Curso WRF-python.ipynb

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    "# Curso WRF-python\n",
    "\n",
    "Gonzalo Zigarán\n",
    "\n",
    "_gjzigaran@gmail.com_\n",
    "\n",
    "#### Links:\n",
    "* https://github.com/NCAR/wrf-python/\n",
    "* https://github.com/NCAR/wrf_python_tutorial/"
   ]
  },
  {
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   "source": [
    "## Temas:\n",
    "1. Introducción a las librerías (python, jupyter notebooks, numpy, pandas, xarray)\n",
    "1. Cómo leer una salida de WRF \n",
    "1. wrf-python\n",
    "1. Librerías para graficar (matplotlib, basemap)\n",
    "1. Ejemplos"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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   "source": [
    "### 1. Introducción a las librerías\n",
    "\n",
    "#### Python\n",
    "\n",
    "- Lenguaje de programación relativamente nuevo y con gran crecimiento\n",
    "- Es interpretado \n",
    "- Licencia de código abierto\n",
    "- De alto nivel, lo que posibilita que su código sea muy legible.\n",
    "- Gran cantidad de documentación y comunidad de usuarios\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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   },
   "source": [
    "#### Jupyter Notebook\n",
    "\n",
    "Es una aplicación web de codigo abierto que permite crear documentos que combinan código, visualizaciones y texto, además de ejecutar el código. Originalmente estaba pensado para el intérprete IPython pero ahora también permite trabajar con otros lenguajes como Julia, R, Octave."
   ]
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   "cell_type": "markdown",
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   "source": [
    "#### Numpy\n",
    "\n",
    "NumPy es un paquete para computación científica con Python. Le agrega un mayor soporte para el trabajo con vectores y matrices eficientemente, integrado con C y Fortran.\n",
    "\n",
    "Los arreglos de numpy pueden ser creados con los tipos comunes de C:\n",
    "- int8, int16, int32, int64 \n",
    "- float16, float32, float64 [default]\n",
    "- bool\n",
    "- complex64, complex128\n",
    "y pueden ser ordenados como en C (más rápido de acceder a derecha, por defecto) o como en Fortran (más rápido de acceder a izquierda)\n",
    "\n",
    "##### Ejemplo de uso:"
   ]
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      "[[[ 0.  5.  0.]\n",
      "  [ 0.  0.  0.]\n",
      "  [ 0.  0.  0.]]\n",
      "\n",
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      "  [ 0.  0.  0.]\n",
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      "\n",
      " [[ 0.  0.  0.]\n",
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      "0.0\n",
      "[ 0.  5.  0.]\n"
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   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "# crea un un arreglo tri-dimensional de ceros\n",
    "my_array = np.zeros((3,3,3), \"float32\")\n",
    "\n",
    "# acceder a un elemento\n",
    "first_element = my_array[0,0,0]\n",
    "\n",
    "# editar un elemento\n",
    "my_array[0,0,1] = 5\n",
    "\n",
    "# acceder a una fila\n",
    "first_row = my_array[0,0,:]\n",
    "\n",
    "print my_array\n",
    "print first_element\n",
    "print first_row"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "#### Pandas\n",
    "\n",
    "Pandas es una librería que proporciona alto rendimiento para el análisis y estructura de datos. Es fácil e intuitivo de usar y conbina muchas funciones con NumPy. (Gran reemplazo del excel)\n",
    "\n",
    "##### Ejemplo de uso:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
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       "      <th>a</th>\n",
       "      <td>1.0</td>\n",
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       "      <th>b</th>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
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       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      "text/plain": [
       "   one  two\n",
       "a  1.0  1.0\n",
       "b  2.0  2.0\n",
       "c  3.0  3.0\n",
       "d  NaN  4.0"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "d = {'one' : pd.Series([1., 2., 3.], index=['a', 'b', 'c']),\n",
    "     'two' : pd.Series([1., 2., 3., 4.], index=['a', 'b', 'c', 'd'])} \n",
    "\n",
    "df = pd.DataFrame(d)\n",
    "\n",
    "\n",
    "df"
   ]
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   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
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       "      <th>d</th>\n",
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       "      <td>4</td>\n",
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      "text/plain": [
       "   one two\n",
       "a    1   1\n",
       "b    2   2\n",
       "c    3   3\n",
       "d  NaN   4"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = pd.DataFrame(columns=['one','two'])\n",
    "\n",
    "df2.loc['a'] = [1,1]\n",
    "df2.loc['b'] = [2,2]\n",
    "df2.loc['c'] = [3,3]\n",
    "df2.loc['d', 'two'] = 4\n",
    "\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    1\n",
       "b    2\n",
       "c    3\n",
       "d    4\n",
       "Name: two, dtype: object"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "df2['two']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "#### xarray\n",
    "\n",
    "Librería que buscar profundizar Pandas para poder trabajar con estructuras de datos más complejas. En particular trabaja con estructuras similares al formato NetCDF. Basicamente busca complejizar la estructura de arreglos de Numpy con metadatos.\n",
    "\n",
    "##### Ejemplo de uso:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<xarray.DataArray 'my_xarray' (bottom_top: 3, south_north: 3, west_east: 3)>\n",
      "array([[[ 0.,  0.,  0.],\n",
      "        [ 0.,  0.,  0.],\n",
      "        [ 0.,  0.,  0.]],\n",
      "\n",
      "       [[ 0.,  0.,  0.],\n",
      "        [ 0.,  0.,  0.],\n",
      "        [ 0.,  0.,  0.]],\n",
      "\n",
      "       [[ 0.,  0.,  0.],\n",
      "        [ 0.,  0.,  0.],\n",
      "        [ 0.,  0.,  0.]]], dtype=float32)\n",
      "Coordinates:\n",
      "  * south_north  (south_north) float64 40.0 50.0 60.0\n",
      "  * west_east    (west_east) float64 -120.0 -110.0 -100.0\n",
      "  * bottom_top   (bottom_top) float64 100.0 200.0 300.0\n",
      "Attributes:\n",
      "    info:     This is my xarray\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import xarray\n",
    "\n",
    "my_array = np.zeros((3,3,3), \"float32\")\n",
    "\n",
    "# Nobre de dimensiones y coordenadas\n",
    "\n",
    "my_name = \"my_xarray\"\n",
    "\n",
    "my_dims = [\"bottom_top\", \"south_north\", \"west_east\"]\n",
    "\n",
    "my_coords = {\"bottom_top\" : [100., 200., 300.],\n",
    "          \"south_north\": [40., 50., 60.],\n",
    "          \"west_east\" : [-120., -110., -100.]\n",
    "         }\n",
    "\n",
    "my_attrs = {\"info\" : \"This is my xarray\"}\n",
    "\n",
    "my_xarray = xarray.DataArray(my_array,\n",
    "                           name=my_name,\n",
    "                           dims=my_dims, \n",
    "                           coords=my_coords, \n",
    "                           attrs=my_attrs)\n",
    "\n",
    "print (my_xarray)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Cómo leer una salida de WRF \n",
    "\n",
    "El archivo que devuelve el WRF tiene el formato NetCDF y contiene toda la información relativa a la simulación. Basicamente se destacan 3 secciones a tener en cuenta: \n",
    "\n",
    "- La primera son datos globales propios del archivo, como puede ser la fecha de inicio, la versión utilizada de WRF, la resolución, la proyección usada, etc.\n",
    "\n",
    "- La segunda son las dimensiones, tanto de la grilla, de la resolución vertical, y del tiempo\n",
    "\n",
    "- Y la tercera son las variables.\n",
    "\n",
    "Hay varias alternativas de programas para analizar un NetCDF (como por ejemplo ncdump, ncl_filedump, netcdf4-python, PyNIO, xarray, etc), algunos en Python y otros directamente por terminal.\n",
    "\n",
    "\n",
    "\n",
    "#### netcdf4-python\n",
    "\n",
    "Vamos a utilizar **netcdf4-python**, para poder verlo directamente desde la consola de Python. \n",
    "\n",
    "\n",
    "##### Datos globales\n",
    "\n",
    "Una vez cargada la salida del WRF, se pueden listar todos los datos globales con * .\\__dict\\__ * , o también accediendo a cada variable particularmente con * .NOMBRE_VARIABLE *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "u'2017-10-26_00:00:00'"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from netCDF4 import Dataset\n",
    "\n",
    "nc_file = Dataset(\"wrfout_d01_2017-10-26_00:00:00\")\n",
    "\n",
    "nc_file.START_DATE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "OrderedDict([(u'TITLE', u' OUTPUT FROM WRF V3.8 MODEL'),\n",
       "             (u'START_DATE', u'2017-10-26_00:00:00'),\n",
       "             (u'SIMULATION_START_DATE', u'2017-10-26_00:00:00'),\n",
       "             (u'WEST-EAST_GRID_DIMENSION', 100),\n",
       "             (u'SOUTH-NORTH_GRID_DIMENSION', 170),\n",
       "             (u'BOTTOM-TOP_GRID_DIMENSION', 35),\n",
       "             (u'DX', 30000.0),\n",
       "             (u'DY', 30000.0),\n",
       "             (u'SKEBS_ON', 0),\n",
       "             (u'SPEC_BDY_FINAL_MU', 1),\n",
       "             (u'USE_Q_DIABATIC', 0),\n",
       "             (u'GRIDTYPE', u'C'),\n",
       "             (u'DIFF_OPT', 1),\n",
       "             (u'KM_OPT', 4),\n",
       "             (u'DAMP_OPT', 0),\n",
       "             (u'DAMPCOEF', 0.2),\n",
       "             (u'KHDIF', 0.0),\n",
       "             (u'KVDIF', 0.0),\n",
       "             (u'MP_PHYSICS', 4),\n",
       "             (u'RA_LW_PHYSICS', 1),\n",
       "             (u'RA_SW_PHYSICS', 1),\n",
       "             (u'SF_SFCLAY_PHYSICS', 1),\n",
       "             (u'SF_SURFACE_PHYSICS', 2),\n",
       "             (u'BL_PBL_PHYSICS', 1),\n",
       "             (u'CU_PHYSICS', 5),\n",
       "             (u'SF_LAKE_PHYSICS', 0),\n",
       "             (u'SURFACE_INPUT_SOURCE', 3),\n",
       "             (u'SST_UPDATE', 0),\n",
       "             (u'GRID_FDDA', 0),\n",
       "             (u'GFDDA_INTERVAL_M', 0),\n",
       "             (u'GFDDA_END_H', 0),\n",
       "             (u'GRID_SFDDA', 0),\n",
       "             (u'SGFDDA_INTERVAL_M', 0),\n",
       "             (u'SGFDDA_END_H', 0),\n",
       "             (u'HYPSOMETRIC_OPT', 2),\n",
       "             (u'USE_THETA_M', 0),\n",
       "             (u'SF_URBAN_PHYSICS', 0),\n",
       "             (u'SHCU_PHYSICS', 0),\n",
       "             (u'MFSHCONV', 0),\n",
       "             (u'FEEDBACK', 1),\n",
       "             (u'SMOOTH_OPTION', 0),\n",
       "             (u'SWRAD_SCAT', 1.0),\n",
       "             (u'W_DAMPING', 0),\n",
       "             (u'DT', 150.0),\n",
       "             (u'RADT', 30.0),\n",
       "             (u'BLDT', 0.0),\n",
       "             (u'CUDT', 5.0),\n",
       "             (u'AER_OPT', 0),\n",
       "             (u'SWINT_OPT', 0),\n",
       "             (u'AER_TYPE', 1),\n",
       "             (u'AER_AOD550_OPT', 1),\n",
       "             (u'AER_ANGEXP_OPT', 1),\n",
       "             (u'AER_SSA_OPT', 1),\n",
