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. There are various plots which can be used in Pyplot are Line Plot, Contour, Histogram, Scatter, 3D Plot, etc.
matplotlib.pyplot.bone() Function
The bone() function in pyplot module of matplotlib library is used to set the colormap to “bone”.
Syntax:
matplotlib.pyplot.bone()
Below examples illustrate the matplotlib.pyplot.bone() 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, 0.5 * 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.colorbar(tpc) plt.bone() plt.title('matplotlib.pyplot.bone() function \ Example\n\n', 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(-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)) % 2 plt.imshow(Z1, cmap ="binary_r", interpolation ='nearest', extent = extent, alpha = 1) def neveropen(x, y): return (1 - x / 2 + x**5 + y**6) * np.exp(-(x**2 + y**2)) Z2 = neveropen(X, Y) plt.imshow(Z2, alpha = 0.7, interpolation ='bilinear', extent = extent) plt.set_cmap("gist_rainbow") plt.bone() plt.title('matplotlib.pyplot.bone()\ function Example\n\n', fontweight ="bold") plt.show() |
Output:
