Set Colorbar Range in matplotlib

Hello Geeks! In this article, we will try to set the color range using the matplotlib Python module. Matplotlib allows us a large range of Colorbar customization. The Colorbar is simply an instance of plt.Axes. It provides a scale for number-to-color ratio based on the data in a graph. Setting a range limits the colors to a subsection, The Colorbar falsely conveys the information that the lower limit of the data is comparable to its upper limit. With the two different limits, you can control the range and legend of the Colorbar.

Requirement: Matplotlib, NumPy

For installation Matplotlib

pip install matplotlib

For installation Numpy.

pip install numpy

Let’s understand with step-wise implementation:

Step 1:

Import required library and set up some generic data. 

Python3

import numpy as np
import matplotlib.pyplot as plt

# setup some generic data
N = 37
x, y = np.mgrid[:N, :N]
Z = (np.cos(x*0.2) + np.sin(y*0.3))

Step 2:

Mask out the negative and positive values.

Python3

Zpos = np.ma.masked_less(Z, 0)
Zneg = np.ma.masked_greater(Z, 0)

Step 3:

Display data as an image, i.e., on a 2D regular raster.

Python3

# plot just the positive data and save the
# color "mappable" object returned by ax1.imshow
pos = ax1.imshow(Zpos, cmap = 'Blues', interpolation = 'none')

Step 4:

Plot both positive and negative values between +/- 1.2

Python3

fig, (ax1, ax2, ax3) = plt.subplots(figsize=(13, 3),
                                    ncols = 3)

pos_neg_clipped = ax3.imshow(Z,
                             cmap = 'RdBu',
                             vmin = -1.2,
                             vmax = 1.2)
# Add minorticks on the colorbar to make
# it easy to read the values off the colorbar.
color_bar = fig.colorbar(pos_neg_clipped,
                         ax = ax3,
                         extend = 'both')

color_bar.minorticks_on()
plt.show()

Output:

Below is the full implementation:

Python3

import numpy as np
import matplotlib.pyplot as plt

# setup some generic data
N = 37
x, y = np.mgrid[:N, :N]
Z = (np.cos(x*0.2) + np.sin(y*0.3))

# mask out the negative and positive values
Zpositive = np.ma.masked_less(Z, 0)
Znegative = np.ma.masked_greater(Z, 0)

fig, (ax1, ax2, ax3) = plt.subplots(figsize = (13, 3),
                                    ncols = 3)

# plot just the positive data and save the
# color "mappable" object returned by ax1.imshow
pos = ax1.imshow(Zpositive, cmap = 'Blues')

# add the colorbar using the figure's method,
fig.colorbar(pos, ax = ax1)

# repeat everything above for the negative data
neg = ax2.imshow(Znegative, cmap = 'Reds_r')
fig.colorbar(neg, ax = ax2)

# Plot both positive and negative values between +/- 1.2
pos_neg_clipped = ax3.imshow(Z, cmap = 'RdBu',
                             vmin = -1.2,
                             vmax = 1.2)

# Add minorticks on the colorbar to make
# it easy to read the values off the colorbar.
color_bar = fig.colorbar(pos_neg_clipped,
                         ax = ax3,
                         extend = 'both')

color_bar.minorticks_on()
plt.show()

Output: