Wang 2014.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def alarm_generate(x, var_list):\n",
    "    '''alarm_generate is a function when return a vector '''\n",
    "    t_delay = 0\n",
    "    t_dead = 0\n",
    "    log_alarm = np.arange(len(x))\n",
    "  \n",
    "    if var_list[\"type_var\"] == 'high':\n",
    "        for i in np.arange(len(x)):\n",
    "            if x[i] > var_list[\"limit\"]:\n",
    "                t_dead = 0\n",
    "                t_delay = t_delay + 3/len(x)\n",
    "                if t_delay >= var_list[\"delay_time\"]:\n",
    "                    log_alarm[i] = 1\n",
    "                else:\n",
    "                    log_alarm[i] = 0\n",
    "            else:\n",
    "                if log_alarm[i-1] == 1:\n",
    "                    t_dead = t_dead + 3/len(x)\n",
    "                    if t_dead >= var_list[\"dead_band_delay\"]:\n",
    "                        log_alarm[i] = 0\n",
    "                        t_delay = 0\n",
    "                        t_dead = 0\n",
    "                    else:\n",
    "                        log_alarm[i] = 1\n",
    "                else:\n",
    "                    t_delay = 0\n",
    "                    t_dead = 0\n",
    "                    log_alarm[i] = 0\n",
    "    else:\n",
    "        for i in np.arange(len(x)):\n",
    "            if x[i] < limit:\n",
    "                t_dead = 0\n",
    "                t_delay = t_delay + 3/len(x)\n",
    "                if t_delay >= var_list[\"delay_time\"]:\n",
    "                    log_alarm[i] = 1\n",
    "                else:\n",
    "                    log_alarm[i] = 0\n",
    "            else:\n",
    "                if log_alarm[i-1] == 1:\n",
    "                    t_dead = t_dead + 3/len(x)\n",
    "                    if t_dead >= var_list[\"dead_band_delay\"]:\n",
    "                        log_alarm[i] = 0\n",
    "                        t_delay = 0\n",
    "                        t_dead = 0\n",
    "                    else:\n",
    "                        log_alarm[i] = 1\n",
    "                else:\n",
    "                    t_delay = 0\n",
    "                    t_dead = 0\n",
    "                    log_alarm[i] = 0\n",
    "\n",
    "    return log_alarm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fcc28d92e10>]"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcc28e4da58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "var_list = {\n",
    "    \"limit\": 1,\n",
    "    \"type_var\":'high', \n",
    "    \"delay_time\" : 10*3/10000, \n",
    "    \"dead_band_delay\" : 10*3/10000\n",
    "}\n",
    "\n",
    "t = np.linspace(0, 50, 5000)\n",
    "ruido = np.random.uniform(-1,1,5000)\n",
    "x = np.sin((t)*.5)+1+ruido \n",
    "\n",
    "log = alarm_generate(x,var_list)\n",
    "plt.figure(figsize=(16,4))\n",
    "plt.plot(log)\n",
    "#plt.plot(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "T1 = []\n",
    "T0 = []\n",
    "count_t1 = 0\n",
    "count_t0 = 0\n",
    "for i in log:\n",
    "    if i == 1:\n",
    "        count_t1+=1\n",
    "        if count_t0 != 0:\n",
    "            T0.append(count_t0)\n",
    "            count_t0 = 0\n",
    "    else:\n",
    "        count_t0+=1\n",
    "        if count_t1 != 0:\n",
    "            T1.append(count_t1)\n",
    "            count_t1 = 0\n",
    "\n",
    "if count_t0 != 0:\n",
    "    T0.append(count_t0)\n",
    "if count_t1 != 0:\n",
    "    T1.append(count_t1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[561, 35, 681, 572, 123, 37, 666]\n",
      "[48, 22, 561, 591, 35, 519, 36, 513]\n"
     ]
    }
   ],
   "source": [
    "print(T1)\n",
    "print(T0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "382.14285714285717 290.625\n"
     ]
    }
   ],
   "source": [
    "Med_T1 = sum(T1)/len(T1)\n",
    "Med_T0 = sum(T0)/len(T0)\n",
    "\n",
    "print(Med_T1, Med_T0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "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": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}