# ifc2duckdb
Convert IFC files to DuckDB format for fast analysis and querying of Building Information Modeling (BIM) data.
[](https://www.python.org/downloads/)
[](https://spdx.org/licenses/LGPL-3.0-or-later.html)
[](https://github.com/psf/black)
## Features
- **Fast Conversion**: Convert IFC files to DuckDB format for high-performance querying
- **Full Schema Support**: Create complete IFC schema tables or only those present in the file
- **Geometry Support**: Extract and store 3D geometry data with materials
- **Property Sets**: Include IFC property set data
- **Inverse Relationships**: Store entity relationships for complex queries
- **Command Line Interface**: Easy-to-use CLI for batch processing
- **Python API**: Programmatic access for integration into larger workflows
## Installation
### From PyPI (recommended)
```bash
pip install ifc2duckdb
```
### From source
```bash
git clone https://github.com/chuongmep/ifc-2-duckdb.git
cd ifc-2-duckdb
pip install -e .
```
### Development installation
```bash
git clone https://github.com/chuongmep/ifc-2-duckdb.git
cd ifc-2-duckdb
pip install -e ".[dev]"
```
## Requirements
- Python 3.10+
- ifcopenshell >= 1.0.0
- duckdb >= 0.7.0
- numpy >= 1.20.0
- ifcpatch >= 0.1.0
## Quick Start
### Command Line Usage
```bash
# Basic conversion
ifc2duckdb input.ifc output.duckdb
# With options
ifc2duckdb input.ifc --database output.duckdb --no-geometry --verbose
# Help
ifc2duckdb --help
```
### Python API Usage
```python
import ifc2duckdb
import ifcopenshell
# Open IFC file
ifc_file = ifcopenshell.open("racbasicsampleproject.ifc")
# Create patcher with default settings
patcher = ifc2duckdb.Patcher(
ifc_file,
database="output.duckdb"
)
# Convert to DuckDB
patcher.patch()
# Get output path
output_path = patcher.get_output()
print(f"Database created at: {output_path}")
```
### Advanced Usage
```python
import ifc2duckdb
import ifcopenshell
# Open IFC file
ifc_file = ifcopenshell.open("racbasicsampleproject.ifc")
# Create patcher with custom settings
patcher = ifc2duckdb.Patcher(
ifc_file,
database="output.duckdb",
full_schema=False, # Only create tables for classes in the file
is_strict=True, # Strict data type validation
should_expand=True, # Expand entity lists into separate rows
should_get_inverses=True, # Include inverse relationships
should_get_psets=True, # Include property set data
should_get_geometry=True, # Include geometry data
should_skip_geometry_data=False # Include geometry for representation tables
)
# Convert to DuckDB
patcher.patch()
```
## Database Schema
The converted DuckDB database contains several types of tables:
### Core Tables
- **`id_map`**: Maps IFC entity IDs to their class names
- **`metadata`**: Contains IFC file metadata (schema, preprocessor info)
### IFC Entity Tables
- One table per IFC class (e.g., `IfcWall`, `IfcDoor`, `IfcWindow`)
- Each table contains all attributes of the IFC entity
- Primary key is `ifc_id` (the IFC entity ID)
### Geometry Tables
- **`shape`**: Contains placement and transformation data
- **`geometry`**: Contains mesh data (vertices, edges, faces, materials)
### Property Set Tables
- **`psets`**: Contains property set data in key-value format
## Querying Examples
```sql
-- Find all walls
SELECT * FROM "IfcWall";
-- Find walls with specific properties
SELECT w.*, p.name, p.value
FROM "IfcWall" w
JOIN psets p ON w.ifc_id = p.ifc_id
WHERE p.pset_name = 'Pset_WallCommon';
-- Get geometry for all elements
SELECT s.*, g.verts, g.faces
FROM shape s
JOIN geometry g ON s.geometry = g.id;
-- Find elements by material
SELECT s.ifc_id, g.materials
FROM shape s
JOIN geometry g ON s.geometry = g.id
WHERE g.materials LIKE '%concrete%';
```
## Development
### Running Tests
```bash
# Run all tests
pytest
# Run with coverage
pytest --cov=ifc2duckdb --cov-report=html
# Run specific test file
pytest tests/test_patcher.py
```
### Code Formatting
```bash
# Format code
black ifc2duckdb tests
isort ifc2duckdb tests
# Check formatting
black --check ifc2duckdb tests
isort --check-only ifc2duckdb tests
```
### Type Checking
```bash
mypy ifc2duckdb --ignore-missing-imports
```
### Linting
```bash
flake8 ifc2duckdb tests
```
## Contributing
1. Fork the repository
2. Create a feature branch (`git checkout -b feature/amazing-feature`)
3. Make your changes
4. Run tests and linting (`make test lint`)
5. Commit your changes (`git commit -m 'Add amazing feature'`)
6. Push to the branch (`git push origin feature/amazing-feature`)
7. Open a Pull Request
## License
This project is licensed under the GNU Lesser General Public License v3.0 or later (LGPL-3.0-or-later) - see the [LICENSE](LICENSE) file for details.
