303 lines
9.4 KiB
Python
303 lines
9.4 KiB
Python
"""
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import_to_mongo.py
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Verze: 1.2
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Datum: 2026-06-02
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Import Clario CSV do MongoDB (databáze: Clario).
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Kolekce: Clario.MayoDiary / Clario.MayoScore / Clario.eCOA_DCRs / Clario.ECG_DCRs
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Filtr: pouze řádky s Country == "Czech Republic"
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Klíč: MayoDiary → Subject ID + Form Number
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MayoScore → Participant ID + Visit
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eCOA_DCRs → Data Correction ID
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ECG_DCRs → Data Correction ID
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Historie: při změně fields se stará verze uloží do pole history[]
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Po importu přesune zpracované CSV do downloads/Zpracovano/
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Použití:
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python import_to_mongo.py # importuje všechny CSV z downloads/
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python import_to_mongo.py downloads/konkretni.csv # jeden soubor
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"""
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import csv
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import re
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import shutil
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import sys
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from datetime import datetime, timezone
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from pathlib import Path
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from pymongo import MongoClient, ASCENDING
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MONGO_URI = "mongodb://192.168.1.76:27017"
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DB_NAME = "Clario"
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DOWNLOADS_DIR = Path(__file__).parent / "downloads"
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PROCESSED_DIR = DOWNLOADS_DIR / "Zpracovano"
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COUNTRY_FILTER = "Czech Republic"
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# ---------------------------------------------------------------------------
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# Konfigurace kolekcí
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# ---------------------------------------------------------------------------
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COLLECTION_CONFIG = {
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"MayoDiary": {
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"collection": "Clario.MayoDiary",
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"subject_col": "Subject ID",
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"key_cols": ("Subject ID", "Form Number"),
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},
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"MayoScore": {
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"collection": "Clario.MayoScore",
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"subject_col": "Participant ID",
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"key_cols": ("Participant ID", "Visit"),
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"outcome_cols": (
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"Site Action",
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"Last Mayo Score Submission",
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"Week I-12 Clinical Responder",
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"Week I-12 Clinical Remission",
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"Clinical Flare",
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"Loss of Response",
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"Partial Mayo Response Post Loss of Response",
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"Partial Mayo Response for Clinical Non-Responders",
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),
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},
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"eCOA DCRs": {
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"collection": "Clario.eCOA_DCRs",
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"subject_col": "Subject ID",
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"key_cols": ("Data Correction ID",),
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},
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"ECG DCRs": {
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"collection": "Clario.ECG_DCRs",
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"subject_col": "Subject Number",
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"key_cols": ("Data Correction ID",),
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},
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}
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DATE_FORMATS = [
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"%d-%b-%Y ",
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"%d-%b-%Y",
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"%d-%b-%Y %H:%M:%S",
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"%d %b %Y %H:%M:%S",
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"%d %b %Y %H:%M:%S:%f",
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"%d %b %Y",
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"%d %B %Y",
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"%Y%m%d %H:%M:%S.%f",
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"%Y-%m-%d %H:%M:%S",
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"%m/%d/%Y %I:%M:%S %p",
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]
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# ---------------------------------------------------------------------------
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# Helpers
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# ---------------------------------------------------------------------------
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def clean_colname(name: str) -> str:
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"""Odstraní BOM a okolní uvozovky/mezery z názvu sloupce."""
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return name.lstrip("").strip().strip('"')
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def parse_date(value: str) -> str | None:
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v = value.strip()
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for fmt in DATE_FORMATS:
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try:
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dt = datetime.strptime(v, fmt.strip())
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return dt.replace(tzinfo=timezone.utc).isoformat()
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except ValueError:
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continue
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return None
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def extract_snapshot_date(filename: str) -> str:
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match = re.match(r"(\d{4}-\d{2}-\d{2})", Path(filename).name)
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return match.group(1) if match else datetime.now().strftime("%Y-%m-%d")
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def detect_collection_type(filename: str) -> str | None:
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"""Vrátí klíč do COLLECTION_CONFIG nebo None."""
