Python: Plots, Images, Contour and Pseudocolor in Matplotlib

In this article, you’ll learn about Line Plot in Matplotlib, Multiple subplots in one figure in Matplotlib, Contouring and pseudocolor in Matplotlib, Images in Matplotlib.

Line Plot in Matplotlib

Here’s how to create a Line plot with text labels using plot().

import matplotlib 
import matplotlib.pyplot as plt 
import numpy as np 

# Data for plotting 
t = np.arange(0.0, 2.0, 0.01) 
s = 1 + np.sin(2*np.pi*t) 

fig, ax = plt.subplots() 
ax.plot(t, s)
 
ax. set (xlabel='time (s)',ylabel=' voltage(mV)' 
              title='About as simple as it gets, folks' ) 
ax.grid() 

fig. savefig("test.png") 
plt. show( )
Code language: PHP (php)

Output:

Multiple subplots in one figure in Matplotlib

Multiple axes (i.e. subplots) are created with the subplot() function:

import numpy as np 
import matplotlib.pyplot as plt 
x1 = np.linspace(0.0, 5.0) 
x2 = np.linspace(0.0, 2.0)
 
y1 = np.cos(2 * np.pi * x1) * np.exp(-x1) 
y2 = np.cos(2 * np.pi * x2) 

fig, (ax1, ax2) = plt.subplots(2, 1) 
fig.suptitle('A tale of 2 subplots') 

ax1.plot(x1, y1, 'o-') 
ax1.set_ylabel('Damped oscillation') 

ax2.plot(x2, y2, '.-') 
ax2.set_xlabel('time (s)') 
ax2.set_ylabel('Undamped') 

plt.show() 
Code language: JavaScript (javascript)

Output:

Images in Matplotlib

Using the imshow() function, Matplotlib can display images (assuming equally spaced horizontal dimensions).

import numpy as np 
import matplotlib.cm as cm 
import matplotlib.pyplot as plt 
import matplotlib.cbook as cbook 
from matplotlib.path import Path 
from matplotlib.patches import PathPatch 
# A sample image 
w, h= 512, 512 

with cbook.get_sample_data('ct.raw.gz') as datafile: 
         s=datafile.read() 

A = np.frombuffer(s, np.uint16).astype(float).reshape((w, h)) 
A /= A.max()
 
fig, ax = plt.subplots() 
extent = (0, 25, 0, 25) 
im = ax.imshow(A, cmap=plt.cm.hot, origin='upper', extent=extent) 

markers=[(15.9,14.5),(16.8,15)]
x, y = zip(*markers) 
ax.plot(x, y,'o')
 
ax.set_title('CT density')
 
plt.show() 
Code language: PHP (php)

Output:

Contouring and pseudocolor in Matplotlib

A colored representation of a two-dimensional array can be done by the pcolormesh() function. Even if there is the uneven spacing between the horizontal dimensions. The contour() is another way for the same data to be represented:

import matplotlib 
import matplotlib.pyplot as plt 
from matplotlib.colors import BoundaryNorm
from matplotlib.ticker import MaxNLocator 
import numpy as np 
np.random.seed(19680801) 
Z = np.random.rand(6, 10) 
x = np.arange(-0.5, 10, 1)   # ten = 11 
y = np.arange(4.5, 11, 1)  # ten = 7
 
fig, ax = plt.subplots() 
ax.pcolormesh(x, y, Z)
plt.show()
Code language: PHP (php)

Output:

Hope you enjoyed this article on Plots, Images, Contour and Pseudocolor in Matplotlib, Happy Programming 🙂