refactor isnull

xarray/core/ops.py

@@ -79,8 +79,20 @@ def _fail_on_dask_array_input(values, msg=None, func_name=None):
 
 around = _dask_or_eager_func('around')
 isclose = _dask_or_eager_func('isclose')
-isnull = _dask_or_eager_func('isnull', pd)
 notnull = _dask_or_eager_func('notnull', pd)
+_isnull = _dask_or_eager_func('isnull', pd)
+
+
+def isnull(data):
+    # GH837, GH861
+    # isnull fcn from pandas will throw TypeError when run on numpy structured
+    # array therefore for dims that are np structured arrays we assume all
+    # data is present
+    try:
+        return _isnull(data)
+    except TypeError:
+        return np.zeros(data.shape, dtype=bool)
+
 
 transpose = _dask_or_eager_func('transpose')
 where = _dask_or_eager_func('where', n_array_args=3)
@@ -125,17 +137,7 @@ def array_equiv(arr1, arr2):
         return False
 
     flag_array = (arr1 == arr2)
-
-    # GH837, GH861
-    # isnull fcn from pandas will throw TypeError when run on numpy structured array
-    # therefore for dims that are np structured arrays we skip testing for nan
-
-    try:
-
-        flag_array |= (isnull(arr1) & isnull(arr2))
-
-    except TypeError:
-        pass
+    flag_array |= (isnull(arr1) & isnull(arr2))
 
     return bool(flag_array.all())
 
@@ -149,17 +151,8 @@ def array_notnull_equiv(arr1, arr2):
         return False
 
     flag_array = (arr1 == arr2)
-
-    # GH837, GH861
-    # isnull fcn from pandas will throw TypeError when run on numpy structured
-    # array therefore for dims that are np structured arrays we skip testing
-    # for nan
-
-    try:
-        flag_array |= (isnull(arr1) | isnull(arr2))
-
-    except TypeError:
-        pass
+    flag_array |= isnull(arr1)
+    flag_array |= isnull(arr2)
 
     return bool(flag_array.all())
 

xarray/test/test_ops.py

@@ -1,8 +1,8 @@
+from pytest import mark
 import numpy as np
 from numpy import array, nan
-from xarray.core import ops
 from xarray.core.ops import (
-    first, last, count, mean
+    first, last, count, mean, array_notnull_equiv,
 )
 
 from . import TestCase
@@ -74,3 +74,34 @@ def test_count(self):
 
     def test_all_nan_arrays(self):
         assert np.isnan(mean([np.nan, np.nan]))
+
+
+class TestArrayNotNullEquiv():
+    @mark.parametrize("arr1, arr2", [
+        (np.array([1, 2, 3]), np.array([1, 2, 3])),
+        (np.array([1, 2, np.nan]), np.array([1, np.nan, 3])),
+        (np.array([np.nan, 2, np.nan]), np.array([1, np.nan, np.nan])),
+    ])
+    def test_equal(self, arr1, arr2):
+        assert array_notnull_equiv(arr1, arr2)
+
+    def test_some_not_equal(self):
+        a = np.array([1, 2, 4])
+        b = np.array([1, np.nan, 3])
+        assert not array_notnull_equiv(a, b)
+
+    def test_wrong_shape(self):
+        a = np.array([[1, np.nan, np.nan, 4]])
+        b = np.array([[1, 2], [np.nan, 4]])
+        assert not array_notnull_equiv(a, b)
+
+    @mark.parametrize("val1, val2, val3, null", [
+        (1, 2, 3, None),
+        (1., 2., 3., np.nan),
+        (1., 2., 3., None),
+        ('foo', 'bar', 'baz', None),
+    ])
+    def test_types(self, val1, val2, val3, null):
+        arr1 = np.array([val1, null, val3, null])
+        arr2 = np.array([val1, val2, null, null])
+        assert array_notnull_equiv(arr1, arr2)

xarray/test/test_variable.py

@@ -288,10 +288,13 @@ def test_equals_all_dtypes(self):
             v2 = v.copy()
             self.assertTrue(v.equals(v2))
             self.assertTrue(v.identical(v2))
+            self.assertTrue(v.notnull_equals(v2))
             self.assertTrue(v[0].equals(v2[0]))
             self.assertTrue(v[0].identical(v2[0]))
+            self.assertTrue(v[0].notnull_equals(v2[0]))
             self.assertTrue(v[:2].equals(v2[:2]))
             self.assertTrue(v[:2].identical(v2[:2]))
+            self.assertTrue(v[:2].notnull_equals(v2[:2]))
 
     def test_eq_all_dtypes(self):
         # ensure that we don't choke on comparisons for which numpy returns
@@ -570,15 +573,9 @@ def test_notnull_equals(self):
 
         self.assertFalse(v1.notnull_equals(None))
 
-        v3 = Variable(('x'), [np.nan, 1, 3, np.nan])
+        v3 = Variable(('y'), [np.nan, 2, 3, np.nan])
         self.assertFalse(v3.notnull_equals(v1))
 
-        v4 = Variable(('y'), [np.nan, 2, 3, np.nan])
-        self.assertFalse(v4.notnull_equals(v1))
-
-        v5 = Variable(('x', 'y'), [[1, 2], [np.nan, np.nan]])
-        self.assertFalse(v1.notnull_equals(v5))
-
         d = np.array([1, 2, np.nan, np.nan])
         self.assertFalse(v1.notnull_equals(d))
         self.assertFalse(v2.notnull_equals(d))