from __future__ import annotations

from pathlib import Path
import re
from typing import Any

import pandas as pd

from allotropy.allotrope.schema_mappers.adm.plate_reader.rec._2025._03.plate_reader import (
    ErrorDocument,
    Measurement,
    MeasurementGroup,
    MeasurementType,
    Metadata,
    ProcessedDataDocument,
)
from allotropy.exceptions import AllotropeConversionError
from allotropy.parsers.constants import NEGATIVE_ZERO, NOT_APPLICABLE
from allotropy.parsers.unchained_labs_lunatic_stunner.constants import (
    CALCULATED_DATA_LOOKUP,
    DEFAULT_DETECTION_TYPE,
    DEVICE_TYPE,
    DYNAMIC_LIGHT_SCATTERING_DETECTION_TYPE,
    INCORRECT_WAVELENGTH_COLUMN_FORMAT_ERROR_MSG,
    MODEL_NUMBER,
    NO_DATE_OR_TIME_ERROR_MSG,
    NO_DEVICE_IDENTIFIER_ERROR_MSG,
    NO_MEASUREMENT_IN_PLATE_ERROR_MSG,
    NO_WAVELENGTH_COLUMN_ERROR_MSG,
    PRODUCT_MANUFACTURER,
    SOFTWARE_NAME,
    WAVELENGTH_COLUMNS_RE,
)
from allotropy.parsers.unchained_labs_lunatic_stunner.unchained_labs_lunatic_stunner_calcdocs import (
    create_calculated_data,
)
from allotropy.parsers.utils.calculated_data_documents.definition import (
    CalculatedDocument,
)
from allotropy.parsers.utils.pandas import (
    map_rows,
    SeriesData,
)
from allotropy.parsers.utils.uuids import random_uuid_str
from allotropy.parsers.utils.values import (
    assert_not_none,
)


def _extract_peak_data(well_plate_data: SeriesData) -> list[dict[str, Any]]:
    """Extract peak data from well plate data and return as list of peak info dictionaries."""
    peak_data = []

    peak_pattern = re.compile(r"peak (\d+) ")
    peak_numbers = set()

    for column in well_plate_data.series.index:
        match = peak_pattern.match(str(column).lower())
        if match:
            peak_numbers.add(int(match.group(1)))

    for peak_num in sorted(peak_numbers):
        peak_info: dict[str, Any] = {}

        mean_dia = well_plate_data.get(float, f"peak {peak_num} mean dia (nm)")
        if mean_dia is not None:
            peak_info["peak mean diameter"] = mean_dia

        mode_dia = well_plate_data.get(float, f"peak {peak_num} mode dia (nm)")
        if mode_dia is not None:
            peak_info["peak mode diameter"] = mode_dia

        est_mw = well_plate_data.get(float, f"peak {peak_num} est. mw (kda)")
        if est_mw is not None:
            peak_info["peak est. MW"] = est_mw

        intensity = well_plate_data.get(float, f"peak {peak_num} intensity (%)")
        if intensity is not None:
            peak_info["peak intensity"] = intensity

        mass = well_plate_data.get(float, f"peak {peak_num} mass (%)")
        if mass is not None:
            peak_info["peak mass"] = mass

        peak_info["peak index"] = str(peak_num)

        if len(peak_info) > 1:
            peak_data.append(peak_info)

    return peak_data


def _get_calculated_value_and_is_na(
    series: SeriesData, key: str
) -> tuple[float | None, bool]:
    """Return value and whether the source cell is literal N/A.

    - If the raw cell value is the string literal "N/A", returns (NEGATIVE_ZERO, True).
    - If the cell is empty/NaN, returns (None, False) so caller can skip the key.
    - Otherwise, returns (float value, False).
    """
    raw_string = series.get(str, key, validate=SeriesData.NOT_NAN)
    if raw_string is None:
        return None, False
    stripped = str(raw_string).strip()
    if stripped == "":
        return None, False
    if stripped.upper() == NOT_APPLICABLE:
        return NEGATIVE_ZERO, True
    return series.get(float, key), False


def _is_literal_not_applicable(series: SeriesData, key: str) -> bool:
    """Return True if the source cell is the literal string N/A (case-insensitive)."""
    raw_string = series.get(str, key, validate=SeriesData.NOT_NAN)
    if raw_string is None:
        return False
    return str(raw_string).strip().upper() == NOT_APPLICABLE


def _create_measurement(
    well_plate_data: SeriesData,
    header: SeriesData,
    wavelength_column: str,
) -> Measurement:
    if wavelength_column not in well_plate_data.series:
        msg = NO_MEASUREMENT_IN_PLATE_ERROR_MSG.format(wavelength_column)
        raise AllotropeConversionError(msg)

    wavelength_match = WAVELENGTH_COLUMNS_RE.match(wavelength_column)
    if not wavelength_match:
        raise AllotropeConversionError(INCORRECT_WAVELENGTH_COLUMN_FORMAT_ERROR_MSG)
    if len(wavelength_match.groups()) > 1:
        wavelength, path_length = wavelength_match.groups()
    else:
        wavelength = wavelength_match.groups()[0]
        path_length = None

    background_wavelength = well_plate_data.get(float, "background wvl. (nm)")
    background_absorbance = None
    if background_wavelength is not None:
        background_absorbance = well_plate_data.get(
            float, f"background (a{int(background_wavelength)})"
        )

    measurement_identifier = random_uuid_str()
    peak_data = _extract_peak_data(well_plate_data)

    error_documents: list[ErrorDocument] = []
    absorbance = well_plate_data.get(float, wavelength_column)
    absorbance_is_na = _is_literal_not_applicable(well_plate_data, wavelength_column)
    concentration_factor = well_plate_data.get(float, "concentration factor (ng/ul)")
    application = header.get(str, "application")
    analytical_method_identifier = (
        well_plate_data.get(str, "application") or application
    )
    experimental_data_identifier = header.get(str, "experiment name")
    detection_type = DEFAULT_DETECTION_TYPE
    if application and "B22 & kD" in application:
        detection_type = DYNAMIC_LIGHT_SCATTERING_DETECTION_TYPE
    # Build calculated data values with special handling for N/A and empty cells
    calculated_data_values: dict[str, float] = {}
    for item in CALCULATED_DATA_LOOKUP.get(wavelength_column, []):
        value, is_na = _get_calculated_value_and_is_na(well_plate_data, item["column"])
        # Only create error docs when the cell is the literal "N/A"
        if is_na:
            error_documents.append(
                ErrorDocument(
                    error=NOT_APPLICABLE,
                    error_feature=item["name"],
                )
            )
        # Skip missing cells entirely; include numeric values
        if value is not None:
            calculated_data_values[item["column"]] = value

    # Append absorbance error last to preserve historical ordering (calculated-data errors first)
    if absorbance_is_na:
        error_documents.append(
            ErrorDocument(
                error=NOT_APPLICABLE,
                error_feature=DEFAULT_DETECTION_TYPE.lower(),
            )
        )

    return Measurement(
        type_=MeasurementType.ULTRAVIOLET_ABSORBANCE,
        device_type=DEVICE_TYPE,
        detection_type=detection_type,
        identifier=measurement_identifier,
        analytical_method_identifier=analytical_method_identifier,
        experimental_data_identifier=experimental_data_identifier,
        detector_wavelength_setting=float(wavelength),
        electronic_absorbance_reference_wavelength_setting=background_wavelength,
        absorbance=absorbance if absorbance is not None else NEGATIVE_ZERO,
        sample_identifier=well_plate_data[str, "sample name"],
        location_identifier=well_plate_data[str, "plate position"],
        well_plate_identifier=well_plate_data.get(str, "plate id"),
        batch_identifier=well_plate_data.get(str, "sample group"),
        firmware_version=header.get(str, "client version"),
        number_of_averages=well_plate_data.get(float, "number of acquisitions"),
        integration_time=well_plate_data.get(float, "acquisition time (s)"),
        compartment_temperature=well_plate_data.get(float, "temperature (°c)"),
        sample_custom_info={
            "path length": float(path_length) if path_length is not None else None,
            "plate type": header.get(str, "plate type")
            or well_plate_data.get(str, "plate type"),
            "nr of plates": header.get(str, "nr of plates"),
            "blanks": header.get(str, "blanks"),
            "plate description": header.get(str, "nan", duplicate_strategy="last"),
            "molar attenuation coefficient setting": well_plate_data.get(float, "e1%"),
            "analyte": well_plate_data.get(str, "analyte"),
            "buffer": well_plate_data.get(str, "buffer"),
            "molecular weight (kda)": well_plate_data.get(
                float, "molecular weight (kda)"
            ),
        },
        device_control_custom_info={
            "path length mode": well_plate_data.get(str, "path length mode"),
            "pump": well_plate_data.get(str, "pump"),
            "column": header.get(str, "column") or well_plate_data.get(str, "column"),
            "Number of acquisitions used": well_plate_data.get(
                str, "number of acquisitions used"
            ),
            "Acquisition filtering": well_plate_data.get(str, "acquisition filtering"),
        },
        error_document=error_documents,
        processed_data_document=ProcessedDataDocument(
            identifier=random_uuid_str(),
            concentration_factor=concentration_factor,
            peak_list_custom_info=peak_data,
        )
        if concentration_factor is not None or peak_data
        else None,
        wavelength_identifier=wavelength_column,
        calc_docs_custom_info={
            **calculated_data_values,
            **{
                "b22 linear fit": well_plate_data.get(str, "b22 linear fit"),
                "kd linear fit": well_plate_data.get(str, "kd linear fit"),
            },
        },
        measurement_custom_info={
            "electronic_absorbance_reference_absorbance": background_absorbance,
            **_filter_empty_string_values(
                well_plate_data.get_unread(
                    # Skip already mapped columns from well plate data (repeated in CSV no header cases)
                    skip={
                        "time",
                        "row",
                        "concentration factor (ng/ul)",
                        "application",
                        "concentration (ng/ul)",
                        "background wvl. (nm)",
                        "plate id",
                        "plate position",
                        "plate type",
                        "instrument id",
                        "column",
                        # Strings to skip since these are already captured as measurements/calculated data
                        # Skip absorbance measurements with a### (10mm) -- spectral scans
                        r"^a\d{3} \(10mm\)$",
                        # a###/a### -- calculated purity values
                        r"^a\d{3}/a\d{3}$",
                        # a### -- raw absorbance measurement
                        r"^a\d{3}$",
                    },
                )
            ),
        },
    )


