{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "45a4ee2a",
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "929bc20d",
   "metadata": {},
   "outputs": [],
   "source": [
    "def lowest_heat_time (t):\n",
    "    t5 = t\n",
    "    if t <= 4 :\n",
    "        t5 = t + 1\n",
    "    elif t <= 6 :\n",
    "        t5 = t - 0\n",
    "    elif t <= 8 :\n",
    "        t5 = t - 1\n",
    "    elif t <= 10 :\n",
    "        t5 = t - 3\n",
    "    elif t <= 12 :\n",
    "        t5 = t - 4\n",
    "    elif t <= 14 :\n",
    "        t5 = t - 5\n",
    "    elif t >= 16 :\n",
    "        t5 = t - 6\n",
    "        \n",
    "    return t5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "70e13998",
   "metadata": {},
   "outputs": [],
   "source": [
    "def lowest_heat_time (t):\n",
    "    t5 = t\n",
    "\n",
    "    if t > 14 :\n",
    "        t5 = t - 6\n",
    "    elif t > 12 :\n",
    "        t5 = t - 5\n",
    "    elif t > 10 :\n",
    "        t5 = t - 4\n",
    "    elif t > 8 :\n",
    "        t5 = t - 3\n",
    "    elif t > 7 :\n",
    "        t5 = t - 2\n",
    "    elif t > 6 :\n",
    "        t5 = t - 1\n",
    "    elif t < 5 :\n",
    "        t5 = t + 1\n",
    "        \n",
    "    return t5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "77a65776",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "c55961eb",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([<matplotlib.axis.XTick at 0x22f2d688df0>,\n",
       "  <matplotlib.axis.XTick at 0x22f2d688dc0>,\n",
       "  <matplotlib.axis.XTick at 0x22f2d6884f0>,\n",
       "  <matplotlib.axis.XTick at 0x22f2d6c20a0>,\n",
       "  <matplotlib.axis.XTick at 0x22f2d6c2700>,\n",
       "  <matplotlib.axis.XTick at 0x22f2d6c2be0>,\n",
       "  <matplotlib.axis.XTick at 0x22f2d6c7370>,\n",
       "  <matplotlib.axis.XTick at 0x22f2d6c7ac0>,\n",
       "  <matplotlib.axis.XTick at 0x22f2d6ce250>,\n",
       "  <matplotlib.axis.XTick at 0x22f2d6ce9a0>,\n",
       "  <matplotlib.axis.XTick at 0x22f2d6cec40>,\n",
       "  <matplotlib.axis.XTick at 0x22f2d6c7a90>,\n",
       "  <matplotlib.axis.XTick at 0x22f2c50ff40>,\n",
       "  <matplotlib.axis.XTick at 0x22f2d6cf6d0>,\n",
       "  <matplotlib.axis.XTick at 0x22f2d6cfe20>,\n",
       "  <matplotlib.axis.XTick at 0x22f2d6dd5b0>,\n",
       "  <matplotlib.axis.XTick at 0x22f2d6ddd00>,\n",
       "  <matplotlib.axis.XTick at 0x22f2d6dd880>,\n",
       "  <matplotlib.axis.XTick at 0x22f2d6cf790>,\n",
       "  <matplotlib.axis.XTick at 0x22f2d6e2340>],\n",
       " [Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, '')])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "t = np.arange (1, 21)\n",
    "t_cont = np.linspace(1, 21, 100)\n",
    "\n",
    "t5 = t + 1\n",
    "\n",
    "offsets = [1, 0, -1, -3, -4, -5, -6]\n",
    "\n",
    "for ll, offset in zip(range(4, 17, 2), offsets):\n",
    "    t5[t>ll] = t[t>ll] + offset\n",
    "\n",
    "plt.plot(t, t5, 'o')\n",
    "plt.grid()\n",
    "\n",
    "plt.xlim(0, 20)\n",
    "\n",
    "\n",
    "\n",
    "plt.plot(t, list(map(lowest_heat_time, t)), ':x')\n",
    "plt.plot(t_cont, list(map(lowest_heat_time, t_cont)),)\n",
    "\n",
    "plt.plot([1, 4, 9, 16, 20], [2, 5, 6, 10, 14])\n",
    "\n",
    "plt.xticks(np.arange (1, 21))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "78d8c049",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
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