ENH: Avoid memory peak when creating a MaskedArray with mask=True/False (#6732).

When the `mask` parameter is set to True or False, create directly a `ndarray` of
boolean instead of going inside `np.resize` which was causing of memory peak of
~15 times the size of the mask.

1 files changed, 14 insertions, 8 deletions

diff --git a/numpy/ma/core.py b/numpy/ma/core.py
index b9f7da092..75c7c001e 100644
--- a/numpy/ma/core.py
+++ b/numpy/ma/core.py
@@ -2756,13 +2756,19 @@ class MaskedArray(ndarray):
                     _data._sharedmask = True
         else:
             # Case 2. : With a mask in input.
-            # Read the mask with the current mdtype
-            try:
-                mask = np.array(mask, copy=copy, dtype=mdtype)
-            # Or assume it's a sequence of bool/int
-            except TypeError:
-                mask = np.array([tuple([m] * len(mdtype)) for m in mask],
-                                dtype=mdtype)
+            # If mask is boolean, create an array of True or False
+            if mask is True:
+                mask = np.ones(_data.shape, dtype=mdtype)
+            elif mask is False:
+                mask = np.zeros(_data.shape, dtype=mdtype)
+            else:
+                # Read the mask with the current mdtype
+                try:
+                    mask = np.array(mask, copy=copy, dtype=mdtype)
+                # Or assume it's a sequence of bool/int
+                except TypeError:
+                    mask = np.array([tuple([m] * len(mdtype)) for m in mask],
+                                    dtype=mdtype)
             # Make sure the mask and the data have the same shape
             if mask.shape != _data.shape:
                 (nd, nm) = (_data.size, mask.size)
@@ -4690,7 +4696,7 @@ class MaskedArray(ndarray):
         See Also
         --------
         numpy.ma.dot : equivalent function
-    
+
         """
         return dot(self, b, out=out, strict=strict)