diff --git a/_preview/465/_images/0f0a40ca2698dc934e5dc65a88e874245abff06a4ba1b9f10dfea226dc0e11b9.png b/_preview/465/_images/0f0a40ca2698dc934e5dc65a88e874245abff06a4ba1b9f10dfea226dc0e11b9.png new file mode 100644 index 00000000..85e4c1d1 Binary files /dev/null and b/_preview/465/_images/0f0a40ca2698dc934e5dc65a88e874245abff06a4ba1b9f10dfea226dc0e11b9.png differ diff --git a/_preview/465/_images/11f5e54967682e1463688896e795e662aa9e1db7ac3d05a2cf15da54bb306081.png b/_preview/465/_images/11f5e54967682e1463688896e795e662aa9e1db7ac3d05a2cf15da54bb306081.png new file mode 100644 index 00000000..a557d9f4 Binary files /dev/null and b/_preview/465/_images/11f5e54967682e1463688896e795e662aa9e1db7ac3d05a2cf15da54bb306081.png differ diff --git a/_preview/465/_images/222dc0b45dae32ffa902da48763a58ce872b828a507c41b514d2e492c6972788.png b/_preview/465/_images/222dc0b45dae32ffa902da48763a58ce872b828a507c41b514d2e492c6972788.png new file mode 100644 index 00000000..7b252d39 Binary files /dev/null and b/_preview/465/_images/222dc0b45dae32ffa902da48763a58ce872b828a507c41b514d2e492c6972788.png differ diff --git a/_preview/465/_images/26f4199b211609377d1a0e08f498812b61b88d9a79b34fe2d7540c2b3cd5fa08.png b/_preview/465/_images/26f4199b211609377d1a0e08f498812b61b88d9a79b34fe2d7540c2b3cd5fa08.png new file mode 100644 index 00000000..9290824b Binary files /dev/null and b/_preview/465/_images/26f4199b211609377d1a0e08f498812b61b88d9a79b34fe2d7540c2b3cd5fa08.png differ diff --git a/_preview/465/_images/505bc3f1ced535682dcb3c38c9cb83885847bfd39fa3f115d02ca36417d21200.png b/_preview/465/_images/505bc3f1ced535682dcb3c38c9cb83885847bfd39fa3f115d02ca36417d21200.png new file mode 100644 index 00000000..87b801a0 Binary files /dev/null and b/_preview/465/_images/505bc3f1ced535682dcb3c38c9cb83885847bfd39fa3f115d02ca36417d21200.png differ diff --git a/_preview/465/_images/ac1b84bc08d7808e6ebdbb3b7e5a0c79effbd1ed00ae37ec49ac30d5f910c051.png b/_preview/465/_images/ac1b84bc08d7808e6ebdbb3b7e5a0c79effbd1ed00ae37ec49ac30d5f910c051.png new file mode 100644 index 00000000..1aee19d0 Binary files /dev/null and b/_preview/465/_images/ac1b84bc08d7808e6ebdbb3b7e5a0c79effbd1ed00ae37ec49ac30d5f910c051.png differ diff --git a/_preview/465/_images/e6879991dae48093de2108b2ce37dc851929698fd793c6e05e19e44fd1dbda11.png b/_preview/465/_images/e6879991dae48093de2108b2ce37dc851929698fd793c6e05e19e44fd1dbda11.png new file mode 100644 index 00000000..0522f4eb Binary files /dev/null and b/_preview/465/_images/e6879991dae48093de2108b2ce37dc851929698fd793c6e05e19e44fd1dbda11.png differ diff --git a/_preview/465/_images/e9b4ed6c613303cb11ea49c87beee6442b74f6b19317a0c547effe2b2d34d031.png b/_preview/465/_images/e9b4ed6c613303cb11ea49c87beee6442b74f6b19317a0c547effe2b2d34d031.png new file mode 100644 index 00000000..42351944 Binary files /dev/null and b/_preview/465/_images/e9b4ed6c613303cb11ea49c87beee6442b74f6b19317a0c547effe2b2d34d031.png differ diff --git a/_preview/465/_sources/core/numpy/numpy-basics.ipynb b/_preview/465/_sources/core/numpy/numpy-basics.ipynb index c1c98d18..b798d213 100644 --- a/_preview/465/_sources/core/numpy/numpy-basics.ipynb +++ b/_preview/465/_sources/core/numpy/numpy-basics.ipynb @@ -700,7 +700,7 @@ "source": [ "### Slices\n", "\n", - "Slicing syntax is written as `array[start:stop:step]'. Note that **all numbers are optional**. Importantly, the **step** parameter is optional and can be omitted, in which case the slice uses a default step of 1.\n", + "Slicing syntax is written as `array[start:stop:step]`. Note that **all numbers are optional**. Importantly, the **step** parameter is optional and can be omitted, in which case the slice uses a default step of 1.\n", "- defaults: \n", " - start = 0\n", " - stop = len(dim)\n", diff --git a/_preview/465/appendix/how-to-contribute.html b/_preview/465/appendix/how-to-contribute.html index 4e911c9b..562626d0 100644 --- a/_preview/465/appendix/how-to-contribute.html +++ b/_preview/465/appendix/how-to-contribute.html @@ -81,7 +81,7 @@ - + @@ -832,7 +832,7 @@

Keeping your dev environment up to date

- Last updated on 28 April 2024. + Last updated on 8 May 2024.

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

Contribute

By the
Project Pythia Community. - Last updated on 28 April 2024. + Last updated on 8 May 2024.

diff --git a/_preview/465/appendix/template.html b/_preview/465/appendix/template.html index bfa845da..c4b054b5 100644 --- a/_preview/465/appendix/template.html +++ b/_preview/465/appendix/template.html @@ -80,7 +80,7 @@ - + @@ -612,8 +612,8 @@

of further and further
header levels

Each section in your notebook can also contain \(\LaTeX\) equations, enabled through MathJax. In the following example, we illustrate some sample MathJax equations. (Rendering instructions, as well as detailed information about MathJax, can be found in this documentation.)

-
-(1)\[\begin{align} +
+(1)\[\begin{align} \dot{x} & = \sigma(y-x) \\ \dot{y} & = \rho x - y - xz \\ \dot{z} & = -\beta z + xy @@ -998,7 +998,7 @@

Resources and references

- Last updated on 28 April 2024. + Last updated on 8 May 2024.

@@ -1127,7 +1127,7 @@

Contribute

By the
Project Pythia Community. - Last updated on 28 April 2024. + Last updated on 8 May 2024.

diff --git a/_preview/465/core/cartopy.html b/_preview/465/core/cartopy.html index d2857509..1e967cdf 100644 --- a/_preview/465/core/cartopy.html +++ b/_preview/465/core/cartopy.html @@ -79,7 +79,7 @@ - + @@ -763,7 +763,7 @@

Cartopy

- Last updated on 28 April 2024. + Last updated on 8 May 2024.

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

Contribute

By the
Project Pythia Community. - Last updated on 28 April 2024. + Last updated on 8 May 2024.

diff --git a/_preview/465/core/cartopy/cartopy.html b/_preview/465/core/cartopy/cartopy.html index 828ffcdc..4f0408ed 100644 --- a/_preview/465/core/cartopy/cartopy.html +++ b/_preview/465/core/cartopy/cartopy.html @@ -79,7 +79,7 @@ - + @@ -571,7 +571,7 @@

Create a map with a specified projection - diff --git a/_preview/465/core/data-formats.html b/_preview/465/core/data-formats.html index a2ea1d3e..e2c1b66d 100644 --- a/_preview/465/core/data-formats.html +++ b/_preview/465/core/data-formats.html @@ -79,7 +79,7 @@ - + @@ -755,7 +755,7 @@

Data Formats

- Last updated on 28 April 2024. + Last updated on 8 May 2024.

