Python faster way to take logarithm of N-dimensional array
Thanks guys,
the problem was a big amount of entries that I had to go through for processing. I just found another cost function for my optimization.
But, to speed up exactly this code - I think the idea with self made log table exactly for my type of signal can make it work.
My question is trivial, nevertheless I need your help. It's not a problem to take a np.log(x) of an array. But in my case this array could be N-dimensional/Tensor (N=2..1024 and 100 samples in each dimension). 
For N=4 calculation of element-wise np.log(x) takes 10 seconds. I need to take this log(x) in a cost function for optimization, thus, all process of optimization takes roughly 2 hours.
So, the question is how this log(x) can be implemented in faster way for N-dimensional arrays. Is it really possible?  
Thanks in advance.