interoperability-analyzer

name	interoperability-analyzer
description	Analyze data interoperability issues in construction projects. Identify format incompatibilities and data loss points.
homepage	https://datadrivenconstruction.io
metadata	{"openclaw":{"emoji":"🔀","os":["win32"],"homepage":"https://datadrivenconstruction.io","requires":{"bins":"[Truncated]"}}}

Interoperability Analyzer

Business Case

Problem Statement

Data interoperability challenges:

• Multiple proprietary formats
• Data loss in conversions
• Incompatible systems
• Missing standard adoption

Solution

Analyze data exchange patterns, identify interoperability issues, and recommend solutions for seamless data flow.

Technical Implementation

import pandas as pd
from typing import Dict, Any, List, Optional
from dataclasses import dataclass, field
from enum import Enum


class ():
    IFC = 
    RVT = 
    DWG = 
    NWC = 
    SKP = 
    EXCEL = 
    CSV = 
    JSON = 
    XML = 
    BCF = 
    COBIE = 


 ():
    NATIVE =            
    LOSSLESS =        
    PARTIAL =          
    DEGRADED =        
    INCOMPATIBLE = 



 :
    : DataFormat
    supports_geometry: 
    supports_properties: 
    supports_relationships: 
    supports_scheduling: 
    supports_costs: 
    open_standard: 



 :
    source_format: DataFormat
    target_format: DataFormat
    interoperability_level: InteroperabilityLevel
    data_preserved: []
    data_lost: []
    recommendations: []


 :
    

     ():
        .capabilities = ._define_capabilities()
        .exchange_matrix = ._define_exchange_matrix()

     () -> [DataFormat, FormatCapability]:
        

         {
            DataFormat.IFC: FormatCapability(
                DataFormat.IFC, , , , , , 
            ),
            DataFormat.RVT: FormatCapability(
                DataFormat.RVT, , , , , , 
            ),
            DataFormat.DWG: FormatCapability(
                DataFormat.DWG, , , , , , 
            ),
            DataFormat.NWC: FormatCapability(
                DataFormat.NWC, , , , , , 
            ),
            DataFormat.EXCEL: FormatCapability(
                DataFormat.EXCEL, , , , , , 
            ),
            DataFormat.CSV: FormatCapability(
                DataFormat.CSV, , , , , , 
            ),
            DataFormat.JSON: FormatCapability(
                DataFormat.JSON, , , , , , 
            ),
            DataFormat.COBIE: FormatCapability(
                DataFormat.COBIE, , , , , , 
            ),
            DataFormat.BCF: FormatCapability(
                DataFormat.BCF, , , , , , 
            )
        }

     () -> [, InteroperabilityLevel]:
        

         {
            (DataFormat.RVT, DataFormat.IFC): InteroperabilityLevel.PARTIAL,
            (DataFormat.IFC, DataFormat.RVT): InteroperabilityLevel.PARTIAL,
            (DataFormat.RVT, DataFormat.DWG): InteroperabilityLevel.DEGRADED,
            (DataFormat.DWG, DataFormat.RVT): InteroperabilityLevel.DEGRADED,
            (DataFormat.RVT, DataFormat.NWC): InteroperabilityLevel.LOSSLESS,
            (DataFormat.IFC, DataFormat.NWC): InteroperabilityLevel.PARTIAL,
            (DataFormat.EXCEL, DataFormat.CSV): InteroperabilityLevel.LOSSLESS,
            (DataFormat.CSV, DataFormat.EXCEL): InteroperabilityLevel.LOSSLESS,
            (DataFormat.JSON, DataFormat.EXCEL): InteroperabilityLevel.PARTIAL,
            (DataFormat.RVT, DataFormat.COBIE): InteroperabilityLevel.PARTIAL,
            (DataFormat.IFC, DataFormat.COBIE): InteroperabilityLevel.PARTIAL,
        }

