personal_site/poem/compute_distance_field.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('PNG', (500, 500), 'RGBA', (500, 500, 4))"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from traceback_with_variables import activate_in_ipython_by_import\n",
    "from PIL import Image\n",
    "import numpy as np\n",
    "im = Image.open(\"image.png\")\n",
    "(im.format, im.size, im.mode, np.array(im).shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 52, 155,  60, ..., 247, 231,  52], dtype=uint8)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.asarray(im)[np.where(np.asarray(im))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "threshold = 150\n",
    "src = np.array(im)[:, :, 3] < 150"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "CPU times: user 21 s, sys: 14.8 s, total: 35.8 s\n",
      "Wall time: 36 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "pixels = np.array(np.where(1 - src))\n",
    "I = np.arange(src.shape[0]).reshape(-1,1,1)\n",
    "J = np.arange(src.shape[1]).reshape(1,-1,1)\n",
    "\n",
    "dist = np.ones(src.shape) * 1000\n",
    "for k in range(10):\n",
    "    print(k)\n",
    "    i, j = pixels[:, k::10].reshape(2, 1, 1, -1)\n",
    "    dist = np.minimum(dist, np.min(np.sqrt((I - i)**2 + (J - j)**2), axis = -1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3\n",
      "2\n",
      "0\n",
      "1\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<timed exec>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/lib/python3.7/multiprocessing/pool.py\u001b[0m in \u001b[0;36mmap\u001b[0;34m(self, func, iterable, chunksize)\u001b[0m\n\u001b[1;32m    266\u001b[0m         \u001b[0;32min\u001b[0m \u001b[0ma\u001b[0m \u001b[0mlist\u001b[0m \u001b[0mthat\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mreturned\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    267\u001b[0m         '''\n\u001b[0;32m--> 268\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_map_async\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0miterable\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmapstar\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mchunksize\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    269\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    270\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mstarmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0miterable\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mchunksize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/lib/python3.7/multiprocessing/pool.py\u001b[0m in \u001b[0;36mget\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    649\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    650\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 651\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    652\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mready\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    653\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mTimeoutError\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/lib/python3.7/multiprocessing/pool.py\u001b[0m in \u001b[0;36mwait\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    646\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    647\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mwait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 648\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_event\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    649\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    650\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/lib/python3.7/threading.py\u001b[0m in \u001b[0;36mwait\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    550\u001b[0m             \u001b[0msignaled\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_flag\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    551\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0msignaled\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 552\u001b[0;31m                 \u001b[0msignaled\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_cond\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    553\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0msignaled\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    554\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/lib/python3.7/threading.py\u001b[0m in \u001b[0;36mwait\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    294\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m    \u001b[0;31m# restore state no matter what (e.g., KeyboardInterrupt)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    295\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mtimeout\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 296\u001b[0;31m                 \u001b[0mwaiter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0macquire\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    297\u001b[0m                 \u001b[0mgotit\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    298\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "\"\"\"\n",
    "%%time\n",
    "pixels = np.array(np.where(1 - src))[:, :]\n",
    "I = np.arange(500).reshape(-1,1,1)\n",
    "J = np.arange(500).reshape(1,-1,1)\n",
    "\n",
    "groups = 10\n",
    "\n",
    "def compute(k):\n",
    "    i, j = pixels[:, k::groups].reshape(2, 1, 1, -1)\n",
    "    dist = np.min(np.sqrt((I - i)**2 + (J - j)**2), axis = -1, initial = 1000)\n",
    "    print(k)\n",
    "    return dist\n",
    "\n",
    "import multiprocessing as mpl\n",
    "with mpl.Pool(4) as p:\n",
    "    dist = np.min(p.map(compute, range(groups), chunksize = 1), axis = 0)\n",
    "dist\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<PIL.Image.Image image mode=RGBA size=500x500 at 0x7FC98E137E50>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "quantised = 255 - (dist / np.max(dist) * 255).astype(np.uint8)\n",
    "#quantised = (quantised % 2) * 255\n",
    "im2 = Image.fromarray(quantised, mode = 'L')\n",
    "im2 = im2.convert(\"RGBA\")\n",
    "im2.save('distfield.png')\n",
    "im2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.LineCollection at 0x7fc98e5189d0>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "f, axes = plt.subplots(ncols = 3, figsize = (15,5))\n",
    "axes[0].imshow(src)\n",
    "axes[2].imshow(im2)\n",
    "vals = np.array(im)[:, :, 3].flatten()\n",
    "axes[1].hist(vals[vals > 1]);\n",
    "axes[1].vlines(threshold, *axes[1].get_ylim(), linestyle = '--')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 357,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<span>What </span><span>is </span><span>the </span><span>value </span><span>of </span><span>a </span><span>fact? </span><br>\n",
      "<span>a </span><span>question </span><span>theoretical </span><br>\n",
      "<span>answers </span><span>are </span><span>asymmetrical </span><br>\n",
      "<span>given </span><span>your </span><span>comfort </span><span>with </span><span>abstracts. </span><br>\n",
      "<br>\n",
      "<span>You </span><span>may </span><span>feel </span><span>personally </span><span>attacked </span><br>\n",
      "<span>or </span><span>held </span><span>within </span><span>the </span><span>metrical </span><br>\n",
      "<span>we </span><span>research </span><span>the </span><span>aesthetical </span><span>– </span><br>\n",
      "<span>how </span><span>the </span><span>world’s </span><span>forces </span><span>interact. </span><br>\n",
      "<br>\n",
      "<span>Boundaries </span><span>expanded </span><span>at </span><span>college </span><br>\n",
      "<span>lead </span><span>to </span><span>discoveries </span><span>anew </span><br>\n",
      "<span>this </span><span>– </span><span>far </span><span>from </span><span>being </span><span>vanity </span><span>– </span><br>\n",
      "<span>increase </span><span>our </span><span>collective </span><span>knowledge </span><br>\n",
      "<span>applied, </span><span>we </span><span>hope, </span><span>life </span><span>will </span><span>improve </span><br>\n",
      "<span>for </span><span>the </span><span>good </span><span>of </span><span>humanity. </span><br>\n",
      "\n"
     ]
    }
   ],
   "source": [
    "poem = \"\"\"What is the value of a fact?\n",
    "a question theoretical\n",
    "answers are asymmetrical\n",
    "given your comfort with abstracts.\n",
    "\n",
    "You may feel personally attacked\n",
