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Posted on • Originally published at rivea0.github.io

LeetCode Meditations: Pacific Atlantic Water Flow

#computerscience #algorithms #typescript #javascript

The description for this problem states:

There is an m x n rectangular island that borders both the Pacific Ocean and Atlantic Ocean. The Pacific Ocean touches the island's left and top edges, and the Atlantic Ocean touches the island's right and bottom edges.

The island is partitioned into a grid of square cells. You are given an m x n integer matrix heights where heights[r][c] represents the height above sea level of the cell at coordinate (r, c).

The island receives a lot of rain, and the rain water can flow to neighboring cells directly north, south, east, and west if the neighboring cell's height is less than or equal to the current cell's height. Water can flow from any cell adjacent to an ocean into the ocean.

Return a 2D list of grid coordinates result where result[i] = [ri, ci] denotes that rain water can flow from cell (ri, ci) to both the Pacific and Atlantic oceans.

For example:

Example image

Input: heights = [
    [1, 2, 2, 3, 5],
    [3, 2, 3, 4, 4],
    [2, 4, 5, 3, 1],
    [6, 7, 1, 4, 5],
    [5, 1, 1, 2, 4]
]

Output: [[0, 4], [1, 3], [1, 4], [2, 2], [3, 0], [3, 1], [4, 0]]

Explanation: The following cells can flow to the Pacific and Atlantic oceans, as shown below:
[0, 4]: [0, 4] -> Pacific Ocean 
        [0, 4] -> Atlantic Ocean
[1, 3]: [1, 3] -> [0, 3] -> Pacific Ocean 
        [1, 3] -> [1, 4] -> Atlantic Ocean
[1, 4]: [1, 4] -> [1, 3] -> [0, 3] -> Pacific Ocean 
        [1, 4] -> Atlantic Ocean
[2, 2]: [2, 2] -> [1, 2] -> [0, 2] -> Pacific Ocean 
        [2, 2] -> [2, 3] -> [2, 4] -> Atlantic Ocean
[3, 0]: [3, 0] -> Pacific Ocean 
        [3, 0] -> [4, 0] -> Atlantic Ocean
[3, 1]: [3, 1] -> [3, 0] -> Pacific Ocean 
        [3, 1] -> [4, 1] -> Atlantic Ocean
[4, 0]: [4 ,0] -> Pacific Ocean 
        [4, 0] -> Atlantic Ocean
Note that there are other possible paths for these cells to flow to the Pacific and Atlantic oceans.
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Although the description can be a challenge in itself to understand at first glance, what we need to do is essentially simple (at least, in theory). We want a cell whose neighbors are less than or equal to it, all the way to the north, south, east, and west until we reach both "oceans."

First, we can initialize two sets to store the cells that can reach "Pacific" and "Atlantic":

const reachableToPacific: Set<string> = new Set();
const reachableToAtlantic: Set<string> = new Set();
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Note
We're initializing them as sets of strings, similarly as we did in the Number of Islands solution. We're going to represent the row and column pair like `${row},${col}`.

Instead of going cell by cell and checking if it can reach the oceans, we can first start by the cells that are adjacent to the oceans, and see which cells can reach us.

Since we're getting the cells that are reachable to oceans in different sets, we can return those that are in both sets (because we need to get those that are reachable to both oceans).

So, at the end, what we'll do is this:

for (const cell of reachableToPacific.values()) {
  if (reachableToAtlantic.has(cell)) {
    const [r, c] = cell.split(',');
    result.push([+r, +c]);
  }
}
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Note
We're converting string values to numbers with the handy unary plus operator.

We can use a breadth-first search to visit and mark the cells.

For the top and bottom edges of the grid, we'll mark the cells that can reach to Pacific and Atlantic:

for (let col = 0; col < colsLength; col++) {
  bfs(0, col, reachableToPacific);
  bfs(rowsLength - 1, col, reachableToAtlantic);
}
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We can do the similar thing for left and right edges of the grid as well:

for (let row = 0; row < rowsLength; row++) {
  bfs(row, 0, reachableToPacific);
  bfs(row, colsLength - 1, reachableToAtlantic);
}
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Now, on to the implementation of bfs.

The purpose of our bfs function is to mark the cells that can reach to an "ocean." So, it'll take three parameters: r for row, c for column, and reachableToOcean for the set that stores the cells that are reachable.

As usual with BFS, we'll keep a queue that has arrays consisting of a row, a column, and the corresponding value in the grid:

let queue = [[r, c, heights[r][c]]];
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As we go over the elements of queue, we'll mark a row-column pair as reachable as long as that pair is not out of bounds, or we haven't already added it as reachable, or the value it has is greater than or equal to the previous "height" we've looked at.

Note
Since we're starting from the edge cells, we're interested in values greater than or equal. In other words, we're not interested in "heights" that are shorter.

While queue is not empty, we'll first pop the current row, current column and previous height from queue:

const [currentRow, currentCol, prevHeight] = queue.pop() as number[];
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Note
It's "previous height," because as we'll see below, the new values we push to queue will be updated row and column values (like currentRow + rowToGo and currentCol + colToGo) while the cell will be the "previous" one (heights[currentRow][currentCol]).

If one of the conditions we mentioned above is true, we want to continue with the next element in the queue. Otherwise, we'll add it to our set:

if (
  isOutOfBounds(currentRow, currentCol) || 
  reachableToOcean.has(`${currentRow},${currentCol}`) || 
  heights[currentRow][currentCol] < prevHeight
) {
  continue;
}

reachableToOcean.add(`${currentRow},${currentCol}`);
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Then, we'll add the neighbors to the queue, as well as heights[currentRow][currentCol], which is going to be the "previous height" for the next element:

// up, down, left, right
const coords = [[-1, 0], [1, 0], [0, -1], [0, 1]];
for (const [rowToGo, colToGo] of coords) {
  queue.push([
    currentRow + rowToGo, 
    currentCol + colToGo, 
    heights[currentRow][currentCol]
  ]);
}
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And, that's it for the bfs function:

function bfs(r: number, c: number, reachableToOcean: Set<string>) {
  let queue = [[r, c, heights[r][c]]];

  while (queue.length > 0) {
    const [currentRow, currentCol, prevHeight] = queue.pop() as number[];

    if (
      isOutOfBounds(currentRow, currentCol) || 
      reachableToOcean.has(`${currentRow},${currentCol}`) || 
      heights[currentRow][currentCol] < prevHeight
    ) {
      continue;
    }

    reachableToOcean.add(`${currentRow},${currentCol}`);

    // up, down, left, right
    const coords = [[-1, 0], [1, 0], [0, -1], [0, 1]];
    for (const [rowToGo, colToGo] of coords) {
      queue.push([
        currentRow + rowToGo, 
        currentCol + colToGo, 
        heights[currentRow][currentCol]
      ]);
    }
  }
}
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Putting everything together, here is what our final solution looks like in TypeScript:

function pacificAtlantic(heights: number[][]): number[][] {
  let result = [];
  const rowsLength = heights.length;
  const colsLength = heights[0].length;
  const reachableToPacific: Set<string> = new Set();
  const reachableToAtlantic: Set<string> = new Set();

  function isOutOfBounds(r: number, c: number) {
    return r < 0 || c < 0 || r >= rowsLength || c >= colsLength;
  }

  function bfs(r: number, c: number, reachableToOcean: Set<string>) {
    let queue = [[r, c, heights[r][c]]];

    while (queue.length > 0) {
      const [currentRow, currentCol, prevHeight] = queue.pop() as number[];

      if (
        isOutOfBounds(currentRow, currentCol) || 
        reachableToOcean.has(`${currentRow},${currentCol}`) || 
        heights[currentRow][currentCol] < prevHeight
      ) {
        continue;
      }

      reachableToOcean.add(`${currentRow},${currentCol}`);

      // up, down, left, right
      const coords = [[-1, 0], [1, 0], [0, -1], [0, 1]];
      for (const [rowToGo, colToGo] of coords) {
        queue.push([
            currentRow + rowToGo, 
            currentCol + colToGo, 
            heights[currentRow][currentCol]
        ]);
      }
    }
  }

  for (let col = 0; col < colsLength; col++) {
    bfs(0, col, reachableToPacific);
    bfs(rowsLength - 1, col, reachableToAtlantic);
  }

  for (let row = 0; row < rowsLength; row++) {
    bfs(row, 0, reachableToPacific);
    bfs(row, colsLength - 1, reachableToAtlantic);
  }

  for (const cell of reachableToPacific.values()) {
    if (reachableToAtlantic.has(cell)) {
      const [r, c] = cell.split(',');
      result.push([+r, +c]);
    }
  }

  return result;
}
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Time and space complexity

The time complexity is O(n∗m)O(n * m) — where nn is the number of rows, and mm is the number of columns, as we're traversing the whole grid but making use of the Set data structure to avoid visiting the same cell.

The space complexity is—I think—also O(n∗m)O(n * m) , again, where nn is the number of rows, and mm is the number of columns.
The size of our queue will grow proportionately to the size of the grid, and we also keep two sets, their sizes can also grow proportionately to the grid we're given.

The next and final problem we're going to look at in this chapter is Course Schedule. Until then, happy coding.

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