In [1]:

    

import numpy as np
import matplotlib.pyplot as plt
import pickle
import sys
sys.path.insert(0,'../code/functions/')


    

In [9]:

    

def meanNorm(volume):
    toStack = []
    for plane in volume:
        normFactor = (65535./float(np.max(plane)))
        toStack.append(normFactor * np.array(plane))
    return np.stack(toStack)


    

In [2]:

    

#get the data to test
data = pickle.load(open('../code/tests/synthDat/realDataRaw_t0.io', 'r'))


    

In [10]:

    

#generate the normalized data
normData = meanNorm(data)


    

In [16]:

    

#display 3 slices, one from top, middle, and bottom
sample = [0, 139, 279]
for idx in sample:
    fig = plt.figure()
    
    subPlt = fig.add_subplot(1,2,1)
    plt.imshow(data[idx], cmap='gray')
    plt.title('z='+ str(idx)+' Pre-Norm')
    
    subPlt = fig.add_subplot(1, 2, 2)
    plt.imshow(normData[idx], cmap='gray')
    plt.title('z='+ str(idx)+' Post-Norm')
    
    plt.show()


    

In [21]:

    

#Generate a line graph of average intensity in each slice, before and after norm
rawValList = [np.average(surface) for surface in data]
normValList = [np.average(surface) for surface in normData]
plt.figure()

plt.title('Average Slice Intensity vs Depth')
plt.scatter(np.arange(len(valList)), rawValList, c='b')
plt.scatter(np.arange(len(valList)), normValList, c='r')

plt.show()


    

In [28]:

    

#Generate a line graph of variance in each slice, before and after norm
rawValList = [np.var(surface) for surface in data]
normValList = [np.var(surface) for surface in normData]
plt.figure()

plt.title('Slice Variance vs Depth')
plt.scatter(np.arange(len(valList)), rawValList, c='b')
plt.scatter(np.arange(len(valList)), normValList, c='r')

plt.show()


    

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