Matplotlib is an amazing visualization library in Python for 2D plots of arrays. Matplotlib is a multi-platform data visualization library built on NumPy arrays and designed to work with the broader SciPy stack.
matplotlib.colors.hsv_to_rgb()
The matplotlib.colors.hsv_to_rgb() function is used to convert hsv values to rgb.
Syntax: matplotlib.colors.hsv_to_rgb(hsv)
Parameters:
- hsv: It is an array-like argument in the form of (…, 3) where all values are assumed to be in the range of 0 to 1.
Returns:
- rgb: It returns an ndarray in the form of (…, 3) that comprises of colors converted to RGB values within the range of 0 to 1.
Example 1:
import matplotlib.pyplot as plt import matplotlib.colors import numpy as np # helper function to find # mid-points def helper(z): k = () for i in range(z.ndim): z = (z[k + np.index_exp[:-1]] + z[k + np.index_exp[1:]]) / 2.0 k += np.index_exp[:] return z # dummy coordinates with rgb # values attached with each s, alpha, x = np.mgrid[0:1:11j, 0:np.pi*2:25j, -0.5:0.5:11j] a = s*np.cos(alpha) b = s*np.sin(alpha) sc, alphac, xc = helper(s), helper(alpha), helper(x) # wobbly torus about [0.7, *, 0] sphere = (sc - 0.7)**2 + (xc + 0.2*np.cos(alphac*2))**2 < 0.2**2 # combining the color components hsv = np.zeros(sphere.shape + (3,)) hsv[..., 0] = alphac / (np.pi*2) hsv[..., 1] = sc hsv[..., 2] = xc + 0.5 #the hsv to rgb function plot_colors = matplotlib.colors.hsv_to_rgb(hsv) # and plot everything figure = plt.figure() axes = figure.gca(projection='3d') axes.voxels(a, b, x, sphere, facecolors=plot_colors, edgecolors=np.clip(2*plot_colors - 0.5, 0, 1), linewidth=0.5) plt.show() |
Output:
Example 2:
from matplotlib.colors import hsv_to_rgb # sample squares for example first_square = np.full((50, 50, 3), fill_value ='698', dtype = np.uint8) / 255.0 second_square = np.full((50, 50, 3), fill_value ='385', dtype = np.uint8) / 255.0 plt.subplot(1, 2, 1) plt.imshow(hsv_to_rgb(first_square)) plt.subplot(1, 2, 2) plt.imshow(hsv_to_rgb(second_square)) plt.show() |
Output:
