MNT: minor code clean ups from review comments

tacaswell · tacaswell · commit fbdf09b9cf92 · 2019-04-01T15:41:29.000-04:00
diff --git a/lib/matplotlib/axes/_axes.py b/lib/matplotlib/axes/_axes.py
@@ -39,7 +39,12 @@
 try:
     from numpy.lib.histograms import histogram_bin_edges
 except ImportError:
+    # this function is new in np 1.15
     def histogram_bin_edges(arr, bins, range=None, weights=None):
+        # this in True for 1D arrays, and False for None and str
+        if np.ndim(bins) == 1:
+            return bins
+
         if isinstance(bins, str):
             # rather than backporting the internals, just do the full
             # computation.  If this is too slow for users, they can
@@ -6630,9 +6635,6 @@ def hist(self, x, bins=None, range=None, density=None, weights=None,
         if bin_range is not None:
             bin_range = self.convert_xunits(bin_range)
 
-        # this in True for 1D arrays, and False for None and str
-        bins_array_given = np.ndim(bins) == 1
-
         # We need to do to 'weights' what was done to 'x'
         if weights is not None:
             w = cbook._reshape_2D(weights, 'weights')
@@ -6667,17 +6669,19 @@ def hist(self, x, bins=None, range=None, density=None, weights=None,
             xmax = -np.inf
             for xi in x:
                 if len(xi):
+                    # python's min/max ignore nan,
+                    # np.minnan returns nan for all nan input
                     xmin = min(xmin, np.nanmin(xi))
                     xmax = max(xmax, np.nanmax(xi))
-            # make sure we have at least one non-nan value
-            # before we
-            if np.isfinite([xmin, xmax]).all():
+            # make sure we have non-nan values before we
+            # reset the bin_range
+            if not np.isnan([xmin, xmax]).any():
                 bin_range = (xmin, xmax)
 
         # If bins are not specified either explicitly or via range,
         # we need to figure out the range required for all datasets,
         # and supply that to np.histogram.
-        if not bins_array_given and not input_empty and len(x) > 1:
+        if not input_empty:
             if weights is not None:
                 _w = np.concatenate(w)
             else:
