wrangling output of obspy coincidence trigger

process_trigs.ipynb

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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Processing output of obspy [coincidence trigger](https://docs.obspy.org/tutorial/code_snippets/trigger_tutorial.html#network-coincidence-trigger-example)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import obspy\n",
    "import pandas as pd\n",
    "from obspy.signal.trigger import coincidence_trigger"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# get data (same as from tutorial)\n",
    "st = obspy.Stream()\n",
    "\n",
    "files = [\"BW.UH1..SHZ.D.2010.147.cut.slist.gz\",\n",
    "         \"BW.UH2..SHZ.D.2010.147.cut.slist.gz\",\n",
    "         \"BW.UH3..SHZ.D.2010.147.cut.slist.gz\",\n",
    "         \"BW.UH4..SHZ.D.2010.147.cut.slist.gz\"]\n",
    "\n",
    "for filename in files:\n",
    "    st += obspy.read(\"https://examples.obspy.org/\" + filename)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "st.filter('bandpass', freqmin=10, freqmax=20)  # optional prefiltering\n",
    "st2 = st.copy()\n",
    "trig = coincidence_trigger(\"recstalta\", 3.5, 1, st2, 3, sta=0.5, lta=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[{'time': UTCDateTime(2010, 5, 27, 16, 24, 33, 209999), 'stations': ['UH3', 'UH2', 'UH1', 'UH4'], 'trace_ids': ['BW.UH3..SHZ', 'BW.UH2..SHZ', 'BW.UH1..SHZ', 'BW.UH4..SHZ'], 'coincidence_sum': 4.0, 'similarity': {}, 'duration': 4.510000228881836}, {'time': UTCDateTime(2010, 5, 27, 16, 27, 1, 260000), 'stations': ['UH2', 'UH3', 'UH1'], 'trace_ids': ['BW.UH2..SHZ', 'BW.UH3..SHZ', 'BW.UH1..SHZ'], 'coincidence_sum': 3.0, 'similarity': {}, 'duration': 3.440000057220459}, {'time': UTCDateTime(2010, 5, 27, 16, 27, 30, 510000), 'stations': ['UH3', 'UH2', 'UH1', 'UH4'], 'trace_ids': ['BW.UH3..SHZ', 'BW.UH2..SHZ', 'BW.UH1..SHZ', 'BW.UH4..SHZ'], 'coincidence_sum': 4.0, 'similarity': {}, 'duration': 4.769999742507935}]\n"
     ]
    }
   ],
   "source": [
    "print(trig)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[{'time': UTCDateTime(2010, 5, 27, 16, 24, 33, 209999), 'duration': 4.510000228881836}, {'time': UTCDateTime(2010, 5, 27, 16, 27, 1, 260000), 'duration': 3.440000057220459}, {'time': UTCDateTime(2010, 5, 27, 16, 27, 30, 510000), 'duration': 4.769999742507935}]\n"
     ]
    }
   ],
   "source": [
    "# get data using a list comprehension (only keep trigger time and duration)\n",
    "desired_columns = ('time', 'duration')\n",
    "\n",
    "new = [{x: sub_trig[x] for x in desired_columns} \n",
    "       for sub_trig in trig]\n",
    "\n",
    "print(new)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   coincidence_sum  duration similarity              stations  \\\n",
      "0              4.0      4.51         {}  [UH3, UH2, UH1, UH4]   \n",
      "1              3.0      3.44         {}       [UH2, UH3, UH1]   \n",
      "2              4.0      4.77         {}  [UH3, UH2, UH1, UH4]   \n",
      "\n",
      "                          time  \\\n",
      "0  2010-05-27T16:24:33.210000Z   \n",
      "1  2010-05-27T16:27:01.260000Z   \n",
      "2  2010-05-27T16:27:30.510000Z   \n",
      "\n",
      "                                           trace_ids  \n",
      "0  [BW.UH3..SHZ, BW.UH2..SHZ, BW.UH1..SHZ, BW.UH4...  \n",
      "1            [BW.UH2..SHZ, BW.UH3..SHZ, BW.UH1..SHZ]  \n",
      "2  [BW.UH3..SHZ, BW.UH2..SHZ, BW.UH1..SHZ, BW.UH4...  \n"
     ]
    }
   ],
   "source": [
    "# use pandas\n",
    "df = pd.DataFrame(trig)\n",
    "print(df)"
   ]
  }
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