modelowanie-jezykowe-aitech-cw/wyk/02_Jezyki.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![Logo 1](https://git.wmi.amu.edu.pl/AITech/Szablon/raw/branch/master/Logotyp_AITech1.jpg)\n",
    "<div class=\"alert alert-block alert-info\">\n",
    "<h1> Modelowanie języka</h1>\n",
    "<h2> 2. <i>Języki</i>  [wykład]</h2> \n",
    "<h3> Filip Graliński (2022)</h3>\n",
    "</div>\n",
    "\n",
    "![Logo 2](https://git.wmi.amu.edu.pl/AITech/Szablon/raw/branch/master/Logotyp_AITech2.jpg)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Języki i ich prawa\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Jakim rozkładom statystycznym podlegają języki?\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Język naturalny albo „Pan Tadeusz” w liczbach\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Przygotujmy najpierw „infrastrukturę” do *segmentacji* tekstu na różnego rodzaju jednostki.\n",
    "Używać będziemy generatorów.\n",
    "\n",
    "**Pytanie** Dlaczego generatory zamiast list?\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Księga pierwsza\\r\\n\\r\\n\\r\\n\\r\\nGospodarstwo\\r\\n\\r\\nPowrót pani'"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import requests\n",
    "\n",
    "url = 'https://wolnelektury.pl/media/book/txt/pan-tadeusz.txt'\n",
    "pan_tadeusz = requests.get(url).content.decode('utf-8')\n",
    "\n",
    "pan_tadeusz[100:150]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Znaki\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['K',\n",
       " 's',\n",
       " 'i',\n",
       " 'ę',\n",
       " 'g',\n",
       " 'a',\n",
       " ' ',\n",
       " 'p',\n",
       " 'i',\n",
       " 'e',\n",
       " 'r',\n",
       " 'w',\n",
       " 's',\n",
       " 'z',\n",
       " 'a',\n",
       " '\\r',\n",
       " '\\n',\n",
       " '\\r',\n",
       " '\\n',\n",
       " '\\r',\n",
       " '\\n',\n",
       " '\\r',\n",
       " '\\n',\n",
       " 'G',\n",
       " 'o',\n",
       " 's',\n",
       " 'p',\n",
       " 'o',\n",
       " 'd',\n",
       " 'a',\n",
       " 'r',\n",
       " 's',\n",
       " 't',\n",
       " 'w',\n",
       " 'o',\n",
       " '\\r',\n",
       " '\\n',\n",
       " '\\r',\n",
       " '\\n',\n",
       " 'P',\n",
       " 'o',\n",
       " 'w',\n",
       " 'r',\n",
       " 'ó',\n",
       " 't',\n",
       " ' ',\n",
       " 'p',\n",
       " 'a',\n",
       " 'n',\n",
       " 'i']"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from itertools import islice\n",
    "\n",
    "def get_characters(t):\n",
    "    yield from t\n",
    "\n",
    "list(islice(get_characters(pan_tadeusz), 100, 150))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Counter({'A': 698,\n",
       "         'd': 11465,\n",
       "         'a': 30979,\n",
       "         'm': 10269,\n",
       "         ' ': 63444,\n",
       "         'M': 585,\n",
       "         'i': 29353,\n",
       "         'c': 14153,\n",
       "         'k': 12362,\n",
       "         'e': 25343,\n",
       "         'w': 14625,\n",
       "         'z': 22741,\n",
       "         '\\r': 10851,\n",
       "         '\\n': 10851,\n",
       "         'P': 1265,\n",
       "         'n': 15505,\n",
       "         'T': 971,\n",
       "         'u': 7699,\n",
       "         's': 15255,\n",
       "         'y': 13732,\n",
       "         'l': 6677,\n",
       "         'o': 23050,\n",
       "         't': 10757,\n",
       "         'j': 6586,\n",
       "         'L': 316,\n",
       "         'I': 795,\n",
       "         'S': 1045,\n",
       "         'B': 567,\n",
       "         'N': 793,\n",
       "         '9': 8,\n",
       "         '7': 2,\n",
       "         '8': 10,\n",
       "         '-': 33,\n",
       "         '3': 3,\n",
       "         '2': 6,\n",
       "         '4': 2,\n",
       "         '5': 2,\n",
       "         'K': 683,\n",
       "         'ę': 5534,\n",
       "         'g': 4775,\n",
       "         'p': 8031,\n",
       "         'r': 15328,\n",
       "         'G': 358,\n",
       "         'ó': 3097,\n",
       "         '—': 720,\n",
       "         ',': 9130,\n",
       "         'ł': 10059,\n",
       "         'W': 1258,\n",
       "         'ż': 3334,\n",
       "         'ś': 2524,\n",
       "         'ą': 4794,\n",
       "         'Ż': 219,\n",
       "         'O': 567,\n",
       "         'ź': 414,\n",
       "         'b': 5753,\n",
       "         'R': 489,\n",
       "         'E': 23,\n",
       "         '!': 1083,\n",
       "         ':': 1152,\n",
       "         'ć': 1956,\n",
       "         '.': 2380,\n",
       "         'D': 552,\n",
       "         'J': 729,\n",
       "         'C': 556,\n",
       "         'h': 3915,\n",
       "         '(': 76,\n",
       "         'f': 386,\n",
       "         ';': 1445,\n",
       "         'ń': 651,\n",
       "         ')': 76,\n",
       "         'Z': 785,\n",
       "         'Ś': 71,\n",
       "         'U': 184,\n",
       "         'F': 47,\n",
       "         'é': 43,\n",
       "         '?': 441,\n",
       "         '…': 157,\n",
       "         '«': 540,\n",
       "         'H': 309,\n",
       "         '»': 538,\n",
       "         'Ó': 13,\n",
       "         'Ł': 24,\n",
       "         'x': 3,\n",
       "         'v': 5,\n",
       "         '*': 150,\n",
       "         'à': 1,\n",
       "         'Ź': 4,\n",
       "         'V': 3,\n",
       "         '/': 19,\n",
       "         'Ć': 1,\n",
       "         'q': 2,\n",
       "         '1': 4,\n",
       "         'æ': 2,\n",
       "         '6': 1,\n",
       "         '0': 1})"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from collections import Counter\n",
    "\n",
    "c = Counter(get_characters(pan_tadeusz))\n",
    "\n",
    "c"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Napiszmy pomocniczą funkcję, która zwraca **listę frekwencyjną**.\n",
    "\n",
    "Counter({' ': 63444, 'a': 30979, 'i': 29353, 'e': 25343, 'o': 23050, 'z': 22741, 'n': 15505, 'r': 15328, 's': 15255, 'w': 14625, 'c': 14153, 'y': 13732, 'k': 12362, 'd': 11465, '\\r': 10851, '\\n': 10851, 't': 10757, 'm': 10269, 'ł': 10059, ',': 9130, 'p': 8031, 'u': 7699, 'l': 6677, 'j': 6586, 'b': 5753, 'ę': 5534, 'ą': 4794, 'g': 4775, 'h': 3915, 'ż': 3334, 'ó': 3097, 'ś': 2524, '.': 2380, 'ć': 1956, ';': 1445, 'P': 1265, 'W': 1258, ':': 1152, '!': 1083, 'S': 1045, 'T': 971, 'I': 795, 'N': 793, 'Z': 785, 'J': 729, '—': 720, 'A': 698, 'K': 683, 'ń': 651, 'M': 585, 'B': 567, 'O': 567, 'C': 556, 'D': 552, '«': 540, '»': 538, 'R': 489, '?': 441, 'ź': 414, 'f': 386, 'G': 358, 'L': 316, 'H': 309, 'Ż': 219, 'U': 184, '…': 157, '\\*': 150, '(': 76, ')': 76, 'Ś': 71, 'F': 47, 'é': 43, '-': 33, 'Ł': 24, 'E': 23, '/': 19, 'Ó': 13, '8': 10, '9': 8, '2': 6, 'v': 5, 'Ź': 4, '1': 4, '3': 3, 'x': 3, 'V': 3, '7': 2, '4': 2, '5': 2, 'q': 2, 'æ': 2, 'à': 1, 'Ć': 1, '6': 1, '0': 1})\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "OrderedDict([(' ', 63444),\n",
       "             ('a', 30979),\n",
       "             ('i', 29353),\n",
       "             ('e', 25343),\n",
       "             ('o', 23050),\n",
       "             ('z', 22741),\n",
       "             ('n', 15505),\n",
       "             ('r', 15328)])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from collections import Counter\n",
    "from collections import OrderedDict\n",
    "\n",
    "def freq_list(g, top=None):\n",
    "  c = Counter(g)\n",
    "\n",
    "  if top is None:\n",
    "     items = c.items()\n",
    "  else:\n",
    "     items = c.most_common(top)\n",
    "\n",
    "  return OrderedDict(sorted(items, key=lambda t: -t[1]))\n",
    "\n",
    "freq_list(get_characters(pan_tadeusz), top=8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_8747/6903746.py:14: UserWarning: Glyph 13 (\r",
      ") missing from current font.\n",
      "  plt.savefig(fname)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'02_Jezyki/pt-chars.png'"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/lib/python3.10/site-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 13 (\r",
      ") missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from collections import OrderedDict\n",
    "\n",
    "def rang_freq_with_labels(name, g, top=None):\n",
    "   freq = freq_list(g, top)\n",
    "\n",
    "   plt.figure(figsize=(12, 3))\n",
    "   plt.ylabel('liczba wystąpień')\n",
    "\n",
    "   plt.bar(freq.keys(), freq.values())\n",
    "\n",
    "   fname = f'02_Jezyki/{name}.png'\n",
    "\n",
    "   plt.savefig(fname)\n",
    "\n",
    "   return fname\n",
    "\n",
    "rang_freq_with_labels('pt-chars', get_characters(pan_tadeusz))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Słowa\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Co rozumiemy pod pojęciem słowa czy wyrazu, nie jest oczywiste. W praktyce zależy to od wyboru **tokenizatora**.\n",
    "\n",
    "Załóżmy, że przez wyraz rozumieć będziemy nieprzerwany ciąg liter bądź cyfr (oraz gwiazdek\n",
    "— to za chwilę ułatwi nam analizę pewnego tekstu…).\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Ty',\n",
       " 'co',\n",
       " 'gród',\n",
       " 'zamkowy',\n",
       " 'Nowogródzki',\n",
       " 'ochraniasz',\n",
       " 'z',\n",
       " 'jego',\n",
       " 'wiernym',\n",
       " 'ludem',\n",
       " 'Jak',\n",
       " 'mnie',\n",
       " 'dziecko',\n",
       " 'do',\n",
       " 'zdrowia',\n",
       " 'powróciłaś',\n",
       " 'cudem',\n",
       " 'Gdy',\n",
       " 'od',\n",
       " 'płaczącej',\n",
       " 'matki',\n",
       " 'pod',\n",
       " 'Twoją',\n",
       " 'opiekę',\n",
       " 'Ofiarowany',\n",
       " 'martwą',\n",
       " 'podniosłem',\n",
       " 'powiekę',\n",
       " 'I',\n",
       " 'zaraz']"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from itertools import islice\n",
    "import regex as re\n",
    "\n",
    "def get_words(t):\n",
    "  for m in re.finditer(r'[\\p{L}0-9\\*]+', t):\n",
    "     yield m.group(0)\n",
    "\n",
    "list(islice(get_words(pan_tadeusz), 100, 130))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Zobaczmy 20 najczęstszych wyrazów.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'02_Jezyki/pt-words-20.png'"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "rang_freq_with_labels('pt-words-20', get_words(pan_tadeusz), top=20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Zobaczmy pełny obraz, już bez etykiet.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'02_Jezyki/pt-words.png'"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from math import log\n",
    "\n",
    "def rang_freq(name, g):\n",
    "   freq = freq_list(g)\n",
    "\n",
    "   plt.figure().clear()\n",
    "   plt.plot(range(1, len(freq.values())+1), freq.values())\n",
    "\n",
    "   fname = f'02_Jezyki/{name}.png'\n",
    "\n",
    "   plt.savefig(fname)\n",
    "\n",
    "   return fname\n",
    "\n",
    "rang_freq('pt-words', get_words(pan_tadeusz))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Widać, jak różne skale obejmuje ten wykres. Zastosujemy logarytm,\n",
    "najpierw tylko do współrzędnej $y$.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'02_Jezyki/pt-words-log.png'"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWoAAAD4CAYAAADFAawfAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAAAUOklEQVR4nO3de5DdZ33f8ff37E3WzbKs9QVdkC/YGUMHWywUR9QtEBObZkJJ6dTMkJJMOmonpANNM4zTzHRC+0ebtmFKC01HCRRoCKYheJpQl1tsYtqA7ZXjK4pAvoCEZWuFLFuWLK1299s/zlmxN2l/kvec33P2vF8zO3v0O789+z3PHH/87PN7fs8TmYkkqVyNuguQJJ2dQS1JhTOoJalwBrUkFc6glqTC9bfjRTds2JBbt25tx0tL0rK0a9euQ5k5vNBzbQnqrVu3Mjo62o6XlqRlKSJ+cKbnHPqQpMIZ1JJUOINakgpnUEtS4QxqSSqcQS1JhTOoJalwxQT11FTyiXv28vgzL9RdiiQVpS03vJyPoycm+I9f28OR4+O89lUX1l2OJBWjmB71hSsHWD3Yz8SUGxlI0kzFBDUAAW44I0mzFRXUUXcBklSgsoI6AvdwlKTZCgtqMKYlabZFgzoiro2Ih2Z8vRgRH2pHMYFj1JI016LT8zJzD3A9QET0AT8C7mxHMRFB2qeWpFnOdejj7cATmXnGBa5fCS8mStJ85xrUtwGfX+iJiNgREaMRMTo2NnbeBTn0IUmzVQ7qiBgEfh7444Wez8ydmTmSmSPDwwtu+1Xhd3gxUZLmOpce9a3Ag5n5XLuKgbBHLUlznEtQv5czDHsslQiwTy1Js1UK6ohYCdwMfKmdxTg9T5Lmq7R6XmYeBy5ucy3NMWqDWpJmKevORJxHLUlzlRXU9qglaZ6yghovJUrSXGUFdTg9T5LmKiqoJUnzFRXUzTFqu9SSNFN5QV13EZJUmLKCGnd4kaS5igrqhj1qSZqnqKCOCKZMakmapaygxouJkjRXUUGNQx+SNE9RQd1w2ockzVNUUAcw5dCHJM1SVlC7KJMkzVNUUDfCZU4laa6ighpwep4kzVFUULt6niTNV3XPxHUR8cWI+OuI2B0RN7ajmACc9iFJs1XaMxH4GPCVzHxPRAwCK9tRTKPhxURJmmvRoI6ItcBNwC8BZOY4MN6OYoJwep4kzVFl6ONKYAz47xHxVxHxBxGxau5JEbEjIkYjYnRsbOy8ivF+F0mar0pQ9wPbgN/LzBuAY8Dtc0/KzJ2ZOZKZI8PDw+dVjBcTJWm+KkG9H9ifmfe1/v1FmsG95LwzUZLmWzSoM/NZYF9EXNs69Hbgu+0oJqIdrypJ3a3qrI9/BnyuNePjSeCX21FMc5nTdryyJHWvSkGdmQ8BI+0txVvIJWkhhd2ZCFNTdVchSWUpK6ixRy1Jc5UV1C5zKknzGNSSVLiygtqhD0map6ygtkctSfMUFdTN6XmSpJmKCuoIbyGXpLmKCmpw6EOS5ioqqBsRpEktSbMUFdQDfcGpSYNakmYqKqj7Gu7wIklzFRfUk1MGtSTNVFRQNyKYtEctSbMUFdT99qglaZ6igrphUEvSPEUFdV8EUwa1JM1SVlA3ggmDWpJmqbQVV0Q8DRwFJoGJzGzLtlxOz5Ok+apubgvw1sw81LZKsEctSQspauijEV5MlKS5qgZ1Al+LiF0RsWOhEyJiR0SMRsTo2NjYeRXT3/BioiTNVTWot2fmNuBW4AMRcdPcEzJzZ2aOZObI8PDweRXj0IckzVcpqDPzmdb3g8CdwJvaUowXEyVpnkWDOiJWRcSa6cfAO4DH2lHMQKO5ep5LnUrST1SZ9XEpcGdETJ//R5n5lXYUMzTQB8D45BRD/X3t+BWS1HUWDerMfBJ4fQdqYai/2cE/ccqglqRpRU3PW9HqUZ+cmKy5EkkqR1FBPd2jPnlqquZKJKkcRQX1dI/6xCl71JI0rdCgtkctSdMKC+rWxUTHqCXptKKCenqmh0MfkvQTRQX1ysFmUB8fN6glaVpRQb16qDmt+9jJiZorkaRyFBXUqwxqSZqnqKCe7lE/deh4zZVIUjmKCurpWR+D/UWVJUm1KioRI4J1Kwcc+pCkGYoKaoBVg/0GtSTNUFxQrx7q5yWDWpJOKy+oV/RzbNyglqRpxQX1qqF+XjrpDS+SNK24oF491OcYtSTNUGBQ9/PSCYNakqZVDuqI6IuIv4qIL7ezoFVDzvqQpJnOpUf9QWB3uwqZtnqoeTHRncglqalSUEfEJuDvAn/Q3nKaPeqphJdd6lSSgOo96v8EfBg449YrEbEjIkYjYnRsbOy8C+pvBADPHz913q8hScvJokEdET8HHMzMXWc7LzN3ZuZIZo4MDw+fd0GXrl0BwP7DLswkSVCtR70d+PmIeBq4A3hbRPxhuwoaXjMEwJGX7VFLElQI6sz8zczclJlbgduAuzPzfe0q6LJWj9opepLUVNw86jUrmmtSv3jCHrUkAfSfy8mZ+U3gm22ppGXNigEAdh94sZ2/RpK6RnE96ulNA8YnzjjBRJJ6SnFBDXD5hSvY9/zLdZchSUUoMqgvWTPEyQlveJEkKDSoh9cMsffgS3WXIUlFKDKo168a5MSpKaamXO9DkooM6g2rmze9PHf0RM2VSFL9igzq6161FoAXvDtRksoM6pWDfQDsefZozZVIUv2KDOqrh9cAcNTbyCWpzKC+ZG1zjPr7z9mjlqQig3rFQHPoo9Fam1qSelmRQQ2wcd0F/L+9h+ouQ5JqV2xQrxrqY7+3kUtSuUF91fBqHPiQpIKD+trL1nBsfJIfv3Sy7lIkqVbFBvWmi1YCcM+e898oV5KWg2KD+u9c29wg96F9z9dciSTVq9ignl7vw1X0JPW6RYM6IlZExP0R8XBEPB4RH+lEYQBb1q9k32FnfkjqbVV61CeBt2Xm64HrgVsi4s1trarl9ZvX8aMjL7s4k6SetmhQZ9P0+MNA66sjC0W/cetFANz/1OFO/DpJKlKlMeqI6IuIh4CDwNcz874FztkREaMRMTo2tjQzNW688mIAdv3AC4qSeleloM7Mycy8HtgEvCkiXrfAOTszcyQzR4aHh5ekuCuHVwOw7/DxJXk9SepG5zTrIzOPAN8EbmlHMXP1NYLXvmot//vRA0xMTnXiV0pScarM+hiOiHWtxxcAPwP8dZvrOu3ay5prUz956FinfqUkFaVKj/py4J6IeAR4gOYY9ZfbW9ZP/P1tmwD48iMHOvUrJako/YudkJmPADd0oJYFbb96A32N4LPffppfv/mausqQpNoUe2fiTBvXXcCR46fI7MisQEkqSlcE9fvevAWAj/3592uuRJI6ryuC+j1v2AzAd578cc2VSFLndUVQr181yNWXrOY7Tx52fWpJPacrghrgH7yhOfvDaXqSek3XBPX1m9cB8IHPPVhvIZLUYV0T1G/cup6rhlfx8qlJZ39I6ildE9SNRvCzr72Moycm+MVP3l93OZLUMV0T1ADve/OrAXjsmRdqrkSSOqergvpV6y7gn9x0JUeOn+IT9+ytuxxJ6oiuCmqAn756AwD/4at7aq5Ekjqj64L6b18zzG+8o7nmx5Hj4zVXI0nt13VBDbD2ggEAtv2br/PwviP1FiNJbdaVQf2u6zfyz3/mGqYS9j/vLuWSlreuDOoLLxjgF7ZtBGD3gRfdqkvSstaVQQ3N4Y9GwMfv2cutH/uWN8FIWra6NqgvvGCAuz74t/iHI5t56eQEJyfcU1HS8tS1QQ3wU5etPb2n4o+PjXPi1GTNFUnS0quyue3miLgnInZHxOMR8cFOFFbV6qHmbmLb/93dXPevvsK3n3DNaknLy6J7JgITwL/IzAcjYg2wKyK+npnfbXNtldzyNy7j+PgEPz42zn+5ey8/PHyMG6+6uO6yJGnJLNqjzswDmflg6/FRYDewsd2FVbV2xQC/tP0K3v/TWwEcq5a07FTpUZ8WEVtp7kh+3wLP7QB2AGzZsmUpajsnQ/3N/+fc+71DjE9MMdTf4N3bNp0eGpGkblU5xSJiNfAnwIcy88W5z2fmTmAnwMjISMfnyq0c7OfStUN8Y/dzfGP3cwCsXtHPu2/Y1OlSJGlJVQrqiBigGdKfy8wvtbek89PXCL714bdxYmKSsaMnefvv/gXHx50FIqn7LRrUERHAJ4HdmfnR9pd0/gb7Gwz2N5iaanboxx2vlrQMVOlRbwd+EXg0Ih5qHfuXmXlX26p6hQZb49V7nj3Kvd8bO338sgtXcM2la+oqS5LOy6JBnZn/F4gO1LJkhvr7WDnYxx0P7OOOB/adPj7Y1+DRj7yDof6+GquTpHOzLKdE9DWCr37oJg4ePXH62J89fIBP/+XTnDg1ZVBL6irLMqgBNq9fyeb1K0//+/FnmhNVTk06bi2pu3T1Wh/nYqCv+VYNakndZtn2qOcabAX1Vx57lotXD816buO6C3jDqy+qoyxJWlTPBPXwmmY4f+TP5i9RMtAX7P7Xt9Df1zN/YEjqIj0T1DddM8y3PvzWeWuBfOGBH/L733qKianEa4ySStQzQQ3Murg47dK1K4Dm2PWKAZNaUnl6/m/9/kZzivjEpFt5SSpTzwd13/RskClng0gqU08NfSxkoNWjvnv3QS5aNXjWc68aXs3Vl6zuRFmSdFrPB/X6Vjjf/qVHFz13y/qV3Pvht7a7JEmapeeD+ubrLuUbv37TojvD/Nd7nuA7T7ofo6TO6/mgjgiuvmTxFfU2rB5kYsoLjpI6r+cvJlbV1/jJOteS1EkGdUX9fWGPWlItDOqK+hrBpEEtqQYGdUX9jWDCudaSatDzFxOr6msEUwn/9q7dr3i/m6G+Br+8/YpF521LElTb3PZTwM8BBzPzde0vqUw/ddkaVg728em/fPoVvU4mjE9OsXXDKn5h26alKU7SslalR/1p4OPAZ9tbStlued3l3PK6y1/x6+x//jhv+Z17vDApqbJFx6gz817gcAdq6QmNaI6bONVPUlVLdjExInZExGhEjI6NjS3Vyy47fa21RSbToJZUzZIFdWbuzMyRzBwZHh5eqpddduxRSzpXTs/rsOketTktqSqDusNaOe3NM5IqWzSoI+LzwLeBayNif0T8SvvLWr4ap3vUBrWkahadnpeZ7+1EIb2irzVGbY9aUlUOfXSYY9SSzpW3kHdYq0PN1777LM++8HK9xZyjt7xmmJuvu7TuMqSeY1B32GBfgxu2rOOpQ8d46tCxusup7KUTE+z64fMGtVQDg7rDIoI7f3V73WWcs3/8mVF+dKS7/gKQlgvHqFVJIyCdqSLVwqBWJRHNlf8kdZ5BrUoaESQmtVQHg1qVRDilUKqLQa1KIsK7KaWaGNSqpBGBIx9SPQxqVRK4PolUF4NalTTsUEu1MahViWPUUn0MalXiPGqpPga1KgnCoJZqYlCrEm8hl+pjUKsSb3iR6mNQq5KGFxOl2lQK6oi4JSL2RMTeiLi93UWpPBHh9DypJlU2t+0DPgHcClwHvDcirmt3YSpLOEYt1abKxgFvAvZm5pMAEXEH8C7gu+0sTGVpBBw+Ns7NH/2LukuRinXRykH+5z+9cclft0pQbwT2zfj3fuBvzj0pInYAOwC2bNmyJMWpHO+6fiPPHzvlUqfSWaxdMdCW160S1LHAsXn/tWbmTmAnwMjIiP81LzNv3LqeN25dX3cZUk+qcjFxP7B5xr83Ac+0pxxJ0lxVgvoB4DURcUVEDAK3AX/a3rIkSdMWHfrIzImI+DXgq0Af8KnMfLztlUmSgGpj1GTmXcBdba5FkrQA70yUpMIZ1JJUOINakgpnUEtS4aId6zdExBjwg/P88Q3AoSUsZ7mxfc7O9jk72+fs6myfV2fm8EJPtCWoX4mIGM3MkbrrKJXtc3a2z9nZPmdXavs49CFJhTOoJalwJQb1zroLKJztc3a2z9nZPmdXZPsUN0YtSZqtxB61JGkGg1qSCldMUPfyBroR8XREPBoRD0XEaOvY+oj4ekR8v/X9ohnn/2arnfZExM/OOP6G1uvsjYj/HBELbfpQvIj4VEQcjIjHZhxbsvaIiKGI+ELr+H0RsbWjb/AVOkP7/HZE/Kj1GXooIt4547lea5/NEXFPROyOiMcj4oOt4937GcrM2r9oLp/6BHAlMAg8DFxXd10dfP9PAxvmHPv3wO2tx7cDv9N6fF2rfYaAK1rt1td67n7gRpq78vwf4Na639t5tsdNwDbgsXa0B/CrwH9rPb4N+ELd73kJ2ue3gd9Y4NxebJ/LgW2tx2uA77XaoWs/Q6X0qE9voJuZ48D0Brq97F3AZ1qPPwP8vRnH78jMk5n5FLAXeFNEXA6szcxvZ/PT89kZP9NVMvNe4PCcw0vZHjNf64vA27vpr48ztM+Z9GL7HMjMB1uPjwK7ae792rWfoVKCeqENdDfWVEsdEvhaROxqbRIMcGlmHoDmBw+4pHX8TG21sfV47vHlYinb4/TPZOYE8AJwcdsq75xfi4hHWkMj03/W93T7tIYkbgDuo4s/Q6UEdaUNdJex7Zm5DbgV+EBE3HSWc8/UVr3ahufTHsuxrX4PuAq4HjgA/G7reM+2T0SsBv4E+FBmvni2Uxc4VlQblRLUPb2BbmY+0/p+ELiT5lDQc60/vWh9P9g6/Uxttb/1eO7x5WIp2+P0z0REP3Ah1YcSipSZz2XmZGZOAb9P8zMEPdo+ETFAM6Q/l5lfah3u2s9QKUHdsxvoRsSqiFgz/Rh4B/AYzff//tZp7wf+V+vxnwK3ta46XwG8Bri/9afc0Yh4c2us7B/N+JnlYCnbY+ZrvQe4uzUG2bWmA6jl3TQ/Q9CD7dN6P58EdmfmR2c81b2fobqv0M64UvtOmldnnwB+q+56Ovi+r6R5xflh4PHp905zvOvPge+3vq+f8TO/1WqnPcyY2QGM0PwP9Ang47TuPO22L+DzNP98P0Wz5/IrS9kewArgj2leNLofuLLu97wE7fM/gEeBR2iGyOU93D5voTkM8QjwUOvrnd38GfIWckkqXClDH5KkMzCoJalwBrUkFc6glqTCGdSSVDiDWpIKZ1BLUuH+P+ntmnLnpoT7AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from math import log\n",
    "\n",
    "def rang_log_freq(name, g):\n",
    "   freq = freq_list(g)\n",
    "\n",
    "   plt.figure().clear()\n",
    "   plt.plot(range(1, len(freq.values())+1), [log(y) for y in freq.values()])\n",
    "\n",
    "   fname = f'02_Jezyki/{name}.png'\n",
    "\n",
    "   plt.savefig(fname)\n",
    "\n",
    "   return fname\n",
    "\n",
