PythonNotebooks/PlatformPlots/Read_TimeSeries_3.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Reading a file using CF module"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The main difference with the previous example is the way we will read the data from the file.\n",
    "\n",
    "Instead of the netCDF4 module, we will use the [cf-python](http://cfpython.bitbucket.org/) package, which implements the [CF data model](http://cfconventions.org/) for the reading, writing and processing of data and metadata. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import cf\n",
    "import netCDF4\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The data file is the same."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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": [
    "We use the function *read*. Doing so, we easily obtain a nice summary of the file content."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "long_name:station name field summary\n",
      "------------------------------------\n",
      "Data           : long_name:station name(ncdim%name_strlen(33)) \n",
      "Axes           : ncdim%name_strlen(33)\n",
      "\n",
      "sea_water_electrical_conductivity field summary\n",
      "-----------------------------------------------\n",
      "Data           : sea_water_electrical_conductivity(time(1451)) S m-1\n",
      "Axes           : longitude(1) = [0.783667] degree_east\n",
      "               : depth(1) = [1.0] m\n",
      "               : latitude(1) = [38.82445] degree_north\n",
      "               : time(1451) = [2015-10-01 00:00:00, ..., 2015-11-30 10:00:00] gregorian\n",
      "Ancillary vars : <CF Field: sea_water_electrical_conductivity status_flag(time(1451)) >\n",
      "\n",
      "sea_water_salinity field summary\n",
      "--------------------------------\n",
      "Data           : sea_water_salinity(time(1451)) psu\n",
      "Axes           : longitude(1) = [0.783667] degree_east\n",
      "               : depth(1) = [1.0] m\n",
      "               : latitude(1) = [38.82445] degree_north\n",
      "               : time(1451) = [2015-10-01 00:00:00, ..., 2015-11-30 10:00:00] gregorian\n",
      "Ancillary vars : <CF Field: sea_water_salinity status_flag(time(1451)) >\n",
      "\n",
      "sea_water_temperature field summary\n",
      "-----------------------------------\n",
      "Data           : sea_water_temperature(time(1451)) C\n",
      "Axes           : longitude(1) = [0.783667] degree_east\n",
      "               : depth(1) = [1.0] m\n",
      "               : latitude(1) = [38.82445] degree_north\n",
      "               : time(1451) = [2015-10-01 00:00:00, ..., 2015-11-30 10:00:00] gregorian\n",
      "Ancillary vars : <CF Field: sea_water_temperature status_flag(time(1451)) >\n",
      "\n"
     ]
    }
   ],
   "source": [
    "f = cf.read(dataurl)\n",
    "print f"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "We see that the file contains 4 variables: \n",
    "1. temperature\n",
    "2. salinity\n",
    "3. conductivity.\n",
    "Each of them has 4 dimensions: longitude, latitude, time and depth."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Read variable, coordinates and units"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the previous commands we cannot know the name of the variables within the file. But that's not necessary. Temperature can be retrived using its *standard name*:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<CF Field: sea_water_temperature(time(1451)) C>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temperature = f.select('sea_water_temperature')\n",
    "temperature"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The number of variables which have a standard name corresponding to **sea_water_temperature** is:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n"
     ]
    }
   ],
   "source": [
    "print len(temperature)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "but in other cases (ex: different sensors measuring temperature with data in a common file), one can obtain more than one variable.\n",
    "\n",
    "To get the temperature values, we select the first element (index = 0 in python, not 1) and convert it into an array."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[23.67 23.67 23.65 23.6 23.56 23.53 23.52 23.54 23.56 23.58 23.63 23.72\n",
      " 23.92 24.13 24.12 24.01 23.83 23.74 23.66 23.65]\n",
      "Temperature units: C\n"
     ]
    }
   ],
   "source": [
    "temperature_values = temperature[0].array\n",
    "temperature_units = temperature[0].units\n",
    "print temperature_values[0:20]\n",
    "print 'Temperature units: ' + temperature_units"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We inspect the corresponding coordinates: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'dim0': <CF DimensionCoordinate: time(1451) gregorian>,\n",
       " 'dim1': <CF DimensionCoordinate: latitude(1) degree_north>,\n",
       " 'dim2': <CF DimensionCoordinate: longitude(1) degree_east>,\n",
       " 'dim3': <CF DimensionCoordinate: depth(1) m>}"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temperature[0].coords()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To extract the time variable:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<CF DimensionCoordinate: time(1451) gregorian>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "time = temperature[0].coord('time')\n",
    "time"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "and to get the values:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[  1.44365760e+09   1.44366120e+09   1.44366480e+09   1.44366840e+09\n",
      "   1.44367200e+09   1.44367560e+09   1.44367920e+09   1.44368280e+09\n",
      "   1.44368640e+09   1.44369000e+09   1.44369360e+09   1.44369720e+09\n",
      "   1.44370080e+09   1.44370440e+09   1.44370800e+09   1.44371160e+09\n",
      "   1.44371520e+09   1.44371880e+09   1.44372240e+09   1.44372600e+09]\n",
      " \n",
      "Time units: seconds since 1970-01-01 00:00:00\n"
     ]
    }
   ],
   "source": [
    "time_values = temperature[0].coord('time').array\n",
    "time_units = temperature[0].coord('time').units\n",
    "print time_values[0:20]\n",
    "print ' '\n",
    "print 'Time units: ' + time_units"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# A simple plot "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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GhQsX2uv33HOPx3Qt3pw1E4CtW7cCnjWT5ORkO4GcOHHCY/vhw4f7fUOlUkqBJpMirUmT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\nmCYTpZRSBabJRCmlVIFpMlFKKVVg/w/wJds/CboHlwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x412b550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "time2 = netCDF4.num2date(time_values, time_units)\n",
    "plt.plot(time2, temperature_values)\n",
    "plt.ylabel(temperature_units, fontsize=20)\n",
    "plt.show()"
   ]
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   "source": []
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