floor plot new.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[10, 10, 10, 5, 5, 5, 5, 5, -5, -5, -5, -10, -10]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "readings = [10] * 3 + [5] * 5 + [-5] * 3 + [-10] * 2\n",
    "readings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f4cb7475e48>]"
      ]
     },
     "execution_count": 3,
     "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": [
    "plt.plot(readings)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t = list(range(len(readings)))\n",
    "t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[10, 10, 10, nan, 5, 5, 5, 5, 5, 5, nan, -5, -5, -5, -5, nan, -10, -10, -10]\n",
      "[0, 1, 2, 2, 2, 3, 4, 5, 6, 7, 7, 7, 8, 9, 10, 10, 10, 11, 12]\n"
     ]
    }
   ],
   "source": [
    "def transform(readings, t):\n",
    "    new_r = []\n",
    "    new_t = []\n",
    "    for i in range(len(readings)-1):\n",
    "        new_r.append(readings[i])\n",
    "        new_t.append(t[i])\n",
    "        \n",
    "        if readings[i] != readings[i+1]:\n",
    "            new_r.append(float('nan'))\n",
    "            new_t.append(t[i])\n",
    "            new_r.append(readings[i+1])\n",
    "            new_t.append(t[i])\n",
    "            \n",
    "    new_r.append(readings[-1])\n",
    "    new_t.append(t[-1])\n",
    "            \n",
    "    return new_r, new_t\n",
    "\n",
    "new_readings, new_t = transform(readings, t)\n",
    "print(new_readings)\n",
    "print(new_t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f4cb741fb70>]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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LDVqTwbAcScYXc7vWWnCi7MuJsh/gvhyvTpR9Wa398FSSJKlhMEiSGusxGMZGXcAKOlH25UTZD3Bfjlcnyr6syn6su2sMkqT5rccjBknSPNZNMCS5IsnDSQ4luWbU9SxVknOSfDbJg0kOJnnrqGtariQbkvyfJB8fdS3LkeS0JLcmeaj793nJqGtaiiS/2P1u3Z/kliT/eNQ1LVaSG5M8meT+GW3PSbI/ySPd9NmjrHGx5tiX3+h+v76S5LYkp/Wx7XURDEk2AO8DXglsB16bZPtoq1qyI8AvV9WPAC8G3ryG9+WotwIPjrqIFfA7wB1V9c+BC1mD+5TkbOAXgEFVvQDYAOwabVXH5EPAFbPargE+XVXnAZ/ulteCD/H0fdkPvKCqfhT4GvCOPja8LoIB2AEcqqpHq+r7wIeBnSOuaUmq6omqureb/y7Tf3zW7Luyk2wBfhL4wKhrWY4kpwL/ku6dIlX1/ar6zmirWrKNwD9JshF4FvO8WfF4U1V3A9+e1bwTuKmbvwn4qVUtaomG7UtV3dW98RLgC0y/+XLFrZdgOBt4fMbyBGv4j+lRSbYBLwS+ONpKluU/A78C/MOoC1mmfwZMAf+1Oy32gSQnj7qoY1VV3wB+E/hz4Angqaq6a7RVLduZVfUETH+xAs4YcT0r5Q3AJ/tY8XoJhgxpW9O3YyX5IeCPgLdV1eFR17MUSa4CnqyqA6OuZQVsBF4EvL+qXgj8DWvnlMX/151/3wmcC2wGTk7yb0dblWZLci3Tp5Vv7mP96yUYJoBzZixvYQ0dHs+W5BlMh8LNVfXRUdezDBcDr0ryGNOn916e5H+MtqQlmwAmquro0dutTAfFWnMp8GdVNVVV/xf4KPATI65pub6Z5HkA3fTJEdezLEl2A1cBP1M9PW+wXoLhHuC8JOcmOYnpi2l7R1zTkiQJ0+exH6yq3x51PctRVe+oqi1VtY3pf5PPVNWa/HZaVX8BPJ7k/K7pEqbfeb7W/Dnw4iTP6n7XLmENXkSfZS+wu5vfDdw+wlqWJckVwNuBV1XV9/razroIhu5izVuAO5n+Jf/Dqjo42qqW7GLgdUx/u76v+7ly1EUJgJ8Hbk7yFeAi4D+OuJ5j1h3x3ArcC3yV6b8Ra+ap4SS3AH8CnJ9kIskbgfcAlyV5BLisWz7uzbEv1wOnAPu7//Zv6GXbPvksSZppXRwxSJIWz2CQJDUMBklSw2CQJDUMBklSw2CQJDUMBklSw2CQJDX+H8DrvhpufKCDAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(new_t, new_readings)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}