2021_12_RobotArchery.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "46823cb5-38bc-44d0-8cd7-22b98fcb380b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from copy import copy\n",
    "import random\n",
    "import time\n",
    "import math\n",
    "import sympy as sy\n",
    "import itertools\n",
    "from fractions import Fraction\n",
    "import requests\n",
    "from bs4 import BeautifulSoup\n",
    "from IPython.display import Markdown, display,IFrame,HTML,Image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "7cc3a8b7-12ee-4ac5-b074-ffe705030f4d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "### Puzzle as published \n",
       " https://www.janestreet.com/puzzles/robot-archery-index/"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div class=\"inner-wrapper\">\n",
       "<p>After a grueling year filled with a wide variety of <a href=\"https://www.janestreet.com/puzzles/robot-weightlifting-index/\" title=\"tug of war\">robot</a> <a href=\"https://www.janestreet.com/puzzles/robot-tug-of-war-index/\" title=\"swimming\">sporting</a> <a href=\"https://www.janestreet.com/puzzles/robot-swimming-trials-index/\" title=\"weightlifting\">events</a>, we have arrived at the final event of the year: <strong>Robot Archery</strong>. Four robots have qualified for this year’s finals, and have been seeded in the following order:</p>\n",
       "<table>\n",
       "<thead>\n",
       "<tr>\n",
       "<th>Robot</th>\n",
       "<th>Seed </th>\n",
       "</tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "<tr>\n",
       "<td>Aaron</td>\n",
       "<td>1</td>\n",
       "</tr>\n",
       "<tr>\n",
       "<td>Barron  </td>\n",
       "<td>2</td>\n",
       "</tr>\n",
       "<tr>\n",
       "<td>Caren</td>\n",
       "<td>3</td>\n",
       "</tr>\n",
       "<tr>\n",
       "<td>Darrin </td>\n",
       "<td>4</td>\n",
       "</tr>\n",
       "</tbody>\n",
       "</table>\n",
       "<p><br/>\n",
       "The robots will take turns shooting arrows at a target<sup>1</sup>, starting with Aaron and proceeding in order by seed. When it is a given robot’s turn, they shoot a single arrow. If it is closer to the center of the target than <em>all</em> previous arrows by all players, that robot remains in the tournament, going to the back of the queue to await their next turn. Otherwise that robot is eliminated immediately. The last robot remaining in the queue is the winner.</p>\n",
       "<p>For example, here is how <em>last year’s</em> finals went, in which <strong>Caren</strong> was the winner. (Oddly enough it involved the same robots in the same seeding.)</p>\n",
       "<table>\n",
       "<thead>\n",
       "<tr>\n",
       "<th>Turn  </th>\n",
       "<th>Robot</th>\n",
       "<th>Distance</th>\n",
       "</tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "<tr>\n",
       "<td>1</td>\n",
       "<td>Aaron</td>\n",
       "<td>10nm</td>\n",
       "</tr>\n",
       "<tr>\n",
       "<td>2</td>\n",
       "<td>Barron  </td>\n",
       "<td>8nm</td>\n",
       "</tr>\n",
       "<tr>\n",
       "<td>3</td>\n",
       "<td>Caren</td>\n",
       "<td>7nm</td>\n",
       "</tr>\n",
       "<tr>\n",
       "<td>4</td>\n",
       "<td>Darrin  </td>\n",
       "<td>1km</td>\n",
       "</tr>\n",
       "<tr>\n",
       "<td>5</td>\n",
       "<td>Aaron</td>\n",
       "<td>9nm</td>\n",
       "</tr>\n",
       "<tr>\n",
       "<td>6</td>\n",
       "<td>Barron</td>\n",
       "<td>2nm</td>\n",
       "</tr>\n",
       "<tr>\n",
       "<td>7</td>\n",
       "<td>Caren</td>\n",
       "<td>1nm</td>\n",
       "</tr>\n",
       "<tr>\n",
       "<td>8</td>\n",
       "<td>Barron</td>\n",
       "<td>1Ym<sup>2</sup></td>\n",
       "</tr>\n",
       "</tbody>\n",
       "</table>\n",
       "<p><br/>\n",
       "To ten decimal places, what is the probability that <strong>Darrin</strong> will be this year’s winner?\n",
       "(Or, if you want to send in the <em>exact answer</em>, that’s fine too!)</p>\n",
       "<p><br/></p>\n",
       "<p>[1] Each robot is equally skilled. Which is to say: for any region <em>R</em> on the target with nonzero area, the robots all have the same positive probability of landing an arrow within <em>R</em> on any given shot.</p>\n",
       "<p>[2] It’s a large target.</p>\n",
       "</div>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "url='https://www.janestreet.com/puzzles/robot-archery-index/'\n",
    "res = requests.get(url)\n",
    "soup = BeautifulSoup(res.content, 'html.parser')\n",
    "display(Markdown('### Puzzle as published \\n https://www.janestreet.com/puzzles/robot-archery-index/'))\n",
    "HTML(str(soup.find('div', {'class' :'inner-wrapper'})))\n",
    "# used a tree to work out the probability. Have worked through the published solution at the bottom in sympy. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "6e08ae17-a7ac-4bdc-a204-99f7eedc0d37",
   "metadata": {},
   "outputs": [],
   "source": [
    "# build out the probability tree\n",
    "def tree(p,stopper):\n",
    "    game = [(np.ones(p),Fraction(1,1),1,1)]  # live players,probability, number of shots, next player\n",
    "    result = []\n",
    "    \n",
    "    while len(game) >0:\n",
    "        (players,prob,num,x) = game.pop()\n",
    "        if np.sum(players) ==1:\n",
    "            result.append([np.argmax(players)+1,prob])\n",
    "        elif num >stopper:\n",
    "            result.append([0,prob])\n",
    "        else:\n",
    "            num +=1\n",
    "            pass_prob = prob * Fraction(1,num)\n",
    "            fail_prob = prob * Fraction(num-1,num)\n",
    "            fail_players = copy(players)\n",
    "            fail_players[x] = 0\n",
    "            nextp = (x +1) % p\n",
    "            while players[nextp] ==0:\n",
    "                nextp = (nextp +1) % p\n",
    "            game.append([players,pass_prob,num,nextp])\n",
    "            game.append([fail_players,fail_prob,num,nextp])\n",
    "    print(len(result),\"total paths\")\n",
    "    return [(n,float(np.sum([j for i,j in result if i==n])))for n in range(p+1)]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "74dd3b48-2ac0-48a9-aed2-53e3875c5ae9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "988 total paths\n",
      "Player 0 has a 0.0000000000 probability\n",
      "Player 1 has a 0.3711532348 probability\n",
      "Player 2 has a 0.2421885526 probability\n",
      "Player 3 has a 0.2032205617 probability\n",
      "Player 4 has a 0.1834376509 probability\n"
     ]
    }
   ],
   "source": [
    "output = tree(4,18)\n",
    "for i in range(5):\n",
    "    print(\"Player {} has a {:.10f} probability\".format(output[i][0],output[i][1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a5598ed3-48f2-4784-bef5-00f2349271e2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "### Solution as published \n",
       " https://www.janestreet.com/puzzles/robot-archery-solution/"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {
      "image/png": {
       "width": 500
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div class=\"inner-wrapper\">\n",
       "<p>[Above is a Mathematica-derived solution to the systems of equations\n",
       "described in the 4-player tournament described below]</p>\n",
       "<p>Without loss of generality we can assume an arrow’s distance to the center of the target is U[0,1] distributed. Let <em>P<sub>j,k</sub></em>(<em>x</em>) denote the probability that Player <em>j</em> out of <em>k</em> in the <strong>current</strong> line will eventually win the game, given that the current best dart is at distance <em>x</em>.</p>\n",
       "<p>In a 2-player archery tournament, we would have <em>P</em><sub>1,2</sub>(<em>x</em>) + <em>P</em><sub>2,2</sub>(<em>x</em>) = 1, <em>P</em><sub>1,2</sub>(0) = 0, and <em>P</em><sub>1,2</sub>(<em>x</em>) = ∫<sub>0</sub><sup><em>x</em></sup> <em>P</em><sub>2,2</sub>(<em>u</em>) d<em>u</em>. Taking derivatives of both sides in that last equation and substituing gives <em>P</em>‘<sub>1,2</sub>(<em>x</em>) = −<em>P</em><sub>1,2</sub>(x). Solving gives <em>P</em><sub>1,2</sub>(<em>x</em>) = 1−e<sup>−<em>x</em></sup>.</p>\n",
       "<p>In the 3-player version of the game, we get the following system of equations:</p>\n",
       "<p><em>P</em><sub>1,3</sub>(<em>x</em>) = ∫<sub>0</sub><sup><em>x</em></sup> <em>P</em><sub>3,3</sub>(<em>u</em>) d<em>u</em></p>\n",
       "<p><em>P</em><sub>2,3</sub>(<em>x</em>) = (1 - <em>x</em>)<em>P</em><sub>1,2</sub>(<em>x</em>) + ∫<sub>0</sub><sup><em>x</em></sup> <em>P</em><sub>1,3</sub>(<em>u</em>) d<em>u</em></p>\n",
       "<p><em>P</em><sub>1,3</sub>(<em>x</em>) + <em>P</em><sub>2,3</sub>(<em>x</em>) + <em>P</em><sub>3,3</sub>(<em>x</em>) = 1</p>\n",
       "<p><em>P</em><sub>1,3</sub>(0) = <em>P</em><sub>2,3</sub>(0) = 0.</p>\n",
       "<p>And for the 4-player version we have the following:</p>\n",
       "<p><em>P</em><sub>1,4</sub>(<em>x</em>) = ∫<sub>0</sub><sup><em>x</em></sup> <em>P</em><sub>4,4</sub>(<em>u</em>) d<em>u</em></p>\n",
       "<p><em>P</em><sub>2,4</sub>(<em>x</em>) = (1 - <em>x</em>)<em>P</em><sub>1,3</sub>(<em>x</em>) + ∫<sub>0</sub><sup><em>x</em></sup> <em>P</em><sub>1,4</sub>(<em>u</em>) d<em>u</em></p>\n",
       "<p><em>P</em><sub>3,4</sub>(<em>x</em>) = (1 - <em>x</em>)<em>P</em><sub>2,3</sub>(<em>x</em>) + ∫<sub>0</sub><sup><em>x</em></sup> <em>P</em><sub>2,4</sub>(<em>u</em>) d<em>u</em></p>\n",
       "<p><em>P</em><sub>1,4</sub>(<em>x</em>) + <em>P</em><sub>2,4</sub>(<em>x</em>) + <em>P</em><sub>3,4</sub>(<em>x</em>) + <em>P</em><sub>4,4</sub>(<em>x</em>) = 1</p>\n",
       "<p><em>P</em><sub>1,4</sub>(0) = <em>P</em><sub>2,4</sub>(0) = <em>P</em><sub>3,4</sub>(0) = 0.</p>\n",
       "<p>In particular we have <em>P</em><sub>4,4</sub>(1) = (-5/4)*(cos(1) + sin(1)) + (1/2)*<em>e</em><sup>-1</sup> + (<em>e</em><sup>-1/2</sup>)*(cos(sqrt(3)/2) + 5/sqrt(3)*sin(sqrt(3)/2)), or approximately 0.18343765086.</p>\n",
