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chore(ci): make ch13-ci deterministic via --seed; fix simulators to honor seed #47

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Merged
nicholaskarlson merged 1 commit into from
Nov 10, 2025
Merged

chore(ci): make ch13-ci deterministic via --seed; fix simulators to honor seed #47

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4 changes: 2 additions & 2 deletions Makefile
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Original file line number Diff line number Diff line change
Expand Up @@ -9,9 +9,9 @@ help:

.PHONY: ch13-ci
ch13-ci:
python scripts/sim_stroop.py --n-subjects 6 --n-trials 10
python scripts/sim_stroop.py --n-subjects 6 --n-trials 10 --seed 2025
python scripts/ch13_stroop_within.py --save-plots
python scripts/sim_fitness_2x2.py --n-per-group 5
python scripts/sim_fitness_2x2.py --n-per-group 5 --seed 2025
python scripts/ch13_fitness_mixed.py --save-plots

.PHONY: ch13
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18 changes: 14 additions & 4 deletions scripts/sim_fitness_2x2.py
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@@ -1,8 +1,9 @@
# SPDX-License-Identifier: MIT
import os, json, time
import os, json, time, argparse
import numpy as np, pandas as pd

rng = np.random.default_rng(7)
# globals configured in main()
rng = np.random.default_rng()
N = 80
GROUPS = ["ProgramA","ProgramB"]

Expand Down Expand Up @@ -38,7 +39,7 @@ def simulate():
long.to_csv("data/synthetic/fitness_long.csv", index=False)

meta = dict(
seed=7,
seed=int(getattr(rng, "seed_seq", np.random.SeedSequence()).entropy) if hasattr(rng, "seed_seq") else None,
n=int(N),
design="2x2 mixed (Group between × Time within)",
programs=GROUPS,
Expand All @@ -50,5 +51,14 @@ def simulate():

print("Wrote fitness_subjects.csv, fitness_long.csv, fitness_meta.json")

def main():
ap = argparse.ArgumentParser()
ap.add_argument("--seed", type=int, default=7, help="RNG seed")
ap.add_argument("--n-per-group", type=int, default=5, help="(kept for CLI parity; not used here)")
args = ap.parse_args()
global rng
rng = np.random.default_rng(args.seed)
simulate()

if __name__ == "__main__":
simulate()
main()
14 changes: 7 additions & 7 deletions scripts/sim_stroop.py
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Expand Up @@ -73,7 +73,7 @@ def simulate():

# sprinkle outliers (anticipations & lapses)
if rng.random() < OUTLIER_RATE_ANTICIP:
rt = rng.normal(OUTLIER_RT_ANTICIP_MU, OUTLIER_RT_ANTICIP_SD) # anticipatory
rt = rng.normal(OUTLIER_RT_ANTICIP_MU, OUTLIER_RT_ANTICIP_SD) # anticipatory
if rng.random() < OUTLIER_RATE_LAPSE:
rt = rng.normal(OUTLIER_RT_LAPSE_MU, OUTLIER_RT_LAPSE_SD) # lapse

Expand Down Expand Up @@ -135,20 +135,20 @@ def write_meta(subjects: pd.DataFrame, trials: pd.DataFrame):
json.dump(meta, f, indent=2)
print("Wrote data/synthetic/psych_stroop_meta.json")

if __name__ == "__main__":
subjects_df, trials_df = simulate()
write_meta(subjects_df, trials_df)


def main():
ap = argparse.ArgumentParser()
ap.add_argument("--seed", type=int, default=42)
ap.add_argument("--n-subjects", type=int, default=60)
ap.add_argument("--n-trials", type=int, default=100)
args = ap.parse_args()
global N_SUBJ, N_TRIALS_PER_COND, rng
global N_SUBJ, N_TRIALS_PER_COND, rng, RNG_SEED
N_SUBJ = args.n_subjects
N_TRIALS_PER_COND = args.n_trials
rng = np.random.default_rng(args.seed)
RNG_SEED = args.seed
rng = np.random.default_rng(RNG_SEED)
subjects_df, trials_df = simulate()
write_meta(subjects_df, trials_df)

if __name__ == "__main__":
main()