I would like to know how to make matplotlib's scatter function colour points by a third variable.
我想知道如何通过第三个变量制作matplotlib的散射函数色点。
Questions gnuplot linecolor variable in matplotlib? and Matplotlib scatterplot; colour as a function of a third variable posed similar queries, however, the answers to those questions don't address my issue: the use of c=arraywhichspecifiespointcolour in the scatter function only sets the fill colour, not the edge colour. This means that the use of c=arr... fails when using markersymbol='+', for instance (because that marker has no fill, only edges). I want points to be coloured by a third variable reliably, regardless of which symbol is used.
在matplotlib中问题gnuplot linecolor变量?和Matplotlib散点图;颜色作为第三个变量的函数提出了类似的查询,但是,这些问题的答案并没有解决我的问题:在散点函数中使用c = arraywhichspecifiespointcolour只设置填充颜色,而不是边缘颜色。这意味着当使用markersymbol ='+'时,使用c = arr ...会失败(因为该标记没有填充,只有边缘)。无论使用哪个符号,我都希望点可以被第三个变量可靠地着色。
Is there a way to achieve this with Matplotlib's scatter function?
有没有办法用Matplotlib的散射函数来实现这个目的?
1 个解决方案
#1
29
This works for me, using matplotlib 1.1:
这适用于我,使用matplotlib 1.1:
import numpy as np
import matplotlib.pyplot as plt
x = np.arange(10)
y = np.sin(x)
plt.scatter(x, y, marker='+', s=150, linewidths=4, c=y, cmap=plt.cm.coolwarm)
plt.show()
Result:
Alternatively, for n points, make an array of RGB color values with shape (n, 3), and assign it to the edgecolors keyword argument of scatter():
或者,对于n个点,使用shape(n,3)创建RGB颜色值数组,并将其分配给scatter()的edgecolors关键字参数:
import numpy as np
import matplotlib.pyplot as plt
x = np.linspace(0, 20, 100)
y = np.sin(x)
z = x + 20 * y
scaled_z = (z - z.min()) / z.ptp()
colors = plt.cm.coolwarm(scaled_z)
plt.scatter(x, y, marker='+', edgecolors=colors, s=150, linewidths=4)
plt.show()
Result: 
That example gets the RGBA values by scaling the z values to the range [0,1], and calling the colormap plt.cm.coolwarm with the scaled values. When called this way, a matplotlib colormap returns an array of RGBA values, with each row giving the color of the corresponding input value. For example:
该示例通过将z值缩放到范围[0,1]并使用缩放值调用colormap plt.cm.coolwarm来获取RGBA值。当以这种方式调用时,matplotlib colormap返回RGBA值的数组,每行给出相应输入值的颜色。例如:
>>> t = np.linspace(0, 1, 5)
>>> t
array([ 0. , 0.25, 0.5 , 0.75, 1. ])
>>> plt.cm.coolwarm(t)
array([[ 0.2298, 0.2987, 0.7537, 1. ],
[ 0.5543, 0.6901, 0.9955, 1. ],
[ 0.8674, 0.8644, 0.8626, 1. ],
[ 0.9567, 0.598 , 0.4773, 1. ],
[ 0.7057, 0.0156, 0.1502, 1. ]])
#1
29
This works for me, using matplotlib 1.1:
这适用于我,使用matplotlib 1.1:
import numpy as np
import matplotlib.pyplot as plt
x = np.arange(10)
y = np.sin(x)
plt.scatter(x, y, marker='+', s=150, linewidths=4, c=y, cmap=plt.cm.coolwarm)
plt.show()
Result:
Alternatively, for n points, make an array of RGB color values with shape (n, 3), and assign it to the edgecolors keyword argument of scatter():
或者,对于n个点,使用shape(n,3)创建RGB颜色值数组,并将其分配给scatter()的edgecolors关键字参数:
import numpy as np
import matplotlib.pyplot as plt
x = np.linspace(0, 20, 100)
y = np.sin(x)
z = x + 20 * y
scaled_z = (z - z.min()) / z.ptp()
colors = plt.cm.coolwarm(scaled_z)
plt.scatter(x, y, marker='+', edgecolors=colors, s=150, linewidths=4)
plt.show()
Result:
That example gets the RGBA values by scaling the z values to the range [0,1], and calling the colormap plt.cm.coolwarm with the scaled values. When called this way, a matplotlib colormap returns an array of RGBA values, with each row giving the color of the corresponding input value. For example:
该示例通过将z值缩放到范围[0,1]并使用缩放值调用colormap plt.cm.coolwarm来获取RGBA值。当以这种方式调用时,matplotlib colormap返回RGBA值的数组,每行给出相应输入值的颜色。例如:
>>> t = np.linspace(0, 1, 5)
>>> t
array([ 0. , 0.25, 0.5 , 0.75, 1. ])
>>> plt.cm.coolwarm(t)
array([[ 0.2298, 0.2987, 0.7537, 1. ],
[ 0.5543, 0.6901, 0.9955, 1. ],
[ 0.8674, 0.8644, 0.8626, 1. ],
[ 0.9567, 0.598 , 0.4773, 1. ],
[ 0.7057, 0.0156, 0.1502, 1. ]])