Fix numpy 1.24 warnings (#1218)

* Avoid numpy warning when converting float array containing NaNs to int

* Avoid numpy warning about all-nan slice

* Fix raster fill_type to be integer if dtype is integer

datashader/core.py

@@ -1070,6 +1070,8 @@ def raster(self,
             mask = array==nan_value
             array = np.ma.masked_array(array, mask=mask, fill_value=nan_value)
             fill_value = nan_value
+        elif np.issubdtype(source.dtype, np.integer):
+            fill_value = 0
         else:
             fill_value = np.NaN
 

datashader/reductions.py

@@ -199,7 +199,11 @@ def apply(self, df):
             values = df[self.column].to_numpy()
             nan_values = np.isnan(values)
 
-        index = ((values - self.bin0) / self.binsize).astype(int)
+        index_float = (values - self.bin0) / self.binsize
+        # NaN values are corrected below, so set them to zero to avoid warnings when
+        # converting from float to int.
+        index_float[nan_values] = 0
+        index = index_float.astype(int)
         index[index < 0] = self.bin_under
         index[index >= self.nbins] = self.bin_over
         index[nan_values] = self.nbins

datashader/tests/test_raster.py

@@ -11,6 +11,10 @@
 except ImportError:
     rioxarray = None
 
+from dask.context import config
+
+config.set(scheduler='synchronous')
+
 open_rasterio_available = pytest.mark.skipif(rioxarray is None and rasterio is None, reason="requires rioxarray or rasterio")
 
 from os import path

datashader/transfer_functions/__init__.py

@@ -307,9 +307,9 @@ def _interpolate(agg, cmap, how, alpha, span, min_alpha, name, rescale_discrete_
     if isinstance(cmap, list):
         rspan, gspan, bspan = np.array(list(zip(*map(rgb, cmap))))
         span = np.linspace(span[0], span[1], len(cmap))
-        r = interp(data, span, rspan, left=255).astype(np.uint8)
-        g = interp(data, span, gspan, left=255).astype(np.uint8)
-        b = interp(data, span, bspan, left=255).astype(np.uint8)
+        r = np.nan_to_num(interp(data, span, rspan, left=255), copy=False).astype(np.uint8)
+        g = np.nan_to_num(interp(data, span, gspan, left=255), copy=False).astype(np.uint8)
+        b = np.nan_to_num(interp(data, span, bspan, left=255), copy=False).astype(np.uint8)
         a = np.where(np.isnan(data), 0, alpha).astype(np.uint8)
         rgba = np.dstack([r, g, b, a])
     elif isinstance(cmap, str) or isinstance(cmap, tuple):
@@ -319,7 +319,7 @@ def _interpolate(agg, cmap, how, alpha, span, min_alpha, name, rescale_discrete_
         r = np.full(data.shape, color[0], dtype=np.uint8)
         g = np.full(data.shape, color[1], dtype=np.uint8)
         b = np.full(data.shape, color[2], dtype=np.uint8)
-        a = interp(data, span, aspan, left=0, right=255).astype(np.uint8)
+        a = np.nan_to_num(interp(data, span, aspan, left=0, right=255), copy=False).astype(np.uint8)
         rgba = np.dstack([r, g, b, a])
     elif callable(cmap):
         # Assume callable is matplotlib colormap
@@ -379,7 +379,9 @@ def _colorize(agg, color_key, how, alpha, span, min_alpha, name, color_baseline,
     color_data = data.copy()
 
     # subtract color_baseline if needed
-    baseline = np.nanmin(color_data) if color_baseline is None else color_baseline
+    with warnings.catch_warnings():
+        warnings.filterwarnings('ignore', r'All-NaN slice encountered')
+        baseline = np.nanmin(color_data) if color_baseline is None else color_baseline
     with np.errstate(invalid='ignore'):
         if baseline > 0:
             color_data -= baseline
@@ -444,7 +446,9 @@ def _interpolate_alpha(data, total, mask, how, alpha, span, min_alpha, rescale_d
     # if span is provided, use it, otherwise produce a span based off the
     # min/max of the data
     if span is None:
-        offset = np.nanmin(total)
+        with warnings.catch_warnings():
+            warnings.filterwarnings('ignore', r'All-NaN slice encountered')
+            offset = np.nanmin(total)
         if total.dtype.kind == 'u' and offset == 0:
             mask = mask | (total == 0)
             # If at least one element is not masked, use the minimum as the offset
@@ -494,8 +498,8 @@ def _interpolate_alpha(data, total, mask, how, alpha, span, min_alpha, rescale_d
     norm_span = array_module.hstack(norm_span)
 
     # Interpolate the alpha values
-    a = interp(a_scaled, norm_span, array_module.array([min_alpha, alpha]),
-               left=0, right=255).astype(np.uint8)
+    a_float = interp(a_scaled, norm_span, array_module.array([min_alpha, alpha]), left=0, right=255)
+    a = np.nan_to_num(a_float, copy=False).astype(np.uint8)
     return a