day 15 part 1 - ATTEMPT
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@ -1,15 +1,26 @@
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import matrix
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import shared
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from pprint import pprint as pp
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import itertools
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import functools
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#@shared.profile
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def part1(rows):
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pass
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def run(SOMETHING):
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pp(SOMETHING)
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#@shared.profile
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def part2(rows):
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pass
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def main():
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spl = lambda y: [int(w) for w in y]
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rows = [row.rstrip().split() for row in shared.load(3)]
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print(run(rows))
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rows = [row for row in shared.load_rows(15)]
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with shared.elapsed_timer() as elapsed:
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part1(rows)
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print("🕒", elapsed())
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with shared.elapsed_timer() as elapsed:
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part2(rows)
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print("🕒", elapsed())
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if __name__ == "__main__":
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100
2022/python/day15.py
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100
2022/python/day15.py
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@ -0,0 +1,100 @@
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import matrix
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import math
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from pprint import pprint
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import shared
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from scanf import scanf
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from dataclasses import dataclass
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from scipy.spatial.distance import cityblock
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def manhattan(a, b):
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return abs(b[0] - a[0]) + abs(b[1] - a[1])
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@dataclass
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class Sensor:
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sX: int
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sY: int
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bX: int
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bY: int
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@property
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def s(self):
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return (self.sY, self.sX)
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@property
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def b(self):
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return (self.bY, self.bX)
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@property
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def distance(self):
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a,b = zip(self.s, self.b)
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return cityblock(a, b)
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def distance_to(self, y,x):
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a,b = zip(self.s, (y,x))
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return cityblock(a, b)
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#@shared.profile
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def part1(rows):
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CHECK_ROW = 10
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CHECK_ROW = 2000000
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sensors = []
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sensor_points = []
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beacon_points = []
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xSet = set()
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ySet = set()
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for row in rows:
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x,y,bx,by = scanf("Sensor at x=%d, y=%d: closest beacon is at x=%d, y=%d", row)
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xSet.add(x)
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xSet.add(bx)
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ySet.add(y)
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ySet.add(by)
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sensors.append(Sensor(sX=x,sY=y,bX=bx,bY=by))
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minX, maxX = min(xSet),max(xSet)
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minY, maxY = min(ySet),max(ySet)
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for sensor in sensors:
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sensor_points.append(sensor.s)
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beacon_points.append(sensor.b)
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ineligible = set()
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for x in range(minX,maxX):
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point = (CHECK_ROW, x)
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if x % 10000 == 0:
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print(point)
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if point in beacon_points:
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continue
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if point in sensor_points:
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continue
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for s in sensors:
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d = s.distance_to(*point)
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if d <= s.distance:
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ineligible.add(point)
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pprint(ineligible)
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print(len(ineligible))
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# mx[sensor.sY][sensor.sX] = "S"
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# mx[sensor.bY][sensor.bX] = "B"
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#@shared.profile
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def part2(rows):
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pass
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def main():
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rows = [row for row in shared.load_rows(15)]
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with shared.elapsed_timer() as elapsed:
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part1(rows)
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print("🕒", elapsed())
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with shared.elapsed_timer() as elapsed:
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part2(rows)
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print("🕒", elapsed())
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if __name__ == "__main__":
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main()
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@ -154,7 +154,7 @@ def get_neighbor_coords(matrix, c, r, diagonals=True):
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return neighbors
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def line_of_sight_coords(matrix, row, col) -> Dict[str, List[Tuple[int, int]]]:
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def line_of_sight_coords(matrix, row, col, distance=None) -> Dict[str, List[Tuple[int, int]]]:
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"""
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Takes a matrix, a row, and a column
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calculates the coordinates to the edge for all four cardinal directions
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@ -167,6 +167,10 @@ def line_of_sight_coords(matrix, row, col) -> Dict[str, List[Tuple[int, int]]]:
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col_ids = list(range(0, height))
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row_ids = list(range(0, width))
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if distance:
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up_ids, down_ids = list(reversed(col_ids[:col])), col_ids[col + 1 :col+distance+1]
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left_ids, right_ids = list(reversed(row_ids[:row])), row_ids[row + 1 :row+distance+1]
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else:
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up_ids, down_ids = list(reversed(col_ids[:col])), col_ids[col + 1 :]
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left_ids, right_ids = list(reversed(row_ids[:row])), row_ids[row + 1 :]
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@ -183,11 +187,11 @@ def line_of_sight_coords(matrix, row, col) -> Dict[str, List[Tuple[int, int]]]:
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}
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def line_of_sight(mx, row, col):
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def line_of_sight(mx, row, col, distance=None):
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"""
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renders a line of sight coord calculation, into the values
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"""
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coords = line_of_sight_coords(mx, row, col)
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coords = line_of_sight_coords(mx, row, col, distance)
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los = defaultdict(list)
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for k, ids in coords.items():
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for _row, _col in ids:
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@ -195,7 +199,6 @@ def line_of_sight(mx, row, col):
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return los
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def coords_between_points(point1, point2):
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y1,x1 = point1
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y2,x2 = point2
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@ -225,9 +228,6 @@ def coords_between_points(point1, point2):
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def get_size(matrix):
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height = len(matrix)
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width = len(matrix[0])
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@ -337,3 +337,5 @@ def highlight(matrix, red=[], green=[], blue=[], blink_green=[]):
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new = f"{colors.BLINK}{colors.GREEN}{mx[y][x]}{colors.ENDC}"
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mx[y][x] = new
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ppmx(mx, pad=False, space=True, zero="0")
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