{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "In recent years, there has been a massive rise in the usage of dating apps to find love. Many of these apps use sophisticated data science techniques to recommend possible matches to users and to optimize the user experience. These apps give us access to a wealth of information that we've never had before about how different people experience romance.\n", "\n", "\n", "In this capstone, you will analyze some data from OKCupid, an app that focuses on using multiple choice and short answers to match users.\n", "\n", "\n", "The dataset provided has the following columns of multiple-choice data:\n", "\n", "- body_type\n", "- diet\n", "- drinks\n", "- drugs\n", "- education\n", "- ethnicity\n", "- height\n", "- income\n", "- job\n", "- offspring\n", "- orientation\n", "- pets\n", "- religion\n", "- sex\n", "- sign\n", "- smokes\n", "- speaks\n", "- status\n", "\n", "And a set of open short-answer responses to :\n", "\n", "- essay0 - My self summary\n", "- essay1 - What I’m doing with my life\n", "- essay2 - I’m really good at\n", "- essay3 - The first thing people usually notice about me\n", "- essay4 - Favorite books, movies, show, music, and food\n", "- essay5 - The six things I could never do without\n", "- essay6 - I spend a lot of time thinking about\n", "- essay7 - On a typical Friday night I am\n", "- essay8 - The most private thing I am willing to admit\n", "- essay9 - You should message me if…\n", "\n", "\n", "### Introduction\n", "\n", "\n", "In this capstone, you will create a presentation about your findings in this OkCupid dataset.\n", "\n", "\n", "The purpose of this capstone is to practice formulating questions and implementing Machine Learning techniques to answer those questions. We will give you guidance about the kinds of questions we asked, and the kinds of methods we used to answer those questions. But the questions you ask and how you answer them are entirely up to you. We're excited to see what kinds of different things you explore.\n", "Compared to the other projects you have completed this far, we are requiring few restrictions on how you structure your code. The project is far more open-ended, and you should use your creativity. In addition, much of the code you write for later parts of this project will depend on how you decided to implement earlier parts. **Therefore, we strongly encourage you to read through the entire assignment before writing any code.**\n", "________________\n", "\n", "### Load in the DataFrame\n", "\n", "\n", "The data is stored in **profiles.csv**. We can start to work with it in **dating.py** by using Pandas, which we have imported for you with the line:\n", "\n", "\n", "```\n", "import pandas as pd\n", "```" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np\n", "from matplotlib import pyplot as plt\n", "import seaborn as sbn\n", "import scipy.stats as stats" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "and then loading the csv into a DataFrame: \n", "\n", "\n", "```\n", "df = pd.read_csv(\"profiles.csv\")\n", "```" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "df = pd.read_csv(\"profiles.csv\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Explore the Data\n", "\n", "\n", "Let's make sure we understand what these columns represent!\n", "\n", "Pick some columns and call `.head()` on them to see the first five rows of data. For example, we were curious about `job`, so we called:\n", "\n", "\n", "```\n", "df.job.head()\n", "```\n", "\n", "\n", "You can also call `value_counts()` on a column to figure out what possible responses there are, and how many of each response there was." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Index(['age', 'body_type', 'diet', 'drinks', 'drugs', 'education', 'essay0',\n", " 'essay1', 'essay2', 'essay3', 'essay4', 'essay5', 'essay6', 'essay7',\n", " 'essay8', 'essay9', 'ethnicity', 'height', 'income', 'job',\n", " 'last_online', 'location', 'offspring', 'orientation', 'pets',\n", " 'religion', 'sex', 'sign', 'smokes', 'speaks', 'status'],\n", " dtype='object')\n", "average 14652\n", "fit 12711\n", "athletic 11819\n", "thin 4711\n", "curvy 3924\n", "a little extra 2629\n", "skinny 1777\n", "full figured 1009\n", "overweight 444\n", "jacked 421\n", "used up 355\n", "rather not say 198\n", "Name: body_type, dtype: int64\n" ] }, { "data": { "text/html": [ "
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135averagemostly otheroftensometimesworking on space campi am a chef: this is what that means.<br />\\n1...dedicating everyday to being an unbelievable b...being silly. having ridiculous amonts of fun w...NaN...oakland, californiadoesn&rsquo;t have kids, but might want themstraightlikes dogs and likes catsagnosticism but not too serious about itmcancernoenglish (fluently), spanish (poorly), french (...single
238thinanythingsociallyNaNgraduated from masters programi'm not ashamed of much, but writing public te...i make nerdy software for musicians, artists, ...improvising in different contexts. alternating...my large jaw and large glasses are the physica......san francisco, californiaNaNstraighthas catsNaNmpisces but it doesn&rsquo;t matternoenglish, french, c++available
323thinvegetariansociallyNaNworking on college/universityi work in a library and go to school. . .reading things written by old dead peopleplaying synthesizers and organizing books acco...socially awkward but i do my best...berkeley, californiadoesn&rsquo;t want kidsstraightlikes catsNaNmpiscesnoenglish, german (poorly)single
429athleticNaNsociallynevergraduated from college/universityhey how's it going? currently vague on the pro...work work work work + playcreating imagery to look at:<br />\\nhttp://bag...i smile a lot and my inquisitive nature...san francisco, californiaNaNstraightlikes dogs and likes catsNaNmaquariusnoenglishsingle
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" ], "text/plain": [ " age body_type diet drinks drugs \\\n", "0 22 a little extra strictly anything socially never \n", "1 35 average mostly other often sometimes \n", "2 38 thin anything socially NaN \n", "3 23 thin vegetarian socially NaN \n", "4 29 athletic NaN socially never \n", "\n", " education \\\n", "0 working on college/university \n", "1 working on space camp \n", "2 graduated from masters program \n", "3 working on college/university \n", "4 graduated from college/university \n", "\n", " essay0 \\\n", "0 about me:
\\n
\\ni would love to think... \n", "1 i am a chef: this is what that means.
\\n1... \n", "2 i'm not ashamed of much, but writing public te... \n", "3 i work in a library and go to school. . . \n", "4 hey how's it going? currently vague on the pro... \n", "\n", " essay1 \\\n", "0 currently working as an international agent fo... \n", "1 dedicating everyday to being an unbelievable b... \n", "2 i make nerdy software for musicians, artists, ... \n", "3 reading things written by old dead people \n", "4 work work work work + play \n", "\n", " essay2 \\\n", "0 making people laugh.
\\nranting about a go... \n", "1 being silly. having ridiculous amonts of fun w... \n", "2 improvising in different contexts. alternating... \n", "3 playing synthesizers and organizing books acco... \n", "4 creating imagery to look at:
\\nhttp://bag... \n", "\n", " essay3 ... \\\n", "0 the way i look. i am a six foot half asian, ha... ... \n", "1 NaN ... \n", "2 my large jaw and large glasses are the physica... ... \n", "3 socially awkward but i do my best ... \n", "4 i smile a lot and my inquisitive nature ... \n", "\n", " location \\\n", "0 south san francisco, california \n", "1 oakland, california \n", "2 san francisco, california \n", "3 berkeley, california \n", "4 san francisco, california \n", "\n", " offspring orientation \\\n", "0 doesn’t have kids, but might want them straight \n", "1 doesn’t have kids, but might want them straight \n", "2 NaN straight \n", "3 doesn’t want kids straight \n", "4 NaN straight \n", "\n", " pets religion sex \\\n", "0 likes dogs and likes cats agnosticism and very serious about it m \n", "1 likes dogs and likes cats agnosticism but not too serious about it m \n", "2 has cats NaN m \n", "3 likes cats NaN m \n", "4 likes dogs and likes cats NaN m \n", "\n", " sign smokes \\\n", "0 gemini sometimes \n", "1 cancer no \n", "2 pisces but it doesn’t matter no \n", "3 pisces no \n", "4 aquarius no \n", "\n", " speaks status \n", "0 english single \n", "1 english (fluently), spanish (poorly), french (... single \n", "2 english, french, c++ available \n", "3 english, german (poorly) single \n", "4 english single \n", "\n", "[5 rows x 31 columns]" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "print(df.columns)\n", "\n", "print(df.body_type.value_counts())\n", "df.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Visualize some of the Data\n", "\n", "\n", "We can start to build graphs from the data by first importing Matplotlib:\n", "\n", "\n", "```\n", "from matplotlib import pyplot as plt\n", "```\n", "\n", "\n", "and then making some plots!\n", "\n", "For example, we were curious about the distribution of ages on the site, so we made a histogram of the `age` column:\n", "\n", "\n", "```\n", "plt.hist(df.age, bins=20)\n", "plt.xlabel(\"Age\")\n", "plt.ylabel(\"Frequency\")\n", "plt.xlim(16, 80)\n", "plt.show()\n", "```\n", "\n", "\n", "Try this code in your own file and take a look at the histogram it produces!" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig = plt.figure(figsize = (12, 8))\n", "ax = sbn.kdeplot(data = df.age, shade = True)\n", "sbn.kdeplot(data = df.age, shade = True, ax = ax, clip = [22,35], alpha = 1, color = '#EE9712')\n", "plt.xlabel('Age', fontdict = {'fontsize': 16})\n", "plt.ylabel('Frequency', fontdict = {'fontsize': 16})\n", "plt.title('Frequency of ages within OK Cupid sample data', fontdict = {'fontweight': 'bold',\n", " 'fontsize': 24})\n", "plt.tick_params(axis = 'y',\n", " which = 'both',\n", " labelleft = False,\n", " length = 0)\n", "plt.text(0.3, 0.7, '63% of all responses fall within the age range of 22 -35.',\n", " fontdict = {'fontsize': 16},\n", " transform=ax.transAxes)\n", "plt.xlim([18, 70])\n", "ax.set_xticks([x for x in range(20, 75, 5)])\n", "ax.get_legend().remove()" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "count 59946.000000\n", "mean 32.340290\n", "std 9.452779\n", "min 18.000000\n", "25% 26.000000\n", "50% 30.000000\n", "75% 37.000000\n", "max 110.000000\n", "Name: age, dtype: float64\n" ] }, { "data": { "text/plain": [ "0.6289616928920507" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "print(df.age.describe())\n", "probs = stats.gaussian_kde(df.age.tolist())\n", "probs.integrate_box_1d(22, 35)\n" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0.5, 0.98, 'User Responses to Partaking in:')" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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X6XV6TJNEZBu0z/JuaMnwYWh33t53M2tvqSmlpotILrSJfu6B9p38Di2xGYESJJ9KqZ9F5Clok109CW323GMAPtITxyHQHl8yCNrf5ykAM/Vle9zXetE2XCLyALQE9BEAy0RkWBk99iZvG/Ei0g1at/ZBAK6BliA/BO1YGYZ/x4YSEZEBpOxmnScioopI7667GsCrSqlXjI2GqPyJyJvQJkC6USn1s9HxEBFVVRzzSURERJVC/rGw+ZZ1gHbHNhlaLwoiIjIIu90SERFRZbFFRA5BG0ecCW2SrVhoF9sf1Cc3IyIigzD5JCIiospiOrSxnbdBmxjrHLTx0e8ppdYYGBcREYFjPomIiIiIiKgccMwnEREREREReRyTTyIiIiIiIvI4Jp9ERERERETkcUw+iYiIiIiIyOOYfBIREREREZHHMfkkIiIiIiIij2PySURERERERB7H5JOIiIiIiIg8jsknEREREREReRyTTyIiIiIiIvI4Jp9ERERERETkcUw+iYiIiIiIyOOYfBIREREREZHHMfkkIiIiIiIij2PySURERERERB7H5JOIiIiIiIg8jsknEREREREReRyTTyIiIiIiIvI4Jp9ERERERETkcUw+iYiIiIiIyOOYfBIREREREZHHMfkkIiIiIiIij2PySURERERERB7H5JOIiIiIiIg8jsknEREREREReRyTTyIiIiIiIvI4Jp9ERERERETkcUw+iYiIiIiIyOOYfBIREREREZHHMfkkiMgYEfmjkPfuEJGV5R0TEVF5E5FpIjK5iGU/F5E3SlsPERFRVcLks4ITkR4iskFEUkUkWUTWi8jV5bV9pdRXSqn+5bU9IqKyICJWEckWkXQROae3ow+KSKH/7ymlHlRKvV7abZdVPUREFUlJ2lWigniwVGAiUhPAjwA+BFAHQDiAVwHkGhkXEZGXGKyUCgHQFEAcgEkAZrkrKCK+5RkYEZGXYrtKpcLks2JrDQBKqQVKKadSKlsptVIptVPvKrteRKbqV5+OiEg3ffkJETkrIvfkVSQioSLyhYgkiMgxEXmxsCtVIvKuiPyhr3NBl1wRUfpVroMikiIiH4uI6O/5isj7IpIoIkdF5BG9vJ+nPygiosIopVKVUssA3ArgHhFpr3eb/VRElotIJoA++bvSikhvETkpIk/p7elpEbnXXf0iEiIiq0XkA9EUuR4RqSsiP4hImoj8KSJvFDYMgoiooihGu7pGRMblrefmvLK/iOzXe/h9IiJr88qLSEv991T93PKbct9RKnNMPiu2AwCcIjJXRAaKSO0C718DYCeAugDmA/gawNUAWgK4E8BHIhKsl/0QQCiA5gB6AbgbwAUnUiLiIyIzAHQE0F8plVpIXDfp24kGMArAAH35/QAGArgSQGcAw0qy00REnqCU+h+AkwB66otuB/AmgBAA7hK+MGjtZjiAsQA+LtgOi0hdAL8BWK+UekwppYpZz8cAMvUy9+gvIiKvUIJ29TwRqQfgOwDPQTuX3Q+gW74irwNYCaA2gAho57Lk5Zh8VmBKqTQAPQAoADMAJIjIMhFpqBc5qpSao5RyAvgGQGMArymlcpVSKwHYALTUuz3cCuA5pVS6UsoK4H0Ad+XbXDUAC6B17x2slMq6RGhxSqlzSqnjAFZDSzYBLRH9r1LqpFIqBVp3DCKiiuQfaO0cACxVSq1XSrmUUjluytqhtal2pdRyABkA2uR7vxGAtQC+VUq9eIltuq1Hb5tHAHhZKZWllNoDYG7pdo+IqNwVp13NbxCA3UqpxUopB4APAJzJ974dWvfeRkqpHKUUe4VUAkw+Kzil1F6l1BilVASA9tBOdv6jvx2fr2i2Xr7gsmAA9QD4AziW771j0K7C52kJYCiAV5VStsuElb9hyNK3AT22E/ney/9vIqKKIBxAsv7vy7VRSfoJUZ787R0AxAIIAjCthPXUB+AHtptE5N2K067md8F5o95z5GS+958FIAD+JyK7ReS+0gZKxmPy6UWUUvsAfA4tCS2ORPx79ShPEwCn8v2+F1o33J9EJP+V/eI4Da1bRJ7GJayHiKjMiTZTeDj+7QrmrotsccwAsALAchGpUYL1EwA4wHaTiLxUEdrVTADV8/0elu/fF5w36nOInP9dKXVGKXW/UqoRgPEAPhGRlmUYPhmAyWcFJiJt9UkqIvTfGwO4DcCm4tSjd8tdCOBNfWKMpgAmAphXoNwCAM8D+FVEWpQg5IUAHheRcBGpBW0GNCIiQ4lITRG5Cdq4+HlKqb/LsPpHoI1T+lFEgoqzot42LwbwiohUF5G20MbjExFVaMVoV7cDGK63cS2hjXvPYwHQQUSG6ZNTPox8yamIjMw7BwaQAi2xdZb1vlD5YvJZsaVDm1Rosz5r2CYAuwA8VYK6HoV29ekItKtT8wHMLlhIKTUXwGsAVomIqZjbmAFtYPhOANsALId2VZ8NBREZ4QcRSYfWresFAP+HAhOtlZbeTewBfRtLRSSwmFU8Am0yojMAvoQ29p6P0yKiiqq47epUaHOQxEMb0/5V3htKqUQAIwG8AyAJQCSALfi3Dbwa2jlwBoBlAB5XSh0t072hcifuJ+YjKj0RGQhgmlKq6WULExERRGQKgDClFGe9JaIqRbRHAJ4EcIdSarXR8ZBn8M4nlRkRCRKRQSLiJyLhAF4GsMTouIiIKip9eEVH/fmgXaB1SWO7SURVgogMEJFaIhIAbeiXoJjDy8i7MPmksiQAXoXWL38btEmMXjI0IiKiii0E2rjPTGjj5t8HsNTQiIiIyk9XAIehTY45GMAwpVS2sSGRJ7HbLREREREREXkc73wSERERERGRxzH5JCIiIiIiIo9j8klEREREREQex+STiIiIiIiIPI7JJxEREREREXkck08iIiIiIiLyOCafRERERERE5HF+xV1h69atDfz8/GYCaA8mrxWVC8Auh8MxLiYm5qzRwRAR206DsC0k8nJsO8sF20oqN8VOPv38/GaGhYW1q1+/foqPj4/yRFBUOi6XSxISEiLPnDkzE8AQo+MhIradRmBbSOT92HZ6HttKKk8luYLUvn79+mlsACouHx8fVb9+/VRoVwmJqGJg21nO2BYSVQpsOz2MbSWVp5Iknz5sACo+/Tti9xSiioNtpwHYFhJ5Pbad5YBtJZUXHmRERFQk69atqz5mzJjGlyrz448/hvTp06clAHzwwQd177777iblEx0RERFVdMUe81mQyWyJKYtA8ljjYreWZX1ERBWRN7ad1113XdZ1112X5entEBEVxhvbzuKw2+2oVq2a0WEQeYxX3vncv3+/f/PmzaNGjx7dtGXLllHdu3dvlZGRIRs2bAiKjo5u27p168gbbrihRUJCgq/RsRIRVRRpaWk+vXv3btmmTZvIVq1aRc2YMaP20qVLQ9q1axfZunXryJEjR5qys7MFANauXVu9U6dObdu0aRPZoUOHdikpKT7572quXr26eqdOndq2a9cuslOnTm137NgRUNh2U1JSfMLDwzvk5uYKACQnJ1/wOxFRRVbYeefu3bsDevbs2SoqKqpdTExMm23btgUmJSX5hoeHd3A6nQCA9PR0n7CwsI65ubluywPAiBEjTOPGjYu45pprWj/00EMRhu4skYd5ZfIJAMePHw987LHHzh46dGh3aGio84svvqg9ZsyYZm+99dbJAwcO7ImKisqeNGlSI6PjJCKqKBYvXlwzLCzMvn///j0HDx7cPXz48LTx48c3++abbw4fOHBgj8PhwLvvvls/JydH7rjjjhb/+c9/ju/fv3/P2rVr9wcHB7vy1xUdHZ3zv//9b9/evXv3vPzyy6eeffbZQk+Yateu7eratWv6woULQwFg9uzZdQYNGpQSEBDAcVxE5BXcnXeOGzeu6SeffHJ89+7de999992TEyZMaFK3bl1n27Zts5YvXx4CAF9//XVor169UgMCApS78nn1Hz58OHD9+vUHZsyYcdK4vSTyvFJ3uzVKeHh4brdu3bIBoFOnTlmHDx8OSE9P942Njc0AgPvvvz9p5MiRzY2Nkoio4ujcuXP2Cy+80HjChAnhQ4cOTQ0NDXVGRETkduzYMRcAxowZk/Txxx83uPHGG9MbNGhg79WrVxYA1KlTx1WwruTkZN9bb721mdVqDRQRZbfbL3kX84EHHkiYMmVK2F133XVu3rx59WbMmGH1yE4SEXlAwfNOq9UasG3btuCRI0e2yCtjs9kEAEaOHJmyYMGC2oMHD05fuHBhnYceeighNTXVp7DyADB8+PAUPz+vPS0nKjKvPcr9/f3PXzH39fVV586dYwd5IqJL6NixY+5ff/21Z9GiRaEvvPBCeJ8+fdLclVNKQUQueVdy0qRJ4b169Ur/5ZdfDu/fv9+/b9++bS5Vvn///pmPPvpogMViCXY6nXL11VfnlGZfiIjKU8Hzzvj4eL+QkBDHvn379hQse9ttt5177bXXwuPj43137dpVffDgwWlpaWk+hZUHgIK9S4gqK6/tdltQaGios2bNms4VK1YEA8CsWbPqdu3aNcPouIiIKgqr1VotJCTE9dBDDyU/8cQT8Zs3bw4+deqU/65duwIA4Isvvqjbs2fP9Ojo6Jz4+Hj/tWvXVge0MZt2u/2CutLS0nwjIiJsADB9+vR6Rdn+6NGjk+69997md955Z2IZ7xoRUbmqWbOmKyIiwjZ79uzaAOByubBx48YgAAgNDXVFR0dnjh8/vkm/fv1S/fz8UKdOnULLE1UllSb5BIA5c+YcnTRpUkTr1q0jd+7cGRQXF/eP0TEREVUUW7duDbryyivbtW3bNnLKlClXvPnmm6emTZtmHTlyZIvWrVtH+vj44Omnn04IDAxUX3311eHHHnusSZs2bSJ79+7dOisr64L/LyZNmnTmlVdeiejcuXPbvIk1Lmfs2LFJaWlpfmPHjk32yA4SEZWjBQsWHJkzZ069vEncFi1aVCvvvVGjRqUsXbq0zm233ZZclPJEVYUoVbz5Hnbs2GGNjo7mVWsvsGPHjnrR0dEmo+MgIradADBnzpzaS5curfX9998fLc/tsi0k8l5sO8sP20oqD1475pOIiLzHPffc03j16tWhP/7440GjYyEiIiJjMPkkIiKPmzt37gkAJ4yOg4iIiIxTqcZ8EhERERERUcXE5JOIiIiIiIg8jsknEREREREReRyTTyIiIiIiIvI4Tjh0CYmJib4zZ86sYzabEwDtAe0PPvhg4xUrVhwxOjZ3XEqJzeHytztdfg6nqpZlVyEms+VVAGH6qwGA6gD8AQQAqAZA8lXhAHAOQEohr5MADgI4bI2LtZXTbhFRBdWlS5c277333onrrrsuy+hYSsNktgQCaAEgHFo7mf9VX38FAfDVX375/u0DIAtAqpvXOWiTLB0FcATASWtcrKu89ouIvIe3nXMCgMls8QPQGIAJQDP9ZxiA0Hyv6tDaz0Bo554KQDa0drPgzywA8QCOAzim/zxujYtNKKddonJQ+uTzldCYMogjX32pW8u0vlJISkrynTVrVoO8hsBkMtkrQiOglEKOwxWQY3MG5didQTkOV1Cuwxlkd6gABXU+mcywuQDgpWJW37QIZZwms+U4gAPQktEDAPYD+NMaF5tSzO0RVU0Gt50ulwtKKfj6+l6ynMPhgJ9f5bhOaTJbggFEA4gC0B5AOwBtAETgwgtxnmIzmS3HoCWjhwBsB/AngF3WuFhHOWyfyPtV0vPOinrOCQAmsyUIQCcAVwO4EkBzaIlmOLSLcJ7efjb+vZC3HcBWAFuscbHl+sxoKhteeUaRlpbmM2TIkOanT5/2d7lc8uyzz/7ToEEDh9lsbux0OhEdHZ31xRdfHAsKClLh4eEdbr755uQ//vgjxOFwyLRp046ZzebwY8eOBTz66KPxzz77bAIATJ48ueGSJUvq2Gw2iY2NPTd16tR/nnrqqYgTJ04EtG3bNrJXr15pEydOPHvTTTe1Onjw4O4PPvig7rJly2q5XC7Zv39/0MMPP3zGZrP5fPPNN3X9/f1dK1euPNiwYUPn7t27Ax588MEmycnJfoGBga6ZM2ce69SpU87s2bNrv/322418fHxUSEiIc8uWLfsL21+Hy+WTkeMIych1hGTZnCG5Dng20X0AACAASURBVFegUsrILtO+0K5wNQMwIN9yZTJb9gJYD2ADgPXWuFg+04+ogti/f7//wIEDW3Xr1i1969atwVFRUVn79u0LysnJ8Rk8eHDK1KlT/wGA8PDwDrfddlvi6tWra44fP/5svXr1nK+99lojm80mTZs2zf3666+toaGh5+/gTZ06td6uXbuCZs2adQIA3n///Xp79+4NnDlz5kmj9hUAbA5XtYxcR3C6zVXHZLb8BaAjyuFE6RL8AbTSX/3zLc8xmS3bAWyBloxussbFHjAgPiIqoKqdc5rMFl9oF+euBtBF/9kexuYMQQBa66/z550msyUZwF/Qk1Fobaeh/+/Q5Xll8rl48eKaYWFh9jVr1hwCtKtFUVFRUStXrtzfsWPH3Jtvvtn07rvv1n/ppZfOAkDjxo1t27dv3zd27NjG9913n2nz5s37srOzfdq3bx/17LPPJixevLjmoUOHAnfu3LlXKYXrr7++5U8//RT8/vvvn7zpppuC9u3btwfQTtzyx3HgwIGgHTt27MnOzvZp06ZN+8mTJ5/au3fvnrFjxzaePn163ZdeeunsuHHjmn722WfHOnTokLtq1aoaEyZMaLJp06YDcXFxV6xcufJAs2bN7ImJiRecDDldyicj1xGckeMIybQ5aubandVVeX24pSMAIvXX/QBgMlsSoCWiqwD8wKtURMayWq2BM2bMsM6bN+94fHy8b8OGDZ0OhwPdunVrs3nz5qBrrrkmGwACAwNdW7du3X/69Gm/wYMHt1i3bt2BmjVrul544YWw119/veF77713Oq/OsWPHJkdFRUXm5uaeDAgIUPPmzas3ffr0Y+W9bw6nyzc12x6anusIzbY5g+1Olz8AZNsVoF21r6gCAVyrvwAA+h3SnwGsAPCrNS423aDYiKq0yn7OCQB2p8svNdsemprjqg8gGUBNz3+yZaIOgOv1FwDAZLbsA7ASwC8AVlnjYr16WEhl5JXJZ+fOnbNfeOGFxhMmTAgfOnRoamhoqDMiIiK3Y8eOuQAwZsyYpI8//rgBgLMAMGrUqHMA0KFDh6zMzEyf2rVru2rXru0KCAhwJSYm+q5YsaLmunXrakZGRkYCQFZWls++ffsCmzdvfslxjd26dUvPqys4ONg5cuTI89vZuXNn9dTUVJ9t27YFjxw5skXeOjabTQDgqquuyrjjjjtMI0aMSLnjjjtSbA6X37lsW+20bEftbJszOH/3WS9XH8BQ/fVfk9myB8APAJZY42I3GxoZURV0xRVX2Pr165cJAHPnzq3z+eef13M4HJKQkFBtx44dgXnJ5913350CAGvWrKlx+PDhwC5durQFALvdLjExMRn566xZs6are/fu6d98801ohw4dcux2u3Tp0iW7PPYn1+70P5dtr5We46hVydrOpgAe0F92k9myEVoiutQaF7vH0MiIqpDKeM6plEKWzVk9TbtYVyvH7qwOALlOL7nVcWlt9ddj0HqVrIJ23vkj74pWDF6ZfHbs2DH3r7/+2rNo0aLQF154IbxPnz5plyofGBioAMDHxwf+/v7n/7J8fHxgt9tFKYUnnnji9DPPPJOYf72CV50KKlhX/u04HA5xOp0ICQlx5F3Fym/+/PnHf/1tVcj3P/zY8MrOMU0W/vy71Kpdp2gfgHfLuzM6SR83uhjA10xEicpH9erVXQCwb98+/48++qjh1q1b99avX985YsQIU05Ozvnu/CEhIS5AG2Peo0ePtB9++OGSvRYeeOCBxDfffDOsdevWOXfeeWfipcqWVq7d6Z+caaubluOonetwBnlyWxVENQDX6a+3TGbLNgDzAMy3xsWeMTQyokquspxzrlq1qsb3S3+od2Wnzo0X/bLeGVKrdrWifQJeLRDAIP31icls+R3A5wC+tcbFZlxqRfIcr3zUitVqrRYSEuJ66KGHkp944on4zZs3B586dcp/165dAQDwxRdf1O3Zs2eRuygNHDgw7csvv6yXmprqAwBHjx6tdurUKb/Q0FBnZmZmiT+jOnXquCIiImyzZ8+uDWgTfGzcuDEoJdNW67fN21s1bBPT6oGnXwqtVaeunPnnVEk3482aAHgCwCaT2bLTZLY8bDJbvKWrB5FXS0lJ8Q0KCnLVqVPHeeLECb81a9aEuivXu3fvzC1btgTnta/p6ek+O3fuDChYrm/fvpmnT5/2X7JkSd2xY8cml3W8LqUkOdNW+9DZjFb749M7JGTkNqoiiac7nQC8D+CkyWz52WS23GkyW2oYHRRRZeTt55x2p8vvj607G4VHXtV03DMv16tVt57PqVMnq0LiWZBAu4A3G8AZk9ky12S29DGZLZWlt4zX8Mo7n1u3bg167rnnInx8fODn56c++eSTYykpKb4jR45skTf4++mnny7ytMzDhw9P2717d+DVV1/dFtDuDHz11VdHo6KicmNiYjJatWoV1bdv39SJEyeeLW6sCxYsOHL//fc3nTJlSiOH0+U3cNgt8kDjdr5vvTIZx48ehlIK1/TohTaR7YtbdWXTAcBHAKaYzJavAUy3xsX+aXBMRJVW165ds9u3b5/VqlWrqCZNmuQW7Eqbp1GjRo7p06dbR48e3TyvC9fLL798Kq/LWX7Dhg1L2blzZ/X69es7yyrObJszMCkzt15qtr2u06W88v8sD/KFNnFRfwAZJrNlHoAPrHGxe40Ni6jy8NJzziscTqfvoGG3OIMbRwa+/OILwnPOC9QAcLf+OmoyW74AMJPdcsuHKFW8/t07duywRkdHe7RLVWWSbXMEJmTYGqRl2+u6ynmG2vjjR3D/stOXL1hxbQPwCYAv+FxR8nZVoe3s06dPyyeeeCJ+6NChpZ4cJzXbXjMhPTcsy+YIKW1dlaAtLK5fAbxvjYtdYXQgRKVVFdrOsqKUQnKmrU5CRu4VNocrsLjrV8G2Mj87gK8ATLHGxe4zOpjKjFeRPUQ/cWqYZXOwG2nJdQIwA8BLJrMlDtpVKSahRBVMYmKi71VXXdWuXbt2WaVJPJVSOJdlr5WQkRuWY3eyG2nJXQ/gepPZ8jeA9wAssMbF2g2OiYg8xKWUJGXY6iZm5IbZna6LhkVQkVQDMAbA3SazZSmAt9kDzzOYfJax9Bx7jTNpORHZNmew0bFUIo0BfAzArCehs6xxsRd1+SMiY9SrV89ptVp3lXT9vKv1iRm5YbkOV1Udx+kJHQDMhXYBbzK0yd0qxXSWRKQ9mi8xI7deUoYtzOFyVcVxnJ7gA+BmADebzJbfAMRZ42J/NTimSsUrJxyqiLJsjqAjCRktjyZmtmXi6TF5SeghfXIiXt0j8nIpmbZa+8+ktz91LrsZE0+PaQFgPoCtJrNlwOUKE1HF5lJKzqTlNNx3Jq1DfFpOYyaeHtMPwC8ms+V3k9kSY3QwlQWTz1LKsTv9rUmZzQ6dzYjMyHW4nS2SylwEtMmJ9pvMlpuNDoaIii8z1xF06GxG6xMpWS1s7CZWXjoBWGEyW1aZzJYuRgdDRMV3LstW88CZ9MizaTkRnISt3PQA8KfJbJltMlvCjA7G2zH5LCGnS/mcTMmKOBif0T4t214lHtBZATUFsNhktiw3mS0tjQ6GiC7P5nD5HU/KbHo4ISOyLCYTohLpA2CzyWxZwBMpIu+QY3cGHE7IaHk8OauVzVn8yYSo1ATAvQAOmsyW59j7ruSYfJZAara95oH49KjkTFtDBcXnAxlvIIBdJrPldZPZwm57RBWQUgpnUnMaHohP73Au217P6HgIADAawF6T2fIAn3VHVDE5XcrnVEpW+MH4jKhM9rCrCIIBvAWt7RxhdDDeqFImn9WrV+/kiXrtTpefNSnTdCwps9V3C+b5v/XiM27LrVm5HLM+nnrJuv7c+AceGXNridYltwIAvAhgj8lsGWZ0METe5I033mjQvHnzqCFDhjT78ssva23durVMr6pn2xyBB89mtDubnhNR3o+cosuqBWA6gHUmsyXS6GCIvIWn203g3zHxSZm2MN7sqHCaAfjOZLZYTGZLuNHBeJNS9xXvMLdDmQ7A/fuev7eWZX1lJSkjt86ZtJzGl+tf73A40Lv/IPTuP6hE2ynNugQAMAFYYjJbvgPwoDUuNsngeIjcqkht56xZs+r/9NNPB9u2bWsbMWKEyeFwpMbExOSUNialFOLTcsISMmyNlOKJUwXXA8A2k9kyBcCbnFGcKqqK0nZ6qt0Ezg/tapKaba9bFvWRRw2C1vvuSWtc7OdGB+MNvO4K9IsvvtjwjTfeaAAAY8eObXzttde2BoClS5eGDB06tFleuUcffTS8TZs2kdHR0W1PnDjhBwD//POP34ABA1q0b9++Xfv27dutXLmyBgBMnDix0ciRI01dunRpExER0SGvfgCwOVzVDp/NaHXqXHazRQvm+Q2+7ircd0sstm/ZfD6myU8+hHdffQFjRw3Gf956BUsXzkfeXdHJTz6EuJcm4e5h/TGo+5X4xbL0on3atf0vjLrxOpw8Zi3Sui6XC28+/xRu7tcVj4y5FQ/fPdJtvVXcLQD+5syORBd65ZVXGrZq1SqqVatWUa+99lqD22+/vcnJkycDhgwZ0nLSpElhv/76a60XX3wxom3btpG7d+8O2L17d0DPnj1bRUVFtYuJiWmzbdu2QAAYMWKEacyYMY07derUNiIiosOcOXNq599Ots0ZePBsRtuz6bnhTDy9hj+AydAm1mhndDBEFUV5tZsAkJ5jDz4Qnx7FxNOr1AIwx2S2LDOZLQ0uW7qK87pZsvr06ZPx3nvvNQRwdvv27dVtNptPbm6urFu3LrhHjx7pAJCdne3TtWvXjA8//PDUgw8+GPHhhx/Wf+edd06PHz++8cSJE+MHDBiQcfDgQf8BAwa0OnLkyG4AOHToUOCGDRv2nzt3zrddu3btn3nmmYQcp4ScPJfdzOlSfgnxZ/Dp/72NBcvXICSkJsbdOhhtojqej+vY0UP4bMH38PX1xdKF8y+IOfFsPD5fvAJHDx3A4/fdjhtih55/b/uWzYh7aRL+O+srXBHeGFs3b7jsur/99AP+OXkci35Zj+TEBAzrew2G3Xqnxz5zL3YFtJkdPwTwDK/kU1X3+++/V58/f37drVu37lVKISYmpt3cuXOPrF27NnTt2rUHrrjiCsehQ4cCb7rpptR77703BQC6du3a+rPPPjvWoUOH3FWrVtWYMGFCk02bNh0AgPj4+GpbtmzZt3379sCbb765Zd468Wk5DZl0erUOALaYzJZHrXGxs40OhshI5dVuupSS0+dyGiVl5nISMO81GMBOk9lynzUudrnRwVRUXpd89ujRI+uee+6pkZKS4hMQEKA6duyY8fvvv1ffuHFjyIcffngcAKpVq6ZGjx6dCgAxMTGZv/76a00AWL9+fc2DBw+en5AmIyPDNyUlxQcA+vfvfy4oKEgFBQU56tSpY9+2/2jj6nXC6ueV/XvbVlzVtQfq1NXmyeg/eDiOHTl0Pq7+scPg6+vrNuY+AwbBx8cHLVq3RVJiwvnlRw8ewGuTnsC0rxajQdgVRV5325+bcEPsMPj4+KBeg4a4umvPknyUVcmjAHqazJZbrXGxB4wOhsgoa9asCR40aNC5mjVrugAgNjY2ZfXq1YXOOJuamuqzbdu24JEjR7bIW2az2c4nlEOGDDnn6+uLmJiYnKSkpGpOl/I5npxlSs+xX3Q1n7xOdQCzTGZLP2hDGNKNDojICJ5uNwGtp8jx5KxmuQ5ndU/uC5WLhgAs+o2Pp6xxsXajA6povC75DAgIUBEREbkff/xxvS5dumRER0dn//rrryHHjh0L6NSpUw4A+Pn5KR8frUexn58fHA6HANr4oy1btuwNDg5W7uoFtG62PtX8/VMybfWrF3iAikjhF/GDqhfeXvj7/zsbs1L/brpew4aw5eRi3+6dhSaf7tbNXwcV2ZXQHrD+gDUudoHRwRAZobhth9PpREhIiGPfvn173L0fGBh4vkKlFA7Gp7fjIwAqndsBdDGZLaOtcbEVck4GIk/ydLuZnGmr/c+5bBMnY6t0HgXQ0WS2jLTGxSZctnQV4pUHerdu3TI+/vjjhr17906//vrr0+fOnVs/MjIyKy/hLEyPHj3SpkyZcr4v9oYNGy54LEdatj3k4Nn0SEAuqqhDpxhs2fgHzqUkw26345cfvy/1foTUDMVHc7/Bh1Nex58b/yjyep2uvha//rQMLpcLSQlnsaUY61ZxwQDmm8yW1/hYAaqK+vbtm7F8+fJa6enpPmlpaT7Lly+v3adPnwvuaAUHBzvT0tJ8AKBOnTquiIgI2+zZs2sD2njzjRs3XvQ4o+RMW22Ijw8Tz0qrJYANJrPlHqMDISpvnmo3lVKAiJxMyWrOxLPS6gVtDP2VRgdSkXjlwd6rV6/0hISEan379s1s3LixIyAgQHXv3j3jcut99tlnJ/76668arVu3jmzRokXURx99dL5brV351DiWlNWqsNls6zcMw4NPmnHX0P4Yf9swtOsQXSb7Urd+A3wwZwHeevEZ7Ny2pUjrXD9oCBpe0Qgjru+G181PokOnGASH1CyTeKqIyQC+5jNBqarp0aNH1u23357UuXPndjExMe3uuuuuhO7du2fnL3PHHXckf/DBB2Ht2rWL3L17d8CCBQuOzJkzp16bNm0iW7VqFbVo0aJaeWWVUjiZktX4ZEpW8/LfGypn/gA+N5ktb/LiHVUlZd1uAtpstkcSM1sCl+hSR5VFUwDrTWbLKKMDqSikuN0JduzYYY2Ojk70UDyG+OdcdqPEjFz3/V4rqKzMDFSvEYxzKcm446Z+mLtkBeo1aHhBmfjjR3D/stMGRegV/gdgqDUu9ozRgVDlV9naTqdL+ViTMpt7w0PP2RaWuW8AjLHGxZbJYyWILqWytZ25dqe/NSmzZa7DVeEugLOt9Li3ALxojYut0uPnvPLOZ1lRSuFYUmZTb0s8AeDRMaMxakBP3DtiEB54/JmLEk8qki4A/mcyW8rmNjZRFWFzuPwOn81o4w2JJ3nErQBWmcyW+pctSUTnZeQ6qh9KyGhXERNPKhfPQ3sWfcBlS1ZiVTb5zOvykJptr2d0LCUx69sfsfDn37Fk1SYMHXW70eF4s8YA/uDzQImKJsfuDDickNEuh7MyVnVdAWw2mS1tjA6EyBukZdtDrImZbQob3kVVxlBos+HWMDoQo1TJ5NPmcPkdTuBVezovGMBSk9kSa3QgRBVZZq4j6HBCRlu70+VvdCxUITQDsNZktkQaHQhRRZaaba95LDmrJScWIl0/AD+bzJYqOWFLlfsjsDlc1Q4nZLTNsfOqPV0gAMBik9ky2OhAiCqijBxHjaO8ak8XawhgtclsaW90IFRpuVwul9dOzHMuyxZ6PDmrpargiadSCgpVeihieesO4DeT2VLnsiUrmQr9h1DW7E6X35HEjNZ2p6tK97WmQvkD+M5ktgwzOhCiiiQz11HdmpTZyqWUr9GxUIXUAFoC2tHoQKhS2pWQkBDqjQloSqat1onk7BZKqQodu1IKjqw0HDtnNzqUquYqAGtMZkuVmrilylzBdjhdvkcSMlvbHHwOHV2SP4CF+gPVFxsdDJHRsmyOQCaeVAT1oE1CdIM1Lnab0cFQ5eFwOMadOXNm5pkzZ9rDi26a5DhUjfRcVz1vuJeooHDsnB0fbk4xOpSqqAOAdSazpZ81Lvak0cGUhyqRfDpcLp8jiZmtcx1Ozi5GRVENwDcms+V2a1zst0YHQ2SUqf/9oOGVPfuH1W0QVuT/K1atsKBp8xZo0bptkbezdOF87N65Dc+/8S4+/b84VK9eA/c8+GiJYiZD1YXWjayvNS52u9HBUOUQExNzFsAQo+MoDpPZciuA+UbHQV6jNbQxoD2scbGV/gpAqZPPvW3bxZRFIHna7du7tSzrc7qUz9GEzFYc40nF5AfgK5PZkmiNi11tdDBU+VT0tjPX7vSf//XC8LCWHaRug7Air7f6Zwuuu35AsZJPqlRqA1huMluutcbFHjc6GKLyZjJbrgMwF150l5YqhEhok1/eYI2LzTU6GE/yuj+Mxx9/vNHrr7/eIO/3Rx99NPyNN95oMHny5Ibt27dv17p168gnn3yyEQCcS0317XN9/w439ekaPLxfV6xYxl6UVCzVoE1C1M7oQIhKa//+/f7NmzePGj16dNOWLVtGde/evVVGRoYAwIYNG4Kio6Pbtm7dOvKGG25ocep0fMAnc+a1271zuzz32AMYNaAncrKzL6hv0fy5uD22L0b274GJD9yN7OwsbN+yGWt++Qn/9+ZLGDWgJ05Yj16wzppffsIdg6/HqBuvwwO3DUNSwtny+wCoPF0B4CeT2VLL6ECIypPJbGkL4HtokxgSFVdPAPNMZovX5WfF4XU799BDDyUuWLCgLgA4nU58//33tRs2bGg/dOhQ4M6dO/fu3bt3z/bt26v/9NNPwfO+/b5F/bBGft+u/AOLf9uI7r37GR0+eZ9a0K7iV6nB4FQ5HT9+PPCxxx47e+jQod2hoaHOL774ojYAjBkzptlbb7118sCBA3siIyOzzS++3KbfoKF+UR2vxNsffIaFP/+OwKALRy30GzgY8y2r8O3KP9C8ZWss+XoerrzqGvS+YSAmvvAaFv78Oxqbml2wTueru2Lesl+wcMU63DhkOOZ8+kH57TyVt0hoD1PnY3moStDPE36CdvefqKRuATDV6CA8yeuSzzZt2thq1arlWL9+fdCSJUtqRkVFZf3555811q1bVzMyMjIyKioq8vDhw4E79hyo36R1VMimP9Zg6lsv46/NGxBSk4/1pBIxAVhmMls4Zpi8Wnh4eG63bt2yAaBTp05ZVqs1ICkpyTc9Pd03NjY2AwCG3X5f0Jb/bax2uboO7duLMcMHYsT13WD5/lscPrD3stuPP30KD94xAiOu74bPp32Iwwf2lXqfqELrDWCOyWyp0DN9EpWWyWypAcAC7XyBqLQeM5ktzxgdhKd4XfIJAPfee2/izJkz682ZM6fevffem6SUwhNPPHF63759e/bt27fn7/2Hj8feek8dU/OW+NqyBq3aRuK/U17DtP+8Y3To5L26QBsD6pV/M0QA4O/vf37iRV9fX+VwOC5ICk6nZodl2V1F6io5+amH8Nwb72DRrxvw4BOTYMu5/BCVuJcm4bYx47Do1w2YHDcVttycYu8DeZ3bAbxpdBBEnmIyW3wBfA2gTMfxU5U3xWS2jDY6CE/wyhPpu+6669zq1atDd+zYUWPEiBGpAwcOTPvyyy/rpaam+uTYnQFbdh9qnpiYgLNnTiMwKAg3Db8V9zzwKPb9vcPo0Mm73QzgXaODICpLdevWddasWdO55MefwhLSc8N/XPwNrrqmOwCgenAwMjMz3K6XlZGBeg3CYLfbsfz7fyeFrh4cjMwM9+ukp6ehQVgjAMCybxeU8Z5QBfacyWwZYXQQRB7yDoCbjA6CKh0BMLsyPj/ZK5PPwMBA1a1bt7QhQ4Yk+/n5Yfjw4WkjR45Mvurqq9t16NAhauL4e3yzMjJwcN8e3DG4H0YN6ImZH76P+x972ujQyftNNJktI40OgqgsTZ85+/TLL70UfssN3bF/998Y/8SzAIChI2/HG89NdDvh0MNPP487h1yPB2+/GaYWrc4vv3HIcMyd/iFG3XjdRRMOTXjSjKcnjMGY4QNRu04dz+8YVSSzTWZLC6ODICpLJrNlMICJRsdBlVYQgEUms6VSjRsUpYr3+NsdO3ZYo6OjEz0UT5E4nU5ERUVFfvvtt4c7dOiQCwBKKRxJzGyZmeuoVF9QacQfP4L7l502OozKKA1AjDUu9pDRgZD3qAhtpztOl/I5GJ8eaXO6Ku3sjGwLK4xtALpZ42LZ35q8nslsaQxgOwBeSSNP+94aF3uz0UGUFa+787l169bApk2bdujZs2daXuIJAPFpuQ2ZeFI5qQlgoclsqbQn61R1nEzJalyZE0+qUDoB+K/RQRCVlsls8QOwAEw8qXwMM5ktjxsdRFnxuuQzJiYm5+TJk3/PmDHjZN6yzFxHUEJ6briRcVGV0wnAFKODICqN5ExbrdRsez2j46Aq5QGT2XKn0UEQldLrALobHQRVKe+YzJbORgdRFrwu+SzI6VI+J5KzmisoTuVO5e0xk9lyo9FBEJVErsNZ7fS5bJPRcVCVNM1ktrS6fDGiisdktvQHMMnoOKjK8QfwtclsCTY6kNLy+uTzn3PZ4TanK9DoOKhKEgCfm8yWBkYHQlQcSimcSM5q7lTK1+hYqEqqAe35n15/DkJVi/7//ZfQ/v8nKm+tAMQZHURpeXXDn5ZtD0nJsvHEn4zUEBzDRF4mPi23YZbN6fVXT8mrdQdQacYwUZXxAQCed5KRJpjMlq5GB1EaXpt8Ol3K5xS7jFHFMNpkttxgdBBERZFjd/onZOQ2MjoOIgBvmsyWlkYHQVQU+mNVbjU6DqryfAB8ZjJbqhkdSEl5XfKZmJjoGxcXV/90anYju9PlX9T1zp45jafG31MmMfy58Q9s37K5TOq6lI/fewubfl8DAJg381NkZ2d5fJtUYp+YzBZ2/6YK79S57CZKqXJv+1f++D2G9bkGY0cNLrc2lCq8IADTjA6C6HJMZksIgE+MjoNI1x7As0YHUVJ+pa3g4wdXxZRFIHkentZ366XeT0pK8p01a1bD2DsnXJR4Op1O+Pq6H8LUIOwKvD99bpnEuGXjH6hevQauvOqaMqmvMA8//fz5f38161PEDh+FoKDqHt0mlVhLAM8DeMnoQMg7lHfbCQApmbZaJXkklVIKSin4+JQ8Z13y9Tw8/+Z76NKtJz79v7hyaUPJK/QzmS13W+NivzA6EKJLeA1AhNFBEOUz2WS2fGuNiz1gdCDFVerks7w99dRTESdOnAwYOaAHru3ZGz37DcC0qVNQv0FD7N+zC/0GDkbtOnVxx9gHAQAfTnkddeo3QO8bBuLRMbdi8W8bsXThNXHGnAAAIABJREFUfKz55SfkZGfj5LGj6HtjLJ584TUAwOKvv8ScT/6LBg3D0KRZC1Tz98fzb7x7fvunThzHt/PmwMfHF5YlC2F+bQrCwiPw8tOPIiUpEbXr1sNr73+EK8IbXxD3uZRkTHp4HFKSE9H+ys7YsOY3LFi+BlmZmefjAoC50z5EVlYmJkw0Y/KTD+G66wfgbPwZnI0/g3GjBqNWnbqYtfCHcvq0qZgmmcyWr6xxsfuNDoSooAcnTIio17hF3VF3jwMALQGsEYx7xj+Cz6d9gJU/fA+bLRd9b7wJDz31HE6dOI6H7x6Jq7v2wI6//kTfAbFIT03FM6+8BQBYNH8ujhw8gGdefvOC7fz0/XeY+dH/QSmFnv3648nnX8W0/7yDbX9uwqnnjqF1uyj89b+NF7Shppat8cZzE3HmlPYErWdeeQudrr4Wn/5fHE6fOolTx604/c9J3DF2Au64b3z5fnBUHt43mS3LrHGx54wOhKggk9nSEcCjRsdBVEAAgOkA+hgdSHF5XbfbV956JzWiqQkLf/4dE198HQCwa/tfeOTZyViyahNuHn0Xln23AADgcrmwYtlixN488qJ69u/5G+98Mgvf/bIeP/+wBGf+OYmzZ05jxn/fxbylv2Da/CU4eujiiwnhjZtg5J334q5xE7Dw59/R+ZpueHvysxg8YjS++2U9Bg0biSkvmS9ab9rUKejU5VosXLEOvW8YiNOnTl5UpjB33DceDRqGYebCH5h4Vmz+AD41Oggid2665Xa/FcuWnL/guPLH79H/pqHYsHYVjh89gq9+/A0Lf/4de/7ejq2b1gMArIcPYvAto7FwxTrcPf4RrPnlJ9jtdgDA0oVfYdio2y/Yxtkzp/Gft1/BjG+WYeHPv2P3jm1YtcKCB594FlEdr8TbH3yG96Z9flEb+s7LZtw5bgLmW1bh/c/m4tVn/52Hxnr4AD6dtwhf/fAbpk+dcn77VKnUA/Cc0UEQFWQyWwRad1vODE4VUW+T2TLG6CCKy6uST5dSkpLtvGiijPZXdkZEk6YAtOSwVu062LtrJzauXYW27TuiVu06F9V1TfdeCKkZioDAQDRv1Qb/nDyBXdv/Qsy13RFauzaqVauGG24aVqS4dm79EwOH3QIAuGnErdj256aLyvy1eQNuunkUAOC6fgNQM7RW0XecvEkfk9lym9FBEOWXbXMENm3TsU5yUiLOnjmN/Xv+Rs3QUFwR3hgb163GxnWrcOuN12H0wF6wHjqIY9YjAIArIhr/f3v3HR5Vlf8P/H3unWTSEwgdAkMXUYr0jl0ZVCxgAY29YV3Lju5Pv3HVdVZd1nV11bXsrru6trWgw4IVsKGiiCOCBRlpCemTOplyz++PCSwlkDYz587M+/U8PGhy5953eJLJ/dxzzudg1FETAAAZGZmYOG0mVr+7Alt++gHBQBBDR4zc5zob1q/D+CnT0TW/GywWC+acPh9ffvZJq/nWfLQKzjtuxYITZ+D6i89DXW0t6utqAQAzjjkBqVYrunTNR9du3VFZXhrhfx0yietsDtcA1SGI9nMOwp2Ziczq7njrORJX02531fh6Bg15wFrP9Ix910Gefs75WPry8ygvLcW8sxe2eK6U1P+dRtN1hEIhSCkjklOIg2z/1MLHLRZ9n+s2NTVFJAMp9dvmefhB1UGIAKCkxtdXQorj5pyKd5YtRUXpLpx46pkAwus5L158I+Yvumif1+zYtvWANeann3s+nv7zEtiGDMVp+4167j5XR0jDwLOvv4209PQDPpeaat3z35qmIRgMdegaZHppAO4FsEh1ECIAsDlcFgB3qc5B1Ip+AK4GsER1kLaKm5FPf9BIqajz987MykJDfd0hjz32pLn4eOV72PDNV5g669g2X+OIMUfhyzUfo6a6GsFgEO8tW9ricRmZWajfK8PocROxfOl/AADLXnsZYyZMPuA1R02aimWvvQwA+OiDd1DjDS9t6dqtByrLy1BdVQl/UxNWv7fi4NesO/TXTaYxBEBkWisTdVJ9UzCj1hfMA4CTTj0DK5b+B+8sW4rj55wKAJg66xi8/uJze95XdxXvREV5WYvnGjV2PEqKd+C/r7+Ck08764DPHzl2HL5c8zGqKisQCoWw/I3/YPzkAwcN9n8PnTLzaLzwjyf3/P+mDe5OfMUUx86zOVwRbcRF1AmFAIaqDkHUBrc1d2SOC3FTfJbW+noaUmp5XbpizPhJOOPYKVhyzx0tHpuSmooJU6fjhLnzDtr9tiU9e/fBJdf8CgtPPQ5XnDsPg4YOR3Z2zgHHzTr+JLy//C0sOHEGvvrsE/z6t7/HGy89j7OOn4a3Xn0Rv77rvgNec+WNv8aXn32Cs0+ehU9XfYDefcNN01JSUnD5Dbdi0SnH4dqLzsHAwS2/z5258EIsvmA+LllwSpu/HlLqDpvD1eatgIiipaTGt2epwpDhI1BfV4cevXqje89eAMLF55x5Z+H8007AmcdNxc1XXoiGQzzoOmHuPIwZPwk5eQcuHejesxeu+/WduHTBKZh/wnSMOGIUjj5xzgHHtfQeumH91zjr+Gk4/ZjJePlfz0TiS6f4IwA80OpRRFHW/Pu75ZtMIvPpBuAm1SHaSrR3mtT69es9o0ePLo9SnhYFQobl+5LaI4027k1nGAbOOXkWHnj87xgwcHC7rtVQX4eMzCwEg0HceNkizFuwCMeePLdDuQ/l5Cmj8LzrA3Tpmh/xc++2a+vPuGxpcdTOT626xuO0P6o6BJmDivfOWl8gc0t5/WGRPOc1F56N8y+9GpOmz4rkaaOK74Vx52SP075cdQhKXjaHazGAR1TnIGqHWgCDPE57TO8zOiIuRj5La5t6tLXw3PzDJsydcRQmTpvV7sITAB5b8nssOHEGzjxuKvoWDMAxJ9nbfQ6iZrfH2yJwSiy7apr6RupcNV4vTpk5Hmlp6XFVeFJcYudbUsbmcKUD+I3qHETtlI04ee80fcOhYMjQq+r9Pdp6/OBhh2HZx193+Ho33XF3h1/bHv/99JuYXIeU6oM4WwROicPbGMhu8AcjtgYkJzcXb65eG6nTER3KTJvDNcnjtH+mOgglpasB9FYdgqgDrrY5XA96nHZTT/Ux/chnWV1TD0NK7q9E8epWrv0kFcprm3qpzkDUCbeoDkDJp/n3Nb/3KF6lAbhKdYjWdKT4NAzDOMheIpEVMqRW2Y5RT/ofKSUkIrN1DHVKTwDzVYeg5NLoD1nr/cEDu6URxY/TbQ7XENUhKOnMR/j3NlG8utzsgx4dKT6/LSsry41FAVpe19QtZEjTTw02Gyklgg01+KU6oDoKhS1WHYCSS3ldE2+eKN5pAG5WHYKSDn9fU7zrCeBs1SEOpd2FXTAYvLSkpOSpkpKSIxDlabuVjaFeQSOaV0hMEhK/VAfw58+qVEehsCk2h2usx2lfpzoIJb6gYWjexkD02mgTxU6hzeG60+O0l6oOQonP5nCNBTBFdQ6iCLgWwD9VhziYdhef48aNKwVwahSy7MPmcE0F8HG0r0MUI4sBXKo6BCW+ijp/t7Z2B39siRMZGZkovPLaaMdqkzdeeh5TZh6NHr0O3evjjhuvxszjTsTx9tNilIwUSQNQCO79SbHBUU9KFBPM3LTNzFNaeaNOieRcm8N1s8dpr1YdhMzhD2fPHRfJ89304ltfSilh9nXyoVAIut5yD7mlLz+PIcNHtFp8UlK5ECw+KcpsDlcXAOepzkEUQdcBWKg6REtM2e3W5nBlA1igOgdRBGUAuEh1CEpsNb5gdiBkWA91zJMPP4hTZ03A5efOg2fzjwCAbZ4tuGrRWThnzmxceMbJ2PLTDwCAt996HWccOwXzT5iOi86cAyA8Onn9xefhqkVn4dRZE/D4H3+/59xvvfoizpt7LBacOAO/ddyAUCgEAJg8vB8effB3WHjKcVj/5ed4/KH7cZ79GJxx7BT89tc3QEqJd1xvYMM3X+O26y7HghNnwNfYiO+++RoXn2XHOXNm48qFZ6JsV8k+X8tnH63CDZcu2vP/n67+ADdedn4E/iXJRA63OVwTVYeghHcxgHTVIYgiaL7N4TJl13tTFp8AzgGQqToEUYRdoToAJbbqBn/XQ33+u2++xvKlr+LF5auw5K/PYsP68DLk3zpugOPu3+OFZSvxqzvuxr2/Cfd5eeKh+/HYv/6Dl9/+CH965t97zvPt+q9w35//ipeWr8bbb72ODevX4ecfv8eKN1/DP15bjpdWfAhd07HstZcBAI0N9RgyfASee/NdHDVxCs4tvAzPu97Hq+99Cp+vEaveXY7j7adh5KgxuO/hv4Zfb7HAeeetePCJf+CFZSsx7+yF+PP99+zz9UycNhNbfvoBlRXlAIA3XnoOpy3g4EUCulB1AEp4/P1MiSYF4XrKdMw67fYS1QGIomC4zeE6yuO0f6U6CCUeQ0pR6wt2OdQxX33+KY45aS7S0zMAALOOPxlNTU1Yv/Zz3HLlhXuO8/v9AIAxEybhzpsW44S583Dsyafs+fzkGbOR1yVc5x578ilY98Ua6BYdG79Zj4VzjwEA+Hw+dO3WHQCg6zqOm/O/VgFffPoh/vbYw/A1NsBbXY3Bww7D7ONP3ierZ/OP+On7TbjyvNMBhKfrduux70NcIQTmnrEArldfwmkLFmL9V1/gnoceb/s/GsWLc20O140ep71JdRBKPDaHawyAoapzEEXBeQAeUh1if6YrPm0O1+EAJqnOQRQl5wBg8UkR520M5BpStryYci9C7LtLliENZOfm4qUVHx5w7B33/RHfrFuLD997GwtOnLHnmP3PIYSAlMAp88/B9Y7/O+A8qda0Pes8m3w+3Pubm/Fv1/vo1acfHlvihL+phZpCSgwedhj++cbbh/x6TluwENddfC6sVitOsJ8Gi8V0v9ao8/IAzAPwouoglJDOVB2AKEom2ByuIR6n/SfVQfZmxmm3XOtJiWyBzeGK+h65lHy8DYFDjnoCwLhJU/H+8rfga2xEfV0tVr+7HOlp6ehb0B9vv/U6gPA+wd9/5wYQXgs6aux4LL75duR1zUfJzh0AgDWrV8JbVQVfYyM+WOHCmPGTMGnaTLzrWoqK8rJwnqoq7Ny+9YAMTc2FZl6XfDTU1+Ed1xt7PpeRlYX6+joAgG3wUFRVlGP9l58DAAKBAH76fuMB5+vRqze69+yFvz78B5w6n1NuE9ii1g8h6pCzVAcgiiLTTb014yPieaoDEEXRAAATAHyuOggllrqmYF5rx4w4cjROPOV0LDhpJnr3LcDYieEt7X738JO49/ab8OTDDyIYDOLEU8/A8MOPxJJ778TWLZshpcSk6bMw/PAj8P0GN8ZOnIzf3HAFtnq2YM68szBy9FgAwOJbfoOrFp4BwzBgSUnB7fc8gD79+u+TISc3F2eeewHOOn4a+hT0x8jRR+353Gnzz8M9t/0KaWlpePb1t/HgE//A7+/8NepqaxAMhbDokisxZPiIA76uOfPmo6qiAoOHHdapf0MytWNtDle6x2lvVB2EEkfzbDu+cVAiOxPAPa0eFUNCSqk6wx42h2sggJ9V5yCKsvs8TvvtqkNQbK1fv94zevTo8micu6K+qcuOqsZB0Tj3/t546Xls+GYdbr/HPLtf/O7/3YLDjhiFM85pudPtrq0/47KlxTFORVFwisdpf0t1CEocNofrTgB3qc5BFGWDPE77FtUhdjPbtNtTWz+EKO6drjoAJZbaxtZHPRPVOXNm48eNGzD3dK7YSAJzVQeghMP1npQMzlAdYG9mm3Y7R3UAohg4zOZwDfM47T+oDkLxT0qJen8wJ1bXO23BeabazuSFZStVR6DYsasOQInD5nANATBKdQ6iGJgL4A+qQ+xmmpFPm8OVAWCW6hxEMXKs6gCUGOr9oYyQIc32IJEoGvrZHK7RqkNQwji59UOIEsJkm8NlVR1iN9MUnwCOBmCafxiiKJuhOgDFnGEYRsQ7Hdf6AjEb9YxHUkpImKe3AXUap95SpBytOgBRjKQBmKg6xG5mKz6JkgWLz+TzbVlZWW6kC9D6phCLz4OQUiLYUINfqgOqo1DkcNYIdVrzlmecbUfJZLbqALuZaarWZNUBiGKon83hGuRx2tndOUkEg8FLS0pKniopKTkCkXvwJ8oaQlkmalpuKhISv1QH8OfPqlRHociZYHO4dI/THlIdhOLaKABdVYcgiqFZAO5WHQIwSfFpc7gsAI5q9UCixDIT3FooaYwbN64UEe7obXO4ZgBYHclzEplcFoAjAKxXHYTi2nTVAYhibIrN4UrxOO3KpwKZZdrtKADpqkMQxdhM1QEo7o1XHYBIAc6Uos6aojoAUYxlAJigOgRgnuKTv0goGbH4pM4aqzoAkQK8Z6DOmqo6AJECpljnbJbic5LqAEQKDLY5XPmqQ1BcY/FJyYijVtRhNoerO4CBqnMQKWCKewYWn0RqjVAdgOKTzeFKA3CY6hxECgyzOVxdVIeguDVSdQAiRQ5XHQAwQfFpc7hyAAxTnYNIERaf1FFHwiRN44hiTIAFBHUcH9pRshpqc7h01SGUF58IF54R33idKE7wlyB1lCmmzxApMlR1AIpbw1UHIFIkFcAQ1SHMUHwOUh2ASCGOfFJHjVIdgEghFp/UUXzoS8lM+dRbFp9EarH4pI5iwwxKZiw+qaNYfFIyU37fyeKTSK0BNocrQ3UIikv9VQcgUojFJ7Vbc6M2vndSMuPIJ4DBqgMQKSTA9SfUMbyBomSmfN0SxaVhMMe9L5Eqyu85zfADyJFPSnZ9VAeg+GJzuHIB5KjOQaRQps3h4nsntRfvOSnZ9VIdQGnxaXO4LAAKVGYgMoFuqgNQ3OGoJxHQW3UAijv8fUvJTvnPgOqRz14AlO83Q6RYd9UBKO6w+CQCuqoOQHEnX3UAIsXSbA5XlsoAqovPXMXXJzID5U+hKO70VR2AyARYSFB78XuGSPGgh+rik2uWiFh8Uvtlqw5AZAIc+aT24vcMEYtPoqTHabfUXkqnzBCZBEexqL34PUOkeNCDxSeRehz5pPbKVB2AyAQ4ikXtxeKTiCOfREmPN1DUXhz5JGIhQe3H7xkixfedLD6J1EtRHYDiDkc+iQCr6gAUdzJUByAyAYvKi6suPtk0g4jbDVH7ceSTiO+d1H5SdQAiE0jq4lPpF09kEryBovZKUx2AyAR4D0HtZagOQGQCSu87Vb9xhxRfP+nlG+XbT/au+qxP+UBrffZASyCte5YQOqcyxVBIoFJ1Boo7AdUBkt1k8e77EzZtru3rTUurs+b4G6zZIZ81U/pTrAhYrFpI13WJAGD4AQQAGRCQfgEZ1ICAJmRQEzJgETKkC4QsQho6AKH664onTVpqCWBXHYPiC4tPxS7X3/jwpNRVqNBSAuV6SqBcsxgVuh6q0nVU65rwapqo06E3aLD4NJnqFzJFCsmH9BEkA7l1Kt87WXwmuQqtW785A97Z+ruhOdmXLDeqhm3HWF9696rq3MHF1XlDG2qyB6T40vK7GVqKDUKkqs6bkCS2qo5AcadJdYBkd0fKuylXnBKyDf9JGJctN5DbgLF7f15CGP6UrIqmtC7VPmvX2sb0fJ8vLT/gs3ZFkzVPC6RkpQUt6ZmGlpIjhdZNAqlAoBEy6JMy0AQEmiADfin9AchAALIpKKU/COk3JAIGpN+Q0i8hA4AMQCIgIANauLgNWqQM6UAoBTKUAsgUwEgFpBWQaQiPnMf96Hl2CMWqM1DcYfGp2NvGRNttxou9hYSlrVVAoxANNZpW59W0+mpda6zWtKYqXW+q1LVgla6HqjQNXk1Dja5pdZpmaRAixSc0q18gPShERii8VCULQqie8WkOKd43VV5edfEZVHx9AlDeONL/ANZmnL2oV6+sRjRd9E75T1O/KzuiT8maPVuAGEIL1mf22VydO6S0Om+IvzarIK3JmtdTCr0/f5g7jT8H1F4sPhXrIbyZT5Y0ZC0Y1qv/58MtKUduMb69ymU0dKvFRAAQkJo1UNvdGqjtnlPb+vOloG6tbbLmVfmsXb2+tPyGxvT8Jp+1q2xK6yL8KTmpgZSM9JBuzZfC0hVC5HU2v5RSAkEfZMAHGfRJBJog/X4pAwGEC96ghD8I6Q9J6TcQ/oPdBW+42A1qkAENCOlSBi1AyAJppABGCmBYAZm6X7Eb6ZFdzgCg9mLxqZhH9i7YKPt/dLjYOr2tr0mXMiM9FMroGQp1+KdeArJeiNoaTav36lp9taY3VulaU5Wu+Ss1PVSla6FqXYdX01CraVqdJlIahJbi00RaQIj0IJBhANkQIhEa/im971RdfPIGygQeDC4Ysqrp074v7Sz5+ew+vbIfPk2f/Zhd+uZ/aKy2fy77pxiwadKwZNdtH5xdt31wwY6Ve14b0lIaa7MKPNV5wyqr8wYH6zL7ZPpTs/tC6L3VfUVxhzMAqL38qgMku2w05Hb3BwbPbPStWp2RPss9UDvi6ms0DNkhv7/mzVBl7ypMEu3oq2AJNWVbGnZlZzbsavVYQ2gBf2puhc+aV+1Ly6/3peU3Nqblh5rSuqApNdcSHlVNyzS0lDxA5EOIAzpqCyEEkJIOkZIOxGa+r5TNxS6aR3dlwA/pD4RHd/2B/xW7gd3FrpTyf9OW5Z5iN6hLGdKFSNkeg9iUWFh8msD1gWv6vZ16a0iI2K09FIDIkjI7KxTK7hMKoaNVbAgI1WlaXfNIbEOVrvmqdM1XpemBKl0LNY/EiprwSKylXmiWRk2kNok9RWymDBexKmefKK2/VBefDYqvTwC2yp79qpD99WH+2jEv7yjZMr9vr5KARfR6/mh95r9nS+PYdXLNwpVGZmYTjtz/tboRSM+r+XlEXs3P2HvyaEBP99bk2H6p6jLM680ZJBsye+UGLJkFEIJ7Wh6IhQS1Fx/cKWZFoBsA/KG0fOKUAf22BYUoAICf+orhN1xpQUGp3HLtm6EdA0oxWUT4d60mjZS0pqpeaU1VvVCz5ZDHSkAGLZnVTdbcSl9afm1jWtcGX1q3gC+ti2yy5gl/SrY1aMnICOmpuVLoXSBE1LrQC2FJg7CkARErdr+JzGkoifBhrwn8KPvZfpJ9PxkqdkxVnaW9dEDPNYzcXMPILejEeQJAoFbTdo/ENlRpuq9K15oqdT1YpWvBKk0zqnVd1GiaqNWEpV7TLI1CpPqFSA8IkR4CspqL2I5s19fYieidxuKTAAB/D55Q96uU/2BYIDDw1R3FnjP69i42hOgthdDePUpMfvcoDaM3G99cvtxo7FaDiaKVe4eUUGNuftXGUflVG/f5eFNqbpk3d9C2qrxhtd4cm96Y3r1LSE8bACGSeeuIKtUBKO6w+FQoA756IZALAGlSpt9dVrHrth7d9rkP2dZDDLz1EsvAnlVy+zVvhn4etgMThYJ1lgIQKcH6vJRgfV5W/c5Wjw9pKY3+1NwKX1qXGl9afl1jWr7fl9Y15LN2gT81NyWQkpkW0q3ZhmbZPaqqctlFrcJrU3zifadJXBe4puey1NsMIZTvvKFECpDS1TC6djWMrp2ZBNsk4KtpHomt0vTG6vBU4qZKrbmIbZ5KHC5iNUuDJlJyDaM8cl9J+6kuPusUX5+aPR2aM/pGy38ahEDG4EDQ9vqO4l9O79t7Z0iIPruPWT9YG7V4sYaCUrnlqmWh7YOLMVG0c5Nvq9/bvUfZuu49ytbt+ZgEZGN69+3VuYN3VucNaww3Oera3dBSBiRJkyN2u6X24g2UQn1FeRmAPet+5tY3jP9LILBmW0rK5P2P3dVF9LvjAku/LrWy9GqX8dmoLXKcMPE+rboRSE/3lfdL95UD+PGQxzY3VSrbr6lS0GftajRZcy2BlOzUoCU9a3dTpShMM6uO8Pko8Sm96ab/2SgHDPbInp8OFLumqM4Sz6wSad1DRlr3kNGtHUs5ld5DqC4+W1/cQjFRj/TsH2S/j4eL7dMAYGAgOOD17cVb5/XrvSMkRN+9j93WQwy8/ULLwLw6WXbpCmPDhB/kaAF06ei1BSAyGsv6ZTSW9etTsmbPx/duclSVN9Rfl9Uvrcma10sKvSDBmhyx+KT2KlEdIJnZRMkBRc/TxaUDTijoU3uwaatV2aLHvefoPbIaZNXly42Vk76XYwTQ6cZBKnW8qVKXmua1qv7GtPxQkzVP86fmpARSMjJCujW7HU2VOGuE2qtCdQD6nxsCi/NfT71TCsFtpmJM6Xun6uJzh+Lr017+FDwz/S+pf9rz/7ZgsP/S7cXbT+3Xe3tIiH77H1+dJbo/eKY+2+qX9eeuMlad8JUcbDFwwHEddcgmR9n9f6nOHVoRbnLUNyvc5EjrFalrxxh/GVJ7cYsJhQaJ4gOeGvcOhXqfU1u3+oWc7JmHem1dhuiy5Ax9dnqTrL3wHWPlrG/lSE2ie/TSmkdHmio1WfO8vrSutY1p3Zp8aflBX7ipkh5IyUq3hHytzyMm2hdHPk1kvRwybLvs9lmBKJ+kOkuSSericycACW6sbQr/NSaMCUqt2CKMPZ1q+weD/d4MF6B7GmrsrylVZP79eH3Ws8fK0Elr5Sdnrza6pAcwIlo5dSOQnufdfFied/O+TY4s6d6anIFbq/KGVntzB8v6jJ65QUtmfwjR4VHZGOEoFrUXb7oVGiiKW2wS5qiomvZGVuamRk07rLVzNFpF9mNz9dlPnygbz1tprDrxSzlUl+jT2uuSxd5NlXIP3lSpAjg3lrEo/nHGncncGFic84r1LtUxko3SB9hKi0+P0x6wOVzlQHI89TU7CU1bZYz+/lh93T7bpBQEg/3e2r5z59x+fX4JCjHgYK83NKEvmyimLpuoYcL3xrpL3jZCXeowrrXmRJGSEmzMza/87sj8yu/2+bgvNbfUmzt4e3Xe0FpvzkCtMb17fki3DjDRXk0sJKi9OPKp0ACxq8X3NB3QH9lVFrqkVw+jrUsD/Cki/e/H67P+eYwMnPmx8eG8T2URvKHXAAAgAElEQVR/i4GDvs/SPjh7itqLv29NZq0cPqJYdv2it6icoDpLkvCiyKt0vbzqkU8g/MuDxadJPBA8u/+x+roDPt43GOrj2r6zeG6/Pp6AELbWzvPFcG3sF8M1DCyRP13lCpUMKMVEAShpHpTm9/ZIK/uqR8+yr/Z8LNzkqMe26tzBxdV5Q/ducmTrYNvqzmAhQe3F7xmFeovKgzZam+hrGjmmyb/66zTrIaff7i+ki5SXZuozXpkuQ3O+kJ+cvdrobg1iaOfTJjQWEtRe/J4xoZsCV2Y8n/o71TGSxS+qAwgppdIANofrLQB2pSFoH27rJRuyRePIlj5Xouslcwr6NAaEGNiec+Z7ZfHly43vx/wsxwqEtygwI0NogfrMvlur84aUVuUO8ddlFaQ1WXN7S6H3i2KTo+GLHz/mhyidmxKUzeGqAMB9cxVYb73UnSsaDtj3eLcaTXhn9O/XZAjRo8MXkVIes15+XviekZ3ux+EdPk/iqhmxaaNpf5eQOdkcrmEAvledgw70hfWqr7oL71GqcySBpSjynqYygFlGPslEXggdXXGZZVmLn+sVCvVavm1n6ckFfX72CzGoreesyBW97ztb753ukzWLPjBWHbNeDtMlerf+ytjSpJGSXbdtcHbdtsEF2z/Y8/GQltpQm93/l6q8IZXe3CHBusw+mf7U7H4RaHIUBODp5DkoOW0Fi08lMuE75DryHEPm/qqy+pMH87t0vPgUQrw/Rkx6f4yGKRuNLy9dYejZjRjT4fMlnoMuBCU6hC0AAgBiPcOJWnFL4ArL31PvVx0jGXDk0+ZwOQDcpzQE7SMPtVXrrFdkCnHwabJlulZ2Ur++NX5NDO7INfSQDJzymfz8zI+N7tYghnU8rVoBS0a1N2fg1uq8oV5v7qCONDn6YfHjxwyPakhKSDaH6zkA56nOkXyk3GJdGDjU++Nuswv6fllh0cdF6sqjNxvfXLXM8Hetw/hInTOO/XPEpo0XqA5B8cfmcH0LoMXZXaTWV9Yrvu4qavmQLbpuQpF3icoAZhj5/EZ1ANpXNbK7bJM91vQXpQdsmL5b95DRfcX2HTipX58fmzSt3euSQrpIeX2qmPb6VA3TNhhrL3zX0HMbMLZzyWMvJdiQ161yQ163yg37fNxnzdvlzRm0oypvWG1Njk1vTO/e9SBNjjbFLi0lmG9VB0hG+aipEALd2nLsMyW7up3Wt7cPQqRF4trrB2ujrrxWw/BtcuPit0LentWYFKuGbibEewfqqA1g8WlKtwUuxROpf1QdI9FtVh3ADMXn16oD0IEeCZ2m3a89echjuoWM7iu27RQnFfT5wadpHR69/HikNv7jkRqGbZebrlwWquhbgUnCHN+bHZbWVN0zreyrnvs3OWpI77nVmze4pCp3aGNtdv+UkJ76hcKYFN9YfCrQX5RWAG0rPgcFggNOrG9YtSIrc1YkM3xfIEZcd5UFA3bJzde+GSopKIv/98wOYPFJHfUtgAWqQ9CBVhgTxnhlxje5omGU6iwJTHndpXzaLQDYHK5SsOOtqVgQDPxgLfRqQrZ6k1WlaZUnFPQp82laRKaP9qyS26/4r7F55C9ynACyInFOE1s0YtPG51SHoPhjc7gGAvhZdY5kc4a2+oslqY+3eUsAP9A02Vaws71N2tqjV6Xcdu2boS1DdmKSAA7aiTfB9BmxaSO7PlO72Ryu0wG8qjoHtewU7ZO1f059hEsLoqMSRd581SGi1b2zvdarDkD7CsKS8rk8bEPrRwJdDKPrO9t29kg3jI2RuPauLqLfb8/TZ11ygx5cdYRYaQiURuK8JsWn99RRHgB1qkMkm0Fasa89x6cC1t+XlldFKw8AlHQVBb8ptMy8erFe5R4gVkmgIZrXM4EyFp7UCZw1YmJvGlPH18r0Nt1/UruZot5i8UkH9WBgQZs7NeYZRpd3tu3snWEY30Xq+nXpIu/RU/TZi27Wc1+ZJj4M6Ak3yuMH13xSB3mcdgkgYj9v1DYDRUmova85vqHxqMH+wMfRyLO3ihzR6+7z9FmXXac3fjFUrJSAN9rXVMStOgDFtc0A2vUQiWLrruAF9aozJKh1qgMA5ik+lc8/pgOtlcNH+GTKj209Ptcw8t7etqNvpmFE9IlV0CKsL83UZyy8RR/4lznaF7VpCfOw4ssRmzYGVIeguPal6gDJpkCU6h153VMlu4ZBypgUgzWZIv+Bs/TZF96oi1VHiJUGUB6L68YQZ4xQh3mcdgMc/TS1V0KzJjZIKx/ORx6Lz72w+DSp10PTd7bn+FxD5r69bUe/LMOI/Bu7EGLlaG3CJTdaRt91nrahJA+fSsCI+HVi50PVASjurVYdINn0ENUd6lzbLWR0v9hbG9OiqTFN5Dx6ij77gpv1jP+OE6tCAokyVTVRHkCSOnzvNLl7ggurVWdIQF+1fkj0maX4/A5AheoQdKCHgmceJiXaNc0sx5C5b2/d0T87ZERtatSGAdrI666yTLnxcn3b932xWgKN0bpWFLH4pM5apTpAsslBfU5HX3tDVfX0qDyYa4U/RWT87QR91vm36PmvThUfBjVsjXWGCPtIdQCKex+oDkCH9nzo2EmNMrXNs++oVWUAItKbpbNMUXw2T4F4X3UOOlAJuvYsRV67h+mzpcx5Z9uOgTmhUFSf9O/MFwPuuMAy87Lr9IZPDhMrjfh5iGGAN1DUSR6nvRjAD6pzJJM0BNq0zUpLBCCeKClNgZTtXjcaCUFdpL4wS5+x6Ba977+O1j726+r3e+uArSM2bfxJdQiKe6uB9j1Yp1gT4oHg2Ym2ZEClD1DkVb/FCUxSfDZ7R3UAatmTQbu/I6/LlDLr7W07B+WGQlGfIlWTKfIfOj08vWzpJLE6qOGXaF+zk74dsWkjp5RQJHD0M0ZSEPQLyK6dOceoJv/wSb4mpQ+eDE3oSydr0xbdog968kRtjS/FHE/D24gjVtRpHqe9Blwzb3p/C504qUlaEq3ZpCrvqQ6wG4tPatW/QseNlRI1HXltcwE6JC8Uism6Xn+KSP/XMfrMhbfqBU+doK2pt5q2qQDXm1CksPiMkT6ivFQIiM6e50+7ysZpUqpffymEeOcobfIFN1tGPHyqtrYuLS4a+XCWFEUKH2SYnISm/TF4lvr3ysRgmvdO0xSfHqfdA4BTaUzIB2u6Ww7s8OhlhpSZb2/bOaxLKBSzLltSCO3tcdrki35lOeJ3C7RvynLwuQRMMd2g2buqA1DCWKk6QLIYIHZFZL/OTCmzflNRZarZGR+N1MZffKNl1H3ztfVVmVirOs9BSPBBNUWOaW7G6eD+Gpo72S91U71fxqGtKPKapsYyTfHZjL9UTGpJcH5uZ16fLmXG29t2HpYfDMV8msvXg7VRixdbJt5yib5lcy98KIGmWGfYjw8sPilCPE77DgDckDsGBoriukida0Ft3eReweDnkTpfpKwboo2+4jrL+P9bqH+3KxdrTPbQbt2ITRs5CkKR8jEAbndmcgY0/dHgvG2qc8Q500y5BcxXfPKG3KRWGmNGBaTeqQ6JaVKmr9i+Y2S3YEjJU/WtPcSg2y6yzLjyGt3bvAG7qjWXK0ds2sgNlCmS3lAdIBkMFsUdWv9+MM8Ul/aBlA2RPGekbOwvDr/2astkx0X65m3d8LE0R3OWZaoDUOLwOO314LKFuPBIaN7kgNS3q84Rx95UHWBvZiw+VY9K0UGsMMZ3etG3VSJtxbYdR/YIBr+IRKaOqMoWPR44S59deJNu+e84sSqkIdZvaK/H+HqU+Pg9FQMDxK6Inq8gGOw3r65e2XthW2zpJYbcdJll2o2X69t/6o0PJRDRArydXAqvTYnpRdUBqHUh6Ja/huxbVOeIU3UA/qs6xN5MVXw2dx9boToHtezB4ILBUnZ+ClYqYP3vtp2jeyqecuZLFVl/O0GftfAWvdezx2ifxKjjowEWChR5awFwWlKU9RHllkif887yymlWwzD9XnY788WA2y+0zFh8tV6xob9YJYFYj9juBGC6acoU9/4DTr2NCw8Fz5oUlNpO1Tni0Jso8vpUh9ibqYrPZi+oDkAt88jeBdXIikg3xFQgddm2nWN6m2DNk6EJy1uTtKkX3GwZ8eAZ2rrKrKg22/hkxKaNkR0+oaTncdolgJdV50h03URNZqTPmQJY/lha3ggpzbS28qDKc0Xvuxbqsy6/Tm9YO0SslOhYJ/QO+OeITRuNGF2LkoTHaa8C8LbqHNS6ACypfw+daJqmOXHEdPcGZiw+lyL2T1SpjZ4NHV8bqXOlAqmubTvH9g0E10TqnJ31+XBt7JXXWsbfdqH+4y/d8ZGM/BPR5yJ8PqLdOH0syjLR2CUa553R6Bs1wh/4OBrnjhZvpuh2/3x99sU36PKjw8VKA6iM8iX/EeXzU/LioEeceCB49sSQjPD6h8Rmuim3gAmLz+YF4JyWaFJPBu2jpERjpM6XAqS8tX3n+IJA4NNInTMSNvcWQ2+51DJ98dV6+fqBYlWEnu43Avh3BM5DdACP0/45AG7GHUUpCHWP1rmfKCk9XEhZEa3zR0t9ush9+DR9duFNuvXtsWKVIRCNG8PPR2zaGItlEZSc3gAid19D0dOE1LTnQ8duUp0jjrxltim3gAmLz2bPqg5ALatDRs5Pss9XkTynBbAs3V48YYDJClAgPMXs3nP0WRfeqOPd0WJVSKAzbf5fG7Fpozdi4YgO9IzqAIkqB3VeIZARrfN3MYyuV1d74/amqilVZD51kj5r0c163huTxepgZBu5cdSTosbjtNeCnZTjxu+CCycYUpSpzhEnTPneadbi812EmwuQCf0peGZapM9pASxvbC+eONAf+CTS546ExjSR89c5+qxFt+jd/j1T+6jJgo40CPlbxIMR7espsHlGVBSIsvJoX+PK6pppuaHQ19G+TjQFLcL63NH6zEW36L3+PVP7KKB3ejS+CZwxQtH3vOoA1DaNsGa8HJrJva1btwUmbeJqyuLT47SHYNJqnQCXMWlsSIqSSJ9XB/TXdhRPGmzitU8hXaS8Nk2bfv4tlqEPn6qt9WZgXRtf+gtMtskvJR6P074LXLYQFTaxKyb7Aj9VUpoNKeP+AYKhCctr07Tpi27RbU8fr33qS8H3HTzV0hGbNlZFNBzRgZYC2KE6BLXNb4MXjDekiPY683j3BIq8pmxkZ8ris9mj4BN8U5LQtNXGqKhMD9MB/dUdxVOG+v0fReP8kfTRSG38Zddbxt6xSN+4oys+aWUT9n+M2LTRlG8ClHAeVx0gEQ0WO2OyJuwwf2DwrMZGU84A6QgphLZivDblgpstwx+Zq31Rb4W7nafgg2iKOo/THgTwV9U5qG3qkZ71hjE1IrsvJCg/TLwMx7TFp8dp3wHgJdU5qGUPBM8uiNa5NUB7ZUfJ1MOazF+AAsD3BWLEjVdYpl53hV68oT9WS6B+v0MCAJ5QkY2Sj8dpfx/o8CgTHcRArfhQD5ci6sHSiokWKRNu39bVR2oTLvqV5cj7z9S+rs5AW3oHbAewPNq5iJr9FRz0iBt3BC46Skqwj0bLXkWR17TrYk1bfDb7g+oA1LLvpG1wnUz7Llrn1wDtpZ0l0w5vavowWteItF1dRb+7FlpmXnKDHlg9Uqw0BHb/4L84YtNGrmGmWOLDjggrEGUiVtdKkzL97rKKhN1OYO0wbczl11uOuus8bUNZDj6XwMFmhTw0YtPGmBX9lNw8TnsJuGVV3KhDRs5/jYlxvUY+ih5THeBQTF18epz2dQA+UJ2DWvZSaHZUG3AIQLywc9f0I31Nq6N5nUirSxd5j5yqz150s57zn6niw7o0PkShmPsbIrM9EDXrJSqtsbze3PqG8QWBgGn2QI6GDQO0kYsXWybefqH+U/PSBWOvT1eD0yAp9vj7Oo7cFrh0tJSI2P7zCeILFHlNfd9s6uKz2RLVAahljwRPGylldKeoCEA8V7xrxpg4K0CBcNfHF2fpjRO+3sgncxRTHqe9GsCfVedIJHmoy471NZ8uLh0AKRP+xmpzbzH0xissU2+6VN/6c098KMNTHx8bsWljwn/tZC4ep/1rAO+rzkFt40VW3nvG2C9V5zCZe1QHaE08FJ8uAHG791kiq0Ru/nbZLaJ7frZEAOKfxbtmHuXzrYr2taLgPtUBKGktAfhEOFLS0dQ11tfsHQr1Premrq0dtePe9u7C5rjYMuO6K/Wt2/PxkOo8lLQeUB2A2u7WwOVHSnlAr41ktR7Am6pDtMb0xafHaZcAHlSdg1r2WOjUmF3rH8WlsyY0xlUB+qm70L1SdQhKTh6nvRLhruHUSRqMkAbZXcW1f11ZNS3dMDaquLYqu7qIt47/eGOp6hyUnDxO+3IApt3yjfZVidz8D40j16rOYRL3mnV7lb2Zvvhs9ndw9NOUXg7NHhvLvZaeKSmdNTl+ClDTT32ghPcHHNh9mdqpFyrLhICu4to6oD+6q0xCSqP1oxOCD8DvVYegpOdQHYDa7ubAlYdLiZhsh2ViGwH8R3WItoiL4tPjtIfANwJTCsCSulYOa+++bZ3yZEnprOkNjStjec0OeM9d6F6mOgQlN4/TXg7gL6pzxLsCUap0M/MJvqbDx8TJ1lMR8IS70F2sOgQlN4/T/hGAt1TnoLYpRZfun8kRX6jOodi9KPLGxUPKuCg+AcDjtL8BIG623UgmDwYWxHw62mO7ymbPamhYGevrtlEIwI2qQxA1ewBc+9kpg7Ri5f9+j+4qHa1JmehTUSsB/FZ1CKJmt2HfDsxkYjf6rx4mJZpU51DkKwDPqw7RVnFTfDa7VXUAOtDncsThPpmyOdbXfWRX+exj6k1ZgD7tLnTHdDSY6GA8TnsZgLtV54hng0Sx8huaHEPm/qqy+ifVOaLsLnehW+koM9FuHqf9WwD/VJ2D2qYY+b2+kkM/U51DkevjYa3nbnFVfHqc9jWIk/nMyWZpaMo2Fdf9U2n57OPrG8y0BrQGwB2qQxDt5yEA36sOEa9sosQUox+FNbVT84OhRN1WYBM4RZzM504gaUfT4s4NgcWDo70FoAm9jCJvXC3LiKvis9ltQNJ9Y5neQ8GzhkupZnrKktLyWSfV1a9Uce0W/M5d6E70qXEUZzxOewDA9apzxKt+otyiOsNuz5Ts6gYpfapzRMFN7kJ3UHUIor15nPatAB5RnYPaZpvs0dctB65RnSOGfIjDWaFxV3x6nPYfATysOgftaye69S5DrrL96B4oq5htV1+AbgG4Nx2Zk8dpXwFgqeoc8aib8GaozrDboEBwwEn1DYk2tWw5G7SRiRUB2Ko6BLXN9YHFA6REsjzIWoIir0d1iPaKu+Kz2R0AEn3tS9x5OjhH6dQUZ1nF7Hm1dSsVRrjVXejm9BwysxvBKWTtlo2GXNUZ9nZvWcWUFCm3qM4RIX6wQRuZmMdprwNwheoc1DZbZJ/+m2T/ZBj93AbgPtUhOiIui0+P094I4FIAcbO4Nhk8GzphjJRqu2reXV45+8yaOhVrQF3uQvcrCq5L1GYep/1ncA/FdrMi0E11hr2lAqn3l5ZXq84RIUXuQjf38SZT8zjtywH8S3UOapvrA4v7qloKFkNXoshbpzpER8Rl8QkAHqd9FYAnVOeg/2mENWODtH2tOkdRReWss2tqY1mAVgO4PIbXI+qMewAo/zmNFxnw1QsBU418AsBxDY1jB/v9n6jO0UlfALhfdQiiNroBQJnqENS6H2TBwM2yTyKPfj6HIm/cLlWI2+Kz2a3gPHxTWRI8K0d1BgD4fxVVs86NXQF6g7vQvTNG1yLqlObmQ+eD02/bpK8oN+3N5lMlpUMhpVd1jg5qAnChu9AdUh2EqC08TnsFgOtU56C2uS5wTU8pE3KGZAnivIFgXBefHqe9FpyHbyrvG2NHBaSuZNuV/d1eUTVrkbcm2gXom+5C9z+ifA2iiGrev45bArWBTZSYdnprt5DR/WJv7Teqc3TQ/7kL3d+pDkHUHh6n/QUAb6nOQa37TtoG/yJ7JlpzNgC4DEXeCtUhOiOui09gzzz8v6vOQbsJ8Y4xbrPqFLv9urJ61oXVNashZTSefu0CcEkUzksUC38AsFp1CLMbJIobVGc4lBuqqqdnGca3qnO002cAHlQdgqiDrgCn38aF6wOLuybY6OczKPLG/cOPuC8+m10LbqBuGg8GFwxUnWFvN1VVz7zMW/NRhAtQifCUMf4CorjkcdoNABcCapuEmd1AUexXneFQBCD+WlyaCinjZWuBWgAXcLotxSuP074TwEIg4RvaxL31csiwHej2ueocEbIB4Xon7iVE8dncBns+gEbVWQj4WfYZUC0zTTUV7Loq74wrqiNagD7iLnQvj9C5iJTwOO1bAFytOoeZDRC7hOoMrTnS7x822ef7WHWONrrIXej+QXUIos7wOO3vAPit6hzUuhv9V5uiF0kn1QE4C0VeU8/EaauEKD4BwOO0u5EgTwQSwb9Cx5muCcY11d4Zi6u9H0PKzj6t/ATAzZHIRKSax2n/F4A/qc5hVr1FpVV1hrZ4aFf5OF1Kszc+e9Bd6P6P6hBEEXI3gBWqQ9ChfSEPG1Eiu3yhOkcnXYYib8JsSZUwxScAeJz2pwE8qToHAU8E546SEj7VOfZ3ZXXN9GurvJ90ogDdCuB0d6Hb1FPxiNrpZgArVYcwoy6iNlN1hrbIlDLr9ooqUzR7O4iVAByqQxBFSvPShUUAzPxzRwBuClyVrjpDJ/wFRd4XVIeIpIQqPptdg3AzA1KoFpm5P8veX6nO0ZLLvTXTb6yq/hRStnfNUT2AU92F7tJo5CJSxeO0BwEsALeuOkAmfF1UZ2irBbV1k3oFg2Zc37QDwNlc50mJxuO0lyP83hlQnYUO7mPjiCPKZK4p70lbsRbAjapDRFrCFZ8ep90P4EyE98EhhR4OnpGqOsPBXOytnXZLZfVn7ShAJYDz3YXu9dHMRaSKx2kvA3AGYL4ZC+pIqcPooTpFezxTXNoHUpppXZAfwAI+tKNE5XHa1wC4QXUOOrRbA5frqjO001YAp6LIm3Az7RKu+AQAj9O+A8AcsIujUm8aU8aGpNilOsfBXFBTO9VRWfV5G7tE3uEudL8W9VBECnmc9i8BXKY6h1nko6ZCCJj2IVpLCoLBfqfX1Ztl9FMi3GDoE9VBiKLJ47T/BcAS1Tno4D4wxo6ulFnxMoDgBTAHRd5i1UGiISGLTwDwOO3rAMwD0KQ6S7IyoOkfG0eYeoH0wpq6Kb+pqFoLKQ81Zebf7kL3vTELRaRQcwMirs0D0F+UxuVG3neWV063GsaPqnMAuN1d6H5edQiiGLkZwMuqQ9DB3Ra4NB62x/EDOB1F3g2qg0RLwhafAOBx2t9HeDF4PHyzJaQHgmf3UZ2hNefU1k2+s6Lyq4MUoJ8CuDjWmYhU8jjtvwfwgOocqg0UxTWqM3SEBbA8VFreGOG9jdvrUXeh26nw+kQx5XHaJYDzAaxWnYVatsKYONYrM9yqc7TiEhR5P1AdIpoSuvgEAI/T/grCTYhIAbccNLReWk09+gkA82vrJ91VXrkOUu49t34dgJPdhW6ugaOk43HabwXwjOocKg3SiuP2Z396o2/U4X6/qr0//w1ufUZJyOO0NwE4BeH7BzKhOwIXmXkN5W9Q5P2X6hDRlvDFJwB4nPbHwM2AlXklNMu06z73dkZd/cR7yivXQ8omABsAnOAudJtuv1KiGLocQNKudR4oSuK6O+sTJWUjhZSxnjq8HEChu9CtctSVSBmP014D4EQA36vOQgdaakwbVyfTvlOdowW/RZH3d6pDxEJSFJ8A4HHa/w/An1XnSEYPB08fKWV8tCE/ra5+wt3llSsAHOcudJerzkOkksdpDwE4F8B7qrOoUCBK46074j7yDKPL1dUx3Zh8GcL7IMfF+z1RtDR3Dz8ewE+qs9CB7gpeUKc6w37uRpH3/1SHiJWkKT4BwOO0XweAa1BirAK53XYiP16moGycV1d/hbvQza16iLDPNLL/qs4Saz1EdZrqDJ11ZXXNtNxQ6OsYXOo1hAvPuJ2qTBRJHqd9G4AZAMy+xjDpvByaPbFBWs0yMn03irx3qg4RS0lVfAKAx2m/DcBtqnMkm8eDp8ZD0yc3gNko8rLwJNqLx2lvBHAagBdVZ4mlHNTnqM4QCU+VlGbvt5490v6N8F6eZl5LRRRzHqe9BMAsAJ+pzkL7+l3wvCrVGZCEhSeQhMUnAHicdieAq8AuuDHzQujoowwJM/ygH8w6AEejyMuN0Ila4HHaAwDOA/BX1VliJQ2BbqozRMJh/sDg2Q2Nn0bp9H8DsMhd6G7LfslEScfjtFcBOA5AQncwjTf/Ch03ySdTVG1JJQHcnoyFJ5CkxScAeJz2xxFuic1fmDEQgCX1KznMrFNPPgBwLIq8cbmnH1GseJx2w+O0XwHgftVZoi0FQb+A7Ko6R6Q8UFYx0SLl1gif9lEAl7gL3XyQS3QIHqe9DsAcAG+pzkK7CfFA8GwVvT38AM5Hkfc+Bdc2haQtPgHA47Q/D+AMAI2qsySDB4Pz81VnaMEjAE5AkdfMo7JEpuJx2n8NwIHw09uE1EeUlwoBoTpHpKRJmX53WUWkZnaEANzgLnRfw662RG3jcdp9AE4H8JzqLBT2TOikSU3S8nMML+kFcBKKvEn9PZDUxScAeJz2NwFMA/CL6iyJbo0xcmSMf8gPxQ/gUhR5r0WRl6PfRO3kcdp/D2A+gAbVWaJhgNiVcA+k5tY3jC8IBNZ08jReAHPdhe4/RSITUTLxOO1Bj9O+COGHd5wxoJiEpj0UPLM4RpfbBmAairxJP/066YtPAPA47esAjAfwvuosie4tY0qkp311RAnCjYWeVh2EKJ55nPb/AJiO8C/VhDJQFJutFX9EPFNcaoOUtR18+WYAU9yF7uWRzESUbJof3s0BTN0LIyk8ETplsl/q0R6A+hzAZBR5N0T5OnGBxWczj9q5E84AAArRSURBVNNeDuAEAEtUZ0lkSwJnDZdS6dO+LwCMR5E3Ws03iJLKXg/vViqOElGDRXFCdm7tFQr1OremriNbX60CMMld6N4Y6UxEycjjtK8AMAHcikUpA5r+l9Bp0RwYeQTADBR5d0bxGnGFxedePE57yOO03wRgIbgONCp2oHvvCuTEYs+5lvwTwEwUeXcouj5RQvI47aUIb6ieMA/vBohdqiNEza8rq6alG0Zbi0gJ4AEAx7sL3WzKRhRBHqd9M4ApAF5WnSWZPRKcNzkg9e0RPm0dgHObl3cl5MPMjmLx2YLmRkRTAfykOksiejp4cqwL+xCAm1HkvQBFXm6AThQFzWuZbgIwD0CZ6jyd1UeUW1RniBYd0B/dVSYhZWuzUIoBnOAudN/qLnQHYpGNKNl4nPZ6j9O+AMCtAPhzpkAQlpSnQnMi2ZPkOwATUOR9IYLnTBgsPg/C47R/DWAMkmhPu1j5R+jEMVIiVuupfgQwC0XeP8ToekRJzeO0vwHgCABvqs7SGd1ETabqDNE0wdd0+Nimpo8OcchbAEa5C93vxioTUTLzOO0PIDwN9xvVWZLRkuD8yUGpRaL50FMAJqLIuykC50pIQkp2SW+NzeGai/A3U0/VWRLFslTHR4drW6dH8RIGgIcR3sSXU6iJFLA5XJchPBU3S3WW9vrBev4vqSI0QHWOaKoVomb6gH6NhhB7/27zAbjFXeh+RFUuomRmc7hSANyJcEfchJ2BYUZ3Wp5dfbFl+cwOvnwbwrsovB3JTImIxWcb2RyurgD+BGCR6iyJ4Hht7ddPpi4ZE6XTbwZwEYq8H0bp/ETURjaHazCAZxFeyhA3tljPaxACGapzRNuzOdmfPpDfZUrz/34C4DJ3ofs7lZmICLA5XOMB/APA4aqzJAsr/L7vrBfV6EL2aOdLnwJwE4q8NdHIlWg47baNPE57pcdpPx/AKQDYsKaT3jHGjQ5KLdKLuwMAnACOZOFJZA7NDTVmArgGcbKtQA7qvMlQeALABTW1U3oHgqsBXA1gOgtPInPwOO1rARyFcMMv7gkaA01ITft36Jj2dPTeBuAkFHkvY+HZdhz57ACbw5UF4NcAbgKQrjhO3HoiZcnKE/W1syN0uo8AXMk9lIjMy+ZwdQNwH4CLYeKHnyPFls0u628Gq84RI88h3JCtRHUQImqZzeE6CuHZd9FcrkQA0tHUsMF6cYMmZLdDHOYD8CAAJ4q89TGKljBYfHaCzeEqQHik7VwAQnGcuDNUbPe8Y73V1snTlCG8LuJvKPLym5koDjRPJ3sEwCTVWVpi19Z8+Wjqw+NU54gyN4BrUORdrToIEbWNzeE6F8D9APqpzpLI7rc8vnKBZfXsg3z6ZQC3osjriVmgBMPiMwJsDtdkAH8EMFl1lniz3nqpO1c0HNmBl5Yh/NTpUT51Ioo/NodLALgQwN0A+qpNs6/r9Fc/+lXKK4k6wrAF4X/zZ1HkDakOQ0TtY3O4MgDciPDWLDmK4ySkTDTWua2X+jUhu+714a8A3MBlXZ1n2mlP8cTjtK9BuJnGeQAiuU9QwnsudFx1O19ShvCU54Eo8t7PwpMoPnmcdulx2v8GYDCAa2GitfQDteJELMq2ArgcwHAUef/GwpMoPnmc9gaP034vgCEIzyDxK46UcOqRnvWmMWX3ljc/INxsdAILz8jgyGeE2RwuHcB8hAukaHVzTRg5qPOut15uFQJprRzKkU6iBGZzuKwALkN4Gr3SkdBXUotWj9d+6Gi7fbPZAeB3AJ5CkZc3qUQJxuZw9QFwA4ArwJHQiMlD7Zdfp13xEIB/82FdZLH4jCKbw3UiwjdSsxVHMbX3U3/16SCtZMpBPs2ikyiJ7FWE3gKgv4oMH1mv+6yfKDfletR22IrwHqtPoMjrUx2GiKLL5nDlArgSwPUAeiuOE8++RLgx3msep51dhqOAxWcM2ByuSQiPhJ4GTnU+wBna6i+WpD4+Yb8PbwbwOIDHWHQSJZ/mWSRzEd4C5HjEsKnbt9aLv8sSvnjcW88AsALAYwBcKPLyxokoydgcrlQA5wO4GcBhiuPECz+ANwA84XHa31MdJtGx+Iyh5u64FwAoBDBUcRzT0BEK/mC9oEoXMgfAqwhv1vsBu9cSEQDYHK4hCD/RvwhA11YO77TN1oUlupC9on2dCCoD8AzCo5xbVIchIvWam7odj/B95+lAcuxd3E6bEL7nfNbjtJepDpMsWHwqYnO4piFchJ4NztH//PeWJx4/27LqDRR5K1WHISJzsjlcaQAWILy91bEAUiJ9DQ1GaLN1EYSAHulzR1gIwGqEb5xe4XpOIjoYm8OVDeBMhAvR2Uju7QEbEd4u5UmP0/6R6jDJiMWnYjaHKx3hJ1LnIXwz1VrjnUSxAeEf/uc9TvuPqsMQUXyxOVxdAMxDuMHbcYhQIdoH5SWfpF1n1lFPH4B3ALwG4E0UecsV5yGiOGNzuPoj3L11IYB4XF7QEV4AywEsBeDyOO1exXmSGotPE2neu+l4hNc5nQyT7X3XSTUA3kP4h3+5x2nfqjgPESUIm8OVh/Ca+nkAZgHo0tFzTRLfffei9R4z3ZB5AbgQLjiXo8hbpzgPESUIm8M1AMCJAE5CeAAkkWbibUG42HwTwGqP0x5QnIeasfg0MZvDdTiAExB+qj8ZQL7aRO0SBPANgLcB/BfAJx6nPag2EhElOpvDpQEYDeBohKeXzQSQ29bXn6u/99l9KU+r7HRbD+AThKfUrgawhlNqiSjabA6XBeF7zd3F6FGIryaZOwCsaf6z3OO0f6s4Dx0Ei884YnO4bADG7/VnHIA8lZmaBRCeRvvlXn++8TjtbO9PREo1d80di/BN1SgARwI4AkBWS8f/xvKv1ZdZlsVqj88gwhuYfw7gs+Y/bhR5+aCOiJSyOVxZCN9njgcwAeH30SEwR0HaAGAtwu+ZawB85nHad6iNRG3F4jPO2RyuwQjfTNkADNjvTyRHShsBbEN477hfmv/einDR+Y3HaW+K4LWIiKKmuQvkQPyvGB2K8J6i/Z9KeeDn4/R1x0bwciEAHgA/7vfnJwAeFppEFC+al4eNRPh9cyCAAgD99vqTGcHLNeJ/95tbEH5Q92Pz35s5my5+sfhMYDaHKxPhIrQLwm8IGfv9nYlwg6Mgwnsc+RFuaFG7159KANvYgpqIkkJRrkD4PbNn859uAKwAUhFuarT/3xrCa9q9AKpb/JsFJhElgeb19wUA+iB8r5mG8Punda//TgOgI7zEoAb73nPuuffkfWfiYvFJREREREREUWeGedtERERERESU4Fh8EhERERERUdSx+CQiIiIiIqKoY/FJREREREREUcfik4iIiIiIiKKOxScRERERERFFHYtPIiIiIiIiijoWn0RERERERBR1LD6JiIiIiIgo6lh8EhERERERUdSx+CQiIiIiIqKoY/FJREREREREUcfik4iIiIiIiKKOxScRERERERFFHYtPIiIiIiIiijoWn0RERERERBR1LD6JiIiIiIgo6lh8EhERERERUdSx+CQiIiIiIqKoY/FJREREREREUcfik4iIiIiIiKKOxScRERERERFFHYtPIiIiIiIiijoWn0RERERERBR1LD6JiIiIiIgo6lh8EhERERERUdSx+CQiIiIiIqKoY/FJREREREREUcfik4iIiIiIiKKOxScRERERERFF3f8HlHy146R/dbsAAAAASUVORK5CYII=\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig_2, (ax1, ax2, ax3) = plt.subplots(1,3)\n", "fig_2.set_size_inches(17, 5)\n", "plt.subplots_adjust(top = 0.8)\n", "ax1.pie(df.smokes.value_counts(),\n", " labels = df.smokes.value_counts().index,\n", " labeldistance = None)\n", "ax1.legend(loc = 2)\n", "ax1.set_title('Smoking')\n", "ax2.pie(df.drinks.value_counts(),\n", " labels = df.drinks.value_counts().index,\n", " labeldistance = None)\n", "ax2.legend(loc = 2)\n", "ax2.set_title('Drinking')\n", "ax3.pie(df.drugs.value_counts(), \n", " labels = df.drugs.value_counts().index,\n", " labeldistance = None)\n", "ax3.legend(loc = 2)\n", "ax3.set_title('Drugs')\n", "fig_2.suptitle('User Responses to Partaking in:', fontsize = 20)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Formulate a Question\n", "\n", "\n", "As we started to look at this data, we started to get more and more curious about Zodiac signs. First, we looked at all of the possible values for Zodiac signs:\n", "\n", "\n", "```\n", "df.sign.value_counts()\n", "```\n", "\n", "\n", "We started to wonder if there was a way to predict a user's Zodiac sign from the information in their profile. Thinking about the columns we had already explored, we thought that maybe we could classify Zodiac signs using drinking, smoking, drugs, and essays as our features." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "No Answer 11056\n", "leo 4374\n", "gemini 4310\n", "libra 4207\n", "cancer 4206\n", "virgo 4141\n", "taurus 4140\n", "scorpio 4134\n", "aries 3989\n", "pisces 3946\n", "sagittarius 3942\n", "aquarius 3928\n", "capricorn 3573\n", "Name: sign, dtype: int64" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.sign = df.sign.replace(np.nan, 'No Answer')\n", "df.sign = df.sign.apply(lambda x: x.split(' ')[0] if x != 'No Answer' else x)\n", "df.sign.value_counts(dropna = True)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0.5, 0.98, 'Zodiac signs of respondants')" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "\n", "\n", "fig, ax = plt.subplots()\n", "fig.set_size_inches(8,8)\n", "plt.pie(df.sign.value_counts(normalize = True, dropna = False),\n", " labels = df.sign.value_counts().index,\n", " textprops = {'color': 'white'})\n", "fig.suptitle('Zodiac signs of respondants', fontsize = 24, color = 'white')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Augment your Data\n", "\n", "\n", "In order to answer the question you've formulated, you will probably need to create some new columns in the DataFrame. This is especially true because so much of our data here is categorical (i.e. `diet` consists of the options `vegan`, `vegetarian`, `anything`, etc. instead of numerical values).\n", "\n", "\n", "Categorical data is great to use as labels, but we want to create some numerical data as well to use for features.\n", "\n", "\n", "For our question about Zodiac signs, we wanted to transform the `drinks` column into numerical data. We used:\n", "\n", "\n", "```\n", "drink_mapping = {\"not at all\": 0, \"rarely\": 1, \"socially\": 2, \"often\": 3, \"very often\": 4, \"desperately\": 5}\n", "\n", "\n", "all_data[\"drinks_code\"] = all_data.drinks.map(drink_mapping)\n", "```\n", "\n", "\n", "These lines of code created a new column called 'drinks_code' that mapped the following `drinks` values to these numbers:\n", "\n", "\n", "| drinks | drinks_code |\n", "|-------------|-------------|\n", "| not at all | 0 |\n", "| rarely | 1 |\n", "| socially | 2 |\n", "| often | 3 |\n", "| very often | 4 |\n", "| desperately | 5 |\n", "\n", "\n", "We did the same for `smokes` and `drugs`." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0 never\n", "1 sometimes\n", "2 NaN\n", "3 NaN\n", "4 never\n", "Name: drugs, dtype: object" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.drugs.head()" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "drink_mapping = {\"not at all\": 0, \"rarely\": 1, \"socially\": 2, \"often\": 3, \"very often\": 4, \"desperately\": 5}\n", "smokes_mapping ={\"no\": 0, \"when drinking\": 1, \"sometimes\": 2, \"trying to quit\": 3, \"yes\": 4}\n", "drugs_mapping ={\"never\": 0, \"sometimes\": 1, \"often\": 2}\n", "\n", "\n", "df[\"drinks_code\"] = df.drinks.map(drink_mapping)\n", "df[\"smokes_code\"] = df.smokes.map(smokes_mapping)\n", "df[\"drugs_code\"] = df.drugs.map(drugs_mapping)\n", "\n", "#We also want to make numeric labels\n", "sign_responses = list(df.sign.value_counts().index)\n", "df['sign_code'] = df.sign.map(dict(zip(sign_responses, list(range(len(sign_responses))))))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We also wanted some numerical data about the short answer essays. We combined them all into one string, took out the `NaN`s, and then created a new column called `essay_len`:\n", "\n", "\n", "```\n", "essay_cols = [\"essay0\",\"essay1\",\"essay2\",\"essay3\",\"essay4\",\"essay5\",\"essay6\",\"essay7\",\"essay8\",\"essay9\"]\n", "\n", "\n", "# Removing the NaNs\n", "all_essays = all_data[essay_cols].replace(np.nan, '', regex=True)\n", "# Combining the essays\n", "all_essays = all_essays[essay_cols].apply(lambda x: ' '.join(x), axis=1)\n", "\n", "\n", "all_data[\"essay_len\"] = all_essays.apply(lambda x: len(x)))\n", "```\n", "\n", "\n", "We also created a column with average word length and a column with the frequency of the words \"I\" or \"me\" appearing in the essays." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "import string\n", "\n", "essay_cols = [\"essay0\",\"essay1\",\"essay2\",\"essay3\",\"essay4\",\"essay5\",\"essay6\",\"essay7\",\"essay8\",\"essay9\"]\n", "def avg_word_length(lst):\n", " words = lst.split()\n", " if not len(words):\n", " return 0\n", " return float(sum(len(word) for word in words)) / len(words)\n", "def calc_i_me_rate(lst):\n", " words = lst.split()\n", " if not len(words):\n", " return 0\n", " return float(words.count('i')+ words.count('me')) / len(words)\n", "\n", "# Removing the NaNs\n", "df[essay_cols] = df[essay_cols].replace(np.nan, '')\n", "df[essay_cols] = df[essay_cols].replace('
|\\\\n', '', regex = True)\n", "df[essay_cols] = df[essay_cols].replace(':', ' ', regex = True)\n", "df[essay_cols].head()\n", "# Combining the essays\n", "all_essays = df[essay_cols].apply(lambda x: ' '.join(x), axis=1)\n", "all_essays = all_essays.apply(lambda x: x.lower())\n", "all_essays = all_essays.apply(lambda x: x.translate(str.maketrans('', '', string.punctuation)))\n", "\n", "\n", "\n", "df[\"essay_len\"] = all_essays.apply(lambda x: len(x))\n", "df['avg_word_length'] = all_essays.apply(avg_word_length)\n", "df['rate_of_i_me'] = all_essays.apply(lambda x: calc_i_me_rate(x))\n", "\n", "\n" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "count 59946.000000\n", "mean 4.789850\n", "std 2.353741\n", "min 0.000000\n", "25% 4.550000\n", "50% 4.832361\n", "75% 5.147612\n", "max 447.250000\n", "Name: avg_word_length, dtype: float64" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.avg_word_length.describe()" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig = plt.figure(figsize = (15, 5))\n", "ax = sbn.kdeplot(data = df.essay_len, clip = [0, 8000], color = '#E7523E', shade = True)\n", "plt.xlim(0, 8000)\n", "plt.xlabel('Number of Characters')\n", "plt.ylabel('Frequency')\n", "ax.get_legend().remove()\n", "plt.tick_params(axis = 'y',\n", " which = 'both',\n", " labelleft = False,\n", " length = 0)\n", "ax.spines['right'].set_visible(False)\n", "ax.spines['top'].set_visible(False)\n", "ax.spines['left'].set_visible(False)\n", "fig.suptitle('Distribution of Total Long Form Response Length', fontsize = 24)\n", "plt.savefig('Essay_len_dist.png', transparent = True)" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "image/png": 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7L/qyXgEoEhRpwOA7TEHz4NK3G2boNhd4WsFRgI8VfJ+3yRnk61PVBuCUuI7EFZxK9i1mtqbadjqey+lVmfP3BHVss07eMzuPvZm/bI6UmXXYKIiZra2g6fHc4Qda9n2272SY7E22+ztTtug6VcEpufm67OmLB+Tu3IZHUG4Kn2avWcsOe32e98xev7Sgr+Hz+LrIds7lO41upzyvPx8+bmhm5XmGyXej6GfU9iPE/M4j9lr2urQpZraBpLUlveWc+yx3oPD5O+Fw6yu4FUDu+FnZ9WktM1tFHdtWbafv9XT9e17BEaL226qvhdfVdfXDSX/JrnvlChpNGSx9Wa+kvm33AAwRFGnAIDKzWZJOC58+7pz7WzfHy3uEKtwxzp4ymXuaUnXO36N7krMPjsxTEB4ZPr6l4F5YkqTwiMb74dO57caRmY2V9INO3i87j72ZvxckvR3+ne9U0cXh4/uSnurFe/RGtrW/3Tq61tDM1lNbC5A3te/fWzkt3UnB9WiV+ToFp9vWKTgC1b6RkOwpjbPMbF21FZsdneooBbdnkKRNzezgLjL2poGDqvBxxQ5apFRY3LRvBCXr7wrms1TS/+tg3JikIzoaMbwO6Jbw6dFmlu9oncws1snOemf+o7ZTPbPrcL6j89lTHv8vfGxV0PJjrr8r+E7F1dagSW7OqKRjsu/TvhjsSth6Z7bRm9/k2Vb8rifT7Avn3OtquybvVDNrf03i18yszMz66xTkXq9Xob5s9wAMERRpwAAzs1FmNsvMrpd0j4LTyT5S2452d5xkZn81s73CZqyz017RzM5VcK2ak/RAtl+4M529+Py7fZ2PbqhXcITn8uzOsJmNNrNTFdzYVZIWd3B6XrbQONrM5mSPzJjZdxS0atnZKZTZVhnnh9dTdVuY4+jw6VwzOy8sCmVmY8PPNXsU6Og8jWgMhBsVtMYnSbeb2U7ZnVkz21HBOhRXMO+9PhLbgexRtPeccy90NqBzrkFtzZK3b0DkCQVHbRIK7sUVVdBa4H/zTOs+BfdYk6QrzOw4M/v6NDAzG2Nmc83sDvWuWfLXFBxpNkk3hkd1ZWZxM5uv4DvTYQt7YaF1Vvj0BDP7mQVN62dPo/yrgu9ePr+TtEzBaW2Pmdm83B19M1vTzA4PM27a0xlzzlUr+LFBarsdQ74i7dF2wz3bvvGO8Hm2Cfyfm9n/ZYvH8Mja9Wq7GfXR6p3FCrZVO0q6Klu8htvJkxTcZLs6/+jdEjGzcV102eXwMwU3S19f0qPh9y27DYqY2XpmdrSCdbpfTsPsh/Uqu93bxszW6o9MAApQRzdPo6Oj61mntps1N0v6LKerU9sNRZ2CnZsbJY3LM52Z6vjGy2e3m06Vgh2Z3Nc6uinqcTn9s0et3pd0eM4wD6kbN47NN1xuZgWNmmTnc5mCX/mz739+numOUbADlB2uUW03ev1AwT27OrxBraQpCnawnIKjiUvCHP/u6jPN6X9CznunO8j9rZv6huN1eKPZnP6Ts8P0Yn1aM5yP7HvUtVuXPpC0dm9y5RnHFNxM2kk6rZvj7JuzvMZ08pk6Sb/pYlopBddW5o5TqbYbK2e7K9uNt1hd3NQ3HG5eu2VanbPedLWOJSTdnzNuc7iOZP+el9NvfAfjb6a2myFn19Ovws8td9626+l6Ek7/zHbTWSPPcOu0G+7UPMNFJV2dM1xrOL+ZnO/IT/uyvis4Spe7TVwWvo+TdIa6uU3qYLqL9c157Kw7NGe83cL1LduvKVxGze3GmdRf24C+rFcKfqR5O+fz+0Jt2/YJ3V0e6mLbSEdH57fjSBrQv+Jqa7Z5nIJ/9u9KulPBaUZrOOcWOee+6uF0z5L0cwWnmb2pYKe6RMERuRslbeuc6+gmsH+Q9FsFR2ZMQYMAkzRAp8k4585W0GDEwwqO1DcqOJ3oQOfc/+QZp0LB/YouUXDkL6Kg5crzFNyH6eOOxgvHfV3Bfb3uU7BDv5KC+ct3fVtH0zhawa/6dyjYMSsP3/9OSTs5547sZPQB4YJbLkxXsPxyb2j9ioKbCU9zzr3Zj2+5nYKdOqntqFZX/qZg/S6RtKhdv9wjfE7BTbDzcs7VOefmSdojfP8lCo44JxTsjF6n4MjzT7uZrf30b1NwlPcBBc2lxxUUZ6cruGdVZ+tYs6TdJf2vgs8/o6BQuUvB9Vn/yhm8soPxn1bwY8JvFZxeWKPg+9eg4Lq1UyVt5pxrf31Yd+WO96lz7p088/GGgp35rPaNhmSHSzvnDlHwef9dwTyVK2gu/npJmzvnLuhl1ux7nKagMPqXgh9kYgo+i4Odc//bl2n3Ms+9Cq7nO0HBdXaNCpZRtYJldqykdZ1zH/Tje/Z6vXJBi8A7KrwVi4IfurLb9sFsAAXAADLnnO8MAAAMSeEpqA9K+sA5N9lzHAwTrFcAOJIGAEDvZRvYeKDToYCeYb0CihxFGgAAeZhZNGy0Z9fcxmnCBiX+qqB1yxZJ53oLiSGH9QpAVzjdEQCAPMKW/lpyXqpWcN1P9mbMGUk/cc5dMtjZMHSxXgHoCkUaAAB5hLc/+LGCIxsbKLgRfVxB662PSDrbOTdYNzrHMMF6BaArFGkAAAAAUEC4Jg0AAAAACghFGgAAAAAUEIo0AAAAACggFGkAAAAAUEAo0gAAAACggFCkAQAAAEABoUgDAAAAgAJCkQYAAAAABYQiDQAAAAAKCEUaAAAAABQQijQAAAAAKCCxAZquG6DpAgAAAMBQYL0dkSNpAAAAAFBAKNIAAAAAoIBQpAEAAABAAaFIAwAAAIACQpEGAAAAAAWEIg0AAAAACghFGgAAAAAUEIo0AAAAACggFGkAAAAAUEAo0gAAAACggFCkAQAAAEABifkOAAAoPJmqSjW99LRaP3hPrqlRrrlJrqVZrrlJag4eXVOTXGuLYuMnKL7mFMXXnKLYamspUpb0HR8AgCHNnHMDMd0BmSgAYGC41la1vPmqml54Wk3PP6nW996SnJMiUVkiIcXisng8fIzJYsHfikSU+fJzZaoqgglZRNEJE5UIi7b4mlMUm7RmMC4AAMXFej0iRRoAFKf0V1+o6bkn1PTCU2p+6Vm5hnopElFswmTFwgIrOn6CLNL1mfGZ2hq1LvlQ6SUfqvWTj5T+5EO5ulpJkpWPVHKXOUruupeiY5cf6NkCAKBQUKQBALon/cVnqr35ajU8dJ+UySgyeoxia/Tv6YrOOWWqKpRe8qGaX35OLa+/LEUiKt1qe6X2WKj4muv2w5wAAFDQKNIAAJ1LVyxV3S3XqP6BuyQnlWy2lUo23UqRcSvKrNf/R7r33su+UtNTj6r5uSflmhoVX2c9pfbcRyWbbyOLcnk0AGBYokgDAHQsU1OlutuvV909t0qtLUpstIXKtttFkVFjBj2La2xU0wtPqunJR5VZ9pUi41ZQavYCJXfdS1ZSOuh5AAAYQBRpAIBvytTXqf7um1V3541yjQ1KbLCxSmfuWhDXhblMRi1v/ldNTzys1vfeUnSlVTTqp79VYr3pvqMBANBfKNIAAG0aHrpf1VeeL1dbrfi601S2/W6Krjjed6wOtbz3lurvvFGZZV8pueteKj/wRzTjDwAYDijSAACSa2pU9WXnqOGf9yg2cXWV7bqXYqtM9B2rS665SQ3/vEdNTzyiyNjlNerHv1bJRpv7jgUAQF9QpAFAsWv96H1VnPF7pT/+QKUzdlbpzFmyaNR3rB5p/eg91d1xgzJffq6y7XfViEP/R5HyEb5jAQDQGxRpAFDMGh66T1WXnCmLxZWaf6Dia07xHanXXEuLGh/+uxr/8w9FRo7WyB/+UqVbzPAdCwCAnqJIA4Bi9I3TGyevqdSCgxQZOcp3rH7R+slHqr/jBqU/W6LSmbM06kf/K0uU+I4FAEB3UaQBQLEZDqc3dsWl08FRtYfvV3yd9TTmtycpMmq071gAAHQHRRoAFJOGRx5Q1UWnBac3LjhI8TXW8R1pQDW/+oLqbr1W0eXGasxRpyq26mTfkQAA6ApFGgAUi7q7/6qaK89TbNIaSi08eNic3tiV1o8/UO31l0uZtEb/6g8qmb6p70gAAHSGIg0AikHtrdeq9tpLFJ86XakFB8liMd+RBlW6cpnqrrtM6S8/08jDjlBylzm+IwEAkA9FGgAMZ8451d54pepuvlqJDTZRct7+w+76s+5yjY2q++vVannrNSXnLNKIA39UtJ8FAKCgUaQBwHDlnFPtNRep7o4blNhoCyXnLJJFIr5jeeXSaTXcd7uannpUJZttrVGHH6NIaZnvWAAA5KJIA4DhyGUyqrniXNXfe5tKNttGZbPnF32BlqvxiUfUcN9tik1eQ2OOOV3RUWN8RwIAIIsiDQCGG5dOq/qSM9Tw4N9UstX2Kttljsx6vb0ftlre/K9qb7pSsfETtNxxZysykib6AQAFgSINAIYTl25V1fmnqPGRB1S67S4q3WE3CrROtLz7pmqvvVSxCZO03OKzFBkx0nckAAAo0gBguHAtLao853g1Pf6wSnfcXWXb7uw70pDQ8vbrqr3uMsUmra7lFp+pSGqE70gAgOLW6yKNCxsAoIA451R14R/V9PjDKpu1FwVaD8TXnKLyfb+n1g/eUcXxv1amvs53JAAAeoUiDQAKSN3NV6vx4b+rdPtdVbrVTN9xhpz42lOV2ue7annnTVWc8GtlGup9RwIAoMco0gCgQDQ8dL9qb7xSiQ03U+l2s3zHGbISU9ZXau+D1fLWa6o48bfKNDb4jgQAQI9QpAFAAWh65XlVXfBHxVZbW8k9F9FISB8lpk5XasGBann9ZVWefJRcU6PvSAAAdBtFGgB41vrx+6o89WhFlhun1KJDZbGY70jDQmL9jZWcd4CaX31eFaf8n1xzk+9IAAB0C0UaAHiUrqpQxYm/lSKm8gMOU6Qs6TvSsFIyfVMl5+6r5pefVeUZx8ml074jAQDQJYo0APDENTWp8uQjla5YpvL9fqDomLG+Iw1LJRttobLd5qvpmf+o5s8X+o4DAECXOKcGADxwmYwqzz1BLW+/rtSi7yo2YZLvSMNa6RYzlFn2lervvlnRlVZRard5viMBAJAXR9IAwIOaay5W0xOPqGyXOUqsO813nKJQNmuu4lPWV83l56rp2Sd8xwEAIC+KNAAYZPX336H6O29QyWbbqGTLmb7jFA2LRJRacJCi41dW5ZmL1fLeW74jAQDQIYo0ABhEzW++qurLz1F8rXVVtts8mtofZJYoUfl+h8lKSlRx0u+UXvql70gAAHwLRRoADJJMTbUqz1isyMjRSi44SBaN+o5UlCIjRym1/w+Vqa1Rxcm/U6ah3nckAAC+gSINAAaBy2RUed5JylQsVWrvQ2hq37PYSiurfJ9D1Pr+O6o86w80zQ8AKCgUaQAwCOrvvFHNzz6usl3mKrbKRN9xICm+1lQlZy9Q87OPq+aqP/mOAwDA12iCHwAGWPNrL6nm2ksUnzpdJVvM8B0HOUo230bpZV+p/p5bFB2/ilKzF/iOBAAAR9IAYCBlqipVeeZxioxeTqm5+9JQSAEq22WO4lM2UM0V56vpxad9xwEAgCINAAZK9obVmepKpfY5VFZa5jsSOhA0zX+goiusqMoz/6D0l5/7jgQAKHIUaQAwQOpuvVbNLzyt5K7zFBs/wXccdMISJUrt812ppVkVpx0j19zkOxIAoIhRpAHAAGh65XnV3nCF4utvpMSmW/mOg26IjltByfkHqPWdN1R9xXm+4wAAihhFGgD0s3TlMlWd9QdFxo5Tas4irkMbQhJTNlDpjJ3U8MBdqv/H33zHAQAUKYo0AOhHLp1W1VnHK1Nbo9Teh8pKSn1HQg+V7jBbsdXXVvWlZ6nl3Td9xwEAFCGKNADoR3V33qjmV55TcvYCxVZa2Xcc9IJFIkotPFiRZLkq/niMMjVVviMBAIoMRRoA9JOW999R7fWXKz51uhIbb+E7DvogkipXap9DlVn2lSrPPl4unfYdCQBQRCjSAKAfuJYWVZ17oqysTMk99uY6tGEgNmGSkrPnq/mFp1V789W+4wAAighFGgD0g9qbrlLrB+8oueciRVLlvuOgnyQ22VKJjTZX3c1Xq/GZx3zHAQAUCYo0AOij5jdfVd1t1ymx4Rj1Q04AACAASURBVOZKTFnfdxz0IzNTcveFio6foKpzTlTrZ0t8RwIAFAGKNADoA9fUqKpzT1Rk1Cgld5vnOw4GgMUTSi36ruQyqjz993Itzb4jAQCGOYo0AOiDmmsuUvrTJUrutb+stMx3HAyQ6JixSu61n1rfe0s1f7nEdxwAwDBHkQYAvdT00rOqv/c2lWyxreKrreU7DgZYYsoGKtlihurvvlmNzz7uOw4AYBijSAOAXsjU1arq/JMVGbeCynbaw3ccDJKynecoutIqqjrvZKWXfuk7DgBgmKJIA4BeqL7iPGWWfaXUvP1liYTvOBgkFo8rtffBck2NqjznBO6fBgAYEBRpANBDjU8+qsaH7lPpjJ0VmzDZdxwMsui4FZWcPV8tr76gutuu9R0HADAMUaQBQA+kqypUddFpio6foNLtdvEdB54kNtxciWmbqPaGK9X82ku+4wAAhhmKNADogepLzpSrq1Nq3gGyWMx3HHhiZkrusbciy41V5Vl/UKam2nckAMAwQpEGAN3U+OQjanriEZXOnKXoiuN9x4FnVlKq1MKDlalcpqoLTpVzznckAMAwQZEGAN2QqatV9aVnKbriyirdegffcVAgYiuvqrKd9lTTU/9Ww/23+44DABgmKNIAoBtq/nKxMpUVSs7dVxaN+o6DAlKy5XaKrzVV1Vf9SS3vv+07DgBgGKBIA4AuNL/2khr+fqdKtthWsVUm+o6DAmNmSs7bX1aaVOWZx8k1NfqOBAAY4ijSAKATrqVZVReepsjo5VS2w2zfcVCgIqlypeYfoPQnH6n66gt8xwEADHEUaQDQidpb/qL0kg+V3GNvWUmJ7zgoYPHV11bJljPVcP8danz2cd9xAABDGEUaAOTR8uF7qrv1WiWmbaL4Wuv6joMhoGzH3RVdcWVVn3+K0lUVvuMAAIYoijQA6IDLZFR94WmykhKV7TrPdxwMERaLKbXgIGXqa1V1wR9plh8A0CsUaQDQgfr771DLm6+qbNe9FEmV+46DISS64niV7bSnmp95TA0P3OU7DgBgCKJIA4B20l99odq/XKzYGusoMW1T33EwBJVsMUOxNdZR9VXnq/WTj3zHAQAMMRRpAJDDOaeqS86US7cGjYWY+Y6EIcgiEaX22l8Wjany7OPlWlt9RwIADCEUaQCQo/Gxf6n52cdVtv1sRZcb5zsOhrDIyFFK7rmPWt95Q7U3X+07DgBgCKFIA4BQprZGNZefq+jKq6rkO9v6joNhIDF1uhIbba66W/6i5tdf9h0HADBEUKQBQKjmukuVqa5Ucs4iWTTqOw6GieRu8xUZs5yqzjlBmfo633EAAEMARRoASGp5+zU1/P3OoMGH8RN8x8EwYiWlSs07QOkvP1f15ef6jgMAGAIo0gAUPZdOq+riM2XlI1W2/WzfcTAMxSauptJtd1bjQ/ep8fGHfMcBABQ4ijQARa/hgTvV+u6bSs6aKyst9R0Hw1TpdrMUXWWiqi46XemKpb7jAAAKGEUagKKWrliqmmsvVWz1tRVffyPfcTCMWTSq1LwD5BobVXXBH+Wc8x0JAFCgKNIAFLWaP18o19Sk5O4LuScaBlx0+RVVtvOean7uCTU8cJfvOACAAkWRBqBoNb3yvBofeUClW++g6LgVfMdBkSjZfBvFVl9bNVf9Sa2fLfEdBwBQgCjSABQl19Ki6kvOVGTMWJVuu5PvOCgiFokotdd+kpmqzj1JLp32HQkAUGAo0gAUpbq7b1Z6yYdKzp4viyd8x0GRiYwao7LZC9Tyxiuqu+MG33EAAAWGIg1A0Ul/8Zlqb75K8XWnKb72er7joEglpm2i+NTpqr3hCrW8/7bvOACAAkKRBqDoVF95npRxSu46z3cUFDEzU3KPvWXJpKrOOUGupdl3JABAgaBIA1BUGp95TE1P/VtlM2cpMnqM7zgocpFUuVJzFqn1w/dUe8MVvuMAAAoERRqAouGaGlV92TmKrrCSSr6zne84gCQpvvZ6SmyyperuuEHN/33RdxwAQAGgSANQNGpv+YsyX36mst33lsVivuMAX0vO2kuRMWNVdd5JyjTU+44DAPCMIg1AUWj95GPV3XG9EtM3VXzyGr7jAN9gJSVKzdtf6S8/V81V5/uOAwDwjCINwLDnnFP1FefKojGV7TzHdxygQ7GJq6t06x3U8ODf1PjMY77jAAA8okgDMOw1PfOYmp9/UqUzd1VkxEjfcYC8SrffTdEVV1b1BX9UprrSdxwAgCcUaQCGNdfUpJorzlV0hfEq2WKG7zhApywWU3L+gcrUVqvq4jPlnPMdCQDgAUUagGGt7o7rlf7iM5XNni+LRn3HAboUW2lllW2/m5qeeFiNjz7oOw4AwAOKNADDVuvnn6r21msVX38jxVdby3ccoNtKtt5BsYmrq/rSs5Re+oXvOACAQUaRBmDYqrnyPMlMyV3m+o4C9IhFIkrO21+utVVV558il8n4jgQAGEQUaQCGpabnnlDT0/9R2bY7KzJqtO84QI9Flxun5Ky5an7pWdXff4fvOACAQUSRBmDYcS3Nqr78XEXGraCSLWf6jgP0WmKTLRVfa13V/PlCtX7yke84AIBBQpEGYNipu+smpT9bouRu82WxmO84QK+ZmZJz95XFYqo690S5dKvvSACAQUCRBmBYSX/5uWr/+mfFp05TfM0pvuMAfRYZMUrJ3Req5a3XVHfb9b7jAAAGAUUagGGl5uoLpIxT2ay9fEcB+k1i/Y0U32Bj1d50pVrefdN3HADAAKNIAzBsNL34jBoff0ilM3ZSdPRyvuMA/So5e4EsVa7Kc0+Ua27yHQcAMIAo0gAMC661VdWXn6PIcuNUutX2vuMA/S6STCk1d1+lP3pfNddd5jsOAGAAUaQBGBbq77lF6SUfqmzXebJ43HccYEDE11xXJZtvo/q7b1bTy8/5jgMAGCAUaQCGvHTFUtXeeKXia09VYp31fMcBBlTZznMUGbu8qs47SZm6Gt9xAAADgCINwJBX8+cL5VpaVLbrPN9RgAFniYRS8w9UpmKpqi87x3ccAMAA6FaRZmbrD3QQAOiN5tdeUuMjD6h06+0VHbu87zjAoIitMlGl2+2ixkceUMN//uk7DgCgn3X3SNpFZvaUmf3UzEYPaCIA6CaXTqv60rMVGTVGpTN28h0HGFSlM3ZWdMIkVV98htJLv/QdBwDQj7pVpDnntpF0gKRVJT1jZteZ2c4DmgwAutDwwJ1q/eAdlc2aK0uU+I4DDCqLRpWaf6Bcc7Oqzj9FLpPxHQkA0E+6fU2ac+4tSUdL+q2k7SSda2avm9n8gQoHAPlkqipVc+1liq22luJTp/uOA3gRHbu8krPmqvmlZ1R/322+4wAA+kl3r0mbZmZnSXpN0g6S9nTOrRv+fdYA5gOADtVcd6lcY0Nwg18z33EAbxKbbqX4WlNV8+eL1Prx+77jAAD6QXePpJ0v6TlJ051z/88595wkOec+UXB0DQAGTcvbr6nhH39TyRYzFF1hJd9xAK/MTMm5+8ricVWefYJcS4vvSACAPupukTZb0nXOuQZJMrOImSUlyTl3zUCFA4D2XCajqkvPlqVGqGzmrr7jAAUhMmKkknMWqfW9t1T71z/7jgMA6KPuFmkPSirLeZ4MXwOAQdXw0H1qfft1le2yp6y01HccoGAk1p2mxEabq+6Wv6j59Vd8xwEA9EF3i7RS51xt9kn4d3JgIgFAxzJ1Naq55iLFVl1NiWmb+o4DFJzkrvMVGT1GVeeeoEx9ne84AIBe6m6RVmdmG2efmNkmkhoGJhIAdKz2hivlaqpVtjuNhQAdsdJSpeYdoPQXn6n68nN9xwEA9FKsm8MdLulmM/skfD5e0qKBiQQA39bywTuqv+82lWy6lWLjJ/iOAxSs2KTVVbrtzmp86D41bLyFyrbewXckAEAPdatIc849bWZTJK0jySS97pyj+SgAg8I5p+pLz5aVlKl0h9m+4wAFr3S7WWp5501VX3S6EmtNpRVUABhiun0za0mbSZomaSNJ+5nZwQMTCQC+qfHRB9Xy2ksq22kPRZIp33GAgmfRqFILDpTSaVWee6JcOu07EgCgB7p7M+trJJ0uaRsFxdpmkrhqH8CAy9TVqubqCxSdMEmJjbfwHQcYMqLLjVPZ7gvU8tpLqrvtWt9xAAA90N1r0jaVNNU55wYyDAC0V3vTVcpUVWjEPofKIj05+A8gMW1Ttbz1mmpvvFKJaZsosfZ6viMBALqhu3s8r0jihHYAg6rlg3dUf88tSmyypWKrTPQdBxhyzEzJ3RcqMnK0qs4+nmb5AWCI6G6RNk7Sf83sfjO7M9sNZDAAxe3rxkJKy1S24+6+4wBDVqQsqdT8A2mWHwCGkO6e7rh4IEMAQHvZxkKSey6isRCgj2iWHwCGlm4dSXPOPSzpfUnx8O+nJT03gLkAFDEaCwH6X+l2sxSdMFnVF52u9Bef+Y4DAOhEd1t3PEzSXyVdHL60iqTbByoUgOKWbSwkOXsBjYUA/YRm+QFg6Oju3s//k7S1pGpJcs69JWmFgQoFoHjRWAgwcL7RLP8t1/iOAwDIo7tFWpNzrjn7xMxikmiOH0C/orEQYOAlpm2qxPRNVXvT1Wp65XnfcQAAHehukfawmR0lqczMdpZ0s6S7Bi4WgGKUbSykbKc9aCwEGCBBs/x7KzJ2nKrO/oMyVZW+IwEA2ulukfY7SV9KelnSjyTdI+nogQoFoPh8o7GQjWgsBBhIVlKi1MKDlampDq5Py2R8RwIA5Ohu644Z59ylzrm9nXMLw7853RFAv6GxEGBwxcZPUHLWXmp+4SnV3XmD7zgAgBzduk+amb2nDq5Bc86t3u+JABSdlvffprEQwIPEZlur5b23VHvtpUqsO02Jddb3HQkAoO6f7rippM3CboakcyX9ZaBCASgeLpNR9UVnyMqSNBYCDDIzU3LOIkVGjVHlmccpU1vjOxIAQN0/3XFpTrfEOXe2pB0GOBuAItDw4N1qeeu/Kps1l8ZCAA8iZcng+rRlS1X1p1PE1QwA4F93T3fcOOdpRMGRtREDkghA0UhXVajmmosVm7ymEtM29R0HKFqxCZNUtvMearj/DtXfe6tSsxf4jgQARa1bRZqkM3L+bpX0vqR9+j0NgKJSc/UFck0NSu6xt8zMdxygqJVsOVOt772tmqsvUGKd9RVfYx3fkQCgaNkAndbAuRIAOtX08nOqWHyESrfdRWU7zvYdB4CkTH2dai46TVaa1NjTL+MUZADom17/At2tIs3MftlZf+fcme1f6m0gAMOfa2nWV0d8V66pUSN/+htZPOE7EoBQ6wfvquaq81W65UyNOuJYjnIDQO/1egPak9YdfyJplbD7saSpCq5L49o0AD1Sd/v1Sn/6cXBPNAo0oKDEJq2u0u13U+N//qn6e27xHQcAilJ3r0kbJ2lj51yNJJnZYkk3O+d+MFDBAAxPrZ9+rNpbrlF8vQ0VX2td33EAdKB0mx2V/vgD1Vx9geJrrKPElA18RwKAotLdI2kTJTXnPG+WNLnf0wAY1pxzqr70bFkkquSu83zHAZCHRSJKzts/uH/a6b9XunKZ70gAUFS6W6RdI+kpM1tsZr+X9KSkPw9cLADDUeNj/1Lzi0+rdIfZiowc5TsOgE5EypJKLfquMrU1qjzzOLl0q+9IAFA0ut26Y3ivtBnh00ecc893MjgNhwD4hkxdrb76+UGyZEojDjtCFunub0QAfGp64SnV33adUnP31YiDf+I7DgAMJQPecIgkJSVVO+fOkfSxma3W2zcFUHxqr79MmaqK4J5oFGjAkFGy4eZKbLqV6u64QY1PPOI7DgAUhW7tKYWnOP5W0pHhS3FJfxmoUACGl5a3X1f9fberZLNtFFtlou84AHooudt8RVeZpKrzTlLrkg99xwGAYa+7P2fPkzRHUp0kOec+EU3vA+gG19qqqgtOlZWP5KbVwBBlsZjK9zlUikRUcdoxyjQ2+I4EAMNad4u0ZhdcvOYkycxSAxcJwHBSd8f1av3gXSV3XyArLfMdB0AvRUaPUWrhwUp//IGqLzxN3b2mHQDQc90t0m4ys4sljTazwyQ9KOnSgYsFYDhoXfKham++WvGp05VYd5rvOAD6KL7GOsGNrv/9D9Xfe6vvOAAwbHV5M2szM0k3SpoiqVrSOpKOdc49MMDZAAxhLpNR1QV/lMXiSs5e4DsOgH5SOmMnpZd8qJqr/qT45DWVmDrddyQAGHa61QS/mT3rnNukB9PlHAigyNXfd7uqLz1Lyb32U8lGW/iOA6AfZRrqVXPZ2XLNTRp36iWKrrCS70gAUIgGvAn+J8xss96+CYDikl76hWquuUix1ddWYsPNfccB0M8iZUmV7/d9qblZFaccpUxDve9IADCsdLdI215BofaOmb1kZi+b2UsDGQzA0OScU9XFZ8ql00ruuY+CM6YBDDfRcSsqtfBgtX74rqrOO1kuk/EdCQCGjU6vSTOzic65DyXtNkh5AAxxjY/9S83PPq6yWXMVXW6c7zgABlB8rXVVtsscNdx/h2pvvlojFn3XdyQAGBa6ajjkdkkbO+c+MLNbnHNc/Q8gr0xNlaovO1vRVSaq5Dvb+Y4DYBCUbDlT6c8/Vd1NVym+6mSVbrW970gAMOR1dbpj7nlKqw9kEABDX/VVf5KrrVVyzr6ySHfPpgYwlJmZknvuo9iqk1V53slqefdN35EAYMjrai/K5fkbAL6h6fmn1PjQ/SrdZkfFVlrZdxwAg8hiMaUWfU+RsjJVnHKU0pXLfEcCgCGtqyJtuplVm1mNpGnh39VmVmNm1YMREEDhyzTUq+qi0xVZfkWVbreL7zgAPIiMGKnUvt9XprpKlX88Wq6l2XckABiyOi3SnHNR59xI59wI51ws/Dv7fORghQRQ2Gqvv1yZpV8oNWeRLNbVpa4AhqvYyqsqtdd+annjVVVfcqa6cy9WAMC3sTcFoE+aX31R9ffcopLNtlZsIpeuAsUusf5GSn/xqRr+ea9iq66m1JxFviMBwJDDlf0Aei3TUK+q809SZMxYle20p+84AApE6cxdFZ86TTV/vlCNjz/sOw4ADDkUaQB6rebPFyr95edK7bW/rKTEdxwABcIiEaXmH6joKpNUec4Jan79Fd+RAGBIoUgD0CtNzz+phr/fqZKttldsEqc5AvgmiydUvv8PFBk5ShUnH6nWTz7yHQkAhgyKNAA9lqmtUdWfTlV0hfEq234333EAFKhIqlzlB/xQchlVnPBrpasqfEcCgCGBIg1Aj1Vffo4yVZVKzttfFo/7jgOggEXHLq/y/X+g9LKlqjjpd8o0NviOBAAFjyINQI80Pv6wGh95QKXb7azYyqv6jgNgCIhNmKzUwoPU+s4bqjrreLl02nckAChoFGkAui1duUxVF5+h6MqrqnTGzr7jABhCElM2UNlu89X0zH9Uc8W53EMNADrBfdIAdItzTtUXnS7XUKfUwT+RRaO+IwEYYkq3mKFM5TLV33e7osuvpNRe+/mOBAAFiSINQLc0Pny/mp7+j8p2mavoCiv5jgNgiCrbeU9lqitVc81FioxbQWXb7Og7EgAUHIo0AF1Kf/WFqi87R7FJa6hky+18xwEwhFkkotRe+6u2plpV552kyIiRKpm+me9YAFBQuCYNQKdcJqOqP50il04rudd+sgibDQB9Y/G4Uvt9X9GxK6ji1P/jZtcA0A57WwA6VX/vrWp+6Vkld5mj6HLjfMcBMExEypIqP+jHipSPVMWJv1HLe2/5jgQABYMiDUBeLe++qZo/X6j42uspselWvuMAGGYiI0ZqxME/lcUTWvaH/1Xrxx/4jgQABYEiDUCHMg31qjzzOFkyFZzmaOY7EoBhKDJ6jMoP/omUyWjZcf+r1i8+9R0JALyjSAPQoZrLz1H6syVKzT9QkVS57zgAhrHouBVUftBP5OrrVLH4l0pXLPUdCQC8okgD8C0Njzyghn/dp9Jtd1Z8tbV8xwFQBGIrrazyA3+odMVSLTvul8rUVPmOBADeUKQB+IbWTz9W9cVnKDZxdZVuN8t3HABFJLbqZJXv932lP/1Yy47/tTL1db4jAYAXFGkAvuZaWlR51h8kM6UWHiSLRn1HAlBk4quvrdQ+h6r1vbdVcdLv5JqafEcCgEFHkQbgazXXXarWd95Qcu6+iowa4zsOgCKVWGd9pebvr5bXX1bFKUdRqAEoOhRpACRJTc89ofo7b1TJZtsose4033EAFLnEBpsoOXdfNb/8rCpO+p0yjQ2+IwHAoKFIA6B0xVJVnneSoiutrLJZc33HAQBJUslGWyg57wA1v/q8Kk74jTIN9b4jAcCgoEgDipzLZFR1zglyDQ1KLTxYFo/7jgQAXyuZvqlSCw5SyxuvqOL4X9GYCICiQJEGFLm6269T88vPKbnbfEWXX8l3HAD4lsQGGyu19yFqeev1oHn+uhrfkQBgQFGkAUWs6cVnVHvd5Yqvv5ESG2/hOw4A5JWYOl2pRYeq9b23tGzxL5WpqfYdCQAGDEUaUKRav/hUlWcep+gKKyo1Z1+Zme9IANCpxJQNVL7v99T6wbta9vvDlamu9B0JAAYERRpQhFxTkyr/eLTU2qLUou/JSkp8RwKAbomvvZ7K9/++Wpd8qGW/P1zpqgrfkQCg31GkAUXGOaeqS85Q6/vvKLXgQEXHLu87EgD0SHzNdVV+wGFq/XSJlh3zc6W/+sJ3JADoVxRpQJGpv/dWNT50v0pnzlJ87fV8xwGAXomvvrbKD/yRMl99oaVH/lQtH77nOxIA9BuKNKCINP/3RdVc9SfFp6yv0m138R0HAPokPnkNlX/vZ3ItTVr2f/+j5tde8h0JAPqFOecGYroDMlEAvZde+oWW/uowKZHQyMOOkJWW+Y4EAP0iXbFUtX+5WJmqSo0+4liVbjHDdyQAkKRet8rGkTSgCLiWZlWedqwyjQ0q3/d7FGgAhpXomLEa8b2fK7rieFWedqzq/36n70gA0CcUaUARqL7sHLW89ZpS8/bnhtUAhqVIqlwjDvmp4mtOUfXFZ6jmxis1QGcLAcCAo0gDhrn6B+5Sw4N3q3TGTkpMne47DgAMGEuUKLXf95XYaHPV3XSVqi86XS7d6jsWAPRYzHcAAAOn+fWXVX3Z2YqvOUWlO8z2HQcABpxFo0rO3U+REaPU8ODdylRWaPQvj5WVlPqOBgDdRsMhwDDV+slHWnrkT2QlpRrxg8MVSaZ8RwKAQdX45KNquPdWxVZfW2N+eyL3hQQw2HrdcAhFGjAMZaoqtfTIHytTW6MRPzhc0eXG+Y4EAF40v/6K6m65RpFkSqN/e6ISa0/1HQlA8aB1RwAB19SkipOPVHrpVyrf7wcUaACKWmLK+hr5g8MlMy079udqeOh+35EAoEscSQOGEZdOq/L036vp6X8rtei7Sqw7zXckACgImbpa1d10lVrff1upufuq/IAfyqJR37EADG+c7ghAqr7yfNXffbPKdt1LpVvO9B0HAAqKS6fVcO9tanr630ps/B2NPvwYRVLlvmMBGL4o0oBiV/e3v6rmivNUssW2Ss6e7zsOABSspqf/o/p7blV0/Coa87uTFVt5gu9IAIYnijSgmDU++agqTztG8SnrK7XPd2URLjcFgM60vP+26m68UpJp9K8Wq2T6Zr4jARh+KNKAYtX81mtaduwvFF1hJY045P/JEgnfkQBgSEhXLFXd9Zcp/cXnKt/nEKUWHMR1agD6E0UaUIxaP/tES4/8sSwW14jv/0KR8hG+IwHAkOKamlT/t5vV/OIzSqy/sUYdfrSiY8b6jgVgeKBIA4pNetlXWnb0z5SpqdKI7/9c0XEr+o4EAEOSc07NLzyl+r/dokgypVGHH6OSaZv4jgVg6KNIA4pJunKZlh37C6W//FwjDv6JYqtO9h0JAIa89Befqvamq5X56nOlFhyk8n0O5fRHAH1BkQYUi0xNlZYd+wu1frpE5Qf9SPFJa/iOBADDhmtuUv3fblHzC08pPnW6Rh9xrKLLjfMdC8DQRJEGFINMXY2W/f4ItX70nsoP+KHiq6/tOxIADEtNLzyl+rv/Kisr0+hfHKOSDWn9EUCPUaQBw12moV4Vx/1SLe++qfL9vq/4WlN9RwKAYS39xWequ/lqpb/8TMk99taI/X4gKynxHQvA0EGRBgxnmcYGVZzwG7W88YpS+xyqxLrTfEcCgKLgmptVf//tan7mMUVXmahRPztKibXW9R0LwNBAkQYMV66pSRUn/07Nrzyv1MKDlFh/Y9+RAKDotLzzhurvuF6Z6mql5u2v8n0OkcW5LyWATlGkAcORa2lWxR+PUfPzTyo5b3+VTOeaCADwxTU2qP6+29T8/FOKTVxNo352FNcGA+gMRRow3LiWFlWeuVhNT/1byT0XqWTTLX1HAgBIan7jVdXfdaNcfZ3KFx6s1PwDZbGY71gACg9FGjCcZBobVHnasWp+4SmV7TZfpd/Z1nckAECOTH2dGu65Vc0vP6vYamtp1M+PUnzi6r5jASgsFGnAcJGpqVbFSb9Ty1v/VXLPfVSyCUfQAKBQNf/3RdXffbNcY4NSey5Sau+DFSkt8x0LQGGgSAOGg/TSL7Xs+F8p/cnHQSMhU6f7jgQA6EKmrlYND9yp5uefUmTcChr5/V+oZLOtZdbr/TMAwwNFGjDUtX7ysZb94X+VqaoI7oPGxegAMKS0fvCu6v92s9Kff6rEJltq5Pd/odiK433HAuAPRRowlLW8+6Yqjv+1XLpV5Qf8ULFVJvqOBADoBZdOq+nJR9Twr/sk54KGReYuorl+oDhRpAFDVfOrL6ji5CNliRKVH/RjRZdf0XckAEAfZaoqVX//7Wp59QVFV15VIw87QiXTNvEdC8DgokgDhqLGp/+jyjN+r8jo5TTioB8rMmqM70gAgH7U8vZrqr/nVmWWfqmSzbbWiAN/pNiESb5jARgcFGnApEzsNgAAGT1JREFUUFP/4N9UffHpio6foPIDfqhIqtx3JADAAHAtLWp8/CE1/fsfci3NKttpD5Xvc6iiY8b6jgZgYFGkAUOFa21VzdUXqP6eWxRbYx2VL/qurKTUdywAwADL1NWq8eH71fT0Y7J4XKm99lNyz30UKUv6jgZgYFCkAUNBpqZKlacvVvMrz6nkO9upbJc5smjUdywAwCBKL/1SDQ/erZb/vqjIqDEq3/d7Kttxtiwa8x0NQP+iSAMKXcsH76jylKOUXvqVknvurZKNtvAdCQDgUetH76vh73eq9cN3FV1lokYccJhKNttGFon4jgagf1CkAYWs8fGHVXXeSVKiROX7flexCZN9RwIAFADnnFreeEUND9ytzFefKzZpdZUvPFgl39mOYg0Y+ijSgELkMhnV3nSV6m6+WtEJk1S+6HuKjBzlOxYAoMC4dFrNrzyvxkf+rsxXXwT/MxYerNKttue0eGDookgDCk2moV5V55ygpqf/o8SGmym5xz6yeNx3LABAAXOZjFpefUGNjzyg9BefBi0ALzxYpTN25Jo1YOihSAMKScuH76nyzMVKL/lQZbvMVcl3tpVZr7+nAIAi4zIZtbz+shoful/pzz9RdMWVlZp/oMq221kWT/iOB6B7KNKAQuCcU/29t6rmzxfKSkqVmn+g4mus4zsWAGCIcpmMWt58VY0P36/0Jx8rMno5JXfdS8ld5ioyarTveAA6R5EG+JauXKaq80/5/+3deXAc533m8e9veg4cPEASPECCJMRDlEiK4mFRJiXKkiWrLFs+di0nVpzLSSXZxJW1K1U5K5uteGs3m9orTuwoSpyN4yPW4Vib+JBsxbJkyyJ1UOIhiqRIiRQJnuCFG5iZnt/+0Q1gCIEEQBLoEfF8qrr6eqfnBV69RT3o9+0m/8rzZJYup+aj95OaNDnpaomIyFXA3Sm+sZfeLc9Q2LcbMhmqb7ubmnvvI7NgUdLVE5GhKaSJJKln62Zav/BneFdnNLxx/a0a3igiImMibDlOz5Yfk9/+EhTyZFe9i9p7P052zXo9EVKksiikiSTBe3tp/8oDdD3xGMGcudR+7BcIZjUkXS0REZkASl2d5LdupueFZ/G2cwRz51Nzz7+n+rb3aSSHSGVQSBMZb4UD+zj3fz5HeOQQuQ3vofrOe/X0RhERGXcehhRe20bP5mcIjxyCTJaqDe+h5q4PkVm+SiM7RJKjkCYyXrxQoPNfH6LjkS9j1TXUfvTnyCy5LulqiYiIUDzWTH7rFvI7XsJ7ewgaGqm5616q7ng/wdRpSVdPZKJRSBMZD/ld22l98H8SHjlEZvmN1Nz7cVK1k5KuloiIyHk8nyf/2jbyW7dQPPQmBAG5m26l5q4Pkl21Tu9cExkfCmkiY6nUeo72rz5A94+eIFU3neoPfIzsshVJV0tERGRYYctxerduIb/9Rbyrk9TUaVTd8l6qNt1FZun1Gg4pMnYU0kTGgpdKdD/1OO1ffQDv6qJq4+1UveduLJtLumoiIiKj4sUihdd3kd/5MoXXd0GxSDC7gapN76N6052kG5uSrqLI1UYhTeRKK7z1Bm0P/m8Ke18lvWARNR/6uJ7cKCIiVwXv6Sa/ewf5nS9TfPN1cCfdtITqTXeR23g7af17J3IlKKSJXCmlznY6vvk1ur77KJarovruD5O98Sa9e0ZERK5KpfY28rteIb/zZcLmtwBINy2h6uZN5NZvIr1wkYZEilwahTSRy+W9PXQ+/i06v/V1vLOD7Jr1VL/vw3owiIiITBjhmVMUdu+ksGcnxcMHwJ1gVgO5mzdRdfMmMteuwIIg6WqKvFMopIlcKi8W6X7qe3Q88mVKZ0+TWbqcqjs/QLqhMemqiYiIJKbU3kZh7y4Ke3ZQeHMfhEVSU+rI3XQLuTU3k121Tn/IFLk4hTSR0fJSiZ7NT9PxjS8RHjtCev41VN11L5mmxUlXTUREpKJ4Tw+F/a+R372T4r7deG8PpFJklq0kt2Y9uTU3k25aoqkBIudTSBMZKXcnv/0l2r/2IMUD+6InW935wWgIh8bci4iIXJSHIWHzWxT276awfzfh0WYAUlPqyK5ZT27NerI3rCOom55wTUUSp5AmMhwPQ3pfeJbOf3mIwr7XSE2bQdUd7yd7wzr95U9EROQSlTraKbyxh+K+PRTe2IN3dQIQzFtA7oa1ZFesJrtiDampdQnXVGTcKaSJXEipp5vuHz1B17cfJjxxjNT0enIbbie39t1YOp109URERK4aXioRHmumeGAfxQP7KRx6E/K9AKTnX0N25WqyK9eSXb6K1BSFNrnqKaSJDBaeO0PX44/R9cRjeEc7QWMTVbfcQea6G3TnTEREZBx4GBIePUzhwD6KB/dTPHQACnkAgrnzyV63ksyylWSXrSSYt0D/PsvVRiFNpE+x+SCd336U7me+D8UimetWUrXxDtILFiVdNRERkQnNi0XCo4coHHyDsPkgxcMH+4dHWs0kMstW9Ae3zOJlpGpqE66xyGVRSJOJrdTdRc9zP6L7h9+lsHcXpDNkV99E1YbbCepnJV09ERERGYK7UzrdQvHwAYqHDxIePkh48lh00oygYT6ZJcvILL6OzJJlpJuWkKqqTrbSIiOnkCYTj7tT2LuL7h9+l56fPoX39pCaOTt6d8vq9Xp3i4iIyDtQqbuLsPktikcPEx49RPFoM952LjppKdKNC6PAtmgZmabFpBcuIlU7OdlKiwxNIU0mjvDcGXqe/j5dT32P8MghyObIrlxDbs3NBPOb9Bh9ERGRq0yprZXiscOERw73hzfv7Og/n6qfTeaaJaQXLibTFK2DOXM1x02SppAmV7ew9Sy9z/+Eni0/Jv/qyxCGpBcsIrtmPdkVa7BcLukqioiIyDhxd7y9lfD4UYrHjxCeOEp44iilUych/n9by1URNDaRnt9Eev5C0o1NpBubCGbNUXiT8aKQJlef8HQLPc//mJ7Nz1DYvRO8RGrGTLLXryK7ej3BzNlJV1FEREQqiBfyhCePEx6PQlvYcpzw1Am8rXWgUDZHet6CKLzNW0h6biPB3PkEc+ZpvptcaQppcnUoHmum94Vno2C27zUAglkNZK5fRWb5jQSzGzScUUREREal1N1F6dSJKMC1HCdsOUGp5Til1nPnlUtNryc9dwHB3EbSc+cTNDSSnjOXYGaDRu3IpVBIk3emUmc7+Z2v0Lv9RfLbXux/olPQ0Ejm+lVkl9+oO2YiIiIyJjzfS3jmFKXTLYSnTkbr0y2UTp/Eu7vOK5uqm04wZy7B7GhJz26ItmfNIVU3AwuChH4KqWAKafLO4MUihf276d0WhbLC/j3gJcjmyFyzlPTiZWSWXk8wvT7pqoqIiMgEVurqpHT6JKWzZwjPnqZ09hSls2conT1Nqe1c/9w3AIKA1PSZBDNnx8scgvpZ/evUjJmkqmuS+2EkKQppUplK3V0UXn+Nwt5Xye95lcLeV/Ge7ujdJ/MWkFkcvfskaFyov0CJiIjIO4IXi5Raz1I6c4rSuTPRdutZSufOUmo7F4W4Uum8z1h1Danp9VF4m14fhboZM6Nj0+tJ1U0nNXUalk4n9FPJGFBIk+S5O+HJ4xT2RmEsv3snxUMHojtlZgSzGgjmN5FZdC3pa5aSqqlNusoiIiIiV5yHId7eRqn1LGHrGbytlVJbaxTg2lujc+2tbwtymGGTpxDUTSc1rZ7UtOnRdhzgUlPr4vU0UpOnKtBVPoU0GV8ehoTHmikc2Efx4H4KB/ZROLBv4OlJ2RzpxoWk519DesE1pBsXYnpikoiIiAgAXirhHe1RcOtoi4JbRxuljvZ4ux3viI4RhkNewyZNJjVlGqm6KLSlpkyN1pOnxNt12JR4f/JUrKZWD2AbXwppMnZKrecoHnmLYvNbFA7up/jmPgqH3oDe3qhAEER3yebMI5g7n/SCawhmNegdJCIiIiKXyd3x7i68q4NSZwfe2RGFu3i71NkeHevupNQVlXvbHbo+lsJqJ5GaNDkKeJOnkKqdTGrSlGi/dlJ0vnZyvJ6E1fStazU1ZfQU0uTyeBgSthyneOQQ4ZFDFJvfioPZIbyjrb+c5aqiMNYwL143EtTP1u12ERERkQrg7nhPN97ViXd34l2dlLo68c5OvKcL7+6m1N0ZlenuipZ4m2FygVVVR2GtuoZUTRzcauLt6poozFVVY9U1/UuqqgarOX+fbHai3NFTSJOLc3e8rZXiyWPRyx1PHiM80bccJTx98rxb6TZpMsGMWaTqZxHUzyaYOYtU/WxSddMnSqcSERERmTC8VIJ8L6We7ii0lS/d3XhvvM73Rsd6e+KlNzrX0wP53pF9maWwqqoo0PUt1TWk+versFwVlivbrqqO1/HxXC7az+biczksW1VpAVAhbSLzMIzGM59uITxzKnq/x5mWgfd+nG6hdLoF7+0573M2aXI0EbVuOsG0GaSm1RPMnE2qfpYe6iEiIiIio9IX9Ly3F8+Xhbh8b/82+Xg/34vn83i+F+K1F/JRmULfuTyExdFVwiwKatkqLJuNQlxfmMvFx3JVkM0NnO9f595+PJMdOJaJj2ey0Xdkslg2C+nMhYKhQtrVxEul6NZ0eyul9mgiadh6llJb/GjX1rPR417PnYn229uiJyiWSwX9k0etbxJp3XRS02cQ1M2I7ojlcsn8gCIiIiIiI+BhCIUosHkhDnWFPF4o4IV8fK6AF3ohPub5PBQLA2XibQoFvG970PkLzuMbqUwmCm3pTH+Am/mFr19ySNNEojHQPxa4b6xvVwelzs6BCZ9dnfE63u9op9T3RJ/2Nryj/e2hq082F0/qnExq0iTSM+dEkzsnTY4eyzqlLgpmtZP04A4REREReUezIICgesyfEu5hOBDcigOBjmJxyHUU9IoQxvvFYvT5YnGg7GWYkCHNw2KcpvMDt2D7lt6yW679t2bjpaenf8xttB+Nxy31TbrsW3p7hp14idnAGNxcFVZTS2raDNLzFmDVtf0TLFM1tdF+35N4srr7JSIiIiJyJUVhMIiGQlaAMQlpxRPHoodQlIpRKg1DKIV4Mdr2UhglzLAYnQvj7WIYl+/bLw6k0r50W5ZOvViMglZfmb7t826BxsfLti/5dmYQlI1B7Rubmonubk2bgc2e2z9pMVrnoG+CY1V1NBkyF/8lIJvVnS4REREREXmbMQlpp37rE1f+omYQpPtTLkGABWlI9x0bWEcpOBc9FjSdicqk09Gkvr7PpDOQDrB0Nh5DmomOxdukowBmmQz0TRLUuyFERERERGSMjUlIm3T/r0bD/VIpSAVRuOnbTqWi7fLAlUphqbLt/iCWjkJVEOiuk4iIiIiITAhj8nRHM3sV6Bm2oCSpHjiVdCXkgtQ+lU9tVPnURpVN7VP51EaVT21U2arcfeWlfHCsHhzS4+7vGqNryxVgZi+pjSqX2qfyqY0qn9qosql9Kp/aqPKpjSqbmb10qZ/VGEIREREREZEKopAmIiIiIiJSQcYqpP3tGF1Xrhy1UWVT+1Q+tVHlUxtVNrVP5VMbVT61UWW75PYZkweHiIiIiIiIyKXRcEcREREREZEKcskhzcyqzOwFM9tuZrvM7E+HKGNm9pdmtt/MdpjZ2surrozGCNvodjNrNbNt8fInSdR1IjOzwMxeMbPvDHFOfagCDNNG6kMJM7ODZrYz/v2/7Ula6kfJGkH7qA8lzMzqzOybZrbHzHab2YZB59WHEjSC9lEfSpCZLSv73W8zszYz++ygMqPuQ5fzCP5e4L3u3mFmGeBZM3vc3beUlbkHWBovNwMPxGsZHyNpI4CfuPu9CdRPIp8BdgNThjinPlQZLtZGoD5UCe5w9wu9K0j9KHkXax9QH0ra54En3P0+M8sCNYPOqw8la7j2AfWhxLj7XmA1RH/UBY4Ajw0qNuo+dMl30jzSEe9m4mXwBLePAF+Jy24B6sys4VK/U0ZnhG0kCTKzRuCDwJcuUER9KGEjaCOpfOpHIhdgZlOA24C/B3D3vLufG1RMfSghI2wfqRx3Am+4+1uDjo+6D13WnLR4CNA24CTwpLs/P6jIPOBw2X5zfEzGyQjaCGBDPCTycTNbMc5VnOj+Avg9oHSB8+pDyRuujUB9KGkO/MDMtprZrw9xXv0oWcO1D6gPJWkR0AL8Qzys+0tmVjuojPpQckbSPqA+VCk+AXxjiOOj7kOXFdLcPXT31UAjsN7MVg4qYkN97HK+U0ZnBG30MrDQ3W8E/gr4f+Ndx4nKzO4FTrr71osVG+KY+tA4GWEbqQ8l7xZ3X0s0nOTTZnbboPPqR8karn3Uh5KVBtYCD7j7GqAT+INBZdSHkjOS9lEfqgDxUNQPA48OdXqIYxftQ1fk6Y7xbdengfcPOtUMzC/bbwSOXonvlNG5UBu5e1vfkEh3/x6QMbP68a/hhHQL8GEzOwg8BLzXzL42qIz6ULKGbSP1oeS5+9F4fZJoHsD6QUXUjxI0XPuoDyWuGWguG2nzTaJQMLiM+lAyhm0f9aGKcQ/wsrufGOLcqPvQ5TzdcaaZ1cXb1cBdwJ5Bxf4V+MX4iSbvBlrd/dilfqeMzkjayMzmmJnF2+uJ/ps4Pd51nYjc/Q/dvdHdm4hujz/l7j8/qJj6UIJG0kbqQ8kys1ozm9y3DdwNvDqomPpRQkbSPupDyXL348BhM1sWH7oTeG1QMfWhhIykfdSHKsb9DD3UES6hD13O0x0bgH+Mn2KSAh5x9++Y2X8AcPe/Ab4HfADYD3QBn7qM75PRG0kb3Qf8ppkVgW7gE643nCdKfajyqQ9VlNnAY/H/n6SBf3L3J9SPKsZI2kd9KHm/DXw9Hq71JvAp9aGKMlz7qA8lzMxqgPcBv1F27LL6kKkNRUREREREKscVmZMmIiIiIiIiV4ZCmoiIiIiISAVRSBMREREREakgCmkiIiIiIiIVRCFNRERERESkgiikiYgIAGb278zMzey6pOtyMWb2GTP7i7L9B83s38r2f9vM/vISr91kZoPfs3bB41eSmf3ReH6fiIhULoU0ERHpcz/wLNGLuy9b/I7GsfAcsLFsfzUwtez7NgI/HcmFzOxy3hd6pf3R8EVERGQiUEgTERHMbBJwC/CrxCHNzO4xs0fKytxuZt+Ot+82s81m9rKZPRp/HjM7aGZ/YmbPAh83s18zsxfNbLuZ/XP8wk/MbLGZbYnPfc7MOsq+53fj4zvM7E+HqO4rwLVmVm1mU4leDLoNuCE+vxF4zsxWx9+xw8weM7Np8fWfNrP/ZmbPAJ8xs3Vx/TYDnx7l722dmT1jZlvN7Ptm1lD2HX9uZi+Y2etmtik+XmNmj8R1etjMnjezd5nZfweqzWybmX09vnxgZn9nZrvM7AdmVj2auomIyDuXQpqIiAB8FHjC3V8HzpjZWuBJ4N1mVhuX+VngYTOrB/4YuMvd1wIvAb9Tdq0ed7/V3R8CvuXuN7n7jcBuohAI8Hng8+5+E3C074NmdjewFFhPdIdsnZndVl5Rdy8ShbKbgHcDzwNbgI1mNhcwdz8MfAX4fXdfBewE/nPZZerc/T3u/r+AfwD+o7tvGM0vzMwywF8B97n7OuD/Av+1rEja3dcDny377t8CzsZ1+i/Auvhn+gOg291Xu/sn47JLgS+6+wrgHPCx0dRPRETeuRTSREQEoqGOD8XbDwH3x2HoCeBD8bDADwL/QhSMlgM/NbNtwC8BC8uu9XDZ9koz+4mZ7QQ+CayIj28AHo23/6ms/N3x8grwMnAdUVgZ7KdEd8w2ApvjZSPR3cDn4jtsde7+TFz+H4HysPcwwBDlvjrkb2doy4CVwJPx7+GPgcay89+K11uBpnj7VuLfs7u/Cuy4yPUPuPu2Ia4hIiJXuUoaiy8iIgkwsxnAe4kClQMB4Gb2e0Rh5tPAGeBFd283MwOedPf7L3DJzrLtLwMfdfftZvbLwO3DVQf4M3d/cJhyzwG/AVQBXwRaiIJjCyObj9ZXRwN8BOWHYsCui9yB643XIQP/3toort9bth0CGu4oIjJB6E6aiIjcB3zF3Re6e5O7zwcOEN31eRpYC/waA3fItgC3mNkS6J9nde0Frj0ZOBYPDfxk2fEtDAzfK39QyfeBXymb4zbPzGYNcd3niO7ozXT3k+7uRAHtI8Bz7t4KnO2bCwb8AvDM4Iu4+zmg1cxujQ99cnCZi9gLzDSzDXFdM2a2YpjPPAv8TFx+OQPz6AAK8e9JREQmOIU0ERG5H3hs0LF/Bn7O3UPgO8A98Rp3bwF+GfiGme0gClwXemz/fyKaM/YksKfs+GeB3zGzF4AGoDW+9g+Ihj9ujodIfpMo6J3H3c8ShbJdZYc3A7OA7fH+LwH/I67jauBzF6jjp4Avxg8O6b5AGYBlZtbctxAFwvuAPzez7UTz5DZe5PMAf00U7HYAv0803LE1Pve3wI6yB4eIiMgEZdEfH0VERMZP/JTHbnd3M/sE0Ry4jyRdr7EWvyYg4+49ZrYY+CFwrbvnE66aiIhUEM1JExGRJKwDvhDPbzsH/ErC9RkvNcCP4mGNBvymApqIiAymO2kiIiIiIiIVRHPSREREREREKohCmoiIiIiISAVRSBMREREREakgCmkiIiIiIiIVRCFNRERERESkgiikiYiIiIiIVJD/D2zUVDOFh/hCAAAAAElFTkSuQmCC\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig_2 = plt.figure(figsize = (15, 5))\n", "ax = sbn.kdeplot(data = df.avg_word_length, clip = (3,7), color = '#E7523E', shade = True)\n", "plt.xlim(3,7)\n", "plt.xlabel('Average Word Length')\n", "plt.ylabel('Frequency')\n", "ax.get_legend().remove()\n", "plt.tick_params(axis = 'y',\n", " which = 'both',\n", " labelleft = False,\n", " length = 0)\n", "ax.spines['right'].set_visible(False)\n", "ax.spines['top'].set_visible(False)\n", "ax.spines['left'].set_visible(False)\n", "fig_2.suptitle('Distribution of Average Word Length', fontsize = 24)\n", "plt.savefig('Avg_word_len_dist.png', transparent = True)" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig_3 = plt.figure(figsize = (15, 5))\n", "ax = sbn.kdeplot(data = df.rate_of_i_me, clip = (0,0.125), color = '#E7523E', shade = True)\n", "plt.xlim(0,0.125)\n", "plt.xlabel('Rate of \"I\" / \"me\"')\n", "plt.ylabel('Frequency') \n", "ax.get_legend().remove()\n", "plt.tick_params(axis = 'y',\n", " which = 'both',\n", " labelleft = False,\n", " length = 0)\n", "ax.spines['right'].set_visible(False)\n", "ax.spines['top'].set_visible(False)\n", "ax.spines['left'].set_visible(False)\n", "fig_3.suptitle('Distribution of Rate \"I\" and \"me\" Are Used', fontsize = 24)\n", "plt.savefig('rate_i_me_dist.png', transparent = True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Normalize your Data!\n", "\n", "\n", "In order to get accurate results, we should make sure our numerical data all has the same weight.\n", "\n", "\n", "For our Zodiac features, we used:\n", "\n", "\n", "```\n", "feature_data = all_data[['smokes_code', 'drinks_code', 'drugs_code', 'essay_len', 'avg_word_length']]\n", "\n", "\n", "x = feature_data.values\n", "min_max_scaler = preprocessing.MinMaxScaler()\n", "x_scaled = min_max_scaler.fit_transform(x)\n", "\n", "\n", "feature_data = pd.DataFrame(x_scaled, columns=feature_data.columns)\n", "```\n", "\n", "**Normalization Model**\n", "\n", "In our case we are going to use a StandardScaler as there are some datapoints which are extreme outliers so this method reduces their impact" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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smokes_codedrinks_codedrugs_codeessay_lenavg_word_lengthrate_of_i_me
01.551600.192311-0.4572070.221055-0.031698-0.051420
1-0.421151.5379831.956334-0.370349-0.2485041.446319
2-0.421150.192311-0.457207-0.7756030.482377-0.806581
3-0.421150.192311-0.457207-0.4732520.848358-0.930372
4-0.42115-2.499033-0.4572070.0719030.0183821.140107
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" ], "text/plain": [ " smokes_code drinks_code drugs_code essay_len avg_word_length \\\n", "0 1.55160 0.192311 -0.457207 0.221055 -0.031698 \n", "1 -0.42115 1.537983 1.956334 -0.370349 -0.248504 \n", "2 -0.42115 0.192311 -0.457207 -0.775603 0.482377 \n", "3 -0.42115 0.192311 -0.457207 -0.473252 0.848358 \n", "4 -0.42115 -2.499033 -0.457207 0.071903 0.018382 \n", "\n", " rate_of_i_me \n", "0 -0.051420 \n", "1 1.446319 \n", "2 -0.806581 \n", "3 -0.930372 \n", "4 1.140107 " ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import sklearn.preprocessing as preprocessing\n", "feature_data = df[['smokes_code', 'drinks_code', 'drugs_code', 'essay_len', 'avg_word_length','rate_of_i_me', 'sign_code']]\n", "feature_data = feature_data.dropna()\n", "sign_labels = feature_data.sign_code\n", "feature_data = feature_data[['smokes_code', 'drinks_code', 'drugs_code', 'essay_len', 'avg_word_length','rate_of_i_me']]\n", "\n", "\n", "x = feature_data.values\n", "standard_scaler = preprocessing.StandardScaler()\n", "x_scaled = standard_scaler.fit_transform(x)\n", "\n", "\n", "feature_data = pd.DataFrame(x_scaled, columns=feature_data.columns)\n", "feature_data.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Use Classification Techniques\n", "\n", "\n", "We have learned how to perform classification in a few different ways.\n", "\n", "\n", "- We learned about K-Nearest Neighbors by exploring IMDB ratings of popular movies \n", "- We learned about Support Vector Machines by exploring baseball statistics\n", "- We learned about Naive Bayes by exploring Amazon Reviews\n", "\n", "\n", "Some questions we used classification to tackle were:\n", "\n", "\n", "- Can we predict sex with education level and income??\n", "- Can we predict education level with essay text word counts?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Split the Data into Training and Test sets" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [], "source": [ "from sklearn.neighbors import KNeighborsClassifier\n", "from sklearn.model_selection import train_test_split\n", "\n", "train_data, test_data, train_labels, test_labels = train_test_split(feature_data, sign_labels, test_size = 0.2)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Classification Teqniques\n", "\n", "First we will use a K Nearest Neighbors classifier and get the score for the test data for k from one to two hundred" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [], "source": [ "kn_classifier_scores = list()\n", "for k in range(1, 200):\n", " kn_classifier = KNeighborsClassifier(n_neighbors = k)\n", " kn_classifier.fit(train_data, train_labels)\n", " kn_classifier_scores.append(kn_classifier.score(test_data, test_labels))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Plot the results" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots()\n", "fig.set_size_inches(15, 6)\n", "plt.plot(range(1, 200), kn_classifier_scores, lw = 2)\n", "plt.xlim(0, 200)\n", "ax.set_yticks([0.1, 0.11, 0.12, 0.13, 0.14, 0.15, 0.16])\n", "ax.set_yticklabels(['10%', '11%', '12%', '13%', '14%', '15%', '16%'])\n", "plt.xlabel('Number of K Nearest Neigbors')\n", "plt.ylabel('Accuracy of Classification Model')\n", "fig.suptitle('Accuracy of classifying Zodiac Sign vs. No. of K Neighbors Used', fontsize = 18)\n", "plt.savefig('Zodiac_accuracy.png', transparent = True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Removing no response data\n", "\n", "We think that our accuracy being so much higher than chance is just a correlation with no response to the essays and no response to zodiac sign\n", "\n", "The solution is to see the correlation if we remove the " ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\sully\\miniconda3\\lib\\site-packages\\pandas\\core\\generic.py:5303: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame.\n", "Try using .loc[row_indexer,col_indexer] = value instead\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", " self[name] = value\n" ] } ], "source": [ "feature_data2 = df[['smokes_code', 'drinks_code', 'drugs_code', 'essay_len', 'avg_word_length','rate_of_i_me', 'sign_code']]\n", "feature_data2.sign_code = feature_data2.sign_code.replace(0, np.nan)\n", "feature_data2 = feature_data2.dropna()\n", "sign_labels2 = feature_data2.sign_code\n", "feature_data2 = feature_data2[['smokes_code', 'drinks_code', 'drugs_code', 'essay_len', 'avg_word_length','rate_of_i_me']]\n", "\n", "\n", "x = feature_data2.values\n", "standard_scaler = preprocessing.StandardScaler()\n", "x_scaled = standard_scaler.fit_transform(x)\n", "\n", "\n", "feature_data2 = pd.DataFrame(x_scaled, columns=feature_data.columns)\n", "\n", "train_data2, test_data2, train_labels2, test_labels2 = train_test_split(feature_data2, sign_labels2, test_size = 0.2)\n", "kn_classifier_scores2 = list()\n", "for k in range(1, 200):\n", " kn_classifier2 = KNeighborsClassifier(n_neighbors = k)\n", " kn_classifier2.fit(train_data2, train_labels2)\n", " kn_classifier_scores2.append(kn_classifier2.score(test_data2, test_labels2))\n" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots()\n", "fig.set_size_inches(15, 6)\n", "plt.plot(range(1, 200), kn_classifier_scores2, lw = 2)\n", "plt.xlim(0, 200)\n", "ax.set_yticks([0.082, 0.084, 0.086, 0.088, 0.09])\n", "ax.set_yticklabels(['8.2%', '8.4%', '8.6%', '8.8%', '9.0%'])\n", "plt.xlabel('Number of K Nearest Neigbors')\n", "plt.ylabel('Accuracy of Classification Model')\n", "fig.suptitle('Accuracy of classifying Zodiac Sign vs. No. of K Neighbors Used', fontsize = 18)\n", "plt.title('Excluding \"No Answer\" as a Possible Zodiac Sign')\n", "plt.savefig('Zodiac_accuracy2.png', transparent = True)" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "DescribeResult(nobs=199, minmax=(0.0798425639583919, 0.09263424233904977), mean=0.08620838236727636, variance=7.2677095125672475e-06, skewness=-0.4197467446441147, kurtosis=-0.5196244906968683)\n", "Ttest_1sampResult(statistic=15.044335813953879, pvalue=1.3020767849862312e-34)\n" ] } ], "source": [ "print(stats.describe(kn_classifier_scores2))\n", "print(stats.ttest_1samp(kn_classifier_scores2, 0.0833333333))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Use Regression Techniques\n", "\n", "\n", "We have learned how to perform Multiple Linear Regression by playing with StreetEasy apartment data. Is there a way we can apply the techniques we learned to this dataset?\n", "\n", "\n", "Some questions we used regression to tackle were:\n", "\n", "\n", "- Predict income with length of essays and average word length?\n", "- Predict age with the frequency of \"I\" or \"me\" in essays?\n", "\n", "\n", "We also learned about K-Nearest Neighbors Regression. Which form of regression works better to answer your question?" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " income age\n", "0 -1 22\n", "1 80000 35\n", "2 -1 38\n", "3 20000 23\n", "4 -1 29\n", "5 -1 29\n", "6 -1 32\n", "7 -1 31\n", "8 -1 24\n", "9 -1 37\n", "10 -1 35\n", "11 40000 28\n", "12 -1 24\n", "13 30000 30\n", "14 50000 29\n", "15 -1 39\n", "16 -1 33\n", "17 -1 26\n", "18 -1 31\n", "19 -1 33" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df[['income', 'age']].head(20) #let's take a look at the data" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.19190604877723283\n" ] } ], "source": [ "income_feature_data = df[['essay_len', 'avg_word_length', 'income']]\n", "income_feature_data.income = income_feature_data.income.replace(-1, np.nan)\n", "income_feature_data = income_feature_data.dropna()\n", "\n", "print(str(float(len(income_feature_data)) / len(df)))" ] }, { "cell_type": "code", "execution_count": 46, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig = plt.figure(figsize = (15, 3))\n", "ax = sbn.kdeplot(income_feature_data.income, color = '#297828', shade = True)\n", "plt.xlim(0, 200000)\n", "ax.set_xticklabels(['$0', '$25k', '$50k', '$75k', '$100k', '$125k', '$150k', '$175k', '$200k'])\n", "ax.set_yticks([])\n", "ax.spines['right'].set_visible(False)\n", "ax.spines['top'].set_visible(False)\n", "ax.spines['left'].set_visible(False)\n", "ax.get_legend().remove()\n", "plt.xlabel('Income')\n", "plt.ylabel('Frequency')\n", "fig.suptitle('Income Disribution of OKCupid Profiles', fontsize = 24)\n", "plt.savefig('Income_distribution.png', transparent = True)\n" ] }, { "cell_type": "code", "execution_count": 51, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[-2.18997615e+00 1.01371491e+04]\n", "-0.0030584891384495268\n" ] } ], "source": [ "from sklearn.linear_model import LinearRegression\n", "from sklearn.neighbors import KNeighborsRegressor\n", "\n", "X_train_income, X_test_income, y_train_income, y_test_income =\\\n", " train_test_split(income_feature_data[['essay_len', 'avg_word_length']], \n", " income_feature_data['income'], test_size = 0.2)\n", "\n", "lr_income = LinearRegression()\n", "lr_income.fit(X_train_income, y_train_income)\n", "print(lr_income.coef_)\n", "print(lr_income.score(X_test_income, y_test_income))" ] }, { "cell_type": "code", "execution_count": 52, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{'algorithm': 'auto', 'leaf_size': 30, 'metric': 'minkowski', 'metric_params': None, 'n_jobs': None, 'n_neighbors': 100, 'p': 2, 'weights': 'uniform'}\n", "-0.014355700539905534\n" ] } ], "source": [ "knr_income = KNeighborsRegressor(n_neighbors = 100)\n", "knr_income.fit(X_train_income, y_train_income)\n", "print(knr_income.get_params())\n", "print(knr_income.score(X_test_income, y_test_income))" ] }, { "cell_type": "code", "execution_count": 54, "metadata": {}, "outputs": [], "source": [ "X_train_age, X_test_age, y_train_age, y_test_age = train_test_split(df.rate_of_i_me, df.age, test_size = 0.2)" ] }, { "cell_type": "code", "execution_count": 60, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "pandas.core.series.Series" ] }, "execution_count": 60, "metadata": {}, "output_type": "execute_result" } ], "source": [ "type(X_train_age)" ] }, { "cell_type": "code", "execution_count": 61, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[-16.77863845]\n", "0.0008748294597701323\n" ] } ], "source": [ "\n", "lr_age = LinearRegression()\n", "lr_age.fit(np.array(X_train_age).reshape(-1, 1), y_train_age)\n", "print(lr_age.coef_)\n", "print(lr_age.score(np.array(X_test_age).reshape(-1, 1), y_test_age))" ] }, { "cell_type": "code", "execution_count": 63, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "-0.011678923051111179\n" ] } ], "source": [ "\n", "knr_age = KNeighborsRegressor(n_neighbors = 100)\n", "knr_age.fit(np.array(X_train_age).reshape(-1, 1), y_train_age)\n", "print(knr_age.score(np.array(X_test_age).reshape(-1, 1), y_test_age))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Analyze the Accuracy, Precision and Recall\n", "\n", "\n", "After you have trained your model and run it, you will probably be curious about how well it did.\n", "\n", "\n", "Find the accuracy, precision, and recall of each model you used, and create graphs showing how they changed.\n", "\n", "\n", "For our question of classifying Zodiac signs, one graph we produced showed classification accuracy versus `k` (for K-Nearest Neighbors):\n", "![accuracy vs k](https://s3.amazonaws.com/codecademy-content/programs/machine-learning/capstone/Zodiac_Accuracy.png)\n", "\n", "\n", "The accuracy we would expect from predicting a Zodiac sign by randomly selecting one would be 1/12, or 0.0833. Our model did not significantly outperform this number. We were unimpressed.\n", "\n", "Precision and Recall are not very useful for the dataset as the accuracy is already low.\n", "\n", "\n", "### Create your Presentation\n", "\n", "\n", "We want to see:\n", "\n", "\n", "- at least two graphs containing exploration of the dataset\n", "- a statement of your question (or questions!) and how you arrived there \n", "- the explanation of at least two new columns you created and how you did it\n", "- the comparison between two classification approaches, including a qualitative discussion of simplicity, time to run the model, and accuracy, precision, and/or recall\n", "- the comparison between two regression approaches, including a qualitative discussion of simplicity, time to run the model, and accuracy, precision, and/or recall\n", "- an overall conclusion, with a preliminary answer to your initial question(s), next steps, and what other data you would like to have in order to better answer your question(s)\n", "\n", "\n", "Good luck!" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "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": "3.7.7" } }, "nbformat": 4, "nbformat_minor": 4 }