ORD output components¶

Oasis produces results in the Open Results Data (ORD) format. The pytools output tools (Output components) emit the ORD tables directly; this page maps each ORD table to the tool that produces it and to the analysis_settings flag that requests it.

Note

The column definitions of each ORD table are part of the ORD standard, single-sourced in the ODS_OpenResultsData repository (and pulled into the aggregated Oasis documentation). This page does not repeat them.

ORD tables, tools and settings¶

ORD table	pytools tool	`analysis_settings` → `ord_output` flag
SELT — Sample Event Loss Table	`eltpy`	`elt_sample`
MELT — Moment Event Loss Table	`eltpy`	`elt_moment`
QELT — Quantile Event Loss Table	`eltpy`	`elt_quantile`
SPLT — Sample Period Loss Table	`pltpy`	`plt_sample`
MPLT — Moment Period Loss Table	`pltpy`	`plt_moment`
QPLT — Quantile Period Loss Table	`pltpy`	`plt_quantile`
EPT — Exceedance Probability Table	`lecpy`	`ept_full_uncertainty_aep` / `_oep`, `ept_mean_sample_aep` / `_oep`, `ept_per_sample_mean_aep` / `_oep`
PSEPT — Per-Sample EPT	`lecpy`	`psept_aep`, `psept_oep`
ALT — Average Loss Table (AAL)	`aalpy`	`alt_period` (`alt_meanonly` for mean only)
ALCT — Average Loss Convergence Table	`aalpy`	`alct_convergence` (+ `alct_confidence`)

EPType in the EPT/PSEPT tables is 1=OEP, 2=OEP TVaR, 3=AEP, 4=AEP TVaR; EPCalc is the calculation basis (1 MeanDamage, 2 FullUncertainty, 3 PerSampleMean, 4 MeanSample). See the ORD standard for full definitions.

Requesting ORD outputs¶

ORD tables are requested per summary set in analysis_settings.json under ord_output. For example, requesting a sample ELT, an EP table (full-uncertainty AEP + OEP) and the period AAL for the ground-up perspective:

{
  "gul_output": true,
  "gul_summaries": [
    {
      "id": 1,
      "ord_output": {
        "elt_sample": true,
        "ept_full_uncertainty_aep": true,
        "ept_full_uncertainty_oep": true,
        "alt_period": true,
        "parquet_format": false
      }
    }
  ]
}

The same ord_output block applies under il_summaries / ri_summaries for the insured and reinsurance perspectives. Global options include parquet_format (write parquet instead of CSV) and return_period_file (use input/returnperiods.bin for the EP curve return periods).

How they are produced in a run¶

oasislmf model run generates a run_kernel.sh pipeline that streams summarypy output into the relevant output tool per requested table (see Workflows). The per-partition outputs are concatenated with katpy. For a worked, stage-by-stage example see the step-by-step pipeline notebook (in the example-models docs), and for analysing the resulting tables see the ORD results notebook.

See also: Output components (the tools and their CLI) · Core components.