Aoc 2022: ojuaf

ojuaf

13800528?v=4

ojuaf
: ojuaf

About me

Nothing here yet. Update your profile at /profiles/ojuaf.adoc

Day 01: python

this documentation is autogenerated. Add a README.adoc to your solution to take over the control of this :-)

python

sol.py

def load_input():
    data = list()
    with open('input') as fd:
        data = list()
        temp = list()
        for line in fd:
            line = line.strip()
            if line:
                temp.append(int(line))
            else:
                data.append(temp)
                temp = list()
    return data


def main():
    data = load_input()

    # Task 1
    calories = list()
    for values in data:
        calories.append(sum(values))
    result = max(calories)
    print("Result 1: ", result)

    # Task 2
    calories.sort()
    result = sum(calories[-3:])
    print("Result 2: ", result)

    return 0


if __name__ == '__main__':
    main()

input

Day 02: python

this documentation is autogenerated. Add a README.adoc to your solution to take over the control of this :-)

python

sol.py

SHAPE2POINTS = {
    'X': 1,
    'Y': 2,
    'Z': 3,
    'A': 1,
    'B': 2,
    'C': 3
}


def load_input():
    data = list()
    with open('input') as fd:
        data = list()
        for line in fd:
            line = line.strip()
            data.append(list(map(lambda x: SHAPE2POINTS[x], line.split())))
    return data


def get_result_task1(rd):
    result = 0
    if rd[0] == rd[1]:
        result = 3
    elif (rd[0] == 1 and rd[1] == 3) or (rd[0] == 2 and rd[1] == 1) or \
         (rd[0] == 3 and rd[1] == 2):
        result = 0
    else:
        result = 6
    result += rd[1]
    return result


def get_result_task2(rd):
    if rd[1] == 2:
        return rd[0] + 3
    elif rd[1] == 1:
        shape = rd[0] - 1 if rd[0] - 1 != 0 else 3
        return shape
    else:
        shape = rd[0] + 1 if rd[0] + 1 != 4 else 1
        return shape + 6


def main():
    data = load_input()

    # Task 1
    score = 0
    for rd in data:
        score += get_result_task1(rd)

    result = score
    print("Result 1: ", result)

    # Task 2
    score = 0
    for rd in data:
        score += get_result_task2(rd)

    result = score
    print("Result 2: ", result)


if __name__ == '__main__':
    main()

input

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Day 03: python

this documentation is autogenerated. Add a README.adoc to your solution to take over the control of this :-)

python

sol.py

import string


def load_input():
    data = list()
    with open('input') as fd:
        data = list()
        for line in fd:
            line = line.strip()
            data.append(line)
    return data


def get_priority(char):
    return string.ascii_letters.index(char) + 1


def main():
    data = load_input()

    # Task 1
    result = 0
    for rucksack in data:
        length = int(len(rucksack)/2)
        common = set(rucksack[0:length]).intersection(set(rucksack[length:]))
        result += sum(list(map(lambda x: get_priority(x), common)))

    print("Result 1: ", result)

    # Task 2
    chars = set()
    result = 0
    for i, rucksack in enumerate(data):
        if i % 3 == 0:
            chars = set(rucksack)
        else:
            chars.intersection_update(rucksack)

        if i % 3 == 2:
            result += sum(list(map(lambda x: get_priority(x), chars)))

    print("Result 2: ", result)


if __name__ == '__main__':
    main()

input

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SHSCbdbddcVWqqFPCLqR

Day 04: python

this documentation is autogenerated. Add a README.adoc to your solution to take over the control of this :-)

python

sol.py

#!/usr/bin/env python3

def load_input():
    data = list()
    with open('input') as fd:
        data = list()
        for line in fd:
            temp = list()
            for values in line.strip().split(','):
                temp.append(list(map(lambda x: int(x), values.split('-'))))
            data.append(temp)
    return data


def main():
    data = load_input()

    # Task 1
    fully_contains = 0
    for a, b in data:
        if a[0] <= b[0] and a[1] >= b[1]:
            fully_contains += 1
        elif a[0] >= b[0] and a[1] <= b[1]:
            fully_contains += 1
        else:
            pass
    result = fully_contains
    print("Result 1: ", result)

    # Task 2
    non_overlaps = 0
    for a, b in data:
        if a[1] < b[0] or a[0] > b[1]:
            non_overlaps += 1

    result = len(data) - non_overlaps
    print("Result 2: ", result)


if __name__ == '__main__':
    main()

input

57-93,9-57
55-55,55-83
55-88,78-88
24-24,24-95
7-92,8-93
25-84,84-85
62-85,62-85
66-78,65-76
28-32,31-33
24-81,2-25
45-80,79-80
75-99,75-91
24-49,23-24
22-69,49-69
15-77,76-78
25-50,21-50
29-77,29-64
47-93,46-48
37-42,70-97
5-95,4-6
37-90,74-95
17-48,18-85
2-97,1-89
2-48,1-48
52-70,42-71
17-90,90-94
76-77,2-76
10-89,10-96
40-52,39-53
9-97,8-96
6-74,5-6
3-89,2-89
35-63,62-79
13-39,18-39
45-81,4-46
8-76,39-67
3-76,1-4
1-96,1-1
11-59,58-60
41-53,25-42
5-54,11-93
15-91,95-95
62-62,2-63
11-41,40-42
52-98,51-53
29-52,47-51
15-86,16-86
13-98,28-99
8-13,14-54
47-61,64-95
27-95,10-76
9-27,10-10
23-89,24-24
98-98,15-99
8-92,92-96
2-38,27-87
22-64,20-63
1-6,5-66
23-49,9-48
63-64,35-63
6-7,7-81
23-91,22-92
32-97,33-69
21-24,20-25
35-42,34-43
82-82,15-83
23-34,20-34
37-92,37-86
35-94,30-36
48-52,51-53
7-74,3-73
41-43,42-86
2-5,5-91
98-99,8-98
68-86,67-67
37-54,36-37
3-95,2-96
50-79,25-78
12-71,11-72
91-92,74-91
10-43,9-44
30-93,2-91
20-44,20-54
36-84,35-85
75-76,75-75
54-54,18-55
36-91,14-39
8-25,8-25
16-97,4-96
7-83,2-8
4-4,4-71
1-98,1-98
87-87,33-88
22-88,21-88
8-94,9-89
99-99,5-96
63-92,63-92
42-68,10-69
18-96,19-19
16-90,16-90
9-93,8-94
48-88,87-89
3-68,67-98
72-93,97-98
11-99,11-99
3-99,2-99
83-87,79-86
14-14,5-14
1-2,1-92
9-90,91-94
53-85,84-84
11-89,3-11
13-33,32-86
40-74,74-91
68-71,69-86
17-51,18-51
47-90,47-90
2-3,3-85
7-97,8-75
21-29,28-75
87-93,53-93
49-74,27-95
56-65,55-97
14-16,15-81
7-7,8-71
24-26,23-82
34-56,50-55
10-97,11-96
52-70,16-69
20-56,20-21
27-96,96-97
23-98,15-24
24-72,86-98
44-77,60-88
6-98,5-99
1-96,99-99
8-95,99-99
10-81,4-62
88-88,69-89
30-67,29-68
14-96,15-96
58-86,73-86
7-55,14-54
3-97,62-98
11-86,9-87
41-79,16-41
2-99,1-96
7-56,5-7
25-61,24-62
25-83,26-82
20-82,7-12
36-58,41-58
98-98,16-89
98-99,86-99
18-50,18-93
44-67,40-48
46-90,47-91
21-49,48-66
2-90,89-94
12-25,13-25
5-9,6-44
68-75,70-76
25-99,26-99
95-95,17-96
13-14,13-90
79-99,76-77
87-88,88-89
77-77,6-78
36-92,35-35
10-11,9-9
9-37,8-38
80-91,81-81
17-97,17-99
4-19,18-71
35-92,8-91
30-31,22-30
19-19,18-47
51-79,52-55
4-8,7-77
3-98,3-99
68-76,67-68
86-86,74-93
20-86,35-86
8-98,7-98
56-58,14-63
41-47,42-47
16-89,41-88
21-22,22-52
2-62,3-33
3-83,3-3
87-94,25-86
80-95,95-98
7-57,8-58
3-24,23-23
5-43,2-4
32-85,86-86
39-82,38-83
73-87,21-79
86-94,39-87
87-88,4-87
41-65,48-60
40-70,62-70
3-44,2-43
3-19,19-98
65-98,66-98
79-84,80-83
93-97,91-98
21-96,21-96
30-77,31-83
5-82,2-5
10-82,9-10
84-94,47-50
26-93,27-39
47-95,77-90
30-33,33-81
45-73,46-72
12-59,12-59
31-60,31-59
23-76,39-75
66-88,80-93
58-76,2-75
9-56,36-55
2-94,2-94
75-81,72-80
9-46,10-41
7-93,6-6
99-99,43-97
33-83,33-83
20-85,84-84
61-76,60-94
16-75,17-75
16-18,17-71
6-88,7-35
38-79,6-38
9-42,8-10
18-60,86-95
3-3,2-98
2-99,3-99
78-82,79-82
34-75,23-76
3-12,3-4
16-95,20-95
81-85,66-75
8-87,15-61
37-58,37-37
2-87,1-86
49-89,48-84
56-58,57-88
77-79,3-78
25-92,25-98
14-73,73-74
15-17,1-16
41-84,1-84
34-64,6-63
86-88,28-87
2-87,2-87
68-76,34-69
42-78,54-83
7-92,2-24
2-12,1-11
72-83,64-82
49-93,48-92
31-32,32-59
4-90,4-86
2-65,71-83
21-88,14-78
5-92,93-94
1-10,2-9
40-66,39-40
47-77,46-46
2-99,25-99
37-95,37-95
60-64,60-64
32-51,33-51
22-90,11-88
13-54,12-54
67-77,16-72
7-37,9-90
2-54,53-53
5-5,6-68
8-9,9-31
4-39,40-61
5-95,6-95
1-11,11-95
30-70,30-70
58-58,59-97
11-41,12-44
2-6,1-7
10-58,10-64
3-95,1-3
23-55,8-23
37-60,52-59
6-46,6-47
8-45,27-53
12-95,94-94
43-52,43-44
9-95,10-94
2-9,5-26
5-8,9-97
2-60,3-60
87-89,45-88
5-9,9-90
47-91,45-47
39-47,27-39
16-72,16-34
89-90,35-89
18-93,8-17
4-35,9-71
64-96,27-62
30-68,7-30
25-70,3-26
41-77,40-77
87-97,24-97
2-97,97-98
41-50,49-67
49-53,50-80
18-18,19-87
36-39,37-40
4-83,27-93
28-92,29-91
33-93,93-94
2-94,2-94
26-65,25-26
5-14,15-27
19-49,19-49
56-56,2-55
79-79,49-80
79-80,55-80
61-82,17-62
61-63,1-62
20-92,32-73
57-57,5-58
19-37,20-20
13-75,75-76
61-99,62-99
19-92,19-91
34-68,35-35
4-76,91-99
30-71,71-72
51-95,28-51
64-70,51-75
28-90,89-91
70-85,42-83
2-91,5-80
45-53,17-52
70-80,20-69
29-88,30-89
5-5,5-89
30-30,30-77
20-71,21-40
11-44,18-43
6-90,6-90
1-98,97-99
3-98,3-98
9-42,24-42
75-75,73-76
20-87,86-88
62-86,63-91
15-61,14-16
46-58,45-58
2-53,47-54
3-89,3-35
3-99,1-78
50-80,9-81
96-98,69-97
6-78,6-78
19-77,18-77
17-75,18-75
54-73,53-73
92-97,73-85
5-76,77-77
43-93,18-94
7-8,8-92
49-87,50-88
34-56,35-57
25-98,24-26
11-93,12-94
68-99,67-67
5-8,9-55
8-83,4-43
52-84,51-53
4-96,3-71
6-94,6-99
19-67,18-68
1-91,2-96
24-35,36-54
13-96,13-99
19-47,32-36
10-62,6-61
3-4,4-98
42-43,43-76
17-58,54-58
57-77,12-77
78-99,25-77
3-91,1-90
13-28,29-83
60-88,23-59
78-95,79-95
16-97,16-17
14-71,12-70
1-99,2-99
11-84,5-11
55-80,55-80
28-67,68-68
60-81,60-81
7-74,7-74
3-68,2-4
5-70,6-70
4-92,30-93
44-79,43-99
31-70,4-98
2-93,1-93
6-95,5-5
16-36,5-36
13-14,13-31
53-84,54-73
41-75,74-91
31-90,48-91
25-93,19-93
9-95,6-9
27-89,25-28
19-86,18-73
11-62,10-11
73-75,23-75
19-90,18-96
5-5,4-51
16-98,42-52
11-81,12-82
65-95,66-95
61-61,61-82
68-74,73-78
31-42,1-32
7-97,7-96
58-59,42-58
99-99,13-92
63-94,20-95
17-38,8-18
60-71,15-52
75-90,9-76
14-99,13-98
9-16,16-81
54-83,54-90
18-18,18-53
16-64,15-16
19-60,18-19
2-90,1-91
79-79,79-98
32-58,31-33
60-65,59-65
13-34,13-34
6-34,33-33
59-64,58-84
13-13,13-71
4-76,3-82
77-86,60-75
63-96,52-95
4-68,3-5
21-45,5-21
11-81,10-12
1-97,96-96
25-78,26-79
17-38,20-48
61-62,62-90
16-99,16-99
12-66,42-65
3-85,2-84
6-47,7-46
3-76,4-85
10-44,11-72
12-47,47-48
47-84,48-84
43-99,24-77
21-44,21-25
75-85,44-84
86-97,32-86
24-92,23-88
86-88,49-87
96-96,2-79
90-95,17-95
11-88,10-89
3-59,1-4
10-56,11-83
46-94,47-94
3-98,98-99
34-86,34-94
9-17,11-16
89-93,21-90
15-98,15-98
32-84,33-85
46-46,45-80
5-69,70-93
8-99,9-98
4-99,5-95
1-35,2-67
1-99,2-98
11-22,14-22
32-95,33-94
48-77,72-77
28-30,22-29
35-86,36-86
56-60,55-60
44-75,44-59
85-91,8-86
21-22,21-59
96-97,8-60
7-50,51-85
32-98,33-33
11-12,11-81
6-7,6-39
32-68,33-69
19-46,18-50
45-96,45-49
1-13,4-94
86-87,14-87
15-15,16-93
29-48,28-28
89-97,90-98
6-59,7-60
13-13,13-64
21-64,81-95
17-34,18-33
3-87,86-90
4-84,2-38
40-76,39-76
17-84,18-85
66-96,25-65
16-97,16-96
64-65,4-66
53-66,10-86
15-78,16-77
75-76,54-75
4-98,20-97
22-75,23-62
42-89,42-89
70-71,69-70
18-90,19-89
40-68,67-69
7-73,6-7
86-95,96-97
40-58,39-72
1-43,5-28
10-60,5-49
30-81,29-91
7-77,8-11
8-65,9-65
3-57,4-11
1-2,2-96
56-58,41-57
34-89,83-90
59-97,58-98
29-30,28-30
54-82,31-57
4-99,98-98
14-36,34-39
88-97,3-98
16-53,17-99
39-39,39-72
39-61,62-80
20-32,31-43
49-70,50-70
65-75,34-66
1-99,1-2
57-90,56-94
28-62,33-62
21-21,22-96
18-65,17-88
93-97,60-94
74-93,92-94
9-18,17-77
46-89,30-45
17-97,16-98
2-99,98-98
11-21,12-20
99-99,4-96
36-48,35-44
70-74,63-81
40-41,40-92
2-94,22-93
67-73,60-78
29-30,22-29
65-99,65-99
7-20,6-21
29-99,30-99
40-98,86-99
20-37,21-53
86-86,7-87
25-39,30-93
55-78,56-88
5-15,6-7
16-17,2-16
71-72,72-83
9-21,8-21
5-44,3-26
77-77,12-84
15-16,16-53
44-69,43-69
6-84,5-5
34-77,33-77
15-78,15-97
28-86,15-29
47-96,46-46
57-59,21-58
2-91,4-92
11-14,13-75
45-46,45-99
26-57,24-26
4-95,67-98
15-54,15-15
41-44,19-42
19-20,19-98
16-86,15-86
13-47,46-82
13-50,12-14
11-98,6-89
44-89,45-99
7-82,82-83
24-61,24-24
23-60,23-59
10-86,9-10
74-86,73-74
1-41,5-82
3-4,4-84
94-98,19-83
2-97,93-96
89-91,45-90
7-12,11-94
52-60,46-51
3-3,2-85
35-43,42-66
29-92,28-92
20-21,21-96
91-91,90-91
8-34,8-21
8-95,95-96
4-81,44-82
2-84,1-2
11-88,11-11
17-69,16-70
12-76,11-12
13-64,66-75
38-68,69-85
86-86,85-91
10-87,11-97
10-78,36-77
8-68,7-68
4-87,3-87
62-64,12-65
32-79,33-79
2-90,2-3
3-8,8-26
63-64,63-70
25-30,7-30
3-92,3-92
4-98,3-99
2-3,2-43
43-65,43-85
17-62,91-94
88-90,48-89
13-96,1-96
44-71,44-93
66-71,70-72
28-63,27-62
19-19,20-99
23-23,24-93
27-84,28-85
77-82,21-78
40-40,24-41
88-89,6-88
1-97,96-98
1-88,2-42
12-39,12-13
47-62,61-65
3-97,7-96
30-66,29-67
71-90,72-78
98-98,6-97
43-43,43-48
36-99,35-95
30-93,30-99
5-28,6-11
3-22,2-96
9-87,8-71
41-79,27-69
6-99,7-98
99-99,45-76
97-99,14-96
6-39,7-92
5-49,13-64
6-41,4-41
93-98,16-94
8-98,25-97
41-64,41-42
5-87,5-6
12-68,13-67
24-75,24-75
41-42,5-41
16-92,13-49
16-31,27-96
27-79,78-78
32-71,33-72
23-24,24-96
4-4,4-78
85-91,40-91
6-10,9-75
38-69,39-69
8-8,8-74
7-40,1-7
33-88,34-88
32-92,33-92
7-19,6-64
21-22,21-22
7-8,7-99
4-48,5-47
27-27,26-61
77-85,76-86
22-39,29-40
4-42,5-63
85-87,43-79
8-88,7-9
55-90,54-90
7-98,6-8
6-85,7-84
18-56,5-19
54-81,42-50
7-27,28-40
96-98,81-97
52-74,52-53
61-64,20-63
45-92,45-60
18-89,19-89
9-51,8-52
19-19,20-95
49-99,49-99
17-78,16-16
20-95,20-21
11-94,96-99
38-57,56-58
94-97,18-90
38-42,10-53
67-86,68-86
2-96,2-91
33-77,33-89
25-26,25-51
86-98,16-97
2-71,2-71
28-29,6-28
30-83,31-84
13-85,13-84
23-61,22-48
32-74,74-92
41-53,10-94
15-89,20-90
25-69,25-25
1-96,1-96
7-96,6-96
1-98,2-2
47-47,11-48
35-94,93-95
67-69,68-77
43-63,44-64
14-94,15-49
7-95,6-96
15-40,41-41
8-83,82-90
66-75,11-67
40-52,45-51
20-57,7-20
4-87,3-87
97-97,56-95
45-45,45-45
58-96,9-95
33-45,44-44
6-26,16-26
46-59,47-60
24-40,23-40
6-91,94-98
40-93,20-84
32-42,41-42
2-94,3-93
59-59,2-60
7-40,3-7
47-89,88-88
35-66,65-67
84-99,52-85
75-99,75-96
8-21,7-8
3-53,4-52
22-62,22-62
72-72,73-78
39-72,38-96
2-14,1-91
4-98,5-76
8-88,7-13
5-6,5-99
20-90,19-90
26-29,27-34
52-69,53-95
56-56,50-57
23-79,22-79
16-93,16-78
13-76,55-92
36-70,35-70
43-81,40-80
4-94,95-99
1-94,1-94
48-84,47-49
32-93,31-92
11-19,18-77
62-80,39-74
62-63,62-96
3-94,93-95
15-66,16-66
23-95,22-88
57-57,25-58
71-86,3-72
2-79,7-78
47-49,13-48
46-94,45-45
19-77,65-76
31-37,38-87
4-79,92-99
17-19,17-19
1-82,41-89
47-91,48-49
6-19,10-37
14-42,18-39
40-79,20-61
2-84,1-31
83-95,47-84
68-71,2-69
37-73,8-72
85-86,36-85
2-9,8-78
17-59,58-68
62-90,78-90
77-94,41-93
11-90,12-94
34-79,33-97
42-56,22-57
38-44,44-45
1-84,98-98
63-74,61-73
28-57,4-58
48-48,47-90
37-38,38-77
72-84,72-72
3-47,47-47
50-91,25-59
5-38,4-38
24-86,23-55
55-74,50-75
33-83,34-83
1-91,73-88
13-13,14-85
54-94,60-99
21-85,84-86
12-98,8-36
89-98,4-90
4-15,8-15
98-98,1-99
21-79,20-78
2-63,62-63
78-80,63-79
24-68,67-86
24-25,25-47
53-68,4-55
11-82,10-82
23-92,5-99
94-99,67-88
11-51,32-86
49-79,24-48
22-73,91-99
4-9,8-89
23-85,23-54
5-86,6-13
13-14,14-94
72-81,73-82
56-98,55-98
3-96,2-96
88-88,20-89
78-79,79-85
29-94,20-30
8-43,85-90
96-98,7-71
45-74,70-71
23-79,94-94
41-97,40-97
73-96,72-72
4-28,4-65
89-98,20-90
37-70,37-98
26-36,26-35
41-41,7-42
7-22,21-90
12-84,13-75
98-98,1-99
47-75,74-87
7-33,33-90
41-68,41-87
64-92,65-88
41-54,10-42
6-89,93-96
20-48,1-21
8-91,8-8
11-95,10-97
72-98,31-90
44-45,33-44
54-56,18-55
7-9,10-88
44-45,45-90
52-56,54-83
68-70,1-69
91-92,91-92
13-74,14-75
4-60,61-91
9-94,10-95
9-95,9-10
50-61,49-68
50-50,5-51
38-46,43-43
4-11,12-22
42-42,43-96
77-91,48-76
23-89,24-94
9-65,10-15
53-81,7-73
8-28,22-37
76-95,31-77
9-84,3-84
12-98,13-99
17-74,17-78
60-89,59-89
1-15,2-14
14-91,13-88
87-97,42-96
77-77,73-76
16-77,15-69
80-95,81-96
40-73,41-90
47-63,46-62
5-98,4-97
46-93,42-92
15-30,7-23
7-90,91-95
1-99,1-2
5-97,4-99
81-87,8-28
56-69,24-68
87-89,20-88
78-99,1-98
32-97,33-97
19-20,20-33
77-78,21-78
2-6,5-42
35-36,35-44
2-83,82-84
48-97,49-58
36-53,53-75
5-92,4-93
49-85,50-86
45-81,43-82
3-97,1-43
97-98,58-97
6-88,3-88
4-67,5-66
43-88,32-44
95-95,13-96
4-54,13-97
1-45,3-45
2-9,8-98
1-12,1-53
13-98,14-84
85-99,86-99
4-16,3-16
15-77,14-15
18-67,67-68
34-69,33-70
97-97,9-92
76-84,58-77
76-84,42-55
96-98,2-97
50-98,51-72
54-94,53-94
23-91,23-91
21-94,22-95
41-89,42-89
48-78,47-78
18-51,51-76
5-49,1-34
40-55,40-47
41-96,41-97
8-10,9-94
74-79,66-80
6-15,15-91
10-99,9-99
12-92,12-96
32-96,20-33
5-67,1-66
21-90,31-94
22-23,22-60

Day 05: python

this documentation is autogenerated. Add a README.adoc to your solution to take over the control of this :-)

python

sol.py

#!/usr/bin/env python3

import re
from collections import deque


def load_input():
    number_list = 9

    data = list()
    commands = list()

    pattern = re.compile('move (\d+) from (\d+) to (\d+)')

    with open('input') as fd:
        data = [deque() for _ in range(number_list)]
        section = 0
        for line in fd:
            if section == 0:
                if line[1] == '1':
                    section = 1
                    continue
                for i in range(number_list):
                    pos = 4*i+1
                    if pos < len(line):
                        char = line[pos]
                        if char != ' ':
                            data[i].appendleft(char)
                    else:
                        break
            elif section == 1:
                section = 2
            else:
                line = line.strip()
                match = pattern.search(line)
                commands.append([int(match.group(1)), int(match.group(2))-1, int(match.group(3))-1])

    return data, commands


def main():
    data, commands = load_input()

    # Task 1
    for command in commands:
        for _ in range(command[0]):
            temp = data[command[1]].pop()
            data[command[2]].append(temp)

    result = "".join([char[-1] for char in data])
    print("Result 1: ", result)

    # Task 2
    data, commands = load_input()
    for command in commands:
        temp = deque()
        for _ in range(command[0]):
            temp.appendleft(data[command[1]].pop())
        data[command[2]].extend(temp)

    result = "".join([char[-1] for char in data])
    print("Result 2: ", result)


if __name__ == '__main__':
    main()

input

        [F] [Q]         [Q]
[B]     [Q] [V] [D]     [S]
[S] [P] [T] [R] [M]     [D]
[J] [V] [W] [M] [F]     [J]     [J]
[Z] [G] [S] [W] [N] [D] [R]     [T]
[V] [M] [B] [G] [S] [C] [T] [V] [S]
[D] [S] [L] [J] [L] [G] [G] [F] [R]
[G] [Z] [C] [H] [C] [R] [H] [P] [D]
 1   2   3   4   5   6   7   8   9

move 3 from 5 to 2
move 3 from 8 to 4
move 7 from 7 to 3
move 14 from 3 to 9
move 8 from 4 to 1
move 1 from 7 to 5
move 2 from 6 to 4
move 4 from 5 to 7
move 1 from 3 to 6
move 3 from 4 to 3
move 1 from 4 to 1
move 5 from 1 to 9
move 1 from 4 to 6
move 4 from 7 to 4
move 15 from 9 to 2
move 7 from 1 to 6
move 3 from 3 to 5
move 1 from 4 to 9
move 2 from 5 to 3
move 2 from 4 to 9
move 4 from 1 to 6
move 1 from 3 to 1
move 1 from 3 to 2
move 4 from 6 to 3
move 24 from 2 to 8
move 4 from 9 to 8
move 1 from 1 to 3
move 2 from 5 to 4
move 1 from 2 to 4
move 19 from 8 to 1
move 5 from 3 to 9
move 8 from 1 to 3
move 3 from 4 to 1
move 6 from 9 to 5
move 2 from 3 to 4
move 1 from 8 to 5
move 2 from 4 to 6
move 11 from 6 to 1
move 8 from 8 to 7
move 1 from 6 to 5
move 13 from 1 to 3
move 1 from 1 to 7
move 2 from 7 to 8
move 5 from 7 to 1
move 2 from 8 to 4
move 3 from 5 to 3
move 11 from 3 to 1
move 2 from 5 to 3
move 2 from 5 to 3
move 2 from 7 to 1
move 7 from 3 to 1
move 1 from 4 to 5
move 1 from 6 to 4
move 3 from 4 to 7
move 3 from 7 to 1
move 6 from 3 to 5
move 1 from 5 to 9
move 4 from 5 to 4
move 2 from 3 to 4
move 8 from 9 to 2
move 5 from 4 to 6
move 1 from 6 to 5
move 1 from 4 to 9
move 39 from 1 to 7
move 7 from 2 to 6
move 1 from 9 to 3
move 1 from 2 to 7
move 1 from 3 to 1
move 5 from 7 to 3
move 4 from 5 to 1
move 19 from 7 to 9
move 1 from 9 to 8
move 1 from 9 to 7
move 5 from 9 to 3
move 6 from 6 to 7
move 1 from 8 to 3
move 4 from 1 to 4
move 23 from 7 to 6
move 1 from 1 to 6
move 21 from 6 to 2
move 3 from 4 to 8
move 7 from 6 to 1
move 1 from 4 to 9
move 1 from 6 to 7
move 6 from 1 to 2
move 1 from 7 to 4
move 15 from 2 to 8
move 5 from 3 to 8
move 22 from 8 to 7
move 1 from 8 to 1
move 5 from 3 to 4
move 1 from 3 to 2
move 1 from 1 to 2
move 3 from 4 to 8
move 3 from 8 to 9
move 11 from 2 to 1
move 2 from 1 to 4
move 15 from 9 to 5
move 22 from 7 to 3
move 2 from 4 to 9
move 3 from 4 to 2
move 8 from 1 to 8
move 6 from 8 to 6
move 1 from 6 to 2
move 3 from 6 to 9
move 3 from 2 to 7
move 4 from 2 to 9
move 2 from 7 to 5
move 1 from 1 to 7
move 2 from 8 to 2
move 2 from 7 to 5
move 9 from 5 to 3
move 8 from 5 to 2
move 1 from 6 to 4
move 1 from 6 to 9
move 1 from 2 to 9
move 2 from 5 to 1
move 7 from 2 to 3
move 1 from 4 to 3
move 1 from 2 to 4
move 5 from 3 to 4
move 6 from 9 to 3
move 1 from 2 to 6
move 6 from 9 to 6
move 2 from 1 to 8
move 3 from 6 to 3
move 2 from 8 to 6
move 6 from 4 to 1
move 14 from 3 to 9
move 1 from 6 to 4
move 3 from 3 to 9
move 1 from 4 to 5
move 10 from 9 to 6
move 6 from 6 to 7
move 2 from 1 to 8
move 1 from 8 to 6
move 16 from 3 to 2
move 1 from 8 to 1
move 1 from 7 to 1
move 7 from 3 to 4
move 1 from 6 to 5
move 4 from 2 to 3
move 5 from 4 to 9
move 2 from 4 to 5
move 4 from 7 to 4
move 5 from 9 to 6
move 2 from 5 to 4
move 11 from 6 to 7
move 1 from 6 to 8
move 5 from 1 to 5
move 2 from 6 to 4
move 7 from 7 to 3
move 1 from 8 to 6
move 2 from 7 to 3
move 1 from 1 to 3
move 3 from 2 to 8
move 9 from 2 to 5
move 1 from 6 to 1
move 1 from 4 to 8
move 7 from 4 to 7
move 8 from 5 to 6
move 1 from 7 to 2
move 1 from 7 to 4
move 3 from 7 to 8
move 1 from 2 to 3
move 1 from 1 to 2
move 1 from 1 to 7
move 3 from 7 to 6
move 11 from 6 to 2
move 4 from 8 to 7
move 2 from 8 to 7
move 15 from 3 to 2
move 7 from 9 to 4
move 3 from 3 to 2
move 4 from 4 to 7
move 5 from 7 to 3
move 3 from 4 to 6
move 3 from 6 to 9
move 1 from 4 to 2
move 1 from 8 to 1
move 2 from 3 to 7
move 2 from 3 to 7
move 23 from 2 to 5
move 1 from 9 to 1
move 1 from 7 to 9
move 1 from 1 to 8
move 8 from 7 to 1
move 1 from 8 to 4
move 1 from 4 to 2
move 3 from 9 to 8
move 1 from 7 to 9
move 22 from 5 to 9
move 1 from 8 to 5
move 1 from 7 to 4
move 1 from 4 to 5
move 1 from 8 to 3
move 2 from 9 to 3
move 5 from 5 to 2
move 5 from 5 to 4
move 3 from 2 to 7
move 1 from 7 to 3
move 6 from 1 to 7
move 4 from 3 to 1
move 6 from 2 to 8
move 1 from 5 to 6
move 2 from 8 to 1
move 12 from 9 to 4
move 8 from 9 to 4
move 1 from 2 to 9
move 2 from 9 to 8
move 3 from 2 to 8
move 5 from 8 to 6
move 7 from 7 to 1
move 4 from 8 to 9
move 1 from 6 to 1
move 17 from 4 to 7
move 1 from 2 to 4
move 2 from 4 to 1
move 6 from 4 to 6
move 1 from 1 to 4
move 7 from 1 to 5
move 9 from 7 to 9
move 8 from 9 to 8
move 5 from 8 to 3
move 1 from 5 to 6
move 2 from 3 to 6
move 1 from 9 to 1
move 1 from 6 to 1
move 10 from 6 to 1
move 1 from 5 to 1
move 2 from 9 to 1
move 1 from 9 to 7
move 2 from 6 to 8
move 2 from 8 to 2
move 1 from 6 to 8
move 22 from 1 to 9
move 9 from 7 to 5
move 1 from 8 to 1
move 2 from 8 to 3
move 4 from 5 to 9
move 1 from 8 to 3
move 5 from 1 to 9
move 2 from 7 to 3
move 2 from 4 to 7
move 1 from 8 to 5
move 2 from 2 to 4
move 1 from 5 to 8
move 9 from 5 to 8
move 2 from 7 to 5
move 2 from 4 to 5
move 3 from 8 to 4
move 3 from 4 to 3
move 2 from 8 to 6
move 1 from 6 to 4
move 3 from 5 to 9
move 1 from 6 to 3
move 12 from 3 to 5
move 1 from 3 to 1
move 7 from 5 to 4
move 1 from 1 to 3
move 1 from 8 to 1
move 7 from 5 to 1
move 6 from 9 to 6
move 29 from 9 to 5
move 2 from 4 to 6
move 26 from 5 to 2
move 24 from 2 to 7
move 1 from 3 to 2
move 8 from 1 to 7
move 7 from 6 to 9
move 2 from 5 to 3
move 1 from 6 to 4
move 3 from 8 to 5
move 2 from 3 to 8
move 2 from 2 to 8
move 5 from 9 to 2
move 27 from 7 to 2
move 2 from 8 to 3
move 2 from 9 to 5
move 3 from 8 to 5
move 2 from 7 to 4
move 3 from 4 to 7
move 2 from 3 to 2
move 4 from 5 to 1
move 5 from 7 to 2
move 29 from 2 to 8
move 9 from 8 to 3
move 2 from 4 to 8
move 7 from 3 to 2
move 3 from 5 to 4
move 1 from 7 to 5
move 3 from 5 to 6
move 2 from 1 to 8
move 2 from 6 to 8
move 3 from 4 to 2
move 4 from 4 to 2
move 1 from 6 to 8
move 8 from 2 to 4
move 2 from 3 to 5
move 1 from 4 to 1
move 3 from 1 to 2
move 4 from 8 to 2
move 3 from 4 to 9
move 3 from 4 to 1
move 2 from 9 to 5
move 1 from 4 to 6
move 4 from 5 to 1
move 1 from 6 to 8
move 1 from 9 to 3
move 4 from 2 to 3
move 15 from 8 to 2
move 9 from 8 to 1
move 1 from 3 to 9
move 5 from 1 to 9
move 3 from 9 to 7
move 2 from 7 to 6
move 3 from 3 to 2
move 1 from 7 to 8
move 1 from 9 to 6
move 1 from 9 to 8
move 2 from 8 to 2
move 1 from 1 to 2
move 1 from 3 to 7
move 4 from 1 to 7
move 19 from 2 to 5
move 1 from 1 to 4
move 1 from 7 to 4
move 1 from 1 to 5
move 3 from 1 to 4
move 1 from 1 to 8
move 6 from 2 to 4
move 7 from 2 to 1
move 2 from 7 to 9
move 8 from 2 to 8
move 2 from 7 to 3
move 1 from 6 to 4
move 10 from 4 to 6
move 5 from 6 to 7
move 2 from 9 to 8
move 6 from 8 to 9
move 1 from 2 to 3
move 2 from 8 to 3
move 5 from 1 to 8
move 8 from 5 to 2
move 8 from 8 to 7
move 7 from 2 to 8
move 1 from 1 to 2
move 1 from 9 to 7
move 1 from 4 to 2
move 2 from 2 to 6
move 5 from 9 to 3
move 2 from 8 to 6
move 2 from 3 to 9
move 4 from 8 to 6
move 7 from 6 to 1
move 8 from 1 to 5
move 1 from 8 to 7
move 1 from 9 to 6
move 12 from 5 to 3
move 1 from 4 to 8
move 2 from 9 to 5
move 1 from 2 to 3
move 3 from 5 to 1
move 1 from 1 to 5
move 21 from 3 to 8
move 2 from 1 to 5
move 6 from 5 to 7
move 2 from 5 to 6
move 10 from 6 to 9
move 1 from 6 to 8
move 13 from 8 to 2
move 2 from 5 to 4
move 2 from 4 to 3
move 4 from 9 to 1
move 5 from 7 to 8
move 12 from 8 to 1
move 5 from 9 to 6
move 1 from 3 to 7
move 2 from 6 to 5
move 11 from 2 to 1
move 1 from 8 to 4
move 16 from 1 to 9
move 1 from 2 to 6
move 1 from 8 to 5
move 12 from 9 to 3
move 14 from 7 to 2
move 1 from 7 to 9
move 1 from 4 to 2
move 1 from 7 to 5
move 3 from 9 to 5
move 4 from 6 to 9
move 3 from 9 to 4
move 1 from 8 to 4
move 2 from 4 to 5
move 1 from 7 to 1
move 5 from 3 to 5
move 2 from 4 to 2
move 8 from 2 to 7
move 7 from 2 to 4
move 1 from 3 to 7
move 3 from 9 to 7
move 2 from 2 to 9
move 3 from 4 to 5
move 6 from 1 to 8
move 6 from 1 to 5
move 3 from 9 to 2
move 22 from 5 to 9
move 1 from 5 to 6
move 2 from 2 to 3
move 5 from 7 to 6
move 5 from 8 to 9
move 2 from 7 to 2
move 20 from 9 to 4
move 1 from 8 to 3
move 2 from 2 to 5
move 1 from 2 to 5
move 15 from 4 to 8
move 1 from 5 to 7
move 6 from 9 to 1
move 5 from 4 to 8
move 2 from 4 to 8
move 1 from 2 to 1
move 5 from 6 to 5
move 5 from 5 to 7
move 1 from 9 to 8
move 5 from 7 to 2
move 2 from 5 to 1
move 4 from 7 to 5
move 1 from 5 to 9
move 1 from 6 to 8
move 1 from 7 to 2
move 6 from 3 to 4
move 3 from 5 to 7
move 1 from 9 to 2
move 6 from 2 to 3
move 1 from 3 to 4
move 13 from 8 to 9
move 7 from 1 to 5
move 6 from 9 to 2
move 1 from 1 to 4
move 6 from 2 to 3
move 1 from 1 to 4
move 5 from 9 to 7
move 11 from 8 to 4
move 7 from 7 to 3
move 2 from 7 to 8
move 1 from 8 to 2
move 8 from 4 to 1
move 2 from 1 to 6
move 2 from 5 to 8
move 3 from 1 to 9
move 1 from 8 to 2
move 11 from 3 to 2
move 2 from 8 to 9
move 9 from 4 to 7
move 11 from 3 to 8
move 7 from 9 to 6
move 5 from 4 to 6
move 3 from 7 to 3
move 1 from 7 to 1
move 5 from 7 to 6
move 2 from 3 to 5
move 1 from 3 to 4
move 5 from 2 to 5
move 1 from 1 to 7
move 1 from 4 to 8
move 1 from 7 to 6
move 7 from 5 to 7
move 2 from 5 to 7
move 3 from 1 to 7
move 1 from 2 to 3
move 1 from 6 to 4
move 1 from 3 to 4
move 1 from 5 to 3
move 18 from 6 to 4
move 9 from 7 to 1
move 14 from 4 to 6
move 3 from 6 to 4
move 12 from 6 to 7
move 2 from 5 to 3
move 3 from 7 to 4
move 6 from 4 to 7
move 5 from 1 to 7
move 5 from 4 to 5
move 5 from 2 to 1
move 9 from 8 to 4
move 9 from 1 to 3
move 2 from 8 to 2
move 4 from 2 to 4
move 1 from 7 to 6
move 1 from 2 to 3
move 1 from 8 to 9
move 1 from 6 to 9
move 2 from 9 to 3
move 3 from 4 to 1
move 13 from 3 to 5
move 12 from 5 to 1
move 7 from 1 to 8
move 1 from 3 to 6
move 4 from 5 to 4
move 1 from 5 to 2
move 8 from 4 to 9

Day 06: python

this documentation is autogenerated. Add a README.adoc to your solution to take over the control of this :-)

python

sol.py

#!/usr/bin/env python3


def load_input():
    with open('input') as fd:
        data = fd.read().strip()
    return data


def start_of_message(data, n):
    """"""
    for i in range(len(data)):
        if len(set(data[i:i+n])) == n:
            break

    return i + n


def main():
    data = load_input()

    # Task 1
    result = start_of_message(data, 4)
    print("Result 1: ", result)

    # Task 2
    result = start_of_message(data, 14)
    print("Result 2: ", result)


if __name__ == '__main__':
    main()

input

rvnvzvhzzjgjgffclllnhhtltptgptgpttjhttsllmbbphbpbzpbpjjcwjwqwccnrrtvrtrfrwffnqffsggwzzhtzhthqhffmrrzsrrnrtrqqbllhrlrjrvvrvvgdgjgfjjtzznffrfvfggswgssccpcwpccstcsstwssgzssswsgwsgsnsshcsscsffcwcmmhmsmrsrddwhhszsfsjfsfccwssvdssmzzwrzzjfzzvzzfsfsvshschhvlhhvmvpvhhstsdtstgtrrtjjcssgfssnsnfsnscnsslvvqbbhthqtqzqmzmjjfmmbdmmzdmdpmpnnfjnnrwnwbwrrwjrrttfzzlwwfmmnhhqlhqqbnqnqznncmmhfmfftsfszssftstggppfddtztltctqcqjcqcmclcnccdgcggvmvvcsstztbzttsccmgcghcczfzszbzmbmjjggsqgghbhjjhhrrjljmllczlzzqssjfjqfjfmfdfgffczfzwwpfwpwbbftbffplphhfcftctlcclhlnlhnhlhnhqnhhjbjmmtpmmlsmllvhhjghhmzzwpzprrjrzzvrvcvqvssgssnccmgmppfhfwhhczhhprhrffpvvqrqvqccdhdvvvssvdvwdwwzggfvfnvvqvwwzwmzzvttgzzwjjfvvpvffqpqhqrrwccjbjppbzbrbrrphrhgrhhzbbhrrblbjjznnlccsffpcfpcfpfddfwddbffczztzsttzsstddrqdqqchqccrlccfcwwghwhnhbhjbhhzrrgprrftrffqlljlslggzdzvvrgvgcgnnngtnggdcgddhjhchqcqfqwfwjffqppblbddgmdgmddrnddjtdjttgzgpzggwvwjvwwhdwwbvvtftcfttpftpfpzfznnchnhrrdtdbtdbdppsjsgsttptsptssnwntwnnsshqqgnnqnddhjjtqqjhhmghmhmsstrrhmmwvvlssprsrbbswsddbnntmtbmtbmmtmrrffvjffdrffnttbffbccgchghjgjwwshwswdswdsdsrdsdjdvjjgbjbssptpddzbdddqdpdspswssjrjwwcbbvsspnspslpplzlqlccvlvfllntlntnqnrqnqpqlqflfrfsfgsfswwptwwgqggrhghzghhrthtzhzsswhhzffpsffhnnrvnrvvhmmlvvqsslddhcddsllpmpggrhrcrbbmnmjmqmlmpmrrqwqhqrqhrrgrqqsrrpjpdjppzhphthddlzdzhddjvdjdcjcncnvnzzvwzvzgzjggcllvpllgdgqqdvvwnvwvwqqvwvvrbbtqqwhdztqfzzcqrshjzwqnpdsshmpjzwqdptbvfqzmfnbtlgbbsjbqgnblhbbpsfdzvcmpzfwczcnbdndsjzccjcqnrdglwfrvwtnjwpvpvvgwtmnpzhbwnbqwznmdvdrsjnlsfrpcnhlbmlgmrcjbbvhqnvbrmlnfttjllstqnqqnhqrzrhfqjbfbwfhhjzwtwzjmszzhjnjbhrlbnnpfvdmlftjnfsfnvddqfhqqlljrthptvmhbqrdmdcmljwgbrqrjwcrtmvjgqtblnslgbjmdsrrpgfsctrhwlwnszpljhrfnsfpcsgczzltgsztclhgcqljrcmbbpdlztncnrnrmgrttplcnldfqddqhznmcczbmwvsztmwmcqnrzmlmqchnhnhrrhhfntjzqcbnttspptqwvphlbtpfcbhdqzbhsbvhmsqbsdntwpcrnzvmbgqsgttbqhhblfjpmvfcrzhfnzwrzbzgsdfqndzzhfdnrvsnvfbptthjnhgljhrvwwrlbnfpvvjdjchcgbhfrqvszhrhqvtzplwsptvdhqwlzhcjpclmmrlccvgvtgsfpnjhqhrbqglznpdhmsqwwsbmhmlsmmvvghsmplqjchrfctltmnqnddzqjfpljwljbdqjcqdqzwsbcclqsmsmlstvljwwtfmpnhqzqfjghjfchjccqrchsvngvrnwfwttsnvrdlfvwfptsjcpslvvpmjclfcpljqjszptsgsmntzrdjbgrzmgvzddqrlsndjzzqbznqnphbnwfhtjjlwjpsvffrdrbsbttpgrvmqrdndqvlgzcpfbttvqdgvrmtvfclcbcwllthmdzjcflwpnrsqzrjdzbvqgzsqvjjpjjpnjtcjqhcfbjdqndlcwzhcbjtgtlvtdwctdnqcbcgsmrcrmwjntdwbjdbzmshbvlspjfdbvmlrbdzlmlggthvphnrqlrcdsqpsgqcmpgmgdzvdqlmcldvztpsbmpwjgjfhswplwrvwpbwbsgsvlhdvmpzwnnbwjwmshwcnqfqjdpchfjjcbdnslqchhnznpqpnnctznbtccclgjhmnngdjlmqnzpsdptqqcrblmrlnnpgvrfrtmbjnmspfrbwpclhgtbsghndrjfbggsplvcjnhjjbqzsfdpfnvchzjgbhdqgddfgddvzdcrjlntnsmscqwmpptqgbnvtsvpjvmhcfpbfrpzqbpfhlbjrmbmvdvvnvfqsndglvhfrqcsbsbbprscrbfthzcwcdrprqrwjzwrpblfllpwzlhmqvltjgpcjzpzwbltgwsrgrrhzcqfpvhcprdhnzcfphqrwcvtpcbppjwzmmwjhbvwbblnbwvcqzvlfzjhgmlnhlhrbsplfctggfbhbgwpncznvtmdtqqmjsvsnrlqswzvflrfsncgpdcndlwrfrqwqnqtjmsphwsgzhdjpnsdgrbrfhbfdrntwvgvbwnvwnrmdbhqgrglbfwprflnrljrwsgwtpgtmfhvvghtzndvwlzjchhmlwcncmpvrslrglzjcnhfdqhcrljhgbzpssvdnmfwzstmvrztgpsscfswltnbwrrtcnvbswmmjbmnnnvqwjzhprfnvlbvzzdvbwlwchrvqnwwpbnttbhfdvjjvzsznhczjcncmcrmwtrlsvbwpsrcwqdvgcfjsbqnwmjmtcgpnmcbfbcqhzrjbtlpvwzhjqqprbdnbgzfwlprlcspwjwnfftldqzbcgqnjtglvbpqffdvjbpslqcdzwdnmncvcwfshdhsmssttqfrsbnjgmhfqzlgrbpdfqtfdwslsgphfzzgbzjssfbgnwztzmczplqwjmhtlflpvqqqmrvlllhngtfgsvbbnvhzqbcgpmnlsmpwqwgqfjpzplzjhrslwzrsrgjgpppjlnhrnggcdzvspsztnschnqftgffbtvrpzndzpqmtsfmwgnrvpmtgpvnmqfmwgcvlznwqnjnjwpgnqfjwtdhhmztlvlcvrlzmlpmjdvdnzwbfsshfsvbbhqsmphjhqtlnvlsmvwlqvfqpnjnzlgmdvwzzrllmcgwtqdwphhbwmlrqhqrfmdvtqswvsllqvwmfbbpllsbjbvgsmrcgqvfgsnszfrdlcjbtfgcbclhmzlmqlnhmslcmgrcvjjlbpsjjcznzqwmztcgdgbmrlgwzjzzjrpndfrdzztzzgmsrcnwrvqrdcczrbhdpfjwvqsmbrcvllvjrrbrsqnzldltdgzscjrssvdhzhnvltpgfdfcvfbtqmphdzhpzgjhjbwsmdlbqqgcrhjrwhgvfgdllmmlnpsbtvdrrzmltfwgrcsfrrsdbdmjtrwhnlgcrgjgmbzvzvqbflvbrsqcssjgsvpjmlhtcggzwbvddmwwtfdrlltjqcpwnthczzlzdszvtmrmhbpcgstvrsnmbdcmjzsvnncmgmlnrzzhfvmblgptwwwbrmtcczjwcqmvdrsvfjgqqvghnhbntqcdrppfmvdzbcjvztrnpdhmdpmnvsnzzldvdfqbdrwqqqqsmswthwpjwdrflszbspqhpfwztjjcbdsrftdsrsfdltnfztcslmbsghgrtcscrfmptqplwpmtqdzthgfjhbqdnsffrpjmgczmrlfvzcjttwtmtqfbtlqrttvjdwhfgcgcclrlswmzhzbfhjrggnhwtnffnqqcvldlttvvgrbcqbmqzvtflfmdblhdbzphrqtbshvp

Day 07: python

this documentation is autogenerated. Add a README.adoc to your solution to take over the control of this :-)

python

sol.py

#!/usr/bin/env python3

import networkx as nx


def load_input():
    data = nx.DiGraph()
    cur_dir = ''
    with open('input') as fd:
        for line in fd:
            temp = line.strip().split()
            if temp[0] == '$' and temp[1] == 'cd':
                if temp[2] == '..':
                    preds = list(data.predecessors(cur_dir))
                    cur_dir = preds[0]
                else:
                    cur_dir = cur_dir + temp[2]
                if not cur_dir.endswith('/'):
                    cur_dir += '/'
                data.add_node(cur_dir)
            elif temp[0] == '$' and temp[1] == 'ls':
                pass
            elif temp[0] == 'dir':
                data.add_edge(cur_dir, cur_dir + temp[1] + '/')
            else:
                size = int(temp[0])
                filename = temp[1]
                data.nodes[cur_dir][filename] = size
    return data


def get_total_size(g, node):
    total = sum(g.nodes[node].values())
    for successor in g.successors(node):
        total += get_total_size(g, successor)
    g.nodes[node]['total'] = total
    return total


def part1():
    data = load_input()
    total = get_total_size(data, '/')

    result = 0
    for node in data.nodes:
        total = data.nodes[node]['total']
        if total <= 100000:
            result += total

    print("Result 1: ", result)


def part2():
    data = load_input()
    total = get_total_size(data, '/')

    needed_space = 30000000 - (70000000 - total)
    temp = [data.nodes[node]['total'] for node in data.nodes if data.nodes[node]['total'] > needed_space]
    temp.sort()
    result = temp[0]
    print("Result 2: ", result)


def main():
    part1()
    part2()


if __name__ == '__main__':
    main()

input

$ cd /
$ ls
dir bntdgzs
179593 cjw.jgc
110209 grbwdwsm.znn
dir hsswswtq
dir jdfwmhg
dir jlcbpsr
70323 qdtbvqjj
48606 qdtbvqjj.zdg
dir tvcr
dir vhjbjr
dir vvsg
270523 wpsjfqtn.ljt
$ cd bntdgzs
$ ls
297955 gcwcp
$ cd ..
$ cd hsswswtq
$ ls
dir bsjbvff
dir dpgvp
267138 grbwdwsm.znn
dir hldgfpvh
dir jdfwmhg
dir jtgdv
93274 ptsd.nzh
268335 qdtbvqjj.dlh
185530 qdtbvqjj.jrw
dir vcbqdj
dir wtrsg
$ cd bsjbvff
$ ls
dir dmnt
148799 grbwdwsm.znn
324931 hzmqrfc.lsd
211089 qdtbvqjj
$ cd dmnt
$ ls
221038 zht
$ cd ..
$ cd ..
$ cd dpgvp
$ ls
dir fzttpjtd
dir jdrbwrc
dir rwz
dir tssm
$ cd fzttpjtd
$ ls
149872 jdfwmhg
$ cd ..
$ cd jdrbwrc
$ ls
149973 hpgg.srm
dir ptsd
$ cd ptsd
$ ls
2594 twzf.pqq
$ cd ..
$ cd ..
$ cd rwz
$ ls
dir jdfwmhg
302808 zzlh
$ cd jdfwmhg
$ ls
229683 cdcrgcmh
218733 nhzt
$ cd ..
$ cd ..
$ cd tssm
$ ls
dir ptsd
37272 qfnnrqsh.qvg
215066 wnvjc.jqf
$ cd ptsd
$ ls
24102 bwtbht.dwq
224035 qdtbvqjj.dmp
$ cd ..
$ cd ..
$ cd ..
$ cd hldgfpvh
$ ls
316712 grbwdwsm.znn
328950 tqvgqjrr
$ cd ..
$ cd jdfwmhg
$ ls
130652 gcwcp
dir jdfwmhg
215427 lfw.zml
dir qdtbvqjj
4181 rgsvgssj.qsr
$ cd jdfwmhg
$ ls
dir bvm
dir hsswswtq
122279 qznt.jhl
dir sjw
dir zpfdtl
$ cd bvm
$ ls
22841 fbcgh.mrp
dir hsswswtq
dir hstg
41317 ndrt
dir nvmvghb
239316 ptsd
dir qtwvdtsp
98555 vzh
$ cd hsswswtq
$ ls
dir ddcjvjgf
127104 plwvb.pbj
dir ptsd
dir qhp
dir rjtrhgwh
$ cd ddcjvjgf
$ ls
135870 bwtbht.dwq
81968 gcwcp
182253 mrbh.wmc
275931 nsrqrts
322128 pfpcp
$ cd ..
$ cd ptsd
$ ls
214981 jsrlsc
dir wpbdrcw
$ cd wpbdrcw
$ ls
197849 mljfb.ggb
173586 ptsd
$ cd ..
$ cd ..
$ cd qhp
$ ls
293198 bnrgl
$ cd ..
$ cd rjtrhgwh
$ ls
224393 clrp.nst
$ cd ..
$ cd ..
$ cd hstg
$ ls
51671 gdsfpc
209216 hsswswtq
97203 jlnr
dir thdhg
57399 tssm
$ cd thdhg
$ ls
201896 jjp.wvw
$ cd ..
$ cd ..
$ cd nvmvghb
$ ls
210047 gfcrzgj
dir rqjbplv
dir rvwd
292931 sgwvcqfr.bpq
dir vtjd
$ cd rqjbplv
$ ls
105204 gcwcp
$ cd ..
$ cd rvwd
$ ls
66170 jdfwmhg
$ cd ..
$ cd vtjd
$ ls
dir ptsd
$ cd ptsd
$ ls
300524 bwtbht.dwq
$ cd ..
$ cd ..
$ cd ..
$ cd qtwvdtsp
$ ls
289574 wctgtq
$ cd ..
$ cd ..
$ cd hsswswtq
$ ls
24935 gcwcp
dir jzpbdcmc
26834 mljfb.ggb
182501 phnmlsjp.pjc
dir pttnl
dir qdtbvqjj
dir vst
$ cd jzpbdcmc
$ ls
297521 grbwdwsm.znn
dir qwc
dir zzswd
$ cd qwc
$ ls
81143 hsswswtq.rjw
54843 mjvvfsz.rgz
273051 pfwgtmtt.ccs
$ cd ..
$ cd zzswd
$ ls
216062 vlbwz.zmh
$ cd ..
$ cd ..
$ cd pttnl
$ ls
257733 mljfb.ggb
250887 pfwgtmtt.ccs
$ cd ..
$ cd qdtbvqjj
$ ls
34667 gcwcp
$ cd ..
$ cd vst
$ ls
70250 pfwgtmtt.ccs
dir zpcqhml
$ cd zpcqhml
$ ls
219936 jdfwmhg.zbm
$ cd ..
$ cd ..
$ cd ..
$ cd sjw
$ ls
152311 nqjtvzff
157117 pfwgtmtt.ccs
118226 ptsd.vsm
$ cd ..
$ cd zpfdtl
$ ls
189042 gcwcp
$ cd ..
$ cd ..
$ cd qdtbvqjj
$ ls
dir ftz
dir hvlffb
dir lzbb
53335 ptsd
dir qdtbvqjj
$ cd ftz
$ ls
dir fft
256058 gcwcp
497 hsswswtq.vqs
103941 hvtcz.fsg
171587 ljlnz.ffg
115101 mljfb.ggb
dir qdtbvqjj
$ cd fft
$ ls
58845 bwtbht.dwq
136040 gcwcp
256973 mljfb.ggb
$ cd ..
$ cd qdtbvqjj
$ ls
dir fgqhdh
304573 ntm.wmc
$ cd fgqhdh
$ ls
317143 gcwcp
26010 lsfpfdqz
$ cd ..
$ cd ..
$ cd ..
$ cd hvlffb
$ ls
6682 vjt.mcf
$ cd ..
$ cd lzbb
$ ls
dir bbvml
324162 bwtbht.dwq
dir fjs
dir pffntc
dir pnltt
dir ptsd
$ cd bbvml
$ ls
dir qdtbvqjj
dir qssdcrp
dir tssm
$ cd qdtbvqjj
$ ls
246275 qdtbvqjj.cgn
$ cd ..
$ cd qssdcrp
$ ls
274399 hsswswtq
$ cd ..
$ cd tssm
$ ls
dir ssqc
$ cd ssqc
$ ls
178904 njrssmlm.gcm
$ cd ..
$ cd ..
$ cd ..
$ cd fjs
$ ls
dir dmvnp
121967 fqlzlvwt
204348 grbwdwsm.znn
102733 jdfwmhg.qsl
240279 ptsd.jwm
228793 ptsd.nsh
dir ssm
$ cd dmvnp
$ ls
dir psj
dir zjw
$ cd psj
$ ls
170665 gcwcp
56058 lsfzc.dcp
40658 tfsllqqw.fgv
$ cd ..
$ cd zjw
$ ls
79989 fggsl.dmz
$ cd ..
$ cd ..
$ cd ssm
$ ls
106263 bwtbht.dwq
106259 jdfwmhg.qtb
6246 rwbnr.tqv
$ cd ..
$ cd ..
$ cd pffntc
$ ls
111475 qbmrdms.ldm
$ cd ..
$ cd pnltt
$ ls
dir nptfhlf
dir zngmf
$ cd nptfhlf
$ ls
223065 qrb.drh
205674 rdgfz
$ cd ..
$ cd zngmf
$ ls
61655 bwtbht.dwq
$ cd ..
$ cd ..
$ cd ptsd
$ ls
dir hrvrt
dir thwtl
$ cd hrvrt
$ ls
152296 pfwgtmtt.ccs
$ cd ..
$ cd thwtl
$ ls
156783 pfwgtmtt.ccs
323304 sltc
$ cd ..
$ cd ..
$ cd ..
$ cd qdtbvqjj
$ ls
320175 pfwgtmtt.ccs
$ cd ..
$ cd ..
$ cd ..
$ cd jtgdv
$ ls
81164 ptsd.tpj
$ cd ..
$ cd vcbqdj
$ ls
dir crng
330203 gvlrg
152022 qdtbvqjj.slq
294095 rthwj.zrf
dir vjsbf
$ cd crng
$ ls
dir gznrh
$ cd gznrh
$ ls
259458 ptsd
$ cd ..
$ cd ..
$ cd vjsbf
$ ls
47331 hlld.fzf
147103 jdfwmhg
$ cd ..
$ cd ..
$ cd wtrsg
$ ls
144344 dtcc
$ cd ..
$ cd ..
$ cd jdfwmhg
$ ls
323973 qdtbvqjj
$ cd ..
$ cd jlcbpsr
$ ls
dir htrdwm
dir jdfwmhg
dir pwmvbhsl
dir vwfdfmcp
$ cd htrdwm
$ ls
dir btn
105731 dlncqrbm.dgl
158267 gqqghldt
242513 hsswswtq.drj
dir jdfwmhg
212816 swsgtv.wbb
228996 tgll.rcs
$ cd btn
$ ls
50419 pfwgtmtt.ccs
$ cd ..
$ cd jdfwmhg
$ ls
dir bwc
$ cd bwc
$ ls
184634 cfwg
$ cd ..
$ cd ..
$ cd ..
$ cd jdfwmhg
$ ls
319749 hsswswtq
dir jdfwmhg
271619 jdfwmhg.znz
dir jhmmt
181217 mljfb.ggb
11297 rcpl.tgf
83423 zwscbcvm.ths
$ cd jdfwmhg
$ ls
267171 cts.hlf
$ cd ..
$ cd jhmmt
$ ls
84473 jdfwmhg
$ cd ..
$ cd ..
$ cd pwmvbhsl
$ ls
dir jsg
171725 mljfb.ggb
152612 qjr
dir vfsqw
$ cd jsg
$ ls
176951 jdfwmhg.fhn
284927 ljvvtw.wcq
153109 vnvtt
$ cd ..
$ cd vfsqw
$ ls
104559 htsrns.gws
$ cd ..
$ cd ..
$ cd vwfdfmcp
$ ls
291404 csmvbjlt.tdf
$ cd ..
$ cd ..
$ cd tvcr
$ ls
dir djtwv
dir hsswswtq
272845 mdds
dir ndshbjzn
65929 scpltww.twm
dir tssm
30516 zdpscm
dir zqdrdzv
$ cd djtwv
$ ls
271696 cwjj.hjp
$ cd ..
$ cd hsswswtq
$ ls
dir djngm
dir hcz
dir ptsd
$ cd djngm
$ ls
317775 ltwjzpjb.rcj
37776 qdtbvqjj.lzf
$ cd ..
$ cd hcz
$ ls
217741 pgdmr
128868 qdtbvqjj
306138 zbmrplsn
$ cd ..
$ cd ptsd
$ ls
304048 ftm
120236 mdcwvvng
$ cd ..
$ cd ..
$ cd ndshbjzn
$ ls
206408 pfwgtmtt.ccs
$ cd ..
$ cd tssm
$ ls
dir mlcnsf
dir nbgjm
204079 pdljvb
185465 rqgdmbjf.rhr
dir sfnlb
$ cd mlcnsf
$ ls
249868 fqrncwd
29146 zdz.jth
$ cd ..
$ cd nbgjm
$ ls
113314 mljfb.ggb
$ cd ..
$ cd sfnlb
$ ls
234917 tjp
$ cd ..
$ cd ..
$ cd zqdrdzv
$ ls
40790 vtdnhzm
$ cd ..
$ cd ..
$ cd vhjbjr
$ ls
dir glv
dir mvns
dir qbrnh
$ cd glv
$ ls
288849 bgvqll.sfj
259105 jdfwmhg
dir qcjlshcv
$ cd qcjlshcv
$ ls
dir nwqqjcmh
$ cd nwqqjcmh
$ ls
137244 grbwdwsm.znn
312904 mzh
dir qdtbvqjj
$ cd qdtbvqjj
$ ls
dir nlqbq
$ cd nlqbq
$ ls
307636 ptsd.vtr
$ cd ..
$ cd ..
$ cd ..
$ cd ..
$ cd ..
$ cd mvns
$ ls
dir gzqlmrdh
dir qjhtlh
dir tssm
dir vthg
$ cd gzqlmrdh
$ ls
274950 mlzdqwm
$ cd ..
$ cd qjhtlh
$ ls
157835 ptsd.lqm
300380 wst.trp
$ cd ..
$ cd tssm
$ ls
15772 gcwcp
$ cd ..
$ cd vthg
$ ls
dir gdndtlnc
$ cd gdndtlnc
$ ls
3175 hsswswtq.bds
320462 mljfb.ggb
305508 mzvtzvqc
dir qdtbvqjj
154575 tssm.vgb
$ cd qdtbvqjj
$ ls
236889 drnnvh
$ cd ..
$ cd ..
$ cd ..
$ cd ..
$ cd qbrnh
$ ls
dir hsswswtq
4623 hsswswtq.rnf
266326 jrmq.ztg
295980 tssm.vzb
dir wnbfzd
dir zjzhncs
dir zttlggt
$ cd hsswswtq
$ ls
48277 gsqjdbhv
$ cd ..
$ cd wnbfzd
$ ls
97133 mljfb.ggb
$ cd ..
$ cd zjzhncs
$ ls
298303 gcwcp
dir ggr
113206 grbwdwsm.znn
$ cd ggr
$ ls
244876 ptsd.zvb
$ cd ..
$ cd ..
$ cd zttlggt
$ ls
dir hdbwrcm
dir mbvpd
dir mtd
dir ptsd
dir tcwqp
$ cd hdbwrcm
$ ls
267323 bwtbht.dwq
$ cd ..
$ cd mbvpd
$ ls
84087 frf.smv
$ cd ..
$ cd mtd
$ ls
158543 mljfb.ggb
$ cd ..
$ cd ptsd
$ ls
112797 vtschwnb.fnp
$ cd ..
$ cd tcwqp
$ ls
90637 lbsqcj.sfn
179097 tssm.dbl
$ cd ..
$ cd ..
$ cd ..
$ cd ..
$ cd vvsg
$ ls
168715 bwtbht.dwq
dir bwv
dir hsswswtq
dir lqmnjrlb
dir mmrfrj
175244 vct.tsc
dir zwvlhs
$ cd bwv
$ ls
201509 gcwcp
62815 grbwdwsm.znn
dir gwdh
dir mfdvcn
166355 pfwgtmtt.ccs
dir ptsd
169681 qdtbvqjj.fgh
250573 wvndzgv
$ cd gwdh
$ ls
306377 sphrj.pjh
$ cd ..
$ cd mfdvcn
$ ls
27796 bvclvtrm.jlf
65045 cghr.vzg
dir hsswswtq
197145 jdqztgh.pvd
$ cd hsswswtq
$ ls
298155 bwtbht.dwq
$ cd ..
$ cd ..
$ cd ptsd
$ ls
27501 grbwdwsm.znn
231999 jdnsv
113528 rmfmb.zzw
dir tssm
dir vgjfsh
$ cd tssm
$ ls
dir dndv
226375 grbwdwsm.znn
$ cd dndv
$ ls
152739 sdjrzcv.tvs
$ cd ..
$ cd ..
$ cd vgjfsh
$ ls
211409 swtbttb.vrp
170879 vvfnf.hrp
$ cd ..
$ cd ..
$ cd ..
$ cd hsswswtq
$ ls
dir qdtbvqjj
dir tssm
86418 vhsgq
$ cd qdtbvqjj
$ ls
118588 bwtbht.dwq
$ cd ..
$ cd tssm
$ ls
113460 gml.wdg
$ cd ..
$ cd ..
$ cd lqmnjrlb
$ ls
dir tssm
$ cd tssm
$ ls
dir jdfwmhg
$ cd jdfwmhg
$ ls
64663 nswd.rwc
$ cd ..
$ cd ..
$ cd ..
$ cd mmrfrj
$ ls
319070 gltlwnlt.jzw
232039 hspr
104688 hsswswtq.jsr
dir jdfwmhg
88712 jdfwmhg.zcw
dir pfr
dir prnnpwcd
45488 qdtbvqjj
dir tssm
dir wcmwrtjn
$ cd jdfwmhg
$ ls
140910 bjjhtzct.stm
$ cd ..
$ cd pfr
$ ls
289538 qdtbvqjj
217502 vvpwf
$ cd ..
$ cd prnnpwcd
$ ls
dir qdtbvqjj
$ cd qdtbvqjj
$ ls
dir pqg
dir tssm
$ cd pqg
$ ls
222392 ptsd.ggr
$ cd ..
$ cd tssm
$ ls
158252 dcnvjj.zfd
10486 jdfwmhg.qmb
4374 qdtbvqjj.vqm
254229 vgqfw
$ cd ..
$ cd ..
$ cd ..
$ cd tssm
$ ls
dir ptsd
$ cd ptsd
$ ls
173766 fvlsgqb
35658 wtc.vvd
$ cd ..
$ cd ..
$ cd wcmwrtjn
$ ls
160089 chfhpc
76202 frgpdnd.ngw
138996 jsfsfpqg.nhf
dir mlm
dir nbdbzsn
dir ptsd
278574 vrnb
$ cd mlm
$ ls
dir gqwhhmvd
dir nrzvzgrt
dir nzplht
dir zzp
$ cd gqwhhmvd
$ ls
dir ddmvjpj
dir jdfwmhg
$ cd ddmvjpj
$ ls
273423 jdfwmhg
43605 pfwgtmtt.ccs
$ cd ..
$ cd jdfwmhg
$ ls
239406 qctw.vzb
$ cd ..
$ cd ..
$ cd nrzvzgrt
$ ls
20712 gcwcp
239372 gjgdvbwb.gcz
dir hdzhl
124814 jdfwmhg
dir jfzr
295071 qwjgwqp
221611 shrzpsj.dwh
dir tssm
dir wdlsvzvl
$ cd hdzhl
$ ls
dir gfwbd
184323 hsswswtq.mln
177147 nqgqz.tnf
4680 pfwgtmtt.ccs
$ cd gfwbd
$ ls
254870 cldm.fft
301411 tssm.cvn
$ cd ..
$ cd ..
$ cd jfzr
$ ls
dir dvvflnnw
dir jdfwmhg
216389 lwtwn.ttt
201727 pfwgtmtt.ccs
107829 prphc.ncb
5816 sdvq.jvn
$ cd dvvflnnw
$ ls
24741 brtrbwh.wwd
27700 mljfb.ggb
$ cd ..
$ cd jdfwmhg
$ ls
325218 bwtbht.dwq
63718 mvl.ngz
162645 vtd.vgp
$ cd ..
$ cd ..
$ cd tssm
$ ls
60903 pfwgtmtt.ccs
332768 qdtbvqjj.jwb
$ cd ..
$ cd wdlsvzvl
$ ls
142213 vgvd
$ cd ..
$ cd ..
$ cd nzplht
$ ls
275904 hsswswtq
157369 jdfwmhg
84363 jvcvmbm.fht
dir qbjqgg
$ cd qbjqgg
$ ls
331934 gcwcp
$ cd ..
$ cd ..
$ cd zzp
$ ls
151335 flsd.zmj
dir gwlhqlp
99086 jdfwmhg.hft
$ cd gwlhqlp
$ ls
201894 glcnpqzp.jvc
$ cd ..
$ cd ..
$ cd ..
$ cd nbdbzsn
$ ls
169929 bwtbht.dwq
$ cd ..
$ cd ptsd
$ ls
128999 bwtbht.dwq
dir jtlrn
dir pszlt
dir ptjnh
dir ptsd
2981 qdtbvqjj.qcn
dir rpb
dir tcjgpqj
dir tmddnh
dir tssm
$ cd jtlrn
$ ls
124888 grbwdwsm.znn
30046 jznz.dwf
$ cd ..
$ cd pszlt
$ ls
154368 dbblsg.mzr
$ cd ..
$ cd ptjnh
$ ls
306974 grbwdwsm.znn
82840 ptsd
$ cd ..
$ cd ptsd
$ ls
dir ftjhsb
dir jdfwmhg
304012 lqgtvmrl.qbj
96971 mljfb.ggb
$ cd ftjhsb
$ ls
56965 dhgds
$ cd ..
$ cd jdfwmhg
$ ls
dir lssbmtms
dir vmwshd
$ cd lssbmtms
$ ls
95453 gcwcp
198402 mljfb.ggb
1507 mzlmp
40526 twlqhml
$ cd ..
$ cd vmwshd
$ ls
267087 pfwgtmtt.ccs
$ cd ..
$ cd ..
$ cd ..
$ cd rpb
$ ls
dir lqbchlbp
dir ptsd
$ cd lqbchlbp
$ ls
151429 ptsd.tjz
$ cd ..
$ cd ptsd
$ ls
28900 gcwcp
55920 llt
$ cd ..
$ cd ..
$ cd tcjgpqj
$ ls
dir cvdlcvq
329232 hcmj.nvp
232764 nvtmgc.qgs
108056 ptsd.gcn
39056 qdtbvqjj
91792 tssm.wqz
$ cd cvdlcvq
$ ls
46978 grbwdwsm.znn
17760 qrdbsdpj.dhm
$ cd ..
$ cd ..
$ cd tmddnh
$ ls
238434 gggvq.tfc
$ cd ..
$ cd tssm
$ ls
dir tlllv
$ cd tlllv
$ ls
198184 trmf.qqw
$ cd ..
$ cd ..
$ cd ..
$ cd ..
$ cd ..
$ cd zwvlhs
$ ls
19923 gcwcp
129179 grbwdwsm.znn
214660 pghcvh
101270 ptsd.gzl
dir srjlz
$ cd srjlz
$ ls
221301 nrcg.pqw

Day 08: python

this documentation is autogenerated. Add a README.adoc to your solution to take over the control of this :-)

python

sol.py

#!/usr/bin/env python3


import numpy as np


def load_input():
    data = list()
    with open('input') as fd:
        for line in fd:
            line = line.strip()
            data.append([int(value) for value in line])
    data = np.array(data)
    return data


def main():
    data = load_input()

    # Task 1
    visibles = np.full(data.shape, True, dtype=np.bool_)
    visibles[1:-1, 1:-1] = False

    inner = data[1:-1, 1:-1]
    it = np.nditer(inner, flags=['multi_index'])
    for value in it:
        i, j = it.multi_index
        i += 1
        j += 1
        for k in range(4):
            highest = 0
            if k == 0:
                highest = max(data[0:i, j])
            elif k == 1:
                highest = max(data[i+1:, j])
            elif k == 2:
                highest = max(data[i, 0:j])
            else:
                highest = max(data[i, j+1:])
            if highest < value:
                visibles[i, j] = True
                break

    result = np.sum(visibles)
    print("Result 1: ", result)

    # Task2
    directions = [(1, 0), (-1, 0), (0, 1), (0, -1)]
    options = visibles.copy()

    options[0, :] = False
    options[:, 0] = False
    options[-1, :] = False
    options[:, -1] = False
    indexes = np.argwhere(options)

    most_scenic = 0

    for i, j in indexes:
        temp = 1
        for d in directions:
            y = i
            x = j
            while True:
                y += d[0]
                x += d[1]
                if x == 0 or x == data.shape[1] - 1 or y == 0 or \
                   y == data.shape[0] - 1 or data[y, x] >= data[i, j]:
                    temp *= abs(j-x) + abs(i-y)
                    break

        most_scenic = most_scenic if most_scenic > temp else temp

    result = most_scenic
    print("Result 2: ", result)


if __name__ == '__main__':
    main()

input

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002220000133232333300400324440200340331442222154322435351134115104433131022400301210231103320120102
002111110023312202140422121302301411425412143121451113531553512221212012141140202223332010130102121
200121112032232121304410232442313124341433152252314544515325125233341244332012020324021110233002001
201022212100303122230042110420411144235142233535353242115241433555244414021310121244221013322222121
011133013013221340022444040034222334222333233513241314324332525223122512421234021412140011030323220
210300120303001122232014221422523555232331235422254211412225214325215241553042040303041423101011002
003120002332341304133020544511125535535541345465624233512532523153125344553310344313112440310203011
131130201012400142322154551413323211211264453362355545322422255141341455331312144311201120100013210
231231113320032112042452344144421222532325455233246353364224262633314514222114310222010411123020310
130132323324003111222144335255232253526222366424453236522644225645542113443452221300403213402211010
113120324042321002433123114511424246663652545323562652666642343346224141215312351154031420431123313
322103103420203222134243312435226546235454342354423362646633563243445363453321134444313221424211210
222002200102244012425542311135622554533554536552565362325262535422342364211432245513321404214403103
020312030211000311145233135522425335364263235236633766423525523653365556233412434131421431421413032
210200210122104332244324435223345455654634767746775377345346266325246256554325112245151433442112120
122021103003325452113155263653362555455536744343555667465337737332644556355541512314415321432122010
302130331324344154351152233526435626553676357536566754666777544543643553226655255322235341014410100
314012231321215344153565545233452474545654556345434346477343637634766266254223421545221444103004003
203142142342444551513532553564436657337357765377547636675373434576744336526346456543411441023143110
012342201025331211122452444533576366457663645436375557334447647673566472636623645223251551503132201
242040141354332534632526224223663666376753663766575676333754654644753446365223356333455541211334020
304212105422512243642223324443434437444774475647656876467367634464365546355325563651434541552020333
411420043555145455366465334765477455434665856478775445748748844536643753776566362432434143454420144
401414045521554536566434665734634475568447478784884574576588667675763475753664255466323144315114340
403203444553445263226363636763747365744764846865845556768875448846334555367546232343531522434303222
320101251535223652234326457665537657586786457478765885578775686476536463546335334642346321452233131
101012145413152534234557444577344864876885844857544755664447444658566665757343254462522531514251101
340341152532244444232667437647346678855466568676685668454547775878787354745333434364264512332351234
034022314552563333466574354365446884777648857896869775765876486687448874547744636623356245124411300
434115222252325233267546336767445666887887795778879879888964665878876674757366746353365644321332143
312035533543623363556753755356877487485687695957956976755556874674844755665746737526623244522514143
420252322135326524253347754684544485747997555758865876756898776645768885645667637664454254124241132
042551521433445422576644675485868857686859585876589677967989879564666758475665333623554365252143351
404455122524562436436545777675857467559889565668857859685895859976758774886545433533522546412531113
402414213444443632667535548848565847669657785977756989557799965677887465558473374753636263414433431
022221152324544456367356377556875457665895668676988667859858876695687554757534736365524353324555445
344111222625453364363635787885744595658769866898866968696568596595864444764473337656423523432142535
225434342232366345537763675578769565995877778767697786787965759585779855884865343633323444422312351
035315413543653543334365848874759855766697967679779767999679566665785768858653734543735645643221454
023235144645352563656735676476699588575688669887679986967789786578779847658656675663653453233341153
142225254342433566337738656784888579869898976766696886867989878587588888556744335476656546565134514
332531546644465434575456856855557977598878768976688677796996689765566874765455375433552656652141222
245115244422246534474538846486987698857687868676878977996687768986655856867454644744464462545223145
534255233266234565357356454786989999898896977688879977977896669857699764786566736463343326443154122
454354332542262477736688458567665797769979688789788788987866776769877696655848467435475444463335241
512543144352562655357675475585978965976868699798788899789679876985786899685774865763673624244331431
324412256535565334657448674688895886877986668889799998798889878866685759475645457456546355544251525
115513233345333374765468446775577596767887988887878879877777968697669577748775734356333445243551434
415151565352224735474558556859799577886898999998979889789968896887667799778755865475767222365331232
341542233524534557363486747546566765697666978897898998779776898687767558456588645347447452326635211
141352533235633637755547684876556558796988688897999988799797887989695558885678545676777233242514541
521224242633256545773565667556868787766779689977797878779776688978856968445584875373474655564453414
212223344626635634464468564667887899889688667779987877977666888978896755878447456437466626246242154
234115532222663767644584477756969899977977999989879987999976887779867996588585775466444223626514313
135352514355535475547475454586885687868789797787897877999696989878678658675857877374766244666335415
245312112534242364465384488548568678767769779778898997999876767795755964548464847565732454524524223
021525515246626734753566444568597959566979986967678896977989678777787774766445653374342254643512525
154333554224543664547576668488558779569899998698676696676768688689695868756558334756732443435414211
241121123663636476553777547778679898575887899887676677898688967655685877474554463766356464665314411
042314546263224256647665654464888598856776787699868899986977858568579785688458575346453652462152344
123331145253442347344577548844655756795759896669869969898669978977675445684486336563564633463521424
441245214645233266655474685847446687769676869796966869876898656576557445456865677774743333555121233
125144145355562535553453348876854556596588579796889867979959976599967847546665344765436664421452124
005214145522463625374665767445668477766757899558986688996855569799556464475877546663265544522533142
424213534345444245575767446587747766879868999866679969656557796695754688786576454364542222333552433
340123443165235626753533465668444668759977876797965776795859756987877778467766435676633663325451242
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424135233343456444347333753548856886767998675667957686789866998677467875563746577654546663332345512
142144412143652453363555366356464857764776776669865675576766767657655474454367647255355333454132411
114044434535536365647343757563768767554848877759975798758895468485667587677463336535634542315521124
243044411234356654655645476665375787765667655868887868786658648788875454747665334553525245122313313
223005135155362266463337346655467578858875455446758445778678456564555447754654655642444335244151100
441031313535133543536645563565447868478877557885475885785484588664864367437333352334646552545540434
230403434533122325663365655775564467566764578557546875654785658764434354637753535553443132323132432
231103055415354435244636576576467643575457665666647886566848556463753663346723326243525141135130424
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001140425351442116425436424534333635755754445857766654848476464365374745573246334553145131221240002
423442125121142242554242262463344446436337646568454446465366647477574476743566434562422322213241112
041120244223522424262622532426564364336575645645643473455773734743736536462532635531424223442140002
333213342044141312256465444645743463747737775353476574446663366477433766453534634424144243324342330
123301211302132311214446544655333545346646756574364363763577337443636433244534466131243245142313041
333302233005545533332445234232634365637756745756665533337455656536362324645335641322232425441330441
210101031320143231223346443232362353735643457655456457447337655745423533326242244322432513011341141
103110441134453455534214566635432365634456656643365735556363677266633236436433421143354400234124322
210103104441245232113425543552436226532532734735737666776674646353226636524623254312414101401413013
020024442231000453252454154546643563663622535463445755356246553442265263346551444421542422240011133
031020232402201014325415255432463233224332466352332362464633465245446636555422514435430143120003112
310302320413232125411435155524536333442626366353663655223253243262552253454113353253031242202102210
221201232400342023452154554354353535536252463345626432342652363255555242425543353242440134432103103
210032311140434341431423452253535242446256556466223466635264232223251545121524142114121403343313003
120033031102121020314222514435233222253442525525522546426665636231521441532113451340400133032210231
210023013101112320231215311434153455555125335452263535435665554254354533423243412221004001101231111
221223223131114201030343552243332413432225514566333324653532145252122232542252133334211422030222312
222331111212014202022011214444443411412221134525134425141412252142514152354243232311422120203312310
110131122101302404410231242111542122454123223331222324435451342542314134420104111021220102332003000
201221202213022021032201013424223253221544343453433115235543553513232252242121443333323311020103222
001200323302010132410244321011414224212135145444322543324442532241244022442341044312322333033210110
012221120210002201233041204141124034134123443133233422423453224352410321301023102020300131022311221

Day 09: python

this documentation is autogenerated. Add a README.adoc to your solution to take over the control of this :-)

python

sol.py

#!/usr/bin/env python3

import numpy as np


direction = {
    'R': (0, 1),
    'L': (0, -1),
    'D': (-1, 0),
    'U': (1, 0)
}


def load_input():
    data = list()
    with open('input') as fd:
        for line in fd:
            a, b = line.strip().split()
            data.append((a, int(b)))
    return data


def next_pos(pos, d):
    next_pos = (pos[0] + d[0], pos[1] + d[1])
    return next_pos


def next_tail_pos(head, tail):
    next_tail_pos = tail
    diff = [i - j for i, j in zip(head, tail)]
    if max([abs(v) for v in diff]) > 1:
        d = [np.sign(i) for i in diff]
        next_tail_pos = next_pos(tail, d)

    return next_tail_pos


def get_tail_pos(pos, value, length):
    tail_pos = list()
    for i in range(value[1]):
        pos[0] = next_pos(pos[0], direction[value[0]])
        for j in range(length):
            pos[j+1] = next_tail_pos(pos[j], pos[j+1])
        tail_pos.append(pos[length])
    return tail_pos


def part_n(number, length):
    data = load_input()
    length = length
    pos = [(0, 0) for _ in range(length+1)]
    tail_pos = list()
    for value in data:
        tail_pos.extend(get_tail_pos(pos, value, length))

    result = len(set(tail_pos))
    print(f"Result: {number}: {result}")


def main():
    part_n(1, 1)
    part_n(2, 9)


if __name__ == '__main__':
    main()

input

L 1
R 1
L 1
U 1
R 2
U 1
D 2
R 2
U 1
D 2
U 2
L 2
D 1
U 1
D 2
L 2
D 1
L 2
D 2
L 1
U 2
R 2
L 1
D 2
L 2
R 2
D 2
L 1
U 1
R 1
U 1
L 1
D 1
R 1
U 2
D 2
R 1
U 1
R 1
L 1
U 1
R 1
D 1
L 1
U 1
R 1
D 1
U 1
D 1
U 1
R 2
L 2
D 2
L 2
R 1
U 2
L 2
R 1
U 1
R 2
D 2
R 2
L 2
U 2
R 2
D 2
R 1
U 1
L 2
R 1
U 1
D 2
U 2
D 1
R 2
L 2
D 2
L 2
R 2
U 1
R 2
L 2
D 2
L 1
D 1
R 2
L 1
U 2
R 1
U 2
D 1
U 2
D 1
L 1
R 2
D 2
R 2
U 2
R 1
U 2
R 2
U 1
R 1
D 2
U 2
R 2
U 1
R 2
D 2
R 1
L 1
U 3
L 2
U 1
L 2
U 1
D 1
U 2
D 1
U 2
R 2
L 1
U 3
R 1
U 1
R 3
L 3
D 3
L 2
U 1
D 3
U 1
R 1
L 1
R 2
L 1
R 3
L 3
D 1
R 2
L 3
U 3
D 1
L 2
U 2
D 2
L 2
U 1
R 1
L 2
R 3
L 3
D 3
R 2
D 3
U 1
L 3
R 1
U 2
L 1
D 3
L 1
U 1
D 3
L 3
U 2
D 1
U 2
L 3
R 1
U 2
D 2
R 3
D 3
U 2
D 3
L 3
R 3
L 1
U 3
L 1
D 1
L 2
D 2
R 1
L 3
R 2
L 3
R 3
L 1
D 1
U 3
L 2
R 2
L 2
D 3
U 3
L 3
U 3
D 1
U 2
R 2
U 1
D 3
L 3
R 1
L 3
U 3
D 1
L 3
D 2
R 1
U 1
R 1
D 2
L 2
U 1
L 2
R 2
L 2
D 3
U 3
D 4
L 3
D 4
R 3
L 2
U 3
R 3
U 1
R 4
U 2
D 1
R 3
L 1
R 1
D 3
R 2
D 1
R 2
L 2
U 2
D 1
R 1
U 2
R 3
L 2
D 1
U 3
R 2
D 4
L 3
U 1
R 1
D 2
U 1
R 3
L 4
D 2
L 1
U 3
L 2
U 3
R 1
D 1
L 2
R 4
D 4
U 1
R 3
U 3
R 1
U 3
L 2
U 3
L 2
D 4
U 1
R 1
L 2
R 2
D 2
L 2
D 2
U 4
D 4
L 2
U 2
D 3
L 4
U 4
D 4
R 1
D 4
R 1
D 1
L 1
R 1
L 1
D 2
L 4
D 2
R 3
L 1
D 2
U 3
L 2
U 3
R 4
D 4
U 1
L 1
U 4
L 4
U 3
D 2
R 3
L 1
D 3
U 3
R 1
L 3
U 1
D 1
R 1
U 3
R 2
L 1
D 1
L 3
U 1
D 1
L 2
R 3
U 4
L 5
D 5
R 5
D 3
U 3
D 1
L 5
R 2
U 1
L 2
D 1
L 1
D 4
R 1
U 1
L 1
U 1
D 3
L 4
D 1
U 5
L 1
R 5
L 2
R 4
D 5
U 2
D 3
U 4
L 1
R 2
U 4
L 4
D 3
R 1
U 1
R 3
D 3
U 5
L 3
D 3
U 5
R 4
L 1
D 4
U 1
L 1
R 4
D 5
L 5
D 3
R 5
U 3
R 2
D 2
U 2
D 5
R 5
D 5
R 2
D 5
U 2
D 2
R 1
L 5
D 2
U 2
R 5
U 1
D 5
U 5
L 2
D 5
L 1
R 4
U 4
D 1
L 5
U 4
L 1
R 3
L 3
R 3
L 3
D 3
R 4
U 5
L 2
D 3
U 2
L 1
R 5
U 1
L 3
U 4
L 4
D 4
R 1
D 4
R 5
D 5
U 5
R 5
L 3
D 3
U 2
R 5
U 5
R 2
D 5
R 1
U 2
D 5
L 3
R 5
U 5
R 4
D 6
L 4
U 4
L 6
R 2
L 4
U 5
L 1
U 6
D 4
R 3
U 2
R 1
D 5
L 4
U 2
R 6
U 1
L 2
D 6
U 1
R 2
L 3
R 2
L 5
U 1
R 3
D 3
L 4
R 5
U 1
D 3
R 6
D 5
R 4
D 3
U 2
D 4
R 5
U 4
R 2
L 2
U 5
D 5
U 6
D 4
U 5
L 5
R 1
D 3
R 3
L 3
D 3
L 3
U 6
R 5
D 6
U 5
L 1
R 4
L 1
U 4
L 5
D 5
U 4
D 3
R 3
L 4
R 4
U 6
L 5
D 2
U 3
R 4
U 5
L 5
D 6
R 5
D 5
L 3
U 6
L 3
U 5
D 2
U 4
R 4
U 3
D 1
R 2
L 6
R 2
L 3
U 5
L 1
R 4
D 2
U 5
D 2
R 4
U 6
R 6
L 6
U 6
L 4
R 5
D 3
U 1
D 6
L 1
D 3
L 4
D 6
U 3
D 2
L 2
U 5
D 2
U 5
D 6
R 4
D 1
U 1
D 5
U 1
L 7
U 3
R 3
U 5
D 2
R 7
D 6
L 7
R 6
L 3
R 3
U 1
L 4
D 4
L 6
R 1
U 4
L 3
R 3
D 3
L 2
D 7
U 7
D 7
R 2
L 6
D 2
R 6
D 6
U 5
L 4
U 3
D 6
U 3
D 1
L 7
U 7
L 4
R 3
D 6
U 2
L 6
D 3
U 7
D 2
R 7
D 4
L 4
R 4
U 2
D 3
U 2
L 4
R 4
L 4
D 6
R 4
U 1
R 7
D 7
U 7
R 6
D 4
U 2
D 4
L 5
U 4
D 6
R 4
U 2
R 1
U 4
D 5
R 1
D 7
R 4
U 1
D 4
U 6
L 6
R 1
D 1
R 1
D 6
R 6
D 3
U 1
D 5
R 4
L 1
D 6
R 6
L 8
U 1
D 7
R 4
L 2
R 6
U 7
L 2
D 5
R 6
D 3
L 4
R 5
U 7
L 3
U 8
L 1
U 6
L 8
R 7
D 2
U 3
R 4
L 8
D 4
L 1
U 1
D 7
U 8
R 7
D 2
L 5
U 6
D 7
R 4
L 1
R 3
U 1
R 2
U 1
D 7
R 6
U 7
D 6
L 2
D 8
U 4
R 8
L 5
D 6
L 7
U 7
R 8
L 5
D 5
R 4
L 5
U 8
D 4
U 6
D 2
U 5
R 7
L 8
U 7
R 3
L 8
R 6
D 4
R 2
D 6
R 5
D 2
U 6
L 4
U 8
R 3
U 1
L 6
R 1
L 3
R 1
U 3
L 8
D 5
U 6
L 8
D 6
R 2
U 6
R 4
L 8
D 1
R 6
D 8
U 6
D 6
L 3
U 4
D 2
L 2
D 7
L 5
U 3
R 8
D 6
R 5
L 8
U 5
L 9
R 7
D 5
R 2
U 5
L 3
D 9
R 4
U 1
L 3
R 5
L 7
R 8
D 1
U 4
L 4
R 3
U 8
L 8
D 8
U 9
R 6
U 3
R 2
U 8
D 9
R 3
L 2
U 9
R 9
L 8
D 2
R 1
L 8
U 7
R 3
L 1
R 9
L 4
R 2
U 3
D 8
R 5
U 9
L 9
R 1
D 5
R 5
U 4
L 9
R 6
D 5
L 2
R 6
U 1
D 5
L 3
R 1
U 5
R 4
D 4
U 8
D 2
U 6
R 6
U 5
R 3
U 5
R 5
L 8
D 9
L 7
R 1
U 7
R 5
U 6
R 8
U 2
R 2
U 8
R 7
D 3
R 6
L 8
R 4
L 8
R 8
U 8
R 3
L 3
U 1
L 9
D 9
R 9
U 4
R 1
L 3
D 4
U 5
R 7
D 6
L 9
U 7
L 4
R 8
U 8
D 4
L 5
D 9
U 2
D 9
R 4
D 8
R 8
D 8
U 9
L 6
R 6
L 2
U 9
L 4
U 10
L 1
R 10
D 3
U 6
R 5
D 3
R 4
L 4
U 7
D 9
R 2
D 10
U 9
L 7
D 4
R 2
L 10
D 5
R 5
U 9
R 4
L 7
R 5
D 3
R 6
D 8
R 6
D 10
R 7
U 4
D 5
U 6
L 3
D 3
U 10
R 10
U 10
D 9
R 6
D 2
R 9
D 8
R 7
U 10
R 4
U 6
R 7
U 2
R 5
U 6
L 4
U 8
L 6
D 8
R 2
U 2
L 4
U 4
R 5
U 10
D 8
U 7
R 5
D 3
L 2
R 9
U 10
L 1
U 5
L 1
D 3
L 9
R 9
U 2
D 3
U 8
R 10
L 2
U 9
D 4
U 4
D 10
L 4
D 3
R 3
L 2
U 8
L 4
D 6
R 2
L 3
D 6
L 4
D 5
L 7
U 9
L 3
D 4
R 6
L 10
U 5
L 3
R 3
D 6
U 1
L 3
R 5
D 8
R 11
U 2
L 11
R 7
U 8
R 4
D 4
U 6
D 4
U 9
D 5
R 9
D 9
U 6
D 1
U 3
L 4
U 3
D 11
U 11
R 4
L 3
U 5
D 1
R 4
U 6
D 4
U 3
D 8
L 7
R 8
L 9
R 3
D 4
U 6
L 2
U 6
R 3
D 8
R 1
L 5
U 8
D 3
R 8
L 8
U 3
R 2
U 11
L 9
U 4
D 10
L 7
D 1
U 2
D 7
R 4
L 2
U 8
L 5
U 5
D 1
L 11
R 4
L 7
U 2
D 9
L 4
U 11
R 8
L 1
D 10
U 5
R 4
L 8
U 8
D 11
U 8
D 3
R 4
U 8
R 2
U 3
R 9
D 4
L 2
D 4
L 4
U 4
R 5
U 4
L 10
U 4
L 4
R 8
D 5
L 2
U 5
D 9
U 10
D 6
R 9
D 4
R 10
U 4
D 12
R 7
L 12
R 8
L 11
U 1
D 4
L 1
D 7
R 1
L 5
R 9
L 3
D 7
L 2
R 6
U 2
D 2
U 9
L 4
U 11
D 4
R 7
D 7
L 11
U 11
L 1
D 9
U 6
R 3
D 3
L 9
R 7
L 6
D 6
R 11
D 10
U 11
R 1
U 2
R 9
L 6
U 7
D 5
U 7
R 4
U 12
R 2
D 1
L 7
R 5
D 6
R 1
D 11
R 9
L 6
U 11
D 7
L 7
R 9
L 7
D 12
U 7
D 12
R 3
L 2
R 10
D 1
L 9
R 3
D 6
R 11
U 2
L 2
U 3
R 3
L 6
U 3
R 1
L 5
R 7
U 2
D 8
L 9
U 7
L 9
D 12
U 3
D 9
U 1
L 10
D 9
L 11
U 6
R 9
U 10
L 5
R 11
U 3
R 9
D 12
U 1
D 11
R 12
U 1
D 7
L 1
U 7
L 5
U 8
L 2
R 6
U 13
R 3
D 8
U 2
L 5
U 7
L 7
U 13
L 3
R 5
D 1
L 9
U 2
L 7
R 9
U 12
R 9
L 13
R 1
U 11
R 4
U 6
D 6
R 13
D 13
U 5
L 9
U 2
D 9
R 12
D 13
U 9
D 10
U 11
R 11
U 12
D 5
L 1
R 2
U 2
D 5
U 1
D 8
R 8
L 1
U 9
L 11
D 10
R 1
D 6
U 5
D 11
L 13
D 2
R 1
L 1
R 7
L 1
U 2
D 6
U 4
L 7
U 8
D 7
L 11
U 5
D 5
R 1
D 6
R 1
U 9
L 11
D 6
U 10
D 10
R 2
U 13
L 5
U 9
L 13
U 7
L 3
D 9
L 13
R 5
L 5
U 5
L 13
U 10
L 1
R 10
L 8
R 13
U 13
R 7
D 6
R 4
L 5
U 3
D 6
R 13
U 9
L 10
U 13
R 3
D 8
L 8
U 5
L 9
U 4
D 11
L 14
U 11
R 1
D 7
U 4
R 11
U 3
R 2
U 11
L 6
R 2
D 5
L 1
U 1
L 11
R 5
U 10
D 12
R 3
L 6
R 3
D 9
L 4
D 11
L 11
D 4
R 10
D 3
L 12
D 8
U 7
R 2
D 1
L 5
U 11
L 9
R 4
U 4
D 11
R 9
D 3
U 6
L 4
U 4
L 8
U 6
L 14
R 13
D 5
R 5
U 5
R 2
U 8
L 12
R 2
U 12
D 4
R 3
L 5
D 11
R 7
U 9
D 10
L 1
D 4
U 4
D 1
R 9
L 10
R 11
L 8
D 10
R 10
L 3
R 1
L 9
D 4
L 8
R 7
U 12
R 5
L 10
R 7
U 6
R 7
U 8
D 1
U 1
L 1
R 2
D 14
U 4
D 5
R 2
D 1
R 5
D 6
R 3
L 12
U 11
D 1
L 7
R 10
U 2
R 6
U 11
R 12
U 2
R 4
L 1
D 7
U 2
D 9
U 12
D 11
U 2
L 4
R 10
U 13
R 2
L 8
R 13
L 11
R 13
L 13
R 5
D 2
U 10
D 14
L 8
U 11
R 1
L 15
U 13
L 4
D 9
L 1
U 14
R 10
D 2
R 15
L 12
R 15
U 15
L 3
U 1
L 1
R 9
D 11
L 9
D 3
U 12
R 1
D 7
L 8
R 10
D 2
L 4
D 2
U 12
D 12
L 15
U 3
D 13
U 13
D 3
R 4
L 7
D 6
U 7
D 7
R 2
D 2
R 10
L 12
U 14
D 1
U 5
L 8
R 1
U 5
D 8
U 1
D 15
U 13
L 15
R 1
L 10
U 14
D 15
R 10
L 13
D 8
L 7
U 11
R 7
U 10
D 15
L 1
U 5
L 2
R 6
L 4
U 15
R 13
U 4
R 7
U 4
R 10
L 3
U 10
R 10
D 13
R 11
L 12
D 12
U 7
R 8
D 14
L 4
R 4
L 8
D 6
R 8
L 5
U 6
L 7
U 14
D 14
R 14
L 9
D 5
U 14
R 16
L 4
R 14
U 3
D 10
L 5
R 9
D 4
R 14
L 16
D 13
L 4
R 13
D 5
R 5
U 1
R 6
L 15
D 16
R 15
D 11
R 11
D 6
U 7
L 10
D 7
U 12
R 6
L 2
U 7
L 8
U 14
L 5
U 6
L 10
R 16
L 13
D 13
U 7
R 9
L 8
U 3
D 6
R 2
L 4
R 15
D 12
U 7
D 4
U 2
L 10
D 4
L 11
D 12
R 5
L 13
U 16
L 12
R 3
L 16
R 14
L 8
R 7
U 7
L 7
D 12
R 2
D 5
R 14
U 9
D 15
U 2
L 10
D 1
U 13
L 2
R 13
D 5
U 1
D 2
L 7
D 14
U 12
D 8
U 10
L 3
R 1
U 14
R 8
L 12
R 8
U 2
R 1
D 14
U 5
D 11
R 16
U 1
R 13
D 2
U 14
R 11
D 12
U 17
R 12
L 6
U 4
R 16
D 4
R 2
D 1
U 6
D 11
R 8
L 7
R 3
U 3
L 14
R 11
L 9
U 9
L 2
R 6
U 11
R 9
L 15
U 7
R 6
D 15
U 11
L 5
U 2
R 8
D 13
L 5
U 6
R 13
D 15
U 11
L 10
R 16
L 14
U 15
R 3
L 9
U 11
L 6
R 7
L 6
D 16
U 8
R 2
U 14
D 14
R 15
D 2
L 16
D 14
U 17
D 6
L 16
D 7
R 2
U 17
D 2
U 2
D 15
U 5
R 9
L 10
D 16
U 1
L 5
U 6
R 10
D 11
R 1
U 7
R 13
U 6
R 2
L 11
D 13
U 8
D 4
R 15
U 8
R 14
L 17
U 5
L 12
U 11
L 14
R 12
L 2
D 10
L 14
D 10
L 11
U 15
D 7
U 5
R 10
U 4
R 14
U 3
D 9
L 6
D 10
L 1
U 4
L 15
R 4
U 13
D 18
R 15
L 3
U 17
R 5
L 3
D 3
L 1
U 10
L 3
R 5
U 13
D 2
R 17
D 3
U 4
L 15
D 5
L 18
U 7
D 7
U 10
L 18
D 14
U 18
L 11
D 3
L 15
D 14
L 16
R 2
D 8
L 12
U 11
R 12
D 2
L 3
R 12
U 7
L 9
D 8
U 7
L 10
D 2
U 10
D 16
L 17
R 13
L 12
R 18
L 6
U 2
D 6
L 15
D 8
U 14
R 12
L 8
R 6
L 17
U 5
D 4
L 8
U 16
R 8
U 12
L 1
R 6
D 11
L 10
U 13
R 4
L 9
R 7
D 1
L 17
R 4
L 13
R 8
D 14
U 3
R 10
L 18
R 11
U 3
D 5
U 2
L 14
R 12
D 12
R 11
U 5
D 17
R 2
D 6
L 13
D 4
L 5
R 7
U 7
L 12
D 16
U 6
L 1
R 18
D 4
R 11
D 10
L 11
D 2
L 1
D 5
U 2
R 19
D 10
U 14
R 1
U 11
R 15
D 2
L 15
U 19
R 3
L 2
D 12
R 2
L 15
R 8
D 3
R 8
D 11
R 17
D 5
R 16
L 8
R 12
L 5
R 14
D 3
R 19
D 4
R 18
L 4
U 12
D 7
L 8
R 11
U 14
L 12
U 17
L 1
D 5
R 8
L 18
U 16
L 15
D 13
U 18
R 11
L 7
R 19
L 15
U 10
L 4
R 9
U 7
R 8
L 16
R 8
L 17
D 8
L 17
D 18
U 9
D 2
L 6
U 6
L 7
D 18
R 1
U 13
L 2
U 1
R 8
U 11
D 14
R 6
U 13
R 2
D 11
L 5
U 12
D 7
U 4
R 4
D 19
R 17
L 10
U 5
L 1
U 4
L 6
D 11
L 16
D 9
R 16
L 3
R 1
D 3
L 5
U 18
D 13
R 10
U 5
D 4
U 17
R 4
D 18
U 1

Day 10: python

this documentation is autogenerated. Add a README.adoc to your solution to take over the control of this :-)

python

sol.py

#!/usr/bin/env python3


def load_input():
    data = list()
    with open('input') as fd:
        for line in fd:
            line = line.strip().split()
            if line[0] == 'addx':
                data.append([line[0], int(line[1])])
            else:
                data.append(line)
    return data


def update_pic(pic, value, cycle):
    if cycle in list(range(value-1, value+2)):
        pic += '#'
    else:
        pic += ' '
    return pic


def part():
    data = load_input()
    value = 1
    cycle = 0
    limit = 20

    result = 0
    pic = ''

    next_add = 0

    for cmd in data:
        value += next_add
        next_add = 0

        pic = update_pic(pic, value, cycle % 40)
        cycle += 1

        if cmd[0] == 'addx':
            pic = update_pic(pic, value, cycle % 40)
            cycle += 1
            next_add = cmd[1]

        if cycle >= limit:
            result += value*limit
            limit += 40

    print("Result 1: ", result)

    # Task2
    print("Result2:")
    for i in range(cycle//40):
        print(pic[40*i:40*i+40])


def main():
    part()


if __name__ == '__main__':
    main()

input

noop
noop
noop
addx 3
addx 20
noop
addx -12
noop
addx 4
noop
noop
noop
addx 1
addx 2
addx 5
addx 16
addx -14
addx -25
addx 30
addx 1
noop
addx 5
noop
addx -38
noop
noop
noop
addx 3
addx 2
noop
noop
noop
addx 5
addx 5
addx 2
addx 13
addx 6
addx -16
addx 2
addx 5
addx -15
addx 16
addx 7
noop
addx -2
addx 2
addx 5
addx -39
addx 4
addx -2
addx 2
addx 7
noop
addx -2
addx 17
addx -10
noop
noop
addx 5
addx -1
addx 6
noop
addx -2
addx 5
addx -8
addx 12
addx 3
addx -2
addx -19
addx -16
addx 2
addx 5
noop
addx 25
addx 7
addx -29
addx 3
addx 4
addx -4
addx 9
noop
addx 2
addx -20
addx 23
addx 1
noop
addx 5
addx -10
addx 14
addx 2
addx -1
addx -38
noop
addx 20
addx -15
noop
addx 7
noop
addx 26
addx -25
addx 2
addx 7
noop
noop
addx 2
addx -5
addx 6
addx 5
addx 2
addx 8
addx -3
noop
addx 3
addx -2
addx -38
addx 13
addx -6
noop
addx 1
addx 5
noop
noop
noop
noop
addx 2
noop
noop
addx 7
addx 3
addx -2
addx 2
addx 5
addx 2
noop
addx 1
addx 5
noop
noop
noop
noop
noop
noop

Day 11: python

this documentation is autogenerated. Add a README.adoc to your solution to take over the control of this :-)

python

sol.py

#!/usr/bin/env python3

from copy import deepcopy
from collections import deque
import math as m


def load_input():
    d = {
        'items': deque(),
        'op': "",
        'cond': list(),
        'count': 0
         }
    data = list()

    with open('input') as fd:
        for i, line in enumerate(fd):
            line = line.strip().replace(',', '')
            temp = line.split()
            if i%7 == 0:
                data.append(deepcopy(d))
            elif (i+6)%7 == 0:
                data[-1]['items'] = deque([int(v) for v in temp[2:]])
            elif (i+5)%7 == 0:
                data[-1]['op'] = "".join(temp[-3:])
            elif (i+4)%7 == 0:
                data[-1]['cond'].append(int(temp[-1]))
            elif (i+3)%7 == 0:
                data[-1]['cond'].append(int(temp[-1]))
            elif (i+2)%7 == 0:
                data[-1]['cond'].append(int(temp[-1]))
            else:
                pass

    return data


def part1():
    data = load_input()
    rounds = 20
    worry_factor = m.prod([monkey['cond'][0] for monkey in data])
    for _ in range(rounds):
        for monkey in data:
            for _ in range(len(monkey['items'])):
                old = monkey['items'].popleft()
                monkey['count'] += 1
                worry = eval(monkey['op'])//3
                if worry % monkey['cond'][0] == 0:
                    data[monkey['cond'][1]]['items'].append(worry)
                else:
                    data[monkey['cond'][2]]['items'].append(worry)

    temp = sorted([monkey['count'] for monkey in data], reverse=True)
    result = temp[0]*temp[1]
    print("Result 1: ", result)


def part2():
    data = load_input()
    rounds = 10000
    worry_factor = m.prod([monkey['cond'][0] for monkey in data])
    for _ in range(rounds):
        for monkey in data:
            for _ in range(len(monkey['items'])):
                old = monkey['items'].popleft()
                monkey['count'] += 1
                worry = eval(monkey['op'])%worry_factor
                if worry % monkey['cond'][0] == 0:
                    data[monkey['cond'][1]]['items'].append(worry)
                else:
                    data[monkey['cond'][2]]['items'].append(worry)

    temp = sorted([monkey['count'] for monkey in data], reverse=True)
    result = temp[0]*temp[1]
    print("Result 2: ", result)



def main():
    part1()
    part2()


if __name__ == '__main__':
    main()

input

Monkey 0:
  Starting items: 97, 81, 57, 57, 91, 61
  Operation: new = old * 7
  Test: divisible by 11
    If true: throw to monkey 5
    If false: throw to monkey 6

Monkey 1:
  Starting items: 88, 62, 68, 90
  Operation: new = old * 17
  Test: divisible by 19
    If true: throw to monkey 4
    If false: throw to monkey 2

Monkey 2:
  Starting items: 74, 87
  Operation: new = old + 2
  Test: divisible by 5
    If true: throw to monkey 7
    If false: throw to monkey 4

Monkey 3:
  Starting items: 53, 81, 60, 87, 90, 99, 75
  Operation: new = old + 1
  Test: divisible by 2
    If true: throw to monkey 2
    If false: throw to monkey 1

Monkey 4:
  Starting items: 57
  Operation: new = old + 6
  Test: divisible by 13
    If true: throw to monkey 7
    If false: throw to monkey 0

Monkey 5:
  Starting items: 54, 84, 91, 55, 59, 72, 75, 70
  Operation: new = old * old
  Test: divisible by 7
    If true: throw to monkey 6
    If false: throw to monkey 3

Monkey 6:
  Starting items: 95, 79, 79, 68, 78
  Operation: new = old + 3
  Test: divisible by 3
    If true: throw to monkey 1
    If false: throw to monkey 3

Monkey 7:
  Starting items: 61, 97, 67
  Operation: new = old + 4
  Test: divisible by 17
    If true: throw to monkey 0
    If false: throw to monkey 5

Day 12: python

this documentation is autogenerated. Add a README.adoc to your solution to take over the control of this :-)

python

sol.py

#!/usr/bin/env python3


import networkx as nx
import string
import math as m


def load_input():
    with open('input') as fd:
        data = list()
        start = None
        end = None
        for i, line in enumerate(fd):
            temp_line = list(line.strip())
            if 'S' in temp_line:
                j = temp_line.index('S')
                start = (i, j)
                temp_line[j] = 'a'
            if 'E' in temp_line:
                j = temp_line.index('E')
                end = (i, j)
                temp_line[j] = 'z'
            temp = [string.ascii_lowercase.index(v) for v in temp_line]
            data.append(temp)
    return data, start, end


def weight_func(n0, n1, attr):
    return 1 if attr['w'] <= 1 else None


def create_graph(data):
    graph = nx.DiGraph()
    for i in range(len(data)):
        for j in range(len(data[0])):
            if i+1 < len(data):
                graph.add_edge((i, j), (i+1, j), w=data[i+1][j] - data[i][j])
                graph.add_edge((i+1, j), (i, j), w=data[i][j] - data[i+1][j])
            if j+1 < len(data[0]):
                graph.add_edge((i, j), (i, j+1), w=data[i][j+1] - data[i][j])
                graph.add_edge((i, j+1), (i, j), w=data[i][j] - data[i][j+1])
    return graph


def part1():
    data, start, end = load_input()
    graph = create_graph(data)
    result = nx.shortest_path_length(graph, source=start, target=end, weight=weight_func)
    print("Result 1: ", result)


def part2():
    data, _, end = load_input()
    starts = list()
    shortest = list()
    for i in range(len(data)):
        for j in range(len(data[0])):
            if data[i][j] == 0:
                starts.append((i, j))

    graph = create_graph(data)

    for start in starts:
        try:
            temp = nx.shortest_path_length(graph, source=start, target=end, weight=weight_func)
            shortest.append(temp)
        except nx.exception.NetworkXNoPath:
            pass

    result = min(shortest)
    print("Result 2: ", result)


def main():
    part1()
    part2()


if __name__ == '__main__':
    main()

input

abcccccccccccccccccaaccccccccccccaaaaaaaacccccccccccaaaaaccccaaaaaaccaaaaaaaaaaaaaaaaaccccccccccccccccaaacccccaaaaaaaacccaaaccccccccccccccccccccccccccccccccccccccccccccccccccaaaaa
abccccccccccccccccaaacaacccccccccccaaaacccccccccccccaaaaaacccaaaaaaccaaaaaaaaaaaaaaaaaaaacccccccccaaacaaacccccaaaaaaaaaccaaaaccccccccccccccccccccccccccccccccccccccccccccccccaaaaaa
abcccccccccccccccccaaaaacccccccccccaaaaaccccccccccccaaaaaaccccaaaacccaaaacccaaaaaaaaaaaaacccccccccaaaaaaaaaacccaaaaaaaaccaaaaccccccccccccccccccccccccccccccccccaaacccccccccccaaaaaa
abcccccccccccccccaaaaaacccccccccccaaacaaccaaccccccccaaaaaaccccaaaacccaaaccccaaaaaaaaaaaaaccccccccccaaaaaaaaaccaaaaaacccccaaacccccccccccccccccccccccccccccccccccaaaccccccccccccccaaa
abcccccccccccccccaaaaaaaacccccccccaacccacaaacaacccccccaaccccccaccaccccccccaaaaaaaaaaaaccccccccccccccaaaaaaacccaaaaaaacccccccccccccaacccccccccccccccccccccccccccaaaccccccccccccccaaa
abcccccccccccccccaacaaaaacccccccccccccccccaaaaacccccccccccccccccccccccccccaaaaaaaaaaaaccccccccccccccaaaaaaccccaaccaaacccccccccccaaaaaaccccccccccccccccccccccccccdccccccccccccccccaa
abccaacccccccaaacccaaacaccccccccccaaacccaaaaaaccccccccccccccccccccccccccccaaacccaaaaaacaaaaccccccccaaaaaaaccccccccaaacccccccccccaaaaaacccccccccccccccccllllllcccdddddcccccccccccccc
abaaaacccacccaaccccaacccccccccccccaaacccaaaaaaaacccccccccccccccccaaccccccccacccaaaaccccaaaaccccccccaaacaaaccccccccccccccccccccccaaaaaaccccccccccccccccllllllllldddddddddddccaaccccc
abaaaaccaaaaaaaacccccccccccccccaaaaaaaacaacaaaaacccccccccccccccccaaacccccccaaacaaccccccaaaacccccccccccccaaccccccccccccccccccccccaaaaaccccccccccccccccclllllllllldddddddddeeaaaccccc
abaaaccccaaaaaaaaccccccccaaacccaaaaaaaacccaaacccccccccccccccccaaaaaaaaccccccaaaaacccccccaacccccccccccccccccccccccccccccccccccccccaaaaccccaaaccccccccckllppppplllmmmmmmmdeeeeaaccccc
abaaaacccaaaaaaaaacccccaaaaaaccccaaaaaccccaaccccccccccccccccccaaaaaaaaccccccaaaaaaacccccccccccccccccccccccccccccccccccccccccccccccccccccaaaacccccccckklpppppppplmmmmmmmmmeeeeaccccc
abaaaacccaaaaaaaaacccccaaaaaacccaaaaaaccccccccaacccccccccccccccaaaaaaccccccaaaaaaaacccccccccccccccccccccccccccccccccccccccccccccccccccccaaaacccccccckkkppppppppqmmmmmmmmmmeeeaacccc
abaaaaaccaaaaaaaaccccccaaaaaacccaaaaaaccccccacaaaacccccccccccccaaaaaaccccccaaaaaaaaccccccccaaaaccccccccaaacccccccccccccccccccccccccccccccaaacccccccckkkpppuuuppqqqqqqqqmmmeeeeacccc
abacccccaaaaaaaccccccccaaaaaccccaccaaaccccccaaaaaacccccacccccccaaaaaaccccccaaaaaacaaaccccccaaaaccccccccaaaacccccccccccccccccccccccccccccccccccccccckkkpppuuuuuuqqqqqqqqqnnneeeccccc
abcccccccaccaaaccccccccaaaaacccccccccccccccccaaaacccaaaacccccccaacaaacccccccccaaacaaaaaccccaaaaccccccccaaaaccccccccccccccccccccccccccccccccccccccckkkkpppuuuuuuuqvvvvqqqnnneeeccccc
abcccccccccccaaacaaccccccccccccccccccccccccccaaaacccaaaaaacccccccccccccccccccccccaaaaaaccccaaacccccccccaaaccccccccccccccccccccccccccccccccccccccckkkkrrpuuuxxxuvvvvvvvqqnnneeeccccc
abcccccccccccccccaacaaaccccccccccccccccccccccaacaacccaaaaacccccccccccccccccccccccaaaaaaccccccccccccccccccccccccccccccccccccccccccccccccccccccccckkkkrrrruuxxxxuvvvvvvvqqnnneeeccccc
abcccccccccccccccaaaaacccccccccccccccccccccccccccaccaaaaacccccaaccccccccccccccccccaaaaaccccccccccccccccaaaccccccccccccccaaacccccccccccccccccccckkkkrrrruuuxxxxyyyyyvvvqqnnneecccccc
abcccccccccccccaaaaaacccccccccccaacaacccccccccccaaaaaaaaacccacaaaacaaccaaccccccccaaacaaccccccccccaaccccaaaaaacccaacaacccaaacacccccccccccccccccjjkkrrrruuuuuxxxyyyyyvvqrqnneffcccccc
abcccccccccccccaaaaaaaacccaaacccaaaaaccccccaacccaaaaaaaaacccaaaaaacaaaaaaccccccccaaacaaacccccccaaaaaacaaaaaaacccaaaaacaaaaaaaaccccccccccccccccjjjrrrtttuuxxxxxyyyyyvvrrnnnfffcccccc
SbccccccccccccccccaaaaacccaaaaccaaaaaaccccaaaaaacaaaaaaaaacccaaaacccaaaaaccccccccaaaaaaacccccccaaaaaaaaaaaaaaccaaaaaccaaaaaaaaccccccccccccccccjjjrrrtttxxxEzzzzyyyvvrrrnnnfffcccccc
abccccccccccaaaccaaccaacccaaaaccaaaaaacccccaaaaacaaaaaaaaacccaaaaccaaaaaacccccccccaaaaaaccccccccaaaacaaaaaaacccaaaaaaccaaaaaacccccccccccccccccjjjrrrtttxxxxxyyyyyyvvrrrnnnfffcccccc
abcccccccccaaaaccaacccccccaaacccaaaaaacccaaaaaaaaaaaaaaaaacccaacacaaaaaaaacccccaaaaaaaacccccccccaaaacccaaaaaaccccaaaacccaaaaacccccccccccccccccjjjrrrtttxxxxxyyyyyyywvrrnnnfffcccccc
abcccccccccaaaacccccccccccccccccccaaaccccaaaaaaaaaaaaaaaaaacccccccaaaaaaaacccccaaaaaaaaaccccccccaccacccaaaaaaccccacccccaaaaaacccccccccccccccccjjjrrrrttttxxxyyyyyyywwrrroooffcccccc
abccccccccccaaaccccccccccccccccccccccccccaaaaaaaaaccaaaaaaaccccccccccaaccccccccaaaaaaaaaaccccccccccccccaacccccccccccccccaaccccccccccccccccccccjjjjqqqqttttxxyywwwwwwwwrrooofffccccc
abcccccccccccccccccccccccccccccccccccccccccaaacacccccaaaaccccccccccccaacccccccccccaaacaaacccccccccccccccccccccccccccccaaacccccccccccccccccccaacjjjjqqqqqttwwwwwwwwwwwrrrooofffccccc
abcccccccccccccccccccccccccccccccccccccccccaaccccccccccaacccccccccccccccccccccccccaaacccccccccccccccccccccccccccccccccaaacccccccccccccccccaaaaaajjjjqqqqttwwwwwwsswwrrrrooofffccccc
abcccccaaaaccccccccccccccaacaaccccccccccccccccccccccccccccccccccccccccccccccccccccaacccccccccccccccccccccccccccccccaaaaaaaaccaaaacccccccccaaaaaacjjjiqqqtttwwwwsssssrrrrooofffccccc
abccccaaaaaccccccccccccccaaaaacccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccaaaaaaaaccaaaaacccccccccaaaaacciiiiqqttswwwssssssrrroooogffccccc
abccccaaaaaacccccccccccccaaaaaacccaaaccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccaaaaaccaaaaaaccccccccaaaaacccciiiqqqssssssspppooooooogggaccccc
abccccaaaaaaccccccccaacccaaaaaaccccaacccccccccccccccccccccccccccccccccaaaaccccccccccccccccccccccccccccccccccccccccccaaaaaaccaaaaaaccccccccaaaaaccccciiiqqsssssspppppoooooggggaacccc
abccccaaaaaccccccaaaaacccaaaaaacaacaaaaaccccccccccccccccccccaacccccccaaaaacccccccccccaaaccccccccccccccccccccccccccccaaaaaacccaaaaacccccccccacccccccciiiqqqpssspppppgggggggggaaacccc
abccccccaaacccccccaaaaaccccaaaccaaaaaaaacccccccaacccccccaaccaacccccccaaaaaacccccccccaaaacccccccccccaaaaccccccccccccaaccaaacccaaacccccaaacaaaccccccccciiqqppppppphhhggggggggaaaacccc
abccccccccccccccccaaaaaccccccccccaaaaaccccccccaaacaaccccaaaaaacccccccaaaaaaaccccccccaaaacccccccccccaaaacccccccccaaaaaacccccccccccccccaaaaaaaccccccccciiippppppphhhhggggggcaaacccccc
abaacccccccccccccaaaaaccccccccccccaaaaaccccccccaaaaacccccaaaaaaacccccaaaaacaaacccccccaaacccccccccccaaaacccccccccaaaaacccccccccccccccccaaaaaacccccccccciiiippphhhhhhcccccccaaacccccc
abaacccccccccccccccaaacccccccccccaaacaaccccccaaaaaaccccccaaaaaaacccaaccaaacaaaaccaaaccccccccccccccccaaacccccccccaaaaaaacccccccccccccccaaaaaaaacccccccciiihhhhhhhhaaaccccccccccccccc
abaaccccccccccccccccccccccccccccccaacccccccccaaaaaaaacccaaaaaaccaaaaaacccccaaaacaaaaaccccccccccccccccccccccccccaaaaaaaaccccccccccccccaaaaaaaaaccccccccciihhhhhhcaaaacccccccccccccca
abaaccccccccccccccccccccccccccccccccaacccccccaacaaaaacccaaaaaaccaaaaacccccccaaaaaaaacccccccccccccccccccccccccccaaaaaaaacccccccccaaacaaaaaaaaaacccccccccccchhhaccccaacccccccccccccca
abaaccccccccccccccccccccccccccccccccaaaaaaccccccaaccccccccccaaccaaaaaaacccccaaaaaaaccccccccccccccccccccccccaaaccacaaacccccccccccaaaaaaacaaacaaaaaacccccccccaaacccccccccccccccaaaaaa
abccccccccccccccccccccccccccccccccccaaaaaccccccaaccccccccccccccaaaaaaaacaaaaaaaaaaccccccccccccccccccccccccaaaaaaccaaaccccccccccccaaaaaacaaacaaaaaacccccccccaaaccccccccccccccccaaaaa
abccccccccccccccccccccccccccccccccaaaaaaaacccccccccccccccccccccaaaaaaaacaaaaaaaaaaaaacccccccccccccccccccccaaaaaacccccccccccccccaaaaaaaaaaaaaaaaaacccccccccccccccccccccccccccccaaaaa

Day 13: python

this documentation is autogenerated. Add a README.adoc to your solution to take over the control of this :-)

python

sol.py

#!/usr/bin/env python3


def load_input():
    data = list()
    with open('input') as fd:
        for line in fd:
            line = line.strip()
            if line:
                data.append(eval(line))
    return data


def check_order(packet1, packet2):
    ordered = True
    stop = False
    if packet1 and not packet2:
        stop = True
        ordered = False
    elif not packet1 and packet2:
        stop = True
    elif not packet1 and not packet2:
        pass
    else:
        for item1, item2 in zip(packet1, packet2):
            if isinstance(item1, list) and isinstance(item2, list):
                stop, ordered = check_order(item1, item2)
            elif isinstance(item1, int) and isinstance(item2, list):
                stop, ordered = check_order([item1], item2)
            elif isinstance(item1, list) and isinstance(item2, int):
                stop, ordered = check_order(item1, [item2])
            else:
                if item1 > item2:
                    ordered = False
                    stop = True
                elif item1 < item2:
                    stop = True
                else:
                    pass

            if stop:
                break

        else:
            if len(packet1) > len(packet2):
                ordered = False
                stop = True
            elif len(packet1) < len(packet2):
                stop = True
            else:
                pass

    return stop, ordered


def part1():
    data = load_input()
    result = 0
    for i in range(len(data)//2):
        _, ordered = check_order(data[2*i], data[2*i+1])
        if ordered:
            result += i + 1

    print("Result 1: ", result)


def part2():
    data = load_input()
    order = [[[2]], [[6]]]

    for i in range(len(data)):
        for j in range(len(order)):
            _, ordered = check_order(order[j], data[i])
            if not ordered:
                break
        else:
            j += 1

        order.insert(j, data[i])

    result = (order.index([[2]]) + 1) * (order.index([[6]]) + 1)
    print("Result 2: ", result)


def main():
    part1()
    part2()


if __name__ == '__main__':
    main()

input

[[0,5,[[],[],2,[7,9]]],[[8,8,3,[6,3,8,9,1]],[2,0,10,7,10],4,10,[9,[1,8],4,[4,0,5,10],[4,0,8,8]]],[9,10],[],[0,[[]],4,10]]
[[],[4,7,3,6,[2,[10],[2,5,10],5,2]],[[1,10,[6,9],3],[6,10,8,[1],[10,7,3]],[[7],[10,9,9,2],1,7],7],[3,7,9,[[4],0]],[[4],[[9],0,9,[]],[7,[],[],[5,7]]]]

[[10,[[10,7,3,0,10],10],[[8,7,7,2,8]],0],[0,7,4],[2,10,[],[8,[7,10,10],1,0],[2,8,6]]]
[[[7,6,5,[]],[[10,0,4,1,6],[5,9,10,6],[1,5]],[7,[6],[10,5,6,7,1]]],[2,[3,[7],[4],7,[4,4]],[],0,[]],[5,[1,6,6],[8,9,[1,1]],[5]]]

[[[[3,9],5,[5],[0],5],[],[4,[6,2,2],[10],[],7]],[[],0]]
[[],[[3],6],[10,[[4,1,10],[2,7,6,0],[0,8,9],[7,0,3,4,4],7],[[7],3,[3,2],[],4]],[[[10,2,7,8]]],[10]]

[[10,6,7,0,[1,7]],[[[3,5,9,1,10],[],1,2,[0,9]]],[4,1,[4,4]],[1,2,2,5],[[5,[8],[]],7,[1,3,[4,1],5],[[10,7,9,2,2],[]],[[3,0,10,7],0,[],[0,3,9,7,3]]]]
[[[],[4,[5],4,[5,5,8,9,1]],[7,10,[8,8,6,2],[],[3,3,6,2]],[3,3]],[],[5,[4,[10,6,6],[]],[[0],[4,7,0],[2,9],[9,4,3,8]]],[[3,[7,0,2,10,3],1,10,10],0,9,[[8,1]],[2]],[8,[[10,2],5,[0],[8,3]],[5,[4,1],[9,8],[0],9]]]

[[8]]
[[[[6,5,8,3],1,[1,5,5,5],[9,5,0,1],[7,0,0,4]],[[6],10,[3,2]],3,6,[10]],[4,[3,[1],10,[5,5,7,3]],[[6,2,5,0,5],[0],[7,5,4],[4,9]],[]],[],[[[4]],[4,[5,3,6,5,0],[2,6,1,6]],2,[[4,2,8,1],[6,2,7],[7,7,10,7],2],2]]

[[],[],[[[7,9,4,8]],[[5,1]],[7,[8],[9,1,5,4],2,[]],4],[2],[[[2,10],[9,7],[3,6,9,9,7],5,2],8,0]]
[[],[0,1,6]]

[[[[2,8],7,[]],[[7,6,10,3,2],3],4],[],[[[4,10],4,[1,3,8]],5,4],[[7],7],[]]
[[[]],[0],[3,[7,[5,3,2,5,6],2,[3,1],10],[8,10,[3,8]],7]]

[[4],[[[7,9,8],3,6],[[],8,[2]]]]
[[[8,3,[3,5]],0],[3,[[9,2],[2],7,1],10],[[3,[0,6,6],10,[5],[1,7,0]]],[],[6]]

[[6,8],[7,[[],[1,4,3,6],[7,8,9,2,10],8,7],8,9],[6,7,[[10]],[[4,8,9]]],[[[6,3,6,1]],[[8,10,10,0,5],[1,4,4,7,5],1,[0,2,1],5]],[5,5,[[2],10,[],6]]]
[[[[],4,7,0,7],[[1,2],0,[7,1,4],[6,2],[8,6,2,1]],0]]

[[2,[[5,4,10],3,[9,0,8,7,4],4,6]],[[[8,10,7]],[2,[]],5],[[]],[4,9,[],7],[9,[[6],0,[]],[7,[],2],0]]
[[[[5,5,0,2,9],6,2,1],[10],[[4],[10,6,6,10],[]]],[],[6,[]],[0,1]]

[[9,6,[3],[1,5,[9,2,4],[4,1,6],[]],[[]]]]
[[5],[9,[],3,8]]

[[8,[[7],1,8,[2,6,4],8]],[]]
[[],[],[[0,[],9,[0]],[[3],[4,6],[8,6,5,8,0]],6],[],[]]

[[10,0,[[],5,[4,4,10,9,8]],8],[],[3,[[0,3]]]]
[[[[0,9,4],[7,3,9,4]],[4,[0,5,7,10,3],0,9]]]

[[[[0,8,8,0,4],[7,10,5,10,6],9,8,10],[[0],[],[0],10]],[9,8,[[8,7,5,5,10]]],[1,[10,4,1,[10,4,10,0,1]]],[[[1,9],9,5],3,1,[[7,6]],[]]]
[[3,1,[[9,5,8],7],[3]],[[9],7,4,[]]]

[[7,3]]
[[[[4,2],[3,3],4,7,[1,2]],5],[[6,[2,9],[10,3,6,4,2],[2],3]]]

[[[6,[]],[[4]]],[],[3,[[7,2,1],[],[8,9,9,10],[9],10],9,1]]
[[[1,7,[7]],10],[[6,8,2,[0,9],2]]]

[[[2],9],[[[2],[5,4,0,9]],[10,3,2,[0,0]],[[1,6,10,0,1],[3,5,6,7],[8],[1]]],[[8],[6,10],8],[9,0]]
[[]]

[[[],[4,3],6,[[],[9,5,3,9],[9,5],2,3],5],[4,[2,2,9,6,5],2],[[],2,[[],[5,6,6]],[10,[7,2,4,2,9],8]]]
[[5,[7,[],[0,4,3,10],4,9],10,[[4,8]],10],[[[2,5,5],6,0,[5],[9,3,2,10]],[[5,4,8],[3],[1]]]]

[[5,[[7,2,1,10,0]],[1,[4,0,5]]],[0],[[],10,[]],[10,[[9,3],[0],3],[7,9,3,[]],1]]
[[[4,2],1,4,7],[[[],9,[3,5,8,7,6]]],[[6,3,4,[8,2,5,2,5],[9,8,3,1,9]],1,[[10,9]],[[1,4,1],1,8,[2],[10,3]],10],[[[],1,1,[]],0,9],[7,[6,[7,2,2,6,0],7,5],[[3,10,10,0],1,[1,8,4],[1,1]],[[7,7,6,2,4],2]]]

[[5,7],[],[6,[4,8]],[8,[3,[10,2,0,5,6],[9,0,7,8,8],[0]]],[2,4,7,[]]]
[[],[4,[5,0,3,6]],[5],[]]

[[1,1,8],[[[1,10,3],1,[6,6,6],6]]]
[[[4,10],0,9,[[1],[3,5],[10],4,[7]],1]]

[[[0,5,10,2]],[[]]]
[[[8]],[[[8,9,0]],10,[[9,7,5,3,6],[9,7,3]]],[6],[2,[[4,7],[6,9,8],[10,0,8,0,0],[9,4,3,10]]]]

[[[],[[],7,[10,1,8,4,0],6,[10,2,2,6]],[[5,7,10],[9,9,5,5,0],6,[6,2],10]],[],[]]
[[],[7],[[[7,3],6,8,9,[2]],[[7,8,6,2],[],9],3,[[9,0,8,10],9,[1,9,7,0],[6,9,1],8]]]

[[8,[[10,8,5],[],[4,0,6]]]]
[[3,4,[0,3,7],2],[[2,6,9,4,0],[1,[6,10],10,[4,5,1,9],0],[10]],[7,[[0,2],[3,5,10,1],9,[6,9,0,1]],8,9],[],[[[1,10,8,10,7],[4,9,5,2,1],5],10,4,7]]

[[[[9,9,4,6,8]],3,2,10,6],[[[2,0],6,[9,10,0,1],[1,0,7,7,1]],[9,10,3,[1]]],[4,6,2,[2,2,5,[9],3],4],[[8],10,[9,[],[],[1]],8,[[],3]],[4,4,[10],7]]
[[[9,[9],[4,0,10],[3,0,4,4]],8,[0,[4,8,0,5],[5,2],[0]],8,1],[6,0],[5,0,3,4],[[[2]],1],[]]

[[[10,3],6,6,10]]
[[[7]],[],[[0,[0,3,7],[],[5,0,2,6]],[2,[8,9,3,9]],[]]]

[[],[[1,8,[0,10,3,7,2],5]],[[9,[4,7,7,6,10],[4,9,3]]],[[[9,4,4,9]]],[[[9,6,8,1]],2,4,[],5]]
[[1,[],[10,6],9]]

[[3,1,4,8],[[],[1]],[]]
[[2,[5,[]],[]],[[2,1,8,6,[4,10,2,7,3]],4,[[9,0,2,3,7],8,10,4],5,[[],[10,9]]],[[4,[9,6,3,9],2,0]],[[[3]],[]]]

[[1,[9],[[],4,[7,9,9],[1,4],[2,6,4]],3,2],[[],6],[[[0,7,3,2],2,2,[5],[3,7,8,3]],10,1],[[[],[],0,[0],[7,4,5]]],[[9,[3,3],[6,8],[10]],2]]
[[9,7,[],[0],0]]

[[],[[5,5],[9],10,2],[],[[6,4,[5,6,9,1,2],2,8]]]
[[[[8,3,5,10,1],[3,4]],[9],[[3,10],9,7,[]],6],[2,10,[10,2,0],[0],[1,1,[2,4,0,8],[7,1,6,3],[8,10,8,1,4]]],[[[0,9]],[[7,2,1,3,4],6,[7,5]]],[8,10],[7,6]]

[[],[[[10,8,8,6,2],2,10,[8,6,7,7,3]],9,1,[[3],9,9],[[5,2,9],2,0,[10,1],5]],[[],6,1],[[[3,2,4,7,3],[],9,[9,2,3],3]],[[[]],1,7]]
[[7,[[7,2,8,9],9],[8,[0,3],[3,5],[5,8]]],[9,[0,3,10,1],[[4,8],4,[3,7,5],[6,2]],[[],6,[5,2]]]]

[[],[0,[[0,0,1,3,8]],6]]
[[10,6,8,8,[]],[9,[[7,4,8,1,4],6,[7,4,5],1],[[7,3,10,5],2],[[1],0,1,7],[6]],[[7,[4,7,9,6],[9],[6,3,3,3]],[6,[10,0,5,8],10,1,6],7,[9,0,2,1,9]]]

[[1,[[4,1]],7],[],[[6,0,5,[9,10,0,6,0]]],[[6,[],[]],[[9,2],9,7],7],[8,10]]
[[[6,4,8],[8],0,[5],2],[7,6,7,1],[10,10]]

[[[[10,8,9,6,10],5,4,0,8],[[7,1,5,0,8],[2,5,5,4,2]],[[2,2]]],[[[6,2],1,[],3],[8,3,5,1],[[7],[0,4,2],4,[0,2,6],[1,6,3]]],[6,[3,0,[6,8]]],[[[9],[6],9,[2,3,9]],5,[7,[8]]]]
[[5,4]]

[[[[],[2,1,4,2],3],[1,9,[8,2,4,10]],[5]],[[1,2,[10],7],10]]
[[7,[],[[10,5],5,1],[5]],[3,3],[1,7],[[[1],9],1],[9,[[7,2,6,6,0],[]],[],[]]]

[[1],[[],3,[4]],[0,9],[[[10,10,6,8,9],4,3]]]
[[[10,10,4,3,[7,10,7]]],[[3,10,9,[8,6,10,5],1],[[7,7],1,0],[5,[4,8,7],6,4,5],1,[[3,5,5,8],[5,9]]]]

[[0,4],[[]],[[4,3,1,4],2,[8,2,1],1]]
[[[0,[4]],[[0,6,0],4,8],[10],5],[],[[4,[10,1,8]],8,[]],[7],[]]

[[[[7,7,3,4],[4,5,0,8],2,[1,6,5,10]],[[2,0,1],10,[2],4],1,2,[10,[7,7,7,2],[]]]]
[[[10,[8,3,0,5,4],6,3,[4]],2],[[10,6,[],[10]],2,5],[[]],[[],[8]]]

[[[7,5]],[[[7,5,4,1,2],8,9,[7,10,6],10],[[8,3,2],[1,1,5,4,4],2],0,5,6],[],[5,7],[5]]
[[3,[[4,2],5,10,8],10,[[5,8,8],3,[6]]],[[[4,7,9,3,4],0,[9],4],[[9,9,3]],[10,6,[3,9,10,1]],2],[9,8]]

[[5],[7,10,[7,[7,5,5,10]],[[3,8,9,10],[],[3,6],5,5]]]
[[[],6,[[],7,5,[6,3,2,10,5]]],[]]

[[[[4],5],[10,4,[6,2,1,3,3]],8,[[]],1]]
[[5,[]],[[[3,7,7],[2],2]],[6,7,[[6,1,3,5,8],[0,0],2,[10,8]],[9,[2,10,5,0]],4],[0,1,[1,[7,1]],9,[6,1,2]]]

[[8],[[[7,7,6,3]],6,5],[[6,10,2,1],[[],9],[5,[],7,3,[9,4,0,2]],[[10,1,9,1,2],9,8]],[]]
[[],[[3,[7,1],0,6,[3,8,3,10,4]],9],[6,4],[10,[9,8,[2,6,0],[2,3,0,0]],[[10,7],[9],3],8],[10,[],[8,[3,5,0,9,1]]]]

[[10,3,4,9,3]]
[[[4,[7,9,9,2,7],[],[],4],3],[1,9],[]]

[[10],[[[0,4,2]],3,8],[7,[0,[2,10,4,3],[5,1,5],2,10],[7,[],1,[],[1,9,7,4,7]]]]
[[5,[3,[7,6]],[6,7],2],[4,[2,[]],[[6,10,4,10],[5,1,0],10,5,1],[[4,10,0,7,5],4]],[]]

[[[[6],10],1,[[10]],9],[7,4,10,10],[2,0,[8,0]],[8,9],[6,[[10,2,7,0,1],1,[0,5,3,7,2],[6]],4]]
[[[[],[2,2,6,0],[5,10,0,10,0]],3,[[3,7],3],5],[],[[],3],[7,[[7,4,8],9,2,[2]],[[1,3],[5,3,2],10],[10,3,[10,6,9,3]],[7,[9,6,0]]]]

[[2,[[3,8,10,9,7],[2,5],[],10,0],[1,8,2,[]]],[10,[5,[2,8,2],5,3]]]
[[],[0,[]],[[1,[],[0,7,0,1,8],[]],[[4,1,8,5],[5,10]]]]

[[[[3,9,9,9,7],3],[0,[6,2,6,4,7],2,[7,9,9,7,8],[0,3,1,5]],[1,[2],[8,5,8,8,10],[7,3,2,10],[2,7,8]],7],[[],7,3,[9,6]],[[2,[0,8]],[1,4,[8,3,0,10]]]]
[[[]]]

[[8,2,3,1,0],[2,[5,[3,5,0,3],7],3,[1]],[10,[[9,4,0],3,[2,0],6],[[]],10]]
[[[[9,3,0],[6,9,6,10],2,[],[5,9]],0],[3,5,[],[4,9,[0,0]]]]

[[6,2,[6,[4,0],[4,5,8,7,1]],[[0,0,1],[9,5,3,1],2,[5],[1,5,7,4]],4],[8,[],[[0],[],[4,5]]],[6,[[10]],1,[1,5,1,10,[7,5,8,2,7]],[[7,1,2,1]]],[6,2,7,[6,[4,3]],[[7],10,[3],4]]]
[[[[3,1,4,10,3],3],8],[3],[1,[5,[5],8,[9,10]],[[]],[4,[1],8,[]],[[],5]]]

[[[10,6],[[1],6,0],[9,2,2],[4,[4,6,10,7,9]],6],[],[[],3,1,4,10],[]]
[[[10,[4,1,7,7],9]],[[[3],[4],[1,3,0]],8],[[7],[6,[0,6,0],[1,8,5,3],[0]],0,6],[],[9]]

[[1,1,[6,[],6,[2,4,10],[9,10,4,3,7]],7,2]]
[[[[0,9,2,6],[0,10,1,8,7],[10,10,6,1],5,[9,2,9,1]],[3],5,10],[0,[[3,1,10,8],[],[],[7,9]],[[10,1,2,10]]],[[1,[10]],[],[[2,4,0,1,5],1,4,3],9,[2,[3,7],[],8]]]

[[[6,1,7,7],4,[[6]],[[6],[9,4],[3]]],[[],[[10,0,2],[9,1,10,1,6],1,[1,8,2],[]]],[],[[],9,[6,0,2],[[],3,9,[6],[1,0,6]]],[[2,[6,7,7,6,7],2,[4,0,0,3,2]],[7],[8,[4,10,0,7,2]],0,4]]
[[4,[[10],2],[]],[[3,[2],[10,0],2],6,2],[[3,[],[],[5,5],6],[0,6],5,[3],[7,[3,8,1,3],6,10]]]

[[],[1,[[],4,[1,10],[5,6,7],[7,8,1,2]]],[[8,1],[0,[]],2,[]]]
[[[[5,5,5],9,9],[],[0,[4,6,9],9,7],4],[[[],0],8,[1,[0,0,10,9,4],[3,5]]],[[8,1],[[]],[[3,5],3,[4,10],4],[9,[8,0,6,10,7],[3,4],10],7]]

[[3,[[0,0,10,10,0],6,[7,8,5,10,9],[3,5,1],[10,1,9,7]],7,[10,3,[0,5,8]],10]]
[[],[[],[[10],8,[3,5,2],[10,4,0]]]]

[[1,8,[[],1,7]],[4],[[[2,6,8,10],0,3,5],[[8,6,7],9,[],[4,2,2,1,0]]],[6,10,0,[[8,0,6,4],[9,7]],5]]
[[2,[3,[5,0,9],7],[[1,4],1,2,[10,8,8,0]],[4,8,2,4]],[9,[],[[3,4,6],[2],[4,6,10,5],0],4]]

[[6,4,[2,[9,5,8,2,1],10,5],[[6,7,8]],2],[],[]]
[[1,[5]]]

[[],[5],[5,[2,[0]],[[5,0,3],4,8,0],3,1]]
[[9],[[[8,8,3,5],[1,1,2],10]],[1],[[[]],[[8,6,8,9,8],7],[10,4,10,[1,3,3,7]],4],[]]

[]
[[[[]],5,[9,[],9,[6,4,1,3]],7],[10,5],[2,9,[[4,1]],6,3],[[6,1,[4,0,10],[3],2],4,[]],[[5],[[2,1,8,7,6],9],[6,6,[10,7,6,5,8],[2,6]],[],7]]

[10,6,2,5]
[10,6,2,5,4]

[[[],6],[],[],[]]
[[],[[],[5,2,[9],[]],2]]

[[1,1,1,2],[],[[9,6,2,9]],[],[7,7,1,[8,4],5]]
[[[7,7,0,4,[2,9,4,7,1]],2,10,[10,[],[5],6],3],[[],[[7],[10,10,4,9,0],[10],1],[],[[10,0,10,9,1],[0,8,2,10],4]],[6],[],[2,5,3]]

[[],[[2,8,[1],[5,2,5,2],[2,9]],[10],[10,6,7,[0,4,8,10,2],[]],[]],[[10,[2,2,0,5],2],[[6,0,3,8,5],[9],[],10],[],[[8,7,10],6,8,3]],[[[5],5,5,[0,3],[10,3]],[1,4,5,[]]],[1,2]]
[[[[],8,4,8]],[],[]]

[[[[10,4,6],[],1,[]],[[3,4],[7,5,4,2],[],0,[10,10]],[[0],[6],[6,5,2,10,4],3,6],[[8,10,4,3],2,0,[3,4,8]],5],[[[3],7,3,[5,5,4,2]]]]
[[[7],1,7]]

[[0,6],[[],[[9,8,10,2],[0,9,5]]],[2,[],9,[[],8,[2,4]]],[4,6,0,3,4],[[[3]],0]]
[[[[6]],5],[[9,[6,10,0,7],[3,8,7,4]],0,[],[[1],[7,4]]],[[[1,5,6],6]]]

[[3],[[[7,7,2,0,7]],[0,4,10,7,[10,0,4,1,6]],[[4,7,9],5],[[6]],7],[[[5],[],[],[1,5,10,6],3]]]
[[5,[7,3,[9,4,10,7],[1,4,10,4],[]],[9,2,1]],[1,7,[[],[0]],7,0],[],[2,[[5,1,0,7]]]]

[[8],[7,0],[6,5,[[],[],8,[5],1],7,[]]]
[[8],[],[7]]

[[[],[[6,1,6,9,8],1],[[8,7,8,0],10,[8],8]],[[[]],[[6,6,9,5],[0,2],7,3,[5,9]]],[]]
[[],[0,[[],[7,5],[3,10,7],7],[[9,4],10],[[5,7,8,7]],[]],[],[8,9,3,6]]

[[[[9,8],6],3,0,[[3,7],[5,1,0,2],10]],[7]]
[[],[],[6,0,10,[],[2]],[[[1]],[7,3,[10,10]]]]

[[8,[8,10,6,4,10],[],1,[[8,0,1],9]],[0,0,3],[],[[[],[3,6],8,[]],1]]
[[9,[[6,3,3,7],5,[2,9,5,8,0],[6,7,8,4],10],[3,4,[8,4,1,10,10]]]]

[[2,[[4],[0,8,3,10,4]]]]
[[[2,[0]],[5,[3,4,1,0],6,[1],1],3,4,9],[6,1,3,6],[7,[10,0,1,[8,10,8,0,6]],9,[[8,4,2],0,6,8,0]],[],[[[6,0,6,8],1]]]

[[6,[[9,10,4,3,9],1],9],[[[9,2],[],[3,2]],[[4,7,8,3,5],0,[5],[10,5,6,0,6],[2,10,3,6,8]],[6,[]],[5,[4,5,9,10],[1,0,1]]],[[[2,1,9,9],[10,6,4,1,8],[],5,[2,10]],8,[[1,1,8],4,[2,4,0]],7]]
[[10,[1,[2,3,0,6,9],1,[],[8,4,1,6,1]],3],[[[2],[]],[],7],[],[2,6],[10,4,6,6,[[],[9,8]]]]

[[10],[[9,[8,9,5],3,9],7],[[[9,8,10,5],3,6],10,5,10],[5,1]]
[[2,3,[]],[1,[[2,1],5,3,[7],0],5,[]],[[],[1,4,[]],[6,[1,4,8,3],9,[2,3],8],10,3]]

[[[],[9]],[[[7,2],9,[4,8,6],[0,3,9],[8,8,1,4,1]],7]]
[[2,[10,6,[5,0,9,5]],10],[[[3,3],6],[[6,8,2,5],[8,6,0,5]],[[1,7],4]],[0,[[3],[]],[[2,6,9,1],9,[4,2,5,0,2],9,[7]],[9,7,6,[2,2,9,5]],6],[[3,4,[6,9]],2,[4],3,[[1,9],[0,2]]]]

[9,0,10,9,1]
[9,0,10,9]

[[[5],[7,0,9,2],2],[[3,[0],[]]],[6,[]]]
[[3,[2,[0,4,5,1],[9,0]]],[9,2,[6],[3]]]

[[[9],[0]],[1],[[[4,5]],3,10,5,10],[8,9,0,1],[[2,[10,5,5,2,3],[9,2,1,0,4]],[1,10,7,[10,9,7,4]],1,1]]
[[0,2],[8,[[9,4,9,2],10,[9,8],4],7,5,6],[6,5]]

[[],[4,[[7,1,3,7]],[]],[],[[[],2,[3],3],[[7,0],6],3,[7,4],[]]]
[[4,8,6,2],[[[]],6,10,[[4,10,5,0,1],6,6],[]],[1]]

[[[[7,8,0,9,3],[8]],[[6,10],[10],2,0,1]],[],[[1,[3,9,4],0,[]],[[],2],[8,[8],[5,7],[],9],[2,10],7],[3,[2,[3,8,5],3,2],6],[[1,[3],[4,10,3],5,[6,5]],[[]],2,[],4]]
[[[],[6,6,[10],7],[10,[10,8],5,[10,8,3]],[5,10,4,8]],[3,10,6],[[[2,2],[5,2],[3,0,10,0,4]],[[8,9,2,9,0],1],1,[[10],6,0,8]],[8,[10,6,[1,3,9]],7,8,[3,[6,10],[7,6,4,5,1],[10],3]]]

[[[[5,9],[1,1]],3],[],[6]]
[[6,[9,7],[[6,0,7,10],[10,6],[4,3,3,7],10,[4,6,4,4,10]]],[1,[[2,0,0],[7,0,10,1,7],[9]],[10],4],[[8,10,9,[6,7,0,2,7],10],[[6]],[],[9],[]]]

[[0,7,[[3],[0,3,6,8],[7,8,4]],[]],[3,[9,5],9,[4,5,[6,8,2],[7,3],7]],[]]
[[],[[[3],[5,7,10,7],9,[3,9,6,5,7]],[[10],[4,3,10,9,3],[]]],[[[0,5,7,3,2]]]]

[[10,7,0],[],[1,5,7,[],5],[1,4,[0,4,[3],[2]]],[[4],7,7,[9,1],9]]
[[2,[2,[],[5,7,2,1,6],[8,9],[7,7]],[]],[1,3,[[],[3,9]],[6,2]]]

[[3,3,0,[4]],[],[[7,0,[2,1,5,4,1],7,[0]]],[8,[6,3,4,7],10,[3]]]
[[[1,[1,8,8,10,0],[5,7,6],[5,1],8],[3,0]],[[7,[],9,6,[9]],4,[[2,3,0],6],[],5],[10,[],[[],10,[5,9,3,2]],[1,3,[],0]],[6,[[2],1,4,3,4],7,2],[1,[[3],10,3],6,9,[[3,8,3,8],2,[4],9,9]]]

[[[8,[3,8],[0,5],[8,8,2,4,7]],[4,[4]]],[1,7,8,2],[[1,7],[[3],3,[8,3],[6,4,2]],3,[[8,3,6,9,1],[4],[6,5,1]],[[3,6,1,5]]]]
[[4],[]]

[[5,[[0,9],[10,10,10,2,2],[1,5,8,2],[8,4,9,0]],9],[[6,[8,8,5,4,7],8,[],[7,9]],8,[],[[7],7,8,[2,2,0]]],[[[2,4],7],[8,6,[],[9,1]],[],[10],[[6,7,0,5,5],4,7,[5,1,9,10]]],[],[[[1,3,1,0],[7,7]],[6]]]
[[[[8]],6,[[6],[],9]],[[[1,3],2,4,9,7],[5,[4],0,1],6],[5],[[],1,[],[[8,1,2,10,7],[7,9],9,5,4],1],[]]

[[[],5]]
[[4,[],9,6],[2,1,[[4,5,10,0,1],10,1]],[],[7,[[2,2]],9,[[3,0,7,5,1],[1,10],2,[]]]]

[[[9,4,[2,2,5,1]],[7,0,[9,2,1,9,6],5,[7,8,0,5]],[[8]]]]
[[[4,6],[[],[],3]],[],[[[4],9,[9,2,5]],[6,[1,7,6],[9,3,1,2,9]],[[2,3,0],4,0],[2,4,[0]],4],[10,[3,10,6],[1,10,5],9]]

[[[0,8,2],6,[[4,0,4,7],[1],[2,8]],[[3],10,5,4,[8,0,9,4,0]],8]]
[[[],[],5,[[8,0]],0],[9,6]]

[[[]],[10,4],[10]]
[[],[[3,[]],[6,[6,6,2,2]]],[[0,9,10,7,[8,4]],6,5,[[1,8,2,2,10],9]],[[[8,7,0],5,3],4]]

[[],[4,[6,[2]],[[3,10],[3,2,0],3],[[],10,[9,8,0,2,9]]]]
[[[[6,1,0,10],9],[6,[2,4,10,10,10],3,6,[6]],[],7,5],[[],[7,[10],5],[[2,3,2],0],1],[[[],10,5],[[7],[],5,[8,1]],3,10]]

[[9],[4,[3,5],7,4,7],[[],[5,2,[1,4,0]],[6,[1,9,3,3],[]],6,10]]
[[[[3,2,0,5],4],[7,[3],[6,7,0,5],0]],[9,4,9,2],[[]],[[[2,7]],[10,9,8],8],[]]

[[],[4,[6,10],6,[[],7],[[3,0,5]]]]
[[[[1,3]]]]

[[],[[[],9],[5,[2,8],[6,7]]]]
[[8,4]]

[[4,[2,2,[1,8,3,6,5]]],[2]]
[[5,4,[[7,4,8]],1,0],[10,[],[[1]],[5,[]]]]

[[10],[],[3,1,[[],0,0],0],[[9,6],2,[[9,9,8,2]]]]
[[10,[4],[[],[5,5],7,[3,1,2,10,0],10],1],[3],[[],9]]

[[[[5,6,8,3]],1,[5],10],[[[2,3,5,3,6],0,10,[2,3,7]],[]]]
[[[[],[2,1,10],[],[4]],[[6,1,1],10,4]]]

[[8,[9,[2],[0,6,2,3,7]],4]]
[[[3,[6,5,1,6,10],[1,5,4,1,3]],[7],[],[10,10,4],[[9,4],[7,4],0,2,[2,5,6,7,4]]],[[],[8,4,3,[0,7,6,7,5],6],[[10]],[[1,7,9,5,8],[1,4,5,9,6]]],[6,2,[]],[]]

[[[[2,7],0,[2],[0,0,0,10],[]]],[2,[[2,8,9,3,3],4,4],9,[1],[10,3,[8,7],[9,6],5]],[[[],[9],[6,5,3]]],[[0,[7,3,0],[],[]]]]
[[],[[4,0,[5,0],[8,9,4],[10,6,9,6,1]],[0,[1,3,10,9],0,5],1]]

[[[10],[[9,1,1,3],8,10,6],[[0,7,6,7],8,[7,10],[10,8,0,2,8],[7,7,10,1]],8,[2,0,[4,9,6],4,[2,5,5]]],[10]]
[[10,[],5],[3]]

[[[[3,5,7,8,5]]],[[[8],[2,7,8,2],[8,6],[5,9,10,2]]],[[],[[]],10,[[7,5],6,[]],[9,[5,3,8,0],5,5,2]]]
[[[[9],4]],[5,9,8,[]],[[]]]

[[],[[[4,2,4]]],[4,9,8,[3,0,[3,6,3,1,5]]]]
[[7,[[1,9,7,5,0]],[]],[1,[6],3,9],[3,2,1,[[2]],8],[[[10,5],[8],10]]]

[[],[8,5,[[]]],[],[[9,0],[],2,9,8]]
[[2,0,[5,3],6],[[],[10,[0,5,9,9,10],7,[5,8,1,8],[8]]]]

[[[]],[[0,6,[7,8,0],3,[9,6,2,5,2]]]]
[[],[[],[]],[[[],2]],[1,9,2,8,6]]

[[],[[[9,2,5],[3],3],1],[7,[[]],[7,6,[7]],[[],[8,0,2,9,9],5,7],1],[[2,[7,4,4],3],[[6,9],[1,10,0,7]],[5],10,3],[]]
[[[],[[0,4,5,4,6],1]]]

[[2,5,6],[[[4,7,4,6]],8,4,4,[[8,4,3],[5,1,8],5,[6,4,0,6,0]]]]
[[6,[[1,5],0,3,7],[],[]],[],[[[1,3,8,0],[6,2,1,0],9,1]],[0,[[0,9,1,7]],[0,1,[1,6]],7]]

[[[3,[4,2,8,0]],0],[0,[[8,3,5]],[7,7,[],10,[0,2,2,4,6]]],[6,[[6,9,8]],[0,[4,9,6,0,9]],[],6]]
[[],[6,10,[],[[0,1,5],1],0],[[]]]

[[[[3,10,1,6],[5,0,9,9],[10,10,10]],6,[3,9],8],[[[1,7,2,6],[],9],6,3,[8,9,[5,5],[0,5,9,4,5],[]]],[10],[[2,0],[[0,1,2],[],[10,5,2],4],2,[[6],[],0,[1,5],2]]]
[[[9,6,[5,8,9]]]]

[[4,[[7,0],1,[8,0],1,7],[],[[2],[1,6],[0,5,3],[0]],[[8,10,5]]]]
[[3,6,[[],2,1],4,[[1],5,9,[10,0,0,5]]],[[1,[2,1],6,10,1],8,[3,[4,0],4,1],[]]]

[[6,9,1],[],[2,4],[[2,5,[]],[[6,7,10,5],4,9],5]]
[[[0,3,4,8,[0,9]],8,10],[[[4,0],[8,8,2,9],[],5,1],[5,[0,0,5,9],[5,10],7],8,[4,[3,0,4,7,0],5],[[9]]],[5,1,6,10],[]]

[[6,[1,2,10,[]]],[[],10,1,9,[[9,5,7],3,[4,5,10,5],5,[1]]],[]]
[[[[10,7],1,1,4],10,6,[7],[10,5,[]]],[1],[[[8,2,5,7],[],[],8],[],[6,4,[],[10,8]]],[9,1]]

[[4,[[],1],[5,[6],0,1,[9,0]],[[7,6,2,1,10],[0,5],[3,4],1,[8,2,4]],[9,3,1,[]]],[]]
[[[[0],1,[5],7,[4,0,1,7,1]],[10,10,[1,9,9,4,6],9],[[4],6,[0,0,10,0]],4]]

[[0,10,[0,[10,10,6,7,1]],4,[[0],[7,10,5]]],[]]
[[10,[],6,[10],[10,[2,5,4],3,3,0]],[9]]

[[],[[[7,1,2,9,4]],8,[8,5,7,3,[9,10,4,9,1]],[3,[6],[1],7],10]]
[[[],[0,8,[3,6]],10,[[10,0],[6,8,5],2,8,[5,10,2,0,5]],10],[[[6,0]],5,8]]

[[7,[[10,0,4],7],[0,[3,7,7,10,3],10,[2,4,2,0,10],6]]]
[[5,5,9]]

[[3,[10,[10],4,0]],[[[3,7,7],[0],[]],0],[[10,6,9,7],[5,[3,7]],[[7,9,8],0,5],10],[],[0,5,10,8]]
[[0,[10,1],10],[8],[7,[4,3,5,[2,5,6,3]],[[2,0,10,4],[7],[9,9,6],[1,5]]],[]]

[[],[],[7,[2,9,0]],[]]
[[[[3,5,0],9],1,7,9],[1,[[9,2],[5,3,0,10],[9],0,[0,8]],[[10,0],7,[4,8],1,0],[[7,7,3]]]]

[[],[],[],[],[[4,1,6,[],10],[[7,2,2,1,1],[],[5],[8,8,7]],1]]
[[0,[[8,9],[10,0,9],5,5]],[[]]]

[[10,[10,9,[10,0,2,2,2]]],[[1,5,[10,9,7,6]],3]]
[[0,6],[10,[[4,5,7,0],3],2],[]]

[[[[8,10,3]],[[10,3,10,4,0],5,[1,5],2],3],[6,[5,[3,10,3,4],[1,1,10,0,6],2],10,[],[[10],7,6,2,10]],[[[7,10],4,2,[],[5,0]]]]
[[],[]]

[[[3,[10,2]],[[2]]],[[[8,5],[0],4,[],[5]],[4,[7,2],[9,1,8,7],[7,7],5],[],[10,[8,7,5,3],3,[],[8,4,2,7,2]],[[1,8,4,5]]]]
[[0,[3,8,9,8],[1,6,1]],[[],[],[0,[4],5,5,[1,7]]],[3,[[6,1],2],[],[[7,10,4,4],[6,9,9]]]]

[[7,[9,9],7,6]]
[[1,[]],[[],0],[3,[6],[[6],1,5,3]],[1,[],[[5,4,9,10],5,5],[],[[1]]]]

[[6,0]]
[[[[2,3],4,[6,4,5,5]],9]]

[[5,10],[10,6,[0,[7,4],[2,3,6]],0]]
[[3,[3,3,1,[5,2,1,5],[8,6,2]],[[9,10,4],2]],[7,[[5]]],[[]],[6,[],[[10,2,7]],[[9,7,3,6]]],[[[8,7,7,5,5]],5,8,2]]

[[6,9],[3,[1,9,[]],10]]
[[[]],[3,10,[[2,4,2,1],7,9,3,[0,7,1,7,4]]],[[8,5,[],[0,2,7],9],7,7,[],[[],[9,7,7,2],[10,0,3,2]]],[[],[4,[6],6,[0,7,1,4,3]],2,[[1,6],0,[6]]],[[],1,[4,10,[3,8,9,2,8],5],[0,1,8,[3,4],8],[[10,8,2],1]]]

[[9],[]]
[[9,[1,7,4]],[[[6,10,3,3,4],8,1,[8,2,2,1,1],2],[[4,9,9,0],[9,1],[7]]]]

[[7,[[0,4,7],[5,1,9,7,6]],5]]
[[5,1,[[4,7,0,8,4],0],3,[]],[[[2,1],[8,3]]],[[],[],[[4,7],3,4,1,[5,6,6,0]]],[[4,9,10],2,[6,[1,10,9]]]]

[[[4,[8,8,4,2],[2]],[[5,8,4,5,6],[2,4]],2],[[],8,[[6,1,0,9,0]],[2,6,[7,1,10,9,7],6,3]],[[0,6,[6,5]],10,10]]
[[3,[9,[6,2,1,7],9,5],8,9],[],[],[]]

[[6],[],[7],[[[6,4,0],[10,7,1,2,0],[7,6,7,7],[3,6,10]],8]]
[[3,4,1],[4,4],[],[[1],5,5,10,[]],[[8,0],[8],[[5,1,10],3],[4],[[10,2,3],3,5]]]

[[3,[1,2,[10,2,4,4],[],5]],[0,[],6],[10,[],[[],[1,4,10]],2]]
[[1,[5,[7,7,6],[10,8,10],9,[8,8,10]]],[],[[[3],[7]],[9,[4]],7]]

[[8,3,[7,4,8,[7,3,0]],10]]
[[[8,0],[7],2]]

[[[0],[6,8,9,2,[6]]],[[[2],10,3,[2]],8,2,2,3],[[5,5,[10,10,3,7],[9,1,7,8,7]],[10,1],[[1,10,7],6,[8,5,1,6],5]],[]]
[[[],2]]

[[],[[4]]]
[[0,10,4],[[2],0,10],[]]

[[[],[4],[[],7,5],0,[10,8]],[[9,[9,1,5,6,4],6,10,[2,1,9]],4],[[],[]],[6,[[]],0,5]]
[[7,[1,2,0]]]

[[9],[[[9,3],8,[4,2,7,2,2],2,[10,6,1,0,8]],[3,2],[9],[7]],[[0],[[4,7],[],3,[],3],2,9,[]],[[],9,[2],3]]
[[3,2,[[7,0,3],[9],[1]],[8]],[],[[[],[10,8,4]],9,[],[]]]

[[4,8,[6,3,[9]]],[[[],3,6,[7,3,3],[5,3,8]],[9,[],5,6,[6,0,2,4,4]],2,8],[]]
[[10,7,8,7,3],[[[9,8,2,1],[0,5],[9]]],[[[7,3,7,7,3]]],[[7,[],8],[9],[2,[1,4,8],[3,4,2,4,5],[],9],[9,[7]]],[[],5,[9,[10,6,5],[0,2,8]]]]

[[1,[0],1],[]]
[[[[7,5,2,5,8],[8,10,1,4,6],10,[10,10,5,1],8],[[2,4,8,1],[],3,0,[2,4]],[[2,0],7,7,[4,3,6]],6],[],[[[6],1],[0,[0,9,3],3]],[5,6,7]]

[[[],[],[[4,0,3],[],[3,3],[1,2,5,4,6],[5,8,0]],[7,[5],10,10,1]]]
[[],[[[1,1,5,3],6],4,8,7],[1,[[7],[]],10,0,[]],[[[],[5,2,2,5,4],8],8,2],[5,[9,[10,5,2,9,8],8],10,10]]

[[3],[[[6,4]],9,1],[6,8]]
[[],[[[5],[5,3,2,10,7],10,[7,9,1,5],2],[6,8,[6,8,1,0,10]],[[]]],[[],4,[[],[5,2,10,1],0,8],4,10],[[],[3,[0,6,0],[5,1,4,4],3,[3,1,9,2]],[3,2,3,2,[6,6,7,6,5]],[],9]]

[[5,9,3,[],[9,7,[]]]]
[[[0,9,8,[10,3],[10]],1,[3,[10,3,4,5],[8,4,2,3,6],[9,8,10,5,8]],[8,[5,3,7],[6,2,2],[5,9],[7,9,4]],[10,5,10]],[3],[1,0,[[8,8],[]]],[9,[],[5,8,[5,2,9,2,0],[10,4]],[[9,9,8,4,0],5],[]],[[[],[8,2]],[[9,9,6,6,7],[4],2,[]],[7,9]]]

[[[6,[1],6,[1],9],[7,[6,2,1],[3,1,1,7]],[[9,5,1],5,[1,2,2,8,5],2,2]],[[]],[[9,6,4,0]]]
[[[],[[2],[1,5],[7,10,0,9,2],8,9]],[4,[8],4,6]]

[[2,6,6],[10,10,4]]
[[[[1,7],5,0],3,10,3],[1,[7],0,[[9,6,3],[10,0,5,7],[10],[5,10]],3],[10,[]],[5,[6,2,10,7,[1]]]]

[[3,[3,2,[8,6,2,6,10],[0],[]],[]],[5,[[],[8],[8],[8,5,2],[9,8,10,8,9]],7,[0,[7,6,7,2]],9]]
[[],[4],[[[10],[2,3,4],[7,3,4,0]],3,[10]],[3,[[4],6,10],[3,10,7,[],3]],[5,4,0,[],6]]

[[[[0,7]],[9],[[],[],5,[7,6,8,0]],3]]
[[3,[[3,5],6,8],7],[[[3,4]],[],[0,[8,3,2,3,3],0,[5,10,3,7],[0,6]],[[2,2,3,10,10],9,[0],[]],[]]]

[[3],[5,7,10,10]]
[[10],[],[[0,[9,2,9,0],[5],[7,6,2],[7,3,6,4,9]],10,[8],2,7],[[[7,3,2,3,9],[7,5,2,9],[10,10,3,4,8],[2,7,9,8],0],[[10,3,1,1],[0],6]]]

[[[10,7,8]],[[],[1,[8,4,0]],2,7],[],[[[8,9,6]]]]
[[[]],[3,0]]

[[6,4,[2,9,10],[[5],4,[],7],2]]
[[],[4,9,3,10],[4],[[[10,7],4,[8,10,4,2]],6,[5,3,5,1,[7,9,4]],[[]],[]]]

[[7,[[4,7],7,[2,8,2]],5,7,[[5,0,7,8,0]]],[[5]],[[0,[3,9]],8,[1,4,2],[[10,1,3,5,0],[4],[],3]],[[[6,4],[4,9,9,4]],[],[]]]
[[5,[0,[6,8,7,3],2],[9,4,[7],[0,3,7]],3],[3,[[9,6]],[[],[4,7,6,8,0]]],[2,5],[9,2,5]]

[[[[1,3]],8],[5,8,6,[0,[6,6,4],[]],7],[[7],[[],3,7,1],[],[[9,10,6],[5],[6,7,6],[3,3,3,8],[]],[[],7]],[],[7,[8,[1,8,1,0]],[4,1,[2,5],1],[[6,10]]]]
[[[10,9,[0,10,10,1]],[[9],[10,0,2],4,9]],[[[4,8]],0,[5,[3],4,3,4],3],[],[],[[]]]

[[4,6,[[10,0,2,6],[3,5,8],3],3],[],[],[],[8]]
[[[],[[],4,10],9],[[3,[],[8,6,9],9,1],1,10,[[1,4,6],[0],2],[[1,7,9,7,0],[3,10],2,[2],[2,9]]]]

[[],[[1,[6,2,10]]],[],[[[7,9,2,8],[5,1,2],9],3,[10]]]
[[[2]],[10,[10,6,8],8,[8,0,10,2],10]]

[[2,8]]
[[[[5],[9,9,6],[1,8],[5,4,6,0,2]],[[5,9],[]],[7,[4,2,3,4],6]],[],[1,9,7,[6]]]

Day 14: python

this documentation is autogenerated. Add a README.adoc to your solution to take over the control of this :-)

python

sol.py

#!/usr/bin/env python3

import numpy as np


def load_input():
    with open('input') as fd:
        data = list()
        for line in fd:
            temp = line.strip().split('->')
            temp = [tuple([int(j) for j in i.split(',')]) for i in temp]
            data.append(temp)
    return data


def get_dir(a, b):
    return tuple([np.sign(j-i) for i, j in zip(a, b)])


def calc_pos(a, b):
    return tuple([i+j for i, j in zip(a, b)])


def create_field(data):
    field = dict()
    for rocks in data:
        for i in range(len(rocks)-1):
            d = get_dir(rocks[i], rocks[i+1])
            pos = rocks[i]
            while True:
                field[pos] = "rock"
                if pos == rocks[i+1]:
                    break
                pos = calc_pos(pos, d)
    return field


def process_sand_part1(field):
    start = (500, 0)
    pos = start
    end = max([key[1] for key in field.keys()])

    while True:
        next_pos = calc_pos(pos, (0, 1))
        if next_pos in field:
            next_pos = calc_pos(pos, (-1, 1))
        if next_pos in field:
            next_pos = calc_pos(pos, (1, 1))
        if next_pos in field:
            field[pos] = 'sand'
            next_pos = start
        if next_pos[1] == end:
            break
        pos = next_pos


def process_sand_part2(field):
    start = (500, 0)
    pos = start
    end = max([key[1] for key in field.keys()]) + 2

    while True:
        if start in field:
            break
        next_pos = calc_pos(pos, (0, 1))
        if next_pos in field:
            next_pos = calc_pos(pos, (-1, 1))
        if next_pos in field:
            next_pos = calc_pos(pos, (1, 1))
        if next_pos in field:
            field[pos] = 'sand'
            next_pos = start
        if next_pos[1] == end:
            field[pos] = 'sand'
            next_pos = start
        pos = next_pos


def part1():
    data = load_input()
    field = create_field(data)
    process_sand_part1(field)

    result = sum([1 for value in field.values() if value == 'sand'])
    print("Result 1: ", result)


def part2():
    data = load_input()
    field = create_field(data)
    process_sand_part2(field)

    result = sum([1 for value in field.values() if value == 'sand'])
    print("Result 2: ", result)


def main():
    part1()
    part2()


if __name__ == '__main__':
    main()

input

516,97 -> 516,94 -> 516,97 -> 518,97 -> 518,87 -> 518,97 -> 520,97 -> 520,92 -> 520,97 -> 522,97 -> 522,94 -> 522,97 -> 524,97 -> 524,95 -> 524,97 -> 526,97 -> 526,89 -> 526,97 -> 528,97 -> 528,89 -> 528,97 -> 530,97 -> 530,94 -> 530,97 -> 532,97 -> 532,91 -> 532,97
495,26 -> 495,27 -> 507,27 -> 507,26
516,97 -> 516,94 -> 516,97 -> 518,97 -> 518,87 -> 518,97 -> 520,97 -> 520,92 -> 520,97 -> 522,97 -> 522,94 -> 522,97 -> 524,97 -> 524,95 -> 524,97 -> 526,97 -> 526,89 -> 526,97 -> 528,97 -> 528,89 -> 528,97 -> 530,97 -> 530,94 -> 530,97 -> 532,97 -> 532,91 -> 532,97
516,97 -> 516,94 -> 516,97 -> 518,97 -> 518,87 -> 518,97 -> 520,97 -> 520,92 -> 520,97 -> 522,97 -> 522,94 -> 522,97 -> 524,97 -> 524,95 -> 524,97 -> 526,97 -> 526,89 -> 526,97 -> 528,97 -> 528,89 -> 528,97 -> 530,97 -> 530,94 -> 530,97 -> 532,97 -> 532,91 -> 532,97
502,42 -> 502,45 -> 496,45 -> 496,51 -> 507,51 -> 507,45 -> 506,45 -> 506,42
516,97 -> 516,94 -> 516,97 -> 518,97 -> 518,87 -> 518,97 -> 520,97 -> 520,92 -> 520,97 -> 522,97 -> 522,94 -> 522,97 -> 524,97 -> 524,95 -> 524,97 -> 526,97 -> 526,89 -> 526,97 -> 528,97 -> 528,89 -> 528,97 -> 530,97 -> 530,94 -> 530,97 -> 532,97 -> 532,91 -> 532,97
516,97 -> 516,94 -> 516,97 -> 518,97 -> 518,87 -> 518,97 -> 520,97 -> 520,92 -> 520,97 -> 522,97 -> 522,94 -> 522,97 -> 524,97 -> 524,95 -> 524,97 -> 526,97 -> 526,89 -> 526,97 -> 528,97 -> 528,89 -> 528,97 -> 530,97 -> 530,94 -> 530,97 -> 532,97 -> 532,91 -> 532,97
506,30 -> 510,30
527,130 -> 527,122 -> 527,130 -> 529,130 -> 529,121 -> 529,130 -> 531,130 -> 531,129 -> 531,130 -> 533,130 -> 533,121 -> 533,130 -> 535,130 -> 535,122 -> 535,130 -> 537,130 -> 537,128 -> 537,130
516,97 -> 516,94 -> 516,97 -> 518,97 -> 518,87 -> 518,97 -> 520,97 -> 520,92 -> 520,97 -> 522,97 -> 522,94 -> 522,97 -> 524,97 -> 524,95 -> 524,97 -> 526,97 -> 526,89 -> 526,97 -> 528,97 -> 528,89 -> 528,97 -> 530,97 -> 530,94 -> 530,97 -> 532,97 -> 532,91 -> 532,97
503,67 -> 503,59 -> 503,67 -> 505,67 -> 505,60 -> 505,67 -> 507,67 -> 507,60 -> 507,67 -> 509,67 -> 509,58 -> 509,67 -> 511,67 -> 511,60 -> 511,67 -> 513,67 -> 513,59 -> 513,67 -> 515,67 -> 515,61 -> 515,67
548,117 -> 553,117
503,33 -> 507,33
527,130 -> 527,122 -> 527,130 -> 529,130 -> 529,121 -> 529,130 -> 531,130 -> 531,129 -> 531,130 -> 533,130 -> 533,121 -> 533,130 -> 535,130 -> 535,122 -> 535,130 -> 537,130 -> 537,128 -> 537,130
539,149 -> 544,149
489,38 -> 489,39 -> 503,39 -> 503,38
527,130 -> 527,122 -> 527,130 -> 529,130 -> 529,121 -> 529,130 -> 531,130 -> 531,129 -> 531,130 -> 533,130 -> 533,121 -> 533,130 -> 535,130 -> 535,122 -> 535,130 -> 537,130 -> 537,128 -> 537,130
533,111 -> 538,111
516,97 -> 516,94 -> 516,97 -> 518,97 -> 518,87 -> 518,97 -> 520,97 -> 520,92 -> 520,97 -> 522,97 -> 522,94 -> 522,97 -> 524,97 -> 524,95 -> 524,97 -> 526,97 -> 526,89 -> 526,97 -> 528,97 -> 528,89 -> 528,97 -> 530,97 -> 530,94 -> 530,97 -> 532,97 -> 532,91 -> 532,97
516,97 -> 516,94 -> 516,97 -> 518,97 -> 518,87 -> 518,97 -> 520,97 -> 520,92 -> 520,97 -> 522,97 -> 522,94 -> 522,97 -> 524,97 -> 524,95 -> 524,97 -> 526,97 -> 526,89 -> 526,97 -> 528,97 -> 528,89 -> 528,97 -> 530,97 -> 530,94 -> 530,97 -> 532,97 -> 532,91 -> 532,97
489,38 -> 489,39 -> 503,39 -> 503,38
516,97 -> 516,94 -> 516,97 -> 518,97 -> 518,87 -> 518,97 -> 520,97 -> 520,92 -> 520,97 -> 522,97 -> 522,94 -> 522,97 -> 524,97 -> 524,95 -> 524,97 -> 526,97 -> 526,89 -> 526,97 -> 528,97 -> 528,89 -> 528,97 -> 530,97 -> 530,94 -> 530,97 -> 532,97 -> 532,91 -> 532,97
527,130 -> 527,122 -> 527,130 -> 529,130 -> 529,121 -> 529,130 -> 531,130 -> 531,129 -> 531,130 -> 533,130 -> 533,121 -> 533,130 -> 535,130 -> 535,122 -> 535,130 -> 537,130 -> 537,128 -> 537,130
510,76 -> 510,78 -> 503,78 -> 503,84 -> 521,84 -> 521,78 -> 516,78 -> 516,76
503,67 -> 503,59 -> 503,67 -> 505,67 -> 505,60 -> 505,67 -> 507,67 -> 507,60 -> 507,67 -> 509,67 -> 509,58 -> 509,67 -> 511,67 -> 511,60 -> 511,67 -> 513,67 -> 513,59 -> 513,67 -> 515,67 -> 515,61 -> 515,67
503,67 -> 503,59 -> 503,67 -> 505,67 -> 505,60 -> 505,67 -> 507,67 -> 507,60 -> 507,67 -> 509,67 -> 509,58 -> 509,67 -> 511,67 -> 511,60 -> 511,67 -> 513,67 -> 513,59 -> 513,67 -> 515,67 -> 515,61 -> 515,67
516,97 -> 516,94 -> 516,97 -> 518,97 -> 518,87 -> 518,97 -> 520,97 -> 520,92 -> 520,97 -> 522,97 -> 522,94 -> 522,97 -> 524,97 -> 524,95 -> 524,97 -> 526,97 -> 526,89 -> 526,97 -> 528,97 -> 528,89 -> 528,97 -> 530,97 -> 530,94 -> 530,97 -> 532,97 -> 532,91 -> 532,97
516,97 -> 516,94 -> 516,97 -> 518,97 -> 518,87 -> 518,97 -> 520,97 -> 520,92 -> 520,97 -> 522,97 -> 522,94 -> 522,97 -> 524,97 -> 524,95 -> 524,97 -> 526,97 -> 526,89 -> 526,97 -> 528,97 -> 528,89 -> 528,97 -> 530,97 -> 530,94 -> 530,97 -> 532,97 -> 532,91 -> 532,97
503,67 -> 503,59 -> 503,67 -> 505,67 -> 505,60 -> 505,67 -> 507,67 -> 507,60 -> 507,67 -> 509,67 -> 509,58 -> 509,67 -> 511,67 -> 511,60 -> 511,67 -> 513,67 -> 513,59 -> 513,67 -> 515,67 -> 515,61 -> 515,67
544,114 -> 549,114
502,42 -> 502,45 -> 496,45 -> 496,51 -> 507,51 -> 507,45 -> 506,45 -> 506,42
503,67 -> 503,59 -> 503,67 -> 505,67 -> 505,60 -> 505,67 -> 507,67 -> 507,60 -> 507,67 -> 509,67 -> 509,58 -> 509,67 -> 511,67 -> 511,60 -> 511,67 -> 513,67 -> 513,59 -> 513,67 -> 515,67 -> 515,61 -> 515,67
533,161 -> 538,161
503,67 -> 503,59 -> 503,67 -> 505,67 -> 505,60 -> 505,67 -> 507,67 -> 507,60 -> 507,67 -> 509,67 -> 509,58 -> 509,67 -> 511,67 -> 511,60 -> 511,67 -> 513,67 -> 513,59 -> 513,67 -> 515,67 -> 515,61 -> 515,67
527,130 -> 527,122 -> 527,130 -> 529,130 -> 529,121 -> 529,130 -> 531,130 -> 531,129 -> 531,130 -> 533,130 -> 533,121 -> 533,130 -> 535,130 -> 535,122 -> 535,130 -> 537,130 -> 537,128 -> 537,130
527,130 -> 527,122 -> 527,130 -> 529,130 -> 529,121 -> 529,130 -> 531,130 -> 531,129 -> 531,130 -> 533,130 -> 533,121 -> 533,130 -> 535,130 -> 535,122 -> 535,130 -> 537,130 -> 537,128 -> 537,130
547,161 -> 552,161
510,76 -> 510,78 -> 503,78 -> 503,84 -> 521,84 -> 521,78 -> 516,78 -> 516,76
527,130 -> 527,122 -> 527,130 -> 529,130 -> 529,121 -> 529,130 -> 531,130 -> 531,129 -> 531,130 -> 533,130 -> 533,121 -> 533,130 -> 535,130 -> 535,122 -> 535,130 -> 537,130 -> 537,128 -> 537,130
531,100 -> 531,102 -> 524,102 -> 524,105 -> 538,105 -> 538,102 -> 535,102 -> 535,100
516,97 -> 516,94 -> 516,97 -> 518,97 -> 518,87 -> 518,97 -> 520,97 -> 520,92 -> 520,97 -> 522,97 -> 522,94 -> 522,97 -> 524,97 -> 524,95 -> 524,97 -> 526,97 -> 526,89 -> 526,97 -> 528,97 -> 528,89 -> 528,97 -> 530,97 -> 530,94 -> 530,97 -> 532,97 -> 532,91 -> 532,97
510,76 -> 510,78 -> 503,78 -> 503,84 -> 521,84 -> 521,78 -> 516,78 -> 516,76
549,146 -> 554,146
527,130 -> 527,122 -> 527,130 -> 529,130 -> 529,121 -> 529,130 -> 531,130 -> 531,129 -> 531,130 -> 533,130 -> 533,121 -> 533,130 -> 535,130 -> 535,122 -> 535,130 -> 537,130 -> 537,128 -> 537,130
546,149 -> 551,149
527,130 -> 527,122 -> 527,130 -> 529,130 -> 529,121 -> 529,130 -> 531,130 -> 531,129 -> 531,130 -> 533,130 -> 533,121 -> 533,130 -> 535,130 -> 535,122 -> 535,130 -> 537,130 -> 537,128 -> 537,130
514,73 -> 524,73 -> 524,72
516,97 -> 516,94 -> 516,97 -> 518,97 -> 518,87 -> 518,97 -> 520,97 -> 520,92 -> 520,97 -> 522,97 -> 522,94 -> 522,97 -> 524,97 -> 524,95 -> 524,97 -> 526,97 -> 526,89 -> 526,97 -> 528,97 -> 528,89 -> 528,97 -> 530,97 -> 530,94 -> 530,97 -> 532,97 -> 532,91 -> 532,97
503,67 -> 503,59 -> 503,67 -> 505,67 -> 505,60 -> 505,67 -> 507,67 -> 507,60 -> 507,67 -> 509,67 -> 509,58 -> 509,67 -> 511,67 -> 511,60 -> 511,67 -> 513,67 -> 513,59 -> 513,67 -> 515,67 -> 515,61 -> 515,67
502,42 -> 502,45 -> 496,45 -> 496,51 -> 507,51 -> 507,45 -> 506,45 -> 506,42
539,155 -> 544,155
527,130 -> 527,122 -> 527,130 -> 529,130 -> 529,121 -> 529,130 -> 531,130 -> 531,129 -> 531,130 -> 533,130 -> 533,121 -> 533,130 -> 535,130 -> 535,122 -> 535,130 -> 537,130 -> 537,128 -> 537,130
557,152 -> 562,152
553,149 -> 558,149
499,53 -> 499,54 -> 509,54 -> 509,53
535,133 -> 535,136 -> 530,136 -> 530,140 -> 547,140 -> 547,136 -> 540,136 -> 540,133
502,42 -> 502,45 -> 496,45 -> 496,51 -> 507,51 -> 507,45 -> 506,45 -> 506,42
510,76 -> 510,78 -> 503,78 -> 503,84 -> 521,84 -> 521,78 -> 516,78 -> 516,76
514,73 -> 524,73 -> 524,72
516,97 -> 516,94 -> 516,97 -> 518,97 -> 518,87 -> 518,97 -> 520,97 -> 520,92 -> 520,97 -> 522,97 -> 522,94 -> 522,97 -> 524,97 -> 524,95 -> 524,97 -> 526,97 -> 526,89 -> 526,97 -> 528,97 -> 528,89 -> 528,97 -> 530,97 -> 530,94 -> 530,97 -> 532,97 -> 532,91 -> 532,97
535,133 -> 535,136 -> 530,136 -> 530,140 -> 547,140 -> 547,136 -> 540,136 -> 540,133
527,130 -> 527,122 -> 527,130 -> 529,130 -> 529,121 -> 529,130 -> 531,130 -> 531,129 -> 531,130 -> 533,130 -> 533,121 -> 533,130 -> 535,130 -> 535,122 -> 535,130 -> 537,130 -> 537,128 -> 537,130
534,117 -> 539,117
497,23 -> 497,21 -> 497,23 -> 499,23 -> 499,17 -> 499,23 -> 501,23 -> 501,20 -> 501,23
502,42 -> 502,45 -> 496,45 -> 496,51 -> 507,51 -> 507,45 -> 506,45 -> 506,42
543,152 -> 548,152
527,130 -> 527,122 -> 527,130 -> 529,130 -> 529,121 -> 529,130 -> 531,130 -> 531,129 -> 531,130 -> 533,130 -> 533,121 -> 533,130 -> 535,130 -> 535,122 -> 535,130 -> 537,130 -> 537,128 -> 537,130
510,76 -> 510,78 -> 503,78 -> 503,84 -> 521,84 -> 521,78 -> 516,78 -> 516,76
527,130 -> 527,122 -> 527,130 -> 529,130 -> 529,121 -> 529,130 -> 531,130 -> 531,129 -> 531,130 -> 533,130 -> 533,121 -> 533,130 -> 535,130 -> 535,122 -> 535,130 -> 537,130 -> 537,128 -> 537,130
503,67 -> 503,59 -> 503,67 -> 505,67 -> 505,60 -> 505,67 -> 507,67 -> 507,60 -> 507,67 -> 509,67 -> 509,58 -> 509,67 -> 511,67 -> 511,60 -> 511,67 -> 513,67 -> 513,59 -> 513,67 -> 515,67 -> 515,61 -> 515,67
503,67 -> 503,59 -> 503,67 -> 505,67 -> 505,60 -> 505,67 -> 507,67 -> 507,60 -> 507,67 -> 509,67 -> 509,58 -> 509,67 -> 511,67 -> 511,60 -> 511,67 -> 513,67 -> 513,59 -> 513,67 -> 515,67 -> 515,61 -> 515,67
542,146 -> 547,146
516,97 -> 516,94 -> 516,97 -> 518,97 -> 518,87 -> 518,97 -> 520,97 -> 520,92 -> 520,97 -> 522,97 -> 522,94 -> 522,97 -> 524,97 -> 524,95 -> 524,97 -> 526,97 -> 526,89 -> 526,97 -> 528,97 -> 528,89 -> 528,97 -> 530,97 -> 530,94 -> 530,97 -> 532,97 -> 532,91 -> 532,97
503,67 -> 503,59 -> 503,67 -> 505,67 -> 505,60 -> 505,67 -> 507,67 -> 507,60 -> 507,67 -> 509,67 -> 509,58 -> 509,67 -> 511,67 -> 511,60 -> 511,67 -> 513,67 -> 513,59 -> 513,67 -> 515,67 -> 515,61 -> 515,67
497,23 -> 497,21 -> 497,23 -> 499,23 -> 499,17 -> 499,23 -> 501,23 -> 501,20 -> 501,23
509,33 -> 513,33
535,133 -> 535,136 -> 530,136 -> 530,140 -> 547,140 -> 547,136 -> 540,136 -> 540,133
516,97 -> 516,94 -> 516,97 -> 518,97 -> 518,87 -> 518,97 -> 520,97 -> 520,92 -> 520,97 -> 522,97 -> 522,94 -> 522,97 -> 524,97 -> 524,95 -> 524,97 -> 526,97 -> 526,89 -> 526,97 -> 528,97 -> 528,89 -> 528,97 -> 530,97 -> 530,94 -> 530,97 -> 532,97 -> 532,91 -> 532,97
502,42 -> 502,45 -> 496,45 -> 496,51 -> 507,51 -> 507,45 -> 506,45 -> 506,42
506,36 -> 510,36
516,97 -> 516,94 -> 516,97 -> 518,97 -> 518,87 -> 518,97 -> 520,97 -> 520,92 -> 520,97 -> 522,97 -> 522,94 -> 522,97 -> 524,97 -> 524,95 -> 524,97 -> 526,97 -> 526,89 -> 526,97 -> 528,97 -> 528,89 -> 528,97 -> 530,97 -> 530,94 -> 530,97 -> 532,97 -> 532,91 -> 532,97
497,23 -> 497,21 -> 497,23 -> 499,23 -> 499,17 -> 499,23 -> 501,23 -> 501,20 -> 501,23
503,67 -> 503,59 -> 503,67 -> 505,67 -> 505,60 -> 505,67 -> 507,67 -> 507,60 -> 507,67 -> 509,67 -> 509,58 -> 509,67 -> 511,67 -> 511,60 -> 511,67 -> 513,67 -> 513,59 -> 513,67 -> 515,67 -> 515,61 -> 515,67
500,36 -> 504,36
535,133 -> 535,136 -> 530,136 -> 530,140 -> 547,140 -> 547,136 -> 540,136 -> 540,133
535,133 -> 535,136 -> 530,136 -> 530,140 -> 547,140 -> 547,136 -> 540,136 -> 540,133
497,23 -> 497,21 -> 497,23 -> 499,23 -> 499,17 -> 499,23 -> 501,23 -> 501,20 -> 501,23
550,152 -> 555,152
527,130 -> 527,122 -> 527,130 -> 529,130 -> 529,121 -> 529,130 -> 531,130 -> 531,129 -> 531,130 -> 533,130 -> 533,121 -> 533,130 -> 535,130 -> 535,122 -> 535,130 -> 537,130 -> 537,128 -> 537,130
503,67 -> 503,59 -> 503,67 -> 505,67 -> 505,60 -> 505,67 -> 507,67 -> 507,60 -> 507,67 -> 509,67 -> 509,58 -> 509,67 -> 511,67 -> 511,60 -> 511,67 -> 513,67 -> 513,59 -> 513,67 -> 515,67 -> 515,61 -> 515,67
536,158 -> 541,158
503,67 -> 503,59 -> 503,67 -> 505,67 -> 505,60 -> 505,67 -> 507,67 -> 507,60 -> 507,67 -> 509,67 -> 509,58 -> 509,67 -> 511,67 -> 511,60 -> 511,67 -> 513,67 -> 513,59 -> 513,67 -> 515,67 -> 515,61 -> 515,67
497,23 -> 497,21 -> 497,23 -> 499,23 -> 499,17 -> 499,23 -> 501,23 -> 501,20 -> 501,23
495,26 -> 495,27 -> 507,27 -> 507,26
535,133 -> 535,136 -> 530,136 -> 530,140 -> 547,140 -> 547,136 -> 540,136 -> 540,133
516,97 -> 516,94 -> 516,97 -> 518,97 -> 518,87 -> 518,97 -> 520,97 -> 520,92 -> 520,97 -> 522,97 -> 522,94 -> 522,97 -> 524,97 -> 524,95 -> 524,97 -> 526,97 -> 526,89 -> 526,97 -> 528,97 -> 528,89 -> 528,97 -> 530,97 -> 530,94 -> 530,97 -> 532,97 -> 532,91 -> 532,97
503,67 -> 503,59 -> 503,67 -> 505,67 -> 505,60 -> 505,67 -> 507,67 -> 507,60 -> 507,67 -> 509,67 -> 509,58 -> 509,67 -> 511,67 -> 511,60 -> 511,67 -> 513,67 -> 513,59 -> 513,67 -> 515,67 -> 515,61 -> 515,67
537,114 -> 542,114
516,97 -> 516,94 -> 516,97 -> 518,97 -> 518,87 -> 518,97 -> 520,97 -> 520,92 -> 520,97 -> 522,97 -> 522,94 -> 522,97 -> 524,97 -> 524,95 -> 524,97 -> 526,97 -> 526,89 -> 526,97 -> 528,97 -> 528,89 -> 528,97 -> 530,97 -> 530,94 -> 530,97 -> 532,97 -> 532,91 -> 532,97
530,114 -> 535,114
502,42 -> 502,45 -> 496,45 -> 496,51 -> 507,51 -> 507,45 -> 506,45 -> 506,42
527,130 -> 527,122 -> 527,130 -> 529,130 -> 529,121 -> 529,130 -> 531,130 -> 531,129 -> 531,130 -> 533,130 -> 533,121 -> 533,130 -> 535,130 -> 535,122 -> 535,130 -> 537,130 -> 537,128 -> 537,130
516,97 -> 516,94 -> 516,97 -> 518,97 -> 518,87 -> 518,97 -> 520,97 -> 520,92 -> 520,97 -> 522,97 -> 522,94 -> 522,97 -> 524,97 -> 524,95 -> 524,97 -> 526,97 -> 526,89 -> 526,97 -> 528,97 -> 528,89 -> 528,97 -> 530,97 -> 530,94 -> 530,97 -> 532,97 -> 532,91 -> 532,97
503,67 -> 503,59 -> 503,67 -> 505,67 -> 505,60 -> 505,67 -> 507,67 -> 507,60 -> 507,67 -> 509,67 -> 509,58 -> 509,67 -> 511,67 -> 511,60 -> 511,67 -> 513,67 -> 513,59 -> 513,67 -> 515,67 -> 515,61 -> 515,67
516,97 -> 516,94 -> 516,97 -> 518,97 -> 518,87 -> 518,97 -> 520,97 -> 520,92 -> 520,97 -> 522,97 -> 522,94 -> 522,97 -> 524,97 -> 524,95 -> 524,97 -> 526,97 -> 526,89 -> 526,97 -> 528,97 -> 528,89 -> 528,97 -> 530,97 -> 530,94 -> 530,97 -> 532,97 -> 532,91 -> 532,97
545,143 -> 550,143
516,97 -> 516,94 -> 516,97 -> 518,97 -> 518,87 -> 518,97 -> 520,97 -> 520,92 -> 520,97 -> 522,97 -> 522,94 -> 522,97 -> 524,97 -> 524,95 -> 524,97 -> 526,97 -> 526,89 -> 526,97 -> 528,97 -> 528,89 -> 528,97 -> 530,97 -> 530,94 -> 530,97 -> 532,97 -> 532,91 -> 532,97
499,53 -> 499,54 -> 509,54 -> 509,53
516,97 -> 516,94 -> 516,97 -> 518,97 -> 518,87 -> 518,97 -> 520,97 -> 520,92 -> 520,97 -> 522,97 -> 522,94 -> 522,97 -> 524,97 -> 524,95 -> 524,97 -> 526,97 -> 526,89 -> 526,97 -> 528,97 -> 528,89 -> 528,97 -> 530,97 -> 530,94 -> 530,97 -> 532,97 -> 532,91 -> 532,97
489,38 -> 489,39 -> 503,39 -> 503,38
503,67 -> 503,59 -> 503,67 -> 505,67 -> 505,60 -> 505,67 -> 507,67 -> 507,60 -> 507,67 -> 509,67 -> 509,58 -> 509,67 -> 511,67 -> 511,60 -> 511,67 -> 513,67 -> 513,59 -> 513,67 -> 515,67 -> 515,61 -> 515,67
531,100 -> 531,102 -> 524,102 -> 524,105 -> 538,105 -> 538,102 -> 535,102 -> 535,100
510,76 -> 510,78 -> 503,78 -> 503,84 -> 521,84 -> 521,78 -> 516,78 -> 516,76
531,100 -> 531,102 -> 524,102 -> 524,105 -> 538,105 -> 538,102 -> 535,102 -> 535,100
503,67 -> 503,59 -> 503,67 -> 505,67 -> 505,60 -> 505,67 -> 507,67 -> 507,60 -> 507,67 -> 509,67 -> 509,58 -> 509,67 -> 511,67 -> 511,60 -> 511,67 -> 513,67 -> 513,59 -> 513,67 -> 515,67 -> 515,61 -> 515,67
495,26 -> 495,27 -> 507,27 -> 507,26
512,36 -> 516,36
503,67 -> 503,59 -> 503,67 -> 505,67 -> 505,60 -> 505,67 -> 507,67 -> 507,60 -> 507,67 -> 509,67 -> 509,58 -> 509,67 -> 511,67 -> 511,60 -> 511,67 -> 513,67 -> 513,59 -> 513,67 -> 515,67 -> 515,61 -> 515,67
531,100 -> 531,102 -> 524,102 -> 524,105 -> 538,105 -> 538,102 -> 535,102 -> 535,100
503,67 -> 503,59 -> 503,67 -> 505,67 -> 505,60 -> 505,67 -> 507,67 -> 507,60 -> 507,67 -> 509,67 -> 509,58 -> 509,67 -> 511,67 -> 511,60 -> 511,67 -> 513,67 -> 513,59 -> 513,67 -> 515,67 -> 515,61 -> 515,67
527,117 -> 532,117
516,97 -> 516,94 -> 516,97 -> 518,97 -> 518,87 -> 518,97 -> 520,97 -> 520,92 -> 520,97 -> 522,97 -> 522,94 -> 522,97 -> 524,97 -> 524,95 -> 524,97 -> 526,97 -> 526,89 -> 526,97 -> 528,97 -> 528,89 -> 528,97 -> 530,97 -> 530,94 -> 530,97 -> 532,97 -> 532,91 -> 532,97
527,130 -> 527,122 -> 527,130 -> 529,130 -> 529,121 -> 529,130 -> 531,130 -> 531,129 -> 531,130 -> 533,130 -> 533,121 -> 533,130 -> 535,130 -> 535,122 -> 535,130 -> 537,130 -> 537,128 -> 537,130
516,97 -> 516,94 -> 516,97 -> 518,97 -> 518,87 -> 518,97 -> 520,97 -> 520,92 -> 520,97 -> 522,97 -> 522,94 -> 522,97 -> 524,97 -> 524,95 -> 524,97 -> 526,97 -> 526,89 -> 526,97 -> 528,97 -> 528,89 -> 528,97 -> 530,97 -> 530,94 -> 530,97 -> 532,97 -> 532,91 -> 532,97
497,23 -> 497,21 -> 497,23 -> 499,23 -> 499,17 -> 499,23 -> 501,23 -> 501,20 -> 501,23
531,100 -> 531,102 -> 524,102 -> 524,105 -> 538,105 -> 538,102 -> 535,102 -> 535,100
531,100 -> 531,102 -> 524,102 -> 524,105 -> 538,105 -> 538,102 -> 535,102 -> 535,100
536,152 -> 541,152
531,100 -> 531,102 -> 524,102 -> 524,105 -> 538,105 -> 538,102 -> 535,102 -> 535,100
497,23 -> 497,21 -> 497,23 -> 499,23 -> 499,17 -> 499,23 -> 501,23 -> 501,20 -> 501,23
497,23 -> 497,21 -> 497,23 -> 499,23 -> 499,17 -> 499,23 -> 501,23 -> 501,20 -> 501,23
535,133 -> 535,136 -> 530,136 -> 530,140 -> 547,140 -> 547,136 -> 540,136 -> 540,133
541,117 -> 546,117
499,53 -> 499,54 -> 509,54 -> 509,53
516,97 -> 516,94 -> 516,97 -> 518,97 -> 518,87 -> 518,97 -> 520,97 -> 520,92 -> 520,97 -> 522,97 -> 522,94 -> 522,97 -> 524,97 -> 524,95 -> 524,97 -> 526,97 -> 526,89 -> 526,97 -> 528,97 -> 528,89 -> 528,97 -> 530,97 -> 530,94 -> 530,97 -> 532,97 -> 532,91 -> 532,97
540,111 -> 545,111
527,130 -> 527,122 -> 527,130 -> 529,130 -> 529,121 -> 529,130 -> 531,130 -> 531,129 -> 531,130 -> 533,130 -> 533,121 -> 533,130 -> 535,130 -> 535,122 -> 535,130 -> 537,130 -> 537,128 -> 537,130
543,158 -> 548,158
503,67 -> 503,59 -> 503,67 -> 505,67 -> 505,60 -> 505,67 -> 507,67 -> 507,60 -> 507,67 -> 509,67 -> 509,58 -> 509,67 -> 511,67 -> 511,60 -> 511,67 -> 513,67 -> 513,59 -> 513,67 -> 515,67 -> 515,61 -> 515,67
536,108 -> 541,108
510,76 -> 510,78 -> 503,78 -> 503,84 -> 521,84 -> 521,78 -> 516,78 -> 516,76
540,161 -> 545,161

Day 15: python

this documentation is autogenerated. Add a README.adoc to your solution to take over the control of this :-)

python

sol.py

#!/usr/bin/env python3


import re


def get_dist(a, b):
    return sum(abs(i-j) for i, j in zip(a, b))


def load_input():
    sensors = list()
    beacons = list()

    pattern = re.compile('Sensor at x=([-\d]+), y=([-\d]+): closest beacon is at x=([-\d]+), y=([-\d]+)')

    with open('input') as fd:
        for line in fd:
            line = line.strip()
            match = pattern.search(line)
            sensors.append((int(match.group(1)), int(match.group(2))))
            beacons.append((int(match.group(3)), int(match.group(4))))
    return sensors, beacons


def part1():
    sensors, beacons = load_input()
    line = 2000000

    positions = list()
    for sensor, beacon in zip(sensors, beacons):
        d = get_dist(sensor, beacon)
        d_y = abs(sensor[1] - line)
        d_x = d - d_y
        if d_x >= 0:
            x_min = sensor[0] - d_x
            x_max = sensor[0] + d_x
            positions.append([x_min, x_max])

    positions.sort(key=lambda x: x[0])
    fp = [positions[0]]
    for pos in positions[1:]:
        if fp[-1][1] >= pos[0]-1:
            if fp[-1][1] < pos[1]:
                fp[-1][1] = pos[1]
        else:
            fp.append(pos)

    result = sum([j - i for i, j in fp])
    print("Result 1: ", result)


def part2():
    sensors, beacons = load_input()
    x_limit = 4000000
    y_limit = 4000000

    for y in range(y_limit+1):
        positions = list()
        for sensor, beacon in zip(sensors, beacons):
            d = get_dist(sensor, beacon)
            d_y = abs(sensor[1] - y)
            d_x = d - d_y
            if d_x >= 0:
                x_min = sensor[0] - d_x
                x_max = sensor[0] + d_x
                positions.append([x_min, x_max])

        positions.sort(key=lambda x: x[0])
        fp = [[0, 0]]
        for pos in positions:
            x_max = pos[1] if pos[1] < x_limit else x_limit
            x_min = pos[0] if pos[0] > 0 else 0

            if fp[-1][1] >= pos[0]-1:
                if fp[-1][1] < pos[1]:
                    fp[-1][1] = x_max
            else:
                fp.append([x_min, x_max])

            if fp[-1][1] == x_limit:
                break

        if len(fp) == 2:
            x_sol = fp[0][1]+1
            y_sol = y
            break

    result = x_sol*4000000 + y_sol
    print("Result 2: ", result)


def main():
    part1()
    part2()


if __name__ == '__main__':
    main()

input

Sensor at x=2302110, y=2237242: closest beacon is at x=2348729, y=1239977
Sensor at x=47903, y=2473047: closest beacon is at x=-432198, y=2000000
Sensor at x=2363579, y=1547888: closest beacon is at x=2348729, y=1239977
Sensor at x=3619841, y=520506: closest beacon is at x=2348729, y=1239977
Sensor at x=3941908, y=3526118: closest beacon is at x=3772294, y=3485243
Sensor at x=3206, y=1564595: closest beacon is at x=-432198, y=2000000
Sensor at x=3123411, y=3392077: closest beacon is at x=2977835, y=3592946
Sensor at x=3279053, y=3984688: closest beacon is at x=2977835, y=3592946
Sensor at x=2968162, y=3938490: closest beacon is at x=2977835, y=3592946
Sensor at x=1772120, y=2862246: closest beacon is at x=2017966, y=3158243
Sensor at x=3283241, y=2619168: closest beacon is at x=3172577, y=2521434
Sensor at x=2471642, y=3890150: closest beacon is at x=2977835, y=3592946
Sensor at x=3163348, y=3743489: closest beacon is at x=2977835, y=3592946
Sensor at x=2933313, y=2919047: closest beacon is at x=3172577, y=2521434
Sensor at x=2780640, y=3629927: closest beacon is at x=2977835, y=3592946
Sensor at x=3986978, y=2079918: closest beacon is at x=3998497, y=2812428
Sensor at x=315464, y=370694: closest beacon is at x=-550536, y=260566
Sensor at x=3957316, y=3968366: closest beacon is at x=3772294, y=3485243
Sensor at x=2118533, y=1074658: closest beacon is at x=2348729, y=1239977
Sensor at x=3494855, y=3378533: closest beacon is at x=3772294, y=3485243
Sensor at x=2575727, y=210553: closest beacon is at x=2348729, y=1239977
Sensor at x=3999990, y=2813525: closest beacon is at x=3998497, y=2812428
Sensor at x=3658837, y=3026912: closest beacon is at x=3998497, y=2812428
Sensor at x=1551619, y=1701155: closest beacon is at x=2348729, y=1239977
Sensor at x=2625855, y=3330422: closest beacon is at x=2977835, y=3592946
Sensor at x=3476946, y=2445098: closest beacon is at x=3172577, y=2521434
Sensor at x=2915568, y=1714113: closest beacon is at x=2348729, y=1239977
Sensor at x=729668, y=3723377: closest beacon is at x=-997494, y=3617758
Sensor at x=3631681, y=3801747: closest beacon is at x=3772294, y=3485243
Sensor at x=2270816, y=3197807: closest beacon is at x=2017966, y=3158243
Sensor at x=3999999, y=2810929: closest beacon is at x=3998497, y=2812428
Sensor at x=3978805, y=3296024: closest beacon is at x=3772294, y=3485243
Sensor at x=1054910, y=811769: closest beacon is at x=2348729, y=1239977

Day 16: python

this documentation is autogenerated. Add a README.adoc to your solution to take over the control of this :-)

python

sol.py

#!/usr/bin/env python3


import networkx as nx
import itertools as it

import re
from copy import deepcopy


limit_combos = dict()


def load_input():
    data = list()
    with open('input') as fd:
        pattern = re.compile('Valve (\w+) has flow rate=(\d+); tunnel[s]? lead[s]? to valve[s]? (.+)')
        for line in fd:
            match = pattern.search(line.strip())
            valve = match.group(1)
            rate = int(match.group(2))
            neighbours = list(map(lambda x: x.strip(), match.group(3).split(',')))
            data.append((valve, rate, neighbours))
    return data


def create_power_set(data):
    result = dict()
    for i in range(1, len(data)):
        for combo in it.combinations(data, i):
            key = tuple(sorted(combo))
            result[key] = [0, 0]
    return result


def create_graph(data):
    graph = nx.Graph()
    for item in data:
        node = item[0]
        graph.add_node(node, flow=item[1], w=0, wm=-1, valves=set())
        for neighbour in item[2]:
            graph.add_edge(node, neighbour)

    return graph


def shortest_valves(graph, start):
    shortest_valves = dict()
    valves = [node for node, values in graph.nodes.items() if values['flow'] != 0]
    valves.append(start)
    for valve in valves:
        shortest_valves[valve] = dict()
        for nv in valves:
            if nv != valve:
                shortest_valves[valve][nv] = nx.shortest_path_length(graph, valve, nv)
    return shortest_valves


def process_part1(nodes, valves, node, pressure, time, remaining, path=list(), depth=0):
    cycles = 30

    if path:
        path.sort()
        key = tuple(path)
        if pressure <= limit_combos[key][0] and time >= limit_combos[key][1]:
            return path, pressure
        else:
            limit_combos[key][0] = pressure
            limit_combos[key][1] = time

    path.append(node)
    path.sort()

    max_pressure = pressure
    max_path = path.copy()

    for valve in remaining:
        length = valves[node][valve]
        tt = time + length + 1
        if tt >= cycles:
            continue
        tr = remaining.copy()
        tr.remove(valve)
        tp = pressure + nodes[valve]['flow']*(cycles-tt)
        tpath, tpressure = process_part1(nodes, valves, valve, tp, tt, tr, path.copy(), depth+1)
        if tpressure > max_pressure:
            max_pressure = tpressure
            max_path = tpath
    return max_path, max_pressure


def process_part2(nodes, valves, node, pressure, time, remaining, path=set(), depth=0):
    cycles = 26

    if path:
        temp = list(path)
        temp.sort()
        key = tuple(temp)
        if pressure <= limit_combos[key][0] and max(time) >= limit_combos[key][1]:
            return path, pressure
        else:
            limit_combos[key][0] = pressure
            limit_combos[key][1] = max(time)

    path.update(set(node))

    max_pressure = pressure
    max_path = path.copy()

    active = 1 if time[0] > time[1] else 0

    for valve in remaining:
        length = valves[node[active]][valve]

        tt = deepcopy(time)
        tt[active] = time[active] + length + 1

        if tt[active] >= cycles:
            continue

        tr = remaining.copy()
        tr.remove(valve)

        tn = node.copy()
        tn[active] = valve

        tp = pressure + nodes[valve]['flow']*(cycles-tt[active])
        tpath, tpressure = process_part2(nodes, valves, tn, tp, tt, tr, path.copy(), depth + 1)

        if tpressure > max_pressure:
            max_pressure = tpressure
            max_path = tpath

    return max_path, max_pressure


def part1():
    data = load_input()
    graph = create_graph(data)

    start = 'AA'
    valves = shortest_valves(graph, start)

    global limit_combos
    limit_combos = create_power_set(list(valves.keys()))

    remaining = set(valves.keys())
    remaining.remove('AA')

    path, pressure = process_part1(graph.nodes, valves, start, 0, 0, remaining)
    result = pressure

    print("Result 1: ", result)


def part2():
    data = load_input()
    graph = create_graph(data)

    start = ['AA', 'AA']
    valves = shortest_valves(graph, start[0])

    global limit_combos
    limit_combos = create_power_set(list(valves.keys()))

    remaining = set(valves.keys())
    remaining.remove('AA')

    path, pressure = process_part2(graph.nodes, valves, start, 0, [0, 0], remaining)

    result = pressure
    print("Result 2: ", result)


def main():
    part1()
    part2()


if __name__ == '__main__':
    main()

input

Valve EG has flow rate=21; tunnels lead to valves WZ, OF, ZP, QD
Valve OR has flow rate=0; tunnels lead to valves QD, CR
Valve VO has flow rate=0; tunnels lead to valves FL, OY
Valve BV has flow rate=0; tunnels lead to valves AA, KK
Valve OF has flow rate=0; tunnels lead to valves EJ, EG
Valve YZ has flow rate=0; tunnels lead to valves EL, AW
Valve EL has flow rate=16; tunnels lead to valves YZ, RD
Valve EJ has flow rate=0; tunnels lead to valves YI, OF
Valve FM has flow rate=0; tunnels lead to valves VX, FX
Valve FL has flow rate=22; tunnels lead to valves VO, FH
Valve QD has flow rate=0; tunnels lead to valves OR, EG
Valve XC has flow rate=0; tunnels lead to valves UA, GV
Valve WZ has flow rate=0; tunnels lead to valves FH, EG
Valve AT has flow rate=0; tunnels lead to valves FX, OZ
Valve MZ has flow rate=0; tunnels lead to valves UA, YI
Valve WI has flow rate=0; tunnels lead to valves OH, WW
Valve YD has flow rate=0; tunnels lead to valves OZ, WW
Valve QX has flow rate=0; tunnels lead to valves OY, YI
Valve AA has flow rate=0; tunnels lead to valves BV, ZE, PE, XL
Valve VX has flow rate=0; tunnels lead to valves FM, GQ
Valve VN has flow rate=0; tunnels lead to valves TU, OQ
Valve RD has flow rate=0; tunnels lead to valves OY, EL
Valve QR has flow rate=0; tunnels lead to valves QQ, OZ
Valve CD has flow rate=0; tunnels lead to valves WW, RJ
Valve VA has flow rate=20; tunnel leads to valve DE
Valve RJ has flow rate=0; tunnels lead to valves CR, CD
Valve UA has flow rate=19; tunnels lead to valves XC, MZ, KY
Valve WW has flow rate=4; tunnels lead to valves YD, PE, WI, DY, CD
Valve MC has flow rate=0; tunnels lead to valves ZP, XY
Valve XY has flow rate=24; tunnel leads to valve MC
Valve FH has flow rate=0; tunnels lead to valves FL, WZ
Valve DE has flow rate=0; tunnels lead to valves VA, FX
Valve DY has flow rate=0; tunnels lead to valves WW, YI
Valve FX has flow rate=14; tunnels lead to valves DE, FM, AT, OQ
Valve UU has flow rate=0; tunnels lead to valves AR, AW
Valve OY has flow rate=13; tunnels lead to valves RD, VO, AR, GV, QX
Valve CS has flow rate=0; tunnels lead to valves MG, OZ
Valve KY has flow rate=0; tunnels lead to valves UA, AW
Valve KK has flow rate=0; tunnels lead to valves BV, TU
Valve GQ has flow rate=18; tunnel leads to valve VX
Valve ZV has flow rate=0; tunnels lead to valves YI, LS
Valve QQ has flow rate=0; tunnels lead to valves CR, QR
Valve AW has flow rate=25; tunnels lead to valves YZ, KY, UU
Valve OH has flow rate=0; tunnels lead to valves WI, TU
Valve CR has flow rate=8; tunnels lead to valves OR, ZE, RJ, LS, QQ
Valve TU has flow rate=7; tunnels lead to valves MG, VN, OH, KK
Valve ZP has flow rate=0; tunnels lead to valves EG, MC
Valve AR has flow rate=0; tunnels lead to valves UU, OY
Valve OZ has flow rate=10; tunnels lead to valves YD, XL, CS, AT, QR
Valve GV has flow rate=0; tunnels lead to valves XC, OY
Valve PE has flow rate=0; tunnels lead to valves WW, AA
Valve ZE has flow rate=0; tunnels lead to valves AA, CR
Valve XL has flow rate=0; tunnels lead to valves OZ, AA
Valve YI has flow rate=15; tunnels lead to valves QX, MZ, EJ, DY, ZV
Valve OQ has flow rate=0; tunnels lead to valves FX, VN
Valve MG has flow rate=0; tunnels lead to valves TU, CS
Valve LS has flow rate=0; tunnels lead to valves CR, ZV

Day 17: python

this documentation is autogenerated. Add a README.adoc to your solution to take over the control of this :-)

python

sol.py

#!/usr/bin/env python3


SHAPES = [[(0, 2), (0, 3), (0, 4), (0, 5)],
          [(0, 3), (1, 2), (1, 3), (1, 4), (2, 3)],
          [(0, 2), (0, 3), (0, 4), (1, 4), (2, 4)],
          [(0, 2), (1, 2), (2, 2), (3, 2)],
          [(0, 2), (0, 3), (1, 2), (1, 3)]]


def load_input():
    with open('input') as fd:
        data = fd.read().strip()
    return data


def calc_start(shape, y_max):
    result = [(y+y_max+4, x) for y, x in shape]
    return result


def move_down(shape):
    return [(y-1, x) for y, x in shape]


def process_jet_gas(shape, gas):
    d = 1 if gas == '>' else -1
    return [(y, x+d) for y, x in shape]


def is_shape_in_field(field, shape):
    result = False
    for pos in shape:
        if pos in field:
            result = True
            break
    return result


def parts():
    data = load_input()
    field = set(((0, j) for j in range(7)))
    y_max = 0
    jet_count = 0

    prev_max = 0

    # Preparation for part2
    value = 0
    steps = 1000000000000
    # Period must be manually determined
    period = 87*100
    period_value = 13405
    init_value = 13404
    init_step = period
    rem_step = steps - ((steps - init_step)//period)*period - init_step
    rem_value = 0

    for i in range(20000):

        # Determine period
        # if i%(100*87) == 0:
        # Example Period
        # if i%(40*7) == 0:
            # print(i)
            # print(y_max - prev_max)
            # print("")
            # prev_max = y_max

        shape = calc_start(SHAPES[i % len(SHAPES)], y_max)
        while True:
            gas = data[jet_count % len(data)]
            jet_count += 1
            next_shape = process_jet_gas(shape, gas)
            xs = [x for _, x in next_shape]
            if not(min(xs) < 0 or max(xs) > 6):
                if not(is_shape_in_field(field, next_shape)):
                    shape = next_shape

            next_shape = move_down(shape)
            if is_shape_in_field(field, next_shape):
                break
            else:
                shape = next_shape
        field.update(set(shape))
        y_s = [y for y, _ in shape]
        y_ms = max(y_s)
        y_max = y_ms if y_ms > y_max else y_max

        # Required for part2
        if i == init_step + rem_step - 1:
            rem_value = y_max - init_value

        # Part 1
        if i == 2022-1:
            result = y_max

    print("Result 1: ", result)

    value = init_value + period_value*((steps - init_step)//period) + rem_value
    print("Result 2: ", value)


def main():
    parts()


if __name__ == '__main__':
    main()

input

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<<>><>>>><<<<>><<<<>><<<<><<<>><<>>><>>>><<<<>><>>><<<<><<<<>><>>><<>>><<<<>>><<<>>><><<<<>

