python_ate_my_homework.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "RkAdqV7WaqrJ"
   },
   "source": [
    "# Python ate my Homework!\n",
    "## Evan Kohilas\n",
    "### @ekohilas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "lW2ymfOibNTG"
   },
   "source": [
    "## Maths Assignments"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "lW2ymfOibNTG"
   },
   "source": [
    "### Traditionally handwritten\n",
    "![example assignment solution written by hand](images/assignment_handwritten.jpg)\n",
    "\n",
    "### Or now, digitally written\n",
    "![example assignment solution written by stylus](images/assignment_stylus.png)\n",
    "\n",
    "### Or re-written with Latex\n",
    "![example assignment solution written in latex](images/assignment_latex.png)\n",
    "\n",
    "### But these could all be:\n",
    "* Messy and illegible\n",
    "* Tedious and time consuming\n",
    "* Error prone and unvalidated\n",
    "* Static and unchangeable\n",
    "\n",
    "### Can we do better?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "iMeG8QH-goXC"
   },
   "source": [
    "## Question 1: Discrete Maths"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "cuo1q6f_td8O"
   },
   "source": [
    "Show whether the following numbers can be represented as a sum of two squares ($a^2 + b^2 = n$):\n",
    "1. 100\n",
    "1. 70\n",
    "1. 4250\n",
    "\n",
    "e.g.\n",
    "\n",
    "$250 = 81 + 169$\n",
    "\n",
    "$250 = 9^2 + 13^2$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "bxfqL5FS7YQl"
   },
   "source": [
    "**Theorem Reminder:**\n",
    "\n",
    "*A positive integer $n$ is the sum of two squares if and only if $p \\equiv 1 \\pmod 4$ for all odd primes $p$ that divide $n$ an odd number of times.*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "91JjZsS5-8GN"
   },
   "source": [
    "Primes that divide n are also known as factors of n."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "e.g. factors of $100$ are:\n",
    "\n",
    "$100 = 2 \\cdot 2 \\cdot 5 \\cdot 5 = 2^2 \\cdot 5^2 $"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Most calculators have a factorisation button\n",
    "\n",
    "![calculator with factorisation button](images/factor_button.png)\n",
    "\n",
    "So let's import one from SymPy (a Python library for symbolic mathematics)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "FAyfNmPX773I"
   },
   "outputs": [],
   "source": [
    "from sympy import factorint"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "Et1XjhQf7YQx"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{2: 2, 5: 2}"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "factorint(100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "91JjZsS5-8GN"
   },
   "source": [
    "```\n",
    "def sum_of_two_squares(n):\n",
    "    for factor, power in factors_of_(n):\n",
    "        if factor is even:\n",
    "            continue\n",
    "        if power is even:\n",
    "            return False\n",
    "        if factor % 4 != 1:\n",
    "            return False\n",
    "    return True\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "JLgbZtyG-i8i"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Q1.1: False \n",
      "\n",
      "Q1.2: False \n",
      "\n",
      "Q1.3: True \n",
      "\n"
     ]
    }
   ],
   "source": [
    "def sum_of_two_squares(n: int) -> bool:\n",
    "    factors = factorint(n)\n",
    "    for prime, power in sorted(factors.items()):\n",
    "        odd_prime = prime % 2 == 1\n",
    "        odd_power = power % 2 == 1\n",
    "        one_remainder_four = prime % 4 == 1\n",
    "        if not odd_prime:\n",
    "            continue\n",
    "        if not odd_power:\n",
    "            return False\n",
    "        if not one_remainder_four:\n",
    "            return False\n",
    "    return True\n",
    "\n",
    "print(\"Q1.1:\", sum_of_two_squares(100), \"\\n\")\n",
    "print(\"Q1.2:\", sum_of_two_squares(70), \"\\n\")\n",
    "print(\"Q1.3:\", sum_of_two_squares(4250), \"\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "lpza11OhCcER"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Factors of 100 are: {2: 2, 5: 2}.\n",
      "100 is not a sum of two squares since the prime factor (5)'s power (2) is even.\n",
      "Q1.1: False \n",
      "\n",
      "Factors of 70 are: {2: 1, 5: 1, 7: 1}.\n",
      "70 is not a sum of two squares since prime factor 7 % 4 == 3 != 1.\n",
      "Q1.2: False \n",
      "\n",
      "Factors of 4250 are: {2: 1, 5: 3, 17: 1}.\n",
      "4250 is a sum of two squares as all of its prime factors % 4 == 1 and all of their powers are odd.\n",
      "Q1.3: True \n",
      "\n"
     ]
    }
   ],
   "source": [
    "def sum_of_two_squares(n: int) -> bool:\n",
    "    factors = factorint(n)\n",
    "    print(f\"Factors of {n} are: {factors}.\")\n",
    "    for prime, power in sorted(factors.items()):\n",
    "        odd_prime = prime % 2 == 1\n",
    "        odd_power = power % 2 == 1\n",
    "        one_remainder_four = prime % 4 == 1\n",
    "        if not odd_prime:\n",
    "            continue\n",
    "        if not odd_power:\n",
    "            print(\n",
    "                f\"{n} is not a sum of two squares since\",\n",
    "                f\"the prime factor ({prime})'s power ({power}) is even.\",\n",
    "            )\n",
    "            return False\n",
    "        if not one_remainder_four:\n",
    "            print(\n",
    "                f\"{n} is not a sum of two squares since\",\n",
    "                f\"prime factor {prime} % 4 == {prime % 4} != 1.\",\n",
    "            )\n",
    "            return False\n",
    "    print(\n",
    "        f\"{n} is a sum of two squares as\",\n",
    "        \"all of its prime factors % 4 == 1\",\n",
    "        \"and all of their powers are odd.\",\n",
    "    )\n",
    "    return True\n",
    "\n",
    "print(\"Q1.1:\", sum_of_two_squares(100), \"\\n\")\n",
    "print(\"Q1.2:\", sum_of_two_squares(70), \"\\n\")\n",
    "print(\"Q1.3:\", sum_of_two_squares(4250), \"\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "id": "HaseuvPA72ZO"
   },
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle 2^{2} \\cdot 5^{2}$"
      ],
      "text/plain": [
       "2**2*5**2"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "factorint(100, visual=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "id": "gcO3-rIxHTLi"
   },
   "outputs": [],
   "source": [
    "from sympy import Eq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "id": "1LqBX9jKHM_r"
   },
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle \\text{True}$"
      ],
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Eq(\n",
    "    100,\n",
    "    factorint(100, visual=True),\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "id": "vTwUBPmNHxQ6"
   },
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle 100 = 2^{2} \\cdot 5^{2}$"
      ],
      "text/plain": [
       "Eq(100, 2**2*5**2)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Eq(\n",
    "    100,\n",
    "    factorint(100, visual=True),\n",
    "    evaluate=False,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "id": "g2IVwjc4J2O_"
   },
   "outputs": [],
   "source": [
    "from IPython.display import Markdown"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "id": "BRB1IZhnH-lb"
   },
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle 100 = 2^{2} \\cdot 5^{2}$"
      ],
      "text/plain": [
       "Eq(100, 2**2*5**2)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "$100$ is not a sum of two squares since the prime factor's power $(5^2)$ is even."
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Q1.1: False \n",
      "\n"
     ]
    },
    {
     "data": {
      "text/latex": [
       "$\\displaystyle 70 = 2^{1} \\cdot 5^{1} \\cdot 7^{1}$"
      ],
      "text/plain": [
       "Eq(70, 2**1*5**1*7**1)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "$70$ is not a sum of two squares since the following prime factor$7 \\pmod 4 \\equiv 3 \\not\\equiv 1$."
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Q1.2: False \n",
      "\n"
     ]
    },
    {
     "data": {
      "text/latex": [
       "$\\displaystyle 4250 = 17^{1} \\cdot 2^{1} \\cdot 5^{3}$"
      ],
      "text/plain": [
       "Eq(4250, 2**1*5**3*17**1)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "$4250$ is a sum of squares as for all of its odd prime factors $p^k$, $p \\pmod 4 = 1$ and $k$ is odd."
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Q1.3: True \n",
      "\n"
     ]
    }
   ],
   "source": [
    "def sum_of_two_squares(n: int) -> bool:\n",
    "    display(\n",
    "        Eq(\n",
    "            n,\n",
    "            factorint(n, visual=True),\n",
    "            evaluate=False,\n",
    "        )\n",
    "    )\n",
    "    factors = factorint(n)\n",
    "    for prime, power in sorted(factors.items()):\n",
    "        odd_prime = prime % 2 == 1\n",
    "        odd_power = power % 2 == 1\n",
    "        one_remainder_four = prime % 4 == 1\n",
    "        if not odd_prime:\n",
    "            continue\n",
    "        if not odd_power:\n",
    "            display(Markdown(\n",
    "                f\"${n}$ is not a sum of two squares since \"\n",
    "                f\"the prime factor's power $({prime}^{power})$ is even.\"\n",
    "            ))\n",
    "            return False\n",
    "        if not one_remainder_four:\n",
    "            display(Markdown(\n",
    "                f\"${n}$ is not a sum of two squares since the following prime factor\"\n",
    "                f\"${prime} \\\\pmod 4 \\\\equiv {prime % 4} \\\\not\\\\equiv 1$.\"\n",
    "            ))\n",
    "            return False\n",
    "    display(Markdown(\n",
    "        f\"${n}$ is a sum of squares as \"\n",
    "        \"for all of its odd prime factors $p^k$, \"\n",
    "        \"$p \\\\pmod 4 = 1$ \"\n",
    "        \"and $k$ is odd.\"\n",
    "    ))\n",
    "    return True\n",
    "\n",
    "print(\"Q1.1:\", sum_of_two_squares(100), \"\\n\")\n",
    "print(\"Q1.2:\", sum_of_two_squares(70), \"\\n\")\n",
    "print(\"Q1.3:\", sum_of_two_squares(4250), \"\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "id": "18H41djXR7CH"
   },
   "outputs": [],
   "source": [
    "from sympy.solvers.diophantine.diophantine import sum_of_squares"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "id": "MLNhV0RJRvMz"
   },
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle 100 = 2^{2} \\cdot 5^{2}$"
      ],
      "text/plain": [
       "Eq(100, 2**2*5**2)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "$100$ is not a sum of two squares since the prime factor's power $(5^2)$ is even."
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Q1.1: False \n",
      "\n"
     ]
    },
    {
     "data": {
      "text/latex": [
       "$\\displaystyle 70 = 2^{1} \\cdot 5^{1} \\cdot 7^{1}$"
      ],
      "text/plain": [
       "Eq(70, 2**1*5**1*7**1)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "$70$ is not a sum of two squares since prime factor $7 \\pmod 4 \\equiv 3 \\not\\equiv 1$."
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Q1.2: False \n",
      "\n"
     ]
    },
    {
     "data": {
      "text/latex": [
       "$\\displaystyle 4250 = 17^{1} \\cdot 2^{1} \\cdot 5^{3}$"
      ],
      "text/plain": [
       "Eq(4250, 2**1*5**3*17**1)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "$4250$ is a sum of squares as for all of its odd prime factors $p^k$, $p \\pmod 4 = 1$ and $k$ is odd.\n",
       "\n",
       " e.g. $4250 = 43 ^ 2 + 49 ^ 2$"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Q1.3: True \n",
      "\n"
     ]
    }
   ],
   "source": [
    "def sum_of_two_squares(n: int) -> bool:\n",
    "    display(\n",
    "        Eq(\n",
    "            n,\n",
    "            factorint(n, visual=True),\n",
    "            evaluate=False,\n",
    "        )\n",
    "    )\n",
    "    factors = factorint(n)\n",
    "    for prime, power in sorted(factors.items()):\n",
    "        odd_prime = prime % 2 == 1\n",
    "        odd_power = power % 2 == 1\n",
    "        one_remainder_four = prime % 4 == 1\n",
    "        if not odd_prime:\n",
    "            continue\n",
    "        if not odd_power:\n",
    "            display(Markdown(\n",
    "                f\"${n}$ is not a sum of two squares since \"\n",
    "                f\"the prime factor's power $({prime}^{power})$ is even.\"\n",
    "            ))\n",
    "            return False\n",
    "        if not one_remainder_four:\n",
    "            display(Markdown(\n",
    "                f\"${n}$ is not a sum of two squares since prime factor \"\n",
    "                f\"${prime} \\\\pmod 4 \\\\equiv {prime % 4} \\\\not\\\\equiv 1$.\"\n",
    "            ))\n",
    "            return False\n",
    "    square1, square2 = next(sum_of_squares(n=n, k=2))\n",
    "    display(Markdown(\n",
    "        f\"${n}$ is a sum of squares as \"\n",
    "        \"for all of its odd prime factors $p^k$, \"\n",
