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a/_images/ff0e812d3550042656cef2a3fae9709d7f2da9ad84a067decf81d100a73ea50d.png b/_images/ff0e812d3550042656cef2a3fae9709d7f2da9ad84a067decf81d100a73ea50d.png new file mode 100644 index 000000000..811224ae7 Binary files /dev/null and b/_images/ff0e812d3550042656cef2a3fae9709d7f2da9ad84a067decf81d100a73ea50d.png differ diff --git a/_sources/core/matplotlib/annotations-colorbars-layouts.ipynb b/_sources/core/matplotlib/annotations-colorbars-layouts.ipynb index 27250dde4..691cd8ab9 100644 --- a/_sources/core/matplotlib/annotations-colorbars-layouts.ipynb +++ b/_sources/core/matplotlib/annotations-colorbars-layouts.ipynb @@ -644,7 +644,7 @@ "## Resources and references\n", "- [Matplotlib text documentation](https://matplotlib.org/stable/api/text_api.html#matplotlib.text.Text.set_math_fontfamily)\n", "- [Matplotlib annotation documentation](https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.annotate.html)\n", - "- [Matplotlib's annotation examples](https://matplotlib.org/stable/tutorials/text/annotations.html#sphx-glr-tutorials-text-annotations-py)\n", + "- [Matplotlib's annotation examples](https://matplotlib.org/stable/tutorials/text/annotations.html)\n", "- [Writing mathematical expressions in Matplotlib](https://matplotlib.org/stable/tutorials/text/mathtext.html)\n", "- [Mathtext Examples](https://matplotlib.org/stable/gallery/text_labels_and_annotations/mathtext_examples.html#sphx-glr-gallery-text-labels-and-annotations-mathtext-examples-py)\n", "- [Drawing fancy boxes with Matplotlib](https://matplotlib.org/stable/gallery/shapes_and_collections/fancybox_demo.html)\n", @@ -679,7 +679,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.9" + "version": "3.10.12" } }, "nbformat": 4, diff --git a/_static/__pycache__/__init__.cpython-312.pyc b/_static/__pycache__/__init__.cpython-312.pyc index ea820d53f..d167a62c9 100644 Binary files a/_static/__pycache__/__init__.cpython-312.pyc and b/_static/__pycache__/__init__.cpython-312.pyc differ diff --git a/appendix/how-to-contribute.html b/appendix/how-to-contribute.html index a05e62a89..a1e0d5a11 100644 --- a/appendix/how-to-contribute.html +++ b/appendix/how-to-contribute.html @@ -900,7 +900,7 @@

Contribute

By the Project Pythia Community. - Last updated on 30 October 2023. + Last updated on 6 November 2023.

diff --git a/appendix/template.html b/appendix/template.html index f2d6d9f72..54c7b326b 100644 --- a/appendix/template.html +++ b/appendix/template.html @@ -895,8 +895,8 @@
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 @@ -1098,7 +1098,7 @@

Contribute

By the Project Pythia Community. - Last updated on 30 October 2023. + Last updated on 6 November 2023.

diff --git a/core/cartopy.html b/core/cartopy.html index 78afc5d27..1d31e86ef 100644 --- a/core/cartopy.html +++ b/core/cartopy.html @@ -813,7 +813,7 @@

Contribute

By the Project Pythia Community. - Last updated on 30 October 2023. + Last updated on 6 November 2023.

diff --git a/core/cartopy/cartopy.html b/core/cartopy/cartopy.html index 2ff2ceeff..aec6d1c1a 100644 --- a/core/cartopy/cartopy.html +++ b/core/cartopy/cartopy.html @@ -881,7 +881,7 @@

Create a map with a specified projection -../../_images/ed554c119dc4bf19f8e545ad44fd7c6e0af5f467cfb6872adebf79fa4e30ff9c.png +../../_images/99f516061984555535d9fcd40c274a3f55785d59434e478008ae6d91ba04275f.png

Although the figure seems empty, it has, in fact, been georeferenced using a map projection; this projection is provided by Cartopy’s crs (coordinate reference system) class. We can now add in cartographic features, in the form of shapefiles, to our subplot. One such cartographic feature is coastlines, which can be added to our subplot using the callable GeoAxes method simply called coastlines.

@@ -892,7 +892,7 @@

Create a map with a specified projection - @@ -924,7 +924,7 @@

Add cartographic features to the map - @@ -964,7 +964,7 @@

Mollweide Projection (often used with global satellite mosaics)
Text(0.5, 1.0, 'Mollweide Projection')
 
-../../_images/fd2234551176d51136c30e35e82915562994413892285c3434865d3dda7b58ae.png +../../_images/c7321ed32e72522f81cf5eb8a4e308a65b577b06a9440d4865f62273128a1338.png
@@ -994,7 +994,7 @@

Add a fancy background image to the map. -../../_images/72eaee3a27921acb82933b56725e17ad30db7348e6ae4e187491d1038325ea30.png +../../_images/1b4efd39352c41f788e76a20ba7e520c955bb14707c9ccfdc5c4c66c845804d9.png @@ -1014,7 +1014,7 @@

Lambert Azimuthal Equal Area Projection -../../_images/7d496c5084eaf9e3f25ffbbce0403e51faefb6ba257390756ad487938c4d6118.png +../../_images/3cc9f95a36b51514a00a382895b64a3e5b2dba6221b603f9d0de572ce35714e5.png @@ -1055,7 +1055,7 @@

Cartopy’s set
-../../_images/595ac53e6a6269258de7d282fd75afbbf5c793558ebd31fd9298951ce373c35c.png +../../_images/5151eb22444a1013d6cbc468ab7441c2c2de8eec93c1322d4af21d6197888519.png
@@ -1080,7 +1080,7 @@

Cartopy’s set

-../../_images/559f8a15cc50294d357ea2132c795b67b764c49b2da6c900c69c1f3d2f6ab6f9.png +../../_images/02fbf65685e35efe2787b1b3c98e9bba015d07843be271259783866340b716b9.png
@@ -1101,7 +1101,7 @@

Cartopy’s set

-../../_images/448b5eaaa77410cca016097147ef7f6c76e847173325d0bb2a91959b17d45a27.png +../../_images/3379e3fa4c8937f6dc1756a1e0b388db4e0cec618947cabca56e69f085ede950.png
-../../_images/e9080139f9a096c3a9a3142140b6a2b3db57e328767a7a000efe09381b52ee73.png +../../_images/eab8c96bb5b029d05c7e9b5f0adb188545a46a61522c9c2446ec1a7d681d3714.png
@@ -1260,7 +1260,7 @@

A figure with two different regional maps -../../_images/17628a9dcd14308a9ac82ea4b77138bd7fffc37854879292e8023f8405620fa8.png +../../_images/639a3002d4d98c8a07bcc68dcb0734e7b3eaba3def43e57c7f9d1be43e4a9342.png @@ -1278,7 +1278,7 @@

An example of plotting data -../../_images/5e69f76528ab2656c2c3d0c1a8fae7242e2117ae7ea24f2281fc78226d634401.png +../../_images/594eeb3e2788e8bd8d010f1e31ddbd9b32a43944097ee4c2816473f569064978.png

Plotting data on a Cartesian grid is equivalent to plotting data in the PlateCarree projection, where meridians and parallels are all straight lines with constant spacing. As a result of this simplicity, the global datasets we use often begin in the PlateCarree projection.

@@ -1294,7 +1294,7 @@

An example of plotting data -../../_images/3ed575ba0c514f02f9dc73e0c7ba0c1090bdb7bbb79cf0d0f90d842c00cce2f6.png +../../_images/e4690e9ed44bfeab062319852df9616b3838c397218545f641e47f0a8d2a0378.png @@ -1476,7 +1476,7 @@

Contribute

By the
Project Pythia Community. - Last updated on 30 October 2023. + Last updated on 6 November 2023.

diff --git a/core/data-formats.html b/core/data-formats.html index 97afa7977..bf9811a6e 100644 --- a/core/data-formats.html +++ b/core/data-formats.html @@ -805,7 +805,7 @@

Contribute

By the Project Pythia Community. - Last updated on 30 October 2023. + Last updated on 6 November 2023.

diff --git a/core/data-formats/netcdf-cf.html b/core/data-formats/netcdf-cf.html index 55e4fc3fa..74a646f59 100644 --- a/core/data-formats/netcdf-cf.html +++ b/core/data-formats/netcdf-cf.html @@ -836,7 +836,7 @@

Gridded Data -
/tmp/ipykernel_2544/2342439358.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_2425/2342439358.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)
 
@@ -882,7 +882,7 @@

Creating the file and dimensions - diff --git a/core/datetime.html b/core/datetime.html index 51026dce2..459dccf5f 100644 --- a/core/datetime.html +++ b/core/datetime.html @@ -811,7 +811,7 @@

Contribute

By the Project Pythia Community. - Last updated on 30 October 2023. + Last updated on 6 November 2023.

diff --git a/core/datetime/datetime.html b/core/datetime/datetime.html index 743aecdc8..92f7ef5b1 100644 --- a/core/datetime/datetime.html +++ b/core/datetime/datetime.html @@ -955,7 +955,7 @@

Some core terminology
-
1698690501.4729056
+
1699290091.5795426
 
@@ -976,7 +976,7 @@

time

-
The benchmark took 1.0003435611724854 seconds
+
The benchmark took 1.0002496242523193 seconds
 
@@ -1105,7 +1105,7 @@

See how many days have elapsed since the strike: -
5969
+
5976
 
@@ -1177,8 +1177,8 @@

Time Zone Naive Versus Time Zone Aware -
I am time zone naive 2023-10-30 18:28:22.522534.
-I am time zone aware 2023-10-30 18:28:22.522571+00:00.
+
I am time zone naive 2023-11-06 17:01:32.640107.
+I am time zone aware 2023-11-06 17:01:32.640148+00:00.
 
