Correct implementation of rescale_discrete_levels

datashader/tests/test_transfer_functions.py

@@ -86,9 +86,9 @@ def create_dask_array_np(*args, **kwargs):
     'a': np.array([[0, 4289306879, 4287070463],
                    [4284834047, 0, 4282597631],
                    [4280361215, 4278190335, 0]], dtype='u4'),
-    'b': np.array([[0, 4289306879, 4287070463],
-                   [4285228543, 0, 4282597631],
-                   [4280755711, 4278190335, 0]], dtype='u4')}
+    'b': np.array([[0, 4291543295, 4288846335],
+                   [4286609919, 0, 4283518207],
+                   [4281281791, 4278190335, 0]], dtype='u4')}
 eq_hist_sol_rescale_discrete_levels['c'] = eq_hist_sol_rescale_discrete_levels['b']
 
 
@@ -499,14 +499,15 @@ 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']
     img = tf.shade(x, cmap=cmap, how='eq_hist', rescale_discrete_levels=rescale)
     if rescale:
-        sol = np.array([[0xff8d85ff, 0xff716bff, 0xff5450ff],
-                        [0xff3835ff, 0xff8d85ff, 0xff1c1aff],
-                        [0xff0000ff, 0xff8d85ff, 0xff8d85ff]], dtype='uint32')
+        sol = np.array([[0xff8981ff, 0xff6d67ff, 0xff524dff],
+                        [0xff3633ff, 0xff8981ff, 0xff1b19ff],
+                        [0xff0000ff, 0xff8981ff, 0xff8981ff]], dtype='uint32')
     else:
         sol = np.array([[0xffcbc0ff, 0xffa299ff, 0xff7973ff],
                         [0xff514cff, 0xffcbc0ff, 0xff2826ff],

datashader/transfer_functions/__init__.py

@@ -169,20 +169,22 @@ def eq_hist(data, mask=None, nbins=256*256):
 
     # Run more accurate value counting if data is of boolean or integer type
     # and unique value array is smaller than nbins.
-    if data2.dtype == bool or (np.issubdtype(data2.dtype, np.integer) and data2.max() < nbins):
+    if data2.dtype == bool or (np.issubdtype(data2.dtype, np.integer) and data2.ptp() < nbins):
         values, counts = np.unique(data2, return_counts=True)
-        vmin, vmax = values.min(), values.max()
+        vmin, vmax = values[0], values[-1]
         interval = vmax-vmin
         bin_centers = np.arange(vmin, vmax+1)
         hist = np.zeros(interval+1, dtype='uint64')
         hist[values-vmin] = counts
+        discrete_levels = len(values)
     else:
         hist, bin_edges = np.histogram(data2, bins=nbins)
         bin_centers = (bin_edges[:-1] + bin_edges[1:]) / 2
+        discrete_levels = None
     cdf = hist.cumsum()
     cdf = cdf / float(cdf[-1])
     out = interp(data, bin_centers, cdf).reshape(data.shape)
-    return out if mask is None else np.where(mask, np.nan, out), data2.max()
+    return out if mask is None else np.where(mask, np.nan, out), discrete_levels
 
 
 
@@ -273,7 +275,7 @@ def _interpolate(agg, cmap, how, alpha, span, min_alpha, name, rescale_discrete_
             masked_data = np.where(~mask, data, np.nan)
             span = np.nanmin(masked_data), np.nanmax(masked_data)
 
-            if rescale_discrete_levels:  # Only valid for how='eq_hist'
+            if rescale_discrete_levels and discrete_levels is not None:  # Only valid for how='eq_hist'
                 span = _rescale_discrete_levels(discrete_levels, span)
         else:
             if how == 'eq_hist':