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Challenge 58: Space Week Day 4: Landing Spot

curriculum/challenges/english/blocks/daily-coding-challenges-python/68c1a929005bf54d342aa8d5.md

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--description--

In day four of Space Week, you are given a matrix of numbers (an array of arrays), representing potential landing spots for your rover. Find the safest landing spot based on the following rules:

  • Each spot in the matrix will contain a number from 0-9, inclusive.
  • Any 0 represents a potential landing spot.
  • Any number other than 0 is too dangerous to land. The higher the number, the more dangerous.
  • The safest spot is defined as the 0 cell whose surrounding cells (up to 4 neighbors, ignore diagonals) have the lowest total danger.
  • Ignore out-of-bounds neighbors (corners and edges just have fewer neighbors).
  • Return the indices of the safest landing spot. There will always only be one safest spot.

For instance, given:

js
[
  [1, 0],
  [2, 0]
]

Return [0, 1], the indices for the 0 in the first array.

--hints--

find_landing_spot([[1, 0], [2, 0]]) should return [0, 1].

js
({test: () => { runPython(`
from unittest import TestCase
TestCase().assertEqual(find_landing_spot([[1, 0], [2, 0]]), [0, 1])`)
}})

find_landing_spot([[9, 0, 3], [7, 0, 4], [8, 0, 5]]) should return [1, 1].

js
({test: () => { runPython(`
from unittest import TestCase
TestCase().assertEqual(find_landing_spot([[9, 0, 3], [7, 0, 4], [8, 0, 5]]), [1, 1])`)
}})

find_landing_spot([[1, 2, 1], [0, 0, 2], [3, 0, 0]]) should return [2, 2].

js
({test: () => { runPython(`
from unittest import TestCase
TestCase().assertEqual(find_landing_spot([[1, 2, 1], [0, 0, 2], [3, 0, 0]]), [2, 2])`)
}})

find_landing_spot([[9, 6, 0, 8], [7, 1, 1, 0], [3, 0, 3, 9], [8, 6, 0, 9]]) should return [2, 1].

js
({test: () => { runPython(`
from unittest import TestCase
TestCase().assertEqual(find_landing_spot([[9, 6, 0, 8], [7, 1, 1, 0], [3, 0, 3, 9], [8, 6, 0, 9]]), [2, 1])`)
}})

--seed--

--seed-contents--

py
def find_landing_spot(matrix):

    return matrix

--solutions--

py
def find_landing_spot(matrix):
    best_spot = None
    lowest_neighbor_sum = float('inf')

    for i in range(len(matrix)):
        for j in range(len(matrix[i])):
            if matrix[i][j] == 0:
                current_neighbor_sum = 0

                if i > 0:
                    current_neighbor_sum += matrix[i - 1][j]
                if j < len(matrix[i]) - 1:
                    current_neighbor_sum += matrix[i][j + 1]
                if i < len(matrix) - 1:
                    current_neighbor_sum += matrix[i + 1][j]
                if j > 0:
                    current_neighbor_sum += matrix[i][j - 1]

                if current_neighbor_sum < lowest_neighbor_sum:
                    lowest_neighbor_sum = current_neighbor_sum
                    best_spot = [i, j]

    return best_spot