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https://github.com/eledio-devices/thirdparty-littlefs.git
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53 lines
1.5 KiB
Python
Executable File
53 lines
1.5 KiB
Python
Executable File
#!/usr/bin/env python
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import matplotlib
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matplotlib.use('SVG')
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import matplotlib.pyplot as plt
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import matplotlib.gridspec as gridspec
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import numpy as np
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matplotlib.rc('font', family='sans-serif', size=11)
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matplotlib.rc('axes', titlesize='medium', labelsize='medium')
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matplotlib.rc('xtick', labelsize='small')
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matplotlib.rc('ytick', labelsize='small')
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np.random.seed(map(ord, "hello"))
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gs = gridspec.GridSpec(nrows=2, ncols=2, height_ratios=[1, 2],
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wspace=0.25, hspace=0.25)
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fig = plt.figure(figsize=(7, 5.5))
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ax1 = fig.add_subplot(gs[0, 0])
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ax2 = fig.add_subplot(gs[0, 1], sharex=ax1, sharey=ax1)
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ax3 = fig.add_subplot(gs[1, :])
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# Convolution (I think?) of two types of uniform distributions under modulo
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w1 = np.random.randint(0, 100, 30)
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w2 = np.arange(0, 30, 1)
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w3 = np.array([w2 + i for i in w1]).flatten() % 100
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ax1.hist(w1, bins=100, range=(0,100), histtype='stepfilled')
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ax2.hist(w2, bins=100, range=(0,100), histtype='stepfilled')
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ax3.hist(w3, bins=100, range=(0,100), histtype='stepfilled')
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ax1.set_title('Random offset')
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ax2.set_title('Linear allocation')
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ax3.set_title('Cumulative allocation')
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ax1.set_ylabel('wear')
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ax1.set_yticks(np.arange(0, 3))
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ax3.set_ylabel('wear')
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ax3.set_xlabel('block address')
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for ax in [ax1,ax2,ax3]:
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ax.set_xlim(0, 100)
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ax.set_xticklabels([])
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ax.set_yticklabels([])
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ax.spines['right'].set_visible(False)
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ax.spines['top'].set_visible(False)
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ax1.text(109.25, 1, '$\\ast$')
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fig.tight_layout()
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plt.savefig('wear-distribution.svg', bbox_inches="tight")
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