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.nipy_spectral() Function:
The nipy_spectral() function in pyplot module of matplotlib library is used to set the colormap to “nipy_spectral”.
Syntax:
matplotlib.pyplot.nipy_spectral()Parameters: This method does not accepts any parameters.
Return value: This method does not returns any value.
Below examples illustrate the matplotlib.pyplot.nipy_spectral() function in matplotlib.pyplot:
Example #1:
# Implementation of matplotlib function import matplotlib.pyplot as plt import matplotlib.tri as tri import numpy as np ang = 40rad = 10radm = 0.35radii = np.linspace(radm, 0.95, rad) angles = np.linspace(0, 4 * np.pi, ang) angles = np.repeat(angles[..., np.newaxis], rad, axis = 1) angles[:, 1::2] += np.pi / ang x = (radii * np.cos(angles)).flatten() y = (radii * np.sin(angles)).flatten() z = (np.sin(4 * radii) * np.cos(4 * angles)).flatten() triang = tri.Triangulation(x, y) triang.set_mask(np.hypot(x[triang.triangles].mean(axis = 1), y[triang.triangles].mean(axis = 1)) < radm) tpc = plt.tripcolor(triang, z, shading ='flat') plt.nipy_spectral() plt.title('matplotlib.pyplot.nipy_spectral() 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, 0.05x = np.arange(-3.0, 3.0, dx) y = np.arange(-3.0, 3.0, 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(6), range(6)) % 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, alpha = 0.7, interpolation ='bilinear', extent = extent) plt.nipy_spectral() plt.title('matplotlib.pyplot.nipy_spectral() function Example', fontweight ="bold") plt.show() |
Output:
