notebooks/DetailEvent.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Third party imports \n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from obspy.io.quakeml.core import Unpickler\n",
    "import pandas as pd\n",
    "\n",
    "# Local imports\n",
    "from libcomcat.dataframes import get_phase_dataframe, get_magnitude_data_frame\n",
    "from libcomcat.search import get_event_by_id"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Contents\n",
    "----\n",
    "- [Getting All Magnitudes](#Getting-All-Magnitudes)\n",
    "- [Getting All DYFI responses](#Getting-All-DYFI-responses)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Getting All Magnitudes\n",
    "\n",
    "\n",
    "A lot of the information about locations and magnitudes can only be found in the QuakeML XML file for an origin or phase-data product. However, it isn't necessary to know how to parse XML in order to extract this information, as Obspy can easily parse the information. In the cells below the *Unpickler* class is used to create an <a href=\"https://docs.obspy.org/packages/autogen/obspy.core.event.Catalog.html\">Obspy Catalog</a> object, which can be easily inspected for events, magnitudes, origins, picks, etc."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "eventid = 'nc73201181'\n",
    "detail = get_event_by_id(eventid, includesuperseded=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "US Magnitudes by version for event nc73201181:\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>version</th>\n",
       "      <th>ML</th>\n",
       "      <th>Mwr</th>\n",
       "      <th>mb</th>\n",
       "      <th>Mww</th>\n",
       "      <th>Ms_20</th>\n",
       "      <th>Mwb</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>5.5</td>\n",
       "      <td>5.6</td>\n",
       "      <td>5.4</td>\n",
       "      <td>5.6</td>\n",
       "      <td>5.2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>5.3</td>\n",
       "      <td>5.6</td>\n",
       "      <td>5.4</td>\n",
       "      <td>5.6</td>\n",
       "      <td>5.4</td>\n",
       "      <td>5.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>5.3</td>\n",
       "      <td>5.6</td>\n",
       "      <td>5.4</td>\n",
       "      <td>5.6</td>\n",
       "      <td>5.4</td>\n",
       "      <td>5.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>5.4</td>\n",
       "      <td>5.6</td>\n",
       "      <td>5.4</td>\n",
       "      <td>5.6</td>\n",
       "      <td>5.4</td>\n",
       "      <td>5.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>5.4</td>\n",
       "      <td>5.6</td>\n",
       "      <td>5.4</td>\n",
       "      <td>5.6</td>\n",
       "      <td>5.4</td>\n",
       "      <td>5.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>5.4</td>\n",
       "      <td>5.6</td>\n",
       "      <td>5.4</td>\n",
       "      <td>5.6</td>\n",
       "      <td>5.4</td>\n",
       "      <td>5.6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   version   ML  Mwr   mb  Mww  Ms_20  Mwb\n",
       "0        1  5.5  5.6  5.4  5.6    5.2  NaN\n",
       "1        2  5.3  5.6  5.4  5.6    5.4  5.6\n",
       "2        3  5.3  5.6  5.4  5.6    5.4  5.6\n",
       "3        4  5.4  5.6  5.4  5.6    5.4  5.6\n",
       "4        5  5.4  5.6  5.4  5.6    5.4  5.6\n",
       "5        6  5.4  5.6  5.4  5.6    5.4  5.6"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unpickler = Unpickler()\n",
    "us_origins = detail.getProducts('phase-data', source='us', version='all')\n",
    "maglist = []\n",
    "mtypes = []\n",
    "for origin in us_origins:\n",
    "    version = origin.version\n",
    "    qbytes, url = origin.getContentBytes('xml')\n",
    "    catalog = unpickler.loads(qbytes)\n",
    "    mdict = {'version':version}\n",
    "    for magnitude in catalog.events[0].magnitudes:\n",
    "        magtype = magnitude.magnitude_type\n",
    "        if magtype not in mtypes:\n",
    "            mtypes.append(magtype)\n",
    "        magvalue = magnitude.mag\n",
    "        mdict[magtype] = magvalue\n",
    "    maglist.append(mdict)\n",
    "\n",
    "magframe = pd.DataFrame(columns=['version'] + mtypes)\n",
    "for magrow in maglist:\n",
    "    magframe = magframe.append(magrow, ignore_index=True)\n",
    "\n",
    "magframe['version'] = magframe['version'].astype(np.int32)\n",
    "print('US Magnitudes by version for event %s:' % detail.id)\n",
    "magframe"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Getting All DYFI responses\n",
    "\n",
    "The above detail event can be used to reproduce the responses vs time plot found [HERE](https://earthquake.usgs.gov/earthquakes/eventpage/nc73201181/dyfi/responses-vs-time).\n",
    "\n",
    "(and add a red vertical line to show when 95% of all responses have occurred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "dyfi_responses = detail.getProducts('dyfi', source='us', version='all')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "response_frame = pd.DataFrame(columns=['Event ID','Version','Update Time','Elapsed (sec)','num-responses'])\n",
    "origin = detail.time\n",
    "tresp = None\n",
    "for response in dyfi_responses:\n",
    "    c1 = 'num-responses' not in response.properties\n",
    "    row = {'Version':response.version, \n",
    "           'Update Time':response.update_time}\n",
    "    if c1:\n",
    "        nresp = np.nan\n",
    "    else:\n",
    "        nresp = int(response['num-responses'])\n",
    "        \n",
    "    eventid = response['eventsource'] + response['eventsourcecode']\n",
    "    row['Event ID'] = eventid\n",
    "    row['num-responses'] = nresp\n",
    "    elapsed = (response.update_time - origin).total_seconds()\n",
    "    row['Elapsed (sec)'] = elapsed\n",
    "    response_frame = response_frame.append(row, ignore_index=True)\n",
    "    \n",
    "# remove any rows where the event ID is not the authoritative one\n",
    "response_frame = response_frame[response_frame['Event ID'] == detail.id]\n",
    "    \n",
    "fig = plt.figure(figsize=(10,8));\n",
    "hours = response_frame['Elapsed (sec)']/3600\n",
    "responses = response_frame['num-responses']\n",
    "\n",
    "# when did 95% of number of ultimate responses come in?\n",
    "r95 = responses.max() * 0.95\n",
    "i95 = np.argmax(responses.values >= r95)\n",
    "t95 = hours[i95]\n",
    "plt.plot(hours, responses, 'b.');\n",
    "plt.axvline(x=t95,color='red');\n",
    "# limit plot to first 24 hours\n",
    "xmin, xmax, ymin, ymax = plt.axis();\n",
    "plt.axis([0,24,ymin,ymax]);\n",
    "plt.xlabel('Elapsed time (hours)', fontsize=14);\n",
    "plt.ylabel('Number of DYFI responses', fontsize=14);\n",
    "tstr = 'Responses vs Time (%s)' % detail.id\n",
    "plt.title(tstr, fontsize=16);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Event ID</th>\n",
       "      <th>Version</th>\n",
       "      <th>Update Time</th>\n",
       "      <th>Elapsed (sec)</th>\n",
       "      <th>num-responses</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>nc73201181</td>\n",
       "      <td>1</td>\n",
       "      <td>2019-06-23 03:57:57.456</td>\n",
       "      <td>294.566</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>nc73201181</td>\n",
       "      <td>2</td>\n",
       "      <td>2019-06-23 04:01:04.309</td>\n",
       "      <td>481.419</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>nc73201181</td>\n",
       "      <td>4</td>\n",
       "      <td>2019-06-23 04:06:19.599</td>\n",
       "      <td>796.709</td>\n",
       "      <td>94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>nc73201181</td>\n",
       "      <td>6</td>\n",
       "      <td>2019-06-23 04:11:49.817</td>\n",
       "      <td>1126.927</td>\n",
       "      <td>192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>nc73201181</td>\n",
       "      <td>8</td>\n",
       "      <td>2019-06-23 04:16:57.519</td>\n",
       "      <td>1434.629</td>\n",
       "      <td>295</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>nc73201181</td>\n",
       "      <td>10</td>\n",
       "      <td>2019-06-23 04:22:22.618</td>\n",
       "      <td>1759.728</td>\n",
       "      <td>405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>nc73201181</td>\n",
       "      <td>12</td>\n",
       "      <td>2019-06-23 04:27:47.202</td>\n",
       "      <td>2084.312</td>\n",
       "      <td>533</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>nc73201181</td>\n",
       "      <td>14</td>\n",
       "      <td>2019-06-23 04:32:49.901</td>\n",
       "      <td>2387.011</td>\n",
       "      <td>658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>nc73201181</td>\n",
       "      <td>16</td>\n",
       "      <td>2019-06-23 04:38:15.459</td>\n",
       "      <td>2712.569</td>\n",
       "      <td>742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>nc73201181</td>\n",
       "      <td>17</td>\n",
       "      <td>2019-06-23 04:39:02.100</td>\n",
       "      <td>2759.210</td>\n",
       "      <td>758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>nc73201181</td>\n",
       "      <td>22</td>\n",
       "      <td>2019-06-23 04:44:15.957</td>\n",
       "      <td>3073.067</td>\n",
       "      <td>871</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>nc73201181</td>\n",
       "      <td>23</td>\n",
       "      <td>2019-06-23 04:49:33.534</td>\n",
       "      <td>3390.644</td>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>nc73201181</td>\n",
       "      <td>24</td>\n",
       "      <td>2019-06-23 04:54:42.245</td>\n",
       "      <td>3699.355</td>\n",
       "      <td>1126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>nc73201181</td>\n",
       "      <td>25</td>\n",
       "      <td>2019-06-23 04:59:47.615</td>\n",
       "      <td>4004.725</td>\n",
       "      <td>1251</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>nc73201181</td>\n",
       "      <td>26</td>\n",
       "      <td>2019-06-23 05:04:48.895</td>\n",
       "      <td>4306.005</td>\n",
       "      <td>1376</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>nc73201181</td>\n",
       "      <td>27</td>\n",
       "      <td>2019-06-23 05:10:11.083</td>\n",
       "      <td>4628.193</td>\n",
       "      <td>1488</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>nc73201181</td>\n",
       "      <td>28</td>\n",
       "      <td>2019-06-23 05:15:24.662</td>\n",
       "      <td>4941.772</td>\n",
       "      <td>1616</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>nc73201181</td>\n",
       "      <td>29</td>\n",
       "      <td>2019-06-23 05:20:30.154</td>\n",
       "      <td>5247.264</td>\n",
       "      <td>1745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>nc73201181</td>\n",
       "      <td>30</td>\n",
       "      <td>2019-06-23 05:25:46.472</td>\n",
       "      <td>5563.582</td>\n",
       "      <td>1875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>nc73201181</td>\n",
       "      <td>31</td>\n",
       "      <td>2019-06-23 05:31:03.013</td>\n",
       "      <td>5880.123</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>nc73201181</td>\n",
       "      <td>32</td>\n",
       "      <td>2019-06-23 05:36:12.351</td>\n",
       "      <td>6189.461</td>\n",
       "      <td>2091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>nc73201181</td>\n",
       "      <td>33</td>\n",
       "      <td>2019-06-23 05:41:29.143</td>\n",
       "      <td>6506.253</td>\n",
       "      <td>2216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>nc73201181</td>\n",
       "      <td>34</td>\n",
       "      <td>2019-06-23 05:46:39.409</td>\n",
       "      <td>6816.519</td>\n",
       "      <td>2324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>nc73201181</td>\n",
       "      <td>35</td>\n",
       "      <td>2019-06-23 05:51:38.741</td>\n",
       "      <td>7115.851</td>\n",
       "      <td>2401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>nc73201181</td>\n",
       "      <td>36</td>\n",
       "      <td>2019-06-23 05:56:46.432</td>\n",
