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+++ b/lab1.ipynb
@@ -0,0 +1,637 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Gutenberg poetry corpus\n",
+    "From: https://github.com/aparrish/gutenberg-poetry-corpus"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current\r\n",
+      "                                 Dload  Upload   Total   Spent    Left  Speed\r\n",
+      "100 52.2M  100 52.2M    0     0  4073k      0  0:00:13  0:00:13 --:--:-- 4693k\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!curl -O http://static.decontextualize.com/gutenberg-poetry-v001.ndjson.gz"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Unzip and load .json\n",
+    "import gzip, json\n",
+    "raw_data = []\n",
+    "for line in gzip.open('gutenberg-poetry-v001.ndjson.gz'):\n",
+    "    raw_data.append(json.loads(line.strip()))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[{'s': 'Through their palisades of pine-trees,', 'gid': '19'},\n",
+       " {'s': 'And the thunder in the mountains,', 'gid': '19'},\n",
+       " {'s': 'Whose innumerable echoes', 'gid': '19'},\n",
+       " {'s': 'Flap like eagles in their eyries;--', 'gid': '19'},\n",
+       " {'s': 'Listen to these wild traditions,', 'gid': '19'},\n",
+       " {'s': 'To this Song of Hiawatha!', 'gid': '19'},\n",
+       " {'s': \"Ye who love a nation's legends,\", 'gid': '19'},\n",
+       " {'s': 'Love the ballads of a people,', 'gid': '19'},\n",
+       " {'s': 'That like voices from afar off', 'gid': '19'},\n",
+       " {'s': 'Call to us to pause and listen,', 'gid': '19'}]"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "raw_data[100:110]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Store the poems separately in a dict by id; this makes it possible to connect verses into whole poems\n",
+    "poems_dict = {}\n",
+    "for object in raw_data:\n",
+    "    if object['gid'] not in poems_dict:\n",
+    "        poems_dict[object['gid']] = object['s']\n",
+    "    else:\n",
+    "        poems_dict[object['gid']] += f\"\\n{object['s']}\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "here the tangled barberry-bushes\n",
+      "Hang their tufts of crimson berries\n",
+      "Over stone walls gray with mosses,\n",
+      "Pause by some neglected graveyard,\n",
+      "For a while to muse, and ponder\n",
+      "On a half-effaced inscription\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(poems_dict['19'][5000:5200])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Total poems: 1191\n",
+      "Average poem word length: 18438\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Check the total number of poems and the estimated average length of a poem in words (estimated since for now punctuation is left as it is)\n",
+    "poems_count = len(poems_dict)\n",
+    "total_word_count = sum([len(v.split()) for v in poems_dict.values()])\n",
+    "print('Total poems:', poems_count)\n",
+    "print('Average poem word length:', total_word_count // poems_count)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Save the entire corpus as one .txt file\n",
+    "with open('gutenberg_poems.txt', 'w', encoding='utf-8') as f:\n",
+    "    for v in poems_dict.values():\n",
+    "        f.write(v + '\\n')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2257117 ,\r\n",
+      " 732414 .\r\n",
+      " 597979 '\r\n",
+      " 517116 -\r\n",
+      " 334015 ;\r\n",
+      " 236376 \"\r\n",
+      " 201702 !\r\n",
+      " 123680 :\r\n",
+      "  97547 _\r\n",
+      "  92513 ?\r\n",
+      "  28887 (\r\n",
+      "  28513 )\r\n",
+      "  27472 ’\r\n",
+      "  14575 1\r\n",
+      "  13943 “\r\n",
+      "   8998 ]\r\n",
+      "   8787 /\r\n",
+      "   8585 }\r\n",
+      "   8300 2\r\n",
+      "   7256 {\r\n",
+      "   6098 3\r\n",
+      "   5744 8\r\n",
+      "   5636 4\r\n",
+      "   5585 ”\r\n",
+      "   5416 6\r\n",
+      "   5180 5\r\n",
+      "   5166 7\r\n",
+      "   4926 þ\r\n",
+      "   4670 0\r\n",
+      "   4507 9\r\n",
+      "   4346 [\r\n",
+      "   3661 &\r\n",
+      "   2542 *\r\n",
+      "   2364 —\r\n",
+      "   2000 `\r\n",
+      "   1985 =\r\n",
+      "   1908 ‘\r\n",
+      "   1338 ~\r\n",
+      "   1211 α\r\n",
+      "   1203 ν\r\n",
+      "   1113 ο\r\n",
+      "   1059 |\r\n",
+      "    974 ε\r\n",
+      "    924 τ\r\n",
+      "    922 Þ\r\n",
+      "    920 ¡\r\n",
+      "    849 ι\r\n",
+      "    666 ρ\r\n",
+      "    621 >\r\n",
+      "    599 ς\r\n",
+      "    593 <\r\n",
+      "    561 +\r\n",
+      "    522 σ\r\n",
+      "    470 π\r\n",
+      "    464 λ\r\n",
+      "    453 μ\r\n",
+      "    447 κ\r\n",
+      "    415 ¿\r\n",
+      "    359 δ\r\n",
+      "    349 €\r\n",
+      "    347 ”\r\n",
+      "    345 υ\r\n",
+      "    282 «\r\n",
+      "    277 η\r\n",
+      "    265 ω\r\n",
+      "    258 θ\r\n",
+      "    246  \r\n",
+      "    220 γ\r\n",
+      "    212 #\r\n",
+      "    181 φ\r\n",
+      "    178 »\r\n",
+      "    138 χ\r\n",
+      "    137 ^\r\n",
+      "    114 ἐ\r\n",
+      "    109 έ\r\n",
+      "    102 ί\r\n",
+      "    101 ά\r\n",
+      "     98 ὶ\r\n",
+      "     97 ί\r\n",
+      "     97 ἀ\r\n",
+      "     96 $\r\n",
+      "     86 έ\r\n",
+      "     84 ὸ\r\n",
+      "     81 β\r\n",
+      "     76 ό\r\n",
+      "     74 ά\r\n",
+      "     72 ῖ\r\n",
+      "     69 ’\r\n",
+      "     65 ὰ\r\n",
+      "     60 \u0001\r\n",
+      "     56 ό\r\n",
+      "     56 ξ\r\n",
+      "     54 ύ\r\n",
+      "     51 ῦ\r\n",
+      "     50 ῶ\r\n",
+      "     49 \r\n",
+      "     48 ὲ\r\n",
+      "     46 ἔ\r\n",
+      "     43 ύ\r\n",
+      "     41 ὐ\r\n",
+      "     40 ἄ\r\n",
+      "     39 ἰ\r\n",
+      "     38 ζ\r\n",
+      "     38 ·\r\n",
+      "     37 §\r\n",
+      "     34 ή\r\n",
+      "     31 Α\r\n",
+      "     30 Τ\r\n",
+      "     30 ῆ\r\n",
+      "     29 ὑ\r\n",
+      "     29 ὴ\r\n",
+      "     28 Π\r\n",
+      "     27 †\r\n",
+      "     26 ώ\r\n",
+      "     26 Μ\r\n",
+      "     25 ­\r\n",
+      "     24 ὺ\r\n",
+      "     24 Ο\r\n",
+      "     24 ἱ\r\n",
+      "     23 Κ\r\n",
+      "     23 ή\r\n",
+      "     22 ᾽\r\n",
+      "     22 ¶\r\n",
+      "     21 Ἀ\r\n",
+      "     21 \\\r\n",
+      "     21 @\r\n",
+      "     20 ὡ\r\n",
+      "     20 ψ\r\n",
+      "     19 ὀ\r\n",
+      "     19 ἡ\r\n",
+      "     19 Ε\r\n",
+      "     18 ἶ\r\n",
+      "     18 ·\r\n",
+      "     17 ὁ\r\n",
+      "     16 ώ\r\n",
+      "     16 †\r\n",
+      "     15 £\r\n",
+      "     15 ̄\r\n",
+      "     15 „\r\n",
+      "     14 Σ\r\n",
+      "     14 ἴ\r\n",
+      "     14 ᾶ\r\n",
+      "     13 ῳ\r\n",
+      "     13 ῷ\r\n",
+      "     13 ῥ\r\n",
+      "     13 ὄ\r\n",
+      "     13 Δ\r\n",
+      "     13 Β\r\n",
+      "     12 ὅ\r\n",
+      "     12 ἁ\r\n",
+      "     11 Θ\r\n",
+      "     11 ῇ\r\n",
+      "     11 ἑ\r\n",
+      "     11 ©\r\n",
+      "     10 ὖ\r\n",
+      "     10 Λ\r\n",
+      "     10 ῃ\r\n",
+      "     10 Ἔ\r\n",
+      "     10 ´\r\n",
+      "     10 °\r\n",
+      "      9 ὼ\r\n",
+      "      9 Ὅ\r\n",
+      "      9 Ν\r\n",
+      "      9 ἵ\r\n",
+      "      9 ¦\r\n",
+      "      8 Ι\r\n",
+      "      8 ἤ\r\n",
+      "      8 ─\r\n",
+      "      7 Φ\r\n",
+      "      7 ὕ\r\n",
+      "      7 Ὑ\r\n",
+      "      7 ὔ\r\n",
+      "      7 ϊ\r\n",
+      "      7 ‧\r\n",
+      "      6 Χ\r\n",
+      "      6 ϕ\r\n",
+      "      6 ΐ\r\n",
+      "      6 ἢ\r\n",
+      "      6 Ζ\r\n",
+      "      6 Ἑ\r\n",
+      "      6 ᾳ\r\n",
+      "      5 ὤ\r\n",
+      "      5 ϑ\r\n",
+      "      5 ἦ\r\n",
+      "      5 Ἄ\r\n",
+      "      5 ½\r\n",
+      "      5 …\r\n",
+      "      5 œ\r\n",
+      "      4 Ὁ\r\n",
+      "      4 Ἠ\r\n",
+      "      4 Η\r\n",
+      "      4 Ἐ\r\n",
+      "      4 Γ\r\n",
+      "      4 ἅ\r\n",
+      "      4 Ἁ\r\n",
+      "      4 ̆\r\n",
+      "      4 –\r\n",
+      "      4 \t\r\n",
+      "      3 ὥ\r\n",
+      "      3 Ὡ\r\n",
+      "      3 ὦ\r\n",
+      "      3 Ὠ\r\n",
+      "      3 ὠ\r\n",
+      "      3 Ω\r\n",
+      "      3 ὗ\r\n",
+      "      3 ῤ\r\n",
+      "      3 ὃ\r\n",
+      "      3 ΐ\r\n",
+      "      3 Ἱ\r\n",
+      "      3 ἲ\r\n",
+      "      3 Ἰ\r\n",
+      "      3 ἂ\r\n",
+      "      3 ¼\r\n",
+      "      3 ;\r\n",
+      "      3 %\r\n",
+      "      3 “\r\n",
+      "      2 ὧ\r\n",
+      "      2 ὒ\r\n",
+      "      2 Υ\r\n",
+      "      2 Ξ\r\n",
+      "      2 ἷ\r\n",
+      "      2 Ἴ\r\n",
+      "      2 ῂ\r\n",
+      "      2 ἣ\r\n",
+      "      2 Ἡ\r\n",
+      "      2 ἠ\r\n",
+      "      2 Ἕ\r\n",
+      "      2 ἕ\r\n",
+      "      2 ̓\r\n",
+      "      2 ⁂\r\n",
+      "      2 ‡\r\n",
+      "      2 ×\r\n",
+      "      2 Ž\r\n",
+      "      1 ᾧ\r\n",
+      "      1 Ὣ\r\n",
+      "      1 ὣ\r\n",
+      "      1 Ὥ\r\n",
+      "      1 ὢ\r\n",
+      "      1 Ὤ\r\n",
+      "      1 ῡ\r\n",
+      "      1 ϋ\r\n",
+      "      1 Ῥ\r\n",
+      "      1 Ρ\r\n",
+      "      1 Ό\r\n",
+      "      1 ὂ\r\n",
+      "      1 Ὀ\r\n",
+      "      1 ἳ\r\n",
+      "      1 Ἵ\r\n",
+      "      1 ᾗ\r\n",
+      "      1 ἧ\r\n",
+      "      1 Ἣ\r\n",
+      "      1 ἥ\r\n",
+      "      1 ᾐ\r\n",
+      "      1 Έ\r\n",
+      "      1 ἒ\r\n",
+      "      1 ᾴ\r\n",
+      "      1 Ά\r\n",
+      "      1 ἆ\r\n",
+      "      1 ¤\r\n",
+      "      1 ̷\r\n",
+      "      1 ☞\r\n",
+      "      1 ‖\r\n",
+      "      1 ΄\r\n",
+      "      1 ®\r\n",
+      "      1 ™\r\n",
+      "      1 —\r\n",
+      "      1 ‘\r\n",
+      "      1 \u001a\r\n",
+      "      1 \u0007\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Checking all potentially undesirable characters\n",
+    "!grep -oE \"[^a-zA-Z ]\" gutenberg_poems.txt | sort | uniq -c | sort -k1 -nr"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Normalizing some characters that should be kept\n",
+    "!sed -i 's/;/;/g' gutenberg_poems.txt\n",
+    "!sed -i 's/…/\\.\\.\\./g' gutenberg_poems.txt\n",
+    "!sed -i 's/[—─–]/-/g' gutenberg_poems.txt\n",
+    "!sed -i \"s/[\\`\\’\\‘\\᾽\\´\\΄]/\\'/g\" gutenberg_poems.txt\n",
+    "\n",
+    "# Nuking the remaining garbage characters\n",
+    "!sed -i \"s/[^a-zA-Z\\ \\,\\.\\'\\;\\!\\:\\?\\-]//g\" gutenberg_poems.txt\n",
+    "\n",
+    "# Removing any remaining multiple spaces\n",
+    "!sed -i \"s/\\ \\ */\\ /g\" gutenberg_poems.txt\n",
+    "\n",
+    "# Finally removing diacritic marks from alphabetic characters\n",
+    "!cat gutenberg_poems.txt | unidecode > gutenberg_poems_clean.txt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2257117 ,\r\n",
+      " 732429 .\r\n",
+      " 629413 '\r\n",
+      " 519492 -\r\n",
+      " 334018 ;\r\n",
+      " 201702 !\r\n",
+      " 123680 :\r\n",
+      "  92513 ?\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Now it looks a lot better - only alphabetic characters, spaces and chosen punctuation are kept\n",
+    "!grep -oE \"[^a-zA-Z ]\" gutenberg_poems_clean.txt | sort | uniq -c | sort -k1 -nr"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Lines: 3085117\r\n",
+      "Words: 21938739\r\n",
+      "Characters: 120840262\r\n",
+      "Size: 116M\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Some basic processed file statistics:\n",
+    "!echo -n \"Lines: \"\n",
+    "!wc -l < gutenberg_poems_clean.txt\n",
+    "!echo -n \"Words: \"\n",
+    "!wc -w < gutenberg_poems_clean.txt\n",
+    "!echo -n \"Characters: \"\n",
+    "!wc -c < gutenberg_poems_clean.txt\n",
+    "!echo -n \"Size: \"\n",
+    "!ls -lh gutenberg_poems_clean.txt | awk '{print $5}'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Sae aft around him flung,\r\n",
+      "A thing so dark that moments of pain\r\n",
+      "A mother and daughter stood together\r\n",
+      "He hath heathen gifts of silver and gold,\r\n",
+      "at secura quies et nescia fallere uita,\r\n",
+      "The grim dim thrones of the east Ep. .\r\n",
+      "Ah tamen illa scelus non lavat unda tuum!\r\n",
+      "A strong emotion on her cheek!\r\n",
+      "Byron sang its funeral dirge. But tenderness, and heroism, and\r\n",
+      "Which now upon my fingers thoughtfully\r\n",
+      "shuf: write error: Broken pipe\r\n",
+      "shuf: write error\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Some random lines from the file (doing this in Jupyter throws a harmless piping error apparently)\n",
+    "!cat gutenberg_poems_clean.txt | shuf | head -10"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "11792288 e\r\n",
+      "7549004 t\r\n",
+      "6755728 a\r\n",
+      "6703094 o\r\n",
+      "6189711 h\r\n",
+      "6158676 n\r\n",
+      "6157618 s\r\n",
+      "5768879 r\r\n",
+      "5576169 i\r\n",
+      "4163693 l\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Simple top 10 frequency histogram of letters (takes a while to run)\n",
+    "!grep -oE \"\\w\" gutenberg_poems_clean.txt | sort | uniq -c | sort -k1 -nr | head"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1110344 the\r\n",
+      " 526755 and\r\n",
+      " 477187 of\r\n",
+      " 367204 to\r\n",
+      " 309477 a\r\n",
+      " 294277 And\r\n",
+      " 283595 in\r\n",
+      " 243898 I\r\n",
+      " 198621 The\r\n",
+      " 182639 his\r\n",
+      "sort: write failed: 'standard output': Broken pipe\r\n",
+      "sort: write error\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Simple top 10 frequency histogram of words (takes a while to run, piping error thrown here as well, but it works)\n",
+    "!cat gutenberg_poems_clean.txt | tr ' ' '\\n' | sort | uniq -c | sort -k1 -nr | head"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "gutenberg_poems_clean.txt (1/1)\r\n",
+      "  100 %        34.6 MiB / 115.2 MiB = 0.300   1.6 MiB/s       1:10             \r\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Compressing the file for uploading\n",
+    "!xz -v gutenberg_poems_clean.txt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "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": "3.10.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/lab2.ipynb b/lab2.ipynb
new file mode 100644
index 0000000..8d88b21
--- /dev/null
+++ b/lab2.ipynb
@@ -0,0 +1,426 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true,
+    "pycharm": {
+     "is_executing": true
+    }
+   },
+   "outputs": [],
+   "source": [
+    "!unxz gutenberg_poems_clean.txt.xz"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "outputs": [],
+   "source": [
+    "text = open('gutenberg_poems_clean.txt', encoding = 'utf-8').read()"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[nltk_data] Downloading package wordnet to /home/ked/nltk_data...\n",
+      "[nltk_data]   Package wordnet is already up-to-date!\n"
+     ]
+    }
+   ],
+   "source": [
+    "import nltk\n",
+    "import matplotlib.pyplot as plt\n",