    "or held within the metrical\n",
    "we research the aesthetical –\n",
    "how the world’s forces interact.\n",
    "\n",
    "Boundaries expanded at college\n",
    "lead to discoveries anew\n",
    "this – far from being vanity –\n",
    "increase our collective knowledge\n",
    "applied, we hope, life will improve\n",
    "for the good of humanity.\n",
    "\"\"\"\n",
    "print(\"<br>\\n\".join(\"\".join(f\"<span>{word} </span>\" for word in line.split()) for line in poem.split('\\n')))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[['There’s', 'chaos', 'in', 'the', 'shimmering', 'heat,'],\n",
       " ['All', 'jives', 'and', 'jostles,', 'structures', 'melt', 'and'],\n",
       " ['Order', 'boils', 'until', 'absence', 'is', 'complete,'],\n",
       " ['Of', 'pattern', 'in', 'the', 'scorching', 'shifting', 'sand.'],\n",
       " [],\n",
       " ['While', 'bitter,', 'on', 'the', 'other', 'side,', 'jutting'],\n",
       " ['Great', 'crystal', 'castles.', 'Lattice', 'works', 'of', 'ice.'],\n",
       " ['Their', 'shear', 'edges', 'almost', 'touching,', 'cutting'],\n",
       " ['Cleaving', 'space', 'apart,', 'to', 'each', 'a', 'separate', 'slice.'],\n",
       " [],\n",
       " ['It’s', 'in', 'between', 'that', 'pattern', 'start', 'to', 'dance,'],\n",
       " ['merging', 'melting', 'bodies,', 'all', 'together.'],\n",
       " ['Hypnotic', 'orchestras', 'dictate', 'their', 'trance.'],\n",
       " ['Connected', 'through', 'some', 'esoteric', 'aether.'],\n",
       " [],\n",
       " ['These', 'littles', 'worlds', 'found', 'only', 'at', 'the', 'borders'],\n",
       " ['create', 'their', 'own', 'unique', 'and', 'gentle', 'orders']]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[line.split() for line in poem.split('\\n')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['There’s chaos in the shimmering heat,',\n",
       " 'All jives and jostles, structures melt and',\n",
       " 'Order boils until absence is complete,',\n",
       " 'Of pattern in the scorching shifting sand.',\n",
       " '',\n",
       " 'While bitter, on the other side, jutting',\n",
       " 'Great crystal castles. Lattice works of ice.',\n",
       " 'Their shear edges almost touching, cutting',\n",
       " 'Cleaving space apart, to each a separate slice.',\n",
       " '',\n",
       " 'It’s in between that pattern start to dance,',\n",
       " 'merging melting bodies, all together. ',\n",
       " 'Hypnotic orchestras dictate their trance. ',\n",
       " 'Connected through some esoteric aether.',\n",
       " '',\n",
       " 'These littles worlds found only at the borders',\n",
       " ' create their own unique and gentle orders ']"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[line for line in poem.split('\\n')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'int'>\n",
      "<class 'str'>\n",
      "<class 'str'>\n",
      "321\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "322"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#definitions\n",
    "example_string = \"It was the best of times, it was the worst of times.\"\n",
    "l = [\"one\",2,'goal',4,5]\n",
    "\n",
    "#functions and methods\n",
    "#functions: print, str\n",
    "\n",
    "#def name_of_the_function(arg1, arg2, arg3):\n",
    "    #lines \n",
    "    #of \n",
    "    #code \n",
    "#    return values\n",
    "\n",
    "def number_reverse(num):\n",
    "    print(type(num))\n",
    "    string = str(num)\n",
    "    print(type(string))\n",
    "    string = string[::-1]\n",
    "    print(type(string))\n",
    "    reversed_num = int(string)\n",
    "    print(reversed_num)\n",
    "    return reversed_num\n",
    "    \n",
    "a = number_reverse(123)\n",
    "a + 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[36mTraceback with variables (most recent call last):\u001b[0m\n",
      "\u001b[36m  File \"\u001b[0m\u001b[36;1m<ipython-input-22-24a588543f2c>\u001b[0m\u001b[36m\", line \u001b[0m\u001b[36;1m18\u001b[0m\u001b[36m, in \u001b[0m\u001b[36;1m<module>\u001b[0m\n",
      "\u001b[36m    \u001b[0m\u001b[35mcode = countryname_to_code(name)\u001b[0m\n",
      "\u001b[36m      \u001b[0m\u001b[32;1m__name__\u001b[0m\u001b[36m = \u001b[0m'__main__'\u001b[0m\n",
