from scipy.interpolate import UnivariateSpline
import matplotlib.pyplot as plt
import pylab as pl
import random
import math
import time
cimport cython
import numpy as np
@cython.boundscheck(False)
@cython.wraparound(False)
def gaussian_2D_distribution(x,y,int mean_x,int mean_y,double sigma):
    #cdef double[:,:]f
    f = np.exp((-0.5*(np.square(x-mean_x) + np.square(y-mean_y))/sigma))/(sigma*np.sqrt(2*math.pi));
    return f

def Centroid2(x,y,f):
    temp_x = np.multiply(x,f)
    temp_y = np.multiply(y,f)
    X_c = (temp_x.sum()/f.sum());
    Y_c = -((temp_y.sum()/f.sum())+1);
    return(X_c,Y_c);

@cython.boundscheck(False)
@cython.wraparound(False)
def benchmark_func(int no_of_frames):
    cdef:
        int i
        double start
        double end
        #int shift[10000][2]
        double Centroid[10000][2]
 #---------------------------------------------Creating Matrices--------------------------------------------------------#
    start = time.time()
    a = np.arange(-50,50,1);
    x = np.matlib.repmat(a,100 ,1);
    y = x.transpose();
    f = np.zeros([no_of_frames,100,100])

    print "Done stage 1 ..."
 #---------------------------------------Creating Gaussian Images------------------------------------------------------#
    shift = np.zeros([no_of_frames,2])
    shift[:,0] = 15
    shift[:,1] = 25

    for i in range(int (no_of_frames)):
        #shift[i][0] = random.randint(-50,50)
        #shift[i][1] = random.randint(-50,50)
        f[i,:,:] = gaussian_2D_distribution(x,y,shift[i][0],shift[i][1],0.1)
        f[i,:,:]= np.flip(f[i,:,:],0)

    print "Done stage 2 ..."
 #---------------------------------------To find the centroid----------------------------------------------------------#

    #start = time.time()
    for i in range(int (no_of_frames)):
        Centroid[i][:] = Centroid2(x,y,f[i])
    end = time.time()

    #print Centroid[2]
    print(end - start),
    print "seconds"
    print "Done stage 3 ..."
    return Centroid