Refactor: eliminate duplicated code in PDF processing

- Remove ~200 lines of duplicate code between single-file and batch processing
- Consolidate all PDF processing logic into process_single_pdf() function
- Add batch_mode parameter to control output formatting
- Single-file and batch modes now use the same code path
- Improves maintainability and reduces chance of inconsistencies

Net reduction: 202 lines deleted, 56 lines added (-146 lines total)

dexa_extract.py

@@ -320,22 +320,39 @@ def append_markdown(path, md_text):
     with open(path, mode) as f:
         f.write(md_text.strip() + "\n\n")
 
-def process_single_pdf(pdf_path, height_in, weight_lb, outdir):
+def process_single_pdf(pdf_path, height_in, weight_lb, outdir, batch_mode=False):
-    """Process a single PDF file and return success status"""
+    """Process a single PDF file and return success status
+    
+    Args:
+        pdf_path: Path to PDF file
+        height_in: Height in inches
+        weight_lb: Weight in pounds (optional)
+        outdir: Output directory
+        batch_mode: If True, use batch-style output messages
+    
+    Returns:
+        bool: True if successful, False otherwise
+    """
     try:
         # Validate PDF file
         pdf_file = Path(pdf_path)
         if not pdf_file.exists():
-            print(f"   ❌ Skipping {pdf_path}: File not found", file=sys.stderr)
+            msg = f"   ❌ Skipping {pdf_path}: File not found" if batch_mode else f"❌ Error: PDF file not found: {pdf_path}"
+            print(msg, file=sys.stderr)
             return False
         if not pdf_file.is_file():
-            print(f"   ❌ Skipping {pdf_path}: Not a file", file=sys.stderr)
+            msg = f"   ❌ Skipping {pdf_path}: Not a file" if batch_mode else f"❌ Error: Path is not a file: {pdf_path}"
+            print(msg, file=sys.stderr)
             return False
         if pdf_file.suffix.lower() != '.pdf':
-            print(f"   ❌ Skipping {pdf_path}: Not a PDF", file=sys.stderr)
+            msg = f"   ❌ Skipping {pdf_path}: Not a PDF" if batch_mode else f"❌ Error: File is not a PDF: {pdf_path}"
+            print(msg, file=sys.stderr)
             return False
 
-        print(f"\n📄 Processing: {pdf_file.name}")
+        if batch_mode:
+            print(f"\n📄 Processing: {pdf_file.name}")
+        else:
+            print("📊 Computing derived metrics...")
         
         # Parse PDF
         d = parse_dexa_pdf(pdf_path)
@@ -515,11 +532,19 @@ def process_single_pdf(pdf_path, height_in, weight_lb, outdir):
         md_text = make_markdown(measured_date, d, derived, total_mass)
         append_markdown(os.path.join(outdir, "summary.md"), md_text)
 
-        print(f"   ✅ {pdf_file.name}: Body fat {d.get('body_fat_percent')}%, FFMI {derived.get('ffmi')}")
+        if batch_mode:
+            print(f"   ✅ {pdf_file.name}: Body fat {d.get('body_fat_percent')}%, FFMI {derived.get('ffmi')}")
+        else:
+            # Single-file mode prints detailed success info outside this function
+            pass
         return True
         
     except Exception as e:
-        print(f"   ❌ Error processing {pdf_path}: {e}", file=sys.stderr)
+        if batch_mode:
+            print(f"   ❌ Error processing {pdf_path}: {e}", file=sys.stderr)
+        else:
+            print(f"❌ Error reading PDF: {e}", file=sys.stderr)
+            print("This tool is specifically designed for BodySpec PDF reports.", file=sys.stderr)
         return False
 
 def make_markdown(measured_date, d, derived, total_mass):
@@ -638,7 +663,7 @@ def main():
                 except Exception:
                     pass  # If we can't extract date, try to process anyway
             
-            if process_single_pdf(str(pdf_file), args.height_in, args.weight_lb, args.outdir):
+            if process_single_pdf(str(pdf_file), args.height_in, args.weight_lb, args.outdir, batch_mode=True):
                 success_count += 1
             else:
                 fail_count += 1
@@ -657,202 +682,31 @@ def main():
         return
 
     # Single file mode
-    pdf_file = Path(args.pdf)
-    if not pdf_file.exists():
-        print(f"❌ Error: PDF file not found: {args.pdf}", file=sys.stderr)
-        sys.exit(1)
-    if not pdf_file.is_file():
-        print(f"❌ Error: Path is not a file: {args.pdf}", file=sys.stderr)
-        sys.exit(1)
-    if pdf_file.suffix.lower() != '.pdf':
-        print(f"❌ Error: File is not a PDF: {args.pdf}", file=sys.stderr)
-        sys.exit(1)
-
     print(f"📄 Reading PDF: {args.pdf}")
     
-    try:
+    # Use the shared processing function
-        d = parse_dexa_pdf(args.pdf)
+    success = process_single_pdf(args.pdf, args.height_in, args.weight_lb, args.outdir, batch_mode=False)
-    except Exception as e:
+    
-        print(f"❌ Error reading PDF: {e}", file=sys.stderr)
+    if not success:
-        print("This tool is specifically designed for BodySpec PDF reports.", file=sys.stderr)
         sys.exit(1)
     
-    # Check if critical data was extracted
+    # Parse the result to show summary info
-    if d.get("body_fat_percent") is None or d.get("total_mass_lb") is None:
+    try:
-        print("⚠️  Warning: Missing critical data from PDF. This may not be a BodySpec report.", file=sys.stderr)
+        # Read the latest entry from overall.json to get the summary data
-        if d.get("body_fat_percent") is None:
+        json_path = os.path.join(args.outdir, "overall.json")
-            print("   - Body Fat % not found", file=sys.stderr)
+        if os.path.exists(json_path):
-        if d.get("total_mass_lb") is None:
+            with open(json_path, 'r') as f:
-            print("   - Total Mass not found", file=sys.stderr)
+                data = json.load(f)
-    
+                latest = data[-1] if isinstance(data, list) and data else data
-    print("📊 Computing derived metrics...")
+                measured_date = latest.get("measured_date", "Unknown")
-    measured_date_raw = d.get("measured_date") or datetime.now().strftime("%m/%d/%Y")
+                body_fat = latest.get("composition", {}).get("body_fat_percent", "N/A")
-    measured_date = convert_date_to_iso(measured_date_raw)
+                ffmi = latest.get("composition", {}).get("derived_indices", {}).get("ffmi", "N/A")
-    total_mass, derived = compute_derived(d, height_in=args.height_in, weight_lb=args.weight_lb)
+        else:
-
+            measured_date = body_fat = ffmi = "N/A"
-    # Overall CSV row
+    except Exception:
-    overall_cols = [
+        measured_date = body_fat = ffmi = "N/A"
-        "MeasuredDate","Height_in","Height_ft_in","Weight_lb_Input","DEXA_TotalMass_lb","BodyFat_percent",
-        "LeanMass_percent","FatMass_lb","LeanSoftTissue_lb","BoneMineralContent_lb","FatFreeMass_lb",
-        "BMI","FFMI","FMI","LST_Index","ALM_lb","SMI","VAT_Mass_lb","VAT_Volume_in3","VAT_Index",
-        "BMDI","Android_percent","Gynoid_percent","AG_Ratio","Trunk_to_Limb_Fat_Ratio",
-        "Arms_Lean_pct","Legs_Lean_pct","Trunk_Lean_pct","Arm_Symmetry_Index","Leg_Symmetry_Index",
-        "Adjusted_Body_Weight_lb","RMR_cal_per_day"
-    ]
-    overall_row = {
-        "MeasuredDate": measured_date,
-        "Height_in": derived["height_in"],
-        "Height_ft_in": derived["height_ft_in"],
-        "Weight_lb_Input": derived["weight_input_lb"],
-        "DEXA_TotalMass_lb": round(total_mass, 1),
-        "BodyFat_percent": d.get("body_fat_percent"),
-        "LeanMass_percent": derived.get("lean_mass_percent"),
-        "FatMass_lb": d.get("fat_mass_lb"),
-        "LeanSoftTissue_lb": d.get("lean_soft_tissue_lb"),
-        "BoneMineralContent_lb": d.get("bmc_lb"),
-        "FatFreeMass_lb": derived.get("fat_free_mass_lb"),
-        "BMI": derived["bmi"],
-        "FFMI": derived.get("ffmi"),
-        "FMI": derived.get("fmi"),
-        "LST_Index": derived.get("lsti"),
-        "ALM_lb": derived.get("alm_lb"),
-        "SMI": derived.get("smi"),
-        "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","LeanPercent","TotalMass_lb","FatTissue_lb","LeanTissue_lb","BMC_lb"]
-    reg_rows = []
-    for name, r in d.get("regional", {}).items():
-        # Calculate lean percentage (lean tissue only, not including BMC - matches BodySpec report)
-        lean_pct = round(100 * r["lean_tissue_lb"] / r["total_mass_lb"], 1) if r["total_mass_lb"] > 0 else None
-        reg_rows.append({
-            "Region": name,
-            "FatPercent": r["fat_percent"],
-            "LeanPercent": lean_pct,
-            "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")
-    df_regional = pd.DataFrame(reg_rows, columns=regional_cols)
-    if os.path.exists(regional_path):
-        df_regional.to_csv(regional_path, mode="a", header=False, index=False)
-    else:
-        df_regional.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 = []
-    for name, data in d.get("regional", {}).items():
-        lean_pct = round(100 * data["lean_tissue_lb"] / data["total_mass_lb"], 1) if data["total_mass_lb"] > 0 else None
-        regional_array.append({
-            "region": name,
-            "fat_percent": data["fat_percent"],
-            "lean_percent": lean_pct,
-            "total_mass_lb": data["total_mass_lb"],
-            "fat_tissue_lb": data["fat_tissue_lb"],
-            "lean_tissue_lb": data["lean_tissue_lb"],
-            "bmc_lb": data["bmc_lb"]
-        })
-    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)
     
+    # Success output
     print(f"\n✅ Success! Wrote files to: {args.outdir}")
     print("   📁 Files created:")
     print("      - overall.csv (time-series data)")
@@ -861,8 +715,8 @@ def main():
     print("      - overall.json (structured data)")
     print("      - summary.md (readable report)")
     print(f"\n   📈 Scan date: {measured_date}")
-    print(f"   💪 Body fat: {d.get('body_fat_percent')}%")
+    print(f"   💪 Body fat: {body_fat}%")
-    print(f"   🏋️  FFMI: {derived.get('ffmi')}")
+    print(f"   🏋️  FFMI: {ffmi}")
 
 if __name__ == "__main__":
     main()