oasislmf.pytools.lec.aggreports.aggreports

Attributes

logger

Classes

LecConfig	Shared per-run configuration passed to output helpers and output_for_summary_idx.
AggReports

Functions

make_output_fn(outmap, output_binary, output_parquet)	Return a callable(data, out_type) that writes data in the correct output format.
output_for_summary_idx(summary_id, outloss_mean_s, ...)	Output all report types for a single summary_id (idx path).

Module Contents

oasislmf.pytools.lec.aggreports.aggreports.logger

class oasislmf.pytools.lec.aggreports.aggreports.LecConfig

Shared per-run configuration passed to output helpers and output_for_summary_idx.

period_weights: numpy.ndarray

max_summary_id: int

sample_size: int

no_of_periods: int

num_sidxs: int

use_return_period: bool

returnperiods: numpy.ndarray

ept_buffer: numpy.ndarray

psept_buffer: numpy.ndarray

oasislmf.pytools.lec.aggreports.aggreports.make_output_fn(outmap, output_binary, output_parquet)

Return a callable(data, out_type) that writes data in the correct output format.

class oasislmf.pytools.lec.aggreports.aggreports.AggReports(outmap, outloss_mean, row_used_mean, outloss_sample, row_used_sample, config, lec_files_folder, output_binary, output_parquet)

outmap

outloss_mean

outloss_sample

lec_files_folder

row_used_indices_mean

row_used_indices_sample

config

output_data

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.

Parameters:

• eptype (int) – Exceedance Probability Type

• eptype_tvar (int) – Exceedance Probability Type (Tail Value at Risk)

• outloss_type (str) – 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.

Parameters:

• eptype (int) – Exceedance Probability Type

• eptype_tvar (int) – Exceedance Probability Type (Tail Value at Risk)

• outloss_type (str) – 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.

Parameters:

• eptype (int) – Exceedance Probability Type

• eptype_tvar (int) – Exceedance Probability Type (Tail Value at Risk)

• outloss_type (str) – 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.

Parameters:

• eptype (int) – Exceedance Probability Type

• eptype_tvar (int) – Exceedance Probability Type (Tail Value at Risk)

• outloss_type (str) – Which loss to output

oasislmf.pytools.lec.aggreports.aggreports.output_for_summary_idx(summary_id, outloss_mean_s, row_used_mean_s, outloss_sample_s, row_used_sample_s, output_flags, hasOCC, hasAGG, outmap, config, output_fn)

Output all report types for a single summary_id (idx path).

Called once per summary_id. config.max_summary_id must be 1; arrays are sized for a single summary. Write generators emit SummaryId=1, corrected here before each write.