oasislmf.pytools.lec.aggreports.write_tables

Functions

get_loss(next_retperiod, last_retperiod, last_loss, ...)	Get loss based on current and next return period
fill_tvar(tail, tail_sizes, summary_id, ...)	Populate the Tail with retperiod and tvar values for summary_id
fill_tvar_wheatsheaf(tail, tail_sizes, summary_id, ...)	Populate the Tail with retperiod and tvar values for (summary_id, sidx) pair
write_return_period_out(next_returnperiod_idx, ...[, ...])	Processes return periods and computes losses for a given summary, updating TVaR and mean map if required.
write_tvar(epcalc, eptype_tvar, tail, tail_sizes)	Get TVaR values for EPT output from tail
write_tvar_wheatsheaf(num_sidxs, eptype_tvar, tail, ...)	Get TVaR values for PSEPT output from tail
write_ept(items, items_start_end, max_retperiod, ...)	Generate Loss Exceedance Curve values and Tail Value at Risk values based on items and epcalc/eptype/eptype_tvar
write_ept_weighted(items, items_start_end, ...[, ...])	Generate Loss Exceedance Curve values and Tail Value at Risk values based on items and epcalc/eptype/eptype_tvar.
write_psept(items, items_start_end, max_retperiod, ...)	Generate Per Sample Exceedance Probability Tables (PSEPT) for each individual sample, producing a separate loss
write_psept_weighted(items, items_start_end, ...[, ...])	Generate Per Sample Exceedance Probability Tables (PSEPT) for each individual sample, producing a separate loss
write_wheatsheaf_mean(mean_map, eptype, epcalc, ...)	Generate Wheatsheaf Mean Exceedance Probability Table (EPT) by averaging losses for each return period

Module Contents

oasislmf.pytools.lec.aggreports.write_tables.get_loss(next_retperiod, last_retperiod, last_loss, curr_retperiod, curr_loss)

Get loss based on current and next return period Args:

next_retperiod (float): Next return period last_retperiod (float): Previous return period last_loss (float): Previous Loss value curr_retperiod (float): Current return period curr_loss (float): Current Loss value

Returns:

loss (float): Loss Value

oasislmf.pytools.lec.aggreports.write_tables.fill_tvar(tail, tail_sizes, summary_id, next_retperiod, tvar)

Populate the Tail with retperiod and tvar values for summary_id Args:

tail (nb.typed.Dict[nb_oasis_int, NB_TAIL_valtype]): Dict of Summary ID to vector of (return period, tvar) values tail_sizes (nb.typed.Dict[nb_oasis_int, nb.types.int64]): Dict of Summary ID to size of each tail array summary_id (int): Summary ID next_retperiod (float): Next Return Period tvar (float): Tail Value at Risk

Returns:

tail (nb.typed.Dict[nb_oasis_int, NB_TAIL_valtype]): Dict of summary_id to vector of (return period, tvar) values tail_sizes (nb.typed.Dict[nb_oasis_int, nb.types.int64]): Dict of summary_id to size of each tail array

oasislmf.pytools.lec.aggreports.write_tables.fill_tvar_wheatsheaf(tail, tail_sizes, summary_id, sidx, num_sidxs, next_retperiod, tvar)

Populate the Tail with retperiod and tvar values for (summary_id, sidx) pair Args:

tail (nb.typed.Dict[nb_oasis_int, NB_TAIL_valtype]): Dict of (summary_id, sidx) pair to vector of (return period, tvar) values tail_sizes (nb.typed.Dict[nb_oasis_int, nb.types.int64]): Dict of (summary_id, sidx) pair to size of each tail array summary_id (int): Summary ID sidx (int): Sample ID num_sidxs (int): Number of sidxs to consider next_retperiod (float): Next Return Period tvar (float): Tail Value at Risk

Returns:

tail (nb.typed.Dict[nb_oasis_int, NB_TAIL_valtype]): Dict of (summary_id, sidx) pair to vector of (return period, tvar) values tail_sizes (nb.typed.Dict[nb_oasis_int, nb.types.int64]): Dict of (summary_id, sidx) pair to size of each tail array

oasislmf.pytools.lec.aggreports.write_tables.write_return_period_out(next_returnperiod_idx, last_computed_rp, last_computed_loss, curr_retperiod, curr_loss, summary_id, eptype, epcalc, max_retperiod, counter, tvar, tail, tail_sizes, returnperiods, mean_map=None, is_wheatsheaf=False, num_sidxs=-1)

