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Documentation improvements (#3328)
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* Add examples for full_like, zeros_like, ones_like

* Add examples for xr.align

* Add examples for xr.merge

* Update xr.where docstring

* Update xr.dot docstring

* Update xarray/core/common.py

Co-Authored-By: Deepak Cherian <dcherian@users.noreply.github.com>

* Update xarray/core/common.py

Co-Authored-By: Deepak Cherian <dcherian@users.noreply.github.com>

* Update xr.combine_by_coords docstring

* Apply black formatting only

* More black formatting

* Remove unnecessary pandas bits

* Fix indentation issues

* Update assign and pipe

* Update `Dataset.reindex` with examples

* Update `Dataset.fillna` with examples

* Address styling issues

* Update docstring

Co-Authored-By: Deepak Cherian <dcherian@users.noreply.github.com>
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@andersy005 @dcherian
andersy005 and dcherian committed Sep 29, 2019
1 parent f3c7da6 commit b51683f
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5 changes: 4 additions & 1 deletion doc/whats-new.rst
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Expand Up @@ -32,7 +32,10 @@ Documentation
~~~~~~~~~~~~~
- Add examples for :py:meth:`Dataset.swap_dims` and :py:meth:`DataArray.swap_dims`.
By `Justus Magin <https://github.com/keewis>`_.

- Add examples for :py:meth:`align`, :py:meth:`merge`, :py:meth:`combine_by_coords`,
:py:meth:`full_like`, :py:meth:`zeros_like`, :py:meth:`ones_like`, :py:meth:`Dataset.pipe`,
:py:meth:`Dataset.assign`, :py:meth:`Dataset.reindex`, :py:meth:`Dataset.fillna`.
By `Anderson Banihirwe <https://github.com/andersy005>`_.
- Fixed documentation to clean up an unwanted file created in ``ipython`` example
(:pull:`3353`).
By `Gregory Gundersen <https://github.com/gwgundersen/>`_.
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130 changes: 130 additions & 0 deletions xarray/core/alignment.py
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Expand Up @@ -116,6 +116,136 @@ def align(
ValueError
If any dimensions without labels on the arguments have different sizes,
or a different size than the size of the aligned dimension labels.
Examples
--------
>>> import xarray as xr
>>> x = xr.DataArray([[25, 35], [10, 24]], dims=('lat', 'lon'),
... coords={'lat': [35., 40.], 'lon': [100., 120.]})
>>> y = xr.DataArray([[20, 5], [7, 13]], dims=('lat', 'lon'),
... coords={'lat': [35., 42.], 'lon': [100., 120.]})
>>> x
<xarray.DataArray (lat: 2, lon: 2)>
array([[25, 35],
[10, 24]])
Coordinates:
* lat (lat) float64 35.0 40.0
* lon (lon) float64 100.0 120.0
>>> y
<xarray.DataArray (lat: 2, lon: 2)>
array([[20, 5],
[ 7, 13]])
Coordinates:
* lat (lat) float64 35.0 42.0
* lon (lon) float64 100.0 120.0
>>> a, b = xr.align(x, y)
>>> a
<xarray.DataArray (lat: 1, lon: 2)>
array([[25, 35]])
Coordinates:
* lat (lat) float64 35.0
* lon (lon) float64 100.0 120.0
>>> b
<xarray.DataArray (lat: 1, lon: 2)>
array([[20, 5]])
Coordinates:
* lat (lat) float64 35.0
* lon (lon) float64 100.0 120.0
>>> a, b = xr.align(x, y, join='outer')
>>> a
<xarray.DataArray (lat: 3, lon: 2)>
array([[25., 35.],
[10., 24.],
[nan, nan]])
Coordinates:
* lat (lat) float64 35.0 40.0 42.0
* lon (lon) float64 100.0 120.0
>>> b
<xarray.DataArray (lat: 3, lon: 2)>
array([[20., 5.],
[nan, nan],
[ 7., 13.]])
Coordinates:
* lat (lat) float64 35.0 40.0 42.0
