__author__ = "Simon Nilsson"

import os
from typing import List

import cv2
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from matplotlib.backends.backend_agg import FigureCanvasAgg as FigureCanvas
from numba import jit, prange

from simba.mixins.config_reader import ConfigReader
from simba.mixins.plotting_mixin import PlottingMixin
from simba.utils.enums import Formats
from simba.utils.errors import NoSpecifiedOutputError
from simba.utils.printing import SimbaTimer, stdout_success
from simba.utils.read_write import get_fn_ext, read_df


class HeatMapperClfSingleCore(ConfigReader, PlottingMixin):
    """
    Create heatmaps representing the locations of the classified behavior.

    .. note::
       `GitHub visualizations tutorial <https://github.com/sgoldenlab/simba/blob/master/docs/tutorial.md#step-11-visualizations>`__.
       For improved run-time, see :meth:`simba.heat_mapper_clf_mp.HeatMapperClfMultiprocess` for multiprocess class.


    :param str config_path: path to SimBA project config file in Configparser format
    :param bool final_img_setting: If True, then create a single image representing the last frame of the input video
    :param bool video_setting: If True, then create a video of heatmaps.
    :param bool frame_setting: If True, then create individual heatmap frames.
    :param int bin_size: The rectangular size of each heatmap location in millimeters. For example, `50` will divide the video into
        5 centimeter rectangular spatial bins.
    :param str palette: Heatmap pallette. Eg. 'jet', 'magma', 'inferno','plasma', 'viridis', 'gnuplot2'
    :param str bodypart: The name of the body-part used to infer the location of the classified behavior
    :param str clf_name: The name of the classified behavior.
    :param int or 'auto' max_scale: The max value in the heatmap in seconds. E.g., with a value of `10`, if the classified behavior has occured
        >= 10 within a rectangular bins, it will be filled with the same color.

    Examples
    -----
    >>> heat_mapper_clf = HeatMapperClfSingleCore(config_path='MyConfigPath', final_img_setting=False, video_setting=True, frame_setting=False, bin_size=50, palette='jet', bodypart='Nose_1', clf_name='Attack', max_scale=20).run()

    """

    def __init__(
        self,
        config_path: str,
        final_img_setting: bool,
        video_setting: bool,
        frame_setting: bool,
        bodypart: str,
        clf_name: str,
        files_found: List[str],
        style_attr: dict,
    ):
        ConfigReader.__init__(self, config_path=config_path)
        PlottingMixin.__init__(self)

        if (not frame_setting) and (not video_setting) and (not final_img_setting):
            raise NoSpecifiedOutputError(
                msg="Please choose to select either heatmap videos, frames, and/or final image."
            )
        self.frame_setting, self.video_setting = frame_setting, video_setting
        self.final_img_setting, self.bp = final_img_setting, bodypart
        self.bin_size, self.max_scale, self.palette, self.shading = (
            style_attr["bin_size"],
            style_attr["max_scale"],
            style_attr["palette"],
            style_attr["shading"],
        )
        self.clf_name, self.files_found = clf_name, files_found
        if not os.path.exists(self.heatmap_clf_location_dir):
            os.makedirs(self.heatmap_clf_location_dir)
        self.bp_lst = [self.bp + "_x", self.bp + "_y"]
        self.timer = SimbaTimer(start=True)
        print("Processing {} video(s)...".format(str(len(self.files_found))))

    @staticmethod
    @jit(nopython=True)
    def __calculate_cum_array(clf_array: np.array, fps: int):
        cum_sum_arr = np.full(clf_array.shape, np.nan)
        for frm_idx in prange(clf_array.shape[0]):
            frame_cum_sum = np.full((clf_array.shape[1], clf_array.shape[2]), 0.0)
            sliced_arr = clf_array[0:frm_idx]
            for i in range(sliced_arr.shape[0]):
                for j in range(sliced_arr.shape[1]):
                    for k in range(sliced_arr.shape[2]):
                        frame_cum_sum[j][k] += sliced_arr[i][j][k]
            cum_sum_arr[frm_idx] = frame_cum_sum

        return cum_sum_arr / fps

    def __calculate_bin_attr(
        self,
        data_df: pd.DataFrame,
        clf_name: str,
        bp_lst: list,
        px_per_mm: int,
        img_width: int,
        img_height: int,
        bin_size: int,
        fps: int,
    ):
        bin_size_px = int(float(px_per_mm) * float(bin_size))
        horizontal_bin_cnt = int(img_width / bin_size_px)
        vertical_bin_cnt = int(img_height / bin_size_px)
        aspect_ratio = round((vertical_bin_cnt / horizontal_bin_cnt), 3)

        clf_idx = (
            data_df[bp_lst][data_df[clf_name] == 1].reset_index().to_numpy().astype(int)
        )

        bin_dict = {}
        x_location, y_location = 0, 0
        for hbin in range(horizontal_bin_cnt):
            bin_dict[hbin] = {}
            for vbin in range(vertical_bin_cnt):
                bin_dict[hbin][vbin] = {
                    "top_left_x": x_location,
                    "top_left_y": y_location,
                    "bottom_right_x": x_location + bin_size_px,
                    "bottom_right_y": y_location + bin_size_px,
                }
                y_location += bin_size_px
            y_location = 0
            x_location += bin_size_px

        clf_array = np.zeros((len(data_df), vertical_bin_cnt, horizontal_bin_cnt))

        for clf_frame in clf_idx:
            for h_bin_name, v_dict in bin_dict.items():
                for v_bin_name, c in v_dict.items():
                    if (
                        clf_frame[1] <= c["bottom_right_x"]
                        and clf_frame[1] >= c["top_left_x"]
                    ):
                        if (
                            clf_frame[2] <= c["bottom_right_y"]
                            and clf_frame[2] >= c["top_left_y"]
                        ):
                            clf_array[int(clf_frame[0])][v_bin_name][h_bin_name] = 1

        clf_array = self.__calculate_cum_array(clf_array=clf_array, fps=fps)

        return clf_array, aspect_ratio

    def __calculate_max_scale(self, clf_array: np.array):
        return np.round(np.max(np.max(clf_array[-1], axis=0)), 3)

    def run(self):
        """
        Creates heatmap charts. Results are stored in the `project_folder/frames/heatmaps_classifier_locations`
        directory of SimBA project.

        Returns
        ----------
        None
        """

        for file_cnt, file_path in enumerate(self.files_found):
            video_timer = SimbaTimer()
            video_timer.start_timer()
            _, self.video_name, _ = get_fn_ext(file_path)
            self.video_info, self.px_per_mm, self.fps = self.read_video_info(
                video_name=self.video_name
            )
            self.width, self.height = int(
                self.video_info["Resolution_width"].values[0]
            ), int(self.video_info["Resolution_height"].values[0])
            if self.video_setting:
                self.fourcc = cv2.VideoWriter_fourcc(*Formats.MP4_CODEC.value)
                self.video_save_path = os.path.join(
                    self.heatmap_clf_location_dir, self.video_name + ".mp4"
                )
                self.writer = cv2.VideoWriter(
                    self.video_save_path,
                    self.fourcc,
                    self.fps,
                    (self.width, self.height),
                )
            if self.frame_setting:
                self.save_video_folder = os.path.join(
                    self.heatmap_clf_location_dir, self.video_name
                )
                if not os.path.exists(self.save_video_folder):
                    os.makedirs(self.save_video_folder)
            self.data_df = read_df(file_path=file_path, file_type=self.file_type)
            clf_array, aspect_ratio = self.__calculate_bin_attr(
                data_df=self.data_df,
                clf_name=self.clf_name,
                bp_lst=self.bp_lst,
                px_per_mm=self.px_per_mm,
                img_width=self.width,
                img_height=self.height,
                bin_size=self.bin_size,
                fps=self.fps,
            )

            if self.max_scale == "auto":
                self.max_scale = self.__calculate_max_scale(clf_array=clf_array)
                if self.max_scale == 0:
                    self.max_scale = 1

            if self.final_img_setting:
                self.make_clf_heatmap_plot(
                    frm_data=clf_array[-1, :, :],
                    max_scale=self.max_scale,
                    palette=self.palette,
                    aspect_ratio=aspect_ratio,
                    file_name=os.path.join(
                        self.heatmap_clf_location_dir,
                        self.video_name + "_final_frm.png",
                    ),
                    shading=self.shading,
                    clf_name=self.clf_name,
                    img_size=(self.width, self.height),
                    final_img=True,
                )

            if self.video_setting or self.frame_setting:
                for frm_cnt, cumulative_frm_idx in enumerate(range(clf_array.shape[0])):
                    frm_data = clf_array[cumulative_frm_idx, :, :]
                    cum_df = pd.DataFrame(frm_data).reset_index()
                    cum_df = cum_df.melt(
                        id_vars="index",
                        value_vars=None,
                        var_name=None,
                        value_name="seconds",
                        col_level=None,
                    ).rename(
                        columns={"index": "vertical_idx", "variable": "horizontal_idx"}
                    )
                    cum_df["color"] = (
                        (cum_df["seconds"].astype(float) / float(self.max_scale))
                        .round(2)
                        .clip(upper=100)
                    )
                    color_array = np.zeros(
                        (
                            len(cum_df["vertical_idx"].unique()),
                            len(cum_df["horizontal_idx"].unique()),
                        )
                    )
                    for i in range(color_array.shape[0]):
                        for j in range(color_array.shape[1]):
                            value = cum_df["color"][
                                (cum_df["horizontal_idx"] == j)
                                & (cum_df["vertical_idx"] == i)
                            ].values[0]
                            color_array[i, j] = value

                    fig = plt.figure()
                    im_ratio = color_array.shape[0] / color_array.shape[1]
                    plt.pcolormesh(
                        color_array,
                        shading=self.shading,
                        cmap=self.palette,
                        rasterized=True,
                        alpha=1,
                        vmin=0.0,
                        vmax=float(self.max_scale),
                    )
                    plt.gca().invert_yaxis()
                    plt.xticks([])
                    plt.yticks([])
                    plt.axis("off")
                    plt.tick_params(axis="both", which="both", length=0)
                    cb = plt.colorbar(pad=0.0, fraction=0.023 * im_ratio)
                    cb.ax.tick_params(size=0)
                    cb.outline.set_visible(False)
                    cb.set_label(
                        "{} (seconds)".format(self.clf_name), rotation=270, labelpad=10
                    )
                    plt.tight_layout()
                    plt.gca().set_aspect(aspect_ratio)
                    canvas = FigureCanvas(fig)
                    canvas.draw()
                    mat = np.array(canvas.renderer._renderer)
                    image = cv2.cvtColor(mat, cv2.COLOR_RGB2BGR)
                    image = cv2.resize(image, (self.width, self.height))
                    image = np.uint8(image)
                    plt.close()

                    if self.video_setting:
                        self.writer.write(image)
                    if self.frame_setting:
                        frame_save_path = os.path.join(
                            self.save_video_folder, str(frm_cnt) + ".png"
                        )
                        cv2.imwrite(frame_save_path, image)
                    print(
                        "Created heatmap frame: {} / {}. Video: {} ({}/{})".format(
                            str(frm_cnt + 1),
                            str(len(self.data_df)),
                            self.video_name,
                            str(file_cnt + 1),
                            len(self.files_found),
                        )
                    )

                if self.video_setting:
                    self.writer.release()

                video_timer.stop_timer()
                print(
                    "Heatmap plot for video {} saved (elapsed time: {}s) ... ".format(
                        self.video_name, video_timer.elapsed_time_str
                    )
                )

        self.timer.stop_timer()
        stdout_success(
            msg="All heatmap visualizations created in project_folder/frames/output/heatmaps_classifier_locations directory",
            elapsed_time="self.timer.elapsed_time_str",
        )


# test = HeatMapperClfSingleCore(config_path='/Users/simon/Desktop/envs/troubleshooting/Two_animals_16bps/project_folder/project_config.ini',
#                      style_attr = {'palette': 'jet', 'shading': 'gouraud', 'bin_size': 75, 'max_scale': 'auto'},
#                      final_img_setting=False,
#                      video_setting=True,
#                      frame_setting=False,
#                      bodypart='Nose_1',
#                      clf_name='Attack',
#                      files_found=['/Users/simon/Desktop/envs/troubleshooting/Two_animals_16bps/project_folder/csv/machine_results/Together_3.csv'])
# test.run()
