oasislmf.pytools.lec.manager
============================

.. py:module:: oasislmf.pytools.lec.manager


Attributes
----------

.. autoapisummary::

   oasislmf.pytools.lec.manager.logger


Functions
---------

.. autoapisummary::

   oasislmf.pytools.lec.manager.read_input_files
   oasislmf.pytools.lec.manager.get_max_summary_id
   oasislmf.pytools.lec.manager.do_lec_output_agg_summary
   oasislmf.pytools.lec.manager.process_input_file
   oasislmf.pytools.lec.manager.run_lec
   oasislmf.pytools.lec.manager.run
   oasislmf.pytools.lec.manager.main


Module Contents
---------------

.. py:data:: logger

.. py:function:: read_input_files(run_dir, use_return_period, agg_wheatsheaf_mean, occ_wheatsheaf_mean, sample_size)

   Reads all input files and returns a dict of relevant data
   Args:
       run_dir (str | os.PathLike): Path to directory containing required files structure
       use_return_period (bool): Use Return Period file.
       agg_wheatsheaf_mean (bool): Aggregate Wheatsheaf Mean.
       occ_wheatsheaf_mean (bool): Occurrence Wheatsheaf Mean.
       sample_size (int): Sample Size.
   Returns:
       file_data (Dict[str, Any]): A dict of relevent data extracted from files
       use_return_period (bool): Use Return Period file.
       agg_wheatsheaf_mean (bool): Aggregate Wheatsheaf Mean.
       occ_wheatsheaf_mean (bool): Occurrence Wheatsheaf Mean.


.. py:function:: get_max_summary_id(file_handles)

   Get max summary_id from all summary files
   Args:
       file_handles (List[np.memmap]): List of memmaps for summary files data
   Returns:
       max_summary_id (int): Max summary ID


.. py:function:: do_lec_output_agg_summary(summary_id, sidx, loss, filtered_occ_map, outloss_mean, outloss_sample, num_sidxs, max_summary_id)

   Populate outloss_mean and outloss_sample with aggregate and max losses
   Args:
       summary_id (oasis_int): summary_id
       sidx (oasis_int): Sample ID
       loss (oasis_float): Loss value
       filtered_occ_map (ndarray[occ_map_dtype]): Filtered numpy map of event_id, period_no, occ_date_id from the occurrence file_
       outloss_mean (ndarray[OUTLOSS_DTYPE]): ndarray indexed by summary_id, period_no containing aggregate and max losses
       outloss_sample (ndarray[OUTLOSS_DTYPE]): ndarray indexed by summary_id, sidx, period_no containing aggregate and max losses
       num_sidxs (int): Number of sidxs to consider for outloss_sample
       max_summary_id (int): Max summary ID


.. py:function:: process_input_file(fin, outloss_mean, outloss_sample, summary_ids, occ_map, use_return_period, num_sidxs, max_summary_id)

   Process summary file and populate outloss_mean and outloss_sample with losses
   Args:
       fin (np.memmap): summary binary memmap
       outloss_mean (ndarray[OUTLOSS_DTYPE]): ndarray indexed by summary_id, period_no containing aggregate and max losses
       outloss_sample (ndarray[OUTLOSS_DTYPE]): ndarray indexed by summary_id, sidx, period_no containing aggregate and max losses
       summary_ids (ndarray[bool]): bool array marking which summary_ids are used
       occ_map (ndarray[occ_map_dtype]): numpy map of event_id, period_no, occ_date_id from the occurrence file_
       use_return_period (bool): Use Return Period file.
       num_sidxs (int): Number of sidxs to consider for outloss_sample
       max_summary_id (int): Max summary ID


.. py:function:: run_lec(file_handles, outloss_mean, outloss_sample, summary_ids, occ_map, use_return_period, num_sidxs, max_summary_id)

   Process each summary file and populate outloss_mean and outloss_sample
   Args:
       file_handles (List[np.memmap]): List of memmaps for summary files data
       outloss_mean (ndarray[OUTLOSS_DTYPE]): ndarray indexed by summary_id, period_no containing aggregate and max losses
       outloss_sample (ndarray[OUTLOSS_DTYPE]): ndarray indexed by summary_id, sidx, period_no containing aggregate and max losses
       summary_ids (ndarray[bool]): bool array marking which summary_ids are used
       occ_map (ndarray[occ_map_dtype]): numpy map of event_id, period_no, occ_date_id from the occurrence file_
       use_return_period (bool): Use Return Period file.
       num_sidxs (int): Number of sidxs to consider for outloss_sample
       max_summary_id (int): Max summary ID


.. py:function:: run(run_dir, subfolder, ept_output_file=None, psept_output_file=None, agg_full_uncertainty=False, agg_wheatsheaf=False, agg_sample_mean=False, agg_wheatsheaf_mean=False, occ_full_uncertainty=False, occ_wheatsheaf=False, occ_sample_mean=False, occ_wheatsheaf_mean=False, use_return_period=False, noheader=False)

   Runs LEC calculations
   Args:
       run_dir (str | os.PathLike): Path to directory containing required files structure
       subfolder (str): Workspace subfolder inside <run_dir>/work/<subfolder>
       ept_output_file (str, optional): Path to EPT output file. Defaults to None
       psept_output_file (str, optional): Path to PSEPT output file. Defaults to None
       agg_full_uncertainty (bool, optional): Aggregate Full Uncertainty. Defaults to False.
       agg_wheatsheaf (bool, optional): Aggregate Wheatsheaf. Defaults to False.
       agg_sample_mean (bool, optional): Aggregate Sample Mean. Defaults to False.
       agg_wheatsheaf_mean (bool, optional): Aggregate Wheatsheaf Mean. Defaults to False.
       occ_full_uncertainty (bool, optional): Occurrence Full Uncertainty. Defaults to False.
       occ_wheatsheaf (bool, optional): Occurrence Wheatsheaf. Defaults to False.
       occ_sample_mean (bool, optional): Occurrence Sample Mean. Defaults to False.
       occ_wheatsheaf_mean (bool, optional): Occurrence Wheatsheaf Mean. Defaults to False.
       use_return_period (bool, optional): Use Return Period file. Defaults to False.
       noheader (bool): Boolean value to skip header in output file


.. py:function:: main(run_dir='.', subfolder=None, ept=None, psept=None, agg_full_uncertainty=False, agg_wheatsheaf=False, agg_sample_mean=False, agg_wheatsheaf_mean=False, occ_full_uncertainty=False, occ_wheatsheaf=False, occ_sample_mean=False, occ_wheatsheaf_mean=False, use_return_period=False, noheader=False, **kwargs)