       "             (u'AER_ASY_OPT', 1),\n",
       "             (u'AER_AOD550_VAL', 0.12),\n",
       "             (u'AER_ANGEXP_VAL', 1.3),\n",
       "             (u'AER_SSA_VAL', 1.4012985e-45),\n",
       "             (u'AER_ASY_VAL', 1.4012985e-45),\n",
       "             (u'MOIST_ADV_OPT', 1),\n",
       "             (u'SCALAR_ADV_OPT', 1),\n",
       "             (u'TKE_ADV_OPT', 1),\n",
       "             (u'DIFF_6TH_OPT', 0),\n",
       "             (u'DIFF_6TH_FACTOR', 0.12),\n",
       "             (u'OBS_NUDGE_OPT', 0),\n",
       "             (u'BUCKET_MM', -1.0),\n",
       "             (u'BUCKET_J', -1.0),\n",
       "             (u'PREC_ACC_DT', 0.0),\n",
       "             (u'SF_OCEAN_PHYSICS', 0),\n",
       "             (u'ISFTCFLX', 0),\n",
       "             (u'ISHALLOW', 0),\n",
       "             (u'ISFFLX', 1),\n",
       "             (u'ICLOUD', 1),\n",
       "             (u'ICLOUD_CU', 0),\n",
       "             (u'TRACER_PBLMIX', 1),\n",
       "             (u'SCALAR_PBLMIX', 0),\n",
       "             (u'YSU_TOPDOWN_PBLMIX', 0),\n",
       "             (u'GRAV_SETTLING', 0),\n",
       "             (u'DFI_OPT', 0),\n",
       "             (u'SIMULATION_INITIALIZATION_TYPE', u'REAL-DATA CASE'),\n",
       "             (u'WEST-EAST_PATCH_START_UNSTAG', 1),\n",
       "             (u'WEST-EAST_PATCH_END_UNSTAG', 99),\n",
       "             (u'WEST-EAST_PATCH_START_STAG', 1),\n",
       "             (u'WEST-EAST_PATCH_END_STAG', 100),\n",
       "             (u'SOUTH-NORTH_PATCH_START_UNSTAG', 1),\n",
       "             (u'SOUTH-NORTH_PATCH_END_UNSTAG', 169),\n",
       "             (u'SOUTH-NORTH_PATCH_START_STAG', 1),\n",
       "             (u'SOUTH-NORTH_PATCH_END_STAG', 170),\n",
       "             (u'BOTTOM-TOP_PATCH_START_UNSTAG', 1),\n",
       "             (u'BOTTOM-TOP_PATCH_END_UNSTAG', 34),\n",
       "             (u'BOTTOM-TOP_PATCH_START_STAG', 1),\n",
       "             (u'BOTTOM-TOP_PATCH_END_STAG', 35),\n",
       "             (u'GRID_ID', 1),\n",
       "             (u'PARENT_ID', 1),\n",
       "             (u'I_PARENT_START', 1),\n",
       "             (u'J_PARENT_START', 1),\n",
       "             (u'PARENT_GRID_RATIO', 1),\n",
       "             (u'CEN_LAT', -38.500008),\n",
       "             (u'CEN_LON', -63.5),\n",
       "             (u'TRUELAT1', -60.0),\n",
       "             (u'TRUELAT2', -30.0),\n",
       "             (u'MOAD_CEN_LAT', -38.500008),\n",
       "             (u'STAND_LON', -63.5),\n",
       "             (u'POLE_LAT', 90.0),\n",
       "             (u'POLE_LON', 0.0),\n",
       "             (u'GMT', 0.0),\n",
       "             (u'JULYR', 2017),\n",
       "             (u'JULDAY', 299),\n",
       "             (u'MAP_PROJ', 1),\n",
       "             (u'MAP_PROJ_CHAR', u'Lambert Conformal'),\n",
       "             (u'MMINLU', u'MODIFIED_IGBP_MODIS_NOAH'),\n",
       "             (u'NUM_LAND_CAT', 21),\n",
       "             (u'ISWATER', 17),\n",
       "             (u'ISLAKE', 21),\n",
       "             (u'ISICE', 15),\n",
       "             (u'ISURBAN', 13),\n",
       "             (u'ISOILWATER', 14)])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nc_file.__dict__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Dimensiones\n",
    "\n",
    "La grilla que utiliza WRF se compone de la siguiente manera:\n",
    "\n",
    "![..](wrf_stagger.png)\n",
    "\n",
    "- Las variables se conputan en los * mass points * que se ubican en el centro de la celda\n",
    "- La componente u del viento se calcula en el limite derecho e izquierdo de la celda. En esta dirección se guarda un punto más.\n",
    "- La componente v del viento se calcula en el limite superior e inferior de la celda. En esta dirección se guarda un punto más.\n",
    "- Las esquinas de la celda son los * staggered points * , tiene un punto más en cada dirección. \n",
    "\n",
    "\n",
    "[link a wrf grid structure del sitio de mmm](http://www2.mmm.ucar.edu/rt/amps/information/configuration/wrf_grid_structure.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "OrderedDict([(u'Time',\n",
       "              <type 'netCDF4._netCDF4.Dimension'> (unlimited): name = 'Time', size = 25),\n",
       "             (u'DateStrLen',\n",
       "              <type 'netCDF4._netCDF4.Dimension'>: name = 'DateStrLen', size = 19),\n",
       "             (u'west_east',\n",
       "              <type 'netCDF4._netCDF4.Dimension'>: name = 'west_east', size = 99),\n",
       "             (u'south_north',\n",
       "              <type 'netCDF4._netCDF4.Dimension'>: name = 'south_north', size = 169),\n",
       "             (u'bottom_top',\n",
       "              <type 'netCDF4._netCDF4.Dimension'>: name = 'bottom_top', size = 34),\n",
       "             (u'bottom_top_stag',\n",
       "              <type 'netCDF4._netCDF4.Dimension'>: name = 'bottom_top_stag', size = 35),\n",
       "             (u'soil_layers_stag',\n",
       "              <type 'netCDF4._netCDF4.Dimension'>: name = 'soil_layers_stag', size = 4),\n",
       "             (u'west_east_stag',\n",
       "              <type 'netCDF4._netCDF4.Dimension'>: name = 'west_east_stag', size = 100),\n",
       "             (u'south_north_stag',\n",
       "              <type 'netCDF4._netCDF4.Dimension'>: name = 'south_north_stag', size = 170)])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nc_file.dimensions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Variables\n",
    "\n",
    "Esta estructura es la más compleja. Se pueden listar todas las variables y para cada una se almacena la siguiente estructura:\n",
    "\n",
    "- atributos (datos generales de la variable, como unidad, coordenadas, descripción, etc)\n",
    "- dimensiones (toma alguna de las dimensiones vistas antes)\n",
    "- valores (los valores que toma para cada dimension)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[u'Times',\n",
       " u'XLAT',\n",
       " u'XLONG',\n",
       " u'LU_INDEX',\n",
       " u'ZNU',\n",
       " u'ZNW',\n",
       " u'ZS',\n",
       " u'DZS',\n",
       " u'VAR_SSO',\n",
       " u'U',\n",
       " u'V',\n",
       " u'W',\n",
       " u'PH',\n",
       " u'PHB',\n",
       " u'T',\n",
       " u'HFX_FORCE',\n",
       " u'LH_FORCE',\n",
       " u'TSK_FORCE',\n",
       " u'HFX_FORCE_TEND',\n",
       " u'LH_FORCE_TEND',\n",
       " u'TSK_FORCE_TEND',\n",
       " u'MU',\n",
       " u'MUB',\n",
       " u'NEST_POS',\n",
       " u'P',\n",
       " u'PB',\n",
       " u'FNM',\n",
       " u'FNP',\n",
       " u'RDNW',\n",
       " u'RDN',\n",
       " u'DNW',\n",
       " u'DN',\n",
       " u'CFN',\n",
       " u'CFN1',\n",
       " u'THIS_IS_AN_IDEAL_RUN',\n",
       " u'P_HYD',\n",
       " u'Q2',\n",
       " u'T2',\n",
       " u'TH2',\n",
       " u'PSFC',\n",
       " u'U10',\n",
       " u'V10',\n",
       " u'RDX',\n",
       " u'RDY',\n",
       " u'RESM',\n",
       " u'ZETATOP',\n",
       " u'CF1',\n",
       " u'CF2',\n",
       " u'CF3',\n",
       " u'ITIMESTEP',\n",
       " u'XTIME',\n",
       " u'QVAPOR',\n",
       " u'QCLOUD',\n",
       " u'QRAIN',\n",
       " u'QICE',\n",
       " u'QSNOW',\n",
       " u'SHDMAX',\n",
       " u'SHDMIN',\n",
       " u'SNOALB',\n",
       " u'TSLB',\n",
       " u'SMOIS',\n",
       " u'SH2O',\n",
       " u'SMCREL',\n",
       " u'SEAICE',\n",
       " u'XICEM',\n",
       " u'SFROFF',\n",
       " u'UDROFF',\n",
       " u'IVGTYP',\n",
       " u'ISLTYP',\n",
       " u'VEGFRA',\n",
       " u'GRDFLX',\n",
       " u'ACGRDFLX',\n",
       " u'ACSNOM',\n",
       " u'SNOW',\n",
       " u'SNOWH',\n",
       " u'CANWAT',\n",
       " u'SSTSK',\n",
       " u'COSZEN',\n",
       " u'LAI',\n",
       " u'DTAUX3D',\n",
       " u'DTAUY3D',\n",
       " u'DUSFCG',\n",
       " u'DVSFCG',\n",
       " u'VAR',\n",
       " u'CON',\n",
       " u'OA1',\n",
       " u'OA2',\n",
       " u'OA3',\n",
       " u'OA4',\n",
       " u'OL1',\n",
       " u'OL2',\n",
       " u'OL3',\n",
       " u'OL4',\n",
       " u'MAPFAC_M',\n",
       " u'MAPFAC_U',\n",
       " u'MAPFAC_V',\n",
       " u'MAPFAC_MX',\n",
       " u'MAPFAC_MY',\n",
       " u'MAPFAC_UX',\n",
       " u'MAPFAC_UY',\n",
       " u'MAPFAC_VX',\n",
       " u'MF_VX_INV',\n",
       " u'MAPFAC_VY',\n",
       " u'F',\n",
       " u'E',\n",
       " u'SINALPHA',\n",
       " u'COSALPHA',\n",
       " u'HGT',\n",
       " u'TSK',\n",
       " u'P_TOP',\n",
       " u'T00',\n",
       " u'P00',\n",
       " u'TLP',\n",
       " u'TISO',\n",
       " u'TLP_STRAT',\n",
       " u'P_STRAT',\n",
       " u'MAX_MSTFX',\n",
       " u'MAX_MSTFY',\n",
       " u'RAINC',\n",
       " u'RAINSH',\n",
       " u'RAINNC',\n",
       " u'SNOWNC',\n",
       " u'GRAUPELNC',\n",
       " u'HAILNC',\n",
       " u'CLDFRA',\n",
       " u'SWDOWN',\n",
       " u'GLW',\n",
       " u'SWNORM',\n",
       " u'OLR',\n",
       " u'XLAT_U',\n",
       " u'XLONG_U',\n",
       " u'XLAT_V',\n",
       " u'XLONG_V',\n",
       " u'ALBEDO',\n",
       " u'CLAT',\n",
       " u'ALBBCK',\n",
       " u'EMISS',\n",
       " u'NOAHRES',\n",
       " u'TMN',\n",
       " u'XLAND',\n",
       " u'UST',\n",
       " u'PBLH',\n",
       " u'HFX',\n",
       " u'QFX',\n",
       " u'LH',\n",
       " u'ACHFX',\n",
       " u'ACLHF',\n",
       " u'SNOWC',\n",
       " u'SR',\n",
       " u'SAVE_TOPO_FROM_REAL',\n",
       " u'ISEEDARR_RAND_PERTURB',\n",
       " u'ISEEDARR_SPPT',\n",
       " u'ISEEDARR_SKEBS',\n",
       " u'LANDMASK',\n",
       " u'LAKEMASK',\n",
       " u'SST',\n",
       " u'SST_INPUT']"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Para listar todos los nombres de variables posibles\n",
    "nc_file.variables.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "OrderedDict([(u'FieldType', 104),\n",
       "             (u'MemoryOrder', u'XYZ'),\n",
       "             (u'description',\n",
       "              u'perturbation potential temperature (theta-t0)'),\n",
       "             (u'units', u'K'),\n",
       "             (u'stagger', u''),\n",
       "             (u'coordinates', u'XLONG XLAT XTIME')])"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Para listar los atributos de una variable\n",
    "nc_file.variables['T'].__dict__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(u'Time', u'bottom_top', u'south_north', u'west_east')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Para listar las dimensiones de una variable\n",
    "nc_file.variables['T'].dimensions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[ 277.00924683,  277.08822632,  277.03015137, ...,  272.36404419,\n",
       "          272.39993286,  272.48239136],\n",
       "        [ 277.13763428,  277.19869995,  277.15005493, ...,  272.50149536,\n",
       "          272.51889038,  272.57196045],\n",
       "        [ 277.28149414,  277.3343811 ,  277.32974243, ...,  272.5993042 ,\n",
       "          272.64575195,  272.74957275],\n",
       "        ..., \n",
       "        [ 289.23294067,  289.30737305,  289.34954834, ...,  302.21768188,\n",
       "          303.20321655,  303.7890625 ],\n",
       "        [ 289.36206055,  289.27319336,  288.97393799, ...,  302.25457764,\n",
       "          304.13796997,  305.05462646],\n",
       "        [ 289.2762146 ,  288.85748291,  288.65142822, ...,  302.10943604,\n",
       "          303.84197998,  304.87857056]],\n",
       "\n",
       "       [[ 276.92785645,  277.03341675,  276.98690796, ...,  272.20181274,\n",
       "          272.21914673,  272.29986572],\n",
       "        [ 277.10531616,  277.20437622,  277.15264893, ...,  272.2911377 ,\n",
       "          272.32626343,  272.47821045],\n",
       "        [ 277.29199219,  277.33847046,  277.35375977, ...,  272.27941895,\n",
       "          272.4704895 ,  272.7432251 ],\n",
       "        ..., \n",
       "        [ 289.22109985,  289.28070068,  289.27587891, ...,  300.08163452,\n",
       "          301.56057739,  301.89996338],\n",
       "        [ 289.32070923,  289.21932983,  288.9382019 , ...,  300.00585938,\n",
       "          301.58093262,  302.41516113],\n",
       "        [ 289.21734619,  288.86428833,  288.61737061, ...,  299.89273071,\n",
       "          301.71636963,  302.53353882]],\n",
       "\n",
       "       [[ 276.96185303,  277.07086182,  277.03182983, ...,  272.1986084 ,\n",
       "          272.21786499,  272.31271362],\n",
       "        [ 277.14953613,  277.22518921,  277.17163086, ...,  272.26559448,\n",
       "          272.32568359,  272.51339722],\n",
       "        [ 277.35125732,  277.36886597,  277.36663818, ...,  272.25152588,\n",
       "          272.47625732,  272.77893066],\n",
       "        ..., \n",
       "        [ 289.19250488,  289.24163818,  289.22442627, ...,  299.02252197,\n",
       "          300.00244141,  300.40179443],\n",
       "        [ 289.27377319,  289.15084839,  288.87280273, ...,  299.46710205,\n",
       "          300.89553833,  301.75939941],\n",
       "        [ 289.15182495,  288.83029175,  288.62765503, ...,  299.30459595,\n",
       "          301.04898071,  301.72994995]],\n",
       "\n",
       "       ..., \n",
       "       [[ 275.22293091,  275.42095947,  275.5760498 , ...,  272.13851929,\n",
       "          272.13751221,  272.11132812],\n",
       "        [ 275.44317627,  275.54550171,  275.69967651, ...,  272.26745605,\n",
       "          272.27154541,  272.38583374],\n",
       "        [ 275.66802979,  275.70672607,  275.86480713, ...,  272.18197632,\n",
       "          272.4190979 ,  272.74188232],\n",
       "        ..., \n",
       "        [ 289.43530273,  289.51046753,  289.47406006, ...,  305.14523315,\n",
       "          305.31842041,  305.1708374 ],\n",
       "        [ 289.64059448,  289.56396484,  289.18157959, ...,  305.29315186,\n",
       "          305.87954712,  306.0413208 ],\n",
       "        [ 289.59872437,  289.03988647,  288.55078125, ...,  305.16357422,\n",
       "          306.15292358,  306.5184021 ]],\n",
       "\n",
       "       [[ 275.17733765,  275.34213257,  275.46594238, ...,  272.10223389,\n",
       "          272.11883545,  272.10494995],\n",
       "        [ 275.375     ,  275.45428467,  275.58007812, ...,  272.22595215,\n",
       "          272.25311279,  272.37438965],\n",
       "        [ 275.57540894,  275.59762573,  275.73724365, ...,  272.14047241,\n",
       "          272.39715576,  272.7286377 ],\n",
       "        ..., \n",
       "        [ 289.4130249 ,  289.47332764,  289.42196655, ...,  303.65786743,\n",
       "          303.81838989,  303.66433716],\n",
       "        [ 289.58828735,  289.51287842,  289.16125488, ...,  303.84500122,\n",
       "          304.43731689,  304.65499878],\n",
       "        [ 289.52862549,  289.05187988,  288.61764526, ...,  303.71472168,\n",
       "          304.6451416 ,  305.11120605]],\n",
       "\n",
       "       [[ 275.13180542,  275.26153564,  275.35733032, ...,  272.06729126,\n",
       "          272.10067749,  272.10083008],\n",
       "        [ 275.30685425,  275.34979248,  275.43521118, ...,  272.19042969,\n",
       "          272.23657227,  272.36505127],\n",
       "        [ 275.4826355 ,  275.45751953,  275.54730225, ...,  272.10855103,\n",
       "          272.3760376 ,  272.71749878],\n",
       "        ..., \n",
       "        [ 289.39077759,  289.43151855,  289.35266113, ...,  302.08737183,\n",
       "          302.31771851,  302.1541748 ],\n",
       "        [ 289.5355835 ,  289.44598389,  289.11257935, ...,  302.35244751,\n",
       "          302.984375  ,  303.25738525],\n",
       "        [ 289.4576416 ,  289.06381226,  288.6875    , ...,  302.27835083,\n",
       "          303.13473511,  303.72470093]]], dtype=float32)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Para ver el arreglo con los valores\n",
    "nc_file.variables['T2'][:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Por ejemplo para las componentes del viento, que toma valores en los bordes de las grillas, podemos ver que toma otras dimensiones"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "OrderedDict([(u'FieldType', 104),\n",
       "             (u'MemoryOrder', u'XYZ'),\n",
       "             (u'description', u'x-wind component'),\n",
       "             (u'units', u'm s-1'),\n",
       "             (u'stagger', u'X'),\n",
       "             (u'coordinates', u'XLONG_U XLAT_U XTIME')])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Para listar los atributos de una variable\n",
    "nc_file.variables['U'].__dict__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(u'Time', u'bottom_top', u'south_north', u'west_east_stag')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Para listar las dimensiones de una variable\n",
    "nc_file.variables['U'].dimensions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. wrf-python\n",
    "\n",
    "**wrf-python** es un paquete desarrollado en Python por la NCAR (National Center for Atmospheric Research) que tiene una colección de cálculos diagnosticos y rutinas para interpolar (a niveles horizontales, * cross section *, a nivel de * superficie * ). Además provee funciones auxiliares para facilitar los gráficos con **cartopy** , ** basemap ** y ** PyNGL **\n",
    "\n",
    "A pesar de que toda la interfaz esté en Python, utiliza rutinas definidas en Fortran para trabajar más eficientemente.\n",
    "\n",
    "Algunas funciones útiles:\n",
    "- getvar: extrae una variable o genera una variable diagnóstico nueva.\n",
    "- interplevel: interpolación lineal de una variable a un plano horizontal de un nivel vertival específico.\n",
    "- vertcross: interpola una variable en 3D a una sección transversal\n",
    "- vinterp: Interpola una variable a una nueva superficie\n",
    "- ll_to_xy, xy_to_ll : funciones para convertir un punto de latitud/longitud a (x,y) de la grilla\n",
    "\n",
    "#### 3.1 getvar\n",
    "\n",
    "Esta función se puede usar para extraer variables que ya estan en el NetCDF o generar variables nuevas. También se puede concatenar una variable a través de varios archivos NetCDF.\n",
    "\n",
    "Por defecto extrae la variable para el tiempo 0, en caso de querer la variable para todos los tiempos del archivo hay que agregar timeidx=wrf.ALL_TIMES\n",
    "\n",
    "En caso de tener la librería xarray cargada, la función devuelve la variable cargada con el tipo de DataArray de xarray.\n",
    "\n",
    "Para ver la tabla de todas las posibles variables diagnositcos que se pueden generar: [link](http://wrf-python.readthedocs.io/en/latest/user_api/generated/wrf.getvar.html)\n",
    "\n",
    "*Ejemplo:*\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.DataArray 'T2' (Time: 25, south_north: 169, west_east: 99)>\n",
       "array([[[ 277.009247,  277.088226, ...,  272.399933,  272.482391],\n",
       "        [ 277.137634,  277.1987  , ...,  272.51889 ,  272.57196 ],\n",
       "        ..., \n",
       "        [ 289.362061,  289.273193, ...,  304.13797 ,  305.054626],\n",
       "        [ 289.276215,  288.857483, ...,  303.84198 ,  304.878571]],\n",
       "\n",
       "       [[ 276.927856,  277.033417, ...,  272.219147,  272.299866],\n",
       "        [ 277.105316,  277.204376, ...,  272.326263,  272.47821 ],\n",
       "        ..., \n",
       "        [ 289.320709,  289.21933 , ...,  301.580933,  302.415161],\n",
       "        [ 289.217346,  288.864288, ...,  301.71637 ,  302.533539]],\n",
       "\n",
       "       ..., \n",
       "       [[ 275.177338,  275.342133, ...,  272.118835,  272.10495 ],\n",
       "        [ 275.375   ,  275.454285, ...,  272.253113,  272.37439 ],\n",
       "        ..., \n",
       "        [ 289.588287,  289.512878, ...,  304.437317,  304.654999],\n",
       "        [ 289.528625,  289.05188 , ...,  304.645142,  305.111206]],\n",
       "\n",
       "       [[ 275.131805,  275.261536, ...,  272.100677,  272.10083 ],\n",
       "        [ 275.306854,  275.349792, ...,  272.236572,  272.365051],\n",
       "        ..., \n",
       "        [ 289.535583,  289.445984, ...,  302.984375,  303.257385],\n",
       "        [ 289.457642,  289.063812, ...,  303.134735,  303.724701]]], dtype=float32)\n",
       "Coordinates:\n",
       "    XLONG    (south_north, west_east) float32 -90.1131 -89.608 -89.1008 ...\n",
       "    XLAT     (south_north, west_east) float32 -59.5115 -59.599 -59.6847 ...\n",
       "    XTIME    (Time) float32 0.0 60.0 120.0 180.0 240.0 300.0 360.0 420.0 ...\n",
       "  * Time     (Time) datetime64[ns] 2017-10-26 2017-10-26T01:00:00 ...\n",
       "Dimensions without coordinates: south_north, west_east\n",
       "Attributes:\n",
       "    FieldType:    104\n",
       "    MemoryOrder:  XY \n",
       "    description:  TEMP at 2 M\n",
       "    units:        K\n",
       "    stagger:      \n",
       "    coordinates:  XLONG XLAT XTIME\n",
       "    projection:   LambertConformal(stand_lon=-63.5, moad_cen_lat=-38.50000762..."
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import wrf \n",
    "\n",
    "# Extrae la temperatura a 2 metros del nivel del suelo\n",
    "t2 = wrf.getvar(nc_file, \"T2\", timeidx=wrf.ALL_TIMES)\n",
    "\n",
    "t2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.DataArray u'temp' (Time: 25, bottom_top: 34, south_north: 169, west_east: 99)>\n",
       "array([[[[  3.687805, ...,  -0.688599],\n",
       "         ..., \n",
       "         [ 15.823792, ...,  31.738434]],\n",
       "\n",
       "        ..., \n",
       "        [[-54.465256, ..., -73.464691],\n",
       "         ..., \n",
       "         [-67.10672 , ..., -67.872711]]],\n",
       "\n",
       "\n",
       "       ..., \n",
       "       [[[  1.543243, ...,  -0.637573],\n",
       "         ..., \n",
       "         [ 16.032043, ...,  31.939972]],\n",
       "\n",
       "        ..., \n",
       "        [[-52.848572, ..., -73.159805],\n",
       "         ..., \n",
       "         [-67.334   , ..., -66.476334]]]], dtype=float32)\n",
       "Coordinates:\n",
       "    XLONG    (south_north, west_east) float32 -90.1131 -89.608 -89.1008 ...\n",
       "    XLAT     (south_north, west_east) float32 -59.5115 -59.599 -59.6847 ...\n",
       "    XTIME    (Time) float32 0.0 60.0 120.0 180.0 240.0 300.0 360.0 420.0 ...\n",
       "  * Time     (Time) datetime64[ns] 2017-10-26 2017-10-26T01:00:00 ...\n",
       "Dimensions without coordinates: bottom_top, south_north, west_east\n",
       "Attributes:\n",
       "    FieldType:    104\n",
       "    MemoryOrder:  XYZ\n",
       "    description:  temperature\n",
       "    units:        degC\n",
       "    stagger:      \n",
       "    coordinates:  XLONG XLAT XTIME\n",
       "    projection:   LambertConformal(stand_lon=-63.5, moad_cen_lat=-38.50000762..."
