Python中的 Matplotlib.colors.to_rgba()

Matplotlib是Python中用于数组二维图的惊人可视化库。 Matplotlib 是一个基于 NumPy 数组的多平台数据可视化库，旨在与更广泛的 SciPy 堆栈一起使用。

matplotlib.colors.to_rgba()

matplotlib.colors.to_rgba()函数用于将 c(color) 转换为 RGBA 颜色。它将颜色名称转换为 RGBA 编码颜色的数组。它返回一个从 0 到 1 的四个浮点数的 RGBA 元组。

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). 
 

示例 1：

Python3

import matplotlib.pyplot as plt
from matplotlib.collections import LineCollection
from matplotlib import colors as mcolors
import numpy as np
  
# simple example showing many
# lines in a single set of axes
x_axis = np.arange(100)
 
# Here are different sets of
# y to plot vs x
y_axis = x_axis[:50, np.newaxis] + x_axis[np.newaxis, :]
  
segments = np.zeros((50, 100, 2))
segments[:, :, 1] = y_axis
segments[:, :, 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 implementation
colors = [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()

输出：

示例 2：

Python3

import matplotlib.pyplot as plt
import matplotlib.colors as mcolors
 
 
# helper function to plot a color table
def 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 figure
 
colortable(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()

输出：