2021.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "2cc6e0d2-4a5e-4c6a-bb3f-d80f2fa12ac3",
   "metadata": {
    "jupyter": {
     "is_executing": true
    }
   },
   "outputs": [],
   "source": [
    "import aoc\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "db341778-e54d-4c2e-bbe5-1312c65adfaa",
   "metadata": {},
   "source": [
    "# Day 6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "4d7732cb-a571-4b0b-95a4-6ec8f70a19a0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1fa3c1f1790>"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sim = \"\"\"Initial state: 3,4,3,1,2\n",
    "After  1 day:  2,3,2,0,1\n",
    "After  2 days: 1,2,1,6,0,8\n",
    "After  3 days: 0,1,0,5,6,7,8\n",
    "After  4 days: 6,0,6,4,5,6,7,8,8\n",
    "After  5 days: 5,6,5,3,4,5,6,7,7,8\n",
    "After  6 days: 4,5,4,2,3,4,5,6,6,7\n",
    "After  7 days: 3,4,3,1,2,3,4,5,5,6\n",
    "After  8 days: 2,3,2,0,1,2,3,4,4,5\n",
    "After  9 days: 1,2,1,6,0,1,2,3,3,4,8\n",
    "After 10 days: 0,1,0,5,6,0,1,2,2,3,7,8\n",
    "After 11 days: 6,0,6,4,5,6,0,1,1,2,6,7,8,8,8\n",
    "After 12 days: 5,6,5,3,4,5,6,0,0,1,5,6,7,7,7,8,8\n",
    "After 13 days: 4,5,4,2,3,4,5,6,6,0,4,5,6,6,6,7,7,8,8\n",
    "After 14 days: 3,4,3,1,2,3,4,5,5,6,3,4,5,5,5,6,6,7,7,8\n",
    "After 15 days: 2,3,2,0,1,2,3,4,4,5,2,3,4,4,4,5,5,6,6,7\n",
    "After 16 days: 1,2,1,6,0,1,2,3,3,4,1,2,3,3,3,4,4,5,5,6,8\n",
    "After 17 days: 0,1,0,5,6,0,1,2,2,3,0,1,2,2,2,3,3,4,4,5,7,8\n",
    "After 18 days: 6,0,6,4,5,6,0,1,1,2,6,0,1,1,1,2,2,3,3,4,6,7,8,8,8,8\"\"\".splitlines()\n",
    "\n",
    "fish = [len(x.split(':')[1].split(',')) for x in sim]\n",
    "\n",
    "fish\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "days = np.array(range(len(fish)))\n",
    "ax.plot(days, fish, label='simulated')\n",
    "ax.plot(days, np.exp((days+16)/10.25), label='fit')\n",
    "ax.set_ylabel('Fish')\n",
    "ax.set_xlabel('Days')\n",
    "ax.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "22c04a9d-8131-4c34-95e9-de33943dc672",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.16825746e+04, 3.34728424e+11, 3.69030946e+11, 9.23164652e+12])"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.exp((np.array([80, 256, 257, 290])+16)/10.25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "441a62f2-b4a1-4871-b4d6-ad8c49c4b601",
   "metadata": {},
   "outputs": [],
   "source": [
    "raw_data = aoc.get_data(2021,6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "91f29005-8318-4848-b6e9-69cbb79f9ceb",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = list(map(int, raw_data.split(',')))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "8a684be2-12c7-416c-9321-2e36f70c48b3",
   "metadata": {},
   "outputs": [],
   "source": [
    "population = [data.count(x) for x in range(9)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "8e016de8-8f5a-46eb-ae1d-7877d917c9ef",
   "metadata": {},
   "outputs": [],
   "source": [
    "def iterate_day(population):\n",
    "    \n",
    "    breeders = children = population[0]\n",
    "\n",
    "    new_population = population[1:] + [0]\n",
    "    \n",
    "    new_population[6] += breeders\n",
    "\n",
    "    new_population[-1] += children\n",
    "\n",
    "    return new_population\n",
    "\n",
    "\n",
    "def simulate(population, days):\n",
    "\n",
    "    pop = population.copy()\n",
    "    \n",
    "    for day in range(days):\n",
    "        pop = iterate_day(pop)\n",
    "\n",
    "    return pop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "23533495-41c0-4132-9af3-b22a14cbedc8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "345793"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(simulate(population, 80))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "e9d2efeb-9a3c-4e22-9160-8d484045e52e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1572643095893"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(simulate(population, 256))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "9ead2c7b-4c80-4108-ad08-4687b32c1b9f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "34.2 μs ± 3.39 μs per loop (mean ± std. dev. of 7 runs, 10,000 loops each)\n"
     ]
    }
   ],
   "source": [
    "%%timeit\n",
    "sum(simulate(population, 80))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "909aa4c4-f579-4467-9490-0737be049653",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "114 μs ± 4.59 μs per loop (mean ± std. dev. of 7 runs, 10,000 loops each)\n"
     ]
    }
   ],
   "source": [
    "%%timeit\n",
    "sum(simulate(population, 256))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "d55c5943-3a9a-4e09-b6ce-c2211df9030b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "179015728399927960558946868532524007926125"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(simulate(population, 2**10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "9aa4538d-f5fd-4672-a2d0-8ecd60de0cb2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10754831429319799002443291789970282230186506118372307458288609473759370273726975991735015661575354811782984143741309977543537900599093636934734706111663064469991703634857361161266066582714282935760604876349990205334482791269976292357933601697958259271447657240803204223076868811396888489729430322390920571235925113059552984674909372718750742159597936959042915081405226339045759082201246324335610311137977059616137556424464837185352913056877930069601706552586566617851691531303592718212817141395467114047656626962488031790780067531340017575484544540066565857596633252657338768108763625380707734540944422224660963513912888738797335784435929444480258022478838034603172927593621756157582507050680848604566195590608834909039598149275646696462779834681619367166474972062223971932548940880176388712507511082585390674383747870461949047252753927591872617677243041647976759991215091718296759319833017913818056002704063875962136463148997760421986308257058518704998502835191684469191588612255944377383418217252406838816811959312078223717081251662674516215970571932927886797813036023868855370822621284175348943942573741055715919411459422821890303039662613497119131983693667758427620568417213992858976351445953023344801952852985076231725710651408810574309167304975205999180378562662842698359697469333576597040018363504859903912882643435351490404395124423072700695798141040255674029842512105734202848677628512254939047543074375595366586192976562162233261720053696000053039226623109657185462011026112592397348626281937606855014048816289542449974035874063492070754375957192589452175356498214746642387794750492811310033674822840317454630655524022055438297423903713745779656371368944984621348297127960693600348443708181686311509854010371487813621557387109447239362916477401802996233027579209115660402240739617878381423531099793341833527408212624565678367932984620908706087512557111385848976073600709030366283027103275133701642093778862675143924181021341694394044257893221333383050537803754393735490990435497022071909674309964953036583419486310398285600482393320570333680485223877372017356308525975093416328942352560757220359406163584641648976454259122594290053455400919988243626547200042038535073639813079865321101913552835719576817705670002828148920684778236052942997490220865223318353239154759580773835568236090025291579337803446094207325244816801765914305725554775033103050969835203078239036341394063868318595340393045248685659791908515911282685479111236768938092879706205448798471296726931176314854"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(simulate(population, 2**16))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "bf0563aa-f0bb-40a7-aa82-4fefa088206a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "56 ms ± 9.55 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
     ]
    }
   ],
   "source": [
    "%%timeit\n",
    "sum(simulate(population, 2**16))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "6ac4ccd3-b12b-41de-909c-b139ae4fead5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "65536"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "2**16"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "d724b279-0ad4-4206-bf05-520c263b85cf",
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Exceeds the limit (4300 digits) for integer string conversion; use sys.set_int_max_str_digits() to increase the limit",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "File \u001b[1;32m~\\anaconda3\\envs\\aoc\\Lib\\site-packages\\IPython\\core\\formatters.py:711\u001b[0m, in \u001b[0;36mPlainTextFormatter.__call__\u001b[1;34m(self, obj)\u001b[0m\n\u001b[0;32m    704\u001b[0m stream \u001b[38;5;241m=\u001b[39m StringIO()\n\u001b[0;32m    705\u001b[0m printer \u001b[38;5;241m=\u001b[39m pretty\u001b[38;5;241m.\u001b[39mRepresentationPrinter(stream, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mverbose,\n\u001b[0;32m    706\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmax_width, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mnewline,\n\u001b[0;32m    707\u001b[0m     max_seq_length\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmax_seq_length,\n\u001b[0;32m    708\u001b[0m     singleton_pprinters\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msingleton_printers,\n\u001b[0;32m    709\u001b[0m     type_pprinters\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtype_printers,\n\u001b[0;32m    710\u001b[0m     deferred_pprinters\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdeferred_printers)\n\u001b[1;32m--> 711\u001b[0m \u001b[43mprinter\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpretty\u001b[49m\u001b[43m(\u001b[49m\u001b[43mobj\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    712\u001b[0m printer\u001b[38;5;241m.\u001b[39mflush()\n\u001b[0;32m    713\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m stream\u001b[38;5;241m.\u001b[39mgetvalue()\n",
      "File \u001b[1;32m~\\anaconda3\\envs\\aoc\\Lib\\site-packages\\IPython\\lib\\pretty.py:394\u001b[0m, in \u001b[0;36mRepresentationPrinter.pretty\u001b[1;34m(self, obj)\u001b[0m\n\u001b[0;32m    391\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m \u001b[38;5;28mcls\u001b[39m \u001b[38;5;129;01min\u001b[39;00m _get_mro(obj_class):\n\u001b[0;32m    392\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mcls\u001b[39m \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtype_pprinters:\n\u001b[0;32m    393\u001b[0m         \u001b[38;5;66;03m# printer registered in self.type_pprinters\u001b[39;00m\n\u001b[1;32m--> 394\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtype_pprinters\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;28;43mcls\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m(\u001b[49m\u001b[43mobj\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcycle\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    395\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    396\u001b[0m         \u001b[38;5;66;03m# deferred printer\u001b[39;00m\n\u001b[0;32m    397\u001b[0m         printer \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_in_deferred_types(\u001b[38;5;28mcls\u001b[39m)\n",
      "File \u001b[1;32m~\\anaconda3\\envs\\aoc\\Lib\\site-packages\\IPython\\lib\\pretty.py:787\u001b[0m, in \u001b[0;36m_repr_pprint\u001b[1;34m(obj, p, cycle)\u001b[0m\n\u001b[0;32m    785\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"A pprint that just redirects to the normal repr function.\"\"\"\u001b[39;00m\n\u001b[0;32m    786\u001b[0m \u001b[38;5;66;03m# Find newlines and replace them with p.break_()\u001b[39;00m\n\u001b[1;32m--> 787\u001b[0m output \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mrepr\u001b[39m(obj)\n\u001b[0;32m    788\u001b[0m lines \u001b[38;5;241m=\u001b[39m output\u001b[38;5;241m.\u001b[39msplitlines()\n\u001b[0;32m    789\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m p\u001b[38;5;241m.\u001b[39mgroup():\n",
      "\u001b[1;31mValueError\u001b[0m: Exceeds the limit (4300 digits) for integer string conversion; use sys.set_int_max_str_digits() to increase the limit"
     ]
    }
   ],
   "source": [
    "sum(simulate(population, 2**17))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3c46ce87-0ed7-44fb-9a6b-32b2dd07a694",
   "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": "3.11.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}