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Mar 14, 2020
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Blacken the doctest code in docstrings #3857

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3e072e4
fix a few erroneous doctest blocks
keewis Mar 11, 2020
8d81203
blacken the doctest code
keewis Mar 11, 2020
6a35f9f
manually remove the trailing comma from doctest lines
keewis Mar 12, 2020
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4 changes: 2 additions & 2 deletions xarray/backends/api.py
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Expand Up @@ -1196,8 +1196,8 @@ def save_mfdataset(

Save a dataset into one netCDF per year of data:

>>> years, datasets = zip(*ds.groupby('time.year'))
>>> paths = ['%s.nc' % y for y in years]
>>> years, datasets = zip(*ds.groupby("time.year"))
>>> paths = ["%s.nc" % y for y in years]
>>> xr.save_mfdataset(datasets, paths)
"""
if mode == "w" and len(set(paths)) < len(paths):
Expand Down
2 changes: 1 addition & 1 deletion xarray/coding/cftime_offsets.py
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Expand Up @@ -938,7 +938,7 @@ def cftime_range(
This function returns a ``CFTimeIndex``, populated with ``cftime.datetime``
objects associated with the specified calendar type, e.g.

>>> xr.cftime_range(start='2000', periods=6, freq='2MS', calendar='noleap')
>>> xr.cftime_range(start="2000", periods=6, freq="2MS", calendar="noleap")
CFTimeIndex([2000-01-01 00:00:00, 2000-03-01 00:00:00, 2000-05-01 00:00:00,
2000-07-01 00:00:00, 2000-09-01 00:00:00, 2000-11-01 00:00:00],
dtype='object')
Expand Down
46 changes: 25 additions & 21 deletions xarray/coding/cftimeindex.py
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Expand Up @@ -268,29 +268,32 @@ def _partial_date_slice(self, resolution, parsed):
>>> from cftime import DatetimeNoLeap
>>> import pandas as pd
>>> import xarray as xr
>>> da = xr.DataArray([1, 2],
coords=[[DatetimeNoLeap(2001, 1, 1),
DatetimeNoLeap(2001, 2, 1)]],
dims=['time'])
>>> da.sel(time='2001-01-01')
>>> da = xr.DataArray(
... [1, 2],
... coords=[[DatetimeNoLeap(2001, 1, 1), DatetimeNoLeap(2001, 2, 1)]],
... dims=["time"],
... )
>>> da.sel(time="2001-01-01")
<xarray.DataArray (time: 1)>
array([1])
Coordinates:
* time (time) object 2001-01-01 00:00:00
>>> da = xr.DataArray([1, 2],
coords=[[pd.Timestamp(2001, 1, 1),
pd.Timestamp(2001, 2, 1)]],
dims=['time'])
>>> da.sel(time='2001-01-01')
>>> da = xr.DataArray(
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I had thought the standard was a blank line after each result; is that a general standard or was I holding that incorrectly?
(no need to do it this round though)

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I had the same impression, but the docs say otherwise:

Expected output cannot contain an all-whitespace line, since such a line is taken to signal the end of expected output. If expected output does contain a blank line, put <BLANKLINE> in your doctest example each place a blank line is expected.

I think that means that we only need the newline if we want to continue with normal text.

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OK. IIUC we can still have blank lines, but it becomes a preference rather than enforced.
FWIW I do find they make it much easier to read the examples!

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sounds good. Let's do that in a new PR, though.

... [1, 2],
... coords=[[pd.Timestamp(2001, 1, 1), pd.Timestamp(2001, 2, 1)]],
... dims=["time"],
... )
>>> da.sel(time="2001-01-01")
<xarray.DataArray ()>
array(1)
Coordinates:
time datetime64[ns] 2001-01-01
>>> da = xr.DataArray([1, 2],
coords=[[pd.Timestamp(2001, 1, 1, 1),
pd.Timestamp(2001, 2, 1)]],
dims=['time'])
>>> da.sel(time='2001-01-01')
>>> da = xr.DataArray(
... [1, 2],
... coords=[[pd.Timestamp(2001, 1, 1, 1), pd.Timestamp(2001, 2, 1)]],
... dims=["time"],
... )
>>> da.sel(time="2001-01-01")
<xarray.DataArray (time: 1)>
array([1])
Coordinates:
Expand Down Expand Up @@ -396,10 +399,10 @@ def shift(self, n, freq):

