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a/appendix/how-to-contribute.html b/appendix/how-to-contribute.html index 385865d62..4226dc1b5 100644 --- a/appendix/how-to-contribute.html +++ b/appendix/how-to-contribute.html @@ -85,7 +85,7 @@ - + @@ -832,7 +832,7 @@

Keeping your dev environment up to date

- Last updated on 24 October 2024. + Last updated on 14 November 2024.

@@ -961,7 +961,7 @@

Contribute

By the Project Pythia Community. - Last updated on 24 October 2024. + Last updated on 14 November 2024.

diff --git a/appendix/template.html b/appendix/template.html index 904e21b77..ca5156b1b 100644 --- a/appendix/template.html +++ b/appendix/template.html @@ -84,7 +84,7 @@ - + @@ -621,8 +621,8 @@

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 @@ -999,7 +999,7 @@

Resources and references

- Last updated on 24 October 2024. + Last updated on 14 November 2024.

@@ -1128,7 +1128,7 @@

Contribute

By the Project Pythia Community. - Last updated on 24 October 2024. + Last updated on 14 November 2024.

diff --git a/core/cartopy.html b/core/cartopy.html index 84cfcaaa7..53494b473 100644 --- a/core/cartopy.html +++ b/core/cartopy.html @@ -83,7 +83,7 @@ - + @@ -761,7 +761,7 @@

Cartopy

- Last updated on 24 October 2024. + Last updated on 14 November 2024.

@@ -890,7 +890,7 @@

Contribute

By the Project Pythia Community. - Last updated on 24 October 2024. + Last updated on 14 November 2024.

diff --git a/core/cartopy/cartopy.html b/core/cartopy/cartopy.html index dfc37ba3b..f5fead866 100644 --- a/core/cartopy/cartopy.html +++ b/core/cartopy/cartopy.html @@ -83,7 +83,7 @@ - + @@ -580,7 +580,7 @@

Create a map with a specified projection -
<cartopy.mpl.feature_artist.FeatureArtist at 0x7fa9981e19d0>
+
<cartopy.mpl.feature_artist.FeatureArtist at 0x7fa3b428be90>
@@ -612,7 +612,7 @@

Add cartographic features to the map -
<cartopy.mpl.feature_artist.FeatureArtist at 0x7fa9982b4f10>
+
<cartopy.mpl.feature_artist.FeatureArtist at 0x7fa3b42e5c50>
@@ -1341,7 +1341,7 @@

Resources and References

- Last updated on 24 October 2024. + Last updated on 14 November 2024.

@@ -1470,7 +1470,7 @@

Contribute

By the Project Pythia Community. - Last updated on 24 October 2024. + Last updated on 14 November 2024.

diff --git a/core/data-formats.html b/core/data-formats.html index a7b623fab..40207c6a0 100644 --- a/core/data-formats.html +++ b/core/data-formats.html @@ -83,7 +83,7 @@ - + @@ -753,7 +753,7 @@

Data Formats

- Last updated on 24 October 2024. + Last updated on 14 November 2024.

@@ -882,7 +882,7 @@

Contribute

By the Project Pythia Community. - Last updated on 24 October 2024. + Last updated on 14 November 2024.

diff --git a/core/data-formats/netcdf-cf.html b/core/data-formats/netcdf-cf.html index 33677134e..a42189eac 100644 --- a/core/data-formats/netcdf-cf.html +++ b/core/data-formats/netcdf-cf.html @@ -83,7 +83,7 @@ - + @@ -648,7 +648,7 @@

Creating the file and dimensions

- Last updated on 24 October 2024. + Last updated on 14 November 2024.

@@ -1681,7 +1681,7 @@

Contribute

By the Project Pythia Community. - Last updated on 24 October 2024. + Last updated on 14 November 2024.

diff --git a/core/datetime.html b/core/datetime.html index 064cb3539..8e10a53c8 100644 --- a/core/datetime.html +++ b/core/datetime.html @@ -85,7 +85,7 @@ - + @@ -761,7 +761,7 @@

Datetime

- Last updated on 24 October 2024. + Last updated on 14 November 2024.

@@ -890,7 +890,7 @@

Contribute

By the Project Pythia Community. - Last updated on 24 October 2024. + Last updated on 14 November 2024.

diff --git a/core/datetime/datetime.html b/core/datetime/datetime.html index 1f7e33d27..100cd54c6 100644 --- a/core/datetime/datetime.html +++ b/core/datetime/datetime.html @@ -85,7 +85,7 @@ - + @@ -602,7 +602,7 @@

Some core terminology
-
1729785890.1775959
+
1731554805.391462
@@ -623,7 +623,7 @@

time

-
The benchmark took 1.0002093315124512 seconds
+
The benchmark took 1.000234842300415 seconds
@@ -752,7 +752,7 @@

See how many days have elapsed since the strike: -
6328
+
6349
@@ -824,8 +824,8 @@

Time Zone Naive Versus Time Zone Aware -
I am time zone naive 2024-10-24 16:04:51.212600.
-I am time zone aware 2024-10-24 16:04:51.212624+00:00.
+
I am time zone naive 2024-11-14 03:26:46.428224.
+I am time zone aware 2024-11-14 03:26:46.428248+00:00.
@@ -848,8 +848,8 @@

Full time zone support with the <

-
I am time zone naive: 2024-10-24 16:04:51.216766.
-I am time zone aware: 2024-10-24 10:04:51.226804-06:00.
+
I am time zone naive: 2024-11-14 03:26:46.431890.
+I am time zone aware: 2024-11-13 20:26:46.442206-07:00.
@@ -871,8 +871,8 @@

Print Time with a Different Time Zone -
The UTC time is October 24, 2024, 4:04PM.
-The 'US/Mountain' time is October 24, 2024, 10:04AM.
+
The UTC time is November 14, 2024, 3:26AM.
+The 'US/Mountain' time is November 13, 2024, 8:26PM.
@@ -1240,7 +1240,7 @@

Resources and References

- Last updated on 24 October 2024. + Last updated on 14 November 2024.

@@ -1369,7 +1369,7 @@

Contribute

By the Project Pythia Community. - Last updated on 24 October 2024. + Last updated on 14 November 2024.

diff --git a/core/matplotlib.html b/core/matplotlib.html index d43de2d86..6c534fc4e 100644 --- a/core/matplotlib.html +++ b/core/matplotlib.html @@ -85,7 +85,7 @@ - + @@ -795,7 +795,7 @@

In this section

- Last updated on 24 October 2024. + Last updated on 14 November 2024.

@@ -924,7 +924,7 @@

Contribute

By the Project Pythia Community. - Last updated on 24 October 2024. + Last updated on 14 November 2024.

diff --git a/core/matplotlib/annotations-colorbars-layouts.html b/core/matplotlib/annotations-colorbars-layouts.html index 9086e2b2d..0f845fc4d 100644 --- a/core/matplotlib/annotations-colorbars-layouts.html +++ b/core/matplotlib/annotations-colorbars-layouts.html @@ -85,7 +85,7 @@ - + @@ -739,7 +739,7 @@

Basic Colorbars -../../_images/412c33793db4ad3dcb5b39394f2c7fb9d241731db7955170d8e9a4af1b5868d0.png +../../_images/ca985ef8b1550c88d88a32a721538239a83e5f39afb947d53e97c9d900345eb9.png

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:

@@ -754,7 +754,7 @@

Basic Colorbars -../../_images/c821ea483ccdee8bfcdcdb87b49a62f8e01c34f824f5ad3d6eece34159452f74.png +../../_images/11c8909ea738ee884954f887836634e17807748ba0e5ddb08aab9ef97a33d1d0.png @@ -773,10 +773,10 @@

Shared Colorbars -
<matplotlib.colorbar.Colorbar at 0x7fd49cddf490>
+
<matplotlib.colorbar.Colorbar at 0x7fc0b5ecff50>
 

 

-../../_images/e668bf433646cb16ed291c71c90bc0b753a0b68f1ec167485c9a9982eadaad5b.png
+../../_images/317812b6874976f9ce1bdc50443e1c2b67f148e87df386edefa58d6848fd86de.png

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. @@ -857,7 +857,7 @@

Custom Colorbars -../../_images/22c6428bdda355290540dc775058f2c80332124fa2b874d49aec7cc3cd6e2b0f.png +../../_images/51b3d5530d50f9ef32676ed68d9cb7babdbc9d637e7cdbce1251882587995359.png
@@ -874,7 +874,7 @@

Custom Colorbars -../../_images/d5e95f4292806a4a0a05afe59060e2291ad5868f29a4a4c5fe5e2e4d11b0d2a0.png +../../_images/f192c88690c165c706d56b0cfca0dfb37ea5ef94f1ce5667060e3b1a1b062b73.png

@@ -911,7 +911,7 @@

Mosaic Subplots -../../_images/c35dfa57016632ec844458471f7da7fae21609b01bf2031babf45c8ba881d4f8.png +../../_images/a0c352991330e359aa25053e8894dada250de94cb47e33fc97d9c7f755a18de0.png

You’ll notice there is not a colorbar plotted by default. When constructing the colorbar, we need to specify the following:

@@ -939,13 +939,13 @@

Mosaic Subplots -
/tmp/ipykernel_2561/1337655102.py:12: UserWarning: Adding colorbar to a different Figure <Figure size 640x480 with 4 Axes> than <Figure size 640x480 with 3 Axes> which fig.colorbar is called on.
+
/tmp/ipykernel_2605/1337655102.py:12: UserWarning: Adding colorbar to a different Figure <Figure size 640x480 with 4 Axes> than <Figure size 640x480 with 3 Axes> which fig.colorbar is called on.
   fig.colorbar(histA[3], ax=[axdict['A'], axdict['B']], location='bottom')
-/tmp/ipykernel_2561/1337655102.py:13: UserWarning: Adding colorbar to a different Figure <Figure size 640x480 with 5 Axes> than <Figure size 640x480 with 3 Axes> which fig.colorbar is called on.
+/tmp/ipykernel_2605/1337655102.py:13: UserWarning: Adding colorbar to a different Figure <Figure size 640x480 with 5 Axes> than <Figure size 640x480 with 3 Axes> which fig.colorbar is called on.
   fig.colorbar(histC[3], ax=[axdict['C']], location='right');
 

 

-../../_images/f301a0a55de19593bcfe661921c6152667edc37b0689266cbca6313e7a66c649.png
+../../_images/88ad8866c4c22eda72a07c661a4629a5733f1372878aa04bd12970319c09f225.png

@@ -1298,7 +1298,7 @@

Resources and references

- Last updated on 24 October 2024. + Last updated on 14 November 2024.

@@ -1427,7 +1427,7 @@

Contribute

By the Project Pythia Community. - Last updated on 24 October 2024. + Last updated on 14 November 2024.

diff --git a/core/matplotlib/histograms-piecharts-animation.html b/core/matplotlib/histograms-piecharts-animation.html index ea0761c0e..effbc6c90 100644 --- a/core/matplotlib/histograms-piecharts-animation.html +++ b/core/matplotlib/histograms-piecharts-animation.html @@ -83,7 +83,7 @@ - + @@ -3630,7 +3630,7 @@

Additional Resources

- Last updated on 24 October 2024. + Last updated on 14 November 2024.

@@ -3759,7 +3759,7 @@

Contribute

By the Project Pythia Community. - Last updated on 24 October 2024. + Last updated on 14 November 2024.

diff --git a/core/matplotlib/matplotlib-basics.html b/core/matplotlib/matplotlib-basics.html index 566c6e4ca..6801f3210 100644 --- a/core/matplotlib/matplotlib-basics.html +++ b/core/matplotlib/matplotlib-basics.html @@ -83,7 +83,7 @@ - + @@ -1493,7 +1493,7 @@

Resources and References

- Last updated on 24 October 2024. + Last updated on 14 November 2024.

@@ -1622,7 +1622,7 @@

Contribute

By the Project Pythia Community. - Last updated on 24 October 2024. + Last updated on 14 November 2024.

diff --git a/core/numpy.html b/core/numpy.html index bb83ac0c7..3499338c4 100644 --- a/core/numpy.html +++ b/core/numpy.html @@ -85,7 +85,7 @@ - + @@ -758,7 +758,7 @@

NumPy

- Last updated on 24 October 2024. + Last updated on 14 November 2024.

@@ -887,7 +887,7 @@

Contribute

By the Project Pythia Community. - Last updated on 24 October 2024. + Last updated on 14 November 2024.

diff --git a/core/numpy/intermediate-numpy.html b/core/numpy/intermediate-numpy.html index 9a2adde3b..90d984890 100644 --- a/core/numpy/intermediate-numpy.html +++ b/core/numpy/intermediate-numpy.html @@ -85,7 +85,7 @@ - + @@ -1426,7 +1426,7 @@

Resources and references

- Last updated on 24 October 2024. + Last updated on 14 November 2024.

@@ -1555,7 +1555,7 @@

Contribute

By the Project Pythia Community. - Last updated on 24 October 2024. + Last updated on 14 November 2024.

diff --git a/core/numpy/numpy-basics.html b/core/numpy/numpy-basics.html index 5846a7a31..8c9c7e867 100644 --- a/core/numpy/numpy-basics.html +++ b/core/numpy/numpy-basics.html @@ -85,7 +85,7 @@ - + @@ -1052,7 +1052,7 @@

Array math functions
-
/tmp/ipykernel_2756/67500735.py:1: DeprecationWarning: `trapz` is deprecated. Use `trapezoid` instead, or one of the numerical integration functions in `scipy.integrate`.
+
/tmp/ipykernel_2724/67500735.py:1: DeprecationWarning: `trapz` is deprecated. Use `trapezoid` instead, or one of the numerical integration functions in `scipy.integrate`.
   sine_integral = np.trapz(sin_t, t)
 

 

@@ -1780,7 +1780,7 @@ 
Resources and references
     

-  Last updated on 24 October 2024.
+  Last updated on 14 November 2024.
@@ -1909,7 +1909,7 @@

Contribute

By the Project Pythia Community. - Last updated on 24 October 2024. + Last updated on 14 November 2024.

diff --git a/core/numpy/numpy-broadcasting.html b/core/numpy/numpy-broadcasting.html index dc63e15c0..978deb0ef 100644 --- a/core/numpy/numpy-broadcasting.html +++ b/core/numpy/numpy-broadcasting.html @@ -85,7 +85,7 @@ - + @@ -1080,7 +1080,7 @@

Blocking
-
array([19.1, 18.7, 13.9, 24.1, 10.9, 21.6, 24.3, 18.1, 21.6, 33.3])
+
array([21.2, 19.4, 22.6, 19.8, 13.8, 17.4,  7.6, 27.5, 23.1, 21.6])
@@ -1103,8 +1103,8 @@

Blocking

-
array([ 0.        , 17.23333333, 18.9       , 16.3       , 18.86666667,
-       18.93333333, 21.33333333, 21.33333333, 24.33333333,  0.        ])
+
array([ 0.        , 21.06666667, 20.6       , 18.73333333, 17.        ,
+       12.93333333, 17.5       , 19.4       , 24.06666667,  0.        ])
@@ -1118,8 +1118,8 @@

Blocking

-
array([17.23333333, 18.9       , 16.3       , 18.86666667, 18.93333333,
-       21.33333333, 21.33333333, 24.33333333])
+
array([21.06666667, 20.6       , 18.73333333, 17.        , 12.93333333,
+       17.5       , 19.4       , 24.06666667])
@@ -1132,7 +1132,7 @@

Blocking

-
array([19.1, 18.7, 13.9, 24.1, 10.9, 21.6, 24.3, 18.1, 21.6, 33.3])
+
array([21.2, 19.4, 22.6, 19.8, 13.8, 17.4,  7.6, 27.5, 23.1, 21.6])
@@ -1151,14 +1151,14 @@

Blocking

-
array([[19.1, 18.7, 13.9],
-       [18.7, 13.9, 24.1],
-       [13.9, 24.1, 10.9],
-       [24.1, 10.9, 21.6],
-       [10.9, 21.6, 24.3],
-       [21.6, 24.3, 18.1],
-       [24.3, 18.1, 21.6],
-       [18.1, 21.6, 33.3]])
+
array([[21.2, 19.4, 22.6],
+       [19.4, 22.6, 19.8],
+       [22.6, 19.8, 13.8],
+       [19.8, 13.8, 17.4],
+       [13.8, 17.4,  7.6],
+       [17.4,  7.6, 27.5],
+       [ 7.6, 27.5, 23.1],
+       [27.5, 23.1, 21.6]])
@@ -1171,8 +1171,8 @@

Blocking

-
array([17.23333333, 18.9       , 16.3       , 18.86666667, 18.93333333,
-       21.33333333, 21.33333333, 24.33333333])
+
array([21.06666667, 20.6       , 18.73333333, 17.        , 12.93333333,
+       17.5       , 19.4       , 24.06666667])
@@ -1186,14 +1186,14 @@

