Initial commit: BodySpec Insights - comprehensive DEXA analytics tool
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Mac DeCourcy
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dexa_extract.py
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dexa_extract.py
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#!/usr/bin/env python3
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"""
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BodySpec Insights - Body composition analytics for BodySpec DEXA scan PDFs
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Extract measurements from BodySpec DEXA reports, compute 30+ derived metrics,
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and output structured data for progress tracking.
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Usage:
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python dexa_extract.py /path/to/bodyspec-report.pdf --height-in 74 --weight-lb 212 --outdir ./data/results
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Note: This script is specifically designed for BodySpec PDF reports.
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Requires:
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pip install pdfplumber pandas
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"""
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import argparse
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import json
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import math
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import os
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import re
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from datetime import datetime
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import pdfplumber
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import pandas as pd
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def read_pdf_text(pdf_path):
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with pdfplumber.open(pdf_path) as pdf:
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pages_text = [page.extract_text() or "" for page in pdf.pages]
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return "\n".join(pages_text)
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def find_one(pattern, text, cast=float, flags=re.IGNORECASE):
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m = re.search(pattern, text, flags)
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if not m:
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return None
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val = m.group(1).replace(",", "").strip()
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return cast(val) if cast else val
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def convert_date_to_iso(date_str):
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"""Convert MM/DD/YYYY to YYYY-MM-DD"""
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if not date_str:
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return None
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try:
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dt = datetime.strptime(date_str, "%m/%d/%Y")
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return dt.strftime("%Y-%m-%d")
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except:
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return date_str
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def inches_to_ft_in(inches):
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"""Convert inches to feet'inches" format"""
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if inches is None:
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return None
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feet = int(inches // 12)
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remaining_inches = int(inches % 12)
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return f"{feet}'{remaining_inches}\""
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def parse_regional_table(text):
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regions = ["Arms", "Legs", "Trunk", "Android", "Gynoid", "Total"]
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out = {}
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for r in regions:
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# Example line: Arms 22.1% 27.4 6.0 20.2 1.1
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pattern = rf"{r}\s+([\d\.]+)%\s+([\d\.]+)\s+([\d\.]+)\s+([\d\.]+)\s+([\d\.]+)"
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m = re.search(pattern, text)
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if m:
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out[r] = {
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"fat_percent": float(m.group(1)),
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"total_mass_lb": float(m.group(2)),
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"fat_tissue_lb": float(m.group(3)),
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"lean_tissue_lb": float(m.group(4)),
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"bmc_lb": float(m.group(5)),
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}
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return out
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def parse_muscle_balance(text):
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names = ["Arms Total", "Right Arm", "Left Arm", "Legs Total", "Right Leg", "Left Leg"]
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out = {}
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for n in names:
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# Example: Right Arm 20.4 13.7 2.8 10.3 0.6
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pattern = rf"{n}\s+([\d\.]+)\s+([\d\.]+)\s+([\d\.]+)\s+([\d\.]+)\s+([\d\.]+)"
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m = re.search(pattern, text)
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if m:
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out[n] = {
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"fat_percent": float(m.group(1)),
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"total_mass_lb": float(m.group(2)),
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"fat_mass_lb": float(m.group(3)),
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"lean_mass_lb": float(m.group(4)),
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"bmc_lb": float(m.group(5)),
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}
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return out
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def parse_bone_density_total(text):
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# Example: Total 1.280 0.8 0.8
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m = re.search(r"Total\s+([\d\.]+)\s+([-\d\.]+)\s+([-\d\.]+)", text)
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if m:
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return {
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"total_bmd_g_per_cm2": float(m.group(1)),
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"young_adult_t_score": float(m.group(2)),
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"age_matched_z_score": float(m.group(3)),
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}
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return {}
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def parse_dexa_pdf(pdf_path):
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text = read_pdf_text(pdf_path)
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data = {}
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data["measured_date"] = find_one(r"Measured Date\s+([\d/]+)", text, cast=str)
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# First try to extract from SUMMARY RESULTS table (more reliable)
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# Pattern: 10/6/2025 27.8% 211.6 58.8 145.4 7.4
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summary_pattern = r"(\d{1,2}/\d{1,2}/\d{4})\s+([\d\.]+)%\s+([\d\.]+)\s+([\d\.]+)\s+([\d\.]+)\s+([\d\.]+)"
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summary_match = re.search(summary_pattern, text)
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if summary_match:
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data["body_fat_percent"] = float(summary_match.group(2))
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data["total_mass_lb"] = float(summary_match.group(3))
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data["fat_mass_lb"] = float(summary_match.group(4))
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data["lean_soft_tissue_lb"] = float(summary_match.group(5))
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data["bmc_lb"] = float(summary_match.group(6))
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else:
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# Fallback to individual patterns
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data["body_fat_percent"] = find_one(r"Total Body Fat %\s+([\d\.]+)", text)
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data["total_mass_lb"] = find_one(r"Total Mass.*?\(lbs\)\s+([\d\.]+)", text)
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data["fat_mass_lb"] = find_one(r"Fat Tissue \(lbs\)\s+([\d\.]+)", text)
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data["lean_soft_tissue_lb"] = find_one(r"Lean Tissue \(lbs\)\s+([\d\.]+)", text)
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data["bmc_lb"] = find_one(r"Bone Mineral\s+Content \(BMC\)\s+([\d\.]+)", text)
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# Supplemental
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data["android_percent"] = find_one(r"Android.*?([\d\.]+)%", text)
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data["gynoid_percent"] = find_one(r"Gynoid.*?([\d\.]+)%", text)
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data["rmr_cal_per_day"] = find_one(r"([\d,]+)\s*cal/day", text, cast=lambda s: int(s.replace(",", "")))
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# A/G Ratio appears after RMR, Android%, Gynoid% on same line: "1,778 cal/day 36.5% 27.8% 1.31"
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ag_match = re.search(r"[\d,]+\s*cal/day\s+([\d\.]+)%\s+([\d\.]+)%\s+([\d\.]+)", text)
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if ag_match:
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data["ag_ratio"] = float(ag_match.group(3))
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else:
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data["ag_ratio"] = find_one(r"A/G Ratio\s+([\d\.]+)", text)
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data["vat_mass_lb"] = find_one(r"Mass \(lbs\)\s+([\d\.]+)", text)
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data["vat_volume_in3"] = find_one(r"Volume \(in3\)\s+([\d\.]+)", text)
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# Tables
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data["regional"] = parse_regional_table(text)
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data["muscle_balance"] = parse_muscle_balance(text)
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data["bone_density"] = parse_bone_density_total(text)
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return data
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def compute_derived(d, height_in, weight_lb=None):
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# Prefer DEXA total mass if available
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total_mass = d.get("total_mass_lb") or weight_lb
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if total_mass is None:
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raise ValueError("Total mass is missing; pass --weight-lb if the PDF lacks it.")
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fm = d.get("fat_mass_lb")
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lst = d.get("lean_soft_tissue_lb")
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bmc = d.get("bmc_lb")
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bf_pct = d.get("body_fat_percent")
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ffm = None
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if fm is not None:
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ffm = total_mass - fm
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elif lst is not None and bmc is not None:
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ffm = lst + bmc
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def idx(value_lb):
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return round(703.0 * value_lb / (height_in ** 2), 2)
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derived = {
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"height_in": height_in,
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"height_ft_in": inches_to_ft_in(height_in),
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"weight_input_lb": weight_lb,
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"bmi": round(703.0 * total_mass / (height_in ** 2), 1),
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"fat_free_mass_lb": round(ffm, 1) if ffm is not None else None,
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"ffmi": idx(ffm) if ffm is not None else None,
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"fmi": idx(fm) if fm is not None else None,
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"lsti": idx(lst) if lst is not None else None,
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"alm_lb": None,
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"smi": None,
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}
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# Lean mass percentage (complement of body fat %)
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if bf_pct is not None:
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derived["lean_mass_percent"] = round(100 - bf_pct, 1)
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else:
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derived["lean_mass_percent"] = None
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# ALM from regional lean masses
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arms_lean = d.get("regional", {}).get("Arms", {}).get("lean_tissue_lb")
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legs_lean = d.get("regional", {}).get("Legs", {}).get("lean_tissue_lb")
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trunk_lean = d.get("regional", {}).get("Trunk", {}).get("lean_tissue_lb")
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if arms_lean is not None and legs_lean is not None:
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alm = arms_lean + legs_lean
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derived["alm_lb"] = round(alm, 1)
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derived["smi"] = idx(alm)
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# Regional lean mass distribution
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if lst is not None and arms_lean is not None and legs_lean is not None and trunk_lean is not None:
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derived["arms_lean_pct"] = round(100 * arms_lean / lst, 1)
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derived["legs_lean_pct"] = round(100 * legs_lean / lst, 1)
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derived["trunk_lean_pct"] = round(100 * trunk_lean / lst, 1)
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else:
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derived["arms_lean_pct"] = None
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derived["legs_lean_pct"] = None
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derived["trunk_lean_pct"] = None
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# Trunk-to-limb fat ratio (health risk indicator)
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trunk_fat = d.get("regional", {}).get("Trunk", {}).get("fat_tissue_lb")
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arms_fat = d.get("regional", {}).get("Arms", {}).get("fat_tissue_lb")
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legs_fat = d.get("regional", {}).get("Legs", {}).get("fat_tissue_lb")
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if trunk_fat is not None and arms_fat is not None and legs_fat is not None:
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limb_fat = arms_fat + legs_fat
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if limb_fat > 0:
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derived["trunk_to_limb_fat_ratio"] = round(trunk_fat / limb_fat, 2)
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else:
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derived["trunk_to_limb_fat_ratio"] = None
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else:
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derived["trunk_to_limb_fat_ratio"] = None
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# Limb symmetry indices (balance indicators)
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mb = d.get("muscle_balance", {})
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right_arm = mb.get("Right Arm", {}).get("lean_mass_lb")
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left_arm = mb.get("Left Arm", {}).get("lean_mass_lb")
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right_leg = mb.get("Right Leg", {}).get("lean_mass_lb")
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left_leg = mb.get("Left Leg", {}).get("lean_mass_lb")
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if right_arm is not None and left_arm is not None and right_arm + left_arm > 0:
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# Symmetry: 100 = perfect, <100 = left stronger, >100 = right stronger
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derived["arm_symmetry_index"] = round(100 * right_arm / (right_arm + left_arm), 1)
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else:
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derived["arm_symmetry_index"] = None
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if right_leg is not None and left_leg is not None and right_leg + left_leg > 0:
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derived["leg_symmetry_index"] = round(100 * right_leg / (right_leg + left_leg), 1)
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else:
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derived["leg_symmetry_index"] = None
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# VAT Index (normalized by height squared, like BMI)
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vat_mass = d.get("vat_mass_lb")
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if vat_mass is not None:
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derived["vat_index"] = idx(vat_mass)
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else:
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derived["vat_index"] = None
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# Bone Mineral Density Index (BMC normalized by height)
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if bmc is not None:
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derived["bmdi"] = idx(bmc)
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else:
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derived["bmdi"] = None
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# Adjusted Body Weight (used in nutrition/health calculations)
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# ABW = IBW + 0.4 * (actual weight - IBW), where IBW differs by sex
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# For simplicity, using a unisex approximation: IBW ≈ height_in * 2.3 - 100 (rough estimate)
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if total_mass is not None:
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ibw_estimate = height_in * 2.3 - 100
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if total_mass > ibw_estimate:
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derived["adjusted_body_weight_lb"] = round(ibw_estimate + 0.4 * (total_mass - ibw_estimate), 1)
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else:
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derived["adjusted_body_weight_lb"] = round(total_mass, 1)
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else:
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derived["adjusted_body_weight_lb"] = None
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return total_mass, derived
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def ensure_outdir(outdir):
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os.makedirs(outdir, exist_ok=True)
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def write_or_append_csv(path, row_dict, columns):
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df_row = pd.DataFrame([{k: row_dict.get(k) for k in columns}])
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if os.path.exists(path):
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df_row.to_csv(path, mode="a", header=False, index=False)
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else:
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df_row.to_csv(path, index=False)
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def write_or_append_json(path, obj):
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if os.path.exists(path):
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with open(path, "r") as f:
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try:
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data = json.load(f)
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except json.JSONDecodeError:
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data = []
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else:
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data = []
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if isinstance(data, dict):
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# convert to list of entries if previous file was a single dict
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data = [data]
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data.append(obj)
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with open(path, "w") as f:
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json.dump(data, f, indent=2)
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def append_markdown(path, md_text):
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mode = "a" if os.path.exists(path) else "w"
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with open(path, mode) as f:
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f.write(md_text.strip() + "\n\n")
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def make_markdown(measured_date, d, derived, total_mass):
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lines = []
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lines.append(f"# DEXA Summary — {measured_date}")
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lines.append("")
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lines.append(f"- Height: {derived['height_in']} in")
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lines.append(f"- Weight: {round(total_mass, 1)} lb")
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if d.get("body_fat_percent") is not None and d.get("fat_mass_lb") is not None:
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lines.append(f"- Body fat: {d['body_fat_percent']}% ({d['fat_mass_lb']} lb)")
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if d.get("lean_soft_tissue_lb") is not None:
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lines.append(f"- Lean soft tissue: {d['lean_soft_tissue_lb']} lb")
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if d.get("bmc_lb") is not None:
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lines.append(f"- Bone mineral content: {d['bmc_lb']} lb")
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lines.append(f"- Fat‑free mass: {derived.get('fat_free_mass_lb')}")
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lines.append(f"- BMI: {derived['bmi']}")
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lines.append(f"- FFMI: {derived.get('ffmi')}; FMI: {derived.get('fmi')}; Lean Soft Tissue Index: {derived.get('lsti')}")
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if derived.get("alm_lb") is not None:
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lines.append(f"- Appendicular Lean Mass: {derived['alm_lb']} lb; Skeletal Muscle Index: {derived['smi']}")
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if d.get("android_percent") is not None and d.get("gynoid_percent") is not None and d.get("ag_ratio") is not None:
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lines.append(f"- Android: {d['android_percent']}%; Gynoid: {d['gynoid_percent']}%; A/G ratio: {d['ag_ratio']}")
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if d.get("vat_mass_lb") is not None and d.get("vat_volume_in3") is not None:
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lines.append(f"- VAT: {d['vat_mass_lb']} lb ({d['vat_volume_in3']} in³)")
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if d.get("rmr_cal_per_day") is not None:
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lines.append(f"- RMR: {d['rmr_cal_per_day']} cal/day")
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lines.append("")
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lines.append("## Regional")
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for name, r in d.get("regional", {}).items():
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lines.append(f"- {name}: {r['fat_percent']}% fat; {r['total_mass_lb']} lb total; {r['fat_tissue_lb']} lb fat; {r['lean_tissue_lb']} lb lean; {r['bmc_lb']} lb BMC")
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return "\n".join(lines)
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def main():
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ap = argparse.ArgumentParser()
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ap.add_argument("pdf", help="Path to DEXA report PDF")
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ap.add_argument("--height-in", type=float, required=True, help="Height in inches (Imperial)")
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ap.add_argument("--weight-lb", type=float, help="Body weight in lb (optional; used if DEXA total mass missing)")
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ap.add_argument("--outdir", default="dexa_out", help="Output directory")
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args = ap.parse_args()
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ensure_outdir(args.outdir)
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d = parse_dexa_pdf(args.pdf)
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measured_date_raw = d.get("measured_date") or datetime.now().strftime("%m/%d/%Y")
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measured_date = convert_date_to_iso(measured_date_raw)
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total_mass, derived = compute_derived(d, height_in=args.height_in, weight_lb=args.weight_lb)
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# Overall CSV row
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overall_cols = [
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"MeasuredDate","Height_in","Height_ft_in","Weight_lb_Input","DEXA_TotalMass_lb","BodyFat_percent",
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"LeanMass_percent","FatMass_lb","LeanSoftTissue_lb","BoneMineralContent_lb","FatFreeMass_lb",
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"BMI","FFMI","FMI","LST_Index","ALM_lb","SMI","VAT_Mass_lb","VAT_Volume_in3","VAT_Index",
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"BMDI","Android_percent","Gynoid_percent","AG_Ratio","Trunk_to_Limb_Fat_Ratio",
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"Arms_Lean_pct","Legs_Lean_pct","Trunk_Lean_pct","Arm_Symmetry_Index","Leg_Symmetry_Index",
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"Adjusted_Body_Weight_lb","RMR_cal_per_day"
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]
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overall_row = {
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"MeasuredDate": measured_date,
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"Height_in": derived["height_in"],
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"Height_ft_in": derived["height_ft_in"],
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"Weight_lb_Input": derived["weight_input_lb"],
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"DEXA_TotalMass_lb": round(total_mass, 1),
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"BodyFat_percent": d.get("body_fat_percent"),
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"LeanMass_percent": derived.get("lean_mass_percent"),
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"FatMass_lb": d.get("fat_mass_lb"),
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"LeanSoftTissue_lb": d.get("lean_soft_tissue_lb"),
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"BoneMineralContent_lb": d.get("bmc_lb"),
