dask: Data.__contains__

cf/data/dask_utils.py

@@ -115,6 +115,32 @@ def allclose(a_blocks, b_blocks, rtol=rtol, atol=atol):
     )
 
 
+def cf_contains(a, value):
+    """Whether or not an array contains a value.
+
+    .. versionadded:: TODODASK
+
+    .. seealso:: `cf.Data.__contains__`
+
+    :Parameters:
+
+        a: `numpy.ndarray`
+            The array.
+
+        value: array_like
+            The value.
+
+    :Returns:
+
+        `numpy.ndarray`
+            A size 1 Boolean array, with the same number of dimensions
+            as *a*, that indicates whether or not *a* contains the
+            value.
+
+    """
+    return np.array(value in a).reshape((1,) * a.ndim)
+
+
 try:
     from scipy.ndimage import convolve1d
 except ImportError:

cf/data/data.py

@@ -104,6 +104,7 @@
 )
 from .dask_utils import (
     _da_ma_allclose,
+    cf_contains,
     cf_dt2rt,
     cf_harden_mask,
     cf_percentile,
@@ -650,44 +651,90 @@ def __contains__(self, value):
 
         x.__contains__(y) <==> y in x
 
-        Returns True if the value is contained anywhere in the data
-        array. The value may be a `cf.Data` object.
+        Returns True if the scalar *value* is contained anywhere in
+        the data. If *value* is not scalar then an exception is
+        raised.
 
         **Performance**
 
-        All delayed operations are exectued, and there is no
-        short-circuit once the first occurrence is found.
+        `__contains__` causes all delayed operations to be computed
+        unless *value* is a `Data` object with incompatible units, in
+        which case `False` is always returned.
 
-        **Examples:**
+        **Examples**
 
-        >>> d = cf.Data([[0.0, 1,  2], [3, 4, 5]], 'm')
+        >>> d = cf.Data([[0, 1, 2], [3, 4, 5]], 'm')
         >>> 4 in d
         True
-        >>> cf.Data(3) in d
+        >>> 4.0 in d
         True
-        >>> cf.Data([2.5], units='2 m') in d
+        >>> cf.Data(5) in d
         True
-        >>> [[2]] in d
+        >>> cf.Data(5, 'm') in d
         True
-        >>> numpy.array([[[2]]]) in d
+        >>> cf.Data(0.005, 'km') in d
         True
-        >>> Data(2, 'seconds') in d
+
+        >>> 99 in d
+        False
+        >>> cf.Data(2, 'seconds') in d
         False
 
-        """
+        >>> [1] in d
+        Traceback (most recent call last):
+            ...
+        TypeError: elementwise comparison failed; must test against a scalar, not [1]
+        >>> [1, 2] in d
+        Traceback (most recent call last):
+            ...
+        TypeError: elementwise comparison failed; must test against a scalar, not [1, 2]
 
-        def contains_chunk(a, value):
-            out = value in a
-            return np.array(out).reshape((1,) * a.ndim)
+        >>> d = cf.Data(["foo", "bar"])
+        >>> 'foo' in d
+        True
+        >>> 'xyz' in d
+        False
+
+        """
+        # Check that value is scalar by seeing if its shape is ()
+        shape = getattr(value, "shape", None)
+        if shape is None:
+            if isinstance(value, str):
+                # Strings are scalars, even though they have a len().
+                shape = ()
+            else:
+                try:
+                    len(value)
+                except TypeError:
+                    # value has no len() so assume that it is a scalar
+                    shape = ()
+                else:
+                    # value has a len() so assume that it is not a scalar
+                    shape = True
+        elif is_dask_collection(value) and math.isnan(value.size):
+            # value is a dask array with unknown size, so calculate
+            # the size. This is acceptable, as we're going to compute
+            # it anyway at the end of this method.
+            value.compute_chunk_sizes()
+            shape = value.shape
+
+        if shape:
+            raise TypeError(
+                "elementwise comparison failed; must test against a scalar, "
+                f"not {value!r}"
+            )
 
-        if isinstance(value, self.__class__):  # TODDASK chek aother type stoo
+        # If value is a scalar Data object then conform its units
+        if isinstance(value, self.__class__):
             self_units = self.Units
             value_units = value.Units
             if value_units.equivalent(self_units):
                 if not value_units.equals(self_units):
                     value = value.copy()
                     value.Units = self_units
             elif value_units:
+                # No need to check the dask array if the value units
+                # are incompatible
                 return False
 
             value = value._get_dask()
@@ -698,10 +745,12 @@ def contains_chunk(a, value):
         dx_ind = out_ind
 
         dx = da.blockwise(
-            partial(contains_chunk, value=value),
+            cf_contains,
             out_ind,
             dx,
             dx_ind,
+            value,
+            (),
             adjust_chunks={i: 1 for i in out_ind},
             dtype=bool,
         )

cf/test/test_Data.py

@@ -8,6 +8,7 @@
 from functools import reduce
 from operator import mul
 
+import dask.array as da
 import numpy as np
 
 SCIPY_AVAILABLE = False
@@ -1094,21 +1095,60 @@ def test_Data_AUXILIARY_MASK(self):
         self.assertEqual(f.shape, fm.shape)
         self.assertTrue((f._auxiliary_mask_return().array == fm).all())
 
-    @unittest.skipIf(TEST_DASKIFIED_ONLY, "TypeError: 'int' is not iterable")
-    def test_Data___contains__(self):
+    def test_Data__contains__(self):
         if self.test_only and inspect.stack()[0][3] not in self.test_only:
             return
 
-        d = cf.Data([[0.0, 1, 2], [3, 4, 5]], units="m")
-        self.assertIn(4, d)
-        self.assertNotIn(40, d)
-        self.assertIn(cf.Data(3), d)
-        self.assertIn(cf.Data([[[[3]]]]), d)
-        value = d[1, 2]
-        value.Units *= 2
-        value.squeeze(0)
-        self.assertIn(value, d)
-        self.assertIn(np.array([[[2]]]), d)
+        d = cf.Data([[0, 1, 2], [3, 4, 5]], units="m", chunks=2)
+
+        for value in (
+            4,
+            4.0,
+            cf.Data(3),
+            cf.Data(0.005, "km"),
+            np.array(2),
+            da.from_array(2),
+        ):
+            self.assertIn(value, d)
+
+        for value in (
+            99,
+            np.array(99),
+            da.from_array(99),
+            cf.Data(99, "km"),
+            cf.Data(2, "seconds"),
+        ):
+            self.assertNotIn(value, d)
+
+        for value in (
+            [1],
+            [[1]],
+            [1, 2],
+            [[1, 2]],
+            np.array([1]),
+            np.array([[1]]),
+            np.array([1, 2]),
+            np.array([[1, 2]]),
+            da.from_array([1]),
+            da.from_array([[1]]),
+            da.from_array([1, 2]),
+            da.from_array([[1, 2]]),
+            cf.Data([1]),
+            cf.Data([[1]]),
+            cf.Data([1, 2]),
+            cf.Data([[1, 2]]),
+            cf.Data([0.005], "km"),
+        ):
+            with self.assertRaises(TypeError):
+                value in d
+
+        # Strings
+        d = cf.Data(["foo", "bar"])
+        self.assertIn("foo", d)
+        self.assertNotIn("xyz", d)
+
+        with self.assertRaises(TypeError):
+            ["foo"] in d
 
     @unittest.skipIf(TEST_DASKIFIED_ONLY, "no attr. 'partition_configuration'")
     def test_Data_asdata(self):