dgn-to-excelConvert DGN files (v7-v8) to Excel databases. Extract elements, levels, and properties from infrastructure CAD files.
Install via ClawdBot CLI:
clawdbot install datadrivenconstruction/dgn-to-excelDGN files are common in infrastructure and civil engineering:
Extracting structured data from DGN files for analysis and reporting can be challenging.
Convert DGN files to structured Excel databases, supporting both v7 and v8 formats.
```bash
DgnExporter.exe
```
| Version | Description |
|---------|-------------|
| V7 DGN | Legacy MicroStation format (pre-V8) |
| V8 DGN | Modern MicroStation format |
| V8i DGN | MicroStation V8i format |
| Output | Description |
|--------|-------------|
| .xlsx | Excel database with all elements |
```bash
DgnExporter.exe "C:\Projects\Bridge.dgn"
for /R "C:\Infrastructure" %f in (*.dgn) do DgnExporter.exe "%f"
Get-ChildItem "C:\Projects\*.dgn" -Recurse | ForEach-Object {
& "C:\DDC\DgnExporter.exe" $_.FullName
}
```
```python
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
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)
```
| Sheet | Content |
|-------|---------|
| Elements | All DGN elements with properties |
| Levels | Level definitions |
| Cells | Cell library |
| 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) |
```python
exporter = DGNExporter("C:/DDC/DgnExporter.exe")
xlsx = exporter.convert("C:/Projects/Highway.dgn")
print(f"Output: {xlsx}")
df = exporter.read_elements(str(xlsx))
print(f"Total elements: {len(df)}")
levels = exporter.get_levels(str(xlsx))
print(levels)
types = exporter.get_element_types(str(xlsx))
print(types)
```
```python
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']}")
```
```python
exporter = DGNExporter()
xlsx = exporter.convert("bridge.dgn")
levels = exporter.get_levels(str(xlsx))
for idx, row in levels.iterrows():
print(f"Level {row['Level']}: {row['Element_Count']} elements")
```
```python
analyzer = DGNAnalyzer(exporter)
xlsx = exporter.convert("utilities.dgn")
coords = analyzer.extract_coordinates(str(xlsx))
coords.to_csv("coordinates.csv", index=False)
```
```python
analyzer = DGNAnalyzer(exporter)
diff = analyzer.compare_revisions("rev1.dgn", "rev2.dgn")
print(f"Elements changed: {diff['element_count_diff']}")
```
```python
from dgn_exporter import DGNExporter, DGNAnalyzer
exporter = DGNExporter("C:/DDC/DgnExporter.exe")
xlsx = exporter.convert("highway_project.dgn")
stats = exporter.get_statistics(str(xlsx))
print(f"Elements: {stats['total_elements']}")
print(f"Levels: {stats['levels_used']}")
analyzer = DGNAnalyzer(exporter)
coords = analyzer.extract_coordinates(str(xlsx))
coords.to_csv("for_gis.csv", index=False)
```
AI Usage Analysis
Analysis is being generated… refresh in a few seconds.
Automatically update Clawdbot and all installed skills once daily. Runs via cron, checks for updates, applies them, and messages the user with a summary of what changed.
Full desktop computer use for headless Linux servers. Xvfb + XFCE virtual desktop with xdotool automation. 17 actions (click, type, scroll, screenshot, drag,...
Essential Docker commands and workflows for container management, image operations, and debugging.
Tool discovery and shell one-liner reference for sysadmin, DevOps, and security tasks. AUTO-CONSULT this skill when the user is: troubleshooting network issues, debugging processes, analyzing logs, working with SSL/TLS, managing DNS, testing HTTP endpoints, auditing security, working with containers, writing shell scripts, or asks 'what tool should I use for X'. Source: github.com/trimstray/the-book-of-secret-knowledge
Deploy applications and manage projects with complete CLI reference. Commands for deployments, projects, domains, environment variables, and live documentation access.
Monitor topics of interest and proactively alert when important developments occur. Use when user wants automated monitoring of specific subjects (e.g., product releases, price changes, news topics, technology updates). Supports scheduled web searches, AI-powered importance scoring, smart alerts vs weekly digests, and memory-aware contextual summaries.