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{
"cells": [
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"DatetimeIndex(['2020-02-10 09:50:23.198098', '2020-02-10 09:50:24.198098',\n",
" '2020-02-10 09:50:25.198098', '2020-02-10 09:50:26.198098',\n",
" '2020-02-10 09:50:27.198098'],\n",
" dtype='datetime64[ns]', freq='S')"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"tseries = pd.date_range('now', freq='s', periods=100)\n",
"tseries[:5]"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"df = pd.DataFrame({'data': np.random.random(100)}, index=tseries)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>data</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2020-02-10 09:50:23.198098</th>\n",
" <td>0.485916</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-02-10 09:50:24.198098</th>\n",
" <td>0.519035</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-02-10 09:50:25.198098</th>\n",
" <td>0.903823</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-02-10 09:50:26.198098</th>\n",
" <td>0.006845</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-02-10 09:50:27.198098</th>\n",
" <td>0.719522</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-02-10 09:51:58.198098</th>\n",
" <td>0.132545</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-02-10 09:51:59.198098</th>\n",
" <td>0.783983</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-02-10 09:52:00.198098</th>\n",
" <td>0.380876</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-02-10 09:52:01.198098</th>\n",
" <td>0.302555</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-02-10 09:52:02.198098</th>\n",
" <td>0.732320</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>100 rows × 1 columns</p>\n",
"</div>"
],
"text/plain": [
" data\n",
"2020-02-10 09:50:23.198098 0.485916\n",
"2020-02-10 09:50:24.198098 0.519035\n",
"2020-02-10 09:50:25.198098 0.903823\n",
"2020-02-10 09:50:26.198098 0.006845\n",
"2020-02-10 09:50:27.198098 0.719522\n",
"... ...\n",
"2020-02-10 09:51:58.198098 0.132545\n",
"2020-02-10 09:51:59.198098 0.783983\n",
"2020-02-10 09:52:00.198098 0.380876\n",
"2020-02-10 09:52:01.198098 0.302555\n",
"2020-02-10 09:52:02.198098 0.732320\n",
"\n",
"[100 rows x 1 columns]"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/klay6683/miniconda3/envs/py37/lib/python3.7/site-packages/pandas/plotting/_matplotlib/tools.py:19: UserWarning: This figure was using constrained_layout==True, but that is incompatible with subplots_adjust and or tight_layout: setting constrained_layout==False. \n",
" fig.subplots_adjust(bottom=0.2)\n"
]
},
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x12200de50>"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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bDMfipvd0I1BAr9lWZqfghxyOzdCo2SmXC1nIqLVpsyLJZR4MnXOOf/rhRzMdB2di6H3h7JlWahrNufTf03RoYuh6wRb93B0FmaKjuRxXmYAeRHDt9KSd1aaLIOLG3RDJh9dTqfs8QQFbD9xyd3QAF74yAd00e0n/xr8fwCMAbgFwL4B/yxhbzbwRY+9gjD3IGHuwytFRQz+Sc/+qYOgf+uuL+Mgjl4zPPXO1hztOtAEgN6A7Nssy9EiwJcHQk5tJD+hVJEWDMEIYB5Uqk6IbXQ9/+MB5fPKpK6nH0z70yYLBdt/HasNFu+7MKLnMJwhJhm6nGbpesEUtFiKeOK7mCemRHsPQXSd9iycdF7PHSJKLFy4HYADJYq7nKfIC/UFAmYB+EcBp5edTEExcxd8D8CEu8DSAcwBerr8R5/x9nPP7Oef3nzhxYtpjzkANgFUw9O4oMDLboR/iwnYfL72hAwBYjwP6Zs9DGHHsDYNYQ7eMlaKOzdCq2anARQF9rSVutFbNxtBPArIJX3txTyZnTVDZapW2xReuCfeDzqRHQYh2XDQ1cVJ04ONoS+QO9GRxGcy7ORctUInkQj50M0MHFqOjezIZnP/9eiFPJUSBhDiYiosoKXoQk337AQrk2d32dZwUBfAlAHcyxm6PE51vB/BR7TXnAXwPADDGbgRwF4CzVR5oEdQAU8VM0b2hOaCf2+iBc+FwAVSGPkI3vonJ5WK6CBzLQsO1MVDYHbk86EajatIilv4L/+XLeM+fPpH7vMraqiz9fyEe86Yf29CPpCVz0sC60/ew1qqJncs0DD1YjIaeJEXNDN0LIthx8nEhAb1EUpQ0dBVUC2EqLtqMayqWLhcgjLgkVfq1JTV07fHeKMC7PvDQXMchjsPYgM45DwD8LIA/B/AkgD/mnD/OGHsnY+yd8cveA+B1jLFHAfwPAL/IOV9YdYIaCKqoFO0OA2MgfFpxuADAelsUbGx2vZR04pokl5DDtVksueRr6K0aWRvNwY1zjrNXe+gXFB+pN3mVtsUXdvIZ+kpsyZzGh77WcqfOHcy7sEjX0ItK/493xAK/iMRomaQ3DeZQUTTkgjqHLpOi6etJjyn0s36evn6li088dhkPPbc9/wPMgVPmRZzzjwP4uPbYe5V/XwLwfdUeWnmo7WZnlVyiiKPrBbCsbOrgmatdMAbcflxo6KtNB47FsNVLAvpqwzEXFkURHNtCq2bjxb3kZro28KW2DiQMvZcT3LZ6HvZGQWFLz7kx9N2YoY+ywYx2K9P40O+8YQVeUK4drI55D7hIGLq4Hoihm3zoN6428OLuaO79XMKIy3YJYzV0LSmaJ7lwzrEhk6LLgK5ex/quP8+2SD9XYTueFoeiUpRYmmuzmZOiXS8A52Z3yIu7Qxzv1CVLY4zhaLuG7X6aoTsWS63enHPRsc+KXS6ppKiH1aYrrWXE0PMuiufiob9FgTPN0CsM6DtxQNfL3v0Qnboz9rhM2On7WGu507tc5jygmv4eCuSObcGxmLFS9HhH7NjmLbmof2vR3+0FWQ1dttDVAnp3FMj3Ooj+6kVDJWQ6Q89LSCcdTvcvqXwoAjoFgiOxJWsWkBY+CqKM/WzghZJBE6hBlwzoLRc1x0pdBKTFOTkuF5okA6gauvmioP4RRa4OVWap8uIibVBPXo6CSG7lJwmsXhChOwqw1qyhWXOmLP2fbxDSNXRAyC76QjkMQhnQ513+XzagC4aevsVJGtOPUW0ydxCTfYtGiqGXLCzyJENfBvSZQIxxteHOfDGq7Eq/aQd+KKURAlWL6gxdPQ76txtLLrrLZa2VFNW268UB/dmN8QzdmxNDvxS7XPTmYSM/SjT0Cc4/nbOjbRdN15quOde8C4sM/VDqjpVaKMNI7MCoCda8NXR191ekd5uSoq5toV2zMwx9U2kyt4hitIMOLxXQzdKKHmvovlsG9BlBjHGl4cycFFVvRp3dDvxIyi0E0XFRT4pamgaX6LC6m2On70tdE1Akl5zGP+dJcimQlkYpDb2aiyuKOF6MNXT1+DnnGAXTSS402GKtVUNrigpZX/Hbz4tVmvqhNFw7lZugG5kS4vMeIKEGm6Kkt6+0/VWx2sz2RCeGbkro/02En5JczAx9KbnMCcQYVyuQXPaUQKp7uIdelqFTg65rA18GbD2g0wXhWEwGA5JzqBiJMC4p+mwJyYUurJW6U9kIus2eBz/kcb/25Bz5oUjQkW2xKKH2yIUd/MXjl+XP27FlUwwLmdy2SDeOnrOoErQ4qky37lqpQEr/brgWVhru/Bl6WQ3d4EMH4o6LQ52hi4B+42pjYQF9b+jjC2c3F/JZkyIoYOheYLYt+kvJpRpQIBABfVaGngQrXdMd+CGauoberuHawMdWb4QjcXJTJGezSRVyuQDJYpEN6OaOjITzm+WToqtNt7LtMxUVnVxrplwuFMzadRsWK5YAfufTZ/FLH3pU/iwZerOGVs2GFxQXVOkglrzadOFPOaB6HEwtaOtOmqHTOa47NlYbzoFJiorS/+wtLnqi6xq6kFxuWm0szIf+R1+6gL/7Ow9MlTuZN1KSS0mXy1JyqQh9L4BtMbRce2bbYlpyGa+hU7Xoc5t96SBwbCvFVH3ZU5vJBWHghbK6tCxD3xv62OyJ3idFgVMy9IZT2faPioruONFB30+m2iTBzDK2DVYxCsS8VCoxp6KqtZYrz+skli/621YaDjjHRItBWeil/4Bg4imG7ifnYMXAfquGH2YXExM8g4YOCLutLrlsdD2sNBy0687CGPpGV1RY76dEkQe/wOVCpFFf+Oi8jZuN++GHL+Kx569VcZgZHJKAHqLl2nGXw2pcLkBWchl4YVZDj4uLzm30pNPDta3Uqi4ZelwpSse8qxUVAVACW/Yif24zGSlWtHBRsFltVMjQ46KiO050EEY8U6lYd2zRlKxACqIL/muX9wAkwy2OtmvJQjfBza0GdGA+BTFSQ9eSomqPHLpO6q6FlQUw9LIj/0w+dCBfcjneqaPmWBMltmcBHcNBLGQqdLkE5sEX9L2M23H84p88iv/+6AtVHGYGhyKgDzwhheT50CfZiqs3Y2bMmB+iWUufsqNtEYyv7I1kYNYTS3RBUC8Xei+9jwsAWJbQ4U1JUQrod96wUsrlstqskKHvDlGzLTmfkmQXev+6a8F1sm2DVdBzT70oAvrOwIdjMbRrduEA7TzQDmqlTvr9ohi6nVrsE4ZuxwH9YGjoeZLLqmEM3VZvhGPtWtwpdDEBlo7hILYaSN+/ZnviNLbFIJ40pe/0q8KhCOg9L0S77sC2DIMlwgjf8et/id/59NlS71XscjFJLul5jQAylaJSh1UqQgd+iB0DQweEHq0X7wBJQvSOE20EEc9t00oX1mrTraxS9IWdIW460khslX56xB0x9OIAI473qZihUx8XxljqvJQFBVXqTzIPpueFiVxGaDh2iqGrSdHVhjt3hl5ecuGZbouAuC72tDa/m10P6+0aXJstLMDuKjUfBw1EwhjLdnDNrRSNr++ia5ieWwb0Agy8QLpL9JM/8ENc2BrgX3/iq3j04njdKuVyUW5aznmuD51wRGroadeFdLmoAd0LM31cCK2aY2To5zf7ON6pS996nnWRgo2QXKqZ4n752hA3H2lkkrajQGXoxZa3kcbQt3ui0yIAKblMklBKJJeYoc8joAfZIRF110ozdEV2WimY2VnlMZn+rcPkQwdEewrO09f6RtfDeiy5LNLlAhxQhh4fU8u1JygsEtdE0S6TAnqjtgzouejHFZyOgaHTlxFEHP/4jx4eu6XfGwYpWYQgBvICdS2gH1XkEgrMNTtdKUqBlwZcAILhmiQXQCRGTUnRZzd7uG29Jdlino6uMvSIV+PRvnRtEAf0OGkrJZfySVG6Sb52eQ+cc2z3PRyNFyda6IYTBHT6LklDn0cgMunQDcdOXRtyUXOEht6LE97zQqqwaMJeLkC2/D+KOLZ6Ixzv1DKW23mCPn8Rc28nBZ2DZi2bJJbDozWJr4zLZRhPK1sy9AL0Yw3dsS0EEU8xUvoy3vzNN+Ps1R7+948/Wfhe3WEgK/7UrVPeF+HYlgzIkqFbFiLFdUGB17UU26IX4lqcFFzNMHTbuPCc3+rHAd1K/W06yHu+Ehf7zHrDUFHRTUeaGSadBDN7rP5Kz/W8EM/vDIRls5XuMjkRQ5dunjkzdE22EC4XRfagRc215HfZnaPs4k8U0M22RSCp1N0Z+Ii42G26trUwCYQklwPJ0ON7t1XLOufktKicQL+UXGYE9Vhx4w6JqgecTv53vuwEfub1t+M/feE5fOpr+dOS9ka+7MmhSi7yizBslUh2kRp6XJ1HN17iQ09rxUWSiz71ZuiHeOHaELcda8ubNI8Nj+K2qXnTdSbFRm8EP+S4Za2BNkkuflr/bLiWcEiMCTAn10RS9Wsv7sUMnSQXcaym3EEeiCWvyta98/Gh60Gx7qYZ+lBZ1JJeKfOTXeicF/n+ZUM4Q0CnxDa1g96Ky/4XLbnIpOhBdLmQ5FKzjdPH1NcQyrhckjgyn9B7KAJ6zwvQrjmwDVKE2ovjn3z/XWi6Nv5SG6GmYm8Y4IQM6MkXU7Sykhed2JlrpRl0yoeu2BKvDXw0XVvOqSSYGPqFuOT/zPGW1EXzpJSRH6HuWFIemtXpQk25blrNSi6qfmwa7KHCDzlecVJMJvzq5T1s9/1EcokXikkkl5EM6Atm6I4Vjz1Mj/mrO5ZcXOYZ0GnR7NSd3AVUn7Sk4uU3rWKl4eCBc1sAhH4OAMely2X+tsWhH8pr50AydCm52NkRdDkDLhIfephrWKD7Wrc/V4VDEdClbTEOpGpiVHWY1B0bq00n1T9dR3cYYL1TA2NaQC/4IigokdtC17hpJXcsSzL8oR9m+rgQhIaePsZnY8vircdacOT7m28EL4wDek7v7klxKW6be8taM1UYRX8HQBp6cQm+F0ZY79Rx85EGvnxhB14QJZLLVIVFiT0TmFNAD7MBnRZK3YvfcG0p/8zT6eIpUlOenJb0oMlq6LbF8Oozx/DFc6Lsnvq4rHfqcG0rNa1nXlDPz0EO6CaGLmeK5lSKAvmjH4dLyWU8ZFLUwNDJSkRbz3bdye2TAogLbaXhZhJfRZLLeictuTiaxh3I5lxJYRG5XPSEKAC06tlWstQ298x6u4SGLtwNedN1JsXluOz/piOJ5NL3dMnFRs3JXvwqyHVx100r+GLMDhOGTlJU+Zt7oLlc5mJbDLJOEX0MnZoUXV1AQKdz3K5n2/jqrzFJLgDwmtuP4ZmrPWx0R7LT4nqnlpEL5wV1B3MgJZc4hjRdJ6uhU3OuHG0dyJddiuJIFbjuA3oUxXbCmpMEUoWh08knP26nns/Qh34ILxStYJu1dL+OopVV19ClJKJ1AnRtBtcWTLYf+9D1hCgAtGt2pkXtc5t9rDYcrLXcREPP0Yy9MELdtZWBxrPdMFRUtK5UdNKiSLJH3bVQG+Nh9gLhurjrxpVUYy5ABEPG8nvYmDD0Q9nwDJhPx0XTGDd9UHS69F8sePMsLiojuXglAjoAfOncFja6op3E0VZNXrvzDrKqtfNg+tALGHqeD135OS+5T4F+ydBzQFubVFLUUNRDW892LT+gd0dJG96Gk+7PXfRFvOzGFaw2HJlMpZ0Cbc0CaVu05HsM4tJ/k+TSrDkZHU5YFttgjKE2hkWN/DCWmCz58yygoiLGGOqOBYsl50Pv5TKOobu2hZfduCIfO9IUiyEVF03mQxcVd3IBnUNg8ExJUXleE8nFthgcOwno8/Sie2EExsRuMy/wSg09J6C/4pYjaLo2Hji3ha3eCEdbNdgWk4vXvMfQ7V6nkkvR+D/1u8hzuixdLmNATLZds+XU9bwqTUDcBN2cAcu0TV5pOHGbW5Pkkj1lb7nnFjzwT98gmRsFAJkNV9rnivewZen/Wg5DVz8TAC5uD3DrsVb8PtlcgQrB0K3KGDoVFQEi8LaV3uUjPwRj4vwWJUVV18VdNyUBnVonAMgM/xiHYRCi7tpzlQlMkosuZQ39UAb5hWjosXRVVJlLAdnUDx0QydJX3raGL57bklWiAMY6qKqCuoM5iAGd5JSG1vCvsCVACcmFrpllYVEOJHOuORCtmcMAACAASURBVJlACmS1xHbdzmXodJF16m5miIGs8DKsrIyxlCZGuwGSRKQPXWXoY5KiAFKJ0at7I+mPHyu5xEEo0XpnY+hUVERo1uyUhi7kElZoW1RdFy+9oQOawX1UmdbUcM3++zwMvRAN15prEDKVz+vJZjoHAKRddK8g8T7zMcXOm+LzXSy5AMBrzqzjycu7OLfRk3kgmZ+ZgwVUhdq+d1EaencUlL6+grgoq+ZYRgkXMPdyoXt/KblMiX7sh04lRVM+dENSNE9ySTH09JixSbLT+sISKJWigFh8rg18DPwwJ6BTeX3CALujpOBprOQSpBl6Xsa9DNSiouT4bKWwKJK2y6JKUXWn1HBtnFlvA0CmdfCkDL2hSC7zYHplGPooSHfhnPeQCzqmmpNfBORpO1MTXnP7MXAuLKTrHbq2FqShz5Ghv/nffBrv/8y5zOP/8A/+Gv/0w48afiMLkgddO119Tv+2mHliEV3PeS10B3Hep2ihnQXXf0CXDN2WUoRpW0RBsFN3pFauY7dIclE+Zxx0l4scNGwRQ7ekt9voctG83lf3hAvhuM6i8myLAblc0lrvNKBJRSpDF+PixLlS5YZawfgyPZfxshtX0FQSt4BY6CbV0FWGPr+kqFb6LwN6lqED4vqZ5xg6ydCLJBfaFRp86IT7bl2T3wdJLjW5u5x/UtSxmDEwzoqnr3Tx1OXdzOMXtvq4uN0v9R5+yOFYDI6Vrj6XDqOaY+zxQiaHXIZu6AdVJa77gE6BtuXaxh4nGcml5mAUREYPt0yK1sXABZXZSsnFKcPQiUEnfWSARM9s1mxcjudzmlwurXq6GpMGQhBDNy1cKkZBiLqTFCzNIrnQYnLDStJVMsPQ44XD1QZ7qNDdRj/5rbfh577npanXTDooehjfHMn5XqxtUXW5qMVhpn7jVYKcN3W33I4oDw3Xxj2n1gAkXUPHkYWqsDsUcqPov17tZ3lBZFxQd4d+6dwG1R/o9zItPq26nTluL4hkTqxIQ5+Xfg4cgoBO8klLsS2O09DF72VPOG2TiaGrX8owrr60LHOSSYV+U6gDLgDhbaVpPSbJpa0xdFnJ19EllwINXSn9nyUpejVeTI6rAb3upHq50CJXdHPKXUp8bl730uP4B9+VDuitWtZ/X4SBL6QOWiTm15wrT3JJfOi0qAGY+5ALct7UbHt8UnTM1p7si6Sh1+Z4LlXsDgKsNJyxLZfzEIQR/tF/fhhPvrCbeTziooVH5jOHQaZgr+j9XdvKxBSVoesFWH7IFcklX0NfMvQC0Ilr1W3pIkmX/qc1dJpO3zV8sXQTdhqOaJGq+dDLlutKDV2WCKd7aquyzZqSFCToDbASyaUciyIJIGHo09+cG9pnA2I3JJOivsbQQ25s1zvORgcgti1OVilKTcGAORYWGZpzAQpD1ySXeTN0VUMf70MvJiAU0DMJ9wUw9NWmi5qTXxxVhCt7I3z0y5cyQ6bpuHWGPvRDeEFUummaH3I4NsvIefT/Vp1qH5JjHyka+lJymRJ04qh9LpBOihJTUW2LAIz9xrujQGqyTddOlVVPsrLScdDFFUTCp0w9tZsKmzMz9HQ1JkkuGSdCkYbuWLAtMbB6lqQofTbp90BachnG8g6gsjtTQB/PGPVirnEY+WmXy1wmFhl96OmFUuQR1KTovBk6T1wuoXk4tq8RmTx8x50n8Bs/eg++664TqdcvQkNfbbioj2nolge6/kyJSSBb2EXfh2lnbgJ977qcl/jTs+0mvCCUEmp+pWg0typR4DAFdNfJJCPVf7tKUhSAMTG6N/Slj1iXXEQ1arkvggIbMfQgTrAQ6GIAzAFdHxS90R1hteHIoEFumbzZjyOFVda16TqTYqM7QsO15HkDBDuRhUVxYhJAoZYtR7kVJOkmZ+hi12RbDLZVPFxjWnga+wYShp64XJJzAMQTgebK0LXCMUNALHO+ATHy8G2vOiWvrXrBolwldocBVpvO1Bq63nqCQH/3rragqvNLy7ST9uNdkKz5IA1dSi7Z6mQ/5Gi4NhoFuaDhQZBcGGNvZIw9xRh7mjH2Szmv+S7G2COMsccZY39V7WHmg0rFaaYoMCYpGgcm00q9OwxkD/GGa8l+20Ci15YBBW/pctFYnvo+1J1PhUyKKgxd1bBrUtLJZ+h0gza06TqTYqMrhgerE3vU9r6qbbHIPljULCp530lti8lsRn2Oa1Uwts910gVb6jkARB/6oR/NjeXSDoyCrykgltkRmbCopOjeUDB0oaFPfn3mMfSRwtDVnYu6YyojuwQRNzN0mRQ1MPTYh15ETCYhhtNg7LfNGLMB/BaANwG4G8CPM8bu1l6zBuDfAXgL5/wbAfzoHI7ViJ4nfJ01xzJWUBKLpSBLSVETQ+8OA1m63XRthBGXX5hwVJS7ObJJUS5ZNb03IG58x3DD0fMyKbrnpTTsopuOc57qEFgFQ1c/m45v6EeIIp6yLRYlJ0tp6LXkfctgEBcWAcUe+GlBCbZMt0VHZ+hhxrYIzK+fi69ILoB5AS2roetwF2ZbDGINfVrJJR6OEZoDuh/ylHynfhdlZBc/jDQNneRTcW0SQ6djpwRpzbbj5L75bzoIGvprADzNOT/LOfcAfBDAD2qv+V8BfIhzfh4AOOf5DccrBg23AJQeKhpDV2dCdiRDHy+5AEnSlVr0loGeSAmiNMuj9gEmyyIg2puq27aNblIlanp/FWpvFSA7/3JSXN3LBnRaFAdxT2sZ0AsSatLlMkZyofcdB865LCwCMJdp9Xk6tBUTiKFqW9QkF2B+5f8yKVpiR1S0gJpQVVL03EYPjz1vnuHrBREGfojVxvSSi+zHr10r6rlQg7iaJDU5YEzHKFwu6bwcHSvJpvRzUu8iWmTnFhZ55Xf606DMt30SwAXl54vxYypeBuAoY+yTjLGHGGM/WdUBjkPfC+TJJRYeaklRlaVIySXH5UIBXx8OMcnKKqUfpZeLq2joNMzBVFQkj1NpIna1O5JDN9T3L2JmFGTFhPrZJJcTK2knDh1/zwtSVZJFgzfKSAC0MJcJ6DTjVe2fU3VStEiHrjtWqjlXOik654AeRnDHMPSytkUd9YJdVllsdkf4sf/n8/iFP/mK8XkKtKtNd2rbIuVwMl5w5WfVaTQpQw8intLQdcmlo0kuI3m+Wco0oGPoh3ObVgQAWQE3C9OeTb9zHACvAvA9AJoAPs8Y+wLn/GupN2LsHQDeAQC33nrr5EdrQF9h6CYpwo8vfgI5SIySyyiRXBpaR72JNHSNOQVhlJJWaGEwJUTla+KpRUM/xN4wSLlMGGNiILahOZfayhUQDH1aH3oohwenGToNoxh4aYY+qwSg9oofB9pOy4DuVK+hJ+Xz2WNuKC4oVXYCkpbAV/aGAI5UekxA2rYImJOiZSpFTUgcQ9OdS845fuFPvoIre6OUEUCFWpFdcyz0+5MvfL0xSVH1c8S/k4DeLcHQ/TCKdxDasBrdthikK0jrjlXYk+ggSC4XAZxWfj4F4JLhNX/GOe9xzjcAfArAPfobcc7fxzm/n3N+/4kTJ6Y95hRUKcTYy0Wbq9hwRftXs+QSoEMausYWJ8lOy6Ql9UOPzBp6UUBvx4nHzV66qIiQ19lQHbkHZCfUT4KtnoeIZz+bJJd+vODU3fwFlVBGApA9bEocr+xap2joo3kF9AKGzjmPq2WTa+MbbzmCmm1lPNJVHheV/gMVa+gz9nL5vc8+i//51Su4YaWe32Ijbi282nAL+9EUQa1UVpEK6AOVoStJ0RIM3QsECcswdJJctKlVauFcXnKfc34gAvqXANzJGLudMVYD8HYAH9Ve898AvJ4x5jDGWgBeC+DJag/VjJ4XJBq6tBilGboaRBhjcYOu9AkPIx4z9FhDdwySS0kNPdMPPYxkHxcgkRaKJJdmvG3Ti4oIbs4wCTlwQnG5TMvQEw+6lhRVfPJpDT3tv1fhFwTH5H2t+H0nCOiKw6bqHt5FskUjbg2hS1yA+O5eddtRfObpOQX0IELNZsoovOz5khKXNRlDHzevtgiPPX8N//oTX8UbvuEG/Oj9p9AdBUaP/K4qucxoW8z2JE/OhRrE1eBexuVCcUPPy0kNXZNcVHkuT3IZBbFMuJ8uF855AOBnAfw5RJD+Y87544yxdzLG3hm/5kkAfwbgKwC+COB3OOePze2oFYikaFpD15OiOksxNeiiLRzZCPXy7klWVnkcysQilaHTe+clRQHBgvtemFRqrpgYej4zU10u0zJ0U1ERkCxI1wZ+SscuGo6gjwI0oemmu0wWISO5zCEpWsTQG3ElsZ6EJnz7ncfx5Au78hxWelxBmqGbJZdINL8q0apCxSwul9/4i6ew2nTxf7ztHnTqLiJurlKmBOVqw0V9Sg2dCFleYRGga+iBlMKKZgoThG2RKbvtNEMn6ZY+TyUseZLLvOeJAiV96Jzzj3POX8Y5v4Nz/qvxY+/lnL9Xec2vc87v5py/gnP+m/M6YB19g8slTLXPzfqI23Un4xOVZf91klzE7wz8UDgq/Ki0hs4YS/midS8zMf1CDd0VSdG8oOraVmbWIZDV0BtaC4NJoDcFI9D53u75qc8qKsEfNxJNfd88h4CKrOTCKi+GkazLcMx1R2jo8nxr18a3v/Q4AOBzz1TP0qk5V2FSVJMay4Ku3WlY85XdEe49fQTH2jUpXZp7qhBDd6a2LSYTs9KBU13c9jQN/cbVRnxMJRh6kO7lkvWhp0v/RyUkl3lPKwIOSaVoU0+Kas25dIZlmlqU9EKnGZeJ5EJf1iQFASLgJj50dZcgJZdmto9LcoziosiTPfKSgCaGXqYyzoSNvVi/zwR0cbNu9734M9K2xcJK0TE+dGAyyaWpMPTKu/bJKuMihk4SV/o1rzh5BKsNB5/9+kalxxRFXBa91AsCOs1vnQbTylfdUeIS65Rogkca+lS2xTzJJUdD3x0GONJ0xe68hOTihTzW0M2dU3WXi8rQ1fbSKiZpwT0trvuAPvAVhp7TnEtnKh3D1CK10yKQHmIwzZQRx2JQfeiOomWePtrC6+88LhsjmdCqCZ1/o+vJ7o8q8gIYMUYKnLMy9JpjyerZ5NjEsWz1KKCnF1TTJCW9BYMJzQlcLsR26orcU7nkQnKKSUOPpayhtiMi2BbD6+44js88vWHUkac+JiVwFLtcskSmLNwpz2V3lJgKSJIwyRu7gwC2Jex987Qt6hr6SsMtnFimQmjoSWGRXvpP9wAVLqrXCk070wvkiqaeVYXrPqD3RokPPZkpqjB0Qz9r06BotdMikKyiQz+caqukBhhdQ2/WbPynn34tXnpDJ/f3WzUbAy/IeNDl++dq6OlAV3enZ+j02WrZPx0bAGzHLYCpqKao9WrZ5lxAWZcLaeiJ3FN9YVE+Qyc7KJ1b0036bXcex/M7Azy7WW6oQhmMlJ1OEUM3tSwoC0EWJl+EusMAnbrY4UrJxcCGd4c+VhuOHFs4TdK+p3T7VEE/H2m6GQ19tekUDrhRQe1zMzUlMVmRzbniY1d3c3R/6AV9B0ZDP6gII2EZoxNE/mw/0pKizvikKOlqq5oPfehHChssf7ocKwkweqVoGbRrNvp+aKzUBES+wKSh69JGwxEMfRqWKPq4ZGUhKbloDL3YRpduwWCC1NBLMHQ9kM61sKiAoeclRYFER//M09XJLr6BoZtdRdNp6ACmYs2j2PFDO9yiimxiy4D4O/S+4mUwjqEf79QyGvpqwy0d0P14lqzefdIPI1gsIRIZycVOArouHVI7gKXkkgMKtOSLBkSgC/WAbkiK5ksu6dL/aSUX10kCrh/wwkBmQrPmgHPg+e0Bjq9kg2qu5EIBxqXCorRfdhLkLSa2xVB3LGyRhk6JyYJZpyQB6GxfBVkQp9LQ5yC5FO0qJEOXkkv22jiz3sLJtWalOrq6yIzzoU+toU9xLruaqaCoqyl1WqTPAiZ31VAn0jwNfb1Tlxp6FFuSVxsOOo3xAZ36IbkWy9S2JLNG04up6kPPK5BbJkXHoC87LSYar2ulL0aThm7yoesuF7WXiwwekyRFrSTg+tMw9HiRunRtYAyqeTa9kcYq68pOY1KYGnMRWjUbO3FAb2gaujGgG6QvHZbWw6YIdLMkDH06Z0YR5Lk0FhYRQ8/fvTHG8O0vPY7PPbMxMQPNg+p3Hlf6P73kMnnVLQXJUgE97oUOFO/qikDzDEwB3WKiWpfu6a4XgHNB1joFQ+IJcmSkErhJxvVif7rO3GkHWlMkF/06pni11NBzoM4TJehShF5YBAg5wwvT7U27wwAWS7b9tiU8qKrkMhFDV2yFusulDOizOEeBhl7QnMulpGjc6nVCL3oUcWz1POPuABCyy1bPrKGb9NeyjLHsGDpqbTxfDT2/Q2TDFV0siyQXQOjou8MAj+Y0qpr8mMpKLjMkRaeQXPQcFP3fzNCTgC57uhuKo4rQV3rRq6AqWnVqFB3batNBuz5++Ajdt65jycIsuqaplYjeN95TroM8yWUaYjgpruuATiy7VVMDupWZKaoHkrZB39sb+ujUnZQkIMbQhRk2WAaOwnL0Xi5l0FacJbptkN7f1A89ybbHSdECJ0QRtvsewogbFxMgzdAzPvQZknSil/TklaJ5rRBmwbjmXF4YyWvDJLkAwN03rwAAntvsVXJMqt9ZFhYZFms/5BN3WiRMYyVMBqwn7afzWmyI1rmzSS79gsKimm1hpZEwdFVOXak7Y+eKqjUTUnLRLMj6btQkuejWxWmk20lxXQd0KkBRVzzVLgiYt56m7eDeMCn7J4i+34rLZUIfuq/0cpmUoauLVJ7kYmLCugSg5gImgRxMbVhM6Phoa6rbFs0+9HJJOtETvZzLpWYnQ7vdeZT+F/RDofNKLLCRkzCXO6SKjk21LUqXSE49wiwul6k19JiZM8bQrpn1apWhTxPQ/TCCF1fC6iP4xLQuG6tN8dlhxFOVqe3Yh15kElC/d1P7XNcW4x0tlu9DB7L33CCWPZcBPQcPn98BAFkBBmSTol6crVYh54oqTHBP6bRIaLjkNZ5GcmGpXi7OhD011DF1JqdJrm1Rd7lMGVDyCpoI6uJGwUy/yFWUlQDKjqEb+mEqiLpO9Rp6EUOnz75G1s0chi6nG83QwliFL3dglvx/7o5oSsklT84rgq6hAyK460U8fhih7yWzN2v25El7um+pF5J6bdPIQCJn3WGQqjHpNBwEsTsuD2oy3M0050p2PqoxIWHoLN/lIvsszS/sXrcBve8FeO9fPYPX3bGOb7h5VT6uJ0WNGrphapEYbqEH9FhDn8blokg/+sSiMhjP0M2Jq1GQ7uGhT9cpi3EBva0sOGowyyvlNklfJlBTsnGgeaLyc+38gcnToqhdAf3NxNDzLK3J/NFqGToF67zzTQ28pkFe47cikO23o9xDbYO8QQGe7MHTMHRa8Kl1hroYeKEI6PT+u0NffkcrDacwWUuQGnq8A1Tn1ao7ftXeqe6cmm6+ht5wrYn760yC6zagf+ALz2Gj6+F/+96XpR43JUVNzbkAXUM3Sy4DP0y2ShNILo4iiUyz/VUDut5Lhd7fJDHoQ431JmNlQV0e8zR09VyowSzPTlmWobdKSy7pgO7aFjhHZW4SYIwPnRj6IN3PJvs62iFVw9D1YypeQKfX0KeVXFbqyT3UMSQgdzV7cFFxVB4oUB5r1zK/6wWhqG6O33936CtJUTcJ6Mpx/X9PvIjf+fTZ5D00qc2xFAtyGElyplpl1e9FLUpUMZjzgGjgOg3ovVGA9/7VWXzHy07g/jPp8nnHMiVFzZKLGtBf3B3ixtV08KprGvokW6WakrQMosl96G3F/mVKxqoavQrqxCf/BpkUnVxDr9mWTF7pUBcc9bzkV7CW1NBLJ0WjtOQi9fvqAnpRx0LJ0GN9Ni8B6cQyVGUMXZOB8hKYsxQWTdMXpzvy5ehEgskiKPXs5vQaOiVE11oioOuSS81Jrtu9YSD96CsNR95XKkP/4Jcu4P2fOSd/1usPVAlKJWc1pZiNXFyMFUsuy4BuwH/4/LPY6nn4+TfcmXnOUbrucc6NF7a+7Rr6omfKzUeaqdeJntdRPCDaLiyKyRxHLP1wLqrgJr25aJU3sXNALBhmySVMSSCzMPT1Ti33byaNn7F0MMur2PSCsHRStFTpf5C+OYp6sU8LfXFUoTL0ekHBFGNM5mIqOSYt2Kij8FKvm8GHPk2laDce36ieB9E3Jf13y06LmuQyyXASklzWSHJRjnUUu1wo6bo7EAy97lioO7Z04agB/creMPWz3upZdaypEq7aIE+ts8grkBv44Vx7oQPXYUDfG/p436fO4m+9/Abcd+vRzPOOlSRFfcXsr0Jn6JevDQEAt6ylA3rTtTCKk6KTekddR/jQk0HDE2rocbAyJUTF++VLLtUw9PyiIiBh6HowK2KMZWx0zYLxXSoGXphqWVvFLEwdRbIFLZQ7A3+snbU+Zb8SE1S/M1B0viM5Pm1STCO57CmdFgmdupvRquW0oub0hUUUKI8aJZfEhw7EDF0Z/p63O+8pwzioW6uUXJR8mGgJkDirZFJUkRSpQE5fxCeZejYtrruA/onHLmOn7+Pn3/Ay4/OObaVWUyAbTCkpSuXDl3YGAIBb1hqp1zVIQ5/ii3BjSxVdCJP60J24+VJeUHUKCouq0NBFQM9v75sEdL0LpNltUjYpmtdLWscwiDIaOn1OVVBvUh10jndjhl6EeTB0KbkUuVxmsi1O6HIZZl1inbqdDejDdEAfp6FzzvHJp66kOhdSojVxuSTnlr6zlVRSNPG96wVPYcSx0RWjFmnR9bU8hat2TtUlF0VDV8+3qYXuUnIx4EdfdQr//R99O77plHn4rmuzVN8F8Vj6z6w7NlybyS/1+Tign9QYOjVgGmgWuTKgSlG6ECbV0AHg5iMN3H68bXyuZjP4UdbVoTP0xGUxOUPPk3uARHLRz0vRzqGshk5DRYow8kPZQI0+F0ClDbq8IH9XIX3oA39s0zaS7qqAPhYvPyk6m4Y+qae/a2Locd8U9btMPOHlXC6PXNjBT/3el1LzWSVDb+UwdDsJ6KShE0PXJZfN3kju6Okxec9KacVSSv+T86pW1OqLvykXNMkYy2lx3QV0xhi+8Zb8SeqOZaX6LgBm25naoOvSjpBcbjqiM3RhW9QdFWVA+lpQcAzj8F/f9Tr8o+/J5gno/UyuDp2hSx/0BDdoFHFsdr2Skkv6vNRztuulfehxUzJ9R3Fld4h3feAh2YM943IpKIOfFmUY+t4oyPWgq6+tyoeeYeiOXXlhUV6xUhHUXuiEdt2RHVEJu0MfjCW216L2BUDiInpxbygfoz4uNFLOJLk4tijBJw2dFpC25nK5sjtS3ld8R/rOXi1WTGnoSr7OC9L26GYtKx0OvMnjyKS47gL6OKgnuagXR7uWNOi6tDPAiZV65sZs1Mi2OPlWiZKiyWo/OUNf79RzLwBXasbFDH0aH/q1gY8g4oUBvalo6KnjyrUtltPQkz7a6dFlX3x2C5947DL+2yPPA8huX8lzXamGHuTLRA2Dfp+HeoUMXbct1g0MXZgBpvehU8J9Ek9/dxik2lUACRtWrYtbPQ9HWzXpHBqnodN1S32DgKSPi8nlMlKu/9W4/F+tTG3VbDCWsPErykKRMPQ0CVMrZ1XpUM1f6ITFJB0OlWE888KhC+i2mhSl7akhOaROLrl0bZBJiAJCcvGCCL3R5FslkVjiycUxYaXoOJCEowdP3eVixU3GJmHol3fFRZ5X9g8kDEuXG/JdLuUY4/E40UWtBwg0LPvjj74AoMi2uCCG7mYXzTw0qmToQZo9igER6fcOIw7Op9sVivee3NO/Nwoyk61MCUg9NzNOciHZgnrvA4JJWyyZLpayLYbJDnWl4UgfOmnojDF0lJYELyoMnbR53UwhigSTmOIYAr1OpHIllyVDnwyObcks9VjJxSPJZYBbNLkFSFjYtRJOhsxxWAxBFCWtOKd0HOSBLh69QdcoyAahuiHjXoS/Pr8NAHjFLau5ryGm0dCTogWuizLngBYRqlQlUIB/8LltvLg7NBYW0edUBX0brcJkDc1DpQw93umQs8hU+i+dVdN2W5xCviLbogqT53uz62G9nRCFcZILsVyaXwuIIN+uOfI7SFWKBpF8fLUpGPqe4nIB0i0JXtxNGHpPY+hEmlTboqqhp5KimsS1lFwqgqtUdakd0HTQ5BLOOS7tDI0MvRmzsK2eN7nLJXYKBPLiqPZU5xXSmLzTkw6KfuDsFm5YqecmZAGgVTcz9PzCopIMvWMO6Ju9EVybgXPgzx67LHIFhoBummc6LYqOubGPDF39fk1J0SIiUwa0iJVNMAehaDGta+gmz/dGd5Ta+cmOkTkLHgXFdEAP0KrbiSVXObfq+VlpONjojjD0I6mhA2kyl2Lo1MFRl1ystORCUpaaFPW1BLpZcomWSdFJ4dhJUlTtgKaD5oru9H0M/NAsuSgd9SYP6OJLpy912ukx+e9vZqR6UhQQwcdUfGIC5xxfOLuJ175kvbCQKi8pWnPMfUBMPXVMWI+345ua5HJ1z8MdJzq484YOPvSw0NHVoFormJY0LYoKiwRLFv8elxRtuHaF3RbDbEAPdYZOOvuUvVwmZOgUCPMYei/D0BPJhTFWWMhEAX1LkVx6XohWzTGye0+5zlYbrrQkpxi60pLg6t5QSjcU5ANNclHbiah2UNdJ7J2jMN0Mremm+/oHoegQuZRcJoRrJzNFC5Oi8dSixLKYL7lwPnlTevrSSUernqGbNXRVQyQI21w5hvjsZh9X9kb4lpccK3wdXZimpKjZ5VLORrdSFzeqiaEf79Txpm+6GV++sJM6BvpcYPK+2kUoWoQYY/JvH2dbrDuTSV6FxxSkWzGbkqJlBnIXoTZh1e3eKCmtV6F7vod+iL1RkKlvyLNeAorkoiRFB16AVs3OJFSDMEIY8RRD36Uee0R+AgAAIABJREFUM8qxqS0JXtwd4SUnxLB2XXJJJUUjZViN1NaZwtCjDENXfegkuS0D+oRIJUULLmwqekiKivIZuv7vMqDECa3S07hcipCMxtJsi35otBKWZejk9/2Wl6wXvo7Yl35eTEUpNAS4jG2RMYYTnTquZjR00Yrgb3/TTfKxhWjoBcfcyFnUTK+rsrBIZ+g6+9dL1ycFvX9ZL3rSOjfd3E5vsbEZs2zdPSV2GebzQ4RoS5FceiPhFqGF1NTxEEiKlwBIlwsdV5IUHeIlsbSY0dCltCJaYctZo4rDSNXQ1etAb2Ehh+QsJZfJ4FjpkwyY5Q7yoT9fGNDTWetJUMtILvPS0LMMXQ9CkzD0L5zdxPFOXV7keWjl2BaLJICy5+B4p2ZwuQhf/F03rshjM7lcqvShj6u2lAy9jA+9QtuiygRrti0XTPkaOt/TJkUnXBz14RYE3fO9GS/S63pAL5Bchn7iciEbZd+PJRdNf9ctnSorTzH0htidiyrREU4fbcK1WTJ4Oky/DzX8I4ODqqGnK0WTOCOG40SywnWamQrT4NAFdFfVuwqSou26aHT/3GYfNcdK6XoElQE2a5OdKicjuVTN0LPbYs65UUMvy9A553jg7Ba+5SXHxjYiEz1cDAHdcHMWLawmHO/UpU0REEmwgR/ieKcOxhjeFLN01WGjz3jU8dmnN3B+s1/q8+Vxl2To46qIiaFX0atdT9SabH8za+gTLo6yF7qmobdc4fkm5pv02C8vudD9E0Rcfk5/FKBdt+HYFiymMHStE6XKylW2LjR0H5vdESIOnFhtoFVLZBi1Hzqg9mUy+dOVgiPNhw4khG6aucTToFSUYoy9kTH2FGPsacbYLxW87tWMsZAx9rbqDnEy6I10AHNSlC6+r724h1uONIwBTD3507hcAIWhT8mW8pA4EZIbIYj9x7ruW5ahP7fZx+Xd4Vi5BRDSyJ03dDJOGNPgDV+70cZhvVNLaegbe558HAB++L6TWKk7OKN89jhW+XP/+WG891PPlPp8wriWv7SgjE+KWoh4MsZsFuj97osC+qwul7L5iK5BpwZEDYQYQyeuPTnW0Ci5FGvoQOJF73shmm7ssnJs+bsj7TrLZeix5EL1Fjeu1GNdPakUtZiQb4GkLxNJWaoPXV1MdNuievxySM6ExHBSmJtdK2CM2QB+C8D3ArgI4EuMsY9yzp8wvO7XAPz5PA60LKiRDlXLAfkMHQC+fqWLO2/oGN9rFg1dulzixEjlhUWkoStBQk6gd/WAbpVqzpXo58UJUcJf/Px3Zh4zJUX9cDJN93injq2ehyjisCwm9XQatvHSG1bwlV/5vtQi7BZUikYRx3bfk2XkZeEFYaE+Tud5bKWokww8KHMOfv6PHsE33rKKv//6lxiOKauhA8AoDAEIFjpzQB+z29FhGj9HEMFTnPcNKbloDL2E5AIIp8tt6230vUA22KspllAK7PR9pDR05d/tuoOICwIDiBGWrVpSaOiF6aHu5HLxtJ1PzU5mmpoKi4AkkFNgPwg+9NcAeJpzfpZz7gH4IIAfNLzu5wD8VwBXKjy+iWHHgTPiYzT0eAW9ujcy6ueApqFP6XIZeOkES1UwSS55E3bK+tAfOLeF450a7jhhXuDKgCpkVXlhcg29jiDiMgBvGsbh6Tsq18BUCV0vAOfITM8BgP/4+WfxBw88ZzwOPywe7i0Z+ljJRTxftuPlX33tKv70Ky8Yn9MlF1O3Qm/GpGhVGjqQ7om+2fXQqtmpebmAObFLEEVE4jyTF73nJZXbKrvXr3/ynjMGdGppDR0Azl7tAQBuWK2nvOm6p5x2/SbJBRCkSs9d0d/Y9w6e5HISwAXl54vxYxKMsZMAfhjAe6s7tOngKEwt0RLzGTpgTogCs0kupJn3/Zihz8uHnupjIS6amt6TpgRDL+s/L3tcujdYPFdSQ9eqRWmrrjM7FVKCMrBKGuTcHWYZ+h8/eAEf+uvnje9ZVPoPqAx9fKUoUK4nPeccuwMfT76wm6kCBrJabdLv3qChT1mdLMlCScmFtO12zcDQG658npxKOooC+sALcfKouD+3er7wcwdR0npC+d08Db1Td1JTpzoxuz+70QVjgiioVsYgSic4aWJRZpKRspiq/ncgkVYokA+l5LL/Ad10Veh3zW8C+EXOeeEVyxh7B2PsQcbYg1evXi17jBOBvogg4mOTogSTBx1AqhJx8m6Lmm2xYsnFtC3Whx8Q6o49tlLx/FYfL1wrp58XHpchsPqh+bjyQP1cSGrZzNmqqyhildSD28TQ94ZBZkwakFgti10u5WyLSYO08QGy74VyKv0zMYNUkXW5mBg6EZnpgse4cnwdvVGAds2WmrOKjtIzKa+Dp8lLTxgqRX/bPU825mopDF0GdHmdieeomGi1odspxc9nr/aw3q7Dta1Yckk0dPV7d+Ih0XpLBXqNSHiniSNp/ORFP0gM/SKA08rPpwBc0l5zP4APMsaeBfA2AP+OMfZD+htxzt/HOb+fc37/iRMnpjzkYlDgDAxfgIpOCYauSi4TB3RLc7lUzNBpB+CbJBdTL5cxbOvZWE98+U0rMx2X1LINAaa05CIZuhf/f4TVhlPIhO14dqcpoJN0kxfQTY8XVRkT6Poo0w8dKNfxclfZRTz2/LXM83kaujEpOu3EoikkF5PcAgjWTpLMRneU6uOifl6Ry+VEpw7HYtjue7LFLUka6u9mGHrckEtP1pL+fvZqFzfE11pHkVy8IL2Qi+pzDt09RHGFFgKjy+UASi5fAnAnY+x2xlgNwNsBfFR9Aef8ds75Gc75GQB/AuAfcM4/UvnRlkCSHONjfOjJic0L6LXYFgVM43JZvA99VMDQvSBKTX3R0S9IbE2CmqFh0jQaOpAw840xvdkJea17aaiCPj2H5A39cfX4i9oV0AKjNyjT0ZCSy/gAqSZuH7tkCOja1j6v/B1YoIZuGG5BoCEXgPgeT6yYJZcil0u77uBouyYCehx06f6tu3ZuQG+6YtegM/SVmKH3vFAOhm8pOwl9uhYNk9GJCQV2+vtMLhepoR+UwiLOeQDgZyHcK08C+GPO+eOMsXcyxt4516ObApQUDaPi1rUphn7EHNBpwC8wufalV4pWHdBNkotu2yLQYlRkXewW6KCTwKS/TpqkW2u6sC2maOjF800J6hR2FcR6u6MgVYAz9EWxiDpPMjnm6hi6qYlUHmjxsS2Gxy/tZp7P9ru3U8cLFLe8KINxLW117I0CdLSgSSDmG0YcW70chl5U+h93KDzWqmGr58kAKVtP2En7YHn9x383YwyrDUcydXlMCmO/cVXIrW3Ftig09DRDF0NX0vcy/Z+Yvfq9UHyhBXpRhUWl7l7O+ccBfFx7zJgA5Zz/1OyHNT30pKhjsVRChEAa+tGWWxisG3Ff48krRTUNfQHNuejC1qUJSgKJkmnzV043irpzmQbJQjN9ks6yGNbbNek/3+iO8LIbx0tBrmPuI7OrsN6eFygDhMXjpFmrslqZXUVS+l89Q/+mk0fwxKVdad0kmEr/gWp96Eliu6RtcehneqETOnUhuWz3xdxO05zaPMlFbWh1tO1iu+cr12ky8YhYe2IKSP7uU0dbOHW0lXpf9RqXkkvNgRcnXLOSCxkc0vcyvYbIkLqA3rTawGrDwePxLmvgh3AsVjmx0zHfd98HpJKiBYUhrm2h5li5cguhoWzfJoF+EVTvQy+voZu63umQDH1WyaUgKTrJxbzeqUuGvtkrK7mYOz2qAV3Vy1W9WtfR8yygKpLS/7JJ0TIMXRzTt710Hd1RgOe20tWtegFLUv6evHfecPSyMBWtFaFIcqGKbOqZpJf9A/mSC+V9WjUbx9o1bPU9yYaLbIvq9/GBn34tfvGNL0+974rSc+aGmKFTO+i+F2QkF7p3iZzR+aH7rG/Q0C2L4Z7Ta3j4/E78u/PvtAgcwoCuJkX1/go6OnVnfECnQQ4TVnglPnRxAVbN0E2BM09DJ1Zu0ooJfS+AbbHSTpQ8mLoelkkw6jgeV4v6YYSdvl9aQy9KigKJZxqA7MQHZM9NklisjqGXqdalReZ1dxwHkE2M6rZFWVhkSkJP28vFmazbYlFSlAI9Jd3zbIumhZiYd6Nm42irhu2eJ4Nqyrbop10u6vk5YtiBN9wkN0aSi9zFemHG5UIxpK/Jp3QPElHSr+/7Tq/hay/uoTcSrSvmrZ8DhzCgq0nRcYOJf+jek/g733Rz4fs1HBsWm1yP1C+C6nu5ZKWNPNtipwRDpw52s3jQAXMvbW/CSlFAVIVudD3ZF73IskjIY3pq4N7LYeX6udH1WBPKts8lrb1MPx1afF5121HUbCuVGI3iXad6TKbCopk19AkZ+l5RUjR+/PymsGCeyGPohs8axkV5LVcw9O2+JxfkVonCojwwxuRxkeSi7mKDMOtyAZSqb01DNyVFAeDeW9cQceDR569huIDxc8AhDOiUFKXKrqIg8s9/4G780H0nc58HxM3YdCcPdKpH1bHYzIFSh8mmlzD09IXTluyjKKDn35STwFSCX/ZGU3F8RUguG4Yq0TzkTUtSGfqeErjzpJjUMRfo/rI519huixPYFgfie2i4Nu66aQWPP58kRk0M1ORymVVDty0Gpl1bP/BvPoP/8uCFzGs55+iOgow1kNDOMHSDDz12J+mJaSrKa8YMPeLAC9dE/xUZ0Atsi0Wga10mRZVdrBemh1Xo5IyuCXo8j6Hfc2oNAPDIhR30vWAZ0KeBU1JDL4tmzZ6qustRLoJ5JUIczaaXd0EnfanzA0ovHhowK5Kuh7MFmOOdGkZBJPttmOxuOky92AERuE/ETGwvxdaLJBdiufnn5M4bOrhhpY5jY3YPsvS/ZFKUStZfcXIVj126JgOdqfLZzNDTzaUmBWMstoDG7Wq9AI8+f83ouul7oqgmjwxQoH9uswfbYlhrZt0weYVMA8XRcrQtfu/5HXE90EJRd/MrRYvQaThxlWgt9X79USy5KOdOH1ajdmEERGsJ8Xj6fK936rj1WAuPnN/BwI+Wkss0cKWGTs3oZ2PGDceeqqGOdLn4YeX6ufoZQUpDzyZnAPViLZZcqmHoRa6L8ueB7G1fvbyb+rn4s7OdHgGhS5+McyXdHPlFl1zKBIfXvfQ4vvjuN4w9b8TQy0guu0NfNpL6xluOYKfvy579pmMyuVzKzm8tPGaF+ZLsZZLsZGOuEgz9WLtmdJzl2SRlMU7M0AHg4vYg1ba5Zis+9HghKyNvtusO1tt1KacQmemOgszOPmmFnZZW6B6npKgp/3Tv6TU8cmEHQy+UM4rniUMX0CVDl0nR2f7EW9aauT714uMQn8t59R50gh7A8jR00/R1Hb1RkGtpnOyY8rX9SZJ0VC361ct7qZ/HfbZJi7028GU/kDwNfS8vKVrBYmxbDK7NyiVFB0lAf8XJIwCAx2LZxVQwZBqyrDeXmgaqBZTmeZokOzqH4zT0q3v5tQR57XpVhn4sbgfx/M4A7ZojJUxVfyePfhl5c71dx6mjyX3dUVwu6pg5AJKtZ5KislKUbItZ4nfv6TVc3h3i3GZvIZLL7HfwAYPUcOPColldG+/+O98wVR9rNRBUnRBNPsMya9V6QK8lPvQ89LwQJ9fGyxpljglIe5inSdLRVvirl3dRdyz5NxSh5lhGFrk7CHDzagOMpRe13aEvmzt1hzlJ0Yr62Dccu3RS9PQx4Zt++U0rcYHRNbzxFTfJoin1mPQhD0Bc6TizW4llArpJsqPzmaehq4He5EEHzNXFQMLQW7Vkl3xpZyDZOhD3gYn191FQbhA5APyLH7g7dV+rSVF9Z58tEjT70E2tFu69VejoV/dGc2/MBRzCgG6nernMztCn7V+s+s7nx9AtWYUJiCBk2nI6toW6Y41Nis5aVAQoGvqMhS7khriwNcDJtWYp1mXS0L0gwsAPcaTppqa9A4Jdnlip49LOILMQFHXqnAain854hr43TAqfGq6Nl57o4IlLxNDNkpru7tF91NNA3e1Ihm6SXCRDN1eKqtdULkPPkVyIETcUhu6HPJXrUW2bouiq3DVMi6Z+nF2poY+3LWaSpYZr5e6bV+PFkc+9FzpwGCUXS+22OHtSdFpYFpNJqXlp6K7N5HQmIKkiNAU/dTCuCWJoQIUaurZzsJXzUQbHlJGAZeQW8dlZDZ183UdaLlYbrlZM5GO14RrPzSQJtjKoO+UGRV8b+KlS9ZecaOPchrD85VkpqVcPoQoNXV0kCgN6PLwit7BIkfFMYx7ps4BsQB8qGnqrZsvXqdKgTArHEuu0O/JmPC5PFBbxFNuWSVHf7EPv5rhcALEY3X3zqvyMeePQBXTZdJ6SohXdkNOAFpdFSS4jP8wtcqGh2Hnoxi1QZz+mrG1xGsbo2BaOtgTrO54TCLKfnfWhkzVRBm61sGgg7HY6c1ePvypC0HDHD4oOwgjdUYAjihPkzPE2zm/1ZU4IyFop9X7iuld9GqgW0E0pueRr6HmSixhDJ64rk2WRPgvItkYgiYPqI47FUovK0FWXzygorjspAmM0Ls+QFLW06WMlSv9V3HtayC7LgD4FEtuikFymHZRbBWpya7YYyaVoIENRQA8jjqEfVcPQK3Rd0Ba9jAcdMPvQyYN+pOlipeFkSv9Xm248BV5j6FNUtxahTE96Oja1O+Dt6+24dH6Ya6WsKQ2qACF3zXrNqfLVVk/UAhS6XAquHXLA5GnoVJilL8ZScolJytF4YW8pn5WSXIJwpoWsXbelbVF9H7qmhQU5qSmRjxcwdCDR0RehoR+6gE7aF1WK7pfkAmSb+FQNXWIQnenMn9VRRoHpIG29CttiXi+XaW40GdBLeNCBOAhp3RapSnS16aRauQIigBJDz5NcqmTo4wZcSHlIYei3rQut99xmL7/fvVZpKZKisxEZ1T2SSC6GpOhwfA8gei5vYa7nuFyGvrieyep4LPaitw0aumiqNT1Dp+PsxpKLKpOqsw1MDqO8SlHCvaePAlgG9Kmg2harKCyaBfTZ89PQrZSGvjtMb9dVqDMTdehDA2Y9JkBzXUyZyyDtvIwHHRAugzyGvtpwsdJwNdtirKErY9II+sDhWdFwx891pda56kDj24+3AYjCHEqK6vKVXjpfhYbuxgOQgSSge4rsQ+iOAtQdqzCQrowJ6EVJUVWmIHdLMyW5JO2Dx40MHId2Tezg9ElVSZFgYOzx0vPMyWrCmfUW/tmb78YPfPMtUx9bWRy+gK4kRavwoc8CmQ2vuNOi+v4qI90d+Jlm/gTSB01IOi3OziAo+ZnR0KdgjJREK5sUVYtMCLua5EJ/axRx7I0CrDYcdOp2Zt6oP+OgZR11ZzxDV+UhwomVOlo1G+c2elJeMzL0TM6iCsklHdCBrOyyV1D2TyCGntePp6iwSA3olChXE61qt0l9PN+kaNdt+R3kNedSF1NRUcsQRhysoKCJMYaf/vbbM86aeeDwBXSZFI019Bm3nrOAvvy5MXTtRt4dFgR0ZSKLDjkFpgKGDhgKnqaUXKhcP097zXyuwzI6rGToTRcrdUdKMD0vAOdi7mRHGW6QHHM4sTOnCA13vMuFJBfV5cIYw23rbTy70cvdNWQY+oyBjT5DVor2PClz6KSgOxzfA6h0QDf40FU2vmZIiuqSy7hGaUXo1B3s9MXildLQFR+6vlCqjbqq7tc0DQ5hQFd7uey3hm6l/l81alrg3B0EmekshLYhaBGq6oWeHFe2x8w03wN1wruhNEPPJkV3hz5qjoWGa2Ol4cRuiDDlzujUXWMvlyqqRAlCcinH0PVF+fbjLTy32c/V9fWAXsWxE0P3ggh7w0CyS122647yW+cSVuoOVgpmwhZViqoB/VjselKlQTWgT1JYZEKr5mCnL74DlYRJkmgYGk4/1/cxzqg4dIVF6aTowdDQ3TnZFh0r3cvlWoHkQqPAOOcZJkEaehWSC2AKMNNpm3/nm2+Ga1u440Sn1Otd20LEhWuHmPXuICnUkU3KhoFkw4Kh2+iOgtR0oCpYrgohuYzT0LOSCwCcWW/jLx5/UVZOZgqLbEvq70CVkguXjPX0sRa+enkvs8srw9D/l1eflk4PE3IlF11Db+fbFkdVaOh1R14XKckl1agrfe/ojbr2GwfjKCpEqpfLPjN03a9a+fsr/TbUikgT2nUHnCdWMBU9r1qGrvvjp11YWzUHP3TfydJbWVMfmV2lUGclDuzdUZBYBGP3C5AUjgDFFtBpUJah2xbLdL08E1sXz10VBUb6QmNMilYwqMQLIulBvzVm6Hr5f1EvdMK3vGQdP/mtZ3Kfl9ZD3bboh2gqbPyYtC3mVIrO6nKp2aAOvuo5VnfY+nWcNAk7GKH0YBxFhaDEBPVy2U8f+vxdLolmvDdMtGITZK8Kg9OFpJiqNPSaky7Br6LrZRnIAdVaXoEWOQrce8NAni/B0ONAr3jUaThxVSjF0ONj1RewM7HT5etXRKOybOm/nUmKzioBUKXolhbQdYZelIgvi7qdHXQNINOhkFwu7TlUigJpQpOuFE3+rZ97eq7KxX8WHIyjqBCMiUTWKAjn2umwDGhxmdcxqJqx6rc2QR0UraNXocsFyHY9XFQuQw6oVj5blaFWUgFd0dAb1I0ycboUdQecBvWYoetDHFQIeSj7/Z05LoLp114UAd3EEquwiaqg/EyWoacD+rWBjyOt2QJ6kctF1cu/4eZV/JPvexm++64b5GNJa+Jw6uQ7Qd1pODm9mPKTovufEAUOYUAHRCCVndH2ceWkC3Vepf9O3PQHSJe4m0A3hsnpQqy9Ch86IC7ykRbQq/Jzj/tc8XlpKycxdBoOvDf0tZYASWMmwpW9YelkbBnIMXQFsss15VhVnOjU0a7ZeHFXVGyaJBf9fM9aWCQssRG24olRp4+JVrPq9eMbWhVMgyIfurpLsi2Gn/1bd6YWENUhM/Jnk1xUKcdU+i8eN2voZZuCzRuHMqC7tpXpjLYfkL1c5lj6TwxdteeZkEwtMkkuYjxWVRY9UWKfsN1FNUkzaejXUhp6wtB3NZcLkJZcru6NpG2yCpQZcqEOt1BB1kVABBR9SMRKnMyL4nawVTbn2ur7YAw4uZaVXPKSuJOC7KF64VWZOZyqQ2bWvEcn1VIgOcc0kg8wMHSpoS8Z+tzg2AlDPwga+ry2Y2nJpZihq/2edfS8sDK5BRAFQWoxyqKS07qGzjlPVc8m0oqQXGq2sDMmi504h14QYbvv44aVRmXHloyhy9fRi1xKVDFqOo8njzbhBZGcvzptqwUV5HLZ7I6w1nTRrNlwbZbaxRCJWJtRcgHSs0EB8d0JyWXMvNb4vPa9EGHEC0cGjoO6Q1XPM2NMuucyltGlhj5/OBaTFq+DYFt05lkpKiUXEajz2FIiK5gZelUOF0DYy7b7SkBfUMVuTWPodJNnNXQfu0Nf/qzPXKXAeMNqhZJLGYY+CHJ3WNTTxRQ4Th8Vz13YFvM2q7Dr0ue8uDtKKjS1Bm+mytZZPi9VuxBGCCM+tv8Jfed5g5ongUpq9Hs26cuUbbsw6+dWiYNxFBXDsazM/L/9gLQtzqla1YnLjqOIG6sMVci5oibb4iisTD8HBEPf7icSwKIqdqXkEpfI6zJU3RE9tfdihr6qM/f4HF7ZEwH9RKVJ0SxDv7DVT7FSIbmYvwdyupiYN+nbF7cHCCOe6UUyDehzXtwdyl467Vo6oO/IgD77pCvdejn0xL/HSS40samoJ3lZ5EkuQL7BQa0UPQg4GEdRMVTJZV/7oS+glwsA+FGE3YEPx2K5N0Ch5DIKJIOvAkdbNYTKIrNol4unyVAqg1yJe5/vKQy9re1eruwOAcyXoY+CEN//m5/Cb3/6LAChF3tBlMt2CyWXWN++sNVP+rjPnBQVv395d4ij7aQwqzsHDR3ISi59X3xOmQ6FdceWrqVZku95kgugMPGcgL70oc8RalL0IGjo8/Khq61qKaGWV4RD3l2T5NL3qhkQTaCeHWR5W1TFrt7p8Vo/m1dYaYghF7uDJKCrzB0ArsaSS5VJUXJrEEPf6nnoeyEeOLcFYLxLiSQXU8Bq1mwc79RxYWtQ2eg8IkIb3RGOEUOv26k6hioll7pjpQqL1AHR41BzLBnQq2LoGcklV0O/DiUXxtgbGWNPMcaeZoz9kuH5v8sY+0r83+cYY/dUf6jl4VjsQLhc5l4pStOBggjXcjzMBNtiaLjmIcrdEtV+k4AKQLaVtquLuOBpm6x789WA04kdOOrsTiBujSAZ+giMlR+sUQZScolzO5tdcW6+fGEHnPOxLiWyLuadx9PHmriw3Zc5lSpK/wGA86TrZbvupJKi1PekMg1dYegDZfxcmd+lhPZshUVqBao+l9csn8rCouuFoTPGbAC/BeBNAO4G8OOMsbu1l50D8J2c828G8B4A76v6QCeBrSRF93PlTJKi89LQkyTgbo6HWUVHuyEJfW+8m2ASUBJts+eB88W1MdZti5L1Krr0StyIi4ZbENTxdFe7Ixxr1So9Zl1yocTrtYGPcxs9ozykgjGGM8fbucd06mgLF7cThj7rsauBkb7PjiEp2ipYZCZBJqBPwNDrKkOfsTkXIY+J52no1xNDfw2ApznnZznnHoAPAvhB9QWc889xzrfjH78A4FS1hzkZhOSy/0lRWtXn122RNHSe62FWkTeGrluxy4UCwFbPQxDR2LQFJkV1b35DZ+ikoacZeldh6FXKLUA2KUoMHQAeubCjHGv+9/CT33ob3vYq8611+mgTl3YGcgdQRbdFQpHLZa0Cdg5kNXQiZGWIhmDos0sutpKD0uNG3vQx+ryDkhQtcxefBHBB+fkigNcWvP6nAXxiloOaFY7N5DCB/TzRyao+r37oieSyO/Bxy5Fm4et1lwIg/L79in3oakCvethyEaSGTlZO2a9FYegN0SK154VZhk4a+t6w8oBODJ2uy814TmfNsfDIhR288lYxpqxol/XTgmOXAAAYAklEQVRjr74197nTx1oIIo7zW335vrPAFND1pOhOfzyJKAu92lXOEy2jodsWrvST8zkL2nUbAz/M5L0SDT2vUvRgBPQyR2GKRsaGFIyx74YI6L+Y8/w7GGMPMsYevHr1avmjnBCqq2Q/ta28REpVUBnp7jC/FzqBWuiqGAXC71slQ2+4Nlo1WwT0iif/FEHuWIKEoXfqTmqHtFJ3cGVPuFh05p4E9OoZelL6nzD0umPhVbcexcPndxTb6XQB8tRRsZif2xAdGavyoQMqQxdDUqgfTRmZb5LPS9kWJ9DQheQizt+s9zvdB1k3SzFDv54C+kUAp5WfTwG4pL+IMfbNAH4HwA9yzjdNb8Q5fx/n/H7O+f0nTpyY5nhLITXgdT8nFsWfPTcN3UpsekVVhoS2YVC0bMxVocsFEEFgu+dJC+Ei7KOunhQdZPuMrDRcxCqQUUPnnONqd1RplSiQlP4PpYbu4XinjntvXcOTL+ziStynZdrOhVRcdPZqNQFdZaLkWmrXHUQ8kUN2Bl4lVaKAQXLxyksudceWyeCZGXp8H5T1m8/b+DApyhzFlwDcyRi7nTFWA/B2AB9VX8AYuxXAhwD8BOf8a9Uf5mSwU8109jEpOmeGTpn43kh4mKfR0GXr3AoZOiAC+qYiueyXhq7Pu+yk5BedoYfY6fvwQ15pYy4g0dAlQ++NsN6p4d7Tawgijs89s4GmO32C8Za1JhgDnrnaBTC7zKcyVHIt6f2A8pqJTfV5WqVof0Lbounf04CkxzwNPc+Hvojmc2Uw9ig45wGAnwXw5wCeBPDHnPPHGWPvZIy9M37ZPwewDuDfMcYeYYw9OLcjLgH1y9hPyWXuM0Xjv20zdkyMDeiGQdFyuEWFLhcgZuh9JaAv4IJv1USDsc88vRH3cclqvGqAX81o6H5SJVp1QHfIthhr6F0P6+0a7jstJvk8cmFnrGRWhJpj4abVhmToVfRyAcR1QTp2W3bsFMG28oA+g22RMGtgJWKTO5mo5OP7hVJXEOf84wA+rj32XuXffx/A36/20KaH8/+3d+7RlVfVHf/se29uMklmMknm/WKeDHYGeQ0zvEelgCjFgshqeUgRC7iE1bpkVUGwXUVQrFaksBjRImCrFiwVrAoCtrAqD0Gw7eLhmgGKnYEODE8nmcnjZvePc869v2RyJ78kv+fN+ayVldzf75fcb05O9m//9tln74x46NWeojHvFN1h8733liEB9Tz0aLsVObpay2zevrNuH8w4aC2XuOT41Vxzz3Nce/9m3tk1sEen9WC+fdDYtzeX2D0wxMtv7wLC9zENi4iYzTPVPPQ+Vs+bzpwZLSycOY1tb+2atHFc3NnK4y+ZjUqTDXE5I9kVaOzsnm56+gbZPVBh98BQtXHzZGkesSi6q79CQcLdmJqHGfTJOSZt5RKlguyxQa9eqznfsSgBhhekT+/OGXuWi/25YT309uYiPf2Vao0VMJUWIbrmFg7nofcnmOUCcOHG5Zx2yCK+/sBmnn9t5x4x6ZG7Rh3O0Ls2b1F76FBrQ6eq7Ojpr8amD7Re+mQ7/yzqmlZtoRbVxiK3SxSGh1zeGWMj1HgxMfTa+s4uWzo3TPvBqEMuo43dWLVcfD30GBm+KJpiyMUtisac5fJG1UMfO4YOw3tnxuWhd7aV6e2vRLLhYzyICFefsj/rl3UxUNFRFkXrxNCdQbdZInNmRLsoCrU2dDv7BukfHGKWNZbOoEfhoTuiiqF3BRY9g/WAqqVzY4qh7xrRTzSM1pFfT4QlXa3M79jzb5+XkEtDGvTgomg20hbjjaG7XYcdY8RgqwY9EHaJK8vFbRffbgtdJRn6KpcKbDrrENYv62L9ss5h59pH5KSPPP7ijh5ay8VISyE4WpqK7B6oVDcVVT30JdZDn6RxdKmLMPl5Xw25DPPQa0XM3oqwjot7v5FZLtPK4X6H5qboPPQLNq7gRxcftcfxehuLstZTNPpZmwGa6vQDTFxHzKU1awY9nIcefGR2XRnj9NAhaNCT9WC62srcfsHhexx3Xvm0puGP1kEPPY5wC5hc9L7BoeqmIpffvXZBB6WCTDoFMLheEFXaYncghl7z0CvVwmeRGfRikSGFwcoQpWKBXf0VWpvCeujBGiyTDzWNNnb1/pfd+2Uly6UhDbq7mxaEyNqqTQSXQxsm9WoiOC8sbAzd6QnmoveMI993PNQ8dKMtzdBXEGe466UzbntrF4cu7dzj+6KguWQ8dHcDdsW/ppWLfPOcdayc3T6pnz/MoE9yvFtsS8Lg4vCwkIv9+ZHloQdKH5eKBXoHKrSEnJPue0Xi2/NRrwhX1uqhN6RBz8ogH7GimxvOOJg1C2bE8vPdjeuNnn7KpcKY26RH6yva0zdIqSCRexjOQ/8/66FnJQugrkEPPKHE7qGPCLkAwzrZT5R5M1ooFYTBIZ30E1Fbc4nvfnwDaxZ21I4FSjC7vqZRhlzAlD5uLcPu/grTmkKGXAKZJmEWUSdCQ5XPzRvuLp22ESkVC3zw3fNjm2Rucr3ZO/YuURi9yYWrtBi1xqqH/rY16BmZ8MWC0FYu7vE0EzToUe8SddRi6MNDLlFRLAgLZpo4evMkems6NizvHjYurniVWxQVGb6wPBmCBh2w/URDhlwS2H5fd1E0Y8W5sqEiYqqdgjJiROIieMMKsymlatAD9VyiroVe1dPSRLEgVQ89KxMejBEaaYiCi6Vxeegmy2WI13v6md5SmnTO9Gi4dnRxlbxos/WA3u7tZ3pzKbKQZqcN3bxiHYDe/sHQocrmBOLYtXaSw9/j0KWdXLBxeTVTKW2y818WIbWc0WykEsVF8J82jIc+Wsilt3+Q1hgMeqEgdLY2VWuUZOlvsXx2G8ttSzdHMMsnNoPeVKRvsMKOnX2RNs8Issi2o4vrBtreXGRnX8XsEo0ofg6wfmkXAI+8YMpA7R4YCrVLFOq3h4uSelv/W8slLj3xXaGqQiZBYxr0jBXMiYvgDtQwsUy3eag3sCi6s68SeYaLo7O1XM0tTjv8FeTWj63nipOG92gpFqS6MBz1LlFHS6loPHS77T8ONizvYtWc9tgWB91uY1MLPbrfYc6MFlbNaefh541BdxuLwlD10GM0qu5/La4yHlHR0IuiWTIicRD0esPkMLeO0le0t28w8joujmCMOEs313pa2ptL9PZXYvTQC/QNVni9p6/a9DlqTj14EaceHF9/mTZbE32gUr+h9UQ5YkU3tz+xlf7BIRNyyZCHnrVGFvXItroJUm+bbqMhIlWjPlYdFxi+qOWIultRkGAWR1YWRfeGi6PHtihaKtLnPPSYQi5x0x7w0CM36CtnsWugwpO/fdOEXEJ63C4PPc45lpVEi7FoSA+9tiia7cejKGgqFhioVELvMnSLWo7e/kpsHnpnazY99Hq020W+qLNPHM1NBXYNVNjZP8ismN4jblzIZWffYKQxdIDDlnVTEPi3514FwlVahEDaYpwGPSOp0GPRmAZ9injoUPtdwxZ2cotajp44PfRhIZfs31zbm0t0t5Vj24zWUipWe6zm10MvVuvGR+2hd7Q2sXZhB/c/ux0IvyEviZDLwpkttDeX9ti/kDWyrW6C1FuRbkTcZA5bS3tkCd2e/vgMuttc1FTcsxxpFlk+uy1WL68lsFEmGI7KE23lEm/09DGk0RXmCnL4im6+8eALQHgPPYk89BPWzOOYfWeHzo1Pi2yrmyCulkse4raTxT2FhPWWggZ9sDLE7oGhyAtzObqqBj0ff4e/Pnnt6M1yIyKYJ93dlk8P3bWhg+h2iQY5csWsmkEfZ5ZLnP/vIpJ5Yw6Nuig6RdIWofY7hg+51GLoroxu1LXQHXkz6IWCxFr7J5irPCunHnpwE1ocBn3d0s5qeC5sfaGsNWpOk4YcgVKdbbqNiLt5jWtR1MbQ46q06MibQY+boEHPaww9OFeiXhQFk1p70GJTHG3ceeh+njWoQZ9Ci6LlqoceMoZeLlbz0J1hj7rSosMZ9KyUFk0bNw4FiSf+nATBp7k4PHSAI1Z2A4SutuhKKHgPvcEN+lRYFK2GXCYQQ3ef46jlArW0xanwpBQG56F3tTVXqxXmjeBciaqf6EhOPmABhy7tZMWscOWEs1aTPE2yH+WfAFkpn5sEtY1F4Q16r+0r6mLpcS32tDQVaSuP3qNxKuI66+Q1fg4jQi4xeejLZ7dzx4VHhL4+ayVs06QhDXp1UXQKbCwqFQtMayqGnsyujdh1P9/Mr1560x6Lbxp0tZe9Qbe40EBeUxahNldcGeIs4G6U3qA3qkGvU4y+ESkXC6Fz0AHm2ubH196/mQUdLZywZi4r50yuU87e6Gotm1Yynmoeel5TFqHmoc+c1pSZvQXlYoFysUB7cz7XJaKkMQ36FNpY1FSU0OEWgJPevYD95s1gwcyWyJoT7I337TeX/kpl7AunAI3gobtF0bjCLROhVCzwvfM3sHLO9LSlpE5jGvQplOVy7pHLeGf3QOjriwVh9bzkJv6f/f6qxN4r67RUY+j59dBdyCXsInxSHLJPV9oSMkFDGvSptCh6zL6z05bgCUl3WzPzO1pYG+jTmTemNRVN2mUMOeieydOQBn0qLYp68sO0cpFHLj02bRmTQkRoK5cyFXLx1AjlworI+0XkNyKyRUQ+O8p5EZHr7Pn/EpGDo5caHrcoOhVi6B5P0px0wALes9o/GWaRMT10ESkCNwDHAVuBx0XkblV9JnDZicAq+7EBuNF+ToWpFEP3eJLmi6fun7YETx3CWLz1wBZVfUFV+4HvAx8acc2HgNvU8CgwU0TmR6w1NE05aRfl8Xg8URLG4i0E/jfweqs9Nt5rEmNBRwuXnrgfx6+Zm5YEj8fjSZwwi6KjrSyOLBsd5hpE5HzgfIAlS5aEeOuJISJcsHFFbD/f4/F4skgYD30rsDjwehHw8gSuQVVvUtV1qrpu9my/qOLxeDxREsagPw6sEpFlIlIG/gi4e8Q1dwMftdkuhwFvq+orEWv1eDwez14YM+SiqoMichFwL1AEblbVp0XkQnt+E/AT4APAFqAXODc+yR6Px+MZjVAbi1T1JxijHTy2KfC1Ap+MVprH4/F4xoPP6/N4PJ4GwRt0j8fjaRC8Qfd4PJ4GQUz4O4U3FnkNeCmVNx+dWcCOtEVMgLzqhvxq97qTxesezj6qOmred2oGPWuIyBOqui5tHeMlr7ohv9q97mTxusPjQy4ej8fTIHiD7vF4PA2CN+g1bkpbwATJq27Ir3avO1m87pD4GLrH4/E0CN5D93g8ngZhyhl0Ecldo1ERyW0DxzyON/gxTxo/3tEwJQy6iLSLyMUisgJosccy80eoh9X9FeAU2wowF+R1vMGPedL48Y6WhjfoIvJe4AlgI/AXwDVQLSiWWUTk48CjmAJqP1bVSsqSQpHX8QY/5knjxzt6Gt6gY1rhfV9VTwOuAI4UkfOg2gA7c4jIakyf1n9W1T9X1R5bi96dT90T2Au5G28AEVmJH/PE8HM8HhrOoIvIEhE5MnBoNdADoKqvYu6oV9rXmfEIRGSp9VhQ1d9gyhXvFpEPi8iNwJUicrY9n7on4BCRxSJycODQfuRgvAFEpNN9rapbgH8lH2Pu53iC5GqOq2pDfABNwFXAM8BdwLX2+HHAlhHX3gVcbr+WtLVbHT8FtgF/YF+vAv4eeB74FHAO8GPgMnu+kAHNX8A0B/8Z8GVgJnAE8EKWxxvTqOVrwGvAxsDxZVkecz/HU9GcqzneSB76Z4ClwAHAacCxIrKvqt4HbBORLwSuvRmYJyJNav8CaRF4RHsC+CZwroi0q+pmjM73qurXVPVW4Fpgg4i0qOpQSpIBEJFZmH/IFcDpQAX4S1V9GHhGRK4OXJ6Z8bacAiwA/g643B1U1Rcxm0EyOeb4OZ4oeZzjuTfogVjbl1X1TFUdAI4BtgO/Z899DDhbRI6xr1cD2+y1qaK1R7T9Mf1bX8Z4KqjqL1T1t4HLDwB+qaq7k1U5Kv3A4cAcVX0L+CdAReQs4ALgzKyNd2Cu3A18FrjaHj7fXaOqj2VtzP0cT43czfHcG3Rqv8MAgIgchHmMuw/4UxH5HOaR6QrgDBF5CDgLM7FSw3ktIlIWkRLwAvDvwHeAU0XkGyLyLnvNGhH5FsYr+0VKkrFaCgCq+g5mgp9hT/038AjmH2A78FdkaLwtAqCq/ar6oqoOAl8FPiEibWCMp2VtVsYcq5v8zfGC/Zy3OV6EnM7xtGNU4/0ALsTcxaeHuHYdcAdwoH3dAnwgi7qBWzCPd1cB7wAP2uMF4HrgcynpXraXcydjDMv+gfH+IdCR9niPpd2ed6UvbsN4v8Fj12VxzEe5NktzfKzxzuocPwdYC8wY5Vym5/geetMWMI5BXwM8hclE2ATcMmKiHFXn++4F1mRY99HADExs8VXgduAkTAbARntdOQXdx2FW8n8OzA4cLwHfthN7IXAZ8K3A+YeA1SnPlb1pv3nkXAHmA7/CeFxfBaYDTVnXHTif9hwfS3dW5/gpwH9iQnC3AdcFzt2a5Tle93dKW8A4Bv89wI3263bgR9S8qrmB6wr289H2mu8BMzOqe17gujOBdYHXZwH7pqS5A/NEcR5wJ/DHBDIOgFmBr+faCX4T8BjmEbUjxfEeS3v3KN9zEPAm8Gvg6KzrztIcH6fuLM3xxRjH5Bj7+hB78znEvs7sHN/r75W2gL0MeBdmB1bZvv4ENk3Lvt7H/hMuHPF9ReBY4Eng3KzrBoqj/IzE07UCupvta6fvdMzTxd5CL7MxXtrZKc+ViWjvxoQsLsyL7gzN8XGPd+BnZGGOHxg4Nwd4YKQ9CZxPdY6H/cjkoqiIfBrzONYGuBSg+4DTRaQLQFVfwoQsrgp833mYFekHgENV9dtZ162qFRE5T0QWuJ+jCadrjdA9KCKiqtusltsx8c7TAguHbrHrPBFZpKqvqep9qvqdJHVHoH2xqr4OnK6qm/Kim+zM8fGOd1bmeMVq+LU9J5gF50GgOZBVlIk5Pi7SvqOMcic8E5MutE/gmFuouhm4PXD8IMyK+Uz7+qOYO23iif2T1H12lnQHzpXs5w2Y+Ogy+3q6/fwnaemOSPvcHI75uX6uRKq7aD8fBTwcON5qP5+T5hwf70cmG1yIyFOYUMV2zETYjHksVuBZ4DOq+gMR+QhmUeWitLQGaSDdzwH3qOqbgWsux2QCzAMeUtXPpyB1D/Kq3etOlrF0i8gZGMP9XUyG04OqemM6aidB2neU4J2S2mLP6cAuzALEJcD9wNepxbE2YUIZzwFned2x6P4KcEDg+juAN4BP5WCuZFK7151J3QfZ818C3sZkO30yTd2T+p1THOwzgSMZkftJLUxxEbDAfr0ck1Z0nH3dBJwAtHvdsem+BTjevl4HfJ4Quf9eu9edI923BnTfjanv05q07ig/Eg+5iMgy4B8wd8OnMHmel6jqDhG5BnhaVW8b5fvuxKT/3Zeo4Nr7T0Xdm1T1Z3bhK/HYXF61e9250n2Tqt4jIq2q2puk7lhI8O7pFk0+CFwVOH4z8I/269FyhNdi7qQPAIuSvuN53cl7GXnV7nV73Wl/xJ62KCIlMS2m/lZENgCHYirGOZ7D1HU4TFVfd2lO9ntXYLbdPq2qx6rq1rj1et3p6M6zdq/b684KsYZcbD7nDZhtv/cCH8YUt7kM+DTQjCkWP4RJczrZft8fAltV9Yk0HoW87uQfPfOq3ev2urNEKeafPx04EDhBVX8nIm9iyn3eCewG3g/8DWbn5MViaiTvxKT5uY4gaQy81508edXudXvdmSHWkIua8pP/g8n7BHgQkwfaC/xUVU9T1ccwf6A+O/Co6l2q+myc2vaG1508edXudSdLXnUnRRJb//8FOFBE5qvq7zA1hfuBpSLSLSJXYnJAH4VMNYf1upMnr9q97mTJq+7YScKg/wfwOvaOqqpPYhYxCpjWTkXgfar6A3s+K1tXve7kyat2rztZ8qo7duKOoaOqr4jID4EvicgWTFePfqBi/xC/jFvDRPC6kyev2r3uZMmr7iRIbGORiJwIfATTMft6Vb0+kTeeJF538uRVu9edLHnVHSeJ7hQVkSbME9BgYm8aAV538uRVu9edLHnVHReZrLbo8Xg8nvGTyQYXHo/H4xk/3qB7PB5Pg+ANusfj8TQI3qB7PB5Pg+ANusfj8TQI3qB7PB5Pg+ANusfj8TQI3qB7PB5Pg/D/nt0jALSMV/cAAAAASUVORK5CYII=\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"df.plot()"
]
},
{
"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": "3.7.6"
},
"widgets": {
"application/vnd.jupyter.widget-state+json": {
"state": {},
"version_major": 2,
"version_minor": 0
}
}
},
"nbformat": 4,
"nbformat_minor": 4
}
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