diff options
Diffstat (limited to 'numpy/lib/function_base.py')
| -rw-r--r-- | numpy/lib/function_base.py | 29 |
1 files changed, 18 insertions, 11 deletions
diff --git a/numpy/lib/function_base.py b/numpy/lib/function_base.py index 0944e0ef0..d30361dab 100644 --- a/numpy/lib/function_base.py +++ b/numpy/lib/function_base.py @@ -195,14 +195,12 @@ def histogram(a, bins=10, range=None, normed=False, weights=None): db = array(np.diff(bins), float) if not uniform: warnings.warn(""" - This release of NumPy fixes a normalization bug in histogram - function occuring with non-uniform bin widths. The returned - value is now a density: n / (N * bin width), where n is the - bin count and N the total number of points. + This release of NumPy (1.6) fixes a normalization bug in histogram + function occuring with non-uniform bin widths. The returned value + is now a density: n / (N * bin width), where n is the bin count and + N the total number of points. """) return n/db/n.sum(), bins - - else: return n, bins @@ -228,7 +226,7 @@ def histogramdd(sample, bins=10, range=None, normed=False, weights=None): A sequence of lower and upper bin edges to be used if the edges are not given explicitely in `bins`. Defaults to the minimum and maximum values along each dimension. - normed : boolean, optional + normed : bool, optional If False, returns the number of samples in each bin. If True, returns the bin density, ie, the bin count divided by the bin hypervolume. weights : array_like (N,), optional @@ -247,8 +245,8 @@ def histogramdd(sample, bins=10, range=None, normed=False, weights=None): See Also -------- - histogram: 1D histogram - histogram2d: 2D histogram + histogram: 1-D histogram + histogram2d: 2-D histogram Examples -------- @@ -285,8 +283,13 @@ def histogramdd(sample, bins=10, range=None, normed=False, weights=None): # Select range for each dimension # Used only if number of bins is given. if range is None: - smin = atleast_1d(array(sample.min(0), float)) - smax = atleast_1d(array(sample.max(0), float)) + # Handle empty input. Range can't be determined in that case, use 0-1. + if N == 0: + smin = zeros(D) + smax = ones(D) + else: + smin = atleast_1d(array(sample.min(0), float)) + smax = atleast_1d(array(sample.max(0), float)) else: smin = zeros(D) smax = zeros(D) @@ -309,6 +312,10 @@ def histogramdd(sample, bins=10, range=None, normed=False, weights=None): nbin[i] = len(edges[i])+1 # +1 for outlier bins dedges[i] = diff(edges[i]) + # Handle empty input. + if N == 0: + return np.zeros(D), edges + nbin = asarray(nbin) # Compute the bin number each sample falls into. |