* lon (lon) float64 100.0 120.0
>>> a, b = xr.align(x, y, join='outer', fill_value=-999)
>>> a
<xarray.DataArray (lat: 3, lon: 2)>
array([[ 25, 35],
[ 10, 24],
[-999, -999]])
Coordinates:
* lat (lat) float64 35.0 40.0 42.0
* lon (lon) float64 100.0 120.0
>>> b
<xarray.DataArray (lat: 3, lon: 2)>
array([[ 20, 5],
[-999, -999],
[ 7, 13]])
Coordinates:
* lat (lat) float64 35.0 40.0 42.0
* lon (lon) float64 100.0 120.0
>>> a, b = xr.align(x, y, join='left')
>>> a
<xarray.DataArray (lat: 2, lon: 2)>
array([[25, 35],
[10, 24]])
Coordinates:
* lat (lat) float64 35.0 40.0
* lon (lon) float64 100.0 120.0
>>> b
<xarray.DataArray (lat: 2, lon: 2)>
array([[20., 5.],
[nan, nan]])
Coordinates:
* lat (lat) float64 35.0 40.0
* lon (lon) float64 100.0 120.0
>>> a, b = xr.align(x, y, join='right')
>>> a
<xarray.DataArray (lat: 2, lon: 2)>
array([[25., 35.],
[nan, nan]])
Coordinates:
* lat (lat) float64 35.0 42.0
* lon (lon) float64 100.0 120.0
>>> b
<xarray.DataArray (lat: 2, lon: 2)>
array([[20, 5],
[ 7, 13]])
Coordinates:
* lat (lat) float64 35.0 42.0
* lon (lon) float64 100.0 120.0
>>> a, b = xr.align(x, y, join='exact')
Traceback (most recent call last):
...
"indexes along dimension {!r} are not equal".format(dim)
ValueError: indexes along dimension 'lat' are not equal
>>> a, b = xr.align(x, y, join='override')
>>> a
<xarray.DataArray (lat: 2, lon: 2)>
array([[25, 35],
[10, 24]])
Coordinates:
* lat (lat) float64 35.0 40.0
* lon (lon) float64 100.0 120.0
>>> b
<xarray.DataArray (lat: 2, lon: 2)>
array([[20, 5],
[ 7, 13]])
Coordinates:
* lat (lat) float64 35.0 40.0
* lon (lon) float64 100.0 120.0
"""
if indexes is None:
indexes = {}
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109 changes: 91 additions & 18 deletions xarray/core/combine.py
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Expand Up @@ -393,7 +393,7 @@ def combine_nested(
--------
A common task is collecting data from a parallelized simulation in which
each processor wrote out to a separate file. A domain which was decomposed
each process wrote out to a separate file. A domain which was decomposed
into 4 parts, 2 each along both the x and y axes, requires organising the
datasets into a doubly-nested list, e.g:
Expand Down Expand Up @@ -505,8 +505,7 @@ def combine_by_coords(
----------
datasets : sequence of xarray.Dataset
Dataset objects to combine.
compat : {'identical', 'equals', 'broadcast_equals',
'no_conflicts', 'override'}, optional
compat : {'identical', 'equals', 'broadcast_equals', 'no_conflicts', 'override'}, optional
String indicating how to compare variables of the same name for
potential conflicts:
Expand All @@ -520,9 +519,21 @@ def combine_by_coords(
of all non-null values.
- 'override': skip comparing and pick variable from first dataset
data_vars : {'minimal', 'different', 'all' or list of str}, optional
Details are in the documentation of concat
These data variables will be concatenated together:
* 'minimal': Only data variables in which the dimension already
appears are included.
* 'different': Data variables which are not equal (ignoring
attributes) across all datasets are also concatenated (as well as
all for which dimension already appears). Beware: this option may
load the data payload of data variables into memory if they are not
already loaded.
* 'all': All data variables will be concatenated.
* list of str: The listed data variables will be concatenated, in
