# Copyright CNRS/Inria/UNS
# Contributor(s): Eric Debreuve (since 2019), Morgane Nadal (2020)
#
# eric.debreuve@cnrs.fr
#
# This software is governed by the CeCILL  license under French law and
# abiding by the rules of distribution of free software.  You can  use,
# modify and/ or redistribute the software under the terms of the CeCILL
# license as circulated by CEA, CNRS and INRIA at the following URL
# "http://www.cecill.info".
#
# As a counterpart to the access to the source code and  rights to copy,
# modify and redistribute granted by the license, users are provided only
# with a limited warranty  and the software's author,  the holder of the
# economic rights,  and the successive licensors  have only  limited
# liability.
#
# In this respect, the user's attention is drawn to the risks associated
# with loading,  using,  modifying and/or developing or reproducing the
# software by the user in light of its specific status of free software,
# that may mean  that it is complicated to manipulate,  and  that  also
# therefore means  that it is reserved for developers  and  experienced
# professionals having in-depth computer knowledge. Users are therefore
# encouraged to load and test the software's suitability as regards their
# requirements in conditions enabling the security of their systems and/or
# data to be ensured and,  more generally, to use and operate it in the
# same conditions as regards security.
#
# The fact that you are presently reading this means that you have had
# knowledge of the CeCILL license and that you accept its terms.

import configparser
from typing import Any, Union


def NutrimorphArguments(parameter_file):
    """"""
    parameters = configparser.ConfigParser(
        allow_no_value=True, inline_comment_prefixes=";"
    )
    parameters.read(parameter_file)

    # [Input]
    data_path = parameters["Input"]["data_path"]
    channel = parameters["Input"]["channel"]
    size_voxel_in_micron = PreferentiallyAsFloat(
        parameters, "Input", "size_voxel_in_micron"
    )
    dilatation_erosion = parameters.getboolean("Input", "dilatation_erosion")
    save_images = parameters["Input"]["save_images"]
    save_csv = parameters["Input"]["save_csv"]
    save_features_analysis = parameters["Input"]["save_features_analysis"]
    statistical_analysis = parameters.getboolean("Input", "statistical_analysis")

    # [Somas]
    soma_low_c = PreferentiallyAsFloat(parameters, "Somas", "soma_low_c")
    soma_high_c = PreferentiallyAsFloat(parameters, "Somas", "soma_high_c")
    soma_selem_micron_c = PreferentiallyAsFloat(
        parameters, "Somas", "soma_selem_micron_c"
    )
    soma_min_area_c = PreferentiallyAsFloat(parameters, "Somas", "soma_min_area_c")
    soma_max_area_c = PreferentiallyAsFloat(parameters, "Somas", "soma_max_area_c")

    # [Extensions]
    adapt_hist_equalization = parameters.getboolean(
        "Extensions", "adapt_hist_equalization"
    )
    ext_low_c = PreferentiallyAsFloat(parameters, "Extensions", "ext_low_c")
    ext_high_c = PreferentiallyAsFloat(parameters, "Extensions", "ext_high_c")
    ext_selem_micron_c = PreferentiallyAsFloat(
        parameters, "Extensions", "ext_selem_micron_c"
    )
    ext_min_area_c = PreferentiallyAsFloat(parameters, "Extensions", "ext_min_area_c")

    # [Connexions]
    max_straight_sq_dist_c = PreferentiallyAsFloat(
        parameters, "Connexions", "max_straight_sq_dist_c"
    )
    max_weighted_length_c = PreferentiallyAsFloat(
        parameters, "Connexions", "max_weighted_length_c"
    )

    # [Frangi]
    scale_range = PreferentiallyAsFloat(parameters, "Frangi", "scale_range")
    scale_step = PreferentiallyAsFloat(parameters, "Frangi", "scale_step")
    alpha = PreferentiallyAsFloat(parameters, "Frangi", "alpha")
    beta = PreferentiallyAsFloat(parameters, "Frangi", "beta")
    frangi_c = PreferentiallyAsFloat(parameters, "Frangi", "frangi_c")
    diff_mode = parameters["Frangi"]["diff_mode"]
    bright_on_dark = parameters.getboolean("Frangi", "bright_on_dark")
    method = parameters["Frangi"]["method"]

    # [Features Extraction]
    hist_min_length = PreferentiallyAsFloat(
        parameters, "Features Extraction", "hist_min_length"
    )
    hist_step_length = PreferentiallyAsFloat(
        parameters, "Features Extraction", "hist_step_length"
    )
    number_of_bins_length = PreferentiallyAsFloat(
        parameters, "Features Extraction", "number_of_bins_length"
    )
    hist_bins_borders_length = PreferentiallyAsFloat(
        parameters, "Features Extraction", "hist_bins_borders_length"
    )
    hist_min_curvature = PreferentiallyAsFloat(
        parameters, "Features Extraction", "hist_min_curvature"
    )
    hist_step_curvature = PreferentiallyAsFloat(
        parameters, "Features Extraction", "hist_step_curvature"
    )
    number_of_bins_curvature = PreferentiallyAsFloat(
        parameters, "Features Extraction", "number_of_bins_curvature"
    )
    hist_bins_borders_curvature = PreferentiallyAsFloat(
        parameters, "Features Extraction", "hist_bins_borders_curvature"
    )

    # [Program running]
    with_plot = parameters.getboolean("Program Running", "with_plot")
    interactive = parameters.getboolean("Program Running", "interactive")
    in_parallel = parameters.getboolean("Program Running", "in_parallel")

    # data_path = "./data/DIO_6H_6_1.70bis_2.2_3.tif"
    # channel = 'G'
    # size_voxel_in_micron = None  # -> list [X,Y,Z]
    # with_plot = False
    #
    # soma_low_c = 0.15
    # soma_high_c = 0.7126
    # ext_low_c = 0.2 #3 #1e-7    ##0.2  # 0.02  # 0.2  # ext_low_c = 9.0e-4
    # ext_high_c = 0.6 #1e-10 #1.2e-7    ##0.6  # 0.04  # 0.6  # high_ext = 8.0e-3
    #
    # # soma_selem_c = mp_.disk(2)
    # # ext_selem_c = mp_.disk(1)
    # soma_selem_micron_c = 0.24050024 * 2
    # ext_selem_micron_c = 0.24050024 * 1
    #
    # max_straight_sq_dist_c = (30 ** 2) * 0.24050024
    # max_weighted_length_c = 20.0 * 0.24050024
    #
    # soma_min_area_c = 1000 * (0.24050024 ** 2)
    # ext_min_area_c = 100 * (0.24050024 ** 2)
    # # soma_min_area_c = 1000
    # # ext_min_area_c = 100
    #
    # in_parallel = False

    arguments = {
        "channel": channel,
        "dilatation_erosion": dilatation_erosion,
        "size_voxel_in_micron": size_voxel_in_micron,
        "soma_low_c": soma_low_c,
        "soma_high_c": soma_high_c,
        "soma_selem_micron_c": soma_selem_micron_c,
        "soma_min_area_c": soma_min_area_c,
        "soma_max_area_c": soma_max_area_c,
        "adapt_hist_equalization": adapt_hist_equalization,
        "ext_low_c": ext_low_c,
        "ext_high_c": ext_high_c,
        "ext_selem_micron_c": ext_selem_micron_c,
        "ext_min_area_c": ext_min_area_c,
        "max_straight_sq_dist_c": max_straight_sq_dist_c,
        "max_weighted_length_c": max_weighted_length_c,
        "scale_range": scale_range,
        "scale_step": scale_step,
        "alpha": alpha,
        "beta": beta,
        "frangi_c": frangi_c,
        "diff_mode": diff_mode,
        "bright_on_dark": bright_on_dark,
        "method": method,
        "hist_min_length": hist_min_length,
        "hist_step_length": hist_step_length,
        "number_of_bins_length": number_of_bins_length,
        "hist_bins_borders_length": hist_bins_borders_length,
        "hist_min_curvature": hist_min_curvature,
        "hist_step_curvature": hist_step_curvature,
        "number_of_bins_curvature": number_of_bins_curvature,
        "hist_bins_borders_curvature": hist_bins_borders_curvature,
        "with_plot": with_plot,
        "interactive": interactive,
        "in_parallel": in_parallel,
    }

    return (
        data_path,
        save_images,
        save_csv,
        save_features_analysis,
        statistical_analysis,
        arguments,
    )


def PreferentiallyAsFloat(
    parameters, section: str, key: str
) -> Union[Union[float, str], Any]:
    """"""
    if (value := parameters.get(section, key)) is None:
        return None

    try:
        output = parameters.getfloat(section, key)
    except:
        output = eval(value)

    return output