Tide_Gauge_Data/GeoNet_Tide_Gauge_Data.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <center>Introduction on Using Python to access and analyse GeoNet's Tide Gauge Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this tutorial we will look at geting waveform data form Tide Gauges. By the end of this notebook you should be able to make a graph like the one below. <img src=\"tide.png\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Table of Contents"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Introduction\n",
    "### 2. Set up\n",
    "### 3. Get the Location of all  Stations with Tide Gauge\n",
    "### 4. Look more at one Station\n",
    "### 5. Get Waveform Data\n",
    "#####  &nbsp; 5. 1 Getting Near real-time waveform data\n",
    "### 6. Save Waveform Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Introduction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this tutorial we will be learning how to use Python to be able to access the data recorded by GeoNets network of water level sensors’. Then we will look at some ways we can interact with this data in Python. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You will need to install the packages obspy (https://github.com/obspy/obspy/wiki) and basemap (https://matplotlib.org/basemap/) useing `pip install (package name)` in the terminal / command prompt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This tutorial assumes that you have basic knowledge of Python.\n",
    "We will be using a package called obspy (https://github.com/obspy/obspy/wiki)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### About Geonet tide gauge data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Geonet has 3 types of data from tide gauges.\n",
    "<ul>\n",
    "<li>Then raw data of the pressure sensor\n",
    "<li>The data corrected to height\n",
    "<li>The data that has been de-tided \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Geonet also samples at 2 rates. 10 Hz and 1 Hz. The 3 letter channel code tells you all of this.\n",
    "All tide gauges are in the form (B/L)T(Z/H/T)\n",
    "<ul>\n",
    "<li>The first letter is the sample rate. B = 10 Hz and L = 1 Hz, <br>\n",
    "<li>The second letter say that is a water level sensor<br> \n",
    "<li>The third letter say what kind of data it is, Z = raw, H = corrected to height and T = de-tided data <br>\n",
    "\n",
    "For a full list see https://www.geonet.org.nz/data/supplementary/channels "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Set Up"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Import obspy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from obspy.clients.fdsn import Client as FDSNClient # Import a module to retrieve data from FDSN Web Services \n",
    "from obspy import UTCDateTime # Import a module to manage time series format and rename as UTCDateTime\n",
    "from obspy import read_inventory # Imports module to read stattion data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For more information on obspy run `help(obspy)`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#import matplotlib\n",
    "#matplotlib.use('TkAgg')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`matplotlib` is the package used to make plots in python. \n",
    "The line `matplotlib.use('TkAgg')` will make all plots open in an interactive window, known as a Tkinter window, so you can zoom in on plots and save them as a png. (The `Tk` is to make it in a Tkinter window and the `Agg` stands for the Anti-Grain Geometry rendering engine that is used to make high-quality images). \n",
    "This is a needed line of code in Python 2 to stop plotting errors but is not need in Python 3."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Set up URL's"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "client = FDSNClient(\"GEONET\")\n",
    "clinet_nrt = FDSNClient('https://service-nrt.geonet.org.nz')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "GeoNet has two FDSN services <br>\n",
    "For waveform data\n",
    "an archive service holds verified data starting 7 days after collection, \n",
    "and a near real-time service holds data that could be incomplete from the last 8 days<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Get the Location of all  Stations with Tide Gauges"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Set the start and end time of the data you want to select"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "starttime = UTCDateTime(\"2011-03-11T17:00:00.000\") # initial time\n",
    "endtime = UTCDateTime(\"2011-03-12T07:00:00.000\") # end time "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that time must be in UTC (Universal Time Coordinated) which is 13 or 12 hours behind nzt (New Zealand Time) (depending on daylight savings time).  Also Note that GMT or Greenwich mean time is the same time as UTC.\n",
    "    In this case we are looking at the 2011 Tōhoku earthquake and tsunami which happened on the 11 March 2011 at 14:46 JST and if it efected new zeeland it would have done so about 12 hours after it happened. \n",
    "    There are online converters if needed.\n",
    "    You can add to the time in number of seconds eg. `stattime + 60` would be the time 2011-03-11 17:01:00.000<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Get all stations that have a tide gauge"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "Inventory = client.get_stations(starttime = starttime,endtime = endtime, channel=\"?T?\" )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`get_stations` gets all stations that where running at some point between `starttime` and `endtime` and hold to any other conditions put in the `()`such as channel, station, latitude and longitude with a maxradius<br>\n",
    "All channels with a code in the form (B/L)T(Z/H/T) are tide gauges <br>\n",
    "The ?s say it will look for data with a channel code in the format (any letter)T(any letter) <br>\n",
    "Therefor the code above finds all stations which have a channel code in the format (any letter)T(any letter)<br> "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Print all stations that have a tide gauge"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Inventory created at 2019-01-27T20:16:56.000000Z\n",
       "\tCreated by: Delta\n",
       "\t\t    \n",
       "\tSending institution: GeoNet (WEL(GNS_Test))\n",
       "\tContains:\n",
       "\t\tNetworks (1):\n",
       "\t\t\tNZ\n",
       "\t\tStations (18):\n",
       "\t\t\tNZ.AUCT (Auckland)\n",
       "\t\t\tNZ.CHIT (Chatham Island)\n",
       "\t\t\tNZ.CPIT (Castlepoint)\n",
       "\t\t\tNZ.GBIT (Great Barrier Island)\n",
       "\t\t\tNZ.GIST (Gisborne)\n",
       "\t\t\tNZ.GLKZ (Green Lake)\n",
       "\t\t\tNZ.KAIT (Kaikoura)\n",
       "\t\t\tNZ.LOTT (East Cape)\n",
       "\t\t\tNZ.MNKT (Manukau)\n",
       "\t\t\tNZ.NAPT (Napier)\n",
       "\t\t\tNZ.NCPT (North Cape)\n",
       "\t\t\tNZ.OTAT (Dunedin)\n",
       "\t\t\tNZ.PUYT (Puysegur)\n",
       "\t\t\tNZ.RBCT (Raoul Island Boat Cove)\n",
       "\t\t\tNZ.RFRT (Raoul Island Fishing Rock)\n",
       "\t\t\tNZ.SUMT (Christchurch)\n",
       "\t\t\tNZ.TAUT (Tauranga)\n",
       "\t\t\tNZ.WLGT (Wellington)\n",
       "\t\tChannels (0):\n"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Inventory"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Plot all stations that have a tide gauge"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "Inventory.plot(projection=\"local\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`projection=\"local\"` makes a plot that is a map and has all the data points in it. More information can be found at https://matplotlib.org/basemap/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Look more at one Station"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this section we will learn how to get more information on a station including the instrument response information."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Get more data on the Raoul Island station"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "Raoul_Island = client.get_stations(starttime = starttime,endtime = endtime,station=\"RFRT\", level=\"response\" )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Having seen the Raoul Island station on the map and its station code, I can get more data about this station. <br>\n",
    "We can use the parameter `level` to pick the \"level\" of detail in data we want. The “level” of the data is about how much on each station you get. `level=\"response\"` is the  most complete \"level\" of data we can get from `get_stations` and includes the instrument response data.<br>\n",
    "`station=\"RFRT\"` say that the code get data for the station with the station code RBCT<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Print Raoul Island station metadata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Station RFRT (Raoul Island Fishing Rock)\n",
      "\tStation Code: RFRT\n",
      "\tChannel Count: 16/16 (Selected/Total)\n",
      "\t2009-05-18T00:00:00.000000Z - \n",
      "\tAccess: open \n",
      "\tLatitude: -29.25, Longitude: -177.90, Elevation: 2.0 m\n",
      "\tAvailable Channels:\n",
      "\t\tRFRT.40.BTZ, RFRT.40.BTH, RFRT.40.BTT, RFRT.40.LTZ, RFRT.40.LTH, \n",
      "\t\tRFRT.40.LTT, RFRT.40.VTH, RFRT.40.VTT, RFRT.41.BTZ, RFRT.41.BTH, \n",
      "\t\tRFRT.41.BTT, RFRT.41.LTZ, RFRT.41.LTH, RFRT.41.LTT, RFRT.41.VTH, \n",
      "\t\tRFRT.41.VTT\n"
     ]
    }
   ],
   "source": [
    "channels = Raoul_Island [0][0]\n",
    "print(channels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Remember the 3 letter channel code.\n",
    "All tide gauges are in the form (B/L)T(Z/H/T)\n",
    "<ul>\n",
    "<li>The first letter is the sample rate. B = 10 Hz and L = 1 Hz, <br>\n",
    "<li>The second letter say that is a water level sensor<br> \n",
    "<li>The third letter say what kind of data it is, Z = raw, H = corrected to height and T = de-tided data <br>\n",
    "</ul>\n",
    "Note that `Raoul_Island [0][0]` says get the first network's first station."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Print Raoul Island Channel metadata including instrument response data for channels at 10Hz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Channel 'BTZ', Location '40' \n",
      "\tTime range: 2011-11-09T00:00:00.000000Z - --\n",
