and the colormap gives a RGB values for each entries in matrix and plots it. 

Which in my head would be similar to 

    import numpy as np

    import matplotlib.pyplot as plt

    import matplotlib

    from PIL import Image

    import librosa

    import librosa.display

    from matplotlib import cm

    

    

    fig = plt.figure(figsize=(12,4))

    min = -1.828067

    max = 22.70058

    data =  np.random.uniform(low=min, high=max, size=(474,40))

    librosa.display.specshow(data.T,sr=16000,x_axis='frames',y_axis='mel',hop_length=160,cmap=cm.jet)

    plt.axis('off')

    plt.show()

    raw_input("sadas")

    

    convert = plt.get_cmap(cm.jet)

    norm = matplotlib.colors.Normalize(vmin=0,vmax=1)

    numpy_output_static = convert(norm(data.T))

    plt.imshow(numpy_output_static,cmap = cm.jet, aspect = 'auto')

    plt.show()

    raw_input("asds”)

Problem here is I need to store as a numpy array, and the data has to represent the plot as the array is being used as an input for a NN, in which input matters, and won't be able learn anything if the input doesn't resemble or show the same patterns which the NN has to learn. 

So how do make it store the actual plot, and keep the dimensions of data that used to make the plot.