@@ -884,7 +884,7 @@

Contribute

By the
Project Pythia Community. - Last updated on 28 April 2024. + Last updated on 8 May 2024.

diff --git a/_preview/465/core/data-formats/netcdf-cf.html b/_preview/465/core/data-formats/netcdf-cf.html index 87c7214f..9a5ce320 100644 --- a/_preview/465/core/data-formats/netcdf-cf.html +++ b/_preview/465/core/data-formats/netcdf-cf.html @@ -79,7 +79,7 @@ - + @@ -564,7 +564,7 @@

Gridded Data -
diff --git a/_preview/465/core/datetime.html b/_preview/465/core/datetime.html index d8cd833a..4e610afa 100644 --- a/_preview/465/core/datetime.html +++ b/_preview/465/core/datetime.html @@ -81,7 +81,7 @@ - + @@ -763,7 +763,7 @@

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

Contribute

By the
Project Pythia Community. - Last updated on 28 April 2024. + Last updated on 8 May 2024.

diff --git a/_preview/465/core/datetime/datetime.html b/_preview/465/core/datetime/datetime.html index 68d6fd2a..3a767805 100644 --- a/_preview/465/core/datetime/datetime.html +++ b/_preview/465/core/datetime/datetime.html @@ -81,7 +81,7 @@ - + @@ -593,7 +593,7 @@

Some core terminology
-
1714313060.0857208
+
1715148371.5298655
 
@@ -614,7 +614,7 @@

time

-
The benchmark took 1.000241756439209 seconds
+
The benchmark took 1.0002434253692627 seconds
 
@@ -743,7 +743,7 @@

See how many days have elapsed since the strike: -
6149
+
6159
 
@@ -815,8 +815,8 @@

Time Zone Naive Versus Time Zone Aware -
I am time zone naive 2024-04-28 14:04:21.123333.
-I am time zone aware 2024-04-28 14:04:21.123359+00:00.
+
I am time zone naive 2024-05-08 06:06:12.566725.
+I am time zone aware 2024-05-08 06:06:12.566752+00:00.
 
@@ -839,8 +839,8 @@

Full time zone support with the <

-
I am time zone naive: 2024-04-28 14:04:21.127492.
-I am time zone aware: 2024-04-28 08:04:21.137918-06:00.
+
I am time zone naive: 2024-05-08 06:06:12.570974.
+I am time zone aware: 2024-05-08 00:06:12.581615-06:00.
 
@@ -862,11 +862,11 @@

Print Time with a Different Time Zone - @@ -924,7 +924,7 @@

Contribute

By the
Project Pythia Community. - Last updated on 28 April 2024. + Last updated on 8 May 2024.

diff --git a/_preview/465/core/matplotlib/annotations-colorbars-layouts.html b/_preview/465/core/matplotlib/annotations-colorbars-layouts.html index 90460ee1..9a298be2 100644 --- a/_preview/465/core/matplotlib/annotations-colorbars-layouts.html +++ b/_preview/465/core/matplotlib/annotations-colorbars-layouts.html @@ -81,7 +81,7 @@ - + @@ -730,7 +730,7 @@

Basic Colorbars -../../_images/6254df1d0092d7c853aa127da32e61fe0954092e7f8154d982cc1e7d4d2c6de5.png +../../_images/0f0a40ca2698dc934e5dc65a88e874245abff06a4ba1b9f10dfea226dc0e11b9.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:

@@ -745,7 +745,7 @@

Basic Colorbars -../../_images/9217dee8974f3742411c380db40a9317ec29fbf13aa182f573dd5f4434c8fedf.png +../../_images/e9b4ed6c613303cb11ea49c87beee6442b74f6b19317a0c547effe2b2d34d031.png

@@ -764,10 +764,10 @@

Shared Colorbars -
<matplotlib.colorbar.Colorbar at 0x7fa7924746e0>
+
<matplotlib.colorbar.Colorbar at 0x7f10314c9550>
 
-../../_images/e0c4b6d578e83100fa169e8b8af64ab5d9eace224520910d9677885e3e214b2e.png +../../_images/222dc0b45dae32ffa902da48763a58ce872b828a507c41b514d2e492c6972788.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. @@ -848,7 +848,7 @@

Custom Colorbars -../../_images/8b26d65276deeb54109c61dd9b07a319c70b819b9da1d7dd572ad41a8bbb9767.png +../../_images/ac1b84bc08d7808e6ebdbb3b7e5a0c79effbd1ed00ae37ec49ac30d5f910c051.png
@@ -902,7 +902,7 @@

Mosaic Subplots -../../_images/849cdde74e770e39fd0f7fe8d41091ce0e813b3b63694e2c3a263aa84f3662f3.png +../../_images/505bc3f1ced535682dcb3c38c9cb83885847bfd39fa3f115d02ca36417d21200.png

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

@@ -930,7 +930,7 @@

Mosaic Subplots -../../_images/e4f3158dc5a5c2510552ac2a1c39ae157be6332e4578167f7b0e8fc5847af01c.png +../../_images/26f4199b211609377d1a0e08f498812b61b88d9a79b34fe2d7540c2b3cd5fa08.png

@@ -1291,7 +1291,7 @@

Resources and references

- Last updated on 28 April 2024. + Last updated on 8 May 2024.

@@ -1420,7 +1420,7 @@

Contribute

By the
Project Pythia Community. - Last updated on 28 April 2024. + Last updated on 8 May 2024.

diff --git a/_preview/465/core/matplotlib/histograms-piecharts-animation.html b/_preview/465/core/matplotlib/histograms-piecharts-animation.html index 4020f4db..3ef458ef 100644 --- a/_preview/465/core/matplotlib/histograms-piecharts-animation.html +++ b/_preview/465/core/matplotlib/histograms-piecharts-animation.html @@ -79,7 +79,7 @@ - + @@ -3212,7 +3212,7 @@

Step 3: Using F 5xvhVm2pMAJrQcAsb9PPR/u8VJlAaDq/i/nw/gCvCDVIVlrnnXju1SkLpnaf0VNSyce0vgfTkK9D HuSR7JUfYcT4Qo36JSzORvsSVEP8/45UM0F5KfDJ58AntsBkgXM2gAW1qtI11YJ2RiRYObBkP0rs Tcuj4WGJsYxJZNyQKYozE/f8EWpeb8CXgEQOKNhI8mz49ILOpmNq74z97xPnpjEtcaDYYjrZcyOv -94rv3S34xbWun6xGSKWAe+yU6yEjA4uPaRTDkdN7Uj6laE9jAQy61lwaN8fwnBqYMCEAAAvybW9v +94rv3S34xbWun6xGSKWAe+yU6yEjA4uPaRTDkdN7Uj6laE9jAQy61lwaN8fwnBqYMCEAAAvxbW9v dgAAAGxtdmhkAAAAAAAAAAAAAAAAAAAD6AAAD6AAAQAAAQAAAAAAAAAAAAAAAAEAAAAAAAAAAAAA AAAAAAABAAAAAAAAAAAAAAAAAABAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAgAACxx0 cmFrAAAAXHRraGQAAAADAAAAAAAAAAAAAAABAAAAAAAAD6AAAAAAAAAAAAAAAAAAAAAAAAEAAAAA @@ -3264,9 +3264,9 @@

Step 3: Using F HAAAAeMAAAL7AAACNQAAAxYAAAHBAAAC3wAAAxQAAAN1AAABeAAAA4AAAAHIAAADAAAAAgsAAALe AAABtAAAAyYAAAMxAAADjwAAAY0AAANlAAACDAAAArYAAAISAAADKAAAAaUAAAK/AAADHAAAA4kA AAGXAAADawAAAcgAAALKAAACAAAAAvsAAAHHAAAC9wAAAzkAAANYAAABhAAAAy0AAAHcAAACzAAA -Af4AAAK9AAABswAAApkAAAJeAAACJQAAAYkAAAAUc3RjbwAAAAAAAAABAAAAMAAAAGJ1ZHRhAAAA -Wm1ldGEAAAAAAAAAIWhkbHIAAAAAAAAAAG1kaXJhcHBsAAAAAAAAAAAAAAAALWlsc3QAAAAlqXRv -bwAAAB1kYXRhAAAAAQAAAABMYXZmNjAuMTYuMTAw +Af4AAAK9AAABswAAApkAAAJeAAACJQAAAYkAAAAUc3RjbwAAAAAAAAABAAAAMAAAAGF1ZHRhAAAA +WW1ldGEAAAAAAAAAIWhkbHIAAAAAAAAAAG1kaXJhcHBsAAAAAAAAAAAAAAAALGlsc3QAAAAkqXRv +bwAAABxkYXRhAAAAAQAAAABMYXZmNjEuMS4xMDA= "> Your browser does not support the video tag. @@ -3629,7 +3629,7 @@

Additional Resources

- Last updated on 28 April 2024. + Last updated on 8 May 2024.

