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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 @@
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.)
-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
.
<cartopy.mpl.feature_artist.FeatureArtist at 0x7ff58ca75d00>
+<cartopy.mpl.feature_artist.FeatureArtist at 0x7f453a4e66c0>
<cartopy.mpl.feature_artist.FeatureArtist at 0x7ff58c4fe240>
+<cartopy.mpl.feature_artist.FeatureArtist at 0x7f451434fc20>
Text(0.5, 1.0, 'Mollweide Projection')
set
-
+
@@ -1080,7 +1080,7 @@ Cartopy’s set
-
+
@@ -1101,7 +1101,7 @@ Cartopy’s set
-
+
@@ -1150,7 +1150,7 @@ Add some predefined features
-
+
-
+
@@ -1260,7 +1260,7 @@ A figure with two different regional maps
-
+
@@ -1278,7 +1278,7 @@ An example of plotting data
-
+
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
-
+
@@ -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
-/tmp/ipykernel_2544/2008125275.py:5: DeprecationWarning: datetime.datetime.utcnow() is deprecated and scheduled for removal in a future version. Use timezone-aware objects to represent datetimes in UTC: datetime.datetime.now(datetime.UTC).
+/tmp/ipykernel_2425/2008125275.py:5: DeprecationWarning: datetime.datetime.utcnow() is deprecated and scheduled for removal in a future version. Use timezone-aware objects to represent datetimes in UTC: datetime.datetime.now(datetime.UTC).
nc.history = str(datetime.utcnow()) + ' Python'
@@ -923,7 +923,7 @@ Creating the file and dimensionsProject Pythia Community.
- Last updated on 30 October 2023.
+ Last updated on 6 November 2023.
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
-The UTC time is October 30, 2023, 6:28PM.
-The 'US/Mountain' time is October 30, 2023, 12:28PM.
+The UTC time is November 06, 2023, 5:01PM.
+The 'US/Mountain' time is November 06, 2023, 10:01AM.
-/tmp/ipykernel_2564/3080495102.py:1: DeprecationWarning: datetime.datetime.utcnow() is deprecated and scheduled for removal in a future version. Use timezone-aware objects to represent datetimes in UTC: datetime.datetime.now(datetime.UTC).
+/tmp/ipykernel_2446/3080495102.py:1: DeprecationWarning: datetime.datetime.utcnow() is deprecated and scheduled for removal in a future version. Use timezone-aware objects to represent datetimes in UTC: datetime.datetime.now(datetime.UTC).
utc = dt.datetime.utcnow().replace(tzinfo=pytz.utc)
@@ -1417,7 +1417,7 @@ Contribute
By the Project Pythia Community.
- Last updated on 30 October 2023.
+ Last updated on 6 November 2023.
diff --git a/core/matplotlib.html b/core/matplotlib.html
index 26d6cb1b9..0a0eef9cb 100644
--- a/core/matplotlib.html
+++ b/core/matplotlib.html
@@ -841,7 +841,7 @@ Contribute
By the Project Pythia Community.
- Last updated on 30 October 2023.
+ Last updated on 6 November 2023.
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
-
+
@@ -851,7 +851,7 @@ Adding Annotations
-
+
@@ -880,7 +880,7 @@ Rendering Equations
-
+
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
-
+
@@ -932,7 +932,7 @@ Add a Box Around the Text
-
+
@@ -1008,7 +1008,7 @@ Basic Colorbars
-
+
We can change which colormap to use by setting the keyword argument cmap = 'colormap_name'
in the plotting function call. This sets the colormap not only for the plot, but for the colorbar as well. In this case, we use the magma
colormap:
@@ -1023,7 +1023,7 @@ Basic Colorbars
-
+
@@ -1042,10 +1042,10 @@ Shared Colorbars
-<matplotlib.colorbar.Colorbar at 0x7f2ef94346e0>
+<matplotlib.colorbar.Colorbar at 0x7f9214858aa0>
-
+
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 ColorbarsText(0.5, 0.98, 'Shared colormaps on data with different ranges')
-
+
@@ -1090,7 +1090,7 @@ Shared ColorbarsText(0.5, 0.98, 'Using the contourf output from the data with a wider range')
-
+
@@ -1126,7 +1126,7 @@ Custom Colorbars
-
+
@@ -1143,7 +1143,7 @@ Custom Colorbars
-
+
@@ -1180,7 +1180,7 @@ Mosaic Subplots
-
+
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
-
+
@@ -1227,7 +1227,7 @@ Resources and references
-
+
@@ -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
-
+
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
-
+
@@ -843,7 +843,7 @@ Pie Charts
-
+
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
-
+
Let’s do a more complicated example.