def _create_measurement_group(
    data: SeriesData,
    wavelength_columns: list[str],
    header: SeriesData,
) -> MeasurementGroup:
    timestamp = header.get(str, "date")
    # Support timestamp from metadata section, but overide with columns in data if specified.
    date = data.get(str, "date")
    time = data.get(str, "time")
    if date and time:
        timestamp = f"{date} {time}"

    return MeasurementGroup(
        measurement_time=assert_not_none(timestamp, msg=NO_DATE_OR_TIME_ERROR_MSG),
        analyst=header.get(str, "test performed by"),
        plate_well_count=96,
        measurements=[
            _create_measurement(data, header, wavelength_column)
            for wavelength_column in wavelength_columns
        ],
    )


def create_metadata(header: SeriesData, file_path: str) -> Metadata:
    asm_file_identifier = Path(file_path).with_suffix(".json")
    device_identifier = header.get(str, "instrument id") or header.get(
        str, "instrument"
    )
    return Metadata(
        model_number=MODEL_NUMBER,
        product_manufacturer=PRODUCT_MANUFACTURER,
        device_identifier=assert_not_none(
            device_identifier, msg=NO_DEVICE_IDENTIFIER_ERROR_MSG
        ),
        software_name=SOFTWARE_NAME,
        software_version=header.get(str, "software version"),
        file_name=Path(file_path).name,
        unc_path=file_path,
        asm_file_identifier=asm_file_identifier.name,
        data_system_instance_id=NOT_APPLICABLE,
        custom_info_doc=_filter_empty_string_values(
            header.get_unread(
                # Skip already mapped columns from well plate data (repeated in CSV no header cases)
                skip={
                    "time",
                    "row",
                    "path length mode",
                    "sample group",
                    "pump",
                    "concentration factor (ng/ul)",
                    "sample name",
                    "application",
                    "concentration (ng/ul)",
                    "e1%",
                    "concentration (mg/ml)",
                    "background wvl. (nm)",
                    "plate id",
                    "plate position",
                    # Strings to skip since these are already captured as measurements/calculated data
                    # Skip absorbance spectrum measurements with a### (10mm)
                    r"^a\d{3} \(10mm\)$",
                    # a###/a###
                    r"^a\d{3}/a\d{3}$",
                    # a###
                    r"^a\d{3}$",
                    # background a###
                    r"^background \(a\d{3}\)$",
                    # a### concentration (ng/uL)
                    r"^a\d{3} concentration \(ng/ul\)$",
                },
            )
        ),
    )


def _filter_empty_string_values(unread: dict[str, Any]) -> dict[str, Any]:
    return {
        key: value
        for key, value in unread.items()
        if not (isinstance(value, str) and value.strip() == "")
    }


def create_measurement_groups(
    header: SeriesData, data: pd.DataFrame
) -> tuple[list[MeasurementGroup], list[CalculatedDocument]]:
    wavelength_columns = list(filter(WAVELENGTH_COLUMNS_RE.match, data.columns))
    if not wavelength_columns:
        raise AllotropeConversionError(NO_WAVELENGTH_COLUMN_ERROR_MSG)

    def make_group(data: SeriesData) -> MeasurementGroup:
        return _create_measurement_group(data, wavelength_columns, header)

    measurement_groups = map_rows(data, make_group)
    return measurement_groups, create_calculated_data(measurement_groups)
