presentation.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "linear-mongolia",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "<div align=\"right\">\n",
    "<strong>Club de Python para Físicos</strong> - Sat 03/04/2021\n",
    "</div>\n",
    "\n",
    "# Manejo de datos n-dimensionales en python: *conociendo xarray*  \n",
    "\n",
    "<br>\n",
    "<div align=\"center\">\n",
    "    <img src=\"http://xarray.pydata.org/en/stable/_static/dataset-diagram-logo.png\">\n",
    "</div>\n",
    "<br>\n",
    "\n",
    "Gerardo Rivera Tello  \n",
    "*Asistente de Investigación*  \n",
    "*Instituto Geofísico del Perú*\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "engaged-beast",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Temas a tratar"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "chubby-terrain",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "**1.** Ciencias del clima - especialmente oceanografía"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "considerable-collective",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "**2.** I/O y análisis de datos - numpy + pandas -> xarray"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "incoming-knight",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "**3.** Escalando la computación - integraciones con dask"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "experienced-corruption",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "**4.** El ecosistema Pangeo en python"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "removable-carry",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### ¿Por qué Python?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "golden-sullivan",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "- Es flexible: permite la rápida iteración de ideas"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "confirmed-justice",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "- Es open-source: crece conjuntamente con la comunidad"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "binding-building",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "- Es complejamente simple: ¿o simplemente complejo?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "handled-counter",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "- Big data frameworks: en gran variedad"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "virtual-spain",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## ¿Cómo se usa python en ciencias del clima?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "resistant-editor",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "**Modelamiento Numérico**  \n",
    "o interfaces a modelos numéricos\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "express-dialogue",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "- Climlab: https://climlab.readthedocs.io/en/latest/  \n",
    "- CliMT: https://climt.readthedocs.io/en/latest/  \n",
    "- PyCPT: https://iri.columbia.edu/our-expertise/climate/tools/cpt/  \n",
    "- Veros: https://veros.readthedocs.io/en/latest/  \n",
    "- xarray-simlab: https://xarray-simlab.readthedocs.io/en/latest/"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "positive-television",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "**Análisis de Datos**  \n",
    "o herramientas de I/O especializadas"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "psychological-gather",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "- xarray: http://xarray.pydata.org/en/stable/  \n",
    "    - xgcm: https://xgcm.readthedocs.io/en/latest/  \n",
    "    - xmitgcm: https://xgcm.readthedocs.io/en/latest/  \n",
    "    - xESMF: https://pangeo-xesmf.readthedocs.io/en/latest/  \n",
    "    - y más: http://xarray.pydata.org/en/stable/related-projects.html  \n",
    "- climpred: https://climpred.readthedocs.io/en/stable/  \n",
    "- wrf-python: https://wrf-python.readthedocs.io/en/latest/\n",
    "- MetPy: https://unidata.github.io/MetPy/dev/index.html  \n",
    "- SatPy: https://satpy.readthedocs.io/en/stable/"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "appointed-recorder",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Entrada y Salida de Datos"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "interesting-chrome",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### ¿Cual es el mejor formato de almacenamiento de datos?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ancient-destruction",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "<div style=\"font-size: 10px\">\n",
    "    <s> y ¿por qué es zarr? </s>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "based-zambia",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "**Formatos más comunes**  \n",
    "\n",
    "<br>\n",
    "  \n",
    "| Datos Tabulares | Data Cubes | Otros tipos de formatos |\n",
    "| :-: | :-: | :-: |\n",
    "| txt (no necesariamente) | HDF5 | pickle (Python) |\n",
    "| CSV | NetCDF | npy (Numpy) |\n",
    "| XLSX | GRIB | TIFF/GeoTIFF |\n",
    "| parquet | zarr | - |"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "concerned-finnish",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### ¿Cómo podemos leer estos formatos?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "agreed-conjunction",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "<img src=\"https://numpy.org/doc/stable/_static/numpylogo.svg\" width=25%>\n",
    "\n",
    "Los datos que puedan ser representados como arreglos (o matrices) son bien manejados por numpy"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "appreciated-wesley",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "Vamos a leer los datos del ONI obtenidos de la página de la NOAA, el cual tiene la siguiente estructura  \n",
    "\n",
    "<br>\n",
    "\n",
    "```\n",
    "year1 yearN\n",
    "year1 janval febval marval aprval mayval junval julval augval sepval octval novval decval\n",
    "year2 janval febval marval aprval mayval junval julval augval sepval octval novval decval\n",
    "...\n",
    "yearN janval febval marval aprval mayval junval julval augval sepval octval novval decval\n",
    "missing_value\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "municipal-thesis",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Con una simple inspección, podemos ver las lineas que debemos omitir para tener un arreglo correcto"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "crude-disco",
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     0\t 1950         2021\n",
      "     1\t 1950  -1.53  -1.34  -1.16  -1.18  -1.07  -0.85  -0.54  -0.42  -0.39  -0.44  -0.60  -0.80\n",
      "     2\t 1951  -0.82  -0.54  -0.17   0.18   0.36   0.58   0.70   0.89   0.99   1.15   1.04   0.81\n",
      "     3\t 1952   0.53   0.37   0.34   0.29   0.20   0.00  -0.08   0.00   0.15   0.10   0.04   0.15\n",
      "     4\t 1953   0.40   0.60   0.63   0.66   0.75   0.77   0.75   0.73   0.78   0.84   0.84   0.81\n",
      "     5\t 1954   0.76   0.47  -0.05  -0.41  -0.54  -0.50  -0.64  -0.84  -0.90  -0.77  -0.73  -0.66\n",
      "     6\t 1955  -0.68  -0.62  -0.69  -0.80  -0.79  -0.72  -0.68  -0.75  -1.09  -1.42  -1.67  -1.47\n",
      "     7\t 1956  -1.11  -0.76  -0.63  -0.54  -0.52  -0.51  -0.57  -0.55  -0.46  -0.42  -0.43  -0.43\n",
      "     8\t 1957  -0.25   0.06   0.41   0.72   0.92   1.11   1.25   1.32   1.33   1.39   1.53   1.74\n",
      "     9\t 1958   1.81   1.66   1.27   0.93   0.74   0.64   0.57   0.43   0.39   0.44   0.50   0.61\n"
     ]
    }
   ],
   "source": [
    "!head data/oni.data | nl -v 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "departmental-bundle",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    10\t 2020   0.50   0.48   0.40   0.19  -0.08  -0.30  -0.41  -0.57  -0.89  -1.17  -1.27  -1.19\n",
      "     9\t 2021  -1.06 -99.90 -99.90 -99.90 -99.90 -99.90 -99.90 -99.90 -99.90 -99.90 -99.90 -99.90\n",
      "     8\t  -99.9\n",
      "     7\t ONI from CPC\n",
      "     6\t  Provided by NOAA/PSL\n",
      "     5\t  From http://www.cpc.ncep.noaa.gov/data/indices/oni.ascii.txt\n",
      "     4\t As of 09/20/2015, uses NOAA ERSST V5 from V3\n",
      "     3\t represents 3 month running mean of ERSST.v5 SST\n",
      "     2\t anomalies in the Niño 3.4 region \n",
      "     1\t (5N-5oS, 120o-170oW)]\n"
     ]
    }
   ],
   "source": [
    "!tail data/oni.data | tac | nl | tac"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "dried-bargain",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "81 data/oni.data\n"
     ]
    }
   ],
   "source": [
    "!wc -l data/oni.data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "completed-zoning",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Con la información recolectada, podemos leer estos datos pasando los parámetros correctos."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "based-thong",
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(72, 13)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "ONI = np.loadtxt(\"data/oni.data\", skiprows=1, max_rows=72)\n",
    "ONI.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "southern-hours",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Digamos que queremos seleccionar el valor del ONI para el año 2010 en el mes de Febrero.  \n",
    "\n",
    "**¿Cómo lo podemos hacer?**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "spectacular-raleigh",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "Indexing al rescate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "north-graphics",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 2.01e+03  1.50e+00  1.22e+00  8.40e-01  3.50e-01 -1.70e-01 -6.60e-01\n",
      " -1.05e+00 -1.35e+00 -1.56e+00 -1.64e+00 -1.64e+00 -1.59e+00]\n",
      "Year  2010.0\n"
     ]
    }
   ],
   "source": [
    "# El primer valor de cada fila es el año, asi que primero\n",
    "# debemos encontrar la fila donde 2010 sea el primer elemento\n",
    "\n",
    "print(ONI[60])  # no siempre se le atina a la primera\n",
    "print(\"Year \", ONI[60, 0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "outdoor-privacy",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.22"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Este es el valor que buscamos\n",
    "ONI[60, 2]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "lovely-baking",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Usando `numpy` es necesario mantener un registro de las dimensiones y operaciones que realizamos para asegurarnos que se esté seleccionando el elemento correcto.  \n",
    "\n",
    "La especialidad de `numpy` en el manejo de arreglos n-dimensionales puede facilmente irse de control en arreglos más grandes al no tener un control explicito de las dimensiones con las que queremos trabajar."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "finite-disposal",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "**¿Existira una forma más facil de manejar esto?**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "chinese-bubble",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "La respuesta es <span style=\"font-size: 50px\">SI</span> <span style=\"font-size: 10px\"><s> y no </s></span>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "opened-change",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "Todo depende de la estructura de los datos que queremos leer"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "colored-offense",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "<img src=\"https://pandas.pydata.org/docs/_static/pandas.svg\" width=25%>\n",
    "\n",
    "Los datos tabulares son muy bien manejados con pandas"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "lucky-savings",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "Como ejemplo, vamos a leer un archivo descargado de la red AERONET de la NASA, especificamente de la estación de Huancayo-IGP.\n",
    "\n",
    "Podemos explorar parte de la estructura usando el comando head en linux"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "dress-florist",
   "metadata": {
    "scrolled": true,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     1\tAERONET Version 3; \n",
      "     2\tHuancayo-IGP\n",
      "     3\tVersion 3: AOD Level 2.0\n",
      "     4\tThe following data are automatically cloud cleared and quality assured with pre-field and post-field calibration applied.\n",
      "     5\tContact: PI=Yamina_Silva_Vidal; PI Email=fsilva@igp.gob.pe\n",
      "     6\tDaily Averages,UNITS can be found at,,, https://aeronet.gsfc.nasa.gov/new_web/units.html\n",
      "     7\tDate(dd:mm:yyyy),Time(hh:mm:ss),Day_of_Year,AOD_1640nm,AOD_1020nm,AOD_870nm,AOD_865nm,AOD_779nm,AOD_675nm,AOD_667nm,AOD_620nm,AOD_560nm,AOD_555nm,AOD_551nm,AOD_532nm,AOD_531nm,AOD_510nm,AOD_500nm,AOD_490nm,AOD_443nm,AOD_440nm,AOD_412nm,AOD_400nm,AOD_380nm,AOD_340nm,Precipitable_Water(cm),AOD_681nm,AOD_709nm,AOD_Empty,AOD_Empty,AOD_Empty,AOD_Empty,AOD_Empty,440-870_Angstrom_Exponent,380-500_Angstrom_Exponent,440-675_Angstrom_Exponent,500-870_Angstrom_Exponent,340-440_Angstrom_Exponent,440-675_Angstrom_Exponent[Polar],N[AOD_1640nm],N[AOD_1020nm],N[AOD_870nm],N[AOD_865nm],N[AOD_779nm],N[AOD_675nm],N[AOD_667nm],N[AOD_620nm],N[AOD_560nm],N[AOD_555nm],N[AOD_551nm],N[AOD_532nm],N[AOD_531nm],N[AOD_510nm],N[AOD_500nm],N[AOD_490nm],N[AOD_443nm],N[AOD_440nm],N[AOD_412nm],N[AOD_400nm],N[AOD_380nm],N[AOD_340nm],N[Precipitable_Water(cm)],N[AOD_681nm],N[AOD_709nm],N[AOD_Empty],N[AOD_Empty],N[AOD_Empty],N[AOD_Empty],N[AOD_Empty],N[440-870_Angstrom_Exponent],N[380-500_Angstrom_Exponent],N[440-675_Angstrom_Exponent],N[500-870_Angstrom_Exponent],N[340-440_Angstrom_Exponent],N[440-675_Angstrom_Exponent[Polar]],Data_Quality_Level,AERONET_Instrument_Number,AERONET_Site_Name,Site_Latitude(Degrees),Site_Longitude(Degrees),Site_Elevation(m)\n",
      "     8\t20:03:2015,12:00:00,79,0.018201,0.023610,0.025333,-999.,-999.,0.031682,-999.,-999.,-999.,-999.,-999.,-999.,-999.,-999.,0.047772,-999.,-999.,0.056059,-999.,-999.,0.074383,0.087858,1.268790,-999.,-999.,-999.,-999.,-999.,-999.,-999.,1.245531,1.641458,1.393531,1.215758,1.808468,-999.,32,32,32,0,0,32,0,0,0,0,0,0,0,0,32,0,0,32,0,0,32,32,32,0,0,0,0,0,0,0,32,32,32,32,32,0,lev20,890,Huancayo-IGP,-12.040200,-75.320900,3313.000000\n",
      "     9\t21:03:2015,12:00:00,80,0.004776,0.012473,0.013893,-999.,-999.,0.020682,-999.,-999.,-999.,-999.,-999.,-999.,-999.,-999.,0.036869,-999.,-999.,0.044473,-999.,-999.,0.059118,0.071017,1.314858,-999.,-999.,-999.,-999.,-999.,-999.,-999.,1.741680,1.705673,1.812615,1.770809,1.838964,-999.,1,1,1,0,0,1,0,0,0,0,0,0,0,0,1,0,0,1,0,0,1,1,1,0,0,0,0,0,0,0,1,1,1,1,1,0,lev20,890,Huancayo-IGP,-12.040200,-75.320900,3313.000000\n",
      "    10\t22:03:2015,12:00:00,81,0.008359,0.012901,0.012335,-999.,-999.,0.015436,-999.,-999.,-999.,-999.,-999.,-999.,-999.,-999.,0.023698,-999.,-999.,0.027255,-999.,-999.,0.039261,0.047811,1.254088,-999.,-999.,-999.,-999.,-999.,-999.,-999.,1.218209,1.903330,1.375605,1.203290,2.286162,-999.,4,4,4,0,0,4,0,0,0,0,0,0,0,0,4,0,0,4,0,0,4,4,4,0,0,0,0,0,0,0,4,4,4,4,4,0,lev20,890,Huancayo-IGP,-12.040200,-75.320900,3313.000000\n"
     ]
    }
   ],
   "source": [
    "!head data/20150101_20211231_Huancayo-IGP.lev20 | nl"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "scheduled-roberts",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Los datos tabulares se almacenan de diferentes formas, por ello es bueno conocer parte de la estructura para obtener el resultado deseado."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "cheap-addition",
   "metadata": {
    "scrolled": true,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date(dd:mm:yyyy)</th>\n",
       "      <th>Time(hh:mm:ss)</th>\n",
       "      <th>Day_of_Year</th>\n",
       "      <th>AOD_1640nm</th>\n",
       "      <th>AOD_1020nm</th>\n",
       "      <th>AOD_870nm</th>\n",
       "      <th>AOD_865nm</th>\n",
       "      <th>AOD_779nm</th>\n",
       "      <th>AOD_675nm</th>\n",
       "      <th>AOD_667nm</th>\n",
       "      <th>...</th>\n",
       "      <th>N[440-675_Angstrom_Exponent]</th>\n",
       "      <th>N[500-870_Angstrom_Exponent]</th>\n",
       "      <th>N[340-440_Angstrom_Exponent]</th>\n",
       "      <th>N[440-675_Angstrom_Exponent[Polar]]</th>\n",
       "      <th>Data_Quality_Level</th>\n",
       "      <th>AERONET_Instrument_Number</th>\n",
       "      <th>AERONET_Site_Name</th>\n",
       "      <th>Site_Latitude(Degrees)</th>\n",
       "      <th>Site_Longitude(Degrees)</th>\n",
       "      <th>Site_Elevation(m)</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>20:03:2015</td>\n",
       "      <td>12:00:00</td>\n",
       "      <td>79</td>\n",
       "      <td>0.018201</td>\n",
       "      <td>0.023610</td>\n",
       "      <td>0.025333</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>0.031682</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>...</td>\n",
       "      <td>32</td>\n",
       "      <td>32</td>\n",
       "      <td>32</td>\n",
       "      <td>0</td>\n",
       "      <td>lev20</td>\n",
       "      <td>890</td>\n",
       "      <td>Huancayo-IGP</td>\n",
       "      <td>-12.0402</td>\n",
       "      <td>-75.3209</td>\n",
       "      <td>3313.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>21:03:2015</td>\n",
       "      <td>12:00:00</td>\n",
       "      <td>80</td>\n",
       "      <td>0.004776</td>\n",
       "      <td>0.012473</td>\n",
       "      <td>0.013893</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>0.020682</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>lev20</td>\n",
       "      <td>890</td>\n",
       "      <td>Huancayo-IGP</td>\n",
       "      <td>-12.0402</td>\n",
       "      <td>-75.3209</td>\n",
       "      <td>3313.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>22:03:2015</td>\n",
       "      <td>12:00:00</td>\n",
       "      <td>81</td>\n",
       "      <td>0.008359</td>\n",
       "      <td>0.012901</td>\n",
       "      <td>0.012335</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>0.015436</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>lev20</td>\n",
       "      <td>890</td>\n",
       "      <td>Huancayo-IGP</td>\n",
       "      <td>-12.0402</td>\n",
       "      <td>-75.3209</td>\n",
       "      <td>3313.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>23:03:2015</td>\n",
       "      <td>12:00:00</td>\n",
       "      <td>82</td>\n",
       "      <td>0.011493</td>\n",
       "      <td>0.019424</td>\n",
       "      <td>0.018699</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>0.025666</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>...</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>lev20</td>\n",
       "      <td>890</td>\n",
       "      <td>Huancayo-IGP</td>\n",
       "      <td>-12.0402</td>\n",
       "      <td>-75.3209</td>\n",
       "      <td>3313.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>24:03:2015</td>\n",
       "      <td>12:00:00</td>\n",
       "      <td>83</td>\n",
       "      <td>0.007365</td>\n",
       "      <td>0.011363</td>\n",
       "      <td>0.011387</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>0.014973</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>...</td>\n",
       "      <td>68</td>\n",
       "      <td>68</td>\n",
       "      <td>68</td>\n",
       "      <td>0</td>\n",
       "      <td>lev20</td>\n",
       "      <td>890</td>\n",
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       "</table>\n",
       "<p>5 rows × 81 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  Date(dd:mm:yyyy) Time(hh:mm:ss)  Day_of_Year  AOD_1640nm  AOD_1020nm  \\\n",
       "0       20:03:2015       12:00:00           79    0.018201    0.023610   \n",
       "1       21:03:2015       12:00:00           80    0.004776    0.012473   \n",
       "2       22:03:2015       12:00:00           81    0.008359    0.012901   \n",
       "3       23:03:2015       12:00:00           82    0.011493    0.019424   \n",
       "4       24:03:2015       12:00:00           83    0.007365    0.011363   \n",
       "\n",
       "   AOD_870nm  AOD_865nm  AOD_779nm  AOD_675nm  AOD_667nm  ...  \\\n",
       "0   0.025333     -999.0     -999.0   0.031682     -999.0  ...   \n",
       "1   0.013893     -999.0     -999.0   0.020682     -999.0  ...   \n",
       "2   0.012335     -999.0     -999.0   0.015436     -999.0  ...   \n",
       "3   0.018699     -999.0     -999.0   0.025666     -999.0  ...   \n",
       "4   0.011387     -999.0     -999.0   0.014973     -999.0  ...   \n",
       "\n",
       "   N[440-675_Angstrom_Exponent]  N[500-870_Angstrom_Exponent]  \\\n",
       "0                            32                            32   \n",
       "1                             1                             1   \n",
       "2                             4                             4   \n",
       "3                             8                             8   \n",
       "4                            68                            68   \n",
       "\n",
       "   N[340-440_Angstrom_Exponent]  N[440-675_Angstrom_Exponent[Polar]]  \\\n",
       "0                            32                                    0   \n",
       "1                             1                                    0   \n",
       "2                             4                                    0   \n",
       "3                             8                                    0   \n",
       "4                            68                                    0   \n",
       "\n",
       "   Data_Quality_Level  AERONET_Instrument_Number  AERONET_Site_Name  \\\n",
       "0               lev20                        890       Huancayo-IGP   \n",
       "1               lev20                        890       Huancayo-IGP   \n",
       "2               lev20                        890       Huancayo-IGP   \n",
       "3               lev20                        890       Huancayo-IGP   \n",
       "4               lev20                        890       Huancayo-IGP   \n",
       "\n",
       "   Site_Latitude(Degrees)  Site_Longitude(Degrees)  Site_Elevation(m)  \n",
       "0                -12.0402                 -75.3209             3313.0  \n",
       "1                -12.0402                 -75.3209             3313.0  \n",
       "2                -12.0402                 -75.3209             3313.0  \n",
       "3                -12.0402                 -75.3209             3313.0  \n",
       "4                -12.0402                 -75.3209             3313.0  \n",
       "\n",
       "[5 rows x 81 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "aeronet = pd.read_csv(\"data/20150101_20211231_Huancayo-IGP.lev20\", skiprows=6)\n",
    "aeronet.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "living-satin",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "A veces se necesitan realizar algunos ajustes para trabajar con los datos de la forma que queremos, como por ejemplo, las fechas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "documented-start",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date(dd:mm:yyyy)              object\n",
       "Time(hh:mm:ss)                object\n",
       "Day_of_Year                    int64\n",
       "AOD_1640nm                   float64\n",
       "AOD_1020nm                   float64\n",
       "                              ...   \n",
       "AERONET_Instrument_Number      int64\n",
       "AERONET_Site_Name             object\n",
       "Site_Latitude(Degrees)       float64\n",
       "Site_Longitude(Degrees)      float64\n",
       "Site_Elevation(m)            float64\n",
       "Length: 81, dtype: object"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aeronet.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "outdoor-housing",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('Date(dd:mm:yyyy)', '20:03:2015', str)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aeronet.columns[0], aeronet.iloc[:, 0][0], type(aeronet.iloc[:, 0][0])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "objective-breed",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Una solución a nuestras fechas podría ser aplicada al momento de la lectura de los datos."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "royal-reggae",
   "metadata": {
    "scrolled": true,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Day_of_Year</th>\n",
       "      <th>AOD_1640nm</th>\n",
       "      <th>AOD_1020nm</th>\n",
       "      <th>AOD_870nm</th>\n",
       "      <th>AOD_865nm</th>\n",
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       "      <th>AOD_675nm</th>\n",
       "      <th>AOD_667nm</th>\n",
       "      <th>AOD_620nm</th>\n",
       "      <th>...</th>\n",
       "      <th>N[440-675_Angstrom_Exponent]</th>\n",
       "      <th>N[500-870_Angstrom_Exponent]</th>\n",
       "      <th>N[340-440_Angstrom_Exponent]</th>\n",
       "      <th>N[440-675_Angstrom_Exponent[Polar]]</th>\n",
       "      <th>Data_Quality_Level</th>\n",
       "      <th>AERONET_Instrument_Number</th>\n",
       "      <th>AERONET_Site_Name</th>\n",
       "      <th>Site_Latitude(Degrees)</th>\n",
       "      <th>Site_Longitude(Degrees)</th>\n",
       "      <th>Site_Elevation(m)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2015-03-20 12:00:00</td>\n",
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       "      <td>-999.0</td>\n",
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       "      <td>32</td>\n",
       "      <td>32</td>\n",
       "      <td>32</td>\n",
       "      <td>0</td>\n",
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       "      <td>3313.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2015-03-21 12:00:00</td>\n",
       "      <td>80</td>\n",
       "      <td>0.004776</td>\n",
       "      <td>0.012473</td>\n",
       "      <td>0.013893</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <td>-75.3209</td>\n",
       "      <td>3313.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015-03-22 12:00:00</td>\n",
       "      <td>81</td>\n",
       "      <td>0.008359</td>\n",
       "      <td>0.012901</td>\n",
       "      <td>0.012335</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>0.015436</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>lev20</td>\n",
       "      <td>890</td>\n",
       "      <td>Huancayo-IGP</td>\n",
       "      <td>-12.0402</td>\n",
       "      <td>-75.3209</td>\n",
       "      <td>3313.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2015-03-23 12:00:00</td>\n",
       "      <td>82</td>\n",
       "      <td>0.011493</td>\n",
       "      <td>0.019424</td>\n",
       "      <td>0.018699</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
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       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>...</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>lev20</td>\n",
       "      <td>890</td>\n",
       "      <td>Huancayo-IGP</td>\n",
       "      <td>-12.0402</td>\n",
       "      <td>-75.3209</td>\n",
       "      <td>3313.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2015-03-24 12:00:00</td>\n",
       "      <td>83</td>\n",
       "      <td>0.007365</td>\n",
       "      <td>0.011363</td>\n",
       "      <td>0.011387</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>0.014973</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>...</td>\n",
       "      <td>68</td>\n",
       "      <td>68</td>\n",
       "      <td>68</td>\n",
       "      <td>0</td>\n",
       "      <td>lev20</td>\n",
       "      <td>890</td>\n",
       "      <td>Huancayo-IGP</td>\n",
       "      <td>-12.0402</td>\n",
       "      <td>-75.3209</td>\n",
       "      <td>3313.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 80 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Date  Day_of_Year  AOD_1640nm  AOD_1020nm  AOD_870nm  \\\n",
       "0 2015-03-20 12:00:00           79    0.018201    0.023610   0.025333   \n",
       "1 2015-03-21 12:00:00           80    0.004776    0.012473   0.013893   \n",
       "2 2015-03-22 12:00:00           81    0.008359    0.012901   0.012335   \n",
       "3 2015-03-23 12:00:00           82    0.011493    0.019424   0.018699   \n",
       "4 2015-03-24 12:00:00           83    0.007365    0.011363   0.011387   \n",
       "\n",
       "   AOD_865nm  AOD_779nm  AOD_675nm  AOD_667nm  AOD_620nm  ...  \\\n",
       "0     -999.0     -999.0   0.031682     -999.0     -999.0  ...   \n",
       "1     -999.0     -999.0   0.020682     -999.0     -999.0  ...   \n",
       "2     -999.0     -999.0   0.015436     -999.0     -999.0  ...   \n",
       "3     -999.0     -999.0   0.025666     -999.0     -999.0  ...   \n",
       "4     -999.0     -999.0   0.014973     -999.0     -999.0  ...   \n",
       "\n",
       "   N[440-675_Angstrom_Exponent]  N[500-870_Angstrom_Exponent]  \\\n",
       "0                            32                            32   \n",
       "1                             1                             1   \n",
       "2                             4                             4   \n",
       "3                             8                             8   \n",
       "4                            68                            68   \n",
       "\n",
       "   N[340-440_Angstrom_Exponent]  N[440-675_Angstrom_Exponent[Polar]]  \\\n",
       "0                            32                                    0   \n",
       "1                             1                                    0   \n",
       "2                             4                                    0   \n",
       "3                             8                                    0   \n",
       "4                            68                                    0   \n",
       "\n",
       "   Data_Quality_Level  AERONET_Instrument_Number  AERONET_Site_Name  \\\n",
       "0               lev20                        890       Huancayo-IGP   \n",
       "1               lev20                        890       Huancayo-IGP   \n",
       "2               lev20                        890       Huancayo-IGP   \n",
       "3               lev20                        890       Huancayo-IGP   \n",
       "4               lev20                        890       Huancayo-IGP   \n",
       "\n",
       "   Site_Latitude(Degrees)  Site_Longitude(Degrees)  Site_Elevation(m)  \n",
       "0                -12.0402                 -75.3209             3313.0  \n",
       "1                -12.0402                 -75.3209             3313.0  \n",
       "2                -12.0402                 -75.3209             3313.0  \n",
       "3                -12.0402                 -75.3209             3313.0  \n",
       "4                -12.0402                 -75.3209             3313.0  \n",
       "\n",
       "[5 rows x 80 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aeronet = pd.read_csv(\n",
    "    \"data/20150101_20211231_Huancayo-IGP.lev20\",\n",
    "    skiprows=6,\n",
    "    parse_dates=[[0, 1]],\n",
    "    date_parser=lambda x: pd.to_datetime(x, format=\"%d:%m:%Y %X\"),\n",
    ")\n",
    "aeronet.rename(columns={aeronet.columns[0]: \"Date\"}, inplace=True)\n",
    "aeronet.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "liked-occasion",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Verificando"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "equal-scientist",
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date                         datetime64[ns]\n",
       "Day_of_Year                           int64\n",
       "AOD_1640nm                          float64\n",
       "AOD_1020nm                          float64\n",
       "AOD_870nm                           float64\n",
       "                                  ...      \n",
       "AERONET_Instrument_Number             int64\n",
       "AERONET_Site_Name                    object\n",
       "Site_Latitude(Degrees)              float64\n",
       "Site_Longitude(Degrees)             float64\n",
       "Site_Elevation(m)                   float64\n",
       "Length: 80, dtype: object"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aeronet.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "greater-ebony",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('Date',\n",
       " Timestamp('2015-03-20 12:00:00'),\n",
       " pandas._libs.tslibs.timestamps.Timestamp)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aeronet.columns[0], aeronet.iloc[:, 0][0], type(aeronet.iloc[:, 0][0])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "occupational-vegetation",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Digamos que queremos seleccionar los datos del 15 de enero del 2017. Para eso, pandas tiene sus propios métodos de selección que facilitan dicha tarea."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "macro-liberty",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Day_of_Year</th>\n",
       "      <th>AOD_1640nm</th>\n",
       "      <th>AOD_1020nm</th>\n",
       "      <th>AOD_870nm</th>\n",
       "      <th>AOD_865nm</th>\n",
       "      <th>AOD_779nm</th>\n",
       "      <th>AOD_675nm</th>\n",
       "      <th>AOD_667nm</th>\n",
       "      <th>AOD_620nm</th>\n",
       "      <th>...</th>\n",
       "      <th>N[440-675_Angstrom_Exponent]</th>\n",
       "      <th>N[500-870_Angstrom_Exponent]</th>\n",
       "      <th>N[340-440_Angstrom_Exponent]</th>\n",
       "      <th>N[440-675_Angstrom_Exponent[Polar]]</th>\n",
       "      <th>Data_Quality_Level</th>\n",
       "      <th>AERONET_Instrument_Number</th>\n",
       "      <th>AERONET_Site_Name</th>\n",
       "      <th>Site_Latitude(Degrees)</th>\n",
       "      <th>Site_Longitude(Degrees)</th>\n",
       "      <th>Site_Elevation(m)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>586</th>\n",
       "      <td>2017-01-15 12:00:00</td>\n",
       "      <td>15</td>\n",
       "      <td>0.016485</td>\n",
       "      <td>0.023729</td>\n",
       "      <td>0.026584</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>0.031273</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>...</td>\n",
       "      <td>38</td>\n",
       "      <td>38</td>\n",
       "      <td>38</td>\n",
       "      <td>0</td>\n",
       "      <td>lev20</td>\n",
       "      <td>929</td>\n",
       "      <td>Huancayo-IGP</td>\n",
       "      <td>-12.0402</td>\n",
       "      <td>-75.3209</td>\n",
       "      <td>3313.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 80 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Date  Day_of_Year  AOD_1640nm  AOD_1020nm  AOD_870nm  \\\n",
       "586 2017-01-15 12:00:00           15    0.016485    0.023729   0.026584   \n",
       "\n",
       "     AOD_865nm  AOD_779nm  AOD_675nm  AOD_667nm  AOD_620nm  ...  \\\n",
       "586     -999.0     -999.0   0.031273     -999.0     -999.0  ...   \n",
       "\n",
       "     N[440-675_Angstrom_Exponent]  N[500-870_Angstrom_Exponent]  \\\n",
       "586                            38                            38   \n",
       "\n",
       "     N[340-440_Angstrom_Exponent]  N[440-675_Angstrom_Exponent[Polar]]  \\\n",
       "586                            38                                    0   \n",
       "\n",
       "     Data_Quality_Level  AERONET_Instrument_Number  AERONET_Site_Name  \\\n",
       "586               lev20                        929       Huancayo-IGP   \n",
       "\n",
       "     Site_Latitude(Degrees)  Site_Longitude(Degrees)  Site_Elevation(m)  \n",
       "586                -12.0402                 -75.3209             3313.0  \n",
       "\n",
       "[1 rows x 80 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Esta forma podría ser una de las mas simples de selección en pandas.\n",
    "# El método query es muy útil para estas tareas\n",
    "\n",
    "aeronet.query(\"Date == '2017-01-15 12:00:00'\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "familiar-retreat",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "un poco mas sencillo que hacer indexing, ¿cierto?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "mediterranean-warren",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "`pandas` es una herramienta altamente versátil que provee muchas operaciones convenientes de selección y agrupación.\n",
    "\n",
    "Pero ... ¿podría ser mejor?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "interpreted-alpha",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Otra vez <span style=\"font-size: 50px\">SI</span> <span style=\"font-size: 10px\"><s> y no</s></span>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "vertical-cooler",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "<img src=\"http://xarray.pydata.org/en/stable/_static/dataset-diagram-logo.png\" width=25%>\n",
    "\n",
    "Es un paquete al que podriamos considerar como una combinación de los arreglos n-dimensionales de numpy con las opciones de seleccion de pandas\n",
    "\n",
    "<span style=\"font-size: 0.5em\">Aunque eso sería una sobresimplificación</span>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cross-luxury",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "Tomado de su [documentación](http://xarray.pydata.org/en/stable/):\n",
    "\n",
    "\n",
    ">Xarray introduce etiquetas en forma de dimensiones, coordenadas y atributos sobre matrices multidimensionales de NumPy, lo cual permite una experiencia más intuitiva, más concisa y menos propensa a errores. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "close-wholesale",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "**¿En qué se diferencia de `numpy` y `pandas`?**\n",
    "<span style=\"font-size: 0.7em\"> o ¿deberíamos preguntar hasta dónde son iguales? </span>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "sporting-stewart",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "Todo depende de los datos que necesitemos leer. Los ejemplos provistos fueron solo para ilustrar una forma típica de ingesta de datos usando los paquetes mencionados: `numpy` con la capacidad de manejar datos n-dimensionales; y `pandas` con el uso de etiquetas en datos tabulares.  \n",
    "\n",
    "`xarray` toma lo mejor de ambos para leer lo que se conoce como Data Cubes\n",
    "\n",
    "<div align=\"center\">\n",
    "<img src=\"imgs/xarray_img.png\" width=60%>\n",
    "</div>\n",
    "<a style=\"font-size: 0.8em\" href=\"https://docs.google.com/presentation/d/16CMY3g_OYr6fQplUZIDqVtG-SKZqsG8Ckwoj2oOqepU/edit#slide=id.g67406e8d7_0_135\">Imagen de Stephan Hoyer</a>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "capable-nelson",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "El modelo de datos usado en xarray esta basado en el formato NetCDF, lo cual lo vuelve la herramienta ideal para el manejo de datos n-dimensionales.\n",
    "\n",
    "<div align=\"center\">\n",
    "<img src=\"https://www.unidata.ucar.edu/software/netcdf/workshops/most-recent/nc3model/images/ncfile.png\">\n",
    "</div>\n",
    "<a style=\"font-size: 0.8em\" href=\"https://www.unidata.ucar.edu/software/netcdf/workshops/most-recent/nc3model/NcFile.html\">Imagen de UNIDATA</a>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "contained-printing",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Las operaciones se vuelven mas simples cuando se conoce explicitamente el nombre de las dimensiones con las que se trabaja"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "blocked-stereo",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre>&lt;xarray.Dataset&gt;\n",
       "Dimensions:  (lat: 25, lon: 53, time: 2920)\n",
       "Coordinates:\n",
       "  * lat      (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n",
       "  * lon      (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n",
       "  * time     (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n",
       "Data variables:\n",
       "    air      (time, lat, lon) float32 ...\n",
       "Attributes:\n",
       "    Conventions:  COARDS\n",
       "    title:        4x daily NMC reanalysis (1948)\n",
       "    description:  Data is from NMC initialized reanalysis\\n(4x/day).  These a...\n",
       "    platform:     Model\n",
       "    references:   http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanaly...</pre>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:  (lat: 25, lon: 53, time: 2920)\n",
       "Coordinates:\n",
       "  * lat      (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n",
       "  * lon      (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n",
       "  * time     (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n",
       "Data variables:\n",
       "    air      (time, lat, lon) float32 ...\n",
       "Attributes:\n",
       "    Conventions:  COARDS\n",
       "    title:        4x daily NMC reanalysis (1948)\n",
       "    description:  Data is from NMC initialized reanalysis\\n(4x/day).  These a...\n",
       "    platform:     Model\n",
       "    references:   http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanaly..."
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import xarray as xr\n",
    "\n",
    "xr.set_options(display_style=\"text\")\n",
    "\n",
    "ds = xr.tutorial.open_dataset(\"air_temperature\")\n",
    "ds"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "invisible-sector",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Las variables se pueden acceder de manera individual por su nombre"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "answering-genesis",
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre>&lt;xarray.DataArray &#x27;air&#x27; (time: 2920, lat: 25, lon: 53)&gt;\n",
       "[3869000 values with dtype=float32]\n",
       "Coordinates:\n",
       "  * lat      (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n",
       "  * lon      (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n",
       "  * time     (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n",
       "Attributes:\n",
       "    long_name:     4xDaily Air temperature at sigma level 995\n",
       "    units:         degK\n",
       "    precision:     2\n",
       "    GRIB_id:       11\n",
       "    GRIB_name:     TMP\n",
       "    var_desc:      Air temperature\n",
       "    dataset:       NMC Reanalysis\n",
       "    level_desc:    Surface\n",
       "    statistic:     Individual Obs\n",
       "    parent_stat:   Other\n",
       "    actual_range:  [185.16 322.1 ]</pre>"
      ],
      "text/plain": [
       "<xarray.DataArray 'air' (time: 2920, lat: 25, lon: 53)>\n",
       "[3869000 values with dtype=float32]\n",
       "Coordinates:\n",
       "  * lat      (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n",
       "  * lon      (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n",
       "  * time     (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n",
       "Attributes:\n",
       "    long_name:     4xDaily Air temperature at sigma level 995\n",
       "    units:         degK\n",
       "    precision:     2\n",
       "    GRIB_id:       11\n",
       "    GRIB_name:     TMP\n",
       "    var_desc:      Air temperature\n",
       "    dataset:       NMC Reanalysis\n",
       "    level_desc:    Surface\n",
       "    statistic:     Individual Obs\n",
       "    parent_stat:   Other\n",
       "    actual_range:  [185.16 322.1 ]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "air = ds.air\n",
    "air"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "likely-asian",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "La selección de datos acorde al nombre de su dimensión vuelve al código más explícito"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "passing-provider",
   "metadata": {
    "scrolled": true,
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre>&lt;xarray.DataArray &#x27;air&#x27; (lat: 25, lon: 53)&gt;\n",
       "array([[256.38998, 254.79999, 253.29999, ..., 242.79999, 244.09999, 246.     ],\n",
       "       [250.59999, 249.29999, 248.59999, ..., 239.5    , 243.29999, 247.89   ],\n",
       "       [248.7    , 247.59999, 247.29999, ..., 238.89   , 244.89   , 251.5    ],\n",
       "       ...,\n",
       "       [297.6    , 297.5    , 297.5    , ..., 292.9    , 292.4    , 291.6    ],\n",
       "       [298.5    , 298.4    , 298.     , ..., 293.     , 292.79   , 292.5    ],\n",
       "       [298.5    , 298.5    , 298.1    , ..., 293.69998, 293.4    , 293.9    ]],\n",
       "      dtype=float32)\n",
       "Coordinates:\n",
       "  * lat      (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n",
       "  * lon      (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n",
       "    time     datetime64[ns] 2014-02-10T06:00:00\n",
       "Attributes:\n",
       "    long_name:     4xDaily Air temperature at sigma level 995\n",
       "    units:         degK\n",
       "    precision:     2\n",
       "    GRIB_id:       11\n",
       "    GRIB_name:     TMP\n",
       "    var_desc:      Air temperature\n",
       "    dataset:       NMC Reanalysis\n",
       "    level_desc:    Surface\n",
       "    statistic:     Individual Obs\n",
       "    parent_stat:   Other\n",
       "    actual_range:  [185.16 322.1 ]</pre>"
      ],
      "text/plain": [
       "<xarray.DataArray 'air' (lat: 25, lon: 53)>\n",
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       "       [250.59999, 249.29999, 248.59999, ..., 239.5    , 243.29999, 247.89   ],\n",
       "       [248.7    , 247.59999, 247.29999, ..., 238.89   , 244.89   , 251.5    ],\n",
       "       ...,\n",
       "       [297.6    , 297.5    , 297.5    , ..., 292.9    , 292.4    , 291.6    ],\n",
       "       [298.5    , 298.4    , 298.     , ..., 293.     , 292.79   , 292.5    ],\n",
       "       [298.5    , 298.5    , 298.1    , ..., 293.69998, 293.4    , 293.9    ]],\n",
       "      dtype=float32)\n",
       "Coordinates:\n",
       "  * lat      (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n",
       "  * lon      (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n",
       "    time     datetime64[ns] 2014-02-10T06:00:00\n",
       "Attributes:\n",
       "    long_name:     4xDaily Air temperature at sigma level 995\n",
       "    units:         degK\n",
       "    precision:     2\n",
       "    GRIB_id:       11\n",
       "    GRIB_name:     TMP\n",
       "    var_desc:      Air temperature\n",
       "    dataset:       NMC Reanalysis\n",
       "    level_desc:    Surface\n",
       "    statistic:     Individual Obs\n",
       "    parent_stat:   Other\n",
       "    actual_range:  [185.16 322.1 ]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "air.sel(time=\"2014-02-10 06:00:00\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "changed-knitting",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "O si preferimos usar los índices posicionales"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "bacterial-monitor",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    precision:     2\n",
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      "text/plain": [
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       "    long_name:     4xDaily Air temperature at sigma level 995\n",
       "    units:         degK\n",
       "    precision:     2\n",
       "    GRIB_id:       11\n",
       "    GRIB_name:     TMP\n",
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       "    dataset:       NMC Reanalysis\n",
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     "execution_count": 18,
     "metadata": {},
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   ],
   "source": [
    "air.isel(time=1621)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "helpful-pioneer",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "hasta incluso si preferimos los métodos antiguos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "steady-legend",
   "metadata": {},
   "outputs": [
    {
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       "    long_name:     4xDaily Air temperature at sigma level 995\n",
       "    units:         degK\n",
       "    precision:     2\n",
       "    GRIB_id:       11\n",
       "    GRIB_name:     TMP\n",
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   ],
   "source": [
    "air[1621]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "rotary-webster",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Incluso se tiene acceso a ciertas operaciones de agregación de `numpy`, pero esta vez usando los nombres de las dimensiones"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "amended-questionnaire",
   "metadata": {
    "scrolled": true,
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
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    "air.sel(time=\"2014-02-10\").mean(dim=\"time\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "metropolitan-usage",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "y muchas cosas más"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "turned-corporation",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.contour.QuadContourSet at 0x7f0ed5136250>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "air_subset = air.sel(time=slice(\"2013-10-01\", \"2013-12-31\"), lon=slice(250, 300)).pipe(\n",
    "    lambda x: x - 273.15\n",
    ")\n",
    "air_subset_masked = air_subset.where(air_subset < 10)\n",
    "air_subset_masked.mean(dim=\"time\").plot.contourf(levels=16, figsize=(10, 6))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "delayed-denial",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "La integración con matplotlib permite generar gŕaficos rápidos de nuestros datos, usando los atributos (o metadatos) para colocar las etiquetas y unidades tanto en los ejes como en la barra de colores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "derived-cyprus",
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x7f0ed4fc7dc0>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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z5BeNU80TNHptDE72b91lM4rHZvHs4vorivo5OouJAule7GXhc64Hx+5lm08rlD25fVHUuHBbZ3szi+ezq9e5F8vpaleLZg16S9oS+AGwKTAMnGFmp0l6EfBNYApwH/AvZvZMXucY4FBgCPh3M7ukKcasIeFCgyAIajDKJU8qmUBpEPi4mT0PeDlwuKQdgO8AR5vZC0h5go4CyJ8dCOwI7AWcLqklEm+EwwiCIKjBSIPeZZaG2zJbYGY35NcLgduBzYHnAL/P1S4F9s+v9wXONbPlZnYvcA+wa3P3cPUIhxEEQVBADJVcgJmSrqtaDqu7VWkOsDNwDXArKe0DpHh6W+bXmwMPVq02P5dNOO0zhhEEQbCWMMak9H7MzHZpVEnSFOA84Egze0bS+4CvSfoUKYJ3JVqm1881jqM15QmHEQRB4NDMjHuSuknO4mwzOx/AzO4A3pA/3x7YO1efz8jTBsAWwENNM2YNiC6pIAiCGszEsHWUWhohScCZwO1mdmpV+cb5fwdwPGnGFKSnjQMl9UraBtgOuLbJu7haxBNGEASBQxN1GLsBBwO3SLoxlx0LbCfp8Pz+fOB7AGY2T9Jc4DbSDKvDzaxc0pZxpn0chrFKL16Hk0AJ6mgVilO/WdHlJDfqLFbscZ5KuxYWJ6x3LlrmNFJM9MNyJ6mPl+inw7lYp04pFA1t5JT1FNd1ExY5Wgjz9B+OjsLVtgAacrQsy4vbdOcIeufO03A4+8fUScXN9TgJpwaL9TwNgTnXh6c9qZe0q5bhScVra6ivaJ+XdMvT7gAM9BfrrphaTnMxOKt4bQ4MOcdBno3FhEedy4vJkpZuVNze4q2L33u9my4plnUXL7AVA0VbBpyyZpASKDVnJquZXUU9kRicVmedE4ETm2JAE1mnuqQ8ZxGsPTxnEYwPnrMImkca9G6aDmPckLRbmbJmEVddEASBQ5vk9P7vkmVNoX26pIIgCNYSFaV3qyLpFcArgVmSPlb10TTq9Pg2g3AYQRAEDsMT//QwGj2kGFRdwNSq8meAd4xXo+EwgiAIajCjpRMomdmVwJWSvm9m96+tdkd1GJL2K7GNZWZ2UZPsCYIgmHAMMTjcEvH+GrFE0imkQIUrp6+Z2WvHo7FGTxjfBn5B/SlhAK8GwmEEQbBO0Uyl9zhyNvAT4C3AB4H3AI+OV2ONHMbFZva+0SpI+lET7QmCIJhwKtNq24CNzOxMSR+p6qa6crwaG9VhmNm7G22gTJ1mYJ0w0D9yAjsGHYGSI1oCWLpxsWzFhkXNgBXz6NCxrChI6nmiKP7qe6y47uRHim1MeqQo+ut+dHGhTEuKCWTcxDyOmMwTNXYMFQVTXsIoL5mTJxzzhG2eEA1AZQVvXoIij5KJpMwRwXnteuJAb10v6ZO8XELO/g67x8sp6/aOdbGNwV7/Wh/2TkHJw9rZXxTzrdiqeN2smOk04oj+6Cmuq0lFQd7gQPHcDSxzBIODXkKsYlFz0FiCD04klZO2QNLepJhTW4xXY6UHvSW9EphTvY6Z/WAcbAqCIJhw2iSn9+clbQB8nKS/mAZ8tLaSpBkltjVsZk+NVqGUw5D0Q2Bb4EZSykBIv1saOgxJ00mZpZ6f13kfcCep320OKTXhO83syTK2BEEQjDdmMNAGg95m9qv88mlgj1GqPpSX0bxgJ7DVaO2VfcLYBdjBzEvk3JDTgN+Y2Tsk9QCTSYG3LjezkyQdDRwN/MdqbDsIgqDptLpwbzW43cx2Hq2CpL822kjZTrpbSQnMx4SkaaRZVGcCmNmK/MizL3BWrnYW8LaxbjsIgmA8GUalljbhFfU+yD/kR61ToZEO45ekbqSpwG2SrgVWjtqa2T711s08izTF63uSXgRcD3wE2MTMFuRtLKjEhXfaPww4DKCnf8NG+xIEQdAU2miWVFmOAj5XW5jHP34B7G5mTsjtVWnUJfWl1bNtle2/GDjCzK6RdBqp+6kUZnYGcAZA/6wtWyJFYRAE6wftMEtK0ibAF4DNzOxNknYAXmFmZ9ZUfZWkE83suKp1NwUuIWUCLMWoR8TMrsxze99ceV1dVmL784H5ZnZNfv8zkgN5WNLsbPRs4JGyBgdBEIw7JUObt8BTyPdJX/qb5fd3AUc69fYBXiTpVABJ2wFXAaeb2QllGyvrQl/vlL2p0Upm9g/gQUnPyUV7krJIXUhSJJL//6KkHUEQBOOOAYPWUWqZYGaa2VxgGMDMBhmZybqS3N30dmBrSecClwFHmdm3xtJYozGMDwEfBraVdHPVR1OBP5Zs4wjg7Dyw8nfgvSRHNVfSocADwAGNNjLUAwu3HvHm3nkanOL3Wg1PLwqSJm9QFMb19xYz33mDWouWFsV8Ty0slj3zZDHL2qSHi6K/KfOLGcymPFjsTux+smhzx5Kizd0DTjZHJ6uchzmZ/qzTEcV5Werq3DvDXvZARyTpiuA8gZ+zK0O9jtDOEeR563rtutn1PFMGnXrD5Y6NV+Zlhxxy0j4O9fnnc8g7rt5tsdwTJhbPszqdgzOleD/ZsLMzXgY/R6Q35AnyvGvJ2ZEOz74m0EZjGIslbUSWZ0p6OWmK7SpUhUC/Fvgk8Adgm0p5da7x0Wg0hnEOcDHwX6w69rDQzJ4o04CZ3UiallvLnmXWD4IgmAjaxGF8jNRjs62kPwKz8MObV4dA/5pTVopGoUGelrQQeMHaDKEbBEEwkbSDDkNSJ/CavDyH9Px8p5kVHgHN7LPNaLOhcM/MhiXdJGkrM3ugGY0GQRC0Oq2usTCzIUn7mtlXgHll1pH0Naf4aeA6M2s4llxW6T0bmJd1GCsj5ZXQYQRBELQf1jZdUn+U9HVSqKXq7+Yb6tTvA54L/DS/35/kbA6VtIeZHTlaY2UdRlMeZ4IgCNoBAwa9gfzW45X5f/XUWAPqJVB6NvDaPJsKSd8AfkuaCXtLo8ZKOQwzuzILRF6ai641s9BOBEGwTtLMMQxJW5ICtW5Kmv56hpmdJmkn4JukX/2DwIfN7Nq8zjHAoaQpsv9uZpe4dpqNFnDQY3Ogn5GZVP0k0d+QpGLuhRrKRqt9J3AKcAVpYOW/JR1lZj8bo7FBEARtgTWvS2oQ+LiZ3SBpKnC9pEuBk4HPmtnFkt6c3++e1doHktKubgZcJml7MyvMl5f0Kd/2umK8k4EbJV1B+i5/NfAFSf0kbcaolO2SOg54aeWpQtKsvPFwGEEQrJM0a9A7x82rxM5bKOl20i99I+WvANiAFH4cUnDWc81sOXCvpHuAXYGrnc1XZ1/rI6VqvX0UW86UdFHenoBjzazS7lGN9qWsw+io6YJ6nPIq8aZg3cayzUZmi6m7KNjp7HUEa0CPI+4Zcvonn1laFNCVvWR6pzpPc07Zis2L7f5ju6LaqvtRL6tfsaxnYbHZ7sVFgVPPIud4LXUyojkiL3My3Hl0ONn6AHCz3DnCMUeg5goEncx3g46QzRPueSK9rmXlshaWFRZ2OGXefmDFDXYMlNzfSf450VDxuHYUdXZ0LS4K6AYnO6K6yc65c+4990ZxBIwuXc7x6nKyQTrpKYZtfHJW2DgNekuaA+wMXEMK4XGJpC+Rvk8r4xGbA3+uWm1+LnPstC/XbP9LJF1GvfZF0sA9y8xOkLSVpF0rXWGNKPul/xtJl0g6RNIhwK+Bi0quGwRB0GaIoeGOUgswU9J1Vcth7halKaRAf0ea2TPAh4CPmtmWpCx5Z65svEjZ4KuTSVHC63E6KYz5Qfn9QuB/Sm679KD3UZL2B3Yj7cwZZnZB2UaCIAjajTGMYTxmZl40i5VI6iY5i7PN7Pxc/B5SugdI01y/k1/PB7asWn0LRrqrard7CyPOpJOk9C6EMa/iZWb24kqyJDN7siofRkNK5/Q2s/MYQxjcIAiCdqWZsaRyN9CZpKx31TGbHiKptK8gTYO9O5dfCJyTI8tuBmxHigHl8Zaq14PAw5Ups3UYyArxSuypWeTAhWUoO0tqP+CLwMakJwwBZmbTRl0xCIKgHTE3/uTqshtwMHCLpBtz2bHA+4HTJHUBy8jJ4sxsnqS5pMjeg8Dh3gypzOfN7ODqAkk/rC2r4mvABcDGkk4kxZ06vuyOlH3COBl4q5nVHX0PgiBYl2jiLKmrqD9/5iV11jkROLHE5nesfpOdj7vNvN2zJV1PGvgW8LaxfK+XdRgPh7MIgmB9wWiqDqPpZGHfscAkSc9UioEV5CylNfVnVL19BPhx9Wdlo4+XdRjXSfoJ8HNWzel9ft01giAI2hYxVHZa8ARgZv8F/Jek/zKzY0qscj3JDwrYCngyv55Oykm0TZl2yzqMacAS4A3VNgNrz2F0GJ2TR8ZyurqLXXq9Pf5YT093sbyj9Cy1IkNO5htvgKyns2hjf09Rm9GzcbHek5tNLpQ9/nR/oWzRE8XETb2PFeemT15QLJv6f45e4yln0r6Dp6PQcJ1j6iUjchMwFbc51O3oK5wkQ2WTJXkaCTdJk4ObIMrRSJTt+3YTG3kJmRz7uur8+u10NCWTHi/W847X8mnFHVy2kVNvQ0+vURw3tW5nBzsczYWbLKlcmTrGJ4EStPYTRgUzO0bShqSB8b6q8t/X1NsGQNI3gQvN7KL8/k3A68q2V3Za7XtH+1zSMdnjBUEQtD1m7eEwJP0baWruFsCNwMtJivB6wQdfamYfrLzJYUlGm4a7Cs1SazdMsRoEQdBODJtKLRPMR0hBYe/PgQh3Bh4dpf5jko6XNEfS1pKOI0XuKEWzHMaEH7UgCIJmYlZumWCWmdkyAEm9ZnYHKftePQ4iifsuyMssRlTfDSkt3GvAxB+2IAiCJmGI4fbIhzFf0nTShKRLJT1JHVU4QJ4N9ZF6nzcinjCCIAgcrOQykZjZ283sKTP7DPCfJEX522rrSfpMo22VqdOsJ4yfNq4SBEHQJrTBoLekDuBmM3s+pER3o1T/tyq9hrs5Ug6Oz4zWZqknDEknS5omqVvS5ZIek/Tuyudm9oUy2wmCIGgbWvwRw8yGgZskbVWi+reBqaMsU3KdUSn7hPEGM/ukpLeTIikeAPwO+FHJ9YMgCNqKVn/CyMwG5km6lqpkSma2T3UlM/tsMxor6zAqUqk3Az82syc84dV40tFh9Pcvr3rvJARyREEAnY5Cypvd4O1StyO+63Qy0pSdXuddhF6y+b7uYhtTpywrlC1y9m1ZTzFa8VBf8VQP9BfLJj1eFGV1rigU4e1u5wr/+HcvLB7DDkeg5pW5oi5nyMwVDXrCPUeX6K3rieqGHZEejgDOixLnJaEacoJKDztCRTc5lCNeBD/JU+dyTwzo2OjkIvK21+Gs6+6zkxhJPU7SKCfBWUenc3049VTnnm8GLTADqgxNcQRlKeswfinpDmAp8OEcErf47RUEQbAOYAbWBrOkzOxKSVsD25nZZZImk/JijAuljoiZHU3K0rSLmQ2QwoTsW2ZdSfdJukXSjZKuy2UzJF0q6e78f8PV3YEgCILxoB10GJLeD/wM+FYu2pw0xXZcKDvoPRk4HPhGLtoMGDXDVA17mNlOVVmpjgYuN7PtgMvz+yAIgtahxQe9M4eT8m08A2Bmd5PyFq2CpJk1798t6WuSDtMYxhfKPnN9jxQ2t5KkfD7w+bKNOOwLnJVfn4UzbzgIgmDiEGbllglmuZmtHGXM+TA8N/bbqjrHkxI6XQ+8HjjVqe9S1mFsa2YnAwMAZraU8mI9A34r6fqq5OibmNmCvK0FOB4RIHu/6yRdN/TMkpLNBUEQNIH2eMK4UlIlL8brSZq4Xzr1qr+v9wP2M7OzgHfR7Gi1wApJkxjJA7stVXkxGrCbmT0kaWOSdP2OssaZ2RnkZCCTnr3ZxJ+aIAjWD9pAuJc5GjgUuAX4AHAR8B2n3iRJO5MeEjrNbDGAmQ1I3pw5n7IO49PAb4AtJZ1N6jM7pMyKZvZQ/v+IpAuAXYGHJc02swWSZpMyQAVBELQObeAwzGxY0lnANaQf9HeauUPxCxjpenqi6vt3I1Le8FKUzYdxqaQbSLHWBXzEzB5rtJ6kfqDDzBbm128ATgAuBN4DnJT//6KswUEQBGuFNujTkLQ38E3gb6Tv5m0kfcDMLq6ul0OfezwFvLpse6UcRh5FfxPwLDM7QdJWknY1s2sbrLoJcEEehO8CzjGz30j6CzBX0qGk9IAN82l0yFbJnOeJ8TrcFGbQ6Yj85NTtclRKnnCvq2SWLy+rn5dY3hP9TekuquVmTVpcKHti0qRC2SO9UwtlS/qKmfkWbVA8/Us3KU7h7lrq/NJyDnXncv8XWd9jxaGySU8458TJLOcJ3rzTLOc3kidu88Ryg31OBjmn3pAjoPOFhUW8em6ZM4N+qHjqGHbsA/9HsbxUo96t4lVz7Bn2vjXcDHmOcM+5d7p6HHGsI9Lr7ipXr2m0gcMAvkyahXoPrBwu+DVwsVdZ0i7AlqSnirtzOPTSA8Rlu6ROB4ZJWZxOABYC55ESd9TFzP4OvMgpfxzYs6yRQRAEaxWjLbqkgEcqziLzd5wufkmvITmXp4CXAH8ENpQ0ABxsZg+Waaysw3iZmb1Y0l8BzOxJSXV+4wRBELQ/Ey3KK8k8SRcBc0lu7gDgL5L2AzCz83O9r5JiAj4qaRvgVDPbLc+sOpM0XNCQstNqByR1MjJLahbpiSMIgmDdZFjllomlD3gYeA2wOyk96wzgrcBbqup1mlkldesDwNaQxqdJ6vBSlH3C+Bopnd/Gkk4E3gEcX7aRIAiCdqPOkOjYtyNtCfwA2JT0Q/sMMztN0k8YSac6HXjKzHbK6xxDmi47BPy7mV3ibdvM3lvSjOsknUmKrLEvcEVuZ0yxpxo6jJyk417gk6RxBwFvM7PbyzYSBEHQVjRXlDcIfNzMbpA0Fbhe0qVm9s+VCpK+DDydX+9ASma0IykM02WStjezwqh/7l46AphD1fd5bXhzkkbj/aRoHZcB361UBd5YdkcaOow8z/fLZvYKoLToLgiCoH1R0wa9czSLSmSLhZJuJ3UD3QYrZ6G+kzSpCNITwLlmthy4V9I9JP3a1c7mf04ag/glowwT5KCxpzvlS4H7y+5L2S6p30raHzi/jigkCIJg3aL8N93MSiTuzBk5SkUBSXOAnUlCuwqvAh7OgQMhOZM/V30+n/rjDMvM7GuNDJQ0hdRLtB9pWu0Kknbjm2b2/UbrVyjrMD4G9AODkpaRuqXMzKaVbWhNMVaV6g863r+ePqLHKe/tLE7c73Eyw5TVXHj0OW14rBgudiF6tkzuKkZjWTJYFBsMDBS3N7ysWObNzzdPu+AFDnB+fA314Xb8ekmBhruL8y16ny6u6yX/8dp223Cu7qEeL0FRsd7g5HJlHm6gBdfmcmXmJAny9BEAOImHvB/KngbES5bkYb1OxT4noZmnm+h29BXdzr3YXVy3r6tYb5KjV2oa5R3GY1WRuOuSv7TPA440s+r82gcBP66uOgZrTpP0aVJwwZVfEGZ2Q029s0nj0HuRnmb6gXOB43N317GN7IfySu+iEiwIamnWKGEQTDRGU2dASeomOYuzq6a6VqLL7kfSRlSYT3oKqLAF8FCdTb+AFHn2tYx0SRkj3VsV5lQ9SZwq6S9m9jlJ7yV1jTXPYUh6sVP8NHC/mZWOQxIEQdAuNHGWlEjjDLebWW0o8dcBd5jZ/KqyC4FzJJ1KGvTeDqgXVePtpAgcjR61Fkv6JzO7StJbgSdg5Rh1ac84FqX3i0kRESF5tZuAjSR90Mx+W3fNIAiCdqR5D8y7kZ4CbpF0Yy471swuIs2Gqu6OwszmSZpL+uU/CBzuzZDK3ESaktsogOsHge9I2h64FXgfrNTU/U/ZHSnrMO4DDjWzebmRHYCjgM8B51OVnCMIgiAYwcyuok7+IDM7pE75icCJJTa/CXBHjs9XPYaxyrRaM7uZNNOqtp1HSTq7UpR1GM+tOIvcyG2Sdjazv4/haSYIgqBtaJMhuU+vzcbKOow7JX2DNKoO8M/AXZJ6yVn4giAI1inaIPigmV0paWtgOzO7bKzK7bFSNpbUIcA9wJHAR0kREQ8hOYt6cdaDIAjaEyPNOSqzTCCS3g/8DPhWLtqcJOYbF8pOq10q6XTgV2Z2Z83Hi5pvVhAEwcTSJl1Sh5PGJq4BMLO7czrsVahEr61H9VTf0Sg7rXYf4BSgh5TRaSfgBCdeybjRIWNS90jvl5cUqdsRu4EvoPMEeb5YrjhbrV47xe0V2+10pl4sdxRmXqIlLyGTKzZ0BFMrupyr3zkGw33FdYedBEMdy8o+nMIKR+hlnZ5yrLjPPQuL1TzR2XBXue4DT/DmlpVNjOQlGPIEeb3F4z802fl5Oql4/DucBEN9k/xZlH09jgjOSQLmJRAbGi7u9KBT5t47jqjOS2jW6agDPfv8e7HY++2JWZtGeziM5Wa2ojKWnHUdnuVvHWUbRpq81JCx5PTelRzh0MxuzBL3IAiCdZP2cBhXSjoWmJRzW3yYFFdqFcYQ1XZUyv5MHDSzp5vRYBAEQasjK79MMEeTcmDcQopIe5GZHVevsqRNJJ0p6eL8foecKrsUZR3GrZLeBXRK2k7SfwN/KttIEARB29EeCZSOMLNvm9kBZvYOM/u2pI+MUv/7wCUkBTnAXaTJTKUo6zCOIMVmX05SJT4zlkaCIAjajTZ5wniPU3bIKPVnmtlc8vyuHNqp3KAs5WdJLQGOy0sQBMG6z8Q7g7pIOgh4F2kS0oVVH00FHh9l1cWSNmIk3fbLyYmbyjCqw5D0S0Y5bGtzllQQBMFaozWeHkbjT6SkTDOBL1eVLwRuHmW9j5OCG24r6Y/ALFLK7VI0esL4Uv6/Hykf7Y/y+4NI8aWCIAjWTVrYYZjZ/aRMea8Y43rXS3oNKZe4gDtzNr5SjOowzOxKAEmfM7NXV330S0m/H4uhQRAEbUULO4zVRdJNwE+An5jZ38a6flkdxixJzzKzv+dGtyE9yqw1OmRMrsqu5Ynx6mW4m9K9rFA2qbPoVLudVGm9jvjOEyQNl4w74607yQll74r5HDXZ9J6lhbLZGzxTKJvvCLCWL+4plHkZ0ZhUPAZDS51LZ8CfQ2FOFjiPzqXF9a3DyaTnRcpxmnZOnRseaKjXqee14a7rXAt9TpkjyOucXDSwv78oRNuwf0mhbPN+v9t5Zs/iQpmXXbLLuda9a9i7Dj26HTFfhxM3w7vHyt5PnmDW216zaPEuqdVlH1IswLmShknOY66ZPVBm5bKzpD4KXCHpCklXAL8DRpu6tQqSOiX9VdKv8vsZki6VdHf+v2HZbQVBEKwVrOQyQeTv1R81rjmCmd1vZieb2UtIg+YvBO4tu37ZWVK/kbQd8NxcdIeZjUWT/xHgdqCSA/xo4HIzO0nS0fn9f4xhe0EQBONH6w96Y2ZDkmZJ6imRcW8lOUrHO0lPGkPAJ8uu22iW1IsrycSzg7hptDp1trEFsDcpGcjHcvG+wO759VmkkCPhMIIgaB1a3GFk7gP+mKfWruyPdFLBAiDpGqAb+ClwQGWYoSyNnjC+J2l36mSLypwJ7DzK518lebCpVWWbmNkCADNb4EVXDIIgmFDaw2E8lJcOVv2Orcd7zOyO1W2skcPYALie0R3Go/U+kPQW4JE8lWv3sRon6TDgMIC+TcociyAIgjVHtH6XFICZfXaMqzwp6UxgMzN7U063/QozO7PMyo2m1c4ZozG17AbsI+nNQB8wLQ/SPCxpdn66mE2dBOZmdgZwBsC052zSBqcvCIJ1AgMnEnvLIOmrZnZkPXH1KKLq7wPfYyRqx12kmVJr7jDWFDM7BjgGID9hfMLM3i3pFFIMlJPy/1+Mpx1BEARjprV/ov4w///SqLWKzDSzuZKOgRRLSio/N3lcHcYonESaB3wo8ABwQKMVujqGmdG7tOq9v49dzs8CT0sxrauozejrKGozhhztg5cExp9fXu4nyhJHCODNfx9wbOl3EshsN63YS+glqXm4r9jN5+1bZ0fxzlnY54gXgIGBooBhcIUjalhULPMSDy2fXiwbLspH8C75TmfeiJcYacjZnjl3hqu5mOScY09fMa14vXl6mc0mF8tmONqKDbuL2gyAyR3FnR5yepT75OmLnIRazgGb3FG85rx7pyzePeYlEPNY5l00zaKFHYaZXZ//X1ldLmlL4EDgSm89xjOWVDMxsysYScD0OLBns9vwnEWw9vCcRRC0K+0whgEgaSbpR/dBpJzeF4xS/WOsQSypUsI9Jd4t6VP5/VaSdi3bSBAEQdvRJOGepC0l/U7S7ZLmVeerkHSEpDtz+clV5cdIuid/9kZnm1Ml/auk3wDXAs8GnmVm25rZJ+ruUpJAvAZ4JSnh0o5mNlqwwlUo+4RxOil++muBE0gREc8DXlq2oSAIgrahuYPeg8DHzewGSVOB6yVdCmxC0qS90MyWV+QFeebSgaQcRJsBl0na3syqO14fITmK44GrzMwkvb2eAZL2q/PR9pIws6bm9H6Zmb1Y0l8BzOxJSU7PbxAEwTpCk7qksuasojtbKOl2UtfR+4GTKlEzzKwyW3Rf4Nxcfq+ke4BdgaurNnssyal8AzhH0k8amPHW0UwEmuowBiR1MjJQMgucyGJBEATrCOMxhpHDcuwMXAOcArxK0onAMtIs0r+QnMmfq1abn8tWYmZfAb4i6VmksYufA5tJ+g/gAjO7q6b+e5thf9ngg18jDaRsnHfuKuALzTAgCIKgJSk/hjFT0nVVy2He5iRNIXXlH2lmz5B+sG8IvBw4ijRzVPhCadd9mdnfzexEM3sBaYhgA+Di1dvhxpQNPni2pOtJM5sEvM3Mbh8vo4IgCCaUsUWifczMdhmtgqRukrM4u2q8YD5wvpkZcG0ONz4zl29ZtfoWpPAfo5tsdgtwC6m7alwY9QkjhyGfIWkGaZDlx8A5JKX2jPEyKgiCYCLRGJaG20pPDWcCt9cEBfw5aSIRkrYHeoDHSNNeD5TUm3MPbUca4J5wGj1hXE/yswK2Ap7Mr6eTBHfbjKdx1XRg9FUlPfKEd56IDXxRkSdw6nXqdTpDNWUTvgw46i9XROWJnpwzs9RRrA057bpCxQ2KP1CePaW4PU+8tdgRFi5YMq1QNuip4oCHFxUFgk8/Udymp8EanOIc6+5iWedy5/g7d/NwnzP01l9U/fX0F6+P6ZOK11dvV/FYT+kp1tt00sJC2ZzJjxfKNujyBXll8QSk3jXsXf/edeivWy6zwTIrnlDvnvDwFD3jmSzJo4mzpHYDDgZukXRjLjsW+C7wXUm3AitIgQENmCdpLnAbaYbV4TUzpNYISc8HdiCFawLAzH5QZt1GsaS2yQ18E7jQzC7K798EvG51DQ6CIGh5mjdL6irqP4y8u846J5JSQoxKDvB6kZmVcm+SPk1KLbEDcBHwJtKYdCmHUXbQ+6UVZwFgZheTxB9BEATrJi2ecS9zIHC3pJMlPa9E/XeQxqL/kWdOvQjw4/w4lHUYj0k6XtIcSVtLOg4oPk8HQRCsC+SMe2WWCTXT7N2kabp/I+UvulrSYVkg6LE0P40MSppGGpt+Vtn2yjqMg0gxRy4gDdRsnMuCIAjWTdrjCYM8Rfc84FxgNvB24AZJRzjVr5M0Hfg2aYz6BsYwoF52Wu0TpLzcQRAE6wXtEMtU0luB9wHbkkKe72pmj0iaDNwO/Hd1fTP7cH75zRyHalrTY0lJ+h1+ko7Xlm0oCIKgnZjo7qaSHAB8xcx+X11oZkskvc9bQdILgTnk739Jz252LKnq6Id9wP6k6V5BEATrHi3S3dQIM/vXUT67vLZM0neBFwLzGAnv1NxYUpVkHVX8UVK9BB1BEATtTws7DEkLWdVCMaKZMzMrCqUSLzezHVa33bJdUtWq7g7gJcCmq9vo6tAhW0Wst2lvMUnUhl3FzGTgi488EVC3k4Wss+Rz6YAVpUZeNrAhZ55Bp3NlevZN7iwKppaXzDjmZTXzBH5TO4uCSE9sOLNnUaGsnrBqQf8GhbLfLy2KBld0TSqu7GSv6+orlsmZ5b7h1KIIbpP+ooBuSnfxuG7c69Rzjr9Ht5MNcopzXP1rsJw+y7umwRfVeddwn8oJ98q2vcIR5LnCVUdo2uFsz7PZFdGO07e6aO0uKTOrNwuqEVdL2sHMbludlct2SVUrvgeBe4FDV6fBIAiCtqCFHYakaWb2TL0QTXmiksdZJKfxD2A5I08kLyzTblmH8TwzW+UnkqTSYo8gCIK2wkDDLewxUky/t7Dqj/kKRn1txXfJYUpYjRQVZR3Gn4AX15Rd7ZQFQRCsE7R4l9Rb8v+xxvN7wMwuXN12R3UYkjYlJe6YJGlnRrzYNGDy6jYaBEHQ8rSww6hG0oakiLbVwQR/X6f6HZLOAX5J6pKq1G/KLKk3AoeQ4rFXh+VdyDjGXA+CIJhoWvkJo4KkfyOJqrcAbiQlY7qaHDbdYRLJUbyhqqw502rN7CzgLEn7m9l5ZTYYBEGwTtAGDoPkLF4K/NnM9pD0XOCz9SqvaarWRl1S7zazHwFzJH3MafxUZ7UgCIL2pgUCC5ZkmZktk4SkXjO7Q9Jz6lWW9DWn+GngOjP7RaPGGnVJ9ef/U5zP2uNwBkEQjBHRHrGkgPk5mODPgUslPcno6Vz7gOcCP83v9yepvg+VtIeZHTlaY426pL6VX15mZn+s/kzSbqOt22wmdyxn5yn3r3zf7wiUvGxxAMOOaM0XOBXX98R3Hn2OcMxrwxMzPeXMHxga9gRORbo7Vz8Rlyf6m9FVFOQ9p2dBoazPEf09MeTPg7ivZ1axcE6x6MFZ0wtlPY4IblpPUQQ3o7so0vPEnZ4wzjvHnoBxaufSQpkniCwrCu3xxJnONdPjrFtPsOZt0xPGeXjXv3e9rnBEqmUpLcgr+W3t2dw0rPV/E5vZ2/PLz+SYfxsAvxlllWcDrzWzQQBJ3wB+C7yeNNV2VMqGN//vkmWrIKlP0rWSbpI0T9Jnc/kMSZdKujv/37CkHUEQBGuFdsiHIekFkg6QdADwuJldaGZFGf8ImzPSc0R+vVlOAdswlEGjMYxXAK8EZtWMYUzDT7tby3KSN1skqRu4StLFwH7A5WZ2kqSjgaOB/yixvSAIgvGnxYMPStoA+AWwFXATqRftBZIeAPbNOTI8TgZulHRFXufVwBck9QOXNWq30RhGD2n8oguojl3yDCnV36jkhOaVPo7uvBiwLymvLCSp+hWEwwiCoIVo8TGMzwHXkX6QDwNI6gBOIuUC95InYWZnSroI2JXkMI41s8qYx1GNGm00hnElcKWk75vZ/aPVrYekTpJ8/dnA/5jZNZI2MbMFuY0Fkjaus+5hwGEAG20WkUiCIFh7tLjDeB3wwoqzADCzYUnH4oxFSHpunkFVic7xYP6/qaRNzeyGMo2WDQ2yRNIpwI6sqiZsmEAp943tlEfyL5D0/JJtYmZnAGcAzHn+lBZ+QAyCYJ3CaPVB7xWVgetqzGxQkjcW8THSj+8vO58Z9YV+q1DWYZwN/IQU7OqDwHuAR0uumywyeyr3m+0FPCxpdn66mE1KRB4EQdAyTPSAdgP6asI1VRBQ6I4xs8Py/z3WpNGyDmOj3Pf1kapuqoYJlCTNAgays5hEeoz6InAhyemclP83FIwEQRCsVVrbYSxg1XBN1fyj3kp5NtVvzGyhpONJAWQ/Z2Z/LdNoWYdRyayyQNLeJGHIFiXWm00KLdJJmsI718x+JelqYK6kQ4EHSHlpR6W3Y5A5PWN6qFmJN1e+9PxtZ+KxNzfdm8u/zJwyp56XfMlLquThreslPPK258119+zz5vZv11Use7TjKddGL7nOazYoPjUPb1A82N658/CS/3jz+x8fKmpQPd3Es3uL99zGncWkSt459nQKfSra5x3XPkdz4eGdYyh/3dRbf3UZcCZNekmaPDxthpdoycM7rs2gmQmUJG0J/ICUdG4YOMPMTpP0GeD9jPTWHGtmF+V1jiHlHBoC/t3MLqne5ho8Kfynmf1U0j+RYgV+Cfgm8LIyK5d1GJ/P07g+TtJfTAOObLSSmd0M7OyUPw7sWbLtIAiCtYtZM8cwBoGPm9kNkqYC10u6NH/2FTP7UnVlSTsAB5LGjDcDLpO0fR4Ppqbu54DPVD6TNA04bZSYUZVt7A18w8x+kR1XKUr9fDOzX5nZ02Z2q5ntYWYvAbYt20gQBEG7oeFySyPMbEFlFpKZLQRuJwno6rEvcK6ZLTeze4F7SNNgPbqAayW9UNIbgL+QZqXW4/8kfQt4J3BRToRXVsBdvqJDIRhhEATBusIYlN4zJV1XtRxWd5vSHFKvyzW56P9JulnSd6siXmzOyLRXgPnUcTBmdgxJw3YN8H1gbzP7+ii79U7gEmAvM3sKmEEJ/UWFsl1SHs3tBA2CIGgVDCifovUxM9ulUSVJU4DzgCNzPu5vkAR4lv9/GXgf/nera4ykVwOnAScALwC+Lul9VWK8VTditoSq3BdZD1cMFleHNXEYrT2HIAiCYE1o4jdcDo10HnB2JbudmT1c9fm3gV/lt/OBLatW34L6EWi/BBxgZrfl7ewH/C8pIm3TaRRLaiH+YRMpc1MQBME6SRNnSQk4E7i9OodQRYuW374duDW/vhA4R9KppEHv7YBr62z+FdWD4WZ2fhnJw+rSKDTI1NE+D4IgWGdp3iyp3YCDgVsk3ZjLjgUOkrQT6Uf5fcAHUrM2T9Jc4DbSDKvDvRlSue7Kckk/MLN/zbNQx4U16ZIKgiBYN7HmxZIys6vwxyUuGmWdE0lBBF0kXVhbBOyRQzBhZvuM3dLGtI3DENZQpFNPtDSk4rlak8QwXj1PwFVadObs15CK6w47YiZPfOeJ+Twme+06E+fuWrFJoWx6xwOFstmddS6nnscKRf8YLCZb8sSU3jl9ZrivUOYJx/od4ZiXBMkTK3oivelOcqNlxXA+dDh9Gf1uYqMia/od5QnyBjwRnHNtlhV8djrXjZekqdtJfuXhnbsO5+gMO2WeLc0gCfdaeph2C9ITyHdITygCdsGPFdU01mRabRAEwbrLcMllYtiFpLc4DnjazK4AlprZlTl807jQNk8YQRAEa5NWfsLIYc2/Iumn+f/DrIXv83AYQRAEtbR4xr0KZjYfOCDH+KuXZa9phMMIgiAoYKi8cG/CMbNfA78e73bCYQRBEHi0cJfURBEOIwiCoJYmTqtdlwiHEQRB4BFPGAXCYQRBEHiEvyjQNg5DQHfVM+KAIzyqJ7zzMpuVFR95Wds8UZ2naBl2bPQEfmXxBHkdJa/qYUeUtUHnkkJZvyNO8wSIjwwVhXczO5e5bW/SUbzMnnDOyePD/YWyqSpu0xM69lEs29jZvw5Hw7ZwuGhfn2Ofx9SOonCv22mjs2Rw56E1/ZbyfhW7TTv9LZ7QtORx8LIWdponKi0ea0+Q6glIvfvbu8eaRStPq50o2sZhBEEQrDUMGAqHUUs4jCAIghqExROGQziMIAgCj3AYBcJhBEEQeITDKBAOIwiCoBZjIgMLtizhMIIgCBxiDKNIOIwgCIICBsPxiFFL2ziMDoweRuZ6e7qCzjpzxr054t48b09zUd3myu05iWG8JDCebmKFMw+9bMIjbw67l0DJ0yl4yYk8XN1DZ1FrsMzRLty2oo9Nu4rah1me+MHhoYENi/Z0FO15Qe9DhbLNOovns1e9hbIlVjw2/fK0FF4iokIRnsjBUwYsc36tdjqHpc/T+DgM1Pv16yWiduouK5kKp9tN0FVuXS/50pBzj5XWErlJn8ZJh2HEGIZD2ziMoPXxnEUQtC3xgFFgXDPuSdpS0u8k3S5pnqSP5PIZki6VdHf+X/xpGQRBMIHIrNSyPjHeKVoHgY+b2fOAlwOHS9oBOBq43My2Ay7P74MgCFoHs3LLesS4OgwzW2BmN+TXC4Hbgc2BfYGzcrWzgLeNpx1BEARjwgyGhsst6xFrbQxD0hxgZ+AaYBMzWwDJqUjauM46hwGHAczevNzAcBAEQVNYz54eyjDeXVIASJoCnAccaWal886a2RlmtouZ7bLhjLViahAEQSK6pAqM+7ewpG6SszjbzM7PxQ9Lmp0/nw08Mt52BEEQlMaAYSu3NKDe5J+qzz8hySTNrCo7RtI9ku6U9Mbm7+DqMd6zpAScCdxuZqdWfXQh8J78+j3AL8bTjiAIgrFhYMPllsbUm/yDpC2B1wMPVCrnzw4EdgT2Ak6X1BJ98uM9hrEbcDBwi6Qbc9mxwEnAXEmHkg7UAY02VJtAqVsrShsx5In8nEdJL9GShycE9MR3Q474y8MT5C3zRHoURWyeIK/bERv2dxSPl9eGR58jbOvvdJIYeaKxOmzWVdyXZfZwoWyx9TjtFPe5V8V6nkjvCUe96/1InOHcnpNL3rMdYxCVFtsod04WOvsGOGfe/1Xoieq8REtevWFXkOclQSqWDa/Bb9TJTnKvsqLX1aJJ3U15vLYyZrtQUmXyz23AV4BPsuqP5n2Bc81sOXCvpHuAXYGrm2LQGjCuDsPMrqJOvi9gz/FsOwiCYLUxxjIDaqak66ren2FmZ3gVqyf/SNoH+D8zu0mr/rjcHPhz1fv5uWzCCaV3EASBR/knjMfMbJdGlaon/5C6qY4D3uBV9awpa8x4Eg4jCIKgQHNnQNVO/pH0AmAboPJ0sQVwg6RdSU8UW1atvgVQDKA2AYTDCIIgqMVoWrRab/KPmd0CbFxV5z5gFzN7TNKFwDmSTgU2A7YDrm2KMWtIOIwgCAKP5j1huJN/zOwiv1mbJ2kuaVB8EDjczMrNmhhnwmEEQRB4NG+W1GiTfyp15tS8PxE4sSkGNJFwGEEQBLWYYUMt8aO+pQiHEQRB4FFCxb2+0TYOIwn3VvMEOqt1ru62gG6vzBGTDVs5MVO/kyGvLB3OfngZ0Tz7+p3sel5yvG7nAPY5osS+kqIzgM07pzplxXqPDD3hrF1s+7FhTwhYTjhZMiEg3U42vGVO17JX5g2feln9/HU98Vx5vMx+k50teNtc7AlSPWFiyVmfHV5Wy5LH38PLLtk01rM4UWVoG4cRBEGw1rDI6e0RDiMIgsAjnjAKhMMIgiAoEIPeHuEwgiAIaqmENw9WIRxGEASBR7nQ5esV4TCCIAhqMMDiCaNAOIwgCIJazOIJwyEcRhAEgUM8YRSRtcnUMUmPAvcDM4HHJticRrS6ja1uH7S+ja1uH7S+jeNp39ZmNmt1V5b0G5J9ZXjMzPZa3bbaibZxGBUkXVcmWclE0uo2trp90Po2trp90Po2trp9QZHVT7AbBEEQrFeEwwiCIAhK0Y4Ow02u3mK0uo2tbh+0vo2tbh+0vo2tbl9QQ9uNYQRBEAQTQzs+YQRBEAQTQDiMIAiCoBQt5zAkbSnpd5JulzRP0kdy+QxJl0q6O//fsGqdYyTdI+lOSW+cIPtOkXSHpJslXSBpeivZV/X5JySZpJlVZWvNvkY2Sjoi2zFP0skTYeMo53gnSX+WdKOk6yTtOhH25fb6JF0r6aZs42dzeavcJ/Xsa4n7JFhNzKylFmA28OL8eipwF7ADcDJwdC4/Gvhifr0DcBPQC2wD/A3onAD73gB05fIvtpp9+f2WwCVkAeRE2NfgGO4BXAb05s82bqVjCPwWeFMufzNwxQQeQwFT8utu4Brg5S10n9SzryXuk1hWb2m5JwwzW2BmN+TXC4Hbgc2BfYGzcrWzgLfl1/sC55rZcjO7F7gH2JVxop59ZvZbMxvM1f4MbNFK9uWPvwJ8klWT1q5V+xrY+CHgJDNbnj97ZCJsHMU+A6blahsAD02EfdkuM7NF+W13XozWuU9c+1rlPglWj5ZzGNVImgPsTPp1somZLYB0QwMb52qbAw9WrTafkS/ItWlfNe8DLs6vW8I+SfsA/2dmN9VUmzD7oHAMtwdeJekaSVdKeulE21hj35HAKZIeBL4EHDOR9knqlHQj8AhwqZm11H1Sx75qWuI+CcrTsg5D0hTgPOBIM3tmtKpO2bjPFa5nn6TjgEHg7FaxL9tzHPApr6pTtlbmWjvHsAvYkNR1cRQwV5ImykbHvg8BHzWzLYGPAmdWqk6EfWY2ZGY7kX6l7yrp+aNUX+s2jmZfq9wnwdhoSYchqZt0o55tZufn4oclzc6fzyb9aoH0S2TLqtW3YKSrYG3ah6T3AG8B/sXMKhd7K9i3Lalf+CZJ92UbbpC06UTYV8dGsi3n5+6Ma4FhUgC4VjiGAO8BKq9/ykiXyYQcwwpm9hRwBbAXLXSf1LGvZe6TYDWY6EGU2oX0S+MHwFdryk9h1cG8k/PrHVl1sOzvjP9gnmffXsBtwKya8pawr6bOfYwMeq9V+xocww8CJ+TX25O6KNQqx5A0lrF7fr0ncP0EHsNZwPT8ehLwB9KXcKvcJ/Xsa4n7JJbVPK8TbUDBIPgn0qPozcCNeXkzsBFwOXB3/j+jap3jSLMq7iTPYpkA++7JX3CVsm+2kn01de4jO4y1bV+DY9gD/Ai4FbgBeG0rHcNcfn3+YrsGeMkEHsMXAn/NNt4KfCqXt8p9Us++lrhPYlm9JUKDBEEQBKVoyTGMIAiCoPUIhxEEQRCUIhxGEARBUIpwGEEQBEEpwmEEQRAEpQiHEQRBEJQiHMY6gKRFjWut0fYvkjQ9Lx9ejfV3l/SrMdZ/WtJFdT7/vqR3jNWOdiQfi1dWvf+opAckfX0i7QrWT8JhBA0xszdbCu8wHRizw1hN/mBmbx7PBiR1jef2m8TuwEqHYWZfwY8JFgTjTjiMdZSqZD+VRDUb5vIrJH0xJ7e5S9KrcvlkSXNz/Z/kiLG75M/uU0q4dBKwbU4gdErtk4Okr0s6JL/eKyfKuQrYr6pOv6TvSvqLpL9K2rfEvihv+zZJv2YkAiuSXpIj214v6ZKqOEovzftydbb11lx+iKSfSvol8Nt69uRIq6fk8pslfSCXz5b0+3wMbq0cvzp2vyG3f0Nuc0ou/1Te7q2SzsgBFpH073kfb5Z0rlKk3A8CH83t1W0rCNYKEy01j2XNF2CRU3Yz8Jr8+gRyXCRSELgv59dvBi7Lrz8BfCu/fj4pkugu+f19pCCAc4Bbq9rYHfhV1fuvA4cAfaTwD9uR4jLNrdQDvgC8O7+eTkpO1F9je+129wMuBTqBzYCngHeQciz8iRyXCPhn4Lv59a3AK/Prkyp2Z/vmk0Nm1LMHOAw4Ppf3AteRYhx9HDgul3cCU+uck5nA7yv7BvwHI+ExqsN1/BB4a379ECPJo6bn/58BPlGz7UOAr0/0dRfL+re0wyN5MEYkbUD6wrkyF51Fiq5aoRJx9XqSE4AUJ+k0ADO7VdLNa2DCc4F7zezubM+PSF/AkDKu7SPpE/l9H7AVKbBfPV4N/NjMhoCHJP1vLn8Oybldmn+kdwILlNJ+TjWzP+V655AC31W41MyeaGDPG4AXVo2VbEBygH8Bvpuj2f7czG6sY/PLSVnk/pht6wGuzp/tIemTwGRgBjAP+CXJyZ8t6efAz0c5HkEwIYTDWD9Znv8PMXINePkIGjHIqt2afVWv6wUpE7C/md05xra87QmYZ2avWKWwKo91HRY3sid3Ex1hZpcUGpVeDewN/FDSKWb2gzq2XWpmB9Ws2wecTnp6e1DSZxg5bnuTnOM+wH9K2rHBfgTBWiXGMNZBzOxp4MmqPu+DgStHWQXgKuCdAJJ2AF7g1FlIynFd4X5gB0m9+almz1x+B7CNpG3z++ovzUuAI6r67XcusUu/Bw7M4wqzSbm/IUU1nSXpFXlb3ZJ2NLMngYWSXp7rHTjKtuvZcwnwofwkgaTt83jH1sAjZvZtUgKlF9fZ7p+B3SQ9O68/WdL2jDiHx/KYxjvy5x3Almb2O1Ia3enAFIrHPAgmjHjCWDeYLGl+1ftTScl+vilpMim3wHsbbON04KzcFVUJS/10dQUze1zSH/MA8sVmdpSkubnu3Xk9zGyZpMOAX0t6jOSMKtnWPgd8Fbg5f0nfx6rdRR4XAK8FbiGNMVyZ21mRu4y+lh1WV972POBQ4NuSFpPGbZ4ubnZUe75D6q67IZc/SsqPvTtwlKQBYBHwr95GzexRpQkAP5bUm4uPN7O7JH0778t9pC4uSN1pP8r7IeArZvZUHpz/WR6MP8LM/tDgWAXBuBHhzQMgzQoCuvOX/bakXArbm9mKCbBld9JAbyNHMto2ppjZovz6aGC2mX2kORZOLNkR7WJm/2+ibQnWL+IJI6gwGfhd7oIR8KGJcBaZFcDzJV1kq6/F2FvSMaRr/H7SzKK2R9JHSVNtz5toW4L1j3jCCII1RNI1pKm31RxsZrdMhD1BMF6EwwiCIAhKEbOkgiAIglKEwwiCIAhKEQ4jCIIgKEU4jCAIgqAU/x9rb36XZFLvsAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "air.sel(time=\"2014-02-10 18:00\").plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "yellow-vocabulary",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Las reglas de propagación de dimensiones (broadcasting) usadas en numpy tambien son aplicadas en xarray\n",
    "\n",
    "<div align=\"center\">\n",
    "<img src=\"http://scipy-lectures.org/_images/numpy_broadcasting.png\" width=60%>\n",
    "</div>\n",
    "\n",
    "<a style=\"font-size: 0.8em\" href=\"http://scipy-lectures.org/intro/numpy/operations.html#broadcasting\">Scipy Lecture Notes</a>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "rubber-consensus",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Manteniendo las dimensiones durante el proceso. `xarray` tambien es capaz de alinear los datos en coordenadas comunes.\n",
    "\n",
    "<div align=\"center\">\n",
    "<img src=\"https://xarray-contrib.github.io/xarray-tutorial/_images/broadcasting.png\" width=60%>\n",
    "</div>\n",
    "\n",
    "<a style=\"font-size: 0.8em\" href=\"https://docs.google.com/presentation/d/16CMY3g_OYr6fQplUZIDqVtG-SKZqsG8Ckwoj2oOqepU/edit#slide=id.g67406e8d7_0_135\">Imagen de Stephan Hoyer</a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "sublime-modeling",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a.shape=(3, 1)\n",
      "b.shape=(1, 4)\n",
      "suma.shape=(3, 4)\n"
     ]
    }
   ],
   "source": [
    "a = np.arange(3).reshape(3, 1)\n",
    "b = np.arange(4).reshape(1, 4)\n",
    "suma = a + b\n",
    "\n",
    "print(f\"{a.shape=}\")\n",
    "print(f\"{b.shape=}\")\n",
    "print(f\"{suma.shape=}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "artificial-childhood",
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre>&lt;xarray.DataArray (time: 3, space: 4)&gt;\n",
       "array([[0, 1, 2, 3],\n",
       "       [1, 2, 3, 4],\n",
       "       [2, 3, 4, 5]])\n",
       "Coordinates:\n",
       "  * time     (time) int64 0 1 2\n",
       "  * space    (space) int64 0 1 2 3</pre>"
      ],
      "text/plain": [
       "<xarray.DataArray (time: 3, space: 4)>\n",
       "array([[0, 1, 2, 3],\n",
       "       [1, 2, 3, 4],\n",
       "       [2, 3, 4, 5]])\n",
       "Coordinates:\n",
       "  * time     (time) int64 0 1 2\n",
       "  * space    (space) int64 0 1 2 3"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# tomado de https://fabienmaussion.info/acinn_xarray_workshop/#/8/1\n",
    "a = xr.DataArray(np.arange(3), dims=\"time\", coords={\"time\": np.arange(3)})\n",
    "b = xr.DataArray(np.arange(4), dims=\"space\", coords={\"space\": np.arange(4)})\n",
    "a + b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "classical-positive",
   "metadata": {
    "scrolled": true,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre>&lt;xarray.DataArray (time: 3)&gt;\n",
       "array([0, 1, 2])\n",
       "Coordinates:\n",
       "  * time     (time) int64 0 1 2</pre>"
      ],
      "text/plain": [
       "<xarray.DataArray (time: 3)>\n",
       "array([0, 1, 2])\n",
       "Coordinates:\n",
       "  * time     (time) int64 0 1 2"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = xr.DataArray(np.arange(3), dims=\"time\", coords={\"time\": np.arange(3)})\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "czech-collaboration",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre>&lt;xarray.DataArray (time: 4)&gt;\n",
       "array([0, 1, 2, 3])\n",
       "Coordinates:\n",
       "  * time     (time) int64 1 2 3 4</pre>"
      ],
      "text/plain": [
       "<xarray.DataArray (time: 4)>\n",
       "array([0, 1, 2, 3])\n",
       "Coordinates:\n",
       "  * time     (time) int64 1 2 3 4"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b = xr.DataArray(np.arange(4), dims=\"time\", coords={\"time\": np.arange(1, 5)})\n",
    "b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "atmospheric-slovak",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre>&lt;xarray.DataArray (time: 2)&gt;\n",
       "array([1, 3])\n",
       "Coordinates:\n",
       "  * time     (time) int64 1 2</pre>"
      ],
      "text/plain": [
       "<xarray.DataArray (time: 2)>\n",
       "array([1, 3])\n",
       "Coordinates:\n",
       "  * time     (time) int64 1 2"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a + b"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "higher-kennedy",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Escalando la computación"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "marine-vienna",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "En la era actual, la cantidad de datos que se generan sobrepasan nuestra habilidad para analizarlos.  \n",
    "\n",
    "Los paquetes usados para la computación científica en python no escalan muy bien con grandes cantidades de datos, especialmente cuando la memoria del sistema no puede contener toda la información al mismo tiempo"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "supported-imaging",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "<div style=\"background-color: black\" align=\"center\">\n",
    "<img src=\"https://dask.org/_images/dask_horizontal_white_no_pad.svg\" width=25%>\n",
    "</div>\n",
    "\n",
    "Dask es la herramienta que escala la computación en python de manera nativa, integrandose perfectamente en el ecosistema científico de python"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "together-profit",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "<div align=\"center\">\n",
    "<img src=\"https://mk0coiled27knr0w73eb.kinstacdn.com/wp-content/uploads/jake-pydata-eco-pycon-2017.png\">\n",
    "</div>\n",
    "\n",
    "<a style=\"font-size: 0.6em\" href=\"https://www.youtube.com/watch?v=ZyjCqQEUa8o\">Jake VanderPlas PyCon 2017 Keynote</a>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "least-dayton",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "**¿Cómo funciona?**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "capital-encoding",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "<div align=\"center\">\n",
    "<img src=\"imgs/dask_img.png\" width=100%>\n",
    "</div>\n",
    "\n",
    "<a style=\"font-size: 0.8em\" href=\"https://dask.org/\">Dask website</a>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "macro-corrections",
   "metadata": {},
   "source": [
    "Dask se basa en el trabajo por *chunks* o bloques, distribuyendo el trabajo de manera eficiente y paralela."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "brief-rough",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.72172915,  0.50733925,  0.91157018, ...,  0.92630041,\n",
       "        -0.15903562,  1.16543549],\n",
       "       [ 0.52157406,  0.3361232 ,  0.66890096, ...,  0.2716334 ,\n",
       "         0.13686391,  0.51428317],\n",
       "       [ 0.92146834,  0.66456431,  1.11890292, ...,  0.10415602,\n",
       "         0.91294043,  0.44757828],\n",
       "       ...,\n",
       "       [ 0.9492168 ,  0.28031498,  0.11717426, ...,  0.43432244,\n",
       "         0.05602311,  0.81794426],\n",
       "       [-0.15117075,  0.13049398,  0.91090714, ...,  0.0409716 ,\n",
       "         0.47604758,  1.10093781],\n",
       "       [ 1.17073311,  0.50534598,  0.44297774, ...,  0.80032549,\n",
       "         1.09837056,  0.18969935]])"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# NumPy\n",
    "x = np.random.random((1000, 1000))\n",
    "x + x.T - x.mean(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "enclosed-permit",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<tr>\n",
       "<td>\n",
       "<table>\n",
       "  <thead>\n",
       "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr><th> Bytes </th><td> 80.00 GB </td> <td> 8.00 MB </td></tr>\n",
       "    <tr><th> Shape </th><td> (100000, 100000) </td> <td> (1000, 1000) </td></tr>\n",
       "    <tr><th> Count </th><td> 53500 Tasks </td><td> 10000 Chunks </td></tr>\n",
       "    <tr><th> Type </th><td> float64 </td><td> numpy.ndarray </td></tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</td>\n",
       "<td>\n",
       "<svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
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       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n",
       "  <line x1=\"0\" y1=\"12\" x2=\"120\" y2=\"12\" />\n",
       "  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n",
       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" />\n",
       "  <line x1=\"0\" y1=\"31\" x2=\"120\" y2=\"31\" />\n",
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       "  <line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
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       "  <line x1=\"25\" y1=\"0\" x2=\"25\" y2=\"120\" />\n",
       "  <line x1=\"31\" y1=\"0\" x2=\"31\" y2=\"120\" />\n",
       "  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"120\" />\n",
       "  <line x1=\"43\" y1=\"0\" x2=\"43\" y2=\"120\" />\n",
       "  <line x1=\"50\" y1=\"0\" x2=\"50\" y2=\"120\" />\n",
       "  <line x1=\"56\" y1=\"0\" x2=\"56\" y2=\"120\" />\n",
       "  <line x1=\"62\" y1=\"0\" x2=\"62\" y2=\"120\" />\n",
       "  <line x1=\"68\" y1=\"0\" x2=\"68\" y2=\"120\" />\n",
       "  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"120\" />\n",
       "  <line x1=\"81\" y1=\"0\" x2=\"81\" y2=\"120\" />\n",
       "  <line x1=\"87\" y1=\"0\" x2=\"87\" y2=\"120\" />\n",
       "  <line x1=\"93\" y1=\"0\" x2=\"93\" y2=\"120\" />\n",
       "  <line x1=\"100\" y1=\"0\" x2=\"100\" y2=\"120\" />\n",
       "  <line x1=\"106\" y1=\"0\" x2=\"106\" y2=\"120\" />\n",
       "  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"120\" />\n",
       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
       "\n",
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       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Text -->\n",
       "  <text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >100000</text>\n",
       "  <text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,60.000000)\">100000</text>\n",
       "</svg>\n",
       "</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "dask.array<sub, shape=(100000, 100000), dtype=float64, chunksize=(1000, 1000), chunktype=numpy.ndarray>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Dask.array\n",
    "import dask.array as da\n",
    "\n",
    "x = da.random.random((100000, 100000), chunks=(1000, 1000))\n",
    "x + x.T - x.mean(axis=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "contrary-watson",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Si corre en tu laptop, puede funcionar en un cluster\n",
    "\n",
    "Escalar la computación a sistemas de alto rendimiento es sencillo gracias al planificador de tareas de `dask`\n",
    "\n",
    "<div align=\"center\">\n",
    "<img src=\"https://raw.githubusercontent.com/dask/dask-org/main/images/grid_search_schedule.gif\">\n",
    "</div>\n",
    "<a style=\"font-size: 0.8em\" href=\"https://dask.org/\">Dask website</a>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "nervous-frederick",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### xarray + dask\n",
    "\n",
    "La combinación perfecta para el manejo de big data en geociencias, todo gracias al ecosistema Pangeo en python."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "desirable-mumbai",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre>&lt;xarray.DataArray &#x27;air&#x27; (time: 2920, lat: 25, lon: 53)&gt;\n",
       "dask.array&lt;xarray-&lt;this-array&gt;, shape=(2920, 25, 53), dtype=float32, chunksize=(30, 25, 53), chunktype=numpy.ndarray&gt;\n",
       "Coordinates:\n",
       "  * lat      (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n",
       "  * lon      (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n",
       "  * time     (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n",
       "Attributes:\n",
       "    long_name:     4xDaily Air temperature at sigma level 995\n",
       "    units:         degK\n",
       "    precision:     2\n",
       "    GRIB_id:       11\n",
       "    GRIB_name:     TMP\n",
       "    var_desc:      Air temperature\n",
       "    dataset:       NMC Reanalysis\n",
       "    level_desc:    Surface\n",
       "    statistic:     Individual Obs\n",
       "    parent_stat:   Other\n",
       "    actual_range:  [185.16 322.1 ]</pre>"
      ],
      "text/plain": [
       "<xarray.DataArray 'air' (time: 2920, lat: 25, lon: 53)>\n",
       "dask.array<xarray-<this-array>, shape=(2920, 25, 53), dtype=float32, chunksize=(30, 25, 53), chunktype=numpy.ndarray>\n",
       "Coordinates:\n",
       "  * lat      (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n",
       "  * lon      (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n",
       "  * time     (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n",
       "Attributes:\n",
       "    long_name:     4xDaily Air temperature at sigma level 995\n",
       "    units:         degK\n",
       "    precision:     2\n",
       "    GRIB_id:       11\n",
       "    GRIB_name:     TMP\n",
       "    var_desc:      Air temperature\n",
       "    dataset:       NMC Reanalysis\n",
       "    level_desc:    Surface\n",
       "    statistic:     Individual Obs\n",
       "    parent_stat:   Other\n",
       "    actual_range:  [185.16 322.1 ]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Esto es solo un ejemplo ilustrativo\n",
    "air.chunk({\"time\": 30})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dressed-jonathan",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "<div align=\"center\">\n",
    "<img src=\"https://repository-images.githubusercontent.com/101820659/4fcdfc00-6c25-11e9-9e30-33a31a285450\" width=70%>\n",
    "</div>\n",
    "\n",
    "Un proyecto de la comunidad dedicado a promover el desarrollo de ciencia de manera libre, reproducible y escalable.  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aggregate-constraint",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Donde comenzó\n",
    "\n",
    "\n",
    "<div align=\"center\">\n",
    "<img src=\"https://pangeo.io/_images/pangeo_tech_1.png\">\n",
    "</div>\n",
    "<a style=\"font-size: 0.8em\" href=\"https://pangeo.io/architecture.html\">Pangeo website</a>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "determined-dylan",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "donde se encuentra\n",
    "\n",
    "<div align=\"center\">\n",
    "<img src=\"https://pangeo.io/_images/interop.jpeg\" width=70%>\n",
    "</div>\n",
    "\n",
    "<a style=\"font-size: 0.8em\" href=\"https://pangeo.io/architecture.html\">Pangeo website</a>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "offshore-world",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Para concluir\n",
    "\n",
    "- xarray es una herramienta muy util que facilita la escritura de código legible y práctico.\n",
    "- La extensión de este paquete no se limita solo a geociencias, el modelo de datos puede ser usado en el area que se desee.\n",
    "- El ecosistema que provee Pangeo busca generar herramientas interoperables, capaces de escalar a computadoras de alto rendimineto.\n",
    "- La curva de aprendizaje no es muy inclinada, pero definitivamente vale la pena aprender los detalles más tecnicos, sobre todo si se busca velocidad de cómputo"
   ]
  }
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