Matplotlib.pyplot.clim() in Python
Last Updated :
19 Apr, 2020
Matplotlib is a library in Python and it is numerical – mathematical extension for NumPy library. Pyplot is a state-based interface to a Matplotlib module which provides a MATLAB-like interface. There are various plots which can be used in Pyplot are Line Plot, Contour, Histogram, Scatter, 3D Plot, etc.
Matplotlib.pyplot.clim() Function
The clim() function in pyplot module of matplotlib library is used to set the color limits of the current image.
Syntax: matplotlib.pyplot.clim(vmin=None, vmax=None)
Parameters: This method accepts only two parameters.
- vmin, vmax : These parameters are used for color scaling.
Below examples illustrate the matplotlib.pyplot.clim() function in matplotlib.pyplot:
Example 1:
import matplotlib.pyplot as plt
import numpy as np
from matplotlib.colors import LogNorm
dx, dy = 0.015 , 0.05
y, x = np.mgrid[ slice ( - 4 , 4 + dy, dy),
slice ( - 4 , 4 + dx, dx)]
z = ( 1 - x / 3. + x * * 5 + y * * 5 ) * np.exp( - x * * 2 - y * * 2 )
z = z[: - 1 , : - 1 ]
z_min, z_max = - np. abs (z). max (), np. abs (z). max ()
im = plt.imshow(z, cmap = 'Greens' ,
vmin = z_min,
vmax = z_max,
extent = [x. min (),
x. max (),
y. min (),
y. max ()],
interpolation = 'nearest' ,
origin = 'lower' )
plt.clim(vmin = 0 , vmax = 2 )
plt.title( 'matplotlib.pyplot.clim Example' )
plt.show()
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Output:
Example 2:
import matplotlib.pyplot as plt
import numpy as np
from matplotlib.colors import LogNorm
dx, dy = 0.015 , 0.05
x = np.arange( - 4.0 , 4.0 , dx)
y = np.arange( - 4.0 , 4.0 , dy)
X, Y = np.meshgrid(x, y)
extent = np. min (x), np. max (x), np. min (y), np. max (y)
Z1 = np.add.outer( range ( 8 ), range ( 8 )) % 2
plt.imshow(Z1,
cmap = "binary_r" ,
interpolation = 'nearest' ,
extent = extent, alpha = 1 )
def geeks(x, y):
return ( 1 - x / 2 + x * * 5 + y * * 6 ) * np.exp( - (x * * 2 + y * * 2 ))
Z2 = geeks(X, Y)
plt.imshow(Z2, cmap = "Greens" ,
alpha = 0.7 ,
interpolation = 'bilinear' ,
extent = extent)
plt.clim( 0 , 2 )
plt.title( 'matplotlib.pyplot.clim Example' )
plt.show()
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Output:
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