In [1]:

    

import matplotlib.pyplot as plt
import numpy as np


    

In [42]:

    

train_data = np.load("../data/images/pr_train_simulated_50.0.npy")
test_data = np.load("../data/images/pr_test_simulated_50.0.npy")
all_data = np.concatenate([train_data, test_data])
np.save("../data/images/project_data.npy", all_data)

train_targets = np.load("../data/targets/train_targets.npy")
test_targets = np.load("../data/targets/test_targets.npy")
all_targets = np.concatenate([train_targets, test_targets])
np.save("../data/targets/project_targets.npy", all_targets)


    

In [43]:

    

train_data.shape


    

    
Out[43]:





(5600, 64, 64, 1)

In [33]:

    

train.shape


    

    
Out[33]:





(2400, 128, 128, 1)

In [35]:

    

plt.imshow(train[4].reshape((128, 128)))


    

    
Out[35]:





<matplotlib.image.AxesImage at 0x11c83b898>






    

In [36]:

    

other_maxs = np.zeros(train.shape[0])

for i, e in enumerate(train):
    other_maxs[i] = e.max()


    

In [37]:

    

other_maxs.shape


    

    
Out[37]:





(2400,)

In [17]:

    

maxs = np.amax(train, axis=(1,2, 3))


    

In [18]:

    

maxs.shape


    

    
Out[18]:





(2400,)

In [38]:

    

plt.hist(other_maxs, bins=20)


    

    
Out[38]:





(array([916., 920., 204.,  69.,  27.,  18.,  37.,  21.,  25.,  19.,  15.,
         38.,  41.,  12.,   6.,   6.,   7.,  10.,   4.,   5.]),
 array([  1.759  ,  13.81965,  25.8803 ,  37.94095,  50.0016 ,  62.06225,
         74.1229 ,  86.18355,  98.2442 , 110.30485, 122.3655 , 134.42615,
        146.4868 , 158.54745, 170.6081 , 182.66875, 194.7294 , 206.79005,
        218.8507 , 230.91135, 242.972  ]),
 <a list of 20 Patch objects>)






    

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