sierra.core.models.interface

Interface classes for the mathematical models framework in SIERRA.

Models can be run and added to any configured graph during stage 4.

• IConcreteIntraExpModel1D: Interface for one-dimensional models.

• IConcreteIntraExpModel2D: Interface for two-dimensional models.

• IConcreteInterExpModel1D: Interface for one-dimensional models.

class sierra.core.models.interface.IConcreteIntraExpModel1D

Interface for one-dimensional models.

1D models are those that generate a single time series from zero or more experimental outputs within a single Experiment. Models will be rendered as lines on a StackedLineGraph. Models “target” one or more CSV files which are already configured to be generated, and will show up as additional lines on the generated graph.

Inheritance

__doc__ = 'Interface for one-dimensional models.\n\n    1D models are those that generate a single time series from zero or more\n    experimental outputs within a *single* :term:`Experiment`. Models will be\n    rendered as lines on a\n    :class:`~sierra.core.graphs.stacked_line_graph.StackedLineGraph`. Models\n    "target" one or more CSV files which are already configured to be generated,\n    and will show up as additional lines on the generated graph.\n\n    '

__module__ = 'sierra.core.models.interface'

__repr__() → str

Return the UUID string of the model name.

legend_names() → List[str]

Compute names for the model predictions for the target graph legend.

Applicable to:

• StackedLineGraph

run(criteria: IConcreteBatchCriteria, exp_num: int, cmdopts: Dict[str, Any]) → List[DataFrame]

Run the model and generate a list of dataframes.

Each dataframe can (potentially) target different graphs. All dataframes should contain a single column named model, with each row of the dataframe containing the model prediction at the Experimental Run interval corresponding to the row (e.g., row 7 contains the model prediction for interval 7).

run_for_exp(criteria: IConcreteBatchCriteria, cmdopts: Dict[str, Any], exp_num: int) → bool

Determine if the model should be run for the specified experiment.

Some models may only be valid/make sense to run for a subset of experiments within a batch, so models can be selectively executed with this function.

target_csv_stems() → List[str]

Return a list of CSV file stems that the model is targeting.

File stem = path sans directory path and extension.

class sierra.core.models.interface.IConcreteIntraExpModel2D

Interface for two-dimensional models.

2D models are those that generate a list of 2D matrices, forming a 2D time series. Can be built from zero or more experimental outputs from a single Experiment. Models “target” one or more CSV files which are already configured to be generated, and will show up as additional lines on the generated graph.

Inheritance

__doc__ = 'Interface for two-dimensional models.\n\n    2D models are those that generate a list of 2D matrices, forming a 2D time\n    series. Can be built from zero or more experimental outputs from a *single*\n    :term:`Experiment`. Models "target" one or more CSV files which are already\n    configured to be generated, and will show up as additional lines on the\n    generated graph.\n\n    '

__module__ = 'sierra.core.models.interface'

__repr__() → str

Return the UUID string of the model name.

run(criteria: IConcreteBatchCriteria, exp_num: int, cmdopts: Dict[str, Any]) → List[DataFrame]

Run the model and generate a list of dataframes.

Each dataframe can (potentially) target a different graph. Each dataframe should be a NxM grid (with N not necessarily equal to M). All dataframes do not have to be the same dimensions. The index of a given dataframe in a list should correspond to the model value for interval/timestep.

run_for_exp(criteria: IConcreteBatchCriteria, cmdopts: Dict[str, Any], exp_num: int) → bool

Determine if a model should be run for the specified experiment.

Some models may only be valid/make sense to run for a subset of experiments within a batch, so models can be selectively executed with this function.

target_csv_stems() → List[str]

Return a list of CSV file stems that the model is targeting.

File stem = path sans directory path and extension.

class sierra.core.models.interface.IConcreteInterExpModel1D

Interface for one-dimensional models.

1D models are those that generate a single time series from any number of experimental outputs across all experiments in a batch(or from another source). Models will be rendered as lines on a SummaryLineGraph. Models “target” one or more CSV files which are already configured to be generated, and will show up as additional lines on the generated graph.

Inheritance

__doc__ = 'Interface for one-dimensional models.\n\n    1D models are those that generate a single time series from any number of\n    experimental outputs across *all* experiments in a batch(or from another\n    source). Models will be rendered as lines on a\n    :class:`~sierra.core.graphs.summary_line_graph.SummaryLineGraph`.  Models\n    "target" one or more CSV files which are already configured to be generated,\n    and will show up as additional lines on the generated graph.\n\n    '

__module__ = 'sierra.core.models.interface'

__repr__() → str

Return the UUID string of the model name.

legend_names() → List[str]

Compute names for the model predictions for the target graph legend.

Applicable to:

• ~sierra.core.graphs.summary_line_graph.SummaryLineGraph`

run(criteria: IConcreteBatchCriteria, cmdopts: Dict[str, Any]) → List[DataFrame]

Run the model and generate list of dataframes.

Each dataframe can (potentially) target a different graph. Each dataframe should contain a single row, with one column for the predicted value of the model for each experiment in the batch.

run_for_batch(criteria: IConcreteBatchCriteria, cmdopts: Dict[str, Any]) → bool

Determine if the model should be run for the specified batch criteria.

Some models may only be valid/make sense to run for some batch criteria, so models can be selectively executed with this function.

target_csv_stems() → List[str]

Return a list of CSV file stems that the model is targeting.

File stem = path sans directory path and extension.