@@ -1201,8 +1201,8 @@

Full time zone support with the <

-
I am time zone naive: 2023-10-30 18:28:22.527848.
-I am time zone aware: 2023-10-30 12:28:22.539815-06:00.
+
I am time zone naive: 2023-11-06 17:01:32.645652.
+I am time zone aware: 2023-11-06 10:01:32.659094-07:00.
 
@@ -1224,11 +1224,11 @@

Print Time with a Different Time Zone - diff --git a/core/matplotlib/annotations-colorbars-layouts.html b/core/matplotlib/annotations-colorbars-layouts.html index ed3e6d5cb..5115a4bd1 100644 --- a/core/matplotlib/annotations-colorbars-layouts.html +++ b/core/matplotlib/annotations-colorbars-layouts.html @@ -835,7 +835,7 @@

Create Some Sample Data

-../../_images/2cf58e27b458c2b7dff468905077a26f7220c8309d8dae410a6625148a943e7a.png +../../_images/a9e45ca51b5e1169fed306d496ac47cb4211a3554164c099fcb2d8a2ca7ea5ca.png

@@ -851,7 +851,7 @@

Adding Annotations -../../_images/d8f93e42258ad34db335f0efe537c9bddd9b903f0dde661e74d6b10a21c36226.png +../../_images/3f70dac830041920a557c78f5985bdb34de20aaa1a13c1c7be7ebc873d2a1139.png

@@ -880,7 +880,7 @@

Rendering Equations -../../_images/33999a4594e7c667974f157477b7ab89033377542983847b986b9438aa58575e.png +../../_images/64e85ebbb008965febf5eb1cd1a9fce4189bebb2193fb7a2d6743cd618c8a4d2.png

As you can see, the equation was correctly rendered in the plot above. However, the equation appears quite small. We can increase the size of the text using the fontsize keyword argument, and center the equation using the ha (horizontal alignment) keyword argument.

@@ -896,7 +896,7 @@

Rendering Equations -../../_images/f331e7661920ed927eb76ffaa343649a77de4860c9af4f3ce2daea7b5c0809e6.png +../../_images/0cd8724851a0b960f9769a30e35c1fa6886e1780c717d4cccfa4724c6ee08231.png @@ -1008,7 +1008,7 @@

Basic Colorbars -../../_images/96edf9c9a0ca6907b5f1eec1d1a24dec178936265b8c24284baa00c1d020e28e.png +../../_images/c9d6872e8ffb11ffa2f71731d20caabacc46ed7fcc4466cd069acad1995c3bb6.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:

@@ -1023,7 +1023,7 @@

Basic Colorbars -../../_images/8b5c153d954c34bc8bc738b5f6a5f0e89c136f571f39618ed4bfa72bce192fd7.png +../../_images/a2ee143099f87356d27b23dfa51ab61e4dbc5f84421f40953d8d3c299ab035a0.png @@ -1042,10 +1042,10 @@

Shared Colorbars -
<matplotlib.colorbar.Colorbar at 0x7f2ef94346e0>
+
<matplotlib.colorbar.Colorbar at 0x7f9214858aa0>
 
-../../_images/1bef42bd708fd6eb98cf73114b05409c3ff781f5211b05d67fff937a1d30a693.png +../../_images/7b92d31955348e3fd67bf8bd24b593d83acbcb58828769de1dce54bffeb7d212.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. @@ -1072,7 +1072,7 @@

Shared Colorbars
Text(0.5, 0.98, 'Shared colormaps on data with different ranges')
 
-../../_images/a21e01ce1dc0aa9b3e8bb76595285c948cca4949d66f8b0620c3e404f21a530a.png +../../_images/b0bb873b2fae434c8781962c6448df0f82f72bea1cdb2a45e80b044994575a0b.png
-../../_images/44ad340df806510aa1e9d46d871d6df52e1cbfdf0064e7422a521d0725f9eb0d.png +../../_images/8eb554b2685a5cc5aded7ccd99041caa020edbc8b14d567516e345a31e164c28.png @@ -1126,7 +1126,7 @@

Custom Colorbars -../../_images/158f0d184f48929ecc2708fabb341c275569ecd6dd8a6c709940736cdddc41af.png +../../_images/bc322ea105481dd7577e286e8420a6df2c11dcb3dba81d89e31ec58c8e603119.png

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

@@ -1208,7 +1208,7 @@

Mosaic Subplots -../../_images/ce52b04f4b6d52c8a5244708cab3975d7bdc3884029e551703cf0dab4ceb5c3d.png +../../_images/e185e921716bf41c4e9d5c8cb54e9d0661c8b14914a61e43266d495c6fb77ada.png @@ -1227,7 +1227,7 @@

Resources and references
  • Matplotlib text documentation

  • Matplotlib annotation documentation

  • -
  • Matplotlib’s annotation examples

  • +
  • Matplotlib’s annotation examples

  • Writing mathematical expressions in Matplotlib

  • Mathtext Examples

  • Drawing fancy boxes with Matplotlib

  • @@ -1391,7 +1391,7 @@

    Contribute

    By the Project Pythia Community. - Last updated on 30 October 2023. + Last updated on 6 November 2023.

    diff --git a/core/matplotlib/histograms-piecharts-animation.html b/core/matplotlib/histograms-piecharts-animation.html index bc0e09cc4..108bcd3a4 100644 --- a/core/matplotlib/histograms-piecharts-animation.html +++ b/core/matplotlib/histograms-piecharts-animation.html @@ -815,7 +815,7 @@

    Histograms -../../_images/b5e079fa13f23aa230cd9fd15932fc4e4c543de6a60a4e0c958698fb8dcc1edf.png +../../_images/4feabe01ad52de3833cd6f2187678759752d4d90131e32872595761c59a7e256.png

    Similarly, we can make a 2-D histogram, by first generating a second 1-D array, and then calling plt.hist2d with both 1-D arrays as arguments:

    @@ -828,7 +828,7 @@

    Histograms -../../_images/a14115e40c47a217d9774d317f53efb9e194f70b175b3e76f454bad00d33aeb8.png +../../_images/5bff64b3fa12b29f60aa1671845b514b165b3f0591f899501811850e93057af9.png @@ -843,7 +843,7 @@

    Pie Charts -../../_images/f82d341224d2db1257d2ef9b54eaa0251ed67c6e9c2a7efed7cf8c310a2167d1.png +../../_images/ee0ee58409271bc89a5e291ffbeda04ec2f46b831eb74ee858ace588120b9f57.png

    Typically, you’ll see examples where all of the values in the array x will sum to 100, but the data values provided to plt.pie do not necessarily have to add up to 100. The sum of the numbers provided will be normalized to 1, and the individual values will thereby be converted to percentages, regardless of the actual sum of the values. If this behavior is unwanted or unneeded, you can set normalize=False.

    @@ -856,7 +856,7 @@

    Pie Charts -../../_images/8f75d1ab70a2f2c16a9b1b504b5c649ce065fc830a67c8e96e6b844765a6d150.png +../../_images/77e78316a00e496a3f839f23af9f2f1648583af71e3026344fe87c0bd6efe52c.png

    Let’s do a more complicated example.

    @@ -876,7 +876,7 @@

    Pie Charts -../../_images/ba28804d53303d1050d8fc5b7b7151729362e17642100c932137f264dc5d398f.png +../../_images/98dacbc00a6bea3f74772b2fd8205a334dd2702489b7a21d12422a00f80481ca.png @@ -918,7 +918,7 @@

    Step 1: Initial State
    -../../_images/dec4a0511bc9fbd647510c97000c2e91f0c7a8152d5f08a128bc2f88fadcb412.png +../../_images/d038c6ff52bf5332b60d1b0a3dcc42d1d7f1cfa1098197968f414dc94ce7e18e.png
    @@ -3712,7 +3712,7 @@

    Contribute

    By the
    Project Pythia Community. - Last updated on 30 October 2023. + Last updated on 6 November 2023.

    diff --git a/core/matplotlib/matplotlib-basics.html b/core/matplotlib/matplotlib-basics.html index 6501c8ba1..1df49cbc5 100644 --- a/core/matplotlib/matplotlib-basics.html +++ b/core/matplotlib/matplotlib-basics.html @@ -968,7 +968,7 @@

    Basic Line Plots -../../_images/5e862e9699331f3bdeb96d591c4a46386d3146aaa3eaa52f68eaab1cd7047578.png +../../_images/352ea94241eb0d85511fae8de81c3272be47772d2253c1382290f0df09111a7c.png @@ -989,7 +989,7 @@

    Adding labels to an -../../_images/8a0dd363e6c89e78aedc4c9d8f4591cc2d5451321b1e52ad59907da48ef00309.png +../../_images/02e720497565ec7b4d4b7785fc2fd15dac014969f573b4160a93eb4ebf570cde.png

    We can also add a title to the plot and increase the font size:

    @@ -1002,7 +1002,7 @@

    Adding labels to an -../../_images/cc31ca5af0497495733221de20290af6d834c8043b5e543467b6af96b3f62f05.png +../../_images/712ccdfbe12febe737288b258627f55b11c794c92371b15af5d5928a1b6890fb.png

    There are many other functions and methods associated with Axes objects and labels, but they are too numerous to list here.

    @@ -1069,7 +1069,7 @@

    Adding labels and a grid -../../_images/cfef758d49c2d8a551f44978aec4848d9fa7055e3b56814ee9a83065ab89ca10.png +../../_images/7ae9939cce0e4345bfabc397681ab22aec8557e01a91ef1a09a357145a7adf65.png @@ -1106,7 +1106,7 @@

    Customizing colors -../../_images/8a40b5e0c5a497ee9cea8a951f1488045db45b669e5b91c5288542b99c261f24.png +../../_images/06cdea6202f31d4737abf2c9078cf791fac1df4276ea5d7b30d128b99fbb7489.png @@ -1142,7 +1142,7 @@

    Using add_subplot to create two different subplots within the figure
    -../../_images/c2f5ab6d0027d13cd84ee791d9827d16503674de1b79725b2d043548b6aaafa1.png +../../_images/59f8a8995321409a0ef35edbef9a49980e0c4e6e0787f861a30b9346ac1e8733.png

    You can also call plot.subplots() with the keyword arguments nrows (number of rows) and ncols (number of columns). This initializes a new Axes object, called ax, with the specified number of rows and columns. This object also contains a 1-D list of subplots, with a size equal to nrows x ncols.

    @@ -1157,7 +1157,7 @@

    Using add_subplot to create two different subplots within the figure
    -../../_images/c2f5ab6d0027d13cd84ee791d9827d16503674de1b79725b2d043548b6aaafa1.png +../../_images/59f8a8995321409a0ef35edbef9a49980e0c4e6e0787f861a30b9346ac1e8733.png
    @@ -1181,7 +1181,7 @@

    Adding titles to each subplot -../../_images/0fa2c2d6c3936530843a020685eb2902cd6c280542cf929801f4f8bd0daa9efb.png +../../_images/f19e1a7886c80a7b342b368f92dcadd5074a23a186ac3ab8b477ac87c40d4ffd.png @@ -1208,7 +1208,7 @@

    Using ax.set_xl
    -../../_images/df379814c983ff37d4718d79e5a700fc04a7ab9b2da25d337dca21683f096f5f.png +../../_images/c40d9dcbd23a6d05a713fe46ff6a9764bc0a1261dea7957244df53ed3b9920a1.png
    @@ -1234,7 +1234,7 @@

    Using sharex
    -../../_images/784375d5825d30f96970b55cc8aefc382c9891ae9bd67a8fdf1f7ca6d5070f11.png +../../_images/455c972aff65226d372b77aa8ff2e65a0a25c51ab00a79569fd8d8231a62aaf0.png
    @@ -1290,7 +1290,7 @@

    Putting this all together -../../_images/6dbfa677a85b4aae6ffe07d0f21a1d460d20f0a2e4571d8f8af0daa634a3c59a.png +../../_images/3d9cacb71bdfdb4fca6fabbcfa49258b664377e26fabce59419d7d2c2138b84b.png @@ -1314,7 +1314,7 @@

    Scatterplot -../../_images/519022c08f02136e9cb490ccddb9563987fd094b16922f8fa5df688d0e44eaa2.png +../../_images/ff0e812d3550042656cef2a3fae9709d7f2da9ad84a067decf81d100a73ea50d.png

    Let’s put together the following:

    @@ -1364,7 +1364,7 @@

    Scatterplot -../../_images/7d63f4d9ddb67250713db61a6ce7f6d90f8cba2e7ddb239dc733d0904de774e5.png +../../_images/5cf328b03f1ef5d31caf4ecef5f866c518e53d1454a60df34c64346bb694f308.png @@ -1394,7 +1394,7 @@

    Displaying Images -../../_images/912d82c9122a8df0718be098fd59d4a8c33c819d829b0940c400313d31d7e750.png +../../_images/78dc2c67ceee8e469e28ac90b4ef43f8d59e876e4a8066d2ffc815ed468c9ca8.png @@ -1413,7 +1413,7 @@

    Contour and Filled Contour Plots -../../_images/1e4a4de2bef92ed19c26a1e2b9130165f4bff269f1dd52bcef3dd4bce69cdaca.png +../../_images/d3ece6ad526ed9fe4f0c2a41a21b49aca0e89c67aceccdfd097795efeb86dc7a.png

    After creating contours, we can label the lines using the clabel method, like this:

    @@ -1426,7 +1426,7 @@

    Contour and Filled Contour Plots -../../_images/f527418c1fb097585f4dd956c7f97c4603792b713f76b810100cda635a4820c2.png +../../_images/999ef8ba454b0c57276a2b5154aff244d09bd0950a5c42be6b27d238aab78a5a.png

    As described above, the contourf (contour fill) method creates filled contours around data, like this:

    @@ -1438,7 +1438,7 @@

    Contour and Filled Contour Plots -../../_images/5ad6c8af7197007720a52868ba2bd2680e6adeea9cf2108a54e44a6c03cfed01.png +../../_images/695917e12c0ae9ed78b70ee2915c36dc9ede565736a459222051b1c6664756dc.png

    As a final example, let’s create a heatmap figure with contours using the contour and imshow methods. First, we use imshow to create the heatmap, specifying a colormap using the cmap keyword argument. We then call contour, specifying black contours and an interval of 0.5. Here is the example code, and resulting figure:

    @@ -1454,7 +1454,7 @@

    Contour and Filled Contour Plots -../../_images/dfa7309ac80233cefd28fa08453f52a7ca2ec0049205d489c974ff81f386061d.png +../../_images/21565e9cf73f9a7de430820e67558dc1f9e234f7934facb1bca0e768ad9e1b33.png @@ -1641,7 +1641,7 @@

    Contribute

    By the
    Project Pythia Community. - Last updated on 30 October 2023. + Last updated on 6 November 2023.

    diff --git a/core/numpy.html b/core/numpy.html index 5a5c43fe0..fcac2ce20 100644 --- a/core/numpy.html +++ b/core/numpy.html @@ -808,7 +808,7 @@

    Contribute

    By the Project Pythia Community. - Last updated on 30 October 2023. + Last updated on 6 November 2023.

    diff --git a/core/numpy/intermediate-numpy.html b/core/numpy/intermediate-numpy.html index 0dd944c83..aee27d606 100644 --- a/core/numpy/intermediate-numpy.html +++ b/core/numpy/intermediate-numpy.html @@ -951,7 +951,7 @@

    Array comparisons -../../_images/c29c2486466456745a7e136b6844d8ddde5a1fc1e1f71203b5904f71016faa48.png +../../_images/7c62ad760e5dc723f6f5187979e1a9d9aa6a6401fa78da243050e5e06ba6e2aa.png

    By doing a comparison between a NumPy array and a value, we get an @@ -1119,7 +1119,7 @@

    Replacing values -../../_images/e65a7950aa1679ecae81c6b2934a12633b025bd8edf0007d8e78c293aa4b2c7d.png +../../_images/a2682a8718016d1e080129bd559ebc3ef1efec89b045431aa4a6f9cf745819a8.png

    and to put this in context,

    @@ -1133,7 +1133,7 @@

    Replacing values -../../_images/63e9553cf50b50014221577a73b4842002a27ea1f3e8319982eb9f1aee4e4a21.png +../../_images/074d6a93362da5fc8bfcffaee25ce07b46dac45f572deff83e30f5ccbd279de0.png

    If we use parentheses to preserve the order of operations, we can combine these conditions with other bitwise operators like the & for bitwise_and,

    @@ -1221,7 +1221,7 @@

    Replacing values -../../_images/5215b8a1e4caf1e157bab2d9cd499db393dd6318015ccbff029be80c4f2cf53d.png +../../_images/428d01f9d85b8f1c5b4e0448e920d3af75b5a82697a8be8e1c58d1412a2f26f4.png @@ -1495,7 +1495,7 @@

    Contribute

    By the
    Project Pythia Community. - Last updated on 30 October 2023. + Last updated on 6 November 2023.

    diff --git a/core/numpy/numpy-basics.html b/core/numpy/numpy-basics.html index 032c22f3c..c0bea8b87 100644 --- a/core/numpy/numpy-basics.html +++ b/core/numpy/numpy-basics.html @@ -1873,7 +1873,7 @@

    Contribute

    By the Project Pythia Community. - Last updated on 30 October 2023. + Last updated on 6 November 2023.

    diff --git a/core/numpy/numpy-broadcasting.html b/core/numpy/numpy-broadcasting.html index 8a7bbfada..7a9e353d5 100644 --- a/core/numpy/numpy-broadcasting.html +++ b/core/numpy/numpy-broadcasting.html @@ -1334,7 +1334,7 @@

    Blocking -
    array([28.7, 28.8, 24.1, 19.6, 28.8, 17.6, 19.3, 22. , 25.4, 21.2])
    +
    array([20. , 22.3, 12.2, 32.1, 28. , 22.1, 10.9, 20.4, 13.3, 25.2])
     
    @@ -1357,8 +1357,8 @@

    Blocking -
    array([ 0.        , 27.2       , 24.16666667, 24.16666667, 22.        ,
    -       21.9       , 19.63333333, 22.23333333, 22.86666667,  0.        ])
    +
    array([ 0.        , 18.16666667, 22.2       , 24.1       , 27.4       ,
    +       20.33333333, 17.8       , 14.86666667, 19.63333333,  0.        ])
     
    @@ -1372,8 +1372,8 @@

    Blocking -
    array([27.2       , 24.16666667, 24.16666667, 22.        , 21.9       ,
    -       19.63333333, 22.23333333, 22.86666667])
    +
    array([18.16666667, 22.2       , 24.1       , 27.4       , 20.33333333,
    +       17.8       , 14.86666667, 19.63333333])
     
    @@ -1386,7 +1386,7 @@

    Blocking -
    array([28.7, 28.8, 24.1, 19.6, 28.8, 17.6, 19.3, 22. , 25.4, 21.2])
    +
    array([20. , 22.3, 12.2, 32.1, 28. , 22.1, 10.9, 20.4, 13.3, 25.2])
     
