Return early in eq_hist() if all data masked out

datashader/tests/test_transfer_functions.py

@@ -501,7 +501,6 @@ def test_shade_zeros(array):
 @pytest.mark.parametrize('agg', aggs)
 @pytest.mark.parametrize('attr', ['d'])
 @pytest.mark.parametrize('rescale', [False, True])
-#@pytest.mark.parametrize('rescale', [True])
 def test_shade_rescale_discrete_levels(agg, attr, rescale):
     x = getattr(agg, attr)
     cmap = ['pink', 'red']
@@ -518,6 +517,29 @@ def test_shade_rescale_discrete_levels(agg, attr, rescale):
     assert_eq_xr(img, sol)
 
 
+empty_arrays = [
+    np.zeros((2, 2, 2), dtype=np.uint32),
+    np.full((2, 2, 2), np.nan, dtype=np.float64),
+]
+if cupy is not None:
+    empty_arrays += [
+        cupy.zeros((2, 2, 2), dtype=cupy.uint32),
+        cupy.full((2, 2, 2), cupy.nan, dtype=cupy.float64),
+    ]
+@pytest.mark.parametrize('empty_array', empty_arrays)
+def test_shade_all_masked(empty_array):
+    # Issue #1166, return early with array of all nans if all of data is masked out.
+    # Before the fix this test results in:
+    #   IndexError: index -1 is out of bounds for axis 0 with size 0
+    agg = xr.DataArray(
+        data=empty_array,
+        coords=dict(y=[0, 1], x=[0, 1], cat=['a', 'b']),
+    )
+    im = tf.shade(agg, how='eq_hist', cmap=["white", "white"])
+    assert isinstance(im.data, np.ndarray)
+    assert im.shape == (2, 2)
+
+
 coords2 = [np.array([0, 2]), np.array([3, 5])]
 img1 = tf.Image(np.array([[0xff00ffff, 0x00000000],
                           [0x00000000, 0xff00ff7d]], dtype='uint32'),

datashader/transfer_functions/__init__.py

@@ -167,6 +167,10 @@ def eq_hist(data, mask=None, nbins=256*256):
         interp = np.interp
         array_module = np
 
+    if mask is not None and array_module.all(mask):
+        # Issue #1166, return early with array of all nans if all of data is masked out.
+        return array_module.full_like(data, np.nan), 0
+
     data2 = data if mask is None else data[~mask]
 
     # Run more accurate value counting if data is of boolean or integer type
@@ -196,6 +200,7 @@ def eq_hist(data, mask=None, nbins=256*256):
 
 
 
+
 _interpolate_lookup = {'log': lambda d, m: np.log1p(np.where(m, np.nan, d)),
                        'cbrt': lambda d, m: np.where(m, np.nan, d)**(1/3.),
                        'linear': lambda d, m: np.where(m, np.nan, d),