clean_data.py

#!/usr/bin/env python3
"""
Data Cleaning Script for Neonatal Brain MRI Dataset
- Removes PII (patient name, hospital ID)
- Translates column headers to English
- Converts xlsx to CSV
- Renames folders to English
- Generates a combined dataset and cleaning report
"""

import os
import shutil
import csv
import json
from datetime import datetime

import openpyxl

# ============================================================
# Configuration
# ============================================================
BASE_DIR = "/Users/yty/Desktop/med_paper/feng_dataset"

# Mapping: original Chinese folder name -> English name
FOLDER_MAP = {
    "正常 2026-2-6":       "normal",
    "扩张 2026-2-6":       "LVM",
    "软化 2026-2-6":       "PVL",
    "扩张+软化 2026-2-7":  "LVM_PVL",
}

# Mapping: original xlsx filename (inside each folder) -> group info
XLSX_MAP = {
    "正常 2026-2-6":       "正常 2026-2-6.xlsx",
    "扩张 2026-2-6":       "扩张 2026-2-6.xlsx",
    "软化 2026-2-6":       "软化 2026-2-6.xlsx",
    "扩张+软化 2026-2-7":  "扩张+软化 2026-2-7 严老师.xlsx",
}

# Group labels
GROUP_LABELS = {
    "normal":   {"label": 0, "cn": "正常",       "en": "Normal"},
    "LVM":      {"label": 1, "cn": "侧脑室扩张",  "en": "Lateral Ventricular Megaly (LVM)"},
    "PVL":      {"label": 2, "cn": "脑白质软化",   "en": "Periventricular Leukomalacia (PVL)"},
    "LVM_PVL":  {"label": 3, "cn": "扩张+软化",   "en": "LVM + PVL"},
}

# Target MRI channels
TARGET_CHANNELS = ["FLAIR", "T1WI", "T2-SAG", "T2WI"]

# Column name translation
COLUMN_MAP = {
    "编号": "patient_id",
    "检查描述": "exam_description",
    "检查结果": "diagnosis",
}


# ============================================================
# Step 1: Process xlsx -> CSV (remove PII, translate headers)
# ============================================================
def process_xlsx(folder_cn, folder_en):
    """Read xlsx, remove PII columns, save as CSV. Returns list of dicts."""
    xlsx_path = os.path.join(BASE_DIR, folder_cn, XLSX_MAP[folder_cn])
    wb = openpyxl.load_workbook(xlsx_path)
    ws = wb["Sheet1"]

    records = []
    for row_idx in range(2, ws.max_row + 1):
        patient_id = ws.cell(row=row_idx, column=1).value
        exam_desc  = ws.cell(row=row_idx, column=4).value
        diagnosis  = ws.cell(row=row_idx, column=5).value

        if patient_id is None:
            continue

        # Clean text: strip whitespace, normalize
        patient_id = str(patient_id).strip()
        exam_desc  = (exam_desc or "").strip().replace("\xa0", " ")
        diagnosis  = (diagnosis or "").strip().replace("\xa0", " ")

        records.append({
            "patient_id": patient_id,
            "group": folder_en,
            "group_label": GROUP_LABELS[folder_en]["label"],
            "group_name_en": GROUP_LABELS[folder_en]["en"],
            "exam_description": exam_desc,
            "diagnosis": diagnosis,
        })

    return records


# ============================================================
# Step 2: Rename folders
# ============================================================
def rename_folders():
    """Rename Chinese folder names to English."""
    renamed = []
    for cn_name, en_name in FOLDER_MAP.items():
        src = os.path.join(BASE_DIR, cn_name)
        dst = os.path.join(BASE_DIR, en_name)
        if os.path.exists(src) and not os.path.exists(dst):
            os.rename(src, dst)
            renamed.append((cn_name, en_name))
        elif os.path.exists(dst):
            renamed.append((cn_name, f"{en_name} (already exists)"))
    return renamed


# ============================================================
# Step 3: Scan data integrity
# ============================================================
def scan_integrity(folder_en):
    """Check each patient folder for target channels and count DCM files."""
    folder_path = os.path.join(BASE_DIR, folder_en)
    patients = sorted([
        d for d in os.listdir(folder_path)
        if os.path.isdir(os.path.join(folder_path, d)) and not d.startswith(".")
    ])

    results = []
    for p in patients:
        p_path = os.path.join(folder_path, p)
        subdirs = [
            d for d in os.listdir(p_path)
            if os.path.isdir(os.path.join(p_path, d)) and not d.startswith(".")
        ]

        channel_info = {}
        missing_channels = []
        extra_channels = []

