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105 lines
2.8 KiB
Markdown
105 lines
2.8 KiB
Markdown
---
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id: 68c1a929005bf54d342aa8d5
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title: "Challenge 58: Space Week Day 4: Landing Spot"
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challengeType: 29
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dashedName: challenge-58
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---
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# --description--
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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:
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- Each spot in the matrix will contain a number from `0-9`, inclusive.
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- Any `0` represents a potential landing spot.
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- Any number other than `0` is too dangerous to land. The higher the number, the more dangerous.
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- The safest spot is defined as the `0` cell whose surrounding cells (up to 4 neighbors, ignore diagonals) have the lowest total danger.
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- Ignore out-of-bounds neighbors (corners and edges just have fewer neighbors).
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- Return the indices of the safest landing spot. There will always only be one safest spot.
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For instance, given:
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```js
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[
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[1, 0],
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[2, 0]
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]
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```
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Return `[0, 1]`, the indices for the `0` in the first array.
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# --hints--
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`find_landing_spot([[1, 0], [2, 0]])` should return `[0, 1]`.
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```js
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({test: () => { runPython(`
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from unittest import TestCase
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TestCase().assertEqual(find_landing_spot([[1, 0], [2, 0]]), [0, 1])`)
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}})
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```
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`find_landing_spot([[9, 0, 3], [7, 0, 4], [8, 0, 5]])` should return `[1, 1]`.
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```js
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({test: () => { runPython(`
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from unittest import TestCase
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TestCase().assertEqual(find_landing_spot([[9, 0, 3], [7, 0, 4], [8, 0, 5]]), [1, 1])`)
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}})
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```
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`find_landing_spot([[1, 2, 1], [0, 0, 2], [3, 0, 0]])` should return `[2, 2]`.
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```js
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({test: () => { runPython(`
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from unittest import TestCase
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TestCase().assertEqual(find_landing_spot([[1, 2, 1], [0, 0, 2], [3, 0, 0]]), [2, 2])`)
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}})
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```
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`find_landing_spot([[9, 6, 0, 8], [7, 1, 1, 0], [3, 0, 3, 9], [8, 6, 0, 9]])` should return `[2, 1]`.
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```js
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({test: () => { runPython(`
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from unittest import TestCase
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TestCase().assertEqual(find_landing_spot([[9, 6, 0, 8], [7, 1, 1, 0], [3, 0, 3, 9], [8, 6, 0, 9]]), [2, 1])`)
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}})
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```
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# --seed--
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## --seed-contents--
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```py
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def find_landing_spot(matrix):
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return matrix
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```
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# --solutions--
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```py
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def find_landing_spot(matrix):
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best_spot = None
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lowest_neighbor_sum = float('inf')
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for i in range(len(matrix)):
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for j in range(len(matrix[i])):
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if matrix[i][j] == 0:
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current_neighbor_sum = 0
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if i > 0:
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current_neighbor_sum += matrix[i - 1][j]
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if j < len(matrix[i]) - 1:
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current_neighbor_sum += matrix[i][j + 1]
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if i < len(matrix) - 1:
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current_neighbor_sum += matrix[i + 1][j]
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if j > 0:
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current_neighbor_sum += matrix[i][j - 1]
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if current_neighbor_sum < lowest_neighbor_sum:
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lowest_neighbor_sum = current_neighbor_sum
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best_spot = [i, j]
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return best_spot
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```
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