      "\u001b[36m      \u001b[0m\u001b[32;1m__doc__\u001b[0m\u001b[36m = \u001b[0m'Automatically created module for IPython interactive environment'\u001b[0m\n",
      "\u001b[36m      \u001b[0m\u001b[32;1m__package__\u001b[0m\u001b[36m = \u001b[0mNone\u001b[0m\n",
      "\u001b[36m      \u001b[0m\u001b[32;1m__loader__\u001b[0m\u001b[36m = \u001b[0mNone\u001b[0m\n",
      "\u001b[36m      \u001b[0m\u001b[32;1m__spec__\u001b[0m\u001b[36m = \u001b[0mNone\u001b[0m\n",
      "\u001b[36m      \u001b[0m\u001b[32;1m__builtin__\u001b[0m\u001b[36m = \u001b[0m<module 'builtins' (built-in)>\u001b[0m\n",
      "\u001b[36m      \u001b[0m\u001b[32;1m__builtins__\u001b[0m\u001b[36m = \u001b[0m<module 'builtins' (built-in)>\u001b[0m\n",
      "\u001b[36m      \u001b[0m\u001b[32;1m_ih\u001b[0m\u001b[36m = \u001b[0m['', '#definitions\\nexample_string = \"It was the best of times, it was the worst of times.\"\\nl = [\"one\",2,\\'goal\\',4,5]\\n\\n#functions and methods\\n#functions: print, str\\n\\n#def name_of_the_function(arg1, arg2, arg3):\\n    #lines \\n    #of \\n    #code \\n#    return values\\n\\ndef number_reverse(num):\\n    print(num)\\n    string = str(num)\\n    print(string)\\n    string = string[::-1]\\n    print(string)\\n    reversed_num = int(string)\\n    return reversed_num\\n    print(reversed_num)\\n    \\na = number_reverse(123)', '#definitions\\nexample_string = \"It was the best of times, it was the worst of times.\"\\nl = [\"one\",2,\\'goal\\',4,5]\\n\\n#functions and methods\\n#functions: print, str\\n\\n#def name_of_the_function(arg1, arg2, arg3):\\n    #lines \\n    #of \\n    #code \\n#    return values\\n\\ndef number_reverse(num):\\n    print(num)\\n    string = str(num)\\n    print(string)\\n    string = string[::-1]\\n    print(string)\\n    reversed_num = int(string)\\n    print(reversed_num)\\n    return reversed_nu...\u001b[0m\n",
      "\u001b[36m      \u001b[0m\u001b[32;1m_oh\u001b[0m\u001b[36m = \u001b[0m{5: 322, 7: <class 'list'>, 8: ['UK', 'Germany', 'France', 'Egypt'], 9: ['UK', 'Germany', 'France', 'Egypt', 'USA'], 11: ['UK', 'USA', 'Germany', 'France'], 12: 'USA', 13: True, 15: True, 16: ['UK', 'USA', 'Germany', 'France'], 19: ['UK', 'Germany', 'France'], 20: ['GB', 'DE', 'FR'], 21: ('PNG', (500, 500), 'RGBA', (500, 500, 4))}\u001b[0m\n",
      "\u001b[36m      \u001b[0m\u001b[32;1m_dh\u001b[0m\u001b[36m = \u001b[0m['/Users/tom/git/tomhodson.github.com/poem']\u001b[0m\n",
      "\u001b[36m      \u001b[0m\u001b[32;1mIn\u001b[0m\u001b[36m = \u001b[0m['', '#definitions\\nexample_string = \"It was the best of times, it was the worst of times.\"\\nl = [\"one\",2,\\'goal\\',4,5]\\n\\n#functions and methods\\n#functions: print, str\\n\\n#def name_of_the_function(arg1, arg2, arg3):\\n    #lines \\n    #of \\n    #code \\n#    return values\\n\\ndef number_reverse(num):\\n    print(num)\\n    string = str(num)\\n    print(string)\\n    string = string[::-1]\\n    print(string)\\n    reversed_num = int(string)\\n    return reversed_num\\n    print(reversed_num)\\n    \\na = number_reverse(123)', '#definitions\\nexample_string = \"It was the best of times, it was the worst of times.\"\\nl = [\"one\",2,\\'goal\\',4,5]\\n\\n#functions and methods\\n#functions: print, str\\n\\n#def name_of_the_function(arg1, arg2, arg3):\\n    #lines \\n    #of \\n    #code \\n#    return values\\n\\ndef number_reverse(num):\\n    print(num)\\n    string = str(num)\\n    print(string)\\n    string = string[::-1]\\n    print(string)\\n    reversed_num = int(string)\\n    print(reversed_num)\\n    return reversed_nu...\u001b[0m\n",
      "\u001b[36m      \u001b[0m\u001b[32;1mOut\u001b[0m\u001b[36m = \u001b[0m{5: 322, 7: <class 'list'>, 8: ['UK', 'Germany', 'France', 'Egypt'], 9: ['UK', 'Germany', 'France', 'Egypt', 'USA'], 11: ['UK', 'USA', 'Germany', 'France'], 12: 'USA', 13: True, 15: True, 16: ['UK', 'USA', 'Germany', 'France'], 19: ['UK', 'Germany', 'France'], 20: ['GB', 'DE', 'FR'], 21: ('PNG', (500, 500), 'RGBA', (500, 500, 4))}\u001b[0m\n",
      "\u001b[36m      \u001b[0m\u001b[32;1mget_ipython\u001b[0m\u001b[36m = \u001b[0m<bound method InteractiveShell.get_ipython of <ipykernel.zmqshell.ZMQInteractiveShell object at 0x7fdc99f3d850>>\u001b[0m\n",
      "\u001b[36m      \u001b[0m\u001b[32;1mexit\u001b[0m\u001b[36m = \u001b[0m<IPython.core.autocall.ZMQExitAutocall object at 0x7fdc992c4f10>\u001b[0m\n",
      "\u001b[36m      \u001b[0m\u001b[32;1mquit\u001b[0m\u001b[36m = \u001b[0m<IPython.core.autocall.ZMQExitAutocall object at 0x7fdc992c4f10>\u001b[0m\n",
      "\u001b[36m      \u001b[0m\u001b[32;1m_\u001b[0m\u001b[36m = \u001b[0m('PNG', (500, 500), 'RGBA', (500, 500, 4))\u001b[0m\n",
      "\u001b[36m      \u001b[0m\u001b[32;1m__\u001b[0m\u001b[36m = \u001b[0m['GB', 'DE', 'FR']\u001b[0m\n",
      "\u001b[36m      \u001b[0m\u001b[32;1m___\u001b[0m\u001b[36m = \u001b[0m['UK', 'Germany', 'France']\u001b[0m\n",
      "\u001b[36m      \u001b[0m\u001b[32;1m_i\u001b[0m\u001b[36m = \u001b[0m'from traceback_with_variables import activate_in_ipython_by_import\\nfrom PIL import Image\\nimport numpy as np\\nim = Image.open(\"image.png\")\\n(im.format, im.size, im.mode, np.array(im).shape)'\u001b[0m\n",
      "\u001b[36m      \u001b[0m\u001b[32;1m_ii\u001b[0m\u001b[36m = \u001b[0m\"#.             0.     1           2\\ncountries = ['UK', 'Germany', 'France']\\n\\ndef countryname_to_code(country):\\n    if country == 'UK': \\n        code = 'GB'\\n    elif country == 'Germany':\\n        code = 'DE'\\n    elif country == 'France':\\n        code = 'FR'\\n        \\n    return code\\n\\n#codes = ['GB', 'DE', 'FR']\\ncodes = []\\nfor name in countries:\\n    code = countryname_to_code(name)\\n    print(name, code, codes)\\n    codes.append(code) #adds to the end of codes\\n\\ncodes\"\u001b[0m\n",
      "\u001b[36m      \u001b[0m\u001b[32;1m_iii\u001b[0m\u001b[36m = \u001b[0m\"#.             0.     1           2\\ncountries = ['UK', 'Germany', 'France']\\n\\ndef countryname_to_code(country):\\n    if country == 'UK': \\n        code = 'GB'\\n    elif country == 'Germany':\\n        code = 'DE'\\n    elif country == 'France':\\n        code = 'FR'\\n        \\n    return code\\n\\n#codes = ['GB', 'DE', 'FR']\\ncodes = []\\nfor c in countries:\\n    code = countryname_to_code(c)\\n    print(c, code, codes)\\n    codes.append(c)\\n\\ncodes\"\u001b[0m\n",
      "\u001b[36m      \u001b[0m\u001b[32;1m_i1\u001b[0m\u001b[36m = \u001b[0m'#definitions\\nexample_string = \"It was the best of times, it was the worst of times.\"\\nl = [\"one\",2,\\'goal\\',4,5]\\n\\n#functions and methods\\n#functions: print, str\\n\\n#def name_of_the_function(arg1, arg2, arg3):\\n    #lines \\n    #of \\n    #code \\n#    return values\\n\\ndef number_reverse(num):\\n    print(num)\\n    string = str(num)\\n    print(string)\\n    string = string[::-1]\\n    print(string)\\n    reversed_num = int(string)\\n    return reversed_num\\n    print(reversed_num)\\n    \\na = number_reverse(123)'\u001b[0m\n",
      "\u001b[36m      \u001b[0m\u001b[32;1mexample_string\u001b[0m\u001b[36m = \u001b[0m'It was the best of times, it was the worst of times.'\u001b[0m\n",
      "\u001b[36m      \u001b[0m\u001b[32;1ml\u001b[0m\u001b[36m = \u001b[0m['one', 2, 'goal', 4, 5]\u001b[0m\n",
      "\u001b[36m      \u001b[0m\u001b[32;1mnumber_reverse\u001b[0m\u001b[36m = \u001b[0m<function number_reverse at 0x7fdc9a0550e0>\u001b[0m\n",
      "\u001b[36m      \u001b[0m\u001b[32;1ma\u001b[0m\u001b[36m = \u001b[0m321\u001b[0m\n",
      "\u001b[36m      \u001b[0m\u001b[32;1m_i2\u001b[0m\u001b[36m = \u001b[0m'#definitions\\nexample_string = \"It was the best of times, it was the worst of times.\"\\nl = [\"one\",2,\\'goal\\',4,5]\\n\\n#functions and methods\\n#functions: print, str\\n\\n#def name_of_the_function(arg1, arg2, arg3):\\n    #lines \\n    #of \\n    #code \\n#    return values\\n\\ndef number_reverse(num):\\n    print(num)\\n    string = str(num)\\n    print(string)\\n    string = string[::-1]\\n    print(string)\\n    reversed_num = int(string)\\n    print(reversed_num)\\n    return reversed_num\\n    \\na = number_reverse(123)'\u001b[0m\n",
      "\u001b[36m      \u001b[0m\u001b[32;1m_i3\u001b[0m\u001b[36m = \u001b[0m'#definitions\\nexample_string = \"It was the best of times, it was the worst of times.\"\\nl = [\"one\",2,\\'goal\\',4,5]\\n\\n#functions and methods\\n#functions: print, str\\n\\n#def name_of_the_function(arg1, arg2, arg3):\\n    #lines \\n    #of \\n    #code \\n#    return values\\n\\ndef number_reverse(num):\\n    print(type(num))\\n    string = str(num)\\n    print(type(string))\\n    string = string[::-1]\\n    print(string)\\n    reversed_num = int(string)\\n    print(reversed_num)\\n    return reversed_num\\n    \\na = number_reverse(123)'\u001b[0m\n",
      "\u001b[36m      \u001b[0m\u001b[32;1m_i4\u001b[0m\u001b[36m = \u001b[0m'#definitions\\nexample_string = \"It was the best of times, it was the worst of times.\"\\nl = [\"one\",2,\\'goal\\',4,5]\\n\\n#functions and methods\\n#functions: print, str\\n\\n#def name_of_the_function(arg1, arg2, arg3):\\n    #lines \\n    #of \\n    #code \\n#    return values\\n\\ndef number_reverse(num):\\n    print(type(num))\\n    string = str(num)\\n    print(type(string))\\n    string = string[::-1]\\n    print(type(string))\\n    reversed_num = int(string)\\n    print(reversed_num)\\n    return reversed_num\\n    \\na = number_reverse(123)'\u001b[0m\n",
      "\u001b[36m      \u001b[0m\u001b[32;1m_i5\u001b[0m\u001b[36m = \u001b[0m'#definitions\\nexample_string = \"It was the best of times, it was the worst of times.\"\\nl = [\"one\",2,\\'goal\\',4,5]\\n\\n#functions and methods\\n#functions: print, str\\n\\n#def name_of_the_function(arg1, arg2, arg3):\\n    #lines \\n    #of \\n    #code \\n#    return values\\n\\ndef number_reverse(num):\\n    print(type(num))\\n    string = str(num)\\n    print(type(string))\\n    string = string[::-1]\\n    print(type(string))\\n    reversed_num = int(string)\\n    print(reversed_num)\\n    return reversed_num\\n    \\na = number_reverse(123)\\na + 1'\u001b[0m\n",
      "\u001b[36m      \u001b[0m\u001b[32;1m_5\u001b[0m\u001b[36m = \u001b[0m322\u001b[0m\n",
      "\u001b[36m      \u001b[0m\u001b[32;1m_i6\u001b[0m\u001b[36m = \u001b[0m\"countries = ['UK', 'Germany', 'France']\"\u001b[0m\n",
      "\u001b[36m      \u001b[0m\u001b[32;1mcountries\u001b[0m\u001b[36m = \u001b[0m['UK', 'Germany', 'France']\u001b[0m\n",
      "\u001b[36m      \u001b[0m\u001b[32;1m_i7\u001b[0m\u001b[36m = \u001b[0m\"countries = ['UK', 'Germany', 'France']\\ntype(countries)\"\u001b[0m\n",
      "\u001b[36m      \u001b[0m\u001b[32;1m_7\u001b[0m\u001b[36m = \u001b[0m<class 'list'>\u001b[0m\n",
      "\u001b[36m      \u001b[0m\u001b[32;1m_i8\u001b[0m\u001b[36m = \u001b[0m\"countries = ['UK', 'Germany', 'France']\\ncountries.append('Egypt')\\ncountries\"\u001b[0m\n",
      "\u001b[36m      \u001b[0m\u001b[32;1m_8\u001b[0m\u001b[36m = \u001b[0m['UK', 'Germany', 'France', 'Egypt']\u001b[0m\n",
      "\u001b[36m      \u001b[0m\u001b[32;1m_i9\u001b[0m\u001b[36m = \u001b[0m\"countries = ['UK', 'Germany', 'France']\\ncountries.extend(['Egypt', 'USA'])\\ncountries\"\u001b[0m\n",
      "\u001b[36m      \u001b[0m\u001b[32;1m_9\u001b[0m\u001b[36m = \u001b[0m['UK', 'Germany', 'France', 'Egypt', 'USA']\u001b[0m\n",
      "\u001b[36m      \u001b[0m\u001b[32;1m_i10\u001b[0m\u001b[36m = \u001b[0m\"#.             0.     1           2\\ncountries = ['UK', 'Germany', 'France']\\ncountries.extend(1, 'USA')\\ncountries\"\u001b[0m\n",
      "\u001b[36m      \u001b[0m\u001b[32;1m_i11\u001b[0m\u001b[36m = \u001b[0m\"#.             0.     1           2\\ncountries = ['UK', 'Germany', 'France']\\ncountries.insert(1, 'USA')\\ncountries\"\u001b[0m\n",
      "\u001b[36m      \u001b[0m\u001b[32;1m_11\u001b[0m\u001b[36m = \u001b[0m['UK', 'USA', 'Germany', 'France']\u001b[0m\n",
      "\u001b[36m      \u001b[0m\u001b[32;1m_i12\u001b[0m\u001b[36m = \u001b[0m'countries[1]'\u001b[0m\n",
      "\u001b[36m      \u001b[0m\u001b[32;1m_12\u001b[0m\u001b[36m = \u001b[0m'USA'\u001b[0m\n",
      "\u001b[36m      \u001b[0m\u001b[32;1m_i13\u001b[0m\u001b[36m = \u001b[0m\"countries[1] == 'USA'\"\u001b[0m\n",
      "\u001b[36m      \u001b[0m\u001b[32;1m_13\u001b[0m\u001b[36m = \u001b[0mTrue\u001b[0m\n",
      "\u001b[36m      \u001b[0m\u001b[32;1m_i14\u001b[0m\u001b[36m = \u001b[0m\"isUSA = countries[1] == 'USA'\"\u001b[0m\n",
      "\u001b[36m      \u001b[0m\u001b[32;1misUSA\u001b[0m\u001b[36m = \u001b[0mTrue\u001b[0m\n",
      "\u001b[36m      \u001b[0m\u001b[32;1m_i15\u001b[0m\u001b[36m = \u001b[0m\"isUSA = countries[1] == 'USA'\\nisUSA\"\u001b[0m\n",
      "\u001b[36m      \u001b[0m\u001b[32;1m_15\u001b[0m\u001b[36m = \u001b[0mTrue\u001b[0m\n",
      "\u001b[36m      \u001b[0m\u001b[32;1m_i16\u001b[0m\u001b[36m = \u001b[0m'countries'\u001b[0m\n",
      "\u001b[36m      \u001b[0m\u001b[32;1m_16\u001b[0m\u001b[36m = \u001b[0m['UK', 'USA', 'Germany', 'France']\u001b[0m\n",
      "\u001b[36m      \u001b[0m\u001b[32;1m_i17\u001b[0m\u001b[36m = \u001b[0m\"#.             0.     1           2\\ncountries = ['UK', 'Germany', 'France']\\n\\ndef countryname_to_code(country):\\n    if country == 'UK': \\n        code = 'GB'\\n    else if country == 'Germany':\\n        code = 'DE'\\n    else if country == 'France':\\n        code = 'FR'\\n        \\n    return code\\n\\n#codes = ['GB', 'DE', 'FR']\\ncodes = []\\nfor c in countries:\\n    code = countryname_to_code(c)\\n    print(c, code, codes)\\n    codes.append(c)\\n    \\n    \"\u001b[0m\n",
      "\u001b[36m      \u001b[0m\u001b[32;1m_i18\u001b[0m\u001b[36m = \u001b[0m\"#.             0.     1           2\\ncountries = ['UK', 'Germany', 'France']\\n\\ndef countryname_to_code(country):\\n    if country == 'UK': \\n        code = 'GB'\\n    elif country == 'Germany':\\n        code = 'DE'\\n    elif country == 'France':\\n        code = 'FR'\\n        \\n    return code\\n\\n#codes = ['GB', 'DE', 'FR']\\ncodes = []\\nfor c in countries:\\n    code = countryname_to_code(c)\\n    print(c, code, codes)\\n    codes.append(c)\\n    \\n    \"\u001b[0m\n",
      "\u001b[36m      \u001b[0m\u001b[32;1mcountryname_to_code\u001b[0m\u001b[36m = \u001b[0m<function countryname_to_code at 0x7fdc9ca8de60>\u001b[0m\n",
      "\u001b[36m      \u001b[0m\u001b[32;1mcodes\u001b[0m\u001b[36m = \u001b[0m['GB']\u001b[0m\n",
      "\u001b[36m      \u001b[0m\u001b[32;1mc\u001b[0m\u001b[36m = \u001b[0m'France'\u001b[0m\n",
      "\u001b[36m      \u001b[0m\u001b[32;1mcode\u001b[0m\u001b[36m = \u001b[0m'GB'\u001b[0m\n",
      "\u001b[36m      \u001b[0m\u001b[32;1m_i19\u001b[0m\u001b[36m = \u001b[0m\"#.             0.     1           2\\ncountries = ['UK', 'Germany', 'France']\\n\\ndef countryname_to_code(country):\\n    if country == 'UK': \\n        code = 'GB'\\n    elif country == 'Germany':\\n        code = 'DE'\\n    elif country == 'France':\\n        code = 'FR'\\n        \\n    return code\\n\\n#codes = ['GB', 'DE', 'FR']\\ncodes = []\\nfor c in countries:\\n    code = countryname_to_code(c)\\n    print(c, code, codes)\\n    codes.append(c)\\n\\ncodes\"\u001b[0m\n",
      "\u001b[36m      \u001b[0m\u001b[32;1m_19\u001b[0m\u001b[36m = \u001b[0m['UK', 'Germany', 'France']\u001b[0m\n",
      "\u001b[36m      \u001b[0m\u001b[32;1m_i20\u001b[0m\u001b[36m = \u001b[0m\"#.             0.     1           2\\ncountries = ['UK', 'Germany', 'France']\\n\\ndef countryname_to_code(country):\\n    if country == 'UK': \\n        code = 'GB'\\n    elif country == 'Germany':\\n        code = 'DE'\\n    elif country == 'France':\\n        code = 'FR'\\n        \\n    return code\\n\\n#codes = ['GB', 'DE', 'FR']\\ncodes = []\\nfor name in countries:\\n    code = countryname_to_code(name)\\n    print(name, code, codes)\\n    codes.append(code) #adds to the end of codes\\n\\ncodes\"\u001b[0m\n",
      "\u001b[36m      \u001b[0m\u001b[32;1mname\u001b[0m\u001b[36m = \u001b[0m'Germany'\u001b[0m\n",
      "\u001b[36m      \u001b[0m\u001b[32;1m_20\u001b[0m\u001b[36m = \u001b[0m['GB', 'DE', 'FR']\u001b[0m\n",
      "\u001b[36m      \u001b[0m\u001b[32;1m_i21\u001b[0m\u001b[36m = \u001b[0m'from traceback_with_variables import activate_in_ipython_by_import\\nfrom PIL import Image\\nimport numpy as np\\nim = Image.open(\"image.png\")\\n(im.format, im.size, im.mode, np.array(im).shape)'\u001b[0m\n",
      "\u001b[36m      \u001b[0m\u001b[32;1mactivate_in_ipython_by_import\u001b[0m\u001b[36m = \u001b[0m<module 'traceback_with_variables.activate_in_ipython_by_import' from '/Users/tom/miniconda3/lib/python3.7/site-packages/traceback_with_variables/activate_in_ipython_by_import.py'>\u001b[0m\n",
      "\u001b[36m      \u001b[0m\u001b[32;1mImage\u001b[0m\u001b[36m = \u001b[0m<module 'PIL.Image' from '/Users/tom/miniconda3/lib/python3.7/site-packages/PIL/Image.py'>\u001b[0m\n",
      "\u001b[36m      \u001b[0m\u001b[32;1mnp\u001b[0m\u001b[36m = \u001b[0m<module 'numpy' from '/Users/tom/miniconda3/lib/python3.7/site-packages/numpy/__init__.py'>\u001b[0m\n",
      "\u001b[36m      \u001b[0m\u001b[32;1mim\u001b[0m\u001b[36m = \u001b[0m<PIL.PngImagePlugin.PngImageFile image mode=RGBA size=500x500 at 0x7FDC9B4BA6D0>\u001b[0m\n",
      "\u001b[36m      \u001b[0m\u001b[32;1m_21\u001b[0m\u001b[36m = \u001b[0m('PNG', (500, 500), 'RGBA', (500, 500, 4))\u001b[0m\n",
      "\u001b[36m      \u001b[0m\u001b[32;1m_i22\u001b[0m\u001b[36m = \u001b[0m\"#.             0.     1           2\\ncountries = ['UK', 'Germany', 'France']\\n\\ndef countryname_to_code(country):\\n    if country == 'UK': \\n        code = 'GB'\\n    elif country == 'Germany':\\n        assert(False)\\n        code = 'DE'\\n    elif country == 'France':\\n        code = 'FR'\\n        \\n    return code\\n\\n#codes = ['GB', 'DE', 'FR']\\ncodes = []\\nfor name in countries:\\n    code = countryname_to_code(name)\\n    print(name, code, codes)\\n    codes.append(code) #adds to the end of codes\\n\\n\\ncodes\"\u001b[0m\n",
      "\u001b[36m  File \"\u001b[0m\u001b[36;1m<ipython-input-22-24a588543f2c>\u001b[0m\u001b[36m\", line \u001b[0m\u001b[36;1m8\u001b[0m\u001b[36m, in \u001b[0m\u001b[36;1mcountryname_to_code\u001b[0m\n",
      "\u001b[36m    \u001b[0m\u001b[35massert(False)\u001b[0m\n",
      "\u001b[36m      \u001b[0m\u001b[32;1mcountry\u001b[0m\u001b[36m = \u001b[0m'Germany'\u001b[0m\n",
      "\u001b[31mbuiltins.AssertionError:\u001b[0m\u001b[91m \u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "UK GB []\n"
     ]
    }
   ],
   "source": [
    "#.             0.     1           2\n",
    "countries = ['UK', 'Germany', 'France']\n",
    "\n",
    "def countryname_to_code(country):\n",
    "    if country == 'UK': \n",
    "        code = 'GB'\n",
    "    elif country == 'Germany':\n",
    "        assert(False)\n",
    "        code = 'DE'\n",
    "    elif country == 'France':\n",
    "        code = 'FR'\n",
    "        \n",
    "    return code\n",
    "\n",
    "#codes = ['GB', 'DE', 'FR']\n",
    "codes = []\n",
    "for name in countries:\n",
    "    code = countryname_to_code(name)\n",
    "    print(name, code, codes)\n",
    "    codes.append(code) #adds to the end of codes\n",
    "\n",
    "\n",
    "codes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "isUSA = (countries[1] == 'USA')\n",
    "isUSA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 351,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9934433327794776 255\n",
      "0.8806781434786721 243\n",
      "0.8333014977294684 236\n",
      "0.810432536599234 232\n",
      "0.7931691928814201 229\n",
      "0.7770441563234508 226\n",
      "0.7669694873848476 224\n",
      "0.7555672298341362 222\n",
      "0.750594457546299 221\n",
      "0.7392962035994641 219\n",
      "0.7392962035994641 219\n",
      "0.7392962035994641 219\n",
      "0.7392962035994641 219\n",
      "0.7392962035994641 219\n",
      "0.7392962035994641 219\n",
      "0.7392962035994641 219\n",
      "0.7392962035994641 219\n",
      "0.7392962035994641 219\n",
      "0.7392962035994641 219\n",
      "0.7392962035994641 219\n",
      "0.7392962035994641 219\n",
      "0.7392962035994641 219\n",
      "0.7392962035994641 219\n",
      "0.7392962035994641 219\n",
      "0.7392962035994641 219\n",
      "0.7392962035994641 219\n",
      "0.7392962035994641 219\n",
      "0.7392962035994641 219\n",
      "0.7392962035994641 219\n",
      "0.7392962035994641 219\n",
      "85.88\n"
     ]
    }
   ],
   "source": [
    "a = np.random.normal(size= 255)\n",
    "\n",
    "for i in range(30):\n",
    "    std = np.std(a)\n",
    "    print(std, len(a))\n",
    "    outliers = abs(a) > 2*std\n",
    "    a = a[~outliers]\n",
    "print(f'{len(a)/255*100:.2f}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 354,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "28.86607004772212"
      ]
     },
     "execution_count": 354,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b = np.arange(100)\n",
    "np.std(b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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