箭型图中箭头的不同颜色 [英] Different colours for arrows in quiver plot

问题描述

我正在绘制箭头图，并且我的代码使用如下外部文件:

将 numpy 导入为 np导入matplotlib.pyplot作为plt将Matplotlib导入为mpl从 pylab 导入 rcParams数据= np.loadtxt(r'data.dat')x = 数据[:,0]y =数据[:，1]u = 数据[:,2]v = 数据[:,3]plt.quiver(x，y，u，v，angles ='xy'，scale_units ='xy'，scale = 1，pivot ='mid'，color ='g')

数据文件基本上看起来像:

1 1 0 00 1 1 00 1 1 01 1 0 1

有没有一种方法可以针对不同的箭头方向绘制不同的颜色?

Ps.:我的数据文件中有更多的箭头，在一个不太合乎逻辑的句子中，就像我使用的例子一样.

解决方案

这可能有用:

  plt.quiver(x，y，u，v，np.arctan2(v，u)，angles ='xy'，scale_units ='xy'，scale = 1，枢轴='mid'，颜色='g')

请注意， plt.quiver 的第五个参数是一种颜色.

<小时>

UPD.如果要控制颜色，则必须使用

您还可以像我的第一个示例一样使用第五个参数(与 colors 相比，其工作方式略有不同)，并更改默认的colormap来控制颜色.

plt.rcParams['image.cmap'] = '配对'plt.figure(figsize=(6, 6))plt.xlim(-2, 2)plt.ylim(-2，2)plt.quiver(x，y，u，v，np.arctan2(v，u)，angles ='xy'，scale_units ='xy'，scale = 1，枢轴='mid')

您也可以创建自己的颜色图，例如参见此处.

I am plotting an arrow graph and my code uses an external file as follows:

import numpy as np
import matplotlib.pyplot as plt
import matplotlib as mpl
from pylab import rcParams

data=np.loadtxt(r'data.dat')

x = data[:,0] 
y = data[:,1] 
u = data[:,2] 
v = data[:,3] 


plt.quiver(x, y, u, v, angles='xy', scale_units='xy', scale=1, pivot='mid',color='g')

The data file basically looks like :

1 1 0 0
0 1 1 0
0 1 1 0
1 1 0 1

Is there a way to plot this with different colours for the different arrow directions?

Ps.: I have got a lot more arrows in my data file in a not very logical sentence like the one I am using as example.

解决方案

This probably do the trick:

plt.quiver(x, y, u, v, np.arctan2(v, u), angles='xy', scale_units='xy', scale=1, pivot='mid',color='g')

Note that the fifth's argument of plt.quiver is a color.

---

UPD. If you want to control the colors, you have to use colormaps. Here are a couple of examples:

Use colormap with colors parameter:

import numpy as np
import matplotlib.pyplot as plt
import matplotlib.cm as cm
from matplotlib.colors import Normalize

%matplotlib inline

ph = np.linspace(0, 2*np.pi, 13)
x = np.cos(ph)
y = np.sin(ph)
u = np.cos(ph)
v = np.sin(ph)
colors = arctan2(u, v)

norm = Normalize()
norm.autoscale(colors)
# we need to normalize our colors array to match it colormap domain
# which is [0, 1]

colormap = cm.inferno
# pick your colormap here, refer to 
# http://matplotlib.org/examples/color/colormaps_reference.html
# and
# http://matplotlib.org/users/colormaps.html
# for details
plt.figure(figsize=(6, 6))
plt.xlim(-2, 2)
plt.ylim(-2, 2)
plt.quiver(x, y, u, v, color=colormap(norm(colors)),  angles='xy', 
           scale_units='xy', scale=1, pivot='mid')

You can also stick with fifth argument like in my first example (which works in a bit different way comparing with colors) and change default colormap to control the colors.

plt.rcParams['image.cmap'] = 'Paired'

plt.figure(figsize=(6, 6))
plt.xlim(-2, 2)
plt.ylim(-2, 2)

plt.quiver(x, y, u, v, np.arctan2(v, u), angles='xy', scale_units='xy', scale=1, pivot='mid')

You can also create your own colormaps, see e.g. here.

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