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

问题描述

我遇到以下问题，我想创建自己的颜色图(red-mix-violet-mix-blue)，将其映射到-2到+2之间的值，并希望使用它来绘制绘图中的点. 然后该图应在右侧具有色标.

到目前为止，这就是我创建地图的方式.但是我不太确定它是否会混合颜色.

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)

这样，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), ..])

然后我将其绘制:

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.我不确定我的色标是否正在创建连续的(平滑的)色标.

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

有如何在此处创建自定义颜色图. 该文档字符串对于理解以下内容的含义至关重要 cdict.一旦掌握了这一点，就可以使用cdict这样:

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，但我发现它很简单 渐变其格式相当不直观.这是一个实用程序功能，可为您提供帮助 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()

顺便说一句for-loop

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

每调用一次plt.plot都得一分.这仅适用于少数几个点，但是对于许多点将变得极其缓慢. plt.plot只能绘制一种颜色，但是plt.scatter可以为每个点分配不同的颜色.因此，plt.scatter是必经之路.

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.

这篇关于使用matplotlib创建自己的颜色图并绘制颜色比例的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！