Björn Steinhagen
243 lines
8.6 KiB
Python
243 lines
8.6 KiB
Python
from collections import defaultdict
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from pydantic import Field
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from speckle_automate import (
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AutomateBase,
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AutomationContext,
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execute_automate_function,
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)
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from typing import Dict, Generator, Any, List
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from src.domain.carbon.databases.enums import SteelDatabase, TimberDatabase
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from src.infrastructure.logging import Logging
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from src.services.carbon_calculator import CarbonCalculator
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from src.services.element_processor import ElementProcessor
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from src.services.material_processor import MaterialProcessor
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def create_one_of_enum(enum_cls):
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"""
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Helper function to create a JSON schema from an Enum class.
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This is used for generating user input forms in the UI.
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"""
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return [{"const": item.value, "title": item.name} for item in enum_cls]
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# TODO: Function inputs
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class FunctionInputs(AutomateBase):
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"""User-defined function inputs."""
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steel_database: str = Field(
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default=SteelDatabase.Type350MPa,
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title="Steel Database",
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description="Database used for the GWP of steel objects",
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json_schema_extra={"oneOf": create_one_of_enum(SteelDatabase)},
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)
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timber_database: str = Field(
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default=TimberDatabase.Binderholz2019,
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title="Timber Database",
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description="Database used for the GWP of timber objects",
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json_schema_extra={"oneOf": create_one_of_enum(TimberDatabase)},
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)
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class RevitCarbonAnalyzer:
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"""Main application for analyzing carbon in Revit models."""
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def __init__(self, steel_database: str, timber_database: str):
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self.material_processor = MaterialProcessor()
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self.element_processor = ElementProcessor(
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material_processor=self.material_processor, logger=Logging()
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)
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self.carbon_calculator = CarbonCalculator(
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steel_database=steel_database, timber_database=timber_database
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)
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def analyze_model(self, model_root) -> dict:
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"""Analyze a Revit model for carbon emissions."""
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results = {
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"processed_elements": [],
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"skipped_elements": [],
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"errors": [],
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"total_carbon": 0.0,
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}
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# Debug: Print number of elements found
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element_count = 0
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# Process each element
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for element in self._iterate_elements(model_root):
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element_count += 1
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try:
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print(
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f"Processing element {getattr(element, 'id', 'unknown')}"
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) # Debug
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element_result = self._process_single_element(element)
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print(f"Result status: {element_result['status']}") # Debug
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if element_result["status"] == "processed":
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results["processed_elements"].append(element_result)
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results["total_carbon"] += element_result["total_carbon"]
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elif element_result["status"] == "skipped":
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results["skipped_elements"].append(element_result)
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else:
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results["errors"].append(element_result)
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except Exception as e:
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print(f"Error processing element: {str(e)}") # Debug
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results["errors"].append(
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{
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"id": getattr(element, "id", "unknown"),
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"error": str(e),
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"status": "error",
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}
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)
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print(f"Total elements found: {element_count}") # Debug
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return results
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def _process_single_element(self, element: Dict) -> Dict:
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"""Process a single element and return its results."""
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element_id = getattr(element, "id", "unknown")
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# Process element
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processed_element = self.element_processor.process_element(element)
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if not processed_element:
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return {
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"id": element_id,
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"status": "skipped",
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"reason": "Invalid element structure",
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}
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# Calculate carbon
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try:
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carbon_results = self.carbon_calculator.calculate_carbon(processed_element)
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return {
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"id": element_id,
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"status": "processed",
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"level": processed_element.level,
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"category": processed_element.category.value,
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"materials": [
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{
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"name": m.properties.name,
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"type": m.type.value,
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"volume": m.properties.volume,
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# Add other material properties as needed
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}
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for m in processed_element.materials
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],
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"carbon_results": carbon_results,
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"total_carbon": sum(r.total_carbon for r in carbon_results.values()),
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}
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except Exception as e:
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return {
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"id": element_id,
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"status": "error",
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"error": f"Carbon calculation failed: {str(e)}",
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}
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@staticmethod
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def _iterate_elements(model_data) -> Generator[Dict, None, None]:
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"""Iterate through all elements in the model."""
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for level in getattr(model_data, "elements", []):
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for type_group in getattr(level, "elements", []):
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for element_group in getattr(type_group, "elements", []):
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for element in getattr(element_group, "elements", []):
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yield element
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def automate_function(
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automate_context: AutomationContext,
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function_inputs: FunctionInputs,
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) -> None:
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"""Program entry point."""
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try:
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# Initialize analyzer
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analyzer = RevitCarbonAnalyzer(
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steel_database=function_inputs.steel_database,
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timber_database=function_inputs.timber_database,
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)
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# Get commit root
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version_id = automate_context.automation_run_data.triggers[0].payload.version_id
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commit_root = automate_context.speckle_client.commit.get(
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automate_context.automation_run_data.project_id, version_id
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)
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# Get model root
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model_root = automate_context.receive_version()
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# Validate Revit source
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if not _validate_revit_source(commit_root):
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automate_context.mark_run_failed("Model must be from Revit")
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return
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# Run analysis - convert Speckle model to dict for processing
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results = analyzer.analyze_model(model_root)
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# Process results
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_process_automation_results(automate_context, results)
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# Mark success
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automate_context.mark_run_success(
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f"Analysis complete. Processed {len(results['processed_elements'])} elements. "
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f"Total carbon: {results['total_carbon']:.2f} kgCO2e"
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)
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except Exception as e:
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automate_context.mark_run_failed(f"Analysis failed: {str(e)}")
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raise
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def _validate_revit_source(commit_root: Any) -> bool:
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"""Validate that the model is from Revit."""
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source_app = getattr(commit_root, "sourceApplication", "").lower()
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return source_app.startswith("revit")
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def _process_automation_results(
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automate_context: AutomationContext, results: dict
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) -> None:
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"""Process results and attach them to the automation context."""
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# Group results by category
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successes: Dict[str, List[str]] = defaultdict(list)
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warnings: Dict[str, List[str]] = defaultdict(list)
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errors: Dict[str, List[str]] = defaultdict(list)
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# Group successful elements
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for element in results["processed_elements"]:
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successes["Carbon Analysis"].append(element["id"])
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# Group skipped elements
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for element in results["skipped_elements"]:
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warnings["Skipped Elements"].append(element["id"])
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# Group errors
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for element in results["errors"]:
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errors["Processing Errors"].append(element["id"])
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# Attach grouped results
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for category, object_ids in successes.items():
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automate_context.attach_success_to_objects(
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category=category,
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object_ids=object_ids,
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message="Carbon calculations completed successfully for these elements!",
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)
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for category, object_ids in warnings.items():
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automate_context.attach_warning_to_objects(
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category=category,
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object_ids=object_ids,
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message="Elements requiring careful review.",
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)
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for category, object_ids in errors.items():
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automate_context.attach_error_to_objects(
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category=category,
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object_ids=object_ids,
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message="Failure processing the following elements.",
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)
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if __name__ == "__main__":
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execute_automate_function(automate_function, FunctionInputs)
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