{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style='background-image: url(\"../share/baku.jpg\") ; padding: 0px ; background-size: cover ; border-radius: 15px ; height: 250px; background-position: 0% 80%'>\n",
    "    <div style=\"float: right ; margin: 50px ; padding: 20px ; background: rgba(255 , 255 , 255 , 0.9) ; width: 50% ; height: 150px\">\n",
    "        <div style=\"position: relative ; top: 50% ; transform: translatey(-50%)\">\n",
    "            <div style=\"font-size: xx-large ; font-weight: 900 ; color: rgba(0 , 0 , 0 , 0.9) ; line-height: 100%\">ObsPy Tutorial</div>\n",
    "            <div style=\"font-size: large ; padding-top: 20px ; color: rgba(0 , 0 , 0 , 0.7)\">Handling Time</div>\n",
    "        </div>\n",
    "    </div>\n",
    "</div>\n",
    "image: User:Abbaszade656 / Wikimedia Commons / <a href=\"http://creativecommons.org/licenses/by-sa/4.0/\">CC-BY-SA-4.0</a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Workshop for the \"Training in Network Management Systems and Analytical Tools for Seismic\"\n",
    "### Baku, October 2018\n",
    "\n",
    "\n",
    "Seismo-Live: http://seismo-live.org\n",
    "\n",
    "##### Authors:\n",
    "* Lion Krischer ([@krischer](https://github.com/krischer))\n",
    "* Tobias Megies ([@megies](https://github.com/megies))\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](images/obspy_logo_full_524x179px.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is a bit dry but not very difficult and important to know. It is used **everywhere** in ObsPy!\n",
    "\n",
    "\n",
    "* All absolute time values are consistently handled with this class.\n",
    "* Based on a nanosecond precision POSIX integer timestamp for accuracy.\n",
    "* Timezone can be specified at initialization (if necessary).\n",
    "* In Coordinated Universal Time (UTC) so no need to deal with timezones, daylight savings, ...\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "from __future__ import print_function\n",
    "import matplotlib.pyplot as plt\n",
    "plt.style.use('ggplot')\n",
    "plt.rcParams['figure.figsize'] = 12, 8"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "## Features of **`UTCDateTime`**\n",
    "\n",
    "#### Initialization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2014-08-24T10:20:44.000000Z\n",
      "2014-08-24T10:20:44.000000Z\n",
      "2014-08-24T10:20:44.000000Z\n",
      "2014-08-24T10:20:44.000000Z\n"
     ]
    }
   ],
   "source": [
    "from obspy import UTCDateTime\n",
    "\n",
    "print(UTCDateTime(\"2014-08-24T10:20:44.0\"))        # mostly time strings defined by ISO standard\n",
    "print(UTCDateTime(\"2014-08-24T01:20:44.0-09:00\"))  # non-UTC timezone input\n",
    "print(UTCDateTime(2014, 8, 24, 10, 20, 44))        # year, month, day, hour, min, sec, musec\n",
    "print(UTCDateTime(1408875644.0))                   # timestamp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2017-09-14T20:37:41.881473Z\n"
     ]
    }
   ],
   "source": [
    "# Current time can be initialized by leaving out any arguments\n",
    "print(UTCDateTime())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Attribute Access"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2014\n",
      "236\n",
      "1408875644.0\n",
      "6\n"
     ]
    }
   ],
   "source": [
    "time = UTCDateTime(\"2014-08-24T10:20:44.0\")\n",
    "print(time.year)\n",
    "print(time.julday)\n",
    "print(time.timestamp)\n",
    "print(time.weekday)\n",
    "# try time.<Tab>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Handling time differences\n",
    "\n",
    "* \"**`+`**/**`-`**\" defined to add seconds to an **`UTCDateTime`** object\n",
    "* \"**`-`**\" defined to get time difference of two **`UTCDateTime`** objects"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2014-08-24T10:20:44.000000Z\n"
     ]
    }
   ],
   "source": [
    "time = UTCDateTime(\"2014-08-24T10:20:44.0\")\n",
    "print(time)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2014-08-24T11:20:44.000000Z\n"
     ]
    }
   ],
   "source": [
    "# one hour later\n",
    "print(time + 3600)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "11194756.0\n"
     ]
    }
   ],
   "source": [
    "# Time differences\n",
    "time2 = UTCDateTime(2015, 1, 1)\n",
    "print(time2 - time)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercises\n",
    "\n",
    "Calculate the number of days passed since the 2014 South Napa earthquake (the timestamp used above)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1117.3867815720603\n"
     ]
    }
   ],
   "source": [
    "print((UTCDateTime() - UTCDateTime(\"2014-08-24T11:20:44.000000Z\")) / 86400)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Make a list of 10 UTCDateTime objects, starting today at 10:00 with a spacing of 90 minutes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[2017-09-18T10:00:00.000000Z,\n",
       " 2017-09-18T11:30:00.000000Z,\n",
       " 2017-09-18T13:00:00.000000Z,\n",
       " 2017-09-18T14:30:00.000000Z,\n",
       " 2017-09-18T16:00:00.000000Z,\n",
       " 2017-09-18T17:30:00.000000Z,\n",
       " 2017-09-18T19:00:00.000000Z,\n",
       " 2017-09-18T20:30:00.000000Z,\n",
       " 2017-09-18T22:00:00.000000Z,\n",
       " 2017-09-18T23:30:00.000000Z]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t = UTCDateTime(2017, 9, 18, 10)\n",
    "\n",
    "times = []\n",
    "for i in range(10):\n",
    "    t2 = t + i * 90 * 60\n",
    "    times.append(t2)\n",
    "\n",
    "times"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Below is a list of strings with origin times of magnitude 8+ earthquakes since 2000 (fetched from IRIS). Assemble a list of interevent times in days. Use matplotlib to display a histogram."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "times = [\"2000-11-16T04:54:56\",\n",
    "         \"2001-06-23T20:33:09\",\n",
    "         \"2003-09-25T19:50:07\",\n",
    "         \"2004-12-23T14:59:00\",\n",
    "         \"2004-12-26T00:58:52\",\n",
    "         \"2005-03-28T16:09:35\",\n",
    "         \"2006-05-03T15:26:39\",\n",
    "         \"2006-06-01T18:57:02\",\n",
    "         \"2006-06-05T00:50:31\",\n",
    "         \"2006-11-15T11:14:14\",\n",
    "         \"2007-01-13T04:23:23\",\n",
    "         \"2007-04-01T20:39:56\",\n",
    "         \"2007-08-15T23:40:58\",\n",
    "         \"2007-09-12T11:10:26\",\n",
    "         \"2009-09-29T17:48:11\",\n",
    "         \"2010-02-27T06:34:13\",\n",
    "         \"2011-03-11T05:46:23\",\n",
    "         \"2012-04-11T08:38:37\",\n",
    "         \"2012-04-11T10:43:10\",\n",
    "         \"2013-05-24T05:44:49\",\n",
    "         \"2014-04-01T23:46:47\",\n",
    "         \"2015-09-16T22:54:32\",\n",
    "         \"2017-09-08T04:49:21\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtUAAAH1CAYAAAA59wLAAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n0Lk1rqSHlwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x111996128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "inter_event_times = []\n",
    "\n",
    "for i in range(1, len(times)):\n",
    "    dt = UTCDateTime(times[i]) - UTCDateTime(times[i-1])\n",
    "    dt = dt / (3600 * 24)\n",
    "    inter_event_times.append(dt)\n",
    "\n",
    "plt.hist(inter_event_times, bins=range(0, 1000, 100))\n",
    "plt.xlabel(\"Magnitude 8+ interevent times since 2000 [days]\")\n",
    "plt.ylabel(\"count\")\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": "3.6.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}