# Constructed data: one trial per mouse, latency to reach a target, cut-off 60 s; 12 control and 12 treated mice.
# True model (model.py): control median 25 s, treatment multiplies latency by 1.6 (median 40 s), log-SD 0.6.
# The seed (8) is the first from 1 upward with at most one censored control and three to five censored treated
# mice, the typical pattern for this model (about 25% of treated mice expected to reach the cut-off).
import numpy as np, pandas as pd
from model import simulate
rng = np.random.default_rng(8)
tc, ec = simulate(rng, 12, 25); tt, et = simulate(rng, 12, 40)
d = pd.DataFrame({"mouse": [f"c{i+1}" for i in range(12)] + [f"t{i+1}" for i in range(12)],
                  "group": ["control"] * 12 + ["treated"] * 12,
                  "latency_s": np.round(np.r_[tc, tt], 1), "responded": np.r_[ec, et]})
d.to_csv("latency.csv", index=False)