Plotting three columns in the form of a matrix in matplotlib

Question

I have three column data file. First and second columns are x and y axes, respectively. I would like to give color to the plot in the form of a matrix as shown in an example figure (with different shades of grey).

I tried the following code, but is not reading the values of column z properly.

import numpy as np
import matplotlib.pyplot as plt


db=np.loadtxt('text.txt')
x=db[:,0]
y=db[:,1]
z=db[:,2]


colors = ['#5BFF00','#99CCFF','#FF5700']

for i in z:
    if i < 9:
        colors.append(1)
    if 9 < i < 25:
        colors.append(2)
    else:
        colors.append(3)

plt.scatter(x, y, c=colors)
plt.show()

A part of my datafile

1   2   5.02
1   3   18.011
1   4   0.31
1   5   24.14

Answer 1

You're appending to the colors list that, I assume, contains the three different colours that you are wanting on the plot. Instead have a new list to contain the colours to be plotted, e.g.,:

colors = ['#5BFF00','#99CCFF','#FF5700']

c = []  # colours to use in the plot

for i in z:
    if i < 9:
        c.append(colors[0])
    if 9 < i < 25:
        c.append(colors[1])
    else:
        c.append(colors[1])

plt.scatter(x, y, c=c)

This will not have the matrix form that you want, for which you should try a seaborn heatmap (you can do it purely with Matplotlib), but the Seaborn interface is simpler. An example using the Seaborn heatmap (and the digitize function from @mozway's answer) is:

import seaborn as sns
import numpy as np

data = np.loadtxt("test.txt")

nrows = data[:, 0].astype(int).max()
ncols = data[:, 1].astype(int).max()

darray = data[:, 2].reshape((nrows, ncols))

bins = [0, 9, 25, np.inf]
darray = np.digitize(darray, bins)

colours = ['#5BFF00','#99CCFF','#FF5700']

ax = sns.heatmap(darray, cmap=colours, cbar=False)

where I have used a test.txt file containing:

1 1 0.3
1 2 5.02
1 3 18.011
1 4 0.31
1 5 24.14
2 1 0.5
2 2 10.1
2 3 5.4
2 4 28.9
2 5 9.1
3 1 0.5
3 2 32.4
3 3 4.3
3 4 12.7
3 5 0.45
4 1 90.4
4 2 20.5
4 3 7.6
4 4 15.8
4 5 21.4
5 1 0.53
5 2 3.4
5 3 15.9
5 4 21.3
5 5 100.3

which gives: