{ "cells": [ { "cell_type": "markdown", "id": "9c6419d4-60db-42d4-a8c4-b19840a216e1", "metadata": {}, "source": [ "(antena_selection)=\n", "# Introducción.\n", "\n", "En este apartado vamos a mostrar dos ejemplos de indudable interés para hacer una optimización de problemas encontrados en el mundo real. En el primero de los ejemplos, vamos a contar con uan serie de antenas con un radio de acción igual en todas ellas. De lo que se tratará es de encontrar la menor cantidad de antenas posibles, de tal manera que entre ellas nos haya ningún punto en comúnn en su radio de acción.\n", "\n", "En el segundo ejemplo, vamos a tratar un conjunto de ubicación de antenas dentro del territorio de Alemanía, y lo que trataremos es de ubicar otras antenas, de manera que se encuentren a una distancia máxima de las antenas ya existentes. \n", "\n", "Procedemos a continuación a resolver el primer problema. En la imagen siguiente, se muestra de forma gráfica lo que pretendemos resolver. Tenemos un conjunto de antenas, con un radio de acción determinado, indicado en el gráfico por un círculo que rodea a cada nodo. Se trata de encontra el máximo número de nodos, pero que no haya interferencias en los nodos seleccionados.\n", "\n", "Para resolver el problema, definimos el gráfo de la derecha, de manera que cuando hay interferencia entre dos puntos, se trazará un arco que una esos dos puntos. Entonces, al final de lo que se trata en de encontrar el máximo número de puntos, pero de tal manera que entre ellos no hay ningún arco que los una\n", "\n", "![Selección de antenas](../img/AntenasSelection.PNG)\n", "```{index} maximum independent set problem\r\n", "```\n", "Este problema de optimización es conocido como *maximum independent set y en este enlace podemos ver más detalles sobre ello . Así pues, nuestro objetivo es maximizar el número de nodos en un conjunto, con la restricción de que el conjunto no contenga aristas. Para resolver en un sistema D-Wave, podemos reformular este problema como un problema de optimización binaria cuadrática sin restricciones (QUBO). \n", "\n", "Para seguir este problema, también puede ser interesante el video que hay detrás de este enlace .\n", "\n", "Importamos las librerías necesarias para poder resolver este problema.*" ] }, { "cell_type": "code", "execution_count": 1, "id": "b790779b", "metadata": {}, "outputs": [], "source": [ "#!pip install dwave-networkx\n", "#!pip install dwave-system --user" ] }, { "cell_type": "code", "execution_count": 1, "id": "a9fb1406-0588-47ce-887b-f913720785e9", "metadata": {}, "outputs": [], "source": [ "# Import networkx for graph tools\n", "import networkx as nx\n", "\n", "# Import dwave_networkx for d-wave graph tools/functions\n", "import dwave_networkx as dnx\n", "\n", "# Import matplotlib.pyplot to draw graphs on screen\n", "import matplotlib\n", "#matplotlib.use(\"tkAgg\") # tkAgg para usar matplotlib con tkinter\n", "import matplotlib.pyplot as plt\n", "\n", "\n", "\n", "# Set the solver we're going to use\n", "from dwave.system.samplers import DWaveSampler\n", "from dwave.system.composites import EmbeddingComposite" ] }, { "cell_type": "code", "execution_count": 2, "id": "84fe7372-8819-43d3-b9ef-0a0bbb8e175b", "metadata": {}, "outputs": [ { "data": { "image/png": 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79mwlJSXp9ttvNzuOS6NQAgBQgk2dOlWPP/64oqOjFRoaanYct3Lx4kU1bdpUN998s9avXy8PDw+zI7ksxswBACihDh48qGeffVaPPvooZbIIlCtXTtOnT9eWLVv0wQcfmB3HpTFCCQBACZSTk6MOHTooPT1du3btUrly5cyO5LZeeOEFffzxx9q5c6caN25sdhyXRKEEAKAEev311/X2229r8+bNatOmjdlx3Nrly5fVqlUreXt7a9u2bfL29jY7ksthyhsAgBJm06ZNeuuttzR+/HjKZDEoU6aMZs6cqZSUFE2cONHsOC6JEUoAAEqQc+fOqVmzZqpZs6bWr18vT09PsyOVGhMmTNCECRO0detWBQUFmR3HpVAoAQAoQR588EEtWLBASUlJuu2228yOU6rk5OSoXbt2unjxohITEzna8jow5Q0AQAkRFRWlWbNm6dNPP6VMmsDLy0szZ87Uzz//rHHjxpkdx6UwQgkAQAmQmpqqJk2aKCQkRJGRkRwHaKIpU6bo+eefV1xcnLp06WJ2HJdAoQQAwGR2u13dunXT4cOHlZSUpMqVK5sdqVRzOBwKDg7W0aNHlZycrAoVKpgdqcRjyhsAAJO999572rBhg2bNmkWZLAGsVqumT5+utLQ0Pf/881d9T0aWTXt/PaddqWe099dzysiyFXPKkoURSgAATLRz5061bdtWo0eP1qRJk8yOgz/5+uuvNWLECC1ZskT33HOPDp68oDnbUxW3/5RS0zP15wJlkRRQxVfBDf01tE2A6tcob1ZsU1AoAQAwSWZmplq0aCE/Pz9t3bqVDbVLGMMwdM8992jXwWPqNPpzbT96Th5Wi+yOvKvTldc71qumtwcEqnYV32JMbB4KJQAAJvnHP/6hGTNmKDExUXfccYfZcXAVX6xK1n9W/iSLh6dkKfiTgh5WizytFr3Zr5EGBwUUYcKSgd1SAQAwwaJFi/TFF1/o888/p0yWUJ/EHdR7a4/J4umlPya1C87uMGR3GBobk6LTF7M0Mrh+0YQsIRihBACgmP32228KDAzU3XffrYULF7JFUAkUlZCqsTEpTrveO6GBGuTGI5UUSgAAipFhGOrTp4927dql5ORk+fv7mx0J/+NYeqa6T1mvLJvjqq87si/p/PYYZf26X9knDshx+aKq9vmXyjXpnuc1fTytWv1cZ7d9ppJtgwAAKEaffvqpli9frmnTplEmS6hxsSmy5bPwxpF5Xuc2Ryon7Zi8/At2opHNYWhcrPNGPEsanqEEAKCY7N27V2PGjNHIkSPVu3dvs+PgKg6evKCNh07n+x6PclV0y8hZ8ihXWVknDuq3Gc9d87p2h6GNh07r0KkLqufvflsKMUIJAEAxyMrK0pAhQ3T77bfr3XffNTsO8jBne6o8rPk/02rx9JJHuevfgN7DatHsbak3Gq1Eo1ACAFAMXnnlFf3444+aM2eOypYta3Yc5CFu/6l895ksDLvDUNyBU0VybbNRKAEAKGKrV6/W+++/r7ffflvNmjUzOw7ycDHLptT0zCK9R2paplse00ihBACgCKWlpemhhx5St27d9Nxz137WDuY5mpahot76xpB0JC2jiO9S/CiUAAAUEcMw9OSTT+rSpUuaMWOGrFZ+7JZk2XlsE+Sq9ylOrPIGAKCITJs2TdHR0Zo/f75q1apldhxcg7dn8RT+4rpPcXK/7wgAgBLg0KFDGjVqlB555BGFhYWZHQcFUKeq33UesHj9LP+9j7uhUAIA4GQ5OTkaNmyYbrrpJn300Udmx0E+Ll++rJUrV+q5555Tq2aByj7za5HeL6Cqr/x83G+C2P2+IwAATDZx4kTt2LFDmzdvVvny7reJtSszDEOHDh3S8uXLtWzZMq1bt06XLl3SLbfcopCQEF1q6K+tv0v2a6zOOb9zsRyXM2S/mC5JunQoXrYLf2yIXqHlvbKW+fsopIfVouAG7nk6EoUSAAAn2rx5syZOnKjx48erTZs2ZseBpIyMDMXFxeWWyMOHD8vLy0udOnXShAkT1Lt3b911112yWCw6ePKCeny44ZrXPL89Vvbz/39PycwDW6QDWyRJ5RoFX7VQ2h2GhrUNcN43VoJYDMMo6hXyAACUCufPn1fTpk1Vs2ZNrV+/Xp6ejNuYwTAM7du3T8uXL9fy5cu1YcMGZWdn67bbblPv3r0VEhKi4OBglStX7qqfHz51u7YcTnPqBuceVova1a2qWY+55y8ZFEoAAJzkoYceUmxsrHbv3q26deuaHadUOX/+vFavXp1bIo8dO6YyZcooODhYISEhCgkJUf369WWxXHvZzbH0THWfsl5ZTtzex8fTqtXPdVbtKr5Ou2ZJwq9OAAA4wdy5czVz5kzNmDGDMlkMDMNQUlKSli1bpuXLl2vLli2y2Wxq2LChwsLCFBISok6dOt3QMZe1q/jqzX6NNDYmxWl5J/Rr5LZlUmKEEgCAQjt27JiaNGminj17KioqqkCjYLh+6enpWrVqlZYtW6YVK1bot99+k5+fn7p166bevXurV69euu2225x2v0/iDuq9lQcKfZ0XejbUM8H1nJCo5KJQAgBQCHa7Xd27d9ehQ4eUnJysypUrmx3Jbdjtdu3cuTN3MU18fLwcDocCAwMVEhKi3r17q3379vL29i6yDFEJqRq/aK9sDuO6nqn0sFrkabVoQr9GGhTkngtx/owpbwAACuH999/X+vXrtXbtWsqkE5w8eVIrV67U8uXLtWLFCqWlpalixYrq0aOHHn/8cYWEhBTrqUODgwLU/vZqGheboo2HTsvDasm3WF55vV3dqnp7QKBbT3P/GSOUAADcoMTERLVt21bPPfec3nnnHbPjuCSbzaZt27blLqbZuXOnJKlly5a5i2natm1bIlbMHzx5QXO2pyruwCmlpmXqzwXKoj82LQ9u4K9hbQNUz7907T9KoQQA4AZkZmaqRYsW8vX11bZt24p02tXd/PLLL1qxYoWWL1+uVatW6dy5c6patap69eqlkJAQ9ezZUzVq1DA7Zr4ysmw6kpahbJtD3p5W1anq55Yn4BRU6f3OAQAohDFjxig1NVU7d+6kTF5Ddna2Nm3alDsKmZKSIqvVqjZt2uj5559XSEiIWrZsKQ8PD7OjFpifj6ca1axodowSgxFKAACu0+LFi9WvXz999tln+sc//mF2nBLpyJEjuVv6rFmzRhkZGbrppptyp7G7d++uqlWrmh0TTkKhBADgOpw8eVKBgYFq06aNFi1axBZB/3Xp0iVt2LAht0Tu379fnp6eateuXe7pNE2aNJHVajU7KooAhRIAgAIyDEN9+/bVjh07lJKSIn9/f7MjmcYwDB08eDB3S59169bp8uXLql27du6WPl27dlXFikwLlwY8QwkAQAF99tln+v7777V06dJSWSYvXryouLi43BL5888/y9vbW506ddLEiRPVu3dv3XnnnYzalkKMUAIAUAD79u1Ty5Yt9dhjj+mTTz4xO06xMAxDe/fuzV1Ms3HjRmVnZ6tu3bq509jBwcHy8/MzOypMRqEEAOAasrKy1KZNG2VnZ2vnzp03dD60qzh37pxWr16dWyJ/+eUXlS1bVsHBwbkLaurXr292TJQwTHkDAHANr776qvbt26f4+Hi3K5MOh0NJSUm5i2m2bNkiu92uO++8UwMHDlRISIg6duzodt83nIsRSgAA8rFmzRp1795dkydP1pgxY8yO4xRpaWl/Od7w5MmTKleunLp3766QkBD16tVLderUMTsmXAiFEgCAPKSnp6tJkyZq2LChVq1a5bJb3tjtdu3YsSN3FDI+Pl6GYahJkya5z0K2a9eODdpxwyiUAABchWEYGjhwoNauXavk5GTdcsstZke6LidPntSKFSu0bNkyrVy5Uunp6apUqZJ69Oih3r17q1evXqpZs6bZMeEmeIYSAICrmD59uqKjozVv3jyXKJM5OTnatm1b7pY+u3btkiS1atVKTz/9tHr37q3WrVvL05Mf/XA+RigBAPgfP/30k5o1a6b7779f06ZNMztOnn755Zfc1dirV6/WuXPnVK1aNfXq1UshISHq2bNnqdwvE8WPQgkAwJ/YbDZ17NhRp06d0u7du1W+fHmzI+XKysrSpk2bckvknj17ZLVa1bZt29wtfVq2bOmyz3rCdTHuDQDAn0ycOFEJCQnauHFjiSiTP//8c+409tq1a5WRkaGbb75ZISEheu2119S9e3dVqVLF7Jgo5RihBACUChlZNh1Jy1C2zSFvT6vqVPWTn89fx1W2bNmijh076vXXX9f48eNNyXnp0iWtX78+d0X2gQMH5Onpqfbt2+euyG7SpAnHG6JEoVACANzWwZMXNGd7quL2n1Jqeqb+/APPIimgiq+CG/praJsA1ShrqFmzZrrpppu0YcOGYlu8YhiGDhw4kDsKuX79el2+fFkBAQEKCQlR79691bVrV1WoUKFY8gA3gkIJAHA7x9IzNS42RRsPnZaH1SK7I+8fdVder3z5Nx2e+5Z2bVqtunXrFmm+ixcvau3atbkl8siRI/L29lbnzp1zS+Qdd9zBKCRcBs9QAgDcSlRCqsYv2ivbf0tkfmXyz6+ne1VT9Yc/Vnyap5zdJw3D0J49e3IX02zcuFE5OTmqV6+e+vbtq5CQEHXp0kV+fn7OvTFQTBihBAC4jU/iDuq9lQcKfZ0xPRtoZHD9Ql3j7NmzWr16dW6JPH78uMqWLauuXbvmrsiuV69eobMCJQGFEgDgFqISUjU2JsVp13snNFCDggIK/H6Hw6Hdu3fnLqbZunWr7Ha77rzzztzFNB07dlSZMmWclhEoKSiUAACXdyw9U92nrFeWzfG317J/P6pzmyKU/dsh2TPOyuLlI6+qtVWhTah867fJ85o+nlatfq6zalfxzfM9p0+f1qpVq7Rs2TKtWLFCp06dUvny5dW9e3eFhISoV69euvXWW53yPQIlGYUSAODyhk/dri2H0676vOSlnxJ0fsdi+dS6Qx7lqsjIyVLm/i3K+mWvqoSMVPlmIVe9pofVonZ1q2rWY/+/dNrtdiUkJOQupklISJBhGGratGnuKOTdd98tb2/vIvtegZKIQgkAcGkHT15Qjw83XNdnDIddJ6b/S4YtR7VGfJHveyOH36UDCRu0fPlyrVy5Uunp6apUqZJ69uyp3r17q2fPnqpZs2ZhvgXA5bHKGwDg0uZsT73m1kD/y2L1kGf5asr67WD+b3TYdc+zk3R2zddq1aqVnnnmGfXu3VtBQUHFtk8l4Ar4rwEA4NLi9p8qUJl0ZF+WYcuSIytTlw5u16XDO+V7Z8f8P2T1UJ1292hFxERVr17dSYkB90OhBAC4rItZNqWmZxbovWfWfqOLu5f/8X8sVvk2uFtVev7j2p/L9pBvhcqFiQm4PQolAMBlHU3LUEEnuisE3SffOzrIfiFNmT9ukmE4JHvONT9nSDqSlqFGNSsWKivgzqxmBwAA4EZlX2WboLx4Va2tsnWaqVxgN/kPHC8j+7JOzZ+ggqxNvZ77AKURhRIA4LK8PW/8x5jvHe2VfeKgbOnHi/Q+QGnAfyEAAJdVp6qfLDf4WSMnS5LkyMrI932W/94HQN4olAAAl+RwOJQYv1Vl7fkXQnvG2b99zbDblLFnrSyePvKqlv/xigFVfeXnw5IDID/8FwIAcBmGYWj37t2KiIjQ3LlzdezYMQX0Hy1rw84yLFcfI0lb/omM7Ez51G4sj/JVZb94Rhn71smW9osqd31MVu+yed7Pw2pRcAP/ovp2ALdBoQQAlHgHDhxQZGSkIiMjtX//flWrVk0DBw5UeHi4/Os3Ua+PNuX5Wb87O+pi8ipd2PW9HJcuyOpdVt431VPlLo/ke5a3JNkdhoa1zX8EEwCFEgBQQv3yyy+aO3euIiMjtXPnTpUrV04DBgzQhx9+qG7dusnLyyv3vR3rVcvzLG+/uzrL767O133/K2d51/MvX6jvAygNOMsbAFBipKWlaf78+YqMjNSGDRvk5eWle+65R+Hh4erbt6/Klr369PSx9Ex1n7JeWU7c3sfH06rVz3VW7Sq+Trsm4K4olAAAU128eFELFy5UZGSkVqxYIYfDoW7duik8PFwDBgxQpUqVCnSdqIRUjY1JcVqud0IDNSiI6W6gICiUAIBil5WVpeXLlysyMlKLFi3SpUuXdPfddys8PFwPPPCAatSocd3XdDgcaj/i3zpRvVWh873Qs6GeCa5X6OsApQWFEgBQLOx2u9avX6+IiAhFR0fr7NmzCgwMVHh4uAYPHqzbbrutUNefOHGiXnvtNb301SLFpnrK5jCu+kxlXjysFnlaLZrQrxEjk8B1olACAIqMYRiKj49XZGSkvvvuO504cUK33XabwsPDFR4ersaNGzvlPkuXLtW9996r119/XW+88YaOpWdqXGyKNh46LQ+rJd9ieeX1jvWq6e0BgTwzCdwACiUAwOn27t2bu83P4cOHVaNGDQ0aNEhDhgxR69atZbHc6Pk2f3fgwAEFBQWpS5cuio2NldX6//ejPHjyguZsT1XcgVNKTcvUn3/gWfTHpuXBDfw1rG0Aq7mBQqBQAgCc4siRI4qKilJkZKSSk5NVsWJF3X///QoPD1eXLl3k4eHh9HueP39ebdu2lcPhUHx8vCpUqJDnezOybDqSlqFsm0PenlbVqerHCTiAk1AoAQA37OTJk5o3b54iIyO1ZcsWlS1bVv369VN4eLhCQkLk4+NTZPd2OBwKCwvTmjVrFB8frzvuuKPI7gUgf/xqBgC4LufOnVNsbKwiIyO1evVqWa1W9ezZU7Nnz1a/fv1UvnzxTB2//fbbWrBggRYuXEiZBEzGCCUA4JouXbqkpUuXKjIyUkuXLlV2drY6deqk8PBwhYWFqVq1asWa58oinPHjx2v8+PHFem8Af0ehBABcVU5OjtasWaPIyEjFxsbqwoULatmypcLDwzVo0CDdcsstpuS6sggnODhYMTExf1mEA8AcFEoAQC6Hw6EtW7bkbvNz+vRpNWzYMHebnwYNGpia78oiHMMwtH379nwX4QAoPjxDCQClnGEYSkpKUmRkpKKiopSamqpatWrp4YcfVnh4uJo3b+7UbX5ulMPh0EMPPaTjx49fc0U3gOJFoQSAUurQoUOKjIxURESEfvzxR1WtWlUDBw5UeHi4OnToUOKmkt966y0tWLBAixYtUsOGDc2OA+BPmPIGgFLk119/1dy5cxUREaEdO3aoXLly6t+/v8LDw9WjRw95eXmZHfGqlixZon79+umNN97Q66+/bnYcAP+DQgkAbi49PV3R0dGKiIjQ+vXr5eXlpT59+ig8PFx9+/aVr2/JPmpw//79at26tbp27aro6OgSN3IKgEIJAG4pIyNDixYtUkREhFasWCG73a6uXbsqPDxcoaGhqlSpktkRC+T8+fNq06aNJLEIByjBeIYSANxEdna2VqxYoYiICC1atEiZmZlq27at3nvvPT3wwAO66aabzI54XRwOhx588EH9+uuvLMIBSjgKJQC4MLvdrg0bNigiIkLR0dE6c+aMGjdurFdeeUWDBw9W3bp1zY54wyZOnKhFixaxCAdwAUx5A4CLMQxDO3bsUEREhObOnasTJ06oTp06uXtFBgYGmh2x0BYvXqx+/fppwoQJeu2118yOA+AaKJQA4CJ++OEHRUREKCoqSocOHVKNGjX0wAMPaMiQIWrTpk2J2CvSGViEA7geCiUAlGBHjx5VVFSUIiMjlZSUpIoVKyo0NFRDhgxRly5d5OnpXk8uXVmEY7FYtH37dpUvX97sSAAKwL3+JgIAN3Dq1CnNmzdPkZGR2rx5s8qUKaN7771Xb7zxhkJCQlSmTBmzIxYJh8Oh4cOH68SJE4qPj6dMAi6EQgkAJcD58+cVGxuryMhIrV69WpLUs2dPzZo1S/fdd1+pKFf//ve/tXjxYi1evNj0M8MBXB+mvAHAJJcvX9bSpUsVGRmpJUuWKCsrSx07dtSQIUN0//33q1q1amZHLDaLFi3Sfffdp3//+9969dVXzY4D4DpRKAGgGNlsNq1Zs0aRkZGKjY3V+fPn1bx5cw0ZMkSDBg1S7dq1zY5Y7H788Ue1bt1a3bt31/z581mEA7ggCiUAFDGHw6GtW7cqMjJS8+bN06lTp1S/fn0NGTJE4eHhpXqPxXPnzqlNmzayWq0swgFcGM9QAkARMAxDycnJioyMVFRUlI4ePapatWpp+PDhCg8PV4sWLdxmm58bdWURzm+//cYiHMDFUSgBwIl++uknRUZGKjIyUvv27VOVKlU0cOBAhYeHq2PHjkzn/smECRO0ZMkSFuEAboApbwAopBMnTmju3LmKjIxUfHy8/Pz81L9/f4WHh6tHjx7y9vY2O2KJs3DhQvXv359FOICboFACwA04c+aMoqOjFRkZqbi4OHl6eqpPnz4KDw9X37595efnZ3bEEotFOID7oVACQAFlZGRo8eLFioyM1LJly2S32xUcHKzw8HCFhoaqcuXKZkcs8c6dO6fWrVvL09NT27Zt47lJwE3wDCUA5CM7O1srV65UZGSkFi5cqIyMDLVp00aTJ0/WAw88oJtvvtnsiC7D4XBo2LBhOnnypBISEiiTgBuhUALA/3A4HNqwYYMiIyM1f/58paen66677tLLL7+swYMH6/bbbzc7okt68803tXTpUi1ZskT169c3Ow4AJ2LKGwD0xzY/O3fuzN3m59dff9Wtt96q8PBwhYeHKzAwsNRv81MYCxYs0IABAzRx4kS98sorZscB4GQUSgCmyciy6UhahrJtDnl7WlWnqp/8fIp34uTHH39UZGSkIiIidOjQIfn7++uBBx5QeHi47r77bkqkE/zwww9q3bq1evbsqfnz5/NnCrghCiWAYnXw5AXN2Z6quP2nlJqeqT//BWSRFFDFV8EN/TW0TYDq1yiaZ+yOHTumqKgoRUREaPfu3apQoYJCQ0MVHh6url27ytOTp4GchUU4QOlAoQRQLI6lZ2pcbIo2HjotD6tFdkfef/Vceb1jvWp6e0CgalfxLfT9f//9d82fP18RERHatGmTypQpo759+yo8PFx9+vRRmTJlCn0P/JXD4dB9992njRs3KiEhgecmATdGoQRQ5KISUjV+0V7ZHEa+RfJ/eVgt8rRa9Ga/RhocFHDd971w4YIWLFigiIgIrVq1SpLUo0cPhYeHq3///qpQocJ1XxMF9/rrr2vixIlaunSpevfubXYcAEWIQgmgSH0Sd1DvrTxQ6OuM6dlAI4OvPcJ1+fJlLVu2TBEREVqyZIkuX76sDh06KDw8XAMHDlT16tULnQXXFhsbq9DQUL311lsaN26c2XEAFDEKJYAiE5WQqrExKU673juhgRp0lZFKm82muLg4RUREKCYmRufPn1ezZs00ZMgQDRo0SAEB1z+6iRu3b98+tWnTRr169dK8efNYhAOUAhRKAEXiWHqmuk9Zryyb45rvPbdlrs5umCWvagGq+fhneb7Px9Oq1c91Vu0qvjIMQ9u2bVNERIS+++47nTp1SvXq1cvd5ufOO+905reDAjp79qxat24tb29vbdu2TeXKlTM7EoBiwFJGAEViXGyKbAV4XtJ2/rTObf1OFq9rL4qxOQz9c9YW3fnbGkVFRenIkSOqWbOmhg0bpvDwcLVs2ZLRMBNdOQnn999/V0JCAmUSKEUolACc7uDJC9p46HSB3nsmbqp8ajaU4XDIcel8vu+1Owzt/i1Lm+YuVljPngoPD1fHjh3l4eHhjNgopPHjx+v777/X999/r3r16pkdB0AxolACcLo521OvuTWQJF1O3aPMHzfr5kc+VvqqLwp0basM/eODCP27fxNnRIWTxMTEaOLEiXr77bcVEhJidhwAxcxqdgAA7idu/6lrlknDYVf6qi9UrmlPefvXKfC1HbJow6G0QiaEM+3bt08PPfSQ7r//fo0dO9bsOABMQKEE4FQXs2xKTc+89vt2LZPt/O+q1Gn4dd8jNS1TGVm2G4kHJzt79qz69++vOnXqaNq0aTzDCpRSFEoATnU0LUPXWopjv3ReZzfOUaV2g+ThW/G672FIOpKWcUP54Dx2u11Dhw7V77//rgULFrAIByjFeIYSgFNlF2CboLMbZslatpzKt7q3SO+DojV+/HgtW7ZM33//vW6//Xaz4wAwEYUSgFN5e+Y/8ZGTflwXd69Q5W5PyH4hPffrhj1HhsMu29mTsvj4yqNs+ULdB0UrJiZGb731lv7zn/+wCAcAG5sDcK6MLJsav7Eiz2nvy0eTdTIy/6P4yrfqpyrdR+T5ukXSnjd6yc+H34nNsHfvXrVp00Z9+vTR3LlzeW4SACOUAJzLz8dTAVV8dTSPhTle1W9V9dBX/vb1sxtmyZF9SVW6j5BnpZvzvUdAVV/KpEnOnDmj/v37q27duvr2228pkwAkUSgBFIHghv6atf3oVbcO8vCtKN8Gd//t6+cTFkrSVV/7y+etFgU38HdOUFyXK4tw0tLSOAkHwF/wEBIApxvaJuCa+1DeKLvD0LC2AUVybeTv9ddf14oVKxQZGckiHAB/wQglAKerX6O8Otarpi2H0wpcLG8aOuma7/GwWtSublXV889/wQ6cLzo6Wm+//bYmTZqkXr16mR0HQAnDohwAReJYeqa6T1mvLCdu7+PjadXq5zqrdhVfp10T17Znzx61bdtW99xzj6KionhuEsDfUCgBFJmohFSNjUlx2vXeCQ3UoCCmu4vTmTNnFBQUJF9fX23dulV+fn5mRwJQAvEMJYAiMzgoQA0u7y/kVf74nfeFng0pk8XMbrdryJAhSk9P14IFCyiTAPJEoQRQZCIiIrTqw9HqXTVdPp5WeVivb6rUIkOOnGyNaOqnZ4LrFVFK5OW1117TypUrFRUVpbp165odB0AJxpQ3gCLxww8/KCgoSP3799esWbP0y5lLGheboo2HTsvDasl3sc6V19vfXlX7Z7+hc8d/0q5du9imphjNnz9fAwcO1DvvvKMXX3zR7DgASjgKJQCny8jIUOvWrWUYhuLj4/9SBA+evKA521MVd+CUUtMy/3KijkV/bFoe3MBfw9oGqJ5/eR08eFDNmjXT8OHD9cUXXxT791IaXVmE07dvX0VGRrIIB8A1USgBOJVhGHrwwQcVExOjhIQE3XXXXXm+NyPLpiNpGcq2OeTtaVWdqn5XPQHnyy+/1FNPPaUlS5bonnvuKcr4pd6VRTh+fn7asmULz00CKBAKJQCn+uqrr/Tkk09qzpw5GjJkiFOuaRiG+vbtq507dyolJUXVq1d3ynXxV3a7Xffcc4/i4+O1Y8cOnpsEUGAsygHgNImJiRo1apSeeuopp5VJSbJYLJo6dapsNptGjBghfg8uGq+++qpWrVqluXPnUiYBXBdGKAE4xdmzZ9WiRQtVrlxZmzdvVpkyZZx+j5iYGIWFhWnatGl6+OGHnX790mzevHl64IEH9O677+qFF14wOw4AF0OhBFBohmEoNDRU69atU2Jiom677bYiu9cjjzyi6OhoJSUlFel9SpOUlBS1bdtW/fr1U0REBItwAFw3CiWAQnv//fc1ZswYLVy4UP369SvSe50/f15NmjRRQECA4uLi5OHhUaT3c3fp6ekKCgpSuXLlWIQD4IbxDCWAQtm0aZNeeuklvfDCC0VeJiWpQoUKmjlzpjZt2qT333+/yO/nzux2u8LDw3X27FlOwgFQKIxQArhhp06dUvPmzXX77bdrzZo18vLyKrZ7v/jii/rwww+VkJCgpk2bFtt93cnYsWM1efJkrVixQt27dzc7DgAXRqEEcEPsdrtCQkKUnJysXbt2qWbNmsV6/6ysLAUFBUmS4uPji2QRkDv77rvvNGjQIE2ePFljxowxOw4AF8eUN4Ab8u9//1tr1qxRREREsZdJSfLx8dHs2bO1f/9+vfbaa8V+f1eWnJysRx55ROHh4Ro9erTZcQC4AUYoAVy3lStXKiQkRG+++abpZe69997Tiy++qLVr16pLly6mZnEF6enpatWqlSpUqKAtW7bI19fX7EgA3ACFEsB1+eWXX9S8eXO1bNlS33//vaxWcyc67Ha7unXrpp9//lnJycmqWLGiqXlKMrvdrj59+mjHjh3asWMH2y4BcBqmvAEUWE5OjgYNGqQyZcpo9uzZppdJSfLw8NCMGTN05swZjRo1yuw4Jdq4ceO0evVqzZ07lzIJwKnM/2kAwGWMHTtW8fHx+u6771StWjWz4+S69dZb9cknn2jmzJmaP3++2XFKpLlz5+rdd9/Vu+++y4puAE7HlDeAArly7OGHH36oZ5991uw4f2MYhgYOHKi4uDjt2bNHN998s9mRSoykpCS1a9dO/fv31+zZszkJB4DTUSgBXNOhQ4fUsmVL9ejRQ/PmzSuxheT06dNq3Lixmjdvru+//77E5ixOaWlpCgoKUsWKFbV582YW4QAoEkx5A8jXpUuXNHDgQPn7+2vq1KkluqRVq1ZN3377rZYvX64vvvjC7Dimu3ISzvnz5xUbG0uZBFBkKJQA8jVq1Cj9+OOPmj9/vkusoO7Tp4+eeuopjRkzRgcOHDA7jqnGjRunNWvWaO7cuapTp47ZcQC4Maa8AeRp5syZeuihhzR16lQ9+uijZscpsIyMDDVr1kxVqlTR5s2b5enpaXakYjd37lwNHjxY77//vp5//nmz4wBwcxRKAFe1Z88etW7dWoMGDdK0adPMjnPdtm/frvbt2+v111/X66+/bnacYnVlEc6AAQM0a9asEv2YAgD3QKEE8DcXLlxQUFCQvL29tW3bNpd99m78+PF66623tHXr1txzv90di3AAmIFCCeAvDMNQeHi4vv/+e+3YsUMNGjQwO9INy8nJUbt27XT+/Hnt2rXL7cuVzWZTnz59lJiYqB07dvDcJIBiw6IcAH/x2Wefae7cuZo6dapLl0lJ8vLy0uzZs3Xs2DG9+OKLZscpcuPGjdPatWv13XffUSYBFCsKJYBc8fHxeu655/TPf/5TAwcONDuOUzRs2FCTJ0/Wp59+quXLl5sdp8hERUVp8uTJmjx5srp27Wp2HAClDFPeACRJ6enpatGihW666SZt2LBB3t7eZkdyGsMwFBISopSUFKWkpKhq1apmR3KqpKQk3X333QoNDWURDgBTUCgByOFwqF+/ftq6dat27dqlgIAAsyM53fHjxxUYGKju3btr7ty5blO60tLS1KpVK1WuXFmbNm1y++dEAZRMTHkD0LvvvqulS5dq9uzZblkmJalWrVr64osvNG/ePEVERJgdxylsNpsGDx6sixcvchIOAFMxQgmUcuvWrVO3bt00duxYvfXWW2bHKXLDhg3TkiVLlJyc7PLl+YUXXtCUKVO0atUqBQcHmx0HQClGoQRKsd9++03NmjXTXXfdpZUrV5aKE2XOnj2rwMBA1a9fX6tXr5bV6poTNZGRkRoyZIimTJmif/3rX2bHAVDKuebfpAAKzWazKTw8XBaLRREREaWiTEpSpUqVNGPGDMXFxemjjz4yO84N2b17tx577DENGzZMzz77rNlxAIARSqC0euWVVzRp0iStXbtWnTt3NjtOsXv++ef12WefaceOHWrcuLHZcQrsyiKcKlWqaNOmTSpbtqzZkQCAQgmURkuXLlXfvn01adIkvfTSS2bHMcXly5fVsmVLeXl5afv27fLx8TE70jXZbDaFhIQoKSlJO3fudPlnQAG4D6a8gVLm6NGjGj58uPr27asXXnjB7DimKVOmjGbPnq19+/bpjTfeMDtOgYwdO1br1q3Td999R5kEUKIwQgmUItnZ2erYsaNOnjypxMREValSxexIpps0aZLGjRunDRs2qEOHDmbHyVNERISGDh3KIhwAJRKFEihFRo0apS+//FKbN29Wq1atzI5TItjtdnXu3Fm//vqrkpKSVL58ebMj/c3u3bvVrl07hYWFaebMmW6zKTsA90GhBEqJuXPnavDgwfr000/19NNPmx2nRDl8+LCaNm2qQYMG6ZtvvjE7zl+cPn1arVq1UtWqVVmEA6DEolACpcD+/fvVqlUr9e3bVxEREYxwXcXUqVP1+OOPa8GCBbrvvvvMjiPpj0U4vXr1UnJyMotwAJRoFErAzWVmZqpNmzbKyclRQkJCiZzSLQkMw1D//v21detWpaSkqEaNGmZH0ujRo/XRRx9p9erV6tKli9lxACBPrPIG3JhhGHr66ad1+PBhRUdHUybzYbFY9PXXX0uSnnjiCZn9u3ZERIQ++OADvf/++5RJACUehRJwY99++61mzJihL774Qo0aNTI7Tonn7++vb775RosXL9bUqVNNy7Fr1y499thjevDBBzVq1CjTcgBAQTHlDbippKQktW3bVsOHD9dXX31ldhyX8sQTTygyMlJJSUm6/fbbi/XeVxbhVKtWTRs3bmQRDgCXQKEE3NC5c+fUqlUrlS9fXlu2bFGZMmXMjuRSLly4oGbNmqlGjRrasGFDsZ1zfmURTkpKinbs2MEiHAAugylvwM0YhqFHH31Uv//+u+bNm0eZvAHly5fXzJkztX37dr377rvFdt8XX3xR69ev5yQcAC6HQgm4mY8++kgxMTGaNm1asU/XupP27dvrpZde0vjx45WYmFjk95s9e7amTJmiDz74gEU4AFwOU96AG9m6das6deqkZ599Vu+9957ZcVxedna22rRpo6ysLO3cubPInmdMTExU+/bt9cADD2j69OnsEwrA5VAoATdx+vRpNW/eXLfeeqvi4uLk5eVldiS3sHfvXrVs2VL/+Mc/NGXKFKdf//fff1erVq1UvXp1FuEAcFlMeQNuwOFwaNiwYbp8+bKioqIok07UqFEjTZo0SR9++KHWrFnj1GvbbDYNGjRIly5dUmxsLGUSgMuiUAJu4K233tLKlSsVERGhW265xew4bmfUqFHq2rWrHn74YZ05c8Zp133hhRe0ceNGzZs3T7Vr13badQGguFEoARe3evVqjR8/XuPHj1ePHj3MjuOWrFarpk+frgsXLmjkyJFOueasWbP04Ycf6oMPPlDnzp2dck0AMAvPUAIu7Pjx42revLmaNWumZcuWycPDw+xIbi0iIkJDhw5VZGSkBg8e/LfXM7JsOpKWoWybQ96eVtWp6ic/n7/vYXllEc6gQYM0bdo0FuEAcHkUSsBF5eTkqGvXrvr555+1a9cuVa9e3exIbs8wDIWHh2vFihXas2ePatWqpYMnL2jO9lTF7T+l1PRM/fkvVIukgCq+Cm7or6FtAlS/RvncRTj+/v7auHEj+4QCcAsUSsBFvfjii5oyZYrWrVun9u3bmx2n1EhPT1dgYKAaNL9btQaM0aZDafKwWmR35P1X6ZXX299eVUfnT9LBXVu1Y8cOnpsE4DYolIALWrhwofr376/3339fzz//vNlxSp3Xpi/XjD2Z8vDylqGCT1dbDIccthw91ryCXh/avQgTAkDxolACLubw4cNq0aKFunbtqujoaJ6/K2afxB3UeysPSIYh3dCfvSHJojE9G2hkcH1nxwMAU1AoARdy+fJltW/fXufOndOOHTtUqVIlsyOVKlEJqRobk+K0670TGqhBQZzZDcD1/X35IYAS67nnntPevXu1bds2ymQxO5aeqfGL9l71tctHk3UyctxVX7tp+HvyqXXHVV97fdFetbu9mmpX8XVaTgAwA4UScBFz5szRF198oa+++krNmjUzO06pMy42RbZ8Ft5IUvmW98r75gZ/+Zpn5ZvzfL/NYWhcbIpmPdbGKRkBwCwUSsAF7Nu3TyNGjNDw4cP1+OOPmx2n1Dl48oI2Hjp9zff51G4kvzs6FPi6doehjYdO69CpC6rnX74wEQHAVJyUA5RwFy9e1P3336/bbrtNn3/+OYtwTDBne6o8rAX7c3dkZcpw2At8bQ+rRbO3pd5oNAAoERihBEowwzD01FNP6dixY0pISJCfn5/ZkUqluP2n8t1n8oq07z+SkX1JsljlU7uRKgc/Kp+b81/JbXcYijtwSm+okbPiAkCxo1ACJdiXX36pOXPmKDIyUnfccfWFHShaF7NsSk3PzP9NHl7ybdhOZeu2ktW3onJOp+p8fKxOznlJNw2bLO+bbs/346lpmcrIsl31mEYAcAVsGwSUUDt37lS7du30xBNP6JNPPjE7Tqm199dzuuf/Nl3353LO/KoTU/8pn9qNVGPQhGu+f+k/O6hRzYo3EhEATMczlEAJdObMGQ0cOFBNmzbV+++/b3acUi3b5rihz3lVrqmy9dvocmpygZ6pvNH7AEBJwPwKUMIYhqGHH35YZ8+e1dq1a+Xj42N2pFLN2/PGf+/2rFBNsttk5GTJ4pP/XpOFuQ8AmI1CCZQw7733nhYtWqTFixerTp06Zscp9aqXka4cl3i9bGd/k8XTWxbvMvm+zyKpTlUWXAFwXfxKDJQgGzdu1Msvv6yxY8eqb9++Zscptc6cOaOZM2eqf//+urXWTco5cyLf99szz/3ta9knDyvzYLzK1GkuiyX/v2oDqvqyIAeAS2NRDlBCnDx5Us2bN1eDBg20evVqeXpSMIrTqVOntGDBAkVHR2vt2rWy2Wxq166dQkND9UuNdlr0w5k8tw76LWKcrF7e8ql1539XeR/TxaTlktVTNw9/T17Vaud5Xw+rRcPb3Ko3+rFtEADXxU8soASw2+0aMmSIHA6HIiMjKZPF5NixY4qNjVV0dLQ2bfpjJXeXLl304YcfasCAAapZs6akP07Kid27Ic/r+DZoq4y963Q+foEc2Zny8K0o3wbtVLFDuLwq18w3g91haFjbAOd9UwBgAn5qASXAm2++qXXr1mn16tW6+ea8z35G4R06dEgxMTGKjo5WfHy8vLy81KNHD3311Ve67777VK1atb99pn6N8upYr5q2HE676ihlhVb9VKFVv+vO4mG1qF3dqhy7CMDlMeUNmGz58uXq06ePJk6cqHHjxpkdx+0YhqG9e/fmlsjk5GSVLVtWvXv3VmhoqPr27auKFa+9/+Ox9Ex1n7JeWU7c3sfH06rVz3VW7Sr5rwAHgJKOQgmY6NixY2revLnatGmjxYsXy2plnZwzGIahnTt35pbIAwcOqHz58rr33nsVFhamXr163dAxllEJqRobk+K0nO+EBmpQENPdAFwfhRIwSXZ2tjp37qxff/1ViYmJqlq1qtmRXJrD4dCWLVsUHR2tmJgYpaamqmrVqrrvvvsUFhambt26OWVPz0/iDuq9lQcKfZ0XejbUM8H1Cn0dACgJeIYSMMlLL72knTt3atOmTZTJG5STk6P169crOjpaCxYs0G+//aabb75ZAwYMUFhYmDp16uT0BU4jg+urWjkfjV+0VzaHkefK76vxsFrkabVoQr9GjEwCcCuMUAImiI6O1v3336+PP/5Y//znP82O41KysrK0atUqRUdHa9GiRUpPT9ett96qsLAwhYWFqW3btsXy6MCx9EyNi03RxkOn5WG15Fssr7zesV41vT0gkGcmAbgdCiVQzA4ePKiWLVsqJCREc+fOlcVy/SewlDYXL17U8uXLFR0draVLl+rChQtq2LBhbols3ry5aX+OB09e0JztqYo7cEqpaZn681+oFv2xaXlwA38NaxvAam4AbotCCRSjS5cuqW3btrp8+bISEhJUoUIFsyOVWGfPntXixYsVExOj5cuX6/Lly2rWrJlCQ0MVFhamu+66y+yIf5ORZdORtAxl2xzy9rSqTlU/TsABUCpQKIFi9PjjjysiIkLbt29XYGCg2XFKnN9//10LFixQTEyM1qxZo5ycHLVt21ahoaEKDQ3V7bffbnZEAMBV8KszUEymT5+uqVOnatq0aZTJPzl+/LhiYmIUExOjDRv+OI2mU6dOev/99zVgwADdcsstJicEAFwLI5RAMUhJSVGbNm00ZMgQffPNN2bHMd3hw4dzt/fZtm2bvLy81K1bN4WFhalfv37y9/c3OyIA4DpQKIEidv78eQUFBals2bLaunWrypYta3YkU+zbty+3RO7evVtlypRRSEiIwsLC1LdvX1WqVMnsiACAG8SUN1CEDMPQE088oRMnTmjnzp2lqkwahqFdu3bllsgff/xR5cqVU9++ffXKK6+od+/eN3RaDQCg5KFQAkXok08+0Xfffaf58+erfv36Zscpcg6HQ9u2bcstkUeOHFHlypV13333afLkyerevbvKlCljdkwAgJMx5Q0Uke3bt6tjx4565plnNGXKFLPjFBmbzab169crJiZGsbGxOnHihGrUqJF7Wk3nzp3l5eVldkwAQBGiUAJFIC0tTS1atFDNmjW1fv16eXt7mx3JqbKysrR69WrFxMRo4cKFSktLU0BAQO4ekXfffbc8PDzMjgkAKCYUSsDJHA6H+vbtq/j4eO3atUu1a9c2O5JTZGRkaPny5YqJidGSJUt0/vx5NWjQQGFhYQoNDVXLli059QcASimeoQScbNKkSVq+fLm+//57ly+T586d05IlSxQdHa3ly5fr0qVLatKkiUaPHq3Q0FA1atSIEgkAYIQScKa4uDh1795dr7zyiiZMmGB2nBty+vRpLVy4UNHR0Vq9erVycnLUunXr3JHIevXqmR0RAFDCUCgBJzlx4oSaN2+uxo0ba8WKFS71DOGvv/6q2NhYRUdHa/369TIMQx07dlRYWJgGDBjg8iOtAICiRaEEnMBms6lbt246dOiQdu3a5RInvfz888+KiYlRdHS0tm7dKk9PT3Xt2lVhYWG67777VKNGDbMjAgBcBM9QAk7w2muvafPmzVq3bl2JLpM//PBDbonctWuXfHx81KtXL82YMUP33nuvKleubHZEAIALYoQSKKQlS5bo3nvv1bvvvqsXXnjB7Dh/YRiGdu/enbvR+A8//CA/Pz/dc889CgsLU58+fVSuXDmzYwIAXByFEiiEI0eOqEWLFurUqZNiY2NLxIpnh8Oh7du355bIn3/+WZUqVVK/fv0UFhamHj16lKojIAEARY9CCdygrKwsdejQQWlpaUpMTFSlSpVMy2Kz2bRx40ZFR0crNjZWv/76q/z9/dW/f3+FhYUpODiY02oAAEWGZyiBGzR69GglJydry5YtppTJ7OxsrVmzRtHR0Vq4cKFOnz6t2rVra+DAgQoNDVX79u1daqU5AMB1USiBGxAVFaVPP/1Un3/+uVq2bFls983MzNSKFSsUHR2txYsX6/z586pXr54ee+wxhYaGKigoqERMuwMAShemvIHr9OOPP6pVq1bq37+/Zs2aVeQF7vz581q6dKmio6O1bNkyZWZmqnHjxrkbjQcGBlIiAQCmolAC/5WRZdORtAxl2xzy9rSqTlU/+fn8dRA/IyNDbdq0kcPhUHx8fJGtkE5LS9PChQsVExOjVatWKTs7W61atcotkQ0aNCiS+wIAcCOY8kapdvDkBc3Znqq4/aeUmp6pP/92ZZEUUMVXwQ39NbRNgOr5l9PTTz+tn3/+WQkJCU4vkydOnFBsbKxiYmK0bt06ORwOdejQQe+8845CQ0MVEBDg1PsBAOAsjFCiVDqWnqlxsSnaeOi0PKwW2R15/2dw5fXbyl7Wpg+e0YzPPtCQIUOckuPIkSOKiYlRTEyMtmzZIqvVqq5duyo0NFT9+/fXTTfd5JT7AABQlBihRKkTlZCq8Yv2yvbfEplfmfzz64czvFT7qa9krd+sUPffv3+/oqOjFR0drcTERPn4+Khnz5769ttv1a9fP1WpUqVQ1wcAoLgxQolS5ZO4g3pv5YFCX2dMzwYaGVy/QO81DEPJycm5JXLfvn3y8/NTnz59FBoaqnvuuUfly5cvdCYAAMxCoUSpEZWQqrExKU673juhgRoUdPXnGh0OhxISEnJPq/npp59UsWJF9evXT6GhoerVqxen1QAA3AaFEqXCsfRMdZ+yXlk2R57vyfrtkM5tilDWL/tk2HLkWamGyjULUYVW/a76fh9Pq1Y/11m1q/hKkux2uzZu3Jj7TOTx48dVvXr1v5xW4+3tXSTfHwAAZuIZSpQK42JTcp+ZvJpLPyfq1PwJ8q5xuyq2GyyLdxnZzv4m+4XTeX7G5jA0NjpJw2ufU0xMjBYsWKDff/9dtWrVUmhoqMLCwtShQwdOqwEAuD1GKOH2Dp68oB4fbsjzdUdWpo5/NUI+te5U9QEvy2KxXtf1j3/9lAIqeissLExhYWEKCgqS1Xp91wAAwJUxQgm3N2d7ar5bA2XsWydHxllV7vSgLBarHNmXZfHyLlCxtMjQiHdn65NHOnFaDQCg1KJQwu3F7T+V79ZAl4/slsXHV7aLaToVM1G29OOyeJWRX+NgVen2hCyeeT/3aMiiPekOyiQAoFSjUMKtXcyyKTU9M9/35KT/Kjns+j363yrXpKfKdH5Il1NTdGHnYjkuZ6j6fS/m+/nUtExlZNn+dkwjAAClBT8B4daOpmXoWg8JGzmXZeRkqVzz3qrS40lJkm/DdjLsObq4e7lyOg6VV5VaeX9e0pG0DDWqWdF5wQEAcCGsHIBby85nm6Arrkxp+93Z+S9f97uriyQp6/iPTrkPAADuikIJt+btee1/xT3KVf3jf/0q/fXrfn+MODouX3TKfQAAcFf8FIRbq1PVT9daLuN90+2SJNuFtL983XYhXZLk4Zv/VLblv/cBAKC0olDCrfn5eCrgvyfZ5PmeOzpKki4mr/zL1y8mr5SsHvIJCMz38wFVfVmQAwAo1fgpCLcX3NBfs7YfzXPrIO+bbpdfkx7KSF6l3x0OlQlorMupKcr8cZMq3D1QnuWr5nltD6tFwQ38iyo6AAAugZNy4PaudVKOJBl2m85t/U4Xk1fLfjFdnhWrq3yLvqoQdN81r7/6uU6q51/eWXEBAHA5jFDC7e3duka2X36UR827ZLFe/Vxti4enKnUYokodhhT4uh5Wi9rVrUqZBACUejxDCbeVkZGhESNGaMCAAWp8aY/KeDn39ydPq0VvD8j/+UoAAEoDCiXcUkJCgpo3b645c+boyy+/1LJ5s/TmfY2deo8J/Rqp9jUW/AAAUBpQKOFW7Ha73nrrLbVr104VK1bUrl27NGLECFksFg0OCtCYng2ccp8XejbUoKAAp1wLAABXx6IcuI0jR45o+PDh2rx5s15++WW98cYb8vLy+tv7ohJSNX7RXtkcRp4rv6/Gw2qRp9WiCf0aUSYBAPgTCiVcnmEYmjNnjp555hlVrlxZs2bNUseOHfP9zLH0TI2LTdHGQ6flYbXkWyyvvN6xXjW9PSCQaW4AAP4HhRIu7cyZM3r66acVFRWloUOH6tNPP1XFivmfbPNnB09e0JztqYo7cEqpaZn6838