Python

Matplotlib colormaps:

In Python, one of the most extensively used libraries is Matplotlib. John Hunter created it in 2002 as a multi-platform library that could run on a variety of operating systems. The cmap() method in the Matplotlib package provides numerous built-in colormaps. The Matplotlib library’s Pyplot component offers a MATLAB-like interface. It also aids in the plotting of lines, 3D charts, bars, contours, and scatter plots, among other things.

Furthermore, colormaps are frequently classified as sequential, divergent, qualitative, or cyclic, depending on their functionality. A reverted form of most standard colormaps may be acquired by including ‘_r’ to the name. Matplotlib offers several developed colormaps that may be accessed through matplotlib.cm.get.cmap. In this article, we’ll talk over the Matplotlib colormaps in Python.

Creating our colormap:

We will modify and develop our colormaps if we require sophisticated colormaps or if the Matplotlib predefined colormaps do not satisfy our requirements. When trying to fit a colormap into a pattern, it’s considerably more difficult to design and create. A visualization incorporated inside a panel or webpage using a pre-existing color theme.

import matplotlib.pyplot as plt

import numpy as np

data = np.random.random([140, 140]) * 20

plt.figure(figsize=(8, 8))

plt.pcolormesh(data)

plt.colorbar()

In this scenario, let’s make some changes with our colormaps. We need to integrate libraries and then use this program to build sample data that would be displayed. The data element is a collection of 140 x 140 integral values ranging from 0 to 20.

We may evaluate it by implementing the subsequent command. After that, we use this method to display the mock dataset with usual colormaps. However, if we did not indicate the colormaps we utilized, the default colormaps will be developed.

Classes of colormaps:

Sequential colormaps, Cyclic colormaps, diverging colormaps, and Qualitative colormaps are some classes of the colormaps available in Matplotlib. We are going to give you representations of every categorized colormap.

import numpy as np

import matplotlib.pyplot as plt

x = np.linspace(-np.pi, np.pi, 60)

y = np.linspace(-np.pi, np.pi, 60)

X,Y = np.meshgrid(x,y)

Z = np.sin(X + Y/6)

fig = plt.figure(figsize = (14,5.5))

fig.subplots_adjust(wspace=0.4)

plt.subplot(1,4,1)

plt.pcolormesh(X, Y, Z, cmap=plt.cm.get_cmap('Greens'))

plt.colorbar()

plt.axis([-2, 2, -2, 2])

plt.title('Sequential')

plt.subplot(1,4,2)

plt.pcolormesh(X, Y, Z, cmap=plt.cm.get_cmap('RdBu'))

plt.colorbar()

plt.axis([-1, 1, -1, 1])

plt.title('Diverging')

plt.subplot(1,4,3)

plt.pcolormesh(X, Y, Z, cmap=plt.cm.get_cmap('RdBu_r'))

plt.colorbar()

plt.axis([-1, 1, -1, 1])

plt.title('Cyclic')

plt.subplot(1,4,4)

plt.pcolormesh(X, Y, Z, cmap=plt.cm.get_cmap('Dark2'))

plt.colorbar()

plt.axis([-3, 3, -3, 3])

plt.title('Qualitative')

Sequential colormaps signify a progressive change in color luminance and intensity, frequently using only one tone; it must be needed to demonstrate data with sequence.

Diverging colormaps: It represents a variation in lightness and perhaps the intensity of two separate hues that reach a consensus at an unsaturated color. This style can be utilized when the mapped data contains a relevant middle value, like topology, or if the data diverges from zero.

Cyclic colormaps: It shows a transition in the intensity of two colors intersecting in the center and starting/ending at an unsaturated tone. These colormaps can be used for elements that revolve around the terminals, like phase angle, wind patterns, or day duration.

Qualitative colormaps: A variety of hues used to depict data that does not contain any sort of order or association.

Colormaps are usually categorized into these groups based on their purposes.

Use of a Matplotlib built-in colormap:

Selecting a suitable colormap is to obtain a good description of our data point in a 3D colormap. A criterion colormap, especially wherein identical stages in data are interpreted as similar stages in color space, is the optimum choice for many purposes.

Researchers discovered that our brain recognizes variations in the brightness parameter as data changes considerably better than color variations.  As a result, the observer will easily understand colormaps with a consistent increase in brightness throughout the color model.

import numpy as np

import matplotlib.pyplot as plt

x, y = np.mgrid[-6:6:0.06, -6:6:0.06]

z = (np.sqrt(x**4 + y**4) + np.sin(x**4 + y**4))

fig, ax = plt.subplots(1,1)

im = ax.imshow(z)

fig.colorbar(im)

ax.yaxis.set_major_locator(plt.NullLocator())

ax.xaxis.set_major_locator(plt.NullLocator())

As a mixin class, objects created by methods like pcolor(), contour(), scatter(), and imshow() subtype ScalarMappable. Mixin classes include common features but are not intended to “stand within their own,” They are not the object’s principal class. This is what allows different objects, such as the Collection provided by poclor() or scatter(), and the Picture produced by imshow(), to share a colormap infrastructure.

Default colormaps in Matplotlib:

Matplotlib includes a vast number of predefined colormaps, as shown here. Different libraries with a large number of additional colormaps are provided in the Matplotlib. Let’s go ahead and try out four different Matplotlib colormaps.

import numpy as np

import matplotlib.pyplot as plt

x, y = np.mgrid[-6:6:0.06, -6:6:0.06]

z = (np.sqrt(x**4 + y**4) + np.sin(x**4 + y**4))

from mpl_toolkits.axes_grid1 import make_axes_locatable

fig, axes = plt.subplots(2,2, figsize=(20,20))

for ax, name in zip(axes.flatten(), cmap_list):

im = ax.imshow(z, aspect='auto', cmap=plt.get_cmap(name))

ax.yaxis.set_major_locator(plt.NullLocator())

ax.xaxis.set_major_locator(plt.NullLocator())

ax.set_aspect('equal', adjustable='box')

divider = make_axes_locatable(ax)

cax = divider.append_axes("right", size="6%", pad=0.2)

plt.colorbar(im, cax=cax)

An inverted variant of most of the preset colormaps may be obtained by inserting ‘_r’ to the label. Matplotlib.cm.get cmap (name), here we pass parameter name to this function which shows the colormap name, can be used to acquire these.

Any specified colormaps are identified by the get_cmap() function. Utilize matplotlib.cm.register_cmap (name, cmap) to register any colormap.

Conclusion:

We covered Matplotlib colormaps in this article. Further, we discussed the use of function cmap () in Python. Because of the human mind’s perception, choosing the proper hue for our colormaps is critical. Color communicates thoughts, sentiments, and feelings. Matplotlib has a variety of colormaps, yet some people get a distinct preference when it comes to colormaps. In Matplotlib, we have the ability to generate and edit our colormaps. We utilized the’RdYlBu_r’colormaps to evaluate the data before modifying the colormaps.

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Kalsoom Bibi

Hello, I am a freelance writer and usually write for Linux and other technology related content