+    "from collections import OrderedDict, defaultdict, Counter\n",
+    "import regex as re\n",
+    "from math import log\n",
+    "from nltk.stem import WordNetLemmatizer\n",
+    "from nltk.stem.snowball import SnowballStemmer\n",
+    "import numpy as np\n",
+    "import matplotlib.ticker as ticker\n",
+    "nltk.download('wordnet')\n",
+    "\n",
+    "def get_characters(t):\n",
+    "    yield from t\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",
+    "def freq_list(g, top=None):\n",
+    "  c = Counter(g)\n",
+    "  if top is None:\n",
+    "     items = c.items()\n",
+    "  else:\n",
+    "     items = c.most_common(top)\n",
+    "  return OrderedDict(sorted(items, key=lambda t: -t[1]))\n",
+    "\n",
+    "def log_rang_log_freq(name, g):\n",
+    "   freq = freq_list(g)\n",
+    "   plt.figure().clear()\n",
+    "   #plt.figure(figsize=(12, 4))\n",
+    "   plt.plot([log(x) for x in range(1, len(freq.values())+1)], [log(y) for y in freq.values()])\n",
+    "   fname = f'{name}.png'\n",
+    "   plt.savefig(fname)\n",
+    "   return fname\n",
+    "\n",
+    "def get_top_word_length(g, top=10):\n",
+    "    d = defaultdict(set)\n",
+    "    for item in g:\n",
+    "        d[len(item)].add(item)\n",
+    "    return {k : sorted(list(v)) for k, v in sorted(d.items(), reverse=True)[:top]}\n",
+    "\n",
+    "def ngrams(g, size):\n",
+    "  ngram = []\n",
+    "  for item in g:\n",
+    "     ngram.append(item)\n",
+    "     if len(ngram) == size:\n",
+    "        yield tuple(ngram)\n",
+    "        ngram = ngram[1:]\n",
+    "\n",
+    "def lemmas(g):\n",
+    "    lemmatizer = WordNetLemmatizer()\n",
+    "    for item in g:\n",
+    "        yield lemmatizer.lemmatize(item)\n",
+    "\n",
+    "def stems(g):\n",
+    "    stemmer = SnowballStemmer('english')\n",
+    "    for item in g:\n",
+    "        yield stemmer.stem(item)\n",
+    "\n",
+    "def get_freq_list(g):\n",
+    "    c = Counter(g)\n",
+    "    freq_list = list(enumerate(sorted([(word, freq) for word, freq in c.items()], reverse=True, key=lambda t: t[1]), start=1))\n",
+    "    return freq_list\n",
+    "\n",
+    "def find_long_popular_words(g, top=None):\n",
+    "    freq_list = get_freq_list(g)\n",
+    "    len_vs_freq = sorted([(freq * len(word), word) for rank, (word, freq) in freq_list], reverse=True, key=lambda t: t[0])\n",
+    "    if top is None:\n",
+    "        return len_vs_freq\n",
+    "    else:\n",
+    "        return len_vs_freq[:top]\n",
+    "\n",
+    "def plot_brevity_law(g):\n",
+    "    freq_list = get_freq_list(g)\n",
+    "    plt.figure().clear()\n",
+    "    #plt.figure(figsize=(12, 4))\n",
+    "    x = [tup[0] for tup in freq_list]\n",
+    "    y = [len(tup[1][0]) for tup in freq_list]\n",
+    "    plt.plot(x[::5000], y[::5000])\n",
+    "    plt.xlabel('Word rank (every 5000th)')\n",
+    "    plt.ylabel('Word length (every 5000th)')\n",
+    "    z = np.polyfit(x, y, 1)\n",
+    "    p = np.poly1d(z)\n",
+    "    plt.plot(x, p(x))\n",
+    "    fname = 'brevity_law_plot.png'\n",
+    "    plt.savefig(fname)\n",
+    "\n",
+    "def plot_heaps_law(g):\n",
+    "    uniq_words = set()\n",
+    "    text_length = 0\n",
+    "    x = []\n",
+    "    y = []\n",
+    "    for word in g:\n",
+    "        text_length += 1\n",
+    "        uniq_words.add(word)\n",
+    "        x.append(text_length)\n",
+    "        y.append(len(uniq_words))\n",
+    "    plt.figure().clear()\n",
+    "    #plt.figure(figsize=(12, 4))\n",
+    "    plt.xlabel('Text length (in words)')\n",
+    "    plt.ylabel('Unique words')\n",
+    "    plt.ticklabel_format(scilimits=(-5, 8))\n",
+    "    plt.plot(x, y)\n",
+    "    plt.savefig('heaps_law_plot.png')"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "43: ninepenceashillingneteighteenpencetwoandsix\n",
+      "36: crownnettwoandeightpencethreeandnine\n",
+      "35: Lebensfeuerversicherunggesellschaft\n",
+      "33: Gottsdonnerkreuzschockschwerenoth\n",
+      "28: Wintztschitstopschinzoudhoff\n",
+      "25: Selbstanschauungsvermogen\n",
+      "24: Aldiborontiphoscophornio, WappenshieldWaffenschild, WunderscheenWunderschoen\n",
+      "22: honorificabilitudinity\n",
+      "21: Voelkerwanderungszeit\n",
+      "20: Alliterationspoesien, Fornminnesfoerenings, Retrogradeacompletes, andkindlystepthisway, richardwashburnchild, xinechoncentlalizqui\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Top 10 longest words\n",
+    "toplist = get_top_word_length(get_words(text))\n",
+    "for k,v in toplist.items():\n",
+    "    print(f\"{k}: {', '.join(v)}\")"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "'zipf_plot_words.png'"
+     },
+     "execution_count": 66,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "text/plain": "<Figure size 640x480 with 1 Axes>",
+      "image/png": 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fo3zAL5jNJj03oK0sZpN+3Jilzs/N1Zvz0lXwlwXsAABVjwGn8CsT52/Xyz9tdW2fFxOmhlE11SCyhm7v1lANo2oamA4Aqi/WdgFOIctWoKXbD+rJ735TbsGfj+HWCQvWZ3d3Vf3aNRQSaDEwIQBUP5QP4Azstx3X4vSDKixxaNL87dpnK3C917FhbU28tYNirSEGJgSA6oPyAVTSliy7Hv7vem3LznWtmNs0OkyP9W2pOmHBahUXIbOZ+UEA4GQoH8BZcjqdSs/J0+B3VyrbXujan5RQSyN7NdOlzaMNTAcA3otJxoCzZDKZdF5MuKYN7aJeLaPVMq7sH9C6zKMa+n6qJs7fbnBCAKj+uPIBnMa27Fy9MS9d36/fL0n69K4L1K1JlMGpAMC7cOUDcKPzYsI14eb2alK37DHc299bqSmLfteGPTZ5WXcHgGqB8gGcAbPZpEm3dVSXxEgVlzr1/A9b1O+tJRr26a9auv2g0fEAoFrhtgtQCSWlDn24fJd++i1LKzMOu/Z3aRSptwa1V3Q4j+YC8E887QJ4wJpdR/Tukh36YUOWJCnOGqJH+7bUVW3ieCwXgN+hfAAetPz3Q3rw87XKspdNUmYNDVSXxEg9fW1rxVlDDU4HAJ7BgFPAg7o1idKChy/RPRc3VmigRbbjxZqzKVvdxv+iV37eyqBUAPgLygfgBiGBFo29sqXWP9lb7wzuqPg/pmWf8Mt2vTEv3eB0AOBdKB+AGwVazOrTOlaLR1+m4T2bSZLeXlD2WC4AoAzlA6gCFrNJD/Zqpq6JkSoqcejfP281OhIAeA3KB1BFTCaTxl/XVpK0cNsBDZuepq1ZuQanAgDjUT6AKtS4bpjuv6SJJGnW+v267u2lemfh78otKDY4GQAYh0dtAQ9YvfOwRn+1Xr8fyJdU9jjugPb1dFPnBNfidQBQnTHPB+CFSh1OfbkmU6/PTdd+W4Fr/9DkRnqiX2sDkwHAuavM93eAhzIBfs9iNummzg107fn19O26ffo6bY9W7Dis95fuVKDFrGGXNVVESKDRMQGgylV6zMeiRYvUr18/xcfHy2QyaebMmeXedzqdevzxxxUXF6fQ0FD16tVL6enMcwCcEBJo0cBOCfrs7m66vFWMJGnKoh268MX5WrHjkMHpAKDqVbp85OfnKykpSRMnTqzw/ZdeeklvvvmmJk+erJUrV6pmzZrq06ePCgoKKjwe8GeTb+uo4T2bKSEyVLbjxRry3irNWr+PWVEB+LRzGvNhMpk0Y8YM9e/fX1LZVY/4+Hg99NBD+te//iVJstlsiomJ0bRp03TzzTef9ncy5gP+yF5QrIGTl2vLH4/itogN1/Ud6uvqpDjWhwFQLRi2tktGRoaysrLUq1cv1z6r1aquXbtq+fLlFf5MYWGh7HZ7uRfgbyJCAvXVfd1190WNVTPIoi1ZuXruh83qNv4X3f/JGu05cszoiADgNm4tH1lZZUuLx8TElNsfExPjeu+vxo8fL6vV6nolJCS4MxJQbdQMDtAjV7XU0jGXadzVrdShQS1J0g8bstT7tUWatX6fsQEBwE0Mn2Rs7NixstlsrldmZqbRkQBD1aoRpDt7JOrr+5P15b3dFB0erGNFpRo2/VeN+nytdh7MNzoiAJwTt5aP2NhYSVJ2dna5/dnZ2a73/io4OFgRERHlXgDKdGoUqV/+dYmuaF327+frX/eq92uLNPbrDTqSX2RwOgA4O24tH4mJiYqNjdW8efNc++x2u1auXKlu3bq581SA3wgLDtDbgzro07suUJdGkSoqdejTVbvV/pk56j9xqb5Zu1elDp6OAVB9VHqSsby8PG3fvt21nZGRobVr1yoyMlINGjTQyJEj9eyzz6pZs2ZKTEzUuHHjFB8f73oiBkDlmc0mdWsSpa6JF2jhtgN65vtN2nEgX2szj2rEZ2v11HebdHW7OP2rT3MmKgPg9Sr9qO2CBQt06aWX/m3/kCFDNG3aNDmdTj3xxBOaMmWKjh49qh49eujtt9/Weeedd0a/n0dtgTOz61C+Pl2VqU9W7lJuQYkkqVl0mD7+Z1fFRIQYnA6Av2FtF8CPFJc6tDj9gO79OE1FJQ7VqhGoET2baWhyotHRAPgRw+b5AOB5gRazLmsRo29SkhUbEaKjx4r11Heb9NAX65gpFYBXonwAPqJlXIQWPHyJ7r6osSTpq7Q9uv29VRQQAF6H8gH4kJBAix65qqUeurxsjNXi9IN6dOZGg1MBQHmUD8AHPdCzmW7qVDZb8PSVu/XPD1YrbfcRg1MBQBnKB+CjXri+rW67oIEkae7mbN38zgqlZ+canAoAKB+AzzKZTHq2f1vNHXWxuiaWTU42+N1VTM8OwHCUD8DHNY0O08s3JMlkkrLsBer56kIWqQNgKMoH4AcaRNXQf+/ppuYx4Sp1ODVs+q+aOH+7CopLjY4GwA9RPgA/0alRpL4ZlqzW8WWT/7z801b1fm2Rvl+/Xw7WhgHgQZQPwI+EBFo0MyVZj/VtqdiIEO0+fEwp09M08J3l2rjXZnQ8AH6C6dUBP3Ukv0j/WbJD7y/dqWNFpTKZpAHt6+n+S5qoaXS40fEAVDOs7QLgjO09elwvzd6ib9b+OQj1rVvb6+p28QamAlDdsLYLgDNWr1ao3ri5vWamJCupvlWS9H9frtfBvEKDkwHwVZQPAJKk8xNq6fN7uqlOWLCOFZVq4DvLlWMvMDoWAB9E+QDgEhJo0cRb2ys8JEA7DuTr6glLtGBrDovTAXArygeAcro2jtKnd12gZtFhyskt1D/eT9VNU1Zo83670dEA+AjKB4C/aVPPqm+GJbsWp1uVcVhXvrFYY75ar31HjxucDkB1x9MuAE7pt302vfXLdv24Mcu1b2hyI42+ooVCAi0GJgPgTXjUFoDb/fRblqYu2qHVu45IkhLr1NSUwR3VLIY5QQDwqC2AKtCnday+vK+7pt7eSbERIco4mK87PkhllVwAlUb5AFApl7eK0ffDe6herVBlHj6u/m8v1atztukQ84IAOEOUDwCVFhUWrBn3d1dSQi0dPVasN+el66o3Fyt152GjowGoBigfAM5KdESIvry3m/59Y5Lq1QpVtr1QA99ZrmdnbVJhSanR8QB4McoHgLMWaDHrho719ePIC3Vjx/pyOqX/LMnQgInLdCS/yOh4ALwU5QPAOYsICdTLNybp7UEdFGgxadN+u3q/vkhbs3KNjgbAC1E+ALjNVW3j9N97u6t2jUAdyC3U1RPKJiZjkToA/4vyAcCtzk+opR9GXKiODWuruNSpz1Iz1fOVhVq07YDR0QB4CcoHALeLs4bqy3u76T+3d1LLuAjZjhdryPur9N6SDBapA0D5AFA1TCaTerWK0cyU7rq+Q9lg1KdnbdLAd5ZzGwbwc5QPAFUqOMCil25op5G9mik00KLUnUfU85WF+jx1t9HRABiE8gGgylnMJo3sdZ7+e283xUaEyHa8WKO/2qB/fpCq7Tk8EQP4G8oHAI9pU8+qxaMv1QOXNZXZJM3dnKNery7SmK/WMz074EcoHwA8KtBi1kO9m+vnBy/SJc3rSpI+S81U3zeX6INlOxmQCvgBygcAQzSNDte0oV30/j86q16tUGXZC/TEt7/p7o/W6OgxZkcFfBnlA4ChLm0RrXkPXaxRl58ni9mkOZuyNeS9VcqxFxgdDUAVoXwAMFxIoEXDezbTR3d2UVhwgNbtsenGd5Zr416b0dEAVAHKBwCv0b1JHc1M6a6EyFDtOnRM/Scu1bfr9hkdC4Cbub18lJaWaty4cUpMTFRoaKiaNGmiZ555hkFkAM5I0+hwfX1fsi4+r65KHE499MVaLdt+0OhYANzI7eXjxRdf1KRJk/TWW29p8+bNevHFF/XSSy9pwoQJ7j4VAB9VNzxY7/2js3q2iFZxqVNDp6Vq/tYco2MBcBO3l49ly5bp2muvVd++fdWoUSPdcMMN6t27t1atWuXuUwHwYRazSW/f1kG9WsaosMShuz9crdkb9xsdC4AbuL18dO/eXfPmzdO2bdskSevWrdOSJUt05ZVXVnh8YWGh7HZ7uRcASGVTs0+6rYP6to1TcalT936cpvE/bFZxqcPoaADOQYC7f+GYMWNkt9vVokULWSwWlZaW6rnnntOgQYMqPH78+PF66qmn3B0DgI8ItJj1xs3nq254sKYt26l3Fu1Q2u4jeuvWDoqJCDE6HoCz4PYrH1988YU++eQTTZ8+XWlpafrggw/073//Wx988EGFx48dO1Y2m831yszMdHckANVcgMWsJ69prUmDOigsOECpO4+o75uLtXDbAQazA9WQyenmf7kJCQkaM2aMUlJSXPueffZZffzxx9qyZctpf95ut8tqtcpmsykiIsKd0QD4gIyD+brv4zXaklW2IF2PpnX0xs3nKyos2OBkgH+rzPe32698HDt2TGZz+V9rsVjkcHCPFsC5S6xTU5/edYFu6pSgQItJS7Yf1GWvLNTXaXuMjgbgDLm9fPTr10/PPfecvv/+e+3cuVMzZszQq6++qgEDBrj7VAD8VO2aQXrxhnb6fviFahodJtvxYo36Yp1em7NNJQxGBbye22+75Obmaty4cZoxY4ZycnIUHx+vW265RY8//riCgoJO+/PcdgFQGaUOp174cbOmLs6QJLVvUEsTbmmv+rVrGJwM8C+V+f52e/k4V5QPAJXldDr1ddpePf7NRuUXlcoaGqhJt3VQ9yZ1jI4G+A1Dx3wAgKeZTCZd37G+Zo+8SK3jI2Q7Xqzb312l1+Zsk8PhVf//CoAoHwB8SEJkDX11X3f1S4pXicOpN+alq/fri7Rm12GjowH4H5QPAD4lJNCiCbe01/MD2io8JEDbc/J0w+Tl+s/iHUZHA/AHygcAn3Rr1wZa8K9LdO358XI6pWe/36z7P1kje0Gx0dEAv0f5AOCzosKC9fpN5+tfvc+T2ST9sCFLfd9czG0YwGCUDwA+zWQyadhlzfTfe7urXq1QZR4+rpunrNCs9fuMjgb4LcoHAL/QsWFtzR55oXq2iFZxqVMPfPqrJsxLZ20YwACUDwB+IzwkUFNu76TbuzWU0ym9Mmeb7vpwtQ7mFRodDfArlA8AfsViNunpa9vo6WtbK9Bi0tzNObri9UWavTHL6GiA36B8APBLt3drpBn3J6t5TLgO5hXp3o/X6MHP1/I0DOABlA8AfqtNPau+GZas+y5pIrNJmvHrXl35+mKtyuBpGKAqUT4A+LWQQItGX9FC/723uxpE1tDeo8d185TlevmnLSpmhVygSlA+AEBlT8P8MOJC3dixvhxOaeL833Xd28u0ca/N6GiAz6F8AMAfwoID9PKNSXp7UAdZQwO1Ya9N109apjmbso2OBvgUygcA/MVVbeP084MX6fyEWioscejuj1Zrwrx0lXAbBnALygcAVCAmIkT/vbebbu3awDUnyO3vrZLtOE/DAOeK8gEAJxFoMeu5/m30yo1JCg4wa9nvh3T9pGVK233E6GhAtUb5AIBTMJlMur5jfU2/q6vqhAVpe06erp+0TK/N2cZtGOAsUT4A4Ax0bBipOQ9erN6tYuR0Sm/MS9cdH6xWlq3A6GhAtUP5AIAzVLtmkN4Z3FGv3JikoACzFm07oKveXKxlvx80OhpQrVA+AKASTtyG+W5YDzWNDtPh/CLdOnWlXv5pixwOVsgFzgTlAwDOQvPYcH07LFk3d06QVDYp2ZD3V+lwfpHByQDvR/kAgLNUIyhAL1zfruw2jMWsxekH1X/iUqVn5xodDfBqlA8AOEfXd6yvr+/vrujwYO0+fEzXvLWUWVGBU6B8AIAbtKln1bfDeqhzo9o6Xlyquz5crYf/u06FJaVGRwO8DuUDANwk1hqij+7sqlu7NpAk/XfNHt3+LrOiAn9F+QAANwoJtOj5AW01ZXBHhQZatDLjsPpPXKoNe1gdFziB8gEAVaB361h9evcFqhserIyD+bp24hJ9nrrb6FiAV6B8AEAVOT+hln4ccaF6tYyRwymN/mqD/v3TVsaBwO9RPgCgCtUJC9aUwR11Z49ESdJb87frpndWKCeXadnhvygfAFDFzGaTxl3dSq8OTFJYcIDWZh7Vze+s0O5Dx4yOBhiC8gEAHnJdh/r6ZliyYiNCtONgvvq9tUTrMo8aHQvwOMoHAHhQk7ph+ur+7moRGy7b8WLd9p+VWpLOwnTwL5QPAPCwerVC9eV93dWxYW3lFpbo9vdWasK8dBamg9+gfACAAcKCAzT9rq7qf368HE7plTnbNPyzX1VQzJMw8H2UDwAwSHCARa/ddL6eG9BGFrNJs9bv1zVvLVHmYQaiwrdRPgDAQCaTSYO6NtSHd3RR7RqB2padp1umrmBlXPg0ygcAeIHkpnU0M6XsSZg9R47ruknLtDj9gNGxgCpRJeVj7969uu222xQVFaXQ0FC1bdtWq1evropTAYDPaBhVU98MS1bnRrWVW1Cif7yfqskLf5fTyUBU+Ba3l48jR44oOTlZgYGB+vHHH7Vp0ya98sorql27trtPBQA+JyaibGXcvm3jVOpw6oUft2jI+6myF7AyLnyHyenmSj1mzBgtXbpUixcvPquft9vtslqtstlsioiIcGc0AKg2nE6nPlqxS099t0mlDqcSIkP1wdAualw3zOhoQIUq8/3t9isf3377rTp16qQbb7xR0dHRat++vaZOnXrS4wsLC2W328u9AMDfmUwm3d6tkT68o4tqBlmUefi4Bry9TCt2HDI6GnDO3F4+duzYoUmTJqlZs2b66aefdN9992n48OH64IMPKjx+/PjxslqtrldCQoK7IwFAtZXctI7mjLpY7epbZTterMHvrtQXqZlGxwLOidtvuwQFBalTp05atmyZa9/w4cOVmpqq5cuX/+34wsJCFRYWurbtdrsSEhK47QIA/yO/sEQjPluruZuzJUnP9G+jwRc0NDgV8CdDb7vExcWpVatW5fa1bNlSu3fvrvD44OBgRURElHsBAMqrGRygKYM7amCn+pKkcTM3atzMjSopdRicDKg8t5eP5ORkbd26tdy+bdu2qWFDGjoAnAuz2aQXrmunO3skSpI+WrFLt05dqcP5RQYnAyrH7eXjwQcf1IoVK/T8889r+/btmj59uqZMmaKUlBR3nwoA/I7ZbNK4q1vp2f5tZDZJq3Ye1jVvLdGuQ/lGRwPOmNvHfEjSrFmzNHbsWKWnpysxMVGjRo3SXXfddUY/y6O2AHBmft19RPd+vEbZ9kLVqhGod4d0UseGkUbHgp+qzPd3lZSPc0H5AIAzt992XIOmrtSOg/kKDjDrrVs76PJWMUbHgh8ydMApAMBz4qyhmpGSrC6JkSosceiej1bryzV7jI4FnBLlAwCqOWtooD68o4uuaB0rh1N6+Mt1+mj5TqNjASdF+QAAHxASaNHEQR10Q8f6cjqlcd/8pjfnpRsdC6gQ5QMAfITFbNLLN7RzTT726pxtGvv1epU6vGpoH0D5AABfYjKZ9Ez/Nhp+WVNJ0qerMnX3h6tVUFxqcDLgT5QPAPBBo3o310s3tJPZJM3bkqObp6yQ7Vix0bEASZQPAPBZAzsl6L1/dFZIoFlrM4/q+snLlGMvMDoWQPkAAF92SfNofXxnV4UFB2h7Tp56vrpQOw8yGyqMRfkAAB/XqVGkPrqziyQpt6BEV09YovlbcgxOBX9G+QAAP9C+QW0tevhSNYqqobzCEg2dlqoZvzIZGYxB+QAAP9Egqoa+faCHa/vBz9dp1vp9BiaCv6J8AIAfiQgJ1MKHL5E1NFCSNGz6r3rrFyYjg2dRPgDAzzSMqqlVj/ZUr5ZlC9D9++dtmjh/u8Gp4E8oHwDgh4IDLJp6e0d1axwlSXr5p616ewEFBJ5B+QAAP2UymTT9rq66sWN9SdJLs7dq6PurDE4Ff0D5AAA/ZjKZ9NIN7XRLlwRJ0vytB3T1hMUGp4Kvo3wAgJ8zmUx6fkBb1asVKknauNeu5Bd+kdPJgnSoGpQPAIBMJpOWjL5UiXVqSpL2Hj1OAUGVoXwAACSVFZD5/7pEzaLDJEn7bAVqMW62HA4KCNyL8gEAKGfOqIvV9I8CUljiUONHfuAKCNyK8gEA+Ju5oy5Wx4a1XduJY3/gCgjchvIBAKjQV/d1V1J9q2u78SM/qLCk1MBE8BWUDwDASX0zrEe5KyDNH5ut40UUEJwbygcA4JS+uq+7rmgd69pu+fhsHckvMjARqjvKBwDgtCYP7qjrO9R3bbd/Zo4O5RUamAjVGeUDAHBGXhmYpH90b+Ta7vjsXO07ety4QKi2KB8AgDP25DWtddeFia7t7i/8whUQVBrlAwBQKY/2baVRl5/n2u747FzZjhUbmAjVDeUDAFBpw3s20z0XN3ZtJz39sw4zCBVniPIBADgrY69sqSHdGrq2OzwzhysgOCOUDwDAWXvq2jblBqEmPf2zDjIGBKdB+QAAnJMnr2mtwRf8eQWk07NzZTvOFRCcHOUDAHDOnunfRv/s8edTMElP/azcAgoIKkb5AAC4xWNXt9KtXRu4tts++TNrwaBClA8AgNs8P6CtBrSv59pmLRhUhPIBAHCr1246X5c0r+vabvn4bJWUOgxMBG9D+QAAuN20oV3Uu1WMa7vpoz8amAbepsrLxwsvvCCTyaSRI0dW9akAAF5kyu2d1Do+wrWd9NTPBqaBN6nS8pGamqp33nlH7dq1q8rTAAC81PfDL1RQQNlXje14sS799wJjA8ErVFn5yMvL06BBgzR16lTVrl27qk4DAPByW56+wvWfMw7m69apKwxMA29QZeUjJSVFffv2Va9evU55XGFhoex2e7kXAMB3mM0mZYy/yrW97PdDGvPVegMTwWhVUj4+++wzpaWlafz48ac9dvz48bJara5XQkJCVUQCABjIZDJp8/9cAfksNVMvzd5iYCIYye3lIzMzUyNGjNAnn3yikJCQ0x4/duxY2Ww21yszM9PdkQAAXiA0yKJ1T/R2bb+94HdNmJduYCIYxeR0Op3u/IUzZ87UgAEDZLFYXPtKS0tlMplkNptVWFhY7r2/stvtslqtstlsioiIOOlxAIDq6XB+kTo8M8e1/dL17TSwM1e9q7vKfH+7/cpHz549tWHDBq1du9b16tSpkwYNGqS1a9eesngAAHxfZM0gpT7653jA//tqvX7csN/ARPA0t5eP8PBwtWnTptyrZs2aioqKUps2bdx9OgBANVQ3PFjzHrrYtX3fJ2mauynbwETwJGY4BQAYokndMH07LNm1/c8PV2tt5lHjAsFj3D7m41wx5gMA/MuaXYd1/aTlru1fHrpYjeuGGZgIZ8PQMR8AAFRGx4aRemdwR9f2Za8s1N6jxw1MhKpG+QAAGK5P61g9fW1r13byC78o215gYCJUJcoHAMAr3N6tkUb0bOba7vr8PNmOFRuYCFWF8gEA8BoPXn6e7rmosWs76emflV9YYmAiVAXKBwDAq4y9qqWua1/Ptd36iZ9U6vCqZyNwjigfAACv88rAJHVrHOXabjlutopLHQYmgjtRPgAAXsdkMunTuy9Q0+iyR26LSh267u1lBqeCu1A+AABe6+eRFynIUvZVtWGvTc99v8ngRHAHygcAwGuZzSatHvfnOjBTF2fok5W7DEwEd6B8AAC8WkRIYLmF6B6dsVFfrM40MBHOFeUDAOD16oYHa+6oPxei+78v12v+1hwDE+FcUD4AANVC0+jyC9ENfT9VGQfzDUyEs0X5AABUG+3q19K0oZ1d25f+e4HsBcyCWt1QPgAA1colzaP1zP+sA9PuyZ9Vwhwg1QrlAwBQ7Qzu1ki3dm3g2r5l6goD06CyKB8AgGrpuf5t1KFBLUlS6s4jGv3lemMD4YxRPgAA1ZLJZNIX93RTREiAJOnz1Zl6afYWg1PhTFA+AADVVoDFrBWP9HRtv73gd32Ryhwg3o7yAQCo1moEBWjV/xSQ//tqvVbuOGRgIpwO5QMAUO1FR4Toy3u7ubZvmrJC6dm5BibCqVA+AAA+oVOjSD11zZ+P4F7+2iL9ts9mYCKcDOUDAOAzhnRvpHsuauzavvmdFdp96JiBiVARygcAwKeMubKFBrSvJ0nKLSzRozM3GJwIf0X5AAD4FJPJpLFXtlDLuAhJ0uL0g7plygoVlTALqregfAAAfE50RIg+vrOLagRZJEnLdxzSZ6m7mYbdS1A+AAA+KSosWMvGXCaTqWz78W9+0ws/MgmZN6B8AAB8Vq0aQXrx+nau7V+25mj2xiwDE0GifAAAfNzATgmaNrSzJGnHgXzd+/EabWMOEENRPgAAPi+5aR2lXNpEtWoESpLe+mW7lv1+0OBU/ovyAQDweYEWsx7u00Lt6teSJH27bp/u+WiNnE6nscH8FOUDAOA3Hr2qpYYmN5Ik5RaUaNqynUxCZgDKBwDAbzSPDddjfVsp0FL2CMxT323S8M9+NTiV/6F8AAD8isVs0vMD2urCZnUkSXuOHFfm4WNyOLgF4ymUDwCA37mxU4JrEbqDeYW68KX5Gjot1eBU/oPyAQDwSw0ia6hLo0gFBZR9FabtPmJwIv9B+QAA+KUAi1lf3NtNix6+VJKUX1iicTM36sPlO40N5gcCjA4AAICRIkIDFGQxq6jUoY9W7JJUNi9Ik7phBifzXW6/8jF+/Hh17txZ4eHhio6OVv/+/bV161Z3nwYAALeoERSgqUM6aUTPZq5JyI4eKzY4lW9ze/lYuHChUlJStGLFCs2ZM0fFxcXq3bu38vPz3X0qAADc4uLz6urBy89TTHiIJOnxbzbqtv+sZB2YKuL22y6zZ88utz1t2jRFR0drzZo1uuiii9x9OgAA3KZ+7VBtzc7Vb/vskqTD+UW6ok2swal8T5WP+bDZbJKkyMjICt8vLCxUYWGha9tut1d1JAAAKvTvG5O07PdD2p6Tp9fmbtPx4lKjI/mkKn3axeFwaOTIkUpOTlabNm0qPGb8+PGyWq2uV0JCQlVGAgDgpGrXDFLfdnG6rEW0JOlgbqFe/mmLXp2zTTsO5BmczneYnFW4qs59992nH3/8UUuWLFH9+vUrPKaiKx8JCQmy2WyKiIioqmgAAJxU5uFjuvCl+eX29WwRrXf/0dmgRN7PbrfLarWe0fd3ld12GTZsmGbNmqVFixadtHhIUnBwsIKDg6sqBgAAlZYQWUMvXNdWW7JytefIMc3dnKPDx4qMjuUz3F4+nE6nHnjgAc2YMUMLFixQYmKiu08BAECVu7lLA0nSom0HNHdzjgqLHQYn8h1uLx8pKSmaPn26vvnmG4WHhysrq+wxJavVqtDQUHefDgCAKnVi+vXtB/LUb8ISSVKbehF6fkBbmUwmI6NVW24fcDpp0iTZbDZdcskliouLc70+//xzd58KAIAqlxBZQyaTVFTi0Ia9Nm3Ya9OnqzK1+/Axo6NVW1Vy2wUAAF9Rr1ao5o662FU2hk//VbmFJSos4TbM2WJtFwAATqNJ3TDXWi81gi3KLSxREeXjrFE+AACohBNjQGat36/1e8om0jwvJkydGlU8mSb+jvIBAEAl1Awq++qcvPB31z6L2aSVj/RUnTCmjjgTlA8AACrh/65ori9S98jxxxjHBVsPqKjUocP5RZSPM0T5AACgEi5rEaPLWsS4trs8N1c5uYWMAamEKl3bBQAAXxdoKfsqLXHwtOeZ4soHAADnINBSNtFYQXGpSkr/vPphMZuYhOwkKB8AAJyDgD+ufNw8ZUW5/U2jwzTrgR4KCbQYEcurcdsFAIBzkNwkqsL923PytOsQs6BWhCsfAACcg6eubaNRvZuXm+G716uLdDCvUCUOBqFWhPIBAMA5soYGltsO+mMcSCmDUCvEbRcAANzM8kf54AmYilE+AABwswBz2dcrVz4qxm0XAADczGIuu/JxJL9IR/KLyr1nNpv+dpvG31A+AABws4A/ysfdH62p8P17LmqssVe19GQkr8JtFwAA3KxXyxidan6xFRmHPRfGC3HlAwAAN/tXn+Z68PLz/rZ//pYc/fPD1XL4+VgQygcAAFXgxLiP/xXAI7iSuO0CAIDHnCgkDiflAwAAeIDFxJUPifIBAIDHmP+48lHKlQ8AAOAJrtsuXPkAAACeYDadGPNhcBCD8bQLAAAecuLKx7GiEs3fknPa4wMtZnVOrK3gAEtVR/MoygcAAB4S+MejtgfzijR0WuoZ/czt3Rrq6WvbVGUsj6N8AADgIS1iI3Rd+3rafiDvtMceyivS3qPHte/ocQ8k8yzKBwAAHmIxm/TqTeef0bFfrM7U/3253ifHhzDgFAAAL3RiflSnDz6WS/kAAMAL+fKTMZQPAAC80IlVcX1xKnbKBwAAXujElQ8f7B6UDwAAvNGJKx9O+V77oHwAAOCFTCfGfDgMDlIFKB8AAHghM2M+AACAJ7nGfBicoypQPgAA8ELM8wEAADzKxDwflTdx4kQ1atRIISEh6tq1q1atWlVVpwIAwOcw5qOSPv/8c40aNUpPPPGE0tLSlJSUpD59+ign5/TLBwMAgD+vfPhg96ia8vHqq6/qrrvu0tChQ9WqVStNnjxZNWrU0HvvvVcVpwMAwOecuPLhi2M+3L6qbVFRkdasWaOxY8e69pnNZvXq1UvLly//2/GFhYUqLCx0bdvtdndHAgCg2jnxtMueI8f11He/ufV31wkLVsqlTd36OyvD7eXj4MGDKi0tVUxMTLn9MTEx2rJly9+OHz9+vJ566il3xwAAoFoLDyn7ij6UX6T3l+506+9uXLemb5WPyho7dqxGjRrl2rbb7UpISDAwEQAAxuvQoLaeH9BWe48ec/vvrl0jyO2/szLcXj7q1Kkji8Wi7Ozscvuzs7MVGxv7t+ODg4MVHBzs7hgAAFRrZrNJt3ZtYHSMKuH2AadBQUHq2LGj5s2b59rncDg0b948devWzd2nAwAA1UyV3HYZNWqUhgwZok6dOqlLly56/fXXlZ+fr6FDh1bF6QAAQDVSJeXjpptu0oEDB/T4448rKytL559/vmbPnv23QagAAMD/mJxe9gCx3W6X1WqVzWZTRESE0XEAAMAZqMz3N2u7AAAAj6J8AAAAj6J8AAAAj6J8AAAAj6J8AAAAj6J8AAAAj6J8AAAAj6J8AAAAj6J8AAAAj6qS6dXPxYkJV+12u8FJAADAmTrxvX0mE6d7XfnIzc2VJCUkJBicBAAAVFZubq6sVuspj/G6tV0cDof27dun8PBwmUwmt/5uu92uhIQEZWZmsm5MBfh8To7P5tT4fE6Nz+fU+HxOrjp9Nk6nU7m5uYqPj5fZfOpRHV535cNsNqt+/fpVeo6IiAiv/y/RSHw+J8dnc2p8PqfG53NqfD4nV10+m9Nd8TiBAacAAMCjKB8AAMCj/Kp8BAcH64knnlBwcLDRUbwSn8/J8dmcGp/PqfH5nBqfz8n56mfjdQNOAQCAb/OrKx8AAMB4lA8AAOBRlA8AAOBRlA8AAOBRflM+Jk6cqEaNGikkJERdu3bVqlWrjI7kFcaPH6/OnTsrPDxc0dHR6t+/v7Zu3Wp0LK/1wgsvyGQyaeTIkUZH8Rp79+7VbbfdpqioKIWGhqpt27ZavXq10bEMV1paqnHjxikxMVGhoaFq0qSJnnnmmTNa98IXLVq0SP369VN8fLxMJpNmzpxZ7n2n06nHH39ccXFxCg0NVa9evZSenm5MWAOc6vMpLi7W6NGj1bZtW9WsWVPx8fG6/fbbtW/fPuMCnyO/KB+ff/65Ro0apSeeeEJpaWlKSkpSnz59lJOTY3Q0wy1cuFApKSlasWKF5syZo+LiYvXu3Vv5+flGR/M6qampeuedd9SuXTujo3iNI0eOKDk5WYGBgfrxxx+1adMmvfLKK6pdu7bR0Qz34osvatKkSXrrrbe0efNmvfjii3rppZc0YcIEo6MZIj8/X0lJSZo4cWKF77/00kt68803NXnyZK1cuVI1a9ZUnz59VFBQ4OGkxjjV53Ps2DGlpaVp3LhxSktL09dff62tW7fqmmuuMSCpmzj9QJcuXZwpKSmu7dLSUmd8fLxz/PjxBqbyTjk5OU5JzoULFxodxavk5uY6mzVr5pwzZ47z4osvdo4YMcLoSF5h9OjRzh49ehgdwyv17dvXeccdd5Tbd9111zkHDRpkUCLvIck5Y8YM17bD4XDGxsY6X375Zde+o0ePOoODg52ffvqpAQmN9dfPpyKrVq1ySnLu2rXLM6HczOevfBQVFWnNmjXq1auXa5/ZbFavXr20fPlyA5N5J5vNJkmKjIw0OIl3SUlJUd++fcv97wjSt99+q06dOunGG29UdHS02rdvr6lTpxodyyt0795d8+bN07Zt2yRJ69at05IlS3TllVcanMz7ZGRkKCsrq9y/L6vVqq5du/J3+iRsNptMJpNq1apldJSz4nULy7nbwYMHVVpaqpiYmHL7Y2JitGXLFoNSeSeHw6GRI0cqOTlZbdq0MTqO1/jss8+Ulpam1NRUo6N4nR07dmjSpEkaNWqUHnnkEaWmpmr48OEKCgrSkCFDjI5nqDFjxshut6tFixayWCwqLS3Vc889p0GDBhkdzetkZWVJUoV/p0+8hz8VFBRo9OjRuuWWW6rFYnMV8fnygTOXkpKijRs3asmSJUZH8RqZmZkaMWKE5syZo5CQEKPjeB2Hw6FOnTrp+eeflyS1b99eGzdu1OTJk/2+fHzxxRf65JNPNH36dLVu3Vpr167VyJEjFR8f7/efDc5ecXGxBg4cKKfTqUmTJhkd56z5/G2XOnXqyGKxKDs7u9z+7OxsxcbGGpTK+wwbNkyzZs3S/PnzVb9+faPjeI01a9YoJydHHTp0UEBAgAICArRw4UK9+eabCggIUGlpqdERDRUXF6dWrVqV29eyZUvt3r3boETe4+GHH9aYMWN08803q23btho8eLAefPBBjR8/3uhoXufE32L+Tp/aieKxa9cuzZkzp9pe9ZD8oHwEBQWpY8eOmjdvnmufw+HQvHnz1K1bNwOTeQen06lhw4ZpxowZ+uWXX5SYmGh0JK/Ss2dPbdiwQWvXrnW9OnXqpEGDBmnt2rWyWCxGRzRUcnLy3x7N3rZtmxo2bGhQIu9x7Ngxmc3l/8RaLBY5HA6DEnmvxMRExcbGlvs7bbfbtXLlSv5O/+FE8UhPT9fcuXMVFRVldKRz4he3XUaNGqUhQ4aoU6dO6tKli15//XXl5+dr6NChRkczXEpKiqZPn65vvvlG4eHhrvurVqtVoaGhBqczXnh4+N/Gv9SsWVNRUVGMi5H04IMPqnv37nr++ec1cOBArVq1SlOmTNGUKVOMjma4fv366bnnnlODBg3UunVr/frrr3r11Vd1xx13GB3NEHl5edq+fbtrOyMjQ2vXrlVkZKQaNGigkSNH6tlnn1WzZs2UmJiocePGKT4+Xv379zcutAed6vOJi4vTDTfcoLS0NM2aNUulpaWuv9WRkZEKCgoyKvbZM/pxG0+ZMGGCs0GDBs6goCBnly5dnCtWrDA6kleQVOHr/fffNzqa1+JR2/K+++47Z5s2bZzBwcHOFi1aOKdMmWJ0JK9gt9udI0aMcDZo0MAZEhLibNy4sfPRRx91FhYWGh3NEPPnz6/wb82QIUOcTmfZ47bjxo1zxsTEOIODg509e/Z0bt261djQHnSqzycjI+Okf6vnz59vdPSzYnI6/XS6PQAAYAifH/MBAAC8C+UDAAB4FOUDAAB4FOUDAAB4FOUDAAB4FOUDAAB4FOUDAAB4FOUDAAB4FOUDAAB4FOUDAAB4FOUDAAB4FOUDAAB41P8DaemyuZAAT8sAAAAASUVORK5CYII="
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Zipf's law plot for words\n",
+    "log_rang_log_freq('zipf_plot_words', get_words(text))"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "# Zbadać prawo Zipfa dla innych jednostek niż wyrazy (n-gramy, rdzenie, lematy, itp.)"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "'zipf_plot_ngrams.png'"
+     },
+     "execution_count": 59,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "text/plain": "<Figure size 640x480 with 1 Axes>",
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Zipf's law plot for n-grams\n",
+    "log_rang_log_freq('zipf_plot_ngrams', ngrams(get_characters(text), 3))"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "'zipf_plot_lemmas.png'"
+     },
+     "execution_count": 60,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "text/plain": "<Figure size 640x480 with 1 Axes>",
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Zipf's law plot for lemmas\n",
+    "log_rang_log_freq('zipf_plot_lemmas', lemmas(get_words(text)))"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "'zipf_plot_stems.png'"
+     },
+     "execution_count": 61,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "text/plain": "<Figure size 640x480 with 1 Axes>",
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Zipf's law plot for stems\n",
+    "log_rang_log_freq('zipf_plot_stems', stems(get_words(text)))"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "# Podać słowa, które najbardziej łamią prawo wiążące długość z częstością"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Word\t\tfrequency * length\n",
+      "------------------------------\n",
+      "1. the\t\t\t3346731\n",
+      "2. and\t\t\t1609434\n",
+      "3. of\t\t\t958674\n",
+      "4. And\t\t\t916899\n",
+      "5. to\t\t\t752068\n",
+      "6. that\t\t\t726440\n",
+      "7. with\t\t\t688892\n",
+      "8. The\t\t\t602034\n",
+      "9. in\t\t\t579134\n",
+      "10. his\t\t\t552441\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Words which violate the length-frequency law the most (highest frequency to length ratio)\n",
+    "toplist = find_long_popular_words(get_words(text), top=10)\n",
+    "print('Word\\t\\tfrequency * length')\n",
+    "print('-'*30)\n",
+    "for i in range(len(toplist)):\n",
+    "    print(f'{i + 1}. {toplist[i][1]}\\t\\t\\t{toplist[i][0]}')"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "# Wymyślić i zbadać 2 zależności dotyczące wyrazów bądź innych jednostek w tekście"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "<Figure size 640x480 with 1 Axes>",
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Brevity law\n",
+    "# https://en.wikipedia.org/wiki/Brevity_law\n",
+    "plot_brevity_law(get_words(text))"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "<Figure size 640x480 with 1 Axes>",
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Heaps' law\n",
+    "# https://en.wikipedia.org/wiki/Heaps'_law\n",
+    "plot_heaps_law(get_words(text))"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/lab2_during_class/top_10.txt b/lab2_during_class/top_10.txt
new file mode 100644
index 0000000..8550364
--- /dev/null
+++ b/lab2_during_class/top_10.txt
@@ -0,0 +1,12 @@
+43: ninepenceashillingneteighteenpencetwoandsix
+36: crownnettwoandeightpencethreeandnine
+35: Lebensfeuerversicherunggesellschaft
+33: Gottsdonnerkreuzschockschwerenoth
+28: Wintztschitstopschinzoudhoff
+25: Selbstanschauungsvermogen
+24: Aldiborontiphoscophornio, WappenshieldWaffenschild, WunderscheenWunderschoen
+22: honorificabilitudinity
+21: Voelkerwanderungszeit
+20: Alliterationspoesien, Fornminnesfoerenings, Retrogradeacompletes, andkindlystepthisway, richardwashburnchild, xinechoncentlalizqui
+
+Komentarz: ponieważ mowa o korpusie języka angielskiego, za rzeczywiście najdłuższe słowo należałoby uznać "honorificabilitudinity" o długości 22 liter. Słowa dłuższe to pojedyncze niemieckie wyrazy lub ciągi angielskich słów, które połączone zostały przy oczyszczaniu korpusu (najpewniej były pierwotnie oddzielone jakimiś niestandardowymi znakami).
diff --git a/lab2_during_class/zipf_plot.png b/lab2_during_class/zipf_plot.png
new file mode 100644
index 0000000..c74fad0
Binary files /dev/null and b/lab2_during_class/zipf_plot.png differ