Processes return periods and computes losses for a given summary, updating TVaR and mean map if required. Args:

next_returnperiod_idx (int): Index of the next return period to process. last_computed_rp (float): Last computed return period last_computed_loss (float): Last computed loss value curr_retperiod (float): Current return period being processed. curr_loss (float): Loss associated with the current return period. summary_id (int): Identifier for the current summary. eptype (int): Type of exceedance probability (0 = OEP, 1 = AEP). epcalc (int): Type of exceedance probability calculation. max_retperiod (int): Maximum return period to be used in calculations counter (int): Counter used for updating TVaR tvar (float): Tail Value at Risk tail (nb.typed.Dict[nb_oasis_int, NB_TAIL_valtype]): Dict of summary_id or (summary_id, sidx) pair to vector of (return period, tvar) values tail_sizes (nb.typed.Dict[nb_oasis_int, nb.types.int64]): Dict of summary_id or (summary_id, sidx) pair to size of each tail array returnperiods (ndarray[np.int32]): Return Periods array mean_map (ndarray[MEANMAP_dtype], optional): An array mapping used for mean loss calculations per Summary ID. Used for EPT output later. Defaults to None. is_wheatsheaf (bool, optional): If True, update the wheatsheaf TVaR structure. num_sidxs (int, optional): Number of sidxs to consider. Defaults to -1 if not is_wheatsheaf.

Returns:

rets (list[EPT_dtype]): Return period and Loss EPT data tail (nb.typed.Dict[nb_oasis_int, NB_TAIL_valtype]): Dict of summary_id or (summary_id, sidx) pair to vector of (return period, tvar) values tail_sizes (nb.typed.Dict[nb_oasis_int, nb.types.int64]): Dict of summary_id or (summary_id, sidx) pair to size of each tail array last_computed_rp (float): Last computed return period last_computed_loss (float): Last computed loss value

oasislmf.pytools.lec.aggreports.write_tables.write_tvar(epcalc, eptype_tvar, tail, tail_sizes)

Get TVaR values for EPT output from tail Args:

epcalc (int): Type of exceedance probability calculation. eptype_tvar (int): Type of Tail Value-at-Risk (TVAR) to calculate (0 = OEP TVAR, 1 = AEP TVAR). tail (nb.typed.Dict[nb_oasis_int, NB_TAIL_valtype]): Dict of summary_id to vector of (return period, tvar) values tail_sizes (nb.typed.Dict[nb_oasis_int, nb.types.int64]): Dict of summary_id pair to size of each tail array

Returns:

rets (list[EPT_dtype]): Return period and Loss EPT data

oasislmf.pytools.lec.aggreports.write_tables.write_tvar_wheatsheaf(num_sidxs, eptype_tvar, tail, tail_sizes)

Get TVaR values for PSEPT output from tail Args:

num_sidxs (int): Number of sidxs to consider. eptype_tvar (int): Type of Tail Value-at-Risk (TVAR) to calculate (0 = OEP TVAR, 1 = AEP TVAR). tail (nb.typed.Dict[nb_oasis_int, NB_TAIL_valtype]): Dict of (summary_id, sidx) pair to vector of (return period, tvar) values tail_sizes (nb.typed.Dict[nb_oasis_int, nb.types.int64]): Dict of (summary_id, sidx) pair to size of each tail array

Returns:

rets (list[PSEPT_dtype]): Return period and Loss PSEPT data

oasislmf.pytools.lec.aggreports.write_tables.write_ept(items, items_start_end, max_retperiod, epcalc, eptype, eptype_tvar, use_return_period, returnperiods, max_summary_id, sample_size=1)

Generate Loss Exceedance Curve values and Tail Value at Risk values based on items and epcalc/eptype/eptype_tvar

The loss calculation follows these principles: - For Aggregate Loss Exceedance Curves (AEP): The sum of all losses within a period is calculated. - For Occurrence Loss Exceedance Curves (OEP): The maximum loss within a period is taken. - TVAR (Tail Conditional Expectation): Calculated as the average of losses exceeding a given return period. Args:

items (ndarray[LOSSVEC2MAP_dtype]): Array mapping summary_id to loss value (and period_no/period_weighting where applicable) items_start_end (ndarray[np.int32]): An array marking where the start and end idxs are for each summary_id in the items array max_retperiod (int): Maximum return period to be used in calculations epcalc (int): Specifies the calculation method (mean damage loss, full uncertainty, per sample mean, sample mean). eptype (int): Type of exceedance probability (0 = OEP, 1 = AEP). eptype_tvar (int): Type of Tail Value-at-Risk (TVAR) to calculate (0 = OEP TVAR, 1 = AEP TVAR). use_return_period (bool): Use Return Period file. returnperiods (ndarray[np.int32]): Return Periods array max_summary_id (int): Maximum summary ID sample_size (int, optional): Sample Size. Defaults to 1.

Yields:

buffer (ndarray[EPT_dtype]): Buffered chunks of EPT data

oasislmf.pytools.lec.aggreports.write_tables.write_ept_weighted(items, items_start_end, cum_weight_constant, epcalc, eptype, eptype_tvar, unused_period_weights, use_return_period, returnperiods, max_summary_id, sample_size=1)

Generate Loss Exceedance Curve values and Tail Value at Risk values based on items and epcalc/eptype/eptype_tvar.

