Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Tencent SkillHub · AI
Ifc Data Extraction
Extract structured data from IFC (Industry Foundation Classes) files using IfcOpenShell. Parse BIM models, extract quantities, properties, spatial relationships, and export to various formats.
skill openclawclawhub Free
Extract structured data from IFC (Industry Foundation Classes) files using IfcOpenShell. Parse BIM models, extract quantities, properties, spatial relationships, and export to various formats.
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Install for OpenClaw
Quick setup
- Download the package from Yavira.
- Extract the archive and review SKILL.md first.
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Package facts
- Download mode
- Yavira redirect
- Package format
- ZIP package
- Source platform
- Tencent SkillHub
- What's included
- claw.json, instructions.md, SKILL.md
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Trust & source
Release facts
- Source
- Tencent SkillHub
- Verification
- Indexed source record
- Version
- 2.0.0
Provenance
- Publisher
- datadrivenconstruction
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Documentation
Overview
This skill provides comprehensive IFC file parsing and data extraction using IfcOpenShell. Extract element data, quantities, properties, and relationships from BIM models for analysis and reporting. Based on Open BIM Standards - Working with vendor-neutral IFC format for maximum interoperability. "IFC является открытым стандартом для обмена BIM-данными, позволяющим извлекать информацию независимо от программного обеспечения." — DDC Methodology
Quick Start
import ifcopenshell import ifcopenshell.util.element as element_util import pandas as pd # Open IFC file ifc = ifcopenshell.open("model.ifc") # Get project info project = ifc.by_type("IfcProject")[0] print(f"Project: {project.Name}") # Extract all walls walls = ifc.by_type("IfcWall") print(f"Total walls: {len(walls)}") # Get wall data wall_data = [] for wall in walls: psets = element_util.get_psets(wall) wall_data.append({ 'GlobalId': wall.GlobalId, 'Name': wall.Name, 'Type': wall.is_a(), 'Level': get_level(wall), 'Properties': psets }) df = pd.DataFrame(wall_data) print(df.head())
Element Extractor Class
import ifcopenshell import ifcopenshell.util.element as element_util import ifcopenshell.util.placement as placement_util import ifcopenshell.geom import pandas as pd from typing import List, Dict, Optional, Any class IFCExtractor: """Extract data from IFC files""" def __init__(self, ifc_path: str): self.model = ifcopenshell.open(ifc_path) self.settings = ifcopenshell.geom.settings() def get_project_info(self) -> Dict: """Extract project metadata""" project = self.model.by_type("IfcProject")[0] site = self.model.by_type("IfcSite") building = self.model.by_type("IfcBuilding") return { 'project_id': project.GlobalId, 'project_name': project.Name, 'description': project.Description, 'site_count': len(site), 'building_count': len(building), 'schema': self.model.schema } def get_all_elements(self, element_types: List[str] = None) -> pd.DataFrame: """Extract all elements of specified types""" if element_types is None: element_types = [ 'IfcWall', 'IfcSlab', 'IfcColumn', 'IfcBeam', 'IfcDoor', 'IfcWindow', 'IfcStair', 'IfcRoof' ] all_elements = [] for ifc_type in element_types: elements = self.model.by_type(ifc_type) for elem in elements: data = self._extract_element_data(elem) data['IFC_Type'] = ifc_type all_elements.append(data) return pd.DataFrame(all_elements) def _extract_element_data(self, element) -> Dict: """Extract data from single element""" # Basic info data = { 'GlobalId': element.GlobalId, 'Name': element.Name, 'Description': element.Description, 'ObjectType': element.ObjectType if hasattr(element, 'ObjectType') else None } # Get level/storey data['Level'] = self._get_element_level(element) # Get material data['Material'] = self._get_element_material(element) # Get type data['TypeName'] = self._get_element_type(element) # Get all property sets psets = element_util.get_psets(element) data['PropertySets'] = psets # Extract common quantities base_quantities = psets.get('BaseQuantities', {}) data.update({ 'Length': base_quantities.get('Length'), 'Width': base_quantities.get('Width'), 'Height': base_quantities.get('Height'), 'Area': base_quantities.get('NetSideArea') or base_quantities.get('GrossArea'), 'Volume': base_quantities.get('NetVolume') or base_quantities.get('GrossVolume') }) return data def _get_element_level(self, element) -> Optional[str]: """Get the building storey for an element""" if hasattr(element, 'ContainedInStructure'): for rel in element.ContainedInStructure or []: if rel.RelatingStructure.is_a('IfcBuildingStorey'): return rel.RelatingStructure.Name return None def _get_element_material(self, element) -> Optional[str]: """Get material name for element""" if hasattr(element, 'HasAssociations'): for rel in element.HasAssociations or []: if rel.is_a('IfcRelAssociatesMaterial'): material = rel.RelatingMaterial if hasattr(material, 'Name'): return material.Name elif hasattr(material, 'ForLayerSet'): layers = material.ForLayerSet.MaterialLayers if layers: return layers[0].Material.Name return None def _get_element_type(self, element) -> Optional[str]: """Get element type name""" if hasattr(element, 'IsTypedBy'): for rel in element.IsTypedBy or []: return rel.RelatingType.Name return None def extract_quantities(self) -> pd.DataFrame: """Extract quantities for all elements""" elements = self.get_all_elements() # Group by category and level quantities = elements.groupby(['IFC_Type', 'Level']).agg({ 'GlobalId': 'count', 'Volume': 'sum', 'Area': 'sum', 'Length': 'sum' }).rename(columns={'GlobalId': 'Count'}).reset_index() return quantities def extract_levels(self) -> pd.DataFrame: """Extract building levels/storeys""" storeys = self.model.by_type("IfcBuildingStorey") level_data = [] for storey in storeys: level_data.append({ 'GlobalId': storey.GlobalId, 'Name': storey.Name, 'Elevation': storey.Elevation, 'Description': storey.Description }) return pd.DataFrame(level_data).sort_values('Elevation') def extract_spaces(self) -> pd.DataFrame: """Extract spaces/rooms""" spaces = self.model.by_type("IfcSpace") space_data = [] for space in spaces: psets = element_util.get_psets(space) base_qty = psets.get('BaseQuantities', {}) space_data.append({ 'GlobalId': space.GlobalId, 'Name': space.Name, 'LongName': space.LongName, 'Level': self._get_element_level(space), 'Area': base_qty.get('NetFloorArea'), 'Volume': base_qty.get('NetVolume'), 'Height': base_qty.get('Height') }) return pd.DataFrame(space_data) def extract_materials(self) -> pd.DataFrame: """Extract material summary""" materials = {} for elem in self.model.by_type("IfcProduct"): material = self._get_element_material(elem) if material: if material not in materials: materials[material] = {'count': 0, 'volume': 0} materials[material]['count'] += 1 psets = element_util.get_psets(elem) volume = psets.get('BaseQuantities', {}).get('NetVolume', 0) if volume: materials[material]['volume'] += volume return pd.DataFrame.from_dict(materials, orient='index').reset_index() def extract_relationships(self) -> pd.DataFrame: """Extract element relationships""" relationships = [] # Spatial containment for rel in self.model.by_type("IfcRelContainedInSpatialStructure"): for elem in rel.RelatedElements: relationships.append({ 'Element': elem.GlobalId, 'Element_Type': elem.is_a(), 'Relationship': 'ContainedIn', 'Related_To': rel.RelatingStructure.GlobalId, 'Related_Type': rel.RelatingStructure.is_a() }) # Aggregation for rel in self.model.by_type("IfcRelAggregates"): for part in rel.RelatedObjects: relationships.append({ 'Element': part.GlobalId, 'Element_Type': part.is_a(), 'Relationship': 'PartOf', 'Related_To': rel.RelatingObject.GlobalId, 'Related_Type': rel.RelatingObject.is_a() }) return pd.DataFrame(relationships)
Extract Geometry Data
import numpy as np class IFCGeometryExtractor: """Extract geometry data from IFC elements""" def __init__(self, ifc_path: str): self.model = ifcopenshell.open(ifc_path) self.settings = ifcopenshell.geom.settings() self.settings.set(self.settings.USE_WORLD_COORDS, True) def get_element_geometry(self, element) -> Dict: """Extract geometry for single element""" try: shape = ifcopenshell.geom.create_shape(self.settings, element) verts = shape.geometry.verts faces = shape.geometry.faces # Calculate bounding box vertices = np.array(verts).reshape(-1, 3) min_coords = vertices.min(axis=0) max_coords = vertices.max(axis=0) dimensions = max_coords - min_coords return { 'GlobalId': element.GlobalId, 'vertices_count': len(vertices), 'faces_count': len(faces) // 3, 'min_x': min_coords[0], 'min_y': min_coords[1], 'min_z': min_coords[2], 'max_x': max_coords[0], 'max_y': max_coords[1], 'max_z': max_coords[2], 'length': dimensions[0], 'width': dimensions[1], 'height': dimensions[2], 'center_x': (min_coords[0] + max_coords[0]) / 2, 'center_y': (min_coords[1] + max_coords[1]) / 2, 'center_z': (min_coords[2] + max_coords[2]) / 2 } except: return {'GlobalId': element.GlobalId, 'error': 'Geometry extraction failed'} def get_bounding_boxes(self, element_type: str) -> pd.DataFrame: """Get bounding boxes for all elements of type""" elements = self.model.by_type(element_type) boxes = [self.get_element_geometry(e) for e in elements] return pd.DataFrame(boxes) def calculate_volumes(self, element_type: str) -> pd.DataFrame: """Calculate volumes using geometry""" elements = self.model.by_type(element_type) volumes = [] for elem in elements: try: shape = ifcopenshell.geom.create_shape(self.settings, elem) # Calculate volume from mesh (simplified) verts = np.array(shape.geometry.verts).reshape(-1, 3) bbox_volume = np.prod(verts.max(axis=0) - verts.min(axis=0)) volumes.append({ 'GlobalId': elem.GlobalId, 'Name': elem.Name, 'BBox_Volume': bbox_volume }) except: pass return pd.DataFrame(volumes)
Export to Various Formats
class IFCExporter: """Export IFC data to various formats""" def __init__(self, extractor: IFCExtractor): self.extractor = extractor def to_excel(self, output_path: str, include_all: bool = True): """Export to Excel with multiple sheets""" with pd.ExcelWriter(output_path, engine='openpyxl') as writer: # Project info project_info = pd.DataFrame([self.extractor.get_project_info()]) project_info.to_excel(writer, sheet_name='Project', index=False) # All elements if include_all: elements = self.extractor.get_all_elements() elements.to_excel(writer, sheet_name='Elements', index=False) # Quantities quantities = self.extractor.extract_quantities() quantities.to_excel(writer, sheet_name='Quantities', index=False) # Levels levels = self.extractor.extract_levels() levels.to_excel(writer, sheet_name='Levels', index=False) # Spaces spaces = self.extractor.extract_spaces() spaces.to_excel(writer, sheet_name='Spaces', index=False) # Materials materials = self.extractor.extract_materials() materials.to_excel(writer, sheet_name='Materials', index=False) return output_path def to_csv(self, output_dir: str): """Export to multiple CSV files""" import os os.makedirs(output_dir, exist_ok=True) exports = { 'elements.csv': self.extractor.get_all_elements(), 'quantities.csv': self.extractor.extract_quantities(), 'levels.csv': self.extractor.extract_levels(), 'spaces.csv': self.extractor.extract_spaces(), 'materials.csv': self.extractor.extract_materials() } for filename, df in exports.items(): df.to_csv(os.path.join(output_dir, filename), index=False) return output_dir def to_json(self, output_path: str): """Export to JSON""" import json data = { 'project': self.extractor.get_project_info(), 'elements': self.extractor.get_all_elements().to_dict('records'), 'quantities': self.extractor.extract_quantities().to_dict('records'), 'levels': self.extractor.extract_levels().to_dict('records'), 'materials': self.extractor.extract_materials().to_dict('records') } with open(output_path, 'w', encoding='utf-8') as f: json.dump(data, f, indent=2, default=str) return output_path def to_database(self, connection_string: str, table_prefix: str = 'ifc_'): """Export to SQL database""" from sqlalchemy import create_engine engine = create_engine(connection_string) tables = { f'{table_prefix}elements': self.extractor.get_all_elements(), f'{table_prefix}quantities': self.extractor.extract_quantities(), f'{table_prefix}levels': self.extractor.extract_levels(), f'{table_prefix}spaces': self.extractor.extract_spaces(), f'{table_prefix}materials': self.extractor.extract_materials() } for table_name, df in tables.items(): # Remove complex columns for database storage simple_df = df.select_dtypes(exclude=['object']).copy() for col in df.columns: if df[col].dtype == 'object': simple_df[col] = df[col].astype(str) simple_df.to_sql(table_name, engine, if_exists='replace', index=False) return list(tables.keys())
Quick Reference
Element TypeCommon PropertiesQuantitiesIfcWallIsExternal, FireRatingLength, Height, Area, VolumeIfcSlabIsExternal, LoadBearingArea, Volume, PerimeterIfcColumnLoadBearingHeight, CrossSectionAreaIfcBeamLoadBearingLength, CrossSectionAreaIfcDoorFireRating, AcousticRatingWidth, HeightIfcWindowThermalTransmittanceWidth, Height, Area
Property Set Lookup
# Common IFC Property Sets PSETS = { 'Pset_WallCommon': ['IsExternal', 'LoadBearing', 'FireRating'], 'Pset_SlabCommon': ['IsExternal', 'LoadBearing', 'AcousticRating'], 'Pset_ColumnCommon': ['IsExternal', 'LoadBearing'], 'Pset_BeamCommon': ['LoadBearing', 'FireRating'], 'Pset_DoorCommon': ['FireRating', 'AcousticRating', 'SecurityRating'], 'Pset_WindowCommon': ['ThermalTransmittance', 'GlazingType'], 'BaseQuantities': ['Length', 'Width', 'Height', 'Area', 'Volume'] }
Resources
IfcOpenShell: https://ifcopenshell.org IFC Standard: https://www.buildingsmart.org/standards/bsi-standards/industry-foundation-classes/ DDC Website: https://datadrivenconstruction.io
Next Steps
See bim-validation-pipeline for validating extracted data See qto-report for quantity take-off reports See 4d-simulation for linking to schedules
Category context
Agent frameworks, memory systems, reasoning layers, and model-native orchestration.
Source: Tencent SkillHub
Largest current source with strong distribution and engagement signals.
Package contents
Included in package
2 Docs1 Config
- SKILL.md
- instructions.md
- claw.json