mirror of
https://github.com/uutils/num-prime.git
synced 2026-06-10 16:12:35 -07:00
Jacob Zhong
56 lines
2.0 KiB
Python
56 lines
2.0 KiB
Python
# This script plot the result generated by profile_factorization.rs
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from re import A
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import pandas as pd
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import numpy as np
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from matplotlib import pyplot as plt
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def plot_n_stats():
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table = pd.read_csv("profile_stats.csv")
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table.drop(columns=["n"], inplace=True)
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pollard_cols = list(k for k in table.columns if k.startswith("pollard"))
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squfof_cols = list(k for k in table.columns if k.startswith("squfof"))
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oneline_cols = list(k for k in table.columns if k.startswith("one_line"))
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# MAXITER = 1 << 20
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# table[table >= MAXITER] = np.nan
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mean_table = table.groupby(table['n_bits'] // 4).agg(np.nanmean)
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fig, ax = plt.subplots()
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mean_table.plot("n_bits", pollard_cols, ax=ax)
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mean_table.plot("n_bits", squfof_cols, ax=ax)
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mean_table.plot("n_bits", oneline_cols, ax=ax)
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ax.set_yscale("log")
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min_table = table.groupby(table['n_bits'] // 4).agg(np.nanmin)
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fig, ax = plt.subplots()
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ax.plot(min_table["n_bits"], np.mean(min_table[pollard_cols], axis=1), label="pollard")
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ax.plot(min_table["n_bits"], np.mean(min_table[squfof_cols], axis=1), label="squfof")
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ax.plot(min_table["n_bits"], np.mean(min_table[oneline_cols], axis=1), label="one_line")
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ax.legend()
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ax.set_yscale("log")
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def plot_n_min_stats():
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table = pd.read_csv("profile_stats.csv")
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table.drop(columns=["n"], inplace=True)
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for k in table.columns: # caculate average time
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if k.startswith("time_"):
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table[k] = table[k] / table[k[5:]]
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print(table[k])
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min_table = table.groupby(table['n_bits'] // 4).agg(np.nanmean)
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# MAXITER = 1 << 24
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# table[table >= MAXITER] = np.nan
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ax = min_table.plot("n_bits", ["pollard_rho", "squfof", "one_line"])
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ax.set_yscale("log")
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ax.set_ylabel("min iters")
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ax = min_table.plot("n_bits", ["time_pollard_rho", "time_squfof", "time_one_line"])
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ax.set_yscale("log")
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ax.set_ylabel("avg time per iter")
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if __name__ == "__main__":
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# plot_n_stats()
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plot_n_min_stats()
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plt.show()
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