Blocking

-
array([[  19.1,   18.7, 2000. ],
-       [  18.7, 2000. ,   24.1],
-       [2000. ,   24.1,   10.9],
-       [  24.1,   10.9,   21.6],
-       [  10.9,   21.6,   24.3],
-       [  21.6,   24.3,   18.1],
-       [  24.3,   18.1,   21.6],
-       [  18.1,   21.6,   33.3]])
+
array([[  21.2,   19.4, 2000. ],
+       [  19.4, 2000. ,   19.8],
+       [2000. ,   19.8,   13.8],
+       [  19.8,   13.8,   17.4],
+       [  13.8,   17.4,    7.6],
+       [  17.4,    7.6,   27.5],
+       [   7.6,   27.5,   23.1],
+       [  27.5,   23.1,   21.6]])
@@ -1220,13 +1220,13 @@

Finding the difference between min and max -
array([[7, 6, 6, ..., 7, 6, 6],
-       [7, 7, 7, ..., 8, 7, 6],
-       [6, 7, 7, ..., 6, 6, 6],
+
array([[7, 6, 7, ..., 6, 7, 7],
+       [7, 6, 6, ..., 6, 7, 6],
+       [6, 7, 6, ..., 6, 6, 6],
        ...,
-       [7, 7, 7, ..., 8, 6, 7],
-       [7, 7, 8, ..., 6, 8, 7],
-       [6, 7, 7, ..., 6, 6, 8]])
+       [6, 6, 7, ..., 7, 7, 7],
+       [7, 6, 6, ..., 6, 7, 8],
+       [7, 7, 7, ..., 7, 8, 6]])
@@ -1251,13 +1251,13 @@

Finding the difference between min and max -
array([[737.5, 775. , 775. , ..., 737.5, 775. , 775. ],
-       [737.5, 737.5, 737.5, ..., 700. , 737.5, 775. ],
-       [775. , 737.5, 737.5, ..., 775. , 775. , 775. ],
+
array([[737.5, 775. , 737.5, ..., 775. , 737.5, 737.5],
+       [737.5, 775. , 775. , ..., 775. , 737.5, 775. ],
+       [775. , 737.5, 775. , ..., 775. , 775. , 775. ],
        ...,
-       [737.5, 737.5, 737.5, ..., 700. , 775. , 737.5],
-       [737.5, 737.5, 700. , ..., 775. , 700. , 737.5],
-       [775. , 737.5, 737.5, ..., 775. , 775. , 700. ]])
+       [775. , 775. , 737.5, ..., 737.5, 737.5, 737.5],
+       [737.5, 775. , 775. , ..., 775. , 737.5, 700. ],
+       [737.5, 737.5, 737.5, ..., 737.5, 700. , 775. ]])
@@ -1286,19 +1286,19 @@

Finding the difference between min and max -
array([[-11.44710595,  -7.22065739,  -7.89588993, ..., -10.82646423,
-         -5.37380055,  -8.87861153],
-       [-10.85520705,  -9.00116433,  -9.99391275, ..., -13.46870204,
-        -12.95425774,  -8.07610409],
-       [-10.49566091, -10.57374805,  -8.88375872, ...,  -3.90684223,
-         -7.88090598,  -8.4823347 ],
+
array([[-13.42481167,  -9.17700948,  -9.37970047, ..., -11.70441647,
+         -7.91347937, -12.10757443],
+       [-13.50636928,  -7.19837053,  -9.35736591, ...,  -8.88210411,
+        -11.15282315,  -6.90731835],
+       [ -8.1434806 , -14.03122444, -10.13179945, ..., -12.51534667,
+        -10.41766898,  -7.11435998],
        ...,
-       [-11.08911142, -13.00460529, -10.88006683, ..., -12.1519193 ,
-         -7.9415901 , -10.34716892],
-       [-11.58423485, -13.24891355, -14.96484073, ...,  -6.12162802,
-         -9.03796517, -11.72144538],
-       [ -7.8272587 , -10.42067982, -12.10214242, ...,  -9.78563562,
-         -8.55425093, -12.33651532]])
+       [-10.34568138, -12.38178421,  -8.80207346, ..., -11.84644993,
+        -14.64804599,  -9.82005014],
+       [-12.46190369,  -7.34290485,  -8.4762359 , ...,  -7.50476032,
+         -9.13744245, -14.80741945],
+       [-11.35426494, -14.47802372, -12.7227316 , ..., -12.01820687,
+        -13.85816499,  -8.13436478]])
@@ -1342,19 +1342,19 @@

Finding the difference between min and max -
array([[-11.44710595,  -7.22065739,  -7.89588993, ..., -10.82646423,
-         -5.37380055,  -8.87861153],
-       [-10.85520705,  -9.00116433,  -9.99391275, ..., -13.46870204,
-        -12.95425774,  -8.07610409],
-       [-10.49566091, -10.57374805,  -8.88375872, ...,  -3.90684223,
-         -7.88090598,  -8.4823347 ],
+
array([[-13.42481167,  -9.17700948,  -9.37970047, ..., -11.70441647,
+         -7.91347937, -12.10757443],
+       [-13.50636928,  -7.19837053,  -9.35736591, ...,  -8.88210411,
+        -11.15282315,  -6.90731835],
+       [ -8.1434806 , -14.03122444, -10.13179945, ..., -12.51534667,
+        -10.41766898,  -7.11435998],
        ...,
-       [-11.08911142, -13.00460529, -10.88006683, ..., -12.1519193 ,
-         -7.9415901 , -10.34716892],
-       [-11.58423485, -13.24891355, -14.96484073, ...,  -6.12162802,
-         -9.03796517, -11.72144538],
-       [ -7.8272587 , -10.42067982, -12.10214242, ...,  -9.78563562,
-         -8.55425093, -12.33651532]])
+       [-10.34568138, -12.38178421,  -8.80207346, ..., -11.84644993,
+        -14.64804599,  -9.82005014],
+       [-12.46190369,  -7.34290485,  -8.4762359 , ...,  -7.50476032,
+         -9.13744245, -14.80741945],
+       [-11.35426494, -14.47802372, -12.7227316 , ..., -12.01820687,
+        -13.85816499,  -8.13436478]])
@@ -1713,7 +1713,7 @@

Resources and references

- Last updated on 24 October 2024. + Last updated on 14 November 2024.

@@ -1842,7 +1842,7 @@

Contribute

By the Project Pythia Community. - Last updated on 24 October 2024. + Last updated on 14 November 2024.

diff --git a/core/overview.html b/core/overview.html index 2c07d9e1d..9c7b67c2d 100644 --- a/core/overview.html +++ b/core/overview.html @@ -83,7 +83,7 @@ - + @@ -886,7 +886,7 @@

Tutorials

- Last updated on 24 October 2024. + Last updated on 14 November 2024.

@@ -1015,7 +1015,7 @@

Contribute

By the Project Pythia Community. - Last updated on 24 October 2024. + Last updated on 14 November 2024.

diff --git a/core/pandas.html b/core/pandas.html index 5f3d35b63..ce30f4564 100644 --- a/core/pandas.html +++ b/core/pandas.html @@ -83,7 +83,7 @@ - + @@ -757,7 +757,7 @@

Pandas

- Last updated on 24 October 2024. + Last updated on 14 November 2024.

@@ -886,7 +886,7 @@

Contribute

By the Project Pythia Community. - Last updated on 24 October 2024. + Last updated on 14 November 2024.

diff --git a/core/pandas/pandas.html b/core/pandas/pandas.html index e9c9b48d0..a54aebeb0 100644 --- a/core/pandas/pandas.html +++ b/core/pandas/pandas.html @@ -83,7 +83,7 @@ - + @@ -1107,7 +1107,7 @@

Indexing a Seri
-
/tmp/ipykernel_2797/737336773.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_2767/737336773.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]
 

 

@@ -1458,7 +1458,7 @@ 
Extending to the 
-
/tmp/ipykernel_2797/541596450.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_2767/541596450.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]
 

 

@@ -3654,7 +3654,7 @@ 
Resampling
-
/tmp/ipykernel_2797/233158901.py:1: FutureWarning: 'Y' is deprecated and will be removed in a future version, please use 'YE' instead.
+
/tmp/ipykernel_2767/233158901.py:1: FutureWarning: 'Y' is deprecated and will be removed in a future version, please use 'YE' instead.
   df.Nino34.resample('1Y').mean().plot();
 

 

@@ -4435,7 +4435,7 @@ 
Resources and References
     

-  Last updated on 24 October 2024.
+  Last updated on 14 November 2024.
   

