.10212211, 281.93321028],
- [276.72439019, 281.86759901, 277.88521001, 291.15900492],
- [283.47892997, 281.69598295, 281.87670232, 280.04253353]],
+array([[[283.19514872, 279.93414516, 288.85955729, 280.30299831],
+ [282.03318795, 283.36237877, 288.28464039, 284.82856563],
+ [282.15998672, 276.06859236, 285.78192464, 280.47703387]],
- [[292.49183408, 286.75694127, 291.1430049 , 279.6708509 ],
- [283.19466798, 285.34236937, 289.30840199, 286.22515585],
- [289.80677567, 281.73710917, 294.57091617, 291.28505114]],
+ [[282.44435897, 282.262194 , 286.34635361, 285.32213341],
+ [283.03737484, 288.57815157, 290.02698113, 278.23328324],
+ [277.06837841, 286.4771544 , 284.58714744, 288.42673576]],
- [[278.59826878, 287.14196565, 284.68298455, 286.59720048],
- [287.51252429, 288.52590266, 273.69692215, 285.39338362],
- [292.66871999, 278.60078099, 280.78168263, 291.92156783]],
+ [[280.62831671, 275.374152 , 283.08613936, 292.56788174],
+ [286.26020096, 282.03531027, 280.16394984, 288.9366629 ],
+ [282.90971416, 278.40998651, 284.98850877, 283.14584888]],
- [[281.24772197, 286.52210588, 282.61809684, 288.82826349],
- [282.48419301, 286.34184212, 277.57835692, 279.66054122],
- [288.97073742, 287.33849395, 289.90083339, 285.95347237]],
+ [[284.34422355, 283.20830934, 278.27414095, 285.21976361],
+ [279.17521323, 292.21743424, 276.974416 , 284.06699408],
+ [274.74247997, 279.63780379, 275.87914515, 277.71758929]],
- [[282.699394 , 286.72856786, 281.89265707, 286.16350665],
- [278.02139036, 284.54696447, 283.16191651, 278.99615563],
- [284.94443941, 287.35039327, 289.38110818, 280.10681174]]])
+ [[282.71726594, 281.99808895, 279.66639367, 276.71709325],
+ [278.30599074, 277.08406243, 273.68999203, 279.75441922],
+ [271.39869986, 286.48138908, 285.22713979, 291.77285885]]])
@@ -982,44 +982,44 @@ Wrap the array: first attempt
@@ -1402,44 +1402,44 @@ Assign dimension names<xarray.DataArray (time: 5, lat: 3, lon: 4)> Size: 480B
-array([[[274.00119189, 291.26874805, 287.10212211, 281.93321028],
- [276.72439019, 281.86759901, 277.88521001, 291.15900492],
- [283.47892997, 281.69598295, 281.87670232, 280.04253353]],
-
- [[292.49183408, 286.75694127, 291.1430049 , 279.6708509 ],
- [283.19466798, 285.34236937, 289.30840199, 286.22515585],
- [289.80677567, 281.73710917, 294.57091617, 291.28505114]],
-
- [[278.59826878, 287.14196565, 284.68298455, 286.59720048],
- [287.51252429, 288.52590266, 273.69692215, 285.39338362],
- [292.66871999, 278.60078099, 280.78168263, 291.92156783]],
-
- [[281.24772197, 286.52210588, 282.61809684, 288.82826349],
- [282.48419301, 286.34184212, 277.57835692, 279.66054122],
- [288.97073742, 287.33849395, 289.90083339, 285.95347237]],
-
- [[282.699394 , 286.72856786, 281.89265707, 286.16350665],
- [278.02139036, 284.54696447, 283.16191651, 278.99615563],
- [284.94443941, 287.35039327, 289.38110818, 280.10681174]]])
-Dimensions without coordinates: time, lat, lon
+array([[[283.19514872, 279.93414516, 288.85955729, 280.30299831],
+ [282.03318795, 283.36237877, 288.28464039, 284.82856563],
+ [282.15998672, 276.06859236, 285.78192464, 280.47703387]],
+
+ [[282.44435897, 282.262194 , 286.34635361, 285.32213341],
+ [283.03737484, 288.57815157, 290.02698113, 278.23328324],
+ [277.06837841, 286.4771544 , 284.58714744, 288.42673576]],
+
+ [[280.62831671, 275.374152 , 283.08613936, 292.56788174],
+ [286.26020096, 282.03531027, 280.16394984, 288.9366629 ],
+ [282.90971416, 278.40998651, 284.98850877, 283.14584888]],
+
+ [[284.34422355, 283.20830934, 278.27414095, 285.21976361],
+ [279.17521323, 292.21743424, 276.974416 , 284.06699408],
+ [274.74247997, 279.63780379, 275.87914515, 277.71758929]],
+
+ [[282.71726594, 281.99808895, 279.66639367, 276.71709325],
+ [278.30599074, 277.08406243, 273.68999203, 279.75441922],
+ [271.39869986, 286.48138908, 285.22713979, 291.77285885]]])
+Dimensions without coordinates: time, lat, lon
This DataArray is already an improvement over a NumPy array; the DataArray contains names for each of the dimensions (or axes in NumPy parlance). An additional improvement is the association of coordinate-value arrays with data upon creation of a DataArray. In the next example, we illustrate the creation of NumPy arrays representing the coordinate values for each dimension of the DataArray, and how to associate these coordinate arrays with the data in our DataArray.
@@ -1849,51 +1849,51 @@ Initialize the
fill: currentColor;
}
<xarray.DataArray (time: 5, lat: 3, lon: 4)> Size: 480B
-array([[[274.00119189, 291.26874805, 287.10212211, 281.93321028],
- [276.72439019, 281.86759901, 277.88521001, 291.15900492],
- [283.47892997, 281.69598295, 281.87670232, 280.04253353]],
+array([[[283.19514872, 279.93414516, 288.85955729, 280.30299831],
+ [282.03318795, 283.36237877, 288.28464039, 284.82856563],
+ [282.15998672, 276.06859236, 285.78192464, 280.47703387]],
- [[292.49183408, 286.75694127, 291.1430049 , 279.6708509 ],
- [283.19466798, 285.34236937, 289.30840199, 286.22515585],
- [289.80677567, 281.73710917, 294.57091617, 291.28505114]],
+ [[282.44435897, 282.262194 , 286.34635361, 285.32213341],
+ [283.03737484, 288.57815157, 290.02698113, 278.23328324],
+ [277.06837841, 286.4771544 , 284.58714744, 288.42673576]],
- [[278.59826878, 287.14196565, 284.68298455, 286.59720048],
- [287.51252429, 288.52590266, 273.69692215, 285.39338362],
- [292.66871999, 278.60078099, 280.78168263, 291.92156783]],
+ [[280.62831671, 275.374152 , 283.08613936, 292.56788174],
+ [286.26020096, 282.03531027, 280.16394984, 288.9366629 ],
+ [282.90971416, 278.40998651, 284.98850877, 283.14584888]],
- [[281.24772197, 286.52210588, 282.61809684, 288.82826349],
- [282.48419301, 286.34184212, 277.57835692, 279.66054122],
- [288.97073742, 287.33849395, 289.90083339, 285.95347237]],
+ [[284.34422355, 283.20830934, 278.27414095, 285.21976361],
+ [279.17521323, 292.21743424, 276.974416 , 284.06699408],
+ [274.74247997, 279.63780379, 275.87914515, 277.71758929]],
- [[282.699394 , 286.72856786, 281.89265707, 286.16350665],
- [278.02139036, 284.54696447, 283.16191651, 278.99615563],
- [284.94443941, 287.35039327, 289.38110818, 280.10681174]]])
+ [[282.71726594, 281.99808895, 279.66639367, 276.71709325],
+ [278.30599074, 277.08406243, 273.68999203, 279.75441922],
+ [271.39869986, 286.48138908, 285.22713979, 291.77285885]]])
Coordinates:
* time (time) datetime64[ns] 40B 2018-01-01 2018-01-02 ... 2018-01-05
* lat (lat) float64 24B 25.0 40.0 55.0
- * lon (lon) float64 32B -120.0 -100.0 -80.0 -60.0
+ dtype='datetime64[ns]', name='time', freq='D'))
latPandasIndexPandasIndex(Index([25.0, 40.0, 55.0], dtype='float64', name='lat'))
lonPandasIndexPandasIndex(Index([-120.0, -100.0, -80.0, -60.0], dtype='float64', name='lon'))
@@ -2273,54 +2273,54 @@ Set useful attributes<xarray.DataArray (time: 5, lat: 3, lon: 4)> Size: 480B
-array([[[274.00119189, 291.26874805, 287.10212211, 281.93321028],
- [276.72439019, 281.86759901, 277.88521001, 291.15900492],
- [283.47892997, 281.69598295, 281.87670232, 280.04253353]],
+array([[[283.19514872, 279.93414516, 288.85955729, 280.30299831],
+ [282.03318795, 283.36237877, 288.28464039, 284.82856563],
+ [282.15998672, 276.06859236, 285.78192464, 280.47703387]],
- [[292.49183408, 286.75694127, 291.1430049 , 279.6708509 ],
- [283.19466798, 285.34236937, 289.30840199, 286.22515585],
- [289.80677567, 281.73710917, 294.57091617, 291.28505114]],
+ [[282.44435897, 282.262194 , 286.34635361, 285.32213341],
+ [283.03737484, 288.57815157, 290.02698113, 278.23328324],
+ [277.06837841, 286.4771544 , 284.58714744, 288.42673576]],
- [[278.59826878, 287.14196565, 284.68298455, 286.59720048],
- [287.51252429, 288.52590266, 273.69692215, 285.39338362],
- [292.66871999, 278.60078099, 280.78168263, 291.92156783]],
+ [[280.62831671, 275.374152 , 283.08613936, 292.56788174],
+ [286.26020096, 282.03531027, 280.16394984, 288.9366629 ],
+ [282.90971416, 278.40998651, 284.98850877, 283.14584888]],
- [[281.24772197, 286.52210588, 282.61809684, 288.82826349],
- [282.48419301, 286.34184212, 277.57835692, 279.66054122],
- [288.97073742, 287.33849395, 289.90083339, 285.95347237]],
+ [[284.34422355, 283.20830934, 278.27414095, 285.21976361],
+ [279.17521323, 292.21743424, 276.974416 , 284.06699408],
+ [274.74247997, 279.63780379, 275.87914515, 277.71758929]],
- [[282.699394 , 286.72856786, 281.89265707, 286.16350665],
- [278.02139036, 284.54696447, 283.16191651, 278.99615563],
- [284.94443941, 287.35039327, 289.38110818, 280.10681174]]])
+ [[282.71726594, 281.99808895, 279.66639367, 276.71709325],
+ [278.30599074, 277.08406243, 273.68999203, 279.75441922],
+ [271.39869986, 286.48138908, 285.22713979, 291.77285885]]])
Coordinates:
* time (time) datetime64[ns] 40B 2018-01-01 2018-01-02 ... 2018-01-05
* lat (lat) float64 24B 25.0 40.0 55.0
* lon (lon) float64 32B -120.0 -100.0 -80.0 -60.0
Attributes:
units: kelvin
- standard_name: air_temperature
+ dtype='datetime64[ns]', name='time', freq='D'))
latPandasIndexPandasIndex(Index([25.0, 40.0, 55.0], dtype='float64', name='lat'))
lonPandasIndexPandasIndex(Index([-120.0, -100.0, -80.0, -60.0], dtype='float64', name='lon'))
- units :
- kelvin
- standard_name :
- air_temperature
@@ -2699,51 +2699,51 @@ Issues with preservation of attributesdocumentation page.
@@ -3134,54 +3134,54 @@ Create a pressure
+ dtype='datetime64[ns]', name='time', freq='D'))latPandasIndexPandasIndex(Index([25.0, 40.0, 55.0], dtype='float64', name='lat'))
lonPandasIndexPandasIndex(Index([-120.0, -100.0, -80.0, -60.0], dtype='float64', name='lon'))
As listed in the rich display above, the new Dataset object is aware that both DataArrays share the same coordinate axes. (Please note that if this page is not run as a Jupyter Notebook, the rich display may be unavailable.)
@@ -3986,54 +3986,54 @@ Access Data variables and Coordinates in a DataArrays through a dictionary syntax, as shown in this example:
@@ -4407,54 +4407,54 @@ Access Data variables and Coordinates in a Dataset objects are mainly used for loading data from files, which will be covered later in this tutorial.
@@ -4838,18 +4838,18 @@ NumPy-like selection<xarray.DataArray (lat: 3, lon: 4)> Size: 96B
-array([[292.49183408, 286.75694127, 291.1430049 , 279.6708509 ],
- [283.19466798, 285.34236937, 289.30840199, 286.22515585],
- [289.80677567, 281.73710917, 294.57091617, 291.28505114]])
+array([[282.44435897, 282.262194 , 286.34635361, 285.32213341],
+ [283.03737484, 288.57815157, 290.02698113, 278.23328324],
+ [277.06837841, 286.4771544 , 284.58714744, 288.42673576]])
Coordinates:
time datetime64[ns] 8B 2018-01-02
* lat (lat) float64 24B 25.0 40.0 55.0
* lon (lon) float64 32B -120.0 -100.0 -80.0 -60.0
Attributes:
units: kelvin
- standard_name: air_temperature
+ standard_name: air_temperature
This example reveals one of the major shortcomings of index selection. In order to retrieve the correct data using index selection, anyone using a DataArray must have precise knowledge of the axes in the DataArray, including the order of the axes and the meaning of their indices.
By using named coordinates, as shown in the next set of examples, we can avoid this cumbersome burden.
@@ -5229,18 +5229,18 @@ Selecting with
fill: currentColor;
}
<xarray.DataArray (lat: 3, lon: 4)> Size: 96B
-array([[292.49183408, 286.75694127, 291.1430049 , 279.6708509 ],
- [283.19466798, 285.34236937, 289.30840199, 286.22515585],
- [289.80677567, 281.73710917, 294.57091617, 291.28505114]])
+array([[282.44435897, 282.262194 , 286.34635361, 285.32213341],
+ [283.03737484, 288.57815157, 290.02698113, 278.23328324],
+ [277.06837841, 286.4771544 , 284.58714744, 288.42673576]])
Coordinates:
time datetime64[ns] 8B 2018-01-02
* lat (lat) float64 24B 25.0 40.0 55.0
* lon (lon) float64 32B -120.0 -100.0 -80.0 -60.0
Attributes:
units: kelvin
- standard_name: air_temperature
+ standard_name: air_temperature
This method yields the same result as the index selection, however:
@@ -5627,18 +5627,18 @@ Nearest-neighbor sampling.sel indeed returned the data at the temporal value corresponding to the date 2018-01-05.
@@ -6017,20 +6017,20 @@ Interpolation
Info
@@ -6414,29 +6414,29 @@ Slicing along coordinates
Info
@@ -6818,18 +6818,18 @@ One more selection method: <xarray.DataArray (lat: 3, lon: 4)> Size: 96B
-array([[292.49183408, 286.75694127, 291.1430049 , 279.6708509 ],
- [283.19466798, 285.34236937, 289.30840199, 286.22515585],
- [289.80677567, 281.73710917, 294.57091617, 291.28505114]])
+array([[282.44435897, 282.262194 , 286.34635361, 285.32213341],
+ [283.03737484, 288.57815157, 290.02698113, 278.23328324],
+ [277.06837841, 286.4771544 , 284.58714744, 288.42673576]])
Coordinates:
time datetime64[ns] 8B 2018-01-02
* lat (lat) float64 24B 25.0 40.0 55.0
* lon (lon) float64 32B -120.0 -100.0 -80.0 -60.0
Attributes:
units: kelvin
- standard_name: air_temperature
+ standard_name: air_temperature
This selection technique is similar to NumPy’s index-based selection, as shown below:
temp[1,:,:]
@@ -7208,29 +7208,29 @@ One more selection method: <xarray.DataArray (time: 3, lat: 2, lon: 2)> Size: 96B
-array([[[291.26874805, 287.10212211],
- [281.86759901, 277.88521001]],
+array([[[279.93414516, 288.85955729],
+ [283.36237877, 288.28464039]],
- [[286.75694127, 291.1430049 ],
- [285.34236937, 289.30840199]],
+ [[282.262194 , 286.34635361],
+ [288.57815157, 290.02698113]],
- [[287.14196565, 284.68298455],
- [288.52590266, 273.69692215]]])
+ [[275.374152 , 283.08613936],
+ [282.03531027, 280.16394984]]])
Coordinates:
* time (time) datetime64[ns] 24B 2018-01-01 2018-01-02 2018-01-03
* lat (lat) float64 16B 25.0 40.0
* lon (lon) float64 16B -100.0 -80.0
Attributes:
units: kelvin
- standard_name: air_temperature
+ [[275.374152 , 283.08613936],
+ [282.03531027, 280.16394984]]])
- time(time)datetime64[ns]2018-01-01 2018-01-02 2018-01-03
array(['2018-01-01T00:00:00.000000000', '2018-01-02T00:00:00.000000000',
+ '2018-01-03T00:00:00.000000000'], dtype='datetime64[ns]')
- lat(lat)float6425.0 40.0
array([25., 40.])
- lon(lon)float64-100.0 -80.0
array([-100., -80.])
- timePandasIndex
PandasIndex(DatetimeIndex(['2018-01-01', '2018-01-02', '2018-01-03'], dtype='datetime64[ns]', name='time', freq='D'))
- latPandasIndex
PandasIndex(Index([25.0, 40.0], dtype='float64', name='lat'))
- lonPandasIndex
PandasIndex(Index([-100.0, -80.0], dtype='float64', name='lon'))
- units :
- kelvin
- standard_name :
- air_temperature
In contrast with the previous example, this example shows a useful advantage of using the .loc attribute. When using the .loc attribute, you can specify data slices using a syntax similar to NumPy in addition to, or instead of, using the slice function. Both of these slicing techniques are illustrated below:
@@ -7604,29 +7604,29 @@ One more selection method: <xarray.DataArray (time: 3, lat: 2, lon: 2)> Size: 96B
-array([[[291.26874805, 287.10212211],
- [281.86759901, 277.88521001]],
+array([[[279.93414516, 288.85955729],
+ [283.36237877, 288.28464039]],
- [[286.75694127, 291.1430049 ],
- [285.34236937, 289.30840199]],
+ [[282.262194 , 286.34635361],
+ [288.57815157, 290.02698113]],
- [[287.14196565, 284.68298455],
- [288.52590266, 273.69692215]]])
+ [[275.374152 , 283.08613936],
+ [282.03531027, 280.16394984]]])
Coordinates:
* time (time) datetime64[ns] 24B 2018-01-01 2018-01-02 2018-01-03
* lat (lat) float64 16B 25.0 40.0
* lon (lon) float64 16B -100.0 -80.0
Attributes:
units: kelvin
- standard_name: air_temperature
+ [[275.374152 , 283.08613936],
+ [282.03531027, 280.16394984]]])
- time(time)datetime64[ns]2018-01-01 2018-01-02 2018-01-03
array(['2018-01-01T00:00:00.000000000', '2018-01-02T00:00:00.000000000',
+ '2018-01-03T00:00:00.000000000'], dtype='datetime64[ns]')
- lat(lat)float6425.0 40.0
array([25., 40.])
- lon(lon)float64-100.0 -80.0
array([-100., -80.])
- timePandasIndex
PandasIndex(DatetimeIndex(['2018-01-01', '2018-01-02', '2018-01-03'], dtype='datetime64[ns]', name='time', freq='D'))
- latPandasIndex
PandasIndex(Index([25.0, 40.0], dtype='float64', name='lat'))
- lonPandasIndex
PandasIndex(Index([-100.0, -80.0], dtype='float64', name='lon'))
- units :
- kelvin
- standard_name :
- air_temperature
As described above, the arguments to .loc must be in the order of the DataArray’s dimensions. Attempting to slice data without ordering arguments properly can cause errors, as shown below:
@@ -8059,10 +8059,10 @@ Access netCDF data with
@@ -8504,17 +8504,17 @@ Subsetting the
positive: down
Grib_level_type: 100
_CoordinateAxisType: Pressure
- _CoordinateZisPositive: down
+ dtype='float32', name='isobaric1'))- units :
- hPa
- long_name :
- Isobaric surface
- positive :
- down
- Grib_level_type :
- 100
- _CoordinateAxisType :
- Pressure
- _CoordinateZisPositive :
- down
(As described earlier in this tutorial, we can also use dictionary syntax to select specific DataArrays; in this case, we would write ds['isobaric1'].)
Many of the subsetting operations usable on DataArrays can also be used on Datasets. However, when used on Datasets, these operations are performed on every DataArray in the Dataset, as shown below:
@@ -8916,7 +8916,7 @@ Subsetting the
geospatial_lat_min: 10.753308882144761
geospatial_lat_max: 46.8308828962289
geospatial_lon_min: -153.88242040519995
- geospatial_lon_max: -42.666108129242815
As shown above, the subsetting operation performed on the Dataset returned a new Dataset. If only a single DataArray is needed from this new Dataset, it can be retrieved using the familiar dot notation:
@@ -9356,7 +9356,7 @@ Subsetting the
Grib1_TableVersion: 131
Grib1_Parameter: 11
Grib1_Level_Type: 100
- Grib1_Level_Desc: Isobaric surface
+ dtype='float32', name='x', length=268))- long_name :
- Temperature @ Isobaric surface
- units :
- K
- description :
- Temperature
- grid_mapping :
- LambertConformal_Projection
- Grib_Variable_Id :
- VAR_7-15-131-11_L100
- Grib1_Center :
- 7
- Grib1_Subcenter :
- 15
- Grib1_TableVersion :
- 131
- Grib1_Parameter :
- 11
- Grib1_Level_Type :
- 100
- Grib1_Level_Desc :
- Isobaric surface
@@ -9790,19 +9790,19 @@ Aggregation operations
+ dtype='float32', name='isobaric1'))
Info
@@ -10196,19 +10196,19 @@ Aggregation operations
+ dtype='float32', name='isobaric1'))
@@ -10226,7 +10226,7 @@ Simple visualization with
-[<matplotlib.lines.Line2D at 0x7f2e0f58d130>]
+[<matplotlib.lines.Line2D at 0x7f505db3d9d0>]
@@ -10250,7 +10250,7 @@ Customizing the plot
-[<matplotlib.lines.Line2D at 0x7f2e0f58eff0>]
+[<matplotlib.lines.Line2D at 0x7f5055a98d40>]
@@ -10268,7 +10268,7 @@ Plotting 2-D data
-<matplotlib.collections.QuadMesh at 0x7f2df9c43b60>
+<matplotlib.collections.QuadMesh at 0x7f5055ad1be0>
@@ -10667,7 +10667,7 @@ Resources and references
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
@@ -10796,7 +10796,7 @@ Contribute
By the Project Pythia Community.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
diff --git a/foundations/conda.html b/foundations/conda.html
index c07684127..6f809cfd0 100644
--- a/foundations/conda.html
+++ b/foundations/conda.html
@@ -79,7 +79,7 @@
-
+
@@ -913,7 +913,7 @@ Resources and References
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
@@ -1042,7 +1042,7 @@ Contribute
By the Project Pythia Community.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
diff --git a/foundations/getting-started-github.html b/foundations/getting-started-github.html
index 4a2af1d14..ec22e00df 100644
--- a/foundations/getting-started-github.html
+++ b/foundations/getting-started-github.html
@@ -81,7 +81,7 @@
-
+
@@ -768,7 +768,7 @@ Getting Started with GitHub
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
@@ -897,7 +897,7 @@ Contribute
By the Project Pythia Community.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
diff --git a/foundations/getting-started-jupyter.html b/foundations/getting-started-jupyter.html
index b1d2107c0..b95652e74 100644
--- a/foundations/getting-started-jupyter.html
+++ b/foundations/getting-started-jupyter.html
@@ -79,7 +79,7 @@
-
+
@@ -905,7 +905,7 @@ Resources and references
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
@@ -1034,7 +1034,7 @@ Contribute
By the Project Pythia Community.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
diff --git a/foundations/getting-started-python.html b/foundations/getting-started-python.html
index 286ad4516..19ac49850 100644
--- a/foundations/getting-started-python.html
+++ b/foundations/getting-started-python.html
@@ -81,7 +81,7 @@
-
+
@@ -779,7 +779,7 @@ Topics
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
@@ -908,7 +908,7 @@ Contribute
By the Project Pythia Community.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
diff --git a/foundations/github/basic-git.html b/foundations/github/basic-git.html
index 441aa5056..32e0b6b24 100644
--- a/foundations/github/basic-git.html
+++ b/foundations/github/basic-git.html
@@ -79,7 +79,7 @@
-
+
@@ -1222,7 +1222,7 @@ References
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
@@ -1351,7 +1351,7 @@ Contribute
By the Project Pythia Community.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
diff --git a/foundations/github/contribute-to-pythia.html b/foundations/github/contribute-to-pythia.html
index d1e1bad12..11ebc0cc2 100644
--- a/foundations/github/contribute-to-pythia.html
+++ b/foundations/github/contribute-to-pythia.html
@@ -79,7 +79,7 @@
-
+
@@ -885,7 +885,7 @@ Summary
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
@@ -1014,7 +1014,7 @@ Contribute
By the Project Pythia Community.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
diff --git a/foundations/github/git-branches.html b/foundations/github/git-branches.html
index 6dac4b319..4e316269f 100644
--- a/foundations/github/git-branches.html
+++ b/foundations/github/git-branches.html
@@ -79,7 +79,7 @@
-
+
@@ -1036,7 +1036,7 @@ Resources and references
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
@@ -1165,7 +1165,7 @@ Contribute
By the Project Pythia Community.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
diff --git a/foundations/github/github-advanced.html b/foundations/github/github-advanced.html
index 9e93b611f..def634b01 100644
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By the Project Pythia Community.
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By the Project Pythia Community.
- Last updated on 15 May 2024.
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29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 46], "support": 7, "switch": [30, 34], "synchron": 37, "system": [5, 39], "templat": 1, "termin": [40, 42, 46], "terminologi": 7, "test": [11, 21], "text": [9, 42, 43], "thi": [1, 8, 11, 28, 45, 49, 50], "thing": [32, 35], "tide": 7, "time": [7, 23, 28], "timedelta": 7, "timestamp": 7, "tip": [35, 36, 37], "titl": 11, "togeth": 11, "token": 36, "topic": [27, 31, 44], "try": [32, 35], "tune": 39, "tutori": [16, 28], "two": [3, 11], "type": 45, "unix": 7, "up": [0, 1, 30, 42], "updat": 30, "upstream": 37, "us": [1, 3, 10, 11, 13, 15, 18, 20, 21, 23, 24, 28, 50], "usag": 7, "user": 32, "utc": 7, "v": 36, "valu": [13, 18, 20, 23], "variabl": [5, 23, 42], "vc": [28, 39], "vector": 15, "veri": 45, "version": [28, 39], "versu": 7, "via": [29, 50], "view": [28, 38], "visual": [22, 23], "wai": 38, "want": 28, "warn": [1, 3, 13, 14, 18, 42], "we": 28, "web": 39, "what": [1, 3, 5, 7, 10, 11, 13, 14, 15, 18, 20, 21, 23, 24, 26, 28, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 45, 46], "when": 38, "where": 20, "why": [8, 21, 28, 47], "window": 32, "within": 11, "work": 37, "workflow": [30, 34, 37], "would": 21, "wrap": 23, "write": [5, 7], "xarrai": [19, 20, 21, 22, 23], "xr": 23, "year": 7, "york": 3, "you": [21, 28, 30, 38, 39], "your": [0, 1, 18, 30, 34, 36, 41, 45], "zero": 45, "zone": 7}})
\ No newline at end of file
<matplotlib.colorbar.Colorbar at 0x7f4e652e9af0>
+<matplotlib.colorbar.Colorbar at 0x7f07e48a40e0>
-
+
fig.colorbar uses the argument hist1[3]. The explanation is as follows: hist1 is a tuple returned by hist2d, and hist1[3] contains a matplotlib.collections.QuadMesh that points to the colormap for the first histogram. To make sure that both histograms are using the same colormap with the same range of values, vmax is set to 0.18 for both plots. This ensures that both histograms are using colormaps that represent values from 0 (the default for histograms) to 0.18. Because the same data values are used for both plots, it doesn’t matter whether we pass in hist1[3] or hist2[3] to fig.colorbar.
@@ -848,7 +848,7 @@
+
@@ -865,7 +865,7 @@ Custom Colorbars
-
Custom Colorbars
-
+
Mosaic Subplots
-
+
You’ll notice there is not a colorbar plotted by default. When constructing the colorbar, we need to specify the following:
@@ -930,7 +930,7 @@ Mosaic Subplots
-
+
+
Resources and references
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
@@ -1420,7 +1420,7 @@ Contribute
By the Project Pythia Community.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
diff --git a/core/matplotlib/histograms-piecharts-animation.html b/core/matplotlib/histograms-piecharts-animation.html
index ea6420ec0..9a591348d 100644
--- a/core/matplotlib/histograms-piecharts-animation.html
+++ b/core/matplotlib/histograms-piecharts-animation.html
@@ -79,7 +79,7 @@
-
+
@@ -3629,7 +3629,7 @@ Additional Resources
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
@@ -3758,7 +3758,7 @@ Contribute
By the Project Pythia Community.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
diff --git a/core/matplotlib/matplotlib-basics.html b/core/matplotlib/matplotlib-basics.html
index 323b54231..22fd1268a 100644
--- a/core/matplotlib/matplotlib-basics.html
+++ b/core/matplotlib/matplotlib-basics.html
@@ -79,7 +79,7 @@
-
+
@@ -1492,7 +1492,7 @@ Resources and References
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
@@ -1621,7 +1621,7 @@ Contribute
By the Project Pythia Community.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
diff --git a/core/numpy.html b/core/numpy.html
index 4fb55d7f5..5c535f0a9 100644
--- a/core/numpy.html
+++ b/core/numpy.html
@@ -81,7 +81,7 @@
-
+
@@ -760,7 +760,7 @@ NumPy
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
@@ -889,7 +889,7 @@ Contribute
By the Project Pythia Community.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
diff --git a/core/numpy/intermediate-numpy.html b/core/numpy/intermediate-numpy.html
index 59706e1a6..38a6afc63 100644
--- a/core/numpy/intermediate-numpy.html
+++ b/core/numpy/intermediate-numpy.html
@@ -81,7 +81,7 @@
-
+
@@ -1425,7 +1425,7 @@ Resources and references
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
@@ -1554,7 +1554,7 @@ Contribute
By the Project Pythia Community.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
diff --git a/core/numpy/numpy-basics.html b/core/numpy/numpy-basics.html
index cb11d7b3d..365a2f855 100644
--- a/core/numpy/numpy-basics.html
+++ b/core/numpy/numpy-basics.html
@@ -81,7 +81,7 @@
-
+
@@ -1168,7 +1168,7 @@ Indexing
Slices
-Slicing syntax is written as array[start:stop[:step]], where all numbers are optional.
+Slicing syntax is written as array[start:stop:step]. Note that all numbers are optional. Importantly, the step parameter is optional and can be omitted, in which case the slice uses a default step of 1.
defaults:
@@ -1775,7 +1775,7 @@ Resources and references
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
@@ -1904,7 +1904,7 @@ Contribute
By the Project Pythia Community.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
diff --git a/core/numpy/numpy-broadcasting.html b/core/numpy/numpy-broadcasting.html
index 78f882658..57e5dd9a6 100644
--- a/core/numpy/numpy-broadcasting.html
+++ b/core/numpy/numpy-broadcasting.html
@@ -81,7 +81,7 @@
-
+
@@ -1070,7 +1070,7 @@ Blocking
-
-
-
-
-array([[ 8.3, 11.6, 33.1],
- [11.6, 33.1, 18.3],
- [33.1, 18.3, 22.5],
- [18.3, 22.5, 24.5],
- [22.5, 24.5, 20.1],
- [24.5, 20.1, 20.1],
- [20.1, 20.1, 18. ],
- [20.1, 18. , 25. ]])
+array([[19.3, 18.8, 28.7],
+ [18.8, 28.7, 27.1],
+ [28.7, 27.1, 17.1],
+ [27.1, 17.1, 16.2],
+ [17.1, 16.2, 15. ],
+ [16.2, 15. , 15.1],
+ [15. , 15.1, 14. ],
+ [15.1, 14. , 19.2]])
@@ -1161,8 +1161,8 @@ Blocking
-
-array([[ 8.3, 11.6, 2000. ],
- [ 11.6, 2000. , 18.3],
- [2000. , 18.3, 22.5],
- [ 18.3, 22.5, 24.5],
- [ 22.5, 24.5, 20.1],
- [ 24.5, 20.1, 20.1],
- [ 20.1, 20.1, 18. ],
- [ 20.1, 18. , 25. ]])
+array([[ 19.3, 18.8, 2000. ],
+ [ 18.8, 2000. , 27.1],
+ [2000. , 27.1, 17.1],
+ [ 27.1, 17.1, 16.2],
+ [ 17.1, 16.2, 15. ],
+ [ 16.2, 15. , 15.1],
+ [ 15. , 15.1, 14. ],
+ [ 15.1, 14. , 19.2]])
@@ -1210,13 +1210,13 @@ Finding the difference between min and max
-array([[6, 6, 7, ..., 8, 7, 6],
- [6, 6, 6, ..., 7, 7, 7],
- [7, 7, 7, ..., 6, 7, 7],
+array([[7, 7, 7, ..., 7, 8, 7],
+ [6, 7, 6, ..., 7, 7, 6],
+ [7, 6, 7, ..., 7, 6, 6],
...,
- [7, 7, 7, ..., 7, 7, 6],
- [7, 6, 6, ..., 6, 7, 7],
- [7, 6, 7, ..., 7, 8, 7]])
+ [6, 7, 6, ..., 6, 6, 7],
+ [7, 7, 7, ..., 6, 6, 7],
+ [6, 7, 7, ..., 8, 8, 7]])
@@ -1241,13 +1241,13 @@ Finding the difference between min and max
-array([[775. , 775. , 737.5, ..., 700. , 737.5, 775. ],
- [775. , 775. , 775. , ..., 737.5, 737.5, 737.5],
- [737.5, 737.5, 737.5, ..., 775. , 737.5, 737.5],
+array([[737.5, 737.5, 737.5, ..., 737.5, 700. , 737.5],
+ [775. , 737.5, 775. , ..., 737.5, 737.5, 775. ],
+ [737.5, 775. , 737.5, ..., 737.5, 775. , 775. ],
...,
- [737.5, 737.5, 737.5, ..., 737.5, 737.5, 775. ],
- [737.5, 775. , 775. , ..., 775. , 737.5, 737.5],
- [737.5, 775. , 737.5, ..., 737.5, 700. , 737.5]])
+ [775. , 737.5, 775. , ..., 775. , 775. , 737.5],
+ [737.5, 737.5, 737.5, ..., 775. , 775. , 737.5],
+ [775. , 737.5, 737.5, ..., 700. , 700. , 737.5]])
@@ -1276,19 +1276,19 @@ Finding the difference between min and max
-array([[ -9.55417349, -9.78584972, -9.92661689, ..., -12.67789616,
- -10.49077377, -6.56515605],
- [-10.00010532, -10.70819797, -7.20985445, ..., -11.10789153,
- -11.73163442, -8.29531203],
- [-11.88696252, -12.29088724, -11.70346147, ..., -7.30859657,
- -8.97849533, -13.84614818],
+array([[-13.35480377, -11.37389473, -6.11490009, ..., -9.69409241,
+ -14.14853765, -11.04724036],
+ [ -6.50332158, -11.09958323, -5.95998607, ..., -13.2604716 ,
+ -13.19971009, -8.49574616],
+ [-12.28051357, -10.19815861, -7.42945122, ..., -11.5798199 ,
+ -8.91153322, -8.6079996 ],
...,
- [-11.90215938, -7.91187899, -13.08679868, ..., -10.41120932,
- -11.35468344, -11.06459146],
- [-11.50292233, -6.26889319, -7.38221861, ..., -10.24036433,
- -10.12594611, -5.63518882],
- [-10.32651953, -6.06759924, -9.54152312, ..., -10.45702773,
- -12.20574751, -8.01746694]])
+ [ -8.10255586, -12.16615351, -11.27253366, ..., -10.44432682,
+ -7.39154896, -10.99574973],
+ [-16.5968518 , -12.57487222, -8.39736923, ..., -4.15595587,
+ -8.86979291, -11.28873531],
+ [ -8.59386363, -9.38047716, -10.46838159, ..., -11.30765026,
+ -10.89295232, -9.24056526]])
@@ -1332,19 +1332,19 @@ Finding the difference between min and max
-array([[ -9.55417349, -9.78584972, -9.92661689, ..., -12.67789616,
- -10.49077377, -6.56515605],
- [-10.00010532, -10.70819797, -7.20985445, ..., -11.10789153,
- -11.73163442, -8.29531203],
- [-11.88696252, -12.29088724, -11.70346147, ..., -7.30859657,
- -8.97849533, -13.84614818],
+array([[-13.35480377, -11.37389473, -6.11490009, ..., -9.69409241,
+ -14.14853765, -11.04724036],
+ [ -6.50332158, -11.09958323, -5.95998607, ..., -13.2604716 ,
+ -13.19971009, -8.49574616],
+ [-12.28051357, -10.19815861, -7.42945122, ..., -11.5798199 ,
+ -8.91153322, -8.6079996 ],
...,
- [-11.90215938, -7.91187899, -13.08679868, ..., -10.41120932,
- -11.35468344, -11.06459146],
- [-11.50292233, -6.26889319, -7.38221861, ..., -10.24036433,
- -10.12594611, -5.63518882],
- [-10.32651953, -6.06759924, -9.54152312, ..., -10.45702773,
- -12.20574751, -8.01746694]])
+ [ -8.10255586, -12.16615351, -11.27253366, ..., -10.44432682,
+ -7.39154896, -10.99574973],
+ [-16.5968518 , -12.57487222, -8.39736923, ..., -4.15595587,
+ -8.86979291, -11.28873531],
+ [ -8.59386363, -9.38047716, -10.46838159, ..., -11.30765026,
+ -10.89295232, -9.24056526]])
@@ -1711,7 +1711,7 @@ Resources and references
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
@@ -1840,7 +1840,7 @@ Contribute
By the Project Pythia Community.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
diff --git a/core/overview.html b/core/overview.html
index 6acc0ae46..fd4752cc2 100644
--- a/core/overview.html
+++ b/core/overview.html
@@ -79,7 +79,7 @@
-
+
@@ -886,7 +886,7 @@ Tutorials
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
@@ -1015,7 +1015,7 @@ Contribute
By the Project Pythia Community.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
diff --git a/core/pandas.html b/core/pandas.html
index 2e4230efc..02509b347 100644
--- a/core/pandas.html
+++ b/core/pandas.html
@@ -79,7 +79,7 @@
-
+
@@ -759,7 +759,7 @@ Pandas
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
@@ -888,7 +888,7 @@ Contribute
By the Project Pythia Community.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
diff --git a/core/pandas/pandas.html b/core/pandas/pandas.html
index 734fecdb6..2e24138b7 100644
--- a/core/pandas/pandas.html
+++ b/core/pandas/pandas.html
@@ -79,7 +79,7 @@
-
+
@@ -1098,7 +1098,7 @@ Indexing a Seri
-/tmp/ipykernel_2735/737336773.py:1: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`
+/tmp/ipykernel_2746/737336773.py:1: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`
nino34_series[3]
@@ -1449,7 +1449,7 @@ Extending to the
-/tmp/ipykernel_2735/541596450.py:1: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`
+/tmp/ipykernel_2746/541596450.py:1: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`
df["Nino34"][3]
@@ -3645,7 +3645,7 @@ Resampling
-/tmp/ipykernel_2735/233158901.py:1: FutureWarning: 'Y' is deprecated and will be removed in a future version, please use 'YE' instead.
+/tmp/ipykernel_2746/233158901.py:1: FutureWarning: 'Y' is deprecated and will be removed in a future version, please use 'YE' instead.
df.Nino34.resample('1Y').mean().plot();
@@ -4434,7 +4434,7 @@ Resources and References
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
@@ -4563,7 +4563,7 @@ Contribute
By the Project Pythia Community.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
diff --git a/core/xarray.html b/core/xarray.html
index e4624108b..6bc787165 100644
--- a/core/xarray.html
+++ b/core/xarray.html
@@ -79,7 +79,7 @@
-
+
@@ -760,7 +760,7 @@ Xarray
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
@@ -889,7 +889,7 @@ Contribute
By the Project Pythia Community.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
diff --git a/core/xarray/computation-masking.html b/core/xarray/computation-masking.html
index b15ad248c..12f19902b 100644
--- a/core/xarray/computation-masking.html
+++ b/core/xarray/computation-masking.html
@@ -79,7 +79,7 @@
-
+
@@ -943,7 +943,7 @@ Data Setup
@@ -1576,7 +1576,7 @@ Arithmetic Operations
+ dtype='float64', name='lon', length=360))
In addition, there are many other arithmetic operations that can be performed on DataArrays. In this example, we demonstrate squaring the original Celsius values of our air temperature data:
@@ -2246,7 +2246,7 @@ Arithmetic Operations
+ dtype='float64', name='lon', length=360))
@@ -2876,7 +2876,7 @@ Aggregation Methodsdim keyword argument; this means that the function was applied over all of the dataset’s dimensions. In other words, the aggregation method computed the mean of every element of the temperature dataset across every temporal and spatial data point. However, if a dimension name is used with the dim keyword argument, the aggregation method computes an aggregation along the given dimension. In this next example, we use aggregation to calculate the temporal mean across all spatial data; this is performed by providing the dimension name 'time' to the dim keyword argument:
@@ -3276,7 +3276,7 @@ Aggregation Methods
@@ -3717,7 +3717,7 @@ Aggregation Methods
@@ -4325,7 +4325,7 @@ Aggregation Methods
@@ -4892,7 +4892,7 @@ Compute climatologyDataArray containing the climatology data, we can plot the data in different ways. In this example, we plot the climatology at a specific latitude-longitude point:
@@ -5412,7 +5412,7 @@ Compute anomaly
@@ -6095,7 +6095,7 @@ Compute anomaly.weighted() method and the newly loaded CESM2 dataset to weight the grid cell data as described above:
@@ -6598,7 +6598,7 @@ Other high level computation functionalityrolling method to compute averages in a moving window of 5 months of data:
@@ -7114,7 +7114,7 @@ Other high level computation functionality
@@ -7783,7 +7783,7 @@ Masking Data
Using where with one condition
@@ -8421,15 +8421,15 @@ Using where
+ dtype='float64', name='lon', length=360))
- cell_measures :
- area: areacello
- cell_methods :
- area: mean where sea time: mean
- comment :
- Model data on the 1x1 grid includes values in all cells for which ocean cells on the native grid cover more than 52.5 percent of the 1x1 grid cell. This 52.5 percent cutoff was chosen to produce ocean surface area on the 1x1 grid as close as possible to ocean surface area on the native grid, while not introducing fractional cell coverage.
- description :
- This may differ from "surface temperature" in regions of sea ice or floating ice shelves. For models using conservative temperature as the prognostic field, they should report the top ocean layer as surface potential temperature, which is the same as surface in situ temperature.
- frequency :
- mon
- id :
- tos
- long_name :
- Sea Surface Temperature
- mipTable :
- Omon
- out_name :
- tos
- prov :
- Omon ((isd.003))
- realm :
- ocean
- standard_name :
- sea_surface_temperature
- time :
- time
- time_label :
- time-mean
- time_title :
- Temporal mean
- title :
- Sea Surface Temperature
- type :
- real
- units :
- degC
- variable_id :
- tos
As shown in the previous example, methods like .isel() and .sel() return data of a different shape than the original data provided to them. However, .where() preserves the shape of the original data by masking the values with a Boolean condition. Data values for which the condition is True are returned identical to the values passed in. On the other hand, data values for which the condition is False are returned as a preset example value. (This example value defaults to nan, but can be set to other values as well.)
Before testing .where(), it is helpful to look at the official documentation. As stated above, the .where() method takes a Boolean condition. (Boolean conditions use operators such as less-than, greater-than, and equal-to, and return a value of True or False.) Most uses of .where() check whether or not specific data values are less than or greater than a constant value. As stated in the documentation, the data values specified in the Boolean condition of .where() can be any of the following:
@@ -8864,7 +8864,7 @@ Using where
+ dtype='float64', name='lon', length=360))- cell_measures :
- area: areacello
- cell_methods :
- area: mean where sea time: mean
- comment :
- Model data on the 1x1 grid includes values in all cells for which ocean cells on the native grid cover more than 52.5 percent of the 1x1 grid cell. This 52.5 percent cutoff was chosen to produce ocean surface area on the 1x1 grid as close as possible to ocean surface area on the native grid, while not introducing fractional cell coverage.
