Datasets:
benchflow
/
frontierphysics-pr32-evidence

Dataset card Data Studio Files
xet
 Community
text
stringclasses
308 values
E ...Full output truncated (64 lines hidden), use '-vv' to show
test_outputs.py:1150: AssertionError
____________ test_submitted_executable_is_rigid_transform_invariant ____________
def test_submitted_executable_is_rigid_transform_invariant() -> None:
data = input_data()
state_a = np.asarray(data["state_a_xyz"])
state_b = np.asarray(data["state_b_xyz"])
rotation_a = Rotation.from_rotvec([0.31, -0.22, 0.47]).as_matrix()
rotation_b = Rotation.from_rotvec([-0.18, 0.39, 0.26]).as_matrix()
transformed_a = state_a @ rotation_a.T + np.asarray([17.0, -11.0, 4.5])
transformed_b = state_b @ rotation_b.T + np.asarray([-8.0, 13.5, 21.0])
fixture = coordinate_fixture(data, transformed_a, transformed_b)
> artifacts, hidden_reference, hidden_nonaffine = run_submitted_executable(
fixture,
PUBLIC_RADIUS,
"rigid-transform",
)
test_outputs.py:1791:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
test_outputs.py:1575: in run_submitted_executable
validate_nonaffine_summary_against_input(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
artifacts = {'arrays': {'compression': array([0.68757631, 0.71798022, 0.70042896, ..., 0.57020443, 0.52854174,
0.02429242])... 0.8885252214093184, 'input_schema_version': 'neutral-protein-transition-v1', 'maximum_neighbor_count': 165, ...}, ...}
fixture = {'residue_key': array(['S000-R00001', 'S000-R00002', 'S000-R00003', ..., 'S025-R00141',
'S025-R00142', 'S025-R0...S025', 'S025', 'S025'], dtype='<U4'), 'segment_position': array([ 1, 2, 3, ..., 141, 142, 143], dtype=int32), ...}
reference = {'deformation_hessian': array([[[[ 0.0382661 , -0.04490413, 0.01117645],
[-0.04490413, 0.04164043, -0.00899..._order_rms_residual': array([2.33638254, 2.44697904, 2.79931571, ..., 4.37409165, 3.38580329,
2.25020394]), ...}
def validate_nonaffine_summary_against_input(
artifacts: dict[str, object],
fixture: dict[str, np.ndarray | str],
reference: dict[str, np.ndarray | float | int],
) -> None:
arrays = artifacts["nonaffine_arrays"]
summary = artifacts["nonaffine_summary"]
residue_key = np.asarray(fixture["residue_key"])
count = residue_key.size
quadratic_signal = np.asarray(arrays["quadratic_signal_rms"])
residual_reduction = np.asarray(
arrays["residual_reduction_fraction"]
)
ordering = sorted(
range(count),
key=lambda index: (
-float(quadratic_signal[index]),
str(residue_key[index]),
),
)
expected_top = [
str(residue_key[index])
for index in ordering[: min(TOP_K, count)]
]
expected = {
"model_radius_angstrom": float(reference["model_radius_angstrom"]),
"model_radius_multiplier": NONAFFINE_RADIUS_MULTIPLIER,
"nonaffine_reduction_threshold": NONAFFINE_REDUCTION_THRESHOLD,
"residue_count": count,
"directed_edge_count": int(reference["directed_edge_count"]),
"minimum_neighbor_count": int(
np.min(arrays["model_neighbor_count"])
),
"maximum_neighbor_count": int(
np.max(arrays["model_neighbor_count"])
),
"minimum_quadratic_design_rank": int(
np.min(arrays["quadratic_design_rank"])
),
"maximum_quadratic_design_condition": float(
np.max(arrays["quadratic_design_condition"])
),
"median_first_order_rms_residual": float(
np.median(arrays["first_order_rms_residual"])
),
"median_second_order_rms_residual": float(
np.median(arrays["second_order_rms_residual"])
),
"median_quadratic_signal_rms": float(
np.median(quadratic_signal)
),
"median_residual_reduction_fraction": float(
np.median(residual_reduction)
),
"fraction_residual_reduction_above_threshold": float(
np.mean(
residual_reduction > NONAFFINE_REDUCTION_THRESHOLD
)
),
"top_nonaffine_residue_keys": expected_top,
}
for key in (
"model_radius_angstrom",
"model_radius_multiplier",
"nonaffine_reduction_threshold",