infrastore

Python bindings for infrastore — time-series storage for power-systems and energy simulations.

Numerical arrays are persisted in HDF5; the metadata associating each array with its owning component lives in SQLite. Identical arrays are stored once and shared through content addressing.

The wheels are self-contained: HDF5 and zlib are statically linked, so there are no system libraries to install.

Install

pip install infrastore

Quick start

from datetime import datetime, timedelta, timezone
import numpy as np
from infrastore import Store, SingleTimeSeries, OwnerCategory

store = Store.create(in_memory=True)
ts = SingleTimeSeries(
    datetime(2024, 1, 1, tzinfo=timezone.utc),
    timedelta(hours=1),
    np.arange(24, dtype=np.float64) + 100,
    "load",   # name (required)
)
key = store.add_time_series(
    owner_id=42, owner_type="Generator",
    owner_category=OwnerCategory.Component,
    time_series=ts,   # name comes from ts
    features={"model_year": 2030}, units="MW",
)
got = store.get_time_series(key)
assert np.array_equal(np.asarray(got.data), np.asarray(ts.data))

Features

One array, stored once — arrays are addressed by a SHA-256 content hash, so a series shared across components is written to disk a single time.
Typed, N-dimensional values — f64, f32, i64, i32, u64, and bool, with an optional per-timestep element shape.
Six time-series types — SingleTimeSeries and NonSequentialTimeSeries read+write; Deterministic, DeterministicSingleTimeSeries, Probabilistic, and Scenarios for forecasts.
Columnar simulation readers — StaticReader / ForecastReader serve every series' value at one timestamp.
Association catalogs — component ↔ supplemental attribute and directed component ↔ component edges, recorded independently of time series.
Type stubs (.pyi), a full exception hierarchy, and keyword-only optional arguments.

Documentation

https://natlabrockies.github.io/infrastore/latest/ — see the Python guide and the Python API reference.

License

BSD-3-Clause. See LICENSE.

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