two_point_functions.py 文件源码

def _bin_results(self, length, results):
        """
        Add hits to the bins corresponding to these results. length_hit_bins
        is flattened, so we need to figure out the offset for this hit by
        factoring the sizes of the other dimensions.
        """
        hit_bin = np.zeros(results.shape[0], dtype='int64')
        multi = 1
        good = np.ones(results.shape[0], dtype='bool')
        for dim in range(len(self.out_labels)):
            for d1 in range(dim):
                multi *= self.bin_edges[d1].size
            if dim == 0 and len(self.out_labels)==1:
                try:
                    digi = np.digitize(results, self.bin_edges[dim])
                except ValueError:
                    # The user probably did something like 
                    # return a * b rather than
                    # return a[0] * b[0], which will only happen
                    # for single field functions.
                    digi = np.digitize(results[0], self.bin_edges[dim])
            else:
                digi = np.digitize(results[:,dim], self.bin_edges[dim])
            too_low = (digi == 0)
            too_high = (digi == self.bin_edges[dim].size)
            self.too_low[dim] += (too_low).sum()
            self.too_high[dim] += (too_high).sum()
            newgood = np.bitwise_and(np.invert(too_low), np.invert(too_high))
            good = np.bitwise_and(good, newgood)
            hit_bin += np.multiply((digi - 1), multi)
        digi_bins = np.arange(self.length_bin_hits[length].size+1)
        hist, digi_bins = np.histogram(hit_bin[good], digi_bins)
        self.length_bin_hits[length] += hist