import numpy as np
import utils
import sys

if len( sys.argv ) < 2:
    raise Exception( 'this command must have a filename of an ETA fits as an argument' )
data = utils.load( sys.argv[1] )

"""
find fill points
"""
points = []

points += utils.find_point( data[ : , 0 ] , 0 ) # x_min
points += utils.find_point(
        data[ : , data.shape[1] - 1 ],
        data.shape[1] - 1            ,
) # x_max

index_min = 0
while data.shape[0] - 1 > index_min:
    index_min += 1
    if len( utils.find_point( data[ index_min , : ] , index_min , 'y' ) ) == 3:
        break

points.append(
    utils.find_point( data[ index_min , : ] , index_min , 'y' )[1]
) # y_min

index_max = data.shape[0] - 1
while index_min < index_max:
    index_max -= 1
    if len( utils.find_point( data[ index_max , : ] , index_max , 'y' ) ) == 3:
        break


points.append(
    utils.find_point( data[ index_max , : ] , index_max , 'y' )[1]
) # y_max

small_data = utils.compress( data , 5 )

points = utils.big_to_small( points , 5 )

# size - 1
points[ 1 ][ 1 ] -= 1
points[ 3 ][ 0 ] -= 1

# little shift to be inside the light
points[ 2 ][ 1 ] += 3
points[ 3 ][ 1 ] += 3

"""
fill data
"""

extremum = []
for point in points:
    if point[0] < points[2][0]:
        point[0] = points[2][0]
    if point[1] < points[0][1]:
        point[1] = points[0][1]
    taken_points = utils.small_to_big(
        np.array( [
            points[2][0],
            points[0][1],
        ] ) + utils.fill(
            small_data[
                points[2][0] : points[3][0] + 1,
                points[0][1] : points[1][1] + 1
            ],
            [
                point[0] - points[2][0],
                point[1] - points[0][1],
            ],
            1000
        ),
        5
    )
    extremum.append( [
        np.min( taken_points[ : , 1 ] ),
        np.max( taken_points[ : , 1 ] ),
        np.min( taken_points[ : , 0 ] ),
        np.max( taken_points[ : , 0 ] ),
    ] )

border = {
    'x': {
        'min': points[0][1] + extremum[0][1] + 1,
        'max': points[0][1] + extremum[1][0]    ,
    },
    'y': {
        'min': points[2][0] + extremum[2][3] + 1,
        'max': points[2][0] + extremum[3][2]    ,
    },
}

"""
label deletion
"""

mean_data = np.convolve(
    np.gradient(
        np.mean(
            data[
                border[ 'y' ][ 'min' ] : border[ 'y' ][ 'max' ],
                border[ 'x' ][ 'min' ] : border[ 'x' ][ 'max' ]
            ],
            axis = 0
        )
    ),
    np.ones(
        int( 0.01 * (
            border[ 'x' ][ 'max' ] - border[ 'x' ][ 'min' ]
        ) )
    ),
    'same'
)

mean_data -= np.min( mean_data )
mean_data /= np.max( mean_data )

top  = utils.consecutive( np.where( mean_data > 0.75 )[0] )
down = utils.consecutive( np.where( mean_data < 0.25 )[0] )

size_top  = [ len( list_ ) for list_ in top  ]
size_down = [ len( list_ ) for list_ in down ]

label_x = {
    'min': border[ 'x' ][ 'min' ] + top[ np.argmax( size_top ) ][0]   ,
    'max': border[ 'x' ][ 'min' ] + down[ np.argmax( size_down ) ][-1]
}

if label_x[ 'min' ] < data.shape[1] // 2:
    if label_x[ 'max' ] < data.shape[1] // 2:
        border[ 'x' ][ 'min' ] = label_x[ 'max' ]
    else:
        raise Exception( 'the label seems to be in the middle of the picture' )
elif label_x[ 'max' ] > data.shape[1] // 2:
    border[ 'x' ][ 'max' ] = label_x[ 'min' ]
else:
    raise Exception( 'for an unkown reason, label_x[ \'min\' ] > label_x[ \'max\' ]' )

import matplotlib.pyplot as plt
plt.imshow( data[
    border[ 'y' ][ 'min' ] : border[ 'y' ][ 'max' ],
    border[ 'x' ][ 'min' ] : border[ 'x' ][ 'max' ]
] )
plt.savefig( 'asset/test.png' )