{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Notbook de Jupyter"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Algunas cosas interesantes para recordar del curso, pero ahora desde un Notbook de Jupyter"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para pedir ayuda, se usa ? (como en iPython) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "%load?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### - Definición y uso de funciones en Python"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# La función se define acá\n",
    "def Multip( x=0,y=0 ):\n",
    "   \"\"\"Se le pasan dos argumentos numéricos, x e y; y la función devuelve su producto y=0 y x=0 por defecto. \n",
    "Ejemplo: Multip(1,3)\"\"\"\n",
    " \n",
    "   return (x*y)\n",
    " \n",
    " \n",
    "# En este punto se llama a la función\n",
    "Multip(3,4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "180"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Definición de la función\n",
    "def Suma( arg1=0, arg2=0 , *vartuple ):\n",
    "   \"Suma todas los argumentos\"\n",
    "   suma=arg1 + arg2\n",
    " \n",
    "   for var in vartuple:\n",
    "      suma=suma+var\n",
    "   return suma\n",
    " \n",
    "# Aqui se usa la función Suma\n",
    "Suma( 70, 60, 50 )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Algo para tener en cuenta, es que las funciones quedan cargadas y pueden ser luego usadas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "192"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Multip(Suma(2,1,5),Suma(9,8,7))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### - El uso de módulos"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "En el módulo new_func está la función fibo, dicha función recibe un entero n, y entrega una lista con los números de la serie de Fibonacci menores que n. Se quiere hacer un programa que sume los números Fibonacci menores que el número 100. Ahora new_func.py luce así:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "la suma de los números de Fibonacci menores que 100 es: 232\n"
     ]
    }
   ],
   "source": [
    "from new_func import fib #Se importa la función fib desde el modulo new_func\n",
    " \n",
    "serie=fib(100)  #La variable serie se llena con la serie de fibonacci hasta antes de 100\n",
    " \n",
    "sum=0\n",
    "for i in serie:\n",
    "     sum=sum+i\n",
    " \n",
    "print('la suma de los números de Fibonacci menores que 100 es:',sum)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "O la otra manera de hacer el import (menos limpia)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "La suma es: 232\n"
     ]
    }
   ],
   "source": [
    "#Importo el módulo\n",
    "from new_func import *\n",
    " \n",
    "a=fib(100)   #Se le asigna a \"a\" una lista con los números de fibonacci menores que 100.\n",
    " \n",
    "b=SumLis(a)   #Uso la función SumLis para sumar las componentes de la lista a, y se las asigno a b\n",
    " \n",
    "print(\"La suma es:\" , b)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "NOTA: Aquí puede pasar un error puesto que el módulo ya había sido importado con otro nombre. La solución es reiniciar el kernel. En el menu superior |Kernel -> Restart\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "También se puede correr un script (o una librería en este caso) desde el notebook, con %load se puede cargar el texto (codigo) en el notebook, y luego ejecutarlo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Serie de Fibonacci hasta el termino exactamente menor que 200:  [1, 1, 2, 3, 5, 8, 13, 21, 34, 55, 89, 144]\n"
     ]
    }
   ],
   "source": [
    "# %load new_func.py\n",
    "def Multi( arg1=0, arg2=0 , *vartuple ):\n",
    "    \"Multiplica todos los argumentos\"\n",
    "    produc=arg1*arg2\n",
    " \n",
    "    for var in vartuple:\n",
    "       produc=produc*var\n",
    " \n",
    "    return produc\n",
    " \n",
    " \n",
    "def fib(n):\n",
    "    \"Recibe un entero n, y entrega una lista con los números de fibonacci menores que n\"\n",
    "    result = []\n",
    "    a, b = 0, 1\n",
    "    while b < n:\n",
    "        result.append(b)\n",
    "        a, b = b, a+b\n",
    "    return result\n",
    " \n",
    " \n",
    "def SumLis(list):\n",
    "    sum=0\n",
    "    for i in list:\n",
    "        sum=sum + i\n",
    " \n",
    "    return sum\n",
    " \n",
    " \n",
    "if __name__ == \"__main__\":\n",
    "    a=fib(200)\n",
    "    print('Serie de Fibonacci hasta el termino exactamente menor que 200: ',a)\n",
    "    b=SumLis(a)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "O directamente corriendolo en el notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Serie de Fibonacci hasta el termino exactamente menor que 200:  [1, 1, 2, 3, 5, 8, 13, 21, 34, 55, 89, 144]\n"
     ]
    }
   ],
   "source": [
    "%run new_func.py"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Las funciones de leer y escribir en pantalla también están en el Notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "¿Cual es tu nombre? camilo\n",
      "Gusto en conocerte  camilo\n"
     ]
    }
   ],
   "source": [
    "Nom=input('¿Cual es tu nombre? ')\n",
    "#Utilizo la variable Nom\n",
    "print('Gusto en conocerte ',Nom)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### -Abrir y Cerrar Archivos."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Método Write"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "¿Cual es tu nombre? camilo\n",
      "Nombre del archivo:  resultados.txt\n",
      "¿Está cerrado? :  False\n",
      "Modo de apertura :  w\n",
      "¿Está cerrado? :  True\n"
     ]
    }
   ],
   "source": [
    "#Pregunto al usuario su nombre, y lo guardo en la variable Nom.\n",
    "Nom=input('¿Cual es tu nombre? ')\n",
    "# Abro el archivo resultados.txt, en modo escritura, y se lo asigno a la variable archivo1\n",
    "archivo1 = open(\"resultados.txt\", \"w\")\n",
    "print (\"Nombre del archivo: \", archivo1.name)\n",
    "print (\"¿Está cerrado? : \", archivo1.closed)\n",
    "print (\"Modo de apertura : \", archivo1.mode)\n",
    "archivo1.write(\"Nombre\\n\") #Escribe la cadena de caracteres \"Nombre\", y \\n es un salto de linea\n",
    "archivo1.write(Nom) #Escribe la cadena de caracteres guardada en la variable Nom\n",
    "archivo1.write(\"\\n\") #Escribe un salto de linea\n",
    "archivo1.close()\n",
    "print (\"¿Está cerrado? : \", archivo1.closed)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nota: Para obtener el directorio de trabajo, se usa os.getcwd() de la librería os."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'C:\\\\Users\\\\casfi\\\\Documents\\\\Python Scripts'"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import os\n",
    "os.getcwd()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Método read"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "los primeros 10 bytes del archivo:   Nombre\n",
      "cam\n"
     ]
    }
   ],
   "source": [
    "# Abro el archivo en modo lectura y escritura y lo asigno a la variable Archivo2 \n",
    "Archivo2 = open(\"resultados.txt\", \"r+\")\n",
    "str = Archivo2.read(10) #leo 10 bytes del archivo\n",
    "print (\"los primeros 10 bytes del archivo:  \", str) \n",
    "# Cierro el archivo\n",
    "Archivo2.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "El método tell() y el método seek(offset , ref)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Los primeros 6 bytes :  Nombre\n",
      "posición actual :  6\n",
      "Apellido: \n",
      "salazar\n",
      "Todo el archivo completo desde el inicio : \n",
      " Nombre\n",
      "camilo\n",
      "salazar\n"
     ]
    }
   ],
   "source": [
    "# Abre el archivo \n",
    "arch = open(\"resultados.txt\", \"r+\")\n",
    "str = arch.read(6)\n",
    "print (\"Los primeros 6 bytes : \", str)\n",
    " \n",
    "# Mira la posición actual\n",
    "pos = arch.tell()\n",
    "print (\"posición actual : \", pos)\n",
    "str2 =arch.read()\n",
    "apell= input('Apellido: \\n')\n",
    "arch.write(apell)\n",
    "# Devuelvo el puntero hasta el inicio\n",
    "pos = arch.seek(0, 0)\n",
    "str = arch.read()\n",
    "print (\"Todo el archivo completo desde el inicio : \\n\", str)\n",
    "# Cierro\n",
    "arch.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sistema de archivos\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Se puede llamar al sistema operativo para cambiar nombres de los archivos, para borrarlos, crear directorios, moverse de directorio de trabajo ..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Cambio el nombre de resultados.txt a resultado.txt\n",
    "os.rename( \"resultados.txt\", \"resultado.txt\" )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['.ipynb_checkpoints',\n",
       " 'new_func.py',\n",
       " 'Repaso-Algoritmos_y_Programacion.ipynb',\n",
       " 'resultado.txt',\n",
       " 'Untitled.ipynb',\n",
       " 'Untitled1.ipynb',\n",
       " '__pycache__']"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# listdir, lista el contenido del directorio actual.\n",
    "os.listdir()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## CLASES Y OBJETOS"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### -Manejo básico de Clases y Objetos\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Creación de Clases"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "class Estudiante:\n",
    "   'Cosas comunes a todo estudiante'\n",
    "   Numero_Estudiantes = 0\n",
    " \n",
    "   def __init__(self, nombre, nivel):\n",
    "      self.nombre = nombre\n",
    "      self.nivel = nivel\n",
    "      Estudiante.Numero_Estudiantes += 1\n",
    "    \n",
    "   def __del__(self):\n",
    "      class_name = self.__class__.__name__\n",
    "      print (class_name, \"destruido\")\n",
    " \n",
    "   def  display_NE(self):\n",
    "     print ('Numero de estudiantes:', self.Numero_Estudiantes)\n",
    " \n",
    "   def display_Estudiante(self):\n",
    "      print ('Nombre:', self.nombre,  \", Nivel: \", self.nivel)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Creando objetos de la clase estudiante"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Creemos el primer objeto estudiante, llamado Camilo en el nivel 10.\n",
    "estu1=Estudiante(\"Camilo\",10)\n",
    "#Esto instancia un segundo objeto estudiante, llamado Alfredo en el nivel 1.\n",
    "estu2=Estudiante(\"Alfredo\",1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Accesando a los atributos y métodos del objeto"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Número de estudiantes:  2\n",
      "Número de estudiantes:  2\n",
      "Nombre: Camilo , Nivel:  10\n",
      "Nombre: Alfredo , Nivel:  1\n",
      "Numero de estudiantes: 2\n",
      "Numero de estudiantes: 2\n"
     ]
    }
   ],
   "source": [
    "print(\"Número de estudiantes: \", estu1.Numero_Estudiantes)\n",
    "print(\"Número de estudiantes: \", estu2.Numero_Estudiantes)\n",
    "estu1.display_Estudiante()\n",
    "estu2.display_Estudiante()\n",
    "estu1.display_NE()\n",
    "estu2.display_NE()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Destruyendo objetos estudiante"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Estudiante destruido\n",
      "Estudiante destruido\n"
     ]
    }
   ],
   "source": [
    "del estu1\n",
    "del estu2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### -Herencia"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "La capacidad de heredar de otras clases tambien se puede usar en el Notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Llamada al método mi_Metodo del padre\n",
      "Llamada al método mi_Metodo del hijo\n"
     ]
    }
   ],
   "source": [
    "class Padre:        # Clase Padre\n",
    "    def mi_Metodo(self):\n",
    "      print ('Llamada al método mi_Metodo del padre')\n",
    " \n",
    "class Hijo(Padre): # Clase hija\n",
    "    def mi_Metodo(self):\n",
    "      print ('Llamada al método mi_Metodo del hijo')\n",
    "\n",
    "p=Padre()\n",
    "h=Hijo()\n",
    "p.mi_Metodo()\n",
    "h.mi_Metodo()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### -Sobrecarga de Operadores y Métodos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class Vector:\n",
    "    '''Es una clase vector de ejemplo'''\n",
    "    def __init__(self, x0=0,y0=0,z0=0):\n",
    "        self.x=x0\n",
    "        self.y=y0\n",
    "        self.z=z0\n",
    " \n",
    "    def __add__(self,other):\n",
    "        return Vector(self.x + other.x, self.y + other.y, self.z + other.z)\n",
    " \n",
    "    def __sub__(self,other):\n",
    "        return Vector(self.x - other.x, self.y - other.y, self.z - other.z)\n",
    "    \n",
    "    def __repr__(self): # metodo para mostrarse\n",
    "        return \"[%s, %s, %s]\" % (self.x, self.y, self.z)\n",
    "        \n",
    "    def __str__(self): #metodo para imprimirse\n",
    "        return \"(%s, %s, %s)\" % (self.x, self.y, self.z)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Z= (-1, -1, -1)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[3, 3, 3]"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X= Vector(1,1,1)\n",