 

   

@@ -4564,7 +4564,7 @@ 
Contribute

         
           By the Project Pythia Community.
         
-          Last updated on 24 October 2024.
+          Last updated on 14 November 2024.
         

       

     

diff --git a/core/xarray.html b/core/xarray.html
index 060e1e886..7fb34f4ed 100644
--- a/core/xarray.html
+++ b/core/xarray.html
@@ -83,7 +83,7 @@
   
   
   
-    
+    
   
   
   
@@ -758,7 +758,7 @@ 
Xarray
     

-  Last updated on 24 October 2024.
+  Last updated on 14 November 2024.
   

 

   

@@ -887,7 +887,7 @@ 
Contribute

         
           By the Project Pythia Community.
         
-          Last updated on 24 October 2024.
+          Last updated on 14 November 2024.
diff --git a/core/xarray/computation-masking.html b/core/xarray/computation-masking.html index c50c34dc2..a081fdbe2 100644 --- a/core/xarray/computation-masking.html +++ b/core/xarray/computation-masking.html @@ -83,7 +83,7 @@ - + @@ -560,7 +560,7 @@

Data Setup
Downloading file 'CESM2_sst_data.nc' from 'https://github.com/ProjectPythia/pythia-datasets/raw/main/data/CESM2_sst_data.nc' to '/home/runner/.cache/pythia-datasets'.

-
/home/runner/miniconda3/envs/pythia-book-dev/lib/python3.11/site-packages/xarray/conventions.py:284: SerializationWarning: variable 'tos' has multiple fill values {np.float32(1e+20), np.float64(1e+20)} defined, decoding all values to NaN.
+
/home/runner/miniconda3/envs/pythia-book-dev/lib/python3.11/site-packages/xarray/conventions.py:287: SerializationWarning: variable 'tos' has multiple fill values {np.float32(1e+20), np.float64(1e+20)} defined, decoding all values to NaN.
   var = coder.decode(var, name=name)
 

 

@@ -958,7 +958,7 @@ 
Data Setup
@@ -1597,7 +1597,7 @@ 
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:

    @@ -2273,7 +2273,7 @@

    Arithmetic Operations + dtype='float64', name='lon', length=360))

    • @@ -2909,7 +2909,7 @@

    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:

    @@ -3315,7 +3315,7 @@

    Aggregation Methods @@ -3762,7 +3762,7 @@

    Aggregation Methods @@ -4376,7 +4376,7 @@

    Aggregation Methods @@ -4479,7 +4479,7 @@

    Split 
    <DataArrayGroupBy, grouped over 1 grouper(s), 12 groups in total:
-    'month': 12 groups with labels 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12>
+    'month': 12/12 groups present with labels 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12>
    @@ -4497,7 +4497,7 @@

    Split 
    <DataArrayGroupBy, grouped over 1 grouper(s), 12 groups in total:
-    'month': 12 groups with labels 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12>
+    'month': 12/12 groups present with labels 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12>
    @@ -4950,7 +4950,7 @@

    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:

    @@ -5476,7 +5476,7 @@

    Compute anomaly
    @@ -6165,7 +6165,7 @@

    Compute anomaly.weighted() method and the newly loaded CESM2 dataset to weight the grid cell data as described above:

    @@ -6234,7 +6234,7 @@

    Other high level computation functionality
    <DataArrayResample, grouped over 1 grouper(s), 15 groups in total:
-    '__resample_dim__': 15 groups with labels 2000-01-01, 00:00:00, ..., 201...>
+    '__resample_dim__': 15/15 groups present with labels 2000-01-01, 00:00:00, ..., 201...>

    @@ -6674,7 +6674,7 @@

    Other high level computation functionalityrolling method to compute averages in a moving window of 5 months of data:

    @@ -7196,7 +7196,7 @@

    Other high level computation functionality
    @@ -7871,7 +7871,7 @@

    Masking Data

    Using where with one condition

    @@ -8515,15 +8515,15 @@

    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:

    @@ -8964,7 +8964,7 @@

    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.

    @@ -9413,7 +9413,7 @@

    Resources and References

    - Last updated on 24 October 2024. + Last updated on 14 November 2024.

    @@ -9542,7 +9542,7 @@

    Contribute

    By the     Project Pythia Community. - Last updated on 24 October 2024. + Last updated on 14 November 2024.

    diff --git a/core/xarray/dask-arrays-xarray.html b/core/xarray/dask-arrays-xarray.html index 6d9310521..2d2475b01 100644 --- a/core/xarray/dask-arrays-xarray.html +++ b/core/xarray/dask-arrays-xarray.html @@ -83,7 +83,7 @@ - + @@ -577,91 +577,91 @@

    Create a dask.a
    -
    array([[[0.85082749, 0.00335599, 0.27139966, ..., 0.33759418,
-         0.18625777, 0.89077574],
-        [0.48448652, 0.01501991, 0.43223704, ..., 0.80560859,
-         0.96929106, 0.56514946],
-        [0.07704013, 0.94453365, 0.13426507, ..., 0.81261223,
-         0.62443486, 0.59473749],
+
    array([[[0.68645777, 0.37393474, 0.07603551, ..., 0.1610494 ,
+         0.96449275, 0.63504158],
+        [0.1986917 , 0.10523146, 0.05839451, ..., 0.22550419,
+         0.09899859, 0.25098441],
+        [0.15387761, 0.54706984, 0.20758795, ..., 0.24279247,
+         0.64220834, 0.68587882],
         ...,
-        [0.14035921, 0.7398968 , 0.05410069, ..., 0.45105955,
-         0.15859272, 0.69022311],
-        [0.89859001, 0.22561364, 0.27119304, ..., 0.14773462,
-         0.0651732 , 0.24673747],
-        [0.69763349, 0.44164523, 0.18032235, ..., 0.99597859,
-         0.22707078, 0.85973697]],
-
-       [[0.69380608, 0.31777153, 0.94997323, ..., 0.82062866,
-         0.95273486, 0.63714666],
-        [0.36054944, 0.56680171, 0.37053102, ..., 0.65654011,
-         0.85638645, 0.08967314],
-        [0.13115314, 0.26677668, 0.91438386, ..., 0.67354052,
-         0.46665008, 0.38629669],
+        [0.38789069, 0.04693531, 0.80290897, ..., 0.13548612,
+         0.67778615, 0.21568401],
+        [0.55284968, 0.2666408 , 0.96215619, ..., 0.63515998,
+         0.98620828, 0.98053863],
+        [0.98693446, 0.7867875 , 0.13694884, ..., 0.53014774,
+         0.85292523, 0.90301122]],
+
+       [[0.71012084, 0.92875495, 0.61827731, ..., 0.22756907,
+         0.42115579, 0.04583442],
+        [0.42647139, 0.90954398, 0.91230578, ..., 0.75767252,
+         0.75836751, 0.26959463],
+        [0.46897044, 0.45985882, 0.29103309, ..., 0.20837473,
+         0.00933414, 0.34138146],
         ...,
-        [0.0406981 , 0.75372342, 0.96462562, ..., 0.78306785,
-         0.13964015, 0.80930759],
-        [0.00544619, 0.29556849, 0.3562885 , ..., 0.20198731,
-         0.8863713 , 0.44525016],
-        [0.8388937 , 0.18107359, 0.85057965, ..., 0.2238808 ,
-         0.79407991, 0.83414925]],
-
-       [[0.81928558, 0.85604873, 0.59062646, ..., 0.21325894,
-         0.83661127, 0.04106256],
-        [0.2564931 , 0.26498671, 0.03963795, ..., 0.42131364,
-         0.88711771, 0.1166075 ],
-        [0.56318233, 0.18884719, 0.21687225, ..., 0.24065192,
-         0.57781694, 0.5175836 ],
+        [0.51084976, 0.71073612, 0.53319859, ..., 0.21540579,
+         0.00790618, 0.84717072],
+        [0.27208792, 0.17389365, 0.93204126, ..., 0.68215896,
+         0.64905643, 0.93330079],
+        [0.1322187 , 0.07714782, 0.29472948, ..., 0.01560498,
+         0.92978229, 0.27962784]],
+
+       [[0.36076081, 0.21039082, 0.09989488, ..., 0.54741398,
+         0.85824712, 0.43258766],
+        [0.73885128, 0.74393814, 0.28374712, ..., 0.19933638,
+         0.21751479, 0.95371197],
+        [0.31723467, 0.5690741 , 0.74545557, ..., 0.06163683,
+         0.89357226, 0.51773771],
         ...,
-        [0.67927131, 0.39541065, 0.37784695, ..., 0.0317081 ,
-         0.18962161, 0.81406631],
-        [0.75549255, 0.75512763, 0.51979596, ..., 0.22500784,
-         0.3683928 , 0.42794544],
-        [0.76955341, 0.23385676, 0.45841997, ..., 0.85393938,
-         0.13953353, 0.97660056]],
+        [0.56891549, 0.93090645, 0.68695621, ..., 0.87329715,
+         0.76797859, 0.28575613],
+        [0.01766151, 0.21148351, 0.95060158, ..., 0.11189488,
+         0.31253911, 0.45582907],
+        [0.30261188, 0.92738623, 0.16019879, ..., 0.75824005,
+         0.43835799, 0.29246901]],
 
