{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Lectura de datos via ftp, manipulación NetCDF y Graficos\n",
    "\n",
    "## Integrantes: \n",
    "\n",
    "* Ines Leyba\n",
    "* Milagros Alvarez Imaz\n",
    "* Leandro Díaz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Abrimos el txt con la informacion de los usuarios de las cosas\n",
    "usuario = open('usuario', 'r')\n",
    "#El ftp de donde quiero descargar\n",
    "FTPusuario='AVISO'\n",
    "\n",
    "while True:\n",
    "    line = usuario.readline()\n",
    "    if line == '':\n",
    "        break\n",
    "    \n",
    "    if FTPusuario in line:\n",
    "        split = line.split(';') #Separamos los elementos por los ;\n",
    "\n",
    "usuario.close() #Cerramos el archivo de usuarios\n",
    "     \n",
    "#Tomamos de la segunda columna que tiene el nombre del usuario\n",
    "user=str(split[1])\n",
    "#Tomamos de la tercera columna que tiene la contraseña\n",
    "pw=str(split[2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'230 User cima_leyba logged in'"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from ftplib import FTP\n",
    "\n",
    "ftp = FTP('ftp.aviso.altimetry.fr') #ftp de donde quiero los datos\n",
    "ftp.login(user, pw)  # 'usuario', 'contraseña'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Año entre 1993 a 2015 = 2014\n"
     ]
    }
   ],
   "source": [
    "anio=input('Año entre 1993 a 2015 = ') #elegimos el año que queremos bajar "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'250 CWD command successful'"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ftp.cwd('/global/delayed-time/grids/msla/all-sat-merged/h/'+ anio) # Poner el directorio donde estan los datos\n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       " 'dt_global_allsat_msla_h_20141220_20150513.nc.gz',\n",
       " 'dt_global_allsat_msla_h_20140503_20141010.nc.gz',\n",
       " 'dt_global_allsat_msla_h_20140105_20140829.nc.gz',\n",
       " 'dt_global_allsat_msla_h_20140623_20150209.nc.gz',\n",
       " 'dt_global_allsat_msla_h_20140502_20141010.nc.gz',\n",
       " 'dt_global_allsat_msla_h_20140514_20141010.nc.gz',\n",
       " 'dt_global_allsat_msla_h_20140213_20140829.nc.gz',\n",
       " 'dt_global_allsat_msla_h_20140110_20140829.nc.gz',\n",
       " 'dt_global_allsat_msla_h_20140409_20140829.nc.gz',\n",
       " 'dt_global_allsat_msla_h_20141111_20150513.nc.gz',\n",
       " 'dt_global_allsat_msla_h_20140819_20150209.nc.gz',\n",
       " 'dt_global_allsat_msla_h_20141226_20150513.nc.gz',\n",
       " 'dt_global_allsat_msla_h_20140204_20140829.nc.gz',\n",
       " 'dt_global_allsat_msla_h_20140903_20150209.nc.gz',\n",
       " 'dt_global_allsat_msla_h_20140521_20141010.nc.gz',\n",
       " 'dt_global_allsat_msla_h_20140513_20141010.nc.gz',\n",
       " 'dt_global_allsat_msla_h_20140713_20150209.nc.gz',\n",
       " 'dt_global_allsat_msla_h_20140508_20141010.nc.gz',\n",
       " 'dt_global_allsat_msla_h_20140913_20150209.nc.gz',\n",
       " 'dt_global_allsat_msla_h_20141009_20150209.nc.gz',\n",
       " 'dt_global_allsat_msla_h_20140106_20140829.nc.gz',\n",
       " 'dt_global_allsat_msla_h_20140318_20140829.nc.gz',\n",
       " 'dt_global_allsat_msla_h_20140805_20150209.nc.gz',\n",
       " 'dt_global_allsat_msla_h_20140528_20141010.nc.gz',\n",
       " 'dt_global_allsat_msla_h_20140316_20140829.nc.gz',\n",
       " 'dt_global_allsat_msla_h_20140728_20150209.nc.gz',\n",
       " 'dt_global_allsat_msla_h_20140308_20140829.nc.gz',\n",
       " 'dt_global_allsat_msla_h_20141216_20150513.nc.gz',\n",
       " 'dt_global_allsat_msla_h_20140923_20150209.nc.gz',\n",
       " 'dt_global_allsat_msla_h_20140403_20140829.nc.gz',\n",
       " 'dt_global_allsat_msla_h_20140225_20140829.nc.gz',\n",
       " 'dt_global_allsat_msla_h_20140813_20150209.nc.gz',\n",
       " 'dt_global_allsat_msla_h_20141130_20150513.nc.gz',\n",
       " 'dt_global_allsat_msla_h_20140517_20141010.nc.gz',\n",
       " 'dt_global_allsat_msla_h_20140916_20150209.nc.gz',\n",
       " 'dt_global_allsat_msla_h_20141010_20150209.nc.gz',\n",
       " 'dt_global_allsat_msla_h_20140817_20150209.nc.gz',\n",
       " 'dt_global_allsat_msla_h_20140124_20140829.nc.gz',\n",
       " 'dt_global_allsat_msla_h_20140617_20150209.nc.gz',\n",
       " 'dt_global_allsat_msla_h_20140420_20140829.nc.gz',\n",
       " 'dt_global_allsat_msla_h_20140518_20141010.nc.gz',\n",
       " 'dt_global_allsat_msla_h_20140428_20140829.nc.gz',\n",
       " 'dt_global_allsat_msla_h_20140203_20140829.nc.gz',\n",
       " 'dt_global_allsat_msla_h_20140410_20140829.nc.gz',\n",
       " 'dt_global_allsat_msla_h_20141120_20150513.nc.gz',\n",
       " 'dt_global_allsat_msla_h_20141003_20150209.nc.gz',\n",
       " 'dt_global_allsat_msla_h_20140524_20141010.nc.gz',\n",
       " 'dt_global_allsat_msla_h_20140516_20141010.nc.gz',\n",
       " 'dt_global_allsat_msla_h_20141102_20150513.nc.gz',\n",
       " 'dt_global_allsat_msla_h_20140108_20140829.nc.gz',\n",
       " 'dt_global_allsat_msla_h_20140810_20150209.nc.gz',\n",
       " 'dt_global_allsat_msla_h_20140621_20150209.nc.gz',\n",
       " 'dt_global_allsat_msla_h_20140319_20140829.nc.gz',\n",
       " 'dt_global_allsat_msla_h_20141026_20150513.nc.gz',\n",
       " 'dt_global_allsat_msla_h_20140211_20140829.nc.gz',\n",
       " 'dt_global_allsat_msla_h_20140925_20150209.nc.gz',\n",
       " 'dt_global_allsat_msla_h_20141019_20150209.nc.gz',\n",
       " 'dt_global_allsat_msla_h_20140628_20150209.nc.gz',\n",
       " 'dt_global_allsat_msla_h_20140710_20150209.nc.gz',\n",
       " 'dt_global_allsat_msla_h_20140828_20150209.nc.gz',\n",
       " 'dt_global_allsat_msla_h_20140322_20140829.nc.gz',\n",
       " 'dt_global_allsat_msla_h_20141207_20150513.nc.gz',\n",
       " 'dt_global_allsat_msla_h_20140415_20140829.nc.gz',\n",
       " 'dt_global_allsat_msla_h_20141115_20150513.nc.gz',\n",
       " 'dt_global_allsat_msla_h_20140520_20141010.nc.gz',\n",
       " 'dt_global_allsat_msla_h_20140722_20150209.nc.gz',\n",
       " 'dt_global_allsat_msla_h_20141228_20150914.nc.gz',\n",
       " 'dt_global_allsat_msla_h_20140111_20140829.nc.gz',\n",
       " 'dt_global_allsat_msla_h_20140104_20140829.nc.gz',\n",
       " 'dt_global_allsat_msla_h_20141206_20150513.nc.gz',\n",
       " 'dt_global_allsat_msla_h_20140507_20141010.nc.gz',\n",
       " 'dt_global_allsat_msla_h_20140523_20141010.nc.gz',\n",
       " 'dt_global_allsat_msla_h_20140525_20141010.nc.gz',\n",
       " 'dt_global_allsat_msla_h_20141011_20150209.nc.gz',\n",
       " 'dt_global_allsat_msla_h_20140324_20140829.nc.gz',\n",
       " 'dt_global_allsat_msla_h_20140716_20150209.nc.gz',\n",
       " 'dt_global_allsat_msla_h_20140827_20150209.nc.gz',\n",
       " 'dt_global_allsat_msla_h_20140714_20150209.nc.gz',\n",
       " 'dt_global_allsat_msla_h_20141017_20150209.nc.gz',\n",
       " 'dt_global_allsat_msla_h_20141204_20150513.nc.gz',\n",
       " 'dt_global_allsat_msla_h_20140317_20140829.nc.gz',\n",
       " 'dt_global_allsat_msla_h_20140820_20150209.nc.gz',\n",
       " 'dt_global_allsat_msla_h_20140208_20140829.nc.gz',\n",
       " 'dt_global_allsat_msla_h_20140826_20150209.nc.gz',\n",
       " 'dt_global_allsat_msla_h_20140624_20150209.nc.gz',\n",
       " 'dt_global_allsat_msla_h_20140310_20140829.nc.gz',\n",
       " 'dt_global_allsat_msla_h_20140424_20140829.nc.gz',\n",
       " 'dt_global_allsat_msla_h_20140604_20141124.nc.gz',\n",
       " 'dt_global_allsat_msla_h_20140816_20150209.nc.gz',\n",
       " 'dt_global_allsat_msla_h_20141221_20150513.nc.gz',\n",
       " 'dt_global_allsat_msla_h_20140223_20140829.nc.gz',\n",
       " 'dt_global_allsat_msla_h_20140101_20140829.nc.gz',\n",
       " 'dt_global_allsat_msla_h_20140102_20140829.nc.gz',\n",
       " 'dt_global_allsat_msla_h_20140407_20140829.nc.gz',\n",
       " 'dt_global_allsat_msla_h_20140304_20140829.nc.gz',\n",
       " 'dt_global_allsat_msla_h_20140515_20141010.nc.gz',\n",
       " 'dt_global_allsat_msla_h_20140122_20140829.nc.gz',\n",
       " 'dt_global_allsat_msla_h_20140329_20140829.nc.gz',\n",
       " 'dt_global_allsat_msla_h_20140619_20150209.nc.gz',\n",
       " 'dt_global_allsat_msla_h_20141031_20150513.nc.gz',\n",
       " 'dt_global_allsat_msla_h_20140529_20141010.nc.gz',\n",
       " 'dt_global_allsat_msla_h_20140704_20150209.nc.gz',\n",
       " 'dt_global_allsat_msla_h_20140201_20140829.nc.gz',\n",
       " 'dt_global_allsat_msla_h_20140402_20140829.nc.gz',\n",
       " 'dt_global_allsat_msla_h_20141125_20150513.nc.gz',\n",
       " 'dt_global_allsat_msla_h_20140210_20140829.nc.gz',\n",
       " 'dt_global_allsat_msla_h_20140611_20141124.nc.gz',\n",
       " 'dt_global_allsat_msla_h_20140811_20150209.nc.gz',\n",
       " 'dt_global_allsat_msla_h_20141219_20150513.nc.gz',\n",
       " 'dt_global_allsat_msla_h_20140501_20140829.nc.gz',\n",
       " 'dt_global_allsat_msla_h_20140809_20150209.nc.gz',\n",
       " 'dt_global_allsat_msla_h_20141222_20150513.nc.gz',\n",
       " 'dt_global_allsat_msla_h_20140430_20140829.nc.gz',\n",
       " 'dt_global_allsat_msla_h_20141005_20150209.nc.gz',\n",
       " 'dt_global_allsat_msla_h_20140118_20140829.nc.gz',\n",
       " 'dt_global_allsat_msla_h_20140217_20140829.nc.gz',\n",
       " 'dt_global_allsat_msla_h_20141208_20150513.nc.gz',\n",
       " 'dt_global_allsat_msla_h_20140803_20150209.nc.gz',\n",
       " 'dt_global_allsat_msla_h_20140723_20150209.nc.gz',\n",
       " 'dt_global_allsat_msla_h_20140116_20140829.nc.gz',\n",
       " 'dt_global_allsat_msla_h_20140626_20150209.nc.gz',\n",
       " 'dt_global_allsat_msla_h_20140727_20150209.nc.gz',\n",
       " 'dt_global_allsat_msla_h_20140227_20140829.nc.gz',\n",
       " 'dt_global_allsat_msla_h_20140920_20150209.nc.gz',\n",
       " 'dt_global_allsat_msla_h_20141103_20150513.nc.gz',\n",
       " 'dt_global_allsat_msla_h_20140130_20140829.nc.gz',\n",
       " 'dt_global_allsat_msla_h_20141105_20150513.nc.gz',\n",
       " 'dt_global_allsat_msla_h_20140909_20150209.nc.gz',\n",
       " 'dt_global_allsat_msla_h_20140612_20150209.nc.gz',\n",
       " 'dt_global_allsat_msla_h_20141223_20150513.nc.gz',\n",
       " 'dt_global_allsat_msla_h_20140708_20150209.nc.gz',\n",
       " 'dt_global_allsat_msla_h_20140222_20140829.nc.gz',\n",
       " 'dt_global_allsat_msla_h_20140929_20150209.nc.gz',\n",
       " 'dt_global_allsat_msla_h_20140908_20150209.nc.gz',\n",
       " 'dt_global_allsat_msla_h_20141024_20150209.nc.gz',\n",
       " 'dt_global_allsat_msla_h_20140720_20150209.nc.gz',\n",
       " 'dt_global_allsat_msla_h_20140205_20140829.nc.gz',\n",
       " 'dt_global_allsat_msla_h_20140107_20140829.nc.gz',\n",
       " 'dt_global_allsat_msla_h_20140421_20140829.nc.gz',\n",
       " 'dt_global_allsat_msla_h_20140302_20140829.nc.gz']"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Generamos una lista con todos los archivos que estan en el directorio.\n",
    "ListaDatos=[]\n",
    "ListaDatos = ftp.nlst()  \n",
    "ListaDatos "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['dt_global_allsat_msla_h_20140901_20150209.nc.gz', 'dt_global_allsat_msla_h_20141001_20150209.nc.gz', 'dt_global_allsat_msla_h_20140801_20150209.nc.gz', 'dt_global_allsat_msla_h_20140701_20150209.nc.gz']\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'221 Goodbye.'"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Bajamos una lista de datos especificos\n",
    "\n",
    "ListaDatosSelec=[]\n",
    "\n",
    "for i in ListaDatos:\n",
    "    if i.endswith('01_20150209.nc.gz'): # Aca decimos que datos queremos seleccionar\n",
    "        \n",
    "        localfile = open(i, 'wb') #Localfile es el nombre del archivo en la maquina local, puede ser el mismo nombre que filename o no.\n",
    "        ftp.retrbinary('RETR ' + i, localfile.write)    # descarga el filename, lo descarga en localfile, velocidad de descarga           \n",
    "        \n",
    "        ListaDatosSelec.append(i)\n",
    "        localfile.close() # Importante cerrar archivos, comentar problema de no cerrarlo\n",
    "        \n",
    "print(ListaDatosSelec)\n",
    "\n",
    "ftp.quit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dt_global_allsat_msla_h_20140901_20150209.nc.gz\n",
      "dt_global_allsat_msla_h_20140901_20150209.nc\n",
      "descomprimí archivo\n",
      "dt_global_allsat_msla_h_20141001_20150209.nc.gz\n",
      "dt_global_allsat_msla_h_20141001_20150209.nc\n",
      "descomprimí archivo\n",
      "dt_global_allsat_msla_h_20140801_20150209.nc.gz\n",
      "dt_global_allsat_msla_h_20140801_20150209.nc\n",
      "descomprimí archivo\n",
      "dt_global_allsat_msla_h_20140701_20150209.nc.gz\n",
      "dt_global_allsat_msla_h_20140701_20150209.nc\n",
      "descomprimí archivo\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "\n",
    "ListaUncompress_file = []\n",
    "\n",
    "for compressed_file in ListaDatosSelec:\n",
    "    print(compressed_file)\n",
    "    \n",
    "    uncompressed_file = os.path.splitext(compressed_file)[0]  # Te separa hasta el ultimo \".\"\n",
    "    print(uncompressed_file)\n",
    "   \n",
    "    ListaUncompress_file.append(uncompressed_file) #Generamos una lsita con todos los nombres sin \".gz\"\n",
    "        \n",
    "    inF = gzip.open(compressed_file, 'rb') # abre el archivo de entrada en modo lectura binario\n",
    "\n",
    "    outF = open(uncompressed_file, 'wb')   # crea el archivo de salida en modo escritura binario\n",
    "    \n",
    "    # try --> Prueba las funciones, si llega a haber un error, continua. \n",
    "    try:\n",
    "        outF.write( inF.read() ) #  Escribe el archivo de salida\n",
    "    # except --> Uno puede printiear el error que uno quiere o por ejemplo generar una lista con todos los archivos que presentaron un problema    \n",
    "    except:\n",
    "        print('error --> no se puede descomprimir este archivo')\n",
    "\n",
    "    inF.close()\n",
    "    outF.close()\n",
    "    \n",
    "    print('descomprimí archivo')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NETCDF3_CLASSIC\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "odict_keys(['time', 'lat', 'lat_bnds', 'lon', 'lon_bnds', 'crs', 'nv', 'sla', 'err'])"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Abrir Netcdf \n",
    "\n",
    "from netCDF4 import Dataset \n",
    "import numpy as np \n",
    "\n",
    "#dataset = Dataset(out_nc, mode='r') #Ver tipo de netcdf \n",
    "dataset = Dataset('dt_global_allsat_msla_h_20140801_20150209.nc', mode='r') #Ver tipo de netcdf \n",
    "\n",
    "print(dataset.file_format) \n",
    "#Dimensiones \n",
    "dataset.dimensions.keys() \n",
    "#Variables del netcdf \n",
    "dataset.variables.keys() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<class 'netCDF4._netCDF4.Variable'>\n",
       "int32 sla(time, lat, lon)\n",
       "    _FillValue: -2147483647\n",
       "    coordinates: lon lat\n",
       "    grid_mapping: crs\n",
       "    long_name: Sea Level Anomalies\n",
       "    scale_factor: 0.0001\n",
       "    standard_name: sea_surface_height_above_sea_level\n",
       "    units: m\n",
       "unlimited dimensions: \n",
       "current shape = (1, 720, 1440)\n",
       "filling off"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Ver informacion de la variable\n",
    "dataset.variables['sla']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<class 'netCDF4._netCDF4.Variable'>\n",
       "float32 time(time)\n",
       "    axis: T\n",
       "    calendar: gregorian\n",
       "    long_name: Time\n",
       "    standard_name: time\n",
       "    units: days since 1950-01-01 00:00:00\n",
       "unlimited dimensions: \n",
       "current shape = (1,)\n",
       "filling off"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Ver informacion de la variable\n",
    "dataset.variables['time']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# EXtraemos las variables\n",
    "\n",
    "lons = dataset.variables['lon'][:]\n",
    "lats = dataset.variables['lat'][:]\n",
    "sla = dataset.variables['sla'][:]\n",
    "time = dataset.variables['time'][:]\n",
    "\n",
    "dataset.close() # Cierra netcdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "01-September-2014\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ines/anaconda3/lib/python3.5/site-packages/mpl_toolkits/basemap/__init__.py:3644: VisibleDeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n",
      "  xx = x[x.shape[0]/2,:]\n"
     ]
    },
    {
     "data": {
      "image/png": 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5ycmTJ79S+ZcvXy5//vlnGRcXJz/XIeDiIH+wQKpOICMOICf11a/bSRbZJzBj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qtbDXseI4pnjquLt0IIPEq5kZmSgRCCzz//nDFjxuDj4/zCn7+/P+Y/lrJy5UrMZjPVqlWzPgsM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\n3F1J5uZK3goJxF3lR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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8a53e0f668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Pasar a fecha calendario\n",
    "import datetime\n",
    "day = time[0]\n",
    "\n",
    "date = datetime.datetime(1950, 1, 1) + datetime.timedelta(float(day)) #This assumes that the year is 2007\n",
    "\n",
    "#Pasamos al formato que queremos (buscar documentación strftime)\n",
    "fecha=date.strftime(\"%d-%B-%Y\")\n",
    "print(fecha)\n",
    "\n",
    "from mpl_toolkits.basemap import Basemap\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "#Con esto muestra el gráfico dentro de esta pantalla y no aparte\n",
    "%matplotlib inline\n",
    "\n",
    "# create Basemap instance for Robinson projection.\n",
    "m = Basemap(projection='robin',lon_0=0.5*(lons[0]+lons[-1]))\n",
    "# compute map projection coordinates for lat/lon grid.\n",
    "x, y = m(*np.meshgrid(lons,lats))\n",
    "# make filled contour plot.\n",
    "#clevs = [-0.5,-0.4,-0.3,-0.2,-0.1,0,0.1,0.2,0.3,0.4,0.5] # defino los intervalos en la barra de colores \n",
    "clevs = [-0.5,-0.3,-0.1,0,0.1,0.3,0.5] # defino los valores \n",
    "\n",
    "cs = m.contourf(x,y,sla[0,:,:],clevs,cmap=plt.cm.jet)\n",
    "#cs = m.contourf(x,y,sla[0,:,:],30,cmap=plt.cm.jet)\n",
    "m.drawcoastlines() # draw coastlines\n",
    "m.drawmapboundary() # draw a line around the map region\n",
    "m.drawparallels(np.arange(-90.,120.,30.),labels=[1,0,0,0]) # draw parallels\n",
    "m.drawmeridians(np.arange(0.,420.,60.),labels=[0,0,0,1]) # draw meridians\n",
    "plt.title('SLA ' + fecha) # add a title\n",
    "cbar = m.colorbar(cs, location='bottom', pad=\"10%\") #Agregamos la barra de colores\n",
    "cbar.set_label('m')\n",
    "\n",
    "\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "\n",
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       "}\n",
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       "#notebook li {\n",
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       "/* draw border around running cells */\n",
       "div.cell.border-box-sizing.code_cell.running {\n",
       "    border: 1px solid #111;\n",
       "}\n",
       "\n",
       "/* Put a solid color box around each cell and its output, \n",
       "   visually linking them*/\n",
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       "    display: block;\n",
       "}\n",
       "\n",
       ".text_cell_render h3 {\n",
       "    font-family: 'Nixie One', serif;\n",
       "    margin-top:16px;\n",
       "    font-size: 22pt;\n",
       "    font-weight: 600;\n",
       "    margin-bottom: 3px;\n",
       "    font-style: regular;\n",
       "    color: rgb(102,102,0);\n",
       "}\n",
       "\n",
       ".text_cell_render h4 { /*Use this for captions*/\n",
       "    font-family: 'Nixie One', serif;\n",
       "    font-size: 14pt;\n",
       "    text-align: center;\n",
       "    margin-top: 0em;\n",
       "    margin-bottom: 2em;\n",
       "    font-style: regular;\n",
       "}\n",
       "\n",
       ".text_cell_render h5 { /*Use this for small titles*/\n",
       "    font-family: 'Nixie One', sans-serif;\n",
       "    font-weight: 400;\n",
       "    font-size: 16pt;\n",
       "    color: rgb(163,0,0);\n",
       "    font-style: italic;\n",
       "    margin-bottom: .1em;\n",
       "    margin-top: 0.8em;\n",
       "    display: block;\n",
       "}\n",
       "\n",
       ".text_cell_render h6 { /*use this for copyright note*/\n",
       "    font-family: 'PT Mono', sans-serif;\n",
       "    font-weight: 300;\n",
       "    font-size: 9pt;\n",
       "    line-height: 100%;\n",
       "    color: grey;\n",
       "    margin-bottom: 1px;\n",
       "    margin-top: 1px;\n",
       "}\n",
       "\n",
       ".CodeMirror{\n",
       "    font-family: \"PT Mono\";\n",
       "    font-size: 100%;\n",
       "}\n",
       "\n",
       "</style>\n",
       "\n",
       "<script type=\"text/x-mathjax-config\">\n",
       "MathJax.Hub.Config({\n",
       "\t\t      TeX: {\n",
       "\t\t      extensions: [\"AMSmath.js\"],\n",
       "\t\t      equationNumbers: { autoNumber: \"AMS\", useLabelIds: true}\n",
       "\t\t  },\n",
       "\t\t      tex2jax: {\n",
       "\t\t\t  inlineMath: [ ['$','$'], [\"\\\\(\",\"\\\\)\"] ],\n",
       "\t\t\t  displayMath: [ ['$$','$$'], [\"\\\\[\",\"\\\\]\"] ],\n",
       "\t\t\t  processEscapes: true,\n",
       "\t\t\t  processEnvironments: true\n",
       "\t\t      },\n",
       "\t\t      displayAlign: 'center',\n",
       "\t\t      \"HTML-CSS\": {\n",
       "\t\t\t  styles: {'.MathJax_Display': {\"margin\": 4}}\n",
       "\t\t      }\n",
       "\t\t  });\n",
       "</script>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Este css es trabajo de @LorenaABarba y su grupo\n",
    "from IPython.core.display import HTML\n",
    "css_file = '../../css/personal.css'\n",
    "HTML(open(css_file, \"r\").read())"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "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": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}