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{"cells": [{"cell_type": "markdown", "metadata": {}, "source": ["<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)\">Handling Station Metadata</div>\n", " </div>\n", " </div>\n", "</div>"]}, {"cell_type": "markdown", "metadata": {}, "source": ["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": [""]}, {"cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": ["%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "plt.style.use('ggplot')\n", "plt.rcParams['figure.figsize'] = 12, 8"]}, {"cell_type": "markdown", "metadata": {}, "source": ["- for station metadata, the de-facto standard of the future (replacing SEED/RESP) is [FDSN StationXML](http://www.fdsn.org/xml/station/)\n", "- FDSN StationXML files can be read using **`read_inventory()`**"]}, {"cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": ["from obspy import read_inventory\n", "# real-world StationXML files often deviate from the official schema definition\n", "# therefore file-format autodiscovery sometimes fails and we have to force the file format\n", "inventory = read_inventory(\"./data/station_PFO.xml\", format=\"STATIONXML\")\n", "print(type(inventory))"]}, {"cell_type": "markdown", "metadata": {}, "source": ["- the nested ObsPy Inventory class structure (Inventory/Station/Channel/Response/...) is closely modelled after FDSN StationXML\n", "<img src=\"images/Inventory.svg\" width=90%>"]}, {"cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": ["!head data/station_BFO.xml"]}, {"cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": ["print(inventory)"]}, {"cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": ["network = inventory[0]\n", "print(network)"]}, {"cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": ["station = network[0]\n", "print(station)"]}, {"cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": ["channel = station[0]\n", "print(channel)"]}, {"cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": ["print(channel.response)"]}, {"cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": ["from obspy import read\n", "st = read(\"./data/waveform_PFO.mseed\")\n", "print(st)"]}, {"cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": ["inv = read_inventory(\"./data/station_PFO.xml\", format=\"STATIONXML\")"]}, {"cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": ["print(st[0].stats)"]}, {"cell_type": "markdown", "metadata": {}, "source": ["- the instrument response can be deconvolved from the waveform data using the convenience method **`Stream.remove_response()`**\n", "- evalresp is used internally to calculate the instrument response"]}, {"cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": ["st.plot()\n", "st.remove_response(inventory=inv)\n", "st.plot()"]}, {"cell_type": "markdown", "metadata": {}, "source": ["- several options can be used to specify details of the deconvolution (water level, frequency domain prefiltering), output units (velocity/displaceme
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