@@ -3758,7 +3758,7 @@

Contribute

By the
Project Pythia Community. - Last updated on 28 April 2024. + Last updated on 8 May 2024.

diff --git a/_preview/465/core/matplotlib/matplotlib-basics.html b/_preview/465/core/matplotlib/matplotlib-basics.html index e37c60d2..0f5fca0f 100644 --- a/_preview/465/core/matplotlib/matplotlib-basics.html +++ b/_preview/465/core/matplotlib/matplotlib-basics.html @@ -79,7 +79,7 @@ - + @@ -1492,7 +1492,7 @@

Resources and References

- Last updated on 28 April 2024. + Last updated on 8 May 2024.

@@ -1621,7 +1621,7 @@

Contribute

By the
Project Pythia Community. - Last updated on 28 April 2024. + Last updated on 8 May 2024.

diff --git a/_preview/465/core/numpy.html b/_preview/465/core/numpy.html index ab329c89..8122832f 100644 --- a/_preview/465/core/numpy.html +++ b/_preview/465/core/numpy.html @@ -81,7 +81,7 @@ - + @@ -760,7 +760,7 @@

NumPy

- Last updated on 28 April 2024. + Last updated on 8 May 2024.

@@ -889,7 +889,7 @@

Contribute

By the
Project Pythia Community. - Last updated on 28 April 2024. + Last updated on 8 May 2024.

diff --git a/_preview/465/core/numpy/intermediate-numpy.html b/_preview/465/core/numpy/intermediate-numpy.html index 6f01acd9..7a14ba6a 100644 --- a/_preview/465/core/numpy/intermediate-numpy.html +++ b/_preview/465/core/numpy/intermediate-numpy.html @@ -81,7 +81,7 @@ - + @@ -1425,7 +1425,7 @@

Resources and references

- Last updated on 28 April 2024. + Last updated on 8 May 2024.

@@ -1554,7 +1554,7 @@

Contribute

By the
Project Pythia Community. - Last updated on 28 April 2024. + Last updated on 8 May 2024.

diff --git a/_preview/465/core/numpy/numpy-basics.html b/_preview/465/core/numpy/numpy-basics.html index 5254e620..dfe65c52 100644 --- a/_preview/465/core/numpy/numpy-basics.html +++ b/_preview/465/core/numpy/numpy-basics.html @@ -81,7 +81,7 @@ - + @@ -1168,7 +1168,7 @@

Indexing

Slices

-

Slicing syntax is written as `array[start:stop:step]’. Note that all numbers are optional. Importantly, the step parameter is optional and can be omitted, in which case the slice uses a default step of 1.

+

Slicing syntax is written as array[start:stop:step]. Note that all numbers are optional. Importantly, the step parameter is optional and can be omitted, in which case the slice uses a default step of 1.

In addition, there are many other arithmetic operations that can be performed on DataArrays. In this example, we demonstrate squaring the original Celsius values of our air temperature data:

@@ -2246,7 +2246,7 @@

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

  • @@ -2876,7 +2876,7 @@

    Aggregation Methodsdim keyword argument; this means that the function was applied over all of the dataset’s dimensions. In other words, the aggregation method computed the mean of every element of the temperature dataset across every temporal and spatial data point. However, if a dimension name is used with the dim keyword argument, the aggregation method computes an aggregation along the given dimension. In this next example, we use aggregation to calculate the temporal mean across all spatial data; this is performed by providing the dimension name 'time' to the dim keyword argument:

    @@ -3276,7 +3276,7 @@

    Aggregation Methods @@ -3717,7 +3717,7 @@

    Aggregation Methods @@ -4325,7 +4325,7 @@

    Aggregation Methods @@ -4889,7 +4889,7 @@

    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:

    @@ -5409,7 +5409,7 @@

    Compute anomaly
    @@ -6090,7 +6090,7 @@

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

    @@ -6593,7 +6593,7 @@

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

    @@ -7109,7 +7109,7 @@

    Other high level computation functionality
    @@ -7778,7 +7778,7 @@

    Masking Data

    Using where with one condition

    @@ -8416,15 +8416,15 @@

    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:

    @@ -8859,7 +8859,7 @@

    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.

    @@ -9445,7 +9445,7 @@

    Contribute

    By the
    Project Pythia Community. - Last updated on 28 April 2024. + Last updated on 8 May 2024.

    diff --git a/_preview/465/core/xarray/dask-arrays-xarray.html b/_preview/465/core/xarray/dask-arrays-xarray.html index 4265fb2e..ca83aedd 100644 --- a/_preview/465/core/xarray/dask-arrays-xarray.html +++ b/_preview/465/core/xarray/dask-arrays-xarray.html @@ -79,7 +79,7 @@ - + @@ -568,91 +568,91 @@

