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python Programming Glossary: np.apply_along_axisUsing numpy.take for faster fancy indexing http://stackoverflow.com/questions/14491480/using-numpy-take-for-faster-fancy-indexing with flatten and similar methods as np.apply_over_axes or np.apply_along_axis that seem to be promising. I used this code below to generate..
Fitting a 3D array of data to a 1D function with numpy or scipy http://stackoverflow.com/questions/15094619/fitting-a-3d-array-of-data-to-a-1d-function-with-numpy-or-scipy numpy scipy fit share improve this question Using np.apply_along_axis solves your problem. Just do this func1d lambda y args optimize.curve_fit.. f xdata x ydata y args 0 # 0 to get only popt param np.apply_along_axis func1d axis 2 arr data See the example below from scipy import..
How to apply numpy.linalg.norm to each row of a matrix? http://stackoverflow.com/questions/7741878/how-to-apply-numpy-linalg-norm-to-each-row-of-a-matrix similarly of course. It is considerably faster than np.apply_along_axis though perhaps not as convenient In 48 timeit np.apply_along_axis.. though perhaps not as convenient In 48 timeit np.apply_along_axis np.linalg.norm 1 x 1000 loops best of 3 208 us per loop In 49.. computed directly too with similar speedups In 55 timeit np.apply_along_axis lambda row np.linalg.norm row ord 1 1 x 1000 loops best of 3..
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