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#!/usr/bin/env python3
"""Mechanics checks for the reconstructed Figure-5 physical grid."""
from __future__ import annotations
from pathlib import Path
import sys
import numpy as np
ROOT = Path(__file__).resolve().parents[1]
sys.path.insert(0, str(ROOT))
from sdil.physical_grid import ( # noqa: E402
EdgePolynomialPredictor,
GridCircuit,
GridSquareLawImperfection,
RingClassificationDataset,
_residual_and_jacobian,
edge_voltage_drops,
fit_edge_predictor,
output_difference,
solve_grid_state,
evaluate_grid_classifier,
train_grid_classifier,
)
def sample_local_states(
circuit: GridCircuit, count: int, seed: int
) -> np.ndarray:
rng = np.random.default_rng(seed)
states = []
for _ in range(count):
gates = rng.normal(2.33, 0.08, circuit.edge_count)
sources = circuit.source_values(
rng.uniform(circuit.low_voltage, circuit.high_voltage),
rng.uniform(circuit.low_voltage, circuit.high_voltage),
)
voltages = solve_grid_state(circuit, gates, sources)
states.append(edge_voltage_drops(circuit, voltages))
return np.asarray(states)
def main() -> None:
circuit = GridCircuit()
assert circuit.edge_count == 32
assert circuit.source_nodes == (15, 13, 7, 5)
assert circuit.target_nodes == (10, 8)
gates = np.full(circuit.edge_count, 2.33)
sources = circuit.source_values(0.2266, 0.2266)
free = solve_grid_state(circuit, gates, sources)
assert np.allclose(free[list(circuit.source_nodes)], sources)
assert abs(output_difference(circuit, free)) < 1e-12
residual, _ = _residual_and_jacobian(circuit, gates, free)
unknown = [
node for node in range(circuit.node_count)
if node not in circuit.source_nodes
]
assert np.linalg.norm(residual[unknown], ord=np.inf) < 1e-10
target_difference = 0.018
target_mean = float(np.mean(free[list(circuit.target_nodes)]))
target_values = np.asarray((
target_mean + 0.5 * target_difference,
target_mean - 0.5 * target_difference,
))
clamped = solve_grid_state(
circuit, gates, sources, target_values=target_values,
initial_state=free)
assert np.allclose(clamped[list(circuit.target_nodes)], target_values)
assert abs(output_difference(circuit, clamped) - target_difference) < 1e-12
ideal = GridSquareLawImperfection.ideal(circuit.edge_count)
free_drops = edge_voltage_drops(circuit, free)
clamped_drops = edge_voltage_drops(circuit, clamped)
assert np.array_equal(
ideal.observed_rate(
circuit.measured_learning_rate, free_drops, clamped_drops),
ideal.ideal_rate(
circuit.measured_learning_rate, free_drops, clamped_drops),
)
hardware = GridSquareLawImperfection.sample_appendix_c(
circuit.edge_count, 20260829)
calibration_states = sample_local_states(circuit, 48, 20260830)
neutral = np.asarray([
hardware.neutral_bias(circuit.measured_learning_rate, state)
for state in calibration_states
])
center = np.mean(calibration_states, axis=0)
scale = np.maximum(np.ptp(calibration_states, axis=0), 1e-3)
constant = EdgePolynomialPredictor.zeros(center, scale, degree=0)
quadratic = EdgePolynomialPredictor.zeros(center, scale, degree=2)
assert fit_edge_predictor(constant, calibration_states, neutral) == 48
assert fit_edge_predictor(quadratic, calibration_states, neutral) == 48
heldout_states = sample_local_states(circuit, 16, 20260831)
heldout_neutral = np.asarray([
hardware.neutral_bias(circuit.measured_learning_rate, state)
for state in heldout_states
])
constant_rmse = float(np.sqrt(np.mean([
np.square(measurement - constant.predict(state))
for state, measurement in zip(heldout_states, heldout_neutral)
])))
quadratic_rmse = float(np.sqrt(np.mean([
np.square(measurement - quadratic.predict(state))
for state, measurement in zip(heldout_states, heldout_neutral)
])))
assert quadratic_rmse < 1e-7
assert constant_rmse > 1e-3
angles = np.arange(8) * (2.0 * np.pi / 8.0)
midpoint = 0.5 * (circuit.low_voltage + circuit.high_voltage)
ring = RingClassificationDataset(
inputs_v=np.column_stack((
midpoint + 0.18 * np.cos(angles),
midpoint - 0.18 * np.sin(angles),
)),
labels_v=np.asarray([-0.018] * 4 + [0.018] * 4),
)
initial_gates = np.random.default_rng(7).normal(
2.33, 0.02, circuit.edge_count)
initial_metrics, _ = evaluate_grid_classifier(
circuit, initial_gates, ring)
trained = train_grid_classifier(
circuit,
initial_gates,
ring,
ideal,
method="clean",
epochs=30,
standard_learning_time_seconds=1e-3,
record_every=10,
)
assert trained["hinge_loss_v2"] < initial_metrics["hinge_loss_v2"]
assert trained["local_updates"] > 0
print({
"nodes": circuit.node_count,
"edges": circuit.edge_count,
"source_nodes": circuit.source_nodes,
"target_nodes": circuit.target_nodes,
"constant_neutral_rmse_v_per_s": constant_rmse,
"quadratic_neutral_rmse_v_per_s": quadratic_rmse,
"clean_hinge_before": initial_metrics["hinge_loss_v2"],
"clean_hinge_after": trained["hinge_loss_v2"],
"autodiff_used": False,
})
if __name__ == "__main__":
main()
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