## Acknowledgments
- Based on the [Ifc2Sql](https://github.com/IfcOpenShell/IfcOpenShell/blob/v0.7.0/src/ifcpatch/ifcpatch/recipes/Ifc2Sql.py) recipe from IfcOpenShell
- Inspired by the [XbimEssentials](https://github.com/xBimTeam/XbimEssentials) project
- Built on top of [IfcOpenShell](https://ifcopenshell.org/) and [DuckDB](https://duckdb.org/)
## References
- [IfcOpenShell Documentation](https://docs.ifcopenshell.org/)
- [IfcPatch Documentation](https://docs.ifcopenshell.org/ifcpatch.html)
- [DuckDB Documentation](https://duckdb.org/docs/)
- [OSArch Community Discussion](https://community.osarch.org/discussion/1535/ifc-stored-as-sqlite-and-mysql)
- [DuckDB Cloud Storage](https://duckdb.org/docs/stable/guides/network_cloud_storage/duckdb_over_https_or_s3.html)
Raw data
{
"_id": null,
"home_page": null,
"name": "ifc2duckdb",
"maintainer": null,
"docs_url": null,
"requires_python": ">=3.10",
"maintainer_email": "chuongmep <chuongpqvn@gmail.com>",
"keywords": "ifc, duckdb, building-information-modeling, bim, database, conversion",
"author": null,
"author_email": "chuongmep <chuongpqvn@gmail.com>",
"download_url": "https://files.pythonhosted.org/packages/75/00/e17448be7e24cf07276a225e8bad6b5c682b16a0ca909a5ac4edc7336639/ifc2duckdb-0.1.1.tar.gz",
"platform": null,
"description": "# ifc2duckdb\n\nConvert IFC files to DuckDB format for fast analysis and querying of Building Information Modeling (BIM) data.\n\n[](https://www.python.org/downloads/)\n[](https://spdx.org/licenses/LGPL-3.0-or-later.html)\n[](https://github.com/psf/black)\n\n\n\n## Features\n\n- **Fast Conversion**: Convert IFC files to DuckDB format for high-performance querying\n- **Full Schema Support**: Create complete IFC schema tables or only those present in the file\n- **Geometry Support**: Extract and store 3D geometry data with materials\n- **Property Sets**: Include IFC property set data\n- **Inverse Relationships**: Store entity relationships for complex queries\n- **Command Line Interface**: Easy-to-use CLI for batch processing\n- **Python API**: Programmatic access for integration into larger workflows\n\n## Installation\n\n### From PyPI (recommended)\n\n```bash\npip install ifc2duckdb\n```\n\n### From source\n\n```bash\ngit clone https://github.com/chuongmep/ifc-2-duckdb.git\ncd ifc-2-duckdb\npip install -e .\n```\n\n### Development installation\n\n```bash\ngit clone https://github.com/chuongmep/ifc-2-duckdb.git\ncd ifc-2-duckdb\npip install -e \".[dev]\"\n```\n\n## Requirements\n\n- Python 3.10+\n- ifcopenshell >= 1.0.0\n- duckdb >= 0.7.0\n- numpy >= 1.20.0\n- ifcpatch >= 0.1.0\n\n## Quick Start\n\n### Command Line Usage\n\n```bash\n# Basic conversion\nifc2duckdb input.ifc output.duckdb\n\n# With options\nifc2duckdb input.ifc --database output.duckdb --no-geometry --verbose\n\n# Help\nifc2duckdb --help\n```\n\n### Python API Usage\n\n```python\nimport ifc2duckdb\nimport ifcopenshell\n\n# Open IFC file\nifc_file = ifcopenshell.open(\"racbasicsampleproject.ifc\")\n\n# Create patcher with default settings\npatcher = ifc2duckdb.Patcher(\n ifc_file,\n database=\"output.duckdb\"\n)\n\n# Convert to DuckDB\npatcher.patch()\n\n# Get output path\noutput_path = patcher.get_output()\nprint(f\"Database created at: {output_path}\")\n```\n\n### Advanced Usage\n\n```python\nimport ifc2duckdb\nimport ifcopenshell\n\n# Open IFC file\nifc_file = ifcopenshell.open(\"racbasicsampleproject.ifc\")\n\n# Create patcher with custom settings\npatcher = ifc2duckdb.Patcher(\n ifc_file,\n database=\"output.duckdb\",\n full_schema=False, # Only create tables for classes in the file\n is_strict=True, # Strict data type validation\n should_expand=True, # Expand entity lists into separate rows\n should_get_inverses=True, # Include inverse relationships\n should_get_psets=True, # Include property set data\n should_get_geometry=True, # Include geometry data\n should_skip_geometry_data=False # Include geometry for representation tables\n)\n\n# Convert to DuckDB\npatcher.patch()\n```\n\n## Database Schema\n\nThe converted DuckDB database contains several types of tables:\n\n### Core Tables\n- **`id_map`**: Maps IFC entity IDs to their class names\n- **`metadata`**: Contains IFC file metadata (schema, preprocessor info)\n\n### IFC Entity Tables\n- One table per IFC class (e.g., `IfcWall`, `IfcDoor`, `IfcWindow`)\n- Each table contains all attributes of the IFC entity\n- Primary key is `ifc_id` (the IFC entity ID)\n\n### Geometry Tables\n- **`shape`**: Contains placement and transformation data\n- **`geometry`**: Contains mesh data (vertices, edges, faces, materials)\n\n### Property Set Tables\n- **`psets`**: Contains property set data in key-value format\n\n## Querying Examples\n\n```sql\n-- Find all walls\nSELECT * FROM \"IfcWall\";\n\n-- Find walls with specific properties\nSELECT w.*, p.name, p.value \nFROM \"IfcWall\" w\nJOIN psets p ON w.ifc_id = p.ifc_id\nWHERE p.pset_name = 'Pset_WallCommon';\n\n-- Get geometry for all elements\nSELECT s.*, g.verts, g.faces\nFROM shape s\nJOIN geometry g ON s.geometry = g.id;\n\n-- Find elements by material\nSELECT s.ifc_id, g.materials\nFROM shape s\nJOIN geometry g ON s.geometry = g.id\nWHERE g.materials LIKE '%concrete%';\n```\n\n## Development\n\n### Running Tests\n\n```bash\n# Run all tests\npytest\n\n# Run with coverage\npytest --cov=ifc2duckdb --cov-report=html\n\n# Run specific test file\npytest tests/test_patcher.py\n```\n\n### Code Formatting\n\n```bash\n# Format code\nblack ifc2duckdb tests\nisort ifc2duckdb tests\n\n# Check formatting\nblack --check ifc2duckdb tests\nisort --check-only ifc2duckdb tests\n```\n\n### Type Checking\n\n```bash\nmypy ifc2duckdb --ignore-missing-imports\n```\n\n### Linting\n\n```bash\nflake8 ifc2duckdb tests\n```\n\n## Contributing\n\n1. Fork the repository\n2. Create a feature branch (`git checkout -b feature/amazing-feature`)\n3. Make your changes\n4. Run tests and linting (`make test lint`)\n5. Commit your changes (`git commit -m 'Add amazing feature'`)\n6. Push to the branch (`git push origin feature/amazing-feature`)\n7. Open a Pull Request\n\n## License\n\nThis project is licensed under the GNU Lesser General Public License v3.0 or later (LGPL-3.0-or-later) - see the [LICENSE](LICENSE) file for details.\n\n## Acknowledgments\n\n- Based on the [Ifc2Sql](https://github.com/IfcOpenShell/IfcOpenShell/blob/v0.7.0/src/ifcpatch/ifcpatch/recipes/Ifc2Sql.py) recipe from IfcOpenShell\n- Inspired by the [XbimEssentials](https://github.com/xBimTeam/XbimEssentials) project\n- Built on top of [IfcOpenShell](https://ifcopenshell.org/) and [DuckDB](https://duckdb.org/)\n\n## References\n\n- [IfcOpenShell Documentation](https://docs.ifcopenshell.org/)\n- [IfcPatch Documentation](https://docs.ifcopenshell.org/ifcpatch.html)\n- [DuckDB Documentation](https://duckdb.org/docs/)\n- [OSArch Community Discussion](https://community.osarch.org/discussion/1535/ifc-stored-as-sqlite-and-mysql)\n- [DuckDB Cloud Storage](https://duckdb.org/docs/stable/guides/network_cloud_storage/duckdb_over_https_or_s3.html)\n",
"bugtrack_url": null,
"license": null,
"summary": "Convert IFC files to DuckDB format for fast analysis and querying",
"version": "0.1.1",
"project_urls": {
"Bug Tracker": "https://github.com/chuongmep/ifc-2-duckdb/issues",
"Documentation": "https://github.com/chuongmep/ifc-2-duckdb#readme",
"Homepage": "https://github.com/chuongmep/ifc-2-duckdb",
"Repository": "https://github.com/chuongmep/ifc-2-duckdb"
},
"split_keywords": [
"ifc",
" duckdb",
" building-information-modeling",
" bim",
" database",
" conversion"
],
"urls": [
{
"comment_text": null,
"digests": {
"blake2b_256": "d697913248bb64b01e03bae70ca7f124d1aac4a0472514c12d23a9e3c90565b1",
"md5": "dc7e6ba53c58b861613a2a4634103008",
"sha256": "6e9440719c111cb0c17458e2447e75be7b73598c0d6e83c57a8a91ccc6aaf82f"
},
"downloads": -1,
"filename": "ifc2duckdb-0.1.1-py3-none-any.whl",
"has_sig": false,
"md5_digest": "dc7e6ba53c58b861613a2a4634103008",
"packagetype": "bdist_wheel",
"python_version": "py3",
"requires_python": ">=3.10",
"size": 12420,
"upload_time": "2025-09-14T15:40:11",
"upload_time_iso_8601": "2025-09-14T15:40:11.295268Z",
"url": "https://files.pythonhosted.org/packages/d6/97/913248bb64b01e03bae70ca7f124d1aac4a0472514c12d23a9e3c90565b1/ifc2duckdb-0.1.1-py3-none-any.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": null,
"digests": {
"blake2b_256": "7500e17448be7e24cf07276a225e8bad6b5c682b16a0ca909a5ac4edc7336639",
"md5": "8753f8a149151b2ded96eb21accc58c7",
"sha256": "220e247ff2223d564941c83cb905c1b6fdb49b71274390c32bf75a655a280424"
},
"downloads": -1,
"filename": "ifc2duckdb-0.1.1.tar.gz",
"has_sig": false,
"md5_digest": "8753f8a149151b2ded96eb21accc58c7",
"packagetype": "sdist",
"python_version": "source",
"requires_python": ">=3.10",
"size": 844477,
"upload_time": "2025-09-14T15:40:12",
"upload_time_iso_8601": "2025-09-14T15:40:12.579236Z",
"url": "https://files.pythonhosted.org/packages/75/00/e17448be7e24cf07276a225e8bad6b5c682b16a0ca909a5ac4edc7336639/ifc2duckdb-0.1.1.tar.gz",
"yanked": false,
"yanked_reason": null
}
],
"upload_time": "2025-09-14 15:40:12",
"github": true,
"gitlab": false,
"bitbucket": false,
"codeberg": false,
"github_user": "chuongmep",
"github_project": "ifc-2-duckdb",
"travis_ci": false,
"coveralls": false,
"github_actions": true,
"requirements": [
{
"name": "ifcopenshell",
"specs": [
[
">=",
"0.8.0"
]
]
},
{
"name": "duckdb",
"specs": [
[
">=",
"0.7.0"
]
]
},
{
"name": "numpy",
"specs": [
[
">=",
"1.20.0"
]
]
},
{
"name": "ifcpatch",
"specs": [
[
">=",
"0.1.0"
]
]
}
],
"tox": true,
"lcname": "ifc2duckdb"
}
JSON