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"FatFreeMass_lb": derived.get("fat_free_mass_lb"),
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"BMI": derived["bmi"],
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"FFMI": derived.get("ffmi"),
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"FMI": derived.get("fmi"),
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"LST_Index": derived.get("lsti"),
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"ALM_lb": derived.get("alm_lb"),
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"SMI": derived.get("smi"),
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"VAT_Mass_lb": d.get("vat_mass_lb"),
|
||||
"VAT_Volume_in3": d.get("vat_volume_in3"),
|
||||
"VAT_Index": derived.get("vat_index"),
|
||||
"BMDI": derived.get("bmdi"),
|
||||
"Android_percent": d.get("android_percent"),
|
||||
"Gynoid_percent": d.get("gynoid_percent"),
|
||||
"AG_Ratio": d.get("ag_ratio"),
|
||||
"Trunk_to_Limb_Fat_Ratio": derived.get("trunk_to_limb_fat_ratio"),
|
||||
"Arms_Lean_pct": derived.get("arms_lean_pct"),
|
||||
"Legs_Lean_pct": derived.get("legs_lean_pct"),
|
||||
"Trunk_Lean_pct": derived.get("trunk_lean_pct"),
|
||||
"Arm_Symmetry_Index": derived.get("arm_symmetry_index"),
|
||||
"Leg_Symmetry_Index": derived.get("leg_symmetry_index"),
|
||||
"Adjusted_Body_Weight_lb": derived.get("adjusted_body_weight_lb"),
|
||||
"RMR_cal_per_day": d.get("rmr_cal_per_day"),
|
||||
}
|
||||
write_or_append_csv(os.path.join(args.outdir, "overall.csv"), overall_row, overall_cols)
|
||||
|
||||
# Regional table
|
||||
regional_cols = ["Region","FatPercent","TotalMass_lb","FatTissue_lb","LeanTissue_lb","BMC_lb"]
|
||||
reg_rows = []
|
||||
for name, r in d.get("regional", {}).items():
|
||||
reg_rows.append({
|
||||
"Region": name,
|
||||
"FatPercent": r["fat_percent"],
|
||||
"TotalMass_lb": r["total_mass_lb"],
|
||||
"FatTissue_lb": r["fat_tissue_lb"],
|
||||
"LeanTissue_lb": r["lean_tissue_lb"],
|
||||
"BMC_lb": r["bmc_lb"],
|
||||
})
|
||||
regional_path = os.path.join(args.outdir, "regional.csv")
|
||||
if os.path.exists(regional_path):
|
||||
pd.DataFrame(reg_rows).to_csv(regional_path, mode="a", header=False, index=False)
|
||||
else:
|
||||
pd.DataFrame(reg_rows).to_csv(regional_path, index=False)
|
||||
|
||||
# Muscle balance
|
||||
mb_cols = ["Region","FatPercent","TotalMass_lb","FatMass_lb","LeanMass_lb","BMC_lb"]
|
||||
mb_rows = []
|
||||
for name, r in d.get("muscle_balance", {}).items():
|
||||
mb_rows.append({
|
||||
"Region": name,
|
||||
"FatPercent": r["fat_percent"],
|
||||
"TotalMass_lb": r["total_mass_lb"],
|
||||
"FatMass_lb": r["fat_mass_lb"],
|
||||
"LeanMass_lb": r["lean_mass_lb"],
|
||||
"BMC_lb": r["bmc_lb"],
|
||||
})
|
||||
mb_path = os.path.join(args.outdir, "muscle_balance.csv")
|
||||
if os.path.exists(mb_path):
|
||||
pd.DataFrame(mb_rows).to_csv(mb_path, mode="a", header=False, index=False)
|
||||
else:
|
||||
pd.DataFrame(mb_rows).to_csv(mb_path, index=False)
|
||||
|
||||
# JSON (overall structured object)
|
||||
# Convert regional and muscle_balance dicts to arrays
|
||||
regional_array = [
|
||||
{"region": name, **data}
|
||||
for name, data in d.get("regional", {}).items()
|
||||
]
|
||||
muscle_balance_array = [
|
||||
{"region": name, **data}
|
||||
for name, data in d.get("muscle_balance", {}).items()
|
||||
]
|
||||
|
||||
overall_json = {
|
||||
"measured_date": measured_date,
|
||||
"anthropometrics": {
|
||||
"height_in": derived["height_in"],
|
||||
"height_ft_in": derived["height_ft_in"],
|
||||
"weight_input_lb": derived["weight_input_lb"],
|
||||
"dexa_total_mass_lb": round(total_mass, 1),
|
||||
"adjusted_body_weight_lb": derived.get("adjusted_body_weight_lb"),
|
||||
"bmi": derived["bmi"]
|
||||
},
|
||||
"composition": {
|
||||
"body_fat_percent": d.get("body_fat_percent"),
|
||||
"lean_mass_percent": derived.get("lean_mass_percent"),
|
||||
"fat_mass_lb": d.get("fat_mass_lb"),
|
||||
"lean_soft_tissue_lb": d.get("lean_soft_tissue_lb"),
|
||||
"bone_mineral_content_lb": d.get("bmc_lb"),
|
||||
"fat_free_mass_lb": derived.get("fat_free_mass_lb"),
|
||||
"derived_indices": {
|
||||
"ffmi": derived.get("ffmi"),
|
||||
"fmi": derived.get("fmi"),
|
||||
"lsti": derived.get("lsti"),
|
||||
"alm_lb": derived.get("alm_lb"),
|
||||
"smi": derived.get("smi"),
|
||||
"bmdi": derived.get("bmdi")
|
||||
}
|
||||
},
|
||||
"regional": regional_array,
|
||||
"regional_analysis": {
|
||||
"trunk_to_limb_fat_ratio": derived.get("trunk_to_limb_fat_ratio"),
|
||||
"lean_mass_distribution": {
|
||||
"arms_percent": derived.get("arms_lean_pct"),
|
||||
"legs_percent": derived.get("legs_lean_pct"),
|
||||
"trunk_percent": derived.get("trunk_lean_pct")
|
||||
}
|
||||
},
|
||||
"muscle_balance": muscle_balance_array,
|
||||
"symmetry_indices": {
|
||||
"arm_symmetry_index": derived.get("arm_symmetry_index"),
|
||||
"leg_symmetry_index": derived.get("leg_symmetry_index")
|
||||
},
|
||||
"supplemental": {
|
||||
"android_percent": d.get("android_percent"),
|
||||
"gynoid_percent": d.get("gynoid_percent"),
|
||||
"ag_ratio": d.get("ag_ratio"),
|
||||
"vat": {
|
||||
"mass_lb": d.get("vat_mass_lb"),
|
||||
"volume_in3": d.get("vat_volume_in3"),
|
||||
"vat_index": derived.get("vat_index")
|
||||
},
|
||||
"rmr_cal_per_day": d.get("rmr_cal_per_day")
|
||||
},
|
||||
"bone_density": d.get("bone_density", {})
|
||||
}
|
||||
write_or_append_json(os.path.join(args.outdir, "overall.json"), overall_json)
|
||||
|
||||
# Markdown summary (append)
|
||||
md_text = make_markdown(measured_date, d, derived, total_mass)
|
||||
append_markdown(os.path.join(args.outdir, "summary.md"), md_text)
|
||||
|
||||
print(f"Wrote files to: {args.outdir}")
|
||||
print("Files: overall.csv, regional.csv, muscle_balance.csv, overall.json, summary.md")
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
Loading…
Add table
Add a link
Reference in a new issue