MFv2xaXlwA38NaxvAam4AAPJAoYTLWrdunR588EGdP39en3/+ucLDwwt1vYwsm46kZSjb5pC3p1V1qvpxAg4AAAVAoYTLyc7O1muvvabJkyerY8eOmjlzpm699VazYwEAUGox/AKX8sMPP2jo0KFKSUnRf/7zH40ZM0YeHlffrBwAABQPtg2CSzAMQ5999platmypzMxMbdu2TS+99BJlEgCAEoBCiRLv5MmTuvfee/XMM8/ooYceUmJiolq2bGl2LAAA8F9MeaNEW7p0qR599FEZhqFFixbp3nvvNTsSAAD4H4xQokTKzMzU008/rb59+6pVq1ZKSUmhTAIAUEIxQokSJzExUUOHDtWRI0f06aef6h//+IcsFovZsQAAQB4YoUSJYbfb9c4776ht27YqW7asEhMT9fTTT1MmAQAo4SiUKBFSU1PVrVs3vfzyy3ruuee0bds23XnnnWbHAgAABcCUN0wXFRWlp556SuXLl9eaNWsUHBxsdiQAAHAdGKGEac6dO6fhw4crPDxcISEhSk5OpkwCAOCCGKGEKTZt2qRhw4YpPT1dM2fO1LBhw3hWEgAAF8UIJYpVTk6OXn31VXXu3Fm33HKLkpKSNHz4cMokAAAujBFKFJsDBw5o2LBhSkxM1JtvvqmxY8fK05N/BQEAcHX8NEeRMwxD33zzjf71r3+pZs2a2rJli1q3bm12LAAA4CRMeaNInT59WgMGDNCIESM0ZMgQ7dq1izIJAICbYYQSRWbFihV6+OGHlZOTo5iYGA0YMMDsSAAAoAgwQgmnu3Tpkp599lmFhISoSZMmSk5OpkwCAODGGKGEUyUnJ2vIkCE6dOiQPvroI40cOVJWK7+3AADgzvhJD6dwOBz64IMPFBQUJA8PD+3YsUOjRo2iTAIAUArw0x6Fdvz4cfXs2VOjR4/WyJEjtX37djVu3NjsWAAAoJgw5Y1CmT9/vkaMGKGyZctq1apV6t69u9mRAABAMWOEEjfkwoULeuSRRzRw4EAFBwcrOTmZMgkAQCnFCCWu29atWzVs2DCdPHlSU6dO1SOPPMLRiQAAlGKMUKLAbDab3njjDXXs2FH+/v5KSkrSo48+SpkEAKCUY4QSBfLTTz9p2LBhio+P12uvvaZXX32Vc7gBAIAkCiWuwTAMTZ8+XaNGjZK/v782bdqku+++2+xYAACgBGHKG3lKS0vTwIED9eijj+r+++/X7t27KZMAAOBvGKHEVa1evVoPPfSQLl26pHnz5un+++83OxIAACihGKHEX2RlZWn06NHq0aOH7rjjDiUnJ1MmAQBAvhihRK69e/dqyJAh+vHHH/Xee+/pueee4+hEAABwTbQFyDAMffzxx2rZsqVsNpvi4+M1evRoyiQAACgQGkMpd+LECfXu3VvPPvusRowYoR07dqhp06ZmxwIAAC6EKe9SbOHChXr88cfl4eGhZcuWKSQkxOxIAADABTFCWQplZGRoxIgR6t+/v9q1a6eUlBTKJAAAuGGMUJYyCQkJGjp0qI4fP64vv/xSTzzxBEcnAgCAQmGEspSw2+1666231K5dO1WsWFG7du3SiBEjKJMAAKDQGKEsBY4cOaLhw4dry5YtevnllzV+/Hh5eXmZHQsAALgJCqUbMwxDc+bM0TPPPKPKlStr3bp16tixo9mxAACAm2HK202dPXtWQ4YM0fDhw9WvXz8lJSVRJgEAQJFghNINrV+/XsOHD9f58+cVGRmpwYMHmx0JAAC4MUYo3Uh2drbGjh2r4OBg1a1bV8nJyZRJAABQ5BihdBM//PCDhg4dqj179ug///mPxowZIw8PD7NjAQCAUoARShdnGIY+++wztWzZUpmZmdq2bZteeuklyiQAACg2FEoXdvLkSd1777165pln9PDDDysxMVEtWrQwOxYAAChlmPJ2UUuXLtWjjz4qwzC0ePFi9e3b1+xIAACglGKE0sVkZmbqmWeeUd++fdWqVSulpKRQJgEAgKkYoXQhu3bt0pAhQ3TkyBF9+umn+sc//sHRiQAAwHSMULoAu92ud955R23atFHZsmWVmJiop59+mjIJAABKBAplCZeamqpu3brp5Zdf1vPPP69t27bpzjvvNDsWAABALqa8S7CoqCg99dRTKl++vNauXasuXbqYHQkAAOBvGKEsgc6dO6fhw4crPDxcISEhSk5OpkwCAIASixHKEmbTpk0aNmyY0tPTNXPmTA0bNoxnJQEAQInGCGUJkZOTo1dffVWdO3fWLbfcoqSkJA0fPpwyCQAASjxGKEuAgwcPaujQodq1a5cmTJigl156SZ6e/KMBAACugRFKExmGoa+//lrNmjXT2bNntWXLFr3yyiuUSQAA4FIolCY5ffq0BgwYoBEjRmjo0KFKTExUUFCQ2bEAAACuG0NhJlixYoUefvhh5eTkKDY2Vv379zc7EgAAwA1jhLIYXbp0Sc8++6xCQkLUtGlTpaSkUCYBAIDLY4SymCQnJ2vIkCE6dOiQPvroI40cOVJWK30eAAC4PhpNEXM4HPrggw8UFBQkDw8P7dixQ6NGjaJMAgAAt0GrKULHjx9Xr169NHr0aI0cOVLx8fFq3Lix2bEAAACciinvIhIdHa0RI0aoTJkyWrVqlbp37252JAAAgCLBCKWTXbhwQY888ojuv/9+BQcHKzk5mTIJAADcGiOUTrR161YNGzZMp06d0rfffquHH36YoxMBAIDbK/UjlBlZNu399Zx2pZ7R3l/PKSPLdt3XsNlseuONN9SxY0f5+/tr9+7deuSRRyiTAACgVCiVI5QHT17QnO2pitt/SqnpmTL+9JpFUkAVXwU39NfQNgGqX6N8vtf66aefNGzYMCUkJOi1117j6EQAAFDqWAzDMK79NvdwLD1T42JTtPHQaXlYLbI78v7Wr7zesV41vT0gULWr+P7ldcMwNGPGDP3zn/+Uv7+/Zs+erbvvvruovwUAAIASp9QUyqiEVI1ftFc2h5FvkfxfHlaLPK0WvdmvkQYHBUiS0tPT9eSTT2r+/Pl65JFH9NFHH6l8+fxHMgEAANxVqSiUn8Qd1HsrDxT6OmN6NtAd9qN66KGHdOnSJX311Ve6//77nZAQAADAdbl9oYxKSNXYmBSnXS/t+4/UprpD06dP1y233OK06wIAALgqty6Ux9Iz1X3KemXZHH977fSSKcrYsybPz9Z6Zro8y1f7y9cMw5CnxdDa0cG6tVo5p+cFAABwRW69HHlcbIpseTwvWb55iMrUafY/XzWUvuJTeVas8bcyKemPbYCsVr26cK9mPdbG+YEBAABckNsWyoMnL2jjodN5vu5T60751LrzL1+7fGyvjJws+d3VJc/P2R2GNh46rUOnLqiePwtxAAAA3HZj8znbU+Vhvb6NxTP2rZdkkd9dnfN9n4fVotnbUguRDgAAwH24baGM23/qurYHMuw2Zf64ST633CnPSjXyfa/dYSjuwKnCRgQAAHALblkoL2bZlJqeeV2fufRzohyXzuc73f1nqWmZN3RMIwAAgLtxy0J5NC1D17t0PWPfesnqKd87OxTo/YakI2kZ150NAADA3bhlocy+yjZB+XFkX9Klg9tU9rbm8ihbocjuAwAA4I7cslB6e17ft5V5YNsfq7sbdSnS+wAAALgjt2xEdar66XrWd2fsWyeLd1mVrV/wvSUt/70PAABAaeeWhdLPx1MBVXwL9F575jldPrJbvvXbyupVpsD3CKjqKz8ft93GEwAAoMDcslBKUnBD/wLtQ5nxwwbJYb+u6W4Pq0XBDfwLkQ4AAMB9uG2hHNomoED7UGbsXSerb6WrHMOYN7vD0LC2AYVIBwAA4D7cds62fo3y6livmrYcTsu3WN784PvXdV0Pq0Xt6lbl2EUAAID/ctsRSkl6e0CgPK/z+MVr8bRa9PaAQKdeEwAAwJW5daGsXcVXb/Zr5NRrTujXSLULuOAHAACgNHDrQilJg4MCNKZnA6dc64WeDTUoiGcnAQAA/sxiGMb1nlLokqISUjV+0V7ZHEaBFutc4WG1yNNq0YR+jSiTAAAAV1FqCqUkHUvP1LjYFG08dFoeVku+xfLK6x3rVdPbAwKZ5gYAAMhDqSqUVxw8eUFztqcq7sAppaZl6s9/ABb9sWl5cAN/DWsbwGpuAACAayiVhfLPMrJsOpKWoWybQ96eVtWp6scJOAAAANeh1BdKAAAAFI7br/IGAABA0aJQAgAAoFAolAAAACgUCiUAAAAKhUIJAACAQqFQAgAAoFAolAAAACgUCiUAAAAKhUIJAACAQqFQAgAAoFAolAAAACgUCiUAAAAKhUIJAACAQqFQAgAAoFAolAAAACgUCiUAAAAKhUIJAACAQqFQAgAAoFAolAAAACgUCiUAAAAKhUIJAACAQqFQAgAAoFAolAAAACgUCiUAAAAKhUIJAACAQqFQAgAAoFAolAAAACgUCiUAAAAKhUIJAACAQqFQAgAAoFAolAAAACgUCiUAAAAKhUIJAACAQqFQAgAAoFAolAAAACgUCiUAAAAKhUIJAACAQvl/v0s+uIqKLNEAAAAASUVORK5CYII=", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Creamos un gráfico vacío\n", "G = nx.Graph()\n", "\n", "# Añadimos nodo y arcos al gráfico.\n", "G.add_edges_from([(1, 2), (1, 3), (2, 3), (3, 4), (3, 5), (4, 5), (4, 6), (5, 6), (6, 7)])\n", "\n", "nx.draw(G, with_labels = True)\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "8958a700-50bd-4083-8887-79590c11f4ec", "metadata": {}, "source": [ "```{index} dnx.maximum_independent_set, maximum_independent_set\r\n", "```\n", "\n", "Procedemos a resolver este problema, bien entendido que Ocean SDK ya tiene el método maximum_independent_set ." ] }, { "cell_type": "code", "execution_count": 3, "id": "debba10d-ae50-4780-8c1f-2e8b018189f1", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Maximum independent set size found is 3\n", "[1, 4, 7]\n" ] } ], "source": [ "sampler = EmbeddingComposite(DWaveSampler())\n", "\n", "\n", "# Encontrar el maximum independent set, S\n", "S = dnx.maximum_independent_set(G, sampler=sampler, num_reads=10, label='Ejemplo - Antenna Selection')\n", "\n", "# Imprimimos la solución para el usuario final\n", "print('Maximum independent set size found is', len(S))\n", "print(S)" ] }, { "cell_type": "code", "execution_count": 4, "id": "6be7c562-aec1-4c1f-b973-1f6a809b8f4d", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "
" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" }, { "data": { "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Visualizamos el resultado\n", "k = G.subgraph(S)\n", "notS = list(set(G.nodes()) - set(S))\n", "othersubgraph = G.subgraph(notS)\n", "pos = nx.spring_layout(G)\n", "plt.figure()" ] }, { "cell_type": "code", "execution_count": 6, "id": "23c594ca-16cd-468b-b635-e9049df1fcf3", "metadata": {}, "outputs": [ { "data": { "image/png": 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b4pM3NXK5TEOhgQPN8kFWDYQUr9erq6++WgcOHNAXX3yhNm3a2F0SAKAegX5/N38YOF5Zmdl9sKLC7DWQlERnwRD397//Xb169dLw4cM1c+ZMu8sBANQj0O/v5nlMUJe4OKlnT+kXvzC/CQIh78ILL9Qf//hHzZo1S3l5eXaXAwCwgL1hAGHpN7/5jfr166fhw4frwIF6m0cDAMIAYQANFhUVpQULFqisrExjxoyxuxwAQBMRBtAonTp10qxZs7R06VItX77c7nIAAE1AGECjDRkyRLfffrsyMzO1d+9eu8sBADQSYQCN5nK5lJubqxYtWig9PV0BLEwBAIQgwgCapEOHDpo/f77WrFmjefPm2V0OAKARCANosltuuUUZGRkaP368CgsL7S4HANBA9jYdQsQoLS3VZZddpo4dO2rdunWKjo4+5Rh6TAFA8wqPpkOIGG3bttWiRYv08ccfa+rUqTXvFxRI48aZL/74eKlXL6l3b/M7Pt68P26cOQ4AYA/uDMBSEyZM0PTp07Vq1WZNm9Zd771n9p3yeus+p/rz/v2lOXOkxMTmqxcAIll47E2AiFNeXq5u3Z7T3r2PKjo6Rl6vK+Bz3W7zM2OG2egSANA0PCaALaZOjdWePU/J52tYEJDM3YHycikjQ8rJCVKBAIBTEAZgGY9Hys6uftWwIHCy7Gxp/vwmlwQACABhAJYoLpbGjq39sx49pOXLpcJC6aefpH37pHXrpF/9qv4xx4wx4wIAgoswAEuMGlX3JMFzz5XatpUWLZIeekh65hnz/ttvm0cCdfF6zbgAgOBiAiGarKBASk5u2DlRUdLGjVJsrNS9u//x/R0DADgVEwjRbGbPNqsAGqKqSvrmG6ldu/qPc7ul3NxGlwYACEAD/y8cONWaNfX3EajWurXUqpWUkCANGiQNHCgtW1b/OV6v9M471tQJAKgdYQBNUloqFRUFduzUqdLo0ebPlZXSm2+aSYL+FBaaVsa0LgaA4OAxAZqksFAKdOfiadOkfv2k++83/9qPjpZatvR/ns9n9jQAAAQHYQBNUlER+LF//7u0dq20ZIn0X/9l/qX/9tvWXwcA0DCEATRJTEzjz12xQrrqKumCC4J7HQBA/QgDaJKkJMnVyGaDrVqZ3wkJ9R/ncpnrAACCgzCAJomLk7p2rf+YM8449T2328wdOHTI//bF3boxeRAAgonVBGiy1FTTC6Cu5YVz5kjx8dKHH0r/+pd01lnSPfeYRkLjx5sWxXVxu80SRABA8NCBEE3mrwPh4MHSiBHSJZdIp59uliNu3Gi2Kg5kAiEdCAGgcQL9/iYMwBIDBkj5+YE1HwqU2y2lpEh5edaNCQBOQjtiNKs5cxrektgft9uMCwAILsIALJGYaG77W2nmTDMuACC4CAOwTHq6NHlyU0cxT61ycsw8AwBA8BEGYKlJk6R588zWxA19bBAd7ZNUrq5dn9Vvf0vLQQBoLoQBWC493awASEkxr/2FgurP+/Z1adWqv2v37qc0bty44BYJAKhBGEBQJCaaVQDbtkmZmbV3KqzuLJiZacJDXp506609lZubq7lz52ru3Ln2FA8ADsPSQjSbsjKz+2BFhdlrICmp7s6Cv/nNb+TxeLRu3Tr16dOneQsFgAhBnwGEtSNHjqhv374qKirSxo0bdfbZZ9tdEgCEHfoMIKy1bNlSK1askMvl0u23364jR47YXRIARCzCAELWWWedpZUrV+qzzz7TQw89ZHc5ABCxCAMIab1799asWbM0e/ZseTweu8sBgIjEroUIeRkZGdq4caMefPBBXXLJJfrFL35hd0kAEFG4M4CwMH36dF1xxRW67bbb9O2339pdDgBEFMIAwkJMTIxWrFihyspK3XHHHUwoBAALEQYQNjp27KiVK1dqw4YNGj9+vN3lAEDEIAwgrPzyl7/UjBkzNGvWLC1YsMDucgAgIjCBEGFn5MiR+vzzz5WZmamLL75YV155pd0lAUBY484Awo7L5dLMmTPVs2dPpaWl6bvvvrO7JAAIa4QBhKWYmBitXLlSXq9Xd955p44ePWp3SQAQtggDCFvnnHOOVqxYoY8//liPPPKI3eUAQNgiDCCsXXPNNZo+fbpefPFFLV682O5yACAsMYEQYS8zM1MbN27UyJEjlZycrCuuuMLukgAgrHBnAGHP5XJp1qxZuvTSS5WWlqZ9+/bZXRIAhBXCACJCbGys3nzzTVVUVDChMFSVlUmbN0sbNpjfZWV2VwTgGMIAIkanTp30xhtvaP369ZowYYLd5UCSCgqkceOkpCQpPl7q1Uvq3dv8jo83748bZ44DYBvCACLKddddpxdeeEHTpk3TK6+8Ync5zlVcLA0YICUnS7m5UmGh5POdeIzPZ97PzTXHDRhgzgPQ7AgDiDgPPvighg4dqoyMDG3atMnucpzH45F69JDy881rr7f+46s/z88353k8wa0PwCkIA4g4LpdLubm5Sk5OVlpamvbv3293Sc6RkyNlZEjl5f5DwMm8XnNeRoYZB0CzIQwgIrVq1UqrVq3S4cOHNXjwYHkb+sWEhvN4pOxsa8bKzpbmz7dmLAB+EQYQsTp37qzly5dr3bp1mjhxot3lRLbiYmns2IAPz5HkknRxfQeNGcMcAqCZEAYQ0W644QZNnTpVU6dO1dKlS+0uJ3KNGhXwY4Hdkp6V1MbfgV6vGRdA0Ll8vpOn+J6qpKRECQkJOnjwoOLj45ujLsAyPp9PQ4cOrdnHoGfPnnaXFFkKCsxqgADdJWmfpEpJ+yV9Gcj43bs3ujzAyQL9/ubOACKey+XSnDlzdNFFFyktLU0//PCD3SVFltmzJXdgnc0/lLRC0rRAx3a7zdJDAEFFGIAjVE8oLC0t1V133cWEQiutWRPQI4JKSWMlpUu6JNCxvV7pnXcaXxuAgBAG4Bjnnnuuli9frvz8fGVlZdldTmQoLZWKigI6dLakXZKeaeg1CgtpXQwEGWEAjtK3b19NmTJFU6ZM0bJly+wuJ/zV1lmwFj9IelzSY5LOaOg1fD5p586G1wYgYIQBOM7DDz+sIUOGaPjw4dqyZYvd5YS3ioqADsuW1F7mMUEwrwOgcQgDcByXy6V58+bpggsuUFpamn788Ue7SwpfMTF+D/la0lxJ4yTtkfSPYz/lko4e+7Pf/wIBXAdA4xEG4EitW7fWqlWrdPDgQd19992qrKy0u6TwlJQkuVz1HvIvSVUyYSDxuJ8NknYc+/PT9Q3gcpnrAAgawgAc67zzztOyZcv0l7/8RdlWtdF1mrg4qWvXeg+5WNKqWn6SJXU59ucR9Q3QrZu5DoCgIQzA0fr166fnn39ef/jDH/TGG2/YXU54Sk2tt89AB0m31vLTQVLbY3+uc6mh2y0NHGhNnQDqRBiA4/32t7/VXXfdpWHDhmnr1q12lxN+Ro9u+A6FgfJ6pczM4IwNoAZhAI7ncrnk8XiUlJSktLQ0HThwwO6SwkuPHlL//gF3Iaz2gfy0Ina7zbi0IgaCjjAASGrTpo1WrVqlH3/8UUOGDGFCYUPNmdPgMOCX223GBRB0hAHgmK5du+r1119XXl6eHn/8cbvLCS+JidKMGdaOOXOmGRdA0BEGgOMMGDBAzz33nJ599lmtXLnS7nLCS3q6NHmyNWPl5Egj6l1jAMBChAHgJL/73e90xx13aOjQodq2bZvd5YSXSZOkefOk2NiGPzZwu815Ho/E3hFAsyIMACdxuVx6+eWX1bVrV6Wlpenf//633SWFl/R0qaBASkkxr/2FgurPU1LMedwRAJodYQCoRVxcnFatWqV9+/bpnnvuUVVVld0lhZfERCkvT9q2zSwNrK1TYXVnwcxMEwLy8pgjANjE5fP533KspKRECQkJOnjwoOLj45ujLiAkvPvuu0pNTVV2draefrreprnwp6zM7D5YUWH2GkhKorMgEGSBfn9bvBYIiCw333yzcnJylJWVpV69eiktLc3uksJXXJzUs6fdVQCoBY8JAD8mTpyo2267Tffff7+++uoru8sBAMsRBgA/XC6XFi5cqHPPPVe33nqrDh48aHdJAGApwgAQgOoJhd99953uvfdeJhQCiCiEASBA559/vpYuXao///nPTCYEEFEIA0ADpKam6plnntFTTz2lP/3pT3aXAwCWIAwADfToo48qLS1N9957r7Zv3253OQDQZIQBoIGioqK0aNEiderUSWlpaSopKbG7JABoEsIA0Aht27bV6tWrtWfPHt1///1MKAQQ1ggDQCNdcMEFevXVV/XWW28pJyfH7nIAoNEIA0AT/OpXv9JTTz2lJ554Qv/7v/9rdzkA0CiEAaCJsrOzNWjQIN1zzz3asWOH3eUAQIMRBoAmioqK0uLFi3X22Wfr1ltvVWlpqd0lAUCDEAYAC8THx2v16tXavXu3hg4dyoRCAGGFMABY5KKLLtIrr7yiVatW6bnnnrO7HAAIGGEAsNCgQYP0xBNP6LHHHtOaNWvsLgcAAkIYACz2+OOP65ZbbtGQIUO0c+dOu8sBAL8IA4DFoqKi9Morr+jMM89kQiGAsEAYAIIgISFBq1ev1q5du/TAAw/I5/PZXRIA1IkwAARJ9+7dtXjxYq1cuVLPP/+83eUAQJ0IA0AQpaWlKTs7W1lZWXr33XftLgcAakUYAILsySef1MCBA3X33XersLDQ7nIA4BSEASDIoqOj9eqrr6pDhw669dZbVVZWZndJAHACwgDQDNq1a6fVq1eruLhYI0aMYEIhgJBCGACaSXJyshYtWqTly5drypQpdpcDADUIA0Azuu2225SVlaVHH31UeXl5dpcDAJIIA0Cze/rppzVgwADdddddKioqsrscACAMAM0tOjpaS5cu1Wmnnaa0tDT99NNPdpcEwOEIA4ANTjvtNK1evVqFhYVKT09nQiEAWxEGAJtccsklWrBggV5//XX9z//8j93lAHAwwgBgozvuuEO///3vNWHCBP3lL3+xuxwADkUYAGyWk5Ojfv36afDgwSouLra7HAAORBgAbBYdHa3XXntNCQkJ+vWvf61Dhw7ZXRIAhyEMACGgffv2Wr16tXbs2KGRI0cyoRBAsyIMACHi0ksv1fz58/Xqq69q2rRpdpcDwEHcdhcA4D/uuusubdq0Sb/73e902WWXqW/fvnaXBMABuDMAhJhnn31WKSkpGjx4sHbt2mV3OQAcgDAAhBi3263XX39dcXFx+vWvf63Dhw/bXRKACEcYAELQ6aefrlWrVumrr75iQiGAoCMMACGqZ8+e8ng8euWVVzRjxgy7ywEQwZhACISwIUOGaOPGjRo/frwuu+wyXX/99XaXBCACuXwB3H8sKSlRQkKCDh48qPj4+OaoC8AxXq9XN910k7Zu3arPP/9cXbp0qfW4sjJp506pokKKiZGSkqS4uGYuFkBICfT7m8cEQIhzu91atmyZWrdufcqEwoICadw488UfHy/16iX17m1+x8eb98eNM8cBQF0IA0AY6NChg958801t27ZNmZmZKiryacAAKTlZys2VCgulk+/x+Xzm/dxcc9yAARJbHwCoDWEACBOXX3655s2bp0WL3Lrookrl55v3vd76z6v+PD9f6tFD8niCWyeA8EMYAMLIrl33SvLo6NFovyHgZF6vVF4uZWRIOTlBKQ9AmCIMAGHC45Gys6tfuZo0Vna2NH9+k0sCECEIA0AYKC6Wxo6t/bPrrzfzA2r7+cUv6h5zzBjmEAAw6DMAhIFRo/zPDZg+XfrssxPf27mz7uO9XjNuXl7T6wMQ3ggDQIgrKJDee8//cR99JK1cGfi4Xq8Z96uvpO7dG18fgPDHYwIgxM2eLbkDjO1xcVJ0dOBju91m6SEAZyMMACFuzRr/jwgkacECqbTUrBh4/33piiv8n+P1Su+80/QaAYQ3HhMAIay0VCoqqv+YI0ekFStMaNi/3/QSeOQR89jgl7+UNm+u//zCQtPKmNbFgHOxNwEQwjZvNq2FG6pbN+lvf5M+/FAaOND/8V98IfXs2fDrAAht7E0ARICKisadV1govfWWlJIiRQXwv/LGXgdAZCAMACEsJqbx537zjTm/TZvgXgdA+CMMACEsKUlyNbLZYNeu0uHDZj5A/Xxyu//RuIsAiAiEASCExcWZL/X6dOhw6nuXXioNGmQaCvmfFbRTl1ySqK5duyojI0Ovv/66vvvuu8aWDCAMsZoACHGpqaYXQF3LC5ctM3cAPv5Y+v57s5pg5Ejp0CFp4sT6x3a7peHDuyg1dbXWrl2rtWvXynNsW8OLL75YN954o2688UZdf/31TB4GIhirCYAQV1AgJSfX/fnYsdI995hHCvHx0r590tq10lNPmYmEgYx/fAfCvXv36v33368JB//85z8VHR2tK6+8siYc9OnTR7GxsU3/ywEIqkC/vwkDQBgYMEDKzw+s+VCg3G6z2qC+vQl8Pp+KiopqgsH777+v/fv3KzY2VldffXVNOLjiiisU3ZDWhwCaBWEAiCDFxeb2f3m5dWPGxpq7AomJgZ9TVVWlrVu31oSDDz/8UGVlZUpISNANN9xQEw66d+8uV2NnPgKwDGEAiDAej5SRYe14I0Y0bYyjR4/qs88+qwkHn3zyiY4cOaKzzjpLffv2rQkH5557rjVFA2gQwgAQgXJypOxsa8bJymr6OCc7dOiQ1q9fXxMONm3aJJ/Pp27dutUEg5SUFJ1xxhnWXxzAKQgDQITyeMykQa+3YXMI3G7zM3Nm0+8IBOrHH3/UunXrasLB9u3bJUmXXnppTTi47rrr1LZt2+YpCHAYwgAQwYqLpVGjpPfeM1/w9YWC6s/795fmzGnYHAGr/etf/zphpcLu3bvldrt11VVX1YSD3r17K4aWiIAlCAOAAxQUSLNnm22ICwtPbDDkcpkNiwYOlDIzT1w+GAp8Pp++/vrrmnDw/vvv68cff1SrVq10zTXX1ISDXr16sVIBaCTCAOAwZWXSzp1m06GYGNN3IJy2Ja6qqtKWLVtOWKlw6NAhnXbaaSesVLjwwgtZqQAEiDAAIKwdOXJEGzZsqLlz8Omnn+ro0aPq2LHjCSsVOnfubHepQMgiDACIKD/99JM++uijmjsHmzdvls/n0/nnn3/CSoXTTz/d7lKBkEEYABDRfvjhB33wwQc14WDHjh1yuVy67LLLasLBtddeq7hwelYCWIwwAMBRdu/eXRMM1q5dqz179sjtdqt379668cYb1bdvX/Xu3VstW7a0u1Sg2RAGADiWz+fTjh07aoJBfn6+Dhw4oNatW+vaa6+tuXPQs2dPRUWxkzsiF2EAAI6prKzU5s2ba8LBRx99pMOHD6t9+/ZKSUmpuXNwwQUXhMVKhXBfOYLmQxgAgDpUVFRow4YNNeFgw4YN8nq96tSp0wkrFc455xy7S61R3VNizRqpqOjUnhJdu0qpqdLo0WZTK0AiDABAwEpLS09YqbBlyxZJ0oUXXlgTDG644Qa1b9++2WsL126TCA2EAQBopP379ys/P78mHOzcuVMul0u9evWqCQfXXHON2rRpE9Q6mroPxYwZUnp68OpD6CMMAIBF/vnPf56wUuHbb79VixYt1KdPn5pwcNVVV6lFixaWXdOqHSonT5YmTWr6OAhPhAEACAKfz6ft27efsFLh4MGDatOmja677rqacHDppZc2eqWCxyNlZFhXs8fTfDtVIrQQBgCgGVRWVmrTpk014WD9+vUqLy9Xhw4dalYq3HjjjerWrVtAKxWKi80EwPLyuo/p1Ut68knpmmuk2FgzoXDuXPNYoDaxsWYCInMInIcwAAA2KC8v16effloTDv7617+qsrJSXbp0OWGlwtlnn13r+QMGSPn5dc8R6N9fevtt6YsvpGXLzDLDbt2kqCjp97+v/Ry3W0pJkfLyLPpLImwQBgAgBJSUlOjDDz+sCQdbt26VJHXv3v2ElQrt2rVTQYGUnFz3WG3bSjt2SB9/LN1++4nLCwNRUBB6W1kjuAgDABCCvv/++xNWKhQVFSkqKkqXX365fL5p+uKLPqqqqn2uwahRptdA9+7S9u1S69bS4cOBhQK3W8rMlF580eK/EEJaoN/f9OEEgGb0s5/9TIMHD9bcuXNVWFio4uJizZ07V+eff742b+5YZxCQpH79pIMHpXPOMWHgp5+kkhLppZdMJ8L6eL3SO+9Y/JdBxODOAACEgNJSKSHBJ5+v7kmGmzeb1sOSNH++9MEH0g03SOPGSa+9Jg0ZUv81XC4THmhd7ByBfn+7m7EmAEAdCgtVbxCQzJd4mzZSbq700EPmvVWrpJYtTRvixx83exbUxeczn/fsaV3diAw8JgCAEFBR4f+Yw4fN79deO/H9pUvN7z59rLkOnIcwAAAhwN8zf0nas8f8/u67E9///nvz+7TTrLkOnIcwAAAhICnJPNOvz8aN5vfJmyl27Gh+79tX//ku13/mHADHIwwAQAiIizPbENdn+XLz++TWwunp0tGjZkJhfbp1Y/IgascEQgAIEampZnJgXd0HN282qwhGjDB9A9atM6sJ7rxTevZZae/eusd2u6WBA4NRNSIBSwsBIET460AomS/1rCzpgQfM44Fdu6RZs6Tp0wMbnw6EzkIHQgAIQ/72JmgM9iZwLjoQAkAYmjPHfHlbye024wJ1IQwAQAhJTKx7K+LGmjmT7YtRP8IAAISY9HRp8mRrxsrJOXX1AXAywgAAhKBJk6R586TY2IY/NnC7zXkej5lsCPhDGACAEJWeblYApKSY1/5CQfXnKSnmPO4IIFCEAQAIYYmJZhXAtm1SZmbtnQqrOwtmZpoQkJfHHAE0DE2HACAM9Oghvfii+XNZmdl9sKLC7DWQlERnQTQNYQAAwkxcHNsQw1o8JgAAwOEIAwAAOBxhAAAAhyMMAADgcIQBAAAcjjAAAIDDEQYAAHA4wgAAAA5HGAAAwOEIAwAAOBxhAAAAhyMMAADgcIQBAAAcjjAAAIDDEQYAAHA4wgAAAA5HGAAAwOEIAwAAOBxhAAAAhyMMAADgcIQBAAAcjjAAAIDDEQYAAHA4wgAAAA5HGAAAwOEIAwAAOBxhAAAAhyMMAADgcIQBAAAcjjAAAIDDEQYAAHA4wgAAAA5HGAAAwOEIAwAAOBxhAAAAhyMMAADgcIQBAAAcjjAAAIDDEQYAAHA4wgAAAA5HGAAAwOHcdhcAAIBjlZVJO3dKFRVSTIyUlCTFxTV7GYQBAACaU0GBNHu2tGaNVFQk+Xz/+czlkrp2lVJTpdGjpR49mqUkHhMAANAcioulAQOk5GQpN1cqLDwxCEjmdWGh+Tw52RxfXBz00ggDAAAEm8dj/pWfn29ee731H1/9eX6+Oc/jCWp5hAEAAIIpJ0fKyJDKy/2HgJN5vea8jAwzTpAQBgAACBaPR8rOtmas7Gxp/nxrxjoJYQAAgGAoLpbGjq3z42GSXPX8/Ku2k8aMCcocAlYTAAAQDKNG1ftYYJSkfie955M0WtJ5ks6p7SSv14ybl2dRkQZhAAAAqxUUSO+9V+8hfY79HG+9pEOS7qnrJK/XjPvVV1L37k0usxqPCQAAsNrs2ZK74f/eXirziGBIfQe53WbpoYUIAwAAWG3NmgavHDgqabmkX8o8JqiT1yu9806jS6sNYQAAACuVlprOgg30/yT9oHoeERyvsNC0MrYIYQAAACvV1lkwAEsltZB0ZyAH+3xmTwOLEAYAALBSRUWDTymT9JakmySdHsTr1IUwAACAlWJiGnzKavlZRWDRdepCGAAAwEpJSWb3wQZ4VVKcpEGBnuBymetYhDAAAICV4uLMNsQB2ifpL5LSJLUO9KRu3cx1LEIYAADAaqmpAfcZWCbJqwY8InC7pYEDG1lY7QgDAABYbfTogPsMvCrpZzq1NXGdvF4pM7ORhdWOMAAAgNV69JD69w/o7sAnkr6TFB3IuG63GdfCVsQSYQAAgOCYM6dRLYnr5XabcS1GGAAAIBgSE6UZM6wdc+ZMM67FCAMAAARLero0ebI1Y+XkSCNGWDPWSQgDAAAE06RJ0rx5Umxswx8buN3mPI9HysoKTn0iDAAAEHzp6VJBgZSSYl77CwXVn6ekmPOCdEegGmEAAIDmkJgo5eVJ27aZpYG1dSqs7iyYmWlCQF5eUOYInMziaY4AAKBePXpIL75o/lxWZnYfrKgwew0kJVnaWTBQhAEAAOwSFyf17Gl3FTwmAADA6QK6M+Dz+SRJJSUlQS0GAABYp/p7u/p7vC4BhYHS0lJJUufOnZtYFgAAaG6lpaVKSEio83OXz19ckFRVVaU9e/aobdu2cjVwj2YAAGAPn8+n0tJSdezYUVFRdc8MCCgMAACAyMUEQgAAHI4wAACAwxEGAABwOMIAAAAORxgAAMDhCAMAADgcYQAAAIf7/wYFYRraDy2cAAAAAElFTkSuQmCC", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Mostramos la solución\n", "# Nota: Los nodos rojos son la solución, los azules no son elegidos.\n", "#solution_name = \"antenna_plot_solution.png\"\n", "nx.draw_networkx(k, pos=pos, with_labels=True, node_color='r', font_color='k')\n", "nx.draw_networkx(othersubgraph, pos=pos, with_labels=True, node_color='b', font_color='w')\n", "#plt.savefig(solution_name, bbox_inches='tight')\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "a78ac897-9260-4f77-839b-73345ce35aab", "metadata": {}, "source": [ "(colocacion_antenas)=\n", "## Colocación de antenas en Alemania.\n", "\n", "Ahora procedemos a resolver el segundo de los problemas planteados. Se trata de lo siguiente.\n", "\n", "Tenemos las ubicaciones (la latitud y la longitud) de las torres de televisión en Alemania (estos datos se pueden descargar desde este enlace ), entonces sabiendo estos datos de lo que se trata es de determinar dónde deberían construirse nuevas torres de televisión para ampliar la cobertura sobre el territorio de alemania, pero con la restricción de que la interferencia entre ellas sea la menor posible.\n", "\n", "Resolveremos este problema utilizando el sampler *LeapHybridCQMSampler* y formulando un problema de tipo Constrained Quadratic Model (CQM) de la manera que vamos a indicar en los siguientes apartados. \n", "\n", "Debemos de tener en cuenta que en lugar de formular este problema como un problema de tipo *independent set problem*, donde no se tolera la interferencia, optimizaremos para encontrar la cantidad mínima de interferencia, ya que **es poco probable que en el mundo real podamos eliminarla por completo**.\n", "\n", "### Resolución del problema.\n", "\n", "Se proporciona un mapa de Alemania con las ubicaciones de 30 torres y se identifican aleatoriamente 100 nuevas ubicaciones potenciales de torres dentro de las fronteras del país (estos datos se pueden descargar desde este enlace ).\n", "\n", "Nuestro objetivo va a ser seleccionar un subconjunto de estas posibles ubicaciones de torres nuevas para que *las distancias por pares entre todas las torres (existentes y nuevas) sean lo más grandes posible*. En el código, hacemos esto calculando primero todas las distancias por pares. Hay que tener en cuenta que si simplemente sumamos estas distancias brutas, podríamos terminar con gran una variedad de distribuciones de distancia. Por ejemplo, podríamos tener pares de torres con distancia 1 y 9, y otros pares con distancia 5 y 5, cada uno de los cuales tiene una suma de 10. En nuestro escenario, preferiremos los pares con distancia 1 y 9.\n", "\n", "Para rectificar esto, elevamos al cuadrado las distancias antes de sumarlas. Observar que elevar al cuadrado las distancias proporciona una distribución más uniforme. En nuestro ejemplo, esto nos proporciona sumas de distancia 1+81=82 y 25+25=50, prefiriendo los pares con distancias 1 y 9 (ya que estamos maximizando la suma).\n", "\n", "Además, dado que la interferencia solo afecta a las torres que se encuentran dentro de cierta proximidad entre sí, vamos a establecemos un radio de corte de la siguiente manera.\n", "\n", "Cada par de torres con una distancia mayor que el radio de corte recibe un sesgo (bias) de valor mínimo para que no se las penalice por seleccionar ambas. Por el contrario, cada par de torres con una distancia menor que el radio de corte recibe un sesgo del negativo de la distancia al cuadrado.\n", "\n", "Por último, agregamos una restricción para elegir exactamente 10 torres nuevas y arreglamos las variables de torre existentes para que tengan un valor de 1.0. Esto garantiza que las torres existentes se identifiquen como ubicaciones donde deben existir torres.\n", "\n", "A continuación procedemos a implementar todo esto. Procedemos a importar los paquetes necesarios:" ] }, { "cell_type": "code", "execution_count": 17, "id": "5bfd387f-3ead-441a-ad73-adf9c3255299", "metadata": {}, "outputs": [], "source": [ "#!pip install pandas\n", "\n", "# para instalar shapefile hacemos lo siguiente\n", "#!pip install pyshp \n", "\n", "#Para instalar shapely. Esta en https://shapely.readthedocs.io/en/stable/\n", "#!pip install shapely" ] }, { "cell_type": "code", "execution_count": 18, "id": "751ad4ca-2678-48a3-b7a4-82f43ac8ebb3", "metadata": {}, "outputs": [], "source": [ "from itertools import combinations\n", "from math import asin, cos, radians, sin, sqrt\n", "\n", "import matplotlib\n", "import numpy as np\n", "import pandas as pd\n", "import shapefile\n", "from dimod import Binary, ConstrainedQuadraticModel, quicksum\n", "from dwave.system import LeapHybridCQMSampler\n", "from shapely.geometry import Point, shape\n", "from shapely.ops import unary_union\n", "\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "markdown", "id": "55b92b7d-1e23-4cd7-9cd1-ff5685a351f6", "metadata": {}, "source": [ "Definimos la función que nos va a servir para medir la distancia entre dos puntos, dadas sus coordenadas de latitud y longitud." ] }, { "cell_type": "code", "execution_count": 19, "id": "936d51ab-a7e3-436c-89b8-f997cd600008", "metadata": {}, "outputs": [], "source": [ "def distance(lat1, long1, lat2, long2):\n", " ''' Calcular la distancia (en millas) entre dos puntos dadas la lat/lon.\n", " Args:\n", " - lat1, long1: float. Lat y long del punyo 1.\n", " - lat2, long 2: float. Lat y long del 2.\n", " Returns:\n", " - dist: float. Distancia en millas entre dos puntos.\n", " '''\n", " # obtenido de https://www.geeksforgeeks.org/program-distance-two-points-earth/\n", "\n", " # El módulo math contiene una función llamada \n", " # radians que convierte grados en radianes.\n", " long1 = radians(long1)\n", " long2 = radians(long2)\n", " lat1 = radians(lat1)\n", " lat2 = radians(lat2)\n", " \n", " # Haversine formula\n", " dlong = long2 - long1\n", " dlat = lat2 - lat1\n", " a = sin(dlat / 2)**2 + cos(lat1) * cos(lat2) * sin(dlong / 2)**2\n", " \n", " c = 2 * asin(sqrt(a))\n", " \n", " # Radio de la tierra en millas\n", " r = 3956\n", " \n", " # Devolvemos el resultado\n", " return(c * r)" ] }, { "cell_type": "markdown", "id": "c6636b04-4395-460f-be22-07cf6b24ce1f", "metadata": {}, "source": [ "Ahora definimos otra función que se encargará de leer los datos donde está la información de las torres existentes" ] }, { "cell_type": "code", "execution_count": 20, "id": "c3d85ebf-0cb7-4582-80e0-3b2a1a71c812", "metadata": {}, "outputs": [], "source": [ "def get_existing_towers(filename):\n", " ''' Cargar las torres existente.\n", " Args:\n", " - filename: string.Nombre del fichero\n", " Returns:\n", " - towers: un data frame contenido la información de las torres existentes.\n", " '''\n", "\n", " with open(filename) as f:\n", " lines = f.readlines()\n", "\n", " points = []\n", " lats = []\n", " longs = []\n", " for line in lines:\n", " temp = line.split(\"\\t\")\n", " points.append(temp[0])\n", " lats.append(float(temp[1]))\n", " longs.append(float(temp[2][:-2]))\n", "\n", " towers = pd.DataFrame({'Name': points, 'Latitude': lats, 'Longitude':longs})\n", "\n", " return towers" ] }, { "cell_type": "markdown", "id": "4c8458a1-00dc-4805-844a-7f4a58e43046", "metadata": {}, "source": [ "Una vez incorporados estos elementos vamos a operar con ellos. Inicialmente cargamos el fichero de tipo shapefile con los datos geográficos de Alemania. Recordar que estos datos se pueden descargar desde este enlace ." ] }, { "cell_type": "code", "execution_count": 23, "id": "2693efba-06c8-4449-a3df-e831b8ee1af1", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Cargando los datos de la planimetría ...\n", "\n" ] } ], "source": [ " # Load and draw country map from geojson file\n", "print(\"\\nCargando los datos de la planimetría ...\")\n", "shp_file = \"data/germany_states.shp\"\n", "germany_map = shapefile.Reader(shp_file, encoding='CP1252')\n", "\n", "print(type(germany_map))" ] }, { "cell_type": "markdown", "id": "2b65b594-b6ec-4160-9a3b-7125b0115b9a", "metadata": {}, "source": [ "Cargamos las torres existentes y mostramos los primeros registros del data frame obtenido" ] }, { "cell_type": "code", "execution_count": 24, "id": "bc31e6a6-c299-4c70-8e8d-a9c802958f1f", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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NameLatitudeLongitude
0Holstein Tower, Sierksdorf, Germany54.07408910.77913
1Scream (Heide Park), Soltau, Germany53.0271119.87851
2Bremerhaven Radar Tower, Bremerhaven, Germany53.5384068.58029
3Brocken Transmitter, Weringerode, Germany51.80019410.61436
4Großer Inselsberg, Thuringia, Germany50.85124610.46551
\n", "
" ], "text/plain": [ " Name Latitude Longitude\n", "0 Holstein Tower, Sierksdorf, Germany 54.074089 10.77913\n", "1 Scream (Heide Park), Soltau, Germany 53.027111 9.87851\n", "2 Bremerhaven Radar Tower, Bremerhaven, Germany 53.538406 8.58029\n", "3 Brocken Transmitter, Weringerode, Germany 51.800194 10.61436\n", "4 Großer Inselsberg, Thuringia, Germany 50.851246 10.46551" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Cargamos las torres existente\n", "existing_towers = get_existing_towers(\"data/locations.txt\")\n", "existing_towers.head()" ] }, { "cell_type": "markdown", "id": "59803949-e1be-4efd-a5b3-2a58d6a1e922", "metadata": {}, "source": [ "Definimos una nueva función que será la que se encargue de generar nuevos puntos de forma aleatoria que serán los candidatos para la ubicación de las antenas." ] }, { "cell_type": "code", "execution_count": 25, "id": "7a342f17-6adb-480d-a358-4568fc39182b", "metadata": {}, "outputs": [], "source": [ "def gen_new_points(num_new_points, region_map):\n", " ''' Generamos puntos aleatorios en la zona geográfica correspondiente.\n", " Args:\n", " - num_new_points: Un entero con el número de puntos a elegir.\n", " - region_map: gdf. Region of interes. En nuestro caso el objeto germany_map anteriormente creado\n", " Returns:\n", " - new_locs: lista de [float, float]. nuevos puntos con formato [lat, long].\n", " '''\n", "\n", " # Load the map boundaries for the region\n", " polys = [shape(p) for p in region_map.shapes()]\n", " boundary = unary_union(polys)\n", " min_long, min_lat, max_long, max_lat = boundary.bounds\n", "\n", " counter = 0\n", " new_locs = []\n", "\n", " while counter < num_new_points:\n", " new_long = (max_long - min_long) * np.random.random() + min_long\n", " new_lat = (max_lat - min_lat) * np.random.random() + min_lat\n", " point = Point(new_long, new_lat)\n", "\n", " # Check that new point is within region before appending\n", " if point.intersects(boundary):\n", " counter += 1\n", " new_locs.append([new_lat, new_long])\n", "\n", " return new_locs" ] }, { "cell_type": "markdown", "id": "05ae4ff3-e520-4b60-8ce7-122d57663695", "metadata": {}, "source": [ "Ahora elegimos 100 puntos" ] }, { "cell_type": "code", "execution_count": 26, "id": "3ba0d5c3-a154-4a15-9a3b-22ab432c68cf", "metadata": {}, "outputs": [], "source": [ "# Seleccione puntos aleatorios dentro de las fronteras del país\n", "num_new = 100\n", "new_locs = gen_new_points(num_new, germany_map)\n", "# Definimos el núemro de puntos que realmente vamos a seleccionar para ubicar las antenas\n", "num_to_build = 10" ] }, { "cell_type": "code", "execution_count": 28, "id": "aa1e9775-ee3e-4c37-a701-fe8c5c29bdf7", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[[54.607001359203274, 13.377615922133907],\n", " [51.57380569840487, 11.182334683288005],\n", " [50.86773756688333, 9.252691648656352],\n", " [52.53996854738223, 7.703400252542739],\n", " [50.73759613854149, 12.030663362624013],\n", " [51.26265388669497, 8.801254770563828],\n", " [53.709061436655816, 7.20536122574036],\n", " [51.25140175633949, 11.301464814378537],\n", " [49.13664181989654, 8.138657995498052],\n", " [48.64721718442703, 13.121211074740703]]" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Vemos la información de los nuevos puntos\n", "new_locs[:10]" ] }, { "cell_type": "markdown", "id": "45a38e0b-7433-4686-97b4-334443314c5d", "metadata": {}, "source": [ "Una vez creado toda la infraestructura procedemos a definir la función que nos creará el objeto de tipo Constrained Quadratic Model o CQM que nos resuelve el problema.\n", "\n", "Pero antes y para una mejor comprensión del código que constituye la función build_cqm, vamos a ver algo de su contenido y cómo se construyen algunos de los elementos de esa función.\n", "\n", "Comenzando viendo el elemento *towers_vars*. Lo que se hace es pasar los datos de un data frame y convertirlos en un diccionario" ] }, { "cell_type": "code", "execution_count": 30, "id": "178ce553-8672-4e8d-b187-60cb0e2a4b8f", "metadata": {}, "outputs": [], "source": [ "tower_vars = {(row['Latitude'],row['Longitude'],row['Name']): Binary(row['Name']) for _, row in existing_towers.iterrows()}" ] }, { "cell_type": "code", "execution_count": 34, "id": "1f3f19c3-b974-4a15-a4ec-13a1b647c6d9", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "key (54.074089, 10.77913, 'Holstein Tower, Sierksdorf, Germany'), value BinaryQuadraticModel({'Holstein Tower, Sierksdorf, Germany': 1.0}, {}, 0.0, 'BINARY') \n", "key (53.027111, 9.87851, 'Scream (Heide Park), Soltau, Germany'), value BinaryQuadraticModel({'Scream (Heide Park), Soltau, Germany': 1.0}, {}, 0.0, 'BINARY') \n", "key (53.538406, 8.58029, 'Bremerhaven Radar Tower, Bremerhaven, Germany'), value BinaryQuadraticModel({'Bremerhaven Radar Tower, Bremerhaven, Germany': 1.0}, {}, 0.0, 'BINARY') \n" ] } ], "source": [ "# Veamos el contenido del diccionario tower_vars\n", "for x in list(tower_vars)[0:3]:\n", " print (\"key: {}, value; {} \".format(x, tower_vars[x]))" ] }, { "cell_type": "markdown", "id": "2ef8ce30-9f5f-4723-97b1-f34e4fdf9b90", "metadata": {}, "source": [ "Algo similar se hace con *new_locs*, con estos datos se construye un diccionario:" ] }, { "cell_type": "code", "execution_count": 35, "id": "a1557d66-3ca1-4eb1-ae16-7faa5e5463fd", "metadata": {}, "outputs": [], "source": [ "new_vars = {(new_locs[n][0],new_locs[n][1]): Binary(n) for n in range(len(new_locs))}" ] }, { "cell_type": "code", "execution_count": 36, "id": "1adbc5be-d617-4307-b788-ae13019b5bb3", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "key: (54.607001359203274, 13.377615922133907), value; BinaryQuadraticModel({0: 1.0}, {}, 0.0, 'BINARY') \n", "key: (51.57380569840487, 11.182334683288005), value; BinaryQuadraticModel({1: 1.0}, {}, 0.0, 'BINARY') \n", "key: (50.86773756688333, 9.252691648656352), value; BinaryQuadraticModel({2: 1.0}, {}, 0.0, 'BINARY') \n" ] } ], "source": [ "# Veamos el contenido del diccionario new_vars\n", "for x in list(new_vars)[0:3]:\n", " print (\"key: {}, value; {} \".format(x, new_vars[x]))" ] }, { "cell_type": "markdown", "id": "797833be-e037-4989-9d9f-4ac08367cd6a", "metadata": {}, "source": [ "Con la expresión *all_vars.update(new_vars)*, lo que hacemos es integrar los dos diccionarios en uno solo. " ] }, { "cell_type": "markdown", "id": "8d27d592-6afc-40de-b5e8-ab04d36a39e2", "metadata": {}, "source": [ "Con la función *combinations* lo que hacemos es obtener todas las combinaciones posibles entre pares de ubicación de las antenas, tanto de las ya existentes, como de las nuevas:" ] }, { "cell_type": "code", "execution_count": 39, "id": "db463a31-6e97-4844-80ab-ae0664664f3f", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[((54.074089, 10.77913, 'Holstein Tower, Sierksdorf, Germany'),\n", " (53.027111, 9.87851, 'Scream (Heide Park), Soltau, Germany')),\n", " ((54.074089, 10.77913, 'Holstein Tower, Sierksdorf, Germany'),\n", " (53.538406, 8.58029, 'Bremerhaven Radar Tower, Bremerhaven, Germany')),\n", " ((54.074089, 10.77913, 'Holstein Tower, Sierksdorf, Germany'),\n", " (51.800194, 10.61436, 'Brocken Transmitter, Weringerode, Germany'))]" ] }, "execution_count": 39, "metadata": {}, "output_type": "execute_result" } ], "source": [ "all_vars = tower_vars.copy()\n", "all_vars.update(new_vars)\n", "list(combinations(all_vars.keys(), 2))[:3]" ] }, { "cell_type": "markdown", "id": "f89623d7-81c1-4cea-90d6-495a645dd8a3", "metadata": {}, "source": [ "```{index} cqm.fix_variables, fix_variables\r\n", "```\n", "Observar la última instrucció n cqm.fix_variable , instrucción que obliga al modelo a dar el valor de 1 a las variables qye representan las torres ya existentes.s" ] }, { "cell_type": "code", "execution_count": 40, "id": "2e054a9c-8744-4136-9bcc-38450588ebb6", "metadata": {}, "outputs": [], "source": [ "def build_cqm(num_to_build, existing_towers, new_locs, radius):\n", " ''' Creación del modelo CQM buscado.\n", " Args:\n", " - num_to_build: entero. Número de nevas antenas a construir.\n", " - existing_towers: dataframe. Indicación donde están ya están las antenas.\n", " - new_locs: Lista de [float, float]. Lista de potenciales sitios donde colocar las nueva antenas lat/long.\n", " - radius: int or float. Radio de distancia de interferencia.\n", " Returns:\n", " - cqm: ConstrainedQuadraticModel representando el problema a optimizar.\n", " '''\n", "\n", " # inicializamos el modelo\n", " cqm = ConstrainedQuadraticModel()\n", "\n", " # construcción de variables para el CQM.\n", " tower_vars = {(row['Latitude'],row['Longitude'],row['Name']): Binary(row['Name']) for _, row in existing_towers.iterrows()}\n", " new_vars = {(new_locs[n][0],new_locs[n][1]): Binary(n) for n in range(len(new_locs))}\n", "\n", " \n", " # Hacemos una combinación de todas las variables para calcular la función objetivo\n", " all_vars = tower_vars.copy()\n", " all_vars.update(new_vars)\n", "\n", " # Objectivo: minimizar interferencias / maximizar distancias\n", " pair_list = list(combinations(all_vars.keys(), 2)) \n", " # Calculamos las distancias entre todos los puntos dos a dos (las combinaciones anteriores)\n", " dist = [distance(a[0], a[1], b[0], b[1])**2 for (a, b) in pair_list]\n", " max_dist = max(dist) \n", " biases = [dist[i] if dist[i] < radius**2 else max_dist for i in range(len(dist))]\n", "\n", " # Definimos la función objetivo. Ponemos un valor negativo porque queremos maximizar\n", " cqm.set_objective(quicksum(-biases[i]*all_vars[pair_list[i][0]]*all_vars[pair_list[i][1]] for i in range(len(pair_list))))\n", "\n", " # restricción: tomamos exactamente num_to_build como nuevos lugares para instalar las antenas\n", " cqm.add_constraint(quicksum(new_vars.values()) == num_to_build)\n", "\n", " # Fijamos el valor de 1 para las torres de antenas ya existentes.\n", " cqm.fix_variables({key[2]: 1.0 for key in tower_vars.keys()})\n", "\n", " return cqm" ] }, { "cell_type": "markdown", "id": "d29d33cc-9a3f-4107-a5fa-e4a8177d9b23", "metadata": {}, "source": [ "Ahora ya utilizamos la función anterior para construir el modelo CQM que se va a utilizar y una vez construido creamos una instancia del solver LeapHybridCQMSampler()." ] }, { "cell_type": "code", "execution_count": 41, "id": "785a03db-50d2-4bc9-830e-5925937f1845", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\\Construyo el modelo CQM...\n" ] } ], "source": [ "print(\"\\Construyo el modelo CQM...\")\n", "cqm = build_cqm(num_to_build, existing_towers, new_locs, radius=75)\n", "\n", "# Initialize the CQM solver\n", "sampler = LeapHybridCQMSampler()" ] }, { "cell_type": "code", "execution_count": 42, "id": "d4fa0beb-f484-40ea-85ae-98a22159ca0e", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Sending problem to hybrid solver...\n" ] } ], "source": [ "# Resolvemos el problema con el CQM solver\n", "print(\"\\nenviando el rpblema al hybrid solver...\")\n", "sampleset = sampler.sample_cqm(cqm, label='Example - TV Towers')\n", "feasible_sampleset = sampleset.filter(lambda row: row.is_feasible)\n", "\n", "try:\n", " sample = feasible_sampleset.first.sample\n", "except:\n", " print(\"\\nNo se han encontrado soluciones factibles.\")\n", " exit()" ] }, { "cell_type": "code", "execution_count": 43, "id": "ba5a91bd-2a65-4140-b87c-f9021c733f2d", "metadata": {}, "outputs": [], "source": [ "def visualize(region_map, existing_towers, new_locs, build_sites, guardar='N'):\n", " ''' Visualizamos las torres encontradas.\n", " Args:\n", " - region_map: gdf. Mapa de la región.\n", " - existing_towers: df. Dataframe conteniendo información de las torres.\n", " - new_locs: lista de [float, float]. Nuevos puntos como [lat, long].\n", " - build_sites: lista de [float, float]. Puntos construidos [lat, long].\n", " - guardar: boolen. 'N' No se guarda el mapa, si en caso otro valor\n", " Returns:\n", " None.\n", " '''\n", "\n", " print(\"\\nVisualizando la solución...\")\n", "\n", " # Inicializando figura y ejes\n", " _, (ax, ax_final) = plt.subplots(nrows=1, ncols=2, figsize=(32, 12))\n", " ax.axis('off')\n", " ax_final.axis('off')\n", "\n", " # Dibujar los bordes de la región\n", " polys = [shape(p) for p in region_map.shapes()]\n", " boundary = unary_union(polys)\n", " for geom in boundary.geoms: \n", " xs, ys = geom.exterior.xy \n", " ax.fill(xs, ys, alpha=0.5, fc='#d3d3d3', ec='none', zorder=0)\n", " ax_final.fill(xs, ys, alpha=0.5, fc='#d3d3d3', ec='none', zorder=0)\n", "\n", " # Dibujando las torres existentes\n", " ax.scatter(existing_towers.Longitude, existing_towers.Latitude, color='r', zorder=2)\n", " ax_final.scatter(existing_towers.Longitude, existing_towers.Latitude, color='r', zorder=2)\n", "\n", " # Dibujar radio de las torres existentes\n", " radius = 30\n", " ax.scatter(existing_towers.Longitude, existing_towers.Latitude, color='r', alpha=0.1, s=radius**2, zorder=1)\n", " ax_final.scatter(existing_towers.Longitude, existing_towers.Latitude, color='r', alpha=0.1, s=radius**2, zorder=1)\n", "\n", " # Dibujando nuevos puntos potenciales\n", " new_locations = pd.DataFrame(new_locs, columns=['Latitude','Longitude'])\n", " ax.scatter(new_locations.Longitude, new_locations.Latitude, color='y', zorder=8)\n", "\n", " # Dibujando los nuevos puntos seleccionados\n", " new_builds = pd.DataFrame(build_sites, columns=['Latitude','Longitude'])\n", " ax_final.scatter(new_builds.Longitude, new_builds.Latitude, color='b', zorder=8)\n", "\n", " # Dibujan do radios\n", " ax_final.scatter(new_builds.Longitude, new_builds.Latitude, color='b', alpha=0.1, s=radius**2, zorder=8)\n", "\n", " # Hacemos la figura\n", " ax.axis('scaled')\n", " ax_final.axis('scaled')\n", " ax.set_title(\"sitios potenciales\", fontsize = 24)\n", " ax_final.set_title(\"Sitios obtenidos\", fontsize = 24)\n", "\n", " if guardar=='N':\n", " plt.show()\n", " else:\n", " # Gyardamos la figura\n", " plot_filename = 'map.png'\n", " plt.savefig(plot_filename)\n", " print(\"\\nOutput guardado como\", plot_filename)" ] }, { "cell_type": "markdown", "id": "cc267908-416d-455e-bfec-8d98876becd9", "metadata": {}, "source": [ "Utilizamos la función anterior para visualizar el mapa con las antenas" ] }, { "cell_type": "code", "execution_count": 44, "id": "ff4ff7f9-fb48-40c4-aa3e-6af5adcf4f3d", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Seleccionados 10 build sites.\n", "\n", "Visualizando la solución...\n" ] }, { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "build_sites = [new_locs[key] for key, val in sample.items() if val == 1.0]\n", "print(\"\\nSeleccionados\", len(build_sites), \"build sites.\")\n", "\n", "visualize(germany_map, existing_towers, new_locs, build_sites)" ] }, { "cell_type": "code", "execution_count": null, "id": "0c7a7f87-1066-47aa-820b-e7a5bc653f27", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "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": "3.8.3" }, "toc": { "base_numbering": 1, "nav_menu": {}, "number_sections": true, "sideBar": true, "skip_h1_title": false, "title_cell": "Table of Contents", "title_sidebar": "Contents", "toc_cell": false, "toc_position": {}, "toc_section_display": true, "toc_window_display": true }, "varInspector": { "cols": { "lenName": 16, "lenType": 16, "lenVar": 40 }, "kernels_config": { "python": { "delete_cmd_postfix": "", "delete_cmd_prefix": "del ", "library": "var_list.py", "varRefreshCmd": "print(var_dic_list())" }, "r": { "delete_cmd_postfix": ") ", "delete_cmd_prefix": "rm(", "library": "var_list.r", "varRefreshCmd": "cat(var_dic_list()) " } }, "types_to_exclude": [ "module", "function", "builtin_function_or_method", "instance", "_Feature" ], "window_display": false } }, "nbformat": 4, "nbformat_minor": 5 }