在 Python 中绘制多个图形的有效方法

Question

我想知道 Python 中有更有效的绘制倍数的方法。例如：

x = np.linspace(0, 2*np.pi, 100)
y = np.sin(x)
y_shifted = np.sin(x+np.pi)
plt.plot(x, y)
plt.plot(x, y_shifted)

这里我们绘制了两个相互叠加的图形。但是，首先保存函数的值（例如，保存到另一个数组），然后将它们绘制在参数上不是更有效吗？如果是，我们怎么能这样做？

谢谢！

Answer 1

我尝试了 furas 的建议：

经过 100 个数字，每个数字 1k 行，这些是我的结果：

Plot one at a time:
Time to plot: Min: 0.504s, Max: 6.728s, Avg: 0.929s
Time to show: Min: 0.727s, Max: 11.425s, Avg: 1.250s

Plot all at once
Time to plot: Min: 0.341s, Max: 7.839s, Avg: 0.776s
Time to show: Min: 0.724s, Max: 8.165s, Avg: 1.294s

苏...差不多吧？也许快一点？与运行之间的差异相比，方法之间的差异很小，因此 IMO 不值得担心。如果您的应用程序感觉很慢，这将无济于事。

要生成的代码：

import numpy as np
from matplotlib import pyplot as plt
from random import uniform
import time

def plot_one_at_a_time():
    start = time.time()
    x = np.linspace(0, 2*np.pi, 100)
    for i in range(1000):
        shift = uniform(0, 100)
        y_shifted = np.sin(x+shift)
        plt.plot(x, y_shifted)
    plotdonetime = time.time()
    plt.show()
    showdonetime = time.time()
    
    plot_times_one.append(plotdonetime-start)
    show_times_one.append(showdonetime-plotdonetime)

def plot_at_once():
    start = time.time()
    x = np.linspace(0, 2*np.pi, 100)
    data = []
    for i in range(1000):
        shift = uniform(0, 100)
        y_shifted = np.sin(x+shift)
        data.append(x)
        data.append(y_shifted)
    plt.plot(*data)  
    plotdonetime = time.time()
    plt.show()
    showdonetime = time.time()

    plot_times_all.append(plotdonetime-start)
    show_times_all.append(showdonetime-plotdonetime)


plot_times_one = []
show_times_one = []
plot_times_all = []
show_times_all = []

for i in range(100):
    plot_one_at_a_time()
    plot_at_once()

print("Plot one at a time:")
print("Time to plot: Min: {:.3f}s, Max: {:.3f}s, Avg: {:.3f}s".format(np.min(plot_times_one), np.amax(plot_times_one), np.mean(plot_times_one)))
print("Time to show: Min: {:.3f}s, Max: {:.3f}s, Avg: {:.3f}s".format(np.min(show_times_one), np.amax(show_times_one), np.mean(show_times_one)))
print()
print("Plot all at once")
print("Time to plot: Min: {:.3f}s, Max: {:.3f}s, Avg: {:.3f}s".format(np.min(plot_times_all), np.amax(plot_times_all), np.mean(plot_times_all)))
print("Time to show: Min: {:.3f}s, Max: {:.3f}s, Avg: {:.3f}s".format(np.min(show_times_all), np.amax(show_times_all), np.mean(show_times_all)))

在 Python 中绘制多个图形的有效方法

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