如何在matplotlib中绘制渐变色线? [英] How to plot a gradient color line in matplotlib?

问题描述

要以一般形式陈述它，我正在寻找一种使用 matplotlib 将一条带有渐变色线的点连接起来的方法，但是我没有找到它在任何地方. 更具体地说，我正在绘制一条带有一条色线的2D随机游动图.但是，由于这些点具有相关的顺序，因此我想查看该图并查看数据已移至何处.渐变色线可以解决问题.或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.

我只是想改善数据的虚拟化.看看R的ggplot2软件包产生的这张精美图片.我正在matplotlib中寻找相同的图片.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(

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，沿线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.

下面的代码示例中包含一个例程(highResPoints)，用于扩展您的随机游走所拥有的点数，因为如果您的点数太少，则过渡可能看起来很激烈.我最近提供的另一个答案启发了这段代码: 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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