Initial commit: AoC solutions 2023-2025

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <noreply@anthropic.com>

2023/13.py

@@ -0,0 +1,62 @@
+import numpy as np
+
+
+filename = "13-input.txt"
+
+def main():
+    with open(filename, "r") as file:
+        arr = []
+        nr_reflections = 0
+        pattern = 0
+        for i, line in enumerate(file):
+            if line == "\n":
+                if arr:
+                    pattern += 1
+                    print(f"Pattern {pattern}")
+                    matrix = np.array(arr)
+                    r = get_reflections(matrix)
+                    print(f"Found {r} reflections")
+                    if r <= 0:
+                        print("Rotating 90 degrees")
+                        r = get_reflections(np.rot90(matrix)) * 100
+                        print(f"Found {r} reflections")
+                    nr_reflections += r
+                    arr = []
+                    continue
+            char_array = []
+            for char in line.strip():
+                char_array.append(char)
+            arr.append(char_array)
+
+        print(nr_reflections)
+
+def get_reflections(matrix: []):
+    f = {}
+    for i in range(0, matrix.shape[0] - 1):
+        for ii in range(i, matrix.shape[1] + 1, 2):
+            part_matrix = matrix[:, i:ii]         
+            first, second = fold_matrix(part_matrix)
+            if first.size > 0 and second.size > 0 and np.all(first == second):
+                #if np.all(np.any(part_matrix == '#', axis=1)):
+                print(f"Found reflection at {i}, {ii}")
+                f[part_matrix.size] = ((ii - i) // 2) + i
+
+    if not f:
+        return 0
+    # Sort the dictionary in descending order of keys
+    sorted_f = dict(sorted(f.items(), key=lambda item: item[0], reverse=True))
+     # Return the value of the first item in the sorted dictionary
+    return sorted_f.popitem()[1]
+
+def fold_matrix(matrix: []):
+    if matrix.shape[1] % 2 != 0:
+        return np.empty(0), np.empty(0)
+    half = matrix.shape[1] // 2
+    first_half = matrix[:, :half]
+    second_half = np.flip(matrix[:, half:], axis=1)
+    # if len(matrix) % 2 != 0:
+    #     second_half = np.vstack([second_half, np.zeros_like(first_half[0])])
+    return first_half, second_half
+
+if __name__ == "__main__":
+    main()