Matplotlib.axes.Axes.imshow() in Python

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.
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:
# Implementation of matplotlib functionimport matplotlib.pyplot as pltimport numpy as npfrom matplotlib.colors import LogNorm     dx, dy = 0.015, 0.05y, 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:
# Implementation of matplotlib functionimport matplotlib.pyplot as pltimport numpy as npfrom matplotlib.colors import LogNorm     dx, dy = 0.015, 0.05x = 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)) % 2ax.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()
Output:
Python-matplotlib
Python
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