oasislmf.pytools.lec.aggreports.aggreports

Attributes

logger

Classes

AggReports

Module Contents

oasislmf.pytools.lec.aggreports.aggreports.logger

class oasislmf.pytools.lec.aggreports.aggreports.AggReports(outmap, outloss_mean, outloss_sample, period_weights, max_summary_id, sample_size, no_of_periods, num_sidxs, use_return_period, returnperiods, lec_files_folder, output_binary, output_parquet)

outmap

outloss_mean

outloss_sample

period_weights

max_summary_id

sample_size

no_of_periods

num_sidxs

use_return_period

returnperiods

lec_files_folder

output_binary

output_parquet

output_data(data, out_type)

output_mean_damage_ratio(eptype, eptype_tvar, outloss_type)

Output Mean Damage Ratio Mean Damage Losses - This means do the loss calculation for a year using the event mean damage loss computed by numerical integration of the effective damageability distributions. Args:

eptype (int): Exceedance Probability Type eptype_tvar (int): Exceedance Probability Type (Tail Value at Risk) outloss_type (string): Which loss to output

output_full_uncertainty(eptype, eptype_tvar, outloss_type)

Output Full Uncertainty Full Uncertainty – this means do the calculation across all samples (treating the samples effectively as repeat years) - this is the most accurate of all the single EP Curves. Args:

eptype (int): Exceedance Probability Type eptype_tvar (int): Exceedance Probability Type (Tail Value at Risk) outloss_type (string): Which loss to output

output_wheatsheaf_and_wheatsheafmean(eptype, eptype_tvar, outloss_type, output_wheatsheaf, output_wheatsheaf_mean)

Output Wheatsheaf and Wheatsheaf Mean Wheatsheaf, Per Sample EPT (PSEPT) – this means calculate the EP Curve for each sample and leave it at the sample level of detail, resulting in multiple “curves”. Wheatsheaf Mean, Per Sample mean EPT – this means average the loss at each return period of the Per Sample EPT. Args:

eptype (int): Exceedance Probability Type eptype_tvar (int): Exceedance Probability Type (Tail Value at Risk) outloss_type (string): Which loss to output output_wheatsheaf (bool): Bool to Output Wheatsheaf output_wheatsheaf_mean (bool): Bool to Output Wheatsheaf Mean

output_sample_mean(eptype, eptype_tvar, outloss_type)

Output Sample Mean Sample Mean Losses – this means do the loss calculation for a year using the statistical sample event mean. Args:

eptype (int): Exceedance Probability Type eptype_tvar (int): Exceedance Probability Type (Tail Value at Risk) outloss_type (string): Which loss to output