exercise_21_Basic_Processing_Exercise.ipynb

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    "<div style='background-image: url(\"../share/images/header.svg\") ; padding: 0px ; background-size: cover ; border-radius: 5px ; height: 250px'>\n",
    "    <div style=\"float: right ; margin: 50px ; padding: 20px ; background: rgba(255 , 255 , 255 , 0.7) ; 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 , 0.8) ; line-height: 100%\">ObsPy Tutorial</div>\n",
    "            <div style=\"font-size: large ; padding-top: 20px ; color: rgba(0 , 0 , 0 , 0.5)\">Downloading/Processing Exercise</div>\n",
    "        </div>\n",
    "    </div>\n",
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    "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",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the this exercise we will download some data from the Tohoku-Oki earthquake, cut out a certain time window around the first arrival and remove the instrument response from the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
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   "source": [
    "from __future__ import print_function\n",
    "\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "plt.style.use('ggplot')\n",
    "plt.rcParams['figure.figsize'] = 12, 8"
   ]
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   "cell_type": "markdown",
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   "source": [
    "The first step is to download all the necessary information using the ObsPy FDSN client. **Learn to read the documentation!**\n",
    "\n",
    "We need the following things:\n",
    "\n",
    "1. Event information about the Tohoku-Oki earthquake. Use the `get_events()` method of the client. A good provider of event data is the USGS.\n",
    "2. Waveform information for a certain station. Choose your favorite one! If you have no preference, use `II.BFO` which is available for example from IRIS. Use the `get_waveforms()` method.\n",
    "3. Download the associated station/instrument information with the `get_stations()` method."
   ]
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  {
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   "execution_count": null,
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   "source": [
    "Have a look at the just downloaded data."
   ]
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   "cell_type": "code",
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   "source": [
    "## Exercise\n",
    "\n",
    "The goal of this exercise is to cut the data from 1 minute before the first arrival to 5 minutes after it, and then remove the instrument response.\n",
    "\n",
    "\n",
    "#### Step 1: Determine Coordinates of Station"
   ]
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  {
   "cell_type": "code",
   "execution_count": null,
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   "source": [
    "#### Step 2: Determine Coordinates of Event"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "source": [
    "#### Step 3: Calculate distance of event and station.\n",
    "\n",
    "Use `obspy.geodetics.locations2degree`."
   ]
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   "cell_type": "code",
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    "#### Step 4: Calculate Theoretical Arrivals\n",
    "\n",
    "```python\n",
    "from obspy.taup import TauPyModel\n",
    "m = TauPyModel(model=\"ak135\")\n",
    "arrivals = m.get_ray_paths(...)\n",
    "arrivals.plot()\n",
    "```"
   ]
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  {
   "cell_type": "code",
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   "source": [
    "#### Step 5: Calculate absolute time of the first arrivals at the station"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
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   "cell_type": "markdown",
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   "source": [
    "#### Step 6:  Cut to 1 minute before and 5 minutes after the first arrival"
   ]
  },
  {
   "cell_type": "code",
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    "#### Step 7: Remove the instrument response\n",
    "\n",
    "```python\n",
    "st.remove_response(inventory=inv, pre_filt=...)\n",
    "```\n",
    "\n",
    "![taper](images/cos_taper.png)"
   ]
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bonus: Interactive IPython widgets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "fcacb0099c0d4194b21064cbc922d0e6",
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      "text/plain": [
       "interactive(children=(Dropdown(description='distance', options=(90, 140, 180, 0), value=90), Dropdown(descript…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from ipywidgets.widgets import interact\n",
    "from obspy.taup import TauPyModel\n",
    "\n",
    "# model = TauPyModel(\"ak135\")\n",
    "model = TauPyModel(model='iasp91')\n",
    "\n",
    "def plot_raypaths(distance, depth, wavetype):\n",
    "    try:\n",
    "        plt.close()\n",
    "    except:\n",
    "        pass\n",
    "    if wavetype == \"ttall\":\n",
    "        phases = [\"ttall\"]\n",
    "    elif wavetype == \"diff\":\n",
    "        phases = [\"Pdiff\", \"pPdiff\"]\n",
    "        #phases = [\"Pdiff\", \"SS\"]\n",
    "    \n",
    "    arrivals = model.get_ray_paths(distance_in_degree=distance, source_depth_in_km=depth, phase_list=phases)\n",
    "    if arrivals:\n",
    "        arrivals.plot_rays(plot_type='spherical', phase_list=phases,\n",
    "                   legend=True)\n",
    "    else:\n",
    "        print(\"No arraivals, can't plot\")\n",
    "    \n",
    "interact(plot_raypaths, distance=[90, 140, 180, 0],\n",
    "         depth=[0, 700], wavetype=[\"ttall\", \"diff\"]);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "outputs": [],
   "source": []
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