""" Sensovation Data Parser

Parsing the numerical output from Sensovation image analysis.
"""

__version__ = "0.0.1"


import re
from pathlib import Path
from collections import namedtuple

import pandas
from defusedxml import ElementTree

REGEX_WELL = re.compile(
    r"""
    (?P<row>([A-Z]+))  # row name containing one or more letters
    (?P<column>(\d+))     # column, one or more decimals
    """,
    re.VERBOSE | re.IGNORECASE,
)

COLUMNS_TO_DROP = ["Rect.", "Contour"]
COLUMNS_RENAME_MAP = {
    " ID ": "Pos.Id",
    "Found": "Spot.Found",
    "Dia.": "Spot.Diameter",
}

CACHE_FILE_NAME = "cached_data.h5"
CACHE_TABLE_NAME = f"raw_data_v{__version__}"

FileInfo = namedtuple("FileInfo", ["row", "column", "exposure"])
ExposureInfo = namedtuple("ExposureInfo", ["channel", "time"])


def _guess_decimal_separator(file_handle):
    """ guesses the decimal spearator of a opened data file """
    file_handle.seek(0)
    headers = next(file_handle)  # noqa: F841
    data = next(file_handle)
    separator = "," if data.count(",") > data.count(".") else "."
    file_handle.seek(0)
    return separator


def _parse_csv(data_file):
    """ parse a csv sensovation data file """
    data_path = Path(data_file)
    with data_path.open("r") as handle:
        decimal_sep = _guess_decimal_separator(handle)
        return pandas.read_csv(handle, sep="\t", decimal=decimal_sep)


def _extract_measurement_info(data_file):
    """ extract measurement meta data from a file name """
    data_path = Path(data_file)
    *rest, well, exposure = data_path.stem.rsplit("_", 2)  # noqa: F841
    matched = REGEX_WELL.match(well)
    if matched is None:
        raise ValueError(f"not a valid well: '{well}'")
    row = matched["row"].upper()
    column = int(matched["column"])
    exposure = int(exposure)
    return FileInfo(row, column, exposure)


def _cleanup_data_columns(data_frame):
    """ renames some data columns for consistency and drops unused columns """
    renamed = data_frame.rename(columns=COLUMNS_RENAME_MAP)
    return renamed.drop(columns=COLUMNS_TO_DROP)


def parse_file(data_file):
    """ parses one data file and adds metadata to result """
    measurement_info = _extract_measurement_info(data_file)
    data_frame = _parse_csv(data_file)
    data_frame["Field.Row"] = measurement_info.row
    data_frame["Field.Column"] = measurement_info.column
    data_frame["Exposure.Id"] = measurement_info.exposure
    return _cleanup_data_columns(data_frame)


def parse_multiple_files(file_list):
    """ parses a list of file paths to one combined dataframe """
    if not file_list:
        raise ValueError("Empty file list provided")
    collection = (parse_file(path) for path in file_list)
    data_frame = next(collection)
    for next_frame in collection:
        data_frame = data_frame.append(next_frame, ignore_index=True)
    return data_frame


def _list_csv_files(folder):
    """ returns all csv files in a folder """
    folder_path = Path(folder)
    files = (item for item in folder_path.iterdir() if item.is_file())
    visible = (item for item in files if not item.stem.startswith("."))
    return (item for item in visible if item.suffix.lower() == ".csv")


def _sanity_check(data_frame):
    """ checks some basic constrains of a combined data frame """
    field_rows = len(data_frame["Field.Row"].unique())
    field_cols = len(data_frame["Field.Column"].unique())
    exposures = len(data_frame["Exposure.Id"].unique())
    spot_positions = len(data_frame["Pos.Id"].unique())
    expected_rows = field_rows * field_cols * exposures * spot_positions
    if expected_rows != len(data_frame):
        raise ValueError("Measurements are missing")
    return data_frame


def parse_folder(folder):
    """ parses all csv files in a folder to one large dataframe """
    file_list = _list_csv_files(folder)
    data_frame = parse_multiple_files(file_list)
    return data_frame


def _search_channel_info_file(folder):
    """ searches for a exposure settings file in a folder """
    folder_path = Path(folder)
    params_folder = folder_path / "Parameters"
    if not params_folder.is_dir():
        return None
    param_files = list(params_folder.glob("**/*.svexp"))
    if len(param_files) == 1:
        return param_files[0]
    else:
        return None


def _parse_channel_info(channel_file):
    """ parses the cannel informations from a settings file """
    file_path = Path(channel_file)
    with file_path.open("r") as file_handle:
        tree = ElementTree.parse(file_handle)
    result = {}
    for child in tree.find("Channels"):
        # child.tag == "ChannelConfig1"
        exposure = int(child.tag[-1])
        channel_description = child.attrib["Description"]
        # channel_description == "Cy3/Cy5 Green"
        channel = channel_description.rsplit(" ", 1)[-1]
        time = int(child.attrib["ExposureTimeMs"])
        result[exposure] = ExposureInfo(channel.lower(), time)
    return result


def _get_valid_exposure_info(folder, data_frame, exposure_info=None):
    """ returns valid exposure information """
    available_exposures = set(data_frame["Exposure.Id"].unique())
    if exposure_info is None:
        params_file = _search_channel_info_file(folder)
        if params_file is not None:
            exposure_info = _parse_channel_info(params_file)
    if exposure_info is not None:
        if available_exposures == set(exposure_info.keys()):
            return exposure_info
    return {c: ExposureInfo(None, None) for c in available_exposures}


def _augment_exposure_info(data_frame, exposure_info):
    data_frame["Exposure.Channel"] = ""
    data_frame["Exposure.Time"] = 0
    for exposure_id, info in exposure_info.items():
        mask = data_frame["Exposure.Id"] == exposure_id
        data_frame.loc[mask, "Exposure.Channel"] = info.channel
        data_frame.loc[mask, "Exposure.Time"] = info.time
    return data_frame


def _process_folder(folder, exposures=None):
    """ parses all csv files in a folder, adds some checks and more data """
    data_frame = parse_folder(folder)
    exposures = _get_valid_exposure_info(folder, data_frame, exposures)
    data_frame = _augment_exposure_info(data_frame, exposures)
    data_frame["Field.Row"] = data_frame["Field.Row"].astype("category")
    data_frame["Exposure.Channel"] = data_frame["Exposure.Channel"].astype(
        "category"
    )
    return data_frame


def process_folder(folder, exposures=None, use_cache=True):
    """ parses all csv files in a folder, adds some checks and more data """
    hdf5_path = folder / CACHE_FILE_NAME
    if use_cache:
        try:
            return pandas.read_hdf(hdf5_path, CACHE_TABLE_NAME)
        except (FileNotFoundError, KeyError):
            # either file or table doesn't exist
            pass
    data_frame = _process_folder(folder, exposures)
    if use_cache:
        try:
            data_frame.to_hdf(hdf5_path, CACHE_TABLE_NAME, format="table")
        except OSError:
            # capturing high level OSError
            # read only filesystems don't throw a more specific exception
            pass
    return data_frame