> > Well I guess, for a slight performance improvement, you could create your > own streamlined histogrammer. > > But, in order to better grasp your situation it would be beneficial to know > how the counts and bounds are used later on. Just wondering if this kind > massive histogramming could be somehow avoided totally. Indeed. Here's what I do. My images come from CCD, and as such, the zero level in the image is not the true zero level, but is the true zero + the background noise of each pixels. By doing the histogram, I plan on detecting what is the most common value per row. Once I have the most common value, I can derive the interval where most of the values are (the index of the largest occurence is easily obtained by sorting the counts, and I take a slice [index_max_count,index_max_count+1] in the second array given by the histogram). Then, I take the mean value of this interval and I assume it is the value of the bias for my row.
I do this procedure both on the row and columns as a sanity check. And I know this procedure will not work if on any row/column there is a lot of signal and very little bias. I'll fix that afterwards ;-) Éric. > > Regards, > eat > Un clavier azerty en vaut deux ---------------------------------------------------------- Éric Depagne e...@depagne.org _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