    Create a dask.a
    -
    array([[[0.77396645, 0.12652206, 0.37508185, ..., 0.43151751,
    -         0.81926924, 0.15333617],
    -        [0.64552196, 0.90718022, 0.83101547, ..., 0.84099636,
    -         0.89045353, 0.32514144],
    -        [0.92497199, 0.00925494, 0.09052796, ..., 0.98124744,
    -         0.22785082, 0.42126014],
    +
    array([[[5.84705213e-01, 7.12855059e-01, 9.39271050e-01, ...,
    +         3.84052306e-01, 3.65539077e-01, 5.20091572e-01],
    +        [1.60220312e-01, 9.48108581e-01, 1.42981901e-01, ...,
    +         4.50692519e-01, 1.95866959e-01, 1.26766664e-01],
    +        [2.35088205e-01, 9.16812416e-01, 6.11024749e-01, ...,
    +         2.71366747e-01, 1.85601580e-02, 3.32883537e-01],
             ...,
    -        [0.72342401, 0.73641571, 0.67794947, ..., 0.17781831,
    -         0.22058084, 0.00356369],
    -        [0.98935282, 0.96219803, 0.02100926, ..., 0.43774496,
    -         0.02350959, 0.2908574 ],
    -        [0.76606839, 0.67126902, 0.2167198 , ..., 0.53406808,
    -         0.1472161 , 0.91769068]],
    -
    -       [[0.02723841, 0.26997918, 0.81001676, ..., 0.66696955,
    -         0.39644252, 0.38142626],
    -        [0.869121  , 0.40911719, 0.56072615, ..., 0.88827351,
    -         0.93945601, 0.02615541],
    -        [0.43435909, 0.53237621, 0.67632178, ..., 0.07002759,
    -         0.28193593, 0.14444255],
    +        [3.32682092e-01, 2.75948970e-01, 2.84868994e-01, ...,
    +         1.74186572e-01, 6.98111939e-01, 4.53791573e-01],
    +        [4.75547968e-01, 6.41457105e-01, 2.63398183e-01, ...,
    +         3.29622362e-01, 7.76015879e-01, 2.63643335e-02],
    +        [8.97842188e-01, 7.87503672e-01, 1.79241460e-01, ...,
    +         4.37498708e-01, 1.78722148e-01, 5.69418965e-01]],
    +
    +       [[4.16258570e-01, 1.59577567e-02, 7.73410080e-01, ...,
    +         2.54124185e-01, 8.70032068e-01, 5.79602262e-01],
    +        [1.16531550e-01, 6.72383069e-01, 2.43667816e-01, ...,
    +         6.44521439e-01, 7.92597459e-01, 4.61951987e-01],
    +        [7.23159298e-01, 1.22116945e-02, 5.48861350e-01, ...,
    +         7.99416287e-01, 6.72057260e-01, 9.58351200e-01],
             ...,
    -        [0.47535577, 0.79737874, 0.03011941, ..., 0.19283898,
    -         0.38565769, 0.21985142],
    -        [0.59495192, 0.07783389, 0.25992055, ..., 0.016069  ,
    -         0.39806558, 0.4211737 ],
    -        [0.44723756, 0.65545788, 0.803289  , ..., 0.40058732,
    -         0.01168566, 0.7434802 ]],
    -
    -       [[0.61687605, 0.85052423, 0.14302337, ..., 0.60526974,
    -         0.0766092 , 0.49564057],
    -        [0.91799729, 0.12576144, 0.83560148, ..., 0.97065688,
    -         0.72514063, 0.15620772],
    -        [0.70723279, 0.15924569, 0.22919368, ..., 0.12862388,
    -         0.93868834, 0.66554452],
    +        [2.86834360e-02, 7.84857963e-01, 7.78160749e-01, ...,
    +         5.97739167e-01, 2.34244033e-01, 6.78034909e-01],
    +        [8.58589470e-01, 3.02917985e-01, 2.79210510e-02, ...,
    +         8.56650661e-01, 8.44018091e-01, 6.38083788e-02],
    +        [4.45319939e-01, 5.43110115e-01, 6.10567829e-01, ...,
    +         6.11691134e-01, 8.06824557e-01, 4.09331569e-01]],
    +
    +       [[2.70866009e-01, 6.20992756e-01, 3.76343788e-01, ...,
    +         1.27321035e-01, 7.91257988e-01, 8.99494610e-01],
    +        [8.94145361e-01, 1.54751155e-01, 9.88312730e-01, ...,
    +         1.13884194e-01, 5.13025635e-01, 2.58387377e-01],
    +        [7.08946310e-01, 5.07292993e-01, 5.19215998e-01, ...,
    +         5.85482422e-01, 5.65680823e-01, 5.67333934e-01],
             ...,
    -        [0.58201658, 0.14700074, 0.71111243, ..., 0.34935515,
    -         0.09434437, 0.80387401],
    -        [0.3816449 , 0.85347178, 0.35261661, ..., 0.73382714,
    -         0.07062029, 0.45632245],
    -        [0.8137755 , 0.76735701, 0.90530655, ..., 0.36422713,
    -         0.17021748, 0.13714801]],
    +        [4.05815564e-01, 6.23645044e-01, 9.46390979e-02, ...,
    +         2.37695191e-02, 5.14334468e-01, 6.42688554e-01],
    +        [8.39960679e-01, 5.15540098e-01, 1.29472619e-01, ...,
    +         9.83397149e-01, 2.40563232e-01, 5.75566582e-01],
    +        [2.31790576e-01, 1.33559897e-01, 8.44705848e-01, ...,
    +         3.30734592e-01, 4.95627535e-01, 1.77778836e-01]],
     
            ...,
     
    -       [[0.53035686, 0.1560838 , 0.98587376, ..., 0.24362439,
    -         0.45247877, 0.70906649],
    -        [0.43249036, 0.22392118, 0.41020788, ..., 0.43692438,
    -         0.77661412, 0.13955031],
    -        [0.95064729, 0.4474999 , 0.07358085, ..., 0.67280012,
    -         0.05404705, 0.88381666],
    +       [[8.90430737e-01, 1.00106886e-01, 9.76457020e-01, ...,
    +         9.84454372e-04, 8.31908881e-01, 5.13815135e-01],
    +        [3.20091435e-01, 7.76798678e-01, 7.15130710e-01, ...,
    +         6.87017977e-01, 4.27971396e-01, 8.02663037e-01],
    +        [4.70927464e-01, 3.94974857e-01, 5.50388098e-01, ...,
    +         7.75072070e-01, 4.87273382e-01, 5.56441508e-01],
             ...,
    -        [0.32108601, 0.98679091, 0.80540896, ..., 0.9246367 ,
    -         0.54306491, 0.17059346],
    -        [0.23343613, 0.59667892, 0.89514881, ..., 0.33473887,
    -         0.88598432, 0.72195913],
    -        [0.3653769 , 0.43502275, 0.07268888, ..., 0.2774776 ,
    -         0.96369511, 0.25619946]],
    -
    -       [[0.48698626, 0.68865839, 0.10729042, ..., 0.68702999,
    -         0.35047128, 0.0765663 ],
    -        [0.17472251, 0.92973318, 0.33530097, ..., 0.15714715,
    -         0.32889593, 0.9409423 ],
    -        [0.60704374, 0.98479759, 0.01087584, ..., 0.61667322,
    -         0.72752146, 0.4425885 ],
    +        [9.77499022e-01, 7.58130709e-01, 6.35926771e-01, ...,
    +         9.78725559e-01, 7.87919066e-01, 5.55648878e-01],
    +        [6.58723962e-01, 8.27643256e-01, 9.95013911e-01, ...,
    +         7.75979689e-01, 7.23155162e-01, 2.72166196e-01],
    +        [5.82375611e-01, 9.92007406e-01, 4.88585059e-01, ...,
    +         3.80687452e-01, 5.03842041e-01, 8.73579819e-01]],
    +
    +       [[9.47144729e-01, 7.93790406e-01, 3.19896551e-01, ...,
    +         7.22946679e-01, 4.33952137e-02, 2.75934144e-02],
    +        [6.72013064e-01, 4.81928595e-01, 5.55723931e-01, ...,
    +         6.57455953e-01, 9.48525236e-01, 8.38818791e-01],
    +        [5.64040668e-01, 7.42987283e-01, 6.56076293e-01, ...,
    +         2.67217439e-01, 4.43655589e-01, 2.53681581e-01],
             ...,
    -        [0.30873117, 0.70745714, 0.78565017, ..., 0.65285402,
    -         0.28607755, 0.21054105],
    -        [0.06396047, 0.81074985, 0.36126222, ..., 0.28065536,
    -         0.03763166, 0.97842859],
    -        [0.73850865, 0.57924842, 0.57793988, ..., 0.56732237,
    -         0.48795221, 0.68503426]],
    -
    -       [[0.61923034, 0.60080548, 0.17757277, ..., 0.58657404,
    -         0.84905093, 0.9945109 ],
    -        [0.27366391, 0.62922694, 0.41755591, ..., 0.03170525,
    -         0.52677539, 0.73674307],
    -        [0.95526902, 0.87909644, 0.33730061, ..., 0.87580362,
    -         0.5718606 , 0.52062666],
    +        [3.40008990e-01, 7.06665855e-01, 2.58763072e-01, ...,
    +         8.84284861e-01, 5.64585649e-01, 1.63210032e-01],
    +        [5.38606143e-02, 1.37338539e-01, 5.17606813e-01, ...,
    +         5.75767085e-01, 7.17846510e-01, 4.72022698e-01],
    +        [8.33354545e-01, 8.91524260e-01, 2.49623733e-01, ...,
    +         3.49682966e-01, 6.78770523e-01, 8.92591690e-01]],
    +
    +       [[2.40291221e-01, 9.68445801e-01, 1.71497172e-01, ...,
    +         1.54280676e-02, 8.49527122e-01, 9.18501235e-01],
    +        [9.75852182e-01, 4.28775105e-01, 4.06872848e-01, ...,
    +         9.43785941e-01, 3.05764848e-01, 1.69684556e-01],
    +        [8.95852921e-02, 3.30451405e-01, 9.30016637e-01, ...,
    +         6.56533330e-01, 1.14648894e-01, 5.76423916e-01],
             ...,
    -        [0.59002888, 0.74681423, 0.38085522, ..., 0.81590749,
    -         0.25793192, 0.26047686],
    -        [0.15708495, 0.63452819, 0.70027426, ..., 0.5222357 ,
    -         0.36298136, 0.69983528],
    -        [0.95177671, 0.06196818, 0.04978718, ..., 0.01916028,
    -         0.25220199, 0.79417708]]])
    +        [7.89303042e-01, 2.04810060e-01, 6.06598033e-01, ...,
    +         9.25029131e-01, 6.94017573e-01, 1.27315744e-01],
    +        [5.76593822e-01, 5.26487687e-01, 3.43520569e-01, ...,
    +         3.20111353e-01, 7.88628916e-01, 1.77809836e-01],
    +        [6.18822465e-01, 8.48057843e-01, 2.55668031e-01, ...,
    +         2.12989271e-01, 6.32617333e-02, 8.30676890e-01]]])
     
