__author__ = "Simon Nilsson"

import os
from copy import deepcopy

import numpy as np
import pandas as pd

from simba.data_processors.interpolation_smoothing import Interpolate, Smooth
from simba.mixins.config_reader import ConfigReader
from simba.mixins.pose_importer_mixin import PoseImporterMixin
from simba.utils.enums import Formats, Methods
from simba.utils.errors import BodypartColumnNotFoundError
from simba.utils.printing import SimbaTimer, stdout_success
from simba.utils.read_write import (find_all_videos_in_project,
                                    get_video_meta_data, write_df)


class MADLCImporterH5(ConfigReader, PoseImporterMixin):

    """
    Importing multi-animal deeplabcut (maDLC) pose-estimation data (in H5 format)
    into a SimBA project in parquet or CSV format.

    :parameter str config_path: path to SimBA project config file in Configparser format
    :parameter str data_folder: Path to folder containing maDLC data in ``.h5`` format.
    :parameter str file_type: Method used to perform pose-estimation in maDLC. OPTIONS: `skeleton`, `box`, `ellipse`.
    :param List[str] id_lst: Names of animals.
    :parameter str interpolation_setting: String defining the pose-estimation interpolation method. OPTIONS: 'None', 'Animal(s): Nearest',
        'Animal(s): Linear', 'Animal(s): Quadratic','Body-parts: Nearest', 'Body-parts: Linear',
        'Body-parts: Quadratic'.
    :parameter dict smoothing_settings: Dictionary defining the pose estimation smoothing method. EXAMPLE: {'Method': 'Savitzky Golay',
        'Parameters': {'Time_window': '200'}})

    .. note::
       `Multi-animal import tutorial <https://github.com/sgoldenlab/simba/blob/master/docs/Multi_animal_pose.md>`__.

    Examples
    -----
    >>> _ = MADLCImporterH5(config_path=r'MyConfigPath', data_folder=r'maDLCDataFolder', file_type='ellipse', id_lst=['Animal_1', 'Animal_2'], interpolation_settings='None', smoothing_settings={'Method': 'None', 'Parameters': {'Time_window': '200'}}).run()

    References
    ----------
    .. [1] Lauer et al., Multi-animal pose estimation, identification and tracking with DeepLabCut, `Nature Methods`,
           2022.
    """

    def __init__(
        self,
        config_path: str,
        data_folder: str,
        file_type: str,
        id_lst: list,
        interpolation_settings: str,
        smoothing_settings: dict,
    ):
        ConfigReader.__init__(self, config_path=config_path, read_video_info=False)
        PoseImporterMixin.__init__(self)

        self.interpolation_settings, self.smoothing_settings = (
            interpolation_settings,
            smoothing_settings,
        )
        self.data_folder, self.id_lst = data_folder, id_lst
        self.import_log_path = os.path.join(
            self.logs_path, f"data_import_log_{self.datetime}.csv"
        )
        self.video_paths = find_all_videos_in_project(videos_dir=self.video_dir)
        self.input_data_paths = self.find_data_files(
            dir=self.data_folder, extensions=Formats.DLC_FILETYPES.value[file_type]
        )
        self.data_and_videos_lk = self.link_video_paths_to_data_paths(
            data_paths=self.input_data_paths,
            video_paths=self.video_paths,
            str_splits=Formats.DLC_NETWORK_FILE_NAMES.value,
        )
        if self.pose_setting is Methods.USER_DEFINED.value:
            self.__update_config_animal_cnt()
        if self.animal_cnt > 1:
            self.check_multi_animal_status()
            self.animal_bp_dict = self.create_body_part_dictionary(
                self.multi_animal_status,
                self.id_lst,
                self.animal_cnt,
                self.x_cols,
                self.y_cols,
                self.p_cols,
                self.clr_lst,
            )
            self.update_bp_headers_file()
        print(f"Importing {len(list(self.data_and_videos_lk.keys()))} file(s)...")

    def run(self):
        import_log = pd.DataFrame(
            columns=[
                "VIDEO",
                "IMPORT_TIME",
                "IMPORT_SOURCE",
                "INTERPOLATION_SETTING",
                "SMOOTHING_SETTING",
            ]
        )
        for cnt, (video_name, video_data) in enumerate(self.data_and_videos_lk.items()):
            video_timer = SimbaTimer(start=True)
            self.add_spacer, self.frame_no, self.video_data, self.video_name = (
                2,
                1,
                video_data,
                video_name,
            )
            print(f"Processing {video_name} ...")
            self.data_df = (
                pd.read_hdf(video_data["DATA"])
                .replace([np.inf, -np.inf], np.nan)
                .fillna(0)
            )
            if len(self.data_df.columns) != len(self.bp_headers):
                raise BodypartColumnNotFoundError(
                    msg=f'The number of body-parts in data file {video_data["DATA"]} do not match the number of body-parts in your SimBA project. '
                    f"The number of of body-parts expected by your SimBA project is {int(len(self.bp_headers) / 3)}. "
                    f'The number of of body-parts contained in data file {video_data["DATA"]} is {int(len(self.data_df.columns) / 3)}. '
                    f"Make sure you have specified the correct number of animals and body-parts in your project."
                )
            self.data_df.columns = self.bp_headers
            self.out_df = deepcopy(self.data_df)
            if self.animal_cnt > 1:
                self.initialize_multi_animal_ui(
                    animal_bp_dict=self.animal_bp_dict,
                    video_info=get_video_meta_data(video_data["VIDEO"]),
                    data_df=self.data_df,
                    video_path=video_data["VIDEO"],
                )
                self.multianimal_identification()
            self.save_path = os.path.join(
                os.path.join(self.input_csv_dir, f"{self.video_name}.{self.file_type}")
            )
            write_df(
                df=self.out_df,
                file_type=self.file_type,
                save_path=self.save_path,
                multi_idx_header=True,
            )
            if self.interpolation_settings != "None":
                self.__run_interpolation()
            if self.smoothing_settings["Method"] != "None":
                self.__run_smoothing()
            video_timer.stop_timer()
            stdout_success(
                msg=f"Video {video_name} data imported...",
                elapsed_time=video_timer.elapsed_time_str,
            )
        self.timer.stop_timer()
        stdout_success(
            msg="All maDLC H5 data files imported",
            elapsed_time=self.timer.elapsed_time_str,
        )

    def __run_interpolation(self):
        print(
            "Interpolating missing values in video {} (Method: {}) ...".format(
                self.video_name, self.interpolation_settings
            )
        )
        _ = Interpolate(
            input_path=self.save_path,
            config_path=self.config_path,
            method=self.interpolation_settings,
            initial_import_multi_index=True,
        )

    def __run_smoothing(self):
        print(
            f'Performing {self.smoothing_settings["Method"]} smoothing on video {self.video_name}...'
        )
        Smooth(
            config_path=self.config_path,
            input_path=self.save_path,
            time_window=int(self.smoothing_settings["Parameters"]["Time_window"]),
            smoothing_method=self.smoothing_settings["Method"],
            initial_import_multi_index=True,
        )


# test = MADLCImporterH5(config_path=r'/Users/simon/Desktop/envs/troubleshooting/two_black_animals_14bp/project_folder/project_config.ini',
#                    data_folder=r'/Users/simon/Desktop/envs/troubleshooting/two_black_animals_14bp/h5',
#                    file_type='ellipse',
#                    id_lst=['Simon', 'JJ'],
#                    interpolation_settings='Body-parts: Nearest',
#                    smoothing_settings = {'Method': 'Savitzky Golay', 'Parameters': {'Time_window': '200'}})
# test.run()
