import pandas as pd
import glob
import os
import image_marker as im 
import numpy as np
import tracking_result as tr
from IPython import embed
import matplotlib.pyplot as plt

# wir estellen eine Tabelle, die fuer jeden Tag die Setup Configuration enthält.
# Folgende Spalten:
# Versuchstag, Datum, light_on_area, feeder_positions, feeder_risks, temperature, salinity
x_limits = np.array([0, 1.24])
y_limits = np.array([0, 0.81])

def get_risk(position, light_area_y, x_limits, y_limits, light_risk=1):
    """Calculates the risk associated with a certain position in the arena

    Args:
        position (iterable): two-element vector of position i.e. (x,y)
        light_area_y (iterable): two element vactor with the start and stop y-coordinates of light area
        light_risk (float, optional): if position is on the bright side, a malus is added. Defaults to 1.
        x_limits : extent of the tank on x axis in cm
        y_limits : extent of the tank on y axis in cm 

    Returns:
        float: the risk for this position
    """
    min_wall_dist_x = min(np.abs(position[0] - x_limits))
    min_wall_dist_y = min(np.abs(position[1] - y_limits))

    risk_x = 1/(max(x_limits)/2) * min_wall_dist_x
    risk_y = 1/(max(y_limits)/2) * min_wall_dist_y
    total_risk = min(risk_x, risk_y)

    is_position_on_the_bright_side = position[1] >= light_area_y[0] and position[1] < light_area_y[1]
    if is_position_on_the_bright_side:
        total_risk = total_risk + light_risk
    return total_risk


def get_feeder_risks(feeder_positions, light_on_area):
    feeder_risks = {}
    if light_on_area['light_center'][1] < light_on_area['left'][1]:
        light_area = [0, light_on_area['left'][1]]
    else:
        light_area = [light_on_area['left'][1], y_limits[1]]
    for k in feeder_positions.keys():
        feeder_risks[k] = get_risk(feeder_positions[k], light_area, x_limits, y_limits)
    embed()


def get_feeder_positions(vid):
    feeder_task = im.MarkerTask("Feeder positions", list(map(str, range(1, 9))), "Mark feeder positions")
    tasks = [feeder_task]
    image_marker = im.ImageMarker(tasks)
    feeder_positions = image_marker.mark_movie(vid, 100)
    for k in feeder_positions[0].keys(): 
        pos = feeder_positions[0][k] 
        new_pos = tr.coordinate_transformation(pos) 
        feeder_positions[0][k] = new_pos 
    return feeder_positions[0]


def get_light_on_area(vid):
    light_on_task = im.MarkerTask("Light ON", ["left", "right", "light_center"], "Mark light on area")
    tasks = [light_on_task]
    image_marker = im.ImageMarker(tasks)
    marker_positions = image_marker.mark_movie(vid, 100)
    for k in marker_positions[0].keys(): 
        pos = marker_positions[0][k] 
        new_pos = tr.coordinate_transformation(pos) 
        marker_positions[0][k] = new_pos
    print(marker_positions) 
    return marker_positions[0]
    

def check_day(path):
    vids = sorted(glob.glob(os.path.join(path, '*.mp4')))
    if len(vids) < 1:
        return
    vid = vids[0]
    la = get_light_on_area(vid)
    fp = get_feeder_positions(vid)
    fr = get_feeder_risks(fp, la)
    
    pass


if __name__ == '__main__':
    folder = '/mnt/movies/merle_verena/boldness/labeled_videos'
    days = sorted(glob.glob(os.path.join(folder, 'day*')))
    x_range = np.arange(0.0, 1.24, .01)
    y_range = np.arange(0.0, .82, .01)
    risk_matrix = np.zeros((len(x_range), len(y_range)))
    
    """
    light_on = [0.5687952439426905, 0.82]
    #light_on = [0, 0.5687952439426905]
    # light_on = [45.5, 81]

    for i, x in enumerate(x_range):
        for j, y in enumerate(y_range):
            risk_matrix[i, j] = get_risk([x, y], light_on, x_limits=x_limits, y_limits=y_limits )
    plt.imshow(risk_matrix.T, origin='lower')
    plt.show()
    exit()
    """
    for d in days:
        check_day(d)
        print(d)
        exit()