Validation Helper

GitHub Link to Code.

Validation helper for structure visualization operations.

This module provides validation utilities for structure visualization, including Jupyter environment checks and visualization data validation.

class mdxplain.structure_visualization.helper.validation_helper.ValidationHelper

Helper class for structure visualization validation.

Provides static methods for validating environment requirements and data availability for structure visualization operations.

Examples

>>> ValidationHelper.validate_jupyter_environment()
>>> viz_data = ValidationHelper.validate_visualization_data(
...     pipeline_data, "my_viz"
... )

static validate_jupyter_environment() → None

Validate that code is running in Jupyter environment.

Parameters

None

Returns

None: Returns normally if running in Jupyter environment

Raises

RuntimeError: If not running in Jupyter notebook environment

Examples

>>> ValidationHelper.validate_jupyter_environment()
RuntimeError: NGLView visualization is only available in Jupyter notebooks...

Notes

Uses EnvironmentHelper.is_jupyter_environment() to check environment. Provides helpful error message for terminal usage.

static validate_visualization_data(pipeline_data: PipelineData, structure_viz_name: str) → StructureVisualizationData

Validate visualization data exists and return it.

Parameters

pipeline_dataPipelineData: Pipeline data object containing visualization data
structure_viz_namestr: Name of visualization session to validate

Returns

StructureVisualizationData: Validated visualization data object

Raises

KeyError: If structure_visualization_data attribute not found
KeyError: If specific visualization session name not found

Examples

>>> viz_data = ValidationHelper.validate_visualization_data(
...     pipeline_data, "my_viz"
... )

>>> # Raises KeyError if not found
>>> viz_data = ValidationHelper.validate_visualization_data(
...     pipeline_data, "nonexistent"
... )
KeyError: Visualization 'nonexistent' not found...

Notes

Provides detailed error messages listing available visualizations if the requested one is not found.

static validate_terminal_environment() → None

Validate that code is running in terminal, NOT Jupyter.

Checks if code is running in Jupyter notebook environment and raises error if true. PyMOL visualization requires terminal execution.

Parameters

None

Returns

None: Returns normally if NOT in Jupyter environment

Raises

RuntimeError: If running in Jupyter notebook environment

Examples

>>> ValidationHelper.validate_terminal_environment()
RuntimeError: PyMOL visualization requires terminal execution...

Notes

Uses EnvironmentHelper.is_jupyter_environment() to check environment. Provides helpful message suggesting NGLView for Jupyter usage.

static validate_pymol_available() → None

Validate that PyMOL Python module is available.

Checks if pymol module can be imported and raises error if not. Provides installation instructions for conda.

Parameters

None

Returns

None: Returns normally if pymol is available

Raises

ImportError: If pymol Python module cannot be imported

Examples

>>> ValidationHelper.validate_pymol_available()
ImportError: PyMOL Python module not found...

Notes

Uses EnvironmentHelper.is_pymol_available() to check availability. Suggests conda installation command if missing.

static validate_data_selectors(pipeline_data: PipelineData, data_selectors: list) → None

Validate that data selectors exist in pipeline data.

Checks that all provided data selector names are present in pipeline_data.data_selector_data and raises errors for missing or empty selector lists.

Parameters

pipeline_dataPipelineData: Pipeline data object containing data selector data
data_selectorslist: List of data selector names to validate

Returns

None: Returns normally if all data selectors are valid

Raises

ValueError: If no data selectors provided or selector doesn’t exist

Examples

>>> ValidationHelper.validate_data_selectors(
...     pipeline_data, ["cluster_0", "cluster_1"]
... )

>>> # Raises ValueError if empty
>>> ValidationHelper.validate_data_selectors(pipeline_data, [])
ValueError: At least one data selector is required

>>> # Raises ValueError if not found
>>> ValidationHelper.validate_data_selectors(
...     pipeline_data, ["nonexistent"]
... )
ValueError: Data selector 'nonexistent' not found...

Notes

This method is used by StructureVizFeatureService to validate data selectors before creating representative PDFs.