MAINT/ENH: Reuse range computation code from np.histogram in np.histogramdd

This also adds support for inferring the range along a subset of the axes, rather than an all or nothing approach.

1 files changed, 15 insertions, 29 deletions

diff --git a/numpy/lib/histograms.py b/numpy/lib/histograms.py
index a8cd9acb8..c0cfa07c7 100644
--- a/numpy/lib/histograms.py
+++ b/numpy/lib/histograms.py
@@ -791,9 +791,12 @@ def histogramdd(sample, bins=10, range=None, normed=False, weights=None):
         * The number of bins for all dimensions (nx=ny=...=bins).
 
     range : sequence, optional
-        A sequence of lower and upper bin edges to be used if the edges are
-        not given explicitly in `bins`. Defaults to the minimum and maximum
-        values along each dimension.
+        A sequence of length D, each an optional (lower, upper) tuple giving
+        the outer bin edges to be used if the edges are not given explicitly in
+        `bins`.
+        An entry of None in the sequence results in the minimum and maximum
+        values being used for the corresponding dimension.
+        The default, None, is equivalent to passing a tuple of D None values.
     normed : bool, optional
         If False, returns the number of samples in each bin. If True,
         returns the bin density ``bin_count / sample_count / bin_volume``.
@@ -849,43 +852,26 @@ def histogramdd(sample, bins=10, range=None, normed=False, weights=None):
         # bins is an integer
         bins = D*[bins]
 
-    # Select range for each dimension
-    # Used only if number of bins is given.
-    if range is None:
-        # Handle empty input. Range can't be determined in that case, use 0-1.
-        if N == 0:
-            smin = np.zeros(D)
-            smax = np.ones(D)
-        else:
-            smin = np.atleast_1d(np.array(sample.min(0), float))
-            smax = np.atleast_1d(np.array(sample.max(0), float))
-    else:
-        if not np.all(np.isfinite(range)):
-            raise ValueError(
-                'range parameter must be finite.')
-        smin = np.zeros(D)
-        smax = np.zeros(D)
-        for i in np.arange(D):
-            smin[i], smax[i] = range[i]
-
-    # Make sure the bins have a finite width.
-    for i in np.arange(len(smin)):
-        if smin[i] == smax[i]:
-            smin[i] = smin[i] - .5
-            smax[i] = smax[i] + .5
-
     # avoid rounding issues for comparisons when dealing with inexact types
     if np.issubdtype(sample.dtype, np.inexact):
         edge_dt = sample.dtype
     else:
         edge_dt = float
+
+    # normalize the range argument
+    if range is None:
+        range = (None,) * D
+    elif len(range) != D:
+        raise ValueError('range argument must have one entry per dimension')
+
     # Create edge arrays
     for i in np.arange(D):
         if np.ndim(bins[i]) == 0:
             if bins[i] < 1:
                 raise ValueError(
                     '`bins[{}]` must be positive, when an integer'.format(i))
-            edges[i] = np.linspace(smin[i], smax[i], bins[i] + 1, dtype=edge_dt)
+            smin, smax = _get_outer_edges(sample[:,i], range[i])
+            edges[i] = np.linspace(smin, smax, bins[i] + 1, dtype=edge_dt)
         elif np.ndim(bins[i]) == 1:
             edges[i] = np.asarray(bins[i], edge_dt)
             # not just monotonic, due to the use of mindiff below