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crosswords/data/better_grid_building.ipynb

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introducing new grid generation
2021-08-31 13:56:29 +02:00
{
"cells": [
{
"cell_type": "markdown",
"source": [
"# trying to create a better grid builging algorithm"
],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 1,
"source": [
"# load stuff\n",
"import json\n",
"import random\n",
"import numpy as np\n",
"from string import digits, ascii_lowercase\n",
"import pathlib\n",
"import logging\n",
"import copy"
],
"outputs": [],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 2,
"source": [
"def get_difficulty_threshold(lang: str, difficulty: int):\n",
" return get_difficulty_threshold.thresholds[lang][difficulty]\n",
"\n",
"\n",
"get_difficulty_threshold.thresholds = {\n",
" 'de': {\n",
" 0: 12,\n",
" 1: 6,\n",
" 2: 0\n",
" },\n",
" 'en': {\n",
" 0: 200,\n",
" 1: 100,\n",
" 2: 10\n",
" }\n",
"}\n",
"\n",
"\n",
"def get_database(lang: str = \"en\") -> dict:\n",
" if lang not in get_database._dbs:\n",
" try:\n",
" file = __file__\n",
" except:\n",
" file = \"./.tmp\"\n",
" current_folder = pathlib.Path(file).parents[0]\n",
" db_file = str(current_folder / f\"{lang}.json\")\n",
"\n",
" logging.info(\"loading database: %s\", lang)\n",
"\n",
" with open(db_file, \"r\") as f:\n",
" db = json.load(f)\n",
" get_database._dbs[lang] = db\n",
"\n",
" logging.info(\"database loaded\")\n",
"\n",
" return get_database._dbs[lang]\n",
"\n",
"\n",
"get_database._dbs = {}\n",
"\n",
"def build_inverted_index(db):\n",
"\n",
" inverted_db = {}\n",
"\n",
" inverted_db['#'] = {}\n",
" number_db = inverted_db['#']\n",
"\n",
" for letter in ascii_lowercase:\n",
" inverted_db[letter] = {}\n",
"\n",
" for key, item in db.items():\n",
" try:\n",
" word = item['word']\n",
" norm_word = normalize_word(word)\n",
"\n",
" n = len(norm_word)\n",
"\n",
" if norm_word.isalnum():\n",
"\n",
" for i, letter in enumerate(norm_word):\n",
" letter_db = inverted_db[letter]\n",
" if i not in letter_db:\n",
" letter_db[i] = {}\n",
" letter_db_i = letter_db[i]\n",
" if n not in letter_db_i:\n",
" letter_db_i[n] = []\n",
" if n not in number_db:\n",
" number_db[n] = []\n",
" \n",
" letter_db_i[n].append(key)\n",
" number_db[n].append(key)\n",
" except:\n",
" pass\n",
" #print(\"error processing \" + word)\n",
" \n",
" return inverted_db\n",
"\n",
"def get_inverted_database(lang: str) -> dict:\n",
" if lang not in get_inverted_database._dbs:\n",
" get_inverted_database._dbs[lang] = build_inverted_index(get_database(lang))\n",
" return get_inverted_database._dbs[lang]\n",
"\n",
"get_inverted_database._dbs = {}\n",
" \n",
"\n",
"remove_digits = str.maketrans('', '', digits)\n",
"\n",
"def normalize_word(word: str):\n",
" word = word.translate(remove_digits)\n",
" return word.lower()\n",
"\n",
"def find_suitable_words(constraints: list, db: dict, inverted_db: dict):\n",
" sets = []\n",
"\n",
" n = len(constraints)\n",
" for i,letter in enumerate(constraints):\n",
" if letter == ' ':\n",
" continue\n",
" \n",
" letter_db = inverted_db[letter]\n",
" if i in letter_db:\n",
" i_list = letter_db[i]\n",
" \n",
" if not n in i_list:\n",
" return set()\n",
" \n",
" sets.append(set(i_list[n]))\n",
" \n",
" else:\n",
" return set()\n",
" \n",
" # at least one constraint must be set\n",
" if len(sets) == 0:\n",
" \n",
" # set first letter random and try again\n",
" if n in inverted_db['#']:\n",
" return inverted_db['#'][n]\n",
" return set()\n",
" \n",
" return set.intersection(*sets)\n",
" \n",
"\n"
],
"outputs": [],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 3,
"source": [
"find_suitable_words(list(\" \"), get_database(\"de\"), get_inverted_database(\"de\"))"
],
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
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" 'qm0',\n",
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" 'wp',\n",
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" 'ct',\n",
" 'ct',\n",
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" 'dg',\n",
" 'dh',\n",
" 'dh',\n",
" 'dt',\n",
" 'dt',\n",
" 'ep',\n",
" 'ep',\n",
" 'hk',\n",
" 'hk',\n",
" 'km',\n",
" 'km',\n",
" 'kp',\n",
" 'kp',\n",
" 'mk',\n",
" 'mk',\n",
" 'mm',\n",
" 'mm',\n",
" 'og',\n",
" 'og',\n",
" 'ot',\n",
" 'ot',\n",
" 'rh',\n",
" 'rh',\n",
" 'as0',\n",
" 'as0',\n",
" 'as1',\n",
" 'as1',\n",
" 'be',\n",
" 'be',\n",
" 'es2',\n",
" 'es2',\n",
" 'fr',\n",
" 'fr',\n",
" 'mo',\n",
" 'mo',\n",
" 'os',\n",
" 'os',\n",
" 'os0',\n",
" 'os0',\n",
" 'po',\n",
" 'po',\n",
" 'po0',\n",
" 'po0',\n",
