在更短的时间内绘制许多图 - python [英] Many plots in less time - python

问题描述

我想在同一个图中绘制大约 50 000 列.这是我使用的代码:

I have about 50 000 columns I want to plot in the same figure. Here is the code I use:

# "Xaxis" is a list containing the x-axis, and "data" a list of the 50 000 data series I want to plot.
for elt in data:
    plt.plot(Xaxis,elt)

这有点耗时(我需要等待大约 15 分钟).有什么优化流程/减少时间的建议吗?

This is a bit time consuming (I need to wait about 15min). Any suggestions to optimize the process/reduce the time ?

谢谢！

推荐答案

一句话回答:使用LineCollection.

绘制多条线有多种选择.

There are several options to draw many lines.

可以遍历数据并在每行创建一个plot.

One can loop through the data and create one plot per line.

import numpy as np
import matplotlib.pyplot as plt
from matplotlib.collections import LineCollection


def loop(N, show=False):
    x = np.random.rand(N,3)
    y = np.random.rand(N,3)
    
    fig, ax = plt.subplots()
    for i in range(N):
        ax.plot(x[i], y[i])
        
    if show:
        plt.show()
    else:
        fig.canvas.draw()
    plt.close(fig)

B.绘制矩阵

无需多次调用 plot，您可以为 plot 提供一个矩阵，其中每一列包含一行的值.然而，这仍然会创建与矩阵中的列一样多的 Line2D 对象.

B. Plot a matrix

Instead of calling plot several times, one can supply a matrix to plot where each column contains the values of a line. This will however still create as many Line2D objects as there are columns in the matrix.

def matrix(N, show=False):
    x = np.random.rand(N,3)
    y = np.random.rand(N,3)
    
    fig, ax = plt.subplots()

    ax.plot(x.T, y.T)
        
    if show:
        plt.show()
    else:
        fig.canvas.draw()
    plt.close(fig)

C.一个 LineCollection

一个集合允许创建一个艺术家，它只被渲染一次.这是最快的选择.

C. A LineCollection

A collection allows to create a single artist, which is rendered only once. This is the fastest option.

from matplotlib.collections import LineCollection
    
def linecoll(N, show=False):
    x = np.random.rand(N,3)
    y = np.random.rand(N,3)
    
    data = np.stack((x,y), axis=2)
    fig, ax = plt.subplots()

    ax.add_collection(LineCollection(data))
        
    if show:
        plt.show()
    else:
        fig.canvas.draw()
    plt.close(fig)

D.带有 nans 的单图.

在数据中nan值的位置截取一行.这允许绘制单个 Line2D，但在构成单个线的每个数据块的末尾带有 nans.

D. Single plot with nans.

A line will be intercepted at the positions of nan values in the data. This allows to plot a single Line2D, but with nans at the end of each data block that makes up an individual line.

def fillednan(N, show=False):
    x = np.random.rand(N,3)
    y = np.random.rand(N,3)
    
    X = np.concatenate((x, np.ones_like(x)*np.nan)).flatten()
    Y = np.concatenate((y, np.ones_like(x)*np.nan)).flatten()
    
    fig, ax = plt.subplots()

    ax.plot(X,Y)
        
    if show:
        plt.show()
    else:
        fig.canvas.draw()
    plt.close(fig)

结果.

针对 N 到 %timeit 的不同值运行这些函数会产生下图.

Results.

Running those functions for different values of N through %timeit results in the following graph.

我们看到 LineCollection 花费的时间最少.对于大 N，差异是显着的.循环效率最低，其次是矩阵.这是因为两者都创建了需要绘制的 N 条单独的线.带有 nans 和 LineCollection 的单行效率更高，LineCollection 仍然胜过 plot.

We see that the LineCollection takes the least amount of time. For large N the differences are significant. The loop is the least efficient, followed by the matrix. This is because both create N individual lines which need to be drawn. The single line with nans and the LineCollection are much more efficient, with the LineCollection still beating the plot.

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