        ...,
 
-       [[0.90620138, 0.40345525, 0.67449356, ..., 0.24795512,
-         0.51052174, 0.09426442],
-        [0.66945977, 0.37796601, 0.77640946, ..., 0.37073494,
-         0.27684663, 0.77683061],
-        [0.9768939 , 0.67277453, 0.95854755, ..., 0.23020094,
-         0.04697688, 0.25734646],
+       [[0.59745792, 0.88678448, 0.723909  , ..., 0.07623182,
+         0.3891411 , 0.21589136],
+        [0.81149816, 0.33531457, 0.03339465, ..., 0.72297896,
+         0.17154819, 0.18474786],
+        [0.34721512, 0.7355664 , 0.47336212, ..., 0.32796123,
+         0.53562576, 0.55962299],
         ...,
-        [0.20812393, 0.26987318, 0.94605267, ..., 0.85385451,
-         0.74365758, 0.51451515],
-        [0.42415816, 0.23285189, 0.50207547, ..., 0.17877892,
-         0.28996782, 0.15869438],
-        [0.19301605, 0.51696316, 0.35245117, ..., 0.01156512,
-         0.22083976, 0.22986193]],
-
-       [[0.57765603, 0.54847294, 0.10713471, ..., 0.11413108,
-         0.83848921, 0.09097996],
-        [0.41578069, 0.31152739, 0.05520071, ..., 0.72717904,
-         0.63295146, 0.8333132 ],
-        [0.65026069, 0.19614582, 0.40163463, ..., 0.8069807 ,
-         0.48231552, 0.47438138],
+        [0.35927629, 0.89846489, 0.53909043, ..., 0.78899758,
+         0.38107048, 0.64670455],
+        [0.38539983, 0.5294017 , 0.08485933, ..., 0.31603783,
+         0.45557065, 0.2666406 ],
+        [0.93963872, 0.47954784, 0.29071576, ..., 0.63912048,
+         0.96493879, 0.48818499]],
+
+       [[0.86068933, 0.87544898, 0.50280602, ..., 0.58870801,
+         0.34810131, 0.11345098],
+        [0.31186757, 0.62415937, 0.89918987, ..., 0.49566383,
+         0.72774826, 0.47508359],
+        [0.0989798 , 0.14391352, 0.15548263, ..., 0.66421861,
+         0.58165482, 0.02490983],
         ...,
-        [0.03618824, 0.3563172 , 0.85706613, ..., 0.39120098,
-         0.84118155, 0.40251902],
-        [0.90384974, 0.85661409, 0.70389707, ..., 0.14674544,
-         0.66012306, 0.84018078],
-        [0.10687958, 0.83456455, 0.17931388, ..., 0.76945325,
-         0.71215871, 0.01635258]],
-
-       [[0.70896681, 0.63727758, 0.270666  , ..., 0.40871586,
-         0.07134823, 0.14724059],
-        [0.04999606, 0.67761766, 0.0492198 , ..., 0.72433312,
-         0.6829474 , 0.12155765],
-        [0.32118084, 0.42899462, 0.996236  , ..., 0.06597821,
-         0.45638411, 0.75904824],
+        [0.41682274, 0.34782555, 0.32388069, ..., 0.90512326,
+         0.50824761, 0.53397015],
+        [0.02314929, 0.25826992, 0.92784893, ..., 0.72089188,
+         0.64656363, 0.06856009],
+        [0.69475663, 0.59096405, 0.13808697, ..., 0.37883402,
+         0.81007946, 0.63602547]],
+
+       [[0.55613391, 0.22254883, 0.73354294, ..., 0.26076197,
+         0.83416559, 0.34439232],
+        [0.50662511, 0.27779658, 0.38382021, ..., 0.1985693 ,
+         0.89890815, 0.86191496],
+        [0.25718988, 0.83334227, 0.4657162 , ..., 0.1396363 ,
+         0.22731616, 0.09770531],
         ...,
-        [0.62981918, 0.28188259, 0.66955694, ..., 0.40793037,
-         0.47414162, 0.43701097],
-        [0.22234111, 0.98235356, 0.85103223, ..., 0.73074805,
-         0.5785452 , 0.15851116],
-        [0.77276264, 0.02011395, 0.00558271, ..., 0.76924589,
-         0.61104929, 0.71552736]]])
+        [0.40583634, 0.54287739, 0.45290771, ..., 0.4560666 ,
+         0.47968049, 0.10728046],
+        [0.79035404, 0.19456124, 0.06837152, ..., 0.12740708,
+         0.5913568 , 0.10410346],
+        [0.4015472 , 0.08119427, 0.45170958, ..., 0.84920496,
+         0.63361087, 0.82045379]]])
    @@ -881,11 +881,11 @@

    Compute the result -
    CPU times: user 309 ms, sys: 32.1 ms, total: 342 ms
-Wall time: 156 ms
+
    CPU times: user 325 ms, sys: 47.6 ms, total: 372 ms
+Wall time: 179 ms
 
    
 
    
-
    np.float64(12002642.188256467)
+
    np.float64(12000903.918981288)
 
    
 
    
 
    
@@ -983,7 +983,7 @@ 
    Exercise with d
    -../../_images/a1927d08cb5b70901df577b99c1cebea9b83a0672ce6103bafc305b12b7f2027.png +../../_images/db31591733408b63d96d6b5b36c0d54f98da4ed42110b5443f30efffbb326207.png
    @@ -1115,7 +1115,7 @@

    Testing a bigger calculation -../../_images/947eb4e35d394433fcf6a2cbc158185e3600f3a8ca36f6bdf7ecf06352a1cff0.png +../../_images/c385274337565fc30cabf5a28416b2dcd3dd7ec108d57e3d2bddec25de301aef.png

    @@ -1126,37 +1126,37 @@

    Testing a bigger calculation -
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+
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+
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+
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+
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@@ -1171,10 +1171,10 @@ 
    Testing a bigger calculation
    [#####                                   ] | 12% Completed | 1.42 s
 
    
 
    
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+
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+
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@@ -1186,22 +1186,22 @@ 
    Testing a bigger calculation
    [########                                ] | 21% Completed | 1.92 s
 
    
 
    
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+
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+
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    [#############                           ] | 33% Completed | 2.53 s
+
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@@ -1219,16 +1219,16 @@ 
    Testing a bigger calculation
    [#############                           ] | 34% Completed | 3.03 s
 
    
 
    
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    [###############                         ] | 37% Completed | 3.13 s
+
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    [################                        ] | 40% Completed | 3.23 s
+
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    [#################                       ] | 43% Completed | 3.34 s
+
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    [###################                     ] | 48% Completed | 3.44 s
+
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@@ -1246,13 +1246,13 @@ 
    Testing a bigger calculation
    [###################                     ] | 48% Completed | 3.94 s
 
    
 
    
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    [####################                    ] | 51% Completed | 4.04 s
+
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    [#######################                 ] | 58% Completed | 4.25 s
+
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@@ -1270,43 +1270,43 @@ 
    Testing a bigger calculation
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    [#########################               ] | 63% Completed | 4.85 s
+
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+
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+
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+
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    [###############################         ] | 77% Completed | 5.76 s
+
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    [################################        ] | 81% Completed | 5.86 s
+
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+
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    [##################################      ] | 85% Completed | 6.06 s
+
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@@ -1321,16 +1321,19 @@ 
    Testing a bigger calculation
    [##################################      ] | 85% Completed | 6.47 s
 
    
 
    
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    [###################################     ] | 89% Completed | 6.57 s
+
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+
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+
    
+
    
+
    [########################################] | 100% Completed | 6.98 s
 
    
 
    
 
    
@@ -1462,7 +1465,7 @@ 
    Reading data with 
-
    /home/runner/miniconda3/envs/pythia-book-dev/lib/python3.11/site-packages/xarray/conventions.py:284: SerializationWarning: variable 'tos' has multiple fill values {np.float32(1e+20), np.float64(1e+20)} defined, decoding all values to NaN.
+
    /home/runner/miniconda3/envs/pythia-book-dev/lib/python3.11/site-packages/xarray/conventions.py:287: SerializationWarning: variable 'tos' has multiple fill values {np.float32(1e+20), np.float64(1e+20)} defined, decoding all values to NaN.
   var = coder.decode(var, name=name)
 
    
 
    
@@ -1854,7 +1857,7 @@ 
    Reading data with 
@@ -1968,7 +1971,7 @@ 
    Reading data with chunks keyword argument, this technique does not specify how to chunk the data, and therefore the resulting Dask Array contains only one chunk.
    
