Hi.
Sorry for not having been clearer. I'll explain a little bit.
I have 4k x 4k images that I want to analyse. I turn them into numpy arrays so
I have 4k x 4k np.array.
My analysis starts with determining the bias level. To do that, I compute for
each line, and then for each row, an histogram.
So I compute 8000 histograms.
Here is the code I've used sofar:
for i in range(self.data.shape[0]):
#Compute an histogram along the columns
# Gets counts and bounds
self.countsC[i], self.boundsC[i] = np.histogram(data[i],
bins=self.bins)
for i in range(self.data.shape[1]):
# Do the same, along the rows.
self.countsR[i], self.boundsR[i] = np.histogram(data[:,i],
bins=self.bins)
And data.shape is (4000,4000).
If histogram had an axis parameter, I could avoid the loop and I guess it
would be faster.
Éric.
> So it seems that you give your array directly to histogramdd (asking a
> 4000D histogram!). Surely that's not what you are trying to achieve. Can
> you elaborate more on your objectives? Perhaps some code (slow but
> working) to demonstrate the point.
>
> Regards,
> eat
>
Un clavier azerty en vaut deux
----------------------------------------------------------
Éric Depagne e...@depagne.org
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