    "Y= Vector(2,2,2)\n",
    "Z= Vector()\n",
    "Z=X-Y\n",
    "print (\"Z=\" ,Z)\n",
    "Z=X+Y\n",
    "Z"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Matplotlib"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "También las capacidades gráficas de Matplotlib pueden ser usados en los Notbooks, hay que tener cuidado de poner en la primera linea de los gráficos el comando mágico %matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x1dafdfaba58>"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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RIEyePBnz5s3b9Jp7hWIoYMKECZg8eXLV+QxWQqIYBFi3DujrAzbfvNElUSia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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1dafc904ef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline #Para ver la gráfica en el notebook\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import calendar\n",
    "# Para generar el lugar del primer días de cada mes en un año\n",
    "dias = [np.array(calendar.mdays)[0:i].sum()+ 1 for i in\n",
    "np.arange(12)+1]\n",
    "# Creamos una lista con los nombres de los meses\n",
    "meses = calendar.month_name[1:13]\n",
    "plt.axes([0.1,0.2,0.8,0.65])\n",
    "# Creamos un plot con 365 valores\n",
    "plt.plot(np.arange(1,366), np.random.rand(365), label = 'valores al azar')\n",
    "plt.xlim(-5,370) # Los valores del eje y variarán entre -5 y 370\n",
    "plt.ylim(0,1.2) # Los valores del eje y variarán entre 0 y 1.2\n",
    "plt.legend() # Creamos la caja con la leyenda\n",
    "plt.title(u'Ejemplo de título') # Ponemos un título\n",
    "# Ponemos un título superior\n",
    "plt.suptitle(u'Ejemplo de título superior')\n",
    "# Pedimos que se vean subrrayas de división en los ejes\n",
    "plt.minorticks_on()\n",
    "# Colocamos las etiquetas, meses, en las posiciones, días,\n",
    "# con color azul y rotadas 45o\n",
    "plt.xticks(dias, meses, size = 'small', color = 'b', rotation = 45)\n",
    "plt.xlabel(u't (días)')\n",
    "plt.ylabel('Media diaria')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x1dafe00e978>"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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CvA64Ptx+OLChTHFJnl3GyxdKKjReXkREYlRKC8WXgNXAq4BZ7v5kuH0qsKRc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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1dafdfab198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "x = np.arange(25) + 1 # Valores de x\n",
    "y = np.random.rand(25) * 10. # Valores de y\n",
    "# Valores de y normalizados. Esta nueva serie tiene media 0 y desvicación estándar 1\n",
    "y_norm = (y - y.mean()) / y.std()\n",
    "# Colocamos los límites del eje x\n",
    "plt.xlim(np.min(x) - 1, np.max(x) + 1)\n",
    "# Colocamos los límites del eje y\n",
    "plt.ylim(np.min(y_norm)-1, np.max(y_norm)+1)\n",
    "# Dibujamos los valores por encima de la media\n",
    "plt.stem(x[y_norm > 0],y_norm[y_norm > 0],linefmt='k-.',markerfmt='go',basefmt='b-') \n",
    "# Dibujamos los valores por debajo de la media\n",
    "plt.stem(x[y_norm < 0],y_norm[y_norm < 0],linefmt='k-.',markerfmt='ro',basefmt='b-') \n",
    "# Colocamos el título del gráfico\n",
    "plt.title(u'Representación de (x, f(x))')\n",
    "plt.xlabel('valores x') # Colocamos la etiqueta en el eje x\n",
    "plt.ylabel('valores f(x)') # Colocamos la etiqueta en el eje y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x1daff121128>"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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k5+d3uuj8/mYS2Yf5+c0UFBQ4aqSX29ZoNL0XKWXSB11+fuLuPRAIsGzZ08yb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\nLBaLxWLJDCtELBaLxWKxZMb/DyRqsniGbxVBAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1dafc8bb668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "plt.ion()\n",
    "x = np.arange(100) \n",
    "y = np.random.rand(100) \n",
    "plt.plot(x,y, color = 'black', label = '(x, f(x)')\n",
    "plt.plot(x[y > 0.9], y[y > 0.9],'bo', label = 'f(x) > 0.9')\n",
    "plt.axhspan(0.9, 1, alpha = 0.1)\n",
    "plt.ylim(0,1.2)\n",
    "plt.legend()\n",
    "plt.title(u'Parejas ordenadas (x, f(x))')\n",
    "plt.xlabel('X')\n",
    "plt.ylabel('f(x)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [default]",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
