MNT: copy logic from numpy

tacaswell · tacaswell · commit 239be7b18e31 · 2019-04-01T18:23:44.000-04:00
as suggested by @eric-wieser We are no longer tracking if the bins kwarg was passed, but if it was passed in is an array we should use as the bin edges. Simplify some internal logic.
diff --git a/lib/matplotlib/axes/_axes.py b/lib/matplotlib/axes/_axes.py
@@ -36,6 +36,25 @@
 from matplotlib.axes._base import _AxesBase, _process_plot_format
 from matplotlib.axes._secondary_axes import SecondaryAxis
 
+try:
+    from numpy.lib.histograms import histogram_bin_edges
+except ImportError:
+    def histogram_bin_edges(arr, bins, range=None, weights=None):
+        if isinstance(bins, str):
+            # rather than backporting the internals, just do the full
+            # computation.  If this is too slow for users, they can
+            # update numpy, or pick a manual number of bins
+            return np.histogram(arr, bins, range, weights)[1]
+        else:
+            if bins is None:
+                # hard-code numpy's default
+                bins = 10
+            if range is None:
+                range = np.min(arr), np.max(arr)
+
+            return np.linspace(*range, bins + 1)
+
+
 _log = logging.getLogger(__name__)
 
 
@@ -6611,10 +6630,8 @@ 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)
 
-        # Check whether bins or range are given explicitly.
-        binsgiven = ((np.iterable(bins) and
-                      not isinstance(bins, str)) or
-                     bin_range is not None)
+        # 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:
@@ -6640,22 +6657,24 @@ def hist(self, x, bins=None, range=None, density=None, weights=None,
                     "sets and %d colors were provided" % (nx, len(color)))
                 raise ValueError(error_message)
 
+        hist_kwargs = dict()
+
         # 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 binsgiven and not input_empty:
-            xmin = np.inf
-            xmax = -np.inf
-            for xi in x:
-                if len(xi) > 0:
-                    xmin = min(xmin, np.nanmin(xi))
-                    xmax = max(xmax, np.nanmax(xi))
-            bin_range = (xmin, xmax)
+        if not bins_array_given and not input_empty and len(x) > 1:
+            if weights is not None:
+                _w = np.concatenate(w)
+            else:
+                _w = None
+            bins = histogram_bin_edges(np.concatenate(x),
+                                       bins, bin_range, _w)
+        else:
+            hist_kwargs['range'] = bin_range
+
         density = bool(density) or bool(normed)
         if density and not stacked:
-            hist_kwargs = dict(range=bin_range, density=density)
-        else:
-            hist_kwargs = dict(range=bin_range)
+            hist_kwargs = dict(density=density)
 
         # List to store all the top coordinates of the histograms
         tops = []