Day 18: python

this documentation is autogenerated. Add a README.adoc to your solution to take over the control of this :-)

python

sol.py

#!/usr/bin/env python3


import networkx as nx

import numpy as np


def load_input():
    data = set()
    with open('input') as fd:
        for line in fd:
            line = line.strip()
            data.add(tuple([int(i) for i in line.split(',')]))
    return data


def get_neighbors(field, point):
    x, y, z = point
    neighbors = []
    x_max, y_max, z_max = field.shape
    for i in range(-1, 2):
        for j in range(-1, 2):
            for k in range(-1, 2):
                if abs(i) + abs(j) + abs(k) == 1:
                    if x+i >= 0 and y+j >= 0 and z+k >= 0 and x+i < x_max and y+j < y_max and z+k < z_max:
                        neighbors.append((x+i, y+j, z+k))
    return neighbors


def parts():
    data = load_input()
    graph = nx.Graph()
    x_max = max((coord[0] for coord in data))
    y_max = max((coord[1] for coord in data))
    z_max = max((coord[2] for coord in data))
    cube = np.full((x_max+3, y_max+3, z_max+3), False, dtype=np.bool_)

    it = np.nditer(cube, flags=['multi_index'])

    for x, y, z in data:
        cube[x+1, y+1, z+1] = True

    side_count = 0
    it = np.nditer(cube, flags=['multi_index'])

    for val in it:
        x, y, z = it.multi_index

        if not val:
            graph.add_node((x, y, z))

        neighbors = get_neighbors(cube, (x, y, z))
        for i, j, k in neighbors:
            if not cube[i, j, k]:
                if val:
                    side_count += 1
                else:
                    graph.add_edge((x, y, z), (i, j, k))

    print("Result 1: ", side_count)

    # Part 2
    src = (0, 0, 0)
    tree = nx.bfs_tree(graph, src)

    lava_nodes = set(graph.nodes).difference(set(tree.nodes))

    side_count = 0
    it = np.nditer(cube, flags=['multi_index'])

    for val in it:
        x, y, z = it.multi_index
        if val:
            neighbors = get_neighbors(cube, (x, y, z))
            for i, j, k in neighbors:
                if not cube[i, j, k] and (i, j, k) not in lava_nodes:
                    side_count += 1

    print("Result 2: ", side_count)


def main():
    parts()


if __name__ == '__main__':
    main()

input

6,7,3
2,9,11
7,5,16
12,12,2
17,8,14
9,4,3
8,10,2
3,14,7
15,14,10
5,13,4
4,6,15
12,16,7
9,6,18
3,10,13
10,12,17
7,3,12
10,3,4
12,17,6
8,8,18
6,14,6
15,6,14
5,16,9
2,5,12
11,16,13
4,8,14
10,8,2
1,9,11
6,17,11
16,13,11
10,1,9
8,4,5
3,10,5
3,4,9
6,5,15
14,3,9
3,15,10
7,4,14
13,7,16
4,15,4
10,3,13
11,17,8
16,12,8
13,12,2
13,4,6
12,13,5
10,2,7
13,16,15
16,12,11
10,2,12
14,4,13
13,13,5
14,7,7
6,4,6
5,16,10
3,9,16
11,3,8
3,6,7
2,12,10
7,14,15
13,13,2
6,16,11
16,13,5
14,3,11
12,5,2
11,8,15
8,12,2
2,10,10
18,12,7
8,16,13
16,12,7
5,2,8
9,9,17
13,6,14
6,14,15
3,8,5
7,10,2
17,14,8
5,14,12
1,8,11
13,5,4
16,9,6
15,6,16
3,4,11
5,4,13
8,15,15
3,3,8
8,9,1
8,2,11
16,10,4
11,1,11
14,14,11
8,3,13
3,6,5
14,16,8
15,15,5
5,12,16
3,14,10
14,5,7
12,2,9
4,7,15
12,3,5
3,13,13
8,8,4
4,11,4
14,17,11
10,17,11
6,11,2
4,9,14
6,16,4
15,15,6
4,10,3
7,7,2
6,5,16
4,7,4
7,3,8
11,2,10
6,8,18
2,6,7
16,14,10
9,2,9
13,4,8
13,9,15
11,5,14
3,11,13
13,12,16
7,3,13
13,13,16
11,2,12
6,15,7
5,12,3
7,4,15
16,4,8
16,15,14
8,2,13
12,10,3
16,13,14
16,7,8
11,4,4
4,10,15
3,13,8
12,17,12
10,4,3
11,6,3
8,15,4
10,17,12
5,9,18
4,2,11
10,14,4
5,15,13
11,7,3
10,13,3
4,15,6
2,10,13
15,15,9
7,17,7
10,15,16
15,16,11
9,3,12
13,11,2
8,6,17
8,5,14
6,11,17
13,17,14
3,9,15
15,7,5
3,10,15
3,15,13
16,13,6
14,6,14
14,3,12
9,1,11
9,7,16
5,13,3
11,9,18
14,15,11
4,14,4
15,14,12
12,16,8
5,6,4
8,16,15
11,16,10
7,15,15
7,2,8
3,8,12
7,8,17
16,11,6
11,11,2
14,4,7
13,16,14
13,7,4
12,16,12
16,7,10
10,14,17
8,4,4
6,10,16
17,11,11
16,5,10
10,17,8
11,10,3
8,16,5
4,6,12
12,15,15
14,7,15
2,13,13
17,9,11
9,18,7
5,7,15
2,12,6
5,9,17
11,17,11
2,11,12
16,4,9
15,6,4
5,4,9
2,7,8
13,12,14
12,1,10
9,5,13
3,9,6
5,3,8
2,13,11
7,17,8
10,15,5
13,7,17
16,10,10
7,5,14
7,8,2
16,9,14
3,13,5
6,11,13
8,5,16
6,4,10
6,6,17
4,15,9
14,9,3
3,14,6
3,12,5
12,15,12
7,17,6
7,9,3
4,17,10
8,7,3
10,16,12
16,5,7
4,8,10
5,6,10
3,7,10
16,7,14
2,8,12
1,8,8
8,7,16
3,7,6
6,15,10
5,10,5
5,3,11
14,5,6
8,5,11
8,13,16
14,15,13
14,2,8
8,15,5
18,7,9
15,3,10
9,2,8
13,15,5
5,16,7
3,5,12
5,6,12
7,4,5
4,11,5
16,12,5
8,3,6
17,9,6
10,9,2
1,11,7
15,11,15
10,4,12
16,10,5
11,12,18
18,12,10
16,14,7
12,13,2
4,3,8
17,11,13
14,6,16
3,7,5
12,11,18
12,2,11
6,6,2
14,5,16
15,7,12
16,5,12
8,6,15
8,17,8
9,15,14
8,3,10
7,4,13
11,11,18
5,5,4
8,11,2
13,10,14
7,6,4
5,16,8
2,9,6
18,12,9
3,8,13
7,8,1
13,16,12
13,6,3
5,15,5
7,15,7
3,11,11
3,5,11
9,1,10
5,4,7
10,7,17
12,10,16
3,10,4
16,10,7
7,16,14
15,10,14
4,6,14
9,16,7
18,11,13
13,4,16
10,14,3
3,12,11
14,11,4
17,11,14
14,16,9
18,12,11
14,14,8
16,12,10
17,5,8
14,13,6
7,15,13
15,12,13
7,11,17
13,10,2
6,4,11
6,14,4
12,10,4
3,6,13
6,16,10
16,12,6
4,12,10
15,17,9
2,8,9
7,3,9
5,11,15
4,5,12
8,8,3
5,9,3
3,8,10
13,15,14
5,6,6
3,7,12
6,14,3
16,7,15
6,12,2
11,16,5
18,7,8
11,3,15
4,13,4
17,7,11
12,9,17
2,7,10
10,16,6
12,7,17
3,12,8
6,12,3
11,5,15
9,6,15
14,4,12
4,10,6
6,17,7
13,2,8
9,1,7
5,6,15
17,12,13
7,6,3
14,8,17
6,2,9
15,4,13
11,15,4
14,16,12
9,18,9
5,6,13
13,17,12
12,17,5
13,13,4
2,9,9
6,10,3
14,13,4
15,4,7
12,4,4
6,10,17
15,6,6
5,15,6
16,6,6
9,11,2
12,14,4
3,14,8
14,9,16
12,3,9
10,15,6
6,15,5
12,16,10
5,14,4
6,6,14
12,9,2
2,10,12
14,10,17
14,17,13
14,7,5
9,7,15
14,16,7
4,14,12
11,5,4
15,6,8
4,11,6
3,12,10
11,6,17
7,9,17
17,7,13
9,15,3
8,16,8
8,14,3
3,9,14
2,11,9
13,2,12
11,15,5
7,5,15
13,3,13
11,4,6
3,10,12
9,10,3
13,5,5
4,12,4
13,4,13
8,16,6
14,8,3
7,9,2
3,7,9
6,3,6
5,15,4
4,13,10
7,7,4
9,2,11
4,7,14
12,6,15
5,14,7
6,15,12
13,14,14
15,4,11
7,15,4
16,14,9
3,11,4
9,18,11
17,9,5
18,11,6
2,10,8
4,14,11
12,10,2
19,8,10
9,17,12
10,17,7
6,12,12
4,13,14
11,4,2
16,11,15
9,13,3
7,8,15
8,14,13
12,3,12
16,13,9
10,16,14
10,15,14
10,17,6
4,11,3
10,3,7
10,11,17
8,18,6
14,3,6
17,12,7
17,10,11
11,7,1
6,16,7
16,9,10
5,2,12
13,3,10
15,4,9
17,9,10
15,13,7
14,4,14
12,8,2
5,16,6
4,15,10
9,12,16
14,17,7
14,12,6
3,8,9
6,3,13
16,8,11
9,7,18
12,4,6
14,6,15
10,10,1
6,15,9
4,6,4
14,4,11
9,3,11
4,15,8
16,9,15
15,3,8
8,3,8
2,5,8
4,10,4
12,15,14
11,16,11
6,8,15
5,3,10
17,9,9
6,4,14
4,5,8
17,9,13
15,13,8
9,13,17
7,17,10
13,9,17
2,13,9
15,10,5
10,2,8
6,11,16
9,1,8
10,8,17
13,5,14
13,13,15
6,3,8
9,8,18
10,16,11
7,13,14
12,6,16
16,14,6
10,16,4
16,7,5
15,12,15
8,5,3
13,13,3
17,6,8
9,6,2
6,7,4
6,3,3
7,18,9
4,4,10
8,4,16
6,9,17
10,1,7
13,16,11
4,16,9
11,12,2
9,6,3
4,16,10
8,13,3
14,12,14
11,18,12
10,4,4
5,11,4
10,10,17
4,15,7
17,12,10
10,3,3
11,8,1
14,13,16
15,13,12
6,8,3
12,6,4
15,5,5
12,14,3
6,15,6
11,13,16
16,8,9
14,10,15
10,17,10
8,4,14
12,7,2
6,7,2
13,15,15
7,6,15
5,16,11
16,16,9
15,14,9
4,5,3
9,4,15
11,2,6
2,9,8
14,17,8
4,16,11
14,6,4
15,6,5
11,13,2
12,4,14
12,3,8
15,12,4
9,2,14
14,9,5
6,2,12
15,16,12
4,10,16
8,6,16
5,12,4
12,8,17
15,11,14
9,18,8
15,14,11
4,9,4
5,13,15
16,4,10
6,14,12
16,12,4
5,8,14
4,4,9
11,3,4
14,5,5
11,3,6
14,3,7
4,5,7
9,9,2
9,2,6
8,18,9
9,3,14
16,8,13
9,17,7
11,2,9
13,9,14
1,8,10
17,8,6
17,12,8
3,6,8
12,14,16
4,13,15
2,6,10
4,4,15
12,9,16
17,7,9
15,13,4
12,4,10
6,8,17
7,10,1
8,4,6
13,14,13
11,18,10
4,7,11
3,8,14
8,1,11
6,8,4
12,14,14
7,2,9
14,14,3
17,9,7
13,5,15
4,13,13
10,8,1
13,11,5
14,17,12
1,9,5
14,15,5
7,4,6
1,11,11
10,5,3
15,5,8
5,12,2
9,16,6
11,3,14
13,10,17
5,14,15
2,10,9
15,5,6
8,7,2
7,11,2
5,14,5
6,3,9
13,3,14
13,15,6
7,8,18
17,9,8
11,14,3
9,9,1
11,3,7
7,6,16
9,16,9
8,4,13
8,13,17
9,3,4
13,5,3
12,4,2
15,14,5
13,13,6
16,15,7
5,2,7
14,14,13
11,17,13
15,9,16
15,12,3
1,12,9
8,11,18
9,17,9
16,14,13
4,7,5
13,8,16
18,9,9
6,13,3
6,16,6
10,17,9
9,15,15
11,17,6
7,6,8
11,15,6
10,1,10
15,8,4
6,16,5
4,14,5
9,7,2
14,14,6
8,14,5
12,11,14
4,8,3
17,11,8
15,11,16
10,18,7
6,13,16
17,13,7
4,14,8
5,5,7
16,12,12
13,14,3
15,15,11
5,5,3
3,9,9
14,3,13
16,14,12
3,11,7
11,9,16
17,7,6
3,7,13
3,12,3
3,11,6
14,15,8
7,3,6
3,7,4
18,8,9
6,8,14
12,17,10
15,5,14
13,7,18
2,12,11
4,12,16
7,15,5
6,4,8
8,15,13
10,10,3
2,13,12
2,7,6
10,9,17
10,18,10
16,5,9
9,16,3
13,7,3
6,17,12
7,3,15
16,11,14
11,16,6
16,12,13
8,11,17
6,10,15
6,11,3
2,8,6
10,16,7
3,10,14
3,11,5
5,10,3
13,2,9
12,11,17
6,16,14
6,13,4
4,11,15
6,15,4
9,13,2
13,12,13
10,10,2
8,3,5
13,9,18
7,13,16
5,14,6
11,3,5
6,3,7
8,2,10
12,12,3
6,13,14
8,7,1
9,16,13
9,17,6
17,7,8
5,4,15
17,13,6
3,11,12
16,8,15
10,4,14
13,7,2
4,12,6
2,9,13
9,18,10
11,18,7
17,10,13
14,13,12
14,16,10
14,10,14
12,16,13
10,6,16
2,5,10
6,4,13
9,14,16
4,12,12
16,13,8
14,10,16
16,5,8
3,15,12
3,8,4
13,16,9
8,13,2
4,11,8
12,3,7
2,11,7
11,1,9
13,9,16
8,3,7
2,6,8
8,10,17
4,3,9
9,18,6
16,10,12
17,8,12
16,6,12
3,8,7
5,13,16
17,11,9
7,13,17
11,17,10
4,5,11
15,13,6
12,14,17
8,8,19
13,17,7
8,17,14
5,4,8
11,15,15
4,13,5
6,13,15
16,6,8
2,12,9
7,4,11
2,10,7
13,10,3
7,17,13
16,10,6
8,6,5
8,7,17
14,6,5
11,5,2
13,3,5
5,8,4
2,11,13
12,8,16
8,10,1
11,17,14
9,5,2
5,11,3
15,7,6
11,8,18
10,4,13
13,11,17
9,2,10
16,8,4
5,4,10
9,17,8
9,13,6
14,14,14
12,2,8
5,11,14
12,1,12
13,12,4
12,6,3
4,8,13
9,9,18
6,2,10
9,2,7
10,2,9
4,3,12
14,14,4
15,14,6
7,3,7
10,2,13
14,13,13
3,14,9
1,11,10
8,18,10
17,13,10
7,2,7
18,8,11
3,15,7
4,9,15
9,6,16
3,14,12
6,5,12
9,14,2
3,8,11
9,11,16
11,17,9
11,14,4
8,12,18
4,12,13
10,13,16
6,15,8
13,15,11
7,16,7
10,5,13
13,12,15
10,2,14
14,16,11
11,18,9
10,17,13
9,1,9
12,11,2
1,8,9
8,7,15
15,4,12
6,10,2
11,10,1
13,2,5
2,11,5
7,10,16
10,2,6
5,3,9
5,7,16
12,5,4
3,10,2
14,16,6
9,7,17
11,14,15
10,16,5
17,10,6
5,9,2
4,3,13
6,16,9
10,13,2
15,5,13
18,8,8
9,3,5
10,6,3
17,11,6
8,15,14
13,3,9
11,7,17
12,9,3
12,11,16
12,5,14
14,15,6
8,3,11
16,13,10
9,6,17
17,10,8
9,3,6
11,5,3
12,16,14
13,6,4
11,9,2
13,6,2
7,10,5
8,17,7
9,5,17
14,15,9
10,14,5
6,2,8
6,15,14
18,11,9
11,2,11
4,5,6
18,11,12
3,14,11
5,15,14
4,6,3
2,11,14
11,10,15
10,18,8
17,8,11
11,3,11
7,13,3
9,2,12
18,9,10
17,13,8
17,10,14
5,5,14
17,6,7
15,5,12
2,8,13
10,1,11
13,2,10
17,15,8
11,10,2
9,10,2
11,5,13
5,5,13
4,12,5
9,15,6
2,9,7
14,2,10
16,4,11
4,11,14
7,4,3
17,13,9
7,2,10
5,16,14
11,14,16
16,4,12
15,12,5
2,13,14
7,16,10
4,13,11
17,7,7
13,3,7
6,8,2
5,16,12
9,15,4
7,12,3
15,11,6
10,5,2
11,1,10
6,17,14
3,6,10
15,6,15
10,11,1
4,16,7
9,2,4
14,4,5
12,11,15
16,5,13
7,5,5
6,18,9
5,9,15
4,4,11
5,6,3
7,14,2
1,11,13
15,14,7
16,9,4
17,10,5
17,6,12
5,13,12
4,16,5
7,6,17
2,14,10
15,9,15
12,4,13
12,17,11
8,9,3
14,5,15
12,12,16
4,4,5
8,17,6
6,4,15
16,7,16
9,7,1
9,17,11
12,4,15
17,10,9
3,10,9
7,10,4
15,6,12
15,7,8
5,6,14
2,5,9
5,15,15
5,8,3
14,5,10
4,14,10
7,10,3
3,9,10
13,16,10
10,18,11
9,8,1
9,10,1
15,15,8
8,7,4
17,5,9
6,6,3
13,14,16
11,9,17
3,12,13
13,17,8
12,14,10
4,7,7
14,13,15
14,10,4
7,4,4
3,3,10
17,7,12
4,4,7
3,15,8
9,4,5
9,5,15
12,2,13
3,6,9
14,13,5
5,3,14
13,16,13
15,3,13
9,4,4
3,7,11
5,7,3
2,6,11
15,13,5
12,15,4
15,6,7
14,3,8
13,13,17
14,5,4
10,4,16
13,14,15
15,12,14
4,13,6
11,15,3
9,1,12
3,5,10
10,16,13
3,7,14
3,12,12
15,5,7
13,15,7
17,11,12
14,3,10
10,11,16
5,14,9
2,12,8
17,8,8
6,5,13
1,12,11
9,16,5
9,17,14
6,15,11
6,14,13
13,14,4
17,12,11
5,4,16
7,16,12
4,5,13
8,6,2
13,8,2
1,11,8
4,4,13
6,12,16
1,10,10
9,10,15
8,12,3
6,17,8
15,5,9
5,15,12
6,3,11
5,14,13
5,4,11
8,9,17
10,2,10
6,4,5
11,6,2
15,13,14
10,3,16
15,8,3
14,13,14
7,16,5
9,5,4
9,16,16
11,12,17
15,9,11
10,3,15
4,15,13
10,12,18
13,16,6
7,2,11
14,6,3
6,5,6
1,9,10
8,17,11
3,7,7
14,11,5
13,9,1
7,13,2
12,12,17
7,16,6
8,9,16
12,1,8
15,14,14
17,12,14
15,3,12
8,18,7
13,12,3
5,8,15
15,8,5
3,10,7
11,17,5
4,11,13
4,16,12
3,8,6
4,7,6
5,7,13
6,17,6
4,8,16
5,7,2
14,2,9
11,4,15
8,14,14
16,10,13
17,11,7
11,10,18
11,6,4
17,8,9
9,4,14
16,11,12
13,10,4
8,5,4
8,17,10
17,9,12
16,9,11
13,13,1
12,7,3
13,2,13
12,8,15
3,15,11
12,2,10
5,4,5
16,15,13
4,15,14
4,15,12
7,13,11
14,5,9
5,13,6
16,7,4
7,14,6
9,15,9
5,14,14
6,17,10
12,1,11
2,10,11
3,11,14
7,17,11
10,3,12
16,13,12
10,9,18
3,9,3
11,7,15
4,4,12
6,7,15
2,14,8
6,2,13
3,13,11
11,11,1
16,7,13
16,6,15
5,3,6
1,9,13
4,7,16
11,12,3
7,4,7
3,13,12
12,18,10
8,17,9
12,17,7
17,12,6
19,9,11
12,3,15
7,11,3
5,14,10
12,13,16
11,7,2
10,15,11
15,9,13
16,9,13
16,4,13
14,16,13
10,15,12
3,4,8
12,7,16
12,3,14
6,16,12
12,17,9
5,10,16
7,16,13
4,11,2
10,2,11
6,9,2
9,13,16
5,4,12
11,2,7
4,15,11
12,2,12
11,3,3
8,8,17
13,4,11
8,9,2
9,12,18
2,11,10
4,10,5
16,11,4
13,15,10
13,14,6
13,16,5
10,14,16
4,10,13
16,10,9
6,12,4
5,10,2
16,7,9
15,9,7
16,10,14
11,13,4
4,11,11
13,12,17
2,13,7
15,11,4
3,13,10
10,1,8
17,8,13
4,3,11
13,16,7
8,17,15
7,10,17
3,5,6
8,16,7
15,15,10
18,11,10
2,12,7
12,6,1
18,5,7
8,16,10
14,5,14
2,11,6
7,12,1
12,3,4
7,14,16
9,6,5
16,15,9
5,17,9
9,17,10
6,6,16
10,9,1
14,12,15
8,2,14
5,15,8
7,7,16
13,13,14
16,12,9
6,14,14
12,11,4
9,13,15
18,6,10
7,8,3
8,2,5
10,5,16
2,9,15
18,7,10
6,16,13
8,2,12
13,14,2
14,11,2
5,13,7
7,5,6
16,6,14
12,4,3
10,3,14
10,4,15
14,4,10
12,13,14
11,6,5
7,12,16
8,16,14
15,4,5
10,12,2
11,10,16
14,9,12
9,14,4
4,8,17
9,11,3
13,17,11
4,4,14
7,7,17
12,15,13
4,13,7
5,8,5
11,16,14
10,3,5
17,10,10
3,8,8
14,7,16
6,2,11
4,14,9
2,8,10
5,16,13
15,5,11
3,4,12
4,14,15
9,14,5
17,8,7
9,2,13
4,8,4
3,12,14
8,15,7
12,16,4
12,2,14
17,6,13
18,8,10
5,6,5
5,10,14
4,6,6
16,14,8
3,4,10
14,11,15
8,16,11
10,12,16
13,8,18
7,5,4
11,15,16
14,14,15
2,8,8
13,6,16
10,5,15
9,10,16
13,16,8
5,9,14
5,5,6
16,5,14
3,11,8
9,11,17
9,3,15
12,3,10
4,6,5
15,3,9
4,12,3
17,7,10
15,13,10
2,7,12
12,18,9
13,15,9
16,6,11
8,8,2
1,9,8
16,11,5
7,13,4
17,12,5
1,9,12
2,14,11
8,15,3
13,5,13
10,4,10
14,9,13
11,11,17
3,14,14
6,9,3
2,12,4
3,9,5
9,8,2
15,7,16
15,7,15
9,19,9
17,12,9
15,13,13
10,5,4
17,8,15
11,13,14
10,7,2
9,1,14
15,3,11
17,14,11
5,17,12
12,5,3
1,10,8
2,11,4
16,9,12
2,6,9
4,5,10
7,17,12
5,12,15
14,6,6
7,3,14
5,10,17
6,3,14
15,10,4
3,6,12
4,9,7
8,1,10
10,6,17
14,12,4
15,16,13
9,15,7
17,6,9
5,11,6
12,10,18
16,6,9
8,13,4
16,15,10
3,13,7
9,9,16
13,9,4
8,2,8
12,3,13
12,6,5
4,12,15
9,3,13
16,6,5
16,13,15
2,8,11
7,14,3
4,11,12
7,3,5
15,12,6
3,5,9
7,10,15
8,4,8
10,10,16
15,10,8
12,9,18
5,5,15
7,15,14
4,7,8
2,8,7
4,14,6
10,12,1
13,15,13
18,11,11
5,9,6
6,11,15
13,8,14
11,16,16
5,4,6
12,15,5
14,13,3
17,13,11
14,7,4
16,11,8
15,5,4
2,12,13
5,14,3
2,7,11
8,3,4
7,9,1
16,6,7
12,14,5
1,12,10
7,4,12
4,5,14
13,4,5
11,7,16
14,9,4
16,11,11
13,5,6
17,11,10
1,10,6
13,11,1
16,8,7
5,10,4
12,1,9
10,1,6
16,3,8
4,6,7
9,14,3
3,6,11
9,5,16
14,14,5
14,12,16
18,9,11
11,7,18
9,4,13
5,9,4
5,9,16
12,4,16
9,15,5
4,7,2
7,11,15
10,8,15
12,3,11
11,2,13
12,15,6
13,9,3
3,9,12
4,9,16
4,14,13
13,10,15
16,5,6
1,12,7
6,9,16
7,1,9
11,13,13
13,5,16
10,1,12
13,15,4
18,6,9
12,13,4
17,14,10
6,15,13
9,3,9
9,17,13
14,8,4
11,3,12
13,14,12
3,4,14
14,15,7
11,4,11
9,4,9
3,10,6
16,7,6
13,17,13
3,10,3
11,4,14
9,15,16
15,15,13
12,13,15
6,7,17
4,14,7
11,17,7
11,15,7
18,9,8
18,10,11
14,15,15
11,12,16
6,5,7
15,10,3
7,15,16
11,6,16
4,8,6
6,15,15
10,9,16
16,10,3
3,15,9
3,9,4
3,5,7
15,4,4
1,8,12
5,14,11
11,4,16
9,11,1
10,8,18
4,6,9
7,3,4
17,10,7
2,12,12
16,9,9
12,15,11
16,9,7
8,6,18
9,14,1
7,14,4
7,17,9
14,12,13
16,5,11
1,12,13
14,4,6
10,15,4
12,14,13
6,17,9
2,7,14
3,5,13
13,11,3
13,10,16
5,2,13
3,13,15
9,16,14
3,13,14
5,6,8
2,11,11
16,8,12
1,7,8
14,7,6
12,8,3
7,15,6
9,12,2
14,10,6
12,7,1
9,13,4
10,3,11
6,3,12
5,12,6
10,17,5
16,4,7
10,11,4
4,11,16
7,4,8
2,10,5
13,4,14
15,3,6
14,1,12
7,1,8
7,11,16
7,15,10
15,4,8
4,5,5
0,11,11
2,12,5
14,17,9
13,18,10
7,16,11
12,5,13
9,10,17
17,5,11
7,4,16
2,5,6
4,2,10
5,3,13
17,12,12
13,6,8
5,12,11
11,14,13
18,10,9
6,5,9
1,10,9
17,8,5
12,10,17
9,5,3
4,8,7
5,7,5
6,12,14
7,6,2
12,13,3
12,6,2
11,5,16
4,13,12
6,4,7
6,9,5
13,2,11
1,9,14
10,12,15
7,3,10
7,11,18
15,8,11
18,9,7
2,8,14
4,7,13
9,14,14
16,14,14
14,11,3
8,16,12
1,8,5
5,17,11
0,9,10
13,5,12
9,14,17
8,9,18
7,8,16
12,5,15
3,11,9
5,10,15
4,9,17
13,3,12
8,15,16
13,3,11
2,9,12
6,12,15
13,8,4
8,15,6
14,5,13
7,5,17
5,15,7
10,16,16
10,11,2
3,11,3
3,13,9
6,8,12
8,3,15
5,4,14
9,15,2
18,10,12
2,10,6
11,18,11
8,14,16
6,2,7
10,14,14
17,9,14
5,13,8
16,11,13
14,9,2
6,7,16
2,7,13
10,13,17
15,4,10
17,13,12
16,14,11
9,10,18
1,11,6
7,3,3
13,17,10
10,6,4
1,9,9
8,3,14
10,15,3
11,2,5
9,19,11
10,14,15
16,8,8
19,11,10
5,8,16
8,11,3
5,7,12
11,15,13
16,9,8
14,16,5
4,11,7
14,10,5
1,11,9
13,1,11
17,8,10
6,11,4
3,12,6
6,7,18
17,5,10
3,7,3
12,5,16
14,10,3
13,6,6
8,1,7
6,14,16
3,12,15
6,8,16
3,9,7
9,4,12
4,16,8
15,13,16
13,17,9
4,16,13
9,12,15
15,16,10
8,17,5
12,19,9
12,5,6
6,9,1
1,11,12
13,3,15
13,9,2
14,12,5
10,14,18
10,7,1
9,8,17
3,9,11
10,15,8
4,5,9
6,16,8
16,11,10
15,14,4
13,2,6
10,14,12
14,5,12
2,8,5
8,4,2
5,17,8
14,15,12
15,7,7
8,10,18
2,7,9
8,17,12
18,9,6
13,4,15
13,15,3
2,16,8
4,6,13
6,10,5
5,12,14
15,5,15
9,13,18
15,15,7
9,17,5
14,6,7
7,16,4
7,18,12
9,11,18
4,12,17
8,8,1
2,13,8
11,3,13
15,11,13
15,7,14
13,11,16
7,2,5
4,9,12
6,5,5
16,8,5
6,1,11
10,16,9
10,7,4
12,12,1
15,8,16
2,14,12
3,7,8
11,17,12
18,8,7
13,3,8
7,16,9
9,5,1
9,0,9
10,18,12
2,15,10
3,4,7
2,11,8
14,9,15
15,4,6
16,8,3
5,3,12
16,7,7
7,11,4
3,11,15
2,7,5
10,18,6
1,7,10
10,5,14
15,10,15
7,3,11
16,15,8
16,5,15
13,6,15
6,4,4
17,10,12
15,8,13
14,15,14
10,8,16
17,7,5
6,5,4
5,6,7
10,3,8
8,10,15
16,10,15
19,10,9
16,6,13
7,14,14
17,4,10
7,16,8
15,9,3
4,7,10
2,5,11
12,4,7
16,8,6
5,13,9
12,10,15
13,14,5
14,8,2
16,11,9
8,2,9
1,7,9
11,14,17
14,9,17
17,11,5
8,7,18
6,3,15
16,13,13
15,12,10
10,5,6
14,11,14
17,14,9
12,17,8
12,18,11
17,5,12
12,3,6
8,6,4
12,5,5
8,18,12
14,8,5
15,13,9
15,2,8
4,3,7
11,5,5
18,12,8
11,8,3
10,3,10
4,4,8
9,16,12
12,1,6
9,12,1
5,2,9
11,3,10
12,5,17
15,14,8
16,3,10
6,12,1
11,11,3
17,14,12
10,4,11
12,11,1
5,5,9
15,6,11
3,6,6
9,17,4
11,12,5
15,14,13
14,6,13
7,7,3
12,7,18
8,14,15
17,6,11
1,5,12
2,12,14
14,7,3
12,6,17
13,11,4
11,6,1
15,7,3
12,2,6
6,13,2
8,1,8
10,7,3
6,3,5
15,7,4
6,5,14
15,15,12
11,7,4
11,4,12
12,15,16
10,7,16
14,8,16
8,12,17
6,4,16
11,14,2
1,10,11