Project_hackathon_dirty_v0.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Subroutine 1\n",
    "Generate points based on the labels (taken as an input from the GUI)\n",
    "structure of the point:\n",
    "    (t0, vel, flag (1=hyperbolic, 0=linear), label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 335,
   "metadata": {},
   "outputs": [],
   "source": [
    "import random\n",
    "import numpy as np\n",
    "import matplotlib.pylab as plt\n",
    "from scipy import signal\n",
    "%matplotlib inline\n",
    "\n",
    "\n",
    "def points_gen(nDA,nR,nCN,nMU=5):\n",
    "    # The function generates random values of t0 - velocity pairs\n",
    "    # based on number of requested waves:\n",
    "    # nDA - number of direct waves (linear moveout, label 'D')\n",
    "    # nR - number of reflections (hyperbolic moveout, label 'R')\n",
    "    # nCN - number of coherent noise events (linear moveout, label 'N')\n",
    "    # nMU - number of multiples (hyperbolic, label 'M')\n",
    "    # outputs (nDA+nR+nCN) * (4) list\n",
    "    # each point in the list has the following structure\n",
    "    # [t0 (intercept time), velocity, flag(1=hyperbolic, 0=linear), label(see above))]\n",
    "    \n",
    "    \n",
    "    # direct arrival\n",
    "    direct = []\n",
    "    for n in range(nDA):\n",
    "        direct.append([random.uniform(0,.1),random.uniform(.5,1.5),0,'D'])\n",
    "    \n",
    "    reflected = []\n",
    "    multiples = []\n",
    "    for n in range(nR):\n",
    "        reflection = [random.uniform(0,3),random.uniform(1.5,3.5),1,'R']\n",
    "        reflected.append(reflection)\n",
    "        for nmult in range(2,nMU):\n",
    "            multiples.append([nmult * reflection[0], reflection[1],1,'M'])\n",
    "        \n",
    "    noise = []\n",
    "    for n in range(nCN):\n",
    "        noise.append([random.uniform(-3,3),random.uniform(-3,3),0,'N'])\n",
    "        \n",
    "    \n",
    "#    for n in range(nMU):\n",
    "#        noise.append([random.uniform(random.uniform(2,2),4),random.uniform(1.5,2.5),1,'M'])\n",
    "\n",
    "    events = direct + reflected + noise + multiples\n",
    "    return events"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 336,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def points_plot(events):\n",
    "    x = [x/1000 for x in range(0,2000,25)]\n",
    "    \n",
    "    fig, ax = plt.subplots()\n",
    "    \n",
    "    # plot waves\n",
    "    for i in events:\n",
    "        if i[3] == 'D':\n",
    "            ax.plot(x,[i[0] + offset/i[1] for offset in x],'r')\n",
    "        if i[3] == 'N':\n",
    "            ax.plot(x,[i[0]+offset/i[1] for offset in x],'b')\n",
    "        if i[3] == 'R':\n",
    "            ax.plot(x,[np.sqrt(i[0]**2 + offset**2 / i[1]**2) for offset in x],'g')\n",
    "        if i[3] == 'M':\n",
    "            ax.plot(x,[np.sqrt(i[0]**2 + offset**2 / i[1]**2) for offset in x],'k')\n",
    "    \n",
    "    plt.ylabel('Time, s')\n",
    "    plt.xlabel('Offset, km')\n",
    "    ax.set_xlim([0,2])\n",
    "    ax.set_ylim([0,4])\n",
    "    ax.invert_yaxis()\n",
    "    ax.set_aspect(1)\n",
    "    return ax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 338,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAKMAAAEKCAYAAAB+COUSAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJztnXdYVcfWxt/FAUFEwYagWDBi72DvJYlRoylqikY/Y9TE\n641JriYaG7HEGGMjJhq7RiOmWmIssSa5V1Qs2HvvYAPpcN7vjzkcASkHOA3Yv+fZD6fMnll7n5eZ\nPTNr1ghJaGjYAw62NkBDIwVNjBp2gyZGDbtBE6OG3aCJUcNu0MSoYTdYVIwi0kVEzojIeREZncH3\nIiJBhu+PikhjS9qjYd9YTIwiogPwDYAXANQG8IaI1E6X7AUAfoZjCID5lrJHw/6xZM3YFMB5khdJ\nJgAIBtAzXZqeAFZSEQLAQ0S8LWiThh3jaMG8KwC4lur9dQDNTEhTAcCt1IlEZAhUzQn3IkX8q9Wr\nZ3ZjNSzHwYMHI0iWzS6dJcVoNkguBLAQAAJEGLpwIdBYe7zML4jIFVPSWbKZvgGgYqr3PobPcpom\nLQ4OwKhRgDanXuCwpBgPAPATEV8RKQLgdQAb0qXZAKC/oVfdHMAjkrfSZ5QGvR7YuRPYssUiRmvY\nDouJkWQSgOEAtgI4BeBHkidE5F0RedeQ7A8AFwGcB7AIwLBsM3Z1VX/ffRdITja/4Ro2Q/KbC1lA\no0YMjY4Gzp0D3nsP+PZbW5ukkQ0icpBkQHbp8t0MzIPESGxcOwn/1CiKC8HzkbByma1N0jAT+a5m\nlPJCDH3yXqcHqrmUR6PqbdHCpwXaVW6HeuXqwUHy3f9ZgcXUmjHfibFOwzpcvmk5HsQ9wM3JH+PC\n9aM4XpYIrVsK15PuAwA8i3nihWovoEeNHuhSrQtcnVxtbHXhpsCKMSAggKGhoerN6dNAnTqAtzdw\n6xauLvoKuxqXwtYLW7Hl/BY8iHuAYk7F0KNGD/St1xfPV3sejg75Ymi1QGGqGEEyXx3+/v5Mw7vv\nkjod2aIFKUIuW0aSTExO5PYL2zlkwxCWml6KCATLzSjHkVtH8nT4aWpYnqQk9RdAKE34bW0urpwe\nT4nx1i2yWDHy5ZfJZ59Vgly8OE2S+KR4rju1ji8Fv0THSY5EINhuWTuuObaG8UnxOb/LGlmi15PB\nwaSvL3noUGESI0kGBqpL2bWL7NJFvV64MMMbdTvqNqf9PY2+c3yJQNDrKy9O2DmBNyNvmnyzNTIn\nJEQ1UgDZoAG5f38BFqOfnx+3b9/Of/75h2FhYbx27RrjIiJILy+yZUsyJobs2lVd2vz5md60ZH0y\n/zj7B7uu7koEgk6TnNj3l748cONA7n6FQs6VK+Sbb6rb7uWlGqcC30wDYEZHGTc3NgbYq0ULjhs9\nmmsaN+ZJgElBQdneyHP3znHE5hEs/nlxIhBsu6wt151ax2R9sum/RiElKoocO5Z0cVHH2LFkZGTa\nNKaKMd/1pmvWrMmFCxciNjYWjx8/xr1793D37l1cv3oVV374AecTEnAxORl6vR4AUAxAQLVqaNm7\nN9q0aYPWrVujePHiGeYdGR+JxYcWI2hfEK48ugK/Un4Y2XIk+jfoDxdHFytepf2TnAysWAGMHQvc\nvg288QYwbRpQufLTaQtPbzo1GzaQAOPmzuWRI0e4YskS/rtqVTYB6OjgQADU6XRs0aIFJ06cyJCQ\nECYnP137JSYnMvhYMP2/8ycCQc8ZnpyyZwrvx9zPvOxCxM6dZMOGql1t3pzcuzfr9CiozXSWYtTr\nybZtSU/PJ21FQgLZqxcfA9wxdCjHjh3Lpk2bUkQIgJ6enhw4cCA3bNjA2NjYdNnpufPiTnZZ1YUI\nBN0+d+N/tv6H1x9dz/ruF1DOniV79lSqqVSJXLNG3fLsKJxiJFO6b+S4cU8+S0gg+/RRn0+bRpKM\niIjg6tWr+cYbb9Dd3Z0A6Obmxtdff52//fbbU8IMux3GN395k7rPdHSa5MRB6wfxTMSZrG0pINy/\nT37wAenoSLq5kVOnqn6iqRReMZLka6+Rrq7kjRtPPktMJN94Q13ylClpksfHx3Pr1q0cPHgwS5cu\nTQB0d3fn22+/zZ07d6Zpyi/ev8hhvw+j82RnSqCwz099eOTWkextyockJJBBQWSpUqSDAzl4MHn7\nds7zKbBirFmzJkNCQnjo0CGeOnWKV69eZWRkJPWp24sLF0gnJ3LQoLR3JTGR7NdPXfakSRneuISE\nBG7ZsoUDBgygm5sbAbBSpUocO3Ysz507Z0x3O+o2P/nzE2MPvNvqbvzf1f9l87PkD/R6cuNGskYN\ndas6diSP5OH/rcCKEZkM7Tg6OtLb25uNGjVit27d+F79+vxShL/Mns1jx44xLi5O3ZmkJHLAAHXp\nEyZk+dATHR3NH374gc8//zwdDB2gtm3bcuXKlYwxtFMPYh9w8p7JLD29NBEIdljegTsu7kj7z5GP\nCAsjO3dWt6d6ddUnzOulmCrGfDe0U716dc6ePRsJCQmIi4vD48eP8ejRI9y7dw/h4eG4ffs2bty4\ngatXruD+gwfG83Q6Hfz8/FC/fn00rF8f/n/9hSbbtqHkuHHApEmASJbl3rhxAytXrsTSpUtx/vx5\neHh44K233sKQIUNQt25dRCdEY+HBhfhq71e4GXUTzX2aY1ybcejq1xWSTd72wJ07wPjxwJIlgLs7\nEBiofJednPKed+Ec2knHw8BAhgJcPXYsx40bx5deeom+vr5patQaAAfWq8clixfz/Pnz2dZoer2e\nO3fu5JtvvskiRYoQAFu1asVVq1YxLi6OsYmxnH9gPivPrkwEgo0WNOIvJ3+x2wH02FjVpyteXHVQ\nRowg790zbxmwh2YaQBcAZ6DWuIzO4Pv2AB4BOGI4JmSXZ07EyJgYsmJF0t+fTNUJuX//Prdv386p\nU6bwxcqVWTqVOH18fDhgwAB+//33vJ3N03p4eDi/+uorVqtWjQBYtmxZjhkzhleuXGFCUgKXHlrK\nakHViECw7rd1GXwsmEnJSabbb0FSnBkqV1Yq6NGDPGOhwQGbixGADsAFAFUBFAEQBqB2ujTtAfye\nk3xzJEaSXLFCXebq1Rl/r9czedgwngD4badO7NWrF0uVKmUUZ+PGjTlu3LhMB8hJMjk5mdu2bWPP\nnj3p4OBABwcHvvLKK9y1axcTkhK4+uhq1ppXiwgEa86ryZVHVjIxOTFn12FG0jsz7Nhh2fLsQYwt\nAGxN9X4MgDHp0lhejMnJarqgShUypROTHr2efP99dTtGjGByUhJDQ0M5ZcoUtm7d2th5KVeuHN95\n5x1u2rTpSYcoHZcvX+bo0aONgq5fvz4XL17Mx9GP+ePxH1l/fn0iEKwWVI1LDy1lQlJCzq4nD6R3\nZliy5IkzgyWxBzH2ArA41fu3AMxLl6Y9gPsAjgLYDKBOdvnWrl2bJ0+e5NmzZ3nlyhXeuXOHjx8/\nzvpZb9s2damzZmWeRq8nP/xQpRs+PE0X8t69e1y1ahX79OnD4sWLEwBLlCjBvn37ZjhATpIxMTFc\ntGgR69Wrpxw5ypTh2LFjef3Gdf526jc2WtCICAR95/hy0cFFFvWrNMWZwZLkFzGWAOBmeN0VwLlM\n8hoCINRwZDi0o9PpWKpUKVarVo0tWrRgz549+e6773Ly5Mlcvnw5dwUE8Kq7O5MjIjK/a3o9OXKk\nui3vvZfmOTOFuLg4btq0iYMGDTLWfsWLF2f//v35xx9/MCEhIV2WqsPTo0cPigidnJzYv39/Hj58\nmBvPbGSThU2IQLDS7Eqcf2A+4xIzqb1zQVKScuXy8lKX9MYbqna0NqaK0WJDOyLSAkAgyecN78cA\nAMlpWZxzGUAAyYjM0jzzzDOcOnUqEhMTkZiYiJiYGERHRyMyMhKPHj1CREQEIiIicOfOHdy6dQv3\n7t1Lc76zTodqNWuiRo0aqF27NurWrYv69evDz88Pjo6OKmzK6NHAl18CQ4YA8+erkCoZkJiYiF27\ndmHt2rX45Zdf8OjRI5QtWxavvfYa+vXrh6ZNm6YZ1jl//jyCgoKwdOlSREdHo1OnTvjoo48gfoJJ\nf01CyPUQ+JTwwZjWYzCo0SA4OzqbfsPTsWsX8NFHwJEjQIsWwKxZQPPmuc4uT9h8aAcqqNRFAL54\n0oGpky6NF54sCmsK4GrK+8yOnD4zxsTE8MyZM9y2bRsXNG/OkTode3TuzOrVqxufBQHQxcWFTZs2\n5bBhw7h82TKeHDyYyQD5zjsZ1pDpiYuL47p169inTx+6uLgQAKtXr84pU6bw6tWradLev3+f06dP\nZ4UKFQiAtWvX5uLFi/n7id/ZakkrIhCsMLMCv973NWMTn34EyIozZ1TPGFA95eDgvA9a5xXYupnm\nk6b3LFSveqzhs3cBvGt4PRzACYNQQwC0zC7PHHdgUnP1qnpoeustkmRsbCwPHz7MFStW8MMPP2S7\ndu2Mz4QA6OHiwhcATm3cmH/v3p1ppyU9Dx8+5JIlS9iuXTsCoIiwc+fOXL16tXHmhlRz4itXrmSD\nBg0IgF5eXpw6dSp/O/Qb2yxtQwSC5WeWZ1BIULaiTO/M8PnnagzRHrALMVriyJMYSXL0aLVo69Ch\nDL9OTk7miRMnuGzZMg4ePJi1ypRJU3t27NiRU6dO5b59+5hkQlf04sWLDAwMZJUqVZTAPTz4r3/9\ni4cPHzam0ev1/PPPP/nss88avYc++OADBv8TzLbL2hKBoPdX3pwbMpcxCWndZTJyZrh1K2+3yNxo\nYsyMhw/J0qXJTp1Mbr8iRo/mOoAjatRgfUPvGABLlizJ3r17c/HixbyR2kMoA5KTk7l9+3a++eab\ndHZ2JgAGBARw4cKFjEzVtT18+DD79u1LnU5HR0dH9u/fn0u3LGW7Ze2MC8jm7J3D6PiYNM4MnTqp\neWV7RBNjVsydqy5982bTz/n8c3XO66/zzo0bXLNmDQcOHMjy5csbxdmwYUOOGzeO+/fvz3SAnFRD\nRXPnzmXdunWNNeHQoUPT1JaXL1/miBEj6OrqSgDs1q0bg34MYoflHYh367GI324CpJ9fslmcGSyJ\nJsasiI8nq1Yl69XL2ajv9OnqlvXurdpHqib26NGjnDZtWpoB8vLly3Po0KHcsmUL4+MzHkPU6/X8\n3//+xwEDBhg7Pc2aNeOKFSuMY5cRERGcNGkSyxgeF8qVa0WRDdS5PiC6/Jvlpvtwzt45TzXf9oQm\nxuxYu1ZdviEChcl89ZU679VXjYJMTUREBL///nu++uqrLFasmNFRt3///tywYUOmnaD79+9zzpw5\nrFGjBgGwVKlSHDVqFC9evMjYWDIwMJpFigQRqEQArFWrLsfOHsv2S9unab7tUZSaGLNDryebNiUr\nVCCjo3N27uzZ6ta9/LKqZTMhJiaG69ev54ABA+jh4WGcuenXrx9///33DGtMvV7PHTt28JVXXqFO\np6OIsGjRbgS2sEePZB4/nsCVK1eyTp06BMAqVarwg0kfsO3CtnYrSk2MprBnj7oFU6fm/NygIBrd\nXUwY8omPj+fmzZs5cOBAozBLlSrFwYMHc9euXU89Y+7bR/r7XyMwno6OnsZxy6CgIEZGRjI5OZkb\nNmxg8+bNjfPmQz4Zwjbz29idKDUxmkqPHsqZ7+7dnJ/7zTfqFnbrZpIgU4iPj+fGjRvZt29fY1Pu\n4+PDUaNGcevWo085M0RHx3HVqlVs1qyZcfpxxIgRPHfuHPV6PXft2mUcFnJ3d2e/f/Vjy69bphkS\nyunguTnRxGgqJ0+qKGbDh+fu/AUL1G184YVcjTJHR0czODiYXbp0p4ODo2GQvAE7d57Jc+eeHjDc\nt28f+/btSycnJ4oIu3fvzu3bt1Ov1/PAgQN89dVXKSJ0dXVlr0G92HxW8xwNnlsCTYw5YehQNXWR\nasFVjli0SA2kP/dcztZwUnXmlyxJcWa4S3//r1m/foDR+aN79+78+eefn3q+vHnzJsePH8+yZcsS\nAOvWrcvFixczNjaWJ06c4FtvvUWdTkcnJyd2e70bA74IME4zzts3z6qi1MSYE1LC6vXqlfs8li5V\nguzc2eQOUerIDC1apI3McPLkSX7yySfGcczSpUtzxIgRDEs3sh0bG8ulS5caXdXKli3LiRMn8s6d\nO7x48SLfe+89Ojs7U6fTsfNLndl4cuM0ojSnl1BmaGLMKSlh9bKL1ZEVy5crQXbsSD5+nGmy9M4M\nWUVmSEpK4ubNm9m7d2/jmpuAgAAuWLCAjx49MqbT6/Xcvn07u3fvTgB0dnbmoEGDePz4cd68eZMf\nffSRcQC99fOt2XBiQyIQrDirIucfmG9Rf0pNjDklKoosV45s1Spv0xkrV6pJ4vbtnxLkvXtPOzPk\npFUPDw/nnDlzjDM3rq6ufPvtt7l37940zsWnT5/me++9x6JFixIAu3Tpwj///JN3797luHHjjBE0\nmrZvyrpj6hr9Kb8L/c4iotTEmBtSOiO//pq3fFavVoJs04aMimJCgpqBzGtkhhT0ej1DQkL4zjvv\nGHvj9erV47x58/jw4UNjuvDwcE6ePJnlypUjDEsgVq5cybt373LKlCnG6BkNWzRkrZG1iIlg5dmV\nuejgIrMuh9DEmBsSE8maNdXq9QxmV3JEcDD1DjpurDWKNfySLObMEBkZyQULFrBx48bG2nLQoEE8\ncOBJ0NPY2FguWbKEtWvXJgBWqFCBM2bM4I0bNzhz5kx6eXkpv0r/2qw+ojoxUS2HWHJoiVlEqYkx\nt6xfr27Lt9/mKZuwMLJzvdsEyBoul7kx+LHFnRkOHDjAd955x/hsGBAQwKVLlzLa0KFKTk7mpk2b\n2KFDB+N45ciRI3nu3DnOmzePFStWJAD61fXjM8OeISaCVedW5bLDy/K0mlETY27R61XzmjqsXg64\nfVs1ww4OZMmS5Ny3jzBB50I2a6bc16zAw4cP+fXXXxtrwpIlS/Kjjz5KEysoNDSUr7/+utFVbcCA\nATx8+DAXLVrEqlWrqqnGGlXoO8SXmKBWM644siJXotTEmBf27VO3Zvx4k0+JjVUdEje3DCIzrFun\nAlE1aaJcsq2EXq/n7t272adPHzo6Oho7Mxs3bjQ6Bl+6dIn//ve/jbVp165duXPnTi5fvtzotOHz\njA8rDaxEjAerf12dq8JW5SgYgSbGvNKnz9Nh9TLA5MgMGzYoQTZubP74ISZw8+ZNfvbZZ/T29iYA\n+vr6csaMGbxv+OdI76rWrFkz/vzzz/zhhx+MvXfvyt706e9DjFfBCH44+oNJotTEmFfOn1fiGTw4\n0yQhIWqDBcDEyAybNpFFiqiR7qyWzFqQhIQErl27lm3atCEAFi1alIMHD+axY8dIqunJb775xhiT\nqEaNGvzuu+8YHBzMhg0bEgA9fTxZvm95YhxY+5vaXHt8bZaxhGwuRgBLAdwFcDyT7wVAEFQcnqMA\nGpuSr9XESKq21sGBPH48zcd5isyweTPp7KzUGx5ufptzwJEjRzho0CCjY2/Hjh25bt06JiUlMTEx\nkWvXrjX20r29vfnll18yODiYTZo0IQCW8S5Dr9e8iHEqltBPJ37KUJT2IMa2ABpnIcauUFEkBEBz\nAPtMydeqYgwPJ93dye7dSZoxMsPWrSqDevVy5y1kZiIiIvjFF18Ye9O+vr6cNWsWHz58SL1ez23b\ntrFTp05MWVA2ZswYBgcHs0WLFsoVzrMUPXt5EmPB+vPr87dTv6UZhLe5GJUNqJKFGL8D8Eaq92cA\neGeXp1XFSJJffMEkOHDxqNPGyAxvvmmGyAzbt5NFi5J16uRtBNyMJCYm8ueff2br1q2Na3OGDx/O\ns2fPkiT379/PV155hSJCFxcXDhs2jN9//72xyXcv484yL5chPlWhANefXk+9Xp8vxPg7gNap3u+A\niiaRUVpjeJNKlSpZ4GfInF1b4tjQ6RgBsnlzPUNCzJj5zp2qk1Srlt2tLz148CD79+9vdFV78cUX\nuWOHish7+vRpDho0iE5OTtTpdOzXrx+XLVtmHL8sUaoES/UoRYwBVxxZUbDEmPqwVs145kyqbSZK\nR3ENXqN+TbD5C9q9W3kM1aiRbc/dFty6dYvjx4839rIbNGjA5cuXMy4ujteuXeOHH35onJLs0aMH\nFyxYYHT0LV6yONdtXJcvxGiXzXRGkRliopJUh8PXN0ce3Sbz119KkH5+5HX73GMmJiaGixcvNq69\nKVeuHCdPnszw8HBGREQwMDDQGAirffv2nDNnDrt27cpLly7lCzF2S9eB2W9KnpYSY7bODFu3qts1\ne7ZFyuc//6jlD9WqkdeuWaYMM6DX67l161Z26dLFODQ0dOhQnj59mlFRUZw5c6bRBzMgIIB79+61\nvRgBrAFwC0AigOsABiFtnB0B8A1UHJ5jpjTRtIAY028z0alTFttMPPecUuuDB2a1wcjevWSJEmpN\nty1i1+WQ48ePc9CgQUY/yxdffJG7d+9mbGwsFy1axGrVqvHQoUO2F6OlDnOKMSxMiQ9QYty4MRtX\nxiNHlPPsxx+bzYanCAlRw0lVqpCXLlmuHDNy+/ZtTpgwweiS5u/vzzVr1hiXSmhizPLmpXVmCArK\ngcfYgAFq0NqSNdf+/aSHh5pjvHjRcuWYmZiYGC5YsIDVq1cnAFauXJnbt2/XxJgR6beZ+OCDXEwT\nX72qxGgIq2cxQkPVf0rFimrHr3xEcnIy169fz7Zt2/L06dOaGFNj9m0mPv44y7B6ZuPwYfWM6uOT\n+5WLdoAmRgMW2WbiwQMlkmefNUNm2XDkCFmmDFm+vOU2arEwpoox42DVBYCrV4G+fVUc60uXgMWL\ngYMHgY4dzZC5h4fa2+zPP4GtW82QYRY0aKACdCcmAu3bA6dPW7Y8W2KKYu3pyK5mtNo2Eylh9erX\nt85mKsePK+/zcuXIEycsX54ZQWFrptNGZjCTM0N2BAerwnIaVi+3nDypLtDTkzT4H+YHCpUYd+xI\nG5nBrM4MWaHXq6UEFSrkOKxJrjl9mvT2Vs+RR49ap8w8YqoY8/Uz47lzQM+eQKdOwP37wJo1wH//\nCzRrZiUDRIAZM4AbN4C5c61TZo0awJ49gLOzegAOC7NOudbAFMXa0+Hv75+xM4MtwxC++KKaxrOm\no+y5c2rIp1Qpyw8x5REU1Ga6YkV/s0VmMBsnTyqD3n/fuuVeuEBWqqQGx0NDrVt2DiiwYgT87XOb\niSFD8hZWL7dcuqTmsT081DSiHVJgxVitmr99bjNx86bySezd2/plX76sfC3d3a3YezMdU8WY7zow\n7u6q32B3eHsDI0cCP/0E7Ntn3bIrVwZ27wZKlwaefRbYu9e65ZuJfCdGu2bkSKBcOfWXtG7ZlSqp\nXraXF/Dcc2pYIZ+hidGcuLkBgYHAP/8AGzZYv3wfH1VDli8PPP888Pff1rchD2hiNDfvvKPGAj/5\nBEhKsn755csrQVasCHTpol7nEzQxmhtHR2D6dODMGWDJEtvY4O2tnCuqVAG6dgV27rSNHTnEYmIU\nkaUicldEjmfyfXsReSQiRwzHBEvZYnV69ADatAEmTgQeP7aNDV5eSpDPPAN06wZs324bO3KAJWvG\n5QC6ZJPmb5INDcckC9piXVKmCe/cAb76ynZ2eHqqWrF6deDFFy3v7pZHLCZGkn8BuG+p/O2eZs2A\n3r2VGG/dsp0dZcsCO3YANWuqifzNm21nSzbY+pmxpYgcFZHNIlIns0QiMkREQkUkNDw83Jr25Y1p\n04CEBNXDtiVlyihB1qkDvPQSsGmTbe3JDFNGxnN7IOtF/CUAuBledwVwzpQ8rR74Ka+8/77aDu7k\nSVtbosJlBASouJMbNlitWNj7DAzJSJKPDa//AOAkImVsZY/FGD8eKFYMGD3a1pYAJUuqpRKNGgGv\nvgqsW2dri9JgMzGKiJeImtgTkaYGW+7Zyh6LUaYMMGaMGgTfs8fW1qj1O9u2Af7+6pn2l19sbdET\nTKk+c3Mg+/AmwwGcABAGIARAS1PyzXfNNKmcLX18lFe4vXh5PHqkYkDrdOSPP1q0KBRUr518KUZS\nrZMB1LoZeyEykmzdWglyzRqLFWOqGG3dmy48vPUWUL8+8OmnQHy8ra1RFC+uhnpatVLrelevtqk5\nmhithU4HfPklcPEisGCBra15gpsb8McfQLt2QP/+wPff28wUTYzW5LnngM6dgUmTgIcPbW3NE4oV\nA37/HejQARgwAFixwiZmaGK0JiKqdnzwAPjiC1tbkxZXV2DjRvXPMnAgsHSp1U3QxGhtGjUC+vUD\n5sxRMVjsiaJFgfXrVQ0+aBCwcKFVi89WjCLSSkSKGV73E5FZIlLZ8qYVYKZMUX/Hj7etHRlRtKga\nDO/aFRg6FJg/32pFm1IzzgcQIyINAPwHKuzxSotaVdCpVAkYMUJ1Fo4csbU1T+PiAvz6K9C9OzBs\nGPDNN1Yp1hQxJhnGinoCmEfyGwDFLWtWIWDMGDU99/HHtrYkY5yd1exMz57A8OFAUJDFizRFjFEi\nMgZAPwCbRMQBgJNlzSoEeHgA48apueJt22xtTcYUKQL8+CPw8suqJp8927LlZTcqDsALwEcA2hje\nVwLQ35QRdUsc+XYGJiPi4tR6Z2uF1cstCQlkr15qBmnGjByfDm06MJ+wZo36GZYvt7UlWZOYSL72\nmrJ12rQcnWqqGLWhHVvTpw8QEKCa7NhYW1uTOY6OwKpVwBtvqOfdqVPNXoQmRlvj4KCWJly/br2w\nernF0RFYuVLNY48bp2aSzIkp1ac9HQWumU6he3cVVs/GG6KbRFIS2b+/arInTszWLQ6WaqZFZIWI\nzBeRuub9tyjkTJ+ulrVOnmxrS7JHp1PThQMHAp99BkyYYJZwLrlppucB2A7grTyXrvGE2rXVFNz8\n+cCFC7a2Jnt0OrWFxDvvqBmlsWPzLkhTqk9V08LV1LSWPApsM02qsHqurmSfPra2xHSSk8mhQ1WT\nPWpUhk02zNVMi0hLETkJ4LThfQMR+TZv/wIaGZISVu/HH60fVi+3ODgA334LvPeeClzwn//kvobM\nTq0A9gGoCOBwqs8yXH6a7ryKAHYBOAm11mVEBmkEQBCA8wCOAmicXb4FumYk1VIAT0+ybVv7WS9j\nCno9+e/nR6IAAAASgElEQVR/qxpyxIg0tsNcg94A9hn+phZjmAnneaeIC2ou+yyA2unSdEXaDdD3\nZZdvgRcjSc6fr36a9ettbUnO0OtV5H+AHD7cKEhTxWhKB+aaiLQEQBFxEpGRAE6ZUOPeInnI8DrK\ncE6FdMl6AlhpuJQQAB4i4m2CTQWbQYNsG1Yvt4gAs2appnrePOBf/wL0etPPz06tAMoAWA3gDoC7\nAFYBKG2K0lPlUQXAVQAl0n3+O4DWqd7vABCQwflDAIQCCK1UqZL5/6Ptkd9+UzXMggW2tiTn6PVq\n51nDNCfsZW4agBuAgwBeyeA7k8SY+igUzTSpftDWrdVegVFRtrYm5+j1aj12UpJZe9O+Bu/uX0Vk\nQ8phSq0rIk4AfgGwmuSvGSS5AdXRScHH8JlG6rB6M2fa2pqcI6IiVuh0Jp/iaEKadQCWANgIwOQH\nAEPokiUATpGclUmyDQCGi0gwgGYAHpG0Yfw4O6N5c/WDzpihlgB4ednaIotiihjjSObGzbcV1CzN\nMRFJ8a3/FMofEiQXAPgDqkd9HkAMgIG5KKdg8/nnwG+/qbB69rTe2gIImfUApYi8CcAPwDYAxlAI\nNPSUrU1AQABDQ0NtUbTteP99NbB87BhQq5atrckxInKQZEB26UypGetB1XAd8aSZpuG9hjUYP14t\nrB89Wi0lLaCYIsbeAKqSTLC0MRqZULasEuKnnwJ//QW0bWtriyyCKYPexwF4WNoQjWwYMQKoUAEY\nNcr6u29ZCVPE6AHgtIhszenQjoYZcXVVvo779ytHigKIKR2Ydhl9TtImYVgLZQcmheRkFR4lOho4\ndUotJc0HmK0DYyvRaWRASli9F15QTrgjRtjaIrOSaTMtIv8Y/kaJSGSqI0pEIq1nokYann9eRQqb\nPNm+wuqZgayeGYsBAMniJEukOoqTLGEl+zTSkxJW7949+wurl0eyEmPB7LIVBFLC6s2dC1y7Zmtr\nzEZWz4yeIvJRZl9mMd+sYQ2mTAF++kkNiC9fbmtrzEJWNaMOyv2reCaHhi2pXFlNE65cCYSF2doa\ns5Dp0I6IHCLZ2Mr2ZEuhHtpJz4MHagvfJk3sesdUU4d2sqoZxYz2aFiCkiVVM71tm/2G1csBWYmx\nk9Ws0Mg9w4YBvr4q6Ghysq2tyROZipFk4d0rOj/h7Kx8HsPCVJSwfIwWhawgkBJWb/x4+w6rlw2a\nGAsCDg5qacK1a8DXX9vamlyjibGg0L692p3g88/V7Ew+xGJiFJGKIrJLRE6KyAkReWpWX0Tai8gj\nETliOCZYyp5CwRdfAFFRT/aZyWdYsmZMAvAfkrWhQpf8S0RqZ5Dub5INDYeZQ6EWMurUAd5+W+3b\ncvGira3JMRYTI00Lb6Jhbj77DHByUvES8xmmrIHJMyJSBUAjqIhm6WkpIkehFu+PJHkig/OHQIU4\ngUN5B3jP9IaDOBgPRwdHODo4wsnBCU46JzjrnFFEVwQuji5wcXRBUaeicHVyRTGnYijmVAxuRdzg\nVsQNxZ2Lo4RzCbg7u8PdxR0eLh4o6VISJYuWRBFd/nBcfYry5VWsm8mTgY8+UrMz+YRsPb3zXICI\nG4A9AKamjyohIiUA6Ek+FpGuAOaS9Msqv7J+ZfnyrJehpx4kkcxkJOmTkKRPQqI+EYnJiUhITkB8\ncjzikuIQlxSHmMQYxCbGIiYxBo8THiM+OfvNx4s5FUNp19Io41rGeHi6esKzmCfKuZWDl5uX8ShX\nrBx0DqZHTrA4UVFqmrBWLWD3buV2ZkNMnQ60qBgN4U1+B7DVFC8fEbkMFWsnIrM05pibTkxORHRi\nNCLjI43Ho7hHeBj3EA/iHuB+7H3cj72Pe7H3EBETgYiYCIRHh+Nu9F1EJ0Y/lZ+DOMDLzQsVileA\nTwkf+JTwQcUSFVHRvSIquVdCFY8q8HLzgoNYcfDi229VFLANG4AXX7ReuRlgczEawpusAHCf5AeZ\npPECcIckRaQpgJ8BVGYWRtnaUSImMQZ3o+/iVtQt3Im+g1tRt3Az6iZuRN3AjagbuB55HdcjryMy\nPq0zfBFdEVR2rwzfkr6o6lEVVUuqo1qpanim1DNwK+JmXkMTE4G6ddVShaNH1bYZNsKci/hziynh\nTXoBeE9EkgDEAng9KyHaA65OrqjiUQVVPKpkmS4yPhJXH13FlYdXcPXRVVx+eBmXHl7C5YeX8ePN\nH3E/Nu1sq7ebN6qXro7qpaujRukaqFGmBmqWqQlfD9/cPQI4OamhnldeUTsTDBmS8zysjMWfGc2N\nrWtGc/Ew7iEuPriIC/cv4Nz9czh3/xzO3juLs/fOIiLmyVOKs84Z1UtXRx3POqhdpjbqetZFXc+6\nqFqyavYiJYE2bdTuCefOAW5mrn1NxObNtKUoKGLMinsx93A64jTO3DuDU+GncDLiJE6Gn8Tlh5eN\naYo6FkUdzzqo71kf9cupo6FXQ5QsWjJtZnv3Ai1bqsBREyda9TpS0MRYAHmc8Binwk/h+N3jOHb3\nGI7dPYaw22EIjwk3pqnkXgmNvBqhsXdj41F+4PvAli3A+fM2CaunibEQcfvxbYTdDkPYnTAcvn0Y\nh28dxtl7Z0HDmjovl7IIOBqBJt7+CBj6GZqUb4KyxcpazT5NjIWcqPgohN0Jw8GbBxF6KxQHD23C\nad0D0DDk6Ovhi2Y+zdC8QnM092mOhl4N4ezobBFbNDFqpOXuXUTVegYHX2iA/e/1wP4b+7Hvxj5c\nj7wOQHWUGns3RsuKLdGyYku0qtgK5dzKmaVoTYwaTzN1qtqa96+/VC8bwI3IGwi5HoKQ6yH43/X/\n4eDNg8YZqmdKPoPWlVqjTaU2aFO5DfxK+UFyMZujiVHjaWJiAD8/oGJF1cvOQFjxSfE4dOsQ/nvt\nv/jn6j/477X/GoeaPIt5om3ltmhXuR3aVW6HOp51TJpV0sSokTFLl6pNj9auVcsVsoEkztw7g7+v\n/I09V/bgryt/4VqkimJRumhptKvSDh2qdEBH346oVaZWhjWnJkaNjElOBho2VGtlTp7MVVi9yw8v\nY8/lPdh1eRd2X96NK4+uAADKFSuHDr4d0Nm3MzpX7YzKHpUBaGLUyIrNm4GuXVWsnvffz3N2Fx9c\nxK5Lu7Dz8k7suLgDd6LvAFDPnPO7zcdz1Z7TxKiRCaQKqxcWpqYK3d3NmDVxMvwktl/cjh2XdmBq\nx6mo71VfE6NGFhw6BPj7q8D106ZZtChzhDfRKMg0bgz07QvMmWM3YfU0MRZmpkxRW/BOsI9FmZoY\nCzNVqqgOzIoVdhFWTxNjYefTTwEPDxU4ysZoYizslCyppgi3bQP+/NOmpmhi1FALt6pUUbWj3uRd\nnM2OJcObuIjIfhEJM4Q3+SyDNCIiQSJyXkSOiojdRcotFDg7KyeKI0dsGlbPkjVjPICOJBsAaAig\ni4g0T5fmBajtg/2gFunPt6A9Glnx+utq3HHcOCAuziYmWDK8CUk+Nrx1MhzpR9h7AlhpSBsCwENE\nvC1lk0YWpA6rF5Sbve7NYIIlMxcRnWGZ6l0Af5JMH96kAoDUI67XkUE8HhEZIiKhIhIaHh6e/msN\nc9Ghg5qztlFYPYuKkWQyyYYAfAA0FZG6ucxnIckAkgFly1pv7Uah5MsvVXiUqVOtXrRVetMkHwLY\nBaBLuq9uAKiY6r2P4TMNW5ESVm/ePODSJasWbcnedFkR8TC8LgrgWQCn0yXbAKC/oVfdHMAjkrcs\nZZOGiXz2mQqH8umnVi3WkjWjN4BdhnB3B6CeGX8XkXdF5F1Dmj8AXARwHsAiAMMsaI+GqaSE1QsO\nBg4csFqxmguZRsZERgLVqgG1awO7duUprJ7mQqaRN0qUUOFQ9uwBNm2ySpGaGDUyZ8gQtZrw44+B\npCSLF6eJUSNzUsLqnToFLFtm8eI0MWpkzcsvqyhmEyYA0U9H7TUnmhg1skZETRPevg3Msux+95oY\nNbKnZUsVAffLL4E7dyxWjCZGDdOYNk1583z2lCeg2dDEqGEa1asDQ4cCCxcCZ85YpAhNjBqmM2EC\n4Oqq1lpbAE2MGqbj6Ql88gmwbh3wzz9mz14To0bO+PBDNXc9apQKk2JGNDFq5AxXV2DSJCAkBPjl\nF7NmrYlRI+f83/8pv8fRo4GEBLNlq4lRI+fodGrM8cIF4LvvzJatJkaN3PHCC0DHjqrJfvTILFlq\nYtTIHSKqdoyIAKZPN0uWmhg1co+/vwqrN3s2cP16nrPTxKiRN8wYVs/W4U3ai8gjETliOOwjUKCG\n6aSE1Vu+XO1rnQdsHd4EAP4m2dBwTLKgPRqW4tNPVVzwTz7JUza2Dm+iURAoWRIYO1bt3Lp9e66z\nsXV4EwBoaYhAtllE6ljSHg0LMnw4ULlynsLq2Tq8ySEAlUjWB/A1gHUZ5aPF2skHuLiokCiHDwM/\n/JCrLKy2btrQOYkh+VUWaS4DCCAZkVkabd20HaPXA02aqLHHM2eUQGEH66ZNCW8iIl5i2GxORJoa\n7LF++CsN85ASVu/qVeDrr3N+ugVMSsGU8Ca9ABwXkTAAQQBeZ34LcaGRlo4d1VTh1Kk5DqunhTfR\nMD/HjwMNGgAffADMnGn7ZlqjEFO3rnIzy2FYPU2MGpZh0iTlEX7+vMmnOFrQHI3CTIUKSog6ncmn\naDWjhuXIgRABTYwadoQmRg27QROjht2giVHDbtDEqGE3aGLUsBs0MWrYDZoYNewGTYwadoMmRg27\nQROjht2giVHDbtDEqGE3aGLUsBs0MWrYDRYXo2Eh/2ER+T2D70REgkTkvGEhf2NL26Nhv1ijZhwB\n4FQm370AwM9wDAEw3wr2aNgplg5v4gOgG4DFmSTpCWClIS5PCAAPEfG2pE0a9oul18DMAfAxgOKZ\nfF8BwLVU768bPruVOpGIDIGqOQEgXkSOm9lOe6EMgEyjaeRjapiSyGJiFJHuAO6SPCgi7fOSF8mF\nABYa8g01ZQ1ufqSgXpuImLTQ3ZLNdCsAPQzxc4IBdBSRVenS3ABQMdV7H8NnGoUQS8ZnHEPSh2QV\nAK8D2EmyX7pkGwD0N/SqmwN4RPJW+rw0CgdWXzedEmeH5AIAfwDoCuA8gBgAA03IYqHlrLM5BfXa\nTLqufBdrR6Pgos3AaNgNmhg17IZ8JUYR6SIiZwzTh5bZgdvKiMhSEblb0MZORaSiiOwSkZOGrVdG\nZHtOfnlmFBEdgLNQEXCvQwUgfYPkSZsalkdEpC2Ax1AzUeljnudbDDNp3iQPiUhxAAcBvJTV75Wf\nasamAM6TvEgyAWrssqeNbcozJP8CcN/WdpgbkrdIHjK8joLyT6iQ1Tn5SYyZTR1q2DkiUgVAIwAZ\nbb1iJD+JUSMfIiJuAH4B8AHJyKzS5icxalOH+QwRcYIS4mqSv2aXPj+J8QAAPxHxFZEiUFOMG2xs\nk0YmGLZUWQLgFMlZppyTb8RIMgnAcABboR6GfyR5wrZW5R0RWQNgL4AaInJdRAbZ2iYz0QrAW1AO\nMim75nbN6oR8M7SjUfDJNzWjRsFHE6OG3aCJUcNu0MSoYTdoYtSwGzQxmoiI+IjIehE5JyIXRGSu\nYbwz5fs1hkAEH4pITcNQxmEReSaH5fyfiJQ3Id1yEemVm2uxVzQxmoBhAPdXAOtI+gGoDsANwFTD\n914AmpCsT3I2gJcA/EyyEckLOSzu/wBkK8YCCUntyOYA0AnAX+k+KwG1UbsrgKMAYgEcATARwG2o\nqcpdAIoB2AQgDMBxAK8ZzvcHsAfKtWor1P7cvaDcyc4Y8iqahU3LAfQyvJ5seK8DcBnANMP5oQAa\nG/K/AOBdW9/LrA5tI0vTqAMlGiMkI0XkKoBqAHoA+J1kQ8BYkz4m+ZWIvArgJsluhu/cDXO2XwPo\nSTJcRF4DMJXk2yIyHMBIkiatNRaRGVBBEgaSpCoaV0k2FJHZUCJtBcAF6p9hQd5uheXQxGh5jgGY\nKSLToQT7t4jUBVAXwJ8G8eiQLoqGiYwHsI/kkHSfp8zZHwPgRuVPGCUi8SLiQfJhrq7EwmhiNI2T\nUE2oEREpAaAS1DJbz8xOJHnWEF2tK4ApIrIDwG8ATpBskUe7DgDwF5FSJFM76MYb/upTvU55b7e/\nudaBMY0dAFxFpD9gXAIxE8BykjFZnWjoGceQXAVgBtQz3BkAZUWkhSGNk4jUMZwShVSxiURkmoi8\nnEn2WwB8AWCTwbU/X6OJ0QSoegkvA+gtIueg1uLEAfjUhNPrAdgvIimdmylUyyZ6AZguImFQnY2W\nhvTLASwwDA0VNZx/OwvbfgKwCMAGQ/p8i+a1Y+eIyFaSz9vaDmugiVHDbtCaaQ27QROjht2giVHD\nbtDEqGE3aGLUsBs0MWrYDf8Pv86q+c6tiOoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xa96ab00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "events=points_gen(2,2,2)\n",
    "ax = points_plot(events)\n",
    "plt.show(ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 339,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def t_linear(x, v, t):\n",
    "    # return a linear event (direct or coherent noise)\n",
    "    return t + x/v\n",
    "\n",
    "def t_reflected(x,v,t):\n",
    "    return np.sqrt(t**2 + x**2 / v**2)\n",
    "\n",
    "def points_to_data(events, dx = 0.005, xmax = 2):\n",
    "    x = np.arange(0, xmax + dx, dx)\n",
    "    t=[]\n",
    "    \n",
    "    for i in events:\n",
    "        if i[3] == 'D' or i[3] == 'N':\n",
    "            t.append(t_linear(x, i[1], i[0]))\n",
    "        if i[3] == 'R' or i[3] == 'M':\n",
    "            t.append(t_reflected(x,i[1],i[0]))\n",
    "    return t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 340,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = points_to_data(events)\n",
    "dataround = np.round(data,decimals=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 341,
   "metadata": {},
   "outputs": [],
   "source": [
    "dt = 0.01\n",
    "tmax = 4\n",
    "t = np.arange(0,tmax + dt,dt)\n",
    "\n",
    "dx = 0.005\n",
    "xmax = 2\n",
    "x = np.arange(0, xmax + dx, dx)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 342,
   "metadata": {},
   "outputs": [],
   "source": [
    "datamatrix = np.zeros((len(t),len(x)))\n",
    "\n",
    "for event in dataround:\n",
    "    for n, i in enumerate(event):\n",
    "        idx = np.where((t >= i - 0.0001) & (t <= i + .0001))\n",
    "        if np.size(idx[0]) != 0:\n",
    "            #print(idx[0],n)\n",
    "            datamatrix[idx[0][0]][n] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 343,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAJUAAAD8CAYAAAB+WebdAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x9099160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAJUAAAD8CAYAAAB+WebdAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nhzutWgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xb69efd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#numpy.apply_along_axis\n",
    "\n",
    "\n",
    "def conv_ricker(array):\n",
    "    points = 100\n",
    "    a = 4.0\n",
    "    ricker = signal.ricker(points, a)\n",
    "    return np.convolve(array,ricker,mode='same')\n",
    "\n",
    "data_ricker = np.apply_along_axis(conv_ricker, 0, datamatrix)\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "plt.imshow(datamatrix, cmap='gray', interpolation='bicubic', extent=[0,2,4,0]);\n",
    "plt.show()\n",
    "\n",
    "plt.imshow(data_ricker, cmap='gray', interpolation='bicubic', extent=[0,2,4,0]);\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Convert data in list format to dictionary\n",
    "    \n",
    "def makeEventsDict(events):\n",
    "    eventsDict = {}\n",
    "    labelsDict = {}\n",
    "    \n",
    "    labelsDict['label'] = []\n",
    "    eventsDict['direct'], eventsDict['reflected'], eventsDict['coherentnoise'], eventsDict['multiples'] = [],[],[],[]\n",
    "    eventsDict['events'] = []\n",
    "    for each in events:\n",
    "#         print(each)\n",
    "#         print(each[0])\n",
    "#         print(each[3])\n",
    "#         eventsDict['direct'].append(each[0])\n",
    "#         eventsDict['reflected'].append(each[1])\n",
    "#         eventsDict['coherentnoise'].append(each[2])\n",
    "         eventsDict['events'].append(each[0:3])\n",
    "         labelsDict['label'].append(each[3])\n",
    "    return(eventsDict,labelsDict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn import svm\n",
    "SVC = svm.SVC()\n",
    "\n",
    "# This function takes a events list, turns it into two dictionaries combined, splits that into two arrays for X and Y\n",
    "# trains a SVM label on them and then returns that model output details\n",
    "# The model will need to be run on a input for a prediction\n",
    "\n",
    "def comboFunctionA(events):\n",
    "    testEvents = makeEventsDict(events)\n",
    "    eventsDict = testEvents[0]\n",
    "    labelsDict = testEvents[1]\n",
    "    X = eventsDict['events']\n",
    "#     print(\"X = \",X)\n",
    "    y = labelsDict['label']\n",
    "#     print(\"y = \",y)\n",
    "    clf = svm.SVC()\n",
    "    output = clf.fit(X, y)\n",
    "    return(output)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
       "  decision_function_shape=None, degree=3, gamma='auto', kernel='rbf',\n",
       "  max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
       "  tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# training on a test set\n",
    "events_train=points_gen(200,200,200,200)\n",
    "\n",
    "tempAnswer = comboFunctionA(events_train)\n",
    "tempAnswer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['D'], \n",
       "      dtype='<U1')"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tempAnswer.predict([[.1,1.5,0]])"
   ]
  }
 ],
 "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
}