Matplotlib.axes.Axes.imshow() in Python

Last Updated : 13 Apr, 2020

Matplotlib is a library in Python and it is numerical - mathematical extension for NumPy library. The Axes Class contains most of the figure elements: Axis, Tick, Line2D, Text, Polygon, etc., and sets the coordinate system. And the instances of Axes supports callbacks through a callbacks attribute.

matplotlib.axes.Axes.imshow() Function

The Axes.imshow() function in axes module of matplotlib library is also used to display an image or data on a 2D regular raster.

Syntax:
Axes.imshow(self, 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.axes.Axes.imshow() function in matplotlib.axes: Example-1:

Python3

# 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()
 
fig, ax = plt.subplots()
   
c = ax.imshow(z, cmap ='Greens', vmin = z_min,
              vmax = z_max, extent =[x.min(),
                                     x.max(),
                                     y.min(),
                                     y.max()],
              interpolation ='nearest', 
              origin ='lower')

fig.colorbar(c, ax = ax)
ax.set_title('matplotlib.axes.Axes.imshow() Examples')
plt.show()

Output:

Example-2:

Python3

# 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)
 
fig, ax = plt.subplots()

Z1 = np.add.outer(range(8), range(8)) % 2
ax.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)

ax.imshow(Z2, cmap ="Greens", alpha = 0.7, 
          interpolation ='bilinear',
          extent = extent)

ax.set_title('matplotlib.axes.Axes.imshow() Examples')
plt.show()