Python: Plots, Images, Contour and Pseudocolor in Matplotlib

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( )

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() 

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() 

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()