On Tue, Sep 10, 2013 at 9:59 AM, Andreas Hilboll <li...@hilboll.de> wrote: > On 10.09.2013 15:52, David Reed wrote: >> Hi there, >> >> Is there a faster way to perform a 2D Histogram from a 2D matrix than >> what I have below: >> >> def spatial_histogram(frame, n_bins): >> shape = frame.shape >> >> h_len = shape[0]/n_bins >> w_len = shape[1]/n_bins >> >> h_ind = range(0, shape[0], h_len) >> w_ind = range(0, shape[1], w_len) >> >> max_val = 255*h_len*w_len >> >> out = np.empty((n_bins, n_bins), np.uint8) >> >> for ii in range(n_bins): >> for jj in range(n_bins): >> out[ii, jj] = np.sum(frame[h_ind[ii]:h_ind[ii]+h_len, >> w_ind[jj]:w_ind[jj]+w_len])/max_val*255 >> >> return out >> >> Should I try implementing this in Cython, or is there something I can do >> in Numpy? >> >> Thanks! > > David, > > are you aware of Scipy's binne_statistic_2d method? > > > http://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.binned_statistic_2d.html
That's modeled after numpy.histogram2d, and just calculates other statistics besides histogram Josef > > At first glance it can do what you're trying to do. > > Andreas. > > _______________________________________________ > NumPy-Discussion mailing list > NumPy-Discussion@scipy.org > http://mail.scipy.org/mailman/listinfo/numpy-discussion _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
