diff options
Diffstat (limited to 'numpy/lib/twodim_base.py')
| -rw-r--r-- | numpy/lib/twodim_base.py | 24 |
1 files changed, 10 insertions, 14 deletions
diff --git a/numpy/lib/twodim_base.py b/numpy/lib/twodim_base.py index 0d206a95f..140d66f1b 100644 --- a/numpy/lib/twodim_base.py +++ b/numpy/lib/twodim_base.py @@ -124,10 +124,6 @@ def vander(x, N=None): X[:,i] = x**(N-i-1) return X - -# David Huard, June 2006 -# Adapted for python from the matlab function hist2d written by Laszlo Balkay, 2006. -# Numpy compatible license. def histogram2d(x,y, bins, normed = False): """Compute the 2D histogram for a dataset (x,y) given the edges or the number of bins. @@ -151,21 +147,23 @@ def histogram2d(x,y, bins, normed = False): xedges = np.linspace(x.min(), x.max(), xnbin+1) else: - xedges = bins[0] + xedges = asarray(bins[0], float) xnbin = len(xedges)-1 if np.isscalar(bins[1]): ynbin = bins[1] yedges = np.linspace(y.min(), y.max(), ynbin+1) else: - yedges = bins[1] + yedges = asarray(bins[1], float) ynbin = len(yedges)-1 elif N == 1: ynbin = xnbin = bins[0] xedges = np.linspace(x.min(), x.max(), xnbin+1) yedges = np.linspace(y.max(), y.min(), ynbin+1) + xedges[-1] *= 1.0001 + yedges[-1] *= 1.0001 else: - yedges = asarray(bins) + yedges = asarray(bins, float) xedges = yedges.copy() ynbin = len(yedges)-1 xnbin = len(xedges)-1 @@ -186,18 +184,16 @@ def histogram2d(x,y, bins, normed = False): # Compute the sample indices in the flattened histogram matrix. if xnbin >= ynbin: xy = ybin*(xnbin) + xbin - shift = xnbin + shift = xnbin + 1 else: xy = xbin*(ynbin) + ybin - shift = ynbin + shift = ynbin + 1 # Compute the number of repetitions in xy and assign it to the flattened # histogram matrix. - edges = np.unique(xy) - edges.sort() - flatcount = np.histogram(xy, edges)[0] - indices = edges - shift - 1 - hist[indices] = flatcount + flatcount = np.bincount(xy) + indices = np.arange(len(flatcount)-shift) + hist[indices] = flatcount[shift:] # Shape into a proper matrix histmat = hist.reshape(xnbin, ynbin) |