Python 中的 Matplotlib.colors.to_rgba()
原文:https://www . geesforgeks . org/matplotlib-colors-to _ rgba-in-python/
Matplotlib 是 Python 中一个惊人的可视化库，用于数组的 2D 图。Matplotlib 是一个多平台数据可视化库，构建在 NumPy 数组上，旨在与更广泛的 SciPy 堆栈一起工作。
matplotlib.colors.to_rgba()
matplotlib.colors.to_rgba()函数用于将 c(颜色)转换为 rgba 颜色。它将颜色名称转换为 RGBA 编码颜色的数组。它从 0-1 返回一个由四个浮点数组成的 RGBA 元组。
语法: matplotlib.colors.to_rgba(c，alpha=None) 参数:
c: 是 matplotlib 颜色或 np.ma.masked 颜色。
alpha: 它是一个接受标量的可选参数。如果 alpha 不是“无”，则强制使用 alpha 值。但是如果 c 是“none”(区分大小写)，它就会映射到(0，0，0，0)。
返回:它以(r，g，b，a)的形式返回标量的元组。
例 1:
Python 3
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, :]
segements = np.zeros((50, 100, 2))
segements[:, :, 1] = y_axis
segements[:, :, 0] = x_axis
#some supported values to test
# masked array :
segements = np.ma.masked_where((segements > 50) & (segements < 60),
segements)
# 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(segements,
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:
例 2:
Python 3
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:
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