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Documentation improvements #3328

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Sep 29, 2019
Merged

Documentation improvements #3328

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53158d0
Add examples for full_like, zeros_like, ones_like
andersy005 Sep 20, 2019
5daf83d
Add examples for xr.align
andersy005 Sep 20, 2019
a2537cc
Add examples for xr.merge
andersy005 Sep 20, 2019
d448daf
Merge branch 'master' of github.com:pydata/xarray into docs-improvements
andersy005 Sep 21, 2019
2c28ac1
Merge branch 'master' of github.com:pydata/xarray into docs-improvements
andersy005 Sep 24, 2019
7504f60
Update xr.where docstring
andersy005 Sep 24, 2019
a60467a
Update xr.dot docstring
andersy005 Sep 24, 2019
7329adb
Update xarray/core/common.py
andersy005 Sep 24, 2019
68aa199
Update xarray/core/common.py
andersy005 Sep 24, 2019
a4ca35d
Update xr.combine_by_coords docstring
andersy005 Sep 24, 2019
5eeaf19
Apply black formatting only
andersy005 Sep 24, 2019
5c36ed5
More black formatting
andersy005 Sep 24, 2019
003fa9e
Remove unnecessary pandas bits
andersy005 Sep 24, 2019
6094f61
Fix indentation issues
andersy005 Sep 24, 2019
7248fe3
Update assign and pipe
andersy005 Sep 25, 2019
e396103
Update `Dataset.reindex` with examples
andersy005 Sep 25, 2019
0c31c34
Update `Dataset.fillna` with examples
andersy005 Sep 25, 2019
54d32df
Merge branch 'master' of github.com:pydata/xarray into docs-improvements
andersy005 Sep 25, 2019
3c703db
Address styling issues
andersy005 Sep 25, 2019
2a021e4
Update docstring
andersy005 Sep 25, 2019
d386247
Merge branch 'master' into docs-improvements
dcherian Sep 29, 2019
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130 changes: 130 additions & 0 deletions xarray/core/alignment.py
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Original file line number Diff line number Diff line change
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
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This looks a bit awkward to me. How about just ValueError: indexes along dimension 'lat' are not equal? Or other ideas welcome / not terrible atm


>>> 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 = {}
Expand Down
111 changes: 94 additions & 17 deletions xarray/core/combine.py
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Original file line number Diff line number Diff line change
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 +519,33 @@ 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
These coordinate variables will be concatenated together:

* 'minimal': Only coordinates in which the dimension already appears
are included.
* 'different': Coordinates 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 coordinate variables into memory if they are not already
loaded.
* 'all': All coordinate variables will be concatenated, except
those corresponding to other dimensions.
* list of str: The listed coordinate variables will be concatenated,
in addition to the 'minimal' coordinates.
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fill_value : scalar, optional
Value to use for newly missing values
join : {'outer', 'inner', 'left', 'right', 'exact'}, optional
Expand Down Expand Up @@ -556,29 +579,83 @@ 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 pandas as pd
>>> import numpy as np
>>> import xarray as xr

>>> x1 = xr.Dataset({"temperature": (("time", "x"), 20 * np.random.rand(6).reshape(2, 3)),
... "precipitation": (("time", "x"), np.random.rand(6).reshape(2, 3))},
... coords={"time": pd.date_range(start="2000-01", periods=2, freq='M'),
... "x": [10, 20, 30]})
>>> x2 = xr.Dataset({"temperature": (("time", "x"), 20 * np.random.rand(6).reshape(2, 3)),
... "precipitation": (("time", "x"), np.random.rand(6).reshape(2, 3))},
... coords={"time": pd.date_range(start="2000-03", periods=2, freq='M'),
... "x": [10, 20, 30]})

>>> x3 = xr.Dataset({"temperature": (("time", "x"), 20 * np.random.rand(6).reshape(2, 3)),
... "precipitation": (("time", "x"), np.random.rand(6).reshape(2, 3))},
... coords={"time": pd.date_range(start="2000-03", periods=2, freq='M'),
... "x": [40, 50, 60]})
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>>> x1
<xarray.Dataset>
Dimensions: (x: 3)
Coords:
* position (x) int64 0 1 2
Dimensions: (time: 2, x: 3)
Coordinates:
* time (time) datetime64[ns] 2000-01-31 2000-02-29
* x (x) int64 10 20 30
Data variables:
temperature (x) float64 11.04 23.57 20.77 ...
temperature (time, x) float64 9.022 4.041 7.246 3.474 16.26 3.05
precipitation (time, x) float64 0.5777 0.3621 0.8043 0.5203 0.03721 0.3805

>>> x2
<xarray.Dataset>
Dimensions: (x: 3)
Coords:
* position (x) int64 3 4 5
Dimensions: (time: 2, x: 3)
Coordinates:
* time (time) datetime64[ns] 2000-03-31 2000-04-30
* x (x) int64 10 20 30
Data variables:
temperature (time, x) float64 1.88 2.184 7.438 3.046 6.719 7.501
precipitation (time, x) float64 0.02079 0.8212 0.9924 0.405 0.16 0.2543

>>> x3
<xarray.Dataset>
Dimensions: (time: 2, x: 3)
Coordinates:
* time (time) datetime64[ns] 2000-03-31 2000-04-30
* x (x) int64 40 50 60
Data variables:
temperature (time, x) float64 15.31 7.169 7.927 16.01 16.58 15.33
precipitation (time, x) float64 0.2915 0.4556 0.2424 0.193 0.3184 0.6775

>>> xr.combine_by_coords([x2, x1])
<xarray.Dataset>
Dimensions: (time: 4, x: 3)
Coordinates:
* x (x) int64 10 20 30
* time (time) datetime64[ns] 2000-01-31 2000-02-29 ... 2000-04-30
Data variables:
temperature (time, x) float64 9.022 4.041 7.246 ... 3.046 6.719 7.501
precipitation (time, x) float64 0.5777 0.3621 0.8043 ... 0.405 0.16 0.2543

>>> xr.combine_by_coords([x3, x1])
<xarray.Dataset>
Dimensions: (time: 4, x: 6)
Coordinates:
* time (time) datetime64[ns] 2000-01-31 2000-02-29 ... 2000-04-30
* x (x) int64 10 20 30 40 50 60
Data variables:
temperature (x) float64 6.97 8.13 7.42 ...
temperature (time, x) float64 9.022 4.041 7.246 nan ... 16.01 16.58 15.33
precipitation (time, x) float64 0.5777 0.3621 0.8043 ... 0.3184 0.6775

>>> combined = xr.combine_by_coords([x2, x1])
>>> xr.combine_by_coords([x3, x1], join='override')
<xarray.Dataset>
Dimensions: (x: 6)
Coords:
* position (x) int64 0 1 2 3 4 5
Dimensions: (time: 2, x: 6)
Coordinates:
* time (time) datetime64[ns] 2000-01-31 2000-02-29
* x (x) int64 10 20 30 40 50 60
Data variables:
temperature (x) float64 11.04 23.57 20.77 ...
temperature (time, x) float64 9.022 4.041 7.246 ... 16.01 16.58 15.33
precipitation (time, x) float64 0.5777 0.3621 0.8043 ... 0.3184 0.6775
"""

# Group by data vars
Expand Down
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