Examples
--------
>>> index = xr.cftime_range('2000', periods=1, freq='M')
>>> index = xr.cftime_range("2000", periods=1, freq="M")
>>> index
CFTimeIndex([2000-01-31 00:00:00], dtype='object')
>>> index.shift(1, 'M')
>>> index.shift(1, "M")
CFTimeIndex([2000-02-29 00:00:00], dtype='object')
"""
from .cftime_offsets import to_offset
Expand Down Expand Up @@ -479,7 +482,7 @@ def to_datetimeindex(self, unsafe=False):
Examples
--------
>>> import xarray as xr
>>> times = xr.cftime_range('2000', periods=2, calendar='gregorian')
>>> times = xr.cftime_range("2000", periods=2, calendar="gregorian")
>>> times
CFTimeIndex([2000-01-01 00:00:00, 2000-01-02 00:00:00], dtype='object')
>>> times.to_datetimeindex()
Expand Down Expand Up @@ -518,9 +521,10 @@ def strftime(self, date_format):

Examples
--------
>>> rng = xr.cftime_range(start='2000', periods=5, freq='2MS',
... calendar='noleap')
>>> rng.strftime('%B %d, %Y, %r')
>>> rng = xr.cftime_range(
... start="2000", periods=5, freq="2MS", calendar="noleap"
... )
>>> rng.strftime("%B %d, %Y, %r")
Index(['January 01, 2000, 12:00:00 AM', 'March 01, 2000, 12:00:00 AM',
'May 01, 2000, 12:00:00 AM', 'July 01, 2000, 12:00:00 AM',
'September 01, 2000, 12:00:00 AM'],
Expand Down
2 changes: 1 addition & 1 deletion xarray/coding/strings.py
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Expand Up @@ -201,7 +201,7 @@ class StackedBytesArray(indexing.ExplicitlyIndexedNDArrayMixin):
"""Wrapper around array-like objects to create a new indexable object where
values, when accessed, are automatically stacked along the last dimension.

>>> StackedBytesArray(np.array(['a', 'b', 'c']))[:]
>>> StackedBytesArray(np.array(["a", "b", "c"]))[:]
array('abc',
dtype='|S3')
"""
Expand Down
4 changes: 2 additions & 2 deletions xarray/conventions.py
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Expand Up @@ -19,7 +19,7 @@ class NativeEndiannessArray(indexing.ExplicitlyIndexedNDArrayMixin):
big endian) into native endianness, so they can be used with Cython
functions, such as those found in bottleneck and pandas.

>>> x = np.arange(5, dtype='>i2')
>>> x = np.arange(5, dtype=">i2")

>>> x.dtype
dtype('>i2')
Expand Down Expand Up @@ -50,7 +50,7 @@ class BoolTypeArray(indexing.ExplicitlyIndexedNDArrayMixin):
This is useful for decoding boolean arrays from integer typed netCDF
variables.

>>> x = np.array([1, 0, 1, 1, 0], dtype='i1')
>>> x = np.array([1, 0, 1, 1, 0], dtype="i1")

>>> x.dtype
dtype('>i2')
Expand Down
10 changes: 5 additions & 5 deletions xarray/core/accessor_dt.py
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Expand Up @@ -250,8 +250,8 @@ class DatetimeAccessor(Properties):
---------
>>> import xarray as xr
>>> import pandas as pd
>>> dates = pd.date_range(start='2000/01/01', freq='D', periods=10)
>>> ts = xr.DataArray(dates, dims=('time'))
>>> dates = pd.date_range(start="2000/01/01", freq="D", periods=10)
>>> ts = xr.DataArray(dates, dims=("time"))
>>> ts
<xarray.DataArray (time: 10)>
array(['2000-01-01T00:00:00.000000000', '2000-01-02T00:00:00.000000000',
Expand Down Expand Up @@ -296,8 +296,8 @@ def strftime(self, date_format):

Examples
--------
>>> rng = xr.Dataset({'time': datetime.datetime(2000, 1, 1)})
>>> rng['time'].dt.strftime('%B %d, %Y, %r')
>>> rng = xr.Dataset({"time": datetime.datetime(2000, 1, 1)})
>>> rng["time"].dt.strftime("%B %d, %Y, %r")
<xarray.DataArray 'strftime' ()>
array('January 01, 2000, 12:00:00 AM', dtype=object)
"""
Expand Down Expand Up @@ -400,7 +400,7 @@ class TimedeltaAccessor(Properties):
>>> import pandas as pd
>>> import xarray as xr
>>> dates = pd.timedelta_range(start="1 day", freq="6H", periods=20)
>>> ts = xr.DataArray(dates, dims=('time'))
>>> ts = xr.DataArray(dates, dims=("time"))
>>> ts
<xarray.DataArray (time: 20)>
array([ 86400000000000, 108000000000000, 129600000000000, 151200000000000,
Expand Down
2 changes: 1 addition & 1 deletion xarray/core/accessor_str.py
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Expand Up @@ -67,7 +67,7 @@ class StringAccessor:
Similar to pandas, fields can be accessed through the `.str` attribute
for applicable DataArrays.

>>> da = xr.DataArray(['some', 'text', 'in', 'an', 'array'])
>>> da = xr.DataArray(["some", "text", "in", "an", "array"])
>>> ds.str.len()
<xarray.DataArray (dim_0: 5)>
array([4, 4, 2, 2, 5])
Expand Down
34 changes: 20 additions & 14 deletions xarray/core/alignment.py
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Expand Up @@ -121,10 +121,16 @@ def align(
--------

>>> 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 = xr.DataArray(
... [[25, 35], [10, 24]],
... dims=("lat", "lon"),
... coords={"lat": [35.0, 40.0], "lon": [100.0, 120.0]},
... )
>>> y = xr.DataArray(
... [[20, 5], [7, 13]],
... dims=("lat", "lon"),
... coords={"lat": [35.0, 42.0], "lon": [100.0, 120.0]},
... )

>>> x
<xarray.DataArray (lat: 2, lon: 2)>
Expand Down Expand Up @@ -156,7 +162,7 @@ def align(
* lat (lat) float64 35.0
* lon (lon) float64 100.0 120.0

>>> a, b = xr.align(x, y, join='outer')
>>> a, b = xr.align(x, y, join="outer")
>>> a
<xarray.DataArray (lat: 3, lon: 2)>
array([[25., 35.],
Expand All @@ -174,7 +180,7 @@ def align(
* 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, b = xr.align(x, y, join="outer", fill_value=-999)
>>> a
<xarray.DataArray (lat: 3, lon: 2)>
array([[ 25, 35],
Expand All @@ -192,7 +198,7 @@ def align(
* 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, b = xr.align(x, y, join="left")
>>> a
<xarray.DataArray (lat: 2, lon: 2)>
array([[25, 35],
Expand All @@ -208,7 +214,7 @@ def align(
* lat (lat) float64 35.0 40.0
* lon (lon) float64 100.0 120.0

>>> a, b = xr.align(x, y, join='right')
>>> a, b = xr.align(x, y, join="right")
>>> a
<xarray.DataArray (lat: 2, lon: 2)>
array([[25., 35.],
Expand All @@ -224,13 +230,13 @@ def align(
* lat (lat) float64 35.0 42.0
* lon (lon) float64 100.0 120.0

>>> a, b = xr.align(x, y, join='exact')
>>> 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, b = xr.align(x, y, join="override")
>>> a
<xarray.DataArray (lat: 2, lon: 2)>
array([[25, 35],
Expand Down Expand Up @@ -674,8 +680,8 @@ def broadcast(*args, exclude=None):

Broadcast two data arrays against one another to fill out their dimensions:

>>> a = xr.DataArray([1, 2, 3], dims='x')
>>> b = xr.DataArray([5, 6], dims='y')
>>> a = xr.DataArray([1, 2, 3], dims="x")
>>> b = xr.DataArray([5, 6], dims="y")
>>> a
<xarray.DataArray (x: 3)>
array([1, 2, 3])
Expand Down Expand Up @@ -706,8 +712,8 @@ def broadcast(*args, exclude=None):

Fill out the dimensions of all data variables in a dataset:

>>> ds = xr.Dataset({'a': a, 'b': b})
>>> ds2, = xr.broadcast(ds) # use tuple unpacking to extract one dataset
>>> ds = xr.Dataset({"a": a, "b": b})
>>> (ds2,) = xr.broadcast(ds) # use tuple unpacking to extract one dataset
>>> ds2
<xarray.Dataset>
Dimensions: (x: 3, y: 2)
Expand Down
6 changes: 3 additions & 3 deletions xarray/core/combine.py
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Expand Up @@ -412,7 +412,7 @@ def combine_nested(
precipitation (x, y) float64 5.904 2.453 3.404 ...

>>> ds_grid = [[x1y1, x1y2], [x2y1, x2y2]]
>>> combined = xr.combine_nested(ds_grid, concat_dim=['x', 'y'])
>>> combined = xr.combine_nested(ds_grid, concat_dim=["x", "y"])
<xarray.Dataset>
Dimensions: (x: 4, y: 4)
Dimensions without coordinates: x, y
Expand Down Expand Up @@ -441,7 +441,7 @@ def combine_nested(
precipitation (t) float64 5.904 2.453 3.404 ...

>>> ds_grid = [[t1temp, t1precip], [t2temp, t2precip]]
>>> combined = xr.combine_nested(ds_grid, concat_dim=['t', None])
>>> combined = xr.combine_nested(ds_grid, concat_dim=["t", None])
<xarray.Dataset>
Dimensions: (t: 10)
Dimensions without coordinates: t
Expand Down Expand Up @@ -650,7 +650,7 @@ def combine_by_coords(
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')
>>> xr.combine_by_coords([x3, x1], join="override")
<xarray.Dataset>
Dimensions: (x: 3, y: 4)
Coordinates:
Expand Down
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