    @@ -1405,14 +1405,14 @@

    Blocking -
    array([[28.7, 28.8, 24.1],
    -       [28.8, 24.1, 19.6],
    -       [24.1, 19.6, 28.8],
    -       [19.6, 28.8, 17.6],
    -       [28.8, 17.6, 19.3],
    -       [17.6, 19.3, 22. ],
    -       [19.3, 22. , 25.4],
    -       [22. , 25.4, 21.2]])
    +
    array([[20. , 22.3, 12.2],
    +       [22.3, 12.2, 32.1],
    +       [12.2, 32.1, 28. ],
    +       [32.1, 28. , 22.1],
    +       [28. , 22.1, 10.9],
    +       [22.1, 10.9, 20.4],
    +       [10.9, 20.4, 13.3],
    +       [20.4, 13.3, 25.2]])
     
    @@ -1425,8 +1425,8 @@

    Blocking -
    array([27.2       , 24.16666667, 24.16666667, 22.        , 21.9       ,
    -       19.63333333, 22.23333333, 22.86666667])
    +
    array([18.16666667, 22.2       , 24.1       , 27.4       , 20.33333333,
    +       17.8       , 14.86666667, 19.63333333])
     
    @@ -1440,14 +1440,14 @@

    Blocking -
    array([[  28.7,   28.8, 2000. ],
    -       [  28.8, 2000. ,   19.6],
    -       [2000. ,   19.6,   28.8],
    -       [  19.6,   28.8,   17.6],
    -       [  28.8,   17.6,   19.3],
    -       [  17.6,   19.3,   22. ],
    -       [  19.3,   22. ,   25.4],
    -       [  22. ,   25.4,   21.2]])
    +
    array([[  20. ,   22.3, 2000. ],
    +       [  22.3, 2000. ,   32.1],
    +       [2000. ,   32.1,   28. ],
    +       [  32.1,   28. ,   22.1],
    +       [  28. ,   22.1,   10.9],
    +       [  22.1,   10.9,   20.4],
    +       [  10.9,   20.4,   13.3],
    +       [  20.4,   13.3,   25.2]])
     
    @@ -1474,13 +1474,13 @@

    Finding the difference between min and max -
    array([[7, 7, 7, ..., 6, 6, 8],
    -       [7, 6, 7, ..., 7, 7, 7],
    -       [7, 7, 6, ..., 8, 6, 8],
    +
    array([[6, 6, 7, ..., 7, 7, 6],
    +       [6, 6, 6, ..., 7, 6, 6],
    +       [7, 7, 6, ..., 7, 7, 7],
            ...,
    -       [7, 6, 7, ..., 8, 5, 6],
    -       [7, 7, 8, ..., 7, 7, 6],
    -       [6, 6, 6, ..., 7, 7, 7]])
    +       [7, 7, 6, ..., 7, 7, 6],
    +       [7, 7, 7, ..., 7, 7, 7],
    +       [6, 8, 7, ..., 7, 7, 7]])
     
    @@ -1505,13 +1505,13 @@

    Finding the difference between min and max -
    array([[737.5, 737.5, 737.5, ..., 775. , 775. , 700. ],
    -       [737.5, 775. , 737.5, ..., 737.5, 737.5, 737.5],
    -       [737.5, 737.5, 775. , ..., 700. , 775. , 700. ],
    +
    array([[775. , 775. , 737.5, ..., 737.5, 737.5, 775. ],
    +       [775. , 775. , 775. , ..., 737.5, 775. , 775. ],
    +       [737.5, 737.5, 775. , ..., 737.5, 737.5, 737.5],
            ...,
    -       [737.5, 775. , 737.5, ..., 700. , 812.5, 775. ],
    -       [737.5, 737.5, 700. , ..., 737.5, 737.5, 775. ],
    -       [775. , 775. , 775. , ..., 737.5, 737.5, 737.5]])
    +       [737.5, 737.5, 775. , ..., 737.5, 737.5, 775. ],
    +       [737.5, 737.5, 737.5, ..., 737.5, 737.5, 737.5],
    +       [775. , 700. , 737.5, ..., 737.5, 737.5, 737.5]])
     
    @@ -1540,19 +1540,19 @@

    Finding the difference between min and max -
    array([[ -9.00860221, -12.57977394, -11.1741251 , ...,  -9.89422967,
    -         -7.73234627, -10.38025907],
    -       [-12.11244473,  -7.56417179,  -7.73392144, ..., -12.47522905,
    -        -13.09199362, -15.07595316],
    -       [ -7.21046684,  -9.09709627,  -9.91873357, ..., -10.17199612,
    -        -10.19868399, -12.22221241],
    +
    array([[ -8.35897323, -12.83713883,  -8.49876152, ..., -11.92190933,
    +         -9.56306376,  -5.85792913],
    +       [-12.03057238,  -7.66845084,  -6.92160308, ..., -11.34017449,
    +         -7.50456939,  -7.64406598],
    +       [-13.35343301,  -9.64634611,  -9.44193425, ..., -13.91805012,
    +        -10.76872851, -11.03802659],
            ...,
    -       [-14.27980992,  -9.01101727,  -7.73676849, ...,  -9.0076362 ,
    -         -4.27344241,  -6.42797267],
    -       [-15.75038015, -11.06479088, -11.43556023, ...,  -5.73052095,
    -        -10.71274543,  -8.27448206],
    -       [ -7.20091588, -10.1672501 , -10.43537197, ..., -12.48069268,
    -        -15.3785921 , -11.0862624 ]])
    +       [-10.67775485, -10.83916639,  -7.60916438, ..., -14.29324445,
    +        -10.91171895,  -9.75391098],
    +       [ -9.00273658,  -9.95457098, -12.67014862, ..., -11.24528357,
    +         -9.03795815,  -4.9495939 ],
    +       [-12.07156199,  -9.81008328, -12.46942577, ...,  -8.44416981,
    +        -12.31411911,  -9.48051666]])
     
    @@ -1596,19 +1596,19 @@

    Finding the difference between min and max -

    diff --git a/core/overview.html b/core/overview.html index 45b523e7a..9cf58b23a 100644 --- a/core/overview.html +++ b/core/overview.html @@ -945,7 +945,7 @@

    Contribute

    By the Project Pythia Community. - Last updated on 30 October 2023. + Last updated on 6 November 2023.

    diff --git a/core/pandas.html b/core/pandas.html index 67c1b8fde..665e915c5 100644 --- a/core/pandas.html +++ b/core/pandas.html @@ -809,7 +809,7 @@

    Contribute

    By the Project Pythia Community. - Last updated on 30 October 2023. + Last updated on 6 November 2023.

    diff --git a/core/pandas/pandas.html b/core/pandas/pandas.html index dc90035a5..f0a9993c8 100644 --- a/core/pandas/pandas.html +++ b/core/pandas/pandas.html @@ -1442,7 +1442,7 @@

    Indexing a Seri

    -
    /tmp/ipykernel_2731/737336773.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_2624/737336773.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]
     
    @@ -1793,7 +1793,7 @@

    Extending to the -
    /tmp/ipykernel_2731/541596450.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_2624/541596450.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]
     
    @@ -2591,7 +2591,7 @@

    Quick Plots of Your Data -../../_images/de73899ad8ed622df31e81a962ac12b819e4257c0452ac1c188054925e9781a6.png +../../_images/35453a8cab4586e5a2f01913d39e0b5ff4e1bfbe1179d9787446ce88be94a2bd.png

    Before, we called .plot(), which generated a single line plot. Line plots can be helpful for understanding some types of data, but there are other types of data that can be better understood with different plot types. For example, if your data values form a distribution, you can better understand them using a histogram plot.

    @@ -2603,7 +2603,7 @@

    Quick Plots of Your Data -../../_images/d4224c88b681e396ff95813af0c38411189a2c57ad77c712e6a68268ecb9d3e4.png +../../_images/f9e6572ca33beae96bb1a79313542f2dd93fa0057d5e9927ca6fc7af08c4e344.png

    The histogram plot helped us better understand our data; there are clear differences in the distributions. To even better understand this type of data, it may also be helpful to create a box plot. This can be done using the same line of code, with one change: we call the box method instead of hist.

    @@ -2614,7 +2614,7 @@

    Quick Plots of Your Data -../../_images/0cbebcedad9328e9ff2a65e8ce663faec77a3040ea2b7129b85e2beaa8d10de2.png +../../_images/5a89bc58a6bdaadbd5f4c5211779dbf97f1b263549b85bd682466bee51344df6.png

    Just like the histogram plot, this box plot indicates a clear difference in the distributions. Using multiple types of plot in this way can be useful for verifying large datasets. The pandas plotting methods are capable of creating many different types of plots. To see how to use the plotting methods to generate each type of plot, please review the pandas plot documentation.

    @@ -2634,7 +2634,7 @@

    Customize your Plot -../../_images/82da9423403141f9144e2b35e5c8b8eee171c16001c55276e0e5ac53c8e5f11b.png +../../_images/94af6ad271d95b1f5261ba4d1b209b40dac6d3104cfaa8dcb6e2162c64d2e26a.png

    Although plotting data can provide a clear visual picture of data values, sometimes a more quantitative look at data is warranted. As elaborated on in the next section, this can be achieved using the describe method. The describe method is called on the entire DataFrame, and returns various summarized statistics for each column in the DataFrame.

    @@ -3318,7 +3318,7 @@

    Subsetting Using the Datetime Column -../../_images/5dee70b70b02f12aaa9512a2e2c5bb5a1d9afc63d66012cebdc17b60c3195821.png +../../_images/83b7a66e7d4752a10a49075385c045e514aa7e6f3ed4bb540942b1fb58561c54.png @@ -3979,7 +3979,7 @@

    Resampling -../../_images/de73899ad8ed622df31e81a962ac12b819e4257c0452ac1c188054925e9781a6.png +../../_images/35453a8cab4586e5a2f01913d39e0b5ff4e1bfbe1179d9787446ce88be94a2bd.png @@ -4590,7 +4590,7 @@

    Contribute

    By the
    Project Pythia Community. - Last updated on 30 October 2023. + Last updated on 6 November 2023.

    diff --git a/core/xarray.html b/core/xarray.html index e638a408e..968fb2dae 100644 --- a/core/xarray.html +++ b/core/xarray.html @@ -810,7 +810,7 @@

    Contribute

    By the Project Pythia Community. - Last updated on 30 October 2023. + Last updated on 6 November 2023.

    diff --git a/core/xarray/computation-masking.html b/core/xarray/computation-masking.html index 75bbc9723..a4c72e171 100644 --- a/core/xarray/computation-masking.html +++ b/core/xarray/computation-masking.html @@ -1245,7 +1245,7 @@

    Data Setup @@ -1878,7 +1878,7 @@

    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:

    @@ -2548,7 +2548,7 @@

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

  • There are many other combinations of aggregation methods and dimensions on which to perform these methods. In this example, we compute the temporal minimum:

    @@ -3578,7 +3578,7 @@

    Aggregation Methods @@ -4019,7 +4019,7 @@

    Aggregation Methods @@ -4627,7 +4627,7 @@

    Aggregation Methods @@ -4715,7 +4715,7 @@

    GroupBy: Split, Apply, Combine -../../_images/a602163d14e1a8a3c230b7394292d4655d874ccf603c1e7914a14197d19f5ab2.png +../../_images/49ec7db6cd6b894e6c207c9af3b92de3d2080f11d4bba02ad597a0bdd46af95a.png

    In this example, we plot the zonal mean climatology:

    @@ -5275,7 +5275,7 @@

    Compute climatology -../../_images/555885cf0bfda940997705539e52b6b66b685e15b7eaaff98973bae1210165d7.png +../../_images/b939aeb1804eab4da9e9c1bc17b5d7a8afe5ac4976d2d0e7a63904c9d13ea5c1.png

    Finally, this example calculates and plots the difference between the climatology for January and the climatology for December:

    @@ -5286,7 +5286,7 @@

    Compute climatology -../../_images/a527e68ca66d142ca69b83a377791c8291c0f3883951ffb414cc843b14194b7e.png +../../_images/fc4841721a120f0e8a262aa8661b0a5e1a45714ba20b1acc20d609e00a4f00b2.png @@ -5711,7 +5711,7 @@

    Compute anomaly

    In this example, we compute and plot our dataset’s mean global anomaly over time. In order to specify global data, we must provide both lat and lon to the mean() method’s dim keyword argument:

    @@ -5993,7 +5993,7 @@

    Compute anomaly -../../_images/342eca018e3efc58971d903034a6e43f4310357b41dfbfb1f972c6701d60adcc.png +../../_images/a4a9939b2c086c2be24e521b7d3177feeb8e87891fe75a7f7e0b8e4b5cdf3043.png

    Many geoscientific algorithms perform operations over data contained in many different grid cells. However, if the grid cells are not equivalent in size, the operation is not scientifically valid by default. Fortunately, this can be fixed by weighting the data in each grid cell by the size of the cell. Weighting data in Xarray is simple, as Xarray has a built-in weighting method, known as .weighted().

    @@ -6392,7 +6392,7 @@

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

    @@ -6892,7 +6892,7 @@

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

    @@ -8077,7 +8077,7 @@

    Masking Data

    Using where with one condition

    @@ -8715,15 +8715,15 @@

    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:

    @@ -9158,7 +9158,7 @@

    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.

    @@ -9206,7 +9206,7 @@

    Using where

    -../../_images/9b0f1fd8a1772f4546cc05e5b34e7f6461b62a2f69f71194ea6caf22b6fd8aac.png +../../_images/18b9a18d6392608931e31fdb2874e87c81c8083c6bb49b9a08f42c1fabf8c20c.png
    @@ -9220,7 +9220,7 @@

    Using where
    -../../_images/836445e3f4c1241ea254ae761311c41a5fcc35432338277ce975a047f9c71efa.png +../../_images/1d73cd72466db5c7cc202ab3693bc67338a7a2065edddd4228c3cba89fdd9643.png

    In addition to using DataArrays and Datasets in Boolean conditions provided to .where(), we can also use coordinate variables. In the following example, we make use of Boolean conditions containing latitude and longitude coordinates. This greatly simplifies the masking of regions outside of the Niño 3.4 region:

    @@ -9234,7 +9234,7 @@

    Using where
    -../../_images/de8a8bfd133f2879ad1d4b08bbdaaefd435b8a1acf1d4b375c5dfcb26290347b.png +../../_images/b991a76b76fc9eac5849ed590a7a3a02241c8b39eaec0b14573bfe96f4708701.png
    @@ -9248,7 +9248,7 @@

    Using where
    -../../_images/0eedd176d3c4d425a944d43cfa6a30ab19cdb68a15d30e5735ec855e1b0430b8.png +../../_images/47c0a8d818d1427783e33b8bc6355e529785b269a752e41b1c1b85046731bc87.png
    @@ -9433,7 +9433,7 @@

    Contribute

    By the
    Project Pythia Community. - Last updated on 30 October 2023. + Last updated on 6 November 2023.

    diff --git a/core/xarray/dask-arrays-xarray.html b/core/xarray/dask-arrays-xarray.html index 58987b6db..dfdfd1533 100644 --- a/core/xarray/dask-arrays-xarray.html +++ b/core/xarray/dask-arrays-xarray.html @@ -896,91 +896,91 @@

    Create a dask.a
    -
    array([[[0.63641451, 0.21836029, 0.16632664, ..., 0.17303799,
    -         0.82476639, 0.23303243],
    -        [0.90723648, 0.18584639, 0.54936424, ..., 0.0306823 ,
    -         0.31359273, 0.02129521],
    -        [0.72257616, 0.36010722, 0.84466446, ..., 0.36743861,
    -         0.98467795, 0.06635553],
    +
    array([[[0.97292215, 0.53215025, 0.92836028, ..., 0.73366963,
    +         0.91786029, 0.71135251],
    +        [0.6434977 , 0.30695394, 0.70667942, ..., 0.9941629 ,
    +         0.86787091, 0.35949455],
    +        [0.97387269, 0.86986211, 0.25889626, ..., 0.69905209,
    +         0.94873798, 0.33205405],
             ...,
    -        [0.22612468, 0.49427944, 0.63134357, ..., 0.57021319,
    -         0.14514794, 0.48235465],
    -        [0.31761759, 0.03659039, 0.05063184, ..., 0.36460367,
    -         0.48574537, 0.82607865],
    -        [0.59460652, 0.68964715, 0.52506008, ..., 0.54934023,
    -         0.85910851, 0.49816226]],
    -
    -       [[0.51342911, 0.16589394, 0.2280175 , ..., 0.4939344 ,
    -         0.60445318, 0.16076763],
    -        [0.21865373, 0.47791321, 0.91515031, ..., 0.51032339,
    -         0.60857955, 0.95153634],
    -        [0.63404539, 0.56673903, 0.57358522, ..., 0.54259419,
    -         0.96632347, 0.53641808],
    +        [0.26863271, 0.87811911, 0.71850512, ..., 0.2411052 ,
    +         0.36773387, 0.21793832],
    +        [0.04201412, 0.59433142, 0.61432897, ..., 0.69909267,
    +         0.57190531, 0.04703371],
    +        [0.43627684, 0.81191429, 0.79340914, ..., 0.1222567 ,
    +         0.09198171, 0.10134061]],
    +
    +       [[0.53176584, 0.85404846, 0.98216003, ..., 0.0152972 ,
    +         0.99312245, 0.38333897],
    +        [0.74332619, 0.40268811, 0.20060048, ..., 0.04245809,
    +         0.45399727, 0.76534164],
    +        [0.92695002, 0.85564142, 0.90788647, ..., 0.17289069,
    +         0.34216355, 0.8315373 ],
             ...,
    -        [0.98622006, 0.10210645, 0.45717348, ..., 0.35248313,
    -         0.20436152, 0.12462653],
    -        [0.3904388 , 0.47325474, 0.26764517, ..., 0.00668907,
    -         0.67522901, 0.58829973],
    -        [0.82622853, 0.53637557, 0.54863351, ..., 0.48278046,
    -         0.59107135, 0.7104121 ]],
    -
    -       [[0.93805962, 0.7469344 , 0.92199294, ..., 0.18207686,
    -         0.77584301, 0.25593086],
    -        [0.33815231, 0.16610531, 0.47824743, ..., 0.24660719,
    -         0.82373287, 0.20450041],
    -        [0.16092187, 0.28134563, 0.43604184, ..., 0.3372316 ,
    -         0.1133594 , 0.30351886],
    +        [0.45028695, 0.96064451, 0.86802682, ..., 0.275698  ,
    +         0.75814712, 0.72808671],
    +        [0.59688502, 0.90595061, 0.61762192, ..., 0.50443671,
    +         0.93286308, 0.31635366],
    +        [0.81844783, 0.79795194, 0.3058952 , ..., 0.57212078,
    +         0.70417908, 0.45355282]],
    +
    +       [[0.00390967, 0.84255258, 0.66829904, ..., 0.17525021,
    +         0.08573297, 0.27883108],
    +        [0.27578842, 0.62996016, 0.7238201 , ..., 0.26418029,
    +         0.73540935, 0.79394157],
    +        [0.41681798, 0.26709248, 0.21578479, ..., 0.97050043,
    +         0.15659464, 0.81454829],
             ...,
    -        [0.84356718, 0.24752371, 0.05606016, ..., 0.40423199,
    -         0.62015351, 0.31789052],
    -        [0.16168344, 0.19401748, 0.45536778, ..., 0.16238046,
    -         0.56640488, 0.57833056],
    -        [0.40997222, 0.16645214, 0.63253308, ..., 0.54775102,
    -         0.92648229, 0.6937621 ]],
    +        [0.93114328, 0.16490346, 0.72532994, ..., 0.31251019,
    +         0.72382845, 0.64061723],
    +        [0.63477577, 0.73682801, 0.51053326, ..., 0.45980768,
    +         0.38910882, 0.61049791],
    +        [0.96642702, 0.52842314, 0.29703334, ..., 0.77745316,
    +         0.29856069, 0.87827057]],
     
