Getting Started¶

The installable package lives in src/unibm and is importable as unibm once the repository environment has been synced. This site focuses on the package layer, not on the full repo orchestration under scripts/benchmark and scripts/application.

Environment setup¶

cp .env.example .env
just verify

Top-level just tasks load .env automatically and sync the development environment before they run. If you prefer raw uv commands instead, load .env into your shell first and then sync:

set -a; source .env; set +a
uv sync --dev

The repo-level workflow details stay in the repository README.md and justfile. Use this site when you want the unibm package API itself.

Package usage¶

import numpy as np
from unibm import estimate_evi_quantile, estimate_design_life_level
from unibm.evi import estimate_design_life_level_interval

sample = np.random.default_rng(7).pareto(2.0, 4096) + 1.0
fit = estimate_evi_quantile(sample, quantile=0.5, sliding=True, bootstrap_reps=120)
design_life = estimate_design_life_level(
    fit,
    years=np.array([10.0]),
    observations_per_year=365.25,
)
design_life_interval = estimate_design_life_level_interval(
    fit,
    years=np.array([10.0]),
    observations_per_year=365.25,
)

The shortest EI package workflow is:

from unibm.ei.preparation import prepare_ei_bundle
from unibm.ei.bm import estimate_pooled_bm_ei

bundle = prepare_ei_bundle(sample)
ei_fit = estimate_pooled_bm_ei(bundle, base_path="bb", sliding=True, regression="OLS")

The scalar/vector outputs from estimate_design_life_level are point estimates on the original response scale. estimate_design_life_level_interval adds the matching conditional interval summary from the fitted coefficient covariance.

For a quick guide to which returned fields matter most, see Reading Returned Objects.

Package boundaries¶

unibm.evi owns the severity-side workflow, design-life-level helpers, and related plotting/bootstrap helpers.
unibm.ei owns the persistence-side workflow, BM-path preparation, and threshold/BM EI estimators.
unibm.cdf contains the public empirical CDF helper used by EI path preparation.