Do not use warnings from numpy (#1176)

numpy has removed the warnings symbol from the __init__.py file so
np.warnings is not valid in newer versions of numpy. This patch justs
imports the warnings module and use it directly.

datashader/core.py

@@ -2,6 +2,7 @@
 
 from numbers import Number
 from math import log10
+import warnings
 
 import numpy as np
 import pandas as pd
@@ -424,7 +425,6 @@ def line(self, source, x=None, y=None, agg=None, axis=0, geometry=None,
 
         if (line_width > 0 and ((cudf and isinstance(source, cudf.DataFrame)) or
                                (dask_cudf and isinstance(source, dask_cudf.DataFrame)))):
-            import warnings
             warnings.warn(
                 "Antialiased lines are not supported for CUDA-backed sources, "
                 "so reverting to line_width=0")
@@ -1255,8 +1255,8 @@ def bypixel(source, canvas, glyph, agg, *, antialias=False):
     canvas.validate()
 
     # All-NaN objects (e.g. chunks of arrays with no data) are valid in Datashader
-    with np.warnings.catch_warnings():
-        np.warnings.filterwarnings('ignore', r'All-NaN (slice|axis) encountered')
+    with warnings.catch_warnings():
+        warnings.filterwarnings('ignore', r'All-NaN (slice|axis) encountered')
         return bypixel.pipeline(source, schema, canvas, glyph, agg, antialias=antialias)
 
 

datashader/transfer_functions/__init__.py

@@ -5,6 +5,8 @@
 from collections import OrderedDict
 from io import BytesIO
 
+import warnings
+
 import numpy as np
 import numba as nb
 import toolz as tz
@@ -457,8 +459,8 @@ def _interpolate_alpha(data, total, mask, how, alpha, span, min_alpha, rescale_d
             a_scaled, discrete_levels = a_scaled
 
         # All-NaN objects (e.g. chunks of arrays with no data) are valid in Datashader
-        with np.warnings.catch_warnings():
-            np.warnings.filterwarnings('ignore', r'All-NaN (slice|axis) encountered')
+        with warnings.catch_warnings():
+            warnings.filterwarnings('ignore', r'All-NaN (slice|axis) encountered')
             norm_span = [np.nanmin(a_scaled).item(), np.nanmax(a_scaled).item()]
 
         if rescale_discrete_levels and discrete_levels is not None:  # Only valid for how='eq_hist'