            ...,
     
    -       [[0.31736805, 0.47920805, 0.61018887, ..., 0.81298372,
    -         0.04236802, 0.41234669],
    -        [0.99577417, 0.75927098, 0.72915076, ..., 0.83218389,
    -         0.46906578, 0.61979856],
    -        [0.4008897 , 0.84334774, 0.60021707, ..., 0.02122637,
    -         0.21514891, 0.33976773],
    +       [[0.01184341, 0.58124918, 0.30718861, ..., 0.95275934,
    +         0.86282587, 0.76261631],
    +        [0.20426825, 0.26998146, 0.55277842, ..., 0.33949996,
    +         0.11307046, 0.96083117],
    +        [0.99743723, 0.12077258, 0.85696536, ..., 0.99378398,
    +         0.27683184, 0.12934769],
             ...,
    -        [0.64688656, 0.73455517, 0.85169907, ..., 0.08330526,
    -         0.33264577, 0.02520771],
    -        [0.18692852, 0.31457968, 0.85199234, ..., 0.77340808,
    -         0.68713744, 0.89603165],
    -        [0.20477326, 0.66256752, 0.76405009, ..., 0.41698327,
    -         0.44073285, 0.63762603]],
    -
    -       [[0.17505611, 0.61789162, 0.11544518, ..., 0.92260935,
    -         0.93297745, 0.93278096],
    -        [0.96342819, 0.32674424, 0.51880039, ..., 0.19298459,
    -         0.36444577, 0.82807148],
    -        [0.61158661, 0.43432253, 0.49733978, ..., 0.86067888,
    -         0.69917924, 0.81791422],
    +        [0.46504216, 0.59434221, 0.66189863, ..., 0.52409965,
    +         0.9993914 , 0.10420245],
    +        [0.9122172 , 0.71420122, 0.97304384, ..., 0.32167899,
    +         0.86764392, 0.46382217],
    +        [0.41704701, 0.42273683, 0.2006334 , ..., 0.85364557,
    +         0.44657324, 0.91370857]],
    +
    +       [[0.41490934, 0.1812414 , 0.2339071 , ..., 0.59540855,
    +         0.30657336, 0.95314024],
    +        [0.37416897, 0.9765443 , 0.55079332, ..., 0.69120861,
    +         0.84715954, 0.07835199],
    +        [0.77245808, 0.7059258 , 0.06246358, ..., 0.9004087 ,
    +         0.62118646, 0.67582599],
             ...,
    -        [0.02993965, 0.64976661, 0.56083635, ..., 0.31242226,
    -         0.83983457, 0.01056781],
    -        [0.07177232, 0.72887083, 0.85632657, ..., 0.63937824,
    -         0.59212759, 0.44606741],
    -        [0.88139692, 0.56913508, 0.50957922, ..., 0.82175255,
    -         0.48829722, 0.29620039]],
    -
    -       [[0.34403296, 0.50126557, 0.31596836, ..., 0.367671  ,
    -         0.98403519, 0.29282912],
    -        [0.4690963 , 0.92952977, 0.95719466, ..., 0.54063072,
    -         0.08915254, 0.54032545],
    -        [0.76524613, 0.94768809, 0.57267291, ..., 0.38323691,
    -         0.21340677, 0.15340354],
    +        [0.19979061, 0.33590635, 0.91539798, ..., 0.82190163,
    +         0.84451931, 0.61221342],
    +        [0.3663617 , 0.20852897, 0.66688964, ..., 0.90823988,
    +         0.48971435, 0.72943188],
    +        [0.82565647, 0.5586544 , 0.38999649, ..., 0.97698086,
    +         0.91609911, 0.96658544]],
    +
    +       [[0.30870518, 0.28515456, 0.19172832, ..., 0.68036419,
    +         0.28476751, 0.497477  ],
    +        [0.15639   , 0.92500676, 0.66989131, ..., 0.12252334,
    +         0.87331071, 0.89695551],
    +        [0.31155052, 0.30215737, 0.12485831, ..., 0.53661526,
    +         0.73170489, 0.06338674],
             ...,
    -        [0.43725928, 0.83979795, 0.11963303, ..., 0.02810452,
    -         0.01317808, 0.13230981],
    -        [0.65455699, 0.35599601, 0.46615921, ..., 0.46764906,
    -         0.35932216, 0.78323752],
    -        [0.98699587, 0.95528827, 0.64705046, ..., 0.81002844,
    -         0.0483785 , 0.05157522]]])
    +        [0.79792459, 0.31798763, 0.25728622, ..., 0.78735608,
    +         0.30258364, 0.30162453],
    +        [0.32531316, 0.126138  , 0.62747899, ..., 0.60239494,
    +         0.41713254, 0.48067513],
    +        [0.4408972 , 0.50849144, 0.2727185 , ..., 0.35826462,
    +         0.30716571, 0.74203337]]])
     
    @@ -1201,11 +1201,11 @@

    Compute the result -
    @@ -1446,577 +1446,637 @@

    Testing a bigger calculation -
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    @@ -2534,7 +2594,7 @@ 

    Reading data with @@ -2648,7 +2708,7 @@

    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.

    @@ -3263,7 +3323,7 @@

    Reading data with @@ -3343,7 +3403,7 @@

    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.

    @@ -3964,7 +4024,7 @@

    Parallel and lazy computation using @@ -4019,7 +4079,7 @@

    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:

    @@ -4419,7 +4479,7 @@

    Parallel and lazy computation using @@ -4474,10 +4534,10 @@

    Parallel and lazy computation using dask.visualize() function, as shown in this example:

    @@ -4499,13 +4559,13 @@

    Parallel and lazy computation using -
    [                                        ] | 0% Completed | 281.00 us
    +
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    -
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    @@ -4736,7 +4796,7 @@ 

    Contribute

    By the Project Pythia Community. - Last updated on 30 October 2023. + Last updated on 6 November 2023.

    diff --git a/core/xarray/enso-xarray.html b/core/xarray/enso-xarray.html index a77676e16..e0043fa11 100644 --- a/core/xarray/enso-xarray.html +++ b/core/xarray/enso-xarray.html @@ -1184,7 +1184,7 @@

    The Niño 3.4 Index @@ -1412,7 +1412,7 @@

    The Niño 3.4 Index -../../_images/899617cd5a5325cc524872a61e4201fe43bf1ced9438cd73e7163b81b09b81c7.png +../../_images/2c9da928f8f62f8ac7b532c3a2ab9d9cc70e629a16c9f92e715db8d252f053b0.png

    @@ -1816,7 +1816,7 @@

    Select the Niño 3.4 regionwhere() method:

    @@ -2865,7 +2865,7 @@

    Compute the anomalies

    -../../_images/480cacbac1e2e405b5fa2d8fa3b82f7dad0e60235764811d395f1f125080527f.png +../../_images/ea0f73d7dbc58cf67979573e6a37938a75f533c721d1dc322588b98d25c67eb2.png

    Since the ENSO index conveys deviations from a norm, the calculation of Niño data requires a standard deviation. In this example, we calculate the standard deviation of the SST in the Niño 3.4 region data, across the entire time period of the data array:

    @@ -3241,7 +3241,7 @@

    Compute the anomalies
    <xarray.DataArray 'tos' ()>
    -array(1.8436452, dtype=float32)

    +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:

    @@ -3458,7 +3458,7 @@

    Contribute

    By the
    Project Pythia Community. - Last updated on 30 October 2023. + Last updated on 6 November 2023.

    diff --git a/core/xarray/xarray-intro.html b/core/xarray/xarray-intro.html index e34658759..9300e5c4a 100644 --- a/core/xarray/xarray-intro.html +++ b/core/xarray/xarray-intro.html @@ -1045,25 +1045,25 @@

    Generate a random numpy array -
    array([[[283.79745057, 281.21963483, 286.69882362, 288.98985995],
    -        [281.37756803, 283.63697388, 282.25510758, 281.73787564],
    -        [284.8369563 , 285.84892274, 288.64286187, 293.84712439]],
    +
    array([[[280.36402988, 283.76496208, 281.23309337, 275.99490763],
    +        [288.20436601, 272.36759236, 288.24162248, 287.08990881],
    +        [285.40602368, 282.81699526, 284.34298676, 286.5381315 ]],
     
    -       [[277.48314626, 279.81829025, 292.40057411, 278.30602076],
    -        [285.88907991, 279.90965722, 273.07631269, 279.70100453],
    -        [282.27790594, 280.34692192, 290.54087666, 269.81010921]],
    +       [[274.04248822, 288.6555151 , 279.23591179, 288.77729917],
    +        [284.81298985, 276.10399142, 277.96778494, 273.41448802],
    +        [288.2695288 , 286.3944557 , 279.67523988, 291.50978292]],
     
    -       [[280.70915085, 279.36714105, 285.82850926, 285.44100885],
    -        [279.05788857, 280.53378651, 285.9302057 , 289.52462105],
    -        [289.50652173, 283.95908228, 283.73152841, 286.27914935]],
    +       [[280.67845929, 284.52484301, 284.35853838, 276.9295114 ],
    +        [277.80180113, 288.79533614, 279.49512995, 284.10506348],
    +        [281.70501279, 287.22264098, 284.80357505, 275.31677028]],
     