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Altura en km, al desactivar la opción msl (Median Sea Level) se calcula AGL (Above Ground Level)\n",
    "z = wrf.getvar(nc_file, 'z', timeidx=wrf.ALL_TIMES, msl=False, units='km')\n",
    "\n",
    "# Reflectividad simulada\n",
    "temp = wrf.getvar(nc_file, 'temp', timeidx=wrf.ALL_TIMES, units='degC')\n",
    "\n",
    "temp"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.2 interplevel\n",
    "\n",
    "Esta función interpola de manera simple y rápida a una altura o nivel de presión específico. Usa  la interpolación lineal, para interpolar con más opciones se puede usar la función ** vinterp **\n",
    "\n",
    "*Ejemplo:*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.DataArray u'temp_10_km' (Time: 25, south_north: 169, west_east: 99)>\n",
       "array([[[-55.038158, -55.444103, ..., -65.221306, -65.245781],\n",
       "        [-54.933277, -55.393635, ..., -65.22551 , -65.266136],\n",
       "        ..., \n",
       "        [-35.15493 , -35.2057  , ..., -36.503113, -36.515377],\n",
       "        [-35.080395, -35.131531, ..., -36.290215, -36.372608]],\n",
       "\n",
       "       [[-54.575459, -54.98531 , ..., -65.278419, -65.270645],\n",
       "        [-54.487331, -54.886112, ..., -65.372292, -65.241173],\n",
       "        ..., \n",
       "        [-35.091316, -35.119965, ..., -36.633789, -36.579304],\n",
       "        [-35.022064, -35.054691, ..., -36.350567, -36.422977]],\n",
       "\n",
       "       ..., \n",
       "       [[-47.371788, -47.546852, ..., -67.385475, -67.119682],\n",
       "        [-47.133289, -47.322968, ..., -67.195137, -66.93277 ],\n",
       "        ..., \n",
       "        [-34.55283 , -34.577953, ..., -37.939671, -37.937889],\n",
       "        [-34.454659, -34.463684, ..., -37.655697, -37.758022]],\n",
       "\n",
       "       [[-47.337444, -47.524189, ..., -67.480545, -67.229797],\n",
       "        [-47.056847, -47.254471, ..., -67.28186 , -67.069817],\n",
       "        ..., \n",
       "        [-34.341293, -34.366409, ..., -37.840408, -37.847954],\n",
       "        [-34.255764, -34.268368, ..., -37.580322, -37.660419]]], dtype=float32)\n",
       "Coordinates:\n",
       "    XLONG    (south_north, west_east) float32 -90.1131 -89.608 -89.1008 ...\n",
       "    XLAT     (south_north, west_east) float32 -59.5115 -59.599 -59.6847 ...\n",
       "    XTIME    (Time) float32 0.0 60.0 120.0 180.0 240.0 300.0 360.0 420.0 ...\n",
       "  * Time     (Time) datetime64[ns] 2017-10-26 2017-10-26T01:00:00 ...\n",
       "Dimensions without coordinates: south_north, west_east\n",
       "Attributes:\n",
       "    FieldType:      104\n",
       "    units:          degC\n",
       "    stagger:        \n",
       "    coordinates:    XLONG XLAT XTIME\n",
       "    projection:     LambertConformal(stand_lon=-63.5, moad_cen_lat=-38.500007...\n",
       "    level:          10 km\n",
       "    missing_value:  9.96920996839e+36\n",
       "    _FillValue:     9.96920996839e+36"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Interpolación de la reflectividad a 10 km sobre la superficie\n",
    "dbz_10 = wrf.interplevel(dbz,z,10)\n",
    "\n",
    "dbz_10"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.3 ll_to_xy, xy_to_ll\n",
    "\n",
    "Esta función sirve para encontrar el par (x,y) de la grilla mas cercano a partir de una latitud y longitud (y viceversa).\n",
    "\n",
    "Una vez teniendo el par (x,y) se pueden obtener los datos de las variables para ese lugar.\n",
    "\n",
    "*Ejemplo:*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<xarray.DataArray u'xy' ()>\n",
       " array(46)\n",
       " Coordinates:\n",
       "     latlon_coord  object CoordPair(lat=-31.524, lon=-64.463)\n",
       "     x_y           <U1 u'x', <xarray.DataArray u'xy' ()>\n",
       " array(109)\n",
       " Coordinates:\n",
       "     latlon_coord  object CoordPair(lat=-31.524, lon=-64.463)\n",
       "     x_y           <U1 u'y')"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Obtenemos los puntos de la grilla para CONAE\n",
    "lat = -31.524\n",
    "lon = -64.463\n",
    "\n",
    "(x, y) = wrf.ll_to_xy(nc_file, lat, lon)\n",
    "\n",
    "(x, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.DataArray u'latlon' (lat_lon: 2)>\n",
       "array([-31.626016, -64.457134])\n",
       "Coordinates:\n",
       "  * lat_lon   (lat_lon) <U3 u'lat' u'lon'\n",
       "    xy_coord  object CoordPair(x=46, y=109)"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Vemos cual es la latitud y longitud exactas que representa ese par (x, y)\n",
    "wrf.xy_to_ll(nc_file, x, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.DataArray 'T2' (Time: 25)>\n",
       "array([ 291.758972,  289.751617,  289.434021,  289.256409,  289.197388,\n",
       "        290.335663,  290.148285,  288.770142,  288.397552,  288.307648,\n",
       "        288.26358 ,  290.179474,  291.391846,  293.708984,  294.565308,\n",
       "        294.934998,  296.092499,  296.751617,  297.311066,  295.188629,\n",
       "        293.687622,  292.103333,  290.11264 ,  290.751801,  290.840485], dtype=float32)\n",
       "Coordinates:\n",
       "    XLONG         float32 -64.4572\n",
       "    XLAT          float32 -31.626\n",
       "    XTIME         (Time) float32 0.0 60.0 120.0 180.0 240.0 300.0 360.0 ...\n",
       "  * Time          (Time) datetime64[ns] 2017-10-26 2017-10-26T01:00:00 ...\n",
       "    latlon_coord  object CoordPair(lat=-31.524, lon=-64.463)\n",
       "Attributes:\n",
       "    FieldType:    104\n",
       "    MemoryOrder:  XY \n",
       "    description:  TEMP at 2 M\n",
       "    units:        K\n",
       "    stagger:      \n",
       "    coordinates:  XLONG XLAT XTIME\n",
       "    projection:   LambertConformal(stand_lon=-63.5, moad_cen_lat=-38.50000762..."
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# El 'x' es el valor en la coordenada de oeste a este, y el 'y' el valor de la coordenada sur a norte\n",
    "\n",
    "t2_conae = t2[:, y, x]\n",
    "\n",
    "t2_conae"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Time\n",
       "2017-10-26 00:00:00    291.758972\n",
       "2017-10-26 01:00:00    289.751617\n",
       "2017-10-26 02:00:00    289.434021\n",
       "2017-10-26 03:00:00    289.256409\n",
       "2017-10-26 04:00:00    289.197388\n",
       "2017-10-26 05:00:00    290.335663\n",
       "2017-10-26 06:00:00    290.148285\n",
       "2017-10-26 07:00:00    288.770142\n",
       "2017-10-26 08:00:00    288.397552\n",
       "2017-10-26 09:00:00    288.307648\n",
       "2017-10-26 10:00:00    288.263580\n",
       "2017-10-26 11:00:00    290.179474\n",
       "2017-10-26 12:00:00    291.391846\n",
       "2017-10-26 13:00:00    293.708984\n",
       "2017-10-26 14:00:00    294.565308\n",
       "2017-10-26 15:00:00    294.934998\n",
       "2017-10-26 16:00:00    296.092499\n",
       "2017-10-26 17:00:00    296.751617\n",
       "2017-10-26 18:00:00    297.311066\n",
       "2017-10-26 19:00:00    295.188629\n",
       "2017-10-26 20:00:00    293.687622\n",
       "2017-10-26 21:00:00    292.103333\n",
       "2017-10-26 22:00:00    290.112640\n",
       "2017-10-26 23:00:00    290.751801\n",
       "2017-10-27 00:00:00    290.840485\n",
       "dtype: float32"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t2_conae.to_pandas()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>T2</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Time</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
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       "      <th>2017-10-26 00:00:00</th>\n",
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       "    <tr>\n",
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       "      <th>2017-10-26 10:00:00</th>\n",
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       "    <tr>\n",
       "      <th>2017-10-26 11:00:00</th>\n",
       "      <td>290.179474</td>\n",
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       "    <tr>\n",
       "      <th>2017-10-26 12:00:00</th>\n",
       "      <td>291.391846</td>\n",
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       "    <tr>\n",
       "      <th>2017-10-26 13:00:00</th>\n",
       "      <td>293.708984</td>\n",
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       "    <tr>\n",
       "      <th>2017-10-26 14:00:00</th>\n",
       "      <td>294.565308</td>\n",
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       "      <th>2017-10-26 15:00:00</th>\n",
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       "      <th>2017-10-26 16:00:00</th>\n",
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       "      <th>2017-10-26 17:00:00</th>\n",
       "      <td>296.751617</td>\n",
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       "    <tr>\n",
       "      <th>2017-10-26 18:00:00</th>\n",
       "      <td>297.311066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-10-26 19:00:00</th>\n",
       "      <td>295.188629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-10-26 20:00:00</th>\n",
       "      <td>293.687622</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-10-26 21:00:00</th>\n",
       "      <td>292.103333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-10-26 22:00:00</th>\n",
       "      <td>290.112640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-10-26 23:00:00</th>\n",
       "      <td>290.751801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-10-27 00:00:00</th>\n",
       "      <td>290.840485</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             T2\n",
       "Time                           \n",
       "2017-10-26 00:00:00  291.758972\n",
       "2017-10-26 01:00:00  289.751617\n",
       "2017-10-26 02:00:00  289.434021\n",
       "2017-10-26 03:00:00  289.256409\n",
       "2017-10-26 04:00:00  289.197388\n",
       "2017-10-26 05:00:00  290.335663\n",
       "2017-10-26 06:00:00  290.148285\n",
       "2017-10-26 07:00:00  288.770142\n",
       "2017-10-26 08:00:00  288.397552\n",
       "2017-10-26 09:00:00  288.307648\n",
       "2017-10-26 10:00:00  288.263580\n",
       "2017-10-26 11:00:00  290.179474\n",
       "2017-10-26 12:00:00  291.391846\n",
       "2017-10-26 13:00:00  293.708984\n",
       "2017-10-26 14:00:00  294.565308\n",
       "2017-10-26 15:00:00  294.934998\n",
       "2017-10-26 16:00:00  296.092499\n",
       "2017-10-26 17:00:00  296.751617\n",
       "2017-10-26 18:00:00  297.311066\n",
       "2017-10-26 19:00:00  295.188629\n",
       "2017-10-26 20:00:00  293.687622\n",
       "2017-10-26 21:00:00  292.103333\n",
       "2017-10-26 22:00:00  290.112640\n",
       "2017-10-26 23:00:00  290.751801\n",
       "2017-10-27 00:00:00  290.840485"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(t2_conae.to_pandas(), columns=['T2'])\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "      <th>T2_degC</th>\n",
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       "      <th>Time</th>\n",
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       "      <th>2017-10-26 05:00:00</th>\n",
       "      <td>290.335663</td>\n",
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       "    <tr>\n",
       "      <th>2017-10-26 06:00:00</th>\n",
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       "    <tr>\n",
       "      <th>2017-10-26 07:00:00</th>\n",
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       "    <tr>\n",
       "      <th>2017-10-26 10:00:00</th>\n",
       "      <td>288.263580</td>\n",
       "      <td>15.113586</td>\n",
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       "    <tr>\n",
       "      <th>2017-10-26 11:00:00</th>\n",
       "      <td>290.179474</td>\n",
       "      <td>17.029480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-10-26 12:00:00</th>\n",
       "      <td>291.391846</td>\n",
       "      <td>18.241852</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-10-26 13:00:00</th>\n",
       "      <td>293.708984</td>\n",
       "      <td>20.558990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-10-26 14:00:00</th>\n",
       "      <td>294.565308</td>\n",
       "      <td>21.415314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-10-26 15:00:00</th>\n",
       "      <td>294.934998</td>\n",
       "      <td>21.785004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-10-26 16:00:00</th>\n",
       "      <td>296.092499</td>\n",
       "      <td>22.942505</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-10-26 17:00:00</th>\n",
       "      <td>296.751617</td>\n",
       "      <td>23.601624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-10-26 18:00:00</th>\n",
       "      <td>297.311066</td>\n",
       "      <td>24.161072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-10-26 19:00:00</th>\n",
       "      <td>295.188629</td>\n",
       "      <td>22.038635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-10-26 20:00:00</th>\n",
       "      <td>293.687622</td>\n",
       "      <td>20.537628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-10-26 21:00:00</th>\n",
       "      <td>292.103333</td>\n",
       "      <td>18.953339</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-10-26 22:00:00</th>\n",
       "      <td>290.112640</td>\n",
       "      <td>16.962646</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-10-26 23:00:00</th>\n",
       "      <td>290.751801</td>\n",
       "      <td>17.601807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-10-27 00:00:00</th>\n",
       "      <td>290.840485</td>\n",
       "      <td>17.690491</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             T2    T2_degC\n",
       "Time                                      \n",
       "2017-10-26 00:00:00  291.758972  18.608978\n",
       "2017-10-26 01:00:00  289.751617  16.601624\n",
       "2017-10-26 02:00:00  289.434021  16.284027\n",
       "2017-10-26 03:00:00  289.256409  16.106415\n",
       "2017-10-26 04:00:00  289.197388  16.047394\n",
       "2017-10-26 05:00:00  290.335663  17.185669\n",
       "2017-10-26 06:00:00  290.148285  16.998291\n",
       "2017-10-26 07:00:00  288.770142  15.620148\n",
       "2017-10-26 08:00:00  288.397552  15.247559\n",
       "2017-10-26 09:00:00  288.307648  15.157654\n",
       "2017-10-26 10:00:00  288.263580  15.113586\n",
       "2017-10-26 11:00:00  290.179474  17.029480\n",
       "2017-10-26 12:00:00  291.391846  18.241852\n",
       "2017-10-26 13:00:00  293.708984  20.558990\n",
       "2017-10-26 14:00:00  294.565308  21.415314\n",
       "2017-10-26 15:00:00  294.934998  21.785004\n",
       "2017-10-26 16:00:00  296.092499  22.942505\n",
       "2017-10-26 17:00:00  296.751617  23.601624\n",
       "2017-10-26 18:00:00  297.311066  24.161072\n",
       "2017-10-26 19:00:00  295.188629  22.038635\n",
       "2017-10-26 20:00:00  293.687622  20.537628\n",
       "2017-10-26 21:00:00  292.103333  18.953339\n",
       "2017-10-26 22:00:00  290.112640  16.962646\n",
       "2017-10-26 23:00:00  290.751801  17.601807\n",
       "2017-10-27 00:00:00  290.840485  17.690491"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Agregar columna con la temperatura en grados celsius\n",
    "df['T2_degC'] = df['T2'] - 273.15\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Máxima 24.1611\n",
      "Mínima 15.1136\n",
      "Media 18.4996700287\n"
     ]
    }
   ],
   "source": [
    "# Temperatura máxima del período de simulación\n",
    "print \"Máxima\", df['T2_degC'].max()\n",
    "\n",
    "# Temperatura mínima del período de simulación\n",
    "print \"Mínima\", df['T2_degC'].min()\n",
    "\n",
    "# Temperatura media del período de simulación\n",
    "print \"Media\", df['T2_degC'].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.  Librerías para graficar\n",
    "\n",
    "\n",
    "#### Matplotlib\n",
    "\n",
    "Es la principal librería para trabajar con gráficos en Python. \n",
    "\n",
    "Galeria con ejemplos:\n",
    "\n",
    "http://matplotlib.org/gallery\n",
    "\n",
