import numpy
import math
import matplotlib.pyplot as plt

#creating a three-component vector matrix of length 2^12:
#v = numpy.zeros(shape=(4096))
v = numpy.zeros(4096)
#insert 1 in the 5th and 30th place 
a = numpy.insert(v,4,1)
b = numpy.insert(v,29,1)
c = a + b    


#Calculate the wavelet transform.

#"4 equations in 4 unknowns..." -NMM: 
numer = math.sqrt(3)
denom = 4 * math.sqrt(2)

#Forward transform lo/smooth coefficients:
c0 = (1 + num)/denom
c1 = (3 + num)/denom
c2 = (3 - num)/denom
c3 = (1 - num)/denom

#Forward transform hi/wavelet coefficients: 
w0 = c3
w1 = -c2
w2 = c1
w3 = -c0

#Inverse transform lo/smooth values:
i_c0 = c2 
i_c1 = w2
i_c2 = c0
i_c3 = w0

#Inverse transform hi/wavelet values:
i_w0 = c3
i_w1 = w3
i_w2 = c1
i_w3 = w1

def invTransform(a, n):
    a = []
    if (n >= 4):
        i,j
        half = n >> 1
        halfPls1 = half + 1

        tmp[0] = a[half-1]*i_c0 + a[n-1]*i_c1 + a[0]*i_c2 + a[half]*i_c3
        tmp[1] = a[half-1]*i_w0 + a[n-1]*i_w1 + a[0]*i_w2 + a[half]*i_w3

        j = 2
        counter = 0

        while counter < half-1:
            counter += 1
            a[i] = tmp[i]

#Inverse Daubechies Transform:

        

    
        
plt.show()