This function calculates weighted exceedance probability tables using cumulative period weightings (period_weighting), which impact the calculation of return periods. The weighting allows for more accurate representation of losses when event periods have different probabilities or frequencies of occurrence.

The loss calculation follows these principles: - For Aggregate Loss Exceedance Curves (AEP): The sum of all losses within a period is calculated. - For Occurrence Loss Exceedance Curves (OEP): The maximum loss within a period is taken. - TVAR (Tail Conditional Expectation): Calculated as the average of losses exceeding a given return period. Args:

items (ndarray[LOSSVEC2MAP_dtype]): Array mapping summary_id to loss value (and period_no/period_weighting where applicable) items_start_end (ndarray[np.int32]): An array marking where the start and end idxs are for each summary_id in the items array cum_weight_constant (float): Constant factor for scaling cumulative period weights. epcalc (int): Specifies the calculation method (mean damage loss, full uncertainty, per sample mean, sample mean). eptype (int): Type of exceedance probability (0 = OEP, 1 = AEP). eptype_tvar (int): Type of Tail Value-at-Risk (TVAR) to calculate (0 = OEP TVAR, 1 = AEP TVAR). unused_period_weights (ndarray[float]): Array of unused period weights use_return_period (bool): Use Return Period file. returnperiods (ndarray[np.int32]): Return Periods array max_summary_id (int): Maximum summary ID sample_size (int, optional): Sample Size. Defaults to 1.

Yields:

buffer (ndarray[EPT_dtype]): Buffered chunks of EPT data

oasislmf.pytools.lec.aggreports.write_tables.write_psept(items, items_start_end, max_retperiod, eptype, eptype_tvar, use_return_period, returnperiods, max_summary_id, num_sidxs)

Generate Per Sample Exceedance Probability Tables (PSEPT) for each individual sample, producing a separate loss exceedance curve for each sample, eptype, eptype_tvar. Args:

items (ndarray[WHEATKEYITEMS_dtype]): Array mapping (summary_id, sidx) to loss value (and period_no/period_weighting where applicable) items_start_end (ndarray[np.int32]): An array marking where the start and end idxs are for each (summary_id, sidx) pair in the items array max_retperiod (int): Maximum return period to be used in calculations eptype (int): Type of exceedance probability (0 = OEP, 1 = AEP). eptype_tvar (int): Type of Tail Value-at-Risk (TVAR) to calculate (0 = OEP TVAR, 1 = AEP TVAR). use_return_period (bool): Use Return Period file. returnperiods (ndarray[np.int32]): Return Periods array max_summary_id (int): Maximum summary ID num_sidxs (int): Number of sidxs to consider

Yields:

buffer (ndarray[PSEPT_dtype]): Buffered chunks of PSEPT data

oasislmf.pytools.lec.aggreports.write_tables.write_psept_weighted(items, items_start_end, max_retperiod, eptype, eptype_tvar, unused_period_weights, use_return_period, returnperiods, max_summary_id, num_sidxs, sample_size, mean_map=None)

Generate Per Sample Exceedance Probability Tables (PSEPT) for each individual sample, producing a separate loss exceedance curve for each sample, eptype, eptype_tvar. Args:

items (ndarray[WHEATKEYITEMS_dtype]): Array mapping (summary_id, sidx) to loss value (and period_no/period_weighting where applicable) items_start_end (ndarray[np.int32]): An array marking where the start and end idxs are for each (summary_id, sidx) pair in the items array max_retperiod (int): Maximum return period to be used in calculations eptype (int): Type of exceedance probability (0 = OEP, 1 = AEP). eptype_tvar (int): Type of Tail Value-at-Risk (TVAR) to calculate (0 = OEP TVAR, 1 = AEP TVAR). unused_period_weights (ndarray[float]): Array of unused period weights use_return_period (bool): Use Return Period file. returnperiods (ndarray[np.int32]): Return Periods array max_summary_id (int): Maximum summary ID num_sidxs (int): Number of sidxs to consider sample_size (int): Sample Size. Defaults to 1. mean_map (ndarray[MEANMAP_dtype], optional): An array mapping used for mean loss calculations per Summary ID. Used for EPT output later. Defaults to None.

Yields:

buffer (ndarray[PSEPT_dtype]): Buffered chunks of PSEPT data

oasislmf.pytools.lec.aggreports.write_tables.write_wheatsheaf_mean(mean_map, eptype, epcalc, max_summary_id)

Generate Wheatsheaf Mean Exceedance Probability Table (EPT) by averaging losses for each return period from a precomputed mean map. Args:

mean_map (ndarray[MEANMAP_dtype]): An array mapping used for mean loss calculations per Summary ID. epcalc (int): Specifies the calculation method (mean damage loss, full uncertainty, per sample mean, sample mean). eptype (int): Type of exceedance probability (0 = OEP, 1 = AEP). max_summary_id (int): Maximum summary ID

Yields:

buffer (ndarray[EPT_dtype]): Buffered chunks of EPT data