       "<p><strong>Congrats to this month’s solvers!</strong></p>\n",
       "<h4 class=\"omit-as-of\" id=\"correct-submissions-from\"></h4>\n",
       "<p class=\"correct-submissions\" data-directory=\"2021-12-01-robot-archery\"></p>\n",
       "</div>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "url='https://www.janestreet.com/puzzles/robot-archery-solution/'\n",
    "res = requests.get(url)\n",
    "soup = BeautifulSoup(res.content, 'html.parser')\n",
    "display(Markdown('### Solution as published \\n https://www.janestreet.com/puzzles/robot-archery-solution/'))\n",
    "display(Image('https://www.janestreet.com/puzzles/2021-12-01-robot-archery-solution.png',width=500))\n",
    "display(HTML(str(soup.find_all('div', {'class' :'inner-wrapper'})[1])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d4d50b18-2292-4d6f-b79e-8df3e342efcc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "***Initial Conditions***"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "\n",
       "&emsp; $\n",
       "\\operatorname{P_{12}}{\\left(x \\right)} + \\operatorname{P_{22}}{\\left(x \\right)} = 1$ &emsp;&emsp;&emsp; ***[ eq1 ]***\n",
       "\n"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "\n",
       "&emsp; $\n",
       "\\operatorname{P_{12}}{\\left(0 \\right)} = 0$ &emsp;&emsp;&emsp; ***[ eq2 ]***\n",
       "\n"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "\n",
       "&emsp; $\n",
       "\\operatorname{P_{12}}{\\left(x \\right)} = \\int\\limits_{0}^{x} \\operatorname{P_{22}}{\\left(u \\right)}\\, du$ &emsp;&emsp;&emsp; ***[ eq3 ]***\n",
       "\n"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "\n",
       "Differentiate $eq3$ and substitute into $eq1$:&emsp; $\n",
       "\\operatorname{P_{12}}{\\left(x \\right)} = 1 - \\frac{d}{d x} \\operatorname{P_{12}}{\\left(x \\right)}$ &emsp;&emsp;&emsp; ***[ eq5 ]***\n",
       "\n"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "\n",
       "Solving differential equation in $eq5$ gives:&emsp; $\n",
       "\\operatorname{P_{12}}{\\left(x \\right)} = 1 - e^{- x}$ &emsp;&emsp;&emsp; ***[ eq6 ]***\n",
       "\n"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/latex": [
       "$\\displaystyle \\operatorname{P_{12}}{\\left(1 \\right)} = 0.632120558828558$"
      ],
      "text/plain": [
       "Eq(P_12(1), 0.632120558828558)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "\n",
       "and from $eq1$:&emsp; $\n",
       "\\operatorname{P_{22}}{\\left(x \\right)} = e^{- x}$ &emsp;&emsp;&emsp; ***[ eq6_1 ]***\n",
       "\n"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/latex": [
       "$\\displaystyle \\operatorname{P_{22}}{\\left(1 \\right)} = 0.367879441171442$"
      ],
      "text/plain": [
       "Eq(P_22(1), 0.367879441171442)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Try to work through the solution given\n",
    "# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
    "\n",
    "def disp(eq,comments=\"\"):\n",
    "    display(Markdown('\\n{}&emsp; $\\n'.format(comments)+sy.latex(eval(eq))+'$ &emsp;&emsp;&emsp; ***[ {} ]***\\n\\n'.format(eq)))\n",
    "\n",
    "#set up symbols\n",
    "x,u= sy.symbols('x u')\n",
    "# set up functions\n",
    "probs = sum([[\"P_\"+str(j+1)+str(i) for j in range(i)] for i in range(2,5) ],[])\n",
    "\n",
    "for i in probs:\n",
    "    locals()[i] =sy.Function(i) \n",
    "\n",
    "# 2 player case,starting conditions\n",
    "eq1 = sy.Eq(P_12(x) + P_22(x),1)\n",
    "eq2 = sy.Eq(P_12(0),0)\n",
    "eq3 = sy.Eq(P_12(x),sy.Integral(P_22(u),(u,0,x)))\n",
    "display(Markdown(\"***Initial Conditions***\"))\n",
    "disp(\"eq1\")\n",
    "disp(\"eq2\")\n",
    "disp(\"eq3\")\n",
    "\n",
    "eq4 = sy.Eq(sy.diff(eq3.lhs,x),sy.diff(eq3.rhs,x))\n",
    "eq5 = sy.Eq(P_12(x),sy.solve([eq1,eq2,eq4])[0][P_12(x)])\n",
    "disp(\"eq5\",\"Differentiate $eq3$ and substitute into $eq1$:\")\n",
    "eq6 = sy.dsolve(eq5,ics={P_12(0):0})\n",
    "disp(\"eq6\",\"Solving differential equation in $eq5$ gives:\")\n",
    "display(eq6.subs(x,1).evalf())\n",
    "eq6_1 = sy.Eq(P_22(x),sy.solve([eq1,eq6])[0][P_22(x)])\n",
    "disp(\"eq6_1\",\"and from $eq1$:\")\n",
    "display(eq6_1.subs(x,1).evalf())              "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "44667fa9-ed7c-4a6a-80aa-4cf6888043e1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "***Expand to 3 player***"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "\n",
       "&emsp; $\n",
       "\\operatorname{P_{13}}{\\left(x \\right)} = \\int\\limits_{0}^{x} \\operatorname{P_{33}}{\\left(u \\right)}\\, du$ &emsp;&emsp;&emsp; ***[ eq7 ]***\n",
       "\n"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "\n",
       "&emsp; $\n",
       "\\operatorname{P_{23}}{\\left(x \\right)} = \\left(1 - x\\right) \\operatorname{P_{12}}{\\left(x \\right)} + \\int\\limits_{0}^{x} \\operatorname{P_{13}}{\\left(u \\right)}\\, du$ &emsp;&emsp;&emsp; ***[ eq8 ]***\n",
       "\n"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "\n",
       "&emsp; $\n",
       "\\operatorname{P_{13}}{\\left(x \\right)} + \\operatorname{P_{23}}{\\left(x \\right)} + \\operatorname{P_{33}}{\\left(x \\right)} = 1$ &emsp;&emsp;&emsp; ***[ eq9 ]***\n",
       "\n"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "\n",
       "&emsp; $\n",
       "\\operatorname{P_{13}}{\\left(0 \\right)} = 0$ &emsp;&emsp;&emsp; ***[ eq10 ]***\n",
       "\n"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "\n",
       "&emsp; $\n",
       "\\operatorname{P_{23}}{\\left(0 \\right)} = 0$ &emsp;&emsp;&emsp; ***[ eq11 ]***\n",
       "\n"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(Markdown(\"***Expand to 3 player***\"))\n",
    "eq7 = sy.Eq(P_13(x),sy.Integral(P_33(u),(u,0,x)))\n",
    "eq8 = sy.Eq(P_23(x),(1-x)*P_12(x)+sy.Integral(P_13(u),(u,0,x)))\n",
    "eq9 = sy.Eq(P_13(x) + P_23(x) + P_33(x),1)\n",
    "eq10 = sy.Eq(P_13(0),0)\n",
    "eq11 = sy.Eq(P_23(0),0)\n",
    "disp(\"eq7\")\n",
    "disp(\"eq8\")\n",
    "disp(\"eq9\")\n",
    "disp(\"eq10\")\n",
    "disp(\"eq11\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "bfbf6307-c51c-4ff5-a1dc-3c79dde9f4dc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "\n",
       "Diffentiate both sides of $eq7$:&emsp; $\n",
       "\\operatorname{P_{33}}{\\left(x \\right)} = \\frac{d}{d x} \\operatorname{P_{13}}{\\left(x \\right)}$ &emsp;&emsp;&emsp; ***[ eq12 ]***\n",
       "\n"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "\n",
       "Solve for $eq6/eq8/eq9/eq12$ gives:&emsp; $\n",
       "\\operatorname{P_{13}}{\\left(x \\right)} = x - x e^{- x} - \\frac{d}{d x} \\operatorname{P_{13}}{\\left(x \\right)} - \\int\\limits_{0}^{x} \\operatorname{P_{13}}{\\left(u \\right)}\\, du + e^{- x}$ &emsp;&emsp;&emsp; ***[ eq13 ]***\n",
       "\n"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "\n",
       "Diffentiate both sides of $eq13$:&emsp; $\n",
       "\\frac{d}{d x} \\operatorname{P_{13}}{\\left(x \\right)} = x e^{- x} - \\operatorname{P_{13}}{\\left(x \\right)} - \\frac{d^{2}}{d x^{2}} \\operatorname{P_{13}}{\\left(x \\right)} + 1 - 2 e^{- x}$ &emsp;&emsp;&emsp; ***[ eq14 ]***\n",
       "\n"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "\n",
       "Solving ...&emsp; $\n",
       "\\operatorname{P_{13}}{\\left(x \\right)} = \\left(x - 1\\right) e^{- x} + 1 - \\frac{2 \\sqrt{3} e^{- \\frac{x}{2}} \\sin{\\left(\\frac{\\sqrt{3} x}{2} \\right)}}{3}$ &emsp;&emsp;&emsp; ***[ eq15 ]***\n",
       "\n"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/latex": [
       "$\\displaystyle \\operatorname{P_{13}}{\\left(1 \\right)} = 0.466492804885307$"
      ],
      "text/plain": [
       "Eq(P_13(1), 0.466492804885307)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "eq12 = sy.Eq(sy.diff(eq7.rhs,x),sy.diff(eq7.lhs,x))\n",
    "eq13 = sy.Eq(P_13(x),sy.solve([eq6,eq8,eq9,eq12])[0][P_13(x)])\n",
    "eq14 = sy.Eq(sy.diff(eq13.lhs,x),sy.diff(eq13.rhs,x))\n",
    "disp(\"eq12\",comments =\"Diffentiate both sides of $eq7$:\")\n",
    "disp(\"eq13\",comments =\"Solve for $eq6/eq8/eq9/eq12$ gives:\")\n",
    "disp(\"eq14\",comments =\"Diffentiate both sides of $eq13$:\")\n",
    "\n",
    "eq15 = sy.dsolve(eq14,ics={P_13(0):0,P_13(x).diff(x).subs(x,0):1})\n",
    "disp(\"eq15\",comments=\"Solving ...\")\n",
    "display(eq15.subs(x,1).evalf())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "b7e1eeb5-7024-4c93-b162-ec5d4aac1b6b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "\n",
       "Now we have $P_{13}$ we can use this to work out $P_{33}$ as:&emsp; $\n",
       "\\operatorname{P_{33}}{\\left(x \\right)} = \\frac{d}{d x} \\operatorname{P_{13}}{\\left(x \\right)}$ &emsp;&emsp;&emsp; ***[ eq12 ]***\n",
       "\n"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "\n",
       "Which gives:&emsp; $\n",
       "\\operatorname{P_{33}}{\\left(x \\right)} = - \\left(x - 1\\right) e^{- x} + e^{- x} + \\frac{\\sqrt{3} e^{- \\frac{x}{2}} \\sin{\\left(\\frac{\\sqrt{3} x}{2} \\right)}}{3} - e^{- \\frac{x}{2}} \\cos{\\left(\\frac{\\sqrt{3} x}{2} \\right)}$ &emsp;&emsp;&emsp; ***[ eq16 ]***\n",
       "\n"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/latex": [
       "$\\displaystyle \\operatorname{P_{33}}{\\left(1 \\right)} = 0.241686482894434$"
      ],
      "text/plain": [
       "Eq(P_33(1), 0.241686482894434)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "disp(\"eq12\",comments=\"Now we have $P_{13}$ we can use this to work out $P_{33}$ as:\")\n",
    "\n",
    "eq16 =sy.Eq(P_33(x),eq15.rhs.diff(x))\n",
    "disp(\"eq16\",comments=\"Which gives:\" )\n",
    "display(eq16.subs(x,1).evalf())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "dd48583e-1fa0-4bf5-af7d-03e3b1f36361",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "\n",
       "Using $Eq9$ now we have $P_{12}$ and $P_{33}$ we get :&emsp; $\n",
       "\\operatorname{P_{23}}{\\left(x \\right)} = - e^{- x} + \\frac{\\sqrt{3} e^{- \\frac{x}{2}} \\sin{\\left(\\frac{\\sqrt{3} x}{2} \\right)}}{3} + e^{- \\frac{x}{2}} \\cos{\\left(\\frac{\\sqrt{3} x}{2} \\right)}$ &emsp;&emsp;&emsp; ***[ eq17 ]***\n",
       "\n"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/latex": [
       "$\\displaystyle \\operatorname{P_{23}}{\\left(1 \\right)} = 0.291820712220259$"
      ],
      "text/plain": [
       "Eq(P_23(1), 0.291820712220259)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "eq17 =sy.Eq(P_23(x),1-eq16.rhs-eq15.rhs)\n",
    "disp(\"eq17\",\"Using $Eq9$ now we have $P_{12}$ and $P_{33}$ we get :\")\n",
    "display(eq17.subs(x,1).evalf())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "3f58d127-9d2d-4750-afc5-1a23e423e6ae",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "172 total paths\n",
      "Player 0 has a 0.0000000000 probability\n",
      "Player 1 has a 0.4664928049 probability\n",
      "Player 2 has a 0.2918207122 probability\n",
      "Player 3 has a 0.2416864829 probability\n"
     ]
    }
   ],
   "source": [
    "#Check vs tree\n",
    "output = tree(3,18)\n",
    "for i in range(4):\n",
    "    print(\"Player {} has a {:.10f} probability\".format(output[i][0],output[i][1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "377ee4bb-4ae9-47f6-9d1a-02e357817246",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "**From the 3 player game we have**"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "\n",
       "&emsp; $\n",
       "\\operatorname{P_{13}}{\\left(x \\right)} = \\left(x - 1\\right) e^{- x} + 1 - \\frac{2 \\sqrt{3} e^{- \\frac{x}{2}} \\sin{\\left(\\frac{\\sqrt{3} x}{2} \\right)}}{3}$ &emsp;&emsp;&emsp; ***[ eq15 ]***\n",
       "\n"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "\n",
       "&emsp; $\n",
       "\\operatorname{P_{23}}{\\left(x \\right)} = - e^{- x} + \\frac{\\sqrt{3} e^{- \\frac{x}{2}} \\sin{\\left(\\frac{\\sqrt{3} x}{2} \\right)}}{3} + e^{- \\frac{x}{2}} \\cos{\\left(\\frac{\\sqrt{3} x}{2} \\right)}$ &emsp;&emsp;&emsp; ***[ eq17 ]***\n",
       "\n"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**And on to 4 player**"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "\n",
       "&emsp; $\n",
       "\\operatorname{P_{14}}{\\left(x \\right)} = \\int\\limits_{0}^{x} \\operatorname{P_{44}}{\\left(u \\right)}\\, du$ &emsp;&emsp;&emsp; ***[ eq18 ]***\n",
       "\n"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "\n",
       "&emsp; $\n",
       "\\operatorname{P_{24}}{\\left(x \\right)} = \\left(1 - x\\right) \\operatorname{P_{13}}{\\left(x \\right)} + \\int\\limits_{0}^{x} \\operatorname{P_{14}}{\\left(u \\right)}\\, du$ &emsp;&emsp;&emsp; ***[ eq19 ]***\n",
       "\n"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "\n",
       "&emsp; $\n",
       "\\operatorname{P_{34}}{\\left(x \\right)} = \\left(1 - x\\right) \\operatorname{P_{23}}{\\left(x \\right)} + \\int\\limits_{0}^{x} \\operatorname{P_{24}}{\\left(u \\right)}\\, du$ &emsp;&emsp;&emsp; ***[ eq20 ]***\n",
       "\n"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "\n",
       "&emsp; $\n",
       "\\operatorname{P_{14}}{\\left(x \\right)} + \\operatorname{P_{24}}{\\left(x \\right)} + \\operatorname{P_{34}}{\\left(x \\right)} + \\operatorname{P_{44}}{\\left(x \\right)} = 1$ &emsp;&emsp;&emsp; ***[ eq21 ]***\n",
       "\n"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "\n",
       "&emsp; $\n",
       "\\operatorname{P_{14}}{\\left(0 \\right)} = 0$ &emsp;&emsp;&emsp; ***[ eq22 ]***\n",
       "\n"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "\n",
       "&emsp; $\n",
       "\\operatorname{P_{24}}{\\left(0 \\right)} = 0$ &emsp;&emsp;&emsp; ***[ eq23 ]***\n",
       "\n"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "\n",
       "&emsp; $\n",
       "\\operatorname{P_{34}}{\\left(0 \\right)} = 0$ &emsp;&emsp;&emsp; ***[ eq24 ]***\n",
       "\n"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "\n",
       "$\\implies$&emsp; $\n",
       "\\operatorname{P_{44}}{\\left(0 \\right)} = 1$ &emsp;&emsp;&emsp; ***[ eq24_1 ]***\n",
       "\n"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "eq18 = sy.Eq(P_14(x),sy.Integral(P_44(u),(u,0,x)))\n",
    "eq19 = sy.Eq(P_24(x),(1-x)*P_13(x)+sy.Integral(P_14(u),(u,0,x)))\n",
    "eq20 = sy.Eq(P_34(x),(1-x)*P_23(x)+sy.Integral(P_24(u),(u,0,x)))\n",
    "eq21 = sy.Eq(P_14(x) + P_24(x) + P_34(x) + P_44(x),1)\n",
    "eq22 = sy.Eq(P_14(0),0)\n",
    "eq23 = sy.Eq(P_24(0),0)\n",
    "eq24 = sy.Eq(P_34(0),0)\n",
    "eq24_1 = sy.Eq(P_44(0),1)\n",
    "display(Markdown(\"**From the 3 player game we have**\"))\n",
    "disp(\"eq15\")\n",
    "disp(\"eq17\")\n",
    "display(Markdown(\"**And on to 4 player**\"))\n",
    "disp(\"eq18\")\n",
    "disp(\"eq19\")\n",
    "disp(\"eq20\")\n",
    "disp(\"eq21\")\n",
    "disp(\"eq22\")\n",
    "disp(\"eq23\")\n",
    "disp(\"eq24\")\n",
    "disp(\"eq24_1\",\"$\\implies$\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "62f86c70-7346-4f8c-8708-f184fda319bc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "\n",
       "Diffentiate both sides of $eq18$:&emsp; $\n",
       "\\operatorname{P_{44}}{\\left(x \\right)} = \\frac{d}{d x} \\operatorname{P_{14}}{\\left(x \\right)}$ &emsp;&emsp;&emsp; ***[ eq25 ]***\n",
       "\n"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "\n",
       "Subsitute the solution for $P_{13}$ into $eq19$:&emsp; $\n",
       "\\operatorname{P_{24}}{\\left(x \\right)} = \\left(1 - x\\right) \\left(\\left(x - 1\\right) e^{- x} + 1 - \\frac{2 \\sqrt{3} e^{- \\frac{x}{2}} \\sin{\\left(\\frac{\\sqrt{3} x}{2} \\right)}}{3}\\right) + \\int\\limits_{0}^{x} \\operatorname{P_{14}}{\\left(u \\right)}\\, du$ &emsp;&emsp;&emsp; ***[ eq26 ]***\n",
       "\n"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "\n",
       "Subsitute the solution for $P_{23}$ into $eq20$:&emsp; $\n",
       "\\operatorname{P_{34}}{\\left(x \\right)} = \\left(1 - x\\right) \\left(- e^{- x} + \\frac{\\sqrt{3} e^{- \\frac{x}{2}} \\sin{\\left(\\frac{\\sqrt{3} x}{2} \\right)}}{3} + e^{- \\frac{x}{2}} \\cos{\\left(\\frac{\\sqrt{3} x}{2} \\right)}\\right) + \\int\\limits_{0}^{x} \\operatorname{P_{24}}{\\left(u \\right)}\\, du$ &emsp;&emsp;&emsp; ***[ eq27 ]***\n",
       "\n"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "\n",
       "Change variable in $eq26$:&emsp; $\n",
       "\\operatorname{P_{24}}{\\left(u \\right)} = \\left(1 - u\\right) \\left(\\left(u - 1\\right) e^{- u} + 1 - \\frac{2 \\sqrt{3} e^{- \\frac{u}{2}} \\sin{\\left(\\frac{\\sqrt{3} u}{2} \\right)}}{3}\\right) + \\int\\limits_{0}^{u} \\operatorname{P_{14}}{\\left(u \\right)}\\, du$ &emsp;&emsp;&emsp; ***[ eq28 ]***\n",
       "\n"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "\n",
       "Substitute into $eq27$:&emsp; $\n",
       "\\operatorname{P_{34}}{\\left(x \\right)} = \\left(1 - x\\right) \\left(- e^{- x} + \\frac{\\sqrt{3} e^{- \\frac{x}{2}} \\sin{\\left(\\frac{\\sqrt{3} x}{2} \\right)}}{3} + e^{- \\frac{x}{2}} \\cos{\\left(\\frac{\\sqrt{3} x}{2} \\right)}\\right) + \\int\\limits_{0}^{x} \\left(\\left(1 - u\\right) \\left(\\left(u - 1\\right) e^{- u} + 1 - \\frac{2 \\sqrt{3} e^{- \\frac{u}{2}} \\sin{\\left(\\frac{\\sqrt{3} u}{2} \\right)}}{3}\\right) + \\int\\limits_{0}^{u} \\operatorname{P_{14}}{\\left(u \\right)}\\, du\\right)\\, du$ &emsp;&emsp;&emsp; ***[ eq29 ]***\n",
       "\n"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "eq25 = sy.Eq(sy.diff(eq18.rhs,x),sy.diff(eq18.lhs,x))\n",
    "eq26 = eq19.subs(eq15.lhs,eq15.rhs)\n",
    "eq27 = eq20.subs(eq17.lhs,eq17.rhs)\n",
    "eq28 = eq26.subs(x,u)\n",
    "disp(\"eq25\",comments =\"Diffentiate both sides of $eq18$:\")\n",
    "disp(\"eq26\",comments =\"Subsitute the solution for $P_{13}$ into $eq19$:\")\n",
    "disp(\"eq27\",comments =\"Subsitute the solution for $P_{23}$ into $eq20$:\")\n",
    "disp(\"eq28\",comments =\"Change variable in $eq26$:\")\n",
    "\n",
    "eq29 = eq27.subs(eq28.lhs,eq28.rhs)\n",
    "disp(\"eq29\",comments =\"Substitute into $eq27$:\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "c83a1432-130c-4e65-b342-1877d2837888",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "\n",
       "Substituting $eq26/29/25$ into $eq21$ gives:\n",
       "\n",
       "&emsp; $\n",
       "\\operatorname{P_{14}}{\\left(x \\right)} = - \\left(1 - x\\right) \\left(\\left(x - 1\\right) e^{- x} + 1 - \\frac{2 \\sqrt{3} e^{- \\frac{x}{2}} \\sin{\\left(\\frac{\\sqrt{3} x}{2} \\right)}}{3}\\right) - \\left(1 - x\\right) \\left(- e^{- x} + \\frac{\\sqrt{3} e^{- \\frac{x}{2}} \\sin{\\left(\\frac{\\sqrt{3} x}{2} \\right)}}{3} + e^{- \\frac{x}{2}} \\cos{\\left(\\frac{\\sqrt{3} x}{2} \\right)}\\right) - \\frac{d}{d x} \\operatorname{P_{14}}{\\left(x \\right)} - \\int\\limits_{0}^{x} \\left(\\left(1 - u\\right) \\left(\\left(u - 1\\right) e^{- u} + 1 - \\frac{2 \\sqrt{3} e^{- \\frac{u}{2}} \\sin{\\left(\\frac{\\sqrt{3} u}{2} \\right)}}{3}\\right) + \\int\\limits_{0}^{u} \\operatorname{P_{14}}{\\left(u \\right)}\\, du\\right)\\, du - \\int\\limits_{0}^{x} \\operatorname{P_{14}}{\\left(u \\right)}\\, du + 1$ &emsp;&emsp;&emsp; ***[ eq30 ]***\n",
       "\n"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "\n",
       " differentiating $eq30$ twice gives:\n",
       "\n",
       "&emsp; $\n",
       "\\frac{d^{2}}{d x^{2}} \\operatorname{P_{14}}{\\left(x \\right)} = - \\frac{\\left(x - 1\\right) \\left(3 e^{- x} - \\sqrt{3} e^{- \\frac{x}{2}} \\sin{\\left(\\frac{\\sqrt{3} x}{2} \\right)} + 3 e^{- \\frac{x}{2}} \\cos{\\left(\\frac{\\sqrt{3} x}{2} \\right)}\\right)}{3} + \\frac{\\left(x - 1\\right) \\left(3 \\left(x - 1\\right) e^{- x} - 6 e^{- x} + \\sqrt{3} e^{- \\frac{x}{2}} \\sin{\\left(\\frac{\\sqrt{3} x}{2} \\right)} + 3 e^{- \\frac{x}{2}} \\cos{\\left(\\frac{\\sqrt{3} x}{2} \\right)}\\right)}{3} - \\frac{\\left(x - 1\\right) \\left(3 \\left(x - 1\\right) e^{- x} - 3 e^{- x} - \\sqrt{3} e^{- \\frac{x}{2}} \\sin{\\left(\\frac{\\sqrt{3} x}{2} \\right)} + 3 e^{- \\frac{x}{2}} \\cos{\\left(\\frac{\\sqrt{3} x}{2} \\right)}\\right)}{3} - \\left(x - 1\\right) e^{- x} - \\operatorname{P_{14}}{\\left(x \\right)} - \\frac{d}{d x} \\operatorname{P_{14}}{\\left(x \\right)} - \\frac{d^{3}}{d x^{3}} \\operatorname{P_{14}}{\\left(x \\right)} + 1 + 4 e^{- x} - \\frac{4 \\sqrt{3} e^{- \\frac{x}{2}} \\sin{\\left(\\frac{\\sqrt{3} x}{2} \\right)}}{3} - 2 e^{- \\frac{x}{2}} \\cos{\\left(\\frac{\\sqrt{3} x}{2} \\right)}$ &emsp;&emsp;&emsp; ***[ eq31 ]***\n",
       "\n"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "eq30 = sy.Eq(P_14(x),1-eq26.rhs-eq29.rhs-eq25.rhs)\n",
    "disp(\"eq30\",comments =\"Substituting $eq26/29/25$ into $eq21$ gives:\\n\\n\")\n",
    "\n",
    "eq31 = sy.Eq(sy.diff(eq30.lhs,x,2),sy.diff(eq30.rhs,x,2))\n",
    "disp(\"eq31\",comments =\" differentiating $eq30$ twice gives:\\n\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "117143f2-132a-4cd0-9c58-cd0b5646f6ee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "\n",
       "**Need one more boundary condition**\n",
       "\n",
       " ... differentiating $eq30$ once and substituting $x=0$ gives:\n",
       "\n",
       "&emsp; $\n",
       "\\left. \\frac{d}{d x} \\operatorname{P_{14}}{\\left(x \\right)} \\right|_{\\substack{ x=0 }} = - \\operatorname{P_{14}}{\\left(0 \\right)} - \\left. \\frac{d^{2}}{d x^{2}} \\operatorname{P_{14}}{\\left(x \\right)} \\right|_{\\substack{ x=0 }} - 2$ &emsp;&emsp;&emsp; ***[ eq31_1 ]***\n",
       "\n"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "\n",
       "Using $P_{14}(0)=0$ and  $\\frac{d}{d x} \\operatorname{P_{14}}{\\left(x \\right)} = \\operatorname{P_{44}}{\\left(x \\right)}$ and $P_{44}(0)=1$ gives:\n",
       "\n",
       "&emsp; $\n",
       "1 = - \\left. \\frac{d^{2}}{d x^{2}} \\operatorname{P_{14}}{\\left(x \\right)} \\right|_{\\substack{ x=0 }} - 2$ &emsp;&emsp;&emsp; ***[ eq31_2 ]***\n",
       "\n"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "\n",
       "$\\implies$&emsp; $\n",
       "\\left. \\frac{d^{2}}{d x^{2}} \\operatorname{P_{14}}{\\left(x \\right)} \\right|_{\\substack{ x=0 }} = -3$ &emsp;&emsp;&emsp; ***[ eq31_3 ]***\n",
       "\n"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "eq31_1 = sy.Eq(sy.diff(eq30.lhs,x,1),sy.diff(eq30.rhs,x,1)).subs(x,0).doit()\n",
    "disp(\"eq31_1\",comments =\"**Need one more boundary condition**\\n\\n ... differentiating $eq30$ once and substituting $x=0$ gives:\\n\\n\")\n",
    "eq31_2 = eq31_1.subs([(P_14(0),0),(P_14(x).diff(x).subs(x,0),1)])\n",
    "\n",
    "disp(\"eq31_2\",comments =\"Using $P_{14}(0)=0$ and  $\"+sy.latex(sy.Eq(eq25.rhs,eq25.lhs))+\"$ and $P_{44}(0)=1$ gives:\\n\\n\")\n",
    "\n",
    "eq31_3 = sy.Eq((eq31_2.rhs+2)*-1,(eq31_2.lhs+2)*-1)\n",
    "disp(\"eq31_3\",comments =\"$\\implies$\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "79356ee7-8e50-4bd2-9712-fa7ad0a26b81",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "\n",
       "Solving $eq31$ using $\\operatorname{P_{14}}{\\left(0 \\right)} = 0$ , $\\left. \\frac{d}{d x} \\operatorname{P_{14}}{\\left(x \\right)} \\right|_{\\substack{ x=0 }} = 1$ and $\\left. \\frac{d^{2}}{d x^{2}} \\operatorname{P_{14}}{\\left(x \\right)} \\right|_{\\substack{ x=0 }} = -3$ solution gives:\n",
       "\n",
       "&emsp; $\n",
       "\\operatorname{P_{14}}{\\left(x \\right)} = - \\frac{x \\cos{\\left(\\frac{\\sqrt{3} x}{2} \\right)}}{\\sqrt{e^{x}}} + \\sqrt{3} x e^{- \\frac{x}{2}} \\sin{\\left(\\frac{\\sqrt{3} x}{2} \\right)} + \\left(- \\frac{3 x^{2}}{4} + 2 x - \\frac{5}{4}\\right) e^{- x} - \\frac{5 \\sin{\\left(x \\right)}}{4} + \\frac{5 \\cos{\\left(x \\right)}}{4} + 1 - \\frac{\\cos{\\left(\\frac{\\sqrt{3} x}{2} \\right)}}{\\sqrt{e^{x}}} - \\frac{\\sqrt{3} e^{- \\frac{x}{2}} \\sin{\\left(\\frac{\\sqrt{3} x}{2} \\right)}}{3}$ &emsp;&emsp;&emsp; ***[ eq32 ]***\n",
       "\n"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/latex": [
       "$\\displaystyle \\operatorname{P_{14}}{\\left(1 \\right)} = 0.371153234771287$"
      ],
      "text/plain": [
       "Eq(P_14(1), 0.371153234771287)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "eq33_4 = sy.Eq(eq25.rhs,eq25.lhs).subs(x,0).subs(eq24_1.lhs,eq24_1.rhs)\n",
    "eq32 = sy.dsolve(eq31,ics={P_14(0):0,P_14(x).diff(x).subs(x,0):1,P_14(x).diff(x,2).subs(x,0):-3})\n",
    "disp(\"eq32\",comments =\"Solving $eq31$ using $\"+sy.latex(eq22)+\"$ , $\"+sy.latex(eq33_4)+\n",
    "     \"$ and $\"+sy.latex(eq31_3)+\"$ solution gives:\\n\\n\")\n",
    "display(eq32.subs(x,1).evalf())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "fea54c72-a0ac-4670-b2b3-c25057af3e7a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "\n",
       "differentiate to get $P_{44}$:&emsp; $\n",
       "\\operatorname{P_{44}}{\\left(x \\right)} = \\frac{\\sqrt{3} x \\sin{\\left(\\frac{\\sqrt{3} x}{2} \\right)}}{2 \\sqrt{e^{x}}} + \\frac{x \\cos{\\left(\\frac{\\sqrt{3} x}{2} \\right)}}{2 \\sqrt{e^{x}}} - \\frac{\\sqrt{3} x e^{- \\frac{x}{2}} \\sin{\\left(\\frac{\\sqrt{3} x}{2} \\right)}}{2} + \\frac{3 x e^{- \\frac{x}{2}} \\cos{\\left(\\frac{\\sqrt{3} x}{2} \\right)}}{2} + \\left(2 - \\frac{3 x}{2}\\right) e^{- x} - \\left(- \\frac{3 x^{2}}{4} + 2 x - \\frac{5}{4}\\right) e^{- x} - \\frac{5 \\sin{\\left(x \\right)}}{4} - \\frac{5 \\cos{\\left(x \\right)}}{4} + \\frac{\\sqrt{3} \\sin{\\left(\\frac{\\sqrt{3} x}{2} \\right)}}{2 \\sqrt{e^{x}}} - \\frac{\\cos{\\left(\\frac{\\sqrt{3} x}{2} \\right)}}{2 \\sqrt{e^{x}}} + \\frac{7 \\sqrt{3} e^{- \\frac{x}{2}} \\sin{\\left(\\frac{\\sqrt{3} x}{2} \\right)}}{6} - \\frac{e^{- \\frac{x}{2}} \\cos{\\left(\\frac{\\sqrt{3} x}{2} \\right)}}{2}$ &emsp;&emsp;&emsp; ***[ eq33 ]***\n",
       "\n"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/latex": [
       "$\\displaystyle \\operatorname{P_{44}}{\\left(1 \\right)} = 0.183437650861763$"
      ],
      "text/plain": [
       "Eq(P_44(1), 0.183437650861763)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "eq33 = sy.Eq(P_44(x),eq32.rhs.diff(x))\n",
    "disp(\"eq33\",\"differentiate to get $P_{44}$:\")\n",
    "display(eq33.subs(x,1).evalf())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "ec8cb3bf-b780-43ce-a751-f2de89932013",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "\n",
       "### Published solution\n",
       "&emsp; $\n",
       "\\operatorname{P_{14}}{\\left(x \\right)} = \\frac{\\left(- 9 x^{2} + 12 \\sqrt{3} x e^{\\frac{x}{2}} \\sin{\\left(\\frac{\\sqrt{3} x}{2} \\right)} - 12 x e^{\\frac{x}{2}} \\cos{\\left(\\frac{\\sqrt{3} x}{2} \\right)} + 24 x - 4 \\sqrt{3} e^{\\frac{x}{2}} \\sin{\\left(\\frac{\\sqrt{3} x}{2} \\right)} - 12 e^{\\frac{x}{2}} \\cos{\\left(\\frac{\\sqrt{3} x}{2} \\right)} - 15 e^{x} \\sin{\\left(x \\right)} + 15 e^{x} \\cos{\\left(x \\right)} + 12 e^{x} - 15\\right) e^{- x}}{12}$ &emsp;&emsp;&emsp; ***[ eq0 ]***\n",
       "\n"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/latex": [
       "$\\displaystyle \\operatorname{P_{14}}{\\left(1 \\right)} = 0.371153234771287$"
      ],
      "text/plain": [
       "Eq(P_14(1), 0.371153234771287)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "\n",
       "\n",
       "differentiate to get $P_{44}$\n",
       "&emsp; $\n",
       "\\operatorname{P_{44}}{\\left(x \\right)} = \\left(\\sqrt{3} x e^{\\frac{x}{2}} \\sin{\\left(\\frac{\\sqrt{3} x}{2} \\right)} + x e^{\\frac{x}{2}} \\cos{\\left(\\frac{\\sqrt{3} x}{2} \\right)} - \\frac{3 x}{2} + \\frac{4 \\sqrt{3} e^{\\frac{x}{2}} \\sin{\\left(\\frac{\\sqrt{3} x}{2} \\right)}}{3} - 2 e^{\\frac{x}{2}} \\cos{\\left(\\frac{\\sqrt{3} x}{2} \\right)} - \\frac{5 e^{x} \\sin{\\left(x \\right)}}{2} + e^{x} + 2\\right) e^{- x} - \\left(- \\frac{3 x^{2}}{4} + \\sqrt{3} x e^{\\frac{x}{2}} \\sin{\\left(\\frac{\\sqrt{3} x}{2} \\right)} - x e^{\\frac{x}{2}} \\cos{\\left(\\frac{\\sqrt{3} x}{2} \\right)} + 2 x - \\frac{\\sqrt{3} e^{\\frac{x}{2}} \\sin{\\left(\\frac{\\sqrt{3} x}{2} \\right)}}{3} - e^{\\frac{x}{2}} \\cos{\\left(\\frac{\\sqrt{3} x}{2} \\right)} - \\frac{5 e^{x} \\sin{\\left(x \\right)}}{4} + \\frac{5 e^{x} \\cos{\\left(x \\right)}}{4} + e^{x} - \\frac{5}{4}\\right) e^{- x}$ &emsp;&emsp;&emsp; ***[ eq0_1 ]***\n",
       "\n"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/latex": [
       "$\\displaystyle \\operatorname{P_{44}}{\\left(1 \\right)} = 0.183437650861764$"
      ],
      "text/plain": [
       "Eq(P_44(1), 0.183437650861764)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### Published soln is.\n",
    "eq0 = sy.Eq(P_14(x),sy.Rational(1,12)*sy.exp(-x)* \\\n",
    "        (-15+12*sy.exp(x)+24*x+-9*x**2+15*sy.exp(x)*sy.cos(x)-12*sy.exp(x/2)*sy.cos(sy.sqrt(3)*x/2) \n",
    "         -12*sy.exp(x/2)*x*sy.cos(sy.sqrt(3)*x/2)-15*sy.exp(x)*sy.sin(x)-4*sy.sqrt(3)*sy.exp(x/2)*sy.sin(sy.sqrt(3)*x/2)+\n",
    "         12*sy.sqrt(3)*sy.exp(x/2)*x*sy.sin(sy.sqrt(3)*x/2)))\n",
    "         \n",
    "        \n",
    "disp(\"eq0\",\"### Published solution\\n\")\n",
    "display(eq0.subs(x,1).evalf())\n",
    "eq0_1 = sy.Eq(P_44(x),eq0.rhs.diff(x))\n",
    "disp(\"eq0_1\",\"\\ndifferentiate to get $P_{44}$\\n\")\n",
    "display(eq0_1.subs(x,1).evalf())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "f51bc1bf-6cce-4fa3-8444-8ec064d0dc41",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle \\left. \\frac{d}{d x} \\operatorname{P_{14}}{\\left(x \\right)} \\right|_{\\substack{ x=0 }} = 1$"
      ],
      "text/plain": [
       "Eq(Subs(Derivative(P_14(x), x), x, 0), 1)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sy.Eq(eq25.rhs,eq25.lhs).subs(x,0).subs(eq24_1.lhs,eq24_1.rhs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "61576104-1e51-4b51-afda-d676e7bfe509",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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