        for ch in TARGET_CHANNELS:
            ch_path = os.path.join(p_path, ch)
            if os.path.isdir(ch_path):
                dcm_count = len([
                    f for f in os.listdir(ch_path)
                    if f.upper().endswith(".DCM")
                ])
                channel_info[ch] = dcm_count
            else:
                missing_channels.append(ch)
                channel_info[ch] = 0

        for d in subdirs:
            if d not in TARGET_CHANNELS:
                extra_channels.append(d)

        results.append({
            "patient_id": p,
            "channels": channel_info,
            "missing_channels": missing_channels,
            "extra_channels": extra_channels,
            "total_target_dcm": sum(channel_info.values()),
        })

    return results


# ============================================================
# Main
# ============================================================
def main():
    print("=" * 60)
    print("  Neonatal Brain MRI Dataset - Data Cleaning")
    print("=" * 60)

    # --- Step 1: Process xlsx files ---
    print("\n[1/4] Processing xlsx files -> CSV ...")
    all_records = []
    group_stats = {}

    for cn_name, en_name in FOLDER_MAP.items():
        records = process_xlsx(cn_name, en_name)
        all_records.extend(records)
        group_stats[en_name] = len(records)
        print(f"  {cn_name} -> {en_name}: {len(records)} records")

    # --- Step 2: Rename folders ---
    print("\n[2/4] Renaming folders to English ...")
    renamed = rename_folders()
    for cn, en in renamed:
        print(f"  {cn} -> {en}")

    # --- Step 3: Save individual and combined CSVs ---
    print("\n[3/4] Saving CSV files ...")
    csv_fields = ["patient_id", "group", "group_label", "group_name_en",
                   "exam_description", "diagnosis"]

    # Individual CSVs
    for en_name in FOLDER_MAP.values():
        group_records = [r for r in all_records if r["group"] == en_name]
        csv_path = os.path.join(BASE_DIR, en_name, "clinical_data.csv")
        with open(csv_path, "w", newline="", encoding="utf-8") as f:
            writer = csv.DictWriter(f, fieldnames=csv_fields)
            writer.writeheader()
            writer.writerows(group_records)
        print(f"  Saved: {en_name}/clinical_data.csv ({len(group_records)} records)")

    # Combined CSV
    combined_path = os.path.join(BASE_DIR, "clinical_data_all.csv")
    with open(combined_path, "w", newline="", encoding="utf-8") as f:
        writer = csv.DictWriter(f, fieldnames=csv_fields)
        writer.writeheader()
        writer.writerows(all_records)
    print(f"  Saved: clinical_data_all.csv ({len(all_records)} total records)")

    # --- Step 4: Integrity scan & report ---
    print("\n[4/4] Scanning data integrity ...")
    report_lines = []
    report_lines.append("# Data Cleaning Report")
    report_lines.append(f"**Generated**: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}\n")

    report_lines.append("## 1. Dataset Overview\n")
    report_lines.append("| Group | Folder | Patients | Label |")
    report_lines.append("|-------|--------|----------|-------|")
    for en_name, info in GROUP_LABELS.items():
        count = group_stats.get(en_name, 0)
        report_lines.append(
            f"| {info['en']} | `{en_name}/` | {count} | {info['label']} |"
        )
    report_lines.append(f"\n**Total patients**: {len(all_records)}\n")

    report_lines.append("## 2. Data Cleaning Actions\n")
    report_lines.append("| Action | Details |")
    report_lines.append("|--------|---------|")
    report_lines.append("| Removed PII | `姓名` (patient name), `住院号` (hospital ID) |")
    report_lines.append("| Column translation | `编号`→`patient_id`, `检查描述`→`exam_description`, `检查结果`→`diagnosis` |")
    report_lines.append("| Added columns | `group`, `group_label` (0-3), `group_name_en` |")
    report_lines.append("| Format conversion | `.xlsx` → `.csv` (UTF-8) |")
    report_lines.append("| Folder renaming | Chinese → English (see below) |")
    report_lines.append("| Text cleaning | Removed non-breaking spaces (`\\xa0`), stripped whitespace |\n")

    report_lines.append("### Folder Renaming\n")
    report_lines.append("| Original (Chinese) | New (English) |")
    report_lines.append("|-------------------|---------------|")
    for cn, en in FOLDER_MAP.items():
        report_lines.append(f"| `{cn}` | `{en}` |")

    report_lines.append("\n## 3. MRI Channel Integrity\n")
    report_lines.append(f"**Target channels**: {', '.join(TARGET_CHANNELS)}\n")

    total_issues = 0
    all_integrity = {}

    for en_name in FOLDER_MAP.values():
        integrity = scan_integrity(en_name)
        all_integrity[en_name] = integrity

        issues = [r for r in integrity if r["missing_channels"]]
        total_issues += len(issues)

