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:
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()
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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)
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()
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Output:
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