- description :
- This may differ from "surface temperature" in regions of sea ice or floating ice shelves. For models using conservative temperature as the prognostic field, they should report the top ocean layer as surface potential temperature, which is the same as surface in situ temperature.
- frequency :
- mon
- id :
- tos
- long_name :
- Sea Surface Temperature
- mipTable :
- Omon
- out_name :
- tos
- prov :
- Omon ((isd.003))
- realm :
- ocean
- standard_name :
- sea_surface_temperature
- time :
- time
- time_label :
- time-mean
- time_title :
- Temporal mean
- title :
- Sea Surface Temperature
- type :
- real
- units :
- degC
- variable_id :
- tos
In this example, we use Matplotlib to plot the original, unmasked data, as well as the masked data created in the previous example.
@@ -9321,7 +9321,7 @@ Resources and References
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
@@ -9450,7 +9450,7 @@ Contribute
By the Project Pythia Community.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
diff --git a/core/xarray/dask-arrays-xarray.html b/core/xarray/dask-arrays-xarray.html
index 0af9daaf3..65793b335 100644
--- a/core/xarray/dask-arrays-xarray.html
+++ b/core/xarray/dask-arrays-xarray.html
@@ -79,7 +79,7 @@
-
+
@@ -568,91 +568,91 @@ Create a dask.a
-array([[[0.08870167, 0.5536724 , 0.86869933, ..., 0.59745109,
- 0.66241166, 0.14712849],
- [0.79424186, 0.53985475, 0.83117213, ..., 0.64752434,
- 0.2068288 , 0.43561572],
- [0.93755297, 0.12641562, 0.55493093, ..., 0.44682309,
- 0.9108161 , 0.28494995],
+array([[[3.73479162e-01, 5.69354087e-01, 8.44121771e-01, ...,
+ 6.95300592e-01, 7.05791578e-01, 3.22757785e-01],
+ [7.34673387e-01, 3.22502093e-02, 2.98660716e-02, ...,
+ 4.48326876e-01, 1.28181189e-01, 6.17129937e-01],
+ [4.61524077e-01, 5.72981031e-01, 6.22455684e-02, ...,
+ 4.66387696e-01, 2.24365962e-02, 9.05528466e-01],
...,
- [0.36681494, 0.55104908, 0.67358062, ..., 0.93120538,
- 0.51164396, 0.2149192 ],
- [0.33220658, 0.18853655, 0.84133513, ..., 0.09990092,
- 0.45265087, 0.48841145],
- [0.54632195, 0.99301667, 0.40513953, ..., 0.5341915 ,
- 0.3220391 , 0.07544213]],
-
- [[0.8398981 , 0.14999579, 0.81431679, ..., 0.45814483,
- 0.81962377, 0.01308931],
- [0.52904343, 0.17038365, 0.49820524, ..., 0.71294698,
- 0.49906669, 0.32305634],
- [0.83597797, 0.12242548, 0.63334391, ..., 0.77669187,
- 0.44530452, 0.02031067],
+ [4.32840675e-01, 6.11791423e-01, 7.77157080e-01, ...,
+ 5.21898060e-01, 5.08370890e-01, 5.88153598e-01],
+ [8.04673850e-01, 7.80296541e-03, 5.00244166e-01, ...,
+ 9.45710973e-01, 5.08700949e-01, 8.89905823e-01],
+ [4.87536782e-01, 4.64576884e-01, 4.97751472e-01, ...,
+ 9.63294478e-01, 8.00438624e-01, 9.39160580e-01]],
+
+ [[9.59798128e-01, 9.67977225e-01, 5.67081374e-01, ...,
+ 1.15551677e-01, 5.93188567e-01, 8.21494821e-01],
+ [7.09798171e-01, 6.86764079e-01, 9.75639983e-01, ...,
+ 6.65980228e-01, 3.63136928e-01, 3.91634965e-01],
+ [2.45312843e-01, 7.92456108e-01, 7.52068086e-01, ...,
+ 8.31396290e-01, 7.34853345e-01, 1.59997513e-02],
...,
- [0.23487395, 0.54185136, 0.84738714, ..., 0.43194404,
- 0.96464114, 0.34661126],
- [0.06216953, 0.87402041, 0.89512514, ..., 0.38706893,
- 0.09187095, 0.88666451],
- [0.46384865, 0.02462827, 0.71575049, ..., 0.47421697,
- 0.30570578, 0.58421292]],
-
- [[0.05704318, 0.36232811, 0.03448708, ..., 0.86748361,
- 0.49546271, 0.39195962],
- [0.62907898, 0.52593284, 0.87418635, ..., 0.97638485,
- 0.63695351, 0.75326941],
- [0.11640535, 0.15534404, 0.80460691, ..., 0.26935451,
- 0.00686985, 0.42474694],
+ [6.29006234e-01, 5.66655665e-01, 9.59798385e-01, ...,
+ 4.64611557e-02, 8.47630616e-01, 9.96868460e-01],
+ [8.65409572e-01, 6.62829465e-01, 5.85935120e-01, ...,
+ 2.76645046e-01, 4.05531947e-01, 1.86970157e-01],
+ [5.88186192e-02, 4.80258125e-01, 7.50053106e-01, ...,
+ 5.82584007e-01, 7.16238302e-01, 3.09095729e-01]],
+
+ [[1.95867406e-01, 8.02997164e-01, 5.92335907e-02, ...,
+ 6.00495084e-02, 3.58568845e-02, 6.14435689e-01],
+ [5.40191024e-01, 7.45240262e-01, 5.83926237e-01, ...,
+ 5.42683512e-01, 7.15062271e-05, 8.15838308e-01],
+ [3.56121359e-01, 6.64103118e-01, 3.19197578e-01, ...,
+ 4.30885292e-01, 5.63274760e-01, 5.58304993e-01],
...,
- [0.53156325, 0.69190957, 0.69352384, ..., 0.77170882,
- 0.44011939, 0.39866217],
- [0.29442052, 0.74080675, 0.46131608, ..., 0.32366183,
- 0.73788577, 0.04341616],
- [0.94117032, 0.92630431, 0.79008536, ..., 0.02443356,
- 0.52042712, 0.83466538]],
+ [3.70429144e-01, 2.28949319e-01, 2.12465225e-01, ...,
+ 8.75347366e-01, 6.88876481e-01, 8.52985255e-01],
+ [8.43544201e-01, 3.50323808e-01, 9.94755069e-01, ...,
+ 2.31937385e-01, 6.46129523e-01, 7.56519847e-01],
+ [9.23292344e-01, 9.06543740e-01, 2.16039623e-01, ...,
+ 6.17841121e-01, 5.83778599e-02, 7.07627581e-01]],
...,
- [[0.1396787 , 0.60143505, 0.57058462, ..., 0.18893598,
- 0.82985107, 0.54242887],
- [0.68157985, 0.63122788, 0.1780818 , ..., 0.55248006,
- 0.63127177, 0.4663941 ],
- [0.55463676, 0.49022722, 0.28183346, ..., 0.64073606,
- 0.37788708, 0.05233016],
+ [[5.50076397e-01, 6.23247920e-01, 6.69863249e-01, ...,
+ 6.66141598e-01, 4.98073488e-01, 9.70545887e-01],
+ [7.38559992e-01, 3.21393458e-01, 4.05421647e-01, ...,
+ 2.52285356e-01, 8.08345653e-01, 7.28764634e-01],
+ [2.68191728e-01, 6.03169049e-02, 6.89258446e-01, ...,
+ 6.21042327e-01, 9.60250603e-01, 9.75257028e-01],
...,
- [0.39020736, 0.85735173, 0.00786958, ..., 0.66161596,
- 0.18152074, 0.17731408],
- [0.93613911, 0.96578828, 0.38973709, ..., 0.06864502,
- 0.5063675 , 0.73631955],
- [0.1671134 , 0.45039599, 0.38253178, ..., 0.74316064,
- 0.20253998, 0.93656703]],
-
- [[0.28018602, 0.84864745, 0.65231976, ..., 0.57238332,
- 0.37536731, 0.04826117],
- [0.47663448, 0.86778224, 0.95451621, ..., 0.76880871,
- 0.89875752, 0.06756555],
- [0.49609048, 0.21693506, 0.23207324, ..., 0.06636032,
- 0.95937476, 0.4827714 ],
+ [6.11978830e-01, 6.32479701e-01, 1.75826196e-01, ...,
+ 2.09161988e-01, 6.71446177e-01, 4.78915946e-01],
+ [6.45044934e-01, 1.15167985e-01, 9.05467373e-01, ...,
+ 7.79598426e-01, 8.76027493e-01, 5.70161686e-01],
+ [8.44173395e-01, 7.14987726e-01, 5.27098062e-01, ...,
+ 6.24560049e-01, 3.32587128e-02, 1.48219268e-01]],
+
+ [[9.16337354e-01, 1.38628117e-01, 2.76572964e-01, ...,
+ 5.68919305e-02, 3.07040211e-01, 6.70607173e-02],
+ [6.78200880e-01, 1.19012654e-01, 8.76844508e-01, ...,
+ 5.78903343e-01, 5.08716762e-01, 3.27430604e-01],
+ [5.19310927e-01, 1.62933592e-01, 6.94104791e-01, ...,
+ 8.28521396e-01, 5.63944660e-01, 9.23353464e-01],
...,
- [0.83197412, 0.22629066, 0.1589711 , ..., 0.55726833,
- 0.40921459, 0.94548168],
- [0.20373662, 0.76138868, 0.56265755, ..., 0.71025427,
- 0.19641998, 0.49627513],
- [0.20384921, 0.34173511, 0.44829 , ..., 0.3999668 ,
- 0.75240336, 0.21357396]],
-
- [[0.05024137, 0.77607526, 0.32452642, ..., 0.26169962,
- 0.2092348 , 0.79937281],
- [0.8834038 , 0.56909665, 0.84006875, ..., 0.09540328,
- 0.66257683, 0.78342945],
- [0.29906903, 0.08192958, 0.51913898, ..., 0.32946737,
- 0.91310302, 0.99198924],
+ [8.75833692e-01, 8.80034882e-01, 3.94957057e-01, ...,
+ 3.88766073e-01, 5.62146961e-01, 4.07401505e-01],
+ [8.83232515e-02, 7.93868945e-02, 7.09396623e-01, ...,
+ 4.00821090e-01, 9.67897242e-01, 3.30712526e-02],
+ [6.21839195e-01, 3.66731583e-01, 9.97239185e-01, ...,
+ 4.06892246e-01, 8.84142338e-01, 1.29206897e-01]],
+
+ [[3.13410458e-01, 7.92759677e-01, 8.24979480e-02, ...,
+ 5.38935776e-02, 4.52819725e-01, 1.48534750e-01],
+ [2.06595218e-01, 8.46746782e-01, 9.06689993e-01, ...,
+ 8.52571008e-01, 4.28351337e-01, 8.06352220e-01],
+ [3.77563013e-01, 9.99637726e-01, 3.37356897e-01, ...,
+ 8.18911136e-01, 8.32562303e-01, 8.45143802e-02],
...,
- [0.89344391, 0.04685752, 0.16870497, ..., 0.70133294,
- 0.2028986 , 0.16916596],
- [0.62721463, 0.12582174, 0.59300888, ..., 0.93023778,
- 0.50053284, 0.63544531],
- [0.89994404, 0.84669077, 0.65421957, ..., 0.19573779,
- 0.56024887, 0.73584493]]])
+ [5.51431397e-01, 8.77465751e-01, 9.63517992e-01, ...,
+ 3.34710544e-02, 9.70363045e-01, 5.42823121e-01],
+ [3.89247115e-01, 3.36500509e-01, 4.31638413e-02, ...,
+ 8.82214294e-02, 2.41024552e-01, 4.19862342e-01],
+ [5.65544128e-01, 6.09859197e-01, 2.77376901e-01, ...,
+ 5.51955058e-01, 6.67783854e-02, 4.08359511e-01]]])
@@ -856,7 +856,7 @@ Why the difference?
-
+
@@ -872,11 +872,11 @@ Compute the result
-CPU times: user 406 ms, sys: 28 ms, total: 434 ms
-Wall time: 232 ms
+CPU times: user 399 ms, sys: 28.5 ms, total: 428 ms
+Wall time: 228 ms
-12002441.41340752
+12000189.444496717
@@ -894,7 +894,7 @@ Exercise with d
-
+
As is obvious from the above exercise, Dask quickly and easily determines a strategy for performing the operations, in this case a sum. This illustrates the appeal of Dask: automatic algorithm generation that scales from simple arithmetic problems to highly complex scientific equations with large datasets and multiple operations.
@@ -974,7 +974,7 @@ Exercise with d
-
+
@@ -1106,7 +1106,7 @@ Testing a bigger calculation
-
+
@@ -1117,34 +1117,34 @@ Testing a bigger calculation
-[ ] | 0% Completed | 169.21 us
+[ ] | 0% Completed | 255.13 us
-[ ] | 0% Completed | 105.92 ms
+[ ] | 0% Completed | 106.98 ms
-[ ] | 0% Completed | 206.66 ms
+[ ] | 0% Completed | 207.72 ms
-[ ] | 0% Completed | 307.49 ms
+[ ] | 0% Completed | 308.36 ms
-[ ] | 0% Completed | 408.13 ms
+[ ] | 0% Completed | 409.00 ms
-[ ] | 0% Completed | 508.89 ms
+[ ] | 0% Completed | 509.67 ms
-[ ] | 1% Completed | 609.51 ms
+[ ] | 0% Completed | 610.39 ms
-[# ] | 4% Completed | 710.49 ms
+[# ] | 3% Completed | 711.30 ms
-[### ] | 7% Completed | 811.54 ms
+[## ] | 6% Completed | 812.41 ms
-[#### ] | 10% Completed | 912.41 ms
+[#### ] | 10% Completed | 913.29 ms
[#### ] | 10% Completed | 1.01 s
@@ -1153,7 +1153,7 @@ Testing a bigger calculation[#### ] | 10% Completed | 1.11 s
-[#### ] | 10% Completed | 1.21 s
+[#### ] | 10% Completed | 1.22 s
[#### ] | 10% Completed | 1.32 s
@@ -1162,10 +1162,10 @@ Testing a bigger calculation[#### ] | 10% Completed | 1.42 s
-[##### ] | 13% Completed | 1.52 s
+[##### ] | 12% Completed | 1.52 s
-[###### ] | 16% Completed | 1.62 s
+[###### ] | 15% Completed | 1.62 s
[####### ] | 19% Completed | 1.72 s
@@ -1186,13 +1186,13 @@ Testing a bigger calculation[######## ] | 21% Completed | 2.22 s
-[######## ] | 22% Completed | 2.32 s
+[######## ] | 21% Completed | 2.32 s
-[########## ] | 25% Completed | 2.43 s
+[######### ] | 24% Completed | 2.43 s
-[########### ] | 27% Completed | 2.53 s
+[########### ] | 28% Completed | 2.53 s
[############ ] | 31% Completed | 2.63 s
@@ -1216,16 +1216,16 @@ Testing a bigger calculation[############## ] | 35% Completed | 3.23 s
-[############### ] | 38% Completed | 3.33 s
+[############## ] | 37% Completed | 3.33 s
-[################ ] | 40% Completed | 3.43 s
+[############### ] | 39% Completed | 3.43 s
-[################# ] | 44% Completed | 3.53 s
+[################# ] | 43% Completed | 3.53 s
-[################### ] | 48% Completed | 3.63 s
+[################## ] | 46% Completed | 3.64 s
[################### ] | 48% Completed | 3.74 s
@@ -1246,10 +1246,10 @@ Testing a bigger calculation[#################### ] | 50% Completed | 4.24 s
-[##################### ] | 53% Completed | 4.34 s
+[#################### ] | 52% Completed | 4.34 s
-[###################### ] | 57% Completed | 4.44 s
+[###################### ] | 55% Completed | 4.44 s
[####################### ] | 59% Completed | 4.54 s
@@ -1276,7 +1276,7 @@ Testing a bigger calculation[######################### ] | 64% Completed | 5.25 s
-[########################### ] | 67% Completed | 5.35 s
+[########################### ] | 68% Completed | 5.35 s
[############################ ] | 72% Completed | 5.45 s
@@ -1300,13 +1300,13 @@ Testing a bigger calculation[############################## ] | 75% Completed | 6.06 s
-[############################### ] | 77% Completed | 6.16 s
+[############################### ] | 78% Completed | 6.16 s
-[################################ ] | 80% Completed | 6.26 s
+[################################ ] | 82% Completed | 6.26 s
-[################################# ] | 83% Completed | 6.36 s
+[################################# ] | 84% Completed | 6.36 s
[################################## ] | 85% Completed | 6.46 s
@@ -1324,16 +1324,16 @@ Testing a bigger calculation[################################## ] | 85% Completed | 6.86 s
-[################################## ] | 86% Completed | 6.96 s
+[################################### ] | 89% Completed | 6.96 s
-[################################### ] | 89% Completed | 7.06 s
+[#################################### ] | 92% Completed | 7.07 s
-[##################################### ] | 92% Completed | 7.16 s
+[###################################### ] | 96% Completed | 7.17 s
-[###################################### ] | 96% Completed | 7.27 s
+[####################################### ] | 99% Completed | 7.27 s
[########################################] | 100% Completed | 7.37 s
@@ -1854,7 +1854,7 @@ Reading data with
- latPandasIndex
PandasIndex(Index([-89.5, -88.5, -87.5, -86.5, -85.5, -84.5, -83.5, -82.5, -81.5, -80.5,
+ 80.5, 81.5, 82.5, 83.5, 84.5, 85.5, 86.5, 87.5, 88.5, 89.5])
- lon(lon)float640.5 1.5 2.5 ... 357.5 358.5 359.5
array([ 0.5, 1.5, 2.5, ..., 357.5, 358.5, 359.5])
- latPandasIndex
PandasIndex(Index([-89.5, -88.5, -87.5, -86.5, -85.5, -84.5, -83.5, -82.5, -81.5, -80.5,
...
80.5, 81.5, 82.5, 83.5, 84.5, 85.5, 86.5, 87.5, 88.5, 89.5],
- dtype='float64', name='lat', length=180)) - lonPandasIndex
PandasIndex(Index([ 0.5, 1.5, 2.5, 3.5, 4.5, 5.5, 6.5, 7.5, 8.5, 9.5,
+ dtype='float64', name='lat', length=180))
- lonPandasIndex
PandasIndex(Index([ 0.5, 1.5, 2.5, 3.5, 4.5, 5.5, 6.5, 7.5, 8.5, 9.5,
...
350.5, 351.5, 352.5, 353.5, 354.5, 355.5, 356.5, 357.5, 358.5, 359.5],
- dtype='float64', name='lon', length=360))
+ dtype='float64', name='lon', length=360))
As shown in the above example, the result of the applied operations is an Xarray DataArray that contains a Dask Array, an identical object type to the object that the operations were performed on. This is true for any operations that can be applied to Xarray DataArrays, including subsetting operations; this next example illustrates this:
@@ -3739,7 +3739,7 @@ Parallel and lazy computation using
@@ -3794,10 +3794,10 @@ Parallel and lazy computation using dask.visualize() function, as shown in this example:
@@ -3819,10 +3819,10 @@ Parallel and lazy computation using
-[ ] | 0% Completed | 122.44 us
+[ ] | 0% Completed | 137.30 us
-[########################################] | 100% Completed | 101.18 ms
+[########################################] | 100% Completed | 101.19 ms
@@ -4236,7 +4236,7 @@ Resources and references
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
@@ -4365,7 +4365,7 @@ Contribute
By the Project Pythia Community.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
diff --git a/core/xarray/enso-xarray.html b/core/xarray/enso-xarray.html
index 316661573..6e86d4355 100644
--- a/core/xarray/enso-xarray.html
+++ b/core/xarray/enso-xarray.html
@@ -79,7 +79,7 @@
-
+
@@ -958,7 +958,7 @@ The Niño 3.4 Index
@@ -1590,7 +1590,7 @@ Select the Niño 3.4 regionwhere() method:
+array(1.8436452, dtype=float32)
The final step of the Niño 3.4 index calculation involves normalizing the data. In this example, we perform this normalization by dividing the smoothed anomaly data by the standard deviation calculated above:
@@ -3396,7 +3396,7 @@ Resources and References
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
@@ -3525,7 +3525,7 @@ Contribute
By the Project Pythia Community.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
diff --git a/core/xarray/xarray-intro.html b/core/xarray/xarray-intro.html
index e17e6628d..308dd9baa 100644
--- a/core/xarray/xarray-intro.html
+++ b/core/xarray/xarray-intro.html
@@ -79,7 +79,7 @@
-
+
@@ -583,25 +583,25 @@ Generate a random numpy array
-array([[[274.00119189, 291.26874805, 287.10212211, 281.93321028],
- [276.72439019, 281.86759901, 277.88521001, 291.15900492],
- [283.47892997, 281.69598295, 281.87670232, 280.04253353]],
+array([[[283.19514872, 279.93414516, 288.85955729, 280.30299831],
+ [282.03318795, 283.36237877, 288.28464039, 284.82856563],
+ [282.15998672, 276.06859236, 285.78192464, 280.47703387]],
- [[292.49183408, 286.75694127, 291.1430049 , 279.6708509 ],
- [283.19466798, 285.34236937, 289.30840199, 286.22515585],
- [289.80677567, 281.73710917, 294.57091617, 291.28505114]],
+ [[282.44435897, 282.262194 , 286.34635361, 285.32213341],
+ [283.03737484, 288.57815157, 290.02698113, 278.23328324],
+ [277.06837841, 286.4771544 , 284.58714744, 288.42673576]],
- [[278.59826878, 287.14196565, 284.68298455, 286.59720048],
- [287.51252429, 288.52590266, 273.69692215, 285.39338362],
- [292.66871999, 278.60078099, 280.78168263, 291.92156783]],
+ [[280.62831671, 275.374152 , 283.08613936, 292.56788174],
+ [286.26020096, 282.03531027, 280.16394984, 288.9366629 ],
+ [282.90971416, 278.40998651, 284.98850877, 283.14584888]],
- [[281.24772197, 286.52210588, 282.61809684, 288.82826349],
- [282.48419301, 286.34184212, 277.57835692, 279.66054122],
- [288.97073742, 287.33849395, 289.90083339, 285.95347237]],
+ [[284.34422355, 283.20830934, 278.27414095, 285.21976361],
+ [279.17521323, 292.21743424, 276.974416 , 284.06699408],
+ [274.74247997, 279.63780379, 275.87914515, 277.71758929]],
- [[282.699394 , 286.72856786, 281.89265707, 286.16350665],
- [278.02139036, 284.54696447, 283.16191651, 278.99615563],
- [284.94443941, 287.35039327, 289.38110818, 280.10681174]]])
+ [[282.71726594, 281.99808895, 279.66639367, 276.71709325],
+ [278.30599074, 277.08406243, 273.68999203, 279.75441922],
+ [271.39869986, 286.48138908, 285.22713979, 291.77285885]]])
@@ -982,44 +982,44 @@ Wrap the array: first attempt
@@ -1402,44 +1402,44 @@ Assign dimension names<xarray.DataArray (time: 5, lat: 3, lon: 4)> Size: 480B
-array([[[274.00119189, 291.26874805, 287.10212211, 281.93321028],
- [276.72439019, 281.86759901, 277.88521001, 291.15900492],
- [283.47892997, 281.69598295, 281.87670232, 280.04253353]],
-
- [[292.49183408, 286.75694127, 291.1430049 , 279.6708509 ],
- [283.19466798, 285.34236937, 289.30840199, 286.22515585],
- [289.80677567, 281.73710917, 294.57091617, 291.28505114]],
-
- [[278.59826878, 287.14196565, 284.68298455, 286.59720048],
- [287.51252429, 288.52590266, 273.69692215, 285.39338362],
- [292.66871999, 278.60078099, 280.78168263, 291.92156783]],
-
- [[281.24772197, 286.52210588, 282.61809684, 288.82826349],
- [282.48419301, 286.34184212, 277.57835692, 279.66054122],
- [288.97073742, 287.33849395, 289.90083339, 285.95347237]],
-
- [[282.699394 , 286.72856786, 281.89265707, 286.16350665],
- [278.02139036, 284.54696447, 283.16191651, 278.99615563],
- [284.94443941, 287.35039327, 289.38110818, 280.10681174]]])
-Dimensions without coordinates: time, lat, lon
+array([[[283.19514872, 279.93414516, 288.85955729, 280.30299831],
+ [282.03318795, 283.36237877, 288.28464039, 284.82856563],
+ [282.15998672, 276.06859236, 285.78192464, 280.47703387]],
+
+ [[282.44435897, 282.262194 , 286.34635361, 285.32213341],
+ [283.03737484, 288.57815157, 290.02698113, 278.23328324],
+ [277.06837841, 286.4771544 , 284.58714744, 288.42673576]],
+
+ [[280.62831671, 275.374152 , 283.08613936, 292.56788174],
+ [286.26020096, 282.03531027, 280.16394984, 288.9366629 ],
+ [282.90971416, 278.40998651, 284.98850877, 283.14584888]],
+
+ [[284.34422355, 283.20830934, 278.27414095, 285.21976361],
+ [279.17521323, 292.21743424, 276.974416 , 284.06699408],
+ [274.74247997, 279.63780379, 275.87914515, 277.71758929]],
+
+ [[282.71726594, 281.99808895, 279.66639367, 276.71709325],
+ [278.30599074, 277.08406243, 273.68999203, 279.75441922],
+ [271.39869986, 286.48138908, 285.22713979, 291.77285885]]])
+Dimensions without coordinates: time, lat, lon
This DataArray is already an improvement over a NumPy array; the DataArray contains names for each of the dimensions (or axes in NumPy parlance). An additional improvement is the association of coordinate-value arrays with data upon creation of a DataArray. In the next example, we illustrate the creation of NumPy arrays representing the coordinate values for each dimension of the DataArray, and how to associate these coordinate arrays with the data in our DataArray.
@@ -1849,51 +1849,51 @@ Initialize the
fill: currentColor;
}
<xarray.DataArray (time: 5, lat: 3, lon: 4)> Size: 480B
-array([[[274.00119189, 291.26874805, 287.10212211, 281.93321028],
- [276.72439019, 281.86759901, 277.88521001, 291.15900492],
- [283.47892997, 281.69598295, 281.87670232, 280.04253353]],
+array([[[283.19514872, 279.93414516, 288.85955729, 280.30299831],
+ [282.03318795, 283.36237877, 288.28464039, 284.82856563],
+ [282.15998672, 276.06859236, 285.78192464, 280.47703387]],
- [[292.49183408, 286.75694127, 291.1430049 , 279.6708509 ],
- [283.19466798, 285.34236937, 289.30840199, 286.22515585],
- [289.80677567, 281.73710917, 294.57091617, 291.28505114]],
+ [[282.44435897, 282.262194 , 286.34635361, 285.32213341],
+ [283.03737484, 288.57815157, 290.02698113, 278.23328324],
+ [277.06837841, 286.4771544 , 284.58714744, 288.42673576]],
- [[278.59826878, 287.14196565, 284.68298455, 286.59720048],
- [287.51252429, 288.52590266, 273.69692215, 285.39338362],
- [292.66871999, 278.60078099, 280.78168263, 291.92156783]],
+ [[280.62831671, 275.374152 , 283.08613936, 292.56788174],
+ [286.26020096, 282.03531027, 280.16394984, 288.9366629 ],
+ [282.90971416, 278.40998651, 284.98850877, 283.14584888]],
- [[281.24772197, 286.52210588, 282.61809684, 288.82826349],
- [282.48419301, 286.34184212, 277.57835692, 279.66054122],
- [288.97073742, 287.33849395, 289.90083339, 285.95347237]],
+ [[284.34422355, 283.20830934, 278.27414095, 285.21976361],
+ [279.17521323, 292.21743424, 276.974416 , 284.06699408],
+ [274.74247997, 279.63780379, 275.87914515, 277.71758929]],
- [[282.699394 , 286.72856786, 281.89265707, 286.16350665],
- [278.02139036, 284.54696447, 283.16191651, 278.99615563],
- [284.94443941, 287.35039327, 289.38110818, 280.10681174]]])
+ [[282.71726594, 281.99808895, 279.66639367, 276.71709325],
+ [278.30599074, 277.08406243, 273.68999203, 279.75441922],
+ [271.39869986, 286.48138908, 285.22713979, 291.77285885]]])
Coordinates:
* time (time) datetime64[ns] 40B 2018-01-01 2018-01-02 ... 2018-01-05
* lat (lat) float64 24B 25.0 40.0 55.0
- * lon (lon) float64 32B -120.0 -100.0 -80.0 -60.0
+ dtype='datetime64[ns]', name='time', freq='D'))
latPandasIndexPandasIndex(Index([25.0, 40.0, 55.0], dtype='float64', name='lat'))
lonPandasIndexPandasIndex(Index([-120.0, -100.0, -80.0, -60.0], dtype='float64', name='lon'))
@@ -2273,54 +2273,54 @@ Set useful attributes<xarray.DataArray (time: 5, lat: 3, lon: 4)> Size: 480B
-array([[[274.00119189, 291.26874805, 287.10212211, 281.93321028],
- [276.72439019, 281.86759901, 277.88521001, 291.15900492],
- [283.47892997, 281.69598295, 281.87670232, 280.04253353]],
+array([[[283.19514872, 279.93414516, 288.85955729, 280.30299831],
+ [282.03318795, 283.36237877, 288.28464039, 284.82856563],
+ [282.15998672, 276.06859236, 285.78192464, 280.47703387]],
- [[292.49183408, 286.75694127, 291.1430049 , 279.6708509 ],
- [283.19466798, 285.34236937, 289.30840199, 286.22515585],
- [289.80677567, 281.73710917, 294.57091617, 291.28505114]],
+ [[282.44435897, 282.262194 , 286.34635361, 285.32213341],
+ [283.03737484, 288.57815157, 290.02698113, 278.23328324],
+ [277.06837841, 286.4771544 , 284.58714744, 288.42673576]],
- [[278.59826878, 287.14196565, 284.68298455, 286.59720048],
- [287.51252429, 288.52590266, 273.69692215, 285.39338362],
- [292.66871999, 278.60078099, 280.78168263, 291.92156783]],
+ [[280.62831671, 275.374152 , 283.08613936, 292.56788174],
+ [286.26020096, 282.03531027, 280.16394984, 288.9366629 ],
+ [282.90971416, 278.40998651, 284.98850877, 283.14584888]],
- [[281.24772197, 286.52210588, 282.61809684, 288.82826349],
- [282.48419301, 286.34184212, 277.57835692, 279.66054122],
- [288.97073742, 287.33849395, 289.90083339, 285.95347237]],
+ [[284.34422355, 283.20830934, 278.27414095, 285.21976361],
+ [279.17521323, 292.21743424, 276.974416 , 284.06699408],
+ [274.74247997, 279.63780379, 275.87914515, 277.71758929]],
- [[282.699394 , 286.72856786, 281.89265707, 286.16350665],
- [278.02139036, 284.54696447, 283.16191651, 278.99615563],
- [284.94443941, 287.35039327, 289.38110818, 280.10681174]]])
+ [[282.71726594, 281.99808895, 279.66639367, 276.71709325],
+ [278.30599074, 277.08406243, 273.68999203, 279.75441922],
+ [271.39869986, 286.48138908, 285.22713979, 291.77285885]]])
Coordinates:
* time (time) datetime64[ns] 40B 2018-01-01 2018-01-02 ... 2018-01-05
* lat (lat) float64 24B 25.0 40.0 55.0
* lon (lon) float64 32B -120.0 -100.0 -80.0 -60.0
Attributes:
units: kelvin
- standard_name: air_temperature
+ dtype='datetime64[ns]', name='time', freq='D'))
latPandasIndexPandasIndex(Index([25.0, 40.0, 55.0], dtype='float64', name='lat'))
lonPandasIndexPandasIndex(Index([-120.0, -100.0, -80.0, -60.0], dtype='float64', name='lon'))
- units :
- kelvin
- standard_name :
- air_temperature
@@ -2699,51 +2699,51 @@ Issues with preservation of attributesdocumentation page.
@@ -3134,54 +3134,54 @@ Create a pressure
+ dtype='datetime64[ns]', name='time', freq='D'))latPandasIndexPandasIndex(Index([25.0, 40.0, 55.0], dtype='float64', name='lat'))
lonPandasIndexPandasIndex(Index([-120.0, -100.0, -80.0, -60.0], dtype='float64', name='lon'))
As listed in the rich display above, the new Dataset object is aware that both DataArrays share the same coordinate axes. (Please note that if this page is not run as a Jupyter Notebook, the rich display may be unavailable.)
@@ -3986,54 +3986,54 @@ Access Data variables and Coordinates in a DataArrays through a dictionary syntax, as shown in this example:
@@ -4407,54 +4407,54 @@ Access Data variables and Coordinates in a Dataset objects are mainly used for loading data from files, which will be covered later in this tutorial.
@@ -4838,18 +4838,18 @@ NumPy-like selection<xarray.DataArray (lat: 3, lon: 4)> Size: 96B
-array([[292.49183408, 286.75694127, 291.1430049 , 279.6708509 ],
- [283.19466798, 285.34236937, 289.30840199, 286.22515585],
- [289.80677567, 281.73710917, 294.57091617, 291.28505114]])
+array([[282.44435897, 282.262194 , 286.34635361, 285.32213341],
+ [283.03737484, 288.57815157, 290.02698113, 278.23328324],
+ [277.06837841, 286.4771544 , 284.58714744, 288.42673576]])
Coordinates:
time datetime64[ns] 8B 2018-01-02
* lat (lat) float64 24B 25.0 40.0 55.0
* lon (lon) float64 32B -120.0 -100.0 -80.0 -60.0
Attributes:
units: kelvin
- standard_name: air_temperature
+ standard_name: air_temperature
This example reveals one of the major shortcomings of index selection. In order to retrieve the correct data using index selection, anyone using a DataArray must have precise knowledge of the axes in the DataArray, including the order of the axes and the meaning of their indices.
By using named coordinates, as shown in the next set of examples, we can avoid this cumbersome burden.
@@ -5229,18 +5229,18 @@ Selecting with
fill: currentColor;
}
<xarray.DataArray (lat: 3, lon: 4)> Size: 96B
-array([[292.49183408, 286.75694127, 291.1430049 , 279.6708509 ],
- [283.19466798, 285.34236937, 289.30840199, 286.22515585],
- [289.80677567, 281.73710917, 294.57091617, 291.28505114]])
+array([[282.44435897, 282.262194 , 286.34635361, 285.32213341],
+ [283.03737484, 288.57815157, 290.02698113, 278.23328324],
+ [277.06837841, 286.4771544 , 284.58714744, 288.42673576]])
Coordinates:
time datetime64[ns] 8B 2018-01-02
* lat (lat) float64 24B 25.0 40.0 55.0
* lon (lon) float64 32B -120.0 -100.0 -80.0 -60.0
Attributes:
units: kelvin
- standard_name: air_temperature
+ standard_name: air_temperature
This method yields the same result as the index selection, however:
@@ -5627,18 +5627,18 @@ Nearest-neighbor sampling.sel indeed returned the data at the temporal value corresponding to the date 2018-01-05.
@@ -6017,20 +6017,20 @@ Interpolation
Info
@@ -6414,29 +6414,29 @@ Slicing along coordinates
Info
@@ -6818,18 +6818,18 @@ One more selection method: <xarray.DataArray (lat: 3, lon: 4)> Size: 96B
-array([[292.49183408, 286.75694127, 291.1430049 , 279.6708509 ],
- [283.19466798, 285.34236937, 289.30840199, 286.22515585],
- [289.80677567, 281.73710917, 294.57091617, 291.28505114]])
+array([[282.44435897, 282.262194 , 286.34635361, 285.32213341],
+ [283.03737484, 288.57815157, 290.02698113, 278.23328324],
+ [277.06837841, 286.4771544 , 284.58714744, 288.42673576]])
Coordinates:
time datetime64[ns] 8B 2018-01-02
* lat (lat) float64 24B 25.0 40.0 55.0
* lon (lon) float64 32B -120.0 -100.0 -80.0 -60.0
Attributes:
units: kelvin
- standard_name: air_temperature
+ standard_name: air_temperature
This selection technique is similar to NumPy’s index-based selection, as shown below:
temp[1,:,:]
@@ -7208,29 +7208,29 @@ One more selection method: <xarray.DataArray (time: 3, lat: 2, lon: 2)> Size: 96B
-array([[[291.26874805, 287.10212211],
- [281.86759901, 277.88521001]],
+array([[[279.93414516, 288.85955729],
+ [283.36237877, 288.28464039]],
- [[286.75694127, 291.1430049 ],
- [285.34236937, 289.30840199]],
+ [[282.262194 , 286.34635361],
+ [288.57815157, 290.02698113]],
- [[287.14196565, 284.68298455],
- [288.52590266, 273.69692215]]])
+ [[275.374152 , 283.08613936],
+ [282.03531027, 280.16394984]]])
Coordinates:
* time (time) datetime64[ns] 24B 2018-01-01 2018-01-02 2018-01-03
* lat (lat) float64 16B 25.0 40.0
* lon (lon) float64 16B -100.0 -80.0
Attributes:
units: kelvin
- standard_name: air_temperature
+ [[275.374152 , 283.08613936],
+ [282.03531027, 280.16394984]]])
- time(time)datetime64[ns]2018-01-01 2018-01-02 2018-01-03
array(['2018-01-01T00:00:00.000000000', '2018-01-02T00:00:00.000000000',
+ '2018-01-03T00:00:00.000000000'], dtype='datetime64[ns]')
- lat(lat)float6425.0 40.0
array([25., 40.])
- lon(lon)float64-100.0 -80.0
array([-100., -80.])
- timePandasIndex
PandasIndex(DatetimeIndex(['2018-01-01', '2018-01-02', '2018-01-03'], dtype='datetime64[ns]', name='time', freq='D'))
- latPandasIndex
PandasIndex(Index([25.0, 40.0], dtype='float64', name='lat'))
- lonPandasIndex
PandasIndex(Index([-100.0, -80.0], dtype='float64', name='lon'))
- units :
- kelvin
- standard_name :
- air_temperature
In contrast with the previous example, this example shows a useful advantage of using the .loc attribute. When using the .loc attribute, you can specify data slices using a syntax similar to NumPy in addition to, or instead of, using the slice function. Both of these slicing techniques are illustrated below:
@@ -7604,29 +7604,29 @@ One more selection method: <xarray.DataArray (time: 3, lat: 2, lon: 2)> Size: 96B
-array([[[291.26874805, 287.10212211],
- [281.86759901, 277.88521001]],
+array([[[279.93414516, 288.85955729],
+ [283.36237877, 288.28464039]],
- [[286.75694127, 291.1430049 ],
- [285.34236937, 289.30840199]],
+ [[282.262194 , 286.34635361],
+ [288.57815157, 290.02698113]],
- [[287.14196565, 284.68298455],
- [288.52590266, 273.69692215]]])
+ [[275.374152 , 283.08613936],
+ [282.03531027, 280.16394984]]])
Coordinates:
* time (time) datetime64[ns] 24B 2018-01-01 2018-01-02 2018-01-03
* lat (lat) float64 16B 25.0 40.0
* lon (lon) float64 16B -100.0 -80.0
Attributes:
units: kelvin
- standard_name: air_temperature
+ [[275.374152 , 283.08613936],
+ [282.03531027, 280.16394984]]])
- time(time)datetime64[ns]2018-01-01 2018-01-02 2018-01-03
array(['2018-01-01T00:00:00.000000000', '2018-01-02T00:00:00.000000000',
+ '2018-01-03T00:00:00.000000000'], dtype='datetime64[ns]')
- lat(lat)float6425.0 40.0
array([25., 40.])
- lon(lon)float64-100.0 -80.0
array([-100., -80.])
- timePandasIndex
PandasIndex(DatetimeIndex(['2018-01-01', '2018-01-02', '2018-01-03'], dtype='datetime64[ns]', name='time', freq='D'))
- latPandasIndex
PandasIndex(Index([25.0, 40.0], dtype='float64', name='lat'))
- lonPandasIndex
PandasIndex(Index([-100.0, -80.0], dtype='float64', name='lon'))
- units :
- kelvin
- standard_name :
- air_temperature
As described above, the arguments to .loc must be in the order of the DataArray’s dimensions. Attempting to slice data without ordering arguments properly can cause errors, as shown below:
@@ -8059,10 +8059,10 @@ Access netCDF data with
@@ -8504,17 +8504,17 @@ Subsetting the
positive: down
Grib_level_type: 100
_CoordinateAxisType: Pressure
- _CoordinateZisPositive: down
+ dtype='float32', name='isobaric1'))- units :
- hPa
- long_name :
- Isobaric surface
- positive :
- down
- Grib_level_type :
- 100
- _CoordinateAxisType :
- Pressure
- _CoordinateZisPositive :
- down
(As described earlier in this tutorial, we can also use dictionary syntax to select specific DataArrays; in this case, we would write ds['isobaric1'].)
Many of the subsetting operations usable on DataArrays can also be used on Datasets. However, when used on Datasets, these operations are performed on every DataArray in the Dataset, as shown below:
@@ -8916,7 +8916,7 @@ Subsetting the
geospatial_lat_min: 10.753308882144761
geospatial_lat_max: 46.8308828962289
geospatial_lon_min: -153.88242040519995
- geospatial_lon_max: -42.666108129242815
As shown above, the subsetting operation performed on the Dataset returned a new Dataset. If only a single DataArray is needed from this new Dataset, it can be retrieved using the familiar dot notation:
@@ -9356,7 +9356,7 @@ Subsetting the
Grib1_TableVersion: 131
Grib1_Parameter: 11
Grib1_Level_Type: 100
- Grib1_Level_Desc: Isobaric surface
+ dtype='float32', name='x', length=268))- long_name :
- Temperature @ Isobaric surface
- units :
- K
- description :
- Temperature
- grid_mapping :
- LambertConformal_Projection
- Grib_Variable_Id :
- VAR_7-15-131-11_L100
- Grib1_Center :
- 7
- Grib1_Subcenter :
- 15
- Grib1_TableVersion :
- 131
- Grib1_Parameter :
- 11
- Grib1_Level_Type :
- 100
- Grib1_Level_Desc :
- Isobaric surface
@@ -9790,19 +9790,19 @@ Aggregation operations
+ dtype='float32', name='isobaric1'))
Info
@@ -10196,19 +10196,19 @@ Aggregation operations
+ dtype='float32', name='isobaric1'))
@@ -10226,7 +10226,7 @@ Simple visualization with
-[<matplotlib.lines.Line2D at 0x7f2e0f58d130>]
+[<matplotlib.lines.Line2D at 0x7f505db3d9d0>]
@@ -10250,7 +10250,7 @@ Customizing the plot
-[<matplotlib.lines.Line2D at 0x7f2e0f58eff0>]
+[<matplotlib.lines.Line2D at 0x7f5055a98d40>]
@@ -10268,7 +10268,7 @@ Plotting 2-D data
-<matplotlib.collections.QuadMesh at 0x7f2df9c43b60>
+<matplotlib.collections.QuadMesh at 0x7f5055ad1be0>
@@ -10667,7 +10667,7 @@ Resources and references
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
@@ -10796,7 +10796,7 @@ Contribute
By the Project Pythia Community.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
diff --git a/foundations/conda.html b/foundations/conda.html
index c07684127..6f809cfd0 100644
--- a/foundations/conda.html
+++ b/foundations/conda.html
@@ -79,7 +79,7 @@
-
+
@@ -913,7 +913,7 @@ Resources and References
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
@@ -1042,7 +1042,7 @@ Contribute
By the Project Pythia Community.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
diff --git a/foundations/getting-started-github.html b/foundations/getting-started-github.html
index 4a2af1d14..ec22e00df 100644
--- a/foundations/getting-started-github.html
+++ b/foundations/getting-started-github.html
@@ -81,7 +81,7 @@
-
+
@@ -768,7 +768,7 @@ Getting Started with GitHub
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
@@ -897,7 +897,7 @@ Contribute
By the Project Pythia Community.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
diff --git a/foundations/getting-started-jupyter.html b/foundations/getting-started-jupyter.html
index b1d2107c0..b95652e74 100644
--- a/foundations/getting-started-jupyter.html
+++ b/foundations/getting-started-jupyter.html
@@ -79,7 +79,7 @@
-
+
@@ -905,7 +905,7 @@ Resources and references
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
@@ -1034,7 +1034,7 @@ Contribute
By the Project Pythia Community.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
diff --git a/foundations/getting-started-python.html b/foundations/getting-started-python.html
index 286ad4516..19ac49850 100644
--- a/foundations/getting-started-python.html
+++ b/foundations/getting-started-python.html
@@ -81,7 +81,7 @@
-
+
@@ -779,7 +779,7 @@ Topics
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
@@ -908,7 +908,7 @@ Contribute
By the Project Pythia Community.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
diff --git a/foundations/github/basic-git.html b/foundations/github/basic-git.html
index 441aa5056..32e0b6b24 100644
--- a/foundations/github/basic-git.html
+++ b/foundations/github/basic-git.html
@@ -79,7 +79,7 @@
-
+
@@ -1222,7 +1222,7 @@ References
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
@@ -1351,7 +1351,7 @@ Contribute
By the Project Pythia Community.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
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By the Project Pythia Community.
- Last updated on 15 May 2024.
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By the Project Pythia Community.
- Last updated on 15 May 2024.
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By the Project Pythia Community.
- Last updated on 15 May 2024.
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index 18d9c718b..f531c0445 100644
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29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 46], "support": 7, "switch": [30, 34], "synchron": 37, "system": [5, 39], "templat": 1, "termin": [40, 42, 46], "terminologi": 7, "test": [11, 21], "text": [9, 42, 43], "thi": [1, 8, 11, 28, 45, 49, 50], "thing": [32, 35], "tide": 7, "time": [7, 23, 28], "timedelta": 7, "timestamp": 7, "tip": [35, 36, 37], "titl": 11, "togeth": 11, "token": 36, "topic": [27, 31, 44], "try": [32, 35], "tune": 39, "tutori": [16, 28], "two": [3, 11], "type": 45, "unix": 7, "up": [0, 1, 30, 42], "updat": 30, "upstream": 37, "us": [1, 3, 10, 11, 13, 15, 18, 20, 21, 23, 24, 28, 50], "usag": 7, "user": 32, "utc": 7, "v": 36, "valu": [13, 18, 20, 23], "variabl": [5, 23, 42], "vc": [28, 39], "vector": 15, "veri": 45, "version": [28, 39], "versu": 7, "via": [29, 50], "view": [28, 38], "visual": [22, 23], "wai": 38, "want": 28, "warn": [1, 3, 13, 14, 18, 42], "we": 28, "web": 39, "what": [1, 3, 5, 7, 10, 11, 13, 14, 15, 18, 20, 21, 23, 24, 26, 28, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 45, 46], "when": 38, "where": 20, "why": [8, 21, 28, 47], "window": 32, "within": 11, "work": 37, "workflow": [30, 34, 37], "would": 21, "wrap": 23, "write": [5, 7], "xarrai": [19, 20, 21, 22, 23], "xr": 23, "year": 7, "york": 3, "you": [21, 28, 30, 38, 39], "your": [0, 1, 18, 30, 34, 36, 41, 45], "zero": 45, "zone": 7}})
\ No newline at end of file
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
Contribute
By the Project Pythia Community. - Last updated on 15 May 2024. + Last updated on 20 May 2024. diff --git a/core/matplotlib/matplotlib-basics.html b/core/matplotlib/matplotlib-basics.html index 323b54231..22fd1268a 100644 --- a/core/matplotlib/matplotlib-basics.html +++ b/core/matplotlib/matplotlib-basics.html @@ -79,7 +79,7 @@ - + @@ -1492,7 +1492,7 @@Resources and References
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
@@ -1621,7 +1621,7 @@ Contribute
By the Project Pythia Community.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
diff --git a/core/numpy.html b/core/numpy.html
index 4fb55d7f5..5c535f0a9 100644
--- a/core/numpy.html
+++ b/core/numpy.html
@@ -81,7 +81,7 @@
-
+
@@ -760,7 +760,7 @@ NumPy
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
@@ -889,7 +889,7 @@ Contribute
By the Project Pythia Community.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
diff --git a/core/numpy/intermediate-numpy.html b/core/numpy/intermediate-numpy.html
index 59706e1a6..38a6afc63 100644
--- a/core/numpy/intermediate-numpy.html
+++ b/core/numpy/intermediate-numpy.html
@@ -81,7 +81,7 @@
-
+
@@ -1425,7 +1425,7 @@ Resources and references
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
@@ -1554,7 +1554,7 @@ Contribute
By the Project Pythia Community.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
diff --git a/core/numpy/numpy-basics.html b/core/numpy/numpy-basics.html
index cb11d7b3d..365a2f855 100644
--- a/core/numpy/numpy-basics.html
+++ b/core/numpy/numpy-basics.html
@@ -81,7 +81,7 @@
-
+
@@ -1168,7 +1168,7 @@ Indexing
Slices
-Slicing syntax is written as array[start:stop[:step]], where all numbers are optional.
+Slicing syntax is written as array[start:stop:step]. Note that all numbers are optional. Importantly, the step parameter is optional and can be omitted, in which case the slice uses a default step of 1.
defaults:
@@ -1775,7 +1775,7 @@ Resources and references
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
@@ -1904,7 +1904,7 @@ Contribute
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- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
diff --git a/core/numpy/numpy-broadcasting.html b/core/numpy/numpy-broadcasting.html
index 78f882658..57e5dd9a6 100644
--- a/core/numpy/numpy-broadcasting.html
+++ b/core/numpy/numpy-broadcasting.html
@@ -81,7 +81,7 @@
-
+
@@ -1070,7 +1070,7 @@ Blocking
-
-
-
-
-array([[ 8.3, 11.6, 33.1],
- [11.6, 33.1, 18.3],
- [33.1, 18.3, 22.5],
- [18.3, 22.5, 24.5],
- [22.5, 24.5, 20.1],
- [24.5, 20.1, 20.1],
- [20.1, 20.1, 18. ],
- [20.1, 18. , 25. ]])
+array([[19.3, 18.8, 28.7],
+ [18.8, 28.7, 27.1],
+ [28.7, 27.1, 17.1],
+ [27.1, 17.1, 16.2],
+ [17.1, 16.2, 15. ],
+ [16.2, 15. , 15.1],
+ [15. , 15.1, 14. ],
+ [15.1, 14. , 19.2]])
@@ -1161,8 +1161,8 @@ Blocking
-
-array([[ 8.3, 11.6, 2000. ],
- [ 11.6, 2000. , 18.3],
- [2000. , 18.3, 22.5],
- [ 18.3, 22.5, 24.5],
- [ 22.5, 24.5, 20.1],
- [ 24.5, 20.1, 20.1],
- [ 20.1, 20.1, 18. ],
- [ 20.1, 18. , 25. ]])
+array([[ 19.3, 18.8, 2000. ],
+ [ 18.8, 2000. , 27.1],
+ [2000. , 27.1, 17.1],
+ [ 27.1, 17.1, 16.2],
+ [ 17.1, 16.2, 15. ],
+ [ 16.2, 15. , 15.1],
+ [ 15. , 15.1, 14. ],
+ [ 15.1, 14. , 19.2]])
@@ -1210,13 +1210,13 @@ Finding the difference between min and max
-array([[6, 6, 7, ..., 8, 7, 6],
- [6, 6, 6, ..., 7, 7, 7],
- [7, 7, 7, ..., 6, 7, 7],
+array([[7, 7, 7, ..., 7, 8, 7],
+ [6, 7, 6, ..., 7, 7, 6],
+ [7, 6, 7, ..., 7, 6, 6],
...,
- [7, 7, 7, ..., 7, 7, 6],
- [7, 6, 6, ..., 6, 7, 7],
- [7, 6, 7, ..., 7, 8, 7]])
+ [6, 7, 6, ..., 6, 6, 7],
+ [7, 7, 7, ..., 6, 6, 7],
+ [6, 7, 7, ..., 8, 8, 7]])
@@ -1241,13 +1241,13 @@ Finding the difference between min and max
-array([[775. , 775. , 737.5, ..., 700. , 737.5, 775. ],
- [775. , 775. , 775. , ..., 737.5, 737.5, 737.5],
- [737.5, 737.5, 737.5, ..., 775. , 737.5, 737.5],
+array([[737.5, 737.5, 737.5, ..., 737.5, 700. , 737.5],
+ [775. , 737.5, 775. , ..., 737.5, 737.5, 775. ],
+ [737.5, 775. , 737.5, ..., 737.5, 775. , 775. ],
...,
- [737.5, 737.5, 737.5, ..., 737.5, 737.5, 775. ],
- [737.5, 775. , 775. , ..., 775. , 737.5, 737.5],
- [737.5, 775. , 737.5, ..., 737.5, 700. , 737.5]])
+ [775. , 737.5, 775. , ..., 775. , 775. , 737.5],
+ [737.5, 737.5, 737.5, ..., 775. , 775. , 737.5],
+ [775. , 737.5, 737.5, ..., 700. , 700. , 737.5]])
@@ -1276,19 +1276,19 @@ Finding the difference between min and max
-array([[ -9.55417349, -9.78584972, -9.92661689, ..., -12.67789616,
- -10.49077377, -6.56515605],
- [-10.00010532, -10.70819797, -7.20985445, ..., -11.10789153,
- -11.73163442, -8.29531203],
- [-11.88696252, -12.29088724, -11.70346147, ..., -7.30859657,
- -8.97849533, -13.84614818],
+array([[-13.35480377, -11.37389473, -6.11490009, ..., -9.69409241,
+ -14.14853765, -11.04724036],
+ [ -6.50332158, -11.09958323, -5.95998607, ..., -13.2604716 ,
+ -13.19971009, -8.49574616],
+ [-12.28051357, -10.19815861, -7.42945122, ..., -11.5798199 ,
+ -8.91153322, -8.6079996 ],
...,
- [-11.90215938, -7.91187899, -13.08679868, ..., -10.41120932,
- -11.35468344, -11.06459146],
- [-11.50292233, -6.26889319, -7.38221861, ..., -10.24036433,
- -10.12594611, -5.63518882],
- [-10.32651953, -6.06759924, -9.54152312, ..., -10.45702773,
- -12.20574751, -8.01746694]])
+ [ -8.10255586, -12.16615351, -11.27253366, ..., -10.44432682,
+ -7.39154896, -10.99574973],
+ [-16.5968518 , -12.57487222, -8.39736923, ..., -4.15595587,
+ -8.86979291, -11.28873531],
+ [ -8.59386363, -9.38047716, -10.46838159, ..., -11.30765026,
+ -10.89295232, -9.24056526]])
@@ -1332,19 +1332,19 @@ Finding the difference between min and max
-array([[ -9.55417349, -9.78584972, -9.92661689, ..., -12.67789616,
- -10.49077377, -6.56515605],
- [-10.00010532, -10.70819797, -7.20985445, ..., -11.10789153,
- -11.73163442, -8.29531203],
- [-11.88696252, -12.29088724, -11.70346147, ..., -7.30859657,
- -8.97849533, -13.84614818],
+array([[-13.35480377, -11.37389473, -6.11490009, ..., -9.69409241,
+ -14.14853765, -11.04724036],
+ [ -6.50332158, -11.09958323, -5.95998607, ..., -13.2604716 ,
+ -13.19971009, -8.49574616],
+ [-12.28051357, -10.19815861, -7.42945122, ..., -11.5798199 ,
+ -8.91153322, -8.6079996 ],
...,
- [-11.90215938, -7.91187899, -13.08679868, ..., -10.41120932,
- -11.35468344, -11.06459146],
- [-11.50292233, -6.26889319, -7.38221861, ..., -10.24036433,
- -10.12594611, -5.63518882],
- [-10.32651953, -6.06759924, -9.54152312, ..., -10.45702773,
- -12.20574751, -8.01746694]])
+ [ -8.10255586, -12.16615351, -11.27253366, ..., -10.44432682,
+ -7.39154896, -10.99574973],
+ [-16.5968518 , -12.57487222, -8.39736923, ..., -4.15595587,
+ -8.86979291, -11.28873531],
+ [ -8.59386363, -9.38047716, -10.46838159, ..., -11.30765026,
+ -10.89295232, -9.24056526]])
@@ -1711,7 +1711,7 @@ Resources and references
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
@@ -1840,7 +1840,7 @@ Contribute
By the Project Pythia Community.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
diff --git a/core/overview.html b/core/overview.html
index 6acc0ae46..fd4752cc2 100644
--- a/core/overview.html
+++ b/core/overview.html
@@ -79,7 +79,7 @@
-
+
@@ -886,7 +886,7 @@ Tutorials
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
@@ -1015,7 +1015,7 @@ Contribute
By the Project Pythia Community.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
diff --git a/core/pandas.html b/core/pandas.html
index 2e4230efc..02509b347 100644
--- a/core/pandas.html
+++ b/core/pandas.html
@@ -79,7 +79,7 @@
-
+
@@ -759,7 +759,7 @@ Pandas
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
@@ -888,7 +888,7 @@ Contribute
By the Project Pythia Community.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
diff --git a/core/pandas/pandas.html b/core/pandas/pandas.html
index 734fecdb6..2e24138b7 100644
--- a/core/pandas/pandas.html
+++ b/core/pandas/pandas.html
@@ -79,7 +79,7 @@
-
+
@@ -1098,7 +1098,7 @@ Indexing a Seri
-/tmp/ipykernel_2735/737336773.py:1: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`
+/tmp/ipykernel_2746/737336773.py:1: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`
nino34_series[3]
@@ -1449,7 +1449,7 @@ Extending to the
-/tmp/ipykernel_2735/541596450.py:1: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`
+/tmp/ipykernel_2746/541596450.py:1: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`
df["Nino34"][3]
@@ -3645,7 +3645,7 @@ Resampling
-/tmp/ipykernel_2735/233158901.py:1: FutureWarning: 'Y' is deprecated and will be removed in a future version, please use 'YE' instead.
+/tmp/ipykernel_2746/233158901.py:1: FutureWarning: 'Y' is deprecated and will be removed in a future version, please use 'YE' instead.
df.Nino34.resample('1Y').mean().plot();
@@ -4434,7 +4434,7 @@ Resources and References
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
@@ -4563,7 +4563,7 @@ Contribute
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- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
diff --git a/core/xarray.html b/core/xarray.html
index e4624108b..6bc787165 100644
--- a/core/xarray.html
+++ b/core/xarray.html
@@ -79,7 +79,7 @@
-
+
@@ -760,7 +760,7 @@ Xarray
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
@@ -889,7 +889,7 @@ Contribute
By the Project Pythia Community.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
diff --git a/core/xarray/computation-masking.html b/core/xarray/computation-masking.html
index b15ad248c..12f19902b 100644
--- a/core/xarray/computation-masking.html
+++ b/core/xarray/computation-masking.html
@@ -79,7 +79,7 @@
-
+
@@ -943,7 +943,7 @@ Data Setup
@@ -1576,7 +1576,7 @@ Arithmetic Operations
+ dtype='float64', name='lon', length=360))
In addition, there are many other arithmetic operations that can be performed on DataArrays. In this example, we demonstrate squaring the original Celsius values of our air temperature data:
@@ -2246,7 +2246,7 @@ Arithmetic Operations
+ dtype='float64', name='lon', length=360))
@@ -2876,7 +2876,7 @@ Aggregation Methodsdim keyword argument; this means that the function was applied over all of the dataset’s dimensions. In other words, the aggregation method computed the mean of every element of the temperature dataset across every temporal and spatial data point. However, if a dimension name is used with the dim keyword argument, the aggregation method computes an aggregation along the given dimension. In this next example, we use aggregation to calculate the temporal mean across all spatial data; this is performed by providing the dimension name 'time' to the dim keyword argument:
@@ -3276,7 +3276,7 @@ Aggregation Methods
@@ -3717,7 +3717,7 @@ Aggregation Methods
@@ -4325,7 +4325,7 @@ Aggregation Methods
@@ -4892,7 +4892,7 @@ Compute climatologyDataArray containing the climatology data, we can plot the data in different ways. In this example, we plot the climatology at a specific latitude-longitude point:
@@ -5412,7 +5412,7 @@ Compute anomaly
@@ -6095,7 +6095,7 @@ Compute anomaly.weighted() method and the newly loaded CESM2 dataset to weight the grid cell data as described above:
@@ -6598,7 +6598,7 @@ Other high level computation functionalityrolling method to compute averages in a moving window of 5 months of data:
@@ -7114,7 +7114,7 @@ Other high level computation functionality
@@ -7783,7 +7783,7 @@ Masking Data
Using where with one condition
@@ -8421,15 +8421,15 @@ Using where
+ dtype='float64', name='lon', length=360))
- cell_measures :
- area: areacello
- cell_methods :
- area: mean where sea time: mean
- comment :
- Model data on the 1x1 grid includes values in all cells for which ocean cells on the native grid cover more than 52.5 percent of the 1x1 grid cell. This 52.5 percent cutoff was chosen to produce ocean surface area on the 1x1 grid as close as possible to ocean surface area on the native grid, while not introducing fractional cell coverage.
- description :
- This may differ from "surface temperature" in regions of sea ice or floating ice shelves. For models using conservative temperature as the prognostic field, they should report the top ocean layer as surface potential temperature, which is the same as surface in situ temperature.
- frequency :
- mon
- id :
- tos
- long_name :
- Sea Surface Temperature
- mipTable :
- Omon
- out_name :
- tos
- prov :
- Omon ((isd.003))
- realm :
- ocean
- standard_name :
- sea_surface_temperature
- time :
- time
- time_label :
- time-mean
- time_title :
- Temporal mean
- title :
- Sea Surface Temperature
- type :
- real
- units :
- degC
- variable_id :
- tos
As shown in the previous example, methods like .isel() and .sel() return data of a different shape than the original data provided to them. However, .where() preserves the shape of the original data by masking the values with a Boolean condition. Data values for which the condition is True are returned identical to the values passed in. On the other hand, data values for which the condition is False are returned as a preset example value. (This example value defaults to nan, but can be set to other values as well.)
Before testing .where(), it is helpful to look at the official documentation. As stated above, the .where() method takes a Boolean condition. (Boolean conditions use operators such as less-than, greater-than, and equal-to, and return a value of True or False.) Most uses of .where() check whether or not specific data values are less than or greater than a constant value. As stated in the documentation, the data values specified in the Boolean condition of .where() can be any of the following:
@@ -8864,7 +8864,7 @@ Using where
+ dtype='float64', name='lon', length=360))- cell_measures :
- area: areacello
- cell_methods :
- area: mean where sea time: mean
- comment :
- Model data on the 1x1 grid includes values in all cells for which ocean cells on the native grid cover more than 52.5 percent of the 1x1 grid cell. This 52.5 percent cutoff was chosen to produce ocean surface area on the 1x1 grid as close as possible to ocean surface area on the native grid, while not introducing fractional cell coverage.
- description :
- This may differ from "surface temperature" in regions of sea ice or floating ice shelves. For models using conservative temperature as the prognostic field, they should report the top ocean layer as surface potential temperature, which is the same as surface in situ temperature.
- frequency :
- mon
- id :
- tos
- long_name :
- Sea Surface Temperature
- mipTable :
- Omon
- out_name :
- tos
- prov :
- Omon ((isd.003))
- realm :
- ocean
- standard_name :
- sea_surface_temperature
- time :
- time
- time_label :
- time-mean
- time_title :
- Temporal mean
- title :
- Sea Surface Temperature
- type :
- real
- units :
- degC
- variable_id :
- tos
In this example, we use Matplotlib to plot the original, unmasked data, as well as the masked data created in the previous example.
@@ -9321,7 +9321,7 @@ Resources and References
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
@@ -9450,7 +9450,7 @@ Contribute
By the Project Pythia Community.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
diff --git a/core/xarray/dask-arrays-xarray.html b/core/xarray/dask-arrays-xarray.html
index 0af9daaf3..65793b335 100644
--- a/core/xarray/dask-arrays-xarray.html
+++ b/core/xarray/dask-arrays-xarray.html
@@ -79,7 +79,7 @@
-
+
@@ -568,91 +568,91 @@ Create a dask.a
-array([[[0.08870167, 0.5536724 , 0.86869933, ..., 0.59745109,
- 0.66241166, 0.14712849],
- [0.79424186, 0.53985475, 0.83117213, ..., 0.64752434,
- 0.2068288 , 0.43561572],
- [0.93755297, 0.12641562, 0.55493093, ..., 0.44682309,
- 0.9108161 , 0.28494995],
+array([[[3.73479162e-01, 5.69354087e-01, 8.44121771e-01, ...,
+ 6.95300592e-01, 7.05791578e-01, 3.22757785e-01],
+ [7.34673387e-01, 3.22502093e-02, 2.98660716e-02, ...,
+ 4.48326876e-01, 1.28181189e-01, 6.17129937e-01],
+ [4.61524077e-01, 5.72981031e-01, 6.22455684e-02, ...,
+ 4.66387696e-01, 2.24365962e-02, 9.05528466e-01],
...,
- [0.36681494, 0.55104908, 0.67358062, ..., 0.93120538,
- 0.51164396, 0.2149192 ],
- [0.33220658, 0.18853655, 0.84133513, ..., 0.09990092,
- 0.45265087, 0.48841145],
- [0.54632195, 0.99301667, 0.40513953, ..., 0.5341915 ,
- 0.3220391 , 0.07544213]],
-
- [[0.8398981 , 0.14999579, 0.81431679, ..., 0.45814483,
- 0.81962377, 0.01308931],
- [0.52904343, 0.17038365, 0.49820524, ..., 0.71294698,
- 0.49906669, 0.32305634],
- [0.83597797, 0.12242548, 0.63334391, ..., 0.77669187,
- 0.44530452, 0.02031067],
+ [4.32840675e-01, 6.11791423e-01, 7.77157080e-01, ...,
+ 5.21898060e-01, 5.08370890e-01, 5.88153598e-01],
+ [8.04673850e-01, 7.80296541e-03, 5.00244166e-01, ...,
+ 9.45710973e-01, 5.08700949e-01, 8.89905823e-01],
+ [4.87536782e-01, 4.64576884e-01, 4.97751472e-01, ...,
+ 9.63294478e-01, 8.00438624e-01, 9.39160580e-01]],
+
+ [[9.59798128e-01, 9.67977225e-01, 5.67081374e-01, ...,
+ 1.15551677e-01, 5.93188567e-01, 8.21494821e-01],
+ [7.09798171e-01, 6.86764079e-01, 9.75639983e-01, ...,
+ 6.65980228e-01, 3.63136928e-01, 3.91634965e-01],
+ [2.45312843e-01, 7.92456108e-01, 7.52068086e-01, ...,
+ 8.31396290e-01, 7.34853345e-01, 1.59997513e-02],
...,
- [0.23487395, 0.54185136, 0.84738714, ..., 0.43194404,
- 0.96464114, 0.34661126],
- [0.06216953, 0.87402041, 0.89512514, ..., 0.38706893,
- 0.09187095, 0.88666451],
- [0.46384865, 0.02462827, 0.71575049, ..., 0.47421697,
- 0.30570578, 0.58421292]],
-
- [[0.05704318, 0.36232811, 0.03448708, ..., 0.86748361,
- 0.49546271, 0.39195962],
- [0.62907898, 0.52593284, 0.87418635, ..., 0.97638485,
- 0.63695351, 0.75326941],
- [0.11640535, 0.15534404, 0.80460691, ..., 0.26935451,
- 0.00686985, 0.42474694],
+ [6.29006234e-01, 5.66655665e-01, 9.59798385e-01, ...,
+ 4.64611557e-02, 8.47630616e-01, 9.96868460e-01],
+ [8.65409572e-01, 6.62829465e-01, 5.85935120e-01, ...,
+ 2.76645046e-01, 4.05531947e-01, 1.86970157e-01],
+ [5.88186192e-02, 4.80258125e-01, 7.50053106e-01, ...,
+ 5.82584007e-01, 7.16238302e-01, 3.09095729e-01]],
+
+ [[1.95867406e-01, 8.02997164e-01, 5.92335907e-02, ...,
+ 6.00495084e-02, 3.58568845e-02, 6.14435689e-01],
+ [5.40191024e-01, 7.45240262e-01, 5.83926237e-01, ...,
+ 5.42683512e-01, 7.15062271e-05, 8.15838308e-01],
+ [3.56121359e-01, 6.64103118e-01, 3.19197578e-01, ...,
+ 4.30885292e-01, 5.63274760e-01, 5.58304993e-01],
...,
- [0.53156325, 0.69190957, 0.69352384, ..., 0.77170882,
- 0.44011939, 0.39866217],
- [0.29442052, 0.74080675, 0.46131608, ..., 0.32366183,
- 0.73788577, 0.04341616],
- [0.94117032, 0.92630431, 0.79008536, ..., 0.02443356,
- 0.52042712, 0.83466538]],
+ [3.70429144e-01, 2.28949319e-01, 2.12465225e-01, ...,
+ 8.75347366e-01, 6.88876481e-01, 8.52985255e-01],
+ [8.43544201e-01, 3.50323808e-01, 9.94755069e-01, ...,
+ 2.31937385e-01, 6.46129523e-01, 7.56519847e-01],
+ [9.23292344e-01, 9.06543740e-01, 2.16039623e-01, ...,
+ 6.17841121e-01, 5.83778599e-02, 7.07627581e-01]],
...,
- [[0.1396787 , 0.60143505, 0.57058462, ..., 0.18893598,
- 0.82985107, 0.54242887],
- [0.68157985, 0.63122788, 0.1780818 , ..., 0.55248006,
- 0.63127177, 0.4663941 ],
- [0.55463676, 0.49022722, 0.28183346, ..., 0.64073606,
- 0.37788708, 0.05233016],
+ [[5.50076397e-01, 6.23247920e-01, 6.69863249e-01, ...,
+ 6.66141598e-01, 4.98073488e-01, 9.70545887e-01],
+ [7.38559992e-01, 3.21393458e-01, 4.05421647e-01, ...,
+ 2.52285356e-01, 8.08345653e-01, 7.28764634e-01],
+ [2.68191728e-01, 6.03169049e-02, 6.89258446e-01, ...,
+ 6.21042327e-01, 9.60250603e-01, 9.75257028e-01],
...,
- [0.39020736, 0.85735173, 0.00786958, ..., 0.66161596,
- 0.18152074, 0.17731408],
- [0.93613911, 0.96578828, 0.38973709, ..., 0.06864502,
- 0.5063675 , 0.73631955],
- [0.1671134 , 0.45039599, 0.38253178, ..., 0.74316064,
- 0.20253998, 0.93656703]],
-
- [[0.28018602, 0.84864745, 0.65231976, ..., 0.57238332,
- 0.37536731, 0.04826117],
- [0.47663448, 0.86778224, 0.95451621, ..., 0.76880871,
- 0.89875752, 0.06756555],
- [0.49609048, 0.21693506, 0.23207324, ..., 0.06636032,
- 0.95937476, 0.4827714 ],
+ [6.11978830e-01, 6.32479701e-01, 1.75826196e-01, ...,
+ 2.09161988e-01, 6.71446177e-01, 4.78915946e-01],
+ [6.45044934e-01, 1.15167985e-01, 9.05467373e-01, ...,
+ 7.79598426e-01, 8.76027493e-01, 5.70161686e-01],
+ [8.44173395e-01, 7.14987726e-01, 5.27098062e-01, ...,
+ 6.24560049e-01, 3.32587128e-02, 1.48219268e-01]],
+
+ [[9.16337354e-01, 1.38628117e-01, 2.76572964e-01, ...,
+ 5.68919305e-02, 3.07040211e-01, 6.70607173e-02],
+ [6.78200880e-01, 1.19012654e-01, 8.76844508e-01, ...,
+ 5.78903343e-01, 5.08716762e-01, 3.27430604e-01],
+ [5.19310927e-01, 1.62933592e-01, 6.94104791e-01, ...,
+ 8.28521396e-01, 5.63944660e-01, 9.23353464e-01],
...,
- [0.83197412, 0.22629066, 0.1589711 , ..., 0.55726833,
- 0.40921459, 0.94548168],
- [0.20373662, 0.76138868, 0.56265755, ..., 0.71025427,
- 0.19641998, 0.49627513],
- [0.20384921, 0.34173511, 0.44829 , ..., 0.3999668 ,
- 0.75240336, 0.21357396]],
-
- [[0.05024137, 0.77607526, 0.32452642, ..., 0.26169962,
- 0.2092348 , 0.79937281],
- [0.8834038 , 0.56909665, 0.84006875, ..., 0.09540328,
- 0.66257683, 0.78342945],
- [0.29906903, 0.08192958, 0.51913898, ..., 0.32946737,
- 0.91310302, 0.99198924],
+ [8.75833692e-01, 8.80034882e-01, 3.94957057e-01, ...,
+ 3.88766073e-01, 5.62146961e-01, 4.07401505e-01],
+ [8.83232515e-02, 7.93868945e-02, 7.09396623e-01, ...,
+ 4.00821090e-01, 9.67897242e-01, 3.30712526e-02],
+ [6.21839195e-01, 3.66731583e-01, 9.97239185e-01, ...,
+ 4.06892246e-01, 8.84142338e-01, 1.29206897e-01]],
+
+ [[3.13410458e-01, 7.92759677e-01, 8.24979480e-02, ...,
+ 5.38935776e-02, 4.52819725e-01, 1.48534750e-01],
+ [2.06595218e-01, 8.46746782e-01, 9.06689993e-01, ...,
+ 8.52571008e-01, 4.28351337e-01, 8.06352220e-01],
+ [3.77563013e-01, 9.99637726e-01, 3.37356897e-01, ...,
+ 8.18911136e-01, 8.32562303e-01, 8.45143802e-02],
...,
- [0.89344391, 0.04685752, 0.16870497, ..., 0.70133294,
- 0.2028986 , 0.16916596],
- [0.62721463, 0.12582174, 0.59300888, ..., 0.93023778,
- 0.50053284, 0.63544531],
- [0.89994404, 0.84669077, 0.65421957, ..., 0.19573779,
- 0.56024887, 0.73584493]]])
+ [5.51431397e-01, 8.77465751e-01, 9.63517992e-01, ...,
+ 3.34710544e-02, 9.70363045e-01, 5.42823121e-01],
+ [3.89247115e-01, 3.36500509e-01, 4.31638413e-02, ...,
+ 8.82214294e-02, 2.41024552e-01, 4.19862342e-01],
+ [5.65544128e-01, 6.09859197e-01, 2.77376901e-01, ...,
+ 5.51955058e-01, 6.67783854e-02, 4.08359511e-01]]])
@@ -856,7 +856,7 @@ Why the difference?
-
+
@@ -872,11 +872,11 @@ Compute the result
-CPU times: user 406 ms, sys: 28 ms, total: 434 ms
-Wall time: 232 ms
+CPU times: user 399 ms, sys: 28.5 ms, total: 428 ms
+Wall time: 228 ms
-12002441.41340752
+12000189.444496717
@@ -894,7 +894,7 @@ Exercise with d
-
+
As is obvious from the above exercise, Dask quickly and easily determines a strategy for performing the operations, in this case a sum. This illustrates the appeal of Dask: automatic algorithm generation that scales from simple arithmetic problems to highly complex scientific equations with large datasets and multiple operations.
@@ -974,7 +974,7 @@ Exercise with d
-
+
@@ -1106,7 +1106,7 @@ Testing a bigger calculation
-
+
@@ -1117,34 +1117,34 @@ Testing a bigger calculation
-[ ] | 0% Completed | 169.21 us
+[ ] | 0% Completed | 255.13 us
-[ ] | 0% Completed | 105.92 ms
+[ ] | 0% Completed | 106.98 ms
-[ ] | 0% Completed | 206.66 ms
+[ ] | 0% Completed | 207.72 ms
-[ ] | 0% Completed | 307.49 ms
+[ ] | 0% Completed | 308.36 ms
-[ ] | 0% Completed | 408.13 ms
+[ ] | 0% Completed | 409.00 ms
-[ ] | 0% Completed | 508.89 ms
+[ ] | 0% Completed | 509.67 ms
-[ ] | 1% Completed | 609.51 ms
+[ ] | 0% Completed | 610.39 ms
-[# ] | 4% Completed | 710.49 ms
+[# ] | 3% Completed | 711.30 ms
-[### ] | 7% Completed | 811.54 ms
+[## ] | 6% Completed | 812.41 ms
-[#### ] | 10% Completed | 912.41 ms
+[#### ] | 10% Completed | 913.29 ms
[#### ] | 10% Completed | 1.01 s
@@ -1153,7 +1153,7 @@ Testing a bigger calculation[#### ] | 10% Completed | 1.11 s
-[#### ] | 10% Completed | 1.21 s
+[#### ] | 10% Completed | 1.22 s
[#### ] | 10% Completed | 1.32 s
@@ -1162,10 +1162,10 @@ Testing a bigger calculation[#### ] | 10% Completed | 1.42 s
-[##### ] | 13% Completed | 1.52 s
+[##### ] | 12% Completed | 1.52 s
-[###### ] | 16% Completed | 1.62 s
+[###### ] | 15% Completed | 1.62 s
[####### ] | 19% Completed | 1.72 s
@@ -1186,13 +1186,13 @@ Testing a bigger calculation[######## ] | 21% Completed | 2.22 s
-[######## ] | 22% Completed | 2.32 s
+[######## ] | 21% Completed | 2.32 s
-[########## ] | 25% Completed | 2.43 s
+[######### ] | 24% Completed | 2.43 s
-[########### ] | 27% Completed | 2.53 s
+[########### ] | 28% Completed | 2.53 s
[############ ] | 31% Completed | 2.63 s
@@ -1216,16 +1216,16 @@ Testing a bigger calculation[############## ] | 35% Completed | 3.23 s
-[############### ] | 38% Completed | 3.33 s
+[############## ] | 37% Completed | 3.33 s
-[################ ] | 40% Completed | 3.43 s
+[############### ] | 39% Completed | 3.43 s
-[################# ] | 44% Completed | 3.53 s
+[################# ] | 43% Completed | 3.53 s
-[################### ] | 48% Completed | 3.63 s
+[################## ] | 46% Completed | 3.64 s
[################### ] | 48% Completed | 3.74 s
@@ -1246,10 +1246,10 @@ Testing a bigger calculation[#################### ] | 50% Completed | 4.24 s
-[##################### ] | 53% Completed | 4.34 s
+[#################### ] | 52% Completed | 4.34 s
-[###################### ] | 57% Completed | 4.44 s
+[###################### ] | 55% Completed | 4.44 s
[####################### ] | 59% Completed | 4.54 s
@@ -1276,7 +1276,7 @@ Testing a bigger calculation[######################### ] | 64% Completed | 5.25 s
-[########################### ] | 67% Completed | 5.35 s
+[########################### ] | 68% Completed | 5.35 s
[############################ ] | 72% Completed | 5.45 s
@@ -1300,13 +1300,13 @@ Testing a bigger calculation[############################## ] | 75% Completed | 6.06 s
-[############################### ] | 77% Completed | 6.16 s
+[############################### ] | 78% Completed | 6.16 s
-[################################ ] | 80% Completed | 6.26 s
+[################################ ] | 82% Completed | 6.26 s
-[################################# ] | 83% Completed | 6.36 s
+[################################# ] | 84% Completed | 6.36 s
[################################## ] | 85% Completed | 6.46 s
@@ -1324,16 +1324,16 @@ Testing a bigger calculation[################################## ] | 85% Completed | 6.86 s
-[################################## ] | 86% Completed | 6.96 s
+[################################### ] | 89% Completed | 6.96 s
-[################################### ] | 89% Completed | 7.06 s
+[#################################### ] | 92% Completed | 7.07 s
-[##################################### ] | 92% Completed | 7.16 s
+[###################################### ] | 96% Completed | 7.17 s
-[###################################### ] | 96% Completed | 7.27 s
+[####################################### ] | 99% Completed | 7.27 s
[########################################] | 100% Completed | 7.37 s
@@ -1854,7 +1854,7 @@ Reading data with
- latPandasIndex
PandasIndex(Index([-89.5, -88.5, -87.5, -86.5, -85.5, -84.5, -83.5, -82.5, -81.5, -80.5,
+ 80.5, 81.5, 82.5, 83.5, 84.5, 85.5, 86.5, 87.5, 88.5, 89.5])
- lon(lon)float640.5 1.5 2.5 ... 357.5 358.5 359.5
array([ 0.5, 1.5, 2.5, ..., 357.5, 358.5, 359.5])
- latPandasIndex
PandasIndex(Index([-89.5, -88.5, -87.5, -86.5, -85.5, -84.5, -83.5, -82.5, -81.5, -80.5,
...
80.5, 81.5, 82.5, 83.5, 84.5, 85.5, 86.5, 87.5, 88.5, 89.5],
- dtype='float64', name='lat', length=180)) - lonPandasIndex
PandasIndex(Index([ 0.5, 1.5, 2.5, 3.5, 4.5, 5.5, 6.5, 7.5, 8.5, 9.5,
+ dtype='float64', name='lat', length=180))
- lonPandasIndex
PandasIndex(Index([ 0.5, 1.5, 2.5, 3.5, 4.5, 5.5, 6.5, 7.5, 8.5, 9.5,
...
350.5, 351.5, 352.5, 353.5, 354.5, 355.5, 356.5, 357.5, 358.5, 359.5],
- dtype='float64', name='lon', length=360))
+ dtype='float64', name='lon', length=360))
As shown in the above example, the result of the applied operations is an Xarray DataArray that contains a Dask Array, an identical object type to the object that the operations were performed on. This is true for any operations that can be applied to Xarray DataArrays, including subsetting operations; this next example illustrates this:
@@ -3739,7 +3739,7 @@ Parallel and lazy computation using
@@ -3794,10 +3794,10 @@ Parallel and lazy computation using dask.visualize() function, as shown in this example:
@@ -3819,10 +3819,10 @@ Parallel and lazy computation using
-[ ] | 0% Completed | 122.44 us
+[ ] | 0% Completed | 137.30 us
-[########################################] | 100% Completed | 101.18 ms
+[########################################] | 100% Completed | 101.19 ms
@@ -4236,7 +4236,7 @@ Resources and references
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
@@ -4365,7 +4365,7 @@ Contribute
By the Project Pythia Community.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
diff --git a/core/xarray/enso-xarray.html b/core/xarray/enso-xarray.html
index 316661573..6e86d4355 100644
--- a/core/xarray/enso-xarray.html
+++ b/core/xarray/enso-xarray.html
@@ -79,7 +79,7 @@
-
+
@@ -958,7 +958,7 @@ The Niño 3.4 Index
@@ -1590,7 +1590,7 @@ Select the Niño 3.4 regionwhere() method:
+array(1.8436452, dtype=float32)
The final step of the Niño 3.4 index calculation involves normalizing the data. In this example, we perform this normalization by dividing the smoothed anomaly data by the standard deviation calculated above:
@@ -3396,7 +3396,7 @@ Resources and References
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
@@ -3525,7 +3525,7 @@ Contribute
By the Project Pythia Community.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
diff --git a/core/xarray/xarray-intro.html b/core/xarray/xarray-intro.html
index e17e6628d..308dd9baa 100644
--- a/core/xarray/xarray-intro.html
+++ b/core/xarray/xarray-intro.html
@@ -79,7 +79,7 @@
-
+
@@ -583,25 +583,25 @@ Generate a random numpy array
-array([[[274.00119189, 291.26874805, 287.10212211, 281.93321028],
- [276.72439019, 281.86759901, 277.88521001, 291.15900492],
- [283.47892997, 281.69598295, 281.87670232, 280.04253353]],
+array([[[283.19514872, 279.93414516, 288.85955729, 280.30299831],
+ [282.03318795, 283.36237877, 288.28464039, 284.82856563],
+ [282.15998672, 276.06859236, 285.78192464, 280.47703387]],
- [[292.49183408, 286.75694127, 291.1430049 , 279.6708509 ],
- [283.19466798, 285.34236937, 289.30840199, 286.22515585],
- [289.80677567, 281.73710917, 294.57091617, 291.28505114]],
+ [[282.44435897, 282.262194 , 286.34635361, 285.32213341],
+ [283.03737484, 288.57815157, 290.02698113, 278.23328324],
+ [277.06837841, 286.4771544 , 284.58714744, 288.42673576]],
- [[278.59826878, 287.14196565, 284.68298455, 286.59720048],
- [287.51252429, 288.52590266, 273.69692215, 285.39338362],
- [292.66871999, 278.60078099, 280.78168263, 291.92156783]],
+ [[280.62831671, 275.374152 , 283.08613936, 292.56788174],
+ [286.26020096, 282.03531027, 280.16394984, 288.9366629 ],
+ [282.90971416, 278.40998651, 284.98850877, 283.14584888]],
- [[281.24772197, 286.52210588, 282.61809684, 288.82826349],
- [282.48419301, 286.34184212, 277.57835692, 279.66054122],
- [288.97073742, 287.33849395, 289.90083339, 285.95347237]],
+ [[284.34422355, 283.20830934, 278.27414095, 285.21976361],
+ [279.17521323, 292.21743424, 276.974416 , 284.06699408],
+ [274.74247997, 279.63780379, 275.87914515, 277.71758929]],
- [[282.699394 , 286.72856786, 281.89265707, 286.16350665],
- [278.02139036, 284.54696447, 283.16191651, 278.99615563],
- [284.94443941, 287.35039327, 289.38110818, 280.10681174]]])
+ [[282.71726594, 281.99808895, 279.66639367, 276.71709325],
+ [278.30599074, 277.08406243, 273.68999203, 279.75441922],
+ [271.39869986, 286.48138908, 285.22713979, 291.77285885]]])
@@ -982,44 +982,44 @@ Wrap the array: first attempt
@@ -1402,44 +1402,44 @@ Assign dimension names<xarray.DataArray (time: 5, lat: 3, lon: 4)> Size: 480B
-array([[[274.00119189, 291.26874805, 287.10212211, 281.93321028],
- [276.72439019, 281.86759901, 277.88521001, 291.15900492],
- [283.47892997, 281.69598295, 281.87670232, 280.04253353]],
-
- [[292.49183408, 286.75694127, 291.1430049 , 279.6708509 ],
- [283.19466798, 285.34236937, 289.30840199, 286.22515585],
- [289.80677567, 281.73710917, 294.57091617, 291.28505114]],
-
- [[278.59826878, 287.14196565, 284.68298455, 286.59720048],
- [287.51252429, 288.52590266, 273.69692215, 285.39338362],
- [292.66871999, 278.60078099, 280.78168263, 291.92156783]],
-
- [[281.24772197, 286.52210588, 282.61809684, 288.82826349],
- [282.48419301, 286.34184212, 277.57835692, 279.66054122],
- [288.97073742, 287.33849395, 289.90083339, 285.95347237]],
-
- [[282.699394 , 286.72856786, 281.89265707, 286.16350665],
- [278.02139036, 284.54696447, 283.16191651, 278.99615563],
- [284.94443941, 287.35039327, 289.38110818, 280.10681174]]])
-Dimensions without coordinates: time, lat, lon
+array([[[283.19514872, 279.93414516, 288.85955729, 280.30299831],
+ [282.03318795, 283.36237877, 288.28464039, 284.82856563],
+ [282.15998672, 276.06859236, 285.78192464, 280.47703387]],
+
+ [[282.44435897, 282.262194 , 286.34635361, 285.32213341],
+ [283.03737484, 288.57815157, 290.02698113, 278.23328324],
+ [277.06837841, 286.4771544 , 284.58714744, 288.42673576]],
+
+ [[280.62831671, 275.374152 , 283.08613936, 292.56788174],
+ [286.26020096, 282.03531027, 280.16394984, 288.9366629 ],
+ [282.90971416, 278.40998651, 284.98850877, 283.14584888]],
+
+ [[284.34422355, 283.20830934, 278.27414095, 285.21976361],
+ [279.17521323, 292.21743424, 276.974416 , 284.06699408],
+ [274.74247997, 279.63780379, 275.87914515, 277.71758929]],
+
+ [[282.71726594, 281.99808895, 279.66639367, 276.71709325],
+ [278.30599074, 277.08406243, 273.68999203, 279.75441922],
+ [271.39869986, 286.48138908, 285.22713979, 291.77285885]]])
+Dimensions without coordinates: time, lat, lon
This DataArray is already an improvement over a NumPy array; the DataArray contains names for each of the dimensions (or axes in NumPy parlance). An additional improvement is the association of coordinate-value arrays with data upon creation of a DataArray. In the next example, we illustrate the creation of NumPy arrays representing the coordinate values for each dimension of the DataArray, and how to associate these coordinate arrays with the data in our DataArray.
@@ -1849,51 +1849,51 @@ Initialize the
fill: currentColor;
}
<xarray.DataArray (time: 5, lat: 3, lon: 4)> Size: 480B
-array([[[274.00119189, 291.26874805, 287.10212211, 281.93321028],
- [276.72439019, 281.86759901, 277.88521001, 291.15900492],
- [283.47892997, 281.69598295, 281.87670232, 280.04253353]],
+array([[[283.19514872, 279.93414516, 288.85955729, 280.30299831],
+ [282.03318795, 283.36237877, 288.28464039, 284.82856563],
+ [282.15998672, 276.06859236, 285.78192464, 280.47703387]],
- [[292.49183408, 286.75694127, 291.1430049 , 279.6708509 ],
- [283.19466798, 285.34236937, 289.30840199, 286.22515585],
- [289.80677567, 281.73710917, 294.57091617, 291.28505114]],
+ [[282.44435897, 282.262194 , 286.34635361, 285.32213341],
+ [283.03737484, 288.57815157, 290.02698113, 278.23328324],
+ [277.06837841, 286.4771544 , 284.58714744, 288.42673576]],
- [[278.59826878, 287.14196565, 284.68298455, 286.59720048],
- [287.51252429, 288.52590266, 273.69692215, 285.39338362],
- [292.66871999, 278.60078099, 280.78168263, 291.92156783]],
+ [[280.62831671, 275.374152 , 283.08613936, 292.56788174],
+ [286.26020096, 282.03531027, 280.16394984, 288.9366629 ],
+ [282.90971416, 278.40998651, 284.98850877, 283.14584888]],
- [[281.24772197, 286.52210588, 282.61809684, 288.82826349],
- [282.48419301, 286.34184212, 277.57835692, 279.66054122],
- [288.97073742, 287.33849395, 289.90083339, 285.95347237]],
+ [[284.34422355, 283.20830934, 278.27414095, 285.21976361],
+ [279.17521323, 292.21743424, 276.974416 , 284.06699408],
+ [274.74247997, 279.63780379, 275.87914515, 277.71758929]],
- [[282.699394 , 286.72856786, 281.89265707, 286.16350665],
- [278.02139036, 284.54696447, 283.16191651, 278.99615563],
- [284.94443941, 287.35039327, 289.38110818, 280.10681174]]])
+ [[282.71726594, 281.99808895, 279.66639367, 276.71709325],
+ [278.30599074, 277.08406243, 273.68999203, 279.75441922],
+ [271.39869986, 286.48138908, 285.22713979, 291.77285885]]])
Coordinates:
* time (time) datetime64[ns] 40B 2018-01-01 2018-01-02 ... 2018-01-05
* lat (lat) float64 24B 25.0 40.0 55.0
- * lon (lon) float64 32B -120.0 -100.0 -80.0 -60.0
+ dtype='datetime64[ns]', name='time', freq='D'))
latPandasIndexPandasIndex(Index([25.0, 40.0, 55.0], dtype='float64', name='lat'))
lonPandasIndexPandasIndex(Index([-120.0, -100.0, -80.0, -60.0], dtype='float64', name='lon'))
@@ -2273,54 +2273,54 @@ Set useful attributes<xarray.DataArray (time: 5, lat: 3, lon: 4)> Size: 480B
-array([[[274.00119189, 291.26874805, 287.10212211, 281.93321028],
- [276.72439019, 281.86759901, 277.88521001, 291.15900492],
- [283.47892997, 281.69598295, 281.87670232, 280.04253353]],
+array([[[283.19514872, 279.93414516, 288.85955729, 280.30299831],
+ [282.03318795, 283.36237877, 288.28464039, 284.82856563],
+ [282.15998672, 276.06859236, 285.78192464, 280.47703387]],
- [[292.49183408, 286.75694127, 291.1430049 , 279.6708509 ],
- [283.19466798, 285.34236937, 289.30840199, 286.22515585],
- [289.80677567, 281.73710917, 294.57091617, 291.28505114]],
+ [[282.44435897, 282.262194 , 286.34635361, 285.32213341],
+ [283.03737484, 288.57815157, 290.02698113, 278.23328324],
+ [277.06837841, 286.4771544 , 284.58714744, 288.42673576]],
- [[278.59826878, 287.14196565, 284.68298455, 286.59720048],
- [287.51252429, 288.52590266, 273.69692215, 285.39338362],
- [292.66871999, 278.60078099, 280.78168263, 291.92156783]],
+ [[280.62831671, 275.374152 , 283.08613936, 292.56788174],
+ [286.26020096, 282.03531027, 280.16394984, 288.9366629 ],
+ [282.90971416, 278.40998651, 284.98850877, 283.14584888]],
- [[281.24772197, 286.52210588, 282.61809684, 288.82826349],
- [282.48419301, 286.34184212, 277.57835692, 279.66054122],
- [288.97073742, 287.33849395, 289.90083339, 285.95347237]],
+ [[284.34422355, 283.20830934, 278.27414095, 285.21976361],
+ [279.17521323, 292.21743424, 276.974416 , 284.06699408],
+ [274.74247997, 279.63780379, 275.87914515, 277.71758929]],
- [[282.699394 , 286.72856786, 281.89265707, 286.16350665],
- [278.02139036, 284.54696447, 283.16191651, 278.99615563],
- [284.94443941, 287.35039327, 289.38110818, 280.10681174]]])
+ [[282.71726594, 281.99808895, 279.66639367, 276.71709325],
+ [278.30599074, 277.08406243, 273.68999203, 279.75441922],
+ [271.39869986, 286.48138908, 285.22713979, 291.77285885]]])
Coordinates:
* time (time) datetime64[ns] 40B 2018-01-01 2018-01-02 ... 2018-01-05
* lat (lat) float64 24B 25.0 40.0 55.0
* lon (lon) float64 32B -120.0 -100.0 -80.0 -60.0
Attributes:
units: kelvin
- standard_name: air_temperature
+ dtype='datetime64[ns]', name='time', freq='D'))
latPandasIndexPandasIndex(Index([25.0, 40.0, 55.0], dtype='float64', name='lat'))
lonPandasIndexPandasIndex(Index([-120.0, -100.0, -80.0, -60.0], dtype='float64', name='lon'))
- units :
- kelvin
- standard_name :
- air_temperature
@@ -2699,51 +2699,51 @@ Issues with preservation of attributesdocumentation page.
@@ -3134,54 +3134,54 @@ Create a pressure
+ dtype='datetime64[ns]', name='time', freq='D'))latPandasIndexPandasIndex(Index([25.0, 40.0, 55.0], dtype='float64', name='lat'))
lonPandasIndexPandasIndex(Index([-120.0, -100.0, -80.0, -60.0], dtype='float64', name='lon'))
As listed in the rich display above, the new Dataset object is aware that both DataArrays share the same coordinate axes. (Please note that if this page is not run as a Jupyter Notebook, the rich display may be unavailable.)
@@ -3986,54 +3986,54 @@ Access Data variables and Coordinates in a DataArrays through a dictionary syntax, as shown in this example:
@@ -4407,54 +4407,54 @@ Access Data variables and Coordinates in a Dataset objects are mainly used for loading data from files, which will be covered later in this tutorial.
@@ -4838,18 +4838,18 @@ NumPy-like selection<xarray.DataArray (lat: 3, lon: 4)> Size: 96B
-array([[292.49183408, 286.75694127, 291.1430049 , 279.6708509 ],
- [283.19466798, 285.34236937, 289.30840199, 286.22515585],
- [289.80677567, 281.73710917, 294.57091617, 291.28505114]])
+array([[282.44435897, 282.262194 , 286.34635361, 285.32213341],
+ [283.03737484, 288.57815157, 290.02698113, 278.23328324],
+ [277.06837841, 286.4771544 , 284.58714744, 288.42673576]])
Coordinates:
time datetime64[ns] 8B 2018-01-02
* lat (lat) float64 24B 25.0 40.0 55.0
* lon (lon) float64 32B -120.0 -100.0 -80.0 -60.0
Attributes:
units: kelvin
- standard_name: air_temperature
+ standard_name: air_temperature
This example reveals one of the major shortcomings of index selection. In order to retrieve the correct data using index selection, anyone using a DataArray must have precise knowledge of the axes in the DataArray, including the order of the axes and the meaning of their indices.
By using named coordinates, as shown in the next set of examples, we can avoid this cumbersome burden.
@@ -5229,18 +5229,18 @@ Selecting with
fill: currentColor;
}
<xarray.DataArray (lat: 3, lon: 4)> Size: 96B
-array([[292.49183408, 286.75694127, 291.1430049 , 279.6708509 ],
- [283.19466798, 285.34236937, 289.30840199, 286.22515585],
- [289.80677567, 281.73710917, 294.57091617, 291.28505114]])
+array([[282.44435897, 282.262194 , 286.34635361, 285.32213341],
+ [283.03737484, 288.57815157, 290.02698113, 278.23328324],
+ [277.06837841, 286.4771544 , 284.58714744, 288.42673576]])
Coordinates:
time datetime64[ns] 8B 2018-01-02
* lat (lat) float64 24B 25.0 40.0 55.0
* lon (lon) float64 32B -120.0 -100.0 -80.0 -60.0
Attributes:
units: kelvin
- standard_name: air_temperature
+ standard_name: air_temperature
This method yields the same result as the index selection, however:
@@ -5627,18 +5627,18 @@ Nearest-neighbor sampling.sel indeed returned the data at the temporal value corresponding to the date 2018-01-05.
@@ -6017,20 +6017,20 @@ Interpolation
Info
@@ -6414,29 +6414,29 @@ Slicing along coordinates
Info
@@ -6818,18 +6818,18 @@ One more selection method: <xarray.DataArray (lat: 3, lon: 4)> Size: 96B
-array([[292.49183408, 286.75694127, 291.1430049 , 279.6708509 ],
- [283.19466798, 285.34236937, 289.30840199, 286.22515585],
- [289.80677567, 281.73710917, 294.57091617, 291.28505114]])
+array([[282.44435897, 282.262194 , 286.34635361, 285.32213341],
+ [283.03737484, 288.57815157, 290.02698113, 278.23328324],
+ [277.06837841, 286.4771544 , 284.58714744, 288.42673576]])
Coordinates:
time datetime64[ns] 8B 2018-01-02
* lat (lat) float64 24B 25.0 40.0 55.0
* lon (lon) float64 32B -120.0 -100.0 -80.0 -60.0
Attributes:
units: kelvin
- standard_name: air_temperature
+ standard_name: air_temperature
This selection technique is similar to NumPy’s index-based selection, as shown below:
temp[1,:,:]
@@ -7208,29 +7208,29 @@ One more selection method: <xarray.DataArray (time: 3, lat: 2, lon: 2)> Size: 96B
-array([[[291.26874805, 287.10212211],
- [281.86759901, 277.88521001]],
+array([[[279.93414516, 288.85955729],
+ [283.36237877, 288.28464039]],
- [[286.75694127, 291.1430049 ],
- [285.34236937, 289.30840199]],
+ [[282.262194 , 286.34635361],
+ [288.57815157, 290.02698113]],
- [[287.14196565, 284.68298455],
- [288.52590266, 273.69692215]]])
+ [[275.374152 , 283.08613936],
+ [282.03531027, 280.16394984]]])
Coordinates:
* time (time) datetime64[ns] 24B 2018-01-01 2018-01-02 2018-01-03
* lat (lat) float64 16B 25.0 40.0
* lon (lon) float64 16B -100.0 -80.0
Attributes:
units: kelvin
- standard_name: air_temperature
+ [[275.374152 , 283.08613936],
+ [282.03531027, 280.16394984]]])
- time(time)datetime64[ns]2018-01-01 2018-01-02 2018-01-03
array(['2018-01-01T00:00:00.000000000', '2018-01-02T00:00:00.000000000',
+ '2018-01-03T00:00:00.000000000'], dtype='datetime64[ns]')
- lat(lat)float6425.0 40.0
array([25., 40.])
- lon(lon)float64-100.0 -80.0
array([-100., -80.])
- timePandasIndex
PandasIndex(DatetimeIndex(['2018-01-01', '2018-01-02', '2018-01-03'], dtype='datetime64[ns]', name='time', freq='D'))
- latPandasIndex
PandasIndex(Index([25.0, 40.0], dtype='float64', name='lat'))
- lonPandasIndex
PandasIndex(Index([-100.0, -80.0], dtype='float64', name='lon'))
- units :
- kelvin
- standard_name :
- air_temperature
In contrast with the previous example, this example shows a useful advantage of using the .loc attribute. When using the .loc attribute, you can specify data slices using a syntax similar to NumPy in addition to, or instead of, using the slice function. Both of these slicing techniques are illustrated below:
@@ -7604,29 +7604,29 @@ One more selection method: <xarray.DataArray (time: 3, lat: 2, lon: 2)> Size: 96B
-array([[[291.26874805, 287.10212211],
- [281.86759901, 277.88521001]],
+array([[[279.93414516, 288.85955729],
+ [283.36237877, 288.28464039]],
- [[286.75694127, 291.1430049 ],
- [285.34236937, 289.30840199]],
+ [[282.262194 , 286.34635361],
+ [288.57815157, 290.02698113]],
- [[287.14196565, 284.68298455],
- [288.52590266, 273.69692215]]])
+ [[275.374152 , 283.08613936],
+ [282.03531027, 280.16394984]]])
Coordinates:
* time (time) datetime64[ns] 24B 2018-01-01 2018-01-02 2018-01-03
* lat (lat) float64 16B 25.0 40.0
* lon (lon) float64 16B -100.0 -80.0
Attributes:
units: kelvin
- standard_name: air_temperature
+ [[275.374152 , 283.08613936],
+ [282.03531027, 280.16394984]]])
- time(time)datetime64[ns]2018-01-01 2018-01-02 2018-01-03
array(['2018-01-01T00:00:00.000000000', '2018-01-02T00:00:00.000000000',
+ '2018-01-03T00:00:00.000000000'], dtype='datetime64[ns]')
- lat(lat)float6425.0 40.0
array([25., 40.])
- lon(lon)float64-100.0 -80.0
array([-100., -80.])
- timePandasIndex
PandasIndex(DatetimeIndex(['2018-01-01', '2018-01-02', '2018-01-03'], dtype='datetime64[ns]', name='time', freq='D'))
- latPandasIndex
PandasIndex(Index([25.0, 40.0], dtype='float64', name='lat'))
- lonPandasIndex
PandasIndex(Index([-100.0, -80.0], dtype='float64', name='lon'))
- units :
- kelvin
- standard_name :
- air_temperature
As described above, the arguments to .loc must be in the order of the DataArray’s dimensions. Attempting to slice data without ordering arguments properly can cause errors, as shown below:
@@ -8059,10 +8059,10 @@ Access netCDF data with
@@ -8504,17 +8504,17 @@ Subsetting the
positive: down
Grib_level_type: 100
_CoordinateAxisType: Pressure
- _CoordinateZisPositive: down
+ dtype='float32', name='isobaric1'))- units :
- hPa
- long_name :
- Isobaric surface
- positive :
- down
- Grib_level_type :
- 100
- _CoordinateAxisType :
- Pressure
- _CoordinateZisPositive :
- down
(As described earlier in this tutorial, we can also use dictionary syntax to select specific DataArrays; in this case, we would write ds['isobaric1'].)
Many of the subsetting operations usable on DataArrays can also be used on Datasets. However, when used on Datasets, these operations are performed on every DataArray in the Dataset, as shown below:
@@ -8916,7 +8916,7 @@ Subsetting the
geospatial_lat_min: 10.753308882144761
geospatial_lat_max: 46.8308828962289
geospatial_lon_min: -153.88242040519995
- geospatial_lon_max: -42.666108129242815
As shown above, the subsetting operation performed on the Dataset returned a new Dataset. If only a single DataArray is needed from this new Dataset, it can be retrieved using the familiar dot notation:
@@ -9356,7 +9356,7 @@ Subsetting the
Grib1_TableVersion: 131
Grib1_Parameter: 11
Grib1_Level_Type: 100
- Grib1_Level_Desc: Isobaric surface
+ dtype='float32', name='x', length=268))- long_name :
- Temperature @ Isobaric surface
- units :
- K
- description :
- Temperature
- grid_mapping :
- LambertConformal_Projection
- Grib_Variable_Id :
- VAR_7-15-131-11_L100
- Grib1_Center :
- 7
- Grib1_Subcenter :
- 15
- Grib1_TableVersion :
- 131
- Grib1_Parameter :
- 11
- Grib1_Level_Type :
- 100
- Grib1_Level_Desc :
- Isobaric surface
@@ -9790,19 +9790,19 @@ Aggregation operations
+ dtype='float32', name='isobaric1'))
Info
@@ -10196,19 +10196,19 @@ Aggregation operations
+ dtype='float32', name='isobaric1'))
@@ -10226,7 +10226,7 @@ Simple visualization with
-[<matplotlib.lines.Line2D at 0x7f2e0f58d130>]
+[<matplotlib.lines.Line2D at 0x7f505db3d9d0>]
@@ -10250,7 +10250,7 @@ Customizing the plot
-[<matplotlib.lines.Line2D at 0x7f2e0f58eff0>]
+[<matplotlib.lines.Line2D at 0x7f5055a98d40>]
@@ -10268,7 +10268,7 @@ Plotting 2-D data
-<matplotlib.collections.QuadMesh at 0x7f2df9c43b60>
+<matplotlib.collections.QuadMesh at 0x7f5055ad1be0>
@@ -10667,7 +10667,7 @@ Resources and references
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
@@ -10796,7 +10796,7 @@ Contribute
By the Project Pythia Community.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
diff --git a/foundations/conda.html b/foundations/conda.html
index c07684127..6f809cfd0 100644
--- a/foundations/conda.html
+++ b/foundations/conda.html
@@ -79,7 +79,7 @@
-
+
@@ -913,7 +913,7 @@ Resources and References
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
@@ -1042,7 +1042,7 @@ Contribute
By the Project Pythia Community.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
diff --git a/foundations/getting-started-github.html b/foundations/getting-started-github.html
index 4a2af1d14..ec22e00df 100644
--- a/foundations/getting-started-github.html
+++ b/foundations/getting-started-github.html
@@ -81,7 +81,7 @@
-
+
@@ -768,7 +768,7 @@ Getting Started with GitHub
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
@@ -897,7 +897,7 @@ Contribute
By the Project Pythia Community.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
diff --git a/foundations/getting-started-jupyter.html b/foundations/getting-started-jupyter.html
index b1d2107c0..b95652e74 100644
--- a/foundations/getting-started-jupyter.html
+++ b/foundations/getting-started-jupyter.html
@@ -79,7 +79,7 @@
-
+
@@ -905,7 +905,7 @@ Resources and references
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
@@ -1034,7 +1034,7 @@ Contribute
By the Project Pythia Community.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
diff --git a/foundations/getting-started-python.html b/foundations/getting-started-python.html
index 286ad4516..19ac49850 100644
--- a/foundations/getting-started-python.html
+++ b/foundations/getting-started-python.html
@@ -81,7 +81,7 @@
-
+
@@ -779,7 +779,7 @@ Topics
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
@@ -908,7 +908,7 @@ Contribute
By the Project Pythia Community.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
diff --git a/foundations/github/basic-git.html b/foundations/github/basic-git.html
index 441aa5056..32e0b6b24 100644
--- a/foundations/github/basic-git.html
+++ b/foundations/github/basic-git.html
@@ -79,7 +79,7 @@
-
+
@@ -1222,7 +1222,7 @@ References
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
@@ -1351,7 +1351,7 @@ Contribute
By the Project Pythia Community.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
diff --git a/foundations/github/contribute-to-pythia.html b/foundations/github/contribute-to-pythia.html
index d1e1bad12..11ebc0cc2 100644
--- a/foundations/github/contribute-to-pythia.html
+++ b/foundations/github/contribute-to-pythia.html
@@ -79,7 +79,7 @@
-
+
@@ -885,7 +885,7 @@ Summary
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
@@ -1014,7 +1014,7 @@ Contribute
By the Project Pythia Community.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
diff --git a/foundations/github/git-branches.html b/foundations/github/git-branches.html
index 6dac4b319..4e316269f 100644
--- a/foundations/github/git-branches.html
+++ b/foundations/github/git-branches.html
@@ -79,7 +79,7 @@
-
+
@@ -1036,7 +1036,7 @@ Resources and references
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
@@ -1165,7 +1165,7 @@ Contribute
By the Project Pythia Community.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
diff --git a/foundations/github/github-advanced.html b/foundations/github/github-advanced.html
index 9e93b611f..def634b01 100644
--- a/foundations/github/github-advanced.html
+++ b/foundations/github/github-advanced.html
@@ -77,7 +77,7 @@
-
+
@@ -810,7 +810,7 @@ References
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
@@ -939,7 +939,7 @@ Contribute
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index 81c713d97..f6f7addda 100644
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By the Project Pythia Community.
- Last updated on 15 May 2024.
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@@ -905,7 +905,7 @@ Contribute
By the Project Pythia Community.
- Last updated on 15 May 2024.
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index 57210f52a..705201479 100644
--- a/search.html
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By the Project Pythia Community.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
diff --git a/searchindex.js b/searchindex.js
index 18d9c718b..f531c0445 100644
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29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 46], "support": 7, "switch": [30, 34], "synchron": 37, "system": [5, 39], "templat": 1, "termin": [40, 42, 46], "terminologi": 7, "test": [11, 21], "text": [9, 42, 43], "thi": [1, 8, 11, 28, 45, 49, 50], "thing": [32, 35], "tide": 7, "time": [7, 23, 28], "timedelta": 7, "timestamp": 7, "tip": [35, 36, 37], "titl": 11, "togeth": 11, "token": 36, "topic": [27, 31, 44], "try": [32, 35], "tune": 39, "tutori": [16, 28], "two": [3, 11], "type": 45, "unix": 7, "up": [0, 1, 30, 42], "updat": 30, "upstream": 37, "us": [1, 3, 10, 11, 13, 15, 18, 20, 21, 23, 24, 28, 50], "usag": 7, "user": 32, "utc": 7, "v": 36, "valu": [13, 18, 20, 23], "variabl": [5, 23, 42], "vc": [28, 39], "vector": 15, "veri": 45, "version": [28, 39], "versu": 7, "via": [29, 50], "view": [28, 38], "visual": [22, 23], "wai": 38, "want": 28, "warn": [1, 3, 13, 14, 18, 42], "we": 28, "web": 39, "what": [1, 3, 5, 7, 10, 11, 13, 14, 15, 18, 20, 21, 23, 24, 26, 28, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 45, 46], "when": 38, "where": 20, "why": [8, 21, 28, 47], "window": 32, "within": 11, "work": 37, "workflow": [30, 34, 37], "would": 21, "wrap": 23, "write": [5, 7], "xarrai": [19, 20, 21, 22, 23], "xr": 23, "year": 7, "york": 3, "you": [21, 28, 30, 38, 39], "your": [0, 1, 18, 30, 34, 36, 41, 45], "zero": 45, "zone": 7}})
\ No newline at end of file
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
Contribute
By the Project Pythia Community. - Last updated on 15 May 2024. + Last updated on 20 May 2024. diff --git a/core/numpy/intermediate-numpy.html b/core/numpy/intermediate-numpy.html index 59706e1a6..38a6afc63 100644 --- a/core/numpy/intermediate-numpy.html +++ b/core/numpy/intermediate-numpy.html @@ -81,7 +81,7 @@ - + @@ -1425,7 +1425,7 @@Resources and references
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
@@ -1554,7 +1554,7 @@ Contribute
By the Project Pythia Community.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
diff --git a/core/numpy/numpy-basics.html b/core/numpy/numpy-basics.html
index cb11d7b3d..365a2f855 100644
--- a/core/numpy/numpy-basics.html
+++ b/core/numpy/numpy-basics.html
@@ -81,7 +81,7 @@
-
+
@@ -1168,7 +1168,7 @@ Indexing
Slices
-Slicing syntax is written as array[start:stop[:step]], where all numbers are optional.
+Slicing syntax is written as array[start:stop:step]. Note that all numbers are optional. Importantly, the step parameter is optional and can be omitted, in which case the slice uses a default step of 1.
defaults:
@@ -1775,7 +1775,7 @@ Resources and references
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
@@ -1904,7 +1904,7 @@ Contribute
By the Project Pythia Community.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
diff --git a/core/numpy/numpy-broadcasting.html b/core/numpy/numpy-broadcasting.html
index 78f882658..57e5dd9a6 100644
--- a/core/numpy/numpy-broadcasting.html
+++ b/core/numpy/numpy-broadcasting.html
@@ -81,7 +81,7 @@
-
+
@@ -1070,7 +1070,7 @@ Blocking
-
-
-
-
-array([[ 8.3, 11.6, 33.1],
- [11.6, 33.1, 18.3],
- [33.1, 18.3, 22.5],
- [18.3, 22.5, 24.5],
- [22.5, 24.5, 20.1],
- [24.5, 20.1, 20.1],
- [20.1, 20.1, 18. ],
- [20.1, 18. , 25. ]])
+array([[19.3, 18.8, 28.7],
+ [18.8, 28.7, 27.1],
+ [28.7, 27.1, 17.1],
+ [27.1, 17.1, 16.2],
+ [17.1, 16.2, 15. ],
+ [16.2, 15. , 15.1],
+ [15. , 15.1, 14. ],
+ [15.1, 14. , 19.2]])
@@ -1161,8 +1161,8 @@ Blocking
-
-array([[ 8.3, 11.6, 2000. ],
- [ 11.6, 2000. , 18.3],
- [2000. , 18.3, 22.5],
- [ 18.3, 22.5, 24.5],
- [ 22.5, 24.5, 20.1],
- [ 24.5, 20.1, 20.1],
- [ 20.1, 20.1, 18. ],
- [ 20.1, 18. , 25. ]])
+array([[ 19.3, 18.8, 2000. ],
+ [ 18.8, 2000. , 27.1],
+ [2000. , 27.1, 17.1],
+ [ 27.1, 17.1, 16.2],
+ [ 17.1, 16.2, 15. ],
+ [ 16.2, 15. , 15.1],
+ [ 15. , 15.1, 14. ],
+ [ 15.1, 14. , 19.2]])
@@ -1210,13 +1210,13 @@ Finding the difference between min and max
-array([[6, 6, 7, ..., 8, 7, 6],
- [6, 6, 6, ..., 7, 7, 7],
- [7, 7, 7, ..., 6, 7, 7],
+array([[7, 7, 7, ..., 7, 8, 7],
+ [6, 7, 6, ..., 7, 7, 6],
+ [7, 6, 7, ..., 7, 6, 6],
...,
- [7, 7, 7, ..., 7, 7, 6],
- [7, 6, 6, ..., 6, 7, 7],
- [7, 6, 7, ..., 7, 8, 7]])
+ [6, 7, 6, ..., 6, 6, 7],
+ [7, 7, 7, ..., 6, 6, 7],
+ [6, 7, 7, ..., 8, 8, 7]])
@@ -1241,13 +1241,13 @@ Finding the difference between min and max
-array([[775. , 775. , 737.5, ..., 700. , 737.5, 775. ],
- [775. , 775. , 775. , ..., 737.5, 737.5, 737.5],
- [737.5, 737.5, 737.5, ..., 775. , 737.5, 737.5],
+array([[737.5, 737.5, 737.5, ..., 737.5, 700. , 737.5],
+ [775. , 737.5, 775. , ..., 737.5, 737.5, 775. ],
+ [737.5, 775. , 737.5, ..., 737.5, 775. , 775. ],
...,
- [737.5, 737.5, 737.5, ..., 737.5, 737.5, 775. ],
- [737.5, 775. , 775. , ..., 775. , 737.5, 737.5],
- [737.5, 775. , 737.5, ..., 737.5, 700. , 737.5]])
+ [775. , 737.5, 775. , ..., 775. , 775. , 737.5],
+ [737.5, 737.5, 737.5, ..., 775. , 775. , 737.5],
+ [775. , 737.5, 737.5, ..., 700. , 700. , 737.5]])
@@ -1276,19 +1276,19 @@ Finding the difference between min and max
-array([[ -9.55417349, -9.78584972, -9.92661689, ..., -12.67789616,
- -10.49077377, -6.56515605],
- [-10.00010532, -10.70819797, -7.20985445, ..., -11.10789153,
- -11.73163442, -8.29531203],
- [-11.88696252, -12.29088724, -11.70346147, ..., -7.30859657,
- -8.97849533, -13.84614818],
+array([[-13.35480377, -11.37389473, -6.11490009, ..., -9.69409241,
+ -14.14853765, -11.04724036],
+ [ -6.50332158, -11.09958323, -5.95998607, ..., -13.2604716 ,
+ -13.19971009, -8.49574616],
+ [-12.28051357, -10.19815861, -7.42945122, ..., -11.5798199 ,
+ -8.91153322, -8.6079996 ],
...,
- [-11.90215938, -7.91187899, -13.08679868, ..., -10.41120932,
- -11.35468344, -11.06459146],
- [-11.50292233, -6.26889319, -7.38221861, ..., -10.24036433,
- -10.12594611, -5.63518882],
- [-10.32651953, -6.06759924, -9.54152312, ..., -10.45702773,
- -12.20574751, -8.01746694]])
+ [ -8.10255586, -12.16615351, -11.27253366, ..., -10.44432682,
+ -7.39154896, -10.99574973],
+ [-16.5968518 , -12.57487222, -8.39736923, ..., -4.15595587,
+ -8.86979291, -11.28873531],
+ [ -8.59386363, -9.38047716, -10.46838159, ..., -11.30765026,
+ -10.89295232, -9.24056526]])
@@ -1332,19 +1332,19 @@ Finding the difference between min and max
-array([[ -9.55417349, -9.78584972, -9.92661689, ..., -12.67789616,
- -10.49077377, -6.56515605],
- [-10.00010532, -10.70819797, -7.20985445, ..., -11.10789153,
- -11.73163442, -8.29531203],
- [-11.88696252, -12.29088724, -11.70346147, ..., -7.30859657,
- -8.97849533, -13.84614818],
+array([[-13.35480377, -11.37389473, -6.11490009, ..., -9.69409241,
+ -14.14853765, -11.04724036],
+ [ -6.50332158, -11.09958323, -5.95998607, ..., -13.2604716 ,
+ -13.19971009, -8.49574616],
+ [-12.28051357, -10.19815861, -7.42945122, ..., -11.5798199 ,
+ -8.91153322, -8.6079996 ],
...,
- [-11.90215938, -7.91187899, -13.08679868, ..., -10.41120932,
- -11.35468344, -11.06459146],
- [-11.50292233, -6.26889319, -7.38221861, ..., -10.24036433,
- -10.12594611, -5.63518882],
- [-10.32651953, -6.06759924, -9.54152312, ..., -10.45702773,
- -12.20574751, -8.01746694]])
+ [ -8.10255586, -12.16615351, -11.27253366, ..., -10.44432682,
+ -7.39154896, -10.99574973],
+ [-16.5968518 , -12.57487222, -8.39736923, ..., -4.15595587,
+ -8.86979291, -11.28873531],
+ [ -8.59386363, -9.38047716, -10.46838159, ..., -11.30765026,
+ -10.89295232, -9.24056526]])
@@ -1711,7 +1711,7 @@ Resources and references
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
@@ -1840,7 +1840,7 @@ Contribute
By the Project Pythia Community.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
diff --git a/core/overview.html b/core/overview.html
index 6acc0ae46..fd4752cc2 100644
--- a/core/overview.html
+++ b/core/overview.html
@@ -79,7 +79,7 @@
-
+
@@ -886,7 +886,7 @@ Tutorials
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
@@ -1015,7 +1015,7 @@ Contribute
By the Project Pythia Community.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
diff --git a/core/pandas.html b/core/pandas.html
index 2e4230efc..02509b347 100644
--- a/core/pandas.html
+++ b/core/pandas.html
@@ -79,7 +79,7 @@
-
+
@@ -759,7 +759,7 @@ Pandas
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
@@ -888,7 +888,7 @@ Contribute
By the Project Pythia Community.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
diff --git a/core/pandas/pandas.html b/core/pandas/pandas.html
index 734fecdb6..2e24138b7 100644
--- a/core/pandas/pandas.html
+++ b/core/pandas/pandas.html
@@ -79,7 +79,7 @@
-
+
@@ -1098,7 +1098,7 @@ Indexing a Seri
-/tmp/ipykernel_2735/737336773.py:1: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`
+/tmp/ipykernel_2746/737336773.py:1: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`
nino34_series[3]
@@ -1449,7 +1449,7 @@ Extending to the
-/tmp/ipykernel_2735/541596450.py:1: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`
+/tmp/ipykernel_2746/541596450.py:1: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`
df["Nino34"][3]
@@ -3645,7 +3645,7 @@ Resampling
-/tmp/ipykernel_2735/233158901.py:1: FutureWarning: 'Y' is deprecated and will be removed in a future version, please use 'YE' instead.
+/tmp/ipykernel_2746/233158901.py:1: FutureWarning: 'Y' is deprecated and will be removed in a future version, please use 'YE' instead.
df.Nino34.resample('1Y').mean().plot();
@@ -4434,7 +4434,7 @@ Resources and References
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
@@ -4563,7 +4563,7 @@ Contribute
By the Project Pythia Community.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
diff --git a/core/xarray.html b/core/xarray.html
index e4624108b..6bc787165 100644
--- a/core/xarray.html
+++ b/core/xarray.html
@@ -79,7 +79,7 @@
-
+
@@ -760,7 +760,7 @@ Xarray
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
@@ -889,7 +889,7 @@ Contribute
By the Project Pythia Community.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
diff --git a/core/xarray/computation-masking.html b/core/xarray/computation-masking.html
index b15ad248c..12f19902b 100644
--- a/core/xarray/computation-masking.html
+++ b/core/xarray/computation-masking.html
@@ -79,7 +79,7 @@
-
+
@@ -943,7 +943,7 @@ Data Setup
@@ -1576,7 +1576,7 @@ Arithmetic Operations
+ dtype='float64', name='lon', length=360))
In addition, there are many other arithmetic operations that can be performed on DataArrays. In this example, we demonstrate squaring the original Celsius values of our air temperature data:
@@ -2246,7 +2246,7 @@ Arithmetic Operations
+ dtype='float64', name='lon', length=360))
@@ -2876,7 +2876,7 @@ Aggregation Methodsdim keyword argument; this means that the function was applied over all of the dataset’s dimensions. In other words, the aggregation method computed the mean of every element of the temperature dataset across every temporal and spatial data point. However, if a dimension name is used with the dim keyword argument, the aggregation method computes an aggregation along the given dimension. In this next example, we use aggregation to calculate the temporal mean across all spatial data; this is performed by providing the dimension name 'time' to the dim keyword argument:
@@ -3276,7 +3276,7 @@ Aggregation Methods
@@ -3717,7 +3717,7 @@ Aggregation Methods
@@ -4325,7 +4325,7 @@ Aggregation Methods
@@ -4892,7 +4892,7 @@ Compute climatologyDataArray containing the climatology data, we can plot the data in different ways. In this example, we plot the climatology at a specific latitude-longitude point:
@@ -5412,7 +5412,7 @@ Compute anomaly
@@ -6095,7 +6095,7 @@ Compute anomaly.weighted() method and the newly loaded CESM2 dataset to weight the grid cell data as described above:
@@ -6598,7 +6598,7 @@ Other high level computation functionalityrolling method to compute averages in a moving window of 5 months of data:
@@ -7114,7 +7114,7 @@ Other high level computation functionality
@@ -7783,7 +7783,7 @@ Masking Data
Using where with one condition
@@ -8421,15 +8421,15 @@ Using where
+ dtype='float64', name='lon', length=360))
- cell_measures :
- area: areacello
- cell_methods :
- area: mean where sea time: mean
- comment :
- Model data on the 1x1 grid includes values in all cells for which ocean cells on the native grid cover more than 52.5 percent of the 1x1 grid cell. This 52.5 percent cutoff was chosen to produce ocean surface area on the 1x1 grid as close as possible to ocean surface area on the native grid, while not introducing fractional cell coverage.
- description :
- This may differ from "surface temperature" in regions of sea ice or floating ice shelves. For models using conservative temperature as the prognostic field, they should report the top ocean layer as surface potential temperature, which is the same as surface in situ temperature.
- frequency :
- mon
- id :
- tos
- long_name :
- Sea Surface Temperature
- mipTable :
- Omon
- out_name :
- tos
- prov :
- Omon ((isd.003))
- realm :
- ocean
- standard_name :
- sea_surface_temperature
- time :
- time
- time_label :
- time-mean
- time_title :
- Temporal mean
- title :
- Sea Surface Temperature
- type :
- real
- units :
- degC
- variable_id :
- tos
As shown in the previous example, methods like .isel() and .sel() return data of a different shape than the original data provided to them. However, .where() preserves the shape of the original data by masking the values with a Boolean condition. Data values for which the condition is True are returned identical to the values passed in. On the other hand, data values for which the condition is False are returned as a preset example value. (This example value defaults to nan, but can be set to other values as well.)
Before testing .where(), it is helpful to look at the official documentation. As stated above, the .where() method takes a Boolean condition. (Boolean conditions use operators such as less-than, greater-than, and equal-to, and return a value of True or False.) Most uses of .where() check whether or not specific data values are less than or greater than a constant value. As stated in the documentation, the data values specified in the Boolean condition of .where() can be any of the following:
@@ -8864,7 +8864,7 @@ Using where
+ dtype='float64', name='lon', length=360))- cell_measures :
- area: areacello
- cell_methods :
- area: mean where sea time: mean
- comment :
- Model data on the 1x1 grid includes values in all cells for which ocean cells on the native grid cover more than 52.5 percent of the 1x1 grid cell. This 52.5 percent cutoff was chosen to produce ocean surface area on the 1x1 grid as close as possible to ocean surface area on the native grid, while not introducing fractional cell coverage.
- description :
- This may differ from "surface temperature" in regions of sea ice or floating ice shelves. For models using conservative temperature as the prognostic field, they should report the top ocean layer as surface potential temperature, which is the same as surface in situ temperature.
- frequency :
- mon
- id :
- tos
- long_name :
- Sea Surface Temperature
- mipTable :
- Omon
- out_name :
- tos
- prov :
- Omon ((isd.003))
- realm :
- ocean
- standard_name :
- sea_surface_temperature
- time :
- time
- time_label :
- time-mean
- time_title :
- Temporal mean
- title :
- Sea Surface Temperature
- type :
- real
- units :
- degC
- variable_id :
- tos
In this example, we use Matplotlib to plot the original, unmasked data, as well as the masked data created in the previous example.
@@ -9321,7 +9321,7 @@ Resources and References
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
@@ -9450,7 +9450,7 @@ Contribute
By the Project Pythia Community.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
diff --git a/core/xarray/dask-arrays-xarray.html b/core/xarray/dask-arrays-xarray.html
index 0af9daaf3..65793b335 100644
--- a/core/xarray/dask-arrays-xarray.html
+++ b/core/xarray/dask-arrays-xarray.html
@@ -79,7 +79,7 @@
-
+
@@ -568,91 +568,91 @@ Create a dask.a
-array([[[0.08870167, 0.5536724 , 0.86869933, ..., 0.59745109,
- 0.66241166, 0.14712849],
- [0.79424186, 0.53985475, 0.83117213, ..., 0.64752434,
- 0.2068288 , 0.43561572],
- [0.93755297, 0.12641562, 0.55493093, ..., 0.44682309,
- 0.9108161 , 0.28494995],
+array([[[3.73479162e-01, 5.69354087e-01, 8.44121771e-01, ...,
+ 6.95300592e-01, 7.05791578e-01, 3.22757785e-01],
+ [7.34673387e-01, 3.22502093e-02, 2.98660716e-02, ...,
+ 4.48326876e-01, 1.28181189e-01, 6.17129937e-01],
+ [4.61524077e-01, 5.72981031e-01, 6.22455684e-02, ...,
+ 4.66387696e-01, 2.24365962e-02, 9.05528466e-01],
...,
- [0.36681494, 0.55104908, 0.67358062, ..., 0.93120538,
- 0.51164396, 0.2149192 ],
- [0.33220658, 0.18853655, 0.84133513, ..., 0.09990092,
- 0.45265087, 0.48841145],
- [0.54632195, 0.99301667, 0.40513953, ..., 0.5341915 ,
- 0.3220391 , 0.07544213]],
-
- [[0.8398981 , 0.14999579, 0.81431679, ..., 0.45814483,
- 0.81962377, 0.01308931],
- [0.52904343, 0.17038365, 0.49820524, ..., 0.71294698,
- 0.49906669, 0.32305634],
- [0.83597797, 0.12242548, 0.63334391, ..., 0.77669187,
- 0.44530452, 0.02031067],
+ [4.32840675e-01, 6.11791423e-01, 7.77157080e-01, ...,
+ 5.21898060e-01, 5.08370890e-01, 5.88153598e-01],
+ [8.04673850e-01, 7.80296541e-03, 5.00244166e-01, ...,
+ 9.45710973e-01, 5.08700949e-01, 8.89905823e-01],
+ [4.87536782e-01, 4.64576884e-01, 4.97751472e-01, ...,
+ 9.63294478e-01, 8.00438624e-01, 9.39160580e-01]],
+
+ [[9.59798128e-01, 9.67977225e-01, 5.67081374e-01, ...,
+ 1.15551677e-01, 5.93188567e-01, 8.21494821e-01],
+ [7.09798171e-01, 6.86764079e-01, 9.75639983e-01, ...,
+ 6.65980228e-01, 3.63136928e-01, 3.91634965e-01],
+ [2.45312843e-01, 7.92456108e-01, 7.52068086e-01, ...,
+ 8.31396290e-01, 7.34853345e-01, 1.59997513e-02],
...,
- [0.23487395, 0.54185136, 0.84738714, ..., 0.43194404,
- 0.96464114, 0.34661126],
- [0.06216953, 0.87402041, 0.89512514, ..., 0.38706893,
- 0.09187095, 0.88666451],
- [0.46384865, 0.02462827, 0.71575049, ..., 0.47421697,
- 0.30570578, 0.58421292]],
-
- [[0.05704318, 0.36232811, 0.03448708, ..., 0.86748361,
- 0.49546271, 0.39195962],
- [0.62907898, 0.52593284, 0.87418635, ..., 0.97638485,
- 0.63695351, 0.75326941],
- [0.11640535, 0.15534404, 0.80460691, ..., 0.26935451,
- 0.00686985, 0.42474694],
+ [6.29006234e-01, 5.66655665e-01, 9.59798385e-01, ...,
+ 4.64611557e-02, 8.47630616e-01, 9.96868460e-01],
+ [8.65409572e-01, 6.62829465e-01, 5.85935120e-01, ...,
+ 2.76645046e-01, 4.05531947e-01, 1.86970157e-01],
+ [5.88186192e-02, 4.80258125e-01, 7.50053106e-01, ...,
+ 5.82584007e-01, 7.16238302e-01, 3.09095729e-01]],
+
+ [[1.95867406e-01, 8.02997164e-01, 5.92335907e-02, ...,
+ 6.00495084e-02, 3.58568845e-02, 6.14435689e-01],
+ [5.40191024e-01, 7.45240262e-01, 5.83926237e-01, ...,
+ 5.42683512e-01, 7.15062271e-05, 8.15838308e-01],
+ [3.56121359e-01, 6.64103118e-01, 3.19197578e-01, ...,
+ 4.30885292e-01, 5.63274760e-01, 5.58304993e-01],
...,
- [0.53156325, 0.69190957, 0.69352384, ..., 0.77170882,
- 0.44011939, 0.39866217],
- [0.29442052, 0.74080675, 0.46131608, ..., 0.32366183,
- 0.73788577, 0.04341616],
- [0.94117032, 0.92630431, 0.79008536, ..., 0.02443356,
- 0.52042712, 0.83466538]],
+ [3.70429144e-01, 2.28949319e-01, 2.12465225e-01, ...,
+ 8.75347366e-01, 6.88876481e-01, 8.52985255e-01],
+ [8.43544201e-01, 3.50323808e-01, 9.94755069e-01, ...,
+ 2.31937385e-01, 6.46129523e-01, 7.56519847e-01],
+ [9.23292344e-01, 9.06543740e-01, 2.16039623e-01, ...,
+ 6.17841121e-01, 5.83778599e-02, 7.07627581e-01]],
...,
- [[0.1396787 , 0.60143505, 0.57058462, ..., 0.18893598,
- 0.82985107, 0.54242887],
- [0.68157985, 0.63122788, 0.1780818 , ..., 0.55248006,
- 0.63127177, 0.4663941 ],
- [0.55463676, 0.49022722, 0.28183346, ..., 0.64073606,
- 0.37788708, 0.05233016],
+ [[5.50076397e-01, 6.23247920e-01, 6.69863249e-01, ...,
+ 6.66141598e-01, 4.98073488e-01, 9.70545887e-01],
+ [7.38559992e-01, 3.21393458e-01, 4.05421647e-01, ...,
+ 2.52285356e-01, 8.08345653e-01, 7.28764634e-01],
+ [2.68191728e-01, 6.03169049e-02, 6.89258446e-01, ...,
+ 6.21042327e-01, 9.60250603e-01, 9.75257028e-01],
...,
- [0.39020736, 0.85735173, 0.00786958, ..., 0.66161596,
- 0.18152074, 0.17731408],
- [0.93613911, 0.96578828, 0.38973709, ..., 0.06864502,
- 0.5063675 , 0.73631955],
- [0.1671134 , 0.45039599, 0.38253178, ..., 0.74316064,
- 0.20253998, 0.93656703]],
-
- [[0.28018602, 0.84864745, 0.65231976, ..., 0.57238332,
- 0.37536731, 0.04826117],
- [0.47663448, 0.86778224, 0.95451621, ..., 0.76880871,
- 0.89875752, 0.06756555],
- [0.49609048, 0.21693506, 0.23207324, ..., 0.06636032,
- 0.95937476, 0.4827714 ],
+ [6.11978830e-01, 6.32479701e-01, 1.75826196e-01, ...,
+ 2.09161988e-01, 6.71446177e-01, 4.78915946e-01],
+ [6.45044934e-01, 1.15167985e-01, 9.05467373e-01, ...,
+ 7.79598426e-01, 8.76027493e-01, 5.70161686e-01],
+ [8.44173395e-01, 7.14987726e-01, 5.27098062e-01, ...,
+ 6.24560049e-01, 3.32587128e-02, 1.48219268e-01]],
+
+ [[9.16337354e-01, 1.38628117e-01, 2.76572964e-01, ...,
+ 5.68919305e-02, 3.07040211e-01, 6.70607173e-02],
+ [6.78200880e-01, 1.19012654e-01, 8.76844508e-01, ...,
+ 5.78903343e-01, 5.08716762e-01, 3.27430604e-01],
+ [5.19310927e-01, 1.62933592e-01, 6.94104791e-01, ...,
+ 8.28521396e-01, 5.63944660e-01, 9.23353464e-01],
...,
- [0.83197412, 0.22629066, 0.1589711 , ..., 0.55726833,
- 0.40921459, 0.94548168],
- [0.20373662, 0.76138868, 0.56265755, ..., 0.71025427,
- 0.19641998, 0.49627513],
- [0.20384921, 0.34173511, 0.44829 , ..., 0.3999668 ,
- 0.75240336, 0.21357396]],
-
- [[0.05024137, 0.77607526, 0.32452642, ..., 0.26169962,
- 0.2092348 , 0.79937281],
- [0.8834038 , 0.56909665, 0.84006875, ..., 0.09540328,
- 0.66257683, 0.78342945],
- [0.29906903, 0.08192958, 0.51913898, ..., 0.32946737,
- 0.91310302, 0.99198924],
+ [8.75833692e-01, 8.80034882e-01, 3.94957057e-01, ...,
+ 3.88766073e-01, 5.62146961e-01, 4.07401505e-01],
+ [8.83232515e-02, 7.93868945e-02, 7.09396623e-01, ...,
+ 4.00821090e-01, 9.67897242e-01, 3.30712526e-02],
+ [6.21839195e-01, 3.66731583e-01, 9.97239185e-01, ...,
+ 4.06892246e-01, 8.84142338e-01, 1.29206897e-01]],
+
+ [[3.13410458e-01, 7.92759677e-01, 8.24979480e-02, ...,
+ 5.38935776e-02, 4.52819725e-01, 1.48534750e-01],
+ [2.06595218e-01, 8.46746782e-01, 9.06689993e-01, ...,
+ 8.52571008e-01, 4.28351337e-01, 8.06352220e-01],
+ [3.77563013e-01, 9.99637726e-01, 3.37356897e-01, ...,
+ 8.18911136e-01, 8.32562303e-01, 8.45143802e-02],
...,
- [0.89344391, 0.04685752, 0.16870497, ..., 0.70133294,
- 0.2028986 , 0.16916596],
- [0.62721463, 0.12582174, 0.59300888, ..., 0.93023778,
- 0.50053284, 0.63544531],
- [0.89994404, 0.84669077, 0.65421957, ..., 0.19573779,
- 0.56024887, 0.73584493]]])
+ [5.51431397e-01, 8.77465751e-01, 9.63517992e-01, ...,
+ 3.34710544e-02, 9.70363045e-01, 5.42823121e-01],
+ [3.89247115e-01, 3.36500509e-01, 4.31638413e-02, ...,
+ 8.82214294e-02, 2.41024552e-01, 4.19862342e-01],
+ [5.65544128e-01, 6.09859197e-01, 2.77376901e-01, ...,
+ 5.51955058e-01, 6.67783854e-02, 4.08359511e-01]]])
@@ -856,7 +856,7 @@ Why the difference?