      "\tLatitude: -29.25, Longitude: -177.90, Elevation: 2.0 m, Local Depth: 0.0 m\n",
      "\tAzimuth: 0.00 degrees from north, clockwise\n",
      "\tDip: 0.00 degrees down from horizontal\n",
      "\tChannel types: CONTINUOUS, GEOPHYSICAL\n",
      "\tSampling Rate: 10.00 Hz\n",
      "\tSensor (Description): Pressure Sensor (Druck PTX)\n",
      "\tResponse information available\n",
      "Channel Response\n",
      "\tFrom m () to count ()\n",
      "\tOverall Sensitivity: 86402.7 defined at 1.000 Hz\n",
      "\t5 stages:\n",
      "\t\tStage 1: PolynomialResponseStage from m to A, gain: 0.0008\n",
      "\t\tStage 2: PolynomialResponseStage from A to V, gain: 250\n",
      "\t\tStage 3: PolynomialResponseStage from V to V, gain: 1.03\n",
      "\t\tStage 4: CoefficientsTypeResponseStage from V to count, gain: 419430\n",
      "\t\tStage 5: FIRResponseStage from count to count, gain: 1\n",
      "\n",
      "Channel 'BTT', Location '40' \n",
      "\tTime range: 2011-11-09T00:00:00.000000Z - --\n",
      "\tLatitude: -29.25, Longitude: -177.90, Elevation: 2.0 m, Local Depth: 0.0 m\n",
      "\tAzimuth: 0.00 degrees from north, clockwise\n",
      "\tDip: 0.00 degrees down from horizontal\n",
      "\tChannel types: CONTINUOUS, GEOPHYSICAL\n",
      "\tSampling Rate: 10.00 Hz\n",
      "\tSensor (Description): Pressure Sensor (Druck PTX)\n",
      "\tResponse information available\n",
      "Channel Response\n",
      "\tFrom m () to count ()\n",
      "\tOverall Sensitivity: 10000 defined at 1.000 Hz\n",
      "\t1 stages:\n",
      "\t\tStage 1: CoefficientsTypeResponseStage from m to count, gain: 10000\n",
      "\n",
      "Channel 'BTH', Location '40' \n",
      "\tTime range: 2011-11-09T00:00:00.000000Z - --\n",
      "\tLatitude: -29.25, Longitude: -177.90, Elevation: 2.0 m, Local Depth: 0.0 m\n",
      "\tAzimuth: 0.00 degrees from north, clockwise\n",
      "\tDip: 0.00 degrees down from horizontal\n",
      "\tChannel types: CONTINUOUS, GEOPHYSICAL\n",
      "\tSampling Rate: 10.00 Hz\n",
      "\tSensor (Description): Pressure Sensor (Druck PTX)\n",
      "\tResponse information available\n",
      "Channel Response\n",
      "\tFrom m () to count ()\n",
      "\tOverall Sensitivity: 10000 defined at 1.000 Hz\n",
      "\t1 stages:\n",
      "\t\tStage 1: CoefficientsTypeResponseStage from m to count, gain: 10000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for i in range (len(channels)): # make a lop over the channels data\n",
    "        if i==1 or i==4 or i==7 : # only print the 3 channel we want to look more into\n",
    "            print(channels[i])\n",
    "            print(channels[i].response)\n",
    "            print(\"\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that the 2nd, 5th and 8th channels are printed as the have the channel code in the form BT(Z/H/T)<br> \n",
    "In other words we know these 3 channels are tide gauges that have a sample rate of 10Hz.<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Get Waveform Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Get Waveform Data from 10Hz Channels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "RFRT= client.get_waveforms('NZ',\"RFRT\", \"40\", \"BT?\", starttime,endtime)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The parameters are in the order network, station, location, channel, starttime, and endtime.\n",
    "The ? is a wildcard character for only a single character, so we will get data with channel codes BTH, BTT and BTZ"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Check basic metadata from miniSEED header"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "               network: NZ\n",
      "               station: RFRT\n",
      "              location: 40\n",
      "               channel: BTH\n",
      "             starttime: 2011-03-11T16:59:54.269536Z\n",
      "               endtime: 2011-03-12T07:00:16.369536Z\n",
      "         sampling_rate: 10.0\n",
      "                 delta: 0.1\n",
      "                  npts: 504222\n",
      "                 calib: 1.0\n",
      "_fdsnws_dataselect_url: http://service.geonet.org.nz/fdsnws/dataselect/1/query\n",
      "               _format: MSEED\n",
      "                 mseed: AttribDict({'dataquality': 'D', 'number_of_records': 2331, 'encoding': 'STEIM2', 'byteorder': '>', 'record_length': 512, 'filesize': 3580416})\n"
     ]
    }
   ],
   "source": [
    "print(RFRT[0].stats)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Plot this data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x750 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "RFRT.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note the differences in the 3 plots and remember the 3rd letter say what kind of data it is, Z = raw, H = corrected to height and T = de-tided data.<br>\n",
    "This can be seen clearly when plotted"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Print what channels we have saved under RFRT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3 Trace(s) in Stream:\n",
      "NZ.RFRT.40.BTH | 2011-03-11T16:59:54.269536Z - 2011-03-12T07:00:16.369536Z | 10.0 Hz, 504222 samples\n",
