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