" 'ra0',\n",
" 'ra0',\n",
" 're0',\n",
" 're0',\n",
" 'tb',\n",
" 'tb',\n",
" 'np',\n",
" 'np',\n",
" 'ps',\n",
" 'ps',\n",
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" 'sv',\n",
" 'xl',\n",
" 'xl',\n",
" 'bw',\n",
" 'bw',\n",
" 'bh',\n",
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" 'ss',\n",
" 'ss',\n",
" 'sw',\n",
" 'sw',\n",
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" 'ac',\n",
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" 'ak',\n",
" 'aw',\n",
" 'aw',\n",
" 'az',\n",
" 'az',\n",
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" 'vr']"
]
},
"metadata": {},
"execution_count": 3
}
],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 4,
"source": [
"class NoDataException(Exception):\n",
" pass\n",
"\n",
"\n",
"class WordInfo(object):\n",
" def __init__(self, word: str, y: int, x: int, is_vertical: bool, database: dict, opposite_prefix: str = \"opposite of\", synonym_prefix: str = \"other word for\"):\n",
" self._dictionary_database = database\n",
" self._y = y\n",
" self._x = x\n",
" self._word = word\n",
" self._hint = None\n",
" self._is_vertical = is_vertical\n",
"\n",
" self.opposite_prefix = opposite_prefix\n",
" self.synonym_prefix = synonym_prefix\n",
"\n",
" self.choose_info()\n",
"\n",
" def get_attribute(self, attr: str):\n",
" attr = self._dictionary_database[self._word][attr]\n",
" if attr is None or len(attr) == 0:\n",
" raise NoDataException\n",
" return attr\n",
"\n",
" def get_best_antonym(self) -> str:\n",
" antonyms = self.get_attribute(\"antonyms\")\n",
" return random.choice(antonyms)\n",
"\n",
" def get_best_synonym(self) -> str:\n",
" synonyms = self.get_attribute(\"synonyms\")\n",
" return random.choice(synonyms)\n",
"\n",
" def get_best_sense(self) -> str:\n",
" senses = self.get_attribute(\"senses\")\n",
" return random.choice(senses)\n",
"\n",
" def choose_info(self, n: int = 1):\n",
" assert n <= 4\n",
" # first choose antonyms, then synonyms, then senses\n",
"\n",
" hints = []\n",
"\n",
" try:\n",
" antonyms = self.get_attribute(\"antonyms\")\n",
" antonyms = [f\"{self.opposite_prefix} {w}\" for w in antonyms]\n",
" hints = hints + antonyms\n",
" except NoDataException:\n",
" pass\n",
"\n",
" try:\n",
" synonyms = self.get_attribute(\"synonyms\")\n",
" synonyms = [f\"{self.synonym_prefix} {w}\" for w in synonyms]\n",
"\n",
" hints = hints + synonyms\n",
" except NoDataException:\n",
" pass\n",
"\n",
" try:\n",
" senses = self.get_attribute(\"senses\")\n",
" hints = hints + senses\n",
" except NoDataException:\n",
" pass\n",
"\n",
" final_hints = []\n",
" for i in range(n):\n",
" choice = random.choice(hints)\n",
" hints.remove(choice)\n",
" final_hints.append(choice)\n",
"\n",
" if n == 1:\n",
" self._hint = final_hints[0]\n",
" return\n",
"\n",
" hint_symbols = ['a)', 'b)', 'c)', 'd)']\n",
"\n",
" self._hint = \"\"\n",
" for i in range(n):\n",
" self._hint += hint_symbols[i] + \" \" + final_hints[i] + \". \"\n",
"\n",
" def get_hint(self) -> str:\n",
" return self._hint\n",
"\n",
" def get_hint_location(self):\n",
" x = self._x if self._is_vertical else self._x - 1\n",
" y = self._y - 1 if self._is_vertical else self._y\n",
" return (y, x)\n",
"\n",
" def is_vertical(self):\n",
" return self._is_vertical\n"
],
"outputs": [],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 64,
"source": [
"def create_base_grid(n:int, min_word_length: int = 4, max_word_length: int = 15, filter_length: int = 2):\n",
" w = n\n",
" h = n\n",
" horizontal_grid = np.full(shape=(h,w), dtype=np.bool, fill_value=False)\n",
" vertical_grid = np.full(shape=(h,w), dtype=np.bool, fill_value=False)\n",
"\n",
"\n",
" for y in range(h):\n",
" x = 0\n",
" while x < w:\n",
" rand_max = random.randint(min_word_length, max_word_length)\n",
" rand_length = random.randint(min_word_length, rand_max)#\n",
"\n",
" x_next = min(x + rand_length, w)\n",
" horizontal_grid[y,x:x_next] = True\n",
" x = x_next + 1\n",
" \n",
" for x in range(w):\n",
" y = 0\n",
" while y < h:\n",
" rand_max = random.randint(min_word_length, max_word_length)\n",
" rand_length = random.randint(min_word_length, rand_max)\n",
" y_next = min(y + rand_length, h)\n",
" vertical_grid[y:y_next, x] = True\n",
" y = y_next + 1\n",
" \n",
"\n",
" \n",
" for y in range(0,h,2):\n",
" horizontal_grid[y,0] = False\n",
" horizontal_grid[y,-1] = False\n",
"\n",
" \n",
" \n",
" for x in range(2,w,2):\n",
" vertical_grid[0,x] = False\n",
" vertical_grid[-1,x] = False\n",
"\n",
" for y in range(2,h,2):\n",
" for x in range(2,w,2):\n",
" if random.randint(0,3) == 0:\n",
" horizontal_grid[y,x] = False\n",
"\n",
"\n",
"\n",
" combined = np.logical_and(horizontal_grid, vertical_grid)\n",
"\n",
" #combined = np.logical_and(horizontal_grid, horizontal_grid.transpose())\n",
" \n",
"\n",
"\n",
" # clean up areas that are to small\n",
" old_combined = combined.copy()\n",
" first_try = True\n",
"\n",
" while np.sum(old_combined.astype(int)) != np.sum(combined.astype(int)) or first_try:\n",
"\n",
" first_try = False\n",
"\n",
" count_x = np.full(shape=(h,w), dtype=np.int, fill_value=0)\n",
" count_y = np.full(shape=(h,w), dtype=np.int, fill_value=0)\n",
"\n",
" for x in range(w):\n",
" last_y = 0\n",
" for y in range(h):\n",
" if not combined[y,x] and last_y != y: \n",
" count_y[last_y:y,x] = y - last_y\n",
" last_y = y + 1\n",
" \n",
" if last_y < h:\n",
" count_y[last_y:h,x] = h - last_y\n",
" \n",
" for y in range(h):\n",
" last_x = 0\n",
" for x in range(w):\n",
" if not combined[y,x] and last_x != x: \n",
" count_x[y,last_x:x] = x - last_x\n",
" last_x = x + 1\n",
" \n",
" if last_x < w:\n",
" count_x[y, last_x:w] = w - last_x\n",
"\n",
" count_mask = np.logical_and(count_x < filter_length, count_y < filter_length)\n",
" old_combined = combined.copy()\n",
" combined[count_mask] = False\n",
"\n",
"\n",
" # clean up areas that are too small\n",
"\n",
" reachable = np.full(shape=(h,w), dtype=np.bool, fill_value=False)\n",
"\n",
" old_reachable = reachable.copy()\n",
"\n",
" reachable[1,0] = True\n",
" reachable[0,1] = True\n",
"\n",
" \n",
" i = 0\n",
" while np.sum(old_reachable.astype(int)) != np.sum(reachable.astype(int)):\n",
" old_reachable = reachable.copy()\n",
" i += 1\n",
" for y in range(h):\n",
" for x in range(w):\n",
" if x > 0 and (reachable[y,x-1] and combined[y,x-1]) or x < w-1 and (reachable[y,x+1] and combined[y,x+1]):\n",
" reachable[y,x] = True\n",
" continue\n",
" if y > 0 and (reachable[y-1,x] and combined[y-1,x]) or y < h-1 and (reachable[y+1,x] and combined[y+1,x]):\n",
" reachable[y,x] = True\n",
" \n",
" #print(\"flooded after \" + str(i) + \" iterations\")\n",
" \n",
" combined = np.logical_and(combined, reachable)\n",
"\n",
" return combined\n"
],
"outputs": [],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 6,
"source": [
"def create_base_grid(n: int):\n",
" grid = np.full(shape=(n,n), dtype=np.bool, fill_value=True)\n",
" for y in range(n):\n",
" for x in range(n):\n",
" if y % 2 == 1 and x % 2 == 1 and not random.randint(0,8) == 0:\n",
" grid[y,x] = False\n",
" \n",
" else:\n",
" if y % 2 != x % 2:\n",
" if random.randint(0,3) == 0:\n",
" grid[y,x] = False\n",
" else:\n",
" if random.randint(0,8) == 0:\n",
" pass\n",
" #grid[y,x] = False\n",
"\n",
" return grid\n",
" #return np.logical_and(grid, grid.transpose())\n",
"\n",
" "
],
"outputs": [],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 65,
"source": [
"%%prun\n",
"import matplotlib.pyplot as plt\n",
"\n",
"base_grid = create_base_grid(20)"
],
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
" "
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
" 2317 function calls in 0.008 seconds\n",
"\n",
" Ordered by: internal time\n",
"\n",
" ncalls tottime percall cumtime percall filename:lineno(function)\n",
" 1 0.006 0.006 0.008 0.008 <ipython-input-64-c07db7426ad7>:1(create_base_grid)\n",
" 18 0.001 0.000 0.001 0.000 {method 'copy' of 'numpy.ndarray' objects}\n",
" 311 0.000 0.000 0.001 0.000 random.py:291(randrange)\n",
" 311 0.000 0.000 0.000 0.000 random.py:238(_randbelow_with_getrandbits)\n",
" 36 0.000 0.000 0.000 0.000 {method 'astype' of 'numpy.ndarray' objects}\n",
" 36 0.000 0.000 0.000 0.000 {method 'reduce' of 'numpy.ufunc' objects}\n",
" 311 0.000 0.000 0.001 0.000 random.py:335(randint)\n",
" 36 0.000 0.000 0.000 0.000 fromnumeric.py:70(_wrapreduction)\n",
" 1 0.000 0.000 0.008 0.008 {built-in method builtins.exec}\n",
" 36 0.000 0.000 0.000 0.000 fromnumeric.py:2105(sum)\n",
" 43 0.000 0.000 0.001 0.000 {built-in method numpy.core._multiarray_umath.implement_array_function}\n",
" 541 0.000 0.000 0.000 0.000 {method 'getrandbits' of '_random.Random' objects}\n",
" 36 0.000 0.000 0.001 0.000 <__array_function__ internals>:2(sum)\n",
" 115 0.000 0.000 0.000 0.000 {built-in method builtins.min}\n",
" 311 0.000 0.000 0.000 0.000 {method 'bit_length' of 'int' objects}\n",
" 36 0.000 0.000 0.000 0.000 fromnumeric.py:71(<dictcomp>)\n",
" 7 0.000 0.000 0.000 0.000 numeric.py:268(full)\n",
" 7 0.000 0.000 0.000 0.000 {built-in method numpy.empty}\n",
" 7 0.000 0.000 0.000 0.000 <__array_function__ internals>:2(copyto)\n",
" 1 0.000 0.000 0.008 0.008 <string>:1(<module>)\n",
" 36 0.000 0.000 0.000 0.000 {built-in method builtins.isinstance}\n",
" 36 0.000 0.000 0.000 0.000 fromnumeric.py:2100(_sum_dispatcher)\n",
" 36 0.000 0.000 0.000 0.000 {method 'items' of 'dict' objects}\n",
" 7 0.000 0.000 0.000 0.000 multiarray.py:1043(copyto)\n",
" 1 0.000 0.000 0.000 0.000 {method 'disable' of '_lsprof.Profiler' objects}"
]
}
],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 66,
"source": [
"\n",
"\n",
"\n",
"plt.imshow(base_grid)\n",
"plt.colorbar()\n",
"plt.show()\n"
],
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 432x288 with 2 Axes>"
],
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},
"metadata": {
"needs_background": "light"
}
}
],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 112,
"source": [
"class GridWord(object):\n",
"\n",
" @classmethod\n",
" def from_base_grid(cls, base_grid: np.ndarray, min_word_length = 2) -> list:\n",
"\n",
" grid_words = []\n",
" # a word starts after each non-letter field, and at the beginning if there is\n",
" # a letter field at the beginning of a row/column\n",
" h, w = base_grid.shape\n",
"\n",
" # first get all horizontal words\n",
" for y in range(h):\n",
" word_start = 0\n",
" for x in range(w):\n",
" if not base_grid[y, x]:\n",
" if word_start < x:\n",
" word = GridWord(\n",
" y=y,\n",
" x=word_start,\n",
" is_vertical=False,\n",
" length=x - word_start\n",
" )\n",
" if word.length >= min_word_length:\n",
" grid_words.append(word)\n",
" \n",
" word_start = x + 1\n",
"\n",
" if word_start < w:\n",
" word = GridWord(\n",
" y=y,\n",
" x=word_start,\n",
" is_vertical=False,\n",
" length=w - word_start\n",
" )\n",
" if word.length >= min_word_length:\n",
" grid_words.append(word)\n",
" \n",
" # then get all vertical word slots\n",
" for x in range(w):\n",
" word_start = 0\n",
" for y in range(h):\n",
" if not base_grid[y, x]:\n",
" if word_start < y:\n",
" word = GridWord(\n",
" y=word_start,\n",
" x=x,\n",
" is_vertical=True,\n",
" length=y - word_start\n",
" )\n",
"\n",
" if word.length >= min_word_length:\n",
" grid_words.append(word)\n",
" \n",
" word_start = y + 1\n",
"\n",
" if word_start < h:\n",
" word = GridWord(\n",
" y=word_start,\n",
" x=x,\n",
" is_vertical=True,\n",
" length=h - word_start\n",
" )\n",
" if word.length >= min_word_length:\n",
" grid_words.append(word)\n",
" \n",
" for i in range(len(grid_words)):\n",
" grid_words[i].id = i\n",
" for j in range(i):\n",
" word_a = grid_words[i]\n",
" word_b = grid_words[j]\n",
"\n",
" if word_a.check_connected(word_b):\n",
" word_a.connect_word(word_b)\n",
" word_b.connect_word(word_a)\n",
"\n",
" return grid_words\n",
"\n",
" def __init__(self, y: int, x: int, is_vertical: bool, length: int, id: int = None) -> None:\n",
" self.x = x\n",
" self.y = y\n",
" self.is_vertical = is_vertical\n",
" self.length = length\n",
" self.word_key = None\n",
" self.connected_words = []\n",
" self.id = id\n",
"\n",
" self.candidate_cache = []\n",
" self.candidate_cache_key = \"\"\n",
" \n",
" \n",
" def get_letters(self, letter_grid: np.ndarray) -> list:\n",
" if self.is_vertical:\n",
" return letter_grid[self.y:self.y+self.length, self.x].flatten()\n",
" return letter_grid[self.y, self.x: self.x + self.length].flatten()\n",
" \n",
" def set_letters(self, letters, letter_grid: np.ndarray):\n",
" if self.is_vertical:\n",
" letter_grid[self.y:self.y + self.length, self.x] = letters\n",
" else:\n",
" letter_grid[self.y, self.x: self.x + self.length] = letters\n",
" \n",
" def set_word_key(self, word_key:str):\n",
" self.word_key = word_key\n",
" \n",
" def connect_word(self, grid_word):\n",
" self.connected_words.append(grid_word)\n",
" \n",
" \n",
" \n",
" def check_connected(self, grid_word):\n",
" if self.is_vertical == grid_word.is_vertical:\n",
" return False\n",
" \n",
" if self.is_vertical:\n",
" if self.y > grid_word.y:\n",
" return False\n",
" if self.y + self.length <= grid_word.y:\n",
" return False\n",
" \n",
" if self.x >= grid_word.x + grid_word.length:\n",
" return False\n",
" \n",
" if self.x < grid_word.x:\n",
" return False\n",
" \n",
" else:\n",
" if self.x > grid_word.x:\n",
" return False\n",
" if self.x + self.length <= grid_word.x:\n",
" return False\n",
" if self.y >= grid_word.y + grid_word.length:\n",
" return False\n",
" if self.y < grid_word.y:\n",
" return False\n",
" \n",
" return True\n",
" \n",
" def get_candidates(self, letter_grid, db, inverted_db):\n",
" letters = self.get_letters(letter_grid)\n",
" word = \"\".join(letters) \n",
" if word == self.candidate_cache_key:\n",
" return self.candidate_cache\n",
" self.candidate_cache = find_suitable_words(letters, db, inverted_db)\n",
" self.candidate_cache_key = word\n",
" return self.candidate_cache\n",
" \n",
" def get_connected_words(self):\n",
" return self.connected_words\n",
"\n",
" \n",
"\n",
"\n",
"class GridCreationState(object):\n",
" def __init__(self, base_grid, db, inverted_db, letter_grid = None, grid_words = None, final_grid_words = None, used_ids = None) -> None:\n",
" self.base_grid = base_grid\n",
" self.shape = base_grid.shape\n",
" self.db = db\n",
" self.inverted_db = inverted_db\n",
" \n",
"\n",
" if letter_grid is None:\n",
" self.letter_grid = np.full(shape = self.shape, fill_value=' ', dtype=np.unicode)\n",
" else:\n",
" self.letter_grid = letter_grid\n",
" \n",
" if grid_words is None:\n",
" self.grid_words = GridWord.from_base_grid(base_grid=base_grid)\n",
" else:\n",
" self.grid_words = grid_words\n",
"\n",
" \n",
" if final_grid_words is None:\n",
" self.final_grid_words = []\n",
" else:\n",
" self.final_grid_words = final_grid_words\n",
" \n",
" if used_ids is None:\n",
" self.used_ids = [[False]] * len(self.grid_words)\n",
" else:\n",
" self.used_ids = used_ids\n",
" \n",
"\n",
"\n",
" def copy(self):\n",
" return GridCreationState(self.base_grid, self.db, self.inverted_db, self.letter_grid.copy(), self.grid_words.copy(), self.final_grid_words.copy(), self.used_ids.copy())\n",
" \n",
"\n",
" def set_random_word(self, last_word = None, n_retries = 1, max_preselected = 10):\n",
" # choose random word\n",
"\n",
" preselected_id = None\n",
" preselected_candidates = []\n",
"\n",
" for word in self.final_grid_words:\n",
" connected_words = word.get_connected_words()\n",
" #random.shuffle(connected_words)\n",
" for connected_word in connected_words:\n",
" if not self.used_ids[connected_word.id]:\n",
" preselected_id = connected_word.id\n",
" for i in range(preselected_id):\n",
" if self.used_ids[i]:\n",
" preselected_id -= 1\n",
" preselected_candidates.append(preselected_id)\n",
" \n",
" \n",
" if len(preselected_candidates) > 0:\n",
" random.shuffle(preselected_candidates)\n",
" break\n",
" \n",
" \n",
"\n",
"\n",
" n_tries = 1\n",
" if preselected_id is not None:\n",
" n_tries = min(len(preselected_candidates), max_preselected)\n",
" else:\n",
" random_index = random.randint(0, len(self.grid_words) - 1)\n",
" \n",
" for i in range(n_tries):\n",
" if preselected_id is not None:\n",
" random_index = preselected_candidates[i]\n",
" grid_word = self.grid_words[random_index]\n",
" \n",
" candidates_raw = list(grid_word.get_candidates(self.letter_grid, self.db, self.inverted_db))\n",
" \n",
" \n",
" if len(candidates_raw) > 0:\n",
" for j in range(min(n_retries, len(candidates_raw))):\n",
" word_key = candidates_raw[j]\n",
" word = normalize_word(self.db[word_key]['word'])\n",
"\n",
" new_grid = self.letter_grid.copy()\n",
"\n",
" grid_word.set_letters(list(word), new_grid)\n",
"\n",
" for word in grid_word.get_connected_words():\n",
" if word.id not in self.used_ids:\n",
" n_cands = len(word.get_candidates(new_grid, self.db, self.inverted_db))\n",
" if n_cands == 0:\n",
" return None\n",
"\n",
"\n",
" self.letter_grid = new_grid\n",
" grid_word.set_word_key(word_key)\n",
" self.used_ids[grid_word.id] = True\n",
" self.final_grid_words.append(grid_word)\n",
" del(self.grid_words[random_index])\n",
"\n",
" return grid_word\n",
" \n",
" return None\n",
" \n",
" \n",
" def fill(self, depth = 0, last_word = None):\n",
" if depth % 5 == 0:\n",
" print(\"depth\", depth)\n",
"\n",
" n_retries = 100 if depth == 0 else 2\n",
" if len(self.grid_words) == 0:\n",
" return self\n",
"\n",
"\n",
" \n",
" state_copy = self.copy()\n",
" grid_word = None\n",
" for _ in range(n_retries):\n",
" \n",
" \n",
" grid_word = state_copy.set_random_word(last_word = last_word, n_retries=20, max_preselected = 2)\n",
" if grid_word is not None:\n",
" \n",
" final_state = state_copy.fill(\n",
" depth = depth + 1,\n",
" last_word = grid_word)\n",
" if final_state is not None:\n",
" return final_state\n",
" return None\n",
"\n",
" \n",
" \n",
"\n",
"\n",
"\n",
" \n"
],
"outputs": [],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 114,
"source": [
"%%prun\n",
"base_grid = create_base_grid(14, min_word_length=3, max_word_length=12)\n",
"\n",
"db = get_database(\"de\")\n",
"inverted_db = get_inverted_database(\"de\")\n",
"\n",
"grid_state = GridCreationState(base_grid, db, inverted_db)\n",
"\n",
"final_state = grid_state.fill()\n",
"\n",
"print(final_state is not None)"
],
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"depth 0\n",
"True\n",
" "
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