        total_dcm = sum(r["total_target_dcm"] for r in integrity)
        avg_dcm = total_dcm / len(integrity) if integrity else 0

        report_lines.append(f"### {en_name} ({GROUP_LABELS[en_name]['en']})\n")
        report_lines.append(f"- Patients: {len(integrity)}")
        report_lines.append(f"- Total target-channel DCM files: {total_dcm}")
        report_lines.append(f"- Average DCM per patient (4 channels): {avg_dcm:.1f}")

        if issues:
            report_lines.append(f"- **Missing channels ({len(issues)} patients)**:")
            for r in issues:
                report_lines.append(
                    f"  - `{r['patient_id']}`: missing {', '.join(r['missing_channels'])}"
                )
        else:
            report_lines.append("- All patients have complete target channels")

        # Extra channels summary
        all_extras = set()
        for r in integrity:
            all_extras.update(r["extra_channels"])
        if all_extras:
            report_lines.append(
                f"- Extra channels present (not used): {', '.join(sorted(all_extras))}"
            )
        report_lines.append("")

    report_lines.append("## 4. Output File Structure\n")
    report_lines.append("```")
    report_lines.append("feng_dataset/")
    report_lines.append("├── clinical_data_all.csv          # Combined dataset (all groups)")
    report_lines.append("├── clean_data.py                  # This cleaning script")
    report_lines.append("├── cleaning_report.md             # This report")
    report_lines.append("├── normal/                        # Normal controls")
    report_lines.append("│   ├── clinical_data.csv")
    report_lines.append("│   └── 001-441/                   # Patient folders")
    report_lines.append("│       ├── FLAIR/  (*.DCM)")
    report_lines.append("│       ├── T1WI/   (*.DCM)")
    report_lines.append("│       ├── T2-SAG/ (*.DCM)")
    report_lines.append("│       └── T2WI/   (*.DCM)")
    report_lines.append("├── LVM/                           # Lateral Ventricular Megaly")
    report_lines.append("│   ├── clinical_data.csv")
    report_lines.append("│   └── 100-xxx/ ...")
    report_lines.append("├── PVL/                           # Periventricular Leukomalacia")
    report_lines.append("│   ├── clinical_data.csv")
    report_lines.append("│   └── 010-xxx/ ...")
    report_lines.append("└── LVM_PVL/                       # LVM + PVL")
    report_lines.append("    ├── clinical_data.csv")
    report_lines.append("    └── 110-xxx/ ...")
    report_lines.append("```\n")

    report_lines.append("## 5. CSV Column Description\n")
    report_lines.append("| Column | Type | Description |")
    report_lines.append("|--------|------|-------------|")
    report_lines.append("| `patient_id` | string | Unique patient identifier (e.g., `001-441`) |")
    report_lines.append("| `group` | string | Group folder name: `normal`, `LVM`, `PVL`, `LVM_PVL` |")
    report_lines.append("| `group_label` | int | Numeric label: 0=Normal, 1=LVM, 2=PVL, 3=LVM+PVL |")
    report_lines.append("| `group_name_en` | string | Full English group name |")
    report_lines.append("| `exam_description` | string | Radiologist's MRI examination description (Chinese) |")
    report_lines.append("| `diagnosis` | string | Final diagnosis conclusion (Chinese) |\n")

    report_lines.append("## 6. Notes\n")
    report_lines.append("- **Medical text** (`exam_description`, `diagnosis`) is kept in the original Chinese "
                        "to preserve clinical accuracy. Machine translation of specialized medical "
                        "radiology reports may introduce errors.")
    report_lines.append("- **Original xlsx files** are retained in each folder as backup.")
    report_lines.append(f"- **Data integrity issues**: {total_issues} patient(s) with missing target channels.")
    report_lines.append("- **Disease description document**: `新生儿侧脑室扩张+脑白质软化疾病描述 AI+医生审核版本.docx` "
                        "is preserved as-is (reference document, not patient data).")

    # Write report
    report_path = os.path.join(BASE_DIR, "cleaning_report.md")
    with open(report_path, "w", encoding="utf-8") as f:
        f.write("\n".join(report_lines))
    print(f"  Report saved: cleaning_report.md")

    # Summary
    print("\n" + "=" * 60)
    print("  CLEANING COMPLETE")
    print("=" * 60)
    print(f"  Total patients:  {len(all_records)}")
    print(f"  Groups:          {len(FOLDER_MAP)}")
    print(f"  Integrity issues: {total_issues} patient(s) with missing channels")
    print(f"  Output files:")
    print(f"    - clinical_data_all.csv (combined)")
    print(f"    - */clinical_data.csv   (per-group)")
    print(f"    - cleaning_report.md    (report)")
    print()


if __name__ == "__main__":
    main()