diff --git a/.binder/environment.yml b/.binder/environment.yml index 67009c4..810c274 100644 --- a/.binder/environment.yml +++ b/.binder/environment.yml @@ -14,11 +14,9 @@ dependencies: - networkx - nglview - notebook - - numpy<1.24 - openff-forcefields - openmm - openmmtools - - openmmforcefields==0.11.2 - pip - plugcli - pymbar @@ -29,5 +27,5 @@ dependencies: - python==3.9.* - rdkit - typing_extensions - - gufe==0.5.* - - openfe==0.5.* + - gufe==0.6.* + - openfe==0.6.* diff --git a/networks/ligand_networks_for_developers.ipynb b/networks/ligand_networks_for_developers.ipynb index dcb76d5..c022ed8 100644 --- a/networks/ligand_networks_for_developers.ipynb +++ b/networks/ligand_networks_for_developers.ipynb @@ -26,9 +26,17 @@ "execution_count": 1, "id": "3bc50f7a", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "WARNING:root:Warning: importing 'simtk.openmm' is deprecated. Import 'openmm' instead.\n" + ] + } + ], "source": [ - "from openfe.setup import SmallMoleculeComponent" + "from openfe import SmallMoleculeComponent" ] }, { @@ -39,7 +47,7 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", "text/plain": [ "" ] @@ -194,7 +202,7 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", "text/plain": [ "" ] @@ -225,7 +233,7 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", "text/plain": [ "" ] @@ -330,7 +338,7 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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dtqNCEQKAQDn+04S5Zfqw/6hXH7iuNZgNpgYXUKzUmTOLtHcvP/7Jrqv2jNdp4NIoAAhUTEwMwzCJiYkGg0Emk/Ed53aFFYaIt+Pzy/VGM3dza3U+lV4hfSWJpaTyJq9+JK5JrjGYF/2WbjSzb03uzU/iDgtFCAAC5e3t3a9fv0uXLp05cyYiIoLvOLdgOe6+FScKKgw3WzDnBJ3dQKUZtxwnkVPQ3RQ6g+QuRFStNy3ZdnlwgOs9g33sHrkDw6VRABAuh71NuPLPzAs5lTcvh178jQ4tptIMcu5K/R+goc/SoH9S91HEmiltB/01m6oLLAdq9ObHvzlTpTPzFr0DQhECgHA5ZhFqDOa3f0urri2z3JN0eh0RR6GP0f3f0dBZ1P9BGjidRi+ke74i5y5UmUPxHxJXc7zOyH66GzcLmwFFCADCZSnC+Ph4juOaPNhuforPMd+8L8jRqbVEHAXFUugMYm59atCtJ41ZTCIpFV2irJpRPxq9+fM9V82sA/2NHByKEACEKzAwsEePHkVFRRcvXuQ7C+WV6XecKnhrU+rrGy9U6/8+HSxOo/JMIoYGTrf+MreeFBBDRJTxV+02M8slppe1d+BOA4NlAEDQoqKiNm3apFarQ0JC7P/ZrxRo1Kml8Wml6tSSizlVVs5LC84TEbl2I+euDb5Ltwi6eoCKLhFntpwyag3mI5eKo/t5tFvwTgVFCACCFhMTs2nTpri4uFmzZtnh05lZ7lJOdXxaiTq19NDF4uvFuiZeUJlDROTavbFjXHsSEZl0pC0hlQ8RGc3cqWuVbZNYAFCEACBodhgvYzRzyVkV+84VWU7+SquNzXlxNRGRzKmxY2R/z5VqqLIUIREVlOtbklWQUIQAIGgDBw709PTMzMzMysrq2bNn3V1Xr141GAzBwcEM0+xpPCt1pmOXy2ove+qMDc4L0wSRhIiIbfTlrOnvg6U3X9f8zIKFIgQAQWMYJjIycufOnWq1+rHHHrNs1Gq1kydPvnz5skqlkkqlf/75Z5cuXZp8q9wyvTq1xFJ+p69VsC0Yiaoro+JUKrxAhRfojkfIf3jN2Z6+vLFX1e6Vu9Ru6+4lb/ZnFyoUYdNOnTo1d+7ca9euhYaGfv7554GBgXwlMeXm2HKYuaRI4t+jvcM0ZP/+/UuXLs26fr3c6EdDniGnxn58ZORr7BYMoCExMTE7d+6Mi4urLcL//ve/FRUVqampUql02rRpH3zwwWeffWb1tXVHu1y4UdWST68tocILlHeaCi9Q+XWiv+szP5n8h5N7LyKi0qtEHFEDJ3mlV4iIlJ4kd7NsEIkoLMCtJWEECY9PNMFgMNx///2TJk1KSEgICgqaPr2BEcztr/y3n/LmvWDLkden3aNRH2jvPFYVFhY+/fTT/3hmdlnUCrPCkxI/afz4OT9dfHdLuiM9wQVCVH/27S1btjz99NMymYxhmFmzZm3durV2l5nlzmdXrT6Q9ciXp31f2Nf79UNPrjq7+kBWM1qQY6k8ky7vocRPaPvTtPWfpF5Ol/dQeRZJ5OQTQiFTafxSGvRPIqIug4lhSFdKBRcafMOsOCIivyG1G1iW/jhbaDLjW8sm7XVGmJGRYTKZunfv7uRk5R5vWVlZfn6+Uqm87Yq8A9q9e7eTk9Prr79OREuXLvX19T137tzAgQPtHKN45Yryn9dxepvufrOa6vx5L3nPW+wy+ZH2DnYbHx+fr/4vccaqc9VGEfW9l3a/3NivsUR6I7ti55XL+ZoNzw/m8Y6G5cu1R48eKpWq/l7Ll6tKperRg7fzbGhX4eHhKpXq/PnzxcXFXl5eRJSdnR0QEGDZGxAQkJOTc+RiceLlMnVqqTq1tEzTnNEuFpyZSq9S4XkqvEB5Z8lQZ0inwp28gsnnDvIZQF79am4K1nLuQl2HUc4JOrOOYj+4fS8R5SRR3hkior731d28/3zRzO9T1j03CPcKm9ReRRgTE5Obm7tjx45JkybV3/vTTz/9+9//joiIOHbsWDsFaCvp6emDBw+2fKxUKvv27Zuenm7nIiz/v/9VbPqB02ltfwmr0xZ9uFjs21U1Mqb9gtV3+lrFjFXnap4Frswl5y6NtKBFtd68NSnvP57K5dOC7RHRmhEjRhQXF+/Zs2fChAn1965fv/61116Ljo52tIm4oK3IZLLhw4cfPnw4ISHh/vvvJyKJRFKpNVjGef6lPm1kmTHvH23yi/l2Ji0VpdaUX+EFMhtu7lJ6kk8I+YSQzx3k2buJdx42iwpSqOgiHXmfRrxCSs+buzIP07EviYh6303e/W973fq4bFeV5MsneHg+smPBPULrWJ3WeCOLiCrzcmWs2fIxESnEovLrmbV/tAPTjevFny6x8VywLlanzX3z38wPf5KznW4VGEzsvSuSNJYWNBso5Uca8A9bXlitN3/xx9WwAJfwIH7ualgmo8ot018pqLln6e0ic1Xiu0NAYmJiDh8+vOuvgxWew9SpJVk6l+nLdnP9pERENy6QysfWFtSVUnFaTfMVp90cz0lEzn7kN4R8Qsg3lJx8mxHOpRuNfofi3qecJNr+NPmEkJMvmfRUnFoz13aPSAq3ft/kqz+veThJ3/1H32Z8OuHBt7p12qPqrMljiEheVJlZqrF8TETZ57PFK29krf+C13S2Mmh0m2e9tajLNHt/YpOO1B+QV3/qfZeNr9AazdO+Ot2uoRqjMRLR098l0+813xFfPXnHS3cH8JYH7M7yNOF3m/74riSWiKhLFF34PwqKJbGcUrdT4NjGXlyVd/O0r+5oF0ZMnn1qTvv8BpPMpbE3aZzfELrvWzq3ia4nUH7y3+/PkFc/6v8ABYxupKff25LuJBe/OSmo5Z+9s2teEWo0mpKSkq5du4rF4qaP7hRGuSiWZpcUGc3eUvEFjaHUxA5xcrgFPBsi44wPlR392GdypUhpv89amUNxS6nbCBr0z2ZfSurgDAZDQUGBr6+vA67yCo2LjIyUSCSmkstk0pNEToHjqOAcbXuaGBH5htCAKbcczbFUcZ0KL1LheSpIoerCm7skSvLuV1N+PiG1q+baTiUThwW6RvfzjAhy/3BnRsr1Sq3BTESk8qGIl2n4S6QpJH0FiaWk9G7iQfu/vbXpkptS8tydjj4mgy/NKMK33377u+++8/f3Lyws/OGHH+66y9Zf9ju0ALl0iqfz9LS8KFfF3jLNK13dVaKONNSWZZg7K1O2udlr0dGyTNo7l+SupC+npJVERKEzbrml0ZjGhtU4vrVr1y5evNhyF/ndd9995pln+E4EzeDs7Dx48OCTJ09ScSp1GUSMiEa8SuEvEMeSREFExJqo7Brlnak5+TPUGSPa+GgXG7gqJRG93WMHekUFew4PcpNLa37I3Bvm8+jK03vPFd1ckolhyMm3sSurrJHiV1DPaAoYXbuN4+jFdeddlJLHIv2bm00IbP0Py8vLy8vLy87Olslka9eunTNnTnJyctMv6xTe6+l1okqXqTdN93YZoOxgv+k7sfpxVefsV4RyFxrxyi1bLD9EbNKBW9BkMr3yyiuHDx8ODw9PSkoaO3bsE088IZHg1kNHEh0dffLkSSo8T10G1WxizVTc6GgXvzDyGUBuPVvw1dvVXR7dzzMq2CO6n0dYoKvViWAUUtHW14f9qM55beN5vYm1abnda0foejzdOEYyJ+o6rHYzy3FPfHtWJhFNjfBrbtROz9ZvVD8/v++//772j0qlTZfakpOTrY5HT0tLq/3YbDZrtc0YD9nedDodEZmJ09SZ0yhEJQtRyYhI0/hER/al5Vgi4ppKFai5RiZ7/QtLleQ/7PaNls9ueVrQrLdfGFuIFVT7A6j0ColqrvmnnSk/IMmwfJyent7k2zAMIxKJ0tLSwsPDNRqNSqVqwaRcwK+YmJgvvviC8s6QRxAVXqC8M1SaQbVPuTIicutZM9TTdxA5+bTgUwT5qizNd1eody8fKz8brXo82v/hEX6/HM39/mBWUka5WMzIxCKW47QGs1wiqtKbbqnhoDupIosu/B8deZ/GvUe+obV7zCw34+szLophEwa1JHwnxjRrOcpffvlly5Ytp0+f3rJlS+OPEPj7++fm5jb+bpbHJ3777bepU6fansEORCIR60iF1wjm5n35joBhyNEenr/nK/IIot+mk76JqfqbfHxi06ZNM2bMmDBhwuXLl3/88ceICHudhUOrXbp0Sa1W792799dff71lh1hGXn3JZ2BN/0ltra5aEjEzuKerpfzGhXh5u7T2khLHUU6ZrqjSoJCKu3konOTiWWtT1hy8fttRdPxruvwHSVU0fil53fJgkkom/vOtCKzQVFfzLt0MGTJEqVRWVFSsXLly1apVTR4/adIkqxOSpaSkHD58uCaBRGL1oXu+mM1mnU4nZkjm8L/Rc0Q6lmOIFKLGozJakQNc0TXrieNILCeH+odl6tzx7T6CVDW3Xkb39xzUs2aM39mzZ5t8glCn03366afvvfdeYGDgypUrX3311YMHDyoUtl8WBrsym82XLl2Kj4/ft2/foUOHCgsLb9ntE0L+w8n3DvIKrjuNtY2c5OIhAa6Wy55jBni27XM4DEPdPBTdPG5+aa16ZmCl1vTL0bonHgwN/zeZtHTtEB16h2I/JLebQ6A1BvOkj5MOLBgxNBBzsNVo3hmhRWZmZq9evaqqqqxe9rSwnBE29ED9N99847AP1G/btm3KlCmxbqpVvZvzoA8fykxseHKWm1h0cnBjg8Euy7tO7LXQbqkatPtFKsuke78m90C+o9RjOSMct4S6DrVsqPv4xBdffNHkA/U7dux4+eWXr127RkRmszkgIODrr79+4IEH2j862Eqj0Zw6dSo+Pl6tVqvV6rKymwu4+/n5hYeHR0dHJyYmbt++nYY+S/0fbNab+7rKInq7R/fziAr2jOjtJpPYdUid0cxN+ezkrtMFt2zlWIr/kLLUpHCn2A9vW9HQx1V2eOHIAd2cCWw/IywoKPjvf/87d+5csVickpLi5uaGAeJ2VmIyn6zSj3dTiZtzQsURc06BicHana+vb0VFRVFRkbe3d3l5uUaj8fHBbRj+VVZWHjt2TK1Wx8fHx8XF6etMTBEUFBQVFRUdHR0VFRUSEmK5p7t+/frt27dT4TlbirDuaJehgW48XumQipnNr4Td+9GJQxeLb25lRBQ5l4xayj1JBxbSXR/VHWtaWGG4+4PjR94Zafutyk7M1iJUqVQnT57s2bOnv79/ZmbmmjVrMCLObspM7Iqc0gPlmkKjOWVIgLI533DVYvkB59Cmj4PWGTFixPPPPz948OCBAweeP39+1qxZkZGRfIcSqNzcXMs5X3x8/OnTp2vv94vF4pCQEEvzjRs3zurMsZbH6qngfEMP8wT5qmIHekcFe4zu7xnoY8fHc5uilIm3zxkWu+xY0pU6CzaJJBSzgA69TQXn6cACuusjUty8NZhdortr+fG4d0Z1dRf6gk22lpmzs/PmzZtLSkrKysp69OghlTb7ujm0mFLETPJwmt/NY8jZZk/tJubYg072niJcmJYtW/bOO+/k5OT4+Pi4uLRiDhFovitXrliaT61WX7hwc5UGqVQaFhYWGxtrOfnz8GhihEhQUFD37t2zs7Op4oblWmLd0S7j7/DycnbcK2GuSslf8yPGvX/sTGbFza0SOY1ZRPvnU0kGHXibYpfXneAmI19z9/Ljh94e4ch/Lzto3lmdp6enp6eND0dDm5GLmFEuCi3b7Lu5OpF0vfsYhxgpIwwKhSIoCBNZERENGTJEp9P9/PPPYWFh9fd+9913n332WUxMTN2HspqlkdEuLi4uI0aMsDRfdHR0c4csRUZG/vrrr3fIMqc/PH50f8/hQW5KWYeZSMtdJf3zrYjR7yWm5lbf3Cp1onFLaO88KrtKhxbT+PdJcvNc9lx2Zeyy4wcXjnBXCff0Bpc3rVNFj+310098pyAiMhXm33jyQbZFj1qq3F3n/7ziP0o73QMwmbmodxMzCjRs8ztbKRPvmTc8tNeOh60AABZOSURBVAc/J1J99shK9PR/r4aNj62ZL0nVcX72OabU1FSdTqfRWF94uaioKDU1tblrXNcd7RIXF1defvMaYNeuXYcNGxYdHR0bGxsWFiZqxfRPMTExv/766zDn7IUP9mnxm/DI11W2d/6ImPcSM4vq/NCQu9H492nfm1R0iQ4vobGL6879diaz4r4VJ/56K8JJLtAvexShdYxEKnJ1iLHFMlc3n0UfFyx+gxr4mdIQRqHs+un3Cm+7/i12vhEe/nZ8pdbU9KF1OMnFSx4OHt2ft4sNlruuzgqJh5Nwfyl2TBUVFcePH9+3b59arT5x4kRDo13uuOOOtvqMltuEHXrJrR5eir3zI0YvOZpXVmfVGpU3jV9Ke9+k/LMU/wFF/6fuVHAJaaVTPju5Y0547exugtJeRZiamsqybEMPCM6cOXPGjBkYbmMj57vuNRUXaL/4wPaXMEpll6VfKkKtXJVqV8Fdnfa8OXziR0lVOpOND+Y4KcQvxga8fk+vdo7WmKtXrzby5frCCy889dRT+HK1m5ycHMtpX0OjXWJjY8eOHdtO43JDQ0Pd3d2vXr16/fr1jrsUc18/pz/nRYxberSkqs4aws5dadz7tH8eZR+jo5/TqDl1H+rdm1L06Mozv74SJmnWwPROob2+txsfLCCTyfD0RbNURcdqtUY6OivbYPKSiDwljV3BEHl4dv30e8WgoXaLV1dksMfRdyPvW5GUWahtsgrlUtFnMwY8O57nSfHx5cq72tEue/fuvXr1au12lUoVFhZmOe2LiYlxd3dv7yQikSgyMnL37t3x8fHTp09v70/Xfgb1dNk9d3js8mO3zFDqHkDjl9H+t+jaQRJJaOSrdQfHbj2R96/vk9c9N8jqxKedGH7J7RjefPPNtLS0sAH95167Mt5N9UrXxn4c9Pz1T7Gnt92y1RfSzfnSijGes/ZqDE3MEfzdM6FPju5mn1TgmFatWrVgwYKSkpLaLV5eXpbai46OHjZsmP3HqMfExOzevTsuLq5DFyERjejjvn12+H0rknTGOnNGegTR2HfpwEK6spekKho2q+5LNsTdcFVKvnqyzS41dwgowo7hp59+IiLD5dTr0yY2ebBIxf+UdXKpSCETNVmEXdxwpiV07u7ullVOLad90dHRrRzt0nqd4DZhrfF3eP3yStjUz08ZzXUu0HgPoNEL6dC7lLqd5K408Ja+X/lXpoeT9L2pwbe/V+eFIgSA9nL33XdbrTSD4eZ6Rvfdd19mZmbPng60Zmx4eLhCoTh37lxJSUkneGBs8tAuP78UNu2r0+a6w7n9wihqHqmXU/JGEstowEN1X7Jk62UnuXje/b3tnZUnQhwgBAAtc+nSpR9++GHbtm2W1cqa5OLi4mFN3XXcXFxcHKoFiUgul0dERHAcl5CQwHeWtvGPCL/vZ4befuOvxyga+SoxDJ3+L13ec9tL5v+Sump/s2fw6KBQhABgk1WrVt1zzz0XL15cvXp1RESELcuI/vbbb1nWzJ071w6BWyM6OpqI1Go130HazNNjun/2eMjtW3vdScOeI+Io6WvKPFJ3D8fRi+vOrTucbb+I/MGlUQCwiYuLS1xcXPfu3VmW7du3719//dWJl9foTLcJa706MbC4yrBk6+VbtgbfT4YqSv6REj8hqZL8h9fu4Th6dk2Ki1LS6Re1xxkhANhkxowZ3bt3JyKRSOTh4WEyNW/ahI4lMjJSLBafOHGioclxOqj3pga/cV+9WQAHPkohU4k1Udwyyk+pu8eyqP2es4W3v6RzQRECQPNcvHgxIyNj9OjRfAdpR66uroMGDTIYDElJSXxnaWMfPdr/2XH1JgoY8hT1vZfMBjryLhWn1d1jMLH/+PxUXGrJ7S/pRFCEANAMxcXF06ZN++ijjzr9goud8uooETEMffvMwOmj/G/bTMNfpN53k1FLBxdS6ZW6+zQG8/0fnzh5tZw6KRQhANgqNTV1zJgxTz311LPPPst3lnbXWYuQiMQiZsMLg+8L8711M0MRL1PPaJKqSHL7CoXlGtM9HyVduFFlt5D2hCIEAJts2bIlMjLy2WefffDBB69cuVJ3LphOyVKECQkJnfJuqGVR+7EDvG7Zyogo8g266xNy6UalGXRoEalvTnFcWGG4e/nxq4U1N01nz55977337tq1y56x2wmKEABs8u233/bq1Wvjxo2PPPLII488snHjxkYOXrt27fr164ODrc9OMnny5PXr1zv4QxRdunTp27dvVVXV2bNn+c7SLpQy8c654VHBty5WLJKSyouISF9BOSco73TdnTdKdXctP55TqiOihISEP/74IzMz036J2w0enwAAm+zdu9f2gx977LFG9oaGhoaGhrY6UbuLjo5OT09Xq9XDhg3jO0u7cJKLd84NH7/02OlrFU0fTUREGfma8UuPHXlnVLsGszOcEQIAWNeJbxPWcldJ98yL6O/vbPtLUnOrJ3xw3Nz89bcdFooQAMA6SxEeOXKEs3F1zY7J11W2Z97wHl4K219ypUBzy4oWHRyKEADAuj59+vj7+xcWFqanp/OdpX0FeCsPLBjp5377YFGrPJykf74V4SRvbFXUjgVFCADQoKioKOrsV0ct+nRR/fVWhKdzE6s/dnGTH1o4cmSfdl8h2Z4wWAYAoEExMTGbN2+Oi4v717/+xXeWdhfaw2X33OF3LT9eqfv7iRGjhnb/u/YAqVjk5qt8XC0iooyMDF5CtgcUIQBAg4QwXqauEX3ct80edt+KpJp1tjiWyq7V7jUSpRXzE6xd4dIoAECDBg0a5ObmduXKlexsQSxIRETj7/DaNnuYRMwQEcmc6dEd9OiOkLkHrxdVm+oYMWIE30nbDIoQAKBBIpFo1KhRRBQfH893FvuZMMhn/uS/l6dnRMN7exx5J7K7l0pcB68B2xiKEACgMZaro51pkV5bxPTztHwwdoDX/gUjvJxl/OZpV7hHCADQGKHdJqzLWSH+Y95whbSTnzJ18r8eAEArRUREKBSKlJSUsrIyvrPYm1Qs6vQtSChCAIDGyeXy8PBwlmUTEhL4zgLtAkUIANAEIV8dFQLcIwQAaEJMTMzy5csFVYS9e/detGiRUqls6ICZM2dOnDhx+PDh9kzVTlCEAABNiIqKEovFJ06c0Gq1jXRDZxIUFLR48eJGDpg5c6a9srQ7XBoFAGiCq6traGioXq9PSkriO4tDe+KJJ8L/1oEWr8cZIQBA06Kjo8+cOaNWq0ePHs13Fsd1+PDhdevWBQYGEpGfnx/fcWyFM0IAgKZhvEyTOI7Ly8uLiIgICgoKCgpSqVR8J7IVihAAoGmWE8GEhASz2cx3FgdVXFzMsuxbb711zz33LFmyRKfT8Z3IVrg0CgDQND8/v969e2dkZCQnJ4eFhfEdxxG5ubn99NNPgYGBYrH41Vdfzc/PX7lyJd+hbIIzQgAAm+DqaOOkUunDDz88fPjwoUOHLliwYOfOnXwnshWKEADAJijCxul0utrVei9fvty1a1d+89gOl0Y7DY6I4TuDFf31N2KqLw7TZCwzFGURLc/7X4VhQIKqX6JTPwODLz/oSGJiYkJDQwcMGMB3EAd1+PDhGTNm3HvvvTKZbMeOHb/88gvfiWyFn0QdCWc0NLyzTguybPtnadq40lMvZG3pYi4Xk1nGmj7ljEQ0SHstuCR3Wlk8Q9wG9zEi3R18xwSwVd++fZOTk/lO4bgmTJhw4cKFxMREjUazbNkyX19fvhPZiuE4ju8MjqWsrOzy5cseHh69e/du+mg70l9MyX3lGXNJUe0WM0cXtHoJwwxQ3rJUmCwo2O/zNdJuPeye8e9gJcX5814sOXtWadbXbrysM2pZtq9CphDVdLaOkcmcFN0//lY5PJKnpABNM5vN69atI6KpU6e6u7vXP+D48ePJycm9e/ceN26c3dNBG0ARdgyaI/vz/vMyp9ORDf9fjEjEqJz8v/2fPCTUDtluY7yemfOvqebycs5ksOVqLaNQes9d5PrgNDtkA2gBrVZreSTu/PnzISEh9Q944403Pvnkk+nTp//88892TwdtAINl6Pnnn/fz81uyZInVvWlpaX5+fn5+fnq93uoBdqA7cyJv/sucVrulqHJkyvV/Xc5v6MgJF26MTLl+rELDVlXlPP+YMeuaHWMSEZlLinP+NdVUWnKlWjMyJXtkynVDA809P7NoZMr1VXnlnE5btGJx9f4/7BwVAMACRUhlZWX5+fkVFRVW9xqNxvz8/Pz8fL5OndmK8rzZMzmdloi0LFdkNJeZG7wFWGwyFxnNBpYj4jiNJvfVZziTyY5hKf/NF8wVZcSyJo6KjOYio7mhf7ZyM1tkNFezLBFxOl3B4jdMuTfsGRUAwAJF6OiKv/qQ07ZkggaOY82F+eW/bmjzSA2pPrBHn3qeMxpb8FpWry9YtqDNIwEANAlF6NDMpSWVu7ayhhZelWW1mrLvv7TbSWHxlx+yGk0Ln+Iwm/WnjhuupLd1KACAJqAIHVrVnt+JadXTgZzZrD1mj+d/DemXzMUFrXkHzmSq2NZhHjwCgE4DzxE6tKr9uy13B1uM1WiqD+1TRbX7qG5N/CHO2KpTT85k1BzeS7MXtlUkgLb19ddf+/j41N+ekJBg/zDQhlCENTiOM1m7hMjvTPPGjDSr2xscglIfx+rPnWqzQA3Tnj1h9Xl/toG0Vv8CxtwczmRiJPiyBEf0zTff8B0B2gV+4tT45JNPPvnkE75T3Irj2MqK+nOnna3W9zudafvbmIqLmj6o1cx5uVa3h55pRlRGKjWXFkt8urRRKIC29Omnn3bv3r3+9o0bN+7YscP+eaCtoAhr+Pj4WJ0iVq/Xp6am2j8PERHHcsTUH3uiFDEBcqnVV6TrDOZ6p1pcw49btKGGhuT0U8qs3uS8YTBV1g/GMITF3sBRTZgwweoD9ceOHbN/GGhDKMIaTz755IoVK+pvP3/+/MCBA+2fh4hIJGakUq7ekNFgpey3ftandQ9Pzioz3d4uYifndol322exNvUUEW3p11UuslKFL14p+KtMc/tWk0Hk6tbm2QAAGoFRow5N0tW/9W8iDerT+jdpkiw4pJUDXIlIpFCJVE5tkgcAwEYoQoemGjmGEYtb8w4ipdIpamwbxWmMckS0qNWnnvKw4W0SBgDAdihCh+Zy34OMXN6ad+DMrNP4iW2VpxGqkdFkatXtPZGTk+v9U9sqDwCAjVCEDk1+x2BJt54tXnCXkUicYsaJvaw8+dTmGJnc5R+Ptaa2GYVKNSa2DSMBANgCRejofOa/L5IrW/ZaRiL1es1+E3h6PvsyI21hEYqUKp957zJijN4ChyMSiUJDQ0NDQxUKhdUD/P39Q0NDAwIC7BwM2gqK0NEpBg9znTpDpFA194UipcpzztsSfyuPPbUTkYtrl2Wfi5TWf1g0glEoVJFjnO68pz1SAbSSXC5PTk5OTk4OCgqyesDs2bOTk5M/+OADOweDtoKFeenkyZM3btzo06eP1SeEKisrDx48SESTJk0SiXj6vYFl8954XnNMnV1ReUlrcBWLhjtbL5sjFVojx4U5yb1cnN3+McPrdR7Wcyj7eV3p1ysqq6qPVumIaJybyuq/WrJGX2g0B8qlfdxcZMEh3Vb/xMhadTcUAKBlUIQdBGsu+mhx5c4trLbes3e3YxiFwmPWqx5PPmePYNZU7tpStHwhZ9A3+Sy/SKlUjhzdZdkXaEEA4AuKsCPRJB4pfH8+W1nOVldb2c0wIqVK4t/dd9EKeUio3dPdwph5peC9efrUC5xeR6yVOhQ5OTFSufebi50n3G//eAAAtVCEHQ3LVqsPVmz9WXfyOGfUM2IJEcOZ9IyTi9PIGJeHHlMOG8F3xJv0F1IqtvyvWn3QXFIsksuJI441EyNShIa5TpnuNH4iI7U+VxwAgN2gCDswtqLMXFlBRGJ3z9Y/zN6uOJ3WVFxIRCKlk9jTi+84AAA3oQgBAFolLy/voYce8vHx2b59O99ZoCVQhAAArfLwww+LxeKkpKSMjAy+s0BL4DnCWyQmJob/7f77HX0QR0pKyuOPPx4eHj5lypS0NOtL+PLr6NGjjz/++Lp162q3bN26dfz48aNGjVq+fDlrbRANQMeya9eu1NTUV155he8g0HKYyOMW6enp/v7+n3/+ORHJZDK+4zQmPT197NixixYteuedd/Ly8qyuF8qvxYsXJyUlVVdXnz9/3rLl9OnTTz311IYNG7p16/bUU08pFIrXX3+d35AArVFRUfHiiy9u2rRJIsHP0g4Ml0ZvsXz58sLCwk8//ZTvIE176aWXdDrdmjVr+A7ShAULFuj1+o8//piInn/+eZlM9uWXXxLRtm3b5s2bx9uixwBt4bnnnpNKpStXrkxKSpo+fToujXZQuDR6i7y8vDNnzkybNm3GjBnHjx/nO05jzp07FxwcvGDBghkzZmzcuJHvODa5ePHisGHDLB8PHTr08uXLRqOR30gALZaYmLhmzRpXV9cPP/xww4YNZWVljv+LKViF0/lbzJw5My0tbcCAAXFxcbGxscnJyYGBgXyHsi43N3fTpk2zZ88eOnTonDlz9Hr9zJkz+Q7VhMrKSpWqZtJUZ2dnlmWrqqo8PDz4TQXQMiqVatmyZXyngDaAIryFZY55IgoJCdmwYcO+ffsctl08PT1fe+21adOmEdGVK1e2b9/usFFr+fn5FRQUWD7Oz89XKBTu7u78RgJoscGDBw8ePNjycVJS0u7dux3/exCswqXRW6SlpVmGMhoMhhs3bvj5+fGdqEFDhw5Vq9WWj9PS0ry9vfnNY4sxY8bs3LnT8vGuXbvGjh3LMC1daxHAkfTu3furr77iOwW0EAbL3MRxXGxsbFVVVVRUVHx8vLe39++//y4Wi/nOZV16evrIkSOnTp1qMpm2bt165MiRgQMH8h3qFpcuXfrhhx+OHDliMpnGjx//xhtviESisLCwkSNHdu/efe3atbt27YqKiuI7JgAIHYrwFizLHjt27OLFi7169XL885WcnJx9+/ZptdrJkyd37dqV7zi3y8rK2rNnT+0fp02b5ubmVlpaunXr1urq6nvuuadPnz48xgMAsEARAgCAoOEeIQAACBqKEAAABA1FCAAAgoYiBAAAQUMRAgCAoKEIAQBA0FCEAAAgaChCAAAQNBQhAAAIGooQAAAEDUUIAACChiIEAABBQxECAICgoQgBAEDQUIQAACBoKEIAABA0FCEAAAgaihAAAAQNRQgAAIKGIgQAAEFDEQIAgKChCAEAQNBQhAAAIGgoQgAAEDQUIQAACBqKEAAABA1FCAAAgoYiBAAAQUMRAgCAoKEIAQBA0FCEAAAgaChCAAAQNBQhAAAIGooQAAAEDUUIAACChiIEAABBQxECAICgoQgBAEDQUIQAACBoKEIAABA0FCEAAAgaihAAAAQNRQgAAIKGIgQAAEFDEQIAgKChCAEAQNBQhAAAIGgoQgAAEDQUIQAACBqKEAAABA1FCAAAgoYiBAAAQUMRAgCAoKEIAQBA0FCEAAAgaChCAAAQNBQhAAAIGooQAAAEDUUIAACChiIEAABBQxECAICgoQgBAEDQUIQAACBoKEIAABA0FCEAAAgaihAAAAQNRQgAAIKGIgQAAEH7f43zWL1GXS9+AAAAnnpUWHRyZGtpdFBLTCByZGtpdCAyMDIyLjA5LjMAAHice79v7T0GIOBlgABGIOYGYi4gbmBkU0gA0izMUJoJxmdk0AArJpfmBtrDyMTAwAw0jIGBlYGRjYGRnYGJg4GJk4GJi0GEQbwP6hYwADrowP4evV2LYQII9gF7BNvhwM9mG1Wo+H6QHBJ7PwMcwNgNqgg1Dg7IZiLptYepFwMAoUMjS8r6gX0AAAD0elRYdE1PTCByZGtpdCAyMDIyLjA5LjMAAHicjZJJDoMwDEX3OcW/ACghTFkyqVQVILW0d+i+91dtEDgIKcJhYTsvlu2PAtuzfXx/2C1plQJ04HPO4WO11moAO6i7231EM1f1lmmm9zi/YAyMxnKObDVPw5YxaBCZONNsiHSsV0+cjUyI3LMh0BJ4qWKKHpE935/AjMG95O6cwfwARgGyIFKmCYClDwY4R5y9sh7SpMeVYUg/Dwz12I3tQdRV5noaW5GZTyJaUgArivFtKrpwmMn2Ocxlx/y2kEVyWMq6DJVzshUOjT/8kjB+636jHG//MvnqD1TClKy3U8ITAAAAuXpUWHRTTUlMRVMgcmRraXQgMjAyMi4wOS4zAAB4nG2PMQ+CMBCF/4ojJKXptbTQY3TBxbgTBmMcSCQl2JEfL62mZ43D3b2X773hhn48FkM/lnH9l/sctqJSTLCuAq7TDfNWcXUiEJFMPJ98LKdOqpTs6t18Wd2Cgk/P07w8ptvkOew2kLPzd64QyAiUeVASq1HlTBHTWJMB1HlQEzNocmaINdiQkdjmwZZYizZnlphF+PkVvp4Fsb0Aoyh324GRCu8AAACbelRYdHJka2l0UEtMMSByZGtpdCAyMDIyLjA5LjMAAHice79v7T0GIOBlgABGIGYDYlYgbmBkU0gA0izMHGCaiZGRQQOsCBfNDdLPxMDADNTEwMjKIMIgHgQ1FAzYos2z9sse/GsL4hjHc9vzCX60A7GrPosfYEhM2Qdi329euD9y+Sl7EPt7muB+0/er9oPYSV0n7TfNkAOzxQAzyh2sXD1kHAAAAPJ6VFh0TU9MMSByZGtpdCAyMDIyLjA5LjMAAHicfZFdbsMgDMffOYUvUOQvIDw2SbVWVRNpy3aHve/+mr0qo5VQDQ+29fvb2ARwe5+v3z/wbzyHAIAvbq0VvgQRww3cgfH0dllg2o7jnpnWz2X7gAzJFHaeyeO23vYMwQQHjJW0YgGMxFJRzME/a1KG1bJJVZnM0YKD9DiBMxw4SlGW7KWZBTl3SHWSImcapADFNAgLd8DkoPXOpZbiEpGknDpkNtIKFU333plqotIBT8v8tIb7YsZ1mdti/HAb30NpU3qobRQPU3u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"text/plain": [ "" ] @@ -351,7 +359,7 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", "text/plain": [ "" ] @@ -434,7 +442,7 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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"text/plain": [ "" ] @@ -455,7 +463,7 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", "text/plain": [ "" ] @@ -498,7 +506,7 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", "text/plain": [ "" ] @@ -531,7 +539,7 @@ "outputs": [], "source": [ "import itertools\n", - "from openfe.setup import Network\n", + "from openfe import LigandNetwork\n", "\n", "def simple_network_planner(three_heavies, two_heavies, mapper):\n", " mappings = []\n", @@ -541,7 +549,7 @@ " # use all suggested mappings\n", " mappings.append(mapping)\n", " \n", - " return Network(mappings)" + " return LigandNetwork(mappings)" ] }, { @@ -565,25 +573,745 @@ "metadata": {}, "outputs": [ { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" + "name": "stderr", + "output_type": "stream", + "text": [ + "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/home/richard/miniconda3/envs/openfe/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf', name='DejaVu Sans', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 0.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/home/richard/miniconda3/envs/openfe/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/STIXGeneralItalic.ttf', name='STIXGeneral', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/home/richard/miniconda3/envs/openfe/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/cmtt10.ttf', name='cmtt10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/home/richard/miniconda3/envs/openfe/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/STIXSizTwoSymBol.ttf', name='STIXSizeTwoSym', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/home/richard/miniconda3/envs/openfe/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/STIXSizFourSymBol.ttf', name='STIXSizeFourSym', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/home/richard/miniconda3/envs/openfe/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/cmex10.ttf', name='cmex10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/home/richard/miniconda3/envs/openfe/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/STIXNonUniBol.ttf', name='STIXNonUnicode', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/home/richard/miniconda3/envs/openfe/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/STIXNonUniBolIta.ttf', name='STIXNonUnicode', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/home/richard/miniconda3/envs/openfe/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSerifDisplay.ttf', name='DejaVu Serif Display', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/home/richard/miniconda3/envs/openfe/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/cmmi10.ttf', name='cmmi10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/home/richard/miniconda3/envs/openfe/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSansMono-BoldOblique.ttf', name='DejaVu Sans Mono', style='oblique', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/home/richard/miniconda3/envs/openfe/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSerif-BoldItalic.ttf', name='DejaVu Serif', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/home/richard/miniconda3/envs/openfe/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/STIXGeneralBolIta.ttf', name='STIXGeneral', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/home/richard/miniconda3/envs/openfe/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/cmss10.ttf', name='cmss10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/home/richard/miniconda3/envs/openfe/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSerif-Italic.ttf', name='DejaVu Serif', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/home/richard/miniconda3/envs/openfe/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSansMono-Oblique.ttf', name='DejaVu Sans Mono', style='oblique', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/home/richard/miniconda3/envs/openfe/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSerif.ttf', name='DejaVu Serif', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/home/richard/miniconda3/envs/openfe/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans-Oblique.ttf', name='DejaVu Sans', style='oblique', variant='normal', weight=400, stretch='normal', size='scalable')) = 1.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/home/richard/miniconda3/envs/openfe/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/STIXGeneral.ttf', name='STIXGeneral', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/home/richard/miniconda3/envs/openfe/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/STIXSizOneSymReg.ttf', name='STIXSizeOneSym', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/home/richard/miniconda3/envs/openfe/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/STIXSizFourSymReg.ttf', name='STIXSizeFourSym', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/home/richard/miniconda3/envs/openfe/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/STIXGeneralBol.ttf', name='STIXGeneral', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/home/richard/miniconda3/envs/openfe/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/cmr10.ttf', name='cmr10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/home/richard/miniconda3/envs/openfe/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSansMono.ttf', name='DejaVu Sans Mono', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/home/richard/miniconda3/envs/openfe/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSerif-Bold.ttf', name='DejaVu Serif', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/home/richard/miniconda3/envs/openfe/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSansDisplay.ttf', name='DejaVu Sans Display', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/home/richard/miniconda3/envs/openfe/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/STIXNonUniIta.ttf', name='STIXNonUnicode', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/home/richard/miniconda3/envs/openfe/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans-Bold.ttf', name='DejaVu Sans', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 0.33499999999999996\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/home/richard/miniconda3/envs/openfe/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/STIXNonUni.ttf', name='STIXNonUnicode', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/home/richard/miniconda3/envs/openfe/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/STIXSizThreeSymBol.ttf', name='STIXSizeThreeSym', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/home/richard/miniconda3/envs/openfe/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/STIXSizThreeSymReg.ttf', name='STIXSizeThreeSym', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/home/richard/miniconda3/envs/openfe/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/cmb10.ttf', name='cmb10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/home/richard/miniconda3/envs/openfe/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/STIXSizOneSymBol.ttf', name='STIXSizeOneSym', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/home/richard/miniconda3/envs/openfe/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/cmsy10.ttf', name='cmsy10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/home/richard/miniconda3/envs/openfe/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/STIXSizFiveSymReg.ttf', name='STIXSizeFiveSym', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/home/richard/miniconda3/envs/openfe/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSansMono-Bold.ttf', name='DejaVu Sans Mono', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/home/richard/miniconda3/envs/openfe/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans-BoldOblique.ttf', name='DejaVu Sans', style='oblique', variant='normal', weight=700, stretch='normal', size='scalable')) = 1.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/home/richard/miniconda3/envs/openfe/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/STIXSizTwoSymReg.ttf', name='STIXSizeTwoSym', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/opentype/urw-base35/URWGothic-Demi.otf', name='URW Gothic', style='normal', variant='normal', weight=600, stretch='normal', size='scalable')) = 10.24\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/freefont/FreeSansBoldOblique.ttf', name='FreeSans', style='oblique', variant='normal', weight=600, stretch='normal', size='scalable')) = 11.24\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/kacst/KacstQurn.ttf', name='KacstQurn', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/opentype/urw-base35/C059-Italic.otf', name='C059', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/Sawasdee.ttf', name='Sawasdee', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/TlwgMono-Bold.ttf', name='Tlwg Mono', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/Laksaman-Italic.ttf', name='Laksaman', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/TlwgTypewriter-Bold.ttf', name='Tlwg Typewriter', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/kacst-one/KacstOne.ttf', name='KacstOne', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/TlwgMono.ttf', name='Tlwg Mono', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/opentype/urw-base35/URWGothic-BookOblique.otf', name='URW Gothic', style='oblique', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/Purisa-BoldOblique.ttf', name='Purisa', style='oblique', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/lohit-tamil-classical/Lohit-Tamil-Classical.ttf', name='Lohit Tamil Classical', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/malayalam/Suruma.ttf', name='Suruma', style='normal', variant='normal', weight=500, stretch='normal', size='scalable')) = 10.145\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/opentype/urw-base35/NimbusSansNarrow-Oblique.otf', name='Nimbus Sans Narrow', style='oblique', variant='normal', weight=400, stretch='condensed', size='scalable')) = 11.25\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/Norasi-Bold.ttf', name='Norasi', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/Norasi-BoldOblique.ttf', name='Norasi', style='oblique', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/dejavu/DejaVuSansCondensed-BoldOblique.ttf', name='DejaVu Sans', style='oblique', variant='normal', weight=700, stretch='condensed', size='scalable')) = 1.535\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/fonts-deva-extra/chandas1-2.ttf', name='Chandas', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/TlwgTypist-Oblique.ttf', name='Tlwg Typist', style='oblique', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/lohit-gujarati/Lohit-Gujarati.ttf', name='Lohit Gujarati', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/opentype/urw-base35/URWGothic-DemiOblique.otf', name='URW Gothic', style='oblique', variant='normal', weight=600, stretch='normal', size='scalable')) = 11.24\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/fonts-beng-extra/LikhanNormal.ttf', name='Likhan', style='normal', variant='normal', weight=500, stretch='normal', size='scalable')) = 10.145\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/freefont/FreeSerifBold.ttf', name='FreeSerif', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/Umpush-BoldOblique.ttf', name='Umpush', style='oblique', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/liberation2/LiberationSans-BoldItalic.ttf', name='Liberation Sans', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/lohit-oriya/Lohit-Odia.ttf', name='Lohit Odia', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/Loma-BoldOblique.ttf', name='Loma', style='oblique', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/dejavu/DejaVuSerif-BoldItalic.ttf', name='DejaVu Serif', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/opentype/urw-base35/URWBookman-Demi.otf', name='URW Bookman', style='normal', variant='normal', weight=600, stretch='normal', size='scalable')) = 10.24\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/ubuntu/Ubuntu-LI.ttf', name='Ubuntu', style='italic', variant='normal', weight=300, stretch='normal', size='scalable')) = 11.145\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/Kinnari-Italic.ttf', name='Kinnari', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/dejavu/DejaVuSansCondensed-Bold.ttf', name='DejaVu Sans', style='normal', variant='normal', weight=700, stretch='condensed', size='scalable')) = 0.5349999999999999\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/liberation/LiberationSansNarrow-BoldItalic.ttf', name='Liberation Sans Narrow', style='italic', variant='normal', weight=700, stretch='condensed', size='scalable')) = 11.535\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/lohit-assamese/Lohit-Assamese.ttf', name='Lohit Assamese', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/opentype/urw-base35/NimbusSans-Regular.otf', name='Nimbus Sans', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/Kinnari.ttf', name='Kinnari', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/padauk/Padauk-Bold.ttf', name='Padauk', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/Garuda-Oblique.ttf', name='Garuda', style='oblique', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/opentype/urw-base35/URWBookman-Light.otf', name='URW Bookman', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/pagul/Pagul.ttf', name='Pagul', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/TlwgTypo-Bold.ttf', name='Tlwg Typo', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/freefont/FreeMonoBold.ttf', name='FreeMono', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/freefont/FreeSerifItalic.ttf', name='FreeSerif', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/liberation2/LiberationMono-Italic.ttf', name='Liberation Mono', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/Sahadeva/sahadeva.ttf', name='Sahadeva', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/lohit-malayalam/Lohit-Malayalam.ttf', name='Lohit Malayalam', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/malayalam/RaghuMalayalamSans-Regular.ttf', name='RaghuMalayalamSans', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/fonts-deva-extra/kalimati.ttf', name='Kalimati', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/Waree-BoldOblique.ttf', name='Waree', style='oblique', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/opentype/urw-base35/URWGothic-Book.otf', name='URW Gothic', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/TlwgTypo-Oblique.ttf', name='Tlwg Typo', style='oblique', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/TlwgTypewriter-BoldOblique.ttf', name='Tlwg Typewriter', style='oblique', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/dejavu/DejaVuSans-ExtraLight.ttf', name='DejaVu Sans', style='normal', variant='normal', weight=200, stretch='normal', size='scalable')) = 0.24\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/fonts-gujr-extra/aakar-medium.ttf', name='aakar', style='normal', variant='normal', weight=500, stretch='normal', size='scalable')) = 10.145\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/kacst/KacstPoster.ttf', name='KacstPoster', style='normal', variant='normal', weight=500, stretch='normal', size='scalable')) = 10.145\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/Waree.ttf', name='Waree', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/dejavu/DejaVuSans-Oblique.ttf', name='DejaVu Sans', style='oblique', variant='normal', weight=400, stretch='normal', size='scalable')) = 1.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/kacst/KacstBook.ttf', name='KacstBook', style='normal', variant='normal', weight=500, stretch='normal', size='scalable')) = 10.145\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/opentype/urw-base35/P052-Bold.otf', name='P052', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/malayalam/Karumbi-Regular.ttf', name='Karumbi', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/TlwgMono-BoldOblique.ttf', name='Tlwg Mono', style='oblique', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/ubuntu/Ubuntu-B.ttf', name='Ubuntu', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/dejavu/DejaVuSerifCondensed-BoldItalic.ttf', name='DejaVu Serif', style='italic', variant='normal', weight=700, stretch='condensed', size='scalable')) = 11.535\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/liberation2/LiberationSerif-Regular.ttf', name='Liberation Serif', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/opentype/urw-base35/D050000L.otf', name='D050000L', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/lohit-devanagari/Lohit-Devanagari.ttf', name='Lohit Devanagari', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/Norasi-Oblique.ttf', name='Norasi', style='oblique', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/malayalam/Keraleeyam-Regular.ttf', name='Keraleeyam', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/ttf-khmeros-core/KhmerOS.ttf', name='Khmer OS', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/opentype/urw-base35/C059-BdIta.otf', name='C059', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/opentype/noto/NotoSansCJK-Bold.ttc', name='Noto Sans CJK JP', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/opentype/urw-base35/C059-Bold.otf', name='C059', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/Norasi-BoldItalic.ttf', name='Norasi', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/liberation/LiberationSans-BoldItalic.ttf', name='Liberation Sans', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/Loma-Oblique.ttf', name='Loma', style='oblique', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/freefont/FreeMonoBoldOblique.ttf', name='FreeMono', style='oblique', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/fonts-gujr-extra/Rekha.ttf', name='Rekha', style='normal', variant='normal', weight=500, stretch='normal', size='scalable')) = 10.145\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/Gubbi/Gubbi.ttf', name='Gubbi', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/Purisa-Oblique.ttf', name='Purisa', style='oblique', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/ubuntu/Ubuntu-BI.ttf', name='Ubuntu', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/Norasi.ttf', name='Norasi', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/Nakula/nakula.ttf', name='Nakula', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/fonts-beng-extra/mitra.ttf', name='Mitra Mono', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/samyak-fonts/Samyak-Gujarati.ttf', name='Samyak Gujarati', style='normal', variant='normal', weight=500, stretch='normal', size='scalable')) = 10.145\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/ubuntu/Ubuntu-Th.ttf', name='Ubuntu', style='normal', variant='normal', weight=250, stretch='normal', size='scalable')) = 10.1925\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/kacst-one/KacstOne-Bold.ttf', name='KacstOne', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/dejavu/DejaVuSans.ttf', name='DejaVu Sans', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 0.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/liberation2/LiberationSans-Italic.ttf', name='Liberation Sans', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/fonts-yrsa-rasa/Rasa-Medium.ttf', name='Rasa', style='normal', variant='normal', weight=500, stretch='normal', size='scalable')) = 10.145\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/dejavu/DejaVuSansMono.ttf', name='DejaVu Sans Mono', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/noto/NotoMono-Regular.ttf', name='Noto Mono', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/freefont/FreeSansOblique.ttf', name='FreeSans', style='oblique', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/liberation/LiberationSans-Regular.ttf', name='Liberation Sans', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/Sawasdee-BoldOblique.ttf', name='Sawasdee', style='oblique', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/fonts-beng-extra/MuktiNarrow.ttf', name='Mukti Narrow', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/kacst/KacstNaskh.ttf', name='KacstNaskh', style='normal', variant='normal', weight=500, stretch='normal', size='scalable')) = 10.145\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/opentype/malayalam/Manjari-Thin.otf', name='Manjari', style='normal', variant='normal', weight=100, stretch='normal', size='scalable')) = 10.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/opentype/noto/NotoSansCJK-Regular.ttc', name='Noto Sans CJK JP', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/malayalam/Uroob-Regular.ttf', name='Uroob', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/ubuntu/UbuntuMono-BI.ttf', name='Ubuntu Mono', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/fonts-yrsa-rasa/Yrsa-Regular.ttf', name='Yrsa', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/dejavu/DejaVuSansMono-Oblique.ttf', name='DejaVu Sans Mono', style='oblique', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/ubuntu/Ubuntu-RI.ttf', name='Ubuntu', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/TlwgTypo.ttf', name='Tlwg Typo', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/liberation/LiberationMono-BoldItalic.ttf', name='Liberation Mono', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/TlwgTypo-BoldOblique.ttf', name='Tlwg Typo', style='oblique', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/dejavu/DejaVuSansMono-BoldOblique.ttf', name='DejaVu Sans Mono', style='oblique', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/Garuda.ttf', name='Garuda', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/freefont/FreeMono.ttf', name='FreeMono', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/opentype/malayalam/Gayathri-Regular.otf', name='Gayathri', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/TlwgTypewriter.ttf', name='Tlwg Typewriter', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/liberation2/LiberationSerif-BoldItalic.ttf', name='Liberation Serif', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/opentype/urw-base35/NimbusRoman-Regular.otf', name='Nimbus Roman', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/liberation/LiberationSerif-Bold.ttf', name='Liberation Serif', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/openoffice/opens___.ttf', name='OpenSymbol', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/liberation2/LiberationSerif-Bold.ttf', name='Liberation Serif', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/Laksaman-Bold.ttf', name='Laksaman', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/abyssinica/AbyssinicaSIL-Regular.ttf', name='Abyssinica SIL', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/fonts-telu-extra/vemana2000.ttf', name='Vemana2000', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/lohit-punjabi/Lohit-Gurmukhi.ttf', name='Lohit Gurmukhi', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/opentype/urw-base35/NimbusSansNarrow-Bold.otf', name='Nimbus Sans Narrow', style='normal', variant='normal', weight=700, stretch='condensed', size='scalable')) = 10.535\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/Laksaman-BoldItalic.ttf', name='Laksaman', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/opentype/urw-base35/NimbusMonoPS-Bold.otf', name='Nimbus Mono PS', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/fonts-beng-extra/ani.ttf', name='Ani', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/malayalam/AnjaliOldLipi-Regular.ttf', name='AnjaliOldLipi', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/lohit-kannada/Lohit-Kannada.ttf', name='Lohit Kannada', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/droid/DroidSansFallbackFull.ttf', name='Droid Sans Fallback', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/opentype/urw-base35/StandardSymbolsPS.otf', name='Standard Symbols PS', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/liberation/LiberationMono-Bold.ttf', name='Liberation Mono', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/liberation/LiberationSerif-BoldItalic.ttf', name='Liberation Serif', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/opentype/urw-base35/Z003-MediumItalic.otf', name='Z003', style='italic', variant='normal', weight=500, stretch='normal', size='scalable')) = 11.145\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/opentype/urw-base35/NimbusMonoPS-BoldItalic.otf', name='Nimbus Mono PS', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/opentype/urw-base35/NimbusRoman-BoldItalic.otf', name='Nimbus Roman', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/freefont/FreeSerif.ttf', name='FreeSerif', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/opentype/urw-base35/NimbusSans-Italic.otf', name='Nimbus Sans', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/fonts-yrsa-rasa/Yrsa-Light.ttf', name='Yrsa', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/opentype/urw-base35/P052-Roman.otf', name='P052', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/opentype/malayalam/Manjari-Bold.otf', name='Manjari', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/dejavu/DejaVuSans-Bold.ttf', name='DejaVu Sans', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 0.33499999999999996\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/lohit-bengali/Lohit-Bengali.ttf', name='Lohit Bengali', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/fonts-deva-extra/samanata.ttf', name='Samanata', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/opentype/urw-base35/P052-Italic.otf', name='P052', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/Gargi/Gargi.ttf', name='Gargi', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/Kinnari-Bold.ttf', name='Kinnari', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/fonts-beng-extra/JamrulNormal.ttf', name='Jamrul', style='normal', variant='normal', weight=500, stretch='normal', size='scalable')) = 10.145\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/fonts-gujr-extra/padmaa.ttf', name='padmaa', style='normal', variant='normal', weight=500, stretch='normal', size='scalable')) = 10.145\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/Norasi-Italic.ttf', name='Norasi', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/padauk/PadaukBook-Bold.ttf', name='Padauk Book', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/fonts-gujr-extra/padmaa-Medium-0.5.ttf', name='padmaa', style='normal', variant='normal', weight=500, stretch='normal', size='scalable')) = 10.145\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/TlwgMono-Oblique.ttf', name='Tlwg Mono', style='oblique', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/TlwgTypewriter-Oblique.ttf', name='Tlwg Typewriter', style='oblique', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/liberation/LiberationMono-Regular.ttf', name='Liberation Mono', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/padauk/Padauk-Regular.ttf', name='Padauk', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/fonts-yrsa-rasa/Rasa-Light.ttf', name='Rasa', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/dejavu/DejaVuSans-BoldOblique.ttf', name='DejaVu Sans', style='oblique', variant='normal', weight=700, stretch='normal', size='scalable')) = 1.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/fonts-guru-extra/Saab.ttf', name='Saab', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/kacst/KacstDigital.ttf', name='KacstDigital', style='normal', variant='normal', weight=500, stretch='normal', size='scalable')) = 10.145\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/freefont/FreeMonoOblique.ttf', name='FreeMono', style='oblique', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/fonts-yrsa-rasa/Rasa-Bold.ttf', name='Rasa', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/malayalam/Rachana-Regular.ttf', name='Rachana', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/Waree-Bold.ttf', name='Waree', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/opentype/urw-base35/NimbusSansNarrow-Regular.otf', name='Nimbus Sans Narrow', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/liberation/LiberationSansNarrow-Italic.ttf', name='Liberation Sans Narrow', style='italic', variant='normal', weight=400, stretch='condensed', size='scalable')) = 11.25\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/opentype/urw-base35/URWBookman-DemiItalic.otf', name='URW Bookman', style='italic', variant='normal', weight=600, stretch='normal', size='scalable')) = 11.24\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/TlwgTypist-BoldOblique.ttf', name='Tlwg Typist', style='oblique', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/Navilu/Navilu.ttf', name='Navilu', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/ubuntu/Ubuntu-R.ttf', name='Ubuntu', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/dejavu/DejaVuSerifCondensed.ttf', name='DejaVu Serif', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/freefont/FreeSerifBoldItalic.ttf', name='FreeSerif', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/opentype/urw-base35/NimbusMonoPS-Regular.otf', name='Nimbus Mono PS', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/liberation2/LiberationMono-BoldItalic.ttf', name='Liberation Mono', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/liberation2/LiberationMono-Bold.ttf', name='Liberation Mono', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/ubuntu/UbuntuMono-R.ttf', name='Ubuntu Mono', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/Sawasdee-Oblique.ttf', name='Sawasdee', style='oblique', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/dejavu/DejaVuMathTeXGyre.ttf', name='DejaVu Math TeX Gyre', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/fonts-kalapi/Kalapi.ttf', name='Kalapi', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/fonts-yrsa-rasa/Yrsa-Medium.ttf', name='Yrsa', style='normal', variant='normal', weight=500, stretch='normal', size='scalable')) = 10.145\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/Umpush-Bold.ttf', name='Umpush', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/Umpush-Oblique.ttf', name='Umpush', style='oblique', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/dejavu/DejaVuSansCondensed.ttf', name='DejaVu Sans', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 0.25\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/ubuntu/Ubuntu-MI.ttf', name='Ubuntu', style='italic', variant='normal', weight=500, stretch='normal', size='scalable')) = 11.145\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/ttf-khmeros-core/KhmerOSsys.ttf', name='Khmer OS System', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/Kinnari-Oblique.ttf', name='Kinnari', style='oblique', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/fonts-yrsa-rasa/Yrsa-Bold.ttf', name='Yrsa', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/opentype/malayalam/Gayathri-Bold.otf', name='Gayathri', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/liberation2/LiberationSans-Regular.ttf', name='Liberation Sans', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/opentype/urw-base35/URWBookman-LightItalic.otf', name='URW Bookman', style='italic', variant='normal', weight=300, stretch='normal', size='scalable')) = 11.145\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/kacst/KacstPen.ttf', name='KacstPen', style='normal', variant='normal', weight=500, stretch='normal', size='scalable')) = 10.145\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/Waree-Oblique.ttf', name='Waree', style='oblique', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/liberation/LiberationSansNarrow-Regular.ttf', name='Liberation Sans Narrow', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/opentype/malayalam/Gayathri-Thin.otf', name='Gayathri', style='normal', variant='normal', weight=100, stretch='normal', size='scalable')) = 10.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/Kinnari-BoldOblique.ttf', name='Kinnari', style='oblique', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/liberation/LiberationSerif-Italic.ttf', name='Liberation Serif', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/malayalam/Meera-Regular.ttf', name='Meera', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/malayalam/Dyuthi-Regular.ttf', name='Dyuthi', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/liberation/LiberationMono-Italic.ttf', name='Liberation Mono', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/kacst/KacstArt.ttf', name='KacstArt', style='normal', variant='normal', weight=500, stretch='normal', size='scalable')) = 10.145\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tibetan-machine/TibetanMachineUni.ttf', name='Tibetan Machine Uni', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/kacst/KacstFarsi.ttf', name='KacstFarsi', style='normal', variant='normal', weight=500, stretch='normal', size='scalable')) = 10.145\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/opentype/malayalam/Chilanka-Regular.otf', name='Chilanka', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/liberation/LiberationSerif-Regular.ttf', name='Liberation Serif', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/fonts-orya-extra/utkal.ttf', name='ori1Uni', style='normal', variant='normal', weight=500, stretch='normal', size='scalable')) = 10.145\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/lao/Phetsarath_OT.ttf', name='Phetsarath OT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/liberation/LiberationSans-Bold.ttf', name='Liberation Sans', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/liberation2/LiberationSerif-Italic.ttf', name='Liberation Serif', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/fonts-yrsa-rasa/Rasa-Regular.ttf', name='Rasa', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/fonts-gujr-extra/padmaa-Bold.1.1.ttf', name='padmaa-Bold.1.1', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/Umpush-Light.ttf', name='Umpush', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/opentype/urw-base35/P052-BoldItalic.otf', name='P052', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/TlwgTypist-Bold.ttf', name='Tlwg Typist', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/kacst/KacstOffice.ttf', name='KacstOffice', style='normal', variant='normal', weight=500, stretch='normal', size='scalable')) = 10.145\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/opentype/urw-base35/NimbusSans-BoldItalic.otf', name='Nimbus Sans', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/kacst/mry_KacstQurn.ttf', name='mry_KacstQurn', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/liberation/LiberationSans-Italic.ttf', name='Liberation Sans', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/lohit-telugu/Lohit-Telugu.ttf', name='Lohit Telugu', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/dejavu/DejaVuSerif-Italic.ttf', name='DejaVu Serif', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/kacst/KacstTitle.ttf', name='KacstTitle', style='normal', variant='normal', weight=500, stretch='normal', size='scalable')) = 10.145\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/malayalam/Rachana-Bold.ttf', name='Rachana', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/dejavu/DejaVuSerif-Bold.ttf', name='DejaVu Serif', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/samyak-fonts/Samyak-Malayalam.ttf', name='Samyak Malayalam', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/dejavu/DejaVuSansMono-Bold.ttf', name='DejaVu Sans Mono', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/freefont/FreeSans.ttf', name='FreeSans', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/opentype/urw-base35/NimbusSansNarrow-BoldOblique.otf', name='Nimbus Sans Narrow', style='oblique', variant='normal', weight=700, stretch='condensed', size='scalable')) = 11.535\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/Laksaman.ttf', name='Laksaman', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/Purisa-Bold.ttf', name='Purisa', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/sinhala/lklug.ttf', name='LKLUG', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/dejavu/DejaVuSerifCondensed-Bold.ttf', name='DejaVu Serif', style='normal', variant='normal', weight=700, stretch='condensed', size='scalable')) = 10.535\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/dejavu/DejaVuSerif.ttf', name='DejaVu Serif', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/kacst/KacstScreen.ttf', name='KacstScreen', style='normal', variant='normal', weight=500, stretch='normal', size='scalable')) = 10.145\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/liberation2/LiberationSans-Bold.ttf', name='Liberation Sans', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/samyak/Samyak-Devanagari.ttf', name='Samyak Devanagari', style='normal', variant='normal', weight=500, stretch='normal', size='scalable')) = 10.145\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/fonts-yrsa-rasa/Rasa-SemiBold.ttf', name='Rasa', style='normal', variant='normal', weight=600, stretch='normal', size='scalable')) = 10.24\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/Loma.ttf', name='Loma', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/opentype/urw-base35/C059-Roman.otf', name='C059', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/padauk/PadaukBook-Regular.ttf', name='Padauk Book', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/dejavu/DejaVuSansCondensed-Oblique.ttf', name='DejaVu Sans', style='oblique', variant='normal', weight=400, stretch='condensed', size='scalable')) = 1.25\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/lohit-tamil/Lohit-Tamil.ttf', name='Lohit Tamil', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/Loma-Bold.ttf', name='Loma', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/liberation/LiberationSansNarrow-Bold.ttf', name='Liberation Sans Narrow', style='normal', variant='normal', weight=700, stretch='condensed', size='scalable')) = 10.535\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/Umpush-LightOblique.ttf', name='Umpush', style='oblique', variant='normal', weight=300, stretch='normal', size='scalable')) = 11.145\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/opentype/malayalam/Manjari-Regular.otf', name='Manjari', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/opentype/urw-base35/NimbusRoman-Bold.otf', name='Nimbus Roman', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/Sawasdee-Bold.ttf', name='Sawasdee', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/fonts-yrsa-rasa/Yrsa-SemiBold.ttf', name='Yrsa', style='normal', variant='normal', weight=600, stretch='normal', size='scalable')) = 10.24\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/Kinnari-BoldItalic.ttf', name='Kinnari', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/opentype/urw-base35/NimbusMonoPS-Italic.otf', name='Nimbus Mono PS', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/Garuda-Bold.ttf', name='Garuda', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/fonts-beng-extra/MuktiNarrowBold.ttf', name='Mukti Narrow', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/opentype/urw-base35/NimbusRoman-Italic.otf', name='Nimbus Roman', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/ubuntu/UbuntuMono-B.ttf', name='Ubuntu Mono', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/opentype/noto/NotoSerifCJK-Bold.ttc', name='Noto Serif CJK JP', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/opentype/urw-base35/NimbusSans-Bold.otf', name='Nimbus Sans', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/Umpush.ttf', name='Umpush', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/ubuntu/Ubuntu-M.ttf', name='Ubuntu', style='normal', variant='normal', weight=500, stretch='normal', size='scalable')) = 10.145\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/ubuntu/Ubuntu-C.ttf', name='Ubuntu Condensed', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/fonts-telu-extra/Pothana2000.ttf', name='Pothana2000', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/Purisa.ttf', name='Purisa', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/freefont/FreeSansBold.ttf', name='FreeSans', style='normal', variant='normal', weight=600, stretch='normal', size='scalable')) = 10.24\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/Sarai/Sarai.ttf', name='Sarai', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/opentype/noto/NotoSerifCJK-Regular.ttc', name='Noto Serif CJK JP', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/ubuntu/UbuntuMono-RI.ttf', name='Ubuntu Mono', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/kacst/KacstDecorative.ttf', name='KacstDecorative', style='normal', variant='normal', weight=500, stretch='normal', size='scalable')) = 10.145\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/kacst/KacstTitleL.ttf', name='KacstTitleL', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/TlwgTypist.ttf', name='Tlwg Typist', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/samyak-fonts/Samyak-Tamil.ttf', name='Samyak Tamil', style='normal', variant='normal', weight=500, stretch='normal', size='scalable')) = 10.145\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/Garuda-BoldOblique.ttf', name='Garuda', style='oblique', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/dejavu/DejaVuSerifCondensed-Italic.ttf', name='DejaVu Serif', style='italic', variant='normal', weight=400, stretch='condensed', size='scalable')) = 11.25\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/ubuntu/Ubuntu-L.ttf', name='Ubuntu', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/kacst/KacstLetter.ttf', name='KacstLetter', style='normal', variant='normal', weight=500, stretch='normal', size='scalable')) = 10.145\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/liberation2/LiberationMono-Regular.ttf', name='Liberation Mono', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/home/richard/miniconda3/envs/openfe/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n", + "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/home/richard/miniconda3/envs/openfe/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf', name='DejaVu Sans', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 0.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/home/richard/miniconda3/envs/openfe/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/STIXGeneralItalic.ttf', name='STIXGeneral', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/home/richard/miniconda3/envs/openfe/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/cmtt10.ttf', name='cmtt10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/home/richard/miniconda3/envs/openfe/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/STIXSizTwoSymBol.ttf', name='STIXSizeTwoSym', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/home/richard/miniconda3/envs/openfe/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/STIXSizFourSymBol.ttf', name='STIXSizeFourSym', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/home/richard/miniconda3/envs/openfe/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/cmex10.ttf', name='cmex10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/home/richard/miniconda3/envs/openfe/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/STIXNonUniBol.ttf', name='STIXNonUnicode', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/home/richard/miniconda3/envs/openfe/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/STIXNonUniBolIta.ttf', name='STIXNonUnicode', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/home/richard/miniconda3/envs/openfe/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSerifDisplay.ttf', name='DejaVu Serif Display', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/home/richard/miniconda3/envs/openfe/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/cmmi10.ttf', name='cmmi10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/home/richard/miniconda3/envs/openfe/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSansMono-BoldOblique.ttf', name='DejaVu Sans Mono', style='oblique', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/home/richard/miniconda3/envs/openfe/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSerif-BoldItalic.ttf', name='DejaVu Serif', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/home/richard/miniconda3/envs/openfe/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/STIXGeneralBolIta.ttf', name='STIXGeneral', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/home/richard/miniconda3/envs/openfe/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/cmss10.ttf', name='cmss10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/home/richard/miniconda3/envs/openfe/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSerif-Italic.ttf', name='DejaVu Serif', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/home/richard/miniconda3/envs/openfe/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSansMono-Oblique.ttf', name='DejaVu Sans Mono', style='oblique', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/home/richard/miniconda3/envs/openfe/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSerif.ttf', name='DejaVu Serif', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/home/richard/miniconda3/envs/openfe/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans-Oblique.ttf', name='DejaVu Sans', style='oblique', variant='normal', weight=400, stretch='normal', size='scalable')) = 1.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/home/richard/miniconda3/envs/openfe/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/STIXGeneral.ttf', name='STIXGeneral', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/home/richard/miniconda3/envs/openfe/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/STIXSizOneSymReg.ttf', name='STIXSizeOneSym', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/home/richard/miniconda3/envs/openfe/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/STIXSizFourSymReg.ttf', name='STIXSizeFourSym', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/home/richard/miniconda3/envs/openfe/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/STIXGeneralBol.ttf', name='STIXGeneral', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/home/richard/miniconda3/envs/openfe/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/cmr10.ttf', name='cmr10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/home/richard/miniconda3/envs/openfe/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSansMono.ttf', name='DejaVu Sans Mono', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/home/richard/miniconda3/envs/openfe/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSerif-Bold.ttf', name='DejaVu Serif', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/home/richard/miniconda3/envs/openfe/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSansDisplay.ttf', name='DejaVu Sans Display', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/home/richard/miniconda3/envs/openfe/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/STIXNonUniIta.ttf', name='STIXNonUnicode', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/home/richard/miniconda3/envs/openfe/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans-Bold.ttf', name='DejaVu Sans', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 0.33499999999999996\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/home/richard/miniconda3/envs/openfe/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/STIXNonUni.ttf', name='STIXNonUnicode', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/home/richard/miniconda3/envs/openfe/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/STIXSizThreeSymBol.ttf', name='STIXSizeThreeSym', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/home/richard/miniconda3/envs/openfe/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/STIXSizThreeSymReg.ttf', name='STIXSizeThreeSym', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/home/richard/miniconda3/envs/openfe/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/cmb10.ttf', name='cmb10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/home/richard/miniconda3/envs/openfe/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/STIXSizOneSymBol.ttf', name='STIXSizeOneSym', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/home/richard/miniconda3/envs/openfe/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/cmsy10.ttf', name='cmsy10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/home/richard/miniconda3/envs/openfe/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/STIXSizFiveSymReg.ttf', name='STIXSizeFiveSym', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/home/richard/miniconda3/envs/openfe/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSansMono-Bold.ttf', name='DejaVu Sans Mono', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/home/richard/miniconda3/envs/openfe/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans-BoldOblique.ttf', name='DejaVu Sans', style='oblique', variant='normal', weight=700, stretch='normal', size='scalable')) = 1.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/home/richard/miniconda3/envs/openfe/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/STIXSizTwoSymReg.ttf', name='STIXSizeTwoSym', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/opentype/urw-base35/URWGothic-Demi.otf', name='URW Gothic', style='normal', variant='normal', weight=600, stretch='normal', size='scalable')) = 10.24\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/freefont/FreeSansBoldOblique.ttf', name='FreeSans', style='oblique', variant='normal', weight=600, stretch='normal', size='scalable')) = 11.24\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/kacst/KacstQurn.ttf', name='KacstQurn', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/opentype/urw-base35/C059-Italic.otf', name='C059', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/Sawasdee.ttf', name='Sawasdee', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/TlwgMono-Bold.ttf', name='Tlwg Mono', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/Laksaman-Italic.ttf', name='Laksaman', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/TlwgTypewriter-Bold.ttf', name='Tlwg Typewriter', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/kacst-one/KacstOne.ttf', name='KacstOne', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/TlwgMono.ttf', name='Tlwg Mono', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/opentype/urw-base35/URWGothic-BookOblique.otf', name='URW Gothic', style='oblique', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/Purisa-BoldOblique.ttf', name='Purisa', style='oblique', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/lohit-tamil-classical/Lohit-Tamil-Classical.ttf', name='Lohit Tamil Classical', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/malayalam/Suruma.ttf', name='Suruma', style='normal', variant='normal', weight=500, stretch='normal', size='scalable')) = 10.145\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/opentype/urw-base35/NimbusSansNarrow-Oblique.otf', name='Nimbus Sans Narrow', style='oblique', variant='normal', weight=400, stretch='condensed', size='scalable')) = 11.25\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/Norasi-Bold.ttf', name='Norasi', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/Norasi-BoldOblique.ttf', name='Norasi', style='oblique', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/dejavu/DejaVuSansCondensed-BoldOblique.ttf', name='DejaVu Sans', style='oblique', variant='normal', weight=700, stretch='condensed', size='scalable')) = 1.535\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/fonts-deva-extra/chandas1-2.ttf', name='Chandas', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/TlwgTypist-Oblique.ttf', name='Tlwg Typist', style='oblique', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/lohit-gujarati/Lohit-Gujarati.ttf', name='Lohit Gujarati', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/opentype/urw-base35/URWGothic-DemiOblique.otf', name='URW Gothic', style='oblique', variant='normal', weight=600, stretch='normal', size='scalable')) = 11.24\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/fonts-beng-extra/LikhanNormal.ttf', name='Likhan', style='normal', variant='normal', weight=500, stretch='normal', size='scalable')) = 10.145\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/freefont/FreeSerifBold.ttf', name='FreeSerif', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/Umpush-BoldOblique.ttf', name='Umpush', style='oblique', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/liberation2/LiberationSans-BoldItalic.ttf', name='Liberation Sans', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/lohit-oriya/Lohit-Odia.ttf', name='Lohit Odia', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/Loma-BoldOblique.ttf', name='Loma', style='oblique', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/dejavu/DejaVuSerif-BoldItalic.ttf', name='DejaVu Serif', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/opentype/urw-base35/URWBookman-Demi.otf', name='URW Bookman', style='normal', variant='normal', weight=600, stretch='normal', size='scalable')) = 10.24\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/ubuntu/Ubuntu-LI.ttf', name='Ubuntu', style='italic', variant='normal', weight=300, stretch='normal', size='scalable')) = 11.145\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/Kinnari-Italic.ttf', name='Kinnari', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/dejavu/DejaVuSansCondensed-Bold.ttf', name='DejaVu Sans', style='normal', variant='normal', weight=700, stretch='condensed', size='scalable')) = 0.5349999999999999\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/liberation/LiberationSansNarrow-BoldItalic.ttf', name='Liberation Sans Narrow', style='italic', variant='normal', weight=700, stretch='condensed', size='scalable')) = 11.535\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/lohit-assamese/Lohit-Assamese.ttf', name='Lohit Assamese', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/opentype/urw-base35/NimbusSans-Regular.otf', name='Nimbus Sans', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/Kinnari.ttf', name='Kinnari', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/padauk/Padauk-Bold.ttf', name='Padauk', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/Garuda-Oblique.ttf', name='Garuda', style='oblique', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/opentype/urw-base35/URWBookman-Light.otf', name='URW Bookman', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/pagul/Pagul.ttf', name='Pagul', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/TlwgTypo-Bold.ttf', name='Tlwg Typo', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/freefont/FreeMonoBold.ttf', name='FreeMono', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/freefont/FreeSerifItalic.ttf', name='FreeSerif', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/liberation2/LiberationMono-Italic.ttf', name='Liberation Mono', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/Sahadeva/sahadeva.ttf', name='Sahadeva', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/lohit-malayalam/Lohit-Malayalam.ttf', name='Lohit Malayalam', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/malayalam/RaghuMalayalamSans-Regular.ttf', name='RaghuMalayalamSans', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/fonts-deva-extra/kalimati.ttf', name='Kalimati', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/Waree-BoldOblique.ttf', name='Waree', style='oblique', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/opentype/urw-base35/URWGothic-Book.otf', name='URW Gothic', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/TlwgTypo-Oblique.ttf', name='Tlwg Typo', style='oblique', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/TlwgTypewriter-BoldOblique.ttf', name='Tlwg Typewriter', style='oblique', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/dejavu/DejaVuSans-ExtraLight.ttf', name='DejaVu Sans', style='normal', variant='normal', weight=200, stretch='normal', size='scalable')) = 0.24\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/fonts-gujr-extra/aakar-medium.ttf', name='aakar', style='normal', variant='normal', weight=500, stretch='normal', size='scalable')) = 10.145\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/kacst/KacstPoster.ttf', name='KacstPoster', style='normal', variant='normal', weight=500, stretch='normal', size='scalable')) = 10.145\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/Waree.ttf', name='Waree', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/dejavu/DejaVuSans-Oblique.ttf', name='DejaVu Sans', style='oblique', variant='normal', weight=400, stretch='normal', size='scalable')) = 1.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/kacst/KacstBook.ttf', name='KacstBook', style='normal', variant='normal', weight=500, stretch='normal', size='scalable')) = 10.145\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/opentype/urw-base35/P052-Bold.otf', name='P052', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/malayalam/Karumbi-Regular.ttf', name='Karumbi', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/TlwgMono-BoldOblique.ttf', name='Tlwg Mono', style='oblique', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/ubuntu/Ubuntu-B.ttf', name='Ubuntu', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/dejavu/DejaVuSerifCondensed-BoldItalic.ttf', name='DejaVu Serif', style='italic', variant='normal', weight=700, stretch='condensed', size='scalable')) = 11.535\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/liberation2/LiberationSerif-Regular.ttf', name='Liberation Serif', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/opentype/urw-base35/D050000L.otf', name='D050000L', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/lohit-devanagari/Lohit-Devanagari.ttf', name='Lohit Devanagari', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/Norasi-Oblique.ttf', name='Norasi', style='oblique', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/malayalam/Keraleeyam-Regular.ttf', name='Keraleeyam', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/ttf-khmeros-core/KhmerOS.ttf', name='Khmer OS', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/opentype/urw-base35/C059-BdIta.otf', name='C059', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/opentype/noto/NotoSansCJK-Bold.ttc', name='Noto Sans CJK JP', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/opentype/urw-base35/C059-Bold.otf', name='C059', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/Norasi-BoldItalic.ttf', name='Norasi', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/liberation/LiberationSans-BoldItalic.ttf', name='Liberation Sans', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/Loma-Oblique.ttf', name='Loma', style='oblique', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/freefont/FreeMonoBoldOblique.ttf', name='FreeMono', style='oblique', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/fonts-gujr-extra/Rekha.ttf', name='Rekha', style='normal', variant='normal', weight=500, stretch='normal', size='scalable')) = 10.145\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/Gubbi/Gubbi.ttf', name='Gubbi', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/Purisa-Oblique.ttf', name='Purisa', style='oblique', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/ubuntu/Ubuntu-BI.ttf', name='Ubuntu', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/Norasi.ttf', name='Norasi', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/Nakula/nakula.ttf', name='Nakula', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/fonts-beng-extra/mitra.ttf', name='Mitra Mono', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/samyak-fonts/Samyak-Gujarati.ttf', name='Samyak Gujarati', style='normal', variant='normal', weight=500, stretch='normal', size='scalable')) = 10.145\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/ubuntu/Ubuntu-Th.ttf', name='Ubuntu', style='normal', variant='normal', weight=250, stretch='normal', size='scalable')) = 10.1925\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/kacst-one/KacstOne-Bold.ttf', name='KacstOne', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/dejavu/DejaVuSans.ttf', name='DejaVu Sans', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 0.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/liberation2/LiberationSans-Italic.ttf', name='Liberation Sans', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/fonts-yrsa-rasa/Rasa-Medium.ttf', name='Rasa', style='normal', variant='normal', weight=500, stretch='normal', size='scalable')) = 10.145\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/dejavu/DejaVuSansMono.ttf', name='DejaVu Sans Mono', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/noto/NotoMono-Regular.ttf', name='Noto Mono', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/freefont/FreeSansOblique.ttf', name='FreeSans', style='oblique', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/liberation/LiberationSans-Regular.ttf', name='Liberation Sans', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/Sawasdee-BoldOblique.ttf', name='Sawasdee', style='oblique', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/fonts-beng-extra/MuktiNarrow.ttf', name='Mukti Narrow', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/kacst/KacstNaskh.ttf', name='KacstNaskh', style='normal', variant='normal', weight=500, stretch='normal', size='scalable')) = 10.145\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/opentype/malayalam/Manjari-Thin.otf', name='Manjari', style='normal', variant='normal', weight=100, stretch='normal', size='scalable')) = 10.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/opentype/noto/NotoSansCJK-Regular.ttc', name='Noto Sans CJK JP', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/malayalam/Uroob-Regular.ttf', name='Uroob', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/ubuntu/UbuntuMono-BI.ttf', name='Ubuntu Mono', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/fonts-yrsa-rasa/Yrsa-Regular.ttf', name='Yrsa', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/dejavu/DejaVuSansMono-Oblique.ttf', name='DejaVu Sans Mono', style='oblique', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/ubuntu/Ubuntu-RI.ttf', name='Ubuntu', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/TlwgTypo.ttf', name='Tlwg Typo', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/liberation/LiberationMono-BoldItalic.ttf', name='Liberation Mono', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/TlwgTypo-BoldOblique.ttf', name='Tlwg Typo', style='oblique', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/dejavu/DejaVuSansMono-BoldOblique.ttf', name='DejaVu Sans Mono', style='oblique', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/Garuda.ttf', name='Garuda', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/freefont/FreeMono.ttf', name='FreeMono', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/opentype/malayalam/Gayathri-Regular.otf', name='Gayathri', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/TlwgTypewriter.ttf', name='Tlwg Typewriter', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/liberation2/LiberationSerif-BoldItalic.ttf', name='Liberation Serif', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/opentype/urw-base35/NimbusRoman-Regular.otf', name='Nimbus Roman', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/liberation/LiberationSerif-Bold.ttf', name='Liberation Serif', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/openoffice/opens___.ttf', name='OpenSymbol', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/liberation2/LiberationSerif-Bold.ttf', name='Liberation Serif', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/Laksaman-Bold.ttf', name='Laksaman', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/abyssinica/AbyssinicaSIL-Regular.ttf', name='Abyssinica SIL', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/fonts-telu-extra/vemana2000.ttf', name='Vemana2000', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/lohit-punjabi/Lohit-Gurmukhi.ttf', name='Lohit Gurmukhi', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/opentype/urw-base35/NimbusSansNarrow-Bold.otf', name='Nimbus Sans Narrow', style='normal', variant='normal', weight=700, stretch='condensed', size='scalable')) = 10.535\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/Laksaman-BoldItalic.ttf', name='Laksaman', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/opentype/urw-base35/NimbusMonoPS-Bold.otf', name='Nimbus Mono PS', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/fonts-beng-extra/ani.ttf', name='Ani', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/malayalam/AnjaliOldLipi-Regular.ttf', name='AnjaliOldLipi', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/lohit-kannada/Lohit-Kannada.ttf', name='Lohit Kannada', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/droid/DroidSansFallbackFull.ttf', name='Droid Sans Fallback', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/opentype/urw-base35/StandardSymbolsPS.otf', name='Standard Symbols PS', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/liberation/LiberationMono-Bold.ttf', name='Liberation Mono', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/liberation/LiberationSerif-BoldItalic.ttf', name='Liberation Serif', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/opentype/urw-base35/Z003-MediumItalic.otf', name='Z003', style='italic', variant='normal', weight=500, stretch='normal', size='scalable')) = 11.145\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/opentype/urw-base35/NimbusMonoPS-BoldItalic.otf', name='Nimbus Mono PS', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/opentype/urw-base35/NimbusRoman-BoldItalic.otf', name='Nimbus Roman', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/freefont/FreeSerif.ttf', name='FreeSerif', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/opentype/urw-base35/NimbusSans-Italic.otf', name='Nimbus Sans', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/fonts-yrsa-rasa/Yrsa-Light.ttf', name='Yrsa', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/opentype/urw-base35/P052-Roman.otf', name='P052', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/opentype/malayalam/Manjari-Bold.otf', name='Manjari', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/dejavu/DejaVuSans-Bold.ttf', name='DejaVu Sans', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 0.33499999999999996\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/lohit-bengali/Lohit-Bengali.ttf', name='Lohit Bengali', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/fonts-deva-extra/samanata.ttf', name='Samanata', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/opentype/urw-base35/P052-Italic.otf', name='P052', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/Gargi/Gargi.ttf', name='Gargi', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/Kinnari-Bold.ttf', name='Kinnari', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/fonts-beng-extra/JamrulNormal.ttf', name='Jamrul', style='normal', variant='normal', weight=500, stretch='normal', size='scalable')) = 10.145\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/fonts-gujr-extra/padmaa.ttf', name='padmaa', style='normal', variant='normal', weight=500, stretch='normal', size='scalable')) = 10.145\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/Norasi-Italic.ttf', name='Norasi', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/padauk/PadaukBook-Bold.ttf', name='Padauk Book', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/fonts-gujr-extra/padmaa-Medium-0.5.ttf', name='padmaa', style='normal', variant='normal', weight=500, stretch='normal', size='scalable')) = 10.145\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/TlwgMono-Oblique.ttf', name='Tlwg Mono', style='oblique', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/TlwgTypewriter-Oblique.ttf', name='Tlwg Typewriter', style='oblique', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/liberation/LiberationMono-Regular.ttf', name='Liberation Mono', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/padauk/Padauk-Regular.ttf', name='Padauk', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/fonts-yrsa-rasa/Rasa-Light.ttf', name='Rasa', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/dejavu/DejaVuSans-BoldOblique.ttf', name='DejaVu Sans', style='oblique', variant='normal', weight=700, stretch='normal', size='scalable')) = 1.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/fonts-guru-extra/Saab.ttf', name='Saab', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/kacst/KacstDigital.ttf', name='KacstDigital', style='normal', variant='normal', weight=500, stretch='normal', size='scalable')) = 10.145\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/freefont/FreeMonoOblique.ttf', name='FreeMono', style='oblique', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/fonts-yrsa-rasa/Rasa-Bold.ttf', name='Rasa', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/malayalam/Rachana-Regular.ttf', name='Rachana', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/Waree-Bold.ttf', name='Waree', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/opentype/urw-base35/NimbusSansNarrow-Regular.otf', name='Nimbus Sans Narrow', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/liberation/LiberationSansNarrow-Italic.ttf', name='Liberation Sans Narrow', style='italic', variant='normal', weight=400, stretch='condensed', size='scalable')) = 11.25\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/opentype/urw-base35/URWBookman-DemiItalic.otf', name='URW Bookman', style='italic', variant='normal', weight=600, stretch='normal', size='scalable')) = 11.24\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/TlwgTypist-BoldOblique.ttf', name='Tlwg Typist', style='oblique', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/Navilu/Navilu.ttf', name='Navilu', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/ubuntu/Ubuntu-R.ttf', name='Ubuntu', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/dejavu/DejaVuSerifCondensed.ttf', name='DejaVu Serif', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/freefont/FreeSerifBoldItalic.ttf', name='FreeSerif', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/opentype/urw-base35/NimbusMonoPS-Regular.otf', name='Nimbus Mono PS', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/liberation2/LiberationMono-BoldItalic.ttf', name='Liberation Mono', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/liberation2/LiberationMono-Bold.ttf', name='Liberation Mono', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/ubuntu/UbuntuMono-R.ttf', name='Ubuntu Mono', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/Sawasdee-Oblique.ttf', name='Sawasdee', style='oblique', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/dejavu/DejaVuMathTeXGyre.ttf', name='DejaVu Math TeX Gyre', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/fonts-kalapi/Kalapi.ttf', name='Kalapi', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/fonts-yrsa-rasa/Yrsa-Medium.ttf', name='Yrsa', style='normal', variant='normal', weight=500, stretch='normal', size='scalable')) = 10.145\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/Umpush-Bold.ttf', name='Umpush', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/Umpush-Oblique.ttf', name='Umpush', style='oblique', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/dejavu/DejaVuSansCondensed.ttf', name='DejaVu Sans', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 0.25\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/ubuntu/Ubuntu-MI.ttf', name='Ubuntu', style='italic', variant='normal', weight=500, stretch='normal', size='scalable')) = 11.145\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/ttf-khmeros-core/KhmerOSsys.ttf', name='Khmer OS System', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/Kinnari-Oblique.ttf', name='Kinnari', style='oblique', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/fonts-yrsa-rasa/Yrsa-Bold.ttf', name='Yrsa', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/opentype/malayalam/Gayathri-Bold.otf', name='Gayathri', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/liberation2/LiberationSans-Regular.ttf', name='Liberation Sans', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/opentype/urw-base35/URWBookman-LightItalic.otf', name='URW Bookman', style='italic', variant='normal', weight=300, stretch='normal', size='scalable')) = 11.145\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/kacst/KacstPen.ttf', name='KacstPen', style='normal', variant='normal', weight=500, stretch='normal', size='scalable')) = 10.145\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/Waree-Oblique.ttf', name='Waree', style='oblique', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/liberation/LiberationSansNarrow-Regular.ttf', name='Liberation Sans Narrow', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/opentype/malayalam/Gayathri-Thin.otf', name='Gayathri', style='normal', variant='normal', weight=100, stretch='normal', size='scalable')) = 10.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/Kinnari-BoldOblique.ttf', name='Kinnari', style='oblique', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/liberation/LiberationSerif-Italic.ttf', name='Liberation Serif', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/malayalam/Meera-Regular.ttf', name='Meera', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/malayalam/Dyuthi-Regular.ttf', name='Dyuthi', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/liberation/LiberationMono-Italic.ttf', name='Liberation Mono', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/kacst/KacstArt.ttf', name='KacstArt', style='normal', variant='normal', weight=500, stretch='normal', size='scalable')) = 10.145\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tibetan-machine/TibetanMachineUni.ttf', name='Tibetan Machine Uni', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/kacst/KacstFarsi.ttf', name='KacstFarsi', style='normal', variant='normal', weight=500, stretch='normal', size='scalable')) = 10.145\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/opentype/malayalam/Chilanka-Regular.otf', name='Chilanka', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/liberation/LiberationSerif-Regular.ttf', name='Liberation Serif', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/fonts-orya-extra/utkal.ttf', name='ori1Uni', style='normal', variant='normal', weight=500, stretch='normal', size='scalable')) = 10.145\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/lao/Phetsarath_OT.ttf', name='Phetsarath OT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/liberation/LiberationSans-Bold.ttf', name='Liberation Sans', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/liberation2/LiberationSerif-Italic.ttf', name='Liberation Serif', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/fonts-yrsa-rasa/Rasa-Regular.ttf', name='Rasa', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/fonts-gujr-extra/padmaa-Bold.1.1.ttf', name='padmaa-Bold.1.1', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/Umpush-Light.ttf', name='Umpush', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/opentype/urw-base35/P052-BoldItalic.otf', name='P052', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/TlwgTypist-Bold.ttf', name='Tlwg Typist', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/kacst/KacstOffice.ttf', name='KacstOffice', style='normal', variant='normal', weight=500, stretch='normal', size='scalable')) = 10.145\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/opentype/urw-base35/NimbusSans-BoldItalic.otf', name='Nimbus Sans', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/kacst/mry_KacstQurn.ttf', name='mry_KacstQurn', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/liberation/LiberationSans-Italic.ttf', name='Liberation Sans', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/lohit-telugu/Lohit-Telugu.ttf', name='Lohit Telugu', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/dejavu/DejaVuSerif-Italic.ttf', name='DejaVu Serif', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/kacst/KacstTitle.ttf', name='KacstTitle', style='normal', variant='normal', weight=500, stretch='normal', size='scalable')) = 10.145\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/malayalam/Rachana-Bold.ttf', name='Rachana', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/dejavu/DejaVuSerif-Bold.ttf', name='DejaVu Serif', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/samyak-fonts/Samyak-Malayalam.ttf', name='Samyak Malayalam', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/dejavu/DejaVuSansMono-Bold.ttf', name='DejaVu Sans Mono', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/freefont/FreeSans.ttf', name='FreeSans', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/opentype/urw-base35/NimbusSansNarrow-BoldOblique.otf', name='Nimbus Sans Narrow', style='oblique', variant='normal', weight=700, stretch='condensed', size='scalable')) = 11.535\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/Laksaman.ttf', name='Laksaman', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/Purisa-Bold.ttf', name='Purisa', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/sinhala/lklug.ttf', name='LKLUG', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/dejavu/DejaVuSerifCondensed-Bold.ttf', name='DejaVu Serif', style='normal', variant='normal', weight=700, stretch='condensed', size='scalable')) = 10.535\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/dejavu/DejaVuSerif.ttf', name='DejaVu Serif', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/kacst/KacstScreen.ttf', name='KacstScreen', style='normal', variant='normal', weight=500, stretch='normal', size='scalable')) = 10.145\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/liberation2/LiberationSans-Bold.ttf', name='Liberation Sans', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/samyak/Samyak-Devanagari.ttf', name='Samyak Devanagari', style='normal', variant='normal', weight=500, stretch='normal', size='scalable')) = 10.145\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/fonts-yrsa-rasa/Rasa-SemiBold.ttf', name='Rasa', style='normal', variant='normal', weight=600, stretch='normal', size='scalable')) = 10.24\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/Loma.ttf', name='Loma', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/opentype/urw-base35/C059-Roman.otf', name='C059', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/padauk/PadaukBook-Regular.ttf', name='Padauk Book', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/dejavu/DejaVuSansCondensed-Oblique.ttf', name='DejaVu Sans', style='oblique', variant='normal', weight=400, stretch='condensed', size='scalable')) = 1.25\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/lohit-tamil/Lohit-Tamil.ttf', name='Lohit Tamil', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/Loma-Bold.ttf', name='Loma', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/liberation/LiberationSansNarrow-Bold.ttf', name='Liberation Sans Narrow', style='normal', variant='normal', weight=700, stretch='condensed', size='scalable')) = 10.535\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/Umpush-LightOblique.ttf', name='Umpush', style='oblique', variant='normal', weight=300, stretch='normal', size='scalable')) = 11.145\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/opentype/malayalam/Manjari-Regular.otf', name='Manjari', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/opentype/urw-base35/NimbusRoman-Bold.otf', name='Nimbus Roman', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/Sawasdee-Bold.ttf', name='Sawasdee', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/fonts-yrsa-rasa/Yrsa-SemiBold.ttf', name='Yrsa', style='normal', variant='normal', weight=600, stretch='normal', size='scalable')) = 10.24\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/Kinnari-BoldItalic.ttf', name='Kinnari', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/opentype/urw-base35/NimbusMonoPS-Italic.otf', name='Nimbus Mono PS', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/Garuda-Bold.ttf', name='Garuda', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/fonts-beng-extra/MuktiNarrowBold.ttf', name='Mukti Narrow', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/opentype/urw-base35/NimbusRoman-Italic.otf', name='Nimbus Roman', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/ubuntu/UbuntuMono-B.ttf', name='Ubuntu Mono', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/opentype/noto/NotoSerifCJK-Bold.ttc', name='Noto Serif CJK JP', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/opentype/urw-base35/NimbusSans-Bold.otf', name='Nimbus Sans', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/Umpush.ttf', name='Umpush', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/ubuntu/Ubuntu-M.ttf', name='Ubuntu', style='normal', variant='normal', weight=500, stretch='normal', size='scalable')) = 10.145\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/ubuntu/Ubuntu-C.ttf', name='Ubuntu Condensed', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/fonts-telu-extra/Pothana2000.ttf', name='Pothana2000', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/Purisa.ttf', name='Purisa', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/freefont/FreeSansBold.ttf', name='FreeSans', style='normal', variant='normal', weight=600, stretch='normal', size='scalable')) = 10.24\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/Sarai/Sarai.ttf', name='Sarai', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/opentype/noto/NotoSerifCJK-Regular.ttc', name='Noto Serif CJK JP', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/ubuntu/UbuntuMono-RI.ttf', name='Ubuntu Mono', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/kacst/KacstDecorative.ttf', name='KacstDecorative', style='normal', variant='normal', weight=500, stretch='normal', size='scalable')) = 10.145\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/kacst/KacstTitleL.ttf', name='KacstTitleL', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/TlwgTypist.ttf', name='Tlwg Typist', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/samyak-fonts/Samyak-Tamil.ttf', name='Samyak Tamil', style='normal', variant='normal', weight=500, stretch='normal', size='scalable')) = 10.145\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/Garuda-BoldOblique.ttf', name='Garuda', style='oblique', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/dejavu/DejaVuSerifCondensed-Italic.ttf', name='DejaVu Serif', style='italic', variant='normal', weight=400, stretch='condensed', size='scalable')) = 11.25\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/ubuntu/Ubuntu-L.ttf', name='Ubuntu', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/kacst/KacstLetter.ttf', name='KacstLetter', style='normal', variant='normal', weight=500, stretch='normal', size='scalable')) = 10.145\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/liberation2/LiberationMono-Regular.ttf', name='Liberation Mono', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/home/richard/miniconda3/envs/openfe/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n" + ] }, { "data": { - "image/png": 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\n", 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v3x9hYWF4//33Rcf6l3379mHcuHHo27cv/Pz8RMfRWmygRERvIEkSYmJiYGRkhOzsbKxevRo7duxQy+H5559/wsbGBk2bNkVsbCyqVq0qOpLWYgMlInqNGzduYPz48YiNjUX37t0RGRmJDz/8UHSsl3rw4AGsra3x4MED7N69G/Xr1xcdSauxgRIRvUJKSgqMjIyQkJCAxYsXIzs7W22HpyRJGDt2LA4fPowNGzbA0NBQdCStxwFKRPQPd+/exbhx42Bubo4GDRogLy8P06dPh56enuhor7RkyRJs3LgRixYtgoWFheg4OoEDlIjoOdnZ2TA1NUVoaCimTZuGvLw8mJqaio71WjKZDLNmzcLw4cMxbdo00XF0BgcoERGAR48eYcqUKejduzcqVaqErKwsLFmyBNWrVxcd7bWOHz+OkSNHokOHDggNDeUbY6sQbyIiIp135MgRODo64ueff4aHhwd8fX01Ysm7GzduwNLSEvr6+khKSsI777wjOpJOYQMlIp1VXFyMhQsXokuXLrh58ya2bduGNWvWaMTwLCoqwtChQ3Hp0iUkJiaq5Z/UaDs2UCLSSb/++iucnZ1x4MABDB8+HKtWrcJ///tf0bHe2tdff409e/YgMjISXbt2FR1HJ7GBEpFOkcvlWLVqFdq3b49Tp05h48aNiI6O1qjhuXbtWgQEBODbb7+Fk5OT6Dg6iw2UiHTGhQsX4OLigrS0NAwaNAghISFo0qSJ6FilkpWVhYkTJ2LgwIFYsmSJ6Dg6jQ2UiLSeJElYv349jI2NsW/fPgQFBSElJUXjhufZs2dha2uLVq1aISYmRq3/LlUXcIASkVa7du0a7Ozs4OTkBGNjYxQUFMDNzU3j/tzj3r17sLS0RFFREWQyGerWrSs6ks7jACUirSWTyWBkZITk5GT4+PggIyMDrVq1Eh2r1ORyOZycnPDzzz8jNjYWbdq0ER2JwGugRKSF7ty5g0mTJiE8PBxmZmZIS0uDsbGx6FhKmzdvHhISErB8+XL0799fdBz6HzZQItIqGRkZMDExQWRkJGbNmoUDBw5o9PDcvHkz5s+fjzFjxuCrr74SHYeewwFKRFrh4cOHmDx5Mvr06YNq1aohNzcXCxcuRLVq1URHU9qRI0fg7OyM7t27Y82aNRp33Vbb8RQuEWm8/Px8ODk54cSJE5gwYQKWLl2KWrVqiY5VJoWFhbCyskL9+vWxZcsWtV+TVxexgRKRxioqKsK8efPQrVs33LlzB6mpqVi1apXGD8/Hjx9jyJAhuH79OpKSkvDee++JjkQvwQZKRBrpxIkTcHJyQn5+PkaNGgV/f3/Uq1dPdKwykyQJ48ePx969e7Fx40Z06NBBdCR6BTZQItIocrkcP/74Izp06IAzZ84gLi4O69ev14rhCQArV65EWFgYZs+eDXt7e9Fx6DXYQIlIY5w7dw6jR49GRkYGLCwsEBQUpFWnN3ft2oWvv/4aVlZWmDdvnug49AZsoESk9iRJQkREBIyNjXHo0CGEhoZq3bXBU6dOwd7eHgYGBli/fj0qV+aPZ3XHBkpEau3q1asYN24ckpKS0Lt3b0RERKB58+aiY5Wr27dvw9LSEpUrV4ZMJsN//vMf0ZHoLfBXHCJSWwkJCTA0NMSOHTvwww8/ID09XeuGZ0lJCUaMGIHTp08jLi4OLVq0EB2J3hIHKBGpnb/++gvOzs4YMmQIPvjgAxw+fBiTJ0/WytOaM2fOxLZt2+Dv749PP/1UdBwqBe37biQijfZ03doNGzZgzpw52L9/PwwMDETHqhBRUVHw8fGBp6cnPD09RcehUuI1UCJSCw8ePMD06dOxcuVKtG3bFvv27UPnzp1Fx6owBw8ehKurKz799FOsWLFCdBxSAhsoEQl38OBBtG/fHitXrsRXX32FI0eOaPXwvHTpEqytrdG4cWNs3rwZVatWFR2JlMABSkTCPHnyBHPmzMHHH3+Mhw8fYvfu3fjxxx/xzjvviI5WYR4+fAhra2vcuXMHMpkM7777ruhIpCSewiUiIX7++Wc4Ojr+/Y4jK1asQJ06dUTHqlCSJMHNzQ15eXlISEjQ6LdZIzZQIlKxkpIS/PDDD+jYsSP+/PNPJCQkICIiQuuHJwD4+vpiw4YNWLBgAaytrUXHoTJiAyUilTlz5gycnZ2RnZ0Na2trrF27Fg0bNhQdSyVSUlIwffp02NvbY9asWaLjUDlgAyWiCidJEkJCQmBiYoKCggJERkZiy5YtOjM8T5w4geHDh8PMzAxhYWF8Y2wtwQZKRBXqypUrcHV1RUpKCvr27Yvw8HB88MEHomOpzM2bN2FpaYl33nkHSUlJqFmzpuhIVE44QImowsTFxcHDwwP379/HihUrMHHiRK1cTehViouLYW9vj3PnziEjIwNNmzYVHYnKke58JxORyty6dQsjR47E0KFD0bJlSxw5cgReXl46NTwB4JtvvkFaWhoCAwPx8ccfi45D5Uy3vpuJqMKlpqbC2NgYsbGxmD9/Pvbu3YuPPvpIdCyVCwkJgb+/PyZNmgQXFxfRcagCcIASUbm4f/8+xo8fjwEDBqBOnTrYv38/vL29UaWK7l0pysnJwfjx49G/f3/4+vqKjkMVhAOUiMps3759MDMzQ2BgIL755hscOnQIHTt2FB1LiPPnz2PIkCFo3rw5Nm7cqJO/QOgKDlAiUtqTJ08wc+ZM9OzZE8XFxdizZw+WLVuGGjVqiI4mxP3792FlZYXHjx9DJpOhXr16oiNRBeKvRkSklGPHjsHJyQkFBQUYO3Ys/Pz8ULt2bdGxhJEkCaNHj0ZBQQFSUlJ08rqvrmEDJaJSKSkpwdKlS9G5c2dcuXIFMpkMISEhOj08AWDBggWIi4uDj48PBg0aJDoOqQAbKBG9td9//x3Ozs7Izc2Fra0tAgMD+W4iALZs2YLvvvsOjo6O+Oabb0THIRVhAyWiN5IkCYGBgTA1NcXx48cRFRWFzZs3c3gCKCgogKOjI7p27YqgoCAu06dD2ECJ6LUuXbqEsWPHYseOHfj8888RFhaG//u//xMdSy1cu3YNVlZWqFu3LhISEnT25ildxQFKRK+0ceNGjB8/Ho8ePUJAQAA8PT3ZsP7nyZMnsLOzQ2FhIbKystC4cWPRkUjFeAqXiP7lxo0bcHBwwPDhw9G2bVsUFBRg/PjxHJ7/I0kSvvzyS2RlZSE0NBSdO3cWHYkE4AAlohds374dxsbG2LJlC77//ntkZ2fjww8/FB1LraxevRpBQUGYPn06RowYIToOCcIBSkQAgHv37sHd3R1ffPEF6tevj4MHD2LmzJlcSecf0tPT8dVXX8Hc3BwLFy4UHYcE4gAlIuTk5MDU1BTBwcGYOnUq8vPzYWZmJjqW2vn9998xdOhQtG3bFhs2bICenp7oSCQQByiRDnv8+DGmTZuGXr16AQCysrKwdOlSVK9eXXAy9XPnzh1YWlpCkiTIZDKdXziCeBcukc46evQoHB0dcfz4cbi7u2PZsmXQ19cXHUstyeVyjBo1Cr/++it27tyJVq1aiY5EaoANlEjHFBcXY9GiRejSpQtu3LiBbdu2ITAwkMPzNby9vbF161b8+OOP6Nevn+g4pCbYQIl0yKlTp+Dk5IT9+/fD3t4eAQEBqF+/vuhYai0mJgaLFi2Cm5sbJkyYIDoOqRE2UCIdIJfLERAQAFNTU/z666+IiYnBxo0bOTzfID8/Hy4uLujZsydWrVrFv4OlF7CBEmm5P//8Ey4uLti1axcGDhyI0NBQNGnSRHQstXf58mVYW1ujYcOGiI+PR7Vq1URHIjXDAUqkpSRJQnR0NCZMmIDi4mIEBgZi3LhxbFFv4dGjR7CxscGtW7ewd+9eNGzYUHQkUkMcoERa6Pr16/Dw8EB8fDx69OiByMhI3jn6liRJgoeHBw4cOIC4uDiYmpqKjkRqitdAibTM1q1bYWRkhK1bt2Lp0qXIzMzk8CyF5cuXIzIyEnPnzoWtra3oOKTGOECJtMSdO3fg6uoKS0tLNGrUCHl5eZg6dSpXyymF7du3Y8qUKbC1tYW3t7foOKTmOECJtEBmZiZMTEwQHh6OmTNnIi8vDyYmJqJjaZSTJ0/CwcEBxsbGiIyMROXK/PFIr8fvECIN9ujRI3zzzTfo06cPqlatipycHHz//fe8Y7SUbt26BSsrK1SvXh1JSUmoVauW6EikAXgTEZGGOnToEJycnPDLL79g/Pjx8PHx4Q9+JRQXF2P48OE4c+YM0tPT0axZM9GRSEOwgRJpmKKiIsyfPx/dunXD7du3sXPnTgQEBHB4KmnatGnYuXMnVq9ejZ49e4qOQxqEDZRIg5w8eRJOTk7Iy8vDyJEjsXLlStSrV090LI0VEREBPz8/fPnll3B1dRUdhzQMGyiRBpDL5fD390f79u3xxx9/IDY2FlFRURyeZbB37164u7ujX79+8PPzEx2HNBAbKJGaO3/+PMaMGYP09HSYm5sjODgY7733nuhYGu3ChQsYMmQImjZtik2bNqFKFf4opNLjdw2RmpIkCevWrYOXlxfkcjlCQkLg4uLCpfjK6MGDB7C2tsaDBw+Qnp7OBfVJaRygRGro6tWrcHd3R2JiInr16oWIiAi0aNFCdCyNJ0kSXFxccOTIEchkMhgYGIiORBqMA5RIDTVs2BAJCQmiY2idxYsXY9OmTViyZAnMzc1FxyENx5uIiEgnJCUlYdasWRgxYgSmTp0qOg5pAQ5QItJ6P/30E0aNGoVOnTohJCSE15GpXHCAEpFWu379OqysrKCvr4/ExES88847oiORluA1UCLSWkVFRRg6dCguXbqEzMxMvP/++6IjkRbhACUirTVp0iRkZGRg3bp16Nq1q+g4pGV4CpeogpWUiE6gmwIDA7F69WpMmTIFjo6OouOQFuIAJSqDs2eBjz4CnJ0BExPAzg548ABo3hyYPx/o2RPYvBmIiQGMjQEjI2DatGev19cHvvkG6NAB6NcPuHZN8XxwMNC5M2BqCtjaKvYJAKNHA15ewMcfAy1bAnFxz/bl66t4jYkJ8N13KvoHUFOZmZn48ssvMWjQICxevFh0HNJSHKBEZfTrr8C4ccCxY0Dt2sDq1Yrna9QAcnKAXr0UQzM9HTh6FMjLAxITFdvcv68YnocPA717A/PmKZ4fMkSxXUEB0K4dEBr67HiXLyv2m5wMTJ+ueC41FTh1Cjh4UHGMQ4eArCwV/QOomTNnzsDW1hatWrVCTEwM9PT0REciLcUBSlRGTZsCPXooPh41SjHcAMDeXvHfvDzg00+BBg2AKlWAkSOfDbfKlZ9t9/xrjx8HPvlE0Vo3bAB+/vnZ8aytFa8zMAAKCxXPpaYq/te+vWIgnzypGKi65u7du7CyskJJSQlkMhnq1KkjOhJpMd5ERFRG//yTwqePn749pySVfl+jRytaqqkpEBEBZGQ826Z69WcfP923JAEzZgDu7m9/LG0jl8vh5OSEn3/+Gdu3b0ebNm1ERyItxwZKVEbnzwP79ik+jolRXPd8XteuQGYmcP264oaimBjF6VoAkMufXceMjn722rt3gcaNgaIiRQN9kwEDgLAw4N49xeOLF4GrV8v+tWmSuXPnIjExET/88AP69+8vOg7pADZQojJq1w6IjFS0vw8/BDw9gZUrn32+cWNg8WKgTx9FU/ziC8DKSvG5WrUUp2c7dgTq1AE2bVI8v2CBYvA2a6Y4jXv37usz9O8PnDgBdO+ueKyvD0RFAQ0blv/Xq45iY2OxYMECuLi44KuvvhIdh3REJakU55c6deok5efnV2AcIs1y9ixgbq64ZqkMff1nrZGUc+TIEfTo0QPt27dHeno6qj9/jpuojCpVqnRIkqROL/scT+ESkcYqLCyElZUV6tevjy1btnB4kkrxFC5RGTRvrnz7BNg+y+Lx48cYMmQIrl+/jpycHDRq1Eh0JNIxHKBEpHEkSYKnpyf27t2LTZs2oUOHDqIjkQ7iKVwi0jj+/v4IDw/H7NmzMWzYMNFxSEdxgBKRRklNTcXkyZNhbW2NeU+XbiISgAOUSElnzpxBw4YN0aZNG9y4cUN0HJ1w6tQp2Nvbw9DQEOvXr0flyvwRRuLwu49ICbdv34a5uTmKi4uRnJyM+vXri46k9W7fvg1LS0vo6ekhKSkJ+vr6oiORjuNNRESlVFxcjGHDhuG3335Damoql4xTgZKSEgwfPhynT5/Grl270KJFC9GRiDhAiUpDkiR4eXkhNTUVoaGh6NOnj+hIOmHGjBnYvn071qxZg08//VR0HCIAPIVLVCr+/v5Ys2YNpk6dChcXF9FxdML69evh6+sLT09PeHh4iI5D9Dcu5Uf0llJSUmBpaQkrKyvExcXxBhYVOHDgAHr37o3u3bsjNTUVVatWFR2JdAyX8iMqo2PHjsHBwQFmZma8+1NFLl68CBsbGzRp0gSbN2/m8CS1w2ugRG9w+fJlmJubo06dOti6dStqPX2jT6owDx8+hI2NDe7evYvU1FS8++67oiMR/QsHKNFrPHjwAFZWVrhx4wZycnLQpEkT0ZG0niRJcHV1RV5eHhITE2FkZCQ6EtFLcYASvYJcLoezszPy8/ORmJiI9u3bi46kE3x8fBAdHY2FCxfC6ukbpxKpIQ5Qolfw9vZGXFwcli1bBktLS9FxdEJycjJmzJgBe3t7zJw5U3QcotfinRBELxEZGYlFixbBzc0NkydPFh1HJ/zyyy8YMWIE2rdvj7CwMFSqVEl0JKLX4gAl+oesrCy4ubmhb9++CAgI4A9yFbh58yYsLS1Rs2ZNJCYmombNmqIjEb0RT+ESPef06dOwsbFBy5YtERcXxz+dUIGnSyNeuHABGRkZaNq0qehIRG+FA5Tof27duoXBgwcDUFyLq1evnuBEumHy5MnYvXs3wsLC0L17d9FxiN4aBygRgKKiItjZ2eHMmTNIS0tD69atRUfSCcHBwVi5ciW+/vprjBkzRnQcolLhACWdJ0kSxo8fj/T0dERGRqJXr16iI+mE7OxsTJgwAQMGDICPj4/oOESlxpuISOf5+fkhJCQEM2fOhJOTk+g4OuHcuXOwtbVFixYtsHHjRlSpwt/lSfNwgJJOS0pKwpQpU2BnZ4cFCxaIjqMT7t+/DysrKzx58gQymQx169YVHYlIKfy1j3TWkSNHMGLECHTq1AmRkZFcIF4Fnq7u9NNPPyE5ORlt27YVHYlIaRygpJMuXrwICwsL1K9fHzKZjH93qCILFy5EfHw8li1bhkGDBomOQ1QmHKCkc+7fvw9LS0vcvn0bubm5eO+990RH0gnx8fH47rvv4OTkxNWdSCtwgJJOkcvlGDVqFI4ePQqZTAYTExPRkXRCQUEBnJyc0LVrV6xdu5arO5FW4AAlnTJjxgwkJibixx9//HvRBKpYV69ehaWlJerVq4eEhATUqFFDdCSicsEBSjojNDQUPj4+8PT0hJeXl+g4OuHJkyews7PD1atXkZ2djcaNG4uORFRuOEBJJ+zZswceHh7o378//P39eQpRBSRJwsSJE5GdnY3o6Gh06tRJdCSicsX79knr/fbbb7C1tUWbNm0QGxvLP9pXkdWrVyM4OBgzZszA8OHDRcchKnccoKTVbty4gcGDB6NKlSpITk5GnTp1REfSCbt378ZXX30FCwsLLFy4UHQcogrBX8VJaz158gS2tra4cOEC0tPT0aJFC9GRdMLvv/+OoUOHom3btoiKiuICFaS1OEBJK0mSBHd3d2RmZmLDhg34+OOPRUfSCXfu3IGlpSUqVaoEmUyG2rVri45EVGE4QEkrLV26FBEREZgzZw5GjBghOo5OKCkpwciRI/Hrr78iNTUVrVq1Eh2JqEJxgJLWiY+Px4wZM+Dg4IC5c+eKjqMzvL29kZycjFWrVqFv376i4xBVOF6cIK2Sn58PR0dHdOvWDeHh4fxzFRWJiYnB4sWLMW7cOIwfP150HCKV4AAlrXHhwgVYWFigYcOGSExM5Io3KpKXlwcXFxd88sknWLlyJX9pIZ3BU7ikFe7duwcLCwvcv38fu3btQqNGjURH0gmXL1+GtbU1GjVqhPj4eFSrVk10JCKV4QAljVdSUoLhw4fjp59+QkpKCoyMjERH0gmPHj2CjY3N3+9q06BBA9GRiFSKA5Q03tSpU/++eWXgwIGi4+gESZIwbtw4HDhwAPHx8TA1NRUdiUjleA2UNFpQUBD8/Pzw5ZdfYsKECaLj6Aw/Pz+sX78e8+bNw5AhQ0THIRKikiRJb71xp06dpPz8/AqMQ/T20tLSMHDgQPTv3x8ymYxr3KrI9u3bYW5ujiFDhmDTpk1caYi0WqVKlQ5JkvTSd0Lgdz5ppBMnTsDOzg7t2rXDxo0bOTxV5OTJk3BwcICxsTEiIiI4PEmn8bufNM7169dhbm6OGjVqIDk5mcvFqcitW7dgaWmJ6tWrIykpCbVq1RIdiUgo/tpOGuXx48ewsbHBpUuXkJGRgWbNmomOpBOKi4thb2+Ps2fPIj09nf/uROAAJQ0iSRJcXV2Rk5ODTZs2oWvXrqIj6YypU6di165dCAkJQc+ePUXHIVILvImINMbChQvh7e2NBQsWYPbs2aLjEJEO4E1EpPFiY2Ph7e0NR0dHzJo1S3QcIiIOUFJ/Bw4cgLOzM3r27Ing4GCutUpEaoEDlNTauXPnYGlpiSZNmiAhIQHVq1cXHYmICABvIiI1dufOHZibm+Px48fIyMjAu+++KzoSEdHf2EBJLRUXF8PBwQEnTpxAXFwc2rVrJzqSVlq06NnHZ88CFb0OvyqOQaQqHKCkliZPnozt27cjICAAn332meg4Wuv5AUpEpcMBSmonICAAK1euxNdffw13d3fRcbRGVBTQpQtgZga4uwNTpgAPHyoejxyp2KakBHBzAwwNgf79FZ8HgOBgoHNnwNQUsLUFHjxQPD96NODlBXz8MdCyJRAXp3hekhT7NzICjI2BTZtU/MUSqQAHKKmVHTt2wMvLCxYWFvD19RUdR2ucOKEYYrm5wNGjgJ6eYrC9847i8YYNiu1OnQImTAB+/hmoWxeIj1c8P2QIkJcHFBQA7doBoaHP9n35MpCTAyQnA9OnK57bskWx34ICIC1NMUwvX1bd10ukCryJiNTG8ePHMWzYMBgbGyM6Ohp6enqiI2mN3buBQ4cULRJQNMuGDf+9XYsWikYKAB07Kq5ZAsDx48Ds2cBffwH37gEDBjx7jbU1ULkyYGAAFBYqnsvJAYYPVwzqRo2A3r0VA9jEpGK+PiIROEBJLRQWFsLc3Bz6+vrYunUr9PX1RUfSKpIEODsDixe/+PyyZS8+fv6vhPT0np3CHT0aSExUnMKNiAAyMl7+mqcLm5VigTMijcVTuCTcw4cPYW1tjatXr0Imk6Fp06aiI2mdfv0U1yevXlU8vnkTOHcOqFoVKCp68+vv3gUaN1Zs+/R07+v06qU4ZVxSAly7BmRlKa6/EmkTDlASSpIkuLi4YP/+/YiKikKnTi9dcpLKyMAAWLhQcWOQiQnw+eeKa5LjxikeP72J6FUWLAC6dlW87qOP3nw8GxvFfk1Ngb59AR8f4L33yudrIVIXXEyehJo7dy7mzZuHxYsXY/rTO1CIiNQEF5MntRQdHY158+ZhzJgxmDZtmug4RESlwgFKQuzduxdjxoxB7969ERgYyAXiiUjjcICSyp05cwbW1tZo1qwZ4uPjUa1aNdGRiIhKjQOUVOr27dswNzdHcXExkpOTUb9+fdGRiIiUwgFKKlNcXIxhw4bht99+Q3x8PNq0aSM6kta5du0ahg0bhitXroiOQqT1OEBJJSRJgpeXF1JTU7F27Vr06dNHdCSts3nzZhgYGCApKQnh4eEoLi4WHYlIq3GAkkr4+/tjzZo1mDp1KlxcXETH0SpXr17FsGHDMGzYMLRo0QKHDx/GjBkzUKUKFxojqkgcoFThUlJSMHnyZNjY2GDxP9eSozLZvHkzDA0NkZSUhMWLF2Pv3r0wNDQUHYtIJ3CAUoU6duwYHBwcYGZmhvXr16NyZX7LlYerV69i6NChL7TO6dOns3USqRB/mlGFuXz5MszNzVG7dm3IZDLUqlVLdCStEBsbC0NDQ8hkMixZsoStk0gQ/rpKFeLBgwewsrLCjRs3kJ2djffff190JI139epVTJgwAXFxcejcuTMiIiJgYGAgOhaRzmIDpXInl8vh7OyM/Px8REdHo0OHDqIjabyXtU4OTyKxOECp3Hl7eyMuLg4+Pj6wsrISHUejPb3WaW9vjxYtWuDIkSOYNm0ar3USqQEOUCpXkZGRWLRoEVxdXfHNN9+IjqOxJEnCpk2bYGBgwNZJpKY4QKncZGVlwc3NDX379sXq1au5QLySnrZOBwcHtGrViq2TSE1xgFK5OH36NGxsbNCyZUvExcWhatWqoiNpnOdb59atW7FkyRLk5uaydRKpKQ5QKrNbt25h8ODBAIDk5GTUq1dPcCLNU1hYyNZJpGE4QKlMioqKYGdnhzNnziAhIQGtW7cWHUmjPG2dhoaGSE5OxtKlS9k6iTQEf70lpUmShAkTJiA9PR2RkZHo1auX6EgapbCwEOPHj8eWLVvQpUsXREREoF27dqJjEdFbYgMlpfn5+SE4OBgzZ86Ek5OT6DgaQ5IkbNy4EYaGhkhJSYGPjw9yc3M5PIk0DAcoKSUpKQlTpkyBnZ0dFixYIDqOxigsLISdnR2GDx+O1q1b48iRI5gyZQqvdRJpIA5QKrUjR45gxIgR6NSpEyIjI7lA/Ftg6yTSPvzJR6Vy8eJFWFhYoH79+pDJZKhZs6boSGrvVa1TT09PdDQiKgMOUHpr9+/fh6WlJW7fvo3k5GS89957oiOpNUmSEBMTAwMDA7ZOIi3ECy/0VuRyOUaNGoWjR49CJpPBxMREdCS1VlhYCE9PTyQkJKBbt24IDw/HRx99JDoWEZUjNlB6KzNmzEBiYiL8/Pz+XjSB/u351rlt2zb4+voiJyeHw5NIC3GA0huFhobCx8cHnp6e8PLyEh1HbV25cgVDhgzBiBEj0KZNGxw9ehTffvstr3USaSkOUHqtPXv2wMPDA59//jlWrFjBBeJf4mnrNDQ0xPbt29k6iXQEByi90m+//QZbW1t8+OGHiI2N5QLxL/F862zbti1bJ5EO4QCll7px4wYGDx4MPT09JCcno27duqIjqRVJkhAdHQ1DQ0Ps2LEDy5YtQ3Z2NlsnkQ7hXbj0L0+ePIGtrS3Onz+P9PR0tGzZUnQktXLlyhV4enoiMTER3bt3R3h4ONq2bSs6FhGpGBsovUCSJLi7uyMzMxNhYWHo0aOH6Ehq41Wtk8OTSDexgdILli5dioiICMyZMwcjR44UHUdtXLlyBR4eHkhKSmLrJCIAbKD0nPj4eMyYMQMODg6YO3eu6DhqQZIkbNiwAQYGBti5cydbJxH9jQ2UAAD5+flwdHT8e9Uc/rkKcPnyZXh6erJ1EtFLsYESLly4AAsLCzRs2BCJiYmoUaOG6EhCPW2dhoaG2LlzJ3744Qe2TiL6FzZQHXfv3j1YWFjg/v372LVrFxo1aiQ6klCXL1+Gh4cHZDIZPv74Y4SFhXFwEtFLsYHqsJKSEowYMQI//fQTYmNjYWRkJDqSMJIkISoqCoaGhkhNTcUPP/yArKwsDk8ieiU2UB02depUbN26FatWrcLAgQNFxxHmn60zPDwcbdq0ER2LiNQcG6iOCgoKgp+fH7788ktMmDBBdBwh/tk6/fz8kJWVxeFJRG+FDVQHpaWlYfz48Rg0aBD8/PxExxGCrZOIyooNVMecOHECdnZ2aNeuHTZu3IgqVXTrdyi2TiIqL7r101PHXb9+Hebm5qhRowaSk5NRu3Zt0ZFU6vLly3B3d8fWrVvRo0cPhIeH48MPPxQdi4g0FBuojnj8+DFsbGxw8eJFJCUloVmzZqIjqYwkSVi/fj0MDAywa9cu+Pn5ITMzk8OTiMqEDVQHSJIEV1dX5OTkYOPGjejatavoSCpz6dIleHh4sHUSUbljA9UB33//PaKiojB//nzY29uLjqMST1unoaEh0tLSsHz5crZOIipXbKBaLjY2Ft7e3hg1ahRmz54tOo5KXLp0Ce7u7khOTmbrJKIKwwaqxQ4cOABnZ2f06NEDISEhWr9AvCRJWLduHQwNDbF79262TiKqUGygWurcuXOwtLREkyZNkJCQgOrVq4uOVKGeb509e/ZEWFgYBycRVSg2UC10584dmJub4/Hjx0hOTkaDBg1ER6ow/2ydP/74I1snEakEG6iWKS4uhoODA06cOIEdO3agXbt2oiNVGLZOIhKJDVTLTJ48Gdu3b0dAQAA+++wz0XEqhCRJiIyMZOskIqHYQLVIQEAAVq5cia+//hru7u6i41SIS5cuYdy4cUhJScEnn3yCsLAwtG7dWnQsItJBbKBaYseOHfDy8oKFhQV8fX1Fxyl3z7fO9PR0/Pjjj8jIyODwJCJh2EC1wPHjxzFs2DAYGxsjOjoaenp6oiOVq4sXL8Ld3Z2tk4jUChuohrt69SrMzc2hr6+PrVu3Ql9fX3SkcvPP1rlixQq2TiJSG2ygGuzRo0ewtrbG1atXkZWVhaZNm4qOVG4uXryIcePGYdu2bWydRKSW2EA1lCRJcHFxwb59+xAVFYVOnTqJjlQuJElCREQEDA0NkZGRAX9/f7ZOIlJLbKAaat68eYiJicHixYsxZMgQ0XHKxfOts1evXggLC0OrVq1ExyIieik2UA0UHR2NefPmYcyYMZg2bZroOGX2sta5Z88eDk8iUmtsoBpm7969GDNmDHr37o3AwECNXyCerZOINBUbqAY5c+YMrK2t8cEHHyA+Ph7VqlUTHUlpkiQhPDycrZOINBYbqIa4ffs2zM3NUVRUhOTkZNSvX190JKX9+eefGDduHLZv387WSUQaiw1UAxQXF2PYsGH47bffsGXLFrRt21Z0JKU8bZ1GRkbIzMxk6yQijcYGquYkSYKXlxdSU1MREhKCPn36iI6klOdbZ+/evREaGsrBSUQajQ1Uzfn7+2PNmjWYMmUKxo4dKzpOqf2zda5cuRLp6ekcnkSk8dhA1VhKSgomT54Ma2trLFmyRHScUvvzzz/h5uaGHTt2sHUSkdZhA1VTx44dg4ODA8zMzBAVFYXKlTXn/ypJkhAWFgZDQ0NkZWVh1apVbJ1EpHXYQNXQ5cuXYW5ujtq1a0Mmk6FWrVqiI721f7bOsLAwtGzZUnQsIqJypzm1Rkc8ePAAVlZWuHHjBrZu3Yr3339fdKS38qrWyeFJRNqKDVSNyOVyODs7Iz8/HwkJCejQoYPoSG/lwoULGDduHHbs2IFPP/0UoaGhHJxEpPXYQNWIt7c34uLi4OPjAysrK9Fx3kiSJISGhsLIyAjZ2dlYtWoVdu/ezeFJRDqBDVRNREZGYtGiRXB1dcU333wjOs4bXbhwAW5ubti5cydbJxHpJDZQNZCVlQU3Nzf07dsXq1evVusF4p9vnTk5OQgICGDrJCKdxAYq2OnTp2FjY4OWLVsiLi4OVatWFR3plf7ZOsPCwtCiRQvRsYiIhGADFejWrVswNzcHACQnJ6NevXqCE72cJEkICQmBoaHhC62Tw5OIdBkbqCBFRUWws7PDH3/8gbS0NLRu3Vp0pJc6f/483NzckJqaij59+iA0NJSDk4gIbKBCSJKECRMmID09HSEhIejVq5foSP/ytHUaGRkhNzcXq1evRlpaGocnEdH/sIEK4Ofnh+DgYMycORNOTk6i4/wLWycR0ZuxgapYUlISpkyZAjs7OyxYsEB0nBdIkoTg4GC2TiKit8AGqkJHjhzBiBEj0KlTJ0RGRqrVAvFsnUREpaM+P8G13MWLF2FhYYH69esjKSkJNWvWFB0JAFsnEZGy2EBV4P79+7C0tMTt27eRk5ODxo0bi44EQNE6XV1dsWvXLvTt2xehoaFo3ry56FhERBqBDbSCyeVyjBo1CkePHkVMTAxMTU1FR3qhde7btw9r1qzBrl27ODyJiEqBDbSCzZgxA4mJiVi+fPnfiyaIdO7cObi5ubF1EhGVERtoBQoNDYWPjw88PDzw1VdfCc0iSRKCgoJgbGz8d+tMS0vj8CQiUhIbaAXZs2cPPDw88Pnnn8Pf31/oAvHnzp2Dq6sr0tLS0K9fP4SEhHBwEhGVERtoBfjtt99ga2uLDz/8ELGxscIWiH++de7fvx+BgYG81klEVE7YQMvZjRs3MHjwYOjp6SE5ORl169YVkoOtk4ioYrGBlqMnT57A1tYW58+fR2JiopD3yJQkCWvXroWRkRFbJxFRBWIDLSeSJMHd3R2ZmZmIiopCjx49VJ7h+db52WefISQkBM2aNVN5DiIiXcAGWk6WLl2KiIgIzJkzByNHjlTpsV/WOlNTUzk8iYgqEBtoOYiPj8eMGTPg4OCAuXPnqvTY586dw9ixY7F79262TiIiFWIDLaP8/Hw4OjqiW7duCA8PV9mfqzzfOg8cOIC1a9eydRIRqRAbaBn8+eefsLS0RMOGDZGYmIgaNWqo5Lhnz56Fq6srWycRkUBsoEq6d+8eLCwscO/ePSQnJ6NRo0YVfkxJkhAYGAhjY2McPHgQQUFBbJ1ERIKwgSqhpKQEI0aMwLFjx5CSkgIjI6MKP+bZs2cxduxYpKen4/PPP0dISAg++OCDCj8uERG9HBuoEqZOnYqtW7dixYoVGDhwYIUeSy6X/9068/LyEBQUhJ07d3J4EhEJxgZaSkFBQfDz88PEiRMxceLECj0WWycRkfpiAy2FtLQ0jB8/HoMGDcLy5csr7DhyuRxr1qyBkZERWycRkZpiA31LJ06cgJ2dHdq1a4eNGzeiSpWK+ac7c+YMXF1d2TqJiNQcG+hbuH79OszNzVG9enUkJyejdu3a5X6Mp62T1zqJiDQDG+gbPH78GDY2Nrh48SIyMjIq5E9Gzpw5g7Fjx2LPnj3o378/goODOTiJiNQcG+hrSJIEV1dX5OTkIDIyEt26dSvX/cvlcqxevRrGxsbIz89HcHAwduzYweFJRKQB2EBf4/vvv0dUVBTmz58Pe3v7ct03WycRkWZjA32F2NhYeHt7Y9SoUZg9e3a57ff51nno0CGEhISwdRIRaSA20Jc4cOAAnJ2d0aNHD4SEhJTbAvHPt84BAwYgODgYTZs2LZd9ExGRarGB/sO5c+dgaWmJJk2aICEhAdWrVy/zPuVyOQICAl5ondu3b+fwJCLSYGygz7lz5w7Mzc3x+PFjZGRkoEGDBmXe55kzZ+Di4oKMjAy2TiIiLcIG+j/FxcVwcHDAiRMnEBcXh3bt2pVpf8+3zsOHD7N1EhFpGTbQ/5k8eTK2b9+OwMBAfPbZZ2Xa1x9//IGxY8eydRIRaTE2UAABAQFYuXIlvv76a7i7uyu9n6et08TEBIcPH0ZoaChbJxGRltL5Brpjxw54eXnBwsICvr6+Su/njz/+gIuLCzIzMzFw4EAEBQVxcBIRaTGdbqA///wz7O3tYWxsjOjoaOjp6ZV6H3K5HKtWrYKxsTGOHDmC0NBQbNu2jcOTiEjL6WwDvXr1KszNzVGzZk1s3boV+vr6pd4HWycRke7SyQb66NEjWFtbo7CwEDKZrNRD75+tMywsjK2TiEjH6FwDlSQJLi4u2LdvHzZv3ozOnTuX6vW///47xo4d+3frDA4Oxv/93/9VUFoiIlJXOtdA582bh5iYGCxatAh2dnZv/Tq5XI6VK1fCxMTkhdbJ4UlEpJt0qoFGR0dj3rx5cHZ2xvTp09/6dc+3zkGDBiEoKIiDk4hIx+lMA927dy/GjBmDXr16ISgo6K0WiH++dR49ehRhYWFISUnh8CQiIt1ooGfOnIG1tTU++OADbNmyBdWqVXvja37//Xe4uLggKyuLrZOIiP5F6xvo7du3YW5ujqKiIiQnJ6N+/fqv3f751llQUIDw8HC2TiIi+hetbqDFxcUYNmwYfvvtN6SmpqJt27av3f706dMYO3YssrKy8MUXXyAoKAjvv/++itISEZEm0doGKkkSvLy8kJqaisDAQPTp0+eV28rlcvj7+7/QOpOTkzk8iYjolbS2gfr7+2PNmjWYMmUKxo4d+8rtTp8+DRcXF2RnZ7N1EhHRW9PKBpqSkoLJkyfD2toaS5Yseek2z7fOY8eOISIigq2TiIjemtY10GPHjsHBwQFmZmaIiopC5cr//h2BrZOIiMpKqxro5cuXYW5ujtq1a0Mmk6FWrVovfF4ul2PFihVsnUREVGZa00AfPHgAKysr3LhxA9nZ2f8ais+3zsGDB2Pt2rUcnEREpDStaKByuRzOzs7Iz89HdHQ0OnTo8MLn/tk6t27dyuFJRERlohUN1NvbG3FxcfD19YWVldXfz58+fRpjxoxBTk4OWycREZWrSpIkvfXGnTp1kvLz8yswDhERkfqoVKnSIUmSOr3sc1pxCpeIiEjVOECJiIiUwAFKRESkBI0eoEePAtu2PXs8dy6wbFnFHlMVxyAiIvWnVQOUiIhIVYQP0LNngY8+AlxdASMjYORIIC0N6NED+PBD4OBB4P59wMUF6NwZaN8eSEoCnjwB5swBNm0CzMwU/wWAX34BPv0UaNkS8Pd/dhxra6BjR8DQEAgKeva8vj4waxZgagp06wYUFiqeP3cO6NcPMDFR/Pf8edX8exARkWYQPkAB4PRp4KuvgGPHgJMngehoICdHcap00SLg+++Bvn2BvDxgzx5gyhSgqAiYPx+wt1c0UXt7xb5OngR27lQM3nnzFNsBQFgYcOgQkJ+vGKw3biiev39fMTgLCoBevYDgYMXzEycCTk6KTCNHAl5eKv9nISIiNaYWA7RFC8DYGKhcWdEQ+/UDKlVSPHf2LJCaCixZomian34KPHr06kY4eDBQvTrw7rtAw4bPGqW//7OWeeECcOqU4vlq1QBzc8XHHTsqjgcA+/YBI0YoPnZ0VAx0IiKip9RiJaLq1Z99XLnys8eVKwPFxYCeHhAfD7Rt++LrDhx4/b709BSvz8hQnBbetw+oWfPZEAaAqlUVw/r57V/m6TZERESAmjTQNxkwAFi5Eni6aNKRI4r//uc/wN27b3797dtAvXqK4XnyJLB//5tf8/HHwMaNio83bAB69lQuOxERaSeNGKDe3oprmSYmihuNvL0Vz/fpo7hp6PmbiF5m4EBFszQxUby2W7c3H9PfHwgPV7xm/XpgxYpy+VKIiEhLcC1cIiKiV+BauEREROWMA5SIiEgJHKBERERK4AAlIiJSAgcoERGREjhAiYiIlMABSkREpAQOUCIiIiVwgBIRESmBA5SIiEgJHKBERERK4AAlIiJSAgcoERGREjhAiYiIlMABSkREpAQOUCIiIiVwgBIRESmhkiRJb79xpUrXAJyruDhERERqpZkkSQ1e9olSDVAiIiJS4ClcIiIiJXCAEhERKYEDlIiISAkcoERERErgACUiIlICBygREZESOECJiIiUwAFKRESkBA5QIiIiJfw/nCo83rl6En4AAAAASUVORK5CYII=\n", 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" + "
" ] }, + "execution_count": 21, "metadata": {}, - "output_type": "display_data" + "output_type": "execute_result" } ], "source": [ @@ -610,7 +1338,7 @@ "outputs": [], "source": [ "import itertools\n", - "from openfe.setup import Network\n", + "from openfe.setup import LigandNetwork\n", "\n", "def scored_network_planner(three_heavies, two_heavies, mappers, scorer):\n", " mappings = []\n", @@ -630,7 +1358,7 @@ " \n", " mappings.append(best_mapping)\n", " \n", - " return Network(mappings)" + " return LigandNetwork(mappings)" ] }, { @@ -678,9 +1406,9 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", "text/plain": [ - "
" + "
" ] }, "execution_count": 24, @@ -800,10 +1528,10 @@ { "data": { "text/plain": [ - "array([[ 0., 26., 26., 0.],\n", + "array([[ 0., 0., 0., 0.],\n", " [ 0., 0., 0., 0.],\n", - " [ 0., 0., 0., 0.],\n", - " [ 0., 26., 26., 0.]])" + " [26., 26., 0., 0.],\n", + " [26., 26., 0., 0.]])" ] }, "execution_count": 28, @@ -833,10 +1561,10 @@ { "data": { "text/plain": [ - "array([[ 0., 26., 26., 0.],\n", - " [26., 0., 0., 26.],\n", - " [26., 0., 0., 26.],\n", - " [ 0., 26., 26., 0.]])" + "array([[ 0., 0., 26., 26.],\n", + " [ 0., 0., 26., 26.],\n", + " [26., 26., 0., 0.],\n", + " [26., 26., 0., 0.]])" ] }, "execution_count": 29, @@ -866,9 +1594,9 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", "text/plain": [ - "
" + "
" ] }, "execution_count": 30, @@ -877,11 +1605,11 @@ } ], "source": [ - "from openfe.setup import LigandAtomMapping\n", + "from openfe import LigandAtomMapping\n", "\n", "propane_to_ethane_dict = {0: 0, 1: 1, 3: 2, 4: 3, 5: 4, 6: 5, 7: 6}\n", "propane_to_methanol_dict = {0: 0, 1: 1, 3: 2, 4: 3, 5: 4, 6: 5}\n", - "custom_network = Network([\n", + "custom_network = LigandNetwork([\n", " LigandAtomMapping(propane, ethane, propane_to_ethane_dict),\n", " LigandAtomMapping(propane, methanol, propane_to_methanol_dict)\n", "])\n", @@ -907,7 +1635,7 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", "text/plain": [ "" ] @@ -934,9 +1662,9 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", "text/plain": [ - "
" + "
" ] }, "execution_count": 32, @@ -949,6 +1677,14 @@ "\n", "plot_atommapping_network(new_network)" ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0915a377", + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { @@ -967,7 +1703,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.0" + "version": "3.9.12" } }, "nbformat": 4, diff --git a/openmm-rbfe/ApplyingProtocolToNetworkQuickrunDemo.ipynb b/openmm-rbfe/ApplyingProtocolToNetworkQuickrunDemo.ipynb index ffb9439..6a290a9 100644 --- a/openmm-rbfe/ApplyingProtocolToNetworkQuickrunDemo.ipynb +++ b/openmm-rbfe/ApplyingProtocolToNetworkQuickrunDemo.ipynb @@ -38,8 +38,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "gufe v0.4\n", - "openfe v0.3.post295+gbd8a2fe\n" + "gufe v0.6\n", + "openfe v0.6.0\n" ] } ], @@ -71,12 +71,12 @@ "metadata": {}, "outputs": [], "source": [ - "def load_ligands(sdf_file) -> list[openfe.setup.SmallMoleculeComponent]:\n", + "def load_ligands(sdf_file) -> list[openfe.SmallMoleculeComponent]:\n", " # load the ligands from multi molecule SDF file\n", " # we just need to load using RDKit\n", " sdf_supp = Chem.SDMolSupplier(sdf_file, removeHs=False)\n", " # and pass the rdkit Mols into openfe\n", - " return [openfe.setup.SmallMoleculeComponent(m) for m in sdf_supp]\n", + " return [openfe.SmallMoleculeComponent(m) for m in sdf_supp]\n", "\n", "ligands = load_ligands('inputs/ligands.sdf')" ] @@ -101,7 +101,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "We have created \"\" with 7 nodes and 6 edges\n" + "We have created \"\" with 7 nodes and 6 edges\n" ] } ], @@ -161,7 +161,7 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", "text/plain": [ "" ] @@ -204,7 +204,7 @@ "metadata": {}, "outputs": [], "source": [ - "from openfe.protocols.openmm_rbfe import RelativeLigandTransform" + "from openfe.protocols.openmm_rbfe import RelativeLigandProtocol" ] }, { @@ -223,7 +223,7 @@ "metadata": {}, "outputs": [], "source": [ - "settings = RelativeLigandTransform.default_settings()" + "settings = RelativeLigandProtocol.default_settings()" ] }, { @@ -243,7 +243,7 @@ "outputs": [], "source": [ "# create the Protocol we'll apply to our network\n", - "prot = RelativeLigandTransform(settings)" + "prot = RelativeLigandProtocol(settings)" ] }, { @@ -265,10 +265,10 @@ "outputs": [], "source": [ "# load our protein model\n", - "protein = openfe.setup.ProteinComponent.from_pdb_file('./inputs/181L_mod_capped_protonated.pdb')\n", + "protein = openfe.ProteinComponent.from_pdb_file('./inputs/181L_mod_capped_protonated.pdb')\n", "\n", "# define the solvent state\n", - "solvent = openfe.setup.SolventComponent(\n", + "solvent = openfe.SolventComponent(\n", " positive_ion='Na', negative_ion='Cl',\n", " neutralize=True, ion_concentration=0.15 * unit.molar\n", ")" @@ -306,32 +306,32 @@ " # define the complete ChemicalSystem for the A and B states\n", " # these are identical except for the ligand component,\n", " # representing the alchemical shift we wish to simulate\n", - " stateA_complex = openfe.setup.ChemicalSystem(\n", + " stateA_complex = openfe.ChemicalSystem(\n", " components={'solvent': solvent,\n", " 'ligand': ligA,\n", " 'protein': protein},\n", " )\n", - " stateB_complex = openfe.setup.ChemicalSystem(\n", + " stateB_complex = openfe.ChemicalSystem(\n", " components={'solvent': solvent,\n", " 'ligand': ligB,\n", " 'protein': protein},\n", " \n", " )\n", " # similarly define the solvent leg\n", - " stateA_solvent = openfe.setup.ChemicalSystem(\n", + " stateA_solvent = openfe.ChemicalSystem(\n", " components={'solvent': solvent, 'ligand': ligA}\n", " )\n", - " stateB_solvent = openfe.setup.ChemicalSystem(\n", + " stateB_solvent = openfe.ChemicalSystem(\n", " components={'solvent': solvent, 'ligand': ligB}\n", " )\n", " \n", - " complex_trans = openfe.setup.Transformation(\n", + " complex_trans = openfe.Transformation(\n", " stateA=stateA_complex,\n", " stateB=stateB_complex,\n", " mapping={'ligand': edge},\n", " protocol=prot,\n", " )\n", - " solvent_trans = openfe.setup.Transformation(\n", + " solvent_trans = openfe.Transformation(\n", " stateA=stateA_solvent,\n", " stateB=stateB_solvent,\n", " mapping={'ligand': edge},\n", @@ -407,7 +407,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "-rw-rw-r-- 1 richard richard 839K Nov 1 11:20 out.json\r\n" + "-rw-rw-r-- 1 richard richard 839K Feb 16 12:24 out.json\r\n" ] } ], @@ -442,7 +442,7 @@ } ], "source": [ - "t2 = openfe.setup.Transformation.load('out.json')\n", + "t2 = openfe.Transformation.load('out.json')\n", "\n", "trans == t2" ] diff --git a/openmm-rbfe/OpenFE_showcase_1_RBFE_of_T4lysozyme.ipynb b/openmm-rbfe/OpenFE_showcase_1_RBFE_of_T4lysozyme.ipynb index 66ee263..f32735d 100644 --- a/openmm-rbfe/OpenFE_showcase_1_RBFE_of_T4lysozyme.ipynb +++ b/openmm-rbfe/OpenFE_showcase_1_RBFE_of_T4lysozyme.ipynb @@ -40,7 +40,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "4a6e22e9e06043f386b6c7c2f3071765", + "model_id": "a046c44eee6d4c089146c57a721df387", "version_major": 2, "version_minor": 0 }, @@ -52,7 +52,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "dd582f5db1b74cb8954db2cacd50d698", + "model_id": "9121490616ec44abb709636e8c6b5fb9", "version_major": 2, "version_minor": 0 }, @@ -110,7 +110,7 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", "text/plain": [ "" ] @@ -217,7 +217,7 @@ } ], "source": [ - "from openfe.setup import SmallMoleculeComponent\n", + "from openfe import SmallMoleculeComponent\n", "\n", "# Create and SDF supplier\n", "# Extract the contents of the sdf file and visualise it\n", @@ -385,7 +385,7 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", "text/plain": [ "" ] @@ -432,8 +432,8 @@ "output_type": "stream", "text": [ "molecule A smiles: c1ccccc1\n", - "molecule B smiles: N#Cc1ccccc1\n", - "map between molecule A and B: {0: 2, 1: 3, 2: 4, 3: 5, 4: 6, 5: 7, 6: 8, 7: 9, 8: 10, 9: 11, 11: 12}\n" + "molecule B smiles: Oc1ccccc1\n", + "map between molecule A and B: {0: 0, 1: 1, 2: 2, 3: 3, 4: 4, 5: 5, 6: 6, 7: 7, 8: 8, 9: 9, 11: 11}\n" ] } ], @@ -466,7 +466,7 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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M2tnZqX2uYdywEH6BNC0lYWAX5WUSgQkZv6XntrPhHXD/7P+h68Ez5j6NCETUDz179rx48eLkyZN37dpFOgsyYOK4mPTQNcKIBxSHK8/PL9YW/71I8u2rJAqo817V6iififYeVGV5iM7DIiOBhfDLMndsEOzbVvRSKuv6MilHJv+ljlNH26IFiBYNm7rsPwWmdzIFAOfPn+/du7eNjU1MTIyzszPpOMgwyWUZ29Zn/3YQJGJaaYJoMVezC94IJZOUGqqx+Hy30+GcyjgJC5URPiP8MvsfpnKcqyleOnDY06vawf+XMSmOC59F5F36g0A+0iQSib+/PwAsW7YMqyAqG1oiSZkxPvvEIVok1FAFAaCLLX/S521F7cZPxSqIygML4ZdRFjyHqXOVj4ypbF3bgvteJDn6KVf5eMaWIHlhgW7Tkbd169aYmBh3d/dp06aRzoIM1cclfoVPH9P/37o5QSQNSRYMi03p8jKp08vEoTEpwUmCeKFE9YNcFze773/UbVh916hRo0aNGik/+VNITU1t1KiRr6+v7lPpM7w1Wjo0nfTjUGHkY8WB69mFP75Js2Kzrni7KK9ksv9xhsNPc0hEJOPTp08eHh5ZWVkXLlzo0aMH6TjIIOWeP/NpbSD9/5PIvWnZG5KzpDTNpqAql8OiIFksY17OcLab/tkKetp5/S7Lzt2JxNZPNE0zPSsyMjIcHByKjSYkJNSoUYPP5+fn55NIp6fwirB0KMpx7s+g1BKloy2vgw0vTybf8vlSiqxDe6TJiTrPR0xgYGBWVla3bt2wCqKyoYWFGeuXKarg4U85QUkCFsACF/vHDatfr+8a7uP6pKHbIlcHNlChKVm7UrOLPszm8Fq2JZMbGREshKVl7tWg2Cqln90cuBT1e3rui4Ki3X1psShjyzqdpyMjMjJy3759XC5369atpLMgQ5X712m5VMp8/VEiW5ckAIANNR1/dLK1/n+XJ0s264cqNhtrVQaA0JSsRPF/76c4nOzTx0ikRkYFC+FXqDRrIcvSSvGyhjn3+8rWig2yFfKunC988kD38XRv9uzZMplsxowZnp6epLMgvSOXy8+ePduhQ4du3boxRxISErp06eLi4lKjRo2dO3cyB3PCjiouB09l5InkdCtri2LbKjG62/Hb2fCkNP1b+n/P5mmRKPf0bxX/V0FGDgvhV2A7ONqNm6J8ZFZVu8pc9pM80d9Zn91wz9iwQnkJsFE6efLkjRs3KleuvGTJEtJZkD56+/btkydPhg8frpi18ccffwwZMiQpKenmzZvz589PSUmhRULx2zjFR+7lFgJAd7sS26T1sOMDwL1cRfcTWpqaJM/JLun9CJUG7j7xdexGT8o9d0qxR7YVmzWzqt2ShIzViYIONnzFHtmimFc5Z0/YDBxBLGgFEwqF8+fPB4BVq1bZ2dl98f3IBNWpU2flypV3795VHJkxYwbzRY0aNapUqZKcnFwpL5syM6cl/00HjRNKAMDDwqykP7MezwwA3hRNH6VY5jzx2ziLRtjMqLjz589bWVkVO5ienk4kjJ7DQvh1KC630oyA1HlFHe6HOVozjwn3f8ye5lxUEjJ3hFh168WytiERs8KtX7/+3bt3jRo1mjBhAuksyPC8fv06NzfX29tb9jxCeQ4a087ehlPinSo7DgsA8mVyGU2zKQoAaKBlWYKKj2x4xowZQzqCwcBC+NUsO3fn+7YvuH+LeclskD0iNnVXavYAB6tqZv99S2WCTMEv2yr5LSKXtKIkJSUFBQUBQGhoKO7qib5Wdnb2oEGDgoKCeDxeIUUp91BjAwWgaUmXjAYAYFHwWT9Mk2zn9EUhISF8lb04BALB4sWLieTRZ1gIy6KS/5LC4T1o2X9PAZtbWXxnZ3kpK39DctaGmo6Kt2X/9qvNwOHcGrUJxawoAQEB+fn5Q4YM6dixI+ksyMAkJiYOGTJk7Nix48ePBwC2gyNNF/WRseewCsTyDGmJz9eZIVv2Z5uGcio5lvR+UzZ+/Hi16wixEKrCyTJlYVa7rvWAz57/LXa157GoPzPzHucVbWJCS6XpG1frPF3Fun///rFjxywsLIKDg0lnQfru2bNnMTEx+fn5T548yc7OvnbtWvPmzbt169axY8cnT56kp6dzq9cCpX2tmUeAL5XWIxXzvEAMAN78ooeItKiQW8u9Iv8SyPhhISyjStP82bb2ipdVzTjjq9jQAKsSM5X7JBbcDi+4c13n6SoKTdOzZs2iaXru3Lk1a9YkHQfpu+3bt1+8eNHb2zsoKCg+Pj48PPybb76Jjo4OCgoKCgp6/vw5xeWa16uveP83NjwAOCfIV9tslAb4IzMPADrYFDW751avzeKrWWuBUOnhrdEyYtnY2U+ckR6yQnHkJ2e7s5n5LwrEZzPyBlYqmqyVvnGVW6t2FMcYvtUHDx58+PChi4tLQEAA6SzIAOzevVv5ZZMmTVTfYzN0jOhNDF1QAAD9HSw3p2TFFIr3pWVP/LyzNgAc/JjzskBsx2EN+v//L8rMzHbI6IrJjkwIXhGWnc3Q0WZ1PBQveSxqTjU7AAhOFuTJis5oJe/e5Jw4RCCftuXl5TFPF9atW6c6LRuhsrH6tjfb4r9LOks2a02NSmwKgpMEPydkKJZJvBVJln/IXJ2YyQJYVb2Szf8fEdJSiWUXbDSKygsLYdlRbI7j3J+Vj/RzsGpuZZEuke1K+2yFb+aezTJBpm7Tad/q1auTk5N9fX1HjRpFOgsyHhSHU3npOpbFf/Mbu9ryt9SqYsdhH0vP/e5Vkte/773/fd/tZdLhTzm2HNaW2pW72yndCJXT2ccPEImNjAl72bJlpDMYMK5LdVHUc0nCW+YlBVCPZ3YiPTcyX9zbwdKO89/SAloskhfkW7bvTC5pecXHx48ZM0Ymk4WFhbm6upKOg4wKt3otWWa6OC4GpFIAcLfgjnC0djPnWLNZlmyWsxmnmaX56Co2q6s7+vDNi31W9CLS6ts+bFvs6vAfiqLMzc27dOnyzTffcFSeyFAUZWNj07Vr1zZt2hCJp59wG6bykiS+/zDkW1pcNM9t3rv0M5l539nxt9euUvQ+Ftvt6DkzDy8CEbVh4MCBZ86cGTt27IEDB0hnQcaIpj8uD8j/53wZdvS07Nzdef3OighliAQCgZ2dHYVrK78G3hotL65rDdthY5WPBLjYW7FZf2cV3M4pLDoql30K+hkM87QjPDz8zJkzVlZWa9asIZ0FGSmKqrIs2GHmApYFj+JyNbzxoiDf/90n5f9I+eGXCh/cruiABoGm6X79+n3zzTdv374lncWQYCHUAvtJs9iORRd/jlz2ZCdbAFiVmClTqnzCyMf54ZcI5CsfmUzm5+cHAIsWLapWrRrpOMiY2Q4d7XYm3LrPYMqCx7KypsyKNx3Nl8mXfcj8IzP/guCzNvfpG1bSMqkOk+qp33///datW7GxsapL6ZEGeGtUO3LO/v5p5QLFSwlN93iV/E4kWe5WaVRla8VxjnO16mH/UBY8dX8GAXFxceHh4czXPj4+bduq2eN0+/bt06dPr1279suXLy0sLHQbEJkoWiwWPv9XHPtKkpKYey5MeX+J39NzFydkOHM5V3xcFG3uAaDywpU2g78nEVZfFBYWenl5vX//fu/evT/++CPpOIYErwi1w6bvEHOfhoqXXIqa52IPAJtSBFnSoqUU0tTkrKP7COQrwd9//338+HGBQCAQCGQyNX2tBAIBM50qJCQEqyDSGcrMjNesle2I8Y5zllRZul55aIijdQO+eapEuvfzudkZ2zfIsk26+3ZQUND79++bNGnCtK9DpYeFUEtYLEf/n5Wb/35nx29nw8uSyrelZim/UbB/hzQ1Wef51EtOTu7Tp09AQEBAQMA333yj+oZly5alp6d36tRpwIABuo+HEABYduzGb130w8kCWOLmQAHsSctOEhfdDpXnZAn2biURUC8kJiaGhIQAtsIvEyyEWmPRqJlVt17KRxa7OrApOPwpJ7awaE4pLSzM3BGi83TqpaSkxMXFLV++fO/evWJx8QaPUVFRO3fuZLPZoaGhROIhxKjkv0S5N1NTS/Me9pZCOb0h+bNLwJwTh8RvYnWeTi/Mnz8/Pz9/2LBhas9okWZYCLWp0uzFLF7Rvid1LbjDHa1lNKxK/Gw1fe6Fs8LIxzpPp8agQYO8vLyaNWt27ty5kSNHFhudM2eORCKZPHlyw4YN1X4cId0wq+VuM+izn8+FLvY8FnUuM/+Rcpt7mSx9wwqVTxu/e/fu/fbbbzweb926daSzGCScLKNlmbtDBXs2K15mSeVdXyVmSeV76lTpbFtUI829GrgeOqu8JSlZmZmZjo6O+fn5PN5/E3n+/PPPfv362dvbx8bGOjriNjeIMHlOdsKATsp78G5OydqakuXNNztbr5ryf6Sqofv4hty84mvJ5XJfX99Hjx4tXboUG6SUjb78IjYa9uOmcKq6KF7acVjTne0AYFVipljpnEMU9Tz3wlkC+T4XHR3NfJGQkGBra2tu/l/bDrFYPG/ePABYtmwZVkGkD1g2tvaTZisfmexk62LGeVUgDsvIUz6eHrKCVrnPb8R+/fXXR48eubq6Mv9nURlgIdQyytyi0vT5ykdGV7b24JkliKSHPuYoH8/YvFaenwfkyOXyadOmtW7detKkSf369QsNDWX9/wp18+bNsbGxXl5eP/30E8GECCmzHTLKzN1T8dKCRflXsweAkCRBrnKb+8T32ScOEshHQm5u7pIlSwAgODjY0hK3oyojvDVaIRa1aPj23bulbg5cigKAO7mFY1+nWbJZV7xdqnCLJnTZ/zDNYdpccjGBpunY2Ni3b982atSoatWqzMGPHz96eHhkZ2dfvHixe3ds7Y/0SOGju8lTinq+0wAjY1Mf5QknO9kyC5YYLL5l9bPX2JUqk8ioUwEBAcHBwa1bt75z5w62VSszvCLUvnPnzt2mzMMy8qT/P8doa83rbMvPl8lDU5SWUrBY8vxcIgkVKIry9PTs3r27ogoCwKJFi7Kzs/v06YNVEOkbXos2lh27KV5SAIGuDiyA/R9z3ookiuPygvzMnRtJBNSpN2/ebN68mcVihYaGYhUsDyyEWpaTk+Pn57f74KFiE2ECXR3MKOpUeu6zAhEAmPs0ctl/ynH+ckIxS/Tvv//++uuvZmZmzJokhPRNpTmByq3XfPhmAytZSWg6KPHzpRR/nBC9eq7zdDo1Z84ckUg0bty4li1bks5i2PDWqJZNnTq1cuXKy5cvNzc3/7dlXfOCoo6Ia5My96Xl2HE5NapX19tdY969e5eZmenv74+FEOmtjK1BWQd2KV6mS2TdXiXlyuS/uju1tynqX2jRqJnLvpNgpJdKV69e7dq1q7W1dUxMjPIdHVQGxXerQuVx79698PDwyMhI5qX9mMkFu4ruz/jYWnHS87Klsqdv4gkF/DKKojgcTpMmTUgHQahE9hOm5/51Wpb+kXnpyGVPcbZdnyRYlZh53qsa5/+VT/j0Sd4/F4q1uTAOilb4gYGBWAXLD68Italx48Y8Ho9Zfv7LL79MnjhxwcdYyfu3AMBt07HL+Rtxb98uWLBg8ODBpJOW6OTJk0FBQdhiG+m53D9PflxeND1b0eZ+qZvD6Mo2iuMcp6rVT1/Vnzb32rJ169aZM2fWrl371atXilVPqMywEGrTjRs3JJL/ntj36NHj3Llz7XkcwZ7NjnN/3njxyuLFi729vZ8+faq6bbT+kMlkTZs2ffbs2Zo1axYuXEg6DkIlkMsTxw0UvXyqOHAlq+Cn+I+2bNY/Pi72nKK52Q5T/OwnziQRsaIIBIK6detmZGScPXu2X79+pOMYAyyEFcXc3FwgEPD5fJDL0z598vDwyMnJ+fvvv7/99lvS0b4gPDy8S5cuVlZWMTExuAEh0lvCZxFJPwxW3ux6fFzarZzC0ZVtlroV7cZHmVtUD/tHuc2FoZs+ffr27ds7d+589epV0lmMBM4arSgikYjP5wMAsFjz5+MDIGcAACAASURBVM/PyckZOHCg/ldBAOjcufOAAQPy8vIWLVpEOgtCJbJo2NTqu77KRxa7OnAo6lh6Toxym3uRMGPbepVPG6pXr17t3r0bW+FrFxbCCvfkyZMjR46YmZkZUD9cZvfBQ4cOPXz4kHQWhEpUadYC5Tb37hbcEera3Of9/afw30c6T1ch/Pz8pFLp1KlTGzRoQDqL8cBCWLFomp49e7ZcLvf3969bty7pOKVVu3bt2bNn0zQ9a9YsvHmO9BanirPd2MnKR2ZXs7PjsO7lCv/JLig6StPpIStALi/+eUNz9uzZy5cv29vbL126lHQWo4LPCCvW4cOHx4wZ4+TkFBsba2NjU2z0/v37q1atIhJMWWBgoK+vb7GDeXl5np6eycnJhw8f/v7774kEQ+iLaJEwYXA3aXKi4sihTzkrPmS6mXP+9nYxU1pEWGVpsHXfISQyaodYLK5fv/7r16+3bt06ffp00nGMChbCClRQUODl5ZWQkHDgwIGxY8eqvuHs2bP6sPP7mTNn+vfvr3r8wIED48ePd3FxiY6OtrKy0n0whEoj7/JfaQtnKF7KaOgbnRxTKJ7nYj/ZyVZxnO3gWP1MOMvKmkRGLVi3bt3ChQu9vb0jIyO5XC7pOEYFC2EFCgwMXL16dbNmzR4+fMhSt/VgWlrao0fkH120aNHCyclJ9ThN076+vg8fPgwMDFy5cqXugyFUSsmThhc+eaB4eS9XOPp1qmqbe7txUyrNCCARsLzS0tKYmeeXLl367rvvSMcxNlgIK8qHDx/q1atXWFh48+bNdu3akY5TRvfv32/Tpo25uXlUVFTNmjVJx0FIPVHMq8Tv+4Jcpjgy5c3Hf7ILBleyWlejaENNist1O3GZW70mgYjlM378+AMHDvTv3//MmTOksxghnCxTUebMmVNQUDBq1CjDrYIA4OvrO3LkSKFQOH/+/C+/GyFCzD29bfp99vxvsauDGUWdzsh7mi9SHKQlkozNa3WeThOxWCz4v4KCArXviYiIOHTokJmZWXBwsI7jmQgshBXi9u3bYWFhfD5/9erVpLOUV1BQkKWl5cmTJ69fv046C0Ilcpg6l2VdNB/NzZwzroqNHGBVYqbyXa/865cL7t3UfbySHD161MvLq1u3bt26ddu7d6/qGxQzz/38/Axo5rlhwUKofXK5nFl7sHDhwurVq5OOU14uLi4BAQEAMHv2bJlM9sX3I0QE26GS/Y+fzaWc5mxbhcv+N190LjNf+XjGxlW0VKrbdCVKSkqaPn3648ePHz9+PGvWLNU3HDt27NatW05OTtjysOJgIdS+vXv3PnnyxM3Nbc6cOaSzaMe8efNq1qz59OnTffv2kc6CUIlsh43j1qiteGnJZvlVsweAoKTMQnnRZaE4/nXO6WME8qmTmprq7Oz87t277Oxs1dHCwkKmwdPatWttbW1V34C0AguhluXk5CxbtgwANm7c+F+LNcNnYWHBPJwIDAzMysoiHQch9Sgu19FvsfKRQZWsGlmap0lke9I+KzOZOzfKsgWgBywtLfft2+fv71+vXr0DBw4UG127dm1CQkLTpk3Vrr9C2oKzRrVszpw5mzZtatu27a1btyjj2hG0Y8eON27cmDNnzoYNG0hnQahEKTPHF9y5rnj5b75oaEyKOYu65O3iala08Yvt8LGO85bpPl5JIiIiOnXqlJmZyWb/t95DMfP8xo0b7du3JxvPuOEVoTbFxcVt376dxWJt3rzZyKogAISGhrLZ7K1bt8bExJDOglCJHOcEUko7nTWxNO9tbymU0+uTPrsEzD5xRBynRz/JNWvWzMvLU+zjBgBz584tKCgYMWIEVsGKhoVQm2bOnCkWiydOnNisWTPSWbSvcePGEyZMkEgkM2bM+PK7ESKEW7OOzZDRykcCXO15LOq8IP9hnrDoqFyWHrJC1+E+J5fLv/322127dt25c2fq1KmDBw9W7IZ9586dkydP8ni8NWvWkA1pCvDWqNZcuHChV69eNjY2MTExzs7OpONUiE+fPnl4eGRlZV24cKFHjx6k4yCknjwn+59vW6dmZvpa8dgUAMCWlKwtKVlePLOz9aqxlW7WOG/ca9mhK6mcAPDy5csjR468e/euZcuWU6dOZbabl8vlrVq1evz48fLly3/++WeC8UwEFkLtkEgkDRo0iImJ2bhxo5+fH+k4FWjjxo3+/v7u7u4vX740MzMjHQchNT58+NChRfOEjx8jG9XgsSgAEMrp7q+SEsXSNdUrDXUsajdq0bCpy69h5JKqt3fv3kmTJrm5uUVHRxvNnDt9hrdGtYN5cubu7j516lTSWSrWjBkzPD09maehpLMgpN6MGTNWrF/Poop+v1mwqHku9gCwITkrVyYHAIrDsRk4wnnTL8RSliA3N5e5CgwJCcEqqBt4RagFihuG58+f79mzJ+k4Fe78+fO9e/c27pvAyHAdP3589+7d165dszA3f+JTjbkiZIyITX2UJ2xjzevRyMeqc3e2YxWCOUvyxx9/XL16tU2bNrdv3za+OXf6CQuhFkyePHnPnj3dunW7fPky6Sw60rNnz4sXL06ePHnXrl2ksyBUJCMjo0mTJpcvX65Xr565uXn0T2Pkt/5RjIZnF/q9Ty+QyfX59x5FUZaWlseOHevTpw/pLKYCC2F5PX36tFmzZhRFRUZG+vj4kI6jI9HR0Q0bNpTJZA8fPjTKKbLIQI0dO1YqlTJNnVq3bn311AmXtQtosQgAWHz+zHzOX/8+8/Ly6tKlC+mkJbp69WpUVNTAgQPDwvTu4aWxwkJYXp06dbp+/bqfn9/GjRtJZ9EpPz+/0NDQdu3a3bx5E2/gID0xYcKE3Nxc5uuwsLBhw4Zta9tUcGCndY9+z5t/07XfACsrq5iYmGrVqpHNqYFi68HLly9369aNdBzTQKNyOHHiBAA4ODhkZGSQzqJrAoGgcuXKAHDy5EnSWRBSw8zMLD8/X5afL4x+KZVKGzZsCABr1qwhnevLmF1rfHx8JBIJ6SwmAWeNlp1il741a9Y4ODiQjqNrdnZ2K1asgP/vvEg6DkLFbd++3czMjMXnm3t67969+9mzZ7Vq1TKI1U2KFUpqN2ZC2ke6Ehswpgw0atRIKpWSzkKGTCZjHhCuXLmSdBaESpSZmeno6AgAYWFhpLOU1qlTpwDAwcEhPT2ddBbjh1eEZZSUlBQUFAT/78BJOg4ZLBYrNDSUoiimRz7pOAipt2zZsvT09E6dOg0cOJB0ltIaNGhQt27dMjMzV65cSTqL8cPJMmU0evToI0eODB48+OTJk6SzEDZ48OCwsLDRo0cfOnSIdBaEiouKimrUqJFcLo+IiGAeEypLTk6+c+cOkWDK2rZtqzp/5+XLl40bNwaAf//9t379+iRymQzSl6QG6d69exRFWVhYxMfHk85C3vv37/l8PkVRt27dIp0FoeK6d+8OAD/99JPa0TNnzpD+HQwAcObMGbXxpkyZAgBdu3atyO8QovGK8KvRNO3r6/vw4cPAwEC8a8EIDAxcvXp1s2bNHj58yGLh/XakL86dO9e3b197e/vY2FjmMWExjx49YjadJmv+/PktWrRQPZ6Zmenh4ZGRkXHu3LnevXvrPpiJwEL41Q4cODB+/HgXF5fo6GgrKyvScfRCQUGBl5dXQkLCgQMHcCttpCfEYnGDBg1iY2NDQ0NnzZpFOk4ZhYaG+vn51alT5+XLl8zeFEjr8OT96+Tl5S1evBgA1q1bh1VQgc/nr1q1CgACAgJycnJIx0EIAGDz5s2xsbFeXl4G3Qp/+vTp9evXf/PmzdatW0lnMVpYCL/OmjVrkpOTfX19R40aRTqLfvn+++/btm2blpbGTKZVlZ2dvXz58uHDh69ZsyYvL0/H8ZCp+fjxI7MsfePGjVwul3ScsuNwOJs2bQKAFStWpKamko5jnLAQfoX4+PhNmzZRFMWsGSAdR79QFLV582YWixUSEvL69WvVN4wbN+79+/ezZ89+9uyZQZ+hI4OwePHi7Ozs3r17M5NlDFrXrl179eql2J4JaR/hyToGZcCAAQAwduxY0kH015gxYwBg4MCBxY4nJiZaW1vn5eXRNJ2RkcHn8wUCAYmAyCRERESw2WwzM7Po6GjSWbTj9evX5ubmLBbr0aNHpLMYIbwiLK3w8PAzZ85YWVmtWbOGdBb9FRwcbGNjc/r06WI7UsXGxtapU8fS0hIAHBwcnJ2d4+PjCWVExm/27NkymWzmzJmenp6ks2iHu7v79OnT5XL5rFmzaJzhqG06LYQjR47s27ev2tvcQqGwb9++ffv2lUqluoxUSjKZjGlRuGjRIn3uW0+ck5NTQEAAAPj5+Sn/UwqFQuUJb+bm5oWFhQTyAUilUuYnTSgUqo6mpqb27dt35MiRug+GtOW33367efNmlSpVAgMDSWfRpqVLlzo7O9+9e5fp9Y+0SZeXn3Z2dgAQFxenOqTYOUUoFOoyUilt374dAGrVqlVYWEg6i74TiUTu7u4AsGPHDsXBp0+furi4MF9LpVJbW9u3b98Siaeof7m5uaqjcXFxAGBnZ6f7YEgrCgoKatSoAQB79+4lnUX79uzZAwCurq75+fmksxgVvDX6ZQKBYOnSpQAQEhJiYWFBOo6+MzMzW7duHQAEBgZmZGQwB+vXr29hYXHhwgUAOHXqVK1atWrWrEkuIzJawcHB79+/b9y48fjx40ln0b4JEyY0b948MTExJCSEdBbjosuqa6BXhDNnzgSATp06kQ5iSJgNRZnnGYyrV6/WqVOnQYMGHh4e9+7dIxUMrwiN2IcPH5jn0NevXyedpaLcvn2boigej/fu3TvSWYyHFgphUFBQ3bp1GzVqtGvXLs3vNMRC+OrVKy6Xy2aznz59SjqLIXnx4gWHw+FwOM+fP1c+rrb86JJWCuGdO3d69erl4eHRpk2blJSUikmKvtqIESMAYNiwYaSDVKyhQ4cCwMiRI0kHMR7lLYQnT56sXbv2u3fvPnz4UKdOHc1tlw2xEDKLkKZOnUo6iOH56aefAKBLly6kg3ym/IXw48ePjo6O58+fl0qlycnJFZYUfZ27d+8yl0qkHj/rTEJCAtPm/ubNm6SzGAlOOe+s3rp1a9CgQczT6VGjRoWFhbVr107zR168eJGVlVXsoPIcQrlcriedRy5evHjp0iV7e/vly5eTzmJ4Vq1adeLEiatXr544cUJPFjUrd8WLjIzk8XjF3pCYmPjFP2Tfvn19+vTp2bMnAFStWlW7CVHZKNYVzJ8/3+gfP7u5uc2dO3fFihWzZs16/PgxtrnXgnIW0m3btrVr1y4/Pz8rK2v48OFDhgzR8GbmilAzoVAYERGhg794KVEUFRwcrPavExMTg4tbaZp+9OhRTEyM2qHg4GC9asETERGhdtVEMZqvCMePH9+/f39fX19XV9cffvhBJBJVzPcVfYV9+/YBgIuLC9O0wegVFBRUr14dAPbv3086izEo7xXhpEmTXrx44e3t7eLi4uzsXJpSN2XKFNW3SSSSDRs2MF+z2Wxra+tyBtMKoVAokUgUt22VhYeHd+/e3cPDIzIyksMp77fRcEml0nHjxsXGxl66dKlz587FRpnbj1wuV09m27LZbMXX/v7+qi0os7Kydu3apfkPyc3NtbS0vHLlCofD6d69+549e6ZPn679rKjUcnNzmSWDwcHBzGQZo8fj8dasWfP9998vXLhw4MCBtra2pBMZOC0W1bFjx65bt07DGwzuGeGdO3dKmqAlEok8PDwAYMuWLUSy6YnNmzcDQJ06dVT/4RRT+G7cuEEkm1rlf0Y4a9asn3/+mfl63rx5s2fPrpCgqNTmz58PAK1bt5bL5aSz6I5cLm/fvj0ABAQEkM5i8LRQCN+/fy8Wi8+ePevo6PjhwwcN7zS4QkjT9PDhwwFg+PDhqkNnz54FAHt7+0+fPuk+mD7IzMysVKkSAPzxxx+qo8OGDQOAESNG6D6YBuUvhLdu3apVq9abN29ev35ds2bNs2fPVlhY9GVxcXFME84HDx6QzqJrT548YbFYZmZmJT2bQKVU3kKYmpraoUMHT0/PAQMGfPGBmf4XQrFYnJycLJFIFEc0X9Z89913ADB9+nQdZtQj06ZNA4DOnTurDmm4mCZLK8snfvnll+bNm9evXz80NLRiYqLS6tu3LwD88MMPpIOQwfQN6NevH+kghg0X1Bc5c+ZMnTp1Wrdu7erqeurUKcVxpq1MkyZNZDJZsY+8fPmSWWX47Nkz3YYl7+XLlxwOR+3fXSaTtWjRAgCWLl1KIpomuKDemFy9ehUArK2tTXYdS2pqqo2NDQBcunSJdBYDhoWwyMKFC5OSkmiavnLliru7u+K4onvhL7/8ovopZqKE2qsi4/btt98CwIwZM1SH9u7dCwCurq56OIUPC6HRkEqlDRo0AADNUxOM3tq1awHA29tbLBaTzmKosBCq8euvv7Zv3175yLFjxwCgSpUqWVlZxd6cmZnp6OgIAGfOnNFhRsJOnz7NPB9NT08vNpSTk8Osrjt+/DiRbJphITQaW7ZsAYDatWvrwy8NgkQiUd26dQFg69atpLMYKp0WQn9//ylTpqidWiIUCqdMmTJlyhTl53O6Fx4e3rhx40qVKr18+bLY0DfffAMA8+bNU/3U1q1bmf+QJrI3heI/3rZt21RH586dCwBt2rTRzyl8EomE+UlT+9vz06dPU6ZM8ff3130w9FUUE7VwshJN02fOnDHxiXvlhDvUF5ebm7tly5YmTZoU+z0eERFR0gQtU7tFo7gVo3rWopjC9/DhQyLZkInQMFHLNJn4xL1ywkJYRFH5mH5vqndBf/jhBwDo27ev6mdN56G94uH833//rTrap08fAJgwYYLugyHTYcqT1EqiYfIa+iIshEW6d+++e/fuiIiI+fPnN27cWPXOXlpaGtPB4eLFi6of79evHwCMHz9eJ2GJGTduHAD0799fdeiff/4xkbMBRBZe/aiFV8llhoWwSEpKysyZMwcMGDB37tzU1FS17wkKCgIALy8v1Qlab968MfqFvYoFvLGxscWGJBJJ/fr1AaCk1qwIaQU+DytJaZ6bxsfHnz9/PiEhQZfB9B8Wwq+jubNaQECAEbd6ksvlzNYiCxYsUB3V0GsNIW3BGZKaaZ5JGxoaWrNmzSlTpri6uqpdDGaysBB+tT/++KOkE1LFyoGjR48SyVahjhw5AgBOTk7Z2dnFhjIyMphT0T///JNINmQi1q1bh2vmNFBM3AsKCio2lJ+f7+DgEB0dTdP0o0ePqlWrht9DBSyEZcE8opg2bZrq0P79+8EYt4NRbPvy66+/qo5OnToV9G8PXmRkUlNTmYf02EVFg5Im7j148MDDw0Px0tnZOSoqSufp9BQWwrJ49eoVM2nt6dOnxYZkMlnLli0BQLFBgXFYsmQJADRt2lRtnzkOh8PhcHC6mv77888/T506pfYsTSqVnjp16tSpU6r/xHoC+2qWktr+q3/99Zevr6/ipY+PD6ltYWQyGfOTJpVKVUfz8vJOnTql43tLWAjLaMaMGSVN0Lp79y7Tb/rt27c6z1UhEhIS+Hw+RVE3b95UHWV6rc2cOVP3wdDXMpTuTqpwp4XSU7sjx927d729vRUvXV1dX7x4QSKdPnZ3wkJYRpo7q40cORIAhg4dqvtgFWHIkCEAMGrUKNUhpteag4ODaq81pIcMtBDi3ntfS3WPxqysLBsbG+Z+6evXrx0cHEh1wsJCaFS2bdtWUme1xMREZvOm69evE8mmRbdv32YucN+/f19sSDGFb/v27USyoa9loIXw6NGjzEQt1TYXSC3FxL1jx44pDgYEBDRr1iwkJKRBgwZr164llQ0LoVFRTNBS+yO1fPlyAGjcuLHa++CGQiaTNWvWDABWrFihOrpmzZqSeq0hXYqLi1u5cuWGDRu+uD7MEAuhYvuX/fv3k85iSPbt26e6CUx4ePiWLVtu3bpFMJhWCqFQKDxx4sSSJUt2796t9s/5KlgIy4WZoGVlZcXs36SsoKCgZs2aALBnzx4i2bRi9+7dAODm5pafn19sSHOvNaQzUVFR9vb2K1asWLVqlZubW0pKioY3G2Ih/Pnnn6GEDUGRBnq7LWj5C6FUKvX19R06dOiRI0fWrl2LhZC8/v37A8C4ceNUh3777TcAqFKlikAg0H2w8svOznZ2dgaA33//XXV07NixADBgwADdB0PKFixYMHHiRObrSZMmrV69WsObDa4Qap6ohTTTz4l75S+Ev/32W7t27bQYiaJpGlA5xMfH+/j4iESi+/fvMwsnlHXs2PHGjRv+/v4hISFE4pWHv7//xo0b27Zte+vWLYqilIciIiJatGjB4XBevHjBPCZEpCxevDg9PZ25dl+3bt2rV68OHTpU0pvt7e2zsrI8PDzMzMyKDcnl8levXgGAUChMTk729/ev0Nil9OTJk4SEhBEjRjB7gqoqLCzk8Xg6TqVvNHwTRo4cefz48erVqzPPOIjbsGFDtWrVLCwsAMDb25vFYhV7g1gsjo2NtbOzEwgEJf0hM2bMsLa2rlq16rt37/r27duhQ4fyxtJiUTVZGjqr/fvvv2w2m8vlMg0dDMjr16+ZGdiPHj0qNqS51xrSsfj4eBcXl1GjRg0cOLBu3brDhg3T8GbmirBy5crOKpycnJjfCUKhMCIiory/WbSHz+cfOnRI9e8SHR3doUOHMWPGVNi31mCMGTOmQ4cOan/JHDp0iM/nk/43LBIREaG4InRyclL9OaxcuTJ86Ypw0KBBnp6eO3fuPHTokLOz87Vr18r5DcQrQi3Izc319PRMSUk5cuTIqFGjio1OmjRp7969vXr1+uuvv4jEK5tevXpduHBh0qRJzKWGsiNHjowePdrJySk2NpZ5TIjIysnJuX//fvXq1Q8ePCiXy5nW8GoxV4RxcXF16tQpNpSXl2dtbQ0AQqFQKBQyz7+J+/PPPw8ePFivXr1nz55xuVzlobdv33p7e4tEonv37rVq1YpUQuKePHnSsmVLDofz8uVLd3d35SGpVNq4ceOXL1+OGzeO2SKNuC5dulhYWDBXhLm5uVZWVsXe8ObNG3d3d81XhBMnTqxXrx5z0yIgIEAsFm/atKlcscpZSBFDQ2c1xeZNFy5cIJKtDK5cuQIA1tbWqjMv8vPzNfRaQwRlZ2fXqFFD89mxwT0jFIlEnp6eALBp0ybV0YULFwKAr6+vUba5Lw25XN62bVsAWLRokeroxo0bAcDd3V2v/k3L/4xw7969PXv2pGlaLpd/991369evL2ckLITaoeistmTJEtXR9evXA0C9evUMosutRCLx8fEBgJCQENXRwMBAKKHXGiIiJSWlU6dOo0ePrlmzptrfhsoMrhDSNH3u3DnmN+PHjx+LDeXm5larVg0A1N47NQUHDx6EklvhOzg4AMBff/1FJFtJyl8ICwoKOnTo0L59+7Zt2zZr1qz8q0uxEGoNM0HLwsJCdYKW5rNafaPhLFIxhY/sOiRUzNu3b+/cuVOaycl6Xgg/fvw4e/bs7777bvr06cpLknr06AEAU6ZMUf3IgQMHAMDZ2Vm1Ehg9xXnAwYMHVUcnT54MAF27dtV9MM20so5QKpW+ePHi0aNHWjkjx0KoTRo6q2k4q9Urms8iNfRaQwZBzwth165dt27dGh0dHRAQ0KlTJ8XxqKgops19ZGRksY/I5XLmAeHixYt1G5a8RYsWAUCzZs1Ui8GLFy+YVvikGopqgJ1ljJzmzmoazmr1h4azSA291pCh0PNCKBKJmC/u3r1bq1Yt5aFZs2YBgHJ1VLh37x5FUebm5q9fv9ZFSv0QHx9vYWFR0u2Zbt26AcDs2bN1H+yLsBAaPw2d1TSc1eoJDWeRmnutIUNRrVo1Ho8XHx+vOpSXl8fj8Xg8nqIaEREXF7dx40ZfX9+TJ08qH1e0uT916pTqp77//nsAGDx4sK5ikjdo0CAAGD16tOrQyZMnQY9b4YtEIuYnTe12YPHx8Twer1q1arqMhIVQyxSd1Xbv3q06quGsVh9oOIvctWsXlNBrDSEtiouL27JlS5cuXQIDA4sN7dixAwCqV6+u+kOYmJjITMS/fPmyrpKSFB4eDgB8Pl/19kxhYWGtWrUAYOfOnUSyGSIshNr3+++/A0DlypVVJy9oPqslS8NZpKLX2okTJ4hkQ6ZGIBCw2exiVwxSqbRRo0YAsGrVKtWPrFy5EgB8fHyMvgU8fh+0DgthhWBa/vj7+6sOaTirJUjzWeScOXMAoG3btia7Wgvpxvr16zMyMmiafvjwobW1tepNWsWVkOo+G6ZzJYRXxlqHhbBCaOispvlsjhQNZ5Eaeq0hpF3bt293c3Nr0KBB9erVS7prMnDgQABQ21lNcVeDqaZGSSAQaLirNHr0aAAYNGiQ7oMZNCyEFWXixIkA0KtXL9UhDff3idB8FtmzZ08AmDRpku6DIRMkl8szMzM1vEHzbMmOHTsCgJ+fX4UFJGz27NkA0K5dO9XbM6Y5e1YrsBBWlLS0NGaqutrOahpmfOmehhl3ly9fBgAbGxvNu9whpEsGun6u/HA9ZQXBQliBmK2X1HZW03xWq0saziIVvdY2bNhAJBtCahloR5Xyww47FQQLYQUSi8UaOqstWrSod+/eapc261JcXFzv3r0NqGMvQrRh9tgsJ+y5WnGwEFYsDT+7etW0WjWMsf42QcZBcSfQRM7hNJ9VM7twqL1XjEoDC2GFM4jOaqqM+P4SMg6PHz9msVgmcle/NM9Z7t+/TySbEcBCWOH0v7OaKuOecYCMhobVAsY0z0vzzDsN60lQKWEh1AVmxnPHjh1JBymtrl27qs5BF4lERrw8CxmilJQUGxsbo1/588W1WFZWVsq7VqGvhYVQFzSvgdU3qquSs7Oz165d6+Li0rJlS7LZECpm1apVxt0LQnN3joYNGwLA6tWriWQzGlgIdUQ/O6up8sua6wAAFO9JREFUUtunKiUl5ejRo+fPn8dCiPSNUCh0d3c34u6AX+zXWKtWrcLCQt0HMyYUTdOAKh6zjdHTp0+///77wYMHk45TopMnTx49etTHxycyMpLD4SgP3b1718/P78GDB6SyIaRWWFjY4MGDHRwcYmNjK1WqpDyUk5Pj6emZmpp64sQJZltpw3LixIlhw4ZVqVIlJiaGeUyoIBAIPDw80tPTw8LCmMeEqOxIV2ITEhYWxkzuIv1vrglFURYWFmFhYar579y5g1eESD9p2EFs9+7dYJg7iCn2dNuzZ4/qqJ7v6WZYOBp/KyJtunDhglAodHV1bdq0KeksJYqIiEhMTLxw4QKeYyIDsmnTpsaNG2/btm3ChAn169dXHvrxxx/37Nnz5MmTDRs2LFmyhFTCMli/fv27d+8aN278ww8/FBuKiorasWMHm83etGkTkWzGhnQlNhUREREsFsvMzCwmJoZ0Fk3i4uKY+QUPHz4sNoRXhEifTZkyBUpY+Xr79m2Kong8np60uS+NxMRES0tLALh+/brqaPfu3QHgp59+0n0wo4SFUEe++eYbAJg3bx7pIF82d+5cAGjTpo3y/IJz586tXLnS3d39xIkTycnJBOMhpFZGRgbzgFBtLyTmAeGoUaN0H6xsRo4cCQBDhw5VHWL6Vdnb23/69En3wYwSTpbRhePHj48cObJKlSqxsbG2trbMwUePHsXFxXl5eTVu3JhsvGJyc3M9PT1TUlKOHz8+fPhw5uCaNWtycnKYr8ePH890e0JIr4SGhvr5+bm7u7948cLc3Fx56MOHD/Xq1SssLLxx40b79u1JJSyle/futW3b1sLC4tWrV8xjQgWxWNywYcOYmJjQ0FDmMSHSAtKV2PgVFBTUqFEDAH755RfFwZ9++ql58+YLFizw8PDQwy5Qe/fuBQBXV9e8vDzSWRAqLUVntZCQENVR5gFh06ZN9bwhp0wma9myJQD8/PPPqqPr16+HEnqtoTLDQljhli5dCgBNmjRR/u93+PDhgoICmqZv375ds2ZNcunUk8lkLVq0AIBly5aRzoLQV7hy5QoAWFtbq3ZWKygoqF69OgD8+uuvJKKV1v79+wHAxcVF9TQ0LS2NuaWkttcaKjMshBXrw4cPzBPvGzduqH3Dw4cP69atq+NUpXHnzh1mfsG7d+9IZ0HoK/Tq1QsAJk6cqDp0+PBhKGHzJj2Rk5NTtWpVADhy5Ijq6I8//ggAvXv31n0w44aFsGIxz9iGDx9e0htGjRq1dOlSHSb6CsOGDQOAESNGkA6C0FfQ0FlNLpe3a9cOABYsWEAk2xcFBAQAQOvWrVVb4URERLDZbDMzM9Vea6icsBBWoC9eVC1fvrx9+/bMPVI99MXLWYT0k7+/P6jMfGY8efKEWcgUGxtLJJsGb968YUr4gwcPVEeZmedz587VfTCjh4Wwoiges6m94BMKhZMmTerRo4fe3qJhqH3AiZCey8nJcXZ2BoDff/9ddXTcuHEA0L9/f90H06xfv34AMH78eNWh3377DQCqVKmSlZWl+2BGDwthRfnll19KmngpFApbtGjh4OAwb968gICAgICA3NxcIiG/SDHldd++faSzIPQVNHRWS01NZTZv+vvvv4lkU+vq1asAYGVlpbpOV3OvNVR+WAgrhOKJ9/Hjx1VHRSLRic8JhULdhyylY8eO4akoMjgymax58+YAsHz5ctXRNWvWAIC3t7fq5k1ESKXSBg0aAMDatWtVR5ctWwYAjRs3lkqlus9mCrAQVoh58+aV9MTb4MjlcmYB8vz580lnQegrKDqrqT6kF4lEdevWBYBt27YRyVbM1q1bAaB27dqqGyopHtWr7bWGtAILofZpaNdpoAylUSpCxQwdOhQARo4cqTp0+vRpALC3t09PT9d9MGWZmZnMxt1nzpxRHR0xYgQADBs2TPfBTAcWQu3r06cPAEyYMIF0EG1i+t/37duXdBCEvkJCQgKfz6co6ubNm6qjXbp04fP5xBenX7hwgc/nd+nSRXXo7t27zEXt27dvdZ7LhGAh1LJ//vmHaWxhZJ2pFS0tLl26RDoLQl9BQ2e1169fJyQkEElVTEJCwuvXr4sd1NxrDWkRFkJtkkgkzBPv4OBg0lm0LygoCAC8vLywySEyIIrOavv37yed5evs27evpF5rSLtY5W/bjRR27Njx/PnzOnXqzJw5k3QW7Zs9e7aHh0dUVNSuXbtIZ0GotHg8HjNHdOHChdnZ2aTjlFZubm5gYCAABAcHM5NlUMXBQqg1mZmZK1asAICNGzcW2wLGOJiZmQUHBwPA0qVL09PTScdBqLRGjhzZvn37tLS0tWvXks5SWqtWrUpJSWndujUzWQZVKNyPUGumTZu2Y8eOLl26MI8JjVX37t3//vvvadOmbdu2jXQWhEorIiKiRYsWHA7nxYsXzMIJfRYfH8+scbx37x7zmBAAPnz4sHXrVrFYHBoaSjae8cErQu149erVnj17OBzOpk2bSGepWJs2beJyubt27Xr+/DnpLAiVVtOmTceMGSMWi+fPn086y5fNmTNHJBKNHTtWUQVfv349ZcqUwsLCe/fukc1mnEg/pDQS3377LQDMmDGDdBBdmD59OgB07tyZdBCEvoKis5qez3xmeq2pnXl+586dli1bEkll3PDWqBacOXNm4MCB9vb2r1+/rlSpEuk4FU4gEHh4eKSnp585c6Z///6k4yBUWmvWrFm8eLG5ubmFhQXpLCUSCoUikWj16tWLFi0qNnT37l0/P78HDx4QCWbEOKQDGDyxWMxsIbZy5UpTqIIAYG9vv3Tp0hkzZvj7+3fv3l2ff6cgpMze3p6iKLFYLBKJSGcpEUVRFEXZ29uTDmJC8IqwvNauXbto0SJvb++nT59yOKZyYiGTyZo0afL8+fO1a9cuWLCAdByEviwnJ8fT0zM1NfXAgQPMhkf66ezZs+PHj69SpUpMTIydnZ3yEF4RVhTS92YNm37u56IbGnaNQUgPzZkzBwDatm2r/63wO3ToAAD+/v7FjuMzwgqChbBc9HaHT93QsI8oQnrl9evXTCv8R48ekc7yZf/++y+bzeZyudHR0YqDvr6+1atXr1y5cu3ata9du0YunRHCQlh2T548YfZkiI2NJZ2FjDdv3jC/XB48eEA6C0Ka9OzZEwAmTZqkOHL58mVvb283N7emTZs+efKEYDa1Jk6cCAC9evUiHcQkYCEsI7lc3q5dOwBYsGAB6SwkMROFjGPnRWSsrly5AgA2NjYpKSnMEblcPmjQoMePH9M0vWvXLl9fX6IB1UhLS2MeEBLfHMMUYCEso8OHDwOAk5NTdnY26Swk5eTkVK1aFQCOHDlCOgtCakgkEh8fHwDYsGGD2je8fPmyatWqOk5VGiEhIQBQr149bHNf0bCzTFkUFhYuXrwYANatW8dMljFZ1tbWq1evBoCAgID8/HzScRAqbuvWrS9fvnR3d582bZraN1y7dq1169Y6TlUaM2bM8PT0jI6O3r59O+ksxo50JTZIGnY4M0G4axrSWxkZGQ4ODgDw119/qX3Do0ePqlatGhUVpeNgpXTu3DkAsLOz+/jxI+ksxgwL4VfTvOe1acJ9tJF+mjx5MgB07dpV7eiNGzfc3d31/D9yjx49AGDKlCmkgxgzLIRfbciQIQAwatQo0kH0y8iRIwFg6NChpIMg9J8XL15wOBxmxwnV0Y0bN1arVu3ChQtv3rx58+aNVCrVfcLSiIqK4nK5bDY7MjKSdBajhZ1lvs6dO3fat29vYWERHR3NbHuNGElJSZ6envn5+devX2eWAyNE1rfffnvlypXZs2er7gmTl5c3YMAA5SMnT54s1sZFf8yePXvz5s2dOnUKDw8nncVIka7EhkQmkzVr1gwAVqxYQTqLPlq+fDkANG7cWG9PrpHpOHnyJAA4ODikp6eTzlJemZmZjo6OAHDq1CnSWYwTXhF+hefPn7dr187e3j4qKorH45GOo3cKCwu9vLwEAsHt27cbNGhAOg4yXSKRqH79+nFxcTt37pwyZQrpOFqwc+fOqVOn1qpV69WrV9jmXuuwEH6dtLS09+/fK3bLRMU8fPiwRo0aTk5OpIMgk7Z69erAwEAfH5/IyEjjaIXP3I56+vSp2u2ZUDlhIfxMRESETCZr1KiRmZlZsSGZTBYREQEALVq0IBHNYDx69AgAmjZtymaziw2JxeKnT5+y2eymTZuSiIZMQlJSUr169fLy8i5fvtytWzfScbTm2rVrnTt35vP50dHRbm5upOMYFSyEn7G0tCwoKHj//r3qRJjMzExmu0G5XE5RFIl0BoCmaRaLBQCK9VvKEhISatSowefzcek9qjhjxow5fPjwoEGDTp06RTqLlg0aNOj06dNjxow5ePAg6SxGBTvLIISMx/37948cOWJubr5u3TrSWbQvJCTEwsLi8OHDt2/fJp3FqGAhRAgZCZqmZ8+eTdP03Llz3d3dScfRvlq1as2ZM4f5a8rlctJxjAcWQoSQkTh8+PCDBw+cnZ3nz59POktFWbhwYbVq1Z48eXL06FHSWYwHFkKEkDHIy8tbuHAhAAQHBxtxK3wrK6u1a9cCwPz583NyckjHMRJYCBFCxmDt2rXJycnNmjUbNWoU6SwVa/To0a1atUpNTQ0ODiadxUjgrNHPMLNGO3furLpkVSKRMNt74qxRDRSzRrt168blcouNCoXC8PBwnDWKtO7t27fe3t4ikejmzZvMjtnG7f79+23atDEzM3vx4oVRPg3VMSyEn2EKoeb3YCHUQFEINcBCiLTOBNcVGPEqEd3DQvgZphC+efNGdb2qQCBgGqZgIdRAUQjT0tLs7e2LjX748KFOnTpYCJF2meZKc2PtG0AEPiNUg8Ph/K+9uwtpqo/jAP5Xs+VWI01hYbabZLGThjFJVhdFSrUlBQuqG4uiKCJqXvSCvaNBDZvikt6giIjoJqLZlTdCkmQQsVw7EpG9MZY7swM74jZ3novxRM/mEw8PPP52nv/3cyU7Xnw5F/vud875n39xjv/Hi5pmDc4hzI7p6Wm3280Ya2tr46cFGWOVlZUnT55kjLnd7lQqRR1H21CEAKBhN2/efPPmTWaBHXWW2ZZZLjkyMnL79m3qLNqGIgQArYrFYmfPnmWMeTweDvdk0Ol0maUUbW1t0WiUOo6GoQgBQKsuXLgwPj6+fv16l8tFnYXG9u3bm5qaJElqb2+nzqJhKEIA0KRQKNTb21tUVJS7AT1XvF7vnDlzfD7fyMgIdRatQhECgCa1trYmk8n9+/evXLmSOgslQRD27duXSqWOHTtGnUWrsHziL6LRqKqqZWVluYvhVFXNXIUvLy+niKYZ4+PjjLFFixblLjJJp9OSJBUUFGQ2tAL41/x+f3Nz88KFC0dHRysqKqjjEJMkqbq6WpIkv9/vdDqp42gPihAANCaRSNTW1oqi6PV6MQZleL3e1tbWZcuWvX37VqfTUcfRGFwaBQCN6enpEUVx+fLlhw8fps6SL44cOSIIwvv3769du0adRXswEQKAlkQiEYvFMjEx8ezZs82bN1PHySP9/f1NTU1Go1EURZPJRB1HS3ifCHt7e+12e0NDQ2dnJ3UWjnR2dtp+cf36depEoBmnT5+emJhwOp1owSyNjY0Oh0OW5XPnzlFn0RiuJ8KBgYG9e/e+fPmyqKhozZo1V65cwX3m2aEoytTUVObvTZs2tbe342WJ8E8EAoG6urrCwsJAIGCxWKjj5B1RFGtqatLp9OvXr2tqaqjjaAbX736MRCLz5s0zGo3FxcUmkykWi1En4oVer9fr9YyxV69eTU5ONjY2UicCbbBarT6f7/v372jBGVksljNnzlRUVFitVuosWsL1RDg5Oblly5ZwOGy32xVFuXv3bu4WevCf2rFjh9PpbGlpoQ4CAPzi+h5hKBT69u3bxYsXS0pK+vv7h4eHqRPx5ePHj0NDQzt37qQOAnmkvr5eEIRwOJx7SJZlQRAEQZj9VNqSOUuyLOceCofDgiDU19fPfqp8xvWl0Z6enl27drlcLpfLVVlZefXqVbvdTh2KI11dXYcOHZo7dy51EMgjwWBQUZREIpF7KJVKBYNBxpiqqtgT9O+oqpo5SzPuzZRIJILBYObGBPzEdRFWVVUFAoHp6enCwkJRFJcsWUKdiCOyLD969CgQCFAHAQDecX1p9Pjx4/Pnz6+trbVarfF4/Pz589SJOPL48eM9e/bgXWsAQI7ridBgMNy5c4c6Bad2795NHQEAgDHOJ0IAAACuJ0IAyE99fX25l83j8ThJGI168uSJwWDI+hAb2c+I63WEAJBvDAaDoii//590Oo2nRv+Oqqq5u8hl0ev1+FXxK0yEAJB3Ll++XFZWlvWhoihHjx4lyaNF3d3ducskJEk6ceIESZ58hokQAPJIZiIcGxtbunRp1iFJkjLXSzER/sbPiTAajeb+mPj06ZPZbMZEmAUPywAAANdQhAAAwDUUIQAAcA1FCAAAXEMRAgAA17B8AgDyyKVLl5LJZGlpae4hvV7v8XgYY3hk9DcKCgoyZ2nGLSZKS0s9Hg82Xs2C5RMAAMA1XBoFAACuoQgBAIBruEcIAJS+fv3q9/vT6bTD4TCbzdRxePHixYtft8VetWqVzWYjzEMLEyEAkBkbG7PZbPF4PJlMNjQ0jI6OUifihaIosT91d3cPDw9TJ6KEh2UAgMyDBw9u3LgxMDDAGNu2bZvD4Thw4AB1KL5IkrRixYpQKGQ0GqmzkMFECABkVq9e/e7du46OjufPn3/48GHjxo3Uibjj8/laWlp4bkGGiRAACMXj8a1bt5aUlAwNDW3YsOHWrVsLFiygDsWRqamp6urqwcHBqqoq6iyUMBECAJmOjg6z2fz06dMvX76kUqlTp05RJ+LLvXv31q1bx3kLMhQhABCKRqOZb2GdTme32yORCHUijqiq2tXV5Xa7qYPQw/IJACBz8ODB5uZmWZZ1Ot39+/cfPnxInYgjfX19ixcvrqurow5CD/cIAYDSjx8/BgcHE4nE2rVry8vLqeNw5PPnz8XFxSaTiToIPRQhAABwDfcIAQCAayhCAADgGooQAAC4hiIEAACuoQgBAIBrKEIAAOAaihAAALiGIgQAAK6hCAEAgGsoQgAA4BqKEAAAuIYiBAAArv0BckbGm6lFlfMAAAC2elRYdHJka2l0UEtMIHJka2l0IDIwMjIuMDkuMwAAeJx7v2/tPQYg4GWAAEYg5oHiBkY2Bw0gzcxCLM3IAKIZGYmluRkYM5gYWBMY2BgYmRIY2RmYmDOYmDiAJiUwczKwsGYwsXAxsHIziDCyMbCyMDMxis+CuhEMgI48YL961SoVCNfB/qHbsv1Q9n4E+8D+Cf1TVJHE7ZHUw9gOQHOgag4AxdUOwPQi2A4HkNSAxB2Q1IPZYgB4SjAE5sbtAQAAAR56VFh0TU9MIHJka2l0IDIwMjIuMDkuMwAAeJyNk81ugzAMx+88hV+AyE7CR44Fqm6aClLL9gCVOOzCaac+fe1W1GGtIpIgEvOL/44dLtN8neYpA2mn7uv3D57NdhnbMTFCCPDjEDE7gkyg2R8+e2jHXbNY2uG7H89AVgZKX7O7cTguFoIWyBQoDdDg/8nCWebQVHcQcjI2BHT1G9AxmG8ivZBbtIuVy4THMg4ywVXwAe5V74WrmXsGmFtThBqr8g0YGMw3kYSCbhEnWjlN+bRxnAlw33er4j+uQzP0nV4Hz4/Vqsuy1NoS90oL6JlwWibeCbUWw/MGrzknXgZNrewuNIF35ThN/FWVSYRZgKIzc2D8is8Wn0TWy0/B8+wGSWmlw+LBJUQAAACtelRYdFNNSUxFUyByZGtpdCAyMDIyLjA5LjMAAHicbY49CsMwDEav0jEBW0iOHdvqBTKV7iFDCR2LS8mYw9cxpbJLl096PPQzT8tKazdPS/8nKedp7zSBU9qAiwGDOmsEn5nAxIg286Ex16Fksa38jrauUZ8lZcfPgeZ8r25belxf6ckIR3tJ2x0Ck4BhIzDwIBDZClh2AoQ8Cjn2lSIOQshRYGSqniCm6gu/vwHc615hPn7L9wAAAL96VFh0cmRraXRQS0wxIHJka2l0IDIwMjIuMDkuMwAAeJx7v2/tPQYg4GWAAEYoG4QbGNkcNIA0MwuxNCMDiGZkJJbmANPMTNwMjBlMDKwJDGwMjEwJjOwMTMwZTEwcQBMTmDkZWFgzmFh4FFi5Gbh4GEQY2RhYWZiZGMWXQZ3LAHX/AXugk5dAuA72D92W7Yey9yPYB4B0gyqSuD2SehjbAUhA1RwAiqsdgOlFsB0OIJsTlFXpgKTeAUk9mC0GAHfELUSFK5jDAAABMHpUWHRNT0wxIHJka2l0IDIwMjIuMDkuMwAAeJyNk+FqhDAMx7/7FHkBS9JWaz+eemxjnMLm9gYHG4xtH/b+LDnRVE6KbcW0/tp/msTfj+v3z1cB0l76588/WJvtC17HzIgxwrtDxOICYkB7fngaoJtO7bLSjW/D9ArkZKD0LXuaxsuyQtABmQqlQYkGZ0uNBbQMognVDJKxMaJrdkDHYHmI9EKu4hntanNk5sQ6dTLDBXgEd693xzXMaXSsqWKDod4BI4PlIZJQ0CPiREKu13GmiYHI75E29TMn7mBM/MyQ56Hf1MlcOe049Fo5nh+r9SHTWquAuAdNtWfCaUJ5JzSaNs8bvGaHeBo1CbK70lDflNOA8ldVJhFmAZdEhx3jVxIFumEuvW16N5kvfxTbxT+Nz7CFVyIp2gAAALp6VFh0U01JTEVTMSByZGtpdCAyMDIyLjA5LjMAAHicbY4xCsMwDEWv0jEBW0h2HNvKBTK13UOGEjoWl5Ixh28cCrJDl48e//HRNM63hZZmGuf2T9Kel63RCN4pCyF6IjVoAqcMuBgw7HSUBCZG7NRQU2VmQDXYHIemK0+fJqXVp/o3pPNSqx5ret0/6c0I+bym9QmETAUZNkIdWwHHXeEROyHkXqBnX3gcBDxHAcNUfBGYii8sU/FF3L5g2GZIkbLUtQAAAABJRU5ErkJggg==\n", "text/plain": [ "" ] @@ -489,7 +489,7 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", "text/plain": [ "" ] @@ -512,7 +512,7 @@ "outputs": [ { "data": { - "image/png": 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dl6z13nbA7PKMmMOa7ys5GuD9jYNAZiTCkqQ9X6b8p9QihLKN+JY0OUIo1FfCMbvBodfrnj+hMT5gEhTCQqXN+oVQ5iKSSNQbn6h1qo93yqbEqPXP1QaEEKnVpM+ZgkuzaY/JsEePHt2/f79gu1KpvHv3bnx8PP2RQEVm17ip776T7ovXsCWu+dvrCHkHanpGVXP35XGeqfUDY9PGvslYnCjNxYnvnYTfiezMv5HBoH1s4Y0NyiUohJap/7msf/2CNBo//UtIo166abX1IgEAPgmL5dill/+fl8QjxpsN+2znJDxbx2eKl9iOhV2Wq8/mqNgYmu4tLvg9CL1OH/eKrsSAYVAILcvZ8SupVn/Wl5B6fe5fxwm1ykqRAACfjuUocpk8WzJ5NvZ+ICiFWpvtXB0fCYfNwTCcRKNepx+VKguOlcg99af8wE4S/4xPw6CMgkJoAaFW6V48/YIvxDgczR2YJAeAreDVCMAE9gXb76l0UiMuZGH1hfx0Az7zbVbvF6nU7k4mpF6XFbEksd+P6ptX6coLmAHTRS3Qv3rB4gtww0ejY+Q4kWUwmz6BzD5FEmq1LuaxfZsfrBzQtpAkmZ6ebtYolUoZCQNAfvyadUi9DiESoQ9jYbQEGZGcgxCa4yvp4+JwTKpclZzzWK3r9zK1u8Rhto+zG/fDSaTh7ZvUySOErdq5zlxottIpKDegEFqAS7MKXif5+bX533qKMP8Aa4IwpplP7C33ZDKZp6cn0ykAsIAtceH6+OnjX+dvjEqXJ+uNdYS83i4OGEI9JQ4/iu2pTZ2OSZXnZKqCmzqp/7mcePsfUe/BRW/qBMooKISWYBauGI/yEPkU2PxzdXKBZTPZbPOW8k4oFK5YscKs8d27d2vWrGEkDwD5iUf+krFopmkbpjSDcXu6HCE031di+j2nbhx2l9hHpMjO5qg2pMqOS1XB3uJOzh8uq5IGg/zgLtWls5JJsxx/6gnrSZUnUAgt4Hh4FrjqiTqL7RvY880a16fIPv5KDte3klWz2SA+nz958mSzxjt37kAhBLYA43IRgZuujq5IytEQZBdn+28cBGY9K/G5G6u43XJ1DEuSvtToJ8dnNs/KDfWV5F+A1JiZnrEwSH5wl+vMBWYrnYKyCwbLWMCrFkDqC19KpnAsgZ2gXqNSzwMA+DKkXpe9cSVCiKqC91W60zkqAQub6VPo5krNHQUnanmF+btIOOxbudruL1LmJWRLzTZ1ev4keVS/9NkTC65xCsoiKIQWYDyeXZMWFi+QFoMkBF81tUIiAMCXkO2JMqYkUY8JhMISpSRCYz2cCt7myI+NYQNcHS8E+gx3FyGEDmbldohJ3pWhwPMv3kaSyotnEvp0kG6LNFvjFJQ5UAgtcx4zBeObXwgtBoYcO/fCuFzrJAIAfB5jRpps11bT4ZFs5WO1zpPLGePhVLAzJhBg/I8uljqxWfN9JWdq+7QW2clxIixJ2ul5yt8fb+pEajU5UesTerTJPfUngnWbyywohJYJ6ja0b93e7BejGCQypiZaLREA4PNkb1hJaPKWxVDhxLoUasqEs51pOCiGMA6HZScUfvudR/iGqv889Vi5iePpnf+bVBNwd1T3iKrm7s/nxGkNo9+kD3+V/ubjlYeN6WkZC4NSxg3Sxz6n44WB0sZetGgR0xlslPDb1soLp0mVSqY3OHFYbZyEIrb554Y0A15byG31fqFCQ8JbQb1GXL/KdGdlTkpKSp06dTp37mzWrtfr5XJ5q1atGjZsyEgwUMFpH9/PXrPUdLguVfaPQtPInh/qKzEN93QePdlz7XbJ+OmOnXvyKldDLBavag1Rr4EYh6t78jD/tmtVBNzBbo5iDvu+SvdGaziYpczBicb2fF6+KRbG1CTF0QPGpHeCBl+z7IQ0vU5QGmAbpqIQClnqL0P1b98QGk1hfYwkmWMkTDNweVWq+x48i3EqxHDctLQ0FxcXLlwNBraGJJOG99TFPKKOEnTGH58lG0nycC2v+sK8Wx5cv0p+h85jPJ7Fb2BMT5VuWp17+qhZe4YB35Aqi87OxUnkzGFP9HTKv6kTheUoch4xwWnQz4V9c2Br4IywKBhf4Ni9H2KxdU8fIB4fGcx3YorV6Ae/Sr+m0PR2yZtji8ukbGeJoG6FOA3q0qXL6tWr27Rp4+7uznQWAD7IPXVEEb3XdDjzXdZrraGPi8Pg91vvIoQ8lq7lVale2HdgOTjat+1o17ip7mUMLs0ytduzWe2chO2chK+1hjit4ZpCc0WhriHgevM4WQb8qFT1UmvwQzj+303lxdNcbz9upSpWeo2gFMEZ4SchVErlhdOqy+f0b17i2Vnv9+kllTjZ4VlypgH/tarbj+K8ubcskZP/0StscaHjs8uH6Ojofv36ubm5xcbGisUW1u8HgBGEWp3Yq50xM28pqJu52mGv0uzZrAt1fNzfX7mxa9rCe/O+T/t2RO6ZY9nrlxXcZO2yXL04UZqsNyKEWons0g3GUe5OOUb8RI7qRC1v0w9yDVrAq1bzw5eRpDEznchVsIT2bHcPs/0xACOgEH42Y3paYu/vqZvw73SGkITsf3O1XjzO+To+J6XK0zlqVy5r6bgx9ZatYzqpFWm12tq1a799+3bbtm1jx45lOg4AH2RvXCXbtYV6jJNk1xepsRr9bB9n02BRjM32PXDmo+JUHCJXkbNri3z/DvLjy0IagtyTqdiUKlcThB0LG+YmGu/p1DYm6d96fuz3S89gHI6ozxDJ+Onapw/lB3Zq7t5GGIZxOCSOI6OBV7OOU98hDj92hwHnDIJC+CWk2yJzotbrCDIiJYfPwg5l5UqNRG8Xh7c6w67qnjdzNVHpipv3H/Bq1GI6qbUsXbp0wYIFDRo0uHfvHrvirSoHbJYhOSGxzw+miX27MxRLk6T+fM5fdXx47yuTePAolxnzvuSbv4vPWhumvn7ZrD3NYIxIlh2XKkmEeBjWw8U+3N/VbAU2jMVGbDZpNBScZcGyE2L2Dh7L1ts1bvYFqUDJQSH8EqROm9C7PbWoxCW5+ki28oJMzcbQLB/Jz+4ihFC7mKST/brW3/Mn00mtIjk5OSAgQKVSXblypU2bNkzHAeCDtKBxqr/PU49lRqL9sySZkdhWzf17p7xhnCyRuNLxKyzRl1/M1/x7IyticcFtex+pdCEJ2S81eoRQfXv+Ql9JwUUZi4AJ7Fymhzr1GfzFwcAXg3mEXwLjC1wmzTIdunHZHcX2RhKZJtt6cDmJd/9VXTnHUEDrmj17tkql6tu3L1RBYFM0/900VUGE0LrUHJmR+NZRYKqCCCGXicElqYIIIbumLXwPnHENXsBycMzfXlvIO1nbe3VlVw6GPVbp+r5MDX6bZdq7LSxJ2uJJotkEREr7mOT+samkVpO9Lkx54XRJsoEvA4XwCzl07Cpo9I3pMNTXmYOhW7mau0otQkiBE2IOK3vdsvK39tLt27d///13gUCwatUqprMAkA+BZ+WbOPhaaziYlcvGUKivxNTIq1bTsWf/kv8ojMNxGjjS/8RVpwEjEIuNEMoy4INi0/ZlKt7pjLXtuJO8xBwMOyZVtotJ3pAq05OkzEikG3C9pStw6QZjhgFHCJFabebiWcaMtJInBJ8FCuGXwjDXmQvR+80IvXic1iIhiVBYkjReZzCQpDuXY0hOkO3fwWzM0kWS5NSpU0mSDA4Orly5MtNxAPhAfni//tUL02FYkhQn0RA3UUC+vSNcg+aX4ihNtpOz68yFvnuPCRp+7cplb6vm7s5lNxTyf6/pNc1LfKaOdydnezVB/JahkBuJT/yepNEgzVslHNAH7hGWyODG9a/HPFfiZKCQt76KW8sniWqC9OFxVld2beIgQAixhEK/Py9z3DyYTlo6du3aNXLkSB8fnxcvXjg4wPakwFYQCnlCz7a4LG9/0PMy9S9xGU5s1qVAXzEn79OqQ/tOHis3WymA6tqlrNWLTAt8m1xVaHKMeA+JQ/DbrGNS5cna3rXtzGfZ13v4zpXLvhLoSx1ifH7li/dYQnsE6AJnhCWy58KVv5vWvlPfb2d1DxGbtcTfBSGkI8k679/rhFot/XU1oxlLjVKpDA0NRQitWLECqiCwKdJt60xV0ECSq5JzEELTvZ1NVRDj8SSTZlsvgP133/sfvuAyebZZAWstEvSQfN4vC8blau7cLNV0oBhQCEuELXFx/nmi6bC7xOFrB0GWAd+aLjc15p7+07TUU5kWHh6ekpLSrFmzwYNhYBuwIfr414rD+02HO9IVb3WG6gLuQNcPFUg8dCzXz7qbZmN8gXjEeL8jFz/ev/6jaRRxWsMztd7sP7PLpqRGo4+NsWpUYAYujZYUaTAk9u9oeBdPHT5V63u9SGFj2Nk63pX5eTNkBfUa+ew8ku93o+yJi4sLDAzU6XS3b99u0qQJ03EA+CB18gj1zavU4ywD3v5ZshIndlX3aPl+NXy2i5v/0csse/ouY+hiHqeHTjUkvjW1UJdGC+vvx+eYLo0ihEQ9+rvNX2HVhCA/WN2npDAu12XK3LSgvNVV6gp53SUOR6XK1ck5m6rmrcCpffIg9+xxx849mItZUsHBwVqtdvjw4VAFgU1RXb2ovnmVuhz6n1KbZsCVOPGDWGiqggghl6lz6ayCCCF+YH3JxJkZC4NInTZ/e4ivxLvAnsAz4jPNWmC1bprBpdFSYN+mg7D5d6bD2T7ODmzWOZn6uuLDnhXSjSsJtZqJdKXg8uXLR48edXBwWLZsGdNZAPiANBiy1y9DCMVrDW5cdpi/q9SAYwjN8vmw0i+/dj3HTt3pz8b19sE45qumNXcU/CgWmv1ntnkFxuPTv5Wb0Wik+SfaFCiEpcMlaL5p6yVXLnuchxPKG8Cdd+XZmJEm272FsXwlgOP49OnTEUIhISHe3t7F9geANvIDO6m7EjXteGM8nMKSpARC9myW6a4EwjDXOUtM05zoxKtRmzR8yTRijMvl1/+q1PMURiqVhoaGent779q1CyH0119/ValSpUOHDh06dHj79i1tMZgFhbB08KpUF/UeZDoc7SGqzOe+1hoOZn24KyDbs73g6Grbt3Xr1sePH1etWpUqhwDYCFyanbPjV9PhCanyrlJrz2b1lHwYt+n4U0+m9kTDeDxh89bY59dgjM8X1KlvjUgW4TjetWvXcePGEQSBEEpJSRk/fvyFCxcuXLhQceYKQyEsNZLxM0xbL3ExbKaPM3q/yBPVSOp12euXM5bvi+Tk5FA7VkZERAgEAqbjAPCBdNNqQplLPdYQ5JoUGULIncOe5ZO3lAxLKJTkWwqRfs7jpmK8z1huFCGE2Qmdx0ym8xTWzc2tWbNmrPc/MTU1NT4+fvXq1VevXqUtA+OgEJYalsjJeew002FHsbClyE5mJH5Nk5kalRfPaO7dZiLdF1q0aFFWVlbbtm179uzJdBYAPtC9jFGciDYdbkmTp+iNIjbrz1pedqz3u0yM/IXZtSz4AYEOnbqxPv0TJJvF9fIR9WZyelKXLl3atGnTpEmTkJCQQ4cOMZiETlAIS5NT38G86gGmw1BfCRtDezMVsRq9qTErYgkicCbSfbbnz59v2bKFzWZHRkYynQWAj2RHLEFE3rWWVL3xf+lyhJA/nzMpLnP4q3QlTnB9/MRDRjOaESGEXGct4VWtgfH47cV2EzydXDkW9iwb4+E01E2E2Cy2g8hr4y5mt+pt0KDBgAEDWrdu/csvv1y8eJHBJHSCQliqWGzX4AWmoxoC7gBXR5xEYUlSU6M+9rni6B9MhPtsM2bMMBgM48aNq1+fvjsWAFhE6vW5Z46lTBoe//1XcV9X1dy/Y3pqWXKOniS7S+yP1fLeXcNjdw0PBzbLZVrI516WtAaMx/OKOij46ptO3m5B3s5uXAuFcIqXeJS/O8fD22fvcY4nA+PRcnJyNBqNWq1WKpUbNmy4c+eOVCo9cuRI06ZN6Q/DCJhQX/rSgsaq/r5APTbtiBZVzb3d+71g2M4S/6NXWI4i5jIW78SJE927d8uacq0AACAASURBVHd2do6NjXV1dWU6DqjQlBdOZy2fRxoMhFpl9pSRJKe/zfpbrr4Q6OPJzTuXsvu6ufe232mPWTiSzD0Rnb1+OWk0EmpV/r15WUJ7EiecBg6XjJ2K8Zm5DT948GCDwYAQaty4cefOnSMjI6VSaceOHceNG4eV5WVAPh0UwtJnSE5I7NOB1OddDt2VoQgruEf2kNEu00OZy1gMvV5fr1692NjY9evXT5kyhek4oAIjiMzloblnjpNaTRG90g24h+lki8X223+SV7M2HfE+B4kbNbeuqa5e0r14SihzMT6fW7WGfYu29m060DzfH5iBQmgV2RtXynZtpR7jJNn1RWqsRj/Hx3m0hxPViLHZvgfO8KrVZCrh9evXtVotQsjPzy8gIMDs2dWrV8+aNat27dqPHj3ics0nBQNAm8xlocozRwlNXhW8p9TdytWkG3AWhnx4nDYiu5oFNnMQ9RniNndpge9Ubv3xxx+3b9+ePn26v7+/2VOhoaF8Pn/BggUWvxCYQCG0CkKtSujZDs/KoA5v5GqGv0q3Z7Mu1PFxf/+51a5pS+/Ne5lKWLly5QkTJiCEmjdv/t133+V/KiMjo2bNmnK5/OzZsz/++CNDAQFAqktn0xcGkRoNQijTgE+Jz/xPqcUQknDYRpKU4wSGUFeJfbi/q2mkKMvewf/ENdNEpopg9OjRO3bs+Pfffwsuf+js7Ozg4JCYmMhIsDIE1hq1CpbQ3mVicMbivDlMLRzt2jkJL8vVkamyZf4uVKPm3+vqG1eELdrSH0+tVjs7O8+ebXlXmpCQELlc3rVrV6iCgEGkwZC5bB5VBTUEOfRV2mutYZibaLynE/Vp8o3WsCJZekKqUuDE9moeVCVkOYgqVBUEpQJGjVqLY5fe/MAGpsN5vhIehh3Oyn2s/rDqUtbqxaTBQH+21NTUnJyc/v37jxkzJjY2Nv9TDx482LlzJ4/Hi4iIoD8YACaqS2cIfd4vy450OVUFF/hJTNdUqgm4W6t6NHUU/C3XnJPlLeSLK2S6l7CHEfg8UAithsVyDV5g2nrJn88Z5i4iEApLlJouRhsS38kP7aE/WtWqVS9evLh///6ePXv269cv/1PTpk0jCGLq1Kk1azJ2/xIAhJDixGHy/RjRw9lKDoZN9HIy68PG0DQvMULocHbeEjNIr1edO0ljTFAeQCG0IkH9rxw6djMdTvZ0cuOy76t0Z3I+DAHP2RaJZ5tvwmJtGIZVr16dw+F07NgxPj7e1H7w4MFr1665u7tTO9EDwCDTdtaZBjxJbwyw47pYmo3+lb3AjoU9UOadO5K4UQ3bu4PPBIXQulymzmHZ5U0ftGezqE+vy5NyNETeaSGhUkq30b1uy99//z18+PBTp05Nnjy5T58+VKNGo5kzZw5CKDw83MnJ/KM3AHQiDQZSk/d5Md2AI4S8uJYHNLAx5M7lyHFCR5h2ekmlJ6RN6dOnT40CFAoF07nKBhgsY10cd0/x8HHSreuow76ujgezlE/Uuu3p8ileYqpRcfSgqOcAfu16tKVq06aN0Wi8e/fuDz/80LVrV6px5cqV7969a9So0ciRI2lLAoBFpNGAMDZCBEKI2suMU/jEbi6GEEIGkuQj6hED990ZFxAQIJFIzBrfvXvHSJgyBwqh1YmHjVUcP2RMTUYIsRCa7yfp/zI1Kl3e28XBh8dBCGEcrv5NLJ2FECHUvn379u3bmw6TkpKo0TGRkZFstoULUADQiSWwM83sEnFYCCE5ThTWWYYTHAyzZ+dd37LxNZusJDw83OL0CUbClDlQCK0O4wtcpsxJnzuZOvzKnt/Z2f50jmpFsnSpn6uweSvJ5NkGLx+pVFr097Gq6dOnq1SqAQMGmM0pBIAZGMb19DYkJyCE/HkcOxb2XKPHScQucF6YbsCzDHigkGd6hlvNfIEIAIoGhZAODj90URzep7n3L3U4y8f5vEx9Xan/+nECerwfbdvPbDyEkEgk4vF4K1asYDoIAHmEbToo/thNGo1sDGstsvtLpr6iULd/v2CvSXRWLkLo+/ftLKG9w/cw/xV8HiiENHEJWpA0pBu1AZPazgHHMJzLE9s72MKatiRJ6vV6o9EIt9aB7XDqNVBxeD8yGhFC4z3FF+TqRQnSGjW5lfgf1vy7o9RuS5c7c9hD3BypFtJotG8HhRB8HiiENOEH1BF165N76ohjt75rLt0iiBdjxoyxnX3+pk6dumHDhqlTp16+fJnpLAAghBC3cjX7lm1U1y6RBkNdIW+Br8vixOwuz1O6SRxq2/Fwkryr0p2XqXgYFlnFVfJ+ZgXL3t4Wdl8CZQusNUofXJpNKGQn7j3s06ePRCKJjY11cXFhOlSenJycmjVrZmVlHTlypFevXkzHAQAhhPAcaWLv73G5HCESIfSfUrs5Tf5vrlZPkgghBzarrchuspe4quCjdeFdZy126j+MmcRMCA0NPXTo0JEjRwruG/r1118LhcJr164xEqwMgUJIK51OV7du3devX2/evJla89p2bN68eeLEiVWqVHn27JlAwMy+aACY0b18ljJmAKFWITJv1KiBJKVGgoWQhMMuOHYGIcQSOfkfvVJBVhyl/oDbwh2WMg0m1NNqzZo1r1+/DgwMHDNmDNNZzFE70cfHx69bt47pLADk4QfU8d17jOPji71fmIKLYR5cthv3QxVM1hsXJWabJtQTCnlOlK3cdLC2Q4cONWvW7M6dO0wHKdvgjJA+6enpNWvWVCgU58+f79ChA9NxLLhy5Uq7du0cHBxevnzp7e3NdBwA8pBGo/zQXtmuLaRGjRBJaNQo39+t3i9TH6l0wd7O4z3fr4jEYvsdOM2rXs7nUWg0mtq1a79792779u2jR49mOk4ZBmeE9Jk1a5ZCoejVq5dtVkGEUNu2bXv27KlUKkNCQpjOAsAHGIcjHjSy8vk7Pr8ddg1eKBkflH/W/ExvZ4TQ5jR5hgHPayLwrIgljESl06pVq2A1qFIBZ4Q0uXfvXpMmTTgcztOnT2vUqMF0nELFxcUFBgbqdLpbt241bdqU6TgAWJZ75ljG/Ommw1/iMs7L1L1cHFZVcjU1eq6Jsm9jox86Sy4pKalWrVoqlerq1auwDkYJQSGkA0mSrVq1unHjRkhISHh4eBE9//jjD2tPYGjXrl3//v2L6BASErJ8+fJmzZrdvHkTbsIDG0WSyaP7aR/epY4SdcYfnyfrCfJwgFcD+7zpE1wff7/DFzAej7mUVjRo0KADBw7079//4MGDTGcp86AQ0mHv3r3Dhg3z8PCIjY0ViYpaCHHy5Mm//vqrVcNMmjRp48aNRXRQKpUBAQEpKSl79+4dMmSIVcMA8MV0L54mDe2OiLzRpBEpOVvT5A3t+dEBXqaPby6TZ4tHjGcqofXcunWrRYsWAoHg2bNnlStXZjpOmQeF0OrUanXt2rUTEhJ27949bFgx05tu3Ljx9OlTq+apW7duixYtiu6ze/fuESNG+Pj4vHjxwsHBwap5APhiGQuDc08doR6rCaJ9THKGAY+o7NpDkvemZQnt/Y9eZru6M5ex9BEE0axZs//++2/hwoWLFi1iOk55AIXQ6ubNmxceHt64ceM7d+6wWGVjdBJJktSY7Hnz5i1dupTpOABYhkuzEnq0JVRK6vDPbOWsd1keXPaFQB/h+981x2593ReuYi5j6fvtt99GjRrl6+v74sULe3t7puOUB2Xj73LZlZCQsG7dOgzDIiMjy0oVRAhhGLZ+/XoMwyIiIvJvYQ+ATWFLXMUjPyxM0dPFoYE9P92Ab0v7sGpu7qkjps3uy4Hc3Nx58+YhhFatWgVVsLSUmT/NZdSMGTPUavWQIUNatmzJdJbP06xZs8GDB2u12lmzZjGdBYBCiYeM4fpXph5jCM33lWAIbU+Xv9O9356XILIilqDycukrLCwsNTW1efPmAwYMYDpL+QGXRq3o77//btu2rVAofP78ub+/P9NxPltycnKtWrWUSuWFCxfy7+ILgE1RXTmXFvxhREzQ26zjUuWPYvtfq7qZGj3CIx1+7M5EutL05s2bwMBAg8Fw69atgtvwgi8GZ4TWguP4tGnTEEIhISFlsQoihHx8fGbPno0QmjZtmtFoZDoOAJbZt+0obNbKdDjLx1nIYv0lU11XaEyN2etXEBo1E+lK04wZM3Q63YgRI6AKli4ohNayffv2R48e+fn5TZ8+vfjetio4OLhy5coxMTE7duxgOgsAhXIJmo+x83Zi8uCyx3mKEELhSVL8/RUvY0aabPc2xvKVhsuXL584ccLR0TEsLIzpLOUNFEKrkMlkCxYsQAitXbtWKDTfU7sMEQgEq1atQgiFhIRIpVKm4wBgGa9qDVGvQabD0e5OvjzOK63hULbS1CjbE2VMSWIiXSkwXWEKDQ318vJiOk55A4XQKhYvXpyZmdmyZcvevXsznaWk+vbt26ZNG6lUCp9DgS2T/DKD7ZS39RKfhc32cUYIrUnJkRnzZtyTOm32xpWM5SuZzZs3P3nypGrVqlQ5BKULBsuUvhcvXtSvXx/H8Tt37jRu3JjpOKXg0aNHjRs3xjDs4cOHgYGBTMcBwDL5wV1ZqxebDge/Svs3VzvSXRTqKzE1ekcdtGtcxhbRzcnJqVGjRnZ29rFjx7p3L/NDfmwQnBGWvhkzZhgMhrFjxxZdBTdv3tyxY8cycb2xQYMGo0ePNhqN8GkU2DJR3yG8ajVNh/N8JWwM7clUvNIaTI3Za5YgArf01bZrwYIF2dnZ7dq1gypoJVAIS9np06fPnj0rEomKXvooNjb2wIEDWVlZOp2OrmglsnTpUrFYfPHixTNnzjCdBQDLMDbHNXiB6bC2Ha+fiyNOorDEDx83dS+fKU4cZiLdF3r27Nm2bdvYbHZkZEXZbZh+UAhLk8FgCAoKQggtWrTIw8OjsG4EQYwbNy4yMpL9fpyb7XNzc5s/fz5CaOrUqWWleIMKyK5JC/vvvjcdBnk7izmsG7mav+UfplJIN60mlLlMpPsS1BWmCRMm1KtXj+ks5RYUwtK0YcOGly9f1qpVa9KkSUV027p1a8OGDcvc7cMpU6YEBga+fv1606ZNTGcBoFAuQfNNWy+JOayJnmKE0NIkqf79eAhcmp3zv6I2YLEdx44dO3funLOz88KFC5nOUp5BISw1GRkZ1LjKtWvXcrncwrqlpKQsXLiwXr160dHRUqn04sWLNGYsEQ6Hs27dOoTQ4sWL09LSmI4DgGVc30pOA0aYDoe5OdYQcN/pDPsyP5wFyg/uMryLYyDc59Dr9dQCh0uWLHF1dS22P/hiUAhLzbx582Qy2U8//dSpU6ciupEkGR4ebjQac3JycBxXKBRFdLY1HTp06Ny5s0KhgM+nwJY5j5li2nqJjWHUqNENqbJMQ94wGdJgyFpX1BbZtmDdunWvXr2qXbv2uHHjmM5SzsH0idLx8OHDr7/+msViPXnyJCAg4BO/6vvvv//999+LuJtog16/fh0YGGg0GsvN5BBQLimO/ZG5dI7pcPSb9L/lmgGujmH+LqZGr427hN+2ZiJd8dLT0wMCAuRy+V9//dWxY0em45RzcEZYOqZNm4bj+OTJkz+9CiKELl26VLaqIEKoevXqEydOJAhi6tSp8CkK2CxRt778wPqmw/m+Eh6GHcrKfarWmxqz14aRtrqI7ty5c+Vyeffu3aEK0gDOCEvBoUOH+vfv7+bmFhsbKxaLmY5jdQqFIiAgIC0t7dChQ3379mU6DgCWaR/dSx7V17QBU3iSdGeGorED/2BNL+x9H9fgBU4DRzKVsDAPHjz4+uuvORzOkydPatasWfwXgJKBM8KS0mg01BYNYWFhFaEKIoRMsySDgoLU6jK/oj8orwQNGjv80MV0ONVL7MZl31Pq/spRmRqlUetxWQ4T6QpFkuTUqVMJgpg2bRpUQXpAISypiIiIt2/fNmzYcNSoUUxnoc+YMWMaN26cmJi4du1aprMAUCiXqSEsu7xV7x3YrCleYoTQsuQcDZF3mkgo5NIttvUePnDgwD///OPu7h4SEsJ0looCCmGJJCcnr1y5EiFUtmbHlxyLxVq/fj2GYcuXL09ISGA6DgCWcTw8nYaOMR32d3WsK+Sl6o070uWmRsWfB/SvXjCRzgKNRkPVv+XLlzs5OTEdp6KAQlgis2fPVqlUffv2bd3aRseeWU+LFi169+6tVqtDQ0OZzgJAoZxHjOd4+VCPWQjN93XBENqaLk/Rvx8mQ+BZEUsYy/exFStWvHv3rlGjRiNGjGA6SwUCg2W+3K1bt1q0aMHn858/f165cmWm4zAgMTGxVq1aGo3m2rVrLVu2ZDoOAJYp/zqRHjrVdDgpLvMvmaq7xGFN5Q+z1D1Xb7Fv9yMT6T5ISkoKCAjQaDRXr15t1aqVxT4GgyE9Pd3X15c6jI+PZ7PZ/v7+NMYsh2z0jLB9+/aDBg0q2H779u1WrVpFRUXRH8kMdSubJMmZM2dWzCqIEPLz85sxY4bp3j7TcVBUVFSrVq1u375d8KlBgwa1b9+e/kjAFjh07Cpo8LXpMNTX2Y6FnZAq7yq1pkb5H3uYiPaR4OBgtVo9YMCAwqrghg0bvvnmmx49elCHgwcPnj59+pgxY4pe0xEUy0YL4Y0bN+7evVuwXSqVXr9+PT4+nv5IZnbv3n3nzh0fHx9qyGiFNXfuXH9///v37+/Zw/zfkfj4+OvXr1vc2eru3bs3btygPxKwCRjmOnsRYuX9ufPicX52dyIRWpokJRDCBHbOY6d6bfiN2Yw3b948dOiQnZ3d8uXLC+vTuXPns2fPUo9v376dnJx87Nixv/766/r16y9fvqQraTnEYTpAmaRUKqkbYytXrrS3t2c6DpOEQmF4ePjQoUPnzJnTq1cvkUjEdCIALOAHBDp27ZN7/BB1ON7T6VB27jO1vt3rbAcPT2x9FFrP8HWm1NRUkiRnzZpVqVKlwvpUr149PT2denzv3j3qxBHDsG+//fbBgweftZoHyI+BQnjt2rXnz5+3aNGibt269P/0UhEeHp6amtqsWTPT9dtbt249ePAgMDCwIoya+e+//x4+fNikSZMGDRoghAYPHrxt27br16+vWLFi2bJlTKf7QtQC6HK5vEuXLl5eXkzHAaXPZdIs1aWz1AZMdizM1cFebySTFLlIYRNbMnE4HBaLVadOnU/sr9Vqee832RAIBBqNpuj+oAh0F8LNmzf//vvvP//888iRIyMiIspi2YiLi4uMjMQwjJo/gBDasWPHmTNnevfuHRYWFhsbO2bMmGK/Sdl16NChDRs2jBkzZvTo0WFhYR07dsQwLDIyskmTJmvWrBk5cmSNGjWYzvjZkpKSfvrppxEjRggEguTkZCiE5RJb4uL888TsDSvYru7/1G/+PGK9s7PzzZs3bWSWwoEDB8LCwubOndu9e3c+n19s/0qVKp0+fZp6/ObNm65du1o5YHlGdyHcvXv31q1bGzVq5OXltWXLliIKIUEQMpnMrFGl+rAkRG5uLiMDNKZNm6bVakeMGNGkSROqZcCAAcOHD+dwOAaD4c6dO/RHolNERMTOnTsDAwOrVKmyfPlyaiHExo0bDx06dPfu3cHBwYzcLGSxWI6OjtRjlUpV8J1T9Ftl7dq148ePnzBhgrXyAdvgNHAkSeD87v0Xfv0NQmjZsmXNmzdnOlSeRYsWHT9+/MmTJ5GRkYWNPEhKSnr+/Llarb5371779u1DQkJOnz6t0Wji4uIKG18DPglJr0GDBu3cudNoNG7fvr1p06aFdRMIBEVknjNnDkmS7u7utP0r5efo6EidN+QPnJiYOHPmzJYtW759+9bq/4iMkkgkOI6TJCmVSqtUqWJqT05OFggEpmpEM3d3d5Ik58yZU0QfgUBQ2Itq167dhAkT2rZt27t3b7P/s6D8Wbp0KUIoMDDQYDAwneUjly5dQgg5OjqmpKRY7HDy5MnZ7+Xk5Lx+/XrOnDnz5s1LSkqiOWo5Q/cZ4bp164KDg/fv3x8YGFj0H01XV9eCH4tevHixY8cO6rFIJNJqtQW+zrpIkjQajTiO5z83RQiJxeJevXqlpqZGR0cHBwcjhPR6vekKfjlgejkYljf3FMdxDufD+0elUuE4jmGYo6MjdcWYTvkH6YwaNapWrVpmHVauXKlUKgv7cpVK1bx5882bNx84cGDGjBkHDx60VlDANNNqUOvWrcv/BrYF7dq16969+/Hjx0NDQ3/7zcIo1i5dunTp8mH1VLFYXMQQU/AZmKrAGzdunD17dmHPCgSCGjVqFGynrolTZ4RM+fnnnxFC3bp1M7Xo9XrqwZ07d1q3bh0fH9+lS5dhw4YxFNAqhg0b1qVLl/j4+ObNmz98+JAkycuXL3ft2tXUgbpFMWrUKOYy5p0Rnj59uuBTNWrUKOKMcMCAAadOnSJJ8u7du82aNbNiRMC0IUOGIIT69OnDdBDL3rx5w+fzWSzWv//+y3SWCoTuQnjjxo21a9eGhYU1atSIGi5skS0XwvT0dOru+tmzZ6mWjRs3jhgxYvfu3R06dNi4cWNcXJxAIMAw7Pbt2wzmLEV3795lsVh8Pv/Vq1fHjh1r2rTppk2bGjVq9Pfff1MdLl68iIq8pEOPLy6E//zzT6NGjdavX//NN9/s2LHDmhkBk27duoVhGPVOZjpLoWbNmoUQat68OUEQTGepKOguhBkZGfv27Ttx4oRCoSiimy0XQpIkqUsrtWvXNp0L3rp1a+fOnf/99x91OHfuXIRQ48aNqdtpZRpBEE2bNkUIhYSEUC1Pnz7du3dvbGwsdWgwGKiZMKtWrWIuJkmWoBCSJPnq1atdu3bdvXvXaukAw0zv5NDQUKazFEWhUFDjlvfv3890lorCRtcatbOz8/Pzi42NNWs/c+bMTz/9NGfOHGavjOv1+nr16sXGxq5fv37KlCkFOyiVyoCAgJSUlD179gwdOpT+hKVoz549w4cP9/DwiI2NtThffv369dOmTatWrVpMTMynDPu2nrlz565YseL06dOdO3c2e6pmzZqJiYkw16oi271794gRIzw9PV++fFnEyg/JycmXL1+2ahKRSNS9e/ciOvz222+jRo3y8fF5+fJlBV+ygyZMV2LLbPyMkCTJ48ePI4ScnZ0zMzMtdti1axdCyNPTUy6X05ytFOXm5np7eyOEdu/ebbFDdna2i4sLQujEiRM0ZyuoJGeEoHwzvZP37NlTdM+TJ09a+69u9erVi86A4zg1O2vBggWl928ACmVbg6ZM+vfvT/15NePj4zNw4MCGDRvSH8lMt27dOnbseO7cuUWLFv36668FOwwbNmzLli3//vvvqlWrwsLC6E9YKpYvX56SktK4cWNqiEFB8+fPz87O/v77721hPm/Dhg0HDhzo4+NT8KmuXbtmZ2fTHwnYiGXLlqWkpDRr1qywd7KJr69vsX1KyNPTs+gOLBYrMjKyRYsWq1evHjlyZIVd1p8+TFfiMuzZs2dcLpfNZj969MhihzJxZ74IplE///zzj8UOMTExHA6Hw+E8fvyY5mwAfLoyOn5t4MCBCKH+/fszHaT8g0JYIpMnT0YItWvXrrAO1A3C3r1705mqtPTq1QshVMQ8kB9++AEhNGXKFDpTAfC5in0n26bExETqBqFpeDawEiiEJSKVSl1dXRFCf/75p8UOSUlJDg4OCKHz58/TnK2EqPECQqEwISHBYocjR44ghCQSSVZWFs3ZAPh01DvZwcGhLK4ZtGjRIoRQw4YNjUYj01nKMyiEJUXdIKxatapGo7HYwWbXcyqC0WikdpYICwuz2EGn01GLa2/atInmbAB8OqPRWL9+fYRQeHg401m+hFqtpm4QRkVFMZ2lPINCWFJGo7FevXoIoeXLl1vsoNFoqlSpghDasmULzdm+2ObNmxFC/v7+KpXKYgdqu6U6deqUoeoOKiDqnVylSpXCPqfaPmrBP3d3d5lMxnSWcgsKYSmglsot4tpLdHR0GbqKaLree/jwYYsd0tLSqGlY586dozkbAJ/O9E4+cuQI01lK5LvvvkMIBQcHMx2k3IJCWDp69OiBEBoxYkRhHTp06IAQmjZtGp2pvszUqVMRQm3bti2sw/DhwxFCPXv2pDMVAJ+r2HdyWXH//n02m83j8V68eMF0lvIJCmHpePPmDTU+u7Clcp8+fUrNNHj69CnN2T7L8+fPqTkh1MraBVHrjvJ4PNMSawDYINPspsLeyWXL6NGjEUJdunRhOkj5BIWw1FDLmjRr1qywpXLHjRuHEGrfvj3NwT5Lp06dEELjx4+3+CxBEC1btkQIzZ07l+ZgAHyWH3/8ESE0YcIEpoOUDtNa/2fOnGE6SzkEhbDU5ObmUkvl7tu3z2KH7OxsiUSCEKJ2/LFB1OJSYrE4IyPDYoe9e/cihDw8PMr0unGg3KPeyUWsgEiS5KlTp+bPn28LSwN+otWrVyOEatWqZVrrH5QWKISlidpL08fHR6lUWuywdu1ahFD16tW1Wi3N2Yql0+kCAgIQQuvWrbPYQaVS+fv7I4R27txJbzQAPoPpnRwZGVlYn507d7Zs2fLUqVPdu3fftm0bnfG+mE6nq1mzZtGvC3wZKISlybRU7vz58y12MBgMgYGBCKGIiAiasxWr2M+b8+bNQwh99dVX5WBvKVCOfcqZU48ePaj9RJ8/f960aVMa05XIiRMnij3TBV8ACmEpu3nzJoZhAoEgPj7eYocLFy4ghBwdHYvYl5h+xd6BSEhIEAqFRaw7CoAt+MR7aQsXLpw+fXpaWtqOHTt8fHxoi1dy5ezep42AQlj6Bg8ejBDq27dvYR2o3fLGjh1LZ6qijRkzBiH0008/FdahT58+CKEhQ4bQmQqAz/WJoyv1ev3ixYv79++/Zs2aunXr0pOtVJSz0bA2Agph6UtKSip6qdxXr17x+XwWi2Xa0Z5ZDx48YLPZXC63sFlK169fxzDMzs7u3bt3NGcDmPt/ogAAHYJJREFU4NNR7+TPmm+3d+/eMWPGWDVVqSs38yNtBxRCq1i8eHHRS+XOmDEDIdSiRYvC5lrQqXXr1gihoKAgi8/iON64cWOE0JIlS2gOBsBnKfqdnN9///03ZcqUoKCgxo0bl7mPd+VmxRzbAYXQKkxL5RY2IE0ul1Obc/7xxx80ZzNjWskwJyfHYoetW7cihPz8/ApbdxQAW1DsOzk/g8Fw7dq127dvl9E1SDdt2lTW11C1KVAIreWPP/5ACLm5uRX2a7lt2zbGC0yxa9ubCvahQ4dozgbAp6touzSU9V01bA0UQiuy/UuOZesSLgCFqYD79pXpfRZtDRRCK7LxQShlblAPABYV+04ur3r16oUQGjZsGNNByjwohNZV7LSEvn37IoQGDx5MZyrKoEGDEEL9+vUrrIMNTvMAoKBi38nlVVxcHLXW/+3bt5nOUrZBIbSu9PR0sVj8KRPVr127RmcwauK/nZ1dYRP/z58/jxASiUQ2NfEfADPFvpPLt7lz5xa91j/4FCwErMnd3Z1amWzGjBkGg6FgBz8/vxkzZpAkeeDAATqD7d27lyTJoKAgaoiBGaPROH36dITQwoULqcEyANgggiCoPT5nzpxp8Z1c7oWEhHh7e9++fXvfvn1MZynLGC7EFYBery96MWulUhkdHU3zBzqCIKKjo8vi4uCAHnv37p0zZ45UKjVrJwhizpw5GzZsYCSVmR07dqAil7mvCHbt2kX9I+Tm5jKdhYyLi5szZ87JkycLPnXixIk5c+bExcXRn6pYUAjpUOz2RjbF9reLAjTo3r07QujNmzdm7UajESHUsGFDRlLlp1AoqI3P9u/fz3QWJhEE0bRpU4RQaGgo01nIK1euIIQmT55c8KlJkyYhWx3QBJdG6dClS5dOnTrJZLIFCxYwnaV4ISEhUqm0ffv2P/30E9NZAChUeHh4ampq8+bNBw4cyHQWJmEYFhkZiWFYRETE69evmY5TJkEhpMnatWu5XO727dsfPXrEdJaixMTE7Nixg8PhREZGMp0FlD6NRpOampq/JS0tLSUlhak8XywuLi4yMpLFYlE1ACFkMBiePn2amJjIdDQ6PH369N69e/fu3UtISEAINWvWbPDgwTqdjho7UxYlJyfrdDrqsUajofk9CYWQJrVq1Zo4cSKO45MmTSJJkuk4hZo0aZLRaJw8eTK1byIoTxYuXNioUaNx48aZWhISEr777rt+/foxmOrLzJgxQ6fTDR8+nNoBVC6Xf/vtt//73/8mTJhQJq67lFCXLl2ioqKioqLu3r1LtaxYscLBweHw4cPURm9lyPPnz3v06FGvXr1r164hhBYvXvzVV19RE8/ow/S12QokJyeHWir38OHDTGexLDo6GiEkkUiys7OZzgJKX2xs7P3797t27Wpq6d69+++//96iRYuCnW35HuGlS5cQQo6OjikpKVTLlStXqNm62dnZvr6+DGajgU6ns7h11NKlSxFCgYGBBoOB/lSUL7hHmJWVlZ2dPXLkyPPnz5MkGRsb++jRo86dO9MR9z0OrVW3YhOLxUuWLPnll19mzJjRqVMnoVDIdKKPaLXaWbNmIYTCw8OpwTKgnKlRo8aDBw9Mh/v27fPw8GjRogW1grNFnTp14nK5hT07b948as90miUlJSGEQkJCqMEyCKFGjRrFxcXNnz9fr9dPmzaN/kh0SktLU6lUEydOFIvFEyZM8PX1pdqDgoKioqJiYmJq1Kjh6OhIfzDqkzRCaP/+/dQKcPmZXZM3cXFxyX9Yo0aNx48fWyNeEaAQ0mrs2LHbtm179OjRunXrQkNDmY7zkYiIiPj4+MDAQGprU4SQXq8/ffp08+bNYSph+ZOdnb18+fKrV6+q1eoiulWqVKngJ7aYmBjqQWJi4pMnT6wVsXB2dnYsFqthw4amFpVK5evrKxaLo6Oj27RpQ38kOnl7e+/bt69atWrnzp3r1avXnTt3qHY7Oztvb2+FQvH27VtGgmk0GuqBWCyuXr16wWelUintoT4JFEJasdnsdevWtWvXbtmyZUOHDvX392c6UZ7k5OSVK1cihNatW8fhcBBCt2/fnj9//rt37zZu3AiFsPxZvXq1QqEYOHCgVquNiYn59ddfqStXZrZu3Vq1atX8LTiOU+8QhFB4eHhQUBAdcT+2c+fOyMjIoKCg9u3bU2GioqLatWsXFBQ0ffr0mjVrjhs37smTJ9HR0Xv27KGG0pQDt27d2rZt2/Lly728vL799luE0NChQydOnEiSJPUaL168+O+//zo4OFy6dIm6C0OzmjVr3r59GyH0008/bdiwwezZyZMn//rrr/Sn+hRQCOnWtm3b3r17HzlyZO7cuVFRUUzHyTNz5kylUtmnT58OHTpQLU2aNLlw4YLp7BCUA3Fxcc+fP5fJZPfu3Vu+fPmKFSsQQgkJCYMGDbJYBYvl6+trui5Hp5UrV54+ffrZs2fbtm2bOHEiQqh27dq//fbb4MGDk5KSCIKwt7cfN25cWlrajz/+OHjwYPoTljpqDZ07d+5UqlTphx9+OHLkSP/+/c+ePdu+fXuqCuZfDapdu3ZM5/0MBoPh8ePHWVlZr169CggIwHH82bNncrn83r17X331FT2fY6AQMmD16tUnT548efKkg4MD01nyODo68ni8VatWmVpYLBhRXN7cvn378ePH3377bXR0dKNGjag/MSKRqMyVCh6Pt3Llyl69es2fP3/AgAEuLi79+/cnCGLhwoVubm4XLlxwd3dftmzZzz//PHv27B49elB7U5Rpe/bsuXPnjo+Pz6xZs4RCYWJi4qFDh2rUqDF79myqw6+//vr06dNq1apNnjyZ2aifS6fTRUdH16lTJyEh4f79+1qt9uHDhy1btoyOjm7QoIHp8oN10TkyB1BiYmLYbLbQxrDZ7JiYGLOoo0aN+uuvvxj5VwLMsuVRo5QffvgBFTJAkSRJHMepmRULFiygOVipy83N9fb2Rgjt27fPYofs7GxqyInFtc3oBCvLgE81Y8YMHMdHjhypshkjRozAcXzKlClM/9sA8Kmo+9lbtmyxOGDHNNd+9erVTA0eKS3h4eEpKSnNmjWjNpwqaN68ednZ2d9//32XLl1ozlY+QCGk2/Hjx8+dO+fs7EztqW0jli5d6uLicunSJdNoeK1WGxUV9eLFi7Nnz/7zzz/MxgP0c3Nz8/PzK3hhCsMwPz8/07wFBtWpU2fs2LGme2MFUauvaTQa0/XDsohaQwfDsPXr11u8YRYTE7N9+3YbWQ1KIBD4+flZnH8lkUj8/PwEAgH9qYrH9ClpxaLT6WrWrIkQMi3eT41inzZt2pUrVxiNRq5fvx4hVK1aNWrHCa1We+i9e/fuMZsNAItMlwSPHz9usUM52Ly+R48eCKERI0YU1oEa4DZ16lQ6U5UzUAhpRU1RqF27tl6vp1o6d+68devW69evBwQEPHnyhMFsBoOhbt26CKFVq1YxGAOAz2L2Aa6gxYsXI4QaNmxoNBppzlZy1Bo6Dg4OycnJFjscPnwYISSRSLKysmjOVp5AIaRPenq6k5MTQujs2bOmRhzHqQeTJ0/evXs3Q9HyXLx4EX28chUANs5gMNSrV6+ID3BqtZraszcqKormbCVkNBrr16+PEFq+fLnFDjqdjpq3vnnzZpqzlTNQCOnz888/I4S6detW8Ckcx7/55puCgzbp17VrV4TQqFGjmA4CwKcq9gPcwYMHEULu7u45OTk0ZysJavp51apVNRqNxQ7h4eEIoTp16jC4uGj5AIWQJvfv32exWDwe7+XLlwWfnTp1anBwMP2pCnr9+jWfz2exWHfu3GE6CwCfqtgPcK1bt0YIBQUF0ZmqJKRSKbU6zJ9//mmxQ1pamkgkQgidO3eO5mzlDxRCmrRq1QohNGvWLLN2jUbz//buPqrH+/8D+OtTn3SzRKUxKffhQ4dKc0Y2qxDWOSpGTJiWm9bdKZZGaiEhJW1u5uawrTbJ7WbHLCT3OikqKoVubMxE6T71++P6/jpbpU+jrvf1ua7n46/OdfXHc51jz+t9Xe+bTz/91M/Pr6GhgUmwlpYvX05E7733nnAiAbRN6QPcjRs31NXVNTQ07ty5w3O218Oti7e1tX3VL7i5uRGRs7Mzn6nECkXIh7i4OO7NzLNnz5rdsrKyMjU19fDw8PDweNWjH8/Kysq4yfHx8fGsswC0l9IHOO6IO+60JoHLzs7W0NBQV1fPyMho9RdSU1O5N0y5ubk8ZxMlFGGnq6ys7Nu3LxHt3r275d3UfyguLuY/Xqu+/fZbIurTp8+LFy9YZwFol6YHuLi4uFZ/oWm22smTJ3nO9l85ODgQ0bJly1q929DQMG7cOCIKCgriOZhYoQg73Zo1a4jIwsKiaYKo8HGTd4hozZo1rLMAtNfu3bvbfoDbtGkTEQ0dOrRp/ZIAHTt2jIj09fX/+uuvVn/hwIEDRNSzZ8/nz5/znE2sUISdq6ioiFvPm5yczDrLf3Px4kWZTKatrX3//n3WWQDaRekDXE1NzZAhQ4goKiqK32jt1bTnxtatW1v9hYqKCu74NuarrcQERdi5Zs2aRUSurq6sg7yO2bNnE9Hs2bNZBwFoL6UPcCdOnCCi7t27v2q8xRZ3Asw/99xohjvQ28rKSoXeMAkfirATqfqgSnWHsyBlSh/guC9wS5cu5TNVe7S658Y/PXjwQEdHRyaTpaSk8JxN3FCEnaXpLU1ISAjrLK9PFT9wgsQpfYBrmpOZnp7Oc7a2LVq0iIgcHR1f9QsuLi5ENG/ePD5TSQGKsLOIY+Jl21NeAYRJ6QOcj48PEX344Yc8B2tD23tuNDY2pqSkyGQyHR2dBw8e8JxN9FCEnUJMS/HaWAQJIExKH+Ca9m05dOgQz9le5f333yei5cuXt3q3vr5+5MiRRLR27Vqeg0kBirBTBAQEENHYsWPFsTlL2/9EAQRI6QPcN998Q0T9+/d/1U6efIqPj2877fbt24nIxMSkoqKC52xSgCLseOLbrlPpSxsAAVI6xuLOdli3bh3PwZpROn4tLS01MjIiooSEBJ6zSQSKsOOJ8gCHNo7OABAmpQ9wZ86cafu0P340fdF81YmJvr6+RGRjYyOON0wChCLsYGI90k/pxG4AAVI6D9PZ2ZmI3Nzc+Ez1T0rnuN6+fVtDQ0NNTS01NZXnbNKBIuxI4j7kPSIiou2lvgBCo/QBrqCgQEtLSyaTXblyhedsHKWrHqdMmUJEixcv5jOV1KAIO1J0dDQRDRw4sLq6mnWWjqd08ycAAVL6ABcUFMRqr5ZLly61vefGzz//TER6enp//PEHz9kkBUXYYf7++29DQ0MiOn78OOssnUXpdsAAQqP0Aa68vLx3795EdODAAT6DKd0Ztba2ltsZdcuWLXwGkyAUYYdZtmwZEdnZ2bEO0rkmT55MRJ6enqyDALSX0ge4/fv3E5GpqWldXR1vqX766Sduz41XrYjYvHkzEQ0aNKimpoa3VNKEIuwYWVlZcrlcLpffvHmTdZbOpfTIUAABUnrCn7e3961bt/iMVFdX9/XXXycmJrZ69/Hjx927d1eJ0xNFAEXYMSZNmkRE3t7erIPwwcvLi4hsbW1ZBwFoL5V7gPPw8CCiqVOnsg4iCbLGxkaCN3P48GEXFxcDA4Pc3FzuM6G4lZaWmpmZPXny5PDhw05OTqzjALSLt7f3tm3bbG1tk5KSWGdRIj09ffTo0Wpqardu3eI+E0KnUmMdQOXV1tYGBgYSUVhYmBRakIj09fVDQkKIKCAgoLq6mnUcgHYJDQ3t0aPHmTNnjhw5wjqLEr6+vi9fvvTy8kIL8gNF+KYiIyPz8vIUCgX3KkMilixZYm5uXlBQwK0YARA+VXmAO3jwYHJyspGR0erVq1lnkQq8Gn0jjx49MjMzKysrO3XqFPeZUDrOnDljZ2enq6ubk5PDzT4HELiXL19aWlrevHlz/fr1K1euZB2nFVVVVQqF4v79+zt37pTUszVbGBG+kfLycgsLi+nTp0utBYnI1tZ2+vTpVlZWlZWVrLMAtIu6unpUVBQRrV+//uHDh6zjtGLz5s33798fNWoUtzkc8AMjwg7w4sULXV1d1ikYkOx/OKg0Jyeno0ePLliwYN++fayz/EtJScmQIUMqKirOnTv3wQcfcBcrKyvv37/fr18/HR0dtvFEDCNC5aKiouzt7XNyclrecnZ2njNnjmTLQFdXd86cOdy2xc3k5OTY29tzT98AghIZGamlpbV///5r166xzvIvX3zxRUVFxcyZM5taMDY21sbGJjIyctWqVWyziZucdQAVkJ2dnZSU9Pz585a3zp07x+0cL1kpKSkVFRUtrz9//jwpKal///78RwJo24ABA3x9fTds2ODj48Pt9sk6ERHR5cuX4+LitLS0Nm7cyF25d+/e9u3bb9y40aVLF7bZRA9FCACSExQUtG/fvitXrpiZmQnkjU5hYWFjY6O/v3+/fv24KxcuXLC2to6Kinrx4sWCBQsGDhzINKCYoQgBQHK6du3aq1evurq6u3fvss7yP9zGNxYWFk1Xnjx5kpWV5ebmVlFR4eDgcOfOHXV1dYYJRQxFCACSc/To0YyMjO7du589e5Y7sJC5+Pj4TZs2BQYGfvTRR5qamkT09ttvjxkzxtbWloi+/PLLBw8eDBgwgHVMcUIRtldtba2QF+Gy1fIvU1tbyyQJgFK1tbUrVqwgorCwsAkTJrCO8z/m5uYnT57MysqKjY319/cnIgcHh+Dg4JSUlKKiourqalNTU9YZRQtF2F7jx49v9brEJ8sQUWlpqba2NusUAO21ZcsWbjeoxYsXE9GlS5dKSkq4W4aGhtwIjH9yuTw6OnrixImhoaFz587t1auXoaFhYmLinj175HL5qVOn5HL877qz4C/bXkuWLDE2Nm52cf369UzCCIq2tjZ3xvc/lZSU7Nixg0kegDY8evQoPDyciLZs2aKhocFdKSgoIKLbt2+Xl5ezKkIisre3nzZt2i+//BIcHLxr1y4iGjVq1LZt21jlkRC2h1+oBHd3dyK6evVqy1v6+vp9+vThP5Jw9OnTR19fv+X1q1evEpG7uzv/kQDasGDBAiKaPn16y1tz5sw5e/Ys74n+JS8vT1NTU01N7fr162yTSAoW1AOAVKSlpR04cKBLly5Na/WaFBcXFxQUMP9kOGjQIE9Pz4aGBl9f30Zs+8UXFCEASEJjY6Ovr29DQ4Ofn9/gwYOb3Y2OjuZOnGZuzZo1vXr1unjxYkJCAussUoEiBABJiIuLS0lJ6dmzZ8tzJ8rLy3/99deZM2cyCdaMnp5eaGgoEQUEBGBHe36gCAFA/KqqqrgpXeHh4S0XDu7cuXPhwoXc3BkhcHd3Hz16dFFRUWRkJOsskoBZo8pZWlo6OTkZGBi0vOXo6MgtfZWsKVOm1NTUtLxuYGDg5ORkaWnJfySAlsLDwwsLCy0tLefPn9/yrpmZWdM+10KgpqYWHR09fvz48PDw+fPnYwVhZ8MxTAAgckVFRUOHDq2qqkpOTn7VgmAB+vjjjxMSEubOnfv999+zziJyeDUKACLHfWxzdXVVoRYkosjISB0dHe7TJussIociBAAx46Zfamtrq9z2FyYmJv7+/k2TXVnHETMUYetiYmIcHR0XLlyYn5/POotqmzp16sT/Fx8fzzoOSEvTgrzAwMC+ffuyjvOfrVy50tTUlFv+yDqLmKEIW5GUlHTs2LHExERXV1duWxl4bT/88MPBgwcPHjyopaVlYmLCOg5Iy549e1JTU01MTAICAlhneR3a2trr1q0josDAwLKyMtZxRAtF2ApNTc3S0tLKykq5XK6lpcU6jmrT19fX19d//vz5kydPbGxsWMcBCSkvLw8ODiaizZs36+josI7zmubOnWtjY9O0RSp0Bswabd2ECRMKCwurqqqSkpIUCgXrOCrPx8fHxsZGIAuWQSICAgIiIyPHjh174cIFmUzGOs7rS0tLs7a2lsvlmZmZLffEgTeHEWErtm3bplAo7t69u2vXLgcHBxyt94ZKS0vPnDnj5OTEOghIyN27d2NjY9XU1LZu3arSLUhElpaWbm5uTccoQodDEbbi3r17CoVCTU1typQp9fX12OXoDe3cudPd3R2nqQGffH19a2pqFi1aNHr0aNZZOsCGDRv09PSOHj3622+/sc4iQng12or8/PxZs2YpFIrCwsJp06YtX76cdSIVVldXN2rUqMuXL+vp6bHOAlLx+++/T5w4sWvXrrm5ub169WIdp2OEh4cHBQUpFIqMjAw8VnYsFOErPXr0yMDAQDjbDwJAe9TX11tYWGRmZm7evNnf3591nA5TW1s7YsSIvLy82NhYT09P1nFEBUUIAKISHR3t5+c3cODArKwskW0FfOTIEWdnZ319/by8PENDQ9ZxxAPfCAFAPJ4+fbp27Voiio6OFlkLEpGTk9OkSZNKS0u/+uor1llEBSNCABCPpUuX7tixw97e/vTp06yzdIrs7OyRI0cSUVpamrm5Oes4IoERIQCIRFZW1u7du+VyeVRUFOssnUWhUHh4eNTX1/v5+bHOIh4oQgAQCT8/v/r6+s8//3zEiBGss3SisLAwQ0PDpKSkEydOsM4iEng1CgBikJiYOGPGDAMDg9zcXNFPJImJifHx8RHlhCAmMCIEAJVXU1MTGBhIRGvXrhV9CxLRsmXLRowYkZ+fHxMTwzqLGGBECAAqb926datWrRo+fHh6erpEFpsnJSXZ29t37do1JyfnnXfeYR1HtWFECACq7c8//9y4cSMRRUVFSaQFicjOzs7R0bG8vHz16tWss6g8jAgBQLW5ubl99913Li4uhw4dYp2FV/n5+cOHD6+rq7ty5Yq1tTXrOCoMI0IAUGFVVVWZmZlaWlrcoFBSBg4c6OXlpaamdu3aNdZZVBtGhACg2l6+fMmd2Mc6CANlZWXFxcU4M/UNYUQIAEKXnZ0dEhKSnJzc8lZiYmJYWFi/fv14DyUIenp6d+/eDQkJKSkpaXk3IiJCxHsLdCAUIQAIXXZ2dmhoaKtFeOjQodDQ0MePH/OfSiCOHz8eGhraahFu2LBhy5Yt/EdSOShCAACQNBQhAABIGooQAAAkDUUIAACSJpVdGABA1e3cufP48ePNLt67d49JGKFZsGCBjo5Os4vl5eW6urpM8qgWFCEAqAYdHZ0ePXo0u/jw4UMmYYSmW7du3bp1a3ZRJpMxCaNyUIQAoBrmzZsXHBzc7KKrq+uPP/7IJI+gbN269d133212UV9fn0kYlYNvhAAAIGkoQgAAkDQUIQAASBqKEAAAJA2TZQBA6LS0tIyMjN56662Wt7p162ZkZCSd83hb0tPTMzIy0tDQaHnrVX80aAbHMAEAgKTh1SgAAEgaihAAACQNRQgAwvLixYt9+/YVFxcTUVVV1d69eyMiIm7cuME6l0pKTU1NSEho+jk8PHz//v21tbVsUwkNihAABOTSpUuzZs2KjIzMzMwkok8++eTZs2fDhg1zdXW9ffs263QqZtmyZeHh4dzZvMnJyV5eXkOHDr127ZqnpyfraMKCyTIAIDifffaZi4uLg4NDTU2NpqYmEc2cOXPhwoVTp05lHU3FFBcXz5w58/Lly42NjdXV1dra2o8fP7axscnNzWUdTUAwIgQA4dLU1Dx16pSLi4umpuakSZNYx1FhMplMW1ubiNLS0szNzVnHERbpLr4BAJVgbW3do0cPb2/vq1evjhs3jnUc1VZcXOzv73/o0CHWQYQFI0IAEK6KigoDAwMrK6vJkyenpKSwjqPa0tPTZ8yYsXv37mHDhrHOIiwYEQKAgFRVVcXExGRkZDQ0NMhksr179xobG/fu3TshIeHgwYOs06mY06dPnz179uHDhxEREYMHD543b978+fPPnz9//vz5FStW4LTCJpgsAwACUldXl5yczP1sbGw8ePDg69evP336dMyYMS1P5YW25eTkFBUVcT8bGxuXlJQ03bKzs0MRNkERAgCApOEbIQAASBqKEAAAJA1FCAAAkoYiBAAASUMRAgCApKEIAQBA0lCEAAAgaShCAACQNBQhAABIGooQAAAkDUUIAACShiIEAABJ+z9rcRe1vNPhYwAAALJ6VFh0cmRraXRQS0wgcmRraXQgMjAyMi4wMy4xAAB4nHu/b+09BiDgZUAAHihuYGRz0ADSzCzE0owMIJqRkViam4Exg4mBNYGBjYGRKYGRnYGJOYOJiQNoUgIzJwMLawYTCxcDKzeDCCMbAysLMxOj+CyQPiSHHrBfvWqVCoTrYP/Qbdl+KHs/gn1g/4T+KapI4vZI6mFsB6A5UDUHgOJqB2B6EWyHA0hqQOIOSOrBbDEAdwswA4ERA5wAAAEeelRYdE1PTCByZGtpdCAyMDIyLjAzLjEAAHicjZPNboMwDMfvPIVfgMhOwkeOBapumgpSy/YAlTjswmmnPn3tVtRhrSKSIBLzi/+OHS7TfJ3mKQNpp+7r9w+ezXYZ2zExQgjw4xAxO4JMoNkfPntox12zWNrhux/PQFYGSl+zu3E4LhaCFsgUKA3Q4P/Jwlnm0FR3EHIyNgR09RvQMZhvIr2QW7SLlcuExzIOMsFV8AHuVe+Fq5l7BphbU4Qaq/INGBjMN5GEgm4RJ1o5Tfm0cZwJcN93q+I/rkMz9J1eB8+P1arLstTaEvdKC+iZcFom3gm1FsPzBq85J14GTa3sLjSBd+U4TfxVlUmEWYCiM3Ng/IrPFp9E1stPwfPsBklppcN31+8NAAAArXpUWHRTTUlMRVMgcmRraXQgMjAyMi4wMy4xAAB4nG2OPQrDMAxGr9IxAVtIjh3b6gUyle4hQwkdi0vJmMPXMaWyS5dPejz0M0/LSms3T0v/Jynnae80gVPagIsBgzprBJ+ZwMSINvOhMdehZLGt/I62rlGfJWXHz4HmfK9uW3pcX+nJCEd7SdsdApOAYSMw8CAQ2QpYdgKEPAo59pUiDkLIUWBkqp4gpuoLv78B3OteYYi1efwAAADqelRYdHJka2l0UEtMMSByZGtpdCAyMDIyLjAzLjEAAHice79v7T0GIOBlQAABKG5gZGNQANIsjAwaQIqRkQNMMzPB+DCazQEszkIsja4fF83NwAhUDnQAAwMTAytbBhMrVwIrNwMbewIbDwM7RwYTOy8DB2cCBx8DE6cCJ1cGExc/gwgjGysXJwc7m/gpkBlIfnLYH5RV6QDiFCozH9DcOsUBInVg/0M3NTD7cC+PfVCWJ1S8QeWh2zKYGntE8DjYA8X3Q9n7EewD+5HUgMTtkdTD2A4INQeA4moHkNwAZTsA6QZVJDVgPWIAUko1FtBEwAgAAAFlelRYdE1PTDEgcmRraXQgMjAyMi4wMy4xAAB4nI1Uy26EMAy88xX+ASI7L8hxgVVbVQtSu+19Dz2sVLWH9v/VGAoOYjcicMBmMnY8A5ev68/350cBvF665+svLEt3Rcxj5g4hwLtBxOIE/ADN8eGph/Z8aOZMO7z151cgzzfytcYezsNpzhC0UKKqXGREMKoOFZEFVDgu2arhEUqt0AY0NVjl7R2ggQFKUm5Ka+VCjZW/AbSREZWbCI2ie4RuxP1nnaLgyekbOB+PstSV1xtcFXHLiWOnOowdbIH1ajY5ZGDkntqEK84MJVHaZg7IyphtyS3QRODSZJlRhliach+U1Sl3lfdp+Qzlse9WFp1M2wx9J6blS4s1ObRiQA6d2IxDI26y0SmVmIZDIjEHcazFAzbmahGaGGISPW3EhEQ1ijHZRB3Lu1IViBMuGTZxz+lILRNLU/wlj9Q+nVE6EY7nX0B8Lv4A4BXRSBZjQ2oAAADgelRYdFNNSUxFUzEgcmRraXQgMjAyMi4wMy4xAAB4nG2Ouw7CMAxFf4URpMTyzaNN3JGFCdgRA0KMqAgx8vGkERAXsVg+vnZyDpvjGeflYXNcfepuXZta1BylLp5LUDTWUcyJkxmY+oIglzMHM0whm8FPpUaz5Htl55n9E3pKuQcKOuIwrQXqQp0wxTLwqezgPTGRkDvE91vld1sd7I+fndmvzOkxXvf38SZMU7sdHxeCEzTqxDWI4tUeJChiiYqidI2y9A2cpAYsWR0JlIYXKI0gUB5JoEVKqEx6gTbxzxfPpoBhr+K5ewAAAABJRU5ErkJggg==\n", + "image/png": 