" 1490 function calls (1488 primitive calls) in 0.004 seconds\n",
"\n",
" Ordered by: internal time\n",
"\n",
" ncalls tottime percall cumtime percall filename:lineno(function)\n",
" 1 0.002 0.002 0.003 0.003 <ipython-input-64-c07db7426ad7>:1(create_base_grid)\n",
" 14 0.000 0.000 0.000 0.000 {method 'astype' of 'numpy.ndarray' objects}\n",
" 14 0.000 0.000 0.000 0.000 {method 'reduce' of 'numpy.ufunc' objects}\n",
" 1 0.000 0.000 0.000 0.000 <ipython-input-112-58b0ffc6eba3>:3(from_base_grid)\n",
" 184 0.000 0.000 0.000 0.000 random.py:238(_randbelow_with_getrandbits)\n",
" 184 0.000 0.000 0.000 0.000 random.py:291(randrange)\n",
" 2 0.000 0.000 0.000 0.000 <ipython-input-112-58b0ffc6eba3>:188(set_random_word)\n",
" 2 0.000 0.000 0.000 0.000 <ipython-input-2-026304d5612b>:92(find_suitable_words)\n",
" 1 0.000 0.000 0.004 0.004 {built-in method builtins.exec}\n",
" 7 0.000 0.000 0.000 0.000 socket.py:438(send)\n",
" 11 0.000 0.000 0.000 0.000 {method 'copy' of 'numpy.ndarray' objects}\n",
" 3/1 0.000 0.000 0.001 0.001 <ipython-input-112-58b0ffc6eba3>:254(fill)\n",
" 184 0.000 0.000 0.000 0.000 random.py:335(randint)\n",
" 22 0.000 0.000 0.000 0.000 {built-in method numpy.core._multiarray_umath.implement_array_function}\n",
" 14 0.000 0.000 0.000 0.000 fromnumeric.py:70(_wrapreduction)\n",
" 14 0.000 0.000 0.000 0.000 fromnumeric.py:2105(sum)\n",
" 3 0.000 0.000 0.000 0.000 {method 'join' of 'str' objects}\n",
" 315 0.000 0.000 0.000 0.000 {method 'getrandbits' of '_random.Random' objects}\n",
" 7 0.000 0.000 0.000 0.000 iostream.py:195(schedule)\n",
" 14 0.000 0.000 0.000 0.000 <__array_function__ internals>:2(sum)\n",
" 1 0.000 0.000 0.004 0.004 <string>:1(<module>)\n",
" 6 0.000 0.000 0.000 0.000 iostream.py:384(write)\n",
" 75 0.000 0.000 0.000 0.000 {built-in method builtins.min}\n",
" 3 0.000 0.000 0.000 0.000 <ipython-input-112-58b0ffc6eba3>:154(__init__)\n",
" 3 0.000 0.000 0.000 0.000 <ipython-input-112-58b0ffc6eba3>:138(get_candidates)\n",
" 8 0.000 0.000 0.000 0.000 numeric.py:268(full)\n",
" 8 0.000 0.000 0.000 0.000 {built-in method numpy.empty}\n",
" 1 0.000 0.000 0.000 0.000 {method 'intersection' of 'set' objects}\n",
" 184 0.000 0.000 0.000 0.000 {method 'bit_length' of 'int' objects}\n",
" 8 0.000 0.000 0.000 0.000 <__array_function__ internals>:2(copyto)\n",
" 3 0.000 0.000 0.000 0.000 {method 'flatten' of 'numpy.ndarray' objects}\n",
" 2 0.000 0.000 0.000 0.000 <ipython-input-112-58b0ffc6eba3>:96(set_letters)\n",
" 2 0.000 0.000 0.000 0.000 {built-in method builtins.print}\n",
" 14 0.000 0.000 0.000 0.000 fromnumeric.py:71(<dictcomp>)\n",
" 3 0.000 0.000 0.000 0.000 <ipython-input-112-58b0ffc6eba3>:91(get_letters)\n",
" 7 0.000 0.000 0.000 0.000 <ipython-input-112-58b0ffc6eba3>:78(__init__)\n",
" 7 0.000 0.000 0.000 0.000 threading.py:1093(is_alive)\n",
" 2 0.000 0.000 0.000 0.000 <ipython-input-112-58b0ffc6eba3>:184(copy)\n",
" 6 0.000 0.000 0.000 0.000 iostream.py:308(_is_master_process)\n",
" 20 0.000 0.000 0.000 0.000 {built-in method builtins.isinstance}\n",
" 7 0.000 0.000 0.000 0.000 iostream.py:91(_event_pipe)\n",
" 7 0.000 0.000 0.000 0.000 threading.py:1039(_wait_for_tstate_lock)\n",
" 2 0.000 0.000 0.000 0.000 <ipython-input-2-026304d5612b>:88(normalize_word)\n",
" 7 0.000 0.000 0.000 0.000 {method 'acquire' of '_thread.lock' objects}\n",
" 14 0.000 0.000 0.000 0.000 fromnumeric.py:2100(_sum_dispatcher)\n",
" 1 0.000 0.000 0.000 0.000 <ipython-input-2-026304d5612b>:19(get_database)\n",
" 2 0.000 0.000 0.000 0.000 {method 'translate' of 'str' objects}\n",
" 6 0.000 0.000 0.000 0.000 {built-in method posix.getpid}\n",
" 17 0.000 0.000 0.000 0.000 {built-in method builtins.len}\n",
" 14 0.000 0.000 0.000 0.000 {method 'items' of 'dict' objects}\n",
" 6 0.000 0.000 0.000 0.000 iostream.py:321(_schedule_flush)\n",
" 8 0.000 0.000 0.000 0.000 multiarray.py:1043(copyto)\n",
" 7 0.000 0.000 0.000 0.000 {method 'append' of 'list' objects}\n",
" 6 0.000 0.000 0.000 0.000 {method 'copy' of 'list' objects}\n",
" 7 0.000 0.000 0.000 0.000 threading.py:529(is_set)\n",
" 1 0.000 0.000 0.000 0.000 <ipython-input-2-026304d5612b>:78(get_inverted_database)\n",
" 2 0.000 0.000 0.000 0.000 <ipython-input-112-58b0ffc6eba3>:105(connect_word)\n",
" 3 0.000 0.000 0.000 0.000 <ipython-input-112-58b0ffc6eba3>:147(get_connected_words)\n",
" 1 0.000 0.000 0.000 0.000 <ipython-input-112-58b0ffc6eba3>:110(check_connected)\n",
" 2 0.000 0.000 0.000 0.000 {method 'lower' of 'str' objects}\n",
" 7 0.000 0.000 0.000 0.000 {method 'append' of 'collections.deque' objects}\n",
