python - Optimizing NumPy with Cython -


I am currently trying to optimize the code written in pure Python. This code uses very heavy because I am working with NumPy arrays. I can see the simplest of my classes below. Which only multiply two Numpi Array here:

  bendingForces = self.matrixPrefactor * membraneHeight   

My question is, if and how when I C Look at, as this can optimize the code "cython-a" that generates a lot of NumPy-calling, which does not seem very efficient.

  import numpy NP as NP cimport numpy as ctypedef np.float64_t dtype_t ctypedef np.complex128_t cplxtype_t ctypedef py_ssize_t index_t cdef class bendingForcesClass (object): cdef dtype_t bendingRigidity cdef np.ndarray matrixpractor cdef np.ndarray bendingForces def __init __ (self, dtype_t bending stiffness, Anpikdhara [dtype_t, ndim = 2] waveNumbersNorm): self.bendingRigidity = bending stiffness self.matrixPrefactor = -self.bendingRigidity * waveNumbersNorm ** 2 cpdef np. calculate ndarray (self, np.ndarray membraneHeight): cdef np.ndarray bendingForces bendingForces = self.matrixPrefactor * membraneHeight bendingForces return   

the Loops Had to use and it was to be repeated on N arrays tries maybe I can use the compiler to customize it with CID-operation? I tried, which I could compile, but gave it strange results and took it forever. The options have been code function:

  cpdef np.ndarray calculation (self Anpikrendra membrane height): cdef index_t index1, index2 matches #: cdef Py_ssize_t index1, index1 in the limit range (self.matrixSize) in index1 to index2: (self.matrixSize) self.bendingForces [index1, index2] = self.matrixPrefactor.data [index1, index2] * membraneHeight.data [index1, index2] return Self.bendingForces   

Although this code, though I said, is really slow and does not work as expected, what's wrong with me? What would be the best way to optimize this and remove NumPy calling operations?

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For multiplying simple matrix, the NumPy code is already only looping and multiply natively, then It would be difficult to beat in Cython Cython is great for those situations where you are doing it in Python with those people in Sithon. Your code is slower than NumPy, one of the reasons is because every time you do an index lookup in your array, then

  self.bending Force [index1, index2] = self.matrixPrefactor.data [index1, index2] * the membraneHeight.data [index1, index2]   

this is valid more calculated index (like checking range). If you put your indices in unsigned inset, you can use the decorator @ cython.boundscheck (wrong) before the function.

See this for more details on how to speed the statement code

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