diff options
| -rw-r--r-- | doc/release/1.15.0-notes.rst | 6 | ||||
| -rw-r--r-- | numpy/lib/histograms.py | 85 |
2 files changed, 47 insertions, 44 deletions
diff --git a/doc/release/1.15.0-notes.rst b/doc/release/1.15.0-notes.rst index 5cb32d929..fc2af11a2 100644 --- a/doc/release/1.15.0-notes.rst +++ b/doc/release/1.15.0-notes.rst @@ -146,6 +146,12 @@ as usual with `errstate`. Dates, times, and timedeltas can now be histogrammed. The bin edges must be passed explicitly, and are not yet computed automatically. +``histogramdd`` allows explicit ranges to be given in a subset of axes +---------------------------------------------------------------------- +The ``range`` argument of `histogramdd` can now contain ``None`` values to +indicate that the range for the corresponding axis should be computed from the +data. Previously, this could not be specified on a per-axis basis. + ``np.r_`` works with 0d arrays, and ``np.ma.mr_` works with ``np.ma.masked`` ---------------------------------------------------------------------------- 0d arrays passed to the `r_` and `mr_` concatenation helpers are now treated as diff --git a/numpy/lib/histograms.py b/numpy/lib/histograms.py index aa067a431..66e2ccda1 100644 --- a/numpy/lib/histograms.py +++ b/numpy/lib/histograms.py @@ -779,10 +779,18 @@ def histogramdd(sample, bins=10, range=None, normed=False, weights=None): Parameters ---------- - sample : array_like - The data to be histogrammed. It must be an (N,D) array or data - that can be converted to such. The rows of the resulting array - are the coordinates of points in a D dimensional polytope. + sample : (N, D) array, or (D, N) array_like + The data to be histogrammed. + + Note the unusual interpretation of sample when an array_like: + + * When an array, each row is a coordinate in a D-dimensional space - + such as ``histogramgramdd(np.array([p1, p2, p3]))``. + * When an array_like, each element is the list of values for single + coordinate - such as ``histogramgramdd((X, Y, Z))``. + + The first form should be preferred. + bins : sequence or int, optional The bin specification: @@ -791,9 +799,12 @@ 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``. @@ -849,53 +860,39 @@ 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.isscalar(bins[i]): + if np.ndim(bins[i]) == 0: if bins[i] < 1: raise ValueError( - "Element at index %s in `bins` should be a positive " - "integer." % i) - nbin[i] = bins[i] + 2 # +2 for outlier bins - edges[i] = np.linspace(smin[i], smax[i], nbin[i]-1, dtype=edge_dt) - else: + '`bins[{}]` must be positive, when an integer'.format(i)) + 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) - nbin[i] = len(edges[i]) + 1 # +1 for outlier bins - dedges[i] = np.diff(edges[i]) - if np.any(np.asarray(dedges[i]) <= 0): + # not just monotonic, due to the use of mindiff below + if np.any(edges[i][:-1] >= edges[i][1:]): + raise ValueError( + '`bins[{}]` must be strictly increasing, when an array' + .format(i)) + else: raise ValueError( - "Found bin edge of size <= 0. Did you specify `bins` with" - "non-monotonic sequence?") + '`bins[{}]` must be a scalar or 1d array'.format(i)) + + nbin[i] = len(edges[i]) + 1 # includes an outlier on each end + dedges[i] = np.diff(edges[i]) nbin = np.asarray(nbin) @@ -930,7 +927,7 @@ def histogramdd(sample, bins=10, range=None, normed=False, weights=None): # Compute the sample indices in the flattened histogram matrix. ni = nbin.argsort() - xy = np.zeros(N, int) + xy = np.zeros(N, np.intp) for i in np.arange(0, D-1): xy += Ncount[ni[i]] * nbin[ni[i+1:]].prod() xy += Ncount[ni[-1]] |