Module pyprotolinc.main

Main module. Configures the logger and provides the entry points of the command line runner.

pyprotolinc.main.create_model(model_class: type[pyprotolinc.models.Model], state_model_name: str, assumptions_file: Optional[str], assumption_wrapper_opt: Optional[AssumptionSetWrapper]) → Model

Create a valuation model.

Parameters

  • model_class – The class of the model.

  • state_model_name (str) – The name of the state model.

  • assumptions_file – Path to the assumptions config file to be used.

  • assumption_wrapper – Object of type AssumptionSetWrapper that is used directly.

Returns

The valuation model.

Return type

Instance of model_class

pyprotolinc.main.main() → None

Entry point of the CLI client. Declares the following subtasks:

  • run to run start a run from the command line

  • profile to run start a run with profile information from the command line

  • download_dav_tables to download soem DAV tables from the R package mortality tables

pyprotolinc.main.print_version() → None

Print the version of the program.

pyprotolinc.main.profile(config_file: str = 'config.yml', multi_processing_overwrite: Optional[bool] = None) → None

Run and and export a CSV file with runtime statistics.

Parameters

  • config_file (str) – Path ot the config file

  • multi_processing_overwrite – Optional boolen parameters that allows overwriting the multiprocessing setting in the config file.

Returns

None

pyprotolinc.main.project_cashflows(run_config: RunConfig, df_portfolio_overwrite: Optional[DataFrame] = None, assumption_wrapper: Optional[AssumptionSetWrapper] = None, export_to_file: bool = True) → dict[str, numpy.ndarray[Any, numpy.dtype[numpy.float64]]]

The main calculation rountine, can also be called as library function. If a dataframe is passed it will be used to build the portfolio object, otherwise the portfolio will be obtained from th run-config.

Parameters

  • run_config – The run configuration object.

  • df_portfolio_overwrite – An optional dataframe that will be used instead of the configured portfolio if provided

  • assumption_wrapper – Optionally an assumption set can be injected here directly instead of getting it via the configured paths

  • export_to_file – Boolean flag to indicate if the results should be written to a file (as specified in the config object)

Returns

Dictionary containing the result vectors.

Return type

dict[str, npt.NDArray[np.float64]]

pyprotolinc.main.project_cashflows_cli(config_file_or_object: Union[str, RunConfig] = 'config.yml', multi_processing_overwrite: Optional[bool] = None) → dict[str, numpy.ndarray[Any, numpy.dtype[numpy.float64]]]

Start a projection run.

Parameters

  • config_file_or_object (str) – Path to the config file or a RunConfig

  • multi_processing_overwrite – Optional boolen parameter that allows overwriting the multiprocessing setting in the config file.

Returns

None

pyprotolinc.main.show_docs_in_browser() → None

Show the readthedocs help pages in the system browser :return: None