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.to_rgba()
The matplotlib.colors.to_rgba() function is used convert c(color) to an RGBA color. It converts the color name into an array of RGBA encoded colors. It returns an RGBA tuple of four floats from 0-1.
Syntax: matplotlib.colors.to_rgba(c, alpha=None)
Parameters:
- c: It is a matplotlib color or a np.ma.masked color.
- alpha: It is an optional parameter that accepts a scalar. It forces the alpha value if alpha is not None. But if c is “none”(case-sensitive) it maps to (0, 0, 0, 0).
Returns: It returns a tuple of scalars in the form of (r, g, b, a).
Example 1:
Python3
import matplotlib.pyplot as pltfrom matplotlib.collections import LineCollectionfrom matplotlib import colors as mcolorsimport numpy as np # simple example showing many# lines in a single set of axesx_axis = np.arange(100)# Here are different sets of# y to plot vs xy_axis = x_axis[:50, np.newaxis] + x_axis[np.newaxis, :] segments = np.zeros((50, 100, 2))segments[:, :, 1] = y_axissegments[:, :, 0] = x_axis #some supported values to test # masked array :segments = np.ma.masked_where((segments > 50) & (segments < 60), segments) # setting the plot limits.figure, axes = plt.subplots()axes.set_xlim(x_axis.min(), x_axis.max())axes.set_ylim(y_axis.min(), y_axis.max()) # colors is sequence of rgba # tuples and .rgba implementationcolors = [mcolors.to_rgba(c) for c in plt.rcParams['axes.prop_cycle'].by_key()['color']] line_segments = LineCollection(segments, linewidths = (0.5, 1, 1.5, 2), colors = colors, linestyle = 'solid')axes.add_collection(line_segments)axes.set_title(' With masked arrays')plt.show() |
Output:
Example 2:
Python3
import matplotlib.pyplot as pltimport matplotlib.colors as mcolors# helper function to plot a color tabledef colortable(colors, title, colors_sort = True, emptycols = 0): # cell dimensions width = 212 height = 22 swatch_width = 48 margin = 12 topmargin = 40 # Sorting colors based on hue, # saturation, value and name. # implementation of to_rgb if colors_sort is True: to_hsv = sorted((tuple(mcolors.rgb_to_hsv(mcolors.to_rgba(color)[:3])), name) for name, color in colors.items()) names = [name for hsv, name in to_hsv] else: names = list(colors) length_of_names = len(names) length_cols = 4 - emptycols length_rows = length_of_names // length_cols + int(length_of_names % length_cols > 0) width2 = width * 4 + 2 * margin height2 = height * length_rows + margin + topmargin dpi = 72 figure, axes = plt.subplots(figsize =(width2 / dpi, height2 / dpi), dpi = dpi) figure.subplots_adjust(margin / width2, margin / height2, (width2-margin)/width2, (height2-topmargin)/height2) axes.set_xlim(0, width * 4) axes.set_ylim(height * (length_rows-0.5), -height / 2.) axes.yaxis.set_visible(False) axes.xaxis.set_visible(False) axes.set_axis_off() axes.set_title(title, fontsize = 24, loc ="left", pad = 10) for i, name in enumerate(names): rows = i % length_rows cols = i // length_rows y = rows * height swatch_start_x = width * cols swatch_end_x = width * cols + swatch_width text_pos_x = width * cols + swatch_width + 7 axes.text(text_pos_x, y, name, fontsize = 14, horizontalalignment ='left', verticalalignment ='center') axes.hlines(y, swatch_start_x, swatch_end_x, color = colors[name], linewidth = 18) return figurecolortable(mcolors.BASE_COLORS, "Base Colors", colors_sort = False, emptycols = 1)colortable(mcolors.TABLEAU_COLORS, "Tableau Palette", colors_sort = False, emptycols = 2)colortable(mcolors.CSS4_COLORS, "CSS Colors")plt.show() |
Output:
