Added an alternative algorithm
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import numpy as np\n",
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"from PIL import Image\n",
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"import random\n",
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"import re\n",
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"from collections import defaultdict\n",
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"\n",
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"#Converts image to \"text\"\n",
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"def img_to_text(img_val):\n",
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" words = []\n",
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" for i in img_val:\n",
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" # r g b\n",
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" outword = str(i[0]) + ',' + str(i[1]) + ',' + str(i[2])\n",
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" words.append(outword)\n",
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" return words\n",
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"\n",
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"#init corpus\n",
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"corpus = []\n",
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"#load first image\n",
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"im = Image.open(\"bauhaus.jpg\", \"r\")\n",
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"#Get img text\n",
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"pix_val = list(im.getdata())\n",
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"img_text = img_to_text(pix_val)\n",
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"corpus = corpus + img_text\n",
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"\n",
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"#load first image\n",
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"im = Image.open(\"bauhaus2.jpg\", \"r\")\n",
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"#Get img text\n",
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"pix_val = list(im.getdata())\n",
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"img_text = img_to_text(pix_val)\n",
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"corpus = corpus + img_text\n",
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"\n",
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"#load first image\n",
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"im = Image.open(\"bauhaus3.jpg\", \"r\")\n",
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"#Get img text\n",
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"pix_val = list(im.getdata())\n",
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"img_text = img_to_text(pix_val)\n",
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"corpus = corpus + img_text\n",
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"\n",
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"\n",
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"#markov model\n",
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"markov_graph = defaultdict(lambda: defaultdict(int))\n",
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"tokenized_text = corpus\n",
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"last_word = tokenized_text[0].lower()\n",
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"for word in tokenized_text[1:]:\n",
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" word = word.lower()\n",
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" markov_graph[last_word][word] += 1\n",
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" last_word = word\n",
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"\n",
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"# Preview graph.\n",
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"limit = 3\n",
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"for first_word in ('the', 'by', 'who'):\n",
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" next_words = list(markov_graph[first_word].keys())[:limit]\n",
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" for next_word in next_words:\n",
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" print(first_word, next_word)\n",
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"\n",
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"def walk_graph(graph, distance=5, start_node=None):\n",
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" \"\"\"Returns a list of words from a randomly weighted walk.\"\"\"\n",
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" if distance <= 0:\n",
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" return []\n",
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" \n",
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" # If not given, pick a start node at random.\n",
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" if not start_node:\n",
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" start_node = random.choice(list(graph.keys()))\n",
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" \n",
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" \n",
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" weights = np.array(\n",
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" list(markov_graph[start_node].values()),\n",
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" dtype=np.float64)\n",
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" # Normalize word counts to sum to 1.\n",
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" weights /= weights.sum()\n",
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"\n",
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" # Pick a destination using weighted distribution.\n",
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" choices = list(markov_graph[start_node].keys())\n",
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" chosen_word = np.random.choice(choices, None, p=weights)\n",
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" \n",
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" return [chosen_word] + walk_graph(\n",
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" graph, distance=distance-1,\n",
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" start_node=chosen_word)\n",
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" \n",
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"chain = []\n",
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"for i in range(10000):\n",
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" chain = chain + walk_graph(markov_graph, distance=12)\n",
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"print(chain)"
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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": 11,
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"metadata": {},
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"outputs": [],
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"source": [
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"def chain_to_rgbbytes(ctext):\n",
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" out_list = []\n",
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" #for each generated pixel\n",
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" for i in ctext:\n",
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" #split out values\n",
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" i = i.split(\",\")\n",
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" #for each value in a pixel, add the int to the list\n",
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" for x in i:\n",
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" out_list.append(int(x))\n",
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" #return and convert to bytes:\n",
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" return out_list"
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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": 12,
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"metadata": {},
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"outputs": [
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{
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"ename": "ValueError",
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"evalue": "not enough image data",
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"output_type": "error",
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"traceback": [
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"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
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"\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)",
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"\u001b[1;32m<ipython-input-12-d1de3a1e6f5d>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m 2\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3\u001b[0m \u001b[0mcolors\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mbytes\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mchain_to_rgbbytes\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mchain\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 4\u001b[1;33m \u001b[0mimg\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mImage\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfrombytes\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'RGB'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m(\u001b[0m\u001b[1;36m1000\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m1000\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcolors\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 5\u001b[0m \u001b[0mimg\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
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"\u001b[1;32m~\\anaconda3\\lib\\site-packages\\PIL\\Image.py\u001b[0m in \u001b[0;36mfrombytes\u001b[1;34m(mode, size, data, decoder_name, *args)\u001b[0m\n\u001b[0;32m 2656\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2657\u001b[0m \u001b[0mim\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnew\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmode\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msize\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2658\u001b[1;33m \u001b[0mim\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfrombytes\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdecoder_name\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2659\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mim\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2660\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
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"\u001b[1;32m~\\anaconda3\\lib\\site-packages\\PIL\\Image.py\u001b[0m in \u001b[0;36mfrombytes\u001b[1;34m(self, data, decoder_name, *args)\u001b[0m\n\u001b[0;32m 795\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 796\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0ms\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m>=\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 797\u001b[1;33m \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"not enough image data\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 798\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0ms\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m!=\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 799\u001b[0m \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"cannot decode image data\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
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"\u001b[1;31mValueError\u001b[0m: not enough image data"
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]
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}
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],
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"source": [
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"#generate images!\n",
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"\n",
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"colors = bytes(chain_to_rgbbytes(chain))\n",
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"img = Image.frombytes('RGB', (100, 100), colors)\n",
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"img.show()"
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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": null,
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.8.5"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 4
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}
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