Python Tutorial

The popular Python library used for data visualisation is called Matplotlib. The Matplotlib add-on for numerical mathematics is called Numpy. Matplotlib can generate excellent graphs, charts, and figures. Matplotlib generates object-oriented API for embedding plots into applications that use GUI toolkits such as "Tkinter", "wxPython", or "Qt". Matplotlib was created by John D.
Hunter is in its first form, and it is made available to users under a BSD-style licence.
It is possible to produce mosaic-like charts or grids that specifically and aesthetically represent data using the Pcolor and Pcolormesh functions.
One of the many functions in Matplotlib that aid in carrying out various tasks is the matplotlib.pyplot.pcolor() function. The Matplotlib library's pyplot module's pcolor() method assists in producing a pseudo-colour plot with an irregular rectangular grid.
The vertices' colours are defined by matrix C. Each face's colour is influenced by the colour of one of the four vertices around it. Which of the four vertices shows first in the x-y grid determines the colour of the face.The vertices' x- and y-coordinates are specified by the formula pcolor(X, Y, C). The dimensions of C and the x-y coordinate grid must coincide. For instance, C must be an m-by-n matrix if X and Y constitute an m-by-n grid.

Examples:

The examples below show how the matplotlib.pyplot.pcolor() function operates.

Example 1: Using the pcolor() method to create images.

We may create 2-D image-style plots using the pcolor() method, as illustrated below.

Code:

# Demonstration of matplotlib function
import matplotlib.pyplot as plt
import numpy as np
from matplotlib.colors import LogNorm
A = np.random.rand(4, 12)
fig, (ax0, ax1) = plt.subplots(2, 1)
d = ax0.pcolor(A)
ax0.set_title('No edge image')
d = ax1.pcolor(A, edgecolors='k', linewidths=5)
ax1.set_title('Thick edges image') 
fig.tight_layout()
plt.show()

Output:

Example 2: Using Log scale with pcolor():

Code:

import matplotlib.pyplot as plt
import numpy as np
from matplotlib.colors import LogNorm
NM = 100
W, Y = np.mgrid[-4:4:complex(0, NM), -4:4:complex(0, NM)]
# The image depicts a low hump with a spike protruding from it.
L1 = np.exp(-(W)**2 - (V)**2)
L2 = np.exp(-(W * 10)**2 - (V * 10)**2)
Z = L1 + 50 * L2
fig, (ax0, ax1) = plt.subplots(2, 1)
d = ax0.pcolor(W, V, L,norm=LogNorm(vmin=L.min(), vmax=L.max()), cmap=plt.cm.autumn)
fig.colorbar(d, ax=ax0)
d = ax1.pcolor(W, V, L, cmap=plt.cm.autumn)
fig.colorbar(d, ax=ax1)
plt.show()

Output:

Example 3: Using pcolor().

Code:

import matplotlib.pyplot as plt
import numpy as np
A = np.random.rand(10, 30)
fig = plt.figure(figsize=(7,2))
plt.pcolor(A, cmap="copper")

Output:

Multiplot Grid:

Combining multiple charts can result in effective visualisations that can be utilised in a variety of projects, including this blog page. If you're interested in learning more about Python's support for multiple charts, you can check out this related tutorial:

Here is an example of a Multiple Pcolor Chart Grid utilising the pyplot.pcolor function and pyplot.subplot() as seen below:

Code:

import matplotlib.pyplot as plt
import numpy as np
fig = plt.figure(figsize=(8,10))
Y = np.random.rand(10, 30)
plt.subplot(4, 1, 1)
plt.pcolor(Y, edgecolors='k', linewidths=2, cmap="inferno",  )
plt.subplot(4, 1, 2)
plt.pcolor(Y, edgecolors='k', linewidths=2, cmap="Blues",  )
plt.subplot(4, 1, 3)
plt.pcolor(Y, edgecolors='k', linewidths=2, cmap="cividis", )
plt.subplot(4, 1, 4)
plt.pcolor(Y, edgecolors='k', linewidths=2, cmap="Spectral",  )

Output:

Edgecolors Parameter:

Edgecolors: This parameter specifies the hue of the lines dividing the grids. By setting the linewidths parameter to 0, as described below, you can also choose not to use the edge option.

Simply put, linewidths are used to specify the width of the lines connecting pcolor grids.

Colormaps can be applied to the entire chart with cmap.

Saving pcolor plots:

Using the code below, you can save pcolor plots just like other Pyplot charts. Just type the complete or relative path as shown below inside the savefig method:

Conclusion:

This Python Visualization Tutorial showed us how to use the pcolor function of PyPlot to create and save Python pcolor charts.

pcolor(C) uses the values in matrix C to produce a pseudocolor plot. In a pseudocolor plot, matrix data is shown as a collection of coloured cells (known as faces). MATLAB® generates this image as a flat surface on the x-y axis.

The surface is defined by an x- and y-coordinate grid corresponding to the corners (or vertices) of the faces.The grid encompasses the area where [m,n] = size and X=1:n and Y=1:m (C).

The vertices' colors are defined by matrix C. Each face's colour is influenced by the colorof one of the four vertices around it. Which of the four vertices shows first in the x-y grid determines the color of the face.

Through a few examples, we proved our understanding of the key pcolor settings and properties.

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