    @@ -873,11 +873,11 @@

    Compute the result -
    @@ -1118,91 +1118,91 @@

    Testing a bigger calculation -
    [                                        ] | 0% Completed | 370.51 us
    +
    [                                        ] | 0% Completed | 192.07 us
     
    -
    [                                        ] | 0% Completed | 101.08 ms
    +
    [                                        ] | 0% Completed | 106.86 ms
     
    -
    [                                        ] | 0% Completed | 203.50 ms
    +
    [                                        ] | 0% Completed | 207.56 ms
     
    -
    [                                        ] | 0% Completed | 304.27 ms
    +
    [                                        ] | 0% Completed | 308.77 ms
     
    -
    [                                        ] | 0% Completed | 404.92 ms
    +
    [                                        ] | 0% Completed | 409.43 ms
     
    -
    [                                        ] | 0% Completed | 505.56 ms
    +
    [                                        ] | 0% Completed | 510.21 ms
     
    -
    [                                        ] | 2% Completed | 606.36 ms
    +
    [                                        ] | 0% Completed | 615.26 ms
     
    -
    [#                                       ] | 3% Completed | 707.28 ms
    +
    [#                                       ] | 3% Completed | 716.14 ms
     
    -
    [##                                      ] | 7% Completed | 808.08 ms
    +
    [##                                      ] | 7% Completed | 816.86 ms
     
    -
    [####                                    ] | 10% Completed | 909.02 ms
    +
    [####                                    ] | 10% Completed | 917.69 ms
     
    -
    [####                                    ] | 10% Completed | 1.01 s
    +
    [####                                    ] | 10% Completed | 1.02 s
     
    -
    [####                                    ] | 10% Completed | 1.11 s
    +
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    -
    [####                                    ] | 10% Completed | 1.21 s
    +
    [####                                    ] | 10% Completed | 1.22 s
     
    -
    [####                                    ] | 10% Completed | 1.31 s
    +
    [####                                    ] | 10% Completed | 1.32 s
     
    -
    [####                                    ] | 10% Completed | 1.41 s
    +
    [####                                    ] | 10% Completed | 1.42 s
     
    -
    [#####                                   ] | 14% Completed | 1.51 s
    +
    [#####                                   ] | 12% Completed | 1.52 s
     
    -
    [######                                  ] | 17% Completed | 1.61 s
    +
    [######                                  ] | 16% Completed | 1.62 s
     
    -
    [#######                                 ] | 19% Completed | 1.72 s
    +
    [#######                                 ] | 18% Completed | 1.72 s
     
    -
    [########                                ] | 21% Completed | 1.82 s
    +
    [########                                ] | 21% Completed | 1.83 s
     
    -
    [########                                ] | 21% Completed | 1.92 s
    +
    [########                                ] | 21% Completed | 1.93 s
     
    -
    [########                                ] | 21% Completed | 2.02 s
    +
    [########                                ] | 21% Completed | 2.03 s
     
    -
    [########                                ] | 21% Completed | 2.12 s
    +
    [########                                ] | 21% Completed | 2.13 s
     
    -
    [########                                ] | 21% Completed | 2.22 s
    +
    [########                                ] | 21% Completed | 2.23 s
     
    -
    [########                                ] | 21% Completed | 2.32 s
    +
    [########                                ] | 22% Completed | 2.33 s
     
    -
    [#########                               ] | 24% Completed | 2.42 s
    +
    [##########                              ] | 25% Completed | 2.43 s
     
    -
    [###########                             ] | 27% Completed | 2.52 s
    +
    [###########                             ] | 27% Completed | 2.53 s
     
    -
    [############                            ] | 31% Completed | 2.62 s
    +
    [############                            ] | 31% Completed | 2.63 s
     
    -
    [#############                           ] | 34% Completed | 2.72 s
    +
    [#############                           ] | 34% Completed | 2.73 s
     
    -
    [#############                           ] | 34% Completed | 2.82 s
    +
    [#############                           ] | 34% Completed | 2.83 s
     
    [#############                           ] | 34% Completed | 2.93 s
    @@ -1214,22 +1214,22 @@ 

    Testing a bigger calculation
    [#############                           ] | 34% Completed | 3.13 s
     

    -
    [#############                           ] | 34% Completed | 3.23 s
    +
    [#############                           ] | 34% Completed | 3.24 s
     
    -
    [##############                          ] | 36% Completed | 3.33 s
    +
    [###############                         ] | 37% Completed | 3.34 s
     
    -
    [################                        ] | 40% Completed | 3.43 s
    +
    [################                        ] | 41% Completed | 3.44 s
     
    -
    [#################                       ] | 43% Completed | 3.53 s
    +
    [#################                       ] | 44% Completed | 3.54 s
     
    -
    [##################                      ] | 46% Completed | 3.63 s
    +
    [###################                     ] | 48% Completed | 3.64 s
     
    -
    [###################                     ] | 48% Completed | 3.73 s
    +
    [###################                     ] | 48% Completed | 3.74 s
     
    [###################                     ] | 48% Completed | 3.84 s
    @@ -1244,16 +1244,16 @@ 

    Testing a bigger calculation
    [###################                     ] | 48% Completed | 4.14 s
     

    -
    [####################                    ] | 50% Completed | 4.24 s
    +
    [####################                    ] | 51% Completed | 4.24 s
     
    -
    [#####################                   ] | 52% Completed | 4.34 s
    +
    [#####################                   ] | 54% Completed | 4.35 s
     
    -
    [######################                  ] | 55% Completed | 4.44 s
    +
    [#######################                 ] | 58% Completed | 4.45 s
     
    -
    [#######################                 ] | 58% Completed | 4.54 s
    +
    [#######################                 ] | 59% Completed | 4.55 s
     
    [#######################                 ] | 59% Completed | 4.65 s
    @@ -1271,19 +1271,19 @@ 

    Testing a bigger calculation
    [########################                ] | 60% Completed | 5.05 s
     

    -
    [#########################               ] | 62% Completed | 5.15 s
    +
    [#########################               ] | 63% Completed | 5.15 s
     
    -
    [#########################               ] | 64% Completed | 5.25 s
    +
    [##########################              ] | 67% Completed | 5.25 s
     
    -
    [###########################             ] | 67% Completed | 5.35 s
    +
    [############################            ] | 71% Completed | 5.36 s
     
