Wgranie zmian do repozytorium

Python3/reader2.py

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+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+# Konwersja .wmt do JSON i csv
+# 22.03.2026 LK
+# python3 reader2.py '/Users/lklich/Desktop/Adam Błachowicz' -a -f -d <-wszystkie, +podfoldery, usuwa stare wmt  
+
+from __future__ import annotations
+import argparse
+import pathlib
+import struct
+import json
+import pandas as pd
+from datetime import datetime
+from typing import Tuple, Dict, Any
+
+HEADER_FMT = "<3sHHIII"   # 19 B
+HEADER_SIZE = struct.calcsize(HEADER_FMT)
+
+SAMPLE_FMT  = "<IBhhhB"   # 12 B
+SAMPLE_SIZE = struct.calcsize(SAMPLE_FMT)
+
+def read_wmt_one(path: pathlib.Path) -> Tuple[pd.DataFrame, Dict[str, Any]]:
+    blob = path.read_bytes()
+
+    if len(blob) < HEADER_SIZE:
+        raise ValueError(f"{path.name}: plik zbyt krótki ({len(blob)} B).")
+
+    magic, version, headerSize, sampleSize, start_unix, reccount = struct.unpack_from(HEADER_FMT, blob, 0)
+
+    if magic != b"WMT":
+        raise ValueError(f"{path.name}: niepoprawny magic {magic!r}.")
+    
+    dt_obj = datetime.fromtimestamp(start_unix)
+    start_time_pl = dt_obj.strftime("%d.%m.%Y %H:%M:%S")
+    start_time_iso = dt_obj.isoformat()
+
+    data = blob[headerSize:]
+    nrec = len(data) // SAMPLE_SIZE
+
+    meta = {
+        "filename": path.name,
+        "full_path": str(path.absolute()),
+        "wmt_version": version,
+        "start_unix": start_unix,
+        "start_time_iso": start_time_iso,
+        "start_time_pl": start_time_pl,
+        "declared_reccount": reccount,
+        "actual_reccount": nrec,
+        "is_incomplete": len(data) % SAMPLE_SIZE != 0
+    }
+
+    rows = []
+    off = 0
+    for _ in range(nrec):
+        rec = data[off:off+SAMPLE_SIZE]
+        offset_us, sensor_id, x, y, z, ready = struct.unpack(SAMPLE_FMT, rec)
+        unix_ts = start_unix + (offset_us / 1_000_000.0)
+        rows.append((start_unix, offset_us, unix_ts, sensor_id, x, y, z, int(ready)))
+        off += SAMPLE_SIZE
+
+    df = pd.DataFrame(rows, columns=["start_unix", "offset_us", "unix_ts", "sensor_id", "x", "y", "z", "ready"])
+    df = df.astype({"sensor_id": "int32", "x": "int32", "y": "int32", "z": "int32", "ready": "int32"})
+    
+    return df, meta
+
+def save_outputs(df: pd.DataFrame, base_path: pathlib.Path, meta: Dict[str, Any], no_header: bool, suffix: str):
+    csv_path = base_path.with_suffix(suffix)
+    json_path = base_path.with_suffix(".json")
+
+    with open(csv_path, 'w', encoding='utf-8') as f:
+        f.write(f"# Plik: {meta.get('filename', 'N/A')}\n")
+        f.write(f"# Wersja WMT: {meta.get('wmt_version', 'N/A')}\n")
+        f.write(f"# Start: {meta.get('start_time_pl', 'N/A')} (Unix: {meta.get('start_unix', 0)})\n")
+        f.write(f"# Rekordy: {meta.get('actual_reccount', 0)}\n")
+        df.to_csv(f, index=False, header=not no_header)
+
+    with open(json_path, 'w', encoding='utf-8') as f:
+        json.dump(meta, f, indent=4, ensure_ascii=False)
+
+def main():
+    ap = argparse.ArgumentParser(description="Konwersja .wmt -> CSV + JSON.")
+    ap.add_argument("input", help="Ścieżka do pliku lub katalogu")
+    ap.add_argument("-a", "--all", action="store_true", help="Przetwarzaj wszystkie pliki .wmt w katalogu")
+    ap.add_argument("-f", "--recursive", action="store_true", help="Przeszukuj podfoldery (wymaga -a)")
+    ap.add_argument("-o", "--overwrite", action="store_true", help="Nadpisuj istniejące pliki wyjściowe")
+    ap.add_argument("-d", "--delete", action="store_true", help="USUŃ plik .wmt po udanej konwersji")
+    ap.add_argument("--out-suffix", default=".csv", help="Sufiks CSV")
+    ap.add_argument("--concat", action="store_true", help="Scal dane do jednego pliku")
+    ap.add_argument("--no-header", action="store_true", help="CSV bez nagłówka kolumn")
+    args = ap.parse_args()
+
+    input_path = pathlib.Path(args.input)
+    files_to_process = []
+
+    if args.all:
+        if not input_path.is_dir():
+            print(f"[ERROR] Ścieżka {input_path} nie jest katalogiem!")
+            return
+        files_to_process = sorted(list(input_path.rglob("*.wmt") if args.recursive else input_path.glob("*.wmt")))
+    else:
+        if input_path.is_dir():
+            print(f"[ERROR] Podano katalog, ale brakuje -a.")
+            return
+        files_to_process = [input_path]
+
+    if not files_to_process:
+        print(f"[INFO] Brak plików do przetworzenia.")
+        return
+
+    dfs = []
+    combined_meta = []
+    successfully_processed = []
+
+    for p in files_to_process:
+        csv_check = p.with_suffix(args.out_suffix)
+        json_check = p.with_suffix(".json")
+
+        if not args.overwrite and not args.concat:
+            if csv_check.exists() or json_check.exists():
+                print(f"[SKIP] Pominięto {p.name} - pliki wyjściowe już istnieją.")
+                continue
+
+        try:
+            df, meta = read_wmt_one(p)
+            
+            if args.concat:
+                df["_source_path"] = str(p.relative_to(input_path))
+                dfs.append(df)
+                combined_meta.append(meta)
+                successfully_processed.append(p)
+            else:
+                save_outputs(df, p, meta, args.no_header, args.out_suffix)
+                print(f"Przetworzono: {p.name}")
+                if args.delete:
+                    p.unlink()
+                    print(f"  [DEL] Usunięto plik źródłowy: {p.name}")
+                    
+        except Exception as e:
+            print(f"[ERROR] Błąd w pliku {p.name}: {e}")
+
+    # Obsługa zapisu zbiorczego (concat)
+    if args.concat and dfs:
+        out_base = input_path / input_path.name if input_path.is_dir() else files_to_process[0]
+        big_df = pd.concat(dfs, ignore_index=True)
+        meta_summary = {
+            "description": "Scalony zestaw danych",
+            "files_count": len(combined_meta),
+            "sources": combined_meta
+        }
+        
+        try:
+            save_outputs(big_df, out_base, meta_summary, args.no_header, args.out_suffix)
+            print(f"\nZapisano scalone dane do {out_base.with_suffix(args.out_suffix)}")
+            
+            # Usuwamy pliki źródłowe dopiero po udanym scaleniu
+            if args.delete:
+                for p in successfully_processed:
+                    p.unlink()
+                print(f"  [DEL] Usunięto {len(successfully_processed)} plików źródłowych po scaleniu.")
+        except Exception as e:
+            print(f"[ERROR] Błąd zapisu pliku scalonego: {e}")
+
+if __name__ == "__main__":
+    main()