vipr_reflectometry.flow_models.postprocess package¶
Subpackages¶
- vipr_reflectometry.flow_models.postprocess.cluster package
- Subpackages
- vipr_reflectometry.flow_models.postprocess.cluster.clustering package
- Subpackages
- Submodules
- vipr_reflectometry.flow_models.postprocess.cluster.clustering.algorithms module
- vipr_reflectometry.flow_models.postprocess.cluster.clustering.clustering module
- vipr_reflectometry.flow_models.postprocess.cluster.clustering.hook module
- vipr_reflectometry.flow_models.postprocess.cluster.clustering.simulation module
- Module contents
- vipr_reflectometry.flow_models.postprocess.cluster.model_selection package
- vipr_reflectometry.flow_models.postprocess.cluster.clustering package
- Module contents
- Subpackages
Submodules¶
vipr_reflectometry.flow_models.postprocess.basic_corner_plot module¶
Basic Corner Plot for Flow Network Posterior Distributions.
Creates clean corner plots WITHOUT clustering overlay. Runs before any filters/clustering to show baseline posterior distribution.
- class vipr_reflectometry.flow_models.postprocess.basic_corner_plot.BasicCornerPlot(app: VIPR)¶
Bases:
objectBasic corner plot visualization for flow network posteriors.
Registers hook on INFERENCE_POSTPROCESS_PRE_PRE_FILTER_HOOK to create clean corner plots showing all posterior samples without clustering.
- create_basic_corner_plot(app, data=None, **kwargs)¶
Create basic corner plot for Flow Network posterior distribution.
Runs BEFORE postprocessing filters (before clustering). Shows clean baseline visualization of ALL posterior samples.
- Parameters:
app – VIPR application instance
data – Prediction data with parameter samples (before postprocessing)
**kwargs – Additional parameters
- Returns:
None (modifies datacollector as side effect)
vipr_reflectometry.flow_models.postprocess.marginal_distributions module¶
1D Marginal Posterior Distributions for Flow Network Predictions.
Creates histogram diagrams for each parameter’s posterior distribution using hook-based architecture for clean separation of concerns.
- class vipr_reflectometry.flow_models.postprocess.marginal_distributions.MarginalDistributions(app: VIPR)¶
Bases:
objectFlow Network postprocessing for 1D marginal posterior distributions.
Registers hook on INFERENCE_POSTPROCESS_PRE_PRE_FILTER_HOOK to create histogram diagrams for each parameter from posterior samples.
- create_marginal_distributions(app, data=None, **kwargs)¶
Create 1D histogram diagrams for each parameter’s posterior distribution.
- Parameters:
app – VIPR application instance
data – Prediction data with parameter samples (before postprocessing)
**kwargs – Additional parameters
- Returns:
None (modifies datacollector as side effect)
- class vipr_reflectometry.flow_models.postprocess.marginal_distributions.MarginalDistributionsParams(*, min_bins: int = 100)¶
Bases:
BaseModelConfiguration for marginal distribution creation.
- model_config: ClassVar[ConfigDict] = {'extra': 'forbid'}¶
Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].