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 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() 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 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) Z1 = np.add.outer(range(8), range(8)) % 2plt.imshow(Z1, cmap ="binary_r", interpolation ='nearest', extent = extent, alpha = 1) def Lazyroar(x, y): return (1 - x / 2 + x**5 + y**6) * np.exp(-(x**2 + y**2)) Z2 = Lazyroar(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:
