SCALODEEP_ContinousData_OverlappingWindows_ReturingLabels_EvenTime.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#################################### Loading Data #####################################\n",
    "from obspy import read\n",
    "# The data should be at least 30s (3000 samples of 100 sampling rate),\n",
    "# and the lenght of the data is multiples of 3000.\n",
    "\n",
    "\n",
    "st = 'Test40Hz.mseed'\n",
    "Data = read(st, debug_headers=True)\n",
    "Data[0].data = Data[0].data[0:14000 ] \n",
    "Data[1].data = Data[1].data[0:14000 ] \n",
    "Data[2].data = Data[2].data[0:14000 ] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n",
      "C:\\Users\\owner\\anaconda3\\lib\\site-packages\\tensorflow\\python\\framework\\dtypes.py:516: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint8 = np.dtype([(\"qint8\", np.int8, 1)])\n",
      "C:\\Users\\owner\\anaconda3\\lib\\site-packages\\tensorflow\\python\\framework\\dtypes.py:517: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_quint8 = np.dtype([(\"quint8\", np.uint8, 1)])\n",
      "C:\\Users\\owner\\anaconda3\\lib\\site-packages\\tensorflow\\python\\framework\\dtypes.py:518: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint16 = np.dtype([(\"qint16\", np.int16, 1)])\n",
      "C:\\Users\\owner\\anaconda3\\lib\\site-packages\\tensorflow\\python\\framework\\dtypes.py:519: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_quint16 = np.dtype([(\"quint16\", np.uint16, 1)])\n",
      "C:\\Users\\owner\\anaconda3\\lib\\site-packages\\tensorflow\\python\\framework\\dtypes.py:520: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint32 = np.dtype([(\"qint32\", np.int32, 1)])\n",
      "C:\\Users\\owner\\anaconda3\\lib\\site-packages\\tensorflow\\python\\framework\\dtypes.py:525: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  np_resource = np.dtype([(\"resource\", np.ubyte, 1)])\n",
      "C:\\Users\\owner\\anaconda3\\lib\\site-packages\\tensorboard\\compat\\tensorflow_stub\\dtypes.py:541: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint8 = np.dtype([(\"qint8\", np.int8, 1)])\n",
      "C:\\Users\\owner\\anaconda3\\lib\\site-packages\\tensorboard\\compat\\tensorflow_stub\\dtypes.py:542: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_quint8 = np.dtype([(\"quint8\", np.uint8, 1)])\n",
      "C:\\Users\\owner\\anaconda3\\lib\\site-packages\\tensorboard\\compat\\tensorflow_stub\\dtypes.py:543: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint16 = np.dtype([(\"qint16\", np.int16, 1)])\n",
      "C:\\Users\\owner\\anaconda3\\lib\\site-packages\\tensorboard\\compat\\tensorflow_stub\\dtypes.py:544: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_quint16 = np.dtype([(\"quint16\", np.uint16, 1)])\n",
      "C:\\Users\\owner\\anaconda3\\lib\\site-packages\\tensorboard\\compat\\tensorflow_stub\\dtypes.py:545: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint32 = np.dtype([(\"qint32\", np.int32, 1)])\n",
      "C:\\Users\\owner\\anaconda3\\lib\\site-packages\\tensorboard\\compat\\tensorflow_stub\\dtypes.py:550: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  np_resource = np.dtype([(\"resource\", np.ubyte, 1)])\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From C:\\Users\\owner\\anaconda3\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:4070: The name tf.nn.max_pool is deprecated. Please use tf.nn.max_pool2d instead.\n",
      "\n",
      "WARNING:tensorflow:From C:\\Users\\owner\\anaconda3\\lib\\site-packages\\tensorflow\\python\\ops\\nn_impl.py:180: add_dispatch_support.<locals>.wrapper (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use tf.where in 2.0, which has the same broadcast rule as np.where\n",
      "WARNING:tensorflow:From C:\\Users\\owner\\anaconda3\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:422: The name tf.global_variables is deprecated. Please use tf.compat.v1.global_variables instead.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#################################### Loading model #####################################\n",
    "from SCALO_Utils import *\n",
    "filname = 'Detection_final1.h5'\n",
    "model = load_SCALODEEP_Model(filname)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Patching \n",
    "w1 =3000\n",
    "w2 =1\n",
    "s1z =1000\n",
    "s2z =1\n",
    "ch0,ch1,ch2 = Patching(Data,w1,w2,s1z,s2z)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0/12\n",
      "1/12\n",
      "2/12\n",
      "3/12\n",
      "4/12\n",
      "5/12\n",
      "6/12\n",
      "7/12\n",
      "8/12\n",
      "9/12\n",
      "10/12\n",
      "11/12\n",
      "12/12 [==============================] - 1s 75ms/step\n"
     ]
    }
   ],
   "source": [
    "n1 = len(Data[0].data)\n",
    "n2 = 1\n",
    "labf = SCALO_Continous_Windows_Overlapping_EvenTime(Data,ch0,ch1,ch2,model,n1,n2,w1,w2,s1z,s2z)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x23bc60ea748>]"
      ]
     },
     "execution_count": 9,
     "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"
    },
    {
     "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": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "thre = 0.3\n",
    "labwin = np.where(labf>thre,1,0)\n",
    "\n",
    "plt.plot(labf[:,0])\n",
    "plt.figure()\n",
    "plt.plot(labwin[:,0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "font = {'family' : 'normal',\n",
    "        'weight' : 'bold',\n",
    "        'size'   : 12}\n",
    "\n",
    "plt.rc('font', **font)\n",
    "\n",
    "fig = plt.figure()\n",
    "ax1 = plt.subplot(411)\n",
    "plt.plot(Data[0].data,label = 'E' , linewidth = 1, markersize=1)\n",
    "ax1.legend(loc='upper right', fontsize = 10)\n",
    "plt.setp(ax1.get_xticklabels(), visible=False)\n",
    "ax1.set_xlim(xmin=0)\n",
    "ax1.set_xlim(xmax=len(labf))\n",
    "#plt.title(str(st[0].stats.starttime))\n",
    "\n",
    "ax2 = plt.subplot(412, sharex=ax1)\n",
    "plt.plot(Data[1].data, label = 'N' , linewidth = 1, markersize=1)\n",
    "ax2.legend(loc='upper right', fontsize = 10)\n",
    "ax2.set_xlim(xmin=0)\n",
    "ax2.set_xlim(xmax=len(labf))\n",
    "\n",
    "plt.setp(ax2.get_xticklabels(), visible=False)\n",
    "\n",
    "# share x only\n",
    "ax3 = plt.subplot(413, sharex=ax1)\n",
    "plt.plot(Data[2].data, label = 'Z' , linewidth = 1, markersize=1)\n",
    "ax3.legend(loc='upper right', fontsize = 10)\n",
    "plt.setp(ax3.get_xticklabels(), visible=False)\n",
    "ax3.set_xlim(xmin=0)\n",
    "ax3.set_xlim(xmax=len(labf))\n",
    "\n",
    "\n",
    "# share x only\n",
    "ax4 = plt.subplot(414, sharex=ax1)\n",
    "    #plt.plot(lab,'b--',label = 'Ground Truth')\n",
    "plt.plot(labwin,'r--',label = 'Output Label')\n",
    "#plt.xlim(0.01, 5.0)\n",
    "ax4.legend(loc='best', fontsize = 10)\n",
    "ax4.set_xlim(xmin=0)\n",
    "ax4.set_xlim(xmax=len(labf))\n",
    "\n",
    "ax4.set_xlabel(\"Samples\",fontsize='large', fontweight='bold')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The event start time is: 2020-02-16T17:03:46.100000Z\n"
     ]
    }
   ],
   "source": [
    "# Starting time of the event.\n",
    "astart = Data[0].stats.starttime\n",
    "StartTime = astart + (np.where(labwin==1)[0][0] /Data[0].stats.sampling_rate)\n",
    "print('The event start time is: ' + str(StartTime))"
   ]
  },
  {
   "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": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}