further cleanup after merging in latest changes

Author: Joe Hamman

xarray/backends/api.py

@@ -98,18 +98,18 @@ def _validate_attrs(dataset):
     def check_attr(name, value):
         if isinstance(name, basestring):
             if not name:
-                raise ValueError('Invalid name for attr: string must be length '
-                                 '1 or greater for serialization to netCDF '
-                                 'files')
+                raise ValueError('Invalid name for attr: string must be '
+                                 'length 1 or greater for serialization to '
+                                 'netCDF files')
         else:
             raise TypeError("Invalid name for attr: {} must be a string for "
                             "serialization to netCDF files".format(name))
 
         if not isinstance(value, (basestring, Number, np.ndarray, np.number,
                                   list, tuple)):
             raise TypeError('Invalid value for attr: {} must be a number '
-                            'string, ndarray or a list/tuple of numbers/strings '
-                            'for serialization to netCDF '
+                            'string, ndarray or a list/tuple of '
+                            'numbers/strings for serialization to netCDF '
                             'files'.format(value))
 
     # Check attrs on the dataset itself
@@ -228,9 +228,11 @@ def maybe_decode_store(store, lock=False):
             try:
                 from dask.base import tokenize
             except ImportError:
-                import dask  # raise the usual error if dask is entirely missing
+                # raise the usual error if dask is entirely missing
+                import dask
                 if StrictVersion(dask.__version__) < StrictVersion('0.6'):
-                    raise ImportError('xarray requires dask version 0.6 or newer')
+                    raise ImportError(
+                        'xarray requires dask version 0.6 or newer')
                 else:
                     raise
 

xarray/core/common.py

@@ -602,7 +602,8 @@ def where(self, cond, other=None, drop=False):
             Unimplemented placeholder for compatibility with future
             numpy / pandas versions
         drop : boolean, optional
-            Coordinate labels that only correspond to NA values should be dropped
+            Coordinate labels that only correspond to NA values should be
+            dropped
 
         Returns
         -------
@@ -677,8 +678,8 @@ def __enter__(self):
     def __exit__(self, exc_type, exc_value, traceback):
         self.close()
 
-    # this has no runtime function - these are listed so IDEs know these methods
-    # are defined and don't warn on these operations
+    # this has no runtime function - these are listed so IDEs know these
+    # methods are defined and don't warn on these operations
     __lt__ = __le__ = __ge__ = __gt__ = __add__ = __sub__ = __mul__ = \
         __truediv__ = __floordiv__ = __mod__ = __pow__ = __and__ = __xor__ = \
         __or__ = __div__ = __eq__ = __ne__ = not_implemented

xarray/core/dataset.py

@@ -28,7 +28,6 @@
                        broadcast_variables)
 from .pycompat import (iteritems, basestring, OrderedDict,
                        dask_array_type, range)
-from .combine import concat
 from .options import OPTIONS
 
 # list of attributes of pd.DatetimeIndex that are ndarrays of time info
@@ -1221,7 +1220,6 @@ def isel_points(self, dim='points', **indexers):
         Dataset.sel_points
         DataArray.isel_points
         """
-        from .dataarray import DataArray
 
         indexer_dims = set(indexers)
 
@@ -2097,7 +2095,7 @@ def reduce(self, func, dim=None, keep_attrs=False, numeric_only=False,
                 if name not in self.coords:
                     if (not numeric_only or
                             np.issubdtype(var.dtype, np.number) or
-                                var.dtype == np.bool_):
+                            (var.dtype == np.bool_)):
                         if len(reduce_dims) == 1:
                             # unpack dimensions for the benefit of functions
                             # like np.argmin which can't handle tuple arguments
@@ -2809,7 +2807,7 @@ def filter_by_attrs(self, **kwargs):
             for attr_name, pattern in kwargs.items():
                 attr_value = variable.attrs.get(attr_name)
                 if ((callable(pattern) and pattern(attr_value))
-                    or attr_value == pattern):
+                        or attr_value == pattern):
                     selection.append(var_name)
         return self[selection]
 

xarray/core/formatting.py

@@ -16,8 +16,7 @@
 from pandas.tslib import OutOfBoundsDatetime
 
 from .options import OPTIONS
-from .pycompat import (
-    PY2, unicode_type, bytes_type, dask_array_type, OrderedDict)
+from .pycompat import PY2, unicode_type, bytes_type, dask_array_type
 
 
 def pretty_print(x, numchars):

xarray/core/npcompat.py

@@ -252,9 +252,9 @@ def nanprod(a, axis=None, dtype=None, out=None, keepdims=0):
 
     def nancumsum(a, axis=None, dtype=None, out=None):
         """
-        Return the cumulative sum of array elements over a given axis treating Not a
-        Numbers (NaNs) as zero.  The cumulative sum does not change when NaNs are
-        encountered and leading NaNs are replaced by zeros.
+        Return the cumulative sum of array elements over a given axis treating
+        Not a Numbers (NaNs) as zero.  The cumulative sum does not change when
+        NaNs are encountered and leading NaNs are replaced by zeros.
 
