Peak+Detection+in+Stocks+Data.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 488,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import csv\n",
    "import numpy as np\n",
    "import math\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.patches import Arc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 489,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def peaks_detection(y, k, h):   \n",
    "\n",
    "    def S1(i, k, a):    \n",
    " \n",
    "        left_bound = k if i-k>=0 else i\n",
    "        right_bound = k if i+k<len(a) else len(a)-i-1\n",
    "        if i == 0:\n",
    "            return max([float(a[i]) - float(a[i+j]) for j in range(1, int(right_bound)+1)])\n",
    "        elif i == len(a)-1:\n",
    "            return max([float(a[i]) - float(a[i-j]) for j in range(1, int(left_bound)+1)])\n",
    "        else:\n",
    "            return (max([float(a[i]) - float(a[i-j]) for j in range(1, int(left_bound)+1)]) + max([float(a[i]) - float(a[i+j]) for j in range(1, int(right_bound)+1)])) * 0.5\n",
    "        \n",
    "    x = np.linspace(0, len(y), len(y))\n",
    "        # Compute peak function value for each of len(y) points in y\n",
    "    vals = [S1(i, k, y) for i in range(len(y))]\n",
    "        # Compute the mean and standard deviation of all positive values in array vals\n",
    "    filtered_vals = list(filter(lambda x: x > 0, vals))\n",
    "    mean = np.mean(filtered_vals)\n",
    "    std = np.std(filtered_vals)\n",
    "        # Remove local peaks which are “small” in global context \n",
    "    peaks = [(i, x[i], y[i]) for i in range(len(y)) if vals[i] > 0 and (vals[i] - mean) > (h * std)]\n",
    "        # Retain only one peak out of any set of peaks within distance k of each other\n",
    "    i = 0\n",
    "    while i < len(peaks):    \n",
    "        for j, peakj in enumerate(peaks):\n",
    "            if peaks[i][0] != peakj[0]:\n",
    "                if abs(peaks[i][0] - peakj[0]) <= k:\n",
    "                    if peaks[i][2] >= peakj[2]:\n",
    "                        del peaks[j]\n",
    "                    else:\n",
    "                        del peaks[i]\n",
    "                        i -= 1\n",
    "                        break\n",
    "        i += 1\n",
    "        # return list with tuples of such form (x_coordinate, y_coordinate)\n",
    "    return peaks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 490,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "vtime, vel = [], []\n",
    "with open('test.csv', 'r') as f:                      \n",
    "    reader = csv.reader(f.read().splitlines())   \n",
    "    for num, row in enumerate(reader):\n",
    "        vtime.append(row[0])\n",
    "        vel.append((row[1]))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 491,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "vtime.pop(0)\n",
    "vel.pop(0)\n",
    "vel1= vel\n",
    "vel2=vel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 492,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "input_data = np.asarray(vel,np.float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 493,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "x = np.linspace(0, len(vtime), len(vtime))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 494,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "peaks = peaks_detection(vel, 1, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 495,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "input_data = np.asarray(vel,np.float)\n",
    "type(vel)\n",
    "lis = vel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 496,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "lis2 = []\n",
    "x =np.array(lis)\n",
    "for e in x:\n",
    "    lis2.append(float(e))\n",
    "vel =-1*np.array(lis2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 497,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "peaks2 = peaks_detection(vel, 5, 1)\n",
    "x = np.linspace(0, len(vtime), len(vtime))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 498,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(0, 0.0, -25.127300000000002)\n",
      "(63, 63.173553719008261, -24.609100000000002)\n",
      "(83, 83.228650137741042, -22.4636)\n",
      "(97, 97.267217630853992, -20.100000000000001)\n",
      "(103, 103.28374655647382, -21.945399999999999)\n",
      "(116, 116.31955922865012, -24.309099)\n",
      "(136, 136.37465564738289, -24.118200000000002)\n",
      "(277, 277.76308539944904, -27.272698999999999)\n",
      "(287, 287.79063360881543, -26.754601000000001)\n",
      "(293, 293.80716253443524, -28.299999)\n",
      "(320, 320.88154269972449, -33.900002000000001)\n",
      "(327, 327.90082644628097, -37.163601)\n",
      "(341, 341.93939393939394, -38.545399000000003)\n",
      "(347, 347.95592286501375, -41.299999)\n",
      "(353, 353.97245179063356, -43.718201000000001)\n",
      "(363, 364.0, -41.336399)\n"
     ]
    }
   ],
   "source": [
    "for i, peak in enumerate(peaks2):\n",
    "    print(peak)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 499,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import operator\n",
    "def find_intermediate_min(points, points1):\n",
    "    for j in range(len(points)-1):\n",
    "        x1 = points[j][0]\n",
    "        y1 = points[j+1][0]\n",
    "        (index1, value) = min(enumerate(vel1[x1:y1]), key=operator.itemgetter(1))\n",
    "        m = vel1.index(value)\n",
    "        value = -1*float(value)\n",
    "        points1.append([m,x[m],value])\n",
    "        \n",
    "find_intermediate_min(peaks, peaks2)\n",
    "peaks2_final = peaks2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 487,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def find_intermediate_max(point, point1):\n",
    "    for i in range(len(point)-1):\n",
    "        x2 = point[i][0]\n",
    "        y2 = point[i+1][0]\n",
    "        (index1, value) = max(enumerate(vel2[x2:y2]), key=operator.itemgetter(1))\n",
    "        m = vel2.index(value)\n",
    "        point1.append([m,x[m],value])\n",
    "        \n",
    "find_intermediate_max(peaks2, peaks)\n",
    "peaks_final = peaks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 396,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2, 2.0055096418732781, '28.0909')\n",
      "(20, 20.055096418732781, '25.781799')\n",
      "(23, 23.0633608815427, '26.318199')\n",
      "(58, 58.15977961432506, '26.572701')\n",
      "(82, 82.225895316804397, '23.6364')\n",
      "(105, 105.2892561983471, '25.0364')\n",
      "(119, 119.32782369146004, '27.181801')\n",
      "(216, 216.59504132231405, '23.6364')\n",
      "(232, 232.63911845730024, '25.236401')\n",
      "(235, 235.64738292011018, '25.0546')\n",
      "(284, 284.78236914600546, '29.563601')\n",
      "(299, 299.82369146005504, '32.709099')\n",
      "(306, 306.84297520661153, '34.1091')\n",
      "(317, 317.87327823691459, '34.072701')\n",
      "(322, 322.88705234159778, '35.636398')\n",
      "(324, 324.89256198347107, '37.118198')\n",
      "(331, 331.91184573002749, '41.209099')\n",
      "(333, 333.91735537190078, '41.318199')\n",
      "(345, 345.95041322314046, '41.9636')\n",
      "(350, 350.96418732782365, '47.209099')\n",
      "(356, 356.98071625344352, '45.136398')\n",
      "[2, 2.0055096418732781, '28.0909']\n",
      "[66, 66.181818181818173, '26.336399']\n",
      "[36, 36.099173553719005, '24.5455']\n",
      "[100, 100.2754820936639, '22.327299']\n",
      "[77, 77.212121212121204, '25.0364']\n",
      "[119, 119.32782369146004, '27.181801']\n",
      "[272, 272.74931129476579, '29.672701']\n",
      "[284, 284.78236914600546, '29.563601']\n",
      "[291, 291.80165289256195, '28.9091']\n",
      "[306, 306.84297520661153, '34.1091']\n",
      "[326, 326.8980716253443, '37.181801']\n",
      "[335, 335.92286501377407, '41.4636']\n",
      "[345, 345.95041322314046, '41.9636']\n",
      "[350, 350.96418732782365, '47.209099']\n",
      "[357, 357.98347107438013, '45.281799']\n"
     ]
    }
   ],
   "source": [
    "for i, peak in enumerate(peaks):\n",
    "    print(peak)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 397,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(0, 0.0, -25.127300000000002)\n",
      "(63, 63.173553719008261, -24.609100000000002)\n",
      "(83, 83.228650137741042, -22.4636)\n",
      "(97, 97.267217630853992, -20.100000000000001)\n",
      "(103, 103.28374655647382, -21.945399999999999)\n",
      "(116, 116.31955922865012, -24.309099)\n",
      "(136, 136.37465564738289, -24.118200000000002)\n",
      "(277, 277.76308539944904, -27.272698999999999)\n",
      "(287, 287.79063360881543, -26.754601000000001)\n",
      "(293, 293.80716253443524, -28.299999)\n",
      "(320, 320.88154269972449, -33.900002000000001)\n",
      "(327, 327.90082644628097, -37.163601)\n",
      "(341, 341.93939393939394, -38.545399000000003)\n",
      "(347, 347.95592286501375, -41.299999)\n",
      "(353, 353.97245179063356, -43.718201000000001)\n",
      "(363, 364.0, -41.336399)\n",
      "[19, 19.052341597796143, -24.9636]\n",
      "[21, 21.057851239669418, -25.0818]\n",
      "[32, 32.088154269972449, -24.0546]\n",
      "[81, 81.223140495867767, -23.1]\n",
      "[97, 97.267217630853992, -20.1]\n",
      "[111, 111.30578512396693, -23.836399]\n",
      "[215, 215.5922865013774, -23.6546]\n",
      "[220, 220.60606060606059, -21.9636]\n",
      "[90, 90.24793388429751, -23.9091]\n",
      "[236, 236.65013774104682, -24.745399]\n",
      "[158, 158.43526170798896, -26.754601]\n",
      "[300, 300.82644628099172, -31.6455]\n",
      "[311, 311.85674931129472, -32.9636]\n",
      "[314, 314.86501377410468, -33.4818]\n",
      "[323, 323.8898071625344, -34.709099]\n",
      "[325, 325.89531680440768, -36.8909]\n",
      "[332, 332.91460055096417, -40.545399]\n",
      "[341, 341.93939393939394, -38.545399]\n",
      "[347, 347.95592286501375, -41.299999]\n",
      "[353, 353.97245179063356, -43.718201]\n"
     ]
    }
   ],
   "source": [
    "for i, peak in enumerate(peaks2):\n",
    "    print(peak)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 500,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pt = np.array(peaks2_final)[:,2]\n",
    "pt = -1*pt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 501,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Z2RXnDE5MTGRkZCTz8/O3PH+w46GHHspHPvKR7N+/P8mb5xWuVyC8fPmy6cEu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\nHo/OnTunyclJ2ouR0wgIAQAAAADAqpE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ZEBqGUWwYxh8Mw3jbMIx2wzC+8f66yzCMw4Zh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\nrjund8/t7+9XcXGxXC7Xks033mEyvi060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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1ee987a9518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "fig = plt.figure(figsize=(18,12))\n",
    "ax = fig.add_subplot(111)\n",
    "    # basic line plot\n",
    "plt.plot(x, input_data, color='grey')\n",
    "    # scatter plot with peaks\n",
    "vel =-1*np.array(peaks2)\n",
    "plt.scatter(np.array(peaks_final)[:,1], np.array(peaks_final)[:,2], color='green')\n",
    "plt.scatter(np.array(peaks2_final)[:,1], pt, color='red')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.margins(0.01)\n",
    "plt.show()"
   ]
  }
 ],
 "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": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}