""" Sensospot Data Parser

Parsing the numerical output from Sensovations Sensospot image analysis.
"""

from pathlib import Path
from collections import namedtuple

import numpy
from defusedxml import ElementTree

from .columns import (
    COL_NAME_EXPOSURE_ID,
    COL_NAME_PARAMETERS_TIME,
    COL_NAME_PARAMETERS_CHANNEL,
)

ExposureInfo = namedtuple("ExposureInfo", ["channel", "time"])


def _search_measurement_params_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_measurement_params(params_file):
    """ parses the cannel informations from a settings file """
    file_path = Path(params_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 = float(child.attrib["ExposureTimeMs"])
        result[exposure] = ExposureInfo(channel.lower(), time)
    return result


def get_measurement_params(folder):
    """ returns measurement parameters """
    params_file = _search_measurement_params_file(folder)
    if params_file is not None:
        return _parse_measurement_params(params_file)
    return None


def _add_measurement_params(data_frame, params):
    """ adds measurement parameters to a data frame """
    for exposure_id, info in params.items():
        mask = data_frame[COL_NAME_EXPOSURE_ID] == exposure_id
        data_frame.loc[mask, COL_NAME_PARAMETERS_CHANNEL] = info.channel
        data_frame.loc[mask, COL_NAME_PARAMETERS_TIME] = info.time
    data_frame[COL_NAME_PARAMETERS_CHANNEL] = data_frame[
        COL_NAME_PARAMETERS_CHANNEL
    ].astype("category")
    return data_frame


def add_optional_measurement_parameters(data_frame, folder):
    """ adds measurement params to the data frame, if they could be parsed """
    data_frame[COL_NAME_PARAMETERS_CHANNEL] = numpy.nan
    data_frame[COL_NAME_PARAMETERS_TIME] = numpy.nan
    params = get_measurement_params(folder)
    if params:
        available_exposures = set(data_frame[COL_NAME_EXPOSURE_ID].unique())
        if available_exposures == set(params.keys()):
            return _add_measurement_params(data_frame, params)
    return data_frame