89 lines
3.0 KiB
Python
89 lines
3.0 KiB
Python
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import math
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import statistics
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from collections import defaultdict
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import matplotlib.pyplot as plt
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def load_results(filename) -> dict:
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results = defaultdict(list)
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with open(filename, "r") as file:
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for line in file:
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if "error" in line:
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continue
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values = line.split()
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keys = [
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("num_servers", int),
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("database_size", int),
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("block_size", int),
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("protocol_name", str),
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("total_cpu_time", int),
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("bytes_sent", int),
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("bytes_received", int)
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]
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d = dict([(a[0], a[1](b)) for a, b in zip(keys, values)])
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results[(d["num_servers"], d["database_size"], d["block_size"], d["protocol_name"])].append(d)
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return results
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def clean_results(results) -> dict:
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cleaned_results = defaultdict(list)
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for test, result in results.items():
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cpu_time = statistics.mean(sorted([int(r["total_cpu_time"]) for r in result])) # [1:-1]
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cleaned_results[result[0]["protocol_name"]].append({**result[0], "total_cpu_time": cpu_time})
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return cleaned_results
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def plot(all_results: dict, y_func: callable, x_func: callable, title=None, y_label=None, x_label=None):
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fig, ax = plt.subplots()
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for protocol_name, results in all_results.items():
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sorted_results = sorted(results, key=lambda r: x_func(r))
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ax.plot(
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[x_func(r) for r in sorted_results],
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[y_func(r) for r in sorted_results],
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)
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ax.set_xscale("log", basex=2)
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#ax.set_yscale("log", basey=10)
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if x_label is not None:
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plt.xlabel(x_label)
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if y_label is not None:
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plt.ylabel(y_label)
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plt.legend(all_results.keys(), loc="upper left")
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if title is not None:
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plt.title(title)
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plt.savefig(title.replace(" ", "_").replace("/", "") + ".pdf")
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#plt.show()
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if __name__ == '__main__':
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plot(
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clean_results(load_results("results_only_with_8_bs.log")),
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y_label="Time",
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x_label="Total Database Size",
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title="Fixed 8-bit block size",
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y_func=lambda r: max(1, r["total_cpu_time"]),
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x_func=lambda r: r["database_size"] * r["block_size"]
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)
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plot(
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clean_results(load_results("results_only_with_8_bs.log")),
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y_label="Time",
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x_label="Total Database Size",
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title="Fixed 8-bit block size - simulated 1MiB/s tx, 5MiB/s rx",
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y_func=lambda r: r["total_cpu_time"] + (r["bytes_sent"]/(1*1024)) + (r["bytes_received"]/(5*1024)), # 1024 B/ms = 1MiB/s
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x_func=lambda r: r["database_size"] * r["block_size"]
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)
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#plot(
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# {n: [r for r in rs if r["block_size"] == 2] for n, rs in clean_results(load_results("results_overflow.log")).items()},
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# y_label="Time",
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# x_label="Total Database Size",
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# title="lol",
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# y_func=lambda r: r["total_cpu_time"] + (r["bytes_sent"]/(1*1024)) + (r["bytes_received"]/(10*1024)), # 1024 B/ms = 1MiB/s
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# x_func=lambda r: r["database_size"] * r["block_size"]
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#)
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