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77 lines
2.2 KiB
Markdown
77 lines
2.2 KiB
Markdown
# MATPLOTLIB UNCHAINED
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脉冲星的假信号频率的比较路径演示（主要是因为Joy Division的未知乐趣的封面而闻名）。
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作者：Nicolas P. Rougier
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```python
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import numpy as np
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import matplotlib.pyplot as plt
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import matplotlib.animation as animation
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# Fixing random state for reproducibility
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np.random.seed(19680801)
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# Create new Figure with black background
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fig = plt.figure(figsize=(8, 8), facecolor='black')
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# Add a subplot with no frame
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ax = plt.subplot(111, frameon=False)
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# Generate random data
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data = np.random.uniform(0, 1, (64, 75))
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X = np.linspace(-1, 1, data.shape[-1])
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G = 1.5 * np.exp(-4 * X ** 2)
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# Generate line plots
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lines = []
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for i in range(len(data)):
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# Small reduction of the X extents to get a cheap perspective effect
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xscale = 1 - i / 200.
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# Same for linewidth (thicker strokes on bottom)
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lw = 1.5 - i / 100.0
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line, = ax.plot(xscale * X, i + G * data[i], color="w", lw=lw)
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lines.append(line)
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# Set y limit (or first line is cropped because of thickness)
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ax.set_ylim(-1, 70)
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# No ticks
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ax.set_xticks([])
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ax.set_yticks([])
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# 2 part titles to get different font weights
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ax.text(0.5, 1.0, "MATPLOTLIB ", transform=ax.transAxes,
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ha="right", va="bottom", color="w",
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family="sans-serif", fontweight="light", fontsize=16)
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ax.text(0.5, 1.0, "UNCHAINED", transform=ax.transAxes,
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ha="left", va="bottom", color="w",
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family="sans-serif", fontweight="bold", fontsize=16)
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def update(*args):
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# Shift all data to the right
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data[:, 1:] = data[:, :-1]
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# Fill-in new values
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data[:, 0] = np.random.uniform(0, 1, len(data))
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# Update data
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for i in range(len(data)):
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lines[i].set_ydata(i + G * data[i])
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# Return modified artists
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return lines
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# Construct the animation, using the update function as the animation director.
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anim = animation.FuncAnimation(fig, update, interval=10)
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plt.show()
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```
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## 下载这个示例
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- [下载python源码: unchained.py](https://matplotlib.org/_downloads/unchained.py)
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- [下载Jupyter notebook: unchained.ipynb](https://matplotlib.org/_downloads/unchained.ipynb) |