497 lines
186 KiB
Plaintext
497 lines
186 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"\n",
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"<div class=\"alert alert-block alert-info\">\n",
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"<h1> Modelowanie języka</h1>\n",
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"<h2> 03. <i>N-gramy</i> [wykład]</h2> \n",
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"<h3> Filip Graliński (2022)</h3>\n",
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"</div>\n",
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"\n",
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"\n",
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"\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## N-gramy\n",
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"\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"W modelowaniu języka często rozpatruje się n-gramy, czyli podciągi o\n",
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"rozmiarze $n$.\n",
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"\n",
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"Na przykład *digramy* (*bigramy*) to zbitki dwóch jednostek, np. liter albo wyrazów.\n",
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"\n",
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"| $n$|$n$-gram|nazwa|\n",
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"|---|---|---|\n",
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"| 1|1-gram|unigram|\n",
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"| 2|2-gram|digram/bigram|\n",
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"| 3|3-gram|trigram|\n",
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"| 4|4-gram|tetragram|\n",
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"| 5|5-gram|pentagram|\n",
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"\n",
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"**Pytanie:** Jak nazywa się 6-gram?\n",
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"\n",
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"Jak widać, dla symetrii mówimy czasami o unigramach, jeśli operujemy\n",
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"po prostu na jednostkach, nie na ich podciągach.\n",
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"\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"#### N-gramy z Pana Tadeusza\n",
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"\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Statystyki, które policzyliśmy dla pojedynczych liter czy wyrazów, możemy powtórzyć dla n-gramów.\n",
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"\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[('k', 'o', 't'), ('o', 't', 'e'), ('t', 'e', 'k')]"
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]
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}
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],
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"source": [
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"def ngrams(iter, size):\n",
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" ngram = []\n",
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" for item in iter:\n",
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" ngram.append(item)\n",
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" if len(ngram) == size:\n",
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" yield tuple(ngram)\n",
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" ngram = ngram[1:]\n",
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"\n",
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"list(ngrams(\"kotek\", 3))"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Zauważmy, że policzyliśmy wszystkie n-gramy, również częściowo się pokrywające.\n",
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"\n",
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"Zawsze powinniśmy się upewnić, czy jest jasne, czy chodzi o n-gramy znakowe czy wyrazowe\n",
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"\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"#### 3-gramy znakowe\n",
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"\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [
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{
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"data": {
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",
|
|
"text/plain": [
|
|
"<matplotlib.figure.Figure>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"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": 1,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"image/png": 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",
|
|
"text/plain": [
|
|
"<matplotlib.figure.Figure>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"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: https://commons.wikimedia.org/wiki/File:Voynich<sub>Manuscript</sub><sub>(135)</sub>.jpg](./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": 1,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"9 OR 9FAM ZO8 QOAR9 Q*R 8ARAM 29 [O82*]OM OPCC9 OP"
|
|
]
|
|
}
|
|
],
|
|
"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": 1,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"image/png": 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",
|
|
"text/plain": [
|
|
"<matplotlib.figure.Figure>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"rang_freq_with_labels('voy-chars', get_characters(voynich))"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 1,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
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"data": {
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"image/png": 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",
|
|
"text/plain": [
|
|
"<matplotlib.figure.Figure>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"log_rang_log_freq('voy-log-log', get_words(voynich))"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 1,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"image/png": 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",
|
|
"text/plain": [
|
|
"<matplotlib.figure.Figure>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"rang_freq_with_labels('voy-words-20', get_words(voynich), top=20)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 1,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"image/png": 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",
|
|
"text/plain": [
|
|
"<matplotlib.figure.Figure>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"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": 1,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"TATAACCCTAACCCTAACCCTAACCCTAACCCTAACCCTAACCCTAACCCTAACCCTAACCCTAACCCTAACCCTAACCCTAACCCTAACCCTAACCCTA"
|
|
]
|
|
}
|
|
],
|
|
"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": 1,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"image/png": 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|
|
"text/plain": [
|
|
"<matplotlib.figure.Figure>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"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": 1,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"image/png": 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",
|
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"text/plain": [
|
|
"<matplotlib.figure.Figure>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
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"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",
|
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" '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": 1,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"image/png": 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",
|
|
"text/plain": [
|
|
"<matplotlib.figure.Figure>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"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 = '03_Ngramy/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": "3.10.9"
|
|
},
|
|
"org": null
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 1
|
|
}
|