CODEWITHSUNDEEP
pythonmatplotlibscatter-plotcolorbar
paula.n
paula.n

Reputation: 191

Change colour of colorbar in Python Matplotlib

I have a code that gives me a scatter plot of predicted vs actual values as a function of concentration. The data is pulled from an excel csv spreadsheet.

This is the code:

import matplotlib.pyplot as plt
from numpy import loadtxt


dataset = loadtxt("ColorPlot.csv", delimiter=',')
x = dataset[:,0]
y = dataset[:,1]
z = dataset[:,2]

scaled_z = (z - z.min()) / z.ptp()

colors = plt.cm.viridis(scaled_z)
sc=plt.scatter(x, y, c=colors)
plt.clim(0, 100)
plt.colorbar()
plt.xlabel("Actual")
plt.ylabel("Predicted")

plt.show()

And with this I get a nice graph: enter image description here

However if I change the color to something like

colors = plt.cm.plasma(scaled_z)

I get the graph below but the colorbar remains unchanged.

enter image description here

I've tried lots of different things like cmap or edgecolors but I don't know how to change it. And I want to keep the code as simple as it currently is because I want to readily change the third variable of z based on my excel spreadsheet data.

Is there also a way for the scale of the colorbar to pick up what the scale is from the excel spreadsheet without me manually specifying 0-100?

Upvotes: 9

Views: 26389

Answers (3)

Valdi_Bo
Valdi_Bo

Reputation: 31011

To get the right color bar, use the following code:

colormap = plt.cm.get_cmap('plasma') # 'plasma' or 'viridis'
colors = colormap(scaled_z)
sc = plt.scatter(x, y, c=colors)
sm = plt.cm.ScalarMappable(cmap=colormap)
sm.set_clim(vmin=0, vmax=100)
plt.colorbar(sm)
plt.xlabel("Actual")
plt.ylabel("Predicted")
plt.show()

For my random generated data I got the following plot:

enter image description here

Now replace 'plasma' with 'viridis' and check the other variant.

Upvotes: 11

Jody Klymak
Jody Klymak

Reputation: 5912

You should not scale your data, unless you want the colorbar to be incorrect. Once you have the PathCollection from the scatter call, you can call set_cmap and set_clim on that and the colorbar should track. (you could also explicitly associate the colorbar with the PathCollection to avoid ambiguity)

import matplotlib.pyplot as plt
import numpy as np


x = np.random.randn(100)
y = np.random.randn(100)
z = np.random.randn(100)

sc=plt.scatter(x, y, c=z, cmap='viridis')
plt.clim(0, 100)
plt.colorbar(sc)
plt.xlabel("Actual")
plt.ylabel("Predicted")
sc.set_cmap('plasma')
sc.set_clim(-1, 1)

plt.show()

enter image description here

Upvotes: 1

Liris
Liris

Reputation: 1511

Your code return for me an error TypeError: You must first set_array for mappable ...

The following is a simplest syntax that works for me:

import matplotlib.pyplot as plt
import numpy as np

a = np.random.random(100)
b = np.random.random(100)

scaled_z = (a + b)/a

plt.figure()
plt.scatter(a, b, c = scaled_z, cmap = 'plasma') ## you can directly change the colormap here
plt.colorbar()
plt.tight_layout()
plt.show()

Upvotes: 0

Related Questions