# Why does NumPy's random function seemingly display a pattern in its generated values?

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I was playing around with NumPy and Pillow and came across an interesting result that apparently showcases a pattern in NumPy `random.random()` results.

Here a sample of the full code for generating and saving 100 of these images (with seed 0), the above are the first four images generated by this code.

``import numpy as np from PIL import Image  np.random.seed(0) img_arrays = np.random.random((100, 256, 256, 3)) * 255 for i, img_array in enumerate(img_arrays):     img = Image.fromarray(img_array, "RGB")     img.save("{}.png".format(i)) ``

The above are four different images created using `PIL.Image.fromarray()` on four different NumPy arrays created using `numpy.random.random((256, 256, 3)) * 255` to generate a 256 by 256 grid of RGB values in four different Python instances (the same thing also happens in the same instance).

I noticed that this only happens (in my limited testing) when the width and height of the image is a power of two, I am not sure how to interpret that.

Although it may be hard to see due to browser anti-aliasing (you can download the images and view them in image viewers with no anti-aliasing), there are clear purple-brown columns of pixels every 8th column starting from the 3rd column of every image. To make sure, I tested this on 100 different images and they all followed this pattern.

What is going on here? I am guessing that patterns like this are the reason that people always say to use cryptographically secure random number generators when true randomness is required, but is there a concrete explanation behind why this is happening in particular?

Don't blame Numpy, blame PIL / Pillow. You're generating floats, but PIL expects integers, and its float to int conversion is not doing what we want. Further research is required to determine exactly what PIL is doing...

In the mean time, you can get rid of those lines by explicitly converting your values to unsigned 8 bit integers:

``img_arrays = (np.random.random((100, 256, 256, 3)) * 255).astype(np.uint8) ``

As FHTMitchell notes in the comments, a more efficient form is

``img_arrays = np.random.randint(0, 256, (100, 256, 256, 3), dtype=np.uint8)  ``

Here's typical output from that modified code: The PIL Image.fromarray function has a known bug, as described here. The behaviour you're seeing is probably related to that bug, but I guess it could be an independent one. FWIW, here are some tests and workarounds I did on the bug mentioned on the linked question.