    -
    [############################            ] | 72% Completed | 5.45 s
    +
    [#############################           ] | 74% Completed | 5.46 s
     
    -
    [#############################           ] | 74% Completed | 5.55 s
    +
    [#############################           ] | 74% Completed | 5.56 s
     
    [#############################           ] | 74% Completed | 5.66 s
    @@ -1295,19 +1295,19 @@ 

    Testing a bigger calculation
    [#############################           ] | 74% Completed | 5.86 s
     

    -
    [#############################           ] | 74% Completed | 5.96 s
    +
    [##############################          ] | 75% Completed | 5.96 s
     
    -
    [##############################          ] | 75% Completed | 6.06 s
    +
    [###############################         ] | 79% Completed | 6.06 s
     
    -
    [###############################         ] | 77% Completed | 6.17 s
    +
    [################################        ] | 81% Completed | 6.16 s
     
    -
    [################################        ] | 80% Completed | 6.27 s
    +
    [#################################       ] | 84% Completed | 6.27 s
     
    -
    [#################################       ] | 83% Completed | 6.37 s
    +
    [##################################      ] | 85% Completed | 6.37 s
     
    [##################################      ] | 85% Completed | 6.47 s
    @@ -1322,25 +1322,19 @@ 

    Testing a bigger calculation
    [##################################      ] | 85% Completed | 6.77 s
     

    -
    [##################################      ] | 85% Completed | 6.87 s
    +
    [###################################     ] | 88% Completed | 6.87 s
     
    -
    [##################################      ] | 86% Completed | 6.97 s
    +
    [####################################    ] | 91% Completed | 6.97 s
     
    -
    [###################################     ] | 89% Completed | 7.07 s
    +
    [######################################  ] | 95% Completed | 7.07 s
     
    -
    [#####################################   ] | 92% Completed | 7.17 s
    +
    [####################################### ] | 98% Completed | 7.17 s
     
    -
    [######################################  ] | 96% Completed | 7.28 s
    -
    -
    -
    [####################################### ] | 99% Completed | 7.38 s
    -
    -
    -
    [########################################] | 100% Completed | 7.48 s
    +
    [########################################] | 100% Completed | 7.27 s
     
    
    @@ -1858,7 +1852,7 @@ 

    Reading data with @@ -1972,7 +1966,7 @@

    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.

    @@ -2587,7 +2581,7 @@

    Reading data with @@ -2667,7 +2661,7 @@

    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.

    @@ -3288,7 +3282,7 @@

    Parallel and lazy computation using @@ -3343,7 +3337,7 @@

    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:

    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:

    @@ -3525,7 +3525,7 @@

    Contribute

    By the
    Project Pythia Community. - Last updated on 28 April 2024. + Last updated on 8 May 2024.

    diff --git a/_preview/465/core/xarray/xarray-intro.html b/_preview/465/core/xarray/xarray-intro.html index 60f93f9c..b492183a 100644 --- a/_preview/465/core/xarray/xarray-intro.html +++ b/_preview/465/core/xarray/xarray-intro.html @@ -79,7 +79,7 @@ - + @@ -583,25 +583,25 @@

    Generate a random numpy array -
    array([[[284.41587778, 278.24039225, 277.12567469, 280.3036116 ],
    -        [291.38671994, 284.07843685, 271.68662433, 284.00738909],
    -        [280.62671666, 285.40633424, 286.63476229, 289.38464724]],
    +
    array([[[288.86970321, 284.7384664 , 282.35852501, 285.92122635],
    +        [282.52331653, 285.6589906 , 285.55553297, 278.33003438],
    +        [279.63501981, 288.14519049, 286.05352077, 288.79783055]],
     
    -       [[289.31264816, 276.87863509, 286.64456698, 284.77258594],
    -        [273.51127397, 283.21851164, 287.41218494, 288.00117461],
    -        [281.69931618, 282.37308577, 280.03249701, 284.14361119]],
    +       [[279.31184742, 288.81020066, 287.24674981, 283.21292832],
    +        [288.02277269, 287.77702705, 283.11280335, 281.09473394],
    +        [286.00638691, 281.87253294, 279.73360744, 289.98233186]],
     
    -       [[283.04559735, 280.78917817, 292.96111744, 278.02258703],
    -        [285.20114621, 278.34000362, 285.86553083, 284.73133541],
    -        [287.42463473, 279.11792656, 286.78351808, 284.55556101]],
    +       [[279.78145268, 290.92864603, 280.18404573, 291.05830313],
    +        [289.82421664, 279.8440422 , 284.27505363, 276.28011894],
    +        [281.19911982, 282.75329696, 283.1504202 , 285.75519209]],
     
    -       [[274.31771102, 278.4600157 , 276.69938539, 284.92340203],
    -        [281.15033359, 276.94539556, 282.4374854 , 283.59988558],
    -        [279.87679517, 284.50906666, 283.20914335, 289.01409305]],
    +       [[278.95406808, 283.81029808, 289.58115229, 283.25837081],
    +        [293.81199489, 290.81157433, 276.52909546, 289.52713825],
    +        [287.58819129, 289.86086944, 275.75143907, 275.23555959]],
     
    -       [[278.09950498, 285.36084784, 286.81385096, 282.64902995],
    -        [284.24927371, 287.03465307, 283.61937887, 287.14922461],
    -        [287.40858438, 292.93218138, 278.34630164, 285.26746923]]])
    +       [[279.3222527 , 271.09745011, 291.62430796, 281.60265432],
    +        [283.40250288, 278.28708635, 281.70213321, 286.89310833],
    +        [283.48952486, 287.22982986, 286.75245209, 281.6099753 ]]])
     
    @@ -982,44 +982,44 @@

    Wrap the array: first attempt @@ -1402,44 +1402,44 @@

    Assign dimension names
    <xarray.DataArray (time: 5, lat: 3, lon: 4)> Size: 480B
    -array([[[284.41587778, 278.24039225, 277.12567469, 280.3036116 ],
    -        [291.38671994, 284.07843685, 271.68662433, 284.00738909],
    -        [280.62671666, 285.40633424, 286.63476229, 289.38464724]],
    -
    -       [[289.31264816, 276.87863509, 286.64456698, 284.77258594],
    -        [273.51127397, 283.21851164, 287.41218494, 288.00117461],
    -        [281.69931618, 282.37308577, 280.03249701, 284.14361119]],
    -
    -       [[283.04559735, 280.78917817, 292.96111744, 278.02258703],
    -        [285.20114621, 278.34000362, 285.86553083, 284.73133541],
    -        [287.42463473, 279.11792656, 286.78351808, 284.55556101]],
    -
    -       [[274.31771102, 278.4600157 , 276.69938539, 284.92340203],
    -        [281.15033359, 276.94539556, 282.4374854 , 283.59988558],
    -        [279.87679517, 284.50906666, 283.20914335, 289.01409305]],
    -
    -       [[278.09950498, 285.36084784, 286.81385096, 282.64902995],
    -        [284.24927371, 287.03465307, 283.61937887, 287.14922461],
    -        [287.40858438, 292.93218138, 278.34630164, 285.26746923]]])
    -Dimensions without coordinates: time, lat, lon

    +array([[[288.86970321, 284.7384664 , 282.35852501, 285.92122635], + [282.52331653, 285.6589906 , 285.55553297, 278.33003438], + [279.63501981, 288.14519049, 286.05352077, 288.79783055]], + + [[279.31184742, 288.81020066, 287.24674981, 283.21292832], + [288.02277269, 287.77702705, 283.11280335, 281.09473394], + [286.00638691, 281.87253294, 279.73360744, 289.98233186]], + + [[279.78145268, 290.92864603, 280.18404573, 291.05830313], + [289.82421664, 279.8440422 , 284.27505363, 276.28011894], + [281.19911982, 282.75329696, 283.1504202 , 285.75519209]], + + [[278.95406808, 283.81029808, 289.58115229, 283.25837081], + [293.81199489, 290.81157433, 276.52909546, 289.52713825], + [287.58819129, 289.86086944, 275.75143907, 275.23555959]], + + [[279.3222527 , 271.09745011, 291.62430796, 281.60265432], + [283.40250288, 278.28708635, 281.70213321, 286.89310833], + [283.48952486, 287.22982986, 286.75245209, 281.6099753 ]]]) +Dimensions without coordinates: time, lat, lon

    This DataArray is already an improvement over a NumPy array; the DataArray contains names for each of the dimensions (or axes in NumPy parlance). An additional improvement is the association of coordinate-value arrays with data upon creation of a DataArray. In the next example, we illustrate the creation of NumPy arrays representing the coordinate values for each dimension of the DataArray, and how to associate these coordinate arrays with the data in our DataArray.