      "NZ.RFRT.40.BTT | 2011-03-11T16:59:54.269536Z - 2011-03-12T07:00:16.369536Z | 10.0 Hz, 504222 samples\n",
      "NZ.RFRT.40.BTZ | 2011-03-11T16:59:54.269536Z - 2011-03-12T07:00:16.369536Z | 10.0 Hz, 504222 samples\n"
     ]
    }
   ],
   "source": [
    "print(RFRT)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data is in order BTH,BTT,BTZ so is in order data corrected to height, de-tided, then raw data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Split the RFRT data up into its channels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "corrected = RFRT[0]\n",
    "detided = RFRT[1]\n",
    "raw = RFRT[2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. 1 Getting Near real-time waveform data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Set new start and end time of the data you want to look at"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "t1 = UTCDateTime.now()-777600 #9 days ago\n",
    "t2 = UTCDateTime.now()-518400 #6 days ago"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that time must be in UTC (Universal Time Coordinated) which is 13 or 12 hours behind nzt (New Zealand Time) (depending on daylight savings time).\n",
    "There are online converters if needed.\n",
    "`UTCDateTime.now()-777600` say take the time the computer says it is now, convert it to UTC and then takes away 777,600 seconds from it, which is $60$(minutes)$*60$(seconds)$*24$(hours)$*9$(days). "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Try both near real-time and  archive service to get waveform data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "try:\n",
    "    stnrt = clinet_nrt.get_waveforms('NZ',\"RFRT\", \"40\", \"BTH\", t1 , t2)#try near real-time\n",
    "except:\n",
    "    print('nrt client not successful')\n",
    "    \n",
    "try:\n",
    "    starc = client.get_waveforms('NZ',\"RFRT\", \"40\", \"BTH\", t1 , t2)#try achive\n",
    "except:\n",
    "    print('arc client not successful')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Remember that GeoNet has two FDSN services <br>\n",
    "An archive service holds verified data starting 7 days after collection<br>\n",
    "A near real-time service holds data that could be incomplete from the last 8 days<br>\n",
    "As The time between 9 and 6 days overlaps this time the code tries to get the data from both services<br>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Merge this data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1 Trace(s) in Stream:\n",
       "NZ.RFRT.40.BTH | 2019-01-08T23:23:41.069500Z - 2019-01-11T23:23:54.869500Z | 10.0 Hz, 2592139 samples"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "st = stnrt #rename near real-time data \n",
    "st += starc #add archive data to this \n",
    "st.merge(fill_value = 'interpolate') #merge this data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If data is not merged then it is harder to plot and manipulate. \n",
    "`st += starc` is the same as `st = st + starc`. \n",
    "`fill_value = 'interpolate'` says that if there is a missing value then it will be filled with a \"best guess\" data point by looking at the two data points next to it. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Plot this data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x250 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "st.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Trim this data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1 Trace(s) in Stream:\n",
       "NZ.RFRT.40.BTH | 2019-01-10T23:23:50.669500Z - 2019-01-11T23:23:50.669500Z | 10.0 Hz, 864001 samples"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t = UTCDateTime.now()-604800 #7 days ago\n",
    "st.trim(t,t+86400)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`st.trim` is used to \"zoom in\" on part of the data that you already have. <br>\n",
    "In this example the code is looking at data from 7 days after now till 8 days after now ($60$(minutes)$*60$(seconds)$*24$(hours)$*7$(days)$= 604800$ and $60$(minutes)$*60$(seconds)$*24$(hours)$= 86400$ or 1 day)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Plot the trimmed data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x250 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "st.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6. Save Waveform Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Save this data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "corrected.write('corrected.csv', format='TSPAIR')\n",
    "detided.write('detided.csv', format='TSPAIR')\n",
    "raw.write('raw.csv', format='TSPAIR')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Files are save into the samed folder that this notebook is in. <br>\n",
    "Files are saved in .csv format to be able to opened by most spreadsheet soft wear.<br>\n",
    "`format = 'TSPAIR'` makes the data into a two column list of time and the sample values <br>\n",
    "For more information on this you can go to http://ds.iris.edu/ds/nodes/dmc/data/formats/simple-ascii/"
   ]
  }
 ],
 "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": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}