Validation Helper

GitHub Link to Code.

Trajectory validation helper for pipeline operations.

Provides validation logic for trajectory-changing operations in the pipeline, ensuring that existing features are properly handled before modifications.

class mdxplain.trajectory.helper.validation_helper.trajectory_validation_helper.TrajectoryValidationHelper

Helper class for validating trajectory operations in pipeline context.

static check_features_before_trajectory_changes(pipeline_data: PipelineData, force: bool, operation_name: str, traj_indices: List[int] | None = None) → None

Check if features exist before trajectory modification.

Parameters

pipeline_dataPipelineData: Pipeline data object containing feature data
forcebool: Whether to force the operation despite existing features
operation_namestr: Name of the operation for error messages
traj_indicesList[int], optional: Trajectory indices to check for features. If None, checks if ANY features exist at all.

Raises

ValueError: If features exist and force=False

static validate_slice_parameters(frames: int | slice | List[int] | None) → None

Validate parameters for trajectory slicing.

Parameters

framesint, slice, list, or None: Frame specification for slicing

Returns

None: Performs validation, does not return value

Raises

ValueError: If frames parameter is invalid

Examples

>>> TrajectoryValidationHelper.validate_slice_parameters(1000)  # OK
>>> TrajectoryValidationHelper.validate_slice_parameters(slice(100, 500))  # OK
>>> TrajectoryValidationHelper.validate_slice_parameters([0, 10, 20])  # OK
>>> TrajectoryValidationHelper.validate_slice_parameters(-100)  # ValueError

static validate_data_selector(pipeline_data: PipelineData, data_selector: str) → Any

Validate DataSelector exists and has data.

Parameters

pipeline_dataPipelineData: Pipeline data object
data_selectorstr: Name of DataSelector to validate

Returns

DataSelectorData: The validated selector data object

Raises

ValueError: If DataSelector does not exist or has no data

Examples

>>> selector_data = TrajectoryValidationHelper.validate_data_selector(
...     pipeline_data, "folded_frames"
... )
>>> print(f"Selector has {len(selector_data.trajectory_frames)} trajectories")

static resolve_and_validate_traj_pair(trajectory_data: TrajectoryData, target_traj: int | str, source_traj: int | str) → Tuple[int, int]

Resolve and validate trajectory pair for join/stack operations.

Parameters

trajectory_dataTrajectoryData: Trajectory data container
target_trajint or str: Target trajectory selector
source_trajint or str: Source trajectory selector

Returns

tuple: (target_idx, source_idx) as integers

Raises

ValueError: If selectors don’t resolve to exactly one trajectory each
ValueError: If target and source refer to same trajectory

static validate_superpose_parameters(pipeline_data: PipelineData, reference_traj: int, reference_frame: int, atom_selection: str, traj_selection: int | str | List[int | str] | str) → tuple

Validate all parameters for superpose operation.

Parameters

pipeline_dataPipelineData: Pipeline data container with trajectory data
reference_trajint: Index of trajectory containing the reference frame
reference_frameint: Frame index within reference trajectory
atom_selectionstr: MDTraj selection string for alignment atoms
traj_selectionvarious: Selection of trajectories to align

Returns

tuple: (reference_trajectory, traj_indices, ref_frame, ref_atom_indices)

Raises

ValueError: If any parameter is invalid

Examples

>>> ref_traj, indices, ref_frame, atom_indices = \
...     TrajectoryValidationHelper.validate_superpose_parameters(
...         pipeline_data, 0, 0, "backbone", "all"
...     )