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/verifier/test_outputs.py:541:
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/verifier/test_outputs.py:97: in load_trajectory
return load_csv(solution / "posterior_trajectories.csv", TRAJECTORY_COLUMNS)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
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path = PosixPath('/root/solution/posterior_trajectories.csv')
columns = ['run_id', 'strategy', 'shot', 'frequency_mhz', 'zeta', 'theta_mean', ...]
def load_csv(path: Path, columns: list[str]) -> list[dict[str, str]]:
> assert path.is_file(), f"missing {path.name}"
E AssertionError: missing posterior_trajectories.csv
E assert False
E + where False = is_file()
E + where is_file = PosixPath('/root/solution/posterior_trajectories.csv').is_file
/verifier/test_outputs.py:89: AssertionError
_________ test_machine_readable_convergence_and_phi_bound_sensitivity __________
def test_machine_readable_convergence_and_phi_bound_sensitivity() -> None:
payload = load_payload()
> rows = [numeric_trajectory_row(row) for row in load_trajectory()]
^^^^^^^^^^^^^^^^^
/verifier/test_outputs.py:548:
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/verifier/test_outputs.py:97: in load_trajectory
return load_csv(solution / "posterior_trajectories.csv", TRAJECTORY_COLUMNS)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
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path = PosixPath('/root/solution/posterior_trajectories.csv')
columns = ['run_id', 'strategy', 'shot', 'frequency_mhz', 'zeta', 'theta_mean', ...]
def load_csv(path: Path, columns: list[str]) -> list[dict[str, str]]:
> assert path.is_file(), f"missing {path.name}"
E AssertionError: missing posterior_trajectories.csv
E assert False
E + where False = is_file()
E + where is_file = PosixPath('/root/solution/posterior_trajectories.csv').is_file
/verifier/test_outputs.py:89: AssertionError
_____________ test_report_and_figure_are_quantitative_and_nonblank _____________
def test_report_and_figure_are_quantitative_and_nonblank() -> None:
payload = load_payload()
> summary = load_json(SOLUTION / "summary.json")
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
/verifier/test_outputs.py:556:
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path = PosixPath('/root/solution/summary.json')
def load_json(path: Path) -> dict[str, Any]:
> assert path.is_file(), f"missing {path.name}"
E AssertionError: missing summary.json
E assert False
E + where False = is_file()
E + where is_file = PosixPath('/root/solution/summary.json').is_file
/verifier/test_outputs.py:70: AssertionError
_______ test_reusable_script_solves_multiple_unpredictable_hidden_inputs _______
hidden_outputs = [({'checkpoint_cases': [{'case_id': 'zero-prefix-cd89de4e4359cddf', 'run_id': 'hidden-cd89de4e4359cddf-adaptive-a', 's...pytest-3/hidden-cases0/hidden_input_1.json'), PosixPath('/tmp/pytest-of-root/pytest-3/hidden-cases0/hidden_output_1'))]
def test_reusable_script_solves_multiple_unpredictable_hidden_inputs(
hidden_outputs: list[tuple[dict[str, Any], Path, Path]],
) -> None:
for payload, _, output in hidden_outputs:
> validate_bundle(payload, output)
/verifier/test_outputs.py:679:
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/verifier/test_outputs.py:509: in validate_bundle
summary = validate_summary_and_design(payload, solution, rows, normalization_error)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
/verifier/test_outputs.py:278: in validate_summary_and_design
assert_close(actual[f"shot_{shot}"][key], from_rows[strategy][f"shot_{shot}"][key], atol=1e-10, rtol=1e-10)
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actual = 0.22320398204402384, expected = 0.049820017600308915
def assert_close(actual: float, expected: float, *, atol: float = 3e-3, rtol: float = 3e-3, message: str = "") -> None:
> assert math.isclose(actual, expected, abs_tol=atol, rel_tol=rtol), message or f"{actual} != {expected}"
E AssertionError: 0.22320398204402384 != 0.049820017600308915
E assert False
E + where False = <built-in function isclose>(0.22320398204402384, 0.049820017600308915, abs_tol=1e-10, rel_tol=1e-10)
E + where <built-in function isclose> = math.isclose
/verifier/test_outputs.py:132: AssertionError
---------------------------- Captured stdout setup -----------------------------
Processing posterior trajectories...
Writing posterior_trajectories.csv...
Writing checkpoint_posteriors.npz...
Computing precision gain...
Writing precision_gain.csv...
Computing strategy statistics...