Skip to content
Related Articles

Related Articles

matplotlib.pyplot.imshow() in Python
  • Last Updated : 22 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.

matplotlib.pyplot.imshow() Function:

The imshow() function in pyplot module of matplotlib library is used to display data as an image; i.e. on a 2D regular raster.

Syntax: matplotlib.pyplot.imshow(X, cmap=None, norm=None, aspect=None, interpolation=None, alpha=None, vmin=None, vmax=None, origin=None, extent=None, shape=, filternorm=1, filterrad=4.0, imlim=, resample=None, url=None, \*, data=None, \*\*kwargs)

Parameters: This method accept the following parameters that are described below:

  • X: This parameter is the data of the image.
  • cmap : This parameter is a colormap instance or registered colormap name.
  • norm : This parameter is the Normalize instance scales the data values to the canonical colormap range [0, 1] for mapping to colors
  • vmin, vmax : These parameter are optional in nature and they are colorbar range.
  • alpha : This parameter is a intensity of the color.
  • aspect : This parameter is used to controls the aspect ratio of the axes.
  • interpolation : This parameter is the interpolation method which used to display an image.
  • origin : This parameter is used to place the [0, 0] index of the array in the upper left or lower left corner of the axes.
  • resample : This parameter is the method which is used for resembling.
  • extent : This parameter is the bounding box in data coordinates.
  • filternorm : This parameter is used for the antigrain image resize filter.
  • filterrad : This parameter is the filter radius for filters that have a radius parameter.
  • url : This parameter sets the url of the created AxesImage.

Returns: This returns the following:



  • image : This returns the AxesImage

Below examples illustrate the matplotlib.pyplot.imshow() function in matplotlib.pyplot:

Example #1:




# Implementation of matplotlib function
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()
  
c = 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.colorbar(c)
  
plt.title('matplotlib.pyplot.imshow() function Example'
                                     fontweight ="bold")
plt.show()

Output:

Example #2:




# Implementation of matplotlib function
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.title('matplotlib.pyplot.imshow() function Example'
                                     fontweight ="bold")
plt.show()

Output:

Attention geek! Strengthen your foundations with the Python Programming Foundation Course and learn the basics.

To begin with, your interview preparations Enhance your Data Structures concepts with the Python DS Course.

My Personal Notes arrow_drop_up
Recommended Articles
Page :