Density plot from plotting multiple arrays

Question

I have a MxN (say, 1000x50) array. I want to plot each 50-point line onto the same plot, and have a heatmap of their density.

Simply doing a plt.pcolor(data) is not what I want, since I don't want to plot the matrix.

This is what I want to plot, but as I said it doesn't provide me with the heatmap I need.

import numpy as np
import matplotlib.pyplot as plt

data = np.random.rand(1000, 50)

fig, ax = plt.subplots()
for i in range(0,1000):
    ax.plot(data[i], '.')

plt.show()

I would like a way of getting this together (I assume it will have something to do with histograms and binning?).

EDIT: simply adding an alpha value to the plot ( ax.plot(data[i], '.r', alpha=0.01)) achieves something similar to what I want. I would like, however, to have a heatmap with different colours.

i am not sure, as to what exactly you want. Does this look like something you were going for (i've used a normal distribution instead of uniform)? — SamProell
– SamProell, Commented Apr 11, 2019 at 11:49

SamProell · Accepted Answer · 2019-04-11 13:15:25Z

1

As you already pointed out in your question, probably one of the simplest approaches involves histograms. A linear approximation of the histogram is probably enough for this application.

You can use np.histogram to calculate bin heights and edges and use scipy.interpolate.interp1d to obtain a function that provides an interpolation of the histogram. We can define a simple helper function to get the approximate density around each value in one column of the data array:

# import scipy.interpolate as interp
def get_density(vals, bins=30, kind="linear"):
    y, bin_edges = np.histogram(vals, bins=bins, density=True)
    x = (bin_edges[1:] + bin_edges[:-1])/2.
    f = interp.interp1d(x, y, kind=kind, fill_value="extrapolate")
    return f(vals)

Then you can use any colormap you want to map the density to a color value. The easiest way to go from here is to use plt.scatter instead of plot, where you can provide a specific color for every data point.

I would do something like this:

fig, ax = plt.subplots()
for i in range(data.shape[1]):
    colors = plt.cm.viridis(get_density(data[:, i]))
    ax.scatter(i*np.ones(data.shape[0]), data[:, i], c=colors, marker='.')

edited Apr 11, 2019 at 13:15

answered Apr 11, 2019 at 13:05

SamProell

3812 silver badges14 bronze badges

Sign up to request clarification or add additional context in comments.

2 Comments

Enzo Over a year ago

I have been using this now for a while, thanks a lot for this answer. However, lately I have been trying to use a custom-made LinearSegmentedColormap as opposed to a ListedColormap like viridis, and I am getting a ValueError: data mapping points must start with x=0 and end with x=1 when I try to compute colors. Is there a way to solve this? Or is it another question I need to open?

SamProell Over a year ago

@Enzo, I am not familiar with custom colormaps.. It might be best to post a new, more specific, question!

Collectives™ on Stack Overflow

Density plot from plotting multiple arrays

1 Answer 1

2 Comments

Your Answer

Hot Network Questions

Collectives™ on Stack Overflow

1 Answer 1

2 Comments

Your Answer

Sign up or log in

Post as a guest

Related