使用 matplotlib 创建自己的颜色图并绘制色标 [英] Create own colormap using matplotlib and plot color scale

问题描述

我有以下问题，我想创建自己的颜色图(红-混合-紫-混合-蓝)，映射到 -2 和 +2 之间的值，并想用它来为我的绘图中的点着色.然后该图应该具有右侧的色阶.

到目前为止，这就是我创建地图的方式.但我不确定它是否混合了颜色.

I have the following problem, I want to create my own colormap (red-mix-violet-mix-blue) that maps to values between -2 and +2 and want to use it to color points in my plot. The plot should then have the colorscale to the right.

That is how I create the map so far. But I am not really sure if it mixes the colors.

cmap = matplotlib.colors.ListedColormap(["red","violet","blue"], name='from_list', N=None)
m = cm.ScalarMappable(norm=norm, cmap=cmap)

那样我将颜色映射到值.

That way I map the colors to the values.

colors = itertools.cycle([m.to_rgba(1.22), ..])

然后我绘制它:

Then I plot it:

for i in range(0, len(array_dg)):
  plt.plot(array_dg[i], markers.next(),alpha=alpha[i], c=colors.next())

我的问题是:
1. 我无法绘制色标.
2. 我不完全确定我的色阶是否创造了连续(平滑)的色阶.

My problems are:
1. I can't plot the color scale.
2. I am not completely sure if my scale is creating a continues (smooth) colorscale.

推荐答案

有一个说明性示例 如何在此处创建自定义颜色图.文档字符串对于理解cdict.一旦你掌握了它，你可以像这样使用 cdict:

There is an illustrative example of how to create custom colormaps here. The docstring is essential for understanding the meaning of cdict. Once you get that under your belt, you might use a cdict like this:

cdict = {'red':   ((0.0, 1.0, 1.0), 
                   (0.1, 1.0, 1.0),  # red 
                   (0.4, 1.0, 1.0),  # violet
                   (1.0, 0.0, 0.0)), # blue

         'green': ((0.0, 0.0, 0.0),
                   (1.0, 0.0, 0.0)),

         'blue':  ((0.0, 0.0, 0.0),
                   (0.1, 0.0, 0.0),  # red
                   (0.4, 1.0, 1.0),  # violet
                   (1.0, 1.0, 0.0))  # blue
          }

尽管 cdict 格式为您提供了很大的灵活性，但我发现对于简单的渐变 其格式相当不直观.这是一个实用函数来帮助生成简单的 LinearSegmentedColormaps:

Although the cdict format gives you a lot of flexibility, I find for simple gradients its format is rather unintuitive. Here is a utility function to help generate simple LinearSegmentedColormaps:

import numpy as np
import matplotlib.pyplot as plt
import matplotlib.colors as mcolors


def make_colormap(seq):
    """Return a LinearSegmentedColormap
    seq: a sequence of floats and RGB-tuples. The floats should be increasing
    and in the interval (0,1).
    """
    seq = [(None,) * 3, 0.0] + list(seq) + [1.0, (None,) * 3]
    cdict = {'red': [], 'green': [], 'blue': []}
    for i, item in enumerate(seq):
        if isinstance(item, float):
            r1, g1, b1 = seq[i - 1]
            r2, g2, b2 = seq[i + 1]
            cdict['red'].append([item, r1, r2])
            cdict['green'].append([item, g1, g2])
            cdict['blue'].append([item, b1, b2])
    return mcolors.LinearSegmentedColormap('CustomMap', cdict)


c = mcolors.ColorConverter().to_rgb
rvb = make_colormap(
    [c('red'), c('violet'), 0.33, c('violet'), c('blue'), 0.66, c('blue')])
N = 1000
array_dg = np.random.uniform(0, 10, size=(N, 2))
colors = np.random.uniform(-2, 2, size=(N,))
plt.scatter(array_dg[:, 0], array_dg[:, 1], c=colors, cmap=rvb)
plt.colorbar()
plt.show()

顺便说一下，for循环

for i in range(0, len(array_dg)):
  plt.plot(array_dg[i], markers.next(),alpha=alpha[i], c=colors.next())

为每次调用 plt.plot 绘制一个点.这将适用于少数点，但对于许多点会变得非常慢.plt.plot 只能绘制一种颜色，但 plt.scatter 可以为每个点指定不同的颜色.因此，plt.scatter 是要走的路.

plots one point for every call to plt.plot. This will work for a small number of points, but will become extremely slow for many points. plt.plot can only draw in one color, but plt.scatter can assign a different color to each dot. Thus, plt.scatter is the way to go.

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