addition to the 'minimal' data variables.
If objects are DataArrays, `data_vars` must be 'all'.
coords : {'minimal', 'different', 'all' or list of str}, optional
Details are in the documentation of concat
As per the 'data_vars' kwarg, but for coordinate variables.
fill_value : scalar, optional
Value to use for newly missing values
join : {'outer', 'inner', 'left', 'right', 'exact'}, optional
Expand Down Expand Up @@ -556,29 +567,91 @@ def combine_by_coords(
they are concatenated based on the values in their dimension coordinates,
not on their position in the list passed to `combine_by_coords`.
>>> import numpy as np
>>> import xarray as xr
>>> x1 = xr.Dataset(
... {
... "temperature": (("y", "x"), 20 * np.random.rand(6).reshape(2, 3)),
... "precipitation": (("y", "x"), np.random.rand(6).reshape(2, 3)),
... },
... coords={"y": [0, 1], "x": [10, 20, 30]},
... )
>>> x2 = xr.Dataset(
... {
... "temperature": (("y", "x"), 20 * np.random.rand(6).reshape(2, 3)),
... "precipitation": (("y", "x"), np.random.rand(6).reshape(2, 3)),
... },
... coords={"y": [2, 3], "x": [10, 20, 30]},
... )
>>> x3 = xr.Dataset(
... {
... "temperature": (("y", "x"), 20 * np.random.rand(6).reshape(2, 3)),
... "precipitation": (("y", "x"), np.random.rand(6).reshape(2, 3)),
... },
... coords={"y": [2, 3], "x": [40, 50, 60]},
... )
>>> x1
<xarray.Dataset>
Dimensions: (x: 3)
Coords:
* position (x) int64 0 1 2
Dimensions: (x: 3, y: 2)
Coordinates:
* y (y) int64 0 1
* x (x) int64 10 20 30
Data variables:
temperature (x) float64 11.04 23.57 20.77 ...
temperature (y, x) float64 1.654 10.63 7.015 2.543 13.93 9.436
precipitation (y, x) float64 0.2136 0.9974 0.7603 0.4679 0.3115 0.945
>>> x2
<xarray.Dataset>
Dimensions: (x: 3)
Coords:
* position (x) int64 3 4 5
Dimensions: (x: 3, y: 2)
Coordinates:
* y (y) int64 2 3
* x (x) int64 10 20 30
Data variables:
temperature (y, x) float64 9.341 0.1251 6.269 7.709 8.82 2.316
precipitation (y, x) float64 0.1728 0.1178 0.03018 0.6509 0.06938 0.3792
>>> x3
<xarray.Dataset>
Dimensions: (x: 3, y: 2)
Coordinates:
* y (y) int64 2 3
* x (x) int64 40 50 60
Data variables:
temperature (x) float64 6.97 8.13 7.42 ...
temperature (y, x) float64 2.789 2.446 6.551 12.46 2.22 15.96
precipitation (y, x) float64 0.4804 0.1902 0.2457 0.6125 0.4654 0.5953
>>> combined = xr.combine_by_coords([x2, x1])
>>> xr.combine_by_coords([x2, x1])
<xarray.Dataset>
Dimensions: (x: 6)
Coords:
* position (x) int64 0 1 2 3 4 5
Dimensions: (x: 3, y: 4)
Coordinates:
* x (x) int64 10 20 30
* y (y) int64 0 1 2 3
Data variables:
temperature (x) float64 11.04 23.57 20.77 ...
temperature (y, x) float64 1.654 10.63 7.015 2.543 ... 7.709 8.82 2.316
precipitation (y, x) float64 0.2136 0.9974 0.7603 ... 0.6509 0.06938 0.3792
>>> xr.combine_by_coords([x3, x1])
<xarray.Dataset>
Dimensions: (x: 6, y: 4)
Coordinates:
* x (x) int64 10 20 30 40 50 60
* y (y) int64 0 1 2 3
Data variables:
temperature (y, x) float64 1.654 10.63 7.015 nan ... nan 12.46 2.22 15.96
precipitation (y, x) float64 0.2136 0.9974 0.7603 ... 0.6125 0.4654 0.5953
>>> xr.combine_by_coords([x3, x1], join='override')
<xarray.Dataset>
Dimensions: (x: 3, y: 4)
Coordinates:
* x (x) int64 10 20 30
* y (y) int64 0 1 2 3
Data variables:
temperature (y, x) float64 1.654 10.63 7.015 2.543 ... 12.46 2.22 15.96
precipitation (y, x) float64 0.2136 0.9974 0.7603 ... 0.6125 0.4654 0.5953
"""

# Group by data vars
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