Add lean percentage to regional data
- 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%)
This commit is contained in:
Mac DeCourcy
parent
b046af5d25
commit
37267fbf34
2 changed files with 46 additions and 15 deletions
|
|
@ -128,7 +128,14 @@ Time-series data with one row per scan. Includes all primary metrics and derived
|
|||
### 2. `regional.csv`
|
||||
Regional body composition breakdown (Arms, Legs, Trunk, Android, Gynoid, Total).
|
||||
|
||||
**Columns:** Region, FatPercent, TotalMass_lb, FatTissue_lb, LeanTissue_lb, BMC_lb
|
||||
**Columns:**
|
||||
- `Region` - Body region name
|
||||
- `FatPercent` - Fat percentage in this region
|
||||
- `LeanPercent` - Lean tissue percentage in this region
|
||||
- `TotalMass_lb` - Total mass of the region
|
||||
- `FatTissue_lb` - Fat mass in the region
|
||||
- `LeanTissue_lb` - Lean mass in the region
|
||||
- `BMC_lb` - Bone mineral content in the region
|
||||
|
||||
### 3. `muscle_balance.csv`
|
||||
Left/right limb comparison for tracking muscle symmetry.
|
||||
|
|
|
|||
|
|
@ -399,22 +399,26 @@ def process_single_pdf(pdf_path, height_in, weight_lb, outdir):
|
|||
write_or_append_csv(os.path.join(outdir, "overall.csv"), overall_row, overall_cols)
|
||||
|
||||
# Regional table
|
||||
regional_cols = ["Region","FatPercent","TotalMass_lb","FatTissue_lb","LeanTissue_lb","BMC_lb"]
|
||||
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(outdir, "regional.csv")
|
||||
df_regional = pd.DataFrame(reg_rows, columns=regional_cols)
|
||||
if os.path.exists(regional_path):
|
||||
pd.DataFrame(reg_rows).to_csv(regional_path, mode="a", header=False, index=False)
|
||||
df_regional.to_csv(regional_path, mode="a", header=False, index=False)
|
||||
else:
|
||||
pd.DataFrame(reg_rows).to_csv(regional_path, index=False)
|
||||
df_regional.to_csv(regional_path, index=False)
|
||||
|
||||
# Muscle balance
|
||||
mb_cols = ["Region","FatPercent","TotalMass_lb","FatMass_lb","LeanMass_lb","BMC_lb"]
|
||||
|
|
@ -435,10 +439,18 @@ def process_single_pdf(pdf_path, height_in, weight_lb, outdir):
|
|||
pd.DataFrame(mb_rows).to_csv(mb_path, index=False)
|
||||
|
||||
# JSON
|
||||
regional_array = [
|
||||
{"region": name, **data}
|
||||
for name, data in d.get("regional", {}).items()
|
||||
]
|
||||
regional_array = []
|
||||
for name, data in d.get("regional", {}).items():
|
||||
lean_pct = round(100 * (data["lean_tissue_lb"] + data["bmc_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()
|
||||
|
|
@ -724,22 +736,26 @@ def main():
|
|||
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"]
|
||||
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):
|
||||
pd.DataFrame(reg_rows).to_csv(regional_path, mode="a", header=False, index=False)
|
||||
df_regional.to_csv(regional_path, mode="a", header=False, index=False)
|
||||
else:
|
||||
pd.DataFrame(reg_rows).to_csv(regional_path, index=False)
|
||||
df_regional.to_csv(regional_path, index=False)
|
||||
|
||||
# Muscle balance
|
||||
mb_cols = ["Region","FatPercent","TotalMass_lb","FatMass_lb","LeanMass_lb","BMC_lb"]
|
||||
|
|
@ -761,10 +777,18 @@ def main():
|
|||
|
||||
# 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()
|
||||
]
|
||||
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()
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue