{ "nbformat": 4, "nbformat_minor": 0, "metadata": { "celltoolbar": "Tags", "kernelspec": { "display_name": "Python 3", "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": "3.6.1" }, "nbTranslate": { "displayLangs": [ "*" ], "hotkey": "alt-t", "langInMainMenu": true, "sourceLang": "en", "targetLang": "fr", "useGoogleTranslate": true }, "toc": { "base_numbering": 1, "nav_menu": {}, "number_sections": true, "sideBar": true, "skip_h1_title": true, "title_cell": "Kazalo", "title_sidebar": "Kazalo", "toc_cell": false, "toc_position": { "height": "368px", "left": "248px", "top": "106px", "width": "165px" }, "toc_section_display": true, "toc_window_display": true }, "colab": { "name": "Periodicni_signali.ipynb", "provenance": [] } }, "cells": [ { "cell_type": "markdown", "metadata": { "id": "--2bMqubIsl_", "colab_type": "text" }, "source": [ "# Periodični signali\n", "Dejan Križaj, 2019" ] }, { "cell_type": "markdown", "metadata": { "id": "n-CUiZ8XIsmE", "colab_type": "text" }, "source": [ "**Namen:** Zvezek (Notebook) je namenjen prikazu uporabe Jupytra za prikaz in analizo periodičnih signalov. \n", "\n", " \n", "\n", "**Prej bi lahko predelal tudi:**\n", "\n" ] }, { "cell_type": "markdown", "metadata": { "id": "ZMdzOZPEIsmF", "colab_type": "text" }, "source": [ "
\n", "Namig: Obstajata dve verziji tega dokumenta. Ena je v obliki html datoteke (končnica html), ki je ni mogoče izvajati, druga pa ima končnico ipny (Jupyter Notebook), ki jo lahko izvajamo z Jupyter aplikacijo. To aplikacijo imate lahko naloženo na vašem računalniku in se izvaja v brskalniku, lahko jo ogledujete s spletno aplikacijo nbViewer, s spletnimi aplikacijami Binder ali Google Colab pa jo lahko tudi zaganjate in spreminjate. Več o tem si preberite v \n", "tem članku.\n", "
\n", "Za izvajanje tega zvezka ne potrebujete posebnega znanja programiranja v Pythonu, lahko pa poljubno spreminjate kodo in se sproti učite tudi uporabe programskega jezika. Več podobnih primerov je na Githubu na https://github.com/osnove/Dodatno/ .\n", "
\n", "\n", "\"Filtri\"" ] }, { "cell_type": "markdown", "metadata": { "id": "Bz8fSIZeIsmG", "colab_type": "text" }, "source": [ "## Nekaj periodičnih signalov\n", "\n", "V zvezku uporabimo funkcije iz modula SciPy za izdelavo signalov. Sicer bi to lahko bil tudi dober programerski izzivček.\n", "\n", "Za izrise uporabimo matplolib funkcijo subplots, ki nadalje omogoča nekaj več možnosti oblikovanja grafa.\n", "\n", "### Žagasti signal " ] }, { "cell_type": "code", "metadata": { "scrolled": true, "id": "ioRsZ2hwIsmH", "colab_type": "code", "colab": { "base_uri": "https://localhost:8080/", "height": 265 }, "outputId": "096b6ed0-8dcd-4d14-d4e8-11f0b5a8a343" }, "source": [ "import matplotlib.pyplot as plt\n", "import numpy as np\n", "from scipy import signal\n", "\n", "t=np.arange(0,16,0.1) # niz od 0 do 16 s korakom 0.1\n", "tri=np.abs(signal.sawtooth( t)) \n", "\n", "fig, ax = plt.subplots()\n", "ax.minorticks_on()\n", "ax.plot(t,tri,linewidth=2,color='b')\n", "plt.show()" ], "execution_count": 2, "outputs": [ { "output_type": "display_data", "data": { "image/png": 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HlFWjmtADwEY7TmfXTVWVik38wDaP9MOHZW/8ww8DTz5pOproY+ssOxeqFA0I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\nZm5i5g/MRvUJJQA6pU7f6gz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" ] }, "metadata": { "tags": [] } } ] }, { "cell_type": "markdown", "metadata": { "id": "mH-ddBvMIsmM", "colab_type": "text" }, "source": [ "### Pravokotni signal" ] }, { "cell_type": "code", "metadata": { "id": "1UKyCJCgIsmN", "colab_type": "code", "colab": { "base_uri": "https://localhost:8080/", "height": 265 }, "outputId": "ff861575-f610-44c7-9406-1f631b2b01b3" }, "source": [ "# pravokotni signal - z dodano grobo mrežo\n", "square = signal.square( t, duty=1/2)+0.2\n", "fig, ax = plt.subplots()\n", "ax.minorticks_on()\n", "ax.plot(t,square,linewidth=2,color='b')\n", "ax.grid(which='major', linestyle='-', linewidth='0.5', color='red')\n", "plt.show()" ], "execution_count": 3, "outputs": [ { "output_type": "display_data", "data": { "image/png": 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QQ2L0tBi2v+Aeg/cInCaI4VGG/hjDsP0F9xi8IXCaIIZHGfpjDMP2F9xj8EtD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\neEPgOI4TOd4QOI7jRI43BI7jOJHz/wExEluC70UkNAAAAABJRU5ErkJggg==\n", "text/plain": [ "
" ] }, "metadata": { "tags": [] } } ] }, { "cell_type": "markdown", "metadata": { "id": "BoFy_q8kIsmR", "colab_type": "text" }, "source": [ "### Sinusni signal" ] }, { "cell_type": "code", "metadata": { "code_folding": [], "id": "M7mdtgDOIsmS", "colab_type": "code", "colab": { "base_uri": "https://localhost:8080/", "height": 280 }, "outputId": "8263af6e-5fa3-4a5e-9247-1e3d8f67c756" }, "source": [ "# Sinusni signal z grobo in fino mrežo\n", "y=np.sin(t)\n", "fig, ax = plt.subplots()\n", "ax.minorticks_on()\n", "ax.plot(t,y,linewidth=2,color='b')\n", "ax.grid(which='major', linestyle='-', linewidth='0.5', color='red')\n", "ax.grid(which='minor', linestyle=':', linewidth='0.5', color='black')\n", "ax.set_xlabel('Čas / s')\n", "plt.show()" ], "execution_count": 4, "outputs": [ { "output_type": "display_data", "data": { "image/png": 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X68XlcmntWWxKLPZ6vWzcuNHInsUZGRlG9yzWEYvPnj3bRyz+yU+66OmZxLXXnkBKyd69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\nrFmqS99ZU19vvrlWjRmjP79qlT1e8eDE4igY8lq8eKcCpa67LrmYt9+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" ] }, "metadata": { "tags": [] } } ] }, { "cell_type": "markdown", "metadata": { "id": "RMyZN4wiIsmV", "colab_type": "text" }, "source": [ "### Zdaj pa ti \n", "(Rešitve so na koncu zvezka)\n", "* Poišči periodo sinusnega signala iz slike in določi njegovo frekvenco.\n", "* Določi periodo signala iz ogleda zgornje celice, ki signal izdela.\n", "* Izdelaj signal s periodo 5 sekund in prikaži tri periode. \n", "* Poskušaj določiti periodo generiranega signala z izdelavo ustrezne kode." ] }, { "cell_type": "markdown", "metadata": { "id": "S2EEYnDjIsmW", "colab_type": "text" }, "source": [ "## Fazni kot\n", "\n", "Običajno imamo opravka s harmoničnimi signali oblike $i(t)=I_m\\sin(\\omega t + \\varphi)$. $I_m$ je amplituda signala, $\\omega=2\\pi f$ je krožna frekvenca in $\\varphi$ je fazni kot signala. \n", "Izdelajmo tri signale:\n", "\n", "$i_1(t)=2\\sin(5 t )$\n", "\n", "$i_2(t)=2\\sin(5 t +\\pi/4 )$\n", "\n", "$i_3(t)=2\\sin(5 t -\\pi/2)$\n", "\n", "in jih poglejmo na grafu." ] }, { "cell_type": "code", "metadata": { "id": "tkT8y5MJIsma", "colab_type": "code", "colab": { "base_uri": "https://localhost:8080/", "height": 280 }, "outputId": "03fef630-b7b3-4d98-c29a-e6348046a649" }, "source": [ "# Sinusni signali\n", "omega=5\n", "t=np.linspace(0,10/omega,200)\n", "i1=np.sin(omega*t)\n", "i2=np.sin(omega*t+np.pi/4)\n", "i3=np.sin(omega*t-np.pi/2)\n", "\n", "fig, ax = plt.subplots()\n", "ax.minorticks_on()\n", "ax.plot(t,i1,linewidth=2,label='i1')\n", "ax.plot(t,i2,linewidth=2,label='i2')\n", "ax.plot(t,i3,linewidth=2,label='i3')\n", "ax.grid(which='major', linestyle='-', linewidth='0.5', color='red')\n", "ax.grid(which='minor', linestyle=':', linewidth='0.5', color='black')\n", "ax.set_xlabel('Čas / s')\n", "ax.legend()\n", "plt.show()" ], "execution_count": 5, "outputs": [ { "output_type": "display_data", "data": { "image/png": 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Wa8Z+t/BdqtqrGB89np+M+4nudj3lpRe/M+NO3qw+l7um3eV1bCU7rW4NOU3t\nXqlHteKtHbBBvv/HBX8Gg9HrM2Xd4XWtXWRtLgPgpilD9N1bHhINF/wJlt8L3z0k72w2BviNV2J0\nKLedPYZX1pfy1+X7+eiOMxX9c6tzWR9aQ1hgWD+BWG/bvtb1d4fPHTWXc0adw6bKTby8+2UePPNB\n33k90v8kLOUjafThx+O26H1lr/UtmuipeTT3hYBVDwICZv0Sa3iS4ku9GSeHQ/D/vpUF4p+MDyYm\nXPnEt16TJFj0JLwyD7a/BnPu9GkODcQCjAb+dMkkbsvK459rDvLUOSGKvo0GC6/veR2Ae2fei3FA\niQ1/8tKLhweFM6tL4WArnbGV7LT8RRASojyAEydO1B9ox+tyPZWEzN7zAdT8tWIPxF9eX4ql28Hi\n9BEsnqN+Pm8/m/E/MGwsNB6CvZ/6ndevL0glOjSQ7YflU87c+Qsh+PeufwNwe/rtxA6JdXmNv3np\nxe+deS+SgM8OfkZNe43feZ1IU5u7TU1N3HTTTbz11ltuD6HROtPYo7wOb5BP2QsZCuf90e/juGJf\nDUW1ZkYODeHXC1xOr1W2EVMh41pwdMPG53yaQ+6wCybGMTc1hpZOG1ubw9x4yZY1tJh2Wztnjzqb\nuSPnetSuN7w8wU+E72n1i8ApFsfFxSmKkJs3b2bs2LGaYvHo+GiSNzyLESiIvYLg4mIiIiJYsWIF\nY8eOdStCNjQ0UFRUpCgWm83m3noxXVIw7+fKS9XmDm1h5cpCFi5cSHV1tfvdnWVlpPXJacTwy5jU\n+E+sq//KlsbhHDpczoIFC9zmtHbtWsaOHasoFrvLadFoiQ8OwPMr9/PToWWMGZPSL6c9LXsw1ZuI\n7A5kQvsERXHLarVSVVXlNqdDhw5x6aWXKorFhw4dUsyprKyMQ4cOcd5557nNaXpdNDvjm3hmwzPc\nd8Z9LoLd8uXLSUlJURynqKio42Lxn4/1E4uFEBiNRkWxGGTBWKsMtc1NGWqLxUJ4eLhLGeq2tjYu\nu+wyHnzwQaZNm4bNZnMRi50iopJYvHr1akaNGqVLLJ6z/68EAYdHXEb59gIAjh075pdxcgjB09vk\nvrlolINVK5Zz5plnuhdWe8TivjkZQ+Yxm4/B9B5bGscRlZKhKKxu2LCB4cOHuxWLlXK6KK6dLaXw\n0c4afjKpmJb6qn457S3dy3vh8sq6axKuIScnp9/cKy4u5tChQ8o5FRVx8OBBzjjjDM/HSSMnrZ3F\nNpuNsWPH/jjE4gkTJiiKIevWrVMVS3rFqXVPysLUfy7uJ8qq+WvF7os/umyfSF6aLe58J88zXk7r\ntgnxjwyZ4+6P/MbLae0Wmy08+boAACAASURBVJj+mCxiv/jJahf81m9vFelZ6eLV5671OLYvvPTi\nB5+4R6RnpYvp70wXNW2uuym1eKmJxa2traq8VHENsbivr7MMtcViEfPnzxdPPvmkqu/OnTtVcdX+\n7Du/yrYc383b2azp6+k4LS+oEslLs8WZf1stumzd+nn1tU9vF+LhSFH5yjUeta0Xu+k/20Ty0mzx\n3KoiF+yF/BdEela6uPO7O72K7QsvTVxjZ7FWbE6kWCxJ0iJJkookSSqRJOl+N/jzkiSZeh7FkiQ1\n98HsfbBl/uCjaV2t8qljIG+g8WFduDura+3ivW3lAPz2Qi+PnTMGwLyetdUb/i6Xw/ajhQYFcPs5\nYwBYVtp/R2teTR55tXlEBEVwfavn96q/Dxtni2RB8gJsDhtZ+7JONh2f7OOPP2bDhg28//77ZGZm\nkpmZiclkOnENbpTLQDPnTvnWkB/N4RC8sOYgAL8+P5XgAC/Pdz73PkBiRM0aaK7wH8Eeu3u+vB/g\nrc2HMXcd32DZam3lgwMfAPCrjF+59f0hms8XAkmSjMC/gcXAZOB6SZL63RAXQtwjhMgUQmQC/wL6\n7pXudGJCiCV62lS7z5qW5loX38V2vi1vaU8+G1LO1u2vFduJv7rhEJZuBxdPiWfyyEj9vAbatOvk\nUsDHiskMLPOZ10C7+axkIkMCKGpysP3w8fMQnAfQ35R2ExFC/b60P/rLW/zOjDsB+KT4E451HuuH\n+cJLbX7pwfX6OstQ33jjjdhsNvLz8zGZTJhMJjIzM118tTQCXXOsMh9KVsulJM48vvrEX+O4qrCG\nAzVmRkSG8LOeMipezf3hEyH9KgyiWz7e0gtuatislGGckRhOa1c372wt733+vf3v0WZrY07n8N4y\nKp7G9oWXHvxE+PrjF8FsoEQIcUgIYQU+BK5Qef31wAe+NKi2fK/v/UEF7+P1fM7+nUf+WrEbGhpo\nare6/TWgzcuNGQNhnlx/JWT7v0Bh+aAeXu4sIiSQW8+WfxW82LPb2FRnIrc6l7DAMG5Iu0GToq/9\n5Qs+cdhE5ifNx2K3kLU3y2+8tOr+a+He+mrF1Vo+qmuObZAPWWfW7RA6TJev3nESQvTOo7v6/Brw\nau4DnPtHBBLs+i+0utbI0oqt1e5PJ8nLd/+z6TAd1m7arG28W/guAHc2q3+g+jJ3fZ33J8LXH2Lx\nKKDvb7ejwBx3L5QkKRkYA6zt83SIJEl5QDfwlBDiSwXfO4A7AEZGRsIjj7glE1pWBj0CiVvb/yWk\nVQLD4P0twFbd/lqxQ8vKeD/xLLq6Eznf0MyU157X7UtOjkJOdiCckNbD8NhtgGsMPbyU8P8RRl5z\nZLChuJ6Ch57mrRFrIAxuqE9k6JPPq/DSju0LL028h9edQbB2FHyy9z3uXN7U+wtGi1fnnXdClfsz\nGgwWCwQrL3dUxc1mxbhavlrtSi0tXo+F3F+/Ab4BAmBzJ2w+Hssf47jNEcFe6yRisHHtyrdhldDJ\n6xH3GNDRnkBYWBU8dzNwtgvuC++5ZWVkJizG1B7O+4+9TuCwjbTGtHJGVyyzVuyDLmVevsxdf8x7\nb3kpmjvhwJMHcDXwRp//3wS8qPDapcC/Bjw3quffsUAZkKrVptdiscMhxMNjZKFs57se+2sJMavW\nrBUzn/hOJC/NFhuK6zzyVRWBNjwrc37nSq94aeF3viyXxv7FeyvE1KypYvo700V9R702L43YJ1Is\n7svrthW3ifSsdPH23rd18zoVxGJP2/VZLP7yf+V5lP0Hj3z1jtMv3t4hkpdmi2cHCLC+iJ/5X77U\ns9s4RQiL665sX3mv3Fstkpdmi7lPrRILenYR5xzJ8VmU/TGKxZVA390oiT3PubPrGHBbSAhR2fPv\nISAHmK7VoNo92gkTVHYwlqwGGiAiAaZe47G/amzgiIil3mxhYnwE54zrv+5ey1fVZtyKI2AIlK6F\nOtfTlrRia+G/PH8CBgk21X6JQHDJmEsU9w14EttXXnr77MY0+SSz9w+8j91h95lXsMq3cj24t75a\ncbU0AvW8OmHPJ/Kfc1xFUF/HsexYO6v31xJkNHDTmcke+avZiJlLYOQZ0NkIBR96xE0P7wvT4kmO\nCaW2eyfV7VWMjhjNvMR5mrx8mbv+mvf+9PXHhWAHMF6SpDGSJAUhf9i7rP6RJGkSEE2fezGSJEVL\nkhTc83cs8m+/Qq0GvS5DvfkF+d8z74IA91X6vC0fK4TgvXx5c9Pt88a47CL2ukQwQOgwWsZcKv+d\n+5JHvPTg4ZKFCycPxTh0BwA3Tr5RN7Xvowy1lp2beC5JEUlUtlWyrmKdz7x+mGWo90B3F4xfCLHj\nPPLVM05vbT6MEHBF5kiGRwS74N6aua0Nzvpf+T+5L7voZL7yNhokbjkrhcBhmwD4edrPMUjaH4vf\nVxlqT+2klaEWQnQDvwFWAvuBj4UQ+yRJekySpL6rgK4DPuz5eeK0NCBPkqTdwDpkjUDzQqB2MI1z\nY4WLtVbB0TwgCGbc6rm/Brb1UAOHGq3EhgdzRaZrTSE1Xz1WHD1f/mP3R9Def4WMVmw9eErKPiSj\nBakrlZQI/Uteve0vf+BOMxqM/Dzt5wC9B937wkttfunBvfXViqtV40gxL7sN6FmO2melkC5fDQyg\n5EgVH+fJB6LcPm+Mx/5qVl1dDZOvgMhRcKwYStfojq13fk0d20pAaBnCHsLEsAv08/IS99e896ev\nX3YWCyGWA8sHPPfQgP8/4sZvCzBVbzvOncWxsbGKO4ud66/d7Sw2XP0tTf/4M025uxR34Tr93e0s\nbmpqUixD/a/d8oqPC5KMbN200WXX4N69e0lLS9O9s7hvTtXV1ZiK68lIOo8hFes5/MlfKE+5tnfX\noJOzNznFx8djKjDx3TBZo++on8tzn21g7gjJ7ZnFA3dCNjY2KpahNplMqmcWm0wm1R3gJpNJ+Szc\nAWcWx1THECKFsLNuJ5sObqKgQN4t624XbmNjY/+dxQPKUHd3d6uWoe7u7va6DHVHR0fvGcXOMtRV\nVVVcd911vW3cdddd3HXXXS47i61Wq+rOYufZ1QPHaWTDFibQTmdECtuOSBirNrrsWHWWVfZmnD7Y\ndoROWywzE8OpObCTmgP9d+GaTCbls6Xd7Cx2l9PYEYsY3fofWlc+yc7KwN5duCUlJb195ElOAHV1\ndZSWlvLyLvlOga15Fm+sOYR5TJHbM4t151RUhMlk8nic9OSktbPYmdOPYmfx1KlTFcWQI0eOqIol\nWmKLmr8SVn6sXSQvzRbj//SNOGbuOnG8SnNk4ezv44WwWXTH1sI/zP9QpGeli3nvXSSSly4Ti/6x\n4fjZxiegv/yCu+H19PanRXpWurh/w/2avNTEYovFoohp4hpisTtfi8Uiurq6hMViEWazWSQnJ4tK\nN3EKChRKOfeYYs6vzZfnzY7/eO6rgXXbHWL24/Jig7X7az321z2/OhqFeGKEnEftfp95O/G69jqR\n+U6myMjKEGP+8o4Y/+fl8nvYh3nvD16K5iMvfkhlqIVQPqrQl/NktfyVsPe3HwHgvDERilUW/cJr\nzLkQN1kulFe0vD+m5atiX1d8DcBtGTcRGx7C/upWtvXZYOZtbF95edpnN6TdgEEysLJsJccG3D7z\nJO73pRE4y1AHBQX1/qqwWCyK8dXmPSjkdTQfKvOAYMi4zjNfHdj64jpq22yMHhbKeRPcV8b0yxnP\nQ6LlYnQA+W/piq1nfn1a/Cndjm7mj57P/NRJWLsdvL/tiH5eXuAn60xsNTstLwRqyTp/Enlrav7u\nMGu3g0/z5ednx5xgXpJ0XN/o82bQiq2aU2sFu5t3E2IM4aoJV3LDHHnFx3s63gyasX3gpQcfaKPC\nR3HOqHOwOWx8Xqx80PfAuFPfntrvMeujWS7P6ca/u7j3b3empANUVFQwc+ZMkpKSWLp0KSNHuupM\nWhqB2/7Kf7Pnj3QIcndEqoqvDuy9XHmeXD97NAaD+1Itvsz9fr4z/0f+d/cHYOvUjK3VbvmRcj47\n+BkA1026jlvmpgDw4Y4K7BrHYvv0nvPzvPeH72l5ITiVbFVhDcfarEyMj2Bc1PfQnRnXQsAQuYRw\nQ6nP4T49KJe5vjjlYiKDIrl2VhIGCVburaGx3bdfMSfDrh5/NQBbzFs0v0GfSpaUlMTWrVspKSnh\n7bffpra21vegXS2w13lB1C3F6baq5k7WFdVhlOCamYnaDr5awjR5KWlXC+z7wudwhZ2F1HbUMjpi\nNLNGzOLs1FhGDwulsrmTjY5IPxA+fey0LEMdHx/v1zOLof/5vjkKZ+GGh4e7iMUvrdoDwLyR8k99\nd2cWm81mGhoaqK2t9Vosrqmp6T03NjT2LBJq1tKy7p80zbxH15nF7nIaGj2095tzantq7xmrGcMD\nMNV188KyXH6tIRaHhYUpisU1NTWqZxb3zcndONXU1CiehTtQLHbmFD0smtjgWOosdbyx8g1mjZjl\nItiFhYX1O7N45w07+4nFzoeSWGwwGDAYDJplqJ1L+fqKxc7y0gPLUBsMBmw2GxEREUyaNIl169Zx\n6aXykmG9ZxY3NTX1KxfetenfJNs6aImeRldFoNszi50iZFhYmKKwqjROb+2owyEgI8pGS+1ROpvc\nC6s1NTXK5/tqiMUDc+ocPp+Uqp20rvsnnQkX0tHRoXhmsVpOALkduQBMN05nw/oNpKSkcPG4cF7f\n3sG/zGGqYrFqTkVF1NTUKJ5ZPDCngWKxWk5aYrGT749eLD58+LCqWKIltqj5D8RK68wieWm2mPTg\nt6Kl0+qRr0+8jmyXRbP/N1YIm0UzthK+8vBKkZ6VLi795NLj4rAQYkXPbsv5z6wTjoc84OUB5jOu\n0l8v7npRpGeli/ty7lOMqyYWd3W5F/x14RpicV9fZxnqiooK0dHRIbq6ukRjY6MYP368W2FYSyzu\n118OhxAvny3Pk4JP/DrvhRDC1m0Xs/8q76L/fNNer2N7zMvSJsTfEuW8avZ6Pf9q2mpERlaGyHwn\nUxzrONb7fG1rp0h94BuRunSZqG3p9Cq2Fn6i5r2e2PyQxGKLxaKIaV75NEzNfyD2QY9IvGTaSCJD\nAj3y9YlX4kyIT4eOY3Dga83YSvgnxfJO01lBs/ptgJs/KY7hEcGU1reTJ8K9iq2F+QNXsqvGXYWE\nxOojq2nschW9teJ+32Le/v37mTNnDjNnzuS8887j3nvvZepU11s5WkXp+uVVtUs+k3jIMPksYA3z\ndBzXHKijttXC2Ngwoqx1Xsf2mFdQ2HHROO8tr+ff5yWf48DB/KT5xAyJ6X0+LiKEC9Pi6MbAJ/lH\nvYqthZ+oee+L72l5ITgVzNJt59OeiXLDnNHfb+N9ReO8t1RfqmQVrRXkVucSbAxmVtisflig0cDP\neu75ftCtfkbqqWgJ4QlMHjIZm8PG16Vfn2w6iuYsQ71gwQIKCgrYsmULBQUF3HHHHb4Hz8+S/828\nAQK9L5utZM6VNdfPHq3vLG5/mlM0LvgIg73LY3e7w87nB+VboldPuNoFv362/H7+cMcRHI7TR2fy\nxU7LC4Hznqs7S0pSPoRbj6n598XW7K+jqcNGWkIkGYlDPfL1C6+Mn0FgKJRtZEyUh77Qu1ri4pSL\nmZjies7ptTPlN8M3jmG0dCjvePUlZ19xNbsiRa6E/mnxpy6isVZcrZo+Wri3vlpxtQ4m783LYoY9\n8iIAzrhFFy9PxrGmpYuNB+sJMhr46YzEEzqObn3jp0DibLC0MtFe5HG7m6s2U9NeQ3xIPHMSXAsl\nzxs/nFFYqGjsZEup+7LOvuT8vfeXDvvBicW7d++moqLCa7E4Pz+fiooKt2Jxd3d3r1j8ZoH8M31m\njI3169cTERGBJEmKYnFTUxPh4eFei8V1dXW9ZwQ7c5o5+kLCS7+mbdPr5DQr7ywemNO+wn18Wil/\nUMyPnc+uXbuoqKhwEewy4oIoqLPyjy83c95I9zshQ0JCFMXiuro6wsPDPcqp7zg5fTwRi52CnbHc\nSKQxkrLWMnKKcghuDO4V7CRJwmAwKO4sliRJdWexwWDwemex8zmDwdB7i9MpFnd0dGCz2QgICCAg\nIMBlZ7HNZlMVi/ft20dFRQWjj+Uw1taOedhU8vdVERR0zOXM4oEiZGRkpKKwOnCcsg9ZcQg4Kzmc\ngh1b1MepuJi6ujosFotXYrEzp4FzLzHyTMaxnYC9H5ETNM2jnF4veB2AsyPO5vChw27n3mzzQb6I\nSOfN9Qforuz2LKeiIqqrqzGbzR7l5BSLDx48SEVFhVdi8ZAhQwB+HGKxX84s9sLfidW2dIqxD3wj\nUh/ov5PYl9KzXvHq2Wnc8eQ4Iex23b6bjm4S6Vnp4pLPLhEOh0OR2zJTpUhemi0u/9dGz3jpwHzG\ndfTXMzueEelZ6eLRLY+6YIWFhf0E8r52Kpahdjgc+stQ/2eRa6l1P5UTdzgc4oJn1onkpdlidWGN\npq8m7i2vzmYhHo+T82wq1+3b3NUspr8zXUzNmio+/+5zxXYrH/qrSLk/W4z/83LR3GHVz0sH/kMt\nQ/2jsy9NldgdgvmT4hR3En8vljIPhiYxpKsOyjfrdvuq9CsALk+9XPX+7oLJ8UTQTcHRFg7W+lA9\n9STZklS55uGKshVY7P0XGISEhNDQ0HBa7DUQQtDQ0KC5oQyAxsNwZIt823CyrpNfPTJTRTOH6tuJ\nDQ/mXIWdxN+LhQw9LoLv/ki324rDK7A5bJyZcCbRAdGKrxspWZmbGoO128E3Bb4VjDwd7LS6NeQ0\ntXupCQkJPsVW809ISEAIwWf58nELP52R6IJ/r7wMBvlc4w1/l3dbjnFfR72vr9lqZu0R+YC4y1Mv\nV+UWEmjkMmMjH9jj+HxXJUsXTdLHSwfmD1zLd3z0eNKGpbG/cT/rKtaxKGVRL5aYmMjRo0epr693\n8bXZbKpzTBVvboaWFq981bCQkBDCwsIU40JPfxX0fCimXQ7BEaqvd/HVgTkXSFyZOZJAo0HTVw/u\ntW/m9bD3U9j9Ppx7r7yIQsN3WalcIX/JuCUk2NR5XTU9kc0lDXy+86jLghBfcj5p/aVip+WFQE00\ni4jQP/k99Y+IiGBvZStFtWaGhQVxwcQ4j3xPCK9p18sXgn1fwuKnIdh1uWdf3+/Kv8NitzAzfiaj\nwkdpcrvK2MAH9ji+2FnJvQsnYhxQRsCXnH3F9fheMe4K9m/fz7KSZb0XgoiICAIDAxkzxrVkMkBV\nVZXbEg+68EceUT1KUM1XT7tqFhEeLn8hAPkLggemZxy7bHa+3i1z6Psl6PsYR7c29gLsoXEYGw9B\nxTYYfaaq7+GWwxQcKyA0IJQLR19IU12TatuL0kfwl6/2klfeRHlDO8kxxy/EvuR80vpLxU6rC4Ge\nMtTffvstmZmZXovFH3/8MZmZmW5FyJKSEpbXyJNh7qhAmhrq+wlB5eXljBo1yq1YvHfvXq655hrv\ny1CbTPz0pz91k5ORIGMSI20V1K7/D9a0q1Rz+rREFokndk9kz549xMfH8+mnn5KRkeFWsEvqqCEu\ncgo1rV28+c0mlsye0E/cqqmpYezYsYplqK+//novcjpehvryyy/3Six25pQUnoRRMrK5cjNfrf6K\nxOhE1XHSygnk8saTJk1yL9iVlZGqsltaLSe1uac1TomJieR+8gJXNZdhDY6lKXQSR/Pzj4uQGmKx\nWk7OcVq1/xitXd0kRxoYYmmkrKxRe5x6SjYvXLjQK7H4yy+/ZPLkyYrCao1xCtOoo3rFsxiv/Ldq\nTpsN8u3TjOAMtm3aRl1dHTNmzHD/GVFWRtLRcs4dE8mKoiae+3wzvz43WV9ORUXk5+dz/vnne5XT\n8uXLGTdunNdlqBVzUjN3woGnD2ARUASUAPe7wW8F6pFPxzABv+iD3QIc7Hncoqe9kyUWr1qzVkx7\nVC65u7ey2SPfE8nrwHsPyKJZ1mWqvkdaj4j0rHQx691Zos3apo/bww+L51YVieSl2eKeD3d5xOtk\ni8VOu3vN3SI9K128teetU4qXX9sVQlS+crU8D1Y9dEJ43fLmNpG8NFu8uemQR7xOpPi5LfsdOee/\nJbqcadzX1+6wi4s+uUikZ6WL7dXbdfPadLBeJC/NFmc/tUbY7ccXFwyKxQNMkiQj8G9gMTAZuF6S\npMluXvqRECKz5/FGj+8w4GFgDjAbeFiSJGUFp8fU7t/GxcUpYnpMzf+INZzmDhuTRkQwZeRQj3xP\nJC8x+UoICIHDG6DZtfqg09e5uerC0RcSFhjmgivZVWfIt5C+3VtDm6X/7lZfcvYV1+t7Raq8p8Ap\nkp8qvPzarq2T+GM9CwamXe93XvVmCxuK6wkwSCyZNtIF9za2L7wAwsfMgFEzwNLarzT7QN/tNdup\naa9hVPgoZsTP0M3rzLExJAwN4WhTJ3nlx28l+ZLzyewvJfPHqqHZQIkQ4pAQwgp8CFyh0/di4Dsh\nRKMQogn4DvnXhaqpaQQxMTGKmB5T88+tlj8El7g5ilLL90Tyik5IhomL5f/scy2/HBMTgxCC7EPZ\nwHGRWC+35JgwZqVE02mz8+2e/isofMnZV1yv77mJ5zI0eCglzSUUNxWfMrz82m7Rcoy2dhg5HeJc\nRX1feS3fU41DwHkThruslDtZ/dWLO0tOODfRufHNLpXn/mVjL+s9k1gPL6NB4ifT5S9Cn/UpOeFL\nziezv5TMHxeCUUDfr6FHe54baD+VJKlAkqRPJUlybn/T69vPnBtt3Jnzfp23puTfYe1mfYlct+by\nDPcXArW2TxSvXmzqNfJ/9nziFt/XsI8KcwWxQ2KZM2KOq7+G/WS6LA4u291fsPQlZ19xvb6BxkAW\nJi8EYPmh5acML7+26/wQdH4oemhavL4yySvl3H0JOln91YtP+QlIBihZDR2N/THAYrew5oh81vGl\nYy/1mNfxX8TVWLrt+nl5gXnCy5++kvBxHbUkSVcDi4QQv+j5/03AHCHEb/q8JgZoE0JYJEm6E7hW\nCDFfkqR7gRAhxBM9r/sL0CmEeMZNO3cAdwCMjIycUXnPPW75lJWV9Qokbi0nB84/XxFW8v/KPozf\n2VI5Q2rj82D3na3W9onidRxLBF4DLMDNQEw//KMzWnh3aAk3toxjaeM0/dx6eDUJI7MsmQgktgWb\niJW6dfJSiOsr7mF/7Qip57aEDYy0hfLKpkmMSXG/Yuj75uWfdruAVxHCgfw2cbPM1AdeWyrquWH4\nJQzBTn6wiVDJodtXE/eBV3/8c6AcuAjn2QtObHVoJffE55JmieLjqgu94rXIMoUDIpQ3Aou5yNji\nU84ns7+kRx/NF0LMdAHcCQeePICzgJV9/v8A8IDK641AS8/f1wOv9sFeBa7XanPy5MmKYohWqV4t\nsUXJ//as7SJ5abZ4a4BQprftE8WrH/bl/8rC2Zon+uGm3SZxwUcXiPSsdLG7brdn3PrwurVHLHxn\na5lnvE4E7mF/2R12Mf/j+SI9K118uuXTU4aXX9rNf1uIhyOF+cULTgivB9/fKJKXZovffuB+Z/PJ\n6q9++M535bn/1qUu2D3r7um3WMAbXi+uPdivD3zJ+WT2FydwZ/EOYLwkSWMkSQoCrgOW9bsKSVLf\nXQ5LAOdX6pXAQkmSontE4oU9z6mamlgcHx/vGXsd/s0dVtYX12OQ4JIM5Q0bam2fCF4u2NSeSop7\nPoE+v/RqAmuo76wnMTyRqbGu5Y31cru8RyT8us/tIV9y9hX3xNcgGVicIusoJovplOHll3Z7bgs5\nplx1QnhtPiqXzr5CQRs7Wf3VD0+7DIzBULYJWip7sTZrG+sr1gOwaMwi9746zHk7eHVhLZ1Wu085\nn8z+UjKfLwRCiG7gN8gf4PuBj4UQ+yRJekySJOce999KkrRPkqTdwG+Rl5MihGgEHke+mOwAHut5\nTtXUNIKiIuVqhHrMnf/KfTXY7IK0YUbiIpRL+qq1fSJ4uWAp8yA8HpoOQ+XOXvyL/fKxfovHLHZb\nUkIvtwWT4wkKMLCjrJGali79vE4Q7qnv4rHyhWDN0TV0O5Rr+3/fvHxq11wLZRvBEMg+R6rfeR2o\naeVQQyfRoYHMG+++pMTJ6q9+eMhQmHAxIHoXTBQVFbG2Yi1Wh5UZ8TMYETbCa16jY0KZlhRFu9XO\nuqI6n3I+mf2lZH6pNSSEWC6EmCCESBVC/LXnuYeEEMt6/n5ACDFFCDFNCHGBEOJAH983hRDjeh66\niusLFV1DVz0WFXPn7xRIZyeolwFWa/tE8HLBDEZI/6n8d49obLVb2dW+C4BLxlziE7eIkEAumDgc\nIeCbntVDvuTsK+6p7+Rhk0mJTMFsN7O9evspw8undvd9AcIB4y7CYhjid15fmeS5f8nUhN6SEnp9\n9eK++PbDByyYsNvtLD8sLyl1N/c95XV5z92Ar3dX+ZTzyewvJTstdxYPHz5ccWdxSUkJgNc7i53+\nzt2dW0372FLSSYABzhhucDmzuO+uQbvdrliGuqyszKczi0tKShRzcnJOSkoiPG4e8byEZdeHlKXc\nzF7LfjocHYwMHIm12kqttdZlJ+ShQ4cA3O/uHHBm8bnJkazcB+9tPECaoRq73a5YhrqkpIS0tDSv\nciorK6OkpERxnLR2FivlNC14GmWU8fb2t7FGW93u7lTLCeQ3m+JZuBo7i9VyGjj39OQ0JfdNhgIt\noxdQcbhC+SxcjZ3F7nJqbW3l41y5zbNGBiqehas6TsXFlJSUKOaktbO4okIlp9RUamtrj5/vmzqd\nmIAwjNW72bb8XVoCIthavRUDBkKPhlIVUdVvF25XV5fy+b5lZS5nFqdFDkcCVhfWkN5Ro5xTUREl\nJSX+ycnDncWqOamZO+HgVH9kZGQoiiE1NTWqYomW2DLQ/81Nh0Ty0mxxe9YOzdhquL95KWIOhxD/\nmCYLZ6U54r6c+0R6Vrp4veB177gN4NVusYm0v3wrkpdmiyMN7T7l7BPuZX8daj4k0rPSxZz35oiu\nbvdnD58MXl6121gmWchbJAAAIABJREFUj/MTI4SwtPmd187yRpG8NFvMfHxlv121enx14z70l1v8\n81/JfbLuKfHq9ldFela6uOu7u/zG65pXtsiLRtbt84yX3na95KU3Nj+kMtRqZxY7r97e2kB/522h\nJZkjNWOr4f7mpYhJkryuGuja9zk5R3MAeguu+cotNCiAC9NkQSq7oNqnnH3FvfEdM3QMScFJtNva\n2VK55ZTh5VW7zs2DExdDUJjfeS3vuf03I86AweCqLan5eoL74uuC98x9Cr9i5WF53cniMYv9xst5\ne+irXcrnGWvFPpn9pWSn5YVAqGgEvhwsPtC/orGDXUeaGRJo5KK0OJ8ONfcnL01ssryxe/OhFXR2\ndzI6aDSJEYluPL3j1vdeqS85f9+HxDtt2hB5H8V35d+dUrw8bnffl/K/PR9+/uQlhGD5nhoAZgxX\nP5P4ZPWXW3zs+RA8lGPH9nOws5hAQyDnJ53vN16LpyZgkKCgzkZzh3djeTL7S8lOywvB91WG+usC\n+dfARZPjCQ0KOKGlZz3hpYklTIPoFFYFyMcinhl9phsv77mdN3E4ESEBFFa30iKUBcpToQy1Oztn\n+DkArKtYh9Xu+sY5Wbw8arepDKpNEBQO4y7yO6/dR1uobO5kRGQImUmudbXUfD3F/Ro7IAgmXcKa\nsFAEMHfkXCKC3MfwhldseDBnj4vFLmDF3hr9vDxod7AMtYY5xeIRI0YoisXl5eWYzWavxWKnf1xc\nHF/mywM91tjAnj17CA4OVhWLg4ODFcXitrY2n8TixsZG4uLi3Obk5Nw3p8TwTNYb8uTc2hLIURFW\nnRz1iMXOnKbFwKZK2FZtI1lBhGxsbCQxMdGrnMrKymhsbCQqKsorsVgtp+TIZBKDEzlqOcrbOW+z\nJH1JP8EuMjJSVSyOjY31WixWy6nv3NPKKbV2ORFAbdR0jhYUkpiYSF1dnddi8cCcVtXJu5OnRndT\nceQII+LjvRun4mIaGxsZMmSIV2Kxak6pqTQ3N7sIq9hT+S4sFIApgVPIyclxHSfkM409EYudOY0P\nbmUj8OHWg1yQHOxWLK6vr8doNPotJ71isWpOauZOODjVH5MmTVIUQ3Jzc1XFEi2xxelffqxdJC/N\nFlMeWiG6bN26Yqvh/uKlF1u763WRnpUurnl9ssjdutnr2Eq8corqRPLSbHHW498qnv3rS395y0uP\nb25urnjF9IpIz0oXf9r4p1OKl27f1+bLoui+L/3Oy+FwiLlPrhHJS7PF9sMNp+w4KuHHzFUi460p\nIvOtKaK5yrV0uq+8mtutYuz92WLM/dmirtX9ggO/jbMHvPTE5ockFjscDkWss7PTp9hO/2/3ylfY\n+ZPiCA4w6oqthvuLl15slVn+FrPQ3EpQzS6vYyvZ3NQYhoUFUdVmp0jhPGNf+stbXnp8Ozs7WZCy\nAJBvD9nstlOGly7f5gqozJPPJR63wO+8CnpuC8VFBDNjdPQpO45K+NqqTTgkiTM7uxh6cLXfeQ0N\nDWRqrBGHgBV73Z9n7Jdx9sK89T0tLwQGgzLtIUO831TT1395z/2/xekjXDBv2vYXLz2Y1W4lpyIH\ngAXtHSQ0bfM6tpIFGg0snCyvHvp2j/t7pb70l7e89PgOGTKEsUPHMi5qHGarmdzq3FOGly7f/T0V\nXMYvgKBQv/Na3vPhtjh9BAaDdMqOoxL+XZm8CGBhewcUfnlCeM0dLfuu2Of53D+Z/aVkPziNoLKy\nkpycHK81gsrKSj77di27KzoJCTBgqDtATk4RMTExhIWFqWoEYWFhJ1QjqFC49+zM2ZnTV3u+os3W\nxpigESR3H6Gzcj0569YSFz/C7X3ampoacpTu0ypoBGazmWSjvJrki7zDTA+scqsRKI2TVk7Oe8+l\nCvdptTQCtZyc4zRejKeEEpYdWEZobej3phEo5eQcR62cZha8TzhQFDCZ6pyc3nvPjY2NPmsEtbW1\nfL5d/lY5NaqbnJwc38apRyMoLCz0SiNQzSk1lba2tn730w9WHmRb9TYMGDjPCtTsYdvyD+gMTfCb\nRlBcXMxI0YhRCia3tIHsVesID5JcNAKTyeSXnAY1AoVHWlqa4j2wvLw81XtkWvfY8vLyxH82ypvI\nfvXfPBdMy/dE8tKL/Wnjn0R6Vrp4xfSKEM+ly/eSy7d6x02Fl8VmF2kPfiOSl2aL0jqzZ3F9xf3Q\nXyVNJSI9K12c/cHZwmq3njK8VPGWSnk8H48TosvsivvIq6CiWd5E9sR3ortnE9mpOo7u8M+KPxPp\nWenijlV3iGOvXSX31YZnTwivG9/IFclLs8VH2494FPtk9hc/JI1ArZ6G2ez+frVeM5vNvcvCFqWP\ncMG0fE8kLz2YzW5jXcU6ABamLITJPXX/9in/RPaWW1CAgWnD5Snk7ieyL/3lCy8tXyeWGpXK2KFj\nabG0sKN6xynDSxXfLx83yriLIDjc77ycNaQWTRmBsWcT2ak6ju7wVeWrAFiYvJCq6Nnykwq3h3zl\ntThd3k/zrRudwOdx9oGXN3ZaXgjcVdB0WlBQkE+xOxwB7ChvJCjAwPxJ/c//1IqthvvKS2/s3Opc\nzFYz46LGMWbomOM7LfcvAwWR3RduZ46Sq7G6W1PtS3/5yktvfy1MkU8uc36AnCq8FPFC+dzl3nH1\nIy8hRO9u4kumJvTDNHn5gPsrdoulhW1V2zBKRuaPnk9b/Bx5n0X1bmg87HdeCybHI0mwuaSB1i6b\nC66Hszf4ifA9LS8EwcHBilhqqveleAHKuociBJw7PpaIkP7nHmjFVsN95aU3tnO3rPNoRkbNwB6e\nAK2V8koTP3O7cs4EhgQae1eaeBLXV9xb377YgmR51c2aI8dLU58KvNzZ+BERUL5Frrs/fqHfee2r\nauVIYwex4cHMHjNMd9yT1V8D8XUV6+gW3cwcMZPokGjGTkiDCT2lVdz8KvCV1/CIYGalDMNqd7Du\nQJ3u2Cezv5TstBSL1aqPbty4kXHjxnktFn+89SAQTMYwB7W1/St11tfXU1xcrCgWNzc3U1ZWplh9\ndPHixT5VH7344ovd5rR69WrGjRtHwqgEVpfJy+Wi6qIoKioiIiKC/8/ee0e3dV3p4t8BSLCITWKn\n2MQiiRQkUd2SXNRdYtlObCexpyQzmUnerMmbTPL75dGZZCLbyUycYjuTcZxmj+nY4yZbsiWqUhKp\nXilBEkmJVWDvYO8A9vvjAhBI3n4vWDxvr4VFEPvufb99zgEucL+z9+5EJpaiGZ2n34R9a8okwu7A\ngQNIS0tTTBabTCYMDQ1hRZw/ztU78Pq+s/jOQ8vGVR/duXOnqpjcVS03b96siiwWi8l7ngIDA5E0\nJwn1A/V448gbWBa+DENDQ8LzBO7WpFBMcqqPCsV08OBBZGRkCMZUtf9VRIPQE70awz1DqL5aMo6E\nPHr0KJKSklRXHz3SzH3BWjbXgdaWZn3myVV9dMOGDarIYtGY0tNRVFSE2NhYGI1G5PdxDepTR1JR\nVFQEh8OB0bBVSMLH6LvwDvrSvjKpUmdmZqYqstgd08bkObh0x4Y/F95EmtHmIYvLy8uxevVqzTGp\nqT4qGJOY8BEHM/2xcOFCQTKksLBQlCwRI1s6+0dowXP5lP6DA9Q9MDpJL+VbTK8Fl1zfZxvOkjnP\nTI/tfWycvvjT33Gk2StmrjqpEmwycH16rYFScvPpqd+dnaSTslWt12G83PIfxf9B5jwzvXDuhRmF\na6LYXt3IzaPlfZ/g2vKrQkrJzaczle2KcE3XeHnre0Z6KOfPObTs7WXUPth+Vzc6SPTTeG7cuup4\nbbXgauwapJTcfFr0o4M0OGKX5Xs6xwu+JIsZYw8xxsoZY1WMsed49N9jjJUxxm4wxo4zxlK8dA7G\nmMX12DfRVuB8gjqxOkRSUlDWAicBGzKiEB48uR2mlG8xvRZccn0frzsOANiWsm2cfmBuFte5rKcO\naLmhKzaj0Ygti2NgMhpwpbYLbX3D43RStlr0am0n6ty3hwrrC+Ek54zBNU4GOhDRXQoY/Llqozrj\nahsCqtsHEBboN+62kBy/0zVe3vpTDadgd9qxMmYlooKi7ur8g4AMV8P68oO640qICMLypAgMjzlx\nsqJtkl7MVq3eF7aaLwSMMSOA3wJ4GEA2gGcYY9kTDrsGYDURLQPwMYBfeOmGiCjH9XgMMiQwULhd\n5KJFi5TAHyeHeJLIlPgW02vBJce3k5yeJLItyVvG6xdnAYtcHZpuH9AV26JFixAa6I97M6NABBwt\nbZXtV6tere1E3eJ5i5EwJwEdQx240X5jxuAaJxWHweDkqmsG8heB04KrzjkXALA1K3ZSJ7KZOo/e\n+hN1JwAAW5O3TrbN2sn9de+40hnXQ0u4z4tDXhsmtHwW+HK8hESPXwRrAVQRUQ0RjQL4AMDj3gcQ\nUSERDbr+vQBAuCayDBkbGxPUtba2CurEpGdoDGerOmBg8GTMKvUtpleLS67v0o5StA21ITY4Ftnz\nsifpkfUo98+tfF2xuW3dW229dw9pGS+9cMnRMcY8F88T9SdmDK5x4r6AL/6COnsJOXaL+zbLt/Zn\n6jy69SOOEZxpPANg/Jcgj23mdsDgxxHtg7bJeo243F8cT9xqw4jdIel7OsdLSPQgi+cDqPf6vwHA\nOpHjvwHgkNf/gYyxKwDsAF4iIt5Nv4yxbwL4JgAkhIUBzz/P6zzUagVcBAmvFBXx2p5wRGLMkYac\nkTZE/upnqnyL6dXikut734p+IALY0hwC9sILPLZJAExAWynw/D8DiJCHTSau7WSEEStwvqod3bt+\nggjm0DReeuGSq9sS2I5344ET13bji2euA6kLZgQuTsYAHAERwPLLgHx+/2pxtZEfyodzYGKE+z/+\nE/DJ+G3GM3Ue3foLWQEYihtC1kgEEn71RwHbBAB1wC++BWCJrrhSASxmS3B7JBjnXvgNNht7NH0W\n+HK8BIWPOFDyAPAUgDe8/v8rAK8JHPuX4H4RBHi9Nt/1Nw2AFUC61Dl9QRb/w7tXKCU3n/4l76hq\n39NJFj++93Ey55npXOM5Ydvdf8uRZmd/Ix+bAlzP/uk8peTm00eX66T9atXrTMqOOcbo3vfvJXOe\nmd478t6MwUVERKWfEe0Ko+6X1/gE13sXayklN5/+5q1Lyv1q1etAFv/47I/JnGem31l+J2x78Y/c\n2n/vGZ/gerWgnFJy8+n7uy2Svj+vZHEjgCSv/xNdr40Txtg2AD8E8BgReXpNElGj628NgCIAK6RO\nKMYRZGVlyYR9V4bHHCgqbwcAPL1B+B6blG8xvRpccu1Dk0NR3VONUP9QrI5bLWzrvq0wgSfQgs3b\n9iFXpqX79pCW8dITlxydn8EPDyQ+AABoCRFuODLVuAB45suQLU6hqcV11JUVLnRLdKbOIwAsXLxQ\nkBsbZ+te+9UngNFB3XG5s4wLylphdzg1fRb4cryERI8LwWUAmYyxBYwxE4CvAhi3+4cxtgLAH8Bd\nBNq8Xp/LGAtwPY8CsBFAmdQJxUpMuPfbKpFz1R0YHHVgSUIYTGP9qn2L6dXgkmt/7A6XO3B/0v3w\nN0ze7eSxzdwOGE1A3QWgv22yXiOuB10fJGeqOjA4atc0XnrikqtzE41FjUUzB5djDKg4DABonTv5\nIq8VV/+IHWerOsEATy9qpX6nax4B4HztediGbUgMSURmRKawbVgCMH8VYB8Cqo/rjmthbAhSI4PR\nNTiGK7Vdmj4LfDleQqL5QkBEdgDfBnAEwC0AHxFRKWPsRcaY+yvMLwGEANg9YZtoFoArjLHrAArB\ncQSSFwIxstidaKFECso4gmVHdpyovZRvLbZSImZ/rp1rwr45abO4bUAot+sEBJQfmqzXiCsmLBA5\nSREYsTtxqqJD03jpiUuubn3CegT5BaFmsAYtA8K/CqYUV+05YLgbiFqEhiHhX8JqcZ2qaMeow4n0\nCAOiQ/kz9mfqPALAycaTALiL+MRt5ZNsF7s2TLh+YemJizGGHa7dQwVlrT77HFGKS67okllMRAcB\nHJzw2o+9nm+bZMS9fg7AUrnncWcWR0VFCWYWWywWAJCdWVxZVYUDFu6n4ooYA66e4Oz5sju7urpE\ny1DbbDbBMtQlJSXIyspSnVlssVh4Y+oa7ULNcA38mB/mj8xHfX39pExI95jExMQgMeF+hFUeRefZ\nt9FkWonY2FjcuMHlFqjJLLbZbONKNm9ICYWlvhvvnLiONc5byMrKUhyTe54sFotgBrhUZrFYTGLz\ntChgESx2C945/w7W+K3hze50l1VWk1ksFpP3PLljyqj8IxIBDCRvFp+nxESUlXHfoZRkFr9zgruL\nuzh0VDAmTfNUUQGLxSI4T1KZxWIxpaWl4WrvVYABkd2RaGtrE52n7Ji1iAEwVrof58KfQluHTXUZ\nar6Y0gK49p77iq0ICatQN0/p6aiqqgIAVZnFbW1t/3PKUC9btkyQDGlsbBQlSyaSLVesnZSSm08b\nXzpOTqdT1F7KtxZbKRJIyN5ddvcfCv5Bnm1fK9GucKIXo4iGe6WxKcRV2dpLKbn5tPyFI1Rb36DI\nVpFe5XhJ6fZV7SNznpm+ceQb04/L6SR6OZsjOeuv6D5eo3YHmXcdppTcfLpQWq3Or1a9hvG63Xmb\nzHlmuv+D+8nusE/S89r+52puPKuLdMdldzhp5YtHKSU3n05dr1JkK1uvYbyIfJxZPNWiZxnqo163\nhRhjPisf66sy1O5Ems3J/LeFJtmGxADJ9wCOUaCyQDO2ibbp0SFYEDUH3YNjuFAl/jN1JpZ7vj/x\nfhhgwJWWK+gZ6ZleXM3Xgd4GIDQeSFih+3hdrLGhb9iOzJgQRBiFb7fO1DLUJ+pdaz9pM4yGyRm1\nvLaeDRP5uuMyGhi2ZnEViwtuCa/9/1eGWicR4wjcP5HkCBF5MmF3LImVtJfyrcVWSvjsB8cGcb7p\nPBiYID/Aazth95AWbBNtGWOe3SfHRN4Mcs6rJy65uvCAcGQEZsBBDpxqODW9uNy7uxY9AhgMuo9X\nQZlrt9CSWJ+tezW45Nq6vwRN3C0karvYlWV8+wCam5p0x7Ujm+MJTlV3K7aVq/eF7ay8EOgl1e39\nuNMxgIhgf6xOmTvdcBTLuaZzGHWOIjUg1VNfRZa4LwSVRwH7qO64trsuBFfbHO5ckVkly4KWAbj7\nQTNtIiObWK0QkefX8PZs/pIqM1ka+xtx23YbASwA6+LF8lcnSMIK7hdWbyNC+6p0x3VvZhSC/I2w\n9jrRNKEs+0yWWVmGOjY2VpAsbmtrk92zePdN7qq9JIJQXVWJ0NBQjz0fuRUeHi5KFoeFhQmSkF1d\nXZp6Fre1tU3qG7tvmNt8tcCxYFzP4onk1uSY6rB6TgpCBmrRbdmPjg5S1bNYqL+vX08LwkxAxxDh\nbKkV8wxDsmPynic38aWGLO7o6BCMSWyeTCYTNsRswMddH+NU/SkcP3kcS7OWjiPswsLCVJPFYjF5\nz1OM3wCi2kphNwbj9sBcxLS1icaUmJiI3t5e2T2LnRGJaO4ZRkQAg63qGiLCwwVj0jRPFRVoa2tT\n3bNYKKbTA6cBAItMi3D+9HleYlVonkJCczC/rxlxPcWort6iiiwWi2nJPIYrrUBeQTG+cX+msnlK\nT8fw8LDqnsVBQUH/c8his9ksSIZUVQmTNEQ0jmx5/LUzlJKbT4dLmmXZS/nWYitFAk20H3WM0ob3\nNpA5z0wnb5xUjuv4TznSbN93xLEpxOWW/7P7OqXk5tOvCyoU28rSq8Ql97xf2f8VMueZ6UTtienB\ndfY/ufnZ/bfyzqsQ16+O3KaU3Hz64d4bynDprVc5Xl8/9HUy55npzXNvKj9v1XGiXWE08kqO7riI\niHZfqaeU3Hz6yzcuKLaV1GvARfQ5I4tHR4VvZ7ivhFLS1jsMS303AvwMuC/z7m0VMXsp31pspWSi\n/bXWa+gd7cWC8AVwdvK3oBQ9t7sIXflB1NfV6obLLW7OpeCW8H58LeOpFpfc87qTy9yE5JTj4rkt\npOd4eefOKMLlA71S267hLlxtuwo/gx/ih+J5rCTOm3ofEBAOU08N0KHu9pBYTFsWx4ABOF/diZ6h\nyXzmdK17MZmVFwI9pOAW90a4LzMawaZZdYcMwPgdE6okbhkQngz0tyKst0JHZJxszIiCyQiUNPbO\nqnulbnETkEX1RZ4WllMm/e1A/QUuCzyDNwVHk9R2DuB2Sx9CA/xwT1qk7v59LScbTsJJTqyNW4sg\nQ5ByB0Z/YOGD3PPbk6vxapV5c0xYONcAu5NQVK4tkXSqZFZeCMR6FrvviUnJxN1CcuylfGuxlRJv\neyJCYV0hAO4DSxUuxjzfNtPHbumCy1sC/Y24JzkUwN1vn3Jt5erV2so5b1p4GlLCUtA90o1rbdem\nFlfFYYCcwIIHgMAw2eeVi8s9H5sWx8DkZ5CPy0d6pbbuBkxbk7eqx501PstYD1zesm1xNIC729OV\n2PpqfYnJrPoqLIcsvnbtGqxWqyhZPHSsEOeqhmBgQKCtCkVF1R4i6MKFC7Barbwk5MjICOrr6wXJ\nYqeT63PMR0J2dnYiKChINVnc0tICo9GI0dFRnK8+j6aBJsw1zUVofyguXOQwC5HFQjElB2QhDYCh\n4iCKCp9AxNy5islif39/dHd388YU6+gAEIS9l6uRMmoVjYlvnlpaWuBwOFSRxZcuXYLValU8T94x\nZSITtahF/u18BLYHegi7OXPmYHBwUBVZLBaTe55WVXyAUADlhkw0FxXJiikxMdGz9qXI4t2XuC5y\nWSEjKCoqkoxJ0zxVVKClpQWDg4OqyOKJMVXXVeNsw1kAwJLAJSgrLYPVauUlVsViMtpN2GAwwdhw\nCeeO7MFowDxFZLFoTOXlCO5uAhCCwtutOHn6LMgxJm+e0tNRViYckxRZbDKZ4HA4/meQxVrLUO+/\n3kgpufn09O9ESjar8D1VZah/e+23ZM4z0/PnnteGyz5G9FIqR0q23daMa6LsP3KC0n5wgOsBPahv\nD2hf9gZ266+1XiNznpke/PhBcnr1evYpruE+ohejuezv3hZVuMVwdfQN04Ln8injXw5Q79DdOfFZ\n2WSZuOTaHrMeI3OemZ7Nf1bStxSu9t9s5db+5f/SjItP/+CrJyklN58Kb7cqthWUGVyGetaJ0G2h\n2SKF9a7bQkn8iTSyxegHLHyIe+6De6UhJoY1qXNn1b1Sb1kWvQyRgZFo7G9ERZf+PAqvVJ8AHCNA\n4hogVP/1efx2G5wErE+PQmjg5Eq1M1083JhIJr1c6Yhy5R+ovD0kJe7ESqFbozNJZuWFwGQyCeqS\nkpIEdQAwSgyFrg+l7Tz118XspXxrsZUSt707kSbYL9iTSKMFFxa7exkfFD5Gpe+kpCTPrhTvXsay\ncMnQa8Elx9bADNiUtAnA+N1DPsUlkkSmx3h5vgRNWPu+WvdyccmxtTvtONnAVRt1fwnSgjtw6RMA\nGHDnJDDcqxqXkN6dqHfsViucThqn0+LbF7az8kIwsdyst4hdJADgojMUfcN2LIoNRUrkHEX2Ur61\n2EqJ295NEt87/16YjCbNuJC+BU5jANB4BehVnp4uFbP7YltUfrefqyxcMvRacMm1de8eco+7T3H5\nMU/vAU/JZJm2cvRDow6cqeIaME38EuSrdS9HL9f2Wts19Iz0ICUsBQvCF0j6ljqvMTzubt2tqmOq\ncQnpzfPDEB8eiNbeEdxo7Bmn0+LbF7azkiwWK0N96NAh5OTkCJLFb3UbgTnAmgQTb8nmTz75BDk5\nObwkZFVVFaxWqyBZXFtbi6amJsEy1E8//bSmMtRPPvkkDlZw39zjB+I9BNCnn36KnJwcQbJYLKbY\n2Fj0G1KR5ihH/fE/wbThW4rI4paWFnR2dvLGZLFY8Mwzz2DBXBPudI3iD3sL8dTG7EkxiZU33rlz\npyqyeO/evVi2bJnieZoYk5M5EWgMxC3bLew5tgfz/ObBZrN5stgnzpOcMtRCMV386NdIGu7GSFgq\nuh2huOUicuXElJiYiP379yM7O1uQLN579BKGx5xIjzCCDffiZm3luJLNQjFpmidXyeYdO3aoIou9\nYzo4xK39DGTg/PnzSE9Px5EjR5CRkSFYLlxwnsCVbA6KvgdRdefReuotDIWs9MQkpwy1YEzl5Sgu\nLsamTZuwMTUUH18fxhuHLuFvVkVKz5NETHLKUAtWVBATPuJgpj/UksVOp5PW5e6hlNx8ulHfrdh+\nusnirqEuWv72csp5O4d6Rnp0wUVEdOu9H3Kk2TtfUoVLSvfyUa6f6w/23FCEa7rJYrd8t/C7ZM4z\n07tl7/oUV/0fn+HmoeB5xbZycH3vQwul5ObTaycqFdnOBLLY6XTSgx8/SOY8M11tvSrLtyxcHVXc\nmP97EtHYiGJcUvrTFe2UkptP214uUmzLKzOZLGaMPcQYK2eMVTHGnuPRBzDGPnTpLzLGUr10P3C9\nXs4Ye1DO+fz9hUmu+HjhTMObjT1ogQnx4YEwzw/jPUbMXkyn1VZK4uPjcarxFBzkwOq41QgzhY3T\nqcUFAH7ZXwB3r/SU4nulcmL2VCMtG3+vVCturbjk6t1Je+7bQz7BRYTYrivcc4Eic1rGy07A8dsc\nP/AgzyYJX617OXo5tpXdlWjsb8S8wHlYFrVMlm9ZuCLTgegsYKQHqD2jGJeUfl3aPIQG+qGyjStw\nqcRWjai11XwhYIwZAfwWwMMAsgE8wxjLnnDYNwB0EVEGgFcB/Nxlmw2ux/ESAA8BeN3lT1SMRuFD\nQkNDBXVuomx7dqwgzyBmL6bTaisloaGh45LI9MIFAMHRKarvlcqJeUlCGBLCA9HWN4LrDd2T9Gp8\n64FLrv7+xPthZEZcaeV6FPgEV/N1+A+2AiFxQMJKZbYy9FcoFN2DY0iLmoP06BBFtlrnSY/xcleC\n3ZS0aVzvAV1wTyjLrgSXlN7faMCWxa4eBa6y39O17sVEj18EawFUEVENEY0C+ADA4xOOeRzA267n\nHwPYyrhP4sdZ3XWNAAAgAElEQVQBfEBEI0R0B0CVy5+oDA8PC+oqKoS3+U2sr6LUXkyn1VZKSm6X\n4GwTl0gzsayEFlwe/SLX7qFyZbuH5MTMGPOQk95b6bTi1opLrj48IByr41Z7ehT4BJd73Bc9DBj4\n35ZaxuuoIwIAsH0J/5cgX617OXo5tu4t00rWvmxcngvBQYDklU1XEvPEtT9d615MGMkMXNABY08B\neIiI/s71/18BWEdE3/Y6psR1TIPr/2oA6wA8D+ACEb3rev1NAIeI6GOe83wTwDcBICEsbFXjd7/L\ni8dqtfKmWdvIDw+MLAXsdlydUwJ/xh+3kL2UTqstioqATZsE1R/1WfCTZdXIHonAh01bdcN1Vx8O\nIA9AAIBvATDKwiU35rOOUPzF2GJksCEcCyjRjlsnXHL1/x1WhZcir2P7wHz806U4H+B6F0A7gC8C\n0A83wH223deTiYbACHxiKsMqw4Ai3/qsLwG9jPEKTI/B9uRDCHIacapuJwLJOE6vHTcBeANAP4Bn\nAMRpWl8T9X1kwKqRFRgDw+UAC/prq6Zl3QMAe+GFYiJaPUnBRxwoeQB4CsAbXv//FYDXJhxTAiDR\n6/9qAFEAXgPwl16vvwngKalzLlmyRJAMKS0tFdSN2h10+19fEiVTxOzFdFptpUig/53/v8mcZ6bf\nW36vK65x+tfWcsRZlVfpZQlccmMetTtoqas/bnVbn3bcOuGSq2/qayJznpnWvLuGrt28pi8u2x2i\nXWFk/0kc0diwMlsZ+rKmHkrJzadVPykgh8PJe4yv1r2kXsZ4vX/rfTLnmemfjv+TIt+KcOV/j1v7\nx16UjUuJ/q/fvEgpufn04aW6aVv3RL4lixsBeGcxJLpe4z2GMeYHIBxAp0zbSSLGEURGCldT9Dca\nsMggXglTzF5Mp9VWTBxOB671coXP+NryacE1Tu/+iazg9pDcmMffK21VhkuF6D2P8SHxyJqXhSH7\nEOpQpy+u8kMAgLGUBwA/4YKKasfrLjcWA4OBnxvz1bqXo5eyFWtJqRtuhTyB0pjdVQyOlrVM27oX\nEz0uBJcBZDLGFjDGTODI330TjtkH4Guu508BOOG6Ou0D8FXXrqIFADIBXJI6oRhH4N7Tq1bE7KV8\na7EVk+vt19E92o3EkERkRGToimucfpHye6VKYt6xhONm3BcCrbj1wiVX7y5rcKBcfUkCXt+uD58a\nU5YqXFL6o+7exCLcmK/WvRy9mFwtvYrLrZdhYAbcn3i/It+KcKXcCwSEA+23gM5qSVxKY96exV0I\nTld2wHKzTJNvX9hqvhAQkR3AtwEcAXALwEdEVMoYe5Ex9pjrsDcBRDLGqgB8D8BzLttSAB8BKANw\nGMA/EpFj4jn+p4untlDyFtGsas3i6efaADRf1939/QujYfIzoLiuC+19I7r797W4yxrcHLwJJ4k3\nA5Itgzag9hxg8INt3ip9fHpJQ9cgSpt6MQcOrE+ffb0HyobKYHfasSJmBeYG+rCvuJ8JWLiDe65w\nw4QciQkLxIrkCIzYnSjpnHkfcbpkFhPRQQAHJ7z2Y6/nwwCeFrD9NwD/Juc87szimJgYwczimpoa\nAJDVs5gva9Btz5exCkC0ZzEAwV649fX1qnoW9/b2er6BmgPNvP1I3ZjVxBQbGwur1erphbswaROC\ny96H9chv0bnsf0lmFgOY1LPYHVNNTQ2ysrLGxbQ6MQTnrL14/dNTSBqrF52nmpoawZikMou9Y1Iy\nT2IxdXR0YJ5xHmwOG/Zd3oeIwYhxa09OZvHEmGJbCpFFDowkrEdFfRvsKmNKTExEY2PjpF64e0ps\nAIB1zk7cLr3Jv/ZcItSzuKamRv08VVSgpqZGMCapzOKLnRe59zOlo7W1dVIWbnt7u2B/X7GYAGBs\nbGzc+yk7ci1isBvdF9+DxbpINLNYNKbyctTU1EyKaUk4wzUAZ+70YZVIz2KxmKQyiyfG9LnOLF6+\nfLkgGdLa2iqoIyJJskXMXsq3FlshXFVdVWTOM9PG/95Idoddd1yT9JUFHGn2+gZRXHJ88+nev1hL\nKbn59DdvXdKGe5rm8aWLL5E5z0wvX3lZH1wf/AU33hf+oO88uuSZP56nlNx82vuj/1Dt2xe4PCIy\nXqP2UVr37joy55mprrdOsW/FuIZ7iV6M4kqA7/q+MlsZ+qq2PkrJzaelPz5EY3aHOt8a1j2RjzOL\np1rEOILy8nJNvsXspXxrsRUSN1GWHZA9LpFGL1yT9Kn3AaZQoLUE6LJK4lMa89asWDAGnKnqgKX0\ntmrfeuOSq+crQqca19gwUOWqarroYX3nEUD34Cgu3rHBz8Cw2dAjYCXtW29ccuVyy2UM2AeQEZGB\npFD+qpq64g4I5brCgQDUKLOVoU+PDkFa9Bz0jjhwyWpT7VsLLiGZlRcCEiEyHQ5t99/E7KV8a7EV\nEveFYGnQUp/gmqT3CwAyt3PPZZSmVhpzdGgAViXPxajdieuto6p9641Lrn5FzAoEG4Jh7bWipkf8\nw0LS952TwNgAEL8ciEjSdx4BnLjdBoeTcE9aJMKZ79auVr2QyOnLrTtuT3kP8ab2amMWK8su17cW\nXEIyKy8EYttHIyIiNPkWs5fyrcWWT1oHWlHSWYJAYyDWxgknXGvBxatXsJVOTczuTMuSLvHlp2Uu\nfTWPfgY/rAznSkCo+VUwzre7GZBrt5be8+jencXXd0OJb93XlwwhIs8mia3JWwWP0x33okcAMAB1\nwEi/vr5xdxtpQVmr4BdaX617MZmVZajj4uJEyeLu7m5NZHF3dzcvEeTn5ydKFgcEBAiSkP39/YiP\nj1dEFpcHcT/zFpoWotHaiPpofhLSjVlNTLGxsaitrUV3d7cnJqstHGuYH1jtObT3L8AdEWI1LCxM\nkCy22WyIj4+fNE+ZQYMAgIsNg7hjrYPTMcY7TzabDaGhoarI4okxyZ0nqZgAYM3cNTjTdQaflnyK\n9M50RWSxJyY/I2JvfgYTAGvgEpiamhTP00Ri1b22goKCEJeYjBO3uAvBvKEGtHr1LOaLKSoqSpBY\ntdlsiIyMlI5JgFi12WwwmUyKyOIOvw60DbYh3BCOtptt6A/u5yVWOzs7BYlVsZgAICwsjJdYnRu2\nEOG95Wg59x7GMh5WHlN5Odrb2z023vOUMH8+wkxAY/cQPjx6DptzMhTFJEUWC8X0uSSLFy1aJEiG\nnD17VpQskSJbxOylfGux5cP190f+nsx5ZtpbuddnuAT1bz/OkZi7nlLtW0y37eUiSsnNp9MV7ars\np2seiYhOnD5Bq95ZRUvzllLbQJs6XLUXuPF91Uzk6oes5zwWlLZQSm4+7fzP08pwaTyvYr0Art9c\n/Q2Z88z0j3v/UbVv1bhOv8LNzSff1N83Ef3t60cpJTefXj5arty3hnkk+pyRxSTCEYyOit93lhIx\neynfWmwnSu9oLy63XIaRGbEpcZPPcAnqPfdKxe+Dq8XlnWmpxl5KfDlezM5wT/w9IBCKGorU4XIn\npS36AuDKDdFzHo+UupPI5PU9nvL1JSFubmxJwBLVvlXjcneHqzgMOMb09Q1geRQ330dL+de+r9a9\nmMzKC4HaMtRyZKaUoT7VcAp2smNV7CpEBEZMfZlgdzVSWIEx4bIcanF5k2ZCF/aZUoaaT+/ePeT+\nwFKMy03Ee/Ue0Gse7Q4njt1y9x4QziaW63uqy1DX99WjqrsKIf4hWBnFX5Jbjm/VuKIyAcwDhruB\nuvP6+gawNiUCIQF+uN3Sh3rboGLfas8rJrPyQiDWmCYxMVGTbzF7Kd9abCfKxPoqvsIlqA+fz2Ua\nww7UFKnyLaZbOj8c0SH+aOkdxs1G/q2NWubS1+N1f+L9YGC42HwRA2OTq3mK+m6vADorgaC5QPJ6\nXXEBwJXaLnQNjmFB1BxkxEzuPaDUt0/Wl4h49+VOTU5V7VsbrnTuj8CGCS2+F6QkYdOiaADA0bLJ\nu4d8te7FZFaSxdHR0YJk8fHjx5Gdna2aLP7000+RnZ3NS9g1NDQgJCREkCxubW1FZGQkLwlZWVmJ\nxx57TBZZ3NDSgJMNJzncY6koKipCWVkZdu7cyRvTwYMHkZ2drSqm2NhYfPrpp1i0aNGkmDJDczAf\n19B2+i2UNQfxEqvd3d1ISEjgjamsrAxPPvmkYB/mRNjQjlB8eOY2bHHDk+aprKwMDz74oCqyWCgm\nqXmSigkABgcHkZqaigUBC1AzUoP9JfuxIniFLLK4rKwMX5nfjEgALWE5uH36jOZ5cpPF+fn5SE9P\nx4eV3BbC5ZHAyZMnPT2Lxchid0x88yS29iTnqaICZWVl2LRpk2yyeG/dXgDAyrCVnpiEsnCPHj2K\npKQkxTEBQG9vL5YsWcIbU2hTMFYlAPaSz3Am8CGAMfkxlZfj5s2b2LBhg+A8JQanAAAOWOqQE2ST\nHZMUWSwWk6jwEQcz/aG2ZzERTWmvWyW23rhO1J4gc56Zvrz/yz7HJapvKeVIs5+nEQlkNWvB9Z+7\nj1FKbj5tf6VIuf00zaO3/q2bb5E5z0y5p3KV4frTNm5cSz/THZfT6aQNPztOKbn5VFxrU4ZLw3lV\n6yfgsg3ZaNnbyyjnzznUO9Lr0/ecOK4fE/1yITdPTRZdfRcWFlLP0Chl/MsBWvBcPnX2j0zSC+Pa\npfq8RJ8zstgg0MEJAIKCgjT5FrOX8q3F1luO1x0HMH7/tK9wiepjsgCEA4MdQD1/UVgtuFbMn4PQ\nQD9UtN7t56rEXkymYrzc1UhPNZzCmJOfVJwoYYYhoOEyYAwA0seXVdYDV2lTLxq7hxATGoCcRPl7\nyqdlffHIqYZTcJITa2LXINQU6tP3nLiecd3iAN7bQ1pxhQX6Y316FJwEHL/VOkmvVtTazsoLgbsw\nGJ+IdeeRI2L2Ur612LrF7rR7bgt5Xwh8hUtUzxg890oFSi9rwZWRtmBSP1cl9mIyFeOVEpaC9PB0\n9I32obi1WBauDEclAALSNwMB4+/f64HLvRNle3asYO8Bpb59tr54xM2NuS+yvnzPSeJy7x7iybDX\nA5c70W8iT+CrdS8ms/JCMDYm/O2roaFBk28xeynfWmzdcq3tGrpHupEaloq08DSf45LWe5FmPLt7\ntOJy7x4q4CHNtMzlVI2X+wNL7u4huuXOJn5kkk4PXEdKle0WkuPbt+vrrgzbh3G+mdul4y4r4cv3\nnCSuBe66Wzcn1d3SA9fdHgXtGBp1TNKrEbW2s5IsjoqKEiSLT58+jb6+PtVksduej7Crq6vD4OCg\nIFlcW1sLu93OS0KWlJSMy/4UIot3X9sNAMhABpqbmz1EkMViQUxMDG9MbsxqYoqNjcXZs2fR19fH\nT0L2h2He3HD422pw4/huxC59YBxh19LSAqPRyBuTxWJBYmKiIFlssVjw8M4n4G9guGLtwr4jhVi2\naIFnniwWCyIiIlSRxWIxic2TVEwAYLPZEBQUhLa2NoSPhAMACmoKsH5wPZgIWVxfdQvr24pBYGgK\nXYbKoiL95ikxEYfPFKO8dT6C/RnSQuwoLi6+S0JKkMXeMfHNk9Dak5ynigpYLBYEBQVJksVWoxVD\n9iEkmZLQeLsRLJHh4sWL6OvrEySLr127hr6+PsUxAUBbWxsiIiL4PyOsViTVNiB+/kYE3zmMqvzf\nwLH2W/JiKi9HcXExjEYjf2ltr5iWxM1BacsAXt9biPVJwZIxSZHFojGJCR9xMNMfn9fMYqfTSdt3\nbydznpmut12fElyS+l27iPb+A0eanfylIlu55/36f3H9XD+4VKsMlwzfWnDJ0TucDtr84WYy55mp\ntKNUHFfJXm4c39juE1w/eJsjif/p/auTlTNkvCaJF64fnfkRmfPM9DvL73TxrQuuG7u5OfuvR3Tz\n7a177UQlpeTm0/c+tPDqBXGpOC+Rj8hixtg8xlgBY6zS9XdSCyHGWA5j7DxjrJQxdoMx9hUvXR5j\n7A5jzOJ65Mg5b0CAcF/X9PR0VbHIsZfyrcUWAMpsZWgeaEZ0UDTMUeYpwSULm0gvYz1wuVtYTqzI\nqGUup2q8DMzguY0heXvo1n7ur1cSmZ64Srq5REuxlpRqfPt8fYHjxtxF5rYlb9PFtx64kLkdMPgD\ndee4bnI643rQlWF//HYr7A6nfFwqzismWjmC5wAcJ6JMAMdd/0+UQQB/TURLADwE4NeMMe/tDN8n\nohzXwyLnpGIcgfsnkloRs5fyrcUWAI7XcruFtiRvgYGNnxpf4ZKFLW0z4BcENBYDveOP1QPX1qwY\nMAacrurAwIhdPi4ZvrXgkqt38wTuDzJesY8AFUe451mP8R6iBVd73whuNvXD5GfwJCspkWldXwCu\ntF5Bz0gPUsNSkR5x98PMl+85WesrMJzjCsjJlZzQGVd6dAjSouage3AMl61d8nGpOK+YaL0QPA7g\nbdfztwE8MfEAIqogokrX8yYAbQCUr1QvEau53d3drcW1qL2Uby22wPjexFOFSxY2U/DdrY4Tdg/p\ngSsmNBArkiIwanfiVEW7fFwyfGvBJVe/Nm4t5vjPQUVXBRr8BLKMa4qA0T70hSwA5i3QHVdBWSsI\nwH0ZUZgToJz6m9b1BeBY7TEAwLaUbeP6cvvyPSd7ffGUZdcLF2MM2yfU3fLVuhcTRiIF3CSNGesm\nogjXcwagy/2/wPFrwV0wlhCRkzGWB2A9gBG4flEQEW9Xc8bYNwF8EwASwsJWNX73u7znqK2tRUpK\nijDooiJg0yZBtZi9lG9NtleP49EnuxHq8MfJukfhP+Ea7StcknrPeJUCOAogGcCTuuP6vT0OL9mT\n8EVDB1413VGAS3lMvhiv/z/6Io6ENOD7F4Px1zEP81gdBVCK7u4sREQ8pDuur49mosgZgZ/73cFX\n/DomHzDDxssbl3PTA9iWdBDtfsP4oHELlozOlWfrS9zjxqsfwJ/A7a35XwD8dcVV7JyDJ0ezMR8j\nOBNwA3V1vln3AMBeeKGYiFZPUvARB94PAMcAlPA8HgfQPeHYLhE/8QDKAdwz4TUGIADcBeLHUniI\nPp89i9985atkzjPTc6eem1Jcknr3eA10Er0wj+j5udxznXFVu/u57jpMo65+rjOxZ7GQ/lDNITLn\nmemvfv/AZAP7GNFLKUS7wqjj1hndcfUOjVLmvxykBc/lU0ffML/xDBsvj+zaRddar5E5z0zbd28n\np6sktx6+de2l/IdNHGl8K193XA6Hk1b9pIBScvOppLF7ZvYsJqJtRGTmeXwGoJUxFg8Arr9tvFch\nxsIAHADwQyK64OW72YVvBMBbAITbcHmJGEfQ2ircAk6OiNlL+dZiezy4CQD/bSFf4pKjBwAEz+P6\nGZNjHGmsF6606BBkxoSgd9iO89Wd8nGpOLcvxuu+xPsQYAzAtcBOtA1OeBvUngGGuoDITDSNCleH\nVIursLwdow4nsqIDEBkivJFCjW8tuOTq3beFtiZvHXdbSKtvXda9Wzy3hw7qjstgYJ6y7IdLWny2\n7sVEK0ewD8DXXM+/BuCziQcwxkwA9gL4MxF9PEHnvogwcPxCiZyT2u12QZ17v61aEbOX8q3Wtm2w\nDTcCbQgwBmBjwsYpxSVH75Hsx7m/ZXenWU9cDy+NBwAcKmlWhkvhuX0xXnP852BDwgYAd0uEeMS9\nWyj7MXTahJuWq8XlziZeNk/9bd7pWl8EwrE67kKwPWW7rr51W/fA3Szj8oOAw647rkfM3No/cLMZ\nHR08t/Zkitr3jNYLwUsAtjPGKgFsc/0PxthqxtgbrmO+DOB+AF/n2Sb634yxmwBuAogC8FONeGal\nuMvurk9Yj2D/4GlGIyKLHwWYAaguBIb5S0drkUeWctsej5Te3Uo3m8T9QVZQW3D3RacTcGcTZ+3U\n/ZzDYw4UlXME+8oY4T4dM1Vum3rQ2N+IyMBILI9ePt1whCV6ETAvHRiyAfUXpI9XKOvS5mFusD9q\n2gfQ1K/+gq5WNGUWE1EngEmdpYnoCoC/cz1/F8C7Avb890EERE7PYpvNhqKiItWZxW57vuzOqKgo\n0Z7FUVFRoj2LW1tbeTOLPy7jfiitCV8j2je2XiBj1Y1ZTUyxsbHo6elBEU/fWC6zuB9Wr5hWxK+B\nqekibn36KrqSdyAqKkq0Z7HQPPHFRERICjehvmcUf/y0EHGsB9XV1aoyi8ViEpsnk8kkGhMg3AvX\n2GiE0clQ3FKM4lvF6GvtQ1jPLazsb4F9TgLOlHfD1tUlGJOaebpQP4j+ETsWxQQjhA2jqKiIPwtX\nY89iobXn7lksOE+u/r5lZWW8Mb01VgYAWD5nOTraOyZl4Q4NDQnHlJ4Oh8Ohe89iT2bxhJiWxm5A\npK0aDQWvwxb5lGBM5eXlsNlssFgsvJnFQjEtj2IoqgMuNo9h/v/rWSz9WLJkiSAZUlpaKkqWSJEt\nYvZSvtXY2oZstPzt5ZTz1lLqHu6eclyS+onjdeEPHGn2/rM+wfWLw7coJTeffrj3hjJcCs7ty/H6\nh9e3kTnPTB+Vf8S9cPhfuPE69AOf4PrO+1cpJTeffltYOSvH6/E/ridznpnONvJnxPriPSdLzzde\nDcXcXP5yIZWW3NQdV+HtVkrJzaf7f3ZEGS6Z5yXyUWbxdIkYWey+UqoVMXsp32psj9cdh4McWDcU\ng/CA8CnHJUc/TrJc90qrjgEj/brjeth1r/RwSStaNJBm0zVe2wfmAwAKrAVckb5b+zhF9mO64xoe\nc+DYLe61LyyN17T2p2O8anpqUG3qQ5gpDGvi1uh+bl3XPcB17ItIBvpbMFJ5UndcG9KjEBboh9ru\nMVS19SvDJuO8YjIrLwSfJzlqPQoAeHBAW4vNKZOwBCBpHWAfBiqP6u5+SUIYkucFo6N/BJVds48n\n2DwYDyMz4lLLJXTXngG664CQWCBR1oY4RXKmsgP9I3YsSQhDSuQc3f37WtyZ9JuSNsHfINx+dsYI\nY0A2lzMb3X5Wd/cmPwO2u8qDHC7RViFBqczKC0FgYKCgbuHChZp8i9lL+VZq2zXchUstl+DH/LBl\nMGFacMnRTxJ3iYSyz3THxRjDwy7SuHJEfRPv6RqvCGcA1sathYMcKLzxFvfi4kcBVzMlPXEdvMl9\nWDzi2m2lZe1Px3i5dwt51xbS89y6r3sAWMJdCOK7LnEbAXTG5d4wcfDm5P4cckTtGpiVZahjYmIE\nSciLFy8iOTlZNVl87NgxJCcn8xJ2fX19qK6uFiSLh4eHUV9fz0tCNjc3Y8uWLePI4qrgKjjIgazA\nLFyrafL0LOYjgurq6rB582bemE6dOoXk5GRVMcXGxqKwsBDz58+XRRanpqaizbEASwE4yg+jNu6v\n0dbWxktC1tXV4cEHH1Qc04pIbr6PlLbiW1VVCAwIUEwWi8UkNk8mkwlEJBgTAPj7+6O7u5ufsLNa\nsfqhDTjffB6HWy7hiwBa5q3DsNUKq9WKuro6bNy4UfM8RUbH4tDNRgBAMutEa2sozpw5g5iYGFVk\nsVhMYmtPKqaKigrU1dVh9erV42JyhDhQ1lmGAIcB9mo7LDZ+YlU0pvR0XLp0CREREcrnySVDQ0Oy\nyeL4+HiEhsRhbmAMggbbYD39AYIWb+Uli+/cuYNly5YpjmlhygIEGAllzb34+PBJ3L8ySxFZLBqT\nmPARBzP98XnpWfz3R/6ezHlm2lOxZ9b0lPXIHx4g2hVGNz/6d93P691394q1k/eYmTxeHYMdtCxv\nKeW8tYR6f55KZB/VHdexshZKyc2nh359SjYuub614JKr/9ONP5E5z0zff+1h3X3rYSs6Xkd+yJHG\nB77vE1xf/fUhSsnNp9cLq5ThkuEbnyey+PMg424LCWQTz2hxJZdFt5/X3TVjDA+btf1Enk6JDIrE\nqoAo2BlDUdpawKj//e8DrttCX1iqvOT0TJAjVq4S60OzhRvzluwvcn9v7RO9PaRWVsdxN2oOTSFP\nMCsvBJ+HfgQn6k5wu4Xi14nuFvI1Ljl6XnHxBNFdV7gSyzqf15NlfLPZXZdKkUzreBFhexeXHXos\ncPxFQA9cI3aHp7Wnmx+QhUuGby245Orv9NzBbdtthPqH4t7BWF1962UrKvNXwh6SAPQ1A/UXdcf1\n+NpMBJuMuNHQg3rboCJo09WPYFpE7INhdHRUk28xeynfSmzd34geTH1wWnHJ0fNKZDoQuxSGsQFu\nK6nO512RFIGoOX5o6hnG9QblWczTOl5N17C1g+sde6brFgbG7pam1gPX2aoO9A3bsTguFGnRIfJx\nyfCtBZdc/WErV9d/c/JmmCCeDa3ne06pXlAYQ3+yK4+27FPdcTGnHVsWxwDgag8pEbUxzUqyWKxn\n8aFDh5CTk6OaLN6/fz9ycnJ4Cbuqqio0NTWJ9izu6OgQ7Fn89NNPo7m5GQ2dDbjYfBFGZkRcfxyK\nioo8PYvF+vs++eSTvDG5MauJKTY2FgcOHMCyZctkk8XumBaErUZK6010nX4D11vm8PYsfuaZZ1TF\nZLVakUjt6MBcfHC2HN1xQ+NikiKLxWISmyd3z+K+vj7R/r4jIyOCZPHKC28jxuGA2RmMEgxi99Xd\n2BS7ydPfd+fOnZrm6Z3b3BehpRFjKCoq8pCQR44cQXZ2tiqyWCwmqXkSi8nd33fHjh2emPY07QEA\n3DP3Hpyu3ePpWcxHrIrGlJ6OwsJC1NfXK58n3N1zr4gsdsXUUheIrwJwlOzB6cAHAWaY1LN406ZN\nqmNKCeGwfHa1DvfMHVDUs1gwJjHhIw5m+mO2k8Ufl39M5jwzfevot6Ydl6ReDJfNypFmP4klGu7T\nHdcf9xyjlNx8Wv/vx8jhGF+eeOaO14+JXs4m2hVG75/7GZnzzPSPx/5RN1wjYw5auuswpeTmU1Vb\n3yS9MK5dkr614JKrr7BVkDnPTBvf30ijjlHN5Oe0kMVEVHjiBNErS7j1X3ted1yDI3bK+leONK7r\nHJCP638SWSzGEbivgGpFzF7Kt1xbpbeFfIlLjl5Q5qZgOCYHsA8B5Yd0P+/WnDQkhAeiqWcYxXVd\niqBN39EMXbkAACAASURBVHg1A70NQFgiti/7BgzMgLNNZ9Ez0qMLrrNVHeh13RZK97otJMdeyrcW\nXHL1h+5w62Rb8jZZSWR6vefU6EVtFyy4W423dPLtIa24gkxGbMvi+JN915vk41IZ06y8EMxmmfW7\nhSbIwAJXt62Sj8UPVCEGxrBzOZdot1/Bm2F6pZz7Y/4iIudEY13cOtiddk/Nfa2Sf2N8EtlsEiK6\nu1toAX+XtlklrixjlH0KOIXb56qVx6Zw7c/KC8HICP8uFUDGvTAJEbOX8i3H9ljdMUW7hXyNS45e\nTG4bs7jS1FXHgcHxtfb1wOW+EBy82ayoNPW0jJfTAaCCe77kSwCAhxdwbSsPWQ9pxlVRfQdHXL0H\n3OOixF5MpmK8btluoa6vDvMC52F17ORuiXqf25fr3mq1AomrudpDfc1A7Tndcd23kKs9dLulD5Wt\nffJxqZDPHVlssVgAQDVZ7LbnI+y6urpEy1DbbDbB8sYlJSXIysrChzc+5PCNpaOtre0uuSWDLBaK\nyY1ZTUyxsbG4ceMGACgmi00mE1r7neiNWomw9iuo3Pcywrd8ZxxZnJWVpSomNwn5aGIikiJMqO8e\nxe/3nsD2pUmyyGKxmMTmyWQyeUoIixGrfOWNh8qOIAeDGAtNQt1QGOqLihDgCIAf88Pl5svYd3wf\nam7WqJ6nfVdq0D8Sh8x5/gh29OPcuevjSMiyMq6ks1qyWKhks5x5EorJTRbHxMRgTwdHEi/xW4Jb\npbc4YrW2VpQsFo0pPR1VVVUAoDgmgCNWlZSh5ospOe0RhFz9PZqO/gadGyI8ZLHFYtElpk0ZEdhX\n0oHf7DuPJzNNssjiKS9DDWAegAIAla6/cwWOcwCwuB77vF5fAOAigCoAHwIwyTmv2WwWJEOqqniy\n8bxFgmwRs5fyLWXb1NdE5jwzrXpnFfWNTCBXpwmXpF4OrqvvcKTZW1/wCa5XjpZTSm4+/X8fWZTh\n0nhexfp93+HG4dgL417+9vFvkznPTP9d9t+acD37ehGl5ObTG6drlNtP83g5nU7asXsHmfPMdKXl\nii64pPQ+X/dERK1l3Jz/LJlobHiyXiOuM5XtlJKbTw/84gTXz1njeMFHZPFzAI4TUSaA467/+WSI\niHJcj8e8Xv85gFeJKANAF4BvyDnpxL6m3mIymWQBV2Mv5VvK1n17YFPSJoSYQgSPnUpccvSStosf\nBYwmwHoG6G0er9MB12M53G2QIyUtGB6Tdy92ysfLMXa3haf5yXGqh1Ndt4fuHFKNq2dwDJfqB8AY\nsHMZPz+geR5V6OTqb3TcQNNAE2KCY7AiZoUuuKT0Pl/3ABCTBcSageFu7vaozrjuSYtEVEgArJ2D\nuNkonU+jNiatF4LHAbztev42uL7DssTVp3gLADfLKNtejCNw/4xTK2L2Ur6lbA/UHAAAPLLgkRmD\nS45e0jYoAsjcAYCA0r2640qPDsGShDD0jdhxsqJdPi6N51Wkv3OSa2OIeUBM9jjVpqRNCDQGwtJu\nwaXyS6rOe7i0GWNOwob0SMSE8Vff1TyPKnRy9YfvcElkO1J2wMDkf+xofc9p8S3bdulT3N+bu3XH\nZTQwPOq68O+zSJPGamNipCJ932PMWDcRRbieMwBd7v8nHGcHd1vIDuAlIvqUMRYF4ILr1wAYY0kA\nDhGRWeBc3wTwTQBICAtb1fjd7/Jislqt4luoioqATZsE1WL2Ur7F9Cfbb+Hba8sQ5vBHUd2j8J94\nDZ4mXJJ62bgqABwAEAfgGd1x/cEeh5/Zk/AFgw2/NVXPwPE6DOAWcCcOWPDMJJv/E30Rh0Ia8Jc1\nCchl6xWf99nRRTjnDMMv/O7gy378zc31mUeFfmXoq2vv4Bv3lqLTbwTvNW7G0tF5uuDyKW5FuHoB\nvAmOcv0WAJOuuIqdc/DkaDZiMYpzF34D46YHZNtOFPbCC8VENJmp57tfROPv7x8DUMLzeBxA94Rj\nuwR8zHf9TQNgBZAOrll9ldcxSQBKpPAQibeqvH37tug9Mql7bGL2Ur7F9P969F/JnGem5889P6Nw\nSerl4hodJPq3BO5+aWe17rgauwYp9bl8yvzhQeoelE5EmtLxGu4j+mk8F/uu7/DanKo/ReY8M+34\nYAd3r1fBeVt6hij1uXzK+EE+F7sa3NM4Xu+df4/MeWZ6dM+jk2PXgEtKPyXr3i1vPsTNv+V93XE5\nnU669+dcNd6zP/qVMlwTBGo5AiLaRkRmnsdnAFoZY/EA4PrL2yeNiBpdf2sAFAFYAaATQARjzL1z\nKRFAoxQegGPThSQ0VH0zEyl7Kd9Ceic5cc7GbS9Tc1vIV7jk6mXZ+gcBi7/APS/5RHdcCRFB2JAe\niVG709OQRa1v3cfrdj4wNgAk3QNg0g9iAMD6hPWIDIxE03ATSjpKFJ13//UmEAEbFoQjPEg4CUuX\neVThV0p/rptb+zvTd4rye3qfe0rWvVvct4dufKQ7LsYYvpjDtUDd44xUhkumaOUI9gH4muv51wB8\nNvEAxthcxliA63kUgI0AylxXp0IAT4nZ88nw8LCgrqKiQi52xfZSvoX0ljYLWodbETcnDqtiV80Y\nXHL1sm3NXm8GIt1xfWkFV7J4z9UGZbg0nldSf/197u/yrwra+Bn8PDkF+2v2Kzrvnqvc9yNzqPC6\nF7OXI74ar/7Rfpxt49o6Ppr2qK64pPRTtu4BLrnM4AfUFAF9LbrjemIFdyE47JiHoVHhDRNqY9J6\nIXgJwHbGWCWAba7/wRhbzRh7w3VMFoArjLHr4D74XyKiMpcuF8D3GGNVACLB3Wj73Mm+aq6B+cML\nHlZElM06Sd8CzIkBOiqAxmLd3T9kjkOQvxGXrV2odQqXGZlS6WkEak5yu6aWiO91eCyd2zB3+M5h\njDnGZLkvbepBWXMvwoP8kRMjXqlzJkpBbQHGaAyrY1cjIUS8HeusljmRwMKHAHJ4fhXoKWnRIVie\nFIF+GFFwq1V3/5oSyoioE8BWntevAPg71/NzAJYK2NcAkN3VW06rSqvViqKiItUJZW57vqQeo9Eo\nmlBmMBgmJSpV11XjQBW3W+jeiHuFE5UkEsqsVivq6+sFYypSGVNsbCzq6+tRJJTUI5FQZjAYxsW0\nMnMn5ljeROPBX8JqelhwnuTEZLVaUT0hqWdDcjCOV/fhFZsRX1QZE988icU0MVHJYDB4EpWS6j5B\nOgh9Cfei+OJ1wGpFukBMLXdaEEmR6BzpxKfXP0V0b7TkPL13i9sht2PhXLQ03haep8REtLS0oKio\nSFVCmXdMesyTO6Y/F/8ZALA6aDVaW1snt3WUSCgTjSk9HZ2dnSgqKlIcEwA4HA7VCWVWqxVlZWXj\n1l7q3I1IRT4Gzv0J1qjvwaJzTOaQMbTREOrqG1DUVaE8JjHhIw5m+mPZsmWCZEhLS4soWSJFAonZ\nS/nm0++r2kfmPDM9tfepGYVLtl4prpZSjjT79yRqaajVHdfpCi7B5t7nPhYkXaV86zZeTifRa2u5\neG8f5F6TGK9fn/s1mfPM9N3C70qed2TMQStePEopufl0vb5raudRrl8RfWNfI5nzzLTyzysnJ1Dq\ngEtKP+XjZR8l+nka0a4w6rhRoDuukTEHOX6sApeX4PNUfVSMI3BfndWKmL2Ubz79Z1Uc7bHcuHxG\n4VKiV2Qbmw0krABGetBx9m1+Iw241qdHIi4sEPUUiCu1whVJp2S8mi1A+20gOBLI2CZq45bkgWQw\nMBTVF3kqkgqdt6i8DbaBUSyMDcHS+eFTO48K/Arp82vyAQDmQLPiBEqt59ZqKyW8tkZ/YNmXAQDD\n54XvcqvFZfIzwCDBtauNaVZeCGaLNPU34WLLRQQYA7ByzsrphjN1kvMXAIC4lhO6uzYaGL64kiPO\nPimWJo19KhYXSbz0adl9ief6zcW6+HUYc47h4J2Dosd+7IrvqVWJinfbTLcQkedL0NoQ2Xd/Z7/k\nPAsAiGk7JdjCdSbKrLwQ+PkJUxuRkeLbq6REzF7K90T9Z9XcG2FL8hYkRSfNGFxK9YptzU8CRhPm\n2SxAr3A2pFpcT7ouBPuvN2FgxK7Yty7jNTYMuAoIut/8ciQyMhJPZnIlKD6p+MSdQzPpvJ39Izhx\nuw1GA8MTrq2DUz6PMv3y6a+0XkFdXx1igmOwcf5Gn+CS0k/LeMUtBeKWwt/ez9ujY9pwScisrD4a\nFxcnSEJWVVWhs7NTNVnstucjIQ0GgyhZHBAQ4CEhAwIDsKeOq7aYNpDGvSliYtDc3KyKLO7q6kJE\nRARvTG7MamKKjY3FnTt30NnZqYosDgsL4yVWY+etRkz7OfSdfQP1KU8pjslqtaKrqwvBwcG8MWWO\ndqASUfjj4WI8e0+qopi850lJTG4SMioqCg3HfofE4W70haSjj6LRV17uaVUpRBa7Y8oyZyHMPwzl\nXeV45+g7WJe6btI8FdTaYXcSlkcb0VpbCUjNU2IiGhoa0NnZqYosjoqKEiRW1czTh/3cRXKl/0rc\nqb4Dk5+Jd+1JkcWiMaWno729XZAsFosJAMLDw1WTxV1dXTAajbwx9YXeg/SWm+g59XsMR63XNSap\n6qOiMYkJH3Ew0x+LFi0SJENOnTolSpZIkUBi9lK+vfWXmi+ROc9M23ZvI7vDPmNwKdarxVV+mCNR\n/2MFR6rqjGv3j35DKbn59PhrZxTb6jJe//UIF9/lN8crZY7Xzy/9nMx5Znrh3AuTdE6nk3a8cpJS\ncvPpwI0mfXBP4frqHu6mlX9eSUvzllJDX4PPcEnpp2u8qL+dHM/PI3o+gqi7Yebgos8ZWUwkXB/J\n4dDWKUjMXsq3t/7jCq6W3mPpj8FoMM4YXGr0qmzTt2LEFAnYqoE7p3TH9QVDF0ID/WCp78btll5F\ntlrHw9RXB9SeAfyD7ybRyRS37y9lcI1rDt45iMGxwXG64toulLf2ISrE5GlXqAdutbZKz5tfk49R\n5yjWJ6zH/JD5PsMlpZ+u8cKcKHRErQPICVx7Z+bgEpFZeSEQKzEREcGf4i9XxOylfLv1tmEbCmoL\nYGAGPJX51IzBpVavytboB1uaq+L4lf/SHVcQc3runX9wqV6RrdbxSLW5LmzmLwGBYaLHCvnOmJuB\n5dHLMTA2gKO1R8fp3rtYBwD48uokmPwMk2zV4lZrq+S8ROT5EvSlzC/5FJeUfrrGCwB6F7q+IBS/\nDTjG81jTiUtIZuWFwN9feIdGfLy2Xq5i9lK+3fq9lXsx5hzDffPvQ3xI/IzBpVav1tZvzd8AzMjV\n4umbnA2pFddX13IE/J6rDZP6FPhsvOyjiGos4J6v/LqoHynfbtJ4T+Uej65rYBT5N5vBGPDM2mT9\ncCvApdSvt/5mx01UdVdhbsBcbEna4lNcUvrpGi8ACF36CBCZAfQ1AZVHZgwuIZmVZLFYq8qCggKY\nzWbVZPGePXtgNpt5idW6ujqEhYUJksXNzc2IjIrEO9Xcz8EdsTtQXFyMvr4+lJeX44knnlBNFpeU\nlODxxx/njSk/Px9ms1lVTLGxsfjkk0+QnZ2tiiy22WxITEzkjamkpAR/l7oZgXeOoWbPi6hLeVp2\nTFarFSUlJXj44Yf5CTurFai4hsxIEyo7R/HyRyewcb6/rJiam5sRHR2tKqao9nMwD3ViOCwNF6r6\ngeqi8YSdBFnsHVNwfTACWACutV3DuYpzuHr0KhpDFmHU7sT61DBU37iEarnzlJiIffv2ITMzUxVZ\n3N/fj7S0NMVrb2JMr116DQBw77x70d7ajoqKCpSUlGDLli2qyGLRmNLTcejQIaSmpiqOCQC6urqw\nbNkyVWSxaEzl5bh+/Tq+GL8D8zur0FXwCq63zNElJimyWDQmMeEjDmb6Y+HChYJkSGFhoShZIkW2\niNlL+S4sLKTTDae5csO7d5DdYZ8xuFTrteKqLOBI1VfNRF7joReu9y7WUkpuPj35+llluNSe9+3H\nuHjOvy6KS67vF8+9SOY8M/3k/E/oxIkTtPmXhZSSm0+HS5r1xT0F66t7uJtWv7OazHlmqu6u9jku\nKf10jZdHP9BJ9GI00a5wItudGYELnyeyeCaXof6wnNs29/Sip2E0GMfpphOXFr1a29DQUCBtCxCR\nAnTXAdUnJus14npseQJCA/xwpbYLJV6t/HwyXm23gZoiOIyBwPLJzWfkyETfzyzm/Oyr3ofyQQdq\nOgYQGxaArYtj9MOtApea8+6t3IthxzA2JGxAWniaz3FJ6adrvDz64HmuQoTEcQUzAJeQzMoLgRhH\nkJiYqMm3mL2Ub1OkCacaTsHP4IcnMsZXopxOXFr1am0TExMBgwFY/TfcC5ffnKzXiGtOgB+eXs1x\nBW+dtcrHpea8F38PABhZ/CWuPacKmeg7Y24G1sWtw5B9CPtbubr9X1mTDD/j5LfmtM6jhK3D6cAH\n5R8AAJ5d/OwkvS9wSemna7zG6Vf/Lff32jueTOPpxCUks/JCMDo6KqiTvBcmIWL2Ur7fLXkXTnJi\nW/I2RAVFzRhcWvVqbT26nL/kyjRXHAY6qyfrNeL6+oZUMMZlGrf3jcjHpUQ/1AVc5z7oKudtkYVL\nru9nslytPR0n4GcgPLOWPwt92udRRH+y4SQa+xuRFJqE+xLvmxJcUvrpGq9x+qR1XHP7gXZPw6bp\nxCUknzuy+Pz58xgaGlJNFrvt+YjVpqYmwcxi0xwTCjsLAQBZQ1m4ePHiOCKopKQEqampqslii8WC\nhIQE3pjcmNXEFBsb68myVUMWt7S0CGbhWiwWpKamorOzE3Oj70d8yzEMHP8VGpb/s2RMVqsVFosF\nUVFRwmSxV8nmnGgjrrU58Or+y/jnbQtFY2psbBTNLOaLyXH6VcTbh2Cbm4PqHiP8hQg7CbKYN6a4\neAQhEkOmTiyeX4muxjQYhhXOU2IiiouLBWOSIottNptgFq6cebqUegkAsMZvDU6dPDWOWLVYLAgN\nDVVFFovGlJ6OmzdvYmhoSHFMANDW1oaoqChVZLFoTOXlKC4uRkBAAOLj49EbtQOZrSUYPPFL9MVt\n0RSTFFksGpOY8BEHM/2xePFiQTLkwoULomSJFNkiZi+me+8W15f12fxnecsjTxcuzXq9cLnLU/80\njiPRdMZ1tpIrT736pwU0MubQd7wcdqJXzBz+8sO6j1fv0CiZX84lc56Zntn3t4psZet9uL4+OfkJ\nmfPMtObdNdQ70jtluKT00zVek/Rjw0S/yODWT3XhtOKCL8hixtg8xlgBY6zS9XcuzzGbGWMWr8cw\nY+wJly6PMXbHS5cj57wmk0lQ574CqhUxeyGdw+nAO2XcltGvLfkab6XI6cCll16t7ThdbDbXwWxs\nECjO0x3X+vRILIoNRXvfCA7ebNZ3vMoPAj11wNwFQMZ23cfroysN6OtYBUb+uGm7hJruGtm2SvRq\nbaX8XrZfBsBl0YeaJpOVvsIlpZ+u8Zqk9wsA1n6Te37+t9OKS0i0cgTPAThORJkAjrv+HydEVEhE\nOUSUA2ALgEEAR70O+b5bT0QWOScdGxNu89fQoK00sZi9kK6ovgj1ffWI9o/G1uRJDdumDZdeerW2\nk3Trv/1/2zvz+Cqqs49/T3YTAllYhUgkgiBh30RFQdFiXy3F5a37hhutWm210NKKVNpXXlutVlv1\ndUEUV2oVqUDYopRNAQNJ2JcLAUIgCyEkIevz/jETeknuzJ27JQHO9/OZT+bOM89zfufMJCdznznn\nGD+/fQNqq4OqSynFPZemAvDWv/ecfDT2J24T++pXjZ8jHoKwsKC2V1298M7KPVAfS5/oiwF4J/cd\nZ7p8tPvra2crrCxk4f6FQNMkcah1ebO3VHt5tA+9DyLOgR0ZFG5Z2WK6rAi0IxgPNLwX9S5gv2ir\nsVD9AhGpCKRQu/k0ysrKAglt629le3ez0QSXt7n8lFdGW1pXsOz++jaxpV0JHfpAWT7kfhZ0XRMG\ndSU5LorsA6WscR31O+4pdtdK2LcaYhJg0B1+6bIrOyP3EPtLKklNjuXG5NGEqTDm75pP/vH8wHQH\nqMup7b3N71ErtVyZciU9Enp4PCdUurzZW6q9PNrjkmGg8VJAovl2VUvoskKJzQRuXp2VOioiCea+\nAkoaPlucvwx4QUTmm59nASOBKswnChHxuJqDUupB4EGAc9u2HXLgiSc8lpGXl0dKis3c/5mZMHq0\npdnO35NtY3QRd5ybSXxdJLNW96dX19RWoSto9qDrygEWA+3Jy7uClJTzPHj6r+tvtZ3539oUBlQf\n4Yu2Lt/jNrF/BuwFLsa4VYPXXiLw4+o+bJQ2/D5iL2Py1/PXwfksaJPH7aVpTCkeaOnrc71CcH+V\nhlXzg5QFlIfV8sGBMfSrTmpWXf7qbjldxcC71NeHERZ2PxDX7LrU9OnrRWRoE4OnxIH7BizB+O1t\nvI0HjjY6t8QmThfgCBDZ6JgCojGeKJ72pkekda1Z/MjSRyR9Vrq8sO6FgNZPPWPWLPZmqzkh8nwv\nkWltpWT1+0HXdayyWvpNWyjdJ8+XtbuLfI/rbt+/zkjw/eHckwluf3V58l2+tUC6T54vg3+fIeVV\nNXLo0CHZWrRV0mely9D3hkphRaF/ugPU5dT2WtZrkj4rXe768q4W0eXN3lLtZWv/4Fbjnlr4mxbR\nhb/JYhEZKyLpHrYvgAKlVBcA8+dhm1D/DfxTRE5+wS8iDePoq4B3AEdr2tnlCBpeo/IXO//GttzC\nXDLzMjkn4hzuvOhOn3xDqSvYdn99PdoiouEy42kuavULxr/FQdQVHxPJPZeeD8Ary3f6FfekfcUL\nxs+h9xmjRAPQ1dhXRPjLkh0APHRFD2KjIsjPz+fCpAsZ3W00J+pOMGfLHP90B6DLqa2ipoL3t7wP\nwLiEcS2iy5u9pdrL1j56svHzu7c8TsQYal1WBJojmAfcbe7fDXxhc+6twIfuB9w6EYWRX8hxUqhd\njuDoUevvhp1g59/Y9mqWkUS8pfcttD+nvU++odQVbLu/vpa2IfdAfBdiS3caM5MGWde9l6QSEw7f\nbD/CxrymMRy1R8FmQ1t49H+S3AHqcvf9evsRsvKOkhwXxR0Xdz/Fdn//+wH4aOtHlFWXNfH1FjsQ\nXU5tc7fP5WjVUQZ0GEDnms4tosubvaXay9beZQCFySOgthJWvdzsuqwItCN4DrhaKbUDGGt+Rik1\nVCn1ZsNJSqlUIAX4upH/HKVUNpANtAdmOCnUbiFvu3mInGDn727beGQjKw6sIDYilnv73uuTbyh1\nhcLur6+lLTIGLvuFsZ85E+rrg6orMS6Kq7obrxh7eipw1B4r/mx8GHwXxHdqaveT8PDwU54GHrzc\neBpwjzugwwCGdx5OWU0ZszfP9k13ALqc2ipqKpiVOwuAB/o9YLuGeCh1ebO3VHt5s+9LM9+usngq\nCKUuKwIaWSwiRUCT9yVFZB1wv9tnF9DVw3k+jdV3smZxYWEhmZmZfo8sbvD3NAo3KSnp5MjiN4rf\nAODy+MvZuGYj8fHxJCYmWo5YLS8vp6CgIKA1i/MsRqw2aPanTp06daKkpIRMq9GdXkYWJyYmWo4s\nLikpsbxOBcdTGRaZyDkF2Rz6+k22ql5NrlNJSQm7rEZ3NhpZ3LhOI5MqWewKZ/HmApZ8v4vYyoKT\ndbK7TlFRUXThMOTMpV5FsC5qJKmHDztfC9fBmsUffb2JrLyjxEdCas0+tm2rbXKdbut+G98e+pa3\nN71N//r+XNjtQvvr1K0bx48fJzMzMyRrFrvfe4tKF3Gk8ggXxF9A/c56io8WW18nc33fzZs3+zWy\n2LZOaWnU1NS02JrFlnXato3CwkKysrI81qlAdeJI+xF0KFxL+ZL/Yet5dzquk16z2G276KKLLJMh\nmzZtsk2WeEu22Pk32DYUbJD0WekyYs4IKaks8ck3lLpCYg+hrv3/nGYkzl4d2WSK6mDoen7hVuk+\neb7c8LeVp4z2to1bXy9lr4wxdC2aahnbX11ZGzfKj/66QrpPni+vZe60jfvo0kdPTlHttdwAdTm9\njkWVRTJizghJn5Uuaw+ubVFd3uytWtfBLOMee7ajSOmBZtPFmTQNdW1traWtoef0Fzv/oqIiRIQX\n1hlJxNv73E5CTMIp9pbS5a+vE7u/vt7i7mx7KbRLgcO5Htd2DVTXQ1f0IDkuivV7S1iYc8hZ3B2L\naXNkvTFuYNQvLWP7y/zsAjbuL6VjfPTJ3IBV3J8P/jlhKoy52+fiKnW1iuv4+sbXKa8pZ1TXUQzv\nMtxRuaHS5c3eqnV1GQB9fgS1J2DJ9GbTZcVp2RG0JPN3zyfrSBbJMckncwMa/5CwSLja/CVY+ixU\nBpZQb0x8TCSPj+0JwMyFW6mu9ZyLOEl9HSx+2ti//Ck4p8mMKQFRXlXLp9uNN94mj+tNXLT9N7Np\nCWlMuGACdVLHy997Tiw2J/uO7eOTbZ+gUDw+5PGWlnP6c/XvjVl5N30Eed+1qJTTsiOIiYmxtPXp\n0yeg2Hb+3Xt258X1LwLwxJAnaBPVxrFvKHV5ix2o3V9fR+X2vQHOuwQqCuHr/w26rluGn0ePDnG4\niiqYs3avfdysOXBkC3Xx3WD4A15j+8rfMndytEoYkJLAhEFNUmYe404aMImY8BgW712MdLYf/Bnq\n6/jShpeolVrGXzCeXom9HJcbKl3e7K1eV9L5/3kjbeHkky9NhFKXFaflNNQdO3a0nYY6NTXV72Rx\nRkYGqampHpOQ7+x6hyOVR0iNSiWlLIWCgoJTEkHl5eW4XC6PScgDBw4wduxYv5PFLpeLq666ymOd\nMjMzSU1N9atOnTp1IiMjg5SUFL+SxbW1tZZ1crlcXHvttV7rRL9H6LZvNbL2NfI7X01Nwvm4XC5c\nLhejRo3yK1nsXqeHR3bmV/N28aeFm0lVhYTXVja5Tvl7tnLR0t8RBWxPuYWqnC2WidWwsDDLOlkl\ni/OPVfP610YdJg5qx4ED+x1fp3EdxvH5oc+ZsXYGki8kJSZ5TEIuX76czp07+5UstquTy+UieVAy\nHwA6JQAAFrhJREFUGXsziFSRDK4cfDL56PU6bd+Oy+Vi+PDhfiWLbeuUlsa///1vkpOTfb9OGK+h\nHz9+3K9ksW2dtm1j165dDBw40Gudzu99JwnfvUvUgfXs+GwGbUc9ZFsnb8li2zrZ4Slx0Nq3lliz\neM/RPTJg1gDpN6ufZB/J9rnss3bNYqe+XzxqJM/eu0HETOwGS1d9fb3c+sZq6T55vvzykyzPcb98\nwij/zWtk+bKlznU71PXQ7HXSffJ8ueUvC3yOW15dLld/erWkz0qXOZvnBFWXE9+MZRly3WfXSfqs\ndHl94+u+lRtCXd7sp42urA+Ne+/5niKVpXrN4taKiDBj7QzqqGNCzwmkt09vaUlnHlf+DqLbwc4l\nkD03qKGVUsz4cTpR4WHMXb+fLUWNBiTmfQvr3oawCLjuRVDB/bVYmJPPwtxDxEaFc/OF1lOoWxEb\nGcuU4cbEvi9//7LHCelCyeLSxbiOuUhtm8o9fe9p1rLPCvr9N3QdCscLYMm0FpFwWnYEdjmCXr16\nWdqc4Mn/0+2fsjZ/Le0i2/HYoMf8KjsUupzGDtTur69P5bbpANc8a+wveAqOHw6qrh4d2vCzMRcA\nMHtbPcerzDfPqivg80mAwCWPQqeLgtpeRyuq+e3nuYCRIL64f2+/4o5JGcOlHS+lvKacZ1Y/0zBX\nl9+6nPpuKdpCxjFj1vinRz5NVHjTjqyl7i9v9tNGV1gY/OhlCIuEdW+T3qYkZLqsOC07guachnrf\nsX38eZ0xynRi6kSSz0n2q2w9DbUD++C7oMcYY33geY9SduxYUHVNGp1G33Pbkn+smhnzNxsHl06H\nop3G9NhXTPFPtwUiwq8/y6bweBXDUhO58+LufreXUop7u91LQnQCqw6u4pNtn/ity2nZVXVVTF05\nlTqp47betzGs8zCfdYdCl1P7aaWrU1/jTTWg3bIpxu9ACHRZcVomi+3WLF6wYAH5+fl+J4sb/Dt2\n7Eh8YjwPZz5MRW0FFydeTEJBguWaxfHx8ezdu5ejR496TKzm5OSQkJAQ0JrFsbGxHuvUoNlJnTwl\ntxYtWkR+fr7faxZXVlZarlmckJDgU53SBk/m3P3rCN++kPw8RUTkH/xKFlvV6f70GJ7KFz76Lo9h\nRfO58eBrSFgEW3v/nIKVa7zWCYy1cOvq6rwmi99btZsFudXERobx+Mj2fPPN12RlZREREeHXdVq7\nfC03pN3A21VvM/O7mah8RUfV8WQScunSpeTn5/u9ZnHjOv1545/ZUbKDtnVtuanTTZYjVu3q1LC+\nb11dnV/JYts6paWxYsUK8vPzfb9OGOv7RkRE+L1msWWdzDWLKysrfapTdMRIhnbsR+ThbI68dStb\n+/+GC3v39nnNYss62eEpcdDat+ZKFj+7+llJn5Uu4+aOk2NVx0KauDrrk8XubJ4nMq2t1D2TJLJv\nbdB1TZ2VIZdNeUuOPt3FSNKt/KtjX692U9fGvBLpNfUr6T55vnz+/X5Hvk7LnbZymqTPSpfrPrtO\nSqtKfdLltOx/7fqXpM9Kl0GzB8nsRbN98vXJrpPFp1K0S2p+38m4L1e9GnRdnEnJ4ujoaEtbWlpa\nQLEb/D/c+iEfb/uYyLBI/nTFn4iPivca284eLF3+xA7U7q+v3+X2uR5GPEyY1MJHt0Op78vv2ZX9\n0CVd+CT+L7RT5fw7fDgl/U8dMxBoex0uO8FD762nqraeW4enMH7gf8YMBKO9Jg+fTM/EnriOuXjq\n66eora915O8kNhgTKv5u5e8A+NWwXzG6z2jHvv7YQxX7tNSV1IOjV5hzb2ZMhV3LgqrLitOyIxAP\nibIGqqurA4pdXV3Nsn3LmPntTACmXzKdvu37OoptZw+GLn9jB2r31zegcq/5AxWdh0P5YXj/Rqgo\nDo6umhMkL32cLtV7cYWfx6TyB3nw/fVUVtd593VgL5Vw7nn7O/JLTzC0eyLTf3TqG2bBaK9zIs7h\nr1f+laSYJFYdXMWza56lXuqDch1dpS4eW/YY1fXV3NzrZn5y4U9a7f3lzX666irtOgZGPQlSDx/f\nBQe/D5ouK07LjsCusnaLljth4faFPPn1k9RJHQ/1f4jr0653HNvOHqiuQGIHavfXN6BywyPYkPYY\ndOgNR7bC+zf41Bl4jF1bBR/fQeyh7yCuI3F3/4O4tkl85yph4rvfnewM/NV97EQN91X3ZHP+MXq0\nj+O1O4cQFRHmyNfXcru26cpLY14iJjyGz3Z8xsxvZ7J3315bf2+x88rymJgxkeITxYzsMpJfj/g1\nSqlWe395s5/WusZMhfQbobrM+EfokKOlWvyu0xmXLM7KygLwK1m8tmwtrxe8jijh2o7XclOXm8jM\nzASMRFBJSYltsri4uNhyeuOcnBz69OkTULLYqk4NdbZKFjfYrZKQmzZtAvArWVxcXGw5DXVWVhZ9\n+vTxq04ul4us3F10u+olumVMJOLg95S/MopDY//OOZ0v8JosblynvVuz6LVhOgmluVSFt2HboGcp\n3n2QXw2P4Q+r6li1q4jrX8jgyRHxVJRY1wmMJGTj6Y1dBSVM/mofeRJPxzaRzLi6MznrVjetU1ZW\n0K5Txa4K7ku+jzeOvMEHWz9gXe066qknPjbe52RxzpEcfjvvt5TWlpIWncaUPlPYvWO3s+tkU6eG\nxKpVnbwlizdvNt7uskoW79xprDfh9Do1Tqz6Ow21bZ22bTv5okQgdYrocAfDzisket/X1P7fNWT3\n+w3nOkgWnzXTUPfr188yGbJnzx7bZImnZEtVbZU8t/Y5SZ+VLumz0uX5b58/Zdpip7Ht7P7oClbs\ngOytQdfR/SKvDDcSaDPPF9mx2Ddd+9eJvDTQHL3ZS/avX3TKuTsKymTkH5dI98nz5dLnlsqXq3N9\n0r1qZ6EMnbFYuk+eL2OmfCp5xeWOfZ3a7Owr96+UYe8Pk/RZ6XLbv26TvGN5TU+yaK/6+nr5x/Z/\nyJDZQyR9VrpMXDRRyqrKgqLLkT2A+8ub/YzQVXNC5OM7jXt3epLItOtOjrz3JzahSBYrpW5WSuUq\npeqVUkNtzhunlNqmlNqplJridvx8pdRa8/jHSinfh10GgIiwYv8Kbv7yZt7f8j4RKoIHL3iQJ4c9\nabsKmqaZadcV7l0APUZDRZHxqMxCKD1g71dRDIumwlvXQPFu6NQPHlhKTdKpg24u6NiGz392Kf27\ntWN/SSWPfbGHZ+blUni8yjZ8wbETTJ67iVv/bw1HyqoY2SOZT6O20i0xNqDq+solXS/hnXHv0D66\nPZuObOLGeTfyZvabnKg9Yeu3s2Qnk5ZOYtqqaVTVVzE+bTx/v+rvTSZT1LQgEdFw0zvG5HT1tcA3\nIRl9HGiOIAe4AfjG6gSlVDjwKnAtcBFwq1LqItM8E3hRRC4ASoCJTgqtqrL+BbV6BBIRKmoq2B9R\nzvqC9byZ/SY3zLuBny79KbtLd3Ne/HnMvnY2/Wr6+Rzbid3ro5kXAokdqN1f36Dqik2COz4zpqII\njwa2wEv94ZO7IOsDOJRtdAyHt0LOP4hZ8AS82BdWv2L8Al38U7h/CbTr5rHcjm1j+PThkTx8RRoi\nMGuVi8tmLuPxj77ni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" ] }, "metadata": { "tags": [] } } ] }, { "cell_type": "markdown", "metadata": { "id": "vd03DOuDIsmf", "colab_type": "text" }, "source": [ "**Ugotovitve:** signal i2 je fazno zamaknjen za $\\pi/4$ v levo relativno na i1, signal i3 pa je glede na i1 fazno zamaknjen v desno za $\\pi/2$. $+\\varphi$ torej predstavlja zamik signala v levo, rečemo tudi da signal i2 \"prehiteva\" signal i1 za fazni kot $\\pi/4$. Signal i3 pa \"zaostaja\" za i1 za $\\pi/2$. Da signal prehiteva rečemo zato, ker npr. max signala nastopi za $\\pi/4$ oz. osmino periode signala pred signalom i1. V resnici seveda ni tako, da en signal prehiteva drugega, saj vsi signali nastopajo hkrati - gre torej le za način razlage časovnega zamika signalov.\n", "\n", "**Zdaj pa ti:** Določi, koliko je to časovno - osmina periode signala? (Rešitev je v spodnji celici)" ] }, { "cell_type": "code", "metadata": { "id": "EvPWfJ76Ismi", "colab_type": "code", "colab": {}, "outputId": "f60095be-d193-4468-ff06-12fbdca9e44b" }, "source": [ "# omega=2*pi*f=2*pi/T\n", "T=2*np.pi/omega\n", "print('i2 je zamaknjen glede na i1 za čas ',T/8,'s')" ], "execution_count": 0, "outputs": [ { "output_type": "stream", "text": [ "i2 je zamaknjen glede na i1 za čas 0.15707963267948966 s\n" ], "name": "stdout" } ] }, { "cell_type": "markdown", "metadata": { "id": "KgOmDnhRIsmp", "colab_type": "text" }, "source": [ "## Srednja (povprečna) vrednost signala\n", "\n", "je določena s površino pod krivuljo signala v eni periodi deljena s periodo signala. Za negativne vrednosti signala je površina negativna. Matematično (za npr. tokovni signal) to zapišemo kot\n", "\t${{I}_{sr}}=\\frac{1}{T}\\int\\limits_{0}^{T}{i(t)\\text{d}t}.$\n", "\n", "Pomembno je, da računamo srednjo vrednost periodičnega signala točno za eno (ali več) period. Če pogledamo zgornje signale, so vsi izrisani za necelo periodo, torej ne smemo računati povprečja celotnega signala pač pa moramo najprej poiskati periodo signala in šele nato računati povprečje.\n", "\n", "Npr. v prejšnji celici smo izdelali časovni signal od 0 do $10/\\omega$ v 200 korakih, torej je en korak $10/200\\omega$. Ena perioda signala je pri $\\omega T=2\\pi$, torej pri $T=2\\pi/\\omega$, za kar potrebujemo x korakov, kjer je $2\\pi/\\omega=x 10/(200\\omega)$. $x=40\\pi$ korakov. Izračun je v spodnji vrstici. Za izračun povprečja niza uporabimo funkcijo *mean*." ] }, { "cell_type": "code", "metadata": { "id": "p7Y-dU7RIsmt", "colab_type": "code", "colab": { "base_uri": "https://localhost:8080/", "height": 52 }, "outputId": "1fcaf51b-8694-4b6b-89d5-dd4e06912252" }, "source": [ "# Izračun srednje vrednosti sinusnega signala\n", "x=int(40*np.pi) # periodo računamo od 0 do x, ki mora biti int vrednost, tako, da je lahko bolj natančen izračun tudi pri x+1\n", "print(i1[x]) # približno 0\n", "Isr=np.mean(y[0:x])\n", "print('Isr = ',Isr)" ], "execution_count": 13, "outputs": [ { "output_type": "stream", "text": [ "-0.0017782710664830663\n", "Isr = 0.00044127844926087034\n" ], "name": "stdout" } ] }, { "cell_type": "markdown", "metadata": { "id": "V5clLtB5Ism4", "colab_type": "text" }, "source": [ "**Ugotovitve:** Srednja vrednost sinusnega signala je seveda (približno) enaka 0, saj je enaka površina signala nad 0 (+) kot pod 0 (-). Približno zato, ker ne moremo natančno \"ujeti\" periode signala, saj ni izrisan tako, da bi imel določeno vrednost ob periodi signala.\n", "\n", "**Zdaj pa ti:** Izračunaj srednjo vrednost žagastega signala iz zgornje (prve) celice. (Rešitev je na koncu zvezka)" ] }, { "cell_type": "markdown", "metadata": { "id": "sacr2P5XIsm5", "colab_type": "text" }, "source": [ "## Efektivna vrednost signala (ang RMS - root mean square)\n", "\n", "je določena kot koren iz srednje vrednosti kvadrata signala:\n", "$${{I}_{ef}}=\\sqrt{\\frac{1}{T}\\int\\limits_{0}^{T}{{{i}^{2}}(t)\\cdot dt}}$$\n", "\n", "Enako kot pri srednji vrednosti, je tudi pri izračunu efektivne vrednosti potrebno zagotoviti, da upoštevamo polno periodo signala. \n", "\n", "Izračunajmo efektivno vrednost sinusnega signala iz prejšnje celice." ] }, { "cell_type": "code", "metadata": { "id": "xVT9cRhyIsm6", "colab_type": "code", "colab": {}, "outputId": "481fcf4a-9826-4677-c95b-b7cf43e04117" }, "source": [ "# Izračun efektivne vrednosti sinusnega signala\n", "omega=5\n", "t=np.linspace(0,10/omega,200)\n", "i1=np.sin(omega*t)\n", "i1_2=i1*i1 # kvadrat signala\n", "\n", "Ief=np.sqrt(np.mean(i1_2[0:x])) # koren iz srednje vrednosti kvadrata signala\n", "print('Ief = ',Ief)\n", "\n", "fig, ax = plt.subplots()\n", "ax.minorticks_on()\n", "ax.plot(t,i1,linewidth=2,label='$i_1$')\n", "ax.plot(t,i1_2,linewidth=2,label='$i_1^2$')\n", "ax.grid(which='major', linestyle='-', linewidth='0.5', color='red')\n", "#ax.grid(which='minor', linestyle=':', linewidth='0.5', color='black')\n", "ax.axhline(Ief,color='k')\n", "ax.set_xlabel('Čas / s')\n", "ax.text(max(1.1*t),Ief,'Ief')\n", "ax.legend()\n", "plt.show()" ], "execution_count": 0, "outputs": [ { "output_type": "stream", "text": [ "Ief = 0.707206771982\n" ], "name": "stdout" }, { "output_type": "display_data", "data": { "image/png": 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h/wTf537xBkmZkDnZZuv+yKn4JpviWXeghKqGZqfyoCNUzGx27J2TovXQ3BBY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\ngFrJHY2VzUXlnL5QF2hxfEKzxcp7tkWjt+QNDLA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"text/plain": [ "" ] }, "metadata": { "tags": [] } } ] }, { "cell_type": "markdown", "metadata": { "id": "wxtq9xG0Ism_", "colab_type": "text" }, "source": [ "**Ugotovitve:** Efektivna vrednost sinusnega signala seveda ni enaka 0, saj je potrebno poiskati povprečje kvadrata signala, ki pa je vedno pozitiven. V konkretnem primeru je rezultat 0.70..., kar je enako $1/\\sqrt2$. Kar je znan rezultat - da je za sinusni signal $I_{ef}=\\frac{I_m}{\\sqrt2}$.\n", "\n", "**Zdaj pa ti:** Izračunaj efektivno vrednost žagastega signala. (Rešitev je na koncu zvezka). \n", "\n", "**Če bi to bil programerski tečaj, bi prav gotovo želeli, da sami napišete funkcijo, ki vrne srednjo in efektivno vrednost signala. Če bi še malo komplicirali, bi si npr. zaželeli, da funkcija sama ugotovi periodo signala.**" ] }, { "cell_type": "markdown", "metadata": { "id": "ndw33A3aIsnA", "colab_type": "text" }, "source": [ "## Še par parametrov\n", "\n", "### Usmerjena vrednost (ANG. RECTIFIED)\n", "\n", "Usmerjena vrednost je določena kot povprečje usmerjenega signala, torej kot povprečna vrednost absolutne vrednosti signala.\n", "\t$${{I}_{r}}=\\frac{1}{T}\\int\\limits_{0}^{T}{\\left| i(t) \\right|\\text{d}t}$$\t\n", "\n", "### Faktor oblike (ANG. FORM FACTOR)\n", "\n", "Faktor oblike pogosto uporabimo za karakterizacijo oblike signala. Določen je kot kvocient efektivne in usmerjene vrednosti\n", "\t$$\\text{faktor oblike}=FF=\\frac{{{I}_{ef}}}{{{I}_{r}}}.$$\n", " \n", "### TEMENSKI FAKTOR (ANG. CREST FACTOR)\n", "\n", "Temenski faktor je definiran kot kvocient maksimalne in efektivne vrednosti\n", "\t$$\\text{temenski faktor}=\\frac{{{I}_{m}}}{{{I}_{ef}}}$$\t\n", "\n", "\n", "Določimo te tri faktorje za sinusni signal.V spodnji celici." ] }, { "cell_type": "code", "metadata": { "id": "PwzIeQa_IsnC", "colab_type": "code", "colab": {}, "outputId": "8a07abd2-c6a4-43de-fafa-184e09cbe14b" }, "source": [ "## Usmerjena vrednost sinusnega signala\n", "# prej morata biti izračunana Ief in Isr\n", "Ir=np.mean(abs(i1[0:x])) # koren iz srednje vrednosti kvadrata signala\n", "print('Ir = ',Ir)\n", "\n", "# faktor oblike\n", "FF=Ief/Ir\n", "print('FF = ',FF)\n", "\n", "# temenski faktor\n", "TF=1/Ief\n", "print('TF = ',TF)\n" ], "execution_count": 0, "outputs": [ { "output_type": "stream", "text": [ "Ir = 0.636759008193\n", "FF = 1.11063489151\n", "TF = 1.41401360906\n" ], "name": "stdout" } ] }, { "cell_type": "markdown", "metadata": { "id": "EmFPmxCZIsnF", "colab_type": "text" }, "source": [ "## Rešitve" ] }, { "cell_type": "markdown", "metadata": { "id": "LhP9Er8jIsnG", "colab_type": "text" }, "source": [ "Rešitve nalog 1.3:\n", "\n", "1. Iz grafa se lahko le približno določi periodo. Nekoli olajša oceno, če uporabimo fino mrežo, kot je na grafu. Recimo da je perioda 6.1 sek, kar pomeni frekvenco 1/6.1=0.16 Hz.\n", "2. Iz celice za izračun ugotovimo, da izrisujemo signal sin(t), ki ima periodo $T=2\\pi\\approx 6.28 s.$ \n", "3. Rešitev izrisa signala s periodo 5 sek je v spodnji celici.\n", "4. Rešitev izračuna periode je spodaj. Uporabimo funkcijo *np.where*, da poiščemo vredosti, za katere je signal manjši od 0.... Za preverjanje lahko kreiramo tabelo vrednosti časa in signala ter poiščemo točko periode. (Če bi želeli bolj natančno določitev, bi tudi morali imeti izračun signala pri T=5 sek. To lahko naredimo tako, da uporabimo za izdelavo niza vrednosti časa funkcijo np.arange, kot je to v prvi celici tega zvezka). " ] }, { "cell_type": "code", "metadata": { "code_folding": [], "id": "R6chK11sIsnG", "colab_type": "code", "colab": { "base_uri": "https://localhost:8080/", "height": 280 }, "outputId": "c1eee020-aed7-4b76-adb8-bfba5d571d0b" }, "source": [ "# Sinusni signal s periodo 5 sek\n", "T=5\n", "freq=1/T\n", "omega=2*np.pi*freq\n", "t=np.linspace(0,3*5,200)\n", "#t=np.arange(0,3*T,0.1) # niz od 0 do 15 s korakom 0.1\n", "y=np.sin(omega*t)\n", "fig, ax = plt.subplots()\n", "ax.minorticks_on()\n", "ax.plot(t,y,linewidth=2,color='b')\n", "ax.grid(which='major', linestyle='-', linewidth='0.5', color='red')\n", "ax.grid(which='minor', linestyle=':', linewidth='0.5', color='black')\n", "ax.fill_between(t, 0, y,facecolor='green', alpha=0.2) # Še malo pobarvamo, za lepši izgled\n", "\n", "ax.set_xlabel('Čas / s')\n", "plt.show()" ], "execution_count": 20, "outputs": [ { "output_type": "display_data", "data": { "image/png": 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ArbzwQhdJQw4psjguLk5pzeLh4WGlNYtdLpdUzWIhhBRZXFubxtTUDopKB6g9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" ] }, "metadata": { "tags": [] } } ] }, { "cell_type": "code", "metadata": { "code_folding": [ 0 ], "id": "zTSFBsiOIsnK", "colab_type": "code", "colab": {}, "outputId": "cf367305-0007-4ffd-9388-a1215e9c2127" }, "source": [ "# Poiščem periodo signala\n", "indeks=np.where(y<0) # indeksi, kjer je y<0\n", "indeks2=np.where(y[indeks[0][0]:len(y)]>0) # nato indeksi od tam, kjer gre iz neg v pozit\n", "indeks12=indeks[0][0]+indeks2[0][0] # skupni indeks\n", "print(t[indeks12]) # čas pri vsoti indeksov je perioda\n", "\n", "# preverim s tabelo \n", "import pandas as pd\n", "pd.options.display.float_format = '{:,.2f}'.format\n", "f=pd.DataFrame([t,y]) # izdelam tabelo\n", "f.T[1:70] # transponiram in izpišem tabelo" ], "execution_count": 0, "outputs": [ { "output_type": "stream", "text": [ "5.05025125628\n" ], "name": "stdout" }, { "output_type": "execute_result", "data": { "text/html": [ "
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4.67 -0.40\n", "63 4.75 -0.31\n", "64 4.82 -0.22\n", "65 4.90 -0.13\n", "66 4.97 -0.03\n", "67 5.05 0.06\n", "68 5.13 0.16\n", "69 5.20 0.25\n", "\n", "[69 rows x 2 columns]" ] }, "metadata": { "tags": [] }, "execution_count": 10 } ] }, { "cell_type": "code", "metadata": { "id": "ntm2DU_-IsnN", "colab_type": "code", "colab": { "base_uri": "https://localhost:8080/", "height": 300 }, "outputId": "90e04809-9141-4915-eca2-093503eb6f3b" }, "source": [ "# Izračun srednje vrednosti žagastega signala\n", "t=np.arange(0,16,0.1) # niz od 0 do 16 s korakom 0.1\n", "tri=np.abs(signal.sawtooth( t)) \n", "\n", "T=2*np.pi # perioda 2pi=0.1x, kjer je 0.1 korak\n", "x=int(T/0.1+1) # indeks periode, x mora biti integer\n", "Isr=np.mean(tri[0:x]) # izračun povprečja od 0 do periode\n", "print('Isr = ',Isr)\n", "\n", "# Drug način izračuna: Isr=površina signala/perioda\n", "Isr2=1*T/2/T \n", "print('Isr2 = ',Isr2)\n", "\n", "fig, ax = plt.subplots()\n", "ax.minorticks_on()\n", "ax.plot(t,tri,linewidth=2,color='b')\n", "ax.fill_between(t[0:x], 0, tri[0:x],facecolor='green', alpha=0.2) # Še malo pobarvamo, za lepši izgled\n", "ax.axvline(t[x])\n", "plt.show()" ], "execution_count": 24, "outputs": [ { "output_type": "stream", "text": [ "Isr = 0.501421905750195\n", "Isr2 = 0.5\n" ], "name": "stdout" }, { "output_type": "display_data", "data": { "image/png": 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bc7dVq1zb5A8+cG0Roq7fb7GI5AF/Dny9p+eq6tOqWqKqJUVFRf3ddCh4iXDX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\n7a3mZjh40B24M8ESeshs7KH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" ] }, "metadata": { "tags": [] } } ] }, { "cell_type": "code", "metadata": { "id": "xGc9kiKDIsnR", "colab_type": "code", "colab": {}, "outputId": "7760be9e-55e3-4f96-84bc-a4b25651bed8" }, "source": [ "# Izračun efektivne vrednosti žagastega signala\n", "t=np.arange(0,16,0.1) # niz od 0 do 16 s korakom 0.1\n", "tri=np.abs(signal.sawtooth( t)) \n", "\n", "tri2=tri**2 # kvadrat signala\n", "\n", "T=2*np.pi # perioda 2pi=0.1x, kjer je 0.1 korak\n", "x=int(T/0.1+1) # indeks periode\n", "Ief=np.sqrt(np.mean(tri2[0:x])) # izračun povprečja od 0 do periode\n", "print('Ief = ',Ief)\n", "\n", "fig, ax = plt.subplots()\n", "ax.minorticks_on()\n", "ax.plot(t,tri2,linewidth=2,color='r')\n", "ax.axvline(t[x])\n", "ax.axhline(Ief)\n", "plt.show()" ], "execution_count": 0, "outputs": [ { "output_type": "stream", "text": [ "Ief = 0.57897379996\n" ], "name": "stdout" }, { "output_type": "display_data", "data": { "image/png": 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FXVEUJYSouCuKooQQFXdFUZQQouKuKIoSQhLWlvHsxkS7AXxexY8a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\nAAAASUVORK5CYII=\n", "text/plain": [ "" ] }, "metadata": { "tags": [] } } ] }, { "cell_type": "markdown", "metadata": { "id": "Z8NsnGUoIsnU", "colab_type": "text" }, "source": [ "## Zaključek\n", "\n", "V zvezku smo prikazali možnost uporabe Jupytra za prikaz in analizi periodičnih signalov. Pokazali smo kako fazni kot vpliva na časovni premik signala. Prikazali smo način izračuna srednje in efektivne vrednosti signala ter nekaj osnovnih parametrov, kot so usmerjena vrednost, faktor oblike in temenski faktor.\n", "\n", "**Naslednje branje:** " ] }, { "cell_type": "code", "metadata": { "id": "eUtpQb8jIsnV", "colab_type": "code", "colab": {} }, "source": [ "" ], "execution_count": 0, "outputs": [] } ] }