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# This script plot the result generated by profile_factorization.rs
from re import A
import pandas as pd
import numpy as np
from matplotlib import pyplot as plt
def plot_n_stats():
table = pd.read_csv("profile_stats.csv")
table.drop(columns=["n"], inplace=True)
pollard_cols = list(k for k in table.columns if k.startswith("pollard"))
squfof_cols = list(k for k in table.columns if k.startswith("squfof"))
oneline_cols = list(k for k in table.columns if k.startswith("one_line"))
# MAXITER = 1 << 20
# table[table >= MAXITER] = np.nan
mean_table = table.groupby(table['n_bits'] // 4).agg(np.nanmean)
fig, ax = plt.subplots()
mean_table.plot("n_bits", pollard_cols, ax=ax)
mean_table.plot("n_bits", squfof_cols, ax=ax)
mean_table.plot("n_bits", oneline_cols, ax=ax)
ax.set_yscale("log")
min_table = table.groupby(table['n_bits'] // 4).agg(np.nanmin)
fig, ax = plt.subplots()
ax.plot(min_table["n_bits"], np.mean(min_table[pollard_cols], axis=1), label="pollard")
ax.plot(min_table["n_bits"], np.mean(min_table[squfof_cols], axis=1), label="squfof")
ax.plot(min_table["n_bits"], np.mean(min_table[oneline_cols], axis=1), label="one_line")
ax.legend()
ax.set_yscale("log")
def plot_n_min_stats():
table = pd.read_csv("profile_stats.csv")
table.drop(columns=["n"], inplace=True)
for k in table.columns: # caculate average time
if k.startswith("time_"):
table[k] = table[k] / table[k[5:]]
print(table[k])
min_table = table.groupby(table['n_bits'] // 4).agg(np.nanmean)
# MAXITER = 1 << 24
# table[table >= MAXITER] = np.nan
ax = min_table.plot("n_bits", ["pollard_rho", "squfof", "one_line"])
ax.set_yscale("log")
ax.set_ylabel("min iters")
ax = min_table.plot("n_bits", ["time_pollard_rho", "time_squfof", "time_one_line"])
ax.set_yscale("log")
ax.set_ylabel("avg time per iter")
if __name__ == "__main__":
# plot_n_stats()
plot_n_min_stats()
plt.show()
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