idfkit 0.14.0

A fast, modern EnergyPlus IDF/epJSON parser with O(1) lookups and reference tracking

idfkit

A fast, modern EnergyPlus IDF/epJSON toolkit for Python.

[!NOTE] idfkit is in beta. The API may change between minor versions. We're looking for early adopters and testers — especially users of eppy who want better performance and a modern API. If you try it out, please open an issue with feedback, bug reports, or feature requests.

idfkit lets you load, create, query, and modify EnergyPlus models with an intuitive Python API. It is designed as a drop-in replacement for eppy with better performance, built-in reference tracking, and native support for both IDF and epJSON formats.

Key Features

• O(1) object lookups — Collections are indexed by name, so doc["Zone"]["Office"] is a dict lookup, not a linear scan.
• Automatic reference tracking — A live reference graph keeps track of every cross-object reference. Renaming an object updates every field that pointed to the old name.
• IDF + epJSON — Read and write both formats; convert between them in a single call.
• Schema-driven validation — Validate documents against the official EnergyPlus epJSON schema with detailed error messages.
• Built-in 3D geometry — Vector3D and Polygon3D classes for surface area, zone volume, and coordinate transforms without external dependencies.
• EnergyPlus simulation — Run simulations as subprocesses with structured result parsing (SQLite, CSV, HTML, and a fast pure-Python .eso/.mtr reader), batch processing, and content-addressed caching.
• Weather data — Search ~17,300 weather stations (~70,000 TMYx datasets), download EPW/DDY files, and apply ASHRAE design day conditions.
• Async & batch simulation — Run simulations concurrently with async_simulate or process parameter sweeps with simulate_batch.
• 3D visualization — Render building geometry to interactive 3D views or static SVG images with no external tools.
• Schedule evaluation — Parse and evaluate EnergyPlus compact, weekly, and holiday schedules to time-series values.
• Thermal properties — Gas mixture and material thermal calculations for glazing and construction analysis.
• Broad version support — Bundled schemas for every EnergyPlus release from v8.9 through v26.1.

Performance

idfkit is designed from the ground up for speed. On a 1,700-object IDF, looking up a single object by name is over 3000x faster than eppy and opyplus thanks to O(1) dict-based indexing:

See full benchmark results for all six operations (load, get by type, get by name, add, modify, write) across four tools.

Installation

Requires Python 3.10+.

pip install idfkit

Or with uv:

uv add idfkit

Optional extras

Extra	Install command	What it adds
weather	pip install idfkit[weather]	Refresh weather station indexes from source (openpyxl)
dataframes	pip install idfkit[dataframes]	DataFrame result conversion (pandas)
s3	pip install idfkit[s3]	S3 cloud storage backend (boto3)
plot	pip install idfkit[plot]	Matplotlib plotting
plotly	pip install idfkit[plotly]	Plotly interactive charts
progress	pip install idfkit[progress]	tqdm progress bars for simulations
all	pip install idfkit[all]	Everything above

Quick Example

from idfkit import load_idf, write_idf

# Load an existing IDF file
doc = load_idf("in.idf")

# Query objects with O(1) lookups
zone = doc["Zone"]["Office"]
print(zone.x_origin, zone.y_origin)

# Modify a field
zone.x_origin = 10.0

# See what references the zone
for obj in doc.get_referencing("Office"):
    print(obj.obj_type, obj.name)

# Write back to IDF (or epJSON)
write_idf(doc, "out.idf")

Note: load_idf() defaults to strict parsing (strict=True) and raises IDFParseError on malformed objects. Use strict=False only as a tolerant migration/compatibility fallback for legacy or noisy files.

Creating a model from scratch

from idfkit import new_document, write_idf

doc = new_document()
doc.add("Zone", "Office", x_origin=0.0, y_origin=0.0)
write_idf(doc, "new_building.idf")

Simulation

from idfkit.simulation import simulate

result = simulate(doc, "weather.epw", design_day=True)

# Query results from the SQLite output
ts = result.sql.get_timeseries(
    variable_name="Zone Mean Air Temperature",
    key_value="Office",
)
print(f"Max temp: {max(ts.values):.1f}°C")

Note: result.sql requires EnergyPlus to produce SQLite output (the default). See the Simulation Guide for details on output configuration.

Weather

from idfkit.weather import StationIndex, geocode

index = StationIndex.load()
results = index.nearest(*geocode("Chicago, IL"))
print(results[0].station.display_name)

CLI

pip install idfkit ships an idfkit command with three subcommands:

• idfkit check — static lint for cross-version EnergyPlus breakage (docs)
• idfkit migrate — forward-migrate an IDF through IDFVersionUpdater (docs)
• idfkit tmy — search and download TMYx weather data from the shell (docs)

Documentation

Full documentation is available at py.idfkit.com.

Key sections:

• Getting Started — Installation, quick start, interactive tutorial
• Simulation Guide — Run EnergyPlus, parse results, batch processing
• Weather Guide — Station search, downloads, design days
• API Reference — Complete API documentation
• Migrating from eppy — Side-by-side comparison

For AI coding assistants

idfkit ships agent-readable reference docs in src/idfkit/.agents/skills/developing-with-idfkit/. The directory is packaged in the wheel, so it's also accessible from an installed copy via importlib.resources.files("idfkit") / ".agents". The companion idfkit-mcp server exposes these references over MCP at idfkit://references/{topic}.

Development

make install    # Install dependencies and pre-commit hooks
make check      # Run linting, formatting, and type checks
make test       # Run tests with coverage
make docs       # Serve documentation locally

Contributing

Contributions are welcome! Please see CONTRIBUTING.md for guidelines.

License

This project is licensed under the MIT License — see LICENSE for details.