         Zeros are returned for slices that are all-NaN or empty.
 
@@ -316,9 +316,9 @@ def nancumsum(a, axis=None, dtype=None, out=None):
 
     def nancumprod(a, axis=None, dtype=None, out=None):
         """
-        Return the cumulative product of array elements over a given axis treating Not a
-        Numbers (NaNs) as one.  The cumulative product does not change when NaNs are
-        encountered and leading NaNs are replaced by ones.
+        Return the cumulative product of array elements over a given axis
+        treating Not a Numbers (NaNs) as one.  The cumulative product does not
+        change when NaNs are encountered and leading NaNs are replaced by ones.
 
         Ones are returned for slices that are all-NaN or empty.
 

xarray/core/utils.py

@@ -189,6 +189,7 @@ def is_scalar(value):
         isinstance(value, (basestring, bytes_type)) or not
         isinstance(value, Iterable))
 
+
 def is_valid_numpy_dtype(dtype):
     try:
         np.dtype(dtype)

xarray/tests/__init__.py

@@ -9,7 +9,7 @@
 from numpy.testing import assert_array_equal
 import pytest
 
-from xarray.core import utils, ops
+from xarray.core import utils
 from xarray.core.pycompat import PY3
 from xarray.testing import assert_equal, assert_identical, assert_allclose
 
@@ -168,58 +168,6 @@ def assertDataArrayAllClose(self, ar1, ar2, rtol=1e-05, atol=1e-08):
         assert_allclose(ar1, ar2, rtol=rtol, atol=atol)
 
 
-def assert_xarray_equal(a, b):
-    import xarray as xr
-    ___tracebackhide__ = True  # noqa: F841
-    assert type(a) == type(b)
-    if isinstance(a, (xr.Variable, xr.DataArray, xr.Dataset)):
-        assert a.equals(b), '{}\n{}'.format(a, b)
-    else:  
-        raise TypeError('{} not supported by assertion comparison'
-                        .format(type(a)))
-
-
-def assert_xarray_identical(a, b):
-    import xarray as xr
-    ___tracebackhide__ = True  # noqa: F841
-    assert type(a) == type(b)
-    if isinstance(a, xr.DataArray):
-        assert a.name == b.name
-        assert_xarray_identical(a._to_temp_dataset(), b._to_temp_dataset())
-    elif isinstance(a, (xr.Dataset, xr.Variable)):
-        assert a.identical(b), '{}\n{}'.format(a, b)
-    else:
-        raise TypeError('{} not supported by assertion comparison'
-                        .format(type(a)))
-
-
-def assert_xarray_allclose(a, b, rtol=1e-05, atol=1e-08):
-    import xarray as xr
-    ___tracebackhide__ = True  # noqa: F841
-    assert type(a) == type(b)
-    if isinstance(a, xr.Variable):
-        assert a.dims == b.dims
-        allclose = data_allclose_or_equiv(
-            a.values, b.values, rtol=rtol, atol=atol)
-        assert allclose, '{}\n{}'.format(a.values, b.values)
-    elif isinstance(a, xr.DataArray):
-        assert_xarray_allclose(a.variable, b.variable)
-        for v in a.coords.variables:
-            # can't recurse with this function as coord is sometimes a DataArray,
-            # so call into data_allclose_or_equiv directly
-            allclose = data_allclose_or_equiv(
-                a.coords[v].values, b.coords[v].values, rtol=rtol, atol=atol)
-            assert allclose, '{}\n{}'.format(a.coords[v].values, b.coords[v].values)
-    elif isinstance(a, xr.Dataset):
-        assert sorted(a, key=str) == sorted(b, key=str)
-        for k in list(a.variables) + list(a.coords):
-            assert_xarray_allclose(a[k], b[k], rtol=rtol, atol=atol)
-
-    else:
-        raise TypeError('{} not supported by assertion comparison'
-                        .format(type(a)))
-
-
 class UnexpectedDataAccess(Exception):
     pass