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Here is my Cython code used for adaptive binning. The calcAdaptiveBinnedRateMap function is called from another Python script. The script is compiled using Cython but the speed I am expecting is still not great. How can I improve the speed of execution?

import numpy as np

import scipy.ndimage.morphology as ndimmor



#define sampline rate and alpha

cdef float samplingRate = 30.0

cdef double alpha = 0.0001 #skaggs and sachin use this value while Jim uses 0.001



"""

runs the iteration for adaptive binning till the criteria mentioned in Skaggs et al 1996 is met

INPUT: spike map, occupancy map, alpha, Number of occupancy (Nocc), Euclidean distance transform (dists)

OUTPUT: Nspikes (number of spikes), Nocc (occupancy count)

"""

def mexAdaptwhile(spikeMap, occMap, alpha, Nocc, dists):

    #initialize the variable

    cdef int Nspikes2 = 1

    cdef double rsq = 0

    cdef int EnoughPoints = 0

    cdef int Nspikes = 0



    #find the row and column count for occupancy map

    cdef int rowLen = int(np.shape(occMap)[0])

    cdef int colLen = int(np.shape(occMap)[1])



    #while the radius is less than 200 and enough points are not covered

    while rsq<200.00 and EnoughPoints==0:

        r = np.sqrt(rsq)

        #need to set these Nocc and Nspikes to zero, otherwise the two for loops above add the same spikes over again.

        Nspikes = 0

        Nocc = 0

        for i in range(rowLen):

            for j in range(colLen):

                #if the distance is less than radius

                if dists[i,j]<=r:

                    #add Nspikes

                    Nspikes = Nspikes + spikeMap[i,j]

                    #add occupancy

                    Nocc = Nocc +occMap[i,j]         

        #if number spikes is greater than 0, then set nspikes2 = number of spikes

        if Nspikes > 0:

            Nspikes2 = Nspikes

        #check for the condition from skaggs et al 1996

        if (alpha*alpha*Nocc*Nocc*rsq*Nspikes2 > 1):

            EnoughPoints = 1 #set the flag to end the loop

        #keep increasing the radius

        rsq = rsq + 1

        #output occupancy = occupancy

        Noccout = Nocc

    return Nspikes, Noccout





"""calculate adaptive binned rate map    

INPUT: spikemap, occupancy map (both of them just binned for 2cm/4cm no other operation applied on them)

NOTE: occupancy is still in terms of number of frames

OUTPUT: adaptive binned rate Map which is color adjusted

"""

def calcAdaptiveBinnedRateMap(spikeMap, occMap):

    #set unoccupied occupancy and spike corresponding to unoccupied position = 0

    spikeMap[0,0] = 0

    occMap[0,0] = 0



    #find the row, col index of minimum occupied and maximum occupied pixel

    row, col = np.where(occMap)

    minrow = np.min(row)

    maxrow = np.max(row)

    mincol = np.min(col)

    maxcol = np.max(col)



    #select the best fitting rectangle according to occupied area

    occMap = occMap[minrow:maxrow+1,mincol:maxcol+1]

    spikeMap = spikeMap[minrow:maxrow+1,mincol:maxcol+1]



    #matrix of zeros same size as of occupancy map

    z = np.zeros(np.shape(occMap))

    #variable to hold adaptive binned rate map value

    abrMap = np.copy(z)

    #variale to hold adaptive binned occupancy map

    abrOcc = np.copy(z)



    #check to endure if number of spikes is greater than 1

    if np.max(np.max(spikeMap))>0:

        #iterate over the values

        for x in range(int(np.shape(occMap)[1])):

            for y in range(int(np.shape(occMap)[0])):

                if occMap[y,x] > 0:

                    #pretend there's atleas 1 spike, and 1 occ.needed to avoid 0 threshold. 

                    Nspikes2 = 1

                    Nocc = occMap[y,x]

                    d = np.copy(z)

                    d[y,x] = 1

                    #computes the Euclidean distance transform of the input matrix.

                    #For each pixel in BW, the distance transform assigns a number that is the 

                    #distance between that pixel and the nearest nonzero pixel of BW. 

                    dists = ndimmor.distance_transform_edt(d==0)

                    # function to keep on iterating while the condition mentioned in Skaggs et al 1996 is met

                    Nspikes, Nocc = mexAdaptwhile(spikeMap, occMap, alpha, Nocc, dists)

                    if Nocc < 12: #occupancy cutoff = 0.4seconds 

                        #if less than 0.4 seconds set it to 0

                        abrMap[y,x] = 0

                        abrOcc[y,x] = 0

                    else:

                        #else equal to number of spikes/occupancy map

                        abrMap[y,x] = samplingRate*float(Nspikes)/float(Nocc)

                        #adaptive binned ocuupancy map = nocc

                        abrOcc[y,x] = Nocc



    #find the maximum value of adaptive binned rate map

    cmax = np.max(np.max(abrMap))

    if cmax > 0:

        #minimum = maximum value found above/60

        cmin = -(cmax/60.0);

    else:

        cmin = -1;

    #set adaptive binned rate map = cmin wherever adaptive binned occupancy map = 0 

    abrMap[abrOcc==0] = cmin



    #return the adaptive binned rate map

    return abrMap

Make sure to declare all variables with Cython (otherwise the lines with such variables basically run with Python-speed). Especially important for the "for"-loop variables (i.e. i, j, x, y).

One can also use cython -a mycode.pyx to create an annotated HTML page that shows, which lines are running with C or Python speed, respectively.