idfpy

Type-safe Pydantic models for all EnergyPlus IDF object types, plus IDF file read/write and simulation execution, optimized for LLM tool calling and IDE auto-completion.

Auto-generated from Energy+.schema.epJSON version 26.1.0.

Features

• 859 object types as Pydantic v2 models with full validation
• 275 reference types with cross-object validation
• Forward navigation — surface.zone resolves a reference field to the target object
• Reverse navigation — zone.referencing("Lights") finds all objects that reference a given object
• Reference validation — idf.validate() batch-checks all cross-object references for existence and type compatibility
• Extension plugin system — surface.area, .normal, .centroid via auto-discovered geometry mixins with full IDE support
• Case-insensitive Literal field matching (EnergyPlus IDF is case-insensitive)
• Extensible field support (vertices, schedule data, etc.)
• IDF read/write with positional field ordering
• epJSON read/write with auto-detection by file extension
• to_dict() / from_dict() for in-memory dict conversion (ideal for LLM tool calls)
• EnergyPlus simulation execution with ExpandObjects support
• Accepts both snake_case and original EnergyPlus schema key names

Why idfpy over eppy?

Installation

pip install idfpy

Quick Start

from pathlib import Path
from idfpy import IDF
from idfpy.models import Version, Building, Zone

# Create an IDF
idf = IDF()
idf.add(Version())
idf.add(Building(name='MyBuilding', north_axis=0.0))
idf.add(Zone(name='Zone1'))

# Save as IDF
idf.save(Path('output.idf'))

# Save as epJSON
idf.save(Path('output.epjson'), output_type='epjson')

# Load (auto-detects format by extension)
idf = IDF.load(Path('existing.idf'))  # IDF format
idf = IDF.load(Path('existing.epjson'))  # epJSON format

# Run simulation
from idfpy.sim import simulate

result = simulate(
    Path('output.idf'), weather=Path('weather.epw'), output_dir=Path('results/')
)
print(result.success)  # True / False

In-memory dict conversion

from pathlib import Path
from idfpy import IDF

idf = IDF.load(Path('model.idf'))

# IDF → dict (epJSON structure)
data = idf.to_dict()
# {
#   "Building": {"MyBuilding": {"north_axis": 0.0, "terrain": "Suburbs"}},
#   "Zone": {"Zone1": {"direction_of_relative_north": 0.0}},
#   ...
# }

# dict → IDF
idf = IDF.from_dict(data)

Logging

idfpy keeps INFO and higher-level logs available through Loguru. Detailed per-object and internal progress messages use Loguru's TRACE level, so they do not appear in a handler configured at DEBUG level. To inspect these messages, configure a TRACE sink explicitly:

from loguru import logger

logger.add('idfpy.log', level='TRACE')

Object navigation

Every reference field generates a @property for forward navigation. Reverse navigation is available via referencing(). All query methods (get / has / all_of_type / remove) accept either an EnergyPlus type string, a Python class name, or the model class itself — passing the class preserves precise typing in your IDE.

from pathlib import Path
from idfpy import IDF
from idfpy.models import BuildingSurfaceDetailed, Zone

idf = IDF.load(Path('model.idf'))

# Forward navigation — resolve reference to target object
surface = idf.get(BuildingSurfaceDetailed, 'Wall1')  # → BuildingSurfaceDetailed | None
surface.zone_name  # "Zone1" (raw string, always works)
surface.zone  # Zone object (resolved via IDF)
surface.construction  # Construction object

# Reverse navigation — find all objects referencing a given object
zone = idf.get(Zone, 'Zone1')
zone.referencing(BuildingSurfaceDetailed)  # → [Wall1, Wall2, ...]
zone.referencing('Lights')  # → [OfficeLights, ...]

# Chained navigation
zone.referencing(BuildingSurfaceDetailed)[0].construction

Strict type-name validation (default)

Query methods raise UnknownObjectTypeError when the type name cannot be resolved — this surfaces typos immediately instead of returning an empty result. Pass strict=False for the legacy silent behavior.

from idfpy import UnknownObjectTypeError

try:
    idf.get('BuildingSurface:detailed', 'Wall1')  # note the lowercase 'd'
except UnknownObjectTypeError as e:
    print(e)  # → Unknown object type: 'BuildingSurface:detailed'. ...

# Opt-in legacy silent behavior
idf.get('BuildingSurface:detailed', 'Wall1', strict=False)  # → None

Reference validation

from idfpy import IDF, RefValidationError

idf = IDF.load(Path('model.idf'))

# Batch check all cross-object references
errors = idf.validate()
for e in errors:
    print(e)
# [missing] Lights/OffLights.schedule_name: "BadSched" not found in any of [ScheduleNames]

# Or raise on first broken reference set
try:
    idf.validate_or_raise()
except RefValidationError as exc:
    print(f'{len(exc.errors)} broken reference(s)')

Real-world Example

from pathlib import Path
from idfpy import IDF

# Load a DOE reference building
idf = IDF.load(Path('LargeOffice.idf'))

# Modify all exterior walls' insulation
for con_name, con in idf.all_of_type('Construction').items():
    layer = con.outside_layer_ref
    if layer and hasattr(layer, 'conductivity'):
        print(f'{con.name}: k={layer.conductivity} W/m·K')

# Validate all references
errors = idf.validate()
print(f'{len(errors)} broken references')

Geometry extensions

Surface models include geometry properties via the built-in ext.geometry plugin — area, normal vector, and centroid are computed from vertices using Newell's method, with full IDE autocompletion.

from idfpy import IDF
from pathlib import Path

idf = IDF.load(Path('model.idf'))

surface = idf.get('BuildingSurface:Detailed', 'Wall1')
surface.area  # 30.0 (m²)
surface.normal  # (0.0, -1.0, 0.0) — outward unit normal
surface.centroid  # (5.0, 0.0, 1.5)
surface.vertices_as_tuples  # [(0,0,3), (0,0,0), (10,0,0), (10,0,3)]

window = idf.get('FenestrationSurface:Detailed', 'Win1')
window.area  # 16.0 (m²)

Supported surface types: BuildingSurface:Detailed, FenestrationSurface:Detailed, Floor:Detailed, RoofCeiling:Detailed, Wall:Detailed, Shading:Building:Detailed, Shading:Site:Detailed, Shading:Zone:Detailed.

Creating custom plugins

Extensions live in idfpy/ext/ as sub-packages. Each plugin exposes a MIXIN_MAP that the code generator auto-discovers:

# idfpy/ext/thermal/__init__.py
from .mixins import ThermalPropertyMixin

MIXIN_MAP: dict[str, type] = {
    'BuildingSurfaceDetailed': ThermalPropertyMixin,
}

# idfpy/ext/thermal/mixins.py
class ThermalPropertyMixin:
    @property
    def u_value(self) -> float:
        """Compute U-value from construction layers."""
        ...

After adding a plugin, re-run idfpy codegen to regenerate models — the mixin is injected into the class hierarchy and IDE autocompletion works immediately.

Container mutation

from idfpy.models import Zone

idf.remove(Zone, 'Zone1')  # unbinds + unregisters references
idf.remove('Zone', 'Zone1')  # string form (EnergyPlus or Python class name)

License

MIT