diff options
Diffstat (limited to 'numpy/lib/histograms.py')
| -rw-r--r-- | numpy/lib/histograms.py | 26 |
1 files changed, 21 insertions, 5 deletions
diff --git a/numpy/lib/histograms.py b/numpy/lib/histograms.py index ad7215504..422b356f7 100644 --- a/numpy/lib/histograms.py +++ b/numpy/lib/histograms.py @@ -812,7 +812,8 @@ def histogram(a, bins=10, range=None, normed=None, weights=None, return n, bin_edges -def histogramdd(sample, bins=10, range=None, normed=False, weights=None): +def histogramdd(sample, bins=10, range=None, normed=None, weights=None, + density=None): """ Compute the multidimensional histogram of some data. @@ -845,9 +846,14 @@ def histogramdd(sample, bins=10, range=None, normed=False, weights=None): 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. + density : bool, optional + If False, the default, returns the number of samples in each bin. + If True, returns the probability *density* function at the bin, + ``bin_count / sample_count / bin_volume``. 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``. + An alias for the density argument that behaves identically. To avoid + confusion with the broken normed argument to `histogram`, `density` + should be preferred. weights : (N,) array_like, optional An array of values `w_i` weighing each sample `(x_i, y_i, z_i, ...)`. Weights are normalized to 1 if normed is True. If normed is False, @@ -961,8 +967,18 @@ def histogramdd(sample, bins=10, range=None, normed=False, weights=None): core = D*(slice(1, -1),) hist = hist[core] - # Normalize if normed is True - if normed: + # handle the aliasing normed argument + if normed is None: + if density is None: + density = False + elif density is None: + # an explicit normed argument was passed, alias it to the new name + density = normed + else: + raise TypeError("Cannot specify both 'normed' and 'density'") + + if density: + # calculate the probability density function s = hist.sum() for i in _range(D): shape = np.ones(D, int) |