灰度颜色（0-255）到MAtplotlib颜色表示

I cannot solve this (what seems easy) problem when using matplotlib to build some plots.

当使用matplotlib构建一些图时,我无法解决这个(似乎很容易)的问题。

I have gray scale colors represented as integers in range(0-255 - higher numbers meaning darker) which can be simplified in this example df:

我有灰度颜色表示范围内的整数(0-255 - 更高的数字意味着更暗),在本例df中可以简化:

colors = pd.DataFrame({'color1': [15], 'color2': [27], 'color3': [89], 
'color4': [123], 'color5': [220], 'color6': [100], 
'color7': [123], 'color8': [247], 'color9': [255]})

Now by looping I want to change the plots background with those colors as:

现在通过循环我想用这些颜色更改绘图背景:

fig, ax = plt.subplots(3, 3, figsize=(10, 10), sharey='row', sharex='col')
fig.subplots_adjust(hspace=0, wspace=0)

column = 0
for i in range(3):
    for j in range(3):
        color = colors.iloc[0, column]
        print(f'{i}, {j}: {color}')

        # here I used (0.15, 0.16, 0.17) as example. 
        #But I want to have variable "color" converted into readable color by set_facecolor
        ax[i, j].set_facecolor((0.15, 0.16, 0.17))

        column += 1

by using matplotlib documentation I can only do it in those colors formats:

通过使用matplotlib文档,我只能用这些颜色格式:

Matplotlib recognizes the following formats to specify a color:

Matplotlib识别以下格式以指定颜色:

an RGB or RGBA tuple of float values in [0, 1] (e.g., (0.1, 0.2, 0.5) or (0.1, 0.2, 0.5, 0.3));

[0,1]中浮点值的RGB或RGBA元组(例如,(0.1,0.2,0.5)或(0.1,0.2,0.5,0.3));

a hex RGB or RGBA string (e.g., '#0F0F0F' or '#0F0F0F0F');

十六进制RGB或RGBA字符串(例如,'#0F0F0F'或'#0F0F0F0F');

a string representation of a float value in [0, 1] inclusive for gray level (e.g., '0.5'); one of {'b', 'g', 'r', 'c', 'm', 'y', 'k', 'w'};

[0,1]中浮点值的字符串表示,包括灰度级(例如,'0.5'); {'b','g','r','c','m','y','k','w'}之一;

a X11/CSS4 color name;

一个X11 / CSS4颜色名称;

a name from the xkcd color survey; prefixed with 'xkcd:' (e.g., 'xkcd:sky blue');

来自xkcd颜色调查的名称;以'xkcd:'为前缀(例如,'xkcd:sky blue');

one of {'tab:blue', 'tab:orange', 'tab:green', 'tab:red', 'tab:purple', 'tab:brown', 'tab:pink', 'tab:gray', 'tab:olive', 'tab:cyan'} which are the Tableau Colors from the ‘T10’ categorical palette (which is the default color cycle);

其中一个{'tab:blue','tab:orange','tab:green','tab:red','tab:purple','tab:brown','tab:pink','tab:grey' ,'tab:olive','tab:cyan'},它们是'T10'分类调色板中的Tableau颜色(这是默认的颜色循环);

a “CN” color spec, i.e. 'C' followed by a single digit, which is an index into the default property cycle (matplotlib.rcParams['axes.prop_cycle']); the indexing occurs at artist creation time and defaults to black if the cycle does not include color.

“CN”颜色规范,即“C”后跟一个数字,这是默认属性循环的索引(matplotlib.rcParams ['axes.prop_cycle']);索引在艺术家创建时发生,如果循环不包括颜色,则默认为黑色。

Using those SO answers:

使用那些SO答案:

RGB Int to RGB - Python

RGB Int到RGB - Python

Converting a [0-255] integer range to a [0.0-1.0] float range

将[0-255]整数范围转换为[0.0-1.0]浮点范围

I converted rewrote my code to:

我转换后将我的代码改写为:

def rgb_int2tuple(rgbint):
    return (rgbint // 256 // 256 % 256, rgbint // 256 % 256, rgbint % 256)


colors = pd.DataFrame({'color1': [15], 'color2': [27], 'color3': [89], 'color4': [123],
                       'color5': [220], 'color6': [100], 'color7': [123], 'color8': [247], 'color9': [255]})

fig, ax = plt.subplots(3, 3, figsize=(10, 10), sharey='row', sharex='col')
fig.subplots_adjust(hspace=0, wspace=0)

column = 0
for i in range(3):
    for j in range(3):
        color = colors.iloc[0, column]
        color = 255 - color
        Blue, Green, Red = rgb_int2tuple(color)
        print(f'{i}, {j}: {color}\t{Blue}{Green}{Red}')
        ax[i, j].set_facecolor((Blue/255, Green/255, Red/255))

        column += 1

But the result is:

但结果是:

Which takes me to step1, how to let python know that my 0-255 scale is gray.

带我到step1,如何让python知道我的0-255比例是灰色的。

[EDIT]:

I read again the matplotlib.colors documentation and found

我再次阅读matplotlib.colors文档并找到了

a string representation of a float value in [0, 1] inclusive for gray level (e.g., '0.5');

[0,1]中浮点值的字符串表示,包括灰度级(例如,'0.5');

using this:

I rewrote my code to:

我把我的代码重写为:

colors = pd.DataFrame({'color1': [15], 'color2': [27], 'color3': [89], 'color4': [123],
                       'color5': [220], 'color6': [100], 'color7': [123], 'color8': [247], 'color9': [255]})

fig, ax = plt.subplots(3, 3, figsize=(10, 10), sharey='row', sharex='col')
fig.subplots_adjust(hspace=0, wspace=0)

column = 0
for i in range(3):
    for j in range(3):
        color = colors.iloc[0, column]
        color = 255 - color
        color = color / 255
        ax[i, j].set_facecolor(str(color))

        column += 1

And this gave me:

这给了我:

But I doubt, that this is the best solution.

但我怀疑,这是最好的解决方案。

1 个解决方案

#1

You may convert the number of a string which denotes the grey level between 0 and 1.

您可以转换表示0到1之间灰度级的字符串数。

import pandas as pd
import matplotlib.pyplot as plt

colors = pd.DataFrame({'color1': [15], 'color2': [27], 'color3': [89], 'color4': [123],
                       'color5': [220], 'color6': [100], 'color7': [123], 'color8': [247], 'color9': [255]})

fig, axes = plt.subplots(3, 3, figsize=(10, 10), sharey='row', sharex='col')
fig.subplots_adjust(hspace=0, wspace=0)

for ax, c in zip(axes.flat, colors.T[0].values):
    ax.set_facecolor(str(c/255.))

plt.show()

Or you may convert it to a RGB tuple, where each channel has the same value

或者您可以将其转换为RGB元组,其中每个通道具有相同的值

for ax, c in zip(axes.flat, colors.T[0].values):
    ax.set_facecolor((c/255.,c/255.,c/255.))

Finally, you may use a colormap and normalization as

最后,您可以使用色彩映射和规范化

norm = plt.Normalize(0,255)
cmap = plt.get_cmap("gray")
for ax, c in zip(axes.flat, colors.T[0].values):
    ax.set_facecolor(cmap(norm(c)))

You get the same result in all three cases.

在所有三种情况下,您都会得到相同的结果。

#1