Blacken the doctest code in docstrings (#3857)

* fix a few erroneous doctest blocks

* blacken the doctest code

* manually remove the trailing comma from doctest lines

Author: keewis

xarray/backends/api.py

@@ -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):

xarray/coding/cftime_offsets.py

@@ -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')

xarray/coding/cftimeindex.py

@@ -269,29 +269,32 @@ 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(
+        ...     [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:
@@ -423,10 +426,10 @@ 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
@@ -511,7 +514,7 @@ 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()
@@ -550,9 +553,10 @@ 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'],

xarray/coding/strings.py

@@ -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')
     """

xarray/conventions.py

@@ -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')
@@ -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')

xarray/core/accessor_dt.py

@@ -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',
@@ -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)
         """
@@ -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,

xarray/core/accessor_str.py

@@ -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])

xarray/core/alignment.py

@@ -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)>
@@ -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.],
@@ -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],
@@ -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],
@@ -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.],
@@ -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],
@@ -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])
@@ -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)

xarray/core/combine.py

@@ -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
@@ -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
@@ -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: