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56 lines
1.8 KiB
Python
56 lines
1.8 KiB
Python
import matplotlib.pyplot as plt
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import scienceplots
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import numpy as np
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import pandas as pd
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from analyze import load
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latencies = pd.DataFrame({
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"2kb": load("raw/paper_latency_2kb_v2.txt", "arrival_diff="),
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"25kb": load("raw/paper_latency_25kb_v2.txt", "arrival_diff="),
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"100kb": load("raw/paper_latency_100kb_v2.txt", "arrival_diff="),
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"500kb": load("raw/paper_latency_500kb_v2.txt", "arrival_diff=")})
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num_bins = 50
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# Best (1 hop) and worst (4 hops) latencies in ms
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# See Table 2 from paper
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multi_host_simulations = {
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"2kb": [364, 709],
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"25kb": [436, 1084],
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"100kb": [471, 1922],
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"500kb": [564, 2988]
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}
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with plt.style.context(['science', 'ieee']):
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fig, ax = plt.subplots(2, 2)
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possitions = [
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("2kb", ax[0][0]),
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("25kb", ax[0][1]),
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("100kb", ax[1][0]),
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("500kb", ax[1][1])
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]
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for (size, pos) in possitions:
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# Plot single host results
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latencies.hist(size, bins=num_bins, ax=pos)
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# Plot multi host results
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pos.axvline(x=multi_host_simulations[size][0], color='red', linestyle='--')
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pos.axvline(x=multi_host_simulations[size][1], color='red', linestyle='--')
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pos.grid(False)
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title = ('size={size}\n' + r'$\mu$={mean:.0f} $p_{{95}}$={p95:.0f} min={min:.0f} max={max:.0f}').format(
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size=size,
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mean=latencies[size].mean(axis=0),
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p95=np.percentile(latencies[size], 95),
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min=latencies[size].min(),
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max=latencies[size].max())
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pos.set_title(title, fontsize=8)
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ax[0][0].set(ylabel='Amount messages')
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ax[1][0].set(xlabel='Latency (ms)', ylabel='Amount messages')
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ax[1][1].set(xlabel='Latency (ms)')
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plt.tight_layout(pad=0, w_pad=0.1, h_pad=0.1)
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fig.set_size_inches(4, 3)
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fig.savefig('paper_distribution.svg', dpi=600) |