       "      <td>7423.542</td>\n",
       "      <td>2441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>nc73201181</td>\n",
       "      <td>37</td>\n",
       "      <td>2019-06-23 06:01:59.460</td>\n",
       "      <td>7736.570</td>\n",
       "      <td>2443</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>nc73201181</td>\n",
       "      <td>38</td>\n",
       "      <td>2019-06-23 06:07:07.057</td>\n",
       "      <td>8044.167</td>\n",
       "      <td>2448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>nc73201181</td>\n",
       "      <td>39</td>\n",
       "      <td>2019-06-23 06:12:18.563</td>\n",
       "      <td>8355.673</td>\n",
       "      <td>2456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>nc73201181</td>\n",
       "      <td>40</td>\n",
       "      <td>2019-06-23 06:17:22.274</td>\n",
       "      <td>8659.384</td>\n",
       "      <td>2462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>nc73201181</td>\n",
       "      <td>41</td>\n",
       "      <td>2019-06-23 06:22:20.971</td>\n",
       "      <td>8958.081</td>\n",
       "      <td>2469</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Event ID Version             Update Time  Elapsed (sec) num-responses\n",
       "0   nc73201181       1 2019-06-23 03:57:57.456        294.566             4\n",
       "1   nc73201181       2 2019-06-23 04:01:04.309        481.419            44\n",
       "3   nc73201181       4 2019-06-23 04:06:19.599        796.709            94\n",
       "5   nc73201181       6 2019-06-23 04:11:49.817       1126.927           192\n",
       "7   nc73201181       8 2019-06-23 04:16:57.519       1434.629           295\n",
       "9   nc73201181      10 2019-06-23 04:22:22.618       1759.728           405\n",
       "11  nc73201181      12 2019-06-23 04:27:47.202       2084.312           533\n",
       "13  nc73201181      14 2019-06-23 04:32:49.901       2387.011           658\n",
       "15  nc73201181      16 2019-06-23 04:38:15.459       2712.569           742\n",
       "16  nc73201181      17 2019-06-23 04:39:02.100       2759.210           758\n",
       "21  nc73201181      22 2019-06-23 04:44:15.957       3073.067           871\n",
       "22  nc73201181      23 2019-06-23 04:49:33.534       3390.644          1000\n",
       "23  nc73201181      24 2019-06-23 04:54:42.245       3699.355          1126\n",
       "24  nc73201181      25 2019-06-23 04:59:47.615       4004.725          1251\n",
       "25  nc73201181      26 2019-06-23 05:04:48.895       4306.005          1376\n",
       "26  nc73201181      27 2019-06-23 05:10:11.083       4628.193          1488\n",
       "27  nc73201181      28 2019-06-23 05:15:24.662       4941.772          1616\n",
       "28  nc73201181      29 2019-06-23 05:20:30.154       5247.264          1745\n",
       "29  nc73201181      30 2019-06-23 05:25:46.472       5563.582          1875\n",
       "30  nc73201181      31 2019-06-23 05:31:03.013       5880.123          2010\n",
       "31  nc73201181      32 2019-06-23 05:36:12.351       6189.461          2091\n",
       "32  nc73201181      33 2019-06-23 05:41:29.143       6506.253          2216\n",
       "33  nc73201181      34 2019-06-23 05:46:39.409       6816.519          2324\n",
       "34  nc73201181      35 2019-06-23 05:51:38.741       7115.851          2401\n",
       "35  nc73201181      36 2019-06-23 05:56:46.432       7423.542          2441\n",
       "36  nc73201181      37 2019-06-23 06:01:59.460       7736.570          2443\n",
       "37  nc73201181      38 2019-06-23 06:07:07.057       8044.167          2448\n",
       "38  nc73201181      39 2019-06-23 06:12:18.563       8355.673          2456\n",
       "39  nc73201181      40 2019-06-23 06:17:22.274       8659.384          2462\n",
       "40  nc73201181      41 2019-06-23 06:22:20.971       8958.081          2469"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "response_frame.head(n=30)"
   ]
  }
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