    "        \"$p \\\\pmod 4 = 1$ \"\n",
    "        \"and $k$ is odd.\"\n",
    "        f\"\\n\\n e.g. ${n} = {square1} ^ 2 + {square2} ^ 2$\"\n",
    "    ))\n",
    "    return True\n",
    "\n",
    "print(\"Q1.1:\", sum_of_two_squares(100), \"\\n\")\n",
    "print(\"Q1.2:\", sum_of_two_squares(70), \"\\n\")\n",
    "print(\"Q1.3:\", sum_of_two_squares(4250), \"\\n\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "QqUoAQmLf7vI"
   },
   "source": [
    "## Question 2: Calculus"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "pohaz5Lo5yph"
   },
   "source": [
    "For the following curve $y = -x^2 + 2x + 3$\n",
    "\n",
    "1. Calculate the $x$ intersection points.\n",
    "2. Calculate the $y$ intersection point.\n",
    "3. Calculate the curve's vertex.\n",
    "4. Plot the curve."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "id": "yz13PPOOTNZf"
   },
   "outputs": [],
   "source": [
    "from sympy.abc import x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "id": "2zRONtn5AyCr"
   },
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle - x^{2} + 2 x + 3$"
      ],
      "text/plain": [
       "-x**2 + 2*x + 3"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_equation = - x ** 2 + 2 * x + 3\n",
    "y_equation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "id": "sSoqnyyMTZRv"
   },
   "outputs": [],
   "source": [
    "from sympy import Function\n",
    "y = Function(\"y\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "id": "D64A8dsCTwv9"
   },
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle y{\\left(x \\right)} = - x^{2} + 2 x + 3$"
      ],
      "text/plain": [
       "Eq(y(x), -x**2 + 2*x + 3)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Eq(\n",
    "    y(x),\n",
    "    y_equation,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Zet3yV1SUwyj"
   },
   "source": [
    "**Reminder:**\n",
    "\n",
    "*The $x$ intersection point is $(0, y(0))$ (where $x = 0$)*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "8fx14u2sHv4f"
   },
   "source": [
    "Thus for Q2.1, we need to substitute for $x=0$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "id": "I3n7S1x9T-p3"
   },
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle y{\\left(x \\right)} = - x^{2} + 2 x + 3$"
      ],
      "text/plain": [
       "Eq(y(x), -x**2 + 2*x + 3)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/latex": [
       "$\\displaystyle y{\\left(0 \\right)} = 3$"
      ],
      "text/plain": [
       "Eq(y(0), 3)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(\n",
    "    Eq(\n",
    "        y(x),\n",
    "        y_equation,\n",
    "    ),\n",
    "    Eq(\n",
    "        y(0),\n",
    "        y_equation.subs(x, 0),\n",
    "    ),\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "id": "MnEt9Tb3UXjH"
   },
   "outputs": [],
   "source": [
    "from sympy import UnevaluatedExpr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "id": "Pf5c0xJWBJYP"
   },
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle y{\\left(x \\right)} = - x^{2} + 2 x + 3$"
      ],
      "text/plain": [
       "Eq(y(x), -x**2 + 2*x + 3)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/latex": [
       "$\\displaystyle y{\\left(0 \\right)} = 3 + 2 \\cdot 0 - 0^{2}$"
      ],
      "text/plain": [
       "Eq(y(0), 3 + 2*0 - 0**2)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/latex": [
       "$\\displaystyle y{\\left(0 \\right)} = 3$"
      ],
      "text/plain": [
       "Eq(y(0), 3)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(\n",
    "    Eq(\n",
    "        y(x),\n",
    "        y_equation,\n",
    "    ),\n",
    "    Eq(\n",
    "        y(0),\n",
    "        y_equation.subs(x, UnevaluatedExpr(0))\n",
    "    ),\n",
    "    Eq(\n",
    "        y(0),\n",
    "        y_equation.subs(x, 0)\n",
    "    ),\n",
    ")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "upVxXWQuVN_e"
   },
   "source": [
    "**Reminder:** *The $y$ intersection point is $(x(0), 0)$ (where $y = 0$)*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "MCmHzB7oJIWf"
   },
   "source": [
    "Thus for Q2.2, we need to re-arrange for $x$, and solve for $y=0$.\n",
    "\n",
    "We can do this using the quadratic formula:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "id": "WptCH3xKVe-s"
   },
   "outputs": [],
   "source": [
    "from sympy import sympify"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "id": "cojcrX2Cn-D3"
   },
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle y{\\left(x \\right)} = a x^{2} + b x + c$"
      ],
      "text/plain": [
       "Eq(y(x), a*x**2 + b*x + c)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "quadratic = \"a * x ** 2 + b * x + c\"\n",
    "Eq(\n",
    "    y(x),\n",
    "    sympify(quadratic)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "id": "fRtu07P7oavJ"
   },
   "outputs": [],
   "source": [
    "from sympy import solve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "id": "a8q_AlXeJIWp"
   },
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle \\frac{- b - \\sqrt{- 4 a c + b^{2}}}{2 a}$"
      ],
      "text/plain": [
       "(-b - sqrt(-4*a*c + b**2))/(2*a)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/latex": [
       "$\\displaystyle \\frac{- b + \\sqrt{- 4 a c + b^{2}}}{2 a}$"
      ],
      "text/plain": [
       "(-b + sqrt(-4*a*c + b**2))/(2*a)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "quadratic_solutions = solve(quadratic, \"x\")\n",
    "display(*quadratic_solutions)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "GqDQkFUMohdL"
   },
   "source": [
    "Subsituting for the respective constants:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "editable": true,
    "id": "gkRv27iVLdUY",
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle x = \\frac{\\left(-1\\right)^{-1} \\left(- \\sqrt{2^{2} - 4 \\cdot 3 \\left(-1\\right)} - 2\\right)}{2}$"
      ],
      "text/plain": [
       "Eq(x, (-1)**(-1)*(-sqrt(2**2 - 4*3*(-1)) - 2)/2)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/latex": [
       "$\\displaystyle x = 3$"
      ],
      "text/plain": [
       "Eq(x, 3)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/latex": [
       "$\\displaystyle y{\\left(3 \\right)} = 0$"
      ],
      "text/plain": [
       "Eq(y(3), 0)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/latex": [
       "$\\displaystyle x = \\frac{\\left(-1\\right)^{-1} \\left(\\sqrt{2^{2} - 4 \\cdot 3 \\left(-1\\right)} - 2\\right)}{2}$"
      ],
      "text/plain": [
       "Eq(x, (-1)**(-1)*(sqrt(2**2 - 4*3*(-1)) - 2)/2)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/latex": [
       "$\\displaystyle x = -1$"
      ],
      "text/plain": [
       "Eq(x, -1)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/latex": [
       "$\\displaystyle y{\\left(-1 \\right)} = 0$"
      ],
      "text/plain": [
       "Eq(y(-1), 0)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "constants = {\n",
    "    \"a\": UnevaluatedExpr(-1),\n",
    "    \"b\": UnevaluatedExpr(2),\n",
    "    \"c\": UnevaluatedExpr(3),\n",
    "}\n",
    "\n",
    "for solution in quadratic_solutions:\n",
    "    x_solution = solution.subs(constants)\n",
    "    display(\n",
    "        Eq(\n",
    "            x,\n",
    "            x_solution,\n",
    "        )\n",
    "    )\n",
    "\n",
    "    x_value = x_solution.doit()\n",
    "    display(\n",
    "        Eq(\n",
    "            x,\n",
    "            x_value,\n",
    "        ),\n",
    "        Eq(\n",
    "            y(x_value),\n",
    "            0,\n",
    "        ),\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "editable": true,
    "id": "BIgBwrw-pUlH",
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "Of course, SymPy also has methods to fast forward this too."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "id": "hRxnXCjaVtLx"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[-1, 3]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "solve(y_equation, x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "upVxXWQuVN_e"
   },
   "source": [
    "**Reminder:** *A function $y(x)$ has a maximum point where $\\frac{d}{dx}y(x) = 0$ if $y(x)\\frac{d^2y}{dx^2} < 0$.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle \\frac{d}{d x} y{\\left(x \\right)} = 2 - 2 x$"
      ],
      "text/plain": [
       "Eq(Derivative(y(x), x), 2 - 2*x)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Q2.3\n",
    "y_diff = y_equation.diff(x)\n",
    "Eq(\n",
    "    y(x).diff(x),\n",
    "    y_diff,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Solving for $y = 0$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "solve(y_diff, x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Substituting $x = 1$ to $y(x)$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle 4$"
      ],
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_equation.subs(x, 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Thus the maximum of point of $y(x)$ is at $(1, 4)$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "9J112lRsq1lU"
   },
   "source": [
    "For Q2.4, we can easily graph as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "id": "yTJaW_sgq_GG"
   },
   "outputs": [],
   "source": [
    "from sympy import plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "id": "yf9R3U465C_T"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<sympy.plotting.plot.Plot at 0x7f74547b85d0>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot(\n",
    "    y_equation,\n",
    "    xlim=(-4, 4),\n",
    "    ylim=(-4, 4),\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "33xeTEpboRtd"
   },
   "source": [
    "[But what can Wolfram Alpha do?](https://www.wolframalpha.com/input?i=y+%3D+-x%5E2+%2B+2x+%2B+3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "4WGixZuef_wP"
   },
   "source": [
    "## Question 3: Multivariable Calculus"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "NGDrb885Km2p"
   },
   "source": [
    "The following profit function $f(u_1, u_2)$ describes the profit that results from selling $u_1$ units of Product 1 and $u_2$ units of Product 2.\n",
    "\n",
    "$f{\\left(u_{1},u_{2} \\right)} = - \\Delta \\left(u_{1}^{2} + u_{2}^{2}\\right) - c_{0} - u_{1} u_{2} \\left(\\Delta_{12} + \\Delta_{21}\\right) + u_{1} \\left(- c_{1} + p_{1}\\right) + u_{2} \\left(- c_{2} + p_{2}\\right)$\n",
    "\n",
    "$\\pi_1$ and $\\pi_2$ represent, respectively, the market price (per unit) for each product.\n",
    "\n",
    "$c_1$ and $c_2$ represent, respectively, the cost (per unit) for each product.\n",
    "\n",
    "$c_0$ represents the fixed cost, regardless of any production or sales.\n",
    "\n",
    "$\\Delta$ represents the decrease in unit price of each product for any additional unit that is sold of that product.\n",
    "\n",
    "$\\Delta_{12}$ represents the decrease in price of Product 1 for each additional unit that is sold of Product 2 (and vice versa for $\\Delta_{21}$).\n",
    "\n",
    "Assuming that a unique solution exists and is extremal, find the optimal profit for the following parameters:\n",
    "\n",
    "$\\pi_1 = \\$400$\n",
    "\n",
    "$\\pi_2 = \\$600$\n",
    "\n",
    "$c_1 = \\$200$\n",
    "\n",
    "$c_2 = \\$300$\n",
    "\n",
    "$c_0 = \\$500,000$\n",
    "\n",
    "$\\Delta = \\$0.02$\n",
    "\n",
    "$\\Delta_{12} = \\$0.001$\n",
    "\n",
    "$\\Delta_{21} = \\$0.002$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "GoVP4cl0otXd"
   },
   "source": [
    "What can Wolfram Alpha do? [1](https://www.wolframalpha.com/input?i=%24f%7B%5Cleft%28u_%7B1%7D%2Cu_%7B2%7D+%5Cright%29%7D+%3D+-+%5CDelta+%5Cleft%28u_%7B1%7D%5E%7B2%7D+%2B+u_%7B2%7D%5E%7B2%7D%5Cright%29+-+c_%7B0%7D+-+u_%7B1%7D+u_%7B2%7D+%5Cleft%28%5CDelta_%7B12%7D+%2B+%5CDelta_%7B21%7D%5Cright%29+%2B+u_%7B1%7D+%5Cleft%28-+c_%7B1%7D+%2B+p_%7B1%7D%5Cright%29+%2B+u_%7B2%7D+%5Cleft%28-+c_%7B2%7D+%2B+p_%7B2%7D%5Cright%29%24+%24%5Cpi_1+%3D+%5C%24400%24+%24%5Cpi_2+%3D+%5C%24600%24+%24c_1+%3D+%5C%24200%24+%24c_2+%3D+%5C%24300%24+%24c_0+%3D+%5C%24500%2C000%24+%24%5CDelta+%3D+%5C%240.02%24+%24%5CDelta_%7B12%7D+%3D+%5C%240.001%24+%24%5CDelta_%7B21%7D+%3D+%5C%240.002%24)\n",
    "[2](https://www.wolframalpha.com/input?i=maximum+of+-+0.02+*+%28u%5E2+%2B+v%5E2%29+-+500000+-+u+*+v+*+%280.001+%2B+0.002%29+%2B+u+*+%28-+200+%2B+400%29+%2B+v+*+%28-+300+%2B+600%29)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "id": "ppcSYRI8t8ta"
   },
   "outputs": [],
   "source": [
    "from sympy import symbols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "id": "w-xFeY3SA0G8"
   },
   "outputs": [],
   "source": [
    "u1, u2 = symbols(\"u_1 u_2\")\n",
    "c0, c1, c2 = symbols(\"c:3\")\n",
    "p1, p2 = symbols(\"p_1 p_2\")\n",
    "D, D12, D21 = symbols(\"Delta Delta_12 Delta_21\")\n",
    "f = Function(\"f\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "id": "8rgfAPxhCGwq"
   },
   "outputs": [],
   "source": [
    "equation = (\n",
    "    -c0\n",
    "    + u1 * (p1 - c1)\n",
    "    + u2 * (p2 - c2)\n",
    "    - u1 * u2 * (D12 + D21)\n",
    "    - D * (u1 ** 2 + u2 ** 2)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "id": "u50BevTp00q9"
   },
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle f{\\left(u_{1},u_{2} \\right)} = - \\Delta \\left(u_{1}^{2} + u_{2}^{2}\\right) - c_{0} - u_{1} u_{2} \\left(\\Delta_{12} + \\Delta_{21}\\right) + u_{1} \\left(- c_{1} + p_{1}\\right) + u_{2} \\left(- c_{2} + p_{2}\\right)$"
      ],
      "text/plain": [
       "Eq(f(u_1, u_2), -Delta*(u_1**2 + u_2**2) - c0 - u_1*u_2*(Delta_12 + Delta_21) + u_1*(-c1 + p_1) + u_2*(-c2 + p_2))"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Eq(\n",
    "    f(u1, u2),\n",
    "    equation,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "id": "U3Kgxz1GzVUc"
   },
   "outputs": [],
   "source": [
    "constants = {\n",
    "    p1: 400,\n",
    "    p2: 600,\n",
    "    c1: 200,\n",
    "    c2: 300,\n",
    "    c0: 500_000,\n",
    "    D: 0.02,\n",
    "    D12: 0.001,\n",
    "    D21: 0.002,\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "id": "AzsryUbvzTnF"
   },
   "outputs": [],
   "source": [
    "from sympy.plotting.plot import plot3d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "id": "Ivt-R08YOfj2"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<sympy.plotting.plot.Plot at 0x7f7438826650>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot3d(\n",
    "    equation.subs(constants),\n",
    "    (u1, 0, 10_000),\n",
    "    (u2, 0, 10_000),\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "4OE3bZb1-TL7"
   },
   "source": [
    "**Reminder:**\n",
    "\n",
    "The maximum value of $f$ is where $\\nabla f = \\bf 0$\n",
    "\n",
    "Where $\\nabla f(x,y) = \\left[\\begin{matrix}\\frac{\\partial}{\\partial x} f(x,y)\\\\\\frac{\\partial}{\\partial y} f(x,y)\\end{matrix}\\right]$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "5S67io3yIZd2"
   },
   "source": [
    "Thus, to find the optimal profit, we must find the maximum, by finding the values $u^\\ast_1$, $u^\\ast_2$ for which $\\nabla f(u^\\ast_1, u^\\ast_2) = \\bf 0$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "id": "314P_oGJBDNa"
   },
   "outputs": [],
   "source": [
    "from sympy import Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "id": "fbVEeku7I97b"
   },
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle \\left[\\begin{matrix}\\frac{\\partial}{\\partial u_{1}} f{\\left(u_{1},u_{2} \\right)}\\\\\\frac{\\partial}{\\partial u_{2}} f{\\left(u_{1},u_{2} \\right)}\\end{matrix}\\right] = \\left[\\begin{matrix}\\frac{\\partial}{\\partial u_{1}} \\left(- \\Delta \\left(u_{1}^{2} + u_{2}^{2}\\right) - c_{0} - u_{1} u_{2} \\left(\\Delta_{12} + \\Delta_{21}\\right) + u_{1} \\left(- c_{1} + p_{1}\\right) + u_{2} \\left(- c_{2} + p_{2}\\right)\\right)\\\\\\frac{\\partial}{\\partial u_{2}} \\left(- \\Delta \\left(u_{1}^{2} + u_{2}^{2}\\right) - c_{0} - u_{1} u_{2} \\left(\\Delta_{12} + \\Delta_{21}\\right) + u_{1} \\left(- c_{1} + p_{1}\\right) + u_{2} \\left(- c_{2} + p_{2}\\right)\\right)\\end{matrix}\\right]$"
      ],
      "text/plain": [
       "Eq(Matrix([\n",
       "[Derivative(f(u_1, u_2), u_1)],\n",
       "[Derivative(f(u_1, u_2), u_2)]]), Matrix([\n",
       "[Derivative(-Delta*(u_1**2 + u_2**2) - c0 - u_1*u_2*(Delta_12 + Delta_21) + u_1*(-c1 + p_1) + u_2*(-c2 + p_2), u_1)],\n",
       "[Derivative(-Delta*(u_1**2 + u_2**2) - c0 - u_1*u_2*(Delta_12 + Delta_21) + u_1*(-c1 + p_1) + u_2*(-c2 + p_2), u_2)]]))"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f_grad = Matrix([\n",
    "    f(u1, u2).diff(u1),\n",
    "    f(u1, u2).diff(u2),\n",
    "])\n",
    "\n",
    "Eq(\n",
    "    f_grad,\n",
    "    Matrix([\n",
    "        equation.diff(u1, evaluate=False),\n",
    "        equation.diff(u2, evaluate=False),\n",
    "    ])\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "id": "6rR3deXWNIkz"
   },
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle \\left[\\begin{matrix}\\frac{\\partial}{\\partial u_{1}} f{\\left(u_{1},u_{2} \\right)}\\\\\\frac{\\partial}{\\partial u_{2}} f{\\left(u_{1},u_{2} \\right)}\\end{matrix}\\right] = \\left[\\begin{matrix}- 2 \\Delta u_{1} - c_{1} + p_{1} - u_{2} \\left(\\Delta_{12} + \\Delta_{21}\\right)\\\\- 2 \\Delta u_{2} - c_{2} + p_{2} - u_{1} \\left(\\Delta_{12} + \\Delta_{21}\\right)\\end{matrix}\\right]$"
      ],
      "text/plain": [
       "Eq(Matrix([\n",
       "[Derivative(f(u_1, u_2), u_1)],\n",
       "[Derivative(f(u_1, u_2), u_2)]]), Matrix([\n",
       "[-2*Delta*u_1 - c1 + p_1 - u_2*(Delta_12 + Delta_21)],\n",
       "[-2*Delta*u_2 - c2 + p_2 - u_1*(Delta_12 + Delta_21)]]))"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f_diff_wrt_u1 = equation.diff(u1)\n",
    "f_diff_wrt_u2 = equation.diff(u2)\n",
    "\n",
    "f_diff = Matrix([\n",
    "    f_diff_wrt_u1,\n",
    "    f_diff_wrt_u2,\n",
    "])\n",
    "Eq(\n",
    "    f_grad,\n",
    "    f_diff,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "id": "bN8ObG7OXGZu"
   },
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle \\left[\\begin{matrix}0\\\\0\\end{matrix}\\right] = \\left[\\begin{matrix}- 2 \\Delta u_{1} - c_{1} + p_{1} - u_{2} \\left(\\Delta_{12} + \\Delta_{21}\\right)\\\\- 2 \\Delta u_{2} - c_{2} + p_{2} - u_{1} \\left(\\Delta_{12} + \\Delta_{21}\\right)\\end{matrix}\\right]$"
      ],
      "text/plain": [
       "Eq(Matrix([\n",
       "[0],\n",
       "[0]]), Matrix([\n",
       "[-2*Delta*u_1 - c1 + p_1 - u_2*(Delta_12 + Delta_21)],\n",
       "[-2*Delta*u_2 - c2 + p_2 - u_1*(Delta_12 + Delta_21)]]))"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Eq(\n",
    "    Matrix([\n",
    "        0,\n",
    "        0\n",
    "    ]),\n",
    "    f_diff,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "x1ppJ7fYDRmP"
   },
   "source": [
    "Solving for $u_1$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "id": "DS7xw0jt43fl"
   },
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle u_{1} = \\frac{- \\Delta_{12} u_{2} - \\Delta_{21} u_{2} - c_{1} + p_{1}}{2 \\Delta}$"
      ],
      "text/plain": [
       "Eq(u_1, (-Delta_12*u_2 - Delta_21*u_2 - c1 + p_1)/(2*Delta))"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f_diff_wrt_u1_solved_for_u1 = solve(\n",
    "    f_diff_wrt_u1,\n",
    "    u1,\n",
    ")[0]\n",
    "\n",
    "Eq(\n",
    "    u1,\n",
    "    f_diff_wrt_u1_solved_for_u1\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "MS1bu_w99YnK"
   },
   "source": [
    "Substituting $u_1$ into $\\frac{\\partial}{\\partial u_2} f(u_1,u_2)$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "id": "LSjNm9Q5-CzT"
   },
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle 0 = - 2 \\Delta u_{2} - c_{2} + p_{2} - \\frac{\\left(\\Delta_{12} + \\Delta_{21}\\right) \\left(- \\Delta_{12} u_{2} - \\Delta_{21} u_{2} - c_{1} + p_{1}\\right)}{2 \\Delta}$"
      ],
      "text/plain": [
       "Eq(0, -2*Delta*u_2 - c2 + p_2 - (Delta_12 + Delta_21)*(-Delta_12*u_2 - Delta_21*u_2 - c1 + p_1)/(2*Delta))"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f_diff_wrt_u2_removed_u1 = f_diff_wrt_u2.subs(\n",
    "    u1,\n",
    "    f_diff_wrt_u1_solved_for_u1\n",
    ")\n",
    "Eq(\n",
    "    0,\n",
    "    f_diff_wrt_u2_removed_u1,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "shmHFK-8GgiX"
   },
   "source": [
    "Solving for $u^\\ast_2$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "id": "PKs8Tl-pGflj"
   },
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle u^{*}_{2} = \\frac{2 \\Delta c_{2} - 2 \\Delta p_{2} - \\Delta_{12} c_{1} + \\Delta_{12} p_{1} - \\Delta_{21} c_{1} + \\Delta_{21} p_{1}}{- 4 \\Delta^{2} + \\Delta_{12}^{2} + 2 \\Delta_{12} \\Delta_{21} + \\Delta_{21}^{2}}$"
      ],
      "text/plain": [
       "Eq(u^*_2, (2*Delta*c2 - 2*Delta*p_2 - Delta_12*c1 + Delta_12*p_1 - Delta_21*c1 + Delta_21*p_1)/(-4*Delta**2 + Delta_12**2 + 2*Delta_12*Delta_21 + Delta_21**2))"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "u2s = symbols(\"u^*_2\")\n",
    "f_diff_wrt_u2_solved_for_u2 = solve(\n",
    "    f_diff_wrt_u2_removed_u1,\n",
    "    u2\n",
    ")[0]\n",
    "\n",
    "Eq(\n",
    "    u2s,\n",
    "    f_diff_wrt_u2_solved_for_u2\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ntkrAuW_Ep8V"
   },
   "source": [
    "Substituting the constants"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "id": "xrLrrO13FNZ7"
   },
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle u^{*}_{2} = 7165.30483972345$"
      ],
      "text/plain": [
       "Eq(u^*_2, 7165.30483972345)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "u2s_value = f_diff_wrt_u2_solved_for_u2.subs(constants)\n",
    "Eq(\n",
    "    u2s,\n",
    "    u2s_value\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "eLZtuvFgE-K8"
   },
   "source": [
    "We can now similarly solve for $u^*_1$ but by using a function!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "id": "4oiNNcARRiC1"
   },
   "outputs": [],
   "source": [
    "from sympy import Expr\n",
    "from sympy import Symbol"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "id": "T3obsVKFE9gK",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle \\left[\\begin{matrix}\\frac{\\partial}{\\partial u_{1}} f{\\left(u_{1},u_{2} \\right)}\\\\\\frac{\\partial}{\\partial u_{2}} f{\\left(u_{1},u_{2} \\right)}\\end{matrix}\\right] = \\left[\\begin{matrix}\\frac{\\partial}{\\partial u_{1}} \\left(- \\Delta \\left(u_{1}^{2} + u_{2}^{2}\\right) - c_{0} - u_{1} u_{2} \\left(\\Delta_{12} + \\Delta_{21}\\right) + u_{1} \\left(- c_{1} + p_{1}\\right) + u_{2} \\left(- c_{2} + p_{2}\\right)\\right)\\\\\\frac{\\partial}{\\partial u_{2}} \\left(- \\Delta \\left(u_{1}^{2} + u_{2}^{2}\\right) - c_{0} - u_{1} u_{2} \\left(\\Delta_{12} + \\Delta_{21}\\right) + u_{1} \\left(- c_{1} + p_{1}\\right) + u_{2} \\left(- c_{2} + p_{2}\\right)\\right)\\end{matrix}\\right]$"
      ],
      "text/plain": [
       "Eq(Matrix([\n",
       "[Derivative(f(u_1, u_2), u_1)],\n",
       "[Derivative(f(u_1, u_2), u_2)]]), Matrix([\n",
       "[Derivative(-Delta*(u_1**2 + u_2**2) - c0 - u_1*u_2*(Delta_12 + Delta_21) + u_1*(-c1 + p_1) + u_2*(-c2 + p_2), u_1)],\n",
       "[Derivative(-Delta*(u_1**2 + u_2**2) - c0 - u_1*u_2*(Delta_12 + Delta_21) + u_1*(-c1 + p_1) + u_2*(-c2 + p_2), u_2)]]))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/latex": [
       "$\\displaystyle \\left[\\begin{matrix}\\frac{\\partial}{\\partial u_{1}} f{\\left(u_{1},u_{2} \\right)}\\\\\\frac{\\partial}{\\partial u_{2}} f{\\left(u_{1},u_{2} \\right)}\\end{matrix}\\right] = \\left[\\begin{matrix}- 2 \\Delta u_{1} - c_{1} + p_{1} - u_{2} \\left(\\Delta_{12} + \\Delta_{21}\\right)\\\\- 2 \\Delta u_{2} - c_{2} + p_{2} - u_{1} \\left(\\Delta_{12} + \\Delta_{21}\\right)\\end{matrix}\\right]$"
      ],
      "text/plain": [
       "Eq(Matrix([\n",
       "[Derivative(f(u_1, u_2), u_1)],\n",
       "[Derivative(f(u_1, u_2), u_2)]]), Matrix([\n",
       "[-2*Delta*u_1 - c1 + p_1 - u_2*(Delta_12 + Delta_21)],\n",
       "[-2*Delta*u_2 - c2 + p_2 - u_1*(Delta_12 + Delta_21)]]))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/latex": [
       "$\\displaystyle \\left[\\begin{matrix}0\\\\0\\end{matrix}\\right] = \\left[\\begin{matrix}- 2 \\Delta u_{1} - c_{1} + p_{1} - u_{2} \\left(\\Delta_{12} + \\Delta_{21}\\right)\\\\- 2 \\Delta u_{2} - c_{2} + p_{2} - u_{1} \\left(\\Delta_{12} + \\Delta_{21}\\right)\\end{matrix}\\right]$"
      ],
      "text/plain": [
       "Eq(Matrix([\n",
       "[0],\n",
       "[0]]), Matrix([\n",
       "[-2*Delta*u_1 - c1 + p_1 - u_2*(Delta_12 + Delta_21)],\n",
       "[-2*Delta*u_2 - c2 + p_2 - u_1*(Delta_12 + Delta_21)]]))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/latex": [
       "$\\displaystyle u_{1} = \\frac{- \\Delta_{12} u_{2} - \\Delta_{21} u_{2} - c_{1} + p_{1}}{2 \\Delta}$"
      ],
      "text/plain": [
       "Eq(u_1, (-Delta_12*u_2 - Delta_21*u_2 - c1 + p_1)/(2*Delta))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/latex": [
       "$\\displaystyle 0 = - 2 \\Delta u_{2} - c_{2} + p_{2} - \\frac{\\left(\\Delta_{12} + \\Delta_{21}\\right) \\left(- \\Delta_{12} u_{2} - \\Delta_{21} u_{2} - c_{1} + p_{1}\\right)}{2 \\Delta}$"
      ],
      "text/plain": [
       "Eq(0, -2*Delta*u_2 - c2 + p_2 - (Delta_12 + Delta_21)*(-Delta_12*u_2 - Delta_21*u_2 - c1 + p_1)/(2*Delta))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/latex": [
       "$\\displaystyle u^{*}_{2} = \\frac{2 \\Delta c_{2} - 2 \\Delta p_{2} - \\Delta_{12} c_{1} + \\Delta_{12} p_{1} - \\Delta_{21} c_{1} + \\Delta_{21} p_{1}}{- 4 \\Delta^{2} + \\Delta_{12}^{2} + 2 \\Delta_{12} \\Delta_{21} + \\Delta_{21}^{2}}$"
      ],
      "text/plain": [
       "Eq(u^*_2, (2*Delta*c2 - 2*Delta*p_2 - Delta_12*c1 + Delta_12*p_1 - Delta_21*c1 + Delta_21*p_1)/(-4*Delta**2 + Delta_12**2 + 2*Delta_12*Delta_21 + Delta_21**2))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/latex": [
       "$\\displaystyle u^{*}_{2} = 7165.30483972345$"
      ],
      "text/plain": [
       "Eq(u^*_2, 7165.30483972345)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/latex": [
       "$\\displaystyle u_{2} = \\frac{- \\Delta_{12} u_{1} - \\Delta_{21} u_{1} - c_{2} + p_{2}}{2 \\Delta}$"
      ],
      "text/plain": [
       "Eq(u_2, (-Delta_12*u_1 - Delta_21*u_1 - c2 + p_2)/(2*Delta))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/latex": [
       "$\\displaystyle 0 = - 2 \\Delta u_{1} - c_{1} + p_{1} - \\frac{\\left(\\Delta_{12} + \\Delta_{21}\\right) \\left(- \\Delta_{12} u_{1} - \\Delta_{21} u_{1} - c_{2} + p_{2}\\right)}{2 \\Delta}$"
      ],
      "text/plain": [
       "Eq(0, -2*Delta*u_1 - c1 + p_1 - (Delta_12 + Delta_21)*(-Delta_12*u_1 - Delta_21*u_1 - c2 + p_2)/(2*Delta))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/latex": [
       "$\\displaystyle u^{*}_{1} = \\frac{2 \\Delta c_{1} - 2 \\Delta p_{1} - \\Delta_{12} c_{2} + \\Delta_{12} p_{2} - \\Delta_{21} c_{2} + \\Delta_{21} p_{2}}{- 4 \\Delta^{2} + \\Delta_{12}^{2} + 2 \\Delta_{12} \\Delta_{21} + \\Delta_{21}^{2}}$"
      ],
      "text/plain": [
       "Eq(u^*_1, (2*Delta*c1 - 2*Delta*p_1 - Delta_12*c2 + Delta_12*p_2 - Delta_21*c2 + Delta_21*p_2)/(-4*Delta**2 + Delta_12**2 + 2*Delta_12*Delta_21 + Delta_21**2))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/latex": [
       "$\\displaystyle u^{*}_{1} = 4462.60213702074$"
      ],
      "text/plain": [
       "Eq(u^*_1, 4462.60213702074)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/latex": [
       "$\\displaystyle \\left[\\begin{matrix}u^{*}_{1}\\\\u^{*}_{2}\\end{matrix}\\right] = \\left[\\begin{matrix}4462.60213702074\\\\7165.30483972345\\end{matrix}\\right]$"
      ],
      "text/plain": [
       "Eq(Matrix([\n",
       "[u^*_1],\n",
       "[u^*_2]]), Matrix([\n",
       "[4462.60213702074],\n",
       "[7165.30483972345]]))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def calculate_maximum(\n",
    "    equation: Expr,\n",
    "    function: Function,\n",
    "    parameter1: Symbol,\n",
    "    parameter2: Symbol,\n",
    "    output_symbol1: Symbol,\n",
    "    output_symbol2: Symbol,\n",
    "    constants: dict[Symbol, float | int],\n",
    ") -> dict[Symbol, float | int]:\n",
    "    f = function\n",
    "    v1 = parameter1\n",
    "    v2 = parameter2\n",
    "    v1s = output_symbol1\n",
    "    v2s = output_symbol2\n",
    "\n",
    "    f_grad = Matrix([\n",
    "        f(v1, v2).diff(v1),\n",
    "        f(v1, v2).diff(v2),\n",
    "    ])\n",
    "\n",
    "    display(\n",
    "        Eq(\n",
    "            f_grad,\n",
    "            Matrix([\n",
    "                equation.diff(v1, evaluate=False),\n",
    "                equation.diff(v2, evaluate=False),\n",
    "            ]),\n",
    "            evaluate=False,\n",
    "        )\n",
    "    )\n",
    "\n",
    "    f_diff_wrt = {\n",
    "        v1: equation.diff(v1),\n",
    "        v2: equation.diff(v2),\n",
    "    }\n",
    "    f_diff = Matrix(\n",
    "        list(f_diff_wrt.values())\n",
    "    )\n",
    "    display(\n",
    "        Eq(\n",
    "            f_grad,\n",
    "            f_diff,\n",
    "        ),\n",
    "        Eq(\n",
    "            Matrix([\n",
    "                0,\n",
    "                0\n",
    "            ]),\n",
    "            f_diff,\n",
    "        )\n",
    "    )\n",
    "\n",
    "    values = {}\n",
    "    for v, u, output_symbol in ((v1, v2, output_symbol2), (v2, v1, output_symbol1)):\n",
    "        f_diff_wrt_v_solved_for_v = solve(\n",
    "            f_diff_wrt[v],\n",
    "            v,\n",
    "        )[0]\n",
    "\n",
    "        display(\n",
    "            Eq(\n",
    "                v,\n",
    "                f_diff_wrt_v_solved_for_v,\n",
    "            )\n",
    "        )\n",
    "\n",
    "\n",
    "        f_diff_wrt_u_removed_v = f_diff_wrt[u].subs(\n",
    "            v,\n",
    "            f_diff_wrt_v_solved_for_v,\n",
    "        )\n",
    "        display(\n",
    "            Eq(\n",
    "                0,\n",
    "                f_diff_wrt_u_removed_v,\n",
    "            )\n",
    "        )\n",
    "\n",
    "        f_diff_wrt_u_solved_for_u = solve(\n",
    "            f_diff_wrt_u_removed_v,\n",
    "            u\n",
    "        )[0]\n",
    "\n",
    "        display(\n",
    "            Eq(\n",
    "                output_symbol,\n",
    "                f_diff_wrt_u_solved_for_u\n",
    "            )\n",
    "        )\n",
    "\n",
    "        value = f_diff_wrt_u_solved_for_u.subs(constants)\n",
    "        display(\n",
    "            Eq(\n",
    "                output_symbol,\n",
    "                value,\n",
    "            )\n",
    "        )\n",
    "        values[u] = value\n",
    "\n",
    "    return values\n",
    "\n",
    "u1s = symbols(\"u^*_1\")\n",
    "u2s = symbols(\"u^*_2\")\n",
    "\n",
    "values = calculate_maximum(\n",
    "    equation=equation,\n",
    "    function=f,\n",
    "    parameter1=u1,\n",
    "    parameter2=u2,\n",
    "    output_symbol1=u1s,\n",
    "    output_symbol2=u2s,\n",
    "    constants=constants,\n",
    ")\n",
    "\n",
    "display(\n",
    "    Eq(\n",
    "        Matrix([\n",
    "            u1s,\n",
    "            u2s,\n",
    "        ]),\n",
    "        Matrix([\n",
    "            values[u1],\n",
    "            values[u2],\n",
    "        ])\n",
    "    )\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "-81JKzIiSdYM"
   },
   "source": [
    "Substituting to get the profit at the maximum point"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "id": "-12dTtyVSUBG"
   },
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle 1021055.93966059$"
      ],
      "text/plain": [
       "1021055.93966059"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_values = {\n",
    "    u1: values[u1],\n",
    "    u2: values[u2],\n",
    "    **constants,\n",
    "}\n",
    "equation.subs(all_values)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Vt6JEuokgGzz"
   },
   "source": [
    "## Appendix"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "v2mJ95pAUx3R"
   },
   "source": [
    "* [Sympy](https://www.sympy.org)\n",
    "* [Contributing](https://github.com/sympy/sympy)\n",
    "* [Jupyter](https://jupyter.org/)\n",
    "* Submitting\n",
    "* Links  \n",
    "<!--[qr code to repository](images/qrcode-.svg)-->\n",
    "* Thank you\n",
    "* Questions?"
   ]
  }
 ],
 "metadata": {
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