" 2 0.000 0.000 0.000 0.000 <ipython-input-112-58b0ffc6eba3>:102(set_word_key)\n",
" 1 0.000 0.000 0.000 0.000 {method 'disable' of '_lsprof.Profiler' objects}"
]
}
],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 115,
"source": [
"\n",
"plt.imshow(base_grid)\n",
"plt.show()\n",
"print(final_state.letter_grid)\n",
"for grid_word in final_state.final_grid_words:\n",
" connected = []\n",
" for word in grid_word.connected_words:\n",
" connected.append(word.word_key)\n",
" print(grid_word.word_key, connected)"
],
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
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},
"metadata": {
"needs_background": "light"
}
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"[[' ' 'm' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ']\n",
" ['a' 'a' 'c' 'h' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ']\n",
" [' ' 'i' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ']\n",
" [' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ']\n",
" [' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ']\n",
" [' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ']\n",
" [' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ']\n",
" [' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ']\n",
" [' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ']\n",
" [' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ']\n",
" [' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ']\n",
" [' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ']\n",
" [' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ']\n",
" [' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ']]\n",
"mai ['aach0']\n",
"aach0 ['mai']\n"
]
}
],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": null,
"source": [
"grid_word"
],
"outputs": [],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 292,
"source": [
"def create_word_grid(w: int, h: int, lang_code: str = \"en\", target_density: float = 0.5, difficulty: int = 0):\n",
" np.full()\n",
"\n"
],
"outputs": [],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 85,
"source": [
"w = 5\n",
"h = 5\n",
"\n",
"hh_index = np.zeros(shape=(h,w), dtype = int)\n",
"vv_index = np.zeros(shape=(w,h), dtype = int)\n",
"\n",
"\n",
"h_index = w - np.arange(w)\n",
"v_index = h - np.arange(h)\n",
"\n",
"hh_index[:] = h_index\n",
"vv_index[:] = v_index\n",
"vv_index = vv_index.transpose()\n",
"\n",
"\n",
"horizontal_starting_points = np.full(shape=(h, w), dtype=int, fill_value=-1)\n",
"horizontal_starting_points[::2,::2] = 0\n",
"horizontal_starting_points[horizontal_starting_points == 0] = hh_index[horizontal_starting_points == 0]\n",
"\n",
"vertical_starting_points = np.full(shape=(h, w), dtype=int, fill_value=-1)\n",
"vertical_starting_points[::2,::2] = 0\n",
"vertical_starting_points[vertical_starting_points == 0] = vv_index[vertical_starting_points == 0]\n",
"\n",
"vertical_starting_points"
],
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"array([[ 5, -1, 5, -1, 5],\n",
" [-1, -1, -1, -1, -1],\n",
" [ 3, -1, 3, -1, 3],\n",
" [-1, -1, -1, -1, -1],\n",
" [ 1, -1, 1, -1, 1]])"
]
},
"metadata": {},
"execution_count": 85
}
],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": null,
"source": [],
"outputs": [],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 82,
"source": [
"w = 10\n",
"h = 5\n",
"\n",
"hh_index = np.zeros(shape=(h,w), dtype = int)\n",
"vv_index = np.zeros(shape=(w,h), dtype = int)\n",
"\n",
"\n",
"h_index = w - 1 - np.arange(w)\n",
"v_index = h - 1 - np.arange(h)\n",
"\n",
"hh_index[:] = h_index\n",
"vv_index[:] = v_index\n",
"vv_index = vv_index.transpose()\n",
"\n",
"hh_index\n"
],
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"array([[9, 8, 7, 6, 5, 4, 3, 2, 1, 0],\n",
" [9, 8, 7, 6, 5, 4, 3, 2, 1, 0],\n",
" [9, 8, 7, 6, 5, 4, 3, 2, 1, 0],\n",
" [9, 8, 7, 6, 5, 4, 3, 2, 1, 0],\n",
" [9, 8, 7, 6, 5, 4, 3, 2, 1, 0]])"
]
},
"metadata": {},
"execution_count": 82
}
],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 87,
"source": [
"l = [[1,2],[2,3]]\n",
"\n",
"[1,3] in l"
],
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"False"
]
},
"metadata": {},
"execution_count": 87
}
],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 53,
"source": [
"import pprint\n",
"\n",
"grid, hints, solution = create_word_grid(10,20)\n",
"\n",
"pprint.pprint(grid)"
],
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"100\n",
"200\n",
"300\n",
"400\n",
"500\n",
"600\n",
"700\n",
"800\n",
"900\n",
"1000\n",
"1100\n",
"1200\n"
]
},
{
"output_type": "error",
"ename": "RecursionError",
