import numpy as np
from ergodic import calibrate, certify, consensus, latent_model, timeseries
from ergodic.latent.likelihoods import spectral_whittle, switching_ar

# 1. Simulate 1,440 ticks. While a hidden switch is on (ticks 500 to 999),
#    the level is 0.5 higher.
rng = np.random.default_rng(0)
switch = np.zeros(1440, dtype=int)
switch[500:1000] = 1
signal = 100 + 0.5 * switch + rng.normal(0, 0.5, 1440)
data = timeseries({"tick": np.arange(1440), "signal": signal}, time="tick")

# 2. Fix the detector's configuration once, then recover with it.
spec = latent_model(model=spectral_whittle(), draws=400, warmup=400, chains=2)
print(spec)
spectral = spec.run(data, "signal")
print(spectral.verdicts())
gate = spectral.gates[0]
print(gate.name, gate.blocks, f"{gate.value:.1f}", gate.passed)

# 3. Does a regime exist at all? Compare against 9 simulated no-switch worlds.
test = certify(
    data, "signal", model=spectral_whittle(), n_null=9, refit_draws=100, refit_warmup=100
)
print(test)

# 4. How often does this configuration fire on worlds with no switch?
report = calibrate(spec, data, "signal", n_controls=3)
print(report.summary())

# 5. Ask a second likelihood and compare the verdicts block by block.
ar = latent_model(model=switching_ar(), draws=200, warmup=200, chains=1).run(data, "signal")
table = consensus([spectral, ar])
print(table)
print(table.to_frame().to_string(index=False))