Matplotlib中的平行坐标图 [英] Parallel Coordinates plot in Matplotlib
问题描述
使用传统绘图类型可以相对直接地查看二维和三维数据.即使具有四维数据，我们也经常可以找到一种显示数据的方法.但是，尺寸大于4的尺寸越来越难以显示.幸运的是，平行坐标图提供了一种查看较大尺寸结果的机制.
Two and three dimensional data can be viewed relatively straight-forwardly using traditional plot types. Even with four dimensional data, we can often find a way to display the data. Dimensions above four, though, become increasingly difficult to display. Fortunately, parallel coordinates plots provide a mechanism for viewing results with higher dimensions.
Several plotting packages provide parallel coordinates plots, such as Matlab, R, VTK type 1 and VTK type 2, but I don't see how to create one using Matplotlib.
- Matplotlib中是否有内置的平行坐标图?我当然不会在画廊中看到一个> .
- 如果没有内置类型，是否可以使用Matplotlib的标准功能来构建平行坐标图?
修改:
基于下面的Zhenya提供的答案，我开发了以下支持任意数量轴的概括.按照我在上面原始问题中发布的示例的绘图样式，每个轴都有自己的比例尺.我通过对每个轴点处的数据进行归一化并使轴的范围从0到1来实现此目的.然后我回过头并在每个刻度线上应用标签，以在该截距处提供正确的值.
Based on the answer provided by Zhenya below, I developed the following generalization that supports an arbitrary number of axes. Following the plot style of the example I posted in the original question above, each axis gets its own scale. I accomplished this by normalizing the data at each axis point and making the axes have a range of 0 to 1. I then go back and apply labels to each tick-mark that give the correct value at that intercept.
该函数通过接受可迭代的数据集来工作.每个数据集被认为是一组点，其中每个点位于不同的轴上. __main__中的示例在两组30行的行中为每个轴获取随机数.在导致线聚集的范围内，线是随机的；我想验证的行为.
The function works by accepting an iterable of data sets. Each data set is considered a set of points where each point lies on a different axis. The example in __main__ grabs random numbers for each axis in two sets of 30 lines. The lines are random within ranges that cause clustering of lines; a behavior I wanted to verify.
此解决方案不如内置解决方案好，因为您的鼠标行为奇特，并且我通过标签伪造数据范围，但是直到Matplotlib添加内置解决方案之前，它才是可接受的.
This solution isn't as good as a built-in solution since you have odd mouse behavior and I'm faking the data ranges through labels, but until Matplotlib adds a built-in solution, it's acceptable.
#!/usr/bin/python
import matplotlib.pyplot as plt
import matplotlib.ticker as ticker
def parallel_coordinates(data_sets, style=None):
dims = len(data_sets[0])
x = range(dims)
fig, axes = plt.subplots(1, dims-1, sharey=False)
if style is None:
style = ['r-']*len(data_sets)
# Calculate the limits on the data
min_max_range = list()
for m in zip(*data_sets):
mn = min(m)
mx = max(m)
if mn == mx:
mn -= 0.5
mx = mn + 1.
r = float(mx - mn)
min_max_range.append((mn, mx, r))
# Normalize the data sets
norm_data_sets = list()
for ds in data_sets:
nds = [(value - min_max_range[dimension][0]) /
min_max_range[dimension][2]
for dimension,value in enumerate(ds)]
norm_data_sets.append(nds)
data_sets = norm_data_sets
# Plot the datasets on all the subplots
for i, ax in enumerate(axes):
for dsi, d in enumerate(data_sets):
ax.plot(x, d, style[dsi])
ax.set_xlim([x[i], x[i+1]])
# Set the x axis ticks
for dimension, (axx,xx) in enumerate(zip(axes, x[:-1])):
axx.xaxis.set_major_locator(ticker.FixedLocator([xx]))
ticks = len(axx.get_yticklabels())
labels = list()
step = min_max_range[dimension][2] / (ticks - 1)
mn = min_max_range[dimension][0]
for i in xrange(ticks):
v = mn + i*step
labels.append('%4.2f' % v)
axx.set_yticklabels(labels)
# Move the final axis' ticks to the right-hand side
axx = plt.twinx(axes[-1])
dimension += 1
axx.xaxis.set_major_locator(ticker.FixedLocator([x[-2], x[-1]]))
ticks = len(axx.get_yticklabels())
step = min_max_range[dimension][2] / (ticks - 1)
mn = min_max_range[dimension][0]
labels = ['%4.2f' % (mn + i*step) for i in xrange(ticks)]
axx.set_yticklabels(labels)
# Stack the subplots
plt.subplots_adjust(wspace=0)
return plt
if __name__ == '__main__':
import random
base = [0, 0, 5, 5, 0]
scale = [1.5, 2., 1.0, 2., 2.]
data = [[base[x] + random.uniform(0., 1.)*scale[x]
for x in xrange(5)] for y in xrange(30)]
colors = ['r'] * 30
base = [3, 6, 0, 1, 3]
scale = [1.5, 2., 2.5, 2., 2.]
data.extend([[base[x] + random.uniform(0., 1.)*scale[x]
for x in xrange(5)] for y in xrange(30)])
colors.extend(['b'] * 30)
parallel_coordinates(data, style=colors).show()
以下是绘制菲舍尔的鸢尾花数据时上述代码的示例.它不如Wikipedia上的参考图像那么好，但是如果您只有Matplotlib并且需要多维图，则可以通过.
Here is an example of what comes out of the above code when plotting Fisher's Iris data. It isn't quite as nice as the reference image from Wikipedia, but it is passable if all you have is Matplotlib and you need multi-dimensional plots.
推荐答案
我敢肯定有更好的方法，但这是一种快速而又肮脏的方法(一种很脏的方法):
I'm sure there is a better way of doing it, but here's a quick-and-dirty one (a really dirty one):
#!/usr/bin/python
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.ticker as ticker
#vectors to plot: 4D for this example
y1=[1,2.3,8.0,2.5]
y2=[1.5,1.7,2.2,2.9]
x=[1,2,3,8] # spines
fig,(ax,ax2,ax3) = plt.subplots(1, 3, sharey=False)
# plot the same on all the subplots
ax.plot(x,y1,'r-', x,y2,'b-')
ax2.plot(x,y1,'r-', x,y2,'b-')
ax3.plot(x,y1,'r-', x,y2,'b-')
# now zoom in each of the subplots
ax.set_xlim([ x[0],x[1]])
ax2.set_xlim([ x[1],x[2]])
ax3.set_xlim([ x[2],x[3]])
# set the x axis ticks
for axx,xx in zip([ax,ax2,ax3],x[:-1]):
axx.xaxis.set_major_locator(ticker.FixedLocator([xx]))
ax3.xaxis.set_major_locator(ticker.FixedLocator([x[-2],x[-1]])) # the last one
# EDIT: add the labels to the rightmost spine
for tick in ax3.yaxis.get_major_ticks():
tick.label2On=True
# stack the subplots together
plt.subplots_adjust(wspace=0)
plt.show()
This is essentially based on a (much nicer) one by Joe Kingon, Python/Matplotlib - Is there a way to make a discontinuous axis?. You might also want to have a look at the other answer to the same question.
在此示例中，我什至没有尝试缩放垂直比例，因为这取决于您要实现的目标.
In this example I don't even attempt at scaling the vertical scales, since it depends on what exactly you are trying to achieve.
这是结果
这篇关于Matplotlib中的平行坐标图的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！
