Allow data_array types to be multi-dimensional numpy arrays.

This is needed for the heatmap `z` property

Author: jonmmease

_plotly_utils/basevalidators.py

@@ -112,17 +112,10 @@ def is_homogeneous_array(v):
     """
     Return whether a value is considered to be a homogeneous array
     """
-    return ((np and isinstance(v, np.ndarray) and v.ndim == 1) or
+    return ((np and isinstance(v, np.ndarray)) or
             (pd and isinstance(v, pd.Series)))
 
 
-def is_homogeneous_ndarray(v):
-    """
-    Return whether a value is considered to be a homogeneous array
-    """
-    return np and isinstance(v, np.ndarray)
-
-
 def is_simple_array(v):
     """
     Return whether a value is considered to be an simple array
@@ -292,7 +285,7 @@ def __init__(self, plotly_name, parent_name, **kwargs):
     def description(self):
         return ("""\
     The '{plotly_name}' property is an array that may be specified as a tuple,
-    list, one-dimensional numpy array, or pandas Series"""
+    list, numpy array, or pandas Series"""
                 .format(plotly_name=self.plotly_name))
 
     def validate_coerce(self, v):
@@ -995,9 +988,7 @@ def validate_coerce(self, v, should_raise=True):
         if v is None:
             # Pass None through
             pass
-        elif self.array_ok and (
-                is_homogeneous_array(v) or
-                is_homogeneous_ndarray(v)):
+        elif self.array_ok and is_homogeneous_array(v):
 
             v_array = copy_to_readonly_numpy_array(v)
             if (self.numbers_allowed() and

_plotly_utils/tests/validators/test_dataarray_validator.py

@@ -23,7 +23,7 @@ def test_validator_acceptance_simple(val, validator: DataArrayValidator):
 
 
 @pytest.mark.parametrize('val', [
-    np.array([2, 3, 4]), pd.Series(['a', 'b', 'c'])
+    np.array([2, 3, 4]), pd.Series(['a', 'b', 'c']), np.array([[1, 2, 3], [4, 5, 6]])
 ])
 def test_validator_acceptance_homogeneous(val, validator: DataArrayValidator):
     coerce_val = validator.validate_coerce(val)
@@ -33,7 +33,7 @@ def test_validator_acceptance_homogeneous(val, validator: DataArrayValidator):
 
 # ### Rejection ###
 @pytest.mark.parametrize('val', [
-    'Hello', 23, set(), {}, np.array([[1, 2, 3], [4, 5, 6]])
+    'Hello', 23, set(), {}
 ])
 def test_rejection(val, validator: DataArrayValidator):
     with pytest.raises(ValueError) as validation_failure: