Bloque A - Sistemas complejos.ipynb

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   "source": [
    "![Aropython_logo](./static/aeropython_name_mini.png)\n",
    "###### Carlos Dorado Cárdenas\n",
    "###### Siro Moreno Martín"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Simplifica tu vida con sistemas complejos y algoritmos genéticos"
   ]
  },
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   "metadata": {},
   "source": [
    "## Bloque A - Sistemas Complejos"
   ]
  },
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   "source": [
    "### 1. ¿Qué es un sistema complejo?"
   ]
  },
  {
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   "metadata": {},
   "source": [
    "Por el momento, no existe una definición formal exacta de qué es un sistema complejo. Sin embargo, de forma general, se puede aceptar que los sistemas complejos tienen una serie de propiedades que los identifican. De manera resumida, podemos decir que los más característicos son:\n",
    "\n",
    "- Están compuestos por múltiples unidades simples\n",
    "- Estas unidades se relacionan entre sí\n",
    "- Estas relaciones producen comportamientos emergentes que no se deducen fácilmente del análisis de los agentes sencillos por separado.\n",
    "\n",
    "Ejemplo: una colonia de hormigas, el cerebro, un animal, la biosfera, una célula... También es relativamente común que las unidades simples de un sistema complejo sean sistemas complejos a su vez."
   ]
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   "source": [
    "Según el profesor **Hiroki Sayama**:\n",
    "<blockquote><strong>Complex Systems</strong>\n",
    " are <strong>networks</strong> made of a number of components that <strong>interact</strong> with each other, typically in a nonlinear fashion.  Complex systems may arise and evolve through  self-organization,  such  that  they  are  neither  completely  regular  nor  completely  random,  permitting  the  development  of  emergent  behavior  at  macroscopic scales.\n",
    "</blockquote>"
   ]
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   "metadata": {},
   "source": [
    "En otras palabras, según el científico **Warren Weaver**:\n",
    "![ 3 tipos de sistemas](./imagenes/SC-Warren.gif)\n",
    "* Los problemas formados por una serie de componentes independientes, como los gases, son fácilemente describibles por las teorías clásicas de la probabilidad. Warren llama a estos sistemas *\"Problems of Disorganiced Complexity\"*\n",
    "* Los problemas opuestos, como el movimiento de los sólidos, están compuestos por elementos completamente acoplados entre sí, de manera que el sistema finalmente tampoco tiene demasiados grados de libertad. Los llama *\"Problems of Simplicity\"*\n",
    "* La mayoria de los sistemas del mundo real, sin embargo, no pertenecen realmente a ninguno de estos dos grupos. Necesitaremos desarrollar modelos nuevos para explorar sus comportamientos: Los Sistemas Complejos."
   ]
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   "source": [
    "### 2. Aplicación de Sistemas Complejos:"
   ]
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   "source": [
    "![Mapa de conceptos](./imagenes/Complex_systems_organizational_map.jpg)\n",
    "\n",
    "*[Fuente: Hiroki Sayama para Wikipedia](https://en.wikipedia.org/wiki/File:Complex_systems_organizational_map.jpg)*"
   ]
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   "source": [
    "Como  hemos visto antes, los sistemas complejos pueden referirse a casi cualquier sistema del mundo real, por lo que naturalmente, su estudio es uno de los más multidisciplinares que existen. El aumento de la potencia informática disponible ha permitido una explosión en el uso y aplicaciones de estos campos, pero algunas de sus raíces ya se investigaban desde antes de la II Guerra Mundial.\n",
    "\n",
    "Desde un punto de vista más simplificado y práctico, podemos observar que el siguiente esquema se ha repetido muy a menudo en la breve historia de los Sistemas Complejos:"
   ]
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   "metadata": {},
   "source": [
    "![1. sistema real. 2. Modelo algorítmico. 3. Aplicación por metáfora](./imagenes/SC-aplicacion.gif)"
   ]
  },
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   "metadata": {},
   "source": [
    "[Otro ejemplo más!](http://www.publico.es/ciencias/hongo-crece-emulando-mapa-trenes.html)"
   ]
  },
  {
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   "metadata": {},
   "source": [
    "En este taller, vamos a intentar poner el foco en los diferentes tipos de **modelos**, para lo que seguiremos el mismo esquema que usa el profesor Hiroki Sayama en su libro:"
   ]
  },
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   "metadata": {},
   "source": [
    "![Tipos de Modelos de Sistemas Complejos](./imagenes/SC-tipos-modelos.gif)\n",
    "\n",
    "Nota: esta clasificación no es ninguna distribución oficial ni objetiva. Es la que el profesor Sayama escogió para su libro y me parece lo bastante correcta como para usarla aquí también. Debe notarse que no es la única posible, ni es exhaustiva."
   ]
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   "source": [
    "**Sistemas con pocas Variables**\n",
    "\n",
    "Los sistemas con pocas variables normalmente son modelos muy simplificados con menos de 5 grados de libertad. Típicamente suelen usarse variables como \"población total\", \"densidad media\" o \"velocidad media\".\n",
    "\n",
    "La diferencia entre tiempo discreto y tiempo continuo (que tendremos que discretizar al programar) no suele ser tan evidente, pero podemos sintetizarla en:\n",
    "\n",
    "- Los modelos de tiempo discreto se ejecutan en pasos o steps, y el tiempo se representa con un número entero. Estan basados en las Ecuaciones en Diferencias: $ X_{t+1} = F(X_t , t) $\n",
    "- Los modelos de tiempo continuo contienen el tiempo con dimensiones (segundos, horas, etc). Estan formulados mediante Ecuaciones Diferenciales: $ \\frac {dx}{dt} = F(x,t) $. Posteriormente, si no se tiene una solución analítica, estos modelos deben ser discretizados para integrarlos numéricamente. Para ello, se deben escoger incrementos de dimensiones y tiempo $ \\Delta x, \\Delta t $, así como un esquema de integración."
   ]
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   "source": [
    "**Sistemas con muchas variables**\n",
    "\n",
    "Los sistemas con muchas variables suelen ser sistemas que modelan cada agente por separado.\n",
    "\n",
    "* Los modelos de Autómatas Celulares están basados en mallas regulares y entornos simples.\n",
    "* Los modelos de Campos Continuos usan variables y funciones continuas sobre el dominio de estudio.\n",
    "* Los modelos de Redes aprovechan las propiedades matemáticas de los grafos y liberan a los agentes de sus reticulas, permitiendo distribuciones libres y desordenadas y conexiones heterogéneas.\n",
    "* Los modelos Basados en Agentes son los más generales y prácticamente sólo requieren contener agente discretos que se relacionan entre sí. Contienen a las Redes y a los Autómatas como casos particulares, pero no a los Campos Continuos."
   ]
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   "source": [
    "### 3. Ejemplo: Ecuaciones Depredador-presa\n",
    "##### Modelo de SC con Pocas Variables y Tiempo Discreto."
   ]
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   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
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   "source": [
    "Tenemos una población de 10 leones y 10 cebras, cuyo número cambia según las siguientes reglas:\n",
    "- Cada año, la población de cebras aumenta de manera proporcional a su población, en ausencia de leones.\n",
    "- Cada año, la población de leones disminuye de manera proporcional a su población, en ausencia de cebras.\n",
    "- El número de encuentros entre leones y cebras es proporcional a los números de ambos.\n",
    "- Cada año, la población de cebras disminuye proporcionalmente al número de encuentros.\n",
    "- Cada año, la población de leones aumenta proporcionalmente al número de encuentros.\n",
    "\n",
    "En su forma diferencial, las ecuaciones derivadas de estas reglas son conocidas como [Ecuaciones de Lotka-Volterra](https://en.wikipedia.org/wiki/Lotka%E2%80%93Volterra_equations)"
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   "source": [
    "### 4. Algunos Conceptos Clave:"
   ]
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   "source": [
    "#### 4.1Emergencia"
   ]
  },
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   "source": [
    "\n",
    "Una de los aspectos más interesantes de estos sistemas son las citadas **propiedades emergentes**: Son características, patrones, variables o comportamientos del sistema que no se deducen de manera fácil a partir del estudio de cada agente del sistema. Por ejemplo, la inteligencia no se deduce fácilmente del estudio independiente de cada neurona. La estructura compleja y eficiente de un termitero no se puede calcular sólo analizando qué hace una termita aislada.\n",
    "\n",
    "Nuestro amigo, el profesor Hiroki Sayama las define de la siguiente manera:"
   ]
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   "source": [
    "<blockquote><strong>Emergence</strong> is a nontrivial <strong>relationship</strong> between the <strong>properties</strong> of a system at <strong>microscopic and macroscopic scales</strong>. Macroscopic properties are called emergent when it is hard to explain them simply from microscopic properties.\n",
    "</blockquote>"
   ]
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   "metadata": {},
   "source": [
    "#### 4.2 Auto-organización"
   ]
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   "source": [
    "Otro aspecto muy característico del estudio de los sistemas complejos es el concepto de **Auto-organización**. A menudo, nuestros modelos de sistemas complejos presentan una tendencia a organizarse de manera espontánea, o crear estructuras o comportamientos macroscópicos de manera no trivial a medida que transcurre el tiempo. Este tipo de comportamiento resultaba muy misterioso para investigadores del pasado, a los que les parecía, por ejemplo, que el crecimiento de las plantas rompía la segunda ley de la termodinámica, al crear estructuras ordenadas a partir de gases y sustancias disueltas.\n",
    "\n",
    "La clave de esta paradoja reside en la energía: Las plantas no son sistemas aislados, sino que por el contrario necesitan del flujo de energía que el Sol les proporciona, desprendiendo calor al ambiente. Sayama describe la **Auto-organización**  simplemente así:"
   ]
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   "source": [
    "<blockquote><strong>Self-organization</strong> is  a  <strong>dynamical</strong>  process  by  which  a  system  spontaneously  forms nontrivial <strong>macroscopic structure</strong>s and/or behaviors <strong>over time.</strong>\n",
    "</blockquote>"
   ]
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   "source": [
    "Si bien numerosos autores hacen una distinción bastante laxa entre **Emergencia** y **Auto-organización**, Sayama considera que la diferencia clave es que la emergencia se basa únicamente en la escala, mientras que la auto-organización es un proceso en el que el tiempo es igual de importante."
   ]
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   "source": [
    "---"
   ]
  },
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   "source": [
    "### 5. Autómatas celulares"
   ]
  },
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   "source": [
    "Tampoco existe una definición formal y matemática aceptada de Autómata Celular. Sin embargo, se puede describir a un A.C. como un conjunto ordenado de objetos caracterizado por los siguientes componentes:\n",
    "\n",
    "- Una rejilla o cuadriculado (lattice) de enteros infinitamente extendida. Cada celda de la cuadrícula se conoce como célula. \n",
    "- Cada célula puede tomar un valor  a partir de un conjunto finito de estados $k$ .\n",
    "- Cada célula, además, se caracteriza por su vecindad, un conjunto finito de células en las cercanías de la misma.\n",
    "- De acuerdo con esto, se aplica a todas las células de la cuadrícula una función de transición $f$ que toma como argumentos los valores de la célula en cuestión y los valores de sus vecinos, y regresa el nuevo valor que la célula tendrá en la siguiente etapa de tiempo. Esta función $f$ se aplica de forma homogénea a todas las células, por cada paso discreto de tiempo."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![Caracol y autómata celular 1D](./imagenes/Caracol y automata.jpg)\n",
    "*Concha de caracol marino y autómata celular 1D. Fuentes: [1](https://commons.wikimedia.org/wiki/File:Textile_cone.JPG), [2](https://commons.wikimedia.org/wiki/File:CA_rule30s.png)*"
   ]
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   "source": [
    "#### 5.1 Condiciones de frontera\n",
    "\n",
    "Por definición, un A.C. consiste de una retícula infinita de enteros. Sin embargo, para cuestiones prácticas (como en modelos de sistemas físicos llevados a cabo en ordenadores de memoria finita), se requiere tomar ciertas consideraciones a la hora de implementar un A.C. Por ello, la definición original se modifica para dar cabida a retículas finitas en las que las células del A.C. interactúen. Esto conlleva la consideración extra de lo que debe de suceder con aquellas células que se encuentren en los bordes de la retícula. A la implementación de una o varias consideraciones específicas se le conoce como condición de frontera.\n",
    "\n",
    "Dentro del ámbito de los A.C., se pueden implementar numerosas condiciones de frontera, en función de lo que el problema real requiera para su modelado. Por ejemplo:\n",
    "\n",
    "- Frontera abierta. Se considera que fuera de la lattice residen células, todas con un valor fijo. En el caso particular del juego de la vida y de otros A.C. con dos estados en su conjunto $k$, una frontera se dice fría si las células fuera de la frontera se consideran muertas, y caliente si se consideran vivas.\n",
    "- Frontera periódica. Se considera a la lattice como si sus extremos se tocaran. En una lattice de dimensión 1, esto puede visualizarse en dos dimensiones como una circunferencia. En dimensión 2, la lattice podría visualizarse en tres dimensiones como un toroide.\n",
    "- Frontera reflectora. Se considera que las células fuera de la lattice \"reflejan\" los valores de aquellas dentro de la lattice. Así, una célula que estuviera junto al borde de la lattice (fuera de ella) tomaría como valor el de la célula que esté junto al borde de la lattice, dentro de ella.\n",
    "- Sin frontera. Haciendo uso de implementaciones que hagan crecer dinámicamente el uso de memoria de la lattice implementada, se puede asumir que cada vez que las células deben interactuar con células fuera de la lattice, esta se hace más grande para dar cabida a estas interacciones. Obviamente, existe un límite (impuesto por la memoria disponible) para esta condición. Es muy importante no confundir esta condición de frontera con la definición original de A.C. cuya lattice es inicialmente infinita. En el caso de un A.C. sin frontera, la lattice comienza con un tamaño definido y finito, y conforme se requiera va creciendo en el tiempo, lo cual no lo hace necesariamente un modelo más cercano a la realidad, pues si se inicializara la lattice aleatoriamente, con esta condición sólo se pueden inicializar las células dentro de la lattice inicial finita, mientras que en el caso de la definición original, en teoría todas las células de la lattice infinita deberían ser inicializadas.\n"
   ]
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   "source": [
    "#### 5.2 Ejercicio: Autómata celular binario de una dimensión"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Programemos uno de los sistemas complejos más sencillos conocidos! Se trata de un autómata celular, como hemos visto antes, pero de una sola dimensión: cada paso del tiempo, nustro tablero consistirá en una sola hilera de células. Esto parece un poco aburrido, pero nos permite representar el tiempo en un eje perpendicular y observar los patrones que se forman.\n",
    "\n",
    "Usaremos: \n",
    "- Numpy\n",
    "- Matplotlib"
   ]
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   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
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  {
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    "#Definamos la dimensión, y creemos nuestro universo con un array de integers\n",
    "dim = \n",
    "universo = \n",
    "#Definimos el estado inicial:\n",
    "universo[] =\n",
    "#Recorremo el tiempo fila a fila:\n",
    "for\n",
    "#Recorremos el espacio célula a célula:\n",
    "    for\n",
    "#Reunimos la información del entorno:\n",
    "        info = \n",
    "#Determinamos el estado de la célula dependiendo del entorno:\n",
    "        if\n",
    "#Pintamos el resultado!\n",
    "plt.figure(figsize=(10,10))\n",
    "plt.imshow(universo, cmap='Greys')"
   ]
  },
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   "source": [
    "Algunas reglas interesantes:\n",
    "* [Regla 30](https://en.wikipedia.org/wiki/Rule_30) (1,2,3,4): Regla 30 : caos desde reglas simplérrimas, morfogénesis genética\n",
    "* [Regla 110](https://en.wikipedia.org/wiki/Rule_110) (1,2,3,5,6): Regla 110 : Demostrada Turing-completa (!!!!!)\n",
    "* [Regla 184](https://en.wikipedia.org/wiki/Rule_184) (3,4,5,7):   #Regla 184: Simula tráfico, partículas y otras cosas (?!?!?)"
   ]
  },
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   "source": [
    "#### 5.3 El autómata celular más famoso:\n",
    "#### el Juego de la Vida de Conway\n",
    "\n",
    "![Un interesante patrón!](./imagenes/Game_of_life_animated_glider.gif)\n",
    "*El planeador o glider es uno de los patrones más reconocidos del juego. [Fuente](https://en.wikipedia.org/wiki/File:Game_of_life_animated_glider.gif)*"
   ]
  },
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   "metadata": {},
   "source": [
    "El juego de la vida es un autómata celular diseñado por el matemático británico John Horton Conway en 1970. Se basa en unas pocas reglas enormemente sencillas:\n",
    "\n",
    "- Se tiene una cuadrícula o matriz en la que cada casilla o célula puede estar en dos estados: Viva o Muerta.\n",
    "- El entorno de una casilla lo componen las 4 con las que comparte lado más las 4 de las diagonales.\n",
    "- Si una célula muerta tiene 3 vivas en su entorno, al turno siguiente pasará a estar viva.\n",
    "- Si una célula viva tiene 1 o 0 células vivas en su entorno, al turno siguiente morirá de aislamiento.\n",
    "- Si una célula viva está rodeada de 4 o más células vivas, al turno siguiente morira de superpoblación.\n",
    "\n",
    "Su comportamiento impredecible, a medio camino entre el orden y el caos, ha fascinado a programadores, hackers y científicos desde su nacimiento. Unos años más tarde se confirmó que este algoritmo es Turing-completo.\n",
    "\n",
    "Podemos probar una versión online [aquí](https://bitstorm.org/gameoflife/)"
   ]
  },
  {
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   "source": [
    "### 6. ¿Por qué son interesantes en ingeniería los sistemas complejos?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Los sistemas complejos pueden ser realmente interesantes en muchos campos de la ingeniería:\n",
    "\n",
    "- Pueden llegar a ser muy robustos: un hormiguero que pierda a 2/3 de sus miembros puede sobrevivir, pero imagina intentar usar un ordenador o un vehículo con 2/3 de sus piezas rotas...\n",
    "- A veces pueden autoregularse a partir de reglas simples.\n",
    "- Algunos son fácilmente escalables: un enjambre de robots colaborando en una tarea, si han sido diseñados con este enfoque en mente, pueden mejorar su rendimiento añadiendo más robots iguales sin necesidad de más cambios.\n",
    "- Pueden usarse para resolver problemas complicados: Problema del viajante, distribución de paquetes en telecomunicaciones, líneas de transporte que se adaptan al tráfico en tiempo real...\n",
    "- Pueden usarse para simular problemas del mundo real a base de agentes simples: tráfico, fluídos, enfermedades..."
   ]
  },
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   "source": [
    "### 7. Ejemplo : Algoritmo de Colonia de Hormigas (ACO) para el problema del viajante"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Imaginemos una distribución de ciudades en un mapa. Somos un vendedor que quiere visitarlas todas, sólo una vez cada una, gastando el menor combustible posible. Este es el conocido Problema del Viajante, que se puede resolver fácilmente con un sistema complejo. Es importante tener en cuenta que aunque este método obtiene resultados muy buenos rápidamente, **no necesariamente encontrará siempre la solución óptima.**\n",
    "\n",
    "Este problema clásico es muy conocido, porque bajo su enunciado simple se esconde una enorme complejidad: \n",
    "\n",
    "Para 10 ciudades hay 181.440 rutas diferentes, pero para 30 ciudades hay más de 4·10^31 rutas posibles. Un ordenador que calcule **un millón de rutas por segundo** necesitaría **10^18 años** para resolverlo. Dicho de otra forma, si se hubiera comenzado a calcular al comienzo de la creación del universo (hace unos 13.400 millones de años) sólo habría calculado el 0,0000001% (**una billonésima parte**) a día de hoy.\n",
    "\n",
    "Suena interesante, ¿verdad? ¡Veamos cómo modelarlo y resolverlo con Hormigas!"
   ]
  },
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   "source": [
    "![Esquema gráfico del algoritmo](./imagenes/hormigas.png)\n",
    "*Las hormigas prefieren las rutas cortas... [fuente](https://es.wikipedia.org/wiki/Algoritmo_de_la_colonia_de_hormigas#/media/File:Aco_TSP.svg)*"
   ]
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   "source": [
    "El algoritmo se basa en varias generaciones sucesivas de hormigas que recorren el mapa viajando de ciudad en ciudad, eligiendo su siguiente ciudad de manera aletoria hasta que las han recorrido todas. En cada etapa del viaje, las hormigas eligen moverse de una ciudad a otra teniendo en cuenta las siguientes reglas:\n",
    "\n",
    "1. Debe visitar cada ciudad exactamente una vez, excepto la inicial en la que estará dos veces (salida y llegada final);\n",
    "2. Una ciudad distante tiene menor posibilidad de ser elegida (Visibilidad);\n",
    "3. Cuanto más intenso es el rastro de feromonas de una arista entre dos ciudades, mayor es la probabilidad de que esa arista sea elegida;\n",
    "4. Después de haber completado su recorrido, la hormiga deposita feromonas en todas las aristas visitadas, mayor cantidad cuanto más pequeña es la distancia total recorrida;\n",
    "5. Después de cada generación, algunas feromonas son evaporadas.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "from Ejercicios.Hormiguero import ants"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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QTlRISr/n7rS0cbj4QzLzXqKiPpuwwAQWT32YSaPcr707WBV12WTsfIDK+hyn\nfUmxi7lm5pOOnNHd1TQW8ObGu52mS81N/S7eRn82HvwDV8/4DeNGXuPYp7WVNTsfxNxRT2p8uq2V\nDGw88AfmpX0Xq7ZwsOg9l/Wc5qb1C32lorz4wtHFd8dCiIuCVVsoqdxFtimD3JKPnZJRdBfsH+PI\nSjU8bOKAuokbW8vJOvoqe4+9SWu7be3dm+b/kzGxi874yj5Wayc7cv/J5kP/h7VXrmUfryCumPoL\nJo26yWW9K+tzefPLu50GSi2a/FN8vYP5JPMRFk56kKndslUB7Mp7kaKKrQT7x7Jk+uMopSir2U9d\nczGp8SvYe+w/Lr/I+HmHMt5FC7yL+0QckgtaCCHOW1prymr2kW3KIMe0xuVc2C4BvhGMH7mc1IR0\nRkROH1AXqNaa0posMvNeIrfkYwwGL9vau8kriQ4deyZvxaG68Rhrdj5IWc1ep32Jw+aydOZThAaO\ncHns8eos3tn0jV4pMRVXz3gcf59wPtj2fWaN/QaXjPt2j+Mq6rLZeOAPgCL9kj/i5xOC1pov9j7B\nFVN/DkBW/qsurzlp9Ff6nMcsqyGdJAFYCHFe01pTWZ/rmKtb32xyW9bXO5ixI2xZqRKj5wy429Ni\nbSfHtJbMvFWU1e4n2D+Wyyb+iMljbsHfJ+xM3UoPWlvJPPoSX+5/2uk5tZfRj0WTf8L0pDvdfnEo\nqtjKfzd/u8ciEAblxfLZfyDAN4J3Nt/LhMTruXzyz3q0nDssZj7c8UMs1nbmjP8OCdGXAJBtWk14\nUCKxEZMpqdpNZX2uy+tOHXNbP/clLeAuEoCFEOelmsYCW9AtzqC68ajbct5Gf5LjriA1YQWjh1+K\nl9F3wNdoNlex99h/yMp/jWZzJSOjZnH93GdJibtySJ9Z1jeX8NGuhyiu3OG0Ly5yGstnPd3nwK68\n0s/5YNv3eyx76GXw5fp5zxLgG8kbX97J6JjLWDrzt07d1hv2/47qhjyGh01gwYT/BWzLKW49/Cy3\nLLSlq8zKf83ldUcNv7TfAWfuR0FLABZCiHNWQ0sp2aY15JgyKK896Lac0eDDmJjLbFmpYhef8pzb\n8tqDZOa9dHLt3YQVzExeyfDwCadV//5ordlf8Bbr9v2G9s6eC9sbDT4smPADZo+7t89gdajoA9bs\nerBHS9PHK5Cb5v+TQL9oXlt/K8PDJnDtnL86fYnIL9vAnqOv4GX0Y8WcvzhSaO7I/ScTEq8n2H84\nzeYqckyogErdAAAgAElEQVRrXV57epL7wVdd3AZgGYQlhBDnlmZzFbkla8k2ZVBStdttOaWMJA6b\nR1pCOilxS/DzCTml61itnRwp/YzMvFWUVO0e9Nq7g9XYeoKPdz/MsfINTvuGhaWRPvsPRIeO6/Mc\nWfmv8emeX9F9LV8/nzBuvvRFAv2ieHXdzQQHxHDTgn86PadtNlexdtdPALhiyiNEBo8BbF96ckvW\nsvLKDwDYX/C200AwgGD/WJJiL+/3Pt2NgpZpSEIIcQ4wtzdw5PgnZJsyKDqxtY+sVDAyahap8emM\nG3nNoAJla1st+wreYs/RV2hsLSM2YgorLvkz40Zec8pr7w6G1pps02o+2/Oo0/rBShmZO/4+5qV9\nt9+6bM95ji8PPN1jW5DfMG6+bJWj5etl9OHmS190mh6ltWbt7p/R3FZFctyVTOn2HHfD/t+zYMIP\nbKshaQt7j73u8vpTk24bUCtWliM86eK7YyHEOam9s8WelSqDgvKNPZ5f9hYTPsmRlaorX/GpqqzP\nJTPvJQ4Vf2Bfe3cpM1O+Rlzk1MHewilraavm0z2/IrfEuUs3IjiJ9Nl/6JGVyhWtNRsP/oHtOc/1\n2B4aMJJbFr5MgG8kb355F+0djdyx+C2XX1L2HvsP+WXrCPSNYunMJx3PhUuqMmk2VzB2xNUAHCv7\n0uXSiwblxeTRNw/oniUV5UkSgIUQHtNpaaOgfCOH7VmpOiytbstGhqSQFp/O+Pjlg84sZdUW8svW\nk5m3iqKKbfj7RDBr7DeYlnT7oNfeHay80s/5ePfDtLRV99qjmDX261w68Uf9LkuotZXPsh51GhQV\nGZzMLQtfwt8nnHc2f5OapkLuuPwNwgLjnc5R3ZDPun2/AWDZ7N8T4BvpOPe6fU9y9YzHHQHZ3dSj\nsSOvdpkAxHWdpQXc5eK7YyGER1mtnRRVbOOwaTV5xz+lraPRbdmwwARS45eTmrCi3+effWnraGR/\nwTvsOWpbe3dYaCpLZz5FWsKKUxoVfSaY2xv4Yu/jHCx612lfaGA8y2f9nvjo2f2ex2Lt4KNdP+Fw\n8Qc9tseET+Srl76In08oH27/X45XZ3LLZS+7fP8s1nZW7/ghnRYzM5JXMiZmoWPfwaL3GBY6juFh\naQDUNhVxrHyjy7pMT7qz3/p2kRbwSRKAhRBDTmsrJVWZZJtWk1uylpa2Grdlg/yGMz7eliAjNnzy\naS1eUNNYQObRlzhY+K597d2rWDbrKUZGzTpjiyKcisITW/ho10M0tpY77Zs65nYun/JTfLwC+z1P\np6WND7bfz9HSz3tsHxk1i68s+H/4eAXxyZ6fk3f8M26c/zwjo2a4PM+mg3/hRN0hokJSWDj5Icf2\nto4mtuc8z+2LTj7v3XvsP3Qf3NUlKiSFkVGz+q1zF6u79YBlFLQQQpwZWmvKaw84slK5Cjpd/H3C\nGTdyKakJ6YyMmnlac0K11hSc2ERm3iqOlX9pW3s36XamJ915RtbeHYz2zhY27H/KZRdukH8MS2f+\nljExlw3wXM28u+XbFFVs67F9TMxCrp/7LN5e/mw8+Ef2HXuTFZf8iaTYRS7PU1yxnR25/8Ro8GHF\nJX/p0d29Pec5poy+2fG8uNPSxoGCd1yeZ1rSHaf0ZUZr1wPqDNICFkKI01NZf8QedDOobSpyW87H\nK4ixI5bYslINm4fR4H1a123vbOZg4XtkHn2ZmsZ8IoOTWTL9cSYkXn9G1t4drJKq3azZ+SB1zcVO\n+yYkXs+VU3/pct1cV1rb63h70zec0lKOG7mMFZf8EaPBh11H/s227L9z5bRfkZZwrcvzmNvrydj5\nY0CzcNKDDAsb79hX12wiv2wdK69837Etp+Qjl7m0vY0BTEi8fkB17+JuyUd5BiyEEINQ21REjmkN\n2aYMtykKwZZCMTl2MakJ6YyJWXRGnr/WNZvYc/QV9he8RVtHI0mxl3PltF8yath8j3Qzd+m0tLHp\n0J/ZmfsCvbtuA3wjWDL9CcaNvHrA52syV/LWxpVO7+/k0Tdz9YwnMCgjBwvfZd2+3zA/7X5mJN/t\n8jxaaz7J/DmNreUkDpvPzJSv9di/ft/vuGzij3tMe8o66jrz1YTE6095xSermxawPAMWQogBamwt\nJ8f0EdmmDMpq9rktZ1DejI65lDR7Vipf76DTvrbWGlPlDnbnreJo6Rd4ewXY196964yuvTtY5bUH\nyNj5INUNeU77xo5YwpLpj5/SnOX65uO8ufEupx6FmSlfZ/GUh1FKcbT0Cz7a/VOmJ93F/LT73Z7r\nUNF75JR8hJ9PGMtnP90j8BVX7qC9s5mk2MXd7uUgpTVZLs81bQCZr3rT0gJ2uPjuWAgxaC1tNeSW\nfEy2KQNT5U5cDcoBUBhIGDaH1IQVjB2x5IwtWNBhMTvW3q2szyE8KJHFUx9h0qibzvjau4NhsXaw\nLfsfbMt+1qmr1dc7mKumPUpawnWn1DKvbjzGm1/e5fQMfcGEHzAv9XsopTBV7uKDbd9n/MhlXDnt\nl27PX9ds4rOsXwOwdOaTBPsPd+yzagvr9z3JsllP9zjeXd7nkVEze3RdD5S0gE+SACyE6FNbRyNH\njn9KtimDwhNb3M7jBBgROd2WlSp+2YDnhQ5EQ0sZWfmvse/YG7S21zJq+AIum/j/SIpddM58cFfW\nH2HNzgc4UXfIad/o4ZdyzczfEhIQe0rnPFF3mLc2rnQaNX7F1J8zM+UeR5l3Nt9LfPRsls/+vdv3\nw2rtJGPHj2jvbGLy6JsdyTW67C94i7jIaT2WVTS3N5BdvNrl+QbT+u2qhysyCloIIbCtfnO0bB05\npgzyyzb0mZVqeNgEUuOXMz4+3e26tIPRtfbu7rxV5JZ8jNHgzcTEG5iRspKokJQzdp3TZdUWdh35\nN5sO/snpffI2BrB46sNMGX3rKT+PLqnazTub7+0xT1ph4JqZTzJ59FcB27P3tzfeQ1RICtfP+3uf\n6Sq35fyD49V7CA9KdKzp26Wto5FdR/7NnZe/1WP7waJ3XSZHCfCNcArgA+VuFLTi3PgidTZJABZC\nALakDAXlm8k2rSbv+Oc91pHtLSJ4DKnxK0hNSHck7T9TOi1t5JasZXfeKsprDxASEMfCSQ8wefTN\nQ7b27mDVNhWyZudDHK/OdNo3MmoWy2f9nrCghFM+b0H5Jt7bel+P4GdQ3qyY82fGj1wKQFNrBW9u\nXIm/bzhfufSFPkd6H6/OYsvhv6GUkfRL/uw013jL4b8xPelO/H3DHdu01m67nyePvmXQA+hcjYJW\nyujRAXOeIgFYiIuYVVsorthOtimDIyWfOC0G0F1IwAhS49NJTUhnWGjqGf/AbDZXkZX/OnvzX6O5\nrYr4qNlcP/fvpMRdcc51T2qt2Xvsddbv+61TC9Fo8GHhpAeZmfK1QXWP55Z8wuodP+i5lq/Rjxvm\n/d2RqcrcXs9bm74GWnPzZav6/GLS1tFExo4fobWFSyf8kLiIKT321zQWUFSxjbuvOJmZ6+jRozzw\ny6/z2ZpMWhtb8Q/2Z+qCAOZf409kjA9Tuy3WcKpctYAvxrWAQQKwEBcdra0cr86yZaUyraW5rcpt\n2UC/aMaPXEZqQjpxEdOGpJVSXnuA3XmryDF9BEBawrXMSL57yNfeHayGllLW7v4ZhSc2O+2LCZ/E\n8tl/ICokeVDnPlD4X9bu+mmP1Z98vIL4yoIXiI+2ZZvq6Gzlnc330myu4o7L3+w3h/UXex+nrrmY\nkVEzmZN6n9P+9ft+y8JJDzrmYa9du5abb7+T+DlfZ+FDLxIQkUhLTRGFm5/jmUf+wQ9/eelpPWpw\n1QKWACyEuGBpramoO0y2KYNsU4bLFW26+HmHMm7kNaQmpBMffcmQfDharZ3kHv+EzLyXOF6dSZDf\nMOalfpepSbc5FgM412itOVj0Lp9nPUZ7Z1OPfQblxfy07zNn/HcG3VrPzHuJz/c+1mObv08EN1/2\nIjHhEwHbKOv3t32Xyvoj3L7o9X6nXOWUrOVA4Tv4eAWRPvuPTv+WBeWb0FgdWbjy8/O59fa7mf2t\nDCKT5jrKBUUnMfGGp4mdfCN/fXwZd197mHFj0wZ1n65GQSsJwEKIC011Q74j6NY0HnNbzscrkJS4\nq0hNSGfU8PlDtg6ube3dN+1r75YTGzH1rK69O1jN5io+yXyEvF65lwGiQsaSPvsPg26xa63Zlv0s\nmw79ucf2IP8YbrnsJUdrWmsrH+36CUUV27j5slX9Xq+hpYxPdj8CwJLpv3ZKw9nW3sjnex9jZso9\n7M57kbqmYv745LuMmPu1HsG3u8ikuSTM+wbf+vFV/OxXK0lLuJb46Nmn1NWuXYyClhawEOKCUN9c\nQrZpDdnFq6moz3ZbzmjwISn2clITVpAUswhvL/8hq1NlfS6781ZxuOgDrNrC+PhlzEheeVbX3h2s\nnJK1fJr5C6dUjAoDl4z/FvPT7h/0gCStNRv2/46dR17osT0sMIFbF77iCJpaa77Y+zjZpgxumPd3\nEqIv6ee8VtbsfBBzRz2Jw+ajtZXNh/5KXXMxdU0m6ptNNJkrAPh0zy8cx238tJyFDzmv0tTdqAX3\n8eXTz7Gv4E32FbxJkH8MafHppCVeN6CxAa5WQzIYJAALIc5TTa0V5JTYslKVVrvOWgS2rtJRwxeQ\nmpBOStyVQ5q8wqot5JeuI/PoSxRVbCPAN4LZ477J1KTbeySAOFe1ttfxedavOVz8odO+8KBRLJ/9\nNCMipw/6/FZt4dPMX7Cv4M0e26NCUrjlspcJ8h/m2LY1+xkyj77MsllPkRJ3pWN7h8VMfXMJdU3F\ntuDabKK+yURZzT7Hs/2iii0UVWwZUJ1aG1sJiEjss0xARAKtjScHnjW1lrPzyAvsPPICkcHJpCVe\nS1r8Crejv10FYOmCFkKcV1rb68gt+ZgcUwbFFTt6DNzpSZEQPduelepqAnwjhrRe5vYG9he+zZ6j\nr1DfbGJYWBrLZj1FavzZX3t3sPLL1vPx7ocdrcTuZiTfzWWTHjytBR4s1g4ydv6YHNOaHttjI6bw\n1QX/ckwH0lqzI/efbD70F1LjV9DQUuZY2KGuyUST+cSg6+CKf7A/LTVFBEUnuS3TUlOMf7Dr3pLq\nxqNsOvgnNh38E3GR00hLuJbxI5f1SLvpsgUsAVgIca5r62jiaOnnZJsyKCjf5HZlGYDYiKmkJaQz\nbuSys9LirG48xp68lzlQ+F86LWZSRixh+aynGRk187yZ49nW0cS6fb9hf8FbTvtCAuJYNuspEofN\nO61rdFjMfLDte+SXre+xPTpkHONGLmVr9rPUNZuoayqmtqnQMR0p2+Q6I9WZNHVBAIWbn2PiDU+7\nLVO4+R9MW9D/44rS6ixKq7P4Yu8TjBo+nwkJ15Ey4iqXmdQu1haw0tp1LtczdgGl9FBfQ4gLWYfF\nzLGyDWSbMsgvXUentc1t2ejQ8Y65umGB8UNeN62tFJzYxO68VRSUb8TPO5QpY25hevJdhATEDfn1\nz6Tiiu18tOsn1LeUOO2bNOqrXDH1kUF12WutaWmrpq7ZRFX9EbZm/50GF9cYCkoZURiw6o4Bla8u\n7+CZR2qYe99nLgdiVedvY9s/ruJ7v4kgMubUl4/0MvpRWWZm00cN7N3c4phjfMmi4fy/P31GUpL7\nlvf5RCmF1rrfb50SgIU4B1msHRSe2OLIStV72kt34UGJjqxUZytFo23t3Xfta+8eIzIkhZkpK0lL\nuM6ja+8ORofFzJcHniYzb5XTvkC/aK6Z8STJcYudD+ym09JGQ0upvWu42NGCrWu2DXhq72weotpD\ngG8kYYEJGAxelFTtQmHAxzuYtj6SqvQlN6uFN55pZNSC+xg1/zsERCTQUlNM8db/R+GW5/nOTyYQ\nnVx2eueefx+jFnzn5BzjLc9xfNsq3nj9ZZYuXTqoc59LJAALcZ6xagsllbs4bFpNbsnHmNvr3JYN\n9o9xtHSHh008a128dU3F7Ml/1b72bhPJsYuZkbKSxGHzzptu5u5Kq/eyZteDLqdojY9fzpJpv8bf\nNxytNa3ttT0GO9U1mRwB17ZS0dB8zhmUN2GBIwkNiic8MJHQoHjCAuMJC0zA28sfU+UuckvWcqx8\nwxm7ZmtdKNUHF/La6/+hvraK0PAo7rj9dn5w/3dJSkqirqmYw6bVHC76kOrGowM650Ba17v+eS1Z\nu7ef9y3hsxKAlVI/BL4BWIEDwD1a6/ZeZSQAC+GG1pqymn1kmzLIMa1xOeinS4BvpCMr1YjI6Wdt\nFSCtNcWV28nMW0Ve6Rf4eAUyafRXmJF8F+FBo85KHc40i7WdLYf+j+05zzsNXvP2CmT8yGX4+4Q6\nBjvVNZv67IU4Xf4+EYQFxRPoG0VRxXb8fcO4atqvGRY2jiD/4Y5BSlprKutzOFr6BUfL1tvXYT7z\nn68rr3ifmIhJ/ZbTWlNRn83hog84VLya5j4GhX24qoFa69f6fL58+IOfcnlyO3/7658GVe9zxZAH\nYKVUHLAZGK+1bldKvQms0Vq/3KucBGAhurF9iOY6EmTUN5vclvX1DmbsCFtWqsToOWc1J3KHxczh\nog/IPPoSlfW5hAclMiN5JRNH3XhOrL17qrTWmNvrOFa+kS8P/IHGVvfZwIba6JiFTBl9C+FBCYQG\njsTXO5hmcxWvrb8FpQzccfkbjoxgHRYzxRXbOFq6jvyydU7rAp9pU8fcxtUzngCgvbOFFnMVzW1V\nNJur7K+raTbbf2+rotlcSbO5ut8vKI/dW87Ch/b3OcK6qTKf7X+ZT03l0N7jUBtoAD7d/5uNQKBS\nygoEAJ77ixbiHFfTWGALusUZfXbbeRv9SY67ktSEdEYPv/SsT91paCklK/819h57A3N7HaOGX8rC\nSQ8yJmbhObP2rjsWa4f9WazpZHexPfFEbVPRELdiwwkLjLd3ESdgUAZ2573kdM2rpj3K9OS7emxr\n62jk7U1fp9Ni5s7Fb2OxdrL32Bvkl66jsGILnRZzj/JeRj+nbWeC0eDNidrDPP/R5TSbq/pcEetU\nDXSOcX2t+9zkF5pBB2CtdalS6o9AMdACfKq1ds7TJsRFrKGl1JGVytVC7V2MBh/GxFxmy0oVu/is\nD2TSWnO8OpPMvJfIPf4JXgYfJo66kenJdw96YYGhYm6vp7apyDHAqfugp4aW0j7mQ58eg/IiJCCO\nsKAEwgITCLMH2rDAeMKCEnr0Cpgqd/LO5m/2CL5KGVk26ykmJt7Q47ydljb+u+Xb1DUXk5ZwHe9u\n+Y7T34pReRPkP5xmcyWd1rYBBF/FYLqmLdYOymr3nfJxAzHQOcah4VFu919oBh2AlVJhwHVAIlAP\nvKOUul1r/Xrvso8++qjj9aJFi1i0aNFgLyvEOa/ZXEVuyVoOF692uU5sF6WMJA6bR1pCOilxS/Dz\nCTmLtbTptLSRU/IRmXmrKK89SEjACBZOepApo2/Gzyf0rNcHbAs1OFqxjmewJ0cXt3U0DNm1/bxD\nTwbWoARCA20DnsKDEgn2jxnQI4D8svW8v/W7PaaLGQ0+XDvnr4wdscSxrb2zhYLyjazb9yQNLccB\nyMp/1bE/0Dea0MB4mtsqqW82uZwe5d6599hvIHOMi7e+wB23334Wa3VmbNiwgQ0bNpzycafzDPgr\nwNVa62/af78LuERr/b1e5eQZsLjgmdvrOXL8U7JNGRSd2NpnK2xk1CzS7FmpumcIOpuazJXszX+d\nvfmv29bejb6EmSkrSY678qxkJTK3N3RLnVhMbbeu4vqW4y6TNZxJPt7BjBl+GcPDJzhasGGB8af9\npSPbtIaMHT/qkSDF2+jPjfOfJ3HYPCrqc8gtWUth+SbKaw+h6Xmf3kZ/QNnXGPbc56avdwiBflEE\n+kYR4BdFoG8kHRYzlfU5VDceo2MQ06oGMgp69z+vZY+Mgh7QBWYD/wJmAW3Ai8AurfWzvcpJABYX\npPbOFntWqjUcK/uyz2QHMeGTSI1PZ3z8Mo8mqCir2U9m3ktkm9aglCIt4TpmpNzN8LDBLS3njtXa\nSWNrOXXNxdQ2FffsKm429TnF6nT5egfj6x1MY+sJp0BuUN5cOvGHzB537xn5oqG1ldb2OprNVRwo\nfIddR/5N98CpMBIUMAxze8OggtZQUMroeF+6XqfGpzNr7DcI9IsiwDcSL6Mv7Z1N7Dv2FoeLV1NZ\nn43FOrBkHn2xzQNuZsyl/0PCvG92m2P8AqatL8g84FO8yK+AW4EOIAu4V+uen0ISgMWFpNPSxrHy\nLx1ZqWwtFdciQ1JIs8/V9eR0HYu1gyOOtXf3EOQ3nGnJdzB1zK2ntfZuW0ej02An2+9FNLSU9pkm\n83QoDPZnsfGE2ruHu7qKvYz+bNj/O5dzYoeFpZE++w9Eh47r8/xWbaG1rZaWtmqazJW2kb/2kcAn\nX1c7RgQPdWu9b30/6w3yG05LWzVW3YnR4EtYUDzVDbYBgEaDDxZrO2GBCXz96rUYDd4cr8pk37E3\nKKzYSrO58ozX1tc7mKvGvchzz73Ca6+/7nKO8YVAEnEIcYZYrZ0UVmwl25RB3vFPaetodFs2LDCB\n1PjlpCas6PeDfqi1tNWw79ib7Ml/labWcuIipjEjZaV97d3+0whatYXGlvIeXcXdMzz1Xp7vTPLx\nCnI72CkkIM6p/lprsk2r+WzPo5h7ZYBSysiccd9mStJttLXX26fQVHabWlPdY5pNS1vNkA3kGiwv\nox/xUbMJ8h/GgcJ3HMHTnRD/EbRbmmhrb0RjxccriMRhc8kr/cxRxtsYQIelhYmJN1FRn01V/ZEh\n+9Jko7h90X+Ij541hNc4N0gAFuI0aG2lpCqTbNNqckvW0tJW47ZskN9wxscvJy1hBTHhkzyeEaqi\nLofMoy/Z19612tbeTVlJXMQUp7JtHU227mEXg53qm48POIfwqVOEBMQ6BjuFBZ5szdqexYb1+z5a\nrB20tNVQ05DP5sN/paRqt1MZo8EHL6Of/UuTpz6HFIG+kQT4RmLuaDyl+cex4ZOZlnwH4+PTaW6t\n4F+fLu1nBLRiRspKSip3OUZS+/tEMC3pdrZmP+Mo5eMVNERTsty3yOen3c+CCf87BNc890gAFuIU\naa0prz1gT5CxhqY+Eh74+4QzbuRSUhPSiY+a5fH5sVZt4WjpF2TmraK4cgcBvpFMTbqdqaNvRWPt\nljqxyNFVXN9c3OcXi9Pl4xXoaMGGBsb3CrYjMBp8nI6xWNtpNlfbEzxU0WyudrRWe3f/DmULfLC8\njP5MGX0zo2MWEhIQQ4BvJG0djewveIus/NcHFPS8DL6kJqxgWtIdxEZMBqC1rZYXPrmalrZqt8dF\nBI3mmpm/5UDhfzlQ+DYAgX7DGDdiCVn5rw9pq15hJMh/GI2trnNEj4yaxW0LXz2riWQ8SQKwEANU\nWX/Engoyg9qmIrflfLyCGDtiiS0r1bB5A+rGHWrm9gb2HH2VrPxXaTKfIMhvOJEhSRiUkfqW49Q3\nl/TZVXl6FMH+MY6g6vivPej6+0SglKLT0kZLt+xJJ4NppSOgdnX/9u4+PpsMypsAv0gCfSMJ9Ism\nwDcSo8GLZnMVtU2F1DQW9BnEYiOmcvOl/8bPJxSrtlBQvpE9R1/lWPmXDKT1HR6UyNSkO5g06ib8\nfcIc2/PLNvDh9v91G7wVBmaN+wbz0+7ni6zH2V/4lv1+vE6rS9nPJxRze0OfdVfKSErcVVTUHaLO\nTUY3P+9Q7lmScd6tjnU6JAAL0YfapiJyTGs4XLyaqoYjbst5Gf1Ijl1MakI6Y2IWeWRBea2tNJkr\negx2OlF3mLKa/bS0DW3WIG9jgP0ZbDxh9u7h0MB4gvyH423wxdzR0CtNYdXJNIX23/t6Zj7UjAYf\nAnwjbVNq/KII9Iu2dQd3/d41zcYvCj/vUKy6A1PlLvLL1pNfts7pC5mvdwheRl+nAUqJw+Zz4/x/\n0Gkxs7/gbbLyX3fM7e2LwkBy3BVMS76TUcPm9ehJMbfX88XexzlY9J7b48ODxzB19K00tpZxsPDd\n0/wCo4gJn4hBGSmrOeA0RapHSWVkYuINjIiczrp9T7r8cqCUAa2t3DDv74wdcfVp1Ov8IwFYiF4a\nW8vJMX1EtinDnsTeNYPytmelSicpdjG+3kFDXrf2zhb7s1hXXcWmIWzFQpB/DOGBCQQHxBLgG4Gv\ndzBeRl8UBjos5m6t10p713D/eX/PNh+vQCYkXk9C9BzbVBp7cPX1Du73WXJLWzXHyr7kaNl6Cso3\nOt1bZHAySXGXkxg9ly2Hn6G0Zk+P/cmxVzJz7D3sL3iL3JK1A/q3CvSNYvKYW5g65laXLcO845/x\nyZ5f9DESWRHkN4zmtqrTGoVtNPiQOGwe8dGzqaw/Qo5pTZ/P/RUGJiRez9zU/+HI8U/58sDTgEZh\n7BGwu54xT0u6gyXTHxt0/c5XEoCFwPbhmlvyCdmmDEyVO3HXnaYwkDBsDqkJKxg7YkmPLsAzwdaK\nrXTMh61tLqa+a9BTs2lIpnx08TL6EegXjb9POD7egRgNPihso7vbOptobas943l/T5W30d8RNAP9\nulqo0fbfo2g2V7Ej93kaWpwHME1IvJ4rp/5ywEk0tNZUNRxxLG5wvDqL7n8XBuVNfPRskuMuJyl2\nMeFBibS0VfPWxnucUkTGRkyl056gYiDio2YzLfkOxo5Y4vIZeEtbDZ9nPUa2afWAzjcYBuXFuJHX\nkDJiCdGh48jMW8X+gncGEHivY27qdwn2j2Ht7p+SbcoAnAd0hQTE0dBSSlRICndf+T7eRr8hu5dz\nlQRgcdFq62h0ZKUqPLGlzxbCiMjppCasYNzIpQT5RZ/WdTssZnuA7dmC7ZrC0z014Znm4xWIt5c/\nBuUNaDotbbR3Ng9py3kgdere/Rvg260b2C+y2+9R+HgFujxHp6WNTYf+zM7cF+j95SnAN4Il059g\n3Mj+uzc7LW0UV24nv3Q9R8vWOXUP+/tEkBS7iKS4xYwevqBHXueGljLe3LiSmsb8HscYDN5YB5Cc\nwq4XkKwAACAASURBVNvoz8RRNzEt6fY+p6blmD7i0z2/7HdwWdf0oVPh6xVMW2cjPl5B3Hv1p2gs\nbM95jn3H3uo38KYlXsu81O8RETyahpbSHrmqQwNG9kiRGR06nsr6HLwMvtx95Xsen4rnKRKAxUWl\no7OVo2XryDFlkF+2oc/AMzxsAqnxyxkfn05o4IgBX0NrTXNbFXX2hQBsqRNPzo3tay3f06cwKCNa\nWz06R9XHK8hFQHV+nhroG4W3l/9pXau89gAZOx+kuiHPad/YEUtYMv3xPlN5Npkrbc9yS9dReGKL\nU9CKDh1PcuzlJMUtJjZiisvMWLVNhbzx5d0Dep7bm8LAnPHf4ZLx33b7GENrTUnVbj7P+jUV9dnu\nz9Ute9Wp8PUOYdnM37Nu3xPUt5SwaPJPqW8uYX/BW33+P6IwkJqwgnlp3yMyeAxgW2Di/W3fdYyc\nHxE5o0eu85jwydQ1FWHuqGfJ9MeZlnT+5XQ+U87WcoRCeIzF2k5B+SZ7gozP+2wVRASPITV+BakJ\n6Y4PFFc6LW0uV9np6ioeiiXgBkYPWZIEp7y/fpGO10F+0Y6u4QC/yLPSnWixdrAt+x9sy37W6Z59\nvYO5atqjpCVc5/RsV2vNibpDjqBbVru/x37b8865JMUuJjlucb+jcivrc/nPl3fSegpTtQzKmyD/\naJrNVdxy2csuk07UN5dQVLGNwhNbOVa+fkCD1AYafLu3jr29Arnj8jfJLv6Q+pYSAv2i2Xjgj/3M\n7VakJaxgXur3iAw5mZUqK/91Ps/6NVbdiUF5kRqfzqHi9x37Y8InYzR4Y+6oZ+yIJUwdc9uA6nux\nkwAszitWbaG4YjvZpgxySz7uc2WckIARpMank5awgujQ8V3fSmk2V7lMPFHXbOpz7u/5xM8nrOfz\nVHtr1RFU7V3DXXl/zxWV9UdYs/MBl0s3jh5+KdfM/C0hAbGObR2drRRVbOVo2TryS9fTZD7R45hA\nv2iSYi8nOXYxicPnD2iZR601e/JfZd3eJwb8pSfYP5apY26zr/m8mhvnP+cIvk2tFRRXbqeoYhtF\nFduodzNdZ7C8jYFMS7qN5rZqDnUbMb181tO0tteyPed5gH7GGShS45czL+37PZaftFjb+TzrMfYe\n+w9g6/afOuZ2tmafTPk/LCyN0cMXsC3n7wT7x3DNzN96PBnN+UK6oMU5T2srx6uzbFmpTGtp7mPq\nTaBfNGNHXM3IqFn4egVR1/L/2TvPwCbu+w8/kvfeGw+88MCb5cEGsxPIAhISkrbZs21G2yTNaNLM\npklDA2kWkLASyCZsDGbYgAceeOCB98J7D0n3fyH7QJYlDyAh/+h5hU6nu5Ml9L3fd3w+ZYNSxeW/\naLPRlWBiaKtcnQ5K/5r2B1plbdUeUyPbIRt8rmcUgpwz5z/lWPY7aqlRAz1T5oT/jbDxq5BIJLR2\nVotjQqW1J9Vq6842E/FxmYOPy2ycbSaOWCSlu7eV7NJdnM7/RKOgxGDGO00nwncNPs6zOJHzPidz\n1/Wnxu36A26yWgrdQN8UmaxH65jPSAh0X4qb/STqmnNws4tiT8oz4nNRvncjCArSij5H+wyyhAD3\nxcQGPYq9pZ/KMx3d9Xyb9LCoMOZkHUyY9yoOpL8orsjtLP2YFfI0X598AASB1bO24O4w5Yre1/8H\ndDVgHdc1RUVFvPPuf9i6bTutTfVY9guy//HxR/Dx8UEQBOqac8gp/4G88t1Ddr8OoC81wsLUFX09\nI7p7m2nrquV69EMdCgM9UyzNXDE3drqslqo6p6oUhbD9f6si1NRewu7TTw/pnTzOfjKLJ71OV18L\nhVWHKKpOoK45R2UffT1jvJzi8HGZjY/LbCxMnEZ1/pqmbNKLtpBT9v2ISgzGBlaEjL+FCJ/bsTH3\nolfWweGM18go3oaFibPa989Q3xx3hylYm3lQWH1oTCtgAz1T5IpeFIIMCVIWTvonAe6L2XRwBXPD\nnuXrkw+INy6Wpm50dF1ELmhvwAsYt5iYoEdxsPJXe66mKYuvTzxAW39GKNB9KRPGLeL75CfEFLaN\nuSc3x37El8fuprWz6jclNTkcugCs47plz549rLr9LsZF/w7P2PswtfWks7GUspOfUHbyfzz/2s1Y\neRTS1FHyS1/qqJEg7VdTulRPNdAzpam9lOrGDHpl7TjbhBDhs4Ygj2XXVfr350YQBM4WbyUh4zU1\nVyk9qSFBHssQBCiuOaImwWhh4oKvq3KV6+EYPeratEzeQ175btKLtlLVmD6i17jYhhHhczu+rvO5\n2JwrppSrGtJVGuP0pUa42Ufh6RiDtZk7Te2lZF74UqVbeCSYmzjj7zofMxNHknLWIVP0IJUYsGza\nvwkYt4jE7H8hQUpmyc7LSifa3ZEAJoxbRGzQoxo7lHPKvmfPmb/0ZxYkzAx5CifrYHaduFcM8lam\n41g9aysJma+RX7HnNyc1ORy6AKzjuqSoqIjISdOYdN/3Gk25k9bP55FXbbFz/uWlHkHZgXqphmqn\nUk81U0kF22FiZCN201Y3ZpJSsJG88p+QSqQEed5IlO9aHK0DfuF39MvT2lnFnpS/UlJ7XO05Q31z\nZPKeQc1CElxtw/FxVdZzB2r6o6W5vYz04q1kXdg5Ii3pgRuBcfaT6ei+SGldEpX1qWppbzMjB0LH\n34anYzRSqT7FNUc5X7lfbXRpeCSEea8idPytuNiEkFu+m92nn0QhyNDXM2ZFzHq8nWfQ3FHOzmP3\nYqhvSnWTZlGZy/F3W0hs0KMav38KQc7RrLc4nf8RoGx4Wzb13xjqm/PlsbvF7IC5iTN3zNpGSd1J\n9qU++5uUmhwOXQDWcV3yyGN/5EiRMUE3vqZxn+xvnsRWbxPL1lpes+sY0P0111hPvbTdxNB6xHVE\nuaKP/Iq9pBZuoqohHXMTZyJ91hDmvRJTI9tr9n5+LQiCQHbp1xxMf3lYNS1DfTO8nKbj6zobb+dZ\nWkeOtKEQ5BRXHyW96AuKaxIZSXlCghR3hylIpQZUNaQPulYJTtZB2FqM53zlfrxdZhHufTtFVYc4\nX3Vg1I18jlZBNHWU0CfrZEHUq4R7rwLgbPE29qU+DwgYGVhwS9zHjLOfBMDO4/dS31JAS+fw6Wx/\nt3higx7D0TpQ4z7dvS18f+oJLtQkAsqpgZtiP6Snt5UdiXfRK+sAlApeq2dvQxDkbDq4HJm8m+XR\nH4xoFvu3hC4A67gusbZzIOZPyZg7aDbebr9YxNG3wvj7R6Or5Ukl+hgbWmNh4oS5idOQ9dTLdX+v\nZqdmZ09Dv/fuFtq7anCziyTKby3+bguuC9OG64GO7np+OvMMxTVHNO5jZeaOr8scfFzn4G4/+YpS\n9B3d9WSV7ByxLvMl1NO4dpZ+eDpOw9MxGneHqTS2XWDH0TsxNLBAJuumR6a5G38ojA2tmez3OyZ6\n3cT+tOcpqk7Az3UeK2I2IJFIOJX/P45kvgEoO49vm7EJJ+sgOnsaOJD+Mnnlu9WucTC2Fj7cMO1d\nnKyDtO5X31rI1yfup6m9BAAfl9ksm/pvmtvL2HZ0jThpYGJow+pZW7Ax92LzoRVcbMkn3Pt2FkT9\nY1Tv/beAbg5Yx3VFr6yDY9nv0trUgKmtp9Z9TW096GobXBM0wsLEGVtzL6zMPbCx8MLC2HHUur9X\nm7rmPFIKNpJT9h2CIBDovoQov7WijZwOaGy7QFLuenLKvhtyrMfCxJkI79vxGxePnYXvFX2GgiBQ\n2ZBKetEW8sr3jNHPWMDKzB1Px2g8HaPxcJiGuYkjXT1NFFYf4rukRymtOwmgVrseDn09Y2aFPE2k\n711IJBLSCr+gqDoBM2MHFk5SZoUSs94mKW89oKx1r5y5GRNDa45kvklqwWZkiuHPaWnqxj3zfxj2\nBqaw6hA/nPqTuMKfFvAg0yf+kYbWInYkrhWDr5GBBbfN2IiD1QQOpL/IxZZ87C39mBP+7Kjevw5V\ndAFYxzXnfOV+Dqa/RFtXDSYWJnQ2lmpdAXc2lmFioaqiJFf00NxRip6eAb5ucwn2XK5RvvBao/Te\nPUhKwSbK+713pwU8QLj3asxNHH+Ra7qekCv6qKxPpbD6MAWVB2juKBtyPzMjBxZPfgNvl5lXfM5e\nWQc5pd+RVrRlxLrMgzE2sCI2+FF8XedhbeYOKGvVeRU/UVC5n/L6lCsyPvBxmc2CqFewMHEGlCvP\nhIx/AsqZXRNDaw6kv0h60ReA0t/3xmnvk12yi9SCzcOOz3k5xlHZmE6frINFk17TGnwFQSA5bz2J\n2e8AAvp6xiye/AaB7kvF1f1AjdxQ34xbp3+Gs81ECqoOklb4OfpSI26Y9t5vUuf5aqILwDquGa2d\nVRxIf4nCqoPitvA4U0qOb2Diirc0vq7k+Hoi4oaWMWxoLWR/2t9JyHyDEK+biPS5U0Wx51rS3dtC\nxoUvSSv8nNbOSpysg1ky+S0C3Jf8pruZQWkYX1yTSGH1IS7UJA6r7hTlexczQp4akTCGNupbC0gv\n2kJ2yTdX5NDkZhfFrdM/wVDfnPrW85zMWcf5yv1DCoKMFmMDK+ZF/F1FvUsm7+GHU39Epughyu9u\nPByn8ePpJ8kp+w4Ae0t/PB2j2XJkpVh/1YSPyxxigx4lpWAjfbIOAt2X4uUUq3H/XlknP515hvyK\nnwClecJNsR/iZB1Ec0c524+uEWft9fWMuSXuY9zsImjrquGnM8pZ4znhz/5mdZ6vJroasI6rjkIh\nI6VgI8fPvad2195Q08e6ZxuJfvDAVeuC9nSMIdJ3Db4uc6/JGER9ayFphZvJLvkamaKXCW4LiPJb\ni5td1G9W8UcQBBraCimqOkxh9WEq69MGaVQPPQ5jaerK4slv4OkYM+ZzyxV9FFQeIL3oC8ounhrR\nayRIsLXw6c+kqDYueTlNZ9qE+yiuTaSgcr+aB/CV4O8Wz/zIl9WMPhIyX+d0/kfYW/pzx+wd7D7z\nlHijamHiTHdvG31y7YHX0TqYhVGv4GIbSmldEtuPrsFQ35x7Fx7QmIlp6ahg14n7xSyBu/0Ulses\nw9TIjtbOarYeWS3OKetJDbk57iPGO8WhEOTsOHonZRdP4ec6nxUx63+z3/2RoGvC0vGLUNVwln2p\nz2kVli/MkLNtXQtesffjEXMvprYedDaWUXJ8PSUnNrDqEQsmRIx+ZWRh4kK4z2rCxq8cc8fsAIKg\noLjmKCkFmyipPYaxoTXh3quI8LnjNztuIZP3UF5/Rgy6gwUlTAxtcLIJpq45RxTsv5wQr1uZG/6s\nitPQaGjtrCajeDsZF3aM0L5RgoOVPxE+d+DnGs83Jx9Um/m1MnOnT9ZFpxZ1taGOa2HiTHtXrUZj\nDBNDW+ZHvkjAuMVqgaq07iTbj96FntSA22dt5WjW25RdTFYeeYSmCxM9b2LJFGUWSa7o5dP9S2ls\nK2Je+N+J8ls75GvK6pL5NukRMbUc6XMnc8KfRU9qQHv3RbYlrKax/QKgbGhcEbMeX9c5AJzM/S/H\nst/BwsSZe+J3X3W7zv9v6AKwjp+V7t5WErPfJr1oK9q6M/1c5zMv4u9crO7i3f/8l42bP6ajtQMT\nCxMi4kyIWWCCnbMBTtbBGOqbU16v2cNXE1KJAQHui4j0vRNX24hR3an39LUra26Fm2lqL8He0p9J\nfncT5Hnjb7Le1dFdT3HNEQqrEiipPaaWDrW39MPXdS6ejrEUVh0itXCj2jHMjB1YGPVP8cd8NAiC\ngtK6JNIKP6ew+hCCMLwTlKmRHRE+a4jwuV30Et6RuHbMteEBXGxC8XKK43zVQRpaz2vcL8B9CfMj\nXsDUyE7tua7eZj7dv4T2rhqmB/+pP82dParr8Haeyc1xH4nz5km5H5CY/S+crIO5a+7Xalkgpbb1\n5xw6+wqCIEcqMWB+5IviuFNnTyPbjtxOfb9kpgQpN0T/h4BxiwCoqE9l65HVOqnJUaALwDp+FgRB\nILf8Bw6ffVWrRrOFiQvzI1/Ez3WeyvZdx++jsPrQkK+5OfZ/uNiGk5DxT3LLfxyTG5CTdTARvmsI\ncl+m1R6vqb2U1MLNZF3YSa+sAz/XuUT5rcXDIfo3lWoTBIGLLXmiuUFV41kuvwHSkxri7jBVVKGy\nNnOnqjGD3aefpLGtWO14Ae5LiI94CRMjm1FdR1dPE6fy/0dWyc4hV9NDMd55BpE+a/B2mSUGp5qm\nc3x17B41Ja2RYKBnhrmJI03tJSya9DrNHWWcyvtQ4/fQzMie+KiX8XcbeiZWEAS+S36M/IqfcLWN\noqnjwoiclqQSffGc1mYerJ33LcaGVgA0d5Tzyd4FyBS93Dl3F662YSqvlcl7OJD+IpkXvhSvcXnM\nf8V54u7eFrYfvfOyWreEpVP+RbDnjf3Pt/LZgSU6qclRogvAOq45Te0l7E97kZLaYxr3kUj0mOR3\nN3HBjw/ZtXwg/UXSCj8f8rV2ln78Ln43UokePX1tJOd9SGrBxlGPfoC6fi8ofxBL606SWrCJwurD\nGOqbETr+NqJ878Ta3GPU5/i10ifvpqwuiaLqBAqrDqsZEZga2SkdhVzn4OkYK3rbyhW9nMh5n+Tc\nDWqpWGNDa+IjXyLQfemIrkEQBJo7Ssm6sJOcsh9GLNtobGhNqNcthPusFj/X5vYyzlcdIKfse2qb\nRre6NDWyw891Hn5u86lpzOJ4zntEBz5MQeV+cYU4FMGey5kb/rzW1GxWyS5+OvM0elJD5Io+hsvs\neDrG0NnTKK7cDfRMuXPuTrH5SRAEdp24j6Lqw0PO47Z31fFN0kNUNSjT7s42E1kRs14sofT0tbMj\ncS3VjWfF1yyc9Bph428Tjz9wwzDOfhKrZ27RSU2OEF0A1nHNkMl7OJ3/ESdz/6vV1NvFNpwFUf/Q\nKgRwOv9jEjI1q2ItmvQGoeNvUTl3RvF2TuSso6t35D6tl+PlFIeNuRdldck0tBViaz6eKL+1TPS6\n6Rcbbfq5aeuq7XcUSqC09oTaTY2jdZAoiOFiE6KmBFbXnMfu008OWev3cZnDwkn/VGs8GkxrZzVl\ndUlcqD1OcfURuvtaRnz9Sl3mO5Qd6FIj6lpyKag8wPnK/aNONVubeeDnNh9/t3hc7SKQSvQ4W7SV\nfWnP424/hYr6FI21XnNjJxZEvTJser22OYfPD93UH3i14+kYS1zwY+SV7ya1cLO4/cbodWJaGJTj\nfd+cfBBTI1vuXXhQXBWDUgb165MPiqpcwR7LWTDpVbGM0ifr4stj91BRf0Z8zbyIF4jyvUt8nFG8\ng72pf8PIwJLfxe/+zfY+jAVdANZxTSi7eIp9qc9r1bg1MrBgZshThHmvElOBmsir2MN3SY9ofN7C\nxJl7Fx1Sq7/KFX3kle/m2Ln3aNEwZzoc+nrGBLovY1bIU5gaq9fr/j8hCApqmrJFG7+aQStDfakR\nnk4x+LrMwdtltorn7uUoFDJO5X/E8XPvqYlcGOqbMzf8eUK8bh4ybd/Z00BpXTJl/SYGA8pLI0Vf\nakSgxw1E+NyOk00wlfWpnK/cT0HlgVEbHThaB+HvFo+/Wzz2lv4q15tX/hPfJT+GsYGl1puC0PG3\nMTv0rxgbapZM7elrI+X8Z5zIXTdsc5WnYzSxQY/j7jCZrJKd4sgPKAUyZoY8KT7ulXXy8d542rqq\nWTL5LSZ63SQ+l13yNXtTn0Wu6EWClFmhzzDZ//cqI1A7j99Lad0J8TWzQp9h6oT7xMf1rYVsOnhj\nv9Tkf5kwbqHWa9ehik4JS8dVpbOnkYTM18ku2aV1v0D3pcwJf27Y1c8AVqZuWp9v66ohrWATUwPu\nV9muJzUg2HM5QR43UFB1kJPn1lHbMrqZTZm8m6ySr8gp+45Aj2VE+qz5f6Vg1SvrpLT2BIXVhymu\nPkJ7d53K8+bGTv3mBnPxdIzWWiMHaGgrZvfpp1RSlgN4OkazaNIbWJld+jx7+toov3hadA0aaxOU\njbkXET5rCHRf0m8duJXCqkOjzIBIcLefjJ9bPP5u87EyGzfkXoVVh/g++XFA0Bh8LU1dWRj1T8Y7\nT9d4tp6+dtIKN3P6/Cd09zZrvTIPh2nEBT8uNjdVN2b2a0ArGe88g+kT/6jympM579PWVY27/RSC\nPVcAypujI1lvcub8JwAYGVhyw7T38HaeIb5Orujl26SHVYJvXPATKsFXJu/h++THkcm7CfderQu+\n1xDdCliHVgRBIKtkF0cyX9fqHmNt5kF85Mtaf5SGoqO7nnU/TBUfG+ib0Teo09bIwJL7Fydora8N\n1HOTcj8QRzrGgottGJE+a3614hqtnVUUVh1WmtXXJamVCFxsQvFxnYOvyxwcrYNG1GAmCApSCzdz\nNOstNb9cfT1jZoU+Q6TPGmTyHioaUsUVbk1jlkrqVirRRyLRQz7ISWgoJEjxc5tHsMcK+uSdFFQd\npLj66LBqUEMdJ9L3LmKCHhqyK/ly0ou2sD/tBbTVZiN87mBmyNNiHXwwysD7OafPfzxs4HV3mEpc\n0GN4OE4Tt3V017Pp4I2iD+/gpiuAiy35bDxwAwB3z/8BByt/unqb+T75cdFdys7Cl5vjPhTr4qAM\n0N8lP875yr3itqkT7mdmyFMq34OD6S+RWrgZO0s/1s79ZtgbMx3q6FLQOq6Y+tYC9qf+vX8UaGik\nEgOmBtxHdOBDYxrTEQSBd76ZKP6wG+qbDan8M2XCvcwO/cuwx2vvqiMx+1/klH03onqbJkwMbQkd\nfysRPrdrXC1dDygEOdWNGf2zuQlqq0wDPVO8nGLx6e9aHmlmYoCWjgp+OvP0kIIXrrbhRPndTVN7\nieiLe3nAl0oMsLfyQxDkNLQWohjBfKuZkT2BHsswNbKj7OIpyuqSx6jnrOxivnX6x8OOzfTKOtib\n8hy55d9r3MfKzJ1Fk17D01FdPGbgGGmFn3M6/+NhbQ7H2U8mLvhxtWPJFX3sOHqX+P9NX2rEXfO+\nUVGcEgSBrUdWU1F/hqkT7mdW6NNcbDnP1yfuFyU/fV3nsXTK2yrz1gpBzu7TT4lKWwBRvmuZG/68\nSvAtrDrErhP3oSc1ZO28b3VqV2NEF4B1jJk+eTdJOes4lf+x1h8/d/spxEf9A3tL3ys630d741Vq\nynpSI7VVkp7UkPsWHdLYCFLVmEGq6L2rR5DnjXg7z+R85X5yyn4ADU00wyPB12UOEb5rGO8UN2Jb\nwmtJT18bF2qPU1R1uN+sXjUVa2nqiq/LXHxc5+DhMHVMK3lBEMgs+YrDZ19Vk3iUSPSwNnOnrbMG\nmeLSiliCFGfbEMbZTUIhyCmtO0m9lnnZy3GxCcXG3Ium9tIR+9sOYKhvrnaNJoY23DbjM5xtQrS+\ntqT2BD+dfpq2bk0WgpJ+2cwnh5TN7JV1kl74OafyPx42Je5gFcDc8Gc1jrYdTH+Z1MJN4uMbp71P\ngPtilX0GOqktTV35/YJ9lNQeZ/fpJ8Wb1pjAR4gLflzleyoICvamPiuOIgGEea9iQeQrKtfR1lXD\nZ/uX0tXbxPyIl4j0XaP1/ejQjC4A6xgTxTWJHEh7QaOAPih/3GaH/YWJnkM324yWLxPv5sJlo0ze\nzrOGtKwL8bqFxZPfEB8rvXf3kFqwmapGzd67ze1lnMr/iKySnVq7todjoBYZ4nWz1saba0FzexmF\n1YcorEqg/OJpNbN6N7sIfFzm4Os6R62paLS0ddWyN+VvWm0DB3CwChBdg0yNbDlX+h3ZpSPTZTbQ\nM8HW0ofu3tZRN9LZmo/Hz20+3X1tZBRvU3nO3NiJlTM3YW/pp/H1PX1tJGS8RsaFHRr3sTH3YvHk\nN8SZ2cvplXWSXvQFp/M/GnZOWSrRY17ES4R7r9L4uWSXfM3uM0+Jj8O9V7Mg6hWVfbp6m/loz3y6\nehtZEbOeiy35HD/3LqD8Wy6e8pZKlzQob6QOnn1JZdQv2HM5Sya/pRKkdVKTVxddANYxKtq76jiU\n8Uq/z6hmQrxuYVboM1fVXH5f6nOcvexHNC74CfGH5XIkSLkn/kfMjO05W7SN9KKttHfX4mYX1e+9\nG6/Ve7e9q44zBZ9ytmjrsAL32jDQMyHI4wYife/UanJ+JSgUMiob0imsPkRRVQINbYUqzxvqmzPe\neQa+LrPxdpk5bH1zZOdUkFa4maPZbyPTMGttbeaJl1Os0qbPcSpGBhacrzxAetEWykeoy2xsaI0g\nKESru5HibBOCv1s8fm7x2Fn4cCTrDU7nfzTo+jxYOXOz6GY0FEXVR9iX+qxYZx2MBCmT/X9H3MQ/\nqpVV+mRdpBdt4VT+/0Yk7mGob85dc7/FznK8xn2qGzPZkrBSvDm0Mh3HfYsPq00Q7E19lozi7Xg7\nz0Rfz5jzlfvE/W+K/RBH6wCV/QVBUPsbTRi3mBum/lttnlcnNXl10XVB6xgRCkHO2aJtHM16S+uq\nxc7ClwVR/7gmMnSWgzqhTQytMTWy6/+BuyTqL6Dgq2O/p7OnQem967GESX53D5tmHMDcxJHZoX9h\nWsADpBV+TkrBxmEbZYaiT95FxoUdZFzYgZtdFJG+a5gwbiF6UsNRH+tyuntbKK45SlF1AsXVR9W6\ncK3NPPB1nYuPy2zcHSZf8flAmXYsq0umqPoIBVUHhwy8RvoWTPK7h1DvW8USQGtnNakFm0esyyxB\ngkSqj0LRN+K/uUSih4fDVPzc5uPnOk88t0KQsy/tOTKKt6vsb2/px20zNmFh4jTk8bp7Wzh09hWy\nS7/WeE47C18WT34DV7twle19si7Si7dyKu/DEatqGeqb8/sFe7TOz3Z01/PNyYfE4CuV6LN61la1\n4FvZkE5G8Q70pUY0tZfR1K/Z7OEwjRuj3x/yhvh4znsqwdfXdR7Lpr6jFnwr6lM5fu49JEhZOvUd\nXfD9GdGtgH/D1DbnsC/1OaobNdfc9KVGRAc9zNQJ916VH/yhyCn7nh9OXRqzmBbwIDJ5DykFnw65\n/0TPW5gZ+uSoG4oG0yvrIKN4B6fzP6a9u/aKjmVmZE+o90rCvVdrnKEdjCAINLZfoLDqEEXVGFW/\nNgAAIABJREFUCUrBh8salSQSPcbZT1KqULnMwdbC+4rTgl09TZRdPEVp3UlK65KGlI+8hIRJfvcw\nI+TPGOgZq+oyVx3SKE5xJejrGePtPAM/1/n4uM5RCwZyRR+7Tz9JbvmPKtudbUK4bfpnGiUvC6oO\nsj/1ebVRrAEkEj2mTbiPmKBHVWrmffJuzhYpA682qVVQruwHbi6kEgP+sHAfNuaeGveXK/rYkbhW\nzBxIJfrEBD5CbPCjKvspFDI2HVpBXXMO+nrGYsNilO9aZof9dcisT3LeBo5mXbL89HKazs2xH6r1\nAyilJpfS2llJTNCjTA9+Qut71DEydCtgHRrplXVw/Nx7pBRs1CoO4OU0nfjIl7T+iFwNBq8QWjoq\niPC5XWMAbmwrwszoytyOQNlxPdn/d0T43MG50m9JztugtfatjY6eepJy/0ty3gb8XOcT6bsGD4dp\nagFTruiloj5FHBUabH1nbGCFt8tMfFxm4+08U2X8ZCz09LVRUZ8izuLWNeeoPH+5zvDlWJm5s2Ty\nm7g7TKG7t4WzRVtJL9oyavGMkWBsaI2vyxz83eLxcorTOPbSJ+/mu6RHKKpOUNnu7jCVm2M/HNJl\nqbOnkUNn/0FOmeYOZwerABZPfl0lk9In7yajeBvJeR8Ou8J3tp6IRKKn0jx2U8yGYf/fJGS8ppK2\ntzRzIyboYbX9Ugs/Fz83mbwbPakh8ZH/UFGIu5yUgs9Ugq+Hw1RuilmvFnwFQWBv6rO0dlbiZhdF\nbKBmQRwd1wbdCvg3xvnK/RxMf0lj/QuU7jVzw58jYNySn6URo62rhg9+vGQgbmZkT09f+6AOWz0E\nLt0srIj5QKPo/VhRKGTkVewhOW/DFTvngFLLOtLnDrydZ1LRkEph1WEu1CSqpfptLXzw7Z/NdbOL\nvCK93T55N1UNaZTWnqS0LpnqpkyVmyx9PWPG2U/C0tSVwqrDQ9rwhXvfzuywv9DYVkxa4RZyy39Q\nm/+9UixMXPB3m4+fWzzu9pOHfc89fe18feI+tXEoH5fZ3Bi9bsgRuLyKPRxIe0Fjylgq0Sc68GGi\nAx8QszsyeQ9ni7dxKu9DjavlAZxtQpg64T7SCj9XGdUbycjc4KYrS1M3lk75F+4Ok1X2a24v56O9\n88SbJDNjB1bErMfNLmLI454t3sa+1OfEx652EaycsWlIidWMC1+yN+WvGBlYcs/8H1VEVHRcGbom\nLB0qtHZWcSD9JdH0e2gkRPjcwYyJf/5Zu3wVChlvfx10WaCQMC3gAUBCct4HADjZTFQR1re18Ob3\n8XuuiTi8IAgU1xwhKfcDKhvSrvrxpRID3B2m4Os6Gx+X2SpiCaNFruijujFTFL+obEhTm8V1tQsX\nO5XtLf04fu490orUDTDMTZyJj3iJ7r4W0ou2aC1NjAV7Sz9Rc9nJeuKIb+66epr46tjvqG7KVNke\n6L6UJVPeVkvBdnTXcyD9RfIr9mg8ppN1MIsnvyk2LsnkPWRc2EFy7oZhyxHONhOJDXocF9swdh7/\nPTVNWZees57Imrk7tTYD1jRlseXwSmT9o3aO1kHYmHmyPGadyn5tXbV8un+xmNZ2sQ1jRcwHWJg4\nD3lcZVB/moGeCWebiaya+cWQmYH61kI2H1xOn7xLTWNax5WjS0HrAJTBLaVgI8fPvadVRcjRKpAF\nUa+oNZ9cS3r62sgq2UVa4Wa1VHhc8GN09jRxKk/ptNPZXY++1ASZQtkk1NhWTGbJV4R7r77q1yWR\nSPpTwLOoqD9DUu4HKmNSV4K9pR9TJ9xHoMcyrT/SmhAEBXXNuf0p5ZOUXzyj8rlKkOJsE4KnYwye\njtG42UeJ86sV9SlsPnTTkGl2X9d5WJq48FPKM2NqTNOEq10E/q7Kla6theZOYE20d9WxI/EuNSei\nsPEriY/6h0qz0oA15sH0lzWKYUgkekwP/iNTJvwBPakBMnkPmRe+JClvg2hcoAkn62Digh/Hx2UO\n7d21bDt6Bw2XXZe+1Jhl097V+rl2dNfz9YkHxeBrZeaOTN7N7LC/quxX2ZDOzmN/oLtP+VkEjFvM\nkilva5zpzi3f3a8drQy+DlYTuG36xiGDr0zeww/JT9An7yLMe5Uu+P6C6FbA/4+pajjLvtTnhnSs\nGcBAz5S4iU8wyXftz2Y11tReQmrBZrJKdvV7786jpbNSpT5536LD2Jh7siPxbtHuMNjzJs5d1sFq\nbuzIvYsODSmQcLWpacoiKXdD/+jHlX+fzY2dCPdZTZj3Kq3NZIIg0NBWJK5wyy6eUguQ9pb+4grX\n3WGKWt1YJu/h+Ll3OZX/kdq1G+qbY23mQV2Lam14rEglBng6TsPfLR5f13mYmziO+VjNHeXsOHqX\n2g3DFP8/MCv0Lyor6PauOvalPa81w2NqZM+qmZ/jYOWPTN5DVslOknI/0FqOAeUKNS7oMXxd5yGR\nSGhqL2VH4lpaOsoBxMaohVH/JMx7pcbjDG66MtAzIdR7FUb6Zipaz5kXdrIv9Tlx1tvfbRHLo9/X\nmDEoqDzAt0mPiGlqWwsfbp+1FTPjofskBgQ/dFKT1w7dCvg3THdvK4nZb5NetBVtwcLPdR7zIl74\nWWzGBEGgpO4EqQWbKKpOwMjAnDDvlUT63om1mTs/nXlGJQC3dlZiY+7JRM/ll/yGBQWmRrai8EF7\ndx2pBRuJDnzoml+/s00Iy6PXcb5yPydz16k1M42W9u5ajp97l5M565gwbiGRvnfiZheFRCKhpaNC\nbJoqq0tSq0Vam3koA65TDB4O0zT+0ALUNGWz+/STQ3rZ6usZ0ytrv+Lga6BnirfLLPzd5uPjMnvI\nVddoqW8tZEfiWrVV6fSJfyI64CExGAmCQHbpLg6dfVXrXLGjdTB3zd0FCKQXbe0PvNUa9wdlVig2\n+DH8XOeL57vYks+OxLViY5aN+Xia2i/g5zqf0H4fXU0kZL6u0nQ1O/xZUs9v5K553wDKAJ2Q8ZqK\nGpathTc3Rr+nMfgW1yTyXfJjYvC1NvNg1czNGr8ThVWHSC3chJ7UkBunvacLvr8wugD8/4iBFNzh\ns69qHZmwMHFhfsQL+LnNv+bX1CvrJKf0W1IKN9PQWoCthTfzI15kotcKlcaQwbPALR1VAPi5xWOg\nZyoK8scGPariH5yc9yFh3quuqjDI5fTJuiitS6KoWqm1PFyacrQoBBm55T+SW/4jxoZWSJCqpU/N\njZ3wdFKucD0cokfULCNX9JGUu56k3P8O2eUMXFFjlamRLb6u8/B3i8fTMeaqGlfUNGXxZeI9an+H\neeF/J8pvrfi4tbOKfanPUVxzVOOxJBIp4+wmccv0j8UVb2tnldbzO1gFECcG3ktqUVWNGXyVeI84\nnz1h3CLyK/ZgbuzIwkn/1FrTzi79htSCjeLjqRPup7ohg+jABzHUN6Wrp4nvkh+ltC5p4MqRIFHO\n7Wqw9CyrS+abEw+INX8LExdWzfxCY424ratGtDicE/asTuf5OkAXgP+f0NReyv60Fy6tFodAItFj\nkt/dxAU/fs2N51s6Kkkr+pzM4i/p7mvB23kmc8L+plFP2cps0ChSv7+rob4pE8YtJLv0a3pl7ZgZ\n22Ft5iGmJXtl7STlrmdu+LNX7dpbO6sprk6gsN+sXjZIl9rJOlgUxLAwcSa1cDNphZ+PSH5RG929\nA8IbEmzMPQjyWE6QxzJszL1G1Y1e31rA7tNPqnn+XilWpuP67fzicbOPHNbreSyUXzzDzuN/UPlb\nSpCyePIbouetIAhkXNhBQsZrGv/m+lIjJFJ9HKwmMMF9EZ/sW0RrZ6XWc9tb+hMX/Dj+bvFq39HS\nuiS+PnE/vbIOsY58Mud9ABZPeUvrDWBNUxb7Ui59P72cpuM/bgGHzv6DRZP+ycWWfHaduF9MaQ+I\n0ET4rtEoMlNRn8rO4/eK301zY0dWz/pC482ZQpDz46k/09XbhJ/rPCJ87tD6t9Dx86CrAf/Kkcl7\nOJ3/EUm5H6gFistxsQ1jQdQrOFkHXbNrEQSBivozpBRspKDyAPp6xoSMv5lI37uws/DW+tqyumS2\nHb30ozDR8yaWTFHOMpbWnWT70TsB8HaeSbDnCn44dUkwQE9qyL0LD4zZtUgQFFQ3ZfU7Ch1WSy/r\n6xnj5TjgKDRryBVGT18baYVfkFLw6bDawKPBy2k6kb5r8HGZPWzAUwhyzpz/lGPZ71yR5vXlOFoF\nip3LDlYB13QsrbjmKN+cfEhlZa4nNeSGae+KI2ctHRXsSfkrpXUnNR7H1TZCucqVSJAipbVL+4rX\n3tKP2ODHmeC2YMibw4Kqg3yX9ChyRS96UkOWTHmb5LwN1DXnMMnvHuaGPzfEUZV09jSw6eBycdVt\nbebBnXO/5puTDzI79C+0ddWw+/RTYiNdwLgl5FXsxszInnsXHRwynV/dmMn2o3eKNx+mRrasnrVN\nqylKUu4HJGb/C3MTZ343/0eNgiU6rg66GvBvgLKLp9iX+ryKk9BgjAwsmBnyFGHeq67JigWUNwE5\nZT+QWriJuuYcrMzcmR32V0LH3zrieqDloDv3y1crHg7TsDBxpq2rhgs1x1g46TWcrIOpbT4HKMUt\njp37N0un/GvE19wr66Ck9jiFVQkUVyeopewtTJzFVa6HY/SwVotGBhZEBz7IJL+7SS/aSnLehlEa\nxg9NSe0xSmqPYWnqRoTP7ZgrJrFhwxa2bN1Ka1M9ljb23HH77fzuDyvIqX//KoxNSRhnP0mpuew6\nD2tzjyt+DyMhr/wnfjj1JxWTCQM9E1bEbmC8UxyCoCC9aAtHMt/U2M1vqG9GdODDpBZspKOnQavI\nDCjntGODHiVg3CKNLlfnSr9l95mnEQQ5Bnqm3BS7gQs1idQ15+BgNYGZIU8N+TpQlgG+S3pUDL4G\neiasiFlPad0JrEzdKKpO4GTuOvHa46Ne4Uim0mxkTvizQ/7fqWvO48vEu8Xga2xgxcoZm7UG38qG\nNI6dexf6U9q64Hv9oAvAv0I6expJyHyd7JJdWvcLdF/KnPDnrliyURNtXbWkF20ho3gbnT2NeDrG\ncFPshyNarQ3GwsQZCVJR3rCl41IAlkikBHsuJ7l/JCmvfDezQp9hR+Jd4j7nSr9jiv8ftJojtHRU\nUFh9mKKqw5RdPDVolSjBxTZMFMQYzWpPoZBR3ZR1aRa3PlVrNmIstHZW8r/NL7F9XRvj4x4i+o8n\nMbX1pLOxlIQT/+PTuMWsesSCCRGj7wjXkxri5RSLn1s8vi5ztDZ1XQsyL3zF3pS/qUhbGhlYcEvc\nJ4yzj6KpvYQ9KX/Tavbg6RiLh8NUjp97d9jVv52FL7HBjxIwbrFWe8m0wi84kP5C//VYcuv0T5HJ\nuzh9/hP0pIYsm/pvrbXvI5lvqAiHLJr8BjYWXnyX/CiWpq6cK/sWUK6Kb4rdQFbJLtq7avB0jCHQ\nfZna8Rpai9iReKdYgzbUN+e2GRu1fue7e1v5PvkJBEFOTOAjeDhM1fq30fHzogvAvyIEQSCrZBdH\nMl/XavptbebB/MiX8HaecU2uo6rhLCkFG8mv2INUokew53Ki/NZeUVOHntQAcxNHcSSkrasGhSAX\nA/lAAAZlQ8s983/AyymOktrj/UcQOJL1JrdN/0w8pkKQU9WQTlF1AoVVh9W8aQ31zfByisPXdQ7e\nzrNGHHgEQcHFlnyxU7n84ulBtUgJTtbB/Y5B0+jubeHM+U/FFftYaKjpY/u6NqIfPICdzyUjd3MH\nH4KXv4FzyHK2r5vPI68aYOc8/Hyxob45Pi6z8XeLZ7zzDIwMzMd8bVfCmfOfcThD1XbP1MiW22Zs\nwsFqAmfOf0pi9r80NowZGVjg77aQ0tokSutOaD2XrYWPcsXrvljrDaIgCCTnbSAx+21Aqcx224xN\nWJg68+n+JYDArNCntX7fs0u/IaXg0ndx6oT7CHRfwqGz/6Crt0XU4PZyiuOGae/R1llDSsFGpBID\n5ke+pHbz19Reyvajd4rlDQM9U26d/gkutqFa38e+tOf6pSYjiQ16VOO+On4ZdAH4V0J9awH7U/+u\nInk3GKnEgKkB9xId+PCwKdPRIlf0kl+xl5SCjVQ3ZmBh4sL0iX8ibPxtVy2lZWU2TgzACkFGe1et\nOCJlb+mHs81EapqyqWvO4WJLPjNDnrosAMOFmkQKqw7RJ++mqPowxdVH1W5UrEzH4es6Bx/Xubjb\nTx5R9+6AaYI4i1uXrHZcO0s/PB2n9c/iTlUzEQjyuJELtcdIzl2v9TPUxIm9XXjFPqgSfFXO7xON\nV+wDnNy3iWVrhw7Apkb2ovyjp+O0a2auMRIEQeBEzn84kfMfle0WJs6snPk5CAJbElZS1ZCu8RiO\nVoH09LWSVfKV1nPZWnj3B94lw2ZmBEHgaNabnMr/H6Dszl81czPWZp58l/wo7V01jHeaTpTvWo3H\nqGnKVmu6mhHyJOdKvye1YLO40p/s/3tmhTyNRCJlZ9ofEAQ50wLvV+uXaO2sYvvRNaJCl77UiJvj\nPhzSp/hyskp2kle+GyMDC5ZNffdnm/PXMXJ0n8h1Tp+8m6ScdZzK/3iQCbsq7vZTiI/6h9Za0Fjo\n6K7nbPE2zhZtpb27jnH2k7hx2vv4u8Vf9f/QylGkFPFxa2eVyoxysOcKsbM3u/QbZof+hSCPG1SE\n9neduE/lmBKkuNlHimb1dha+I0ott3ZWiY5BpXXJauNHVmbuoviFh8O0YQUnJBIJ3s4z8HaeQUV9\nCsl5G9RMBbRx9ngnM59+QOs+XnEPcvStDSxbe0lG1NLUlQD3Jfi7xeNqG6415fpzIQgChzNeVVkh\nAtiYe3Jr3GfkV+7Vmko20DPBQN9Mq8AMgK35eGKCHiXQY+mISiIKQc7+tL+LNoe2Fj6snLEJS1MX\nskp2kl+xBxNDWxZPflPj37Gzp4FvTqoqXS2b+m9Szn8mjs/pSQ1ZGPWq2NWdceFLqhrSsTJzJzpQ\n1YyhrauWbUfuEOvIUokBK2LX4+kYo/W9NLQWcTD9JQAWTnpNp/N8naILwNcxxTWJHEh7QatDj4mh\nDbPD/sJEz5uvaodqbdM5Ugo3kVv2AwCB7kuI8ls7Yu/dsWA1hCvS5Xf5ge5LSch4DYUgI+vCV6Jp\n/WD09YzxdZ2Lr8tcvJ1njGiF3tFdLwpflNYlqf3NzY0d8RgIuI7TtBq+D8c4+0ncEvcxdc25JOdt\nILd8N8Opa3W1dWFqq91dx9TWg662LvT1jAkfv4pQ75XYW/r9LIYaI0UhyNmb8qzaqtXBKoC54c/z\nw6kn1DSfL2dgJrxvCN/iAWzMvYgJeoQg92UjvkmUK3r58fST5JXvBpSjZrfN+AxTIzua2ks5mP4y\nAIsm/VPjzZZCIeO7pMdUmq5umPYeh8++ItZ7zYwcuSluA662YYCyn2Og8Wp+xAsqmauO7nq2H71T\n/C5KJHrcGP0fvJ1nan0vMnkP3yc/rpOa/BWgC8DXIe1ddRzKeEX8MdBEiNctzAp95qqJUCgUMgqq\nDpJSsJGK+jOYGTsQHfgQ4d6rf5bGHMtBY0Qtl3VCd/U2U1J7AlMjO9q7a+nqbVZbQQ1gbuzE0in/\n0qrJ293bSvnFU2Idd3B92NjACo/+lLKnY8xV8eIF5Y9jbXMONY2ZVDdlUtusfRU3gImFCZ2NpZg7\n+Gjcp7OxDBMLE2TybjJLdmJt7oGdpQ8Srk33+2iRK3r54dSf1EwSXGzC8XKK4cvEuzVmeSQSKYKg\n0KpnbmPuSUzgIwR53DCq7EyfrItvkx6huOYIAOPsJ3NL3EcYGVigUMj48dSf6ZV1EOa9Sqt4TULm\n65RdTBYfzwr9CwfSXhDNGuytJrBy+kaVAH4k8026e5vxd4vHx2W2uL2rt5kdiWvFCQcJUpZNfQd/\nt/hh38+RzDeoa8nFzsKXuWGaR6R0/PLoAvB1hEKQc7ZoG0ez3tIq6mBn4Ut81MtXraOxq7eZzOId\npBV9QWtnFS42oSyd8g4B7ot+1jqh1SA1rLrmXE7lfUhhdQKV9alqBvCmRvZMDbgPN7tIldGM5o5S\nMop3EOm7Rty3V9ZJRX2KuMKtbTqncjxDfTPG2U8W08qO1oFXnK5VKGTUtxZS3ZSpDLiNmVxsydeo\nTKWN8DhTSo5vYOKKtzTuU3J8PRFxSmnBXlk7B8++zLnSb1kw6dVrOv89EvpkXXyT9BAXahJVtrvY\nhCJTdJPU73qlCUFQaHzO2syDmKBHCPa4cdRlkZ6+NnYdv0+sy3s7z2R59H9FicaTuf+lqjEdW/Px\nzAnTLPZyrvRblRvCQPdlnMx5XxxvszEfz9q536j0HFTUp5BV8hUGeqbMDX9e5Zq+TLxbxRJz0eTX\nCXRfOuz7uVxq8gad1OR1jy4AXyfUNuewL/U5rRZw+lIjooMeZuqEe69KYLzYcp7Uwk2cK/0WhULG\nhHELuWHae7jaRvwiacvBqb38ij0qqyVjQ2vGO02noOogMnkXfbIOwsavxMjAnCkT/sDxc++K+57I\n+Q/WZh5UNaZTWpdEVcNZldWVntQQN7vI/pRyNC62oWNyJxpAEASa2kuoacqiuj/Y1jXnaE2VjobY\nhSase3Y9LqE3DdmI1VCURMmJDSx9VTUbUt2UyaaDy/sV0J74WYwrBtPT18bO43+goj5FZbut+Xhq\nm3PGdEMCypumOWHPM9Fr+Zg+u86eBr5MvEfsTg9wX8LSKW+L/7cq6lM5mbMOqUSfpVPf0fi3q2nK\nZm/K38THdha+5JXvQUAGSDE1suX2WdtUgq9c0ce+VGXQjQ1+TOx16JV18NWx36lYHMZH/oMQr5uH\nfT9tXbWXSU3+TbRa1HH9ogvAvzC9sg6On3uPlIKNWoUDvJymEx/5Ejbm2uuAwyEICoqqE0gp2ERp\n3QlMDG2Y7HcPEb5rNGrIXks6uusprjlCYVUCF2rUZTTtLf3EBipXuwikEj32pj5LRvF2+uRdnK/c\nR4jXzUT53kVKwUbRKaizp4Gvjt8jHkci0cPVLkJc4braRV5Rp3hbVw3Vjf0r2/6gq80MYKTYmHvh\nYhuKs00o9pY+VDdlkVawGZzrWfWIBdvXzccr9gG84h7E1NaDzsYySo6vp+TEBlY9YjHkCJIgyDlz\n/hPyK/YQH/mySqrzWjM4yA1gZGBJY/uFMR5VgrPNRG6fvX3Mn2FrZ7VKijfMexXxkS+LzVo9fW38\nePrPCCiYHvwnjeM+g5uuDPXNaWgrBJT9GeOdZ+BoNQFzE9VZ/NSCTdS3nsfe0p9JfncDyobLXcfv\nUxFTmRP2HBE+tw/7fpRSk3+iq7cJX9d5RPisGfY1On55dFKUvyDnK/dzMP0lrXZoZkb2zAl/jkD3\npVe0KlV67+4ktWAzzR1lOFgFMMnvbgI9ll31kSVtCILAxZa8fkGMBKoaz6KpAUlPasifb8pRe98V\n9SlsSVDavtmYe2Fn6Uv5xVP09LWp7CdBSpj3Knxd5zDOftKYXXq6eptVgm1NY6aaQ9FYMDdxxsUm\nRBlwbUNxsQlRsxIEZd34RM77JOetp6Gmj5P7ukg/3kVXWxcmFiZExJkQs8BkRPO/ABPGLWZu+HNY\nmDhd8XvQRltXDduP3jWEUpuEsVg6mhs70d3XgptdJLfEfTxmA4im9hK2H71LVFubMuFeZoU8o/I9\n2336KbJLv8bdfgqrZn0xZBe1QiFjR+Jase57uZCMg1UA8yNfZH/q31k771uVa23trOLjvQvok3dy\nx+wdjLOfhEzew9cnH1BJ0c+Y+CTRgQ+O6D0l5a4nMfttndTkdYJOivI6prWzigPpL2n1LgUJET53\nMGPinzE2tNSyn3Ya2y6QVqj03u2TdeHnNo9Fk1/H3X7Kz5Zmlsl7LjkKVR1Ws4EzNbLDx2U2Pi5z\nSMr7gNr+USO5opfOngbMjO0RBIHmjlJKa09SUntS/LFrai+hqb0EUNbZOnvqxUAsoEBPqj+qFV+v\nrJPapnNUN2b0126ztHahjxRjQ2tcbEKUgdY2FBeb0BF75errGYkB387ZgGVrDVRGjUZLfsVPXKhJ\nZGbIU0T43H5NRpOa2kvZcfQu0VRDldEFX0tTV8K8V5NWuBkHS39WxKwfc/C92JLPjqN3ibXZGROf\nZFrAAyr/F3LLd5Nd+jVGBhYsmfK2xhGmhMw3VJquBoLvhHGLWDz5TfaceYbpE/+odq2Hzr5Kn7yT\nEK9bGGc/Cbmij++TH1MJvtGBD484+FY2pHPs3L8BCcum/EsXfH9F6ALwz4hCISOlYCPHz72ntZvT\n0SqQBVGv4GoXPqbzCIJASe1xUgs3UVR9pN97dxVRvneN2bBgtLR11VJcfYTC6sOU1p5Qq4U6Wgfh\n4zIbX5c5uNiGikEgt/xHMQCDUg6wtbOS0rokjf6tAeMWMyf8WSxMnMmr2MN3SY+Iz6UXbSPK7+4h\nU/dyRS91zXkqdduG1kK1Zq/RYqBnipNNsDLQ9gdbKzP3Md/wdPY0iONgV4teWTsH0l9QNmlFvXJV\n64WDPXPHiqWpK9GBD+HtPJPtR9dgZGDJLdM/GbNqV2VDOjuP/V6Ucpwf8ZJKox5csjgEiI98WeP8\nrLLp6tNBWyVMn/hHogMeoqL+DF29zfi5qnZNF1Uf4XzlXowNrZkV+owydXz6zxRcdjM+2f/3TA/+\n44jeU09fGz/0S01GBz6Mh+O0Eb1Ox/WBLgD/TFQ1nGVf6nNaxQMM9EyJm/gEk3zXjknkolfWybnS\nb0gt2ExDWyG2Fj7ER75EsOfya24/KAgKapvPUVh1mKLqw2pWePpSIzydYvBxUToKWQ6a+QVloJHJ\nVTWUT+a+L/7bzMhenMW1NvNge6Lyx/Niy3nMjZXp1AluC3GxDROb2RRCH8ey32Hp1HdoaC2ipimT\n6kZlGrmuJe+KXYOkEgMcrQPEuq2LbQh2lr5X1fgio3jHVXM3GkxVYzqbDt7IZP/fExv06BV3zVY1\nZvDVsd+JtfixYGHiTHTgw4R43Yxc0cvWI7cjk/eyZs4XYx65K6k9wdcnHqBP3olEoscUyFg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CeucpOzdzJ+0HVVICtLO8YPepYDcdfVi85d+YHgPg+YNOv6e6Fr9Ghwz7vEx5YWulLQygAsCALN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\nir17T4P72Lv3oKFGd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W8a/EIorU5j/dG7iUz+7IabcSQSCd29RjN3zKc8Mfs0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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2142dab2a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mapa1 = ants.Mapa()\n",
    "mapa1.draw_distances()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "generación: 0·1·2·3·4·5·6·7·8·9·10·11·12·13·14·15·16·17·18·19·20·21·22·23·24·25·26·27·28·29·30·31·32·33·34·35·36·37·38·39·40·41·42·43·44·45·46·47·48·49·50·51·52·53·54·55·56·57·58·59·60·61·62·63·64·65·66·67·68·69·70·71·72·73·74·75·76·77·78·79·80·81·82·83·84·85·86·87·88·89·90·91·92·93·94·95·96·97·98·99·"
     ]
    },
    {
     "data": {
      "image/png": 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BMwATtaKqKi0OHsxGUlIaNm/OQFFRper7aNfOAxMmKAkybr89kEHXThUXV2L9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\nhw2qCAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2142e140cc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mapa1.swarm_create(100)\n",
    "print('generación:', end=' ')\n",
    "for i in range(100):\n",
    "    print(i, end='·')\n",
    "    mapa1.swarm_generation()\n",
    "mapa1.draw_best_path()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### Demo - (Si hay tiempo):\n",
    "### Vecindario de Schelling\n",
    "\n",
    "Se puede encontrar en la carpeta de ejercicios con el nombre de  \"el vecindario racista\"."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Si te está gustando este taller:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<a href=\"https://twitter.com/share\" class=\"twitter-share-button\" data-url=\"https://github.com/AeroPython/Taller-Algoritmos-Geneticos-PyConEs16\" data-text=\"Aprendiendo Algoritmos Genéticos y Sistemas Complejos con\" data-via=\"AeroPython\" data-size=\"large\" data-hashtags=\"PyConEs\" data-dnt=\"true\">Tweet</a>\n",
       "<script>!function(d,s,id){var js,fjs=d.getElementsByTagName(s)[0],p=/^http:/.test(d.location)?'http':'https';if(!d.getElementById(id)){js=d.createElement(s);js.id=id;js.src=p+'://platform.twitter.com/widgets.js';fjs.parentNode.insertBefore(js,fjs);}}(document, 'script', 'twitter-wjs');</script>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%html\n",
    "<a href=\"https://twitter.com/share\" class=\"twitter-share-button\" data-url=\"https://github.com/AeroPython/Taller-Algoritmos-Geneticos-PyConEs16\" data-text=\"Aprendiendo Algoritmos Genéticos y Sistemas Complejos con\" data-via=\"AeroPython\" data-size=\"large\" data-hashtags=\"PyConEs\" data-dnt=\"true\">Tweet</a>\n",
    "<script>!function(d,s,id){var js,fjs=d.getElementsByTagName(s)[0],p=/^http:/.test(d.location)?'http':'https';if(!d.getElementById(id)){js=d.createElement(s);js.id=id;js.src=p+'://platform.twitter.com/widgets.js';fjs.parentNode.insertBefore(js,fjs);}}(document, 'script', 'twitter-wjs');</script>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Siro Moreno, Aeropython, 7 de Octubre de 2016\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<link href='http://fonts.googleapis.com/css?family=Source+Sans+Pro|Josefin+Sans:400,700,400italic|Ubuntu+Condensed' rel='stylesheet' type='text/css'>\n",
       "\n",
       "\n",
       "<style>\n",
       "\n",
       "#notebook_panel { /* main background */\n",
       "    background: #f7f7f7;\n",
       "}\n",
       "\n",
       "div.cell { /* set cell width */\n",
       "    width: 900px;\n",
       "}\n",
       "\n",
       "div #notebook { /* centre the content */\n",
       "    background: #fff; /* white background for content */\n",
       "    width: 950px;\n",
       "    margin: auto;\n",
       "    padding-left: 0em;\n",
       "}\n",
       "\n",
       "#notebook li { /* More space between bullet points */\n",
       "    margin-top:0.7em;\n",
       "}\n",
       "\n",
       "/* draw border around running cells */\n",
       "div.cell.border-box-sizing.code_cell.running { \n",
       "    border: 1px solid #111;\n",
       "}\n",
       "\n",
       "/* Put a solid color box around each cell and its output, visually linking them*/\n",
       "div.cell.code_cell {\n",
       "    font-family: 'Source Sans Pro', sans-serif;\n",
       "    background-color: rgb(256,256,256);\n",
       "    font-size: 110%;\n",
       "    border-radius: 0px; \n",
       "    padding: 0.5em;\n",
       "    margin-left:1em;\n",
       "    margin-top: 1em;\n",
       "}\n",
       "\n",
       "div.text_cell_render{\n",
       "    font-family: 'Josefin Sans', serif;\n",
       "    line-height: 145%;\n",
       "    font-size: 125%;\n",
       "    font-weight: 500;\n",
       "    width:750px;\n",
       "    margin-left:auto;\n",
       "    margin-right:auto;\n",
       "}\n",
       "\n",
       "\n",
       "/* Formatting for header cells */\n",
       ".text_cell_render h1, .text_cell_render h2, .text_cell_render h3,\n",
       ".text_cell_render h4, .text_cell_render h5 {\n",
       "    font-family: 'Ubuntu Condensed', sans-serif;\n",
       "}\n",
       "\n",
       ".text_cell_render h1 {    /*Use this for Title*/\n",
       "    font-weight: 500;\n",
       "    font-size: 38pt;\n",
       "    line-height: 100%;\n",
       "    color: #ffbc29;\n",
       "    text-align: center;\n",
       "    margin-bottom: 0.1em;\n",
       "    margin-top: 0.3em;\n",
       "    display: block;\n",
       "}\n",
       "\n",
       ".text_cell_render h2 {    /*Use this for Subtitle*/\n",
       "    margin-top:16px;\n",
       "    font-size: 32pt;\n",
       "    font-weight: 500;\n",
       "    margin-bottom: 0.1em;\n",
       "    margin-top: 0.3em;\n",
       "    text-align: center;\n",
       "    font-style: regular;\n",
       "    color: #376a94;\n",
       "}\t\n",
       "\n",
       ".text_cell_render h3 {   /*Sections*/ \n",
       "    font-size: 30pt;\n",
       "    font-weight: 300;\n",
       "    text-align: left;\n",
       "    margin-bottom: 0.1em;\n",
       "    margin-top: 0.3em;\n",
       "    font-style: regular;\n",
       "    color:  #252525;\n",
       "}\n",
       "\n",
       ".text_cell_render h4 {    /*Subsections*/\n",
       "    font-size: 28pt;\n",
       "    font-weight: 200;\n",
       "    text-align: left;\n",
       "    margin-bottom: 0.1em;\n",
       "    margin-top: 0.3em;\n",
       "    font-style: regular;\n",
       "    color:  #376a94;\n",
       "}\n",
       "\n",
       ".text_cell_render h5 {    /*Subsubsections*/\n",
       "    font-size: 20pt;\n",
       "    font-weight: 300;\n",
       "    font-style: italic;\n",
       "    margin-bottom: .1em;\n",
       "    margin-top: 0.8em;\n",
       "    display: block;\n",
       "    color:  #ffbc29;\n",
       "}\n",
       "\n",
       ".text_cell_render h6 {    /*Author*/\n",
       "    font-family: 'Ubuntu Condensed', sans-serif;\n",
       "    font-weight: 100;\n",
       "    font-size: 14pt;\n",
       "    line-height: 100%;\n",
       "    color: #252525;\n",
       "    text-align: right;\n",
       "    margin-bottom: 1px;\n",
       "    margin-top: 3px;\n",
       "}\n",
       "\n",
       ".CodeMirror{\n",
       "        font-family: 'Duru Sans', sans-serif;\n",
       "        font-size: 100%;\n",
       "}\n",
       "\n",
       "</style>\n",
       "<script>\n",
       "    MathJax.Hub.Config({\n",
       "                        TeX: {\n",
       "                           extensions: [\"AMSmath.js\"],\n",
       "                           equationNumbers: { autoNumber: \"AMS\", useLabelIds: true}\n",
       "                           },\n",
       "                tex2jax: {\n",
       "                    inlineMath: [ ['$','$'], [\"\\\\(\",\"\\\\)\"] ],\n",
       "                    displayMath: [ ['$$','$$'], [\"\\\\[\",\"\\\\]\"] ]\n",
       "                },\n",
       "                displayAlign: 'center', // Change this to 'center' to center equations.\n",
       "                \"HTML-CSS\": {\n",
       "                    styles: {'.MathJax_Display': {\"margin\": 4}}\n",
       "                }\n",
       "        });\n",
       "</script>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Notebook style\n",
    "from IPython.core.display import HTML\n",
    "css_file = './static/style.css'\n",
    "HTML(open(css_file, \"r\").read())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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