     () -> ExchangeAnalysis:
        

        level = .exchange_matrix.get(
            (source, target),
            InteroperabilityLevel.INCOMPATIBLE  source != target  InteroperabilityLevel.NATIVE
        )

        source_cap = .capabilities.get(source)
        target_cap = .capabilities.get(target)

        preserved = []
        lost = []

         source_cap  target_cap:
             source_cap.supports_geometry  target_cap.supports_geometry:
                preserved.append()
             source_cap.supports_geometry:
                lost.append()

             source_cap.supports_properties  target_cap.supports_properties:
                preserved.append()
             source_cap.supports_properties:
                lost.append()

             source_cap.supports_relationships  target_cap.supports_relationships:
                preserved.append()
             source_cap.supports_relationships:
                lost.append()

             source_cap.supports_scheduling  target_cap.supports_scheduling:
                preserved.append()
             source_cap.supports_scheduling:
                lost.append()

             source_cap.supports_costs  target_cap.supports_costs:
                preserved.append()
             source_cap.supports_costs:
                lost.append()

        recommendations = ._get_recommendations(source, target, level)

         ExchangeAnalysis(
            source_format=source,
            target_format=target,
            interoperability_level=level,
            data_preserved=preserved,
            data_lost=lost,
            recommendations=recommendations
        )

     () -> []:
        

        recommendations = []

         level == InteroperabilityLevel.INCOMPATIBLE:
            recommendations.append()
            recommendations.append()

         level == InteroperabilityLevel.DEGRADED:
            recommendations.append()
            recommendations.append()

         level == InteroperabilityLevel.PARTIAL:
            recommendations.append()
            recommendations.append()

         source == DataFormat.RVT  target == DataFormat.IFC:
            recommendations.append()
            recommendations.append()

         target == DataFormat.COBIE:
            recommendations.append()
            recommendations.append()

         recommendations

     () -> [, ]:
        

         (formats) < :
             {: }

        exchanges = []
        cumulative_lost = ()

         i  ((formats) - ):
            analysis = .analyze_exchange(formats[i], formats[i+])
            exchanges.append({
                : i + ,
                : formats[i].value,
                : formats[i+].value,
                : analysis.interoperability_level.value,
                : analysis.data_lost
            })
            cumulative_lost.update(analysis.data_lost)

        
        levels = [e[]  e  exchanges]
           levels:
            overall = 
           levels:
            overall = 
           levels:
            overall = 
        :
            overall = 

         {
            : .join(f.value  f  formats),
            : (exchanges),
            : exchanges,
            : overall,
            : (cumulative_lost),
            : ._get_workflow_recommendations(formats, overall)
        }

     () -> []:
        

        recommendations = []

         overall  [, ]:
            recommendations.append()
            recommendations.append()

         (formats) > :
            recommendations.append()

         DataFormat.DWG  formats  DataFormat.RVT  formats:
            recommendations.append()

         recommendations

     () -> pd.DataFrame:
        

        formats = (DataFormat)
        matrix = []

         source  formats:
            row = {: source.value}
             target  formats:
                 source == target:
                    row[target.value] = 
                :
                    level = .exchange_matrix.get((source, target), InteroperabilityLevel.INCOMPATIBLE)
                    row[target.value] = level.value
            matrix.append(row)

         pd.DataFrame(matrix)

     () -> :
        

         pd.ExcelWriter(output_path, engine=)  writer:
            
            matrix = .generate_compatibility_matrix()
            matrix.to_excel(writer, sheet_name=, index=)

            
            caps_data = [{
                : cap..value,
                : cap.supports_geometry,
                : cap.supports_properties,
                : cap.supports_relationships,
                : cap.supports_scheduling,
                : cap.supports_costs,
                : cap.open_standard
            }  cap  .capabilities.values()]
            caps_df = pd.DataFrame(caps_data)
            caps_df.to_excel(writer, sheet_name=, index=)

         output_path