solutions/4_Matplotlib_1_Solution.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Matplotlib Challenge Problem"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1. Create a pie chart like the one below of the number of bachelor's degrees awarded in different fields in 2014"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "majors = [ \"Agricultural Sciences\", \"Biological sciences\", \"Computer sciences\", \n",
    "          \"Earth sciences\",\"Mathematics and statistics\", \n",
    "          \"Physical sciences\", \"Psychology\", \"Social sciences\",\"Engineering\"]\n",
    "number_of_degrees = [28796,109520,56130,22226,6730,22222,118405,177936,93950]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Bachelors Degrees awarded in 2014')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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OgfN03K6204CeQHegC1CAu9Ys3Pbi+L8YcAg4EPwdDP7vxW1kKAaK196w9lCTXZDH04zxxiQ91DTNdaGkrwHtgK44/5IqY/JIHTIfD/4vxz0Qq7MM+EOwpfdvZrZK0jRgoZltBTCz/QnqzQSGBwYLoFPc6OEpMysDyiS9i3v4Tgf+amZ7q8msSc5i4P/JhWZ53Mx21nGdaWHkfSPPAM7HOS+ehTMgg4DcWqqlo939OMOyBVgFrBhfUrrsf2/ecrgx2/V4mhvemDQSciFMfg2MM7MdkiKc7Ch4rA4RVQ6JCR32zGyhpKk4Z8H7Jf0Y13uua3teFjDRzEqq6RvfZny7NTkeJpQDzJX0FHAJsFTSTDPbVIdOKTHyvpFtcP43E3EGZDzQLZ1tpEDX4O883GiRGw4dXk2koA2wFCgCniFyqEmMqscTFt6YNB5VhmNvMN1zLVDTdt0juOmWpAmc+94ys3sktcc55H0PuFvS+6qmuRKMTqocCX8cyBljZquomeeBJyT91Mz2xclMKEfSEDNbC6yVNBE3ddZgYzLyvpGDcIbzEtxUXNuGymwUzKLjSstOx8UWOwu4AYBIwQbgadx04EIih0Kb/vN4GoM6jYkkAx4ws48H73OAXcDLZjarlnpjgD5m9s/gfQQ4amY/SYfidSFpEHCBmf05eD8OuN7MvlhbvXpSfc3kaTObI+kenJPgNty01DRJX0lQ/2HgnmDRuyrMyplyHt01eb9PA74afB6VwGVmtkfS/wKLJO3Fxae6qFq93wN3ysWzygG2BxsBEmJm6yV9D1ggKYpzSrwR+CLOcK0J5CzEBXi8TdKFuJHNBpzjZL0Yed/IMbjYXleQWnyx0Mg3e6Od2RkJTg0P/v4TKCFS8C/gQeApIofKEpT3eDKKOp0WJR3FzQdfYGYlkj4I/ADYWYcxuRE3xXNr8D5C0xqTacBXatOxqUnlHlS/fw0tV9/yTc3GocN6VWTzsY9/Jfv6WJZGha1PqgwtK1/06NvvTE6hykHc+tiDwHwih2KNo5nH07gkuzX4X7gpBoDrgIeqTkgaL2lJsA10iaSzJOUBd+JiTK1SkJMDt2CbaOtpbTktfihpuaR/B21V1b88KDNILnfHiuDvgkDsXGBKIPN2udwdTwZ1TsmzoSRyX0i6TNLLwbX+W+/l2KhtW+3XJW2W9G/ctMcpSPpQ0O5que2+p9y/ZO+zTt4GnIzc+PKn5AqR1F7SU8H7dXGfZdrYOHRYzsahw67YOHTY/wE7cqP8ZMImq0h3O03BhJLSVI1BZ1zwyeeBbUQK/ptIQVjrPx5PvUnWmDwMfERuUXkU8HLcuU24rajnAN8Cvm9m5cHrR4I8GVU7l4biIriOB74tKVd157SYb2ZjcesK38VN21yFeyhCMJVjZucGcn4RHJ8DFAXt/7Ta9STKs5FM7otFwITgWh/GBTWsItG1jcVN05yDy1NyXuLby7eAi4McHZfXcP9Suc+pyI3nlFwhwAeAt81stJmdjZv3Twsbhw7rsHHosK8A23Fh6y8nmHq9ZnEsI6d+CktKujegen/g+8AOIgX3EikYmSa1PJ5GJ6kFeDNbE6xBXAf8s9rpAuA+OV8Jo/atmIm2ntaV06Lq4bUWKDOzCrkgf4OC47nAr+TWaKI4v4i6OCXPhlzej7pyX/QDHpFLVpWH8zeo7dqmAE+Y2XEASTV5oS8G5kn6C+9tCa5OKvc5FbnxJMoVshb4iaQf4nxhGpxWeOPQYd1xARtvwfl7nEK/vYxpU25HS/OUUjrhUDErH+UW3xtKW+BTwKeIFLwIfI/IoefTINfjaTRS8YD/Oy7a7EPVjn8HeDHotV5GzXkyoOatp/cFPeUxZnaWmUWCMvE5LU7k7wgSP1UZwtuB3bgshONwD/m6OGW7a5C4aTQwH/eQuzdBvV/iHBFHAp/l5GtNdG1UbycRZnYzLuptf2CVpETTHKnc51Tk1iXjNZyxXwv8QNK3UpVRxcahw7ptHDrsR7iRyDeowZAACNpdssxW17etMGhv9npect+/VLgQ+DeRgheJFKSyFuPxNCmpGJM/AHcG2z7jKQDeCl7fGHc82e2u9clpUb39XYGB+TguHHpd7Z+SZ0PJ5b6Iv9YbktBtIXCVpLZyDn2XJSokt532ZTP7Fs7Dun8C/VO+z0nKjeeUXCGS+gDHzewBXGci5ZwgG4cO67Rx6LAI8AbwVZwTZ5188NVY89z+WwNnlFfsbUTx04AiIgVPEymoabrU4wmNpI2Jme00s58nOPUjXI91Me89yAFexC24xy/AJ5Jbn5wW8fwauEHSUtwUV5Uz4BqgMlg4rr6YnijPRjK5LyLAo5KKcA/nWjGzFThP91W4dLc1TRH9OFj0X4czQKs59f7V5z4nIzeeU3KF4PLNvxLcl6/j7l1SbBw6LHvj0GFfwE0HfhvolGxdgE7HGdP9kEtTnAlMKilpilh3FwOvECl4iEhBnyZoz+NJCp/PxNMobBw67DzgtzQwu+H8kVrw61nZhenRqnF55K1drw8vr0jHmkmyHMFtRPkZkUOVTdiux3MKGRk1WO/lD1kn6VFJ7eS2CK9Lg+ybJV1fz7pHG9j2OEm/qLtk8+XFwus7bhw67G5cKJEGp8m9YIP1a7hWTYDZ0aHlFYObuNWOuAgEq4gUTEun4Ljf2GrFbbmX1EdSrYnXFLcNv55tL6lnvYgSOwWnIuNexeUW8iRPRhoTgsCKwWJ0Oc7zOi2Y2W/N7E/pkpdi2682kod+k3D3zS9ctmHoDesPdD6jkDR9t/KiDBm6I72xvRqDglisOCu839MI4EUiBfcQKUjX7req39ho3JTvDwDM7G0zu7b2qg3DzC6ou1Sjtf3pYOrdkyKZakziKcJFiAXIlnSPpPWSng0WvodIOpHvXNIZkpYHr+dK2iDnuPiT4NiJ3o2k0+WcE6t6Z0PkHB6fD96vlXRFbcrV5PQn6Tw558PVcg6bHXWyY2V7OUfIZXKOilcEx2+U9LikpyVtkfSjuLYS5TKpSc4IvecoukZuy3G9uPvmFzrdffMLfwT+jtR/9cjP58WUlTanw9kLY7vTJauxGF5efjBsHYBP40YpE9IstxMu/H6Vk/C64HUbvef8u1IujM5JyG2o+VvwHVsquagGcvlwngu+r7+TtF1uE8xJI3xJXwvkr5Y0Nzh2U/B9Xi2XU6fWDR2q5rwbHMuW9BO957j8heD4iSynkt4v6aVAx0flYuwhaZukO+KeAUOD46c4Q9ch55TnTyaT0YEe5eJSfZD3fFHOAK4zs5vkfCuuMbMHJB3SewENP4HzveiKc34camYmqXOCJh4E5prZE3IOm1m4kdBVZnY4+PIvlfR3q3nxqcrp79JA5wI5T/RHgNlmtkxSJ6B69N2vAy+Y2ScD3V6R86IH52B5Dm478mY535hSEucyqUnOzcDPzezBQJ/4Rf2kufvmFyYG9+l9Vcdi2XlnbBx6/fwRG+dNq4/M6gzbYcOzYlYZy1Kz/b5OOV7aqKHuU2AIsIhIwfeBOxuwllIVb64NbkPM9ARlbgEws5HBA/VZSdX9vO4AVprZlZKmA3/CfX+/jfte/kDSB4DPVBcuF7rpSuB8Mzse951+3MzuCcp8F+eT88tarqXKefetuN/5Z3Df2XPMrFLVcv8Ev+1vADPN7Jik/8LFVatylt5rZudK+jwuh8+niXOGDmR0qUmOXNSJup4/GUWmjkyqvuivAm8C/xsc32rvRcCNzwNyL/AJuTAts4E/A4dxD+B7JV0NHI9vQG4rb18zewLAzEoD50MB35fbefZv3C6w02rRdS0wUy4szBQzO4QLq7LLzJYFsg+bWfUf/fuBOcF1zsf9qAcE5543s0NmVooLpjgQmEDiXCY1yXkJ+J/gyz3QTg0lXyd3zZ711cqytXcRZ0iq2N1z3MSj7fu8karMRGQZPaast5XpkNVYTDle0pzWdrJxD7YlRAoG1VNG1TTXUFyH6E/Se8lrAiYD9wOYSzOwnVOdhuPLvAB0k1QQHH84OP40wcinGjOBP1Y5/cZ9p8+WC6G0FhctY0Qd11LlvHsT73WaZgK/rfrd2anRtSfgAnMuDn47N+B+Z1Ukyjc0E7i7qkDgu1aTnFqfP5lIs+3p1cEpyaiUOB9HlZ/CYwQ9IWC5me0L6ozHeeB/BOd3Et/7qv7DqeJjQA9gbOCNv41aHAjN7DW5sCqX4Lb2PosLHVLXNjrhRlabq13n+Qmus7a8IwnlABslvYyLufaMpE8HP/Y6uWv2rALc9ukrK48/tz87Z+AuZXc6eTu3lL9izG1Hpyz+mqnme5k0Vy2JVS4Y2Tz7PjLbP6iyckDdJZuc84BXiRR8mMihpD7bRJjZS0EPu0e1U8l8ronKWAp1E32n5wFXmtlqucCl02oTYmY3B7+bS3HOu2NqkR3f9nNmdl0N5xPlG0oks0Y5dTx/Mo7m+etMM0EP/hngNwQxt4J5ywJzIfJvww294+scBnZKujIonx/MzRYA7waG5EJO7q2cghI7/W0C+kg6LyjTMZiyi+cZ4AtVvUFJ59RxmS8BhZLeF5SvGrYnlCNpMPCGmf0CF90gqQi9d82eNQbXG7syONS17Mj9e8xi0eplK3PbjyoefOWiZOTWRe/9nNOu1Jplitxu0VhaRmCNRDfgWSIFpwQuTZZgCisb2Fft1EKCOHrB9NYAoHqnJb7MNNz00GFcnLsPB8ffT+JoCM8Cnwx+d/Hf6Y7ALrksox9LUK+6/omcd58Fbq763VWf5sLtRpwk6fTgfLsEU3iJ9D3JGbomOXU9fzKRVmFMAh7E9RqqYm51BJ4MpqsW4MKyVOfjwBeDMkuAXoGccZJexX2R69ppdIrTn7mAi7OBX8o5TT7HqaOb7+Dib62RW/D8Tm2NmNke3Dzw44HMqiCONcmZDawL9BqKm8uulbtmz7ocdx+GnNx42aiKY/9IaDTe7D9zdGl+lwY7HgraXPZybE1D5TQGo8rKGrQlvAnIBv4fkYL7iRQkG1WgrdzmjFW479INQby2eH6N2/SyNihzYxCfLp4I7veyBhfJuypyxB3A++U2x3wQlyPpSHzFYPrr78CrgR5V236/iQs2+xzJJV5L5Lx7L26KfE3we/lotbb34CJNPBTovpS6c+qc4gxdi5xknj8ZRatxWpTboVVgZt8MW5dM5K7Zsz5D8PCooUgst/2sVdl5Z57iW5Jfuv+VSUu/Ob6hOhxpw5pP3Z7T7HKc3LFn3ytXHz3W4OtrIpYBlxI5tCdMJSTlA9Fg8Xsi8JvqU9eezKJVjEwkPYGLhpsoHIynDu6aPes7wO+ofcdXVsWxp/pZ7NgpD6myNl3H7+hbuLShenQoZeRp+63Z5VKfVFKaSiy5sDkPWEyk4JRNE03MAFyk8NW41Ac3hayPp4G0mpGJJ3Xumj0rC7fOdMq2zRpRu+X5BZ8995SdPxbbM2Xxf+XmVh5v0BbIRcO14BdXNJ/wKllmu1Zv25FKLLnmwi7gIiKH1oetiKdl0CpGJp7UuWv2rGzcOkryhgTAjo+tOP7MqTnlldVj5egvNDjczfmbrFmNAnpVRt8MW4d60htY4CMQe9KFNyaeU7hr9qxc3IJqnTtlEhEr3zA5WrGteqoCjnYcMPnd7qMb5C+SG2PQ2dtizaY3fW5ZWcr+Oc2IbsDz3qB40oE3Jp6TCAzJ48A1DRCTU3H0ia4WKzklxMj64Z/sGs3KbdADePbCWPUtqqFReLwkmZw9zZmOwNM+RbCnoXhj4jnBXbNnCZcEbVbDpVnfsiMPVvc5wLJyBq4dcdMrDZF85luMzIlaeUNkpIuJJaVD6i7V7OmK80Wpd3w2j8cbE088PwL+I23SYofPrzj+4inrJ/u7Dp98sNPgekcCFnSZtib88Co5ZtsLYrGMj6kU0AuXHrh/2Ip4MhNvTDwA3DV71u285xSWNqJlKyfEKt/aeNJBKXvV6FuJKaveCZ2ueCkW+jbE/hWVb9VdKqMYgDMo3cJWxJN5eGPiYeecomsm97z6MuqOF1Yf8suPPNrOrOwk7+ZYdv7QzWdet7i+Qnse4twOxy1RcMAmY3xpadrC7DcjzgQeJVKQqXH7PCHhjUkrZ+econHA/X3bn3HhB/t++qUssquHw9EYvUQAACAASURBVEgDsYHlhx86JRTKrl4Txx9re9r2+kgU5F35UuyUHWNNSeHxkkTxpFoCF+IcCT2epPHGpBWzc05Rb1zso7YAnfK6XXD5gFs25WW1SXuP32L7J1WULCk66aDUdsU5t1cP/Z00M1Zb9eB8TYdZdFxpWVPme29qPkek4HNhK+HJHLwxaaXsnFOUjcvrcpL3dn5229GXD7jlYMecLml3xouWLh0bq3y3OP5YRV7Hc94YdEm9Igu3L+PsfntsW1qUS5F8szfamtWa4a8F8AsiBadkT/R4EuGNSevl29SQByJbOe/7YL+b2p3WZmCDPdar0a78yENmVnGSn8m2gZecXZbXqV6BB2cvjG1Li2YpMrii8p0w2m1icoBHiBT0ClsRT/PHG5NWyM45RTNw4fBrRFL3wl6zhwzpOKbBARpPJnp6+ZG/vFqtsc4rxtz+en2kjX3dhhBCgLkJJaWh7yZrInoA84gUNDjBmadl441JK2PnnKKqnCx1fvaS2o7t9v7xY7tdfGqsrQZg0d1TKkuXL4k/VtKu58S3e01M2ZkxJ0b/c4qtyfOcFB4vqZ51sCVzMS0g34ancfHGpBWxc06RgAeoPWf9SUjKOr3TmMLpvT+2UOiUbIr1pbJkwdmx6P6TdnJtOuuj/Suz2xxOVdaHi2Ip12kQZmWjylr04nsifkCkwOcb8dSINyati8/gck6nTI82/abO6n/z8mzlHkuTLp3KDz94zCz6XlgUZfVeNeqW1akKGvwOo3IrrTRNetVJe7PXc132ytZEHvBQCpkaPa0Mb0xaCTvnFPUBftgQGe1yOo2/YsCtb7bN7rA7PVpVDK84+vhL8UcOd3rf5H1dh6c0bSUomLmy6cKrnFle3mwCTTYxQ3Epcz2eU/DGpPXwS6CgoUJys/KGzer/ucoueafVa8G8OrHKHYWVZeveWyuRtObsz3SIKScl58nLX441mcf2pJLS1vy7+QqRghFhK+FpfrTmH0WrYeecoquAq9MlL0tZfS/qc0OPfu3OXJEOeZXHnz3dood3Vb23rNzB64Z/IqVdZF2PcE7BUWuSvOZTj5dkYmbFdJEL/Nbv7vJUxxuTFs7OOUWdgF+lW66kggt6XjlyeOcL6uVwWI2uZUfu32MWO7HAv7f76AsOd+i/JWl9IOfqJbGNdZdsIGZHzyqvCDt/ethMBj4ZthKe5oU3Ji2fbwJ9GkOwpNyRXaZMvqDnlQ3fOmxloyqO/eO9cCtS7soxXyo3FEtWxLQ11ujbdQtisdez/O8G4EdECrqHrYSn+eB/FC2YnXOK+gO3NnY7/dufVXhx308uziKrQQmrYhXFU6Plr52YOovmtB2x5fRrkx75tK1g2KB3rLjukvVnRFn5ocaUn0F0pQ7HV0/rwhuTls2dQJumaKhzXo9Jlw24ZX1uVn5DHrZZFcee6mexYyfWPnb2LTy3pE33nckK+MjCWNJl68OUktK8xpSfYXyOSMGAsJXwNA+8MWmh7JxTdDZwfVO22Sa73TlX9L91b4eczg14oFvPssP3v2lVIVKkDsvPuT3pOFij37AzZZb01FiqTDle0q+xZGcg+cAdYSvhaR54Y9JymUsIn292Vs6QS/rdlN+jTf8N9RZix8dWHH/mxDpMeX7ncdv7X7Sktion2jd6j99sKTs+JoPM9g2srPRpbU/m40QKhoWthCd8vDFpgeycUzQJuDSs9qWsHhf2um7Q4A6jXq6vjFj5hsnRim0nkl8VD778rPLcDkk5C167OJYuL/2T6B6Nbm0MuRlONvC9sJXwhI83Ji2T/wpbAUntxnX/wHnndJ1R351eORVHn+hqsZKDTmBWt5VjbtucTMUB7zImr8KO17PdGhlVVn403TJbCFd5R0aPNyYtjJ1zis4CZoWtB7ggkWcWjCu8sNdHFgD1WMewvmVHHjxhQI61733BOz3HvVpbDQBBhw8sT394lcLjJS09GVZD8FGFWznemLQ8bgealXdyz7YDC2f1++yybOWkPlqIHT6/4viLC6vebhx6fa9oVl6d01iXvhJL+y62SSWlg9ItswXxH0QKeoathCc8vDFpQeycU9SdJt7BlSztczuff8WAW7e2yW6fcsiTaNnKCbHKtzYCWFZ2v9UjP7e8rjqdjzGm22FLWzbELLNdPaNR/7CsmXzglrCV8ISHNyYti88DzTZEeG5W/ojL+n+utCC3xxspVs0rP/JoO7OyIwAHO58x+UDnM9bXVkGQfc2iWFJrLMnQuzK6ve5SrZ7PESloEr8mT/PDG5MWws45RTk4Y9KsyVJ2/4v7fqJrn3anr0qtZmxg+eGHXGh6KWv1yM/nxZRVUVuNKestbWFkzi0tbbJ8KRlMD+C6sJXwhIM3Ji2Hi0khg2KYSOo8uefVw4cWnL84lXoW2z+pomTJIoBYdt4ZG4deX2v9/ErOOP0tS8voZNrxkk7pkNMKuCFsBTzh4I1Jy+HjYSuQCpLyRnedNmlCj8vmp1IvWrr03Fjlu8UAu3uOm3i0fZ9ap8w+sjDW8HUTMzu/tHRIg+W0DqYSKRgYthKepscbkxZAEGb+irD1qA8DOwyf9v4+Ny4StU9ZxdGu/MhDZlZRgpS/YsxtRw2spsJnb7dhWTFrUO76XNheELMGJxZrJYgM69h40oM3Ji2Da2migI6NQZf80yZfPuDza5MPEhk9vfzIX14FqMxtP6p48JU1RhbOMnpO3Ggprs+cTP+KyrcaUr8V0ix3FHoaF29MWgYZ3xNsk93+3Mv73/Ju+5yCpB7cFt09pbJ0+RKAN/vPHF2a32VXTWWvWRxLKQVwdcaXllY2pH4r5AwiBRPCVsLTtHhjkuHsnFPUGygMW490kJOVe8al/T6T0z2/X1IZEytLFoyMRfdvR+q0/Jz/3FFTub77OKdtmR2pr16Fx0u61LduK+basBXwNC3emGQ+H6CZebw3BCnrtOm9PzpgUIezX0mieMfyww8eN4uWl7XpOn5H38KEeeMFbS9dVs9IwmbRsaVlp9erbuvm8rAV8DQt3phkPh8MW4F0I6n9+O6XjB3d9cKFdZeuGFZx9PGXALacfu2Qipx2BxOV+sCrsfb10aWNWXFbMx+TK0UqLLvtz79x4xlh6+FpOrwxyWB2zinKBmaGrUdjICl7aMH4qYW9Zi+glt1aALHKHYWVZeteQVk9Vo7+wrpEZTqWMKbnQUt5If19FZW7U63TGjGjcr91WP1w5bT5l5R9v/iMsvv7/bTyQ80i4KinafDGJLOZALTo+fxebQcVXtrvMy9nK6ektnKVx589IxY99PbRjgMmv9t99CkRgwX6UFHs9VTbn1hSUqsha83ETHs2xgYs+nbF9S+NKrvn2Lllvx89p/Iz0zbYoCqfnItDVdDTpHhjktm0uCmuRHTI7TLh8gG3FOdntdtbS7Eu5Uce2GsWi64f/smu0azcU4zPBRst5SyJhcdLeqRap6Vihh21Nhv/GR0//yPlX18/uOyB7h8snzv5vugHJh6hfSI/nMJBc57K2C3rntTwxiSzaZFTXInIy2pz9uUDPn+8U273mrMdWtmoimP/KLKsnIFrR9x0ygJ+bpTBw9+05NMJm5WNLCtv1YvvZhzZHuu59KcV1yyaUParPWeX/WHY5ytum7Y0NmIEqK6NH22Ac5tCT0/4eGOSoeycU5QHnBO2Hk1JlrIHfKDvJzv3bju4xp1ZsYriqdHy11bs7zp88sFOgzdVP//hhdGkQ+C3N3s9F3Lrq2+mUmY5WxdFz17w2fLbVp5R9qc2heU/m/Dz6DWTd9O1PiH4z0+7gp5mSU7YCnjqzSggL2wlmhpJXaacdm271ftfXLL58LILEhTJqjj2VL+sgpv2rxp9K1MXfaUyy2InvudDdzAiO2oV0WzVaSTOKi+vbVqtxWBG2R46r30yOuHYA9GZg96wPu8D3pcm8d6YtBL8yCRzOS9sBcJCUv7orhdOPL/7pTXkl7eeZYcfeDOalXfW5jOvOymycBZ0n7IuuZS+k46XZjdc2+ZJpWXtWhUbUvS1ipteGVH2h8rxZb8ed2fl9YVvWJ90B2lMyRNe0lWSTNLQ+jQm6U5J9Zr+lbRNUndJnSWlNZ2DpIikryQ4fpak+ZJWSdoo6fd1yLlX0vB06pYuvDHJXFqtMQGQpEEdzy68qM/1RUKnhjux42Mrjj+zYFevieOPtT3tpMRWV70USyof/dSSkt5pUjd0zIgesnZrHotOmX9F2Z2vnV72QO8ry78z5S/RC8cfp029fHCSZOCgOU+lkhrhOmAR8JFUG5KUbWbfMrN/p1q3Gp2pR24gSfXpfPwC+KmZjTGzYcAvaytsZp82S2HdrwnJWGMiKRpY83WSHpWUFseymnoQddQ5mo62U6RVG5Mquub3nnJZ/8+vzlHeKeFSYuUbJkcrt7++4pzb98cf73WAc9qXWO1BJc2OnFleka6pnlCIGQe2xPos/n7FRxefU/a7w6PL7h315YrPTVttp5/ZxKokNdUlqQMwCfgUccZEUpakX0taL+lJSf+UdG1wbpukb0laBHxI0ry4c+dJWiJptaRXJHWUdKOkX8XJflLStGqqzAWGBM+XH0uaJunJuDq/knRjDe3fJGlZ0OZjSTyXegM7q96Y2dpAbrakn0haK2mNpC8Ex+dLGhe8fr+klyStCJ6BHeJ0uiM4vrZqlCepg6Q/xsm8pg45cyVtCMr+pK7PL2ONCVASWPOzgXLg5rAVaip2zilqBwwLW4/mQtucDmOvGHDrrnY5naoHe8ypOPpE1/KcnPe9MeiSE5GFBfmXvxxbU5vMzrFYcVYG/j6OW97m56Lnzv94+Zy1p5c90Omi8p9M+n101qSDdAzTH+nsJMtdCTxtZq8B+yVV7QS7GhgEjAQ+DUysVq/UzCab2cNVByTlAY8AXzKz0bidj7X6KsUxBygOni9fTaJ8fPuPm9l5QZsbcYaxNn4KvCDpX5Jul9Q5OP4Z3LrVOWY2CngwvpKk7sA3gJlmdi7wKvCfcUX2Bsd/A1R1jr8JHDKzkYHMF2qSI6krcBUwIij73bpuQsb9WGqgCDhdUntJTwW9gnWSZkuaIemJqoKSLpL0ePD6A4E1Xi3p+Th5w4MewBuSvhhX9z8Duesk3VZdCTl+HJxfK2l2cDxhz6o23ergDKDFzufXh5ys3DMv7fdZuub3rpZZ0fqWHXlw87aBl4woy+t0YifXRSutc3UZ8YwoK08YlqW5Ycaxt6zbK3dXXl40qfTnu4aXzTvrpoqvTCuKjRoZI6u5fEeSXf+4DqgyCA/zXgrgycCjZhYzs3eAF6vVeySBrLOAXWa2DMDMDptZY0V/jm//bElFktYCHwNG1FbRzP6I6xg+CkwDlkrKxxm/31bpbGb7q1WdAAwHFktahctwGb/eVfUcWY4zxAQy745r+0Atcg4DpcC9kq4Gjtd+C1rAbi5JOTjnvadxQQ/fNrNLg3MFuJtyt6QeZrYH+ATwR0k9gHuAqWa2NbDEVQwFLgQ6Apsl/Qa3e+oTuCG7gJclLTA7aTH3amAMMBroDiyTtBA3dB+E61n1xPVY/gC8kEi3JC7bxzxKQJayes/s/fEOS/c8+eqbxzaMO3Eidvj8ipIXF6wYc3vexFfu6AHQoZSRvffZm7u6aUAiWVNKSvKbSO2UqbDs7cvtzG0PVM7s8Gxs3Nnl5I4PW6c6OKuuApK6AdNxD2PDdZZM0teoO5DpsUQiSRyGp5KTO9HJOFXWVSe+/XnAlWa2OpgKm1aXcDN7G/c8+IOkdbiRXE36VyHgOTO7robzVWkXorz3nE8ks0Y5ksYDM3BTjrfiPp8ayeSRSdvAkr4KvAn8L7AWmCnph5KmmNkhMzPgfuA/giHkROBfOIu80My2wimW/ykzKzOzvcC7uNzqk4EnzOyYmR3FWf4p1XSaDDxkZlEz2w0swK1tJOxZ1aJbXbRqR7rakNRxQo9ZY0Z2mVoUfzxatmrisbyKzm/3mnjCmXH2wliNDpBTjpem7C3fWJhRsc86rnygcsaCi8vmbj2j7P6BHyn/ZuGTsYljy8lttkYvjmRSHl8L/MnMBprZIDPrD2zF/XYWAdcEI/zTSOIBDWwC+kg6D9z3Iuh4bgPGBLL6A4kM8RFcR7KK7bjZivyggzqjlnY7Arsk5eJGJrUSzI7kBq97Ad2At4BngZsDnanW2QVYCkySdHpwvp2kutbCnsUZhaq2u9QkJ1g3KTCzfwK34TrJtZLJI5MSM6t+ga9JGgtcAvxA0rNmdieut/8P3LDtUTOrlFSb5Y9PplRl2ZMJ815TmdrqnqJbEu1k9MJwYyMpZ3jniVM65/VcULT7r1Nx9z+v/Mij7Tad8Zm8nntWHs6JlnYa/5oNSljfbN+Aysp+Tap0NaKmdzfawM0PRy/MfSI6ecQx2mayg2q3QXOe6rRt7qWHaylzHW7hO57HgI8Ct+Ae4OuA14CXgVo3UJhZeTDN/EtJbXHrJTOBxTgjtTaQtyJB3X2SFgejhH+Z2Vcl/QVYA2wBatta/s1Av+1BGx1rKQvwfuDnkkqD9181s3ck3QucCayRVIGbRTmxccDM9gQjn4eCaTFwax+v1dLWd3EzIetwz7U7zOzxGuQcAf5PUhvc7+f2Oq4Duc5x5iHpqJl1qHasD7DfzEolXQncaGZXBuf+gQvtcJGZbQimuVYQN81lZvslRYCjZvaToN46YBbQFTeEnUAwzQV83MxWVukSzC1+FmfMuuJGTefjRjA34HI89MBNc33GzP6aSLe6rn3nnKJ/4ab0PHVwpGL/S0/v/MO5MaL5AMrquqQHUyvHrbxrKsB3Z2etWzM466QF4h6Vla++sOPtcYnkNRZm2FHabpgfG73nT5XvP22ZnTU0iXAlmcSYbXMvrV9OGdxOJDM7GkyHvQJMCkb5nmZCJo9MEjES+LGkGFABfC7u3INAj6qHdWDZPwM8LikLN511UU2CzWyFpHm4LzLAvdXWSwCewE1VrcaNer4W9DIeo/ae1Um6JUHCeX7PqXTM7TrxigG3rnlq5+/7lcdKulps/wX78nct3Nd1+Jpu+zeMml0UO7Bm8MmzvaPLyptkq7cZh7Zarw2PRadGH4lOO2svnWtdrM1weuF+F/XlyWAqOA/4jjckzY+MHZmkitze8pVm9r8htV9jzypV3XbOKdqDW+D3JEnUotueeeuPOlKxbyBwvG3bq1YWvvyzcbLKko99NbttZc6JIT7f2bPvlSuPHmuURe1Syy1+OTZsx5+iF3WeHxtzdpTsltahq4mPbpt76UNhK+FpPFrFF1nSctyOiy+HqEbCnlU9devUCPq1aLKVPeiDfT+1b8Huv6zdXbJtZEnJ309bP/S6RSM33j9j+mpb+uxYnQj7MamkJG1rUmaU7qbL2r9HJ5Y8GJ05eLv1GkJyC9ItjW5hK+BpXFqFMTGzsc1Ah2k1HE9Jt51zitrQCgM8pgNJ3QpP+3D7lfv//dKWwysm7myz5e2BHfpvuWLpDj071k11ZZu93SMa69OQdiot663VNqT4z5Uz2j4VO//sUvJ9tAK3huhpwbQKY9LC8KOSBiCpzTldZ07onNdzwbK9TxeuPH30P6es2jm943Hbf6SduvaujL4JpGRMzKg8SIf1z0bHHrg/elG/dTb4dKBv41xBxuKNSQvHG5PMI1FGO08KSNLgjqMLO+V2X/jCrgcnFg84f9FVS17O+9PM7KnnlpaW1i0BYqZ9W6zvpkeiF+qv0SkjDtNhdGPrneF4Y9LC8cYk8/AjkzTRvU3fqbP6f27Z05rX7dziLkf/NPMw046XJLy/Zthx8jcVxUbtvj96UY8lseHDjKxJTa1zBtOYkYk9zQBvTDKPtERH9jja5XQ87/IBn920pPLPb/XfvfSN80tLTyyOm3F0h/VY90RscsVDlTPOfIeuw/ABNutLJkfb8CSBNyaZR1K5ODzJk5OVN3TykOs7lb5VvrhN53fGLY6dteqB6MxO/46NHVFBTkrJnTw10lyCTnoaCW9MMo+KsBVoaRhmm3LefvPdboMGvzCxe2l21u6RV/MgV/NgGHlqWiTRWE4JXBq2Gp5GxBuTzKOxwmi3OgyLrcve8fKrOcXdo4pNIJbNpvWFa88e+Xwnyf820klWdqW/ny0c/wFnHt6YNJAYFl2TvX3pipw3esdkJyVaOniwz8jd75w+v1fv16eFpF5LxY+oWzjemGQe3pjUkxixipU525auzt42ICarcSfWli0TCrt0fXtZfv5x72yYPrwxaeH4HRaZR3nYCmQaUWLlr+S8vvCP+fN3r8zZOiUmG1h7DWnF8kuHmOntptGwVeCNSQvHG5PMo3r6Tk8NVBItfSnntQXz8l/ctyZn+1STJZ2jpLKyTdd166bvM/MjwTRRWy4TTwvAT3NlHgdwvbzcsBVprlQQPf5yzpZlm7PfGmaisL5yDh7oM3L37iELevUqrrcMzwl2hK2Ap3HxI5MMo9/cKYbLveKpRgWVRxfkbFhwX/7845ty3io00bOhMre8NnFqWVnbZenQr5XzZtgKeBoXb0wyk91hK9CcKKPi0Au56+bfl7+gYkvOrkKUzlwv0soVswabaVf6ZLZKvDFp4Xhjkpl4YwKUUn7g37lr5t+fv5A3sndPQ3RpjHYqKtp0W7/uwj1+/aRBeGPSwvFrJplJqzYmJZTvXZi7Yf2OrH3nIqY1RZsHDvQd9e7uwfNP6/VGk7TXwigHfJrdFo43JpnJ1rAVCINjlL27IHf9xrezDpxHAxbW68trr11Q2LnLrlfz80vGNXXbGc7OGdOLW0d+8FaMn+bKTDaGrUBTcoSSXf/IW77wofxFnd7OPlCIwoqcLK1cMet9fv0kZfwUVyvAj0wykw1hK9AUHNbxnS/mrn9jjw5PQPQOWx8I1k/WT1s9YsSLPSUfCTdJvDFpBXhjkpm8hgur0iI/v4M6tv3F3PVv7tORCYikHQ2bigP7+41+9933LTjttK3e/yQ5vDFpBfhprgyk39wpFcDrYeuRbvbr6NbH8pYu/mve0n77so5MQc3XMfO1zZOmlpe1fTVsPTKEVjUt21rxxiRzaTFTXXt1+PVH81566fG8lwceyDo2iYyYPpJWrLh0kJn8LqW6WRS2Ap7GxxuTzGVV2Ao0lN06tPmRvMVL/5a3bMihrOMTUWZ9Hysq2nbfsH7abjOiYevSjNkxY3qxn+ZqBbTIOfdWQsb29nbpwIb5eeuPHlPZ+LB1aSj79/cbvWfPoAU9e27z6yeJKQpbAU/T4I1J5rKUDAv4uDNr39oFuRvKS1Q+Nmxd0snmTZOndO78zvK8vNIWdV1pImM7PZ7UyKhpBc979Js7pQTIiAXg7Vl7Vj+Qv3Dl03mrRrY0Q+JQ1orlswaYqVVHJqgBPzJpJfiRSWZTBEyss1RIvJG1e/ni3E05ZaocHbYujU1FRdseGzYUrho+fH53739yggPA+rCV8DQN3phkNguBr4WtRHW2ZO1atiR3c9sKRVvgKKRm9u/rP2bPnkHze/bcNi1sXZoJi30YldaDNyaZzSKaifOiYbY5++1XluZs6VSpaKvNnb550+SpnTu/syIvr/TcsHVpBvj1klaEXzPJYPrNnXIImB+mDobF1mfveGle/vwti3I3nV+p6LAw9QkfZa1YPqu/Xz8B/HpJq8Ibk8zniTAajWHR1dnbl8zLn7/1pdzXJkYVOzMMPZojFRVte2zcMPVtM2Jh6xIiu4GXw1bC03R4Y5L5PAE02bx0jFjliuyti+blv7hjWe7rF0QVG9JUbWcS+/YNOGfv3oGtuWf+0Izpxd6ZsxXhjUmG02/ulF04n5NGJUqs/NWc4qI/5s/ftSL3jckx2aDGbjPT2bRxypTy8jYrwtYjJB4IWwFP0+KNScug0aa6osTKlua8tnBe/ot7V+Vsm2Ky/o3VVstDWStXXNrfjD1ha9LEbJwxvXh52Ep4mhZvTFoGfyXNU12VREsW52xeMC//xQPrcnZMNdEnnfJbC+Xl7Xps3FC4s5Wtn9wftgKepscbkxZAv7lTtgIvpkNWBZXHinI2zp+XP//oxpydhSZ6pUNua8atnwxYGLYeTYQBD4athKfp8cak5fC7hlQup/Lwi7nrF9yXv6Bsc87b0xA90qWYBzZtnDq1ojx/Zdh6NAELfJTg1ok3Ji2HJ4B3U61USsXB53PXzv9T/oJYcfY7hYiujaCbB2WtWDGrbytYP/EL760Ub0xaCEH2xT8mW76E8v3P5q6e/0D+wqyt2e9OQ3RuRPU8QHl5u56bNk7d0YLXT0px63eeVog3Ji2Le6hjIf44ZXv+lbtywYP5RflvZu+dhujURLp5gL17B567b++Alup/8viM6cWHwlbCEw4y83HYWhI75xT9C/hA9eNHKX1nQe76zbuyDo5HtA1BNc8JLDZh4qNrcnPLxoStSZoZPWN68ZqwlfCEgx+ZtDx+GP/msEre+nveqwsfzl/cZVf2wUJvSJoDylqxfFbvFrZ+8pQ3JK0bPzJpgeycU7TkoI71nZ+7fvteHZmAMicbY2uie/dty4cOKzpHahGdugtmTC9+KWwlPOHREr7Enmo8n7v2W3/NW9pnb9aRKd6QNF/27h00dt++/i3B/2S+NyQeb0xaIDd85/P/RpmR0re1s3HD1CkVFfmrwtajgUTqKiApKmlV3N+c+jYmaUl966bQxuUN0bE14qe5WiiRSOQi4Nmw9fDUTV7esXfGn/94jkT3sHWpB8/OmF58cV2FJB01sw5NoVBDkZRjZpVh65Fp+JFJCyUSiTyHT06UEZSXt++1adOU7WZNl0ogjfxPQypL2ibpDkkrJK2VNDQ43kPSc8Hx30naLql7cO5o8H+apPmS/ippk6QHJSk4N1bSAknLJT0jqXdwfIikp4PjRXHtzZP0/yS9CPxQ0o2SfhV37heSlkh6Q9K1wfEsSb+WtF7Sk5L+WXWuNeKNScvm62Er4EmON0pNtwAAC21JREFUvXsGjd2/r1+mrZ88nkJ04LbVprlmx53ba2bnAr8BvhIc+zbwQnD8CWBADXLPAW4DhgODgUmScoFfAtea2VjgD8D3gvK/B74QHP8K8Os4WWcCM83sywna6Q1MBmYBc4NjVwODgJHAp4GJdd+Glos3Ji2YSCRSBNwXth6e5NiwoXByRUXe6rD1SJJyUuuslJjZmLi/R+LOPR78X457OIN7cD8MYGZPAwdqkPuKme00sxiwKqh/FnA28JykVcA3gH6SOgAXAI8Gx3+HMxJVPGpmNSX0+puZxcxsA3BanI6PBsffIU3BVjOVnLAV8DQ6XwYuAR+4sfmTlb1yxaye541/fG8GrJ98f8b04k1pklUW/I/y3jNJKdaNry9gvZmdNFKQ1Ak4aGY1OYseS7IdVfvvwY9MWjyRSGQfcHvYeniSo6ysfe/NmyZva+brJ2uA7zdyG4uADwNIej/QJYW6m4EekiYG9XMljTCzw8BWSR8KjkvS6AbqeE2wdnIaMK0BsjIeb0xaAZFI5EHgmbD18CTHnj3vG7d/f9/mun5SCXxixvTiihTrVV8zmVtH+TuA90taAXwQ2AUcSaYhMysHrsUtpK/GTX9dEJz+GPCp4Ph64IoUryOex4CdwDrclNnLQKuNTea3BrcSIpHI+3Bf+nZh6+JJhlh0wsRH1+fmlo8KW5NqzJ0xvfi/G7sRSflA1MwqgxHGb2qZngoNSR3M7KikbsArwKRg/aTV4UcmrYRIJLIVt0PGkxFkZa9cMauHGfvC1iSOTSThoJgmBgDLghHEL4CbmqjdVHkyWMwvAr7TWg0J+AX41sZPgeuAc8NWxFM3ZWXte2/ePOnVs85a3FUKfbE3BnxyxvTisjpLpgEz24Lb9tusMbNpYevQXPAjk1ZEJBKJ4np4qc53e0Jiz7uDxx3Y33dB2HoAv/Dxtzy14Y1JKyMSiawAvhi2Hp7kWb9+2uSKirwww7sX4x1gPXXgjUkrJBKJ/Ba4N2w9PMmSlbNyxaU9zNgfQuPHgGtnTC8+HkLbngzCG5PWyy3A0rCV8CRHWVmH3q9tnlTcxP4nBvzHjOnFmR7V2NMEeGPSSolEIuXANbj9+54M4N13B5934ECfpvQ/+fqM6cV/a8L2PBmMNyatmEgk8jbOuas8bF08ybF+3YWTKitz1zZBU/fPmF78gyZox9NC8MaklROJRJbgF+QziKycFctndTOrMfBhOlhC8/Xr8DRTvDHxEIlEfgfcE7YenuQoK+vQ57XXJm5ppPWT7cBVTeVP4mk5eGPiqeJW4MmwlfAkx7u7Tx9/8EDvdK+fHAUumzG9+N00y/W0Arwx8QAnLch7g5IhrFs3PZ3rJ5XAdTOmFzfFeoynBeKNiecE3qBkGlk5K1dc2jUN6ydlwNUzphf7z91Tb7wx8ZyENyiZRWlpx75bXpu4pQEijgGXzphe/I906eRpnXhj4jmFOIPyVNi6eOpm9+7Txx840Ks+8bsOAhfNmF78fLp18rQ+vDHxJCQwKFfjDUpGsH7djAsqK3PXp1BlDzDdB2/0pAtvTDw1EmdQ/JRXM8csK3fliksLzDiYRPG3gMIZ04tXNrZentaDNyaeWgkMylXAr8PWxVM7paUd+72+ZcLmOoptBabMmF68sSl08rQefNpeT9JEIpFbgJ/hk6o1a0aOfG5B5y7vFCY4tRb4wIzpxW83tU6elo8fmXiSJhKJ3A18EJKaSvGExLrE6ycPAhO8IfE0Fn5k4kmZSCQyGHgMGBO2Lp7EtGlzeOe48/6vo0Q74PYZ04vvDlsnT8vGj0w8KROJRN4AJgJ/DFsXT2JKSzv1e/3185fgFtq9IfE0On5k4mkQkUjkU8AvgbZh6+I5iceBmyKRSBjZGT2tEG9MPA0mEomcDvwOmB62Lh6OA7dHIpHfh62Ip3XhjYknbUQikRuBu4CuIavSWnkJ+GQkEtkUtiKe1odfM/GkjUgkMg8YBjwUsiqtjXeA64FJ3pB4wsKPTDyNQiQS+SDwG2Bg2Lq0YCqAnwN3RiKRI2Er42ndeGPiaTQikUh74E7gS0B2yOq0NJ4FvuRHIp7mgjcmnkYnEomcBXwd+CjeqDSUrcB/RiKRv4WtiMcTjzcmniYjEokMAf4H+DiQG7I6mcZbuCmtX0YikdKwlfF4quONiafJiUQiA4H/Bj4B5IWsTnNnFW6H3CORSKQibGU8nprwxsQTGpFIpB/wX8CngTYhq9OcMOBp4K5IJOITV3kyAm9MPKETiUR64aa+/gMYFbI6YVKGC8h4VyQS2RC2Mh5PKnhj4mlWRCKRUTij8lGgb8jqNAWGczZ8DPhzJBJ5J2R9PJ564Y2Jp1kSiUSy+P/t3V2IlUUcx/HvL7YsWTNMiMxiy3whiaQyK4gsvQmjCDIvLIvqQuimiyUCIR6IKAq6WCG8KjUIanvHCys0NTQsF3d1V8xiJYIi2qIiREX9dzGzdTjunl18dvc8K78PDGfOPM8zM2cXzv/M8zKTpmd5lLTa47Tm9mhMnQZ2kubP+qgoCk8Lb5Oeg4lVXlEUU4EHSGupLGNyjlhOAttII5BPiqIYaHJ/zMaUg4lNOkVRzCMFlaXAncDspnZoaMeBLmAP6TTW9qIo/mpul8zGj4OJTXr5rrDbSWus3AhcC1zDxN12fIy0JG4P6VbeLqC7KIqTE9S+WdM5mNh5KV9zmUUKLG11r1eTbkVuyenCmnwL/0+Aegr4jTSR4lDpV9LDhP1FUZwZ/09lVl0OJmZ1ciBqAU45SJiNjoOJmZmV5vVMKkTSOkl9kg5I6pa05BzquFVSxwj7LJW05dx7CpLWSlpTpg4zO3+0NLsDlki6A7gfuDkiTkiayTlcQI6IfcC+se7fEO1sGO82zGzy8MikOq4EBiLiBEBEDETEzwCSlknaL+mgpDclTcnliyXtkdQj6RtJ02pHHZJuy9v359f5jTogaWGupzuPjubm8jX5fY+kt3NZIak95+dI2iqpS9JXkhbk8o2SOnLb/ZIermnrufx5eiS9MkI9KyX15n13jelf3czGRkQ4VSABraTbSo8AbwB35/KLgZ+Aefn9ZuBZ0qilH1icyy8ljTSXAltqy3J+OfBBzv+3T10f1gOrc/4i4BJgIfAdMDOXz8ivBdCe89uAuTm/BNie8xuBTtKPlhuAH3L5faTnL6bW1TlcPQeBq3L+smb/r5ycnM5OPs1VERHxj6RbgLuAe4B3JT0P7AeORsSRvOsm4BnSF+8vEfFtPv5vAEm11U4HNuURRjDyGiJfA+skzQY+jIjvJd0LvB8RA7mdP2oPkNRKenCws6btKTW7fBwRZ4BDkq7IZcuBtyLi2GCdI9SzG9go6T3SFCRmVjEOJhUSEaeBHcAOSQeBx0mjlaGIFCAaeRH4MiIektSW627U/juS9gIrgM8kPT2Kdi4A/oyIRcNsP1HX5+H6Pmw9EbE234ywAuiWtCgifm/0WcxsYvmaSUVImj94jSJbBPwIHAbaJF2fyx8jTRJ4GJglaXE+fpqk+h8H00kP1QE8MYo+XAf0R0QH8ClpOvhtwCOSLs/7zKg9Jo+IjkpambdL0k0jNPU58KSkqYN1NqpH0pyI2BsRLwADpIcOzaxCHEyqo5V0SuqQpAOkawxFRBwnrUjYmUcrZ4ANEXESWAWsl9QDfMHZC0y9CrwsaTejW3t9FdArqRtYAGyOiD7gJWBnbuf1IY5bDTyVt/cBDzZqJCK2koLVvtxW+wj1vJYv1vcCu0jTlphZhfihRTMzK80jEzMzK83BxMzMSnMwMTOz0hxMzMysNAcTMzMrzcHEzMxKczAxM7PSHEzMzKw0BxMzMyvNwcTMzEpzMDEzs9IcTMzMrDQHEzMzK83BxMzMSnMwMTOz0hxMzMysNAcTMzMrzcHEzMxK+xdQ/GQh+aFE2AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot = plt.pie(number_of_degrees,labels=majors)\n",
    "plt.title(\"Bachelors Degrees awarded in 2014\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2. Make a bar chart of the number of women and men who got bachelor's degrees in each of these fields in 2014. \n",
    "\n",
    "Hint 1: Use `?plt.bar` if you are stuck. \n",
    "\n",
    "Hint 2: If you're still stuck, try Googling what you're trying to figure out.\n",
    "\n",
    "Hint 3: This might help you out: https://matplotlib.org/examples/pylab_examples/barchart_demo.html\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "male = [13271,44811,45986,4134,12715,13407,27554,80813,75324]\n",
    "female = [15525,64709,10144,2596,9511,8815,90851,97123,18626]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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VXvCjEfFoWfdCRDxV2f/9pMm/X9TUQDXGGNMBijvjGdKs/D+Ks2ZZ1gBOAzZotmLULMdOwF4R8b6ZbmtlZowxnaF8rK8kzYvfJns163VSgXQr3WpmNMaYuYGdSdPfI6Rpd5wV2ezhnpkxxpiuxz0zY4wxXY+VmTHGmK6nbVmYJf0MeBcZKr5WKRtOhhevQKaoel8jC4Ckw8jxVq8Cn4hMXIoyIeupZJjvxWSIbCiT5Z5Ohs0/SaZDebDssw+Zdw4yNcppM5N3qaWWihVWWGFOT9sYY15X3HjjjU9ERG9ZWGqlbT6zMgjyeTL/WUOZ/R85UPY4SYeSudm+UMJAfwVsQKZ4uQJYJSJeVU5Q+UlyPMXFwPcj4hJJHwXWiYiPSBoHvCci9igKcwIwlhzoeCOwfj+pcwAYO3ZsTJjQ8bnojDGmq5B0Y0SM7bQcbTMzRsSfycF5VXYmxy5Q/u9SKT8rIl4q4ysmAhso59VZNCKuKxE+pzft0zjWucA2JeXK9sDlEdHI/XY5mZnAGGPMXErdPrORJZVNI6VNI4nrKGZMqDm5lI1ixtH0jfIZ9olMSvosmdesr2MZY4yZSxksASC95SOLfspnd58ZK5UOkDRB0oSpU6cOSFBjjDGDj7qV2WPFdNiYmvvxUj6ZGTN0jyYHEU6mklW6Uj7DPiUx6mKkWbOvY/0PEXFKRIyNiLEjRnTcf2mMMWY2qVuZXUhOFEf5f0GlfJxySvQVyZHwNxRT5DTlVOAiE8Fe0MuxdgP+WPxqlwHblWzcS5BJgS9r94kZY4zpHO0Mzf8VOSXJUpImkxnojwPOkTQeeJjM0k1E3CHpHDJb+XTg4OiZMPIgekLzL6FnKoCfAmdImkj2yMaVYz0l6RgyizfA0U0ZmI0xxsxlOJ1VwaH5xhgz68z1ofnGGGNMXbTNzGiMMa9ndFTfk0bHEbaItRr3zIwxxnQ9VmbGGGO6HiszY4wxXY+VmTHGmK7HyswYY0zXY2VmjDGm67EyM8YY0/VYmRljjOl6rMyMMcZ0PVZmxhhjuh4rM2OMMV2PlZkxxpiux8rMGGNM1+Os+caYrqe/DPXgLPWvB9wzM8YY0/VYmRljjOl6rMyMMcZ0PVZmxhhjuh4rM2OMMV2PlZkxxpiux8rMGGNM12NlZowxpuuxMjPGGNP1WJkZY4zpeqzMjDHGdD1WZsYYY7oeKzNjjDFdj5WZMcaYrsfKzBhjTNdjZWaMMabrsTIzxhjT9ViZGWOM6Xo6oswkfUrSHZJul/QrSQtIGi7pckn3lv9LVLY/TNJESfdI2r5Svr6k28q670tSKZ9f0tml/HpJK9R/lsYYY+qidmUmaRTwCWBsRKwFDAHGAYcCV0bEysCV5TeS1ijr1wR2AE6QNKQc7kTgAGDl8rdDKR8PPB0RKwHfBb5Rw6kZY4zpEJ0yMw4FhkkaCiwIPALsDJxW1p8G7FKWdwbOioiXIuIBYCKwgaRlgEUj4rqICOD0pn0axzoX2KbRazPGmFYg9f9n6qV2ZRYR/wK+BTwMTAGejYg/ACMjYkrZZgqwdNllFDCpcojJpWxUWW4un2GfiJgOPAss2Y7zMcYY03k6YWZcguw5rQgsCywkac/+dumlLPop72+fZlkOkDRB0oSpU6f2L7gxxphBSyfMjNsCD0TE1Ih4BTgP2AR4rJgOKf8fL9tPBsZU9h9NmiUnl+Xm8hn2KabMxYCnmgWJiFMiYmxEjB0xYkSLTs8YY0zddEKZPQxsJGnB4sfaBrgLuBDYp2yzD3BBWb4QGFciFFckAz1uKKbIaZI2KsfZu2mfxrF2A/5Y/GrGGGPmQobWXWFEXC/pXOAmYDpwM3AKsDBwjqTxpMLbvWx/h6RzgDvL9gdHxKvlcAcBpwLDgEvKH8BPgTMkTSR7ZONqODVjjDEdonZlBhARRwBHNBW/RPbSetv+WODYXsonAGv1Uv4iRRkaY4yZ+3EGEGOMMV2PlZkxxpiux8rMGGNM12NlZowxpuuxMjPGGNP1dCSa0RhjTOvpLyfk3D7S1j0zY4wxXY+VmTHGmK7HyswYY0zXY2VmjDGm67EyM8YY0/VYmRljjOl6rMyMMcZ0PVZmxhhjuh4rM2OMMV2PlZkxxpiux8rMGGNM12NlZowxpuuxMjPGGNP1WJkZY4zpeqzMjDHGdD1WZsYYY7oeKzNjjDFdj5WZMcaYrsfKzBhjTNdjZWaMMabrsTIzxhjT9QzttACmfnSU+lwXR0SNkhhjTGtwz8wYY0zXY2VmjDGm67EyM8YY0/VYmRljjOl6rMyMMcZ0PVZmxhhjup6OKDNJi0s6V9Ldku6StLGk4ZIul3Rv+b9EZfvDJE2UdI+k7Svl60u6raz7viSV8vklnV3Kr5e0Qv1naYwxpi461TM7Hrg0IlYD3gzcBRwKXBkRKwNXlt9IWgMYB6wJ7ACcIGlIOc6JwAHAyuVvh1I+Hng6IlYCvgt8o46TMsYY0xlqV2aSFgW2AH4KEBEvR8QzwM7AaWWz04BdyvLOwFkR8VJEPABMBDaQtAywaERcFxEBnN60T+NY5wLbNHptxhhj5j460TN7IzAV+LmkmyX9RNJCwMiImAJQ/i9dth8FTKrsP7mUjSrLzeUz7BMR04FngSWbBZF0gKQJkiZMnTq1VednjDGmZjqhzIYC6wEnRsRbgBcoJsU+6K1HFf2U97fPjAURp0TE2IgYO2LEiP6lNsYYM2iZqTKTNI+k21tY52RgckRcX36fSyq3x4rpkPL/8cr2Yyr7jwYeKeWjeymfYR9JQ4HFgKdaeA7GGGMGETNVZhHxGnCrpOVaUWFEPApMkrRqKdoGuBO4ENinlO0DXFCWLwTGlQjFFclAjxuKKXKapI2KP2zvpn0ax9oN+GPxqxljjJkLGWjW/GWAOyTdQJoFAYiId89mvR8HzpQ0H3A/sB+pWM+RNB54GNi91HGHpHNIhTcdODgiXi3HOQg4FRgGXFL+IINLzpA0keyRjZtNOY0xxnQBA1VmR7Wy0oi4BRjby6pt+tj+WODYXsonAGv1Uv4iRRkaY4yZ+xmQMouIqyUtD6wcEVdIWhAYMrP9jDHGmDoYUDSjpA+TgRonl6JRwPntEsoYY4yZFQYamn8wsCnwHEBE3EvPODBjjDGmowxUmb0UES83fpRwd0cHGmOMGRQMVJldLemLwDBJbwd+DfyufWIZY4wxA2egyuxQMgXVbcCBwMXAl9sllDHGGDMrDDSa8TVJpwHXk+bFezwI2RhjzGBhQMpM0juBk4D7yLyHK0o6MCIu6X9PY4wxpv0MdND0t4G3RcREAElvAi6iJ+OGMcYY0zEG6jN7vKHICvfTkwjYGGOM6Sj99swkvbcs3iHpYuAc0me2O/D3NstmjDHGDIiZmRl3qiw/BmxZlqcCS7RFImOMMWYW6VeZRcR+dQlijDHGzC4DjWZckZy2ZYXqPnMwBYwxxhjTMgYazXg+OUfY74DX2ieOMcYYM+sMVJm9GBHfb6skxhhjzGwyUGV2vKQjgD8ALzUKI+KmtkhljDHGzAIDVWZrA3sBW9NjZozy2xhjjOkoA1Vm7wHeWJ0GxhhjjBksDDQDyK3A4u0UxBhjjJldBtozGwncLenvzOgzc2i+McaYjjNQZXZEW6Uwxhhj5oCBzmd2dbsFMcYYY2aXgWYAmUZGLwLMB8wLvBARi7ZLMGOMMWagDLRntkj1t6RdgA3aIpGZK5H6Xuc5y40xc8pAoxlnICLOx2PMjDHGDBIGamZ8b+XnPMBYesyOxhhjTEcZaDRjdV6z6cCDwM4tl8a0jP7MehxZlxTGGFMPA/WZeV4zY4wxg5Z+lZmkw/tZHRFxTIvlMcYYY2aZmfXMXuilbCFgPLAkYGVmjDGm4/SrzCLi241lSYsAnwT2A84Cvt3XfsYYY0ydzNRnJmk48Gngg8BpwHoR8XS7BTPGGGMGSr/jzCR9E/g7MA1YOyKObJUikzRE0s2Sfl9+D5d0uaR7y/8lKtseJmmipHskbV8pX1/SbWXd96WM4ZM0v6SzS/n1klZohczGGGMGJzMbNP0ZYFngy8Ajkp4rf9MkPTeHdX8SuKvy+1DgyohYGbiy/EbSGsA4YE1gB+AESUPKPicCBwArl78dSvl44OmIWAn4LvCNOZTVGGPMIKZfZRYR80TEsIhYJCIWrfwtMid5GSWNBt4J/KRSvDNpxqT836VSflZEvBQRDwATgQ0kLQMsGhHXRUQApzft0zjWucA2jV6bMcaYuY/ZSmfVAr4HfB54rVI2MiKmAJT/S5fyUcCkynaTS9mostxcPsM+ETEdeJaMvjTGGDMXUrsyk/Qu4PGIuHGgu/RSFv2U97dPsywHSJogacLUqVMHKI4xxpjBRid6ZpsC75b0IBniv7WkXwCPFdMh5f/jZfvJwJjK/qOBR0r56F7KZ9hH0lBgMeCpZkEi4pSIGBsRY0eMGNGaszPGGFM7tSuziDgsIkZHxApkYMcfI2JP4EJgn7LZPsAFZflCYFyJUFyRDPS4oZgip0naqPjD9m7ap3Gs3UodToxsjDFzKQNNNFwHxwHnSBoPPAzsDhARd0g6B7iTTHJ8cES8WvY5CDgVGAZcUv4AfgqcIWki2SMbV9dJGGOMqZ+OKrOIuAq4qiw/CWzTx3bHAsf2Uj4BWKuX8hcpytAYY8zcT6eiGY0xxpiWYWVmjDGm67EyM8YY0/VYmRljjOl6rMyMMcZ0PVZmxhhjuh4rM2OMMV2PlZkxxpiux8rMGGNM12NlZowxpuuxMjPGGNP1WJkZY4zpeqzMjDHGdD1WZsYYY7oeKzNjjDFdj5WZMcaYrsfKzBhjTNdjZWaMMabrsTIzxhjT9ViZGWOM6XqszIwxxnQ9VmbGmK5A6vvPGCszY4wxXY+VmTHGmK7HyswYY0zXY2VmjDGm67EyM8YY0/VYmRljjOl6rMyMMcZ0PVZmxhhjuh4rM2OMMV2PlZkxxpiux8rMGGNM12NlZowxpuupXZlJGiPpT5LuknSHpE+W8uGSLpd0b/m/RGWfwyRNlHSPpO0r5etLuq2s+76UKUclzS/p7FJ+vaQV6j5PY4wx9dGJntl04DMRsTqwEXCwpDWAQ4ErI2Jl4Mrym7JuHLAmsANwgqQh5VgnAgcAK5e/HUr5eODpiFgJ+C7wjTpOzBhjTGeoXZlFxJSIuKksTwPuAkYBOwOnlc1OA3YpyzsDZ0XESxHxADAR2EDSMsCiEXFdRARwetM+jWOdC2zT6LUZY4yZ++ioz6yY/94CXA+MjIgpkAoPWLpsNgqYVNltcikbVZaby2fYJyKmA88CS7bjHIwxxnSejikzSQsDvwEOiYjn+tu0l7Lop7y/fZplOEDSBEkTpk6dOjORjTHGDFI6oswkzUsqsjMj4rxS/FgxHVL+P17KJwNjKruPBh4p5aN7KZ9hH0lDgcWAp5rliIhTImJsRIwdMWJEK07NGGNMB+hENKOAnwJ3RcR3KqsuBPYpy/sAF1TKx5UIxRXJQI8biilymqSNyjH3btqncazdgD8Wv5oxxpi5kKEdqHNTYC/gNkm3lLIvAscB50gaDzwM7A4QEXdIOge4k4yEPDgiXi37HQScCgwDLil/kMryDEkTyR7ZuHaflDHGDGZ0VN8xcHFE97f1a1dmEXEtvfu0ALbpY59jgWN7KZ8ArNVL+YsUZWiMMWbuxxlAjDHGdD1WZsYYY7oeKzNjjDFdj5WZMcaYrqcT0YyvO+b2KCJjjOk07pkZY4zpeqzMjDHGdD1WZsYYY7oeKzNjjDFdj5VZi5D6/jOmG/EzbboJRzMaM0joT0k4TbYx/eOemTHGmK7HyswYY0zXY2VmjDGm67EyM8YY0/VYmRljjOl6HM1oOk5/uSvB+SuNMTPHysyYLsDJqo3pHyszY8wsY+VqBhv2mRljjOl6rMyMMcZ0PVZmxhhjuh4rM2OMMV2PlZkxxpiux8rMGGNM12NlZowxpuuxMjPGGNP1WJkZY4zpepwBxLwu6W9WZ/DMzsZ0G+6ZGWOM6XqszIwxxnQ9VmbGGGO6HvvMjOkFZ4U3prtwz8wYY0zXM1crM0k7SLpH0kRJh3ZaHmOMMe1hrlVmkoYAPwJ2BNYA3i9pjc5KZYwxph3MtcoM2ACYGHPfv6YAACAASURBVBH3R8TLwFnAzh2WyRhjTBtQzKWjQyXtBuwQEfuX33sBG0bExyrbHAAcUH6uCtzTzyGXAp5ok7izy2CTyfL0z2CTBwafTJanfwabPADLR8SITgsxN0cz9haONoPmjohTgFMGdDBpQkSMbYVgrWKwyWR5+mewyQODTybL0z+DTZ7BxNxsZpwMjKn8Hg080iFZjDHGtJG5WZn9HVhZ0oqS5gPGARd2WCZjjDFtYK41M0bEdEkfAy4DhgA/i4g75uCQAzJH1sxgk8ny9M9gkwcGn0yWp38GmzyDhrk2AMQYY8zrh7nZzGiMMeZ1gpWZMcaYrsfKrGakmU0LaYzpNvxedx4rszYhaZ7yfztJoxrlUbOTsvGSSVpC0kK9ydgpJC1c/nfkQ9Dp858TJM1fc33Duvl6tZu632vzv/jhbBMR8ZqkocCXKddZ0l6SdqxZlMY9/jzwriLHGyQtEBGv1SxLVcm/Ffg45IegWdG2sf6Gcl8F+F25R0haTNIydcgwp0gaBhwoafHye4E21dO4V2sB36+Uj6w20NpJ5X59XNLKVbkGA+W5+WbdjYsmGRrXaMG63qPByKB5KOYmKi/bB4EHImKSpI8AHwW+LemddckSEa9KmhfYFfi9pDHAScAzkvatS45e+ALwKICk/YFrJH2thg9V4/h7ARPKEI6PAtcBH+3kR2lmlOTZAB8G1o+IZyRtAPxI0nGV9S2rsvzfh3yOX5P0ceAXwN4trqtXSkNnKLA0JbdqJxphzVSu9Z7A0hHxkqStJF0gaZ+6xSn/dwE+LWlTScs1GmqvF6zM2kDlZVsIeLlMP7M68G7gCDKTf53mtU2A24BXgM8CfwbWBt5Rdyu3fBCHAMsBZ0v6APA24DOl7I1trv/Vsrgx2TP7DLAieV3WJO/RYKXxXG0PnCxpVeATwEPks7ZhKyurXKvNgaskfQpYGTgV2ETSu1pZXz9yTAd+CWwo6QZJm0Fn/VSVa7MH8A1JWwDvAx4ExknaukZZXisN1j2Bg0hr0CeB8ZLWrUuOTmNl1l7OAaYC7wB+EBFTgf2Bi8r6ul7GB4DpwOPAkxHxHWAsOc7wtQ6YbRYCrgKuBg4hX77rgPWASTXJcHKpdwfgJxFxMalM/15T/bNMpZdyI7AfmdHmzIg4GliH9j1PPyB7YrsB34mIM4FlgAlQj1KJiLsiYnfgcGA7Sat02k9V3puLyAbFMcBFEfFJYAHguZpkaPQQ30/OErIs8DngWeBo4OMlecRcjwdNtxhJarxkkkYAIyLizvJ7VeDEiKit1VaRa2FgQeDpiHhF0nXAURFxqaR56jLdNK5PuTajgJcj4s7SQ9ogIvaoS57iN/tPMQO/H9gzImozAc8Kles2D/AGYDNgekScJ2k94LSIWLtNdQ8FNiVNjQ+Xa/WBiNip3fdK0mjgeOCfwIvAtqRV4RHgmIj4VbvqHgiSliBT5U2OiN9JejtwdERsXLMc3wGejYijKmUHk9aGFchrdV2dMtXN68qmWgcVRXYi8DSwo3KqmdvIqRveU9a3+yMwpPjLtiYf6C3IVvz5khYFPt14uGv2QSwoaTuyF/TviPhxCWS4DbikDgEkjSN7yLcCXyrFt9L/FEAdpdIL+SbwXeDXRbktCIwkewYtpSjO95A9suuBv5VV9wNfaXV9fbAIcB6ZKPxW4C/AkqTJ/F2S7o+I62uSZQYkrUT6ov8REY1ndyGy1183ZwNHKKe6uoO0CO1O9tiOAFYjrR9zLTYztpBKVNE6pG/hl8B8wC1lk/2AV6H9CqRi0/8iqVSrMwhsBtzbzvqbaQpeeCewJenLgzRZ3dnowbbj2lQi894P7AT8FNgxIl4sfoUFIuKmVtfbCiqybwhsGRGTK8rtNVK5nNfC+hr3ajxphr0X+GAJclgdeCwiboFaGkIrA3+MiG9ExKURcUVEnB0R55GWhg+0uf4ZqNyLzYHvkcrrpFI2BrgiIk6tUyaAotC/DWxFBpr9hFRq00j/8K/rlqlurMxaSOUDsxXwK/JB/3tEvAK8FdgjIp6vSx5JawBDIuIXwPzApWXVV4DF65IDZlCuHyBftimkzwzgQ7Q/8KJxb3YCTiBNdeeWsi1IJTtYafiktiN9jZTeNWTD5FMlSKJVNBTUtsAPycbQb0rZ+8lhHm2jojDeAhwLXCrpi5J2lLS8cmgC5Dt2Ujtl6Ye9gJ8DE+mZjWNz0g9bK5LWlHQg8M+IGE++3+8lfXkrAn+o87vTKazM2sPvyA/Pr8sypAP9HJih5dtupgHXS/o6cGPphWxE+lom1iTDfykmsavJaLxNKi3YzUnzUduCCYpJTsBNpIN+N+C4snoHcnaFQUmlIfAHYISkURHRCDD4AC02j1YCTe4h/S17ki19yFb+b6GW8V77AqeTCnVR4Ouk/+wdRc4LIuKuNsswA5We6HTS9Po+4MRS9k4ysrTtVBT+emR06dbALZJuJSMsF4iIVyPitoj4XB0ydRoHgLQYSfNFxMuStgU+QrZyVwZuBg6NiMerQSI1yLM3aWq8FbiLtJ3/PSK+3fCr1SFHRZ5NSL/Py2QPaXlg54jYvKb6lyNb9OuQfrN1gI07EZQzEIoJdEpEPFZ+fw04kIy6vJ9see9RUW6tqLMRbLIS8B1SoX2SHMe0RkS8vVV19SPDfOQ4wHUqZWuTz85IsvFxeES83G5Z+pDvrcDPSPP9GqRf+pvAZnX0gho+d0lHkr7n/yvlu5A956ERsUEn3vFOYWXWIioBFx8lfWTXAasAw8iewA01Reg1HvIVgZER8TdJG5Mv27qkH+/vJaKxNqVaZFudHIezIdmSXJp0VJ8REf9s14tX+Th/ADiL9GPuR473+x3w54gYlMEfJXjoPLKnP5Ts3Q8hewHPk7K3/OMp6Z0RcVGJ+BxPNjr+CFwXEbfVEMC0FNkjuxf4eUTcX8ovJ03FFwG7RsQz7ZKhF5kaz9Ha5RqMBQ4m/b9XApdGxG/6P0rLZfoq2WD+YUQ8XilvfAeszMysoxy4eA/wloh4tpQNJQMcprTYr9GXDA2leiZwZUT8rFre7vp7kafxUm0HfDhyrBCSFiHD8l9qc/3VXsapwPYR8UI762wlyhRbj5G9/NWAhYH7gCuA+yLiiVY1Sir3am3yWm3cqZ5PkWct0pc5mfRxrkX2SM8mP95bdkCmxYDTyGd5at31N8myHPAj8pm4jIwIfgB4pE4lP1iwz6wFVPw82wK3R8Sz6smXtyDwNXoCENpK9KSvWrOiyIaW8k9JamuGjX54HyUARdKwiJgGrKdMx9ROGvdmPHB1RLzQCCCQtLEyO8ugJSKmkOOqbiM/XBeS/poPkQOI25Hkdjzw22IubySD3lI5bqkWJL2BHMpyLWnZuIeMHvwGaWb9ZV2yFHka38rxZOKBqSqpzyStIKm2zDGSDiwBQI+TKb4+S35n9iDTxG1VlyyDCY8zawGVj8mDwL8lbRYR15ayXYEFizKpa3DyKOAhSVtGxNWVHuHHyA9ibZSWvsgPUqNH1OiNHUa2sm9ol8mzcr0fAFaRNG9E/KeU7UZ+EAYlxS/zMhn08PFiCr2n9Go3IgN8WkblWj1MiWismDDHkT3Cto2RrFgVdiUDW9Ykg6juBX7ZeI4lfYk0sdZG5XwXB/5RyhrP8e5k7+jCXnZtKaUh9lREPKccyzoFuJzM9jGMDK56uGxbqxuh01iZtZCIuEvSNcC3JP2F7PmuQw5arFOOByVdCBxSHv7XSKV6ZWlt12pyLGa+U4AjJb0E/EE5mHsMZfxLDS/dH8hw5S9JeojsKW9dygYd5b4tSQaprAZsWXoCN0fEtOLLOre/Y8wBVwBnSVqe9P8+Qg5fOLqsb8u9qjyTR5FJuh8kI10/D2wg6dMR8XKxfHQqL+NFZLLwR4HbyQbF+8gw+LZTGmK/Lvf/RmAEmQh6H+BO4LKG//f1pMjAPrM5ptr6kTQSeIqMtno32QO5OjoQBl/k2ZUMdHieDH3/fUQ8UGMPsSqLSJPIl8isDleQQTGn1yVPMV3tBwwnfTA/i4g/tbve2aX4Zz5EBn/8iQwoepY0ne4YEau3se7lyCmD1geWAr4fEVe2q7Vf8W2OIrPyv6fq95H0JzJqs+M9aeU0TruXn28iFcjXaqq74dc8ATiF7LWuCaxEjmW9MyJ+XIcsgw0rsxYh6fukqWEMGYb/a3JsVy0RRRUTzYbkQ70G2Yq8ot1BFn3I0/g4DSvyrEm2HK8F5o+If9coy97A28lIxpuBx+sIxmkFpYH0JKl830Te16WBuyPi7BbXNQ+p7JcjZ1Z4otRTS5BOWZ4fOJIMmjqRVN7LAV+PiPXbKcfMUEbjrk9GwP6b7DlPA16s09LRCEKJiF2aypcj8zM++3ozMYKVWUtQZtq4gDRZDSOnNNkYWBZ4d0Q8WqMst5Mv20NkuqhRZG/xqxFxa41yNFqQh5EmzstIf8PiRbarI6JtA5Ur9X8QOIAcrL0FJSsLGeBwebvqnxMqsm9KZqnfsJSvDTwfEQ+0qb59SWX2ALAY2aO/mwz/v6aVdTbVvzAZ1HFSCdBpTGo7muxFz0P2oi+s20ReuTZbkIFcL5D+ypvJlGhn1mXlqDQQdyQDPW4m80D+qwRUva6xz2wOqLxYY8gpXm4rL+ItZOaC5epQZJWH/A3A3yLisCLHWeSA1x1IhVabU7jygr+BjAC7jfT9LE8q2cXaLE/jmBuSrfpGJOWaZJj79qTjfDDS8Ad9GPhxuZeHk9PlXCnpCxHxzxbW17hWmwKfiYgJJRp3a9IfNJ2cPLVd92okqUS/KOk24NsRcaSk4WTj8ElK0FCdiqzQON/3kEEoJ5Qe7P5k2qj9yOvUfkF6rv3z5Li2tchxbndJuh+4PiKerkOWwYiV2RxQebGOAxaS9ERENEKGp5S/OhD50u0FvFHS24ov6Bkyxc3tDbNaHYqsolxXI+dNu6L0Cu8E7pR0U5GtbfJUTJyLAXtLegK4LSLuAD7ejjpbReW5modsfX8DeC4iFlWOH1ydnBKlVfVFiZAcA3xY0jPFz3sxcLHaPGNxRNwHrCVpIbKhcaykU8nB4qdHxL+UKeBqHydZeT6nA/NLWrz48k4BTlHPEJw6ZbqmWGD+Q1qDNiD9qoMyUXZd2Mw4B2jGbBvbky3peUmT2plRMovXKM84Mo/eGsBfgTOByzvlH1KmYvo5+fG9BvhRRJxfY/1vJHuFbwT+RQ4+vg+4JUpGicGGcpD0o0XBbEn6jW4k00k9R/b6N4sWD4otz/DeZJaYR0l/2U2kifHJVtbVS93zkN+iVytlo8hGx+eA90bEBe2UYSbyrUlOuzMfqWDvJE3lj0RNA/ArPvGDyEbHwcDBkUnEkfSGOt0ZgxErs9mk0vuYnzTlDYmcZHJD8kEbEhEf7IRcpGP6o2T04BLAOtHBrNmlxX0wqWjXAnaKiIv636ul9S9KJqddlQyiOD9yCpFBh6TPkr6YN5MDhR+tBEd8HNionc9VUWrrktFxbyF9i22bPqTykV6djNwcSfrsrhoMkaaV93wh8tkdR75TLwPnRMQVNctzMxkp/TPSD361pM8VWWpJcjxYsTKbA5SZNs4hw2O3A3Zobh21O+y80jvcn/RPbQF8OSJuKOvfFBH31Rj+3pDnTaQvYWVybNSvyvpVSYf18+3wwTT5D99PBhb8JiK+VO7XyqSCeKqV9baCYs5bKyJuUc50sAw5xusWMgr0MWB4tCiNUuVevYH0je0B/CIiTixBGcuRMyg/1y5/WUWGK0gfZqPHuQtwdkScWtez24d885HRuHuSQ1suKj3+7YFrIuL2GmXZgjTDHkjO8fbWUv4P4G3t7kEPdpzOajZQT2qbvciPzWWkT+NRSasp514S1DIJ52uld3ggcAMZAfaspHkl7V7kq3M26UbwwhdJ5boxGfTRyJj/r0YvsU3+ssa9+WyR5edk4Alkmp9hg1GRAUTE9KLIVqYMXCbNfZsAXyVnB29lPsDGvTqK9AndRfpfIEPQX46Sjb+Nvs3XJI0AloycgPPkiDiZzNb/LklLdkKRqWeapo+SjaIAPlXK5iGVfm2KrHAz2Vs/lZ7ppPYFHoiIJ9X+KXkGNa/rk59dKi/XGmR3f23KHE9k5ODapXfQ1uvbUJhkK/Y6svX+RGQGgMXIAcqvtFOGZorJaD5gw4g4hoxCa0wpfyg5mWRb6y+LY4EzSNPQmaVst3bXPyeoJ2/mEeT4rktJZfwTclLOf7Syvsq1WpX8QC5AKlBIs/D2rayvP1GA+yV9vZg5IX2+a3eqt1G5Nu8mc0K+RFEg9GTcqFumaaT/dDngYyVI5l30zDXXqawogwIrsznjl2Sk2eHk9BiQIbw/raPySmv5NtKG/yt6UhztQQY6TFd9k4E2WIKcHfhjwHwRcWsx8S1Hz3VqG6Wu35Pj/Zar+Mc2JMcDDjqKWW9DSb8me5BjlHkkny49gGlkz7vV9S5KZhcZB6wYPWP/VqM8S5VGU1uIiCfInufCwD6Sfku+W6eW+ut+fin1LkQGe7xENs5OKaveThvuRR8yNCbhnF85nvVlcsqZD5IN2PER8TvoyLCFQYV9ZrOIcir3e4D/lN7XDqQZYjg5QPmyiDisBjmGkfrsxfL7I6Q9/RbygR8FfCsi/tQJn4Okd5Lmq3vJVvaqwEIRsZ/aN2/Z6lFmHq5EUi5HDsAdC8wbEXu3ut5WUBTwKNK8thKp9IeSpqWXgAOjhROYSlo2Ih4py28t9Q4Bvk8GDv07Isa369mp+DaHAEtETmWzOhn48irpH7whcnb0uufdW7hhCldmwz+ZtHC8jZwDb/eoafqZynU6g/zujCfNzb9VDqdop8m+q7Aym0WUaas+Q9rS/0kqj+HkmI+hUQaztvsFlPRpelIODQEmkT2P0WTE3i8i4sF21d+LPOuRJtbjSKXxkqTNyYCUZcgw7/Mj4ql2fCDLi31wRBwnaTxp2pxCjsPZisyKcnOLfU4tpzRSFibzIW5FBqysQjr8v9OiOpYCPh8Rn5e0FxluPi/5oRxDBmLcWHzA7Q5gOpAMrliGNJddAvyjUx/nEoTz4RIEs05E/EOZ1Pcgcoqn3wAXNwKs2ixLQ5GNIc2/7yIbOdsWH9l3yAHm/2q3LN2AldlsULr+3yBbkdPIVEnXkX6OWtLKKHP2PQH8Hxkg8CcyK8DtEfFYHTJUZBGZJmo0OUzhXPLDdDpwR9SQG7KY6ZYEXiQV6nRyFutLKZnm2y3D7FKJ6FuEHBO3BfC7yNkPGutaplSKwlyEVGAnkT6r20if0G3t7sWX92do5AwO95Af6aXJgKqxRa4PRcSN7ZSjD9mWIcdF3kqOLbuHbLDe0KmGkKRdikwXAV+JiN1LkNBvI2KtTsg0GLEymwWUk1xOl7RC+dCIHL+0E7AeGar7mRrkaLTYGvKsQ4ZWb0gmQL0dOKZhgqwb5SDT/ciP1NPA9cDXoqaM56UluyYZoLM8GVV5fpThAYMN9Yy1+iEZmr4lsCI5MPc3ZCDLE+3orZSP95vIqVbWIfOJ/igizul3xzmrc1MyuGQysHVEjGtavxtwaXR2bOS8ZPDHquTz8wqZVuvqiPhLzbIsBPyYHP5zbER8V9LxwAsR8cV2me27DSuz2UDS+cAXI+LOStmiwMiIuLfdD1fl43cgmYXgd5V12wLrR8Q32lV/L/I0eg/LAVtGxBmVdVuRkXFfjIh721R/Q7kvQfbKDiryLEwqs7WBCdGhqXgGinJA7BbA+eTQhveSpr9DomR6aEEd1XF4BwNHlGu1IDlg+S2UDCntMpVL2o4cg7gA2Qi8h+wV3h0Rk6pytrruAconYK/I6YlENorWJRMM/zIiruuATMuTro23kqbgC8hpeR7u5LUaTFiZDZDKB3s78sO8VXFeBxm9twk5qLJOR/Xd5LxWD5TIt1fUgbQ2FeV6Iqlcj6nI0/bgk0r9HwfWK0Emjfs1P/BK3QEws0oJwhhPDl+4KiLWLQrnE8CREfFyi+ppXKvPAatHxIcqCm5Y9MzC3VZKz2ckOVRiXVKxTSMTYp8RGeFYK2qa6Toidm1aX5vS0Iyp8oaTZvNXyZnRF4iIh+uQo5twaP6ssyI5LQZA40O9KTlxYJ2KbH0yO8MDAEVxDAF+VHqJtVHpha5FCacmg1IAvlJ6Z3XUvx1wWlluJMf9CjnebrDzONmrXBi4QdI2ZGb2tVulyGCGa7UDOUYSMjM9wFGSDm5VXb2hnqTF25DK9CxyapXzSB/wsqQ5rxM0Gjz7kP7ehn+xMTj5Q3UJUml8nQ78kMxR+X7SpbF8sTqYClZmA6TycF0ALC5pT2BpSWuR/qErodYxMVOBpyV9qPHCkWHDw6OkH6pJDuC/dv1bSNMYFX/dnvQo/3bzG+BzkqoKYGsy+/ugo+lZ+QDwcERMJoMxvkgGtHyzTdX/BhgnaYnomSh1czKYqZ1jyxrK9BCyx0FEPBcRf46I44FDSy+x9gHAlXpvAMZKWqjSU30fmay67ahnbNkWwKSI2JgcOjGF7MXuSwdmEBjs2Mw4AJQh5o3Bq0h6Bznh4zQyim8icFjdTljlGJgPkkMERpGt2h9GxO874RRWJlk+howEu4/ssb4aEfu229xYPkILkrMUN6YzeYlMyTSun107hqSVSB/ZVsDCEfHeyrrR5Li8e9pQryjPCnnNXiGDMUZGxHtaXV8v9S9DTgu0Zvk9X4lsPB/4bKd9m8qpi44D/kb21uYDdo6SC7GG+huBXUeS9+SgyroFgFUihwzYV1bBymwASLqGfLgvKw/ZcDJd1NLAvVFTrr+KTX9rUklcrZx9eGNyevkbO/EhKKaj7SLiYmUk486kP+QK4KbI+ajarczeR4bhTyf9MK+QH6E/1+UHmlUkrUAqs5PIoRXXkEEQ5ytnyF4sIk5oQ727k2Hd0yWNJYNkXiGnC/pPDfdqMTI679dRMvKXa3F2lFm1O0VpYNxPBg1tTw4TmK/Idmd/+7ZYDpGZhQ4ge+qXARdFaydlnavw5JwzofQ2IjJb9lBJ25NjqJ4gQ+DbHopfodHy+DwZrg05qPZV4No6lEaVSu9vHDlE4WJy8PZNwN+jklevHTJVnOTbAJ+MiHMkLUmmYoqI+EGr62wlkcM7niJ7kpPI4QTblx73jsCu/e0/K1Su1buA/SLi15KWJv2Mk6ISgdru5ycinlVOMnqgpI3I3uEwsvFTfa5qoRIAswbw02LWu7WY74dFjVPRKIclXB05pu0oSSeT/sVNgO9LmhwR+9clTzdhZTZzdiHHSUG21PYlE4+eRE6c+A56gh7aSvkYLQuMiYgzJe1BZsufDmwi6VNRspzXROOjdyBwSDGNfYX8EN8n6UONAJU2Mw74sTK8/GjSxPigpK0i4qoa6p9lGqYkMkXSpZHT9FxFpt8aCfw8Iv7ahqp3B04uUZ7HkEpkjKR/RM4G3laUUwMdTQa3/JPM0r8mGQByTdms7sjTecgG4V7ksAgkfZnMb/onSQ9FfZO5rghcqUwi/ASZku5M4Exllp3hRb6OTYszWHEAyMw5jwz42Bz4Fpm5/MzIGWYfIl/E/zpta2A+4O/KMPQPkB+Fd5PzYNWpyBoO8/nJyRRHkVFp/4iI5chMHMPbXH/jZX6czD7yJzKDxbvJdFDLt7P+OSF6Zv8+khyUC9lQ2ozMWHJNK4MgSkNI5H3ZhExZdTM5HOANlGlyagi8eJp8b74KPBkRp0XE5yPi8kbQUN1+oEovcAzwkKQTSPPie4HFyd5rXbJ8MyKeJpOGrwTcI2mCcr7C26NMBmpF9r9Ymc2c28iP5VFkfrSvR884ru2pqVdW4WGyp7gJcFxpMX4Z+AvUn2E8MlXVL8nW9uMR8aMS4Tky6ktH9EPS5/PLiDipRFauT88MAoOKhsJQZsJ4LiL+IukA0qz4LuDLJSiipR/1crzDgUeBP0XESaSJczUyurHtiqT4l08ih24cr0wuPFj4DpkRf1EykOpeMitKLdGwxfeMpAUj4rKI2CUiFgN+AHyMzOBv+sABILNAJQBjMTJUd7eIqGXOp74ilyQtTuaQOy4i7ulkhJN6BkofSQ7sPLQu/0clAmwIaeZcJyK+1u565wRJbwY+SfZsVyVnPRgDfDcidmhDfc1p0IYA7wTWjYij23WvKv66JYEXI+IF5dxtu5Jm1h+W4KGOR+cVU/kzkTOhvwfYNSL2rFmGK8hB5KcAZ0UZZqIcKvBC3T7FbsHKbCY0WksVs1CjZb1uFsctdQddkIO1a510sz8q/p9q2QJR0/Qdzde/OO7nKabgQUt5tj5DmpP+LzIV2unA9RHxoxbXNYR8Xl9rKl+UzJDyn3bfK+U0RZ8jQ95fpCeX6GTgB3UGWjTJNQ/8r+lOmZ5tSB1+30pD442kFWgBsoGzNDnp7nERMaHdcnQzVmZ9oF5S+3Si5aictfkdpC/q/qZ1Ij/adY8na7S01wUeKjb+jtLXx3ow0k8vezQ5BOQj0aIku5LeEhE3N5UNJYd21JmxptEAvJc0a/6D9P8uRWaO+TSZg7Kls2nPRKZR0TR9Sqd6PRWrz/HAxCiRuJI2I82MbyCnf9nbvbLesc+sb/aTdLCkjYpZ8b/+BCV1Xbt1yIjKD0v6hKRtlTn7iKT2B7uiMH5AT9qq2qj4nBaVtFlpeLzakKvGezNbVJ6jIcochY3yyRGxZ6sUWeGnkp6TdLoy/yMRMb30Aoa0+1pVfLg7Am+JiOdLD2PxsvxgRPyenDqo7kbRYZIukvRp5UDp/waDlHe8tme78h4vAsxfZJg3Iq4FziazDD1NJjs2veCeWS8oI/Q+QXbx5yUfonvJYJB7ooW58gYgy1Ayk/mbSXPUwuQA6fvI0OaboicdUR3yNFqQa5G54r7a6MHW0eKvmGNWAY4nQ6pXIbOO/DwiBmXqKoDiM9qSHNz+UNO6tvWylXNffZL0Ub0CnAmcECVDfTup3K9rgcMj4o+SvkD6Le6vMgAAHPVJREFUyi6MMihcZVqldstTlYucI2wNMphqFfI9/yM5+WatcwJW5FqFTGF2NTlQemXg22Rv9krgU9GBed66ASuzflCO6VqXNIOMpidr9R0RcWEH5FmAfAE3JidxXJr0tdzeAVn+D/gsGQ7/6ahhjFKpd+HinD8GWCQiDlFOjzGOzAP5aES8vQ5ZZhVJO5JjlyaRefZuBB6INs3z1pvpVdLbyFmTdyOzgLRsYHYv9TcU2bLkDNKbkkl8tyATQu8HfKxTiqPIOJTsCa1ADovYgBzrd2tEfLZDMq1FNj5WJYdPTCAn//1tRKzdCZm6ASuzXqgGFFR6IiPIuYS2JpXZz2twmDd8U6PIaeVXJX0Nd9IzF9S1nYoAU6YgOpgcbPo8mU7qc2TEWjvmwZoPOJlUoMsD/4yIs5u2qX0KnIFSAlNWIsOs30ZmkLmdTMT8T3KMXMtn5S6t/fXIzBJTKuWrRcTdvQXwtKDOhiITIHL4yObkgOjdyDGI50XE+q2sd1blK8vrkWO4Xi7KbVNgvoi4vEZ55gXeQzZU7wN+03SvViaTJfyxLpm6DSuzJiov4RJkXrQlSFPjzWSmhickzR8RL9WgzBqynEVm7N4EeIw0h9wEnFt94NuNZgyxHkkqlMsjw7y3JFv8e7b6w1ipfwFygsJG6/lV0gRzIzmLwEudUuwzo3LtliV7JZ8mewPrkL2V64HPt+p+VurbAdibVKIrk63884FzIlMmtQ1lKri3kAmf/wW8AEyJnPjzWwAR8dm6gy4q79VKwBfIPKtrkbM+nBQRf65RlsZ9Ogj4MOmHXp1sfPyHfL++X5c83YzTWf0vjdQ2B5AfzFfIB2txYH9JV0XEkVDLANNQRritFBHjJN1Jpov6KJnM9wrSXFUXjfP9OvmBehvZ4j4f+FtEXN3WyjNDxHeUWdeHk1GeHyHv0Z1k1oRb2inDHNC4djsD90fEbaQP9neSHiPnLWvHvdyLHK+0CvAmcsD0D8he/ifaUB8AknYmn40byN7Y6Ig4pqwbTobn/6Vs3qn0VfuTs1w/Q6aEexi4WNI1EbFjTbI0noslgc9FxJXKQf9Lk+bYRrCT01fNBCuzJiotxF3Ij+Vx5CSGN5XlG6HWh2sj4OxiCpkcEVdIehb4UETcVUP9/6Uo1zHA+hGxflGujQ/S8ZK+GjkfV8sp/p9lyA/RJZHTh9wBfFMZvrwvPWmhBh2Vhs8E4ABJnyLP426y0dRSv1lp7c9H+lZvIhtB+0XEw+UengdtDUX/APlxvkqZmf9YSTtExKUR8ZSk8xrvTwd60436tiSjLE8GflzeLZGBF/UIku/USGBb8nt8ZeT4yAckPdyQ1Yps5gzqEOZOUcxovwZeJhN/nh85/mU+0scBPS9Eu/kjGZr7AvCkpHXI7CPTi6x138PVgEskbQDcFRFTldnGN26XIiuMIqPxfgA8I2mUeoYoXEtmO7+0jfW3hIj4O/Apcj6xj0i6ERhLzywIrazrZTIw5mWyB3KQpJ1Iv+91ZZt2RE8OJxMaP1XqmED2MJ4t64d08uNcFP385ODkV8nrs7AyUfU2ZCOp7UgaXd7f+ciB4++T9CdJH5M0MirDTczMsTKrUFEM69DTQroc+Kuk75HRcw9Ae1uTpXVI+VgvHxEPk07hB8hW4+Jk0uNOcCU5FuYCcpqM+ahnpu1JZLjyEqRZ8XPAPpK2kPQD0qQ1KKncz6WV889dS/rNziUT/e5f7nGr61sdeKyYZ08g79vOZOPs1Tbeq/9v77yj5SyrNf7bJBHpiy5NpQQSBAOhiIFEeg1SBaUJSkCUXu4FkaJSriAgCiplCYTQLtFIBzFUg1SRZug1QEAw1IgQeO4fzzvJl9yQBJ355pwz+7cWy+TMSd7tZM63392e/SZeGjs8Ih4LC/cuKKllDnRWiYhDwhJwUaLEt4HhuHvwV3jbd13dlcfhFTMvyPOFK+IocWMcmX2zJjt6BNkAMh0i4iZga0lvlQfDntiBjJL0ZKsL1pUOyhOwsvgprTrrkxCW2vkcnrnbF++h2hC4Cvi5vE+tZU0x5eF7JI7O1sHdcQtjaaQRksbM4I+3nfAm5ZWwvVcB1wB3q4lzi5XmhqXwhWMQpTGmNDy80OiYbHUDUzljYTyPeBSuZ/5WFoNuh5pOH2CopFERMQpfkK7FF8TlyreNUw3LXMMLUr8labPwWpxvSDqu8vq8AI1nUFdtbOpKpDMrVLqK+uFI4zdqwcr6T2jTWGCQpAkxRetwH9xiXefW2/6SxkbE0VgZ/9flYTkPnutq6abtyr/NVsD6kg6ovLYo8K6aq5rRNCq2bw3sLmnrsMDwzvgi8C9giJqktVk57zBgCXkObw5Ze3FJLCp8dTPO+jdsWx6vvLlM0hVtsqEXbjjZCGdg1sBzZrcBt0j6S012jMAdpVeWzs7ZJB1cXlsVGCDp/Dps6SlkmrFQyU1vDxwI/CIivlJ3TaqSIloM1zmWLfa9V77lW7g9v05WCW9EPhpHQpTUyN9wM0NLV91X/m3WApaMiC+F2/SR9EpXdWSFxm1xGWAsgKQH5B1eA7EOY9NEoyvv1XzA0+VrjUhjP9yBOvlzVieSHpe0UxsdWZSMysp4VvHXwOHAOfjnrK4NGAvgBpmG1uoAvMaowffw0tQuL83WlcjIbDoUR7I7VmsQbiM+XNKbNduxPx7gPBuYC8+frFtSE7W26pZazzFYGHYgfk9+BRxfbGqJikXl/E/jnWkr45v1rfiB9LSk11t59n9KaTY4DTvjq3BH7COSnmryOX0ajjE80H41HmQ/B89KDgd2kgelO6rVu5J+XRh34K6ussw2PCg9G1bIryPF2As3cf037sD9FLCIynxmRNwLbNLVP9ddjXRmTPVBXwOnH24C7pWHgdfAQ6cHtrpwXXLnO6nM45Sv7YkfgvPgHP95kh5pdd2ucn6jfrcCVvZ4rtws98Z1qyslnVXnwzEihmC1hLWBMZIOquPcf4fKZ6sv7owdjJtY3gcelXR2E8/6Ov7s9sbR+3L4lr8Z1q78QzPP605U0q8HAP0lfSe8BHNiqQWvKenSNti1AJ4FPAJ3UY4D5pK0faddOP5T0plViIjNga9iJfgJWGZoTB21s5L2mR83CADcgluHz5T0WqvPnxGlcD4Sq2zcAdwm6cny2mTZolYWqUvNaWHcSv2ApPvCw6VLSHq8Vec2i1IHeVHSq2EtyU2B1yT9tkl/fy98EbqwNA69j9vv7y9nTo7aOpnizHpJOrXytWOAz0jap32WTa4pHo6Vhv63rgtrTyGd2XQID3kOwSogzwB7q4mt07Now8bl/I1wrWUUTlV9WOcHvHKjXRa3DA/GM1+vAedLuqqGs9fDP+RP45rPGJwWugsPu3a5H/iKk18aOAlHSmvhEYtLgJsaaa4WnL0tngdcDKdkn8MXs+s6vSuuRGGjcZr6cvyZGoHHI+6f0Z9NujYd78wqD8zBwCSVWZjy2mBgL0m71mBH4+E3B7CAKksDI2JnPEc1TB4QrpWYZlFpeV/OAC6SdFKrorIoArgRcQEeKl4ct5pfCvwMGF69YXclYurxijewEHPDER+A18Bs38TzGu/VIOAeHMEug+usG+CFj2fM6O/oFCJiEZx63QbLov2yHSnGpLl0vJxVJSe9IrBNREzAQ62XA+viHHYd8lWBm012BX4dEXdgGa1LJV2Ed1DVTngx6SkR8TBOW90j6faIuAcPlMMU25tKeTgHVj/5KxYy/pGkByLibhxtdEkq0eIaWA3jDOBiecZpWaAp6cXKeZPKRehEYGdJ40qd7ik8SzUf1DNb1lWJiG2w8PHiwLN4YP1t1bifMGkd2fYJhGemXsL7ua7Bt9kxOAo4t3xbq0WFG46yL+5yGo1FdF+NiPPC2oztYG68MWBh3AhzVkSch+eV7ofW6MZFxICIWKw8eI8tdcObgbMjYm8c5dzc7HObSXEuJ2PFkjfwsDT4ktS0TsZK+/Y38fLYceU9OhF/ntdSmQXsNEfWeG9K09BB+GJ0FRZBOCwdWc+ho9OMEbGcrOhxJF6SeHFEzI3rDAG8X0fRvJJiHIA1BlevvDYMdw7OC+wjaXSr7fkYG5fC0etnsML3fZJubVWROiKuwCtRHisNEy/iy9dhuEvvWkmXN/vcVhFexXIG7iqc2IrUdUR8H6fNnsPv0XHAFkA/SYd3YlRWKSNcgHf/ndNoHMLC4ZdLuqS9VibNoGPTjCV9tVpEjMQ/+N8HUBnALU5kDF4t0lIqD5jZgRciYkVNUfi4D6eJ/oAjtZY7s8oD4LO4CWVH3IAyWtIF09jeCkc2EFisOLJG6mwPWYbp+Cj75Jp9brOpOg9J14fV/ZegdUK2J2MNxt2BHWQR6J3xQxtalA7uylSyBqOBNSJiHlmP8fGIeG8GfzTpZnSsMysPmcvKB/oIYKOI+DYunl+O53NG1WzT3RExBjgzIq7CHXDfxc0Oy1FUx2vkSOBtXPNZDzgxIr6FZZlaOVy6Ex70BdcQP60peoJL4WaYvVt4flOoRkElgh2P94k1jUpUvw+wtKRhlddWw+/d6GJPR80slSzLh+WzOhprRI6MiPFYX7QfrpslPYCOrZlFxKkRsQuuu6wraUssFTUet4H/Xt4qXdt7FFbzPhVHiZ/Frdw/xcsvd8MKDi2n8tCbHThL0l8lnSbLL83JlFm4VnEAMLjMt22EI7MG38Z1jy5JifiJiM9HRO/G79UilfqKw7wamL1cNho8CGxb7OnEn/WfAv3LjN2LOOV6DJ7h7A1s1+JLWVIjHVkzKw+Y/8LrMObH6cQLVdmUXGl1bvUwcCOlNww3BnwFpxXPkXdfNRoJlpO3E9dCWL7qF8DreE7qBezoHwbWkRcstqolfy28BXlTXKg/BDhb0rsRcR1whKSuulG60QF6OnBweZ/649b4ltVfy2d6bSz59QKWX6tzC3mXokSl55QLGKXuujvWYBwp6co2mpe0gI50ZlXCazH2wMofH+La1PmqV5V+Dtx6vjVeaLgLMAx3W7VsKHkmNi2P35OV8CLQubD46X2qyG3VYMdQ3GU6ADvWDyT1r+v8T0JltuxQnPL7XkTsiLvo3gd2bKaDqZy3QHGac+FGoWE4sv+BpPEd2vhxBvCcpJPDknR7A1/A6esNgO9Leq6dNibNpSOdWeUhMAR4vNQyKAOnB+MFfQfXGJVtAewnadPKa18GDlETB2v/DfvmlvROWBFlXZz2fBl3y42U9GyNtsyF58xekdT0rczNJLze42qcDh2KFSY2AZ6SdF4LzmvoMT6A9TInYWHh8cD+6gZyX80mIo7HF59jI+Iy3A37K0lPRMSFWHv19PZamTSTjnRmMLk4PBIrMzwI3I73hH1U+Z5ahD7DG6VPwO3mI0t9Y388y7V7jXY0nPyWwJY4xfcmnlUajeeltsGO7XBJE1ptU3ckIjbAqdGV8DLIByPiZpwevbOJ5zSaPxbA/1b/lPRyWEJrArAdbtzZW96a3jGUjMuJOFLti/fgPVte+xO+PKZ8VQ+iI51ZJSKaDaccNsU32k8BP5u2/bxFNiyDI50rS/SzKS5O/xPrxS2Bh4XvqsuZVWy7CdftbgZWKXauhN+by+qyoztR+UwtgztAZwP6yAPM6+BN3E0bfJ/mvEVxZPaSKmtlImIVXGtcs1nndifKJXFB4J1GSrFc1I7q1PekJ9ORzgzcbVZNk4X12i7CQ5Rnt9qBRMSGOPr6CM+yXSSvdhmIm1LubMdtOiI+hccSLpb0SnjX0/x4YPppSS/U7Vy7ExFxLXCQyqaFkh5dHLfIN7WBJyLmxXvlHsdNHx/gNPCTuPb7AbCMpC4r+1Un4a3kQ/HQeg5K9zA6cs4sIpbAM1NPYammMaVQ/i5T9AZb7eX/hB9CfXEH43ER8SFwA3BH6dyrbQVE5ayBOEpdLyKOxnWev2OVcaDz5pVmRiXdNwiYVx72btRbG5uNm9bIExGflbc4rIrV9/ctTQ798Dzi2pIaM5LpyArlcnYBvkAmPYyOjMzKjXZj3PH1OSzRtAjwr2oTRs029cWpvK8AX8RisbW3VocFfK/ED8bP4Bv/fVjwuK171boqlZTfHrg2s2sUlZJSPztI0tAmnnc1jvbG44vPcZXXFgHmLw4192ElHUOnOrOtcLfZvMDnsZ5db6zP+HirHwIx9fbmPfAg8g146eXbEdFX0hOtOn869lS3IR8labfy9YXxuMC2eBXOC3XZ1B2JiHmAMylzizjqPgq4UdJZTT5nFTxQfiDOLlwEXCapbpWYJOkSdIwzm6YN/khJgyqvteUGGxEPAmfjZo8VgaWA3SQ9XLctxZ7dsb7fmcC5ksa1w47uRHgR5jjgYUkTy7DuD/GWgWfwqpFj1CQtydLwsQLOKNyBJc+2ADbHs3ijJX2nGWclSXeiE53ZCOAWSedGER0tg60LSvplDXY0oqD1sCr8ZpXX9gfmkXR8q+34GNtWwrf9Abjp4yX8wLwE+ChrZf+fiFgf+AuW2VobX07+iIfMP5JFbZt53hj87/IssBC+mL1UXusPLCTvm8sUY9JRdIxeW+VBfD9+0FB50OxIEfGNFmvYVYawBXwUEYPKzBt4x9WQYke00o6P4TEsYXUK8BMsxjoUmCMd2fSRdBMwETv96/Fn6UosXr1JMz9PEbEdlsX6GnAW7lacLLgsaayk28uv05ElHUVHdDNGxPzAW+UH/EpgVFH/uBt4Fa+XvxRa26kXEXNKmljOuaWkpPYBbi0zMevjnVfgi0bLH0iViPVrWAbpfeA24GZJp0bEQiV67ThJpJkREZ+SlzuuC8xXRjquxSnAdXAzyMgmHnkQ7oJF3sN3B7BasaW3pElNPCtJuhUdkWaMiIPwnqe+eCB5djwsvRkecD2nzHi1erZsLxwBPoBbuO+OiHXx0PZbwAOSrmnV+TOwa068Y2sortutjjcIfFfS9XXb012IiK/iWufOeHHqnyqvrQK8KemZJp53LLBn+e0IYENgJxW5qrxwJJ1Mj3dmETE7MBhHG7/B9Ya/Yk3Ge2u2pQ9ekLgnljt6EkeEoyS9Ub6ntgdSpX63GTBM0raV1zbAwrh71WFLdyO8zmVp/O/4DTyfeC9wjaSHI+Jc4ARJT7fg7C/jPXeb4+aT4Vjpo6n1uSTpTvR4Z9YgLJa7PO4yG4AHJ8fjVeq1RB/VVFB4TcjGWD+vH44Qt5f0Sh22TGPXMriD8TrgaklPlyhyQ0k7pOLHx1MGpVcHxuItA8sCc+M64xo1nL89rs+Nk7RVq89Lkq5Kj3dmlejjWjyIPKFEa4OwmO5dki5rdUQUEZvgdOLSwIGaWkprSbwgdESrzp+OPYvhtOsESQ9FxEZYRPhNPLzdG8+c3ZvObMZEWcFSfr0cHsb/h2rcuVaZXcwuxqQj6dHOrNLcsATWGxyhafaU1fGgLp2Jj+MbdD/gX0yZKxsuqZYN0tPY9Au8O+30yoN4BRxlPI+75jp2ueOMqHyuVsX1si1xlD8Kp/smttXAJOlAenRrfsVJ7QAcBpxcOgin9z2tZGssHDwSt3AfC5yH9fqOiYhtarBhMsW5boSV3P/R+Losjnsr8J68SqQd4wHdgcb7cgCW+joXt+fvArwTEae0y7Ak6VR6tDNrIOk0nDp7EBgREXdGxE+KRmMdHIpv7hQ7TpZ0m7wc8FDg6zXZ0WBz4AVJr5eUKzDZyfUCDo2IBbMzbvqUdF7gtvhROH28q6TV8SbjP7TTviTpRHqsM2sMq0bEsqWZ4V1JR0jqDxyJa1d1/f+/EfhGRDyP5aLurry2Po7WGh1ydfAcMC4i5m/ILEVEn+K8FgMWkfR6TbZ0V+bFslWfxsPLXwyvGFkGuKWNdiVJR9Jja2aVusYPgIPxKowbsfTQTY025jqbGyLiS1hYeAe8xn04sBtu/qjVeUTE7/CD+HTgbpWt0RFxEXC/pJ9mM8GsERG74LqZACTVHWknScfTY50ZQETMgRU/DsCR2CA843UDcK2kS9to23Y4xfimpE3b0TEYEYfgVvJeONIAq47sK+mNHMKdPuGFpWvgNOMV8sLSwbih5sXGzGCSJPXRI51ZpR1/KzwMPLTy2vp40HVxPNR6ebvsLPY0Ish2OLM+WIF9RWA+3PhxYZ02dCcq7e+H4vTwxPK/Y3Gt7BI1SR0/SZJPRo/UZqxEE08AvSLiu8ANkp7CkcgteOfU94C2OrOGA2vHHJekD4CHy3/JTKikXFcDfiTpToCI2BXvLRuA9ROTJKmZHhmZVYmIhsrGW9iRLY7rVPviHVRnt9G8pJtQifZ742hsFeDyqvZiDpcnSfvokc5s2lpPeJX8QOA93AACcAWwuaR/tsHEpJtRSTEejjdvf4Abil7GGpv3V2f2kiSplx6XZoyII4BJ4UWT92INxnnxjNk6wDtFomnjkmZLkplSSTGug9PTvbFaytLAl7Fzu6091iVJ0qOcWUQMBI7HxfjrcMfZa8AxwNW4hvY8TK4XJclMqTTpDABewRsX3gT+XKTS1mBKxJ8kSRvoaUPTD+E9XEOARSUdgoVzfydpe+DHkl5tp4FJ96NSB1sTzwk+EhH7RcRckl6U9HtJ77TRxCTpeHpqzWxlYH9c09gf+KGkG7NAnzSDspTzUOALeD/eltUtCEmS1E9PSzMGQFlpch5OLw4C3i1fT0eWfCIqKcal8D68fsD1koZExDx4CH/8DP+SJElaTo9yZtUORkl3AJtExDDg2xHxtqSH2mdd0s35Ea6/bgq8AzwFLCjptNwukCTtp6fVzKbH+cADwKIwJXpLklmhRGULAatJOgx3Ld5cXj4pIlZKya8kaT89KjKbHqVr8eeV3+eDJ/mkLAHcEBGbAS9Jer6kHVeQlOopSdIF6ITILEn+Ux4F3sAjH0+XEZD9sSRakiRdgB7ZzZgkrSAi9gU+h1v0rwaGS3qlvVYlSQLpzJJkukREb0mTIqI/8FXgMeAu4MOcVUySrkc6sySZARHxF+A+rIgP8GfgJuAaSZPaZliSJFPR4xtAkuSTEhHL4/1ucwLjJA0rX+8H7AL8GLgD+HvbjEySZCoyMkuSaSi1sS8Cr2OVj1OAeyRNbKthSZJ8LOnMkmQaImJJvIDzC8DKWEFmLB6UfkLSI200L0mS6ZDOLEkqVPaWLY3lq8YCg4G+wALA28DRlZUwSZJ0AbJmliRTs2ZELAz8F/A/kp4HLgKIiC2A2dKRJUnXI4emk6QQEX2AAIbimtngiNiuqH0A9Af+1i77kiT5eDLNmCQVIqIXnivbGit8DMSXvtmATSQtNYM/niRJm0hnliTTISL6SPqgbJJeE9fLxku6ps2mJUkyHdKZJQnepiBJJaW4PbAh8DxwraSr2mtdkiQzI51ZkjBVF+PJwETgQ7y7rBewOHCBpB+008YkST6e7GZMEtPYQr4RbsW/ADgQeAk4Ge/ES5Kki5LdjEmC99yVlvzzsWNbEHhb0jgsa/XHNpqXJMlMyDRj0vFExBDg0aoafkTsh1VA3sBbpge3y74kSWZOOrOkoynSVc8BtwMPAbcANwCTgL2AV4G7JD3TLhuTJJk56cySjiUiAlgUOBM3fNwFLAUsW349KnUYk6R7kM4s6XgiYkEsX7U8cDEwAVgHGAJcL+mkNpqXJMkskM4sSZgsZTUUWBW4WNKjZX/ZP3KzdJJ0fdKZJR1LRAzFM2RjgZWAubAm40Bgx0wxJkn3IZ1Z0rFExPt41vIK4AycZvw88Cxwo6QnG8ogbTMySZJZIp1Z0rFExJpYumo34DXgBOB3kt5rq2FJknxi0pklCRARXwP2x+nGx4EfSrq2vVYlSTKrpDNLkgoRMRdwMPCypHPbbU+SJLNGOrMkSZKk25PajEmSJEm3J51ZkiRJ0u1JZ5YkSZJ0e9KZJUmSJN2edGZJkiRJtyedWZIkSdLt+T99EBf+gMFpPgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "plt.bar(np.arange(len(male))-0.2,male,width=0.2,color='b',align='center',label='male')\n",
    "plt.bar(np.arange(len(female)),female,width=0.2,color='g',align='center',label='female')\n",
    "plt.title(\"Number of Women and Men awarded Bachelor's degrees (2014)\")\n",
    "plt.ylabel(\"Number\")\n",
    "ax.set_xticks(np.arange(len(female))-0.5)\n",
    "ax.set_xticklabels(majors,rotation=70)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3. If you still have time, try to replicate this: https://matplotlib.org/examples/shapes_and_collections/path_patch_demo.html. Once you've done that, try making your own shape!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\u001b[0;31mSignature:\u001b[0m\n",
       "\u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbar\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mheight\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mwidth\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.8\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mbottom\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0;34m*\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0malign\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'center'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;31mDocstring:\u001b[0m\n",
       "Make a bar plot.\n",
       "\n",
       "The bars are positioned at *x* with the given *align*\\ment. Their\n",
       "dimensions are given by *width* and *height*. The vertical baseline\n",
       "is *bottom* (default 0).\n",
       "\n",
       "Each of *x*, *height*, *width*, and *bottom* may either be a scalar\n",
       "applying to all bars, or it may be a sequence of length N providing a\n",
       "separate value for each bar.\n",
       "\n",
       "Parameters\n",
       "----------\n",
       "x : sequence of scalars\n",
       "    The x coordinates of the bars. See also *align* for the\n",
       "    alignment of the bars to the coordinates.\n",
       "\n",
       "height : scalar or sequence of scalars\n",
       "    The height(s) of the bars.\n",
       "\n",
       "width : scalar or array-like, optional\n",
       "    The width(s) of the bars (default: 0.8).\n",
       "\n",
       "bottom : scalar or array-like, optional\n",
       "    The y coordinate(s) of the bars bases (default: 0).\n",
       "\n",
       "align : {'center', 'edge'}, optional, default: 'center'\n",
       "    Alignment of the bars to the *x* coordinates:\n",
       "\n",
       "    - 'center': Center the base on the *x* positions.\n",
       "    - 'edge': Align the left edges of the bars with the *x* positions.\n",
       "\n",
       "    To align the bars on the right edge pass a negative *width* and\n",
       "    ``align='edge'``.\n",
       "\n",
       "Returns\n",
       "-------\n",
       "container : `.BarContainer`\n",
       "    Container with all the bars and optionally errorbars.\n",
       "\n",
       "Other Parameters\n",
       "----------------\n",
       "color : scalar or array-like, optional\n",
       "    The colors of the bar faces.\n",
       "\n",
       "edgecolor : scalar or array-like, optional\n",
       "    The colors of the bar edges.\n",
       "\n",
       "linewidth : scalar or array-like, optional\n",
       "    Width of the bar edge(s). If 0, don't draw edges.\n",
       "\n",
       "tick_label : string or array-like, optional\n",
       "    The tick labels of the bars.\n",
       "    Default: None (Use default numeric labels.)\n",
       "\n",
       "xerr, yerr : scalar or array-like of shape(N,) or shape(2,N), optional\n",
       "    If not *None*, add horizontal / vertical errorbars to the bar tips.\n",
       "    The values are +/- sizes relative to the data:\n",
       "\n",
       "    - scalar: symmetric +/- values for all bars\n",
       "    - shape(N,): symmetric +/- values for each bar\n",
       "    - shape(2,N): Separate - and + values for each bar. First row\n",
       "        contains the lower errors, the second row contains the\n",
       "        upper errors.\n",
       "    - *None*: No errorbar. (Default)\n",
       "\n",
       "    See :doc:`/gallery/statistics/errorbar_features`\n",
       "    for an example on the usage of ``xerr`` and ``yerr``.\n",
       "\n",
       "ecolor : scalar or array-like, optional, default: 'black'\n",
       "    The line color of the errorbars.\n",
       "\n",
       "capsize : scalar, optional\n",
       "   The length of the error bar caps in points.\n",
       "   Default: None, which will take the value from\n",
       "   :rc:`errorbar.capsize`.\n",
       "\n",
       "error_kw : dict, optional\n",
       "    Dictionary of kwargs to be passed to the `~.Axes.errorbar`\n",
       "    method. Values of *ecolor* or *capsize* defined here take\n",
       "    precedence over the independent kwargs.\n",
       "\n",
       "log : bool, optional, default: False\n",
       "    If *True*, set the y-axis to be log scale.\n",
       "\n",
       "orientation : {'vertical',  'horizontal'}, optional\n",
       "    *This is for internal use only.* Please use `barh` for\n",
       "    horizontal bar plots. Default: 'vertical'.\n",
       "\n",
       "See also\n",
       "--------\n",
       "barh: Plot a horizontal bar plot.\n",
       "\n",
       "Notes\n",
       "-----\n",
       "The optional arguments *color*, *edgecolor*, *linewidth*,\n",
       "*xerr*, and *yerr* can be either scalars or sequences of\n",
       "length equal to the number of bars.  This enables you to use\n",
       "bar as the basis for stacked bar charts, or candlestick plots.\n",
       "Detail: *xerr* and *yerr* are passed directly to\n",
       ":meth:`errorbar`, so they can also have shape 2xN for\n",
       "independent specification of lower and upper errors.\n",
       "\n",
       "Other optional kwargs:\n",
       "\n",
       "  agg_filter: a filter function, which takes a (m, n, 3) float array and a dpi value, and returns a (m, n, 3) array\n",
       "  alpha: float or None\n",
       "  animated: bool\n",
       "  antialiased or aa: unknown\n",
       "  capstyle: {'butt', 'round', 'projecting'}\n",
       "  clip_box: `.Bbox`\n",
       "  clip_on: bool\n",
       "  clip_path: [(`~matplotlib.path.Path`, `.Transform`) | `.Patch` | None]\n",
       "  color: color\n",
       "  contains: callable\n",
       "  edgecolor or ec: color or None or 'auto'\n",
       "  facecolor or fc: color or None\n",
       "  figure: `.Figure`\n",
       "  fill: bool\n",
       "  gid: str\n",
       "  hatch: {'/', '\\\\', '|', '-', '+', 'x', 'o', 'O', '.', '*'}\n",
       "  in_layout: bool\n",
       "  joinstyle: {'miter', 'round', 'bevel'}\n",
       "  label: object\n",
       "  linestyle or ls: {'-', '--', '-.', ':', '', (offset, on-off-seq), ...}\n",
       "  linewidth or lw: float or None\n",
       "  path_effects: `.AbstractPathEffect`\n",
       "  picker: None or bool or float or callable\n",
       "  rasterized: bool or None\n",
       "  sketch_params: (scale: float, length: float, randomness: float)\n",
       "  snap: bool or None\n",
       "  transform: `.Transform`\n",
       "  url: str\n",
       "  visible: bool\n",
       "  zorder: float\n",
       "\n",
       ".. note::\n",
       "    In addition to the above described arguments, this function can take a\n",
       "    **data** keyword argument. If such a **data** argument is given, the\n",
       "    following arguments are replaced by **data[<arg>]**:\n",
       "\n",
       "    * All positional and all keyword arguments.\n",
       "\n",
       "    Objects passed as **data** must support item access (``data[<arg>]``) and\n",
       "    membership test (``<arg> in data``).\n",
       "\u001b[0;31mFile:\u001b[0m      ~/miniconda3/envs/gwc/lib/python3.7/site-packages/matplotlib/pyplot.py\n",
       "\u001b[0;31mType:\u001b[0m      function\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "?plt.bar"
   ]
  }
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