-
+
@@ -872,11 +872,11 @@ Compute the result
-CPU times: user 406 ms, sys: 28 ms, total: 434 ms
-Wall time: 232 ms
+CPU times: user 399 ms, sys: 28.5 ms, total: 428 ms
+Wall time: 228 ms
-12002441.41340752
+12000189.444496717
@@ -894,7 +894,7 @@ Exercise with d
-
+
As is obvious from the above exercise, Dask quickly and easily determines a strategy for performing the operations, in this case a sum. This illustrates the appeal of Dask: automatic algorithm generation that scales from simple arithmetic problems to highly complex scientific equations with large datasets and multiple operations.
@@ -974,7 +974,7 @@ Exercise with d
-
+
@@ -1106,7 +1106,7 @@ Testing a bigger calculation
-
+
@@ -1117,34 +1117,34 @@ Testing a bigger calculation
-[ ] | 0% Completed | 169.21 us
+[ ] | 0% Completed | 255.13 us
-[ ] | 0% Completed | 105.92 ms
+[ ] | 0% Completed | 106.98 ms
-[ ] | 0% Completed | 206.66 ms
+[ ] | 0% Completed | 207.72 ms
-[ ] | 0% Completed | 307.49 ms
+[ ] | 0% Completed | 308.36 ms
-[ ] | 0% Completed | 408.13 ms
+[ ] | 0% Completed | 409.00 ms
-[ ] | 0% Completed | 508.89 ms
+[ ] | 0% Completed | 509.67 ms
-[ ] | 1% Completed | 609.51 ms
+[ ] | 0% Completed | 610.39 ms
-[# ] | 4% Completed | 710.49 ms
+[# ] | 3% Completed | 711.30 ms
-[### ] | 7% Completed | 811.54 ms
+[## ] | 6% Completed | 812.41 ms
-[#### ] | 10% Completed | 912.41 ms
+[#### ] | 10% Completed | 913.29 ms
[#### ] | 10% Completed | 1.01 s
@@ -1153,7 +1153,7 @@ Testing a bigger calculation[#### ] | 10% Completed | 1.11 s
-[#### ] | 10% Completed | 1.21 s
+[#### ] | 10% Completed | 1.22 s
[#### ] | 10% Completed | 1.32 s
@@ -1162,10 +1162,10 @@ Testing a bigger calculation[#### ] | 10% Completed | 1.42 s
-[##### ] | 13% Completed | 1.52 s
+[##### ] | 12% Completed | 1.52 s
-[###### ] | 16% Completed | 1.62 s
+[###### ] | 15% Completed | 1.62 s
[####### ] | 19% Completed | 1.72 s
@@ -1186,13 +1186,13 @@ Testing a bigger calculation[######## ] | 21% Completed | 2.22 s
-[######## ] | 22% Completed | 2.32 s
+[######## ] | 21% Completed | 2.32 s
-[########## ] | 25% Completed | 2.43 s
+[######### ] | 24% Completed | 2.43 s
-[########### ] | 27% Completed | 2.53 s
+[########### ] | 28% Completed | 2.53 s
[############ ] | 31% Completed | 2.63 s
@@ -1216,16 +1216,16 @@ Testing a bigger calculation[############## ] | 35% Completed | 3.23 s
-[############### ] | 38% Completed | 3.33 s
+[############## ] | 37% Completed | 3.33 s
-[################ ] | 40% Completed | 3.43 s
+[############### ] | 39% Completed | 3.43 s
-[################# ] | 44% Completed | 3.53 s
+[################# ] | 43% Completed | 3.53 s
-[################### ] | 48% Completed | 3.63 s
+[################## ] | 46% Completed | 3.64 s
[################### ] | 48% Completed | 3.74 s
@@ -1246,10 +1246,10 @@ Testing a bigger calculation[#################### ] | 50% Completed | 4.24 s
-[##################### ] | 53% Completed | 4.34 s
+[#################### ] | 52% Completed | 4.34 s
-[###################### ] | 57% Completed | 4.44 s
+[###################### ] | 55% Completed | 4.44 s
[####################### ] | 59% Completed | 4.54 s
@@ -1276,7 +1276,7 @@ Testing a bigger calculation[######################### ] | 64% Completed | 5.25 s
-[########################### ] | 67% Completed | 5.35 s
+[########################### ] | 68% Completed | 5.35 s
[############################ ] | 72% Completed | 5.45 s
@@ -1300,13 +1300,13 @@ Testing a bigger calculation[############################## ] | 75% Completed | 6.06 s
-[############################### ] | 77% Completed | 6.16 s
+[############################### ] | 78% Completed | 6.16 s
-[################################ ] | 80% Completed | 6.26 s
+[################################ ] | 82% Completed | 6.26 s
-[################################# ] | 83% Completed | 6.36 s
+[################################# ] | 84% Completed | 6.36 s
[################################## ] | 85% Completed | 6.46 s
@@ -1324,16 +1324,16 @@ Testing a bigger calculation[################################## ] | 85% Completed | 6.86 s
-[################################## ] | 86% Completed | 6.96 s
+[################################### ] | 89% Completed | 6.96 s
-[################################### ] | 89% Completed | 7.06 s
+[#################################### ] | 92% Completed | 7.07 s
-[##################################### ] | 92% Completed | 7.16 s
+[###################################### ] | 96% Completed | 7.17 s
-[###################################### ] | 96% Completed | 7.27 s
+[####################################### ] | 99% Completed | 7.27 s
[########################################] | 100% Completed | 7.37 s
@@ -1854,7 +1854,7 @@ Reading data with
- latPandasIndex
PandasIndex(Index([-89.5, -88.5, -87.5, -86.5, -85.5, -84.5, -83.5, -82.5, -81.5, -80.5,
+ 80.5, 81.5, 82.5, 83.5, 84.5, 85.5, 86.5, 87.5, 88.5, 89.5])
- lon(lon)float640.5 1.5 2.5 ... 357.5 358.5 359.5
array([ 0.5, 1.5, 2.5, ..., 357.5, 358.5, 359.5])
- latPandasIndex
PandasIndex(Index([-89.5, -88.5, -87.5, -86.5, -85.5, -84.5, -83.5, -82.5, -81.5, -80.5,
...
80.5, 81.5, 82.5, 83.5, 84.5, 85.5, 86.5, 87.5, 88.5, 89.5],
- dtype='float64', name='lat', length=180)) - lonPandasIndex
PandasIndex(Index([ 0.5, 1.5, 2.5, 3.5, 4.5, 5.5, 6.5, 7.5, 8.5, 9.5,
+ dtype='float64', name='lat', length=180))
- lonPandasIndex
PandasIndex(Index([ 0.5, 1.5, 2.5, 3.5, 4.5, 5.5, 6.5, 7.5, 8.5, 9.5,
...
350.5, 351.5, 352.5, 353.5, 354.5, 355.5, 356.5, 357.5, 358.5, 359.5],
- dtype='float64', name='lon', length=360))
+ dtype='float64', name='lon', length=360))
As shown in the above example, the result of the applied operations is an Xarray DataArray that contains a Dask Array, an identical object type to the object that the operations were performed on. This is true for any operations that can be applied to Xarray DataArrays, including subsetting operations; this next example illustrates this:
@@ -3739,7 +3739,7 @@ Parallel and lazy computation using
@@ -3794,10 +3794,10 @@ Parallel and lazy computation using dask.visualize() function, as shown in this example:
@@ -3819,10 +3819,10 @@ Parallel and lazy computation using
-[ ] | 0% Completed | 122.44 us
+[ ] | 0% Completed | 137.30 us
-[########################################] | 100% Completed | 101.18 ms
+[########################################] | 100% Completed | 101.19 ms
@@ -4236,7 +4236,7 @@ Resources and references
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
@@ -4365,7 +4365,7 @@ Contribute
By the Project Pythia Community.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
diff --git a/core/xarray/enso-xarray.html b/core/xarray/enso-xarray.html
index 316661573..6e86d4355 100644
--- a/core/xarray/enso-xarray.html
+++ b/core/xarray/enso-xarray.html
@@ -79,7 +79,7 @@
-
+
@@ -958,7 +958,7 @@ The Niño 3.4 Index
@@ -1590,7 +1590,7 @@ Select the Niño 3.4 regionwhere() method:
+array(1.8436452, dtype=float32)
The final step of the Niño 3.4 index calculation involves normalizing the data. In this example, we perform this normalization by dividing the smoothed anomaly data by the standard deviation calculated above:
@@ -3396,7 +3396,7 @@ Resources and References
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
@@ -3525,7 +3525,7 @@ Contribute
By the Project Pythia Community.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
diff --git a/core/xarray/xarray-intro.html b/core/xarray/xarray-intro.html
index e17e6628d..308dd9baa 100644
--- a/core/xarray/xarray-intro.html
+++ b/core/xarray/xarray-intro.html
@@ -79,7 +79,7 @@
-
+
@@ -583,25 +583,25 @@ Generate a random numpy array
-array([[[274.00119189, 291.26874805, 287.10212211, 281.93321028],
- [276.72439019, 281.86759901, 277.88521001, 291.15900492],
- [283.47892997, 281.69598295, 281.87670232, 280.04253353]],
+array([[[283.19514872, 279.93414516, 288.85955729, 280.30299831],
+ [282.03318795, 283.36237877, 288.28464039, 284.82856563],
+ [282.15998672, 276.06859236, 285.78192464, 280.47703387]],
- [[292.49183408, 286.75694127, 291.1430049 , 279.6708509 ],
- [283.19466798, 285.34236937, 289.30840199, 286.22515585],
- [289.80677567, 281.73710917, 294.57091617, 291.28505114]],
+ [[282.44435897, 282.262194 , 286.34635361, 285.32213341],
+ [283.03737484, 288.57815157, 290.02698113, 278.23328324],
+ [277.06837841, 286.4771544 , 284.58714744, 288.42673576]],
- [[278.59826878, 287.14196565, 284.68298455, 286.59720048],
- [287.51252429, 288.52590266, 273.69692215, 285.39338362],
- [292.66871999, 278.60078099, 280.78168263, 291.92156783]],
+ [[280.62831671, 275.374152 , 283.08613936, 292.56788174],
+ [286.26020096, 282.03531027, 280.16394984, 288.9366629 ],
+ [282.90971416, 278.40998651, 284.98850877, 283.14584888]],
- [[281.24772197, 286.52210588, 282.61809684, 288.82826349],
- [282.48419301, 286.34184212, 277.57835692, 279.66054122],
- [288.97073742, 287.33849395, 289.90083339, 285.95347237]],
+ [[284.34422355, 283.20830934, 278.27414095, 285.21976361],
+ [279.17521323, 292.21743424, 276.974416 , 284.06699408],
+ [274.74247997, 279.63780379, 275.87914515, 277.71758929]],
- [[282.699394 , 286.72856786, 281.89265707, 286.16350665],
- [278.02139036, 284.54696447, 283.16191651, 278.99615563],
- [284.94443941, 287.35039327, 289.38110818, 280.10681174]]])
+ [[282.71726594, 281.99808895, 279.66639367, 276.71709325],
+ [278.30599074, 277.08406243, 273.68999203, 279.75441922],
+ [271.39869986, 286.48138908, 285.22713979, 291.77285885]]])
@@ -982,44 +982,44 @@ Wrap the array: first attempt
@@ -1402,44 +1402,44 @@ Assign dimension names<xarray.DataArray (time: 5, lat: 3, lon: 4)> Size: 480B
-array([[[274.00119189, 291.26874805, 287.10212211, 281.93321028],
- [276.72439019, 281.86759901, 277.88521001, 291.15900492],
- [283.47892997, 281.69598295, 281.87670232, 280.04253353]],
-
- [[292.49183408, 286.75694127, 291.1430049 , 279.6708509 ],
- [283.19466798, 285.34236937, 289.30840199, 286.22515585],
- [289.80677567, 281.73710917, 294.57091617, 291.28505114]],
-
- [[278.59826878, 287.14196565, 284.68298455, 286.59720048],
- [287.51252429, 288.52590266, 273.69692215, 285.39338362],
- [292.66871999, 278.60078099, 280.78168263, 291.92156783]],
-
- [[281.24772197, 286.52210588, 282.61809684, 288.82826349],
- [282.48419301, 286.34184212, 277.57835692, 279.66054122],
- [288.97073742, 287.33849395, 289.90083339, 285.95347237]],
-
- [[282.699394 , 286.72856786, 281.89265707, 286.16350665],
- [278.02139036, 284.54696447, 283.16191651, 278.99615563],
- [284.94443941, 287.35039327, 289.38110818, 280.10681174]]])
-Dimensions without coordinates: time, lat, lon
+array([[[283.19514872, 279.93414516, 288.85955729, 280.30299831],
+ [282.03318795, 283.36237877, 288.28464039, 284.82856563],
+ [282.15998672, 276.06859236, 285.78192464, 280.47703387]],
+
+ [[282.44435897, 282.262194 , 286.34635361, 285.32213341],
+ [283.03737484, 288.57815157, 290.02698113, 278.23328324],
+ [277.06837841, 286.4771544 , 284.58714744, 288.42673576]],
+
+ [[280.62831671, 275.374152 , 283.08613936, 292.56788174],
+ [286.26020096, 282.03531027, 280.16394984, 288.9366629 ],
+ [282.90971416, 278.40998651, 284.98850877, 283.14584888]],
+
+ [[284.34422355, 283.20830934, 278.27414095, 285.21976361],
+ [279.17521323, 292.21743424, 276.974416 , 284.06699408],
+ [274.74247997, 279.63780379, 275.87914515, 277.71758929]],
+
+ [[282.71726594, 281.99808895, 279.66639367, 276.71709325],
+ [278.30599074, 277.08406243, 273.68999203, 279.75441922],
+ [271.39869986, 286.48138908, 285.22713979, 291.77285885]]])
+Dimensions without coordinates: time, lat, lon
This DataArray is already an improvement over a NumPy array; the DataArray contains names for each of the dimensions (or axes in NumPy parlance). An additional improvement is the association of coordinate-value arrays with data upon creation of a DataArray. In the next example, we illustrate the creation of NumPy arrays representing the coordinate values for each dimension of the DataArray, and how to associate these coordinate arrays with the data in our DataArray.
@@ -1849,51 +1849,51 @@ Initialize the
fill: currentColor;
}
<xarray.DataArray (time: 5, lat: 3, lon: 4)> Size: 480B
-array([[[274.00119189, 291.26874805, 287.10212211, 281.93321028],
- [276.72439019, 281.86759901, 277.88521001, 291.15900492],
- [283.47892997, 281.69598295, 281.87670232, 280.04253353]],
+array([[[283.19514872, 279.93414516, 288.85955729, 280.30299831],
+ [282.03318795, 283.36237877, 288.28464039, 284.82856563],
+ [282.15998672, 276.06859236, 285.78192464, 280.47703387]],
- [[292.49183408, 286.75694127, 291.1430049 , 279.6708509 ],
- [283.19466798, 285.34236937, 289.30840199, 286.22515585],
- [289.80677567, 281.73710917, 294.57091617, 291.28505114]],
+ [[282.44435897, 282.262194 , 286.34635361, 285.32213341],
+ [283.03737484, 288.57815157, 290.02698113, 278.23328324],
+ [277.06837841, 286.4771544 , 284.58714744, 288.42673576]],
- [[278.59826878, 287.14196565, 284.68298455, 286.59720048],
- [287.51252429, 288.52590266, 273.69692215, 285.39338362],
- [292.66871999, 278.60078099, 280.78168263, 291.92156783]],
+ [[280.62831671, 275.374152 , 283.08613936, 292.56788174],
+ [286.26020096, 282.03531027, 280.16394984, 288.9366629 ],
+ [282.90971416, 278.40998651, 284.98850877, 283.14584888]],
- [[281.24772197, 286.52210588, 282.61809684, 288.82826349],
- [282.48419301, 286.34184212, 277.57835692, 279.66054122],
- [288.97073742, 287.33849395, 289.90083339, 285.95347237]],
+ [[284.34422355, 283.20830934, 278.27414095, 285.21976361],
+ [279.17521323, 292.21743424, 276.974416 , 284.06699408],
+ [274.74247997, 279.63780379, 275.87914515, 277.71758929]],
- [[282.699394 , 286.72856786, 281.89265707, 286.16350665],
- [278.02139036, 284.54696447, 283.16191651, 278.99615563],
- [284.94443941, 287.35039327, 289.38110818, 280.10681174]]])
+ [[282.71726594, 281.99808895, 279.66639367, 276.71709325],
+ [278.30599074, 277.08406243, 273.68999203, 279.75441922],
+ [271.39869986, 286.48138908, 285.22713979, 291.77285885]]])
Coordinates:
* time (time) datetime64[ns] 40B 2018-01-01 2018-01-02 ... 2018-01-05
* lat (lat) float64 24B 25.0 40.0 55.0
- * lon (lon) float64 32B -120.0 -100.0 -80.0 -60.0
+ dtype='datetime64[ns]', name='time', freq='D'))
latPandasIndexPandasIndex(Index([25.0, 40.0, 55.0], dtype='float64', name='lat'))
lonPandasIndexPandasIndex(Index([-120.0, -100.0, -80.0, -60.0], dtype='float64', name='lon'))
@@ -2273,54 +2273,54 @@ Set useful attributes<xarray.DataArray (time: 5, lat: 3, lon: 4)> Size: 480B
-array([[[274.00119189, 291.26874805, 287.10212211, 281.93321028],
- [276.72439019, 281.86759901, 277.88521001, 291.15900492],
- [283.47892997, 281.69598295, 281.87670232, 280.04253353]],
+array([[[283.19514872, 279.93414516, 288.85955729, 280.30299831],
+ [282.03318795, 283.36237877, 288.28464039, 284.82856563],
+ [282.15998672, 276.06859236, 285.78192464, 280.47703387]],
- [[292.49183408, 286.75694127, 291.1430049 , 279.6708509 ],
- [283.19466798, 285.34236937, 289.30840199, 286.22515585],
- [289.80677567, 281.73710917, 294.57091617, 291.28505114]],
+ [[282.44435897, 282.262194 , 286.34635361, 285.32213341],
+ [283.03737484, 288.57815157, 290.02698113, 278.23328324],
+ [277.06837841, 286.4771544 , 284.58714744, 288.42673576]],
- [[278.59826878, 287.14196565, 284.68298455, 286.59720048],
- [287.51252429, 288.52590266, 273.69692215, 285.39338362],
- [292.66871999, 278.60078099, 280.78168263, 291.92156783]],
+ [[280.62831671, 275.374152 , 283.08613936, 292.56788174],
+ [286.26020096, 282.03531027, 280.16394984, 288.9366629 ],
+ [282.90971416, 278.40998651, 284.98850877, 283.14584888]],
- [[281.24772197, 286.52210588, 282.61809684, 288.82826349],
- [282.48419301, 286.34184212, 277.57835692, 279.66054122],
- [288.97073742, 287.33849395, 289.90083339, 285.95347237]],
+ [[284.34422355, 283.20830934, 278.27414095, 285.21976361],
+ [279.17521323, 292.21743424, 276.974416 , 284.06699408],
+ [274.74247997, 279.63780379, 275.87914515, 277.71758929]],
- [[282.699394 , 286.72856786, 281.89265707, 286.16350665],
- [278.02139036, 284.54696447, 283.16191651, 278.99615563],
- [284.94443941, 287.35039327, 289.38110818, 280.10681174]]])
+ [[282.71726594, 281.99808895, 279.66639367, 276.71709325],
+ [278.30599074, 277.08406243, 273.68999203, 279.75441922],
+ [271.39869986, 286.48138908, 285.22713979, 291.77285885]]])
Coordinates:
* time (time) datetime64[ns] 40B 2018-01-01 2018-01-02 ... 2018-01-05
* lat (lat) float64 24B 25.0 40.0 55.0
* lon (lon) float64 32B -120.0 -100.0 -80.0 -60.0
Attributes:
units: kelvin
- standard_name: air_temperature
+ dtype='datetime64[ns]', name='time', freq='D'))
latPandasIndexPandasIndex(Index([25.0, 40.0, 55.0], dtype='float64', name='lat'))
lonPandasIndexPandasIndex(Index([-120.0, -100.0, -80.0, -60.0], dtype='float64', name='lon'))
- units :
- kelvin
- standard_name :
- air_temperature
@@ -2699,51 +2699,51 @@ Issues with preservation of attributesdocumentation page.
@@ -3134,54 +3134,54 @@ Create a pressure
+ dtype='datetime64[ns]', name='time', freq='D'))latPandasIndexPandasIndex(Index([25.0, 40.0, 55.0], dtype='float64', name='lat'))
lonPandasIndexPandasIndex(Index([-120.0, -100.0, -80.0, -60.0], dtype='float64', name='lon'))
As listed in the rich display above, the new Dataset object is aware that both DataArrays share the same coordinate axes. (Please note that if this page is not run as a Jupyter Notebook, the rich display may be unavailable.)
@@ -3986,54 +3986,54 @@ Access Data variables and Coordinates in a DataArrays through a dictionary syntax, as shown in this example:
@@ -4407,54 +4407,54 @@ Access Data variables and Coordinates in a Dataset objects are mainly used for loading data from files, which will be covered later in this tutorial.
@@ -4838,18 +4838,18 @@ NumPy-like selection<xarray.DataArray (lat: 3, lon: 4)> Size: 96B
-array([[292.49183408, 286.75694127, 291.1430049 , 279.6708509 ],
- [283.19466798, 285.34236937, 289.30840199, 286.22515585],
- [289.80677567, 281.73710917, 294.57091617, 291.28505114]])
+array([[282.44435897, 282.262194 , 286.34635361, 285.32213341],
+ [283.03737484, 288.57815157, 290.02698113, 278.23328324],
+ [277.06837841, 286.4771544 , 284.58714744, 288.42673576]])
Coordinates:
time datetime64[ns] 8B 2018-01-02
* lat (lat) float64 24B 25.0 40.0 55.0
* lon (lon) float64 32B -120.0 -100.0 -80.0 -60.0
Attributes:
units: kelvin
- standard_name: air_temperature
+ standard_name: air_temperature
This example reveals one of the major shortcomings of index selection. In order to retrieve the correct data using index selection, anyone using a DataArray must have precise knowledge of the axes in the DataArray, including the order of the axes and the meaning of their indices.
By using named coordinates, as shown in the next set of examples, we can avoid this cumbersome burden.
@@ -5229,18 +5229,18 @@ Selecting with
fill: currentColor;
}
<xarray.DataArray (lat: 3, lon: 4)> Size: 96B
-array([[292.49183408, 286.75694127, 291.1430049 , 279.6708509 ],
- [283.19466798, 285.34236937, 289.30840199, 286.22515585],
- [289.80677567, 281.73710917, 294.57091617, 291.28505114]])
+array([[282.44435897, 282.262194 , 286.34635361, 285.32213341],
+ [283.03737484, 288.57815157, 290.02698113, 278.23328324],
+ [277.06837841, 286.4771544 , 284.58714744, 288.42673576]])
Coordinates:
time datetime64[ns] 8B 2018-01-02
* lat (lat) float64 24B 25.0 40.0 55.0
* lon (lon) float64 32B -120.0 -100.0 -80.0 -60.0
Attributes:
units: kelvin
- standard_name: air_temperature
+ standard_name: air_temperature
This method yields the same result as the index selection, however:
@@ -5627,18 +5627,18 @@ Nearest-neighbor sampling.sel indeed returned the data at the temporal value corresponding to the date 2018-01-05.
@@ -6017,20 +6017,20 @@ Interpolation
Info
@@ -6414,29 +6414,29 @@ Slicing along coordinates
Info
@@ -6818,18 +6818,18 @@ One more selection method: <xarray.DataArray (lat: 3, lon: 4)> Size: 96B
-array([[292.49183408, 286.75694127, 291.1430049 , 279.6708509 ],
- [283.19466798, 285.34236937, 289.30840199, 286.22515585],
- [289.80677567, 281.73710917, 294.57091617, 291.28505114]])
+array([[282.44435897, 282.262194 , 286.34635361, 285.32213341],
+ [283.03737484, 288.57815157, 290.02698113, 278.23328324],
+ [277.06837841, 286.4771544 , 284.58714744, 288.42673576]])
Coordinates:
time datetime64[ns] 8B 2018-01-02
* lat (lat) float64 24B 25.0 40.0 55.0
* lon (lon) float64 32B -120.0 -100.0 -80.0 -60.0
Attributes:
units: kelvin
- standard_name: air_temperature
+ standard_name: air_temperature
This selection technique is similar to NumPy’s index-based selection, as shown below:
temp[1,:,:]
@@ -7208,29 +7208,29 @@ One more selection method: <xarray.DataArray (time: 3, lat: 2, lon: 2)> Size: 96B
-array([[[291.26874805, 287.10212211],
- [281.86759901, 277.88521001]],
+array([[[279.93414516, 288.85955729],
+ [283.36237877, 288.28464039]],
- [[286.75694127, 291.1430049 ],
- [285.34236937, 289.30840199]],
+ [[282.262194 , 286.34635361],
+ [288.57815157, 290.02698113]],
- [[287.14196565, 284.68298455],
- [288.52590266, 273.69692215]]])
+ [[275.374152 , 283.08613936],
+ [282.03531027, 280.16394984]]])
Coordinates:
* time (time) datetime64[ns] 24B 2018-01-01 2018-01-02 2018-01-03
* lat (lat) float64 16B 25.0 40.0
* lon (lon) float64 16B -100.0 -80.0
Attributes:
units: kelvin
- standard_name: air_temperature
+ [[275.374152 , 283.08613936],
+ [282.03531027, 280.16394984]]])
- time(time)datetime64[ns]2018-01-01 2018-01-02 2018-01-03
array(['2018-01-01T00:00:00.000000000', '2018-01-02T00:00:00.000000000',
+ '2018-01-03T00:00:00.000000000'], dtype='datetime64[ns]')
- lat(lat)float6425.0 40.0
array([25., 40.])
- lon(lon)float64-100.0 -80.0
array([-100., -80.])
- timePandasIndex
PandasIndex(DatetimeIndex(['2018-01-01', '2018-01-02', '2018-01-03'], dtype='datetime64[ns]', name='time', freq='D'))
- latPandasIndex
PandasIndex(Index([25.0, 40.0], dtype='float64', name='lat'))
- lonPandasIndex
PandasIndex(Index([-100.0, -80.0], dtype='float64', name='lon'))
- units :
- kelvin
- standard_name :
- air_temperature
In contrast with the previous example, this example shows a useful advantage of using the .loc attribute. When using the .loc attribute, you can specify data slices using a syntax similar to NumPy in addition to, or instead of, using the slice function. Both of these slicing techniques are illustrated below:
@@ -7604,29 +7604,29 @@ One more selection method: <xarray.DataArray (time: 3, lat: 2, lon: 2)> Size: 96B
-array([[[291.26874805, 287.10212211],
- [281.86759901, 277.88521001]],
+array([[[279.93414516, 288.85955729],
+ [283.36237877, 288.28464039]],
- [[286.75694127, 291.1430049 ],
- [285.34236937, 289.30840199]],
+ [[282.262194 , 286.34635361],
+ [288.57815157, 290.02698113]],
- [[287.14196565, 284.68298455],
- [288.52590266, 273.69692215]]])
+ [[275.374152 , 283.08613936],
+ [282.03531027, 280.16394984]]])
Coordinates:
* time (time) datetime64[ns] 24B 2018-01-01 2018-01-02 2018-01-03
* lat (lat) float64 16B 25.0 40.0
* lon (lon) float64 16B -100.0 -80.0
Attributes:
units: kelvin
- standard_name: air_temperature
+ [[275.374152 , 283.08613936],
+ [282.03531027, 280.16394984]]])
- time(time)datetime64[ns]2018-01-01 2018-01-02 2018-01-03
array(['2018-01-01T00:00:00.000000000', '2018-01-02T00:00:00.000000000',
+ '2018-01-03T00:00:00.000000000'], dtype='datetime64[ns]')
- lat(lat)float6425.0 40.0
array([25., 40.])
- lon(lon)float64-100.0 -80.0
array([-100., -80.])
- timePandasIndex
PandasIndex(DatetimeIndex(['2018-01-01', '2018-01-02', '2018-01-03'], dtype='datetime64[ns]', name='time', freq='D'))
- latPandasIndex
PandasIndex(Index([25.0, 40.0], dtype='float64', name='lat'))
- lonPandasIndex
PandasIndex(Index([-100.0, -80.0], dtype='float64', name='lon'))
- units :
- kelvin
- standard_name :
- air_temperature
As described above, the arguments to .loc must be in the order of the DataArray’s dimensions. Attempting to slice data without ordering arguments properly can cause errors, as shown below:
@@ -8059,10 +8059,10 @@ Access netCDF data with
@@ -8504,17 +8504,17 @@ Subsetting the
positive: down
Grib_level_type: 100
_CoordinateAxisType: Pressure
- _CoordinateZisPositive: down
+ dtype='float32', name='isobaric1'))- units :
- hPa
- long_name :
- Isobaric surface
- positive :
- down
- Grib_level_type :
- 100
- _CoordinateAxisType :
- Pressure
- _CoordinateZisPositive :
- down
(As described earlier in this tutorial, we can also use dictionary syntax to select specific DataArrays; in this case, we would write ds['isobaric1'].)
Many of the subsetting operations usable on DataArrays can also be used on Datasets. However, when used on Datasets, these operations are performed on every DataArray in the Dataset, as shown below:
@@ -8916,7 +8916,7 @@ Subsetting the
geospatial_lat_min: 10.753308882144761
geospatial_lat_max: 46.8308828962289
geospatial_lon_min: -153.88242040519995
- geospatial_lon_max: -42.666108129242815
As shown above, the subsetting operation performed on the Dataset returned a new Dataset. If only a single DataArray is needed from this new Dataset, it can be retrieved using the familiar dot notation:
@@ -9356,7 +9356,7 @@ Subsetting the
Grib1_TableVersion: 131
Grib1_Parameter: 11
Grib1_Level_Type: 100
- Grib1_Level_Desc: Isobaric surface
+ dtype='float32', name='x', length=268))- long_name :
- Temperature @ Isobaric surface
- units :
- K
- description :
- Temperature
- grid_mapping :
- LambertConformal_Projection
- Grib_Variable_Id :
- VAR_7-15-131-11_L100
- Grib1_Center :
- 7
- Grib1_Subcenter :
- 15
- Grib1_TableVersion :
- 131
- Grib1_Parameter :
- 11
- Grib1_Level_Type :
- 100
- Grib1_Level_Desc :
- Isobaric surface
@@ -9790,19 +9790,19 @@ Aggregation operations
+ dtype='float32', name='isobaric1'))
Info
@@ -10196,19 +10196,19 @@ Aggregation operations
+ dtype='float32', name='isobaric1'))
@@ -10226,7 +10226,7 @@ Simple visualization with
-[<matplotlib.lines.Line2D at 0x7f2e0f58d130>]
+[<matplotlib.lines.Line2D at 0x7f505db3d9d0>]
@@ -10250,7 +10250,7 @@ Customizing the plot
-[<matplotlib.lines.Line2D at 0x7f2e0f58eff0>]
+[<matplotlib.lines.Line2D at 0x7f5055a98d40>]
@@ -10268,7 +10268,7 @@ Plotting 2-D data
-<matplotlib.collections.QuadMesh at 0x7f2df9c43b60>
+<matplotlib.collections.QuadMesh at 0x7f5055ad1be0>
@@ -10667,7 +10667,7 @@ Resources and references
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
@@ -10796,7 +10796,7 @@ Contribute
By the Project Pythia Community.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
diff --git a/foundations/conda.html b/foundations/conda.html
index c07684127..6f809cfd0 100644
--- a/foundations/conda.html
+++ b/foundations/conda.html
@@ -79,7 +79,7 @@
-
+
@@ -913,7 +913,7 @@ Resources and References
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
@@ -1042,7 +1042,7 @@ Contribute
By the Project Pythia Community.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
diff --git a/foundations/getting-started-github.html b/foundations/getting-started-github.html
index 4a2af1d14..ec22e00df 100644
--- a/foundations/getting-started-github.html
+++ b/foundations/getting-started-github.html
@@ -81,7 +81,7 @@
-
+
@@ -768,7 +768,7 @@ Getting Started with GitHub
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
@@ -897,7 +897,7 @@ Contribute
By the Project Pythia Community.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
diff --git a/foundations/getting-started-jupyter.html b/foundations/getting-started-jupyter.html
index b1d2107c0..b95652e74 100644
--- a/foundations/getting-started-jupyter.html
+++ b/foundations/getting-started-jupyter.html
@@ -79,7 +79,7 @@
-
+
@@ -905,7 +905,7 @@ Resources and references
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
@@ -1034,7 +1034,7 @@ Contribute
By the Project Pythia Community.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
diff --git a/foundations/getting-started-python.html b/foundations/getting-started-python.html
index 286ad4516..19ac49850 100644
--- a/foundations/getting-started-python.html
+++ b/foundations/getting-started-python.html
@@ -81,7 +81,7 @@
-
+
@@ -779,7 +779,7 @@ Topics
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
@@ -908,7 +908,7 @@ Contribute
By the Project Pythia Community.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
diff --git a/foundations/github/basic-git.html b/foundations/github/basic-git.html
index 441aa5056..32e0b6b24 100644
--- a/foundations/github/basic-git.html
+++ b/foundations/github/basic-git.html
@@ -79,7 +79,7 @@
-
+
@@ -1222,7 +1222,7 @@ References
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
@@ -1351,7 +1351,7 @@ Contribute
By the Project Pythia Community.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
diff --git a/foundations/github/contribute-to-pythia.html b/foundations/github/contribute-to-pythia.html
index d1e1bad12..11ebc0cc2 100644
--- a/foundations/github/contribute-to-pythia.html
+++ b/foundations/github/contribute-to-pythia.html
@@ -79,7 +79,7 @@
-
+
@@ -885,7 +885,7 @@ Summary
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
@@ -1014,7 +1014,7 @@ Contribute
By the Project Pythia Community.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
diff --git a/foundations/github/git-branches.html b/foundations/github/git-branches.html
index 6dac4b319..4e316269f 100644
--- a/foundations/github/git-branches.html
+++ b/foundations/github/git-branches.html
@@ -79,7 +79,7 @@
-
+
@@ -1036,7 +1036,7 @@ Resources and references
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
@@ -1165,7 +1165,7 @@ Contribute
By the Project Pythia Community.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
diff --git a/foundations/github/github-advanced.html b/foundations/github/github-advanced.html
index 9e93b611f..def634b01 100644
--- a/foundations/github/github-advanced.html
+++ b/foundations/github/github-advanced.html
@@ -77,7 +77,7 @@
-
+
@@ -810,7 +810,7 @@ References
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
@@ -939,7 +939,7 @@ Contribute
By the Project Pythia Community.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
diff --git a/foundations/github/github-cloning-forking.html b/foundations/github/github-cloning-forking.html
index c03c6f72a..18b821529 100644
--- a/foundations/github/github-cloning-forking.html
+++ b/foundations/github/github-cloning-forking.html
@@ -79,7 +79,7 @@
-
+
@@ -988,7 +988,7 @@ References
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
@@ -1117,7 +1117,7 @@ Contribute
By the Project Pythia Community.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
diff --git a/foundations/github/github-issues.html b/foundations/github/github-issues.html
index fe56bf8f1..cf5e4670a 100644
--- a/foundations/github/github-issues.html
+++ b/foundations/github/github-issues.html
@@ -79,7 +79,7 @@
-
+
@@ -875,7 +875,7 @@ References
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
@@ -1004,7 +1004,7 @@ Contribute
By the Project Pythia Community.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
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index c8dc2806e..344643277 100644
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index 81c713d97..f6f7addda 100644
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By the Project Pythia Community.
- Last updated on 15 May 2024.
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By the Project Pythia Community.
- Last updated on 15 May 2024.
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- Last updated on 15 May 2024.
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index 57210f52a..705201479 100644
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By the Project Pythia Community.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
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index 18d9c718b..f531c0445 100644
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"Introducing the DataArray and Dataset": [[23, "introducing-the-dataarray-and-dataset"]], "Introduction to Cartopy": [[3, "introduction-to-cartopy"]], "Introduction to Pandas": [[18, "introduction-to-pandas"]], "Introduction to Xarray": [[23, "introduction-to-xarray"]], "Investigating Extreme Values": [[18, "investigating-extreme-values"]], "Issues": [[33, "issues"]], "Issues and Discussions": [[33, "issues-and-discussions"]], "Issues with preservation of attributes": [[23, "issues-with-preservation-of-attributes"]], "Jupyter Hub": [[26, "jupyter-hub"]], "Jupyter Kernels": [[26, "jupyter-kernels"]], "Jupyter Lab": [[26, "jupyter-lab"]], "Jupyter Notebooks": [[26, "jupyter-notebooks"], [40, "jupyter-notebooks"]], "JupyterLab": [[42, "jupyterlab"]], "Keeping your dev environment up to date": [[0, "keeping-your-dev-environment-up-to-date"]], "Labels and Grid Lines": [[11, "labels-and-grid-lines"]], "Lambert Azimuthal Equal Area Projection": [[3, 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29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 46], "support": 7, "switch": [30, 34], "synchron": 37, "system": [5, 39], "templat": 1, "termin": [40, 42, 46], "terminologi": 7, "test": [11, 21], "text": [9, 42, 43], "thi": [1, 8, 11, 28, 45, 49, 50], "thing": [32, 35], "tide": 7, "time": [7, 23, 28], "timedelta": 7, "timestamp": 7, "tip": [35, 36, 37], "titl": 11, "togeth": 11, "token": 36, "topic": [27, 31, 44], "try": [32, 35], "tune": 39, "tutori": [16, 28], "two": [3, 11], "type": 45, "unix": 7, "up": [0, 1, 30, 42], "updat": 30, "upstream": 37, "us": [1, 3, 10, 11, 13, 15, 18, 20, 21, 23, 24, 28, 50], "usag": 7, "user": 32, "utc": 7, "v": 36, "valu": [13, 18, 20, 23], "variabl": [5, 23, 42], "vc": [28, 39], "vector": 15, "veri": 45, "version": [28, 39], "versu": 7, "via": [29, 50], "view": [28, 38], "visual": [22, 23], "wai": 38, "want": 28, "warn": [1, 3, 13, 14, 18, 42], "we": 28, "web": 39, "what": [1, 3, 5, 7, 10, 11, 13, 14, 15, 18, 20, 21, 23, 24, 26, 28, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 45, 46], "when": 38, "where": 20, "why": [8, 21, 28, 47], "window": 32, "within": 11, "work": 37, "workflow": [30, 34, 37], "would": 21, "wrap": 23, "write": [5, 7], "xarrai": [19, 20, 21, 22, 23], "xr": 23, "year": 7, "york": 3, "you": [21, 28, 30, 38, 39], "your": [0, 1, 18, 30, 34, 36, 41, 45], "zero": 45, "zone": 7}})
\ No newline at end of file
Slices
-Slicing syntax is written as array[start:stop[:step]], where all numbers are optional.
Slicing syntax is written as array[start:stop:step]. Note that all numbers are optional. Importantly, the step parameter is optional and can be omitted, in which case the slice uses a default step of 1.
defaults:
-
@@ -1775,7 +1775,7 @@
Resources and references
- Last updated on 15 May 2024. + Last updated on 20 May 2024.
@@ -1904,7 +1904,7 @@
Contribute
By the Project Pythia Community. - Last updated on 15 May 2024. + Last updated on 20 May 2024. diff --git a/core/numpy/numpy-broadcasting.html b/core/numpy/numpy-broadcasting.html index 78f882658..57e5dd9a6 100644 --- a/core/numpy/numpy-broadcasting.html +++ b/core/numpy/numpy-broadcasting.html @@ -81,7 +81,7 @@ - + @@ -1070,7 +1070,7 @@Blocking
-----@@ -1161,8 +1161,8 @@array([[ 8.3, 11.6, 33.1], - [11.6, 33.1, 18.3], - [33.1, 18.3, 22.5], - [18.3, 22.5, 24.5], - [22.5, 24.5, 20.1], - [24.5, 20.1, 20.1], - [20.1, 20.1, 18. ], - [20.1, 18. , 25. ]]) +
array([[19.3, 18.8, 28.7], + [18.8, 28.7, 27.1], + [28.7, 27.1, 17.1], + [27.1, 17.1, 16.2], + [17.1, 16.2, 15. ], + [16.2, 15. , 15.1], + [15. , 15.1, 14. ], + [15.1, 14. , 19.2]])
Blocking
--diff --git a/core/pandas.html b/core/pandas.html index 2e4230efc..02509b347 100644 --- a/core/pandas.html +++ b/core/pandas.html @@ -79,7 +79,7 @@ - + @@ -759,7 +759,7 @@@@ -1210,13 +1210,13 @@array([[ 8.3, 11.6, 2000. ], - [ 11.6, 2000. , 18.3], - [2000. , 18.3, 22.5], - [ 18.3, 22.5, 24.5], - [ 22.5, 24.5, 20.1], - [ 24.5, 20.1, 20.1], - [ 20.1, 20.1, 18. ], - [ 20.1, 18. , 25. ]]) +
array([[ 19.3, 18.8, 2000. ], + [ 18.8, 2000. , 27.1], + [2000. , 27.1, 17.1], + [ 27.1, 17.1, 16.2], + [ 17.1, 16.2, 15. ], + [ 16.2, 15. , 15.1], + [ 15. , 15.1, 14. ], + [ 15.1, 14. , 19.2]])
Finding the difference between min and max -
@@ -1015,7 +1015,7 @@@@ -1241,13 +1241,13 @@array([[6, 6, 7, ..., 8, 7, 6], - [6, 6, 6, ..., 7, 7, 7], - [7, 7, 7, ..., 6, 7, 7], +
array([[7, 7, 7, ..., 7, 8, 7], + [6, 7, 6, ..., 7, 7, 6], + [7, 6, 7, ..., 7, 6, 6], ..., - [7, 7, 7, ..., 7, 7, 6], - [7, 6, 6, ..., 6, 7, 7], - [7, 6, 7, ..., 7, 8, 7]]) + [6, 7, 6, ..., 6, 6, 7], + [7, 7, 7, ..., 6, 6, 7], + [6, 7, 7, ..., 8, 8, 7]])Finding the difference between min and max -
diff --git a/core/overview.html b/core/overview.html index 6acc0ae46..fd4752cc2 100644 --- a/core/overview.html +++ b/core/overview.html @@ -79,7 +79,7 @@ - + @@ -886,7 +886,7 @@@@ -1276,19 +1276,19 @@array([[775. , 775. , 737.5, ..., 700. , 737.5, 775. ], - [775. , 775. , 775. , ..., 737.5, 737.5, 737.5], - [737.5, 737.5, 737.5, ..., 775. , 737.5, 737.5], +
array([[737.5, 737.5, 737.5, ..., 737.5, 700. , 737.5], + [775. , 737.5, 775. , ..., 737.5, 737.5, 775. ], + [737.5, 775. , 737.5, ..., 737.5, 775. , 775. ], ..., - [737.5, 737.5, 737.5, ..., 737.5, 737.5, 775. ], - [737.5, 775. , 775. , ..., 775. , 737.5, 737.5], - [737.5, 775. , 737.5, ..., 737.5, 700. , 737.5]]) + [775. , 737.5, 775. , ..., 775. , 775. , 737.5], + [737.5, 737.5, 737.5, ..., 775. , 775. , 737.5], + [775. , 737.5, 737.5, ..., 700. , 700. , 737.5]])Finding the difference between min and max -
@@ -1332,19 +1332,19 @@array([[ -9.55417349, -9.78584972, -9.92661689, ..., -12.67789616, - -10.49077377, -6.56515605], - [-10.00010532, -10.70819797, -7.20985445, ..., -11.10789153, - -11.73163442, -8.29531203], - [-11.88696252, -12.29088724, -11.70346147, ..., -7.30859657, - -8.97849533, -13.84614818], +
array([[-13.35480377, -11.37389473, -6.11490009, ..., -9.69409241, + -14.14853765, -11.04724036], + [ -6.50332158, -11.09958323, -5.95998607, ..., -13.2604716 , + -13.19971009, -8.49574616], + [-12.28051357, -10.19815861, -7.42945122, ..., -11.5798199 , + -8.91153322, -8.6079996 ], ..., - [-11.90215938, -7.91187899, -13.08679868, ..., -10.41120932, - -11.35468344, -11.06459146], - [-11.50292233, -6.26889319, -7.38221861, ..., -10.24036433, - -10.12594611, -5.63518882], - [-10.32651953, -6.06759924, -9.54152312, ..., -10.45702773, - -12.20574751, -8.01746694]]) + [ -8.10255586, -12.16615351, -11.27253366, ..., -10.44432682, + -7.39154896, -10.99574973], + [-16.5968518 , -12.57487222, -8.39736923, ..., -4.15595587, + -8.86979291, -11.28873531], + [ -8.59386363, -9.38047716, -10.46838159, ..., -11.30765026, + -10.89295232, -9.24056526]])Finding the difference between min and max -
@@ -1840,7 +1840,7 @@@@ -1711,7 +1711,7 @@array([[ -9.55417349, -9.78584972, -9.92661689, ..., -12.67789616, - -10.49077377, -6.56515605], - [-10.00010532, -10.70819797, -7.20985445, ..., -11.10789153, - -11.73163442, -8.29531203], - [-11.88696252, -12.29088724, -11.70346147, ..., -7.30859657, - -8.97849533, -13.84614818], +
array([[-13.35480377, -11.37389473, -6.11490009, ..., -9.69409241, + -14.14853765, -11.04724036], + [ -6.50332158, -11.09958323, -5.95998607, ..., -13.2604716 , + -13.19971009, -8.49574616], + [-12.28051357, -10.19815861, -7.42945122, ..., -11.5798199 , + -8.91153322, -8.6079996 ], ..., - [-11.90215938, -7.91187899, -13.08679868, ..., -10.41120932, - -11.35468344, -11.06459146], - [-11.50292233, -6.26889319, -7.38221861, ..., -10.24036433, - -10.12594611, -5.63518882], - [-10.32651953, -6.06759924, -9.54152312, ..., -10.45702773, - -12.20574751, -8.01746694]]) + [ -8.10255586, -12.16615351, -11.27253366, ..., -10.44432682, + -7.39154896, -10.99574973], + [-16.5968518 , -12.57487222, -8.39736923, ..., -4.15595587, + -8.86979291, -11.28873531], + [ -8.59386363, -9.38047716, -10.46838159, ..., -11.30765026, + -10.89295232, -9.24056526]])Resources and references
- Last updated on 15 May 2024. + Last updated on 20 May 2024.
Contribute
By the Project Pythia Community. - Last updated on 15 May 2024. + Last updated on 20 May 2024.Tutorials
- Last updated on 15 May 2024. + Last updated on 20 May 2024.
Contribute
By the Project Pythia Community. - Last updated on 15 May 2024. + Last updated on 20 May 2024.Pandas
- Last updated on 15 May 2024. + Last updated on 20 May 2024.
@@ -888,7 +888,7 @@
Contribute
By the Project Pythia Community. - Last updated on 15 May 2024. + Last updated on 20 May 2024. diff --git a/core/pandas/pandas.html b/core/pandas/pandas.html index 734fecdb6..2e24138b7 100644 --- a/core/pandas/pandas.html +++ b/core/pandas/pandas.html @@ -79,7 +79,7 @@ - + @@ -1098,7 +1098,7 @@Indexing a
Seri-diff --git a/core/xarray/computation-masking.html b/core/xarray/computation-masking.html index b15ad248c..12f19902b 100644 --- a/core/xarray/computation-masking.html +++ b/core/xarray/computation-masking.html @@ -79,7 +79,7 @@ - + @@ -943,7 +943,7 @@/tmp/ipykernel_2735/737336773.py:1: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]` +
@@ -1449,7 +1449,7 @@/tmp/ipykernel_2746/737336773.py:1: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]` nino34_series[3]
Extending to the
-@@ -889,7 +889,7 @@diff --git a/core/xarray.html b/core/xarray.html index e4624108b..6bc787165 100644 --- a/core/xarray.html +++ b/core/xarray.html @@ -79,7 +79,7 @@ - + @@ -760,7 +760,7 @@/tmp/ipykernel_2735/541596450.py:1: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]` +
@@ -3645,7 +3645,7 @@/tmp/ipykernel_2746/541596450.py:1: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]` df["Nino34"][3]
Resampling -
@@ -4563,7 +4563,7 @@/tmp/ipykernel_2735/233158901.py:1: FutureWarning: 'Y' is deprecated and will be removed in a future version, please use 'YE' instead. +
@@ -4434,7 +4434,7 @@/tmp/ipykernel_2746/233158901.py:1: FutureWarning: 'Y' is deprecated and will be removed in a future version, please use 'YE' instead. df.Nino34.resample('1Y').mean().plot();Resources and References
- Last updated on 15 May 2024. + Last updated on 20 May 2024.
Contribute
By the Project Pythia Community. - Last updated on 15 May 2024. + Last updated on 20 May 2024.Xarray
- Last updated on 15 May 2024. + Last updated on 20 May 2024.
Contribute
By the Project Pythia Community. - Last updated on 15 May 2024. + Last updated on 20 May 2024.Data Setup @@ -1576,7 +1576,7 @@
Arithmetic Operations + dtype='float64', name='lon', length=360))
In addition, there are many other arithmetic operations that can be performed on DataArrays. In this example, we demonstrate squaring the original Celsius values of our air temperature data:
@@ -2246,7 +2246,7 @@
Arithmetic Operations + dtype='float64', name='lon', length=360))
Aggregation Methodsdim keyword argument; this means that the function was applied over all of the dataset’s dimensions. In other words, the aggregation method computed the mean of every element of the temperature dataset across every temporal and spatial data point. However, if a dimension name is used with the dim keyword argument, the aggregation method computes an aggregation along the given dimension. In this next example, we use aggregation to calculate the temporal mean across all spatial data; this is performed by providing the dimension name 'time' to the dim keyword argument:
@@ -3276,7 +3276,7 @@ Aggregation Methods
@@ -3717,7 +3717,7 @@ Aggregation Methods
@@ -4325,7 +4325,7 @@ Aggregation Methods
@@ -4892,7 +4892,7 @@ Compute climatologyDataArray containing the climatology data, we can plot the data in different ways. In this example, we plot the climatology at a specific latitude-longitude point:
@@ -5412,7 +5412,7 @@ Compute anomaly
@@ -6095,7 +6095,7 @@ Compute anomaly.weighted() method and the newly loaded CESM2 dataset to weight the grid cell data as described above:
@@ -6598,7 +6598,7 @@ Other high level computation functionalityrolling method to compute averages in a moving window of 5 months of data:
@@ -7114,7 +7114,7 @@ Other high level computation functionality
@@ -7783,7 +7783,7 @@ Masking Data
Using where with one condition
@@ -8421,15 +8421,15 @@ Using where
+ dtype='float64', name='lon', length=360))
- cell_measures :
- area: areacello
- cell_methods :
- area: mean where sea time: mean
- comment :
- Model data on the 1x1 grid includes values in all cells for which ocean cells on the native grid cover more than 52.5 percent of the 1x1 grid cell. This 52.5 percent cutoff was chosen to produce ocean surface area on the 1x1 grid as close as possible to ocean surface area on the native grid, while not introducing fractional cell coverage.
- description :
- This may differ from "surface temperature" in regions of sea ice or floating ice shelves. For models using conservative temperature as the prognostic field, they should report the top ocean layer as surface potential temperature, which is the same as surface in situ temperature.
- frequency :
- mon
- id :
- tos
- long_name :
- Sea Surface Temperature
- mipTable :
- Omon
- out_name :
- tos
- prov :
- Omon ((isd.003))
- realm :
- ocean
- standard_name :
- sea_surface_temperature
- time :
- time
- time_label :
- time-mean
- time_title :
- Temporal mean
- title :
- Sea Surface Temperature
- type :
- real
- units :
- degC
- variable_id :
- tos
As shown in the previous example, methods like .isel() and .sel() return data of a different shape than the original data provided to them. However, .where() preserves the shape of the original data by masking the values with a Boolean condition. Data values for which the condition is True are returned identical to the values passed in. On the other hand, data values for which the condition is False are returned as a preset example value. (This example value defaults to nan, but can be set to other values as well.)
Before testing .where(), it is helpful to look at the official documentation. As stated above, the .where() method takes a Boolean condition. (Boolean conditions use operators such as less-than, greater-than, and equal-to, and return a value of True or False.) Most uses of .where() check whether or not specific data values are less than or greater than a constant value. As stated in the documentation, the data values specified in the Boolean condition of .where() can be any of the following:
@@ -8864,7 +8864,7 @@ Using where
+ dtype='float64', name='lon', length=360))- cell_measures :
- area: areacello
- cell_methods :
- area: mean where sea time: mean
- comment :
- Model data on the 1x1 grid includes values in all cells for which ocean cells on the native grid cover more than 52.5 percent of the 1x1 grid cell. This 52.5 percent cutoff was chosen to produce ocean surface area on the 1x1 grid as close as possible to ocean surface area on the native grid, while not introducing fractional cell coverage.
- description :
- This may differ from "surface temperature" in regions of sea ice or floating ice shelves. For models using conservative temperature as the prognostic field, they should report the top ocean layer as surface potential temperature, which is the same as surface in situ temperature.
- frequency :
- mon
- id :
- tos
- long_name :
- Sea Surface Temperature
- mipTable :
- Omon
- out_name :
- tos
- prov :
- Omon ((isd.003))
- realm :
- ocean
- standard_name :
- sea_surface_temperature
- time :
- time
- time_label :
- time-mean
- time_title :
- Temporal mean
- title :
- Sea Surface Temperature
- type :
- real
- units :
- degC
- variable_id :
- tos
In this example, we use Matplotlib to plot the original, unmasked data, as well as the masked data created in the previous example.
@@ -9321,7 +9321,7 @@ Resources and References
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
@@ -9450,7 +9450,7 @@ Contribute
By the Project Pythia Community.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
diff --git a/core/xarray/dask-arrays-xarray.html b/core/xarray/dask-arrays-xarray.html
index 0af9daaf3..65793b335 100644
--- a/core/xarray/dask-arrays-xarray.html
+++ b/core/xarray/dask-arrays-xarray.html
@@ -79,7 +79,7 @@
-
+
@@ -568,91 +568,91 @@ Create a dask.a
-array([[[0.08870167, 0.5536724 , 0.86869933, ..., 0.59745109,
- 0.66241166, 0.14712849],
- [0.79424186, 0.53985475, 0.83117213, ..., 0.64752434,
- 0.2068288 , 0.43561572],
- [0.93755297, 0.12641562, 0.55493093, ..., 0.44682309,
- 0.9108161 , 0.28494995],
+array([[[3.73479162e-01, 5.69354087e-01, 8.44121771e-01, ...,
+ 6.95300592e-01, 7.05791578e-01, 3.22757785e-01],
+ [7.34673387e-01, 3.22502093e-02, 2.98660716e-02, ...,
+ 4.48326876e-01, 1.28181189e-01, 6.17129937e-01],
+ [4.61524077e-01, 5.72981031e-01, 6.22455684e-02, ...,
+ 4.66387696e-01, 2.24365962e-02, 9.05528466e-01],
...,
- [0.36681494, 0.55104908, 0.67358062, ..., 0.93120538,
- 0.51164396, 0.2149192 ],
- [0.33220658, 0.18853655, 0.84133513, ..., 0.09990092,
- 0.45265087, 0.48841145],
- [0.54632195, 0.99301667, 0.40513953, ..., 0.5341915 ,
- 0.3220391 , 0.07544213]],
-
- [[0.8398981 , 0.14999579, 0.81431679, ..., 0.45814483,
- 0.81962377, 0.01308931],
- [0.52904343, 0.17038365, 0.49820524, ..., 0.71294698,
- 0.49906669, 0.32305634],
- [0.83597797, 0.12242548, 0.63334391, ..., 0.77669187,
- 0.44530452, 0.02031067],
+ [4.32840675e-01, 6.11791423e-01, 7.77157080e-01, ...,
+ 5.21898060e-01, 5.08370890e-01, 5.88153598e-01],
+ [8.04673850e-01, 7.80296541e-03, 5.00244166e-01, ...,
+ 9.45710973e-01, 5.08700949e-01, 8.89905823e-01],
+ [4.87536782e-01, 4.64576884e-01, 4.97751472e-01, ...,
+ 9.63294478e-01, 8.00438624e-01, 9.39160580e-01]],
+
+ [[9.59798128e-01, 9.67977225e-01, 5.67081374e-01, ...,
+ 1.15551677e-01, 5.93188567e-01, 8.21494821e-01],
+ [7.09798171e-01, 6.86764079e-01, 9.75639983e-01, ...,
+ 6.65980228e-01, 3.63136928e-01, 3.91634965e-01],
+ [2.45312843e-01, 7.92456108e-01, 7.52068086e-01, ...,
+ 8.31396290e-01, 7.34853345e-01, 1.59997513e-02],
...,
- [0.23487395, 0.54185136, 0.84738714, ..., 0.43194404,
- 0.96464114, 0.34661126],
- [0.06216953, 0.87402041, 0.89512514, ..., 0.38706893,
- 0.09187095, 0.88666451],
- [0.46384865, 0.02462827, 0.71575049, ..., 0.47421697,
- 0.30570578, 0.58421292]],
-
- [[0.05704318, 0.36232811, 0.03448708, ..., 0.86748361,
- 0.49546271, 0.39195962],
- [0.62907898, 0.52593284, 0.87418635, ..., 0.97638485,
- 0.63695351, 0.75326941],
- [0.11640535, 0.15534404, 0.80460691, ..., 0.26935451,
- 0.00686985, 0.42474694],
+ [6.29006234e-01, 5.66655665e-01, 9.59798385e-01, ...,
+ 4.64611557e-02, 8.47630616e-01, 9.96868460e-01],
+ [8.65409572e-01, 6.62829465e-01, 5.85935120e-01, ...,
+ 2.76645046e-01, 4.05531947e-01, 1.86970157e-01],
+ [5.88186192e-02, 4.80258125e-01, 7.50053106e-01, ...,
+ 5.82584007e-01, 7.16238302e-01, 3.09095729e-01]],
+
+ [[1.95867406e-01, 8.02997164e-01, 5.92335907e-02, ...,
+ 6.00495084e-02, 3.58568845e-02, 6.14435689e-01],
+ [5.40191024e-01, 7.45240262e-01, 5.83926237e-01, ...,
+ 5.42683512e-01, 7.15062271e-05, 8.15838308e-01],
+ [3.56121359e-01, 6.64103118e-01, 3.19197578e-01, ...,
+ 4.30885292e-01, 5.63274760e-01, 5.58304993e-01],
...,
- [0.53156325, 0.69190957, 0.69352384, ..., 0.77170882,
- 0.44011939, 0.39866217],
- [0.29442052, 0.74080675, 0.46131608, ..., 0.32366183,
- 0.73788577, 0.04341616],
- [0.94117032, 0.92630431, 0.79008536, ..., 0.02443356,
- 0.52042712, 0.83466538]],
+ [3.70429144e-01, 2.28949319e-01, 2.12465225e-01, ...,
+ 8.75347366e-01, 6.88876481e-01, 8.52985255e-01],
+ [8.43544201e-01, 3.50323808e-01, 9.94755069e-01, ...,
+ 2.31937385e-01, 6.46129523e-01, 7.56519847e-01],
+ [9.23292344e-01, 9.06543740e-01, 2.16039623e-01, ...,
+ 6.17841121e-01, 5.83778599e-02, 7.07627581e-01]],
...,
- [[0.1396787 , 0.60143505, 0.57058462, ..., 0.18893598,
- 0.82985107, 0.54242887],
- [0.68157985, 0.63122788, 0.1780818 , ..., 0.55248006,
- 0.63127177, 0.4663941 ],
- [0.55463676, 0.49022722, 0.28183346, ..., 0.64073606,
- 0.37788708, 0.05233016],
+ [[5.50076397e-01, 6.23247920e-01, 6.69863249e-01, ...,
+ 6.66141598e-01, 4.98073488e-01, 9.70545887e-01],
+ [7.38559992e-01, 3.21393458e-01, 4.05421647e-01, ...,
+ 2.52285356e-01, 8.08345653e-01, 7.28764634e-01],
+ [2.68191728e-01, 6.03169049e-02, 6.89258446e-01, ...,
+ 6.21042327e-01, 9.60250603e-01, 9.75257028e-01],
...,
- [0.39020736, 0.85735173, 0.00786958, ..., 0.66161596,
- 0.18152074, 0.17731408],
- [0.93613911, 0.96578828, 0.38973709, ..., 0.06864502,
- 0.5063675 , 0.73631955],
- [0.1671134 , 0.45039599, 0.38253178, ..., 0.74316064,
- 0.20253998, 0.93656703]],
-
- [[0.28018602, 0.84864745, 0.65231976, ..., 0.57238332,
- 0.37536731, 0.04826117],
- [0.47663448, 0.86778224, 0.95451621, ..., 0.76880871,
- 0.89875752, 0.06756555],
- [0.49609048, 0.21693506, 0.23207324, ..., 0.06636032,
- 0.95937476, 0.4827714 ],
+ [6.11978830e-01, 6.32479701e-01, 1.75826196e-01, ...,
+ 2.09161988e-01, 6.71446177e-01, 4.78915946e-01],
+ [6.45044934e-01, 1.15167985e-01, 9.05467373e-01, ...,
+ 7.79598426e-01, 8.76027493e-01, 5.70161686e-01],
+ [8.44173395e-01, 7.14987726e-01, 5.27098062e-01, ...,
+ 6.24560049e-01, 3.32587128e-02, 1.48219268e-01]],
+
+ [[9.16337354e-01, 1.38628117e-01, 2.76572964e-01, ...,
+ 5.68919305e-02, 3.07040211e-01, 6.70607173e-02],
+ [6.78200880e-01, 1.19012654e-01, 8.76844508e-01, ...,
+ 5.78903343e-01, 5.08716762e-01, 3.27430604e-01],
+ [5.19310927e-01, 1.62933592e-01, 6.94104791e-01, ...,
+ 8.28521396e-01, 5.63944660e-01, 9.23353464e-01],
...,
- [0.83197412, 0.22629066, 0.1589711 , ..., 0.55726833,
- 0.40921459, 0.94548168],
- [0.20373662, 0.76138868, 0.56265755, ..., 0.71025427,
- 0.19641998, 0.49627513],
- [0.20384921, 0.34173511, 0.44829 , ..., 0.3999668 ,
- 0.75240336, 0.21357396]],
-
- [[0.05024137, 0.77607526, 0.32452642, ..., 0.26169962,
- 0.2092348 , 0.79937281],
- [0.8834038 , 0.56909665, 0.84006875, ..., 0.09540328,
- 0.66257683, 0.78342945],
- [0.29906903, 0.08192958, 0.51913898, ..., 0.32946737,
- 0.91310302, 0.99198924],
+ [8.75833692e-01, 8.80034882e-01, 3.94957057e-01, ...,
+ 3.88766073e-01, 5.62146961e-01, 4.07401505e-01],
+ [8.83232515e-02, 7.93868945e-02, 7.09396623e-01, ...,
+ 4.00821090e-01, 9.67897242e-01, 3.30712526e-02],
+ [6.21839195e-01, 3.66731583e-01, 9.97239185e-01, ...,
+ 4.06892246e-01, 8.84142338e-01, 1.29206897e-01]],
+
+ [[3.13410458e-01, 7.92759677e-01, 8.24979480e-02, ...,
+ 5.38935776e-02, 4.52819725e-01, 1.48534750e-01],
+ [2.06595218e-01, 8.46746782e-01, 9.06689993e-01, ...,
+ 8.52571008e-01, 4.28351337e-01, 8.06352220e-01],
+ [3.77563013e-01, 9.99637726e-01, 3.37356897e-01, ...,
+ 8.18911136e-01, 8.32562303e-01, 8.45143802e-02],
...,
- [0.89344391, 0.04685752, 0.16870497, ..., 0.70133294,
- 0.2028986 , 0.16916596],
- [0.62721463, 0.12582174, 0.59300888, ..., 0.93023778,
- 0.50053284, 0.63544531],
- [0.89994404, 0.84669077, 0.65421957, ..., 0.19573779,
- 0.56024887, 0.73584493]]])
+ [5.51431397e-01, 8.77465751e-01, 9.63517992e-01, ...,
+ 3.34710544e-02, 9.70363045e-01, 5.42823121e-01],
+ [3.89247115e-01, 3.36500509e-01, 4.31638413e-02, ...,
+ 8.82214294e-02, 2.41024552e-01, 4.19862342e-01],
+ [5.65544128e-01, 6.09859197e-01, 2.77376901e-01, ...,
+ 5.51955058e-01, 6.67783854e-02, 4.08359511e-01]]])
@@ -856,7 +856,7 @@ Why the difference?
-
+
Aggregation Methods
@@ -3717,7 +3717,7 @@ Aggregation Methods
@@ -4325,7 +4325,7 @@ Aggregation Methods
@@ -4892,7 +4892,7 @@ Compute climatologyDataArray containing the climatology data, we can plot the data in different ways. In this example, we plot the climatology at a specific latitude-longitude point:
@@ -5412,7 +5412,7 @@ Compute anomaly
@@ -6095,7 +6095,7 @@ Compute anomaly.weighted() method and the newly loaded CESM2 dataset to weight the grid cell data as described above:
@@ -6598,7 +6598,7 @@ Other high level computation functionalityrolling method to compute averages in a moving window of 5 months of data:
@@ -7114,7 +7114,7 @@ Other high level computation functionality
@@ -7783,7 +7783,7 @@ Masking Data
Using where with one condition
@@ -8421,15 +8421,15 @@ Using where
+ dtype='float64', name='lon', length=360))
- cell_measures :
- area: areacello
- cell_methods :
- area: mean where sea time: mean
- comment :
- Model data on the 1x1 grid includes values in all cells for which ocean cells on the native grid cover more than 52.5 percent of the 1x1 grid cell. This 52.5 percent cutoff was chosen to produce ocean surface area on the 1x1 grid as close as possible to ocean surface area on the native grid, while not introducing fractional cell coverage.
- description :
- This may differ from "surface temperature" in regions of sea ice or floating ice shelves. For models using conservative temperature as the prognostic field, they should report the top ocean layer as surface potential temperature, which is the same as surface in situ temperature.
- frequency :
- mon
- id :
- tos
- long_name :
- Sea Surface Temperature
- mipTable :
- Omon
- out_name :
- tos
- prov :
- Omon ((isd.003))
- realm :
- ocean
- standard_name :
- sea_surface_temperature
- time :
- time
- time_label :
- time-mean
- time_title :
- Temporal mean
- title :
- Sea Surface Temperature
- type :
- real
- units :
- degC
- variable_id :
- tos
Aggregation Methods
@@ -4892,7 +4892,7 @@ Compute climatologyDataArray containing the climatology data, we can plot the data in different ways. In this example, we plot the climatology at a specific latitude-longitude point:
@@ -5412,7 +5412,7 @@ Compute anomaly
@@ -6095,7 +6095,7 @@ Compute anomaly.weighted() method and the newly loaded CESM2 dataset to weight the grid cell data as described above:
@@ -6598,7 +6598,7 @@ Other high level computation functionalityrolling method to compute averages in a moving window of 5 months of data:
@@ -7114,7 +7114,7 @@ Other high level computation functionality
@@ -7783,7 +7783,7 @@ Masking Data
Using where with one condition
@@ -8421,15 +8421,15 @@ Using where
+ dtype='float64', name='lon', length=360))
- cell_measures :
- area: areacello
- cell_methods :
- area: mean where sea time: mean
- comment :
- Model data on the 1x1 grid includes values in all cells for which ocean cells on the native grid cover more than 52.5 percent of the 1x1 grid cell. This 52.5 percent cutoff was chosen to produce ocean surface area on the 1x1 grid as close as possible to ocean surface area on the native grid, while not introducing fractional cell coverage.
- description :
- This may differ from "surface temperature" in regions of sea ice or floating ice shelves. For models using conservative temperature as the prognostic field, they should report the top ocean layer as surface potential temperature, which is the same as surface in situ temperature.
- frequency :
- mon
- id :
- tos
- long_name :
- Sea Surface Temperature
- mipTable :
- Omon
- out_name :
- tos
- prov :
- Omon ((isd.003))
- realm :
- ocean
- standard_name :
- sea_surface_temperature
- time :
- time
- time_label :
- time-mean
- time_title :
- Temporal mean
- title :
- Sea Surface Temperature
- type :
- real
- units :
- degC
- variable_id :
- tos
@@ -5412,7 +5412,7 @@ Compute anomaly
Compute anomaly
@@ -6095,7 +6095,7 @@ Compute anomaly.weighted() method and the newly loaded CESM2 dataset to weight the grid cell data as described above:
@@ -6598,7 +6598,7 @@ Other high level computation functionalityrolling method to compute averages in a moving window of 5 months of data:
@@ -7114,7 +7114,7 @@ Other high level computation functionality
@@ -7783,7 +7783,7 @@ Masking Data
Using where with one condition
@@ -8421,15 +8421,15 @@ Using where
+ dtype='float64', name='lon', length=360))
- cell_measures :
- area: areacello
- cell_methods :
- area: mean where sea time: mean
- comment :
- Model data on the 1x1 grid includes values in all cells for which ocean cells on the native grid cover more than 52.5 percent of the 1x1 grid cell. This 52.5 percent cutoff was chosen to produce ocean surface area on the 1x1 grid as close as possible to ocean surface area on the native grid, while not introducing fractional cell coverage.
- description :
- This may differ from "surface temperature" in regions of sea ice or floating ice shelves. For models using conservative temperature as the prognostic field, they should report the top ocean layer as surface potential temperature, which is the same as surface in situ temperature.
- frequency :
- mon
- id :
- tos
- long_name :
- Sea Surface Temperature
- mipTable :
- Omon
- out_name :
- tos
- prov :
- Omon ((isd.003))
- realm :
- ocean
- standard_name :
- sea_surface_temperature
- time :
- time
- time_label :
- time-mean
- time_title :
- Temporal mean
- title :
- Sea Surface Temperature
- type :
- real
- units :
- degC
- variable_id :
- tos
Compute anomaly.weighted() method and the newly loaded CESM2 dataset to weight the grid cell data as described above:
@@ -6598,7 +6598,7 @@ Other high level computation functionalityrolling method to compute averages in a moving window of 5 months of data:
@@ -7114,7 +7114,7 @@ Other high level computation functionality
@@ -7783,7 +7783,7 @@ Masking Data
Using where with one condition
@@ -8421,15 +8421,15 @@ Using where
+ dtype='float64', name='lon', length=360))
- cell_measures :
- area: areacello
- cell_methods :
- area: mean where sea time: mean
- comment :
- Model data on the 1x1 grid includes values in all cells for which ocean cells on the native grid cover more than 52.5 percent of the 1x1 grid cell. This 52.5 percent cutoff was chosen to produce ocean surface area on the 1x1 grid as close as possible to ocean surface area on the native grid, while not introducing fractional cell coverage.
- description :
- This may differ from "surface temperature" in regions of sea ice or floating ice shelves. For models using conservative temperature as the prognostic field, they should report the top ocean layer as surface potential temperature, which is the same as surface in situ temperature.
- frequency :
- mon
- id :
- tos
- long_name :
- Sea Surface Temperature
- mipTable :
- Omon
- out_name :
- tos
- prov :
- Omon ((isd.003))
- realm :
- ocean
- standard_name :
- sea_surface_temperature
- time :
- time
- time_label :
- time-mean
- time_title :
- Temporal mean
- title :
- Sea Surface Temperature
- type :
- real
- units :
- degC
- variable_id :
- tos
Other high level computation functionalityrolling method to compute averages in a moving window of 5 months of data:
@@ -7114,7 +7114,7 @@ Other high level computation functionality
@@ -7783,7 +7783,7 @@ Masking Data
Using where with one condition
@@ -8421,15 +8421,15 @@ Using where
+ dtype='float64', name='lon', length=360))
- cell_measures :
- area: areacello
- cell_methods :
- area: mean where sea time: mean
- comment :
- Model data on the 1x1 grid includes values in all cells for which ocean cells on the native grid cover more than 52.5 percent of the 1x1 grid cell. This 52.5 percent cutoff was chosen to produce ocean surface area on the 1x1 grid as close as possible to ocean surface area on the native grid, while not introducing fractional cell coverage.
- description :
- This may differ from "surface temperature" in regions of sea ice or floating ice shelves. For models using conservative temperature as the prognostic field, they should report the top ocean layer as surface potential temperature, which is the same as surface in situ temperature.
- frequency :
- mon
- id :
- tos
- long_name :
- Sea Surface Temperature
- mipTable :
- Omon
- out_name :
- tos
- prov :
- Omon ((isd.003))
- realm :
- ocean
- standard_name :
- sea_surface_temperature
- time :
- time
- time_label :
- time-mean
- time_title :
- Temporal mean
- title :
- Sea Surface Temperature
- type :
- real
- units :
- degC
- variable_id :
- tos
Other high level computation functionality
@@ -7783,7 +7783,7 @@ Masking Data
Using where with one condition
@@ -8421,15 +8421,15 @@ Using where
+ dtype='float64', name='lon', length=360))
Masking Data
Using where with one condition
@@ -8421,15 +8421,15 @@ Using where
+ dtype='float64', name='lon', length=360))
where with one conditionwhere
+ dtype='float64', name='lon', length=360))- cell_measures :
- area: areacello
- cell_methods :
- area: mean where sea time: mean
- comment :
- Model data on the 1x1 grid includes values in all cells for which ocean cells on the native grid cover more than 52.5 percent of the 1x1 grid cell. This 52.5 percent cutoff was chosen to produce ocean surface area on the 1x1 grid as close as possible to ocean surface area on the native grid, while not introducing fractional cell coverage.
- description :
- This may differ from "surface temperature" in regions of sea ice or floating ice shelves. For models using conservative temperature as the prognostic field, they should report the top ocean layer as surface potential temperature, which is the same as surface in situ temperature.
- frequency :
- mon
- id :
- tos
- long_name :
- Sea Surface Temperature
- mipTable :
- Omon
- out_name :
- tos
- prov :
- Omon ((isd.003))
- realm :
- ocean
- standard_name :
- sea_surface_temperature
- time :
- time
- time_label :
- time-mean
- time_title :
- Temporal mean
- title :
- Sea Surface Temperature
- type :
- real
- units :
- degC
- variable_id :
- tos
.isel() and .sel() return data of a different shape than the original data provided to them. However, .where() preserves the shape of the original data by masking the values with a Boolean condition. Data values for which the condition is True are returned identical to the values passed in. On the other hand, data values for which the condition is False are returned as a preset example value. (This example value defaults to nan, but can be set to other values as well.).where(), it is helpful to look at the official documentation. As stated above, the .where() method takes a Boolean condition. (Boolean conditions use operators such as less-than, greater-than, and equal-to, and return a value of True or False.) Most uses of .where() check whether or not specific data values are less than or greater than a constant value. As stated in the documentation, the data values specified in the Boolean condition of .where() can be any of the following:where
+ dtype='float64', name='lon', length=360))- cell_measures :
- area: areacello
- cell_methods :
- area: mean where sea time: mean
- comment :
- Model data on the 1x1 grid includes values in all cells for which ocean cells on the native grid cover more than 52.5 percent of the 1x1 grid cell. This 52.5 percent cutoff was chosen to produce ocean surface area on the 1x1 grid as close as possible to ocean surface area on the native grid, while not introducing fractional cell coverage.
- description :
- This may differ from "surface temperature" in regions of sea ice or floating ice shelves. For models using conservative temperature as the prognostic field, they should report the top ocean layer as surface potential temperature, which is the same as surface in situ temperature.
- frequency :
- mon
- id :
- tos
- long_name :
- Sea Surface Temperature
- mipTable :
- Omon
- out_name :
- tos
- prov :
- Omon ((isd.003))
- realm :
- ocean
- standard_name :
- sea_surface_temperature
- time :
- time
- time_label :
- time-mean
- time_title :
- Temporal mean
- title :
- Sea Surface Temperature
- type :
- real
- units :
- degC
- variable_id :
- tos
In this example, we use Matplotlib to plot the original, unmasked data, as well as the masked data created in the previous example.
@@ -9321,7 +9321,7 @@ Resources and References
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
@@ -9450,7 +9450,7 @@ Contribute
By the Project Pythia Community.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
diff --git a/core/xarray/dask-arrays-xarray.html b/core/xarray/dask-arrays-xarray.html
index 0af9daaf3..65793b335 100644
--- a/core/xarray/dask-arrays-xarray.html
+++ b/core/xarray/dask-arrays-xarray.html
@@ -79,7 +79,7 @@
-
+
@@ -568,91 +568,91 @@ Create a dask.a
-array([[[0.08870167, 0.5536724 , 0.86869933, ..., 0.59745109,
- 0.66241166, 0.14712849],
- [0.79424186, 0.53985475, 0.83117213, ..., 0.64752434,
- 0.2068288 , 0.43561572],
- [0.93755297, 0.12641562, 0.55493093, ..., 0.44682309,
- 0.9108161 , 0.28494995],
+array([[[3.73479162e-01, 5.69354087e-01, 8.44121771e-01, ...,
+ 6.95300592e-01, 7.05791578e-01, 3.22757785e-01],
+ [7.34673387e-01, 3.22502093e-02, 2.98660716e-02, ...,
+ 4.48326876e-01, 1.28181189e-01, 6.17129937e-01],
+ [4.61524077e-01, 5.72981031e-01, 6.22455684e-02, ...,
+ 4.66387696e-01, 2.24365962e-02, 9.05528466e-01],
...,
- [0.36681494, 0.55104908, 0.67358062, ..., 0.93120538,
- 0.51164396, 0.2149192 ],
- [0.33220658, 0.18853655, 0.84133513, ..., 0.09990092,
- 0.45265087, 0.48841145],
- [0.54632195, 0.99301667, 0.40513953, ..., 0.5341915 ,
- 0.3220391 , 0.07544213]],
-
- [[0.8398981 , 0.14999579, 0.81431679, ..., 0.45814483,
- 0.81962377, 0.01308931],
- [0.52904343, 0.17038365, 0.49820524, ..., 0.71294698,
- 0.49906669, 0.32305634],
- [0.83597797, 0.12242548, 0.63334391, ..., 0.77669187,
- 0.44530452, 0.02031067],
+ [4.32840675e-01, 6.11791423e-01, 7.77157080e-01, ...,
+ 5.21898060e-01, 5.08370890e-01, 5.88153598e-01],
+ [8.04673850e-01, 7.80296541e-03, 5.00244166e-01, ...,
+ 9.45710973e-01, 5.08700949e-01, 8.89905823e-01],
+ [4.87536782e-01, 4.64576884e-01, 4.97751472e-01, ...,
+ 9.63294478e-01, 8.00438624e-01, 9.39160580e-01]],
+
+ [[9.59798128e-01, 9.67977225e-01, 5.67081374e-01, ...,
+ 1.15551677e-01, 5.93188567e-01, 8.21494821e-01],
+ [7.09798171e-01, 6.86764079e-01, 9.75639983e-01, ...,
+ 6.65980228e-01, 3.63136928e-01, 3.91634965e-01],
+ [2.45312843e-01, 7.92456108e-01, 7.52068086e-01, ...,
+ 8.31396290e-01, 7.34853345e-01, 1.59997513e-02],
...,
- [0.23487395, 0.54185136, 0.84738714, ..., 0.43194404,
- 0.96464114, 0.34661126],
- [0.06216953, 0.87402041, 0.89512514, ..., 0.38706893,
- 0.09187095, 0.88666451],
- [0.46384865, 0.02462827, 0.71575049, ..., 0.47421697,
- 0.30570578, 0.58421292]],
-
- [[0.05704318, 0.36232811, 0.03448708, ..., 0.86748361,
- 0.49546271, 0.39195962],
- [0.62907898, 0.52593284, 0.87418635, ..., 0.97638485,
- 0.63695351, 0.75326941],
- [0.11640535, 0.15534404, 0.80460691, ..., 0.26935451,
- 0.00686985, 0.42474694],
+ [6.29006234e-01, 5.66655665e-01, 9.59798385e-01, ...,
+ 4.64611557e-02, 8.47630616e-01, 9.96868460e-01],
+ [8.65409572e-01, 6.62829465e-01, 5.85935120e-01, ...,
+ 2.76645046e-01, 4.05531947e-01, 1.86970157e-01],
+ [5.88186192e-02, 4.80258125e-01, 7.50053106e-01, ...,
+ 5.82584007e-01, 7.16238302e-01, 3.09095729e-01]],
+
+ [[1.95867406e-01, 8.02997164e-01, 5.92335907e-02, ...,
+ 6.00495084e-02, 3.58568845e-02, 6.14435689e-01],
+ [5.40191024e-01, 7.45240262e-01, 5.83926237e-01, ...,
+ 5.42683512e-01, 7.15062271e-05, 8.15838308e-01],
+ [3.56121359e-01, 6.64103118e-01, 3.19197578e-01, ...,
+ 4.30885292e-01, 5.63274760e-01, 5.58304993e-01],
...,
- [0.53156325, 0.69190957, 0.69352384, ..., 0.77170882,
- 0.44011939, 0.39866217],
- [0.29442052, 0.74080675, 0.46131608, ..., 0.32366183,
- 0.73788577, 0.04341616],
- [0.94117032, 0.92630431, 0.79008536, ..., 0.02443356,
- 0.52042712, 0.83466538]],
+ [3.70429144e-01, 2.28949319e-01, 2.12465225e-01, ...,
+ 8.75347366e-01, 6.88876481e-01, 8.52985255e-01],
+ [8.43544201e-01, 3.50323808e-01, 9.94755069e-01, ...,
+ 2.31937385e-01, 6.46129523e-01, 7.56519847e-01],
+ [9.23292344e-01, 9.06543740e-01, 2.16039623e-01, ...,
+ 6.17841121e-01, 5.83778599e-02, 7.07627581e-01]],
...,
- [[0.1396787 , 0.60143505, 0.57058462, ..., 0.18893598,
- 0.82985107, 0.54242887],
- [0.68157985, 0.63122788, 0.1780818 , ..., 0.55248006,
- 0.63127177, 0.4663941 ],
- [0.55463676, 0.49022722, 0.28183346, ..., 0.64073606,
- 0.37788708, 0.05233016],
+ [[5.50076397e-01, 6.23247920e-01, 6.69863249e-01, ...,
+ 6.66141598e-01, 4.98073488e-01, 9.70545887e-01],
+ [7.38559992e-01, 3.21393458e-01, 4.05421647e-01, ...,
+ 2.52285356e-01, 8.08345653e-01, 7.28764634e-01],
+ [2.68191728e-01, 6.03169049e-02, 6.89258446e-01, ...,
+ 6.21042327e-01, 9.60250603e-01, 9.75257028e-01],
...,
- [0.39020736, 0.85735173, 0.00786958, ..., 0.66161596,
- 0.18152074, 0.17731408],
- [0.93613911, 0.96578828, 0.38973709, ..., 0.06864502,
- 0.5063675 , 0.73631955],
- [0.1671134 , 0.45039599, 0.38253178, ..., 0.74316064,
- 0.20253998, 0.93656703]],
-
- [[0.28018602, 0.84864745, 0.65231976, ..., 0.57238332,
- 0.37536731, 0.04826117],
- [0.47663448, 0.86778224, 0.95451621, ..., 0.76880871,
- 0.89875752, 0.06756555],
- [0.49609048, 0.21693506, 0.23207324, ..., 0.06636032,
- 0.95937476, 0.4827714 ],
+ [6.11978830e-01, 6.32479701e-01, 1.75826196e-01, ...,
+ 2.09161988e-01, 6.71446177e-01, 4.78915946e-01],
+ [6.45044934e-01, 1.15167985e-01, 9.05467373e-01, ...,
+ 7.79598426e-01, 8.76027493e-01, 5.70161686e-01],
+ [8.44173395e-01, 7.14987726e-01, 5.27098062e-01, ...,
+ 6.24560049e-01, 3.32587128e-02, 1.48219268e-01]],
+
+ [[9.16337354e-01, 1.38628117e-01, 2.76572964e-01, ...,
+ 5.68919305e-02, 3.07040211e-01, 6.70607173e-02],
+ [6.78200880e-01, 1.19012654e-01, 8.76844508e-01, ...,
+ 5.78903343e-01, 5.08716762e-01, 3.27430604e-01],
+ [5.19310927e-01, 1.62933592e-01, 6.94104791e-01, ...,
+ 8.28521396e-01, 5.63944660e-01, 9.23353464e-01],
...,
- [0.83197412, 0.22629066, 0.1589711 , ..., 0.55726833,
- 0.40921459, 0.94548168],
- [0.20373662, 0.76138868, 0.56265755, ..., 0.71025427,
- 0.19641998, 0.49627513],
- [0.20384921, 0.34173511, 0.44829 , ..., 0.3999668 ,
- 0.75240336, 0.21357396]],
-
- [[0.05024137, 0.77607526, 0.32452642, ..., 0.26169962,
- 0.2092348 , 0.79937281],
- [0.8834038 , 0.56909665, 0.84006875, ..., 0.09540328,
- 0.66257683, 0.78342945],
- [0.29906903, 0.08192958, 0.51913898, ..., 0.32946737,
- 0.91310302, 0.99198924],
+ [8.75833692e-01, 8.80034882e-01, 3.94957057e-01, ...,
+ 3.88766073e-01, 5.62146961e-01, 4.07401505e-01],
+ [8.83232515e-02, 7.93868945e-02, 7.09396623e-01, ...,
+ 4.00821090e-01, 9.67897242e-01, 3.30712526e-02],
+ [6.21839195e-01, 3.66731583e-01, 9.97239185e-01, ...,
+ 4.06892246e-01, 8.84142338e-01, 1.29206897e-01]],
+
+ [[3.13410458e-01, 7.92759677e-01, 8.24979480e-02, ...,
+ 5.38935776e-02, 4.52819725e-01, 1.48534750e-01],
+ [2.06595218e-01, 8.46746782e-01, 9.06689993e-01, ...,
+ 8.52571008e-01, 4.28351337e-01, 8.06352220e-01],
+ [3.77563013e-01, 9.99637726e-01, 3.37356897e-01, ...,
+ 8.18911136e-01, 8.32562303e-01, 8.45143802e-02],
...,
- [0.89344391, 0.04685752, 0.16870497, ..., 0.70133294,
- 0.2028986 , 0.16916596],
- [0.62721463, 0.12582174, 0.59300888, ..., 0.93023778,
- 0.50053284, 0.63544531],
- [0.89994404, 0.84669077, 0.65421957, ..., 0.19573779,
- 0.56024887, 0.73584493]]])
+ [5.51431397e-01, 8.77465751e-01, 9.63517992e-01, ...,
+ 3.34710544e-02, 9.70363045e-01, 5.42823121e-01],
+ [3.89247115e-01, 3.36500509e-01, 4.31638413e-02, ...,
+ 8.82214294e-02, 2.41024552e-01, 4.19862342e-01],
+ [5.65544128e-01, 6.09859197e-01, 2.77376901e-01, ...,
+ 5.51955058e-01, 6.67783854e-02, 4.08359511e-01]]])
@@ -856,7 +856,7 @@ Why the difference?
-
+
Compute the result
-CPU times: user 406 ms, sys: 28 ms, total: 434 ms
-Wall time: 232 ms
+CPU times: user 399 ms, sys: 28.5 ms, total: 428 ms
+Wall time: 228 ms
-12002441.41340752
+12000189.444496717
@@ -894,7 +894,7 @@ Exercise with d
-
+
As is obvious from the above exercise, Dask quickly and easily determines a strategy for performing the operations, in this case a sum. This illustrates the appeal of Dask: automatic algorithm generation that scales from simple arithmetic problems to highly complex scientific equations with large datasets and multiple operations.
@@ -974,7 +974,7 @@ Exercise with d
-
+
@@ -1106,7 +1106,7 @@ Testing a bigger calculation
-
+
@@ -1117,34 +1117,34 @@ Testing a bigger calculation
-[ ] | 0% Completed | 169.21 us
+[ ] | 0% Completed | 255.13 us
-[ ] | 0% Completed | 105.92 ms
+[ ] | 0% Completed | 106.98 ms
-[ ] | 0% Completed | 206.66 ms
+[ ] | 0% Completed | 207.72 ms
-[ ] | 0% Completed | 307.49 ms
+[ ] | 0% Completed | 308.36 ms
-[ ] | 0% Completed | 408.13 ms
+[ ] | 0% Completed | 409.00 ms
-[ ] | 0% Completed | 508.89 ms
+[ ] | 0% Completed | 509.67 ms
-[ ] | 1% Completed | 609.51 ms
+[ ] | 0% Completed | 610.39 ms
-[# ] | 4% Completed | 710.49 ms
+[# ] | 3% Completed | 711.30 ms
-[### ] | 7% Completed | 811.54 ms
+[## ] | 6% Completed | 812.41 ms
-[#### ] | 10% Completed | 912.41 ms
+[#### ] | 10% Completed | 913.29 ms
[#### ] | 10% Completed | 1.01 s
@@ -1153,7 +1153,7 @@ Testing a bigger calculation[#### ] | 10% Completed | 1.11 s
-[#### ] | 10% Completed | 1.21 s
+[#### ] | 10% Completed | 1.22 s
[#### ] | 10% Completed | 1.32 s
@@ -1162,10 +1162,10 @@ Testing a bigger calculation[#### ] | 10% Completed | 1.42 s
-[##### ] | 13% Completed | 1.52 s
+[##### ] | 12% Completed | 1.52 s
-[###### ] | 16% Completed | 1.62 s
+[###### ] | 15% Completed | 1.62 s
[####### ] | 19% Completed | 1.72 s
@@ -1186,13 +1186,13 @@ Testing a bigger calculation[######## ] | 21% Completed | 2.22 s
-[######## ] | 22% Completed | 2.32 s
+[######## ] | 21% Completed | 2.32 s
-[########## ] | 25% Completed | 2.43 s
+[######### ] | 24% Completed | 2.43 s
-[########### ] | 27% Completed | 2.53 s
+[########### ] | 28% Completed | 2.53 s
[############ ] | 31% Completed | 2.63 s
@@ -1216,16 +1216,16 @@ Testing a bigger calculation[############## ] | 35% Completed | 3.23 s
-[############### ] | 38% Completed | 3.33 s
+[############## ] | 37% Completed | 3.33 s
-[################ ] | 40% Completed | 3.43 s
+[############### ] | 39% Completed | 3.43 s
-[################# ] | 44% Completed | 3.53 s
+[################# ] | 43% Completed | 3.53 s
-[################### ] | 48% Completed | 3.63 s
+[################## ] | 46% Completed | 3.64 s
[################### ] | 48% Completed | 3.74 s
@@ -1246,10 +1246,10 @@ Testing a bigger calculation[#################### ] | 50% Completed | 4.24 s
-[##################### ] | 53% Completed | 4.34 s
+[#################### ] | 52% Completed | 4.34 s
-[###################### ] | 57% Completed | 4.44 s
+[###################### ] | 55% Completed | 4.44 s
[####################### ] | 59% Completed | 4.54 s
@@ -1276,7 +1276,7 @@ Testing a bigger calculation[######################### ] | 64% Completed | 5.25 s
-[########################### ] | 67% Completed | 5.35 s
+[########################### ] | 68% Completed | 5.35 s
[############################ ] | 72% Completed | 5.45 s
@@ -1300,13 +1300,13 @@ Testing a bigger calculation[############################## ] | 75% Completed | 6.06 s
-[############################### ] | 77% Completed | 6.16 s
+[############################### ] | 78% Completed | 6.16 s
-[################################ ] | 80% Completed | 6.26 s
+[################################ ] | 82% Completed | 6.26 s
-[################################# ] | 83% Completed | 6.36 s
+[################################# ] | 84% Completed | 6.36 s
[################################## ] | 85% Completed | 6.46 s
@@ -1324,16 +1324,16 @@ Testing a bigger calculation[################################## ] | 85% Completed | 6.86 s
-[################################## ] | 86% Completed | 6.96 s
+[################################### ] | 89% Completed | 6.96 s
-[################################### ] | 89% Completed | 7.06 s
+[#################################### ] | 92% Completed | 7.07 s
-[##################################### ] | 92% Completed | 7.16 s
+[###################################### ] | 96% Completed | 7.17 s
-[###################################### ] | 96% Completed | 7.27 s
+[####################################### ] | 99% Completed | 7.27 s
[########################################] | 100% Completed | 7.37 s
@@ -1854,7 +1854,7 @@ Reading data with
- latPandasIndex
PandasIndex(Index([-89.5, -88.5, -87.5, -86.5, -85.5, -84.5, -83.5, -82.5, -81.5, -80.5,
+ 80.5, 81.5, 82.5, 83.5, 84.5, 85.5, 86.5, 87.5, 88.5, 89.5])
- lon(lon)float640.5 1.5 2.5 ... 357.5 358.5 359.5
array([ 0.5, 1.5, 2.5, ..., 357.5, 358.5, 359.5])
- latPandasIndex
PandasIndex(Index([-89.5, -88.5, -87.5, -86.5, -85.5, -84.5, -83.5, -82.5, -81.5, -80.5,
...
80.5, 81.5, 82.5, 83.5, 84.5, 85.5, 86.5, 87.5, 88.5, 89.5],
- dtype='float64', name='lat', length=180)) - lonPandasIndex
PandasIndex(Index([ 0.5, 1.5, 2.5, 3.5, 4.5, 5.5, 6.5, 7.5, 8.5, 9.5,
+ dtype='float64', name='lat', length=180))
- lonPandasIndex
PandasIndex(Index([ 0.5, 1.5, 2.5, 3.5, 4.5, 5.5, 6.5, 7.5, 8.5, 9.5,
...
350.5, 351.5, 352.5, 353.5, 354.5, 355.5, 356.5, 357.5, 358.5, 359.5],
- dtype='float64', name='lon', length=360))
+ dtype='float64', name='lon', length=360))
As shown in the above example, the result of the applied operations is an Xarray DataArray that contains a Dask Array, an identical object type to the object that the operations were performed on. This is true for any operations that can be applied to Xarray DataArrays, including subsetting operations; this next example illustrates this:
@@ -3739,7 +3739,7 @@ Parallel and lazy computation using
@@ -3794,10 +3794,10 @@ Parallel and lazy computation using dask.visualize() function, as shown in this example:
@@ -3819,10 +3819,10 @@ Parallel and lazy computation using
-[ ] | 0% Completed | 122.44 us
+[ ] | 0% Completed | 137.30 us
-[########################################] | 100% Completed | 101.18 ms
+[########################################] | 100% Completed | 101.19 ms
@@ -4236,7 +4236,7 @@ Resources and references
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
@@ -4365,7 +4365,7 @@ Contribute
By the Project Pythia Community.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
diff --git a/core/xarray/enso-xarray.html b/core/xarray/enso-xarray.html
index 316661573..6e86d4355 100644
--- a/core/xarray/enso-xarray.html
+++ b/core/xarray/enso-xarray.html
@@ -79,7 +79,7 @@
-
+
@@ -958,7 +958,7 @@ The Niño 3.4 Index
@@ -1590,7 +1590,7 @@ Select the Niño 3.4 regionwhere() method:
+array(1.8436452, dtype=float32)
The final step of the Niño 3.4 index calculation involves normalizing the data. In this example, we perform this normalization by dividing the smoothed anomaly data by the standard deviation calculated above:
@@ -3396,7 +3396,7 @@ Resources and References
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
@@ -3525,7 +3525,7 @@ Contribute
By the Project Pythia Community.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
diff --git a/core/xarray/xarray-intro.html b/core/xarray/xarray-intro.html
index e17e6628d..308dd9baa 100644
--- a/core/xarray/xarray-intro.html
+++ b/core/xarray/xarray-intro.html
@@ -79,7 +79,7 @@
-
+
@@ -583,25 +583,25 @@ Generate a random numpy array
-array([[[274.00119189, 291.26874805, 287.10212211, 281.93321028],
- [276.72439019, 281.86759901, 277.88521001, 291.15900492],
- [283.47892997, 281.69598295, 281.87670232, 280.04253353]],
+array([[[283.19514872, 279.93414516, 288.85955729, 280.30299831],
+ [282.03318795, 283.36237877, 288.28464039, 284.82856563],
+ [282.15998672, 276.06859236, 285.78192464, 280.47703387]],
- [[292.49183408, 286.75694127, 291.1430049 , 279.6708509 ],
- [283.19466798, 285.34236937, 289.30840199, 286.22515585],
- [289.80677567, 281.73710917, 294.57091617, 291.28505114]],
+ [[282.44435897, 282.262194 , 286.34635361, 285.32213341],
+ [283.03737484, 288.57815157, 290.02698113, 278.23328324],
+ [277.06837841, 286.4771544 , 284.58714744, 288.42673576]],
- [[278.59826878, 287.14196565, 284.68298455, 286.59720048],
- [287.51252429, 288.52590266, 273.69692215, 285.39338362],
- [292.66871999, 278.60078099, 280.78168263, 291.92156783]],
+ [[280.62831671, 275.374152 , 283.08613936, 292.56788174],
+ [286.26020096, 282.03531027, 280.16394984, 288.9366629 ],
+ [282.90971416, 278.40998651, 284.98850877, 283.14584888]],
- [[281.24772197, 286.52210588, 282.61809684, 288.82826349],
- [282.48419301, 286.34184212, 277.57835692, 279.66054122],
- [288.97073742, 287.33849395, 289.90083339, 285.95347237]],
+ [[284.34422355, 283.20830934, 278.27414095, 285.21976361],
+ [279.17521323, 292.21743424, 276.974416 , 284.06699408],
+ [274.74247997, 279.63780379, 275.87914515, 277.71758929]],
- [[282.699394 , 286.72856786, 281.89265707, 286.16350665],
- [278.02139036, 284.54696447, 283.16191651, 278.99615563],
- [284.94443941, 287.35039327, 289.38110818, 280.10681174]]])
+ [[282.71726594, 281.99808895, 279.66639367, 276.71709325],
+ [278.30599074, 277.08406243, 273.68999203, 279.75441922],
+ [271.39869986, 286.48138908, 285.22713979, 291.77285885]]])
@@ -982,44 +982,44 @@ Wrap the array: first attempt
@@ -1402,44 +1402,44 @@ Assign dimension names<xarray.DataArray (time: 5, lat: 3, lon: 4)> Size: 480B
-array([[[274.00119189, 291.26874805, 287.10212211, 281.93321028],
- [276.72439019, 281.86759901, 277.88521001, 291.15900492],
- [283.47892997, 281.69598295, 281.87670232, 280.04253353]],
-
- [[292.49183408, 286.75694127, 291.1430049 , 279.6708509 ],
- [283.19466798, 285.34236937, 289.30840199, 286.22515585],
- [289.80677567, 281.73710917, 294.57091617, 291.28505114]],
-
- [[278.59826878, 287.14196565, 284.68298455, 286.59720048],
- [287.51252429, 288.52590266, 273.69692215, 285.39338362],
- [292.66871999, 278.60078099, 280.78168263, 291.92156783]],
-
- [[281.24772197, 286.52210588, 282.61809684, 288.82826349],
- [282.48419301, 286.34184212, 277.57835692, 279.66054122],
- [288.97073742, 287.33849395, 289.90083339, 285.95347237]],
-
- [[282.699394 , 286.72856786, 281.89265707, 286.16350665],
- [278.02139036, 284.54696447, 283.16191651, 278.99615563],
- [284.94443941, 287.35039327, 289.38110818, 280.10681174]]])
-Dimensions without coordinates: time, lat, lon
+array([[[283.19514872, 279.93414516, 288.85955729, 280.30299831],
+ [282.03318795, 283.36237877, 288.28464039, 284.82856563],
+ [282.15998672, 276.06859236, 285.78192464, 280.47703387]],
+
+ [[282.44435897, 282.262194 , 286.34635361, 285.32213341],
+ [283.03737484, 288.57815157, 290.02698113, 278.23328324],
+ [277.06837841, 286.4771544 , 284.58714744, 288.42673576]],
+
+ [[280.62831671, 275.374152 , 283.08613936, 292.56788174],
+ [286.26020096, 282.03531027, 280.16394984, 288.9366629 ],
+ [282.90971416, 278.40998651, 284.98850877, 283.14584888]],
+
+ [[284.34422355, 283.20830934, 278.27414095, 285.21976361],
+ [279.17521323, 292.21743424, 276.974416 , 284.06699408],
+ [274.74247997, 279.63780379, 275.87914515, 277.71758929]],
+
+ [[282.71726594, 281.99808895, 279.66639367, 276.71709325],
+ [278.30599074, 277.08406243, 273.68999203, 279.75441922],
+ [271.39869986, 286.48138908, 285.22713979, 291.77285885]]])
+Dimensions without coordinates: time, lat, lon
This DataArray is already an improvement over a NumPy array; the DataArray contains names for each of the dimensions (or axes in NumPy parlance). An additional improvement is the association of coordinate-value arrays with data upon creation of a DataArray. In the next example, we illustrate the creation of NumPy arrays representing the coordinate values for each dimension of the DataArray, and how to associate these coordinate arrays with the data in our DataArray.
@@ -1849,51 +1849,51 @@ Initialize the
fill: currentColor;
}
<xarray.DataArray (time: 5, lat: 3, lon: 4)> Size: 480B
-array([[[274.00119189, 291.26874805, 287.10212211, 281.93321028],
- [276.72439019, 281.86759901, 277.88521001, 291.15900492],
- [283.47892997, 281.69598295, 281.87670232, 280.04253353]],
+array([[[283.19514872, 279.93414516, 288.85955729, 280.30299831],
+ [282.03318795, 283.36237877, 288.28464039, 284.82856563],
+ [282.15998672, 276.06859236, 285.78192464, 280.47703387]],
- [[292.49183408, 286.75694127, 291.1430049 , 279.6708509 ],
- [283.19466798, 285.34236937, 289.30840199, 286.22515585],
- [289.80677567, 281.73710917, 294.57091617, 291.28505114]],
+ [[282.44435897, 282.262194 , 286.34635361, 285.32213341],
+ [283.03737484, 288.57815157, 290.02698113, 278.23328324],
+ [277.06837841, 286.4771544 , 284.58714744, 288.42673576]],
- [[278.59826878, 287.14196565, 284.68298455, 286.59720048],
- [287.51252429, 288.52590266, 273.69692215, 285.39338362],
- [292.66871999, 278.60078099, 280.78168263, 291.92156783]],
+ [[280.62831671, 275.374152 , 283.08613936, 292.56788174],
+ [286.26020096, 282.03531027, 280.16394984, 288.9366629 ],
+ [282.90971416, 278.40998651, 284.98850877, 283.14584888]],
- [[281.24772197, 286.52210588, 282.61809684, 288.82826349],
- [282.48419301, 286.34184212, 277.57835692, 279.66054122],
- [288.97073742, 287.33849395, 289.90083339, 285.95347237]],
+ [[284.34422355, 283.20830934, 278.27414095, 285.21976361],
+ [279.17521323, 292.21743424, 276.974416 , 284.06699408],
+ [274.74247997, 279.63780379, 275.87914515, 277.71758929]],
- [[282.699394 , 286.72856786, 281.89265707, 286.16350665],
- [278.02139036, 284.54696447, 283.16191651, 278.99615563],
- [284.94443941, 287.35039327, 289.38110818, 280.10681174]]])
+ [[282.71726594, 281.99808895, 279.66639367, 276.71709325],
+ [278.30599074, 277.08406243, 273.68999203, 279.75441922],
+ [271.39869986, 286.48138908, 285.22713979, 291.77285885]]])
Coordinates:
* time (time) datetime64[ns] 40B 2018-01-01 2018-01-02 ... 2018-01-05
* lat (lat) float64 24B 25.0 40.0 55.0
- * lon (lon) float64 32B -120.0 -100.0 -80.0 -60.0
+ dtype='datetime64[ns]', name='time', freq='D'))
latPandasIndexPandasIndex(Index([25.0, 40.0, 55.0], dtype='float64', name='lat'))
lonPandasIndexPandasIndex(Index([-120.0, -100.0, -80.0, -60.0], dtype='float64', name='lon'))
@@ -2273,54 +2273,54 @@ Set useful attributes<xarray.DataArray (time: 5, lat: 3, lon: 4)> Size: 480B
-array([[[274.00119189, 291.26874805, 287.10212211, 281.93321028],
- [276.72439019, 281.86759901, 277.88521001, 291.15900492],
- [283.47892997, 281.69598295, 281.87670232, 280.04253353]],
+array([[[283.19514872, 279.93414516, 288.85955729, 280.30299831],
+ [282.03318795, 283.36237877, 288.28464039, 284.82856563],
+ [282.15998672, 276.06859236, 285.78192464, 280.47703387]],
- [[292.49183408, 286.75694127, 291.1430049 , 279.6708509 ],
- [283.19466798, 285.34236937, 289.30840199, 286.22515585],
- [289.80677567, 281.73710917, 294.57091617, 291.28505114]],
+ [[282.44435897, 282.262194 , 286.34635361, 285.32213341],
+ [283.03737484, 288.57815157, 290.02698113, 278.23328324],
+ [277.06837841, 286.4771544 , 284.58714744, 288.42673576]],
- [[278.59826878, 287.14196565, 284.68298455, 286.59720048],
- [287.51252429, 288.52590266, 273.69692215, 285.39338362],
- [292.66871999, 278.60078099, 280.78168263, 291.92156783]],
+ [[280.62831671, 275.374152 , 283.08613936, 292.56788174],
+ [286.26020096, 282.03531027, 280.16394984, 288.9366629 ],
+ [282.90971416, 278.40998651, 284.98850877, 283.14584888]],
- [[281.24772197, 286.52210588, 282.61809684, 288.82826349],
- [282.48419301, 286.34184212, 277.57835692, 279.66054122],
- [288.97073742, 287.33849395, 289.90083339, 285.95347237]],
+ [[284.34422355, 283.20830934, 278.27414095, 285.21976361],
+ [279.17521323, 292.21743424, 276.974416 , 284.06699408],
+ [274.74247997, 279.63780379, 275.87914515, 277.71758929]],
- [[282.699394 , 286.72856786, 281.89265707, 286.16350665],
- [278.02139036, 284.54696447, 283.16191651, 278.99615563],
- [284.94443941, 287.35039327, 289.38110818, 280.10681174]]])
+ [[282.71726594, 281.99808895, 279.66639367, 276.71709325],
+ [278.30599074, 277.08406243, 273.68999203, 279.75441922],
+ [271.39869986, 286.48138908, 285.22713979, 291.77285885]]])
Coordinates:
* time (time) datetime64[ns] 40B 2018-01-01 2018-01-02 ... 2018-01-05
* lat (lat) float64 24B 25.0 40.0 55.0
* lon (lon) float64 32B -120.0 -100.0 -80.0 -60.0
Attributes:
units: kelvin
- standard_name: air_temperature
+ dtype='datetime64[ns]', name='time', freq='D'))
latPandasIndexPandasIndex(Index([25.0, 40.0, 55.0], dtype='float64', name='lat'))
lonPandasIndexPandasIndex(Index([-120.0, -100.0, -80.0, -60.0], dtype='float64', name='lon'))
- units :
- kelvin
- standard_name :
- air_temperature
@@ -2699,51 +2699,51 @@ Issues with preservation of attributesdocumentation page.
@@ -3134,54 +3134,54 @@ Create a pressure
+ dtype='datetime64[ns]', name='time', freq='D'))latPandasIndexPandasIndex(Index([25.0, 40.0, 55.0], dtype='float64', name='lat'))
lonPandasIndexPandasIndex(Index([-120.0, -100.0, -80.0, -60.0], dtype='float64', name='lon'))
As listed in the rich display above, the new Dataset object is aware that both DataArrays share the same coordinate axes. (Please note that if this page is not run as a Jupyter Notebook, the rich display may be unavailable.)
@@ -3986,54 +3986,54 @@ Access Data variables and Coordinates in a DataArrays through a dictionary syntax, as shown in this example:
@@ -4407,54 +4407,54 @@ Access Data variables and Coordinates in a Dataset objects are mainly used for loading data from files, which will be covered later in this tutorial.
@@ -4838,18 +4838,18 @@ NumPy-like selection<xarray.DataArray (lat: 3, lon: 4)> Size: 96B
-array([[292.49183408, 286.75694127, 291.1430049 , 279.6708509 ],
- [283.19466798, 285.34236937, 289.30840199, 286.22515585],
- [289.80677567, 281.73710917, 294.57091617, 291.28505114]])
+array([[282.44435897, 282.262194 , 286.34635361, 285.32213341],
+ [283.03737484, 288.57815157, 290.02698113, 278.23328324],
+ [277.06837841, 286.4771544 , 284.58714744, 288.42673576]])
Coordinates:
time datetime64[ns] 8B 2018-01-02
* lat (lat) float64 24B 25.0 40.0 55.0
* lon (lon) float64 32B -120.0 -100.0 -80.0 -60.0
Attributes:
units: kelvin
- standard_name: air_temperature
+ standard_name: air_temperature
This example reveals one of the major shortcomings of index selection. In order to retrieve the correct data using index selection, anyone using a DataArray must have precise knowledge of the axes in the DataArray, including the order of the axes and the meaning of their indices.
By using named coordinates, as shown in the next set of examples, we can avoid this cumbersome burden.
@@ -5229,18 +5229,18 @@ Selecting with
fill: currentColor;
}
<xarray.DataArray (lat: 3, lon: 4)> Size: 96B
-array([[292.49183408, 286.75694127, 291.1430049 , 279.6708509 ],
- [283.19466798, 285.34236937, 289.30840199, 286.22515585],
- [289.80677567, 281.73710917, 294.57091617, 291.28505114]])
+array([[282.44435897, 282.262194 , 286.34635361, 285.32213341],
+ [283.03737484, 288.57815157, 290.02698113, 278.23328324],
+ [277.06837841, 286.4771544 , 284.58714744, 288.42673576]])
Coordinates:
time datetime64[ns] 8B 2018-01-02
* lat (lat) float64 24B 25.0 40.0 55.0
* lon (lon) float64 32B -120.0 -100.0 -80.0 -60.0
Attributes:
units: kelvin
- standard_name: air_temperature
+ standard_name: air_temperature
This method yields the same result as the index selection, however:
@@ -5627,18 +5627,18 @@ Nearest-neighbor sampling.sel indeed returned the data at the temporal value corresponding to the date 2018-01-05.
@@ -6017,20 +6017,20 @@ Interpolation
Info
@@ -6414,29 +6414,29 @@ Slicing along coordinates
Info
@@ -6818,18 +6818,18 @@ One more selection method: <xarray.DataArray (lat: 3, lon: 4)> Size: 96B
-array([[292.49183408, 286.75694127, 291.1430049 , 279.6708509 ],
- [283.19466798, 285.34236937, 289.30840199, 286.22515585],
- [289.80677567, 281.73710917, 294.57091617, 291.28505114]])
+array([[282.44435897, 282.262194 , 286.34635361, 285.32213341],
+ [283.03737484, 288.57815157, 290.02698113, 278.23328324],
+ [277.06837841, 286.4771544 , 284.58714744, 288.42673576]])
Coordinates:
time datetime64[ns] 8B 2018-01-02
* lat (lat) float64 24B 25.0 40.0 55.0
* lon (lon) float64 32B -120.0 -100.0 -80.0 -60.0
Attributes:
units: kelvin
- standard_name: air_temperature
+ standard_name: air_temperature
This selection technique is similar to NumPy’s index-based selection, as shown below:
temp[1,:,:]
@@ -7208,29 +7208,29 @@ One more selection method: <xarray.DataArray (time: 3, lat: 2, lon: 2)> Size: 96B
-array([[[291.26874805, 287.10212211],
- [281.86759901, 277.88521001]],
+array([[[279.93414516, 288.85955729],
+ [283.36237877, 288.28464039]],
- [[286.75694127, 291.1430049 ],
- [285.34236937, 289.30840199]],
+ [[282.262194 , 286.34635361],
+ [288.57815157, 290.02698113]],
- [[287.14196565, 284.68298455],
- [288.52590266, 273.69692215]]])
+ [[275.374152 , 283.08613936],
+ [282.03531027, 280.16394984]]])
Coordinates:
* time (time) datetime64[ns] 24B 2018-01-01 2018-01-02 2018-01-03
* lat (lat) float64 16B 25.0 40.0
* lon (lon) float64 16B -100.0 -80.0
Attributes:
units: kelvin
- standard_name: air_temperature
+ [[275.374152 , 283.08613936],
+ [282.03531027, 280.16394984]]])
- time(time)datetime64[ns]2018-01-01 2018-01-02 2018-01-03
array(['2018-01-01T00:00:00.000000000', '2018-01-02T00:00:00.000000000',
+ '2018-01-03T00:00:00.000000000'], dtype='datetime64[ns]')
- lat(lat)float6425.0 40.0
array([25., 40.])
- lon(lon)float64-100.0 -80.0
array([-100., -80.])
- timePandasIndex
PandasIndex(DatetimeIndex(['2018-01-01', '2018-01-02', '2018-01-03'], dtype='datetime64[ns]', name='time', freq='D'))
- latPandasIndex
PandasIndex(Index([25.0, 40.0], dtype='float64', name='lat'))
- lonPandasIndex
PandasIndex(Index([-100.0, -80.0], dtype='float64', name='lon'))
- units :
- kelvin
- standard_name :
- air_temperature
In contrast with the previous example, this example shows a useful advantage of using the .loc attribute. When using the .loc attribute, you can specify data slices using a syntax similar to NumPy in addition to, or instead of, using the slice function. Both of these slicing techniques are illustrated below:
@@ -7604,29 +7604,29 @@ One more selection method: <xarray.DataArray (time: 3, lat: 2, lon: 2)> Size: 96B
-array([[[291.26874805, 287.10212211],
- [281.86759901, 277.88521001]],
+array([[[279.93414516, 288.85955729],
+ [283.36237877, 288.28464039]],
- [[286.75694127, 291.1430049 ],
- [285.34236937, 289.30840199]],
+ [[282.262194 , 286.34635361],
+ [288.57815157, 290.02698113]],
- [[287.14196565, 284.68298455],
- [288.52590266, 273.69692215]]])
+ [[275.374152 , 283.08613936],
+ [282.03531027, 280.16394984]]])
Coordinates:
* time (time) datetime64[ns] 24B 2018-01-01 2018-01-02 2018-01-03
* lat (lat) float64 16B 25.0 40.0
* lon (lon) float64 16B -100.0 -80.0
Attributes:
units: kelvin
- standard_name: air_temperature
+ [[275.374152 , 283.08613936],
+ [282.03531027, 280.16394984]]])
- time(time)datetime64[ns]2018-01-01 2018-01-02 2018-01-03
array(['2018-01-01T00:00:00.000000000', '2018-01-02T00:00:00.000000000',
+ '2018-01-03T00:00:00.000000000'], dtype='datetime64[ns]')
- lat(lat)float6425.0 40.0
array([25., 40.])
- lon(lon)float64-100.0 -80.0
array([-100., -80.])
- timePandasIndex
PandasIndex(DatetimeIndex(['2018-01-01', '2018-01-02', '2018-01-03'], dtype='datetime64[ns]', name='time', freq='D'))
- latPandasIndex
PandasIndex(Index([25.0, 40.0], dtype='float64', name='lat'))
- lonPandasIndex
PandasIndex(Index([-100.0, -80.0], dtype='float64', name='lon'))
- units :
- kelvin
- standard_name :
- air_temperature
As described above, the arguments to .loc must be in the order of the DataArray’s dimensions. Attempting to slice data without ordering arguments properly can cause errors, as shown below:
@@ -8059,10 +8059,10 @@ Access netCDF data with
@@ -8504,17 +8504,17 @@ Subsetting the
positive: down
Grib_level_type: 100
_CoordinateAxisType: Pressure
- _CoordinateZisPositive: down
+ dtype='float32', name='isobaric1'))- units :
- hPa
- long_name :
- Isobaric surface
- positive :
- down
- Grib_level_type :
- 100
- _CoordinateAxisType :
- Pressure
- _CoordinateZisPositive :
- down
(As described earlier in this tutorial, we can also use dictionary syntax to select specific DataArrays; in this case, we would write ds['isobaric1'].)
Many of the subsetting operations usable on DataArrays can also be used on Datasets. However, when used on Datasets, these operations are performed on every DataArray in the Dataset, as shown below:
@@ -8916,7 +8916,7 @@ Subsetting the
geospatial_lat_min: 10.753308882144761
geospatial_lat_max: 46.8308828962289
geospatial_lon_min: -153.88242040519995
- geospatial_lon_max: -42.666108129242815
As shown above, the subsetting operation performed on the Dataset returned a new Dataset. If only a single DataArray is needed from this new Dataset, it can be retrieved using the familiar dot notation:
@@ -9356,7 +9356,7 @@ Subsetting the
Grib1_TableVersion: 131
Grib1_Parameter: 11
Grib1_Level_Type: 100
- Grib1_Level_Desc: Isobaric surface
+ dtype='float32', name='x', length=268))- long_name :
- Temperature @ Isobaric surface
- units :
- K
- description :
- Temperature
- grid_mapping :
- LambertConformal_Projection
- Grib_Variable_Id :
- VAR_7-15-131-11_L100
- Grib1_Center :
- 7
- Grib1_Subcenter :
- 15
- Grib1_TableVersion :
- 131
- Grib1_Parameter :
- 11
- Grib1_Level_Type :
- 100
- Grib1_Level_Desc :
- Isobaric surface
@@ -9790,19 +9790,19 @@ Aggregation operations
+ dtype='float32', name='isobaric1'))
Info
@@ -10196,19 +10196,19 @@ Aggregation operations
+ dtype='float32', name='isobaric1'))
@@ -10226,7 +10226,7 @@ Simple visualization with
-[<matplotlib.lines.Line2D at 0x7f2e0f58d130>]
+[<matplotlib.lines.Line2D at 0x7f505db3d9d0>]
@@ -10250,7 +10250,7 @@ Customizing the plot
-[<matplotlib.lines.Line2D at 0x7f2e0f58eff0>]
+[<matplotlib.lines.Line2D at 0x7f5055a98d40>]
@@ -10268,7 +10268,7 @@ Plotting 2-D data
-<matplotlib.collections.QuadMesh at 0x7f2df9c43b60>
+<matplotlib.collections.QuadMesh at 0x7f5055ad1be0>
@@ -10667,7 +10667,7 @@ Resources and references
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
@@ -10796,7 +10796,7 @@ Contribute
By the Project Pythia Community.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
diff --git a/foundations/conda.html b/foundations/conda.html
index c07684127..6f809cfd0 100644
--- a/foundations/conda.html
+++ b/foundations/conda.html
@@ -79,7 +79,7 @@
-
+
@@ -913,7 +913,7 @@ Resources and References
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
@@ -1042,7 +1042,7 @@ Contribute
By the Project Pythia Community.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
diff --git a/foundations/getting-started-github.html b/foundations/getting-started-github.html
index 4a2af1d14..ec22e00df 100644
--- a/foundations/getting-started-github.html
+++ b/foundations/getting-started-github.html
@@ -81,7 +81,7 @@
-
+
@@ -768,7 +768,7 @@ Getting Started with GitHub
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
@@ -897,7 +897,7 @@ Contribute
By the Project Pythia Community.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
diff --git a/foundations/getting-started-jupyter.html b/foundations/getting-started-jupyter.html
index b1d2107c0..b95652e74 100644
--- a/foundations/getting-started-jupyter.html
+++ b/foundations/getting-started-jupyter.html
@@ -79,7 +79,7 @@
-
+
@@ -905,7 +905,7 @@ Resources and references
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
@@ -1034,7 +1034,7 @@ Contribute
By the Project Pythia Community.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
diff --git a/foundations/getting-started-python.html b/foundations/getting-started-python.html
index 286ad4516..19ac49850 100644
--- a/foundations/getting-started-python.html
+++ b/foundations/getting-started-python.html
@@ -81,7 +81,7 @@
-
+
@@ -779,7 +779,7 @@ Topics
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
@@ -908,7 +908,7 @@ Contribute
By the Project Pythia Community.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
diff --git a/foundations/github/basic-git.html b/foundations/github/basic-git.html
index 441aa5056..32e0b6b24 100644
--- a/foundations/github/basic-git.html
+++ b/foundations/github/basic-git.html
@@ -79,7 +79,7 @@
-
+
@@ -1222,7 +1222,7 @@ References
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
@@ -1351,7 +1351,7 @@ Contribute
By the Project Pythia Community.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
diff --git a/foundations/github/contribute-to-pythia.html b/foundations/github/contribute-to-pythia.html
index d1e1bad12..11ebc0cc2 100644
--- a/foundations/github/contribute-to-pythia.html
+++ b/foundations/github/contribute-to-pythia.html
@@ -79,7 +79,7 @@
-
+
@@ -885,7 +885,7 @@ Summary
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
@@ -1014,7 +1014,7 @@ Contribute
By the Project Pythia Community.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
diff --git a/foundations/github/git-branches.html b/foundations/github/git-branches.html
index 6dac4b319..4e316269f 100644
--- a/foundations/github/git-branches.html
+++ b/foundations/github/git-branches.html
@@ -79,7 +79,7 @@
-
+
@@ -1036,7 +1036,7 @@ Resources and references
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
@@ -1165,7 +1165,7 @@ Contribute
By the Project Pythia Community.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
diff --git a/foundations/github/github-advanced.html b/foundations/github/github-advanced.html
index 9e93b611f..def634b01 100644
--- a/foundations/github/github-advanced.html
+++ b/foundations/github/github-advanced.html
@@ -77,7 +77,7 @@
-
+
@@ -810,7 +810,7 @@ References
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
@@ -939,7 +939,7 @@ Contribute
By the Project Pythia Community.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
diff --git a/foundations/github/github-cloning-forking.html b/foundations/github/github-cloning-forking.html
index c03c6f72a..18b821529 100644
--- a/foundations/github/github-cloning-forking.html
+++ b/foundations/github/github-cloning-forking.html
@@ -79,7 +79,7 @@
-
+
@@ -988,7 +988,7 @@ References
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
@@ -1117,7 +1117,7 @@ Contribute
By the Project Pythia Community.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
diff --git a/foundations/github/github-issues.html b/foundations/github/github-issues.html
index fe56bf8f1..cf5e4670a 100644
--- a/foundations/github/github-issues.html
+++ b/foundations/github/github-issues.html
@@ -79,7 +79,7 @@
-
+
@@ -875,7 +875,7 @@ References
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
@@ -1004,7 +1004,7 @@ Contribute
By the Project Pythia Community.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
diff --git a/foundations/github/github-pull-request.html b/foundations/github/github-pull-request.html
index 566d2d767..a2f449208 100644
--- a/foundations/github/github-pull-request.html
+++ b/foundations/github/github-pull-request.html
@@ -79,7 +79,7 @@
-
+
@@ -946,7 +946,7 @@ References
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
@@ -1075,7 +1075,7 @@ Contribute
By the Project Pythia Community.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
diff --git a/foundations/github/github-repos.html b/foundations/github/github-repos.html
index 7d26de757..389418ea0 100644
--- a/foundations/github/github-repos.html
+++ b/foundations/github/github-repos.html
@@ -79,7 +79,7 @@
-
+
@@ -885,7 +885,7 @@ References
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
@@ -1014,7 +1014,7 @@ Contribute
By the Project Pythia Community.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
diff --git a/foundations/github/github-setup-advanced.html b/foundations/github/github-setup-advanced.html
index f2123372f..71344fd0b 100644
--- a/foundations/github/github-setup-advanced.html
+++ b/foundations/github/github-setup-advanced.html
@@ -79,7 +79,7 @@
-
+
@@ -924,7 +924,7 @@ References
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
@@ -1053,7 +1053,7 @@ Contribute
By the Project Pythia Community.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
diff --git a/foundations/github/github-workflows.html b/foundations/github/github-workflows.html
index a5c6b6dfc..17e8fd580 100644
--- a/foundations/github/github-workflows.html
+++ b/foundations/github/github-workflows.html
@@ -79,7 +79,7 @@
-
+
@@ -1253,7 +1253,7 @@ References
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
@@ -1382,7 +1382,7 @@ Contribute
By the Project Pythia Community.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
diff --git a/foundations/github/review-pr.html b/foundations/github/review-pr.html
index f5b0820fb..fe5674aa9 100644
--- a/foundations/github/review-pr.html
+++ b/foundations/github/review-pr.html
@@ -79,7 +79,7 @@
-
+
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- Last updated on 15 May 2024.
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- Last updated on 15 May 2024.
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- Last updated on 15 May 2024.
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By the Project Pythia Community.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
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index c8dc2806e..344643277 100644
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- Last updated on 15 May 2024.
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By the Project Pythia Community.
- Last updated on 15 May 2024.
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diff --git a/foundations/quickstart.html b/foundations/quickstart.html
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By the Project Pythia Community.
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By the Project Pythia Community.
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- Last updated on 15 May 2024.
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By the Project Pythia Community.
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By the Project Pythia Community.
- Last updated on 15 May 2024.
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By the Project Pythia Community.
- Last updated on 15 May 2024.
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- Last updated on 15 May 2024.
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@@ -884,7 +884,7 @@ Contribute
By the Project Pythia Community.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
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- Last updated on 15 May 2024.
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By the Project Pythia Community.
- Last updated on 15 May 2024.
