Parte4-Seba-Ideas-y-librerias.ipynb

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    "![banner](images/banner.png)\n",
    "\n",
    "### Programar y Reprogramar Recursos para Docentes\n",
    "#### Fecha: 29 Octubre 2021, 13:00 a 15:00\n",
    "####  Sofía Martin, Ariel Ramos, Liliana Hurtado, Sebastián Flores"
   ]
  },
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   "source": [
    "### Ideas y Librerías\n",
    "\n",
    "**Agenda:**\n",
    "* Introducción\n",
    "* Presentaciones interactivas\n",
    "* Gráficos\n",
    "* Ecuaciones\n",
    "* Análisis de datos\n",
    "* Machine Learning"
   ]
  },
  {
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   "source": [
    "### Introducción \n",
    "\n",
    "¿Porqué ocupamos librerías?"
   ]
  },
  {
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   "metadata": {
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   "source": [
    "En Jupyter puedes usar cualquier librería de Python (incluso otros lenguajes).\n",
    "\n",
    "La comunidad de Python continuamente desarrolla librerías para todo tipo de temas.\n",
    "\n",
    "Con toda seguridad, existe una librería de python que puede ayudarte a enseñar **ese** tema (desde algebra a astrofísica). "
   ]
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   "source": [
    "### Algunos enlaces a considerar\n",
    "\n",
    "* https://github.com/usantamaria: repositorios de matemática, informática y mecánica de la UTFSM - Chile.\n",
    "* https://github.com/sebastiandres: \n",
    "* https://sebastiandres.xyz: Blog con tutoriales y consejos.   \n",
    "* Comunidades y personas en Discord, Twitter y Github."
   ]
  },
  {
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   "source": [
    "# 1. Presentaciones Interactivas\n",
    "\n",
    "La librería RISE (Reveal.js Interactive Slide Extension), permite  convertir un jupyter notebook en una presentación interactiva. Es compatible con todas las otras librerías.\n",
    "\n",
    "###### Instalación\n",
    "```bash\n",
    "pip install rise\n",
    "```\n",
    "\n",
    "###### Utilización\n",
    "¡Muy simple! Sólo requiere configurar el tipo de cada celdas."
   ]
  },
  {
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   "source": [
    "##### ¿Qué es RISE?\n",
    "\n",
    "En lugar de mostrar una larga página web con las celdas, las celdas **se agrupan** en diapositivas. \n",
    "\n",
    "Las distintas celdas pueden ser editadas y ejecutadas *DURANTE LA PRESENTACIÓN*.\n"
   ]
  },
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   "source": [
    "Con **herramientas** de *markdown*, <font style=\"color:green\">html</font> y latex: $ e^{i \\pi} + 1 = 0 $\n"
   ]
  },
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   "source": [
    "y python:"
   ]
  },
  {
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   "execution_count": null,
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   },
   "outputs": [],
   "source": [
    "print(\"Hola \\U0001F30E\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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    }
   },
   "source": [
    "##### ¿Porqué usar RISE?\n"
   ]
  },
  {
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   "metadata": {
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    }
   },
   "source": [
    "#### Ventajas\n",
    "\n",
    "* Simplificar la **generación** de material. \n",
    "\n",
    "* Simplificar la **distribución** del material."
   ]
  },
  {
   "cell_type": "markdown",
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   },
   "source": [
    "#### Desventajas\n",
    "\n",
    "* RISE funciona sólo con Jupyter Notebook. No funciona con Jupyter Lab ni con Google Colaboratory.\n",
    "* Es completamente interactivo, con todos los riesgos y beneficios que eso representa."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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    }
   },
   "source": [
    "##### ¿Cómo usar RISE?"
   ]
  },
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   "source": [
    "Instalar la librería\n",
    "\n",
    "```\n",
    "pip install rise\n",
    "```\n",
    "\n",
    "Permitirá que se agregue el botón de iniciar presentación (destacado en rojo).\n",
    "\n",
    "![](images/install2.png)"
   ]
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   "source": [
    "**Paso 2**: \n",
    "\n",
    "En el menú de jupyter notebook, es necesario seleccionar `View/Cell Toolbar/Slideshow` para que permita configurar el tipo de celda para diapositiva. \n",
    "![](images/install3.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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    }
   },
   "source": [
    "![](images/install4.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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   },
   "source": [
    "##### ¿Cómo aprender?\n",
    "\n",
    "Existe una comunidad material (en español) sobre cómo utilizar RISE:\n",
    "\n",
    "* Presentación Pycon Latam, Octubre 2021: https://sebastiandres.github.io/talk_2021_08_pylatam/\n",
    "* Tutorial RISE: https://sebastiandres.github.io/blog/tutorial-rise/\n",
    "\n",
    "¡En caso de duda, pregunte! Creo que rise es el GRAN diferenciador para jupyter notebook."
   ]
  },
  {
   "cell_type": "markdown",
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   "source": [
    "# 2. Gráficos\n",
    "\n",
    "Para mostrar gráficos existe un amplio espectro de opciones. Se diferencian en aspectos de usabilidad y flexibilidad.\n",
    "* matplotlib\n",
    "* seaborn\n",
    "* plotly\n",
    "* altair\n",
    "* bokeh\n",
    "\n",
    "¡Y muchas otras que no conozco!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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   "source": [
    "##### ¿Cuál librería gráfica elegir?\n",
    "Todas las librerías cuentan con buena documentación y ejemplos.\n",
    "Se trata más bien de preferencias personales: ¿Control fino de aspectos gráficos o píntamelo a tu pinta pero bonito? ¿Estático o interactivo?\n",
    "\n",
    "**Consejo:** \n",
    "\n",
    "Elegir una y dominarla en profundidad. Saber graficar datos es un superpoder, para aprender y enseñar."
   ]
  },
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   "source": [
    "### Algunos ejemplos\n",
    "Todas las librerías tienen galerías con ejemplos. \n",
    "\n",
    "Al principio suele ser más fácil copiar y personalizar un ejemplo.\n",
    "\n",
    "* matplotlib: https://matplotlib.org/stable/gallery/index.html\n",
    "* seaborn: https://seaborn.pydata.org/examples/index.html\n",
    "* plotly: https://plotly.com/python/\n",
    "* altair: https://altair-viz.github.io/gallery/\n",
    "* bokeh: https://docs.bokeh.org/en/latest/docs/gallery.html"
   ]
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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "dt = 0.1\n",
    "t = np.arange(0.0, 2.0, dt)\n",
    "s = np.sin(2*np.pi*t)\n",
    "\n",
    "plt.figure(figsize=(12,6))\n",
    "plt.plot(t, s)\n",
    "plt.title(r'$\\alpha_i > \\beta_i$', fontsize=20)\n",
    "plt.text(1, -0.6, r'$\\sum_{i=0}^\\infty x_i$', fontsize=20)\n",
    "plt.text(0.6, 0.6, r'$\\mathcal{A}\\mathrm{sin}(2 \\omega t)$',\n",
    "         fontsize=20)\n",
    "plt.xlabel('time (s)')\n",
    "plt.ylabel('volts (mV)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x576 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "def f(t):\n",
    "    return np.exp(-t) * np.cos(2*np.pi*t)\n",
    "\n",
    "t = np.arange(0.0, 3.0, 0.01)\n",
    "\n",
    "plt.figure(figsize=(14,8))\n",
    "ax1 = plt.subplot(212)\n",
    "ax1.margins(0.05)           # Default margin is 0.05, value 0 means fit\n",
    "ax1.plot(t, f(t))\n",
    "\n",
    "ax2 = plt.subplot(221)\n",
    "ax2.margins(2, 2)           # Values >0.0 zoom out\n",
    "ax2.plot(t, f(t))\n",
    "ax2.set_title('Zoomed out')\n",
    "\n",
    "ax3 = plt.subplot(222)\n",
    "ax3.margins(x=0, y=-0.25)   # Values in (-0.5, 0.0) zooms in to center\n",
    "ax3.plot(t, f(t))\n",
    "ax3.set_title('Zoomed in')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Fixing random state for reproducibility\n",
    "np.random.seed(19680801)\n",
    "\n",
    "# some random data\n",
    "x = np.random.randn(1000)\n",
    "y = np.random.randn(1000)\n",
    "\n",
    "\n",
    "def scatter_hist(x, y, ax, ax_histx, ax_histy):\n",
    "    # no labels\n",
    "    ax_histx.tick_params(axis=\"x\", labelbottom=False)\n",
    "    ax_histy.tick_params(axis=\"y\", labelleft=False)\n",
    "\n",
    "    # the scatter plot:\n",
    "    ax.scatter(x, y)\n",
    "\n",
    "    # now determine nice limits by hand:\n",
    "    binwidth = 0.25\n",
    "    xymax = max(np.max(np.abs(x)), np.max(np.abs(y)))\n",
    "    lim = (int(xymax/binwidth) + 1) * binwidth\n",
    "\n",
    "    bins = np.arange(-lim, lim + binwidth, binwidth)\n",
    "    ax_histx.hist(x, bins=bins)\n",
    "    ax_histy.hist(y, bins=bins, orientation='horizontal')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# definitions for the axes\n",
    "left, width = 0.1, 0.65\n",
    "bottom, height = 0.1, 0.65\n",
    "spacing = 0.005\n",
    "\n",
    "\n",
    "rect_scatter = [left, bottom, width, height]\n",
    "rect_histx = [left, bottom + height + spacing, width, 0.2]\n",
    "rect_histy = [left + width + spacing, bottom, 0.2, height]\n",
    "\n",
    "# start with a square Figure\n",
    "fig = plt.figure(figsize=(8, 8))\n",
    "\n",
    "ax = fig.add_axes(rect_scatter)\n",
    "ax_histx = fig.add_axes(rect_histx, sharex=ax)\n",
    "ax_histy = fig.add_axes(rect_histy, sharey=ax)\n",
    "\n",
    "# use the previously defined function\n",
    "scatter_hist(x, y, ax, ax_histx, ax_histy)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "vegetables = [\"cucumber\", \"tomato\", \"lettuce\", \"asparagus\",\n",
    "              \"potato\", \"wheat\", \"barley\"]\n",
    "farmers = [\"Farmer Joe\", \"Upland Bros.\", \"Smith Gardening\",\n",
    "           \"Agrifun\", \"Organiculture\", \"BioGoods Ltd.\", \"Cornylee Corp.\"]\n",
    "\n",
    "harvest = np.array([[0.8, 2.4, 2.5, 3.9, 0.0, 4.0, 0.0],\n",
    "                    [2.4, 0.0, 4.0, 1.0, 2.7, 0.0, 0.0],\n",
    "                    [1.1, 2.4, 0.8, 4.3, 1.9, 4.4, 0.0],\n",
    "                    [0.6, 0.0, 0.3, 0.0, 3.1, 0.0, 0.0],\n",
    "                    [0.7, 1.7, 0.6, 2.6, 2.2, 6.2, 0.0],\n",
    "                    [1.3, 1.2, 0.0, 0.0, 0.0, 3.2, 5.1],\n",
    "                    [0.1, 2.0, 0.0, 1.4, 0.0, 1.9, 6.3]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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rFz2uZ2a/Ro9PMbM3zexDM/vVzC4ws3+Z2fdmNtTM6qSFcaKZDTezkWa2S/T6qmb2pJl9Hb2mW9r7vm1mA4GP/+Y2Wq/qNqjB7BnzVj3Pnzmfug1rlCjz/MMfs88hO/LcR5dz08Mn8fDt/TIdZsY1qV2D1k3rM+K3GSWWN6hRlX2325w+Q35IKLLMalK7Bq2b1GfE5BlrrGvTojGvX3QCj556GJs1qJtAdPFLYby571l8echlfDlrAiN+n1pi/c9zZ7J/060B2K/J1lTLq0itCpWTCDVW9ZrWYfaUOaue508poF7Tkv/zuk3rMHtyPgBFhUUsmreYGnWrZzTOTLhq+wO596cPKCqdcUYaVKrOjCXzASj0IhasXEatClUyGWLsNuT9YZ2TDDPbFrgW2MfddwQu+oOyBwHdgF2jsnetw5/YDjgCaA/cCix2952AIcBJaeWquHsb4DzgyWjZNcBAd98F2Bu428yqRut2Bo5y907r9EHLgc5dduDDN7/nxH/czfXnPcvltx2FmSUdVmwqV8ij58mHcOdbn7Jo2fIS667s1pme7362xhVtNqpSIY9exx/CHe+suR1GTZ3Ffnf25oj7n+eFL4fz4EldE4oyXkU4h338OJ3e68kOtZuyRY36Jdbf9eOHtK+3CW/seya71N+EGYvnU+hFCUUrcevUcEsKli1i1LzpSYcif9NfaULYB3jV3fMB3L3gD058/wCecvfFxWXX4f0/cfcFwAIzmwe8Ey3/EdghrdxL0XsONrMaZlYL2B841Mwui8pUAlpEjz9c2983s7OAswAq5dUoq8h6N2fWfOo3qrnqeb2GNZgzc36JMgcc3pZrznkWgNE/TKZCxVxq1K7CvIJFGYkxk3JTKXqdfAjvfvczH40ct8b6bZs35O4TugBQu2pl9mzdisLCIgb+ND7TocYqN5Wi1wmH8O7wn/nopzW3Q3rS8dmYX7nusBS1qlRi7uKlmQwzYxasWMZXs39lz4ab88v81f2VZi1dSI+hob29Sk4e+zdpzYIVy5IKMzb5Uwuo32z1lWq9ZnXInzqnRJk5Uwuo37we+VMLSOWkqFqzCvPnZFfT6k51WtC50Vbs2XALKqZyqZpbkTt2PoKrvnt9VZlZSxfQqHINZi6dT46lqJ5bkbnLs6tpdUPeH/6/fTJWFr+HmaWACutanpAIpEs/UhSlPS+iZDJU+prWAQOOdPc20U8Ldx8drV/rmdndH3f3du7erkJOZqrXxoycSpNN6tKwaW1yc3PodND2DB30c4kys2bMY6cOmwLQvFV9KlTIzcoEA+Cmf+7HhJkFPDu47PbWA297kgOinw9G/MItrw/MugQD4Kaj9mPCrAKe+bzs7VCv2ur9c/tmDUmZZV2CUbtCFarnhb5IFVO5dGy4KRMW5JcqU5niS5uztt6D1yYNz2yQGTLmm3E03aIxjVo2IDcvl87dd2fI2yX7uw95Zxj7nxwqaPc6qgPDB45MItRY9Rr9Eft+cB/7f9iLy4b15av8iSUSDIBPZoyhW/M2AOzfZBu+yp+YQKTx2pD3h79SkzEQeMPM7nP3OVE/iV+BtsArwKFAXlT2Q+B6M3vB3RebWZ2oNqG4/NfAUaX/wDrqDnxiZnsA89x9npm9D/Qwsx7u7ma2k7uXPRYyYUWFRTx8Wz9uffRkUjkpPnjjWyaNn8WJ5+/LLz9NZeign3ni7v5cdMNhHH5iR9zh3mtf//M33gDt1LIJh7bbhrHTZtP3kuMBuL//FzSuHWqVXhkyIsnwMmbnTZrQbedtGDN9Nq9dGLZDr/e/oHGtaDt8NYL9t9+C7h12pLCoiKUrVnLZi+8lGXIsGlSqxh3tu5FjKQxjwJRRDJrxCxdu05mRv09j4PSx7FK/Jf/abh/cYVj+JG4c3j/psGNRVFjEQz16c/uAa0jlpHj/qU+YNGoKJ9/YnbHDxjPknWH07z2Qq57twdNjH2RBwcJyMZIgUy7Yem9+mjuNT2aM4bVJ33HHzkfQf98LmbdiCZcN65t0eOvdhrw/mP+Fxm4zOxm4HCgEvgeuBN4CKgMDgPPdvVpU9ipCX4rlwHvufrWZbU1ISAqBd4ET3L2lmZ0CtHP3C6LX/ho9z09fFw1hHQ50IiQ0p7n712ZWGegFdCTUlEx090NKv+8fqVm5se+2+enrvC2y1ZQDsrND4d/h5WI8UvJWtk++yrU8aHH0j0mHUG5Me2ObpEMoF5ocPirpEMqNj7zvt+7ervTyv5RkZDMlGYGSjNWUZARKMgIlGaspyQiUZKy2tiRjo5knQ0RERDJLSYaIiIjEQkmGiIiIxEJJhoiIiMRCSYaIiIjEQkmGiIiIxEJJhoiIiMRCSYaIiIjEQkmGiIiIxEJJhoiIiMRCSYaIiIjEQkmGiIiIxEJJhoiIiMRCSYaIiIjEQkmGiIiIxEJJhoiIiMRCSYaIiIjEQkmGiIiIxEJJhoiIiMRCSYaIiIjEQkmGiIiIxEJJhoiIiMRCSYaIiIjEQkmGiIiIxEJJhoiIiMRCSYaIiIjEQkmGiIiIxCI36QDKi+U1c5lyQN2kw0hc4/u+TDqEcmPaG9skHUK5sMltOUmHUC78dl3HpEMoN5ofruOErBvVZIiIiEgslGSIiIhILJRkiIiISCyUZIiIiEgslGSIiIhILJRkiIiISCyUZIiIiEgslGSIiIhILJRkiIiISCyUZIiIiEgslGSIiIhILJRkiIiISCyUZIiIiEgslGSIiIhILJRkiIiISCyUZIiIiEgslGSIiIhILJRkiIiISCyUZIiIiEgslGSIiIhILJRkiIiISCyUZIiIiEgslGSIiIhILJRkiIiISCyUZIiIiEgslGSIiIhILJRkiIiISCyUZIiIiEgslGSIiIhILHKTDmBj06hmNW479kDqVq+CO/Qd+iPPf/59mWW3a96Q5y84hstfeI8PR/yS4Ugzo90BbTiv16mkclL07/0xfe58s8T6vAq5XPFMD7Zouynz5yzg1mN6MnPS7GSCjVkK45VOZzNz6XzO/+rFEuvyUjncvvMRbFuzMXNXLOHSb15l2pK5yQQao39d25UOu2/J3N8XcdZxj5ZZ5rx/HUD7jluwbOkK7rn5LcaNmZHhKOPXqEY17jziQOpWrYIDr3z7I88NLXmcOG33tnTdfmsAclIpNqtfh453Pcq8JcsSiDg+OkYEG+p2iL0mw8xqmdl5Mf+NzmbWMc6/sb6sLHLufmcw3e5+luMefIljdt+RTRvWWaNcyoxLDt6DL8dOSiDKzEilUvR46HSu7nIrZ2x7CXsfszstWjcrUebA0/dh4dyFnLJlD17v1Y8z7jghoWjjd+JmHZiwsOyDwpEtdmb+8iUc9PEDPDt+CP/adr8MR5cZH/b7gasvfmGt69t33Jymzety6lEP0euOflx4xcEZjC5zCoucO98fzCH/fZZjnniJ49vvyGb1Sx4nnvziWw5/9AUOf/QFen70Bd/8OiXrEgwdI4INeTtkormkFhBrkgF0BjaIJCN/wSJGT50FwOJlK5gws4CGNaqtUe64Pdrw4YhxFCxcnOkQM2arXTZn2rgZzJg4i5UrVjKozxd07NauRJmOh7bng2c+BWBw36HstO92SYQau4aVarBXwy15bdJ3Za7fp/HWvDV5OAAfTBtFh3qtMhhd5vw4/DcWzF+y1vUd99qKD/v/AMDPI6dStXpF6tRd8/uzoZu9cBGjpofjxKLlKxifX0DD6mv/nAdvvxXvjhyTqfAyRseIYEPeDplIMu4ANjOz4WZ2d/Qz0sx+NLPusKom4lMze8vMJpjZHWZ2vJl9HZXbLCrX1cy+MrPvzewjM2toZi2Bc4BLor+xp5m1NLOBZjbCzD42sxYZ+Jx/WZPaNWjdtD4jfitZ3dugRlX23W5z+gz5IaHIMqNe0zrMnjJn1fP8KQXUa1q3RJm6Teswe3I+AEWFRSyat5gadatnNM5MuGr7A7n3pw8oci9zfYNK1ZmxZD4AhV7EgpXLqFWhSiZDLBfq1q/O7JnzVz3Pn7WAuvWzb39I17RWDVo3qs8PU8tuFqqUl8sem7fkg1HZ16SqY0SwIW+HTCQZVwHj3b0NMBRoA+wI/AO428waR+V2JCQLrYETgS3dfRfgf0CPqMznQAd33wl4GbjC3X8FHgV6unsbd/8MeBB4xt13AF4AHigrMDM7y8yGmdmwwsWL1u+n/hOVK+TR8+RDuPOtT1m0bHmJdVd260zPdz9jLecbyTKdGm5JwbJFjJo3PelQpJypUiGPB7ofwu0D1jxOFNt7y035fvK0rGsqkeyQ6Y6fewAvuXshMNPMPgXaA/OBb9x9OoCZjQc+iF7zI7B39LgZ0CdKTCoAE9fyd3YDjogePwfcVVYhd38ceBygcqPmGTul56ZS9Dr5EN797mc+GjlujfXbNm/I3Sd0AaB21crs2boVhYVFDPxpfKZCzIj8qQXUb7Y6G6/XrA75U+eUKDNnagH1m9cjf2oBqZwUVWtWYf6cBZkONVY71WlB50ZbsWfDLaiYyqVqbkXu2PkIrvru9VVlZi1dQKPKNZi5dD45lqJ6bkXmLs/eprS1mTN7AfUb1lj1vF6D6syZnV37Q7HcVIoHuh/COyN+5sPRax4ninXZfive/fHnDEaWOTpGBBvydihPQ1jT0/CitOdFrE6GHgQecvftgbOBSpkLb/256Z/7MWFmAc8OLrv9/cDbnuSA6OeDEb9wy+sDsy7BABjzzTiabtGYRi0bkJuXS+fuuzPk7WElygx5Zxj7n9wJgL2O6sDwgSOTCDVWvUZ/xL4f3Mf+H/bismF9+Sp/YokEA+CTGWPo1rwNAPs32Yav8teWX2e3IZ+NZb+DdgRg6+2asmjhMgrmLEw4qnjc0m0/xs8u4OkhZR8nAKpVrED7TZrx8c/Zd3wAHSOKbcjbIRM1GQuA4oahz4CzzewZoA6wF3A5sPU6vldNYGr0+ORSf6NG2vMvgWMItRjHR3+3XNipZRMObbcNY6fNpu8lxwNwf/8vaFw7hP/KkBFJhpdRRYVFPNSjN7cPuIZUTor3n/qESaOmcPKN3Rk7bDxD3hlG/94DuerZHjw99kEWFCzk1mN7Jh12xlyw9d78NHcan8wYw2uTvuOOnY+g/74XMm/FEi4b1jfp8GLx75uPYIedN6FmrSq88M7FPPf4IHJycwB4941v+fqLX9il4+Y8/doF0RDWtxOOOB47t2jCYW22YcyM2bxxTjhO9Pz4CxrXDMeJPsPCcWK/1pvzxfhJLFmxMrFY46RjRLAhbwfzDDT8m9mLwA5A/2jRQYADt7h7HzPrDFzm7odE5QdFz4elrzOzbkBP4HdgINDe3Tub2ZZAX0KtRw/gN+ApoB4wGzjV3X/7oxgrN2rum53wr/X2mTdUje/7MukQyo1pb2yTdAjlQtPbcpIOoVz47cDkO9GVF81v1nFCSvrI+37r7u1KL89Inwx3P67UostLrR8EDEp73rmsde7+FvBWGe8/lpDEpNvnbwcsIiIi/2/lqU+GiIiIZBElGSIiIhILJRkiIiISCyUZIiIiEgslGSIiIhILJRkiIiISCyUZIiIiEgslGSIiIhILJRkiIiISCyUZIiIiEgslGSIiIhILJRkiIiISCyUZIiIiEgslGSIiIhILJRkiIiISCyUZIiIiEgslGSIiIhILJRkiIiISCyUZIiIiEgslGSIiIhILJRkiIiISCyUZIiIiEgslGSIiIhILJRkiIiISCyUZIiIiEgslGSIiIhILJRkiIiISi9ykAygv8hYW0WjooqTDSNz0f3VMOoRyo/m1c5IOoVxYeN+SpEMoF6q/UC3pEMqNoo+bJx1CuZDad3LSIZR7qskQERGRWCjJEBERkVgoyRAREZFYKMkQERGRWCjJEBERkVgoyRAREZFYKMkQERGRWCjJEBERkVgoyRAREZFYKMkQERGRWCjJEBERkVgoyRAREZFYKMkQERGRWCjJEBERkVgoyRAREZFYKMkQERGRWCjJEBERkVgoyRAREZFYKMkQERGRWCjJEBERkVgoyRAREZFYKMkQERGRWCjJEBERkVgoyRAREZFYKMkQERGRWCjJEBERkVgoyRAREZFYKMkQERGRWOQmHcDG6LJ/H8KuHbdg7u+LOPOkx9dY37xFXS6/uiubb9mIp54YxKsvDU0gyvg1qlmN2449kLrVq+AOfYf+yPOff19m2e2aN+T5C47h8hfe48MRv2Q40vi13X0Lzr2yC6mcFANe/5ZXeg8usb5+o5pcduuRVK1eiZycFE/2+oBvPhubULTxSmH8b9cLmb1sPlcOf6rEum7NOnBEs90owlmychl3jX6NXxfNSijS+Fx32v7s0WZTfp+/mGOufXaN9dWrVOS60w+gWYOaLF9RyM2932f81DkJRJoZKYyH213CnGXzuObH3mWW2bP+Dtyw3SmcO+w+xi6YkuEI49fugDac1+tUUjkp+vf+mD53vllifV6FXK54pgdbtN2U+XMWcOsxPZk5aXYywabJSE2GmS38k/W1zOy8tOctzey4+CNLxvvvjeDfl7601vUL5i/hv73e59WXszO5KLayyLn7ncF0u/tZjnvwJY7ZfUc2bVhnjXIpMy45eA++HDspgSjjl0oZ51/TlWvPe5azuj1A54O2p8Wm9UuUOfbszgx+fyQX/PNhbr+8Dxdc0zWhaON3dIs9mLSWxOHD6d9z8tCenDq0Fy9M+pQeW2bnduj3+U9ceO/ra11/atddGfvbLI677jn+80R/Lj1+7wxGl3lHNN+L3xavPZmsnFORI5rtyah52XqMSNHjodO5usutnLHtJex9zO60aN2sRJkDT9+HhXMXcsqWPXi9Vz/OuOOEhKItqbw0l9QCzkt73hLI2iTjxx9+Y8H8JWtdP3fuYsb8PJ3ClUUZjCrz8hcsYvTUcOBYvGwFE2YW0LBGtTXKHbdHGz4cMY6ChYszHWJGbLV9M6b/NocZU35n5cpCPu3/I7vt3bpkIYcq1SoCULV6JebMXpBApPGrX7Emu9Xbmnemfl3m+sWFy1Y9rpxTAcczFVpGfT92KvMXLV3r+lZN6jBs9GQAJk3/ncb1alCnRpVMhZdR9SrWZNe6rXlv2tovuk5tdRAv/zaQ5UUrMhhZ5my1y+ZMGzeDGRNnsXLFSgb1+YKO3dqVKNPx0PZ88MynAAzuO5Sd9t0uiVDXkPEkw8wuN7NvzGyEmd0YLb4D2MzMhpvZ3dHzPaPnl5jZKWb2UNp79DOzztHjA83sOzP7wcw+jpZVNbMnzexrM/vezLpl9lPKX9Wkdg1aN63PiN9mlFjeoEZV9t1uc/oM+SGhyOJXt0ENZs+Yt+p5/sz51G1Yo0SZ5x/+mH0O2ZHnPrqcmx4+iYdv75fpMDPiwq268sgv7/1h8nBEs93os/uVnLtFF3qNeTuD0ZUfv/w2m73bbg7ANq0a0ahuDRrUXjNBzwbnb34Yj4/rt9Z9YotqTalfsRZfzRmd4cgyp17TOsyesro5LH9KAfWa1i1Rpm7TOsyenA9AUWERi+Ytpkbd6hmNsywZTTLMbH9gC2AXoA3Q1sz2Aq4Cxrt7G3e/PHr+WfS85x+8X33gCeBId98RODpadQ0w0N13AfYG7jazqmW8/iwzG2Zmw1asXLT+Pqj8JZUr5NHz5EO4861PWbRseYl1V3brTM93P8Oz84J1nXXusgMfvvk9J/7jbq4/71kuv+0ozCzpsNarjvVaM3f5QsYsmPqH5V6fMoTuX9zJo7+8x8mt9slQdOXLM+9+Q/UqFXnhphPovl8bxk6aRVEWfkk61N2G31cs5JeFZfexMIxzNu/Go+PfynBksq4y3fFz/+inuHdfNULS8dvffL8OwGB3nwjg7gVpf+dQM7ssel4JaAGUSHXd/XHgcYAa1Zpm3zd0A5CbStHr5EN497uf+WjkuDXWb9u8IXef0AWA2lUrs2frVhQWFjHwp/GZDjU2c2bNp36jmque12tYgzkz55coc8DhbbnmnNABcPQPk6lQMZcataswryB7kuPta23C7vW3oUO9ramQyqNqbkWu2+4Ybh75cpnlP5rxA5dufXiGoywfFi1dzk29P1j1/K17TmfqrHl/8IoN07Y1W9Gx7rbs2qE1FVK5VMmtxL9bH8/to18AoEpORVpVbcR9bc4HoE6F6ty8/elc92PvrOr8mT+1gPrNVtdc1GtWh/xSHX3nTC2gfvN65E8tIJWTomrNKsyfk3yzaqaTDANud/fHSiw0a/knr1tJyVqXSuvwd4509zF/OULJqJv+uR8TZhbw7ODvylx/4G1Prnp8S/f9+XT0xKxKMADGjJxKk03q0rBpbebMnE+ng7bnzitfLVFm1ox57NRhUz5863uat6pPhQq5WZVgADw2bgCPjRsAwE61N+WYTTqtkWA0q1KPKYtDlXDHelszZUn2jqj4I9WqVGTpshWsLCzisE7b8/2YqSxauvzPX7iB6T3hXXpPeBeAHWttxj+bd16VYAAsKlzKEV9cv+r5vW3O47Hxb2dVggEw5ptxNN2iMY1aNiB/agGdu+/O7cffX6LMkHeGsf/JnRg9dCx7HdWB4QNHJhRtSZlOMt4HbjazF9x9oZk1BVYAC4D0xqPSz38FzjOzFNCU0NwCMBR42MxauftEM6sT1Wa8D/Qwsx7u7ma2k7uXPTYyAVffcDg7tmlBzVpVeOn1C3mm92Byc0MO1e+t76hdpyoP/+90qlStiBc5Rxy9C6ef8CiLF2fXQWSnlk04tN02jJ02m76XHA/A/f2/oHHt0B/hlSEjkgwvY4oKi3j4tn7c+ujJpHJSfPDGt0waP4sTz9+XX36aytBBP/PE3f256IbDOPzEjrjDvdeufeRBtjl9s/35ef4Uvpg9iiObd6Rdnc1Z6UUsWLGEW0f2STq8WNxyThfabt2MWtUq0+++M3n8zSHk5oRjxOufjKBV4zr858wDwZ0JU+dw85Mf/Mk7ZpdTWh3ImPmTGTLnp6RDyYiiwiIe6tGb2wdcQyonxftPfcKkUVM4+cbujB02niHvDKN/74Fc9WwPnh77IAsKFnLrsWvtaZBR5hloxzOzhe5eLXp8EXBGtGohcIK7jzezF4EdgP7A1YREoS7wNNALeB5oS2jyqA3c4O6DzOwg4DZCTccsd9/PzCpHr+kYLZ/o7of8UYw1qjX1Xdqcu94+84ZqRoc1uq5stJq9v3FeJZe28L7sSm7/rmUvNEo6hHKjxsnZVVPwd6X2nZx0COXGR973W3dvV3p5RmoyihOM6PH9wP1llCk9ZLV0j67j1/Le/QmJSfqyJcDZfytYERERWS/KyzwZIiIikmWUZIiIiEgslGSIiIhILJRkiIiISCyUZIiIiEgslGSIiIhILJRkiIiISCyUZIiIiEgslGSIiIhILJRkiIiISCyUZIiIiEgslGSIiIhILJRkiIiISCyUZIiIiEgslGSIiIhILJRkiIiISCyUZIiIiEgslGSIiIhILJRkiIiISCyUZIiIiEgslGSIiIhILJRkiIiISCyUZIiIiEgslGSIiIhILJRkiIiISCyUZIiIiEgslGSIiIhILHKTDqC8sBWF5E2fm3QYiWt83w9Jh1B+1K6ddATlQuUDfk86hHJh2j0Nkw6h3Ki17+SkQ5ANhGoyREREJBZKMkRERCQWSjJEREQkFkoyREREJBZKMkRERCQWSjJEREQkFkoyREREJBZKMkRERCQWSjJEREQkFkoyREREJBZKMkRERCQWSjJEREQkFkoyREREJBZKMkRERCQWSjJEREQkFkoyREREJBZKMkRERCQWSjJEREQkFkoyREREJBZKMkRERCQWSjJEREQkFkoyREREJBZKMkRERCQWSjJEREQkFkoyREREJBZKMkRERCQWSjJEREQkFkoyREREJBa5SQewMWq711acc103UjkpBvT5ilcf+2SNMnt22ZETLtwfd2fCz9O465IXE4g0fu0OaMN5vU4llZOif++P6XPnmyXW51XI5YpnerBF202ZP2cBtx7Tk5mTZicTbIza7rMt597enVQqxYDnP+eV+weUWN/llL3oevreFBUWsXTRMu7/13P8NmZ6QtHGR/tDUCEnhz7du1MhJ4ccSzHgl1/oNeTLEmXaN23KdZ33Zuv69bno3X70/+WXhKKNl/aJYEPdDqrJyLBUyjj/hsO57rT/cfYBd9O560602LxhiTJNWtaj+zn7cOk/H+Kcg+7hsVveTijaeKVSKXo8dDpXd7mVM7a9hL2P2Z0WrZuVKHPg6fuwcO5CTtmyB6/36scZd5yQULTxSaWM8+86jmv/+QBndfwPnY9oT4utGpcoM+i1rzl3zxs5v/PNvPrg+5x189EJRRsf7Q+rLS8s5PhXX+Xg557jkOefY6+WLWnTuOQ+MW3BAq54fwBv/zw6oSjjp30i2JC3Q7lJMsxso6hV2XLHFkybNIcZkwtYuaKQT/sNp8M/ti1R5sDuu/LO81+wcP4SAObNWZhEqLHbapfNmTZuBjMmzmLlipUM6vMFHbu1K1Gm46Ht+eCZTwEY3HcoO+27XRKhxmqrnVsxfeIsZkzKD/vEG9+w20E7liizeMHSVY8rVamAe6ajjJ/2h5IWr1gBQG4qRW4qhZf6p0+dP5+f8/MpysadIaJ9ItiQt8M6JRlm9qaZfWtmP5nZWWaWY2ZPm9lIM/vRzC6Jyg0ys/vNbHi0bpdo+S5mNsTMvjezL81sq2j5KWb2tpkNBD42s2pm9rGZfRe9b7e0GK4zszFm9rmZvWRml6X9zXbR43pm9mv0eFsz+zqKZYSZbbE+N9zfVa9hTWZPn7vqef6MudRtWLNEmaat6tO0VX3ueeV8evbtQdu9tspwlJlRr2kdZk+Zs+p5/pQC6jWtW6JM3aZ1mD05H4CiwiIWzVtMjbrVMxpn3Oo2rsXsqQWrnudPm0vdxrXXKNf19M48OexWTr/hSB7598uZDDEjtD+UlDKj3wkn8s055/LFb5P4YcaMpEPKOO0TwYa8Hda19uA0dy8ws8rAN8C3QFN33w7AzGqlla3i7m3MbC/gSWA74GdgT3dfaWb/AG4DjozK7wzsEL1/LnC4u883s3rAUDN7G2gXld8RyAO+i2L4I+cA97v7C2ZWAchZx8+auJycFE1b1uPK4x6hXqNa3P3yeZx70D0sSrualY3PO70H8U7vQXQ+cheOvbQL957/dNIhSYyK3Dnk+eeoXrEijx56KFvWrcvYOXP+/IUi5ci6NpdcaGY/AEOB5kAFYFMze9DMDgTmp5V9CcDdBwM1ogSkJvCqmY0EegLp7QMfunvxZZwBt5nZCOAjoCnQENgdeMvdl7r7AuCddYh5CHC1mV0JbOLuS0oXiGplhpnZsOVFi9dtS/w/5c+cR/3GtVY9r9eoFnNmzitZZsY8hn40isKVRcycUsDUibNp2rJ+RuLLpPypBdRvtjobr9esDvlTSx5E50wtoH7zegCkclJUrVmF+XMWZDTOuM2ZPpf6Teusel6vSS3mTP99reU/ff0bOnbZKROhZZT2h7ItWLaMoZMns1fLVkmHknHaJ4INeTv8aZJhZp2BfwC7ufuOwPdARUKtwiBCjcH/0l5SuoHQgZuBT6Kaj65ApbT1i9IeHw/UB9q6extgZqmyZVmZ9jlWlXX3F4FDgSXAe2a2T+kXuvvj7t7O3dtVSFX5kz+zfowdMZkmLevRsFkdcvNy6HRIG4Z+/FOJMkM+HMkOHTYDoEbtKjRtVZ/pk7PvCmbMN+NoukVjGrVsQG5eLp27786Qt4eVKDPknWHsf3InAPY6qgPDB45MItRYjfn+V5ps2oCGLeqGfeLw9gzt/0OJMk02bbDq8S77b8/UCTMzHWbstD+sVqdyZapXrAhAxdxc9mixCRMKCv7kVdlH+0SwIW+HdWkuqQn87u6LzWxroANQD0i5+2tmNgZ4Pq18d+ATM9sDmOfu88ysJjA1Wn/Kn/ytWe6+wsz2BjaJln8BPGZmt0cxHwI8Hq37FWgLfA0cVfxGZrYpMMHdHzCzFsAOwMB1+LyxKios4pEb3+CWp88kJ2V80PcbfvtlJidefABjf5zMVx+P4tvBY9h5jy15bMDlFBYV0fuOfiyYm5malkwqKizioR69uX3ANaRyUrz/1CdMGjWFk2/szthh4xnyzjD69x7IVc/24OmxD7KgYCG3Htsz6bDXu6LCIh6+8iVuffViUjkpPnjxCyaNmc6JVx3KL8MnMXTADxx6xt7s1Kk1K1cUsnDuYu4976mkw17vtD+s1qBqVe4+8CByzDAz3hs7hoETJ3Bxx478OGMmH08Yzw4NG/LIod2oWakS+266GRft1pEDn30m6dDXK+0TwYa8Hax0j+U1CphVBN4EWgJjgFrAG8DJrK5B+Le79zezQcBwoBOh78Rp7v61me0GPEOotXgXOMHdW5rZKUA7d78g+lv1CE0h1YBhhITmIHf/1cxuAI4j1G7MAga4+xNR4vMKUFjqva8CTgRWADOA49KaZdZQs2Ij79isfAz5SdLKiZOSDqHcyKm9ZufLjVHh72tvutmYjL+nQ9IhlBubXTY06RCknPnI+37r7u1KL//TJOOviJKMy9x92J+V/RvvXc3dF5pZFWAwcJa7f7e+3l9JRqAkYzUlGYGSjEBJxmpKMqS0tSUZG9LcFI+b2TaEfhfPrM8EQ0RERNa/9ZpkuHvn9fl+pd77uLjeW0RERNa/cjPjp4iIiGQXJRkiIiISCyUZIiIiEgslGSIiIhILJRkiIiISCyUZIiIiEgslGSIiIhILJRkiIiISCyUZIiIiEgslGSIiIhILJRkiIiISCyUZIiIiEgslGSIiIhILJRkiIiISCyUZIiIiEgslGSIiIhILJRkiIiISCyUZIiIiEgslGSIiIhILJRkiIiISCyUZIiIiEgslGSIiIhILJRkiIiISCyUZIiIiEgslGSIiIhILJRkiIiISi9ykAyg3zPA8bY6F/+yQdAjlRq1vpicdQrkw5ZzWSYdQLlSfkHQE5ccbU75OOoRy4fBmuyQdQrmnmgwRERGJhZIMERERiYWSDBEREYmFkgwRERGJhZIMERERiYWSDBEREYmFkgwRERGJhZIMERERiYWSDBEREYmFkgwRERGJhZIMERERiYWSDBEREYmFkgwRERGJhZIMERERiYWSDBEREYmFkgwRERGJhZIMERERiYWSDBEREYmFkgwRERGJhZIMERERiYWSDBEREYmFkgwRERGJhZIMERERiYWSDBEREYmFkgwRERGJhZIMERERiYWSDBEREYmFkgwRERGJRW7SAWyM2u6xJede05VUyhjQ9xteeeLTEuvPuuoQdtx1UwAqVs6jVp1qHLXLjUmEGqtrzj6Ajjtvyu/zF3PC5c+ssf74Q9qx/x6tAcjJSdGyaR26nPkI8xctzXSosWu711acc103UjkpBvT5ilcf+2SNMnt22ZETLtwfd2fCz9O465IXE4g0Po1qVuOOow6kbrUq4PDKNz/y3JDv1yjXvlUz/n1wJ/JSOfy+eAkn/e/VBKKNV8Na1bj1pAOpU70KAH2/+JEXB5XcFl3abc2p+7XDzFi0dDm39vmYsVPzkwg3XlaDSrXuJJW7JQBL515O0YrvVq3OrXwYedXOAQx8EcvmXkPRytEJBRufdge04bxep5LKSdG/98f0ufPNEuvzKuRyxTM92KLtpsyfs4Bbj+nJzEmzkwk2TblPMszsYuBxd1+8PsolLZUyzr++G1ef1pv8mfN44NULGDpwNL+Nn7WqzON39Fv1+NATOrJZ6yZJhBq7dz8dyavvf8/15x9U5voX+g3jhX7DANhj503p3qVtViYYqZRx/g2Hc/XJj5M/Yx73v3ERX308it/GzVxVpknLenQ/Zx8u/edDLJy/hJp1qyUYcTwKi5y7+g9m1LRZVKmQx2vnH8+X4yYxfnbBqjLVK1Xk+kP34ayn32D6vAXUqVo5wYjjU1jk3PP6YH6eMosqFfN4+crjGfrzJCbMWL0tps6Zx2m9XmXBkmXsvk1Lrj/2H5xwz8sJRh2PijX/w8pln7Ly93OBPLCS//OilZNZkv9P8PnkVOxMxVq3syT/sERijUsqlaLHQ6dz5f43kz+lgIe+vp0hbw/jt9FTVpU58PR9WDh3Iads2YPO3Ttyxh0ncOuxPROMOtgQmksuBqqsx3KJ2mqH5kz/bQ4zphSwckUhn773A7vtu81ay3c+eEcGvTs8cwFm0PCfp65z0rDf7lvz4Zc/xxxRMrbcsQXTJs1hxuRon+g3nA7/2LZEmQO778o7z3/BwvlLAJg3Z2ESocZq9oJFjJoWku3Fy1cwfnYBDWuUTKYO2XErPvppHNPnLQCgYNGSjMeZCfnzF/HzlGhbLFvBhBkFNKhVclv8MHE6C5YsA2DExOk0rFU943HGzqqTU2FXVi4uTp5WgM8vUaRoxberlhUu/w7LaZzhIOO31S6bM23cDGZMnMXKFSsZ1OcLOnZrV6JMx0Pb88EzoVZ8cN+h7LTvdkmEuoaMJxlm1tLMfjazF8xstJn1NbMqZravmX1vZj+a2ZNmVtHMLgSaAJ+Y2SfR6x8xs2Fm9pOZ3RgtK6vcsdF7jTSzOzP9OdembsMazJ4+b9Xz/BnzqNuwRpllGzSpRaOmtflh6PhMhVcuVayQS4cdWzLoq1+SDiUW9RrWZPb0uaue58+YS92GNUuUadqqPk1b1eeeV86nZ98etN1rqwxHmVlNatWgdeP6/DBlRonlLevWpkblijxz+lH0Pe84urVpnVCEmdOkTg22blafH3+dsdYyh3fcjs9HTcxgVJmRymmOF82hYq17qFz/PSrWvHONmox0eVWOoXDpoMwFmCH1mtZh9pQ5q57nTymgXtO6JcrUbVqH2ZNDc1lRYRGL5i2mRt3kE8+kajK2Ah5299bAfOBfwNNAd3ffntCMc667PwBMA/Z2972j117j7u2AHYBOZrZD6XJm1gS4E9gHaAO0N7PDSgdhZmdFCcuw5YXlr5WlU5cd+eyDkRQVedKhJGqPtpsxYsy0rGwqWVc5OSmatqzHlcc9wh0Xv8BFtx1N1eqVkg4rFlUq5PHAcYdwx7ufsmjZ8hLrcnJSbNukIec8+yZnPP065+69Ky3r1kom0AyoXCGPe884hLtf+5RFS5eXWab9Fs04fLdt6fXW5xmOLgMsh1TedqxY9DxLZnfBfTEVqp1XZtGcCruRV6U7y+bfnuEg5Y8klWRMdvcvosfPA/sCE919bLTsGWCvtbz2n2b2HfA9sC1QVltDe2CQu89295XAC2W9n7s/7u7t3L1dhZzMtLTMmTmf+o1XX6XWa1STOTPnl1m2U5fsbSr5K/bbbausbSoByJ85j/qNa616Xq9RLebMnFeyzIx5DP1oFIUri5g5pYCpE2fTtGX9DEcav9xUivuPO4R3fviZD0eNW2P9jHkL+XzcJJasWMncxUsZ9utUtmqcfdsBwra478xDeG/Yz3z8w5rbAmCLJvX4z3H7cfHjbzMvC5NwL5yBF06naMVwAFYufY9U3prNAKncralY606WFJwBPjezQWZA/tQC6jdbXXNRr1kd8qfOKVFmztQC6jevB0AqJ0XVmlWYP2dBRuMsS1JJRulL87nr8iIzawVcBuzr7jsA7wIb1OXcmB+n0GSTujRsWpvcvBw6ddmRoQNHrVGuWav6VK9ZmdHf/5ZAlOVH1coV2GmbZgweVvZBNhuMHTGZJi3r0bBZnbBPHNKGoR//VKLMkA9HskOHzQCoUbsKTVvVZ/rkOWW93QbtliP2Y8KsAp754rsy1w8cPZ6dN2lCTsqolJfLDs0bMWFWQZllN3Q3HL8fE2YU8NzAsrdFo9rVue/Mrlzz7AAmzZqb2eAyxItm44XTsZww2i634u4UrSzZbGo5TahU5zGW/n4JXph9TUYAY74ZR9MtGtOoZQNy83Lp3H13hrw9rESZIe8MY/+TOwGw11EdGD5wZBKhriGp0SUtzGw3dx8CHAcMA842s83dfRxwIlA8rnMBUB3IB2oAi4B5ZtYQOAgYVEa5r4EHzKwe8DtwLPBgJj7YnykqLOLhm9/m1t6nkUql+OC1YUwaN4sTe+zHLyOnMPSTMPQqdPj8IeFo43Vjj4PZeZtm1Kpembf+exb/6/sluTkh733joxEAdNplC74aMYmly1YmGWqsigqLeOTGN7jl6TPJSRkf9P2G336ZyYkXH8DYHyfz1cej+HbwGHbeY0seG3A5hUVF9L6jHwvmlr8mvv+PnTdpQredtmHMjNm8fsHxAPT64Asa1wp9lvp8PYIJswv4fOyvvNnjRNydvsNG8sus7Eu2dtq0CV133YaxU2fT56qwLR58+wsa1wnb4tXPR3D2QbtSq2olru6+DxBGpBx3V3YNawZYNu8/VKp9P1gevvI3ls69jNwqYZusXPwCFapdhKVqU7HWzeEFXsiS/K4JRrz+FRUW8VCP3tw+4BpSOSnef+oTJo2awsk3dmfssPEMeWcY/XsP5Kpne/D02AdZULCwXIwsATD3zLb3m1lLYAAhsWgLjCIkFbsB9xASn28IfTKWmVkP4AJgWtTf4mmgIzAZmAe87e5Pl1HuWOBqwIB33f3KP4qrZqXGvlvLk9f7593QzGuTnVXPf0etb6YnHUK58OsxTZMOoVyoMO/Py2wsvri6V9IhlAuHN9sl6RDKjY+877dRf8kSkqrJWOnuJ5Ra9jGwU+mC7v4gabUQ7n5KWW9YRrmXgJfWR7AiIiLy120I82SIiIjIBijjNRnu/itQPmYJERERkdioJkNERERioSRDREREYqEkQ0RERGKhJENERERioSRDREREYqEkQ0RERGKhJENERERioSRDREREYqEkQ0RERGKhJENERERioSRDREREYqEkQ0RERGKhJENERERioSRDREREYqEkQ0RERGKhJENERERioSRDREREYqEkQ0RERGKhJENERERioSRDREREYqEkQ0RERGKhJENERERioSRDREREYqEkQ0RERGKhJENERERikZt0AOVFUYUcljWvlXQYiasyc1nSIZQbKydOSjqEcqHZ7doOUtJ2W12YdAjlwmkjBiUdQrnx0fZlL1dNhoiIiMRCSYaIiIjEQkmGiIiIxEJJhoiIiMRCSYaIiIjEQkmGiIiIxEJJhoiIiMRCSYaIiIjEQkmGiIiIxEJJhoiIiMRCSYaIiIjEQkmGiIiIxEJJhoiIiMRCSYaIiIjEQkmGiIiIxEJJhoiIiMRCSYaIiIjEQkmGiIiIxEJJhoiIiMRCSYaIiIjEQkmGiIiIxEJJhoiIiMRCSYaIiIjEQkmGiIiIxEJJhoiIiMRCSYaIiIjEQkmGiIiIxEJJhoiIiMQiN+kANkZXXNqFDrtuxty5izntrN5rrN99ty049ZQ9cXcKC4t46OGPGfnTlAQijddlVxxMhw6bM3fuYs447Yk11u/7j2055pjdwGDJ4uX06jWACeNnJRBp/Nod0Ibzep1KKidF/94f0+fON0usz6uQyxXP9GCLtpsyf84Cbj2mJzMnzU4m2BhpO6ymbQEVc3Loc2R3KubkkGMp+o//hZ5ffVmizOlt2nLMttuzsqiIgiWLueLj95m6YEFCEcfnwi3/x7KiJbgXUUQh/xv/rxLr61ZoRrdmF9Go0mZ8MvM5hsx5I6FISyqXNRlmtnA9vU8bM+uyPt5rfRrwwY9cefUra13/7fe/csbZT3LmOU9x1z3vcfm/DspgdJnz/oAR/PvKl9e6fvr0uVxy8fOcefr/eP65z/nXpdm5HVKpFD0eOp2ru9zKGdtewt7H7E6L1s1KlDnw9H1YOHchp2zZg9d79eOMO05IKNr4aDuspm0RLCss5Lg3XuWgl56jy8vP0alFS3Zq2LhEmVGzZ9G1z/Mc9NKz9B/3C//evVNC0cbv2YnX8Pj4i9ZIMACWFC5gwPTHGZJfPpKLYuUyyViP2gDlLskY8eNk5i9Yutb1S5euWPW4UqU8HM9EWBn344jJzJ+/9u0w6qepLFwY1o8aNY369WpkKrSM2mqXzZk2bgYzJs5i5YqVDOrzBR27tStRpuOh7fngmU8BGNx3KDvtu10SocZK22E1bYvVFq8Ix8PcVIrcVGqN4+GQqZNZunIlAN/PmE6jqtUyHmN5sLhwHtOW/EIRK5MOpYREkgwzu9zMLowe9zSzgdHjfczshejxrWb2g5kNNbOG0bL6ZvaamX0T/eweLd/FzIaY2fdm9qWZbWVmFYCbgO5mNtzMuifxWf+uPXbfkmd6n8nttxzNXfe8l3Q4iTuoy458/fX4pMOIRb2mdZg9Zc6q5/lTCqjXtG6JMnWb1mH25HwAigqLWDRvMTXqVs9onHHTdlhN22K1lBnvHXMi355+Lp9PnsTwmTPWWvaf227HoEkTMxhd5jhwQsubOGOznuxc+4Ckw1lnSdVkfAbsGT1uB1Qzs7xo2WCgKjDU3XeMnp8Zlb0f6Onu7YEjgf9Fy38G9nT3nYDrgdvcfXn0uI+7t3H3PqWDMLOzzGyYmQ1bsWJRLB/07/r8i7GcfPoTXHfD65x2yl5Jh5OoNm024aAuO/LE458kHYqIZFiRO11efo7dnnqcHRs2Yss6dcssd9hWrdmhQUMe/25YhiPMjKcnXMET4y/mxV9voF2dg2lRZdukQ1onSSUZ3wJtzawGsAwYQkg29iQkIMuBfmllW0aP/wE8ZGbDgbeBGmZWDagJvGpmI4GewDptfXd/3N3buXu7vLyq6+NzrXcjfpxM48a1qFGjctKhJGLTTetz6WVduP7avsyfvyTpcGKRP7WA+s1WHzjrNatD/tQ5JcrMmVpA/eb1AEjlpKhaswrz52RX5zZth9W0LdY0f/kyhkyZTKdNWq2xbvfmLbig3a6c0e9NlhcVJhBd/BasLABCs8iYBUNoWnnLhCNaN4kkGe6+ApgInAJ8SUgs9gY2B0YDK9y9uOGtkNWjYFJAh6hmoo27N3X3hcDNwCfuvh3QFaiUsQ8TgyZNaq16vMXmDcnLy8naE+wfadCgBjfcdCS33/42U6YUJB1ObMZ8M46mWzSmUcsG5Obl0rn77gx5u+TV2JB3hrH/yaFD215HdWD4wJFJhBorbYfVtC2COpUqU6NCRQAq5uSyR4tNGP97yWPBtvUacNve+3FGvzeZsyQ7j5N5VpEKqcqrHm9abSdmLZuUcFTrJskhrJ8BlwGnAT8C9wHfurub2dpe8wHQA7gbwugRdx9OqMmYGpU5Ja38AqDcNVJee/WhtNmhBTVrVuaVF8/j6Wc/Jyc35Hvv9BvOXntuxQH/2I6VhUUsW7aSm255K+GI43HNtd3Ysc0m1KxZmZdfuYBnnv6MnJywHfq98z0nnrQHNWpU5qKLDwSgsLCI8855KsmQY1FUWMRDPXpz+4BrSOWkeP+pT5g0agon39idscPGM+SdYfTvPZCrnu3B02MfZEHBQm49tmfSYa932g6raVsEDapW5d79DiJlRsqMd38Zw8BfJ3DJrh35cdZMPpo4nn/vsRdV8vJ4+KCuAExdsIAz330z2cDXs6q5tfhni2sASFkOI+d9yviF39G2djg2fvv7AKrm1uLMzXpSMVUFp4hd6x3Kw7+cx/KiZBMvW11hkOE/bLYvMACo5e6LzGws8Ki732dmC929WlTuKOAQdz/FzOoB/wVaExKkwe5+jpntBjwDLALeBU5w95ZmVgd4H8gDbi+rX0ax6jWaebtdL4jxE28YUiuLkg6h3Eh9+n3SIYiUS7/c3yHpEMqF0/YelHQI5cZ/tu/3rbu3K708sZoMd/+YcPIvfr5l2uNqaY/7An2jx/nAGqNE3H0IkN5AdW20vABov75jFxERkT+X7fNkiIiISEKUZIiIiEgslGSIiIhILJRkiIiISCyUZIiIiEgslGSIiIhILJRkiIiISCyUZIiIiEgslGSIiIhILJRkiIiISCyUZIiIiEgslGSIiIhILJRkiIiISCyUZIiIiEgslGSIiIhILJRkiIiISCyUZIiIiEgslGSIiIhILJRkiIiISCyUZIiIiEgslGSIiIhILJRkiIiISCyUZIiIiEgslGSIiIhILJRkiIiISCyUZIiIiEgslGSIiIhILHKTDqC8sIVLyBv8Y9JhJG7OiW2TDqHcqPNp0hGUD8sPbJ90COVChQHfJB1CuVH/W0s6hHLh2qN/TjqEcuM/a1mumgwRERGJhZIMERERiYWSDBEREYmFkgwRERGJhZIMERERiYWSDBEREYmFkgwRERGJhZIMERERiYWSDBEREYmFkgwRERGJhZIMERERiYWSDBEREYmFkgwRERGJhZIMERERiYWSDBEREYmFkgwRERGJhZIMERERiYWSDBEREYmFkgwRERGJhZIMERERiYWSDBEREYmFkgwRERGJhZIMERERiYWSDBEREYmFkgwRERGJhZIMERERiYWSDBEREYmFkgwRERGJhZIMERERiUVu0gFsjNrtvwPn3nsiqZwUA54cRJ973imxfvs9tuace05g0+1bcNsJD/HZG18nFGm8Gtauxk2nH0SdGlVwd94Y/CMvffz9GuUuP3Zvdt++FUuXr+CGJ9/n599mJRBtvNod0Ibzep1KKidF/94f0+fON0usz6uQyxXP9GCLtpsyf84Cbj2mJzMnzU4m2BhdefGB7LbLZvw+dzGnnvfUWsttvUUj/nvfCdx0x9t8+sXYDEaYOdon4LrT9mePNpvy+/zFHHPts2usr16lItedfgDNGtRk+YpCbu79PuOnzkkg0gyw6ljN2yB3CwB83lWwYvjq9RX3xapdDDiwEp9/K6z4NoFAS0qkJsPMWprZyP/H628ws8vWZ0yZkkoZF9x/Ctccehdn7ngFnbvvRoutm5YoM2tyPvec8RgDX/4yoSgzo7DI6fnKpxx9/TOccttLHL13G1o1rlOizO7bt6J5g1ocdvWT3PLsR/z7hH0TijY+qVSKHg+dztVdbuWMbS9h72N2p0XrZiXKHHj6Piycu5BTtuzB6736ccYdJyQUbbz6fzSSy6/r+4dlUinj7NM6Mey7iRmKKvO0TwT9Pv+JC+99fa3rT+26K2N/m8Vx1z3Hf57oz6XH753B6DLLalyLLxuM5x+I53eFleNLFlg+BJ/TFZ9zKD7v31jNW5MJtJQNrrnEzDbo2pet2m/GtPEzmTFxNitXFPLpK0Pp2LVtiTIzJ+UzceRkvMgTijIz8uctWlUrsXjZCiZOn0OD2tVKlOnUZjPeHTIKgJETplOtSkXq1aya8VjjtNUumzNt3AxmTJzFyhUrGdTnCzp2a1eiTMdD2/PBM58CMLjvUHbad7skQo3diJFTWLBgyR+WOaLrznz6xVh+n7s4Q1FlnvaJ4PuxU5m/aOla17dqUodhoycDMGn67zSuV4M6NapkKrzMsWqQ1x6WvBotWAG+oGQZT/s+WOWMhfZnkkwycs3sBTMbbWZ9zayKmV1vZt+Y2Ugze9zMDMDMBplZLzMbBlyU/iZmtpmZDTCzb83sMzPb2syqm9lEM8uLytRIf56kek3qMHvy6uq82VMLqNu0doIRlQ+N69Zg6xYNGDlhRonlDWpVY2bB6i/TrN8XUr9WtdIv36DVa1qH2VNW7xP5Uwqo17RuiTJ1m9Zh9uR8AIoKi1g0bzE16lbPaJzlQb261diz45a89e6azWrZRPvEuvnlt9ns3XZzALZp1YhGdWuscaGSFXKaQ1EBVvNOrO5bWI1by04kKu6H1RuA1X4iNKeUA0kmGVsBD7t7a2A+cB7wkLu3d/ftgMrAIWnlK7h7O3e/t9T7PA70cPe2wGXRey4ABgEHR2WOAV539xXpLzSzs8xsmJkNW+Frz5YlXpUr5nH3eV25p88gFi1dnnQ4Uo71OGsfHntyEJ7dlXyyjp559xuqV6nICzedQPf92jB20iyKsnLnyIG8bfHFL+JzuoEvwaqevWaxZR+G5pTfz4v6ZyQvyaaHye7+RfT4eeBCYKKZXQFUAeoAPwHFvSL7lH4DM6sGdARejSo9ACpGv/8HXAG8CZwKnFn69e7+OCFJoUaqbkb2zPxpBdRvvvqKpH7TOsyZ+nsm/nS5lJuT4u5zu9J/6Gg++W7cGutnzV1Iwzqrr84a1K7G7LkLMxli7PKnFlC/2ep9ol6zOuSX6rw2Z2oB9ZvXI39qAamcFFVrVmH+nAWl3yrrbbVFI66/6lAAataoTIf2m1JYVMTnQ9bcdzZk2ifWzaKly7mp9wernr91z+lMnTUvwYhiUjQj/Kz4AQBfOqDsJKPYim8gpwVYbfBkzy9J1mSUPqk78DBwlLtvDzwBVEpbv6iM90gBc929TdpPa4AogWlpZp2BHHf/2x1N16cxwybQdPNGNGpZn9y8HDr9swND+iXfAzgp1528PxOnF/DCh9+VuX7w8PEcvNs2AGy3aWMWLllO/ryydoUN15hvxtF0i8Y0atmA3LxcOnffnSFvDytRZsg7w9j/5E4A7HVUB4YPLBe7c8Ydc9rjHHPqYxxz6mN8+vkYev73w6xLMED7xLqqVqUiuTnhNHZYp+35fszU7KwNLcqHwumQ0woAq7gbFJba73NarH6cuw1YXuIJBiRbk9HCzHZz9yHAccDnhFqJ/KiG4ijgD7uZu/v8qK/F0e7+atSHYwd3/yEq8izwInBzfB/jrykqLOKhi5/mtn5XkspJ8f7TnzJp9FROuv5Ixn43kaH9vmPLtpvyn1cuoXrtKnQ4eCdOvP5IztrpyqRDX+/abN6EQzpuwy9TZvPi9aFn/H/f+IJGUc3Fa5+O4PMfJ7L79q1467bTWLp8JTc89X6SIceiqLCIh3r05vYB14R94qlPmDRqCiff2J2xw8Yz5J1h9O89kKue7cHTYx9kQcFCbj22Z9Jhx+L6K7rSZofm1KxRmVefPZennv+c3NwcAN5+b3iywWWQ9onglnO60HbrZtSqVpl+953J428OWZVUvP7JCFo1rsN/zjwQ3JkwdQ43P/nBn7zjhsvn34zVuhfIg8LJoc9F5WPDyiUvQaUDsUqHASvBl+JzL04u2DTmCbRfmVlLYAAwDGgLjAJOBK4GjgVmAGOBSe5+g5kNAi5z92HR628AFrr7PWbWCngEaAzkAS+7+01RuUbARKCxu8/9o5hqpOp6h7wD1+8H3QDNObHtnxfaSNR5ckjSIZQLyw9sn3QI5UKFAd8kHUK5Mfek3ZIOoVz46o5Hkg6h3MhpPO5bd29XenkiNRnu/iuwdRmrro1+SpfvXOr5DWmPJwJryw72APr+WYIhIiIi698GPefEHzGzB4GDgC5JxyIiIrIxytokw917JB2DiIjIxmyDm/FTRERENgxKMkRERCQWSjJEREQkFkoyREREJBZKMkRERCQWSjJEREQkFkoyREREJBZKMkRERCQWSjJEREQkFkoyREREJBZKMkRERCQWSjJEREQkFkoyREREJBZKMkRERCQWSjJEREQkFkoyREREJBZKMkRERCQWSjJEREQkFkoyREREJBZKMkRERCQWSjJEREQkFkoyREREJBZKMkRERCQWSjJEREQkFkoyREREJBbm7knHUC6Y2WxgUsJh1APyE46hvNC2CLQdAm2H1bQtAm2H1crDttjE3euXXqgkoxwxs2Hu3i7pOMoDbYtA2yHQdlhN2yLQdlitPG8LNZeIiIhILJRkiIiISCyUZJQvjycdQDmibRFoOwTaDqtpWwTaDquV222hPhkiIiISC9VkiIiISCyUZIiIiEgslGSIiIhILJRkiIiISCyUZCTMzCz6vYeZXWJme5tZ06TjkuSZWWsza5N0HOVJ8fdlY5N2nLD0x8lGlVlmtlnSMchfpyQjYe7uZnYg8ASwCHgUONLM9L/ZCKWdQHYGLgcuM7Ntko0qGWnbYgsza2hmVaPvy0b13TAziz73wcCTwENmdrBvJEMDo7yqMvC2md2VdDzlTXlPNjeqL2t5E315qgFdgUOAH4AlwKvuXmRmFRMNMEPST6xmtn3S8SQpOpl0BZ4G5gC1gQvMbKdEA0tAtC0OAD4F7gQeM7OG0Xdjozl2RdthP+BG4AGgBnC5meUlG1nGpNx9CbAHcJCZXVG8YmPaD6DEsbKGmVWFVfuHlddkY6P6B5U3HiwEJgP3Eg4gh7r7dDM7DCiXc9Gvb9GX5DDgEaBW6fVmtsZNd7JVlFgeBlzk7pcD1wLTgXPNrHWSsWWame0IdAK6E7bDJODxjS3RMLNcQrJ5JtAU2Aw42d1XmFnDRIPLAHcvjB7uDowCbjWzu6N1RQBm1tnMqicUYsZEx8puwIdAXzO7s3h5tK5logGWYaP4kpYnaZloEzPbIlo8EagJ3OPuv5lZO+AOIDehMDMqamu9Bjja3T8zs+Zm1jFa1wC4ZGPpp+Luy4AqhJot3P174HtgR+BUM2uSYHgZE1WPv0RIMkYA04D7gR+B54sTjQRDzIioJudWYFugN/Av4Eh3n2RmXYDzzKxSkjFmgpkdCdxD+PydgcPM7I60IodSxgVKtjGzLQnJ5uXARcA+ZnZvtK4ScH55uxhRkpFhaZloP+AZM3sCWAp8AxxiZv0IB5Mr3P3TBEPNpIpAEbBvdOB4APjQzI4F5gEPu/vUJAOMS1rSuaWZtY0W3wlUMbMzoue/EU6ymwLNMh9lZpnZpkAFQjNiVeBMdy9y91nAQ8B3bBzbYUvgYuBRd/8P4TgxParp/AdwH/CFuy9NMMxYRElm8eMUsBh4z92nuvsXwL6E2r17Adz9X+4+OZlo45V2jNgGeIywH3zl7mOBfxDOG8dG+8HN7j46uWjXtFFcKSfNzCq4+3IzyyEcHC8DTnD3UWZ2E7AzYe75JUArIN/dfy7u8JVc5PFI68jWHJgLjAF6AWcTEoxrgaOAxtGV/ZSEQo1dWsff+wDM7G3gBeAjwkH0CKA1cCBwDrAd8HVC4cYqOplUB64CZgB3A0cD75hZkbvf5+4zzOw6d1+eZKxxM7PawO2E/hfFfS+6Am+Y2cuE48i/3P2DhEKMTdRPbR8zmw5sTvj884CtzKy2u/8e1fg+C+xvZnXcvSDJmOMUHSP+ATQBBgIHAe3N7Ht3n2dmzwAelZ2fYKhlUpIRMzOrBXxjZt3d/TszKwRWAMXtjLcCbwJF7n4jsOqKPRsTDFj1pTkEuBKYDcwC/uvunQHMrBNwPXBhYkHGKD15NLPtgPOBLsDvhCuVbsBzwOHAToS+CM2jMg8lEXNcihNwWNW+Ps/MngOOAXoADxJOrp+YWY67370RJBitCf1w7gWuAPY0s/nuPgPYK+p7UDmq2clmjxGakXdw90VRh+jnzOwpoCXhpHtQNicYAGa2LXAKcLe7P2tmKwmJ+GAzG0O4+DgluQj/mJpLYubucwnNH++ZWRt3n0JoY97NzJpFV+q9gZXltXfw+hb1r7iHUBV8E/AVoTNX+6iq/H7gSnf/KLko4xF11PuvmeVEvcOPB7YHKrn7PEJb6zaE9tYa7j6YcDV7GXCUu49PKPT1zsx2AN40szwz29rMnjCzPHf/DHiecCK5kpBk7UOW1uCkixKIEwk1e6OAnoSmgQOK++O4+4JsTjCizvAzgPqE//mu0fIzgE8ItXl7Af+JjqdZKTpG1AMGAHWAXwHc/XbgY8KxY1/gRHcfWF7PH7oLa4xKXbFeBFwH7EY4aZwbFRtNuJI9z90HJBJoBkXtinWAy9z9sGhZPUITyRB372NmLaLq0KxrLrIw7LAVsJxQg1ObUGuzBHjI3cdFzUj3Ade6+5jodXXdfU5CYa930XZ4GhgOPAtsDZxEaHu/NGpePBD4H2HU0Z3uvjJL94kSnynqm9ONcJK9CmhDSMjfAZ5z9xUJhBmrqIP33PRaKjNrRUgw/gH0d/fXzGxzQgKy1N1XJhNtvMrYH/YhNKdf4+590pb/C9gb+A/wQ9oonHJFNRkxipoFcqLH9wO3AF8ABYTRFF8QTjKnZXOCkdZxaTdCdf90oL6ZXQfg7vnAfGDL6Plv0e+sOpkAuPuKqMPWVYRhaL8TRhItIfTB2DLqwHaCu49J23+yKcHIiU6U3xD+5+8C+YTtkEe4eofQV2cE8HbxCSVL9wk3sz3N7J7o+bfAa4R5Um4CviU0G32XbQmGBQ2BV4Fuljb3h7tPBD4j7CcHm9nzwMOEWr+sTjCi/eEWMzuZ8D04Hrgj6qMFgLvfR5hb6QpCR+nyyd31s55/WF1DVCH6nUpbdwmhH8J20fPcpOPN0DbZFfgvcEb0vC3hQPo0oa/BKKBT0nFmYJ9I3xceJlSFViY0DfQkdHytnF4um34I7eiHRo8PJ5xIewPVomVbAy8SqsnHENrcE487hu1QldCvAkLNVi1CwnlLWpkjCCNpHgVyko455u1xPPBBtE8UHzeLvzNVCFfsDxYfN7P5BzgA+JlQ892LMBJxB8I8IbMJQ5jTy9dNOuY/+lFzSUwsjGE/idC+WuIqLGo6uQr4h7v/lFiQMTKzFsAuwPfuPt7MTiA0ibwK3ORhIqFGwM2E6s+v3L1fchHHL9on9iRU9d4YLXuMMMHSP4FGQJ5HTSTZyMw6E2otphD2jyLCiWU68JJHfU7MbH9gprv/kEyk8TKzPYHTgP6EmTzbEJpHhgLPuvvVZtae0Knvviw+TqQ3Kf+T8HkfJDSPLI2WdyfUXjyTXKTxMbPGwCbAN+5eaGZXA2Pdva+Z1QH2B/Zz99MtTH8w390/MbOUbwhzxSSd5WTjD6Ej3wfAzqWWW9rjiwlXrpbJ2DL0+bcmTJr0JqGj1ubR8uMIQ7AOILpaWdv2ybYfQs3NKOBUwonk7bR1zxKaTrKy9qKMbdGUcA+Oc9L2l6cJnTxbJx1fzJ+9AbBL9Pg1wkizg0ttm8mEYcxTydKanFLbJP24+M/omHF49Px0wmSF2yYdZ0yffStCc9CVwO7Rsp7AG2lltoyOpfXL2mbl/Ud9MtYzM6tBmAZ5O8KkKavm13cPc8xHRRcRrlzKZY/gvyvqmPU6YVKYw4BxhNn56rj7i4Qmk0sJ49tL3HvBo29PtrFwP5bTCcN0n3L3DkA9M3sDwN1PAi7xDeGq5P8pqqHoRmhr72hmpxP2kdsIw3WPiEbdZKvTgYsszGj7NvAyYUbbBgAeJp3bjtB3qau7908s0piUHgWRflx091cIHX3PsTBR4bXAYZ6FNTnRSLo3gAfd/U4Pk4wB3AAssdUzmlYkJKerpk3fkI6VSjLWg7SOjTkeJkN5gpB5nm9mm3q4z0Lxl8ijk2shof01a04sFu6xcDJhhsqPo8VtCTUXvS3cQfFdwnTRlxHGwG8MGgJbADtZdG8Bd+8IbGpm70XPRyYXXmZEQ1aPBr5196cIyejehCbFCYRe8u+4+6LkoozdvcBYwnYY5+4nEvqevGZmKTPbCzjG3Ye4+3dJBhqHUs0jXcxsaygz0XiScAXf1bO0yYww/PRtd38WVnWCzfEwlP0WYAcz+4DQR+kud5+QYKx/m/pkrCdmdihwMCHrvIvQee9QoBrwiLuPK1V+w2hP+4uiE8lRQA7hBPKZu18ZtcWfCvR193eKh6kmF2l80nqI70AYNTKX0LHvRkJz0QfFn93MOrr7l0nFmilR7cT3rO64NiNafihhIqH+7v5EchFmhplVIewT1xA6+z7u7l+b2SOEG5+1AK5y9zcTCzIGad+JVHTRdQFhht9DPYwiKS6X69HIETOr4u6Lk4o5btHIkfbufoGlTUoXrdvS3ceaWTPCeXryhjp8WzUZ60F09XEtIfvcgnBF9j2hKqyIUD1aOf012ZZgpDUJjQD6EpKtZYS2dtx9ELCQ0P4OWTpVeHQQdTM7iNCu3o0wDHMRYVvsARxqZpsAbCQJRjPCvnAEofbq6OJ17v42YXbTb5KJLnMszH9yD+Hmd7cSJlc608zaufu5hHb5w9z9zdJNCllgCwjHPTPbg9Bs1MndJ5pZRzPbx8yqeTQXSlQ2axOMyCKiO217mBcmz1bfWXgfM9vD3ad4dE+WDTHBAE0rvr60IYxn357Qx+LK6Ms0gjC5kLn7kgTji03UB2WFuy8pvkpx9xFm9j/ChFPHWJhbvzJhlr5XICuTrCruvjj6v29CuFI9FGhPmHRrhbt/YGFa+TOBtxIMN3ZpV667ErbFt+5+o5kdR2gaWOnujwC4+xuJBhujUlefywn9T/5B6PB5K/BvwkXI4x5mOgU23BNKaVHCUAH42MzedvfzgfHAIMK8DwsJw9snEUZXvZgtn/3PeBg9cpyZfebue3o0B4qZ7Q5cQGhG3OCpJuNvKOMq43dC1d9VhEmUfjWzEwntr794lg5JjBKMqwi3IK8SnWCLazR+JnRqSxGaCfoRqoGz7s6y0TCzKy3cjwXCEM33gQ6EW1Mf5e6zLdx74TPgXM/SO0YWixKMLoSbnM0AjjOzi6KariOB66Mq86wWbYfdzKy1u88kTJc+hnA/lr0IE5CNIxxDslHKw60Ttga6mtl/3H068CUh6XoW6ETYJg2SCzOzbPUke0cAs83sazO71swuJNR4XuXu3ycZ4/qiJONviA4ce5nZYWa2C6GXeH3CmPd5Fma2vBL4MMuz8kWETmybAt3NrGKpRGMEYV6MOcDZ7v5ucqHGqgLhLoi7m9l+hDb3fQmTTO3l7r9EV/T/Blq5e7aeUFYxs7qEG5zd4O5nRY93N7Oro/2iC5D1nV0jexLunrq1h3uOvE6YYOpawvwHN2Zrx19fPdX1boRp0a+2cBfdV939AncfTujDdQhhYrqNgof5MHKjx0cQBgssJkzSdra798uWJjM1l/wNZrYzod/Bs4Q29nsJ7cz3EmZxrAf8293f21A76/yZqBd0oZlNJjQL7AasMLPX3X1xqaaTq6NlWbctou0ww8y+JcxaeA7hyv0owlwpD1i4U+JJhBNuttZqbUUYgvqFu0929znRvtHazD6NmooaAreZ2TR3fzp6XTbuE8VNRbtHi3oS+mY9Z2YnuftoM/uQkGhlZefndGZ2LKE5+VDC6LJHzKyGu18eXZAdR7i1ws9JxhmXUk2HVYAl7j406n+S4+6FZXV6zpbvhWoy1lFxVmnhToh1gePc/TLCnA/XALu5+z+BEwhNJu9k4wG0WJRg7EkY0/4EYfKt3Qh9MCqVqtFYHP3Oum0RbYf9CdXegwlj2c8ANieMrplCOMFc6u5vZcvVSbroM51F6MB5l5ndb2bVCDc/q0ZIxCHcg2MccJmlDV3MfMTxSev4uz+haQSg0N3vIfRH+sjMLibcFO9xdx+dUKiZZEBvdx/t7u8BnQlNrDe7+xDC8fLHRCOMUbQ/HEqY+6QjIdE+JlpXmH5MyMrjQ5Z9x2ORlol2IWTklQh9DO5w97lRNv408Ji735fNyUW66GDZ2MMQ1VzCNMlHEebB6ONZ3Ds8bZ/IBe4ERrl7bwvTqZ9KaIO+192HJRpohkTNRP8hzJNyH6EppAHhBFM88dxOhGHe5xOGrH6UTLTrn5nVcve50eM6hGbCO9z9w/TjQXRVvxXwpbt/kFjAMSnr2Bf1RbrR3XdOW/YIoYmkjWfRzf/KYma1CAn4CYQp9M8mjDqb4+X0zqnrk2oy1kF0MulAmMnzXMKslQ2AzmZWPcrGTwW+Ki6fWLAxSqvNKc62fwbamtkO7r7S3R8njCLpSLide9ZKSzr3A6YBB1q4HftvhGa0dsCJFs3kmO3c/UNCh9fj3L0bocaiC6F2ayXhzsMHEeaB6BqtzwpmVp1wB92mAO5eQJgafHxUpFJUrrm7v0S4d0/WJRhQ4v5Mp5nZrWZ2M2Fivo/MbLiZ7WJmPQjHibbZnmBEUoT9/1zC0N0To745+0TNjFlNScYfSDup1iOMkNjW3b/xMPRuGOFgeVDUvvilr54WNitFJ9aOwOlmth3h4DGUMG14JzPbgtA08F93z8p5MIpFHX7PJlylv06Y8+AYWz0l9q/AE9HBJKulJZ23AhXMbEdCM+J/gAuBecBjhLlT7geOcPdfEwg1LiuB/wFuq0fM5BGaRPAwvHsX4P4oEc2q4dulmdmZhOazkYRJ6H4g1AA/QxiW2ZVw07es/G6knTeaWZhcrICwLS4mNJuOM7NOhDus5q31jbKEmkvWIq06/ABCp85HCdO7vuru10RlLiZMm32Zh+FpWc3CrJ1PEoZnHkUYijif0Pfgn4Sp0u/1LJ73AFZVf34CTHf3LtFB5WjCKIJdCSfT6909q+fCKC2qtXmOMDTzYnd/LFq+auZGM2uYTd+VtONEivB9OJwwB8qrwOfATGA0oVbnxmz/bgCYWS9goIeJ1jCzmwjzxRwc9dWq6GFYa9ayMJz9EsJEfAuBTwl9UfYh7B8nA1d4lt95GpRk/KGod/g5hE5Lg8xsG8IV2Sfufn1UJmunx05nZlsSrsyedPeBFm7dfikhwfrYzGoT7qw6c2Pok2JmBxP64VwTNRNh4Z40WxBu5T5hY9gOpVm4PfkDhNqK6bZ6Gumsm0Y/LcGo4e7zo0SjK3AYYTj7q4TksxIwwd0/z7Z9Yi19MO4H5qUdI2sSRticm+3JBUBUy/scoXnwBqAZcAyhf9JBhNreGdm4P5RFQ1jXIjpgHEE4SFwL4O6joqrAl6JqsKuzPcGItoMBBxJuWNTJzAa7+/MWZq983MwuKb5qgezrk5J2MtmVMLLoN3d/N+rEd4+ZFbp7bw8z9o0qfl22bYd1NBz4CdjTzPoWJxbZlmBAiVED15jZ18AQd38x+s50AWoX1+akvyaJWONQqkNrN0Kz0QLC7RV+MLN8wuizrkBrwvDNrEsyzKwVcIC7PxotqgP0AbYlzAZ9grsvNLPtPdz8bZVs2h/WRklGmvQvTXRQvNTCfReeNrMu7r7E3X82s+PJ8juIpm2Lqu6+wMz+S2gaaUu4HXdfd38pOqBm7eRSaQnGQYQ21EeBl83s2CjRuBj4X1Tuf0nGWh64+wozewzIy8bEIp2ZbQ4cS5gfp5BwH5Iq7v4/C6OODjKzAe4+KdFAY5KWYJxHGLb9DuGi7EmgeC6hnQi3rj/Fs3cSukrALRaG7vcidPi9m9DJc08P8+h0AU4zszOzeDuUSUlGJO1k0oVwIs1x9xsIE8U8RLjfwtHuvsjdR/3Re23oSp1YLzGzIcDP7v60mVUEdgcqmtkL7v5C+muSjHt9MrM8d18RbYfNgZsJQ+5aEu6q+qCFkUUvm9nZZOEV2t/l7ll9s7OoD84WhBkqX3b3V8ysEmFm2yujfeeRqMYva/qfFLOSd0qtS+ifdaK7/2RmdxFqs2YQZr3NAyq5e35S8cYp2hajLXSIf9PC5Fr3mllfoBVwiJlNBG4HrtvYEgzQ6BLMrFF0sihOMO4gHDzONLMXCVV8FxBucvW2rb5LXtax1fPpu5n9gzD/w/WE4bpXW7j3xGPAL4ShiQ2LX5tlCcaWwHVmdhSAu48jXKHVI8x90IIwkuJFM+vq7h+7++fJRSyZ5MFYwqiiw8ysqbsvBb4gmv3XzJplaYKxN+FYsH90wbEUmE3oZ4C7LyJMIb+juy9z94XZmmAAeJi1swPh4qMrcHZ00dGbcB45hJCEXevub0cJ6kYla0+Y68LCrIODgZ3NrAph6udTCBMH/Uo4ib5KSDTOBC7M1ipgC8N0b4jaFyG6HwmhWagdoV/K0WZ2vrs/BNzu4UZHWcXMWhNmapxJaF8GwN0nErbJ8GjRz8BHhKtXyXLFJwcz28HMDoiqxi8jTMr3RpRorCCMIjjKs3AIt62+4d2vwJQoiVhEaB7oEx1DATYDmkdNRhuDJoQ+GeMJx8zLgO5RP7XDCeeNrJ4B+g+5+0b5A2xDGIZ4RtqyHMKJ5CugYrRsISErzU065pi3x86EPgd3EWbxBKhNOIjuFD1/HRgCtEw63pi2QRPC9OjHllq+e/R7R8LU0I8QOjd2jJZb0rHrJyP7R1fCkMTehHtwdI6W30bo8Ns06Rhj/Oy7EtVgllq+V/T7LuBrwui774Ftko45g9umLWHepJ2j59sTJui7POnYysPPRlmTYWGo4dvAQo86aZnZa4Sx3EXAVKCxmbUh1GQ85VEbZLZy9+8I84AUEu4t0cJXtx/mRSMrUsBJnl0TKaVrBnznoUNrDoCZnQt8ZmavuPsPhKF444F/ufuXkF1NRVI2M9ueMGR7P+A9QofGU81sX3e/mtDpsWVyEcauEfA/dx+S9t14ALjPzO4m3L/pbMKw7sM9+/utbWNmD5lZTXf/ltBv74yo6f1H4ABC0rXR2yiTDA/VmscCu5rZ+YQxzb+6+1DCDI7jCX0z3gFe9Gg8c2IBxyj9c7n714SDxErgIgvTJb9L6H/QG3je3X9JIs4MqUXUz8TDjYtaEYbtbgI0NbP73X2Iu9/j7u9n6z4hQan/7xTgIsJ9R64mdH5eCPQ0s4Pd/UrP7hl/GwL7RZ1aC81sM8IU8RcQLszOBIZH349fE4wzNqX2hyLCpHsvWJg6vQGwHCgenfiju3+qY8RGPhmXmbUDPgRGu3vHtOUtCH0RKkcn3qxmYWKpPQmjjW4mNBucSvgi3Ur48tT2MBQra9sVozblT4CXPAxFw8zquXu+me1F6MB1hYdOfrIRMLOdgda+ehTVBUAjd7/Wwo2/zgcucvcxScYZt2iE1VWE4anfRBdqxetuAJYAd2XrsaFYWifPj6Ljwg6EOy5fSZgL5HEPfXUkslHWZBTzcIfMzkBrMzs9bdWUKBPdGBKMHQmduUYTOry+Txii+RhQlZB0mLvPgOxtGrAwI+Viwv022pnZRQDRgWRXQptzPyUY2S+tk2cnwnfjGTM7K1r9PXCCmd1GSMDvzMYEo4wr8AmEYaknAx2jIbvFd5XdG3gtW48NxaL94SnC3ab/G12cjXX31wlTyp8CVIuG9Upko67JKBbVaLwH3OLuDyQdT5yiL0A9dx8Tfe5LgWHufm+0/i5C4tWB0KFpfjYeRNfGwn1J9iSMpplF6El/IOHGRm+v/ZWSTSzcUuAJwjw57QmTTT1CGHnUCTgUeNez8G6q6bWVZnYOYb6HPMLw3EsJo+12AgYRbll+lLuPTCbazLAwVXgv4Pzo2HkpYZDAB4RajUVRuY+Af3uWzxXzV2wsQ4z+kLsPi6o+PzKzNwk1GVk3VDUa194DqGxmTxCGaLYCCs2svrvPdvcrLEwk0zibvygWJs0pLP3c3ecC75jZYMKJZDbwTLSPZG1TkZSYhC4HqA587e7DgeFmNhV4ljBJX28zGxT1Tci6fSItwTiBcDfVCwjDMq8B/kvon9KV0EH+UQ/Du7NWNACgIeGiay9gDHAf4a6q3YAcMyvu+Ls5GtZewkbdXJLO3b8iDEH7LRsTDAAPNyf6kNA56USgADiB0AfjODPb0cItqdsDlRMLNEZmVr04oTCzPczsDDPbtlTCkXL3ee7+nLsPiJrVsrapSIIowehCGKbshLyjVdTZ8T3gDcJkS/sW7y/ZtE+Y2V5R02CxDsB/3f1Ldz+C0CH83ui78by7f7IRJBh7EP7v04HLgcPN7DAPehKamcd4mB34F2AXd5+QYMjljpKMkhZAme2RGzQzq1b8OOoB/zqhv8UFhPuOXEi4an+IcOVyvruPy8LtUIvQee1wM9uHcGW6M/CpmR1YXC5bk0z5Y1GV+KXADe7+PuHGgGcD+1uYAbcxoXr8HMvOiaaqA9PMrHgm3/FACwt3UcXdLwSWbSx9DixM1ngXcJ67jyAkGy8Qhi4fDeDud3uYTr14tuRZiQVcTmXjF+VvK74qybKrkyrAe2bW292fgTBUNcofjgJOdfd7ojbGewkdmfpF5bJmO0RWEm7y1h5oSpiIbaCZfQ7cGVV99080QskYM9uUcOvthYSRVNUJk/QVJ+UXA5cQEvAdCEl5faA50VDFbBHt+++aWUtgmIWbQL4DPAB0NbPvCKMnmhG+RxuDmkANwki7/u4+x8zeI/RPOT1qUp3t7kXpNaFSkjp+bgTM7HBCR8a73L1P2vI9Cb3nz3T3H82sPeEuo48AT2bLFX3UE75aNFJkC0I7ahfCvSZu9HD/gWMIowUuKk6yJHuZ2TaECfneJdzc7mjCLQYmERKJR939h6ipZEVUG7gvcANwcnRlu8GzcBPEgwhJ1h0ehqmfSqjdPIVQm3MuYZvUBC72MNlU1knrk9OQ0Pdmmpm1JSSbv7n7NVG5OoSbvk1LMNwNhpKMjYStvvnbre7eJ+p3UGRmDxGy9HejcjsDczyLbk8dtau2J5xMtiPc+K0LYWKlL4G+0bY4nnAw+SyxYCV2UfX/O4SZfJ+KljUhTDg3GxhKmITtKQ+zvBa/7iLgkyxKMPYD7iHUVuwALHH3q6J1pwL/ItR0DjOzGkCeu2d1p0Yz60aYbM0IM3a+TJgF+Swg390vTzC8DZL6ZGwkoo5r/wauNbMTo5NqB8LV2TRYlcl/ly0JhpltYmHW0vGEYbn/Ab6KPt8LwERC57bjo6TrBXf/LNv6osgalhFqLF6BUNMVXZWeAexBGCWQT+jkWbV4f3D3+7MowdiH0DfrSHfvTajVa2JmF5jZZlHydQ/h9uX7uPv8bEwwzKxC2uNWhEm1TiEcFxcSLkYmEuYNam7hDs3yF6hPxkYkanNdADwfJRi7A5e5+/fR+myr1jocGOTuw83sM8KshFub2XbuPtLM/ku4WmsDfEyUbGXhdpCSKhOGI3YmzHWx1MwquPtUM3uEMMPt+0CRR/MfZKF8wnwXmwPjCFfvXxCGtH9kZge5+zNmtoQwV0zWiRKGyy0M2f+S0GS0jDA30AIzux14C5jp7veb2dnuPi/BkDdIai7ZCJlZc6AC4c6yWT3Rlpk1JYwiOTJadAWhY99NhINKZ8I9FzTsbCNiZmcT7iz6QJSEFg9r/jehPf6WhEOMXdQH6wNCc8B57l5cs3MnYVj7SdmacEd9cp4l3KvpVXefGTUJ3UJIOD6Jlp0INHT3e5KLdsOm5pKNkLtPdvfx2ZpgpDd3uPtUQnPJq9GihwlDlZ8FRgLTlGBslF4nzH1wTvG8F2bWkdD2/mWyoWWGh8n29gJyCCMmik0mzKGTlaJk4iHgYXd/yN1nArj7fML/vjPwHzM7j3Ax8sPa3kv+nGoyJKuk9RDfGqjj0e3Yzew+wlTIhxHaWvcHFrn74MSClURFowi6E25y9g1hiOZN7v5WooFlWFqNxjmEqfTvBk7xLJ0q3MzygP8BF7r7vGiOi6LiWhszO4QwmmZb4D13H5hctBs+JRmSdaKRNP8ldO5z4AB3X25mdxOuUg7WpDlSzMwaRQ8rufuvxYlqokFlmIX7GH1NGF3T2d1HJxxSbKJJ+T4DrkobVZcijCipCHR094+KR+AlF2l2UHOJZIXiJpLoKqUpodd8Z0K17xvRCILLgc+BzRILVModd58R/fwaPd+oEgxYdUfq7YC9sjnBAPBwf6IHgSMt3JcEwgV3IbAPcFI0Km2j2w/ioJoMyRoWbnJ3EaGN+Tl3fzJa3gdoAHRx9yUJhigi5YCZ1SdMslWXMJT5E6Aj4c67l2rm3/VHSYZkhWg42p2EWRw3J7Spvl08e6eZvQHc5ll8Z1kRWXdRn5x/AucB3xFqOO9w9zc3xiazuCjJkA1SNENjReA3YFNgAKH24obo/gtdCLMYDnD3N5OKU0TKtyjZKAIquvsUJRjrl/pkyAYnGjnyIdAOqOzhFssDgCPMrEHUtt4PGEu4uVN9zeIpImVx95nuPtvdp0TPlWCsR6rJkA1KVEvRD+jp7r3Te4CbWU/CNOGHuvtsM9sEIFumSRcR2dAoyZANSnTjpjbuflE07GwHQoetye7+jpndRJgLY193n51gqCIiGz3du0Q2NBOAM8zsAMJESpUJQ+++N7ND3P1sM2tM6PypJENEJEGqyZANiplVIUz9fArhxk73E6YHbwZc4e4nJhediIikU5IhGyQzq+PuBWnPOwG3EoakTVfnLRGR5Gl0iWyQihMMM8uLphF/ALjd3acpwRARKR+UZMgGK5pCfBfgX8C1xfchEBGR8kHNJbJBixKNuu4+Q5PoiIiUL0oyREREJBZqLhEREZFYKMkQERGRWCjJEBERkVgoyRAREZFYKMkQERGRWCjJEBERkVj8H6QdHp8J65eoAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(8,8))\n",
    "im = ax.imshow(harvest)\n",
    "\n",
    "# We want to show all ticks...\n",
    "ax.set_xticks(np.arange(len(farmers)))\n",
    "ax.set_yticks(np.arange(len(vegetables)))\n",
    "# ... and label them with the respective list entries\n",
    "ax.set_xticklabels(farmers)\n",
    "ax.set_yticklabels(vegetables)\n",
    "\n",
    "# Rotate the tick labels and set their alignment.\n",
    "plt.setp(ax.get_xticklabels(), rotation=45, ha=\"right\",\n",
    "         rotation_mode=\"anchor\")\n",
    "\n",
    "# Loop over data dimensions and create text annotations.\n",
    "for i in range(len(vegetables)):\n",
    "    for j in range(len(farmers)):\n",
    "        text = ax.text(j, i, harvest[i, j],\n",
    "                       ha=\"center\", va=\"center\", color=\"w\")\n",
    "\n",
    "ax.set_title(\"Harvest of local farmers (in tons/year)\")\n",
    "fig.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "#  Matrices, Ecuaciones y Ciencia\n",
    "## Librerías\n",
    "* numpy\n",
    "* scipy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "¿Cuál es la diferencia? \n",
    "* **numpy**: Permite definir vectores, matrices y arreglos multidimensionales de datos.\n",
    "* **scipy**: Corresponde a la implementación numérica de diversos algoritmos de corte científicos: algebra lineal, estadística, ecuaciones diferenciales ordinarias, interpolacion, integracion, optimización, análisis de señales, entre otros.\n",
    "\n",
    "#### OBSERVACIÓN IMPORTANTE:\n",
    "Las matrices y arrays de numpy deben contener variables con el mismo tipo de datos: sólo enteros, sólo flotantes, sólo complejos, sólo booleanos o sólo strings. La uniformicidad de los datos es lo que permite acelerar los cálculos con implementaciones en C a bajo nivel."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "##### ¿Porqué numpy?\n",
    "\n",
    "Python no tiene operaciones matemáticas para vectores o matrices. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "x = [2, 3, 4, 5, 6]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "x + 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "y = [a + 2 for a in x]\n",
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "# En cambio numpy...\n",
    "import numpy as np\n",
    "x = np.array([2, 3, 4, 5, 6])\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "y = x + 2\n",
    "y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "¡El caso de las matrices es peor!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "X = [[1, 2, 3], \n",
    "     [4, 5, 6]]\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "X*2 + 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "# Muy ineficiente!!!\n",
    "Y = []\n",
    "for i in range(len(X)):\n",
    "    y_i = []\n",
    "    for j in range(len(X[i])):\n",
    "        y_i.append(2*X[i][j]+5)\n",
    "    Y.append(y_i)\n",
    "Y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "# En cambio numpy... \n",
    "import numpy as np\n",
    "X = np.array([[1, 2, 3], \n",
    "     [4, 5, 6]])\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "X*2 + 5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Numpy es como matlab, pero en python.\n",
    "\n",
    "Usar numpy no solo es más natural para trabajar con matrices y vectores, es muchísimo más eficiente (rápido)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Es posible combinar funciones de numpy:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "x = np.linspace(0, 1, 101)\n",
    "y = np.exp(2*np.pi * np.sin(x))\n",
    "np.floor(y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "En numpy hay muchos submódulos interesantes, entre otros, los que permiten generar números aleatorios:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "t = np.random.randint(low=0, high=10+1, size=30)\n",
    "t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_random = np.random.rand(4, 3)\n",
    "X_random"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.set_printoptions(precision=3)\n",
    "print(X_random)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "En scipy existen muchos submódulos específicos para ciencia:\n",
    "\n",
    "\n",
    "* `scipy.cluster`\tVector quantization / Kmeans\n",
    "* `scipy.constants`\tConstantes físicas y matemáticas\n",
    "* `scipy.fftpack`\tTransformadas de Fourier\n",
    "* `scipy.integrate`\tRutinas para integración\n",
    "* `scipy.interpolate`\tInterpolación\n",
    "* `scipy.io`\tEntrada y salida\n",
    "* `scipy.linalg`\tRutinas para algebra lineal\n",
    "* `scipy.ndimage`\tn-dimensional image package\n",
    "* `scipy.odr`\tOrthogonal distance regression\n",
    "* `scipy.optimize`\tOptimización\n",
    "* `scipy.signal`\tProcesamiento de señales\n",
    "* `scipy.sparse`\tMatrices dispersas\n",
    "* `scipy.spatial`\testructuras de datos espaciales y algoritmos\n",
    "* `scipy.special`\tFunciones matemáticas especiales\n",
    "* `scipy.stats`\tEstadística"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "##### Array\n",
    "\n",
    "Un array de numpy es simplemente un \"contenedor\" multidimensional.\n",
    "\n",
    "**Pros**:\n",
    "* Es multidimensional: 1D, 2D, 3D, ...\n",
    "* Resulta consistente: todas las operaciones son element-wise a menos que se utilice una función específica.\n",
    "\n",
    "**Contras**:\n",
    "* Multiplicación maticial utiliza la función **dot()** o **@**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "# Operaciones con np.matrix\n",
    "A = np.array([[1,2],[3,4]])\n",
    "B = np.array([[1, 1],[0,1.01]], dtype=float)\n",
    "x = np.array([1,2]) # No hay necesidad de definir como fila o columna!\n",
    "print(\"A =\\n\", A)\n",
    "print(\"B =\\n\", B)\n",
    "print(\"x =\\n\", x)\n",
    "\n",
    "print(\"A+B =\\n\", A+B)\n",
    "print(\"AoB = (multiplicacion elementwise) \\n\", A*B)\n",
    "print(\"A*B = (multiplicacion matricial, v1) \\n\", np.dot(A,B))\n",
    "print(\"A*B = (multiplicacion matricial, v2) \\n\", A.dot(B))\n",
    "print(\"A*B = (multiplicacion matricial, v3) \\n\", A @ B)\n",
    "print(\"A*A = A^2 = (potencia matricial)\\n\", np.linalg.matrix_power(A,2))\n",
    "print(\"AoA = (potencia elementwise)\\n\", A**2)\n",
    "print(\"A*x =\\n\", np.dot(A,x))\n",
    "print(\"x.T*A =\\n\", np.dot(x,A)) # No es necesario transponer."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Ecuaciones\n",
    "\n",
    "Para resolver un sistema de ecuaciones usamos numpy y scipy:\n",
    "\n",
    "$$\n",
    "x + y + z = 1 \\\\\n",
    "x - y + z = 2 \\\\\n",
    "x + y - z = 3 \\\\\n",
    "$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importar las librerías\n",
    "import numpy as np\n",
    "from scipy.linalg import solve\n",
    "\n",
    "# define matrix A using Numpy arrays\n",
    "A = np.array([[1, 1, 1],\n",
    "              [1,-1, 1],\n",
    "              [1, 1,-1]])\n",
    "\n",
    "#define matrix B\n",
    "b = np.array([1, 2, 3]) \n",
    "\n",
    "# linalg.solve is the function of NumPy to solve a system of linear scalar equations\n",
    "x = np.linalg.solve(A, b)\n",
    "print(\"Solución:\\n\", x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Verifiquemos la solución"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Verifiquemos\n",
    "print(\"A*x=\", A@x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "También es posible calcular la matriz inversa y multiplicar, pero para sistemas de ecuaciones grandes es muy ineficiente."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.linalg import inv\n",
    "# Invertir explícitamente una matrix\n",
    "A_inv = inv(A)\n",
    "print(\"A^{-1}=\\n\", A_inv)\n",
    "print(\"A^{-1} * x =\\n\", A_inv @ b)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Ejemplo para clases:\n",
    "\n",
    "![ecuacionmemistica](images/ecuacion.jpg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "# Solución\n",
    "import numpy as np\n",
    "from scipy.linalg import solve\n",
    "\n",
    "# Buscaremos resover x = [manzana, banana, coco]\n",
    "\n",
    "# define matrix A using Numpy arrays\n",
    "A = np.array([[3, 0, 0],\n",
    "              [1, 8, 0],\n",
    "              [0, 4,-2]])\n",
    "\n",
    "#define matrix B\n",
    "b = np.array([30, 18, 2]) \n",
    "\n",
    "# linalg.solve is the function of NumPy to solve a system of linear scalar equations\n",
    "x = np.linalg.solve(A, b)\n",
    "print(\"Valores individuales:\\n\", x)\n",
    "print(\"Solución:\\n\", np.dot(x, np.array([1,3,1])))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Tips\n",
    "* La práctica y la necesidad hace al maestro.\n",
    "\n",
    "* Preguntar: en línea y en persona, pero tratar de solucionar los problemas antes.\n",
    "\n",
    "## Enlaces útiles:\n",
    "* http://www.labri.fr/perso/nrougier/teaching/numpy.100/ : Lista con 100 recetas prácticas."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "#  Análisis de Datos\n",
    "## Librerías\n",
    "* pandas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "El nombre de la librería pandas viene de \"Panel Data\". \n",
    "\n",
    "¡Gran parte del éxito de python en Data Science es atribuible a esta librería!\n",
    "\n",
    "###### Instalación\n",
    "```bash\n",
    "pip install pandas\n",
    "```\n",
    "\n",
    "###### Utilización\n",
    "```python\n",
    "from pandas import dataframe\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Aprender haciendo\n",
    "\n",
    "Consideraremos el siguiente archivo `data.csv` que contiene datos incompletos:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%bash\n",
    "cat data/data.csv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "#### ¿Porqué utilizar pandas?\n",
    "\n",
    "Porque en numpy no es posible mezclar tipos de datos, lo cual complica cargar, usar, limpiar y guardar datos mixtos.\n",
    "Trabajar con pandas permite automatizar procesamiento de datos, como se haría en excel, pero todo desde python."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "df = np.loadtxt(\"data/data.csv\", delimiter=\";\", dtype=str)\n",
    "print( df )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'pandas'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
      "\u001b[0;32m/tmp/ipykernel_20567/1418217510.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mpandas\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mdf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"data/data.csv\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msep\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\";\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'pandas'"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df = pd.read_csv(\"data/data.csv\", sep=\";\")\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting pandas\n",
      "  Using cached pandas-1.4.2-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (11.7 MB)\n",
      "Requirement already satisfied: numpy>=1.18.5 in /home/sofia/github/pycon_ar/env/lib/python3.8/site-packages (from pandas) (1.21.2)\n",
      "Requirement already satisfied: python-dateutil>=2.8.1 in /home/sofia/github/pycon_ar/env/lib/python3.8/site-packages (from pandas) (2.8.2)\n",
      "Collecting pytz>=2020.1\n",
      "  Using cached pytz-2022.1-py2.py3-none-any.whl (503 kB)\n",
      "Requirement already satisfied: six>=1.5 in /home/sofia/github/pycon_ar/env/lib/python3.8/site-packages (from python-dateutil>=2.8.1->pandas) (1.16.0)\n",
      "Installing collected packages: pytz, pandas\n",
      "Successfully installed pandas-1.4.2 pytz-2022.1\n",
      "\u001b[33mWARNING: You are using pip version 21.2.4; however, version 22.0.4 is available.\n",
      "You should consider upgrading via the '/home/sofia/github/pycon_ar/env/bin/python -m pip install --upgrade pip' command.\u001b[0m\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install pandas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Manipulemos el dataframe para convertir unidades y arreglar el contenido."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "inch2m = 0.0254\n",
    "feet2m = 0.3048\n",
    "df.diametro = df.diametro * inch2m\n",
    "df.altura = df.altura * feet2m\n",
    "df.volumen = df.volumen * (feet2m**3)\n",
    "df.tipo_de_arbol = \"Cherry Tree\"\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "print( df.columns )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "df.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "df[\"diametro\"]**2 * df[\"altura\"] / df.volumen"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "#### Lectura y escriturra de datos\n",
    "  1. **Archivo csv**: `pd.read_csv` y `pd.to_csv`\n",
    "  1. **Archivo de excel** `pd.read_excel` y `pd.to_excel`\n",
    "  1. **Archivo json** `pd.read_json` y `pd.to_json`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "#### Inspeccionando datos\n",
    " 1. Accesando las columnas\n",
    " 1. shape\n",
    " 1. head, tail, describe\n",
    " 1. histogram\n",
    " 1. pd.scatter_matrix\n",
    " 1. count_values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'pd' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m/tmp/ipykernel_20567/3274408022.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"data/data.csv\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msep\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\";\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'pd' is not defined"
     ]
    }
   ],
   "source": [
    "df = pd.read_csv(\"data/data.csv\", sep=\";\")\n",
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "df['altura']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "df.describe(include=\"all\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "df.describe(include=\"all\").fillna(\"\").T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "df.hist(figsize=(10,10), layout=(3,1))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "pd.plotting.scatter_matrix(df, figsize=(10,10), range_padding=0.2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "df.tipo_de_arbol.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "#### Otras operaciones\n",
    "   1. Agregando columnas\n",
    "   2. Borrando columnas\n",
    "   3. Agregando filas\n",
    "   4. Borrando filas\n",
    "   5. Mask\n",
    "   6. Grouping\n",
    "   7. Imputación de datos\n",
    "   8. Apply\n",
    "   9. Merge (a la SQL)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "##### Merge: Uniendo tablas (como en SQL)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1 = pd.read_csv(\"data/data.csv\", sep=\";\")\n",
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "df2 = pd.DataFrame(data={\"tipo_de_arbol\":[\"Cherry Tree\", \"Apple Tree\", \"Pear Tree\"], \n",
    "                         \"fruto\":[\"guinda\",\"manzana\", \"pera\"], \n",
    "                         \"precio_pesos_por_kg\":[500, 2000, np.nan]})\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "df3 = df1.merge(df2, how=\"left\", on=\"tipo_de_arbol\")\n",
    "print(df3.shape)\n",
    "df3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "#  Machine Learning\n",
    "## Librerías\n",
    "* sklearn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "La librería sklearn (scikit learn - _\"scientific kit for machine learning\"_) es una librería para entrenar modelos de Machine Learning desde python. ¡Tienes el poder de los últimos algoritmos, listos para entrenar en tu computador!\n",
    "Está construída para interactuar con numpy, pandas, scipy y matplotlib.\n",
    "\n",
    "###### Instalación\n",
    "```bash\n",
    "pip install -U scikit-learn\n",
    "```\n",
    "\n",
    "###### Utilización\n",
    "```python\n",
    "from sklearn import datasets\n",
    "```\n",
    "\n",
    "Observación: \n",
    "\n",
    "Notar que el nombre de la librería al instalar **es distinto** a la librería que se ejecuta. ¡Suele ser una confusión común!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.datasets import make_moons\n",
    "\n",
    "# Obtener dataset (datos)\n",
    "X, y = make_moons(noise=0.3, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.figure(figsize=(18,6))\n",
    "ax = plt.subplot(1, 3, 1)\n",
    "ax.scatter(X_train[:, 0], X_train[:, 1], c=y_train)\n",
    "ax.set_title(\"Dataset Completo\")\n",
    "ax = plt.subplot(1, 3, 2)\n",
    "ax.scatter(X_train[:, 0], X_train[:, 1], c=y_train)\n",
    "ax.set_title(\"Dataset Entrenamiento\")\n",
    "ax = plt.subplot(1, 3, 3)\n",
    "ax.scatter(X_test[:, 0], X_test[:, 1], c=y_test)\n",
    "ax.set_title(\"Dataset Test\")\n",
    "print(X.shape, X_train.shape, X_test.shape)\n",
    "print(y.shape, y_train.shape, y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "# Inicializar con metaparámetros\n",
    "clf = KNeighborsClassifier(n_neighbors=3)\n",
    "\n",
    "# Entrenar el modelo\n",
    "clf.fit(X_train, y_train)\n",
    "\n",
    "# Evaluar el modelo\n",
    "score = clf.score(X_test, y_test)\n",
    "print(score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "help(clf.score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "# Predecir: -1 a 2, -1 a 2\n",
    "x_desconocido = np.array([[1.0, 1.0]])\n",
    "y_predicho = clf.predict(x_desconocido)\n",
    "y_predicho"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "¿Cómo es la predicción en cada punto? \n",
    "\n",
    "¡Podemos graficar para averiguarlo!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "x1_min, x1_max = X[:, 0].min() - 0.5, X[:, 0].max() + 0.5\n",
    "x2_min, x2_max = X[:, 1].min() - 0.5, X[:, 1].max() + 0.5\n",
    "h = 0.20\n",
    "X1, X2 = np.meshgrid(np.arange(x1_min, x1_max, h), np.arange(x1_min, x2_max, h))\n",
    "X = np.c_[X1.ravel(), X2.ravel()]\n",
    "y = clf.predict(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure(figsize=(6,6))\n",
    "ax = plt.subplot(1, 1, 1)\n",
    "ax.scatter(X[:, 0], X[:, 1], c=y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Acá hemos probado el método de Nearest Neighbors, pero en sklearn existen muchísimos más:\n",
    "* Árboles de Decisión: DecisionTreeClassifier\n",
    "* Random Forests: RandomForestClassifier\n",
    "* Redes neuronales: MLPClassifier\n",
    "* AdaBoost: AdaBoostClassifier\n",
    "* y muchos otros...\n",
    "\n",
    "Nuevamente, la librería tiene una galería de ejemplos: https://scikit-learn.org/stable/auto_examples/index.html"
   ]
  }
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