 
    Correct usage of the chunks keyword argument specifies how many values in each dimension are contained in a single chunk. In this example, specifying the chunks keyword argument as chunks={'time':90} indicates to Xarray and Dask that 90 time slices are allocated to each chunk on the temporal axis.
    
@@ -2197,7 +2200,7 @@ 
    Reading data with 
-
    /home/runner/miniconda3/envs/pythia-book-dev/lib/python3.11/site-packages/xarray/conventions.py:284: SerializationWarning: variable 'tos' has multiple fill values {np.float32(1e+20), np.float64(1e+20)} defined, decoding all values to NaN.
+
    /home/runner/miniconda3/envs/pythia-book-dev/lib/python3.11/site-packages/xarray/conventions.py:287: SerializationWarning: variable 'tos' has multiple fill values {np.float32(1e+20), np.float64(1e+20)} defined, decoding all values to NaN.
   var = coder.decode(var, name=name)
 
    
 
    
@@ -2589,7 +2592,7 @@ 
    Reading data with 
@@ -2669,7 +2672,7 @@ 
    Reading data with .chunks method on an Xarray DataArray. In this example, we show that the tos DataArray now contains two chunks on the time dimension, with each chunk containing 90 time slices.
    
 
    
@@ -3296,7 +3299,7 @@ 
    Parallel and lazy computation using 
@@ -3351,7 +3354,7 @@ 
    Parallel and lazy computation using 
  • lon
    (lon)
    float64
    0.5 1.5 2.5 ... 357.5 358.5 359.5
    array([  0.5,   1.5,   2.5, ..., 357.5, 358.5, 359.5])
    • lat
      PandasIndex
      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)
      float64
      0.5 1.5 2.5 ... 357.5 358.5 359.5
      array([  0.5,   1.5,   2.5, ..., 357.5, 358.5, 359.5])
    • lat
      PandasIndex
      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))
    • lon
      PandasIndex
      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))
    • lon
      PandasIndex
      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:
    
 
    
@@ -3757,7 +3760,7 @@ 
    Parallel and lazy computation using 
@@ -3812,10 +3815,10 @@ 
    Parallel and lazy computation using dask.visualize() function, as shown in this example:
    
 
    
@@ -3837,10 +3840,10 @@ 
    Parallel and lazy computation using 
-
    [                                        ] | 0% Completed | 121.98 us
+
    [                                        ] | 0% Completed | 161.94 us
 
    
 
    
-
    [########################################] | 100% Completed | 101.23 ms
+
    [########################################] | 100% Completed | 101.50 ms
 
    
 
    
 
    
@@ -4246,7 +4249,7 @@ 
    Resources and references
     
    
-  Last updated on 24 October 2024.
+  Last updated on 14 November 2024.
   
    
 
    
   
    
@@ -4375,7 +4378,7 @@ 
    Contribute
    
         
           By the     Project Pythia Community.
         
-          Last updated on 24 October 2024.
+          Last updated on 14 November 2024.
         
    
       
    
     
    
diff --git a/core/xarray/enso-xarray.html b/core/xarray/enso-xarray.html
index d37808cb9..daf1eb311 100644
--- a/core/xarray/enso-xarray.html
+++ b/core/xarray/enso-xarray.html
@@ -83,7 +83,7 @@
   
   
   
-    
+    
   
   
   
@@ -574,7 +574,7 @@ 
    The Niño 3.4 Index
-
    /home/runner/miniconda3/envs/pythia-book-dev/lib/python3.11/site-packages/xarray/conventions.py:284: SerializationWarning: variable 'tos' has multiple fill values {np.float32(1e+20), np.float64(1e+20)} defined, decoding all values to NaN.
+
    /home/runner/miniconda3/envs/pythia-book-dev/lib/python3.11/site-packages/xarray/conventions.py:287: SerializationWarning: variable 'tos' has multiple fill values {np.float32(1e+20), np.float64(1e+20)} defined, decoding all values to NaN.
   var = coder.decode(var, name=name)
 
    
 
    
@@ -973,7 +973,7 @@ 
    The Niño 3.4 Index
@@ -1611,7 +1611,7 @@ 
    Select the Niño 3.4 regionwhere() method:
    
 
    
@@ -2217,7 +2217,7 @@ 
    Select the Niño 3.4 region
@@ -3048,7 +3048,7 @@ 
    Compute the anomalies
    <xarray.DataArray 'tos' ()> Size: 4B
-np.float32(1.8436452)
    
+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:
    
 
    
@@ -3421,7 +3421,7 @@ 
    Resources and References
     
    
-  Last updated on 24 October 2024.
+  Last updated on 14 November 2024.
   
    
 
    
   
    
@@ -3550,7 +3550,7 @@ 
    Contribute
    
         
           By the     Project Pythia Community.
         
-          Last updated on 24 October 2024.
+          Last updated on 14 November 2024.
         
    
       
    
     
    
diff --git a/core/xarray/xarray-intro.html b/core/xarray/xarray-intro.html
index e22a90feb..bd59c1912 100644
--- a/core/xarray/xarray-intro.html
+++ b/core/xarray/xarray-intro.html
@@ -83,7 +83,7 @@
   
   
   
-    
+    
   
   
   
@@ -592,25 +592,25 @@ 
    Generate a random numpy array
-
    array([[[283.9549397 , 286.68241951, 282.55284073, 275.24932891],
-        [287.29350585, 279.31262997, 278.12271498, 280.95543026],
-        [281.55480501, 277.46384362, 277.78716451, 280.9345857 ]],
+
    array([[[281.60692786, 276.67084132, 284.36460496, 279.96577049],
+        [287.45959099, 280.08886007, 285.69945014, 288.52639985],
+        [278.89825409, 288.47511333, 290.0604668 , 284.09985762]],
 
-       [[278.26257316, 288.74800757, 285.62461731, 285.39099085],
-        [275.36233549, 278.59458061, 291.63845286, 283.41055414],
-        [288.43239376, 289.20478126, 287.5639616 , 282.82470589]],
+       [[282.43442104, 289.47031305, 279.74014994, 285.45981026],
+        [279.1009808 , 281.61318622, 281.4071121 , 273.85259586],
+        [282.58048205, 287.13933247, 289.53343033, 276.61722972]],
 
-       [[285.83603353, 279.13892842, 281.58902794, 281.65454346],
-        [277.17810038, 282.87544493, 277.93951768, 285.51319384],
-        [280.63097391, 284.45121542, 279.91773552, 270.83582213]],
+       [[285.93195483, 288.28583787, 283.29440162, 277.26141412],
+        [286.81758112, 284.82754318, 287.88285676, 290.23029913],
+        [283.77733194, 279.67327444, 277.71928484, 281.2909813 ]],
 
-       [[275.95283182, 290.37521519, 283.14132549, 277.85842485],
-        [283.08387261, 284.24122436, 281.58200762, 284.35252815],
-        [278.56108768, 285.80547976, 281.66830617, 285.55731036]],
+       [[279.50934726, 289.8841102 , 276.14092316, 288.30036967],
+        [279.19429076, 279.66644356, 286.9585296 , 284.1208639 ],
+        [279.61076646, 287.85807502, 286.03787045, 280.80605988]],
 
