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  1. PO
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    <p>I wrote a routine that cut your matrix in blocks. The example is very easy to understand. I wrote it in an easy form to display the result (only for checking purpose). If you are interested in it, you could include in the output the number of blocks or anything.</p> <pre><code>import matplotlib.pyplot as plt import numpy as np def cut_array2d(array, shape): arr_shape = np.shape(array) xcut = np.linspace(0,arr_shape[0],shape[0]+1).astype(np.int) ycut = np.linspace(0,arr_shape[1],shape[1]+1).astype(np.int) blocks = []; xextent = []; yextent = [] for i in range(shape[0]): for j in range(shape[1]): blocks.append(array[xcut[i]:xcut[i+1],ycut[j]:ycut[j+1]]) xextent.append([xcut[i],xcut[i+1]]) yextent.append([ycut[j],ycut[j+1]]) return xextent,yextent,blocks nx = 900; ny = 650 X, Y = np.meshgrid(np.linspace(-5,5,nx), np.linspace(-5,5,ny)) arr = X**2+Y**2 x,y,blocks = cut_array2d(arr,(10,10)) n = 0 for x,y,block in zip(x,y,blocks): n += 1 plt.imshow(block,extent=[y[0],y[1],x[0],x[1]], interpolation='nearest',origin='lower', vmin = arr.min(), vmax=arr.max(), cmap=plt.cm.Blues_r) plt.text(0.5*(y[0]+y[1]),0.5*(x[0]+x[1]),str(n), horizontalalignment='center', verticalalignment='center') plt.xlim([0,900]) plt.ylim([0,650]) plt.savefig("blocks.png",dpi=72) plt.show() </code></pre> <p>The output is:</p> <p><img src="https://i.stack.imgur.com/Sd0dj.png" alt="enter image description here"></p> <p>Regards</p> <p>Note: I think you could optimize this routine using np.meshgrid instead a lot of appends with the xextent &amp; yextent.</p>
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    1. POSplitting numpy array into blocks
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    1. COAmazing, exactly what I was looking for. I wish I could rep you more for this incredible answer.
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      1. USDan Doe
    2. CO@DanDoe, I believe that you can start a bounty (2 days after you posted) on your question, and award it to the above answer.
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