    "rang_log_freq('pt-words-log', get_words(pan_tadeusz))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "****Pytanie**** Dlaczego widzimy coraz dłuższe „schodki”?\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Hapax legomena\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Z poprzedniego wykresu możemy odczytać, że ok. 2/3 wyrazów wystąpiło\n",
    "dokładnie 1 raz. Słowa występujące jeden raz w danym korpusie noszą\n",
    "nazwę *hapax legomena* (w liczbie pojedynczej *hapax legomenon*, ἅπαξ\n",
    "λεγόμενον, „raz powiedziane”, żargonowo: „hapaks”).\n",
    "\n",
    "„Prawdziwe” hapax legomena, słowa, które wystąpiły tylko raz w *całym*\n",
    "korpusie tekstów danego języka (np. starożytnego) rzecz jasna\n",
    "sprawiają olbrzymie trudności w tłumaczeniu. Przykładem jest greckie\n",
    "słowo ἐπιούσιος, przydawka odnosząca się do chleba w modlitwie „Ojcze\n",
    "nasz”. Jest to jedyne poświadczenie tego słowa w całym znanym korpusie\n",
    "greki (nie tylko z Pisma Świętego). W języku polskim tłumaczymy je na\n",
    "„powszedni”, ale na przykład w rosyjskim przyjął się odpowiednik\n",
    "„насущный” — o przeciwstawnym do polskiego znaczeniu!\n",
    "\n",
    "W sumie podobne problemy hapaksy mogą sprawiać metodom statystycznym\n",
    "przy przetwarzaniu jakiekolwiek korpusu.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Wykres log-log\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Jeśli wspomniany wcześniej wykres narysujemy używając skali\n",
    "logarytmicznej dla ****obu**** osi, otrzymamy kształt zbliżony do linii prostej.\n",
    "\n",
    "Tę własność tekstów nazywamy ****prawem Zipfa****.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'02_Jezyki/pt-words-log-log.png'"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from math import log\n",
    "\n",
    "def log_rang_log_freq(name, g):\n",
    "   freq = freq_list(g)\n",
    "\n",
    "   plt.figure().clear()\n",
    "   plt.plot([log(x) for x in range(1, len(freq.values())+1)], [log(y) for y in freq.values()])\n",
    "\n",
    "   fname = f'02_Jezyki/{name}.png'\n",
    "\n",
    "   plt.savefig(fname)\n",
    "\n",
    "   return fname\n",
    "\n",
    "log_rang_log_freq('pt-words-log-log', get_words(pan_tadeusz))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Związek między frekwencją a długością\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Powiązane z prawem Zipfa prawo językowe opisuje zależność między\n",
    "częstością użycia słowa a jego długością. Generalnie im krótsze słowo, tym częstsze.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'02_Jezyki/pt-lengths.png'"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def freq_vs_length(name, g, top=None):\n",
    "    freq = freq_list(g)\n",
    "\n",
    "    plt.figure().clear()\n",
    "    plt.scatter([len(x) for x in freq.keys()], [log(y) for y in freq.values()],\n",
    "                facecolors='none', edgecolors='r')\n",
    "\n",
    "    fname = f'02_Jezyki/{name}.png'\n",
    "\n",
    "    plt.savefig(fname)\n",
    "\n",
    "    return fname\n",
    "\n",
    "freq_vs_length('pt-lengths', get_words(pan_tadeusz))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### N-gramy\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "W modelowaniu języka często rozpatruje się n-gramy, czyli podciągi o\n",
    "rozmiarze $n$.\n",
    "\n",
    "Na przykład *digramy* (*bigramy*) to zbitki dwóch jednostek, np. liter albo wyrazów.\n",
    "\n",
    "| $n$|$n$-gram|nazwa|\n",
    "|---|---|---|\n",
    "| 1|1-gram|unigram|\n",
    "| 2|2-gram|digram/bigram|\n",
    "| 3|3-gram|trigram|\n",
    "| 4|4-gram|tetragram|\n",
    "| 5|5-gram|pentagram|\n",
    "\n",
    "**Pytanie:** Jak nazywa się 6-gram?\n",
    "\n",
    "Jak widać, dla symetrii mówimy czasami o unigramach, jeśli operujemy\n",
    "po prostu na jednostkach, nie na ich podciągach.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### N-gramy z Pana Tadeusza\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Statystyki, które policzyliśmy dla pojedynczych liter czy wyrazów, możemy powtórzyć dla n-gramów.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('k', 'o', 't'), ('o', 't', 'e'), ('t', 'e', 'k')]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def ngrams(iter, size):\n",
    "  ngram = []\n",
    "  for item in iter:\n",
    "     ngram.append(item)\n",
    "     if len(ngram) == size:\n",
    "        yield tuple(ngram)\n",
    "        ngram = ngram[1:]\n",
    "\n",
    "list(ngrams(\"kotek\", 3))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Zauważmy, że policzyliśmy wszystkie n-gramy, również częściowo pokrywające się.\n",
    "\n",
    "Zawsze powinniśmy się upewnić, czy jest jasne, czy chodzi o n-gramy znakowe czy wyrazowe\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3-gramy znakowe\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'02_Jezyki/pt-3-char-ngrams-log-log.png'"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "log_rang_log_freq('pt-3-char-ngrams-log-log', ngrams(get_characters(pan_tadeusz), 3))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2-gramy wyrazowe\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'02_Jezyki/pt-2-word-ngrams-log-log.png'"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "log_rang_log_freq('pt-2-word-ngrams-log-log', ngrams(get_words(pan_tadeusz), 2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tajemniczy język Manuskryptu Wojnicza\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Manuskrypt Wojnicza](https://pl.wikipedia.org/wiki/Manuskrypt_Wojnicza) to powstały w XV w. manuskrypt spisany w\n",
    "tajemniczym alfabecie, do dzisiaj nieodszyfrowanym. Rękopis stanowi\n",
    "jedną z największych zagadek historii (i lingwistyki).\n",
    "\n",
    "![Źródło: Wikipedia Commons](./02_Jezyki/voynich135.jpg)\n",
    "\n",
    "Sami zbadajmy statystyczne własności tekstu manuskryptu. Użyjmy\n",
    "transkrypcji Vnow, gdzie poszczególne znaki tajemniczego alfabetu\n",
    "zamienione na litery alfabetu łacińskiego, cyfry i gwiazdkę. Jak\n",
    "transkrybować manuskrypt, pozostaje sprawą dyskusyjną, natomiast wybór\n",
    "takiego czy innego systemu transkrypcji nie powinien wpływać\n",
    "dramatycznie na analizę statystyczną.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'9 OR 9FAM ZO8 QOAR9 Q*R 8ARAM 29 [O82*]OM OPCC9 OP'"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import requests\n",
