问题描述
以一般形式表示，我正在寻找一种使用 matplotlib 将多个点与 渐变色线 连接起来的方法，但我没有找到它在任何地方.更具体地说，我正在绘制一个带有单色线的 2D 随机游走.但是，由于点具有相关序列，我想查看绘图并查看数据移动的位置.渐变色线可以解决问题.或者一条透明度逐渐变化的线.
To state it in a general form, I'm looking for a way to join several points with a gradient color line using matplotlib, and I'm not finding it anywhere.To be more specific, I'm plotting a 2D random walk with a one color line. But, as the points have a relevant sequence, I would like to look at the plot and see where the data has moved. A gradient colored line would do the trick. Or a line with gradually changing transparency.
我只是想改进我的数据的可视化.看看这个由 R 的 ggplot2 包生成的美丽图像.我正在 matplotlib 中寻找相同的图像.谢谢.
I'm just trying to improve the vizualization of my data. Check out this beautiful image produced by the ggplot2 package of R. I'm looking for the same in matplotlib. Thanks.
推荐答案
我最近回答了一个类似请求的问题 ( 使用 matplotlib 创建超过 20 种独特的图例颜色 ).在那里我展示了您可以将绘制线条所需的颜色循环映射到颜色图.您可以使用相同的过程为每对点获取特定颜色.
I recently answered a question with a similar request ( creating over 20 unique legend colors using matplotlib ). There I showed that you can map the cycle of colors you need to plot your lines to a color map. You can use the same procedure to get a specific color for each pair of points.
您应该仔细选择颜色图，因为如果颜色图是彩色的，那么沿着您的线条的颜色过渡可能会显得很剧烈.
You should choose the color map carefully, because color transitions along your line might appear drastic if the color map is colorful.
或者，您可以更改每个线段的 alpha，范围从 0 到 1.
Alternatively, you can change the alpha of each line segment, ranging from 0 to 1.
包含在下面的代码示例中的是一个例程 (highResPoints)，用于扩展随机游走的点数，因为如果点太少，转换可能看起来很激烈.这段代码的灵感来自我最近提供的另一个答案:https://stackoverflow.com/a/8253729/717357
Included in the code example below is a routine (highResPoints) to expand the number of points your random walk has, because if you have too few points, the transitions may seem drastic. This bit of code was inspired by another recent answer I provided: https://stackoverflow.com/a/8253729/717357
import numpy as np
import matplotlib.pyplot as plt
def highResPoints(x,y,factor=10):
'''
Take points listed in two vectors and return them at a higher
resultion. Create at least factor*len(x) new points that include the
original points and those spaced in between.
Returns new x and y arrays as a tuple (x,y).
'''
# r is the distance spanned between pairs of points
r = [0]
for i in range(1,len(x)):
dx = x[i]-x[i-1]
dy = y[i]-y[i-1]
r.append(np.sqrt(dx*dx+dy*dy))
r = np.array(r)
# rtot is a cumulative sum of r, it's used to save time
rtot = []
for i in range(len(r)):
rtot.append(r[0:i].sum())
rtot.append(r.sum())
dr = rtot[-1]/(NPOINTS*RESFACT-1)
xmod=[x[0]]
ymod=[y[0]]
rPos = 0 # current point on walk along data
rcount = 1
while rPos < r.sum():
x1,x2 = x[rcount-1],x[rcount]
y1,y2 = y[rcount-1],y[rcount]
dpos = rPos-rtot[rcount]
theta = np.arctan2((x2-x1),(y2-y1))
rx = np.sin(theta)*dpos+x1
ry = np.cos(theta)*dpos+y1
xmod.append(rx)
ymod.append(ry)
rPos+=dr
while rPos > rtot[rcount+1]:
rPos = rtot[rcount+1]
rcount+=1
if rcount>rtot[-1]:
break
return xmod,ymod
#CONSTANTS
NPOINTS = 10
COLOR='blue'
RESFACT=10
MAP='winter' # choose carefully, or color transitions will not appear smoooth
# create random data
np.random.seed(101)
x = np.random.rand(NPOINTS)
y = np.random.rand(NPOINTS)
fig = plt.figure()
ax1 = fig.add_subplot(221) # regular resolution color map
ax2 = fig.add_subplot(222) # regular resolution alpha
ax3 = fig.add_subplot(223) # high resolution color map
ax4 = fig.add_subplot(224) # high resolution alpha
# Choose a color map, loop through the colors, and assign them to the color
# cycle. You need NPOINTS-1 colors, because you'll plot that many lines
# between pairs. In other words, your line is not cyclic, so there's
# no line from end to beginning
cm = plt.get_cmap(MAP)
ax1.set_color_cycle([cm(1.*i/(NPOINTS-1)) for i in range(NPOINTS-1)])
for i in range(NPOINTS-1):
ax1.plot(x[i:i+2],y[i:i+2])
ax1.text(.05,1.05,'Reg. Res - Color Map')
ax1.set_ylim(0,1.2)
# same approach, but fixed color and
# alpha is scale from 0 to 1 in NPOINTS steps
for i in range(NPOINTS-1):
ax2.plot(x[i:i+2],y[i:i+2],alpha=float(i)/(NPOINTS-1),color=COLOR)
ax2.text(.05,1.05,'Reg. Res - alpha')
ax2.set_ylim(0,1.2)
# get higher resolution data
xHiRes,yHiRes = highResPoints(x,y,RESFACT)
npointsHiRes = len(xHiRes)
cm = plt.get_cmap(MAP)
ax3.set_color_cycle([cm(1.*i/(npointsHiRes-1))
for i in range(npointsHiRes-1)])
for i in range(npointsHiRes-1):
ax3.plot(xHiRes[i:i+2],yHiRes[i:i+2])
ax3.text(.05,1.05,'Hi Res - Color Map')
ax3.set_ylim(0,1.2)
for i in range(npointsHiRes-1):
ax4.plot(xHiRes[i:i+2],yHiRes[i:i+2],
alpha=float(i)/(npointsHiRes-1),
color=COLOR)
ax4.text(.05,1.05,'High Res - alpha')
ax4.set_ylim(0,1.2)
fig.savefig('gradColorLine.png')
plt.show()
此图显示了四种情况:
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