    "La librería * pandas * trae una integración con matplotlib para graficar rápidamente "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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8sgJ1SZLUJZdcAtdcA6NHw8CBH328V680UO9ql3LrdOgKIQwEbgBOARYBPwB+1PIh5S1NkqTOueUW+PGP4bbbYMUVP/l5W4yqBp26ezGE0IcUuK6OMd4cQtgYGA48HkIIwDDg0RDCNjHG11t//RlnnPGv95uammhqaup55ZIkAQ89BMceC7feCmuv3fZjdtwRTjml2LpUG8aMGcOYMWMKuVan9ukKIVwFvBljPK2dz08Btogxzmnjc+7TJUmqiMmToakJLr8c9t67/cctWADLLw+vvQaDBhVWnmpQJffp6syWETsARwG7hRAmhhAmhBD2avWwiO1FSVKBZsxIQeusszoOXAD9+8MWW8DYscXUJrVlie3FGOMDQO8lPGatslUkSdISzJ0L++wDX/taOuanM5r369pjj4qWJrXLsxclSTVlwQI4+GDYYQf43vc6/3U77ph2ppdy8exFSVLNWLwYjjoqBa8//hF6d9iH+bi334bVVoO33vpoDy+ptawzXZIkVYt//3eYNi3tx9WVwAWw7LKw1lowcWJlapOWxNAlSaoJv/hF2hbilltgqaW69xy2GJWToUuSVPV+/3v4+c/h73+HwYO7/zwefq2cnOmSJFW1u+6CI4+EO+6AkSN79lyvvgqbbw6vv54Ow5Zac6ZLktSQHn8cjjgC/vCHngcugGHD4NOfhuee6/lzSV1l6JIkVaWXX4Z994ULL0y7zpeLc13KxdAlSao6s2fDXnvB6afDYYeV97k9/Fq5GLokSVVl/nzYf//0VolDqh2mVy4O0kuSqsbChXDIITBwIFx1FfSqwNLA4sUwZAg89RSsvHL5n1+1zUF6SVLdixFOOgneew8uu6wygQvS8+6wg6tdKp6hS5JUFc48E8aNgxtvhH79KnstW4zKwdAlScru0kvh8svhb3+DpZeu/PW8g1E5ONMlScrqH/+AY46Be++FESOKueaCBbD88jBjRjEhT7XDmS5JUl2aPh2+8AW47rriAhdA//6w5ZYwdmxx15QMXZKkLD78MO02f9JJsOuuxV/fuS4VzdAlScriP/4jbQ3xve/lub5zXSqaM12SpMLdckta4ZowAVZYIU8Nc+fCqqvCW29V/m5J1Q5nuiRJdePll+HYY+H3v88XuACWWQbWWQcmTsxXgxqLoUuSVJgFC9JZit/9Lmy/fe5qbDGqWIYuSVJhTj8dVlkFvvWt3JUkHn6tIhm6JEmF+OMfYfRouOIKCBWZmOm65uOAHD1WEQxdkqSKe+45OPFEuP56WHbZ3NV8ZNgwGDQIJk/OXYkagaFLklRR8+fDoYfCf/1X2pC02thiVFEMXZKkijr5ZNhwQ/j613NX0jY3SVVRDF2SpIq56qp0puKvf109c1ytGbpUFDdHlSRVxNNPQ1MT3HUXjByZu5r2xQhDhsATT6Q7K9XY3BxVklRT5s1Lc1znnFPdgQvSClzzXYxSJRm6JEllFSMcfzyMGgVf/nLuajrHFqOK0Cd3AZKk+vKb36RW3bhxuSvpvJ12ghNOyF2F6p0zXZKkspk4ET7zmbRqtN56uavpvA8+gMGDYfr0dCajGpczXZKkqjd3bprjOv/82gpcAP36wVZbwdixuStRPTN0SZJ6LEb4ylfSKteRR+aupns8/FqVZuiSJPXY+efDyy/Dz3+eu5Luc2d6VZozXZKkHhk7FvbfP/261lq5q+m+d95J+3S99VZqN6oxOdMlSapKs2fD4YenHedrOXABLL00rLsuTJiQuxLVK0OXJKlbFi+GL3wBDjkEDjwwdzXlsdNOznWpcgxdkqRuOeccmDMHzjordyXl4yapqiRnuiRJXXbPPamt+MgjsNpquaspnxkzYJNN4PXXoZfLEg3JmS5JUtWYNQuOOgquuKK+AhekQfplloHJk3NXonpk6JIkddqiRSlwfelLsNdeuaupDFuMqhRDlySp0/77v1PwOuOM3JVUjqFLlWLokiR1yu23p60hrr0W+vTJXU3leAejKsXQJUlaounT0/YQ11wDK6+cu5rKWm89ePddePXV3JWo3hi6JElLdPrpcNxxsOuuuSupvBBSi/GBB3JXonpj6JIkdWj6dPj73+H//b/clRTHuS5VgqFLktShiy6Cz38+baXQKJzrUiW4OaokqV3vvw9rrAH33ptmnRrFBx/A4MFpla+RwqbcHFWSlMl118EWWzRW4ALo1w+23hoeeih3Jaonhi5JUptihPPOg1NOyV1JHrYYVW6GLklSm+67D+bPh898JncleThMr3JzpkuS1KbPfQ522w2+8Y3cleTxzjvpLMbZs6F//9zVqCjOdEmSCjV1KowZA1/8Yu5K8ll66TTL9uijuStRvTB0SZI+4Ze/TIFr4MDcleRli1HlZOiSJH3Me+/BZZfBSSflriQ/Q5fKydAlSfqY3/0OdtgB1lordyX5NR8HtHhx7kpUDwxdkqR/iRHOP79xt4lobeWVYbnlYNKk3JWoHhi6JEn/cued0KtXYxxs3Vm2GFUuhi5J0r+cdx6cfDKEitwwX5sMXSoX9+mSJAHwwguw3XZpu4hPfSp3NdXj2Wdhzz3h5ZdzV6IiuE+XJKniLrwQvvpVA1drI0akOzqnTctdiWqdoUuSxDvvwFVXNe7u8x0J4aO7GKWeMHRJkrjySth9d1httdyVVCcPv1Y5GLokqcEtXgwXXJAG6NU2h+lVDoYuSWpwf/sbDBqUNkRV2zbfHF56Cd5+O3clqmWGLklqcOef7zYRS9K3L2yzDTz0UO5KVMsMXZLUwCZNgscfhyOOyF1J9dtxR+e61DOGLklqYBdcAMcfD/37566k+jnXpZ5yc1RJalBz5qRDrZ95Jp0xqI69+256nWbPNqTWMzdHlSSV3aWXwr77Grg6a9AgWH99GD8+dyWqVYYuSWpAixalHejdJqJrbDGqJwxdktSAbrklrXBts03uSmrLTjsZutR9hi5JakDN20Soa3bYIR0HtHhx7kpUiwxdktRgnngCnnsODjkkdyW1Z6WVYPnl080HUlcZuiSpwZx3Hpx4YtrwU13nXJe6y9AlSQ3kzTfhppvguONyV1K7nOtSdxm6JKmB/PrXcNBBMGRI7kpqlzvTq7vcHFWSGsSHH8Kaa8Jf/wqbbZa7mtoVY5rteuQRWH313NWo3NwcVZLUYzfdBGuvbeDqqRDSatcDD+SuRLXG0CVJDeK88+CUU3JXUR9sMao7DF2S1AAeeQSmT4f9989dSX3wDkZ1h6FLkhrA+efDSSdBnz65K6kPm28OL7+cDg2XOsvQJUl1bubMNDz/1a/mrqR+9OmTjlB68MHclaiWLDF0hRCGhRDuCiE8E0J4MoTwzdLHzwkhTAohPBZCuDGEsHTly5UkddXFF8Phh8PgwbkrqS+2GNVVnVnpWgicFmPcENgOOCmEsD5wO7BRjHEz4Hnge5UrU5LUHQsWpNDlOYvl5yap6qolhq4Y48wY42Ol9+cBk4BVY4x3xBibj/wcCwyrXJmSpO64/noYORI23DB3JfVn221h4kR4//3clahWdGmmK4QwHNgMGNfqU18B/laekiRJ5RCj20RU0sCBsPHG7telzut06AohDARuAE4prXg1f/wHwIcxxmsrUJ8kqZseegjefhv22Sd3JfXroIPghhtyV6Fa0ambh0MIfUiB6+oY480tPv5FYB9gt46+/owzzvjX+01NTTQ1NXWjVElSV5x3Hnzzm9DL+9Qr5tBDYbvt4IIL3I6jVo0ZM4YxY8YUcq1Onb0YQrgKeDPGeFqLj+0F/AzYOcY4u4Ov9exFSSrYtGmw6aZpL6mlvbe8orbaCs45B3brcPlBtSLr2YshhB2Ao4DdQggTQwgTQgh7AxcAA4F/lD72q0oUKEnquosugqOPNnAV4dBD4Y9/zF2FakGnVrp6dAFXuiSpUPPnw+qrp4071103dzX176WXUotx+nRbjPUg60qXJKm2XHtt2i3dwFWMtdaCYcPg3ntzV6JqZ+iSpDriNhF5HHaYLUYtmaFLkurImDGwcCHssUfuShrLoYfCTTfBokW5K1E1M3RJUh05//y0TUSoyESK2mOLUZ1h6JKkOjFlSvqh/4Uv5K6kMR16aDp2SWqPdy9KUp047bS0Eer//V/uShrTiy/C9tvDjBnQu3fuatRd3r0oSWrXBx/A6aenQW4H6PNZe21bjOqYoUuSatiUKbDTTjB5MkycCKutlruixuZGqeqIoUuSatQNN8C228IRR8Att8AKK+SuSIceCjfe6F2Mapt750pSjXn//TS/ddttMHo0bL117orUrGWLcdddc1ejauNKlyTVkMmT0+rW7NkwYYKBqxrZYlR7DF2SVCOuuirNb33jG/D738Myy+SuSG2xxaj22F6UpCo3b14KWg8/DHfdBSNH5q5IHbHFqPa40iVJVezxx2GrrdK+T+PHG7hqhS1GtcXNUSWpCsUIF18MP/wh/OIXcPTRuStSV7z4IuywA0yf7kaptaaSm6PaXpSkKvP223DssekH9wMPwIgRuStSV629Nqyyii1GfZztRUmqIg8/DFtsASuvDA89ZOCqZYcdZotRH2d7UZKqwOLF8POfwznnpLbiwQfnrkg9ZYuxNtlelKQ69sYb8MUvwpw5aaVr+PDcFakcbDGqNduLkpTRPfekduLIkemHs4GrvngXo1qyvShJGSxaBGeemVqJl18Oe+2VuyJVwgsvwI472mKsJbYXJamOzJgBRx2V3n/00dSCUn1aZ53053vffdDUlLsa5WZ7UZIK9Pe/w5ZbphmfO+4wcDWCQw+F66/PXYWqge1FSSrII4/AZz+bfgDvskvualQUW4y1pZLtRVe6JKkAixfDySfDWWcZuBpNyxajGpuhS5IKcM01aXj+i1/MXYlysMUosL0oSRX37ruw/vpw002w7ba5q1EOthhrh+1FSaphP/kJ7LGHgauR2WIUuGWEJFXU88/Db38LTz6ZuxLl1rxRqltHNC7bi5JUQfvtBzvvDKefnrsS5WaLsTbYXpSkGvS3v8Gzz8Ipp+SuRNVgnXVg5ZVtMTYyQ5ckVcAHH8Cpp8K550K/frmrUbU47DDPYmxkhi5JqoALLkgrG/vsk7sSVZNDD4Ubb0zbh6jxGLokqcxmzoT//V/4xS9yV6JqY4uxsRm6JKnMvv99+MpXYMSI3JWoGjXfxajG492LklRGDz8MBx4IkyfD0kvnrkbV6PnnYaedvIuxWnn3oiTVgObzFf/3fw1cat+669pibFSGLkkqk6uvhhjhmGNyV6JqZ4uxMdlelKQyeOeddL7in/8M22yTuxpVu+efT5vmvvqqLcZqY3tRkqrcmWfCnnsauNQ5664LK60E99+fuxIVybMXJamHnnsOLr/c8xXVNYceCtdfD7vskrsSFcX2oiT10L77wq67wre/nbsS1RLvYqxOthclqUqNHp0OMj755NyVqNY038Voi7FxGLokqZs++AC+9S3PV1T3NbcY1RhsL0pSN/30p3DvvfCXv+SuRLXKFmP1sb0oSVXmtdfg7LPh5z/PXYlqmXcxNhZDlyR1w/e+B8cem35oSj1x2GFulNoobC9KUheNGwcHH5zOVxw0KHc1qnW2GKuL7UVJqhKLF8M3vwlnnWXgUnnYYmwchi5J6oIrr0yrEUcdlbsS1RPPYmwMthclqZPmzoUNNoCbb4att85djerJc8+lsxhtMeZne1GSqsB//zfsvbeBS+U3YoQtxkbg2YuS1AmTJ6fW4lNP5a5E9aq5xehZjPXL9qIkLUGMsM8+sMcecNppuatRvXruuRS4Xn3VFmNOthclKaPRo2HKFDjppNyVqJ6NGAFDh9pirGeGLknqwIIFcOqpnq+oYngXY32zvShJHTj7bHjgAbjlltyVqBHYYszP9qIkZTBjRjrU2vMVVRRbjPXN0CVJ7fjud+FrX4N11sldiRqJLcb6ZXtRktrw0EPph9/kyTBwYO5q1EhsMeZle1GSCtTyfEUDl4o2YgSsuGKaJVR9MXRJUiuXX57uVPR8ReVy2GFw/fW5q1C52V6UpBaFXlaRAAAgAElEQVTmzoX114e//hW23DJ3NWpUthjzsb0oSQX5059g++0NXMrLFmN9MnRJUgtjx8LOO+euQko3ctxwQ+4qVE6GLklqYdw4GDUqdxUSHHQQ/PnP6exP1QdDlySVvPdemqXZbLPclUiw4YbQvz9MmJC7EpWLoUuSSh59FDbeOP2gk3ILIa12/elPuStRuRi6JKlk3DjYdtvcVUgfMXTVF0OXJJU4z6Vqs+22MGdOanur9hm6JKlk7FhXulRdevWCAw5IA/WqfYYuSQKmT4cFC2CttXJXIn3cgQfaYqwXhi5JIrUWt9kmDS9L1WTXXdPB6zNm5K5EPWXokiSc51L16tcP9tkHbr45dyXqKUOXJOE8l6pb80apqm0eeC2p4S1cCMstB9OmwbLL5q5G+qR582CVVeCVV/xvtNI88FqSKujpp2HVVf1hpuo1cCDssguMHp27EvWEoUtSw3OeS7XAjVJrn6FLUsNznku1YL/94B//gPnzc1ei7jJ0SWp4Hv+jWjBkCGy+OdxxR+5K1F2GLkkN7Z13YOpUGDkydyXSktlirG2GLkkN7ZFH0upB3765K5GW7IAD4C9/SXfcqvYYuiQ1NOe5VEuGD4fVVoP7789dibrD0CWpoTnPpVrjRqm1a4mhK4QwLIRwVwjhmRDCkyGEk0sfXy6EcHsI4dkQwm0hhGUqX64klU+Mhi7Vnua5Lvcdrz2dWelaCJwWY9wQ2A74RghhfeC7wB0xxvWAu4DvVa5MSSq/qVOhd+/UrpFqxUYbpfMYJ07MXYm6aomhK8Y4M8b4WOn9ecAkYBhwAHBl6WFXAgdWqkhJqoTmea5QkQM/pMoIAQ480LsYa1GXZrpCCMOBzYCxwNAY4yxIwQwYUu7iJKmSbC2qVrl1RG3qdOgKIQwEbgBOKa142U2WVNMMXapVo0bB7Nnw/PO5K1FX9OnMg0IIfUiB6+oY482lD88KIQyNMc4KIawEvN7e159xxhn/er+pqYmmpqZuFyxJ5fDBB/D447DVVrkrkbquV6+0Z9ef/wynn567mto2ZswYxowZU8i1QuzE7Q8hhKuAN2OMp7X42NnAWzHGs0MI3wGWizF+t42vjZ25hiQVafx4+MpX4Iknclcidc9tt8GPfwwPPpi7kvoSQiDGWJFJz85sGbEDcBSwWwhhYghhQghhL+BsYI8QwrPA7sBZlShQkirBTVFV63bdFSZNgtdey12JOmuJ7cUY4wNA73Y+vXt5y5GkYowbB7vskrsKqfv69YO994abb4avfz13NeoMd6SX1JAcolc9cHf62tKpma4eXcCZLklVZvZsWHNNmDMnbY4q1ap582CVVWDaNFjGc2HKIutMlyTVm4cfTnctGrhU6wYOTG3y0aNzV6LOMHRJaji2FlVP3J2+dhi6JDUcQ5fqyf77w+23w/vv565ES2LoktRQYkztRUOX6sWQIbDZZnDHHbkr0ZIYuiQ1lOefT3MwK6+cuxKpfDyLsTYYuiQ1FFuLqkcHHgi33AILF+auRB0xdElqKIYu1aPhw2HYMHjggdyVqCOGLkkNZdw4GDUqdxVS+blRavVzc1RJDWP+fFh++bQ56lJL5a5GKq8nn4T99oMpUyBUZGvPxuDmqJJUBhMnwgYbGLhUnzbeGPr0gccey12J2mPoktQwnOdSPQvBuxirnaFLUsNwnkv1zt3pq5uhS1LDGDvWlS7Vt+22gzfegBdeyF2J2mLoktQQZs2CuXNh3XVzVyJVTq9ecMAB3sVYrQxdkhrCuHGwzTbph5JUz5zrql7+9SOpITjPpUax227wzDMwc2buStSaoUtSQ3CeS42iXz/Ya690LJCqi6FLUt1btAjGj0/tRakR2GKsToYuSXVv8mQYMgRWWCF3JVIx9t47ncM4d27uStSSoUtS3XOeS41m0CDYeWe49dbclaglQ5ekuuc8lxqRLcbqY+iSVPc8/keNaL/94Pbb4f33c1eiZoYuSXVt3ry0O/emm+auRCrWiivCJpvAnXfmrkTNDF2S6tqjj6YfPP37565EKp4txupi6JJU15znUiM78MC0X9eiRbkrERi6JNU557nUyNZcE1ZZJW0fofwMXZLqmqFLje6ggzwAu1oYuiTVrVdfhQ8/TP/alxpV81xXjLkrkaFLUt1qnucKIXclUj4jR0KvXvD447krkaFLUt2ytSilf3R4F2N1MHRJqluGLik58EBDVzUIscJN3hBCrPQ1JKm1hQth2WVh+nRYZpnc1Uh5LVqU7mJ88EFYe+3c1VS3EAIxxooMJbjSJakuPfkkrL66gUsC6N0bDjjAuxhzM3RJqku2FqWPc64rP0OXpLpk6JI+brfd4KmnYNas3JU0LkOXpLo0bhyMGpW7Cql69O8Pe+2VjgVSHoYuSXXn7bfhlVdg441zVyJVF1uMeRm6JNWdRx6BLbaAPn1yVyJVl733hvvvh3feyV1JYzJ0Sao7znNJbVt6adhpJ7j11tyVNCZDl6S6Y+iS2meLMR83R5VUV2KEoUNhwgQYNix3NVL1mTUL1lsv/dq/f+5qqo+bo0pSJ02ZAn37Grik9gwdmg7BvvPO3JU0HkOXpLpia1FaMluMeRi6JNUVQ5e0ZHvvDXfckbuKxmPoklRX3BRVWrL114f58+Hll3NX0lgMXZLqxoIF8MQTsOWWuSuRqlsI0NQEY8bkrqSxGLok1Y3HH4d11oGBA3NXIlU/Q1fxDF2S6obzXFLnGbqKZ+iSVDec55I6b731Ukveua7iGLok1Y2xY13pkjqrea7r7rtzV9I4DF2S6sKbb8Ibb6S7siR1ji3GYhm6JNWFhx+GrbaC3r1zVyLVjubQ5Wl9xTB0SaoLznNJXTdiBHz4YTo+S5Vn6JJUF5znkrrO/bqKZeiSVPMWL07tRUOX1HWGruIYuiTVvOefh2WWgaFDc1ci1Z5dd013MDrXVXmGLkk1z3kuqfvWWSetFr/0Uu5K6p+hS1LNc55L6j7nuopj6JJU8zz+R+oZN0ktRogVbuKGEGKlryGpcc2fD8svD7Nnw1JL5a5Gqk0vvJCC17RpaeWrkYUQiDFW5FVwpUtSTZswATbayMAl9cTaa6dfX3wxbx31rpDQ9corRVxFUiNynkvqOee6ilFI6LrnniKuIqkROc8llUfz1hGqnEJCl8lZUqUYuqTy8BzGynOlS1LNeu01mDcP1l03dyVS7VtrLejVKw3VqzIKCV3vvJPuiJCkcho3DrbZxrutpHIIwRZjpRUSunbe2dUuSeVna1EqL4fpK6uQ0OUfoqRKMHRJ5eVcV2UZuiTVpEWLYPz41F6UVB5rrgl9+sBzz+WupD4VEro23BDmzoVXXy3iapIawTPPwEorpd3oJZVH81yXCyWVUUjo6tXLuS5J5WVrUaoMu1OVU9gxQP4hSionQ5dUGc2HXzvXVX6GLkk1adw4GDUqdxVS/VlzTRgwAJ59Nncl9aew0LXRRjBnDkyfXtQVJdWrd99NB/NusknuSqT65EJJZRQWunr1gl12ca5LUs+NHw+bbgr9+uWuRKpPhq7KKCx0QQpd/iFK6qmxY53nkirJ/boqo9DQZXKW1FMxwnXXwb775q5Eql/Dh8NSS8HkybkrqS+Fhq6NN4bZs2HGjCKvKqmePPAAvP8+7LZb7kqk+uZCSfkVGrqc65LUUxddBCeckP4+kVQ5zVtHqHwK/2vLuS5J3fX66zB6NHzpS7krkeqfc13lV3jocrlSUndddhkcfDAst1zuSqT6t8YaMHAgTJqUu5L6UXjoGjkS3nwTXnut6CtLqmWLFsHFF8OJJ+auRGocthjLa4mhK4RwaQhhVgjhiRYf2zSE8FAIYWII4eEQwladvqDnMErqhr/9DYYMga06/beNpJ7y8Ovy6sxK1+XAnq0+dg7woxjj5sCPgJ925aLOdUnqqosucpVLKlrzzW/OdZXHEkNXjPF+YE6rDy8Glim9vyzQpcN9nOuS1BVTpqSzFg8/PHclUmNZfXUYNAiefjp3JfWhTze/7lvAbSGEnwEB2L4rX7zJJukupNdeg5VX7mYFkhrGJZfAF74An/pU7kqkxtPcYtx449yV1L7uDtKfAJwSY1ydFMAu69JFS3Nd997bzatLahgLFqS7Fr/+9dyVSI3J7lT5dHel64sxxlMAYow3hBAu7ejBZ5xxxr/eb2pqoqmp6V9zXbYLJHXkhhvS4dYjRuSuRGpMu+wC3/oWLF5cn5sSjxkzhjEFpcoQOzEdF0IYDvwlxjiy9PungRNjjPeEEP4NOCvGuHU7XxvbusbEifD5z7v/h6SO7bADfPvbcNBBuSuRGtc668Cf/pS2fap3IQRijKESz73Ela4QwrVAE7B8COEV0t2KXwPODyH0Bt4HjuvqhTfZBGbOTG8rrdTVr5bUCB5/HKZOhf32y12J1Nia57oaIXRVUmfuXvx8jHGVGGP/GOPqMcbLY4wPxhi3ijFuHmPcLsY4sasX7t3buS5JHbvoIjjuOOjT3UEISWXhXFd5ZO3O+ocoqT3vvAN/+AMce2zuSiQ1NaX9uhYvzl1JbcsautwkVVJ7rr4adt8dVlkldyWSVl0VBg+Gp57KXUltyxq6Nt007dU1a1bOKiRVmxjhV79yB3qpmtid6rmsoat3b9hpJ89hlPRx992X2hhNTbkrkdTMw697LvuOG819Yklq9qtfwQknQKjITduSuqOpKd385lxX92UPXc51SWpp5ky47bZ07I+k6rHKKrDCCvDkk7krqV3ZQ9dmm8H06eksRkm69FI45BBYdtnclUhqzRZjz2QPXc51SWq2aFE63PqEE3JXIqktDtP3TPbQBc51SUpGj04tjC22yF2JpLY419UzVRG6nOuSBG4TIVW7lVeGFVeEJ57IXUltqorDNTbbDF59Nc11rbhi7mok5fDii/Doo/DnP+euRFJHmue6NtssdyU9s3gxvPxyujHgySdTkKz0TQJVsdLVpw/suKPnMEqN7OKL4UtfggEDclciqSPNh1/XkrfeSmNMF14Ixx8P220HyyyTOm2XXALz5sEBB6SjxyopxBgre4EQYmeu8X//B1OnwgUXVLQcSVVo/nxYfXUYOxbWXjt3NZI6MnMmbLABvPlmuhmumnzwAUye/PGVqyeegLlzYeTI9LbJJh+9v9xyn3yOEAIxxorsElgV7UVIy5Vf/nLuKiTl8Mc/wpZbGrikWrDSSunt8cfz3fQSI0yb9snW4AsvwPDhHwWr449P76+xBvSqgt5e1ax0LVyYNl17/nkYMqSiJUmqMqNGwfe/D/vvn7sSSZ1x4omwzjpw2mnFXvfDD+GLX4Rbb4Wllvrk6tUGG6SP90RDrHT16QM77JDmuj73udzVSCrKhAkwYwbsu2/uSiR1VlMT/O53xYeu73wH5syp3QWaKlhs+4ibrkmN56KLUgug2mZDJLVvl13SwfSLFhV3zT/8Id3dfM01tRm4oApDl5ukSo3j7bfhhhvgq1/NXYmkrhg6NO3Z9dhjxVzv6afhpJPgxhth8OBirlkJVRW6Nt883cH45pu5K5FUhKuugj33TEO5kmpLUVtHvPMOHHww/PSnKSfUsqoKXS3nuiTVtxhTa9Ed6KXaVMTh1zGm/ft23TX9WuuqKnSBc11Soxgz5qMD7yXVnl12gfvvT7sPVMo558D06XDeeZW7RpGqMnQ51yXVv1/9Ck44AUJFbsyWVGkrrgjDhlVuruvOO+Hcc9PcZ//+lblG0aoudG2xRToLafbs3JVIqpQZM+COO+CYY3JXIqknKtWdmjYNjj46bUux2mrlf/5cqi509ekD22/vXJdUz377Wzj8cFh66dyVSOqJSsx1LVgAhxwCp54K//Zv5X3u3KpmR/qWzj47/Uu4Xnq4kj6ycGE6pmP0aNh009zVSOqJN95IO9PPnp0WTcrh61+H119P20PkGD+o5I70VbfSBc51SfXsL39J56AZuKTaN2RIOqx+4sTyPN/ll6eVsyuuqM95z6oMXVtsAVOmwFtv5a5EUrn96lduEyHVk3K1GCdMgH//d/jTn+p39KAqQ1ffvrDdds51SfXmuefg8cfTvIak+lCOYfq33kp/L/zyl7DhhuWoqjpVZegC9+uS6tHFF8NXvlI/t39LSvt1PfBA9/frWrwYjjoKDjoIDjusvLVVG0OXpEL885/p2J/jj89diaRyWmGFNKf56KPd+/of/zj9/XDWWeWtqxpVbejackt46SXnuqR68Yc/wLbbwppr5q5EUrl1d6Hkr3+FSy9Nfz/07VvuqqpP1Yau5rmu++7LXYmkcvCcRal+defw6xdfTOMG11/fOIfeV23oAluMUr145JG0785ee+WuRFIl7Lxzmuv68MPOPf6f/4TPfQ5++MO0IXqjMHSp02bPTmdg/fOfuStRrbnoorThYe/euSuRVAnLL59GBzoz1xVj+vtg443hG9+ofG3VpKpD11ZbpeXHOXNyVyJIJwV8+9vpgNOvfS39q6bCBxqoDsyZAzfdlNoIkupXZ1uMF12UDsm+5JL63AC1I1Uduvr2hVGjnOuqBvPmwWWXpQ3wnnoK1l03Ba8RI+AnP4FXXsldoarVFVfAvvvCiivmrkRSJXWmO/XQQ3DGGekfYp/+dAFFVZmqDl1gi7FaXHll2otlzTVhlVXSrsFPPw3XXpvOydxiC9h993QivO1HNVu82AF6qVHsvDM8+GD7c12zZqV9uC69NJ3X2IgMXVqixYvT4eOnnvrxj4cAW2+ddhB+9VU47rgUwlZdFY49Fu6/3/Zjo7vrLhgwoLEGZaVGNXgwrLUWjB//yc8tXAhHHAFf+hLst1/hpVWNqg9dW20Fzz/vXFdOt96azsHaccf2HzNgQPoXzK23phWwESNSCBsxAs480/Zjo2pe5Wq0uQ2pUbU31/W976WTKM44o+iKqkvVh65+/dJc1/33566kcZ17blrl6uwPztbtx9des/3YiKZPTzOARx2VuxJJRWnr8Osbbkhv11zjHcxVH7rAFmNOTzwBkyZ17zys1u3H44+H666z/dgofvMbOPJIGDQodyWSirLzzmlY/oMP0u8nTYITTkiha/nl89ZWDQxd6tB556X2UL9+PXueAQPg0ENh9Oi0Arbeeqn9uO66th/r1dVXp3AtqXEst1z6e338eHj3XTj44LTd0JZb5q6sOoRY4aWGEELs6TU++CAl5GnTYNlly1SYluj111M4ev75dKBpucWY/se84op07taOO6bbiHvVxD8F1JEpU9IxXq+95jyX1GhOOy0N1T/2WPrZfckluSvqmhACMcaK/M1VEz/enOvK4+KL0+pUJQIXfLL9+MIL7slWL+66C3bbzcAlNaKmprS69corcP75uaupLjURuiDtEWWLsTgLFqQ7z045pZjrDRgAX/hCakmp9t15ZwpdkhrPzjvDRhulOa7+/XNXU11qor0IaZXr1FPb3v9D5XfllenOw9tuK+6ar74Km2ySNlsdMKC466q8YoSVV07DtGuumbsaSeqahm8vQmpDPfsszJ2bu5L6F+NH20QUadiwtLXEX/5S7HVVXk8/DZ/6lIFLklqrmdDVvz9su61zXUW45x54/33Yc8/ir3300bYYa92dd8K//VvuKiSp+tRM6ALnuopy7rlplivHXYSf+1wKfW+8Ufy1VR533WXokqS21FTocr+uynvhBXjggTTUnsOgQbDvvmkLCdWehQtTaN5119yVSFL1qanQtc02MHmyc12VdMEFaUPLT30qXw3HHGOLsVY9+iisthoMHZq7EkmqPjUVuvr3T8HLua7KmDs3hZ1vfCNvHXvsAVOnphsnVFuc55Kk9tVU6II013XPPbmrqE+XXgp77ZXuIsypT590Zt811+StQ11n6JKk9tXMPl3N7r0Xvv1tePjhsj2lSLM466wD11+fVhNzmzABDjkEXnzRXc1rxfvvp9MLZsyApZfOXY0kdY/7dLWwzTbwzDPwzju5K6kvN98Mq65aHYELYPPNYaml0lC/asODD8LGGxu4JKk9NRe6BgxwrqsScmyG2pEQ3LOr1thalKSO1VzogrR1hHNd5TN+fDqY9KCDclfycUcdlc7uev/93JWoMwxdktSxmgxdbpJaXueeC9/8Zhpgryarr57OYhw9OnclWpK5c9PxP9tvn7sSSapeNRm6tt02/QXvXFfPTZ8Ot96a9uaqRu7ZVRvuvTf9f+lB5ZLUvpoMXQMGpAOwHbLuuV/9KrXxll02dyVtO+QQuPtumD07dyXqiK1FSVqymgxdkOa67r47dxW17Z//hF//Gk4+OXcl7Vt6adh777SVharXnXfCbrvlrkKSqlvNhq5DDoErr4TTT4f33stdTW363e9gu+1g3XVzV9IxW4zVbdYsmDYNttwydyWSVN1qNnRttBE8+WTaiHHkSLj99twV1ZYY0wD9t76Vu5Il+8xn0iapL7yQuxK15a670s0t1XYjhiRVm5oNXQArrpiOivnlL+H449OKyBtv5K6qNtx+O/Ttm9q01a5vXzjiiLQyp+pz113Oc0lSZ9R06Gq2997w1FMwdGjaEfuqq9JKjtr3i1+kVa5aOWLn6KNT6PLPtfo4RC9JnVNzZy8uyaOPwte+BssvDxdfDGuvXdila8Yzz6Sh55dfrp1b/GOEDTaAyy5zL6hqMmVKmgt87bXaCfCS1BHPXuyCLbdMh2HvuWfaN+icc+DDD3NXVV3OPx++/vXaCVyQfqA7UF99mu9aNHBJ0pLV3UpXSy+9lMLF66/Db38LW22VpYyqMns2rLMOTJ6c2rG15OWX05/hjBnQr1/uagRw5JGw++7w1a/mrkSSysOVrm5aay247Tb49rfhs59NM0zz5uWuKq9f/xoOPLD2AhfA8OHprtVbb81diSC1fB2il6TOq+vQBantcfTRadD+rbfSoH2j/tD+4AO48EI49dTclXSfLcbq8fTTMHBgCsOSpCWr+9DVbIUV0maqv/lNOtz5yCPTpo6N5IYbYL31YNNNc1fSfYccAnfcAXPm5K5E7kIvSV3TMKGr2R57pE1VV189bap62WWNsQ1BjB9tE1HLll023SThsUD5uVWEJHVNXQ/SL8nEiWl7iUGD0qxTtR+H0xMPPABf+hI8+yz0qvGofcst6a7U++/PXUnjWrgwrR4/91zapFiS6oWD9BWy+eYwdizsv3/aa+h//ifNPdWjX/wCTjml9gMXwF57pfD40ku5K2lcjz6aVosNXJLUeXXwI7hn+vRJLbfx4+G++9I+X+PG5a6qvF5+GcaMSStd9aBfPzj88HQElPKwtShJXdfwoavZ8OHprsYf/CBtqXDiiemOx3pwwQXw5S+nO83qRfNdjFXaua57hi5J6rqGnulqz1tvwdlnp5WU5ZeHo45Kdzuutlruyrru3XdToJwwAdZYI3c15RNjuhPz6qvTyQMqzvz5MGRI2qR26aVzVyNJ5eVMV8EGD06ha+pUOPfcNCy86abQ1JS2nKil7QquuCKtSNRT4AKPBcrpwQfTnb8GLknqGle6Oun991P78Zpr0j5Ru+2WVsA++9nqPcNw0aK0GnTVVfV5SPSUKbDNNjB9uscCFekHP0ih98wzc1ciSeXnSlcVGDAADj4YbrwxrYDttx9cdBGssgp85StpxmXRotxVftzo0WnVbrvtcldSGWuuCeuvD3//e+5KGovzXJLUPa509dD06fD736cVsFmz4Igj0grY5pun1YCcdt0VjjsuzaPVq0suSSuPf/xj7koaw9y5MGwYvPFG9a7wSlJPVHKly9BVRpMmpfB17bXQv38KX5//fDp4u2iPPZZan1OmQN++xV+/KHPmpBsFpk5Nu9Wrsm65Bc4/PwVdSapHthdrxAYbpDmXF1+ESy+F116DUaPSPNUvf5lWB4py7rlw0kn1HbgAllsOdt89nSupyrO1KEnd50pXhX34Idx+e1r9Gj0adtghrX5tsUXa0fvTny7/NWfOTAHwxRfTTFe9+/Of047799yTu5L6N3Jk+gfFNtvkrkSSKiNrezGEcCnwWWBWjHGTFh//JvAN4ENgdIzxu+18fUOHrpbmzYObb06HNU+aBNOmpdC1+uofva2xxsd/P3Ro14/u+dGP4PXX06B/I/jgg3RDw/jxqdWoypg1K9248Oab0Lt37mokqTJyh64dgXnAVc2hK4TQBHwf2CfGuDCEsEKM8c12vt7Q1Y4YU8vxlVfS29SpH73f/NY8uNxeMFttNfjUpz56zvffT5+/5570A7JRnHgirLpq2s5AlXHddfCHP6SVRUmqV9kH6UMIawB/aRG6/gBcEmO8qxNfa+jqgfnz04pYyyDWMpxNmwaDBn0UxBYvTis/t96au/JiPfRQOupo0qT8d43Wq2OPhU02gZNPzl2JJFVOJUNXn25+3Qhg5xDC/wDzgdNjjOPLV5aaLbUUjBiR3tqyePFHq2XNYeyAA4qtsRqMGpX2SRs/HrbeOnc19enOO9Ph8JKk7ulu6OoDLBtjHBVC2Bq4HsiwMYJ69UpzX0OHNnbYCAGOPjodC9TIr0OlTJmSWtcbbpi7EkmqXd0NXdOAmwBijI+EEBaHEJaPMc5u68FnnHHGv95vamqiqampm5eV2nfUUenu0J/9rP63yijanXemo69s3UqqN2PGjGHMmDGFXKuzM13DSTNdI0u/Pw5YNcb4oxDCCOAfMcY2j1R2pktF2n77NEy/7765K6kvRx4Je+yRjrySpHqWdXPUEMK1wIPAiBDCKyGELwOXAWuFEJ4ErgW+UInipK465pjUYlT5xAh33eWmqJLUU26OqroyezasvXa6qWCZZXJXUx+efBIOPDBttitJ9c5jgKROWn75dND3jTfmrqR+uMolSeVh6FLdscVYXp63KEnlYXtRdWfBgrQ7/YQJacNYdd/ChbDCCvD88zBkSO5qJKnybC9KXdC/PxxyCFxzTe5Kat/48em0AwOXJPWcoUt1qXmjVAQaPb0AABA1SURBVBdZe6Z5fy5JUs8ZulSXdtgh7aA+cWLuSmqbQ/SSVD6GLtWllscCqXvmz4eHH4add85diSTVB0OX6tYxx8B116VhcHXdgw/CyJGw9NK5K5Gk+mDoUt1ad10YPhz+8Y/cldQmt4qQpPIydKmuuWdX9zlEL0nl5T5dqmtvvgnrrAPTpsGgQbmrqR1z58KwYfDGGzBgQO5qJKk47tMlddMKK8Auu8BNN+WupLbccw+MGmXgkqRyMnSp7nkXY9c5zyVJ5WfoUt3bb790JNAjj+SupHYYuiSp/AxdqnsDBsBvfwv77AMXXugu9UsycyZMnw5bbJG7EkmqL4YuNYSDD4aHHoLLLoNDD4W3385dUfW6++40B9e7d+5KJKm+GLrUMNZZJ234ufLKaRXHdmPbbC1KUmUYutRQBgyACy6An/4U9t0Xzj3XdmNrhi5Jqgz36VLDmjIFDj88rXxdfjkMHpy7ovxeegl23DHNdIWK7FIjSdXNfbqkClhzTbj/flh7bdh88zTz1eiad6E3cElS+Rm61ND69YOf/zy1HA88EM45BxYvzl1VPnfd5dE/klQpthelkqlT4cgjYdll4aqr0m72jSRGWGklePhhWGON3NVIUh62F6UCrLFGOv5m5MjUbrzvvtwVFeupp9L5lAYuSaoMQ5fUQt++cPbZcMklaT+vn/ykcdqN3rUoSZVl6JLasM8+MH48/P3vsNdeMGtW7ooqz9AlSZVl6JLaMWxY2p19m23SZqp33527ospZuBDuvRd23TV3JZJUvwxdUgf69IEzz4QrroCjjoIf/xgWLcpdVfmNHw/Dh8OQIbkrkaT6ZeiSOmGPPeDRR9Og/R57wGuv5a6ovGwtSlLlGbqkTlp5ZfjHP9Jh0Ftumd6vF4YuSao89+mSuuHuu+Hoo+HLX4YzzkhtyFo1fz6suCLMmJG2jJCkRuY+XVKV2XVXmDAhbSS6227wwAPw7ru5q+qeBx6ATTYxcElSpdXwv8+lvIYOTVtK/OxncOqp8MwzaUf3TTb5+Nvaa0OvKv7njUf/SFIxbC9KZbJoEbzwAjzxxMff3ngDNtro40Fs5EgYPDh3xcm226YNYZuaclciSflVsr1o6JIqbO7cdMROyyD25JOwzDKfXBUbMSLtil+Ut9+G1VaDN9+E/v2Lu64kVStDl1RnFi9OB2y3XhWbNg3WW+/jQWz99dOgeyVC0c03wy9/CbffXv7nlqRaZOiSGsR776XZsJZB7NlnU4ty4MAUvoYO/ejXlu+3/NjAgRA68VfGySfDqqvCd75T+e9NkmqBoUtqcIsXw5w58Prr6RzIWbM+er/1r7Nmpce3F8hafuyww+DKK2HrrXN/h5JUHQxdkrrkvffaD2QtP9a3b9r6onfv3BVLUnUwdEmSJBXAzVElSZJqnKFLkiSpAIYuSZKkAhi6JEmSCmDokiRJKoChS5IkqQCGLknS/2/v/oM9q+s6jj9fIZiGugpNS6CApQEZIohtSoCG7tbEaGUTOZMI02iOgqOlRjbBNKM4RCQ1Y4UEiTMLOFiUlEmAWCqsCKwsv4mMBZSQxi2WDF1498f5XPbL5XzvD3b37L3f7/Mxc4bv/ZzP59zzee85h/c93885H0kDMOmSJEkagEmXJEnSAEy6JEmSBmDSJUmSNACTLkmSpAGYdEmSJA3ApEuSJGkAJl2SJEkDMOmSJEkagEmXJEnSAEy6JEmSBmDSJUmSNACTLkmSpAGYdEmSJA3ApEuSJGkAJl2SJEkDMOmSJEkagEmXJEnSAEy6JEmSBmDSJUmSNACTLkmSpAGYdEmSJA3ApEuSJGkAJl2SJEkDMOmSJEkagEmXJEnSAEy6JEmSBmDSJUmSNACTLkmSpAGYdEmSJA3ApEuSJGkAJl2SJEkDMOmSJEkagEmXJEnSAEy6JEmSBmDSJUmSNACTLkmSpAGYdEmSJA1g3qQryV8l+c8kN/Ws+50kjyd5wY7ZPUmSpMmwkDtd5wOrZxcm2Qc4Brhne++UJEnSpJk36aqqLwHf6Vn1J8D7t/seSZIkTaCnNaYrybHAvVW1YTvvz9S4+uqrd/YuLDnGpJ9x6Wdc+hmXpzIm/YzL8BaddCV5FvAh4NTR4u22R1PCg/2pjEk/49LPuPQzLk9lTPoZl+GlquavlOwLfLaqDk7yMuAK4H/pkq19gPuBV1XVgz1t5/8FkiRJS0RV7ZCbSc9YYL20haq6GVj5xIrkG8ChVdU37muH7bgkSdJyspBXRqwFvgK8NMnGJCfMqlL49aIkSdKcFvT1oiRJkrbNogbSJ1mT5PYkdyb5YCvbL8m1Se5IcmGS3q8sk5yS5K4ktyV5w1zbXG5m9eEDrezcJOvb8ukkzx7Tdlri8sGR8g+34+WWJO8e0/b41u6OJG8dKT80yU1t3ceG6Mf2NuZ4eV2S61vfzk/Se25Oalz6XsKc5Ix2XqxP8pkkzx3TdtxxtqBr01I2Ji6nJrkvyQ1tWTOm7UTGZUxMXp7kmiQ3JvlqkleOaTuR5w90785MclWSW5NsSHJyK39zkpuTPJbk0DnaT+rxsrTylqpa0EKXoP0bsC+wK3AjcCBwMfCrrc6fA+/oaXtgq/8MYL+2nfRscz1wwEL3aSks4/oA7D5S54+BDxgXDgDeBvz1SL09e9o+H7gbeB6wYuZzW7eO7qENgH8EVu/svm5jXGbOo43Aj7U6pwEnTllcjgAOAW4aKTsG+IH2+aPA6Qs9ztq6ea9NS30ZE5dTgfct8jibmLiMicnngTe0zz8PfKGn3cSeP22/VwKHtM+7A3e0a+5PAC8BrqIbfz01x8sc19udlrcs5k7Xq4C7quqeqvo+cBHwRuC1wGdanU8Cv9TT9o3ARVW1par+A7irbW/cNpeT3j5U1WaAJAGeRTf2bbZpisuFwJuAdwJ/OFOpqh7qabsauLyq/ruqNgGXA2uSrASeU1VfbfUuaNtcTvr+bX8F+L+qurvVuaKVzTaxcamelzBX1RVV9Xj78Vq6J6Vnm+tceR3zX5uWtL64NPONo53YuIyJyeN0yRR0CdX9PU0n9vwBqKoHqmp9+7wZuA3Yu6ruqKq7mPuYmdTjZcnlLYtJuvYG7h35+f5Wtmnkwngf8KPQvUA1yWnztJ1dfl8rW07G9iHJecC36P7S+LNWNq1xmenbi4HjklyX5B+S/DhAksOSnDNP273pYjFjUuKyEth15Nb/m2kJxhTFZT4nAp8DSLJXkstaee+5kmQP4Dt916YJ8a72teu5SVbA1MflvcCZSTYCZwCnwPSeP0n2o7sbuG6OOtNwvCy5vGUxSVdflrxLT1kBVNVnq+q0OdqOe+pxuY3sH9uHqjoR2IvuL47jWtm0x+WZwHer6nDgXOA8gKq6vqrePk/bSY3LY3THx8eSXAv8D7AFpiouYyX5EPD9qloLUFXfqqpfnFnd02QmJrPXTUpMPk73VfQhwAN0wxemPS7vBN5TVS+iS8Cm7bryhCS7A5fQxWPzuHpTcrwsubxlMUnXfcCLRn7eh24cyopsHfS7D/DNMW1fOKvtN8dss6/9UjZnH6r7cvjTwC+PaTttcbmPdlu3qv4WOHiRbfvitZz09q2q1lXVkVW1CvhXulvZC2rLZMSlV5LjgV8A3jKmyrh4PsTCrk3LTlV9u11XAD4BHN5TbdricnxVXQpQVZfQfQU028SfP21A+CXAp6rq7xbRdFKPl6WXtyxiQNoubB08thvd4LGZAWm/NjIg7bd62h5ENyBtN2B/tg5I693mQvdpKSxzxGVmUHSAPwLOMC4cCHwEOKHVORpY19N2dMDrzOcVbd06ugtq6Aa8rtnZfd1Ocfnhtv6ZdGO6jp6muLQ+7AdsGPl5DXALsMci4zk6AHjOa9NyWHrisnLk83uBtdMWl56Y3AIc1T7/HHBdT5uJPn9aPy4Azhqz7gvAYWPWTeTxMsf1dqflLYvtwBq6JyLuAn63le3fDtg7W0d2beXHAqeNtD2l7ehttKdMxm1zuS2z+9D+Yb4EfB24CfgU7WnGaY5LK3secFmLy5eBn2rlhwHnjLR9W2t3J/DWkfLDgA1t3dk7u4/bMS5nALe24+CkWf2d+LgAa+n+WnyU7i/RE1pf7gFuaMvHW929gMvmimcr7702LadlTFwuaOfPeuBS4EemKS5jYvJq4Gt0/5O8BnhFqzsV50/rw2vohiqsb3G4oR0Db6Ibg/RdujHGn5uy42VJ5S2+HFWSJGkAi3o5qiRJkp4eky5JkqQBmHRJkiQNYN6ka9x8Tm3dSW3+oQ1JPjrHNn6/zVF0e5Irkxw0z+88KsnPLK4rkiRp2vXkLSe18ouydc7SbyS5YY5t7JC8ZSETV26hm+drfXvp2vVJLqd7i/axwMuqakuSPcfsyLuBVXRPqT2a5PXA3yc5qKq+N+Z3Hg1spnsKRZIkaaH68pZ/rqrjZiokORPY1Nd4R+Yti356McmldFPavB34y6q6ap76G4Ejq5u7aKbsk8DVVXV+kjXAh+nuuj0E/CbdPGtbgG/TPT7/5UXtpCRJElvzlqq6cqRsI/Da2jrnLbPW7ZC8ZSF3ukZ3ZD+2zud0JnBkko/Qvf/j/VX1tVn1nwM8e3THm+uBn2x3x84BjqiqjUlWVNWmJH8BPFxVZy1m/yRJkmb0zUOZ5GeBB8YkXDs0b1lw0jV7Pqc23cCKqlqV5HC6qW5evNDN0c1VtAr4YlVtBKhu5ndJkqRtMsc8lL8OXLjYzbEd8pYFPb04Zj6ne4G/ab/0OuCxJHskOS/JjUkuq6qHgUdapjnqULq3b/dNHClJkvS0jZuHMskudHMhXzxSNljestBXRpwH3FpVZ4+UXUo3xxVJXgrsVlX/VVUnVtUrauvs5WcCf5rkB1vdY+imK1hLN+DsyCT7tnXPb20eBp67Df2SJEnTqy9vAXg9cFtVPTFJ9ZB5y7wD6ZO8BvgXunmpqi2/B1zZOnUI3RxYv11VXxyzjT8AfoNukNkDwLuq6ta2bjVwOl32+GBVrU7yEroM9TEcSC9JkhZoXN5SVf+U5Hzgmqo6Z55t7JC8xbkXJUmSBuAb6SVJkgZg0iVJkjQAky5JkqQBmHRJkiQNwKRLkiRpACZdkiRJA1jU3IuStKMkeQHd+/8K2IvufTcP0r0L55GqOmIn7p4kbTPf0yVpyWkvJtzspPeSJolfL0paip40v1mSh9t/j0pydZKLk9ye5PQkb0myLsnXk+zf6u2Z5JJWvi7Jq3dGJyRplF8vSloORm/JHwwcAGwC/h34RFX9dJKTgZOA9wFnA2dV1VeSvBD4PHDQwPssSU9i0iVpubmuqh4ESHI3cHkr3wAc3T4fAxyYZOaO2e5JfqiqHhl0TyVphEmXpOXm0ZHPj4/8/Dhbr2kBVlXV94bcMUmai2O6JC0Hmb/Kk1wOnPxE4+Tl23d3JGnxTLokLQfjHrMeV/4e4JVtcP3NwDt2zG5J0sL5yghJkqQBeKdLkiRpACZdkiRJAzDpkiRJGoBJlyRJ0gBMuiRJkgZg0iVJkjQAky5JkqQBmHRJkiQN4P8B5by2Yva53dMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff18169f250>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline \n",
    "\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "# Creamos la figura\n",
    "plt.figure(figsize=(10,10))\n",
    "\n",
    "df['T2_degC'].plot()\n",
    "\n",
    "plt.title('Temperatura a 2m del suelo en CONAE')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Basemap\n",
    "\n",
    "Basemap es una librería para graficar información en 2D sobre mapas. Tiene varias proyecciones de mapas e información vectorial cargada (como limites políticos, cuerpos de agua, límites continentales, etc.)\n",
    "\n",
    "**wrf-python** trae varias funciones auxiliares para facilitar la carga de datos a basemap. Por ejemplo con **get_basemap** cargamos el mapa del dominio, con **latlon_coords** cargamos las variables latitud y longitud.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.  Ejemplos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff181acc090>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from mpl_toolkits.basemap import Basemap\n",
    "from matplotlib.cm import get_cmap\n",
    "\n",
    "t2_degC = t2.copy()\n",
    "t2_degC.values = t2.values - 273.15\n",
    "\n",
    "# Nos quedamos con las latitudes y longitudes\n",
    "lats, lons = wrf.latlon_coords(t2_degC)\n",
    "\n",
    "# Creamos el basemap\n",
    "bm = wrf.get_basemap(t2_degC)\n",
    "\n",
    "# Creamos la figura\n",
    "plt.figure(figsize=(12,12))\n",
    "\n",
    "# Se agregan los vectores de datos geográficos\n",
    "bm.drawcoastlines(linewidth=0.25)\n",
    "bm.drawstates(linewidth=0.25)\n",
    "bm.drawcountries(linewidth=1)\n",
    "\n",
    "# Definimos la grilla x, y para graficar \n",
    "x, y = bm(wrf.to_np(lons), wrf.to_np(lats))\n",
    "\n",
    "# Se grafica la variable\n",
    "bm.contourf(x, y, t2_degC[8], 10, cmap=get_cmap(\"jet\"))\n",
    "\n",
    "# Se agrega la escala de colores\n",
    "plt.colorbar()\n",
    "\n",
    "plt.title(\"Temperatura a 2m a las 14hs del 29/11/2017\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff183bd16d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from mpl_toolkits.basemap import Basemap\n",
    "from matplotlib.cm import get_cmap\n",
    "\n",
    "# Creamos la figura\n",
    "plt.figure(figsize=(12,12))\n",
    "\n",
    "# Se agregan los vectores de datos geográficos\n",
    "bm.drawcoastlines(linewidth=0.25)\n",
    "bm.drawstates(linewidth=0.25)\n",
    "bm.drawcountries(linewidth=1)\n",
    "\n",
    "# Se grafica la variable\n",
    "bm.contourf(x, y, dbz_10[8], 10, cmap=get_cmap(\"jet\"))\n",
    "\n",
    "# Se agrega la escala de colores\n",
    "plt.colorbar()\n",
    "\n",
    "plt.title(\"Reflectividad a 10km de altura sobre la superficie a las 14hs del 29/11/2017\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para ver más ejemplos en la documentación de wrf-python:\n",
    "http://wrf-python.readthedocs.io/en/latest/plot.html"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.14"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}