脉冲星假信号频率的相对路径论证。

首先看一下演示结果：

实例代码：

import numpy as npimport matplotlib.pyplot as pltimport matplotlib.animation as animation# Fixing random state for reproducibilitynp.random.seed(19680801)# Create new Figure with black backgroundfig = plt.figure(figsize=(8, 8), facecolor='black')# Add a subplot with no frameax = plt.subplot(111, frameon=False)# Generate random datadata = np.random.uniform(0, 1, (64, 75))X = np.linspace(-1, 1, data.shape[-1])G = 1.5 * np.exp(-4 * X ** 2)# Generate line plotslines = []for i in range(len(data)): # Small reduction of the X extents to get a cheap perspective effect xscale = 1 - i / 200. # Same for linewidth (thicker strokes on bottom) lw = 1.5 - i / 100.0 line, = ax.plot(xscale * X, i + G * data[i], color="w", lw=lw) lines.append(line)# Set y limit (or first line is cropped because of thickness)ax.set_ylim(-1, 70)# No ticksax.set_xticks([])ax.set_yticks([])# 2 part titles to get different font weightsax.text(0.5, 1.0, "MATPLOTLIB ", transform=ax.transAxes, ha="right", va="bottom", color="w", family="sans-serif", fontweight="light", fontsize=16)ax.text(0.5, 1.0, "UNCHAINED", transform=ax.transAxes, ha="left", va="bottom", color="w", family="sans-serif", fontweight="bold", fontsize=16)def update(*args): # Shift all data to the right data[:, 1:] = data[:, :-1] # Fill-in new values data[:, 0] = np.random.uniform(0, 1, len(data)) # Update data for i in range(len(data)): lines[i].set_ydata(i + G * data[i]) # Return modified artists return lines# Construct the animation, using the update function as the animation# director.anim = animation.FuncAnimation(fig, update, interval=10)plt.show()

脚本运行时间：（0分0.065秒）

总结

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