-       [[278.06365261, 281.76367666, 284.74985889, 274.01713495],
-        [291.32648655, 279.98683867, 281.64157256, 276.54718015],
-        [284.51699889, 287.89021979, 278.02511928, 289.34766056]]])
+       [[287.39726957, 284.59693181, 283.23145735, 285.93181661],
+        [289.25032653, 277.43791197, 279.19899207, 281.89627415],
+        [280.87136895, 287.63899777, 280.34971586, 282.27292707]]])
 
    
 
    
 
    
@@ -997,44 +997,44 @@ 
    Wrap the array: first attempt
@@ -1423,44 +1423,44 @@ 
    Assign dimension names
    <xarray.DataArray (time: 5, lat: 3, lon: 4)> Size: 480B
-array([[[283.9549397 , 286.68241951, 282.55284073, 275.24932891],
-        [287.29350585, 279.31262997, 278.12271498, 280.95543026],
-        [281.55480501, 277.46384362, 277.78716451, 280.9345857 ]],
-
-       [[278.26257316, 288.74800757, 285.62461731, 285.39099085],
-        [275.36233549, 278.59458061, 291.63845286, 283.41055414],
-        [288.43239376, 289.20478126, 287.5639616 , 282.82470589]],
-
-       [[285.83603353, 279.13892842, 281.58902794, 281.65454346],
-        [277.17810038, 282.87544493, 277.93951768, 285.51319384],
-        [280.63097391, 284.45121542, 279.91773552, 270.83582213]],
-
-       [[275.95283182, 290.37521519, 283.14132549, 277.85842485],
-        [283.08387261, 284.24122436, 281.58200762, 284.35252815],
-        [278.56108768, 285.80547976, 281.66830617, 285.55731036]],
-
-       [[278.06365261, 281.76367666, 284.74985889, 274.01713495],
-        [291.32648655, 279.98683867, 281.64157256, 276.54718015],
-        [284.51699889, 287.89021979, 278.02511928, 289.34766056]]])
-Dimensions without coordinates: time, lat, lon
    
+array([[[281.60692786, 276.67084132, 284.36460496, 279.96577049],
+        [287.45959099, 280.08886007, 285.69945014, 288.52639985],
+        [278.89825409, 288.47511333, 290.0604668 , 284.09985762]],
+
+       [[282.43442104, 289.47031305, 279.74014994, 285.45981026],
+        [279.1009808 , 281.61318622, 281.4071121 , 273.85259586],
+        [282.58048205, 287.13933247, 289.53343033, 276.61722972]],
+
+       [[285.93195483, 288.28583787, 283.29440162, 277.26141412],
+        [286.81758112, 284.82754318, 287.88285676, 290.23029913],
+        [283.77733194, 279.67327444, 277.71928484, 281.2909813 ]],
+
+       [[279.50934726, 289.8841102 , 276.14092316, 288.30036967],
+        [279.19429076, 279.66644356, 286.9585296 , 284.1208639 ],
+        [279.61076646, 287.85807502, 286.03787045, 280.80605988]],
+
+       [[287.39726957, 284.59693181, 283.23145735, 285.93181661],
+        [289.25032653, 277.43791197, 279.19899207, 281.89627415],
+        [280.87136895, 287.63899777, 280.34971586, 282.27292707]]])
+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.
    
 
@@ -1876,51 +1876,51 @@ 
    Initialize the 
   fill: currentColor;
 }
 
    <xarray.DataArray (time: 5, lat: 3, lon: 4)> Size: 480B
-array([[[283.9549397 , 286.68241951, 282.55284073, 275.24932891],
-        [287.29350585, 279.31262997, 278.12271498, 280.95543026],
-        [281.55480501, 277.46384362, 277.78716451, 280.9345857 ]],
+array([[[281.60692786, 276.67084132, 284.36460496, 279.96577049],
+        [287.45959099, 280.08886007, 285.69945014, 288.52639985],
+        [278.89825409, 288.47511333, 290.0604668 , 284.09985762]],
 
-       [[278.26257316, 288.74800757, 285.62461731, 285.39099085],
-        [275.36233549, 278.59458061, 291.63845286, 283.41055414],
-        [288.43239376, 289.20478126, 287.5639616 , 282.82470589]],
+       [[282.43442104, 289.47031305, 279.74014994, 285.45981026],
+        [279.1009808 , 281.61318622, 281.4071121 , 273.85259586],
+        [282.58048205, 287.13933247, 289.53343033, 276.61722972]],
 
-       [[285.83603353, 279.13892842, 281.58902794, 281.65454346],
-        [277.17810038, 282.87544493, 277.93951768, 285.51319384],
-        [280.63097391, 284.45121542, 279.91773552, 270.83582213]],
+       [[285.93195483, 288.28583787, 283.29440162, 277.26141412],
+        [286.81758112, 284.82754318, 287.88285676, 290.23029913],
+        [283.77733194, 279.67327444, 277.71928484, 281.2909813 ]],
 
-       [[275.95283182, 290.37521519, 283.14132549, 277.85842485],
-        [283.08387261, 284.24122436, 281.58200762, 284.35252815],
-        [278.56108768, 285.80547976, 281.66830617, 285.55731036]],
+       [[279.50934726, 289.8841102 , 276.14092316, 288.30036967],
+        [279.19429076, 279.66644356, 286.9585296 , 284.1208639 ],
+        [279.61076646, 287.85807502, 286.03787045, 280.80605988]],
 
-       [[278.06365261, 281.76367666, 284.74985889, 274.01713495],
-        [291.32648655, 279.98683867, 281.64157256, 276.54718015],
-        [284.51699889, 287.89021979, 278.02511928, 289.34766056]]])
+       [[287.39726957, 284.59693181, 283.23145735, 285.93181661],
+        [289.25032653, 277.43791197, 279.19899207, 281.89627415],
+        [280.87136895, 287.63899777, 280.34971586, 282.27292707]]])
 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'))
  • PandasIndex
    PandasIndex(Index([25.0, 40.0, 55.0], dtype='float64', name='lat'))
  • PandasIndex
    PandasIndex(Index([-120.0, -100.0, -80.0, -60.0], dtype='float64', name='lon'))
    • 
 
    
 
 
    
@@ -2306,54 +2306,54 @@ 
    Set useful attributes
    <xarray.DataArray (time: 5, lat: 3, lon: 4)> Size: 480B
-array([[[283.9549397 , 286.68241951, 282.55284073, 275.24932891],
-        [287.29350585, 279.31262997, 278.12271498, 280.95543026],
-        [281.55480501, 277.46384362, 277.78716451, 280.9345857 ]],
+array([[[281.60692786, 276.67084132, 284.36460496, 279.96577049],
+        [287.45959099, 280.08886007, 285.69945014, 288.52639985],
+        [278.89825409, 288.47511333, 290.0604668 , 284.09985762]],
 
-       [[278.26257316, 288.74800757, 285.62461731, 285.39099085],
-        [275.36233549, 278.59458061, 291.63845286, 283.41055414],
-        [288.43239376, 289.20478126, 287.5639616 , 282.82470589]],
+       [[282.43442104, 289.47031305, 279.74014994, 285.45981026],
+        [279.1009808 , 281.61318622, 281.4071121 , 273.85259586],
+        [282.58048205, 287.13933247, 289.53343033, 276.61722972]],
 
-       [[285.83603353, 279.13892842, 281.58902794, 281.65454346],
-        [277.17810038, 282.87544493, 277.93951768, 285.51319384],
-        [280.63097391, 284.45121542, 279.91773552, 270.83582213]],
+       [[285.93195483, 288.28583787, 283.29440162, 277.26141412],
+        [286.81758112, 284.82754318, 287.88285676, 290.23029913],
+        [283.77733194, 279.67327444, 277.71928484, 281.2909813 ]],
 
-       [[275.95283182, 290.37521519, 283.14132549, 277.85842485],
-        [283.08387261, 284.24122436, 281.58200762, 284.35252815],
-        [278.56108768, 285.80547976, 281.66830617, 285.55731036]],
+       [[279.50934726, 289.8841102 , 276.14092316, 288.30036967],
+        [279.19429076, 279.66644356, 286.9585296 , 284.1208639 ],
+        [279.61076646, 287.85807502, 286.03787045, 280.80605988]],
 
-       [[278.06365261, 281.76367666, 284.74985889, 274.01713495],
-        [291.32648655, 279.98683867, 281.64157256, 276.54718015],
-        [284.51699889, 287.89021979, 278.02511928, 289.34766056]]])
+       [[287.39726957, 284.59693181, 283.23145735, 285.93181661],
+        [289.25032653, 277.43791197, 279.19899207, 281.89627415],
+        [280.87136895, 287.63899777, 280.34971586, 282.27292707]]])
 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'))
  • PandasIndex
    PandasIndex(Index([25.0, 40.0, 55.0], dtype='float64', name='lat'))
  • PandasIndex
    PandasIndex(Index([-120.0, -100.0, -80.0, -60.0], dtype='float64', name='lon'))
  • units :
    kelvin
    standard_name :
    air_temperature
    • 
 
    
 
 
    
@@ -2738,51 +2738,51 @@ 
    Issues with preservation of attributesdocumentation page.
    
