simuran.analysis.analysis_handler module¶

This module provides functionality for performing large batch analysis.

class simuran.analysis.analysis_handler.AnalysisHandler(fns_to_run: list = <factory>, fn_params_list: list = <factory>, verbose: bool = False, handle_errors: bool = False, pickle_path: Union[str, Path, None] = 'simuran_analysis.pkl', _was_error: bool = False)¶

Bases: object

Hold functions to run and the parameters to use for them.

fns_to_run¶

The functions to run

Type:	list of functions

fn_param_list¶

The arguments to pass to these functions. The arguments are passed in order, so these are positional.

Type:	list of tuples

fn_kwargs_list¶

Keyword arguments to pass to the functions to run.

Type:	list of dicts

results¶

The results of the function calls

Type:	indexed.IndexedOrderedDict

verbose¶

Whether to print more information while running the functions.

Type:	bool

handle_errors¶

Whether to handle errors during runtime of underlying functions, or to crash on error.

Type:	bool

Parameters:	verbose (bool, optional) – Sets the value of the verbose attribute, defaults to False. handle_errors (bool, optional) – Sets the value of the handle_errors attribute, defaults to False.

add_analysis(fn, args)¶

Add the function fn to the list with the given args and kwargs.

Parameters:	fn (function) – The function to add. args (positional arguments) – The list of arguments to run the function with. See https://slimmer-ai.github.io/mpire/usage/map/map.html For more information on how arguments are handled.
Returns:
Return type:	None

handle_errors = False

load_results_from_pickle()¶

pickle_path = 'simuran_analysis.pkl'¶

reset()¶: Reset this object, clearing results and function list.

run(pbar: bool = False, n_jobs: int = 1, save_every: int = 0, force_mp: bool = False)¶

Run all of the established functions.

Parameters:	pbar (string or bool, optional) – Whether to have a progress bar. Options are False (default) no progress bar. True a tdqm progress bat “notebook” a progress for notebooks n_jobs (int, optional) – The number of jobs to run in parallel, by default 1 Uses mpire.WorkerPool along with a mapping. For more complex multiprocessing, directly use mpire.WorkerPool. save_every (int, optional) – Save the results to a pickle file every n jobs, by default 0 If 0, then no saving occurs. force_mp (bool, optional) – Force multiprocessing, by default False
Returns:
Return type:	None

save_results_to_pickle()¶

save_results_to_table(filename=None, columns=None, from_dict=True)¶

Dump analysis results to csv file.

Parameters:	filename (str or Path) – The output path.
Returns:	The resulting dataframe
Return type:	Dataframe

verbose = False