Fixed bug in 41e8cae running doctest & format issue for docs

Author: shazelquist

xarray/core/common.py

@@ -1390,10 +1390,10 @@ def isin(self, test_elements, tolerance=None):
         Parameters
         ----------
         test_elements : array_like
-        tolerance : dtype
             The values against which to test each value of `element`.
-            This argument is flattened if an array or array_like.
             See numpy notes for behavior with non-array-like parameters.
+        tolerance : dtype
+            Optional parameter for acceptible equal tolerance.
 
         Returns
         -------
@@ -1409,7 +1409,7 @@ def isin(self, test_elements, tolerance=None):
         Dimensions without coordinates: x
 
         >>> array = xr.DataArray([1, 2, 3], dims="x")
-        >>> array.isin([1.1, 2.9], tolerance = 0.2)
+        >>> array.isin([1.1, 2.9], tolerance=0.2)
         <xarray.DataArray (x: 3)>
         array([ True, False,  True])
         Dimensions without coordinates: x
@@ -1435,7 +1435,7 @@ def isin(self, test_elements, tolerance=None):
             test_elements = test_elements.data
 
         if tolerance:
-            # non-zero & None arguments
+            # non-zero tolerance arguments
             return apply_ufunc(
                 duck_array_ops.isin_tolerance,
                 self,

xarray/core/duck_array_ops.py

@@ -13,12 +13,12 @@
 import pandas as pd
 from numpy import all as array_all  # noqa
 from numpy import any as array_any  # noqa
-from numpy import zeros_like  # noqa
-from numpy import around, broadcast_to  # noqa
+from numpy import zeros_like  # noqa; noqa
+from numpy import around, broadcast_to
 from numpy import concatenate as _concatenate
-from numpy import einsum, isclose, isin, isnan, isnat, pad  # noqa
+from numpy import einsum, isclose, isin, isnan, isnat, pad
 from numpy import stack as _stack
-from numpy import take, tensordot, transpose, unravel_index  # noqa
+from numpy import take, tensordot, transpose, unravel_index
 from numpy import where as _where
 
 from . import dask_array_compat, dask_array_ops, dtypes, npcompat, nputils
@@ -472,20 +472,21 @@ def isin_tolerance(self, test_elements, tolerance):
     Parameters
     ----------
     self : numpy.array_like
-    test_elements : numpy.array_like
+    test_elements : array_like
     tolerance : dtype
-        Absolute value of accemptable range between self and test_elements.
+        Absolute value of acceptable range between self and test_elements.
 
     Returns
     -------
     array_like : Same shape as self, but contains bool values.
 
     Notes
     -----
-    Vectorized comparasons elementwise require immense memory for larger datasets
-    because it generates np.array with shape (self.shape, test_elements.shape)
+    Vectorized comparisons elementwise require immense memory for larger datasets
+    because it generates np.array with shape (*self.shape, *test_elements.shape)
 
     """
+    test_elements = np.asarray(test_elements)
     merge_axis = (
         *[
             mergeaxis

xarray/tests/test_duck_array_ops.py

@@ -15,6 +15,7 @@
     count,
     first,
     gradient,
+    isin_tolerance,
     last,
     least_squares,
     mean,
@@ -23,7 +24,6 @@
     push,
     py_timedelta_to_float,
     stack,
-    isin_tolerance,
     timedelta_to_numeric,
     where,
 )