SLF: PFA inventory with correlations

This example generates storey loss functions from a PFA-based component inventory with a separate correlation file and no grouping (Weibull regression, seed 1).

Corresponds to test case: ('pfa_inv', 'pfa_corr', False, False, "weibull", 1).

Input files

Download the input files from the repository:

• pfa_inv.json — component inventory (PFA-driven, non-structural components).

• pfa_corr.json — damage-state correlation matrix for the PFA components.

The inventory structure is identical to the PSD case. Components here use "EDP": "PFA" and median demands expressed in units of g (peak floor acceleration).

Running the example

import json
from djura.slf import SLF
from djura.slf.utilities import filter_args

with open("pfa_inv.json", encoding="utf-8") as f:
    inventory = json.load(f)

with open("pfa_corr.json", encoding="utf-8") as f:
    correlations = json.load(f)

data = {
    "inventory": inventory,
    "correlations": correlations,
    "do_grouping": False,
    "include_correlations": False,
    "conversion": "1.0",
    "realizations": "20",
    "replacement_cost": "1.0",
    "regression": "weibull",
    "storey": None,
    "directionality": None,
    "seed": "1",
}

# Correlations are only used when include_correlations=True.
data["correlations"] = None

outs = {}
n_prev = 0

unique_edp = set(item["EDP"] for item in inventory)
for edp in unique_edp:
    data["edp"] = edp.lower()
    data["inventory"] = [item for item in inventory if item["EDP"] == edp]

    id_map = {old: new for new, old in enumerate(
        sorted(item["id"] for item in data["inventory"]))}
    for item in data["inventory"]:
        item["id"] = id_map[item["id"]]

    filtered_data = filter_args(SLF, data)
    filtered_data["n_prev"] = n_prev

    slf_obj = SLF(**filtered_data)
    out = slf_obj.generate_slfs()

    outs.update(out)
    n_prev += len(data["inventory"])
    del slf_obj

print(outs)