""" Sensospot Data Parser

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

from pathlib import Path
from collections import namedtuple

import numpy
import pandas
from defusedxml import ElementTree

from . import columns



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 _get_channel_data(channel_node):
    # child.tag == "ChannelConfig1"
    exposure_id = int(channel_node.tag[-1])
    # channel_description == "[Cy3|Cy5] Green"
    description = channel_node.attrib["Description"]
    exposure_channel = description.rsplit(" ", 1)[-1]
    # floats can be used for exposure times, not only ints
    exposure_time = float(channel_node.attrib["ExposureTimeMs"])
    return {
        columns.EXPOSURE_ID: exposure_id,
        columns.PARAMETERS_CHANNEL: exposure_channel.lower(),
        columns.PARAMETERS_TIME: exposure_time,
    }


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)
        data = [_get_channel_data(child) for child in tree.find("Channels")]
    return pandas.DataFrame(data)


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_optional_measurement_parameters(data_frame, folder):
    """adds measurement params to the data frame, if they could be parsed"""
    params = get_measurement_params(folder)
    if params is not None:
        params_exposures = params[columns.EXPOSURE_ID].unique()
        data_exposures = data_frame[columns.EXPOSURE_ID].unique()
        if set(data_exposures) == set(params_exposures):
            return data_frame.merge(params, how="left", on=columns.EXPOSURE_ID)
    
    # only executing if the parameters were not merged to the data frame
    data_frame[columns.PARAMETERS_CHANNEL] = numpy.nan
    data_frame[columns.PARAMETERS_TIME] = numpy.nan
    return data_frame