"evalue": "maximum recursion depth exceeded while calling a Python object",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mRecursionError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-53-d31de1543123>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mpprint\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mgrid\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhints\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msolution\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcreate_word_grid\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m20\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mpprint\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgrid\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m<ipython-input-52-64ae6dfdecba>\u001b[0m in \u001b[0;36mcreate_word_grid\u001b[0;34m(w, h, lang_code, target_density, difficulty)\u001b[0m\n\u001b[1;32m 279\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 280\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 281\u001b[0;31m \u001b[0msolution_word_locations\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_solution_word\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 282\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 283\u001b[0m \u001b[0mlogging\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minfo\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"crossword generation done after %s iterations\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m<ipython-input-52-64ae6dfdecba>\u001b[0m in \u001b[0;36mget_solution_word\u001b[0;34m(min_length, max_length)\u001b[0m\n\u001b[1;32m 173\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mchar\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mlocations_cpy\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlocations_cpy\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mchar\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 174\u001b[0m \u001b[0;31m# next try:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 175\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mget_solution_word\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmin_length\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmin_length\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmax_length\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmax_length\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 176\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 177\u001b[0m \u001b[0mlocation_candidates\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlocations_cpy\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mchar\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"... last 1 frames repeated, from the frame below ...\n",
"\u001b[0;32m<ipython-input-52-64ae6dfdecba>\u001b[0m in \u001b[0;36mget_solution_word\u001b[0;34m(min_length, max_length)\u001b[0m\n\u001b[1;32m 173\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mchar\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mlocations_cpy\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlocations_cpy\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mchar\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 174\u001b[0m \u001b[0;31m# next try:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 175\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mget_solution_word\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmin_length\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmin_length\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmax_length\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmax_length\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 176\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 177\u001b[0m \u001b[0mlocation_candidates\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlocations_cpy\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mchar\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mRecursionError\u001b[0m: maximum recursion depth exceeded while calling a Python object"
]
}
],
"metadata": {}
}
],
"metadata": {
"orig_nbformat": 4,
"language_info": {
"name": "python",
"version": "3.9.5",
"mimetype": "text/x-python",
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"pygments_lexer": "ipython3",
"nbconvert_exporter": "python",
"file_extension": ".py"
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3.9.5 64-bit"
},
"interpreter": {
"hash": "916dbcbb3f70747c44a77c7bcd40155683ae19c65e1c03b4aa3499c5328201f1"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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