 
    
@@ -3179,54 +3179,54 @@ 
    Create a pressure 
@@ -3617,48 +3617,48 @@ 
    Create a Datase
   * lat          (lat) float64 24B 25.0 40.0 55.0
   * lon          (lon) float64 32B -120.0 -100.0 -80.0 -60.0
 Data variables:
-    Temperature  (time, lat, lon) float64 480B 284.0 286.7 282.6 ... 278.0 289.3
-    Pressure     (time, lat, lon) float64 480B 1e+03 1.002e+03 ... 1.002e+03
    
+              dtype='datetime64[ns]', name='time', freq='D'))
  • PandasIndex
    PandasIndex(Index([25.0, 40.0, 55.0], dtype='float64', name='lat'))
  • PandasIndex
    PandasIndex(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.)
    
 
@@ -4043,54 +4043,54 @@ 
    Access Data variables and Coordinates in a DataArrays through a dictionary syntax, as shown in this example:
    
 
    
@@ -4470,54 +4470,54 @@ 
    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.
    
 
@@ -4907,18 +4907,18 @@ 
    NumPy-like selection
    <xarray.DataArray (lat: 3, lon: 4)> Size: 96B
-array([[278.26257316, 288.74800757, 285.62461731, 285.39099085],
-       [275.36233549, 278.59458061, 291.63845286, 283.41055414],
-       [288.43239376, 289.20478126, 287.5639616 , 282.82470589]])
+array([[282.43442104, 289.47031305, 279.74014994, 285.45981026],
+       [279.1009808 , 281.61318622, 281.4071121 , 273.85259586],
+       [282.58048205, 287.13933247, 289.53343033, 276.61722972]])
 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.
    
@@ -5304,18 +5304,18 @@ 
    Selecting with 
   fill: currentColor;
 }
 
    <xarray.DataArray (lat: 3, lon: 4)> Size: 96B
-array([[278.26257316, 288.74800757, 285.62461731, 285.39099085],
-       [275.36233549, 278.59458061, 291.63845286, 283.41055414],
-       [288.43239376, 289.20478126, 287.5639616 , 282.82470589]])
+array([[282.43442104, 289.47031305, 279.74014994, 285.45981026],
+       [279.1009808 , 281.61318622, 281.4071121 , 273.85259586],
+       [282.58048205, 287.13933247, 289.53343033, 276.61722972]])
 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:
    
 
    
+    standard_name:  air_temperature
    
 
    
 
    This selection technique is similar to NumPy’s index-based selection, as shown below:
    
 
    temp[1,:,:]
@@ -7313,29 +7313,29 @@ 
    One more selection method: 
    <xarray.DataArray (time: 3, lat: 2, lon: 2)> Size: 96B
-array([[[286.68241951, 282.55284073],
-        [279.31262997, 278.12271498]],
+array([[[276.67084132, 284.36460496],
+        [280.08886007, 285.69945014]],
 
-       [[288.74800757, 285.62461731],
-        [278.59458061, 291.63845286]],
+       [[289.47031305, 279.74014994],
+        [281.61318622, 281.4071121 ]],
 
-       [[279.13892842, 281.58902794],
-        [282.87544493, 277.93951768]]])
+       [[288.28583787, 283.29440162],
+        [284.82754318, 287.88285676]]])
 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
    
+       [[288.28583787, 283.29440162],
+        [284.82754318, 287.88285676]]])
    • 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)
      float64
      25.0 40.0
      array([25., 40.])
    • lon
      (lon)
      float64
      -100.0 -80.0
      array([-100.,  -80.])
    • time
      PandasIndex
      PandasIndex(DatetimeIndex(['2018-01-01', '2018-01-02', '2018-01-03'], dtype='datetime64[ns]', name='time', freq='D'))
    • lat
      PandasIndex
      PandasIndex(Index([25.0, 40.0], dtype='float64', name='lat'))
    • lon
      PandasIndex
      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:
    
 
    
@@ -7715,29 +7715,29 @@ 
    One more selection method: 
    <xarray.DataArray (time: 3, lat: 2, lon: 2)> Size: 96B
-array([[[286.68241951, 282.55284073],
-        [279.31262997, 278.12271498]],
+array([[[276.67084132, 284.36460496],
+        [280.08886007, 285.69945014]],
 
-       [[288.74800757, 285.62461731],
-        [278.59458061, 291.63845286]],
+       [[289.47031305, 279.74014994],
+        [281.61318622, 281.4071121 ]],
 
-       [[279.13892842, 281.58902794],
-        [282.87544493, 277.93951768]]])
+       [[288.28583787, 283.29440162],
+        [284.82754318, 287.88285676]]])
 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
    
+       [[288.28583787, 283.29440162],
+        [284.82754318, 287.88285676]]])
    • 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)
      float64
      25.0 40.0
      array([25., 40.])
    • lon
      (lon)
      float64
      -100.0 -80.0
      array([-100.,  -80.])
    • time
      PandasIndex
      PandasIndex(DatetimeIndex(['2018-01-01', '2018-01-02', '2018-01-03'], dtype='datetime64[ns]', name='time', freq='D'))
    • lat
      PandasIndex
      PandasIndex(Index([25.0, 40.0], dtype='float64', name='lat'))
    • lon
      PandasIndex
      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:
    
 
    
@@ -8176,10 +8176,10 @@ 
    Access netCDF data with 
  • Originating_or_generating_Center :
    US National Weather Service, National Centres for Environmental Prediction (NCEP)
    Originating_or_generating_Subcenter :
    North American Regional Reanalysis Project
    GRIB_table_version :
    0,131
    Generating_process_or_model :
    North American Regional Reanalysis (NARR)
    Conventions :
    CF-1.6
    history :
    Read using CDM IOSP GribCollection v3
    featureType :
    GRID
    History :
    Translated to CF-1.0 Conventions by Netcdf-Java CDM (CFGridWriter2)
 Original Dataset = DatasetScan#narr-a_221_19930313_0000_000.grb; Translation Date = 2018-01-04T23:57:16.043Z
    geospatial_lat_min :
    10.753308882144761
    geospatial_lat_max :
    46.8308828962289
    geospatial_lon_min :
    -153.88242040519995
    geospatial_lon_max :
    -42.666108129242815
    • 
 
    
 
@@ -8627,17 +8627,17 @@ 
    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:

    @@ -9045,7 +9045,7 @@

    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:

    @@ -9491,7 +9491,7 @@

    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
    • @@ -9931,19 +9931,19 @@

    Aggregation operations + dtype='float32', name='isobaric1'))

    • Info

    @@ -10343,19 +10343,19 @@

    Aggregation operations + dtype='float32', name='isobaric1'))

    • @@ -10373,7 +10373,7 @@

    Simple visualization with
    -
    [<matplotlib.lines.Line2D at 0x7fea36012d90>]
+
    [<matplotlib.lines.Line2D at 0x7fbbee69e650>]
 
    
 
    
 ../../_images/ca2e0c5a4a29bfb2704a6c998687b070753a4493d011510c506f49cdee82f0d1.png
@@ -10397,7 +10397,7 @@ 
    Customizing the plot
    -
    [<matplotlib.lines.Line2D at 0x7fea2b8a2d90>]
+
    [<matplotlib.lines.Line2D at 0x7fbbe6513c50>]
 
    
 
    
 ../../_images/371cb749acf39f890ddbda648cea4d5ac3565267c5364d084f7d914ad6031033.png
@@ -10415,7 +10415,7 @@ 
    Plotting 2-D data
-
    <matplotlib.collections.QuadMesh at 0x7fea2b905290>
+
    <matplotlib.collections.QuadMesh at 0x7fbbe6592710>
 
    
 
    
 ../../_images/1c765f54dd8ab497cc54e712e6e28ac82cddc894000a0507d13d40d479ca20b7.png
@@ -10806,7 +10806,7 @@ 
    Resources and references
     
    
-  Last updated on 24 October 2024.
+  Last updated on 14 November 2024.
   
    
 
    
   
    
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