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index 57210f52a..705201479 100644
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By the Project Pythia Community.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
diff --git a/searchindex.js b/searchindex.js
index 18d9c718b..f531c0445 100644
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32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 45, 46], "when": 38, "where": 20, "why": [8, 21, 28, 47], "window": 32, "within": 11, "work": 37, "workflow": [30, 34, 37], "would": 21, "wrap": 23, "write": [5, 7], "xarrai": [19, 20, 21, 22, 23], "xr": 23, "year": 7, "york": 3, "you": [21, 28, 30, 38, 39], "your": [0, 1, 18, 30, 34, 36, 41, 45], "zero": 45, "zone": 7}})
\ No newline at end of file
CPU times: user 406 ms, sys: 28 ms, total: 434 ms
-Wall time: 232 ms
+CPU times: user 399 ms, sys: 28.5 ms, total: 428 ms
+Wall time: 228 ms
-12002441.41340752
+12000189.444496717
@@ -894,7 +894,7 @@ Exercise with d
-
+
+
d
-
+
@@ -1106,7 +1106,7 @@ Testing a bigger calculation
-
+
@@ -1117,34 +1117,34 @@ Testing a bigger calculation
-[ ] | 0% Completed | 169.21 us
+[ ] | 0% Completed | 255.13 us
-[ ] | 0% Completed | 105.92 ms
+[ ] | 0% Completed | 106.98 ms
-[ ] | 0% Completed | 206.66 ms
+[ ] | 0% Completed | 207.72 ms
-[ ] | 0% Completed | 307.49 ms
+[ ] | 0% Completed | 308.36 ms
-[ ] | 0% Completed | 408.13 ms
+[ ] | 0% Completed | 409.00 ms
-[ ] | 0% Completed | 508.89 ms
+[ ] | 0% Completed | 509.67 ms
-[ ] | 1% Completed | 609.51 ms
+[ ] | 0% Completed | 610.39 ms
-[# ] | 4% Completed | 710.49 ms
+[# ] | 3% Completed | 711.30 ms
-[### ] | 7% Completed | 811.54 ms
+[## ] | 6% Completed | 812.41 ms
-[#### ] | 10% Completed | 912.41 ms
+[#### ] | 10% Completed | 913.29 ms
[#### ] | 10% Completed | 1.01 s
@@ -1153,7 +1153,7 @@ Testing a bigger calculation[#### ] | 10% Completed | 1.11 s
-[#### ] | 10% Completed | 1.21 s
+[#### ] | 10% Completed | 1.22 s
[#### ] | 10% Completed | 1.32 s
@@ -1162,10 +1162,10 @@ Testing a bigger calculation[#### ] | 10% Completed | 1.42 s
-[##### ] | 13% Completed | 1.52 s
+[##### ] | 12% Completed | 1.52 s
-[###### ] | 16% Completed | 1.62 s
+[###### ] | 15% Completed | 1.62 s
[####### ] | 19% Completed | 1.72 s
@@ -1186,13 +1186,13 @@ Testing a bigger calculation[######## ] | 21% Completed | 2.22 s
-[######## ] | 22% Completed | 2.32 s
+[######## ] | 21% Completed | 2.32 s
-[########## ] | 25% Completed | 2.43 s
+[######### ] | 24% Completed | 2.43 s
-[########### ] | 27% Completed | 2.53 s
+[########### ] | 28% Completed | 2.53 s
[############ ] | 31% Completed | 2.63 s
@@ -1216,16 +1216,16 @@ Testing a bigger calculation[############## ] | 35% Completed | 3.23 s
-[############### ] | 38% Completed | 3.33 s
+[############## ] | 37% Completed | 3.33 s
-[################ ] | 40% Completed | 3.43 s
+[############### ] | 39% Completed | 3.43 s
-[################# ] | 44% Completed | 3.53 s
+[################# ] | 43% Completed | 3.53 s
-[################### ] | 48% Completed | 3.63 s
+[################## ] | 46% Completed | 3.64 s
[################### ] | 48% Completed | 3.74 s
@@ -1246,10 +1246,10 @@ Testing a bigger calculation[#################### ] | 50% Completed | 4.24 s
-[##################### ] | 53% Completed | 4.34 s
+[#################### ] | 52% Completed | 4.34 s
-[###################### ] | 57% Completed | 4.44 s
+[###################### ] | 55% Completed | 4.44 s
[####################### ] | 59% Completed | 4.54 s
@@ -1276,7 +1276,7 @@ Testing a bigger calculation[######################### ] | 64% Completed | 5.25 s
-[########################### ] | 67% Completed | 5.35 s
+[########################### ] | 68% Completed | 5.35 s
[############################ ] | 72% Completed | 5.45 s
@@ -1300,13 +1300,13 @@ Testing a bigger calculation[############################## ] | 75% Completed | 6.06 s
-[############################### ] | 77% Completed | 6.16 s
+[############################### ] | 78% Completed | 6.16 s
-[################################ ] | 80% Completed | 6.26 s
+[################################ ] | 82% Completed | 6.26 s
-[################################# ] | 83% Completed | 6.36 s
+[################################# ] | 84% Completed | 6.36 s
[################################## ] | 85% Completed | 6.46 s
@@ -1324,16 +1324,16 @@ Testing a bigger calculation[################################## ] | 85% Completed | 6.86 s
-[################################## ] | 86% Completed | 6.96 s
+[################################### ] | 89% Completed | 6.96 s
-[################################### ] | 89% Completed | 7.06 s
+[#################################### ] | 92% Completed | 7.07 s
-[##################################### ] | 92% Completed | 7.16 s
+[###################################### ] | 96% Completed | 7.17 s
-[###################################### ] | 96% Completed | 7.27 s
+[####################################### ] | 99% Completed | 7.27 s
[########################################] | 100% Completed | 7.37 s
@@ -1854,7 +1854,7 @@ Reading data with
- latPandasIndex
PandasIndex(Index([-89.5, -88.5, -87.5, -86.5, -85.5, -84.5, -83.5, -82.5, -81.5, -80.5,
+ 80.5, 81.5, 82.5, 83.5, 84.5, 85.5, 86.5, 87.5, 88.5, 89.5])
- lon(lon)float640.5 1.5 2.5 ... 357.5 358.5 359.5
array([ 0.5, 1.5, 2.5, ..., 357.5, 358.5, 359.5])
- latPandasIndex
PandasIndex(Index([-89.5, -88.5, -87.5, -86.5, -85.5, -84.5, -83.5, -82.5, -81.5, -80.5,
...
80.5, 81.5, 82.5, 83.5, 84.5, 85.5, 86.5, 87.5, 88.5, 89.5],
- dtype='float64', name='lat', length=180)) - lonPandasIndex
PandasIndex(Index([ 0.5, 1.5, 2.5, 3.5, 4.5, 5.5, 6.5, 7.5, 8.5, 9.5,
+ dtype='float64', name='lat', length=180))
- lonPandasIndex
PandasIndex(Index([ 0.5, 1.5, 2.5, 3.5, 4.5, 5.5, 6.5, 7.5, 8.5, 9.5,
...
350.5, 351.5, 352.5, 353.5, 354.5, 355.5, 356.5, 357.5, 358.5, 359.5],
- dtype='float64', name='lon', length=360))
+ dtype='float64', name='lon', length=360))
As shown in the above example, the result of the applied operations is an Xarray DataArray that contains a Dask Array, an identical object type to the object that the operations were performed on. This is true for any operations that can be applied to Xarray DataArrays, including subsetting operations; this next example illustrates this:
@@ -3739,7 +3739,7 @@ Parallel and lazy computation using
@@ -3794,10 +3794,10 @@ Parallel and lazy computation using dask.visualize() function, as shown in this example:
@@ -3819,10 +3819,10 @@ Parallel and lazy computation using
-[ ] | 0% Completed | 122.44 us
+[ ] | 0% Completed | 137.30 us
-[########################################] | 100% Completed | 101.18 ms
+[########################################] | 100% Completed | 101.19 ms
@@ -4236,7 +4236,7 @@ Resources and references
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
@@ -4365,7 +4365,7 @@ Contribute
By the Project Pythia Community.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
diff --git a/core/xarray/enso-xarray.html b/core/xarray/enso-xarray.html
index 316661573..6e86d4355 100644
--- a/core/xarray/enso-xarray.html
+++ b/core/xarray/enso-xarray.html
@@ -79,7 +79,7 @@
-
+
@@ -958,7 +958,7 @@ The Niño 3.4 Index
@@ -1590,7 +1590,7 @@ Select the Niño 3.4 regionwhere() method:
+array(1.8436452, dtype=float32)
The final step of the Niño 3.4 index calculation involves normalizing the data. In this example, we perform this normalization by dividing the smoothed anomaly data by the standard deviation calculated above:
@@ -3396,7 +3396,7 @@ Resources and References
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
@@ -3525,7 +3525,7 @@ Contribute
By the Project Pythia Community.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
diff --git a/core/xarray/xarray-intro.html b/core/xarray/xarray-intro.html
index e17e6628d..308dd9baa 100644
--- a/core/xarray/xarray-intro.html
+++ b/core/xarray/xarray-intro.html
@@ -79,7 +79,7 @@
-
+
@@ -583,25 +583,25 @@ Generate a random numpy array
-array([[[274.00119189, 291.26874805, 287.10212211, 281.93321028],
- [276.72439019, 281.86759901, 277.88521001, 291.15900492],
- [283.47892997, 281.69598295, 281.87670232, 280.04253353]],
+array([[[283.19514872, 279.93414516, 288.85955729, 280.30299831],
+ [282.03318795, 283.36237877, 288.28464039, 284.82856563],
+ [282.15998672, 276.06859236, 285.78192464, 280.47703387]],
- [[292.49183408, 286.75694127, 291.1430049 , 279.6708509 ],
- [283.19466798, 285.34236937, 289.30840199, 286.22515585],
- [289.80677567, 281.73710917, 294.57091617, 291.28505114]],
+ [[282.44435897, 282.262194 , 286.34635361, 285.32213341],
+ [283.03737484, 288.57815157, 290.02698113, 278.23328324],
+ [277.06837841, 286.4771544 , 284.58714744, 288.42673576]],
- [[278.59826878, 287.14196565, 284.68298455, 286.59720048],
- [287.51252429, 288.52590266, 273.69692215, 285.39338362],
- [292.66871999, 278.60078099, 280.78168263, 291.92156783]],
+ [[280.62831671, 275.374152 , 283.08613936, 292.56788174],
+ [286.26020096, 282.03531027, 280.16394984, 288.9366629 ],
+ [282.90971416, 278.40998651, 284.98850877, 283.14584888]],
- [[281.24772197, 286.52210588, 282.61809684, 288.82826349],
- [282.48419301, 286.34184212, 277.57835692, 279.66054122],
- [288.97073742, 287.33849395, 289.90083339, 285.95347237]],
+ [[284.34422355, 283.20830934, 278.27414095, 285.21976361],
+ [279.17521323, 292.21743424, 276.974416 , 284.06699408],
+ [274.74247997, 279.63780379, 275.87914515, 277.71758929]],
- [[282.699394 , 286.72856786, 281.89265707, 286.16350665],
- [278.02139036, 284.54696447, 283.16191651, 278.99615563],
- [284.94443941, 287.35039327, 289.38110818, 280.10681174]]])
+ [[282.71726594, 281.99808895, 279.66639367, 276.71709325],
+ [278.30599074, 277.08406243, 273.68999203, 279.75441922],
+ [271.39869986, 286.48138908, 285.22713979, 291.77285885]]])
@@ -982,44 +982,44 @@ Wrap the array: first attempt
@@ -1402,44 +1402,44 @@ Assign dimension names<xarray.DataArray (time: 5, lat: 3, lon: 4)> Size: 480B
-array([[[274.00119189, 291.26874805, 287.10212211, 281.93321028],
- [276.72439019, 281.86759901, 277.88521001, 291.15900492],
- [283.47892997, 281.69598295, 281.87670232, 280.04253353]],
-
- [[292.49183408, 286.75694127, 291.1430049 , 279.6708509 ],
- [283.19466798, 285.34236937, 289.30840199, 286.22515585],
- [289.80677567, 281.73710917, 294.57091617, 291.28505114]],
-
- [[278.59826878, 287.14196565, 284.68298455, 286.59720048],
- [287.51252429, 288.52590266, 273.69692215, 285.39338362],
- [292.66871999, 278.60078099, 280.78168263, 291.92156783]],
-
- [[281.24772197, 286.52210588, 282.61809684, 288.82826349],
- [282.48419301, 286.34184212, 277.57835692, 279.66054122],
- [288.97073742, 287.33849395, 289.90083339, 285.95347237]],
-
- [[282.699394 , 286.72856786, 281.89265707, 286.16350665],
- [278.02139036, 284.54696447, 283.16191651, 278.99615563],
- [284.94443941, 287.35039327, 289.38110818, 280.10681174]]])
-Dimensions without coordinates: time, lat, lon
+array([[[283.19514872, 279.93414516, 288.85955729, 280.30299831],
+ [282.03318795, 283.36237877, 288.28464039, 284.82856563],
+ [282.15998672, 276.06859236, 285.78192464, 280.47703387]],
+
+ [[282.44435897, 282.262194 , 286.34635361, 285.32213341],
+ [283.03737484, 288.57815157, 290.02698113, 278.23328324],
+ [277.06837841, 286.4771544 , 284.58714744, 288.42673576]],
+
+ [[280.62831671, 275.374152 , 283.08613936, 292.56788174],
+ [286.26020096, 282.03531027, 280.16394984, 288.9366629 ],
+ [282.90971416, 278.40998651, 284.98850877, 283.14584888]],
+
+ [[284.34422355, 283.20830934, 278.27414095, 285.21976361],
+ [279.17521323, 292.21743424, 276.974416 , 284.06699408],
+ [274.74247997, 279.63780379, 275.87914515, 277.71758929]],
+
+ [[282.71726594, 281.99808895, 279.66639367, 276.71709325],
+ [278.30599074, 277.08406243, 273.68999203, 279.75441922],
+ [271.39869986, 286.48138908, 285.22713979, 291.77285885]]])
+Dimensions without coordinates: time, lat, lon
This DataArray is already an improvement over a NumPy array; the DataArray contains names for each of the dimensions (or axes in NumPy parlance). An additional improvement is the association of coordinate-value arrays with data upon creation of a DataArray. In the next example, we illustrate the creation of NumPy arrays representing the coordinate values for each dimension of the DataArray, and how to associate these coordinate arrays with the data in our DataArray.
@@ -1849,51 +1849,51 @@ Initialize the
fill: currentColor;
}
<xarray.DataArray (time: 5, lat: 3, lon: 4)> Size: 480B
-array([[[274.00119189, 291.26874805, 287.10212211, 281.93321028],
- [276.72439019, 281.86759901, 277.88521001, 291.15900492],
- [283.47892997, 281.69598295, 281.87670232, 280.04253353]],
+array([[[283.19514872, 279.93414516, 288.85955729, 280.30299831],
+ [282.03318795, 283.36237877, 288.28464039, 284.82856563],
+ [282.15998672, 276.06859236, 285.78192464, 280.47703387]],
- [[292.49183408, 286.75694127, 291.1430049 , 279.6708509 ],
- [283.19466798, 285.34236937, 289.30840199, 286.22515585],
- [289.80677567, 281.73710917, 294.57091617, 291.28505114]],
+ [[282.44435897, 282.262194 , 286.34635361, 285.32213341],
+ [283.03737484, 288.57815157, 290.02698113, 278.23328324],
+ [277.06837841, 286.4771544 , 284.58714744, 288.42673576]],
- [[278.59826878, 287.14196565, 284.68298455, 286.59720048],
- [287.51252429, 288.52590266, 273.69692215, 285.39338362],
- [292.66871999, 278.60078099, 280.78168263, 291.92156783]],
+ [[280.62831671, 275.374152 , 283.08613936, 292.56788174],
+ [286.26020096, 282.03531027, 280.16394984, 288.9366629 ],
+ [282.90971416, 278.40998651, 284.98850877, 283.14584888]],
- [[281.24772197, 286.52210588, 282.61809684, 288.82826349],
- [282.48419301, 286.34184212, 277.57835692, 279.66054122],
- [288.97073742, 287.33849395, 289.90083339, 285.95347237]],
+ [[284.34422355, 283.20830934, 278.27414095, 285.21976361],
+ [279.17521323, 292.21743424, 276.974416 , 284.06699408],
+ [274.74247997, 279.63780379, 275.87914515, 277.71758929]],
- [[282.699394 , 286.72856786, 281.89265707, 286.16350665],
- [278.02139036, 284.54696447, 283.16191651, 278.99615563],
- [284.94443941, 287.35039327, 289.38110818, 280.10681174]]])
+ [[282.71726594, 281.99808895, 279.66639367, 276.71709325],
+ [278.30599074, 277.08406243, 273.68999203, 279.75441922],
+ [271.39869986, 286.48138908, 285.22713979, 291.77285885]]])
Coordinates:
* time (time) datetime64[ns] 40B 2018-01-01 2018-01-02 ... 2018-01-05
* lat (lat) float64 24B 25.0 40.0 55.0
- * lon (lon) float64 32B -120.0 -100.0 -80.0 -60.0
+ dtype='datetime64[ns]', name='time', freq='D'))
latPandasIndexPandasIndex(Index([25.0, 40.0, 55.0], dtype='float64', name='lat'))
lonPandasIndexPandasIndex(Index([-120.0, -100.0, -80.0, -60.0], dtype='float64', name='lon'))
@@ -2273,54 +2273,54 @@ Set useful attributes<xarray.DataArray (time: 5, lat: 3, lon: 4)> Size: 480B
-array([[[274.00119189, 291.26874805, 287.10212211, 281.93321028],
- [276.72439019, 281.86759901, 277.88521001, 291.15900492],
- [283.47892997, 281.69598295, 281.87670232, 280.04253353]],
+array([[[283.19514872, 279.93414516, 288.85955729, 280.30299831],
+ [282.03318795, 283.36237877, 288.28464039, 284.82856563],
+ [282.15998672, 276.06859236, 285.78192464, 280.47703387]],
- [[292.49183408, 286.75694127, 291.1430049 , 279.6708509 ],
- [283.19466798, 285.34236937, 289.30840199, 286.22515585],
- [289.80677567, 281.73710917, 294.57091617, 291.28505114]],
+ [[282.44435897, 282.262194 , 286.34635361, 285.32213341],
+ [283.03737484, 288.57815157, 290.02698113, 278.23328324],
+ [277.06837841, 286.4771544 , 284.58714744, 288.42673576]],
- [[278.59826878, 287.14196565, 284.68298455, 286.59720048],
- [287.51252429, 288.52590266, 273.69692215, 285.39338362],
- [292.66871999, 278.60078099, 280.78168263, 291.92156783]],
+ [[280.62831671, 275.374152 , 283.08613936, 292.56788174],
+ [286.26020096, 282.03531027, 280.16394984, 288.9366629 ],
+ [282.90971416, 278.40998651, 284.98850877, 283.14584888]],
- [[281.24772197, 286.52210588, 282.61809684, 288.82826349],
- [282.48419301, 286.34184212, 277.57835692, 279.66054122],
- [288.97073742, 287.33849395, 289.90083339, 285.95347237]],
+ [[284.34422355, 283.20830934, 278.27414095, 285.21976361],
+ [279.17521323, 292.21743424, 276.974416 , 284.06699408],
+ [274.74247997, 279.63780379, 275.87914515, 277.71758929]],
- [[282.699394 , 286.72856786, 281.89265707, 286.16350665],
- [278.02139036, 284.54696447, 283.16191651, 278.99615563],
- [284.94443941, 287.35039327, 289.38110818, 280.10681174]]])
+ [[282.71726594, 281.99808895, 279.66639367, 276.71709325],
+ [278.30599074, 277.08406243, 273.68999203, 279.75441922],
+ [271.39869986, 286.48138908, 285.22713979, 291.77285885]]])
Coordinates:
* time (time) datetime64[ns] 40B 2018-01-01 2018-01-02 ... 2018-01-05
* lat (lat) float64 24B 25.0 40.0 55.0
* lon (lon) float64 32B -120.0 -100.0 -80.0 -60.0
Attributes:
units: kelvin
- standard_name: air_temperature
+ dtype='datetime64[ns]', name='time', freq='D'))
latPandasIndexPandasIndex(Index([25.0, 40.0, 55.0], dtype='float64', name='lat'))
lonPandasIndexPandasIndex(Index([-120.0, -100.0, -80.0, -60.0], dtype='float64', name='lon'))
- units :
- kelvin
- standard_name :
- air_temperature
@@ -2699,51 +2699,51 @@ Issues with preservation of attributesdocumentation page.
@@ -3134,54 +3134,54 @@ Create a pressure
+ dtype='datetime64[ns]', name='time', freq='D'))latPandasIndexPandasIndex(Index([25.0, 40.0, 55.0], dtype='float64', name='lat'))
lonPandasIndexPandasIndex(Index([-120.0, -100.0, -80.0, -60.0], dtype='float64', name='lon'))
As listed in the rich display above, the new Dataset object is aware that both DataArrays share the same coordinate axes. (Please note that if this page is not run as a Jupyter Notebook, the rich display may be unavailable.)
@@ -3986,54 +3986,54 @@ Access Data variables and Coordinates in a DataArrays through a dictionary syntax, as shown in this example:
@@ -4407,54 +4407,54 @@ Access Data variables and Coordinates in a Dataset objects are mainly used for loading data from files, which will be covered later in this tutorial.
@@ -4838,18 +4838,18 @@ NumPy-like selection<xarray.DataArray (lat: 3, lon: 4)> Size: 96B
-array([[292.49183408, 286.75694127, 291.1430049 , 279.6708509 ],
- [283.19466798, 285.34236937, 289.30840199, 286.22515585],
- [289.80677567, 281.73710917, 294.57091617, 291.28505114]])
+array([[282.44435897, 282.262194 , 286.34635361, 285.32213341],
+ [283.03737484, 288.57815157, 290.02698113, 278.23328324],
+ [277.06837841, 286.4771544 , 284.58714744, 288.42673576]])
Coordinates:
time datetime64[ns] 8B 2018-01-02
* lat (lat) float64 24B 25.0 40.0 55.0
* lon (lon) float64 32B -120.0 -100.0 -80.0 -60.0
Attributes:
units: kelvin
- standard_name: air_temperature
+ standard_name: air_temperature
This example reveals one of the major shortcomings of index selection. In order to retrieve the correct data using index selection, anyone using a DataArray must have precise knowledge of the axes in the DataArray, including the order of the axes and the meaning of their indices.
By using named coordinates, as shown in the next set of examples, we can avoid this cumbersome burden.
@@ -5229,18 +5229,18 @@ Selecting with
fill: currentColor;
}
<xarray.DataArray (lat: 3, lon: 4)> Size: 96B
-array([[292.49183408, 286.75694127, 291.1430049 , 279.6708509 ],
- [283.19466798, 285.34236937, 289.30840199, 286.22515585],
- [289.80677567, 281.73710917, 294.57091617, 291.28505114]])
+array([[282.44435897, 282.262194 , 286.34635361, 285.32213341],
+ [283.03737484, 288.57815157, 290.02698113, 278.23328324],
+ [277.06837841, 286.4771544 , 284.58714744, 288.42673576]])
Coordinates:
time datetime64[ns] 8B 2018-01-02
* lat (lat) float64 24B 25.0 40.0 55.0
* lon (lon) float64 32B -120.0 -100.0 -80.0 -60.0
Attributes:
units: kelvin
- standard_name: air_temperature
+ standard_name: air_temperature
This method yields the same result as the index selection, however:
@@ -5627,18 +5627,18 @@ Nearest-neighbor sampling.sel indeed returned the data at the temporal value corresponding to the date 2018-01-05.
@@ -6017,20 +6017,20 @@ Interpolation
Info
@@ -6414,29 +6414,29 @@ Slicing along coordinates
Info
@@ -6818,18 +6818,18 @@ One more selection method: <xarray.DataArray (lat: 3, lon: 4)> Size: 96B
-array([[292.49183408, 286.75694127, 291.1430049 , 279.6708509 ],
- [283.19466798, 285.34236937, 289.30840199, 286.22515585],
- [289.80677567, 281.73710917, 294.57091617, 291.28505114]])
+array([[282.44435897, 282.262194 , 286.34635361, 285.32213341],
+ [283.03737484, 288.57815157, 290.02698113, 278.23328324],
+ [277.06837841, 286.4771544 , 284.58714744, 288.42673576]])
Coordinates:
time datetime64[ns] 8B 2018-01-02
* lat (lat) float64 24B 25.0 40.0 55.0
* lon (lon) float64 32B -120.0 -100.0 -80.0 -60.0
Attributes:
units: kelvin
- standard_name: air_temperature
+ standard_name: air_temperature
This selection technique is similar to NumPy’s index-based selection, as shown below:
temp[1,:,:]
@@ -7208,29 +7208,29 @@ One more selection method: <xarray.DataArray (time: 3, lat: 2, lon: 2)> Size: 96B
-array([[[291.26874805, 287.10212211],
- [281.86759901, 277.88521001]],
+array([[[279.93414516, 288.85955729],
+ [283.36237877, 288.28464039]],
- [[286.75694127, 291.1430049 ],
- [285.34236937, 289.30840199]],
+ [[282.262194 , 286.34635361],
+ [288.57815157, 290.02698113]],
- [[287.14196565, 284.68298455],
- [288.52590266, 273.69692215]]])
+ [[275.374152 , 283.08613936],
+ [282.03531027, 280.16394984]]])
Coordinates:
* time (time) datetime64[ns] 24B 2018-01-01 2018-01-02 2018-01-03
* lat (lat) float64 16B 25.0 40.0
* lon (lon) float64 16B -100.0 -80.0
Attributes:
units: kelvin
- standard_name: air_temperature
+ [[275.374152 , 283.08613936],
+ [282.03531027, 280.16394984]]])
- time(time)datetime64[ns]2018-01-01 2018-01-02 2018-01-03
array(['2018-01-01T00:00:00.000000000', '2018-01-02T00:00:00.000000000',
+ '2018-01-03T00:00:00.000000000'], dtype='datetime64[ns]')
- lat(lat)float6425.0 40.0
array([25., 40.])
- lon(lon)float64-100.0 -80.0
array([-100., -80.])
- timePandasIndex
PandasIndex(DatetimeIndex(['2018-01-01', '2018-01-02', '2018-01-03'], dtype='datetime64[ns]', name='time', freq='D'))
- latPandasIndex
PandasIndex(Index([25.0, 40.0], dtype='float64', name='lat'))
- lonPandasIndex
PandasIndex(Index([-100.0, -80.0], dtype='float64', name='lon'))
- units :
- kelvin
- standard_name :
- air_temperature
In contrast with the previous example, this example shows a useful advantage of using the .loc attribute. When using the .loc attribute, you can specify data slices using a syntax similar to NumPy in addition to, or instead of, using the slice function. Both of these slicing techniques are illustrated below:
@@ -7604,29 +7604,29 @@ One more selection method: <xarray.DataArray (time: 3, lat: 2, lon: 2)> Size: 96B
-array([[[291.26874805, 287.10212211],
- [281.86759901, 277.88521001]],
+array([[[279.93414516, 288.85955729],
+ [283.36237877, 288.28464039]],
- [[286.75694127, 291.1430049 ],
- [285.34236937, 289.30840199]],
+ [[282.262194 , 286.34635361],
+ [288.57815157, 290.02698113]],
- [[287.14196565, 284.68298455],
- [288.52590266, 273.69692215]]])
+ [[275.374152 , 283.08613936],
+ [282.03531027, 280.16394984]]])
Coordinates:
* time (time) datetime64[ns] 24B 2018-01-01 2018-01-02 2018-01-03
* lat (lat) float64 16B 25.0 40.0
* lon (lon) float64 16B -100.0 -80.0
Attributes:
units: kelvin
- standard_name: air_temperature
+ [[275.374152 , 283.08613936],
+ [282.03531027, 280.16394984]]])
- time(time)datetime64[ns]2018-01-01 2018-01-02 2018-01-03
array(['2018-01-01T00:00:00.000000000', '2018-01-02T00:00:00.000000000',
+ '2018-01-03T00:00:00.000000000'], dtype='datetime64[ns]')
- lat(lat)float6425.0 40.0
array([25., 40.])
- lon(lon)float64-100.0 -80.0
array([-100., -80.])
- timePandasIndex
PandasIndex(DatetimeIndex(['2018-01-01', '2018-01-02', '2018-01-03'], dtype='datetime64[ns]', name='time', freq='D'))
- latPandasIndex
PandasIndex(Index([25.0, 40.0], dtype='float64', name='lat'))
- lonPandasIndex
PandasIndex(Index([-100.0, -80.0], dtype='float64', name='lon'))
- units :
- kelvin
- standard_name :
- air_temperature
As described above, the arguments to .loc must be in the order of the DataArray’s dimensions. Attempting to slice data without ordering arguments properly can cause errors, as shown below:
@@ -8059,10 +8059,10 @@ Access netCDF data with
@@ -8504,17 +8504,17 @@ Subsetting the
positive: down
Grib_level_type: 100
_CoordinateAxisType: Pressure
- _CoordinateZisPositive: down
+ dtype='float32', name='isobaric1'))- units :
- hPa
- long_name :
- Isobaric surface
- positive :
- down
- Grib_level_type :
- 100
- _CoordinateAxisType :
- Pressure
- _CoordinateZisPositive :
- down
(As described earlier in this tutorial, we can also use dictionary syntax to select specific DataArrays; in this case, we would write ds['isobaric1'].)
Many of the subsetting operations usable on DataArrays can also be used on Datasets. However, when used on Datasets, these operations are performed on every DataArray in the Dataset, as shown below:
@@ -8916,7 +8916,7 @@ Subsetting the
geospatial_lat_min: 10.753308882144761
geospatial_lat_max: 46.8308828962289
geospatial_lon_min: -153.88242040519995
- geospatial_lon_max: -42.666108129242815
As shown above, the subsetting operation performed on the Dataset returned a new Dataset. If only a single DataArray is needed from this new Dataset, it can be retrieved using the familiar dot notation:
@@ -9356,7 +9356,7 @@ Subsetting the
Grib1_TableVersion: 131
Grib1_Parameter: 11
Grib1_Level_Type: 100
- Grib1_Level_Desc: Isobaric surface
+ dtype='float32', name='x', length=268))- long_name :
- Temperature @ Isobaric surface
- units :
- K
- description :
- Temperature
- grid_mapping :
- LambertConformal_Projection
- Grib_Variable_Id :
- VAR_7-15-131-11_L100
- Grib1_Center :
- 7
- Grib1_Subcenter :
- 15
- Grib1_TableVersion :
- 131
- Grib1_Parameter :
- 11
- Grib1_Level_Type :
- 100
- Grib1_Level_Desc :
- Isobaric surface
@@ -9790,19 +9790,19 @@ Aggregation operations
+ dtype='float32', name='isobaric1'))
Info
@@ -10196,19 +10196,19 @@ Aggregation operations
+ dtype='float32', name='isobaric1'))
@@ -10226,7 +10226,7 @@ Simple visualization with
-[<matplotlib.lines.Line2D at 0x7f2e0f58d130>]
+[<matplotlib.lines.Line2D at 0x7f505db3d9d0>]
@@ -10250,7 +10250,7 @@ Customizing the plot
-[<matplotlib.lines.Line2D at 0x7f2e0f58eff0>]
+[<matplotlib.lines.Line2D at 0x7f5055a98d40>]
@@ -10268,7 +10268,7 @@ Plotting 2-D data
-<matplotlib.collections.QuadMesh at 0x7f2df9c43b60>
+<matplotlib.collections.QuadMesh at 0x7f5055ad1be0>
@@ -10667,7 +10667,7 @@ Resources and references
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
@@ -10796,7 +10796,7 @@ Contribute
By the Project Pythia Community.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
diff --git a/foundations/conda.html b/foundations/conda.html
index c07684127..6f809cfd0 100644
--- a/foundations/conda.html
+++ b/foundations/conda.html
@@ -79,7 +79,7 @@
-
+
@@ -913,7 +913,7 @@ Resources and References
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
@@ -1042,7 +1042,7 @@ Contribute
By the Project Pythia Community.
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+ Last updated on 20 May 2024.
diff --git a/foundations/getting-started-github.html b/foundations/getting-started-github.html
index 4a2af1d14..ec22e00df 100644
--- a/foundations/getting-started-github.html
+++ b/foundations/getting-started-github.html
@@ -81,7 +81,7 @@
-
+
@@ -768,7 +768,7 @@ Getting Started with GitHub
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
@@ -897,7 +897,7 @@ Contribute
By the Project Pythia Community.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
diff --git a/foundations/getting-started-jupyter.html b/foundations/getting-started-jupyter.html
index b1d2107c0..b95652e74 100644
--- a/foundations/getting-started-jupyter.html
+++ b/foundations/getting-started-jupyter.html
@@ -79,7 +79,7 @@
-
+
@@ -905,7 +905,7 @@ Resources and references
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
@@ -1034,7 +1034,7 @@ Contribute
By the Project Pythia Community.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
diff --git a/foundations/getting-started-python.html b/foundations/getting-started-python.html
index 286ad4516..19ac49850 100644
--- a/foundations/getting-started-python.html
+++ b/foundations/getting-started-python.html
@@ -81,7 +81,7 @@
-
+
@@ -779,7 +779,7 @@ Topics
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
@@ -908,7 +908,7 @@ Contribute
By the Project Pythia Community.
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+ Last updated on 20 May 2024.
diff --git a/foundations/github/basic-git.html b/foundations/github/basic-git.html
index 441aa5056..32e0b6b24 100644
--- a/foundations/github/basic-git.html
+++ b/foundations/github/basic-git.html
@@ -79,7 +79,7 @@
-
+
@@ -1222,7 +1222,7 @@ References
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
@@ -1351,7 +1351,7 @@ Contribute
By the Project Pythia Community.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
diff --git a/foundations/github/contribute-to-pythia.html b/foundations/github/contribute-to-pythia.html
index d1e1bad12..11ebc0cc2 100644
--- a/foundations/github/contribute-to-pythia.html
+++ b/foundations/github/contribute-to-pythia.html
@@ -79,7 +79,7 @@
-
+
@@ -885,7 +885,7 @@ Summary
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
@@ -1014,7 +1014,7 @@ Contribute
By the Project Pythia Community.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
diff --git a/foundations/github/git-branches.html b/foundations/github/git-branches.html
index 6dac4b319..4e316269f 100644
--- a/foundations/github/git-branches.html
+++ b/foundations/github/git-branches.html
@@ -79,7 +79,7 @@
-
+
@@ -1036,7 +1036,7 @@ Resources and references
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
@@ -1165,7 +1165,7 @@ Contribute
By the Project Pythia Community.
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+ Last updated on 20 May 2024.
diff --git a/foundations/github/github-advanced.html b/foundations/github/github-advanced.html
index 9e93b611f..def634b01 100644
--- a/foundations/github/github-advanced.html
+++ b/foundations/github/github-advanced.html
@@ -77,7 +77,7 @@
-
+
@@ -810,7 +810,7 @@ References
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
@@ -939,7 +939,7 @@ Contribute
By the Project Pythia Community.
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+ Last updated on 20 May 2024.
diff --git a/foundations/github/github-cloning-forking.html b/foundations/github/github-cloning-forking.html
index c03c6f72a..18b821529 100644
--- a/foundations/github/github-cloning-forking.html
+++ b/foundations/github/github-cloning-forking.html
@@ -79,7 +79,7 @@
-
+
@@ -988,7 +988,7 @@ References
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
@@ -1117,7 +1117,7 @@ Contribute
By the Project Pythia Community.
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+ Last updated on 20 May 2024.
diff --git a/foundations/github/github-issues.html b/foundations/github/github-issues.html
index fe56bf8f1..cf5e4670a 100644
--- a/foundations/github/github-issues.html
+++ b/foundations/github/github-issues.html
@@ -79,7 +79,7 @@
-
+
@@ -875,7 +875,7 @@ References
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@@ -1004,7 +1004,7 @@ Contribute
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+ Last updated on 20 May 2024.
diff --git a/foundations/github/github-pull-request.html b/foundations/github/github-pull-request.html
index 566d2d767..a2f449208 100644
--- a/foundations/github/github-pull-request.html
+++ b/foundations/github/github-pull-request.html
@@ -79,7 +79,7 @@
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+
@@ -946,7 +946,7 @@ References
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+ Last updated on 20 May 2024.
@@ -1075,7 +1075,7 @@ Contribute
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diff --git a/foundations/github/github-repos.html b/foundations/github/github-repos.html
index 7d26de757..389418ea0 100644
--- a/foundations/github/github-repos.html
+++ b/foundations/github/github-repos.html
@@ -79,7 +79,7 @@
-
+
@@ -885,7 +885,7 @@ References
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
@@ -1014,7 +1014,7 @@ Contribute
By the Project Pythia Community.
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+ Last updated on 20 May 2024.
diff --git a/foundations/github/github-setup-advanced.html b/foundations/github/github-setup-advanced.html
index f2123372f..71344fd0b 100644
--- a/foundations/github/github-setup-advanced.html
+++ b/foundations/github/github-setup-advanced.html
@@ -79,7 +79,7 @@
-
+
@@ -924,7 +924,7 @@ References
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
@@ -1053,7 +1053,7 @@ Contribute
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- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
diff --git a/foundations/github/github-workflows.html b/foundations/github/github-workflows.html
index a5c6b6dfc..17e8fd580 100644
--- a/foundations/github/github-workflows.html
+++ b/foundations/github/github-workflows.html
@@ -79,7 +79,7 @@
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+
@@ -1253,7 +1253,7 @@ References
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+ Last updated on 20 May 2024.
@@ -1382,7 +1382,7 @@ Contribute
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+ Last updated on 20 May 2024.
diff --git a/foundations/github/review-pr.html b/foundations/github/review-pr.html
index f5b0820fb..fe5674aa9 100644
--- a/foundations/github/review-pr.html
+++ b/foundations/github/review-pr.html
@@ -79,7 +79,7 @@
-
+
@@ -918,7 +918,7 @@ Resources and References
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
@@ -1047,7 +1047,7 @@ Contribute
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diff --git a/foundations/github/what-is-github.html b/foundations/github/what-is-github.html
index 27ef2dcd4..d0e70d102 100644
--- a/foundations/github/what-is-github.html
+++ b/foundations/github/what-is-github.html
@@ -81,7 +81,7 @@
-
+
@@ -916,7 +916,7 @@ References
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@@ -1045,7 +1045,7 @@ Contribute
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diff --git a/foundations/how-to-run-python.html b/foundations/how-to-run-python.html
index 8b1aab94d..d7e56e4bc 100644
--- a/foundations/how-to-run-python.html
+++ b/foundations/how-to-run-python.html
@@ -79,7 +79,7 @@
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+
@@ -871,7 +871,7 @@ Resources and References
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@@ -1000,7 +1000,7 @@ Contribute
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diff --git a/foundations/jupyter.html b/foundations/jupyter.html
index 0d2537ca8..901251796 100644
--- a/foundations/jupyter.html
+++ b/foundations/jupyter.html
@@ -79,7 +79,7 @@
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+
@@ -885,7 +885,7 @@ Resources and References
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diff --git a/foundations/jupyterlab.html b/foundations/jupyterlab.html
index 1565427e6..94676c432 100644
--- a/foundations/jupyterlab.html
+++ b/foundations/jupyterlab.html
@@ -79,7 +79,7 @@
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+
@@ -1161,7 +1161,7 @@ Resources and references
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@@ -1290,7 +1290,7 @@ Contribute
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diff --git a/foundations/markdown.html b/foundations/markdown.html
index 80c1a4850..364430a39 100644
--- a/foundations/markdown.html
+++ b/foundations/markdown.html
@@ -79,7 +79,7 @@
-
+
@@ -754,7 +754,7 @@ Formatted Text in the Notebook with Markdown
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@@ -883,7 +883,7 @@ Contribute
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diff --git a/foundations/overview.html b/foundations/overview.html
index c8dc2806e..344643277 100644
--- a/foundations/overview.html
+++ b/foundations/overview.html
@@ -79,7 +79,7 @@
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+
@@ -778,7 +778,7 @@ Topics
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@@ -907,7 +907,7 @@ Contribute
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diff --git a/foundations/quickstart.html b/foundations/quickstart.html
index c1a4cabac..8e51f8ba9 100644
--- a/foundations/quickstart.html
+++ b/foundations/quickstart.html
@@ -81,7 +81,7 @@
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+
@@ -1267,7 +1267,7 @@ Resources and references
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diff --git a/foundations/terminal.html b/foundations/terminal.html
index 634495814..c4cb654ea 100644
--- a/foundations/terminal.html
+++ b/foundations/terminal.html
@@ -79,7 +79,7 @@
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@@ -881,7 +881,7 @@ Resources and References
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diff --git a/foundations/why-python.html b/foundations/why-python.html
index abbbd234f..b955e39c2 100644
--- a/foundations/why-python.html
+++ b/foundations/why-python.html
@@ -79,7 +79,7 @@
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+
@@ -791,7 +791,7 @@ Open source
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diff --git a/genindex.html b/genindex.html
index 81c713d97..f6f7addda 100644
--- a/genindex.html
+++ b/genindex.html
@@ -76,7 +76,7 @@
-
+
@@ -648,7 +648,7 @@ Index
@@ -777,7 +777,7 @@ Contribute
By the Project Pythia Community.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
diff --git a/landing-page.html b/landing-page.html
index ff147bc66..276e728be 100644
--- a/landing-page.html
+++ b/landing-page.html
@@ -80,7 +80,7 @@
-
+
@@ -776,7 +776,7 @@ A community learning resource for Python-based computing in the geosciences<
@@ -905,7 +905,7 @@ Contribute
By the Project Pythia Community.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
diff --git a/preamble/how-to-cite.html b/preamble/how-to-cite.html
index 6055d6478..8dfbb5b1d 100644
--- a/preamble/how-to-cite.html
+++ b/preamble/how-to-cite.html
@@ -79,7 +79,7 @@
-
+
@@ -755,7 +755,7 @@ How to Cite This Book
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
@@ -884,7 +884,7 @@ Contribute
By the Project Pythia Community.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
diff --git a/preamble/how-to-use.html b/preamble/how-to-use.html
index 626d2a29a..a79c9377d 100644
--- a/preamble/how-to-use.html
+++ b/preamble/how-to-use.html
@@ -79,7 +79,7 @@
-
+
@@ -895,7 +895,7 @@ Interacting with Jupyter Books locally
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
@@ -1024,7 +1024,7 @@ Contribute
By the Project Pythia Community.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
diff --git a/search.html b/search.html
index 57210f52a..705201479 100644
--- a/search.html
+++ b/search.html
@@ -78,7 +78,7 @@
-
+
@@ -673,7 +673,7 @@ Search
@@ -802,7 +802,7 @@ Contribute
By the Project Pythia Community.
- Last updated on 15 May 2024.
+ Last updated on 20 May 2024.
diff --git a/searchindex.js b/searchindex.js
index 18d9c718b..f531c0445 100644
--- a/searchindex.js
+++ b/searchindex.js
@@ -1 +1 @@
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image to the map.": [[3, "add-a-fancy-background-image-to-the-map"]], "Add cartographic features to the map": [[3, "add-cartographic-features-to-the-map"]], "Add in the cartographic shapefiles": [[3, "add-in-the-cartographic-shapefiles"]], "Add some predefined features": [[3, "add-some-predefined-features"]], "Adding Annotations": [[9, "adding-annotations"]], "Adding labels and a grid": [[11, "adding-labels-and-a-grid"]], "Adding labels to an Axes object": [[11, "adding-labels-to-an-axes-object"]], "Adding titles to each subplot": [[11, "adding-titles-to-each-subplot"]], "Additional Resources": [[10, "additional-resources"]], "Advanced GitHub Topics": [[31, "advanced-github-topics"]], "Aggregation Methods": [[20, "aggregation-methods"]], "Aggregation operations": [[23, "aggregation-operations"]], "An example of plotting data": [[3, "an-example-of-plotting-data"]], "Animations": [[10, "animations"]], "Annotations, Colorbars, and Advanced Layouts": [[9, "annotations-colorbars-and-advanced-layouts"]], "Another content subsection": [[1, "another-content-subsection"]], "Apply & Combine": [[20, "apply-combine"]], "Applying operations to a DataFrame": [[18, "applying-operations-to-a-dataframe"]], "Approximate selection and interpolation": [[23, "approximate-selection-and-interpolation"]], "Arithmetic": [[14, "arithmetic"]], "Arithmetic Operations": [[20, "arithmetic-operations"]], "Array comparisons": [[13, "array-comparisons"]], "Array math functions": [[14, "array-math-functions"]], "Arrays of numbers with NumPy": [[45, "arrays-of-numbers-with-numpy"]], "Assign dimension names": [[23, "assign-dimension-names"]], "Auxiliary Coordinates": [[5, "auxiliary-coordinates"]], "Basic Colorbars": [[9, "basic-colorbars"]], "Basic Line Plots": [[11, "basic-line-plots"]], "Basic Python data types": [[45, "basic-python-data-types"]], "Basic Statistics": [[18, "basic-statistics"]], "Basic Version Control with git": [[28, 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commit, push": [[37, "clone-branch-change-commit-push"]], "Cloning a repository": [[32, "cloning-a-repository"]], "Cloning and Forking a Repository": [[32, "cloning-and-forking-a-repository"]], "Cloning and forking": [[32, "cloning-and-forking"]], "Cloning the remote repository": [[37, "cloning-the-remote-repository"]], "Code Cells": [[42, "code-cells"]], "Colormap Overview": [[9, "colormap-overview"]], "Command Mode Shortcuts": [[42, "command-mode-shortcuts"]], "Commands we used in this tutorial": [[28, "commands-we-used-in-this-tutorial"]], "Committing": [[28, "committing"]], "Compare across branches": [[28, "compare-across-branches"]], "Compare across commits": [[28, "compare-across-commits"]], "Comparing versions": [[28, "comparing-versions"]], "Complete Workflow": [[30, "complete-workflow"]], "Computations and Masks with Xarray": [[20, "computations-and-masks-with-xarray"]], "Compute anomaly": [[20, "compute-anomaly"]], "Compute climatology": [[20, "compute-climatology"]], 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"id1"]], "GitHub\u2019s distributed repositories": [[35, "githubs-distributed-repositories"]], "Giving NumPy room for broadcasting": [[15, "giving-numpy-room-for-broadcasting"]], "Going back in time": [[28, "going-back-in-time"]], "Gridded Data": [[5, "gridded-data"]], "GroupBy: Split, Apply, Combine": [[20, "groupby-split-apply-combine"]], "HTTPS vs SSH: Either is fine!": [[36, null]], "Have you forked the repository?": [[30, null]], "High level languages": [[47, "high-level-languages"]], "High-level libraries": [[16, "high-level-libraries"]], "Histograms": [[10, "histograms"]], "Histograms, Pie Charts, and Animations": [[10, "histograms-pie-charts-and-animations"]], "How to Cite This Book": [[49, "how-to-cite-this-book"]], "How to Use This Book": [[50, "how-to-use-this-book"]], "How to Use This Page": [[1, "how-to-use-this-page"]], "Imports": [[1, "imports"], [3, "imports"], [5, "imports"], [7, "imports"], [9, "imports"], [10, "imports"], [11, "imports"], [13, "imports"], [14, 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