| """Shared CSV validation and non-overwriting output helpers."""
|
| import argparse
|
| import os
|
| from contextlib import contextmanager
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| from pathlib import Path
|
| import tempfile
|
|
|
| import numpy as np
|
| import pandas as pd
|
|
|
|
|
| def parser(description):
|
| return argparse.ArgumentParser(description=description)
|
|
|
|
|
| def require(df, columns):
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| missing = set(columns) - set(df.columns)
|
| if missing:
|
| raise ValueError(f"Missing columns: {sorted(missing)}")
|
|
|
|
|
| def numeric(df, columns, integer=False):
|
| require(df, columns)
|
| for col in columns:
|
| values = pd.to_numeric(df[col], errors="raise")
|
| if not np.isfinite(values.to_numpy(dtype=float)).all():
|
| raise ValueError(f"Non-finite values in {col}")
|
| if integer:
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| if not (values == np.floor(values)).all():
|
| raise ValueError(f"Non-integer values in {col}")
|
| values = values.astype("int64")
|
| df[col] = values
|
|
|
|
|
| def unique(df, keys):
|
| require(df, keys)
|
| if df[keys].isna().any().any() or df.duplicated(keys).any():
|
| raise ValueError(f"Missing or duplicate keys: {keys}")
|
|
|
|
|
| def lane_direction(df, allow_missing=False):
|
| """Validate lane indices and direction labels; reconstruction may fill missing observations."""
|
| require(df, ["Lane number", "Direction"])
|
| lanes = pd.to_numeric(df["Lane number"], errors="raise")
|
| observed = lanes.dropna().to_numpy(dtype=float)
|
| if not np.isfinite(observed).all() or np.any(observed <= 0) or np.any(observed != np.floor(observed)):
|
| raise ValueError("Lane number must be a positive integer")
|
| if not df.Direction.dropna().isin(["Left", "Right"]).all():
|
| raise ValueError("Direction must be Left or Right")
|
| if not allow_missing and (lanes.isna().any() or df.Direction.isna().any()):
|
| raise ValueError("Lane and Direction cannot be missing")
|
| df["Lane number"] = lanes
|
|
|
|
|
| def read_csv(path):
|
| df = pd.read_csv(path, encoding="utf-8-sig")
|
| if df.empty:
|
| raise ValueError(f"Empty input: {path}")
|
| return df
|
|
|
|
|
| def preflight_outputs(*paths):
|
| """Reject colliding or existing destinations before a multi-output run."""
|
| resolved = [Path(path).resolve() for path in paths]
|
| if len(set(resolved)) != len(resolved):
|
| raise ValueError("Output destinations must be distinct")
|
| for path in resolved:
|
| if path.exists():
|
| raise FileExistsError(f"Output exists; choose a new filename: {path}")
|
|
|
|
|
| @contextmanager
|
| def output_path(path):
|
| path = Path(path)
|
| if path.exists():
|
| raise FileExistsError(f"Output exists; choose a new filename: {path}")
|
| path.parent.mkdir(parents=True, exist_ok=True)
|
| handle, temporary = tempfile.mkstemp(prefix=path.name + ".", dir=path.parent)
|
| os.close(handle)
|
| try:
|
| yield temporary
|
| if path.exists():
|
| raise FileExistsError(path)
|
| os.replace(temporary, path)
|
| finally:
|
| if os.path.exists(temporary):
|
| os.unlink(temporary)
|
|
|
|
|
| def save_csv(df, path):
|
| with output_path(path) as temporary:
|
| df.to_csv(temporary, index=False, encoding="utf-8-sig")
|
| print(f"Saved {len(df):,} rows: {path}")
|
|
|
