AoC 2024: day 1

README.md

@@ -16,3 +16,5 @@ My own solutions to [Advent of Code](https://adventofcode.com/).
   → [solutions in Python 3](https://github.com/loociano/advent-of-code/tree/master/aoc2022)
 * [Advent of Code 2023](https://adventofcode.com/2023)
   → [solutions in Python 3](https://github.com/loociano/advent-of-code/tree/master/aoc2023)
+* [Advent of Code 2024](https://adventofcode.com/2024)
+  → [solutions in Python 3](https://github.com/loociano/advent-of-code/tree/master/aoc2024)

aoc2024/README.md

@@ -0,0 +1,7 @@
+# Advent of Code 2024
+
+My own solutions to [Advent of Code 2024](https://adventofcode.com/2024).
+
+> Advent of Code is an Advent calendar of small programming puzzles for a variety of skill sets and skill levels that can be solved in any programming language you like. People use them as a speed contest, interview prep, company training, university coursework, practice problems, or to challenge each other.  —Eric Wastl
+
+* [Day 1: Historian Hysteria](https://adventofcode.com/2023/day/1) → [Python3 solution](https://github.com/loociano/advent-of-code/blob/master/aoc2024/src/day01/python/solution.py)

aoc2024/src/day01/python/solution.py

@@ -0,0 +1,77 @@
+# Copyright 2024 Google LLC
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# https://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from collections import defaultdict
+from typing import Sequence
+
+type LocationIds = Sequence[int]
+
+def _parse_input(input: Sequence[str]) -> tuple[LocationIds, LocationIds]:
+  """Converts puzzle input into 2 lists of location IDs.
+
+  n = len(input)
+  Time complexity: O(n)
+  Space complexity: O(n) + O(n) = O(n)
+
+  """
+  location_ids1 = []
+  location_ids2 = []
+  # Parse input:
+  for line in input:  # t:O(n)
+    # Line format is: '<int>\s\s\s<int>'
+    id1, id2 = map(int, line.split('   ')) # Assumes valid input.
+    location_ids1.append(id1)
+    location_ids2.append(id2)
+  return location_ids1, location_ids2
+
+def calculate_distance(input: Sequence[str]) -> int:
+  """
+  Calculates total distance between 2 sequences of location IDs.
+
+  n = len(input)
+  Time complexity: O(n) + O(2nlogn) + O(n) = O(nlogn).
+  Space complexity: O(n) + O(n) = O(n)
+
+  Args:
+    input: Sequence of location ID pairs.
+  Returns
+    Total distance.
+  """
+  location_ids1, location_ids2 = _parse_input(input)  # O(n)
+  # Calculate total distance:
+  location_ids1.sort()  # t:O(nlogn)
+  location_ids2.sort()  # t:O(nlogn)
+  return sum(
+    abs(location_ids1[i] - location_ids2[i])
+    for i in range(0, len(location_ids1)))  # t:O(n)
+
+def calculate_similarity_score(input: Sequence[str]) -> int:
+  """
+  Calculates similarity score between 2 sequences of location IDs.
+
+  n = len(input)
+  Time complexity: O(n) + O(n) + O(n) = O(n)
+  Space complexity: O(2n) + O(n) = O(n)
+
+  Args:
+    input: Sequence of location ID pairs.
+  Returns
+    Total distance.
+  """
+  location_ids1, location_ids2 = _parse_input(input)  # t:O(n)
+  histogram = defaultdict(int)
+  # Generate histogram:
+  for id in location_ids2:  # t:O(n)
+    histogram[id] += 1
+  # Calculate similarity score:
+  return sum(id * histogram.get(id, 0) for id in location_ids1)  # t:O(n)

aoc2024/test/day01/python/example1.txt

@@ -0,0 +1,6 @@
+3   4
+4   3
+2   5
+1   3
+3   9
+3   3