    @@ -1849,51 +1849,51 @@

    Initialize the fill: currentColor; }
    <xarray.DataArray (time: 5, lat: 3, lon: 4)> Size: 480B
    -array([[[284.41587778, 278.24039225, 277.12567469, 280.3036116 ],
    -        [291.38671994, 284.07843685, 271.68662433, 284.00738909],
    -        [280.62671666, 285.40633424, 286.63476229, 289.38464724]],
    +array([[[288.86970321, 284.7384664 , 282.35852501, 285.92122635],
    +        [282.52331653, 285.6589906 , 285.55553297, 278.33003438],
    +        [279.63501981, 288.14519049, 286.05352077, 288.79783055]],
     
    -       [[289.31264816, 276.87863509, 286.64456698, 284.77258594],
    -        [273.51127397, 283.21851164, 287.41218494, 288.00117461],
    -        [281.69931618, 282.37308577, 280.03249701, 284.14361119]],
    +       [[279.31184742, 288.81020066, 287.24674981, 283.21292832],
    +        [288.02277269, 287.77702705, 283.11280335, 281.09473394],
    +        [286.00638691, 281.87253294, 279.73360744, 289.98233186]],
     
    -       [[283.04559735, 280.78917817, 292.96111744, 278.02258703],
    -        [285.20114621, 278.34000362, 285.86553083, 284.73133541],
    -        [287.42463473, 279.11792656, 286.78351808, 284.55556101]],
    +       [[279.78145268, 290.92864603, 280.18404573, 291.05830313],
    +        [289.82421664, 279.8440422 , 284.27505363, 276.28011894],
    +        [281.19911982, 282.75329696, 283.1504202 , 285.75519209]],
     
    -       [[274.31771102, 278.4600157 , 276.69938539, 284.92340203],
    -        [281.15033359, 276.94539556, 282.4374854 , 283.59988558],
    -        [279.87679517, 284.50906666, 283.20914335, 289.01409305]],
    +       [[278.95406808, 283.81029808, 289.58115229, 283.25837081],
    +        [293.81199489, 290.81157433, 276.52909546, 289.52713825],
    +        [287.58819129, 289.86086944, 275.75143907, 275.23555959]],
     
    -       [[278.09950498, 285.36084784, 286.81385096, 282.64902995],
    -        [284.24927371, 287.03465307, 283.61937887, 287.14922461],
    -        [287.40858438, 292.93218138, 278.34630164, 285.26746923]]])
    +       [[279.3222527 , 271.09745011, 291.62430796, 281.60265432],
    +        [283.40250288, 278.28708635, 281.70213321, 286.89310833],
    +        [283.48952486, 287.22982986, 286.75245209, 281.6099753 ]]])
     Coordinates:
       * time     (time) datetime64[ns] 40B 2018-01-01 2018-01-02 ... 2018-01-05
       * lat      (lat) float64 24B 25.0 40.0 55.0
    -  * lon      (lon) float64 32B -120.0 -100.0 -80.0 -60.0
    + dtype='datetime64[ns]', name='time', freq='D'))
  • 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'))
  • @@ -2273,54 +2273,54 @@

    Set useful attributes
    <xarray.DataArray (time: 5, lat: 3, lon: 4)> Size: 480B
    -array([[[284.41587778, 278.24039225, 277.12567469, 280.3036116 ],
    -        [291.38671994, 284.07843685, 271.68662433, 284.00738909],
    -        [280.62671666, 285.40633424, 286.63476229, 289.38464724]],
    +array([[[288.86970321, 284.7384664 , 282.35852501, 285.92122635],
    +        [282.52331653, 285.6589906 , 285.55553297, 278.33003438],
    +        [279.63501981, 288.14519049, 286.05352077, 288.79783055]],
     
    -       [[289.31264816, 276.87863509, 286.64456698, 284.77258594],
    -        [273.51127397, 283.21851164, 287.41218494, 288.00117461],
    -        [281.69931618, 282.37308577, 280.03249701, 284.14361119]],
    +       [[279.31184742, 288.81020066, 287.24674981, 283.21292832],
    +        [288.02277269, 287.77702705, 283.11280335, 281.09473394],
    +        [286.00638691, 281.87253294, 279.73360744, 289.98233186]],
     
    -       [[283.04559735, 280.78917817, 292.96111744, 278.02258703],
    -        [285.20114621, 278.34000362, 285.86553083, 284.73133541],
    -        [287.42463473, 279.11792656, 286.78351808, 284.55556101]],
    +       [[279.78145268, 290.92864603, 280.18404573, 291.05830313],
    +        [289.82421664, 279.8440422 , 284.27505363, 276.28011894],
    +        [281.19911982, 282.75329696, 283.1504202 , 285.75519209]],
     
    -       [[274.31771102, 278.4600157 , 276.69938539, 284.92340203],
    -        [281.15033359, 276.94539556, 282.4374854 , 283.59988558],
    -        [279.87679517, 284.50906666, 283.20914335, 289.01409305]],
    +       [[278.95406808, 283.81029808, 289.58115229, 283.25837081],
    +        [293.81199489, 290.81157433, 276.52909546, 289.52713825],
    +        [287.58819129, 289.86086944, 275.75143907, 275.23555959]],
     
    -       [[278.09950498, 285.36084784, 286.81385096, 282.64902995],
    -        [284.24927371, 287.03465307, 283.61937887, 287.14922461],
    -        [287.40858438, 292.93218138, 278.34630164, 285.26746923]]])
    +       [[279.3222527 , 271.09745011, 291.62430796, 281.60265432],
    +        [283.40250288, 278.28708635, 281.70213321, 286.89310833],
    +        [283.48952486, 287.22982986, 286.75245209, 281.6099753 ]]])
     Coordinates:
       * time     (time) datetime64[ns] 40B 2018-01-01 2018-01-02 ... 2018-01-05
       * lat      (lat) float64 24B 25.0 40.0 55.0
       * lon      (lon) float64 32B -120.0 -100.0 -80.0 -60.0
     Attributes:
         units:          kelvin
    -    standard_name:  air_temperature
    + dtype='datetime64[ns]', name='time', freq='D'))
  • 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'))
  • units :
    kelvin
    standard_name :
    air_temperature
  • @@ -2699,51 +2699,51 @@

    Issues with preservation of attributesdocumentation page.

    @@ -3134,54 +3134,54 @@

    Create a pressure @@ -3566,48 +3566,48 @@

    Create a Datase * lat (lat) float64 24B 25.0 40.0 55.0 * lon (lon) float64 32B -120.0 -100.0 -80.0 -60.0 Data variables: - Temperature (time, lat, lon) float64 480B 284.4 278.2 277.1 ... 278.3 285.3 - Pressure (time, lat, lon) float64 480B 1.002e+03 1.01e+03 ... 1.004e+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.)

    @@ -3986,54 +3986,54 @@

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

    @@ -4407,54 +4407,54 @@

    Access Data variables and Coordinates in a Dataset objects are mainly used for loading data from files, which will be covered later in this tutorial.

    @@ -4838,18 +4838,18 @@

    NumPy-like selection
    <xarray.DataArray (lat: 3, lon: 4)> Size: 96B
    -array([[289.31264816, 276.87863509, 286.64456698, 284.77258594],
    -       [273.51127397, 283.21851164, 287.41218494, 288.00117461],
    -       [281.69931618, 282.37308577, 280.03249701, 284.14361119]])
    +array([[279.31184742, 288.81020066, 287.24674981, 283.21292832],
    +       [288.02277269, 287.77702705, 283.11280335, 281.09473394],
    +       [286.00638691, 281.87253294, 279.73360744, 289.98233186]])
     Coordinates:
         time     datetime64[ns] 8B 2018-01-02
       * lat      (lat) float64 24B 25.0 40.0 55.0
       * lon      (lon) float64 32B -120.0 -100.0 -80.0 -60.0
     Attributes:
         units:          kelvin
    -    standard_name:  air_temperature

    + standard_name: air_temperature

    This example reveals one of the major shortcomings of index selection. In order to retrieve the correct data using index selection, anyone using a DataArray must have precise knowledge of the axes in the DataArray, including the order of the axes and the meaning of their indices.

    By using named coordinates, as shown in the next set of examples, we can avoid this cumbersome burden.

    @@ -5229,18 +5229,18 @@

    Selecting with fill: currentColor; }
    <xarray.DataArray (lat: 3, lon: 4)> Size: 96B
    -array([[289.31264816, 276.87863509, 286.64456698, 284.77258594],
    -       [273.51127397, 283.21851164, 287.41218494, 288.00117461],
    -       [281.69931618, 282.37308577, 280.03249701, 284.14361119]])
    +array([[279.31184742, 288.81020066, 287.24674981, 283.21292832],
    +       [288.02277269, 287.77702705, 283.11280335, 281.09473394],
    +       [286.00638691, 281.87253294, 279.73360744, 289.98233186]])
     Coordinates:
         time     datetime64[ns] 8B 2018-01-02
       * lat      (lat) float64 24B 25.0 40.0 55.0
       * lon      (lon) float64 32B -120.0 -100.0 -80.0 -60.0
     Attributes:
         units:          kelvin
    -    standard_name:  air_temperature
    + standard_name: air_temperature

    This method yields the same result as the index selection, however:

    In contrast with the previous example, this example shows a useful advantage of using the .loc attribute. When using the .loc attribute, you can specify data slices using a syntax similar to NumPy in addition to, or instead of, using the slice function. Both of these slicing techniques are illustrated below:

    @@ -7604,29 +7604,29 @@

    One more selection method:
    <xarray.DataArray (time: 3, lat: 2, lon: 2)> Size: 96B
    -array([[[278.24039225, 277.12567469],
    -        [284.07843685, 271.68662433]],
    +array([[[284.7384664 , 282.35852501],
    +        [285.6589906 , 285.55553297]],
     
    -       [[276.87863509, 286.64456698],
    -        [283.21851164, 287.41218494]],
    +       [[288.81020066, 287.24674981],
    +        [287.77702705, 283.11280335]],
     
    -       [[280.78917817, 292.96111744],
    -        [278.34000362, 285.86553083]]])
    +       [[290.92864603, 280.18404573],
    +        [279.8440422 , 284.27505363]]])
     Coordinates:
       * time     (time) datetime64[ns] 24B 2018-01-01 2018-01-02 2018-01-03
       * lat      (lat) float64 16B 25.0 40.0
       * lon      (lon) float64 16B -100.0 -80.0
     Attributes:
         units:          kelvin
    -    standard_name:  air_temperature
    + [[290.92864603, 280.18404573], + [279.8440422 , 284.27505363]]])

    • 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:

    @@ -8059,10 +8059,10 @@

    Access netCDF data with

    @@ -8504,17 +8504,17 @@

    Subsetting the positive: down Grib_level_type: 100 _CoordinateAxisType: Pressure - _CoordinateZisPositive: down + dtype='float32', name='isobaric1'))
  • units :
    hPa
    long_name :
    Isobaric surface
    positive :
    down
    Grib_level_type :
    100
    _CoordinateAxisType :
    Pressure
    _CoordinateZisPositive :
    down
  • (As described earlier in this tutorial, we can also use dictionary syntax to select specific DataArrays; in this case, we would write ds['isobaric1'].)

    Many of the subsetting operations usable on DataArrays can also be used on Datasets. However, when used on Datasets, these operations are performed on every DataArray in the Dataset, as shown below:

    @@ -8916,7 +8916,7 @@

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

    As shown above, the subsetting operation performed on the Dataset returned a new Dataset. If only a single DataArray is needed from this new Dataset, it can be retrieved using the familiar dot notation:

    @@ -9356,7 +9356,7 @@

    Subsetting the Grib1_TableVersion: 131 Grib1_Parameter: 11 Grib1_Level_Type: 100 - Grib1_Level_Desc: Isobaric surface + dtype='float32', name='x', length=268))
  • long_name :
    Temperature @ Isobaric surface
    units :
    K
    description :
    Temperature
    grid_mapping :
    LambertConformal_Projection
    Grib_Variable_Id :
    VAR_7-15-131-11_L100
    Grib1_Center :
    7
    Grib1_Subcenter :
    15
    Grib1_TableVersion :
    131
    Grib1_Parameter :
    11
    Grib1_Level_Type :
    100
    Grib1_Level_Desc :
    Isobaric surface
  • @@ -9790,19 +9790,19 @@

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

    @@ -10196,19 +10196,19 @@

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

  • @@ -10226,7 +10226,7 @@

    Simple visualization with @@ -897,7 +897,7 @@

    Contribute

    By the
    Project Pythia Community. - Last updated on 28 April 2024. + Last updated on 8 May 2024.

    diff --git a/_preview/465/foundations/getting-started-jupyter.html b/_preview/465/foundations/getting-started-jupyter.html index 77737ab8..c5d490db 100644 --- a/_preview/465/foundations/getting-started-jupyter.html +++ b/_preview/465/foundations/getting-started-jupyter.html @@ -79,7 +79,7 @@ - + @@ -905,7 +905,7 @@

    Resources and references

    - Last updated on 28 April 2024. + Last updated on 8 May 2024.

    @@ -1034,7 +1034,7 @@

    Contribute

    By the
    Project Pythia Community. - Last updated on 28 April 2024. + Last updated on 8 May 2024.

    diff --git a/_preview/465/foundations/getting-started-python.html b/_preview/465/foundations/getting-started-python.html index a18704f9..ad34c4c6 100644 --- a/_preview/465/foundations/getting-started-python.html +++ b/_preview/465/foundations/getting-started-python.html @@ -81,7 +81,7 @@ - + @@ -779,7 +779,7 @@

    Topics

    - Last updated on 28 April 2024. + Last updated on 8 May 2024.

    @@ -908,7 +908,7 @@

    Contribute

    By the
    Project Pythia Community. - Last updated on 28 April 2024. + Last updated on 8 May 2024.

    diff --git a/_preview/465/foundations/github/basic-git.html b/_preview/465/foundations/github/basic-git.html index 63bae98a..04355766 100644 --- a/_preview/465/foundations/github/basic-git.html +++ b/_preview/465/foundations/github/basic-git.html @@ -79,7 +79,7 @@ - + @@ -1222,7 +1222,7 @@

    References

    - Last updated on 28 April 2024. + Last updated on 8 May 2024.

    @@ -1351,7 +1351,7 @@

    Contribute

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    A community learning resource for Python-based computing in the geosciences< @@ -905,7 +905,7 @@

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