mortal_rabbits.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Mortal rabbits\n",
    "\n",
    "\n",
    "- Pair of rabbits produce another pair.\n",
    "- Takes 1 month to reach maturity\n",
    "- Pair dies after $m$ months\n",
    "- Find how many rabbits after $n$ months"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "### How do I make this method work??"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def fib(n, m = 1):\n",
    "    if n < 2:\n",
    "        return 1\n",
    "    else:\n",
    "        return fib(n - 1) + fib(n - 2) - fib(n - m)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Since I can't figure out a recursive method:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def mortal_rabbits(n, m):\n",
    "    ls = [1] + [0] * (m - 1)\n",
    "    for i in range(1, n):\n",
    "        ls = [sum(ls[1:])] + ls[:-1] \n",
    "    return sum(ls)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "669908100703859665L"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mortal_rabbits(87, 16)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Explanation:\n",
    "\n",
    "Vector of rabbits, each element is a month,\n",
    "\n",
    "```python\n",
    "ls = [1] + [0] * (m - 1)\n",
    "```\n",
    "\n",
    "so if the lifespan is 6 months:  \n",
    "```python\n",
    "[1, 0, 0, 0, 0, 0]\n",
    "```\n",
    "\n",
    "With the first element being the starting pair.  \n",
    "Then loop through the months\n",
    "\n",
    "Then sum of all the elements (except the first (since they're not mature)) are totaled and create the new first element.  \n",
    "All the others are shifted along by one month\n",
    "\n",
    "```python\n",
    "for i in range(1, n):\n",
    "    ls = [sum(ls[1:])] + ls[:-1]\n",
    "```\n",
    "\n",
    "\n",
    "So the second month will be:  \n",
    "```python\n",
    "[0, 1, 0, 0, 0, 0]\n",
    "```\n",
    "\n",
    "\n",
    "Third month:  \n",
    "```python\n",
    "[1, 0, 1, 0, 0, 0]\n",
    "```\n",
    "\n",
    "After $m$ months, total everything to find the number of rabbits.\n",
    "\n",
    "```python\n",
    "return sum(ls)\n",
    "```\n",
    "\n",
    "#### e.g:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0, 1, 0, 0]\n",
      "[1, 0, 1, 0]\n",
      "[1, 1, 0, 1]\n",
      "[2, 1, 1, 0]\n",
      "[2, 2, 1, 1]\n",
      "[4, 2, 2, 1]\n",
      "[5, 4, 2, 2]\n",
      "[8, 5, 4, 2]\n",
      "[11, 8, 5, 4]\n",
      "\n",
      "Sum of last row: 28\n"
     ]
    }
   ],
   "source": [
    "def mortal_rabbits_(n, m):\n",
    "    ls = [1] + [0] * (m - 1)\n",
    "    for i in range(1, n):\n",
    "        ls = [sum(ls[1:])] + ls[:-1] \n",
    "        print(ls)\n",
    "    print \"\\nSum of last row: %i\" % sum(ls)\n",
    "\n",
    "mortal_rabbits_(10, 4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0xb18827ac>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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5RdcRvUpR6m737t3MmjXrqGnPWHH48GHuu+++0gfuVkS/EyIikl+UT9uft6WouIj99+2n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\nh1s+ZO7nc1mYuZC8Q3kANLNmXDTgIia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      "text/plain": [
       "<matplotlib.figure.Figure at 0xb4b3a2ec>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### Procrastination\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "plt.figure(figsize=(10, 6))\n",
    "\n",
    "# immortal rabbits function from previous week\n",
    "def n_rabbits(n):\n",
    "    old = 0\n",
    "    young = 1\n",
    "    total = None\n",
    "    for i in range(1, n + 1):\n",
    "        total = old + young\n",
    "        buffer_young = old * 2\n",
    "        old += young\n",
    "        young = buffer_young     \n",
    "    return total\n",
    "\n",
    "months = range(1, 20)\n",
    "mortal_rab = [mortal_rabbits(i, m = 16) for i in months]\n",
    "immortal_rabbits = [n_rabbits(i) for i in months]\n",
    "\n",
    "plt.plot(months, mortal_rab, label = \"mortal $(m = 16)$\", lw = 2)\n",
    "plt.plot(months, immortal_rabbits, label = \"immortal\", lw = 2)\n",
    "plt.title(\"Number of rabbits\")\n",
    "plt.legend(loc = 0)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}

protein_translation.ipynb