    "\n",
    "voynich_url = 'http://www.voynich.net/reeds/gillogly/voynich.now'\n",
    "voynich = requests.get(voynich_url).content.decode('utf-8')\n",
    "\n",
    "voynich = re.sub(r'\\{[^\\}]+\\}|^<[^>]+>|[-# ]+', '', voynich, flags=re.MULTILINE)\n",
    "\n",
    "voynich = voynich.replace('\\n\\n', '#')\n",
    "voynich = voynich.replace('\\n', ' ')\n",
    "voynich = voynich.replace('#', '\\n')\n",
    "\n",
    "voynich = voynich.replace('.', ' ')\n",
    "\n",
    "voynich[100:150]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_8747/6903746.py:14: UserWarning: Glyph 9 (\t) missing from current font.\n",
      "  plt.savefig(fname)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'02_Jezyki/voy-chars.png'"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/lib/python3.10/site-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 9 (\t) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "rang_freq_with_labels('voy-chars', get_characters(voynich))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'02_Jezyki/voy-log-log.png'"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWoAAAD4CAYAAADFAawfAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAAAcd0lEQVR4nO3deXzV5Z328c/3LNkXloRIwhLZRHYwsio4SitVtFaty9NS2zql9bFUHVttp9NOp+3Y2lZKndqp1O1x7ShaOypQcCkoKBr2fd8iwSQgBAhkvZ8/EqggISckJ7/fybnerxcvknNOjpcHcnHnPvfvvs05h4iI+FfA6wAiInJmKmoREZ9TUYuI+JyKWkTE51TUIiI+F4rGk2ZlZbn8/PxoPLWISLu0dOnSMudc9unui0pR5+fnU1hYGI2nFhFpl8xsZ2P3aepDRMTnVNQiIj6nohYR8TkVtYiIz6moRUR8TkUtIuJzKmoREZ/zVVE/+MZmVhcd9DqGiIiv+KaoD1RU8dz7u/jCHxbx+zc3U1unfbJFRMBHRd0hJYE5d1zMpEHn8Jt5m7jx4XfZvb/C61giIp7zTVFDfVn/183DmXHjMDbuPcSkGQt5vnA3OoVGROKZr4oawMy4Zngec+68mEF5mdwzaxXfenop+49UeR1NRMQTvivq47p1TOHZb4zmB5/rz5sbSrh8xkL+vrHE61giIm3Ot0UNEAwY35zQm7/efhEdU8J89fEP+PFf13C0qtbraCIibcbXRX3cgNwM/vfbF/H1cefy5Ls7mfxfb7PmQy3jE5H4EFFRm1kHM5tlZhvMbL2ZjYl2sFMlhYP8+KoBPH3rKI5U1nLNQ4t46K0tWsYnIu1epCPq3wFznXP9gaHA+uhFOrOL+mYx986LuXzQOfz6bxu1jE9E2r0mi9rMMoDxwKMAzrkq59yBKOc6ow4pCfz+5uH89sahbNx7iM/97m1e0DI+EWmnIhlR9wJKgcfNbLmZPWJmqac+yMymmlmhmRWWlpa2etDT/Pf4wvBuzLnzYgbmZvC9Wau47ellWsYnIu1OJEUdAkYA/+2cGw4cAb5/6oOcczOdcwXOuYLs7NOezxgVn1zG98aGj7h8xkIWbIr+PxQiIm0lkqIuAoqcc0saPp9FfXH7xvFlfC/fPo6OKWFueex9/l3L+ESknWiyqJ1ze4HdZnZew02XAeuimuosDczNPLGM7/9pGZ+ItBORrvqYBjxjZquAYcB9UUvUQlrGJyLtjUVjpURBQYErLCxs9edtrgMVVfzw5TW8tqqYC/M7Mv2GYXTvlOJ1LBGRTzGzpc65gtPdFxNXJp6tTy7j21Bcv4zvqXd3UHqo0utoIiIRa9cj6k8q+riCu59fyZLt+wE4LyedsX06M653FqN6dSI9KexxQhGJZ2caUcdNUQPU1TlWf3iQxVv3sXhrGe9v309lTR3BgDGkWyZje9cX94ieHUkKB72OKyJxREXdiMqaWpbtPMDirWUs2lLGyqKD1NY5EkMBCvI7MrZ3FmN7d2ZwXiahYLueJRIRj6moI3ToWDXvb9/Poi31I+4New8BkJ4YYuS5nejROYWcjCS6pCfSJT2JnIz63zOSQ5iZx+lFJJadqahDbR3Gz9KTwlx2fg6XnZ8DQNnhShZv3ce7W8v4YMfHvLdtH0dOcxFNYihAl4xEctKTyMlIok+XNAbmZjAwL5PczCSVuIi0iEbUzXS4soaS8mOUHKrko/JjlB6qPPFxSXkle8uPsXPfEY4v2+6YEmZgbiYDczMYkJvB0G4dyM/61FYpIhLnNKJuRWmJIdKy0+iVndboY45W1bJ+bzlr95Sz9sODrN1TzuOLdlBVWwfAT64awFfHndtWkUUkxqmooyA5IciIHh0Z0aPjiduqa+vYUnKYB+Zt5GevradfTjpj+2R5mFJEYoWWMrSRcDDA+V0zmHHTcHplpXL7s8t04IGIRERF3cbSEkPM/EoBNXWOqU8t1Q5/ItIkFbUHzs1K5cGbh7Nhbzn3vLhKJ9OIyBmpqD3yT+d14XuXn8crK/cwc+E2r+OIiI+pqD1024TeXDm4K/fP3aBTaUSkUSpqD5kZv/7iEPrlpDPt2WXsKDvidSQR8SEVtcdSEkL86SsFBALGFQ++zXdfWMmSbfs0by0iJ2gdtQ9075TCC98cw2OLtvPKymJmLS0iv3MK11/QjauH5pGZfPIWrIEAJIeD2ihKJE7oEnKfqaiqYe6avTxfuJv3tu0/42MTggGSE4KkJARJTgiSHK7/uEenVCae34WL+2WTlqh/i0VigXbPi1G79lWwYFMJ1bUn/xnV1jmOVtdSUVXL0aqa+t+razlaVX/b+r3lHKioJiEY4IKeHcnPSqFrZjLnZCSRmRImPSlEz86p5HVI9uj/TEROpb0+YlSPzilMGZPf7K+rqa1j6c6PeWNDCe9t28f8dSWUHT75+LFQwLj/uiFcd0G3VkorItGiom6HQsEAo3p1ZlSvziduq6yppaS8kvJj1Rw8Ws1Db23h7hdWUna4kqnje2krVhEfU1HHicRQ8KQT2C/o2ZG7n1/JL+ZsoOxwJf96xfkqaxGfiqiozWwHcAioBWoam0eR2JEYCvLgTcPpnJrAn97ezrHqOv7j6oEEAiprEb9pzoj6n5xzZVFLIm0uEDB+cvVAkhKCPLxgG5U1tfzy2iEqaxGf0dRHnDMzvj+pf/0I+43NZCSF+bfJA7yOJSKfEGlRO2CemTngYefczFMfYGZTgakAPXr0aL2EEnVmxl0T+1J+tJpH3tlOTkYS3xjfy+tYItIgonXUZpbrnNtjZl2A+cA059zCxh6vddSxqa7OMe255by2uphLzsvmMwNyyO2QTMCMgp4dSdXFMyJR0+J11M65PQ2/l5jZX4CRQKNFLbEpEDCm3ziUnp1TeHVVMT/8y5oT9/Xtksajt1xIj84pZ3gGEYmGJkfUZpYKBJxzhxo+ng/81Dk3t7Gv0Yg69jnn2FZ2hPKj1RQfPMYPXlpNMGBcPTSXvA7JhIJGh5Qw4/tm0zkt0eu4IjGvpSPqHOAvDWtsQ8CzZyppaR/MjN4NJ60PB/qfk869L67ihcLdHPnE8WFmMKJHRz47IIebLuxBZkq4kWcUkbOlvT6kWZxzlB+rwTlH0cdHeX39R7y+/iPWfFhOemKIX1w3mMlDcr2OKRJztCmTRN26PeXc9T8rqKqt4827J+gqR5FmOlNRa0NjaRUDcjO4ZWw+28uOsK643Os4Iu2KilpazeUDcwgGjNmri72OItKuqKil1XROS2R0r068uqqYLSWHvY4j0m6oqKVV3XRhD3buq2Di9AU89s52r+OItAsqamlVVw3N5Y27JzAyvxN/XLCVqpo6ryOJxDwVtbS63tlp3HZJb0oOVTJnjearRVpKRS1RMaFfNr2yUvnNvI1s+uiQ13FEYpqKWqIiEDB+/cUhHK2q44rfvc2ND7/L9rIjXscSiUkqaomaC3p2YvZ3LuIb43uxYe8hvvPccqprNWct0lwqaomqLhlJ3DupP7+8djCrPzzIF/6wiJkLt7J7f4XX0URihi4hlzbzzJKdPPf+LtZ8WH/l4pBumYzp1Zl+OelcOyJPl51LXGvxftQireFLo3rypVE92bWvgtlripm9uphH39lOTZ2j7HAl35zQ2+uIIr6kEbV4qq7OMe3Py3ltVTFfGtWDaZf25ZzMJK9jibQ5jajFtwIBY8aNw8hJT+KJxdt5vnA3Vw3J5Z8v7sWA3Ayv44n4gkbU4hu79lXw2KL6sq6oqiUhGCAzJcyjtxQwpFsHr+OJRJX2o5aYcrCimpeWF1FyqJIXlxbRISXMH798Ab0aTpwRaY9U1BKzFmwq5fZnlnG0upbBeZn8/JpBDMrL9DqWSKvTwQESsyb0y+at717CV8fms2L3Af7t5TXa6EnijopafC87PZEfTR7AA18cyordB+j/oznc+eflHKyo9jqaSJvQqg+JGddd0I2s9ERmryrmxWVFrNh9gNe+czGpifprLO2bRtQSUyb0y+b+64fw5NdHsnN/Bb+Zt9HrSCJRp6KWmDS2TxZTRvfkicU7WLbrY6/jiERVxEVtZkEzW25mr0YzkEik7pnUn3Mykrj2D4v5xZz11NW1/gomET9ozoj6DmB9tIKINFdaYohHb7mQS/t34eEF27j92WVsLdWhutL+RFTUZtYNuBJ4JLpxRJpnQG4Gj95SwL2T+jNnzV4mTl/At59dxtGqWq+jibSaSEfUM4B7gEYXsJrZVDMrNLPC0tLS1sgmEhEz47ZLejPnjou5uG82r64q5v8+s5TDlTVeRxNpFU0WtZlNBkqcc0vP9Djn3EznXIFzriA7O7vVAopE6vyuGTz59ZFMPL8Lb20s5banl7J4axk1OlVGYlwkI+pxwNVmtgP4M3CpmT0d1VQiLfDILRfyq+uH8PbmMv7Pn5bwvVmriMZWCSJtpVl7fZjZJcB3nXOTz/Q47fUhfrC97Aj/88Fu/rhgK326pDHvzvEEAjpFRvxJe31IXDo3K5V7Lj+Pa4fnsaXkMIu37vM6kshZaVZRO+f+3tRoWsRPAgHjvmsH0zUziS8/uoTp8zayYvcBr2OJNItG1NLuJYWDPHXrSHp0SuHBN7dwzUOLmPLoEtbtKfc6mkhEtJuNxIU+XdKZ/y/jWbunnAff2MzfN5YC63nq1lFeRxNpkkbUEjcSQ0FG9OjIE18byVfG9OTtzWX8/NV1uvRcfE9FLXFp2qV96ZWVyiPv1J/RKOJnKmqJS9npifztrvHkdUjmhaVFXscROSMVtcStcDDAVUNzWbrzY15f95HXcUQapaKWuHbzyO4APPXeTo+TiDRORS1xrWfnVK4amsuCTaX8+f1drNx9gGPV2nlP/EXL8yTu3XP5ecxdU8z3X1oNwJWDu3LDhd0ZmJtBVlqix+lEVNQidO+Uwpt3X8KWksM8X7ib11YX89rqYrqkJ/Ly7ePI7ZDsdUSJc83alClS2pRJYtWx6lrWFZczd81eZi7cRkZSiLl3jldZS9RpUyaRCCWF6y+KuXdSf6aM7kn5sRq+8WQh64t1ubl4R0UtchrBgPGTqwdy88gerN1Tzud/v4iDFdVex5I4paIWaUQwYPzs8wP53U3DqKqt47kPdulyc/GEilrkDELBAJOH5NIxJcwv52xgxhubvY4kcUhFLdKEYMB46tZRJIeDPPjGZsoOV3odSeKMilokAoPyMvnPLwwC4BezN3icRuKNilokQtcMyyMzOcwbGz7i315ezY//uoaNew95HUvigIpaJEKBgPGr64cQDgaYs3ovT7+3k/tmr2fe2r3sP1LldTxpx3RlokgzXD7wHC4feA4A33pqKXPX7mXBplKuHZHH9BuGeRtO2i2NqEXO0vQbh/LqtIsY16czLy37kH4/nMMjb2/zOpa0QxpRi5yllIQQg/Iy+eEVA3h11R7+umIPTyzewY59R+iUksAdE/sRDJjXMaUdaLKozSwJWAgkNjx+lnPu36MdTCRWDMjNYEBuBulJYR55ext/XbGHQ8dqGNq9A2N6dyYlQeMhaZkmN2UyMwNSnXOHzSwMvAPc4Zx7r7Gv0aZMEs+2lR7m0gcWAND/nHTm3jne40QSC1q0KZOrd7jh03DDL11HK9KIXtlpPPbVAj436Bw27D3EY+9s9zqSxLiI3kw0s6CZrQBKgPnOuSWnecxUMys0s8LS0tJWjikSWy7tn8PXLzoXgJ+9tk57hEiLRFTUzrla59wwoBsw0swGneYxM51zBc65guzs7FaOKRJ7LszvxH1fGIxz0P/Hc1m6c7/XkSRGNWt5nnPuAPB3YFI0woi0N1cO7sodl/WlqqaOJxbrAF05O00WtZllm1mHho+TgYmANjsQiUBmSpg7J/YF4JWVeyj6uMLjRBKLIhlRdwXeMrNVwAfUz1G/Gt1YIu2HmTHjxmEA/Hb+Zp4v3O1tIIk5TS7wdM6tAoa3QRaRdmtcnyw6pSbw0vIiXlxWxJWDu5KaqPXVEhldQi7SBrLTE1n2o8/wwBeHAnDPrFXcN3s9NbV1HieTWKB/0kXa0LDuHeiVncq72/ax/0gVVw/NZVBeptexxOdU1CJtqFd2Gm/efQmFO/Zz/R/f5bfzN9ElI4l+OWl8bdy5XscTn1JRi3igT5c0+nRJY9WHB6nYto8jVbVMGd2TUFCzkfJpKmoRD3RISeD1f5kAwBOLtvOTV9bx8MJtJIWDJIYCXDeiG8kJQY9Til+oqEU81i8nHTP49d82nritU2oCVwzu6mEq8RMVtYjHxvbJYs1PLqemzlF2uJLLHljA9rIjHKyoJjMl7HU88QFNiIn4QGpiiMzkMHkdkgkGjF//bSMX/Hw+ew8e8zqa+ICKWsRHksJBnvr6SG696Fxq6hzFB496HUl8QEUt4jNj+2QxaVD9AbozXt/MvbNWsbrooMepxEuaoxbxod7ZafTLSWPTR4d4e3MpCaEAg7vpwph4paIW8aFOqQnMu6t++d5F97/JkaoajxOJl1TUIj6XmhDitVXFvLt1H8N7dOAPX7rA60jSxjRHLeJz0y7rw+eH5ZKZHObvG3XMXTxSUYv43OQhufzq+qF8dkAOR6trcU7nL8YbFbVIjEhOCOEcXPifrzPqvtd5c8NHXkeSNqI5apEYMXlIV4oPHqWmzvHskl2s2HWAS/vneB1L2oCKWiRGdO+Uwk8/PwiAF5cWUVmjQwfihYpaJAYlhYO8uaGEAxXVACQnBLlrYj/tDdJOqahFYtCEftm8v30/CzaVUlNXR9nhKkb36sSkQdpxrz1SUYvEoAdv/sd507v3V3Dxr97i0DFdFNNeadWHSIxLT6ofbx2uVFG3V00WtZl1N7O3zGy9ma01szvaIpiIRCY1sb6o75+7geE/nceIn83nxaVFHqeS1hTJiLoGuNs5dz4wGrjdzAZEN5aIRCocDPAfVw/khoLuXDU0l6NVtSzd9bHXsaQVNTlH7ZwrBoobPj5kZuuBPGBdlLOJSIRuGZt/4uO3NpZwtKrWuzDS6po1R21m+cBwYMlp7ptqZoVmVlhaqv0IRLySEg5Rod322pWIV32YWRrwInCnc6781PudczOBmQAFBQXajEDEI8kJQZZs38/NM987cVtWeiLTbxhKOKj1A7Eooj81MwtTX9LPOOdeim4kEWmJ60bk0a9LOrV1jto6x0flx3hl5R6dvxjDmhxRm5kBjwLrnXPTox9JRFpiyph8pozJP/H5Kyv3MO255Ryr1rx1rIpkRD0OmAJcamYrGn5dEeVcItJKEkP13+baGyR2RbLq4x3A2iCLiERBUjgIoBF1DNMl5CLt3PGi/trjHxAOnfxD9F0T+540TSL+pKIWaeeGdMvktkt6c/iUvUBeXv4hy3YdYMoYj4JJxFTUIu1cUjjIvZP6f+r2xVvLqKrVvHUs0KJKkTgVDgao1huMMUFFLRKnEkIBjahjhKY+ROJUOBjg4yNVrNh94FP3dc1MIicjqe1DyWmpqEXiVEZSiLc2lnLNQ4s+dV+n1ASW/egzHqSS01FRi8Sp+68bwto9n9q2hxeXFTF7dbEHiaQxKmqRONUlI4kup5neWLH7AHUO6uocgYCudfMDvZkoIicJB+vLudZpE0y/UFGLyEmCgfpaqK1TUfuFilpEThJqmO6oUVH7huaoReQkwYaiLtyx/8QJ56fqnZ1Gh5SEtowV11TUInKS4+X81cc/aPQx4/p05pl/Ht1WkeKeilpETnLN8Dy6dUyhupGrFh+Yv4mDR6vbOFV8U1GLyEnCwQBjendu9P6n3tvJ7v0VbZhI9GaiiDRLKGBaEdLGVNQi0ixBFXWbU1GLSLOEAqale21MRS0izRIMBDSibmMqahFplvoRtfaxbkta9SEizRIMGseq61i26+MmH9spJYH8rNQ2SNW+NVnUZvYYMBkocc4Nin4kEfGz9KQQB49Wc+0fFjf52IDBsh99RlcxtlAkI+ongN8DT0Y3iojEgmmX9mVs7yxcE7vrLdxUxmOLtnO4skZF3UJNFrVzbqGZ5bdBFhGJAWmJISb0y27ycaWHKgHQbqktpzcTRSQqjm/upBUiLddqRW1mU82s0MwKS0tLW+tpRSRGnShqDalbrNWK2jk30zlX4JwryM5u+sciEWnfAlZf1HUaUbeYpj5EJCqOj6jV0y3XZFGb2XPAu8B5ZlZkZrdGP5aIxLrj5+JqjrrlIln1cXNbBBGR9uXE1IfmqFtMUx8iEhVa9dF6VNQiEhWBgEbUrUVFLSJRoamP1qNNmUQkKoINRf3kuzuZv67krJ4jr2MyU0b3bM1YMUlFLSJR0b1TMh1TwsxZs/esvr62zlFb57hmWC7pSeFWThdbVNQiEhU9O6ey/MefPeuvf+yd7fz01XVo62vNUYuITx1fh+3QHLeKWkR8yUxXNh6nohYRXzoxotaqERW1iPiURtQnqKhFxJc0ov4HFbWI+JJR39SqaRW1iPjU8RG1rmxUUYuIT9mJqQ9vc/iBilpEfMm0V8gJKmoR8aWGAbVG1KioRcSnju++p6JWUYuIT5neTDxBRS0ivnRiRO1xDj9QUYuIL2lE/Q8qahHxJdMc9QkqahHxJV1C/g8qahHxpeOXkGtTpgiL2swmmdlGM9tiZt+PdigRER0c8A9NFrWZBYGHgM8BA4CbzWxAtIOJSHw78WaijuKK6MzEkcAW59w2ADP7M/B5YF00g4lIfDv+ZuK3nl5KYig2Zmk7piTw/LfGtPrzRlLUecDuT3xeBIw69UFmNhWYCtCjR49WCSci8euCnh25dkQex6prvY4SsYwonZYeSVHbaW771KSRc24mMBOgoKBAk0oi0iJZaYlMv2GY1zF8IZKfJ4qA7p/4vBuwJzpxRETkVJEU9QdAXzM718wSgJuA/41uLBEROa7JqQ/nXI2ZfRv4GxAEHnPOrY16MhERASKbo8Y5NxuYHeUsIiJyGrGx5kVEJI6pqEVEfE5FLSLicypqERGfs2hsIWhmpcDOs/zyLKCsFeO0F3pdGqfX5vT0ujTOj69NT+dc9unuiEpRt4SZFTrnCrzO4Td6XRqn1+b09Lo0LtZeG019iIj4nIpaRMTn/FjUM70O4FN6XRqn1+b09Lo0LqZeG9/NUYuIyMn8OKIWEZFPUFGLiPicb4paB+ienpl1N7O3zGy9ma01szu8zuQnZhY0s+Vm9qrXWfzEzDqY2Swz29Dwd6f1z4eKQWZ2V8P30Roze87MkrzOFAlfFLUO0D2jGuBu59z5wGjgdr02J7kDWO91CB/6HTDXOdcfGIpeI8wsD/gOUOCcG0T9ts03eZsqMr4oaj5xgK5zrgo4foBu3HPOFTvnljV8fIj6b7g8b1P5g5l1A64EHvE6i5+YWQYwHngUwDlX5Zw74Gko/wgByWYWAlKIkdOq/FLUpztAV2V0CjPLB4YDSzyO4hczgHuAOo9z+E0voBR4vGFa6BEzS/U6lNeccx8CvwF2AcXAQefcPG9TRcYvRR3RAbrxzMzSgBeBO51z5V7n8ZqZTQZKnHNLvc7iQyFgBPDfzrnhwBEg7t/3MbOO1P+kfi6QC6Sa2Ze9TRUZvxS1DtA9AzMLU1/SzzjnXvI6j0+MA642sx3UT5VdamZPexvJN4qAIufc8Z+8ZlFf3PFuIrDdOVfqnKsGXgLGepwpIn4pah2g2wgzM+rnGtc756Z7nccvnHM/cM51c87lU//35U3nXEyMjqLNObcX2G1m5zXcdBmwzsNIfrELGG1mKQ3fV5cRI2+yRnRmYrTpAN0zGgdMAVab2YqG2/614RxLkcZMA55pGPhsA77mcR7POeeWmNksYBn1q6mWEyOXkusSchERn/PL1IeIiDRCRS0i4nMqahERn1NRi4j4nIpaRMTnVNQiIj6nohYR8bn/D2zTRLc1/1QcAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "log_rang_log_freq('voy-log-log', get_words(voynich))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'02_Jezyki/voy-words-20.png'"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "rang_freq_with_labels('voy-words-20', get_words(voynich), top=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'02_Jezyki/voy-words-log-log.png'"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWoAAAD4CAYAAADFAawfAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAAAcd0lEQVR4nO3deXzV5Z328c/3LNkXloRIwhLZRHYwsio4SitVtFaty9NS2zql9bFUHVttp9NOp+3Y2lZKndqp1O1x7ShaOypQcCkoKBr2fd8iwSQgBAhkvZ8/EqggISckJ7/fybnerxcvknNOjpcHcnHnPvfvvs05h4iI+FfA6wAiInJmKmoREZ9TUYuI+JyKWkTE51TUIiI+F4rGk2ZlZbn8/PxoPLWISLu0dOnSMudc9unui0pR5+fnU1hYGI2nFhFpl8xsZ2P3aepDRMTnVNQiIj6nohYR8TkVtYiIz6moRUR8TkUtIuJzKmoREZ/zVVE/+MZmVhcd9DqGiIiv+KaoD1RU8dz7u/jCHxbx+zc3U1unfbJFRMBHRd0hJYE5d1zMpEHn8Jt5m7jx4XfZvb/C61giIp7zTVFDfVn/183DmXHjMDbuPcSkGQt5vnA3OoVGROKZr4oawMy4Zngec+68mEF5mdwzaxXfenop+49UeR1NRMQTvivq47p1TOHZb4zmB5/rz5sbSrh8xkL+vrHE61giIm3Ot0UNEAwY35zQm7/efhEdU8J89fEP+PFf13C0qtbraCIibcbXRX3cgNwM/vfbF/H1cefy5Ls7mfxfb7PmQy3jE5H4EFFRm1kHM5tlZhvMbL2ZjYl2sFMlhYP8+KoBPH3rKI5U1nLNQ4t46K0tWsYnIu1epCPq3wFznXP9gaHA+uhFOrOL+mYx986LuXzQOfz6bxu1jE9E2r0mi9rMMoDxwKMAzrkq59yBKOc6ow4pCfz+5uH89sahbNx7iM/97m1e0DI+EWmnIhlR9wJKgcfNbLmZPWJmqac+yMymmlmhmRWWlpa2etDT/Pf4wvBuzLnzYgbmZvC9Wau47ellWsYnIu1OJEUdAkYA/+2cGw4cAb5/6oOcczOdcwXOuYLs7NOezxgVn1zG98aGj7h8xkIWbIr+PxQiIm0lkqIuAoqcc0saPp9FfXH7xvFlfC/fPo6OKWFueex9/l3L+ESknWiyqJ1ze4HdZnZew02XAeuimuosDczNPLGM7/9pGZ+ItBORrvqYBjxjZquAYcB9UUvUQlrGJyLtjUVjpURBQYErLCxs9edtrgMVVfzw5TW8tqqYC/M7Mv2GYXTvlOJ1LBGRTzGzpc65gtPdFxNXJp6tTy7j21Bcv4zvqXd3UHqo0utoIiIRa9cj6k8q+riCu59fyZLt+wE4LyedsX06M653FqN6dSI9KexxQhGJZ2caUcdNUQPU1TlWf3iQxVv3sXhrGe9v309lTR3BgDGkWyZje9cX94ieHUkKB72OKyJxREXdiMqaWpbtPMDirWUs2lLGyqKD1NY5EkMBCvI7MrZ3FmN7d2ZwXiahYLueJRIRj6moI3ToWDXvb9/Poi31I+4New8BkJ4YYuS5nejROYWcjCS6pCfSJT2JnIz63zOSQ5iZx+lFJJadqahDbR3Gz9KTwlx2fg6XnZ8DQNnhShZv3ce7W8v4YMfHvLdtH0dOcxFNYihAl4xEctKTyMlIok+XNAbmZjAwL5PczCSVuIi0iEbUzXS4soaS8mOUHKrko/JjlB6qPPFxSXkle8uPsXPfEY4v2+6YEmZgbiYDczMYkJvB0G4dyM/61FYpIhLnNKJuRWmJIdKy0+iVndboY45W1bJ+bzlr95Sz9sODrN1TzuOLdlBVWwfAT64awFfHndtWkUUkxqmooyA5IciIHh0Z0aPjiduqa+vYUnKYB+Zt5GevradfTjpj+2R5mFJEYoWWMrSRcDDA+V0zmHHTcHplpXL7s8t04IGIRERF3cbSEkPM/EoBNXWOqU8t1Q5/ItIkFbUHzs1K5cGbh7Nhbzn3vLhKJ9OIyBmpqD3yT+d14XuXn8crK/cwc+E2r+OIiI+pqD1024TeXDm4K/fP3aBTaUSkUSpqD5kZv/7iEPrlpDPt2WXsKDvidSQR8SEVtcdSEkL86SsFBALGFQ++zXdfWMmSbfs0by0iJ2gdtQ9075TCC98cw2OLtvPKymJmLS0iv3MK11/QjauH5pGZfPIWrIEAJIeD2ihKJE7oEnKfqaiqYe6avTxfuJv3tu0/42MTggGSE4KkJARJTgiSHK7/uEenVCae34WL+2WTlqh/i0VigXbPi1G79lWwYFMJ1bUn/xnV1jmOVtdSUVXL0aqa+t+razlaVX/b+r3lHKioJiEY4IKeHcnPSqFrZjLnZCSRmRImPSlEz86p5HVI9uj/TEROpb0+YlSPzilMGZPf7K+rqa1j6c6PeWNDCe9t28f8dSWUHT75+LFQwLj/uiFcd0G3VkorItGiom6HQsEAo3p1ZlSvziduq6yppaS8kvJj1Rw8Ws1Db23h7hdWUna4kqnje2krVhEfU1HHicRQ8KQT2C/o2ZG7n1/JL+ZsoOxwJf96xfkqaxGfiqiozWwHcAioBWoam0eR2JEYCvLgTcPpnJrAn97ezrHqOv7j6oEEAiprEb9pzoj6n5xzZVFLIm0uEDB+cvVAkhKCPLxgG5U1tfzy2iEqaxGf0dRHnDMzvj+pf/0I+43NZCSF+bfJA7yOJSKfEGlRO2CemTngYefczFMfYGZTgakAPXr0aL2EEnVmxl0T+1J+tJpH3tlOTkYS3xjfy+tYItIgonXUZpbrnNtjZl2A+cA059zCxh6vddSxqa7OMe255by2uphLzsvmMwNyyO2QTMCMgp4dSdXFMyJR0+J11M65PQ2/l5jZX4CRQKNFLbEpEDCm3ziUnp1TeHVVMT/8y5oT9/Xtksajt1xIj84pZ3gGEYmGJkfUZpYKBJxzhxo+ng/81Dk3t7Gv0Yg69jnn2FZ2hPKj1RQfPMYPXlpNMGBcPTSXvA7JhIJGh5Qw4/tm0zkt0eu4IjGvpSPqHOAvDWtsQ8CzZyppaR/MjN4NJ60PB/qfk869L67ihcLdHPnE8WFmMKJHRz47IIebLuxBZkq4kWcUkbOlvT6kWZxzlB+rwTlH0cdHeX39R7y+/iPWfFhOemKIX1w3mMlDcr2OKRJztCmTRN26PeXc9T8rqKqt4827J+gqR5FmOlNRa0NjaRUDcjO4ZWw+28uOsK643Os4Iu2KilpazeUDcwgGjNmri72OItKuqKil1XROS2R0r068uqqYLSWHvY4j0m6oqKVV3XRhD3buq2Di9AU89s52r+OItAsqamlVVw3N5Y27JzAyvxN/XLCVqpo6ryOJxDwVtbS63tlp3HZJb0oOVTJnjearRVpKRS1RMaFfNr2yUvnNvI1s+uiQ13FEYpqKWqIiEDB+/cUhHK2q44rfvc2ND7/L9rIjXscSiUkqaomaC3p2YvZ3LuIb43uxYe8hvvPccqprNWct0lwqaomqLhlJ3DupP7+8djCrPzzIF/6wiJkLt7J7f4XX0URihi4hlzbzzJKdPPf+LtZ8WH/l4pBumYzp1Zl+OelcOyJPl51LXGvxftQireFLo3rypVE92bWvgtlripm9uphH39lOTZ2j7HAl35zQ2+uIIr6kEbV4qq7OMe3Py3ltVTFfGtWDaZf25ZzMJK9jibQ5jajFtwIBY8aNw8hJT+KJxdt5vnA3Vw3J5Z8v7sWA3Ayv44n4gkbU4hu79lXw2KL6sq6oqiUhGCAzJcyjtxQwpFsHr+OJRJX2o5aYcrCimpeWF1FyqJIXlxbRISXMH798Ab0aTpwRaY9U1BKzFmwq5fZnlnG0upbBeZn8/JpBDMrL9DqWSKvTwQESsyb0y+at717CV8fms2L3Af7t5TXa6EnijopafC87PZEfTR7AA18cyordB+j/oznc+eflHKyo9jqaSJvQqg+JGddd0I2s9ERmryrmxWVFrNh9gNe+czGpifprLO2bRtQSUyb0y+b+64fw5NdHsnN/Bb+Zt9HrSCJRp6KWmDS2TxZTRvfkicU7WLbrY6/jiERVxEVtZkEzW25mr0YzkEik7pnUn3Mykrj2D4v5xZz11NW1/gomET9ozoj6DmB9tIKINFdaYohHb7mQS/t34eEF27j92WVsLdWhutL+RFTUZtYNuBJ4JLpxRJpnQG4Gj95SwL2T+jNnzV4mTl/At59dxtGqWq+jibSaSEfUM4B7gEYXsJrZVDMrNLPC0tLS1sgmEhEz47ZLejPnjou5uG82r64q5v8+s5TDlTVeRxNpFU0WtZlNBkqcc0vP9Djn3EznXIFzriA7O7vVAopE6vyuGTz59ZFMPL8Lb20s5banl7J4axk1OlVGYlwkI+pxwNVmtgP4M3CpmT0d1VQiLfDILRfyq+uH8PbmMv7Pn5bwvVmriMZWCSJtpVl7fZjZJcB3nXOTz/Q47fUhfrC97Aj/88Fu/rhgK326pDHvzvEEAjpFRvxJe31IXDo3K5V7Lj+Pa4fnsaXkMIu37vM6kshZaVZRO+f+3tRoWsRPAgHjvmsH0zUziS8/uoTp8zayYvcBr2OJNItG1NLuJYWDPHXrSHp0SuHBN7dwzUOLmPLoEtbtKfc6mkhEtJuNxIU+XdKZ/y/jWbunnAff2MzfN5YC63nq1lFeRxNpkkbUEjcSQ0FG9OjIE18byVfG9OTtzWX8/NV1uvRcfE9FLXFp2qV96ZWVyiPv1J/RKOJnKmqJS9npifztrvHkdUjmhaVFXscROSMVtcStcDDAVUNzWbrzY15f95HXcUQapaKWuHbzyO4APPXeTo+TiDRORS1xrWfnVK4amsuCTaX8+f1drNx9gGPV2nlP/EXL8yTu3XP5ecxdU8z3X1oNwJWDu3LDhd0ZmJtBVlqix+lEVNQidO+Uwpt3X8KWksM8X7ib11YX89rqYrqkJ/Ly7ePI7ZDsdUSJc83alClS2pRJYtWx6lrWFZczd81eZi7cRkZSiLl3jldZS9RpUyaRCCWF6y+KuXdSf6aM7kn5sRq+8WQh64t1ubl4R0UtchrBgPGTqwdy88gerN1Tzud/v4iDFdVex5I4paIWaUQwYPzs8wP53U3DqKqt47kPdulyc/GEilrkDELBAJOH5NIxJcwv52xgxhubvY4kcUhFLdKEYMB46tZRJIeDPPjGZsoOV3odSeKMilokAoPyMvnPLwwC4BezN3icRuKNilokQtcMyyMzOcwbGz7i315ezY//uoaNew95HUvigIpaJEKBgPGr64cQDgaYs3ovT7+3k/tmr2fe2r3sP1LldTxpx3RlokgzXD7wHC4feA4A33pqKXPX7mXBplKuHZHH9BuGeRtO2i2NqEXO0vQbh/LqtIsY16czLy37kH4/nMMjb2/zOpa0QxpRi5yllIQQg/Iy+eEVA3h11R7+umIPTyzewY59R+iUksAdE/sRDJjXMaUdaLKozSwJWAgkNjx+lnPu36MdTCRWDMjNYEBuBulJYR55ext/XbGHQ8dqGNq9A2N6dyYlQeMhaZkmN2UyMwNSnXOHzSwMvAPc4Zx7r7Gv0aZMEs+2lR7m0gcWAND/nHTm3jne40QSC1q0KZOrd7jh03DDL11HK9KIXtlpPPbVAj436Bw27D3EY+9s9zqSxLiI3kw0s6CZrQBKgPnOuSWnecxUMys0s8LS0tJWjikSWy7tn8PXLzoXgJ+9tk57hEiLRFTUzrla59wwoBsw0swGneYxM51zBc65guzs7FaOKRJ7LszvxH1fGIxz0P/Hc1m6c7/XkSRGNWt5nnPuAPB3YFI0woi0N1cO7sodl/WlqqaOJxbrAF05O00WtZllm1mHho+TgYmANjsQiUBmSpg7J/YF4JWVeyj6uMLjRBKLIhlRdwXeMrNVwAfUz1G/Gt1YIu2HmTHjxmEA/Hb+Zp4v3O1tIIk5TS7wdM6tAoa3QRaRdmtcnyw6pSbw0vIiXlxWxJWDu5KaqPXVEhldQi7SBrLTE1n2o8/wwBeHAnDPrFXcN3s9NbV1HieTWKB/0kXa0LDuHeiVncq72/ax/0gVVw/NZVBeptexxOdU1CJtqFd2Gm/efQmFO/Zz/R/f5bfzN9ElI4l+OWl8bdy5XscTn1JRi3igT5c0+nRJY9WHB6nYto8jVbVMGd2TUFCzkfJpKmoRD3RISeD1f5kAwBOLtvOTV9bx8MJtJIWDJIYCXDeiG8kJQY9Til+oqEU81i8nHTP49d82nritU2oCVwzu6mEq8RMVtYjHxvbJYs1PLqemzlF2uJLLHljA9rIjHKyoJjMl7HU88QFNiIn4QGpiiMzkMHkdkgkGjF//bSMX/Hw+ew8e8zqa+ICKWsRHksJBnvr6SG696Fxq6hzFB496HUl8QEUt4jNj+2QxaVD9AbozXt/MvbNWsbrooMepxEuaoxbxod7ZafTLSWPTR4d4e3MpCaEAg7vpwph4paIW8aFOqQnMu6t++d5F97/JkaoajxOJl1TUIj6XmhDitVXFvLt1H8N7dOAPX7rA60jSxjRHLeJz0y7rw+eH5ZKZHObvG3XMXTxSUYv43OQhufzq+qF8dkAOR6trcU7nL8YbFbVIjEhOCOEcXPifrzPqvtd5c8NHXkeSNqI5apEYMXlIV4oPHqWmzvHskl2s2HWAS/vneB1L2oCKWiRGdO+Uwk8/PwiAF5cWUVmjQwfihYpaJAYlhYO8uaGEAxXVACQnBLlrYj/tDdJOqahFYtCEftm8v30/CzaVUlNXR9nhKkb36sSkQdpxrz1SUYvEoAdv/sd507v3V3Dxr97i0DFdFNNeadWHSIxLT6ofbx2uVFG3V00WtZl1N7O3zGy9ma01szvaIpiIRCY1sb6o75+7geE/nceIn83nxaVFHqeS1hTJiLoGuNs5dz4wGrjdzAZEN5aIRCocDPAfVw/khoLuXDU0l6NVtSzd9bHXsaQVNTlH7ZwrBoobPj5kZuuBPGBdlLOJSIRuGZt/4uO3NpZwtKrWuzDS6po1R21m+cBwYMlp7ptqZoVmVlhaqv0IRLySEg5Rod322pWIV32YWRrwInCnc6781PudczOBmQAFBQXajEDEI8kJQZZs38/NM987cVtWeiLTbxhKOKj1A7Eooj81MwtTX9LPOOdeim4kEWmJ60bk0a9LOrV1jto6x0flx3hl5R6dvxjDmhxRm5kBjwLrnXPTox9JRFpiyph8pozJP/H5Kyv3MO255Ryr1rx1rIpkRD0OmAJcamYrGn5dEeVcItJKEkP13+baGyR2RbLq4x3A2iCLiERBUjgIoBF1DNMl5CLt3PGi/trjHxAOnfxD9F0T+540TSL+pKIWaeeGdMvktkt6c/iUvUBeXv4hy3YdYMoYj4JJxFTUIu1cUjjIvZP6f+r2xVvLqKrVvHUs0KJKkTgVDgao1huMMUFFLRKnEkIBjahjhKY+ROJUOBjg4yNVrNh94FP3dc1MIicjqe1DyWmpqEXiVEZSiLc2lnLNQ4s+dV+n1ASW/egzHqSS01FRi8Sp+68bwto9n9q2hxeXFTF7dbEHiaQxKmqRONUlI4kup5neWLH7AHUO6uocgYCudfMDvZkoIicJB+vLudZpE0y/UFGLyEmCgfpaqK1TUfuFilpEThJqmO6oUVH7huaoReQkwYaiLtyx/8QJ56fqnZ1Gh5SEtowV11TUInKS4+X81cc/aPQx4/p05pl/Ht1WkeKeilpETnLN8Dy6dUyhupGrFh+Yv4mDR6vbOFV8U1GLyEnCwQBjendu9P6n3tvJ7v0VbZhI9GaiiDRLKGBaEdLGVNQi0ixBFXWbU1GLSLOEAqale21MRS0izRIMBDSibmMqahFplvoRtfaxbkta9SEizRIMGseq61i26+MmH9spJYH8rNQ2SNW+NVnUZvYYMBkocc4Nin4kEfGz9KQQB49Wc+0fFjf52IDBsh99RlcxtlAkI+ongN8DT0Y3iojEgmmX9mVs7yxcE7vrLdxUxmOLtnO4skZF3UJNFrVzbqGZ5bdBFhGJAWmJISb0y27ycaWHKgHQbqktpzcTRSQqjm/upBUiLddqRW1mU82s0MwKS0tLW+tpRSRGnShqDalbrNWK2jk30zlX4JwryM5u+sciEWnfAlZf1HUaUbeYpj5EJCqOj6jV0y3XZFGb2XPAu8B5ZlZkZrdGP5aIxLrj5+JqjrrlIln1cXNbBBGR9uXE1IfmqFtMUx8iEhVa9dF6VNQiEhWBgEbUrUVFLSJRoamP1qNNmUQkKoINRf3kuzuZv67krJ4jr2MyU0b3bM1YMUlFLSJR0b1TMh1TwsxZs/esvr62zlFb57hmWC7pSeFWThdbVNQiEhU9O6ey/MefPeuvf+yd7fz01XVo62vNUYuITx1fh+3QHLeKWkR8yUxXNh6nohYRXzoxotaqERW1iPiURtQnqKhFxJc0ov4HFbWI+JJR39SqaRW1iPjU8RG1rmxUUYuIT9mJqQ9vc/iBilpEfMm0V8gJKmoR8aWGAbVG1KioRcSnju++p6JWUYuIT5neTDxBRS0ivnRiRO1xDj9QUYuIL2lE/Q8qahHxJdMc9QkqahHxJV1C/g8qahHxpeOXkGtTpgiL2swmmdlGM9tiZt+PdigRER0c8A9NFrWZBYGHgM8BA4CbzWxAtIOJSHw78WaijuKK6MzEkcAW59w2ADP7M/B5YF00g4lIfDv+ZuK3nl5KYig2Zmk7piTw/LfGtPrzRlLUecDuT3xeBIw69UFmNhWYCtCjR49WCSci8euCnh25dkQex6prvY4SsYwonZYeSVHbaW771KSRc24mMBOgoKBAk0oi0iJZaYlMv2GY1zF8IZKfJ4qA7p/4vBuwJzpxRETkVJEU9QdAXzM718wSgJuA/41uLBEROa7JqQ/nXI2ZfRv4GxAEHnPOrY16MhERASKbo8Y5NxuYHeUsIiJyGrGx5kVEJI6pqEVEfE5FLSLicypqERGfs2hsIWhmpcDOs/zyLKCsFeO0F3pdGqfX5vT0ujTOj69NT+dc9unuiEpRt4SZFTrnCrzO4Td6XRqn1+b09Lo0LtZeG019iIj4nIpaRMTn/FjUM70O4FN6XRqn1+b09Lo0LqZeG9/NUYuIyMn8OKIWEZFPUFGLiPicb4paB+ienpl1N7O3zGy9ma01szu8zuQnZhY0s+Vm9qrXWfzEzDqY2Swz29Dwd6f1z4eKQWZ2V8P30Roze87MkrzOFAlfFLUO0D2jGuBu59z5wGjgdr02J7kDWO91CB/6HTDXOdcfGIpeI8wsD/gOUOCcG0T9ts03eZsqMr4oaj5xgK5zrgo4foBu3HPOFTvnljV8fIj6b7g8b1P5g5l1A64EHvE6i5+YWQYwHngUwDlX5Zw74Gko/wgByWYWAlKIkdOq/FLUpztAV2V0CjPLB4YDSzyO4hczgHuAOo9z+E0voBR4vGFa6BEzS/U6lNeccx8CvwF2AcXAQefcPG9TRcYvRR3RAbrxzMzSgBeBO51z5V7n8ZqZTQZKnHNLvc7iQyFgBPDfzrnhwBEg7t/3MbOO1P+kfi6QC6Sa2Ze9TRUZvxS1DtA9AzMLU1/SzzjnXvI6j0+MA642sx3UT5VdamZPexvJN4qAIufc8Z+8ZlFf3PFuIrDdOVfqnKsGXgLGepwpIn4pah2g2wgzM+rnGtc756Z7nccvnHM/cM51c87lU//35U3nXEyMjqLNObcX2G1m5zXcdBmwzsNIfrELGG1mKQ3fV5cRI2+yRnRmYrTpAN0zGgdMAVab2YqG2/614RxLkcZMA55pGPhsA77mcR7POeeWmNksYBn1q6mWEyOXkusSchERn/PL1IeIiDRCRS0i4nMqahERn1NRi4j4nIpaRMTnVNQiIj6nohYR8bn/D2zTRLc1/1QcAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "log_rang_log_freq('voy-words-log-log', get_words(voynich))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Język DNA\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Kod genetyczny przejawia własności zaskakująco podobne do języków naturalnych.\n",
    "Przede wszystkim ma charakter dyskretny, genotyp to ciąg symboli ze skończonego alfabetu.\n",
    "Podstawowe litery są tylko cztery, reprezentują one nukleotydy, z których zbudowana jest nić DNA:\n",
    "a, g, c, t.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'TATAACCCTAACCCTAACCCTAACCCTAACCCTAACCCTAACCCTAACCCTAACCCTAACCCTAACCCTAACCCTAACCCTAACCCTAACCCTAACCCTA'"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import requests\n",
    "\n",
    "dna_url = 'https://raw.githubusercontent.com/egreen18/NanO_GEM/master/rawGenome.txt'\n",
    "dna = requests.get(dna_url).content.decode('utf-8')\n",
    "\n",
    "dna = ''.join(dna.split('\\n')[1:])\n",
    "dna = dna.replace('N', 'A')\n",
    "\n",
    "dna[0:100]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'02_Jezyki/dna-chars.png'"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "rang_freq_with_labels('dna-chars', get_characters(dna))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Tryplety — znaczące cząstki genotypu\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nukleotydy rzeczywiście są jak litery, same w sobie nie niosą\n",
    "znaczenia. Dopiero ciągi trzech nukleotydów, *tryplety*, kodują jeden\n",
    "z dwudziestu aminokwasów.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'02_Jezyki/dna-aminos.png'"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "genetic_code = {\n",
    "     'ATA':'I', 'ATC':'I', 'ATT':'I', 'ATG':'M',\n",
    "     'ACA':'T', 'ACC':'T', 'ACG':'T', 'ACT':'T',\n",
    "     'AAC':'N', 'AAT':'N', 'AAA':'K', 'AAG':'K',\n",
    "     'AGC':'S', 'AGT':'S', 'AGA':'R', 'AGG':'R',\n",
    "     'CTA':'L', 'CTC':'L', 'CTG':'L', 'CTT':'L',\n",
    "     'CCA':'P', 'CCC':'P', 'CCG':'P', 'CCT':'P',\n",
    "     'CAC':'H', 'CAT':'H', 'CAA':'Q', 'CAG':'Q',\n",
    "     'CGA':'R', 'CGC':'R', 'CGG':'R', 'CGT':'R',\n",
    "     'GTA':'V', 'GTC':'V', 'GTG':'V', 'GTT':'V',\n",
    "     'GCA':'A', 'GCC':'A', 'GCG':'A', 'GCT':'A',\n",
    "     'GAC':'D', 'GAT':'D', 'GAA':'E', 'GAG':'E',\n",
    "     'GGA':'G', 'GGC':'G', 'GGG':'G', 'GGT':'G',\n",
    "     'TCA':'S', 'TCC':'S', 'TCG':'S', 'TCT':'S',\n",
    "     'TTC':'F', 'TTT':'F', 'TTA':'L', 'TTG':'L',\n",
    "     'TAC':'Y', 'TAT':'Y', 'TAA':'_', 'TAG':'_',\n",
    "     'TGC':'C', 'TGT':'C', 'TGA':'_', 'TGG':'W',\n",
    "  }\n",
    "\n",
    "def get_triplets(t):\n",
    "  for triplet in re.finditer(r'.{3}', t):\n",
    "    yield genetic_code[triplet.group(0)]\n",
    "\n",
    "rang_freq_with_labels('dna-aminos', get_triplets(dna))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### „Zdania” w języku DNA\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Z aminokwasów zakodowanych przez tryplet budowane są białka.\n",
    "Maszyneria budująca białka czyta sekwencję aż do napotkania\n",
    "trypletu STOP (\\_ powyżej). Taka sekwencja to *gen*.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'02_Jezyki/dna_length.png'"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def get_genes(triplets):\n",
    "  gene = []\n",
    "  for ammino in triplets:\n",
    "    if ammino == '_':\n",
    "       yield gene\n",
    "       gene = []\n",
    "    else:\n",
    "       gene.append(ammino)\n",
    "\n",
    "plt.figure().clear()\n",
    "plt.hist([len(g) for g in get_genes(get_triplets(dna))], bins=100)\n",
    "\n",
    "fname = '02_Jezyki/dna_length.png'\n",
    "\n",
    "plt.savefig(fname)\n",
    "\n",
    "fname"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.2"
  },
  "org": null
 },
 "nbformat": 4,
 "nbformat_minor": 1
}