Python: Creating a 2D histogram from a numpy matrix

histogram, matplotlib, matrix, numpy, python

Solution

If you have the raw data from the counts, you could use `plt.hexbin` to create the plots for you (IMHO this is better than a square lattice): Adapted from the example of `hexbin`:

import numpy as np
import matplotlib.pyplot as plt

n = 100000
x = np.random.standard_normal(n)
y = 2.0 + 3.0 * x + 4.0 * np.random.standard_normal(n)
plt.hexbin(x,y)

plt.show()

If you already have the Z-values in a matrix as you mention, just use `plt.imshow` or `plt.matshow`:

XB = np.linspace(-1,1,20)
YB = np.linspace(-1,1,20)
X,Y = np.meshgrid(XB,YB)
Z = np.exp(-(X**2+Y**2))
plt.imshow(Z,interpolation='none')

Problem

I'm new to python. I have a numpy matrix, of dimensions 42x42, with values in the range 0-996. I want to create a 2D histogram using this data. I've been looking at tutorials, but they all seem to show how to create 2D histograms from random data and not a numpy matrix. So far, I have imported: ``` import numpy as np import matplotlib.pyplot as plt from matplotlib import colors ``` I'm not sure if these are correct imports, I'm just trying to pick up what I can from tutorials I see. I have the numpy matrix `M` with all of the values in it (as described above). In the end, i want it to look something like this: obviously, my data will be different, so my plot should look different. Can anyone give me a hand? Edit: For my purposes, Hooked's example below, using matshow, is exactly what I'm looking for.

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