Python: Plots, Images, Contour and Pseudocolor in Matplotlib

Line Plot

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

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

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

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: