diff --git a/22-01-PAI.ipynb b/22-01-PAI.ipynb index 6106e73eddb91e957c85e49eb34e1641e55cc6c3..1c1cadadbfb1316906774f758cb08fff683ce788 100644 --- a/22-01-PAI.ipynb +++ b/22-01-PAI.ipynb @@ -1382,6 +1382,184 @@ "# And to come full circle, everything at once" ] }, + { + "cell_type": "code", + "execution_count": 243, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "m_const = set_model_constants_22()\n", + "da_const = set_da_constants_22(method='LETKF',ncyc=30)\n", + "sat_operator = reflectance_simulator\n" + ] + }, + { + "cell_type": "code", + "execution_count": 244, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 1min 31s, sys: 1.71 s, total: 1min 33s\n", + "Wall time: 23.4 s\n" + ] + } + ], + "source": [ + "%%time\n", + "\n", + "#this takes about 30 seconds because LETKF is so poorly implemented\n", + "states_1 = run_linear_advection_KF_22(m_const,da_const,reflectance_simulator)" + ] + }, + { + "cell_type": "code", + "execution_count": 245, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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LBYvFwrZt24pcr1mzZnz33XckJSUB8MUXXzBw4MDbtn3mzBk6dOhAeHg4Q4cOZdCgQZw4cYKKFStiMBgKBbAg+/DvZvDd6rgUcKfbiYmJIScnh9GjR9O6dWv279+P1WotPF63onnz5oXiFRcXx/79+4tcr1mzZqxduxZZlrFaraxfv54mTZoUfl7gIZ88eZLLly9Tt25dFixYwPLly3n88ceZMmUKlStX5ty5c3/7OPTo0YOZM2dStWpVQkJCAG553oK4yVz/EADifL148SLHjx8H4Ny5cxw8eJBHHnnkltu12+20bt0ak8nEM888w/Tp0zl79uwdRV7+Cb///juPPfYYffr0oVatWvz000+F+3QrIiMj8fX1ZcSIETRr1qxQZP/6PUmS6NOnD1OmTKFDhw7o9fo72r+dO3cyaNAgHnroIUaNGkWXLl2KPMebNWvGmjVrCs+P4cOHs3bt2tvafrfOj1vx3XffYbVasVgsbN68mRYtWtxk851s5+8cl4Jzz93dnXr16rF69WpAiNYzzzzDzz//XKzdxd0DC7LoT548yaVLl3j44YdveQ/MysrixIkTjBs3jrZt25KQkMDly5cL7wvXXyv/5BjeCx5oj7ZSpUosW7aMK1eu4Ofnx1tvvQWIJ9oXX3wRV1dX3N3defjhh7l8+TI6nY533nmHOXPmMG/ePLRaLe+8884NT5OVKlVixIgRjB8/nq+++opFixYxceJEVCoVtWrVQqPR3CAaBRiNRuLj4+nRowdOp5OwsDAWLFgAwPDhw5k0aRK7d++mUqVKNGzYsNh96927N5cvX6ZDhw54e3sTFhZW5HrNmjVj8ODBPPfcc0iShLu7O8uWLbvtU1u1atV46qmn6N69O66urhgMBqZOnYpOp2P58uXMnj2bVatWYbfbefnll2nQoMEtxaoobnVcCrjddq6natWqtGrViqeeegqdTkdERASVK1cmJibmJg/geqZPn86rr77KU089RXBwcGHo/69MnTqVWbNm0bFjR2w2G82bN2fYsGGFn1+5coUuXbogSRKLFi3C29ubgQMHMmnSpMKQVtWqVWnfvj06ne5vHYcuXbqwaNEiFi1aVPjerc5bgNatW7No0aIbPAFfX1+WLFnCzJkzMZvNSJLEm2++ScWKFTly5EiR29VoNEyePJlx48ah0WiQJIk5c+bc9vf8f+jduzevvPIKHTt2xG6307RpU7Zv337bh6WChLF27dohSRKPPPIIvr6+xMTE3LRu165dmTt3bmGW6Z3sX4sWLdizZw8dOnTA1dUVLy8vZs6ceVPbU6ZMYfbs2YXnR5MmTXjhhRduu7936/y4FeXKlaNPnz7k5ubyxBNP0LVr1xs+v9P7wd85Lu3ataN///688847LFiwgJkzZ9KxY0esVisdOnSgU6dOXL169bZ2F3cPPHz4MOvXr8fpdLJ48WK8vLxueQ/09PRkyJAhdO3aFVdXV4KCgqhfvz4xMTE0btyYJ554gvHjx/P666//o2N4L5Dk28Wm/uPk5OSwfPlyRo0ahYuLCydPnmTo0KH8+uuvJRJ+UFBQuJHvvvuOzZs3s2rVqpI2ReEfUrVqVfbt24evr29Jm3LPeKA92nuNu7s7Wq0Wo9GIRqNBo9Hw9ttvKyKroFAK6N+/P2lpaSxfvrykTVFQuC2KR6ugoKCgoHAPeaCToRQUFBQUFEo7itAqKCgoKCjcQx7oPlqn00lubi5arVbpN1VQ+AfIsozNZsPFxaWwjKNyLSko/D0KriM3N7eb6gjAAy60ubm5REVFlbQZCgoPPOXKlSt2iIaCgsLtiYiIKLIm/QMttFqtFhA7d6/GAf4dIiMjqVWrVkmbASi23ArFlhuxWq1ERUUVVmhSrqXSaQcottyK0mBLwXVUoEl/5YEW2oIQl06nKyyHVtKUFjtAseVWKLbcTEG4S7mWbqa02AGKLbeitNhyq24XJRlKQUFBQUHhHqIIrYKCgoKCwj1EEVoFBQUFBYV7SKkS2pycHDp06KBkPyooKCgo/GsoNUJ77NgxnnnmGaKjo0vaFAUFBQUFhbvGbbOOf/jhB7755hsSEhJQq9UEBwfTpk0bunTpctcNWb9+PdOnT2fChAl3ve37jc1mK5xg/XYTISso/GcpmI2t4FFfvu59G2ABh9WBw+IAB+j0OlDnr1/wHSn/JQP2/L9V+d9XAUWPtFBQuO/cUgWWLVvG/v376datG8HBwciyTFJSEps2beLcuXOMHz/+rhoye/bsu9re/cThcPDjjz+yZs0aLly4QEJCAiBSvUNDQ2ncuDFdu3aladOmStUdhf8mdiAdyAZM+cs5QC5wFTgDxMHxi8fZEL+BXeZdREvROCQxGbmf0496cj06qDvQKbAThvIG8EbcwXKAFCArv10N4A4EA2FAfaA5UOa+7a2Cwg3ccvaeJ598kq1bt940eN1qtdKxY0d+/PHHe2JQ69at+eSTTyhXrlyx61osFiIjI++JHXfKwYMHWb16NVevXsXLy4t69epRpkwZ1Go1NpuNK1eucOTIEUwmE9WqVWPw4MFUqVKlRG1WUPgrERER96bKmhM06Ro06eKZXtbLYAVNtgZNkga3I27o4/VcSb/C3Iy57JZ3o5N1NLU1pZqzGm6yGw4cXFVfZZ9mH1dVVwl2BjPGPIZudBOiKoHT1Ynd247NywZaUNlUSBYJWSMjqSVsPjZy6+WS0SID2UuZsEzh3lCrVq0ix/Te0qNVqVQUpcEOh+OW1S9Kilvt3L3EZDLx6quv8tVXXxEREcH7779PQEAAjzzyyE3rms1mvvrqK95++23Gjx/PyJEjGTduHGq1+p7Zd+jQIRo0aHDP2v87KLYUTWmw5a8Pq3f1WjIBlwEDUB3wBZKATOAwEAOyq8zHho+ZmTETvaznVe2rDPQfSG5gLt51vNmRvIMd53dwIuEE6bnpPOv6LCfMJ5iomsgqxypWOVZRuVxleBRoAgQBfogQ9DngApCB8JzPAmbACDRAhJqLoTQcowIUW4qmNNhSnNN3S6Ht0qULzzzzDB06dCA4OBhJkkhKSmLr1q106tTpnhj7oBAfH8+gQYM4efIko0eP5uWXX0an03Ho0KEi1zcYDPTv35/OnTszffp0li5dSmRkJCtWrMDNze0+W6+gcB/IQoisBqiU/380kAf8DpwAu8HO5NOT+ez8Z7TWtWZ+hfkElwkGP7jiuMJTW54iKSsJHw8fGpZpSPPc5jxle4oZ7jOYnj6dj6wf0VLdkq65XZmRPAPfM75gBXSAK0J8I4B9iJC1BEQBqxGC345SlA6q8G/mlkI7dOhQqlWrxk8//cTevXuRZZmQkBBefPFFHnvssftpY6kiNjaWnj17kpKSwscff0ybNm3u+Luenp4sXryYhx56iKlTp9KvXz8+/fRT3N3d76HFCgr3mXREv6sroo/UivAsc4G9QCTYQmyM/GUk3176llEeo5hQfQKqUBWxnrGUbVYWw2EDA5oOoH6b+jR1b4rmTw1YgHjgEMwMmEn98PqM/208m3M289MfP7EkdwlPap4UgloNSEV40d2A7xGebSPgBLAV4d12QRFbhXvObVNiW7ZsScuWLe+XLQD88ssv93V7f4fY2FiMRiPp6el8/vnn/zhcMWDAAHx8fHjxxRd55pln+Pzzz4uc8UFB4YGjQGTduSay0YhM4P3ASbBXtjPi2xFsu7SNaT7TGFZvGHKIzAcJH/DmljfZWm0rVIIxHcfAr4gQcChwEeEptwa00NXUlXov1KPrx11Js6Qx8/RM2gS2QaPSiEzkmkAaQmy7AlsQYtsQOAbsyLe5C4rYKtxTlNPrDsnKyqJ///6kp6ezbt26/7tPoGPHjqxYsYJjx44xfPhw7Hb7XbJUQaGEyAZiESJbASGulwAHcAA4DnKEzJSdU9h2cRuv+73OsIeGYQuxMebYGF7/5XVaP9SasCphWMtYRXg3HuGhxue/6gE9gR6AL1RMqciWwVsIcgkiy5lF4uVETFdN5FzOEV40iD7hDIRn6414GGiE8Lh/AbZzbXiRgsI9QBHaO8BmszF06FAuXLjABx98QL169e5Ku08//TRvvvkmO3fuZNq0aUUmnykoPBBcn/gUhkhGisn/7DRwCKgMK4+vZO2JtYz0HsngeoOxBlsZfnA4X538ild6vcL7r7+PezV3dJd1cBSomN/Gkfx2RyD6fN2ADoALhKWGsXbYWqySlQHRA3hx74v02NiDjHMZEJdvSybiQaAzQmBTgIcRIr4D8SCgoHCPUIT2Dpg5cyZ79uxh3rx5NG/e/K623bdvX0aMGMEnn3zCxx9/fFfbVlC4LzgQIqtGeLIScAURNo5DJD+FwC/pvzDr51l0cO/AxFoTIRg2JG/g+3PfM+P5GYwdNBZVWRWkgcdRD/BAJDZdQWQTN0F4pCGAJ0JsnwTsUDW7Ku8PeJ/ztvMkmBI4nX6afpv6kR2ZLbzZgnG8TkQSlAMRhn4U0Xe8FTh/738qhf8mxQptbm4u8+bNo1OnTnTt2pWlS5ditVrvh22lgm3btvHhhx/ywgsv0KtXr3uyjVdffZXWrVvzxhtvcOzYsXuyDQWFe8YVRJg4FJH1kYTwHi3AT4AO4oLieGndS1TTV+Ptqm8LQfWHZ7o+w4Y5G3i+6/OiL1UHbAen2imWDyEEty+i4MTl/G2WB/T577UEkqGFXwtef+p1jlmO0dmzMyfSTjBw00BMx0ziOxYgAfAHWiEEVoMY6pMMbEaIr4LCXaZYoZ06dSqJiYm8+uqrjB8/nosXLzJr1qz7YVuJExMTwyuvvMJDDz3ElClT7tl2VCoVS5YsISAggKFDh5Kenn7PtqWgcFcpENUQREg2N/89HbATyAJ7HTsjPhiB1W5lRegKXMJd+CD2Ay7qLiKFSjSu3Vj06wYBPwImsARb4CQiM7glwpstixgelIC4cxWEqGsAtYEzMKjJIDpGdGRT1iZG+o1kf+J+pnw/BY4jRNWCeDCojshMTsjfbjiiT3cL18pDKijcJYoV2lOnTrFw4UIaN25MkyZNWLRoEQcPHrwftpUoFouFYcOGIUkS77333k0Vsu42vr6+rFixgoSEBCZNmqT01yqUfvKAREQ41w8Rnr2C8DQPI7KF68H8b+ZzMOEg84LnUbl6ZTYmb+T1315n7fm1QtT0CG/4ECJjuTzo4/Qi7FwTER6WrttOCkLc9QjxtQItxN/SMYkFzy2ggkcF1mWsY2qZqQwKGySG+lxEhLdNiKStNgiv+TJCeH3ybfjtnv1iCv9RihXawMBA0tLSCpfz8vLw8fG5p0aVBhYvXszx48dZtGgR5cuXvy/brF+/PuPGjePbb79l8+bN92WbCgr/CBkhilqE2IEQLzvCo90PhMH+pP0s27uMvt596VKtC5FyJBN+nUDjGo15ddCrwvMNRojnAYSQ2sDlkovwNLsgBLWA4Pzlq4h+Vu/8l4wY9qMC90vuvPv8u6Q4UojMiaSOuQ5kw5VjV0Ro2IkQ6iyEiJO//Qb59u/gWiKXgsJdoFihDQ4Opnv37sydO5cFCxbQo0cP1Go1s2bN+teGkE+cOMHy5cvp2bMn7dq1u6/bHj58OA8//DBTpkwhNjb2vm5bQeGOSUKEYcsi7iJpCOFSAT8DajBVMvHKp68Qqgvl9Yqvk+aXxvO7nsfHw4cV01aIUq5uiD7YHYjQri9wHJw6p0haKvuX7aoQ/bMOhLCD6KfVAAFAUyAe6njXYUyrMXyd9TVfn/+aNX+u4bGPHyNqd5Twam2IsLE3YqhPRn4btRH9tVsRnrKCwl2gWKENCwuje/fuuLm5YTAYaN++PY8++ije3t54e3vfBxPvLzabjbFjx+Ln58f06dPv+/bVajVLlizB4XAwevRonE6lw0ihlGFCiJEPQiRtiDGuBuAPhIDVh4VfLeRS9iXmhczDNcKVRWcWkWxKZtX0Vfj7+AthK4cI1WYghvJcANLAVNUkMoKLwgXh7RaMj1UjxNcC1EH0tx6HkZ1HUj+oPpMTJlPfUh83tRtDvxpK3om8a9PpXUWMzS2HCCGHIYQ7CjHGVkHhLlDsZKkjR47EbDYTExNDlSpVsFgsuLi43A/bSoR3332XU6dO8dFHH5XYg0RYWBhvvPEG48aN45NPPmHQoEElYoeCwk0UhIzViAQoEH2pIPprjwPhcDThKCv/WElfn740j2gOnjD1ial07tWZetXqifWDEKJ8AhESzkIIbVnIfDTz9m6Af/76cYhEKrf891KAx4B1oDmtYemwpTwx6wnmx81naZWl9I3sy9RPp7Jo8iIxRCgXIdZPAJ/n21MH2AX8CtpHtSKkrKDwf1CsR3vs2DEef/xxhg4dSlJSEq1ateLw4cP3w7b7ztmzZ3n77bfp1KkTTz75ZPFfuIf07t2bli1bMmfOHCWErFB6SEZkApdFiG0GQvDUiCxjDVgqWhj70VgCtYFMDZvKebfzZKuzMVQ28HDDh4VYuwJeCEHTIkK4kfl/twanbzGRHAnhhTq5JvRBiD5fd0SWcjxUVFXk1Xav8kvOL6Smp/JyxMt8efJL1q9bL9qwI8TVJf87BekoNYFU8PrNSwkhK/zfFCu0c+fOZc2aNXh7exMcHMy8efMe6Enab4Xdbmfs2LF4eHiUir5nSZKYO3cusiwrWcgKpQMzom/WG+EN2hEhY1fgIELw6sI7X73D2YyzzA2ei6qiioE7B/LsD89eq/KkQojkPoSwVUCEalMRAnerkPFf0XMthJx5XbtWYQfhwFEY9MQgGgQ14LWE1xigGcATZZ9Ab9eLrGgVor/3CqJ/Niz/71CgLOgv68UDhILC/0GxoWOz2UzlypULl1u2bMnixYtvWm/79u13tMG2bdv+DfPuH6tWreLo0aMsX74cPz+/kjYHgPLly/Pqq68ybdo0Nm3aRPfu3UvaJIX/KteHjMvkvxePEKkERInEinAy7STv/PYO3b2783i1xxkTOYaYzBgWjll4ra2CLOOjiHBvDiJkHAw8lb+NO8UfIbLXh5B9EQL+OPAZqE+rWfDcAp6c+yRvxL7B6rqrkcpKYn+CEA8OJkTlqNbAF/n7Vgfk07KY2KAaQoQVFP4BxQqtRqMhMzMTSRKzJF+8eLHI9SZOnEitWrVu29bJkydLpdBevHiR+fPn8+STT5a6uXYHDhzI119/zWuvvUbLli3x9/cvaZMU/oukIMQoFCGEWYiwsR7YLVaxV7bzyuuv4K325vUKr7PFtIX159bz8pMv0+jRRmIlV4Q3/CXi7uOHGAokIQpThPD3kBBh7AsIcSyHEOxsRHJWE2A7RJSJ4KWWL7Fg5wK6RHehrW9bPo36lCv7rjB56mQRgk4AqiA86j2AL5grmIWn/R3wAiI0raDwNylWaIcNG0a/fv1ISUlh7Nix/P7778yYMeOm9cLCwvj0009v21aXLl3+saH3CqfTybhx49Dr9cyZM6fwgaK0oFarWbBgAU8++STTpk3jvffeK2mTFP5rWBCJTl75r4KhNQZEyPgK0AhWfL2CE6kneL/8++SG5DLp50nUL1efMS+OEYJY0K96ACFeVRBFJJIRCUhN/qF9LgjPNhnhnbojvO4YREbxBeA4vNjxRb47+h2vxr9KowuNOGc7x4dnPuSRrx/h8S6PX9uvuvnfuQzWEKvYzzMI8X38H9qo8J+m2D7a1q1bs2zZMkaNGkX9+vX5/PPPi0wU2rRpU7Ebu5N17jeffPIJ+/fvZ/r06QQHB5e0OUUSERHByy+/zDfffMOPP/5Y0uYo/JcoCBmruDFkbEeI5WEgDM5ln2PRzkW092xP+6rtUXupeaTcI7w74V0xXhZEmDYDEWb2R3jI5/L/fhqRCPVPCUJ417EI79QTIbq5iApQetCd07Fg0AKSbEnMjp3N5KDJ1PCtwdhPx5IYkyjayUWEolsBEmjTtaLvVkII7dX/w0aF/yzFCu3kyZMJCwujT58+9OvXj/DwcF566aWbG1KJprZt28aTTz5J48aNady4MY0aNaJx48Y3rFNauHLlCrNnz6ZVq1b07NmzpM25LSNGjKB69epMnjyZzMzMkjZH4b9CKqLUYkFRiBxEX6YLQnjs4Ihw8MqHr+AiuTA7bDaEQJngMnz88seEVg4V7bgghG8XQrQCENPngSgy8f8WXysIIVsR3jeIMLQKIboPA1ehnm89BjcezNq0tRyOOczyBsvJteUyev5onLJTPFgUFLJoAJpMjQiVV0d4zNsQ428VFP4GtwwdT58+ncTERA4dOnRDCUa73c6VK1du2eD8+fOZOnUqoaGhd9fSu4wsy4wfP74wu7e0hYz/ik6nY+HChXTo0IGZM2eyYMGCkjZJ4d9OwWw3Bd6hE+Ex6hGJTJeAR+Cj7z7iUNIhlpZdSpx/HBN+ncD8bvPxr+wvBLAgG/goImmpMiKsGw/UQniPdwM3rtVC9kL0B5dBhLYfRoSpj8P47uP5MfJHxsWN4yevn3ij4RtM/GMi+77fR9OnmwpvPRZoALafbeL7lfN/i5PAXkR/soLCHXJLoTUajZw7d46zZ8/eECpWq9W3nfi8bNmytGnT5q4aeS/48ssv+fXXX5kzZw7lypUraXPuiLp16zJ8+HDeffddOnXqRIsWLUraJIV/M7EIkSwog5iA8BgtiASmshBtjeat7W/RxqMN7Sq1o92hdphkE5pq+bcWCQhEeMUHERnBTkSfpy+izOLdTDAqKHxxFdEH7I0IBecgQshfiDrK8/vNp8d7PZh3dR7T606ndtfa1DXUFeu556+fA9kNssU8tcmIEPJuxHCfqvnbUlC4A24ptLVr16Z27do0adKksO/SarWSkpJCmTJlbvU1unTpwty5c2nRogUazbXmH3744bto9v9HfHw8b7zxBo0bN6Z///4lbc7fYsyYMfzwww+MHz+en3/+GXd395I2SeHfSCqiv7Ic4i6Rm/+eK7AdMIPzESfj541Hi5a3yr/FrIRZXMq6xLoR6/Au5y3aMSC8zK8RnmIIIhnKBjQDKt1luwu850uIEHIwwqs9l2/HI8AuaNKoCQPqD2DV4VV0uNSBhg0bggkO/3iYqu2r4mZwg3hw+DhEQtWfCOGuiiis8R0wkDtIJ1VQuIM+2hMnTjBz5kxycnJo164dnTt35uOPP77l+vv372fTpk3MmTOHGTNmFL5KC7IsM2HCBKxWK/Pnzy91/cbF4eLiwsKFC4mNjeXNN98saXMU/o1YEd6rB6KesRPhIeoQodPzQB345OdP2Bu3l2mB0zjlcYpPzn3C0EZDafZUM9HXqUaI3klEqDgMUU84Fojg7oWM/4p7vt0FQ5K0CIHPRQhteeAoTOkzhTJuZXgl7hXMF8zEJcbRfU13ps6bKtqxgy5ZJ77jD0QjimD4I8T23z9bqMJdoliVWblyJT179mT79u3Uq1ePnTt3smXLlluuf+rUKfbs2cPWrVtveN1Lvv32W0BMCGA0Gtm4cSMAJpMJo9FYaG9WVhYtW7bkl19+YfLkyXh5eWE0GguLbSQlJWE0Gtm5U5SCiY2NxWg0smfPHkBMBG80Gtm3bx8A58+fx2g0Fs7PW/D50aNHAYiMjMRoNBIZGQnA0aNHMRqNnDlzBoCDBw9iNBo5f/48APv27cNoNBITI+bo2rNnD0ajsbAE486dOzEajYSFhfHcc8+xZs0a2rZtW9iHvm3bNoxGI1lZWQBs2bIFo9GIyWQCYOPGjRiNRmw2kc3x5ZdfYjQaC3/Hzz77jF69ehUur1mzhn79+hUur1q16oa6yytWrGDw4MGFy8uWLWP48OGFy4sXL2bUqFGFy/Pnz2fMmDGFy2+++SYTJkwoXJ4xYwaTJ08uXH7ttdd47bXXCpcnT558w0PbhAkTbnjYGDNmDPPnzy9cHjVq1A3FVYYPH86GDRsKlwcPHsyKFSsKlwcNGsSqVasKl/v168eaNWsKl3v16sVnn31WuGw0Gvnyyy+BOzv3jEYj27ZtAyAtLY3JkyfftXPvzJkz/+jcKzjXCvj222/hKtjsNoxj8/cnEUxZJowvG9ny2RYIgpN5J5m2dRo1DDV4pvIzvBX1Fq5aV+o/Uh8kSMpIwjjZyM5fdsLvECvFYlxsZM8ve8AHYurGYOx3D6+lmH0YJxiJORADMuw5sQfjJCOxMbHQBnYm7GTQ4kFM6TqF85bzvHTuJV76+SUG1RjE+j/W8/ry1zFOMGJKMUEebLFtwfipEVO8CerCxksbMY4yYou7f9fSwoXXin6U9LU0f/58li1bVrhcktdSwXJJXksjRozgdhQrtLIsU7VqVfbu3UuLFi1wd3e/bTlAf39/7HZ7cc2WCAkJCURHRxMREcGzzz5b0ub8X0yaNImAgAAuXLhQKKQKCv83eQjPryBj14rwDN0QQ3PM4KzpZMonU5CQGOg7EFWYig86fEDVkKpovfPH6BgQnuRxRJg4KL9dK2K87L2usqRCeOAWRP8qXJvXNhjhmZqhcWhjetftzffZ35NjymF4heE0CG7A2h/XYraahWcez7XxuQW/RVD+vvyA8PgVFG6DJBdTRLdHjx48++yzzJgxg61bt3Lq1CmWLFlyyzGxEyZM4PDhwzRp0gSd7lqWw9SpU++u5YDFYiEyMpJatWqh1+tvu64sy/Tv358//viDn376iQoVKtx1ew4dOkSDBvdvqo/ffvuNXr16MXTo0BueVkvCltuh2FI0pcGWgmsoIiKCqKgoaulqoffQi/rDTkSY2ImoRbwNeAhWH1rN1O+msjBkIeGVwqlXox5afy00zm9Uj8jSjQJ+QrSVjgi1VgOGIob73Ia79ttcQSRDVUaIfyoi89kVMVtPBmTWz+Sx6Y/hK/uyreY2Eiom0PabtkSUjWDWsFnUqV1HJG4FAusQ4egwRPZxDvAMIrx8jykN50sBii03UpwWFevRTpw4kfXr1zN27FgCAgJ47733biuaoaGhdOnShcDAwMI5a0vDvLXr1q1j586dTJky5Z6IbEnQrFkz+vXrx/vvv18YBlFQ+L8oqN4EIpnIghCT34BgiCaa2T/MprV7a2oE1aDnvp7MOzJPCKgK4cUWZBn/hvAEJUQ/rS/QnmJF9q4SgugrvoLwTv0QHqkZMZ2eA7zivXir91ucNp9mYfRCQjNDmdtsLk+UewLJLIkHjQzEb9Eq//90xNAkKyI5LPU+7pPCA0exOXMNGza8Iba+bt26Itdbvnw5I0aMYOTIkbdsq2Cd+82lS5eYPn06jRs3ZuDAgfd9+/eS1157jd9//51Ro0bx008/lYqHGoUHmGCuFaZIQXin2wAT2OrbGDV3FFpZy7Ry0xh0bhB+rn4M7zhcJAjBtaL+GxECVZBlbEdkGVe5z/tTMLl8NCLBKyR/+Rwi/FsX2A9tm7WlT50+vHv8XVpGtaTzI50hEC7FXEJuICNZJZHEVRkxzOd4/r5UBs4ispD7cQeui8J/kbuWnL5x40aqVKly2/7bTZs23XehtVqtjBw5Eo1Gw5IlSx64LOPicHNzKxxXO2HCBFauXFnqi28olGI8EKJ4BSGyhxCFHh6GRZsWcTjpMMtDlrMgawFX866yceBGfBv6ClHVI0TsT0R4Npxrxf7rUHJ1gj24VsjCPX+5LGIfH0FkVP8Jb/R9gz8u/cGouFH8dOYnfHQ+HL58mOHDh/PF3C/wUfkIsW2O+P8SQmxTEKUoKwDK0Pb/Jo7bf3zXhLZMmTJ88sknt10nJOTvTs3x/7NgwQKOHj3K+++/T9myZYv/wgNI3bp1mTBhAnPmzOHLL7+kd+/eJW2SwoNMLOLGkYYoTBEGe+P38s7v79DbuzfpHul8F/UdU1pM4eGnHhbCrEd4iykID9YX0Zd5GuE5dqVkZ74JRiRjXV/IIhsREm4NbATXi668+/y7dFrSiQnRE3jf5328LF6ciT3DhMUTeH/K+0hZkhDrtsBXiESruoip9LYjZjeqcJ/3TaFksSMeum7DXRPa4mbuKQn27NnD8uXL6du3L+3bty9pc+4pw4cPZ/fu3UybNq1UFQdReMBIR1RWkoEfATWklUlj1KxRVNRVZGbFmVwOucwA/QCG9RgmkookRMlDA7BZfAcvRLKQDniSvz/93d1GhRg/ex4hthUQXm0ewtttAvwEderWYWKbicz6aRafR33O46GPM7HhRGYdmMXKLSsZ1mWYCEGHI5K/diM85NoI738zItnL9T7vn0LJICPGhheTeX5HE7///PPPZGRk3PB+3759/7lx94HY2FhGjhxJ5cqVef3110vanHuOSqViyZIlPPHEEwwZMoQ33nijpE1SeBBJRiQ0/SD+tj9qZ8R7I0gzp7E8dDkuFVyoFlKNN5u8KcRKRvTLhiD6KbOBisCJ/L9bAo1KYkeKwICwMw5IQiRtlUeExmvlv38chj42lN1ndvNa7GuEOcMY2nQohysdZvaa2VQJrUKbBm3EzbUOwvu/iNjnUESm9TdAT5T+2v8CSYhISTDinLgFxZ4Kw4YN48MPP2T//v2FrwMHDtw1O+8FJpOJF154AYvFwqpVq3B1/W88XoaEhLB8+XKioqJYsmTJbfvLFRSKRIPwzE4DtWDu1rn8evVXpgdMZ0raFGadnyXCrnW4FjIORQzducS1vs/LQA2gE6VLcPwQ9iciHgRcEeJrQzwUBILqqIp3n30XXxdfRqeOJv1cOkvqLKGGfw127Ngh2slDeMaPIyZduALURHi3fyA8XYV/N9kIofVFnAO3oViPNjExke+///6u2HU/kGWZSZMmcfz4cVavXk3lypVL2qT7SosWLZgyZQozZ85k+fLlvPjiiyVtksKDxGVgHxAK35z/huV/LKefVz92OXZxNucsUyOmXhNZV0Ty0xWE0Hoi+mWPIwS3B6UzhFoWMbynYFYeP4Td6Yi+183gd9mPD5//kM7LOjP0wlDWua9jfcv1eJb1FF6/HyLE7gJ0ADYgxLsBYljTD4i+6Rr3ed8U7g9mxLVSECUpZurEYp81IyIiSE5OLm61G0hMTOTUqVOcPHmy8HW/WLlyJRs2bGDs2LG0bdv2vm23NDF06FCaN2/Om2++WViWTEHhjvgd8ICj8lHGbhhLQ0NDTBoTO1J3MLPFTFq1bSW8WD1iSE8esCN/2QWRPOWNSH4qrbPbqLhWmSoakfhVlmtz5rYCsqCOXIdxzcaxz7SPaaen4ZnsiZQmEfN7DC/MfoHM1Ezh0egQY3JNiN+jPkK0NyAyrhX+XdgR540K0X0icW0O5FtQrEfbrl07nnrqKSIiIm6YjedWGcaLFy9m9erV+Pv7F74nSRI///xzcZv6v9m4cSMzZ86kffv2N9QB/a8hSRKjRo0iOzub4cOHs27dOiVBSuHOcMJF34v0X94ff7U/td1qszp1NWMfHsugroOEeBYkO7kC68V3CEJ4clrgaYTXW5rRIcT2EuKmWTF/+QLi5vkwsBfaB7Un/eF03jv4HkEnghitGU0MMfx06CcGzR7E51M/x0XlIsLnDyMeNPwQfb7HgLXA84jwosKDjxMxQYYDMfOUFtFPn3H7rxUrtO+++y5Dhw6944nct27dyi+//HKD0N4Pdu/ezdixY2ncuDFLly79142X/bsYDAY++eQTOnfuzKBBg9i8eTMRERElbZZCKSc5OJm+S/uCAz4L+4wL/hfQlNUwtutYIUQyoh8yAJFhm4OYhm4/okrSUzw4k6K7IZKhLiPCyKEIwb2ACAHngct2Fya3mExiViLzz84n8Hggfer1YWnzpYzYPYIXFrzAqgmrcJFcxHR6eYiZfXwRMxSdAT5FiK0yo+WDTUGGcR7iWnBBRDTSEEp6m/BxsULr4uJyw6wSxeHj43PfRfbgwYO88MILRERE8NFHH2EwGO7r9ksrfn5+fP7553Tu3Jk+ffqwYcOGf035SYW7T1ZWFhNXTiQ5L5m3gt4ivHI44RXCaRvcVvQ1OhGh1SDE/LLJiMzdI4j+ypYIb/ZBqpfihXhQiEN4JmURHu1FoAmYz5tRnVGxqNMi0r5IY+LViXgc96BTvU7kNsll/N7x9J/TnzWT1uAuuYthQiaEWJdF3HwLPNuB3N/ykwp3DxnxMJaNOK6eiPM/EfGwuY/bPmAW6/Y1adKEzz77jKSkJDIyMgpff6WgL7ZGjRrMmjWLY8eO3Zc+2hMnTvDss88SHBzM2rVr8fQsJv3rP0ZoaCifffYZFouF7t27F04jpqDwV6ZMmcLF9Iu0cmnFmIQxHDEcEUJaFyGyXojQ8VauZVtGIur8NkEkP6lLxvb/Cz/Ew0M6IpPYBeGxqCH7oWyoDNpTWt7v/D4NAxsyInEEm49s5hmXZ1jabCnWdCuyUxbhwxhEH295xE04JP/vA8AaxFAQhQeLApHNRBxPX0RhlgSE8G7L//s2FOvRrl69GqvVysyZMwvfkySJ06dP37De9fOOAvzyyy83rH+v+mjfeOMNwsLCWL9+PYGBgfdkGw86NWrU4KuvvqJXr14YjUY++eQT6tQp7Z1oCveDgrmOQUwj2SSwCd8nfs/zNZ6nXs16IhwqIyZS90OEi1MQT/QnEJ5sM6APor/qQaXg1pGIeKgoD4SBdFISiU6A2zk31nZdy8AtAxmVMIrsI9kMqDWAzi06oz6hJrt6NilxKVSkIjwB/IzoAy6D6NM7gPBwByAiAwqlHyciXJyNEFl/hKgm57+2AxmQUSPjts0UK7THjx+/I3uuF9b7idFoZPTo0YrIFkO1atXYuHEjffr0oVu3bixfvvw/m5WtAE6nk4ULF/L222/zzTffADB79mzmz5vPtIbTGPr0UKSakoh5+SCGMXyN8PrcgKMI8Xgc6MaDLbIFBCL2Nx4hkGFgLWsVSV8tAQ24nXbj0yc+ZcgvQ3g19lViDscwxTQFbDDt22lsj9nOey+9R8tWLUXdYy1i0oEghLd/COHVDkCEIBVKLzZEhMKEOFY+CM82A5FAtwuwwC55F8MWDeOzpp8V3Q63EdotW7bQuXNnVq9eXeTnpWXi9N69e9/3PuEHlcqVK/Ptt98yaNAgnnvuOcaOHcvLL7+MWv0gxvsU/ilZWVmMHTuW77//HqPRWPh+REQE85vNp81DbUSJQR3iCT4bUY4xFyFEfyLEpxPQjtJVkOL/xR+x31eA8yBLsvgtohHCqQeX4y6sbrqa1469xoqzK4g6HsWS7CW8EvgKkUmR9Jvbj9FnR/PSgJfQNtCKJKgjiP/DEcVAliFC7Q/xYPVp/1fIQ4isk2uJTxcQ/bFHgD/BiZP3495n9oHZVDJUum1zt7xEYmJiAIiKiirypfBgEhgYyIYNG+jatSsLFy6kT58+xMcrg/3+Kxw8eJC2bduyfft2pk+fzty5c2/4vFmrZuLGUpBZfAYRHstEhImPITy/ZxEZxv8mkS3AEzF0QwL9Vb140AhH9M01FS+NTcPsyrOZ03AOv1l/o21UW2KjYvmm0jd0q9iNRZsX0WlMJ6L3R4swdHNEVECDmNQgHngfMZ2guQT2UaFoZET+wUXEuR2e/945RP/998BvkGRPov/e/sw8MJNgVTAmD9Ntm72lR/vSSy8B8Oabbxa+Z7VaSUlJoUyZMv/fziiUKK6urixdupSmTZsyZcoUHnvsMaZOnUqfPn3+88Oi/q3k5OSwYMECPvzwQ8qVK8emTZto2LAhFovlxhX9EX2xVuAXRCaulWuTwNdH9MeW9CQB9xoXoDI4zzlFn1wWoq/VDREC9gNpj8RA20DqN6/PsAPD6J7QnQEZA5hVbhZt67Zl/uX5uKW7CW84AGiDGAaVgHiYSUD0eZ9BhN+VKlIliwlxvpsQfeiBiPM+FdFVchScGU6+SvmKmZEzybHn4IorSc4kxlUed9umi72r7tixg5kzZ5KTk0O7du3o3LkzH3/88f+3QwoljiRJ9O7dm59//pnatWszceJEnn76aXbv3q3USP4XYbPZ+Pzzz2nZsiWrVq2ib9++bN++nYYNGxb9BTfgFCJUfBrxFH8eUfmpN/AS/36RLUAN1hCr8EitiNChHTHeNgIROq8Ftd1rs+PRHQwuO5i15rU0vdCUxPOJfO/3PQH7A3AecTJw3EDWrluLpY5FZHF7Ix5qvBElKxcCKxDDjBTuLzaEwJ7P/7scIvpwHlFadB2wC/ad30e3P7ox9uhYKsuVGew+mBruNdjefjtDOg+57SaKFdqVK1fSs2dPtm/fTr169di5cydbtmz5P/dMobRQoUIF1q9fz9KlS0lPT6dPnz707NmTX375BaezmLmfFEotJpOJL774ghYtWjB+/HhCQkL45ptveOutt/Dw8Lj1F38F9iAyimO4Vmx/MmKM7L8h6env4o0I9/pybQiQG6K4xVNAe3At68rrVV/n21rfUtW1KtNyp/HYhcf4+KePid0cS3pcOhM/nEjjvo1Z/vNyMmtmCrEui3hwMSEebl4DViF+e4V7ixXxYHMWcVy981+xiGjOxyBvlvn9wO8Y9xoxHjVyJOcIvbS92FRpE5NaTmLzwM1UbVO12DmIi806lmWZqlWr8sEHH9CiRQvc3d0Vj+dfhiRJdO/enQ4dOrB27VqWLVtG//79qVChAv369aNDhw6UL1++pM1UKAZZljl58iRffvklGzduJDMzk7p16zJz5kzatGmDJN1B1s0RRFalD/AoItkpAiVhR4MIHfsjwolpiL47V8SsPRWB01D3j7qs91rP7rjdzL86n8nmyXjEeGBUGenk2okd8g5mr5vNwg0L2TBuAw/VeUgItzei3zYRUSN5J1AVMSa3IaVzcoYHESeizz0NkdgEIlqjQUQsjgCnITEukc2XN/NR+kfEOmOR8i+AEFUILau0RNVEJY55QH47UYihcLegWKFVqVRs27aN3377jYkTJ7J79+47u2D/Jlu3buW9997DbrczcODAUj/f7b8RvV7P888/T//+/fn+++9ZvXo1s2bNYtasWdSpU4cnnniCRo0a8dBDD+HiopS4KQ2kpaXx559/smfPHrZv305sbCw6nY6nn36aPn360KRJk793vQYhbu5tuFZ4X+EaOkQoORhxs05DhJNVQHWgAkjREq2Ot6Ll+ZYcjj3Mmrg1fGb9DGuelWA5mI5SR8xOM8GfB0NVWJKxhKOZR2kV2oqGIQ2paq2KJk4DPyGGkASLtj3cPYTQl+PBLAxSUlgQGfPZCFG0II6ZE/GQcwHMp8ycuHCCHxN+5Efzj1yULwLgLrujklS0MrTiucrP0bJBS1RBKvH7X0VUhEpEKGm9W5tQrNBOnDiRZcuWMXbsWAICAnjvvfeYOnXq/7HXN5OYmMjixYvZtGkTOp2O3r178+ijj/7nprgrLeh0Ojp37kznzp2Jjo7m+++/57vvvmPRokXIsoxOp6Nq1apUr16datWqERERQdmyZQkJCbl9WFLhHyHLMunp6SQkJHDx4kXOnTtHVFQUJ0+e5MKFC4Cobd2yZcvCWat8ff9hFfvXEBnHCrdHi3goCUTcxLMQmdkqxNR7ZUDKkGhwtgENLjVg1oVZ7Liyg21Z29jh3IHZaWbH5R1UvFQRHTriVfFsjxIzbenR09K7JaurroYcOHb6GG4n3PC0eoosZR+E2IYhvN5QRAjaj/9maB9EdMGOCAdb8l85iOOSgcgkToa8y3lcvniZ2CuxnE85zx/WPzjmPEaSlCSGcuW39YrzFTq5d8Lp6yQgKAAfdx/R/hFElnhu/jb1iIpp4bc3T5L/Rhz4XmUdb968mYMHDzJnzhxATGQgyzIjR4687fcsFguRkZHUqlULvV5/V236Jxw6dIgGDRqUtBnAvbElMzOTAwcOsH//fk6ePMmZM2dISkq6YR0PDw9CQkLw9vbG09MTT09PLBYL4eHh6HQ6DAYDOp2u8KXX69FqtahUKlQqFZIkIUnSDctFvf9XijqN//qeLMtERUXdMLnCnXzvVu/dyTpOpxObzYbdbi/8v+DvixcvEhISgsPhwGQykZOTQ3Z2NtnZ2eTk5JCVlUVSUhKJiYk3ZAdLkkT58uWpWrUqDRs2pGHDhtStW/cfRRkKrqGIiAiioqKUa+n/taPAe8pD3JBz8pdzEENGLoL1ipVjF4/xR9IfHLcc5zSniZair93oAbWsppyjHL5OX85ozmBSieEjGlmDm9ONKo4qdDB1QIeO7frtSJKEG24YMKCRNJSlLLWl2qhUKvar96NChUalQSNpUKvUBKoDCdWEIiNzXBZFiSRJQkbYEKgOJEQdgh07Z+xnxOeIa1BCQmfRUdG9IlasnLeeR0a+4fwPVgfjq/LF7DRzznFOvOkUY08Byknl8FX5kivnEuWIAplrbchQUV0RH3zIdGZyznkOWZZxyA4cTgcOHIQ6Q3HBhSRnEicdJ3GqnFhlKxbZggkToY5QnDiJVkVzUXMRM2askhWndGPeiavTFZNkwlf2paqjKk2cTWijbkNNbU3UBrV4cJIQDzBaRDa6F6JPNhyRKV4LLAEWIk/dWouK9Wh37NjBH3/8wZgxY+jUqRPZ2dmMHDmSgQMHFvfVOyYpKYmAgIDC5cDAwDuuSKVw//Dy8uKJJ57giSeeKHwvNTWVCxcuEBcXV/hKSEggMzOThIQEoqKiSEtL4/vvv1eSq4rBxcUFDw8P3N3d8fT0xN3dnYYNGxIUFERwcDBBQUFUrFiR8PBwJXRfWimYq7cgoCAjvCwrYho9G+jydDxsepiHMx8WhTFiwRRr4vyZ88TGxxKXGUecKY444siQMijvLE+GnEGOlINZMpOpzuRP9Z/8qfvz79lWcPk5KHai8juiNIz/VVFkSu8Z9Rl8nD7IyFix4ia7EewMxtvpTbAcjNFq5FEexaAzoDVo0bpqhYi6IgqLeCDGUwcgogXlEElrFRCRjL8q519Gyf2VYoV25cqVzJ49uzDreMaMGQwYMOCuCq3T6byhH0mW5b/VrxQZGXnXbPl/OXToUEmbUMj9skWtVlO+fPliE6YcDgc2mw2bzYbVar3B05Nl+aaX0+m85ftFUdQ589f37vS8upO27uR7kiShVqvRaDQ3/a9SqQqXdTrdDfM93wqLxcKpU6fuaB/+Ccq1dDP3zA4dwiu6LuwYIAcQIAdQ11kXAMkpXRNGG0gWCafZiTPTiSPVgTXbiiPbgTPbidVkxWayYbfYUTlVuDhdcDqdJOYk4rA7sDvt2B12ZLuMp+yJn+SHw+Hgkv1SoSdb8J+PyocAVQB22c555/n8j655rQFSAIGqQCyyhYtO0Z8p5f8DCFIF4S15Y5WtXJYvi8+vuzaC1EF4Sp6YMBHniLv2/XyPOVAdiKvaFZNsIklOAkncZ1SSCpVGRRl9GTwMHpi0JnKlXPQuegyuBlzcXXDxdEHlqkJ2lYUXqgOHzgE6kPUyTq0Tp8bJJbdL4hjoEEp4JyUEEih2AoGiKBVZx8HBwfz557Wns+Tk5L9Vu1gJd92MYkvRKLbcSEHouADlWiqddoCwpX6D+iVtBlD6fpeStuWv19FfKVbDr886btq06T3JOm7SpAn79u0jLS0Nk8nE9u3badGixV3dhoKCgoKCQklwx1nHY8aMKcw6njJlyl01IigoiDFjxjBgwABsNhtGo1GZxk1BQUFB4V9BsULbsGFD1qxZU7i8bt26e2JIx44d6dix4z1pW0FBQUFBoaQoVmiPHDnC+++/T15eXmEiytWrV9m1a9d9ME9BQUFBQeHBptg+2qlTp/LQQw+Rk5NDx44dcXd3VyYMV1BQUFBQuEOK9WglSWLIkCGkp6dTqVIlOnbsSPfu3e+HbQoKCgoKCg88xXq0bm5uAISGhnLu3DkMBoMyZ6mCgoKCgsIdUqxHW6dOHUaPHs3LL7/M0KFDiY6OvqOB9QoKCgoKCgp34NFOnjyZQYMGUbFiRSZPnozT6WThwoX3wzYFBQUFBYUHnjvqo61Xrx4ArVq1olWrVvfYJAUFBQUFhX8PSmergoKCgoLCPUQRWgUFBQUFhXuIIrQKCgoKCgr3EEVoFRQUFBQU7iEP9Dgdh8MBQE5ODlartYStEWRnZ5e0CYUothSNYss1Cq6bgv+Va+lmSosdoNhyK0raloJrpkCT/ook3+3JZe8jiYmJXL16taTNUFB44PH09CQrK6ukzVBQeKApV64cQUFBN73/QHu0Hh4eAERERKDT6UrYGoiMjKRWrVolbQag2HIrFFtuxGq1EhUVhZ+fH1lZWcq1VErtAMWWW1EabCm4jgo06a880EKrVqsB0Ol06PX6ErZGUFrsAMWWW6HYcjNarRZQrqWiKC12gGLLrSgtthRo0l9RkqEUFBQUFBTuIYrQKigoKCgo3EMUoVVQUFBQULiHPNB9tAoKCgr3BAdwEdz2uUEc4AIEAR6AN+AFSCVnnsKDRakS2pycHHr37s2KFSsoV65cSZujoKDwX8MEbAd+ApIgICEA1EA6kAPIiGUdQnDDgRb5rwqUsjuqQmmh1JwWx44dY+rUqURHR5e0KQoKCv81LEAU8AVwBjAA3mDLsQnPNR0w579AiG0CEAlsBTyBMKAh0AioBlQFtPdvFxRKL6VGaNevX8/06dOZMGFCSZtSapAdDpx2OyCmK5Q0GiTVtW51WZaRbTYKao5IajWyzY41JxtbcjI5ly+jdnNDrdVhNps4f/YsOenpyE4nIX5+BPr5o3F3Q9JoQKUS21CpUOv0qA0iXV52ykgq6ZoNWi2SJCE7HNhycnFYLUiShEqrA40G7DYcFgv2PBOSSkKt1+Mwm5FlGUlSYm0KpZB04DSwCREmfhQoB5wH9UU15CFCxbXB4XQQHRNNpiUTKopCHxUuVkATo4EL+d//DQjNf0UAzRDerut93zOFUsIthfbkyZO3/WLNmjXvqiGzZ8++q+09qDjtduzZ2dhz85DzRfZ6VDodqCSQwWm1gixjN5nIu3KFvKux2NLTkR1OHOkZXDh4iO3Hj/PTuShOJSfj5MYiYL56PY2DQugUXoUy/n5oPTzQeHmi9XBH0mrRurqjD/BD4+aGymBArddjy8rGmpKC3WQClQqnxYwtOxdbVhaOnBycdiuoNGh0OlCpUOm02PNMXElIxD00DLfKldD7+Nyvn1NB4fYkAReBH4BM4HFEP+xZ4BKoc9VQEfaa9vLp2U/5KfEn8mx54rux4j8XrQttwtrQT9ePZrnNkCyS8HazgSvAXoS32wyoBYTczx1UKA3csgRjp06diI6OJiAggL+uIkkSP//88z0xqHXr1nzyySd31EdrsViIjIy8J3bcb2RZhtxc5Lz8i1inQ9LpoGAAtCwjm0zIaenIFiuSRo3TywspIwNHfAJkZyGrVKA3gASHz57lw1/3kJybS1k3Nx71CyDMzR2DQY+s05GYm0tkchJHkpJwItO2fCh9qtfE4OEBej24uSLp9KBRI3l7IesNSElJYLEi63RIep3wqJ1OyM6F7Cyw2YStLgbw9ARvL1QOB3JuLljtInfEzRVVSDBSWCjqUjLIXEFUV4uKiippM+4r6nQ1uqs6PA54oL+sJ6dWDnZ/O4ZoA7qrOnTJOi66XmRWwiwOph3Ey+BFs9rNqF6xOr4aX1zPupKZkckR1RF2xe4iIy+DBh4NmO41nYpSRWQXGaufFaeLE7VDjWSTMIWZMEeYMVU24fRylvRPoHCXqVWrVpHFM27p0a5du5aePXuyYMGCEi9vVRy32rn7zaFDh2jQoMHf/p7TZsOSnIzTakPj5obW2wuV9sbOHWtGBraMTCSNBrWLC5bUVDIOHcGanY3aPwC3hg1wDauAKTuLGXPmsGnPbsJ8fZnVsjWNqkag0mqRAVt6JjhsGMqVw7N6NZJTklnx+ed8vW8fR9PTmfrkk1Tz90cf4I9L2XKotGpyoi5gTUrGtUwILuXKoTIYcOTmYk5JxZaWiqw34Fq/Ht61aqL198N06RK5l68gqTW4Vw7nzMULhPv6Yk5JxZqeiWyzoM/MwuehergEB9+lX//O+KfH6F5QGmz568Pqg34t3REOIBq4ivBo04EICAwLFB6oWazzlctXTDwzEb1Wz+vDX6ffc/1wOeEiEqbqIfppPwbOg7m1mS/MXzB/w3x6JvRkVo1ZPOP/jEieCkCEkWUgGUgF/IFgsd077cArDedLAYotN1Kc03fLQ+zp6cmrr77KkiVL+OCDD+6JcQrgsFiwJCYBoA8MROPqcsPnstOJNTUVe24eGnd3dL4+mJOTyTobhS07E7XBFbcKYXjVqkHi6TMMnzKZo+fP06luPV5s9xQapxOtmxveDz2EJSOdvAuXcNosSE4nlsQkykRU5q0FC3l682Ymv7+SV77+mmndutHM04O8yzGAE6cEWhcXJK0OlUaHLS0NW3YO9rxcVDod+sBAvOvUxrVMGSS1Gl3t2hhCQsiMPEn2mTPIdjvujSLQeMRjcUvBlpuDLS2N1H378apbG/eKFZX+W4X7QzZwCREutiCEthzQEpEMlQBytMycjDksT1xO/cD6rFy+kjLly/DrT7+y6+guUlJT4Evw9fOlakBVelTugSHHwLM1n+Xpj57m5bkvM+7wOM5GnGV66HSkNAmsiJBxCJACHEe8l4sIJ7vcbKrCv4fbPku1bNmSli1b3i9b/nPY80xYkpNRaTToAwNu8mJlpxNLUhIOswWdrw9aT0/yYuNI+/NP7JnZuFWqhCE4BNQqonf/yoj58zgfe5XpPXpSMzAIV08P9L7+GEKCyTl3HpVWQ1Dbx7FlZZNx5DC29HTM8Yk4c/No3qkj62vVYtQbrzN9w1e81s1I48BAnCYzrmGh+LZuRdbxE6QdPowuOBhHdjY4HXjXq4tHRATW9AzMSUkYAgOR1Gr0/v74NXqU9KPHcB4+Ql7MZdwqh6PS6VElJqHWGzAnp5Bx9Bg4nLhXDlfEVuHekgzE5/9fBtiPELq2+f9ngJwn81rya3yU9hEDIgbQpncbQoJDIB1+jvyZTzZ/gr+3P9ggNTcVV3dXen3XCzbDuq3rMFQw8P7r7zP/o/l88PUHmE1m5njPQWVViWxmd8AHMVToHFA5356WgNt9/0UU7hOlrjLUL7/88p8YQ+swm7GmJKPSaTEEB90ssrKMJTkZh9mC3t8fracnpoQE0g8dwZGXi0toeTyrRuBeqSIZ8fEMe3MOF2KvMqv3MzzRoCEqtQaX4BC8G9bHlpEBgGtYGCpJQu/liX+Txmh9fcm9cBFTUjKSWk2lpk1YNXceNStU5I2NX7Hr1Gk0fr7IajUZhw+jdnNHpdaQc+Y0kkqFT/0G6Pz8kZ1O9AH+OK02zEnJot8WUBsM+DxUD5WPD7nR0ZguXcJQJgRDSBBaN1dcQ4JBlsmMPEnOhQs35QIoKNw14hEJSk5E2DYVkfBUEzGUJwbkeJnXTguRHVJ9COHVwhk8ZzA7tu+AsjD2lbGc/eYsB9Ye4MC3Bzj39Tl2fLEDKVSC9vDx+Y958bMXadi9IZmXMukU0YlPr3zKpPhJyMjCazUgxuBWQYSSjwEbgHeFDQr/Tkqd0P4XcFqtWJKSkNSaQg/wr1hTUnCYzOj9/dC4u2FJTSX9z0PYc7MxhJTFvWIY+sBA0s+cYexbb3IhIZ4ZXbvTOLwyyDKSlyeedWuTey4Kp8WCf/Om6Lw8McXHo9LpcQsNxb1iBbRenjhMeeReicWWkUlw7VosHjKE6sEhzP91F2cz0sFqwRIbh+nqVSQ3F9T5Q3xcypVB6+GBLSMT2eHIF1srluTkQtFU6/WoKlXAEBREzsVo8i5fxqVMGfRBgagNegzBwSA7yYw8hUmZW1jhXhCLCNd6IGJ4MnAQ4V1WBS4DCbDyxEo+SvmI5ys/T6JrItO/mU6D8g2oWaUmaMAz2ROtTiu80Aqg8lMRLAcLbzgAvnvtOzY23Ehnz87sOLmDb6K+oU6FOnyW8hnveL0jvGjf/O0agPZAnfy/LwDvIIpl3DzYQOEBp9SMo/2vIDscmJOSQKVCH3QLkc3IwJ6bh87XB427Ow6TibSDh7Dn5mEIDsG1XDlcypYl5/wFXp8/n/0nTzK5u5HmDRviyMpGpdZCeCXMl69gz8rCq05d9N5emG1WIazmPPJic4kzm/kjJYmLx46TlpWJwcMTDw8PnmvWggXDRzD4nSVM/vwz3unZi2BvX+xx8ag9PfBv3gx7djaZR47h26QRaocL1rR0DIEB6P18saSkYk1LR+/nC4Ck0eBdowZpR46Qc+48Glc3DMHByA4HltQ09MFBmOMSyDh+ApWLCwZ///t9WBT+rSQCaYiEpDyEa3EB4eG2ArLE39+d+I6ZV2bydMDTXOQiOw/tZNKgSbR8qCVl9WXhKFAWMUyn4K5ZJr/9nwAzqFJUNKrfiEZlGjHTcyY/qn4kvEU4KzeuZO73c9lfdj+LKiwiSBskhD8BkRClQhS2yAW+QSRkGRFjdxX+FSge7X2kIBwsO5zoAwJQaW5+zrHn5mLLyETj7o7W0xOHw0Hq/gPYs7PQentjCPDDtWIYpqtX+eTTT/l6z276t3yM9k2bolKrUXt74h5eAZKSMSck4lqpMvqgQCypaWhcXUlSqbBmZGBNS2f1hq+YPn8+a3/awd5Tp9jx++9s37UTt+BAIjq0p2H16pisVl79ejN5WZnITgcSMmq9C161a+G028k4fDQ/S1oj+pv1erSentizs7FlZxful9rFBe/atVEbDGSdPIUjLw9DcDA6by9UWh36oEDsublkHD6KPTf3Ph4VhX8taYhkJ1+E15iLuOMdRNQt9gWuQNSFKF4+8zL13evTuV5ndl3YxbyX5zHqpVE4AhyiPzUPkchUcMnmIYpcnEb09cYjqkK9DAwAQ6CBzr6dqZVTiwUDFlAppBK7YnfRaG8j5pydQ3paurAvGpG9nIrIhjYDO4FliKxohX8FxQptbm4u8+bNo1OnTnTt2pWlS5ditVrvh23/OmzpGaLP1c+vyDGkTpsNa2oqKr0eXb43mHU8EktqGio3N/T+fnhEVMWelsbh335nwdpPaVS1GkPat0clqUCS8KlfH5WbK1y5itbHB88q4VhTUjgfE8OISZNo1aYNZ+Pi0Hp6MrRff/bt28el6Gh+fGcZXz33Ah+/MASVToek1ZJns2N3OIjNyGD4+i+Jt1sxlAkh9/w5nGYzHtWqYsvMIvv0GXT+/oCEJSUFjbcXahfh5TqvO1e0Hh541ayB7HSQefKUSJoKCEDr5obG1RVDYBC2jAwyTpwsrIiloPCPyEWEjD0QXmMCQmyPIASuDpAIeVfyGLpvKG4qN1Y1WkWH3h3Y+f5O+vbsCy6gS9BBRaB8fns2RNby9whBdCASmRojQsre+X/XQQj0IdDv17N56Gb8PfzRarS8G/8uzY81Z/2V9cKWuPx2LyOGGqUBO4BJiIcChQeeYoV26tSpJCYm8uqrrzJ+/HguXrzIrFmz7odt/yrseSZsWVloPDzQuN+cXijLMpaUFEBCH+CPJEnkXo0l73KMECQfXzwiquC0WUk4fZpxCxfg4+7OtF690RlccdrsuFcOxyU4CFNsPLgYcCkTQuaVWOYsfpsOffvw+++/8/ILg6lUqzYuISGU9fMjxMsLbDYkrVYM1fHxxp6bR8aJEyyYMJGts+ZQPTiE+JxsXlj7KbtOnsTpdJIZeRqNuzuu5ULIu3IZc0IiOn8/nBYrtvQM9P5+SCoVluSUG5Kc9IGBeERUwZaeQdaZs2g8PdH5+Qpv3M0NjZcXpthYci5cVJKjFP4ZdkT4VYcYv5qGEDIncBghmnrgKkz+dTLnTOcYFDSIS2UuQXmoEl5FhJqvgKyXhYCGIrzjr4DvEIL4MNAdeBLhIccjhg/FI8LK/ogxt+ngn+HPB30/wOww0yykGZX0lYhMjRRtpuTb55X/f0E/7nFgGDAT4UErPLAU20d76tQpfvzxx8LlRo0a0b59+3tq1L8N2eEQnqpOi87Hu8h1bOkZOC1WMcxHo8GWm0vmseM4rXZ0wUG4hpYTYdeoc0xfvJj4lBSWDR6Kn78/DqsJfYA/7pXDyT59Fux2pGrVsJst9HpxOCfPn6df376M6tcP/8BA9EFBSJKE02LGkpaOPTMTZ1Y2/s2bYY6PJ+/yZVRaHS5lQ/D39mHZs88xZt3nnIyJwVOrRaU3YM/JIvvsWbxq18aWmU3WyVP4NWmE1tMTW1YWahcD+gB/zAmJcF0IWZIkXENDsaZnYroai9bHB7eyZXCYLTiThTfsMJvIOReFzscbQ2DgfTpKCv8ariLENhwhrkmIov+7EULYGEiA9QfW81XsVzwf8DzvJ73P7v272fzMZqTykmhDBZYQi2gzHvgDIdqNgSYIQSwgGDiACAWH5G+3MWKs7BXABo/IjzC5zmRmHp3J/Lrz6ebeDWxwmMMkqhJ5qv5Toi0Z8TCQhOiz/Qo4Ah5NPYSnrExU8MBRrEcbGBhIWlpa4XJeXh4+Sq3av4UlJQVk0S97/aQABdzg7bq6IssyGYcO4zCZ0Ph4Y/D1xbVsWUzx8XzzzTfsOHCAwW2fpF7VajgddlR6PV41qmNJSsKSnIJ75UqovT1RqSQGdu7K2g8/4vUxY/D19kHn7184XlXn64s1LY3Mk6fRBfjjW78ermXL4rRYsGVnkxMdg8Och+9D9Vjw6mRcDQZWbv8RU042U9auZcvWrZiuXsWjVk1k2Unm8ROoPdxR6XVYU1JRabVovb2QTWbsOdf6XVUaDZ5VI9C4upJzNgp7Xp4IIXt5otZqMQQE4rDYyDwRid1svun3UlC4JSkIMQ1BDKdJRAhXDnAC4ZlqIPZiLNP+nMajro9y1HwUWSWz9NWlSEGS6C+1ISYCACGwPyCEtTlQg2sia0eI8AVEX2s2Ikytzt/uo/l2XAAuwpAKQ2hasSmvR71OYqtEqA4rL6/khX0vsPCXhcja/ChOLlAbWAi0Efvhv9lfeLf/rUqZ/wqKFdrg4GC6d+/O3LlzWbBgAT169ECtVjNr1iwlhHwH2LKycZjMaH18bhorC0V7u9nnzmNOTUPj6oLeyxOPKuFYM7OIO32WuR+voUZoKH0eawNqFTiceFSrhqTXkXP+AlpfH+avXctP332H1sODbs/04qFyZUUWs7/fDQlYDpMZa2oqkkaNS5kyOK02tN5eqPMznS3JKchWO74N6lOxbh0mP/c8kTExrN7+I2lZWbz++WcsevttnBYL7lWqYE3LIPfiJfT+/iIUnpqK1ssLtFqsaWnIDkfhtjXubnhUi8Bps5J1+gwqvQ6dry9qvQ6VXofe3w9rejrZZ88WjstVULgteQiR8wT8EIlFafl/70OIV02QE2Qm/TgJh+zgIbeHOJR9iLeGvUVohVAxJV4OIvSrBvf97sJTrQD0QAhnLteKX5xB9LHmIgQ1EJHJHIvoyz2K8HZrABVBFaZicd/FqFDx8taXcdR0sLT5UnqW7cmiyEUM3zAck90kHhDSEaHngUAnsHvYxdjfpQhPV0mVeWAoVmjDwsLo3r07bm5uGAwG2rdvz6OPPoq3tzfe3t73wcQHF6fNhi0jHbWLC1oPjyLXsaZnIDud6PxEn6Y1K4ucs1FIgNbbG9eKFUGlwnTlCnNWvkeuycSr3XugdXdFtllxKVsG17JlyD51FrtTZvqnH/PR6tVcvRqLzs8Pnbc3jlwTTosVtcu1Om+yw0H2xQs4bXY8alRHttsxx8Vhz8nGrUIFnDY7st2G1s8XW0YGhuBgOnfqRLtGjfls9y5GtGtHh0aN+fCH7xkzdizk97XmnL+ALTubbb/9SvN27ShfvjyDx49jyw8/YElNvWHfDYGBuIWFYklKIi86Bp2PN1ovbzFVn6sbaoMredGXMSUk3MvDpPBvwIFIJtIiSiqCEEI1wss9jQgly7Dh1w38Ev8LQ/2Gsjp1Ne3rtKfLU12El5qE6Cv1AL4Hw2WDCNc+jeg39UCI6l5EgYmCQI0K4d1K+dvbiyjz6IdIlnoV6AfooGxuWWZ3ms3B4wd579x76BvoWdRwEVOrTOXbmG/ptaEX2dZs4bmmw6ZfNvHIF49Q/3h9HjnyCJuubhIe9ur8bSmUeortox05ciRms5mYmBiqVKmCxWLB5bobtsKtseaH3AsyiP+KPc+EPSdHeJF6PbIsk3nkKA6rFZ2vD3o/Pwz+fuRdjeWHH35k+/79DH7iScIrhIHNhtrFBY+IKpjjE8hKTGTKF2v57cABxo94kaaNGqF2MWBOSEDn44OkUWPPyhIeJmBOSMR0JRZ9UCCe1auRc/YclqQkNB7uyA4HOl9vcDiQZJm8mMtC9EPLM/XFFzl05gzzvt7MqlEvUdbfn5XfbsUx+mWWv/suqfsP8MWKlcz66ENMJhMAycnJTJv7FiDTo0/fwmQwSa3GvVIlLKlp5Fy4iD4wAH2APw6zGXtWFjp/X8xx8SKr2ccHjXLeKdyKgn7ZSlwL4eYgkpQ2IOoaV4aks0m8/tvrPOz2MKODRuNf2Z/2g9sLkU1F3BGDgB+BS5BXPQ8eQ4SBkxCerC2/fS3XPOcMriVDqRFjbkMQk8CXzbfxIYRQ/wDdArvxY80fWbh6IU+++yRV6ldhuG04YS5hbErchN6iBy1s2rqJCdsnYLKKayk2N5YJRyaAO3Q72U148V0QmdEKpZZiPdpjx47x+OOPM3ToUJKSkmjVqhWHDx++H7Y90NiyrwsZFzFeVnY6sabl92Pmi1/2+QuY09LRuLqgdXfHrWJFbNnZJJ45y5yPVlGlbFn6tXkclVqNbHfgUbUqkkZDxtkoXlm1kr1//smb06YxZMAAJE8P7FlZOC3W/ApO7lgzMnHabNjz8sg+fwGVXo9n1Qiw20HKLwuZlo41NQ2P8HA8qlXDbrFgy8kh9+IltB4eBFWNYMrzL3AhPp7Pdu5i0BNtea1vP55p1BiHyYxHRATvfPF5ocgWYDKbWbRyJda0tBuG7hQ8LDjtdrJOn0HSaND7+aLS61HpdOj8/LCmZ5J1NkoJISsUTSoiXBuMmFxdRoieHpGIlF9TWLbLTN4yGZPNxILgBWiDtDzb/Vkxaw8IMS4L7AHOAw0gr26eELNziFBwGiJE7I0ID58F/kSEkO1iOzwJPIsQ1jSECBdQGegBkq/E7Edn46p15ZW5r+Co4YDa8HTQ03xQ4wN02TrSktOY88ucQpEtwGQ38dbRt8RDRRziQeJE/n4rlEqKFdq5c+eyZs0avL29CQ4OZt68ecok7cXgtNuxpaejdjHcJmScjmx3oPP3Q5IkETI+cxZJktB6eeFaIQxUEnmXrzB31fukZ2czuZsRnZsLTrMFQ5kQXMqEkH36DFqtjhZt2rD4rbfo3u4pdL4+4HRizchE4+aKxtUVna8vkiRhSUom9+Il7NnZuIdXROPmhiUlFdlmQzLoMCcmguzEo0Y1/B59GI3BgC0zC0taOubEJAxBQbRt9yRPNmrEml9+4kJ0DO2bNKVKYBDZZ6P4evcuEq9Lnrue+ESRmWJNvfFzQ0AAbmHlsSQlC+/ZywutlxcqlQq1mwtqVxfyYmKUELLCzZgQouqBGE4DQtws+cu/IzKAK8G3v3zL99HfMyJgBEOuDuEn7U9QDREqTkN4tVEI0aoJNAVNukaIaSwiPE3+9hLz143M//7DQAfEBAVVERMEVMj//wrCEy4gCHgGAqoE8EazNzh08RAfr/9YzOJTDSSHhJwu8+yuZ4nPiy9yt+PS40QoPBzhRX+H6EtWxLZUUmzo2Gw2U7ly5cLlli1bsnjx4pvW2759+x1tsG3btn/DvAeTAiHR+RYdMnaYTNizc9B6ehYWrsg8eqwwZKzz9cUQEIApNpadP//E1l9/ZeBjralauTKyw4Far8cjogpZV2M5d/ostVq3YswTrTHFxaN2MaBxc0POykYKKVNog6RWo/P1IevsWXJjruBariyuoaHYMjKxZ2UhO51Ikgqn1YYhOBhDYCAqtRqvGjVIO3oUe0Ymufn9qK7lyvHq0GHsj4zkrc0beG/ESHSurkRFnmDqwgVotFpsBZPAX0eZMmXQentjTUvHnpN7QwjZrWLFayHkAH/0/n44zGbk7Gx0Pt6Y4xNFCNnXF43BcC8Om8KDhhPRL6vhWr+sAyGC7gghvAhUgbSUNKb8MIU67nXIsGVwznKO4DrBwjMtCANbEMJcHtGvegX0V/TX3JGriGSrLITnXOe6/ysj+mevR4UQ24LvWRBJVirEg0Fv6O7ana/Pfs2bm9/kiZpPUL5WecgG6YLEqOBRDEwbWOSul/EsI5KlKiAKccQhCmg4EQlbSs2/UkWxQqvRaMjMzCwcEnLx4sUi15s4cWKxE8SfPHnyXy+09pwcHCYTOl/forOMnU4sqWlIGg1abxEyzjp3AXNqGmoXV7RubrhXqogtO4ekc+eZ8cEHVAgKYtATbVGpVDjNNtyrV0et1zN5+nR+O3GcPV06YklJRZIk9H5+2LKywWZD5+tzQy1lSa3GFJeAbLHiXjkc2W7Hkp6Ow2LBYbFiS8vAEByEa/ly2LOz0Xl741YxjLy4OMzx8dhycsi5eAnPalUpW6M64wYMYOry5Wz87Vd6PfEEYSEhzBj4LJM//ACVJOG8ruCEQW/glREj0Hh4YM/Nw5qWhtrFUGifxtUVj8qVST96lKwzZ/FpUB+djzdOixlkGZ2fD5bkFLLPRuFdp7YypZ6C8DKtiBBqwZ0sCSG2vsAmhPiFwmvvvEaWJYt5AfMYcmUIfWr2odZjtcT3chAFKr5BeKDtEAIeA5JJEglHVxGC7IfwXmtwreJUZr4dNxd7E4JXUOwiERGGLo8IP+tA6ibxluUtWk9pzcQPJvLZ2M+Q6kqQCY/bHqdfcD/WJqy9oUkXgwuTBkwSIXNfRKazLt/GPVwbVnRzGXWFEqJYoR02bBj9+vUjJSWFsWPH8vvvvzNjxoyb1gsLC+PTTz+9bVtdunT5x4Y+CIh+13TUBj1az6JDxraMTGS7HUNwEJJKhS03l5yzZ0SWsZcHrmFhQhCvXGHxRx+SlJ7OiuEjcPX0xGE2YwgJxjW0PHOnvcZ3+/Yy+sUX8TK4YE1LQ+/vh+x0YstIB70Ojbv7DbblRMeA04FLaDmcVhtWcyqO7BycDoeYSk8F3nXroM4PF2tcXVHpdHjXq0Nyehr2zEwsBj3m5BQMQUF07dyZ73/7nRU/fE/z6jUpGxZKq+rVmDFsBK+tWI5ep8NqteLv78+UCRN4umkzHLl56P39MMXFC5sDAgptNAQF4lquHHlXrmK6ehXXsmXFnL2pqahd3dC45JEXE4MhMBCXkOB7fDQVSjUFfZ9BXJvH1YIQHx/EsJrLQHXYfmA7m09tZmz5sXyc+jHuGncmDJoghgFlIv7fiwhDd0GEhi8CZjBcNAgx9UUkRdXOb7/gOS8EIcBXEYJ/q+e/QIT3exUxpjYg/6WCcn3LMTlqMlNWTWHDVxvo0bEH1Ad2w9zQueQ6c9mctBmAsr5lmfT8JLoN6Qbf5rfVANE/XAHx8FEQLm+MIralhGIDDK1bt2bZsmWMGjWK+vXr8/nnn/Pkk0/etN6mTZuK3didrPNAk52NLMvo/PyK/NhhsRQWplDnhz8zjh3HbhGz6ui8fTAEBWJOSuK3Pbv56uef6dmsObWrROC021DrdXhUjeDzjz9mxYav6NauHWPHjSscQqRxdy/MdJb+0jdsSU0lLzoGfVAQnjWqY0lOFpO+y04Rys7JwaVcOVzKhKDzEUNsLKmpyLIsvOxq1ZAlCVtGJnnR0SCDa7lyTBvxIiqVirmbN+LIy0MyGHi8cmWmjXoJm93OsrGvsPKV8XTv1QuVXo81PR1JpULn7YU9N++GCQREFrLoN845fxG7yYTezxetuzsqjQadny+y3UHWmTNKIYv/MiZEqNQdIVYFJCCEzgNRZEIPmZ6ZvPrVq1T3rE5jTWP25O1hbNOx+NXzE2Ik5X8vGjEpgA2R+JQIHAFNtkYIWX9EONmXG8VUgwgH5yG81tvhjpiH1jt/3XMIbxoYMH0AD9d6mNePvE7yvmThIdcB1LAsdBmvVX6N7+t/z4HnDtDtoW7iYaAtwpM9ixB8A2J2IT1iooO9KFPulRKKFdrJkycTFhZGnz596NevH+Hh4bz00ks3N5Rf8Wjbtm08+eSTNG7cmMaNG9OoUSMaN258wzr/Rux5echmS/5sNEWEjGUZa4ooDlFQmCI3JgZrcjJqgwGNmytu4RWx5+WRev4Cr69YSVl/f4a0bYdaq0O2O3CvHM6xqLO8Nn8+jWrXZsE772BLTwfEECIRtjaj9b4xZOwwm8mOOoekUuFZNQKtm5soSJGSXFiYQuvpgVf1akgqVWF/rtNixZ6VBYBn5XAMfr44TGaRhRwdg9bLi0p1avFS72c4EHWWbQf2gyyDSkWHatX5/qsNtO7QHjk7i9zoaPR+voVev8bTU1SQSkv/SyELd9wrh+Mwmcg+G4XKYEDn5YVKp0HSadH6+mBNTSf33HmlFvJ/kYLxshpECLZA9HIQfaeBiKSgq0AVmLl+Jsm5ySyqsIgmuiase2QdAwcMFN5lLkKUDiLEMgSRPRyDGHfrBpmtMqEj1xKtisIb4eUmcW1c7a1QI/qTC7zfS8AVUDlVLHh3ASbZxJRzU0RCFoikLBmGug8lxBqC45KDi0cviv5ZO9Caax51WYTwFojtAYTgKmJb4twydDx9+nQSExM5dOjQDSUY7XY7V65cuWWD8+fPZ+rUqYSGht5dS0sxorpTGmg0aDw9i1zHlimG1ugDA5FUKhwmM1mnzuB0yug8PXEtH4paqyX73HmWrllNbHISywYPxd3HG4dZ1DJ2DatAaGYm/Z9oy5hpU1E7nVhM5sKM4qLC1rLTSW7MZWzpGbhXqYLW2xtzQgKSWo3T4cQWFwdqDR5VI9C4XZvsQOPmhj03F2tGJmpXV1RaLV716mHbswdbegaW/BmGDMHB9O3Vix/37mXp1q08UiWC4DJlceTlEWiz4VKmLEfi4vhhwUImz5uL1ssLW0YGajdX9H5+mOITxNy1AdfuZIbgIFxSymCKi8MUG4drmRDsJhNOWxpqNzfUubnkXIpGHxSo1EL+r3EV4XVe3y8rIzxcXf57+wF32JO8hy8OfsGL4S9S2VIZXKB5q+ZizKklf/3jCM+2DkJkoxDh11CgB5jiTXcWfi3wai8jEqOKq0fshvBuC8bmZkPl4MqMGTuGt956i++qf0f78+2FHaHARVCZVLyx7w02fbuJb32+pYKhgmijTv5+FISj0xBiexXxECEjxvMqs4+XGLd0MY1GI23btsXd3Z0nn3yy8NWpUyc++OCDWzZYtmxZ2rRpQ5UqVW54/ZspqO4keXoUmaTjtFpFn6ebGxpXUXQhMzISR54JrYcnBm8vXMqEYE5J4eDevXz+4490bdyE+jVrilrGGi15Pj6kXL4C6VmMHzsG33LlbhDWgjDvX8PWlvR0ci9dQufjg3ulCtizsrBlZIqayxo11rQM9L6+uJYrd5Pdej8x9MiSkt+2pwfuVSOQJQlrfruSWo1b+XK8PmokVruN+V9vxmHKQzLoMMXGYoqL5VDsVT7Y+g3vvPkmGnc3VDot1tQ0JLUarZcn9txc7HnXxgqqNBrcK1VC42Ig5/wFHBYrej9fNG5uqLVa9L5+yDY7WadO4ywiu1nhX0oywmsNQXikBRQM5wkBfgMSIDcsl/GfjSfcM5w+Hn145PwjbJY2i75PHSI0m4MIGVdFCPUpxFCcysAAhHjeKQVJT878Nu9kyLeE6GOuggj7xsKwJ4dRu1ZtJu+ZTFrZNOHxegO+IFklnvV/Ftkh8+yCZ8mOzRZ2N0d48scQAu6J8GjD8tv9E/HwoVwqJcYthbZ27dp069aNL774gq5du9K1a1fat29Pw4YNb+utdunShblz57Jv3z4OHjxY+Pq3UtC/qfX0QLpFyNiSmir6JX3FZAx5cfGY4uKQ9Lr8kHEl7HkmMi5GM/295QR5ezP8qadRazXINjvO4CCGTBjPsyOGoXF3xbNKFVE7OF9Y7Tm5OPJM6Hy8bwhbOywWcs6eA0nCo3pVACypaThMJlG4IjcXrZcnhqCivUIRQvbFabFgzxadSe7h4eh9fUQIOTuH3OjL6Ly9qV6/ASOMPfjtZCQ7Dh9CklRIKjVZZ87yfL9+PNWqFYs++YSP33sPnZ9I2rKmZ4jxsjot1rTUG0LIWk8P3MLDceTmkn02CrWrK1ovTySNGkmvQ+frjTU1jewoJYT8n6CgWL8XIvO3ADvXhvPkIETFH+bsmkNsRiwLayxk0cVF5DpzeajxQ0LY8hBC9CdClNwQyVOXEJ5yH0Qo+O9iQIitJb8tx+1XL0Sfv91yoLVrWThiIRlZGUy/PF28n4Cw2wAVrRVZWWklF9IuMGrBKJypTvHw0RYh3EcRXq2ea322rvn7egBFbEuIYjtNT5w4wcyZM8nJyaFdu3Z07tyZjz/++Jbr79+/n02bNjFnzhxmzJhR+LqXfPvttwDYbDaMRiMbN24EwGQyYTQa2bJlCwBZWVkYjUa2bdsGQFpaGkajsXAMcFJSEkajkZ07dwIQGxuL0Whkz549AMTExGA0Gtm3bx8A58+dw9izJ0dPn0Lr5VX4+dGjRwGIjIyke9euRJ6IROfrw7ETJ+jerRuHtn2PbHdwMiGBwbNnEn1VZNlOWTifS3FxPNfmcbx8ffnj+AlefH8Fo+fPJSoqiua1azPs7cUkxMXhyDOx58hhevTuTcLFi6j0erb/+itGo5GsrCxkWear1R/z7JRXUQUGofPx4ctPPqXfiOFYrXasGRl8t28fL328pnCS9s8++4xevXoV/q5r1qxh0LChqF1csGWk88HKlTz33HN416uLSqdn9YaveHHCOGyZmRhCgnHotHi6ubH4my2kJCWy+uefmLbqA6S4eJYsXUq5oCCmvvUW3337LVpPD96aP5/xr7wihNfu4PWpU5k8eXLh9ue+/z5LvtuKOSEec3w8MxYvZuGaNUiShNrDgzfXf8ncRQsKayiPGTOG+fPnF35/1KhRN4z5Hj58OBs2bChcHjx4MCtWrChcHjRoEKtWrSpc7tevH2vWrClc7tWrF5999lnhstFo5Msvv/zH597kyZP/+bl3/jxGo7HwIfbMmTM3nXtGo5HIyEgAjh49itFo5MyZMwAcPHgQo9FITEwM11Ni19Jf9ueGa8kMkTsjMU4yEpn5l/35/Qw44WD8QYy9jZyPOc/v0u+s+XUNQS5BJKUksTFrI+3KtGPc7nHEJsWCDDs378S42kiSXxIche17t2P800ha+zTwErkmRqOR3PxkvS1btmA0Ggsrnm3cuBGj0Vg4XvzLL7/EaDSKRKxQ+GzjZ/Tq0kuILuJa6tevX+HvvGrVKgYNGlS4vGLFCgZPGAwRUPOhmjxa61E2fbeJHRV2QCgsPriYCWkTQAPNpGY0d2/OjlM7WPDhAkiAN997kwmHJ4ihSJdgxpYZTP54shhGVAFe2/Mar817TYSSrSL35vr78oQJE3jzzTcLl4u7lubPn8+yZcsKl0vyWipYLslracSIEdyOYoV25cqV9OzZk+3bt1OvXj127txZuMNFcerUKfbs2cPWrVtveP0bsWVlgyyj8fQscvo72W5HttsLi0gA2HPzcJjNaDw90Li65GfiZnDgt9/Z/scf+Hl48FDVaqLcoARXU1I4cOgQr/btR936DVFptdgyMlHpdahdXZFtNmRZFhOtXxe2dmbnYElORNJocK9UEXt2No68XGQZ7KZc7FnZ6H18RPWq/PCt01r0dCAFtZrteWL2aZ2XFx5VwsV2bDZyLogQst7bm9oREeSYTCz+5htkux1JpUZOTsWRmkb7p57Cx8ODXdu2ofHwQFKrcJrN+WUoPXHabMjXlWdEpcr3eHVknzuH0+HIz652Q1KpUOn1yHYH2aeVEPK/FgciOUlCeGnSXz7LQnhwiUAu5Hnn8cqGVwh2C6a8rjyLriwiSBtEl1ZdRB+lFeHVFfTpJiL6N70RXqP3XbDZE5E8JSNKOabefvUb0ACh0LR5UzzcPJg0ZxKZT2eCO0hOSdgoQ11tXaq5VKO1R2vh7Wfn214D0ddsQ3iyTsTv5pnf9iGEd3un3rbCXUGSi4m7de/enY0bNzJu3DiaNWtGly5d6Nq1K5s3by5y/YEDB7J8+XLcrkusuVdYLBYiIyOpVasWen1Ro8XvHQ6zGXNCIhoPd/T5/aKHDh2iQYMGQH7IODFRVFoqE4JKo8GUkEDa/gMgqdAHBOBVqwaSWkPysWN0HzkSi8XMJ6PH4uXnj8OUx9dnTjPvw1W80KkzI7p1x695UxzZOdjz8nAJCcZps2FJTsmf9cbrmm0WC39u2ECoXwC+Deuj9fIi78rVwoSsvLg41AYDAU2aoPX0QJZlzPHxyE4nLiEhN2QsF2DLzsaamobOzxethwdOp5PkXXuwpqWh8/PDvUIYrmGh5MbEsOTtJSzf8BVz+g+g9aONiI2OJrRKZXwbNSI9+hKOq/H41K+LPjAQS0IiGg+P/OpPCciyjEuZkMIHF1mWyY2+TNbJSFzLl8e7bh3MiUlYkpJwWG3YMjKxZmbgVaM6ntWqFnvcrj9GJU1psKXgGoqIiCAqKqpErqWiOHToEA0eaiBCsCZECPX6flknYniMlP/Zx8BReDX+VT7d9ymbHtuEKkFFt9PdePvRt+k2rds1of4DkR0cBmxDhJ2fA6rfwo5/eoxsiISkHISo+3Kt//QOOHrgKB27d6R3297MHzafhEUJBEvB4vdIRiRclQG6QlZIFp4RnsKjXo9Ypz1irHEyIqHLiRgfnIcYslT/zm35K6Xh3C2gNNhSnBYV69GqVCq2bdvGb7/9RtOmTdm9e/dtq/IEBQXRuXNnXnvttcI5a/9t89bKTqeoxKTRoPMpujPHnpWFw2wRFaI0GhxmCxlHjyPbHWh8vHEpWwaNqyumq1dZ8tGHxCTEM6mbEU9fXxxmMzofH7r27cOIZ/ow5Kmn8ahZAxyOwn5VSa3GmpaOSq+7IdNZlmXyLl9BzsrGLSwUvb8/1tRU7NmizKI1IxOcMp5VqxZmJ0uSVBi+tWZkFLk/Wg8P1AY9tvR0nHY7KpUK74fqodLpsWakY4qPx56bi0tICIMHDKBqaCgLvt5MekoyTq0GW1Y2OefO4V+tGjovT07u2k37Tp24lJSEPTsbp9WaP07WjjX9mg2SJOFaNgR9UDCm+HhMCQno/HzReHiAJKHxcEelUZNzQUzIoPAvQUZk8BZUUnL9y+eJCO+0LHAYOAu/qn7lk32f8EKtF3hE/QgNHQ3ZWXcnXXt0Ff2VDkRGcToicWp3/nbaU6TI/t9oERnOYfl/JyCyms8ivPREhBDmUeQQnHqP1GPYkGF8/sPn7Dm1h8zGmUJIVYiHAzMQBys+XMHjwx8n+Uyy2J/H8/f1t/z99MnfTwlRG9kVOILwbC33YL8VbqJYoZ04cSLr169n7NixBAQE8N577zF16tRbrh8aGkqXLl0IDAwsnLP23zZvrTUtHdluRx/gX2TI2Gm1Xivon1/PN/PECRy5eag9PdB7eeFSrizmlBT++PVX1n7/PZ0fbcQjtWohOxxcSkrAJaIyvnoXBrVoJQQzwB9rahoqvR6tl5fIMnY6CzODC7CkppFz8SKSmyvu4eHYMjNFVrTdiT0vD3tWJi5lQnALLX+DzWq9XoSQ8z3moijIaLampIhwtY837uEVQAZrZjY55y4gaTR4hYXyxsiRZOblMXfjRrBYkfQ6cqNjMMXF41G9GjgcJMbFMWDYUGKTkrCkpKLS6USJxuxsHNcVpFDpdHhGVEal1pB9JkpM4+fni9bdFUmjRh8QIMbdnjx1w8xACg8u2kStCIeWRSRAXU8eoi+yoJT4r5BjzmHcL+Oo6FORiRUnEh0bDSoIrxuOVEMSQuZAZBa7IzKDkxAZuy3v8c54IrzuqggP1AUhcEmILOcLiHG7pxCh5sv5n+XB2FfGEh4ezpi3x5DmngYtEGKrpbB8ZNOcpqRmpjL49cFYL1pFachGCGE/gRi364EQ2gKxNSASpw4gBFvhnlKs0DZs2JA1a9bQs2dPANatW0f9+vVvWm/58uWAmL+2qNf16zzI2PPyRJax17UJAa5HlmUsKSn5WcbiTpB75arIMtZpxBCZyuE4TSaSzpxhyjtLCfbx4cWnO6DSajkbHc2Qd5ay6MMPyT57Bq2HG57Vq2FLSwNEX6w9J0dkGXuL/ssCHGYz2WfOAqAqH4osO7GkpuLIy8Nus2BOTkbj7oFXzRpFhoe13t6iiERKapGCpdJq0fn64jBbCgtZuOd7qI7cXKxZWeReikbn68tDTZryorEHu04c5+djx5CQQKUSU+GpVETUb8DSkSMx55l4dvTLJCUmYk3PEFWpNBoxpOi6afE0np64VQ7HnpNN1unTaNzdhb1qDSqtTowPTkwk58IFJQv5QScO1Dlq0R/513k5nAhx0iIEZSdwFSb/j72zjrOqzv/wc+r2zJ0uuhEBCztRVl07sHtdE7HFwFobO9cOVGyx1lhdWRsLRbpzOm7M7ZO/P773zgwlrj9B0Pvwui9m5px77vfWeZ9PL7yK+nA99+x0D4vqFrH73N151XkVdqWzDnYuQnAVhEU3GDicdbdN/K1xITKmewIDEdN6BiJaJ1YjYqwqwu3bBCwG73IvD17/IG3hNu5+626cYY5w+foRYqnAsNQw7u59N9/N+46r77oaZ6UDWyHKhmYgukh1Q1xgyNnn2z97jJ+AqeTFdgPzm7Vqev311/noo4/48MMP13nb3Fsw5hpTyC4NbR1WuhGOYOsGrtJSJEXBTCaJzpyFbVqoRcV4u/dEdrlILF/BjQ88SGNbG9cffSzBkhLq6uu55OknKSws5Jidd8UxTAqHbomVznTMthWPkR3B1yUu61gW8cWLMSJh/H37IBUEyLS0irisbWGEwsiSTHDLLdY5uk+SJNxlIotDb1t7BocaCKD6feiRKFYmg6IoFG2zDbJLwwiHybS0kGlrw9ethjNOOpnttxjC0599wpLly5A1F1YyQfvcefh69WTwoC24d+xYmpqb+ftllxKqr8dKp0XPZtNE7+IKliQJX4/ueGu6kaprILGyFndpqRjMIEmowUJkl0Z84UIyLS1rXXuezYA6oA3MIlPUhq5OPcJl3APhfv0BJrdM5vUZr3PhDheynbId4+eNp0QtYd/d9xWi5iDcxQ0IsZmKELwTWNMlvTHJJXjlRvzVIER3ECKpqSfgh+EVw7n85Mv56vuvePHLF4VVuwVi7RpgwaGZQxnbbyyT/j2Jic9PFD2c98ke91OEeFdm76Mizvz9so89s8s+eTYIv1mvkJqaGp599tmf3ae6uvq3erjfhZyV5amsWGuc2unoZRxA9XmxbZvw99OwkknUoiI8JSV4a6pIrljJa6+/zvtTv+KMv+zLsEGDCEfCXPLkExi2zdO33EJRxsQ/sB+uYJBUQ2NHL+N0o2jounpjinRjE4nlK3BXVBDo2xc+/zzbSMPBjCexkikCA/rh7dbtZ5+jrGloxcXobSGM9na0tXS6cpWUYGUayLS04q2pxl1STMHgQURnz0EPhUksWYa61TACfXpxy0UXc/gFY7nh5Zd4ZMx5eAJ+UnV1uEqKCWw5hKGpJHdfdAnPTvkPkktDb23DU1ONVhTEiERFe8qs+11WVQoGDcCIRokvWIi7uAhPRTm2nsFoj+EuLSPV0EB05izUXXfJj9PbnHDojJ9WgKmvJQQQ6dyOBnwgSjWu/OpKtu+xPRf0uoDnvn+Oaclp3L3l3QT3zF6Imoi5sQ4iGcgEjkDETjdVFITLPAiYcNY5Z/HuZ+9y7R3XssNjO9D/gP5CTFMIN3QCxqnjWNpvKT7FJy5I+gJ/RUwx+ggxMKEC4ZZO0Sm2SxDWvo5wo6+9uV2e/we/mdCub3LP5o4eiYrxd6Ulq7hrc9imidPejty7d0eCVPucOaRb25C9XtzZkhg9FGLud99x69NPsU3fvpw4ahQSDtc8N5G6UBvPPPAgVaaFu7yUQL++ZFqEG9pdVooeCmNndNwV5chq51unR9tpnzsP2e2hcIvB2Kk0drQdK1CAncmgt7biKiujYNCgtcaUV0crKMBKpdDDEWS3ew0XuaQouMvKSDc2obeFcJeXUThwAJmWVlKNDUjtURILF1Gw5RB6bz2ccw8/gttfmMSj77/H2MOOAEUhNm8+pUVBAgMGsJ1hsfs//kFhnz60L1+O2dBAoFs37HQaPdSG7HZ1NOJQ/X4KBg8i/MN0orNmU7LddrhLyzpKk1xlpWRaWonOnEXJttus1UWeZxMj5w7O9SquRGTrdiWV/Zs/u8+HoC/UGTNjDLIk8+CuD9JY18jNy25mj4I9OPqQo4U1JyGEpB0hsMsR/YH32CjP7LdBBbla5vzLz+fiSy5mzA1jeOu2t/Ac6BHPKwEYIEdkHql5BKlKAh3spTbyQBn2A95BZFgfhnhNcmKrIKxojY6JRYzk53s75/mfWe9ZN51O8+677zJp0qRVbn8mrFQKIxJB9fvX6nZ1HIdMSys44C4vR5JlUg0NJBaL2b3ukhICA/phmxbNc+Zy0YTbcKkq1xx9DC6vF8cwOf+0v/HPu+9msMeD4nIR3Go4ZjSKnU26slJpzFgMrbAQ1dfp77IyGdpnz8U2dIJDxJzadHMzTjKFreukm5pRfF6Cw4f+Txaeu7QUSZHJtLSs0rEph+LxoBUVYSYSop4YKNl2GzS/HyMcId0WIrlsOd6qKnbefTeO3HtvXvzsMz7+7ltRP2vqRGbMRAsG8VRXklq6jER9PWdcfjnnj7ucZPbiACSxhi7xWk9lBYF+fci0hmifPw+1KCj6Pcsyis+H6veTXL6S2KIl+Xjtpo6FKOHJtVasXMs+Jp2DBHoiEoa+hWtnXMuP9T9yx1/uoHu6O/Mb51MgF3DHbncgjZCEBduOEFcTkWzUDziOjReX/Q0prSjl7nvvZtbiWYx/cjyOxxGzc7sjno8DUqME78I7777DAeccQOinkHBJ74MoM/oXwmLNuZFtROy4W/Y49QhBXrHxn98fmfUK7dlnn82TTz7JN99803H79ttvN8baNgls0xQZsZrW0bhhdYSlmUEqLEDWNPT2GOEff8LSDVzlZfi614jRb0uWcNUdd7CsoYEbjzue0vIypnz3Pa6yUnb+61/ZoaoGO50hOHx4tpQn2TGyTg+1ib7G2ck/IOKysQUL0dta8fftmx2x1yJcxo6N0SaGwQe33BLPOkb3rQtJEdm8jmWTyWYZr46rKCi6SmWHxyteD8UjtkPxeMi0tJCoqxevXXk548+/gGH9+nHL66+xcOlSJEXFCEdonz2HQL++qH4/iTnz+cvIkXz02aeccc65JCNRXGVlYnZul5ixJMsE+vXFU11JcsVKkitX4i4vF4PuZRmtpBhJlYnPn0e6qel/et55NiI6IuM2hRDQtVlRNkIojew+SeB9eOH7F3hu3nOcu9u5HBQ4CMKwj2sfvtrpK7rv210kCjkIl3Gup3EAMe4usJbH2UzYd999ueCCC3jpg5d49ptnxcXJPnT2ZdaBpVA2vYwFKxZw8riTScxPiMSokYi+0O8gxLYGEaM1Ea9XGcLdHAE+RJQh5a9TfxPWK7RNTU1MnjyZ+++/v+N23333bYy1/e44tk2muRkcG3dF+VrdrkYs1mFpSh4PViZD6NtvMWMx1KIgnrJSvN26kVixgoefeIIp077n3L8ewLD+Axj/5FOMf24idZpKuraOTEsrgYED0QoL0cMRFJ8X1e8n3dwCsmhykYsNO45DYvkKksuW46muwt+vL5nWVjFFyLYgkcTKZAj064tvtVKeX4riduPOjcbrUtvaFXeZSPrKNLdgmyaesjLRiENV0VtaSSxbimPbFA8cyL1Xjcfn9XLV888RjUTBrZGqryOxdBmFQ7fEkRwOG7Ilt918M59/8zWn/O00Upk0ruIizEQSIxrteFxZ0wgO2QK1oIDY3PmiDWRlJe6iIhRFxVNRgakbhKf/hJ7NkM6zCZFAiKyJqDVdvYQHxEl+JZ21tG7gX/DDtB8Y/+N49hy4J1cMuIKlK5fy/OLncVQH9zC3sFplhLi2Zm9JxLi79fc02eS59NJL2Xvvvbn2vmv5NvatGKW3G6JDlgXEYef6nXl4t4f5aeFP/H3c38ksyYgs6z0RnareRliyPRCvvYUoOwog9ksD/0F0kspXzP2/Wa/QDhw4kJb/MYuzqamJOXPmMHv27I7b5kauTMfWDVxl5WudMWsmU2KCjteLVlyEZVmEvpsmmuUXFuItLyfQvz+p+gbefn0yD732Kn/ZamsO23lXLn/qCb6YM5trL76YATXdSK5cia9nN/y9eqC3tiBrKq6SEtLNwnXrLi9fJd6Yqm+gfe58tKIghVsOwYy2k2lpwcqk0aNRiMfw1tRQmJ0x+2tRAwHUggKM9naMWGyN7ZKi4KkoB0dclDi2TaBvX/y9eoFti/WvrEV2u+i9zTbcefElNIbDjJ/0HOlkCklSaF+wkHRLC4VDtsBMpvjroMHce9ddfDd9OhddcAGK34/q96OHI6vU+Ko+H4VbDgFJIvLTjOwYwnLUggCSpuEuK8OMthP58cf8oPhNiTaEu1hBiOLamsjlkqPaEZZXEPgIVkxdwelTT6eqqIqH9n4Is8nknB/O4daGW2mrboNdEGe1BJ3tDxsRQ90P2ODPbKMgyzIPPvggPXr04O/j/s7SgqWiHnh7RImQCYRgvxX7cedf7uSzHz7jnMvPwVhpwDBEjDoMvImwgHMXOg5CbN0IsZWBzxElVPmM5P8X602G2n///fnrX//KwIEDUbsk4Kwrw/iee+7h6aefpqys0w8kSRIff/zxb7DcjYcRjoha1ZKSjtF2XbF1vUMQ3eVlwtJctoy0rCG7XbjLyygYPAg9FOKTf73L+IceZGjPnpx7wEFc8tQTzFy2lBsvvpijR48mOmMW7rISCrbYgkxrK8gyrvJy9FAIO5PBXV62SkJSuqWV6KzZKF4XRVtvhWMYZJqaMeMJzESCTFsIgkWic9NaLhD+V1wlxaLcJjvarmuMGERDCVdZuSjtaWnBXVFBcKthWMk4qYZmrFCY2Lx5FG4xhF33/QvXNTRwzSMPc9OLL3L98SegKAqxWbMp2nZbAv37EV+wiJH9BvDwAw9SUxAg09yMu7wc2xQtJ6XKCpRsvNlTWkpw6JZEps8gMv0nirfbTuxrmOA4OMXFpBqaCU/7gZLtR/y/X4s8/w9yc2NDCJdlD9Y+69XJNqxwI2KJpcDX0PZJG8d/fDy6o/PykS9T3FzM1T9ezczkTJ4e+jRle5UJoZGA2Yha1DqEUJ+6jsfaTAkGg0ycOJHDDjuME84+gTdffJOKIyqEgH6DsOCb4BjXMaT+mmK5bzlqVBWv/xDEa/sxwrLdB1FXuwLhNjay2wcgPAozEMfdh1UnJ+X5xaxXaB966CHOOuusXzzI/Z133mHKlCmrCO3mhh6JdpS2dB2insM2DNLNzaJncXaQe2TWLKyGZqipwVtZQcGggVjxBD98PIUL75hAt5JSJpx4MjNqVzJ7xXImXHIph40eTeSnmWh+vxiqHgqBbeOpqsJoj2WFvniVgeyZSJTojBkAFG21FZIkk6qvQ4/FsFJpMi2tuIuLkWuqUL1rXiD8GiRJwl1eJnoMryZ0OVSfF0pLyLS2obe24S4vo2SHHWj98iuoXUG6oQlZUQkMGsTRx59ASzjM/S+/RPHbfi469AgswyI8fQYlI7bF26sHqeUr2LVPHwL9+pJuaeWWG25g1F//yla9epNpbsZTVdWR/e2tqcY2dNpnzSU8/SdKttsGT2U5qYZGtGAQ27ZI1dUTdc3AyQedfh8yiBN5ms7M4rVhA7VdGlZUALMg8e8EJ793Mg3JBl465SUGRgfy0pyXeLrhac6oPIN999xX1JaqCEt2JaJu1gOcwpqNL/4A9OvXj4kTJ3L00Udz0pkn8doLr1FweoGISU9HWKsr4VTXqWL0nwZLZy2lor0C/yC/ENOPEMlPIxD9j72I3shRxGvXHbHfcuAthOu578Z9nn8E1utT9Hq9nHHGGasMf99vv/3WuX9xcfFmLbJGe3tHhnFufmxXbNMUCTa2g6eyAllVaZ87j9iCRUi2hbd7NwoGDsTWDX6cMoWzb7wBv9vNTcceR2FBAbttMYQ373+AQ0cfSWTmTBS3i6LtthEZxoaJu7xcWKbZuG/XOtZMNEr0xx+wdZ3gsKEoHg+p+joy4RBmKimGHAQCFG27NcpqVuf/F0mW8WTLijLNzViZNZuk5ro1Cau6DcXlomynnZADBRiRKIm6euILF+Ht0Y1zzj6b4/fdj9enTuXR997FsS2sdJLQtB/QioJ4a6pILF1GYvkKMi6ND/7zH447/nje+vQTkGXSTc0dE3skScLfsyeBgf0w2tqI/PiTcN1XVqC4NbRgENXvI750GdaSpWvNos6zAQkjxM9ElJKsS2RNhEs5CkaZ0SGyyTeTnPrWqcwIzeDh4x5m+8z2NNQ1cOX8K9mzYE/G7zm+swNUGNFPeClCaA4EttmwT+/3ZNttt+Wxxx5j3rx5nPz3k4kPicP5dLp+M4iynVcg9X2Koy4/isPOPIy6b+qEpX8EIuP4a+ADOttFliM8Ay7E+9AP4Yn4F8Jizsdt/yfWK7S77LILkyZNorm5mUgk0nFbnVwsdsiQIdx000389NNPm12M1ohG0UNhVL8PV9maPhLbMEg3CpF1V1Ygu1xEZ80hOnsudjqDU1pCwcABOLbN9x/+m9OvuRrJcThut90Y88RjzFi6FH/v3gzcemuiM2cjqxpFI7bFSiaxdSPbszedbXpRsIrQZyJRoj/8gJXOEBw+DFewkFRdPZlQCDOZJtPYhFYQoGSHEXg20IWOpCi4KytAVsg0Na1VbF1FQbSiIGYsLjKOPW6kIYPRiosxwm0kVq4gvnAhvh49GD/uMg7bYw+e++wTHvrXO9iWhRmPE/n+R7TiUtyVFcQXLkJpCzP5pZfYdvhwLrnsMq656y7S2elJufpZSZYp6NePwID+ZFqaiUyfgVoYxF1RieJyi3aNLhd2fQPhH6fneyJvDHL1sbUIS6k/wmW8NpIIMc5mIFtFFsyA+JtxTnztRL5u/pp7j76XfaV9oQWq9Wqe6vsUj+z6CNpemojzWgg353yERTYCISR/cPbee2/uv/9+pk2bxvEnHU/7kHa4DJH45SDi1QvA+6qXOw69g5VNKznwjAP54Z0fhLV6AMKaXYqY/FOPcBt3Q7yuEuJ965893qdQ+HmhiJ/n+UWs13X89NNPo+s6N954Y8ffJEli7ty5q+w3duzYVX6fMmXKKvtv6jFaPRzGiLYLS3a12a5AR00q0CGy4ek/EV+0BNvQ8fbqiZJJ45gG/33rbS6+43bcqsp2fftx37vvMrhbd/pvsw2eqkoRX3W7CG63LXYyhWNla2WzfZTVggLcXUqJMi2t2WQfneDwYWgFhSRXrEQPhTESCfTWFrRgkRDZ8vIN+jrJqoqnqpJMUzOZpiZcZWVrxGxd2faURiSKY1vIbjelW21FaOrXZJpbSRgWjmUT6NePm6+9Du2Wm3lxyhRiqTSXHHIoxNoJfT+Noq2HAZBYsgRfr55MeuYZ7rzzTh6eOJEVK1bwzL33km5swl1ZgeJ2IykKBQP6AxBfuJDwjz9StPVWeEF01CorQ1pZS2zhYmzLonibrVHW0nwkz29AHCGwBsKCzVlIq+PQmbCkISwnL/h/9NO8pJlT3zmVWZFZPHjCgxyqHcrsebNpbGtkn6J9GNl/pLBkKxFnsh8Qs1hbECJzNr9hS55Nm0MPPRRN0zj33HMZffRoJk6cSPW11XADosViApgHIxnJ26e/zamvnsroC0dzzRnXcOpZpyLtJgk38WeI0p5+iMSyIEJ4WxCu5H5AI3h+9MDLiElBvdks65I3Juv9GM7IxgPXR1dh3ZxwbFuMkUsk1xC4HGYyiZ5NUvJUVuI4Dq1Tvya1ohbHcfhk5QoevOsOGhoaKAwEiCWTVAWD+FxuPpj+I0fuvAtXXHAhnmAhsXnzUX0+gttshZ1IguPgKisTE2tSabSiIlxFotbBcRyStXW0z54NkkJwm21QXS6SK1aQCYUx2tsxo6LWtGTEth1zcTc0ObFNNzeTaW7BKSlZI5btKsrV/4ZxwhE0r5eyXXehbeo3pBsbiS3IYBsGgQH9ufHa6yjw+XnqX+/QFAlz/VFHE7BMwtN+oHDolnhqqkkuX4GVSnPZpZexzbDhpPUM7tJSMm0hEnX1+CoqxED4nNiqCrF5Cwh/+x3BrbfCU1NDuqERCgPIXi+JxUuwUilKRmyHFtiMCys3NWxEbDSEsJZyY9nWRgYhxkmgECZ/N5nbTryN+vp6StwlODiknBRPnPgE+0r78sPsHzj5h5MJKkF277Y7rmGuzp6/CxAJUMsQtaXn8adrJXjAAQfw9NNPc9ZZZ3HwwQczceJEtrxlSxiPENs4MBcG6gP512H/4oLvL+Dj7z/m1INP7ZyUdBSiF/QchDdia0SmciHifW0DqkGv18XF0euIbOed+NWzbf8MrFNo33rrLQ499FCefvrptW4/7bTTNtiiNhZicHqLKOEpKV6jr6/jOBjRKEYkms0kLseMxwlNm0a6vhHF6+PT5Uu58dFHSGXLR6LxOJIk0be8gh+XL+PGE0/isGOOwc5kSC1dhru8nMDA/lixOJKioBUFhRhZFu6yUtTsSd82TdrnLySxZAmqz0/RVsOwdZ3E0qVk2tvR28I4mTSemmpKRoxAK9i4YiEpCp7KSlG7m82OdpWWrFJKpBUWIqkqzJ1Dqr4Bd3kZZbvvSuj7aaRW1NI+dz5mOkNB/75cMX48PaqruenJJzjz8Ue5/ojRDKyqJjr9J9GMo2cP0ivrsJIJ9t5rL5EF3drGi+/+izffeovrLr6YrUZsj1ZcJMS2Xz8Ut5v2WXMIffMdBVsMwtezB/KSxbiLizBkmdTKWlpSKYq33hpvddVGff3+kEQQJ18DYcFWsPbglI2wkFqy23vA5CmTGTduHKmMqCNpy4gGJZeNuox9zX15a8ZbXDT7IqpcVTy/zfNCZHdAWFlLEdbsbETThbMQJSt/QkaOHMkbb7zBySefzCGHHMItt9zCMfcdA1cgRuLFgYVQkizh2V2eJTkqieSRWLpgKV+99hXHnnwsykhFeAS+zt7mIdzwAxAu5CZw/I64oFkBfIK4wNkHYRXnWYN1Cu3y5csBWLBgwUZbzMbEjMfRQyFAwl1RsUYJj20YZFrbsDMZ1EAAV2kJsUVLaJ81Ez0cwVNRgb9Pb+6/7eYOkc3hOA4LGht5cfw19N5ma/TWNqx0Cl+vXngqK7DiCWSPB8XtQg+FkRQZT1VlRwmPHo3SPmsOmdZW3JWVBAb0wwhHSDU1YbTHyYTakFWFwsGDKRy25e/m/hQJUhUiSzsSEcPby0pXKUVSfT6kbHvEdFMzWrCQkh13IFZYQGzeQhLz52PGYgT69eHEs8+id88ejLvzLsY8/RRnjNybw7fdFmveXLyV1bi7VaM3tRCe9gP+fn1wFQYp1Fwsr63l8L/9jaMPOYSLzzuPmgEDkDUNf48eqD4f0ZmzaJ8xC2+P7lBSjKeqAkmRQZHItLTS+tVXFA4aRGDQQJTfoBzqT0d2ADkJRCy2J2u3YnNTdJoQyTRFiJN1Bm677rYOke3KC1++QLQ0ymO1j7F9wfY8NeIpSrYoERaUF2FlfYcYYl4EnIawsP7EbLnllrz//vuMGTOGiy++mC+O+IIbJ9xI0a1FMAUhtktBSkr42/wwCl7+7mUeePEBnn/3ea4fcz077rsjHAosRAj0fxClPdsCg0Gv08VAeRXRN3kB4n3dAfHe5Od5rMI6hfb8888H4NZbb+34m67rtLa2UlNTs667bfLYhoEeCmGl0iheD67S0lUa9DuOg9ne3tGFyF1Whm2ZtH72BYkVK3EsA3/fPvh79yKdTFLf0LDWx2lpj9JjwADStfXIbo3CwYORPR5sXUcN+LENEyPajuLzZvsKK9iGQWLpMuKLl+DYNv7+/XEVB0mtqCXT0komEsJOpnAVFREcPhR/797/r2YUvxWuoiCK20WmrY10Q6PIli4KdqxNUlU81VViIlAkipVMUjBgAO6yMkLTppOub0BvC+Hv24dd/7Ifk/sP5Iqbb+Sh/3zIlNmzuPgv+9EvmUEPRfD27oFjmsQXLEQtKuaAffZh9x135KGJz/Dcq6/yr48+4qoLLuD4E09ECwZxl5ZSuvNORGfPIbliJU5zM3KfPnh7dEfWXEiKSqa1lfCPP5JqaKBoq+GrdODK8zPoiJNsGHEm6YY4+a7+0tnZfVoQ1q6fzsk5n4PznUNdqG6tD1GfqsfWbU6rOo1rtrkG92C3SNwpQLidpyKyYEsQZTz7/IbPbzOmoqKCl156iXvvvZf77ruPzz//nBuuvYGDux2M9Jok3rd6hOjWw+XbX86gcwdx08s3ccSFR7Dz8J255PRL2PkvO8OxCKv2R0Q5UDE4iiMymxsQXgk34v34N2ISUG6U3+9/etokWG+M9qOPPuLrr7/moosu4pBDDiEWi3HeeedxyimnbIz1/WY4loUeiWJmXbuu1eKKjuNgJZMYkSi2YaD4vEhuN+3z5xNbtAgz0o6rvAR/7y2QfV5em/Qidz83cZ2PV1lSghEJ4yotxVNdjSzLSLKEpGiY8UTHBBw14MexLJK1tcQWLBKtG4uL8PXojp1M0z53HkZrCD0WQ/V6CfTvR9FWW210V/H6ULxevNXV6JGIiB0nEmhFwQ5XuCTLovGG34ceCpFuaET1+6nYc3di8+cTX7iY6E8zSdfVEezTl8fve4DJr7/G7c88zVnPPcOowUM4efsdqImE8VRVogULMSMR2tsjaGXlXH72ORx10ME8+PTT1FR3w4hEaVi+HENV6TdkCMVbb4WnspLa998nOmMmrhLxvqiBAIpbI93cJkqJmlvw9+5F4ZZDOpK68qxGBtHWMDcuuAzhJl69IUQmu08YYcH6EGKcBr4ApsOPs37k5lk3r/Ohalw1XN//eqRqSbguhyDihYuALxGDy8uBM4G//BZP7o+Doihccskl7LffflxyySWcM/YcntjuCa46+Sp2+tdOnZ2zkiA1SBw+5HD2O3E/nm97nkfee4SPPvmInQftjK3axIIxgscEhYU7HQp+KBCx9X4IwW2j08OwGBHfHYhIqOrPn15w1yu0jz76KDfffDMffvghW2+9NTfccAMnn3zyZiO0tmFgtMcw43FAjFlzZeN4IJKhrGRKDEg3DGRNQ3K7iK+sJTF/IUY0ilLgwz94IJaq8sQLL/Dy++/TGAlTXViIIolPkOV0mS6juRhz5Gh8ffrgKiiA7DbHsnEsXVh8wUIcxyG+bBmJJcswY+0oHjf+Xj2xJZnEwsWkm5swE2kUl4avew3BoVvi7dZtk7W2JEXBXSrizEY4LCzYaBQnkcCxrI6uUorHI8Q4K8j+nj3wVFcRm7+QZO1Kwt99h7ushIP22Yc9dtyJRyc9z6sff8x/F8xjnwGDOGToUAb17I2rogzV48aorUNvbaOmqJg7LrtMJDdJEk9PeoHHJz3PHjvvzFFHHcV+Bx6IMmwogUABiWUr0EMhtGABrtIylEABemsrmaZmIjNnEV+2An/vngQG9MeTt3CF2zeOOKHGEFZrCULkunrbLUTZRyS7f640xIuweL4Ae77NlPlTeHTpo3zV9BWl/lJGDx7NW/PfwnCMjkNpksYV/a9A6ifBUET8z4VwZX6OaKLQBzgXcULPs1aGDh3Ku+++yyuvvMJdd93FkfcdyW4jduP07U9nnzn7oIQV8d5EwLfAx5mDz+TkE07G6GeACZ9N/YzTbz2dkduPZP9R+zNq/1FEC6LCOzETcYFViajLLUN041qGcOfPRVwg7YgQ5D9pZGa9Qus4DoMGDeLxxx9njz32IBAIbPKjxxzLwkqlMONxrHQGJEmMuAsWImuasF4zGcx4AiuRwLFtHMvCTKdIrqwlXd+AlUyiBApwdavhs7mzefGxx5i5dCmWbVPq83HVXnuz2xaDMd0evqtdyWMf/4fmcJjKkhIuPPU0DjvsMBzTxDZNJEVBliXUQAFKwI8RbSf800xRi5rOILlU1NJSJMchunARRiiMYxiofj/+Xj0oHDwIb/duq7i4N2UUtxulqgozmcKMtePEE6Tq6lC8PhS/X7jsi4rQsj2UzVgcx7YpHDQAT00NyRXLSdfWE532A0pBgPOPOJJj99+fJ15/jQ+++YYP5s1hSEUle/XpzR6Dt6CishrF6yXTFkIJ+HH5faiFQU489BDcLo1X3n6HsRdfjPeqq9h955154vHH8fbqRWrZMpIrazEiS5HdHhSfH1/vXhixGJnGJsLTfqB93ny8lVX4+/XB2707rk3Mk7BBcRB1rVGEcJqIM0YlQmRzH0cdIart2f8dxMnXRFizU4HFsKBuAW/WvslbDW+xLLaMKn8V44eP5yT3SZw06yQMx0BGxsamQq3gmq2v4YjdjhAn6jJEDPA7RIKOg4gHXoSICef5WVRV5fjjj+fwww/nmWee4YknnuC0xtPoXtKdo7ofxQHhA9gisgVSuwR14JnuwdPTAwOge2V3jtv1ON7/6X3e//J91JtUhvQZwvMPPU/piFJh5S5FCKwL4XEYTOfgiK8RbuduwFbA8OzPfyIrV3LWo5pHHXUUp512GjfccAPvvPMOc+bM4b777mPy5Mm/6ULeeecdHn74YUzT5JRTTuGEE05Y730ymQyzZs1iyyFD0AAro2OnU0JcEbFBrSCAGgjg2DZ2JoOVzggRTgkr1ojFMcIh9HAEK5UioxvMD7WwMhJl2sIFfDZnboe16lIUtq2q5vgR27NF794oPj9asADF5UIrLGRFOMzgYcNAkpBVFdntRtZUHMDOiFFvmdZWrGQSyzCQZRlHAiudwWyP4WR0ZI8LV3Exvl49CfTtg6ey8lfFYadNm8Z22233P99vQ/D9118zbMAArEQSx7aRZBnZ40bxeFE8blAUrEQSMxHHzogGFGYyQaq5mdSKWqx4Atsykb0e4rrJez98z9vffM3KUAgJGFxWxvDySoZ3786W3bvjKyxE9ftRfT7cwSKkAh8/LV/Oh19/TSQS5f4bb0TxeBhz9dWUl5exRc9eDCgrpWcgiCw5YrSnI94XIxYTZViSg+L14aosx1/TDU91Ne6Ksv9pxi/A5MmTue02UcJSVlbGtddeyxFH/H5dFXLfoYEDB7JgwQKGDhiK23SLk2QMYaHmrNJixHSXDEKAkwhhNei0YkNAGziNDg0tDfxY/yNfhL7g09CnLE8uR0Ki3FWOYiskzAQzKmagSRpvO2+juTX2rtkbd6WbWqeW7jt1h6rscX9EtBUMIRKoDgFOZIMn3WxK36Pfci2mafLvf/+biRMnMnXqVGzbpo+rD3uYe7Bjekd2MnaiUqkUsdcioALsnjbTC6bzQfMHfLnsS965/h3koMx1L1zH7GWzGdZ9GMMKhzHMN4y+gb4otiIuuHSEJySM+OwEEO/rYER2c2/Ehdv/4DTaVL9HQ4cOxe1es85pvSbS5ZdfzoMPPsjFF19MeXk5Dz/8MFdfffVvusimpibuueceJk+ejMvl4thjj2XHHXekf//+v+j+qfoGTFkWJ3FFRnEJgbMMk0RjE0Z4LlYsjhGLEw+FaWtpwUknqW9p5fMFC1jU2kJrKkkolSLTpT1fidtNN7+fnoVBDh40iGE9e4nB64EAqs+LGijEFQwg+QMoiozT1obR3o5j2dimiZkUo92seBwrlcJK60g4ot+uJCE5gCKjeL24Sorxdu+Br3t3vDVV//MJfFNG0jTcpaU4JSXidcjdktksU0lCdmnImgtZc2FldLSAhOLx4u3WHSMcIdPUSKa5BZ9hcPigIRw+YCBLWlr5fME8vlm2jFfnzeHluaIDWaXXR6/CQnoVFlLhL6Dc76e8qIhjK6owyypo+vxLbJdGe2MjX33zNS+kxDo0VePvhx/O3/bbj0x7jDe++pJSv5+g20WRqlLQHsPV0EB0+k9Imobi8aEWFuAuKcFdViIG0AeLxcWdX7jIu7qcJ0/OlrBkH6+lpYVx48YBbJSThGNZ4oLTMHEsU5RIrd7daykieUlCiJgPYXlkM1VpQwhfFBINCRKRBM1tzcxsncl34e+oTdWy0lhJk9lEBnFsn+SjShLlUw4O6DBEGcJWvq3IuDJopRqHBA8RWa01QCEYtYbo8PQ2omQoiXBTHwSczJ+2fOe3QlVVDjzwQA488EBaW1v54IMP+Pd7/+a1r19joj0RXFDqlDLYGszg5sH0aehD9fRqauQazlTPZKw2Fnm8DMVQlapiWmwaz856lrQlKjCGdhvKv0/7N7TDczOfQzEVKtVKyp1yyhvLKastQ/taE65kH8JjUYO4iKpGeE3KEJ8JP6tYv7/39wgb4a0xEReZuQvPn2G9Fm1XNlTW8RtvvMF3333HLbfcAohBBo7jcN555/3s/TKZDDfffDNfvfU2iUQCy3EwHQfTthkUKMB0HGpTSdp0HcOxsX/mWBLgV1TKPW76+Qs4sHsPagIBFC0rAC4VWVGF+1ZRkRQh7LnLMAlIJGL4fD4c2xEiKgGyguRShfi7PageD2rAj7usGHd5Je7KCtzlZbiCwd80g3hzuBK3DQM7o2PrOrahY+vGKn2IHcfG0Q1sy8IxLeHej8fJRCOYYZF0ZSWSGKkUiVicmfV1LGhuZFk4zPL2duqTiTVGCKiShF9R8akKAUXFqyjYQMa2SVkWpW431V4fGdviv02Na6x5m6JiBhUUEtd1vmxrRZNlVFlCk2QUSWJQoIBKt5t2w2Ruoh0ZCUmSUIA5sXaMtXzdNEliWJFot9nHH8CvqkR1g9pUIruHRE6ve/sDeBSFiJ6hIZ3uCOM44gWjf6AAl6LQmsnQmBbffju7DWCLgkJUWaYxnaIxnaYiGOTihx5k4MCBXHvttcx/cz6pZAoDAxOTflY//I6fZrmZuepcdEnHxMTGXrcF4oDH8VDkFDE+MZ6DjIOoU+tYqa1kEIOodGctJQ1xEnUhflcQiVIGxKIxCnwFneVCeyHKTX7daOVfzebwPfotMU2TmTNm8v0n3zPv83nMWzSP+eH5pJxVlUR2ZAqdQoJ2kEKnkEK7EC9edHQSUgIFhS2sLXBJLl52vUy7vGq/xr5mX44yjkKyJV5wv4AlWbgcF27HjeZo9DH7sJ0pnusHng+QkJCRUVD4Wv2ajLxm+1e37WY3azckJLrb3elp98TA4Hv1ewCkjnO12F7j1JAixXRl+irDRhwc+tp9qXQqiRNfZXvu/4HWQCrtSiJyhB+VH3EFXFzy6iXrtGjXK7QbI+v40UcfJZlMctFFFwHw6quvMmPGjFXaPq6NTCbD0KFDSWZnlEq5myRR7nKhSTJJy0K3bdyKjFdWOk6wWweLKHe5KVQ1qn0+/C4XSBLIsrgpMkiy+JuigCyJn1fZRwFFBVUR+ysauF3gdSP7fOByg9cNbjdSoADJ70Py+VA2k1jr74HjOGBZ4mbb2ZsDji0ubLLbHbNzH1s3IJPCTmUglYRMBkc3MJNJQpEooWiE1mg7bYkY8YxOQs+Q0A2SpkHCNDFsG92xMW0Hw7ExbBvTcTBsGwf+0PN+JOCtt99m4MCBbLHFFuSesJT9V2qXUmgXYmHRprThcTz4HB8BO0CBU8D2me0ZbA2mwClARqa/3Z8SqQRFUnBkB0d2hDWSu6ni/46/S+CoDrZi42gOVsDCDJroVTrJAUmSQ5KYvc18i7/fEduyaW9sJ7QgRHRJlFBtiGgoSrI9SSKVIK7HaTfbSdtp9LX9y16cOdIf+ZsEb7/99q93HW+MrGPbtldxsTmO84uzPF988UV6BAJ4fAER+1MUcV9JQlKzdZw5YQQhlKrauV8WWZaF5aGsXqPwy/mzXf3+Un7PtXRcR2b/nzZtGtttu+1at+d+ti0bx8m6+C0b2xahANu2MQwdSzewbRvTMHB0HQcwMhnhmgVxcdD1cR3xOEePOYfmtrY11lheUsLEu+4GJDRVRVZk9HSajJ4Ra3KcrPcEfF4vqqqhGwaZTAZZlpFlcUEoSeDR3CiKgmXb2LaFLEkggaJoQuuyn3MARVVwfH5aLZHp++abbzLMOwy3N3uiyJ0d3HQKXU4wc+SySFf/urq6/E1ay3bWsq3LPpvK53dTWQf8MdbimA62YeNkxP92ysaxHJyUg63bOLr43zZtYYHqdE4KsgELRo0fRUN4zf4FVUVVvH/1+zi2g6qouFQXtmGT0BPiO2hnv+MyuD1ufC4flmmRyCSQFAlZEt8NSZXQFA1N0XBsB1MykezspackiTJNWUJSxHdLlmUMxWA+89f5vDeJrOOqqiq+//77jt9bWlqoqKj4RfeVJImiPn3WehWRJ0/HxVT2f0mWO0q7YO3n/w01H/ya669fJbYEYgzltf/4B1vt+/sUgWYyGVpnzQKyr1V/8j1r82wwJFVCURUREviVXGVdtdbv0fgbx1NxxJq6EST4s8cr/C2aYmeAWevevN6goCzLvPfee3zxxRfsuuuufPrpp795TeEuu+zC1KlTCYVCpFIpPvzwQ/bYY4/f9DHy5Pm9OeKII7j99tvplq2FLi8v5/bbb/9dsyXz5Nnc2By/R7846/iiiy7qyDoeP378b7qIyspKLrroIk4++WQMw2D06NEMHz78N32MPHk2BY444oiOE8Km5ArMk2dzYnP7Hq1XaEeMGMEzzzzT8ftLL720QRZy8MEHc/DBB2+QY+fJkydPnjy/F+sV2h9//JHHHnuMZDKJ4zjYtk1tbS2ffPLJRlhenjx58uTJs3mz3hjt1VdfzTbbbEM8Hufggw8mEAiw7777boy15cmTJ0+ePJs967VoJUnizDPPJBwO07dvXw4++GCOPPLIjbG2PHny5MmTZ7NnvRat3+8HoGfPnixcuBCPxyPq9vLkyZMnT54862W9Fu3w4cO58MILueCCCzjrrLNYtmwZar6zUZ48efLkyfOLWK9petVVV3HqqafSp08frrrqKmzb5q677toYa8uTJ0+ePHk2e35RjHbrrbcGYK+99mKvvfbawEvKkydPnjx5/jjkg6158uTJkyfPBiQvtHny5MmTJ88GJC+0efLkyZMnzwZks04fzk0R0nX9d15JJ5nMmgOJfy/ya1k7+bV0kvvu2NkxfPnv0ppsKuuA/FrWxe+9ltz3Zl2T7dY7+H1TJhqNsmjRot97GXnybPZUVVXR2Nj4ey8jT57Nmv79+xMMrjmWL+86zpMnT74JTZ48G5DN2nWcG/Y+cOBAXC7X77wamDVrFkOHDv29lwHk17Iu8mtZFV3XWbBgAT6fD8h/lzbVdUB+LetiU1hL7nuU06TV2ayFNjeA3uVyrfMJbmw2lXVAfi3rIr+WNclZtPnv0ppsKuuA/FrWxaaylpwmrc5mLbR58uTJ86fFAXK5awlgOZAG/EAFUApov8/S8qxKXmjz5MmTZ3NCBxYAS7K3r7L/G4AH8CLEthewA7AdUIMQ3jy/C3mhzZMnT57NACktwRxgGtCE+Pk7IIY4k7uAJEJwTeAn4L/AAGAEMAzYHSjb6Ev/05MX2jz/LxzbxkqlsA0DHAdJUVG8HmRNE9vSaZx4HD0cRlJUwMHKZLBSaRzbRvG40QoKkFQVM5HATutImoLi8aL6vBjxOHprG1Y6BbKCVhjEXVGGouV9Ynn+wNiAhagLMYE68M/wC9dwHUJkFyBcw3sCQ4HFwCIgBbgRruUwsAIhvk3AfOBAhOjm2WhsUkIbj8c59thjeeSRR+jevfvvvZw8P4PjOJjt7RjRdhzbhlwSQLYs2zEtUGQkScJJJMm0tpFpbUUPRUAG2ePBSiTJNDcTX74cKxZHkhVQVRSXhuR24dg2qtuDWlCApMhgWSDJKF4Pvp49KRw8CFdJMcomkgiRJ8//CwdoB9oQlqmDiL2GxM+uWpcQ3nqgEegH7IewZOuB7sBIhBjPzN6vNLstnP0d4BngcGC3jfGk8sAmJLQ//fQTV199NcuWLfu9l5JnPTiWRaa1FSuVRvF60YKFyG43kiRhpTMkV9ait7UiuVz4enSHgoCwbC0LxefDiEZIrlhJsrEJvakZ2zSRAFQZWdUwIgZmOgm2g+L24CorpXCLwRQMHIiViJNuaiY2dx6JpUsJDBiAr0c3tEABsteDJElIioKsaUj52tA8mwsGsBIhrC6Ee9egQ2SZCb55PihAWLR9gSOBQuBLhDD3BXoirNmewFygBQgiLN1aRJKUAkxCxHr33jhP78/OOoU2HA5z2223UV9fz1//+leOP/74jm1jx47lgQce+E0X8sorr3Ddddcxbty43/S4eX5bHMsi3dyMrRu4y0pRA4HObbaNHg4juzQ+XrSQO+69l4bGRkqDQS46/gQO/stf0GNtpFtaSK5YiRFtp8Wr0RgoISmBGUugRaL0UBR6VNeg+HzY6TRWKkl05iyseILyffaiZNttiS9dSmTGTMLTfiS5Yjne6iq0QCFqsFBYuJKE4naheL0ofj+yuslcU+bJsyppYCnCXdwdKEKI4CKgCiZPmsxtL91GfaKeGrWGK6qv4Ih9j4BiYDagQGtZKzNmzqBuXh1OmYNbcdNP7sfwwHBc3V2QARYi4rujgWbgNcAH7LTRn/GfjnW2YDz//PPp0aMHQ4cO5Z///CcjRozguuuuA+Cwww7jzTff3CAL2nvvvXn22Wd/kes4k8kwa9asDbKOPGviOA5OJAKGgRQMInVx2YptUTB0Ppsxg4cefXSV/qMuTeOcAw9k19590Fva+GjObD5auoT6SHitj1VVVMSoYcPZd9gwPOkMhCOQTIHHAz26oXTrBjjYLW2g61DgRyktBZ8P2eMBtwsMAwwTJJA8XvD7kBRlw75ImykDBw5kwYIFv/cy/nRIGQl3nRtHdtBrdBzNQWlX8Cz1IBkSH/30Efe8ew9pK91xH4/s4bK9L2P/kv159ftXea7+OdrSbWs9fsATYO/+e3Ny75Ppt7wfvgU+rIBF03FNBGYHkHWZ5mOaMXobG+sp/6EZOnToWmt613mZv2zZMu6//34AdtllF0488USeeeYZTj311A22yF/Lup7cxmbatGlst912v/cygA2zlkxbCDMWw11WhhrwA2DrOlY6Q6atFcvjxVNZyZjrrlujybduGLz0ySd0O7CAO956g5Z4nG2HD+f0E09gq0GDCbpcpGrriAOz62v54PPPef7zz3h/xk9cecqp7DpsONHFi9EbGpFa2igoLqZst11Q3W4Si5eQCUfRioN4KsrRgkWoHg/uinIAjPYYZjyOJEnMWbmCEbvu+pu+Lr+WTeHzsvrF6h/tu2QYBrW1taTT6fXvvBZ0Xd9wnbIcRDLTIEAFr+MVCVDF4AQdHNlhSP8hPHLEI2vcta2ljTHPjGH6kumUlZVx5AFHMnyr4ZSXlWNZFgFfgOXLl/P5F5/z/ufv89689zju8OM4ftzxaLYGKkT3i0IKNElDc2vCpfwL+f+8Lh6Ph+7du6P9RgmNm+L3aHXWKbS2bWOaJqqqEgwGeeihhzj22GPp37//Ortf5PnjYiaTmLEYWrAQNeDHSqfRw2HsjN7xs+rzYSYS1NfXr/UYTZEI4yY9R5+qKu65+2522HIoakEBiZUrCH3zPV6Xmx59ezNsu+046eRTmfbjD9xw//1cev99jB65Nxcccgje0hJS9Q2kVqykbeo3VOyxG4GBA3AWLkQPR5EASZKRgHRDA66yMtylJWiFBWRa23Ci7WTaQrhKivOf4z8BtbW1FBQU0Lt371/1ficSCfx+/wZYGcKdayNishYiu1jK/uwCEqDLa05T+vLLL7nrrrvQFI2br7mZ4089HpfHJe5vgpN2kBwJZ0+HvUbuRV19HS889wLPvfocM2fP5InrnqC6sFrU2/YQj4MPkTj1C8X2174ujuPQ1tZGbW0tffr0+Z/vv7myzmyRPfbYgzPOOIO5c+cC0LNnT+6++24uuugiamtrN9oC8/z+OJaF3hZCdrtQg0EybSHSjU04loVWVITi8eDv1ZPCLYfgLi+jqmzdhXqHbrsdbzz7LDtsObQjBpuqr8exTGS3C9nlpnDoEAr692WXv4zixbvu4uQDDuS1/07hsokT0fr1I9CvL2pRkOTyFdR/8BGZSARv9+5IqkqqsZH4suWkGhowUxkyzS3okSiypuGpqkTy+zDa20k3NXeMhsvzxyWdTlNaWrrpXVSZCEFV6RBINITQKtnfU+CSV7UaJ0+ezM0330zvXr35+M2POfXEU3HZLogjBNMAySOBGyRZonuP7nTv0Z0xF4zh5htvZuGyhRww5gDm180XseFE9vESiHrcDfyVkCSJ0tLSX+1h2FxZp9BedtllHHDAAavMp9xpp5145plnGD58+EZZXJ5NAz0UwrFtXEXFZJqahWVbWIi3pgbHNEGScJWWIqsqju0w5rDD8azFrbT/8K34x1XjkWNxHNsCILZ4Eem6BjxVlXj69GZZNMwrr7zC7bffzg0338yrH/6bcWPHcsvll/PtT9MZe+ftSJWVuIJBJFUhuWQpta++QXTmbLTCAuyMQWpFLbGFi4jNn0+mrZVUQwOJ5SvQW1tx0mmsZJLEkiVEp/9EYuVKMi0tmPGEKFPK84djkxNZG5FRrNBZL+vqsk1FCKcJVf4qZEmcpl955RWeeOIJdt9tdyY9OoluPbsJsQwhyoJyjSpSiL/HwWt56denH9VV1Ww7YlvuvfdekOCoy45i7oq5Yt+cXzNCZ1nRBmSTez82Aut0HUuSxFFHHbXG37fcckuefPLJDbagKVOmbLBj5/nfMZMpzEQSJRBAD4WwTRN3eRmq34+VSmHG42hFQRS3G9s0icycwb4jRqAGC7nniSdpyiY7HThsK24aPx5/756k6xowk0lS9Q3EFi1FCwQIDt2Sw848k/kLFwLi8+fzetl/330J9O7FkQcfwo333ce0GTM44LxzOWfffdmjR288/gBGOEJ8+QoqutVQsfeeRKbPwIhGSTU1kWlpxVVchOLz4qmqBFXD260bWlEReiiMlUghSRJmIokky2hFQVG3+yc8GeTZSHT1BudEVkGIY+5/XfxcTDG44b4X7uOZZ55h5F4juefqeygvKBf7OQgXsJY9jpo9pkWHxSplJCq8Ffhr/BT6Cnn12Vc55tRjOOqyo3jz7jfp7+oPAYS4RxDmlxdhXef5TcgXGuZZJ47joIdCSLKMnU7hWCaeygpUvz+7LYykqmjZQceJZcvR20K4y8oYNWw4O2+1FQBn77ATVx57LAUDB+BkdHy9epBuaGLp99N4+r8f4+3fFy0Q4KxzzuH+++7j3SeeZNqLLzNz6lTue/BB7IxOvK6eq084kR0Gb0E0keC2N97g2Cce5avmJvy9e+Gk07R+8x16OELxtlujBQIgSdiOjRGPAxJWMg2ZNFqwEF+3Gvw9u6N43KiBAJ6qSmS3Cz0UJlVXj/Unc23l2UiYCKtVRoihhhBHAyGaEsKN69DRFerLr77k8SceZ9Seo5h440TKg+Vie05kC7ocIyfcPqAEUWerisf0y356F/amf1V/XnnqFWRZ5uRrTqatqU2sy5M9Rhxh6W5gy/bPRL64MM86MdvbsXUdSQIJBXdlZUcXJjMWxzYM3BXlSJKEHm0nsWwZqlfMNX3mnXd489NPOGXPvdh7wCD8fftgtIVQCwpINzXz7tQvuf2ZiWRMgwOOOYYdhw/nmGOOQQ+F0SMRbMMgWVtHfMlSHNPE7fNyxIkncvixx/DIs89x/3PPUhIMUllQgG0YRAFn5Qr0N94iOHwo/j59UFpakVQVx7KwMhmkVBK7tY34osUE+vdDCwaxTRMjEkUuU/FUVmImU+ghEYPWgoW4iot/x3cgzx8KBx579DHe//B9LNtit11247IrLqNuZR3njTmPAQMGMHfOXEqDpdz3j/vwa37OuPIM/vPlfwj4A+yz/T4ostJpHplwxbVX4PV5mTNvDu3t7Vx89sW89eFbzFs4j1F7j+KKcVdgWRa333s73077Fsu0OGK/Ixi13yiuHH8ll4+7nF1P3ZXqimq2GLQFd19+N60rWjnv+vMYMHAAcxfMpbS0lPvuu4+ioqLf89XbrMkLbZ61YpsmeiSClUqhBgK4yso7RNaxLIxIBMXrQfX5hCguX4adzuAqK+XLr77ioddfY98R23PKjjsRKvDjLi8nEwqhmyYT7r+fF957l6379uPac85mcJ++yKqKGU+gR6M4to0Zj5Gub0R2uygYsgVaYSFme4xMSwsn7zWS5cuW8tbnnxNXZCRZ4fEvPuP7ZUs5Y/jW7GFZoqFGaSlGOIIaLETSNOx0GscwSTU04NgWBQMG4CopwTEMMm0hZJcL1edF8VSjh8MY0XZxMVFWlu8y9Qdh9OjRa/ztoIMO4tRTTyWVSnHSSSetss2yLI499liOOeYYQqEQZ5555hr3P+mkkzj00EPX+9if/fczZs2ZxWuTXkOSJC679jLefvttthu+HfMWzOOWG29hSMUQxl49lnf+8w6VwUo+/fZTaqpreHbCszz58pNCZG063boWNDc38/KTL/PGu29w5c1X8u+X/o3b72aPg/ZgzN/G8K9//wsseOP5N9AlndPPPJ0thm6BR/MwYvsRfPPNNxy6xaHUh+v59MdP2bL3lsxbNI9brruFIcOGMPbSsbzzzjtrvDZ5fjnrFdpEIsFDDz3EF198gaIojBw5krPPPnvD1Zbl2SQwolGMWBzF48FVUozq83bZJvobu0pKAMi0tJBuaEL2emhqbOTqJx6nT7fuXLT3PrhLS1EqypA1DdswuPof/+C9qV9x3Mi9ufi00yjs3RtJlkmurMWMx0QvZFVBcXsIbrs1ZjRGuqEJMxzBNkxkjwdfzx5cd9ElLKqv5x/PPM1TF1zEUbvuxopImNu//oopK5YzNpOiW59+SI6NtXQJWlERkgN2NEKmqAgjFsNKZ/D364ukqOjhJvSQcHtLqoqsKkiahtEew7FsPJUVebHN8+uxYepXU5kxawZHHHcESJDOpKmprmG7LbejtKSUIb2GQAwG9BtAJBzh9fdfR9d1ulV145vvv+Hi0y4WQivROWfWgT1G7AEG1FTUMKDfAErLxTy8osIiovEoU7+dytz5c/n6u69BgmQqyaIlizjm8GMIFAQwTZPn33ue8tJykrEkyFBaVMqQAUPAhAF9BxCNRH+vV+4PwXqF9uqrr0aWZa688kocx+GVV17hpptu4oYbbtgY68vzO2BnLTzHsnAFg2gFBZ3bTBMjFkMNBJA1DTMeJ1lXDzg4qspVDzxAKp3mgbPOwauqFAwaREs0ghWPowUCnHzQQWzXvx8H7bQLBT174qmswLZt2r74kkw4QqB3b3w9e6AGAhixOI6hY4RCWF4P/t69cJeU4kjA8pXccuZZnPiP67nmxUk8cu5YHj17DK9/+QWPf/wRY995m+v2+Qvb7bSTSHaKtqMWBXFSaeLLlqF63KTqG0guW46/X1/UggBmtB0rmUArKsLK6DimiWOapOrqsJJJfL17Iec7S23WvPbaa+vc5vV619jetV60pKTkZ+//sxjCOj7l+FM47bTTwAXt7e0otkK4NSyahCTFrpIk8cX3X/Ddj99x6dhL2brb1nz6/accffHRvPvsuxSWFYoztwxooHlFAwocUCVVxGlzyGA5FpddfBn77r4vmBCKh/C7/Xz66ac88MQDjPrLKGLtMRYvWUxDbQMMA7fLLRKyVJCQcKx8wPb/w3ov0efMmcNdd93FzjvvzC677MLdd9/Nd999tzHWlud3Qm8LYUYiuEuKcZWWrLLNiIorW60oKPoet7ahhyJIisrjr77K9/PmcsXpp9NN0/BUV+Hv2RMzFOLfX36J7PUxqFdv9h00GLUoiBrw49g27bPmYKYzeCoqUAJ+rIxBur4Bva0N2eOhYItBeKurwYFMOER80SKsRIK+Q4dy43ljmbdsGU/950MkPcPonXbiyfMuYHCPHvTs3h3ZsqnYey9huToOeFxIkoyRSGElU+jtUcx4HMXlFvFml0iO8nXvhrd7N7w1NbiKikg3NROdOQsjkfg93pI8mzPZLOCdRuzEW+++RUJPYJomY84dw78/+Lc4Czt01NXW1tXy5fdfMmzoMJbOX8pe2+zF1edfjdfrpaG9QSRA+bK33BxaF2KYgIRIaMo1w2iHnbbZiVdeewXDMUhkEhz/9+OZPn86U3+YygEjD+DMo8/ktnG34TgOE9+eiJEyxHrS2WORPZa58V6yPxrrtWgrKioIhUKUZN2EyWSS4nyCyB8WW9dJ1tUhe0U7xa7uUtswMOMJYc2qKpm2EJmmJsBhfu1KnnjrTQ7eex9G9e6HGY8R6NcfIx7jiVde4YOpXzGpWw0DXV6QFVSPB0vXSa5ciR4J4+/VE7WggFRdHZmmFjzVlXgqK0Q7xexc2vB30zBiMdzVVXjKy3BMi5E77sihu+/Ocx99yJ477sCwbtUM7teXB/r1w0inSNbVc+dtE/jb3/+OkkhCa4twA2saVjKBlUiSWLQExSOmEBnt7eA4+Hv1RFZV5MICtMIC1GAhyWXLiS9chLeqUtQN52fi5vklZBtS7D1yb+Ytm8fRxxyNZVnsvuvuHH7I4dQ11HU0ikjpKd6d8i5+n5/Hb3qcB594kAMvPBC3x82B+x3IoOGD1jxrOwhhtRDlQUUIa1QS24498FiW1y7n8GMPx7RMjjjwCHbcakeKioq49IpLeffjd9EUje2Hb88307/hyVez5Zs6ndnHMkJ0ZfK1Kr+C9QptVVUVRx55JPvvvz+KovDxxx9TVlbGTTfdBAjXcp4/DokVK3AMA1+/fsirxeGNSBRJAldREFvXRcJQewzTNLj+sUcpDga55JhjMJavxNezO57yUh5/+BE+mPoVpx93HMN79haTdrrVYESj2LourEmvD1nTsBJJ3BUVOIaB7NLQCgpR3C5SDQ0kV6xE0jTcpaXYqRSpunpUjxt3aSn/uOEmfjjhOK57/HFeuvkWPLqOEghgJhMsjER4/vPPmDJ7Fo/ccw+KIYoYHdNAKyoG0yIdaiM6azbFI7ZFDRSQaW4GCQJdWsR5ysuRVJV0QwOZthC2ruMqKe3o+Zwnz1qxEAKVjaueO+Zczh1zrhDWDCBD99LuTJk0BTS45957CEfDPHfnc/QI9GDChRNEiY4Pkkqy84ztACbcdv1tHb/vuOOO7LjnjkIIPTDlP1OEOzoNV4+5WljCXTpRDeo9iHdefqej0UUkGuGCmy5g4tsTeeOBN4Rop2DsKWNFCZGNEN/fvxX2Zsd6r0169erFkUceid/vx+PxcOCBB7LjjuJqKJ/u/cdCj0bJNLfgKq/AVRRcZZut65iJBGphIZKioIdC6Nk47nMffcT85cu59vzz0SIRZE3F37cfX33yCXc+8zQ7bLstl559jhAwBxSfDyuVJrFiJbZhonq9KB4P3poqCvr1pWjYULRAgRiFN30G8aVLAXCVlCB73DiGKdo/Fpfg79Ob8oH9uffee6lvbubul1/CymTQ21pxJBjWvy93/+10IokEx59zDktsC29NDZKsYIYjOLKDr6YGW88Q+XE6OBaukhLSDY3ioqPLcCt3cTGe8gpktxvHtsm0topY9toHYOX5s+MghMlEuHa7OkC6umGzsdnpc6bz8KSHOf7A49l7u72FsLnpcBU7irPqcXMCrtLZtjHR5TFVxCzaIOJMn8reJ7e/lb1/VgWCgSDjxoyjrKyMsTePJZnJLix3zGw9bt6F/L+zXov2vPPOI51Os3z5cgYMGEAmk8Hr9a7vbnk2MxzLIrl0GbLLjb9XjzW26+GI6JxUUCBqTdtjGLEYS+preeyNyfx1zz3ZtV9/4vPm4+vdGxOHcbdNoKa6mgtOPRUzGsFMxJFklUx9PZKq4Rg6qs+Lv29vtGC2u5Suo4cjWIaBEYuRbmhEdrlwV5Rjp1JowUL8ffuAZWPG49kGGaXssOOOnHnqqTzy1FPsNXgLRnTrgSso4sg7FpfyaLCICx9/lOvuuJ2au+9meL9+xJctwwxHcQpFVrERiRL5aQZFW2+FVlhIur4BWVHwVFV1jNdzlZZgGzq2aaJ4vJixGI6ZLQHKJ0rl6YqFsFpVVhXZXNvFnPhZoKNz0XUXUVxczLlnnCvETAH8dMZgc80udDoTnmw6y30UhJDqdGYnZxOmKEE0ojDobJiRW2PWcSVlJAZVDOLySy7nkssvYcI/J/CPS/4h7hNjzeYaeX4x67Vof/rpJ0aNGsVZZ51Fc3Mze+21Fz/88MPGWFuejUiytg4zmcLfu9caQ9KtTAYrK3KSomBEwuihEEYmw43PTMTv9XLl2eeQXFGL7Pbg7d0LKRbn+osu4sE778LrcpNqEO0Q9UhIxGgDAQoHDsLfsydqoADHskk3NZGqF65ZPRLGjLYjOSBla2X9fXsT6NMHd3Ex7rJSXKUl6JEI0TlzSa5YyZgTT6JPz55MeOUVnNIScBwUjxs7k6b/kC146PQzqS4M4rTHcZWVUjB4MGpBAUZ7O0Z7DCUQwHEgtmAhKDKKx0O6pZV0czOOJc5skizjLi8X4S/LwlVagpXOkG5q6tgnTx4chIiC6LjU9Uzb1SLM7vPAUw+wYMkCzj/vfLoHsrO4vXRatNl4Kxk6WzgqCPHzZPfxZ/9XsvfLWaAZhDjmkqhyGctkt6XpuBhQPSoH7XQQBx54IE+98RQ//PiDeOwUwvLOCrhsyHmx/R9Yr0U7YcIEnnnmGS699FKqqqq4/fbbufnmm3n99ddX2e/DDz/8RQ+47777/rqV5tlgGNEomZYWXGvJMgYwwmEkRUEtKMCMJzBiccxYgpf+8xEzFy/i9iuuxJ9KE0smCPTrTzQaJSDJ7HfE4RjhCDM+/5x4awiQcJWWEOjbRzTBKC/DaGklNm8uSqAAxzSxdB0nO99W8foIDtsSxefDjMUxE0nUQECsKRbHjMcAsFJpJFmioFdv7rr3Xo444gge/+hDxowaha0bSG4XRihMj2FDueWww6k2TDJtrURlhdKhQ4nMnCksU8tC8XpxdINMc0u257GMGYuRth3clRUiQUrTcJWWkmlpxXa5cJeXo7e2kGpoxFNZkU+SytPpvs3FRXOsbs2aMGfpHO5/8n5GjhzJwbsejEZ2NqyPTuF0QM7IneKa62u8ej9iDSGeJp1imxvBZ9KZ2JQT6DSd4p1tARlwB7jq7KuY9s00Lr39Uj54+gNckksMLnBl75ddO/mP+i9ivUKbTqfp379/x+977rkn99xzzxr7XX755QwdOvRnjzV79uy80G5iWJkMqcZGJFnB261mjWb6ViqFlc6I5hSShB4JkwmFWF63koffmMxe2+/AAXvsQej7H1D8PupMnWNPPJFbrrqSQ/v3RW9vx160GNPtoaB/X4q22QbF7QLTxIonMBMJMq2tSO0xVK8PSZFxbBtfr14U9OuLkg1TqH4/qbp62ufMRfZ6UVwuFK8HX/duSH16k2luwQi1sd2wYZxyyilMfPZZ9t9rL/ogCZd0Ko0VDiFVVmAlE7zw/PPc9/bbPPPIowwduiXRmbOwkklsy0QNFKCk09heDxgmkqogu3TSjU2iJ7Kqovr92Bkdo70dxevBXVlJpqmZdFMTnsrKvNj+mbERQpSzLLuSs2azFq9pmlxywyUUFBRw3tnnUaaUifvnRNZFR6mNZEid1uu6fJEyne7dXJy2a0zWoLMncq5PskZnRnHWwu5d1JsJl0zglCtP4YFnHuCSsy4R+0SAUnBkp9O9nc9CXi/rFVpVVYlGox0n4CVLlqx1v169evHcc8/97LEOO+yw/32FeTYYjmWRaWnBSiRxlZV1WItd0SNRJFURDR1iMWFZxuLc9OxENE3j+osvJllXh53O4B3Ql/G33IzH7WL3ffclWV9P5McZkEzg7z+Awi2G4AoGSdXVZYvgw6BqyG4vtm7g6l4iEo1MMbwgJ7K2aWLG4uA42IbIAHGXleEu6Swz81ZXkW5uIdPcwqXnncdHH33Etffczct33IlR34BWUkqmqRFSKdzdurNDIk55MMjfxpzLpGefpd+A/sQWLMZKxjFCYSRVQUllkD0e9HAYxedFkZVVxFYrLsLKpNFb2/BUV+GpqiTd2JQX2z87uZIYH6tanF2t2TRgwqMvPcqMuTO4+qqrGVg8EMVWhPD56ayLTYubozk/L7I5clZs13KcrklT2Wxi4nTGgHMDDQqz29th1K6j2G/kftz/wv0ctPdBDOo/SFi+MToaZGCQz0L+Baz3WuTss8/mxBNPpLGxkYsvvpjjjjuOc845Z439Jk+evN4H+yX75Nl4ZFrbOupi3SUla1izZjKFncmI6Ty2jRGJokfCvPLRh/wwfz7j/n4GJQUBMvX1qAV+Xvjkv8xauJB/jB+PL5EkvmgJmbZWCAbx9+2Dp7Kc+MJFGKE2HAlkvw/F7cLXuxf+Xt2RJAnHNHEFg6g+MZzAjCdI1zdgJhK4iosp2mo4nqpKzPZ29C5t4SRFyU4W8uHWDW4afzULFizg2f9OESVDelrEVi0bSVYoLS3lzqOPJejzccppp9Goqvh6VKMU+JEUmUxdPXo8hpQVy1R9AygK2BbpxiZs00SShOCDQ6a1DSk7XB7bId3UlL0oyPOnIpeMlHPbdmW1TONFyxZx12N3ccAeB3DGyDMoVrIXjl6E5apmj5UGNLDd9i+zHiU659vmXMKrbwsgBDVJZ8JWbhyzjw4BHjdmHD6fjwtvuxDLssTj69kYbW6ubj41Yb2s923be++9efDBBxk7dizbbrstL7zwAvvtt9+aB8o2NnjvvffYb7/92Hnnndl5553Zaaed2HnnnVfZJ8/vjx6JYiaTSJKMGvCj+H1r7GNExBg80Q4xhh6LsWLpMh54/TV2HjacIw86iNTKOizdoM62eWjSJPbfYw/2HzmS+MKFZCIRZK8XuaQUV3ExscVL0EMhXFXVaIEAqseLu7QUf/duuKuqyLS0YBs6WnGRKJ9paRVuZU3DW1ONq6QYWdNwV5SLNUUi6KFwx3pziUpqQQG7bb01hx54IA8+9BANmoJWWAi2g+TzIikSrtIySl0e7jzmWDRZ4ZTT/4Zr4EDcJSViCIHXS3LJMtLNLbjLy3AyGVIra5E8HrAtMk0iQSoXr7UzGYxIBNnlyovtn5Wcyzhbx7r6tlgkxpiLx0AarIzFxTdfjMft4aZzb0KVVSRbEiJYALggFo0x5rwxIENtuJYDDzrwl68lF4fNieHq5DKac27mXCZxzgrOCv3AHgMZe85YZsybwRMvPtHRwUoypY4xfh1zcfOsk/W6jq+66ipuueUWevXq1fG3888/n/vvv3+t+99xxx1cffXV9OzZ87dbZZ7fFCuVwohEkGQJye0SDfdXt2YTCTEBp6wMbBuzvZ1MKMxNTz2FJElcf9FFWMkUmaZGtMIg81cso6SwkOuvvpr2GTOxLQtkWcRdJQe9PYoRCuHv3QutoADHtnGXlnS4q+10GsXjQVZUrGQSoz0mrOmiIpHt3GV9wpIsBUnCaG/HcWzcpaUd292lJUiyxJXnjuHzr77i8muu4ZXHHyc+bz5IMhISarAAX8/uVC5dxq1HH02DpuEvLMS91daEvv0WCQfdcYgvWojqc+OuriZdV0+msRlPVSV2OtXhRlb9fqx0GiPajuz2oPq8uCsrsjHb5g5Xc54/MLmWhbn46uo2hQnR9ihz582FFDz96tNMmzmNyy69DLOoi6nrQ1jDCkQbo8xdMFcIYvuvWFPXutdc7LYruRhyTlxz9blW9u8ayLbMKQefwkf//YgJT09gv133o3ff3ki6JI7tQljECmteXOTpYJ3f/uuuu46mpiamTZtGKBTq+LtpmqxcuXKdB+zWrRv77LPPb7vKPL8ZtmkKN6eq4tg2skvtcNPmcBxHzGjVNBS/TzSniMZ448N/883cOVz5t9Pp0b0H8YULcHQLT58qDu3biwP32w8lHEG3RGGfo2fQqqpwWlqxYnE83buhFhaC4+CpKO+IwZrJJFYyha9XT8x4nPa583GXFuOurFxjbV1xl5YgKTJGJApIuLtkTLuKi6mUZK658CIuuu5ann3jDU4+8CCkpUtwHAczFsdTXY1tmPSpr2dgQYDI9BnUe9306N+f5Pz5uIqCZAyD9rnzKdm+AFdZKZnWFmSPG1dRkRDbbDzWVVyMncmgt7Yi11SjuN2dYtslrpvnD0qugYTGmpm42djsTbffRHNzM6deeCr/+eI/FAWL+PyLz6nSqpg+azq3XXYbBOCkv5/EeWeex9MTn6a5pZkx54/hyiuvJJPJcNFFF7Fw4UIKCwt56KGH1t8OV6NTQGHNM37O6s2JZi4hKkVHBrPf5ee6i6/jqL8dxaV3Xsord72CpEnC7VxMpyUvkY/XroN1fvNHjx7NwoULmT9//iquYkVR2Hrrrdd5wMMOO4wJEyawxx57oHY5sWy//fa/zYrz/GocxyHT0gqOjeIPYLbH0IJFa+xnJRIdQ90d08SIxaldsph7XnyBbQcP5rgjjkCPxUg2NbFCTxKZPYs9dtgezbbR22OowUISixYhqxqSYSJpqnD3er1IOKsMkHdsGz0UQnZpoqY12o5jGTiS1LHPz+EqKgLHwYi2I0kSri4JUq6iIIeNPpJ3P/4Pd959N6P22guprALV60EPhTFi7QT69sYxRJOMpd99z7F338G+f/kLN51zLsnly/GUV5CsqyUyfQYlu+2MktHRm5uRNQ3V5+2oofVUVuIuLyfd0IDe2trxHIXYNuXFdhPg1Vdf5eWXX/6f7mNZFsr6GpHYcMwRx3DUoUcJsVq95Cbrur368qs56dSTCIVFN7HrrruOXYbswreffSt2yCVAZYXx6iuv5uQzT+ahhx6itraWcDjMaaedxvDhwzn//PN57733OOGEE35+bbmYrM66xdZF5wCBrDWd61aFDGRgeO/hXHPuNVx555VMemcSxx18nLhPEuHqjov9VokP5+lgnd/6YcOGMWzYMHbZZReqqqoA0HWd1tZWampq1nnAb775hs8++4wvvvhilb+/8847v9GS8/xa9FAIO5PBVVqCEYmieNyrzJmFrPBFIshuF6rPR6alBSMa4abHHsWwLP5xzrmobjfRxUswDYtbX3uNpnCYfw3dEo8s4yovI1Vbi5lMo/o8aKWlSKoCjo3sdq0isiA6TjmmhauylExzs6iHHTQII9pOurkZT0XFejsuuYqLcWxHDASQJSG+uW1FRdz8j3/wl0MO4bIrr2T8+Rfgra7BNi2MplZUrxd/797Y+kKKHYvT9/8rD739FpVlZYw54CAyzc14utWQWrGSyPc/ULrDDqSbmjDCEQBUrwcro3es1VVSSqa1FSMaxVVUlBXbSjJNTWSamjtqcfP8QXC63HKlNF3JTu5BAXThPl6wZAFFRUVsMXgLKtVKYRHmYqYKnb+vdp1ZXl7O8OHDAejfvz/hcJhfxOpi67Cq1Z2bb5ur/dWyj20hRFQH2ZQ56dCTeHfKu9z0+E3ssf0e9OreSwith86a3Fw8OC+2q7De7KSZM2dy4403Eo/H2X///Tn00EOZOHHiOvefM2cOn332Ge+8884qtw3Jv/71LwAMw2D06NEdzTRSqRSjR4/mrbfeAsT8x9GjR/Pee+8BEAqFGD16dEezjebmZkaPHs1///tfAOrq6hg9ejSfffYZAMuXL2f06NFMnToVgEWLFjF69OiOsYG57dOnTwdg1qxZjB49mlmzZgEwffp0Ro8ezbx58wD47rvvGD16NIsWLQJg6tSpjB49muXLlwPw2WefMXr0aOrq6gD473//y+jRo2lubgZEk5DRo0d3uPbfe+89Ro8eTXu7COi89dZbjB49mlQqhdHezuuvvcZJF5yPnk7jWBZvfvwxo0eP7ngdJ02axDFHHSWEr7iYp594glPOOovJ7/yLz6dPZ+dhw7nrhUnooRCZ5maueeN15i5bxhWnnIrX5eaZD97n0gm3kW5pwzF0nps6leuefQYpo6N4fTw46QUuveKKjse7+cYbueLq8aJ0qD3GLXffzY0PPoirqAh3eTk33n474y8bh62LS/GrrrpqlTnI48aN49ZbbwWEG/nKCRO48447O0b5jR07lnvuuYfuAwdyzbjL+f6HH7jl/vtwFRbg61bD+Fde4IlJk0Bz4e1Ww5UvvoBi2Rw96i88PnEiR467lMnffA2WjbumhrEPPMDERx/BVVaKlUlz4rnn8urrk1HcLjLJFEccdjhvfvA+aiBAe2MTRx5+OG+99RaK203G6+X4s87k7ZdewrEsQqEQV1111W/22Zs3b96v+uzlPmt/1O+SaYr4ZywW60jmfO2113jqqaeYMGECL774Iq+99hpPPvkkEyZM4KWXXlrl92effZbXXnuNJ554ggkTJvDKK6/w2muv8fjjjzPhtgm88twrvPb8a+zzl31YXLe4w5pta2tj8eLFQrgkaA23Mm3WNJrbmhmx1QiKiorQJA0NDUmWSDkplrUsg4x47SPpCHX1dR3vS2tr6yrvUyKRIBKJdPze0NBAbW1tx+/19fWr/F5XX0dda11Hm8baFbXU19d3bK9trKUh1NDR5nFl00oaQ43iwqEAVqRX0Gw0c+WFV2LaJhfdexFN6SYhtHFYVr+M5nhzx7CDpYuX0tLS0nH8JUuWrPIcWlpamDRpUsfvo0eP7vA2/K+fvdzvv+d36dxzz+XnWK/QPvrooxx99NF8+OGHbL311vz3v//teMJro6ysrOPDnWfTwEym0ENhZJcLSVUx2mOofj/SapaVY9s4pilqRj0erGSSVCLJHROfYasBA9hu6JZIkkSitpblbW18u2A+5cXFjNphBxSXC1nRsFIpzGgYLViIp6oSxxTtaLzVVcha5+M5joOVSomSHlv8rHg8HWtSfV4Unw9wSDU0oofDsJ7m/YrHjezS0MMRzPiqc2OPO+1UysvK+PGnn6hraRG9lb1eZCTSDfW4K0RHJ8c0uOCgg9h7551ZvHw5jYmEeG6A5FIxIlExOSgQwLEtbMvCSmeQXRo4Nka0Ha0oiKxp2IYJtqiZUNxuZJcLx7ZJN+bbNW72OHSWw+R6Cq/uMs7NmFXAztg8/PjDAFx99tWokiq6LdlQXFbM8trlOI7DymUrmb94PsgiTPebn0uzSU4da7O7bMs5jnIj9mQ6k7skcRvafyinn3Y633z/De/8Jzv5J05nfXDuljt+HgAkZz2jR4488khef/11Lr30UnbbbTcOO+wwDj/8cN5444217j9u3Dh++OEHdtllF1xdxqxtiHF6mUyGWbNmMXToUNy/IJ63oZk2bRrbbbfd770MoHMtViZDpqlJ1HhWVoqh7skk3m41a7gwM22irtZbU41tGCTr6jnjwgv5bvZsXr7tNgZuuSWZtjbCM2dz/ouTWNnawis33UJVr55owUJSK2qJzpmLY5uU7rwzvm41pGrrWZqIs/1fRq3yWEY0ih6OoPj8WMkEWmHhKvHVHI5lZYUzjiRLKH4/iteH4nGvMiu3Y3/HIdPUhJXRRdMLT2cqZG1tLXvtuSdbDx3KM/fdj+TYxJevJLFkCd5u3dAKg7TPn4dj6EhlZXywZDEnH3MsqRXLSdY2ADZmpB1HguCwoWheH5JLQ9E0JFVFcrlwDAMlm8mdbmpG8bjxVFZ2rMFKp4WLXFWZXVfHiN85dyH3HRo4cCALFiz4w32X5s6dyxZbbPGr759IJPD7VxuFmOsfDEJULNbsZ+x02QeY+PhErrrtKvr27kvvqt7UNdUx5fEpAOhBnUtuvoQF8xfQp3cfTEzOOOMMtt12W0466SQ0TePWW2/lxBNP5JNPPgHggQceAITX5ldhsWrMNtfO0cz+PdfiMTdAXgdEziGtiVaOP/94VixfwadPfkplaaVwMVfSKa650p+1JYfx/39furIpnHfXp0XrDRbJssx7773HF198weWXX86nn366RilIV3r27Jkv7dlEsA3Rs5dsfaltGJiJhLC4VhNZW9cxY3G0wkJh9Ta38Nrrr/PF9B+5+PgT6Nu7N44sicEBmsZhu+9OIBCgoqoKV0kJeihEfGUtZiJBwZDBFPTvT2LxIlwlxUgudY11GdEosqZip5MoHjdacdFan4OkKLjLStEKRaKUlUiILlGArGnILiFysqYhaRqyqoqkpMYmMs0teKqrOjo0de/enVNPO42HH36Yl998g2MOPphArx5YiTiplbWoQwL4evYgvngpUjTG6O1G4A4W0hwooM1YTjkOcsCHGY4Qm7+A4NChKLKEo2lgWaDrYqB8OoMTjqAVBTHCEfRItGPsoOLx4C4vJ9PSghMOY5tmPma7OdFVZHNxza5N+nPkJu1osGLeCm667yZ233F3rr3+Wrx46ePqI0QpAK4iFw9MeEBYhwWsclZ+6aWXOn5+9913O37+1QKbI1eOk8uWzq51la5Sub7KGYQlnE2aKi0o5cqLruRv5/6NcfeN45kbnkFKSNCGGDqfizHnanRzr9WfmPV+wy+//HIefPBBLr74YsrLy3n44YfXap3+85//5Nxzz+W8885b57Fy++TZ8DiWRbqpCQBPZSWSomQtqWzzhtXItIXEGLxgIWY8zopFi5jw5BNsM2AgJxxwIEpBIen6OvRIBFdpCYfuNRJJlnGVFmMbBqn6BvSGRlzFQYq32Zp0YyNICv7evZFmz1rlsfRQGMfO+t6k7DScn7l4A8SovPKyrMs5ja1nsHUdWzewk6lV3MqSLIMEeiSCmU7h79Vb9FcG9ttvP2bOmMGtDzzAiKFD6denL4EBAzATCRKLl1A4ZAs85aWkm1tI1jei+Odz9lVXEo/FeOyiSwjoOorfjxlrJ7F4EQWDB+NYNpJLE6IJoCjZuLKD4vFgRCIoHneHda14vbgrKmD2HNKNjfl2jZsLXUXWTaeLdW3lPNk+wFbG4uLxFyNLMldcfAWGY9BT7Sn2UREdmiSEIOW6QW0scuU4ZvbxM3RaoLmJP7k5ujoiWUsHyZLYof8O/O2Uv/HI44/wwgcvcMKBJ4h4bc4StrL7Q15s+QVv64gRI3jmmWc6fu96hdWV119/nQEDBvzsEOzJkyfnhXYjYJsmTiQCVVV4qoRFZ7S3iwYU5WVruFyNmGgOkWsCkW4LcdmNN2JZFteddRaugB9bTxNbuowrXn+Fv+y4M4fsvhue6hq0gkJiS5aSWLoESVUo3m4bZFkhE4vh79sHxbOqG8WMJ7BSQhgd28ZTVfk/zXGVJElkSnfJlnYcB8c0sQ0TxzQ6/lf9fjItzUTbY3i7d8dVFESWZe697z5GjRrFuFtv5bnb78RXUkxgQH+iP80isWyZmKebTGElkqTq6rh2zBhOueQSLnviMR4451zkaDuSqpJpaUPSlhAcPAhJ8WTjzbYIZykKVkZHVhQkWV7DulY8HqTiInAQpT+VFciufKrmJktXV2suIzdXe7qOch5kePihh5k6bSq3X307SoFCgVRAQAmI++cGB/zeDR9yFnmufWSuH7LZ5edcDNoNJEHTNK468Spm/zCbax+4lh2H70j/nv1FPW1h9r5xxHPMzcnN9Vv+E/KbPe2amhqeffbZn92nurr6t3q4POvANk3SjU0iU7ayEtnlwjZNYVV5vairxZscy8IIC4tLDQTQIxEeeuRhps2dw/VnnEnP6mpkr5f44kU8998pfLNoEfvvsiuuoiI8NVWkG5tILF6EGU9SOGQw/p69SNbWogWDq8Qmc4+lZ92lkqKI8hfP///sIkmScBtrGuLMlaUSXKWlJFesIN3QiJVKYYfDVAwdyl133cXpp5/OQy+9wIUnnIiruBhf3z4kFiwkE2jD16snsfkLMMJR+lZVcO9NN3HuuHFc/+IL3HziSdhNLVjpJOnmZmRFpXDIYLTCQqxkUiSVOQ6SonQkPTm2LcS2y4VFrjdyOltn666s+EW1w3k2MjmLr2uNaE6E1lXOo8GP3/3IHQ/dwcGjDma3PXej3WinRqvp2E4BnaPrVh9AsLGR6ewS1TX/Kmdp56zcAMKqTUsoXoV7rryHUX8bxZgbx/D2P9/GnXZ3ztE16Jx1K7HuOt4/Ab/ZU17f5J48Gx7bMEg3NYNtIxUXdZy09TZR/rO2ZCM9JIrnXaWl2IbB159+xj8nTWK/HXfkoN12FzNo29uZ/t00nvnic0Ztux0H7bEn7uoq7IxOYsVKUnWNuIuLKdl6K/RwGEmS8PXquYY7WA9HsDPC96b6vB1xyw2JqyiIpPQm09IqLHnTIt3QyMgR23PiCSfwxKRJ7LLNtuwwZAiBXj0xYzEydfWoA/rjq6kmubIOPRRmx241/GP8eK658UZeG7Ilx26/I4kVyzHjcVJNDciaSnDYUNRgEDMaxbEtHFOILY4j2lgmk2RaWnBXVna8NnI2SS3d1EymqUlMUfqZblh5NiJOtqevhBDVnMh2jdGutj86IItyojEXjaGqvIpbr7yVkBGiTC7DlTtIrqm/ycZ3Gf8cGh2DAzo6RnUdt2ciLgqy/Y2LKou44MIL+McN/2DCExO49txrxXSfXLw3dwrIZSFn+FOS7/L/B8HWxbxUbBt3ZUXH1BmjPYaVSqEVFa8RBzTjCcxEUrhUNY3mpcu45LprqSwp4arT/47qciFrKs1z53Hjm5MpLShg3LHH4amswFVURKq2jti8BciaSvFO2wMSRiyGp1oMDeiKlU5jxNqxDUM0cSgr21gvDVpBAa7iIiF4Pm9HHHrcmWfSv18/rrzjdlojEcxYjMKBA1EL/CRXLEcrLBAx6EyGdGsrh2y9DbfdfDOnnXsOgb59KOjTB1lV0UMRkrV1tC9YAJaJq7QESVZAosOilVRVWPSRqEiE6hJikTUNb3UVkuYSc3XbYxvttcmzDrKZtpIlCcHIuYi7JDmtcfbMWmyO6nDF5Vewsn4lD97wIMWuYvrKfanUss0pPAixylnFm1qP4NyacsKaGxqg0eE+djQHdPC6vRy4+4EccMABPPryo0z5dooQ1DidjTxyFym54yVY1Wr+E7BeoU2n07z77rtMmjRplVueTQczmcomH4GnqrPzkm0YGJEwiteDVliwyn1s00QPtQmXcWEhmVicsZdcQks4zK1jzqPA40UtLCDd2MTn331DXTjCtSedTEXPnvh79STT2EhkxgxsPUPhFoPwFBdjtLdnXcYVqzyW4zjobSGsRArZ7cZVWvo/xWV/C1zFxah+H8QTYsJOdRU+r4+7rrmWaHs74+66i3Q8hmMZBAYNBMshWVePq6wc2eXGTCZINzZx8DbbUVxWBsFCPpw/j8DAAciqTKa1lcTiJUTnzQck3BXlQmxxOsRW8XrFDODmFvTW1lXENjfmT/F5RW/pLlOJ8mxkcpaXnRWUnMjmsnHX1gHKzG5X4fHHHufN997ksjMvY8CgAehpHUmWkG1ZnHG7XoP+3i7jdZGLx3Z1AecSv2SwPXZH7LUqUMW5Z5xL3959Oe8f57Gsbpl4/XIDCrJWfscFRs6N/D2/bljCZsgvmkf75JNP8s0333Tcvv32242xtjy/ADMeJ9PSgqSqIvEpm1Aj+hq3ABKuLpNtOreJLi2u0lJwHG698Ua++mEal590MkP79UPxezF1ndjChew5aAsmXXEFO227Lb6ePbGSKSIzZ5NpacNTUU7B4MGYGR0kCV9N9ZoZtIkERiyGpKm4iorWaPu4sXCVloKmkWltA8BTXcWwrbfi5suv4NsfpnHPSy9hRNpRfT78/fpixhNkIhE81ZVIloMRi5GoXUFs0WJe+tc7XHXH7Tz/1ZcUbDEEWVVJN7cQnTmb6Nw5QjirKpE1l7BsTRPHtlF9fpDEfFsn2r6q2HYZ82e0ixaUjm2v6+nk+a3JuX5zPXvd4MjZ9ydXS5obP9eV3Cg6Bb6Y+gU33XYTB+x1AGeceAYrEytptBo7j+9FCLctfq5trGXvvffe0M9svVx55ZUdHejOOOMMmpqaxGvgobMXcm5urQKOkh1Cb4EiKfQr7sf4a8bjSA5/v+rvJJNJkRiVG72Xe01zsWkVqAemAwsQpUF/4I/6eiMDTU1NvP/++//TQZuammhra1vlJLLlllv+76vLs07EhJ0IRrQdxStqM1fJJo7FRJZxxZq9dfVQWGQZl5chaxqTX3iBxyc9z5EjR3Lkvvti6waSy813//438YYmRuwwgn7BIJ6qatTCAsLTphFfuAjF76N4xHbgONjpFJ6KCjGdpwtWJoMdi2MX6bgrytZZL7sxkGQZqSiYLXUSiUnu8nKOPOZoZs2fx9MvvcSQfv05cPvt8VSWi85YtbU4bjeeyipS9fUY7QkSy5Zz3KhRzJ41i/ufegqAE3fcmdj8+WRaW2n79nskRaV4q+F4qsWcXSuVxjENUDUx0CEWx2lp6Sjvyb13kiSmEMmaih4Kk25sxF1eni//2dDkhDRXdpNzk8Kq029WzzLusq22qZZzzj2Hvj37ctc1d1GbqEWSJKrV6k7Xq5/OnzehvLdvvvmGMWPGAPD44493bpDoEFQydF5o5Gpss5nKhZ5Ctuq5Ffdedy9/H/d3Lrn1Ev554z+RMlLnKL2ur6sL2AqoA8LZ7Y2IOtwSVslp/COwXqEdOHAgLS0tlJeX/6ID3nPPPTz99NOUdYnBSZLExx9//OtXmWcVHMsi09qKlUqjFhSIphBdEo+M9hhOKo1WFFzDejTjccxYDK2wENXv5+svv+TS8eMZ3r8/l5/+d+xUGrW4iIb58xk/8RlUTePVffbGXVSMr2d34suWEfrhR5BlirYajur14hgGWmHhGvWwjuMI6zGZRAsW4C4rW2+97IZGkmU8lRWkGxo7xFYLBrn6uuuZt3Ah1951JxU33Mj20mD8PXtgZdKk6urw9OqBq7yMTGsruqqgLF/BTZdciuM43P/UU7TH45yzz1+IL5iP0Rai5bMvkIDirbfKduRqw4gnsHUdWdPQCgM4Lo3kipXYuoGve7dV3OlaYSGyppFpaSXd0Ii7vKxjrGCe35Bsb96OmKEbJr81mdtuu436+nqqq6u58rIrOeLgIzrHyHW9b7ZJfzgZ5qSTTsLQDZ6c8CRJkiStJL3UXmhO9iLJB4889whv//ttFE1h19125fjjjyeTyXDBBRewdOlSevbsyc0330wwGGTChAl8+eWXyLLMqFGjOO2000gkEtxwww0sXLgQy7I444wzOOigg5g8eTJvvPEGkUiEbbfdlv/85z988sknaJrGggULuPTSS3n77be55557mDp1KtFolIqKCu655x4mT55Mc3MzZ555JpMmTeLII4/k2WefpaamhltuuYWpU6ciIXHI/odw5t/O5Jvp3/DPR/+J3+Nn8ZLFDOo1iDuvupMCu4DX//U63Sq78faUt3FweOSWRzpVJoGwZnMf857Z1zMiXhtc2Z9DCKEtAYJd9t+MWa/Q7r///vz1r39l4MCBq4y9W1cpzzvvvMOUKVNWEdo8vx22rpNubsGxLNxlpR2D03OYySR6KARu1ypTbEAMfM+0hUTMtriIeXPn8rfTT6eyqIh7LhuHbBiilCcc5sI77iCcSvHo2AtweTz4+/XFiEQJffUVViJF4eBBeKsqQZKEy7O0bI1SHSPaLmKNLhfusrJNxiqTNQ13RTnppmbSzS14KivwFBbw6BNPMHr0aM6/8QaeuulmhvTti79PH+yMTrq2Dk+NsOjN9nZSsgzLlnPblVdRGAjwzr/+xWlHH0PBoEEkFi0hE43QPOUTbMuiZLtthVXqcqGHI1jpNJKiIHm8qAWFpOobsHUdf6+eq9TSKl5vh0WcbmrGVVK81mYjeX4la7FiJ78xmXHjxpFKpQDRnH/cleNAhiNGH9F53y5D0lN2itP+dhrLli9j0r2TqKqqYnF8McVyMUVykdjXDZ/+8ClTvpjC6y+/jhbQGDt2LJ9//jltbW2cdNJJjBgxggkTJvDQQw9xyimn8Nlnn/Huu++SSqU65tE+9dRTbLnllkyYMIF4PM6xxx7LVlttBQhP4nvvvYeqqjQ3N/PFF18wcuRI3n33XQ455BCWL1/OkiVLeOmll5BlmXHjxvH2229z5pln8tJLL/HYY4+tMt/2xRdfpKGhgbfffhs9o3PSiScxsP9AvH4vP834iQ/e/ICKwgqOPu1ovvj2C6LJKN0qu3HN+GsYf+N43pnyDju8tAN/O+FvwgWdq63NnbIkoId43WnJvgcDEXHbEMLarc/uH2SNrlmbE+td9kMPPcRZZ531i9sqFhcX50V2A2G0x0T5jCKvkvSUw0qnybS0IrvdSMFVS2esTIZMSwuypuIuK6OhoYETjj8eVZL55xXjKSkowM7oWNhcfvPNzGtq5LZzzmVo7974evYESaL5sy9IN7fhrammYNAAZJeGnc7gLi9HK1rL4zU34ZgGcmHhGhcEvzeiFWIZmZZWMs3NuCsqKK2o4PlJkzj00EM598YbmHjjTfTs3oNA3360L5iP3tiEu6oSDAszEkFXVKTFixl//vmc+/e/U6RqGCUlJNIZPG4NvaWNpv9MwYzFKNttV7RgENntJtPSipVKgp5B9XqQZBm9LYSdTuHr3QetoPO1kjUNT1WVsKRDYeyMns1qzhcM/GpWt2JzGbHAbbfd1iGyOVLpFLfdflun0ObijhaYksm5553L99O+55GbH2GXbXbByliUKqVUKVWdIu6Fr3/4mgP/eiDeIuGZOPLII3nzzTfp06cPI0aMAODQQw/liiuuYNy4cbjdbo499lhGjhzJpZdeitvt5quvviKdTndMtkkmkyxcuBCAIUOGdBhDhxxyCO+++y4jR47k/fff57nnnqOyspLLL7+cV199laVLlzJ9+vSfPa9/8803HH744SiKgtfn5eCDD2bqd1PZe6+96d+3P1XdqiAN/fr0I5qMss0W23D3k3ezrHkZw7cdjmmYXHvftZSVlnHIQYd0tmRMsurAgSqE2NYjLl56AaWIuHAke8sl4nsRguvP/ryZWLvr/bZ6vV7OOOMM9ttvv1VuqzN79mxmz57NkCFDuOmmm/jpp586/jZ79uwNsvg/C45lkW5uRg+FUDwevNXVaxfZ5mZkVcVTsaoLVwwWaBY9jysqqG9sZPQRRxCLxXjw0kvp3qMbZiIJmsqrzz3PF4sXceFRxzBy2+1wl5XgLish9PXXJJYuQSsOEhw6REz3SWdQCwtxlZWucuLPTagxou24y8tgtYznTQXV58NdWoqVznSU3HTr0YMXXngBy7Y588YbWL50KbZlEOjbF0nVSDe1oJWIRht6JIweiZJYtIRitwc1EOCfzz7LqbffRovLhaemG7Ki0Pbl1zS++z6Z3PtXU40WDIKqYSYSIkmsrBRb14nNm0eqqWmVJChJlvFUVKAVFWEmEqQbGjtGB+b5H8m1UTTozITtYm50HR3XlY6/5yxZEwwMxl48lg8/+pCbLr2JA/Y8AFu3UVDopnZDcRQhBFlBsLHXmNNqmuYqnkLHcVBVFVVVefXVV7nggguIRCIce+yxLF++HNu2ueOOO3jrrbd46623eOWVV9h9990B8HTxKO2zzz589913fPfdd1RXV1NZWcmsWbM4/fTTsW2b/fbbj1GjRv1sJz97tUQ8R3KwbKGQLperY2iAJEs4ikPvbr15/9n3Gb3PaBYuWEh9Sz3bDd2OsdeP5b1/v9d5oIx4/VhBp+CWAr2z78sihLB6ECI8GOiHGFogIazfpcAcYD64GlwivhtCWMxG9n3ahFiv0O6yyy5MmjSJ5uZmIpFIx211xo4dy9ixY/nyyy+ZMmUKF110Ucffzj///A2x9j8FVjpNqqEBK5XGVVKMp3LNQehmMiX6GCuqqKHtst1Kp8k0NYEs46mspK6hgSOPOILW1lYevOhihgwbJiwlbGJz5jGyRy9uP/NsTjz4QFSPB2/3HoS+/5HIjNkoXj/BocPQCgqzGbRe3GVla4h+pqWFTEszruIikejzO8dlfw414MddVoqVSneI7cDBg3nxpZdI/l975x0fVZk97ufe6TPpIYGEgPRioYk0AUUUC0RKVGwgoivuV1gFFRXUdXXh56KrK8UCusuuujYIHRXUxbJYAFlcpCOBhJDeZibT7/v742ZmEkEpm4a+Tz4Dc+e2M++dc899z3vec7xefjPnKQ7u3QcKODq2R1EUfCUlmOLj9KCqkhL8zircOTkEnFUMHToUp8vFpP83l89yD2HPaI0xLpaKXbs5umINlbv1GpaW1FTUuFgMDgchdzUiFMKUlIxqseD+IQfngYOEvN46spoT4vXSg0LDc6yAgFPOtz1lake/CnSDZ+G4qTXp6ekn3D09Pb2Ou9gv/Px22m9ZvXo1j/3uMW4bcxt57jwOeA/oBkoQjbI16ecbMHgA69atw+v1EgwGWb58OQMGDODgwYPs2rUL0FPZDho0iF27dnHrrbdy0UUX8dBDD9GxY0dycnIYMGAAb731FqDXXb322ms5duzYcfKazWaGDBnC3LlzufbaawG9bmq/fv246aabaNeuHZs2bSIUnnpmMETehxkwYAArV64kFArh8XhYs2YN/Qf211fWDhSrSejxxpo3WPDXBYy8ciRz/m8OVc4qHnn4EXp268nds+9m1epV0RJ9AeAwsJ9obzUW6FTTXjnoPdywrbcDqegGtzvQHt0I20DxK1CC7mo+BOwBvkePZj4MFKL3isPzgZuAk7qO//a3v+H3+3nqqacinymKwu7du+ts98knn9S/dL9ihKbp5eGczpqpO8e7iiHqTlZNxkjxgDBBlwtfaZle0aZlKjt37eK2iRPxuN0smnE/fS7qhyc/n+pqNwveeYdxXbrTacBFXJGSglbtxt7xHCp3fk/5t9tRrRbizz8Xa4skFKM+P9ScmHTc/NxAZSWe/AIMVhvW9PRmMy77c+i1ZUWkoL0lJYUePXrw3rJljL/hBu6Y8xTP/e4+evfuhaNjB9wHf8BXWo4xIZZQpRNvQUGkpkHPdu3JXvp3Zjz+GI8uWcLIQRdzX2Ym1thY/KXFFG78CE9uHol9L0RxOLClp+EvKcVfWUHQ6cMYG4tqsRCsqKSquhpbejqW5KTIdQ17NHwlpTXuZi/mpKRGn5d8VhGe/xquKnOi/MQ12z18/8PMnDUTjzfqPrbZbDz80MMRI13hqeA3d/+GzZs38+QDTzJ57GSOVh+lPFhOK2Mr1HD/JXwuM+CAYZcPY/e+3WRlZREMBhk8eDDDhg3j7bffZtGiRRw5coQuXbowffp0HA4HvXr1YtSoUdhsNvr06cPFF1/M0KFDeeKJJxg1ahShUIgHH3yQtm3bsnXr1uO+zujRo1m9enXEA3nNNdcwdepUMjMzATj//PMjxeEvvfRS7rrrLl599dXI/uPHjycnJ4fRo0cTCATIzMzkiiuv4Osvvo5GTodTUyowJnMMMx6dQebkTAwGA9MmTcOR5OC1F17j7vvv5p5H76G4rJg77rhD3/8oes+2Gr1H2wr94acjeg+1FN0IZxAtUBBu1xgiY72+Yh+cRzRXc/jlq3k5iRpYA7rRjkHPydxI6cVPWo+2OfNLrUcbDloSwaAeVZyYcNyYnNA0/GXlBF0uDDU9y/A2QtPY9tlndG/XXp/606IFH27cyNR77iHe4eD5ab/j/N69ceXkUF5axiNL/8qOI0eYdeNN3JB1HYHycswpKfiKCqn6fhcChdhOHYhp3w6DzY4W0IsT1J6WAnrP2nXgACIYwtGhfSTFYnOoFxnm52SJPJiYTVhTdc/AwYMHmXDrrRQUFPDEnb/hmssuQwhB9eHDhNxe1MR4hMul179tlYoluQXmxAQ0ReGlN15nyZtv8vdHH6N9fCIaAn9xMSGfH0tiEoUOKxddm4lqMuEvL9evp9uFwWZHNRoJuFz6mGyinjfaGBtbN7q8shJ/RSWKQcWSnHxGUcm/6Hq0oZq6px26R6eUnOh5pPaYrQLZ67J5+k+1oo5nPsK4Ufr4bE5+DrdNvo3DOYd59rFnybo8i2PVxygOFJNiTCHNkIYilGjuYDt6T60eAsZPWBu3KdDAU+HB5rDp7VnjSo/UsvWhf18P+H1+zHYzHq+H3z32O9Z/tp5J10/ixltu5IKdF+jt06rmuPHoBjcZ/XM3kFdzzPia7U5gGE/6WwnXBvbWHNNNNBWkDX1KUSL/03jvGdejXbVqFaNHj+Zvf/vbCdfffvvtZy6V5IToyf8r9SLn4V7sCZLuh7xefCWliGAQU0J8nejikM+Hv6QUUe3RI1RjHDz1xz/yyuLFnNuuPX/+3b20ymiNc88+vju4n9+//RZlLjdPTrqdMWPH4S04hmq24D58GE9uHhhNONLScLRpg2qzE/J6dSObmlrHyIZ8Ptw5h9G8Puztz2mUPMb1jTEmBlQD/pJiPMcKsKam0LFjR9auW8ftkybxyIuL2HFgPzMmTsJ+TjuqDx8hUFyCIS4WFfAeO4bm84OqYIqJ4Z5bbmHc5VeQltYKz5E8/rl2NRf36ElqYiKhyiq0nEPkqwYSevXC0a4tBptdd0WXl+sxNDEOhM1GsMqFu9qLKTEBS1ISBocdRVH04CqrFX9JKd7CIoyxMZgTE2WglECfm1lIJFtTnXmxtand263ZblzWOMZljYMQVDursZvtoMK6jeu4/4H7MSgG3lr4FgN7DOSYWzeyLYwtSDOmoWg1RtaIPsYYDtr5JaHqaSbrtG34YcVENBuUFcwhM/jBrbh55slnaPty3olk3wAAKCVJREFUW15++2W++e4blk5dSutAa33MtRX6NStF720mohvANujjrsXo0cjxQAtOr03DiTesNcekRr4q9GL2x9B70GFD3wCpxn9SIw8fPgzAvn37TviSRMnOzqZfv36MHj2afv36kZ2dfVr7CyEIVFbizc/XC7PHx2FLTzvOyOrzZ0v1nMbo6RbDRlaEQvhKS/EeK0AIvajAoeIixo4ewyuLF3PdpZeyeNYsWsTF4dy7j807/sM9ry5BAV55+CFGj7uO6pwcAi63Xk3mWCGoCtbUFBydOmCIjSVU7cGcnHyci1rz+3H/cIig04kto3Wj5jGub4x2G5aWLUEIvAUFBF1ukpKSeG/ZMu684w7e3rCBiY/OYs++vTjatcOSkkywogIR0jCYrfiKS3Dt3YuvrAwRDJGW2pKQy025QWHRmjWMf/IPzF+1Ek9CPCTG4y8ppfCjjyh4/0P85eXY0tOwZ2SgWiwEyiv0OcoJCahmE/6SEqrz8vDmHyPociOEwGCxYE1PwxQXR9DpwlPzGzobCetRRkYGd9xxx2nrEaD3Vg6guyUtRMZHjzOy4aCo8JitpdZ24Wk/PlA0hSpvFTNnz+SuKXfR6ZxOfPD3Dxh4wUDwQ7KSTLopnXRTerQnGw6yiqVBbtrNAWEQ0Tq6YWMbHiWyEC1IYANNaJR5y8j15DLj/2bw4u9fJOdIDiMeGEH299m60a4k+mDiRx9r/S+wAygialiPAbvRx3aL0XupZ4IZ3WB3BDqjz9mtAg6i/37Kqdfx3NNyHfv9fkpKSn4yYKCxaQ6u4+zsuvPuQB/TmTdvHuPGjfuZPXUDG3K78VdUIoJBDHYb5sTjk/9rwSDBKidBlxMhwBQXiyk+HkVV9TJ3TifBqiqE0HtlQbOJxx59lOyVK3HYbDx4401c3qcPIY+XoryjxKoK7ionf936NTN+dx9xDgeuPXsJ+rwYrFa0QADNH8CcEE9st64Y7HY0jwdrSkqdmqpQ05M9+AMBpxNbmwxsaWnHBT+dLa7j2ohQSM/o5PXV6Sl+8P77zJw5k4qKCiZccw13Xj8es9ej9/4FYDSiBQKoioIlLQ1Hu3YYbRaCLif5xwp48Z9vsubzz7GYTFzSoycP3H47MULBX1AABgO2jNbE9zgfS1IS/tIyfGWliEAQg8OBajTqtYaDAQw2OwarVU88EuNAUVXdm1FaiuYPYLBZ9d/SSWrcNhfX8f+iR4BuYIvQez8mIA2Ir3Edd+8e3S5cjSYc9xPOW6wcv04YBNmrs5k7dy5FxUVMuXUKD971INWBapx+J20MbVAMSjQPcs1YJXb0npODes1j3Gxcx9TIYnVEi8Ub0d9XE63U40U3nn4IeAMc9B4koAVoZ2/HV9u+4oXHX2C7azuXdb+MJ255go5tOuoGL1TrGCH0VI4h6majqplehRH2Ht5L1z5d9fYOexBsteQK19M9GRp60FQJ0UxWSei93JNEM53MFp3U0G7cuJGvvvqK6dOnc+211+J0Opk6dSq33XbbKUjesDQHQ9uvX79IjtDatG7d+idzQgtNI+h2E6isQgSDqDXJJX48xhby+Qg6XZEeitFux1RTaSfk8+lF1N0uhCYwOuyELBaWL1vG8/PnU1hczJX9+3PvtaOJM1vZ+v33/O3jDZRUVfHarROJSU8j9oLz8Rw+QvWhw6CqmFumgs9P0KNX9Inp1h1VVSAUwtKqJbZWrer0ZANuN+4DPxDyerG3aY31BEYWzk5DCzWehvIKAlVVKEYD5qRkjHYb5eXlPPHooyxbuZKE2FjuHDuW6y4dRvDoMXxVlXoxAU2/WxhtduydOmLPyEAEAgSqqti/dx+vZi/ni+93kj37MZLPaUtBdTXx/gBacQmoCra0NGK7dcOSnEigohJ/eTkCBaPVjGIyIUJ6ZKtqMWMwmzHGxuhjuAYDQaeLQEWFHhnusOsu5p8wuM3F0J6JHgFRt6IL/WaYgn5zrLmx7t69m+7dukdv2mGDGL5pw3HrhCr48psveXre02z7dhvdO3fn2dnP0qVjF/Ld+bg1N3bFTntbe4wY9Rt0OPrWjn5jtlHvxQKanaF1OCLFFyIR3B70h55wbzdYs84LAX+AH3w/4Av5CFQE6PWXXvw196/MK5yHT/Nx06CbmHHbDFp2ahl94Al7HGofL2yEa8aED3x/gE7ndNIFU4mOtYavcTjC3I5u+G3ov5WwF8PM8XNyXegG11lz7pO4rM94jDbMK6+8wpw5c9iwYQO9evXiySefZOLEic3C0DYHTjrvrhZaIKAbTpcLoWm6gU1KrZMmUfP7CVZ7CLndaIEAiqpgjHFEMgKFqqvxud1o/gAoCga7nSpPNW+89hr/ePNNisvK6NGxI9Muv4IenTrz/udfsGbL1+SUlpJos3Fz/4HYu3TCEhtH+Rdf4nc6MSUlYGnZkmBlFSGPF3tGOvaM1ohAAMxmbOe0xZKcHDGiQtPwFRdTnatHLMZ06oDlR4ULfgkoioI5KRGDw46/pBRfURFBm434xAReWLSISZMnM/epp3j2H/9g6Zo1XDd8OKN6X4jD5SLkD6EFQwTdZfgqynEfOIijU0fsbdpwboyDp9u14/uvvsIe0nDtO8CMpa9RWFnJiCFDuaRrV7oFg3hy8zAlJeJo3w5LSiqa10vQ5QJFzyxlsFkRwSDBYIBgdTWGmrzXRkcM1vQ0Pd1mVRVBdzUGmw1jbCwGm7VZTrc6HT0iiO5qLCXa80ijjoElhH6TrF3mrXae3nAhgFB0XUAEWLd+HYuXLGbHzh20bNGSpx95mqyrsjjmPcZ+536MGMkwZpBkTdLHY8P7K+hjiy1oVjmMG5xwQfgA+ve2Eu2Fmoi2swVMwkRHtSM5vhzKTeUYhhj4zZbfMCZuDH8p/wtvbH6D9756j9EDRnP7b26nR78eeg+5uuZ4VqJTtMKlCq0QSgxB2Mla27MgarYLyxM22qD/ZsIufivRCHE7ujG1oyfO8KP/zsqJpopsgR6xfBpqdFJDK4Sga9euLFmyhKFDhxITE/Ozk5x/baSnp5/wSTzsXtf8fkIeD8FaxtFot2GMi8NgsSBCIYLV1YQ8XkIeDyKop6tRLRa9ULuqIvwBfEXFaIGAfnBVxR0IsGnzv1m9Zi2fb/mGYDBIv27dGD9oMIM6tMdYWsG2L79kwUcb6JSUxP3DLmPEoItxJCYQKCiibPc+DFYr9nbnoBqMBErKQIGYju0wxyboruOkROxt2tQpRB5wufDkHcVfXo4pJgZ7h/aYmslTdkMRHgcNOp0EKirx5B/DYLPR49xzeSc7m083bOSVl1/m5WXLeHXFCgb16MnQbt25qEULbIQIuP34K5xUHz2KKS4OR7u22Nueg71jR2xxcXiO5nN7/4Fs/H4nqz/ayHvvr8dmtTJ5VCY39e5D2TdbEUYDttQULKmp+th90E+gwquP05rNKDYbwu2OeEoMVgsGmx1TQgJaMEjIXa3PtTYaMNodGBz2E04XaypOpkcE0Q1nJdFap3b0YJn4muVwb8pF1IUZrocavimG3cM1t7BgKMiWb7ewcvVK1r6/lorKCjqe05HfP/B7xlw+hiQ1CcWjENSCpKlpJFuTMaiGaI8K9LtoIrqh/7XNsgo/vIQjj41Ea+2Gja+XSB1eo9dIB2sH9hr2wiDY4N1Ap6ROzDkyh9/E/IaXXS+z/KvlvPvvd+nRrgejxozimuuuof157aNj6uESfH79HMFjQf0hp/b0nvA1Vqhb1jDsRg4PFYTQfzPhB7Qy6iYysaG7ozOIZqo6UrMuDv23dwq3v5MaWlVVWb9+PV988QUPPfQQn376aYM8Ea9Zs4aXXnqJYDDIbbfdxi233HLK+4b8foTJ1CTRluF0aXXGlqxWHrhnKtV5eYigPuCjWiy627emAHiwyonfXxoxnkLT9Py3ZhOqwYgIBvUpPqEQWiBAUVk5u/fvY8fOnXz57TZ27t9PSNOIsVrJSEhEC4XYvm8f3+zZQ3m3cxmd0ZYLYmJ4eUwWnTt1RLXZCLncVJeX6xHNqSkYY+IIelwE/AGMMQ6sqS0x2m0Y7FY9qClZz/gkNI2gy4W3oFDPXawq2DNaY0tP/9XM31QUJVKIIeDUCzN4C3XDdXG/fgwePJiDe/fw+j/+wQebNvHZ9m8xGAx0z8jggrTWdEtKoJ3NQUJFFe4juRhMWwii4ezcGXNSEpcOu4RBvXpRVVDItwcPsiU/lxaawJiURJnRwI2PP0b31q3pnJJK+5QUOmRk0KFNWxxxcSg2K0ajCVQFg9kCRgOqakC1mFBNZlSLBYPNpmfwCQTxV1SgVFWhGAyEflTZqU5qvEbkxHpk4+EpD+uBL+GgFxO6QQvHCVaj9zjCJdk0ookizOg30RAR967T5WTXnl1899/v+PfX/+arLV/hdDmx2WwMGTiESy+5lB4X9kBVVSq1SpJDyRhMBrqYu+jjseGI2nAP2Y7urm6udWUbAyPR6O2w6zYG/aEo7Fb2EqlYpPpUfdx2W4DZb82mtKqUaVdO4652d/H0D08zK3YW71W/R3ZxNnP/Mpe5f5lLh/QODOw/kP79+9OzZ0/adWqH0WoEFbwuL/Qgamhr/+9Ff/Dy1loOG2KVqLENP5CFfzdVNcvh8Xsz0epL4XHiIuoa5p/hpGO0W7duZeHChVxzzTXccMMN3HjjjcycOZM+ffqctP1PlcLCQm666Says7Mxm83ceOONPPfcc3Tq1Oln9wv7xduazJhNJoxWq57n12TCYDHrhsto1MujqSqo6hk/JAghQNMQmoYIhWpeGlogwMrVq3h24UKOFRSQltqS++6YTObwy1GMRlAVVINB3zYYBK3m/5pmd3mqKSkto6i4hMLiIkpLSqmsqiQUCJBfWMi3B/ZT7nLhr5W1Jd5s4Yo2bbkgPoGntm/FYTTRNiaGdrFxnJvcgh4ZrTEZTcSnp2MwGNBCQYQmUAwqqsmEarHqxciDQVSbDVvLFMzJyViSk7G0TMWSlIQWChFyuwlUVhKorNIT4asGLCnJWNPTMZ5g2tFPcbaO0f4cQghC1dUE3W5CHm/keipGAyGfj+3bvuXjzz5ly87v+f6HgwRrrl+sxcI5CYm0stpIUBVSHDGkWKwk2GzEWq3E2e1YTWb9dxbUhw6K/QFWHDzA3tISjpSXE6hJgzfrsssZ2qkTe4tLWP7df2iZEE9KXALJsTHE2R2c264d8QkJhAwqBpMJiyNGn6NrNqEoCorRQNBoJE9o0TFa3/lYrJZoUEntcSxDrdf/8kwb7lXWfgVqqua89DT5xfmkxKfw2MTHGDd4XDSiNXxOL9Ex1ZqeiyfgoaS8hIKyAopKiiguLuZY8TG853jxe/wcOnqIQ0cOkZefFxEjPT2dy3pextDeQ+nQtwNYwYoVu7ATo8ZgU21oQsOo1hqHhWhPJwk9sriBnu/fffdd7HY7o0aNAqLjov/617/Iycnh9ttvZ8GCBYCema++yMvLY+LEiT+bhOjH48WXDbuMfyz5BxkZGdHxWh9MmDiBqZOn0r9Pf/26mQEBuw/spvvfulPQpoDHtz3Oum/XkRyfzJTLp3Bz7M0k5iRCFRytOsr7Ve/zufdzvnF/Q1VIrxRvMVjonNKZzi07Y1fsnNv+XNIS02iV0Ip4RzxxjjjiHHEYLboxrhNcFfZ0hN3QYdd3+CGqJoWjUAQaGqpZRbHU2I1whLUd/XvWTOXyGXzsTPkfxmj79u3L0qVLAT3q+Lnnnqv3qOPNmzczYMAAEmqmqlx55ZV88MEHTJ069aT7vvbaa3z58cf4vT4UJfpQeX5sPChQ4PVSFvDXfB79t0tMDAJ9fWUwEDme/mCj0KHmR3TM68UZDFIbk6rQ1mZHAPleD9XBEAKINRpxlpbyp2f/zNsvvgTAoWo31aEQQghdVwUYVQW7wUBAE7hCdY8dRgESTWacwUDNexNJJjMZVhu9EhK4ODEZVJXFfS4ixmIBRUUxm1FNRvAHCbmqqfZ5URQDGAy6e9FsBKMRo9GMIdaBJaUF5uQkLPGJkaT/3qP5VOccQWghEEIfC3Q4cKS1wpKcfFZkemoMFEXB6HBgdDgQmqYPEXi9+jxaFC7s148+F15I0OPBVVbGdzt3sv/QIfYfOcKB3Fy2l5ZQ5nKecAaBAtiNRqyqilFRMakqJkXBpCh0djjQhMCvaazduoUN326jKhDgiKcan6bVOV6vuHjiTCYKvF72uV36zBNFQUXBoCj0io8nzu7gnpcWAfDCCy+w5/M9BD3BGjkUuge6k6QlUaFWsMe4ByWiQ/r/nYOdidPiKDWUcshwCFFLAoGga7ArdmGnRC3hiPFInfUA3QLdsGChWC0mz5CHQJBMMsHSII8//zjvzXsPEybyDHnkGnLR0NAUjfBfqpZKgAAVagUh5QTd8c8hISaBli1b0r5De0YMH0G3jt04t8O5pCemk6qloqCgKRpqSI1egPD0lDDhBBThSjKNMDf222+/pV+/fsd9vnPnzoY/+ekS9iKEH6LC9Xb1JHI6FqJRyiYgDlrltmJxn8VsHbCVP3/6Z+Yun8uQ+UNIvDqRo1uOYj1m5U7fndxZeSchb4g9zj3s8uxij3cPe6r3sG3/NvI9+QT/c+L7aIwxBpvBhkExoKBgUS0kmhOxqBb2O/cTEAGCWpCQCBEUQdJIo6foiRJSWG9Yj6bUBB3W/HUIdqB3oDcCwXrregwYUIWKzWbj5WUv/2TznNTQNkbUcVFRUZ16t6mpqXz33XentO/27dup8HiOS4C9y1WFAlSHQnjDuUfDV1xROFATyesMBvT1tVCBXI+udBUBP74frTcoChbVhwJUBQL4NC16+1FAaILygG4gNQEmRUVV9f0MikKMwUgHhwOjolLi92FUFGKMRmKMJmINRs6x2zg/Nh6jqoKigKqAUvPeYNCXjboBjak5L4qKUFVC4e0tsQRMRjCZwGwCkxmsFoiJRXXYUM1mnAYDiscLnmM1x1bBYEAxmcBsBosFxWhA8Xnh6FH9dYZs27btjPetbxpDlrAHBE0Do4HUHheQcv55DAoG9XH4QJCA10t5aSnFZWVUVlXiqqrC7XLjcjpxu6vxeT0E/X78gQDBUJBAMEQwFCKohRCoOEMh3VshBCkWK5oQhGq98r1e8r1e/ELDpup3vPDDfAjBHpcTc61Ugzt27MAZcCJUETGI/zX9F6uw4lf8lKllx33PAAGsWPEoHkrV0sjnYUNswIBFWHAprsh6hahX6YjhCGbMVClVVKgVKCgoQiH8V2QowiRMeBQPZmHGgAGDMGAURkyY6O3vjUM4qFQq8Spe4kU8iVoiiVoi6SKd9q+358K2F4aF0r9XOFhGAc2o6Z8rQr+pqnrUMSoIk0AzaWhWDWERdcd5z2CqcmFhIbNnz8bj8aCqKg8++CA9evRg48aNvP766/h8PgKBAL///e/xer18/PHHfPnll8TGxjJo0CD9evz3v5Fcx8nJyfj9fr7//nuuv/76SO7ju+++m9WrV7N27VoqKioYMmQIN998M3PmzKGgoABVVZk2bRr9+/fn66+/5oUXXkBRFOLi4pg7dy4ejwePx8O0adM4ePAgsbGxPPfccyQkJPDZZ5/x4osvomkaGRkZzJ49m+TkZDRNw+P3UOGu4Mk/PsmuPbtIS0+jtKIUn9GHz+vT209FH/MOBskx5GAOmTH810C6LZ2nezzNgV4HsOZZ2V2xm2c2PcOHWz+kdUJruiZ0pbW1Na0trRnTYQwD1YHsce2hjDKcOCn2F1NQXYBf8dO1dVfcPjfLtiyjqLIIV9AVuQYJxgTSRBpBT1A3sIQwYsQszKiohESIfWIfGhrxWjwCEenZCgTFajGfmz9HIAjW/AlV4Al4TnTJo4iTkJWVJfbs2SOWL18upk+fLpxOpxg7duzJdjstXnzxRfH8889Hlt955x3x2GOPnXQ/r9crtm7dKrxeb73Kc6Zs3bq1qUWIIGU5MVKWuoR1qKqq6hepS7t27aqznJWVJd5++20hhBB+v19kZWWJZcuWCSGEqK6uFllZWWLlypVCCCEqKyvFmDFjxLp164QQQpSWloqsrCzx4YcfCiGEKCwsFFlZWeKTTz45JVkWLFgglixZIoQQ4tNPPxWvvvqqCIVCYuLEiaK0tFQIIcR7770npkyZIoQQ4qGHHhLLly+P7O9yuYQQQsyfP1/Mnz8/8n7s2LHC5/OJ0tJS0bNnT+F0OsXy5cvFFVdcIQKBgBBCiPvuu0989NFHEbmHDx8unE6nuPXWW8WOHTuEEEIsXrxYfP755yI3N1d07do18vm0adPEG2+8IUpKSsTgwYNFbm6ucLlcYsmSJWLatGlCCCGGDRsmcnNzxatLXhUPzHhAiGohDh08JC644ALx1ZdfCeEWQhQIIQqFEMVC7Nq8S4hHhBDzhRB/EELcI4S4WwjxqBDiFSHEW0LsfH6nmP/b+eLOkXeKAT0GiHPanCMGnT9IiOeFEE8IccOFN4j09PQ6r+FdhgtxixBivBB/6fcXMbfHXPHXLn8V77d8X2yP2y6KTEVCKEIIVQhhEEKYhBAOIURLIUQ3IcQlQoiJNTK9IYT4XAhxUOjy/wwns0XNIuq4VatWdZJiFxcXk5qaWq/nkEgkkqZk4MCBTJs2jd27d3PJJZdw6623oqoqixYt4pNPPuHQoUN88803qKcZ1DlkyBDMZjNJSUkkJiZSWVkJ1K1Pu3nzZn744Qfmz58P6OX5cnNzGT58OFOnTuXyyy9n+PDhXHzxxeTl5ZGamkqPHj0A6NSpE+Xl5Xz33Xf06NGDjIwM3G4348ePZ/HixXVk+WbLN4y/YTwA7dLa0bt37+j8YhU9yCicujEBPQNUInr0eBl6gFGR/tl5bc/jvKHnRWICNL9GVaBKX46DJ0Y8gavKhbvITaekTsSEYogvj4dcIB/uLb9XD5YLBz6p6OPqGUAvYDQwnEbJ3tUsoo4HDRrEggULKCsrw2azsWHDhjrVgiQSiaQ+WLZsWeS9yWSqs2yz2eosx8XF8cYbb0SCfpKSkuqsT01NrbN8Mi688ELWrVvHpk2bWL9+PStWrGDhwoVcd911XHvttVx00UV07dqVN99887S+U+16toqiRDpCtevTaprG3//+90gcTFFREcnJyXTv3p1hw4bxr3/9i2eeeYbvvvuOzMzMEx7zuPq0QhD8UfyKoii6e75mPNaoGKNjtOH5quFo5BboRvYweuS4Db3mbFXN8jGi85PjQI1VSTAn6AYzAN2t3cEDReVFpOan6vmKndSdZ5uKPh/2MuBm9DJ8TRAdftJHp4ceeoh3332X6dOnk5KSwksvvcTs2bPrVYiWLVsyffp0Jk6cyJgxYxg1alTkaUoikUh+CcybN4/Vq1czduxYHn/8cXbt2kVOTg6KonD33XfTv39/Nm7c+LM1YsOf/9jAnYwBAwbwz3/+E4ADBw6QmZmJx+Ph+uuvx+12M2nSJCZNmhSpi3sievbsyY4dOyKl9d555x369+9fZ5uBAweyZs0aNDSOlhzl2x3f6pG8YRsdnp5lRu9t+oEO6PmGw0Xd/UTnuvrQ561uBTYA2cAbwN+BfwDLIObbGD0/sRc9UK0vMA14D/gPsAn4fa1zNAGnFXUM8PbbbzeIIJmZmZE6iRKJRPJLY8KECdx///1kZ2djMBj405/+RLdu3ejevTtXX301iqIwePDgSLDeoEGDeO6554iNjeWqq66KHCdcDL7FaRTvePTRR3n88ccj99h58+YRExPDjBkzePjhhzEajdjtdv74xz/+5DFatGjBk08+ydSpU/H5fGRkZDBnzpw629x8883s37+fq6++mtatW9Olcxd9Re2cyOF5p2OBbeil8FT0BP9BdBdyKdF50daa9T6iGZziouuqQ9XYe9r1mrS90HutcafcNI3CSefRbt++ncWLF1NdXR1xH+Tl5bFp06ZGEvGnaQ65jmvzS5wvWh9IWU5Mc5ClueQ6/jH11TbHFRU4TZplfuFmwGnJItB7qbUKA+zeW3NdQugu4v3orl8P0V5nONGIl2it23C+YyN6Tul0+N77PeeNPE+f09xE/M+5jh999FFGjx7Nhx9+yI033sjHH3/MiBEjGkRYiUQikfzCCGdWCicn8dX8X40eiJQBtEY3ssXoeYWriFYDCieWoGb7JPTgqVb6snebt0mN7KlwUkOrKAp33XUX5eXldOjQgczMTLKyshpDNolEIpH8EggntDAQzUN8EN0CWWutC4/hxqAb1/AQtQ3dHRxHoyQLqW9OamjD7oG2bduyf/9+LrzwwtMOP5dIJBKJBBW9dxuuuORGN6hOonmqw+OwSeg9WAenYKmaNycVv0ePHtx3333ce++9TJkyhZycnDqh3xKJRNKcEUI0y9KAv1aEqMmylVjz+hVw0q7prFmzmDRpEu3bt2fWrFlomsaf//znxpBNIvnFkZ2dTb9+/cjIyOCOO+4gOzu7qUX6RWO1WiktLZWlPZsJQghKS0vrzPE9E842PTqlMdpevXoBcOmll3LppZc2sEgSyS+T7OzsOqXgiouLmTlzJgDjxo1rStF+sWRkZJCXl0dxcfEZ7e/3+zGbzfUs1ZnxS5HFarXqVX7OkLNRj6QPWCJpJJ5++uk69VYBPB4PTz/9dLO9QZztmEwm2rdvf8b7b9u2jZ49e9ajRGeOlEXnbNQjGdUkkTQS+fn5p/W5RCI5nrNRj6ShlUgaiZ+q41zf9Z0lkl8yZ6MendWu43CAg9/vb2JJovh8vpNv1EhIWU5MU8nywAMPMGvWrDpuL5vNxgMPPNBkMoV1J5wwXurS8TQXOUDKAs1bj34q6O6kKRibM06nk3379jW1GBLJKbNp0yZef/11SkpKaNGiBRMmTGgWAYbhoCGJ5GyguepRly5diI09Pk3VWW1oNU3D7XZjMpnkPDmJ5AwQQhAIBLDZbHg8HqlLEskZENYjh8NxwoROZ7WhlUgkEomkuSODoSQSiUQiaUCkoZVIJBKJpAGRhlYikUgkkgZEGlqJRCKRSBoQaWglEolEImlApKGVSCQSiaQBkYZWIpFIJJIG5Kw2tGvWrOGaa65hxIgRvPnmmw1yjgkTJjBy5EhGjx7N6NGj2bFjB5s3byYzM5MRI0bw/PPPR7bdvXs348aN48orr2T27NkEg0FAT3Z9yy23cNVVV/Hb3/4Wt9t9WjK4XC5GjRoVydxTX+evqqrirrvu4uqrr+aWW245pVJiP5blkUceYcSIEZH22bhxY6PIsnDhQkaOHMnIkSOZN29ek7bLiWRpqnY5ExpDj6DpdUnq0fFIPWokxFlKQUGBGDZsmCgvLxdut1tkZmaK/fv31+s5NE0TgwcPFoFAIPKZx+MRl1xyiThy5IgIBAJi8uTJYtOmTUIIIUaOHCm2b98uhBDikUceEW+++aYQQoi77rpLrF27VgghxMKFC8W8efNOWYb//Oc/YtSoUeK8884Tubm59Xr+P/zhD+KVV14RQgixYsUKce+9956WLEIIMWrUKFFYWHjctg0py7///W8xfvx44fP5hN/vFxMnThRr1qxpknY5kSwbNmxoknY5ExpDj4Roel2SenQ8Uo8aj7PW0GZnZ4tHHnkksrxw4UKxYMGCej3HgQMHxODBg8WECRNEZmameP3118XXX38tJk6cGNlmxYoV4uGHHxZ5eXli+PDhkc+3bNkiJkyYIPx+v+jdu3fkBpOfny8uu+yyU5Zh1qxZYsuWLWLYsGEiNze3Xs8/bNgwkZ+fL4QQIhAIiN69ewu/33/KslRXV4s+ffqIO+64Q4waNUq88MILIhQKNbgs+/btiyiZELoSLViwoEna5USyLF26tEna5UxoDD0Soul1SerR8Ug9qj89Ohlnreu4qKiIlJSUyHJqaiqFhYX1eo6qqioGDhzIokWLWLp0KW+//Tb5+fknPO+P5UlJSaGwsJDy8nJiYmIwGo11Pj9V5syZQ9++fSPLP/W9z+T8tfcxGo3ExMRQVlZ2yrKUlJQwYMAA5s6dy7vvvsvWrVtZtmxZg8vSuXNnevXqBUBOTg7vv/8+iqI0SbucSJYhQ4Y0SbucCY2hR9D0uiT16HikHtWfHp2Ms9bQappWJ/m5EKLek6H37t2befPmERsbS1JSEtdddx3z588/4Xl/Sp4TyfW/yPlT56mP8wshTpgQ+6do06YNixYtIjU1FZvNxoQJE/j0008bTZb9+/czefJkZs6cSZs2bZq0XWrL0qFDhyZtl9OhMfQImp8uST2KIvWo4TlrDW2rVq3qDGgXFxeTmppar+fYunUrX375ZWRZCEHr1q1PeN4fy1NSUkJqaipJSUk4nU5CoVC9yPlT3/tMzp+amkpJSQkAwWAQt9tNQkLCKcuyd+9ePvzww8iyEAKj0dgosmzbto1JkyZx//33M3bs2CZtlx/L0pTtcro0hh5B89MlqUc6Uo8ah7PW0A4aNIgvv/ySsrIyPB4PGzZsYOjQofV6DqfTybx58/D5fLhcLlasWMGMGTM4dOgQhw8fJhQKsXbtWoYOHUrr1q2xWCxs27YNgFWrVjF06FBMJhN9+/Zl/fr1AKxcufJ/krNnz571dv5LLrmElStXArB+/Xr69u2LyWQ6ZVmEEMydO5fKykoCgQDvvPMOV1xxRYPLcuzYMe655x6effZZRo4c2aTtciJZmqpdzoTG0CNofrok9UjqUX3q0UlpiIHfxmL16tVi5MiRYsSIEWLx4sUNco7nn39eXHXVVWLEiBFi6dKlQgghNm/eLDIzM8WIESPEnDlzhKZpQgghdu/eLbKyssSVV14pZsyYIXw+nxBCiLy8PHHrrbeKq6++WkyePFlUVFScthzhwIn6PH95ebmYMmWKuOaaa8T48eMjxz8dWd544w1x9dVXiyuuuEI888wzkW0aUpannnpK9OrVS1x77bWR1z//+c8maZefkqUp2uVMaQw9EqJ56JLUoyhSjxoPWY9WIpFIJJIG5Kx1HUskEolEcjYgDa1EIpFIJA2INLQSiUQikTQg0tBKJBKJRNKASEMrkUgkEkkDIg2tpEGYMGECl112GQsXLjyl7UOhEKNHj6Z379588MEHDSydRHJ2IPXol4E0tJIGY+bMmUydOvWUtjUYDKxatYrzzz+/gaWSSM4upB6d/RibWgDJ2cWKFStYtGgRq1atQlEUsrKymDJlCmPGjPnJfRYsWMCRI0coLCykuLiY8847j/79+7Ny5Ury8vJ48MEHGTVqVON9CYmkiZF69OtCGlrJaTF27Fi++OILnnnmGfx+P3379v3Zm0OYbdu2sWrVKkwmE0OHDqVFixa8+eabfPTRRzzzzDPyBiH5VSH16NeFNLSS0+YPf/gDo0ePxmq1kp2dfUr7DBo0iNjYWEBP8D1kyBAA2rZtS0VFRUOJKpE0W6Qe/XqQY7SS06a0tBSfz0dVVRVFRUWntI/ZbK6zHK4ZKZH8WpF69OtBXiXJaREIBJgxYwb33nsvmqYxffp03nrrrcathCGRnOVIPfp1IXu0ktPiueeeo0WLFlx//fWMHz+exMREnn/++aYWSyI5q5B69OtC9mglp8VDDz1UZ3nJkiUn3WfatGl1lteuXRt536VLF77++uv6EU4iOUuQevTrQvZoJQ3GvHnzTnui/c6dOxtYKonk7ELq0dmPrEcrkUgkEkkDInu0EolEIpE0INLQSiQSiUTSgEhDK5FIJBJJAyINrUQikUgkDYg0tBKJRCKRNCDS0EokEolE0oD8fxP2AWhjvxKAAAAAAElFTkSuQmCC\n", + "text/plain": [ + "<Figure size 540x324 with 6 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = ensemble_plotter_22(states_1,m_const,da_const,t_end=3,h_c=0.5)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 246, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "<Figure size 540x324 with 6 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = ensemble_plotter_22(states_1,m_const,da_const,t_start=da_const['ncyc']-3,h_c=0.5)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 247, + "metadata": {}, + "outputs": [], + "source": [ + "#plotting the \n", + "t_step = 25\n", + "bg = states_1[0]['bg'][t_step]\n", + "an = states_1[0]['an'][t_step]\n", + "truth = states_1[0]['bg'][t_step][:,18]\n", + "truth = states_1[0]['truth'][t_step]\n", + "obs = states_1[0]['obs'][t_step] #truth + np.random.normal(0,da_const[\"used_std_obs_sat\"],size=bg.shape[0])\n", + "obs_sat = states_1[0]['obs_sat'][t_step] #truth + np.random.normal(0,da_const[\"used_std_obs_sat\"],size=bg.shape[0])" + ] + }, + { + "cell_type": "code", + "execution_count": 248, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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8cwt6KoXW1oaeTGJpOr+5/XZUVeWMM87g8MMPL+xv2zb51lYEQcQ3oBrJ60UOBvFVVWFpOlpHnGuvvZZwOMx///tf3n77bQDuuOMOcrlcr2vncjnuuOOOHXPzLi4ufaK7YqDnE3q3E9BdSbAt7IiIwpfBdQBc9knMbBYAaQvZ/1pnJ0YqhRKNEhw6FF91FYIis/STT3nm2WdRFKWQAFg4ZyaDlVfxlJYiyp+l18gBP3IkjJ5MUhwKcfnllwMUSgcbGxs3a8OW1ru4uOwctqVioC/siIjCl2G30wFwcdnRWIZBtq4BPZlE9HiQgsFeSn+WYaB1dCAFg3hLSwCcJ/mKSv7fi89j2zbnnnsugwYN6nVerSOO6FFQIptGFTwlJc50QnsH06ZN4+GHH2bhwoW8/fbbDBgwgIaGhk2OGTBgQD/fuYuLy65kWzQIdgZuBMBln8LM58nW1aG2xVDCIQRZQo/HsdvasHQdAD0eBxu8ZaW9jm3LpPnP228jCgIXX3gh4DgLRjpNrrEJPZlELira7HVFWUYpKsbMZPBKEhdffDEAf/jDH7juuut65REA+P1+rrvuuv6+fRcXl11If0cUviyuA+Cyz2CqKrnGJixVw1dRQXDoEPwDBuCvGQCWTb6pCTOvoidTKJEIoqL0Ov6RRx5BNwxOOPwIygWBfHMz2Q0byTe3kN2wAS0eR2vvQOvs3OxcnlIUAUFATySYOnUqkUiEuXPnMnz4cO68805qamoQBIGamhruvPNOzjrrrJ310ri4uOyDuA6Ayz6BbZrkm5oRRAGluNgJ/Xc9dUt+P0K0GEvTyaxfD7aNUtz7ST6ZTBY0KP7vW1NILl2Gnk6jFBfhraxwcgWGDMHSddKr15JZsw7bNHudQ5Rl5HAIPZUiHAzyne98B4C//e1vnDbpZN5+/nlmPfpX5rz5VmHwN1UVNRYj19SE2t6+yTldXFxcthfXAXDZJ1BjMWzTxFddjW3oiF5vL11/wetFiUZRY63YAps8/T/zzDOk02mOmDCBkSVRtHgnpqrjiUaxNA1EwTmvJCEqMtn6OjoXLcH6XBdBpagILBs9lea73/0u4MhaNy9fjpnPg66Tb25Gbe9Ai8fJ1Tegp1LYuoEe7yRbV1+YqtjdSafTnHrqqdTX12+ybdmyZZx11lmcdNJJ/PznP8cwjF1goYvLvo3rALjs9RjpNEY6g6ckiijLWKpWePrviejxIAgitm5s0hL08ccfB+C0gw9BUBQ8paXkNm4k19SElkhiZrLYNvgGVFN04DhCI4ajx+Okl6/sdR5BFBE8HoxkkqFDh3LM176GqqrMeuN1AoMGIVSUI4VCpFevJltXjxwKEhw8mMCggfhra7Ati3xTc5/aE+9KFixYwHe+8x3Wr1+/2e1XX301N910E6+88gq2bfPkk0/uXANdXFxcB8Bl78Y2TdRYG6LPi1JcjKmqYNtI/k21/81sBk9pKYIkoScShfWffvopS5cupSgY5OsHjSdQW4sSjmBqGpmN9WTXrAYbfBXlyIEAgiAQqK0lMGgg+ZYWsg1NGNks2Q0byW7YiN4ZR21vw8zn+c5ppwHwz1mzsAEsC0vTsA0n1O8tL0eQJAAknw9fZQWWpqF3Jj5v/m7Fk08+yYwZMzbbsbOhoYF8Ps/48eMBOOuss3Z6+ZOLi4tbBuiyl6O2t2NbFv6KCgRBwMzlQRCQPtf8x7ZtjEwWT2mJE27v7ESJRBBEkf/Xpdd96lFfoeakbyB6PGjxOJ0LF5Jevx5JlCg+OICeTGFbFoKsIPl9BIcNRevsJLl8Kf4BNUgeD1IwgG1bZDZspHPRYr6y/wEMqKpiw4YNvP3a6wQNA7usjPB++6F3xtE7E3hKogU75WAQKRhE6+xEKYoUnIPdjV/96ldb3Nba2kp5eXlhuby8nJaWlp1hlouLSw9cB8Blr8VUVYxkqpD0B2DmcoheT6/5fwA0DSwLORgEQSTf2IieTJEzdF7oakH93WnTkAMBsnX1mPkcciCA1t6OFQhg5HKkVq5C8nrwlJUheb34qioJjRhO8yuvoSeSFI8di5nJAgJGKk38k08JDh7M5K9+jT899W+eeOwxvnfB+fhrapC8XixdQ+uMbzLQe0qi5OoyaJ2deEt7lyruCViWtUkv9O0pf1q8eHF/mrXddLcu39W4dvTGteOLcR0Al70WNdaGIEl4osUA2JaFpapOIt7nyedBFJyKAFFE9PnQOzt59rnnyKkqh+y/PwdMPBK1NUZ2w0Z8ZeXIxVHa330HkNCaW4mM3R/R50WQRGzTJNfYiKB4QBSx8iqCouCrqiTf3IyntBSluQUpGOCsM8/gT0/9m9fmvMd3zjm74Kx4SqJkN6bINjQgIDjJf4IzFSBIInoiiSca3dSZ2c2pqqoiFosVltva2jY7VfBFjB07Fu9W+jjsDObPn8+ECRN2qQ2uHa4dW0JV1a06ynvWL4eLSx8x0mmsfB5PaUnh6dnayvy/nVed+fuuwdRTXISWSPL8f14E4Kwzz8Q2TbIbNyJ5vYRGDEcSBUTZixItwrJMbMvGW1YOlo0UDmFqGp0LFuCvGYB/QDVqeztaRweYFr6qSiJjRuMpjjLqwIM49NBDyeXzvP/+B6gtLU4Som2jxTvJbawDUUApLkIJR7B0AyObQ421oafSO+9F7Sdqamrwer2FJ6PnnnuOo48+ehdb5eKy7+E6AC57HbZloba1I3q9yOHPZHnNXB5gk/l/M58H00QKftYWWAoGaVy5gg+XLkWWZU49/QyyG+swczkCQwc7EYJ0BkGW8Uaj+AcMILuxDsuyEH0+jGQKM5vHSKXxVZQTGjEctbmFXH0DSlERoigSHDIIUZLIx2JM6ar7/+9H8zDSGbJ19eQaG5GDQZTiKJ6SErylpXjKSgkMGoivqgpb18isXbtLuohtDxdffDGLFi0C4Le//S233347kyZNIpvNMnXq1F1snYvLvoc7BeCy16EnU9iGgbcr8a8bM5/DsizUtjYQBJSiIiSvFyOTAUFADnzWGMhIJnl17lws2+aYww+nuLiIxLr1eEpL8ZaVYaRSGMkknpIS5FAIb1k56VWryaxaRXTCIU7Gf30dvopyBATkUAjbNDGSSayumndfdTVmLo+RTDHpmGOY4fWycMkSWuJxwpkMvuoqwqNGkt1YR66hCVGRsQ0DQZZQiovxDxpEZs1a8s3N+Kurd/rr3BfefPPNwt8PP/xw4e/Ro0fz1FNP7QqTXFxcunAjAC57FbZlocfjSH4/cuCzWn/btsnHYhiJJGY2h5HOkKurR08kMDMZ8Ci9Eu0yG+t45aOPADj1G98gV9+AbVkEBg1EEATyLS2Y2Rz+QQORAwFEr4K3ugK1NYbaGsPSVMx0luDIkdi2TXbDRkdyWFbIbtiA2BWFUCIRbNvCb1ocf/zx2LbNS6+/hhwKIUiS02sgmyPf2OjkM5SUOFUIbe2OsmAwSK6xGSPbu52wi4uLyxfhOgAuexV6ZwLbNPF0dfHrRovH0Ts68ZRECQwZTHDwIKRgwGnik0gi9JgW0BJJ1q1YzqI1q/H7fBx9yCHkW5rxlJWihMPYlkWuvgHR6yM8bChSMIiZzeGrqkLy+UitWoXa1oESCSFgAzZaPE5gyBAkn4/0mnVkNmwgvWoNanuH038gkeDk448H4JX//peicWMRJInkkqWAhRyJ4IlG8ZQ40w2ekhLMbBYp6MfSNLS22B4zFeDi4rJ7sEumAF544QUeeughDMPgwgsv5Pzzzy9sW7ZsWa8uaB0dHRQVFfHiiy8ya9Ys7r77bkq7Sp++/vWvc9VVV+10+112T2zTROvsRAoGe83z27ZNrqEJQZbxD6x1pgUkCV9lJVpHHLUjjq04XwXbssg3NvLynLkAfOPoo5ESKUxNI3joYACMTAa1vYPA4IF4SqIYySRaPI5gg3/wIBKLl2DnVUqOOhIjncHI5xx7bAstkSDf3IIUDqKEQtiGiZnJoCcSHFpZjUdR+GTxYprb2igWJYxUmkDJQARBxEinkENOnoKnJIqlaxjZDJLHg57JoKScJkYuLrs7anuH03XzC2h95z0yq9d84X7JlSudUt4evLcN9ti2zaJUEr8oMjLk5A0ZlsWydIoxoTDyl6i06WmH4Ns0AfnzKMXFRA868Av3C+8/hsqvf7nk2Z3uALS0tHDvvffyzDPP4PF4OPfcczniiCMYMWIEAGPGjOG5554DIJfLcfbZZ3PzzTcDTt3vddddx6mnnrqzzXbZA9BTKbCsXsI5AEYqhZlJ460oQ+oqsQMQRBE5GEBPJiGZwLZt9GQKPZ1i9hznazt50snkW1vwlpcVcgSy9fVgWo50ryAgBQIIySSWruOJhJ25fjWL5PWitrVjZrMEBg8ksXgJWlsb/poBeEtKEQMBBAHkdIrk6jVIySSHHjSeOR/N4/mnn+a73zwFf20NWDZIAkY216tm3slFSJNvjSF6PWgdHcjh8E5vKerisifTqub584Z1LEwmOK6souAALEkl+dWq5YQkmUOLo5xcWcXQQPALzrZnsdOnAObMmcORRx5JcXExgUCAk046aYsyoH/605847LDDOPTQQwFYtGgRs2bNYvLkyfzsZz8jkdi95VBddh62baN3diL5/Uifqw3XO53B3RPt7RhYhoFtmPhrakDTnXa+8Q7WbaxjTV0dkUCA0ablNP7RdEcASNfJ1jUghQJ4u9Ts5GAQye+E4s2uRj3ekjJyTY2Y6TSS34eWSJJevRZBlgkMHoga7yBXX4+tGwiKByuvYiSSHLX/GABefPFFRJ+P4PBhiF4vZi6LpeuFSgYAQZK6Eh3B1HRsw8TYA8sCXVx2FR93xrl66SIWJhMEJIkhPRKBNctigM9H2jT4b3uMXyxbzD/rN9Kcz2/ljHsWOz0C8HkZ0IqKChYuXLjJfqlUiieffJIXXnihsK68vJzvfe97HHLIIdxzzz3ceuut3H333TvFbpfdGyOVwjZMlIriXuvNfB4jk0EOBJB8vRsAmZkMAL7KCvD7SK9ajZHNMvv1NwD46oEH4Q0GsYNBJEUh19yC1tmJ1t5OZPRoxK6woBTwI8gS6DpqSyuiLOMfUO0kHKoadkec7MYNWKaJ6PWgJ5LYqgZBAVsUkCMhAgNryTe3ML64BJ/Xy6dLltCSTjFEGICvsoKMmkdra8eIRnslN8rhEHKkCD0exw6H0BMJlEgYF5fdGW9pCd7P5elsjtCI4dt1/r4I8Hz00Ufce845qKbJpEmTuOuuuygp+cymrwBXAqtWreK+++5j1qxZzGpuZFZzI9/97ne54447+sWOXclOdwD6KgP6/PPPc8IJJxTm+wEeeOCBwt/Tp0/nG9/4xjZf35UP3b1sgP6xw2qNgSAgJjp7rbc7E1gdHeD1Iqp5BEnCNgwQBOzOBJgGQrwD27RY9cEHWLrJa+/PAeCAwUOoa2pCHFgLrTHs9nasZBKaYzRHi5Hnf9bC1uqIY6fT2M0tWNgIuSxmfYMTvs+kIZuDSARBU5FKo2AY2GvXwKoVCOEIlJdipZN4WmMcXFPL3LVrmPngQ5wxaRJCJAy2jbWhDhoakIYM7n3v6QzW2nUIsVYEWUaor0PoMdXh4rK70dccABuhK5F22/azGptIF22aO9C9X2NLC9/7wfdRVZVvT57MLT/9GUJHnHSHY5MSjRYclJEjR/KHP/yB8847r/BQOmbMmL7e6m7NTncAqqqq+KirvAogFottVgb09ddf5wc/+EFhOZVK8fTTT3PRRRcBjuMgbUcjFFc+dPexob/sMLJZ8kXFeCsrUHoI/9iWRWb9esyqSuRgCF9lBfnWGLauY1sWGk7XPtswWLxxIyMOPZT1ixayurUVn8fDGccfj1cUKD54fEGcJ6ub2NUiRUVFBGoH4q90Prt6MkWmro54MoXo86I2t2DIitO9L5dHSyURLBtPWQUB2UPgwKF0LliEmcvhDYfxl5ShT5jA6tbZnDh2LHPXruGTlSv4+TVXY+VVPGWl5CuryDc1UbLffsih0Gf3adt0BAJg2XjLS5FDIXzbIa0LXywd6uKyN/D0S/+hPR5n4oQJ3HjlVV+YNyMIAkcddRRHHXUUN954I8EeomH//ve/kWWZ0047bbvGpF3JTs8BOOqoo5g7dy4dHR3kcjleffXVTWRAbdtmyZIlHHzwwYV1gUCAmTNnsmDBAgAee+yx7YoAuOx9GMkkgiQ5jXx6YGZzzhO4KIEAucYmALwV5Uh+H0Y6Q2btWkxNBY8Hb3k5//34YwCOmjCBcHkZ/gED0NrjyKEg+aZm8vX1BIYOAVkmuWgxiaXL0RIJjGyGxOLF5JtbyTc2I8gKxePG4YlG8dVUExo2HG91Nf6qckxVRe3oQPL58ZWXoxSFESQRye9HHDyYrxx0MD6PhwWLFhEXBORQEK2tHU9ZKSCQ2VjX6z4FQcBXWYmZzYIgYKQz2Ja1M156F5c9kssv+j9uuvIq7rvlVhR5256DS0tL8XVl8y9dupSrr76ayy+/nKOPPnqTvDTbtnn55Zd32xLdnR4BqKys5KqrrmLq1Knous6UKVM48MADufjii/nRj37EuHHj6OjoQFGUXk/qkiTxu9/9jptvvpl8Ps+QIUO48847d7b5LrsZlmFgZLIoRUWbNMUxMs5AKIgiRiqNHA4TqK1BkCSnK6DHg6lrzj4IGKkUby9ZCsAx4w7ENkzwOvkFmQ0byLe1YRkG3opy5EAQvaOTxMJFZDasJ22YrFm6jGw6zYhR+1G930gEj4fM2nUIskRwoKM7oLW3Y8Y70RNJIqNHYWNjWzZaWztgI5WVUqx4OHzkKN5esphXXniB//v+97G0esxkCm9ZKUYigdaZwFP8WVMjX2UFmXXrMNJZZL8PI5PpFQ1xcdmb8USLe+UUiInOzeYPmKZZeEr/wdU/+9LX3W+//bj1hp9z5733sH79er565JH85Z57GdMlAPbMQw/x9tvvIN54I18/5ZQ+5T3sTHaJDsDkyZOZPHlyr3U9ZUJLS0t5771NqzgPPfRQZs2atcPtc9lzMFIpsO1NEt9s28bIZBBECb0zjhwM4quscHIAbJtcUzOiV3Ga9LS1YeZzNK1cyacb1iOJIkeNHo3a0kzgsMOQAn6yb/0Xvb0dRAG1NYYxJMBL61bz1iuvMH/xEhLZTOHad13+I2oDB5FramLO8mX8b+lSvv71r3PG/12EkU5hpDJIhulUDpgGucYmbMvCP6Aa2tqRi4o4ZuxY3l6ymNkvv8xF06bhKSsn39iI5PdjpFKobTHkYABRUQCQvF480WL0ZBI5FMBIp10HwGWfwrZtZs+ezaRJkza7XhRF7rrrLv72t79RU1PTL9eUJIlvf+tbHDZ6NOf84Pt0JJN86/sXc+1ll5GNtfH22+9wxumnc9TEif1yvf7G7QXgskejJ1NO6PxzSW9mLgeWhWVZmNkcgUEDC+JAans7erzTkfEtihD/5FOshibeWLQI07I4fOw4ikNhJ6Jg2xiZLKZhIioKgtdLeyLJ6ZMmkct9Jr8b9PkoDwQJeL2EvR5yLS3kmpp5e/FiXpz3IS9+8D6//tMfOev447ng6GPweH2k169H9PqwNQ0lGsVTXIxYEkXyeDhq/wOQRJEPPvmE2Lr1VI4a6UQQ4p3IkQiWqqK1t+OrqirY4KuqQmtfhqVpYNnYptlL3tjFZW9m9uzZTJ8+nWnTpnHaaacBzuA/Y8YMHnnkESRJwjRNnn32WS677LJ+vfbAAQN448l/M/Wyy1i0ZjW//sMfCMsyZ51wAj/4wQ92W20O1wFw2WMxczlsXUf+nPBP9zYEASOVRPJ58XSV91iaRnb9BpBEbMOg48N5WPk8dirNO8uXAfD1Qw7GzGQJDhtMvrWV1IqV6B2dKBXlCLaN1dDAfjU1KB4Ppx53PEcecQSBjRvJNzfjLavAtkxSS5ZjaiqnjzmA2tJSXlvwKaubmpj59NM8/dprXDL5dL45dhyeaBGBQYOQ/X6MdBqCAUJDhlDa0cFBgwfz8bp1vDb7Zc6tceR/jVQaI5dDDgQw0hnMXA7J75QFektLET0e9HQab1ExZi7XK1nQxWV3oa9lgNvCpEmTmDZtGo888gitra1MmDChMPiLoohpmlx88cVceuml/Xrd7nsJAS//77/U1tYCkDIMzrrkEsIjR/Tr9fqTrToAo0eP3qrnsmzZsn43yMWlrxjpNIjCJsl/0OUcAEYqg7+2BlGWsQyDbEMjZtaZ/9fa25G8XgLjxrF2wUI+qnOS60742tH4i6N4y8tJLVtJrKGBu1/6D9NOOIGBxcXkG5u4Y/IZ1B53DFIwhG3btCxfgeTxOMmCbe1Yto0SDjNm5AgOEMdz0RlnsGD5ch54/jkWrF3Lrx//O4mzpvC9005DTyYxc3kEAey2NryHHUbR/mM4etyBjgPwxpucceqpBIcMQQr40RNJxCIn9K+2teOvrUEQBKdZUGkJ+dYYVijs6B+4DoDLHkxfywW7y/ZuueUWAB555JGChkwwGCSTyfB///d/XHf5FWTWrO3z+bbFRtu2+dOf/tRLLfCZhx5iv+uuRxCEPp8ztXotubq6L9zPP3Ag4RHD+mTjlthqFcDcuXN57733OOecc7jqqqt4//33mTdvHtdffz0XXnjhl7qwi8uXwbZtjHQGORjcJPnPNk2svOoI/dg2vsoKR+O/udmpDBBFbNNE8gfwVVSCbbOwvh7VNBgzZAilHi+BwQNRIhHq4u1c8ve/8fbKFdzz6isUH3gg4dGjCRYV4a2sIlAzAEvTkBQZT0UlejyOFoshyjLeygpsXccTLabkkEM4+SdX8e9//INf/+jHDKusZNKo/cg1NpJZvQa1uRlT1bBaY2TXb8A3oJpJ3zwFgPcWLqRj1Wq0zk4n2VGWMNJp5CJnKqCn+p+vssKZ+lBVjEx2t80+dnHZEQiCUHACuslkMkyYMIGbb755h4Xiuwf/Z597jjNOP52XX3qJM04/nf/9720uuvxynvrPizvkul+WrToA0WiUkpISFi9ezPe//32KiooIhUJMnTqVDz/8cGfZ6OKyCWY2i22am33CNbukOvVkylnO5QslemY+h55KISgyklcBbNL1dcxrrAfgmLHjMHN5zFyOpR/OY/ovb6OxvZ3RgwZx+5VXUnLQQZR95UhsyyS7YaMTSeiIY5sWoiQhR8LI4RCiLKGEQsiBALZpFpyR4MCBTP3ZT5n154cJYWPpGigyTa0tjj5BMIDW2YnaGmPUkUey36BB5DSVOXPmklqxEtHrdcL/2SyiJCN6vWhdTx/gtBeWA36nFNA0ekkHu7js7XTP+fckGo3y17/+FXkby/22hTlz5xYG/+45/x/84AeMOGB/3l+8iBl3381Hn3yyw66/vfTpFcnlcqxdu5Zhw5xww4oVK9C7NM9dXHYFRjqNIElIPbS7u9ETSXItzeSbWlCKw6RWrgTLxlNSQnrdWoxMFlGWMNMZbGzysTY+qtsIwDGHTEAOBlj1yQL+b8ZNtCU6mTBqFL+78idUHXwwSiSM6PWQKC0ltXIlSrSI9JrVmNksSiiMKEkEa2swVQM9lSIweBB2XiWzYQNSMOhEDAwDTzhEcNBABMXDnU/8kzc+/JA/XnYFks+LqChYqort8/GNY49jxd/+yttLF3PUAQcgBQP4q6rIt7ZiZNJ4SqLkm5oxkkknOiBJeErLyDXUY+ZymLlsL+lgF5e9lZ4Jf5MnT+ahhx7i29/+Nu+99x6/+93vNokM9Bfe0hJO/O4FWOVlTJo0qVeU4Yobb8S67z7efPNNLr7icp79zV0Uf0F1jn/gQCqOPWaH2Pp5+iQEdOWVV/Ltb3+bCy64gPPOO4+pU6dyzTXX7GjbXFw2i21ZXfPbwU1CenoqRWbdeoxkCgSQI8X4awZQctgEAgNrsQ0TQRAwczmydfWosXY+XrKEVD7P4NpaRgwaSCqf40d3/cYZ/IcN43c/voqSYUNRuuruJa+X8Oj9UJtbaHtvLnoqjegPIpcWI/q8BIcNp/jAA/BVV2NrOsgKZjZL56cLSK9bT2b9BiSvl9B+ozAlibqWVjqSSX726CMkGpvINDZgd+kSnDzZ6Xz5zqJFCD4f6RUr0To6EBUPetwpbxR9PicK0CX+4y0rQRAlp3ohm8PFZV9g9uzZPPLII0ybNo3p06cjCAJPPPFEITFwS03n+gNBEDj55JM3+T0SBIFHH32UiRMnkkgm+eerr+wwG7aHPjkAJ554IrNnz2bq1KlcdNFFvPzyy3zta1/b0ba5uGyWboU/Kdg7/K8nk+SbmrEtC9uwEGUFX3kZgYEDET0eEksWY6Yz+GqqkQJBlGAIRPGz7P/x45H8Ad6e9yHrGhoYVlXFL885F5/Hi5VXsW3H8ci3tiJ6PNgIZOsaMDUdye/DSmdQQiFCw4cRGb0fwdoapIAfrT2GqenkGptoffMtsnX1IMkEBg7C6/Fw3003MWrgQDY2N3PnC8/TuXwVqZUrMXI5Rg4YwMCaGjpSKZY01iMGgiRXrMLIZtATCUxVxVNSgm2YhSkPKRhECoUwVRU9ncI2zZ3+Hrm47GwmTZrEww8/jKqqLF3qCHp15wTMnDlzE32AnYUsy4UH5n++MptUNrtL7NgcfZ4CePvtt0kknLaqDQ0NAPzf//3fDjXOxWVzGJmu8L/f12NdBrU1hqUbGPkclq7iGzCAwJDBiKJIrqkJtaUNpbQUJRBGNxKERo2kc8kS3lu9GoBjDjwII5vhxAPHI0y9kJHREqIDqrFyGbIbMl1VByKiR0GUFYJDhxB7912EZIJQbS1yOELRgeMKmb5CV7KhqHiwsfFWV6E2NjkJeukUoizjKYliNjbyux//mIt+9SuWNzbw5/fe4ceRCEpRBCuX58QTTuCRv/2NN9+dw4RLD0TvTKAlk6gdcQJD4virqxB9PvTOTpSiiJOAWF6KFm/HSKcxsllXFMhlj2RbywVTqRSPPfYYJSUlfPvb38br9RaezrfnfP3F4YcfzsSJE5k7dy4vrV3NFVdcsdNt2Bx9cgCuueYaGhoaGDVq1G4raOCyb2B3CfPIwc/C/5auk29pxbZMzFwGW9dRolECgwYiiiJ6KkW+uQVkCSUcxMhmCi18V7fGaEkmKA6FOOjAcaAb5No6ONznRw4E8dXWIvu8yP4gUjAIloFtWeTqGxB9HixVRTQtpGgx4REj8FVWAmCqKkY+B6ZF0fgDSXyyACyT8Jj90GJt5OrqnQQ9y0JPpKgZNoJ7fvpTLpoxg3+/+w4HDRnKSaVRxJJSjh43jkeAt5cu5kftHSihECCgpTtIrVyJHAriiRY7uQCpNEokjKeoGCUYQksknXWuA+CyB9LXMkAtkSTWUM+tN90EwAXHHkdizvub7CdHIniKIl94vm0pA+wr0791NnPnzmXuq6/x7bEHbnG//ijv6yt9cgBWrFjBSy+9tEOzKF1c+kL3oNmz9l9tjWHmcgiy7CT7FRc7zYECAWzbRmvvwEhlEGUF27YRJRElEkFPJPjfIqe51PjhI7jw+uv50elnMHbgYMRACCUYQBREREnGX1NduGauqdm5liDiKS7GBsx4HH/tAMDpT5BvakIOhpB8AYx4J0pxMUY2Q6C2BjkUIrdhI6kVK5y2xJaFmU5zwPARXHjyyTzy4ov8a867HHvQQSilpexfUUlJNEpdSwsbYzFGhCNYuo6lauSamlHWrnMcD0FAa29DDoeQggHkSAQ1Hkdrb3dkhl1c9mIefOrfdKbTHH7AARwzfvyuNmcTjjr0UP528y2MG777CAP1KQegqofcqIvLrsTMOOI/Uldmu5FOoyUT2KaB6PEUqgIkv7+gm29pGrZlAE6SnBwOYWNj5vK8vdBxAGLJBIvXreOPzz6Lp6yM0NBBeMvLsS0Tta0dPZF0Egfr60mtXAWCgNrSjKe8nMDAWrREklyzk3/QnYfgq65CEAW0jjiBQbX4KyrQOjoJDR5McPgwMEwnPB8pxkimsHWdk0cfwLUXXcSfb74VTBO1I47s8XD0hEMBeHv5MgRRcMSNFBkjkUDviJNathw1FiOzfiOpZcvR2tpRIhFESUbr7MRU1Z3/Zrm47CTqmpt59r//RRQErp160W4ZqRYEgQNHjNytbOvTI/2oUaOYOnUqX/va1wptEMHNAXDZ+RiZDJI/4MyvWxb5llaMZBpvRRlKcRF6RwfgOACCoqA1t2DpOrZlYekGHq8X0evFSKfZ2NjI6oYGvIqHJevW4ZUVbrrkEux8FjkcciIJshclGCTX3ExyxXKMZAZvRSlSMISecrrzhUeOIL12LYnFSzGSKeRAAE9ZGXpHB7ZpokSLERUFb3U1+fp68rEYks9HcPhQ1FgHVj6HUhTBU16GUFbKuQcfjOhVyHg9GO0deMIhjhl3IM++/hr//fRTvnfyKUiKQnj0aFIrVyCFAnhLStAzWZBkzLyKnkw6PQFEES2RROtwcgVcXPZG/vjPf2CYJpO/djTDampYt37drjZpEzzRYgQcvY7V69cTa29n4oQJm+ynRIt3mk19igBkMhkGDx7Mxo0bWblyZeGfi8vOxFRVbMMshOL1ZBK1zQl5+6ursXUdU1URZBk5FHI0/nUdS9cxMjkknw85UoSZyaC1t/PaO/8DoNshv/Tb57DfEUegdsTBAm95BVqsFTOnInm9CIKIEgkhh8J0zPsQK5cnMHAQRQfsT3DYMNTWGIklS8m3taG1tWHm8/gqKwgNG4aVV5EUGaWoCLWlhXxzE5EDDqD44AOdaEJbG3p7B4LPi6jI+AdUYxcXc8+sp1m3fAVHjhuH3+tl0ZIltJsmWjyOHPAj+fzoHZ14SkvxFhcj+33IwQCe8jI8JSWIHgW9o4NcU9OuettcXHYo6WyW1+bOQRJFfnDmWbvanC/ko4ULOeXCqfz8zt+gG8YutaVPEYDbb799R9vh4vKFGOk0CAJyMOAk4jU0gmXhr65C8vnI1jdi2yApHiS/o5lv27ZTwqerCEVhjFQCNdaOpWu8s2QJAHlNY1hlJd//0Y+wEymUYBDR6yHf3OyE9YFg0RACgwbhq6okuXo1erwTRAkEJwnRGy0h5/c6QkGShOj14KuqKrTr1RMJpy5fVbEtGyVajN6ZwFddRXjkSDoXLiQfa4N8DkGWMLM5/jT7ZZ6e9yHNnZ3ce+21HHXQQbzx4Ye8u2oFZxwwtpDdb2SzaG3t+AZUY2qa0/44FMJfMwDbMsk1NJJeuZrw6P1QNiOc5OKyJxMKBHh15l/4aP58aioqdrU5X8jBBxzA0EGDWLdxI/95/XXO2EXlidBHB+CHP/zhZtf/8Y9/7FdjXFy2hpnJIPl8CJLkPGW3t+OrGYBSXOw85aeSAEh+H4KiYLa3A2CkktiGiZXLY2gaUiBA2vSwYM2awrm/f9a38IXCJNdvdCIKtbVk165DCgQQvR4QIFBbg6mqaO0dCIJAcMhgwiOHo8baUFtjyKEwSlExntISLFXDSDlKfQDeslLSq9egJRJIfh/BIYPR2jvINzTiq67E31ZNvqUVK5lydA4EgUt/eAlPv/AC761ayZtv/4+vj3McgFfffJNzvn4saksLgscDmTR6Ko2SyeAtK8VIJdE6OvCUleKrqiI0Yhidny4ktWQp0UMn7FZzkC4uW6OvZXuhEcMZ1kM9r25+iIrNhNd3JT3v5UdXXcVVV13Fn598gu9c8kOkXdS2u09TACeddFLh33HHHYeu6+y333472jYXlwKWpmFpOlIwiG1ZZNatQ/R6CA4c2KXs5+j3i7KE5PM6uvqWhaVrqPE4pqoih0LI4RBKJMJbH3wAgCxJnHjwwex38HgsVUWNxxF9fgTAW1FO+Ve/QnjUKKeboGmSb24hV1ePoCiER43EW1aGoMjIwSC+6mrMdAojm0P0edG6Qu+2aTqZ/rksgiAgh8PYukFgYC2CJGIkkoheL6LHC7pGrrkZPZOmxOPhpz/+MQD3Pf884yqrkCWJ999/n3wo5FQ1WBaWbmIbhtMl0OfDUxxFSybR4nEEQSAwaDBKOEyusYl8S8sufBddXPqXVatWoWnarjZjmznzzDMZOHAga9as4T//+c8us6NPDsCZZ55Z+HfOOefwxz/+kQ+6fkBdXHYGRiYDgBwMkK1vwExnCQwZiujxONvTaSz9s0oAI53GNrsy+DsTeMvL8JRGQRARJZE35s4B4IpTJ/OzqRchRCLkYzEwDARJRJAVp5wuEMBXWYEgSqRXriK1ajVWPo8SjSIHg+SbmpH9AYoOPohAbQ2iP0C+vgErryJ6PWjxTjoXLSazcSOeigr8VVVYqpOkJ8gy/oED8ZSVoYTDCLaNLcloHR2kli4nuWw55xx/AiMHD6aps5Nn57zLIcNHYFkW/53zHt7KCqeFsG0UeiPoHR14KyuQPF7yza1O34FoMb6aAVi6TnbDxkKzJBeXPZlsNsu5557L4YcfzoYNG3a1OduEoihcdtllAPz+97/H6pLx3tn0yQH4PLZt09raut0XfeGFF/jmN7/JiSeeyOOPP77J9vvvv59jjz2W008/ndNPP72wT2NjI+effz6TJk3ikksuIdM1KLjs/RiZDKLXi21ZZDdsQCkuwl/52XyfFo8jiI5Ov+j1YuXzmNk8+YZGRJ8P/6BB2JqOKCuk8nk+WLgQURA44cCDGDBqpJMtH4thGQbekhKUSAhBEJED/kJr31xDI5kNG7BU3WnYo2nIoSD+AdVIioK/qpLQ8KEIikK+NYalaiCAkUphZrJ4iqP4qioRZRm9sxMz40QEPMVFRMaMxj+wFqkkSmj0KGzLSfQTdJ2bfnwlAI+/8zbjBw4E4KWXXsJfU4Ps9wMians7YsDfpf0v4K0ow+iKAoiyjLe0FNHvbM9sqHPbBLvs8fzhD3+gubmZiooKBnZ9L/YkzjnnHKqqqli2bBmvv/76LrFhu3IAVq5cyeGHH75dF2xpaeHee+/lmWeewePxcO6553LEEUcwYsRn4giLFy/mnnvu4eCDD+517C233MJ5553HKaecwgMPPMCDDz7I1VdfvV12uOw5WIaBlVdRolHSq9diWzahEcM/267rGKk0gui0x8WyMFWVfHsbpmHgr6hAUmS0VBrR6+HPTz6BYZocOHgIpeVl+AcMgAULyHd0IoeDeCvKsXUdQZYQPR5MVSWzdh02Np6SUmxDw1deRmjY0E3aEftrapzqhNYYYiiIL1xFaOgwtPZ2tLY2PGWleMrLSK9aTT4WIxRyKhpERcFbVoqw0qZ43DiMeIJ8LIZtmhxzxulMevopZr/3HqqmIQgCb//vf2TzObyVleRaWtHiHeipNJIsoXV04KuocKYrGpvwlpbiq6wg19gIsozW1oZeXlbIT3Bx2dPo7OzkL3/5CwC/+tWvEMXtepbdpXi9Xq644goWLVrEqFGjdokN25wDMGnSJGbMmMGvfvWr7brgnDlzOPLIIykuLiYQCHDSSSdt0qVp8eLF/OlPf2Ly5MnceuutqKqKruvMmzePk046CYCzzjprh3Z3ctl9MLsiPZaaR2tvw19d1Uva1shkMPN5RK+C5PNh5nJOQp2axxOJoESLsXUdsEEQ+feLLwIQDQbxlJTgKS7C6mjHzOfwVVZ1qfblEH1+1PZ2snX1js5+KIw3Woy3pJTAoEGbDP7giH2ER45E8nnJrl3vTEn4vPgGVCOHgmht7UgeL0o44sgB5z8T6PFEo9i2ja2qRMcfiIBNasUqzFyeX/zqV/z+4h8w9YiJHDBwIJqu8+qsWcihEL6Kcsxsnlx9A55o1Cl/tG285eUYiSRawmkVLPn9CJIIoki2oQFrF5cgubhsL48++ijpdJqvfe1rHHbYYbvanO3moosu4u6772bIkCG75Pp9zgHofuLXdZ0hQ4Zsd9Zia2sr5eXlheWKigpaeiQmZTIZxowZw9VXX82sWbNIJpM8+OCDxONxQqFQQY64vLy813F95cWuH39d15kyZQpPP/004DQ8mjJlCs899xwAyWSSKVOm8NJLLwHQ0dHBlClTePXVVwv3MWXKFN566y0AGhoamDJlCm+//TYAGzZsYMqUKcydOxeA1atXM2XKFObNmwfA8uXLmTJlCp9++ingOD1Tpkxh8eLFAHz66adMmTKF5cuXAzBv3jymTJnC6q7GNXPnzmXKlCmFua+3336bKVOmFBo1vfXWW0yZMqUwVfPqq68yZcoUOrqEcubMmcOUKVNIJp3M+eeee44pU6aQyzntY59++mmmTJmCrusAPPHEE0yZMqXwOj7++ON8+9vfLiz/9a9/5YILLigsz5w5k4suuqiw/Mc//pGLL764sHz//fdzySWXFJbvvffeXg0y7rrrLq666ioAjHSGvz7yF+7/1a8R/T4CgwZy6623csMNNwCgdXTw1L//zd8e/yeS30++tY1nnniCl155BUQJKRjkjw88wJNPP8Nrc98j1eVQHF5dg2/AACzD4I1/P8UH8z/GXzsAS9W465e/5J8PP4we70SUJH73pz/xStd7LXq9XHvzDGbOnFmw94ILLuCvf/2rs11R+Mndd/PuW2+RXu1UGpx99tk899//IgUDZGMxfnzLLXzwwQdk1q8nm80yZcoUXn7rLQSPl3hdPdOvuZa1qST51haaPv2Uq6+/nqKaGiRF4YhBg5335LnnMJJJ4pkML736GivmzAVFoTnWyiXf/S4LN2xAkCXWzPuIs88+mzWtrQiWTWNnnN/MmMEnb7+z2c+ei8vuTCaTKXz3fvSjH+1ia/qPXTEt16cpgHfeeYerr76aCRMmYJomd955J7fffjsnnHDCNl/QsqxeZUi2bfdaDgaDPPzww4Xl733ve9xwww2cd955m+21vK00NDQwf/58DMMglUqxbt065s+fj6qqvZYzmQypVIo1a9Ywf/58kslkr+V4PE4qlWLVqlVEIhFisVhhORgM0tzcTCqVYuXKlXg8Hurr60mlUqxYsYIxY8awdOlSUqkUy5cvxzRN1q5dSyqVYtmyZaiqyqpVq0ilUixdupRMJsOKFStIpVIsWbKERCLBypUrSaVSLF68mLa2tsL+ixYtorm5ubC8cOFCotEoa9asIZVKsWDBAiIRpxlGKpXi008/JRgMsm7dOlKpFJ988gler7ew/PHHHyPLMhs2bCCVSjF//nyATZbr6upIJBJbXK6vrycejxeWGxoaiHc1+Zg/fz6NjY20t7cXtjc1NdHe3s5H8+Zh1dWTbG1FzWdZG48jLllCS0sLmqYxf/58zFWrSSaTJBWJTz/5GGvtOuLpNLIsUd/ciGBoZDo7yasqD7/yMgARjwdZlljZ0gyrVhKwoTmfZfGGDdgNjdjJFE3t7SyPtWInU+TSadpjMdZvWI8QDtOWTFFXV1ewN5FI9FqOZTJszKRZ9v77iF2fhQ0bN7Kkvh6juQUrl6U+0cmyj+ZjrHde63V1G6keNpw1y5aSz6TZoOuE8p1o77+PmUqxNpsmaEIY53P/8eLFLFq3llRdPRktT6KhnvlvvklHPo+eybJ67Rrk0lLa1m9AT6dZ29oCnQlacjmS2RwbFyzADgZYt3Fjr8+ei8vuzN///nc6Ozs59NBDmThx4q42p1944403uOuuu7jllls44ogjdtp1BbsPbseUKVO44447CvP0q1at4uqrr+bZZ5/d5gvOmjWLjz76qDCF8MADD2DbNpdffjngJPp1P50CLFmyhBkzZvDPf/6TI444gnnz5iFJEk1NTVxwwQW88cYbfbquqqosXryYsWPH4vV6t9nu/mT+/PlM2MU1qruDDX2xI9vQRHrVSuRwGCUcJjB4EFKP98/I54l/MA9BlggMHoyeSJBesxZ/7QDUtg7kcBAlFMbMZHhn0SIuu3kGANOOP4EfXXIp0UPG0z7vI9a8N4cxJ09CiUTI1jfgKSkhMmY/LE0js249Zi6Hkc1hZjMEagcS3m8kwhfMO1qGQXzefBAgeugExK7olZl3wvWWZWKm0kihkDOtEYnw0VtvMVj2EBw2FF95GU0vv0q2vp7SiUcSPWgcT/3+fq787Z14JAnNNHn88cf56qGHEZszh44PPyJ6yHiqTjiebH0DcjCA6PORWLgYX3U1gYE1JJcucxolhYLkGhoJDRuKv6amYHN/fk9eeOEFHnroIQzD4MILL+T888/vtf3+++/n6aefLjik55xzzib7bI496bts2zazZ89m0qRJmzz4bG79jrJjZ7Ez7Hj55Ze58847+cUvfsHxxx+/Q+xIrV5Lrq6uT/turXtfTzu2ds6Hnn6KP896hqPGHciffvXrfutY+EXflT5NAei63itJb+TIkZim2ZdDN+Goo45i7ty5dHR0kMvlePXVVzn66KML230+H3fddRd1dU6m8uOPP843vvENFEXh0EMPLYTkn3322V7Huexd2LaNGmsjV1eH6PWiRCIoRZFegz+AHu/E6hL3ERQZtbUVORhwpvstE8njwzYMkBXuf+zvheMmHXUUvsoKtHgcLdYOSldNvWHgKYkSqKlGEAT0RAIzk3XKDW0bUZZRIuEvHPwBRFkmNGoEZl4ls359Yb3k86EUFWGbJqLPBzaoMUc6GK8XyedD74wjyDLhkcMRRJHsxo1o8U6OP/00KouL0bq+fy+99BJyKEjJoROQA36Sy1egJZMokQhGOtN1rQhaWxvYNko4jKWpiB6v05WwsXmHNArqTvb9xz/+wbPPPssTTzxRmL7qpjvZ97nnnuO5557r0+C/pzF79mymT5/OjBkzCiFe27aZMWMG06dPd/OYtoOTTz6ZN954g+OOO25Xm9JvfOfEkwj4fMxZtJAlq1bttOv2yQHw+XwsWrSosLxo0SL8fv92XbCyspKrrrqKqVOncsYZZ3Dqqady4IEHcvHFF7No0SJKSkq49dZbueSSS5g0aRK2bReaDs2YMYMnn3ySb37zm3z00UdceeWV22WDy+6NbZrkG5vQugZBT3EUQRTxlGzq7WodHZi6hhwKYaYzmLk8cnGxM+gjIsgStq7xwaKFLOsagAaXlzN8v/2QIxGnVXAmDeAkBEaLkbq0BGzLQk+msG2nRtfSHUejuxNhX/CWluKrKCff2ITelW/hXCuK7PNj5fNIfq+jbtjcjC05OQtmJoeZzRIcNhRPNIoWayPX1ESkuoofnnFm4TyzX3oJ0zTxlpQQHjMaM50hsXAxcsRJkjRSaXxVVQUFQzkURJAULFXFW1GBmcuifomS3i2xvcm+exuTJk1i2rRpPPLIIwUnYMaMGTzyyCNMmzaNSbtQBnZPo2ewWhTFvUrRsjgc5uzjnSn13/2/v+20fIA+5QBcffXV/PCHP2Tw4MEIgsDatWu57777tvuikydPZvLkyb3W9Zz37644+Dw1NTX8/e9/32S9y96DpevkGpuwDQMpGMLWDaejXqS0oKvfE7WjA1HxOKI7be1OrwCfz6mL9/tAELAtOOKA/Zk4dixzFy/m+EMmIAeCWPk8ans7RjqDUFpKoLaGXGMToseDqCjoqRRmOo3k64oiWDZyMFhoOdxXAkOHoHV2klm7jsjYAxBlGUGSUIqL0DrjGOkMgcGDnIE4kUAZOAg93oGRdgbv0OhRdMyZS76lBW9pCWefey5/+89/WB9rpb0rr+Lwww+n+MBxpJYuI7VqFUVj93cEkVIpfDUDkAMB8q2tBAYNQvL7nKhJKOREAZqa8VVWFkSV+oPNJfsuXLiwsNwz2Xfw4MFcd911PPjgg4XEz70FQRC45ZZbAHjkkUd45JFHAJg2bRq33HLLXjWIddOf0x5qewd6V67QPX/+M8l0mssvuoiyzz0M9CUcvi2ERwzbYlh/R53zZweN47n33uX9BZ/y9soVnHLKKf16/c3RJwfA7/fzn//8hwULFmBZFuPHjycadWuIXfoXS9OcWnUb/DUDUDs60JMpfFUVm61ZV+NxzHTG2WaaaMmUU5oniViq5nQNNE1sQyefSLK8q2Li2AmHFdQC1dYYkteDOKgW2zSx8vlCpEFPpjByOXyVlWhtbQiShBQMbDIN8UXIfj+BgbWk164n39xCoNaZc1eKipBDYbT2dmxdx1tWDkuWgmUiejxonZ14y8uJ7LcfiYWLyDU2ERw0CH9NDZeecQbXPPxnAJ7591Mcfvjh+Coq8A0aRG79epLLVhA9ZDxqJoOt6Xgryx0VQE1zZI0tCyubxVdVTWrVKvKxGIEeuQBflu1N9t0WB2B3qVroTvzcGqeddlph8O9e/vjjj3e6HTuDBx54gNtvv53Jkyczffp0BEHAtm1mzpzJCy+8wPXXX9/n5D07lcJOpWlpa+ORJ/6FYRhM2H9/Rgwe3Gs/IRxC6FEaDLvP67Etdpx77rn88Y9/5IYbbiAaje7wHJc+OQA/+9nPePnllznmmGO+eGcXl+3ANk3yzc2FwV+QJNTmFgRFdvT2N1N2qsVimLkcctFgTE0HQ0cMBrE1HQEQZBE9nWbDyhU0JtPEUykGlpczZuRwbNtC60xg5nL4a2sQA4HP5IbDISzDQGtvR/J4ED0KZj7vtBPezm56vspK1LZ28k3NKEURR/pXFPGWlqLGYmgdcULDh4HPh5nNOQJE2TxGJoMSiRAeOZz4pwtQ2zuQ/H5OPPEkHnp2FutiMZ6Z9Qy//s0diIpCaMggtKYmsnV1hEePQpAl9FQSb0kJ+cZmjHgc0e9DlKSuHARPQdLY14+d1Kqqqvjoo48Ky7FYjIoe5/98sq9t24US376yJyQBwmdz/j15/vnn+zUCsDslAV522WW0trbyyCOPUFFRwS233MKMGTN44YUXmDZtGpdddtk2RwBm3nYrhmFw6gknMHkz0yafjwDsTq/Httgxfvx43nnnHVpaWgiHw4wbN+5LXb87CXBL9CkHYL/99uOFF16gsbGRzs7Owj8Xl/4i3xrD0g1HKtfjId8aQ08k8VZUbFZwxzZNck1NSD4fkseHlct1tQr2Y+bz2KKIkVN54623+NbNN/O7fz8JwDcOmYAoyViahm0aiLKCr7oKQZYxUmmnKY+iYKTTGMkUSnERZt5p4SsHA0j+7XMARI8HX3W105yotRW7K4lPKYogB4NOLoOqIhRFEGQZyzCwTQM9mQKg6MADESWFbF0dlqrir67ip2edjV9RyORyvD/H6W0QqKnFU1qClkiQWbvOySfI5hC8XuSiiHM+2wbbdjompjP4BlRhZDKoXd0T+4PtTfbd2/j8nH99ff0mOQHd+7388subzP1uaX1/2tff1+2e9ui+z9ra2sL9b4/Ts2j5cl58/XUUReGq6Rd/8QF7MJIk8dBDD/Huu+9+4eCvtneQXr1mq/+yX9AjoU8OwOuvv87VV1/Ncccdx5FHHsmRRx6519Rfuux6jHQaM5PBU1qC5Pc70rvr1yMFgwRqBmz2GC2ZQu/oRCmNYtsWRj6PICuIXi9GPo+ZSWOmUzz80kuYlsWGpkYATjhkAoIodYUlLQSPjK+8AtswsFQVOew4G/mWVhAElGiJMwcpiYhe7zYlAH4eb2kJSnExWrwTrcuBFiQJb0UFRjbnNAiSJDwlJQg4+RBmNoul6yjBIMHhQ1Hb2zCyWQAmfmUipx/sPF08+9RTgONQeAdUI0oymQ0bsXUDbBszk8FXUY5tmZjZPFhWIRdACYeR/X7yjU0Fx+TL8mWSffcmZs+evcng13Nw7E6M3FXVAjvquj1zH7rZnsHftm3ufOghAL77rW9RW129XfbsSQwfPpxwj+mMHZkQ2KeY2+4y1+ay92HbNmpbm1PqV1SEkc2Rq6vDNgxCI0dsttzOaQe8HtuyncHStrHyeeSQE/7X29qwdJ25dRtZUbeRSChEMp1mWHU1w6urAaccTuuId7UIDkMmC2UCSjjsZMy3taMURRAk0aks8PuQfL5CLf/2ICoK3tISspkMWnsHSiSCqCh4SqJIHgW1xcnGV4oiyJEwemMaI5dDT6bwlpYQPeRg0ivXkN2wkciY0XijJZx4+GH868P3+c/s2Vwfj1NcVIS3OIoWjaInOsk1NeMtL8VIpvBVVyEHQ5j5nPMaFBWBKGDmcngrK8hu3FiIOPQH25vsuzcxadIkZs6c2SvxrXtwnDhxYqEKoGe1AFAIm+/oaoEddd3NTXvMmDFjm52A1956kw8//YSicJgfXvDd7bJlT0VVVe666y4Mw+Dmm2/eIdfo06/Z/fff32tZEAT8fj8jR47ka1/72g4xzGUfIZPBCkfwlIXJNzdjZLKYeadEzVNcvNlD9EQCtaXFKdnz+tATCbBB9PnJ1tdjqhqe8jL+dN/vABg0YACLV67k+LEHIigySnERoteDmcviq6pC9Hqwc7muEjmJfF09lq7hr6lBi8exsRG9vm3O/t8cSnExcmcCI5N2mvZUViJ5vU5koKMDWxQQBAFfVRX5xmasvIqRSuEpieKNRgkMriFX30hg5AgERWbc+PGUh8PEUikuu+QS/vGvfznljOWlmNkMmbVr8VVWYOXzYNt4ykrJrt8AoujkTwRDGOk03vJy8k3NqLHYl75Hl88QBIGTTz75C9fvqmqBHXHdz0979HQqYNsiAUvWOFLaP7vmGmoOHr/NtuzJrF69mocffhjDMDj++OM3GWu9pSV9EgJiKw/wfXIAVq5cySeffMJJJ52EJEm89tpr1NTU8PLLL7Nw4cJCX2MXl21BT6cxN9SRUTXyLS3YpoUcCmAj4C0r2ezTtm2aZNZvwDYMfEMHY2taoe2unkhiZLLIwQDvLFnMig0bKC8pYe3GjQAcP/6grq57ZRi5HLZh4i2vcJoNWRZyOIJlWeQaGlHCEZRImPTatU72v+/Lhf+7kbpEjcxMFj2RxFNS4thUUUG+tRU77/RicCITQbRECtswMDMZ5FCI4gMPIrexnnxdHcHBg5F8fr66/wHM+uB9/vfOO6xevZpBZWXIXh+esjLyzc3k2mJ4Q2GMdAZPUQTV78NSVYxMBn9xEUYqhW3ZKNEo2Y4O8Gxabumy4+kejHtWC+yMUsH+vu6Wpj3AcTImTpy4Wadoc1x33XUcf/zxu0VC35bYmsKfsX4drUlHZ2RrioGb44ADDuDKK6/kt7/9LVdeeSUvvvgi1T2mQHqWSG4J3dr6lF6fcgDa29t55pln+MUvfsH111/P008/jSAIPP74466Slcs2YVsWRjpDcsVK2t+bg93egRwO46+toWjcWDwlpZiZDFpnEq0jjm1Znx1r22Q2OIp4ntISBFlxSve0PJauYakqUsCHLYg88I9/AvD1iRPJ5vOMGlDDoOoBeMvLnfr7tjYEjwcl6szJoyjIAT/55hYsTcM/qBYz54jxyH4fgiwhbaf41efxlESRggGMbAatw/kCe4qLkP0+7M5EoWTOW1kF2Bi5PFpnAnAqJDwVFeQampwkRo/COSeeWDj3r3/5SyS/HykQwFNWiuj1kF29FnByLaRgEDkS6Xof0k4lgEdx9ALKy50EQZddwpbC5j3ngHdE0l5frrstdE979HQiup2A7umQbeGwww7bI9v99gdXXHEFRxxxBM3NzZxxxhn9Ph3fp1e1s7Ozl6hHNBqls7MTj8ezzaU7LvsmlmGgxtrIrF9PYulS1JYWBI8HYchASg8/jNCwYXjLHLGfwMCBeEujaB0dZDduRI21oScSZDdsIFdfj1IUxltW5gz+lonWmcS2BUf4x7TJpFMMr61lQGUlbV3z6ieMPwg5EMBXUYFtWhjJFJ6iIgTbxtZ1hHDIESFqaEAK+vGWlTlZ8baN4PEiBwL99iQmeb0o4RBYNnoyiaXrTvJfWQV2LlcoR/RWlCNKEqaax8rnMXM5BEGgaOz+WJpKrrkVJRJh+IABDCp3Suxeee01Fi5dihIJIUkivqpKtHgn+bY2p/JB1/GWlCAoCkY+j55MIofDWPk8ot9XSIJ02TI7chD+omqBLSXtzZw5c7uS9vp63W2he3pjc83bNrf+86xatYrTTjut0Dl1X0aWZWbOnMnBBx9MfX09Z511FuvWrStst22b9+bM2exn0Vn/BefvixEDBw7k7rvv5pxzzgHgqaeeYtCgQSxYsGCf9cxc+o6eTKK2tYENlqYiBwIoA2uxMlkkSSzU+BuZDJam4a2oQImEMfN5tHgcPZnENk20jg6UkhI8xcUY6TSCJKF1xrHyObxdA6AaixGpquSem2+mvrGBUy6+GFEQOHHCYXjLyvCWl5FvbcXMq/hratATCUSvF8Hnc9bncoS7+l6osTYERemSBg7262uiFEfRU2nMbBa9S/DHV1UBCKitMZRQCElRUIqjGOkkNjZqeweB2hrHQSovJ9/SgqesFEGSOf2oo/jDc88CcPttt/H/HvojakccT2kpWls7+S6FQ086gxxxGiuZuRxaRxxPaRkAVi6PUhyF1m1vs70v0T0IT5s2jdNOOw3oPZDOnDmzzyHunufsS9h8S0l73TX22/p03Z/h+m1hS2HzVDbLeT+/nsZYjLtvvoUHb72tT41xbAQEPhvtrMYm0kVrNtmvvxUDYesKf3XzQ1R0TV90l+19EVoiidFDNhzgoSt+zHUP/IH/zp/PA7fexjVTL8Q/cCDvrl7F9Ouv6/X+9fws/uUvf6GsrGyL1+rT6P3rX/+axsZGzjzzTM4++2xaWlr45S9/yZIlS7j22mv7cgqXfRQ11taltudDjkQQFQ++6ipESQJRgB5hdS3uaP93P4VKPh/+6moCQwYjBfx4y8sJDRuKreuOM6Hr6O1xBI8XKRwkt34jtmUTHDQYAZtXZ7+CYZocMXoM1RXl+CrLEWQZtb0DQZZAErANA295GXYuhxZrRwmH8ZREMTIZpwmQ348gik6DoX5EDviRgwFsy0TrcnDkQADB70eNtRamPryV5Vi609fAyucxsllkv5/g0CFY+Rx6PI5SFOGkgw9G7Hqyeu+DD3jvg/eR/X4krw9PaYlTTdDZSb6lFVGW8USLERUPejKFmc0i+f3oqRRKqH8dnb2RnoPwzJkz+0Xfv69h8y3V2E+ePHm75u37O1y/vdi2zey5c5hy7TU0xmKMGTKE31zxo51y7T0Br8fDLd//Aad97Wiu+Pa5hfVf1Gvii8T7+iwFfPfdd/da9+mnn3Leeedtx63sWro7rrnseNS2dvREwpG8DYfINTQiBYN4SkrIrFuPHAwidCWxGNkcVl7FW162yY+YHu/stc02TWzbQk8mMdJp5EiEfEMjuXSaX774HBdIAuNKS3mxSxznmxMOxVMcxV9djZHJYCSSCJIIgohSVISoKNiJJJbfj7esHMnnI79+PTY2st+P6PNtVonwy+Ipjjr9BjJZ9FQKT3ExQnERZjaPlkjgjUZRwk4HREtTkQIB1Fgb0sBaArW1ZDdsRG1rx19ZSWmkiIn7H8B7SxYT8PqIN7cgjNoPW9UcLYNEElt31BY9pSVI4QhKOITa1o4ai+GvqcHskjt22Tqff0p+4YUXgC+XOd/XaoGe1++ZtNctubsjr7ujSKTT3Pzwn/hvl2TumKFD+c3lP8Lv/k73IhIMccsPflhYtm2bVCq11SoOTdO2es4+OQCXXnopDz/8MLIsY5omv//97/nXv/7FBx98sL33ssvQOxNQVLSrzdjr0ZNJ9M5OlKIiPKUlZOvqESQJX0U5ZjbrZN33UPjT43EEWXJq8nudJ1U4j1JUhNrejqVpWLruaNurqiPjm07z7PtzePOjj1jf1MRt06azor6OkN/PV8eOw1czAMnvR21rR0slESURJRzCU1qCGothGwZyIIjS1UXPiVp4ESWp35/+u5FDQZRgkFxLK3oiiVJUhFBchKAZ5Bub8EajSH6f0+kwk3EEgzo70eJxPKUl+KoqSa9dh9HlHJx8yCG8t2QxA0pL+frYcRjpNKLPhycSJu/xYpoGsj9MvrHJSaJUPCBJ6IkE/gHVjiZAJrtD7nVvY1dl7MPmk/ZmzpzJhAkT9pjmQt1hc9u2mdbVFyESiXDjjTdy7rnnfumpZTHRSWjE8H6ytn/oS9leX4nFYlz+058WEvS397PYJwdg5MiR/OQnP+GKK67g2muvpaioiFmzZm2/9bsQS3WSqforo9tlU0xVdZ5UA348ZY7Wva3rBY1/I50GUSzU1RuZjCNGU17WS/jHzOdRY7HCeQCMZAq1vYNcU5MTDg+HAJvOWIy/vfM2AD+5+GJenjMXgG8cfAiRijICNTVOkl9TM3q8k/DIkfirqzGzOYyUk08gKgpyKISeSmFksni7El/l4I4LiyvFUfREAiOZxMxmEWQZb1ERaqy98DlVosVoiQSYhiMQFO90pkdqa1FjbVi5HIIsc9SIkUQCAVY3NrBy43pG1g7ENk2kkhKUaDFqSytSVRVyOIhtGoXvgqEb5FtaUYoi5LsSEF22Tn8J3WzvdTdXY9+tu7+nOAHgOFLXXXcdv/nNb7j//vsZNGjQrjZph7G1csGe2IKA8AXZe5lcjiULF9Eca+Xcc89l6NChvbZ3fxa/iD65WTfccAPl5eWcccYZfOtb3+LRRx9lwIDNS7Tu7ti2XSi9cul/bNtGbY0hiCK+ykpngEmmUIqLkfx+p/wsk3FEd7oSVrT2dkSPghz5LNmnuzmQIMt4y8sx0mlSq1YT/+RT0mvXkm9pdqYNcnn0VIp/zJ1DIpPhsEMOYcKgwbw01wn/n3LEkQQGDkRQZFIrV5Gtq0MOBonsPxoAtS2G6PFgiyJSwI+oKOSbW0AUnGWPp1/b5H4eORxCCoUxsln0rsQfX1U12Ba5ZicZTwmFED0KWmfC0Q3wep0BO1qMUhTB1g0EScTj8/ON8QcD8NSrr/GnZ57h+VdewdQ0vKWl0KW6iCDgHziQwNAhKOEQRiZNauVKtM6E0/bYZav0HIQnT57cL5nzfWVLSXuTJ0/uJS28u2LbNv/4xz96ict95Stf4bnnnturB//+Juj389Dtd1BeXs6HH37IE088wdlnn73Nn8WtRgAeffTRwt8DBgwgGo3y8ccfF+YV9kTtbkGSnUEpk9mhT3b7KnoigaWq+KoqQRSdevsuuVvoysGw7M9e+2wWS9PxVVX1enLpbg4kh0Nk6+oxUmlyjQ0gCUjBIJaqokSjiF4vG5ct5cn33gXgijPO5M3XXqMjlWJIVTUHjz8IweshV1ePnkgier14S6J4iosd4R3DRCmPIpgmciiMbVmosZgTWbCsHV4WJwgCnmgUrSPu6B6YJkokjBwKocXasGprkAIBZH8AM5srvLa5hga0llbHOUqlEEQJ0aNw0thxPD3nPV58+39kcjkqolG+cfTRRCorkcMhtPZ2zNoap+1xJEJk/zGAQD7WSr65GUtRwO/bofe8s+nP/vTQexA+7bTTdlrmPGxZWnj69OmcfvrpOy1pb1uxbZs5c+Zw33338d577wFO57uvfvWrAHtU1GJ3YczIkb1aaL/yyis8+uij/PSnPwWcz+JXvvKVrVYBbNUBWLlyZa/lbinCz6/f0xAUxWn12o+13S50lerFkQJ+ZwDriDuD+4DqQmjfzGSdp2u/30nmSznz1HKP7HM9mUSPx7Es03l/RAFBEp2QvCDQuXARvopyfAMHkqur50//+x+aYfDNE09k/wEDuP/vfwfgrK8fg63pYNmIwQBKtBg96TxFG9muyES0GCuvOp0EQ0HUtjYsVcNT0RX+30wnwv5GiYRRImG09g5Qc06b4KpKMqvXOEl+1VXI4TD55haMdAZfZRB/TQ25hgYQBASPgqXryEE/o6qqGDWghpWNDVRXVNLU2sI/n3+BaRdcgKe0DD2RINfYiK+8DDkYRIlEnCoIQ0fweLG+IGlod+JHk04muZkOhhOjpUwodhzOVekUL7W2gG3x6DXX0fPbbgPfG1DL//44EwV4pqmBuly2sK0no4IhvlnpqLBZusYR0RKWPjOLpc/M6rXvEdES4vfcx3t/nAnA2+0x5nVuPuJYJCtMH/xZ6Pb+davJdzVj+vz1jy4t54ioM3/cmEpyxs+u/uw+euz80E+v5sfDRuDrSub8V0Md67OZzZ5zdCjMmdU1AMQ1jT+uX9vjtem993dqBzG0qxT2zVgrc+PtPfb9jGJF4fKhIwrLd61eQd40acjn6dCdz5Zfkpg2aAj2nffw3l33bu6l6U1f8gF8PiLDPivFy6eTLHrm+U12ixw4lsHfPvuLz7cNdMz/hPSq1Zvdpjc1sHGV87p2LlqM2tr6hefT2tqghwDa1qgBZh58GH9ct5qPOuPceOONSI8/wUmhEMcdeiT/vfc+Jv/qti0ev1UH4Pbbby/8PW/ePA477DA6Ozv56KOPOOGEE/pk4O6GbZrI0WL0eKcjxVrsJgT2F93COd6yMixdR+uMI4eCyD009I2ukjNBFMm3tjr7l3/moVq6TrauDiOdxldZiaekBLWtHcnvhOMTK1aCYRAYNpR8UwstGzby0do1eL1erv3Rj1j7/od8uGolPo+Hkw44EF9VFYFBAzHSaXKplFMLX+rkJQiKghKJkN1YhxDwgyCQa2xG9HqQZAVBdvICdjSCKOLtssnqasbjjUbJB4Kora14S0ucEkFRRE+lHIEgRcFfU0O+uQVR8qAn2lEiEWS/jzMOOYQ7Gxvwe52pi0dffJ4pk0+huNiJmKitMfR0Bm95uXPtslK09g5sy0QuLgJ9z3ACPuyM09retsn6Wp+/4ADENI33Ojbdp5sLBw2h+x1ekkywKJXc7H6WbRccgLxp8UG8Y4vn/EZ5ZeHvjbnsFvet/FyW+/zOOJktdGMcGfzMEe3QdT7aglMBYPTwCFalU1u8J2+PgVW1LD5Jdm7xnKf2mBpqVHMsSCY2u9/n72lpKlm4pyJZ4aSKSiZVVBFyBeT6jYgkcfXwUbwf7+DdjjaGdUVXFWBZOsXkrRzbp3fh3nvv5eOPP+bvf/87+XyeP//5z6xcuZJLL710uwx+4YUXeOihhzAMgwsvvJDzzz+/1/bXX3+dP/zhD9i2TW1tLbfffnsh8fDuu++mtNRJCPv6179eCH/0FT2dwo4WIwX8aB0dyKHgl+rw5uJgqmphrl/0eJx2ujZ4ut4roKBEJxcXYebzGMkUQjCA1ONHI1tXh9rWTmDQIAIDB5JragZACgXJNzahtbTgqawAwyC7YQNlZWW88eKLLN24keJsjoe6EgG/MWECJQOqKDpwLKIsk29uwcxmEb1e6FL/89cMcFT3bBsCAScRL53CU1aGbRgoO9E5VIoiyKEwrF9fSP7zlERRW1tR2ztQImEkn7egCCgHAl1OwAAs00Rti6ElOpHDYY4dM5YHX3+NtXV1HDBmDEuWLeOBxx7j2u9Nx1NcTLaunlxTE/4B1Y4qYVERnmgx+Vhss90Xd1e+WzuIXMCp2uj5ZD+0x9TeiGCQy4c62eAGgGFSCPpJEsEe93tGdQ3HllVs9lplPfJAihWFK4eN+Nwen1kwqEeC8ddKyhgR2HwUySv1fq0vGzocs8fjdM97qu1xztGhMFcPH9X76j129vW4p2/XDOSbPQbvnucsVnrf03Uj9tviOYf4P3PijyurYGy493eje1fP5z4/Px0+Csu2iXo81Pj8Ba0Kl/5FEAQmlpQysaS01/qezujm6NPI98YbbxSy/quqqnjsscc466yztssBaGlp4d577+WZZ57B4/Fw7rnncsQRRzCiS30tnU5z88038/TTT1NZWcl9993HH/7wB37xi1+wePFirrvuOk499dRtvm43tmmhtrUTGj6MXH0DWlsbvqqq7T6fi4Mac2rIPdFixxlIdTkDPZ6gu/vYi34/anMLgixDj7I/LR4nV9eAt7yM4KCBjjJgPo9c7GSwJ1asQE8kCY0aSWrFamzLIjJ2f4qHDaOktJS6V1/jPx9+CMCUY48nMnIkoixj5h0pXUvT8UajGKkUciTslAXGYog+L0iS47SIIqLX52Tc74TwfzeCJOGrqsD+WEONxwn4/XiKi9ETneiJBHI4hOj3OxUD6XQhqiKIIqEhg9E6OkguWQKiRKgoxEnjDuTpeR9SU1nJshUreOKVVzjjuBMYNWgQgqyQ21iHsd9+SF6vk7BZXY3a1o5t6Dvtnr8slz31JN5tqBXvfqDopr6+vtcU4Fe24drHd/0/f/78rTaq2ZZzbsu+n2dLdmzPPX0ZPm/Hl7mnL2vHuJ3UQKhkwsGUTDh4s9ti8+czqMuOQef279RDXzhUVbfaP6BP7r6u6yg9fsgVRdnuufM5c+Zw5JFHUlxcTCAQ4KSTTuqVuarrOjNmzKCy0vFc9ttvP5qamgBYtGgRs2bNYvLkyfzsZz8jkdh8GGpriD4fWlu7M88bjWKkMxhpt/Tpy2Ck005SWWmJI8/bEQdRxBMt7rWfmckiehTMbNaR/C37TPTHMk1SK1YgeDyERozAtiyyGzaidXaSb2h0egI0NKIUFyF5PLS0tfL4wk9RampAEEitWs3z//sfqVyWscOGc+DYA/ANcEK2RiqNnkw6IX1JAkHAW1rqVBFoOkqkCDIZ9FTa+dsykIKBnR4Z8kSj4PWSb2rGtizkSNjpUKjm0do7kP1+wMbMZDfJ7o2MHo1/0GDMXA5L1znj0MMAeHvOHM4+80ws2+aVOe9h6xpyOICRyZBraCwcr0TCeMrLMLo6l+1t9HfDGxeXvYE+OQCHHHIIP/3pT5k7dy7vv/8+119/PQcddNB2XbC1tbVXY6GKigpaWj7THo9Go3zjG98AKEw3dOcblJeXc+mll/L8889TXV3Nrbfeuu0GmAaWaZCtr0eJFjtzom0x7C3MvblsHdtyIiqix4McdvT7zUzGUbXroSpnmyZmPo/g8aB1dCAFA70S/zLrN2DmVML7jQTTJLlsBWpbu3MeWcLUNKRAkMjYAzBUld8+9xx/fu457vjdvajt7aQ2bOSJd51KgPO/eTLesjJkvx/btjHSaSfUb9lO690uR0VPJJz6f4+Cnc4ANpLPCzYokS/WH+9vREVBLC52HJZEElGWUcJhBNmDpapYmo7k8XSpJuZ7HSsH/ISGDsFfU4NtGNREoxw5bBh5TaM4HOb+227jyunTwQZR9oAgkF67Bkv/7Ik/OGQwgrznTAH0lR3R8MbFZW+gT484N954I/fddx+33347siwzceJELr/88u26oGVZm5TibC6akEqluOyyyxg9ejRnnnkmAA888EBh+/Tp0wuOwrawbvUayGRg3XrERKdTi97WDqtWI3aVqu0M5nfJXu5K+sMGO5XCTqURSksQYq1Y7e2gGwiVFQjrerzP+Tx2RxxbFBFsG6G8DKHReQKdN2cO1spVCOEw4pIlWPFO7EQCIVqM2NyE2dKKtXwl2BZtDQ28+8YbvLtsKQG/n6995St8+uprzH3jDera26gsKWFEeQUrO9oR58/HzuUwW2PY7e0IsoJo6AipJJgmdmsMQkFYuQLb0FldXw8NDQiyjNAZ3yUVIkJJlHUrViLE25EGDcLWdexYGwjO9JWt6wiGidjajPA5J8XO5TDzTqmg1Zlg0n6jeX/tWv7x5JM8fPc9rKmvw1Y17KZmrHweobWVRkVBqvpsntDK5pw8ib2IXdXwxsVld6dPDkAgEOD666/vlwtWVVXx0UcfFZZjsRgVFb0Tb1pbW5k2bRpHHnkkN9xwA+A4BE8//TQXXXQR4DgO0nbolg8bvR+KDenVa4lES4mMHoXW2YnW1o63vAxlJ8gEf9G84c6gP2ywdJ3sxjrkUBBfZSVGNuvIzJaVbVJdkW9tRW2NOXX45WV4iosLdoyKFKEOHUZ41EjMTAarrAxBkvEPGojW2kp7WzsZj4eisQdgDKji0euuA+DmW27ha+PGEXv3PV5eugSAC085lTGHHUb0oAMByDU2kpEUNMVLYFAt4f1GOVr/ra0Y5eUo4TB6IsnSFcsZPWIEgiA69kV3njPYk/nz5zPm8MNRW1spGTsWyesl19iImcsjdCUzWqZBsLaGwOeEU2zbJldXR7aqitTKVZRVVrDf/PmsaG1h0aKFTP32uayJtfLsW29y4ZFHYWZzhHw+yg88sJCroX7BnOGeyJZq52+55RYmTpy429bOu7jsaPoU7/vkk0+45JJLuPDCC5k6dSoXXHABX//617frgkcddRRz586lo6ODXC7Hq6++ytFHH13YbpomP/zhDzn55JP5+c9/XvjCBgIBZs6cyYIFCwB47LHHtisCYKRSyEUR5GCA9KpVmKqKp9ipClDb2zFVdbvua1+kW1WuO9Nfa+9AkGWUzbTvNFJpzHweyefr5WRZmQxqrA05FMTMZJCCQUSfHzkYxEylSK5cRa6uHk9xlOIJBzPj17fTmUkz8fDDOXvyZNJr1/PxihUs2biR4nCY048+hsDAgc65dR0jnUFPJQALb1Ulks+HZRgYqTSioqAnkkihIILtTFMIsrRLwv898dcMwDYt8l0VEJ6SErBtJL8fOejHTKXRk6lNavYFQUApjuKJRvFWVqCEQpx75EQAHv3Xv2hdv4Hzpk3jz88+y38XLcKybfKtbU7y417Ml+1P7+Kys7Ftm5dffnmT6aktrd9e+uQA/OIXv+Dggw8mnU4zefJkQqEQJ5544nZdsLKykquuuoqpU6dyxhlncOqpp3LggQdy8cUXs2jRIt58802WLl3KK6+8wumnn87pp5/Oz3/+cyRJ4ne/+x0333wzJ598MkuWLOHqq6/+4gt+DjOnkm9oxD90CGY2R3L5CgC8FRUIgoja8lkrVpctY6TTmJksnpIooiw7iYCqiqekZJMfVFNV0To6nPn3ivJe262mFsyso8qoFEWQggEwTSzTpOPjT8g3NCL6/RQdfCCzZs/mlXffIeDzceedd5JraCTX1MzMN98E4NvHHU/RgGq8XVM5ejKFnkpjJNJ4yivwdSli6YkElqZh5vKIHgXJ5++SwBVQIkW7vCOeEg6jFBWRa2pyIl0+H1LQaXjk75I1ztbVoyU2re+WwyHkgB9POIK3pIQTjppIbbSExo4Onv3nP/j+KacA8Nvnn6W9rQ0jn3WUFt1EWBeX3YbZs2czffr0Xjkq3bks06dP7zfJ5z5NAQiCwPe//33i8TjDhg1j8uTJfOtb39rui06ePJnJk3vLEzz88MMAjBs3juXLl2/2uEMPPfRLNyHyFBdhJpLI+TxSMEh23XqCQwbjKSrCW1FOvqkZrb290AjGZVMsw0CNtSF21ZDbto3a3tGVCLhp6ZzW0YGRzhAeM7BXzb/a1g6trQilYxAkCds0ya5djxqLYabT2IAcjaKIIv6qSv53/x8AuPEnP6HS5yexeAnzVq/k09WrKAqFOO/kbxIcMhhwkhONZJJ8SzOIEB41onANraMDM5fDE43iq64m39yCrWpIPu9Orf3fGv6aASSXLkNta8NXXo63tJRsXT1WPk9wyBASixaRXbcOT3FRr1JLQRDwlJSitrcjqwEQRL4/eTI3/b+/8fc57/Ho+VN5ffgIPl6zmntenc2Nx52At6SUfGsLQf/gXXjHLi4u3UyaNKmQqAr0avo0bdq0fpu26lMEINglrDFo0CBWrVqFz+f70u0adxVyKITk9WJqOt6KMrTOThJLl2Hbjj69UlyMnnD6zLtsHjXWhm1Z+CorEAQBI5XC1vXNPv3btk2uvqFL7teRB863tJKtq6PlzTexOjoQFBkzlyW1dgOxOXNJLF6Cnkrjq6pCVGS8JVHkUJhbLvguv//Rj/nWaaeR3bABS9N4+NVXAJj6jROpGDGsML1gpDOonZ3oiU781QMKc/pqewdqaxtSIICvugrbNNHTKSd8XlS824hCectKEb0e8l2leqLHg9JVISAHg/gqKtASSbIbN2Lmcr2OlUPBruoJGTng5/ix4xg+YACtySRvdcS4bvLpBH0+3luzmlkfzye1ciXpNevI1jegdykRurjsaHZWmHtPpDtHpdsJqK2t3SSRtT/o0yg+btw4rrzySo488kj+8pe/cMcddyDvJj+U24qlqY5slWXir6lBKSoivXw5ubp6ADylJYg+b1czmj1HFGVnoScSTplfaYnTRc+ynPC+z9urrK8bra0dta0NQRRQm5vROjrQOjvp+HQh2Q0bsUMhlK4WvOlVq5A8MqUTJ1J+3DHoiU60tjYMAYxUEjOV4mtf+YqjiZ9K8s6KZSxevZrSSITzJk/GX1tb+GLonZ1k129AUrxExu6PIAiYmkZ67RpERSEwsBbJ50Pv7ETvTEDAv1vJQjvCQJVoiSR6yhmUPSWOlK+RTiNFIkgBP2ZeJdfYhP45TQxfVRWiKDrthIuLmH68U0r719deo/aQg5nxw0sA+Msn81m0eAntH3xI0xtvkl67bufeqMsOYU8YXHdWmHtPpWe1Sjf93e65zzkAF110ER6Ph0svvZR169Zxzz339JsROxM5EnGaAXXEMdIZoodNwEagfd6HqO0dCIKAr0uEKN/Sss1fFNu20RMJsvUNZDZsJNfktK3dGzCyOUeXPxgoPGnriQS2YeL5nASls3+WzoWLsFQdT1kp3opy5HDI0QpIJAiNGI58wBgnMU/T8ZaVUnnssZQdeRjekhIEWeb5BQs4/+qrWfra6+Ta4xiJJFo8ji7K3PuPfwAw7dRTKRk1sqDcZ2SzZBrqMVIpgsOH4olEsC2L9MqVWHmN0MjhyMEglq47WvqKgli86+f+P4+3stLpmdDoCGEJguB0WRQE0A1s03IiWn4faqzN+bx26VlIfj+e8grMXA5/VSXHHDSesYMH09bRwV9e/g+nnXUW3zntNCRJpl1XndeisYn0urVbM2mvZk8YNPvKnjC49gxzd9u5I8Lceyo7Q7xqqw7Axx9/zEknncR3v/tdUqkUp556Kn/961/5+OOP99iOgL6KCvw1AxAVmez69YiSRNHYsRiJFInFi9E64oiKgq+iAiuvOh3a+ohtmuQaGlBjbU7WtteDpebJNzaibsN5dkecwbIZUZHxVTihf9s00eKdjqhPwN9rfyOfJ7FoEWY2Q2DIYHxVVWgdcdSODqdFsMeDp7wccnnMfB5veQWh4cOcAQ7INTTy8dJl3P3kEyxft44PP5yHEY87zpRh8v+ef46GWIyRgwZx7lnfwlf+WXJhrq6BzOq1KMVFhMeMwTZNMhs2osbaCQ4eVJgOyLe2oiWTzv34e9u/OyAHAnhKitHi8UKSnqgo+KoqEb1erGwOtaMdb1fTJCOdcRL6uhzOwMCagvKhr6qKK8908nb++uSTrPjkE356wXd57KabOHb8IfirqggPH4aZ2nenvvaEQbOv7AmD684Kc++J7Czxqq06AL/5zW+48sorOfnkk7n00kt58MEHefbZZ3nyySd58MEH+8WAnY1tmvgrKwkOHYKl62TWrsNXWYGnqoJ8c0uhGY0cCqIUFaF3dm6SId2tLqe2t6O2t2Nks04CW30Dlqbhq6okMLDW6UQ3eDByJIwejzvd8vZAbNMk39TkPIFWVxeelLWOuNPNr7T307+pqqRXrMRUNXwDahA9CmqXvK1tWlh51WlCEwoWZHm7m91IgQCWYbDq0wVc8+ADGKbJuUd/neNGjCA6YTzR8QcRlwQefeklAK696P8IlJUhh0LO+7mhjuTKlQiiRNHYA5AUhVxTE2pzC97ycgKDnBJBU9PIbtiIHAzhq949e0EIgoC3stJ5/VtbC196ORDAXzsA0e8lt6EOI53BUxLFXzMABKHL4XQ6KHpKS7FUDcnrYfxBB3HaV7+KYZrc9f/+RvGI4Yz/5sn4qqowc1k6RBF/1+uzL7InDJp9ZU8ZXHdGmHtPZEviVd3vZ385o1t1ALLZLCeffDLnnXce5eXlTJzo1BQPHTp0j32DkitWYlkW/poa/DUDMPN59HgnvtJyEAWyDU3kmprJtbTgKSsFSSKzcSP5tna0eJxsQxOZNWvJN7egdybQOxPkG5voXLAAS1XxV1f3aiIjCAK+ioouJ6DTkaTdg7Btm3xLC5ZuOE+eXRnnlqahJ5MokTBij05plqaRXb8RPZnCW1aGmc06jXWKixA9ClpbG6amohQVIQeCCB4P3ooKbE1DDkcQBIH6JUu45Fe/JJ5KcuSYMUw/6ijC+42i5PDDQYCbH3yQvKYy6eijOeyAsXiiUfLNzWQ31pGrr8PMZQkMGYR/wAByDY3o8QRyKIi/tqbgvKRXrMLSdELDh+2Ulr/biycSQQ6HnSZAPZ7OlXCY8H77YWoaySVLsU0TyecjMLC28FnL1TegRB1JZtHnRQCuOPc8Qn4/786fz3P/+Q/+qiqKDhrHq4sXcdY1P+O/XYJK+yJ7yqDZV/aEwdXt0bB5usWrer5f3e9nt6hVf7BVB6Cn0l7kc+Iou9OHaFvIt7aSXLwUQZYJ1NYi+f3YpoESCTviM7kMaixGatlyOuZ9hJnPo8XaSK1YQXLFKtIrV5JvbUX0KAQGDyI4dAii14OeTGNpmtPhrge2baPG46jxOOl1a4n97x2M9evJtbRskr29O6K1tWFmc3jLy5F6hMnVmCMCpPRQzLNNk2xjE1q8HaUogtmlrx8cOgxBEMk3t2JkMkh+pw+A6FEQSksKuvZKJEyys5PvXXop9bFW9qsdyK+/N51gZSVFY8diZjI8+cLzvPfRRxRHIvxkytlYlulEabI5BEXBtmyUUAj/gGryTc1YuoagOOJE3QJFucYm1LY2AoMG7laJf5tD9HjwlESxdR2to6PXD6O/qpLgoFryLc1k6uqxDMPp7FdRga+qCtsw0DuTCJKM5PViWxalxUX85Dyn/fYtv/kNjQ0NRPYfgx0MoBkGv3/ssV11q7sFe8Kg2Vd298HV7dGwZXaWeNVWHYCeF9kTvwCbxXKeaJNLlyIXFzkhUk1D8vvx19QgSsr/Z++846Qqz4Z9nTO9be9LV3qxgDQ7iiwoqIixxhIwxhS/RKOvJRFR8xoTW2KPkPhaYoKiFCOLvSQQFKT3Dtv77uz0mXO+P+7ZgYVF2jbgXPzmx5wy53nO7Mzc93NXMUtnZaGaTKgWM+6+vVFQUM0qnv598fQ+FS0cIVBcIgVxgiEc+bno0Rj1a9cRKC0lUFpKw+YtVHzxNbVLvyFYXILJ4SQWDqGXllG/ajW1K1fRsGkT4bq6Tll8KFJfT6S+AUtKCpakvW17Iw0NxAIBSVWLKzy6rhMsKydcVQVm6Rapoyfy8kMVFYQqK1BMFlSbFUuSB0deHorZTKShAZPDgaKqvPvqTDbs3El+ejrP3fs/uN1u7NlZWFJS2LF5M7+P94N4YNpteKIxbOnpWDPScXbrihYOEa6uxuR2o0djKCYTJod09bPFYwTC9fX4tm/HkuRJzK2zY/F4MDmdRP1+IvsV/3H17InZ6SJcWUWwpAQt3vvd7HaJgpOaghbwE/U2Yk1JJtrg5fILL+TswUNoaGzk13fdhWo2c/PtP+GqoUOJdcLP4cFoi6C9zi40D5fjQbi2l5nb4OB8by7fpk2bOPPMMwHpzNf0XNd1wvuVIT1e0KNRVKeDUHklqsmELTcPLRgi2tiIPSMDLRQiWFZOzOPC0bUb0bo6QmVlKGYzqsWKxSOWArPbjX9PEdX/XSo55VlZKGYT4apawnV1gKTAoarY0tOwJCfLNcxmqKxCsdmI1NYRqa0jWFKGJSUZR16epHp1ApN01O9PRPxb09MS+7VoVPY7HM1K5oYqKwlVV6NrGorJhNnjQbXZMDmcBCsqCJSWyb2bVMwOB64ePSSNMBRCj0YxpabgLy5h7CmnUjyugPGXXEJWdibh6hpc3bvjr63h/z30EP5AgLFnn8O53brhyM4mqV9fFJOJUG0dvu070WJR+Ru53ViSkwiWlEqFQYeDSIMX39ZtKCYT7r59UI6TWhZmt1sUgMZGwjU1WDzuhCvDlpGB2eNG12LE4kqpIz8P1WxGMZlw5OagRcL4duzE5HTJ6zSd+6+7nuu2beXLf/+b5597jttvuZW7fngz4X+83cF3e/h8+eWXTJs2rZkA2VfwzZw584ia/OwvNPctvgLHlyXgeGiAZPRo6Hi+VwH4+OOP22se7cbibVu4oF9/rKmpBMoq0FUVc5JHVvKBgPSw13XCtfWgFqMoCpH6eqkjb7MTqqhE6WIlFo4QqqoiUt+A2e3GlpUJKGiRKMHSUnQFbNlZ2NLSwaRANCZlXR12FOIV2zIyiIWCMobXS2znTmzeBiniopqkL4GuiQnXYcfscrVLqpoWDifS45oi/iHuziivAPT4/QqR+nrC1TXEgkH0mIY9Jwezy0m4poZIQwPh6mr0cATFZkY1mXGd0isRN6D7/YQjEWp27sLR6CMWCPCjKybj7CL+e2t6OmaPm/vu/R/Wbd5Ml+wcHrzph5gVE8mnDUlU92vcspVASQmubl1xde+OJTWFwJ4iFLMJa1oakYYGAsUlaOEIzh7dsbgPrFjYWVFMJiweD1o4jBaNEKquxh5voKWYTNhzcwgUlcTTKcMES0px5OclPiuO3Fxi/oAUPbJYiPkayc3P46Frr+Oev87iD3/8I0NOO43BPXtx95WTObDAcOfk/PPPb9VqaceD0Dxcjgfh2mTOPtz9Bq3P9yoA+fn57TWPduOpt95CuWoKY88/H0uyh1BpOSaPR1ZVVit6MIiiqChmM74dO7CkpODp3Qc9GpbAt7o6gpWVidVj2ojhaOEQ3i1bAYiFwkR9PhSTiqt/fyzJSbKib1ptahpqSQlmpwtbZjrhqmoiXi+KyRK3BpSgR6NYkpKxZ2eh2u0oJhNRr5eQUoUlORlrPLCrLdBjMQKlZfGI/5xm4zSV0LVlZSWsFLFAgEBZOeGGBskIyEjHkZtDsLycaKMP1WYjXFeHFotg96SJspQmFgUtGiXS6OPO3/6Gbbt28cKd/49MhxN7fi7hmlp0LYa7Vy9enTWL9woXYrfZePruu/FYbNLsJm6B8O3aTf3aNVhSU0k54wysqSmEqmviGRk5ROobCFVVEfX7sGVlJoTn8YQlJTlR7Cfa4CWWlITJbgfAlpZGuKqaqNeLNTOTaH09wbJy7Hm5KIqCarGIAhGN4u5zKg0bNoGqcv7wEdy6Yyd/+/xTfvqTn/D2X/5CSsaB9Rw6K/sL6CZF4GiD9o4HoXm4tLZw1XWdwsLCZu/N9+03OD44PmygrYimadz/yst8/c1SQMGalUXU66Vx2zYidXVYkpOJhYPxyPcIeiRCqLICPaYRqZOVbuO2bQTLy6VLGzr+PcX4tu0gUl2DyWLGfcopePr1w+zxYE1Nxex2Y3Y65eF2o2RkYE1LxWSzkXLG6Th79EB12lEtZkx2B7a8PGmKoyhiylUUUOXLFa6txb9nT5tkEzT58fVo84h/iMcD1NZJMF08HkCLRgmUlhKprcNksWBJTkrk8QdLy9E1jVB1DdEGL478fMwedzPlxVdVxR9fepHPlyyhvqGBBm8j1rQ0tFCYSH09ji5deG9RIY//6U8APDTtNvr26InqsOPMz0NRFAJlZVQtXoJqsZIxcgTW1BSi/gCR2lpMbhdRn5jNtUhElKr9lJrjBdVikc+EqoBJJVRRmfDjmpxSmEmPxYh5vVhSUogFAoQqKhOvN3s86JEItvQMXN26YbbbUS0WbrtqMucMHERdQwO3/OxnVDYcL+t/oTWD9oyugQfnRKqRYLCXk04BGH/WcCLRKHf/+c98+fW/UU0mkvr3RbVaaVi/Ee+mzdgyMnH36oUtMxM9phEoKsK3axdmjxtPv75Y09PRIzFCtbX4d+3G4nGTNHig+LztNpIHDcCWlkqkvi4RlNUMTQMUfLt249u5S8rllpZhdntwn3oK7h498Jwq1eqs6WlY09LEf67rKArEwlGCpWWSG96KQVvBsjLCdXWSix+JEGnwEm1sJFBair+kBNVuxxrvqKfrOoGSEkIVlZgcdlSbDVtaGhaPh0BRsXQH1KSlrS0rC2fXLqgmM2aPrNobGxu5edo0vlm7liS3m5f+5z769OyJYjYTrqzA5HLx2aaN3PfQQwDcc8utjD/7HFSrBVtqCubkZAKlZVT/9xv0SJi0EcNx5Oaix2KE4jnzWjBE1NuIYrFgstmwpqVhdjpb7f1qbyzJyaBJW2AtHCZcI8WlVIsFk8OBKd6zI+ptwOR2E/V6E/EoZrcLVJVYwI81PVVqInTvhqrDY7f8iDP69aO8uprfvvJyR93eUXGiBO11dk6kGgkGeznpFIBrT+nDxKHDCIXD/PLpJ1nx3/+ix2Jknn8erl490KJRVKuVpP79SB7QH1f37lKS1WwmUFSMb9dObBmZ2DLTIRLF3esU3Keeitlhx5LsQbXaiAVDWNPTQSfxIw0QCwYJlJSgV1SiRyPE/H68W7aihYKYkzzE/P6Ez9zkdIr5vLYOs8eNs1s37Dk5KBYrigI6OuHaOnw7dxJpbESLRo+qbHG00UewvIK6VavxbtyMFgwS8/kIVVRKtsTGTXg3biZa30DM78e3Yyf+omIa1q2nces2MJlQLBbMLhe2jAxCVdUEiktQzCYCu/dgcTtJO2sYWjAk5zkd1NTUcPVVV7F0xQpSPEm8+uBv6NejB4pJJVxbgx6LMWfVSv7fPfegaRo/vf4GrrvookQanzk5hVBFJfXr1xOpq8MzYCCeU08BIFhWTqimRtr76jrm5GQJMnQ6sKalft/b0ekxO52odpsUUvI0rythdrsgFpPqijpSf0GRQNSoz4eiqlg8HqKNkoapKArO/DyS+/fDqir84aZbGdC7N6U1rVexcsGCBUyYMIFLLrmEt95664DjGzZsYPLkyYwbN44HH3yQaEvK8vdwPES6nyicaDUSDITjs6PPMaC5Xdw56mwcFgte4NScLBq3bcfsdpPUvx/+3XvQwiFC5eWoDgfWeFe2cF0toWANui8Eigmzy4EejhAoKcaSkoLJ4SBp0CDClVWEKiqwZWUlKglGXC5iXi/RRh+KyYSS5MGSlkakwYsSCJB82hBMNhveLVslxc7vp2HjJpIG9BdBXFYuQV1mE2aXi4C3gUhNHbqugQ7+3UVx03wSqsUsloi4y6Elc7euaeg+nzTjiUaJxsvxOrrk4ciXYjlaJEK4qgqTy4k9JzthYtYjEfwlpfh27EQ1W1BUE1oggJqfT+O2bUQbfVJcyduAoqqkjx6FYlIlwDItjWAwyFWTJ7N5yxbys7J44JZb6dOtmwRVVlQSUVVe+OwT3lm4EIB777iD6887X9whFgt6vPtguLaWUFk5zi5dSDvjdLRIhEBxMYGiYnG9pKVh9rjj5Yst2LOzT4gfKWtqKsHSMkxWC7rNRrC8Akd+nnRarK6RYlRd8gnG60xE6hqIBgK4Tz0FS3KS9G6IRRPpl55+fdHCYfTVa3j2ttv5/QfzW2We5eXlPPPMM7z33ntYrVauvfZaRowYwamnnpo455577uGxxx7j9NNP54EHHmD27Nlcf/31hz3Gl19+ecIE7R0PNL2/TbEWcHxlRhgcyEmnADy0YC63jxjN7Wefh+pxoVrtRKpr2PmfJfS48HwsHje6TqKaGrqOJTUFZ7euKGYzWjgi5u1AQEzk3gYUi1kEdDxwLlhWRqiiArPHTdTnI7R2HbaMDDHl223omzcTravDlp2JFgiiBUNY3G5cPboTLCklFokQrqjEt2075iRPwtxvjTfgsSYlY3Y4iTQ0SCMbi4VYIEAsFEK1Woj5A0S9jYRAuvQ5XZhcTmmDHAgQqqxEr2+QdrFuFzpgTU3BkZ+HFgoRideeV0wmXF27NqtsGKyoRPP7cXbpgi0rg0htPZgUwlWVRL0+wnX1hGtrUa1mkocMQTWbCVVLARuzx41iNnPx2WcTCwR4+bfTadizR9wMJaXsDgZ45J//YPPOnVgtFh695x4Khg4jXFODPTOLYFU1scZG8YGXlWN2OHD26I6/qEisFlXVWNPT8PTpDUjBH0U1STDccej3bwmzy4VqtxOpq8eem0uwrIxgSSn23BxMTgdRrxdbehqO/HyiXi8oKsHSEmqXfYerezdikQixikrs2VlEamqJBYIkDxpILBKBdRv47bXX0hoNgRcvXszIkSNJSUkBYNy4cRQWFvLzn/8cgOLiYoLBIKeffjoAkydP5s9//vMRKQDnn3/+CRO0dzxwMHeLoQQcv5x0CsCe8nJun/UXLh86jJuHDcPqsKPk53P7/9xL5kvZvPjSS9iDIVSzGWe3roSra4jW1RNrbMSani455g574nqhqmqpROdtxJLkkUpsubkEyyvw7dhJqLYWNB1raqooDjU1EI1iy8rEkpREsLycSEM91tQUCRL0uNFq6jC5nfiLijA5nRDTMLtFiNuzshLCLOr3ywrXbMaank6koR4tHJF52GwQjRH1+6SCXHU1Ub8PPRyRin5uF2aXU4L+dLC43fh370GPREBVsaSmSDpifCxd0wiWl+PftRvFbMHVqweR2joUkypjaTqqXYrVqDYL9txcLEkeAiWlBMvK8IbDKCaVUHUNNw45nUk9emErLUOrqaYiFOSNJUt4Z/F/iGkaPbp04fGf/pw+OTn4duxEMZvxB4sI1dZgstnQIhFMFiue/v0w2W3yXvjA2bM7rm7diAVDBMtKUUxmHHm5iWJFJwq2zAwCe4qIehtw5Eu6ZKC4BNVmRQtLForZ5cKSJFYhW3oa3m3b8O0pEoWssjIeIKgTDQRIHtCf5P79IKZRv2tXq8yxoqKCzMy9qaJZWVmsXr36oMczMzMpLy8/ojHWrVtHVlYW33333QHHDra/LVi+fHm7jHMo2nIeuq4zc+ZMFixYwMSJE5k2bRozZ85k1qxZVFRUMG3atIQScDK8H0dCZ5lHS5xYv4yHwQ8mT+alV19l7rJv+Wj1KiYNHMzokcOpaGhgc3Ex4y6byFMzZjDcasEcr0IXS04iVFlFqLyCSH0DtswMTDYRPNb0NLRQiFBVJarNimqxxM34Pqmgl55OuLqKUHUNzm5dsGZkoNTWJFLYLCkpRL2NhOvqUM1mooEAwYoKQMOWni718S1mcSXUN2BNS0uUaDY7nThycwmUlqE3NmLPziHqa5T+8fUNUgPeakW1WvDvKRazvMmEJRpDKyqmOhBCtdlw5OfFS/TaMackS6bCPivmWCBAsLyCYGkpqt2Gq1cvwtU1BMvKJTI9HMHscqLFoujRCM6uXUkfOQKT3U7llq089NJLLF27htenzyA5GiPW6MUeCVPt9fLPZctYtGY13kAARVG4/sorueuWW7BENaL19dAUcaxFsWdkiq8bSBrQP17kRkz/Fo8HR36e1CSoqZWuhXl5J5zwB6SRUrKkBZqcTpxduxCsqCTa2EiwooKoz4+zSx66rqNHomiRMGabjZDXByYTJpeTaEMDitlEQ2k5ofJyXKecgj03h4iuUdsKc9Q07YB0sX23D3X8cBg0aBC2+Pewo1i+fDlDhw7t0Dm0xzwWLlzIggULmrlbhg4dSlZWFrNmzeLyyy9n/PjxJ837cbzMIxQKsXbt2oMe75BfxwULFvDSSy8RjUa5+eabueGGG5od37BhAw8++CA+n49hw4YxY8YMzGYzJSUl3HPPPVRXV9OzZ0+efPJJXPHI58PlxpGjmXTFFTz6yCN8vXgx/1ixnHdWrWBY374UV1ZSVFnBjT/7KTdccSX3PnA/6Xl5mOx2HF3EpBquriGwpwiTyyWrcpsNa3oa/l27adi4STrc6WByOXF27YpqsRCN16U3u9xYU5KbV6DTdaIBP4GyUuzZUmzI1bMHWiAgfm+rBVtmJgFVJVhWCrqGp0+fhICWEsZ5BEtKCVVUYM/LxZaeTtTnJxYIEPU1JlLG7Hn5mB02It5GdG8jWlISJpeLcF0tZpdbAvrMIXRNQ7VaUVQTkQZpeBSurcOclIQjP59QRQX+PUUoFgsWkwlzigdLSgq1y5aDopI0YAB6JMLiL77g7gcfZE95OXabjU2V5QzrdSr/Wb+Oj5Z9w1dr1xKN968/67TT+M1993PGiOH4du4iUFKCb88eFFXF0VTtLhgERcHdq9de4V9SCjpYM9IIVVQSCwQwu11S+vcEMfu3hDU9LZHq58jPw5GbQyzeUyFUXk6ophaTzYpitmByOLGkpGBNSyfa2IijRze0Rh8mp5NgZSXB0jLJcrE79tarOEZycnJYtmxZYruyspKsfeov5OTkUFm5N02xqqqq2XGDzsWJVCPBYC/trgAcS3DQjBkzuP7667n00kt54YUXePHFF7nnnnuOaHzfrl2kazHe/sc/+OY/i3numaf58ttvWbphAwAKshp58/33mPfRIv7nrru59oor0MMhSenTxGwa8TZgstkTFe20aIxIbS3oGu7efbC49qabmV0uTE4HocpKVKsVLRolXFdHpKFBKuSpZkxWO5bkFOxZmZJiV1RM1NeIFpbyxa4e3VEUBX9REVokQtKAAYnVrRrvqOffswfv5q3YMtJQzRbxx1dXg65jTU/FZLESCwaJeRtRXE7cvXuDpqOFQ0QbvETq6lFUBUU1oaOjhUIoVrFqWDOkIl+gpBj/zl0oNjvOnBysaalYkpNpWL+BYGUVrp7dqSwq4qlXXubdf/0LgK55+Zw3cgRvL1jAr9euJRIX+qqqMnTQIO784Q8ZOWo01swMvJu24N2yhVB5ObqqkHra6Ti6d6Vh5SoUswVP3z7xuv9hAiWlRP0BzG6nVChU1YRr5URHXE05BIqKCZSUYM/NxWS34+7VM94DwYFjnzbHUoXSQTTgJ1xWDqpCLBzB2b0bZpeTWCAoKaW+RjAd+6p69OjRPPfcc9TU1OBwOPjoo4949NFHE8fz8/Ox2WyJFdK8efM477zzjnlcg7bBqNp3YtLuCsDRBgddffXVfPvtt7wQbwYzefJkbrzxxiNWABSgduUqwnV19B0wgOd+O53da9fx/ry5LN29i3VlpYmGKF6fj988+gj/+8c/0L1bN7p26UpudhapSUmkuN2kut24PR6SMjNxJSdjS07C3ODFv30bVo8HVdNRYzHJtYxGpWXtrl1oxSXU+YMoZrN0IfR4xI1QXiZWBYcDW6b0JYgGAoRr63C43Ti65BMLS133UGUNrp7dUc0mtEg0YSqP1tcRqqiQvgMmEya3SyoKWm1oIYn2t3fJQ7VZSB7QHxD/fsTrJVRRRbC8jHBjo1gfnA6igQCxhgbUQICIplG3ezdRkxklOwutsoJg0R6qduxgz/IV1IdCrK0oY+nKlcTiQh5gT0kxb733XuL9H9yzJxePGcNlF11E3ebN9MzNJ+rzEa6ro3HbNmK+AGaXi6TTh+Du0YOqr/6DFg2TdtoQnPn5RBobadyyjZjfhzUtDT0akwqJaakn9Kp/f1SLRWIASkoI7CnCHO9oaXY5CVVVg6KgqApaSKpYouug6cRCIaI+HzF/gFBNDY4cyZAwuVxodjs0HnsYYHZ2Nr/61a+46aabiEQiTJkyhSFDhnDbbbdx5513MnjwYJ588kl+85vf0NjYyMCBA7npppta4V0xMDA4XNpdATja4KDa2lrcbjfm+Kr3aIKGAKzJyQSrqqlfs45wbQPJA/vT/czTuNXj5prt22kIBFi5ZzcbiopYtmc3VT4f3mCQDZs3s2Hz5mO4c1AVFVVVUFU1cR/N0HWIm9dUVZU85rhgP8A7qmnxH3hVhJ6ixAsFKegxDdDF1aCqaJqGrmnyv66j6TpaXMnRNQ1N18Vf3AZ50x6Hg55ZWfTNzKJ/Th7Dh55Jt9OGYEtPx19cQr2u4MjNRnV7aFi/HsVkxpKShD07G1taGtX/XUosGCB1xFlYPB68W7cRLC4BVZFud2lp0iPhOGns09qoVivOrl2l74LXS7TBi65rhGtqidTWSnaAw4ElJRmTzYZqs+Hq1ZNQZRWBomKClRUEojEsqclEyiuI2a2tNreJEycyceLEZvteffXVxPN+/frx7rvvttp4BgYGR0a7KwBHGxzUUpDQ0aSelKNDfg5acSkNO7dTvnMHmM2ADo0+iGkMSkphUL8kru7bD1weGu1Wiisr+f38ufhCoRav67bZcNnshLQYwXCYYAvdEjVdQ4sBsRjhSOSI594eKIqSaFYEoDb9j4LZpGIxm1FVlZiuE41ECUTCOK1Whg8YSEpmBsluD9FQkH6uJHIcDjwuF4oWA5cTNTWNcGY621UFduxAKy1Fyc1hY2kp+q5v0H0+dIcNNFAaGmHjRnSfDzU9jbLVa6RzoN+P4nSi5OWiVlVBVVWr3XtnidY92nnoug7xlb4eDUuqZzCAmtpyASQ9EkILhdDLysBkAodDiifFiyoZGBic2LS7AnC0wUFpaWl4vV5isRgmk+mA1x0uuXY7RDViOblEnC4pghKNoigKmtONFgqgqCYU1US4vg6tphqny0X3bt15/2d3squqko01NZT5Gimrq8UXChEIhhgzaDBXnH46WiTMyqIi7v/H24SjUbQWVtV/+tVdDBs0CLPTyW9fepEvly4lJzOTzOQUMmxWMpNTyEpNpVtaBqempxJp8KJFY9gy01EtNmKhIHokCtEI4YZ6ol4fKCqq04HZKSmKWkwnFgyi+RohqmGymFFUE+gxFB0Cfh92p5vtdbX4w0H80Rj+aAR/IEhjKIgvEuXCHj3pm54BqsL7Wzbzfyu/w9eCYpOXlMz9F12CxW5DtVjRIiGIaVhSkiWIMSMTW0YmZrcDR34+uqZRt2IlSr9+bK2ooAcq0fQMbP37SSvmYFD8+CYVk9WKPScHk8OOFpCKiY7c1s/r7+ho3baYR7iujnBVNSaXC3vWgUGReixGLBTCX1SMf+cOYsEQms1BRauMbmBg0NlpdwXgaIODLBYLw4YN48MPP2TixInMnTv3qIKGYoEgSjgM0QiqSYSmNSWVWChA1B8kUiOCFZMZV/duhOsbiNTWEq6swt4lj749e9F/wABs6elYk5NQTNLpT4tFCBaVECgr4bTsXD6e/ghmpwNvLEZdMEhFVSVVdbVU1jeQlZqCJzMTk9lMo9dLKBRiV1ERu4qKms313D59mV4wAcUfoKKsnEff+QdZbg9umx2HxYLNZMJmMkEsygWZOaSaTeiqyuelpayuqcQXjuCLxB/RCP5olO5uD/87bATWSIRoJMK9nyw66HvVMzmZvlmZWFxOUtLSiOk6aXYH2U4npyYl0Sc9ndN69KJrn96oNptUAKypQYtGMXtcmDUPji5dpAFPTMOWkYliNtOwchXRRmn8Q3ExWtduWLLSaVi/UXLbu3fDnp2FHothTUvFmp5BpKYGS3Iy9pzsk8rPfyxY43E24eoafLt2YXI4JWYkGhPFN26FUlUVV89e0jK5obEDZ2xgYNCetLsCcCzBQdOnT+e+++7jpZdeIjc3l6effvqIx08bcRZmTYOYBL75du1G12K4enQjFo0SKi0jUFaGHgyhR6K48vKIpacTqqkh2tCAmppK1Osl1thILDMTa0Y6ejhMpEEqAiYPGECotp5wVRWhRi9Ws4Ucp4O87FxiySlo4TCVW3dSWlSKrsV4eNQ5+IePprKxgcrGRsqqqymrqqTSH6BfSipaOIQtNY0Gn49tdXVsizd32Z8zxuaTlZRM1Odnc00VXxYXt3heIJ4REPX58XiSGJCZic1swWW347Lb8djtuKxW3DY7Z/bvhyczk5jXx/ldunDedTdiT/JIuqLFRiwYQI9GQNfQoxHMVitqahqq1YzF48GSnoYWDOLbvFXS8qxWvEuXSoEjh4NwbZ0EOoYjeL9ZjhaOkDL0NFIGD0GPSK8Es8dNpKZGVrHZWSetr/9oscbLVEfq64kFgmhBLVEfwuTxSO0Kmy2RURL0+ymLZ8QYGBic2HRIHYCjDQ7Kz8/njTfeOKaxHTk52Gw2YqEQVr8fS3ISvp270EJhrEkerH1ORXU6CJaUEqmtx19aisXjxpqRhtboJ9JQj8nhJOqX6Hxl504xeyMp1JFGH7o/QNTnQwsE0DQNk82OJTUFxWRGi0agoZ5IwELUH5BgPrOJHJudHJudIXm50KUr1qQkbHm5sorToZ/HyZ8uv5Ky2loCQDAaIaRphKIxdAVy8vKxWq2YnE7G9OlL36xsPA4HySmppKQk47JZcVltOB0OLA4n/uoqbJ4k/nztDWgxDVQVs8eJLTsbe2YmiqISrq4i2uBFi0RxJqeArqGaVLRGH5YsJ65TB6DabYRKyiSlMRLG4rRjdjpQHXbCVVXSiEZR8ZeWEK6sJubzYfJ4sOfYpCyyBqHyUkxOB1mXXERK//5E/QGCJSXSrMjbiNnjxpaVZZQbPUpMNhumw3SXGdYVA4OThxOvTNphYrLZpIhPquSxB4pLpNua1YLZ5caWkkywvJJgeQUxnw89EMTkcqJqJqIBP4qixqOuG0CLgckCaChmC2anHWtqKnpqGtFGL5GGBrSKSikeZLeB1YotLxd3ShqKFiVc3wCxKCaXC1eP7ji65BOuqoFYFKxWog0NuJKSGTpiBMHKahSrFXtaiswHCHt9xEJSBMaanMy4yyZgz8sjXFuLb9t2Yj4/qsuByeFAD8eIBf2oPh+OrAz0mEbU55MKemUVhEor8Nlt2FJTMSV7UMxmnDk50vDI6URXFWINXkIVFTRu2iI+/qxM6YWg6yg2O2a3m1ggkKhPoIVChCursKamYB/QD3tODlokSizgRynegz0rm+TBg3Gf0lPa+VZKVTuT04k5OUmsB4bwNzAwMGhVTloFYF9s6elo4TAxfwBrRoYUUenahUBREeG6eoIVlYRratH8PhSrC5PZSsTrxZaVhbtPbxQFTDaHFKFxOtFNKnoshhYIEPUHJOWquBjVasGek0tDdRXOrBwghsmWjLv3qUTqG9BjUUwuNyarDUdeLo2btxCrq8fk8WA2qegxHbuqoOiSAx7zB4jGq/bZMtJxdOmCq1sXLMnJaKEQqsmE2WYjFg4TCwSk6JDZLKmF4RBml1t88akpOHv0wJ6eRjQQIFRZTSwcQlUULCnJWFNSsXjcYiq2WjH1sqFYLASrq/Fu2Ix/504pwpORidkVI+prxGSxYk1Px2Q2E/X7saVnJBoSRRu8aNEIMV8A1W4n5fQhuLp1Q1EUAhUVhCoqMDmdWFNTsWVmdPTHw8DAwOCExFAA4tizsggUlxAsK8ORm4vZ4cDdqxehqmpUsxmL00nE7yPW6MOUmYnbbpMCPDHpYa5arJjdLiwpKeJP1XVQFbEqOJ2Eq2uoX72GqD+AKV4PQLHY0SIRfFu3ScGenBxCFZV4N2yUAC1FAS2GWluDPTMba2Y6jpxsguXlKCYT9uRkLG439vx87BnpktMfCuHbuZNwZRUoCqYkDwSD0h8eBVt6GvbsLMzeBlL69cOSlIRisRBt9KIFQzisFlzdu9G4dbu4O5xO7Lk52DIz0KMxtGCAWDBEtKaWSE2tuE1SBxLzBwnXVhP2B8CkEFVNhGqqUU1mTE4H5qQk1HCYYIMXYlFiDV5MLgdq1664undHUVWC8RLDEvmfjS09vWM/FAYGBgYnMIYCEEcxSdvYYEkJgdJSbBmZWJI82LOzsKQk49+9B1NdHXomEI2IPz8clu53ZhM6EK1vQAuEsKYmY0lJxexxoaCg+f1SjjcjHb2sHFJSSBoyiJjfh39XEVjMxLw+6qvWQSRKLBIGFFSbFVtaGorNjmIxE/P50YMhTDYbuqZjSUnBkZODHovi370HLRIhXFNLLBjE5HSgms1EqmukbGxODloggG/bdho3bUb3B7BmpO9tMZySTKS+Ae+WrUS9Xpz5ediGnoF/5y78O3clFCOT24ViiqA0ld1NScZkt6NrGrFgiHBVNaHqKqL+AFo4jGI2oZotaP6AKAcK6DENa3oqnt69KdqxA4BAWRn+nbtRLWac3bphTWs5d93AwMDAoHUwFIB9UM3SQS5ULmboqNeL2eMR031eLlo4TKiyCl3XMNnA07cv9qwMYoEAkYYGIt5GYvESq9FGnwRUmRRUsxWT044zv4u04v3uO0IVlYneAc6cHLRolEhjI4piwp6ViS0zHVCJehuI+n1o4SiKqmBO8mBJTiHm9xGprkELBbFnZoHJhObzYU5yY8vJRtF19FgMR34+JruNSEMDUasVt9OJHgqir1lL3YpV2DLTcXTtKr0DamuwJCeJ8NU0og1ekvr3I+r307h1Ow0b1qPHpKa8JSkJS2oyMZ+faKNPLB66jsluw5mfD0gdgkg8PsFksWJy2KXVcJIHZ9euKCYTut+Pb9duQmVl8Sp1vbCmJHf0R8HAwMDghMdQAPZDlIBcIvUNROqkrn7imMUijX0cdkxuF9GGekImBWtaGnaHA1tGhjTbCQbRwhGpsqbFiLcYQtdiWJOTwG4n6pM69rbcbCzuJMwuB2a3Gz3e4a5pJa+YVDnudmH2uFGtNkw2K6rVFi8C1CgpXsEgiqpiil+jqe1rzO+Pt34148jNwezxoCgKaiiEIyMTf2kZjdt2oChgy87B2a0LZqeLWDBAsLySUEUFqtWGJTlJiiVFo5hcDnkf7A5UqwVFUUFVxPVhMoGmEQsEQVVRzBa0aJSY30+kvgGT3QZJSQSKSyROoraOkN2B2ePB2a3rSdHIx8DAwKAzYCgALaAoCtaUZCzJSfGCKVEUk4pqtaLHYviLikWIupxEauuINvqwpqVidrvjzVgObFH83nvv8fvf/56SkhIG5uRw57XXc86E8Xh69kS1WqVBi9ebWE2rNiuoCia7A7PLiWI2i5nd5yPm80nOoapKu97aOolB8IjwbyqbHPMHMDnsWDLSMblczSLpVZsEGqKoBBRFGgUF/Ph378GSlIRqNmOyW9EjYVEuLGaSBg9ENZkI19ZJp8JgUDoGms0oikJU09Cj0fgAKma3G5PTSaSujpjNhik3F5PTgR5tahQkVRJtafG4BLe7rf+0BgYGBgZxDAXge1AUBZPNBra97VEVkwl7Tg7B0lJ0UwxbTjaRmlpxG1RWYXY5MTkcqBaLCEZV5f25c7n3f/6HYCBAls1GpMHLH/46C81uZ7SvUQSuKd7a127DmpGBy+MGVZUVfl0dWjAobV5dLjCpRBsapGVuTMORn4fZ6UIxqSgmM6rVimq3YXY6W8zrjvoDaNXVBMvKMdmspJ81FNVqJVxbR7i2Fl3TMLlc2NLTUa0Won4/oYoKgiWlWFJSsGVmYMtIJxaQgEA9FkXXdGl0ZLGi2u2odhvR+vq4BUUR68M+Aj4WDBIsK5P3MzcHs9N5wDwNDAwMDNoOQwE4CsxOB/ZsicaP1NXjyM8jFgwSbfSJyd3bvJzqm089TY6ikpqcgtNkwhuN0hCJ8Prf32L0sKFE673YcrKlc5vF0uy1liQPliQPUb+fSH09ocoqog316DpY09JwdO2CxdWyoN8XLRIh6vMT9XrRQiGIxrBmZCRM+wC2jHQsyUmEa2uJNnjxBwJYU1KwJCfh7NqVUFUVkdpaol6vdOHzuA9YtTcV7wkWVaKFI6JIZKSjxu9L13XpVFdXJwpSepoh/A0MDAw6AEMBOErMbhc2PZNQRSWB4hLsuTnYs8X0r0Ui4jqIxdA1ja2lJaRZrEQ0nZ0hP9WRMFFNY9fuXaSdNYxgRSUxn09W2MnJmF3OvQJT06RGQSCAFgpjsttQzakoVqtU66uoIIy0hVWtFhSzRdoEK4qMH4sSC+1T991mw5aZgVJX22KwnWqxYM/KQktJIVRdQ7imhnBtLWa3SwrzuFyEayU2IlxTI1kATifEYqJgNDaiR6Oo8SY+Zvded0i00Ue4pgYtHMbs8WDLSEepqjxgDgYGBgYGbY+hABwDFo8HxWQiWFaOf88erGlpYs63WFAtlsRquGduHtWVFewKBGlo8pEjpY0VkwlHbg5Rn49wTS3hqirCVYCqAIqUCgbJ6XdKoGGTUG3q5qYFQ8RCQbRwWFIOtXgHwnhgnmq1YkpOlhK9Vmv8ct9fWU+1WnHk5khsQoNXqh7GLRuKyYQWixGprcW/pwg9GsVkt6HYbFiTkrGkpWLxeNA1TawiwWBCMVAslgMUAwMDAwOD9sdQAI4Rs9OJs2sXQpWVhKuqCdfUoFolZkALh0HT+NG0adz/+OPNhL/D4eC+++7be5148KAI9KAUGdL1vY1bHPYDzPyKySTm8xZM6E2BgMeKyWbDlGnDmpGOFgoRCwbRIxHUqB1LUhJ6NEosFEKPxZDRdCLxIkF7J6pgctgxp6dhdrtRFKVZUGReXh733XcfkydPPub5GhgYGBgcHoYC0AqoFguOvHgcgLeRWCiEoiiSuudycfmPbiWWkpwQeBkZGTz00EMtCrymHgXHSmvXzlcUBZPdjsluP+g5eiyGFomgR8X1oagKStwCsW8Xv/fee497772XQCAAQHFxMffeey+AoQQYGBgYtBOGAtCKfJ+AnDx5ckK4LV++nKFDh7bn1NoFxWTCdBjd5H7/+98nhH8TgUCA3//+94YCYGBgYNBOGM3VDdqdkpKSI9pvYGBw8qLrOgsXLkTX9cPab3D4GAqAQbuTl5d3RPsNDAxOXgoLC5k2bRrTp09PCHtd15k+fTrTpk2jsLCwg2d4/GIoAAbtzn333YfD4Wi2b/+gSAMDAwOAgoICpk6dyqxZsxJKwPTp05k1axZTp06loKCgo6d43GLEABi0O01+fiMLwMDA4FAoisKMGTMAmDVrFrNmzQJg6tSpzJgxo9UDnk8m2l0BKCkp4Z577qG6upqePXvy5JNP4tqvdn5FRQX3338/VVVVqKrKvffey6hRo4hEIowYMYKuXbsmzn3vvfcOK/DMoHPRFBR5ogZEGhgYtB5NSkCT8AcM4d8KtLsLYMaMGVx//fUUFhYyaNAgXnzxxQPO+cMf/sCYMWOYN28eTz31FL/+9a+JxWJs2rSJM844g3nz5iUehvA3MDAwOLFpMvvvy74xAQZHR7sqAJFIhG+//ZZx48YBsgpsKYBj7NixXHbZZQB0796dUCiE3+9nzZo11NTUMHnyZH7wgx/wzTfftOf0DQwMDAzamf19/kVFRQfEBBgcHe3qAqitrcXtdmM2y7CZmZmUl5cfcF6TggDi8+nfvz+eeB/7iy66iNtvv50tW7Zw2223sWDBAtLS0trtHgwMDAwM2o/CwsKE8G8y++8bEzBq1CjGjx/fwbM8PmkzBWDhwoU8/vjjzfZ17979AJ/N9/lwXnvtNf75z3/y5ptvAnDttdcmjg0YMIAhQ4bw3XffcfHFFx/2vNauXXvY57Yly5cv7+gpdIo5gDGP/eks8zAw6AwUFBQwc+ZMCgoKEvKiSQkYNWqUkQVwDLSZAjB+/PgDtLKmIL5YLIbJZKKyspKsrKwWX/+HP/yBL7/8krfeeoucnBwA5s6dy5lnnkm3bt0AMQ1Z9mufezCazER9+vTBGm+I01GsXbuWQYMGnfRzMObR+eYRDofZvHlzpzarNs0tHA538EyEUCjU0VMATux5jBkzpsW/98H2t9U8joaOnEfTe3Ow77Oit/M3/cc//jETJ05k4sSJvPTSS1RUVBwQ3PHaa6+xYMEC/va3v5GUlJTY/8QTTxAIBHj44YfZvn07t956K//6179w79eTviW8Xi+bN29u9fsxMDgR6dOnDx6Pp6On0SLGd9nA4Mg42Pe53RWA4uJi7rvvPqqrq8nNzeXpp58mOTmZt99+m4qKCu68806GDx+O2+1uJvz/8pe/4HK5eOCBB9i+fTuKovDggw8ycuTIwxpX0zR8Ph8Wi8VIHTEwOAi6rhOJRHC5XKhq56wTZnyXDQwOj0N9n9tdATAwMDAwMDDoeDqnim9gYGBgYGDQphgKgIGBgYGBwUmIoQAYGBgYGBichBgKgIGBgYGBwUmIoQAYGBgYGBichBgKgIGBgYGBwUmIoQAYGBgYGBichBgKgIGBgYGBwUmIoQAYGBgYGBichBgKgIGBgYGBwUmIoQAYGBgYGBichBgKgIGBgYGBwUmIoQAYGBgYGBichBgKgIGBgYGBwUmIoQAYGBgYGBichBgKgIGBgYGBwUmIoQAYGBgYGBichBgKgIGBgYGBwUmIoQAYGBgYGBichBgKgIGBgYGBwUmIoQAYGBgYGBichBgKgIGBgYGBwUmIoQAYGBgYGBichBgKgIGBgYGBwUmIoQAYGBgYGBichBgKgIGBgYGBwUmIuaMn0F5omobP58NisaAoSkdPx8CgU6LrOpFIBJfLhap2zvWB8V02MDg8DvV9PmkUAJ/Px+bNmzt6GgYGxwV9+vTB4/F09DRaxPguGxgcGQf7Pp80CoDFYgHkjbBarR06l7Vr1zJo0KCTfg7GPDrfPMLhMJs3b058XzojxnfZmIcxj8PjUN/nk0YBaDIVWq1WbDZbB88GYw77YMyjOZ1hHp3ZtG58l1vGmEdzjHns5WDf587p5DMwMDAwMGgDdF1n4cKF6Lp+WPtPZAwFwMDAwMDgpKGwsJBp06Yxffr0hLDXdZ3p06czbdo0CgsLO3iG7cdJ4wIwMDAwMDAoKChg6tSpzJo1C4AZM2Ywffp0Zs2axdSpUykoKOjgGbYfHWYBaGxs5LLLLqOoqOiAYxs2bGDy5MmMGzeOBx98kGg0CkBJSQk33HADBQUF3HHHHfh8vvaetoGBgYHBcYyiKMyYMSOhBHTp0iUh/GfMmNGp419amw5RAFatWsV1113Hzp07Wzx+zz338NBDD7Fo0SJ0XWf27NmAaGrXX389hYWFDBo0iBdffLEdZ21gYGBgcCLQpATsy8km/KGDFIDZs2czffp0srKyDjhWXFxMMBjk9NNPB2Dy5MkUFhYSiUT49ttvGTduXLP9BgYGBgYGR0KTz39f9o0JOFnoEAXgd7/7HcOGDWvxWEVFBZmZmYntzMxMysvLqa2txe12Yzabm+03MOgs1NTU8Pzzz3P77bdz00038ctf/pIFCxagaVpHT83AwCBOk/BvMvsXFRUl3AH7KwEnesZApwsC1DStmRlG13UURUn8vy9HY65Zu3btMc+xNVi+fHlHT6FTzAFOjHksWrSIv/3tb/j9/mb733nnHXp268ndd99Nt57d2nweBgYG309hYeEBPv8md8CsWbMYNWoU48ePT5w7bdq0Zufuq0DMnDkzce7xSKdTAHJycqisrExsV1VVkZWVRVpaGl6vl1gshslkorKyskUXwqEYNGhQhxdmWL58OUOHDj3p53AizEPXdX7/+9/zwgsvAHD++eczefJkksxJ7Fixg1fnvsqO3Tt48IEHee3F1xh+8fA2mUdrEQqFOo2SbGDQFhQUFDBz5kwKCgoSi8gmJWDUqFHNsgBaO2NA13UKCwubjf19+9uaTlcHID8/H5vNllgFzZs3j/POOw+LxcKwYcP48MMPAZg7dy7nnXdeR07V4EQnApQA5UCs5VP+8pe/8Pzzz2M2m3n66af5+9//zpTxU7ik2yXcfs3tfP3p14wfO556Xz23/PQWtq3c1o43YGBgsD+KojB+/PgWLcr772/tjIHOVoOg0ygAt912G2vWrAHgySef5PHHH6egoAC/389NN90ESJDG7NmzmTBhAsuWLeOXv/xlB87Y4IQmBKwHSoEiYCMQbX7Kv//9bx577DEAnnvuOa655hrQgR2ABegDjgwHr8x6hYJxBdT76rn19lvx1zZ3EyQIgrnWDA1tdE8GBgZHTGtmDOxrUWhSAjqyBkGHugA+++yzxPNXX3018bxfv368++67B5yfn5/PG2+80S5zMzjJ2YkI84FAGNgK7AJOkcM+n4+7774bTdO48847mTRpkhwoR5SH3iS+XSaTiT8/92cmXjaRTZs38YeH/8DDf3q42XAf/O0DZj85m8rKSjIzM7nhpzcw7o5xogyEAVf8YWBg0K4cLGPgaJSA/eMNmlwLHVWDoNNYAAwMOg31QCPQBbADSUAeUBc/BjzxxBMUFRUxePBg7rrrLtkZQSwGKfHX7IPL5eLZPz2LyWRi5pyZfPf5d4lj89+Yz6sPvcq2qm1sNG9kVfUqZv7vTL5++GvYBuxBLBC72+qGDQwMWuJIMgYOl85Ug8BQAAwM9qccsALp++zLBmxAKWzdupXXXnsNVVV56qmn9rbarAA0IL/lyw4ZMoTbb78dXdeZ8b8z0GM66PD3J/5OQ6iBXaZdhJUwISWEKWii8K1CyASGxMevlPENDAzah4NlDDQpAUfjs+9MNQgMBcDAYF9CgBfIAPZVyPX4Ph88/ujjxGIxrrvuOgYOHCjHY4iATkGsBgfhzjvvJD0tnWXrl1H4z0KohPqKekpNpeiKTkYsgx7RHpSqpSzzLgM/Ek/QBUhDFIBgK9+zgYFBizRlDOy7Qm9SApoyCY6EtrAoHAuGAmBgsC+18f/3Xf1XA6uAPbBq6SoKPynE4XBw9913y/EAsEGOE4z/X0OLmQMexcNdt4rL4Ilnn0DbrZGamQo6dI90JzeWS71azzbLNrR8DXzsDQrsiiglJa16xwYGBgfhSDIGDoe2sCgcC52uDoCBQYdShwTbWePbXiQg0AM44cVF0n/i5h/eTHZSNmyOn7MNcRHE2OurtyNWg9z49Srl2PXnXs8L//cCW4q3sOgvi/jZpJ/xz7/+k1gkxmbzZnabduNwOvj53T8XYV8E9EKUknSgCok3sLTpO2FgYNDKHEkNgvbAsAAYGDQRQ1bc+wbw7UEE+6mwM7qTD7/7EIvJwrRh0yQwzw+kAt2BvkgMgA1xGVQD24F1iBKxG0gB61Ard9xyBwDPffUco4eO5oq7r6A6vxqzYmZ4xnD+fO+fubz/5aKMmAETYv6vQYR/VZu+EwYGBm1Aa1sUjhXDAmBg0ESTqb1JAahDzPs9AVWK/miaxtXDryZ3fa7s7w0Ux1+rIjEAWYgyUY5kDVQhykImkka4Bq7zXMez9mdZ5V3F0m1LGXPXGMbcO4ZVH6/itNhpEouQjqQh/hdxLaQgLopaxLqQ24bvhYGBwQmPYQEwMGjCi6y0m/LtqxAze6o0+vnnP/8JwE+G/0TM8jsQi8EeRGBnAKfGX9sksDMRq0BD/HpfAyXgSHFw44QbAfi/L/9PLAWAbtLlW2lC1PPdyKp/K6KQBBHrQlF8vgYGBgZHiaEAGBg00YD4+hWk6l8DsgpXpIV1MBhkzMAx9MnsIyv/PcBSxMTvRoT9BkRYlyL++xrE9x9GVu71iADPhRsvvxGTycSHaz6k4pMKqAJrqVXcCf2AJUhq4SAkBqAbkBx/fRVQ1vZvybHQ2NjIZZddRlFR0QHHNmzYwOTJkxk3bhwPPvgg0Wi0hSsYGBi0JYYCYGAAKBFFVvGe+I4mQZ0qqTtvvfUWAD/s+kMx8WchAr4QWIuszP+FKAXdgDOB0xF3QghRKpKRAkOVQBHkRfO4ZOglRLUob33yFrwFakCFHojy0YC4EnrEXx9BLAxZiEKxoc3ejmNm1apVXHfddezcubPF4/fccw8PPfQQixYtQtd1Zs+e3b4TNDAwMBQAAwOIC16QlTyIud0KOOG///0v27dvJ8eVw5jzxsAYwIH49Vch5vpKRNgnIav/xvj+AOIK6B8/Fo2f+1+gGm66SPpcvLn2TaLLo1hKLKJUrEdcCnuAFTIP6uLjdkEsExVInEEnZPbs2UyfPr3Fjp3FxcUEg0FOP/10ACZPntzu6U8GBgZGEKCBARBXAFIQAQviX0+Vp2+9Iav/awZeg3mCWfzyDUgQIEicwBZgBCKctyPpgUnIuWnAKMT/nwKsRoR5JZzT9xx65fRie9l2Pt/9OUMah0jMwLnAaGAl4lLohigBIcQC0DV+7c1IlcBOxu9+97uDHquoqCAzMzOxnZmZSXn5kWsynaVtcVPn0o7GmEdzjHkcGkMBMDAgrgA4EVN7ADG9u6G2tpYPF36IgsJ1k66T45uAHCQAMIis1IOIK6ArMAARzsuQBkJXIBaBFCRwcFf8NbtAReWaS6/h8VmP827oXc6sP1NcD8TnMCz+2jJEkahnrwLgQSwEjey1XBwHaJp2QC/0o0l/GjRoEDabrTWndsQsX76coUOHdugcjHkY8zgYoVDoexVlwwVgYKCDGlT3Rv83Rdd7YMH8BYTCIc7rfh5dx3QV4a8hKXhmRBi7kEC9LYhLAERAh5CaALXAGiRYcCXiv4/I9SmCK7deiYLCx6UfU51cLccqkbEUpBeAirgW6uLXz0QaFFUhroDjiJycHCorKxPbVVVVLboKDAwM2hZDATAw8IOiK80VABtghbnvzgVg8ujJEhNQggj3ppTBrsjqux8ilJchVQErkJX8+YjQ/nf8mApcGH8MAiyQH87n7K5nE4qEWBhbKIpFU0nhXfHr5iMr/T3x65oQd4Me33ccBdHn5+djs9kSptF58+Zx3nnndfCsDAxOPgwFwMDAF/+/SQGIm9SLi4tZ+t1S7GY7BZMKpJqfgkTllyDfni6IgPYiMQAKsBjx91uQlL46xJwfjr8mDYnm9yJugXNhSvcpACzcvVAsBjsQob4LMfufgsQn7Nlnvt2QOIMyJN2wk3PbbbexZs0aAJ588kkef/xxCgoK8Pv93HTTTR08OwODkw8jBsDAIBAvwGNFVt5RwA3z/jEPgLG9x+LOc4t5Pwcx0dchgvwURBmoix87EzH3L0f89/+OH48hQYUaErjXGH+NClhhQsME7lfvZ03FGnYGd9JjfQ9Z3Q9GlIBBiDLxTfx6fZC0wgz21hvohFb0zz77LPH81VdfTTzv168f7777bkdMycDAII6hABgYBECzafLcH9/ngvfnvA/AledcKfuDiMDfifj3m/zwEUQA1yMR+duR1XoYUQaajimACapzq3nv0/f4eNPHbG7YTESLkGnJJNeWy/bAdhbYFvALzy8kz9+CBCdmIMGFy5D0wz7xeeazt1pgU8yBgYGBwWFgKAAGBvsrACps2rWJ9ZvXk2xL5oKLLpAAvOT4oxT55mQjAjoTWYXXIoK6GjHNq/GHhUQ3wdJTSjlvxnn4g372pS5Wl3j+wVcf8IurfwHfIvUAPPHrnoYEH25FrBRmJAZhNRI0WIPRH8DAwOCw6RAFYMGCBbz00ktEo1FuvvlmbrjhhsSxDRs2cN999yW2a2pqSE5O5oMPPuD999/nqaeeIj1dmrVfcMEF/OpXv2r3+RucQIQADTR7XAHwAQ6YO28uAJf2vxRbsk0a/vRjb3OfbEQogwj7VKQ+fzFiEXAi/vpyJLYgHciF3FNyGXXKKLSgNBUaah2K0+Rke+p2Pln3CX9d9FfWVq9le2w7vU7tJZkFGxHFIxMpKLQ9/ugTn4MLqUtQi6EAGJycLELcY3FySnLgwxbOe4RmAbM6OkusS5jrmMsO8w4q1Apu9N/Ibb7bAFhiXcJ9yfehoqK08O+t6rfI1KSmxaNJj7LUuhQFRc7XJbU1U8tkdGg0t/hvQeEw0l1twIOHcc/DgXGHcd730O4KQHl5Oc888wzvvfceVquVa6+9lhEjRnDqqdJFpX///sybJ77XQCDA1VdfzcMPPwxI4Y/77ruPyy67rL2nbXCiEpD/dJsuPnc/6Ck6896Vz+AVI66QYD0N8cFvRVwBOeztGqggwrkGMdtH449SCKthnln1DJOGT6J/XX8ogVcvelXy1wNIV7/BkLY+jWEjhrFpxyY+2vQRb3z1Blsbt/Jsr2dJL08XgZ8FnIN8a9chCoASn8smRHkx3AAGBofFcstypidPZ4V1RbP99Wp94nmj0shWy9aDXiO6jzax1bz1gGs1EVSC3Oq/9Rhn3Pq0uwKwePFiRo4cSUpKCgDjxo2jsLCQn//85wec+8orr3DWWWcxbNgwANasWcPOnTt55ZVX6Nu3L7/97W9JTk5uz+kbnGjEFQDNponwbIR1q9exq2QXmfZMRmaOFJ+/DVECNsg52BGFoUmhz0Si8Xciq34d1gTXcNfqu1hfvp7Piz/nw2kfolap2Kw2OT+GBA1WISb8CFycfjEf8RFvbX0LX8zH7b7b+Uf/f2DebRarQw9EEWnKEjAjcQhbEetEU6EgAwODg/Kl7UtuTruZiBIhI5bBDf4bGBEeQXYsm+zY3tKao8Kj+Lzic3R0NDT0ff8pOulaeuLc39b/lju9d6KhgQIaGmHClJhK6BXrlThvnXkdpaZSLg5d3K733BLtrgDsXwY0KyuL1atXH3Ce1+tl9uzZLFiwILEvMzOTH/3oR5x55pk8/fTTPPLIIzz11FNHNL5RPrRzzQE6dh7WIqsUAToVVn2xCs9yD29uehOA0Xmj2VG/A/fXbgI9A8R2xEj+OplIcgRvqpfo5ijBbsHEt8i+zk724mwaQg28En2Fv+/6O5quke/M5ycjfsJGz0acW5zEPDG0co1IRoTInghJXybhXuMGBc5OORuXyYUv5iPFmsKSuiU8tvExfp77c0JLQwQbg4SyQiRvSab6X9VEukRQAgopJSloVRrBhiCh7qEOez8NDDqEcTQzh5ctLyN/aP6B5/1W/vN+4CVye4Trr7+ehx9+GJfLdeC5gBs3fRIRt9/PqYle4HtZvnw51wy9JrEdi8W4Z+I9bNy4kXfffZczzzzzsK7dVrS7AnC4ZUDnz5/PxRdfnPD3A7zwwguJ59OmTWPs2LFHPL5RPrTzzKFTzMMuj+V1yznNcRqcAss+WwbAlJFTONV8qqz00xErQHckva8HYnK3I/n4FeDf5uelypeYVTuL+kg9qqJyW85t3Dv6XpynO2XVnowUAapErA/rkWyBs2U6dWod4+vG8+6KdxmXNo53yt9hVu0srsq5isG2wRJbcIq8Pt+aD0OR+dUirooeSLCg6ejejkOVDjUw6JSUIMG5cZwbnC2fpwA6XJZ7GW9Of5PzzzgfdWML5XByEctaK6MoCgMGDGDVqlVcf/31vPHGG5x11lnNT9rvXg5KK8yx3QsB7V8GtLKyssUyoJ988gkTJkxIbHu9Xl577bXEtq7rmExH+StnYABi0g8CDjA1mKAWdtl2sWHjBtw2N2enni1fxjORH44ViAJwLuL/r4Hg3CC8ASyVa8yqE+E/Oms0C/ot4OEBD+PEKcWB9iAZAR8jdQJKETfAaOCXcl1VUZn4o4kArK5bzdTuU9HQuG/nfcT8MUn5K0EKCBXF568g7oFofLuhzd85A4PjDl3XqfXWJrYvHHohqtq+IlBVVR5//HEmTpyI1+vl5ptvZvfu3e06h2bzae8BR48ezZIlS6ipqSEQCPDRRx8dUAZU13XWrVvHGWeckdjndDqZOXMmq1ZJsfU333zzqCwABgYJgvH/HWCpsoAOhRukLe2YHmOwBWxS5vdMZJXvQ3zslbBk9RJum3sbQ18fiq/IB7Vg89q4v8/9zDljDu/0fIfTLafvXZ3XIFaEKKIImJCI/jOAscjKvr8cP898Hh6nhw3BDVytXU2OLYeVgZW8u+NdiT8oR4R+AxJ3ALIaUBArQFMvAwMDgwSF/y3k3NvP5YP/fNDq19Z1nYULF6Lr+iH3WywWXnjhBcaOHUt9fT2/+c1vWn0+h0u7KwDZ2dn86le/4qabbuKKK67gsssuY8iQIc3KhNbU1GCxWJqZ6k0mE88++ywPP/ww48ePZ926ddxzzz3tPX2DE4l4ACAhUP0qJMOixYsAKMgoEOHdFXGURQAbVO6q5Obf3cyUB6bw4ZIPaQg3sMy1TJr87IEf9vohI88aKYWAMpBI/RxgILLST0ci+UciwjwLUQb2ADvBWmHFusrKRWddBMC/zf/mgVMewK268df6JVCxJD53HbEI6EhVQlt8v2EBMDA4gNcXvk6tt5ay6rJDn3yEFBYWMm3aNKZPn54Q9rquM3PmTKZNm0ZhYWGz800mE08++SQul4tPP/2UJUuW7D2Yh7j2DvVoBRdFh9QBmDhxIhMnTmy2b98yoenp6fznP/854HXDhg3j/fffb/P5GZwkBJBVczzrp0qr4puV32A1WRmTNEby682IqX0jrC1fy82bb6YsWIbb4eb2S27n+pHXkxPLkWI8+cBVSOneSkQoOxF3wQWI734z0j+gAclTDiBZANWADtHkKMRgXJ9xzP1yLouii3gn6R0uvORC0pakybnO+JxNSNOhhvgYyfFrBTDSAQ1OOnRdZ/HixYwePfqA/fMWzuPrVV9js9iYMmZKq49dUFDA1KlTmTVrFgAzZsxg+vTpLFiwgKlTp1JQUHDAazIyMrjjjjt4+umnWbFiBaNGjWr1eR0KoxKgwclLABGijRBzxPjo24/QdZ1zup6Dx+GRVfx2wArb123n+g3XUx2pZkTqCF664SWyu2eL0LUgwXdOJE6gUl6DFSnikxr//1vEBVCMrOLrkRgAO2L+7wo1PWroubsnF+64EKvZyrdF31J7Si0ZpgxRKIriY9XGX9fkXkhGrAmVSDXDpo6GBgYnCYsXL+bRRx/l8isu5/zzzgdE+L/8ysv8Zf5f0M0640eNJ8Wd0upjK4rCjBkzAJg1a1ZCEZg4cSIzZsxoMdAd4Pbbb2f8+PH069ev1ed0OBjdAA1OXgIkrACaXaNwqZjpCrIKZPVfRqIy4FLfUrwxLxfkXcA/ev2D7LJs8d9fhDT1URAFYAtiznchuf6jgEsQJSCElPWtQ/z4ufH9IDEGKYhKfiZ40jyc0+ccNE3jk8xPoB4C2QHeaHiDrzZ/JePVxR9VSEBjTnwePgw3gMHJRR6M/sVoht8xnMc+fIw//+fP6GfqTJ8/ncc+fAx/ipTevuaOa9rMvL6vEtDEtGnTDir8QWLbOkr4g2EBMDhZiSJ+/QCQCcFNQb5e+TWKonCJ+xI5px4p/6vDdRnXMWzSMPLS87AGreJ31xGhvi5+bjLiBnDEX2dBfP6nI26BDGTVvjN+7rD4+MuBz4Dx8bllAV1g3IBxfLb+MwqLC7k2+1re2fgO9wfvZ+iWoZw36DxJHyxDYgnqETeAi70WAAODk4W/g/K1wgx9BhNyJ7Dx9Y383+v/Ry968fApD/NwzcPkO/I5Z/c5bTYFXdeZPn16s30zZ85k6NChogR8h6T9toCmaXy35zuGdhuKMlCRwON2wLAAGJycBEg0/iEVlq9eTjgSZlj3YWRaMuV4U5W9zUA99O7bG9eNLmhqXbEQWIp0/HMiKYIeJCUvFxHyOYigrkSEsj9+zumIsnAqcF58/78Rl4BHXnfJ6ZegKApfffsVvjN8TMmeQrKazPLQctaWrZX5VSLWhBr2xhtE4tdr3m/IwOCER1EURgwb0WzfnuQ9AEzpOqXN0v6ahP+sWbOYOnUqRUVFTJ06lQULFjQLDDwYV71yFZe/eDnLdi1rk/kdDEMBMDg5aYqWdwAe+M9qCTot6FIg1oEwkAkPvvYg//zvP4mmR+EaJKp/EBLFvw54CTG/D45fLxI/x42s9nOQMr17EIUgHVml71v6IhexBpSDY6tD9mVCVk4WZ/Y8k1A4xJfhL3FmOZmcMRmAt9e8LcLej1gU6uPzyEYsE43xh4HBSYSu6yxdtrTZvnGBcbw3+j2u63Zdm41bWFiYEP5NPv8ZM2YwceJEZs2adUAWwP6c0VVS3uetmtdmc2wJwwVgcHLStEJOh7A3zNIN8qNR4CmQVbgZVlSt4LWVr+FQHFw0/iIysuPt/0xIKt8/kOj/c5GaAhWI/3AAsA0x+ZciikJXJNBvC7LCd+w3nz5AOdi/tstr0uRRMKyA5duXU7i0kAmjJnBd6XX8reJvvF/zPr9p/A0Os0PcC41IPEC6zB0fe9MMDQxOApqE/8b1G+nRowfnn3d+Yruf0o8uw7pI0OzhVB4/wip7BQUFzJw5k4KCgoTPX1EUpk2bxuWXXy5ZAAoHNe1fMeQKXhn/Cgs2L+DhjIcxLz8M0Xw8VgI0MOgUVCOCPBUWf70Yf8hP/x796RHuIV9UM7z47xcBuLXvrWScnbH3tU1Nf9yIpWAXssLvj/jx65BVeAyJ/PcgwYCB+PktCWUFGB5vSvQtEj/ggnEjpcD5p4s/JTIgwsAuAxliG0K9Xk/hrkK5h4b4fGriY7kwKgIanHTs2r1LhP2AfvTv3x9FURg+dDj9BvRj4/qN7Nq9q83GVhSF8ePHHxDwd7D9+zN48GB69uxJVVUVi5cvbrN57o+hABicnJQjq/AUWPjxQkCC7pr85uV7yllUvgiTYmLqRVNlZV2L+OkXA/9BOgM6EdO7Begdv+5GRBhvjY8xOn68HEkNVBHffTV7ixEB2MHfzy+xAmuBNDilxyn0zutNXWMdS/cshRy4tvu1APx9299ltV+HKCC18esnx+dUjwQpGhicBHTv1p0LLriAEcNGJATuzd/czJ8if+KUUafQvVv3Dp7hwVEUhSuuuAKADz5t/UqFB8NwARicfDStjk+Vlp0fffkRAOOzx0t+vgn+UfQPYsQY32U8Ob1yYBMSDNhUge8bRJBPQmIGPIhAL0EEeD9EmeiJuAIqkGZATsQKsC9uJIDQDpHciFxrHWIpMMO4c8axZfYWFn25iHMuPYcrBl/BB9s/YIplCrpdRwkrYpUojo+XjVgEmuIAjHoABic614NyvUIPegCwZ/ke0k9PZ0nfJQQCAZ7/7HmU9O9fhXc0F154Ic888wzfrP1GXIntgKEAGJx8VCJCOwe+++47KmoqyE7NZmBoIARAs2r8vfLvAPyw7w9lhb0eWV13A1Yh5v2hwBhE6Afj+wJIDICGmPU14L9IpkAwfsyCrNKd8dc0WQ2aWoYPRwT6KqArjDt3HM/Pfp7CfxfyyM8fITktmXd6vSP5//b4vMrYawVIRZSKekQB2NtQ08DgxGS/FLu0HWls/XwrgUCALqldSF8Y/xIMoN1S7A7gEF3+BkcGY7faqSyrxP8fP077QToaNtEKMQBHrABEIhEsFsuxjWpg0JGUIcI5Bxb9TWr/jx40GmWrAhosjy2nKFhErjOXc03nyoo9CVmRNyKC142U9E0F+gKfIoI+hJj+keuzk72leQchq/NY/BogPnwPErS3DZSgIm6DMxArgxtO73Y62enZlFSXsHbjWgZ3HyzXLkMsC025/7vj4+XH51eHWCU6r+XTwKDNWFUsjeOG5A/p4JkcHlaLlc+f/5wu2V1Qlfbxzh9ylGXLlvHiiy8SDoe5+uqrGTZsGB9++GF7zM3AoPXRSdTp103SqQvgvLzzRFhaoG9OX54+62nu7nI3qq5KDf98ZCW9BynHqyPqs4Ksuj1ICmB/YAQSDHg+cCGS8z82/nwAkjJ4BlIDIBlZqQflf/seu1gnesfH2wVqSOWSMVKcaOHHC6UUcA6sZCUPrX2IHZYdcm8BZCXki79WQ+4p2srvoYHBccDqotUAnNbltA6eyeHTLadbu7YoPuRIf/zjHzn99NP55JNPSElJ4V//+hd//etf22NuBgatT5NfPBs2b97Mjl07SPWkMsw7TAS4B5IcSVzjuobrUq8TIZ2DrPaXISv9RmTlPxRZXZ+KpPENR4T92YiZsQcSa2DmwFW4igj/nkinwHTAApZii6zkm1KGTMA2KBghzUQWLVskroMc+Gv0r8yqmcW82nlipTAj7oTVSBliS3x8ox6AwYlODqJ8xx/BnkFWVYsF4LSzT9t7LKfjpngkhCP7Bwq1DYdUAGKxGKNHj2bx4sVcfPHFdOnSBU3T2mNuBgatTzUi6LNIFOe4ZMAlOBvi/rYU9kbPpyL5+zakc99nyDfmNKQHwJnID4oXsQicEz++Acn3X4esynshvvqDYUOsDL0Rf/4KxKqQFX9tA4z2jCbJk8TGso3s2LwDusHleZcDMH/nfHEbVCNKxQrEJZCBZCPUHd1bZWBwvBKNRVm/Q4ICBp8yuINnc/hEY1Em3TOJgTcMJBAKHPoFx8ghFQBN01i9ejVffPEFo0ePZvPmzUQikTafmIFBq6MjfnM34NqrABT0KUD1qqDCyztf5v5197Mpskl86zYkGG8HIlDPQKwBWYjZvx5ZdWcgq+7+8eeR+Dj9EKF8OGSBb6BPYgT+G59vH7mudY+Vi0ZdBEDhskKwwbkDziVFSWFT/SY2ejaKsM9AFJIdiDKh8b2BRwYGJyIxLcb0qdP56eSftkn3v7bCbDLjD/rxB/2s3rq67cc71Ak/+clPuPvuu5kyZQpdu3ZlzJgxPPjgg20+MQODVseL+MeTobi2mNWrV+O0Ojm3/7kE/h0AFWYXz2ZT4yYuzbmUvmpfWVU7EcuAFRH6dkTYB9mb2tc1PoYFyRQ4SmLJMbnWUsSCMBhRIj6FglMLeP+j91m4YiF3jLkDaw8rE+wT+Hvg78yvn0+/SD+ZrxsJMkxDlIhyRBEwqn4YnKjk0SwiPkaMm269qcOm0yL7zfFgDD9/OBt2buCb+m8YMXTEoV9wDBzyJ+GSSy7h448/5pe//CUAH3/8MRdddFGbTsrAoE2oIZGzv+hzif6/oM8FOJwOTD4TRbEiNjVuwm1yM9w1XMzoWYggVRAlQIlvW5C6ACpipm9N4dobSfFZj/jwuwN5cGHahdgsNpZvW055pBxSYWLmRADmb52P7tIlQNGOxASoiDWhGrFUGBgYdHqGDx8OwDfffNPmYx3UAvC73/2OBx98kJ/85CctHn/55ZfbbFIGBq2OhvjVbYCTRCbL+LPGQxGofpVPbJ8AcF7SeVhrrbLKz0Ai692IQA0iVoRSRND2oW0K7ZwBfIwE9I0G8sFV4+K8gefx8cqPWbRpETel38ToU0aTuieVHQ072OjYSP+a/nKvDUhwYVMFwmIkpsHA4CRg/vz5lJaWctFFF+Fw7N94o3Nz1llnAbB8+XI0TWvTrICDKgCjRo0CYNy4ca0+6IIFC3jppZeIRqPcfPPN3HDDDc2OP//888yZM4ekpCQAfvCDH3DDDTdQUlLCPffcQ3V1NT179uTJJ5/E5XK1+vwMTkCauuXZoDpYzdJvlmIxWcSv/hEoUYUvAl8AcLFysUTO90JW4UVIkN5OxA2QgeTz5yNR+m1BWnz8nUjcQh9gO4wfOJ6PV35M4beF3PTDmzBnmrnBcQONtkYcKQ45tzE+Tz+SibAFSV8c1EZzNTDoRDQ2NvLXv/6Vv/3tb2zYsKGjp3PE5Ofnk5+fT3FxMZs3b6Zfv35tNtZBVYsxY8YAcOWVVzJ27FiuvPJK+vbti6IoXHbZZUc9YHl5Oc888wx///vfmTt3Lv/85z/ZunVrs3PWrl3L008/zbx585g3b15CQZgxYwbXX389hYWFDBo0iBdffPGo52FwklET/98CHy/+GE3TOGfIOSTbk2EbxPQYS+ukI+A5tnNE4BcggvR0RNinISl/IxE/f1sJ/yZ6IdaFbUggYSqMPWUsqqLyn+/+Q11OHaTB/en38zvr7+jh7iFWiaZGR7uQ+/AglQiNvgAGJwFNK+fBgwcftwvE9nIDHNK28Oc//5np06dTUlLCtGnTeO+993j44YePesDFixczcuRIUlJScDqdjBs37oBeyWvXruWVV15h4sSJPPLII4RCISKRCN9++23CIjF58uRD9lg2MAAkIr8eCdZT4MNP4+b/88aLsKyADeoGGqINdLd0J9+aL6vlpuC5ruwt35tD+5XWTUViAeqQlX0vSHOmMeKUEURjUT7d86mc40F8/g3x5w1yn9TF599b7pHjbzFkYHDENAnNJiF6PHLjjTfy/PPPSxvhNuSQCsAXX3zBY489xkcffcSll17K66+/fkxmlYqKCjIzMxPbWVlZlJeXJ7Z9Ph/9+/fnnnvu4f3336ehoYEXX3yR2tpa3G43ZrN4LTIzM5u97nD54APptBSJRJgyZQpz5swBIBAIMGXKFObNmwdAQ0MDU6ZMSfiKa2pqmDJlCh999FHiPqZMmcLnn38OQHFxMVOmTOGrr74CYNeuXUyZMoUlS5YAsHXrVqZMmcK3334LwMaNG5kyZQorV64EROmZMmUKa9euBWDlypVMmTKFjRs3AvDtt98yZcqUhLVkyZIlTJkyhV27pMXlV199xZQpUyguLgbg888/Z8qUKVRUVADw0UcfMWXKFGpqZCm8ePFipkyZQkOD9IydN28eU6ZMIRCQ3NM5c+YwZcqURMrnP//5T6ZMmZJ4H9966y2uueaaxPZrr73GjTfemNieOXMmt9xyS2L75Zdf5rbbbktsP//889xxxx2J7WeeeYZf/OIXie0//vGP/OpXv0psP/7449x7772J7UceeYQHHnggsf3QQw/x0EMPJbYfeOABHnnkEdmogj89+yde+NsLeBu8fL38awDqttdJk58GqPZWM9kzmWsd14ILXvnoFea/PV9SAd3w8tMvs/DLhRIIaIZbbrmFmTNnJsa78cYbee211xLb11xzDW+99VZie8qUKfzzn/8EjuCzV/gh5EKDv4H7fnQfn234DGxwYe8LAXjz9TehL3jtXlbtWsXLS19mXtU8GuoaeGPmG+zYsAOKYGfyTt5f8D7b3tsG2qE/ewYGxzNNn+uhQ9upo04bMHLkSK688kqyslrqHd56HFYvAIfDweLFixM/+MdSB0DTtGa9kXVdb7btcrl49dVXE9s/+tGPeOCBB7j++utb7LV8pBQXF7N8+XKi0Sher5cdO3awfPlyQqFQs22fz4fX62Xbtm0sX76choaGZtu1tbV4vV62bNlCUlISlZWViW2Xy0VZWRler5fNmzdjtVopKirC6/WyadMm+vfvz/r16/F6vWzcuJFYLMb27dvxer1s2LCBUCjEli1b8Hq9rF+/Hp/Px6ZNm/B6vaxbt476+no2b96M1+tl7dq1VFVVJc5fs2YNZWVlie3Vq1eTmprKtm3b8Hq9rFq1KhFb4fV6WblyJS6Xix07duD1elmxYgU2my2x/d1332E2m9m1axder5fly5cDHLC9Z88e6uvrD7pdVFREbW1tYru4uJja2lpATHYlJSVUV1cnjpeWlh6wve945eXlhMPhxHaTotO0XVlZidVqZfmy5di32Sn1lWIvt/PWu28RjoZJS0ojXB+mcnslTp8Tu93OzbGb6av2pVKtpMZfg73aztaKrUQiEQLeAKX1pazatYpoY5T6+nr27NmTGG//ba/Xy65du1rc/r7PXpcuXVi5cmXis/Zd6ndo1RpKvULxxmI2Zm+kT1IfAFauXcnS8FLSLemsNK3k0cpH6R3ozRldzsDms1FZWwlfw8auGylSiuixoQdr31/LZmXz9372DAyOOxYB34g8WfVfqQB4+lenS1GsfRkOtH5YW+tyiKZBCVqhGRD6IfjRj36kT58+XR81apTu9/v1P/7xj/qPfvSjQ73soLz33nv6Aw88kNh+/vnn9eeeey6xXVxcrL/zzjuJ7bVr1+pXXXWVHg6H9TPOOEOPRqO6rut6SUmJPmbMmMMeNxgM6suWLdODweBRz721WLZsWUdPoVPMQdfbYR41uq4v03W9Vtf11br+48t+rOfl5el/+dNfdP0dXdfP03X9dF0vuapE1y+Q5/qvdV3/Wtf1Obquf6XreqGu62/ruv5fXdcDbTvdFt+PzbquL4rfxyZd19/R9fFnjdfz8vL0wvcLdf1VXQ8ODep98vvoeXl5+vbrt+v6z3Vdf1LX9T/pur5F1/UFuq5Pj99T+ODjt+b3ZP78+fr48eP1sWPH6m+++eYBx5977jn9ggsu0CdNmqRPmjSpxXPaeo7HyqE+v5qm6R9++KGuadph7W+rebQXHTaPQl3XH9H1Xb/epefl5ekDewzUtRmarj+iN38Utu+0jur9KNZ1fZmufz3ra/2eW+/RP3vlM/nu7/8oPvSlDvVdOaQL4IknniArK4tXXnkFh8OBoig88cQTR61wjB49miVLllBTU0MgEOCjjz7ivPPOSxy32+388Y9/ZM+ePei6zltvvcXYsWOxWCzNGhHNnTu32esMDFqkKV3PA8GiIJ+t+wyA8eeMh5VAALa7tvNOyTtsr9wu53ZHAv4C8f8b4tdw8f0lfduKDCQNsRHJ60+GgiHiG1y4YCEMBFuWjUvM0jDow+oPxffvQ+5hFxLEmIrEBWxp+ykfS7DviURhYSHTpk1j+vTp6LoOyCp1+vTpTJs2zYhjamVqg7X0T+/P4LTBR2Uh7kx8t+k73lr0Fp98+0mbjXFIBWDmzJlMmDCBwYOlnvLdd99NRkbGUQ+YnZ3Nr371K2666SauuOIKLrvsMoYMGcJtt93GmjVrSEtL45FHHuGOO+6goKAAXde59dZbAZg+fTqzZ89mwoQJLFu2LFGcyMCgReoQAZgLhOCrpV/hD/kZMmQIXZQukhoHfBT5iCd2PcGLjS9K29/TkEp6TcI+jAjgwy3p29okIwGMMaQOQTKMP208AB//+2OieVHoAZeaLgXgX0X/knPrkW94KaJEpCLBgZsQ5aANOdpg3xONgoICpk6dyqxZsxJKwPTp05k1axZTp05t8yCvk4ZxwG/htOdO45PVn3DXy3fBbznw0dnN/yBm/aFw1hVSD+Cbnd9I47H9H8dq/ucwYgCSk5OZOnUqXbt25dprr2Xs2LGYTMeW/zRx4kQmTpzYbN++fv9x48a1WH8gPz+fN95445jGNjhJ0JHiNzZE8O2Bhd9J69+CggLYiqzsLbC4fjEAo9RRkjefixT/yUFaByvI6r+jFAAVuYcGpJKhAr1P7c0pGaewrWobS75bwrkDz+X8xefjDDlZVbuKPaE9dLV1lbnXI4qQG8mICCFKwJltN+WWgn1Xr95b23zfYN/u3btz33338eKLLzYL/DwRUBSFGTNmADBr1ixmzZoFwNSpU5kxY8Zxv0ptCV3XKSwspKCg4IB4r5b2fy+H6w/fhKTLxskryYOWjCvHQwxAPJ7h9MjpWFQLG9ZvoOE3DSTZkpqf1wr3ckgF4I477uAnP/kJX3/9NXPmzOGpp57i0ksv5brrriM7O/vYRjcwaCvKkdXyqYAC4V1hFq2X8r8Tzp4gnf00iJqjfNMgaUMjXSOl9n4QWfWnIz8sNhJuhA4jHVFGgiTKEo8fNJ7nv3ief334L8794bk4UhxcVHcRCyIL+LDyQ25PuV2UBhNSUCgfcQckIT0MetJm1QGPNtj3SBSAzpK10BTo+X1MmjQpIfybtr/77rt2n0d78MILL/D4448zceJEpk2bhqIo6LrOzJkzWbBgAffff3+i0NyhsFRasFRZDnmedZcVa7GVskAZuY5cFEWhuKT4gPN8W3x4M7xHfE/HwpH+XTxbPLhKpH5B3+S+rK1dy6L1ixidPbrZea1xL4eVBaAoCtnZ2Qktftu2bdxwww1MmzaNa6+99pgmYGDQ6gSQVUMKsmrX4MsvvqQ+UE///v3pbe0tefExWKutxRvz0o1u5GfnS858Ux69K36tdERoduRizY0oIqH4PBSYeNZEUQD+/S8e+9ljmLuamVg8kSpLFV098e5E9cj7UIkUL9Li243ARqSoURvcV05ODsuWLUtsV1ZWNktpKikpSaSigigITSm+h8ugQYOw2dqiDvPhs3z58kOmmzWZ/fdl/vz5rWoBOJx5tAfLly/nZz/7GRUVFcyaNYusrCxmzJjB9OnTWbBgAVOnTuVnP/tZ61sAVNhSvYUrFlzBwPSB/O3sv5Gfl3/geb0R83k7cVR/lyqkbDlwQY8LWFu7lrX+tVydd3Xz8w7jXkKh0PcqyoeMAXjnnXe4+uqrueOOO8jMzGTOnDk899xzzJ49mz//+c+HermBQfuiAduRT3ZTV756mP/NfAAmjZ8kwt8LRGBJSOo0DFeGi8k/A6kamIYUCdLoWPP/vqQjq3kTEICBfQZySuYp1DTU8J9V/4FT4FLXpbyb9i4TUieI1SKKWDP8yI9Kcnw7BylxfOSlNA6Low32PdHY3+dfVFR0QExA03kLFy5MbO/7+pb2t+b8WnvcJrdH03126dIlcf9HrPTE/eGHfKTDyoqVAHRLOoZ2nG1JCbD8MB4g1rmeMGaEVOT9rPSzxL7EI5Nj5pAKwMKFC7n99tv55JNP+PGPf0xaWhoAaWlpJ5y/zuA4R0dM20HkCxK3HAZ2BFi0Tsz/k0ZOkuDAoBxr8v8Pt8cVADOyas5DhKMKOOgcCkBTF8KYPBSnwqTTJwEw/7P50BexVAQQYW9BrAa1iCJThdxHU3aDGakOGG39qR5LsO+JRGFh4QHCb1/h2BQY2VHZAm017r6xD020dczD8nKRnqdlntZmY7Q3Q7sNZVSvUUwcMpFI7Ojr7xyMg9rc6urqAHj66acBEhXjmkhJSeHqq6/e/2UGBh1HESLcuyKCECAKn338Gb6Qj9MGnUYPZw8xf8d96c6oExcuzkw+U4L/vIgikQKsQ6LvXSSUiQ6lKRUxTEIRmHTWJJ75+BkWLlnI4zc9jjXDil6rs2LXClZrq7nllFtEoQki95aBWBCiSMDjJiRYsnvrT/dog31PJAoKCpg5c2azwLcm4Thq1KhEFsC+2QJAwmze1tkCbTVuS26P6dOnt5kSoOs6n++Wqqzn5J8jSvIJgNlk5t3b32276x/swMiRIxN/qP3NQIqiHJddlgxOYCrij6z4o4lamP+fuPn/4kmyEq5AouFj8IrrFaIpUerd9SIE69lrOo8iArcjg//2p6kugQVohD49+tA/vz8bijfw5aYvGdt1LI3bG7lq11VEdkWYcOoEsiJZe4V/A6LUhJGOhm4kFiCHtmlrfJKjKArjx48/5P6OyhZoi3H3d3vsq1RA21gCtvTaQlFjEenp6Zz259NYsWIF+UNbiAHoSPJoldS91uSgCkBTDXoDg05PPZLTnwJ0aX7IV+zjk9VSSGPimfHVaCWyyvcCKpgdZjSnJhHx1YhgLGNvwFxnUgDiKY1o8YciVoANxRuY/+/5jB06Fk+yh/Prz+fj8McU7irkpuybxDLSFAeQgqyQfEgg0XeI66Ttuo4aHAZNwnjfbIH2SBVs7XEP5vYAUTJGjRrVolJ0LHz2vhT4unDIhagrVJwbnC2f2Brlc9uaFgIfiyqKmPPFHK4eczV5GfEbaIV7OWQMQDgc5uOPP2bu3LnMnTuXOXPm8MwzzxzbqAYGrUUQEV5OxO+/729WCD7+7GOCkSBnDTiL/JT4iqAGCMDG4EZC5hDYQXNp0vo3gigA5fFrmulcCoCFve4NJxCEScMlDmDR0kUEMgLggUsd8aJA2/4lMQw+ROEJI8qAA1EIMhCrwlbENWLQYRzMbL6vBbYtgvYOZ9wjocntsa8S0aQENLlDWpvF30ksz5hhY1r92p2BR/76CH948w+8/+X7rXrdQyoAv/rVr3juued44oknWLBgAY8//jhbtrRDLVEDg0MRQwSXgvS93//TXANzPpOOe5ePulxM+7WAF6K+KJOqJjGgbAC15loiqRERjrBXOLrij0N+S9qZpgA+OxCBHlk9OP2U0/EFfXy0+yPIhLGesZgxs2TPEqpN1fIeBZB7rGFvNkE90B8R/ltbHs6gOW0phA+VLXCwoL2ZM2ceVdDe4Y57JDS5N1pq3tbS/tZg1h9nMed/5zBm6ImpAEwZI+my7372bqsqhIf8aduwYQPvvfceF110EQ888ABvv/029fX1R3MPBgatSxGSF38Ksnrfj7ItZXyx8gssJguXn3O5xAZsBnywxrcGn+4jz5xHanIq0ZSorJDdyP8xZIXdmVb/TaQg31wrogh44eoLJSB39tLZkAMpzhTOtZ5LTI+xqHjRXutGELEGVCOWhDoknSgHKRJU1763cjzSFpHzh5stcLDSwk059ke6uj7ccTuMw0yds1RZGDloJB5nZ/zCxjncNMA1SHbOPo8LnReS4c5g857NfD73c9lfdujP4pdffvm9UzqkApCVlYXZbKZHjx5s3ryZ3r174/W2byUlA4MDaEDS2nIQob0/fniv8D00XWPsaWNJy0oTgb4TaITF4Xj5X9cocEDMHRMBmYxYCXREQCa1cO2OxoTMU0cUFC9MOncSVrOVr1Z9Ram1FNxwqTPuBtj4L7EWBBAXQlX8YYtfowbog9z/7na/m+OOfYXwzJkzW6W+/+GazQ+WYz9x4sSj8tt3hLm+NQlFQpTXtFExi06ExWThjvPvAOCJRU+gaRpw6F4T559//vde95AKgNPpZMGCBfTr14+FCxeyadMm/H5/K9ySgcFRoiOCys5Bg2D0ap3Zn80G4OoRV8sqtxIJrtmnANDo1NFgA82uSVBd03lO5NvhatM7OXrSEEXABUQgTU1j7MixaJrGnKI5kArj0saRY8qhp7UnepK+NwDQgVhC6uLbVUgsQA6SIWHwvewrhBcsWHBshW72uebhms1byrFvKrnbluN2Rv71n38xfOpwfv/67zt6Km3OzaNuJicph7Ula1mwegFw7EWXDqkAPPTQQ2zYsIGzzz4bVVW58cYb+dGPftQ6d2RgcDRUIKb/LrRcxlaHlf9dyZY9W0j3pHPhWReKYN8ir42oEb4Jxuv/544EO+i2uK/Mhvj/7YhlobP+/iUjCoADWdWXww8m/ACA2atmo3t00lxpLMtaxmPZj6EkKaI4VSMFg7zAMkSRiCIWlZ6IEmRwSDqi0E0TLQXtNVkiTji+pxKgfqbOXz//K9FYlC7DujQ75u/vb7MOekfN4VY1HAfceODDcauDu35zFwAzPptBoH8AOLbP4iEVgB49enDvvfeiKArPPvss3377Lddff/1h3a+BQaujISl6SRy8Op8X3vnoHQAmnz4ZSzeL+MDXAHWwUlmJT/PRy9KLnKQc8IASVUTgNyL+/86W/78/KrJ6B1m9V8IFwy8gMyWTbaXbWBFcAcmgWBQJ9Asj97QdUZy6Iu6QDYgi0VINBYOD0tqR80c67v5BewsWLGiX8TsTf/nLX1ixYgVpaWlMnjy5o6fTeiwCHm35cd2267htyG28OvpVHF85gGP7LB5SAVixYgU//OEPmTRpUqKy1/7VvQwM2o1KZMX6PZp8sDTIvK/mAfCDs34gPQECwGpAg+Xxgtvnes5NVPpTiCsADXIOHjqn/39f0hALRQbgB3PAzFVjrgJgdslsuR83eOu9vP3129S4aiS9sQEYgNz7DiQGoAgJEDQUgEOyrxCeOHFiq0TOHy4HC9qbOHFi5wjaawdqamr4n//5Hx599FEAnnjiCZzOg+T9n2CoisrDox9maLZ0AQqHwzz00ENHncVxyPZbDz30EJMnT2bAgAGd3h90WDTQKk0UDDoADRFgHg7um9ekf0VdYx0D8wcyYMgAOXcZsuJNgtuzb+eS6ksw9TPtbfYTQlbUOxHzvxUxr3dmPIj5X0HmugN+cMUPePm9l5m7eS6/zf4trnQXv9j+Cz7e+TFBZ5Bb1VvFEnIxEgegInUPVgP/Bc5EFIITiFbtT09zITxp0qR2KXTTxMFKC0+bNo3LL7+80wftHSs1NTWMHTuWsrIyTCYTDzzwABMmTOjoaXUYt956K1988QVXX311i0WXzj77bDIyMg76+kMqAGaz+cRq0lHC3gAqg+OLaiRSvef3nFMPr//rdQB+OPyHe89diPj2B4JSpdDL2ktM4WFEkEYQYdjk//fQef3/TShIZcByxAqwB/qe05ez+p/Ftxu+5f3y97nxlBu5MvlKPg5+zOy1s7l1xK2wFrgCyEdS/6zAYEQJ2MphNgnveP7fGf+PhvKGA/Zf67+WSUEpjrTEuoRnPc8C8BqvNTtPR+eVmleI6lEsWHgk6RHWWNa0ONao0CjuahT/62nqaYxIHcG6F9ex9sW1Yj2KX284w8m9PFc+T8DrzteZ65jbbMwmsrVsXq59ObF9Q9oNNKotV2O6yXcTVwWuQkHBbXUzKWlSs2vq6CgovMALvF39Nh5d/FcPJj/IKsuqA8YGOCd0Dvd77wegRC1hatrUFscG+F397zgzciYAs1yzmO2YvXd8Ze91s2JZvFnzZmL7yvQr8aotZ41Na5zGtQFpJ/+F7QseTXq02T3ty9yquSTpSaSSSnpmOvl6Pk/VPUXvO3rDHch3YR8vQM/anvLd2J8LgZ8d9DY7B48BKw99Wp2vjhXZK0CF+bPnU/5GOWrcqK+js7BhIX1u7cO6BesOeo1DftV79+7Npk2b6Nu37+FOv3MTRZqfdNKOkQbfQzmH9M2v/3Y936z/BrfDzeSzJouvey0i3DygpWmom1QxjzuRz4MDNJsmJvAQYiHq7Ob/JtIQ/30OYs6vh5svvZlvN3zLa3te44buNzCu2ziSKpNYXbWaDWygf0V/caV0i79mI2IR8CHvx3GiACy1LaXCfmDawtnhsxPPK9VK/m3790GvESOGOX7D6yzrWGxb3OJ5aVpa4nlYCbPUtvSg12xQ9iole0x7Dnpu92jzDkwrrCuoV1uusXJJ8JLE81q1lu+s3x10/Ng+nXC2mreywrqixfO6xPbWzQ4rYVZbVx/0mo3KXsWkTC1jrbXlHvP73jvAJsumg95Ttam62es2Wg5efl6LR6cqKMysmUluLBdLp+jQ1XGk6Cl8WfEljyU9xrvOd/nK/lWz4z9v/HlCOT0Yh/yq79mzh6uuuoq8vDxsNukWoigK8+fPP+qJL1iwgJdeeoloNMrNN9/MDTfc0Oz4J598wnPPPYeu63Tp0oXHH3+c5ORk3n//fZ566inS09MBuOCCC468JbED+fFLpnO0eDU4PBoQ4fx9/T2i8PpsWf1ffebVuFJcsA34Elnp58Bj3zzG18Vfc/8p9zMmNka+AU6IOWISANgUWd+ZAwD3xYVkLiQh7oDNMKFgAumvprOhdgPLKpZxVtZZXJ5yOW/UvME7297hodSH4GvgB8j7WYooV5lIauBxEkf2bO2z6NUHTrZndK+JaGR4JG9Xv42OnhCMir73RzFJT0r8SP62/rfUqXUtjpWh7TWjZseyeadKgkyjRDFjbvZD2z/SP/H8Rv+NXBS6qNm1ms616c27L71R/Qaa0nIaRn507wf/nPA5zKuc1+z4voqMW99bGOOx+sfwKntX4PvOM0VLSTzPieXwYeWHLY4N0CvaK/H8Vt+tCQtL4rrx9zS2Xxu+OVVzEsJ7f7K17MTz80Pn80nFJ82vuc9cmywaAN1ixuqtiUwtkz/V/YmfNf6MYlNxs2P7fg4PxiEVgH0FbDQapaamhtdee+3IZxqnvLycZ555hvfeew+r1cq1117LiBEjOPXUUwFobGzk4YcfZs6cOWRnZ/OnP/2J5557jt/85jesXbuW++67j8suu+yox09EO+9EAqFObiXy+KES+bSm7LdfR5SDWvBu9jLnUyn9e1PXm0SYlyFR8C7ACV+VfcWG6AbsOXbx/9vj1zUj5n8FsQ7Y2/6WWo0MxKqVCuwG25k2rht7Hc+/+zz/t+f/OKvHWfwg5we8UfMGc7bN4b4R92FdYYXbkQ6IxUjr41FIrYDjhJGbRiYWJQcjK/4PSCwomigqKmrm+x/EoMMa14GD0YwGYPny5QwdOvSg53aP/zschnLw6+xLWvzfvhxsHr3pfVjXtGPnNE47rHPz4v9aYvny5ex7G/05tBACSI7/ay12LN9B2tC0Q5/YGfn6yF/SJ/5vf0KhkFhAD8IhswCGDx9O3759WblyJffffz8zZszgvPPOO/IZxlm8eDEjR44kJSUFp9PJuHHjmkWuRiIRpk+fTna2aId9+/altFRaI61Zs4b333+fiRMn8utf//roShJXICs8DTF/HiernZOapgY2GTT3y3uRNLatcvzvi/6OP+xn1Kmj6HNWH+iFmLSR11UoFWzwb8Cu2Bnad6iY/FPYqwYHkM/G8WYZSkfel0xE2WmEH47/Iaqq8kHZB5Q2lHJG/hn0c/WjKlLFhyUfSvGfNYgboCsSG1PCCdsSuKPS9gwMOjPfqwBs376d6dOnc8EFFzB//nyCwSCfffYZd95551EPWFFRQWbm3jD8rKwsysv3lnJMTU1l7NixAASDQf7yl79w8cUXA5CZmclPf/pT5s+fT25uLo888siRT8COFITJRgRIyVHfikF7URX/f99g1lJgE+K3ToVIUoRXF70KwO1DbxfFzo8oABHADJ/tlpaho92jsTltogBkIpaCGLLdWev/fx8W9rYttgBboUuvLlw6+lIiWoRZm2ahJCvc0uUWzkw/kzQ1TVb6nyMxBE0L1K2ckApxWzS8MTA4ETioC+DHP/4xa9euZcKECbz++usMHjyYMWPG4PEc26+jpmkHpOK0lILj9Xr52c9+Rr9+/bjyyisBeOGFFxLHp02bllAUjoTN0c24lrvw+X3oFh3LBguhvBCx5NihX9yKLF++vF3H66xzgEPMQwfHVgeaXSNECHSwb7NjLbKCCaJpUVDh068/pbS2lB6pPeit9GZjYCPh4jD2IjuubS4s1RY+2PEBACNTR7J7127slXbqquuIqTHUgMr24u1EfBF8qT70nR0nFI7m76I2qth32bHX2FH+o+ANeSk4o4AF/17A6ztf5+ouV3OJ8xLOHXMurk0uvNu9BD8OsmfYHpSYgjvoxrzSjN/rF9fYCURH9Kc3MDgeOKgCsH79egYOHEjv3r3p3l2WCK1RByAnJ4dly5YltisrK8nKal59pKKigqlTpzJy5EgeeOABQBSCOXPmcMsttwCiOJhMR57L1+e8PthcNlkpDUWCnxqR8qjtVPf9UH7Dk2UOhzWPOsS83R1xBaxDVvRDkAY2SVLG9xcv/QKAO6+8kx4X94DhSNW7EOCEYCTIf33/BeDKoVeSl54HMcgamgUW2LxiM7169oKBwBltc6+HwzH9XdIQF0Y1kAmDuw3mzU/fZMn6JXxa8yk/7fdT+dxfDLwLnnIPmaFMuAhJifwGQikh1n6f0/A45GC58zNmzGDUqFEnfO68gcHBOKgL4IsvvuDKK6/kgw8+4JxzzuHOO++UgIJjZPTo0SxZsoSamhoCgQAfffRRs5iCWCzGT37yE8aPH8+DDz6Y+MI6nU5mzpzJqlWS0/rmm28elQWAeiRlqhEx//dCfhS3kcjdNehEFCEugF2Iv78ROB0RYt2BVPj000/ZsHMDOak5XHHaFfL3DSHugRqgHhZXLiagBxjkGERe1zw5loz4vKOgRBT5HKS39w22ItmIC8OCfLYt8NOJPwVg5paZBMwBCMAGfQP/o/8PZbVl8BbyXnVHlABfB829DTneG94YnHzous7ChQsPcE8dbP/RclAFwGw2M2HCBN544w3ee+89srKyCIVCXHLJJbz99ttHPWB2dja/+tWvuOmmm7jiiiu47LLLGDJkCLfddhtr1qzhs88+Y/369SxatIjLL7+cyy+/nAcffBCTycSzzz7Lww8/zPjx41m3bh333HPPkU+gqee5CVkpeYFTER/wVoxmKJ2FECLwV7K30p0LGISs0uO/2bqu88en/wjAj8f/GFuqTQRhGRIDsEf+H+keyayes7jrlLtE8Dci5YQVQAM1rB5f6X8tkYHMv6n1by1cePqFDOo2iPJQOW9seAM88PSHT/Pmnjd50fSi9B//ELEFDsPIijEw6AQUFhYybdq0ZjEqTbEs06ZNa7WSz4dV8uPUU0/lN7/5DXfffTfz58/nH//4B9ddd91RD9pSP4FXX5UArsGDB7NxY8sFIYYNG8b7779/1OMCskKyISukIJI6NQCxBGxF0gN7HezFBt9LFFGqGuPPbcj7eyRR9ToS4FeGZGxkAmchipsH+dvss2Bb+K+FrN28lpzUHG4afpOcr8bnUIr8PXVwupwUZBaIcmCKzy8j/jyMKH7HuwKgIiv5kvjzclC6K/x68q+55dlbeH7D89ww5AZ+efYv+XDTh7wVeIufmX9G9pvZ8r6exl63mIGBQYdRUFCQCFQF6e63byBra7mtDpkGuC8Oh4Nrrrnm2IVwR1KC/MjnIMKqFDETJyMm0FqMzICjoRxJK2tqKgPibtmKrOSDB3ndvkSQyP5S5O+RjgimEkTon0qzEs6xWIwn//AkAL8o+AWOZIf8DYuQOvdrkRoBKciqWEVqQNSwt3iOP34xHfGhHydV8A5KN+Qe7IhiUwsXD7qYM7qfQXW4mr+u/isDMwcy/ozxBLUgL/CCWEmeA/6D4QYzaDfay8x9PNIUo9KkBHTp0uWAQNbW4IgUgBOCYqQxTDLyI7kV8f/ryOownb1KgcGhCSPCtggRqAPjj75IoF6P+DkbENfL911nE2K67oX8fRRkJR8CTuGAHPU5c+awadsmuqR14bozrxPhV4Okt21GFLwUuHfbvfx05U/ZFtsmQt6PuBPS4uPp8apjJ0InPCvi2oggFo0y+TG59wf3AvDS+peoKavhl7f/EoC3Gt5ij3uPWEpeQ943g+Oe40G4tpeZ+3hl32yVJlpT+MPJqAC4kKCyBsQ8XYn4QSvjx7sjleB2ckIGRLUqtcB65H3qjgjpfSvoKYhCNQARRtto2bwcRQRPFInsb/q71CCm+aa/yT54vV4e/9/HAbjnvHuwYRMFYn58jHR51CbVMmf3HOaVz8OSbJHVcZREq1yiQFgyCY6b+v+HohcS05Ia/78ezh18Luf2PJf6SD1/+PcfGKQM4vJxlxPUgzxc87AEVpo4qRWA40FoHi7Hg3Dd18zdNM+2MHMfr7RH8aqTTwEYgqz6ypCgp16IYJqHmKkVRJBZkDSyaMuXOamJIQrSdkTgD6B5kZ79URHTvAlJ4/sOef/LwVxtFotMNSLoXciqf0f8dXm0GJn/p2f+REVlBUOzhjI5Y7IoIWHEijM0/jozzN4+m6AW5ILUC+iW201WyBHEBeSPzykIMVvsACXjuCUDsaBoiPujDJRGhUdueQSTYuLNbW+ydslafvvgb3FZXXxa9Snbte1SEfAk9v8fD0LzcDkehGt7mbmPR9qreNXJpwD0Q3qeN/mmC5BWqNuRlrG1yCrxFERQ7OyQWXZeGpFVfzWQi5j6Wyof6wN2x89diazOY4g5fz3ib94Bjs0O+VtEkb/BWuBj5O/QOz5GEzGgBrZ+tpWZs2aioPDokEdRe6oSKHgaopC4AQ00r8br26Q50E09b9obCxBGggUbSQQURpOjJ06LaAeiAPkQhQigBPr07sOtZ92Kjs5vPvgN2d5snnngGT459xNp9pKBvC8nKceD0Dxcjhfh2h5m7uORgxWvavp7tmsWwAnFduB8xM+/BFldjkbMzRuRH8weyA9oV0SIlSErxiZ0RJDFEOF3MryLOhKMV4bccz9aLpzUiMQD+BD10o2You3I6nsQ4n7ZjVhiokjMQFck6n89opj1iV9fR97nlcAWiIVi3PXqXUSiEa7vej2nDT9Nmth0Bz6Nj+EE1sLHOz9mp28nudZcLjrlIrleDLHupCFKRtwNEE0/wUw9Gchq3oZ8xjcDPeCuH97F3NVz+bbqW/5v5v9x6x9vldiMNYgF7CRutLZ/hcCmCOzOJjQPl6b7aboP6HzC9WBm7s42z/amvYpXnXwWgI3Iqv4q5IfxC0RoDURMphsR4bQTEVw2RCBtQQTgRqS3/Lr481U0LyLkRZSMpuOlHP+1BSKIAClDBEt/Whb+5chKP4wIkiHsXcWnsrd1bSaiCATA7DfL9XfJNknAyPjxHcAHwJ+AQvj/7Z15fFTV+f/fk8xMtglZyEogBMsmoLJVlioIyibki0ZsfYHYVtBq6/KFql9cfiqtSkXEBZBa5Vu+tWjRVqBRBNuqWBGVRRFQ9jUJZA9JJpNZ7++P59zcsNWAWSA5b155hZm5c89zz+TO5znPec5zKIOX17/M5oObSXOm8UjGI/LZHQE+RaISadK+ccDg2Z3PAnBnlzuxx9vl865RNsSp6woADpq9FHSTE484ObVAFyS68S3EJccx5zrJnXgy90kObDoAQ8GINVj+4XLW7VvXYiafD7SmEen5vgGS3qPhzDRX8aq25wAEgM+RRMBrEFE6hHxhxiEifwD4BBGfamRk+hmyr/wmxEFIQJalpSMj2h3I6HU34gREIeHlAvXahZpQWIVcVw0iJJ05NVRuIA5THtKPvbE22TkTbiARKgdUipNgnttMGrwI6fcNiONxEexsv5Nn1krRn3l95hF3dZyM/guBt5UNu4DPYMexHeys3EmaI40p/abI59EFSS5MVtcVTd0GQKGoC91LOwkXVjQkEunbA0A1XDvxWq7Puh5PwMOMR2YQ6BDgw7APmblxJk+89URLWn1WNEXS3vkumg3lQhDX5gpza85M23MAqpEvxk+Q0WYWIm7xyIjVgYhCD+TLMwxJFgxHnIXhiPCXq/d1UI8PImHUFCSn4CJ1jh6II7CHC88JKEXstiOj/tNtr21GB8ycgB/w3XPpIcRZiIZgUlCcKTMkb0fE6wtE2HsDN0Fl90qm/246vqCPyf0mM2L0CGkrEXl/FtL3B4F90CehD//u/2+e7/k8kR0iZYohhHz+qUiNgkjks07hxG2GWwNhiEMbjkQ8spDr3QJEwW/v/S2pjlQ27t3InBfmMOKaEYzpPIYqX1XL2XyWrFu3rlGT9i4E0WwoF4K4mmHu+hEW004z/K1pWtrC7PWJqO1jcSDh/yHI/P8+JBfAHPEWI8K/HYkQpCEOgROZ/z6EVTCoEhGRAOIU1BcTFzKfvRtJdrtQdlo7htRMaIc4M6cT9Rqk3wLqmIQGnrsIcRwuQkbsIJ9BJTLF8I06Jh7oCqFeIf57yn9zoPgAvTJ78Zuc34j490GiMgeRzy4CmX45DhjQ2d+ZzkM7i5OQjkR9bEgEoFidP4BVHKi10R5xqhKRnJUuSH/vgYQhCSy+ejE/Xvtjfv/W7+mb0ZcXb3yRn6/8eUtafFYMHz68UaultaZdAy+EDZDMcHZDn9c0Pm0uArA9cruIljnq+wL5YqxERPoSRLAPIg5CABH9BKSCYD4ifJ0RAfkImeuPR75wjyCh1pMHCzGIA/AORO6JFAeiGHFIDE7NE6hGjtmBJMDtUDZVfp+rbyCm+JvTHKcT/3Is8e5Bw8U/hIzs47CW3QWQfotU5z2KOB4ZQBY8/cTTrP1kLXHRcbxyxytERUTJ51StbItGnLGjsOaLNSw9thTjiCHnvxxx9lKRqZt2yF+92gMAGycmeLYm1QTSXAAALL9JREFU2iEufhD5++yiHu8EjsGgmYN49KJHAZjx0gx2B3bzyk9eaSFjz57GznRvTSPSxp5Dbk01EjQWbc4BuPe1e/my9ktJ7DNr/pth7uOIKEdhhbarkSiBWi+OD4kKHEDOYa4SqFSvVyPz1uuQaYaVwOvAx8gIdAe4PnNJfkGu+nlTHb8VK8lwJyKUQUQY7Ui29h7VblNNJxQh4p+ICMbJ3xPmaoD9SD/1RAS4oRQjgm+KrqHOZebg5SOOlEt+v/Tnl1j4ykLsYXYWzVxEliNLwvkBdS4XEg3Ig62fbeWet+7h4Q8e5sNDH8Jodc4o5PMrQyIMat4fN+LURZ2F/RcSNsQxq0CcKTMSUovksnSAWyfdyo87/BiPz8PUxVMpKL+w6mA3ZtKe3jXwzLSmGgkaizbnALg9bia/PpkvDnwhkYAByJdkHhJ6PoqIUBUycixHRuDhiDh+iyQEvo+I1lhk//gERAy7IV+w5nFfY5WezQQ6ghFlyIgsiGS+hyNOxDZgLbI72w5khOpHhKoaq8Z7LeIg5NG4KwyKETGOFjtPEX8/4oAcRUS6B2e3e5w5+o9FhNsA51Gn9HUc0r8OZOQaCa+sfYUnn3wSm83G83c8z4juI8Rh6IxEDELqpwA2ujdy07KbcPvdXJ9wPSN6jJDVBKXIZ1KEOG9pSHtmzkFrHf2bJCF9dBxxlMwdA4uB9WC71sbcH81lVJdRVHgruPedexut6dzcXK699lpGjx7NsmXLTnn922+/JScnhzFjxvDwww8TCJz9UszWkrR3vtOaaiRoLNqcAzAydSSV7kpueuMmVn+2WoT2KmAYIsgFSFnUcYg4HEeEeSMijF0RkSpCei8REaWPgb8iYf4g1ih6GJLIFk/dZjRGuCHn6oZMLwQRcT+CzGMPQkLWQSR5qysiyE5E/A3EaShAnJMixJE4241cQsio+CASrViLiLsbcVy+QhyNfCTiYa5myFI/pxsQhZD5Zg/WqN6kVNmYro47APbjdpmOKUdGqh3AwODJ157k8aceB+CJUU9wfZ/rxYZ4ZbNdrjv0bYg/bv4jN758I5W+SiYkT+C5lOewXWITpyoeEb4j6j12xBGIUDa0dgcgGhH8IiynzYY4YNvkEMcgB4t/uJihXYdSUlvSKM0WFhby3HPP8frrr7Ny5UqWL1/O3r17Tzjm/vvv59FHH2Xt2rUYhsGbb755Vm20pqS9850LpbCQ5uxoc0mAM5Nn4ujk4LUjr3H78tt5lVcZ224sXIF8UW5FxCsFcQwOIkLiQUavSYiDkI98qZrLpr1IWDoRa5vZbkiimjnnjPx217jlmJ6Ic/A1Io4/QoSpAzIi3ocIck/1OEXZYCYphhBn4FvVZnvkE41GRtFxnD68HQJHsUPaDSIRinJkZN1D2RtCnI2jyJRHLSKml6jfJ52PcqytgOt/70ZTV5efY0gkJKSuwQu+FJ/YkAekQYVRwa8f/jVrtq7BHm7n2UnPMqnfJDn3EdWvKupQtLOIO1bdwef7PwdgWq9pPJr+qKz5z1E2RavfJcpuv+pjv7rO1u4AgPzd7EP6oR/izNYifxvrgMsg6nAUfxr1J+7/+v5GafLTTz9l8ODBxMfHAzBmzBjWrFnDXXfdBUB+fj61tbX07dsXgJycHF588UUmT57c4DbWrVvXapL2LgQuhMJCmrOjzTkAP9v3M2Ylz6JDbAdWl67mio5XyHxoIiJ+lYiQm6uh0rBqBRxERqFdkDnVAOI0DEJE5SNETC9HhCcPEayTNpnxdvJaRYfC1TnLkBF+LCLMlyEOxB5ELOORL/AAIqYxSHQiGvkir0RGtrFYEYh8dc549eNCRtlHwFHikGTHKETkk1V74VgOwXHkL8SsyhdAhLQCq0Z/GRKJ8CPTEylYNRC86hxHEMeqColkuFV/dYfQ1pBcYwSsP7qeGY/NIL8sn3audiz+f4u5KuoqcUx2YO0ToOoSxIXiKKsoIyk6iSdHPsmEhAniKNyo7DMz4PeotrMQR66Tuo72qn9aO6YjWIA4nJcjU1ipSL8cArpB1I4onvnRM3zDN9+7yaKiIpKTrbrCKSkpfP3112d8PTk5mcLCs9uIYPjw4ed9pntrQlfta320OQegxFPCLVtu4dq0a1nYZyGuRBe4wbPSQ9nYMjLSM0QQzUI0lYhwXoOVrX4METdzbnk7IvIpWCWCk7ES6uo7AMchIi9CzmmGtLthFSFyIgL7b8Qh8SCORxQy+k5R57Mp2/Yhn2JP1V61Omcy4mRUItGCQkQca4EE8Cf6xXkwEyBTECGoRsTcptrpxIkjfre6pu2IeCRhJQy61HNlqh9CyCjbTJz0qWMuRcTHBs48J/kV+cz5cA4rPlkBQN+L+7L45cVk7soUof4CiXJkwvaK7XR0diS+IJ6IQASvjH6F5Mxk4u3x0heXIHkdReoa4tRnVj/zPwlJ3ryMtoENcbz2IP0yEIkCHEOcOzfi+KVBWGXjzAqGQqETRMEwjBMef9frDWHHjh2kpKSwZcuWU1470/NNwebNm5ulne+iKe0wDINXX32V3NxcsrOzmT59Oq+++ipLliyhqKiI6dOn131+baE/zobzxY7T0eYcgLvuuIvfPf07Vh9bzdrCtUz6dhK3T76dRR8s4p9L/skTjz5BzmU52EpsIsxm6Hk/8iXaFxHRY4jYhSMilYYcX6aOz0OiBOboPlo9Xwm2gM1al54IXIkIVLxqbwhS2vYLrCx1lzrGFH/U/3sgeQdFiNNShQh+hTouQv3er84dCQQgele0PBeDOA+FWBEIc/rgdH8dZuIdSKSgRF3bDsQx8NQ71oaE6yOQUfxF6r0qe3/v7r0sWrKI1TtX4w/5iXREclf2XfzqoV/h3OeUqIHK3g91CvH8/ud54d0XyBmUw3P9n4NE6JbZTY7ZpfrpR+o6SrGmHUwnztwHwOy/Dqe5vtaK+ZkWqN9DkfLKJYjzVo70z+lKPJ8DaWlpbNq0qe5xcXExKSkpJ7xeXFxc97ikpOSE1xtCnz59iIg43U5UzcfmzZsZMGBAi9rQHHa899575ObmnjDdMmDAAFJSUliyZAkTJ05k3LhxbaY/LhQ7vF4v27dvP+PrLZIEeK7ZwQUFBUyZMoWxY8dy55134naf/Vq4m8fezLp/rWPS4EkALC9YztXzruZfh/5FpbeSex6+hzvm3UHxgWIRuPZI2NSFjBr3IIKThYw2R6vf5sg/BRndmiJsR6qvmQl06RCKCMkouzviAJiXkYk8vwERqjQkk707IlrfIA5FfaKx5u33IgJu1uBPRaIWh9XrvZWt7cBR4ZAReSQiCpWIcMao5+qv/fep6/kGa6WEQ/WBD/gQEet4ZHQ5GrgO+C9EkA1lRwjyj+fzf8v/j5zxOQzPHs6qb1YRCAX4rx/+Fx/O/JAZP5qB8wOnLJNUGw9507z88tNf8uzfnyUQDBBbGkuIkIzgu6o+K0CqFV6GOEMhdf17EKckTn1GGer1KBpeu6C10Bn5OziA/C10RhylUiRRMo5TEzfPkaFDh7JhwwbKysrweDy8//77DBs2rO71jIwMIiIi6kZHq1atOuF1zflFa6qRoLFodgfg+2QHz549m8mTJ7NmzRr69OnDSy+9dPYGvAMdYjrwwl9eYN3cdUztNRVXmIvjnuPWIR+9w8BbB3L7z27nyy+/xF2lkvZiEKH5EqtynA2rnsB+ROw6IuK9Tx27E3EGYoFCCHeHyzEDkNGxOXLei5XYl4YIbBD5su6FCPQXSDi9fqKdHQnVe5Clh2YlPXPLYx8y2o1BnI0iCCQGJPExCxkdliC5EP9Wv9cjYeJt6me/uvZqxBlKQaIYWYjwdkEcmO7qNRfUhNew4/AOlu9azqyPZ3HNf1/D5TdfzkN/fIjP935OdFg02VnZfLT8IxY/vZjM9pnWMscKIADHK48z5a0p5G7MJTYqljeufoPfXP4bwiaGiTNTgZXDMUr1VyEi7rWqH8zVE06sPe9TaXtrYByI6NcgEarLkH46hny2l9Jo2wGnpqYyY8YMbrnlFq677jomTJjApZdeym233ca2bbL8YN68ecyZM4exY8dSU1PDLbfc0jiNaxodXSOhddLsUwDnmh184403snHjRhYtWlT3/M0338z9959l1vI3wDJgAnQZ1oXfxf6OR1c/yntb3uMfoX/wQfEHuP1uAsEA765/l3cnvAtASkIKWelZpMSl0D68PUmxSSR2TMTV2UV0UjRRRBFdHk30gWgi4iMIqwjD7rYT7ggnPCyc8H3h2G12wpLCKEstw+1yi2DHIE7CIbDF22SEmqkeY5Mv61hE4H6ICPzHSKJgD7AZNmv5XxBspTaZy1d7FxipBkYnA8NpEKoIYfgMQlkh8lLzSOgqQ+BQMIRRZRA6HMLYbxDaF8IIGRgRBka8QSguRIgQXpsXT6QHj9tDbX4tnsMePAEPbpebsuIySt4qobSmlKKaIg4dPsSxY8dO6f6YqBiG9xrONUnXMP6y8Rw1jtJ1V1cpiNQOWTVxHGgP+f3ymfrEVHYV7yLNlcZr2a/RK7yXJPllqX7bgDhDI7FG/DZE6Lcgwh+BjP7NKoA+xAFri8QjTsAhJArSAav2RQTizPkap6ns7Gyys7NPeO6VV6xKgz179uSvf/1r4zSm0WjOmmZ3AM41O7i8vByXy4Xdbj/h+bMmHJn73IWE1zMg+ppobnDewA35N+Af6Oeroq94a/9brCpdRZgtDC9eisqLKCovOreLbqM47U4yEzPp0bkH/S/uT9+ufemX2I+IgggR5m4Qti1Moh4RSKQjBnntEvj9m79nV/EuuiV0Y9mEZWTsy5CpDT+SxZ6PRCQuR6Ip5lRGJuJc5SPOgA8Z/Wep5xxI9KKtkoT0y2GsLZoNJGLlQZxQjUbT6ml2B+Bcs4NPlyV8LmGngugCXE4X9h12vCVePN09+JP8RIYiiaqMwn7MThdHF2bFzGJG3AwqOlUQFRdFka+IFUdX8KctfyIlPoXU9qnERMUQTTRevxeP4aE2WIvX7cUf9BOwBwiFQgSDQUKhECF/iFAwRICAjLhDKoZvXoJ6aNjqxfbN6YCTX7PVe96wzmFgnFCcxzAMwmwS5w6zhYFNfttsNsx/9Z8/4XcoTH6MMDnSZsPpcBJpjyTCHkFEZATOKCcRjggiHZHER8cTFxtHoiORpGASaQlpJCckExYRhj/ZT5g3DEepg7yCPOw1dvzxfvzFfkJZIb71fEuoQwhvspeYr2OIPhpNoCbArVG34unhIefmHEJbQuTF5uFOdRP+cTg2v40wdxihhBAVsRU4c53Yj9sJxAfw4SPq2ygiDkVg89qwV9nxp/mpLqwmZlsMgdgA7u2n5o+cL9m6zWWHrdaGs8xJ5LFInCVOAtEBmQ7QDoBG0yZodgfgXLODExMTqaqqIhgMEh4efsr7GkqHPh2I6B0h4eFDyPy2mS0fwtqOVlWaS9+VDt0hq3MWX+d9TYw9hqKKIooqJBoQGR5J74TeDMoYxCNTHrE2nXEhuQGmIAdUW1VwoPIAXXp1kWNjsEZjQSQZy0vdrm3sV+/PQka2lVgFd8ziRKXK9mgkkctsz4NVO8AOQYK4g27a2dpRVVNFrCuWzwOfU2VUgV0qFNqCNly4aGdvR0p8CkmxSWJXAAmfd0XqHnRB5vodWNX/fOq3G9iMrHrIQkL6Ycio24mM4C+W69myawv9o/pLyD4S/vXHfzE4eTAxPWLgIpg3aJ4kraUDE5GkzH8jSyNdyJRBtOrH/oh4FSNz3F3kM6Qjki/QEcl1GKz6uR4tna3bYnYYSH99CuSDN8pLOeXN175Go2kxmt0BGDp0KAsWLKCsrIyoqCjef/99fvvb39a9Xj87eMCAAXXZwQ6Hg4EDB7J69Wqys7NZuXLluWUNH0LmPM15zkhEPIKIeJTWe609IsybgWMwPXU6UwZP4aPaj1jvWc8nBZ+wp3wPm0s2ExYIk6mFDPCn+7nq2avoktqFH3T+AV3TunJRwkWkRqaSRBK16bUwARHDMMCAUGaI6g3VVG2tovJgJdUHqgmrDWNA9AA4AMZWg4XBhXjDvfgMH16b/PYFfPj8PiaFJjHcPxwM+If9H7wYehGf4aOWWqqNaqqowm1zYzNsHA4exm7YwQuPBR9jm7HttF11Q+ENvBj7IkRCUVgRz1Q+Q5/SPvQL9aOn0ROn2ylOSRriCJjJdl8iuQvm9sjdkbnmRMQp6IAsPQQcnzskVyHGYNEdi5jzwRxGXjKSpZcvJdwZLs7C18j0QBaSnGjW9U9Qn10sMq9tVvjbq14vxyo9nIE4DZG07fD/ydgQR24C0s9HWtYcjUbTfDS7A1A/O9jv9zNp0qS67OB77rmHSy65hHnz5vHII49QXV1N796967KDH3vsMWbNmsXixYtJT09n/vz5Z29AH2T0V4uMpguQkWQCIhgxiBNgQwTrUmQJXw3ggKhOUYwLjGNc2jhIg3LK2V68HcNnyGhqDxzae4iDFQc5WHGQD3d9eIoJT497motTL4Z0eOYvz/C/uf9LlafqlNrlPSJ78EGPD8AJtkQb8/fPx2ecPkPr0sRLGW4fDmVwvPY4W+z1iqDYzF82XGEuPB09BGoCRMVGcUX5FSQHkyU3Qh1XHaimMlhJZrtMGTVHwb7APl7/4vW6bYoj/hZB/8T+DMsYxrA+w7ik3yWEV4VLVr5ZIGiy6rd4ZMRfiLUtLYAPwo+H40n3MHPyTP6+6e8ADL18KGHuMBH8DUhUYzAy739EfV7pSD6AodrbiwjZMUToq9XnaS6HdGLVazib3QvbCnYkgqISUDUaTeunRQoBnWt2cEZGBq+99tr3a3wMIv5lSGj6MCIYcciXXxYSqj+qfiqQ8HQ+VnSgHRKir4aErglcmXylCE4FkABZtVmsG7SOfYF97PXtZU9oDweOH6CkpoQSdwlx7eIk4lAG7kI3lTWVAESHR9NO/XOFu8iKzBJR6yDH3n38bkK2EM4IJ86QkwhnBE6nE6fdSV9n37rtX69yX8XK6pVEREYQ0T6C2PhYYo1YYsJiCHOGQRQUlhUSFxfHI+5HZJRs7sKXhTg+IUQwi4BiyDqexW86/IavfF+xtXwr+4L72FC6gQ2lG3j666f54vAXZPTJgHbgjfbKNEu86rsiZHTpV31s1m4phG1fb+O2Z2/jYOFBYhwxLHh8AWNcY0SwDyAOxAhkqV8+IuhRyIi+O+IAlCPCvwlJZPOrzzYLcTbMnf98SKRAc2ZiW9oAjUbTXLS5SoDYsGrjhxCBaI+IvQsRiwRk1F+gnj+CfDFGI4JUiQj4UWTkaY4qlYDak+10PdCVrrVdGRM9xlp+pubti7YWyXLEarjffz/3ZtxLbCgWu9cu4hWLrPu/BAlZlwExMLPfTBGyFERYzfr8Fcjcu5pnT+qRRFLvJBnJfYa8J1XZ5xb7g4Gg2B1ExPIYsFtdTxIilDHq/z5Ir05nmmea9FE/KK8s59Pdn/Kx92OO2I+QkZAhdibC2NfGEh0bzcj+IxnWZxg9MnvQ7lA7EXUV+scLd997N29//jYAPZN68tKsl+iR1kPm700HoKeyLw/JqTA3POpU7/NMVP1wEKs+QldkROvB2uQpXr1fo9FoNG3QAaiPWX53CFIwx484BH5EAA8gEYL9iNibpVKr1f+rkBFnABmVOpFQuprXr9ugJwIJWasNfCKLIuX1WogJiyHGGWMlDQ5Vx5ob+5jTEtGI8Ju7tfqxtiR2ISPj3kiovR3iFBxHtiP2qMc1iFgCbqfbci5S1PuTkTD9AXVtYUhkIBNZamcgDsVhSIhJYPyA8YwvGi/XeUDOU2KUcOjoIbxHvHz1zVfMR6Zp2se0xxXhYn7xfAaPGwz7INGeSIwjhjuH3ckvJ/2SiNgIOU+Y6u909flsx9pvwKPsjFL21KjPYLuyvR0i8r0QZycOa1qiF6ffHVGj0WjaIG3bATAJR8R3JyKqXRGRSUfmwLsgImIKo4EIVJw61oMIkR9xDMKRnk3D2ohnHyJO0RCMCVqCG6neV4uMavsjArYdK2weoc6XjIh1AeIcpCMj9O7ICL9W2WdGL5zKxirEkQhDohsZ4Pf4xVkwd+8rRgTTTED/F9b2xz3rXVcPxClahzhCFykbj0o/JPmT2H7zdtazng/yP2DTnk3sz99PqbuUUncpRXuL4DUgAL+87Jdc3/16+o7oKzYeVna2V3ZnIY6ZDcndMMsT25EVEsfVdeZjRXOSVD+ZVf5cyHRNnOojjUaj0QDaAbCIRgTnACIuP8DadteFiHEHxBEIIUJtjvANRGw7IA5DLSJkFcgI+xAS8g+T89aU15CQmGAJtB1rG99/IWJahRVZ6IgIYKxqzyzkko5EKqoRx8WLiGEAGTGHKTucyHRCEVLedz1E2iLhKqw13+2RHIct6rieqs0CdT19VJsVSKThB0g0YLe6hiQkBH8UoiuiGZUwilE9R8EPIGgEKUkpocZVQ2psqjg3hyA1MhWv3SvL+mrV9avlgLiUrYVIhMaNOFEqgoEPa0OmjkikwOzPWNWHZlKgB3HG4tFoNBqNQjsA9THF5SAi2OZWuN3U/7ciI2UfIroJSEg+hIyCDyGj0jREEDsgQhaPCNJqoAJCgZCE1UPqPTXqONNx8CDRgo6IQJvCHqke95TzcFC1l4mIX7V63XQUQEb5DqzpglR1/vXAR8rWXlii2U61E6H6oxsixBvV/zOwkij7qXPvVdffXrVThrUXQTsI7xBOammqHGuOzHvIe52bnNKHcaqPOyj7vkUcsQ7q9wfqvabDElA2piLOW7Tq81isGgHV6qcd4iy1tdr/Go1G8x/QDsDJJCICeACZ+7dz4rzxcWSk2g4R3onI6PQAMlotUj9+JBTtUa+bxW8OgjfWK2F7c/TcHhF0EJHtiwhfFda+9knquCisLXyPq/flIU6EUx1Xra4hSr0/iAhkV+p2wCt3lsvrO4Hlqo2uyEg/BXFEjiJiPwxxfrYiYpyk+qVStRGBteVxhmrbdJTiEPFth0QTjqj3xkg7ni4e2eMgGQnhH0RWYexU5+uICHcXZAqgEGtDogRlS6Zq18xFMMv/mtMnETTaJjcajUbTWtAOwOmIRkbFpsDWIqKSighJCSK2IWR+OQsZDdciwndU/T6szmeHjzd/zJI3l2AcM+ga1ZXrRl7Hpb0vtebv05ARcKU6537VZgYiyGGIM1GrfhzI1IPpdJiOgQcRYLMITzziOJy8vMuBiGsIEdZKRPTNnQTDEMdlNzL9YeYbFHDiqoLLkCmBLUi4PQq5JifW/Hy16rdeSI5BtHr9IIQHwiXCYWBtcnQIcRwmqXY3YGX790GcHycyZVGh2jB3PTQjAeHKvuOqD/XoX6PRaE5AOwBnov5ywZOJRkQnHhHZXYjopCMi3gkRIw/ghdV/X81TS5+itraWgCMAHnh9zescG3CM0TePFhEuR5yGSqxRrR0R4VqsbH8H1kg4D3lvL0QwwxFhjFLHmmWG62MAFRB5IFJeiwGuVe/LQ0Tbi1V/IBMZtQeQokiXY0UGIrHyH36IVAA8XO8aeipb9kibDFHHZSDz+UXgTfHKsbsQ4a9GHIhJ6v+rlT2Xq/YTkYhGJdZqC5+69gzE0WinbNqHXvqn0Wg0Z0A7AOdCGiKApUgSngcR7xJEfMwStaou/+N/eJx8fz6EQ0owhZKwEmqDtVQtrmL0oNEipOaOeB0QETM/GQOrqt1xRHyL1PNmMZx4xDE4EwbWvgBliGCGkAz+hHrHmfX9DyPTDyAOQCYSmi9Tx3dTx9Sq63QoG/thCf5+dcwPVDtHkKTBSiR6UALEQsgVksjBcdVfLtUH36j+DUOmIDogDsouJLQfqX7M5YEJWM7OcdW+E134R6PRaM6AdgDOlUxEAI8gImeKWilWKVUb4ICYQzF0NbrSPtSetGAabpubwrBCokuiRSTTkND26UaqNiR8r+oG4EAchFisTYQOq+cjsPYXsCGjdj8SpQjVO1cnqKX2RPE3MfMLSpAoxzeIU9AZK/JRq665DIlE1CjbnYgzYo7o9yOj8H5IfsEG4F31/lR5Lro42irpexhxIMzNfcJV31SoPg1iVSwMx3KczLr/QXWsmZvQDf0XrtFoNGdAfz2eK+bOeGb1vM7IKLUDInBm6N4P8SnxGEcN2oXaURBewKHwQ9SG1VKZUQkjkZGtuYIgDhE2EGFzY5WxdWDVKLAh4mcmGdZg7cRnbiNsVz9JWDsFNuQTt6k2EhDBN3ccTEVG84eRJL0srKWCparNWGTJYRekHsEmZMVBknrOXNWg+ibMHSZORhniRAxQbbgRQY9HHJseiDNgbjhUo45xYyVQol6vnzeh0Wg0mtOiHYDvgx0Jwe9HQuSViANghqcBquHO2+5k0TOL2OHbQUF4AdggKiqK+x66T4Q2HgmDl8EpO7Gac//mWnfbSa/FqJ+mwI44NknI1EM+MvKORa51n3qtE9Y2vGZUAGQEnoI4AYXqPeny/s/Wf8a6N9cRUxZDgiuB7pO688OHfij9cRhxrMzplC6cuIGPS/2YBLC2K3Y2ag9oNBpNq0U7AN8XOyJ0R7FEPFo9Xwv4YPRVo/HGefntot9iK7CRlJTEo48+Sk5OjpzDgYhoR2R07FXndiKj35YeycYgkYca5BorkChDFValw16IuKdjVSR0I9c2BImSlAFe2PzZZta+tRaf38ch5yG2hbZRtKaIOSPnkNM7RwoFmZUAzWWA/wkz0qHRaDSaBqO/NhsDG1byXgnWXgHRSNi6PWRfmk32T2UHxM2bNzNgwIDTn6d+9OB8IxoJzwewohVFWNUT07ByBcy1/jFYqyl2ynv+9v7f2GPsoTCikPzwfGrDanF4HOQ+kkvOIzlynoHUJVFqNBqNpvHRDkBj4kBGwK0dOxLaT0EiAxXIdEABUiwoEQnf18eBzONXwY7KHdSE11BrqyXOiKODvwNdA12J8cRINMWsMKjRaNo8hmGwZs0axo4di81m+87nNQ2npYPLmgudcCR573JgFFLtMBaZvuiEJAT2R9bwXyzHVWRWUG2rJi4URw9/D3oFeuGz+TiYeVDOo8Vfo9Eo1qxZw/Tp03nssccwDNnv2zAMHnvsMaZPn86aNWta2MILF+0AaBqPGETkL0KmM44gFfqOIFMGqjb/fffchyPSgc1mo9xWznbHdrbEb+HWx25tMdM1Gs35ydixY5k2bRpLliypcwIee+wxlixZwrRp0xg7dmxLm3jBoqcANI2LWbI3Acn6N2sjFFuHZPfJJvruaBa8toAdxTtI6JjAk7OetJIiNRqNRmGz2Zg9ezYAS5YsYcmSJQBMmzaN2bNn6/D/96DZHYCCggLuv/9+SktL6dKlC/PmzSMm5sR1bEVFRTz44IOUlJQQFhbGAw88wJAhQ/D7/QwaNIhOnTrVHfv2228THh5+cjOalsaGJPHFYRUx8lFXHOnq/ldz9X1XnzkhUqPRaBSmE2CKP6DFvxFo9imA2bNnM3nyZNasWUOfPn146aWXTjlm7ty5jBw5klWrVvHss89y3333EQwG2bVrF/369WPVqlV1P1r8LwBsSGW+OCQ5MIpT9yjQaDSaM2CG/etTPydAc240qwPg9/vZuHEjY8aMASAnJ+e0CRyjRo1iwoQJAHTu3Bmv10tNTQ3btm2jrKyMnJwcfvzjH/PFF180p/kajUajaWZOnvPPy8s7JSdAc2406xRAeXk5LpcLu12aTU5OprCw8JTjTAcBZM7n4osvJjY2FpvNxtVXX80vfvEL9uzZw2233UZubi6JiYnNdg0ajUajaT7WrFlTJ/5m2L9+TsCQIUMYN25cC1t5YdJkDsB7773HnDlzTniuc+fOp8zZ/Kc5nKVLl7J8+XL+/Oc/A3DTTTfVvdarVy8uvfRStmzZwjXXXNNgu7Zv397gY5uSzZs3t7QJ54UNoO04mfPFDo3mfGDs2LG8+uqrJ6z3N52AIUOG6FUA34MmcwDGjRt3ildmJvEFg0HCw8MpLi4mJSXltO+fO3cu69atY9myZaSlpQGwcuVK+vfvT2ZmJiChIYejYYvGzTBR9+7dcTpbtmD89u3b6dOnT5u3Qdtx/tnh8/nYvXv3eR1WNW3z+XwtbIng9Xq/+6BmoDXbMXLkyNN+3md6vqnsOBda0g6zb850P9uMZr7Tb7/9drKzs8nOzmbx4sUUFRWdktyxdOlScnNz+eMf/0i7dlZJuaeffhqPx8Pjjz/O/v37+fnPf867776Ly+U6uZlTqKqqYvfu3Y1+PRpNa6R79+7Exsa2tBmnRd/LGs3Zcab7udkdgPz8fGbNmkVpaSnp6enMnz+fuLg43njjDYqKirjnnnu4/PLLcblcJ4j/H/7wB2JiYnjooYfYv38/NpuNhx9+mMGDBzeo3VAohNvtxuFw6KUjGs0ZMAwDv99PTEwMYWHnZ50wfS9rNA3ju+7nZncANBqNRqPRtDznp4uv0Wg0Go2mSdEOgEaj0Wg0bRDtAGg0Go1G0wbRDoBGo9FoNG0Q7QBoNBqNRtMG0Q6ARqPRaDRtEO0AaDQajUbTBmkTDkBubi7XXnsto0ePZtmyZU3SxtSpUxk/fjwTJ05k4sSJbN26lU8//ZTs7GxGjx7Nc889V3fst99+S05ODmPGjOHhhx8mEAgAUFBQwJQpUxg7dix33nknbre7QW1XV1czYcIE8vLyABqt3crKSm6//XbGjRvHlClTKC4uPis7HnzwQUaPHl3XJ//4xz+a3I6FCxcyfvx4xo8fz9y5c1usP05nR0v0R2ukqe/nlryXQd/P9dH3cxNjtHKOHTtmjBgxwigvLzfcbreRnZ1t7Nmzp1HbCIVCxhVXXGH4/f665zwejzF8+HDj8OHDht/vN2699Vbjo48+MgzDMMaPH298+eWXhmEYxoMPPmgsW7bMMAzDuP3224133nnHMAzDWLhwoTF37tzvbPurr74yJkyYYPTu3ds4cuRIo7Y7e/Zs4+WXXzYMwzBWrFhh3HvvvQ22wzAMY8KECUZhYeEpxzaVHevXrzd+8pOfGF6v1/D5fMYtt9xi5ObmNnt/nM6O999/v9n7ozXS1PdzS97LhqHv5/ro+7npafUOwNtvv208+OCDdY8XLlxoLFiwoFHb2Lt3r3HFFVcYU6dONbKzs43XXnvN+Pzzz41bbrml7pgVK1YYs2bNMvLy8oyrr7667vmNGzcaU6dONXw+n9GvX7+6L56CggJj5MiR39n2Qw89ZGzcuNEYMWKEceTIkUZtd8SIEUZBQYFhGIbh9/uNfv36GT6fr0F21NTUGP379zemTZtmTJgwwXjhhReMYDDYpHbs3r277sYzDLmxFixY0Oz9cTo7li5d2uz90Rpp6vu5Je9lw9D3c330/dz0tPopgKKiIpKTk+sep6SkUFhY2KhtVFZWMmTIEBYtWsTSpUv5y1/+QkFBwWnbPdme5ORkCgsLKS8vx+VyYbfbT3j+u3jyyScZOHBg3eMzXe+5tFv/PXa7HZfLRVlZWYPsKCkpYfDgwTz11FO8+eabbNq0ib/+9a9Nake3bt3o27cvAAcPHuS9997DZrM1e3+czo4rr7yy2fujNdLU93NL3sug7+f66Pu56Wn1DkAoFDphwxDDMBp9A5F+/foxd+5cYmNjSUxMZNKkSbz44ounbfdM9pzOrnOx80znb4x2DcNo8AYxnTp1YtGiRaSkpBAVFcXUqVNZt25ds9ixZ88ebr31Vh544AE6derUYv1R346LLrqoxfqjNdHU9/P5dC+Dvp9B389NSav/1khLSzshqaK4uJiUlJRGbWPTpk1s2LCh7rFhGGRkZJy23ZPtKSkpISUlhcTERKqqqggGg9/LzjNd77m0m5KSQklJCQCBQAC32018fHyD7Ni1axdr166te2wYBna7vcnt2Lx5Mz/72c/49a9/zfXXX99i/XGyHS3VH62Npr6fz6d7GfT9rO/npqXVOwBDhw5lw4YNlJWV4fF4eP/99xk2bFijtlFVVcXcuXPxer1UV1ezYsUKZs6cyYEDBzh06BDBYJB33nmHYcOGkZGRQUREBJs3bwZg1apVDBs2DIfDwcCBA1m9ejUAK1euPCc7L7vsskZrd/jw4axcuRKA1atXM3DgQBwOR4PsMAyDp556iuPHj+P3+1m+fDmjRo1qUjuOHj3Kr371K+bNm8f48eNbrD9OZ0dL9EdrpKnv5/PpXgZ9P+v7uWlpE9sB5+bm8vLLL+P3+5k0aRK33XZbo7fx/PPPs3btWkKhEJMnT+anP/0pGzZsYM6cOXi9XoYPH86DDz6IzWZj586dPPLII1RXV9O7d2/mzJmD0+kkPz+fWbNmUVpaSnp6OvPnzycuLq5B7Y8cOZI//elPdOzYsdHaraioYNasWRw5coTY2FjmzZtHx44dG2zHsmXLWLZsGYFAgNGjR3PfffcBNJkdTzzxBH/729/IzMyse+6mm24iKyurWfvjTHaEQqFm7Y/WSlPfzy19L4O+n0Hfz81Bm3AANBqNRqPRnEirnwLQaDQajUZzKtoB0Gg0Go2mDaIdAI1Go9Fo2iDaAdBoNBqNpg2iHQCNRqPRaNog2gHQNDtTp05l5MiRLFy4sEHHB4NBJk6cSL9+/VizZk0TW6fRaBqKvpcvbLQDoGkRHnjgAe66664GHRseHs6qVavo06dPE1ul0WjOFn0vX7jYW9oATethxYoVLFq0iFWrVmGz2bjhhhv4xS9+wXXXXXfG9yxYsIDDhw9TWFhIcXExvXv3ZtCgQaxcuZK8vDzuv/9+JkyY0HwXodFo9L3cRtAOgKbRuP766/nkk0945pln8Pl8DBw48D9+YZhs3ryZVatW4XA4GDZsGElJSSxbtox//vOfPPPMM/pLQ6NpZvS93DbQDoCmUZk9ezYTJ04kMjKSt99+u0HvGTp0KLGxsYBsjHHllVcCkJmZSUVFRVOZqtFo/gP6Xm796BwATaNSWlqK1+ulsrKSoqKiBr3H6XSe8NjcM1uj0bQc+l5u/ehPR9No+P1+Zs6cyb333ksoFGLGjBm88cYbbWanOo2mtaDv5baBjgBoGo358+eTlJTEjTfeyE9+8hMSEhJ47rnnWtosjUZzluh7uW2gIwCaRuN//ud/Tnj8yiuvfOd77r777hMev/POO3X/7969O59//nnjGKfRaBqMvpfbBjoCoGkR5s6de9bFQ7Zv397EVmk0mrNF38sXLjbDMIyWNkKj0Wg0Gk3zoiMAGo1Go9G0QbQDoNFoNBpNG0Q7ABqNRqPRtEG0A6DRaDQaTRtEOwAajUaj0bRBtAOg0Wg0Gk0b5P8D3dPef9YB2AoAAAAASUVORK5CYII=\n", + "text/plain": [ + "<Figure size 576x432 with 4 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig,ax = plot_ensemble_sat_analysis(bg,an,obs,obs_sat,truth,reflectance_simulator,m_const,da_const,h_c=0.5)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 249, + "metadata": {}, + "outputs": [], + "source": [ + "K_bg, K_HXa, K_Ya, pai_HXa, pai_Ya, x_ol_an, x_ol_bg, obs_loc = triple_kalman_pai(states_1[0],time,m_const,da_const,reflectance_simulator)" + ] + }, + { + "cell_type": "code", + "execution_count": 250, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "<Figure size 1080x360 with 3 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig,ax= plot_triple_K(K_HXa, K_Ya, K_bg)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 251, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "<Figure size 720x720 with 3 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plot_triple_K_lines(K_HXa, K_Ya, K_bg,da_const,obs_loc)" + ] + }, + { + "cell_type": "code", + "execution_count": 252, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "<Figure size 720x432 with 2 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plot_double_pai(pai_HXa,pai_Ya,da_const,obs_loc)" + ] + }, { "cell_type": "code", "execution_count": null, diff --git a/22-paper-collection.ipynb b/22-paper-collection.ipynb index b24b6429e8b4982125aa560f12e4bdd7d7abfc2c..efaf2f4f703648d6bb94545b22d5a13539c807e5 100644 --- a/22-paper-collection.ipynb +++ b/22-paper-collection.ipynb @@ -96,8 +96,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "CPU times: user 5.76 s, sys: 153 ms, total: 5.91 s\n", - "Wall time: 1.53 s\n" + "CPU times: user 6.72 s, sys: 188 ms, total: 6.9 s\n", + "Wall time: 1.91 s\n" ] } ], @@ -187,8 +187,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "CPU times: user 7 µs, sys: 0 ns, total: 7 µs\n", - "Wall time: 10 µs\n" + "CPU times: user 10 µs, sys: 1e+03 ns, total: 11 µs\n", + "Wall time: 16.9 µs\n" ] } ], @@ -768,7 +768,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 96, "metadata": {}, "outputs": [ { @@ -780,7 +780,9 @@ "completed timesteps: 21 seconds spent computing so far: 1421.0\n", "completed timesteps: 31 seconds spent computing so far: 2131.0\n", "completed timesteps: 41 seconds spent computing so far: 2775.0\n", - "completed timesteps: 51 seconds spent computing so far: 3465.0\n" + "completed timesteps: 51 seconds spent computing so far: 3465.0\n", + "CPU times: user 1h 7min 30s, sys: 59 s, total: 1h 8min 29s\n", + "Wall time: 17min 9s\n" ] } ], @@ -840,7 +842,7 @@ }, { "cell_type": "code", - "execution_count": 94, + "execution_count": 97, "metadata": {}, "outputs": [], "source": [ @@ -868,12 +870,12 @@ }, { "cell_type": "code", - "execution_count": 95, + "execution_count": 98, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "<Figure size 648x288 with 2 Axes>" ] @@ -919,7 +921,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 99, "metadata": {}, "outputs": [ { @@ -933,7 +935,7 @@ }, { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "<Figure size 576x396 with 6 Axes>" ] @@ -1438,7 +1440,7 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 100, "metadata": {}, "outputs": [], "source": [ @@ -1462,7 +1464,7 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 101, "metadata": {}, "outputs": [], "source": [ @@ -1481,7 +1483,7 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 102, "metadata": {}, "outputs": [ { @@ -1542,7 +1544,7 @@ }, { "cell_type": "code", - "execution_count": 85, + "execution_count": 22, "metadata": {}, "outputs": [], "source": [ @@ -1557,9 +1559,20 @@ }, { "cell_type": "code", - "execution_count": 86, + "execution_count": 25, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 4, 8, 16, 32, 64, 128, 256, 512])" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "\n", "# slight difference now, instead of running a new state for each ensemble size, i instead first run the max size ensemble, and then randomly select the desired number of ensembles from the large state.\n", @@ -1568,28 +1581,21 @@ "n_steps=8\n", "ens_values = 2**np.arange(2,n_steps+2)\n", "ens_values = ens_values.astype(int)\n", - "#da_const['nens'] = ens_values[-1]" + "#da_const['nens'] = ens_values[-1]\n", + "ens_values" ] }, { "cell_type": "code", - "execution_count": 87, + "execution_count": 26, "metadata": {}, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/pgriewank/pgriewank/code/2021-linear-advection/da_functions.py:328: ComplexWarning: Casting complex values to real discards the imaginary part\n", - " bg[:,i] = linear_advection_model(analysis[:,i],u_ens,dhdt_ens,m_const[\"dx\"],da_const[\"dt\"],da_const[\"nt\"])\n" - ] - }, { "name": "stdout", "output_type": "stream", "text": [ - "CPU times: user 19.3 s, sys: 296 ms, total: 19.6 s\n", - "Wall time: 4.95 s\n" + "CPU times: user 16.4 s, sys: 371 ms, total: 16.7 s\n", + "Wall time: 4.25 s\n" ] } ], @@ -1726,7 +1732,7 @@ }, { "cell_type": "code", - "execution_count": 89, + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -1735,7 +1741,7 @@ "(8, 900)" ] }, - "execution_count": 89, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } @@ -1752,12 +1758,13 @@ "ens_values =dict_raw['ens_values']\n", "\n", "counter = vr_real.shape[1]\n", - "vr_real.shape" + "n_steps = vr_real.shape[0]\n", + "vr_real.shape\n" ] }, { "cell_type": "code", - "execution_count": 90, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ @@ -1775,17 +1782,19 @@ }, { "cell_type": "code", - "execution_count": 91, + "execution_count": 14, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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DGXmL+bVwJX7Nh3Mg/OZfg8heX0jKsNh6DZIFK10jetIjagA9owbQPWoAya40rM2cnlvVgyg66GP/Gg/71rjZt8bN4a0N3x7ev8ZTp1HqNsLFSb/vRPd0F2nDXUTEtZ+YjaJ1MWOUXgSeAJBSbhBCPADMBsaHSglj2cqzgLNK8yxguWEMJwNPA1eGSiaEx93Pc2ez5si3rDnyLQXenFrHuwzqVMsYdbIn0CPoBQnSIvviskWGTKd9GW5+nd2JjL0HKNjXtKD3vjVuhlwSXavd1cnK6X+ND5GGCkUlZoxStJRyUcWOMZnyHyHW49/AU8CpVdouAE43tt8DnhdCOKSU3hDLPmq8ATebCn4i48gSdhZvaLCvBSsJgVQGdRlFjyhBj6gBxDmSmrQEoKkU7PeR/WMkZlcHdepqo3u6i26jXPQ80RU2vRSKujC79m2YlHI9gBBiECFc+yaEmAhESSnnCyGqHkpDz3qJlNInhCgEOgMHQiX7aNA0jf1l28nIW8L6/OWUB0ob7J/oTCE9cQIj48ezfcMe0tNCG4zN2eElMs5KdHLtIHL39IYNS/Jx+l2v7qNcdE/X40EKRWth5tN3H7BUCFHhAgwErmmqICHEb4H/1GjeAsQCdc17quk6WMD8GoGNGzea1i0jw3xKBjclZFo3sdu6jkJLw3k5bJqdbtogegdGkOTtgaXEwvbMPU2WWReaBsV77Rxa5eLwahelWXb6XVFMrwtqG0dNA1dCEu4jNiw2jdg+XuKEl/gBXuL6e3HE6EGlMmDbAUJq9o/2dSqZHU+mmbVvnwrdhTkVfTrASillk7PkSCnnAfOqtgkhpgH3AssqvCQhxFpgLLAfSAH2CSHsQCcg16y8IUOG4HI1PvTQ7xA17LX4NT/bi9aScWQxWwpXN5qErEfUANITzmRo/KlE2Grf4jYjsy40TSNrg4etX5ex9evSWvGhkk0JpP+j7sIzOZM3MCR9AF2Pd+CIaJmgdHNfp5J57Mp0u91NcgjqwkzmyYol13uM5+5CiO5SyjVHJRmQUr4KvFpFlialHGFsfw5MAR5GD3Avb+l4Uq47i4wjS1iT922j68qi7XGMjB9PeuIEukT0CJkOAb/GgbUetn5dytavyyg6WP/IOXuTl4IDPuLSav9bO6d7Gh3GKRRtATPDt6rTnZ1AKrAaODEsGlVyH/CmEGITkE8zhozNwRMoZ2P+CjKOLGZ3ya8N9rViZUCnUaQnnomITcdmCU0sJuDTyFzt1g3RN2WU5jY+arXaoedJEXiKm5JuQ6Foe5gZvlVLyiuEGE+YDISUsmpZ8DxgYjjk1ETTNDJLt5JxZDEb8n/AHShrsH+yM430xDMZkTCOWEdiyPWZd/Nh9v7UeBZCmxN6nxLBgLOj6D8+Us0VUrQLmvzTLqX8TgjxZDiUaWmKvflstf7E8q1vcdi9r8G+TmsEQ+NOJT1xAj2jBob1Fn6vkyLqNUr2CAt9x0Yw4OxI+p4eiStGGSJF+6IpMSXQ74CNBkI3s68VCGgBvjn4HssPfUjA5ocGnJJeUYNIT5zAkLhTQjKh0VMa4NDPLj7+bw6n/zWe+O61/wUDzo5k+dOVGdmc0Rb6jYtkwNmR9DktosnpNRSKY4mmxpQ09CUffwyPOi3D3lLJ0kP1ZwaIscczKuEMRiVMoHNEt5DILDjgY8WLhWz+ohRfeRxQRupQJydMrb1+LbG3gx6jXcR2syHOjqLXmAjsLrXSXdExaHJMqT3gD9S+iWfFxsDY0aQnnslxnUZis9S9kr2plOT6+enlQtZ9UIy/htitX5fVaZQArnqzS0jkKxTHGg3lU2pwKYmU8sHQq9My9IkZwgmJ57AufxkR/lhOTTuPEfHjiHHEh0yGuyjAqreKWP1WEd6yule8HljnoSjbR6cUNYNaoaigoW9DZ+N5ICCAReiTJy8G6kgpfuxgtVi5pPvNXNL9Zn1CWOfQTULzlgf45b1iVr5aRHlB3bfnXUl+hl4Yx4CzourNLaRQdFQayqd0K4AQYgkwSkqZY+zPQi/jrahCwKex4cMSfnyxkOJ6JjjGpto45U+xlHeTnHBi75ZVUKE4RjAzbkitMEgG+YAKeFQhc3U5/7v/CEf21L38JCrRysk3xjL8yhjsTgutsGRJoThmMGOU1gsh3gDeRp8S8HtgZVi1OsZwRlvrNEjOaAsnTO3E6Cmd6iz/rFAoamPGKE1Dr5L7tLH/BfBAuBQ6Fuk6yMnAcyPZ8qU+E9zmhJG/68RJ0zoRlaBiRgpFUzAzJaBICPF39OolG4EIKWXD6zDaKUUHfXTqWvdbdtqtcWxbUsbxF0Uz5uZYlZNIoWgmjY4phBAnAzuAT9ETr2UKIU4Jt2JtifxMH5/encvsc7I4vLXuCh4JvRzc/E0av5mZqAySQnEUmAl0PI6ehC1XSrkPqMiX3e4pPuzn61lHeO2iLDZ/Vormh+XPFNTbPypRDdUUiqPFjFGKklIGc3hIKT+nGQt5jyXKCwIseyqfV87LYu37xQSqxLB3fFfO/l8aX8GvUCiahxnj4hVCJGAU4hE1Emm3J7xlAda8W8zK1wtxF9Y9Czv5OAdaAyWJFArF0WHGKD0ELAVShBDvAecAfwirVi2M36uxfkExK14qpCSn7lnYcd1tnHpLHIPOj8JqU4tjFYpwYcYofQlsRi/hbQMelFJuDqtWLYQW0Mj+0cVr07PqrYcWlWRlzM2xDL88RtWkVyhaADNGaZWRN3t7mHVpUQr2+1j45xxytsVRV8UoVycLJ94Qy6hrYnBGqYmPCkVLYcYolRiFAhpOzXiMEdPFhq+8dnDIHmFh1DUxnHhDJyLj1N00haKlMVUhF9glhMgEiisapZTDwqZVC2BzWDjt1lg+vUuvUmK1w7DLohlzcxwxnVvWGBUWFnLo0CG83vAVa7Hb7Wze3LKjbiWz/cqMjo6me/fuWK2hH0WYMUp/CbnUNsLAc6P47vmD9BgSx6m3xJHQs+VnOhQWFnLw4EG6detGZGRk2HJ/l5SUEB0dHZZrK5kdS2YgEGD//v3k5OTQpUvo1+abWWayNORS2wgWq4X0GUc4cUzfVtPh0KFDdOvWjaio2kUrFYq2iNVqpWvXruzZsycsRqnDR3BtztaV7/V6iYw8puswKDogDocDn6/hStHNpcMbpbZAOMs1KRThIJyfWdNGSQgRHzYtFG2GDRs2MH369KO+zsKFC7n//vsBuPHGGzl48GC9fSuOZ2Zm8ve///2oZXd0ioqKuOWWW5rVf9++fUyYMCFcqpnCTN23ivzccUKIE4FvgElSyi3hVk7R8gwdOpShQ4eG9JqvvPKKqeMrV64kMzMzpLI7IgUFBU26M9fU/uHGzO2mZ9HvwD0mpdwvhHgWeBk4PayadUB2Fm/g4/0vc9i9PyzX7+zqxsRuf6BvTP1GZ+XKlTz33HPMmTOHyZMnM3jwYDIyMnC73dxxxx28/fbb7Nixg6lTpzJ16lSeffZZDhw4wI4dOzhy5AhXXnkl06ZNq3bNCRMm8Pbbb9O5c2dmzpxJRkYGDoeDP/3pT5x//vnB47NmzWLfvn3MnDkz6GUBZGdnc8cdd1BaWorVamXGjBmMGDGC9evX88gjj1BeXk5CQgIzZ84kMTGRyZMnM3ToUDIyMsjLy2PGjBmMGzeOTz75hFdffRWbzUb37t15/PHHcblcYXmvzbJ69WpWrFjBmDFjGD16dEiuOWvWLA4dOsQtt9zCjh07SEhIICIigosuuoiff/6Zf/3rXwBMnjyZP//5z7zxxhvB/vfeey/l5eX89a9/Zdu2bcTGxvL888+TkJAQEt3MYMYoJUkpv65YhyulfEEI0a7WvrUVPtz3ErmerLBd/7B7Px/ue4n/G/i86XM0TWP+/Pk899xzzJo1i48//pi8vDwuueQSpk6dCsDGjRt5//33CQQCXHrppYwZM6bOa82ZM4fS0lK++OILcnNzmTp1KmeddVbw+IwZM3juueeqGSSA+fPnM378eKZNm8ayZcvIyMhg8ODBzJgxg5deeom0tDSWL1/Offfdx/PP66/N6/Uyd+5clixZwtNPP824ceN46qmn+OCDD0hKSuLRRx9l586dDBo0qInvYuNMnjyZJUuWhPy6oBv4OXPmNNhnxowZTJkyhXvvvZczzzyTV199le7du7Nw4cIG+z///PPs27ePvLw8rr/+eoYNG8Ztt93G559/zjXXXBOOl1MnZoySJoSIoDJLQAr6GriQIIRIBV5FTyBXClwjpdxtxLDeBfqiV+W9QkqZHSq5CnOcfrruEKelpTF8+HAiIyPp1q0bhYWFwT4XXnhhcM7MhAkT+Omnn+r8ZV21ahVXXHEFVquVzp0789lnn5nSYcyYMdx6661s3ryZcePGce2117J7924yMzP54x8rizUXFwfn9jJ27FgAjjvuOPLz8wE444wz+N3vfsdZZ53Fb37zm7AYpLZGUlIS3bt3b9I5Xbp0YdgwfW50//79OXLkSDhUqxczRukF4H9AFyHEI8DvgEdDqMMcYL6U8iUhxM3Gta8EZgHLpZQXCCEqEstdGUK5bY5Lut/Mx/tf4bA7PCt6Oru6M7HbjU06x+FwBLft9ro/LjZb5W9UIBCotl8Vu91e7a7Nnj17SE1NbVSH9PR0PvvsM7777js+//xzFi1axN1330337t356CO92pff7ycnp7LoTsWwrKq8GTNmsGXLFpYuXcqdd97Jn//8Zy6++OJG5TeVxjyZClavXs2VV16J1+vF4XAwd+7ckA3hKoiIiAhuWywWtCp5d+pbQVD1/1zznJbAzOTJ14UQ24ELAAcwTUr5TSiECyGSgeHoGQgA3gAWG9sXUBm3eg94XgjhkFKGby1GK9M3Zii3i2fCcu1wzgD+5ptvmDJlCmVlZXz77be89NJLrF9fu17pCSecwOeff84ZZ5xBXl4e1157LV988UXwuM1mq3Puy2OPPUbXrl257rrrOOmkk5g0aRJ9+/aloKCA1atXM3r0aBYsWMAnn3zCSy+9VKeOPp+P888/nzlz5nDTTTfh9XrZvHlzWIySWUaPHs3cuXNDHlOy2+11vo8JCQns2LEDTdPYt28fUsoG+7cWZu6+dQJOlVLeLYToBfxVCLFCSlkSAvn9gL3Av4UQY4Fs4M/GsTQgC0BK6RNCFKJX7T0QArmKEOJyubj66qspLi7mpptuon///nUapauvvppZs2YxceJEAO677z5iYmKCx/v160dRURF33nknjz/+eLB98uTJ/O1vf2PhwoXYbDYeffRRnE4nTz/9NA899BBut5uYmBgefbR+B95ut3Pbbbdxww034HK5SEpKCgZ8W5PRo0eH3DtKSkoiLS2Ne++9t1r7KaecwoIFCzj33HPp06cP6enp1fpPnjyZRx55JKS6NAdLY66ZEGIBsEtKeYcQIg69vFJXKeXVTREkhPgt8J8azduAccBEKeWnQohpwLVSyvFCCA96Kl6fcf5+IL2xuFJGRkZvYFdTdGtN7HY7/fv3b201mk2FZ3LzzTe3siaKlmb79u0NeVh90tPTdzfnumZiSsdJKS8DkFIWoHtK65oqSEo5D5hXtU0I0Q9YI6X81Gj6L1AxftkPpAD7hBB2oBOQa1bekCFDTN3uzcjICP5itBRVZW7evLlFFlaGa/jmdOrrdOq6dkdbqNrRZDqdToYPH16tj9vtZuPGjUclx8yMbocQIrZiRwgRg14p96iRUu5ANzrnGU0XARVFrT8HphjbV6IHvdttPOlY5dZbb+XWW29tbTUU7QgzntLbwEohxDz0aQGXogekQ8WlwGwhxONAIXCd0X4f8KYQYhOQD7TcRAmFQtFqmLn79ohhGM4EfMBdUsovGjnNNFK/BTC+jvY8YGKo5CgUimMDs1nNvgSWYwzbhBCJhtFQKBSKkGKmbPfNQAGQgz6zuuJZoVAoQo4ZT+lO9HlKa8KtjEKhUJi5+5atDJKiqTz77LPBOUyNzZquOL5+/fpqkyYVzWPlypVMnjyZ6dOns2HDBtPntZX/gxlP6SshxB+Bj4GyikYVU1KYpWJ9WmPHt2/fTm6u6aloikZ46KGHmtS/rfwfzBilewAXUDXfhUYIMwUoqvP4kOYlOus62MGUD1LqPDZvSgFTF5ibbPfyyy/zxRdf4Pf7Oe2007jzzjtZsmQJjz32GB9//DHZ2dlMnjyZefPm8Z///AeXy8WGDRsoKSnhj3/8I5dcckm16wkhkFKSn5/P9OnT2blzJ06nk3vuuYcxY8YghGDVqlU888wzlJaW8uKLL1Zb/b9lyxb+8Y9/4PP5cLlcPPLII/Tu3Ztly5bxzDPP4PP56N69O//85z9xOp1MmDCBiRMn8v3331NWVsajjz7KkCFDeOONN1i0aBFWq5Vhw4bx4IMPNut9DiXhyKdUQUW+JNBn3jscjmBmyaioKL75Rl/C+vLLL5OcnNzo/6GlMDMlQGW170AsW7aMjRs3Mn/+fCwWC3feeScff/wxF198MV999RUvvfQSK1eu5O6776Zr164AZGZmMnfuXHJzc7n00ks59dRT67z2008/Tc+ePXn++eeRUvKPf/wjmHspNjaW2267jZ9//rnWF+Gtt97i+uuv57zzzmPRokWsXbuW2NhY/v3vf/P2228TFxfH+++/zxNPPBFMpxsfH8/8+fOZM2cOs2fP5qmnnmL27NksX74cm83G9OnTOXjwYPA1hJpu3brVe+zRRx/l2muvZfXq1Vx++eUN1vvbv78y4d+5557Ll19+2Sx91q1bx2effUZ8fDynnHIKd999NwsXLuTee+/ls88+47rr9OmBDf0fWgozC3Kd6Cv2K2Zy24D+UsqjT+SsaHOsWLGC9evXc+mllwJQXl5OWloaANOnT+f8889n1KhRXHDBBcFzLr30UhwOBykpKYwaNYqMjIw6r71q1SqeeOIJQPee5s6da0qncePG8eCDD7J8+XImTJjAGWecwbJly8jKymLKFH3SfyAQIC4uLnhO1XxKX331FTabjZEjR3L55Zdz5plncv3114fNIJllxYoVLbY6f8CAAcE0MQkJCcEfg7S0tGq5sdoCZoZvc9ETraUCvwAnAd+FUSdFK+L3+7nuuuu4/vrrAb1YZkV+pJycHGw2Gzt37sTtdgfXFtbMp1Rf3qWa+ZR27NhBnz59GtXp3HPPZeTIkXz77be8+eabfPfdd4wfP55Ro0YFg+lut5uSksrEFXXlU3rhhRdYu3Yty5YtY9q0aTzxxBOceOKJpt6XplLVw6mPMWPG4HK5TOdTaq6XBNXzYgH15rxqC5gxSiOA44AXgSfR79i9GEadOjx3buwR8mv+9u24xjsBJ598Ms888wxXXHEFLpeLW265hUmTJnHxxRdz7733Mn36dH766Seefvpp7rrrLgC++OILzj33XA4cOMD69et56KGH6kxEP3r0aD777DOEEOzYsYMbb7yRxYsXB4/Xl0/p9ttv58ILL+Sqq66iX79+PPLII/zlL39hxowZ7Nq1iz59+vDCCy9w8OBB7rvvvjpfV15eHtdccw3z589n5MiRZGdnI6UMm1EyQ7jyKR0t9f0fWgozRinLyGe0FRgipZxnpDBRtEMmTJjAli1buOKKK/D7/YwdO5ZJkybx6quvkpSUxDnnnMMpp5zChRdeyDnnnAPoQ7zLLrsMj8fDgw8+WG+S+dtuu40ZM2YwceJE7HY7jz32WDVPZtiwYTz33HM88cQT3HHHHcH2m2++menTp/P888/jcDh44IEH6Ny5Mw8//DC33347gUCArl27NngbOzExkSuvvJLLL7+cyMhI+vTpw2WXXRaid635hCOf0tFS3/+hpTCTT+kr4E30mdw3Ag8CH0op+4Vdu2ZQkU/pWEpd0hK5osOV6uKee+7hxBNPDMagWkJmQyiZLSezrs9uldQlzc6nZGby5J/Rh3BfAwFgGfBEc4QpFApFY5iZErAVuMvYbdeJ+xVNpy2klFW0L+o1SkKID6SUVwghNmCUV6qKlHJYWDVTKBQdkoY8pYos7H8D3C2gS4clEAhgtZoZSSsUbYNwll2q1yhJKStmwD0mpRwRNg06ONHR0ezfv5+uXbvicDiq3Y1SKNoimqaRm5tbraZcKDEzJaBECNFdShmeCokdnO7du5OTk8OePXvCOjfE4/EEk/y3FEpm+5UZERHR5Mq7ZjFjlKKBXUKITCBYF1nFlEKD1WqlS5cudOnSJaxyMjIyalWeCDdKppLZHMwYpb+EXQuFQqEwaDS6KqVcCmwAdqIXedwLtKzfqFAoOgxmsgQ8CFTU//WhG6RfgaFh1EuhUHRQzNyHngL0BOajL8ydCmwKo04KhaIDY8YoHZJSZgGbgeFSyjkoL0mhUIQJM0bJK4ToB0hgrBDCDoRngoJCoejwmDFKjwAvA5+il9jOBJaEUymFQtFxMTMlYLmU8lMAIcQI9LjS+nAqpVAoOi5mjNIuIcSnwCtSyuXAujDrpFAoOjBmjFIf4HfAE0bGyVeBt6SUqnS3QqEIOWbyKRUALwEvCSGGA7OBWYQo2C2E6A28DcQC+cB1Uso9Qoh44F30ogWHgSuklNmhkKlQKNoupvJlCCFGCSGeAb4EDgG/DaEO/wTeMzIRLAAqynrOQo9nDQJeAZ4OoUyFQtFGMTOjez36otw3gHQp5YEQ62BD95Iw5FSUBr8AON3Yfg94XgjhkFLWX7lPoVAc85iJKf1NSvl1GHW4D/hRCHEb+hKWMUZ7GpAFYFRTKQQ6A6E2igqFog3RaDWTUCGE+C3wnxrNW9BjU49LKT8SQlwGPAAMQ892GSWl9Bnn70f31BqMK1VUMwmt9gqFook0u5qJGU8pJEgp5wHzqrYJIToDm6WUHxl9FgghXgKSgf1ACrDPmEXeCcg1K+9YKbGkZCqZ7UlmlRJLzaa1E0PnAOVCiLEAQohTgSJjusHn6IuBQa+islzFkxSK9o8pT0kI0QtIBIIJpKWUa45WuJRSE0JcCjwrhIgEioCKsqX3AW8KITahTxW45mjlKRSKto/ZfEp3oE8FqAhAaejzh44aKeXPwEl1tOcBE0MhQ6FQHDuY8ZQmA/3DMBVAoVAoamEmppSpDJJCoWgpzHhKi4UQjwEfUTmxMSQxJYVCoaiJGaM01XiuurQkZDElhUKhqIqZBbl9WkIRhUKhAHN335LRg90x6FMCbOiBb3WLXqFQhBwzw7cP0GNJxwNfA2cDy8OplEKh6LiYufvWS0p5AfoM6+eAU4GBYdVKoVB0WMwYpYoFsNuAIVLK/YAjfCopFIqOjJnh2yEhxJ3ACmCmkUIkKrxqKRSKjooZT+kmwC2l/B5YDTwI3B1WrRQKRYelUaMkpTwEvCKEGArcC5wqpVwUds0UCkWHpFGjJIQ4GdgBfIaeDTJTCHFKuBVTKBQdEzPDt8eBs4BcKeU+9DlLKom/QqEIC2aMUpSU8teKHSnl57RgxkqFQtGxMGOUvEKIBIxcSkIIEV6VFApFR8aMxzMLWAqkCCHeA84B/hBWrRQKRYfFzILcT4UQW9CXl9iAB6WUm8OumUKh6JCYLRxQjO4tLQEsQojB4VNJoVB0ZMxkCXgSuAUooLJwgAZ0CaNeCoWig2ImpnQpkCalNF1zTaFQKJqLmeHbVvQSRwqFQhF2zHhKzwBLhRDfAsFikFLKB8OmVQvx/fff88EHH6BpGqNHj25tdRQKBeaM0j1AIRAfXlValtWrV3Pttdfi9XpZuHAhEydO5KqrruLEE08kIiKitdVTKDosZoxStJTytLBr0sKsWLECn88HgKZpfPTRR3z00UdEREQwZswYzjzzTK6//vpW1lKh6HiYiSlJIcSwsGvSwowZMwaXy4XVasXpdHLZZZdx/PHHU15ezrfffsuiRZWJEAKBAF988QX5+fmtp7BC0UEw4yn1BFYLIXYB7opGKeUxbahGjx7N3LlzmT9/PpdffnkwpnTo0CGWLVtGdHR0sO+vv/7KtGnTsFqtjBw5kvHjxzNu3DhGjBiBzWZrrZegULRLzBile8OuRSsxevRoLBYL6enpwbYuXbpw+eWXV+vndrsZM2YMq1atIiMjg4yMDP79738THx/PaaedxiOPPEJiYmJLq2+a1atXM3/+fBXQVxwTmFlmsjSUAoUQ/wT8UsoHjP144F304paHgSuklNlCCCfwGjAavZrK1VLKLaHUxSzp6enMnz+f4uJifvzxR7777juWLl3K7t27+f7774mLiwv2ffPNN+nduzcnnXQSkZGRYdVL0zQALBZ9TuuqVavYuHEjhw8fJicnh8OHD7N79262bduGpmnMmzePuXPnkp6ezs0330xERASRkZHBR1RUFJGRkYwdO5YBAwYAkJWVRWZmJlFRUcH+Ff1cLldQdk2UIVQ0lxZLQSKEiAOeBH4HPFbl0CxguZTyAiFERa6mK4HbgBIp5SAhxOnAm8DJLaVvXcTExHDOOedwzjnnALB79252794dHMKVlJTwwAMP4PV6iYiI4KSTTmLcuHGMHz+eAQMG1PsFrkogEMDtdgcNWk5ODosWLQoamaoGJzc3l08//ZTjjz8egLlz5/Lee+/Ve22v18uKFSsYOnQon376ab39nnzyyaBR+t///sf06dPr7Ge329m9e3fwdd14440cOHAAn8/Hr7/+SiAQ4L333uPOO+9k4sSJ9OzZs9HXr1C0ZF6ki9Erovy7RvsFwOnG9nvA80IIh9H+DwAp5TIhRGchRE8p5d6WUrgxevfuTe/evYP7brebm266iaVLl7JhwwaWLl3K0qVLefDBB0lNTeX555/HZrMxf/58lixZQnl5eTUjk5OTQ25uLpMmTeLpp/U8ekeOHOGBBx6oV4fc3MqJ9qeeeip2u53OnTuTnJxMcnIyubm5QUPpcDgYM2YMVquV2bNnU1paSllZWa1HhUECSExMJD09vc5+DoejmqHdvHkzu3btqqafz+fjkUceITs7m1mzZgGwfft2nnvuOXr16kXv3r2DzwkJCaYMt6J902JGSUr5NoAQ4oEah9KALKOPz6iW0rlqu0EW0B1oM0apJomJidx7773ce++95OTksGzZsqBhysrK4siRI9xyyy14PB5A94rqorS0NLjdtWtXrr/+epKTk+ncuXPQ4FQ8Vx0iTpo0iUmTJtW63uDBg2sF9C+88EJTr2nixIlMnDixzmMVUyoqePPNNyksLGTdunXMnDkTn8+H1WplxIgRjBgxIthv06ZNzJs3r9b1YmNj6dWrF++88w7JyckAbNu2jaioKFJTU7Faza4fb9+096GxpSIuESqEEL8F/lOjeYuU8izj+AMAVWJKHvTslj5jfz+Qjl6F91wp5Q6j/Qfgb1LKnxqSn5GR0RvY1VCflkbTNDIzM1m5ciXvvvsugUAAi8XCyJEjOe2004iPjw8+4uLicDiO/bJ6W7ZsYcOGDQwdOpSBA6vXLs3OzmbdunVkZ2eTlZVFVlYW2dnZlJWVBT3JiiHxXXfdxZYtW7Db7aSkpJCSkkJqaiopKSkMHDiQ4447zpTMln6dPp8Pj8eD2+0mEAiQlJQUPLZ+/XrKy8uDxz0eT3B7yJAhDB48OHjtTz75pFqfgoICDhw4gKZpOJ1OZs2axcCBA/nyyy85ePBgtRhhxSM5OTk4dA4EAvj9/pb4jPVJT0/f3ZwTQ+4pSSnnAbV/ButnP5AC7BNC2IFOQC6wD0hFL1qA0eeA2YsOGTIEl8vVaL+MjIxqd9/CxejRo+nRowfz5s3D4/HgdDq5//77W+yXrqVeZwXp6ekNyrzggguq7WuaRl5eHgcOHGDo0KHB9p49e5Kbm8vhw4fZt28f+/btCx6bNm0aV111FQAffPAB99xzD4FAAKvVyvHHH09sbGzwZsDLL79MQkICAA899BAZGRlomhY8XrF90kknBWNoOTk5TJ06tc5+AFdffTUzZ87E4/FgtVqJjo7G5/NRXl6O3+8P6tm9e3dWrlwZ3J8yZUq9c97uuuuu4HuWnZ3N8uXL632P/X4/eXl5pKen8+ijj/LDDz/U2e/888/nlVdeASAzM5OTTz4Zh8NBdHQ00dHRxMTEBJ///ve/B9//ZcuWsXbtWmJiYoiIiGD9+vXVvO26cLvdbNy4sd7jZmgLubY/B6YAD6MHuJdLKb1CiIr274UQpwHlbSme1Bzqmxul0O8gJiUlVfMoAN544w1Av4mwZ8+e4GP37t2cckplUZ0lS5YEh8OBQIANGzZUu07VoeaWLVuqGYmqVJXv8Xj45Zdf6tV5zZo1eL1eAoEAmqZRUFAQPGa1WomIiCAiIiJoDCs47bTTKCsrCx6veERGRlb7TIwYMYLnnnuuWp9du3YxY8YMPB5PMEYIMHnyZMaOHUtxcTElJSXVnocMGRK8ZkUs0Ov1kp+fX8s43n777cHtb775htdeew3Qb2oEAoHgHdxwfnbbglG6D3hTCLEJPRvBNUb7s8Bso92NXkXlmKeuuVGKxomOjmbw4MHBoU1NrrjiCv73v/8FA/rTp0+nIp28xWIhNjY22Pfee+/lT3/6ExaLJfio6BcfHx/sl5yczCeffBI8VrWfxWLhyJEjfPLJJ8Gh1OzZsznppJOIiIhocHg0e/ZsU6+5W7dutWKEY8aMYcCAAbV+2C666CJT1xwwYAC7d+/G7XZTUlISNFwVRqzqEPT0008nMjKSH3/8kbVr1xIIBIJ3cNuVUaqIJVXZzwNqRVKllOXAdS2kluIYZ8KECcybN8+UF1qfYauJ0+lk1KhRDfZpDc83FD9sLpcLl8vV4KTfs846i7POOovVq1dz5ZVX1vLOwkVb8JQUipDQGl5oR/B8WzrsoIySQqFolJY0vmrih0KhaFO0R0/JBgQnKJrB7XY33inEKJlKZnuUWeV71+z0GSGfPNnaZGRknIY+8VKhULQeY9PT079vzont0VNaBYxFX5bib6SvQqEILTb0Sc+rmnuBducpKRSKYxsV6FYoFG0KZZQUCkWbok3FlIQQTwDJUsqpQoiz0JPCRQJzpZQzWlc7hULRErQZT0kIcSbGshIhRCTwOnpiuEHACUKI81pRPYVC0UK0CU9JCJEIPISeKWA4cCKwTUq5yzj+DvBb4IvGrpWRkeECTkDdfVMoWoPg3bf09PRmTaRqE0YJmA1MB3oY+/VlnTTDCah5SgpFazMWaNY8pVYfvgkhpgGZUsrFVZqtQNW5Chag7tyxtclqvItCoQgz1b6HQoipQoh/mTmxLXhKVwKpQoi1QCIQA/Si+tCrKVkn/dD2Mk8qmUpmR5BZJfNks0MnrW6UpJRnV2wLIaYC44GbgW1CiP7o+bavRg98KxSKY5cxQojFQCzwgJTys7o6tfrwrS6MBG9TgQXAr8AWYH5r6qRQKI6aEuAs9PJpzwkh6rQ/re4pVUVK+SZ60UmMGNPwlpCraZqqN6ZQhJ/vpZQacEgIUQAkoVfFrkab9JRaAi2gseuHMjY8F8tn9+S1tjoKRUfgBAAhRAp67Dinrk5tylNqKYoO+vjv5EMUHvADEeTaSznjrniik5qdAkahUDROpBBiCbpBusnwmmrRIY1STBcbdlflcC3gg00fl3Di9bENnKVQKJpL1dBMY3TI4ZvFYmHopdHV2jYsLEGlcVEoWp8OaZQAjp8YjbWKn5i3y8f+X8yn0FUoFOGhwxql6CQb/SdEVmtbP7+4lbRRKBQVdFijBDDssupDOPlVGeWFZlezKBSKcGAq0C2EGIe+BCQYHZZSLgyXUi1Fr5MjcCX5cefqd9185RqbPy9l5FUxrayZQtFxadQoCSFeAc4DtlO5SFYDjnmjZLVZSDu9jF2LKo3Q+gXFyigpFK2IGU/pTGCQlLIo3Mq0Bqljy9n1YUzQ3B7a7OXgrx66Dna2rmIKRQfFTEwps70aJICIpAB9Touo1rZOBbwVilbDjKf0gxDifeAToKyisT3ElCoYdmk0u5aXB/c3f17K+DvicUZ16PsACkWrYMYojTGep1VpaxcxpQr6jY8kKtFKaZ5+581TrLH16zKGXBzdyJkKhSLUNGqUpJRnAAgh7IBFSukNu1YtjM1hYcgl0fz8euUodecyZZQUitag0fGJEKKLEOIL9Fwo5UKIJUKItPCr1rIMvTQamwPEbyL57cuduejxpNZWSaHokJgZvj0H/AT8Dr1SwW3Ai+jlj9oNib0d3LKsG65OKo6kULQmZozSACnlFVX27xdCbAqXQq2JMkgKRetj5lvoEEIE75kLIaKoXmlEoVAoQoYZT+l94BshxBvoxugGQpwvWwjxIHC5cf3XpJRPGvJOQ49lAcyUUi4KpVyFQtH2MHP37Z9CiH3AuegxpTeB10KlgLGubgIwDHAAvwohPgNGA6dLKVu1jlt5QQANjcg4lZVSoWgJ6h2+CSFijedE4CPgj8Af0OcnJYRKASnlUuAMKaUP6IJuKMuAnsDrQoj1QoiZ9VU+CAeaprH353I+vTuXF87YT8YcNcNboWgpGvqif2c856BXHKh4VOyHDCmlVwgxE72c0mJ0j2kJ+lDxZPQSwL8PpcyG+PWTUubecJjNn5Xi98DGRSUE/CqMplC0BJbGUsAKIaxSykCNtkQpZchLgBhB9E+AuVLKl6u0TwKmSCknNXaNjIyM3ugFLJuNr8zC97clE/BU5vEe9n/5JA9XmSkVCpP0SU9P392cE80EulcDo2q0LQOGNEdgTYQQA4EIKeVaKWWpEGIhcKUQIldKucDoZgGaNJP8aMt2552fx8YPS4L7ZetSSb8huSkqNFlmOFEylcyWkFmlbHezqdcoGeV1TwCihBCFVQ7ZgFVHJbU6fYGZQojT0O++XQwsBZ4yyrEUo8ey3gqhzEYZdnl0NaO0Y2kZxTl+YpJVwFuhCCcNxZQmod8RWwYMrfIYgH63LCRIKT8HPgN+ATKAH6WUDwKPAD+gx5nWSinfC5VMM6QNd5LUr9JmB3yw6aOSBs5QKBShoF5PSUpZCBQKIS4BbpFSPiKE6AXcCUyncv7QUSOlfAB4oEbbC8ALoZLRVCwWC8Mui+Hbx/KDbesXlHDiDZ1UiW+FIoyYuc3+BnrNb4B89CHWK+FSqC0x+KIobI7K/fy9PjJXuVtPIYWiA2DGKB0npbwDQEpZIKX8K3B8eNVqG0Ql2DjuzBplmBaoIZxCEU7Mrn0L1rMWQsRQpapJe2foZdWLCGz9upSyAn8raaNQtH/MTAl4G1gphJiHPnS7FH1I1yHodZKLuO42Cvbphsjvgc2fljLqmk6trJlC0T5p1FOSUj4C3A3EATHAXVLKJ8OtWFvBYrUwdFJ1b2nd/BIam3SqUCiah6lilFLKj4GPw6xLm2XIJVH88HwBmjGvPWebl+yNHlKHNj45U6FQNA0zxSiLqCN/kpQyto7u7ZJOXe30HRvBjqV6xZOYLjaKD6vy3gpFODDjKVVdTuJCT4vb4W5BDf9tDBabhWGXRtPntAis9g4T61coWhQz+ZT21GiaKYRYCTwRHpXaJv3GR9JvfGTjHRUKxVHR5BxFxgLarmHQRaFQKJocU7Ki5zq6K5xKKRSKjktTY0oakG+si1MoFIqQ01DqktMbOIaUcll4VDp2cBcHkF+WMviiaOwuFfhWKEJBQ57S88ZzFNAL2AT40NOX/AqMCKtmbZisDW7Wzi1B/q8Ub5mGI8rKoPOjWlsthaJdUG+gW0o5VEo5FD3z5OlSyuFSynT0nNk7WkrBtsjmz0rZ+GEJ3jI91LZ+gSosoFCECjN334SU8seKHSnlGqB/+FRq+wy9LLra/t6Vbo7s9bWSNgpF+8JMoLtMCDEVmIOeHWAael6lDkvn45ykDneSta6ykMDGRcWM/Ut86ymlULQTzBilG4B3gVfR775lAFeHUol6KuSeBTwJRKJXN5kRSpk7ijewPn85miWSdJqegH3YpdHVjNKGD0s49ZY4NdNboThKzMzo3gyMMopSEurSSvVUyF0MvA6MAzKBz4QQ50kpvwiFzFx3Fm/sfAANDeyQlpPCycnnNekaA8+LYsmj+XhL9bhSyeEAO5eX0/8MNetboTgaGo0pCSFSjDLaPwF2IcT/hBCpoVKgngq58cA2KeUuo/0d4LehknmwPFM3SAZfZb9LkfdIk67hjLIy6Lzqd9xUwFuhOHrMBLpfAD5EL6V9BFiLPpQLGXVUyE0Dsqp0yQK6h0pen+jBRFgrDYo7UMr/st9p8nWGXV494L1zWTlFB1XAW6E4GsxUyF0jpRwlhPhFSjnSaNtgTBcIKVUq5C4D+kspJxvtZwN/k1Ke29g1zFbI3W79mfW2r6u1jfNdR5Jm3vZpGvw8I5GSfZWj4L6XFdN7YqnpaygU7ZSwVsgNCCGCHpUQohPNWMhbH/VUyL0cqJoIOwU40JTrNlYhd4Q2goPbJAfL9wbbtsYs40/HPYbVYr7gpOXaIpb8Kz+4n7syjkvvH4jFWn/Au61WN1UylcyjlRmKCrlmjMtC9LtvcUKIm4AlwAdHJbU6fYFXhBAuIYQTvULubEAIIfoLIWzod/tCEuSuwGaxcWHatGptWeW7WJX3TZOuM/iiKGzOyv2CfX72/qzKMCkUzcVMju6Hgc/RS3WfDbwMPBgqBeqpkPs+MBVYgB5n2gLMD5XMCvrGDKF7YHC1tq+z36XUV2T6GpFxNgacXSPgPV8FvBWK5mImdcnbUsop6JMnw0I9FXIXA8PDJbOCIf4zOWTfiSegp7ot8xfzdfZ/ubj7TaavMeyyaDZ/VhlH2ra4jNIjfqISzA8DFQqFjpnh2wghRLudERhFLOO7XF6tbVXeV+wvNb+8r8doF/E9dPselWhl1LWd6shqrlAozGAm0H0A2CSE+AkIjkuklLeFTasW5tTki8jIW0yuR5+FoKHx6YFXubHfQ1gtjdtti9XC6X+Nw2LR0+baHO3WhisUYceMp7QCmAvsAXKrPNoNdquDC7v9vlrb3lLJ2iNLTV9DnBPFgLOjlEFSKI4SM8tMZraEIq3NgE6jGBR7IpsLfw62/S97DoPjTiTCFt3AmQqFIpSEbL5Re+D8tOuxWxzB/WJfPksOhnL2g0KhaAxllKqQ6OzK6Z0nVWtbkfNZtQmWCoUivCijVIPTu0wi3tE5uB8gwKf7X6Wx5Tg18ZYF2PhRCV/NDGlSBYWi3WNmnlIM8CgwEH2l/iPo69Da5QxBh9XF+WnX8989jwXbdpZsZGPBjwyNP7XR87WAxuJH8tn0SQmeYt2Qjbgyhi4DnY2cqVAowJyn9Ax6psmuQDkQiz6ru90yOPYk+seMqNb2RdabwQmWDWGxWsjb7Q0aJID1CztclXOFotmYMUojpZTTAa+UshS4hnZeycRisXBh2u+xWSodyQJvLt8dWmDq/GGXxVTb//XTErzlgZDqqFC0V8wYJX+NfRvQ7r9hnSO6cUryhdXavj/8ETnuxpMV9J8QSWRC5VvrLtTY9k1ZyHVUKNojZozSMiHEo0CkEOI3wCLg2/Cq1TY4o8tv6WRPDO77NR+fHXi90aC33Wnh+Ik1F+mqIZxCYQYzRulu9OUlBcBD6Jkn7wyjTm0Gly2S81KnVGvbWrSGLUWrGz132KXVh3CZq93k7faGVD+Foj1iJnWJF1gqpTwJOAdYLaVsPOLbThgWP5be0dXTm3x+4HW8AU89Z+gk9XPQbWT1O24bFilvSaFoDDOFAx4CKpaaRAH3CCFCWu6oLWOxWLgobRqWKm9Vnucg3x/+qNFzawa8N35Ygt+r0gcoFA1hZvh2MbqHhJRyH3rZo6vCqVRbIyWyNycnVU8PvvTQAo54DjV43oBzInHGVC7QLc0NsGOpCngrFA1hxig5jCFcBR46wN23mpyZ8juibbHBfa/m4YusNxs8xxllZdD51QPeGxaoIZxC0RBm8in9IIR4F3gNPXXZdcDKUCohhLgfuMLY/UxKeZcQ4g3gNKDiWzxTSrkolHKbQqQtmnNSr2XRvheCbZsKfmJ70Vr6dxpR73nDLoth3QeVhmjXD+WkTFKrexSK+jDz7bgVOAj8B3jC2P5LqBQwynOfA4xEn5SZLoSYBIwGTpdSjjAerWaQKhiVMIFukf2rtX164DV8gfrvqqUc76TLoMrMA1oAspZHhE1HheJYx0w+pRLg/8KoQxb6WjoPgBBiM9DTeLwuhOiGPjdqppSyVYeNVouVid1u5KXt9wQr7B527+en3M85rfPF9Z437LJovpmVH9zPWh6JFtAaLMOkUHRUzCzIHQM8DCQCwW+RlHJYKBSQUm6qIus49GHcWGA88Cf0+VGfAr8HXgmFzKOhe9RxpCeeyeoqpZgWH5zLsPixxDoS6zxn0PnRfPdEAXanhcEXRWEfuAeLtXcLaaxQHFuYqZC7HngTWEOVdPhSSvO5Yk0ghDgevdTS/VLKt2ocmwRMkVJOqvPkKpitkHs0uCnhK/tLeC2V07V6BIZwgr9+b6lgu52Ynr5qNeIUinZMWCvk+qSUTzbn4mYRQpyKXuPtdinl+0KIocAAKWXFClgL0KTp0I1VyK2guZVGrTklfHKg0nHLtG7k3OOuqjXRMkgVEW21uqmSqWQercyWqpC70TASYUEI0QP4ELjaKEIJuhF6SgiRIIRwAH9Ajyu1GU5IOoeUiF7V2j7Z/yoBreb6ZYVC0RTMeEp9gQwhxB4gOPMvVDEl4A4gAnhSCFHR9hJ6MrkfAAewQEr5XojkhQSbxcZF3W7klR2Vk9uzy3fzc+5XnJx8nunraJpGfqafhJ5m/hUKRfvHzDdhejgVkFL+hfqnGLxQT3uboHf0YIbHn866/GXBtq8P/peh8acQbY9r9HxveYDFD+Wz5ctSrnm3C50HqICTQmFmQe5SYAOwEz2AvBdQ3x6Dc1On4LRWzjsq95fwVfZ/Gz2vYL+P96YcYsOiErxlGh/enou7qMNNlFcoamFmQe6D6BMmdwIS2A6ENfB9LBHrSGRC1yuqtWXkfcO+0m0Nnpe5ys3BXytj9/l7fXz+91y0gFqwq+jYmAl0T0GfyDgfOA6YCmxq6ISOxpikC+js6hbc19CMoHf9ns+QS6IZdnn1Ipfbvy1n5atFYdNToTgWMGOUDkkps4DNwHAp5RwgbHfjjkXsVgcXplUv+72vbBu/HGk4QeeZ9yaQcryjWtvyZwvY9UOHSVelUNTCjFHyCiH6oQ/dxgoh7Oh3yxRV6N9pBMfHnlyt7X9Zcyjz158VwO6ycPFTyUTGV/k3aPDpXbkU7PeFS1WFok1jxig9gl5S6VPgMiCTDpKju6mclzYVu6XyHkCJv5DF2e83cAbEptq58PEkLFX+E+UFAT76aw4+t4ovKToeZu6+fSqlPNNYmDscOFdKOS38qh17JDi7MK7LpdXafsr9guyy3Q2e13tMBKfdWn0KwcFfvXzz0JFQq6hQtHnqNUpCiLuM52eFEM8IIZ4B/gX83thW1MHYzpeQ6Owa3NcI8MmBxst+nzStE/0nRFZr27CwhHXz22UhYoWiXhrylAqM5xwgt46Hog4cVifnp15frW13ya9sKPihwfMsFgvnP5RIQu/q81kXP3SErA3ukOupULRV6p3RLaWcbWz2k1JOqa+fojYDY09gQKdRbC1aE2z7/MCbiE7puGyR9Z7n6mTlkqeSeed3B/GW6Z6V3wsf/TWXKR90JSrRFnbdFYrWxkyge7gQQmUjawIWi4UL0m6oVva7yJfHd4fmN3pucn8Hv5lZPS9TWX6AQ1tUzThFx8DM2rcsYJMQ4if0opQASClvC5tW7YBkVxqnJU9k6eGFwbYfcj5hVMKERs8ddH4UWRvcZMwpJq67jUueSqbLQLWyR9ExMGOUVhgPRRMZ3/Vy1uYvpcCrh+D8mo9PD7zGUM5v9Nxx/xePI9LKCdd1IiJOFRpQdBzM5OieWbNNCBFdV19FdZzWCM5Lncr7e/8dbNtevJZkS3/0ugj1Y3NYGHtb45kGFIr2hpkc3RcDDwIx6MnXbOj5ujuFV7X2wZC4U+gb/T92llRm41tv+5rfBC7DaW08M6ZC0dEwMy54Ar1wwF70RP5foidhU5jAYrFwYbffY63yVpdaCnh710OU+0ubdU1PaYA17xU1OvdJoTgWMWOUSqSUc4GfgHLgj8CFYdWqndE1ohcnJ1ePI+0q2cirO+6j2JvfpGsd2ePl3WsOsfihfDLmqImVivaHGaNULoRwoedRGmHUXlM/0U3kzK5X0cXVo1pbVvkuZu/4O3nubFPX2PNTOW9feZCcbfr0gO/+nU/mapVRQNG+MGOUPkYvffQl8H9CiAXos7xDhhDifiHEJuPxmNF2lhBivRBimxBiVijltQYRtihu7DeLHlHHVWvP82Qze8ffOVDWeFWo2DQbliozxjQ/fPy3XIoPqWIFivaDmQW5DwM3SCn3A5cAy4DLQ6VAPWW7fwe8DlwMDAJOEEKYz8bfRomyd+KGvjPpGuhbrb3Yl8+rO+5jZ3HDpWkSejo4/5Gkam2luQE++r8c/F7lvCraB2bS4a4AzhRCREkp10gpn5ZSHgqhDsGy3VJKL3oyuQHANinlLimlD3gH+G0IZbYaTmsEY/xXMDz+9Grt7kApb+36J5sKfmrw/P7jIxlzU2y1tgNrPXz7eH6oVVUoWgUzw7cHgd8Au4QQs4UQDU+waSJSyk1Syp+gWtnuALqxqiAL6B5Kua2JFRuX97iNU5Mvqtbu07y8t+cJVuV+3eD5p/wplt6nVs+z98t/i9n0Sf0J5RSKY4VGy3ZXIISIB64GpgEWKeXIUCpStWw34EPP2zTZOHY2ujd1bmPXaYmy3aFCQ2OrdQWbbLVz5g32j0METsVC3csOvcUWVt2fSHlO5SJdq1Mj/b4jdOqpslYqWp2wlu3GSIE7Ad1j6gLMbY6wBq5fs2z3OCC1SpcU4EBTrhnust1HQ1WZoxnN6rzBfLjvRTQqCw38altKbJcoLkj7PVZL3Q5tnxc8vHvtQfwefT/gsbB1dmemzE2ptTSltV+nktkxZLZI2W4jodt+9PlJ/wX6Sin/dlRSq1+/rrLdK/VDor8QwobuoX0RKpltjdGJZ3JNr7uqpdIFPWvlvL1P4QvUnSGg62AnZ9+XUK2tYJ9flWpSHNOY8ZSKgJOllOEaEtVXtnsquvcUAXyOXuKp3TIo7kSu7/sP5ux6mPJA5Uzv9QXfU+ov4uped9WZi2nopBiy1ntYN68ynrRjaTkrXi7klJvV2jnFsYeZBbmtWbZ7eDhltzV6Rw/mxn4P8eauBynyVebn3l68jtd2/oPr+syosxz4hHsTOLTFS9YGT7Dth+cLSTneSd+x9SeVUyjaIionRhsjJbIXN/V/hCRnarX2/WU7eHn7dI54as/GsDstTHwyiciEyn9nYm87cd1MhQwVijaFMkptkARnF/7Q/yHSIvtVa8/xHGD29r+TXban1jmxqXYuekIv1TTgnEgmv9+VpL6OWv0UirZOs4ySECI51IooqhNjj2da3wfpFzOsWnuRL49Xds5gd8nmWuf0OimCa97twsR/J+GMVr83imOThkosfVVl+94ah79CEXZctkim9J7O0LhTq7WX+0t4Y+dMNheuqnVO6lAXFkvtuU0qy4niWKGhn9POVbZrLvFQhQRaCLvVwRU9/8rJSdWX/vk0D//d/SgZeYtNXSfnFydvXJLFsv/ks3+tm4BfWSlF26ShSGjVT21NI6Q+0S2I1WLlwrRpRNvjWHywsgx4gAAL9z1Pia+QsZ0vqdNDqiDnFxc5233kbC9i5WtFRCVa6Xt6JP3GR9D7lAicUWq4p2gbNGSUqn7ClRFqZSwWCxO6XkGMPY6P979Sbfb3/7LnUOzL59zU6+qc/a0FNHLXVZ+YWZoXYOOHJWz8sASbE3qeFEH/8ZH0GxdBpxR1107Repj1lBRthBOTfkO0PY65e5/Er1Wucfsh5xNKfIVc2uOWavXmAHJ3+fAW1e8J+T2wa3k5u5aX8/U/oetgB/3GRdL/jEi6DHI06IEpFKGmIaM0UAix3tjuX2XbAvSt5xxFC3B83MlM7XMf7+z+F+5AWbB9bf5SSv1F/K7XHTitlVkEkvs5OO25HGKLBDu+K2fX92W4i+r/zTn4q5eDv3r58cVCko9zMHVhV2WYFC1GQ0bpmE+q1p7pGzOUaf3+yVu7ZlHsyw+2by1aw+s7H2BK7+lE2SsLzjiiNQafHs3gC6LxezX2rXGz49sytn9XRsG++jNXdh6gPCVFy1KvUZJSLq3ZJoRIBI5IKdXQrg2QFtmXP/R7mDd3zSTPczDYnlm6lZd3TGdqn38Q76w9pczmsNDrpAh6nRTBGXfHk7vDx/Zvy9jxXRkH1nuqDdz7j697mYrPo7Hg5sP0OjmCfmdEkNxfGS9FaGhonlKsEOIdI40IQoj3gMPAdiFE/5ZSUNEwSa4U/tDvYVIj+lRrP+zex8s77uVQ+b4Gz7dYLCT3d3DyjbFc825X/vRdGuf+M4H+EyJxxVroc1pEnedlripn789ulj9TwJuTDvLKeVksfuQIe34qV6l5FUdFQ8O3J9AzBGwSQpwPnAn0BoYYxy4Jt3IKc3RyJDCt34O8s/tRdlUpelngzeXlHX9nSm/za6qjk2wMnRTD0Ekx+L0aNkfd3s/2b6tXUSnY52fNu8WsebcYZ4yFvqdFQGoEveK8JPW1Y7EqL0phjoaM0hhgmJRSM5L2L5RSZgKZQoh/N3CeohWIsEVzXZ8ZzNv7FJsKK/N8l/mLeX3nAwy3nMsg34BqcabGqM8gaZrGzqVldR4D8BRrbPmyDIhlyxvZuDpZSB3qIm24k7ThTrqnu3BEqnlRirppyCj5qsSOTkH3jipQP3ttEIfVyVW9/sbH+19hVV7lSiCv5ma1/SNW//oRcY5kUiP7kBbRh9TIvqRF9iHOkdykeJDFYuHqd7qwY2k5O74tY8/K8mD2y7pwF2ns/rGc3T/q3tXvP0khsY8ySoq6acgo+YUQcUAMMAz4FkAI0Q1o4COoaE2sFhsXd7uJGHs83x76oNbxAm8OBd4ctlRZNxdl60RqZB9SI/qQZhiqJFcqVout1vkVdOpqZ8QVMYy4IgZPaYA9K8rZ8V05O5aWUZoXqPe8iDgrCb3r/tht/KiE/Ewf3UY4SR3qqpXSV9ExaMgoPQesQfeK5kops4UQFwH/Ap5tCeUUzcNisXBWylXE2OP49MCraI3Mgy31F7GjeD07itcH2xwWF6mRvasZq64RPbFba6dDcUZZOe7MKI47MwotoJG1wcPuFeVsWX6Ykt2RlBdUGqm0Yc56vbJNH5ewd6U7uJ/U107acGPYN8LV5mJTfs2Hx1/e6PuraBoNTQl4UwixCT1pf0V+7GTgMSnlW6FWRAgRC/wIXCil3C2EeAM4DajI8zpTSrko1HLbMycnn0e3qH78nPsVO/I2UmTJJYC5arpezc3eUsneUhlss2KjS0T34LAvNaIPqZF9iLBFBftYrBbDkLhwnbCdUaP6cWS3jwPrPOxf5yZ1iLMucQT8WrXMmQC5O33k7vSxYZH+EXDGWEgd6iRtuCvk3lRA81PmL6bEV0Spv5ASXyGlviJK/UWU+gopMZ71Y3p7uV/Xy2q38YNMI9HZlSRXKonOlOBzvLMztgY8TkVtGlzkJKVcVWP/jXAoIYQ4CXgFvQhlBaOB06WUWXWfpTBDj6gB9IgaQMahDIaPHMbB8r1kle/iQNkussp2kl2+B0+gvPELAQH8ZJfvIbt8D78cqSwLlehM0Y1UZB9SI3SD1cmhFzSwWCwk9nGQ2MfBkEui6712zjYv3tKGPQ5PscaeFW72rKjuTfU8OYKz/l5ZQCGgBSj3l1BYWkD2thJK3aWUeksp95RS7imnzFNGuacMt9eN2+PG7XPj9fggYAG/Ffw2Ap3z8I38tU49IuZegKXcRaTfBpqFQEI+R1IOk5tykC0pGyGq8v20YiPB2YVEZ1cSXakkOVNIdKWQ5EwlwdkFh7VuI91a+AJeSvyFlPgKjEchxb4CApofLy2ja71GqcqykjqRUg5r6HgTuRG4BZhjyI4CegKvGzGsReieUv3BCkWj2K0OukX1o1tUZUbLgOYn151tGKqdZJXpz6X+ItPXzfNkk+fJZmPBimBbjD0e7FaWbqm7zFXNenZaUQTWW/qjbe2GJtMgszNojQ/Vcnf6yHNtZqN8j4AWoMhewKIN5WgEsB5KJPb2+4GoBq/hMB5V8Zywrl6j5Fx8CtbimHqvF+hUTCDlMIGUw/i7HqYw5TD5KTlsS9lSzWBZsBDrSDIMVSpJVQ2XM6XOQhFNJaAFDA+wgGLD0NR8LvHpRqjYXxD0/urCardzfNnxpET2Pmq9GqIhTykGiEQ3FF+CSb+/GUgppwFUqWaSAiwB/gQUAJ8Cv0f3phQhxGqx0TmiG50jujEs/jRAv+Vf4M0lq2wnB8p3kWV4VfneHNPXLfblgwWKzd4ScQGn7oSKfHalLuw7emHb1hv7tj7YtvfCWlK3p1XWX1Lu3q/vVLFjmq35H1mLv4Fhoa3h30ZrUQzWohjYVn1Cq2bzU/DmHcHzNbTgjYedBZvBXl3fGHt80LNKdKYEjVe8I5li8thTsqWKcan0bor9lUan1FdULaPE0RCw+Nhdsrn1jJKUsq8QYixwHfAC8DHwppSy7p+PECKl3AlMqtgXQjwLTKEJRqkpBfEyMjKaol5IODZk2oinP/H0ZxDgppQCy0HyLQcpsGSTbzlIEblgCUOgN8qNb+hWfEO34gYIWLBmddYN1Lbe2Lf3xrovBYtmxd9/dz3qN//L6PJ3IikwDJcWhZPIymei+NUaSd2V+Bom0Dm3Xp2in56KbWtfAl0NDyslB3fKYfZ1zWFvykq06BrzwhzADpOC/VZwO7GUu7AYz7X2q2xrtgDuy76sdRmrZsO9x07GnvB+dhuLKS0HlgshItGNxH+MgPTbUsoXw6WUEGIoMEBKucBoskDTPgfHSoXcY12mJ+Amu2x3tTjVwfK9+LTmfG0bwKoR6HYIT7dDMH6l3mZ4U/6+mbW6R1ijiIxOxNL3EFabFZvDgs1mxW63YnPYsNvtOBw2HHYHDocdh8OBzWbF6gCrzUJy/xGMHDG2TlVS/lKMt1zDagfND1vXHMDpTuTIHh8F+3z463npiT1diMSz9eGuO4sCb27wzp31YOdKD2t7n1rnBmIqh4SBznmgWbCUu/B3zsNzXq1lqgB0uvdOrAe6YvE2rYCEFl1G/DW/Em2PI8YWR7Q9jlhHAmTGcXr6mQ2eG4oKuaayeUkpy4QQHwDFwN+Ah4GwGSV0I/SUEGKJIfMPQMjv+CmOHqfVRc9oQc/o4NAbv+ajwJPD+o3rGTJkSK1ztEYThtd/vNaREdV3t23awUkjT6mcuvBxI6KawbDLq8eTtIHFpKfrrz/g1yjK9nNkr48je3zkZ3r1570++h7fnfHd/xg8zxvwkO85RE55Np8f6tzgIMtaHIN1e22D5Ru4vV6jhNfeZIMEYHNHctuAp2q1Z2S2jHffqFESQpyMPnS6FMhAN0YfhlMpKeV6IcQjwA/ojuoCKeV74ZSpCB02i51EVwqd2E+yK61FZWdysM65VC2F1WYhrptec6/3mIb7OqxOOkd0J7I4FYt2oFnyIrwJDEiYQIxd92ii7bHB7a/iXRw+0PQhbMBHg+sew01Dd9/uB65F91TeBoZLKQ/W1z8USCl7V9l+AT2WpVC0a2KSbdy+unstDys/uF3/kLCTvyuX9fhzncciog4BbrCAM8qCI9KCI8qKI9Ji7FtxGO3OqOrHWrP6TUOe0v3AXmAfcAZwRpW7Y0gpJ4ZXNYWi49CQhxXwaxQd9AeNVNFBPwdzDtCrX3eik+q/Szjp2WSsdrBHWI6pXFcNGaXrW0wLhUJRL1abhbg0O3FpdnqdrLdlZGwnPb3hjA+uTsfm2sGGpgTUG1gWQpwdHnUUCkVHp6GY0ij0hbd5wPVSyhwhRE/gKfT83Uc/3VShUChq0JB/9yKwENgJzBBCXAysB6KB4S2gm0Kh6IA0FFOKk1L+WwhhA7YCVwA3Synfb+AchUKhOCoa8pRKAaSUfiACuEAZJIVCEW4aMkpV7yHmSCl/CbcyCoVC0dDwzSqESMAwTlW3AaSUeWHWTaFQdEAaMkpDgRwqDVFulWMaoNLpKRSKkNPQPKVjc+aVQqE4plGGR6FQtCmUUVIoFG0KZZQUCkWbQhklhULRplBGSaFQtCmUUVIoFG0KUzm6W4I6KuSeBTyJno1grpRyRqsqqFAoWoQ24SkZFXK/x6iQa1RPeR24GBgEnCCEOK/1NFQoFC1FmzBKVFbIrciefiKwTUq5S0rpA94BfttayikUipajTQzf6qiQmwZkVemSBXQ3eTkbgMdjtjSrXquqpVEylcz2KLPK967Zy9DahFGqAyvVS3xZwHTt4VSArVu3mhZ2tMXzmoOSqWS2c5mpmK/hW422apT2YRgXgxQqh3aNsQoYi+5dNb+YvEKhaA429O/uquZeoK0apZWAEEL0B3YBV6MHvhslPT3djR40VygUrUOzPKQK2kqguxpSynJgKrAA+BXYAsxvTZ0UCkXLYGm8rrtCoVC0HG3SU1IoFB0XZZQUCkWbQhklhULRplBGSaFQtCmUUVIoFG2KtjpPqcUQQjwBJEspp7aArGuBe43dL6SUd4RRVs2sC2OA/wCd0MuvXyelNL8Wp3F5DwKXo8/Ef01K+aQQ4g/AbUbbauCmEMu8CLgfvZT8V1LKv1Q59mfgcinl+BDJqvl+1vnahBCjgNmAE8gErpVS5jdD3v3oVakBPpNS3iWEeAM4DSgx2mdKKRcJfX3WbCAByAauklIeaYbMb4EugNdouklKuVII4QC+BP4ppfzO6HsxMBN9tcUu4PrmyKyLDu0pCSHOBK5rIVlRwDPAOGA4MNZIzxIOWTWzLsQCC4E/SCmPN7r9PoTyxgETgGHAaOBW44tyJ3CK0W5FX3QdKpl9gZeAS4zrj6rIJCGEGAzcE0JZNd/PAdT/2p4G/iGlHA5IoMk/PMbn4hxgJDACSBdCTEJ/b0+XUo4wHouEEBbgY+BfhsxfaMZrN64zABhe5forjf/jd8ZrregbC7yIXjV7OPqP3ANNlVkfHdYoCSESgYeAh1tIpA39/Y4GHMajLEyyamZdOBtYIaVcb+zfCiwKlTAp5VLgDCOjQxd0D7wc+JOUslBKqQEbgJ6hkglMQs+ztU9K6QWuBFYKIVzoXsM/Qiir5vvppv7XZgNije0omvc/zgL+JqX0GK9ts3H9nsDrQoj1QoiZQggrMAookVJ+aZz7MPB8M2RWrIb/SgixzvA0Qf/xehx9lUUFDuAWKeV+Y389IfzfduTh22xgOtCjJYRJKYuEEPehz04vBZaiDwfCIatm1oX+QLEQ4n1gIPAD8LcQy/QKIWaiewbzgL1Syj2GHp2BP6PP0g8V/QGPEOJj9C/Ep8B9wL/RlyTtCpWgmu+n8brqe23/h/7Ffgp9mHVSM+RtqtgWQhyHPowbC4wH/gQUoL/e3wOFQLYQ4jV0z2oz+o9OU0kAFhvnOoDvhBBSSnmXocftVfTLxfhRM3Kf3QM82wyZddIhPSUhxDQgU0q5uAVlDgNuAHqhp2bx0wzXvpnYgd+gx7PS0b21kA1vKpBS3g90Rjf0NwIIIbqhf9hfq4hHhAg7cBb6F3MM+pd/GtBTSvlGCOXUS83XZnxBXwPOklKmAi8Abx/F9Y8HvgbulDqTpJRZUspSdCNwPvr7MB54UUo5CtiJnrG1SUgpV0gpp0gpC6SUOcbrOL8R/eKAz4B1Usq3miqzPjqkUUJ39c8RQqwFHgQmCiH+E2aZvwEWSykPSSndwJvoH6aWIBv4yUia5wc+QE+kFxKEEAOFECMAjC/MQmCYEGIgujf4lpTyn6GSZ5ANfCOlPCylLEP/5R4DHG/8X18FRgsh5oZYLqC/Zmq/tiFAmZTyZ2N/Ns38HwshTkU3ePdIKd8SQgwVQlxWpYsFPSCdjZ4QcbXR/h7N+N8KIU4zYqw1r19f/1RgOfrQbVpT5TVEhxy+SSnPrtgWQkwFxksp/xpmseuAx4QQ0ejDt4s4ivQOTeQrYKYQooeUMhO4EMgI4fX7Gtc/Df1u1MXAHEPudCnlnBDKquBT4C0hRDxQBJwHfCilvAFACDEeeEBKeWWoBQshOlH3a9sO9BDGuAf9fWjy/1gI0QP4ELhSSrnEaLYATwkhlgDFwB+At9ANY2chxHAp5Tr0z1Vz/rfxwINCiFPQh2/XATfXo58N+AT4QEo5qxmyGqRDGqXWQEr5lRBiJPoHxgv8DPyrhWRnCiFuAj4RQkQAawnh0FFK+bkQ4kT0Oz9+9OwOyUBX4G9CiIr41cdSypAEoI07Q4+h3xVzoA9zWmTYhu4Z1PnajB+5D4y7WYeA65tx/TuACODJKnHBl4BH0OOBDmCBlPI9AOPO3CvGD94+YHJTBUopPzXuMv6CHqx/Xkq5op7uE9ED7HYhxOVG2+qK2NvRorIEKBSKNkVHjSkpFIo2ijJKCoWiTaGMkkKhaFMoo6RQKNoUyigpFIo2hTJKijaDEKK3EKK4nmMPCCGeC5GcV8O1GFpx9Kh5SooOR6jm0yjCgzJKHRwjJ9EM9Pw/pcAdUsoVQogHgN7ohQV7AfvRcwNlCSH+iD7b14OeDeAmKeWvxlqw59AXyDqA96WUDwshegNL0Cc4pqN/7v4B3IS+QHg18DtDJasQ4lWjnxe4TUr5Uw2d65RTx2u71HhtAfRJnXdKKZcJIb4zzvej52OqoD+wSEo52ZjZ/Cj6OkE/eu6iT5vw1iqaiRq+dWCMFegPA+dLKUeiL11YaMwMBn1l+m+llAPRV7zfbCwxeAo4V0p5AvAyeuIx0JeWvC6lTEdff3WWEKIiUVkf9GRlo4EV6HmHfgccb8g52egXCXxt6DMDmCeEcNZQvSE5VXkcPcXIaPQMAuOrHpRSLqrIHWQczwbuEEIkoM8On2wscr0YeFEIEcrUK4p6UEapY3M2uie02FjE+i66V9HfOP6dlLLQ2P4FSDQW9M4DfjRiPPnAa4YhGwf807jWT+iezAjjfC/6einQK6j+aOQjKkfPU5RoHMuXUs4FfWmO0TawQmETcqryPrDI8LwSgMfqehOEECejJy27SEp5EH1hbyrwoSHjc/Q1fcPqOl8RWtTwrWNjQ89cEFy0aiwGPYCeRK1qgjINfVEoUsprhRBD0FOH3IO+1mqacfwUI1MAQohk9OFdMuAxEqJVUN8KdH+NfWuNvrYG5FRDSjldCPE6uvGdip5DqtoKeiOL5ALgGinl5ioyNkspT6rSLw04XI/OihCiPKWOzWL0FC4DAYQQ56Onoois7wQhRLIQIhPIlVI+hT7EOsHwqH5CT3KGsXr/B/ShT1NIEkJcaFzjInTDuK3ioFk5Qgi7EGI3ECWlfAk9OdowIzNlRZ8U4Av0WNN3VU7/CThOCHG60W+EoUO3Jr4WRTNQRqkDI6X8FT2O9L4QYh3wT2CilLLO2/LGOTnALPQhXwZ6poMbjcNXAycLITagp099T0r5bhPVOgRcZgyb7gUuM9LsVqVROcY5twP/FUKsQR9y3mDksqpgJnr63tuFEGuNx+dSysPAZcDjxvsyBz2+tLuJr0XRDFSWAIVC0aZQnpJCoWhTKKOkUCjaFMooKRSKNoUySgqFok2hjJJCoWhTKKOkUCjaFMooKRSKNoUySgqFok3x/+Qk9D6AVpaxAAAAAElFTkSuQmCC\n", "text/plain": [ "<Figure size 288x432 with 2 Axes>" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -1822,6 +1831,355 @@ "label_axes_abcd(fig,loc=(1.02,0.95))" ] }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "completed nens: 4\n", + "completed nens: 8\n", + "completed nens: 16\n", + "completed nens: 32\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/pgriewank/pgriewank/code/2021-linear-advection/da_functions.py:2275: ComplexWarning: Casting complex values to real discards the imaginary part\n", + " fc[:,i] = linear_advection_model(an[:,i],u_ens,dhdt_ens,m_const[\"dx\"],da_const[\"dt\"],da_const[\"nt\"])\n", + "/home/pgriewank/pgriewank/code/2021-linear-advection/da_functions.py:2423: ComplexWarning: Casting complex values to real discards the imaginary part\n", + " an_response[n] = func_J(quad_state[\"an\"][:,n])\n", + "/home/pgriewank/pgriewank/code/2021-linear-advection/da_functions.py:2698: ComplexWarning: Casting complex values to real discards the imaginary part\n", + " an_response[n] = np.sum(response_s * np.power(quad_state[\"an\"][:,n],response_c))\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "completed nens: 64\n", + "completed nens: 128\n", + "completed nens: 256\n", + "completed nens: 512\n", + "CPU times: user 25min 52s, sys: 30.6 s, total: 26min 23s\n", + "Wall time: 6min 35s\n" + ] + } + ], + "source": [ + "%%time\n", + "da_const_vr = set_da_constants_22(obs_loc=np.arange(25,299,50)) #changed direct point observations for variance reduction tests\n", + "\n", + "t_start= 40\n", + "t_end = 100\n", + "n_rand = 15\n", + "n_samples = (t_end-t_start)*n_rand\n", + "counter =n_samples\n", + "vr_real = np.zeros([n_steps,n_samples]) \n", + "vr_es = np.zeros([n_steps,n_samples]) \n", + "vr_is_ca = np.zeros([n_steps,n_samples]) \n", + "var_total = np.zeros([n_steps,n_samples]) \n", + "for i in range(n_steps): \n", + " n_ens=ens_values[i]\n", + " for t in range(t_start,t_end):\n", + " for r in range(n_rand):\n", + " n = (t-t_start)*n_rand+r\n", + " np.random.seed(n)\n", + " # selecting random ensemble members for the ensemble\n", + " idx_ens = randomized_obs_loc(n_ens,start=0,end=ens_values[-1],seed=n)\n", + " truth_idx = r\n", + " \n", + " da_const_vr['nens'] = n_ens\n", + " \n", + " \n", + " vr_t, vr_i, vr_r, J_dict_LA,bla, quad,dx_ni =vr_reloaded_22(states_512[0]['bg'][t][:,idx_ens],\n", + " states_512[0]['bg'][t][:,truth_idx],m_const,da_const_vr,sat_operator,\n", + " obs_seed=counter,model_seed=counter,alpha=alpha_default)\n", + " \n", + " var_total[i,n] = np.var(J_dict_LA['bf'],ddof=1)\n", + " vr_es[i,n] = vr_t\n", + " vr_real[i,n] = vr_r\n", + " vr_t, vr_r, quad, J_dict_LA = vr_individual_loc_22(states_512[0]['bg'][t][:,:],\n", + " states_512[0]['bg'][t][:,truth_idx],m_const,da_const_vr,sat_operator,\n", + " advect_flag=1,quad_state=quad,\n", + " obs_seed=counter,model_seed=counter)\n", + " vr_is_ca[i,n] = vr_t\n", + " \n", + " \n", + " print('completed nens:',ens_values[i])" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [], + "source": [ + "es =np.sum(vr_es ,axis=1)/counter#/var_total\n", + "is_ca =np.sum(vr_is_ca ,axis=1)/counter#/var_total\n", + "real =np.sum(vr_real ,axis=1)/counter#/var_total\n", + "total =np.sum(var_total,axis=1)/counter#/var_total\n", + "\n", + "me_es =np.sum((vr_es -vr_real),axis=1)/np.sum(vr_real,axis=1)\n", + "me_is_ca =np.sum((vr_is_ca -vr_real),axis=1)/np.sum(vr_real,axis=1)\n", + "\n", + "rmse_es =np.power(np.sum(np.power((vr_es -vr_real),2),axis=1)/counter,0.5)/np.sum(vr_real,axis=1)*counter\n", + "rmse_is_ca =np.power(np.sum(np.power((vr_is_ca -vr_real),2),axis=1)/counter,0.5)/np.sum(vr_real,axis=1)*counter" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + 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\n", + "text/plain": [ + "<Figure size 288x432 with 2 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#Lets see what changes when we change the localization used to create the background\n", + "fig,ax = plt.subplots(2,1,figsize=(4,6),sharex='all')\n", + "ax[0].plot( is_ca , lw=4,alpha=1.,label='implicit sens',color=plt.cm.viridis(0.8))\n", + "ax[0].plot( es , lw=4,alpha=1.0,label='explicit sens' ,color='blueviolet',ls='--') \n", + "ax[0].plot( real , lw=2,alpha=1, color='k',ls='-',marker='.',label='truth')\n", + "ax[0].plot(-total , lw=2,alpha=1, color='k',ls='--',marker='.',label='limit')\n", + "ax[1].plot(rmse_is_ca*real , lw=4,alpha=1.0,label='implicit sensitivity',color=plt.cm.viridis(0.8))\n", + "ax[1].plot(rmse_es*real , lw=4,alpha=1.0,label='explicit sensitivity' ,color='blueviolet',ls='--',zorder=2) \n", + "\n", + "\n", + "ax[1].set_xlabel('ensemble size');\n", + "ax[0].set_ylabel('mean variance reduction');\n", + "ax[1].set_ylabel('RMSE variance reduction');\n", + "\n", + "ax[0].set_xticks(np.arange(n_steps));\n", + "ax[0].set_xticklabels(ens_values);\n", + "ax[1].set_ylim(bottom=10)\n", + "ax[0].set_ylim(top=-28,bottom=-50)\n", + " \n", + "lines_labels = [ax.get_legend_handles_labels() for ax in fig.axes]\n", + "lines, labels = [sum(lol, []) for lol in zip(*lines_labels)]\n", + "#fig.legend(lines, labels, loc='upper center',ncol=6)\n", + "#ax[0].legend(bbox_to_anchor=(-0.3,1.05),loc='lower left',ncol=5);\n", + "ax[0].legend(loc='center right',ncol=2);\n", + "#ax[1].legend(lines,labels)#ncol=2,bbox_to_anchor=(1.3,.05),loc='lower left').set_zorder(100);\n", + "plt.subplots_adjust(hspace=0.05)\n", + "fig.align_labels()\n", + "ax[0].yaxis.get_label().set_verticalalignment(\"baseline\")\n", + "ax[1].yaxis.get_label().set_verticalalignment(\"baseline\")\n", + "label_axes_abcd(fig,loc=(1.02,0.95))" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "completed nens: 4\n", + "completed nens: 8\n", + "completed nens: 16\n", + "completed nens: 32\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/pgriewank/pgriewank/code/2021-linear-advection/da_functions.py:2275: ComplexWarning: Casting complex values to real discards the imaginary part\n", + " fc[:,i] = linear_advection_model(an[:,i],u_ens,dhdt_ens,m_const[\"dx\"],da_const[\"dt\"],da_const[\"nt\"])\n", + "/home/pgriewank/pgriewank/code/2021-linear-advection/da_functions.py:2423: ComplexWarning: Casting complex values to real discards the imaginary part\n", + " an_response[n] = func_J(quad_state[\"an\"][:,n])\n", + "/home/pgriewank/pgriewank/code/2021-linear-advection/da_functions.py:2698: ComplexWarning: Casting complex values to real discards the imaginary part\n", + " an_response[n] = np.sum(response_s * np.power(quad_state[\"an\"][:,n],response_c))\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "completed nens: 64\n", + "completed nens: 128\n", + "completed nens: 256\n", + "completed nens: 512\n", + "CPU times: user 25min 51s, sys: 31.1 s, total: 26min 22s\n", + "Wall time: 6min 35s\n" + ] + } + ], + "source": [ + "%%time\n", + "da_const_vr = set_da_constants_22(obs_loc=np.arange(25,299,50)) #changed direct point observations for variance reduction tests\n", + "alpha_default =0.000001\n", + "t_start= 40\n", + "t_end = 100\n", + "n_rand = 15\n", + "n_samples = (t_end-t_start)*n_rand\n", + "counter =n_samples\n", + "vr_real = np.zeros([n_steps,n_samples]) \n", + "vr_es = np.zeros([n_steps,n_samples]) \n", + "vr_is_ca = np.zeros([n_steps,n_samples]) \n", + "var_total = np.zeros([n_steps,n_samples]) \n", + "for i in range(n_steps): \n", + " n_ens=ens_values[i]\n", + " for t in range(t_start,t_end):\n", + " for r in range(n_rand):\n", + " n = (t-t_start)*n_rand+r\n", + " np.random.seed(n)\n", + " # selecting random ensemble members for the ensemble\n", + " idx_ens = randomized_obs_loc(n_ens,start=0,end=ens_values[-1],seed=n)\n", + " truth_idx = r\n", + " \n", + " da_const_vr['nens'] = n_ens\n", + " \n", + " \n", + " vr_t, vr_i, vr_r, J_dict_LA,bla, quad,dx_ni =vr_reloaded_22(states_512[0]['bg'][t][:,idx_ens],\n", + " states_512[0]['bg'][t][:,truth_idx],m_const,da_const_vr,sat_operator,\n", + " obs_seed=counter,model_seed=counter,alpha=alpha_default)\n", + " \n", + " var_total[i,n] = np.var(J_dict_LA['bf'],ddof=1)\n", + " vr_es[i,n] = vr_t\n", + " vr_real[i,n] = vr_r\n", + " vr_t, vr_r, quad, J_dict_LA = vr_individual_loc_22(states_512[0]['bg'][t][:,:],\n", + " states_512[0]['bg'][t][:,truth_idx],m_const,da_const_vr,sat_operator,\n", + " advect_flag=1,quad_state=quad,\n", + " obs_seed=counter,model_seed=counter)\n", + " vr_is_ca[i,n] = vr_t\n", + " \n", + " \n", + " print('completed nens:',ens_values[i])" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [], + "source": [ + "es =np.sum(vr_es ,axis=1)/counter#/var_total\n", + "is_ca =np.sum(vr_is_ca ,axis=1)/counter#/var_total\n", + "real =np.sum(vr_real ,axis=1)/counter#/var_total\n", + "total =np.sum(var_total,axis=1)/counter#/var_total\n", + "\n", + "me_es =np.sum((vr_es -vr_real),axis=1)/np.sum(vr_real,axis=1)\n", + "me_is_ca =np.sum((vr_is_ca -vr_real),axis=1)/np.sum(vr_real,axis=1)\n", + "\n", + "rmse_es =np.power(np.sum(np.power((vr_es -vr_real),2),axis=1)/counter,0.5)/np.sum(vr_real,axis=1)*counter\n", + "rmse_is_ca =np.power(np.sum(np.power((vr_is_ca -vr_real),2),axis=1)/counter,0.5)/np.sum(vr_real,axis=1)*counter" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "<Figure size 288x432 with 2 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#Lets see what changes when we change the localization used to create the background\n", + "fig,ax = plt.subplots(2,1,figsize=(4,6),sharex='all')\n", + "ax[0].plot( is_ca , lw=4,alpha=1.,label='implicit sens',color=plt.cm.viridis(0.8))\n", + "ax[0].plot( es , lw=4,alpha=1.0,label='explicit sens' ,color='blueviolet',ls='--') \n", + "ax[0].plot( real , lw=2,alpha=1, color='k',ls='-',marker='.',label='truth')\n", + "ax[0].plot(-total , lw=2,alpha=1, color='k',ls='--',marker='.',label='limit')\n", + "ax[1].plot(rmse_is_ca*real , lw=4,alpha=1.0,label='implicit sensitivity',color=plt.cm.viridis(0.8))\n", + "ax[1].plot(rmse_es*real , lw=4,alpha=1.0,label='explicit sensitivity' ,color='blueviolet',ls='--',zorder=2) \n", + "\n", + "\n", + "ax[1].set_xlabel('ensemble size');\n", + "ax[0].set_ylabel('mean variance reduction');\n", + "ax[1].set_ylabel('RMSE variance reduction');\n", + "\n", + "ax[0].set_xticks(np.arange(n_steps));\n", + "ax[0].set_xticklabels(ens_values);\n", + "ax[1].set_ylim(bottom=10)\n", + "ax[0].set_ylim(top=-28,bottom=-50)\n", + " \n", + "lines_labels = [ax.get_legend_handles_labels() for ax in fig.axes]\n", + "lines, labels = [sum(lol, []) for lol in zip(*lines_labels)]\n", + "#fig.legend(lines, labels, loc='upper center',ncol=6)\n", + "#ax[0].legend(bbox_to_anchor=(-0.3,1.05),loc='lower left',ncol=5);\n", + "ax[0].legend(loc='center right',ncol=2);\n", + "#ax[1].legend(lines,labels)#ncol=2,bbox_to_anchor=(1.3,.05),loc='lower left').set_zorder(100);\n", + "plt.subplots_adjust(hspace=0.05)\n", + "fig.align_labels()\n", + "ax[0].yaxis.get_label().set_verticalalignment(\"baseline\")\n", + "ax[1].yaxis.get_label().set_verticalalignment(\"baseline\")\n", + "label_axes_abcd(fig,loc=(1.02,0.95))" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "<Figure size 288x432 with 2 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#Lets see what changes when we change the localization used to create the background\n", + "fig,ax = plt.subplots(2,1,figsize=(4,6),sharex='all')\n", + "ax[0].plot( is_ca/total , lw=4,alpha=1.,label='implicit sens',color=plt.cm.viridis(0.8))\n", + "ax[0].plot( es/total , lw=4,alpha=1.0,label='explicit sens' ,color='blueviolet',ls='--') \n", + "ax[0].plot( real/total , lw=2,alpha=1, color='k',ls='-',marker='.',label='truth')\n", + "# ax[0].plot(-total , lw=2,alpha=1, color='k',ls='--',marker='.',label='limit')\n", + "ax[1].plot(rmse_is_ca*real , lw=4,alpha=1.0,label='implicit sensitivity',color=plt.cm.viridis(0.8))\n", + "ax[1].plot(rmse_es*real , lw=4,alpha=1.0,label='explicit sensitivity' ,color='blueviolet',ls='--',zorder=2) \n", + "\n", + "\n", + "ax[1].set_xlabel('ensemble size');\n", + "ax[0].set_ylabel('mean variance reduction');\n", + "ax[1].set_ylabel('RMSE variance reduction');\n", + "\n", + "ax[0].set_xticks(np.arange(n_steps));\n", + "ax[0].set_xticklabels(ens_values);\n", + "ax[1].set_ylim(bottom=10)\n", + "# ax[0].set_ylim(top=-20)\n", + " \n", + "lines_labels = [ax.get_legend_handles_labels() for ax in fig.axes]\n", + "lines, labels = [sum(lol, []) for lol in zip(*lines_labels)]\n", + "#fig.legend(lines, labels, loc='upper center',ncol=6)\n", + "#ax[0].legend(bbox_to_anchor=(-0.3,1.05),loc='lower left',ncol=5);\n", + "ax[0].legend(loc='center right',ncol=2);\n", + "#ax[1].legend(lines,labels)#ncol=2,bbox_to_anchor=(1.3,.05),loc='lower left').set_zorder(100);\n", + "plt.subplots_adjust(hspace=0.05)\n", + "fig.align_labels()\n", + "ax[0].yaxis.get_label().set_verticalalignment(\"baseline\")\n", + "ax[1].yaxis.get_label().set_verticalalignment(\"baseline\")\n", + "label_axes_abcd(fig,loc=(1.02,0.95))" + ] + }, { "cell_type": "code", "execution_count": null, diff --git a/LinAdvFunc/LinAdvFunc.egg-info/PKG-INFO b/LinAdvFunc/LinAdvFunc.egg-info/PKG-INFO new file mode 100644 index 0000000000000000000000000000000000000000..b878b94e234defcbf6f8bc50b0488d01f51786d0 --- /dev/null +++ b/LinAdvFunc/LinAdvFunc.egg-info/PKG-INFO @@ -0,0 +1,3 @@ +Metadata-Version: 2.1 +Name: LinAdvFunc +Version: 0.1 diff --git a/LinAdvFunc/LinAdvFunc.egg-info/SOURCES.txt b/LinAdvFunc/LinAdvFunc.egg-info/SOURCES.txt new file mode 100644 index 0000000000000000000000000000000000000000..68db2505a135ad3014b9457102b3749e36fc851e --- /dev/null +++ b/LinAdvFunc/LinAdvFunc.egg-info/SOURCES.txt @@ -0,0 +1,5 @@ +setup.py +LinAdvFunc.egg-info/PKG-INFO +LinAdvFunc.egg-info/SOURCES.txt +LinAdvFunc.egg-info/dependency_links.txt +LinAdvFunc.egg-info/top_level.txt \ No newline at end of file diff --git a/LinAdvFunc/LinAdvFunc.egg-info/dependency_links.txt b/LinAdvFunc/LinAdvFunc.egg-info/dependency_links.txt new file mode 100644 index 0000000000000000000000000000000000000000..8b137891791fe96927ad78e64b0aad7bded08bdc --- /dev/null +++ b/LinAdvFunc/LinAdvFunc.egg-info/dependency_links.txt @@ -0,0 +1 @@ + diff --git a/LinAdvFunc/LinAdvFunc.egg-info/top_level.txt b/LinAdvFunc/LinAdvFunc.egg-info/top_level.txt new file mode 100644 index 0000000000000000000000000000000000000000..8b137891791fe96927ad78e64b0aad7bded08bdc --- /dev/null +++ b/LinAdvFunc/LinAdvFunc.egg-info/top_level.txt @@ -0,0 +1 @@ + diff --git a/LinAdvFunc/__init__.py b/LinAdvFunc/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..ed9d2c6d9f9c31430a82b4ff32c69afffb2bd0fb --- /dev/null +++ b/LinAdvFunc/__init__.py @@ -0,0 +1,5 @@ +from LinAdvFunc.da_functions import * +from LinAdvFunc.misc_functions import * +from LinAdvFunc.model_functions import * +from LinAdvFunc.plot_functions import * +from LinAdvFunc.efsoi_functions import * \ No newline at end of file diff --git a/LinAdvFunc/efsoi_functions.py b/LinAdvFunc/efsoi_functions.py index 3fedbf4e588211a79f51e948ce999dc680832038..415ee105443a75beb72b811e27dc6cf96be5f050 100644 --- a/LinAdvFunc/efsoi_functions.py +++ b/LinAdvFunc/efsoi_functions.py @@ -15,7 +15,7 @@ def set_obs_v_dict( obs_loc_sat = np.array([]), True_std_obs_h = 0.01, True_std_obs_sat= 0.01, - loc =True, + loc =False, loc_length=2000): """ @@ -136,13 +136,15 @@ def quad_plotter_paper_efsoi(quad_state,m_const,da_const,obs_v_dict): ax[0,1].plot(m_const['x_grid']/1000.,np.mean(quad_state['an'][:,:],axis=1),'k',alpha =1,zorder=2,lw=2,ls='-',label='ens mean') ax[1,0].plot(m_const['x_grid']/1000.,np.mean(quad_state['bf'][:,:],axis=1),'k',alpha =1,zorder=2,lw=2,ls='-',label='ens mean') ax[1,1].plot(m_const['x_grid']/1000.,np.mean(quad_state['fc'][:,:],axis=1),'k',alpha =1,zorder=2,lw=2,ls='-',label='ens mean') + ax[0,1].legend(loc='upper right') if da_const['n_obs_h']: ax[0,0].scatter( m_const["x_grid"][da_const["obs_loc"]]/1000.,quad_state['obs'][da_const["obs_loc"]] ,zorder=3,s=10,label='verification obs',color='k') ax[0,1].scatter( m_const["x_grid"][da_const["obs_loc"]]/1000.,quad_state['obs'][da_const["obs_loc"]] ,zorder=3,s=10,label='verification obs',color='k') ax[0,1].errorbar(m_const["x_grid"][da_const["obs_loc"]]/1000.,quad_state['obs'][da_const["obs_loc"]] ,yerr=da_const['used_std_obs'] ,zorder=3,color='k',capsize=3,capthick=2,ls='none') ax[0,0].errorbar(m_const["x_grid"][da_const["obs_loc"]]/1000.,quad_state['obs'][da_const["obs_loc"]] ,yerr=da_const['used_std_obs'] ,zorder=3,color='k',capsize=3,capthick=2,ls='none') - + + ax[0,0].set_title('background') ax[1,0].set_title('free-forecast') @@ -165,9 +167,11 @@ def quad_plotter_paper_efsoi(quad_state,m_const,da_const,obs_v_dict): return fig,ax -def single_step_analysis_forecast_23(background,truth,da_const,m_const,sat_operator,model_seed=0,obs_seed=0): +def single_step_analysis_forecast_23(background,truth,da_const,m_const,sat_operator,model_seed=0,obs_seed=0,flag_mean=True): """ Revisited version of single step analysis forecast to deal with sat data as well. + + If the LETKF is used, the ensemble weighting matrix W_a is also returned. """ @@ -200,12 +204,32 @@ def single_step_analysis_forecast_23(background,truth,da_const,m_const,sat_opera bf = np.zeros_like(background) fc = np.zeros_like(background) - for i in range(da_const["nens"]): - np.random.seed(i+model_seed) - u_ens = np.random.normal(m_const["u_ref"],da_const["u_std_ens"]) - dhdt_ens = np.random.normal(m_const["dhdt_ref"],da_const["dhdt_std_ens"]) - bf[:,i] = linear_advection_model(background[:,i],u_ens,dhdt_ens,m_const["dx"],da_const["dt"],da_const["nt"]) - fc[:,i] = linear_advection_model(an[:,i],u_ens,dhdt_ens,m_const["dx"],da_const["dt"],da_const["nt"]) + # In this version the mean of the ensemble deviations is not forced to be zero + if flag_mean==False: + for i in range(da_const["nens"]): + np.random.seed(i+model_seed) + u_ens = np.random.normal(m_const["u_ref"],da_const["u_std_ens"]) + dhdt_ens = np.random.normal(m_const["dhdt_ref"],da_const["dhdt_std_ens"]) + bf[:,i] = linear_advection_model(background[:,i],u_ens,dhdt_ens,m_const["dx"],da_const["dt"],da_const["nt"]) + fc[:,i] = linear_advection_model(an[:,i],u_ens,dhdt_ens,m_const["dx"],da_const["dt"],da_const["nt"]) + + # This version normalizes the mean ensemble deviations to zero + if flag_mean==True: + u_ens = np.zeros(da_const["nens"]) + dhdt_ens = np.zeros(da_const["nens"]) + for i in range(da_const["nens"]): + np.random.seed(i+model_seed) + u_ens[i] = np.random.normal(m_const["u_ref"],da_const["u_std_ens"]) + dhdt_ens[i] = np.random.normal(m_const["dhdt_ref"],da_const["dhdt_std_ens"]) + # print('dhdt_ens mean before',dhdt_ens.mean()) + # print('u_ens mean',u_ens.mean()) + u_ens = u_ens - u_ens.mean()+m_const["u_ref"] + dhdt_ens = dhdt_ens - dhdt_ens.mean() + m_const["dhdt_ref"] + # print('u_ens mean after',u_ens.mean()) + # print('dhdt_ens mean',dhdt_ens.mean()) + for i in range(da_const["nens"]): + bf[:,i] = linear_advection_model(background[:,i],u_ens[i],dhdt_ens[i],m_const["dx"],da_const["dt"],da_const["nt"]) + fc[:,i] = linear_advection_model(an[:,i],u_ens[i],dhdt_ens[i],m_const["dx"],da_const["dt"],da_const["nt"]) quad_state['bg'] = background @@ -368,11 +392,11 @@ def quad_plotter_sat_v_efsoi(quad_state,m_const,da_const,obs_v_dict,sat_operator ax[1,1].hlines( sat_fc [obs_v_dict["obs_loc_sat"],i], xpixmin/1000,xpixmax/1000, - color='cyan',alpha=0.2,lw=3) + color='c',alpha=0.2,lw=3) ax[1,0].hlines( sat_ff [obs_v_dict["obs_loc_sat"],i], xpixmin/1000,xpixmax/1000, - color='blue',alpha=0.2,lw=3) + color='b',alpha=0.2,lw=3) ax[1,0].hlines(sat_ff [obs_v_dict["obs_loc_sat"],:].mean(axis=1),xpixmin/1000,xpixmax/1000,color='white',alpha=1,lw=4,zorder=1) ax[1,0].hlines(sat_ff [obs_v_dict["obs_loc_sat"],:].mean(axis=1),xpixmin/1000,xpixmax/1000,color='black',alpha=1,lw=2,zorder=2) @@ -416,14 +440,14 @@ def quad_plotter_sat_v_efsoi(quad_state,m_const,da_const,obs_v_dict,sat_operator def EFSOI_LETKF(background,truth,m_const,da_const,obs_v_dict,sat_operator, - quad_state = None,exp_num=0,obs_seed=0,model_seed=0,obs_v_seed=1000,alpha=0.1,sigprop_error=1.0): + quad_state = None,exp_num=0,obs_seed=0,model_seed=0,obs_v_seed=1000,alpha=0.1,sigprop_error=1.0,flag_mean=False): """ EFSOI, specifically for the LETKF. It has 3 different versions of computing Ya 1. Ya = H Xa (HXa, not great for non-linear H) 2. Ya = Wa_l Yb_l (WlYbl, notation is a bit strange, but the _l indicates the l gridpoint of where the observation is. ) - 2. Ya_l = Wa_l Yb (WYb, this Ya needs to calculated separately for each observation point.) + 3. Ya_l = Wa_l Yb (WYb, this Ya needs to calculated separately for each observation point.) Shows how saving the ensemble weights leads to the correct answer for dt=0, because the Kalman gain is correctly captured. Is like calculating the Kalman from the background instead of from the analysis. @@ -431,6 +455,8 @@ def EFSOI_LETKF(background,truth,m_const,da_const,obs_v_dict,sat_operator, If the observation operator is non-linear, option 2 is better than option 1, option 3 is always better than 2, but not feasible for large systems. For now I've thrown out all the localization stuff for the explicit sensitivity. Not sure if or how it should be included in the paper + + Finally we also have the Sommer formulation, which is the same as the WYb formulation, but with a different error in front. Should be closer to a data denial experiment. """ @@ -442,15 +468,16 @@ def EFSOI_LETKF(background,truth,m_const,da_const,obs_v_dict,sat_operator, #A precomputed quad_state can be supplied to quickly use different response functions ########################################################################### if quad_state==None: - quad_state = single_step_analysis_forecast_23(background,truth,da_const,m_const,sat_operator,obs_seed=obs_seed,model_seed=model_seed) + quad_state = single_step_analysis_forecast_23(background,truth,da_const,m_const,sat_operator,obs_seed=obs_seed,model_seed=model_seed,flag_mean=flag_mean) # if random_truth_u_flag: - # np.random.seed(model_seed+da_const['nens']) - # u_tr = np.random.normal(m_const["u_ref"],da_const["u_std_ens"]) - # dhdt_tr = np.random.normal(m_const["dhdt_ref"],da_const["dhdt_std_ens"]) - # tr_ff = linear_advection_model(truth,u_tr,dhdt_tr,m_const["dx"],da_const["dt"],da_const["nt"]) + np.random.seed(model_seed*da_const['nens']) + u_tr = np.random.normal(m_const["u_ref"],da_const["u_std_ens"]) + dhdt_tr = np.random.normal(m_const["dhdt_ref"],da_const["dhdt_std_ens"]) + tr_ff = linear_advection_model(truth,u_tr,dhdt_tr,m_const["dx"],da_const["dt"],da_const["nt"]) + # print('warning,truth uses random speed') # else: - tr_ff = linear_advection_model(truth,m_const['u_ref'],m_const['dhdt_ref'],m_const["dx"],da_const["dt"],da_const["nt"]) + # tr_ff = linear_advection_model(truth,m_const['u_ref'],m_const['dhdt_ref'],m_const["dx"],da_const["dt"],da_const["nt"]) quad_state['tr_ff'] = tr_ff ########################################################################### @@ -505,12 +532,13 @@ def EFSOI_LETKF(background,truth,m_const,da_const,obs_v_dict,sat_operator, # Initializing the matrices that will be used to calculate the explicit sensitivity of the observation efsoi_Jd_WYb = np.zeros([n_obs_a,n_obs_v]) + efsoi_Jd_som = np.zeros([n_obs_a,n_obs_v]) efsoi_Jd_WlYbl = np.zeros([n_obs_a,n_obs_v]) efsoi_Jd_HXa = np.zeros([n_obs_a,n_obs_v]) - if da_const['loc']: L= localization_matrices_obs_obs(m_const,da_const,obs_v_dict,sigprop_error=sigprop_error) - else: L = np.ones([n_obs_a,n_obs_v]) #Already includes advection + if da_const['loc']: L= localization_matrices_obs_obs(m_const,da_const,obs_v_dict,sigprop_error=sigprop_error)#Already includes advection + else: L = np.ones([n_obs_a,n_obs_v]) # Just saving the locactions into a single array @@ -556,18 +584,21 @@ def EFSOI_LETKF(background,truth,m_const,da_const,obs_v_dict,sat_operator, HXaRY_loc = np.dot(np.dot(np.diag(gc_loc)*np.linalg.inv(da_const['R']),HXa),Yf[o,:]) init_error =quad_state['e_bf'][o]+quad_state['e_fc'][o] + sommer_error = 2*quad_state['e_fc'][o] for a in range(n_obs_a): if a < da_const['n_obs_h']: dbg = quad_state['obs'][da_const['obs_loc'][a]]-bg_obs_ol[a] else: dbg = quad_state['obs_sat'][da_const['obs_loc_sat'][a-da_const['n_obs_h']]]-bg_obs_ol[a] - efsoi_Jd_WYb[a,o] = init_error*WYbRY_loc[a]*dbg/(da_const['nens']-1) - efsoi_Jd_WlYbl[a,o] = init_error*WlYblRY_loc[a]*dbg/(da_const['nens']-1) - efsoi_Jd_HXa[a,o] = init_error*HXaRY_loc[a]*dbg/(da_const['nens']-1) + efsoi_Jd_WYb[a,o] = init_error *WYbRY_loc[a] *dbg/(da_const['nens']-1) + efsoi_Jd_som[a,o] = sommer_error*WYbRY_loc[a] *dbg/(da_const['nens']-1) + efsoi_Jd_WlYbl[a,o] = init_error *WlYblRY_loc[a]*dbg/(da_const['nens']-1) + efsoi_Jd_HXa[a,o] = init_error *HXaRY_loc[a] *dbg/(da_const['nens']-1) quad_state['efsoi_WYb'] = efsoi_Jd_WYb + quad_state['efsoi_som'] = efsoi_Jd_som quad_state['efsoi_WlYbl'] = efsoi_Jd_WlYbl quad_state['efsoi_HXa'] = efsoi_Jd_HXa diff --git a/misc_functions.py b/LinAdvFunc/misc_functions.py similarity index 100% rename from misc_functions.py rename to LinAdvFunc/misc_functions.py diff --git a/da_functions.py b/da_functions.py deleted file mode 100644 index b062a10d7464e728ab4dbea3863116812265667b..0000000000000000000000000000000000000000 --- a/da_functions.py +++ /dev/null @@ -1,3121 +0,0 @@ -#!/usr/bin/env python - -#Contains all functions related to setting up and running the ensemble filter. There is quite a bit of overlap with the model functions, but I felt it was a bit cleaner to try to separate them a bit. - -import numpy as np -from model_functions import * -from misc_functions import * -from numba import jit - - - -def set_da_constants_22(ncyc=100,nt=1,dt=550,u_std=2.0,dhdt_std=0., - True_std_obs=0.15,used_std_obs=0.15,pert_std_obs=0.15, - obs_loc=np.arange(49,299,100).astype(int), - True_std_obs_sat=0.15,used_std_obs_sat=0.15,pert_std_obs_sat=0.15, - obs_loc_sat=np.arange(7,299,15), - nens=32,nexp=1, - init_noise=0.,init_spread = False, init_spread_h=0.5,init_spread_x = 500., - ens_seed=0, obs_seed=0, fixed_seed = False, - loc=True,loc_length = 3200,loc_type='gaspari_cohn', - method='sqEnKF'): - """ - Sets constants needed for data assimilation and stores them in a dictionary. - Is now expanded to also use "satellite" observations, so that all observation statements are doubled, once for the default h point measurements, and once for the sat measurements. - - There is some confusting misnaming going on, e.g. "u_std_ens" = u_std, but nothing that needs to be changed immediately - - nt is currently really dangerous because of how the u perubation of the linear advection modell are rerolled, please leave at 1 for now. - - - - TODO: - - """ - DA_const = {} - - - DA_const["ncyc"] = ncyc # Number of assimilation cycles - DA_const["nens"] = nens # number of ensemble members - DA_const["nexp"] = nexp # number of parallel experiments to run - - - DA_const["nt"] = nt # Number of model timesteps between observations - DA_const["dt"] = dt # time duration of model timesteps - - #Assimilation Method - DA_const['method'] = method # Options so far include EnKF and LETKF, both with and without localization. - - #Ensemble Errors and ensemble - DA_const["u_std_ens"] = u_std # Standard deviation of model u - DA_const["dhdt_std_ens"] = dhdt_std # Standard deviation of model dydt - DA_const["ens_seed"] = ens_seed # Is added to the seed when generating ensemble paramter deviations - DA_const["fixed_seed"] = fixed_seed # If True then the ensemble errors will always be constant - - DA_const["True_std_obs_sat"] = True_std_obs_sat # Standard deviation of true observation error of y - DA_const["used_std_obs_sat"] = used_std_obs_sat # Standard deviation of assumed observation error used to calculate R - DA_const["pert_std_obs_sat"] = pert_std_obs_sat # Standard deviation of pertubations added to the true observation for each ensemble member individually when updating - - #Ensemble initialization - DA_const["init_noise"] = init_noise # spread of noise added to initial conditions to avoid singular matrices - DA_const["init_spread"] = init_spread # If True, adds an x and h displacement to initial ensemble spread - DA_const["init_spread_h"] = init_spread_h # initial spread of ensemble in h - DA_const["init_spread_x"] = init_spread_x # initial spread of ensemble in x - - #Point measurement observations - DA_const["True_std_obs"] = True_std_obs # Standard deviation of true observation error of y - DA_const["used_std_obs"] = used_std_obs # Standard deviation of assumed observation error used to calculate R - DA_const["pert_std_obs"] = pert_std_obs # Standard deviation of pertubations added to the true observation for each ensemble member individually when updating - DA_const["obs_loc"] = obs_loc # index array of which state elements are observed - - # Satellite observations - DA_const["True_std_obs_sat"] = True_std_obs_sat # Standard deviation of true observation error of y - DA_const["used_std_obs_sat"] = used_std_obs_sat # Standard deviation of assumed observation error used to calculate R - DA_const["pert_std_obs_sat"] = pert_std_obs_sat # Standard deviation of pertubations added to the true observation for each ensemble member individually when updating - DA_const["obs_loc_sat"] = obs_loc_sat # index array of which state elements are observed - - DA_const["obs_seed"] = obs_seed # Is added to the seed when generating observations - - #Localization - DA_const["loc"] = loc # localization yes or no, no for now - DA_const["loc_length"] = loc_length # localization yes or no, no for now - DA_const["loc_type"] = loc_type # localization yes or no, no for now - - #Observations variance matrix - n_obs_h =len(DA_const["obs_loc"]) - n_obs_sat =len(DA_const["obs_loc_sat"]) - n_obs = n_obs_h+n_obs_sat - r =np.ones(n_obs) - r[:n_obs_h] = DA_const["used_std_obs"]**2. - r[n_obs_h:] = DA_const["used_std_obs_sat"]**2. - DA_const['R'] = np.diag(r) - DA_const['n_obs_h'] = n_obs_h - DA_const['n_obs_sat'] = n_obs_sat - return DA_const - -def set_da_constants(ncyc=10,nt=1,dt=500,u_std=0.5,dhdt_std=1e-4,True_std_obs=0.1,used_std_obs=0.1,pert_std_obs=0.1,obs_loc_h=np.arange(5,101,15),nens=20,nexp=1,init_noise=0.,fixed_seed=True, - loc=None,init_spread = False, init_spread_h=0.5,init_spread_x = 500., - loc_length = 1000,loc_type='gaspari_cohn',method='EnKF'): - """ - Sets constants needed for data assimilation and stores them in a dictionary. - There is some confusting misnaming going on, e.g. "u_std_ens" = u_std, but nothing that needs to be changed immediately - - nt is not really relevant as long as we use the linear advection model, but I left it in from Yvonne in case we start applying it to different models. - - TODO: - - """ - DA_const = {} - - - DA_const["ncyc"] = ncyc # Number of assimilation cycles - DA_const["nens"] = nens # number of ensemble members - DA_const["nexp"] = nexp # number of parallel experiments to run - - - DA_const["nt"] = nt # Number of model timesteps between observations - DA_const["dt"] = dt # time duration of model timesteps - - #Assimilation Method - DA_const['method'] = method # Options so far include EnKF and LETKF, both with and without localization. - - #Ensemble Errors and ensemble - DA_const["u_std_ens"] = u_std # Standard deviation of model u - DA_const["dhdt_std_ens"] = dhdt_std # Standard deviation of model dydt - DA_const["fixed_seed"] = fixed_seed # If True, then the ensemble number is used as a seed, so that the u and dhdt values are always randomized the same way - - #Ensemble initialization - DA_const["init_noise"] = init_noise # spread of noise added to initial conditions to avoid singular matrices - DA_const["init_spread"] = init_spread # If True, adds an x and h displacement to initial ensemble spread - DA_const["init_spread_h"] = init_spread_h # initial spread of ensemble in h - DA_const["init_spread_x"] = init_spread_x # initial spread of ensemble in x - - #Observations - DA_const["True_std_obs"] = True_std_obs # Standard deviation of true observation error of y - DA_const["used_std_obs"] = used_std_obs # Standard deviation of assumed observation error used to calculate R - DA_const["pert_std_obs"] = pert_std_obs # Standard deviation of pertubations added to the true observation for each ensemble member individually when updating - DA_const["obs_loc"] = obs_loc_h # index array of which state elements are observed - - - #Localization - DA_const["loc"] = loc # localization yes or no, no for now - DA_const["loc_length"] = loc_length # localization yes or no, no for now - DA_const["loc_type"] = loc_type # localization yes or no, no for now - - return DA_const - - -def sine_initial_condition(x,n): - """ - Generates a sin curve on the x grid with overlapping waves defined by n. - """ - mu = 0 - y = np.zeros_like(x) - for ni in n: - y = y+ np.sin(x/np.max(x)*2*np.pi*ni) - - return y - - - -def create_states_dict(j,states,m_const,da_const): - """ - Generates the initial analysis and truth. - Also creates the "states" dictionary where the analysis ensemble, background ensemble, truth and observations are all going to be stored. - Very memory hungry, as everything from all assimilation time steps and all experiments is stored. - Works find for simple model though. - - A fixed seed is used by default so that the model errors of all ensemble members is constant. But this can also be randomized. - Alternative version would be to generate the model errors and store them, but this has not happened yet. Would be necessary to test some parameter estimation tests. - - Modified version of the Yvonne setup to work with the linear advection model - - Todo: - - Describe states dictionary here. - - make model errors stored variables so enable parameter estimation - - Maybe make a more sensible name, such as init_da_ensembles - - I am not convinced this is the best way to generate the initial analysis. - - Might be best to change to dictionary time axis. Forecast and anaylis at the same time have difference of 1 in the time integer. - """ - nx = m_const["nx"] - - #initial conditions - if m_const['init_func']=='gaus': h_initial = gaussian_initial_condition(m_const["x_grid"],m_const["h_init_std"]) - if m_const['init_func']=='sine': h_initial = sine_initial_condition( m_const["x_grid"],m_const["sine_init"]) - - - #Generate truth - if da_const["fixed_seed"]==True: np.random.seed(j) - - u_truth = np.random.normal(m_const["u_ref"],m_const["u_std_truth"]) - dhdt_truth = np.random.normal(m_const["dhdt_ref"],m_const["dhdt_std_truth"]) - truth = linear_advection_model(h_initial,u_truth,dhdt_truth,m_const["dx"],da_const["dt"],da_const["nt"]) - - an = np.zeros((nx,da_const["nens"])) - - #First rows full of nans :) - bg = np.zeros((nx,da_const["nens"])) - bg[:] = np.nan - obs = np.zeros(nx) - obs[:] = np.nan - - for i in range(da_const["nens"]): - if da_const["fixed_seed"]==True: np.random.seed(i+j*da_const["nens"]) - if da_const["init_noise"]>0: - h_ens = np.random.normal(h_initial,da_const["init_noise"]) - else: - h_ens = h_initial - if da_const["init_spread"]>0: - #initial spread generated by moving waves forward and backward and up and down using - #da_const["init_spread_h"] and da_const["init_spread_x"] - x_displace = np.random.normal(0.,da_const["init_spread_x"]) - h_displace = np.random.normal(0.,da_const["init_spread_h"]) - u_tmp = x_displace/da_const["dt"] - h_ens = semi_lagrangian_advection(h_ens,m_const["dx"],u_tmp,da_const["dt"]) - h_ens = h_ens+h_displace - - u_ens = np.random.normal(m_const["u_ref"],da_const["u_std_ens"]) - dhdt_ens = np.random.normal(m_const["dhdt_ref"],da_const["dhdt_std_ens"]) - an[:,i] = linear_advection_model(h_ens,u_ens,dhdt_ens,m_const["dx"],da_const["dt"],da_const["nt"]) - - states[j]={} - states[j]['bg']=[bg] - states[j]['an']=[an] - states[j]['truth']=[truth] - states[j]['obs']=[obs] - return an, truth, states - - - return DA_const - - -def generate_obs(truth,states,m_const,da_const,j,t): - """ - Generates the truth and observations for the next assimilation step. - If a fixed_seed is used the model "errors" of the truth remain the same, but noise of the obs is still reseeded differently each time. - - To avoid diffusion over time in the truth I take advantage of the linearity of the model to compute it directly from the initial conditions. - This only works if the truth is not perturbed each timestep though. - """ - #Generate new truth constants and integrate in time - if da_const["fixed_seed"]==True: np.random.seed(j) - u_truth = np.random.normal(m_const["u_ref"],m_const["u_std_truth"]) - dhdt_truth = np.random.normal(m_const["dhdt_ref"],m_const["dhdt_std_truth"]) - if da_const["fixed_seed"]==True: - #t starts at zero, that is why we need a plus 1 - truth = linear_advection_model(states['truth'][0],u_truth,dhdt_truth,m_const["dx"],da_const["dt"]*(t+1),da_const["nt"]) - - else: - truth = linear_advection_model(truth,u_truth,dhdt_truth,m_const["dx"],da_const["dt"],da_const["nt"]) - - #make obs by adding some noise, a fixed seed should change over time so that the differences are not always the same for each measurement location - if da_const["fixed_seed"]==True: np.random.seed((j+1)*(t+1)) - obs = truth + np.random.normal(0,da_const["True_std_obs"],m_const["nx"]) - states["truth"] = states["truth"]+[truth] - states["obs"] = states["obs"]+[obs] - return truth, obs, states - - - -def generate_obs_22(truth,truth_init,m_const,da_const,j,t,sat_operator): - """ - Generates the truth and observations for the next assimilation step to be used when running the model forward with assimilation steps. - In contrast to the old generate_obs this now includes the possibility to generate sat_obs. - - Another new difference is that the new da_const has seeds for the obs and the ensembles, so that we can set them independantly without messing around with flags - - To avoid diffusion over time in the truth I take advantage of the linearity of the model to compute it directly from the initial conditions. - This only works if the truth is not perturbed each timestep though. - - Should replace the standard function at some point - """ - #Generate new truth constants and integrate in time - if m_const["u_std_truth"]+m_const["dhdt_std_truth"]==0: - #t starts at zero, that is why we need a plus 1 - truth = linear_advection_model(truth_init,m_const["u_ref"],m_const["u_std_truth"],m_const["dx"],da_const["dt"]*(t+1),da_const["nt"]) - - else: - np.random.seed((j+1)*(t+1)+1000+m_const['truth_seed']) - u_truth = np.random.normal(m_const["u_ref"],m_const["u_std_truth"]) - dhdt_truth = np.random.normal(m_const["dhdt_ref"],m_const["dhdt_std_truth"]) - truth = linear_advection_model(truth,u_truth,dhdt_truth,m_const["dx"],da_const["dt"],da_const["nt"]) - - #make obs by adding some noise, a fixed seed should change over time so that the differences are not always the same for each measurement location - np.random.seed((j+1)*(t+1)+10000+da_const['obs_seed']) - obs = truth + np.random.normal(0,da_const["True_std_obs"],m_const["nx"]) - if len(da_const['obs_loc_sat'])>0: - truth_sat = sat_operator(truth) - obs_sat = truth_sat + np.random.normal(0,da_const["True_std_obs_sat"],m_const["nx"]) - obs_sat[obs_sat>1.]= 1. - obs_sat[obs_sat<0.]= 0. - else: - obs_sat = np.zeros(m_const['nx']) - - return truth, obs, obs_sat - - - - - -def predict(analysis,states,da_const,m_const,j): - """ - Runs the analysis ensemble forward in time using the model to generate the next forecast/background ensemble for the next assimilation step. - - Outlook: - - Add different models than the linear_advection_model? - """ - bg = np.zeros((m_const["nx"],da_const["nens"])) - for i in range(da_const["nens"]): - if da_const["fixed_seed"]==True: np.random.seed(i+j*da_const["nens"]) - u_ens = np.random.normal(m_const["u_ref"],da_const["u_std_ens"]) - dhdt_ens = np.random.normal(m_const["dhdt_ref"],da_const["dhdt_std_ens"]) - bg[:,i] = linear_advection_model(analysis[:,i],u_ens,dhdt_ens,m_const["dx"],da_const["dt"],da_const["nt"]) - - states["bg"] = states["bg"]+[bg] - return bg, states - -def predict_LA_22(analysis,states,da_const,m_const,j,t): - """ - Runs the analysis ensemble forward in time using the model to generate the next forecast/background ensemble for the next assimilation step. - """ - bg = np.zeros((m_const["nx"],da_const["nens"])) - for i in range(da_const["nens"]): - if da_const["fixed_seed"]==True: - np.random.seed(i+j*da_const["nens"]+da_const['ens_seed']) - else: - np.random.seed(i+j*da_const["nens"]+(da_const['ens_seed']+1)*t) - u_ens = np.random.normal(m_const["u_ref"],da_const["u_std_ens"]) - dhdt_ens = np.random.normal(m_const["dhdt_ref"],da_const["dhdt_std_ens"]) - bg[:,i] = linear_advection_model(analysis[:,i],u_ens,dhdt_ens,m_const["dx"],da_const["dt"],da_const["nt"]) - - states["bg"] = states["bg"]+[bg] - return bg, states - - - - -def update(background,obs,R,H,C,states,da_const,m_const): - """ - Computes the analysis by individually changing each ensemble member of the forecast/background through the shared ensemble Kalman Gain and the observations. - Now also used the localization matrix,C - """ - # Compute the background error covariance matrix - P = np.cov(background)*C - - # define relative weights of observations and background in terms of the Kalman Gain matrix of size - K = KalmanGain(P, R, H) - - # Compute the analysis for each ensemble members - an = np.zeros((m_const["nx"],da_const["nens"])) - for i in range(da_const["nens"]): - an[:,i] = get_analysis(background[:,i],obs,K,H,da_const) - states["an"] = states["an"]+[an] - return an, states - -#def update_noloc(background,obs,R,H,states,da_const,m_const): -# """ -# Computes the analysis by individually changing each ensemble member of the forecast/background through the shared ensemble Kalman Gain and the observations. -# -# Todo: -# -Figure out exactly which version of the EnKF is used here, so that I can refer to it properly -# """ -# # Compute the background error covariance matrix -# P = np.cov(background) -# -# # define relative weights of observations and background in terms of the Kalman Gain matrix of size -# K = KalmanGain(P, R, H) -# -# # Compute the analysis for each ensemble members -# an = np.zeros((m_const["nx"],da_const["nens"])) -# for i in range(da_const["nens"]): -# an[:,i] = get_analysis(background[:,i],obs,K,H,da_const) -# states["an"] = states["an"]+[an] -# return an, states - - - -def KalmanGain(P,R,H): - """ - Computes the Kalman gain matrix: K = PH^T(HPH^T+R)^(-1) - """ - P_H = np.dot(P,H.transpose()) - S = np.dot(H,P_H)+R - K = np.dot(P_H,np.linalg.inv(S)) - - return K - -def get_analysis(bg,obs,K,H,da_const): - """ - Computes analysis: an = bg + K(H*bg-obs_pert), where obs_pert are perturbed observations - """ - obs_pert = np.dot(H,obs+np.random.normal(0,da_const["pert_std_obs"],len(obs))) - bg_obs = np.dot(H,bg) - #obs_orig =np.dot(H,obs) - #print('obs_orig-bg_obs',np.sum(obs_orig-bg_obs),'obs_pert-bg_obs',np.sum(obs_pert-bg_obs),'update/np.sum(x)',np.sum(np.dot(K,obs_pert-bg_obs) )/np.sum(bg),'max(K)',np.max(K)) - an = bg + np.dot(K,obs_pert-bg_obs) - return an - -def get_analysis_v2(bg, obs, K, H, obs_error_vec): - """ - Computes analysis: an = bg + K(H*bg-obs_pert), where obs_pert are perturbed observations. v2 comes with a perturb_obs vector to enable differing observation erros. - Mostly made to deal with the shallow water model, but I guess it could be used to make some fun tests. - """ - #print(obs) - #print(obs_error_vec) - obs_pert = np.dot(H,obs+np.random.normal(0,obs_error_vec,len(obs))) - bg_obs = np.dot(H,bg) - #obs_orig =np.dot(H,obs) - #print('obs_orig-bg_obs',np.sum(obs_orig-bg_obs),'obs_pert-bg_obs',np.sum(obs_pert-bg_obs),'update/np.sum(x)',np.sum(np.dot(K,obs_pert-bg_obs) )/np.sum(bg),'max(K)',np.max(K)) - an = bg + np.dot(K,obs_pert-bg_obs) - return an - - -def run_linear_advection_KF(m_const,da_const): - """ - The heart and soul of the whole linear advection EnKF filter. - Steps: - - Computes constant Matrices H and R - - Initializes states dictionary with first truth and analysis ensemble - - And so on - - Todo: Improve the documentation - """ - - """ - constant matrices that follow from previously defined constants - """ - H = np.identity(m_const["nx"])[da_const["obs_loc"],:] # Observation operator - R = da_const["used_std_obs"]**2*np.identity(len(da_const["obs_loc"])) # Observation error corvariance matrix - - - - - """ - create dictionary to store background, analysis, truth and observations for verification - """ - states = {} - - #Construct localization matrix C if loc!=None - if da_const["loc"]: - C = loc_matrix(da_const,m_const) - else: - C=np.ones([m_const["nx"],m_const["nx"]]) - - """ - Loop over experiments - """ - for j in range(da_const["nexp"]): - - """ - Initialize a random truth and a random analysis ensemble stored in "states" - """ - analysis, truth, states = create_states_dict(j,states,m_const,da_const) - - - """ - START DATA ASSIMILATION CYCLE - """ - for t in range(0,da_const["ncyc"]): - """ - Run the truth forward in time until the next assimilation step and create observations. - Note that this step is usually provided by nature and measurements obtained from weather stations, - wind profilers, radiosondes, aircraft reports, radars, satellites, etc. - """ - truth, obs, states[j] = generate_obs(truth,states[j],m_const,da_const,j,t) - - """ - Predict - """ - # Predict the state at the next assimilation step by running the analysis forward in time - background, states[j] = predict(analysis,states[j],da_const,m_const,j) - - """ - Update - """ - ## Combine background and observations to get the analysis - np.random.seed(t) - if da_const['method'] == 'EnKF': - analysis, states[j] = update(background,obs,R,H,C,states[j],da_const,m_const) - if da_const['method'] == 'LETKF': - analysis,bla = LETKF_analysis(background,obs,m_const,da_const) - states[j]["an"] = states[j]["an"]+[analysis] - """ - END DATA ASSIMILATION CYCLE - """ - """ - end loop over experiments - """ - return states - - -def run_linear_advection_KF_22(m_const,da_const,sat_operator): - """ - The heart and soul of the whole linear advection EnKF filter, now updated in the 22 version to work with satelitte data and the seeds. - - Todo: Improve the documentation - """ - - """ - constant matrices that follow from previously defined constants - """ - # should not be needed anymore H = np.identity(m_const["nx"])[da_const["obs_loc"],:] # Observation operator - R = da_const["R"] # Observation error corvariance matrix - - - - - """ - create dictionary to store background, analysis, truth and observations for verification - """ - states = {} - - #Construct localization matrix C if loc!=None - if da_const["loc"]: - C = loc_matrix(da_const,m_const) - else: - C=np.ones([m_const["nx"],m_const["nx"]]) - - """ - Loop over experiments - """ - for j in range(da_const["nexp"]): - - """ - Initialize a random truth and a random analysis ensemble stored in "states" - """ - analysis, truth, states = create_states_dict_22(j,states,m_const,da_const,sat_operator) - - - """ - START DATA ASSIMILATION CYCLE - """ - for t in range(0,da_const["ncyc"]): - """ - Run the truth forward in time until the next assimilation step and create observations. - Note that this step is usually provided by nature and measurements obtained from weather stations, - wind profilers, radiosondes, aircraft reports, radars, satellites, etc. - """ - truth, obs, obs_sat = generate_obs_22(truth,states[j]['truth'][0],m_const,da_const,j,t,sat_operator) - states[j]["truth"] = states[j]["truth"] +[truth] - states[j]["obs"] = states[j]["obs"] +[obs] - states[j]["obs_sat"] = states[j]["obs_sat"]+[obs_sat] - - """ - Predict - """ - # Predict the state at the next assimilation step by running the analysis forward in time - background, states[j] = predict_LA_22(analysis,states[j],da_const,m_const,j,t) - - """ - Update - """ - ## Combine background and observations to get the analysis - np.random.seed(t) - if da_const['method'] == 'EnKF': - np.random.seed((j+3)**2+(t+4)**3) - analysis = ENKF_analysis_22(background,obs,obs_sat,da_const,m_const,sat_operator) - states[j]["an"] = states[j]["an"]+[analysis] - - if da_const['method'] == 'LETKF': - # analysis,bla = LETKF_analysis_22(background,obs,obs_sat,m_const,da_const,sat_operator) - analysis,bla,W_a = LETKF_analysis_23(background,obs,obs_sat,m_const,da_const,sat_operator) - states[j]["an"] = states[j]["an"]+[analysis] - - if da_const['method'] == 'sqEnKF': - analysis = sqEnKF_analysis_22(background,obs,obs_sat,da_const,m_const,sat_operator) - states[j]["an"] = states[j]["an"]+[analysis] - """ - END DATA ASSIMILATION CYCLE - """ - """ - end loop over experiments - """ - return states - - -def sqEnKF_analysis_22(background,obs,obs_sat,da_const,m_const,sat_operator): - """ - Computes the analysis using the square root kalman Gain as introduced by Whitaker 2002. It used the modified Kalman gain to compute the change in deviations, - and the normal Kalman gain to compute the change in the mean. - """ - #Step 1, preparing all the basics - an = np.zeros((m_const["nx"],da_const["nens"])) - bg_obs_deviations,bg_obs_ol = state_to_observation_space(background,m_const,da_const,sat_operator) - bg_obs = (bg_obs_deviations.T+bg_obs_ol).T - bg_ol = np.mean(background,axis=1) - if da_const['n_obs_sat']* da_const['n_obs_h']>0: - obs_stack = np.hstack([obs[da_const['obs_loc']],obs_sat[da_const['obs_loc_sat']]]) - else: - if da_const['n_obs_sat']>0: - obs_stack = obs_sat[da_const['obs_loc_sat']] - if da_const['n_obs_h']>0: - obs_stack = obs[da_const['obs_loc']] - - dbg= (background.T-bg_ol).T - # calculating the analysis ensemble mean using the normal Kalman gain - K = Kalman_gain_observation_deviations(background,m_const,da_const,sat_operator) - an_ol = bg_ol + np.dot(K,obs_stack-bg_obs_ol) - - # Calculating change in ensemble deviations using squar root Kalman gain - sqK = square_root_Kalman_gain_observation_deviations(background,m_const,da_const,sat_operator) - - dan = dbg -np.dot(sqK,bg_obs_deviations) - - an = (dan.T + an_ol).T - #print(obs_stack) - #print(mean) - #print(bg_obs[0,:]) - #for i in range(da_const["nens"]): - # an[:,i] = background[:,i] + np.dot(K,obs_stack-bg_obs[:,i]) - return an - - -def ENKF_analysis_22(background,obs,obs_sat,da_const,m_const,sat_operator): - """ - Computes the analysis by individually changing each ensemble member of the forecast/background through the shared ensemble Kalman Gain and the observations. - """ - # define relative weights of observations and background in terms of the Kalman Gain matrix of size - K = Kalman_gain_observation_deviations(background,m_const,da_const,sat_operator) - - # Compute the analysis for each ensemble members - an = np.zeros((m_const["nx"],da_const["nens"])) - bg_obs_deviations,mean = state_to_observation_space(background,m_const,da_const,sat_operator) - bg_obs = (bg_obs_deviations.T+mean).T - for i in range(da_const["nens"]): - obs_pert_h = obs[da_const['obs_loc']] +np.random.normal(0,da_const["pert_std_obs"] ,len(da_const['obs_loc'])) - obs_pert_sat = obs_sat[da_const['obs_loc_sat']]+np.random.normal(0,da_const["pert_std_obs_sat"],len(da_const['obs_loc_sat'])) - obs_pert = np.hstack([obs_pert_h,obs_pert_sat]) - an[:,i] = background[:,i] + np.dot(K,obs_pert-bg_obs[:,i]) - return an - - - -def get_spread_and_rmse(states,da_const,m_const): - """ - computes RMSE over space and time respectively, averaged over all experiments for the background ensmeble mean and analysis ensemble mean. - Structure of the output is as follows: rmse['dim']['quan'] where dim = space or time, quan = bg or an, bf will also be calculated when available - - Returns spread and rmse over time and space averaged over all experiments. So increasing the number of experiments acts smooths out the time errors. - """ - - - n = m_const["nx"] - ncyc = da_const["ncyc"]+1 - nexp = da_const["nexp"] - nens = da_const["nens"] - time = np.arange(0,ncyc) - - quantities = ['an','bg'] - if 'bf' in states[0].keys(): quantities.append('bf') - tr_matrix = np.zeros([nexp,ncyc,n]) - for j in range(nexp): - tr_matrix[j,:,:] = np.array(states[j]["truth"][:]) - rmse = {} - spread = {} - - - for dim in ['time','space']: # Choose dim to average results - rmse[dim]={} - spread[dim]={} - for quan in quantities: # Loop over fields - - - rmse[dim][quan]={} - spread[dim][quan] = {} - rmse[dim][quan]['mean']=0. - spread[dim][quan]['mean']=0. - - for j in range(nexp): # Loop over experiments - ens_matrix = np.array(states[j][quan][:]) - if dim == "space": # Average over time to get one value per gridpoint - rmse[dim][quan][j] = np.zeros(n) - spread[dim][quan][j] = np.zeros(n) - for x in range(n): # Loop DA cycles - rmse[dim][quan][j][x] = np.nanmean(L2norm(ens_matrix[:,x,:].T-tr_matrix[j,:,x])) - spread[dim][quan][j][x] = np.nanmean(np.std(ens_matrix[:,x,:], axis=1, ddof=1)) - - if dim == "time": # Average over space so that one value remains per timestep - rmse[dim][quan][j] = np.zeros(ncyc) - spread[dim][quan][j] = np.zeros(ncyc) - for t in time: # Loop DA cycles - rmse[dim][quan][j][t] = np.mean(L2norm(ens_matrix[t,:,:].T-tr_matrix[j,t,:])) - spread[dim][quan][j][t] = np.mean(np.std(ens_matrix[t,:,:], axis=1, ddof=1)) - rmse[dim][quan]['mean'] = rmse[dim][quan]['mean'] + rmse[dim][quan][j]/float(nexp) - spread[dim][quan]['mean'] = spread[dim][quan]['mean'] + spread[dim][quan][j] / float(nexp) - - rmse[dim][quan] = rmse[dim][quan]['mean'] - spread[dim][quan] = spread[dim][quan]['mean'] - return rmse, spread - - -def L2norm(error): - """ - Computes the l2 norm over the first dimension of the input array - """ - return np.sqrt(np.average(error**2,axis=0)) - - - -def predict_blind(background,states,da_const,m_const,j): - """ - Runs the background ensemble forward in time using the model to predict the truth at the next assimilation step. - This is than saved as 'bf', for blind forecast. - """ - bf = np.zeros((m_const["nx"],da_const["nens"])) - for i in range(da_const["nens"]): - if da_const["fixed_seed"]==True: np.random.seed(i+j*da_const["nens"]) - u_ens = np.random.normal(m_const["u_ref"],da_const["u_std_ens"]) - dhdt_ens = np.random.normal(m_const["dhdt_ref"],da_const["dhdt_std_ens"]) - bf[:,i] = linear_advection_model(background[:,i],u_ens,dhdt_ens,m_const["dx"],da_const["dt"],da_const["nt"]) - - #bg[:,i] = linear_advection_model(analysis[:,i],da_const["dt"],da_const["std_model"],m_const["C"],m_const["a"]) - states["bf"] = states["bf"]+[bf] - return bf, states - - - -def add_blind_forecast(states,m_const,da_const): - """ - Takes a states dictionary created by run_linear_advection_EnKF and adds a 'bf' field which stands for blind forecast, also known as free forecast. - This blind forecast is generated directly from the background ensemble, without assimilating first. - Only makes sense if the randomized errors are carefully generated from the same seed, otherwise the model errors used to make the background ensemble will change when creating the blind forecast. - - This feels dangerously close to philipp making a custom solution for everything himself again. - """ - """ - Loop over experiments - """ - for j in range(da_const["nexp"]): - - #initializing new dictionary field, this is where things can easily go wrong, so pay attention - states[j]['bf'] = [] - #add two first initial nan states, which we take from the initial forecast field - states[j]['bf'].append(states[j]['bg'][0]) - states[j]['bf'].append(states[j]['bg'][0]) - - """ - START blind forecasting - """ - for t in range(2,da_const["ncyc"]+1): - """ - Predict - """ - #print('blind forecast time',t) - # Predict the state at the next assimilation step by running the analysis forward in time - blind_forecast, states[j] = predict_blind(states[j]['bg'][t-1],states[j],da_const,m_const,j) - - """ - END blind forecasting - """ - """ - end loop over experiments - """ - return states - -def sum_mid_tri(x): - """ - Default response function, is a simple sum over the middle of the state vector - """ - nx = len(x) - idx_str = int(nx/3.) - idx_end = int(2*nx/3.) - return np.sum(x[idx_str:idx_end]) - -def sum_mid_tri_abs(x): - """ - Is a sum over the absolute values over the middle of the state - """ - nx = len(x) - idx_str = int(nx/3.) - idx_end = int(2*nx/3.) - x_mid = x[idx_str:idx_end] - return np.sum(np.abs(x_mid)) - - -def mid_ma(x): - """ - mean absolute values over the middle - """ - nx = len(x) - idx_str = int(nx/3.) - idx_end = int(2*nx/3.) - x_mid = x[idx_str:idx_end] - return np.sum(np.abs(x_mid))/np.float(len(x_mid)) - -def mid_rms(x): - """ - root mean square ober mid - """ - nx = len(x) - idx_str = int(nx/3.) - idx_end = int(2*nx/3.) - x_mid = x[idx_str:idx_end] - ms = np.sum(np.power(x_mid,2.))/np.float(len(x_mid)) - return np.sqrt(ms) - -def randomized_obs_loc(n_obs,start=0,end=100,seed=0): - """randomly assignes n_obs within the window given""" - np.random.seed(seed) - randomized_obs_loc = np.random.choice(np.arange(start,end), n_obs, replace=False) - randomized_obs_loc.sort() - return randomized_obs_loc - -def add_response(states,func_J=sum_mid_tri): - """ - Goes through the analysis, background, truth, and blind forecast if it exists and applies the given response function to the ensemble members. - Isn't pretty, could be made more elegantly to loop over the given variables instead of hard coding them. - Also poorly handles that there are differing number of bg, an, and bf fields. - Also so far doesn't allow computing the truth as a response function, as only the individual ensemble members are passed to func_J with no other information - - ToDo: - - Allow using more diverse func_Js - - tidy up, is a mess - """ - nexp = len(states) - ncyc = len(states[0]["bg"]) - nx = states[0]["bg"][0][:,0].shape[0] - nens = states[0]["bg"][0][0,:].shape[0] - - #Poor way of checking if a blind forecast also exists - bf_flag = 0 - if 'bf' in states[0]: bf_flag = 1 - - for e in range(nexp): - states[e]["response"]={} - - #Initialize dictionary fields, not the cleanest solution, but fuck it - c = 0 - - an_response = np.zeros(nens) - bg_response = np.zeros(nens) - if bf_flag: bf_response = np.zeros(nens) - for n in range(nens): - an_response[n] = func_J(states[e]["an"][0][:,n]) - bg_response[n] = func_J(states[e]["bg"][0][:,n]) - if bf_flag: bf_response[n] = func_J(states[e]["bf"][0][:,n]) - - - states[e]["response"]["an"] =[an_response] - states[e]["response"]["bg"] =[bg_response] - states[e]["response"]["truth"]=[func_J(states[e]["truth"][0])] - - if bf_flag : states[e]["response"]["bf"] =[bf_response] - - - for c in range(1,ncyc): - an_response = np.zeros(nens) - bg_response = np.zeros(nens) - bf_response = np.zeros(nens) - for n in range(nens): - an_response[n] = func_J(states[e]["an"][c][:,n]) - bg_response[n] = func_J(states[e]["bg"][c][:,n]) - if bf_flag: bf_response[n] = func_J(states[e]["bf"][c][:,n]) - - states[e]["response"]["bg"] =states[e]["response"]["bg"]+[bg_response] - if bf_flag: states[e]["response"]["bf"] =states[e]["response"]["bf"]+[bf_response] - - states[e]["response"]["an"] =states[e]["response"]["an"]+[an_response] - states[e]["response"]["truth"]=states[e]["response"]["truth"]+[func_J(states[e]["truth"][c])] - - - - return states - -def var_reduction_estimate(states,m_const,da_const,j=0,obs = [],ncyc=0,n_start = 10): - """ - just loops over a full experiment - Calculates an estimated var reduction of the response fucntion for all observations, as well as for each observation individually - if a sepcific obs list is applied, that will be used instead of all observations - - following naming of Hakim 2020 - - """ - - #For now I am not worried about being efficient - #j=0 - #t=3 - nobs = len(da_const["obs_loc"]) - if obs == []: - obs = np.arange(nobs) - - - nens = da_const["nens"] - if ncyc ==0: ncyc = len(states[j]['response']['bf']) - - - var_reduction_total = [] - var_reduction_individual = [] - real_reduction = [] - - for t in range(n_start,ncyc-1): - - R = da_const["used_std_obs"]**2*np.identity(len(obs)) # Observation error corvariance matrix - H = np.identity(m_const["nx"])[da_const["obs_loc"][obs],:] - - x = states[j]['bg'][t][:,:] - dx = x.T-np.mean(x,axis=1) - dx = dx.T - A = np.dot(dx,dx.T)/(dx.shape[1]-1) - - J= states[j]["response"]['bf'][t+1][:] - dJ = J-np.mean(J) - - HAHt = np.dot(H,np.dot(A,H.T)) - HAHtRinv= np.linalg.inv(HAHt+R) - dJHdxt = np.dot(dJ,np.dot(H,dx).T)/(nens-1) - - vr_total = -np.dot(dJHdxt,np.dot(HAHtRinv,dJHdxt)) - var_reduction_total.append(vr_total) - - #And now the loop for the indivudual reductions - vr_individual = np.zeros(nobs) - for o in range(nobs): - R = da_const["used_std_obs"]**2*np.identity(1) # Observation error corvariance matrix - H = np.identity(m_const["nx"])[da_const["obs_loc"][o],:] - HAHt = np.dot(H,np.dot(A,H.T)) - HAHtRinv= np.linalg.inv(HAHt+R) - dJHdxt = np.dot(dJ,np.dot(H,dx).T)/(nens-1) - vr_individual[o] = - np.dot(dJHdxt,np.dot(HAHtRinv,dJHdxt)) - var_reduction_individual.append(vr_individual) - - J_f=states[j]["response"]['bg'][t+1][:] - dJ_f = J_f-np.mean(J_f) - - real_reduction.append(np.var(dJ_f) - np.var(dJ)) - return var_reduction_total,var_reduction_individual,real_reduction - -def var_reduction_estimate_iterative(states,m_const,da_const,j=0,dJ_update_flag=0): - #from scipy.linalg import sqrtm - - """ - Iteratively goes through all observations, estimates the impact on the response function, updates dx and dJ, and continues on. - Currently starts with the location with the largest estimated impact, and then goes to the next highest, and so on. But the ordering should be unimportant. - It return the total variance reduction estimate for all the individual observations. - - Following naming of Hakim 2020. - - It was a bit unclear in the paper, but we decided that we would update dx and dJ simultaneously by bringing dJ into the state vector. This was also confirmed by Hakim via email. - This results in reduction estimates which are pretty much identical down to machine precision with those reached by including all observations at once. - - Is coded very inefficiently, does not make benefit of only looking at the problem in observation space "reduced model space". - Currently is not tested for inputing multiple observations at once. - - Output: - Individual variance reduction per each observation - - Options, dJ_update_flag - 0: Hakim version, updates dJ using "appended state" approach - 1: Modified Hakim. Updates dJ, but then scales dJ to perfectly match the estimated variance reduction perfectly. Should be a very small tweak - 2: Scales dJ to match the reduced variance estimate. Should be extremely cheap and robust, but totally neglects covariance information. - 2: Doesn't update dJ at all, only used to check with what Tanya did - - - """ - - #For now I am not worried about being efficient - nobs = len(da_const["obs_loc"]) - #if obs == []: - obs = np.arange(nobs) - - j=0 - - nens = da_const["nens"] - ncyc = len(states[j]['response']['bf']) - - - var_reduction_total = [] - var_reduction_individual = [] - observation_ranking = [] - real_reduction = [] - - #R is always the same - R = da_const["used_std_obs"]**2*np.identity(1) # Observation error corvariance matrix - - for t in range(1,ncyc-1): - #for t in [10]: - - - x = states[j]['bg'][t][:,:] - dx = x.T-np.mean(x,axis=1) - dx = dx.T - A = np.dot(dx,dx.T)/(dx.shape[1]-1) - - J= states[j]["response"]['bf'][t+1][:] - dJ = J-np.mean(J) - - - obs_ordered = [] - obs_remain = list(obs) - vr_individual = np.zeros(nobs) - for o in range(nobs): #loop over the number of observations - #for o in range(1): #loop over the number of observations - - vr_ind_tmp = np.zeros(nobs) - for oo in obs_remain: #loop over all observation locastions which have not been used yet used - - H = np.identity(m_const["nx"])[da_const["obs_loc"][oo],:] - HAHt = np.dot(H,np.dot(A,H.T)) - HAHtRinv= np.linalg.inv(HAHt+R) - dJHdxt = np.dot(dJ,np.dot(H,dx).T)/(nens-1) - vr_ind_tmp[oo] = - np.dot(dJHdxt,np.dot(HAHtRinv,dJHdxt)) - #print(vr_ind_tmp) - - ind_min = np.where(vr_ind_tmp==np.min(vr_ind_tmp))[0][0] - #print(ind_min) - vr_individual[ind_min] = vr_ind_tmp[ind_min] - obs_remain.remove(ind_min) - - H = np.identity(m_const["nx"])[da_const["obs_loc"][ind_min],:] - - #New we add in Tanyas reduction - E = np.matmul(A,H.T) - E = np.matmul(H,E) - E = E + R - alpha = 1./(1.+np.sqrt(R/E)) - - - - if dJ_update_flag==0 or dJ_update_flag==1: - - #Moded Tanyas code because I had en error. - #Turned out it was a matrix multiplication issue I solved by switching the final matmul with an np.outer. It has the right dimension, but I fear my version will only work if its single point measurements. - #Now we include dJ into dx to include it in the update - dxJ = np.vstack([dx,dJ]) - AxJ = np.dot(dxJ,dxJ.T)/(dxJ.shape[1]-1) - HxJ = np.hstack([H,np.array(0)]) - HAHt = np.dot(HxJ,np.dot(AxJ,HxJ.T)) - HAHtRinv= np.linalg.inv(HAHt+R) - - - K = np.dot(AxJ,HxJ.T)*HAHtRinv - - - # Update state vector - HdxJ = np.matmul(HxJ,dxJ) - dxJ = dxJ - alpha*np.outer(K,HdxJ) - - dx = dxJ[:-1,:] - old_var_dJ = np.var(dJ,ddof=1) - dJ = dxJ[-1,:] - new_var_dJ = np.var(dJ,ddof=1) - - if dJ_update_flag==1: - var_scaling = (old_var_dJ+ vr_ind_tmp[ind_min])/new_var_dJ - dJ=np.sqrt(var_scaling)*dJ - #print(old_var_dJ+vr_ind_tmp[ind_min],new_var_dJ,np.var(dJ)) - - if dJ_update_flag==2 or dJ_update_flag==3: - - if dJ_update_flag==2: - var_scaling=(np.var(dJ,ddof=1)+vr_ind_tmp[ind_min])/np.var(dJ,ddof=1) - #New dJ - dJ=np.sqrt(var_scaling)*dJ - - #Update dx - #Moded Tanyas code because I had en error. - #Turned out it was a matrix multiplication issue I solved by switching the final matmul with an np.outer. It has the right dimension, but I fear my version will only work if its single point measurements. - HAHt = np.dot(H,np.dot(A,H.T)) - HAHtRinv= np.linalg.inv(HAHt+R) - K = np.dot(A,H.T)*HAHtRinv - - # Update state vector - Hdx = np.matmul(H,dx) - dx = dx - alpha*np.outer(K,Hdx) - - #Recalculating A makes things worse if you don't rescale dJ as well - A = np.dot(dx,dx.T)/(dx.shape[1]-1) - - var_reduction_individual.append(vr_individual) - return var_reduction_individual - - - -def loc_matrix(da_const,m_const): - """ - Creates the localization matrix, using either a gaussian or gaspari cohn function - """ - C = np.zeros([m_const['nx'],m_const['nx']]) - # I cheat a bit by mirroring the functions to accomodate for the repeating boundary conditions, but this should only lead to a maximum dx/2 error. - if da_const['loc_type']=='gaussian': - C[:,0] = gaussian_initial_condition(m_const["x_grid"],da_const["loc_length"]) - if da_const['loc_type']=='gaspari_cohn': - C[:,0] = gaspari_cohn(m_const["x_grid"],da_const["loc_length"]) - - for i in range(1,m_const['nx']): - C[:,i] = np.hstack([C[-1,i-1],C[:-1,i-1]]) - return C - -def gaspari_cohn(x,loc_length): - """Gaspari-Cohn function intended to be applied to the x grid, which mirrors the matrix to account for periodic boundary domains.""" - - ra = x/loc_length - gp = np.zeros_like(ra) - i=np.where(ra<=1.)[0] - gp[i]=-0.25*ra[i]**5+0.5*ra[i]**4+0.625*ra[i]**3-5./3.*ra[i]**2+1. - i=np.where((ra>1.)*(ra<=2.))[0] - gp[i]=1./12.*ra[i]**5-0.5*ra[i]**4+0.625*ra[i]**3+5./3.*ra[i]**2-5.*ra[i]+4.-2./3./ra[i] - - #Now we mirror things to zero for the periodic boundary domain - half_idx = int(ra.shape[0]/2) - gp[-half_idx:] = gp[half_idx-1::-1] - return gp - -def gaspari_cohn_non_mirrored(x,loc_length): - """Gaspari-Cohn function, with no mirroring.""" - - ra = np.abs(x)/loc_length - - gp = np.zeros_like(ra) - i=np.where(ra<=1.)[0] - gp[i]=-0.25*ra[i]**5+0.5*ra[i]**4+0.625*ra[i]**3-5./3.*ra[i]**2+1. - i=np.where((ra>1.)*(ra<=2.))[0] - gp[i]=1./12.*ra[i]**5-0.5*ra[i]**4+0.625*ra[i]**3+5./3.*ra[i]**2-5.*ra[i]+4.-2./3./ra[i] - - return gp - -# def single_step_analysis_forecast(state,da_const,m_const,j,t,seed_add=1): -# """ -# This function takes a given background state, to which is computes an analysis for provided observations. A blind forecast is computed from the background, and a forecast is computed from the analysis. - -# The main purpose of this approach of having a little isolated timestep is to enable applying different test to the same backgound state. - -# """ - -# """ -# constant matrices that follow from previously defined constants -# """ -# H = np.identity(m_const["nx"])[da_const["obs_loc"],:] # Observation operator -# R = da_const["used_std_obs"]**2*np.identity(len(da_const["obs_loc"])) # Observation error corvariance matrix - - -# #Construct localization matrix C if loc!=None -# if da_const["loc"]: -# C = loc_matrix(da_const,m_const) -# else: -# C=np.ones([m_const["nx"],m_const["nx"]]) - -# """ -# Generate obs -# """ -# #make obs by adding some noise, a fixed seed should change over time so that the differences are not always the same for each measurement location -# #if da_const["fixed_seed"]==True: -# #Changed to the following -# np.random.seed((j+1)*(t+1)*(seed_add+1)) - - -# #changing obs to only exist where observations are to enable having multiple observations at the same space -# #obs = np.zeros(len(da_const['obs_loc'])) -# #for o in range(len(da_const['obs_loc'])): -# # obs[o] = state[j]['truth'][t][0] + np.random.normal(0,da_const["True_std_obs"]) - -# obs = state[j]['truth'][t] + np.random.normal(0,da_const["True_std_obs"],m_const["nx"]) - -# #Generate new truth constants and integrate in time -# if da_const["fixed_seed"]==True: -# np.random.seed(j) -# else: -# np.random.seed((j+1)*(t+1)*(seed_add+1)) - - -# u_truth = np.random.normal(m_const["u_ref"],m_const["u_std_truth"]) -# dhdt_truth = np.random.normal(m_const["dhdt_ref"],m_const["dhdt_std_truth"]) -# truth_forecast = linear_advection_model(state[j]['truth'][t],u_truth,dhdt_truth,m_const["dx"],da_const["dt"],da_const["nt"]) - - -# """ -# EnKF -# """ -# if da_const['method'] == 'EnKF': -# # Compute the background error covariance matrix -# P = np.cov(state[j]['bg'][t])*C - -# # define relative weights of observations and background in terms of the Kalman Gain matrix of size -# K = KalmanGain(P, R, H) - -# # Compute the analysis for each ensemble members -# an = np.zeros((m_const["nx"],da_const["nens"])) -# for i in range(da_const["nens"]): -# an[:,i] = get_analysis(state[j]['bg'][t][:,i],obs,K,H,da_const) -# """ -# LETKF -# """ -# if da_const['method'] == 'LETKF': -# an,bla = LETKF_analysis(state[j]['bg'][t],state[j]['obs'][t],m_const,da_const) - - -# """ -# Predict blind forecast and forecast -# """ -# bf = np.zeros((m_const["nx"],da_const["nens"])) -# fc = np.zeros((m_const["nx"],da_const["nens"])) -# for i in range(da_const["nens"]): -# if da_const["fixed_seed"]==True: np.random.seed(i+j*da_const["nens"]) -# u_ens = np.random.normal(m_const["u_ref"],da_const["u_std_ens"]) -# dhdt_ens = np.random.normal(m_const["dhdt_ref"],da_const["dhdt_std_ens"]) -# bf[:,i] = linear_advection_model(state[j]['bg'][t][:,i],u_ens,dhdt_ens,m_const["dx"],da_const["dt"],da_const["nt"]) -# fc[:,i] = linear_advection_model(an[:,i],u_ens,dhdt_ens,m_const["dx"],da_const["dt"],da_const["nt"]) - - -# """ -# create dictionary to store this single step -# """ -# quad_state = {} -# quad_state['bg'] = state[j]['bg'][t] -# quad_state['an'] = an -# quad_state['bf'] = bf -# quad_state['fc'] = fc -# quad_state['tr_fc'] = truth_forecast -# quad_state['tr_bg'] = state[j]['truth'][t] -# quad_state['obs'] = obs -# return quad_state - -def LETKF_analysis(bg,obs,m_const,da_const): - """ - Follows the recipe and notation of Hunt 2007, e.g. what is normally called B is now P_a, and _ol refers to over line, so the mean. - P_tilde_a refers to B but in ensemble space. - The application when not localized is pretty straight forward. - The localized version is coded maximully inefficiently. If needed two available options to speed it up are parallelization and batch processing. - Currently also only uses gaspari-cohn. - """ - from scipy.linalg import sqrtm #Needed to calculate the matrix square root. Can lead to complex values due to numrical noise, which I deal with by only using real values. - - - L = da_const['nens'] - H = np.identity(m_const["nx"])[da_const["obs_loc"],:] # Observation operator - R = da_const["used_std_obs"]**2*np.identity(len(da_const["obs_loc"])) - x_b = bg - x_ol_b = np.mean(x_b,axis=1) - X_b = x_b.T-x_ol_b - X_b = X_b.T - - y_obs = obs[da_const["obs_loc"]] - y_b = np.dot(H,x_b) - delta_y = y_obs-np.mean(y_b,axis=1) - Y_b = y_b.T-np.mean(y_b,axis=1) - Y_b = Y_b.T - - if da_const['loc']==False: - """ Now that all the variables are set, we start by computing the covariance matrix in ensemble state """ - YRY = np.dot(Y_b.T,np.dot(np.linalg.inv(R),Y_b)) - P_tilde_a = np.linalg.inv((L-1)*np.identity(L)+YRY) - - """Next step, computing the enesemble mean analysis via the weighting vector w_ol_a""" - w_ol_a = np.dot(P_tilde_a,np.dot(Y_b.T,np.dot(np.linalg.inv(R),delta_y))) - x_ol_a = x_ol_b+np.dot(X_b,w_ol_a) - - """We now get the ensemlbe by calculating the weighting matrix through a square root of the error covariance matrix, and adding the mean values to the ensemble deviations""" - W_a = np.real(sqrtm((L-1)*P_tilde_a)) - w_a = W_a+w_ol_a - - x_a = np.dot(X_b,w_a).T+ x_ol_a - x_a = x_a.T - - else: - """ - Localization baby! - Works by recalculating the whole process for every single grid point. For each point the inverse observation error is multiplied with the gaspari-cohn function. - Only implemented for diagonal R. - Accordingly, observations outside 2*the loc radius have no impact. - As mentioned, this is not computationally efficient, at all. While it does skip grid points that are completely unaffected by observations, - localization still increases the cost linearly with grid size. - - """ - x_grid_neg = -m_const['x_grid'][::-1]-m_const['dx'] - x_grid_ext = m_const['x_grid']+(m_const['dx']*m_const['nx']) - N = m_const['nx'] - x_a_loc = np.zeros((N,L)) - x_ol_a_loc = np.zeros((N)) - - for g in range(N): - - # first step is to get the disance of the current grid point to the observations. - # I know this isn't the quickest way, but when I coded it I was not feeling really smart and it works. - dist_reg = np.abs(m_const['x_grid'][da_const['obs_loc']]-m_const['x_grid'][g]) - dist_neg = m_const['x_grid'][g]-x_grid_neg[da_const['obs_loc']] - dist_ext = x_grid_ext[da_const['obs_loc']]-m_const['x_grid'][g] - dist = np.minimum(dist_reg,dist_ext)#apparently minimum doesn't like 3 variables ,dist_neg) - dist = np.minimum(dist,dist_neg) - - #And now we calculate the gaspari cohn weighting and apply it to inverse R - gc_loc = gaspari_cohn_non_mirrored(dist,da_const['loc_length']) - if np.max(gc_loc) == 0.: - #If no observations are within the double gc radius no need to bother computing things - x_ol_a_loc[g] = x_ol_b[g] - x_a_loc[g,:] = x_b[g,:] - else: - R_inv = np.linalg.inv(R) * np.diag(gc_loc) - - YRY = np.dot(Y_b.T,np.dot(R_inv,Y_b)) - """ Now that all the variables are set, we start by computing the covariance matrix in ensemble state """ - P_tilde_a = np.linalg.inv((L-1)*np.identity(L)+YRY) - - """Next step, computing the enesemble mean analysis via the weighting vector w_ol_a""" - w_ol_a = np.dot(P_tilde_a,np.dot(Y_b.T,np.dot(R_inv,delta_y))) - x_ol_a = x_ol_b+np.dot(X_b,w_ol_a) - W_a = np.real(sqrtm((L-1.)*P_tilde_a)) - w_a = W_a+w_ol_a - x_a = np.dot(X_b,w_a).T+ x_ol_a - x_a = x_a.T - - x_ol_a_loc[g] = x_ol_a[g] - x_a_loc[g,:] = x_a[g,:] - x_a = x_a_loc - x_ol_a = x_ol_a_loc - - - - return x_a,x_ol_a - - - - -def L2_regularized_inversion(A, b, alpha_init=1,alpha=None,mismatch_threshold=0.05): - """Instead of solving for Ax=b, which isn't possible if A is not invertible, the regularization instead minimizes ||Ax-b||^2 + || alpha x ||^2. - The solution is unique and well defined: x = (AA.T + alpha*alpha I )^-1 A.T b. - - While this works fine with np.linalg.inv, I use .solve instead because it is roughly a factor 3 quicker. - - I tried to find a easy way to link the starting alpha to the model space or ensemble size, but after not finding anything easy I just start with a prescribed value - which is 0.1 by default. It is checked that the mismatch between sum(Ax-b)/sum(b) does not exceed the mismatch_threshold, and if it does alpha is reduced by a factor of 2 until it does. - The mismatch_threshold is given in percent, and is used to check if ||(Ax-b)||/||b|| falls below the theshold. - - alternative would be to try to use the things presented by Shu-Chih Yang's talk at the ISDA-online - kappa_req = 10000. - n = numbers of non-diagonal componentts of correlation matrix C - r = average of non-diagonal componentts of correlation matrix C - lamda_max = 1+(n_cols-1)*r - alpha = lamda_max/kappa_req - print(kappa_req,r,lamda_max,alpha) - - - """ - n_cols = A.shape[1] - I = np.identity(n_cols) - #n_ens = b.size - #this is just a guess, but alpha should decrease with size, is not applied if a value other than 1 is applied - if alpha == None: - #alpha= 1./np.sqrt(n_cols) - alpha= alpha_init #1#n_cols/n_ens - #x = np.linalg.inv(A.T.dot(A) + alpha**2 *I).dot(A.T).dot(b) - x = np.linalg.solve((A.T.dot(A) + alpha**2 *I),(A.T).dot(b))#,rcond=-1) - while np.sum(np.abs(A.dot(x)-b))/np.sum(np.abs(b))> mismatch_threshold: - alpha = alpha/2. - x = np.linalg.solve((A.T.dot(A) + alpha**2 *I),(A.T).dot(b))#,rcond=-1) - #x = np.linalg.inv(A.T.dot(A) + alpha**2 *I).dot(A.T).dot(b) - #print('reducing regularization:',alpha,np.sum(np.abs(A.dot(x)-b))/np.sum(np.abs(b))) - else: - x = np.linalg.solve((A.T.dot(A) + alpha**2 *I),(A.T).dot(b))#,rcond=-1) - #x = np.linalg.inv(A.T.dot(A) + alpha**2 *I).dot(A.T).dot(b) - - - return x - - - -def single_step_analysis_forecast_v2(background,truth,da_const,m_const,model_seed=0,obs_seed=0): - """ - The idea is that this should be merged into single_step_analysis_forecast, for a uniform processing chain for the paper. So all SWM changes will be included in flags. - - What is a bit annoying is that the original was built around the states dictionary, I am not really a fan of the states setup, will switch to background and truth matrixes. - - for now the perturbed observations in the EnKF for the SWM are the same as the actual errors. - """ - - """ - constant matrices that follow from previously defined constants - """ - if m_const['model'] == 'LA': - H = np.identity(m_const["nx"])[da_const["obs_loc"],:] # Observation operator - R = da_const["used_std_obs"]**2*np.identity(len(da_const["obs_loc"])) # Observation error corvariance matrix - if m_const['model'] == 'SWM': - H = np.identity(m_const["nx"]*3)[da_const["obs_loc"],:] # Observation operator - obs_error_vec = np.ones(len(da_const["obs_loc"]))*da_const['used_h_std_obs'] - obs_error_vec[obs_error_vec<m_const['nx']] = da_const['used_u_std_obs'] - obs_error_vec[obs_error_vec>2*m_const['nx']] = da_const['used_r_std_obs'] - R = np.diag(obs_error_vec**2) - - - #Construct localization matrix C if loc!=None - if da_const["loc"]: - C = loc_matrix(da_const,m_const) - else: - C=np.ones([m_const["nx"],m_const["nx"]]) - if m_const['model'] == 'SWM': - C = np.hstack([C,C,C]) - C = np.vstack([C,C,C]) - - """ - Generate obs - """ - #make obs by adding some noise, a fixed seed should change over time so that the differences are not always the same for each measurement location - np.random.seed(obs_seed) - - if m_const['model']=='LA': obs = truth + np.random.normal(0,da_const["True_std_obs"],m_const["nx"]) - if m_const['model']=='SWM': - u_obs_noise =np.random.normal(0,da_const["True_u_std_obs"],m_const["nx"]) - h_obs_noise =np.random.normal(0,da_const["True_h_std_obs"],m_const["nx"]) - r_obs_noise =np.random.normal(0,da_const["True_r_std_obs"],m_const["nx"]) - obs = truth + np.hstack([u_obs_noise,h_obs_noise,r_obs_noise]) - - #Generate new truth constants and integrate in time - if m_const['model']=='LA': - if da_const["fixed_seed"]==True and model_seed==0: np.random.seed(model_seed) - if da_const["fixed_seed"]==False and model_seed!=0 : np.random.seed(model_seed) - - u_truth = np.random.normal(m_const["u_ref"],m_const["u_std_truth"]) - dhdt_truth = np.random.normal(m_const["dhdt_ref"],m_const["dhdt_std_truth"]) - truth_forecast = linear_advection_model(truth,u_truth,dhdt_truth,m_const["dx"],da_const["dt"],da_const["nt"]) - - if m_const['model']=='SWM': - #I assume we are going to have to add an empty second dimension here - truth_double = np.vstack([truth,truth]).T - truth_double_forecast = shallow_water(truth_double,m_const) - truth_forecast = truth_double_forecast[:,0] - - - """ - EnKF - """ - if da_const['method'] == 'EnKF': - # Compute the background error covariance matrix - P = np.cov(background)*C - - # define relative weights of observations and background in terms of the Kalman Gain matrix of size - K = KalmanGain(P, R, H) - - # Compute the analysis for each ensemble members - an = np.zeros_like(background) - if m_const['model'] == 'LA': obs_pert_vec = np.ones(len(obs))*da_const['pert_std_obs'] - if m_const['model'] == 'SWM': - obs_pert_vec = np.hstack([np.ones(m_const['nx'])*da_const['pert_u_std_obs'], - np.ones(m_const['nx'])*da_const['pert_h_std_obs'], - np.ones(m_const['nx'])*da_const['pert_r_std_obs']]) - for i in range(da_const["nens"]): - an[:,i] = get_analysis_v2(background[:,i],obs,K,H,obs_pert_vec) - """ - LETKF - """ - if da_const['method'] == 'LETKF': - an,bla = LETKF_analysis(background,obs,m_const,da_const) - - - """ - Predict blind forecast and forecast - """ - bf = np.zeros_like(background) - fc = np.zeros_like(background) - - if m_const['model'] == 'LA': - for i in range(da_const["nens"]): - if da_const["fixed_seed"]==True: np.random.seed(i+model_seed*da_const["nens"]) - u_ens = np.random.normal(m_const["u_ref"],da_const["u_std_ens"]) - dhdt_ens = np.random.normal(m_const["dhdt_ref"],da_const["dhdt_std_ens"]) - bf[:,i] = linear_advection_model(background[:,i],u_ens,dhdt_ens,m_const["dx"],da_const["dt"],da_const["nt"]) - fc[:,i] = linear_advection_model(an[:,i],u_ens,dhdt_ens,m_const["dx"],da_const["dt"],da_const["nt"]) - if m_const['model'] == 'SWM': - bf = shallow_water(background,m_const) - fc = shallow_water(an,m_const) - - - """ - create dictionary to store this single step - """ - quad_state = {} - quad_state['bg'] = background - quad_state['an'] = an - quad_state['bf'] = bf - quad_state['fc'] = fc - quad_state['tr_fc'] = truth_forecast - quad_state['tr_bg'] = truth - quad_state['obs'] = obs - return quad_state - - - -def vr_reloaded(background,truth,m_const,da_const,func_J=sum_mid_tri, - reduc = 1,reg_flag=1, - quad_state = None,dJdx_inv=None,alpha=None,mismatch_threshold=0.1, - iterative_flag=0,explicit_sens_flag = 1,exp_num=0,obs_seed=0,model_seed=0): - - """ - New version takes only the background and truth, then caculates the quad, finaly calculates the response function and the variance reduction. - - The quad can be supplied to save time, which makes sense when you are chaning the response function. - Also is the way to go if you don't need the real reduction value, only the estimate. - - Should also return the dJ values of all 4 ensembles (analysis, background, forecast, blind forecast) - - exp_num x(experiment number j) needs to passed on to the forecast of LA somehow, because it determines the random model error of the ensembles - This will become a major part of the paper, and will see lots of refining. Important options are: - - not implemented: - - state model reduction - - response functions planned: - - mid tri, sum over middle of domain. - - sum over first and last third - - Triangle sum - - right/left rain amount. - - Speed up options: - - If the same forecast can be recycled, the quad can be calculated once and than passed on. - - If the sensitivity can be recycled, that can be calculated once and then reused. - - implemented: - -iteratively and all at once - -explicit vs implicit - -reduced model spacing by subsampling the grid. Reduc is the spacing, eg reduc 3 means every third grid point is used. - should find the nearest reduced model grid point for each observation. - -reg_flag added to use L2 regularization - """ - if iterative_flag ==0: from scipy.linalg import sqrtm - ########################################################################### - #First, need we calculate the quad (analysis, forecast, blind forecast) - #A precomputed quad_state can be supplied to quickly use different response functions - ########################################################################### - if type(quad_state)== type(None): - quad_state = single_step_analysis_forecast_v2(background,truth,da_const,m_const,obs_seed=obs_seed,model_seed=model_seed) - - ########################################################################### - # Next we need the response functions. - # We only really need the forecast and blind forecast, but for fun I'll calculate all for now - ########################################################################### - - #For now I am not worried about being efficient - nobs = len(da_const["obs_loc"]) - obs = np.arange(nobs) - nens = da_const["nens"] - nstate = len(truth) - - bf_response = np.zeros(nens) - fc_response = np.zeros(nens) - an_response = np.zeros(nens) - bg_response = np.zeros(nens) - for n in range(da_const["nens"]): - bf_response[n] = func_J(quad_state["bf"][:,n]) - fc_response[n] = func_J(quad_state["fc"][:,n]) - an_response[n] = func_J(quad_state["an"][:,n]) - bg_response[n] = func_J(quad_state["bg"][:,n]) - - J_dict = {} - J_dict['bf'] = bf_response - J_dict['bg'] = bg_response - J_dict['an'] = an_response - J_dict['fc'] = fc_response - J_dict['tr_bg'] = func_J(quad_state['tr_bg']) - J_dict['tr_fc'] = func_J(quad_state['tr_fc']) - - ########################################################################### - # Creating the R,H, and C matrices we will need for the VR estimate - ########################################################################### - - if m_const['model'] == 'LA': - H = np.identity(m_const["nx"])[da_const["obs_loc"],:] # Observation operator - R = da_const["used_std_obs"]**2*np.identity(len(da_const["obs_loc"])) # Observation error corvariance matrix - if m_const['model'] == 'SWM': - H = np.identity(m_const["nx"]*3)[da_const["obs_loc"],:] # Observation operator - obs_error_vec = np.ones(len(da_const["obs_loc"]))*da_const['used_h_std_obs'] - obs_error_vec[obs_error_vec<m_const['nx']] = da_const['used_u_std_obs'] - obs_error_vec[obs_error_vec>2*m_const['nx']] = da_const['used_r_std_obs'] - R = np.diag(obs_error_vec**2) - - if da_const["loc"]: - C = loc_matrix(da_const,m_const) - else: - C=np.ones([m_const["nx"],m_const["nx"]]) - if m_const['model'] == 'SWM': - C = np.hstack([C,C,C]) - C = np.vstack([C,C,C]) - - - - - - - if reduc>1: #Does currently not work for - #Defining reduced model domain/state, and then defining obs location on new reduced grid - #For the SWM model this only really makes sense if nx is a multiple of reduc - reduced_model_domain = np.arange(0,nstate,reduc) - reduced_model_size = len(reduced_model_domain) - reduced_obs_loc = np.zeros(nobs).astype(int) - for o in range(nobs): - reduced_obs_loc[o] = (np.abs(reduced_model_domain-da_const['obs_loc'][o])).argmin() - #print('reduced_model_domain:',reduced_model_domain) - #print('reduced_model_size :',reduced_model_size ) - #print('reduced_obs_loc :',reduced_obs_loc ) - - #Getting the localization matrix in reduced model space - H = np.identity(m_const["nx"])[reduced_model_domain,:] - C = np.dot(H,np.dot(C,H.T)) - x = quad_state['bg'][reduced_model_domain,:] - - else: - x = quad_state['bg'][:,:] - - #x = states[j]['bg'][t][:,:] - dx = x.T-np.mean(x,axis=1) - dx = dx.T - dx_orig = dx+0 - - A = np.dot(dx,dx.T)/(dx.shape[1]-1) - - J = bf_response - dJ = J-np.mean(J) - dJ_orig = J-np.mean(J) - - - ############################################################################################### - # Sensitivity - ############################################################################################### - #Covarianz between reponse function dJ and state ensemble dx - cov_dJdx_vec = np.dot(dJ,dx.T)/(dx.shape[1]-1) - - if explicit_sens_flag==1: - #If a sensitivity is provided it is used instead of calculating it - if type(dJdx_inv) == np.ndarray: - #testing supplied sensitivity - rel_error_sens = np.sum(np.abs(A.dot(dJdx_inv)-cov_dJdx_vec))/np.sum(np.abs(cov_dJdx_vec)) - if rel_error_sens>0.05: - print('using supplied sensitivity has a relative error of:',rel_error_sens) - #Computing the sensitivity, highly recommend using the regularized version before doing so. - else: - if reg_flag == 1: - dJdx_inv = L2_regularized_inversion(A,cov_dJdx_vec,alpha=alpha,mismatch_threshold=mismatch_threshold) - else: - A_inv = np.linalg.pinv(A) - dJdx_inv = np.dot(A_inv,cov_dJdx_vec) - - estimated_J = bf_response + 0. - - - if iterative_flag ==0: - - vr_individual = 0. - - H = np.identity(m_const["nx"])[da_const["obs_loc"],:] # Observation operator - R = da_const["used_std_obs"]**2*np.identity(len(da_const["obs_loc"])) # Observation error corvariance matrix - - - R_obs = R # Observation error corvariance matrix - H_obs = H # Not the cleanest coding I know - - if explicit_sens_flag ==1: - #Tanjas approach of calculating the square root K following Kalman gain, formula (10) Whitaker and Hamil 2002 - E = np.matmul(C*A,H_obs.T) - E = np.matmul(H_obs,E) - E = E + R_obs - Esqrt = sqrtm(E) - #alpha = 1./(1.+np.sqrt(R_obs/E)) - # Kalman gain, formula (10) Whitaker and Hamil 2002 - K1 = np.matmul(C*A,H_obs.T) - K2 = np.linalg.inv(Esqrt) - K2 = K2.T - K3 = np.linalg.inv(Esqrt + sqrtm(R_obs)) - K = np.matmul(K1,K2) - K = np.matmul(K,K3) - - Hdx = np.matmul(H_obs,dx) - dx_prime = dx - np.dot(K,Hdx) - - #estimated J calculated by updating the original dJ - estimated_J = estimated_J -np.dot(dJdx_inv,np.dot(K,Hdx)) - - #Using the cheaper variance calculation instead of going to A-B - new_J = np.dot(dJdx_inv.T,dx_prime) - vr_individual = np.var(new_J,ddof=1)-np.var(np.dot(dJdx_inv.T,dx),ddof=1) - vr_total = vr_individual - #print('all at once:',vr_individual) - dx = dx_prime - - if explicit_sens_flag ==0: - HAHt = np.dot(H_obs,np.dot(C*A,H_obs.T)) - HAHtRinv= np.linalg.inv(HAHt+R_obs) - - dJHdxt = np.dot(dJ,np.dot(H_obs,dx).T)/(nens-1) - vr_individual = -np.dot(dJHdxt,np.dot(HAHtRinv,dJHdxt)) - vr_total = vr_individual - - - if iterative_flag: - vr_individual = np.zeros(nobs) - for o in range(nobs): #loop over each observation individually - - #New A after dx was updated - A = np.cov(dx,ddof=1) - - #Selecting the single R value for this observation - R_obs = R[o,o]*np.identity(1) # Observation error corvariance matrix - H_obs = H[o,:] #Not sure about this :( - #if - ##H_rms = np.identity(nobs)[o,:] - #H_rms = np.identity(reduced_model_size)[reduced_obs_loc[o],:] - ##H = np.identity(m_const["nx"])[da_const["obs_loc"][o],:] -# - ##HAHt = np.dot(H,np.dot(A,H.T)) - ##HAHtRinv= np.linalg.inv(HAHt+R) - ##dJHdxt = np.dot(dJ,np.dot(H,dx).T)/(nens-1) - - #Now we get the change in dx, which uses the localized A matrix and a square root - E = np.matmul(C*A,H_obs.T) - E = np.matmul(H_obs,E) - E = E + R_obs - alpha = 1./(1.+np.sqrt(R_obs/E)) - - - HAHt = np.dot(H_obs,np.dot(C*A,H_obs.T)) - HAHtRinv= np.linalg.inv(HAHt+R_obs) - #print((C*A).shape,H_obs.T.shape,np.dot(C*A,H_obs.T).shape,HAHtRinv.shape) - ## - - if explicit_sens_flag ==1: - - K = np.dot(C*A,H_obs.T)*HAHtRinv - # Update state vector - Hdx = np.matmul(H_obs,dx) - dx_prime = dx - alpha*np.outer(K,Hdx) - - #Update expected J for each ensemble member - estimated_J = estimated_J -np.dot(dJdx_inv,alpha*np.outer(K,Hdx)) - - #A_new = np.dot(dx,dx.T)/(dx.shape[1]-1) - #Now we get the variance estimate using the difference between new and old B - #dSigma = np.matmul(A_new-A,dJdx_inv.T) - #print('wtf: np.sum(np.abs(A_new-A_old)) ',o,np.sum(np.abs(A_new-A))) - #dSigma = np.matmul(dJdx_inv,dSigma) - #vr_individual[o] = dSigma#np.matmul(np.atleast_2d(np.diag(A_new-A)),(dJdx_inv.T**2)) - - vr_individual[o] = np.var(np.dot(dJdx_inv.T,dx_prime),ddof=1)-np.var(np.dot(dJdx_inv.T,dx),ddof=1) - dx = dx_prime - if explicit_sens_flag==0: - dJHdxt = np.dot(dJ,np.dot(H_obs,dx).T)/(nens-1) - vr_individual[o] = -np.dot(dJHdxt,np.dot(HAHtRinv,dJHdxt)) - - #Still needs localization! - - #Now we include dJ into dx to include it in the update - dxJ = np.vstack([dx,dJ]) - AxJ = np.dot(dxJ,dxJ.T)/(dxJ.shape[1]-1) - HxJ = np.hstack([H_obs,np.array(0)]) - CxJ = np.ones([nstate+1,nstate+1]) - CxJ[:nstate,:nstate] = C - HAHt = np.dot(HxJ,np.dot(CxJ*AxJ,HxJ.T)) - HAHtRinv= np.linalg.inv(HAHt+R_obs) - - #print(AxJ.shape,CxJ.shape) - #print(AxJ.shape,HxJ.T.shape,np.dot(AxJ,HxJ.T).shape,HAHtRinv.shape) - K = np.dot(CxJ*AxJ,HxJ.T)*HAHtRinv - - - # Update state vector - HdxJ = np.matmul(HxJ,dxJ) - dxJ = dxJ - alpha*np.outer(K,HdxJ) - - dx = dxJ[:-1,:] - #old_var_dJ = np.var(dJ) - dJ = dxJ[-1,:] - estimated_J = dJ+np.mean(bf_response) - #new_var_dJ = np.var(dJ) - - #Final var reduciton estimate - if explicit_sens_flag==0: vr_total=np.sum(vr_individual) - - if explicit_sens_flag==1: - vr_total=np.var(np.dot(dJdx_inv.T,dx),ddof=1)-np.var(np.dot(dJdx_inv.T,dx_orig),ddof=1) - - #Checking different formulation - - - # - - J_dict['es'] = estimated_J - - J_fc= fc_response - dJ_fc = J_fc-np.mean(J_fc) - real_reduction=np.var(dJ_fc,ddof=1) - np.var(dJ_orig,ddof=1) - - return vr_total,vr_individual,real_reduction,J_dict,dJdx_inv,quad_state,dx - - -def vr_individual_loc(background,truth,m_const,da_const,response_func=1,abs_flag=0, - quad_state = None,exp_num=0, - advect_flag = 0,obs_seed=0,random_samples=0,seed_samples=0,error_u=100,error_u_ens=100): - - """ - Based on vr_reloaded, but adapted to individually localize the covariances of the response functions at each point. - - important parameters: - response_func: controls the response, 1 results in the default sum over the mean, 2 in the mean over the mean - abs_flag: determines if the absolute value of the state vector is used for the response function. - - If advect_flag==1, the localization matrix is advected using the mo_const advection speed which should be close to the ensemble mean. The same advection function used as in the linear advection model. - If advect_flag==2, the localization matrix is advected using the individual ensemble members, so that in the end the localization vector is broadened accoring to ensemble disperstion. - - random_samples=0,seed_samples=0,error_u=100,error_u_ens=100: these all are various ways of adding an error to the advection term. - - - For now only using the all at once method, and it probably won't work for only a single observation. - - The equations should be in the paper draft, but I made quite the mistake by thinking that the A at the end of A'-A = -KHA is also localized. - - Possible speed ups not used so far: - * I could speed this up a bit by only calculating the localized correlations where they will be needed (so where H says they should be), but that is a bit of work to do well so for now I will leave it out. - * Given that we use Gaspari-Cohn localization most of the time, I might be able to save some time by only calculating covariances where C_i is not zero. But probably won't make a difference for toy model - * We currently assign j_i a value at every grid point, even though we skip calculations where response_s = 0. It would be more memory efficient to allocate J_i in a restricted area, but again I think this won't matter in a toymodel - * Possibly the biggest speed up would be to avoid the loop when calculating the response functions, which I haven't bothered with yet. - - - - """ - ########################################################################## - #We begin by computing the vecors which determine the response function - ########################################################################### - response_s = np.zeros(m_const['nx']) - response_c = np.ones(m_const['nx']) - nx = m_const['nx'] - if response_func ==1: - idx_str = int(nx/3.) - idx_end = int(2*nx/3.) - response_s[idx_str:idx_end] = 1 - if response_func ==2: - idx_str = int(nx/3.) - idx_end = int(2*nx/3.) - response_s[idx_str:idx_end] = 1./np.float(len(response_s[idx_str:idx_end])) - - - - ########################################################################### - #First, need we calculate the quad (analysis, forecast, blind forecast) - #A precomputed quad_state can be supplied to quickly use different response functions - ########################################################################### - if type(quad_state)== type(None): - quad_state = single_step_analysis_forecast_v2(background,truth,da_const,m_const,obs_seed=obs_seed) - - ########################################################################### - # Next we need the response functions. - # We only really need the forecast and blind forecast, but for fun I'll calculate all for now - ########################################################################### - - #For now I am not worried about being efficient - nobs = len(da_const["obs_loc"]) - obs = np.arange(nobs) - nens = da_const["nens"] - nstate = len(truth) - - bf_response = np.zeros(nens) - fc_response = np.zeros(nens) - an_response = np.zeros(nens) - bg_response = np.zeros(nens) - for n in range(da_const["nens"]): - if abs_flag==0: - bf_response[n] = np.sum(response_s * np.power(quad_state["bf"][:,n],response_c)) - fc_response[n] = np.sum(response_s * np.power(quad_state["fc"][:,n],response_c)) - an_response[n] = np.sum(response_s * np.power(quad_state["an"][:,n],response_c)) - bg_response[n] = np.sum(response_s * np.power(quad_state["bg"][:,n],response_c)) - if abs_flag==1: - bf_response[n] = np.sum(response_s * np.power(np.abs(quad_state["bf"][:,n]),response_c)) - fc_response[n] = np.sum(response_s * np.power(np.abs(quad_state["fc"][:,n]),response_c)) - an_response[n] = np.sum(response_s * np.power(np.abs(quad_state["an"][:,n]),response_c)) - bg_response[n] = np.sum(response_s * np.power(np.abs(quad_state["bg"][:,n]),response_c)) - - J_dict = {} - J_dict['bf'] = bf_response - J_dict['bg'] = bg_response - J_dict['an'] = an_response - J_dict['fc'] = fc_response - J_dict['tr_bg'] = np.sum(response_s * np.power(quad_state["tr_bg"][:],response_c)) - J_dict['tr_fc'] = np.sum(response_s * np.power(quad_state["tr_fc"][:],response_c)) - if abs_flag==1: - J_dict['tr_bg'] = np.sum(response_s * np.power(np.abs(quad_state["tr_bg"][:]),response_c)) - J_dict['tr_fc'] = np.sum(response_s * np.power(np.abs(quad_state["tr_fc"][:]),response_c)) - - ########################################################################### - # Creating the R,H, and C matrices we will need for the VR estimate - ########################################################################### - - if m_const['model'] == 'LA': - H = np.identity(m_const["nx"])[da_const["obs_loc"],:] # Observation operator - R = da_const["used_std_obs"]**2*np.identity(len(da_const["obs_loc"])) # Observation error corvariance matrix - if m_const['model'] == 'SWM': - H = np.identity(m_const["nx"]*3)[da_const["obs_loc"],:] # Observation operator - obs_error_vec = np.ones(len(da_const["obs_loc"]))*da_const['used_h_std_obs'] - obs_error_vec[obs_error_vec<m_const['nx']] = da_const['used_u_std_obs'] - obs_error_vec[obs_error_vec>2*m_const['nx']] = da_const['used_r_std_obs'] - R = np.diag(obs_error_vec**2) - - if da_const["loc"]: - C = loc_matrix(da_const,m_const) - else: - C=np.ones([m_const["nx"],m_const["nx"]]) - if m_const['model'] == 'SWM': - C = np.hstack([C,C,C]) - C = np.vstack([C,C,C]) - - - x = quad_state['bg'][:,:] - - dx = x.T-np.mean(x,axis=1) - dx = dx.T - - A = np.dot(dx,dx.T)/(dx.shape[1]-1) - - #Now we need the vector of individual js for all ensembles - J = np.zeros_like(dx) - for n in range(da_const["nens"]): - if abs_flag==0: - J[:,n] = response_s * np.power(quad_state["bf"][:,n],response_c) - if abs_flag==1: - J[:,n] = response_s * np.power(np.abs(quad_state["bf"][:,n]),response_c) - #J[:,n] = response_s * np.power(quad_state["bg"][:,n],response_c) - dJ = J.T-np.mean(J,axis=1) - dJ = dJ.T - - dJ_sum = bf_response - np.mean(bf_response) - - - H = np.identity(m_const["nx"])[da_const["obs_loc"],:] # Observation operator - R = da_const["used_std_obs"]**2*np.identity(len(da_const["obs_loc"])) # Observation error corvariance matrix - - - R_obs = R # Observation error corvariance matrix - H_obs = H # Not the cleanest coding I know - - - HAHt = np.dot(H_obs,np.dot(C*A,H_obs.T)) - HAHtRinv= np.linalg.inv(HAHt+R_obs) - - dJHdxt = np.dot(dJ_sum,np.dot(H_obs,dx).T)/(nens-1) - CdJdX = np.zeros(m_const['nx']) - - #Since we no longer need C for the HCAHT which was calculated above, we can just advect the C matrix for the correlations and not make a new one. - if advect_flag > 0: - if advect_flag ==1: - u_advect =m_const['u_ref']*error_u/100. - C[:,0] = semi_lagrangian_advection(C[:,0],m_const['dx'],u_advect,da_const['dt']) - #for c in range(C.shape[0]): - # C[c,:] = semi_lagrangian_advection(C[c,:],m_const['dx'],-m_const['u_ref'],da_const['dt']) - - if advect_flag ==2: - C_adv = C[:,0]*0. - if random_samples == 0: - for i in range(da_const["nens"]): - if da_const["fixed_seed"]==True: np.random.seed(i) - u_ens = np.random.normal(m_const["u_ref"],da_const["u_std_ens"]) - u_ens = m_const["u_ref"]*error_u/100.+(u_ens-m_const["u_ref"])*error_u_ens/100. - #print('u_ens',u_ens) - C_adv = C_adv + semi_lagrangian_advection(C[:,0],m_const['dx'],u_ens,da_const['dt']) - #C_adv[:,0] = C_adv[:,0] + semi_lagrangian_advection(C[:,0],m_const['dx'],10.,da_const['dt']) - C[:,0] = C_adv/np.float(da_const["nens"]) - else: - np.random.seed(random_samples+seed_samples) - randomized_ens = np.random.choice(np.arange(0,da_const["nens"]), random_samples, replace=False) - for r in randomized_ens: - #print(r) - if da_const["fixed_seed"]==True: np.random.seed(r) - u_ens = np.random.normal(m_const["u_ref"],da_const["u_std_ens"]) - u_ens = m_const["u_ref"]*error_u/100.+(u_ens-m_const["u_ref"])*error_u_ens/100. - #print('u_ens',u_ens) - C_adv = C_adv + semi_lagrangian_advection(C[:,0],m_const['dx'],u_ens,da_const['dt']) - #C_adv[:,0] = C_adv[:,0] + semi_lagrangian_advection(C[:,0],m_const['dx'],10.,da_const['dt']) - C[:,0] = C_adv/np.float(random_samples) - - for i in range(1,m_const['nx']): - C[:,i] = np.hstack([C[-1,i-1],C[:-1,i-1]]) - - - #for n in range(m_const["nx"]): #Not sure if the if loop is much quicker than just calculating the dot product everywhere ;) - # if response_s[n] != 0: CdJdX += C[n,:]*np.dot(dJ[n,:],dx.T)/(da_const['nens']-1) - # Somewhat quicker way that avoids the loop - CdJdX=np.sum(C*np.dot(dJ,dx.T)/(da_const['nens']-1.),axis=0) - - CdJdXH = np.dot(CdJdX,H.T) - vr_total = - np.dot(CdJdXH,np.dot(HAHtRinv,dJHdxt).T) - J_fc= fc_response - dJ_fc = J_fc-np.mean(J_fc) - real_reduction=np.var(dJ_fc,ddof=1) - np.var(bf_response,ddof=1) - - - return vr_total,real_reduction,quad_state,J_dict#,C - - -#========================================================== -# 22 version with satelitte data -#========================================================== - - - -def LETKF_analysis_22(bg,obs,obs_sat,m_const,da_const,sat_operator): - """ - Revised version of LETKF_analysis that allows for a satelitte observation - - Follows the recipe and notation of Hunt 2007, e.g. what is normally called B is now P_a, and _ol refers to over line, so the mean. - P_tilde_a refers to B but in ensemble space. - The application when not localized is pretty straight forward. - The localized version is coded maximully inefficiently. If needed two available options to speed it up are parallelization and batch processing. - Currently also only uses gaspari-cohn. - """ - from scipy.linalg import sqrtm #Needed to calculate the matrix square root. Can lead to complex values due to numrical noise, which I deal with by only using real values. - - - - n_obs_h =len(da_const["obs_loc"]) - n_obs_sat =len(da_const["obs_loc_sat"]) - n_obs = n_obs_h + n_obs_sat - #obs_sat = sat_operator(quad['tr_bg'],window=window)+ np.random.normal(0,da_const["used_std_obs_sat"],size=bg.shape[0]) - obs_loc = np.hstack([da_const["obs_loc"],da_const["obs_loc_sat"]]).astype(int) - - - L = da_const['nens'] - x_b = bg - x_ol_b = np.mean(x_b,axis=1) - X_b = x_b.T-x_ol_b - X_b = X_b.T - -# r =np.ones(n_obs) -# r[:n_obs_h] = da_const["used_std_obs"]**2. -# r[n_obs_h:] = da_const["used_std_obs_sat"]**2. - R = da_const['R'] #np.diag(r) - - # this is the function that ports the background ensemble state to pertubations in observation space - Y_b, y_ol_b = state_to_observation_space(bg,m_const,da_const,sat_operator) - - # ineligant way to merge the obs vector depending on which observations occur - if n_obs_h>0: y_obs = obs[da_const["obs_loc"]] - if n_obs_sat>0: sat_obs = obs_sat[da_const["obs_loc_sat"]] - if n_obs_h>0 and n_obs_sat>0 : y_obs = np.hstack([y_obs,sat_obs]) - if n_obs_sat>0 and n_obs_h==0 : y_obs = sat_obs - - delta_y = y_obs-y_ol_b - - if da_const['loc']==False: - """ Now that all the variables are set, we start by computing the covariance matrix in ensemble state """ - YRY = np.dot(Y_b.T,np.dot(np.linalg.inv(R),Y_b)) - P_tilde_a = np.linalg.inv((L-1)*np.identity(L)+YRY) - - """Next step, computing the enesemble mean analysis via the weighting vector w_ol_a""" - w_ol_a = np.dot(P_tilde_a,np.dot(Y_b.T,np.dot(np.linalg.inv(R),delta_y))) - x_ol_a = x_ol_b+np.dot(X_b,w_ol_a) - - """We now get the ensemble by calculating the weighting matrix through a square root of the error covariance matrix, and adding the mean values to the ensemble deviations""" - W_a = np.real(sqrtm((L-1)*P_tilde_a)) - w_a = W_a+w_ol_a - - x_a = np.dot(X_b,w_a).T+ x_ol_a - x_a = x_a.T - - else: - x_grid_neg = -m_const['x_grid'][::-1]-m_const['dx'] - x_grid_ext = m_const['x_grid']+(m_const['dx']*m_const['nx']) - N = m_const['nx'] - x_a_loc = np.zeros((N,L)) - x_ol_a_loc = np.zeros((N)) - for g in range(N): - #for g in range(1): - dist_reg = np.abs(m_const['x_grid'][obs_loc]-m_const['x_grid'][g]) - dist_neg = m_const['x_grid'][g]-x_grid_neg[obs_loc] - dist_ext = x_grid_ext[obs_loc]-m_const['x_grid'][g] - dist = np.minimum(dist_reg,dist_ext)#apparently minimum doesn't like 3 variables ,dist_neg) - dist = np.minimum(dist,dist_neg) - #And now we calculate the gaspari cohn weighting and apply it to inverse R - gc_loc = gaspari_cohn_non_mirrored(dist,da_const['loc_length']) - if np.max(gc_loc) == 0.: - #If no observations are within the double gc radius no need to bother computing things - x_ol_a_loc[g] = x_ol_b[g] - x_a_loc[g,:] = x_b[g,:] - else: - R_inv = np.linalg.inv(R) * np.diag(gc_loc) - - YRY = np.dot(Y_b.T,np.dot(R_inv,Y_b)) - """ Now that all the variables are set, we start by computing the covariance matrix in ensemble state """ - P_tilde_a = np.linalg.inv((L-1)*np.identity(L)+YRY) - - """Next step, computing the enesemble mean analysis via the weighting vector w_ol_a""" - w_ol_a = np.dot(P_tilde_a,np.dot(Y_b.T,np.dot(R_inv,delta_y))) - x_ol_a = x_ol_b+np.dot(X_b,w_ol_a) - W_a = np.real(sqrtm((L-1.)*P_tilde_a)) - w_a = W_a+w_ol_a - x_a = np.dot(X_b,w_a).T+ x_ol_a - x_a = x_a.T - - x_ol_a_loc[g] = x_ol_a[g] - x_a_loc[g,:] = x_a[g,:] - x_a = x_a_loc - x_ol_a = x_ol_a_loc - return x_a, x_ol_a - - -def LETKF_analysis_23(bg,obs,obs_sat,m_const,da_const,sat_operator): - """ - Same as LETKF_analysis_22 but speed things up by putting the middle loop into a separate numba function. - - It seems roughly 10 times faster than the 22 version. - - Further speed up could probably be achieved by reducing the size of the state arrays to not include areas outside the localized observations influence. - - Follows the recipe and notation of Hunt 2007, e.g. what is normally called B is now P_a, and _ol refers to over line, so the mean. - P_tilde_a refers to B but in ensemble space. - """ - - - - n_obs_h =len(da_const["obs_loc"]) - n_obs_sat =len(da_const["obs_loc_sat"]) - n_obs = n_obs_h + n_obs_sat - obs_loc = np.hstack([da_const["obs_loc"],da_const["obs_loc_sat"]]).astype(int) - - - L = da_const['nens'] - x_b = bg - x_ol_b = np.mean(x_b,axis=1) - X_b = x_b.T-x_ol_b - X_b = X_b.T - - R = da_const['R'] - - # this is the function that ports the background ensemble state to pertubations in observation space - Y_b, y_ol_b = state_to_observation_space(bg,m_const,da_const,sat_operator) - - # ineligant way to merge the obs vector depending on which observations occur - if n_obs_h>0: y_obs = obs[da_const["obs_loc"]] - if n_obs_sat>0: sat_obs = obs_sat[da_const["obs_loc_sat"]] - if n_obs_h>0 and n_obs_sat>0 : y_obs = np.hstack([y_obs,sat_obs]) - if n_obs_sat>0 and n_obs_h==0 : y_obs = sat_obs - - delta_y = y_obs-y_ol_b - - if da_const['loc']==False: - """ Now that all the variables are set, we start by computing the covariance matrix in ensemble state """ - YRY = np.dot(Y_b.T,np.dot(np.linalg.inv(R),Y_b)) - P_tilde_a = np.linalg.inv((L-1)*np.identity(L)+YRY) - - """Next step, computing the enesemble mean analysis via the weighting vector w_ol_a""" - w_ol_a = np.dot(P_tilde_a,np.dot(Y_b.T,np.dot(np.linalg.inv(R),delta_y))) - x_ol_a = x_ol_b+np.dot(X_b,w_ol_a) - - """We now get the ensemble by calculating the weighting matrix through a square root of the error covariance matrix, and adding the mean values to the ensemble deviations""" - W_a = np.real(sqrtm((L-1)*P_tilde_a)) - w_a = W_a+w_ol_a - - x_a = np.dot(X_b,w_a).T+ x_ol_a - x_a = x_a.T - - else: - # print('making sure that the fix is being used') - # print('using the old version') - # print('trying the third version') - # print('sigh, what is not working now?') - # bla, blub,bleurgh=LETKF_numba_loop(m_const['x_grid'],da_const['loc_length'],obs_loc,x_ol_b,x_b,R,Y_b,L,delta_y,X_b,m_const['nx'],m_const['dx']) - # print('this is annoying, I hate numba') - x_a, x_ol_a,W_a=LETKF_numba_loop(m_const['x_grid'],da_const['loc_length'],obs_loc,x_ol_b,x_b,R,Y_b,L,delta_y,X_b,m_const['nx'],m_const['dx']) - return x_a, x_ol_a,W_a - -# @jit -def LETKF_numba_loop(x_grid,loc_length,obs_loc,x_ol_b,x_b,R,Y_b,L,delta_y,X_b,N,dx): - x_grid_neg = -x_grid[::-1]-dx - x_grid_ext = x_grid+dx*N - x_a_loc = np.zeros((N,L)) - W_a_loc = np.zeros((N,L,L)) - x_ol_a_loc = np.zeros((N)) - - for g in range(N): - dist_reg = np.abs(x_grid[obs_loc]-x_grid[g]) - dist_neg = x_grid[g]-x_grid_neg[obs_loc] - dist_ext = x_grid_ext[obs_loc]-x_grid[g] - dist = np.minimum(dist_reg,dist_ext)#apparently minimum doesn't like 3 variables ,dist_neg) - dist = np.minimum(dist,dist_neg) - #And now we calculate the gaspari cohn weighting and apply it to inverse R - """Gaspari-Cohn function, with no mirroring.""" - - ra = np.abs(dist)/loc_length - - gp = np.zeros_like(ra) - i=np.where(ra<=1.)[0] - gp[i]=-0.25*ra[i]**5+0.5*ra[i]**4+0.625*ra[i]**3-5./3.*ra[i]**2+1. - i=np.where((ra>1.)*(ra<=2.))[0] - gp[i]=1./12.*ra[i]**5-0.5*ra[i]**4+0.625*ra[i]**3+5./3.*ra[i]**2-5.*ra[i]+4.-2./3./ra[i] - - gc_loc=gp - - if np.max(gc_loc) == 0.: - #If no observations are within the double gc radius no need to bother computing things - x_ol_a_loc[g] = x_ol_b[g] - x_a_loc[g,:] = x_b[g,:] - W_a_loc[g,:,:] = np.identity(L) - else: - R_inv = np.linalg.inv(R) * np.diag(gc_loc) - - YRY = np.dot(Y_b.T,np.dot(R_inv,Y_b)) - """ Now that all the variables are set, we start by computing the covariance matrix in ensemble state """ - P_tilde_a = np.linalg.inv((L-1)*np.identity(L)+YRY) - - """Next step, computing the enesemble mean analysis via the weighting vector w_ol_a""" - w_ol_a = np.dot(P_tilde_a,np.dot(Y_b.T,np.dot(R_inv,delta_y))) - x_ol_a = x_ol_b[g]+np.dot(X_b[g],w_ol_a) - # Trying to replace sqrtm with numpy eigenvectors so jit can work. - evalues, evectors = np.linalg.eigh((L-1.)*P_tilde_a) - W_a = evectors * np.sqrt(evalues) @ np.linalg.inv(evectors) - # w_a = W_a+w_ol_a - # x_a = np.dot(X_b[g,:],w_a).T+ x_ol_a #this seems to be wrong - # x_a = np.dot(X_b[g,:],w_a).T+ x_ol_b[g] # this should be right but isn't returning the right stuff - # This fix somehow makes things worse. I'll try something slightly different - # Well try Xa = Xb Wa, and then add x_ol_a to that - X_a = np.dot(X_b[g,:],W_a) - x_a = X_a+x_ol_a - x_ol_a_loc[g] = x_ol_a - x_a_loc[g,:] = x_a - W_a_loc[g,:,:] = W_a - # print(W_a_loc.shape) - # print(x_a_loc.shape) - # print(x_ol_a_loc.shape) - - return x_a_loc,x_ol_a_loc, W_a_loc - - -def state_to_observation_space(X,m_const,da_const,sat_operator): - """ - Computes Y_b, so the ensembles transfered to observation space, when a satetellite operator is used. - - Should be used for LETKF, as well as for the variance reduction estimate using the modified Kalman gain - - Important, X must not be the ensemble deviations, but the full ensemble state - - Returns the ensemble deviations as well as the mean - - """ - if np.max(np.mean(X,axis=1))<1e-10: - print('I think you accidentally used the ensemble deviations when calculating from state to observation space') - n_obs_h =len(da_const["obs_loc"]) - n_obs_sat =len(da_const["obs_loc_sat"]) - n_obs = n_obs_h + n_obs_sat - - if n_obs_h>0: - H = np.identity(m_const["nx"])[da_const["obs_loc"],:] # Observation operator - y_b = np.dot(H,X) - - if n_obs_sat>0: - #here we will use sat instead of Y and y - sat_b = sat_operator(X)[da_const["obs_loc_sat"],:] - - if n_obs_h>0 and n_obs_sat>0 : - y_b = np.hstack([y_b.T,sat_b.T]) - y_b = y_b.T - - if n_obs_sat>0 and n_obs_h==0 : - y_b = sat_b - - y_ol_b =np.mean(y_b,axis=1) - Y_b = y_b.T-y_ol_b - Y_b = Y_b.T - - return Y_b, y_ol_b - - - -def Kalman_gain_observation_deviations(bg,m_const,da_const,sat_operator): - """ - Calculates Kalman gain, but using YYT instead of HBH, and XYT intead of BHT, - This is important for satellite data because we don't need a linearized version of the satellite operator - - Important fix! Localization was applied incorrectly. In stead of localizing BH the whole Kalman gain was localized :( - """ - n_obs_h =len(da_const["obs_loc"]) - n_obs_sat =len(da_const["obs_loc_sat"]) - n_obs = n_obs_h + n_obs_sat - obs_loc = np.hstack([da_const["obs_loc"],da_const["obs_loc_sat"]]).astype(int) - L = da_const['nens'] - - dY_b, y_ol_b = state_to_observation_space(bg,m_const,da_const,sat_operator) - x_b = bg - x_ol_b = np.mean(x_b,axis=1) - X_b = x_b.T-x_ol_b - X_b = X_b.T - - R = da_const['R'] - if da_const['loc']: - L_obs, L_obs_state = localization_matrices_observation_space(m_const,da_const) - YYlocR_inv = np.linalg.inv(L_obs*np.dot(dY_b,dY_b.T)/(L-1)+R) - # K = L_obs_state*np.dot(X_b,np.dot(dY_b.T,YYlocR_inv))/(L-1) - K = np.dot(L_obs_state*np.dot(X_b,dY_b.T),YYlocR_inv)/(L-1) - else: - YYR_inv = np.linalg.inv(np.dot(dY_b,dY_b.T)/(L-1)+R) - K = np.dot(X_b,np.dot(dY_b.T,YYR_inv))/(L-1) - return K - - -def square_root_Kalman_gain_observation_deviations(bg,m_const,da_const,sat_operator): - """ - Calculates modified square root Kalman gain, but using YYT instead of HBH, and XYT intead of BHT, - This is important for satellite data because we don't need a linearized version of the satellite operator - """ - from scipy.linalg import sqrtm #Needed to calculate the matrix square root. Can lead to complex values due to numrical noise, which I deal with by only using real values. - n_obs_h =len(da_const['obs_loc']) - n_obs_sat =len(da_const["obs_loc_sat"]) - n_obs = n_obs_h + n_obs_sat - - obs_loc = np.hstack([da_const["obs_loc"],da_const["obs_loc_sat"]]).astype(int) - L = da_const['nens'] - - dY_b, y_ol_b = state_to_observation_space(bg,m_const,da_const,sat_operator) - x_b = bg - x_ol_b = np.mean(x_b,axis=1) - X_b = x_b.T-x_ol_b - X_b = X_b.T - R = da_const['R'] - - - #Tryig to do this directly from the formula because Tanyas implementation works but slightly confuses me. - if da_const['loc']: - L_obs, L_obs_state = localization_matrices_observation_space(m_const,da_const) - else: - L_obs = np.ones([n_obs,n_obs]) - L_obs_state = np.ones([m_const['nx'],n_obs]) - - YYR = L_obs*np.dot(dY_b,dY_b.T)/(L-1)+R - XY = L_obs_state*np.dot(X_b,dY_b.T)/(L-1) - # Separating last two terms is modified Kalman gain into terms I and II - I = (np.linalg.inv(sqrtm(YYR))).T - II = np.linalg.inv(sqrtm(YYR)+sqrtm(R)) - K = np.dot(XY,np.dot(I,II)) - - return K - - -def localization_matrices_observation_space(m_const,da_const): - """ - Creates the two localization matrices needed to calculate the localized Kalman gain with oberservation space ensemble pertubations. - - The distance calculation is still far from elegant, but I can't be bothered to make it faster as long as the LETKF is still the numerical bottleneck. - """ - n_obs_h =len(da_const["obs_loc"]) - n_obs_sat =len(da_const["obs_loc_sat"]) - n_obs = n_obs_h + n_obs_sat - obs_loc = np.hstack([da_const["obs_loc"],da_const["obs_loc_sat"]]).astype(int) - L_obs = np.zeros([n_obs,n_obs]) - L_obs_state = np.zeros([m_const['nx'],n_obs]) - - x_grid_neg = -m_const['x_grid'][::-1]-m_const['dx'] - x_grid_ext = m_const['x_grid']+(m_const['dx']*m_const['nx']) - - for o in range(n_obs): - g = obs_loc[o] - # First the one using the distance between observations and the state - dist_reg = np.abs(m_const['x_grid']-m_const['x_grid'][g]) - dist_neg = m_const['x_grid'][g]-x_grid_neg - dist_ext = x_grid_ext-m_const['x_grid'][g] - dist = np.minimum(dist_reg,dist_ext)#apparently minimum doesn't like 3 variables ,dist_neg) - dist = np.minimum(dist,dist_neg) - gc_loc = gaspari_cohn_non_mirrored(dist,da_const['loc_length']) - L_obs_state[:,o] = gc_loc - - - L_obs[o,:] = L_obs_state[obs_loc,o] - - return L_obs, L_obs_state - -def generate_obs_22_single(truth,m_const,da_const,sat_operator,obs_seed): - """ - Generates the observations for the single forecast analysis, including the sat obs, for a provided seed - - """ - np.random.seed(obs_seed) - obs = truth + np.random.normal(0,da_const["True_std_obs"],m_const["nx"]) - if len(da_const['obs_loc_sat'])>0: - truth_sat = sat_operator(truth) - obs_sat = truth_sat + np.random.normal(0,da_const["True_std_obs_sat"],m_const["nx"]) - - obs_sat[obs_sat>1.]= 1. - obs_sat[obs_sat<0.]= 0. - else: - obs_sat = np.zeros(m_const['nx']) - - return obs, obs_sat - -def single_step_analysis_forecast_22(background,truth,da_const,m_const,sat_operator,model_seed=0,obs_seed=0): - """ - Revisted version of single step analysis forecast to deal with sat data as well. - - For now I will remove the SWM stuff, which can still be found in the older version - """ - obs, obs_sat = generate_obs_22_single(truth,m_const,da_const,sat_operator,obs_seed) - - """ - create dictionary to store this single step - """ - quad_state = {} - - #Getting the analysis - if da_const['method'] == 'EnKF': - np.random.seed(obs_seed+100000) - an = ENKF_analysis_22(background,obs,obs_sat,da_const,m_const,sat_operator) - - if da_const['method'] == 'LETKF': - # an,bla = LETKF_analysis_22(background,obs,obs_sat,m_const,da_const,sat_operator) - an,bla,W_a = LETKF_analysis_23(background,obs,obs_sat,m_const,da_const,sat_operator) - quad_state['W_a'] = W_a - - if da_const['method'] == 'sqEnKF': - an = sqEnKF_analysis_22(background,obs,obs_sat,da_const,m_const,sat_operator) - - - - - - """ - Predict blind forecast and forecast - """ - bf = np.zeros_like(background) - fc = np.zeros_like(background) - - for i in range(da_const["nens"]): - np.random.seed(i+model_seed) - u_ens = np.random.normal(m_const["u_ref"],da_const["u_std_ens"]) - dhdt_ens = np.random.normal(m_const["dhdt_ref"],da_const["dhdt_std_ens"]) - bf[:,i] = linear_advection_model(background[:,i],u_ens,dhdt_ens,m_const["dx"],da_const["dt"],da_const["nt"]) - fc[:,i] = linear_advection_model(an[:,i],u_ens,dhdt_ens,m_const["dx"],da_const["dt"],da_const["nt"]) - - - quad_state['bg'] = background - quad_state['an'] = an - quad_state['bf'] = bf - quad_state['fc'] = fc - quad_state['tr_bg'] = truth - quad_state['obs'] = obs - return quad_state - - -def create_states_dict_22(j,states,m_const,da_const,sat_operator): - """ - Generates the initial analysis and truth. - Also creates the "states" dictionary where the analysis ensemble, background ensemble, truth and observations are all going to be stored. - Very memory hungry, as everything from all assimilation time steps and all experiments is stored. - Works find for simple model though. - - A fixed seed is used by default so that the model errors of all ensemble members is constant. But this can also be randomized. - Alternative version would be to generate the model errors and store them, but this has not happened yet. Would be necessary to test some parameter estimation tests. - - Modified version of the Yvonne setup to work with the linear advection model - - Todo: - - Describe states dictionary here. - - make model errors stored variables so enable parameter estimation - - Maybe make a more sensible name, such as init_da_ensembles - - I am not convinced this is the best way to generate the initial analysis. - - Might be best to change to dictionary time axis. Forecast and anaylis at the same time have difference of 1 in the time integer. - """ - nx = m_const["nx"] - - #initial conditions - if m_const['init_func']=='gaus': h_initial = gaussian_initial_condition(m_const["x_grid"],m_const["h_init_std"]) - if m_const['init_func']=='sine': h_initial = sine_initial_condition( m_const["x_grid"],m_const["sine_init"]) - - - #Generate truth and first observations - truth, obs, obs_sat = generate_obs_22(h_initial,h_initial,m_const,da_const,j,0,sat_operator) - - - an = np.zeros((nx,da_const["nens"])) - - #First rows full of nans :) - bg = np.zeros((nx,da_const["nens"])) - bg[:] = np.nan - obs = np.zeros(nx) - obs[:] = np.nan - - for i in range(da_const["nens"]): - np.random.seed(i+j*da_const["nens"]+da_const['ens_seed']) - if da_const["init_noise"]>0: - h_ens = np.random.normal(h_initial,da_const["init_noise"]) - else: - h_ens = h_initial - if da_const["init_spread"]>0: - #initial spread generated by moving waves forward and backward and up and down using - #da_const["init_spread_h"] and da_const["init_spread_x"] - x_displace = np.random.normal(0.,da_const["init_spread_x"]) - h_displace = np.random.normal(0.,da_const["init_spread_h"]) - u_tmp = x_displace/da_const["dt"] - h_ens = semi_lagrangian_advection(h_ens,m_const["dx"],u_tmp,da_const["dt"]) - h_ens = h_ens+h_displace - - u_ens = np.random.normal(m_const["u_ref"],da_const["u_std_ens"]) - dhdt_ens = np.random.normal(m_const["dhdt_ref"],da_const["dhdt_std_ens"]) - an[:,i] = linear_advection_model(h_ens,u_ens,dhdt_ens,m_const["dx"],da_const["dt"],da_const["nt"]) - - states[j]={} - states[j]['bg']=[bg] - states[j]['an']=[an] - states[j]['truth']=[truth] - states[j]['obs']=[obs] - states[j]['obs_sat']=[obs_sat] - return an, truth, states - - - return DA_const - - -def vr_reloaded_22(background,truth,m_const,da_const,sat_operator, - func_J=sum_mid_tri, - reduc = 1,reg_flag=1, - quad_state = None,dJdx_inv=None,alpha=0.01,mismatch_threshold=0.1, - iterative_flag=0,explicit_sens_flag = 1,exp_num=0,obs_seed=0,model_seed=0): - - """ - Version of vr_reloaded that can now also use sat obs. - Takes only the background and truth, then caculates the quad, finaly calculates the response function and the variance reduction. - - The quad can be supplied to save time, which makes sense when you comparing different methods for the same experiment calculating. - - Should also return the dJ values of all 4 ensembles (analysis, background, forecast, blind forecast) - - - -reg_flag added to use L2 regularization - - not implemented: - - state model reduction - - - Speed up options: - - If the same forecast can be recycled, the quad can be calculated once and than passed on. - - If the sensitivity can be recycled, that can be calculated once and then reused. - - Don't use LETKF - - First getting the explicit all at once verion working, will then add the various approaches - - to be implemented: - -iteratively and all at once - -explicit vs implicit - -reduced model spacing by subsampling the grid. Reduc is the spacing, eg reduc 3 means every third grid point is used. - should find the nearest reduced model grid point for each observation. - - - - """ - if iterative_flag ==0: from scipy.linalg import sqrtm - ########################################################################### - #First, need we calculate the quad (analysis, forecast, blind forecast) - #A precomputed quad_state can be supplied to quickly use different response functions - ########################################################################### - if type(quad_state)== type(None): - quad_state = single_step_analysis_forecast_22(background,truth,da_const,m_const,sat_operator,obs_seed=obs_seed,model_seed=model_seed) - - ########################################################################### - # Next we need the response functions. - # We only really need the forecast and blind forecast, but for fun I'll calculate all for now - ########################################################################### - - #For now I am not worried about being efficient - nobs = len(da_const["obs_loc"]) - obs = np.arange(nobs) - nens = da_const["nens"] - nstate = len(truth) - - bf_response = np.zeros(nens) - fc_response = np.zeros(nens) - an_response = np.zeros(nens) - bg_response = np.zeros(nens) - for n in range(da_const["nens"]): - bf_response[n] = func_J(quad_state["bf"][:,n]) - fc_response[n] = func_J(quad_state["fc"][:,n]) - an_response[n] = func_J(quad_state["an"][:,n]) - bg_response[n] = func_J(quad_state["bg"][:,n]) - - J_dict = {} - J_dict['bf'] = bf_response - J_dict['bg'] = bg_response - J_dict['an'] = an_response - J_dict['fc'] = fc_response - J_dict['tr_bg'] = func_J(quad_state['tr_bg']) - - - - - - - - #if reduc>1: #Does currently not work for - # #Defining reduced model domain/state, and then defining obs location on new reduced grid - # #For the SWM model this only really makes sense if nx is a multiple of reduc - # reduced_model_domain = np.arange(0,nstate,reduc) - # reduced_model_size = len(reduced_model_domain) - # reduced_obs_loc = np.zeros(nobs).astype(int) - # for o in range(nobs): - # reduced_obs_loc[o] = (np.abs(reduced_model_domain-da_const['obs_loc'][o])).argmin() - # #print('reduced_model_domain:',reduced_model_domain) - # #print('reduced_model_size :',reduced_model_size ) - # #print('reduced_obs_loc :',reduced_obs_loc ) - # - # #Getting the localization matrix in reduced model space - # H = np.identity(m_const["nx"])[reduced_model_domain,:] - # C = np.dot(H,np.dot(C,H.T)) - # x = quad_state['bg'][reduced_model_domain,:] - # - #else: - x = quad_state['bg'][:,:] - - dx = x.T-np.mean(x,axis=1) - dx = dx.T - dx_orig = dx+0 - - A = np.dot(dx,dx.T)/(dx.shape[1]-1) - - J = bf_response - dJ = J-np.mean(J) - dJ_orig = J-np.mean(J) - - - ############################################################################################### - # Sensitivity - ############################################################################################### - #Covarianz between reponse function dJ and state ensemble dx - cov_dJdx_vec = np.dot(dJ,dx.T)/(dx.shape[1]-1) - - if explicit_sens_flag==1: - #If a sensitivity is provided it is used instead of calculating it - if type(dJdx_inv) == np.ndarray: - #testing supplied sensitivity - rel_error_sens = np.sum(np.abs(A.dot(dJdx_inv)-cov_dJdx_vec))/np.sum(np.abs(cov_dJdx_vec)) - if rel_error_sens>0.05: - print('using supplied sensitivity has a relative error of:',rel_error_sens) - #Computing the sensitivity, highly recommend using the regularized version before doing so. - else: - if reg_flag == 1: - dJdx_inv = L2_regularized_inversion(A,cov_dJdx_vec,alpha=alpha,mismatch_threshold=mismatch_threshold) - else: - A_inv = np.linalg.pinv(A) - dJdx_inv = np.dot(A_inv,cov_dJdx_vec) - - estimated_J = bf_response + 0. - - - #if iterative_flag ==0: - - vr_individual = 0. - - - if explicit_sens_flag ==1: - #Tanjas approach of calculating the square root K following Kalman gain, formula (10) Whitaker and Hamil 2002 - sqK = square_root_Kalman_gain_observation_deviations(x,m_const,da_const,sat_operator) - bg_obs_deviations,bg_obs_ol = state_to_observation_space(x,m_const,da_const,sat_operator) - dx_prime = dx - np.dot(sqK,bg_obs_deviations) - - #estimated J calculated by updating the original dJ - estimated_J = estimated_J -np.dot(dJdx_inv,np.dot(sqK,bg_obs_deviations)) - - #Using the cheaper variance calculation instead of going to A-B - new_J = np.dot(dJdx_inv.T,dx_prime) - vr_total = np.var(new_J,ddof=1)-np.var(np.dot(dJdx_inv.T,dx),ddof=1) - #print('all at once:',vr_individual) - dx = dx_prime - - #if explicit_sens_flag ==0: - # HAHt = np.dot(H_obs,np.dot(C*A,H_obs.T)) - # HAHtRinv= np.linalg.inv(HAHt+R_obs) - - # dJHdxt = np.dot(dJ,np.dot(H_obs,dx).T)/(nens-1) - # vr_individual = -np.dot(dJHdxt,np.dot(HAHtRinv,dJHdxt)) - # vr_total = vr_individual - # - # - #if iterative_flag: - # vr_individual = np.zeros(nobs) - # for o in range(nobs): #loop over each observation individually - - # #New A after dx was updated - # A = np.cov(dx,ddof=1) - - # #Selecting the single R value for this observation - # R_obs = R[o,o]*np.identity(1) # Observation error corvariance matrix - # H_obs = H[o,:] #Not sure about this :( - # #if - # ##H_rms = np.identity(nobs)[o,:] - # #H_rms = np.identity(reduced_model_size)[reduced_obs_loc[o],:] - # ##H = np.identity(m_const["nx"])[da_const["obs_loc"][o],:] -# - # ##HAHt = np.dot(H,np.dot(A,H.T)) - # ##HAHtRinv= np.linalg.inv(HAHt+R) - # ##dJHdxt = np.dot(dJ,np.dot(H,dx).T)/(nens-1) - # - # #Now we get the change in dx, which uses the localized A matrix and a square root - # E = np.matmul(C*A,H_obs.T) - # E = np.matmul(H_obs,E) - # E = E + R_obs - # alpha = 1./(1.+np.sqrt(R_obs/E)) - - - # HAHt = np.dot(H_obs,np.dot(C*A,H_obs.T)) - # HAHtRinv= np.linalg.inv(HAHt+R_obs) - # #print((C*A).shape,H_obs.T.shape,np.dot(C*A,H_obs.T).shape,HAHtRinv.shape) - ### - - # if explicit_sens_flag ==1: - # - # K = np.dot(C*A,H_obs.T)*HAHtRinv - # # Update state vector - # Hdx = np.matmul(H_obs,dx) - # dx_prime = dx - alpha*np.outer(K,Hdx) - # - # #Update expected J for each ensemble member - # estimated_J = estimated_J -np.dot(dJdx_inv,alpha*np.outer(K,Hdx)) - - # #A_new = np.dot(dx,dx.T)/(dx.shape[1]-1) - # #Now we get the variance estimate using the difference between new and old B - # #dSigma = np.matmul(A_new-A,dJdx_inv.T) - # #print('wtf: np.sum(np.abs(A_new-A_old)) ',o,np.sum(np.abs(A_new-A))) - # #dSigma = np.matmul(dJdx_inv,dSigma) - # #vr_individual[o] = dSigma#np.matmul(np.atleast_2d(np.diag(A_new-A)),(dJdx_inv.T**2)) - # - # vr_individual[o] = np.var(np.dot(dJdx_inv.T,dx_prime),ddof=1)-np.var(np.dot(dJdx_inv.T,dx),ddof=1) - # dx = dx_prime - # if explicit_sens_flag==0: - # dJHdxt = np.dot(dJ,np.dot(H_obs,dx).T)/(nens-1) - # vr_individual[o] = -np.dot(dJHdxt,np.dot(HAHtRinv,dJHdxt)) - # - # #Still needs localization! - # - # #Now we include dJ into dx to include it in the update - # dxJ = np.vstack([dx,dJ]) - # AxJ = np.dot(dxJ,dxJ.T)/(dxJ.shape[1]-1) - # HxJ = np.hstack([H_obs,np.array(0)]) - # CxJ = np.ones([nstate+1,nstate+1]) - # CxJ[:nstate,:nstate] = C - # HAHt = np.dot(HxJ,np.dot(CxJ*AxJ,HxJ.T)) - # HAHtRinv= np.linalg.inv(HAHt+R_obs) - - # #print(AxJ.shape,CxJ.shape) - # #print(AxJ.shape,HxJ.T.shape,np.dot(AxJ,HxJ.T).shape,HAHtRinv.shape) - # K = np.dot(CxJ*AxJ,HxJ.T)*HAHtRinv - - - # # Update state vector - # HdxJ = np.matmul(HxJ,dxJ) - # dxJ = dxJ - alpha*np.outer(K,HdxJ) - - # dx = dxJ[:-1,:] - # #old_var_dJ = np.var(dJ) - # dJ = dxJ[-1,:] - # estimated_J = dJ+np.mean(bf_response) - # #new_var_dJ = np.var(dJ) - # - # #Final var reduciton estimate - # if explicit_sens_flag==0: vr_total=np.sum(vr_individual) - # - # if explicit_sens_flag==1: - # vr_total=np.var(np.dot(dJdx_inv.T,dx),ddof=1)-np.var(np.dot(dJdx_inv.T,dx_orig),ddof=1) - # - # #Checking different formulation - - - # - - J_dict['es'] = estimated_J - - J_fc= fc_response - dJ_fc = J_fc-np.mean(J_fc) - real_reduction=np.var(dJ_fc,ddof=1) - np.var(dJ_orig,ddof=1) - - return vr_total,vr_individual,real_reduction,J_dict,dJdx_inv,quad_state,dx - -def vr_individual_loc_22(background,truth,m_const,da_const,sat_operator,response_func=1,abs_flag=0, - quad_state = None,exp_num=0, - advect_flag = 0,obs_seed=0,model_seed=0, - random_samples=0,seed_samples=0,error_u=100,error_u_ens=100): - - """ - Based on vr_individual_loc, but adapted to take sat measurements as well. - The main purpose is to do variance reduction estimates by individually localizing the covariances of the response functions at each point. - - important parameters: - response_func: controls the response, 1 results in the default sum over the mean, 2 in the mean over the mean - abs_flag: determines if the absolute value of the state vector is used for the response function. - - If advect_flag==1, the localization matrix is advected using the mo_const advection speed which should be close to the ensemble mean. The same advection function used as in the linear advection model. - If advect_flag==2, the localization matrix is advected using the individual ensemble members, so that in the end the localization vector is broadened accoring to ensemble disperstion. - - random_samples=0,seed_samples=0,error_u=100,error_u_ens=100: these all are various ways of adding an error to the advection term. - - - For now only using the all at once method, and it probably won't work for only a single observation. - - The equations should be in the paper draft, but I made quite the mistake by thinking that the A at the end of A'-A = -KHA is also localized. - - Possible speed ups not used so far: - * I could speed this up a bit by only calculating the localized correlations where they will be needed (so where H says they should be), but that is a bit of work to do well so for now I will leave it out. - * Given that we use Gaspari-Cohn localization most of the time, I might be able to save some time by only calculating covariances where C_i is not zero. But probably won't make a difference for toy model - * We currently assign j_i a value at every grid point, even though we skip calculations where response_s = 0. It would be more memory efficient to allocate J_i in a restricted area, but again I think this won't matter in a toymodel - * Possibly the biggest speed up would be to avoid the loop when calculating the response functions, which I haven't bothered with yet. - - - - """ - ########################################################################## - #We begin by computing the vecors which determine the response function - ########################################################################### - response_s = np.zeros(m_const['nx']) - response_c = np.ones(m_const['nx']) - nx = m_const['nx'] - if response_func ==1: - idx_str = int(nx/3.) - idx_end = int(2*nx/3.) - response_s[idx_str:idx_end] = 1 - if response_func ==2: - idx_str = int(nx/3.) - idx_end = int(2*nx/3.) - response_s[idx_str:idx_end] = 1./np.float(len(response_s[idx_str:idx_end])) - - - - - ########################################################################### - #First, need we calculate the quad (analysis, forecast, blind forecast) - #A precomputed quad_state can be supplied to quickly use different response functions - ########################################################################### - if type(quad_state)== type(None): - quad_state = single_step_analysis_forecast_22(background,truth,da_const,m_const,sat_operator,obs_seed=obs_seed,model_seed=model_seed) - - ########################################################################### - # Next we need the response functions. - # We only really need the forecast and blind forecast, but for fun I'll calculate all for now - ########################################################################### - - #For now I am not worried about being efficient - nobs = da_const["n_obs_h"] + da_const["n_obs_sat"] - obs = np.arange(nobs) - nens = da_const["nens"] - nstate = len(truth) - - bf_response = np.zeros(nens) - fc_response = np.zeros(nens) - an_response = np.zeros(nens) - bg_response = np.zeros(nens) - for n in range(da_const["nens"]): - if abs_flag==0: - bf_response[n] = np.sum(response_s * np.power(quad_state["bf"][:,n],response_c)) - fc_response[n] = np.sum(response_s * np.power(quad_state["fc"][:,n],response_c)) - an_response[n] = np.sum(response_s * np.power(quad_state["an"][:,n],response_c)) - bg_response[n] = np.sum(response_s * np.power(quad_state["bg"][:,n],response_c)) - if abs_flag==1: - bf_response[n] = np.sum(response_s * np.power(np.abs(quad_state["bf"][:,n]),response_c)) - fc_response[n] = np.sum(response_s * np.power(np.abs(quad_state["fc"][:,n]),response_c)) - an_response[n] = np.sum(response_s * np.power(np.abs(quad_state["an"][:,n]),response_c)) - bg_response[n] = np.sum(response_s * np.power(np.abs(quad_state["bg"][:,n]),response_c)) - - J_dict = {} - J_dict['bf'] = bf_response - J_dict['bg'] = bg_response - J_dict['an'] = an_response - J_dict['fc'] = fc_response - J_dict['tr_bg'] = np.sum(response_s * np.power(quad_state["tr_bg"][:],response_c)) - if abs_flag==1: - J_dict['tr_bg'] = np.sum(response_s * np.power(np.abs(quad_state["tr_bg"][:]),response_c)) - - ########################################################################### - # Creating the localization matrix which we will need for the VR estimate - ########################################################################### - - - x = quad_state['bg'][:,:] - - dx = x.T-np.mean(x,axis=1) - dx = dx.T - dx_obs,x_obs_ol = state_to_observation_space(x,m_const,da_const,sat_operator) - - A = np.dot(dx,dx.T)/(dx.shape[1]-1) - - #Now we need the vector of individual js for all ensembles - J = np.zeros_like(dx) - for n in range(da_const["nens"]): - if abs_flag==0: - J[:,n] = response_s * np.power(quad_state["bf"][:,n],response_c) - if abs_flag==1: - J[:,n] = response_s * np.power(np.abs(quad_state["bf"][:,n]),response_c) - #J[:,n] = response_s * np.power(quad_state["bg"][:,n],response_c) - dJ = J.T-np.mean(J,axis=1) - dJ = dJ.T - - dJ_sum = bf_response - np.mean(bf_response) - - L_obs, L_obs_state = localization_matrices_observation_space(m_const,da_const) - - # calculating HAHt with the background deviations in obs space - HAHt = L_obs*np.dot(dx_obs,dx_obs.T)/(nens-1) - HAHtRinv= np.linalg.inv(HAHt+da_const['R']) - # Again replacing Hdx with the background deviations in obs space - dJHdxt = np.dot(dJ_sum,dx_obs.T)/(nens-1) - - #missing is all the advection shit, for now we use only a prescribed error - if advect_flag ==1: - u_advect =m_const['u_ref']*error_u/100. - for o in range(nobs): - L_obs_state[:,o] = semi_lagrangian_advection(L_obs_state[:,o],m_const['dx'],u_advect,da_const['dt']) - - - - dJdYT=np.dot(dJ,dx_obs.T)/(nens-1) - LdJdYT=np.sum(L_obs_state*dJdYT,axis=0) - - vr_total = - np.dot(LdJdYT,np.dot(HAHtRinv,dJHdxt).T) - J_fc= fc_response - dJ_fc = J_fc-np.mean(J_fc) - real_reduction=np.var(dJ_fc,ddof=1) - np.var(bf_response,ddof=1) - - return vr_total,real_reduction,quad_state,J_dict#,C - #return dJ,dx_obs,dx - - # # calculating HAHt with the background deviations in obs space - # HAHt = L_obs*np.dot(dx_obs,dx_obs.T)/(nens-1) - # HAHtRinv= np.linalg.inv(HAHt+da_const['R']) - # - # CdJdX = np.zeros(m_const['nx']) - # - # #Since we no longer need C for the HCAHT which was calculated above, we can just advect the C matrix for the correlations and not make a new one. - # if advect_flag > 0: - # if advect_flag ==1: - # u_advect =m_const['u_ref']*error_u/100. - # C[:,0] = semi_lagrangian_advection(C[:,0],m_const['dx'],u_advect,da_const['dt']) - # #for c in range(C.shape[0]): - # # C[c,:] = semi_lagrangian_advection(C[c,:],m_const['dx'],-m_const['u_ref'],da_const['dt']) - # - # if advect_flag ==2: - # C_adv = C[:,0]*0. - # if random_samples == 0: - # for i in range(da_const["nens"]): - # if da_const["fixed_seed"]==True: np.random.seed(i) - # u_ens = np.random.normal(m_const["u_ref"],da_const["u_std_ens"]) - # u_ens = m_const["u_ref"]*error_u/100.+(u_ens-m_const["u_ref"])*error_u_ens/100. - # #print('u_ens',u_ens) - # C_adv = C_adv + semi_lagrangian_advection(C[:,0],m_const['dx'],u_ens,da_const['dt']) - # #C_adv[:,0] = C_adv[:,0] + semi_lagrangian_advection(C[:,0],m_const['dx'],10.,da_const['dt']) - # C[:,0] = C_adv/np.float(da_const["nens"]) - # else: - # np.random.seed(random_samples+seed_samples) - # randomized_ens = np.random.choice(np.arange(0,da_const["nens"]), random_samples, replace=False) - # for r in randomized_ens: - # #print(r) - # if da_const["fixed_seed"]==True: np.random.seed(r) - # u_ens = np.random.normal(m_const["u_ref"],da_const["u_std_ens"]) - # u_ens = m_const["u_ref"]*error_u/100.+(u_ens-m_const["u_ref"])*error_u_ens/100. - # #print('u_ens',u_ens) - # C_adv = C_adv + semi_lagrangian_advection(C[:,0],m_const['dx'],u_ens,da_const['dt']) - # #C_adv[:,0] = C_adv[:,0] + semi_lagrangian_advection(C[:,0],m_const['dx'],10.,da_const['dt']) - # C[:,0] = C_adv/np.float(random_samples) - # - # for i in range(1,m_const['nx']): - # C[:,i] = np.hstack([C[-1,i-1],C[:-1,i-1]]) - # - # - # #for n in range(m_const["nx"]): #Not sure if the if loop is much quicker than just calculating the dot product everywhere ;) - # # if response_s[n] != 0: CdJdX += C[n,:]*np.dot(dJ[n,:],dx.T)/(da_const['nens']-1) - # # Somewhat quicker way that avoids the loop - # CdJdX=np.sum(C*np.dot(dJ,dx.T)/(da_const['nens']-1.),axis=0) - # - # #This is where shit gets real! - # CdJdXH = np.dot(CdJdX,H.T) - # vr_total = - np.dot(CdJdXH,np.dot(HAHtRinv,dJHdxt).T) - # J_fc= fc_response - # dJ_fc = J_fc-np.mean(J_fc) - # real_reduction=np.var(dJ_fc,ddof=1) - np.var(bf_response,ddof=1) - - # - # - -######################################################################################################################## -# 2023 -######################################################################################################################## -def vr_reloaded_22_locsens(background,truth,m_const,da_const,sat_operator, - func_J=sum_mid_tri, - sens_loc_flag=0,sens_loc_length = 2000,sens_loc_adv_error=100, - reduc = 1,reg_flag=1, - quad_state = None,dJdx_inv=None,alpha=0.01,mismatch_threshold=0.1, - iterative_flag=0,explicit_sens_flag = 1,exp_num=0,obs_seed=0,model_seed=0): - - """ - Version of vr_reloaded_22 that includes the possibility to apply localization to the the sensitivity. - Takes only the background and truth, then caculates the quad, finaly calculates the response function and the variance reduction. - - The quad can be supplied to save time, which makes sense when you comparing different methods for the same experiment calculating. - - Should also return the dJ values of all 4 ensembles (analysis, background, forecast, blind forecast) - - - -reg_flag added to use L2 regularization - - not implemented: - - state model reduction - - - Speed up options: - - If the same forecast can be recycled, the quad can be calculated once and than passed on. - - If the sensitivity can be recycled, that can be calculated once and then reused. - - Don't use LETKF - - First getting the explicit all at once verion working, will then add the various approaches - - to be implemented: - -iteratively and all at once - -explicit vs implicit - -reduced model spacing by subsampling the grid. Reduc is the spacing, eg reduc 3 means every third grid point is used. - should find the nearest reduced model grid point for each observation. - - - - """ - if iterative_flag ==0: from scipy.linalg import sqrtm - ########################################################################### - #First, need we calculate the quad (analysis, forecast, blind forecast) - #A precomputed quad_state can be supplied to quickly use different response functions - ########################################################################### - if type(quad_state)== type(None): - quad_state = single_step_analysis_forecast_22(background,truth,da_const,m_const,sat_operator,obs_seed=obs_seed,model_seed=model_seed) - - ########################################################################### - # Next we need the response functions. - # We only really need the forecast and blind forecast, but for fun I'll calculate all for now - ########################################################################### - - #For now I am not worried about being efficient - nobs = len(da_const["obs_loc"]) - obs = np.arange(nobs) - nens = da_const["nens"] - nstate = len(truth) - - bf_response = np.zeros(nens) - fc_response = np.zeros(nens) - an_response = np.zeros(nens) - bg_response = np.zeros(nens) - for n in range(da_const["nens"]): - bf_response[n] = func_J(quad_state["bf"][:,n]) - fc_response[n] = func_J(quad_state["fc"][:,n]) - an_response[n] = func_J(quad_state["an"][:,n]) - bg_response[n] = func_J(quad_state["bg"][:,n]) - - J_dict = {} - J_dict['bf'] = bf_response - J_dict['bg'] = bg_response - J_dict['an'] = an_response - J_dict['fc'] = fc_response - J_dict['tr_bg'] = func_J(quad_state['tr_bg']) - - - - x = quad_state['bg'][:,:] - - dx = x.T-np.mean(x,axis=1) - dx = dx.T - dx_orig = dx+0 - - A = np.dot(dx,dx.T)/(dx.shape[1]-1) - - J = bf_response - dJ = J-np.mean(J) - dJ_orig = J-np.mean(J) - - if sens_loc_flag==1: - X_J =quad_state['bf'][:,:] - dX_J = X_J.T - np.mean(X_J,axis=1) - dX_J = dX_J.T - - dji = dX_J*1 - dji[0:100,:] = 0. - dji[200:300,:] = 0. - - - ############################################################################################### - # Sensitivity - ############################################################################################### - #Covarianz between reponse function dJ and state ensemble dx - cov_dJdx_vec = np.dot(dJ,dx.T)/(dx.shape[1]-1) - - if explicit_sens_flag==1: - #If a sensitivity is provided it is used instead of calculating it - if type(dJdx_inv) == np.ndarray: - #testing supplied sensitivity - rel_error_sens = np.sum(np.abs(A.dot(dJdx_inv)-cov_dJdx_vec))/np.sum(np.abs(cov_dJdx_vec)) - if rel_error_sens>0.05: - print('using supplied sensitivity has a relative error of:',rel_error_sens) - #Computing the sensitivity, highly recommend using the regularized version before doing so. - else: - if reg_flag == 1: - dJdx_inv = L2_regularized_inversion(A,cov_dJdx_vec,alpha=alpha,mismatch_threshold=mismatch_threshold) - else: - A_inv = np.linalg.pinv(A) - dJdx_inv = np.dot(A_inv,cov_dJdx_vec) - if sens_loc_flag==1: - da_const_wide = da_const.copy() - da_const_wide['loc_length'] = sens_loc_length - C_sens = loc_matrix(da_const_wide,m_const) - C_adv = C_sens*1. - for nn in range(m_const['nx']): - C_adv[:,nn] =semi_lagrangian_advection(C_sens[:,nn],m_const['dx'],+m_const['u_ref']*sens_loc_adv_error/100. ,da_const['dt']) - - sum_loc_cov_adv_djidX=np.sum(C_adv*np.dot(dji,dx.T),axis=0)/(da_const['nens']-1) - dJdx_inv = L2_regularized_inversion(C_sens*A,sum_loc_cov_adv_djidX,alpha=alpha) - - estimated_J = bf_response + 0. - - - #if iterative_flag ==0: - - vr_individual = 0. - - - if explicit_sens_flag ==1: - #Tanjas approach of calculating the square root K following Kalman gain, formula (10) Whitaker and Hamil 2002 - sqK = square_root_Kalman_gain_observation_deviations(x,m_const,da_const,sat_operator) - bg_obs_deviations,bg_obs_ol = state_to_observation_space(x,m_const,da_const,sat_operator) - dx_prime = dx - np.dot(sqK,bg_obs_deviations) - - #estimated J calculated by updating the original dJ - estimated_J = estimated_J -np.dot(dJdx_inv,np.dot(sqK,bg_obs_deviations)) - - #Using the cheaper variance calculation instead of going to A-B - new_J = np.dot(dJdx_inv.T,dx_prime) - vr_total = np.var(new_J,ddof=1)-np.var(np.dot(dJdx_inv.T,dx),ddof=1) - #print('all at once:',vr_individual) - dx = dx_prime - - - J_dict['es'] = estimated_J - - J_fc= fc_response - dJ_fc = J_fc-np.mean(J_fc) - real_reduction=np.var(dJ_fc,ddof=1) - np.var(dJ_orig,ddof=1) - - return vr_total,vr_individual,real_reduction,J_dict,dJdx_inv,quad_state,dx \ No newline at end of file diff --git a/getting-started.ipynb b/getting-started.ipynb index ecea1d5874e1520287cfe7485ffa8a7751834d1d..b0a511edcd34cadafb027dc2f2286698cea83e02 100755 --- a/getting-started.ipynb +++ b/getting-started.ipynb @@ -19,7 +19,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -39,7 +39,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -54,7 +54,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -63,12 +63,12 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "<Figure size 540x540 with 10 Axes>" ] @@ -107,7 +107,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "<Figure size 720x288 with 1 Axes>" ] @@ -117,7 +117,7 @@ }, { "data": { - "image/png": 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\n", + "image/png": 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YsSIbN27M1/vKD3Xq1OH27dt8/vnn7Nq1i3feeYc+ffoU2v7zYuXKlYwYMYJr167RqVMnatasiVKpxM/Pj5SUFMzNzfnmm2/ylaAikZRXpECTSMoxs2fPpnfv3iQlJTFv3jw2b9783PXr1atH5cqVefjw4QsHvI8cOZKOHTuya9cuzpw5w6NHj/D19cXU1JRatWrRtm1b+vfvn2Oph3SCg4MJDg7O1+vl1N8yJ9555x3q1avHli1bOH/+PD4+PtjY2NCpUycGDhz4XK9W06ZNqVSpEo8ePcrRe5ZOzZo12bNnD1u2bOHo0aNaYebi4kLbtm15//33CzUhYurUqcTHx/Pvv//i7+9f7OUsHB0d2blzJ1u3buXAgQNaYebg4MCrr77KBx98oC0BI5FIckah0UWQgkQikUgkEokkV2QMmkQikUgkEkkJQwo0iUQikUgkkhKGFGgSiUQikUgkJQwp0CQSiUQikUhKGGUqi1OtVhMXF4eBgUG++8ZJJBKJRCKR6AKNRkNKSgpmZmbZ2s2VKYEWFxeXa8sWiUQikUgkkpJI7dq1sbCwyDJWpgRaeqXv2rVrF3mbGEnB8Pb2xsvLS9dmSPJAHqfSgzxWpQN5nEoHxXWckpOTuX37drZOJVDGBFr6tKahoSFGRkY6tkaSF/IYlQ7kcSo9yGNVOpDHqXRQnMcpp7AsmSQgkUgkEolEUsKQAk0ikUgkEomkhFGmpjglEolEIiltqNVqgoKCiIuL07UpkjT09fXx8fEptP2ZmZlRpUqVbJmaz7Wh0F5dIpFIJBLJCxMeHo5CoaBOnTovdAGXFB1xcXGYmZkVyr7UajUPHz4kPDwcBweHfG8nvwkSiUQikeiQJ0+e4OjoKMVZGUWpVOLo6Eh0dPQLbSc9aBKJJBsajYaHu3aTfOEisRVsMHetrmuTJJIyi0qlyrHMgqTsYGBgQGpq6gttI+W6RCLJRuD2ndz/4SfUPr54z5hNzC1ZAFoiKUpk95uyzcscXynQJBJJFh6f/IfAbdu1z1Xx8dyYM1+KNImkHHDu3DneffddXZvxUvj4+NCnTx86derEjBkznuux2rlzJ1OnTtU+T05OZuHChfTq1YuuXbty9uxZ7bLFixfTtWtXunXrxt69e4v0PWRGCjSJRKLlqY8vd9Z9kW1clZDAjTnzeep7SwdWSSQSSd5MnjyZ2bNnc+jQITQaDTt27Mi2TlJSEitWrGDx4sVZxr/99luioqL4/fffWbNmDXPnzkWj0XD27FmuXbvGH3/8wZYtW5g3bx4JCQnF8n5kDJpEIgEgMTQU3yVL0aSkAGBSpQqqDm3R/L6HlOinWpHmOXcWlu5uOrZWIpEUFVFRUYwYMYKwsDDq1avHnDlzMDQ0ZP/+/axbtw5TU1Pc3d1RqVR8/vnnWbZt164d9erVw8fHh+XLlzNr1ixcXV25e/cuHh4eNGzYkN9//53o6Gi++OILatSowdKlSzl9+jRKpZIOHTowbtw44uLimD9/Pnfu3EGlUvH+++/TrVs3tm7dmk14NWvWjKFDh5KYmEiDBg0A6NOnD+vWrWPQoEFZ1r1w4QJqtZrJkydz7do17fiBAwdYvnw5CoWCWrVqsWnTJjQaDSqViqSkJFJTU0lISCjWNpJSoEkkElJj47g5fzEp0U8BMLCyxGP2dG4EBeG+cB7eM+eSEh2NOjGRG3MX4DlnJpYe7jq2WiIpezzc/QcPtm1HnZhY6PtWGhvjMrA/lXv1eO56QUFBbNiwgapVqzJx4kS2bdtG9+7dWbx4Mb/99hv29vZMmDABc3PzHLdv3bo1a9asISgoiFu3brFkyRLc3Nzo1KkTDg4ObN++nQ0bNrB9+3aGDh3KyZMn2bdvHwkJCUybNo2kpCQ2bdqEp6cnS5cuJTY2lgEDBlC/fn0GDx7M4MGDs73mlStXsLe31z63t7cnNDQ023qvvvoqr776Krt27coyfv/+fS5cuMD8+fNRqVSMHj0apVLJq6++yo4dO2jdujXx8fFMmjQJExOT/HzcBUYKNImknKNOTeXW8pUkBAUBoDAwwG3aZxg7OkJQEKYuLngtnIf3rLmkPHkiRNq8hXjMnoGVp4eOrZdIyhYPd/9RJOIMQJ2YyMPdf+Qp0Jo0aUK1atUA6N69O7t27cLJyYmGDRvi6OgIQK9evThy5EiO29evX1/7v52dHR4e4jxRsWJFWrRoAUClSpUICgrC0dERIyMjBgwYQNu2bZk0aRJGRkacOXOGxMREfvvtNwDi4+O5c+cOJ0+ezNGD1qlTpyyB+BqN5oUC81UqFSEhIWzdupVbt24xYsQIDh48yP79+9HT0+PUqVM8efKEIUOGUL9+fa2nriiRAk0iKcdoNBr8vt7Mk/+uasdqTRibbQrT1MVZiLSZc7Qi7eb8RXjMno6Vp2dxmy2RlFkq9+pRpB60vMQZiCr66Wg0GvT19VEqlajV6ny9TuYm489OCerp6WV7rZ07d3L+/HlOnjzJgAED+PHHH1Gr1SxfvhzPtPNLeHg4VlZWGBgY5OhBe/jwIY8fP9Y+f9GisHZ2dnTt2hWFQoGbmxuOjo74+/tz9OhRBg4ciIGBAfb29rz++utcvHhRCjSJRFK0PPpjL6GHDmufOw/sj33r13Jc19S5Cl6L0kRaVJpIm7dIeNK8pEiTSAqDyr165EtEFSWXLl3i0aNHVKxYkd27d/Paa6/RqFEj5s+fT1hYGPb29uzfvz+LEHtZbt68yYIFC/jxxx9p0aIFN2/exN/fn+bNm7Nt2zYWLlxIWFgYvXr14pdffsHFxSXH/VSuXBkjIyMuXbpE48aN2bNnD61bt863HW3btmX//v14eHgQGBhISEgI1atXx83NjSNHjtC2bVvi4+P5999/+eyzzwr8vvODzOKUSMopEecuEPDd99rndq1fw7n/28/dxrRKFbwWzsegQgUA1ElJ3Jy/iOjr3kVqq0QiKT5q1qzJ9OnT6d69O46Ojrz11lvY2Ngwc+ZM3nvvPfr27UtqamqhCDQPDw8aNGhAt27d6N27NzVr1qR169aMGzeOxMREunXrxtChQ5k8eXKu4iydFStWsGTJEt58803i4+MZMmQIAGvXrmXbtm3P3XbSpEmEhYXRtWtXPvzwQ2bNmoWFhQUffvghqampdO7cmX79+tGzZ0+aN29e4PedHxQajUZTLK9UDCQlJeHt7Y2Xl1ehfHEkRUf6XY5EN8T6+XF92iztNIqFuxte8+egfGY6IrfjFB/0MM2TFgWA0tAQ91nTsa5Xt+iNl+SI/E2VDnI6Tj4+Pri7l+ykm6ioKH788UfGjRuHUqlk4cKFVK1atdTWTMuLwuzFmU5Ox/l5ukV60CSSckZSRAQ+C5doxZlxRUfcp03JJs6eh2mVytRdNB9DGxsA1MnJ+CxYzJOr1/LYUiKRlEasra15+vQp3bp1o3v37sTGxtKvXz9dm1WmkTFoEkk5QpWYiM+iz0mOiARAz8wU95nTMbCyeuF9mVSupI1JS46IFCJt4RLcZ0zFukH9vHcgkUhKDQqFgpkzZ+rajHKF9KBJJOUEjUrF7VVriLvnJwaUStymTMLUucpL79OkUiW8Fs3H0NYWSPOkLfo8S1aoRCKRSF4cKdAkknJCwA8/EXnugvZ5jdEfFIqny8TJSYg0OztAiLSbC5cQdflKgfctkUgk5RUp0CSSckDI4b94tPsP7fNKvXpQseMbhbZ/E6eK1F00DyN7IdI0KSn4LF4qRZpEIpG8JFKgSSRlnCdXr+H35Tfa5zbNXqHakHcK/XWMK1bEa9H87CLt0uVCfy2JRCIp60iBJpGUYeIDg/BduhyNSgWAmWt1an/yMYpnqnkXFsaOjkKkOYieeOkiLfLipSJ5PYlEIimrSIEmkZRRUqKj8Vm4GFVcPACGNja4z5yGnrFxkb6usaMjXgvnY5TWZkWTmorvkmVEXrhYpK8rkUgKzrlz50ptbbN3332Xc+fOPXedR48eMXjwYN58801Gjx5NXFxcruuePn2aoUOHZhtPTU2lf//+2obrKpWKOXPm0K1bN7p27cqWLVsK9D7SkQJNIimDqFNS8FmyjMSQUACURka4z5qGUVq2ZVFj7OiA16J5GDlmEmmfLyfy/IU8tpRIJJKiY968eQwaNIiDBw/i5eXFxo0bs62jVqv58ccf+eSTT3LsP/rFF18QEBCgfb5r1y6ePHnCH3/8wc6dO9mxYwc3btwosK2yDppEUsbQaDTcXb+RGB9fMaBQUPvTiZi7uharHcYODtRdNB/vmXNIDAkVIm3pCupMmYRts1eK1RaJpDTxYNt2An/Zka91HTt2oObY0VnG7n6xidDDR7Kt6zygHy4D++e5z6ioKEaMGEFYWBj16tVjzpw5GBoasn//ftatW4epqSnu7u6oVCo+//zzLNsuXbqU06dPo1Qq6dChA+PGjWP9+vU8evSIe/fuERUVRf/+/Rk5ciS7du3i999/58mTJ7Rt25YhQ4Ywe/ZsQkJCUCgUfPrpp7Rs2ZLQ0FCmT59OTEwMYWFh9O7dm48++ojk5GRmzJiBt7c3lStXJiqts8lff/3Fhg0bsthVvXp1li9fzoULF/jiiy8A6NOnD++88w6TJ0/Osu69e/fw9/fX9gjNzOXLl/H19aVt27basVq1alG/fn2USiWmpqY4OzsTHBysbfT+skiBJpGUMQK37+TxiZPa59WGD9GZIDKyt8dr4Xy8Z87WirRby1ZQZ/In2DZvphObJBLJ8wkKCmLDhg1UrVqViRMnsm3bNrp3787ixYv57bffsLe3Z8KECZibm2fZ7uHDh5w8eZJ9+/aRkJDAtGnTSEpKAsDb25tffvkFtVpNnz59aNGiBQChoaHs378ffX19Jk6cSN++fWnfvj1hYWEMGjSI3bt3s3fvXm2vzpiYGNq0acO7777L77//DsCBAwcICAigRw/RZP6NN97gjTeyZ6mHhYVhbm6Ovr6QPvb29oSGhmZbr1atWsyePRtv76w9hmNjY1myZAmbNm1ixYoV2vEGDRpo/798+TLXrl1j2bJlL/qxZ0MKNImkDPH45D8Ebtuufe7YqSOVenTXoUVgZG+H16IFeM+YTWJISJpIW0mdyZ9i20KKNImkpNGkSROqVasGQPfu3dm1axdOTk40bNgQR0dHAHr16sWRI1m9dI6OjhgZGTFgwADatm3LpEmTtP0lu3Xrpu1t2a5dO/79918qVKiAh4eHVjCdOXMGPz8/1q1bB4hYr8DAQEaMGMG///7L5s2buXPnDikpKSQkJHD+/Hn69xcewWrVqtGwYUMgdw/atGnTUCgUWcafff485s2bx6hRo7BLq/n4LBcuXGDixImsWLECq5fozvIsUqBJJGWEpz6+3Fn3hfa5dYP6uH4w4oVOQEWFkZ0tXovnC5EWHIJGpeLW8pXCk9aiua7Nk0hKFC4D++drKjI3ao4dnW3a80VIF0wgQib09fVRKpU5xmM9u93OnTs5f/48J0+eZMCAAdopQr1MmeNqtVr73DhT0pJareb777/H2toaEB4vW1tbPv/8cwIDA+nWrRsdOnTgzJkzaDQaFAoFGo0mm925edBSUlKIiYlBpVKhp6fH48ePcUhLZsqL2NhYzp49y+3bt1m/fj3BwcH8+++/6Ovr06NHDw4fPszcuXNZvXo1zZoVzo2nTBKQSMoAiaGh+C5ZiiYlBQCTKlWoM/lTlPol5x7MyNYWr0XzMa7kBIjWU77LVhJ++qyOLZNIJJm5dOkSjx49Qq1Ws3v3blq2bEmjRo24fv06YWFhaDQa9u/fn+3m7+bNm7zzzju88sorfPbZZ9SoUQN/f38Ajhw5QnJyMtHR0fz999+8+uqr2V63efPm/PzzzwDcvXuX7t27k5CQwOnTpxkxYgSdO3fG39+f0NBQ1Go1LVq04M8//0StVvPw4UMuX35+zUUDAwOaNGnC/v37Adi9ezetW7fO12dibm7OqVOn2LNnD3v27KFdu3ZMmDCBHj16cO3aNebOncv//ve/QhNnID1oEkmpJzU2jpvzF5MS/RQAfUtLPGZNQ9/cTMeWZcfI1jYtJm0OiY8egVrNrRWrgInYtWqpa/MkEglQs2ZNpk+fzuPHj2nevDlvvfUWenp6zJw5k/feew9DQ0OqVKmCpaVllu08PDxo0KAB3bp1w8TEhEaNGtG6dWtu3LiBkZERgwYNIjY2llGjRlGzZk2uXbuWZfuZM2cye/ZsuncXYRnLli3D3NycUaNGMWXKFIyNjalYsSJeXl4EBQUxaNAg7ty5Q+fOnalcuTK1a9fO873NmTOHqVOnsmnTJpycnFi1ahUA27ZtIywsjI8++uiFP69NmzahUqn47LPPtGMTJkygffv2L7yvzCg0mf2DpZykpCS8vb3x8vLSzntLSiaXLl2icePGujaj1KNOTcVnwWJtc3KFgQFeC+Zi6e5WKPsvquOUHBmF98zZJDx8JAaUSup8+jF2r7Yq9NcqL8jfVOkgp+Pk4+ODu7u7jizKH1FRUfz444+MGzcOpVLJwoULqVq1ar5qpq1fvx6A8ePHF7WZhUZcXJw2Zq6wyOk4P0+3yClOiaSUotFo8Pt6s1acAdSaMLbQxFlRYmhTAa+F8zGpUlkMqNXcWrmGxydP6dYwiUSSI9bW1jx9+pRu3brRvXt3YmNj6devn67NKtPIKU6JpJTy6I+9hB46rH3uPLA/9q1f06FFL4YQafPwnjmXhKAgUKu5vXotoClV70MiKQ8oFApmzpz5UtuWJs9ZSUJ60CSSUkjEuQsEfPe99rld69dw7v+2Di16OQwrVMBr0TxMnKuIAbWa26vX8fjEP7o1TCKRSHSMFGgSSSkj1s+P26vWQFr4qIVbHWqNH1Miymm8DIbW1ngtnIepi7MYUKu5vWYdYcdP6NYwiUQi0SFSoEkkpYikiAh8Fi5BnZgIgJGjA+7TP0NpaKhjywqGobU1ngvmYVrVRQyo1dxZs56wv4/r1C6JRCLRFfkWaIGBgYwbN46mTZvStGlTpkyZQmRkZJ7b/fPPPwwaNIj69evTsGFDhg0bxn///Vdo+5dIyguqxER8Fi4hOUL8LvTMTPGYNQODQqhYXRIwtLbCa8HcDJGm0XBn7QbCjv2tW8MkEolEB+RLoEVFRTF06FD+++8/Ro4cyfDhwzl27BjDhw8nOTk51+3Onz/P+++/T0xMDBMnTmTs2LE8ePCAd955J0v9k5fdv0RSXtCoVNxetYY4P1H0EaUStymTME2P3SojGFilibRqVcWARsOddV8QevSYbg2TSCQlnjNnztC9e3c6duzI6tWrn7vumjVrtOU/0jl9+jRDhw7NMhYaGppjUd3iIF8CbcuWLYSEhPD999/zwQcfMHr0aNatW4evry+7d+/OdbvFixfj5OTEjh07GDZsGCNHjmTHjh2Ymppm+fBedv8SSXkh4IefiDx3Qfu8xugPsG5QX4cWFR3pIs2sejUxoNFwd/1GQo8c1aldEomk5JKYmMj06dPZuHEj+/fvx9vbmxMnssexxsTEMH36dL777jvtmFqt5n//+x+ffPJJlnZWJ06cYMiQITx+/LhY3sOz5KvMxr59+2jatCk1atTQjrVs2ZLq1auzb9++HGuhREdH4+vry/DhwzExMdGO29nZ8corr3D69OkC7V8iKS+EHP6LR7v/0D6v1KsHFTtm7zNXljCwtMRz/lxuzJ5HnL+/VqSh0eD4RgddmyeRlFlSU1OZO3cud+7cITw8nDp16rBq1SrCw8MZN24ctWrVwsfHB1tbW9auXavtm5lO8+bN8fLy4vHjx0yZMoVvv/0WAwMDgoKCaNeuHaamptom619//TVWVlZMnz6dO3fuADBo0CD69etHeHg4s2fPJiQkBIVCwaeffkrLli1ZvXo1x48fz/Ka3bt3p169elStWhVnZ2ft2MGDB2nTpk2WdY8ePUq1atUYPny4duzevXvcu3ePBQsWaHuHAvz666+sX79e29mguMlToEVHRxMYGEinTp2yLfP09Mz2QaVjbm7OwYMHs4izdKKiorSNUl92/xJJeeDJf1fx+/Ib7XObZq9Qbcg7OrSo+DCwtMBz/hxuzJmnndq9u2ETGo2mzAtUSTnmB+B/RbTv94Ahz1/lypUrGBgYsH37dtRqNUOHDuXEiRN4enri6+vL4sWL8fDwYPz48fz555/ZOglERUXx/vvv06xZM86dO8fVq1fZt28f1tbWtGzZks8++4xdu3Yxbdo09u3bh7u7O9HR0ezevZvQ0FBWrlxJv379WLRoEX379qV9+/aEhYUxaNAgdu/ezcSJE5k4cWI2u/fu3Yu9vb32uYODA6GhodnW69WrF0CW6c1atWqxaNEizp07l2XdZ6dAi5s8BVr6G3R0dMy2zN7entjYWGJiYrCwsMiyTE9Pj2rVqmXbxtfXl8uXL2vndF92/xJJWSc+MAjfZSvQqFQAmLlWp/YnH6NIu7kpD2SItPnE3fMD4N4XX4JGQ8VOHXVsnURS9njllVewtrZm69at+Pn5ERAQQHx8PAC2trZ4eHgAQtRER0fnuI/69TPCL2rXro2TkxMAFSpUoEWLFgBUqlSJp0+fUqtWLfz9/RkxYgStW7dmypQpgIgn8/PzY926dYDw7AUGBnLw4MEcPWgODg5ZSg1pNJpSW3oonTwFWlxcHECOnrD0vlHx8fH5ElBxcXHaZqIffPBBoe9fIikrpERH47NwMao4cWI0tLHBfeY09IyNdWxZ8WNgYYHX/Dl4z55P3L17ANzb+BUatQanztk97xJJqWYIeXq5ipKjR4+ybt06hgwZQp8+fYiKiiK9ZXfmXpEKhYLcWnkbZzpPGRgYZFmm98wNZoUKFdi3bx+nT5/mxIkT9O7dm3379qFWq/n++++1U6hhYWHY2tri7u6eowft/PnzWWLFHj9+jIODw4u9+RJGngItc8BcbiiVeecaJCQkMHr0aHx9fRk1ahRNmzYt1P1nxtvb+4XWl+iGS5cu6dqEEokmNZXkH39GE5LmnjcwgL698A4IgICAYrenpBwnTd+eKH7ahuZRMAB+X37Ngwf30W8iG4SnU1KOleT5PHuc9PX1tc4KXXPy5Enat2/Pm2++SVBQEP/++y+NGzcmISEBtVqttTO9wkJOdqePJSYmolKptM/VajUJCQnExcVpt9+/fz/79+/n888/p1GjRlrPWZMmTfj+++8ZOXIkfn5+jBgxgr179+bawLxmzZr4+fnh4+ND5cqV2bNnDz179sz1c83J/mftzbysMI5PcnLyC/1G8xRo6R9GUlJStmXpY3l1fH/69CmjRo3i8uXL9O3bN4v6LYz9P0tOXeElJYtLly7RuLG8sD6LRqPhzup1PA4MEgMKBW6TP8W22Ss6saekHafU+g24MXc+sXfuiuf7D+FSxRmnrp11bJnuKWnHSpIzOR0nHx+fF77OFRWDBg1i0qRJ/PXXXxgYGNC4cWPCwsIwMTFBqVRq7TRMK46dk93pY8bGxujp6WmfK5VKTExMMDMz027/xhtvcOLECfr164eRkRE9e/akQYMGzJ07l9mzZzNgwAAAli9f/lyPmJmZGUuXLuWzzz4jKSmJNm3a0LNnTxQKBTNmzKBdu3a0b99eu35O9me2Ny4uLsuywjg+hoaGWaZ/Qeic3JxKeQq0SpUqAeSYZhoWFoalpSWmpqa5bh8REcGIESPw8fGhf//+zJs3L8u8cEH3L5GUJQK37+TxiZPa59WGD9GZOCuJ6Jub4TlvNjfmLiD2tsj68vv6W0CDU9cuujVOIikD1KlThz///DPHZceOZdQjzK0B+q1bt7T/N2vWjGbNmuW5/dKlS7Ptx9HRka+++ir/hgMtWrTgjz/+yDa+aNGibGM52f+svelkfk/FSZ5zh5aWllSpUoUbN25kW3bz5k28vLxy3TY2NlYrzoYNG8b8+fOzBe0VZP8SSVni8cl/CNy2XfvcsdMbVOqhm/Tukoy+mRmec2dhUae2dszv6808+nOfDq2SSCSSwiVfwV0dO3bk7Nmz3EsL0AWRYeHv70+XLrnftc6fPx8fHx+GDBnCtGnTCn3/EklZ4amPL3fWfaF9blW/Hq4fjCz1WUhFhb6ZGR5zZ2FRp452zP/b//Hoj706tEoikUgKj3wVqn3//ffZs2cPw4YN47333iMpKYlvv/0WT09PevbsCYhempcvX6ZRo0Y4Oztz79499uzZg4WFBe7u7uzZsyfbftO3zc/+JZKySmJoKL5LlqJJSQHApEoV3KZMQqmfr59nuUXf1BSPuTO5OW8hMb5iCsJ/83doNBoq95SeR4lEUrrJ1xXAxsaGn376iSVLlrBu3TqMjY3p0KEDU6ZM0QbaXbhwgWnTprFkyRKcnZ05f/48INoq5OY9Sxdf+dm/RFIWSY2N4+b8xaREPwVA39ISj1nT0DcvGQHDJR19U1M85szi5vyFxPj4AhDwvy2Ahso9e+jUNolEIikI+b5Fd3V15Ztvvsl1eZ8+fejTp4/2+cCBAxk4cGC+Dclr/xJJWUOdmsqt5StJCBIZmwoDA9ynf4ZxxYo6tqx0oW9qgsfsmfgsWMTTmz4ABPzve1BrqNxbeuAlEknp5MUKjEkkkkJBo9Hg9/Vmnvx3VTtWa/xYLN3ddGhV6UWItBlYerhrxwK2/EDQrt26M0oikUgKgBRoEokOePTHXkIPHdY+dx7YH/s2r+nQotKPnkmaSPP00I7d//5Hgn77XYdWSSQSycshBZpEUsxEnLtAwHffa5/btX4N5/5v69CisoNWpHl5asfu//ATQb/u0qFVEomkOGjXrh1BaSEjueHj40OfPn3o1KkTM2bMIDU1Nds6T58+ZcKECXTu3JnBgwdr67QmJyfz6aef0r17d3r27MmZM2cASElJoVGjRvTs2VP7UKX1UC4IUqBJJMVIrJ8ft1etgbQedhZudag1fowsp1GI6Bkb4zFrOlZ1M2oo3v9xK4E7ftWhVRKJpCQwefJkZs+ezaFDh9BoNOzYsSPbOmvWrKFhw4YcOHCAt99+W1vods+ePajVav7880+WLVvG1KlTAVHItmHDhuzZs0f7eLbn6Msg8/glkmIiKSICn4VLUCcmAmDk6ID79M9QykzlQkfP2Bj3WdPxWbiE6GvXAXiwdRtoNNJbKSn5vJ7DWD9gDBAP5FQedFjaIxx4K4flo4H+eb90amoqc+fO5c6dO4SHh1OnTh1WrVpFeHg448aNo1atWvj4+GBra8vatWu1zcxBeJKmT5/OnTuiy8egQYPo168fU6dOxcjIiOvXrxMXF8fo0aPp1asX69ev57///iM4OJh33nmHVq1aMXfuXJ48eYKxsTGzZs3Cw8OD27dvs2DBAuLj44mMjOSDDz5g4MCBPHnyhMmTJxMSEkKNGjW07SG3bt2aTXg1a9aMoUOHkpiYSIMGDQCR3Lhu3ToGDRqUZd3jx49rkxa7devG/PnzSUlJ0fYSValUJCQkaJvCX79+ncjISPr06YO+vj6TJk3S9hsvCFKgSSTFgCoxEZ+FS0iOiARAz8wUj1kzMLCy0rFlZRc9IyPcZ07LKtJ+/gWNRoPLgH46tk4iKZlcuXIFAwMDtm/fjlqtZujQoZw4cQJPT098fX1ZvHgxHh4ejB8/nj///JN33303y7bR0dHs3r2b0NBQVq5cSb9+4rcWGBjI9u3biYiIoE+fPrRq1QoQ04b79+8HYMCAAcyePRsPDw/u3r3L2LFjOXToEDt37mTMmDG0aNGCwMBAevTowcCBA1m3bh0eHh588803XLhwgQMHDgAwePBgBg8enON7s7e31z63t7cnNDQ023phYWHY2dkBopG9ubk5kZGR9O7dm99//53XXnuNp0+fsmrVKgAUCgXt27dn1KhR3Llzh/fff58///wTGxubAh0LKdAkkiJGo1Jxe9Ua4vz8xYBSiduUSZg6V9GtYeWAdJHmu3ipNmM2cNt2kiMicBk8CENrKZAlJZDjz1lmmsdyuzyW58Err7yCtbU1W7duxc/Pj4CAAOLj4wGwtbXFw0Mk4dSqVYvo6Ogs29aqVQt/f39GjBhB69atmTJlinZZnz59MDAwoGLFijRq1IhLly4BUK9ePQDi4uLw9vbOUjc1Pj6eqKgopk6dyj///MNXX33F7du3tfacP3+elStXau12dnYGcvegderUKUs4iUajyVd4iUajQalUsmHDBho0aMC2bdsICAhg2LBheHp6ahu6A3h4eFCvXj0uX75Mhw4d8tz385ACTSIpYgJ++InIcxe0z2t8+D7WDerr0KLyhZ6REW7TP8si0kIPH+HxyVNU6dubSj26oZc2VSGRlHeOHj3KunXrGDJkCH369CEqKgpNWsyskZGRdj2FQqEdT6dChQrs27eP06dPc+LECXr37s2+faJHbuaYLLVajX5ap5T0aUK1Wo2hoWGWrkMhISFYW1szYcIELC0tadu2LV26dGHv3r052pD+Grl50B4+fKgN+AcIDw/HwcEh23oODg5ERERgZWVFamoqcXFxWFtbc/ToUVavXo1CoaB69erUr1+fa9euceHCBRo1aoSLiwsgBJ2BgUGen3VeyCQBiaQICTn8F492/6F9XqlXDyp26qhDi8on6SLNpllGXIg6MZEHW7dxefR4Qv86gqYQsq4kktLO2bNn6dy5M3379sXS0pJz587lOyPx6NGjTJ48mddff52ZM2diampKcHAwAAcOHECj0fDw4UOuXbtG48aNs2xrYWFBtWrVtALt9OnTWpF1+vRpJkyYQIcOHTh58iQAKpWKFi1aaNe/du0aDx48eK59lStXxsjISOu927NnD61bt862Xps2bbQicP/+/TRp0gQDAwPc3Nw4cuQIAJGRkXh7e+Pu7s6tW7f43//+B4Cfnx8+Pj7Z3t/LID1oEkkR8eS/q/h9mdEdw6bpK1Qb8o4OLSrf6BkZ4TZtClGXLhOw5QcSAkU6fnJkJHc3bOLRH3upOvRdKjRuJLNqJeWWt99+m0mTJrFv3z4MDAxo1KhRnqUr0mndujWHDx+ma9euGBkZ0aNHD+rUqQNAYmIiffv2JTk5mfnz51OhQoVs2y9fvpy5c+fy7bffYmBgoPVWjR8/nkGDBmFkZISbmxuVK1cmKCiICRMmMHXqVLp27Yqrq6t2ivN5rFixgpkzZxIbG4unpydDhgwBYO3atTg4ODBw4EA++ugjJk+eTNeuXbGwsGDFihUATJs2jVmzZtG1a1eUSiWffPIJ1apVY+zYsUyfPp1u3bqhUChYunQp5ubm+f3Ic0WhedZHWYpJSkrC29sbLy+vLK5YScnj0qVLhXKHUVKJDwzi2mfTUMWJWAkz1+rUXbwAPRMTHVv2YpTV46RRqQg79jf3t/5CSlRUlmWWXp5UGzYEi1o1dWTdy1FWj1VZI6fj5OPjg7u7ey5blH6mTp1K06ZNs7SDLOnExcVhZla4PZFzOs7P0y1yilMiKQwSgDDxb0p0ND4LF2vFmaGNDe4zp5U6cVaWUejp4fhGBxp/uQGXwQNRZopBe+p9g2uTPuPWytUk5pDhJZFIJMWBnOKUSAqKBugGnAX1qlR8/JeRGCIu7Mq0LEIjW1udmijJGT1jY5z7vYVjxzcI3L6D0EN/aWPRwk+eIuLMvzh1eZMqb7+FgaWFjq2VSEonn3/+ua5NKJVID5pEUlA2A8dA46xBOVofp586o5diCgoFtT/9GPMarrq2UJIHhtZW1Bj1Pg3Xr8G2RXPtuCY1lUd/7OXSh2MI2rUbdXKyDq2UlGXKULSRJAde5vhKD5pEUhAeAZOANhD4wa+oFyZR1XcgFk9q8XTOLWybFbyatKT4MKlcCbepk3nq40vAlh+I8b0FgCounvvf/0jI/gO4DB6IfZvWKJTy/lZSOOjp6ZGSkoKh7CpSZklJSdGWFskv8gwjkRSEcUASRH5wgcDtv/Cw1u9cbzULPSNT7Ge8BisBta6NlLwolu5u1P18EW5Tp2BcyUk7nvQ4nDtr1nP1kynammoSSUGxtrYmNDQUtVqeLMoiarWa0NBQrF6wc4z0oEkkL8su4HdI+CgY310rtcPKtkbo7TJBMUohvGvHgC2Afc67kZRMFAoFti2aUeGVxoQePkLgL9tJiX4KQJy/PzfmzMe6YQOqDX0Xs+rVdGuspFRjZ2dHUFAQt27d0rUpkjSSk5ML1aNpZmambR+VX6RAk0hehihgLKjcErgaOBlNagoAJlWq4DZlEkpzffgN2AR8AtQHtgJtdWax5CVR6uvj1OVN7F9vw8Pfd/Noz5+o05oyP7nyH//9dxWHtm1wGTQQI/sXOwFLJABKpVJbhV5SMrh06RL16+u244uc4pRIXoYpoHms4YbzQlSpCQAY2triMXs6+uZptXMUwBjgHGAJtAfmAKk6sVhSQPRNTag6eCCNNm3A8Y0OkB6DptEQduw4l8eMJ+D7H0mNi9OtoRKJpEwgPWgSyYvyN/AtBLvtJcbEFwCDChXwWjgXY0fH7OvXBy4h4tXmp23/M1DCe6UnhoWhunad0KgnaNRqNCo1qNVo1GrQaNCo1Ti+0R79TMUcVUlJBO34VWQspa2rUatBrcn0f/o+1NQYOxplpsDZxLAw/L7eLNbJto+s/+uZmOC1cF4Wm6Nv3OTOmvUZ62ue3U6DwsAA5359qdS920t9Lka2NtQcN5pKPboS8MNPRF0QbWPUyck83LWb0L+O4tz/LSq+2QllIfTjk0gk5RMp0CSSFyEe1MOSSTaP4r7rzwAYWFvjtXAuJpUq5b6dGfAdwov2IUK0bQG6F7XBL45GpeLh73t4sG07mtRU7j5nXZtmTbMINE1KCkG/7sr3a7l++AFkEmjqxESiLlzM17Z6ZqbZbU9JISks7PkbJiTg/+136JuZ49Du9Xzb+iymLi54zJxO9HVvArb8QOzdewCkxsTg/+13BO/dj8s7g7F7taVsHSWRSF4YOcUpkbwAyROfoHxgyN16G1HrJ6NvaYnXgjmYVsmnO+wd4DJQFegBTASSiszcFyY1No7r02dx/8etaFLzMRf7bNbZi5aeKMD2GlUOGW8vsH3Qb7tQ5+c95oFVXS/qLf+c2p9OxMjRQTueGBLK7RWruDZ5KtHeNwr8OhKJpHwhPWgSST5J2BuM8TcOhLj8RbSdN/oW5njNn4Ppiwb31gbOAlOANcBJ4BegViEb/BLomZmin8kzpajoiL2HByiVKPSUKJTigUKBQqlE75ledUoDA1wGD9QuVyiVYlulEoVSken/tPFn6gIZ2driNv2zLK+hXVdPiUKRaX/6etnst6hTm8ZffZG2jp7YRya71ckp3Jg7Hz1jY9xnTM0yvVoQFEol9q1fxbZFM0IOHCJwx05SY2IBiL1zF+8Zs6nwShOqDXkHU5e8GzpLJBKJFGgSST6IvxeIZoiaFMOnBHj8iJ6ZGZ7z5rx8eQUjYC1iynMY0Aj4ChhUOPa+LAqFgprjxvLfxEk4de1MaPWq1HrllXxvrzQwwLnfWy/9+nomJgUq7qtnZIRexYrPXcdz7mz0zEzRe6YxcWGgNDCgUo9uOLRrS9Bvu3j05z40KSLDN+rCRaIuXcaxQztcBg7A0KZCob++RCIpO8gpTokkD+KDHhLR7xxmUVW5V/cbsNbgOW924bRw6gFcBRoAg4H3gGJMAow8fwF1moBIx9CmAo2/+gLnfm+VyWr5hjYVikScZUbf3IxqQ9+l8ab12Ld9HdJj0NRqQg8f4dKHY3nw8y+kxicUqR0SiaT0UvbOvhJJIZIQHMzdjzZS6b/uhDv9yxPXq3jMmYlFrZqF9yLOiMzOmYjEgSbAtcLbfU6kxMRwa/kqfBZ9zoNt27Mt1zM2LloDShgRZ89xa/kqbaP0wsLI3p7aH4+n/qrlWDfIqKmkTkoicPtOLn84luADBwslFk5SMtGo1SQ8fMTjf05zf+s2Ui9fITU+XtdmSUoBcopTIsmFxNBQvGfMo/aJcWiUqQQ0/gGP2TOwdKtT+C+mDyxAFLIdDDRFxKeNQtRTK0SiLl/hzrovSImKAuDh73uwafpK0byvUsCjP/bi/78toNFgYGVJ9fdHFHrWpblrdTznzSbqyn8EbPmB+ID7AKRER+P35TcE/7mPqkPewaZZU5nxWYpRp6aSEBREnJ8/sff8xF8/f9SJiVnWu3j0OA7t2uLUtTMmlZ+T/S0p10iBJpHkQNLjx3jPnEOFy/WxivDiXuOvqbV4DFaeHkX7wu0QU55DgNHAEeBbwLrgu1YlJBCw5QdCDh7OMu7Qrm25DlxPjooCjQaA4H0HMHJ0pHLPoql/UqFhA6zr1eXxiZPc/2kbyRERACQ8fITvkmVYuLtRbdiQciuWSxOqpCTi7z9IE2F+xN3zI+7+A23M4XO3TUggeN9+gvftx7phA5y6daFCo4ZlMqRA8vJIgSaRPENSeATeM+egeZBKtRtDiLb3xvabZljV9SoeAxyA/YhG69OBi4gsz+Yvv8unPr7cWbOexJAQ7ZiBlRU1xo7Gtln+kwDKIlXfHUxiaBgRp88AEPDd9xg72GPbogAf+HNQ6Onh0K4ttq1aErx3P0G/7kKVNuUV4+PL9c+mY9uiOVWHDH5+bT1JsZEaH0+cv3+aZ8yfOD8/4gODspeJyQUDKyvMarhiWqUywWfOogmP0C57cuU/nlz5D2Onijh16YxD+7ZZagtKyi9SoEkkmUiOjMJ71hwSg0NxuzYFhdoAvlFg3bCYe7IpgclAa2AA8CqwKG3sBW6y1SkpPNi2nYe/78lyMbFp3oyaY0ZhYGVVmFaXShRKJbU/Ho93RAQxvrdAo+H2qrV4LbTBok7tIntdPSMjqvTtjeMb7Qnc8RshBw5qa89FnP2XyPMXcOz0Bs79+2FoLY9TcZESHU2sn3+maUo/EoND8t4wDSMHe8yqV8e8hitmrtUxc3XF0KaCduo6on5dauob8GjvfqIuXtJ6bxODQ/Df/B33t27DoW0bnLp0LteebYkUaBKJluQnT4Q4exSMbXBzbEOaET8uCKuenrozqhlwBfgAmAocA34Acugo9SxJERHcnL9IG+8EoGdqiusHI7B/vY2MdcqE0tAQ9xlTuTZlGonBIaiTk/FZtIR6y5ZgnEfZjoJiYGmJ68jhOHXtzIOffib81GlAdHQI2X+Qx3+foHKfXlTq2b3Is0/LExqNhuSISK0ISxdk6dPOeaJQYFLJSSvCzNP+Glha5LGZAusG9bFuUJ/EkBCCDxwi9K+jqNJ6uKoTEwk5cIiQA4ewql8Pp65dsGnSCIVe9rp/krKNFGgSCeKu+casuSQEPUQv2QzX6yNJrR2H6eoS0DDTGtgOdAA+QpTk+DHt+XMwsLLKclK3qleXWhPGYWRvV0SGlm4MLC3xmDOTa5OnkRoTQ0r0U27OX0TdpYsxsHj+RbcwMHGqSJ3Jn1CpZ3cCtvzA0xs3ARGv9GDrNkIOHMJl8AAc2r4uL9YviEatJjEkJFvwfurTp/naXqGnh6mLM2bVq2NWQ4gx02rV0Dc1KZBdxhUrUn34UFwG9ufx8ZME79tP/INA7fLoq9eIvnoNI0cHnDq/KXrfmpsX6DUlpQeFRpPmXy0DJCUl4e3tjZeXF0byTrNEc+nSJRo3bqxrMwBRcuLGrLnE+QcAUPO/sTg8bIviggIa6ta2bFwH+gO+wDRgHs+9zYp/EMj1aTNxHtgfpy5vvnAQckk6TsXFUx9fvGfN1QZ7W3q44zl/TrE2PtdoNERdvETAlh9JCArKsszUxZmqQ9+lQuNGWbyg5fFY5YRGpSI+6CFx9/xE8H7adKUqIX8155SGhphWqyo8YjVchSir6oLS0LBQ7HvecdJoNERf9yZ43wEiz1/IFuOmNDLC/vXWOHXtglnVF+xgInkhiuv39DzdIj1oknJNamwcN+bM14ozq4h6OD5oB59R8sQZQF3gAsKTthg4DmwDjbOGyPMXsHmlSRYRZuriTONvvizwnX55wtLdjdoTJ3Br2UoAnt704cnVa9g0KT7xo1AosHmlCRUaNST06DEe/PwLKVFPACG6fRYsxqquF1WHvlu4NflKGerkZOLuPyDOzy8teN+f+Pv3UScn52t7PVNTMUVZvTrmNUTcmEnlyjrzUCoUCqzr1cW6Xl0Sw8IIOXCI0L+OaNuGqZOSCD30F6GH/sLSy5NK3bpg0/QV6VEto0iBJim3pMbFcWPuAuLu+QGgVBnh7jcFagJzdGvbczFDlN5oD4wCTT01QZ1/40H8L7h+MBKnrp2zrC7F2Ytj16oliUPDCNy2nVofTyhWcZYZhZ4eFTu+gX3r13i050+Cdu3W1tSKvu7NtUmfYdf6Vaq+o+MeYcVAanyCNpMyzs+PWD9/MR2Y70xKS8xcReB+egC/saNjiS1tYezgQLWh7+I8oB/hJ//h0d79WeJJn3rf4Kn3DYzs7ajY+U0c3+iQZ/ybpHQhBZqkXJIan8DNeYuIvXNHO1bPdDF6wSaiqn9p0DQDIVJ5CcMxFXD+5W30q5lzf/M2rBvWl+UZCoHKvXti17J5kScJ5Ac9Y2Oc+7+NY6c3CPxlByGH/tIKk/CTp4g48y9YWXLZxOSZBvVKUKT9r5cxnnczez3xv0KR0ag+1+1y+P+Z7TLbkXXd7PtDkWarWp1pqtKfxOBgbcZjXhja2WFeI1Pwfg1XDG1sSmVijJ6REY5vdMChQ3ue3rxJ8N4DRPx7Tnv8kx6Hc/+Hnwj8ZQd2rV+jUrcuL98jWFKikAKtBBNz6za+S1dgYG1FtWFDsK5XV9cmlQlUiYn4LFhEzK1b2rHa7SdhNrEavA+8rivL8k9KTAx+X39L+MlTKFrqU/XmO1T2646Nqin6ERYg9VmBUSgUJUKcZcbQ2poaH36AU7eu3P/hJyLPnQcQ5TkiIimPnT2NKzlh7uqqDd43c62OgaWlrs0qdBQKBVaenlh5epL0OJyQg4cIOXxEm+igTk4m7MhRwo4cxdLDHaduXbBt3kxOf5ZipEArwYSfPkNyRATJERHcmDUXhw7tqD58qMziKQCqpCR8Fi7h6U0f7Vj14SOxn9tClK5Ypjvb8kvUlf+4u+4LkiMjAdAoUwluuxerj+tiPq8atAA2IroRSAqVlKdPubNmHVXffUenXgrTKpVxn/4ZT2/6ELDlxyw3G2UWpRJT5ypp05NpNcaqV0Pf1FTXlhU7RvZ2VH13MM793+bxP6cI3ndAG6oBIm7y6U0fDG1tqdi5ExU7dpA1D0shUqCVYBw7tCf0yDFtfZywI8eIungZ1w9GYtuyeal01+sSdXIyvouXEn3dWztW7b2hVPLpLNor7aJQWioVFarERAK2/EjIgYNZxh3at6P6yOHiQtUH0ctzKKJN1EZA6vlCIeHRI27OX0RicAhxAfept/xzjGxtdWqTpYc7dZcuIjkikmuXLuLp7oFGrUajVoNajUajEX/VajRqFRqVGjSajOXadTXPbJd1ee7bqZ/zeunb5bTu819P/K/ByMFBO1VZmJmUZQWloSGO7dvh0K4tMb63CN67n4iz/6JRqQBIjojgwU8/E7h9J/avtcKpaxfMa9bQsdWS/CIFWgnG1MWZRhvW4vfNZiLOnAUg5ckTbi1bgU2zV3Ad9b7OLxClBXVKCr6fL+PJf1e1Y1WHvENl9x6ibEVfoLfOzMuT+KAgfBYuyVLR3MDKMq1VU9OMFSsDR4GFwHzgX0QNtZKYkVrKUCenkBItppOSIyLxWbAYr8ULdZ6EoVAoMLKzRWlnJyvPl1MUCgWW7m5YuruRFBFJyMFDhB76i5ToaAA0KSmEHTtO2LHjWLjVwalrF2xbNkepLyVASaZkpq9ItBjaVMDts0m4TZuCoY2Ndjzy3AWujPuYkIOHxR2nJFfUKSn4Ll1B1KUr2jGXQQOo0ru3iDkzAdbrzLx8YWhjo70rBrBp1pQG69ZkFWfp6CGyUI8BcYgenuuBMlPxUDeYVauK22eTtDE9cf4B3Fq+EnVaeyaJpCRgZGtD1cEDabL5K2pNnID5M2VYYnxvcXvlai6O/JDA7TtJfvJEN4ZK8kQKtFKCbfNmNNywBsdOHbVjqvh47m36Cu8Zs4kPeqhD60ou6tRUbq9cTdSFi9qxKv3ewrn/26JUxUlgBeCkKwvzh76pKbU+Go+emRm1PhovBHte/RnbIKZu3wAmIKY/I4vc1DKNdYP61Bjzofb5k8tX8PvqG8pQvW9JGUFpYIDD622ov2Ip9ZYtwb5NaxSZPGYpUVE8+PkXLo4Yxe3Va4m5fec5e5PoAinQShH6ZmbUHDMKr0XzMa6UoSie3vThv48/JXDHr/JuPhMalYo7q9cRcfacdqxyn164DBoADxGNx9sB7+nKwpzRqFREXryUbdzKy5Mm336JQ7vX8x9/aAf8CawC9iHaRJ0uNFPLJY4d2lGl31va56GHj/Bw127dGSSR5IFFndrU/uQjmnz7Jc4D+2NQwVq7TJOayuPjJ7k2eSpXJ00l7PhJ1GldNCS6Jd8CLTAwkHHjxtG0aVOaNm3KlClTiIx8sdvxmTNn8u677+a47K233qJOnTrZHhMmTHih1ygPWHl50nDtKqq81Uc73aJJSeHB1m1c/WQyMXfu6thC3aNRqbizdoO28TRApZ7dqTrkHRQoYCyQDHwFlKBci8SQELxnzsFnweIswjKdl8pYUwATgTOAIcKztghQPW8jyfNwGTQA+9dba5/f/+EnHp88pUOLJJK8MaxQAZcB/WjyzZfU/vRjLOrUybI89s4d7qxey8WRH/Lg519IjozSkaUSyGeSQFRUFEOHDiU5OZmRI0eiUqnYvHkzt27dYufOnRjmI7Nm586d7Ny5k6ZNs8fMaDQa7t27R4cOHejYsWOWZZUrV87nWylfKA0NqfruYOxebcXdDRuJvXsPgPj7D7g2ZRqVunXBZfBA9IyNdWxp8aNRq7m7YROPT5zUjjl17UK14UOF5+lXYA+ipEYJ6ZKj0WgI/eso/pu/01aKv7vxSyzc3fKeyswvTYDLwChgJqIg74+U+OndkohCoaDmuDEkhUfw1PsGAHfWrsfIzhZLD3cdWyeRPB+lgQH2rV/DvvVrxNy5S/C+A4T/c0rU00MkowVu30nQr7uwbdUCp65dsKhTW1YOKGbyJdC2bNlCSEgIf/75JzVqiBTd+vXrM3z4cHbv3k2/fv1y3ValUrFp0yY2bNiQ6zpBQUHEx8fTvn17evbs+YJvoXxjVr0a9ZYt4dHefTz4aZvoQadW8+iPvUT8e54aY0ZRoWEDXZtZbGjUau5t+oqwY39rxyq+2ZHq778nTi6RwDigEcKrVAJIjori7oZNRGWe1lQqceryJvrmZoX7YpbAz0AHYDxQHyHSOhXuy5QHlAYGuE+bwrXPppMQ9BBNaiqBO3/Dc85MXZsmkeQbi1o1sfh4PNWGDSH08F+EHDxEckRajUWVivCTpwg/eQqzGjWo1K0Ldq+1QmlgoGOrywf5muLct28fTZs21YozgJYtW1K9enX27duX63ZJSUn07t2b9evX07NnTxwdHXNc7+5dMSWXef+S/KPQ06Nyzx40XL8a6wb1teNJYWHcnLuA22vWk/I0RocWFg8ajQa/rzcTeviIdsyhQ3tcR72fcec3CQgHNlMiisyEnz7LlfETs4gzk8qVqLdsCS4D+xdNGrwCGAFcRBTnfRPRHF6Gnbww+ubmeMyegYGVFTbNXsFt6mRdmySRvBSG1lY493uLxl9vos7kT7J5guPu3ePO2vVcHPEB93/6maSICB1ZWn7I8+wfHR1NYGAgnTplv8X29PTk+PHjuW6blJREbGwsq1evpkuXLrRr1y7H9e6k9UNMF2jx8fGYlsPq0AXFuGJFPObO4vHfx/HfvIXU2FgAHv99nCeXL1N95HvYvfZqmXRTazQa/Dd/l6WIq33b16k59sOMZshHgO+AqYhgeR2SGhuL39ebs0zDAjh170rVdwejZ2RU9EZ4AOcRnsRlwAlgG1C96F+6LGHs6Ei9ZYsxsreXbXUkpR6lvj52r7bC7tVWxPr5EbzvAI9P/IMmLXEgJfopQTt/I+i337Ft0ZxK3bpg4e5WJq8ruiZPgRYaGgqQo/fL3t6e2NhYYmJisLCwyLbc3Nycw4cPo5+HF+DOnTuYmZmxZMkS9u/fT3x8PM7OzkycOJGuXbvm971IELExDu3aYt2oIf7f/o/wf0SQfEr0U26vXMPjE/9Q48P3MbK317GlhYdGoyFgyw8E/5nhzbVr/Rq1xo/JEGfxiNirWsBsXViZQcztO/h+vkw7jQCiuXOtj8YVf79VE+BLoD0wElHQ9lvgredtJHmWktazUyIpDMxdXak1fizVhr5L6OEjBB84RHJ4uFioVhNx+gwRp89g5OiAiZMThnZ2GNnbYexgj6Fd2l9bWzkl+pLkKdDi0toMmZhkr5ZtlHaXHx8fn6NAUyqVKJV5z6LevXuXuLg4YmJiWLZsGU+fPuWHH37gk08+ISUlhV69euW5D0lWDK2tqTPpE+zbtObepq9JTnNHR128xOVxN6g2ZDAVO7+ZIWBKKRqNhgc//cyj3X9ox2xbtaD2x+OzejNmA37AcYQo0SGGtjaoEpO0z+3bvo7r+++hb1bI8WYvwtuIJIIBaf9/iCjNIXlpHu3dT3J4ONWGyaaoktKNgaUlVd7qQ+XePYk8f4FHe/drk2MAkkLDSAoNy3ljhQIDa+ssos3I3g4je/u0hx16ZmbSA5cDeQo0dT6q1OdHhD2Pfv36oVarGTx4sHasa9eudOvWjeXLl9O9e3f0XmDqwNvbO++VygtKBYqRw9A7dhzVBRHnpE5MxO/rzfjvP4hB9y4odeRNu3Qpe62vFyX1xD+knvhH+1xZpzaxr7fm8n//acdMb5rittqN8N7hPDB/AAV/2QKj7NQB1cHDGHTtTIy7G1d9fXVtEgCKtQoqbaxExS8rEn8kHuMlxlwqCR9YKUKjVpP611FU5y4AEJqYgP4rTYrltQvjNyUpekr1cTI0gD49MWzVAtWFi6iuecPz6m9qNKRERZESFQW3bueyT0MUVpYorKwy/c34HwtznTgTdH2c8hRoZml39UlJSdmWpY+ZFfDOf+DAgdnGjI2N6dmzJxs2bODu3bvUeaZey/Pw8vLSevfKAlGXr2BSpTLGDg4vv5OWLXnq48vdDZtICAoCQBP0kJRvvqPK232p0rd3sbqhL126ROPGjQu0j8Cdv/Egkzir8Epj3D6bnPV9pCAK0TqC/Xf22FsVrxhVp6Tw9MbNLMkbAJpGjVD17Vv4WZqFQXNgEJgOMcX9HXeUs5UiuUL2qc4XGpUK37+OaZs2pB46Qq3GjbEpYpFWGL8pSdFTpo5Tl86okpJIDA4hKTycpLDHJD1Of4ST9Dic5KgoyMvRk5yM5nE4msfhOS9XKjGys8XIzk7rdTOyt8fIwT5tzA69HGb5CkJxHaekpKRcnUp5CrRKlSoB8Pjx42zLwsLCsLS0LLKAfpu03pPx8fFFsv/SQMLDR/guXYFSX5+aE8bm3Hsxn1i6u9FgzQptgKcmNVWUBti2nfBTp6k5bgyWbvkXwrokaNduHvz0s/a5daOG2cUZiDZO14DfgUIqJ5Zf4h884PbqdcQF3Kfe54uwqFNbu0yhUJRMcZbOm8BViH4nmgozKsBPiFi11nlsJ0Ghp0ftTz/Ge8YcYu/cAbWaW8tXUXfxAsxrykx1SdlCz8gIs2pVMatWNcfl6tRUkiMinxFumf6GPRbloZ6HWi3EX9hjwCfHVfQtzDGys8fIwS7tb5qQs7PDyMEeAyurUhfSk6dAs7S0pEqVKty4cSPbsps3b+Ll5VUgA0JDQ3nvvffo3Lkz48aNy7LM398fgCpVqhToNUorGo2G26vXok5MRA34Ll6KU/euVBv67kt7u5QGBrgMGoBtq5aiwG1a/7WEwCCuT52BU5fOuLwzCH1THQdqPYdHf+zl/vc/ap9b1a+H29QcxNktYB4i4L1X8dmnUal49Oc+7v/0szbz6faadTRYs7J4sjMLCyfwW+ZH45DGonZcG2AYsBzRQkqSK3pGRrjPnMq1KdNICg1DnZTEzYWLqbdsScE84RJJKUOpr4+xowPGjjl/7zUaDakxMUKwhT0mKfxxpv/F35To6DxfJzUmltSYWOLSdMOzKAwMhBcuU+xbRiycEHLKfBTdL07yVWSpY8eO/PDDD9y7d09bCuPMmTP4+/szYsSIAhng6OhITEwMO3fuZNiwYZibmwMQHBzMrl27aNasGfZlKOPwRVAoFFQf+R63V6wiKc31G/znPmJ8fKk96RNMnF4+c8ysqgv1Pl9E8IFD3P9xq6her9EQvG8/EefOU2P0B9g0KXlu+OB9B/Df/J32uaWXJ+4zpmYXPmrgfURCwPrisy8xNJQ7azfw9MZN7ZjCwICKb3YqvZlMXYG2wAKER/IPhEgbhuzm+xwMra3xmD2Da1Omo4qLIyXqCT4LFlN3yaKS7T2VSIoRhUKBgaUlBpaWmNdwzXEdVVISyeERwuOmnUrN5IkLD9d2QcgNTUoKicEhJAaH5LqOgbW1VrSlmpmirltXp6JNodFoNHmtFBkZSbdu3dDT0+O9994jKSmJb7/9FhcXF3755RcMDQ0JDAzk8uXLNGrUCGdn5xz3065dOypXrsyPP/6YZfzIkSOMHTuWWrVq8fbbbxMXF8fWrVtJSUlh27Zt+S5gmz6XW9Zi0FJiYri77gsiz1/QjumZmlJz3GjsWrUs8P4Tw8Lw+/Jroi5dyTJu1/pVXEe+h4FV4c8Nvsz8fsihw9zb+JX2uaWHOx6zZ+Qce/AVIhNxM8XSDF2j0RB25Ch+32a0agIwq+FK7Y8nYOqS82+ipJPtON1AfK6ngNeATYCnTkwrNUR73+DGnPnaC4hVvbp4zJ5R6IK9TMU2lWHkcSp8NGo1KU+ic5lGFf+n1wV9EaoNH0rlXj2KwOIMnqdb8uVBs7Gx4aeffmLJkiWsW7cOY2NjOnTowJQpU7R9OC9cuMC0adNYsmRJrgItNzp06MAXX3zBV199xYoVKzA2NqZp06Z88sknsrsAYGBhgdv0zwjeu4+ALT+iSU1FFR/PrWUrie7sTfX3hhVI5Rs7OOA+awbhJ0/h9+3/SH36FIDwk6d4cuUq1UcMw/71NjpNgw49ciyLOLOoUxv3WbmIs4fAFERtr+FFb1tSRCT3Nn1J1IWsrZqc3+5LlX5vFU03AF3hiShouwWYjCj4OwmYBcja0jli5eVJzfFjubN6LQDR165zb+OX1JwwTpYWkJQqNBoNqrh4UmJiSH36FEM7W4xsbXVtFgqlEkObChjaVMgS65uZ1PgEktOmTxPDHpMcHp5JyD0mKSIyWzKDno4L5ufLg1ZaKKsetMzE3LnLreUrs9ScMatejdqTPsG0SsEby6c8fYr/5u94fDxrhXvrBvWpMebDXOMIXpQXuYsMO36CO2vWQ9pX1bxmDTznz8m5bpgGEW/2F3AdKGJ9H3nhIrdXr0UVl5HIYlypErUnTsCidq2iffFi4LnH6TFCCG8BqgFfAF2KybBSSOCOX3mwdRsgpr3rr1yGWVWXQtu/9MyUDkrKcdJoNGhUqmw3kBHnzhN7+45WhKXExIq/T2NIjYlBo1Jp160+YjiVenTLsn3Alh+IvnETEycnjCs6YuxUEWMnJ0ycKqJvaVlib0o0KhXJkZEkpmWiBoSH80rvXkXeHaTAHjRJycGiVk0arF7B3Q2biDhzFoA4/wCSQkMLRaAZWFpSe+JHaQVuv0rLmoEn/13lyviPqfrOIJy6di62ljaPT57iztoNWnFm5lodz3mzcy/q+isZMVLF4Hw1dnJCnZzRxNKpaxeqDn2ndCUDvCz2iNZZwxDTnl0RCRlrgIJ/FcscVd7uS2JoKJHnzuM+fWqhijOJJJ3EsDASgh6S8vQpqU9jxN+YGFKy/P+U1JhYKnbqiOsHWePII89dIOzosXy9VkrabEtmYu/5EXv7jjYBLTN6pqZCsFWsiIlTRexebYVZ9Wov9T4LG4WenjaBACDw0iWdt26TAq0Uom9mRp0pnxJy8BD+m7dQqXtXKjRuVKivUaFRQxquW839rb8QvHcfaDSok5KEd+3kP9QcNxqzatUK9TWfJfzMWW6vXqt1O5tWdcFz3hz00xJJshGJyDZsDHxcpKZpMa1SGef+bxN29Bg1x43ByqscBmS1Aa4iRPFC4GDa33GAbE2pRaFQUGP0KJz7vV1onmhJ2UOj0aBOTHxGUMWQGpPmxcrkzTJ1cckmsB7/fYIHP/+Sr9dKiYnJNqZvkcv5NQ2lsTEGlpboW1jkGJ+cGJJ7EL4qPp64e37E3fMDxOzPswLN79vvUBroZxJyThja2pS6EhmFgRRopRSFQoFT5zex8vTEuJJTtuXq1NQCxz7pmZjgOnI49q+14u4Xm4i//wCA2Dt3ufrJFCr36YVzv7eKJMsl4tx5bq9YrRVnJs5V8Jw/FwPL7C3FtHwKRACHKZJvdlxAAPH3A7Fv81qW8cq9e1KpR7fy4TXLDUNgBqJV1FiEQP4BkaxRPEX0SwXpJQck5Y/U2Fji/ANQXb/Bo0chpMbEoNDXx7lf1sa3j0/8o41XzIvM3vt09J93jnwGVUL2GqMVGjZAz8QkLbPSAv30vxYWGFhY5Hm+r7tkEYnBwSQEh5AYEqLNnEwIDs6SQAViBiIzGo2G0EOHs9VFUxgYiOnSNM+bsZMTxk4VsfRwL9PnXSnQSjk5ZQemREdz7bPpVOnbG4cO7Qs8529Rpzb1Vy7j4e97CNy+UxS4VakI2vkbEWfOUmPsaKw8PQr0GpmJvHiJW8tWamMdTCpXwmvBXAytn5NNegQRCzUNqJ/7ai+DOiWFwB2/8vC331Ho6WFeuyYmmU4sSn19KEuJAAWhBnAA2Al8BDRFCLaFFHuh4NJCQnAIfl9+Ta2Px2NYoYKuzZEUAsmRUcT6+RHn50+cnx+x9/xJCsuIG06v1GVQwTqbQDPIw4OVmZSY7FOMJk5OWDeoj76lBQYWlml/04SWhXma4LJE38I8R3Fj3aB+ts4nL4Ko+G+LVd2sNVI1Gg0p0dFpgk0IuGcFWnJkVI5FazUpKSQEBpEQGERUpvEmm7/O8h5UiYmE/nVECLiKFTF2dCi95Y2QAq3MoVGrubN2PYnBIdzdsIno6zdw/fCDAheeVRoY4NzvLWxbNufeF1/y9Kao5pzw8BHe02dR8c2OVB3yToEbfkdd+Q/fz5drSxIYO1XEc8G851+44oAPgNqIpuiFSLb2WCoVfl99i+fcWYX7QmUJBdAP6ATMRCQP/IaITXs7bbkEgJhbt/FZtISU6KfcXLCEuovno2dsrGuzJPlEo9FkuwGOuXWba1Om5Wv71Kcx2fahb2mJ0tBQeKws0wRWugdL680Sfw2srbPts6ACq6hQKBQYWltjaG2NpbtbjuvomZhQ+5OPSQgO1nreEkOCSYnOLkSVhoYY2mS9LiQ8Csb/2+8yraTEyM4OY6esnjcTp4oYOTqWeO+bFGhljJSnMSSGZbTlenziJDF37lJn8ieYu1Yv8P5Nq1TBa9F8Qg4d5v73P6FKSAAg5OBhIi9cxHXUB9g2e+Wl9v3k2nV8Fy/VVt83cnTAa8E8jGxtnr/hbMQt6QmgkK5tqoQE7v/0M8H7DmgTFAAs3N2oPrIYaneUBawQRYKHAqOA/oikgg0USwJHaSA1NpaUGFGfKe7ePW6tWI37tCk6D06WZEejUpHwKJg4P/9M3jF/Gm1clyUWy7SqCygUWc4bAAp9fUyrupBgZIRDtaraOC7Uash0vM1r1qDFzm3F9r5KEvqmJtlCSABS4+OzTZdqVOpscWnZ4t/UapLCwkgKCyP66rUsi4zs7Wjy7VdZxpKfPCE5Mgrjii9fBL4wkQKtjGFobUX9FUvx+2YzYUdEJk7io0dcmzKN6iOGUfHNTgWe8lQolTh1fhObJk2499XX2vpfyRGR+C7+HNtWLXD9YCSGOdzd5Ub0jRv4LFyidW8b2dsJcWafR0+hCwjPzCgKrU9k1JX/uLfxS20GK4jA2GpD36Ximx3LZbBqgWgCnEd40mYCXml/J1PuG7BXaNyIGqPe594mcaGIunAR/83fUf39ESW2HEF5QJ2SQnxgIHH3/Ij18yfunj9xAQGok5KyrRvr50+Fhg20z/WMjTGv4YrCwABz1+qYubpi5lodU+cqKA0MuHTpEjWeU2ZDHvfs6JuaYu7qirlrzp0G0jGytcWx0xvaadSk8IhsQjmdnERYxNl/8fvyGwAM3hkIOi6HIgVaGUTP2Jha48diVdeLe5u+Rp2YiCYlBb8vvyH6ujc1x44u8FQkCBHlPmMaEafP4Pf1Zm2/tIjTZ4m+ep1qw4fi0L5tniecpz6+3Jy/WHvyM7S1xWvhvLyDqVOAkUBFYGmB3w4pMTEE/G8LYceOZxmv0LghNUaP0qZfS14CPWAC0BeRQDAT2IroRNBGd2aVBCq+2ZHE0FAe7toNiHZmRo6OVO7ZXbeGlWOufjpFmxSVF/EB97MINIB6K5ZKoaUDLOrUzlKoVp2cTGJoWEbSQppwSwwJxaRypWzbZ24DpXgBB0NRIQVaGcbh9TaY16zJ7RWriPMPAIR4ir17jzqTP8WiVs0Cv4ZCocDu1VZY1a9HwP++J+zY34CYurm7/gsenzhJjTEf5to3NObWbW7OW6jN7jGoUAGvhXPz52JeBlwD9lAoAei+i5dqY+sA9C0sqD5yOPZtWsuTbWFRGZFAsB+RPPA6Ygp0OaKuWjml6ruDSQwNI+L0GQACvvseYwd7bFs017FlZYvU2DgxPekvvGKx9/xw7NCOyr17ZlnPtKpLjgLNoEIFzGsIj5h5mmfMyCH7F1eeL0oGSkNDTJ2rYOpcJduynGr065ubY1KlMknhESiel5RWTEiBVsYxrVKZesuW4P+/LYQcOARAUmgY16fOEJXMq1UtlNcxsLCg1kfjsG/zGnc3fqntdBB97Tr/TZiIy6ABVOrRLUtsTezde9yYt0Abx2ZgbY3XwrmYVMp+Z5MNX2A+Iui8kFqlubwzEO/pIsvA7rVWVB854vmZo5KXpwuir+dChDj7EyG4h1MuG7ArlEpqfzwe74gIYnxvgUbD7VVr8Vpok2vrGsnzSX7yRMSLpdXdivP3JzEkNNt6sXfvZRszd3Ul5tZtrQhLF2Uyy7bskJOIdu73Fs793kKjVnP5ypUctipepEArBygNDanx4QdY1fXi7oZNqOLjsWnWVASzFjLWDerTcN1qHmzbzqM/9oJajTo5mYAtP/D4n1PUHDcGc9fqqENCubFqnbZFkr6lJV4L5mBaJfudTjbUwPuAGSII/SVIv3vK/CO18vTEuf/bmNWo8dKJDpIXwBRYDAxGdCIYiSiV8iXlsgG70tAQ9xlTufbZdBIfBaNOTsZn0RLqLVtSYoKWSwvB+/bj9/XmfK0b6+efbaxSrx7ZvGqS8kNJiTOWAq0cYdeqJeY1XAn44SdqjhtdZG54PWNjqg8fit2rrbj3xSbt9GrcPT+ufjoFp86dSD52HNI8Z/oW5njNn4OpSz4F49fAKeB/gOOL25cQHMK9jV/i2PEN7F9rlWWZy6ABL75DScGQDdi1GFha4jF7BtcmTxMV5KOf8uiPfdmqxZdXNGp1Wialn/CM+fmjTkqi3rIlWdYzyeVGT6Gvj6mLM2auriKAv4ZrjrMIcopSUhKQAq2cYVyxIm5TJmUbVyUl8fjESRw7tC+0uweLWjWpt2Ipj3b/wYNfdojyGWq1KF2Rhp6ZGZ7z5uS/H1sQokF3B0QPyBdAo1LxaO8+Hvy0DXVyMvH372Ndvy4GlpYvtiNJ4aME3kNMV08GPgd+QZTk6KpDu3SAiZMT7jOm4j1rLhU7dqD6iGG6NkknqFNTSQgM0goxETsWkK0aPUBqfEKWWo9mrtVRGhlhVq1a2hSlEGOmzs6lunCppHwhBZoEAP/NWwg9dJiIs+eo/fH4HHusvQxKfX2qvNUH2xbNubvxS55639Au0zM1xXPebMxrPD91WosGGAOkIloIvcBNblzAfe5u2ETsnYwGvikxsTy5ej2bF02iQ+zIaMA+GuiGyPxcA+Rj9rusYOnuRsN1qzB2cirz3hyNRgMaTZYbQ1VSEucGD9XWRMyL+IAALD3ctc8NLCxovu1HWU9OUqqRAk1C1OUrhB46DMCTy1f47+NJ1P7040Jt/J3erin0yFEe/LydVI0aj2mfvVgm6U5EMPkKIJ+aTp2SQtDO3wj6dZe2dRSAabWq1Bw3plAyWSVFQBvgP8SxXgAcQiQUjKXcnLXylSxTykh5+pT4+w+Iu/+A+AcPiA8Qf92mf4Z1vbra9fSMjDCsYJ2lFmE6BtbWwiPm6qoN3jdyyF6SR4ozSWmnnJzqJM/Dql5dKvfppa3DlBwZifesubgM6EeVt/oU2olOoVRSseMbOL7RgUsXL2LpVif/G0cA4xFFTz/K3yYxt25zZ/0XJAQGZdigr49z/7ep3KdXgZvJS4oYQ2A62RuwfwmUwxwOjVpNwPc/YlK5MhU7dtC1OXkSF3Cf2Lt3swiylKgnOa4bf/9BFoEGaRX5UWTJojR3dc3W3kciKavIK5QEpb4+1Ya+i5WXJ7fXrCf16VNQq3nw8y9Ee9+g9icfFWp6uUKhePE4t0+BSOAweX5rVYmJok3T3v1Z2zS51aHmuDE51sSRlGBcEXXTfkWI82aIqe5FlJsG7KqkJO6sWU/EmbNp/QVtqdCooa7NQp2aSuKjYFAqsmVgB27fKezNB4nBwdnG3KZOkTdRknKN/PZLtFRo3IgGa1Zwe+Uant64CaTVMfvoU2p/8pHuGvD+BXyP8KbkwwR1UhKPj5/UijOlsTHVhgymYuc3S0z6tOQFUSBq3nVEZHduIKMBez/KfgN2tTqjz6Baza1lK6m7ZGH+k2sKiEajIenxY+LvP8jiEUsIeogmNRX7Nq2p/UlW17ZpVZdsAk1paIiJszNmVZ0xrVoV06oumLq45OgVk+JMUt6RvwBJFoxsbfFaMJcHv+wgaOdvoNGQEh3NjbkLqNK3Ny6DBhRvbEcc8AFQB3FhzgcGVla4vj+C26vWYN2wATXGjMI4hxgVSSnEClgHDEH0Xx2ASCr4gjLdgF3PxAT3mdO5NmUayeHhqBISuLlgEfWWf46RrW2RvGbsPT9CDh4i/n4g8Q8eaAtK50T8g+xV9y3d3bBt2QLTqi6YVXXBtKoLxo6OMjZMIsknUqBJsqHQ06Pq4IFYeXpwe/U6Up48AY2G6DSvWrEyCwgATgLGOa8S6+ePuWv1LGN2rV9F39IC6wb1y3wWXKGhQXzWZzIeHjEesAuop0vDciCnBuwzECU6jHRoVxFiZGuDx+wZXJ86A1V8PMkRkfgsWIzX4gXom754wThVQgLxgUHE379PctQTnPu9lWV5SnQ0oYeP5LkfQzs7jBwd0Wg0WX5r1vXrYV2/pH1xJJLSgxRoklyxblBfTHmuWkucvz91Pp1YvHe/54G1iCrzr2VfnBwZxb2vviHy33N4LV6AlaeHdplCocjWwFjyDEnAFcRZoAnwmIzsWAugOSgiFZC740S3ZG7APhEh5rcikgjKaAN2s6ouuH02iZvzF6FRqYjzD+DWspW4z5yW6zYiTuwRcfcDib9/X0xTPniQte2RUkmlnt3RM8pQt88Wjta3MBfTki7OmGmnJ53RNzMr9PcpkUikQJPkgWGFCnjOnUVCcAhG9nZZlmnUajRqddHEiiQjWv84AUuzLtJoNIQdPYb//75HFRcHwN0Nm2i4diVKQ8PCt6UssRfhjTwDXESItN4IL5kDItavAaK6vx7cOHeDxs0ai219AbditzhvKgM7EIkE4yjzDditG9SnxpgPubv+CwCeXPkPv6++QfNMezJVUhLXJk8l4eEjNKmpz9+pWk1C0MMsNQkNbW2oPnI4JlWqaOPEpDdaIik+pECT5IlCTw/TKpWzjT/ctZuIc+epM+kTjB0LOcZrGXAd2ANkKvSfGBLC3S++JPra9SyrW3l5oE5VoZT6TKBCNCM/gyhRMiNtfAGivlhjRNmSlkCLTNsNeWY/6WeInYh4r02ImMCSSBfAm3LRgN2xQzsSQ0MJ2vErAKGHj6BvoA9NmmjX0TMyIuVpTO7iTKnEpFIlTKsKj5i+hXmWxQqFgkrduxXZe5BIJM9HCjTJS/HUx5f7W7eBWs1/EydRa/xYbFs0K5yd+yKERD9E6x/S2zTt58HWbaiTkrSrGlesSI2xH2aroVRu2Yboafkv8DRtrBowDSFSdiD6l+YSz5crXYBOiMD8EMR0Ykl0pmRuwD6ajAbsmxBxamUIl0EDSAp7zOPjJ3Jdx6yqC0+iojC0s9MG6oug/aqYVK4kPc4SSQlGCjTJSxEfGIhCoUADqOLi8P18GU5du1Bt+JCC9bpTA+8DZohsPSDu/gPubthI7O2MNk0olVTq0Q2XQQOyxM2UCzSAHxnB/GeBv4EKiCD/UIRAaZn2qE6GmMreFzp/mCG8mSOBOQiRth4RB1YSebYBe0NELb1ZiPdSBlAoFNQcN5qUJ0948t9VNDlU3a8x9kP0zcxknJhEUgqRAk3yUlTs+AamLi7cXrGKpMfhAATv289TX1/qTP4EEyenl9vxl8ApxIXVEcL+Ps7dDZuyTNOYVnWh5vix5adNUxJClBkDBxHxVWFpyyyB5ohpzArAVIS3rCgwQByXioipw7eAdkX0WoWBAjG92R2YgohlzNyAvSR6AF8QpYEBHrNnEP8gEJ/A7KUuZHkZiaT0UsYiMyTFiaVbHRqsWYlNs6basbh7flydOJnwU6dffIeBCIHxBtpYKIvatSEtMFmhr4/LoAHUX7msbIuzEOB3hOenFUKE/Zq2rCpiqvFL4Bqiu8IhIP3jKGrRoUAInQtkiDNN7quXCOyA/yE8aqYIwdYUMd2bR+x8aUChp4dZ9WoopJdMIilTSIEmKRD65ua4TZtC9ZHvoUjL5lQlJHBr+SrubvwKVaZ4seeiQbTvUQFfoRUaJpUr4TKgHxZ16tBg9Qqc+79dsCnUkoYKuIoQWwDBiMzVPogpRAWivVF633p3RD/KUUBddDfFmB6LfgIxjRqiIztehNaIBIkvgWigP1Ab4VGL051ZEolEkhNSoEkKjMj26krdzxdhXNFROx566DB3Vq/L3052AHvh6bu3RcxUJir37kndJQswdXEuPKN1yVFgLsJTWAFR1mJR2jInRPHVMwgRcQoxnaj7tos5k4TItm0J3NWxLfnBECFufRClRSoislmrImLrsodxSSQSiU6QAk1SaFjUqkn9VcuxbdUSAIWBAVWeqU6eE6n3Y0kdGU+M9R1uhM8l4eGjLMsVenqlsz2MBiFafgBWZxqfjMhSDQfeBX4ia623MYjSF6Uh96EjcAyIQYi0S7o1J9/oIeq/nUGI4FbAfMAFkf1ZGsSmRCIp08gkAUmhom9mRp3JnxBary4oyNaC6Vkizv6L5l0VNnHNuNt6E+qUJAK2/ID7jKnFZHERsBNR0f4MGR6ZysDHiCnLXxCeMgtdGFcENAVOI8Ta64gOEO66NOgFaYXIUPUFViLi1b5CCLgpQCFVj5FIJJIXQXrQJIWOQqGg4psdqdipY7ZlkecvEHf/AZrYWHw/X07IJ4exu9eKh7V+J97qPratWlBj7Ic6sLoAJADzyGiJ5I0oEtsFcaG/DjwgI4C/NmVHnKVTGyFIP0I0ti+NuAHfAPcRmbDHEBmyrRFFb9W6M00ikZQ/pAdNUmwkPHzErZVrQK1GrVSSEgsNr60i3vwhIU2P4jZ2CrbNS5m74h6i3MR/QH2gFyKWaZ7uTNIZlRBV/EGInMOImnaljYqImMCpwGbE9HQPhICbBLxD6Zh+Lo1oEEWWtyPakpkjxH/mRx1E7KZEUsaRAk1SLGg0Gu6s/wJ1YqJ2rKrvMIzjHQn6+HcaLliJvnkpKxOwGxiG8EPvQ3jMQPqlQTS5Xw34I8ROaaw5ZoGYlh6LmLZejijUOxPRpP1DpFAoDDSI2MXtiGShB4hkjjfSll9BJHSoMm1jR87CrQZgUixWSyRFjhRokmJBoVBQc8yH3Fq+kvgHgZhH1cLJrytJ/cKpsrq3rs17cTYgsv8aI2qUVdOpNSWPZYjSFUsQnQ2+ovSebQyAQcBARAbucmA6oqXU+wgR56Ir40opGkR5mR0IYeaH+H50RCTQ9ASsMq2fjBD7tzM9biFqAG7JtJ4CcSyeFW6108ZLYa6RpPxSWk+ZklKIqYsz9VYsJeTPv6jwUQuorMDoGztdm/VydELEW33Oi/e1LA/oI+qNVURkR4YhLsSmujSqgCiADmmP/4AViHZk6xCN5CcjprkluXMD8T3YjhBZekB7YAYiPMAml+0MEUIrp/jGGOAOWYXbbeBHMvrRpu+jJtmFW23AntLp5ZWUaaRAkxQreoZGVL7UDR4hAq8tdW3RC3ASMdW1DqgFrNGpNSUfBSIWzxH4jjJRtV9LA0R5lMWI78E3iMzdNxBCrQPygp/OLTKmL28gQgDaAJ8gCjLbF3D/FkCjtEdmNIgbg2e9brcQIQkpmda1ImevWy1EHJxEogOkQJMUH3HAB8DPEN49HLtupcR7pkF4S6YhYlwiEDEwkvwxBnHc9RHfgSeIsiNlARdgFaIJ+1eI2LuOCAE3GXgbMUVa3vAjw1N2FSFWX0WEBvRFeFaLGgXi5sAReO2ZZamIWLdnvW7/IIR2ZiqRs9etOuXz2EqKDSnQJMXDLcSJ2QdYBPffuI9daVA50YhEgN2IbM3NlC6vX0kh/UwzElEz7RClq1ZaXqQ3qp+IuMAvBwYjRP1ExPsu656YB2TElF1MG2uOSBZ5m5IlyvUB17THm88si0dkZz8r3n5D3Jylo5e2/bPCrTZC1EkPqqSASIEmKXp+BYYjYrUOIaZ/SkPFeQ3i5H0RcZH5CHnSLSifIT7TVxFlFFro1pxCxwh4DyHq9yGE2kTEVO9oRPZncXiPiouHiGn/7YjyGCD6tC5HiLKqOrKrIJgi+tzWzWFZBBnxbunC7TaiZl5CpvXMyJ5lmv6QSPKJFGiSoiMF4VVYhbiT3glU0alF+UeDEGMLEcKylW7NKTM0QBS07YQIDt8JdNWlQUWEEuie9jiHECyfIzoVDAE+RdRVK42EILxJ2xFtsjSI5IjFQD9EGEBZxTbt0fyZcTVCrD4r3C4ivuOZihx7VfQSNyhNgVcQmeBlrXC1pFCQAk1SNAQD/RExHeMRMVyGOrUofyQi7HVFTE+11605ZRJXxDRnF0Rs2l3Kdu2qZggv8h3EzcoW4FtE8dvJCPFf0j2z4WSIshMIweGJ8Az2o/R2jygslIBz2uPZc0YyIibvNuALcUfjMLpsJL4TII69B0KwpT/qIuPbJFKgSYqAEwhxFgP8jKgfVRrwQ8SZXUEUI5UUHQ7A34i4JROEFwZKvlApCLWATQhR80Xa4w+EN2YKQrCVpDpdUcDvCFF2FFEotjaiJEZ/hECT5I0hwlvqBvQA//b+2DS2EaL3AqJ37XlEVvt3adsYAw3JEGyvIEqElOXfhyQb+a55HhgYyLhx42jatClNmzZlypQpREZGvtCLzZw5k3fffbfI9i/RMenZju0Ba8RJp7SIsz8RUw3+af8v0K055QILMi7yMxDTfuWh36UDQqQ9QGQ1hiHKTbgjMkETct+0yIkGfkBMOzsCIxAezsmIGxdfRF07Kc4Kjh3QGdEabh/ie+AH/ILoXqGPKN8yGCGMbRGhAbMQ56jQ4jdZUrzky4MWFRXF0KFDSU5OZuTIkahUKjZv3sytW7fYuXMnhoZ5z13t3LmTnTt30rRp0yLZv0THRCOCo3dR+rIdHyAukPUQ0w7VdWtOuUODKL+xDnHR+Y7SMR1eUEwRF+JRiN/NckT7qFmIafYxiItyUROLuOBvBw4CSYjyIR8hPGWNkZ6b4kCBOPdUR3zuIMqB3CTDy3Ye0Z0jve2VC1mnRhtT9rOFyxH5EmhbtmwhJCSEP//8kxo1RARo/fr1GT58OLt376Zfv365bqtSqdi0aRMbNmwokv1LSgDXESU0/BAxNh9TOk7o8YiLpAviDrY1siuALlAgir06IeL+0uOdysuFRh8Rx/U2IjxgOTAbkVTwHqKga2HfNMQjvvM70v4mIEpDjE6zpTml4zdc1tFH3DjWQ5RqAXEzc4Ws06Pp8WxKRDzbK8h4tjJAvqY49+3bR9OmTbXiCaBly5ZUr16dffv25bpdUlISvXv3Zv369fTs2RNHR8dC3b+kBPATIgg6FhFTNJHScWI/hYgJ+jPteUekONMlCkTG72bgCGIqpzxMd2ZGAbyOEEzeCKH0FSL2aAAFL02TiKjnNxAxzdoPkcTzHqJLRiCinEwLSsdvuLxihsgCnQhsQ9RsewzsRwh7F8R5bTTCo2YJtETcOP+MmLLWPLtTSUkkT4EWHR1NYGAgnp7Zgw48PT3x9vbOddukpCRiY2NZvXo1S5cuRV8/u8OuIPuX6JAkxBTMu4i7tMtkr9ZdEtEgSh28jvCelcY6TWWZ9xAiYjwvECFbBvFETPX6I2LzDiDqi7VL+z+/F9hkhOAbgogp640QwO8gAv8fIuLgXqN8f96lnefFs41BJJ+kx7PVQsazlRLynOIMDRVHLifvl729PbGxscTExGBhkb2Qi7m5OYcPH85RmBXG/iU64gFiOuY8Inh4MaUjHzhznFwf4H+IHnySkkX3TP/vRkzvldcm5JWBZYis4q8RU8FdAC9gEsIb9my8XgqicOp2RBbmE0TSzluI2Ka2yCmvso6MZysT5HlZjYuLA8DEJHuhIiMjIwDi4+NzFFBKpRKl8vm3ZQXZf25Ir1vRYfGvBa4zXFGkKghYFsCTdk9Er72X4NKl4m0nYLPPhmp7qhH0cRBhg8OEq1+SJ8V9nNJRpCjwGO+BQZQBd1fdJbZxrE7sKDG0BcWrCiocqkDFHytiMsyE5CnJhA0MI7xnOBa3LHi86DEVjlVAP1oflZmKJ22eENkxkphmMWgM0txu13T7NiS6+00BonxHQ2AUKBOUmNwywczbDLMbZpidNcPoV3Hd1Sg1JFZPJM4jjjjPOOK84kiomVA6bsYLCZ0eJ/LxUavVeQeC5CXCinv/Xl5eWnEnKSTUwCKEC90T+A1q1H75kuGXLl2icePGhWRcHjxA3B02AvqBs5szzjgXz2uXcor1OOVEWteBOhPqiHib3rozpcTQHBFrdAgMlxtSZW0VqqxNa9Fhhqin1h/0Oulha2yLbbGkgkryi85/U8/y6jPPHwMXQXFegcl5E0zOmmD3Z1rf5GfrszVFdI4ogzGLxXWckpKScnUq5SnQzMzMtDvJaceZ13kZinr/kkIgEhGzciDt75eIC0FJJxFRKmAr8B8i2Lq0ttcprzgjEjq6IaboNiLKUpR3FIiepm8ikgd2wr0K96gxvoaIrZRIXhZ7RDxb57TnGiCArFOjXwNr05ZXQNz81k971EPU9JM+kgKTp0CrVKkSAI8fP862LCwsDEtLS0xNX/6MUNT7lxSQS4gL40PExfFDSsfdUgDC7kuIBt3VdGmMpEDYIALb+yEy1iRZaSweTy49keJMUvjkFc92DlH24wtE8hgIZeGOEGv1M/2tWGxWlwnyFGiWlpZUqVKFGzduZFt28+ZNvLy8CmRAUe9f8pJoECUPxiFS8k8h3NmlgX2I7FI1sAcx5SMp3ZgiEgbSox0CEN61ktQaSSIpL+RUny0V0W/2KiLO8Sqirt/WTNs5kCHWMnvbykNh6pcgX8FdHTt25OzZs9y7l3H7eubMGfz9/enSpUuBjSjq/UtekAREi5f3EcVbL1N6xBmIekBVEd4zKc7KDvqIM1YkIg6rH2IaWyKR6J50r9kARGb/PkRtvXBEVvEaRAuxKERplyFAA0SmaH3ETfUK4DCy7Eca+crHeP/999mzZw/Dhg3jvffeIykpiW+//RZPT0969uwJiF6aly9fplGjRjg7v1gAdn72Lykm7iGmBv9DBCLPpnR4KcIQJwIPRDcDNaIJt6TsYYMoajsREYO1B1kuRSIpqdgiSru0zTSWCtwmw9N2FVHo/KdM6ziSfYrUjXLlbcuXQLOxseGnn35iyZIlrFu3DmNjYzp06MCUKVO0fTIvXLjAtGnTWLJkyQsLtPzsX1IM/Im4i1Ei7n5Ki/PyDMKbYo34wcvg1LLPx4jpkqFAG0QCi5MuDZJIJPlGH3Ez7YHwuKUTjmgdmC7argHryYhtM0B46TKLtvqIc0EZJN8VTVxdXfnmm29yXd6nTx/69Onz3H0cO3bspfcvKUJSEZ6yJYhsnNLSMFyDyCSajJjS/AlZDb08MQhRQb0PoiL6t7o1RyKRFBA7cve2ZRZtR4EfM63jSNa4tnRvWykvyFyOSs5JciQMUY38GCLmbB2loydlHDAc2An0BLYgPGiS8kVH4DSiFhMI0V4asowlEkn+yOxtG5hpPJyMKdL0v2sR7c1AiDMPsnvb7IvF6kJBCrTyzFlEy6YIRNuj4bo154UwQvxAlyI8aPKiXH5JbwMVgxDr04A3dGeORCIpBuwQvWnbZRpLIbu37S/gh0zrVCS7aKtDifS2SYFWHtEgsmg+QVTYP4vIpikNbEe4vx0QP7zSkMAgKR7iETcbXYHvyXq3LZFIyj4GiE43nogQiHQe83xvmyHC25ZJtCkNdR8vIwVaeSMWMZX5C6Ip9feIStAlnSREYPiXwBSE50yKM0lmHIGTCC/aIMT0/Uc6tUjyMmgQJXL+QBSYfk+n1kjKAvZA+7RHOinALbKKtkzeNtcWriIBTYdIgVae8AX6pv1djKiwr/ubhLwJQEzFXkRMZy7SqTWSkowVcBAYjBD0akQ5DknJxw+R6PMz4sIJ0IQMgbYeUZy4LbKsiqTgGABeaY/BmcbDgGsQ9CQIKx1/0aRAKy/sRJzoTBCFANs/f/USw7+IKatU4Hegl06tkZQGjIEdwEzk96WkE4bw4BsA3wELEWVTPkXcTGrS1ksF5iPiTvUQhbPfQGTw1kciKTwcgA6QeEn3VbBLg/9EUhBSELFm/RB3CpcpPeIMRIPzVogpj166NUVSitBDlI2pjvCirUHEqEl0TyzCU9YZqITweIJoK/cAOI4Iw7BBFDkF4Up4iGgdNBVQIcTcH2nLYxBxtbfIEHUSSSlHCrSyzCPEdMBqYDzi5FZFpxblj8eIqcwURKbOHwihJpG8DP8iblI6INpESXRDNGIqyRFREPsm4nfumbbcETGFmRuGiNZzCxENusOBMWnLziDOcW6ImogjEHG2Twv1HUgkxYoUaGWVE4iis1cQMR3rKB0tMv5F2L0eEXMmkRSUlogp/kvAq4j+gJKiR4MQTjvTnlsg4l+HAP8A/ggvp+tL7r8CGR62TsBdYBPwCrALkcXrn7b8KqK4qe5nrSSSfCMFWllDAyxHTGNaA+cpHeUGNAhR1hoRj3IGaKFTiyRlib7AIcQ0WVMypsYkhY8PIv6vBiI8YTLi961E3HRtQgjlwr761AA+BH5DeOH/BeqmLfsC4UG1QfRvXYnI3JPToQUnkYxyFZJCpWwKtNNACOXvxxeNCJqdAvQGLpAxfVDSmQpMQJw8LyG8aBJJYfI6ogxHXcA0bewkIjZzLCKxIFQnlpUd5iHqSS0BaiHK+Fwno5B0cRWU1geakXGFW4noNfw+Is5tEtAj0/r/IEJCJDmTCtxD9Lw9lGm8PuK3ZI7IuP0Q2Fvs1pVZymYW5ztAMML9nZ5G65np/9JQ9+tFuYbwEgQgYs4+onRV1x+MuLudTFm9bZCUBOojspjTUSLiMr8HNqaNuQH7EQkGqZTVs2TBiUZ4q7Yi6hI2QWRcWyEaYFfUnWnZsAC6pT0AghBCTYG4kX8bIc49ENmhHRHZpGbFbqnu0CCum2FkFC6fgPi9+CFigkF4oDul/d8D4RRIQHhHtyOyqLshvGrtEL+5JkBjxOcrf0/5pmx+VFsRd23eaY8fEFk+6VQiu3DzQNwFlEZ+BEYhpjT/RkwflAa2IYJ91yAqONfTqTWS8siriCzCFESG8wnE9HrltOWfITwvbTI9nhfIXtZJQXhItqb9TUJMLYanLW+S9ijpVCFrwtRBRJHSv4CvEFXmxyNid1MRsbyNKBvFsZ8Clmn//wjsQ7RHuo3ocVwJEQoAQiF4ImZkamd6pLPgmX1rEGINRFcPQ0TGbvrNj0na/8PSXus+os1SWfhci4CyKdBaIrIX09EgAoNvkCHavBFflMxBo9XJEG7p4s0N0fexJJK5un4bRNZSSbprzY0kcF7qLIKHX0X8oE10bJOkfGOAmBJr9sx4E+AO8CvwbdpYM0R8E8ATxI1RWUaNmP6rghArQxG/1w8Qnu+mlC5v/bMoEB6jBggPfgIiTMYpbflFRDysDcIj9Ebao3ox2/kyXEJ4wG5nekQhSs4YIm5KLiJEV2uyC7BVL/h6CjLCB5yAY4jvz92017lIRtjNP4hSK2ZAQzK8bF0Qn7WkjAq0Z1Egek66IL4Q6agQrltvsoq3A4gTEQhlX4vswq0muv307iPc8hcQMWeLdGxPfogDvgE2gsMdB1GMcgklskmtRAKIBJuBiHPFNYSHLXNA9CsIr1JmD5srpVuwpHMN4f3YhvC4eCOE2WnAnZJ/vnlZTBAJBem4ITLh0z1sv6aNn0CImseIc5h18ZmoJRyRCHb7mccpxPXuGDAd4RWrTYYnLAUh0FYhQmKKEiUZwi9zf8yGiNmtdOH2FUIcX0MItEMIcZnula1BuQt/Kas/sfyRLr5qIb646SQjvuSZhdt/iHiL9MQDQ8RJyuuZhwtF/yU6hPiipyDSyXs/f3WdokG4uu3Sns8F3OHumLvU/FgWN5OUEvQQF5SGmcbUiFjP44iburQefnyMuOhpEJ6DmpQuwfYb4nfqjbhCvInwlGkQ76NurluWTazJEOoaRDHcwwjPIYhjvZSM7gZvAM0pnBvPdO/lswJsPmLK9QgZWfoVECLo9bTtQIS+fIiIwcsJXX4v0+vhvZv2PBWRAeye9vwaWWe5rBAetn2IOLcYRFhSafptvSDlW6DlhiEZgisz8YgvUGbhdhIRj5GOGVkTEtIfFSn4F0mNKNI4N+01fiOrO7okEYO46/wS8QO7ifhsfIGKEH0pWofGSSSFgBJR/X4c4sLtg/CqpE/h+CJiW53I6mFzo2RdVCIQ4QbdENOYyYiL4UaEl94u903LHQrE8XPLNPY24kr6F2ImYwEihvEB4jsSgfAIPe+YR5BVgHVFhOqcAV7LtJ4JwqHwJO15h7R1apNREy4zljmMlVT0ySr+JyNudm6S4WULRIgzgOEID2H61Gi6p61q8ZhbHEiB9iKYIr4IjZ8ZjyZDsKX/3Qv8L9M6Fcgu2jzJ+UeVExGIO40DiCzVLymZGUa3EAG2PyFEWn3Ej0yF+LaVhhg5ieRFUSDEmEemMQfEtM0JhJftl7TxPYjst2DEFJUnxT91E49IfthKRkiHAuFxGUDpqJ1YUkj3rM5HCKe/EWWe0o9pG8Q1Is27Zv3AWsx+NE9bz5OsHS70EN6llojrxCaEAKuFEH6Zvyt2lG0BbYC4htRHdIfIzNsI4XsRWIH4DjdBhP0AfI34DTZBfG4l6aYon5RNgfYV0J3iC+K0QvyYWj4zHkZ24fYz4seaTkWyCzcPsrqkLyFKaAQjfqyjKFlftgSEADNHTAV/B/RHuNabUbJslUiKC1tEIP0HCA/bPYRYS/eI/ICo/2eLiGVK97DVo2gFWxzCU/YEceH6GDGFmd50XP5eXx5rsoacaMgoVfE78B3UoIb4TjRHCIj+iGnw9Dit6mRMj1ojzqOS7PRPe4CYpblORr9dNSI2O/1a64hwrAwmaxxcCadsCrTPEbEhjREqeyy6KaHhkPZ4NqP0IVmzSW+QESCZTlWEWHNGeOIcEVkvTSk53EbYvQUR8D8dcXJ6A5mFI5FkRoG4CGcOu0zvS3ki7fE7IrziCWIq61Ta3wa8fBkCDcLDsBURzL4V4Xmfnbbf1gXYtyRvFGSIdBXwH9y8fROPHmmuViUZJSgkL48xImEnHSXCoXGVjOnRS4ibJBDCzZOsU6ONEdfrEkTZFGj/IE52OxFibWKm8cq8fO+3wkBBRg2eNzONqxBFZp8Vbn8h2jb9QMlxZe9CtE45hvgG9UEEpoK4wEhxJpHkTRVEPahhac8fIH7z6SVnpgBnER76VxHetfbkr8uGP+Kc8TPiRsoQMa2qQgiyiblvKiki9IDGkEBCyQxPKWuYILyUzTONpSf5xSJKplxETPWnj3+H+D0+Bv1I3csj3VtQFFRBtPKYhKj5kt4k/ANE4G4jhGftbUTqbklAD2FLDaBnpnE1JSO1+DFgn/b/d4g7kUXAe8i4MomkMEgvBZTOTjK8aycQ2WtdyWil8xViOrQJYAD6EfpiiscUcRM1DyHqJiNCJMpiBxWJ5EVIn76vTEbWdQyiEPFFMoq8HwGFle7n+sumQMtM5pPSQUQNmx3AtLTHp4gAw5KKLsWZCvGZfZn29w5QDTHlaoOcGpFIipLKiHiZ9JiZEDJiaiKB0Yg7f1PADepdrSdunt5F3Dj1J2u1fIlEkh0LxFR/60xj3fh/e/cf1/O9/3/8lmQy82uk+bVZ886PlFKZGFMMKR2X9IOlNVxkwhZHhcY+zo78mHaonR3m13Tmx+kic/a1ZjObE8IZNmMyLYdiqwgl6efr+8fL+6V3P4iZ97s8rpdLF72fr9f79X68eqfuPZ/P1/NF6ZnSWp7w6JhC38yj8yxqIDuMOpz4HneGGTNQe9ZiUNcuepxdAxaj9uZ5oV4VE8mdFaLbIeFMiEfNGvW2OKD+gZSN+gfnJOBJ+C34tzsXKrVGwpkQD+opTOJimYbfg1YbfVjT0983bN7tD0fUIdBQHo85VZUXlC1GHR4ZiBpifZDV/oUwNe1Qhy591YeXjl7iGZtn7vYMIUQ98nj1oN2NC+r99c4DK1Dvv7mQO5MHj9Iwe9byUFfC7o72g572qBOWvwbGIuFMCCGEeMQkoFXVBZiFevXUJe4sJDsbdaFARxrGMOhx1KtVOqKe79OoQyX6QNreOGUJIYQQQgLa3VVe1iIB9caylqhDoN2ovrKxqSvgzlpr+1FvFRWCurjsQSAYkxh3F0IIIR53EtDqqjPq2kEHUYf/YoGRt7ddQx0i/SvqmkOm5gTqFV8dUG/BBOpVXpdQ70zgUMvzhBBCCGEUEtAehD6sjb39+FfUeVrRqFdZ9UENa9nGKO42BXWdFzfUALYRdY6Z/k4ET2J4OykhhBBCmAwJaA9DD+70rL2PuhxFNHfWLPoJ9Sbij0Lu7X/NgHjuXARwETWkSW+ZEEIIYfIkoD1MnVFvPHwQdfhQd7v9/1CvknQA3uXhh7VS1PWQPFCXD8m73f7/gNO3a3oclgoRQgghGggJaH+UyssRrQD+hjqk+DZqWPvTQ3iNbNSeui6oa7b9cvuxfhFZK2TSvxBCCFEPPb4L1T5KnYA3b39koV49qV+VvxS152sYasjqfo9jlaNelPA06n1GlwCewFRgOLLCvxBCCNEASEB71PRhTe831An9C25/2KEGtYkY3qrlN2AdsAZ1on8iapi7iKxZJoQQQjQwMsRpbJ2BFNSetZVAK9Q7GJy7vf0n1MDWGXX4sht3bp4MEs6EEEKIBkh60ExFR2Dm7Y+L3JnDthrYi9rrNoU7Fx4IIYQQosGSgGaKOlb6fAGwFGhqpFqEEEII8chJQDN1T997FyGEEEI0LDIHTQghhBDCxEhAE0IIIYQwMRLQhBBCCCFMjAQ0IYQQQggTIwFNCCGEEMLESEATQgghhDAxDWqZDUVRACgpKTFyJaIuiouLjV2CqAN5n+oPea/qB3mf6odH8T7p84o+v1RmptTUWk8VFBTw888/G7sMIYQQQog60+l0PPXUUwZtDSqgVVRUUFhYiIWFBWZmZsYuRwghhBCiVoqiUFpaypNPPkmjRoazzhpUQBNCCCGEaAjkIgEhhBBCCBMjAU0IIYQQwsRIQBNCCCGEMDES0IQQQgghTIwENCGEEEIIEyMBTQghhBDCxEhAE0IIIYQwMQ0ioGVmZjJ9+nRcXV1xdXUlIiKCvLw8Y5clqkhJSWH8+PE4ODjg6OhISEgI33//vbHLEveQlpaGnZ0dcXFxxi5FVJGXl0d0dDRubm44OTkxYcIE+T9lok6ePMnrr79Onz59cHJyYurUqWRkZBi7LHFbdHQ0EyZMqNZuzHxR7xeqvXr1Kr6+vpSUlBAcHEx5eTnr1q2jY8eOJCYm0qRJE2OXKIAjR44QHBxMt27d8PX1paysjM2bN5OTk8PmzZuxt7c3domiBmVlZfj5+fHTTz8xffp0ZsyYYeySxG03btzAz8+PnJwcQkJCaNGiBZ988gnZ2dkkJiai0+mMXaK4LSMjA19fXywtLQkJCQFgw4YNKIrCzp07ad++vXELfMwlJiYSHR2Nq6srCQkJWrvR84VSz8XGxio9evRQ0tPTtbYDBw4oOp1O2bZtmxErE5X5+PgoL7/8snLz5k2tLTc3V3FxcVFCQkKMWJm4m/j4eKVXr16KTqdTVq1aZexyRCWxsbGKra2tcuTIEa0tJydHsbe3V+bMmWPEykRVCxYsUHQ6nXLq1Cmt7YcfflB0Op2yZMkSI1b2eCsrK1Pi4uIUW1tbRafTKUFBQQbbjZ0v6v0Q565du3B1dcXGxkZrc3Nzo2vXruzatcuIlQm969evk5aWxogRI7C0tNTa27Zti4uLC8ePHzdidaI2Z86c4cMPP2TatGnGLkVUoSgKO3bs4OWXX8bFxUVrb9euHRERETg7OxuxOlFVVlYWrVu3pmfPnlqbvb09rVq14ueffzZiZY+v4uJixowZQ1xcHD4+PjX2Yho7X9TrgHb9+nUyMzPp1atXtW29evXi5MmTRqhKVNW8eXO++OILrWu/sqtXr2Jubv7oixJ3VVZWxty5c3Fzc2P06NHGLkdUkZWVRXZ2Nm5uboAa2AoLCwF49dVX8ff3N2Z5oopnn32W69evG8xdunbtGgUFBVhZWRmxssdXcXExN27c4P3332fp0qU0btzYYLsp5It6HdCys7MBaky+7dq148aNGxQUFDzqskQV5ubmPPfcc9Xep7S0NI4dO4ajo6ORKhO1+eijjzh//jyLFi0ydimiBufPnwfg6aefZunSpTg7O+Pk5MSwYcPYu3evkasTVU2ePBlra2tmzZpFWloaZ86cYfbs2VhYWNQ4MV388Zo3b86XX36Jp6dnjdtNIV/U64Cm/4ux8rCZ3hNPPAHAzZs3H2lNom4KCwuJjIwEYMqUKUauRlR29uxZPvjgAyIjI7G2tjZ2OaIG+fn5AKxcuZJ9+/Yxf/58li5dStOmTQkLC+PgwYNGrlBU1qFDB0JDQ/nvf/+Lj48Po0ePJjU1lRUrVhgMe4pHp1GjRtV6zSozhXxRe3X1QEVFxT33adSoXmfQBqmoqIg33niDtLQ0QkNDcXV1NXZJ4rby8nLmzp1L3759ZZjMhJWUlABqUNu9ezctW7YEwN3dnWHDhrFixQpt+FMY39/+9jc+/PBDXF1d8ff3p7y8nK1bt/LWW2+xatUq3N3djV2iqMIU8kW9DmhPPvkkoI4lV6Vv0+8jTEN+fj6hoaEcO3YMX19fwsPDjV2SqGTdunWkpaWxefNmbb6MvremqKiIvLw8WrVqJX/4GFmzZs0AeOWVV7RwBtCiRQvc3d3ZsWMHhYWF8vPPBOTn57Nu3Trs7OzYuHGjNud21KhRjB07lrfffpuBAwfKklAmxhTyRb3+KduhQwcAcnNzq23LycmhRYsW2g8yYXxXrlwhODiYY8eOERAQwF//+lfMzMyMXZaoJCUlhdLSUvz8/Ojfvz/9+/dnzJgxgBre+vfvz6VLl4xcpdDPi2nTpk21bW3atEFRFJneYSL+97//UVJSgpeXl8EFURYWFnh7e3P58mVZsNYEmUK+qNc9aC1atKBTp06cOnWq2raffvoJOzs7I1QlanLjxg0mTZrE6dOnCQkJYe7cucYuSdQgMjJS6zHTu3z5MnPmzMHHx4c//elPtGvXzkjVCb1u3brRpEkT0tPTq23LysriiSeeqDG8iUdP3zNWXl5ebZt+GK0uw2ni0TKFfFGve9BA7eJPTU3ll19+0doOHjzIuXPnar06Qzx6ixYt4vTp0wQHB0s4M2F2dna4ubkZfDg5OQHQuXNn3NzctAmywniaNWuGu7s73377LWfPntXaMzMz2bt3Lx4eHrJ8jYno1q0bVlZW7Nixw2C4rLi4mE8//ZTWrVvTrVs3I1YoamPsfFHvb/WUl5endR1PnDiR4uJi1q5dS5cuXdi6dauM65uAX375BU9PT5566inmzZtX4y8OHx8fI1Qm6iIrKwsPDw+51ZOJycrKws/PD4Dg4GAsLCzYtGkTRUVFJCUl0blzZyNXKPS++uorZs6cyQsvvMDYsWOpqKhg+/btpKens2zZMllr0AS4u7vTsWNHg1s9GTtf1PuABup9zmJiYvjuu+9o2rQpgwcPJiIiQrr4TcSWLVt455137rrPmTNnHk0x4r5JQDNdmZmZLF++nIMHD6IoCs7OzkRERBisfC5MQ2pqKn//+9/58ccfAejZsydTp05l0KBBRq5MQM0BDYybLxpEQBNCCCGEaEjq/Rw0IYQQQoiGRgKaEEIIIYSJkYAmhBBCCGFiJKAJIYQQQpgYCWhCCCGEECZGApoQQgghhImRgCaEEEIIYWIkoAlhYq5cuWJwo+uoqChsbW2NWJFp1fEgDh8+jK2tLUlJSQ/9WL/32JmZmQaPbW1tiYqK+t11mqqGfn5CPCz1+mbpQjQ0+/bt489//jM7duygWbNmAAQEBNC/f38jV2Y6dTwIGxsbli1bpt1X1FSOtWDBAs6dO2ewevmyZcvo0qXL7z62EKJ+k4AmhAk5ceIE+fn5Bm2Ojo44OjoaqSLTq+NBtG3b9qHd7/VhHmv//v107NjRoE3uSyuEABniFEIIIYQwORLQhDARUVFRxMfHA+Dh4cGECRO09spzv6KiovDy8uLo0aMEBARgb2+Ph4cHO3bsoLS0lBUrVjBgwABcXV156623uHr1qsHrpKenExYWhrOzMw4ODgQGBpKSklKn+qrWMWLECE6cOEFQUBAODg64ubnx7rvvcuvWrXseLzU1lcmTJ9OvXz969erFSy+9xIIFCwx6EBVFIT4+nuHDh9O7d2/c3NyYM2cOv/76q8GxtmzZgre3Nw4ODvTr14+wsDDOnj2rba9t3tjBgweZP38+Li4u9O3bl7lz53Lz5k327duHj48PDg4O+Pj4kJqaWuuxapKbm8uiRYvw8PDAzs6Ovn37EhwczNGjR7V9bG1tuXjxIkeOHDE4Xk1ztPbs2UNgYCD29vY4OzszdepU0tLSDPaxtbVlzZo1bNiwgaFDh2JnZ4e3tzfJycn3fC9AHV4PCgrC0dGRAQMGEB4eTlZWFgCzZs3Czs6uWu9uQUEBvXv3ZtmyZXU6Tm2++eYbAgMDcXBwwMXFhRkzZnDu3DmDfS5dusSMGTMYOHAgvXv3xtPTk48++oiKioo6nZ8Q9Y0ENCFMREBAAMOGDQNg7ty5TJ06tdZ9c3NzmTp1Kn379iUyMpLGjRszb948QkNDOXToENOmTcPLy4vk5GSDX55nzpwhICCA9PR0QkNDCQ8Pp6ysjClTpvD555/fd815eXlMmjSJ559/nvnz5+Pk5ERCQgKrVq266/P279/PxIkTKSoqYubMmcyfPx97e3u2bdtGTEyMtt8//vEPPvjgAy28+fn5sWfPHiZOnEh5eTkA//73v3nnnXfo2bMn8+fP5/XXX+fYsWNMmDCBgoKCu9YRFRXFr7/+yuzZsxk8eDBJSUmEhYURERHBsGHDmDVrFpcvX+bNN9+sFk5qc+vWLV599VWSk5MZM2YMCxcuJDAwkJMnTzJlyhRu3LgBqHPNWrduzfPPP8+yZctwcXGp8XiffPIJYWFhlJaWMmvWLEJCQjhx4gTjxo3jxIkTBvtu2bKFjRs34u/vT0REBDdv3iQ8PJyff/75rjXv2rWL0NBQrl+/zowZMwgODubgwYOEhISQn5+Pl5cXpaWl7Nmzx+B5X375JSUlJXh5edXpODVJSkrijTfewNLSkjlz5hASEsLx48fx9/fXQlppaSmTJ0/m1KlThISE8Pbbb9O1a1fee+891qxZc+83RYj6SBFCmIxVq1YpOp1OyczM1NoiIyMVnU5X7XFCQoLW9u233yo6nU4ZMmSIUlxcrLUHBgYqAwcO1B4HBQUpQ4cOVQoLC7W20tJSZfz48Yqbm5vBc6uqrY5NmzYZ7Ddy5EiD16zJpEmTqtWqKIri7++vODo6GhxrypQpBvts2bJFGT16tHL+/HlFURRl8uTJyqhRowz2+fbbbxVPT0/lu+++UxRFUQ4dOqTodDpl+/btBo99fX2V8vJyRVEUpby8XBkwYICi0+mUffv2acf617/+peh0OmX//v13PZb+8a5duxSdTqf85z//qVa3TqdTdu/erbUNGTJECQoKMthPp9MpkZGRiqIoSl5enuLg4KCMHTvW4GuVmZmptVd+Xp8+fZScnByt7fvvv1d0Op0SGxur1EZ/3t7e3kpRUZHWfuDAAUWn0yn//Oc/lZKSEsXV1VWZPHmywXMnTpyojBgxos7HqXp+BQUFipOTkxIeHm5w3JycHMXFxUWZNm2aoiiK8sMPPyg6nU5JTk7W9qmoqFAmTpyoRERE1HpuQtRn0oMmRD2l720DeO655wB46aWXaNKkidbeqVMncnNzAbh69SpHjhxh8ODB3Lp1i7y8PPLy8sjPz2fYsGFcvnyZH3/88b7rGDlypMHj7t27c+XKlbs+Z/Xq1Wzfvt2g1qtXr9K8eXODJUasra05fPgwH3/8MZcvXwYgMDCQnTt3alc6Wltbk5GRQXx8vDaUNnjwYHbt2kXfvn3vWoeHhweNGqk/Bhs1akTnzp1p2rQpgwYN0vbp1KkTgPZ1vBdPT09SU1MZOHCg1lZSUqJ9Xvn87iU1NZWioiJef/31au/r6NGjOXHiBDk5OVp73759adeunfa4R48e96z95MmT5Obm4u/vT9OmTbV2Nzc3EhMT8fHxwcLCguHDh5Oamsr169cBtff00KFDjBo1qs7HqerAgQPcuHGDoUOHat+PeXl5mJub8+KLL7J//37KysqwsrLCzMyM1atXk5KSQklJCWZmZqxbt46lS5fW+espRH0iV3EKUU89/fTT2ufm5ubV2vTtiqIAd9bbSkhIMFjWobKqc7vqok2bNgaPmzRpog0/1sbc3JzMzExWrlxJeno6Fy5cIDs7u9p+ERERvPHGGyxevJiYmBh69eqFu7s7/v7+WhAJCwvj+++/Jy4ujri4OF544QXc3d3x8/O753IVbdu2NXjcuHHjauejD3D3M9fJzMyMNWvWcPz4cS5cuMCFCxcoLS297+PoA+fzzz9fbZuNjQ2gzs2ysrICan4v7vWaFy9eBODZZ5+tts3e3l773Nvbm23btrFnzx58fX354osvKCsr04Y363qcyi5cuABAeHh4rfXl5eVhbW3NnDlziI2NZfLkyTRr1oz+/fvj6enJyJEjte9/IRoSCWhC1FONG1f/72tmZlbr/vrQ9OqrrzJ06NAa93nhhRfuuw59gLkfW7duZeHChXTt2hVnZ2deeeUVHBwcSEhI4LPPPtP26969O7t37yYlJYVvvvmGlJQUVq1axcaNG9m6dSs2NjZYW1uzc+dODh8+zNdff01KSoo2WX79+vW4urrWWkdNv9jv9jWsi4sXLxIQEMDNmzcZOHAgnp6e9OjRA0VRCAsL+13HrkwfvC0sLLS2B3kv9OHtXs91dnamQ4cOJCcn4+vrS3JyMnZ2dlrvbV2PU9Nr/+Uvf9F6Kqtq2bIlAJMmTcLLy4uvvvqKffv2ceDAAb7++ms+/fRT1q5dW+fXFKK+kIAmxGNCv96Wubk5bm5uBtvS09PJysrC0tLyD6+juLiYJUuW0K9fP9avX28QNFeuXKl9Xl5eTlpaGs2bN8fDwwMPDw8APv/8c8LDw0lMTCQqKoozZ84A0L9/f20h3aNHj/Laa6+RkJBw14D2R4iPj+fKlSskJydr4QUwCJ51pX/PMjIy6N69u8G2jIwMQB3i/T2eeeYZAM6fP8+AAQMMts2dOxcnJyf8/PwwMzPD09OTjz/+mEuXLnH06FEiIiLu+zg1nV+bNm2qfU8ePnyYiooKmjRpwrVr10hLS8PJyYmgoCCCgoK4efMmUVFR7N69mzNnztTbu1wIURuZgyaECdH3Puh7Rx4mKysr7Ozs2LFjh8FwYmlpKfPmzWPmzJmUlZU99Net6tatWxQVFfHcc88ZhLPTp09z5MgRAMrKyigvLyc4OJjFixcbPN/BwQG487V68803iYiIMBhW7dmzJxYWFg/Uo/R7Xbt2DUtLSzp06KC1lZSUsHXrVgCDOhs1anTX4Uc3NzeeeOIJNmzYYDCP7bfffuOzzz7D3t6+2rD2/bKzs6NNmzYkJSUZvMaxY8dISkoymDPn7e1NaWkpy5cvR1EUg/mH93Ocque3du1abQgYIDs7m2nTpvHee+9hZmbGgQMHeO2119i7d6+2T7NmzdDpdEDNPaFC1HfSgyaECdHPIVq7di2DBg3Seo0elujoaF577TV8fX0ZN24crVq1YteuXfzwww/Mnj2b1q1bP9TXq0nLli1xcHAgKSmJ5s2b07VrV86ePUtiYqIWqAoLC2nZsiUTJkzgww8/JCwsjJdeeolbt26xbds2LC0t8fX1BdShr+joaEJCQhgxYgSKorBz506Ki4sZP378H34+VQ0aNIi9e/cSGhrKiBEjKCgo4NNPP9XmWxUWFmr7tmnThrS0NDZv3oyrq2u1IebWrVsza9YsYmJiGDduHN7e3hQWFrJlyxYqKiqIjo7+3fU2adKEqKgoIiMjGTduHKNHj6awsJBNmzZhY2Nj0OvVvXt3unXrxueff06/fv1o3779Ax2n8vnrzy8gIIDRo0dTVlbG5s2bKS4uJjIyEoAhQ4bQtWtX5s+fz6lTp+jSpQsZGRl88sknvPjiiw80NC+EqZOAJoQJGTVqFF9++SVJSUkcOXLkoQc0R0dHtmzZQlxcHBs2bKCsrIyuXbuyZMkSxowZ81Bf625WrlxJTEwM27dvp6SkhI4dOzJlyhRsbGyYMWMGhw4dYvjw4cycOZNWrVqxfft2li5dirm5OU5OTixfvlybJO/n54eFhQWbNm0iNjaWiooK7Ozs+Oijj+jXr98jOye9wMBA8vPzSUxM5N1336Vt27b06dOH+Ph4AgMDOXToECEhIQDMmDGDhQsXsnjxYsLCwmoMGiEhIVhZWbF+/XpiY2OxtLTE1dWV6dOnP7RhPR8fH5o3b87q1atZsWIFLVq0YMiQIcyePVu7J6yet7c3sbGx2sUBD3qcyufXvn17NmzYwPvvv0/Tpk3p1asXy5cv167CbdasGevXr2fVqlV89tlnXL58mXbt2jF+/HimT5/+UL4GQpgaM+WPGEsRQgjRIK1Zs4a4uDj279+vTeAXQjx8MgdNCCFEnZSUlJCUlMTQoUMlnAnxB5MhTiGEEHeVnZ1NTEwM6enpnD9/nuXLlxu7JCEaPAloQggh7qpFixZ89913lJWVsXDhQnr37m3skoRo8GQOmhBCCCGEiZE5aEIIIYQQJkYCmhBCCCGEiZGAJoQQQghhYiSgCSGEEEKYGAloQgghhBAmRgKaEEIIIYSJ+f/S5D9IKThF4gAAAABJRU5ErkJggg==\n", "text/plain": [ "<Figure size 720x288 with 1 Axes>" ] @@ -134,8 +134,10 @@ ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": null, "metadata": {}, + "outputs": [], "source": [ "## Adding a blind forecast and calculating a response function" ] @@ -189,7 +191,7 @@ }, { "data": { - "image/png": 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Ro4epLMtumwmjM4aSmppCVlaWW9pzp/PnzzL99Sls3PgHLU0B3BUcRnkv9s8bwmujKE62b9/itWN6i8ViwejmRKoProyhXAP+/HOdVwbV830n3H///YwaNYoBAwYgSRIrV67koYce8nhgguApWVmZXLx4gfZm923mIZvDATh3Lo4GDRq5rd3i0DSNDRvWs/D7bzCiMSQ4lGpuHvwtCNkcgWyOYNOmP322KtZTbDYrBtn9+0iYIpujZl1h3rwvePPNeoSFhbv9GNnyvRIYNWoUr7/+Og6HA6vVyrRp07j33ns9FpAgeFpMzCkAKrrxbFhnjgBcpZj9QUZGOp9++iELFsyjsk7H3cFhPkkA2fRhdTh3Lo64uFifxeBuDocDh9OJyQNdapJOxlSlI1kWC/O+/sKj1VjzTAKnT7t2vDl8+DAhISEMHDiQwYMHExYWxuHDhz0WkCB42qlTJ5GAKHcmAX0AsjGI06dPua3Nojp0aD+vTn2RfXtdg7+DgkIILOYUxuIyhNVE0sn89defPo3DnbK3zzTrPDOuIpvCMEY25+CBfWzdutkjx4BbdAe9++67zJkzhwkTJtz0O0mSWL9+vceCEgRPOnXyOOX0BoxuHNADkMzlOXHyOJqm+aTYotVqZfHi79iwYT3l9HqGB4cR6Sdz8yXZiBxcjS1b/2bkyPt8VjHTnbJng4V4cHaVIaI+zrRzLFy4gBYtWhWqLlFB5fkOmTNnDgDff//9TfWDTp486fZABMEbnE4np06dINoDxd7kgEjS4s+SmJhAZGSU29u/ldOnT/HFnI9JSEyghSmA9gGB6P2s6q8hvA6Ws3Hs3r2DTp26+jqcYjt37iwAER5MApKkw1SpDZln1rBs2U/cd98Ytx8jz1OhlJQUUlJSeOKJJ0hNTSUlJYXU1FQSExNzvToQhJLg5Mnj2B2O4q8SzoUcGAnA8eNH3d72rWzYsJ63334Na3IyQ4PD6BwY5HcJAFxlJGRTCBs3/uHrUNzi+PGjBMkywR7uapPNERjC6/Lnn+uIj7/s9vbzjH7SpEl07NiREydO0KFDBzp27EiHDh3o0aMHTZo0KfABMjIyGDx4MOfPu7ate/nll+nbty9Dhw5l6NChrF27tvjPQhAKaN++PciSRFWD+5OAzhSGTm/yahJYv34N33wzl6qyzN3BoVTxwPNyF0mSkEPrcOLEMZ9vqVhcNpuVgwf2UkPWe6Xrz1ihCRo6VqxY5va28+wOmjt3LuD60s6uH1RY+/fvZ+rUqcTGxubcdujQIb799luiorx7uSwIqqqyY/sWqnlgPABcX3K6gEiOHjvi9rZzc+VKPD8sXEBNg5F+QSFuW/PgSYbw2jgSD/Lnn+u5994HfR1OkW3btgWb3U50cJhXjqczBKAPq8PWrZsZNuxut+7Wlu8n4dlnn+W1114DICYmhqeffprExMQCNb548WKmTZuW84VvsVi4ePEiU6ZMYciQIcyePbtE12AXSpZDhw6QmpZKAw9uAykHRnE1KdErJSSOHDmEoip0CQgqEQkAXCti5ZBqbN68EavV6utwisTpdPLbimVE6g1U9uLAu7FcNKqqsmmTe2dY5ZsEJk+eTJ06dQCoWrUq7du35+WXXy5Q42+99RZt27bN+TkxMZGOHTsyY8YMFi9ezK5du/jpp5+KGLogFM66dasJlGW3bZqSm+xxAW/sqBUS4toDIVEpWfsYGCOisVotbN1aMusJrV27isSkRNqbA7w6C0xnDEYOqsRff21067qBfNNYcnIyDz7oumwzmUyMGTOGZcuWFelg1atX59NPP835+YEHHmDZsmWMHDmywG2UL+/+KVJC6XfmzBkOHTpAe3Nggc6aQ0LMREbeuM2ioij5Pk5nCkenN3L27GmGDh1Y5HgLolevbqxauYwNcXEYJIkaXloMFhRkvOm1AQpch0gXUB59QDn++GMtd989rETtXX7hwgV+WfYTtQzGm15vCTCbDbm+NrLePd2PhrCaJF/cztWrF2nYsKFb2sw3CSiKQnx8PBUruna7SUxMLHIWOn78OLGxsfTr1w9wLWsvbI3xpKQMVNVzq+eE0mn+/AUYdTqaFHADmfR0KwkJ6TfcVpAkIEkSkrkC+/YfuOnxnjBu/CRm/Xcmv104TwuTmXYBQRg8/KWamWnP9bmpBfxekCQJfUR9LlzYzoYNf9O0aQt3h+gRDoedGW+9jU5R6XbtKux6GmC1OnJ9bRSne7q99cFVQZLYsOEvypevWqDH6HTSLU+e801PY8aM4c477+TFF1/kpZdeYtiwYTz22GMFj/o6mqYxY8YMUlNTcTgcLFq0iD59+hSpLUEoqNOnT7Fnz06aG82YvbByVg6I5Er8ZdLS0jx+rIiIckx5ZTq33dab/TYri9JTiLHbPFpmwB30oTXQGQJYs2aVr0MpEE3TWLDga86ei+O2gECPlt++FUk2IgdU4NChg25rM9/T8BEjRtC0aVO2bduGLMs8+uijREdHF+lgDRs25IknnuCee+7B6XTSt29fBg8eXKS2BKEgNE3jhx++IVCWaWl2V+X3W5MDKwBw+vQJWrVqm8+9i89sNvPgg4/QoUMnFiyYx+8XL1DNYKBrQBARsn+sGP43SZLRh9Xl0KEDXL58iUqVKvs6pFv6/fff2Lx5I23MAdT24MSCgpADIjl79hg2mxVTMbdGhQJcCQCEhITQvn17WrdujcPhKHTtoD/++INq1aoBcN9997Fy5UrWrFnD888/X/iIBaEQtm37m9OnT9HeFODxbpJssrkckqTj5MnjXjletgYNGvH66zO5776HSJL1LE5PZaclC8VPrwoMEfVA0rFhg3+XoNm6dTOLF39PXYORdm6sPFtUuoByaJrK+fPn3NJevqcJH330EfPmzaN8+X/mpYraQUJJkJWVyQ8LFxClN9DQi2dvkk5GF1COkydPeO2Y2WRZplevfrRr15GFCxewffsWzjkd9PeDInL/ptOb0QdXZfPmjYwYMdov9yDeu3c3c+d+ThWDgduDQvxiEFs2udYmXLhwnrp16xe7vXxf9V9++YU1a9bkDAwLQkmxePH3ZGSk0y8k3OsfXp25ArGxp3A47Bh8UMI5NDSMsWPH06pVG+bN/ZylGWkMCw51WyJYuXI5H388K6crZ8KEiUVqxxBWm6zz5/xyD+J9+/bw2WcfUkEnMyAoxG9KcUiGIACuXk1yS3v5viMqV64sEoBQ4hw5cohNm/6kuSnAJ5U09YGRKIqTM2d8u79A+/adeOHFqVh1En9kZbhlwHj//r1Mn/4qly5dRNM0Ll26yPTpr3IlPr7QbcnBldDJRvbu3V3suNxpz56dfPrJfykv6RgcFOKRFeZFJUk6dHpTTinr4sr309GpUyfeffddevXqhdn8zyBEYeoHCYI3ZWVlMW/u54TLetoH+KYPVw5wDQ6fOHGM6Gj3zOcuqrp163PnsLtZvPh7EhWl2Elx7drfb1rta7VaOXXqJNW0AMwWCb3BhCkwnJDyrrFAuyUdnd6ArDciXfeFKkk6dIEVOXjwQLFicqfNmzcy/+sviJT1DAoKweRn3WgAkk7vto3o8303LFmyBIDVq1f/E4AYExD8lKZpfPvt1yQnX2VYSJjPLuElvQnZHMbx40cZPPhOn8RwvU6durJ48fdcdDqKnAQcikJSUhKpqSm5/l5RFOJO7AX2AlCuSkOa9XwEgN2rPsJucZ25ynoTssFEZI3m1Gt7B3JgBfZu28SLL04kIiKCoKBggoKCaNq0OR06dAJg585tBAQEERwcRGBgEMHBwQQEBLq1m0/TNFas+IWlSxdTzWCgf1Co1yYTFJqmIbupHHq+74Y//igdZV+FsuHvvzexbdvftDMHUtED5aILQxcQxYkTx3E6nT4f9AwNDUPWyVi0oi1a2n75Ej+cOEaGw4FOp8u15pfJZKJJu37oyzVCcdjQXTc9tXaL/jhsmShOG06HFcVhIzDUVWJDZwrDarWyZ88u7HY7WVmZOJ1ORo++jw4dOmG323n88TE3He/hhx/n2WcnkZ6ezkMPjSYwMCgngQQFBdGv30C6du1ORkYGK1b8QnBw8A2/r1q1GuHhEWiahsPhYMGCr/n7743UN5roGRhc5HpM7hovyYumaaiKDbObpjzn+878+uuvc7394YcfdksAguAuZ8/GseCbeVQ1GGjtpTUBtyIHVsSafJKYmFM+7xKSJMn15V3AIQFV0ziclEjFwCCiAgOJMJmpHx5BelgoLVq05rfffr2hS8hsNlOzZi0MRjPmkJsrXFaqm/d6CZ0+kEaNGvH440/TqVNXNE3DZrOhXUtYOp2OuXMXkJmZQWZmZs7/mzRp5opVVahbt37O7UlJCWRmZtKsmWslckLCFWbOfOOm406d+hojRoxm167tPP74GHQ6HQF6PZf0BnboZe6sU5/G5ctzOSuTP8+dw6yXMct6AvR6zLJMw3LlCDeZsTidpNpt2B0ODh06wMaNG3Jem+zxksaNmyIHV7ophqLQFBuaqtwwY7M48k0CJ078M83Nbrezc+dOOnXq5JaDC4K7pKen8cnHH2DUVHoHhRW4jo0n6YOiAIlDhw74PAkAGAx6nPlkgQyHnb8vXmTjhXMkWCz0q1GLEfWjiY6IoG54OF+kJNGyZWvatGl309nuqtUrKFIvtc71NWSz2QBXwrp+/FGv19OmTbs8Hx4WFs57732Y5+9r1KjJ+vWbycjIICsrM+f/9epFc+rUCebNnUOVSpWpotNh0jSsioLF6UR/be/gZKuVbZcvYlWUG0pjPNeyNeEmM0euJvH5wf15Ht9qtXL06GGatnNPElCtyQBUq1bDLe3lmwT+vZdAfHw8r7zyilsOLgju4HA4+PTTD0m+msRQN06DLC5JNiIHlufAgX0MH17wIomeUqFCJKmXLuX5+++PH2XzxQs4VJXo8AiG1a1Pqzy2yRw4cAgDBw654bZVq1cULbBrZ/zu6uP+N1mWKV++AuXLV/jnkJrGunW/s2jRt4RIOp5p0IjyeXTZNSpXno963I6madhVFZvTiUVxEm5yrT2pFRrKY02a8UdGGjFxcbm2YbFY3PZ8lKwr6HQ6atWq7Zb2Ct1RWbFiRS5cKNm7Agmlh6ZpzJ//JSdOHKNXULDPxwH+TQ6qzNmzB0lNTSEsLNynsTRs1JT1589hUVUCdDrsisKhpERaR7mmgMuSROfKVbitWnWqBd9cCdNTNMV1/RAY6J2ZXBaLhflff8HOXdupZTBye2BwgWYASZKESZYxyTKh/LP4sLw5gPKVAjiaZibhanKuUzcDAtzTPalpGkrGRerXb0iAm2a+FWpMQNM0Dh065La+KEEorp9++oGtWzfT3hxItLH4dVTcTR9cBXvCQfbv30v37j19Gkv37j1Zu2YlG1Oukp6UxN8XL5LpdDC1XUdqhoYyykddVqojC4By5Srkc8/iu3jxAh/P/oArVy7TMSCQlib37gnQrVt3/vhj3U3jJY0auWdKvWpNRrGl0qHDCLe0B4UcEwDX4rEXX3zRbQEIQlGtWbOSVauW09ho9ouB4NzoTOHIxiD27Nnl8yQgSRKJSVfZHReLDolWUVHcVrU6NUK8d9afG9WeCuDxInJ79+7iizmfoFMU7ggO88h+zI0aNaZr1x43jZds376Fi8mOYrfvSDmF3mCgffuObojWJc8k8Nhjj/HVV1/RsGFDHnroIbcdUBDcYcuWv/jhh2+pYzDSLTDIL2q65EaSJHRBVTl85CBWq/WGAU9vSE5O5uLF8zRp0ozw8Ah0Oh1169alXHAwoyMqEOqhfvjCUK0plCtXwW1dJrlZu3YVPyxcQKTeQL/gUI+Wgs5tvGT79i3Fbld1WnGmxdG9Ww8CA4OK3V62PJPA6dOnWb58OQsWLKBKlSo3LTfv27ev24IQhMI4cGAv8+Z+TlW9gd5BIX4xE+hW9CHVsCSf4ODBfbRr574zuLxomsbBg/tZvHgha9asokqVqixdupKAgACWLPmNixcvMOOtaazITGdocChBPh5I1+wp1Krvua6o5cuXsnTpj9Q2GOkVFOK/C8Dy4bh6Ak1V6dt3gFvbzTMJPPPMM/z0008kJSXxzTff3PA7SZJEEhB84syZ03z26YeUl/X0Dw4pERusy4EV0BnM7Nq1w+NJYPPmTXz66YccPXqEoKAghg0bwciR9+ZcKUmSRNWq1Zj4n5d4//0ZLM9M444g382o0hQ7ii3dbTNd/m3Tpj9ZuvRHoq8tAPP3E4a8aIodZ8opWrduS+XKBdtRrKDyTALDhg1j2LBhvP322wXeWF4QPOnq1SQ++vA9zKrGwOBQvyrqdSuSpEMOqsr+/Xs9UlU0Li6W0NAwIiIiyMrKwm53MGXKNAYNGkJQUO7bCtarF83EiS8xa9Y7/JqZxpAg31wRKNYUAGrUqOX2ti9cOM+Cb+ZR3WAs0QkAwJ58ClWxM3jwULe3ne9fXSQAwR84nU4+/WQW1swMBgQF+81agILSh1TDbrdx+PAht7SnKAobNvzBU089ytCh/fnxx4UA9O7dl59++pWRI+/JMwFka9CgEf/5z2QydTqWZ6aRlUspCE9Tba5B4erV3bPw6XqLFn2LHo1eJTwBaKqCM+UkTZo0o1atOm5vv2R9koQy65dffuJMbAy3BQRRzk+3TLwVOSgKnWxg377ilUzWNI2vv/6KwYP78NxzT3P69CmefvoZhg1zTRnU6XSFGiSPjm7IxIkvkYHEb5np2ItYW6ioVHs6RqOJ8PAIt7abnHyVw4cO0MxoIqCEnTD8mzMtDtVhYeDAOzzSfsl+dYQy4fz5c6xatYKGRhN1fby/a1FJkowuqDJ79uzKtfjarWiaRkzM6WvtSOzfv4caNWrywQezWblyPU888TSReazsLYgGDRoxbvxzXFWcrM3MuKE0gqdpziwiIsq5fXbX2bNxaEB1H2zo426OlNNUrlyVhg0be6T9AiWBy5cvs3HjRhRF4eLFix4JRBDy8uuvP6MHOgW4b1qcL+iDq5CRkU5c3JkC3d9iyeLnnxczevRw7rprcM6esu+99yFz5nxNr1593VadtHnzVtx3/xjOOuzssma5pc2C0BQbYeHhbm/X4XCtQtZTcruBABRbGooliR49bvfYNOh8k8CGDRsYPXo0r7/+OklJSQwaNIh169Z5JBhB+Le0tDR2795JY4MJcwm/rJeDXIuhDt6i2BhAUlIi7747gz59evDGG/+Hpqm88sprOSv1PbVd5W239aZr1x7stlqIc7hnw5J8qU4CPLB2olw512uVoipub9ubnOnnAMmti8P+Ld9P1aeffsrixYsJDQ0lKiqK77//ntmzZ3ssIEG43uHDB9A0jXoltBvoejq9CTmgHIcO3byLltPpJP7a9oySJLFs2U907dqdr7/+nkWLljFixCi31YrJiyRJ3H//w1SrWo0/sjLJ8MIXqISG3gP1nmrWrE2AOYBYbyUzD1EyL1OzZm23j5lcL98koCgKUVH/9Dc2atSoUJclGRkZDB48mPPnzwOwZcsWhgwZQt++fZk1a1YRQhbKkosXL6BDorwfrGx1BzmwIjExp7DZXLVlkpIS+fLL/8egQb2ZPPk/gOssdt26v5g58wNatWrt1dXQRqORp8dNRNPLrMnMQPH4+IBn2pdlmU6duxLjsJPpg1lP7qCpCqoliUaNPDMWkC3fJBAQEMDFixdz3oi7du3CZCrYWdn+/fu55557iI2NBVx1tadMmcJnn33GypUrOXToEBs3bix69EKpZ7PZ0OukEj3F73pyYBSqqrJ27WomT55Ev349+fTTj6hVqw4PPPBwzsp8d5YFKKxKlSrz6GNPEe90sDkr08NHk26qRuAuffsORAP2enGMw51UWyqaplK7tvunhV4v3yTw/PPP88gjj3D27FlGjRrFuHHjeP755wvU+OLFi5k2bVrOlcSBAweoWbMm1atXR6/XM2TIkBv2LhaEfwsKCsKuqji8OGPFUxSHDckYBsDWrX+zefMmRo68h2XLVjJnzjxuv72339RAatu2AwMGDOGI3coRmzX/B/ihqKiKdOnag8N2G2lKyRsbUO2uktRVqlTz6HHynVrQqlUrFi9ezN69e1FVlRYtWlCuXLkCNf7WW2/d8POVK1eIjIzM+TkqKiqnH1QQcpO9e1KC0+mRqo/ekJV6hYsnt3I5Zjd1Wg0k3BRKuXLlWbt2o8f7+YvjrrtGcTYuls1HDxEl64nwSJec5tHEd+edd7N9+xa2WDLpHxzqseN4gupwXYUVZ/pvQeSbBCwWCzExMfTo0YOFCxeydu1axo8fT5UqVQp9MFVVb/iDa1rh3wDly996FaRQunTr1oHPP5eJddi8mgRCQsxERt5YYlkpxNmkpmkknjvExRNbSYk/haSTiazRnJDy1ZAsGhcvnqdGjYruDtvtXp7yEhPGj2d9RgbDg11XMUFBxpteG6BoXXaaQlhYcK7tuUNkZAgjR47k22+/5YLDTlUPrhuQALPZkOtzkfWFn9mmObIIDg6halXP7t+SbxJ4+eWXqV69OrIs89VXX3HnnXfy6quvMnfu3EIfrFKlSiQkJOT8nJCQcMOgc0EkJWWgFnS3bKFUaNGiNUf27aG9pqH3UndJerqVhIT0G24rSBJQHDZkgwlJkjh3ZCN2Sxq1W/SnUr32GM2uExhbUjrJV+I4c+YSwcH+flKj44EHH2P27Pc5bHNtkZiZab/ptQEKvchM0zQUhwWDISDX9tyla9ferFq5is1pqdytN3hsfEkDrFZHrs9FcRZ+cFp1WggPjyj2a6PTSbc8ec43PZ07d45Jkybxxx9/MGzYMCZMmEBKSkqRgmnRogVnzpwhLi4ORVFYsWIF3bt3L1JbQtnRp09/rKrCMT/tm9Y0jZT4GI789S1bl7yJw+a6jG/S/QE6DJ1Mjaa35yQAAPnauMDlyyVj4WXLlq2Jrt+AA3abW9vVnFY0Vblh719PMBqNjBr9AFedTg776XsoV04LEREF63ovjnyTgNPpBGDz5s107NgRRVHIyiraaLvJZGLmzJlMmDCBgQMHUqdOHfr371+ktoSyIzq6IfXq1meP3YrTjwaIFYeNiye2svu3Wexf9znJl09SuV77nNkupsAwpFwWuOmMroQQH3/Zq/EWR/cet5Pp5sFV1ZoMeKZ43L+1adOORo2asNNm8UmhvKLQnFle2cq3QAPDAwcORJZlWrduzUMPPUTnzp0LdZA//vgj59+dOnXi119/LXykQpklSRJ3jRjNO++8wUGbhVZm3w6mqtcWUdksqZzcuZTgclWJ7ng3UTVbIOvz73OWDK7pn1evJnk0TneK9sD+w86sy+j1BmrXruv2tv/NtRBuDP/36ktstWTSK8i3W2rmR1PsqE4bUVGeHzfKNwm8+uqr7N27lwYNGqDT6Xj00Ufp0aOHxwMT3MNms3H8+JGbxlHMZjPR0Q3RlZBSDA0aNKJF85bsOXSARkazV0pIrFy5PGev2IoVK9Gjx+2cOHECY1ASzXo3JzA0iraDJxEYGlWoCQ6STkanN5GcfNWD0btXdhkGd9E0DTXjAo0bNcFo9E6Rt8qVqzJg4B2sWLGMaKPJr4vLqTbX9NCKFT277zIUIAnIsoyqqmzbti3nMnft2rViZ7ESQFVVPpz1DsdPHMv191OmvEa9etFejqroRtx9L/93cD+7rVl0CfTsgOqOHdtZtOg7rFZXH/Lly5dYtOg7ZFmmXNV/pjkHhRXtTE3Sm8nI8NxgqLu5+2RBybyMYs+kc+eubm03P0OG3MnOHVvZmJTESL3ebzcmUrzYVZZvEpg6dSqbNm2iZs2aObeJ7SVLhuXLl3L8xDE6BwRR+bpqk5ecTrZYMrHZ3DvQ52lVq1ajS5fubP17E83NAYR4cLPwX39dmpMArqfT6ahSq0nxDyAZyMjw9Gpc/+W4epzQ0DBat27n1eMaDEYeefRJZr79OluzsuiRz8Y7vqJYEgkJCfP4oDkUIAls3bqVlStXloCpbML1jh07wq+/LiHaaKK5yXxDd0XJWzv5j6FD72Lr1s3ssVg89gF2Op15dtU4HA73HEQnY3fzbJuSQslKxJl5mX5334PBBwsA69dvQL/+g1m9egW1jEZq+lm3kKZpaJYEGrRo5pUV5PleC1WuXFkkgBImIyOdL+Z8QphOpntgsN+UInCH8uUr0LVrD447bB4pDHY6JZmjR4/m2f3hti8tScZxbeZdSVGndl30ej3VqhW9jIGmadgS9hMcEsrtt/dxY3SFM2zYCKpUqcoGSyYWP5stpNrTURxZNG7c1CvHyzcJtG7dmokTJ/Lrr7+yZs2anP8E/7Xgm3mkpaXSOzAYQylKANn69x+Mqmk5i5fcQVFVfok5xf/btwedTsfAgUMw/6vOvdlsLtJK+VxpWokrijf11Tf44otvaNWqbZHbcKafR8lKYPiwuzGZ3L+PQEEZDEaeeGI8Nk3jr6wMn8WRGyXDtX6kadPmXjlevt1Be/fuBeDHH3/MuU2MCfivXbt2sHPXdjqYA4l0065T/qZixUo0a96So4cP0sYciFzML9NMh4NP9u/lVGoKbStWxlEpigEDBtG5c9ec2UGVKlVm3LhnWbJkkXuehKYil9K/T1401YkjYT9VqlSjW7fbfB0ONWrUZOidI1iyZDGn7Ta/2brUmXGJypWrUqFCZP53doN834ULFiwAXP2kmqb5pA9PKBibzcr3382ngl5PS3OAr8PxqB49enHgwD7OOx3F7tMN0OsJNOh5tEkzGkdG8V2aa2bGwIFDGDhwSM79FEVxYxJwEOjHxeM8wX71OIo9g/vuexbZT/aHGDBgCLt37eCvC+eopjdg8vGUaU1xoFgSaNljkNeOme8zTkpK4rHHHqNly5Y0b96cBx98UFT+9FPr168hJTWFrgFBJa6robCaNWtBUGAgJ4s4uGpTFH46eYI0mw2dJDG+eSs6VvL8nOwcqoPg4JK9Z3JhqI4sHElHc1bu+gtZlnn4kSewqirb/WDfAWdWPGgqzZu39Nox800C06dPp2XLlmzZsoUtW7bQtm1bXnvtNS+EJhSGoiis+X0l1Q1GKntguz5/o9frad2mPXFOR6FLSVzISGfGzm38fjaWg0mJAF4dPNc0DdWeSUSE50sC+AtbwkF0EowceZ+vQ7lJjRq16NmzN0dsVq4qvh2sVzLjMRpN1K1b32vHzDcJxMbGMn78eEJDQ4mIiOCZZ57h7Nmz3ohNKIRjx46Qlp5GYz/p1/SGNm3aY1dVLjgLNm1T0zQ2nD/HWzu3k+Fw8FzL1nSpUtXDUeYSh9OCpqlUqOD5OeD+QLGl4kw9Q+/e/TxeG7+ohg69C5PJzA6Lb68GVEsC9erVR+/F8aICFZC7flGRxWIpVVMOS4tjx46gkyS/Xgrvbo0aNcFsMhNTwC6hNWfj+O74URpERDCtQyeaeGEhTm5UWyrg+R2j/IU98TBGo+mG8RV/ExISSt9+AznjsJPko6sBTVVQbKnUqVPPq8fNN90MHDiQMWPGMHz4cCRJ4ueff6Zfv37eiE0ohPj4y4TKcqmcEpoXg8FAy1Zt2LdjG4qm5TlLSFFVZJ2OLlWqYNTp6FGtuk/HTLJLAlStWt1nMXiLas/AmXaOAQMGExLi3zt79e7dj9WrlrPfauF2HxSYU+3poGlUq+bd90W+VwLjxo1jxIgR/P3332zatInhw4czfvx4b8QmFILdbsdA2UkA2dq374hVVTiXy0peVdNYHnOa9/bswqmqBBuM9Kxew+eD5oolkaiKlcrEIkx78ikkSaJ3b/8/cQwODqFL1x6ccth9soBMu7adpLemhmYrUMfT7bffTkhICLIs0759e9Ed5IfMZhN2/KfWvrc0bdqC4KBgjtmt1LquGuVVq5W5hw9yIiWZjpUqo2gq+vzPeTxO01Q0SyINWhWuHHtJpGkqSnocLVq09MrmKO5w++19+fPPdRyzW71eslxTXN2aoaFhXj1uvp+K7Iqh33zzDV999RV9+vRh27Zt3ohNKITy5SNJV5RCb/FX0un1erp170msw076tTr/e67E8/r2LcSlp/FI46Y82qQZJtk/Fmaplquoip0mTbyzGtSXFEsSqsNCx45dfB1KgVWtWo369aI5arfnVE32Fu3a+9fg5XG9fJPArFmz+Pbbb/n2229ZuHAhc+fO5Z133vFGbEIhVK1aDVXTSFFLcnm4orn99j5IOh0HrBacqsrS06eIDAjk/9p3olNlN5V5cBNnxgUkSee1ujC+pGRcQpJ0NG3awtehFMptPXuTqjgLPOvMba71sGiad7ui8k0CZrOZBg0a5PzcpEkT0R3kh6pVc9UdT3LzFoAlQfnyFYiObshBSxY2NJ5r1ZrJbdsTFehfK3I1TUPJuEDDho3LxHiAYkmkRo2aBPrZ3yE/bdq0JzAgkCNe3o9YulYa3dsl3vNNAt27d+eLL74gKysLm83GokWLqF+/PqmpqUXecF5wv4oVKwGQVsaSgKZp/PTTD/z002LOX7rELouF8uYA9H64Y5pqTUaxpdGuXQdfh+Jxmqah2ZK9Pt3RHYxGI1273cYZp8MjlWrzIsmuNT7p6WleOyYUYGD4yy+/RFEU/vvf/95w+y+//IIkSRw9etRjwQkFZzKZMBqNWL18KelLaWmpTJ/+KuvWraFjx860bt2G7dv+pqnZTHk/GQO4niMtDlmWy0YScFpQFUexyk77Us+evVmzZiVHbBbaBXinvIekd9X7SklJ9srxsuX7STl8+LA34hCKSdM0FEVBp/ePwlyedvjwQSZNeobExASee+4FHnzwYbKysji4fy9/Z2UyJDjUr7otNU1BSYujVcvWBPnpblbupNpd5Zmjoir5OJKiqVixEs2ateDIkUO0dkOl2oLQGVzJJjEx0ePHuuG4+d0hMTGR9evXA/D+++/z0EMPcexY7nvWCr5z9WoSiqIQIvtfN4gnhIWFEx4ewfz5Cxkz5lF0Oh3BwcHcOfxuLjgdxDrsvg7xBs70i6hOK9269fR1KF6hOV1z3r2xPaKn9O7dnyxF4bTDO330kmxEpzeSkHDFK8fLlu83xuTJkzl37hxbt25l06ZNDB06lDfffLNYB33ggQcYNGgQQ4cOZejQoezfv79Y7QmuM2OASnLpLR4XH3+Zzz//BO3aqsqFC3+madNmN9znttt6U7lSFbZaLSh+NF3WmXKK8IhyXtsoxNdUh2tQNSIiwseRFF2TJs2IioziiBcHaiV9EImJfpYEUlJSGDNmDJs2bWLw4MEMHz4ci6XoOzppmkZsbCy//PJLzn8tWpSsKWT+aPNfGwiT9ZT3kzrt7vbnn+sZOXIo//vfPOLizgC5V/6UZZm7R95LquLkmN27szvyotrTcWbG0/O2XnluW1naaIoFo9Hk093Dikun09Gt++1ccjpI9dKEC0kfSEJCgleOlS3fd6TD4cDhcPDXX3/RuXNnLBYLWVlFr7QXExMDwCOPPMIdd9zBt99+W+S2BJdTp05w6vRJmhhNftUP7g5Wq5W3357OxInjqFy5Kj/88DO1atW55WNatGhFndp12Wuz+cXVgKt0go6uXW/zdSheozmtXl/56gkdO7pWdsd4q0vIEOj1geF8k0CvXr3o1KkTERERNG3alLvvvpvBgwcX+YBpaWl06tSJTz/9lPnz5/PDDz/w999/F7m9sk7TNJYsWUyALNO4BJ915eW5555m0aLveeCBMXzzzQ/UrFk738dIksSgwUNJV5zE+XhsQFMVlLRYWrduU6K7RgpLc1oJDw/3dRjFVr58BapVrcb5XGpTeYIkG7FaLahenJqa7+ygZ555hpEjR1KpkmuU//3336dhw4ZFPmCrVq1o1apVzs8jRoxg48aNdOlSsKXl5cuX/pkVhbF3716OHTtCl4CgQlcQDQ8PJDLS+9US86NpGpqmodPpePbZCTz99JP06tWrUG306tWdbxfM43hmFnWKsMdCSIj5ptdGKUKXgDP9HKrTxp133uGXr7U7XV+YT1JtREVVKBXPuUnTJmy8fCnf+0mA2WzI9TnL+oJ1A0o615heSIjBa4vs8k0Cqqry22+/cfLkSaZOncrGjRupX79+kfcI3bVrFw6Hg06dOgGuD3xhNlBISspAVX1/ie8PNE1j7tx5hMh6mhThKiAlJYuEhHQPRFZ0rrn//0f9+tGMHTuORo1cJwxFibN1m/Zs/GMtTk1DX8gEmZ5uvemYRUoCKTGUrxBJ5cq1/e61drfr61apDgsBASGl4jkHB0dgVRQcmnbLEy0NsFoduT5nxVnQM3tX+wkJ6QQGumccQqeTbnnynG96evfddzl+/HjODJ6//vqLt99+u8gBpaen8+6772Kz2cjIyGDp0qX06dOnyO2VZfv37yUuLpY2JrNX5jF72r59exg1ahgbNqzHbC5+11ajRk1wahqJPtokRLVn4My6QvduPcvMgDCApjpRFTvh4aWj+0t/be2NN4ozalp2ETnvzfLL9xR869atLF26lOHDhxMcHMy8efMYOnRokQ/Ys2dP9u/fz5133omqqtx77703dA8JBffbimWEynqiS/iWkoqiMG/el3z++cdUrlyFr7/+nmbNij+VskaNWgBcVRQq+WDfZUfqGUCiS5duXj+2L2kO18SR0jIGkpGRjgQYvXCipSl2ZL3ev5KAXq+/4SzGaDQWe//L5557jueee65YbZR1p0+f5HTMKboGBJX4q4CYmFPMmfMJffr0Z+rU191WXK1cufLIOtkn9ZQ0TUNJP0eDBo0oV67sbCgPoDpdU8hLyh4C+Tl37iwRer1XZt5pziyvv275fptHR0fz3XffoSgKMTExzJ8/v1gDw4J7rF+/BqNORwNTyb0KOHXqJPXq1ad+/Qb88MNS6tat59YPmk6nIyQkhKxirGspKtWWimJLo0OHTl4/tq9p15JAaZgd5HQ6OXH8KHV03qlFpdnTqVTDz7aXfOWVVzh8+DBJSUnce++9ZGVlMWXKFG/EJuQhLS2NnTu3EW0wYpRKXl+zzWbjnXfeZMSIIWzbtgWAevXqe+RMKzg4GJsP1go4My4A0LJla68f29f+SQIlvzvo8OGDWG02anphoxdNU1HtaV7fezrf9PbLL78wY8YMb8QiFNCmTX+gKApNfbAZdnGdORPDSy/9hxMnjnHffQ/RunVbjx4vOCSUlPjLHj1GbpTMy1SvUatUfBEWluq0YjAYMZsDfB1KsW3Z8hdmnUx1L/TRq7ZUNFWhVq3818K4U76nkQsXLvRGHEIBOZ1O1q/7nWoGAxF+WC75VpYvX8Y999xFQkI8s2d/zgsvvIzR6NkzrNDQMLzdGaSpCqolicaNmnj5yP5Bc1oJDvGvKq5FkZWVxb69u6hnMHhl3E3JclUP9fYeDPl+i9SuXZupU6fStm3bGxYv9O3b16OBCbnbunUzqWmpdA0O9XUoheZ0OmnRoiVvvvkOkZFRXjlmZGQkuxQFRdO8NoCu2lLQNJV69aK9cjx/oyk2wsqVvPfnv+3evQOH00m0l/YTUCwJhIVFUKFCpFeOly3fJJCSkkJKSgpxcXE5t0mSJJKADyiKwm8rllFBr6e6D6Y8FsX+/XuJj4+nb9/+3HnnXQwdOtyrc+arVauBqmlcVRQiizmrraAUawoANWrU9Mrx/I5qJySkoq+jKLY9e3YSIuuJ8sIVt6ZpaJYEGrZu5fUrqHyf3YIFC7wRh1AAW7b8xZWEK/QPCvH7S21FUZg//ys++2w2tWvXoVevPsiy7PW4GzRoBMA5h91rSUC1p6HXG0p0Lf1iUR0EBXnn7NlTVFXl+LGj1JG9NDXUno7isOS8X72p5E0tKaMcDge/LvuJKL2BWl6YqVAcV67E89RTj/Lxx7Po3bsfX3/9fZHLjBRXeHgEdWrX5aTTjualWUKaI4vwiIgytUr4eppiJ8BLXSiecvVqElab1WsnDs4sV/noBg28P/2+bL5LS6BNm/4kKfkq7cwBfn0VkJyczKhRd3LgwH5ee+0tZs78gJAQ385i6nFbL646nZx3eqcSpKbYCSsFZZSLRkNTHAQElOyZQdmbvQd5KZErlgSCgkOoVKmKV453vTyf4bp16wCw2/1rm76yyOGws2L5UirrDX47FpB9lh0REcHDDz/OwoU/c+edd/lFwurYsQvhYeHstFq8czWgOd1S+6gk0jQVTVNLfBLI5rWrR0si0fUb+OTzkmcS+OijjwAYNWqU14IRcrd580ZS01Jp66dXAbGxMdx//0gOHXJtcfngg49Qu/atN37xJoPBwLDhI4l3Ojjprc1B/PDv5BWqq1hfSd5RDFx7WANkeKGuv+q0otgzqFevvsePlZs8O7yCgoLo168f8fHxDBky5KbfL1++3KOBCS6KorBq5XIq6g1U9bOrAE3T+OWXJcyc+SZmsynnEtofdenSnT/+WMPW8+eoqTdi8uhlvoTT6ZvKpb6m5SSBklvOBFx1j4ICArnihQq0qiUJgLp1/SwJfPXVVxw9epRXXnmFV1991ZsxCdfZs2cniUmJ9POzGUHp6em89dY0Vq9eSbt2HXjzzXepWNF/pwXqdDrGjHmc6dOnss2SRY8gD25OpDMUawvWEq2UXAlIkkTjps04tGcXqqbdsGGOuynWq0iSRM2atTx2jFvJ83QoODiYdu3aMWfOHJo0ca18dDqdNG7cmPbt23stwLJM0zRWr1pBmKz3uxlBS5f+yNq1vzN+/HN8/vk8v04A2WrWrE3fvgM5YrdyyYODxJLeRHp6yd9MpSg0zZUEPL0S3Bvat++ERVE45+EJBYrlKpUrV/VZ4sx3/lN6ejoPPPAAFSpUQFEU4uPj+fzzz2nduuwVxvK248ePciY2hm6BQR49EykoVVW5ePEC1apV5957H6R9+440bNjY12EVytChd7Fzx1b+Sk9jRHCYR15XnT6A1JRzqKpa9qaJqq6y3SW9OwigRYvWhIaEctBq8VgBOU3T0GzJ1KnT0SPtF0S+79B33nmH999/n2XLlrF8+XI++ugjZs6c6Y3YyjRN0/hl2c8EyjINjb6/tE5IuMJTTz3GQw/dQ1paKnq9vsQlAACz2cyo0feT5HRyzO6ZQWLJEIiqKKSmpnikfX9WWsYEwLWXSu8+/TnncJDooTEezZGJ6rRSu7Z3i8ZdL98kkJmZSceO/2SpTp06YfFBffay5uDB/Rw/cZTWRnOh98d1t7/+2sjIkUPZv38P48Y9S0hIya4L07ZtB2rXqsMemxXFA1MAdQbXeENCwhW3t+3vtFIyJpDt9tv7EGA2s8vqmTEexXoV8H7RuOvlmwQkSeLChQs5P58/f95nqz/LCofDzvffzSdM1tPYhx8mp9PJ+++/zYQJY4mMjOL7739m+PC7/WqAuigkSWLQ4KGkK07OOty/DkZndCWBeB+UsPY51dV/XlrWCQQGBtGn70DOOOwkeOBqQMlKRG8weH0PgevlOyYwbtw4Ro0aRadOnZAkic2bNzNt2jRvxFZm/frrUq4kXGFwcKhPt46UZZnz588zevR9TJz4Yqm4xM+W3d97wmqltpv3aJYMQSDpuHKlDCYBXFdWpSUJAPTtO5D161az3ZrFYDdX71WtidSpXbfYW/YWR75H7t27N3Xq1GHbtm2oqsrYsWOpW7euN2Irk06fPsnKlb/SwGiiug9mBGmaxooVv9C6dVuqVq3G++9/5NM3qKfIskzzFq3ZuWUTmqa59epGknTIxiDi4+Pd1mZJIklSqekOAggMDGTQ4DtZvPh7LjocVHHTBjOa4kCxJtMgurtb2iuqAn2669SpQ506/rMCtLSyWq18MecTgnU6ugR6pwDXypXL+fjjWVy+fImoqIpUrFiJAwf2ce+9D/Lii1NKZQLIVqdOXTZv3kC6qhLq7i5OOZDExAT3tunnjEYjmUBoaHipmxV1++19WbtmJdsyMhmmd8+GOYolATTNJ5VDr1d6P+El0OLF35OQmMDQ4DBMXtg7+O+//2LevC+wWq2Aqw87Pv4yffr0Y9Kklzx+fF/L3tgmwwNJQDIEknQ1ya1t+ruJz73I5cuXqFSpsq9DcTuj0cjQO+9m/vwvOeOwU8cNXYjOzCvIst7nmw/5JF0vX76cgQMH0rdvX7777jtfhOB3Tp06wYYN62huMrvtcjObpmlkOhwkW63EZ2VyOSODzMxMFi5ckJMArnfo0MEyMfif3W/twAMzhPRmMjMzvFaAzB9Uq1adtm3bU62a7wY5PalLl+5UrlSF7VaLW2aVqVnx1KsX7fOxtjyvBA4fPpyzUtid4uPjmTVrFkuWLMFoNDJ69Gg6dOhAvXqemSJ1fXdHpUqVmTBhIgMH3lwLyducTidOp6vapKZpzJ37/1CtVsqh45AlEYeqEqjX0yCiHAB/nj9Lut2OXVGxqwp2RaV6SAi9qtcAYNbe3WQ47NgVBbuqYlcU2lWsxL3XLjUnbvqzwF91ly9f8sRT9jse/X6WjaiKgt1uK1X942WZLMuMuPsePv74A47ZrTQxFX3wW3VYUKzJNG3q+x0a80wCU6dOZenSpQB89tlnPP3002454JYtW+jYsSPh4eEA9OvXj9WrVzN+/Hi3tH+9lSuXM336qzlnu5cuXWT6dFcdpNwSgaZp2O12rFYLVqsVh8ORc1Zz4sRxLl++iMVixWazYrVa0ev1DB9+NwA//vgDx44dwWazYbVasFislC9fnunT3wbg+eefZf/+PVgsrsc6nQ6aNWvBggWLiI2N4c8//8RqtbLvungaRZTLSQJrz8aRYLFg1Okw6GSMsg5Z90+/pFmW0etMGHUyBlnGpNNR51olREmSuCe6IbJOh1GnIwONXTYrSUlXSUlJvul1KI2X87mxWl3rXQy4fwaWdK07z+FwUoomVZV5LVu2pl7d+uyOjSHaaMZQxLEBJdN1otWsWQt3hlckeSaB6y9j165d67YkcOXKFSIj/9lIOSoqigMHDril7X/7+ONZN3V3WK1Wpk2bwjfffI3NZmXJkt+QJIkZM6bz448Lb3jegYGBbNmyB4B5875g9erfbmirfPkKOUlg9+6d7Ny5HbPZjNkcgMlkIiTknyJlDRs2IiQkFLPZTECA6z7ZX7b79++lerVq3B4QRKBej/Hal3zQdVVDX+/QGVmny7PMwVPNW97yteh57YoB4JLTwcn0VPr1G3TDmAC4VtROmDDxlm2VFhkZrvo+ATpPTMN1talpni9FLHiPJEncPfJe3n77dQ7aLLQ2BxapHWfGJUJDw6he3ff7UOeZBK4f/XZnv6aqqje1XZiR9vLlC179Ma9uDYfDQfXqVTGbzUREBGAwGOjXrxdVqkRd+5IOICAggMDAQCIjXbtivfzyi0yY8DQBAQE33Cd716y5c7+4ZSwvv/xCnr9LSUmkWrnytA3Jezcqgwf66AcN6kejRvWZOXMmFy9epEqVKkyePJnhw4e7/Vj+SFVdZSMCchmEDwkx5/ztsymKUuC2s1fOVq1awed9voJ7RUa2pW3btuzbs4cmJnPOJA4JMJsNN71vAGT9P+8xTVNRs+Jp37M7UVG+X31foNlB7pxDXalSJXbt2pXzc0JCAlFRUQV+fFJSBqpasKRUqVJlLl26eNPtlStX4b33PgYgJcUKWGnTpgtt2nS56b4JCa6zxbCwioSF3Vgp02oFq7X41SIdDhXVA4OT+UlJyaJbtz5069bnhtuzn7OvKIqT5OQEnE7P7moXFVWR//xnEiHXTWesAPxHVQkODuHgwUM33F/T4D//mYQkG5B0t17DoSm1QOvD8eMn8dcF1jqdTEBAMMHBYSV+Fbi3DRo0nF27dnHQaqVtgOtqQAOsVkeunx/F+c8VoWJJRFXsREc38cpnTaeTbnnynGcSuHz5Mm+++eZN/842derUIgXUuXNnPv74Y65evUpAQABr1qzhjTfeKFJb+ZkwYeINYwLgn90dVapUY6vTSYaqEKwr/bNy8pOcnIDZHEhQUCWPfjklJiagl9OpIP/zMVA0DUlVqFAhkuDgG8/oNE3DZlOQ9AFI+lsPCqr2DPQ6lcqVa9zyfr6iaRqK4iQ9PYXk5ATKlSv4iZgANWvWomXL1hw4sI8W5oBCjQ0oGZfQ6XQ0btzMgxEWXJ5J4L777sv138VVsWJFJk6cyIMPPojD4WDEiBE0b97cbe1fL3vw1x9nB12vTZt2/PzzDxyxWWkf4J1FYv7M6bR7PAHAta5IDwwKuxpX0Ov9t6a+JEno9QbCw8sTH3/e1+GUSIMGDWXfvj18m5aCLElYFAVu9X7SFNdWkpmXqVu3PoGBRRtPcLc8k4AnZutkGzJkSK5bVnrCwIFD/O5L/98qVapMmzbt2L9nN42MZkLKwBz9/JTs7gkNNKVEbKzimsVUdtYyuFPduvUZOfLenLFHSZLo1q1HrvfV6/UomfFknlwGQNOmfXK9ny/cckzgwIED/O9//+PEiROYzWaio6N56KGHiI727Qq30mjUqPs5eHA/67MyuCM41C82kSntXInGA1+ApWhjFeHW+vcfXKD7Pfjgo8TEnARAlvW0a9fBk2EVSp4rhrdu3crTTz9NdHQ0zz//POPHj6dy5co88sgj7Nixw5sxlgkVKkTy0EOPccnp4G9LZplaaeorsiyjcus0kJGRwcsvP1/gNrMyM/jve68BcPXqVUaM8O+rUME7atasRc+efejZsw/du/ckIMA/uoLgFlcCc+bM4auvvqJhw4Y5t/Xo0YPu3bvzwQcfiH2GPaBTp66cPRvH77//RrBOR6sizkEui4qyMjy7OJ6qaXmW7E5PT+PkyeMFjiMzM4PYM6cxGIylroiaUDrlmQSSkpJuSADZmjdvXmY30faGu+++h5Tkq2zbsRUZiebm0lOX3VMKuzI8m+FaqW4nkNcozIcfvkdiYgIvv/w8cXFnMJvNGE0BdOneh6OH9/Pk+BcBeOP//sNdIx9k5fKfSE6+yqxZ7zJp0mRsNhvTpr1MTMxpQkJCefvt9wm7tpJbEPxBnkngVgXERFeF5+h0Oh597CkcTid/79mJikbLMn5F8OijD9x0W9++Axg16l4sFguvvfYKdvuNawqsVisffzyLTp268vzzz9z0+JEj76FPn/4AODQNUx5XAs899wITJozlmWf+w91338GsWZ8QVbkmm/7alOv9HxzzBG++fpq33nqXq1ddZTlGjbqPxo2bMnXqi6xbt4a77hpZ2JdAEDwmz+vVkj07o2TT6/U8+eQE2rXtwFZLFtvFGMEt/TsBZMuvEJ5Op8NkMuMo4GsbEVEup/x0XrRr2ysar5UarlAhksaNmwJQu3bdMrn5vODf8rwSiImJyXMa57lz5zwWkOCi1+sZ++QEAr4JZNOmP7GoKt0Dg8vkrKG5cxfk+buAgAAqV66S68rwSpUqExERkc/jA0mxWVHQkPNZM3D9bB9Jkm5IzK6SEhqoDnQ6Xc5J1L+vqEUyF/xNnkngyy+/9GYcQi50Oh0PPfQYoaFhrFixDIum0ScoBH0ZTAS3UpyV4YGBgaSkXMWuarkWkpNlOdeaQSEhoVy4cBZN00i4cpmzcTFoigOdLhhVFUXjhJIjzyTQvn17Tpw4QWxsLC1atKBixYp53VXwIEmSGD58JKGhYSz8/n/8lpFG/+AQr+w8VlIUZ2W40WjEoDdgczrJbQi+XLnyVKxYiRkzXr/h9qbNW7Phj9VMemYMVarWoEHDpmiqkwoVIq8dfyxTpkxzx9MTBI+StDyuT3/++WfeeecdatasydmzZ/nggw/o2rWrt+O7SWEKyJU227dv4csvP6OCTmZQUAjmIk5BvOR0sCw9lUmTXqZJE/+oX3K9y5fjqFTJeyV2U1KSSUlJJkjSoQFZmppn7aC4uDO51w7SFFRbKhER5Urc7B9vv96CdxW5gNyCBQtYvnw5FStWZO/evcyaNcsvkkBZ1qFDZ0wmM59+OosVmekMEVcEbhEUFExKSjKZ19X+L+z2mppiz2lLEEqSW36DZHcBtWrViuTkm3egEryvZcvWjBs3kSRV4feMdLfsdVrWGQwGatSoRfXqNa79V7PQKzo1xY7JZM5ZgCYIJUWBp4iWhY3HS4qWLVvz8MNPcMHpYKsl09fhlAo6nQ5Z1l/7r5DvddUJmiKuAoQSqcB9CWLdgH/p0qU7ffoM4KDNylmHZzdfEW4te21AUFDZXtQnlEx5XrseP36c1q1b5/xstVpp3bp1znaQe/bs8UqAQt5GjBjFwQN7+SsxkdF6Q571bwTPyu4KkmXRFSSUPHm+a9euXevNOIQiMBiM3Hf/w3zwwdscsVlpJuoMeZ+mgKYQGJj3/tCC4M/yTAJVq1b1ZhxCETVp0oz69aLZd+Y0TUzmUrmi+MOZb5CWnOT2dkMjyvPc5Ffzvd+MGa+zd+8ennjiKerXv3EvDU1xdQV5qzTwhAlj+fjjOTfdnpGRwVtvvcbbb7+fc5vT6WTSpAk89NCjtG7d1ivxCSWPuH4tBfr1H8Qnn8wi1mGnjrH0bWSSlpzEAKf7V+GuKmBiWbVqBX/8sQW9Xk9c3JkbfqepTmRZxmAwuD2+3OzduzvX2/9d8vrs2Vjefns6x48XvAy2UDaJJFAKtGzZhojwcI5kZpXKJOBLL700EU3TePzxh+jduy8rVvwCko46dRsw5tEJGHVOxj75CI0aNSUpKZGvvvqGhQu/5c8/16IoKh06dOSpp55BkiQWLfqOZct+RpZlOnfuxtNPP0NMzClmzXoPi8VCcvJVHnhgDHfeOYJdu3bw2WezkSSJkJAQXnttBvPnu0q5PP74Q3z55f9uiPP6ktdvv/0+K1b8wj33PMjixd/74mUTShCx0qgU0Ol09LitN+ccdlJzqXMjFN0778wC4NVXp7N8+TJeeeU13nnvE0wmMz8v/h9oKmlpadx334PMn/89u3bt4Pjxo3z55Td8/fV3JCQksGbNKo4ePczSpT/x5ZffMH/+Qo4fP8axY0dZvvwXHnroUb766htmz/6cTz+dDcD//jeXF154mblzF9CuXQdOnDjGc8+9AHBTAgBXyesKFSJzuoOefvpZune/zTsvklCiiSuBUqJbt578+usSDtksdAkU89Xdbd++3XTu3I2QEFcpidv7DGLOJ+/CKNfeAE2auMpF79q1gyNHDuXsgWCzWalYsRJJSUl06dKN4GDX3+ajjz4DoH79aLZv38qCBV9z+vQpLJYsALp27c6UKS/QrVsPunXrQbt2Hb36fIWyQySBUiIiIoL27Tuxe8dW2qoqJrG1oVv9u16VpmkoijPnZ5PJfO1+CiNH3sPo0fcDkJ6ejizLrm6k60pVJyYmYDKZmTlzOiEhoXTp0o1evfqybt3vAIwadR9dunRny5a/+Oyz2dx222EeeujRnMdv3ryRr75yDRB37dqdQYPu8MjzFko/8U1RivTvPxiHqnLAZs3/zkKhtGrVhr//3kRGRgYAf65bSaPGTXO2qMzWunU7fv99JVlZWTidTl5+eRIbNqynRYtWbNv2d87tr732CseOHWHnzh089tiTdOt2G9u2bQFcexM8/vhDZGVlMnLkvYwceS8nThwDXCv3nU4nXbv2YP7875k//3see+zJPEteC0J+vH4lsHTpUj744APKly8PwG233cbEifnXfRfyV6NGTVq1asPB/XtpZjIXucqocLN69epz//1jePPNaSiqSu06DXh4zCM3zQrq2rU7p06d4IknxqCqCh06dGbAgME5JcGffPJhVFWjR4+etGvXgUceeZynnnoMk8lI3br1czbIGTt2HG+99TqyLBMYGMhLL03NaX/MmHuZO3fBDZvcZJe8zmsKqSDkJc9S0p7yxhtv0KpVKwYPHlykx5flUtIFcf78Wab932RamALoFBiU631KWilpX68TyHZjKWkzqjWZsLBwIiLKuT02bxKlpEu3IpeS9pSDBw8SGxvLnDlzaNCgAa+++iphYWK1pbtUq1aDTp27sX3rZpqazYToSn7hv8J8UXvNtbLTomqoUNJ5/R0cGRnJI488QuvWrfnvf//L9OnT+eCDDwr8+FtlNMHl0UfHsHPHVnZYsugVFJLn/cLDA4mMzPv3vnLlig693v+6sq6/ZtauJQGTyeiXsRaGTqfzy/eB4B0eSwKrVq3i7bffvuG2OnXqMH/+/JyfH3vsMfr06VOodkV3UP4kKYBevfuzevUKWpgCqJDH2WpKShYJCeleji5/qqri9MAK4eK6oec0ZwManV/GWhiqqvrl+0BwD591Bw0YMIABAwbccFt6ejrz589nzJgxgOtDJfYp8IxBg+5g44b1bLdmMSg41NfhlD7XEoJ4/wolnVevYwMDA/nqq6/Yv38/AN9++22hrwSEggkKCmbQ4KGcddi57HT4OpzSR1MBCZ2YgSWUcF59B8uyzIcffshrr73GgAEDOHz4MC+88II3QyhTevXqS0hwCDutWb4OpdTRUJFlndhsSSjxvD4w3LZtW5YuXertw5ZJJpOZ/gMG8+OPC7nidBCl906lyzJB09DpRVeQUPKJ+W2l3G239WLF8qXss1roG1wyk8DMd2eSnJLq9nYjwsOY/OLkfO+X634Cmoas8/7Hp6D7CfzyyxJ++ukHJEmiYcPGvPDCFK+VuxZKFpEESrmAgEB63Nab31evIENVCC6B6waSU1KxRnRyf7vJWwt0v9z3E1B9MihckP0Ezp6NY+HCBcydu4DAwCDeeus1lixZzKhR93kzVKGEEEmgDOjZ05UEjtqstAvIfRWxkLu89hOoXasW48a5yp0MHtybBg0a+81+As8+O4lJk14iKMg1LbBOnXrEx1/27gsnlBhiakMZEBkZRePGzTjucODlKiElXq77Cbz7MSaTicWLvwMgJSXFr/YTqFSpck7p6eTkZJYsWUzXrj288XIJJZC4EigjOnfpxuEjB7nkdCImtBTejfsJaPTs2Zt5877I+b0/7ieQkHCF559/hsGDh4o9hoU8iSRQRrRq1RaDXk+Mw0ZdsQVloV2/Sj27ZISq/rNS2N/2E4iLi+U//xnPiBGjueee+934SgiljegOKiPMZjONmzQjzulE9AgV3g37CWgaf/65jpYtW990P3/YTyArK5OJE8fx+ONPiQQg5EtcCZQhLVq0Zv/+vfyS4ZpuWVJWu0aEhxV4Jk9h2y2oG/YTUBRq167DxIkv3nQ/f9hPoGvX7iQnX+WHH77jhx++y3ncY489WcxXTCiNvL6fQHGJAnJFZ7VaWbduNQ6HA7PZTO/e/W7aGcsf+Gt9++z9BFzdOho1atQqMYn0Vvz19Rbcw+/2ExB8x2w2M3jwnb4OoxTQkCRRN0goHUQSEIQCkiSJ8PByOBz2nIFgQSjpRBIQhEIIDw/3dQiC4FbielbwSyVsqKrE0q6VxBbKLpEEBL+j1xvJzEwTicCDNE3D6XSQkpKI0Si6tsoy0R0k+J2IiEiSkxPIyEjxdSilmk4nExAQTHBwwafKCqWPSAKC35FlPRUqVPZ1GIJQJojuIEEQhDJMJAFBEIQyrMR1B+l0YiaDIAhCQeX3nVniykYIgiAI7iO6gwRBEMowkQQEQRDKMJEEBEEQyjCRBARBEMowkQQEQRDKMJEEBEEQyjCRBARBEMowkQQEQRDKMJEEBEEQyrAykQSWL1/OwIED6du3L999952vw/E7GRkZDB48mPPnz/s6FL/yySefMGjQIAYNGsS7777r63D8ykcffcTAgQMZNGgQX3/9ta/D8UvvvPMOkydP9nUY+Sr1SSA+Pp5Zs2bx/fffs2zZMhYtWsSpU6d8HZbf2L9/P/fccw+xsbG+DsWvbNmyhc2bN7N06VKWLVvG4cOHWbt2ra/D8gs7duxg27Zt/Prrr/z8888sWLCAmJgYX4flV7Zu3crSpUt9HUaBlPoksGXLFjp27Eh4eDiBgYH069eP1atX+zosv7F48WKmTZtGVFSUr0PxK5GRkUyePBmj0YjBYKBu3bpcvHjR12H5hfbt2/PNN9+g1+tJSkpCURQCAwN9HZbfSElJYdasWTz55JO+DqVASlwV0cK6cuUKkZGROT9HRUVx4MABH0bkX9566y1fh+CX6tevn/Pv2NhYVq1axcKFC30YkX8xGAzMnj2befPm0b9/fypWrOjrkPzG//3f/zFx4kQuXbrk61AKpNRfCaiqiiT9U0pV07QbfhaEWzl58iSPPPIIL774IrVq1fJ1OH7lmWeeYevWrVy6dInFixf7Ohy/8OOPP1K5cmU6derk61AKrNRfCVSqVIldu3bl/JyQkCC6PoQC2b17N8888wxTpkxh0KBBvg7Hb5w+fRq73U6jRo0ICAigb9++HD9+3Ndh+YWVK1eSkJDA0KFDSU1NJSsrixkzZjBlyhRfh5anUp8EOnfuzMcff8zVq1cJCAhgzZo1vPHGG74OS/Bzly5dYty4ccyaNatEndV5w/nz55k9e3ZO99j69eu56667fByVf7h+ptSSJUvYsWOHXycAKANJoGLFikycOJEHH3wQh8PBiBEjaN68ua/DEvzc3LlzsdlszJw5M+e20aNHc8899/gwKv/Qo0cPDhw4wJ133oksy/Tt21dcKZVgYmcxQRCEMqzUDwwLgiAIeRNJQBAEoQwTSUAQBKEME0lAEAShDBNJQBAEoQwTSUAo9T766COWLVtWqMcMHTqUtLQ0lixZwtixY/O9/9SpUzl06BAAr7zyClu2bClKqAV27tw5JkyY4NFjCGVDqV8nIAjPPvtsoR/zyy+/FOr+W7ZsYdSoUYB36jFdvHiRM2fOePw4QuknkoDgl1RVZcaMGezfv5/MzEw0TePNN9+kTZs27Nq1i5kzZ6KqKgBjx46lX79+ed4+efJk6tevz6OPPkqzZs14+OGH2bJlC1lZWYwfP57Vq1dz4sQJoqKi+PzzzwkMDKRBgwZs3br1hpj27dvHe++9h91uJyEhgc6dOzNjxgxmzZrFlStXeP7553n33Xd5//33ue++++jfvz/r1q3jk08+QVVVgoKCePnll2nevDkff/wxFy5cICEhgQsXLlCxYkXee++9m0qanD59mldeeQW73Y6maYwYMYLRo0czdepU4uPjefTRR5k7dy579uzh/fffx2KxoNPpGD9+PD179mTJkiWsXr0aVVW5ePEiFStWZObMmaLgm/APTRD80J49e7QJEyZoiqJomqZpc+bM0caOHatpmqY9+OCD2ooVKzRN07SjR49qr7322i1vf+mll7SvvvpK0zRNi46O1v73v//ltNmqVSvt8uXLmqIo2rBhw7Rff/01535JSUnazz//rD3xxBOapmnaxIkTtW3btmmapmkZGRlahw4dtIMHD2qapmk9e/bUDhw4oGmapt1///3aqlWrtFOnTmmdO3fWzp49q2mapm3ZskXr0qWLlp6ers2ePVvr1auXlp6ermmapo0dO1b76KOPbnodXn75ZW3OnDmapmnalStXtOeee05TFEXbtm2bNmjQIE3TNC0lJUXr27evdu7cOU3TNO3y5cta9+7dtQsXLmg///yz1rJlSy0mJkbTNE177733tAkTJhT9DyOUOuJKQPBLrVq1IiwsjB9++IFz586xfft2goKCABgwYADTp0/njz/+oHPnzvznP/+55e3/1q9fPwBq1KhBdHR0zllxtWrVSE1NzTOmmTNnsmnTJj7//HNiYmKw2WxkZWXlef9t27bRsWNHqlevDkCnTp0oV65czthB+/btCQ4OBqBx48a5HrtPnz689NJLHDhwgE6dOjF16lR0uhuH8vbt20dCQgLjxo3LuU2SpJyibl26dKF27doAjBw5kqFDh+YZs1D2iIFhwS9t2LAhZ0C2V69eN9TsGT16NL/++itdunRh8+bN3HHHHdhstjxv/zeDwZDrv/Nz//33s3HjRurUqcO4ceOIiopCu0XVlX+XMQdXKXOn0wmA2WzOuV2SpFzb6tmzJ7///jsDBgzg6NGjDBkyhMuXL99wH0VRqFu3Lr/88kvOf4sWLaJr164AyLJ8Q0zX/ywIIgkIfunvv/+mZ8+e3HvvvTRt2pR169ahKArgSgJHjx5l+PDhvPHGG6SlpZGQkJDn7e6QlpbGwYMHef755+nbty+XL1/m7NmzOeMPsiznfLln69SpE5s3b+bcuXMAObX3W7RoUeDjTpo0iZUrVzJo0CCmTZtGcHAwZ8+eRZZlHA4HAC1btiQuLo6dO3cCcPToUfr160d8fDzguiLJ/vcPP/xAz549i/diCKWK6A4S/NLo0aOZNGkSQ4YMwel00qVLF9asWYOqqjz//PPMmDGDDz/8EEmSGD9+PNWqVcvzdncIDQ3liSeeYNiwYQQGBlKxYkVat25NXFwcnTp1ok+fPrzwwgu89tprOY+pV68e06ZNY/z48SiKgtls5vPPPyckJKTAx3366ad55ZVXWLRoEbIs07t3b9q1a0dqaiomk4kRI0bw448/Mnv2bN59911sNhuapvHuu+9SrVo1duzYQcWKFXnhhRdISEigXr16TJ8+3S2viVA6iCqiglCKLVmyhN9//505c+b4OhTBT4nuIEEQhDJMXAkIgiCUYeJKQBAEoQwTSUAQBKEME0lAEAShDBNJQBAEoQwTSUAQBKEME0lAEAShDPv/eUVmXp1pf0AAAAAASUVORK5CYII=\n", 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] @@ -203,8 +205,10 @@ ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": null, "metadata": {}, + "outputs": [], "source": [ "## Compute and plot RMSE and ensemble spread with blind forecast as well" ] @@ -636,94 +640,6 @@ "\"\"\"\n", "f = plot_scores_spread(rmse,spread)" ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Response funtion variance reduction should be moved to its own notebook" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Variance reduction, with lots more cycles \n" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "da_const['ncyc'] = 1000" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "states = run_linear_advection_EnKF(m_const,da_const)" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "states = add_blind_forecast(states,m_const,da_const)" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [], - "source": [ - "states = add_response(states,sum_mid_tri)" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [], - "source": [ - "var_red_est,var_red_ind,var_red =var_reduction_estimate(states,m_const,da_const)" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "<Figure size 216x216 with 1 Axes>" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig,ax = plt.subplots(1,1,figsize=(3,3))\n", - "plt.scatter(var_red,var_red_est,alpha=0.2,s=10)\n", - "min_val= 1.05*min(np.min(var_red),np.min(var_red_est))\n", - "max_val= 1.05*max(np.max(var_red),np.max(var_red_est))\n", - "plt.xlim(min_val,max_val)\n", - "plt.ylim(min_val,max_val)\n", - "plt.plot([min_val,max_val],[min_val,max_val],'k--')\n", - "plt.xlabel('actual variance reduction of dJ')\n", - "plt.ylabel('estimated variance reduction of dJ')\n", - "sns.set_style(\"whitegrid\")" - ] } ], "metadata": { diff --git a/model_functions.py b/model_functions.py deleted file mode 100644 index c6b6ccd9220458a77e6abaa379ecdc38e6a4124b..0000000000000000000000000000000000000000 --- a/model_functions.py +++ /dev/null @@ -1,118 +0,0 @@ -#!/usr/bin/env python -#collection of all functions which determine the model setup and time evolution - -import numpy as np - -def set_model_constants_22(nx=300,dx=100,u=10,u_std=0,dhdt=0,dhdt_std=0,h_init_std = 3000, init_func='gaus',sine_init=[1],truth_seed=0): - """ - Sets all the constants used to describe the model. The 2022 version (_22) is only new in that a truth seed is added so that - truth perturbations can be added systematically, but that hasn't been tested yet anyway. - - """ - const = {} - - #grid - const["nx"] = nx - const["dx"] = dx - const["x_grid"] = np.arange(nx)*dx - - #properties of truth wave - const["h_init_std"] = h_init_std # width of the initial gaussian distribution, or number of sine waves - const["sine_init"] = sine_init # width of the initial gaussian distribution, or number of sine waves - const["init_func"] = init_func # currently a gaussian ('gaus') or a sine wave ('sine') are available - const["u_ref"] = u # reference u around which ensemble is generated, truth can - const["u_std_truth"] = u_std # standard deviation of u for truth - const["dhdt_ref"] = dhdt # reference dhdt around which ensemble is generated - const["dhdt_std_truth"] = dhdt_std # standard deviation of dhdt for truth - const["truth_seed"] = truth_seed # added to seed used to generate truth deviations - - #model - const["model"] = 'LA' - - return const - -def set_model_constants(nx=101,dx=100,u=2.33333333,u_std=0,dhdt=0,dhdt_std=0,h_init_std = 500, init_func='gaus',sine_init=[1]): - """ - Sets all the constants used to describe the model. - Currently has only gaussian initial conditions. - - ToDo: Add different initial conditions as an option. - """ - const = {} - - #grid - const["nx"] = nx - const["dx"] = dx - const["x_grid"] = np.arange(nx)*dx - - #properties of truth wave - const["h_init_std"] = h_init_std # width of the initial gaussian distribution, or number of sine waves - const["sine_init"] = sine_init # width of the initial gaussian distribution, or number of sine waves - const["init_func"] = init_func # currently a gaussian ('gaus') or a sine wave ('sine') are available - const["u_ref"] = u # reference u around which ensemble is generated, truth can - const["u_std_truth"] = u_std # standard deviation of u for truth - const["dhdt_ref"] = dhdt # reference dhdt around which ensemble is generated - const["dhdt_std_truth"] = dhdt_std # standard deviation of dhdt for truth - - #model - const["model"] = 'LA' - - return const - -def gaussian_initial_condition(x,sig): - """ - Generates a gaussian which is commonly used for the initial conditions. - It always uses a mean of zero, and then mirrors values along the x axis to account for periodic boundary conditions. - This is not very flexible, and also not super clean because it just assumes that the number of x grid points is even - But since it works making it nice is very far down the priority list. - """ - mu = 0 - y = np.exp(-np.power(x - mu, 2.) / (2 * np.power(sig, 2.))) - half_idx = int(x.shape[0]/2) - y[-half_idx:] = y[half_idx-1::-1] - - return y - -def linear_advection_model(y,u,dydt,dx,dt,nt): - """ - This is a simple wrapper for the advection and growth routines which make up the time evolution of the model. - """ - for n in range(nt): - y = semi_lagrangian_advection(y,dx,u,dt) - y = linear_growth(y,dydt,dt) - - return y - - -def semi_lagrangian_advection(y,dx,u,dt): - """ - Advects a 1D field for a given time difference and speed. - A linear polation is applied between the nearest grid points. Getting the periodic boundary right is a bit of annoying book keeping. - Works by calculating the fractional displacement and the integer displacement. - Only works with a constant dx. - """ - dx_frac = u*dt/dx-np.floor(u*dt/dx) - dx_int = int(np.floor(u*dt/dx)) - - ##initial quality check, shouldn't be needed anymore - #if np.abs(u*dt - (dx_frac+dx_int)*dx)>0.00001: - # print(u,u*dt,(dx_frac+dx_int)*dx) - - if abs(dx_int)>len(y): - #this is what to do if things move through the whole domain in one step - if dx_int >0 : - dx_int = dx_int - int(dx_int/float(len(y)))*len(y) - if dx_int<0: - dx_int = dx_int + int(-dx_int/float(len(y)))*len(y) - - - y = np.hstack([(1-dx_frac)*y[0]+(dx_frac)*y[-1],(1-dx_frac)*y[1:]+(dx_frac)*y[:-1]]) - y = np.hstack([y[-dx_int:],y[:-dx_int]]) - return y - - -def linear_growth(y,dydt,dt): - """ - Applies linear growth rate. Very highly advanced math. - """ - return y + dydt*dt diff --git a/plot_functions.py b/plot_functions.py deleted file mode 100644 index a5112e8737323bec40faedafed967c464177a912..0000000000000000000000000000000000000000 --- a/plot_functions.py +++ /dev/null @@ -1,1125 +0,0 @@ -#!/usr/bin/env python - -# Collection of various plot scripts. -# All plot scripts return the figure and axes object. Final tweaks and saving are intended to happen in notebooks -import seaborn as sns -import matplotlib.pyplot as plt -import numpy as np - -from da_functions import * - -def ensemble_plotter(states,m_const,da_const,j=0,t_start=0,t_end=0): - """ - Plots the full forecast/background ensemble with observations, as well as the resulting analysis. - Plots all timesteps from t_start till t_end. - Initial starting conditions have no background state - - - ToDo: - intital state needs to be added as an exception with no backbround state. - - Input: - j : experiment number - t_start and t_end: time frame to plot - - Returns: - figure and axes - """ - sns.set() - sns.set_style('whitegrid') - nc = da_const['ncyc'] - if t_end ==0: t_end = nc - - na = t_end-t_start - - alpha = np.sqrt(1/da_const['nens']) - - fig, ax = plt.subplots(na,2,figsize=(7.5,na*1.5),sharex='all',sharey='all') - - #left bg, right analysis - - - #for c in range(nc): - for c in range(na): - for i in range(da_const["nens"]): - ax[c,0].plot(m_const['x_grid'],states[j]['bg'][c+t_start][:,i],'r',alpha =alpha,zorder=1) - ax[c,1].plot(m_const['x_grid'],states[j]['an'][c+t_start][:,i],'magenta',alpha =alpha,zorder=1) - ax[c,0].plot(m_const['x_grid'],np.mean(states[j]['bg'][c+t_start][:,:],axis=1),'k--',alpha =1,zorder=2,label='ens mean') - ax[c,0].plot(m_const["x_grid"],states[j]['truth'][c+t_start],'k',zorder=10,label='truth') - - ax[c,1].plot(m_const['x_grid'],np.mean(states[j]['an'][c+t_start][:,:],axis=1),'k--',alpha =1,zorder=2,label='ens mean') - ax[c,1].plot(m_const["x_grid"],states[j]['truth'][c+t_start],'k',zorder=10,label='truth') - - ax[c,0].scatter(m_const["x_grid"][da_const["obs_loc"]],states[j]['obs'][c+t_start][da_const["obs_loc"]],zorder=3,label='observations') - ax[c,1].scatter(m_const["x_grid"][da_const["obs_loc"]],states[j]['obs'][c+t_start][da_const["obs_loc"]],zorder=3,label='observations') - ax[c,0].set_ylabel('assim step '+str(c+t_start) + '\n h [m]') - - - ax[0,0].set_title('background ensemble and observations') - ax[0,1].set_title('analysis ensemble after update') - ax[0,1].set_xlim([m_const["x_grid"][0],m_const["x_grid"][-1]]) - plt.subplots_adjust(wspace=0.01,hspace=0.01) -# ax[0].set_xticklabels([]) - ax[na-1,1].set_xlabel('x [m]') - ax[na-1,0].set_xlabel('x [m]') - plt.legend() - return fig,ax - - -def quad_plotter(quad_state,m_const,da_const): - """ - Plots the initial background and blind forecast, as well as the analysis and forecast with observations - - Input: - j : experiment number - t_start and t_end: time frame to plot - - Returns: - figure and axes - """ - sns.set() - sns.set_style('whitegrid') - - - alpha = np.sqrt(1/da_const['nens']) - - fig, ax = plt.subplots(2,2,figsize=(10,5),sharex='all',sharey='all') - - - - for i in range(da_const["nens"]): - ax[0,0].plot(m_const['x_grid'],quad_state['bg'][:,i],'r',alpha =alpha,zorder=1) - ax[0,1].plot(m_const['x_grid'],quad_state['bf'][:,i],'b',alpha =alpha,zorder=1) - ax[1,0].plot(m_const['x_grid'],quad_state['an'][:,i],'magenta',alpha =alpha,zorder=1) - ax[1,1].plot(m_const['x_grid'],quad_state['fc'][:,i],'c',alpha =alpha,zorder=1) - - - ax[0,0].plot(m_const["x_grid"],quad_state['tr_bg'],'k',zorder=10,label='truth') - ax[1,0].plot(m_const["x_grid"],quad_state['tr_bg'],'k',zorder=10,label='truth') - #ax[0,1].plot(m_const["x_grid"],quad_state['tr_fc'],'k',zorder=10,label='truth') - #ax[1,1].plot(m_const["x_grid"],quad_state['tr_fc'],'k',zorder=10,label='truth') - - ax[0,0].plot(m_const['x_grid'],np.mean(quad_state['bg'][:,:],axis=1),'k--',alpha =1,zorder=2,label='ens mean') - ax[1,0].plot(m_const['x_grid'],np.mean(quad_state['an'][:,:],axis=1),'k--',alpha =1,zorder=2,label='ens mean') - ax[0,1].plot(m_const['x_grid'],np.mean(quad_state['bf'][:,:],axis=1),'k--',alpha =1,zorder=2,label='ens mean') - ax[1,1].plot(m_const['x_grid'],np.mean(quad_state['fc'][:,:],axis=1),'k--',alpha =1,zorder=2,label='ens mean') - - ax[0,0].scatter(m_const["x_grid"][da_const["obs_loc"]],quad_state['obs'][da_const["obs_loc"]],zorder=3,label='observations',color='k') - ax[1,0].scatter(m_const["x_grid"][da_const["obs_loc"]],quad_state['obs'][da_const["obs_loc"]],zorder=3,label='observations',color='k') - - - ax[0,0].set_title('background with obs') - ax[0,1].set_title('free forecast') - ax[1,0].set_title('analysis with obs') - ax[1,1].set_title('forecast') - ax[0,1].set_xlim([m_const["x_grid"][0],m_const["x_grid"][-1]]) - plt.subplots_adjust(wspace=0.03,hspace=0.30) - ax[1,1].set_xlabel('x [m]') - ax[1,0].set_xlabel('x [m]') - ax[0,0].set_ylabel('h [m]') - ax[1,0].set_ylabel('h [m]') - ax[1,0].legend() - return fig,ax - -def quad_plotter_v2(quad_state,m_const,da_const): - """ - Plots the initial background and blind forecast, as well as the analysis and forecast with observations - - Input: - j : experiment number - t_start and t_end: time frame to plot - - Returns: - figure and axes - """ - sns.set() - sns.set_style('whitegrid') - - - alpha = np.sqrt(1/da_const['nens'])+0.1 - - fig, ax = plt.subplots(2,2,figsize=(7.5,4),sharex='all',sharey='all') - - - - for i in range(da_const["nens"]): - ax[0,0].plot(m_const['x_grid']/1000.,quad_state['bg'][:,i],'r',alpha =alpha,zorder=1) - ax[0,1].plot(m_const['x_grid']/1000.,quad_state['bf'][:,i],'b',alpha =alpha,zorder=1) - ax[1,0].plot(m_const['x_grid']/1000.,quad_state['an'][:,i],'magenta',alpha =alpha,zorder=1) - ax[1,1].plot(m_const['x_grid']/1000.,quad_state['fc'][:,i],'c',alpha =alpha,zorder=1) - - - ax[0,0].plot(m_const["x_grid"]/1000.,quad_state['tr_bg'],'k',zorder=10,label='truth') - ax[1,0].plot(m_const["x_grid"]/1000.,quad_state['tr_bg'],'k',zorder=10,label='truth') - #ax[0,1].plot(m_const["x_grid"],quad_stat/1000.e['tr_fc'],'k',zorder=10,label='truth') - #ax[1,1].plot(m_const["x_grid"],quad_stat/1000.e['tr_fc'],'k',zorder=10,label='truth') - - ax[0,0].plot(m_const['x_grid']/1000.,np.mean(quad_state['bg'][:,:],axis=1),'k--',alpha =1,zorder=2,label='ens mean') - ax[1,0].plot(m_const['x_grid']/1000.,np.mean(quad_state['an'][:,:],axis=1),'k--',alpha =1,zorder=2,label='ens mean') - ax[0,1].plot(m_const['x_grid']/1000.,np.mean(quad_state['bf'][:,:],axis=1),'k--',alpha =1,zorder=2,label='ens mean') - ax[1,1].plot(m_const['x_grid']/1000.,np.mean(quad_state['fc'][:,:],axis=1),'k--',alpha =1,zorder=2,label='ens mean') - - ax[0,0].scatter(m_const["x_grid"][da_const["obs_loc"]]/1000.,quad_state['obs'][da_const["obs_loc"]],zorder=3,label='observations',color='k') - ax[1,0].scatter(m_const["x_grid"][da_const["obs_loc"]]/1000.,quad_state['obs'][da_const["obs_loc"]],zorder=3,label='observations',color='k') - - - ax[0,0].set_title('background with observations') - ax[0,1].set_title('free-forecast') - ax[1,0].set_title('analysis with observations') - ax[1,1].set_title('forecast') - ax[0,1].set_xlim([m_const["x_grid"][0],m_const["x_grid"][-1]/1000]) - plt.subplots_adjust(wspace=0.03,hspace=0.20) - ax[1,1].set_xlabel('x [km]') - ax[1,0].set_xlabel('x [km]') - ax[0,0].set_ylabel('h [m]') - ax[1,0].set_ylabel('h [m]') - ax[1,0].legend() - - # now to add in shading for response domain - ylimits = ax[0,0].get_ylim() - ax[0,1].fill_between([10,20], ylimits[0], y2=ylimits[1],color='grey',alpha=0.2,zorder=-1) - ax[1,1].fill_between([10,20], ylimits[0], y2=ylimits[1],color='grey',alpha=0.2,zorder=-1) - ax[1,1].set_ylim(ylimits) - - return fig,ax - -def quad_plotter_paper(quad_state,m_const,da_const): - """ - Plots the initial background and blind forecast, as well as the analysis and forecast with observations. - - Swapped free forecast and analysis figure locations, also changed h to phi - - Returns: - figure and axes - """ - sns.set() - sns.set_style('whitegrid') - - - alpha = np.sqrt(1/da_const['nens'])+0.1 - - fig, ax = plt.subplots(2,2,figsize=(7.5,4),sharex='all',sharey='all') - - - - for i in range(da_const["nens"]): - ax[0,0].plot(m_const['x_grid']/1000.,quad_state['bg'][:,i],'r',alpha =alpha,zorder=1) - ax[1,0].plot(m_const['x_grid']/1000.,quad_state['bf'][:,i],'b',alpha =alpha,zorder=1) - ax[0,1].plot(m_const['x_grid']/1000.,quad_state['an'][:,i],'magenta',alpha =alpha,zorder=1) - ax[1,1].plot(m_const['x_grid']/1000.,quad_state['fc'][:,i],'c',alpha =alpha,zorder=1) - - - ax[0,0].plot(m_const["x_grid"]/1000.,quad_state['tr_bg'],'k',zorder=10,label='truth') - ax[0,1].plot(m_const["x_grid"]/1000.,quad_state['tr_bg'],'k',zorder=10,label='truth') - #ax[0,1].plot(m_const["x_grid"],quad_stat/1000.e['tr_fc'],'k',zorder=10,label='truth') - #ax[1,1].plot(m_const["x_grid"],quad_stat/1000.e['tr_fc'],'k',zorder=10,label='truth') - - ax[0,0].plot(m_const['x_grid']/1000.,np.mean(quad_state['bg'][:,:],axis=1),'k--',alpha =1,zorder=2,label='ens mean') - ax[0,1].plot(m_const['x_grid']/1000.,np.mean(quad_state['an'][:,:],axis=1),'k--',alpha =1,zorder=2,label='ens mean') - ax[1,0].plot(m_const['x_grid']/1000.,np.mean(quad_state['bf'][:,:],axis=1),'k--',alpha =1,zorder=2,label='ens mean') - ax[1,1].plot(m_const['x_grid']/1000.,np.mean(quad_state['fc'][:,:],axis=1),'k--',alpha =1,zorder=2,label='ens mean') - - if da_const['n_obs_h']: - ax[0,0].scatter(m_const["x_grid"][da_const["obs_loc"]]/1000.,quad_state['obs'][da_const["obs_loc"]],zorder=3,label='observations',color='k') - ax[0,1].scatter(m_const["x_grid"][da_const["obs_loc"]]/1000.,quad_state['obs'][da_const["obs_loc"]],zorder=3,label='observations',color='k') - - - ax[0,0].set_title('background') - ax[1,0].set_title('free-forecast') - ax[0,1].set_title('analysis') - ax[1,1].set_title('forecast') - ax[1,0].set_xlim([m_const["x_grid"][0],m_const["x_grid"][-1]/1000]) - plt.subplots_adjust(wspace=0.03,hspace=0.20) - ax[1,1].set_xlabel('x [km]') - ax[1,0].set_xlabel('x [km]') - ax[0,0].set_ylabel(r'$\phi$') - ax[1,0].set_ylabel(r'$\phi$') - ax[0,1].legend() - - # now to add in shading for response domain - ylimits = ax[0,0].get_ylim() - ax[1,0].fill_between([10,20], ylimits[0], y2=ylimits[1],color='grey',alpha=0.2,zorder=-1) - ax[1,1].fill_between([10,20], ylimits[0], y2=ylimits[1],color='grey',alpha=0.2,zorder=-1) - ax[1,1].set_ylim(ylimits) - - return fig,ax - -def B_plotter(states,j=0,ncyc=0,matrix='bg'): - """ - Plots the covariance plotter of either the forecast/background ensemble ('bg'), analysis 'an', or the free/blind forecast 'bf' - - """ - - - fig, ax = plt.subplots(1,1,figsize=(5,5)) - - ax.set_ylabel('[h1,h2,h3,...]') - ax.set_xlabel('[h1,h2,h3,...]') - - #B = np.cov(ensemble_diff.transpose()) - B = np.cov(states[j][matrix][ncyc],ddof=1) - limit = np.max(np.abs(B))/4.*3. - pm = ax.pcolormesh(B,vmin=-limit,vmax=limit,cmap='RdBu_r') - plt.colorbar(pm,shrink = 0.8) - ax.set_aspect('equal') - - return fig,ax - -def plot_scores_spread(rmse, spread): - """ - Plots the evolution of the RMSE and spread over time and space. - Could be expanded to also plot the blind forecast if available - - Todo: - plot next to each other with a shared y axis - """ - sns.set_style('whitegrid') - figs = {} - for dim in ["space", "time"]: - figs[dim], ax = plt.subplots(1, sharex='col', figsize=(10, 4)) - - #for i, var in enumerate(['RMSE over ' + dim + ' h', 'RMSE over ' + dim + ' u']): - ax.set_title('RMSE over ' + dim, fontsize=24) - ax.tick_params(labelsize=18) - if 'bf' in rmse['time'].keys(): - ax.plot(rmse[dim]['bf'], 'b', label="bf rmse="+str(round(np.nanmean(rmse[dim]['bf']),3))) - ax.plot(spread[dim]['bf'], 'b', label="bf spread="+str(round(np.nanmean(spread[dim]['bf']),3)), ls='--') - ax.plot(rmse[dim]['bg'] , 'r' , label="bg rmse=" +str(round(np.nanmean(rmse[dim]['bg']),3)),lw=3) - ax.plot(spread[dim]['bg'], 'r' , label="bg spread="+str(round(np.nanmean(spread[dim]['bg']),3)), ls='--',lw=3) - ax.plot(rmse[dim]['an'] , 'magenta', label="an rmse=" +str(round(np.mean(rmse[dim]['an']),3))) - ax.plot(spread[dim]['an'], 'magenta', label="an spread="+str(round(np.mean(spread[dim]['an']),3)), ls='--') - - - ax.set_xlim(left=0) - ax.legend() - if dim == "space": - ax.set_xlabel("space in grid points", fontsize=18) - if dim == "time": - ax.set_xlabel("time in assimilation cycles", fontsize=18) - return figs - - -def plot_response_function_pdf_split(states,j=0,t_start=0,t_end=0,left_var='bg',right_var='an'): - """ - Makes a split Seaborn violin plot of the respone function values. By default plots the analysis and forecast, but can also plot the free/blind forecast - Is not super efficient because it first transforms the response values into a panda dataframe to get the seaborn plot to work. - """ - import seaborn as sns - import pandas as pd - sns.set() - fig,ax = plt.subplots(1,1,figsize=(6,4)) - - ncyc,nens = np.array(states[j]['response']['bg']).shape - if t_end==0: t_end=ncyc - - #Got to figure out how to make sure the times line up, since the time integers of an, bg, and bf are all 1 step delayed - dict_label ={'an':'analysis','bg':'forecast-t1','bf':'forecast-t2'} - dict_color ={'an':'magenta','bg':'r','bf':'b'} - #t_diff = t_adjust[right_var]-t_adjust[left_var] - - nt = t_end-t_start - - - plot_data = { - 'response' : np.hstack([np.array(states[j]['response'][left_var ][t_start:t_end]).ravel(), - np.array(states[j]['response'][right_var][t_start:t_end]).ravel()]), - 'cyc' : np.hstack([np.repeat(np.arange(nt),nens),np.repeat(np.arange(nt),nens)])+t_start, - 'type' : [dict_label[left_var]]*nt*nens+[dict_label[right_var]]*nt*nens} - - - - sns.violinplot(data=plot_data,y='response',x='cyc',hue='type', inner=None, orient="v",split=True,palette={dict_label[left_var]:dict_color[left_var],dict_label[right_var]:dict_color[right_var]},cut=0,bw=0.25) - - - plt.plot(states[j]['response']['truth'][t_start:t_end],'k--',marker='o',label='truth') - - plt.legend(loc='lower center') - ax.set_xlabel('assimilation step') - ax.set_ylabel('PDF of response function J') - - #Moves the left and right pdf a bit appart so you can see where they end. Thank you stackoverflow - inner=None - width = 0.75 - delta = 0.05 - final_width = width - delta - offset_violinplot_halves(ax, delta, final_width, inner, 'vertical') - return fig,ax - - - - -def offset_violinplot_halves(ax, delta, width, inner, direction): - """ - From Stackoverflow! - - This function offsets the halves of a violinplot to compare tails - or to plot something else in between them. This is specifically designed - for violinplots by Seaborn that use the option `split=True`. - - For lines, this works on the assumption that Seaborn plots everything with - integers as the center. - - Args: - <ax> The axis that contains the violinplots. - <delta> The amount of space to put between the two halves of the violinplot - <width> The total width of the violinplot, as passed to sns.violinplot() - <inner> The type of inner in the seaborn - <direction> Orientation of violinplot. 'hotizontal' or 'vertical'. - - Returns: - - NA, modifies the <ax> directly - """ - import matplotlib.collections - # offset stuff - if inner == 'sticks': - lines = ax.get_lines() - for line in lines: - if direction == 'horizontal': - data = line.get_ydata() - print(data) - if int(data[0] + 1)/int(data[1] + 1) < 1: - # type is top, move neg, direction backwards for horizontal - data -= delta - else: - # type is bottom, move pos, direction backward for hori - data += delta - line.set_ydata(data) - elif direction == 'vertical': - data = line.get_xdata() - print(data) - if int(data[0] + 1)/int(data[1] + 1) < 1: - # type is left, move neg - data -= delta - else: - # type is left, move pos - data += delta - line.set_xdata(data) - - - for ii, item in enumerate(ax.collections): - # axis contains PolyCollections and PathCollections - if isinstance(item, matplotlib.collections.PolyCollection): - # get path - path, = item.get_paths() - vertices = path.vertices - half_type = _wedge_dir(vertices, direction) - # shift x-coordinates of path - if half_type in ['top','bottom']: - if inner in ["sticks", None]: - if half_type == 'top': # -> up - vertices[:,1] -= delta - elif half_type == 'bottom': # -> down - vertices[:,1] += delta - elif half_type in ['left', 'right']: - if inner in ["sticks", None]: - if half_type == 'left': # -> left - vertices[:,0] -= delta - elif half_type == 'right': # -> down - vertices[:,0] += delta - -def _wedge_dir(vertices, direction): - """ - Args: - <vertices> The vertices from matplotlib.collections.PolyCollection - <direction> Direction must be 'horizontal' or 'vertical' according to how - your plot is laid out. - Returns: - - a string in ['top', 'bottom', 'left', 'right'] that determines where the - half of the violinplot is relative to the center. - """ - if direction == 'horizontal': - result = (direction, len(set(vertices[1:5,1])) == 1) - elif direction == 'vertical': - result = (direction, len(set(vertices[-3:-1,0])) == 1) - outcome_key = {('horizontal', True): 'bottom', - ('horizontal', False): 'top', - ('vertical', True): 'left', - ('vertical', False): 'right'} - # if the first couple x/y values after the start are the same, it - # is the input direction. If not, it is the opposite - return outcome_key[result] - - -#Little subplot labeling script found online from https://gist.github.com/tacaswell/9643166 -#Put here for no good reason -#Warning, if you define your colorbar using an additional axes it will give that a label as well, which is pretty funny but also very annoying. - -import string -from itertools import cycle -from six.moves import zip -def label_axes_abcd(fig, labels=None, loc=None, **kwargs): - """ - Walks through axes and labels each. - kwargs are collected and passed to `annotate` - Parameters - ---------- - fig : Figure - Figure object to work on - labels : iterable or None - iterable of strings to use to label the axes. - If None, lower case letters are used. - - loc : len=2 tuple of floats - Where to put the label in axes-fraction units - """ - if labels is None: - labels = string.ascii_lowercase - - # re-use labels rather than stop labeling - labels = cycle(labels) - if loc is None: - loc = (.9, .9) - for ax, lab in zip(fig.axes, labels): - ax.annotate(lab, xy=loc, - xycoords='axes fraction', - **kwargs) -########################################################################################### -# 2022 baby, now with sat data -########################################################################################### - - -def ensemble_plotter_22(states,m_const,da_const,j=0,t_start=0,t_end=0,h_c=None): - """ - Plots the full forecast/background ensemble with observations, as well as the resulting analysis. - Plots all timesteps from t_start till t_end. - Initial starting conditions have no background state - - - - Input: - j : experiment number - t_start and t_end: time frame to plot - - Returns: - figure and axes - """ - sns.set() - sns.set_style('whitegrid') - nc = da_const['ncyc'] - if t_end ==0: t_end = nc - - na = t_end-t_start - - alpha = np.sqrt(1/da_const['nens']) - - fig, ax = plt.subplots(na,2,figsize=(7.5,na*1.5),sharex='all',sharey='all') - - #left bg, right analysis - - - #for c in range(nc): - for c in range(na): - for i in range(da_const["nens"]): - ax[c,0].plot(m_const['x_grid'],states[j]['bg'][c+t_start][:,i],'r',alpha =alpha,zorder=1) - ax[c,1].plot(m_const['x_grid'],states[j]['an'][c+t_start][:,i],'magenta',alpha =alpha,zorder=1) - ax[c,0].plot(m_const['x_grid'],np.mean(states[j]['bg'][c+t_start][:,:],axis=1),'k--',alpha =1,zorder=2,label='ens mean') - ax[c,0].plot(m_const["x_grid"],states[j]['truth'][c+t_start],'k',zorder=10,label='truth') - - ax[c,1].plot(m_const['x_grid'],np.mean(states[j]['an'][c+t_start][:,:],axis=1),'k--',alpha =1,zorder=2,label='ens mean') - ax[c,1].plot(m_const["x_grid"],states[j]['truth'][c+t_start],'k',zorder=10,label='truth') - - if da_const['n_obs_h']>0: - ax[c,0].scatter(m_const["x_grid"][da_const["obs_loc"]],states[j]['obs'][c+t_start][da_const["obs_loc"]],zorder=3,label='observations',c='k') - ax[c,1].scatter(m_const["x_grid"][da_const["obs_loc"]],states[j]['obs'][c+t_start][da_const["obs_loc"]],zorder=3,label='observations',c='k') - if h_c is not None: - ax[c,0].hlines(h_c,m_const['x_grid'][0],m_const['x_grid'][-1],color='k',ls=':',label='sat threshold') - ax[c,1].hlines(h_c,m_const['x_grid'][0],m_const['x_grid'][-1],color='k',ls=':',label='sat threshold') - - #if da_const['n_obs_sat']>0: - # #sat obs location will be added as well, I'll add them in with their value for now Maybe I'll get fancy and use a full circle where cloudy and empty circle where empty, because why not - # ax[c,0].scatter(m_const["x_grid"][da_const["obs_loc_sat"]],states[j]['obs_sat'][c+t_start][da_const["obs_loc_sat"]],zorder=3,label='sat obs loc',marker='x',c='k') - # ax[c,1].scatter(m_const["x_grid"][da_const["obs_loc_sat"]],states[j]['obs_sat'][c+t_start][da_const["obs_loc_sat"]],zorder=3,label='sat obs loc',marker='x',c='k') - ax[c,0].set_ylabel('assim step '+str(c+t_start) + '\n h [m]') - - - ax[0,0].set_title('background ensemble and observations') - ax[0,1].set_title('analysis ensemble after update') - ax[0,1].set_xlim([m_const["x_grid"][0],m_const["x_grid"][-1]]) - plt.subplots_adjust(wspace=0.01,hspace=0.01) -# ax[0].set_xticklabels([]) - ax[na-1,1].set_xlabel('x [m]') - ax[na-1,0].set_xlabel('x [m]') - plt.legend() - return fig,ax - - - - -def quad_plotter_22(quad_state,m_const,da_const): - """ - Plots the initial background and blind forecast, as well as the analysis and forecast with observations. - - Only difference to previous versions is that it can deal with no obs being present - - - Returns: - figure and axes - """ - sns.set() - sns.set_style('whitegrid') - - - alpha = np.sqrt(1/da_const['nens'])+0.1 - - fig, ax = plt.subplots(2,2,figsize=(7.5,4),sharex='all',sharey='all') - - - - for i in range(da_const["nens"]): - ax[0,0].plot(m_const['x_grid']/1000.,quad_state['bg'][:,i],'r',alpha =alpha,zorder=1) - ax[0,1].plot(m_const['x_grid']/1000.,quad_state['bf'][:,i],'b',alpha =alpha,zorder=1) - ax[1,0].plot(m_const['x_grid']/1000.,quad_state['an'][:,i],'magenta',alpha =alpha,zorder=1) - ax[1,1].plot(m_const['x_grid']/1000.,quad_state['fc'][:,i],'c',alpha =alpha,zorder=1) - - - ax[0,0].plot(m_const["x_grid"]/1000.,quad_state['tr_bg'],'k',zorder=10,label='truth') - ax[1,0].plot(m_const["x_grid"]/1000.,quad_state['tr_bg'],'k',zorder=10,label='truth') - #ax[0,1].plot(m_const["x_grid"],quad_stat/1000.e['tr_fc'],'k',zorder=10,label='truth') - #ax[1,1].plot(m_const["x_grid"],quad_stat/1000.e['tr_fc'],'k',zorder=10,label='truth') - - ax[0,0].plot(m_const['x_grid']/1000.,np.mean(quad_state['bg'][:,:],axis=1),'k--',alpha =1,zorder=2,label='ens mean') - ax[1,0].plot(m_const['x_grid']/1000.,np.mean(quad_state['an'][:,:],axis=1),'k--',alpha =1,zorder=2,label='ens mean') - ax[0,1].plot(m_const['x_grid']/1000.,np.mean(quad_state['bf'][:,:],axis=1),'k--',alpha =1,zorder=2,label='ens mean') - ax[1,1].plot(m_const['x_grid']/1000.,np.mean(quad_state['fc'][:,:],axis=1),'k--',alpha =1,zorder=2,label='ens mean') - - if da_const['n_obs_h']: - ax[0,0].scatter(m_const["x_grid"][da_const["obs_loc"]]/1000.,quad_state['obs'][da_const["obs_loc"]],zorder=3,label='observations',color='k') - ax[1,0].scatter(m_const["x_grid"][da_const["obs_loc"]]/1000.,quad_state['obs'][da_const["obs_loc"]],zorder=3,label='observations',color='k') - - - ax[0,0].set_title('background') - ax[0,1].set_title('free-forecast') - ax[1,0].set_title('analysis') - ax[1,1].set_title('forecast') - ax[0,1].set_xlim([m_const["x_grid"][0],m_const["x_grid"][-1]/1000]) - plt.subplots_adjust(wspace=0.03,hspace=0.20) - ax[1,1].set_xlabel('x [km]') - ax[1,0].set_xlabel('x [km]') - ax[0,0].set_ylabel('h [m]') - ax[1,0].set_ylabel('h [m]') - ax[1,0].legend() - - # now to add in shading for response domain - ylimits = ax[0,0].get_ylim() - ax[0,1].fill_between([10,20], ylimits[0], y2=ylimits[1],color='grey',alpha=0.2,zorder=-1) - ax[1,1].fill_between([10,20], ylimits[0], y2=ylimits[1],color='grey',alpha=0.2,zorder=-1) - ax[1,1].set_ylim(ylimits) - - return fig,ax - - -def plot_ensemble_sat_analysis(bg,an,obs,obs_sat,truth,sat_operator,m_const,da_const,h_c=None): - fig ,ax = plt.subplots(2,2,figsize=(10,6),sharey='col',sharex='all') - """ - Plots the background and analysis ensemble, as well as the satetlitte obs equivalents. Includes observations as well. - - Is still quite rough around the edges, and will hopefully be improced upon - """ - - sat_an = sat_operator(an) - sat_bg = sat_operator(bg) - sat_tr_bg = sat_operator(truth) - n_obs_h =len(da_const["obs_loc"]) - n_obs_sat =len(da_const["obs_loc_sat"]) - - dY_b, y_ol_b = state_to_observation_space(bg,m_const,da_const,sat_operator) - y_b = dY_b.T + y_ol_b - y_b = y_b.T - - - # for plotting the pixels - window = m_const['x_grid'][da_const["obs_loc_sat"][1]]-m_const['x_grid'][da_const["obs_loc_sat"][0]] - xpixmin = m_const['x_grid'][da_const["obs_loc_sat"]]-window/2 - xpixmax = m_const['x_grid'][da_const["obs_loc_sat"]]+window/2 - - for i in range(bg.shape[1]): - #ax[0].plot(m_const['x_grid'],x_a[:,i],'r',alpha=0.2) - ax[1,0].plot(m_const['x_grid']/1000,an [:,i],'magenta',alpha=0.2) - ax[0,0].plot(m_const['x_grid']/1000,bg [:,i],'r',alpha=0.2) - - ax[1,1].hlines( - sat_an [da_const["obs_loc_sat"],i], - xpixmin/1000,xpixmax/1000, - color='magenta',alpha=0.2,lw=3) - ax[0,1].hlines( - sat_bg [da_const["obs_loc_sat"],i], - xpixmin/1000,xpixmax/1000, - color='r',alpha=0.2,lw=3) - ax[1,0].plot(m_const['x_grid']/1000+1000,an [:,-1],'magenta',alpha=1,label='background ensemble') - ax[0,0].plot(m_const['x_grid']/1000+1000,bg [:,-1],'r',alpha=1,label='analysis ensemble') - - #ax[0].plot(m_const['x_grid'],x_ol_a,'r') - ax[1,0].plot(m_const['x_grid']/1000,truth,'k',linewidth=2,label='truth') - ax[0,0].plot(m_const['x_grid']/1000,truth,'k',linewidth=2) - - - if n_obs_h>0: - ax[1,0].scatter(m_const['x_grid'][da_const['obs_loc']]/1000,obs[da_const['obs_loc']], - c='k',label='h point observation') - ax[0,0].scatter(m_const['x_grid'][da_const['obs_loc']]/1000,obs[da_const['obs_loc']],c='k')#,label='h point obs') - if n_obs_sat>0: - ax[1,1].scatter(m_const['x_grid'][da_const['obs_loc_sat']]/1000,obs_sat[da_const['obs_loc_sat']], - marker='x',s=50,c='k',label='reflectance observation') - ax[0,1].scatter(m_const['x_grid'][da_const['obs_loc_sat']]/1000,obs_sat[da_const['obs_loc_sat']],marker='x',s=50,c='k') - - - ax[0,0].plot(m_const['x_grid']/1000,np.mean(bg,axis=1),'k--',lw=2,label='ensemble mean') - ax[1,0].plot(m_const['x_grid']/1000,np.mean(an,axis=1),'k--',lw=2)#,label='ens mean') - - if h_c is not None: - ax[0,0].hlines(h_c,m_const['x_grid'][0]/1000,m_const['x_grid'][-1]/1000,color='k',ls=':') - ax[1,0].hlines(h_c,m_const['x_grid'][0]/1000,m_const['x_grid'][-1]/1000,color='k',ls=':',label=r'cloud threshold $h_c$') - - label_axes_abcd(fig) - - ax1 = ax[0,1].twinx() - ax1.set_yticks([0.3,0.7]) - ax1.set_yticklabels(['clear \n sky','cloud']) - ax2 = ax[1,1].twinx() - ax2.set_yticks([0.3,0.7]) - ax2.set_yticklabels(['clear \n sky','cloud']) - ax1.set_ylim(-0.05,1.05) - ax2.set_ylim(-0.05,1.05) - ax[1,1].set_ylim(-0.05,1.05) - ax[0,0].set_yticks([0,0.5,1]) - ax[0,1].set_yticks([0,0.3,0.7,1.0]) - ax[0,0].set_yticklabels(['0',r'$h_c$','1']) - - ax[1,0].set_xlabel('x [km]') - ax[1,1].set_xlabel('x [km]') - ax[1,1].set_xlim(m_const['x_grid'][0]/1000,30)#m_const['x_grid'][-1]/1000) - ax[0,0].set_ylabel('background \n h [m]') - ax[1,0].set_ylabel('analysis \n h[m]') - ax[0,1].set_ylabel('reflectance') - ax[1,1].set_ylabel('reflectance') - plt.subplots_adjust(wspace=0.2,hspace=0.1) - - ax[0,1].step(m_const['x_grid'][da_const['obs_loc_sat']]/1000,np.mean(sat_bg[da_const['obs_loc_sat']],axis=1),'k--',lw=2,where='mid')#,marker='s',markersize=4,label='ens mean') - ax[1,1].step(m_const['x_grid'][da_const['obs_loc_sat']]/1000,np.mean(sat_an[da_const['obs_loc_sat']],axis=1),'k--',lw=2,where='mid')#,marker='s',markersize=4,label='ens mean') - - lines_labels = [ax.get_legend_handles_labels() for ax in fig.axes] - lines, labels = [sum(lol, []) for lol in zip(*lines_labels)] - fig.legend(lines, labels, loc='upper center',ncol=4) - return fig, ax - - - -def plot_ensemble_sat_analysis_paper(bg,an,obs,obs_sat,truth,sat_operator,m_const,da_const,h_c=None): - fig ,ax = plt.subplots(2,2,figsize=(7.5,5),sharey='row',sharex='all') - """ - Plots the background and analysis ensemble of the state, as well as the satetlitte obs equivalents. Includes observations as well. - """ - - sat_an = sat_operator(an) - sat_bg = sat_operator(bg) - sat_tr_bg = sat_operator(truth) - n_obs_h =len(da_const["obs_loc"]) - n_obs_sat =len(da_const["obs_loc_sat"]) - - dY_b, y_ol_b = state_to_observation_space(bg,m_const,da_const,sat_operator) - y_b = dY_b.T + y_ol_b - y_b = y_b.T - - - # for plotting the pixels - window = m_const['x_grid'][da_const["obs_loc_sat"][1]]-m_const['x_grid'][da_const["obs_loc_sat"][0]] - xpixmin = m_const['x_grid'][da_const["obs_loc_sat"]]-window/2 - xpixmax = m_const['x_grid'][da_const["obs_loc_sat"]]+window/2 - - for i in range(bg.shape[1]): - #ax[0].plot(m_const['x_grid'],x_a[:,i],'r',alpha=0.2) - ax[0,1].plot(m_const['x_grid']/1000,an [:,i],'magenta',alpha=0.2) - ax[0,0].plot(m_const['x_grid']/1000,bg [:,i],'r',alpha=0.2) - - # plotting the pixels - - #ax[1,1].plot(m_const['x_grid'],sat_an [:,i],'magenta',alpha=0.2) - #ax[0,1].plot(m_const['x_grid'],sat_bg [:,i],'r',alpha=0.2) - ax[1,1].hlines( - sat_an [da_const["obs_loc_sat"],i], - xpixmin/1000,xpixmax/1000, - color='magenta',alpha=0.2,lw=3) - ax[1,0].hlines( - sat_bg [da_const["obs_loc_sat"],i], - xpixmin/1000,xpixmax/1000, - color='r',alpha=0.2,lw=3) - ax[0,1].plot(m_const['x_grid']/1000+1000,an [:,-1],'magenta',alpha=1,label='analysis ensemble') - ax[0,0].plot(m_const['x_grid']/1000+1000,bg [:,-1],'r',alpha=1,label='background ensemble') - - #ax[0].plot(m_const['x_grid'],x_ol_a,'r') - ax[0,1].plot(m_const['x_grid']/1000,truth,'k',linewidth=2,label='truth') - ax[0,0].plot(m_const['x_grid']/1000,truth,'k',linewidth=2) - - - if n_obs_h>0: - ax[0,1].scatter(m_const['x_grid'][da_const['obs_loc']]/1000,obs[da_const['obs_loc']], - c='k',label=r'$\phi$ point observation') - ax[0,0].scatter(m_const['x_grid'][da_const['obs_loc']]/1000,obs[da_const['obs_loc']],c='k')#,label='h point obs') - if n_obs_sat>0: - ax[1,1].scatter(m_const['x_grid'][da_const['obs_loc_sat']]/1000,obs_sat[da_const['obs_loc_sat']], - marker='x',s=50,c='k',label='reflectance observation') - ax[1,0].scatter(m_const['x_grid'][da_const['obs_loc_sat']]/1000,obs_sat[da_const['obs_loc_sat']],marker='x',s=50,c='k') - - ax[0,0].plot(m_const['x_grid']/1000,np.mean(bg,axis=1),'k--',lw=2,label='ensemble mean') - ax[0,1].plot(m_const['x_grid']/1000,np.mean(an,axis=1),'k--',lw=2)#,label='ens mean') - - if h_c is not None: - ax[0,0].hlines(h_c,m_const['x_grid'][0]/1000,m_const['x_grid'][-1]/1000,color='k',ls=':') - ax[0,1].hlines(h_c,m_const['x_grid'][0]/1000,m_const['x_grid'][-1]/1000,color='k',ls=':',label=r'cloud threshold $\phi_c$') - - label_axes_abcd(fig) - - ax1 = ax[0,1].twinx() - ax1.set_yticks([0.,0.5,1]) - ax1.set_yticklabels(['0',r'$\phi_c$','1']) - ax1.set_ylim(-0.3,1.3) - ax[0,0].set_ylim(-0.3,1.3) - ax2 = ax[1,1].twinx() - ax2.set_yticks([0.3,0.7]) - ax2.set_yticklabels(['clear \n sky','cloud']) - ax2.set_ylim(-0.05,1.05) - ax[1,1].set_ylim(-0.05,1.05) - ax[0,0].set_yticks([0,0.5,1]) - ax[1,0].set_yticks([0,0.3,0.7,1.0]) - ax[0,0].set_yticklabels(['0',r'$\phi_c$','1']) - - - ax[1,0].set_xlabel('x [km]') - ax[1,1].set_xlabel('x [km]') - ax[0,1].set_xlim(m_const['x_grid'][0]/1000,30)#m_const['x_grid'][-1]/1000) - ax[0,0].set_title('background') - ax[0,1].set_title('analysis') - ax[1,0].set_ylabel('reflectance') - ax[0,0].set_ylabel(r'$\phi$') - plt.subplots_adjust(wspace=0.05,hspace=0.05) - - ax[1,0].step(m_const['x_grid'][da_const['obs_loc_sat']]/1000,np.mean(sat_bg[da_const['obs_loc_sat']],axis=1),'k--',lw=2,where='mid')#,marker='s',markersize=4,label='ens mean') - ax[1,1].step(m_const['x_grid'][da_const['obs_loc_sat']]/1000,np.mean(sat_an[da_const['obs_loc_sat']],axis=1),'k--',lw=2,where='mid')#,marker='s',markersize=4,label='ens mean') - - lines_labels = [ax.get_legend_handles_labels() for ax in fig.axes] - lines, labels = [sum(lol, []) for lol in zip(*lines_labels)] - fig.legend(lines, labels, loc='upper center',bbox_to_anchor=(0.5,1.1),ncol=3) - return fig, ax - - -def plot_ensemble_sat_analysis_abstract(bg,an,obs,obs_sat,truth,sat_operator,m_const,da_const,h_c=None): - fig ,ax = plt.subplots(2,2,figsize=(5.5,4.5),sharey='row',sharex='all') - """ - Modified for paper visual abstract - Plots the background and analysis ensemble, as well as the satetlitte obs equivalents. Includes observations as well. - """ - - sat_an = sat_operator(an) - sat_bg = sat_operator(bg) - sat_tr_bg = sat_operator(truth) - n_obs_h =len(da_const["obs_loc"]) - n_obs_sat =len(da_const["obs_loc_sat"]) - - dY_b, y_ol_b = state_to_observation_space(bg,m_const,da_const,sat_operator) - y_b = dY_b.T + y_ol_b - y_b = y_b.T - - - # for plotting the pixels - window = m_const['x_grid'][da_const["obs_loc_sat"][1]]-m_const['x_grid'][da_const["obs_loc_sat"][0]] - xpixmin = m_const['x_grid'][da_const["obs_loc_sat"]]-window/2 - xpixmax = m_const['x_grid'][da_const["obs_loc_sat"]]+window/2 - - for i in range(bg.shape[1]): - #ax[0].plot(m_const['x_grid'],x_a[:,i],'r',alpha=0.2) - ax[0,1].plot(m_const['x_grid']/1000,an [:,i],'magenta',alpha=0.2) - ax[0,0].plot(m_const['x_grid']/1000,bg [:,i],'r',alpha=0.2) - - ax[1,1].hlines( - sat_an [da_const["obs_loc_sat"],i], - xpixmin/1000,xpixmax/1000, - color='magenta',alpha=0.2,lw=3) - ax[1,0].hlines( - sat_bg [da_const["obs_loc_sat"],i], - xpixmin/1000,xpixmax/1000, - color='r',alpha=0.2,lw=3) - ax[0,1].plot(m_const['x_grid']/1000+1000,an [:,-1],'magenta',alpha=1,label='analysis ensemble') - ax[0,0].plot(m_const['x_grid']/1000+1000,bg [:,-1],'r',alpha=1,label='background ensemble') - - #ax[0].plot(m_const['x_grid'],x_ol_a,'r') - ax[0,1].plot(m_const['x_grid']/1000,truth,'k',linewidth=2,label='truth') - ax[0,0].plot(m_const['x_grid']/1000,truth,'k',linewidth=2) - - if n_obs_h>0: - ax[0,1].scatter(m_const['x_grid'][da_const['obs_loc']]/1000,obs[da_const['obs_loc']], - c='k',label=r'$\phi$ point observation') - ax[0,0].scatter(m_const['x_grid'][da_const['obs_loc']]/1000,obs[da_const['obs_loc']],c='k')#,label='h point obs') - if n_obs_sat>0: - ax[1,1].scatter(m_const['x_grid'][da_const['obs_loc_sat']]/1000,obs_sat[da_const['obs_loc_sat']], - marker='x',s=50,c='k',label='reflectance observation') - ax[1,0].scatter(m_const['x_grid'][da_const['obs_loc_sat']]/1000,obs_sat[da_const['obs_loc_sat']],marker='x',s=50,c='k') - - - ax[0,0].plot(m_const['x_grid']/1000,np.mean(bg,axis=1),'k--',lw=2,label='ensemble mean') - ax[0,1].plot(m_const['x_grid']/1000,np.mean(an,axis=1),'k--',lw=2)#,label='ens mean') - - if h_c is not None: - ax[0,0].hlines(h_c,m_const['x_grid'][0]/1000,m_const['x_grid'][-1]/1000,color='k',ls=':') - ax[0,1].hlines(h_c,m_const['x_grid'][0]/1000,m_const['x_grid'][-1]/1000,color='k',ls=':',label=r'cloud threshold $\phi_c$') - - - ax1 = ax[0,1].twinx() - ax1.set_yticks([0.,0.5,1]) - ax1.set_yticklabels(['0',r'$\phi_c$','1']) - ax1.set_ylim(-0.3,1.3) - ax[0,0].set_ylim(-0.3,1.3) - ax2 = ax[1,1].twinx() - ax2.set_yticks([0.3,0.7]) - ax2.set_yticklabels(['clear \n sky','cloud']) - ax2.set_ylim(-0.05,1.05) - ax[1,1].set_ylim(-0.05,1.05) - ax[0,0].set_yticks([0,0.5,1]) - ax[1,0].set_yticks([0.3,0.7]) - ax[1,0].set_yticklabels([]) - ax[0,0].set_yticklabels([]) - ax[1,0].set_xticklabels([]) - ax[1,1].set_xticklabels([]) - - - ax[1,0].set_xlabel('x',labelpad=-8) - ax[1,1].set_xlabel('x',labelpad=-8) - ax[0,1].set_xlim(m_const['x_grid'][0]/1000,30)#m_const['x_grid'][-1]/1000) - ax[0,0].set_title('background') - ax[0,1].set_title('analysis') - ax[0,0].set_ylabel(r'$\phi$',labelpad=-10) - ax[1,0].set_ylabel('reflectance',labelpad=-8) - plt.subplots_adjust(wspace=0.05,hspace=0.05) - - ax[1,0].step(m_const['x_grid'][da_const['obs_loc_sat']]/1000,np.mean(sat_bg[da_const['obs_loc_sat']],axis=1),'k--',lw=2,where='mid')#,marker='s',markersize=4,label='ens mean') - ax[1,1].step(m_const['x_grid'][da_const['obs_loc_sat']]/1000,np.mean(sat_an[da_const['obs_loc_sat']],axis=1),'k--',lw=2,where='mid')#,marker='s',markersize=4,label='ens mean') - - lines_labels = [ax.get_legend_handles_labels() for ax in fig.axes] - lines, labels = [sum(lol, []) for lol in zip(*lines_labels)] - return fig, ax - -def plot_J_quad_paper(J_dict,quad,sens,dx,bw=0.3,dJ=True): - """ - Plots the forecast metric distributions of the free forecast, forecast, and their linear approximations for the given sensitivity - """ - - fig = plt.figure(figsize=(4,3)) - nens = len(J_dict['bf']) - dX_bg=(quad['bg'].T-np.mean(quad['bg'],axis=1)).T - dX_an=(quad['an'].T-np.mean(quad['an'],axis=1)).T - dX_an=dx - dJ_ff=np.dot(sens,dX_bg) - dJ_fc=np.dot(sens,dX_an) - print('vr_reductions:',np.var(dJ_fc,ddof=1)-np.var(dJ_ff,ddof=1 ),np.var(J_dict['fc'],ddof=1)-np.var(J_dict['bf'],ddof=1)) - print('variance:',np.var(J_dict['bf'],ddof=1),np.var(dJ_ff,ddof=1),np.var(J_dict['fc'],ddof=1),np.var(dJ_fc,ddof=1 )) - #'response' : np.hstack([J_dict['bf']-np.mean(J_dict['bf']),dJ_ff,J_dict['fc']-np.mean(J_dict['fc']),J_dict['es']-np.mean(J_dict['es'])]), - if dJ: - plot_data = { - 'response' : np.hstack([J_dict['bf']-np.mean(J_dict['bf']),dJ_ff,J_dict['fc']-np.mean(J_dict['fc']),dJ_fc]), - 'x_pos' : np.hstack([np.zeros(nens)+0,np.zeros(nens)+1,np.zeros(nens)+2,np.zeros(nens)+3]), - 'type' : ['blindforecast']*nens+['estimated']*nens+['forecast']*nens} - else: - plot_data = { - 'response' : np.hstack([J_dict['bf'],dJ_ff+np.mean(J_dict['bf']),J_dict['fc'],dJ_fc+np.mean(J_dict['fc'])]), - 'x_pos' : np.hstack([np.zeros(nens)+0,np.zeros(nens)+1,np.zeros(nens)+2,np.zeros(nens)+3]), - 'type' : ['blindforecast']*nens+['estimated']*nens+['forecast']*nens} - - my_pal = ["blue", "peru","cyan","orange" ] - - PROPS = { - 'boxprops':{'facecolor':'none', 'edgecolor':'black'}, - } - #ax = sns.violinplot(data=plot_data, inner='quartile', orient="v",cut=0,bw=bw,y='response',x='x_pos',palette=my_pal)#sns.color_palette('cool',n_colors=3))#,x='type')#,y='response',x='cyc',hue='type',,split=True,palette={dict_label[left_var]:dict_color[left_var],dict_label[right_var]:dict_color[right_var]} - ax = sns.stripplot(data=plot_data, y='response',x='x_pos',alpha=0.7,jitter=0.15,size=5,palette=my_pal)#color='0.0')# - #ax = sns.boxplot(data=plot_data, y='response',x='x_pos',showfliers=False,**PROPS)#,patch_artist=False)#color='0.0')#,palette=my_pal - #plot errorbars - plt.errorbar(np.arange(4),np.zeros(4),[np.std(J_dict['bf'],ddof=1),np.std(dJ_ff,ddof=1),np.std(J_dict['fc'],ddof=1),np.std(dJ_fc,ddof=1 )],fmt='.',capsize=15,lw=3,color='k') - - #if dJ == False: ax.hlines(J_dict['tr_fc'],-0.5,2.5,'k',ls='--',label='truth'); plt.legend() - #if dJ: ax.hlines(0,-0.5,3.5,'k',ls='--') - ax.set_xlim(-0.5,3.5) - if dJ == False: ax.set_ylabel(r'$j$') - if dJ: ax.set_ylabel(r'$\delta j$') - ax.set_xticklabels(['free-\nforecast','estimated \n free-forecast','\n forecast','estimated \n forecast']) - return fig, ax - -def vr_scatter_v6(vr_tot1,vr_rea1,vr_tot2,vr_rea2,vr_tot3,vr_rea3,vr_tot4,vr_tot5,vr_tot6,alpha=0.3,alpha2=0.5,color1='blueviolet',color2=plt.cm.viridis(0.8), - label1='',label2='',label3='',llabel1='explicit sens',llabel2='implicit sens'): - """ - Just a 2x3 scatterplot with shared axis and a linear regressions """ - - fig, ax = plt.subplots(2,3,figsize=(8,5.5),sharex='all',sharey='all') - - color = color1 - vr_rea = vr_rea1 - vr_tot = vr_tot1 - m, b = np.polyfit(vr_rea, vr_tot, 1) - ax[0,0].plot([-1000,1000],[-1000,1000],'k--',zorder=0) - ax[0,0].scatter(vr_rea,vr_tot,c=color,alpha=alpha,s=5,zorder=1,label=llabel1) - ax[0,0].plot(vr_rea, m*np.array(vr_rea) + b,'k',zorder=2) - ax[0,0].scatter(np.mean(vr_rea),np.mean(vr_tot),c='w',s=100,zorder=2) - ax[0,0].scatter(np.mean(vr_rea),np.mean(vr_tot),c='k',s=50,zorder=3) - ax[0,0].scatter(np.mean(vr_rea),np.mean(vr_tot),c='w',s=10,zorder=4) - ax[0,0].set_aspect('equal', 'box') - ax[0,0].legend(loc='lower center') - - - vr_rea = vr_rea2 - vr_tot = vr_tot2 - m, b = np.polyfit(vr_rea, vr_tot, 1) - ax[0,1].plot([-1000,1000],[-1000,1000],'k--',zorder=0) - ax[0,1].scatter(vr_rea,vr_tot,c=color,alpha=alpha,s=5,zorder=1) - ax[0,1].plot(vr_rea, m*np.array(vr_rea) + b,'k',zorder=2) - ax[0,1].scatter(np.mean(vr_rea),np.mean(vr_tot),c='w',s=100,zorder=2) - ax[0,1].scatter(np.mean(vr_rea),np.mean(vr_tot),c='k',s=50,zorder=3) - ax[0,1].scatter(np.mean(vr_rea),np.mean(vr_tot),c='w',s=10,zorder=4) - ax[0,1].set_aspect('equal', 'box') - - vr_rea = vr_rea3 - vr_tot = vr_tot3 - m, b = np.polyfit(vr_rea, vr_tot, 1) - ax[0,2].plot([-1000,1000],[-1000,1000],'k--',zorder=0) - ax[0,2].scatter(vr_rea,vr_tot,c=color,alpha=alpha,s=5,zorder=1) - ax[0,2].plot(vr_rea, m*np.array(vr_rea) + b,'k',zorder=2) - ax[0,2].scatter(np.mean(vr_rea),np.mean(vr_tot),c='w',s=100,zorder=2) - ax[0,2].scatter(np.mean(vr_rea),np.mean(vr_tot),c='k',s=50,zorder=3) - ax[0,2].scatter(np.mean(vr_rea),np.mean(vr_tot),c='w',s=10,zorder=4) - ax[0,2].set_aspect('equal', 'box') - - color = color2 - vr_rea = vr_rea1 - vr_tot = vr_tot4 - m, b = np.polyfit(vr_rea, vr_tot, 1) - ax[1,0].plot([-1000,1000],[-1000,1000],'k--',zorder=0) - ax[1,0].scatter(vr_rea,vr_tot,c=color,alpha=alpha2,s=5,zorder=1,label=llabel2) - ax[1,0].plot(vr_rea, m*np.array(vr_rea) + b,'k',zorder=2) - ax[1,0].scatter(np.mean(vr_rea),np.mean(vr_tot),c='w',s=100,zorder=2) - ax[1,0].scatter(np.mean(vr_rea),np.mean(vr_tot),c='k',s=50,zorder=3) - ax[1,0].scatter(np.mean(vr_rea),np.mean(vr_tot),c='w',s=10,zorder=4) - ax[1,0].set_aspect('equal', 'box') - ax[1,0].legend(loc='lower center') - - - vr_rea = vr_rea2 - vr_tot = vr_tot5 - m, b = np.polyfit(vr_rea, vr_tot, 1) - ax[1,1].plot([-1000,1000],[-1000,1000],'k--',zorder=0) - ax[1,1].scatter(vr_rea,vr_tot,c=color,alpha=alpha2,s=5,zorder=1) - ax[1,1].plot(vr_rea, m*np.array(vr_rea) + b,'k',zorder=2) - ax[1,1].scatter(np.mean(vr_rea),np.mean(vr_tot),c='w',s=100,zorder=2) - ax[1,1].scatter(np.mean(vr_rea),np.mean(vr_tot),c='k',s=50,zorder=3) - ax[1,1].scatter(np.mean(vr_rea),np.mean(vr_tot),c='w',s=10,zorder=4) - ax[1,1].set_aspect('equal', 'box') - - vr_rea = vr_rea3 - vr_tot = vr_tot6 - m, b = np.polyfit(vr_rea, vr_tot, 1) - ax[1,2].plot([-1000,1000],[-1000,1000],'k--',zorder=0) - ax[1,2].scatter(vr_rea,vr_tot,c=color,alpha=alpha2,s=5,zorder=1) - ax[1,2].plot(vr_rea, m*np.array(vr_rea) + b,'k',zorder=2) - ax[1,2].scatter(np.mean(vr_rea),np.mean(vr_tot),c='w',s=100,zorder=2) - ax[1,2].scatter(np.mean(vr_rea),np.mean(vr_tot),c='k',s=50,zorder=3) - ax[1,2].scatter(np.mean(vr_rea),np.mean(vr_tot),c='w',s=10,zorder=4) - ax[1,2].set_aspect('equal', 'box') - - - - plt.subplots_adjust(wspace=0.05,hspace=0.05) - - ax[1,0].set_xlabel('variance reduction') - ax[1,1].set_xlabel('variance reduction') - ax[1,2].set_xlabel('variance reduction') - ax[1,0].set_ylabel('estimated var reduction') - ax[0,0].set_ylabel('estimated var reduction') - - ax[0,0].set_title(label1) - ax[0,1].set_title(label2) - ax[0,2].set_title(label3) - - x_max = np.max(np.hstack([vr_rea1,vr_rea2,vr_tot1,vr_tot2])) - x_min = np.min(np.hstack([vr_rea1,vr_rea2,vr_tot1,vr_tot2])) - - - ax[0,0].set_xlim(x_min,x_max) - ax[0,0].set_ylim(x_min,x_max) - plt.locator_params(axis='y', nbins=4) - plt.locator_params(axis='x', nbins=4) - return fig, ax - - -def vr_scatter_v4(vr_tot1,vr_rea1,vr_tot2,vr_rea2,vr_tot3,vr_tot4,alpha=0.3,alpha2=0.5,color1='blueviolet',color2=plt.cm.viridis(0.8), - label1='',label2='',label3='',llabel1='explicit sens',llabel2='implicit sens'): - """ - Just a 2x2 scatterplot with shared axis and a linear regressions """ - - fig, ax = plt.subplots(2,2,figsize=(5.5,5.5),sharex='all',sharey='all') - - color = color1 - vr_rea = vr_rea1 - vr_tot = vr_tot1 - m, b = np.polyfit(vr_rea, vr_tot, 1) - ax[0,0].plot([-1000,1000],[-1000,1000],'k--',zorder=0) - ax[0,0].scatter(vr_rea,vr_tot,c=color,alpha=alpha,s=5,zorder=1,label=llabel1) - ax[0,0].plot(vr_rea, m*np.array(vr_rea) + b,'k',zorder=2) - ax[0,0].scatter(np.mean(vr_rea),np.mean(vr_tot),c='w',s=100,zorder=2) - ax[0,0].scatter(np.mean(vr_rea),np.mean(vr_tot),c='k',s=50,zorder=3) - ax[0,0].scatter(np.mean(vr_rea),np.mean(vr_tot),c='w',s=10,zorder=4) - ax[0,0].set_aspect('equal', 'box') - ax[0,0].legend(loc='lower center') - - - vr_rea = vr_rea2 - vr_tot = vr_tot2 - m, b = np.polyfit(vr_rea, vr_tot, 1) - ax[0,1].plot([-1000,1000],[-1000,1000],'k--',zorder=0) - ax[0,1].scatter(vr_rea,vr_tot,c=color,alpha=alpha,s=5,zorder=1) - ax[0,1].plot(vr_rea, m*np.array(vr_rea) + b,'k',zorder=2) - ax[0,1].scatter(np.mean(vr_rea),np.mean(vr_tot),c='w',s=100,zorder=2) - ax[0,1].scatter(np.mean(vr_rea),np.mean(vr_tot),c='k',s=50,zorder=3) - ax[0,1].scatter(np.mean(vr_rea),np.mean(vr_tot),c='w',s=10,zorder=4) - ax[0,1].set_aspect('equal', 'box') - - color = color2 - vr_rea = vr_rea1 - vr_tot = vr_tot3 - m, b = np.polyfit(vr_rea, vr_tot, 1) - ax[1,0].plot([-1000,1000],[-1000,1000],'k--',zorder=0) - ax[1,0].scatter(vr_rea,vr_tot,c=color,alpha=alpha2,s=5,zorder=1,label=llabel2) - ax[1,0].plot(vr_rea, m*np.array(vr_rea) + b,'k',zorder=2) - ax[1,0].scatter(np.mean(vr_rea),np.mean(vr_tot),c='w',s=100,zorder=2) - ax[1,0].scatter(np.mean(vr_rea),np.mean(vr_tot),c='k',s=50,zorder=3) - ax[1,0].scatter(np.mean(vr_rea),np.mean(vr_tot),c='w',s=10,zorder=4) - ax[1,0].set_aspect('equal', 'box') - ax[1,0].legend(loc='lower center') - - - vr_rea = vr_rea2 - vr_tot = vr_tot4 - m, b = np.polyfit(vr_rea, vr_tot, 1) - ax[1,1].plot([-1000,1000],[-1000,1000],'k--',zorder=0) - ax[1,1].scatter(vr_rea,vr_tot,c=color,alpha=alpha2,s=5,zorder=1) - ax[1,1].plot(vr_rea, m*np.array(vr_rea) + b,'k',zorder=2) - ax[1,1].scatter(np.mean(vr_rea),np.mean(vr_tot),c='w',s=100,zorder=2) - ax[1,1].scatter(np.mean(vr_rea),np.mean(vr_tot),c='k',s=50,zorder=3) - ax[1,1].scatter(np.mean(vr_rea),np.mean(vr_tot),c='w',s=10,zorder=4) - ax[1,1].set_aspect('equal', 'box') - - - plt.subplots_adjust(wspace=0.05,hspace=0.05) - - ax[1,0].set_xlabel('variance reduction') - ax[1,1].set_xlabel('variance reduction') - ax[1,0].set_ylabel('estimated var reduction') - ax[0,0].set_ylabel('estimated var reduction') - - ax[0,0].set_title(label1) - ax[0,1].set_title(label2) - - x_max = np.max(np.hstack([vr_rea1,vr_rea2,vr_tot1,vr_tot2])) - x_min = np.min(np.hstack([vr_rea1,vr_rea2,vr_tot1,vr_tot2])) - - - ax[0,0].set_xlim(x_min,x_max) - ax[0,0].set_ylim(x_min,x_max) - plt.locator_params(axis='y', nbins=4) - plt.locator_params(axis='x', nbins=4) - return fig, ax \ No newline at end of file