#!/usr/bin/env python3
"""
Generate SP2XR configuration files by automatically detecting PbP and HK files in a directory.

This script scans a directory for SP2XR data files (CSV/ZIP/Parquet) and generates
configuration files with proper schemas and column mappings.
"""

from __future__ import annotations

import argparse
import pandas as pd
import yaml
import os
from pathlib import Path
from typing import Any


def infer_general_dtype(dtype: Any) -> str:
    """Infer general data type from pandas dtype."""
    if pd.api.types.is_integer_dtype(dtype):
        return "float"  # Use float to handle potential NA values
    elif pd.api.types.is_float_dtype(dtype):
        return "float"
    elif pd.api.types.is_datetime64_any_dtype(dtype):
        return "datetime"
    else:
        return "string"


def find_sp2xr_files(directory: str | Path) -> tuple[list[Path], list[Path]]:
    """
    Find PbP and HK files in the given directory and all subdirectories.

    Parameters
    ----------
    directory : str | Path
        Directory to search for SP2XR files (searches recursively)

    Returns
    -------
    tuple[list[Path], list[Path]]
        Lists of PbP files and HK files found
    """
    directory = Path(directory)
    if not directory.exists():
        raise FileNotFoundError(f"Directory not found: {directory}")

    # Common SP2XR file patterns
    pbp_patterns = ["*PbP*", "*pbp*", "*Pbp*"]
    hk_patterns = ["*hk*", "*HK*", "*Hk*"]
    file_extensions = ["*.csv", "*.zip", "*.parquet"]

    pbp_files = []
    hk_files = []

    # Search for files matching patterns (including subdirectories)
    for ext in file_extensions:
        for pattern in pbp_patterns:
            pbp_files.extend(directory.glob(f"**/{pattern}{ext}"))
        for pattern in hk_patterns:
            hk_files.extend(directory.glob(f"**/{pattern}{ext}"))

    # Remove duplicates and sort
    pbp_files = sorted(list(set(pbp_files)))
    hk_files = sorted(list(set(hk_files)))

    return pbp_files, hk_files


def load_schema(input_file: str | Path, nrows: int = 100) -> dict[str, str]:
    """
    Load schema from input file by inferring column types.

    Parameters
    ----------
    input_file : str | Path
        Path to the input file
    nrows : int
        Number of rows to read for type inference (for CSV files)

    Returns
    -------
    dict[str, str]
        Mapping of column names to data types
    """
    ext = os.path.splitext(str(input_file))[1].lower()

    if ext in [".csv", ".zip"]:
        df = pd.read_csv(input_file, nrows=nrows)
    elif ext == ".parquet":
        # For parquet, we can just read the schema without loading data
        pf = pd.read_parquet(input_file, engine="pyarrow")
        df = pf.head(0)  # Empty dataframe with schema
    else:
        raise ValueError(f"Unsupported file format: {ext}")

    schema = {col: infer_general_dtype(dtype) for col, dtype in df.dtypes.items()}
    return schema


def get_canonical_schemas() -> dict[str, dict[str, str]]:
    """Return canonical column schemas for SP2XR data."""
    pbp_canonical = {
        "Time (sec)": "float",
        "Packet Time Stamp": "float",
        "Flag": "float",
        "Dropped Records": "float",
        "Record Count": "float",
        "Record Size": "float",
        "Particle Time Stamp": "float",
        "Particle Flags": "float",
        "Scatter relPeak": "float",
        "Scatter Transit Time": "float",
        "Scatter Peak Time": "float",
        "Scatter FWHM": "float",
        "Scatter Size (nm)": "float",
        "Incand relPeak": "float",
        "Incand Transit Time": "float",
        "Incand Peak Time": "float",
        "Incand FWHM": "float",
        "Incand Delay": "float",
        "Incand Mass (fg)": "float",
        "Reserved": "float",
    }

    hk_canonical = {
        "Time Stamp": "datetime",
        "Time (sec)": "float",
        "Sample Flow Controller Read (sccm)": "float",
        "Sample Flow Controller Read (vccm)": "float",
        "Time Stamp (UTC sec)": "float",
        "Elapsed Time": "float",
        "Error Code": "float",
        "Packet Time Stamp": "float",
        "Laser TEC Temp (C)": "float",
        "Crystal TEC Temp (C)": "float",
        "Inlet Air Temp (C)": "float",
        "Computer Heatsink Temp (C)": "float",
        "Laser Heatsink Temp (C)": "float",
        "Outlet Air Temp (C)": "float",
        "YAG Output Monitor (V)": "float",
        "Cavity Pressure (hPa)": "float",
        "Laser Driver Power Monitor (uA)": "float",
        "Laser Driver Current Limit Monitor (A)": "float",
        "Laser Driver Current Monitor (A)": "float",
    }

    return {"pbp_canonical": pbp_canonical, "hk_canonical": hk_canonical}


def generate_basic_config(
    pbp_file: Path,
    hk_file: Path,
    schema_output: str = "new_data_schema.yaml",
    ini_file: str = None,
    instrument_output: str = None,
) -> None:
    """Generate basic config schema file with data type definitions only."""
    print(f"Reading PbP schema from: {pbp_file}")
    pbp_schema = load_schema(pbp_file)

    print(f"Reading HK schema from: {hk_file}")
    hk_schema = load_schema(hk_file)

    config = {
        "pbp_schema": pbp_schema,
        "hk_schema": hk_schema,
    }

    # Create output directory if it doesn't exist
    schema_path = Path(schema_output)
    if schema_path.parent != Path("."):
        schema_path.parent.mkdir(parents=True, exist_ok=True)
        print(f"Created config directory: {schema_path.parent}")

    with open(schema_output, "w") as f:
        yaml.dump(config, f, sort_keys=False)

    print(f"Data schema config saved to: {schema_path.absolute()}")

    # Generate separate instrument settings config from INI file
    if ini_file:
        # Determine instrument settings output filename
        if instrument_output:
            instrument_path = Path(instrument_output)
        else:
            instrument_path = schema_path.parent / "new_instrument_settings.yaml"

        try:
            from sp2xr.helpers import export_xr_ini_to_yaml_with_source

            export_xr_ini_to_yaml_with_source(ini_file, str(instrument_path))
            # print(f"Instrument settings config saved to: {instrument_path}")
        except ImportError:
            # Fallback to original function if new one doesn't exist yet
            from sp2xr.helpers import export_xr_ini_to_yaml

            export_xr_ini_to_yaml(ini_file, str(instrument_path))
            # print(f"Instrument settings config saved to: {instrument_path}")
        except Exception as e:
            print(f"Warning: Could not convert INI to YAML: {e}")
            # Still reference the original INI file as fallback
            config["calibration_file"] = ini_file
            with open(schema_output, "w") as f:
                yaml.dump(config, f, sort_keys=False)
            print(f"Added INI file reference as fallback: {Path(ini_file).name}")


def generate_mapping_config(
    pbp_file: Path,
    hk_file: Path,
    schema_output: str = "new_data_schema_with_mapping.yaml",
    ini_file: str = None,
    instrument_output: str = None,
) -> None:
    """Generate enhanced config schema with column mapping templates."""
    print(f"Reading PbP schema from: {pbp_file}")
    pbp_schema = load_schema(pbp_file)

    print(f"Reading HK schema from: {hk_file}")
    hk_schema = load_schema(hk_file)

    # Get canonical schemas
    canonical_schemas = get_canonical_schemas()

    # Create column mapping templates
    pbp_mapping = {}
    hk_mapping = {}

    # For PbP: map file columns to canonical columns
    for canonical_col in canonical_schemas["pbp_canonical"]:
        matching_file_col = None
        for file_col in pbp_schema.keys():
            if file_col.lower() == canonical_col.lower():
                matching_file_col = file_col
                break

        pbp_mapping[canonical_col] = (
            matching_file_col or canonical_col  # Use canonical name as default
        )

    # For HK: map file columns to canonical columns
    for canonical_col in canonical_schemas["hk_canonical"]:
        matching_file_col = None
        for file_col in hk_schema.keys():
            if file_col.lower() == canonical_col.lower():
                matching_file_col = file_col
                break

        hk_mapping[canonical_col] = (
            matching_file_col or canonical_col  # Use canonical name as default
        )

    # Build enhanced config
    config = {
        "# INSTRUCTIONS": [
            "This config file contains both schema definitions and column mappings.",
            "1. The *_schema sections define the data types for your input files.",
            "2. The *_column_mapping sections map your file columns to canonical names.",
            "3. Update column mappings if your files use different column names.",
            "4. If your file doesn't have a particular canonical column, set it to null or remove the line.",
            "5. The output parquet files will use the canonical column names for consistency.",
        ],
        "pbp_schema": pbp_schema,
        "hk_schema": hk_schema,
        "pbp_canonical_schema": canonical_schemas["pbp_canonical"],
        "hk_canonical_schema": canonical_schemas["hk_canonical"],
        "pbp_column_mapping": pbp_mapping,
        "hk_column_mapping": hk_mapping,
    }

    # Create output directory if it doesn't exist
    schema_path = Path(schema_output)
    if schema_path.parent != Path("."):
        schema_path.parent.mkdir(parents=True, exist_ok=True)
        print(f"Created config directory: {schema_path.parent}")

    with open(schema_output, "w") as f:
        yaml.dump(config, f, sort_keys=False, default_flow_style=False)

    print(
        f"Enhanced data schema config with column mapping saved to: {schema_path.absolute()}"
    )

    # Generate separate instrument settings config from INI file
    if ini_file:
        # Determine instrument settings output filename
        if instrument_output:
            instrument_path = Path(instrument_output)
        else:
            instrument_path = schema_path.parent / "new_instrument_settings.yaml"

        try:
            from sp2xr.helpers import export_xr_ini_to_yaml_with_source

            export_xr_ini_to_yaml_with_source(ini_file, str(instrument_path))
            # print(f"Instrument settings config saved to: {instrument_path}")
        except ImportError:
            # Fallback to original function if new one doesn't exist yet
            from sp2xr.helpers import export_xr_ini_to_yaml

            export_xr_ini_to_yaml(ini_file, str(instrument_path))
            # print(f"Instrument settings config saved to: {instrument_path}")
        except Exception as e:
            print(f"Warning: Could not convert INI to YAML: {e}")
            # Still reference the original INI file as fallback
            config["calibration_file"] = ini_file
            with open(schema_output, "w") as f:
                yaml.dump(config, f, sort_keys=False, default_flow_style=False)
            print(f"Added INI file reference as fallback: {Path(ini_file).name}")


def parse_args():
    """Parse command line arguments."""
    parser = argparse.ArgumentParser(
        description="Generate SP2XR configuration files from data directory",
        formatter_class=argparse.RawDescriptionHelpFormatter,
        epilog="""
Examples:
  # Generate basic config schema from files in current directory
  python sp2xr_generate_config.py .

  # Generate config schema from specific directory
  python sp2xr_generate_config.py /path/to/sp2xr/data

  # Generate config schema with column mapping support
  python sp2xr_generate_config.py /path/to/data --mapping

  # Specify custom schema and instrument settings filenames
  python sp2xr_generate_config.py /path/to/data --schema-output my_schema.yaml --instrument-output my_settings.yaml

  # Generate mapping config with custom names
  python sp2xr_generate_config.py /path/to/data --mapping --schema-output campaign_schema.yaml --instrument-output campaign_settings.yaml
        """,
    )

    parser.add_argument(
        "directory", help="Directory containing SP2XR files (PbP and HK files)"
    )

    parser.add_argument(
        "--schema-output",
        "-s",
        default="config/new_data_schema.yaml",
        help="Output filename for data schema config (default: config/new_data_schema.yaml)",
    )

    parser.add_argument(
        "--instrument-output",
        "-i",
        default=None,
        help="Output filename for instrument settings config (default: {schema_output}_instrument_settings.yaml)",
    )

    parser.add_argument(
        "--mapping",
        "-m",
        action="store_true",
        help="Generate config with column mapping support (creates config_with_mapping.yaml)",
    )

    parser.add_argument(
        "--pbp-file", help="Specify specific PbP file instead of auto-detection"
    )

    parser.add_argument(
        "--hk-file", help="Specify specific HK file instead of auto-detection"
    )

    return parser.parse_args()


def main():
    """Main entry point."""
    args = parse_args()

    try:
        # Use specific files if provided, otherwise auto-detect
        if args.pbp_file and args.hk_file:
            pbp_file = Path(args.pbp_file)
            hk_file = Path(args.hk_file)

            if not pbp_file.exists():
                raise FileNotFoundError(f"PbP file not found: {pbp_file}")
            if not hk_file.exists():
                raise FileNotFoundError(f"HK file not found: {hk_file}")

        else:
            # Auto-detect files in directory
            print(f"Searching for SP2XR files in: {args.directory}")
            pbp_files, hk_files = find_sp2xr_files(args.directory)

            if not pbp_files:
                raise FileNotFoundError(
                    "No PbP files found. Looking for files with 'PbP', 'pbp', or 'Pbp' in the name."
                )
            if not hk_files:
                raise FileNotFoundError(
                    "No HK files found. Looking for files with 'hk', 'HK', or 'Hk' in the name."
                )

            # Use the first file found for each type
            pbp_file = pbp_files[0]
            hk_file = hk_files[0]

            print(f"Found {len(pbp_files)} PbP file(s), using: {pbp_file.name}")
            print(f"Found {len(hk_files)} HK file(s), using: {hk_file.name}")

        # Check for INI files in the directory
        try:
            from sp2xr.helpers import find_and_validate_ini_files

            ini_file = find_and_validate_ini_files(str(args.directory))

            if ini_file:
                print(f"Found consistent INI calibration file: {Path(ini_file).name}")
            else:
                print("No INI calibration files found in directory")

        except ValueError as e:
            print(f"WARNING: {e}")
            print("You should process data with different calibrations separately.")
        except ImportError:
            print("Could not import INI validation function")

        # Generate configuration
        if args.mapping:
            schema_file = (
                "config/new_data_schema_with_mapping.yaml"
                if args.schema_output == "config/new_data_schema.yaml"
                else args.schema_output
            )
            generate_mapping_config(
                pbp_file,
                hk_file,
                schema_file,
                ini_file if "ini_file" in locals() else None,
                args.instrument_output,
            )
        else:
            generate_basic_config(
                pbp_file,
                hk_file,
                args.schema_output,
                ini_file if "ini_file" in locals() else None,
                args.instrument_output,
            )

        print("\nConfiguration generation completed successfully!")

    except Exception as e:
        print(f"Error: {e}")
        return 1

    return 0


if __name__ == "__main__":
    exit(main())