Promote NumPy arrays to masked arrays during combining.

This ensures that the masking status is properly preserved, otherwise
the regular numpy.concatenate() will just throw it away.

src/biocutils/combine_columns.py

@@ -42,6 +42,9 @@ def combine_columns(*x: Any):
 def _combine_columns_dense_arrays(*x: numpy.ndarray):
     _check_array_dimensions(x, active=1)
     x = [convert_to_dense(y) for y in x]
+    for y in x:
+        if numpy.ma.is_masked(y):
+            return numpy.ma.concatenate(x, axis=1)
     return numpy.concatenate(x, axis=1)
 
 

src/biocutils/combine_rows.py

@@ -42,6 +42,9 @@ def combine_rows(*x: Any):
 def _combine_rows_dense_arrays(*x: numpy.ndarray):
     _check_array_dimensions(x, active=0)
     x = [convert_to_dense(y) for y in x]
+    for y in x:
+        if numpy.ma.is_masked(y):
+            return numpy.ma.concatenate(x)
     return numpy.concatenate(x)
 
 

src/biocutils/combine_sequences.py

@@ -44,6 +44,9 @@ def _combine_sequences_lists(*x: list):
 
 @combine_sequences.register(numpy.ndarray)
 def _combine_sequences_dense_arrays(*x: numpy.ndarray):
+    for y in x:
+        if numpy.ma.is_masked(y):
+            return numpy.ma.concatenate(x, axis=None)
     return numpy.concatenate(x, axis=None)
 
 

tests/test_combine_columns.py

@@ -19,6 +19,18 @@ def test_combine_columns_dense():
     assert z.shape == (20, 15)
 
 
+def test_combine_columns_masked():
+    num_rows = 20
+    x = np.ones(shape=(num_rows, 10))
+    y0 = np.zeros((num_rows, 5))
+    y = np.ma.array(y0, mask=True)
+
+    z = combine_columns(x, y)
+    expected = np.concatenate([x, y0], axis=1) == 0
+    assert (z.mask == expected).all()
+    assert z.shape == (20, 15)
+
+
 def test_combine_columns_sparse():
     num_rows = 20
 

tests/test_combine_rows.py

@@ -19,6 +19,18 @@ def test_combine_rows_dense():
     assert z.shape == (15, 20)
 
 
+def test_combine_rows_masked():
+    num_cols = 20
+    x = np.ones(shape=(10, num_cols))
+    y0 = np.zeros((5, num_cols))
+    y = np.ma.array(y0, mask=True)
+
+    z = combine_rows(x, y)
+    expected = np.concatenate([x, y0]) == 0
+    assert (z.mask == expected).all()
+    assert z.shape == (15, 20)
+
+
 def test_combine_rows_sparse():
     num_cols = 20
 

tests/test_combine_sequences.py

@@ -22,17 +22,26 @@ def test_basic_list():
 def test_basic_dense():
     x = [1, 2, 3]
     y = [0.1, 0.2]
-    xd = np.array([1, 2, 3])
-    yd = np.array([0.1, 0.2], dtype=float)
+    xd = np.array(x)
+    yd = np.array(y)
 
     zcomb = combine_sequences(xd, yd)
-
     z = x + y
     zd = np.array(z)
+    assert (zcomb == zd).all()
+
+
+def test_basic_dense_masked():
+    x = [1, 2, 3]
+    y = [0.1, 0.2]
+    xd = np.array(x)
+    yd = np.ma.array(y, mask=[True]*2) 
 
-    assert all(np.isclose(zcomb, zd)) is True
-    assert isinstance(zcomb, np.ndarray)
-    assert len(zcomb) == len(zd)
+    zcomb = combine_sequences(xd, yd)
+    z = x + y
+    zd = np.ma.array(z, mask=[False]*3 + [True]*2)
+    assert (zcomb == zd).all()
+    assert (zcomb.mask == zd.mask).all()
 
 
 def test_basic_mixed_dense_list():