Python 中的散点图和颜色映射
声明：本页面是StackOverFlow热门问题的中英对照翻译，遵循CC BY-SA 4.0协议，如果您需要使用它，必须同样遵循CC BY-SA许可，注明原文地址和作者信息，同时你必须将它归于原作者（不是我）：StackOverFlow
原文地址: http://stackoverflow.com/questions/17682216/
Warning: these are provided under cc-by-sa 4.0 license. You are free to use/share it, But you must attribute it to the original authors (not me):
StackOverFlow
Scatter plot and Color mapping in Python
提问by Vincent
I have a range of points x and y stored in numpy arrays. Those represent x(t) and y(t) where t=0...T-1
我有一系列点 x 和 y 存储在 numpy 数组中。那些代表 x(t) 和 y(t) 其中 t=0...T-1
I am plotting a scatter plot using
我正在绘制散点图
import matplotlib.pyplot as plt
plt.scatter(x,y)
plt.show()
I would like to have a colormap representing the time (therefore coloring the points depending on the index in the numpy arrays)
我想要一个表示时间的颜色图（因此根据 numpy 数组中的索引为点着色）
What is the easiest way to do so?
最简单的方法是什么？
采纳答案by wflynny
Here is an example
这是一个例子
import numpy as np
import matplotlib.pyplot as plt
x = np.random.rand(100)
y = np.random.rand(100)
t = np.arange(100)
plt.scatter(x, y, c=t)
plt.show()
Here you are setting the color based on the index, t, which is just an array of [1, 2, ..., 100].
在这里，您将根据索引设置颜色，t，它只是[1, 2, ..., 100].
Perhaps an easier-to-understand example is the slightly simpler
也许一个更容易理解的例子是稍微简单一点的
import numpy as np
import matplotlib.pyplot as plt
x = np.arange(100)
y = x
t = x
plt.scatter(x, y, c=t)
plt.show()
Note that the array you pass as cdoesn't need to have any particular order or type, i.e. it doesn't need to be sorted or integers as in these examples. The plotting routine will scale the colormap such that the minimum/maximum values in ccorrespond to the bottom/top of the colormap.
请注意，您传递的数组c不需要具有任何特定的顺序或类型，即不需要像这些示例中那样进行排序或整数。绘图例程将缩放颜色图，使得最小值/最大值c对应于颜色图的底部/顶部。
Colormaps
颜色图
You can change the colormap by adding
您可以通过添加更改颜色图
import matplotlib.cm as cm
plt.scatter(x, y, c=t, cmap=cm.cmap_name)
Importing matplotlib.cmis optional as you can call colormaps as cmap="cmap_name"just as well. There is a reference pageof colormaps showing what each looks like. Also know that you can reverse a colormap by simply calling it as cmap_name_r. So either
导入matplotlib.cm是可选的，因为您也可以调用颜色图cmap="cmap_name"。有一个颜色图的参考页面，显示了每个颜色图的样子。还知道您可以通过简单地将颜色图调用为cmap_name_r. 所以要么
plt.scatter(x, y, c=t, cmap=cm.cmap_name_r)
# or
plt.scatter(x, y, c=t, cmap="cmap_name_r")
will work. Examples are "jet_r"or cm.plasma_r. Here's an example with the new 1.5 colormap viridis:
将工作。例子是"jet_r"或cm.plasma_r。这是新的 1.5 颜色图 viridis 的示例：
import numpy as np
import matplotlib.pyplot as plt
x = np.arange(100)
y = x
t = x
fig, (ax1, ax2) = plt.subplots(1, 2)
ax1.scatter(x, y, c=t, cmap='viridis')
ax2.scatter(x, y, c=t, cmap='viridis_r')
plt.show()
Colorbars
颜色条
You can add a colorbar by using
您可以使用添加颜色条
plt.scatter(x, y, c=t, cmap='viridis')
plt.colorbar()
plt.show()
Note that if you are using figures and subplots explicitly (e.g. fig, ax = plt.subplots()or ax = fig.add_subplot(111)), adding a colorbar can be a bit more involved. Good examples can be found here for a single subplot colorbarand here for 2 subplots 1 colorbar.
请注意，如果您明确使用图形和子图（例如fig, ax = plt.subplots()或ax = fig.add_subplot(111)），则添加颜色条可能会更复杂一些。可以在此处找到单个子图 colorbar和2 个子图 1 colorbar 的好示例。
回答by Nathan
回答by Audelia
Subplot Colorbar
子图颜色条
For subplots with scatter, you can trick a colorbar onto your axes by building the "mappable" with the help of a secondary figure and then adding it to your original plot.
对于散点图，您可以在辅助图形的帮助下构建“可映射”，然后将其添加到原始图中，从而将颜色条欺骗到轴上。
As a continuation of the above example:
作为上面例子的延续：
import numpy as np
import matplotlib.pyplot as plt
x = np.arange(10)
y = x
t = x
fig, (ax1, ax2) = plt.subplots(1, 2)
ax1.scatter(x, y, c=t, cmap='viridis')
ax2.scatter(x, y, c=t, cmap='viridis_r')
# Build your secondary mirror axes:
fig2, (ax3, ax4) = plt.subplots(1, 2)
# Build maps that parallel the color-coded data
# NOTE 1: imshow requires a 2-D array as input
# NOTE 2: You must use the same cmap tag as above for it match
map1 = ax3.imshow(np.stack([t, t]),cmap='viridis')
map2 = ax4.imshow(np.stack([t, t]),cmap='viridis_r')
# Add your maps onto your original figure/axes
fig.colorbar(map1, ax=ax1)
fig.colorbar(map2, ax=ax2)
plt.show()
Note that you will also output a secondary figure that you can ignore.
请注意，您还将输出一个可以忽略的辅助数字。
