DGN to Excel Conversion

Business Case

Problem Statement

DGN files are common in infrastructure and civil engineering:

• Transportation and highway design
• Bridge and tunnel projects
• Utility networks
• Rail infrastructure

Extracting structured data from DGN files for analysis and reporting can be challenging.

Solution

Convert DGN files to structured Excel databases, supporting both v7 and v8 formats.

Business Value

• Infrastructure support - Civil engineering focused
• Legacy format support - V7 and V8 DGN files
• Data extraction - Levels, cells, text, geometry
• Batch processing - Process multiple files
• Structured output - Excel format for analysis

Technical Implementation

CLI Syntax

DgnExporter.exe <input_dgn>

Supported Versions

Version	Description
V7 DGN	Legacy MicroStation format (pre-V8)
V8 DGN	Modern MicroStation format
V8i DGN	MicroStation V8i format

Output Format

Output	Description
.xlsx	Excel database with all elements

Examples

# Basic conversion
DgnExporter.exe "C:\Projects\Bridge.dgn"

# Batch processing
for /R "C:\Infrastructure" %f in (*.dgn) do DgnExporter.exe "%f"

# PowerShell batch
Get-ChildItem "C:\Projects\*.dgn" -Recurse | ForEach-Object {
    & "C:\DDC\DgnExporter.exe" $_.FullName
}

Python Integration

import subprocess
import pandas as pd
from pathlib import Path
from typing import List, Optional, Dict, Any
from dataclasses import dataclass
from enum import Enum


class DGNElementType(Enum):
    """DGN element types."""
    CELL_HEADER = 2
    LINE = 3
    LINE_STRING = 4
    SHAPE = 6
    TEXT_NODE = 7
    CURVE = 11
    COMPLEX_CHAIN = 12
    COMPLEX_SHAPE = 14
    ELLIPSE = 15
    ARC = 16
    TEXT = 17
    SURFACE = 18
    SOLID = 19
    BSPLINE_CURVE = 21
    POINT_STRING = 22
    DIMENSION = 33
    SHARED_CELL = 35


@dataclass
class DGNElement:
    """Represents a DGN element."""
    element_id: int
    element_type: int
    type_name: str
    level: int
    color: int
    weight: int
    style: int

    # Geometry
    range_low_x: Optional[float] = None
    range_low_y: Optional[float] = None
    range_low_z: Optional[float] = None
    range_high_x: Optional[float] = None
    range_high_y: Optional[float] = None
    range_high_z: Optional[float] = None

    # Cell/Text specific
    cell_name: Optional[str] = None
    text_content: Optional[str] = None


@dataclass
class DGNLevel:
    """Represents a DGN level."""
    number: int
    name: str
    is_displayed: bool
    is_frozen: bool
    element_count: int


class DGNExporter:
    """DGN to Excel converter using DDC DgnExporter CLI."""

    def __init__(self, exporter_path: str = "DgnExporter.exe"):
        self.exporter = Path(exporter_path)
        if not self.exporter.exists():
            raise FileNotFoundError(f"DgnExporter not found: {exporter_path}")

    def convert(self, dgn_file: str) -> Path:
        """Convert DGN file to Excel."""
        dgn_path = Path(dgn_file)
        if not dgn_path.exists():
            raise FileNotFoundError(f"DGN file not found: {dgn_file}")

        cmd = [str(self.exporter), str(dgn_path)]
        result = subprocess.run(cmd, capture_output=True, text=True)

        if result.returncode != 0:
            raise RuntimeError(f"Export failed: {result.stderr}")

        return dgn_path.with_suffix('.xlsx')

    def batch_convert(self, folder: str,
                      include_subfolders: bool = True) -> List[Dict[str, Any]]:
        """Convert all DGN files in folder."""
        folder_path = Path(folder)
        pattern = "**/*.dgn" if include_subfolders else "*.dgn"

        results = []
        for dgn_file in folder_path.glob(pattern):
            try:
                output = self.convert(str(dgn_file))
                results.append({
                    'input': str(dgn_file),
                    'output': str(output),
                    'status': 'success'
                })
                print(f"✓ Converted: {dgn_file.name}")
            except Exception as e:
                results.append({
                    'input': str(dgn_file),
                    'output': None,
                    'status': 'failed',
                    'error': str(e)
                })
                print(f"✗ Failed: {dgn_file.name} - {e}")

        return results

    def read_elements(self, xlsx_file: str) -> pd.DataFrame:
        """Read converted Excel as DataFrame."""
        return pd.read_excel(xlsx_file, sheet_name="Elements")

    def get_levels(self, xlsx_file: str) -> pd.DataFrame:
        """Get level summary."""
        df = self.read_elements(xlsx_file)

        if 'Level' not in df.columns:
            raise ValueError("Level column not found")

        summary = df.groupby('Level').agg({
            'ElementId': 'count'
        }).reset_index()
        summary.columns = ['Level', 'Element_Count']
        return summary.sort_values('Level')

    def get_element_types(self, xlsx_file: str) -> pd.DataFrame:
        """Get element type statistics."""
        df = self.read_elements(xlsx_file)

        type_col = 'ElementType' if 'ElementType' in df.columns else 'Type'
        if type_col not in df.columns:
            return pd.DataFrame()

        summary = df.groupby(type_col).agg({
            'ElementId': 'count'
        }).reset_index()
        summary.columns = ['Element_Type', 'Count']
        return summary.sort_values('Count', ascending=False)

    def get_cells(self, xlsx_file: str) -> pd.DataFrame:
        """Get cell references (similar to blocks in DWG)."""
        df = self.read_elements(xlsx_file)

        # Filter to cell elements
        cells = df[df['ElementType'].isin([2, 35])]  # CELL_HEADER, SHARED_CELL

        if cells.empty or 'CellName' not in cells.columns:
            return pd.DataFrame(columns=['Cell_Name', 'Count'])

        summary = cells.groupby('CellName').agg({
            'ElementId': 'count'
        }).reset_index()
        summary.columns = ['Cell_Name', 'Count']
        return summary.sort_values('Count', ascending=False)

    def get_text_content(self, xlsx_file: str) -> pd.DataFrame:
        """Extract all text from DGN."""
        df = self.read_elements(xlsx_file)

        # Filter to text elements
        text_types = [7, 17]  # TEXT_NODE, TEXT
        texts = df[df['ElementType'].isin(text_types)]

        if 'TextContent' in texts.columns:
            return texts[['ElementId', 'Level', 'TextContent']].copy()
        return texts[['ElementId', 'Level']].copy()

    def get_statistics(self, xlsx_file: str) -> Dict[str, Any]:
        """Get comprehensive DGN statistics."""
        df = self.read_elements(xlsx_file)

        stats = {
            'total_elements': len(df),
            'levels_used': df['Level'].nunique() if 'Level' in df.columns else 0,
            'element_types': df['ElementType'].nunique() if 'ElementType' in df.columns else 0
        }

        # Calculate extents
        for coord in ['X', 'Y', 'Z']:
            low_col = f'RangeLow{coord}'
            high_col = f'RangeHigh{coord}'
            if low_col in df.columns and high_col in df.columns:
                stats[f'min_{coord.lower()}'] = df[low_col].min()
                stats[f'max_{coord.lower()}'] = df[high_col].max()

        return stats


class DGNAnalyzer:
    """Advanced DGN analysis for infrastructure projects."""

    def __init__(self, exporter: DGNExporter):
        self.exporter = exporter

    def analyze_infrastructure(self, dgn_file: str) -> Dict[str, Any]:
        """Analyze DGN for infrastructure elements."""
        xlsx = self.exporter.convert(dgn_file)
        df = self.exporter.read_elements(str(xlsx))

        analysis = {
            'file': dgn_file,
            'statistics': self.exporter.get_statistics(str(xlsx)),
            'levels': self.exporter.get_levels(str(xlsx)).to_dict('records'),
            'element_types': self.exporter.get_element_types(str(xlsx)).to_dict('records'),
            'cells': self.exporter.get_cells(str(xlsx)).to_dict('records')
        }

        # Identify infrastructure-specific elements
        if 'ElementType' in df.columns:
            # Lines and shapes (often roads, boundaries)
            lines = df[df['ElementType'].isin([3, 4, 6, 14])].shape[0]
            analysis['linear_elements'] = lines

            # Complex elements (often structures)
            complex_elements = df[df['ElementType'].isin([12, 14, 18, 19])].shape[0]
            analysis['complex_elements'] = complex_elements

            # Annotation elements
            annotations = df[df['ElementType'].isin([7, 17, 33])].shape[0]
            analysis['annotations'] = annotations

        return analysis

    def compare_revisions(self, dgn1: str, dgn2: str) -> Dict[str, Any]:
        """Compare two DGN revisions."""
        xlsx1 = self.exporter.convert(dgn1)
        xlsx2 = self.exporter.convert(dgn2)

        df1 = self.exporter.read_elements(str(xlsx1))
        df2 = self.exporter.read_elements(str(xlsx2))

        levels1 = set(df1['Level'].unique()) if 'Level' in df1.columns else set()
        levels2 = set(df2['Level'].unique()) if 'Level' in df2.columns else set()

        return {
            'revision1': dgn1,
            'revision2': dgn2,
            'element_count_diff': len(df2) - len(df1),
            'levels_added': list(levels2 - levels1),
            'levels_removed': list(levels1 - levels2),
            'common_levels': len(levels1 & levels2)
        }

    def extract_coordinates(self, xlsx_file: str) -> pd.DataFrame:
        """Extract element coordinates for GIS integration."""
        df = self.exporter.read_elements(xlsx_file)

        coord_cols = ['ElementId', 'Level', 'ElementType']
        for col in ['RangeLowX', 'RangeLowY', 'RangeLowZ',
                    'RangeHighX', 'RangeHighY', 'RangeHighZ',
                    'CenterX', 'CenterY', 'CenterZ']:
            if col in df.columns:
                coord_cols.append(col)

        return df[coord_cols].copy()


class DGNLevelManager:
    """Manage DGN level structures."""

    def __init__(self, exporter: DGNExporter):
        self.exporter = exporter

    def get_level_map(self, xlsx_file: str) -> Dict[int, str]:
        """Create level number to name mapping."""
        df = self.exporter.read_elements(xlsx_file)

        if 'Level' not in df.columns:
            return {}

        # MicroStation levels are typically numbered 1-63 (V7) or unlimited (V8)
        level_map = {}
        for level in df['Level'].unique():
            level_map[int(level)] = f"Level_{level}"

        return level_map

    def filter_by_levels(self, xlsx_file: str,
                         levels: List[int]) -> pd.DataFrame:
        """Filter elements by level numbers."""
        df = self.exporter.read_elements(xlsx_file)
        return df[df['Level'].isin(levels)]

    def get_level_usage_report(self, xlsx_file: str) -> pd.DataFrame:
        """Generate level usage report."""
        df = self.exporter.read_elements(xlsx_file)

        if 'Level' not in df.columns or 'ElementType' not in df.columns:
            return pd.DataFrame()

        # Cross-tabulate levels and element types
        report = pd.crosstab(df['Level'], df['ElementType'], margins=True)
        return report


# Convenience functions
def convert_dgn_to_excel(dgn_file: str,
                         exporter_path: str = "DgnExporter.exe") -> str:
    """Quick conversion of DGN to Excel."""
    exporter = DGNExporter(exporter_path)
    output = exporter.convert(dgn_file)
    return str(output)


def analyze_dgn(dgn_file: str,
                exporter_path: str = "DgnExporter.exe") -> Dict[str, Any]:
    """Analyze DGN file and return summary."""
    exporter = DGNExporter(exporter_path)
    analyzer = DGNAnalyzer(exporter)
    return analyzer.analyze_infrastructure(dgn_file)

Output Structure

Excel Sheets

Sheet	Content
Elements	All DGN elements with properties
Levels	Level definitions
Cells	Cell library

Element Columns

Column	Type	Description
ElementId	int	Unique element ID
ElementType	int	Type code (3=Line, 17=Text, etc.)
Level	int	Level number
Color	int	Color index
Weight	int	Line weight
Style	int	Line style
RangeLowX/Y/Z	float	Bounding box minimum
RangeHighX/Y/Z	float	Bounding box maximum
CellName	string	Cell name (for cell elements)
TextContent	string	Text content (for text elements)

Quick Start

# Initialize exporter
exporter = DGNExporter("C:/DDC/DgnExporter.exe")

# Convert DGN to Excel
xlsx = exporter.convert("C:/Projects/Highway.dgn")
print(f"Output: {xlsx}")

# Read elements
df = exporter.read_elements(str(xlsx))
print(f"Total elements: {len(df)}")

# Get level statistics
levels = exporter.get_levels(str(xlsx))
print(levels)

# Get element types
types = exporter.get_element_types(str(xlsx))
print(types)

Common Use Cases

1. Infrastructure Analysis

exporter = DGNExporter()
analyzer = DGNAnalyzer(exporter)

analysis = analyzer.analyze_infrastructure("highway.dgn")
print(f"Total elements: {analysis['statistics']['total_elements']}")
print(f"Linear elements: {analysis['linear_elements']}")
print(f"Annotations: {analysis['annotations']}")

2. Level Audit

exporter = DGNExporter()
xlsx = exporter.convert("bridge.dgn")
levels = exporter.get_levels(str(xlsx))

# Check for unused standard levels
for idx, row in levels.iterrows():
    print(f"Level {row['Level']}: {row['Element_Count']} elements")

3. GIS Integration

analyzer = DGNAnalyzer(exporter)
xlsx = exporter.convert("utilities.dgn")
coords = analyzer.extract_coordinates(str(xlsx))

# Export for GIS
coords.to_csv("coordinates.csv", index=False)

4. Revision Comparison

analyzer = DGNAnalyzer(exporter)
diff = analyzer.compare_revisions("rev1.dgn", "rev2.dgn")
print(f"Elements changed: {diff['element_count_diff']}")

Integration with DDC Pipeline

# Infrastructure pipeline: DGN → Excel → Analysis
from dgn_exporter import DGNExporter, DGNAnalyzer

# 1. Convert DGN
exporter = DGNExporter("C:/DDC/DgnExporter.exe")
xlsx = exporter.convert("highway_project.dgn")

# 2. Analyze structure
stats = exporter.get_statistics(str(xlsx))
print(f"Elements: {stats['total_elements']}")
print(f"Levels: {stats['levels_used']}")

# 3. Extract for GIS
analyzer = DGNAnalyzer(exporter)
coords = analyzer.extract_coordinates(str(xlsx))
coords.to_csv("for_gis.csv", index=False)

Best Practices

1. Check version - V7 and V8 have different capabilities
2. Reference files - Process all reference files separately
3. Level mapping - Document level standards for your organization
4. Coordinate systems - Verify units and coordinate systems
5. Cell libraries - Export cells separately if needed

Resources

• GitHub: cad2data Pipeline
• DDC Book: Chapter 2.4 - CAD Data Extraction
• MicroStation: Infrastructure-focused CAD software