    -       [[281.81964926, 284.54211097, 287.21639175, 284.8093394 ],
    -        [283.85038846, 284.56261645, 277.87751685, 283.74649987],
    -        [275.9536332 , 285.98690958, 279.32301921, 280.80220966]],
    +       [[291.30461272, 289.25261588, 284.31600354, 290.04466468],
    +        [283.03874546, 269.89719002, 284.72602009, 290.21792553],
    +        [276.33407199, 288.19869931, 277.60084883, 286.47770559]],
     
    -       [[279.11198521, 283.58562898, 289.13728249, 281.36707722],
    -        [284.6353974 , 286.03046709, 276.69632347, 278.1893575 ],
    -        [279.51548572, 283.22707062, 279.80229521, 280.49415031]]])
    +       [[291.98339602, 274.37715704, 288.03360854, 287.44478907],
    +        [280.92518353, 281.17004065, 287.33803458, 283.21496466],
    +        [281.90103706, 278.69208077, 275.01390959, 282.99321442]]])
     
    @@ -1444,44 +1444,44 @@

    Wrap the array: first attempt @@ -1864,44 +1864,44 @@

    Assign dimension names
    <xarray.DataArray (time: 5, lat: 3, lon: 4)>
    -array([[[283.79745057, 281.21963483, 286.69882362, 288.98985995],
    -        [281.37756803, 283.63697388, 282.25510758, 281.73787564],
    -        [284.8369563 , 285.84892274, 288.64286187, 293.84712439]],
    +array([[[280.36402988, 283.76496208, 281.23309337, 275.99490763],
    +        [288.20436601, 272.36759236, 288.24162248, 287.08990881],
    +        [285.40602368, 282.81699526, 284.34298676, 286.5381315 ]],
     
    -       [[277.48314626, 279.81829025, 292.40057411, 278.30602076],
    -        [285.88907991, 279.90965722, 273.07631269, 279.70100453],
    -        [282.27790594, 280.34692192, 290.54087666, 269.81010921]],
    +       [[274.04248822, 288.6555151 , 279.23591179, 288.77729917],
    +        [284.81298985, 276.10399142, 277.96778494, 273.41448802],
    +        [288.2695288 , 286.3944557 , 279.67523988, 291.50978292]],
     
    -       [[280.70915085, 279.36714105, 285.82850926, 285.44100885],
    -        [279.05788857, 280.53378651, 285.9302057 , 289.52462105],
    -        [289.50652173, 283.95908228, 283.73152841, 286.27914935]],
    +       [[280.67845929, 284.52484301, 284.35853838, 276.9295114 ],
    +        [277.80180113, 288.79533614, 279.49512995, 284.10506348],
    +        [281.70501279, 287.22264098, 284.80357505, 275.31677028]],
     
    -       [[281.81964926, 284.54211097, 287.21639175, 284.8093394 ],
    -        [283.85038846, 284.56261645, 277.87751685, 283.74649987],
    -        [275.9536332 , 285.98690958, 279.32301921, 280.80220966]],
    +       [[291.30461272, 289.25261588, 284.31600354, 290.04466468],
    +        [283.03874546, 269.89719002, 284.72602009, 290.21792553],
    +        [276.33407199, 288.19869931, 277.60084883, 286.47770559]],
     
    -       [[279.11198521, 283.58562898, 289.13728249, 281.36707722],
    -        [284.6353974 , 286.03046709, 276.69632347, 278.1893575 ],
    -        [279.51548572, 283.22707062, 279.80229521, 280.49415031]]])
    -Dimensions without coordinates: time, lat, lon
    + [[291.98339602, 274.37715704, 288.03360854, 287.44478907], + [280.92518353, 281.17004065, 287.33803458, 283.21496466], + [281.90103706, 278.69208077, 275.01390959, 282.99321442]]])

      • 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.

        @@ -2311,51 +2311,51 @@

        Initialize the fill: currentColor; }
        <xarray.DataArray (time: 5, lat: 3, lon: 4)>
        -array([[[283.79745057, 281.21963483, 286.69882362, 288.98985995],
        -        [281.37756803, 283.63697388, 282.25510758, 281.73787564],
        -        [284.8369563 , 285.84892274, 288.64286187, 293.84712439]],
        +array([[[280.36402988, 283.76496208, 281.23309337, 275.99490763],
        +        [288.20436601, 272.36759236, 288.24162248, 287.08990881],
        +        [285.40602368, 282.81699526, 284.34298676, 286.5381315 ]],
         
        -       [[277.48314626, 279.81829025, 292.40057411, 278.30602076],
        -        [285.88907991, 279.90965722, 273.07631269, 279.70100453],
        -        [282.27790594, 280.34692192, 290.54087666, 269.81010921]],
        +       [[274.04248822, 288.6555151 , 279.23591179, 288.77729917],
        +        [284.81298985, 276.10399142, 277.96778494, 273.41448802],
        +        [288.2695288 , 286.3944557 , 279.67523988, 291.50978292]],
         
        -       [[280.70915085, 279.36714105, 285.82850926, 285.44100885],
        -        [279.05788857, 280.53378651, 285.9302057 , 289.52462105],
        -        [289.50652173, 283.95908228, 283.73152841, 286.27914935]],
        +       [[280.67845929, 284.52484301, 284.35853838, 276.9295114 ],
        +        [277.80180113, 288.79533614, 279.49512995, 284.10506348],
        +        [281.70501279, 287.22264098, 284.80357505, 275.31677028]],
         
        -       [[281.81964926, 284.54211097, 287.21639175, 284.8093394 ],
        -        [283.85038846, 284.56261645, 277.87751685, 283.74649987],
        -        [275.9536332 , 285.98690958, 279.32301921, 280.80220966]],
        +       [[291.30461272, 289.25261588, 284.31600354, 290.04466468],
        +        [283.03874546, 269.89719002, 284.72602009, 290.21792553],
        +        [276.33407199, 288.19869931, 277.60084883, 286.47770559]],
         
        -       [[279.11198521, 283.58562898, 289.13728249, 281.36707722],
        -        [284.6353974 , 286.03046709, 276.69632347, 278.1893575 ],
        -        [279.51548572, 283.22707062, 279.80229521, 280.49415031]]])
        +       [[291.98339602, 274.37715704, 288.03360854, 287.44478907],
        +        [280.92518353, 281.17004065, 287.33803458, 283.21496466],
        +        [281.90103706, 278.69208077, 275.01390959, 282.99321442]]])
         Coordinates:
           * time     (time) datetime64[ns] 2018-01-01 2018-01-02 ... 2018-01-05
           * lat      (lat) float64 25.0 40.0 55.0
        -  * lon      (lon) float64 -120.0 -100.0 -80.0 -60.0
        + dtype='datetime64[ns]', name='time', freq='D'))

      • PandasIndex(Index([25.0, 40.0, 55.0], dtype='float64', name='lat'))
      • PandasIndex(Index([-120.0, -100.0, -80.0, -60.0], dtype='float64', name='lon'))
      • @@ -2735,54 +2735,54 @@

        Set useful attributes
        <xarray.DataArray (time: 5, lat: 3, lon: 4)>
        -array([[[283.79745057, 281.21963483, 286.69882362, 288.98985995],
        -        [281.37756803, 283.63697388, 282.25510758, 281.73787564],
        -        [284.8369563 , 285.84892274, 288.64286187, 293.84712439]],
        +array([[[280.36402988, 283.76496208, 281.23309337, 275.99490763],
        +        [288.20436601, 272.36759236, 288.24162248, 287.08990881],
        +        [285.40602368, 282.81699526, 284.34298676, 286.5381315 ]],
         
        -       [[277.48314626, 279.81829025, 292.40057411, 278.30602076],
        -        [285.88907991, 279.90965722, 273.07631269, 279.70100453],
        -        [282.27790594, 280.34692192, 290.54087666, 269.81010921]],
        +       [[274.04248822, 288.6555151 , 279.23591179, 288.77729917],
        +        [284.81298985, 276.10399142, 277.96778494, 273.41448802],
        +        [288.2695288 , 286.3944557 , 279.67523988, 291.50978292]],
         
        -       [[280.70915085, 279.36714105, 285.82850926, 285.44100885],
        -        [279.05788857, 280.53378651, 285.9302057 , 289.52462105],
        -        [289.50652173, 283.95908228, 283.73152841, 286.27914935]],
        +       [[280.67845929, 284.52484301, 284.35853838, 276.9295114 ],
        +        [277.80180113, 288.79533614, 279.49512995, 284.10506348],
        +        [281.70501279, 287.22264098, 284.80357505, 275.31677028]],
         
        -       [[281.81964926, 284.54211097, 287.21639175, 284.8093394 ],
        -        [283.85038846, 284.56261645, 277.87751685, 283.74649987],
        -        [275.9536332 , 285.98690958, 279.32301921, 280.80220966]],
        +       [[291.30461272, 289.25261588, 284.31600354, 290.04466468],
        +        [283.03874546, 269.89719002, 284.72602009, 290.21792553],
        +        [276.33407199, 288.19869931, 277.60084883, 286.47770559]],
         
        -       [[279.11198521, 283.58562898, 289.13728249, 281.36707722],
        -        [284.6353974 , 286.03046709, 276.69632347, 278.1893575 ],
        -        [279.51548572, 283.22707062, 279.80229521, 280.49415031]]])
        +       [[291.98339602, 274.37715704, 288.03360854, 287.44478907],
        +        [280.92518353, 281.17004065, 287.33803458, 283.21496466],
        +        [281.90103706, 278.69208077, 275.01390959, 282.99321442]]])
         Coordinates:
           * time     (time) datetime64[ns] 2018-01-01 2018-01-02 ... 2018-01-05
           * lat      (lat) float64 25.0 40.0 55.0
           * lon      (lon) float64 -120.0 -100.0 -80.0 -60.0
         Attributes:
             units:          kelvin
        -    standard_name:  air_temperature
        + dtype='datetime64[ns]', name='time', freq='D'))

      • PandasIndex(Index([25.0, 40.0, 55.0], dtype='float64', name='lat'))
      • PandasIndex(Index([-120.0, -100.0, -80.0, -60.0], dtype='float64', name='lon'))
      • units :
        kelvin
        standard_name :
        air_temperature
      • @@ -3596,54 +3596,54 @@

        Create a pressure @@ -4028,48 +4028,48 @@

        Create a Datase * lat (lat) float64 25.0 40.0 55.0 * lon (lon) float64 -120.0 -100.0 -80.0 -60.0 Data variables: - Temperature (time, lat, lon) float64 283.8 281.2 286.7 ... 279.8 280.5 - Pressure (time, lat, lon) float64 995.4 995.1 ... 998.2 1.007e+03

        + dtype='datetime64[ns]', name='time', freq='D'))
      • lat
        PandasIndex
        PandasIndex(Index([25.0, 40.0, 55.0], dtype='float64', name='lat'))
      • lon
        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.)