@@ -876,7 +876,7 @@ Pie Charts
-
+
@@ -918,7 +918,7 @@ Step 1: Initial State
-
+
@@ -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
-
+
@@ -989,7 +989,7 @@ Adding labels to an
-
+
We can also add a title to the plot and increase the font size:
@@ -1002,7 +1002,7 @@ Adding labels to an
-
+
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
-
+
@@ -1106,7 +1106,7 @@ Customizing colors
-
+
@@ -1142,7 +1142,7 @@ Using add_subplot to create two different subplots within the figure
-
+
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
-
+
@@ -1181,7 +1181,7 @@ Adding titles to each subplot
-
+
@@ -1208,7 +1208,7 @@ Using ax.set_xl
-
+
@@ -1234,7 +1234,7 @@ Using sharex
-
+
@@ -1290,7 +1290,7 @@ Putting this all together
-
+
@@ -1314,7 +1314,7 @@ Scatterplot
-
+
@@ -1337,7 +1337,7 @@ Scatterplot
-
+
Let’s put together the following:
@@ -1364,7 +1364,7 @@ Scatterplot
-
+
@@ -1394,7 +1394,7 @@ Displaying Images
-
+
@@ -1413,7 +1413,7 @@ Contour and Filled Contour Plots
-
+
After creating contours, we can label the lines using the clabel
method, like this:
@@ -1426,7 +1426,7 @@ Contour and Filled Contour Plots
-
+
As described above, the contourf
(contour fill) method creates filled contours around data, like this:
@@ -1438,7 +1438,7 @@ Contour and Filled Contour Plots
-
+
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
-
+
@@ -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
-
+
By doing a comparison between a NumPy array and a value, we get an
@@ -1119,7 +1119,7 @@
Replacing values
-
+
and to put this in context,
@@ -1133,7 +1133,7 @@ Replacing values
-
+
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
-
+
@@ -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
-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]])
@@ -1797,7 +1797,7 @@ Contribute
By the Project Pythia Community.
- Last updated on 30 October 2023.
+ Last updated on 6 November 2023.
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
-
+
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
-
+
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
-
+
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
-
+
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
-
+
@@ -3979,7 +3979,7 @@ Resampling
-
+
@@ -3989,7 +3989,7 @@ Resampling
-
+
@@ -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
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:
@@ -3178,7 +3178,7 @@ 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:
@@ -3188,7 +3188,7 @@ Aggregation Methods
-
+
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
-
+
@@ -5191,7 +5191,7 @@ 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:
@@ -5264,7 +5264,7 @@ Compute climatology
-
+
In this example, we plot the zonal mean climatology:
@@ -5275,7 +5275,7 @@ Compute climatology
-
+
Finally, this example calculates and plots the difference between the climatology for January and the climatology for December:
@@ -5286,7 +5286,7 @@ Compute climatology
-
+
@@ -5711,7 +5711,7 @@ Compute anomaly
@@ -5981,7 +5981,7 @@ 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
-
+
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()
+ dtype='float64', name='lon', length=360))
method and the newly loaded CESM2 dataset to weight the grid cell data as described above:
@@ -6435,7 +6435,7 @@ Compute anomaly
-
+
@@ -6892,7 +6892,7 @@ Other high level computation functionalityrolling
method to compute averages in a moving window of 5 months of data:
@@ -7408,7 +7408,7 @@ Other high level computation functionality
@@ -7674,7 +7674,7 @@ Other high level computation functionality
-
+
@@ -8077,7 +8077,7 @@ Masking Data
Using where
with one condition
@@ -8715,15 +8715,15 @@ Using where- 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- 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
-
+
@@ -9220,7 +9220,7 @@ Using where
-
+
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
-
+
@@ -9248,7 +9248,7 @@ Using where
-
+
@@ -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
-CPU times: user 395 ms, sys: 80.8 ms, total: 476 ms
-Wall time: 364 ms
+CPU times: user 444 ms, sys: 83.2 ms, total: 527 ms
+Wall time: 392 ms
-12000937.694931177
+11997988.894554757
@@ -1435,7 +1435,7 @@ Testing a bigger calculation
-
+
+ dtype='float64', name='lon', length=360))
+ dtype='float64', name='lon', length=360))