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stem = Path(filename).stem
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for key in COLLECTION_CONFIG:
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if key in stem:
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return key
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return None
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# ---------------------------------------------------------------------------
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# CSV → dokument
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# ---------------------------------------------------------------------------
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def map_row(row: dict, col_type: str) -> dict:
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cfg = COLLECTION_CONFIG[col_type]
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doc: dict = {}
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fields: dict = {}
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cleaned = {clean_colname(k): v.strip() if v else "" for k, v in row.items()}
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subject_col = cfg["subject_col"]
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doc["subject"] = {"id": cleaned.get(subject_col, "")}
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# ECG DCRs používají "Site ID" místo "Site"
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site_name = cleaned.get("Site") or cleaned.get("Site ID", "")
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doc["site"] = {"name": site_name}
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doc["country"] = cleaned.get("Country", "")
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doc["study"] = cleaned.get("Protocol", "")
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key_parts = [cleaned.get(c, "") for c in cfg["key_cols"]]
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doc["recordKey"] = "_".join(key_parts)
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outcome_cols = set(cfg.get("outcome_cols", ()))
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for col in outcome_cols:
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value = cleaned.get(col, "")
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if value and value != "-":
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parsed = parse_date(value)
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doc[col] = parsed if parsed else value
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else:
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doc[col] = None
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skip_top = {"Protocol", "Country", "Site", subject_col} | outcome_cols
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for col, value in cleaned.items():
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if col in skip_top:
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continue
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if not value or value == "-":
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continue
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parsed = parse_date(value)
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fields[col] = parsed if parsed else value
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doc["fields"] = fields
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return doc
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# ---------------------------------------------------------------------------
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# Import jednoho souboru
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# ---------------------------------------------------------------------------
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def import_file(csv_path: str, db) -> dict:
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filename = Path(csv_path).name
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col_type = detect_collection_type(filename)
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if col_type is None:
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print(f" Preskakuji (neznamy typ): {filename}")
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return {"skipped": True}
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cfg = COLLECTION_CONFIG[col_type]
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col_name = cfg["collection"]
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snapshot_date = extract_snapshot_date(filename)
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collection = db[col_name]
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inserted = changed = unchanged = filtered_out = 0
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with open(csv_path, encoding="utf-8-sig", newline="") as f:
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reader = csv.DictReader(f, delimiter=",", quotechar='"')
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for row in reader:
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cleaned_row = {clean_colname(k): v for k, v in row.items()}
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country = cleaned_row.get("Country", "").strip()
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if COUNTRY_FILTER not in country:
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filtered_out += 1
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continue
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doc = map_row(row, col_type)
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record_key = doc.get("recordKey")
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if not record_key:
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continue
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doc["sourceFile"] = filename
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existing = collection.find_one({"recordKey": record_key})
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if existing is None:
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doc["firstSeen"] = snapshot_date
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doc["lastSeen"] = snapshot_date
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doc["history"] = []
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collection.insert_one(doc)
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inserted += 1
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elif existing.get("fields") != doc["fields"]:
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old_entry = {
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"date": existing.get("lastSeen", snapshot_date),
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"fields": existing["fields"],
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}
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update_doc = {k: v for k, v in doc.items()}
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update_doc["lastSeen"] = snapshot_date
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collection.update_one(
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{"_id": existing["_id"]},
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{
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"$push": {"history": old_entry},
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"$set": update_doc,
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},
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)
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changed += 1
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else:
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collection.update_one(
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{"_id": existing["_id"]},
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{"$set": {"lastSeen": snapshot_date, "sourceFile": filename}},
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)
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unchanged += 1
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collection.create_index([("recordKey", ASCENDING)], unique=True)
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collection.create_index([("subject.id", ASCENDING)])
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collection.create_index([("site.name", ASCENDING)])
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if col_type == "MayoScore":
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collection.create_index([("Site Action", ASCENDING)])
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if col_type in ("eCOA DCRs", "ECG DCRs"):
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collection.create_index([("fields.Status", ASCENDING)])
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collection.create_index([("fields.Type", ASCENDING)])
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stats = {
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"collection": col_name,
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"snapshot": snapshot_date,
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"inserted": inserted,
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"changed": changed,
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"unchanged": unchanged,
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"filtered_out": filtered_out,
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}
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print(f" {col_name} [{snapshot_date}]: +{inserted} new, ~{changed} changed, ={unchanged} same, -{filtered_out} non-CZ")
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return stats
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# ---------------------------------------------------------------------------
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# Main
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# ---------------------------------------------------------------------------
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def main():
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paths: list[Path] = []
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if len(sys.argv) > 1:
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for arg in sys.argv[1:]:
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p = Path(arg)
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if p.is_file():
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paths.append(p)
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else:
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print(f"Soubor nenalezen: {arg}")
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else:
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paths = sorted(DOWNLOADS_DIR.glob("*.csv"))
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if not paths:
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print("Zadne CSV soubory k importu.")
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return
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print(f"Nalezeno {len(paths)} souboru.\n")
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client = MongoClient(MONGO_URI, serverSelectionTimeoutMS=5000)
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client.admin.command("ping")
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db = client[DB_NAME]
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PROCESSED_DIR.mkdir(exist_ok=True)
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total = {"inserted": 0, "changed": 0, "unchanged": 0}
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for csv_path in paths:
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print(f"Import: {csv_path.name}")
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stats = import_file(str(csv_path), db)
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if not stats.get("skipped"):
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for k in total:
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total[k] += stats.get(k, 0)
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dest = PROCESSED_DIR / csv_path.name
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shutil.move(str(csv_path), str(dest))
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print(f" -> presunut do Zpracovano/")
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client.close()
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print(f"\nCelkem: +{total['inserted']} new, ~{total['changed']} changed, ={total['unchanged']} same")
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if __name__ == "__main__":
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main()
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