        @@ -4448,54 +4448,54 @@

        Access Data variables and Coordinates in a DataArrays through a dictionary syntax, as shown in this example:

        @@ -4869,54 +4869,54 @@

        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.

        @@ -5300,18 +5300,18 @@

        NumPy-like selection
        <xarray.DataArray (lat: 3, lon: 4)>
        -array([[277.48314626, 279.81829025, 292.40057411, 278.30602076],
        -       [285.88907991, 279.90965722, 273.07631269, 279.70100453],
        -       [282.27790594, 280.34692192, 290.54087666, 269.81010921]])
        +array([[274.04248822, 288.6555151 , 279.23591179, 288.77729917],
        +       [284.81298985, 276.10399142, 277.96778494, 273.41448802],
        +       [288.2695288 , 286.3944557 , 279.67523988, 291.50978292]])
         Coordinates:
             time     datetime64[ns] 2018-01-02
           * lat      (lat) float64 25.0 40.0 55.0
           * lon      (lon) float64 -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.

        @@ -5691,18 +5691,18 @@

        Selecting with fill: currentColor; }
        <xarray.DataArray (lat: 3, lon: 4)>
        -array([[277.48314626, 279.81829025, 292.40057411, 278.30602076],
        -       [285.88907991, 279.90965722, 273.07631269, 279.70100453],
        -       [282.27790594, 280.34692192, 290.54087666, 269.81010921]])
        +array([[274.04248822, 288.6555151 , 279.23591179, 288.77729917],
        +       [284.81298985, 276.10399142, 277.96778494, 273.41448802],
        +       [288.2695288 , 286.3944557 , 279.67523988, 291.50978292]])
         Coordinates:
             time     datetime64[ns] 2018-01-02
           * lat      (lat) float64 25.0 40.0 55.0
           * lon      (lon) float64 -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:

        @@ -6479,20 +6479,20 @@

        Interpolation

        Info

        @@ -6876,29 +6876,29 @@

        Slicing along coordinates

        Info

        @@ -7280,18 +7280,18 @@

        One more selection method:
        <xarray.DataArray (lat: 3, lon: 4)>
        -array([[277.48314626, 279.81829025, 292.40057411, 278.30602076],
        -       [285.88907991, 279.90965722, 273.07631269, 279.70100453],
        -       [282.27790594, 280.34692192, 290.54087666, 269.81010921]])
        +array([[274.04248822, 288.6555151 , 279.23591179, 288.77729917],
        +       [284.81298985, 276.10399142, 277.96778494, 273.41448802],
        +       [288.2695288 , 286.3944557 , 279.67523988, 291.50978292]])
         Coordinates:
             time     datetime64[ns] 2018-01-02
           * lat      (lat) float64 25.0 40.0 55.0
           * lon      (lon) float64 -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,:,:]
        @@ -7670,29 +7670,29 @@ 

        One more selection method:
        <xarray.DataArray (time: 3, lat: 2, lon: 2)>
        -array([[[281.21963483, 286.69882362],
        -        [283.63697388, 282.25510758]],
        +array([[[283.76496208, 281.23309337],
        +        [272.36759236, 288.24162248]],
         
        -       [[279.81829025, 292.40057411],
        -        [279.90965722, 273.07631269]],
        +       [[288.6555151 , 279.23591179],
        +        [276.10399142, 277.96778494]],
         
        -       [[279.36714105, 285.82850926],
        -        [280.53378651, 285.9302057 ]]])
        +       [[284.52484301, 284.35853838],
        +        [288.79533614, 279.49512995]]])
         Coordinates:
           * time     (time) datetime64[ns] 2018-01-01 2018-01-02 2018-01-03
           * lat      (lat) float64 25.0 40.0
           * lon      (lon) float64 -100.0 -80.0
         Attributes:
             units:          kelvin
        -    standard_name:  air_temperature
        + [[284.52484301, 284.35853838], + [288.79533614, 279.49512995]]])

        • 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:

        @@ -8066,29 +8066,29 @@

        One more selection method:
        <xarray.DataArray (time: 3, lat: 2, lon: 2)>
        -array([[[281.21963483, 286.69882362],
        -        [283.63697388, 282.25510758]],
        +array([[[283.76496208, 281.23309337],
        +        [272.36759236, 288.24162248]],
         
        -       [[279.81829025, 292.40057411],
        -        [279.90965722, 273.07631269]],
        +       [[288.6555151 , 279.23591179],
        +        [276.10399142, 277.96778494]],
         
        -       [[279.36714105, 285.82850926],
        -        [280.53378651, 285.9302057 ]]])
        +       [[284.52484301, 284.35853838],
        +        [288.79533614, 279.49512995]]])
         Coordinates:
           * time     (time) datetime64[ns] 2018-01-01 2018-01-02 2018-01-03
           * lat      (lat) float64 25.0 40.0
           * lon      (lon) float64 -100.0 -80.0
         Attributes:
             units:          kelvin
        -    standard_name:  air_temperature
        + [[284.52484301, 284.35853838], + [288.79533614, 279.49512995]]])

        • 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:

        @@ -8521,10 +8521,10 @@

        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
      • @@ -8966,17 +8966,17 @@

        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:

        @@ -9378,7 +9378,7 @@

        Subsetting the geospatial_lat_min: 10.753308882144761 geospatial_lat_max: 46.8308828962289 geospatial_lon_min: -153.88242040519995 - geospatial_lon_max: -42.666108129242815

      • 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
      • 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:

        @@ -9818,7 +9818,7 @@

        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
      • @@ -10252,19 +10252,19 @@

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

      • Info

        @@ -10658,19 +10658,19 @@

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

      • @@ -10688,10 +10688,10 @@

        Simple visualization with

        -
        [<matplotlib.lines.Line2D at 0x7ff558358e90>]
        +
        [<matplotlib.lines.Line2D at 0x7f957660e4e0>]
         
        -../../_images/af3cb5da339f579aade0f2b621c5b13b96337d133d134203c97375d7eb93e260.png +../../_images/eb496d8fdeeb149deaf794d8b93cb8049dace6688a85b73b496e402a0672a856.png
        @@ -10730,10 +10730,10 @@

        Plotting 2-D data -
        <matplotlib.collections.QuadMesh at 0x7ff55834a150>
        +
        <matplotlib.collections.QuadMesh at 0x7f957666d670>
         
        -../../_images/492b84248b589ae963546e7dcbcf782d8957490d5494354daedc5a351222ef78.png +../../_images/73af137dec3dde744e9f620a6c2dbe633e8356e1e14987c08a27f7efb225e936.png

        The figure above is generated by Matplotlib’s pcolormesh method, which was automatically called by Xarray’s plot method. This occurred because Xarray recognized that the DataArray object calling the plot method contained two distinct coordinate variables.

        @@ -10920,7 +10920,7 @@

        Contribute

        By the
        Project Pythia Community. - Last updated on 30 October 2023. + Last updated on 6 November 2023.

        diff --git a/foundations/conda.html b/foundations/conda.html index ebdfcdb84..bcb3aca4e 100644 --- a/foundations/conda.html +++ b/foundations/conda.html @@ -989,7 +989,7 @@

        Contribute

        By the Project Pythia Community. - Last updated on 30 October 2023. + Last updated on 6 November 2023.

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Foundations Contributor\u2019s Guide","Project Pythia Notebook Template","Cartopy","Introduction to Cartopy","Data Formats","NetCDF and CF: The Basics","Datetime","Times and Dates in Python","Matplotlib","Annotations, Colorbars, and Advanced Layouts","Histograms, Pie Charts, and Animations","Matplotlib Basics","NumPy","Intermediate NumPy","NumPy Basics","NumPy Broadcasting","Overview","Pandas","Introduction to Pandas","Xarray","Computations and Masks with Xarray","Dask Arrays with Xarray","Calculating ENSO with Xarray","Introduction to Xarray","Installing and Managing Python with Conda","Getting Started with GitHub","Getting Started with Jupyter","Getting Started with Python","Basic Version Control with git","Contribute to Project Pythia via GitHub","Git Branches","Advanced GitHub Topics","Cloning and Forking a Repository","Issues and Discussions","Opening a Pull Request on GitHub","GitHub Repositories","Configuring Your GitHub Account","GitHub Workflows","Reviewing Pull Requests","What is GitHub?","Installing and Running Python","Python in Jupyter","JupyterLab","Formatted Text in the Notebook with Markdown","Overview","Quickstart: Zero to Python","Python in the Terminal","Why Python?","Pythia Foundations","How to Cite This Book","How to Use This 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