CHANGES:
- Added MeasuredDate as first column in regional.csv
- Added MeasuredDate as first column in muscle_balance.csv
- Updated README to document new column structure
BENEFITS:
✅ Track regional changes over time (e.g., Arms fat % across scans)
✅ Easy time-series analysis with pandas/Excel
✅ Filter by date range for progress tracking
✅ Consistent date column across all 3 CSV files
✅ Enables queries like: 'Show me trunk fat % over last 6 months'
EXAMPLE USAGE:
import pandas as pd
regional = pd.read_csv('regional.csv')
arms = regional[regional['Region'] == 'Arms']
# Now you can track Arms progress over time!
Each scan now adds:
- 1 row to overall.csv
- 6 rows to regional.csv (one per region)
- 6 rows to muscle_balance.csv (one per limb comparison)
- Add LeanPercent column to regional.csv matching BodySpec reports
- Calculate lean percentage from lean tissue (excluding BMC) for accuracy
- Update JSON output to include lean_percent for each region
- Document new column in README
- Values now match BodySpec regional reports (e.g., Arms: 73.7%, Legs: 72.7%, Trunk: 64.4%)
- Change --batch to accept directory instead of glob pattern
- Automatically skip already-processed scan dates
- Add --force flag to reprocess all files
- Fix date extraction regex to parse from client info line
- Display helpful tips about skipping/forcing
- Better user feedback with skip counts and suggestions
Usage:
python dexa_extract.py --batch data/pdfs --height-in 74 --outdir data/results
This will process only new scans, skipping any dates already in the output.
