PythonNotebooks/PlatformPlots/Read_TimeSeries_2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Reading a remote file using OPeNDAP"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we will read a remote file using the [OPeNDAP protocol](http://www.opendap.org/). The advantage is that the file has not to be downloaded on your computer, while you can access the variables you want using it as it were on your computer. Furthermore, if the file is automatically updated on the remote server, you can run the same script without having to bother downloading it everytime it is updaded.\n",
    "\n",
    "We first import the modules needed to load and plot the data. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import netCDF4\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will use data from a mooring located in the Ibiza Channel.<br/>\n",
    "To get the file name, browse the directories in the thredds daa server until you find the file you want (http://thredds.socib.es/thredds/dodsC/mooring/conductivity_and_temperature_recorder/buoy_canaldeibiza-scb_sbe37006/L1/dep0003_buoy-canaldeibiza_scb-sbe37006_L1_latest.nc.html). Then copy the field called **Data URL**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dataurl = \"http://thredds.socib.es/thredds/dodsC/mooring/conductivity_and_temperature_recorder/buoy_canaldeibiza-scb_sbe37006/L1/dep0003_buoy-canaldeibiza_scb-sbe37006_L1_latest.nc\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Read the file"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The method is exactly the same as the previous example, except the file has not been downloaded on your machine. In the present case, the file is updated several times a day, meaning that running the same code at different time of the day could produce different results, as more data will be included.\n",
    "\n",
    "To be sure the file is closed, we will use the [context manager](https://en.wikibooks.org/wiki/Python_Programming/Context_Managers) *with ... as ...*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "with netCDF4.Dataset(dataurl) as ds:\n",
    "    temperature_values = ds.variables['WTR_TEM_SBE37'][:]\n",
    "    time_values = ds.variables['time'][:]\n",
    "    temperature_units = ds.variables['WTR_TEM_SBE37'].units\n",
    "    time_units = ds.variables['time'].units"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The variable storing the temperature is not called *temp* or *TEMP*, but *WTR_TEM_SBE37*. The variable names can be obtained by checking the [link on the thredds server](http://thredds.socib.es/thredds/dodsC/mooring/conductivity_and_temperature_recorder/buoy_canaldeibiza-scb_sbe37006/L1/dep0003_buoy-canaldeibiza_scb-sbe37006_L1_latest.nc.html) or by running:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[u'station_name', u'time', u'LAT', u'LON', u'DEPTH', u'WTR_TEM_SBE37', u'QC_WTR_TEM_SBE37', u'COND_SBE37', u'QC_COND_SBE37', u'SALT_SBE37', u'QC_SALT_SBE37']\n"
     ]
    }
   ],
   "source": [
    "with netCDF4.Dataset(dataurl) as ds:\n",
    "    print ds.variables.keys()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In another example we will see how to do it in a more clever way."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# A simple plot "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x3d33690>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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TJ8jIyKB06dLevjdv3syUKVO8msju3buDahL33nsvEBxIZs6cyZw5c0LeN7Ea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\nJMYYY2JigcQYY0xMLJAYY4yJyf8DQ9gsHms3Zi8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x17d0810>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(time_values, temperature_values)\n",
    "plt.xlabel(time_units, fontsize=20)\n",
    "plt.ylabel(temperature_units, fontsize=20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With the time units in seconds since 1st January 1970, it is not easy to see which period the curve represents.<br/> \n",
    "The solution is to change the time format using the *units* attribute of the variable and the *num2date* function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[datetime.datetime(2015, 10, 1, 0, 0) datetime.datetime(2015, 10, 1, 1, 0)\n",
      " datetime.datetime(2015, 10, 1, 2, 0) datetime.datetime(2015, 10, 1, 3, 0)\n",
      " datetime.datetime(2015, 10, 1, 4, 0)]\n"
     ]
    }
   ],
   "source": [
    "time2 = netCDF4.num2date(time_values, time_units)\n",
    "print time2[0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ZLdk5t2bNGiIjIwkJCSnyUyMXddrTplQWNW/enLNnz7J7926effbZDLft378/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\nKaVUrmkyUUoplWuaTJRSSuXa/w885JLozaNANAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x3e5bc90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(time2, temperature_values)\n",
    "plt.ylabel(temperature_units, fontsize=20)\n",
    "plt.show()"
   ]
  },
  {
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    "Now that's clearer. In another chapter we will see how to further improve the figure."
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