Initial import after prompting claude

analyzer/parser.py

@@ -0,0 +1,107 @@
+import csv
+import io
+import pandas as pd
+from datetime import datetime, timedelta
+
+CANONICAL_COLS = ["idx", "speed", "std_dev", "energy", "power_factor", "time"]
+TIME_FORMATS = ["%H:%M:%S.%f", "%H:%M:%S", "%H:%M:%S,%f"]
+
+
+def parse_csv(stream) -> pd.DataFrame:
+    raw = stream.read()
+    if isinstance(raw, bytes):
+        raw = raw.decode("utf-8-sig")
+    # Strip BOM characters that may appear anywhere in the file
+    raw = raw.replace("\ufeff", "")
+
+    data_rows = []
+    for line in raw.splitlines():
+        fields = _split_line(line)
+        if len(fields) >= 6 and _is_index(fields[0]) and _is_time(fields[5]):
+            data_rows.append(fields[:6])
+
+    if len(data_rows) < 2:
+        raise ValueError(
+            "Could not find valid data rows in the CSV. "
+            "Expected rows with: integer index, 4 numeric values, and a time (HH:MM:SS)."
+        )
+
+    df = pd.DataFrame(data_rows, columns=CANONICAL_COLS)
+
+    for col in ("speed", "std_dev", "energy", "power_factor"):
+        df[col] = _parse_numeric(df[col])
+
+    df["time"] = _parse_time_column(df["time"])
+    df = df.sort_values("time").reset_index(drop=True)
+    return df[["speed", "std_dev", "energy", "power_factor", "time"]]
+
+
+def _split_line(line: str) -> list:
+    """Parse one CSV line, respecting quoted fields."""
+    for row in csv.reader([line], quotechar='"', doublequote=True, skipinitialspace=True):
+        return [f.strip() for f in row]
+    return []
+
+
+def _is_index(val: str) -> bool:
+    """True if the value is a non-negative integer (auto-increment row index)."""
+    try:
+        return int(val.strip()) >= 0
+    except (ValueError, AttributeError):
+        return False
+
+
+def _is_time(val: str) -> bool:
+    """True if the value parses as HH:MM:SS or HH:MM:SS.fff."""
+    cleaned = val.strip()
+    for fmt in TIME_FORMATS:
+        try:
+            datetime.strptime(cleaned, fmt)
+            return True
+        except ValueError:
+            continue
+    return False
+
+
+def _parse_numeric(col: pd.Series) -> pd.Series:
+    """Parse a numeric column, accepting both '.' and ',' as decimal separator."""
+    result = pd.to_numeric(col, errors="coerce")
+    if result.isna().any():
+        result = pd.to_numeric(
+            col.astype(str).str.replace(",", ".", regex=False),
+            errors="coerce",
+        )
+    if result.isna().any():
+        bad = col[result.isna()].tolist()
+        raise ValueError(f"Non-numeric values in column: {bad}")
+    return result
+
+
+def _parse_time_column(col: pd.Series) -> pd.Series:
+    today = datetime.today().date()
+    cleaned = col.astype(str).str.strip()
+
+    parsed = None
+    for fmt in TIME_FORMATS:
+        candidate = pd.to_datetime(cleaned, format=fmt, errors="coerce")
+        if candidate.notna().all():
+            parsed = candidate
+            break
+
+    if parsed is None:
+        candidate = pd.to_datetime(cleaned, errors="coerce")
+        if candidate.notna().all():
+            parsed = candidate
+
+    if parsed is None:
+        raise ValueError(
+            "Could not parse time column. Expected format: HH:MM:SS or HH:MM:SS.fff"
+        )
+
+    parsed = parsed.apply(lambda t: datetime.combine(today, t.time()))
+
+    times = parsed.tolist()
+    for i in range(1, len(times)):
+        if times[i] < times[i - 1]:
+            times[i] += timedelta(days=1)
+    return pd.Series(times, index=col.index)