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v02dJz5Wb7D7rXs7RUcWDhfANNPdvp0/6Lk+uPFOgbCDktbazvL/1l1SdpTd87eHEETv47DxafTaeoChq8+bN3t7evXv3tnjp2rVrMTExAwcOdHGxbsccVM1p7t7MiVigjXtoMX5BqlqQnJeiMwBAF0fRPF+JD890wYYCsJz/maIz9HiUqiOpPQ09P7QX0IPCth28N+yyan5UQeCs0dcx5uVkBIwh1eo0nWHOi9yjeYrix/ySKZ3zItdoMJByWdqkEVZtEFOhGAwGf3//FStWFH/pt99+8/f3f/Hihe1ToWpF0Oojn51H3MNXsl3czMe7OIpON6kxvYazHZt1QarqHpP6U0qe0kj3sShhFcTSlDwtSX0psTdVQWCxXKfNtXZ+VEFgIXyd7OXzybfeBYoyGg3ZmQW7t1g1EkLoFSyWuM/AmkcuOvsHmN/ME7CI8R6O55rUGOoqNlLU9ixZ90epFk2daP/INacLVEIWEeT9cltRlr2YV6+hTb4AxDwshKUyZGaoL58v0yc8Sq0q+HUDVWxXe4SQVbGEIsn4qb6/n7Xv9oX5uDuXvbCmy/7GXq3t+Fl6Y+iL3EFx6XfMmjoZKViUkgcAEzydvHlm8930evWt67aKjxiGhbBUyvMnqbdrnPYKitLc/tcKcRBCb8D1reWxbJ33hl28+o3Mx1uI+Psaea6t4+bF4zxQaYfEp//4LDtNZwCAvTnyWLXOm8cZ7e5g/hZSo5Yf22/T9Ig5WAhLpfrnb0qrffNxryLVKvX9W9bIgxB6G8K2HXx+O+42ZzHb6eXuMAQQvZztzpo1derxKHVFan5kej4AzPaRCFmv3jukKPWdGzZOjpiCS99KVXwP+6N5ymtyyxUUaa82CwXSqH/+1KrBKpQbN240btzYYhB3J0fMItgch4HD7Lv1youOkv6+HYpaGApZxBQvp/4S+yWpeecKVNGZUgBoKOT1dCqhrYQxN9umoRFzsBCWiiItb/W5cdktRJbLJ2RGUvNqR1B6uX014eDg0K5dO4vBGzduFBQUMJIHIROWg5Pr9DCCxS7Ys9V8uWFNPmdDXfcDuYqZSTlsAhLUum8fZ4T6SBoLX1k4T+l0maGBLlNmcNw9bZ4d2RQWwlKxJW76pOfmI+3Fgvm+lgvjhidk5OjNKh9BcL28rJ+uomjcuPH27dstBidPnhwXF8dIHoQs8Br5EUIRpVJajJ/MV1IAH9kLEtT6f+SafrFp/ST2s3yczbcqVZw6rPzrjNMIf+dRPxB8AaAqCu8RlkrYqi2Ufdc0QiTiN21pjTwIoXfA92tOFJv0dkGquiRTO7BZUXXczjatMd7DkU0Qh/MUn8ekRmdK9WYfHymNOn9T1IsB/5MfPwi4/UgVhYWwVHZde7K4Zd9j0GAQte9khTgIoXfBq9uAsHvlFqCBopam5gPAj15OEg7bkW7q5Of9maNQSjd1ik37S/rKAmJDZkZW2LTUUQM1D+/ZND2yCSyEpeI3bsapWbus3ThFHT5j2YutFAkhVGYE4fTtWPMf5O1Z8qcafT0B9zu3lz+qdQXczfU8tjfwqC/gPtXoxyVmjnyc+UTzyjJizcN7qaMHZc0LMubl2C4/sj4shK/jPncpwStb8zyqlI25EUJMMWRnma5q5hqM6zIKAGC2j4RT7DS3g1h43M871EciZrOuytV9YtN+SsmTG822oyFJ+YlDL77snBcdSel0tvoKkHVhY97X4bh5EEI7zb0bWr0+W2/8yF7QRGR5sTRNZ3Dlsns62dHLkPTJSYIWrbm+tRiIa3Px8fFt27bt3LmzxXhmZqZAIOjbt6+Tk1OJb0TIZvQvnmfPDwaysJgtSsm/o9T+z1H4o1fJ35wsgmhpxx/qKtZS1H8q7V2ldl+unEcQzUV801JDSq/X3P5X8edJrk9Nbs06tvlCkPVg94k3K9i9JW/9Sqr0TUcpgAydwatofyZe3QY+e05W+faEycnJPj4+RBkvHSNkY+lTv1f9fYF+HKvW9Y9LI4A46eddV8B943tjVLqFKYVNnZqIeHN9JB/ZW84dFbbr4DptHm5MWqnhpdE3c/rme++Nu7l16rFEdgTL8l8sW2/8Kj59WEKGtmjRru7pY9mB32we06Y0Gk3nzp3bt2+fkZHBdBaESqW+cdVUBQHgp+Q8IwUj3cWWVZDFYons2RJXrk9N8+GmIt6ehp6b6rn78DiPVLphCRn+iVkpr+6hof73asqwL7IXzTYW5FvzS0FWhJ8Iy0Bz75bi3HHNnRv69FRSLqMHjRR8GZcWp9ZN83ae4OlID7IcHGse+st8h6cqZtGiRaGhoU2bNr137x6nqn/2RZUUZTSkDOutS0ygn57MV055li3hsP9sWsOBXXhGy3Jw5Hr58Ju3tO/aS/hhewCQnzycG7XEYjqMhqS2Z8vWpUtVJClgESPdHCZ5OYpePS1mOThJ/Kc4DhkBLDagSgUL4TuhqNRxQzR3b9LP/pFrvn2cIWKx/mxaw51b+DPgOGSEa0g4cxGtKCMjo1GjRjKZ7OzZs927Y/9uVEFJ9/yaE7GAfqwhqZ6PUlN0hoU1XYa6Fk4W5Xh4+h44zxJa7q9GqlQFOzcVbNtgMR0mU29ckZp/JE9BAXhw2cE1nPtL7C3uDfDq1HeZNhfXUFUueGn0nRCEa/A8KDof/Fgs6OEkUpHkitSX10akf+zWPYlnKJ91hYSEyGSyQYMGYRVEFRYpK8j/Za3p6S+Z0hSdwU/IG+zycsmEy9Q5xasgALBEIsn4qb77zlg0dfLgsiNqu+5v5NXSjp+pN05/nvNVfPpd5Stb8+uePUmfPDJ96vf6VGxMXWngJ8J3lxUeIj/6B/04RWfo8ShVR1L7G3l9YFe44kL40SfeG3czF9Aqbt++3bZtWy6X+/Dhw/r16zMdB6GSZS+ZK9u/i36cqTd2i0lRk9RvDT3bFs12EXzQpsaWP964UFj979WclQtM11dpFMDhPMXy1PxsvZEA+FJiP6OGsxv3lSuiBJfrMOgbycRpLDv78vuykFXg8ol3J2jRSnZoL33xxIHNUpPULYX2kVr3tauY/tkypCXzGzXl1a7HbM5yRFHUwIEDU1JSZsyYMXjwYKbjIFQy3dPHOQtnmtYOzkvOjVHpvnC2G+tReBcfWCzP5RveZjdtrk9NhwHDOB5e2gd3KE3hKmECwE/I+8bNgUsQd5XaGLXutxy5gYJW9ny2qbKSpPbhPcXxgyyRHb9RUyAIoCht3EPlhTOKU4eVl87pnsRTahXHw4tg4112huEnwvdSsG1j7tpl9GMVSXaLSc3SGyNqu/aXFJ4DcmvU9N1/juCVfau2CmnHjh0jR4708PBISEhwcHB48xsQYkL6pBGqf/6mH99Var+OT+exiDNNavgUrXFy6D/Ebe7SMv2ZpKwgLzpK+vtOeLXbTJJWH5FWcCpfCQC1+Nxgb6deznYW7+X7NRN93El2/ACplIPRWNjolM1hCQWUwegwcKjEfypLjD9QjMFC+F4ovT7568/1L57TTw/mKkKScty47HNNatgXTUtzmTLTaeR4xiKWH4VC0ahRo7S0tO3bt48YMYLpOAiVTHnhdMb0CfRjEmBwfPp9pXayl9PUohX0LJFdzUMX2K7u7/CH6549yV35k+r6ZYvx63LNwpS8eLUOANqLBaE+kkbCtz39Jfh8lkDguWqzoOWH7xAJvT+cLPNeCC7XJWCW6ekAF/sP7PjZeuOmTKlpMH/zWmNOFhPpytmSJUvS0tLatGnz7bffMp0FoZJRer3pIg0AHMxV3FdqPbjs8aaLogDO/gHvVgUBgFenvtfP2z1Xb+bWeGXFYXux4Ghjr4U1XSQc9nW5pl9cWuiL3DzDy8+OR/OUF6Sq4n9grFp3IC03IycvbfII9Y2r75YKvScshO/L7rPPTVOlCYC5PhICYHOmLElbuF0vqVLmrl/JXMDy8ezZs1WrVhEEERkZySq2qwBCFUTBzk2mKzRKI7kqLR8AQmpIhEXbo3F9ajkOGfmef4tdp66+B/50DZ7HEr28CsomiKGu4nNNa4x0dwCAvTny7jGp27JkRooCgDkvclalldCt+kyBKiQp57FGT6nVGdPGGzLS3jMbegf4G60cuASFmjZUa2nH/1Jir6Mo86UU8mP7tTH3GUpXPoKDgzUazXfffdexY0emsyBUMmNeTsG2jaan6zOkWXpjKzt+P8nLcuUybW653LMnuFzHYaN9D/wp/qK/+dRTRzZrro/kaGPv9mKB1EguTMn79nHmW/6ZpFaTtXDWm49D5Q0LYTng1W3gMHC46en0Gs4iFut0geqKrGh7UpLMiVhQebt6/vXXXwcPHhSJRAsXLmQ6C0Klyo1aSioV9ONkrWFbtowACPWRmMqUsG0Hu05dy/Fv5Lh7uv+02mfHEcEHbczHGwl5Oxt4bqrnXpPP6eVcwlLFkhmNmrs39c8TyzEhehtYCMuHZEIQ27FwQzUPLnu8pwMALErJMxYVP81/dxRnjjGW7z0YjcbAwEAAmD17tq+vL9NxECqZNvaB/OQh09PFqXlakhroYm9a10uw2a7B86zxV/ObNK+x5Q+PZes4nt7m410cRaeb1PjGtSwNSg0G+akj5ZwPvQkWwvLBcnB09g8wPR3r7ujD4zzW6PflKkyDuWuWkOoS7pZXcJs2bbp//37NmjXpcohQRURRORELTL2Wrss15wpUIhZrmvfL/X4dvh5hxR4RBGHf7Qvf/eec/QPMm5jyCIJdlg4tlEGvLlr4gWwGl0+UH9KYPKy3aVu10wXKyU+znTisP5v4OHEKTzic/QMk46cyF7HM8vPzGzZsmJOTs3///kGDBjEdB6GSyU8cypoXRD82UtAvLi1erQv2dv7h5T74TjUPXzBdtrEqQ2Z63roV8hOHzAeb30siAWrzLXs/5eiNuQbjjgaen4gLt7xhu3nUPv2PDXIiE/xEWH5Yr1x46elk19FBWGAg6XbYtILt0Ya0FCbCvaPw8PCcnJz//e9/WAVRhUVp1HnrI0xP9+TI49U6Xz5ntPvLJeouk6bZpgoCAMfDy33BKrsuPSzG+QTRQMC1+E/CKfZL2Gi0HHkniYmJixYtkkqlAJCVlTWzSFoaTky1hFv7lCfhR5/Yde6mvPQn/XSOj6RPbOqObNnXruIGAi4AUFpN7tplHkvWvvaPqSji4uLWr1/PZrNXr17NdBaESpW/db1p1YHUSEalFwDA7BoSU0d5Xt0G4gFDbZxK0OQD1eXzlOFl80JvHieyjpvFYZHpBY/TX1lWUS7t286ePbtv374rV64MHz7c0dExISEhMTFx5syZACCRSN7/z69i8BNhOXMJCjVNzm4g4H7tIjZSsDA5z3SA4uxx9e1/GUpXNkFBQXq9fty4cR988AHTWRAqmSEzvWD3FtPTqLSCfIOxvVjQ3enlXE2XaXNtv58nz68ZIbBsZ/9mBEvQovX7/+2ff/75li1bTLPb0tPTmzdv3rRp0zZt2gjeIVVVh4WwnHF9ajkOG216Os3b2YnDuipX/yVVmwZzVy6w2K6wAjp+/PipU6ecnJwWLFjAdBaESpW76idKU/jDlajR/5YjZxMwx+flhx67Lj1FH39q+2DC1m3BQJb1XSyRyK5773IPIxAI/vnnn1GjRnXs2DE5Obnc//zKDgth+XMe+6NpAycnDmuSpxMALEzJ0xXNS9LGP5Id3c9Yvreg0+mCg4MBICwszM3N8mIOQhWE5t4txfnTpqcLU/IMFDXMVdy4aJ9Pgst1+XEGI9kIHl88aCjB57/5UDMsB0dR2w704/Pnz69Zs+batWvvH6Zv374nT57cu3fvt99+Gx0d/f5/YBWDhbD8sUR2LhOnmZ6OcBM3EHCTtPqdWXLTYN66FaRCXtK7K4Q1a9bEx8c3btx40qRJTGdBqBSv7lNxXqr6W6Z2ZLMCijbXBgCnb8dxa9ZmJh6AZFwAqyzXIQmB0H3uUrrj988///zbb7/VrFkzJCTk5MmT75nk2bNnWq0WAFQqFa4UKA77EVoFv47szhUAACAASURBVKGf6tpFY3YmALAIoo6AezhPeVel/crF3o7NAgBKrQajgZErNm+UlZU1ePBgrVa7Y8eORo0aMR0HoZLJDu6RHdpDP9ZT1ITE7AIjOcPH+WOxkB5kS1w9lv3MYBM0gs8XfPCh4txxMBgeq/WNhLxODkKLY3IMRiNAF0ehm4O945CRDoMK96j64IMPBgwY4Ofnl5KSIpfL27dvX6a/OikpafDgwXfv3v3777/d3NyePHni7+9/5MiRhISEiIgIkeitN7upHnAdobVo/ruTOuYr0+nq2MTMi1L1UFfxwpou9AjB5fruO82tVdf22fbt27dq1SoHBweBQHDsmOV+N/7+/r/88kvv3r2PHz9u+2wIvYYhM0Nx5ojq6kV9cpIhO9O0gn5TpnR5an49AfeEnzenaAG7e3iEuA/zy37U/17NCB5P6TSUodSZAYRA6DhkhMuUmeaDDx8+XLRoUVpa2okTJ+zty6HNvVar5ZfxUm01gYXQijLnBChOH6UfJ2n1vR6lGSjqQGOv5qLC70XRp128IreU/gdYy4IFCxo1ajRkyJDiL927d+/DDz9ksVgPHjzAj4Oo4iBl0pyIcOWfpyigChvbmglJyjmYq/i1vsenRR+5+H7NfXYchorRKcWQnpr10wzt/TuU0UDp9S9fIAhCaMcSi93nLjU1sTHR6/UZGRlz585t3779+PFVoadphYWXRq1I0KyV/OAeyqAHACcOW2ok7yi1TzT6r1zE9Cmr/sUzQfNWXN/aNg62b98+giCePn3q4uJi0Wh+6NChz549CwwMHD58eGlvR8jGdIkJKSO+1MY9pHTaEtebd3cSdXMUfWhfdEOOIDyX/szxqmHTlKVjiR3EvQfafdadJbKjtBqgSJZAyPHwsvu0q2R8gNuM8OI3MrOysuzt7Z2cnPR6/eXLl/v168dE8OoCPxFaV/6mqLzoSPqxwkh2f5SarTeuqeP2hXNhXxhenfo+e0+ZujjZxrlz51JTU3k83pIlS44dO1a7dm16fN++fUOHDnV3d4+Pj3dycnrtn4GQjehTklK+6Ueq5EBSAJBrMF6Sqp9r9ToK3DjstmK+6RKLibj3APcFq5gIW2527ty5devW1q1bX7hwITo6um3btiUetmPHjqdPn4aGhnJe/R3y+PHj3bt3d+3a9dNPK+JEhIoGC6F1UVrNi0HdDOmp9NO9OfLQF7lePM7ZJjVMnUJdg+eZLz20paCgoBYtWowaNQoA1Gp1kyZNnj9/vmnTpnHjxjGSByELlNGQPLiHPvk5fTswOlP6c3qBmqSELILPIgoMJAB0EAtX1nZ15bLptxA8fs1DFywaQVRGMpksKSmpfv36QqHlFBuTbt26nT9/XqVSWRxz4sSJPn36LFq0aPbs2dZPWulViAvoVRjBF5gvY/raVdxMxEvXGbZkSk2DeZuijAX5Jb3bWpYtW/bo0aMXL1789ddfrVq1ogdXrFjx/Pnzli1bjhkzxpZhEHoN+aF9hqwMUxVckZrfQMD7o5HXfy1r3WpR83pz39HuDlfl6u8eZ2jIwnN6lsiO4+HFaOry4eDg0Lx589dUQVResBBanX2Pvgq/Fg9VOgBgAcz1cSEANmZK03SFmxCSMmneBptexunUqdPKlStDQ0OXLFlCb5+Wmpq6fPlyAIiMjGSz2bYMgxAAaLXaq1evxsXFWYznbV5LqVUAkKU3RqYV+PA4Oxp4tLLj05dT3LjsOT6S0e4OjzX6bVky+i2UTqu+c8Om6VElh4XQ6vR6/YTY5FFPMujz1Tb2/J7OdhqSWpn28lOg8sIpUqW0WaT27dtv2bJlx44dPXv2pEdCQkKUSuXXX3/duXNnm8VAiJaZmdm+fftDhw4FBgaab+mnS0yglIX7ThzPV+oparS7gz3b8rfWJE8nLkEczCvs/Ulp1IrT2NsWlQEWQqtbtmzZ0JGjnMwmZ86u4SxkEUfzlDcVGoLNdhg4zPf3syyRHVMJr1+/vmfPHqFQuGzZMqYyoOpMLBbv2bMnIiJi3759v/76q2lc+/Ce6fE9pRYA2olL2KjFicNqLOQ90+hlRhIAKJLU4CdCVBbYhsm6EhISzpw5c/HixTWRq1n2YkohBwAvHmeku8PGDOl3T7J4fB7ERMHiKAZD6nQ6iqKmTp1qmj6KkC2JRCJ60WpqaqqpYQIAGDLTSVXhhtrZeiMAeHJL/pXlyWM/UEGO3ujAZgGAMS/H6qErkuIbAhvLqalhNYGF0IpIkvT394+IiGCz2cBiO4+ZlLdmKf2SvZOEyJQZSNKg1jAbkkYQBO66hJilVqv9/f3Dw8NNI5TRCATAW0xspy9tmQ6kqlkZmDhxosXyicTExN9//52pPJUOFkIrevTokUqlCg0NBYC0tLS5/94PqVXHmJWp+3LoL4tWUhR1+PDhbt26MR0TLl261Lt37+XLl3///fdeXlVhuh2qdLKzs0eMGDFhwoQuXbqYBtkurgSfT2k0AODMYQFArsHoVLylO0COwQgApm7vLAdHW4SuMMLDw4svn8BC+PawEFpRs2bNbtwovFdRv379tevW6eJjOG4e46bPkEqlX3755ZdffslsQtoXX3zRr1+/o0ePhoaGbtnCwJZvqJqLi4v7/PPPu3TpolAoNm3aNGbMGPrzDb9hE4LDpUADAE2EvLMFqntKbT0B1+LtapJ6pNJ58zjOnMIJzwK/5jb+ElClhpNlbGTt2rUEQfAbN7uflLx9+3Yej0cvV6ggVq1axefzt23bZqrcCNmMUChcuXJl7969nZ2dnZ2diaJds/nNWpq21e7lbEcA7M6WF+91uz9XriapPkW7NREiO2v0tkVVGBZCG+nVqxcA0HNSSJIMDAxs2LAh06FeqlevXkBAAEmSU6dOxc2GkI3VqlVrsBnTSlaCwxEPGEb3tq0n4H7lYv+fSjsrKUdFvqyGpwtUy1PzXbnssR6FE7MJo9Hus89t/1WgygsvjdrUnj17/v77bw8Pj1mzZlm8pFTabh0hzc7ulQUboaGhO3fuvH79+t69e4cNG2bjMAiVSDJ2svzI73S7iTBflwIjeSBXcbZA1dKOL2QRj9X6Z1q9O5e9qZ6HpOi6KFAUKStgu7ozmRtVKrjXqO2o1Wo/P7+kpKStW7eOHv3K5qJyudyiC4QNyGQysVhsPrJ169bvv//ex8cnLi7OokwixBTV9cuZwT+QmsJ1FJdl6lP5yqdavZ4ENy77E7FgsKu9iMUCoAAKr6k69B/iNncpc5FtJzo6OiEhYdmyZRazRmNjYzdv3vzFF1907dqVqWyVCBZC2wkLC1uwYEGrVq1u3brFerVNmkKh8PDwsHGezMxMi26fJEl+/PHHN2/eDAsLw/5cqOJQnDqUtXAOVVQLS6SjKF7RzUVgsXy2HeI3bWGLcMxJTU0FgBo1Kkq3qcoLC6GNJCcnN27cWK1WX7p0qSI3Rrl+/XqHDh0EAsGjR49wfT2qODQP7mbN+tEoLShxM8IT+crFKXmraruZtp4RtGhdY+t+MJXGqmjQoEFnzpzZs2dP3759mc5SueFkGRsJDg5WqVTDhg2ryFUQANq3bz906FC1Wj1z5kymsyD0kqB5K98jF91ClwhatSV4fILLNV0IBYDnWn2m3jg/OddYdGav+e+O4swxhsLawl9//XXw4EGCINq0acN0lkoPPxHawrVr1zp27CgQCGJjY2vVqsV0nDdISUlp3LixUqm8dOlSp06dmI6DUAlImVT6x8689Svpp1qS6vEoNUVn+KmmyzDXwjvfHA9P3wPnWcIquGWS0Whs3br1f//9hx0HywV+IrQ6kiQDAgIoipo5c2bFr4IA4OPjM336dACYOnUq7liIKiaWg6PTqB949RvRT/ksYkYNZwBYmZZPd+sFAENmRsGOTYxFtKbo6Oj//vuvTp06QUFBTGepCrAQWt2WLVtu3brl4+Mzbdo0prO8rZCQkFq1at29e9e8FQBCFQrB5rhOm2t62svZrp1YUGAg12UUmAYLtkcb0lOZSGdF+fn5YWFhABARESEQlNCOA5UVFkLrksvl8+bNA4CIiIhKtCBBKBQuXboUAObMmSOVSpmOg1DJhG072HV+uVtvqI+ETcCObNljjZ4eobSa3LVVrbnY/Pnzc3Jy/ve//w0cOJDpLFUEFkLrCg8Pz8jI+OSTT77++mums5TN0KFDO3XqlJWVtXDhQqazIFQql6BQgsejH/sJeYNdxEYKFqXkmQ5QnDlWlRrWx8bGbtiwgc1mR0ZGMp2l6sBCaEWJiYk///wzi8WKiooiKuE07sjISDabvWbNmoSEBKazIFQyrk8tx6GjTE+DvJ0c2KwrMvVF6ctFh7kRC4CsIne7g4KC9Hq9v79/ixZVfJWkLWEhtKKpU6dqtdoxY8Z8+OGHTGd5F61atRo1apROpwsODmY6C0Klch43xbShmoTDnuzlBAA/peTpiqbEa+NjZEf3M5av/Bw7duz06dPOzs4LFixgOkuVgoXQWv7888/jx4+LxeJK/S27ePFiR0dH+seP6SwIlYwlspNMeDl5coSbuK6Am6TV78qWmwbz1q0gFfKS3l1pmE5Jw8LCXF1dmY5TpWAhtAqDwRAYGAgA8+bNq9Stbt3d3efMmQNFF2SYjoNQyRz6DTZtqMYhiHk+EgBYk16QrS+8ImrMy83f8jNj+cpDVFRUQkKCn5/fxIkTmc5S1WAhtIqff/754cOH9erV+/HHH5nO8r4CAgIaNmwYGxu7fv16prMgVAoWyzU4zLShWkcH4WeOQoWRjEp/uZRCuudXfdIzhvK9r6ysrEWLFgHAqlWruFzL1sToPWEhLH95eXn0TMvIyEg+n890nPfF4/EiIiIAIDw8PCcnh+k4CJVM0KK1/ed9TE/n+ki4BPF7jvyhSkePUHp9buQihtK9L3ohU58+fXr27Ml0lioIC2H5Cw0Nzc3N7dq1a58+fd58dGXQt2/fnj17mpbxIlQxuQTMNm2oVovP/dZNTAL8lJJr2kZSefm86tolpuK9M3prC9MpKSp3WAjLWUxMzC+//MLhcKrYKh/6ggy9sRPTWRAqGcfD0/G7caanAV5Oblz2bYX2dP7LhhW5qxZSBgMT6d4dvdnhlClTGjVqxHSWqgkLYTkLDAw0GAyTJk1q1qwZ01nKk5+f34QJE4xG49SpU5nOglCpnEf9wPEq7M9nz2ZN8XICgMWp+Wqy8GOh7tkT2f5djOUru717916+fNnd3T00NJTpLFUWFsLydODAgXPnzkkkkrlz57756Mpm/vz5rq6udPMXprMgVDKCL3CZHGJ6OsRV3EzES9cZtma93CkwLzrSWJDPRLoyMzVEW7RokaOjI9NxqiwshOVGq9XS37ILFy50cXFhOk75c3Z2Dg8PB4Dg4GCNRsN0HIRKZt+zn7B1W/oxC2CujwsBsDFDmqYrvCJKyqT5G1czF7AMli9fnpSU1LJly9GjRzOdpSrDQlhuVq5c+eTJk6ZNm44bN+7NR1dO48ePb9GixbNnz1avrhy/R1D15BI8D1iFv9za2PN7ONmpSWplmtlSigO/6R7HMZTubaWkpKxYsQKKNjtkOk5VhoWwfGRmZi5btgwAVq9ezeFwmI5jLaatfhcvXpyWlsZ0HIRKxm/U1KHfYNPTOT7OQhZxNE9xS1F0JYM05qz8iZlwby0kJESpVA4ZMqRz585MZ6nisBCWj5CQEJlMNnDgwO7duzOdxbr+97//DRgwQKFQYF9sVJFJJk1n2Re2qvficca4O1IAP6XkkUUHqG9eU/51hql4b3T9+vW9e/eaGqIhq8JCWA5u3769a9cuHo9XTb5l6XagO3bs+Pfff5nOglDJ2BIX5zGTTE9/8HT05nFiVLpDuQrTYO7qxZROy0S6NyBJMiAggKKokJCQ2rVrMx2n6sNC+L4oigoICCBJMjg4uEGDBkzHsYW6desGBgZSFDV16lSKoixejYyM7NChQ/v27XFyKWKW4/Ax3Fp16MdCFjHN2wkAVqTlK4yFHwv1qS8Kdm9hLF/ptm3bdvPmTR8fn+nTpzOdpVogiv8iQ2Wyc+fOESNGeHh4JCQkODg4MB3HRhQKRaNGjdLS0nbu3Pntt9+axp88efLVV1/duHFDoVB8+OGHDx48sLOzYzAnquaUl/7MCCqcvEYBDEvIuKXQ/ODpGOztTA+yRCLfgxc4bh7MZbQkl8sbNWqUnp7+22+/DRs2jOk41QJ+InwvKpWKXuW6fPny6lMFAcDe3n7x4sUAMHPmTIXi5bWmK1eu9OjRg8fjSSSSDz74ALehQcyy69xN9EnhTBMCINRHwgLYkil7ri1spUKqVHk/r2AuYAkWLlyYnp7evn37oUOHMp2luqjQhTAuLm7KlClnzpRwQ3vJkiX0oj1mLV68+MWLF23atDH/VFRNjBgxom3btqmpqfR0WZpMJjOdEIjFYqlUWsq7y9nMmTOXLFlSfPzMmTNTpkyJi6voE+WR9bgEhRJFE7mbiXj9Xez1FLU89eWCevmJg9qY+wyls5SYmBgVFcVisSIjI4miZhrI2ip0IUxKSlq7dm2JMzJ27dq1ceNG20cy9+LFi9WrVxMEERkZyWJV6H9JayAIIioqiiCIiIiIZ88Ku9t4e3snJyfTj5OTk318fGwTZuPGjbt2lbBv1r///rt27dqkpCTbxEAVEK9OfYevvjE9DfF2tmezzhaorsjUhUMUlbMiHCrGTaKgoCCtVjtq1Ki2bdsynaUaqXa/vstRUFCQSqX69ttvO3bsyHQWZnz88cfffPONRqMJCSnc1Kpnz54XLly4cePGiRMn5HJ506ZNmU2IEABIxgeynQpvCrpy2T94OALAwpQ8Y1Hx0zy4Kz91hLF8RS5cuHD06FGxWEz3cUM2w2QhjI+PP3LkSGZmJoMZ3tmVK1cOHjwoEomq+bfs0qVL7e3t9+/ff/HiRQCwt7c/cODA9u3b6R/pin9tR6vVXrly5cSJE0ql8s1Ho8rpaVb2lVYdU4q2WPvew6E2n/tEo9+b8/L2dt7aZaRKxVBAAADTjvahoaFeXl4MJqmGGCuE+/btGz58eGxsbM+ePSvdLRyj0Th58mSKombPnl2zZk2m4zCpRo0aM2bMAIDJkycbDAYAaN68+bp161auXOnt7c10ujeQyWRdunQ5ceLE3bt37927x3QcZBXnzp0bMGBAhodvQJr8tkILAFyCmF7DGQBWp+cXGAqXUpAKmTbuIYM5169f/+DBg7p16wYEBDAYo3pibDOw6OjoDRs2tG3b1sfHJzo6+jV7VxqNRp1OZzFoWvWh0+mKv2ptW7duvX//vq+vb2BgoI3/6gooODh4y5YtMTExGzZssP3WwDwej8fjAQBFUcW/E4xG42veu2nTpl69emF3m6pt69atixcv7tu3bxM7/vrQ2W3s3QCgh5Ooo4Pwiky9Oj0/qIbEvlM3h4nTNO6emnxmulIUFBTQO9qvWrWKz+czkqFaoxgyatSoHTt2GAyGyMjIfv36lXjM6dOnX5Pc0dGRoqjg4GCb/VuZCAQCANi3b59FYKPR+Mcff5AkafV/PoaQJPnHH38YjUaL8X379pn+WWwsODiYoqjXd6g5ffp0iV/OsGHD/P39e/bsOXz48MzMTKv/8yEmzJo1a/HixQaDYefOna083Z+0rk3/d8LPmwVgz60oOwMLBIJ27dox/a9VTTH2TbBixYrg4OA9e/bUqlXL3t7+NUf26NGjW7duFoMRERF0JyA+n2/7JdsURQGA+fo5Wr9+/U6cOLF9+/YRI0bYOJJt7Ny5c+TIkb179z5+/Lj5OP1PwWKxbP//wnT67OHhUfys6M8//yxx+Q1NqVR26NBhw4YNe/bsmT59+vbt260YFDEkNDR05syZffr0adasmXP9BoQhh95WTSl2IiEN+AInO+ZroV6vV6vVSqWSJMlqOAWdeUxXYmr+/PmrV68u8SX6E2F4eHjxl5o0aUJ/ImTEb7/9BgDu7u4FBQXm4zt27AAADw8PqVTKVDbrkcvl9G2/nTt3mo/LZDL63v6ePXuYyubo6NikSZPi4/TlptI+EQYGBtJfyz///NO1a1frRkRMW79+/axZs3LWLEts1yBzWVibVq0AYMGCBUznoiiKUqlUtWrVAoDNmzcznaU6YqwQXrp0KSwsLCgo6NNPP5XL5SUeU2ELIUVRn376KQCEhISYD5Ik2aFDBwCYPXs2U8GsZ9asWQDw8ccfW1z7pbdDbN++PYPXhN+tEMbFxX3wwQfh4eEtW7Y8cOCAlTMiZty7d2/atGnTp0//+OOPMzMzjUqFLuVFdHQ0APj6+iqVSqYDFtqzZ0+Jp9fIBhgrhCqV6vTp0//8849OpyvtmIpTCB88eDB27NgRI0ZcunSJHrlz5w6LxeLxePHx8eZH3rp1ix5PSEiwWTwbSExMFAgEBEH8888/5uNPnjzh8/ksFuvGjRtMZaPetRBSFJWVlXXy5MknT55YMx1ikl6vv3z58r///qvRaOgRqVTq6ekJJd3mZ1anTp0AYPr06UwHqXaYvzT6GhWkEJIkOXTo0AcPHsTFxdWsWVOhUNDjY8aMAYDiM33oG4QDBw60TTzbGDBgAACMHDnSYrxv374A8P333zMR6qV3LoSoGgoKCgKADh06VLR5baWdXiNrY/4uccVHEAR91UIqldIz9WlLliw5cODA0aNHT58+3bNnT9P48uXLDx8+fPDgwbNnz37++ecMJC5vFy5cOHTokGmjbZPz588fO3ZMLBb/9FNFb/aNEO3Jkyfr1q0rcTPP5ORklW3X1Ht7e4vFYtPTVq1ajR49esuWLcHBwUePHrVlkuqO6Ur8OomJiQsXLvz777+LvxQdHR0REWHLMMuXL/f19V25cqX5IL3ftJ+fn8UF3kWLFgFAkyZN9Hq9LUNag8FgaNGiBQAsXrzYfFyv1zdr1gwAli9fzlQ2k4iIiOjo6OLjf//998KFCxMTE20fCVVMvXv3BoBx48YVf4m+wW9Lxa/NZmZm0muBTp06ZZN/D0RRFIX9CMtAq9W2a9fuwIED9erVo0d0Ol3z5s0TEhLWrFnz448/mo7U6XRNmzZ98uTJ+vXrJ0yYwFDe8rF+/fpJkybVqVPn0aNH5isF16xZExAQUK9evZiYGFwCjCqFP//8s3v37mKxOCEhgb5NaG748OG3b9+2ZZ6oqCjzi0m05cuXz5gxw8/P7/79+1wu15Z5qi+mK3ElYDQajx8/TlEUSZLt27d/9OiR+auHDx8GAGdn5+zsbPPx/fv3A4BEIsnJybFp3HKVl5fn6uoKABaTKnNzc11cXADgyJEjTGVDqEz0ej29C7yNLyaVlVarbdiwIQBERUUxnaW6wEL4Zkajcf78+V27dv3ss8/mzZtX/IAePXoAwKRJkyzGu3fvDgABAQE2iWkVU6ZMAYD//e9/FuMTJ04EAFx7hyqRVatWAUD9+vVN00cZZDQaDQZDaa8eOXKkxNNrZCVYCMvBo0ePuFwum82+f/+++fjDhw85HA6Hw3nw4AFT2d5HaV9XTEwM/XX9999/TGVDqExyc3MlEgkA0Fd3bEmv12/cuNHPzy80NJQeWb9+/UcfffTxxx+/5v46fcl04sSJtopZrWEhLB/0DcIuXbpYjP/www8A0K1bN0ZSvSf6R3HChAkW4/RU2ClTpjCSCqF3wOBPotFoPHPmzM6dO2fMmEFRVGZmZsOGDVUqlU6na9as2fPnz0t8V2mnocgasBCWD9O9tEOHDpmPm+6lHTt2jKls74aevV384szBgwerwL1PVK1UhGszv//+O10IT506ZVqPO3ny5N9//720t5R2eo3KHe7uWj6cnZ3nz58PANOmTaN3A6dJJBK6y8/UqVO1Wi1T8cpKp9PRG1iHhYXRBd40Tncf/Omnn+gCj1DFFxgYaDAYJk+eTC/4YZZCoRCJRPRjOzu74nv3m4SHh7u6utKreG2VrprCQlhufvjhh+bNmz99+jQyMtJ8nP7xS0xMXLt2LVPZyioqKiohIcHPz4+eFGOycuXKx48fN2nSxN/fn6lsCJXJ/v37z507ZzolZZyvr+/z58/px8+fP39NZ2/T6XVwcLD56TUqf0x/JK1Szp8/DwD29vapqanm4+fOnQMAsVicnp7OVLa3V9qS3oyMDAcHBwA4c+YMU9kQKhONRlO/fn0A2LBhA4Mx4uLili5dOnLkyJiYGKPR2KpVq/379x87dqx58+av33PDtJ3FkiVLbJa2GsJPhOWpS5cu/fv3VygUc+bMMR/v1q1b79695XL5vHnzmMr29ubMmSOVSvv06WOx1HfGjBkymWzAgAFVY984VB1EREQ8efKkadOmY8eOZTDGxYsX8/PzPT09z507x2KxTpw48eDBgxs3bpw4cYLDed0+l2w2e/Xq1QCwaNGitLQ0W+WtfpiuxFWNqUvDv//+az7++PFjukvDzZs3mcr2Nu7cucNms3k8XlxcnPn47du3q2RXDVSFpaen09cwzp49y3SW99K/f38AGDVqFNNBqiwshOWPnk5SvD/ftGnTAOCTTz6paHvem6MbwQQHB5sPkiTZsWNHAJg5cyZTwRAqq++++w4ABg0axHSQ91Xa6TUqL1gIy5+pY/uuXbssxuntDffu3ctUttcrrTXozp07AcDDw0MqlTKVDaEyoTuD8vn8x48fM52lHMycORNKaouNygUWQqvYunUrANSoUcPUvJC2adMmAPDx8ak4fbFNVCpVrVq1AOCXX34xH1cqlfTEtl9//ZWhaAiVDUmS7dq1A4A5c+YwnaV8yOVyb2/v4qfXqFxgIbQKo9HYtm1bAJg7d67F+IcffgildBtmFj1Ru2XLlhZbINKTzlu3bm00GpnKhlCZbN++vepdw/j1119LPL1G7w8LobVcu3aNIAiBQPDs2TPz8StXrhAEIRQKS9taiRHJycl2dnYAcPHiRfPxFy9eiEQigiBK7AqJmygo2wAAHTBJREFUUAVk+vC0fft2prOUp9JOr9H7w0JoRcOHDweAr7/+2mL866+/BoDhw4czkqpEw4YNA4AhQ4ZYjA8ePBgAvvnmG0ZSIfQOZs+eDQBt2rSpetcwSju9Ru8JC6EVpaSkvP5j1uXLl5nKZo7+6RIKhaV9eE1KSmIoGkJl8/TpU3qCZVW9hvHNN98AwODBg5kOUqVgIbSu8PDwEm+80SvrW7VqxfhJq9Fo/OijjwAgLCzMYrxNmzYAsGDBAoaiIVRmAwcOBIARI0YwHcRaSju9Ru8DC6F1qVSq2rVrA0B0dLTFOD0Vc+vWrUxlo23ZsqXEO/AbN24EAF9f3wo4wRWhEl24cAEARCLRixcvmM5iRQsWLCjx9Bq9MyyEVrdv3z4AcHNzy8/PNx/ftWsXPbHNYtGeLZmWPO7evdt8XCqV0kseX9MjBqEKxWAwfPDBBwCwcOFCprNYV2mn1+idYSG0hc6dOwPAtGnTzAdJkvz0008BgO5SxoiQkJASN8EJCgoCgA4dOuDqXVRZrF+/HgBq1qxZHa5hlHZ6jd4NFkJbuHv3LpvN5nK5FWoDz8TERHr700q6LSpCJvn5+XTjzP379zOdxUbo0+ugoKDiL2m12rwitg9WGWEhtJFx48YBQO/evS3GR40aBQD9+/e3faQvv/wSAEaPHm0x/sUXXwCAv7+/7SMh9G6mTp0KAJ999hnTQWyntNNriqKio6PbtGkzePBgXPj0lrAQ2khmZqaTkxMAnDx50nzc1OTv9OnTtsxDt04Ui8VpaWnm42fPngUABweHStE6ESGKomJjY7lcLpvNvnfvHtNZbKq00+uwsLDq88m4XGA/Qhtxd3en9yoLCgrS6/WmcQ8Pj9mzZxMEcf36dVvmuXz5MkEQc+bMoSfL0AwGQ2BgIACEhYXRk2UQqvjon6lx48bRk2Wqj4ULFzo5OZ04ceLUqVPm4+np6WfOnAkMDKTPa9GbMV2JqxGdTteoUSMAWL16tfm4RqO5ceOG7fPcuHFDo9GYj6xatQoA6tevbzGOqrNBgwaV2H5r27ZtAwYMiI+Pt30kc8eOHQMAJyenrKwsZpMwIiIiAgAaN26s0+lMg48fP75//35sbGzLli0fPHhgmySrVq0aMGBA8f8Ljx8/HjBgAOPrxF4PC6FNVeQf2tzcXIlEAgDHjx9nOguqQAiC+Pjjj4uP0/01r127ZvtIJqWdXFYfr/8XmDZt2rZt22yTZOjQoQBQfAvlGzduAEBAQIBtYrwbvDRqU3369OnVq1dBQQG9s0yFMnv27Ly8vG7duvXu3ZvpLAi9lTVr1sTHxzdu3HjSpElMZ2EGl8tduXIlAISHh2dnZ9ODEyZM2Llz55EjR06cOPHZZ58xma+SwEJoa6tWreJyub/88sv9+/eZzvJSTEzMli1bOBxOZGQkPUKS5KlTp548ecJsMGQNFEWdP3/+3r175oMXLlzYu3cvU5HeQVZW1sKFC6HoZ4rpOIzp3bu3xen1/Pnz5XL5s2fPTpw4QTcZrZjkcvlvv/2mUCjop8+ePTtw4AAzUZj+SFod0VO9O3bsWHGWq9OnjYGBgfTTzMzM3r17t23bdv369cwGQ+VOrVZ/+eWXnTt3Nu/mk5iY2KlTp7p16xY/vsJeGi1tzmQ1RM+bZbFYt27dYipDWS+N/vnnn3379q1Vqxa9inrZsmVDhgypV6+ejeK+isNM+a3ewsLCdu3adeXKlfXr1/fs2ZPpOHD69OmLFy9KJBJ6XisAuLu7Hz9+fMWKFcwGQ9YgEAgOHz68e/fu+Ph40+DkyZNXr15Nd90qTqvVPnv2zGJQJpPRD1QqVXJyspXSliY2Nnbr1q2mC4PVHH1xODIycuLEidu3bycIwpZ/O5/Pp7d8A4Dk5GSSJM1fTUtLK/FdXbt27dq1a69eveinISEhFEU1aNDAmklLhYWQAU5OTrNmzZoxY8bkyZOZzlKIw+HMmTOHniyDqptff/21cePGrVu3Lu2Au3fv1q1bt7RX//nnn65du1on2usIhcLBgwfTU0XM/fHHH82bN2/cuLHtI9nGjh07OnfubHHNc968eZs3b05MTPTz87NxnqZNmz58+JB+TG8bWelgIWRGUlKSwWBwcHCg94ViVk5OjkwmS0pKYjoIYkBmZuaiRYsOHDjw9OlTvV6flZXl7u5ucUyDBg3ojgfmdu/effz4cQCws7Nr2LChjeIWyc/Pz87OfvHihcV4RETE9OnTe/bsabG0rsp49OjR2LFj+Xz+8+fPXVxcTOMpKSlqtVqv19erV4/NZtsyUp06dUyP165da/E77enTp3PmzLFlnneAhZABsbGxGzZsYLPZf//9d4sWLZiOAzExMS1btly/fv24ceOaNWvGdBxkU0+fPu3atSu9Y7VUKj169OjYsWMtjnFxcaHvAJm7desWXQjbtWtnfpXVNvLz8xs0aHDx4sUjR47QmwXSxowZs2TJktOnT584caJKzn+eNm0avXuAeRUEgODgYKPROGnSpKioKKayAQB928985ObNmxW/EOJkGQbQ9wUnTpzIdJCXJkyYAABdu3Y1jcyYMaNdu3YdO3Zct24dg8GQNSxZsqRLly6tW7devHix+XjlmiyzZs0aAKhbt2712RfiyJEjAODs7JydnW0+Tk+2lEgkOTk5TGUr62SZjIwMf39/X1/fIUOGXLhw4fz58/7+/g4ODv7+/pmZmbZKXQgLoa0dPXrU/FuZJMk1a9Z07dp12LBhT548YSpVbm4ufYJ57NgxeiQpKSkxMTExMRE3Ha16UlJS6P+5KSkp5uMl9rOtIIUwOTl5xIgRffr0mTVrFt2Q1mAwNG/eHACWLl1qfqRer2/atCkARERE2CabbWi1WvoSdFRUlMU4PceE2XPWshZCnU6XWEQqlRYUFJiemu+SYxtYCG2q+LdyXFxcQECAQqE4duxY9+7dGcxGryCsV69e1TuPRu+jghTC0aNH79q1i6Kob7/9dt++ffRgaXvHnzt3jh6vSqdxy5cvBwA/Pz+LOrF48WIAaNKkiV6vZyobhTvLoLcXFRWVkJDg5+dHX4oEgEaNGkVGRtrZ2bVr1y49PZ3BbJMmTWrWrFliYuLatWsZjIFQiby8vK5cuSKXy3Nzc+vVq0cPdunSpV+/fnK53LTyh9atW7cvvvhCLpeHhYUxEbb8ZWVlLVq0CIrtHpCZmbl06VIAWL16NYeDcz7eFdOVuBrJzMx0dHQEgFOnThV/denSpaGhobZPZc50Hm1xfo2qs+3bt1v0DqPdunVr27ZtNts199GjRw0bNmzYsGH37t3Ne9A/efKkOvSX/v777wGgb9++FuMjR44EgAEDBjCSytzly5e3bdumUCgsxnNycrZt28ZIX4G3h4XQdkr7VqYo6o8//ujSpYtKpbJ9Kgt9+vQBgLFjxzIdBKFXNGvW7PHjxyRJzp49e9q0aeYvhYSEAED79u0ttmoKCgoCgA4dOlScLZzezZ07d1gsFo/Hs+j1cevWLXqc3pwFvTMshDZS2rey0WicN2/esGHDzE9yGWQ6v64a59GoymjSpElsbCxFUWvWrJk6dar5SzKZjG6ruXv3bvNxqVRKt9U03VOspDp16gQA06dPNx8kSbJjx44AMGvWLKaCVRlYCG2E/lYODg62GD927FibNm26du3arVs3ZifLmNCTID755JPKfh6NqpIbN2706dOnf//+33//fUFBgcWrW7ZsAYAaNWpYXJqLjo4GAF9f3wpyovkO9uzZAwDu7u4WX/XOnTsBwMPDQyqVMpWtysBCaAulfStXTDKZjD6P3rt3L9NZEHorRqPxo48+AoCwsDCL8TZt2gDAggULGIr2XlQqFb0+ffPmzebjSqWyZs2aAGCzdoNVGxZCqzN9K//yyy9MZ3lbmzZtAgAfH5/it74RqpiuXbtGEIRQKHz27Jn5+JUrV+jxpKQkhqK9O3rWa6tWreilkyb0LNnWrVsbjUamslUlWAitrrRv5YrMaDR++OGHADB//nymsyD0toYNGwYAQ4YMsRinu2p88803jKR6Z8nJyXZ2dgBw6dKl/7d3pzFRnWscwJ9hAFm1ii1RJFYHFMW6xY0iLUqrNoJbiVute9uoqKBSl7i1tCqroKWlVWJjxKKmsoiVFrGiJagg4sIIZUsVFARBQWbYMnM/nJQ7d2As3jLzzjnn//uEz/HDH3PMc95z3kWz/uDBAysrK4lEcvXqVVbZBAaNUL903crGLzMzk3uO7rhCFsA4tf93u3z5sma9vXNcuXKFVbb/A7dEfeHChVp1X19fIlqyZAmTVIKERqhfuh5ReWH+/PlEtHjxYtZBALpq7969RDR69GitFzC7du3i17tEXU+iV69elUgkVlZWfHzTa7TQCPVI10cLvuDpczSImUKh4A6J1fokr1AouNklx44dYxTtFfzj3J+goCBG0YQJjVBfdN3K/LJ7927uAydfnqMB4uPjO52kfeLECb6sNzh69Gins9ViYmL4vhrEOKER6ouuW5lf2qe8xsbGss4C0FWdLtttX4G+bds2VsG6on1/gJMnT2rW2/cHOH36NKtsQoVGqBe6bmU+iouL49EiSAC1Wp2bmyuVSs3NzQsKCjTrN2/eNP49yQIDAzvdMS4gIEAYO8YZITRCvdC1+SEfqVQqDw8PItq6dSvrLABdtXr1aiLy9vbWqi9fvpyI5syZwyTVP2rf41Brl+qioiJzc3MTE5OcnBxW2QQMjbD76doOn7/an6O1NkoFMFq6DnuprKzs2bMnEf3666+ssr2Ej48PEa1atUqr/sEHHxDRZ599xiSV4KERdr9Zs2YR0cqVK1kH6U4rVqwgotmzZ7MOAtBVoaGh1NlJtvv37ycjOMm2o4sXL3Z6Dtpvv/1GRD179hTSOcNGBY2wm+m6lfmusrKSe75OTU1lnQWgS5qbm4cMGUJEkZGRWnVnZ2ci+uabb1hl66i1tfWtt94iouDgYM16S0vL0KFDiSg8PJxVNsFDI+xObW1tnd7KwsAdhN3x+RrAaCUnJxNR7969q6urNetnz57l6jU1NayyaYmKiiIimUzW1NSkWQ8PDyciJycnrTp0IzTC7nTo0CEiGjx4sCBv2fbn6MOHD7POAtBVM2bMIKI1a9Zo1adNm0ZE69evZ5JKy9OnT+3s7IgoKSlJs/7kyZPXXnuNiM6fP88qmxigEXab2tpa7lZOTExknUVfEhISOn2+BjBacrnczMxMKpXevn1bs56fn29qaiqVSu/cucMqW7u1a9cSkZeXl1b9008/JSIjOalUwNAIu826deuIaOrUqayD6Nf06dOJyM/Pj3UQgK7asGEDEU2ZMkWrrqv9GJiulpyXlyeVSk1NTe/du8cqm0igEXYPo3q61Kv8/Hzu+VrwvykIRm1tbd++fYno559/1qzreiFpYLpe0np6ehJRQEAAk1SigkbYPYzqe4O++fn5iWHsC0ISHR1NRIMGDVIqlZp1XVNUDEbXtJ3Tp08TUZ8+fZ4+fcokmKigEXaD9i9nxjMDTa/E8DUUBKatrW3kyJFE9PXXX2vWdS1aMAxdCzmUSiV3hkZMTIzhU4kQGuG/Jc65lMKeHwuCdOnSJSKysbGpqKjQrDNc+6traX9QUBARubq6GtuSf6FCI/y3qqqqfHx8xHbLtra2urq6+vj4VFVVsc4C0FVz584lomXLlmnVdW1sple6NnsrLy+3sbEhot9//92QecRMolarCf6158+fcxuviIcIf2Xgu9LSUldX1+bm5qysrIkTJ7bXi4qKRowYMXPmzDNnzkilUsOEyczMXLBgwbhx4xITEzXrH3/88YkTJ3x9fc+cOWOYJGDCOgA/FBcXe3p6ct/Vtfj7+3t6enJPcKJiY2Pj6enp7+/f8VJUVJSnp2dxcbHhUwG8xODBg7lJmP7+/ppjAGdnZ7lcfvbsWYN1QSJyd3cvLCzkztptd+3atbi4OAsLi5CQEIMlATTCLmloaMjIyCgqKup4KS8vLyMjQ6VSGT4VWyqVKiMjIy8vr+OloqKijIyMhoYGw6cCeLkdO3b079//2rVr3IH17WQymeHDWFtbc2ftctRq9caNG9Vq9ZYtWwYNGmT4PKKFRggAImJjY7Nv3z4i2r59+4sXL1jH+R/Hjx+/ceOGg4PD1q1bWWcRFzRCABCXpUuXTpgwoaKiIjg4mHWW/3rx4sWOHTuI6MCBA+2fWpKTk/fs2RMXF8c0mvChEQKAuEgkkqioKIlEEhoaajxfsvft2/fo0aNJkyZ99NFHXGXz5s0XLlzw8vKytbVlm03wTFkH4JPq6uqcnBytosi/hDU0NHT8N6murmYSBqCLuH5z4sSJtWvX7ty5k3UcevToUUREhEQiiYyMlEgkRFRXV3fu3Ln79+8bcv6OaGH5RJfcunVr7NixL/kLLS0tZmZmBstjDFpbW83NzV/yF3Jzc8eMGWOwPACvpLy8XCaT9erVy0ie2/r06TN69Oj09HTujzdv3lywYIGrq2tdXd348ePDwsK4Bgn6gBHhK5g1axZ3xISmwMDAO3fuMMljDEaOHBkaGqpVjI6O5g5EBTBamZmZLS0tTU1NkydPZt5jampq7t+/X1RUpFQqLS0tuaK9vX1CQoKJicnkyZMLCgqGDRvGNqSAoRG+AkdHR25zbU3cDDTR6t27d8d/E3RBMHJKpZKbmXnw4MFVq1axjkMqlWrixIk5OTlhYWG7du0iIicnp+rqapVKJZFImpqa2traWGcUMkyWAQDRCQ4O/uuvv8aMGbN8+XLWWYiITExMuK+D+/fvf/DgARH16tVrzZo17u7uHh4e48aN43YGBz1BIwQAcSkvLw8LCyOiyMhIqVT6/Pnz9/82cuTI7OxsJqnc3d19fX2VSiW3iIKIAgICMjIykpOTtXafgW6HRggA4hIYGNjY2Lhw4cJ33nmHiHr16pWWlpaWlpaSkiKRSBiOvcLDw62srE6ePHn16lWuYmFh0adPH1Z5xAONEABEJCsr69SpU5aWlgcOHNC6dPz48fnz51tYWDAJRkSOjo6bN2/mtkIV4a6NDGH5RJfU19dnZGQMGjRoxIgRWpeysrJqamq8vb2ZTzwzMLVanZKS0rdvXzc3N61L9+7dKysre/fdd7lTZgCMhEqlmjRpUnZ29p49e/bu3at5Sa1WT5gw4cKFC3379mWUjohIqVS6uLg8ePDg2LFjRvL9UgzQCAFALGJjY1evXj1gwICCggJra2vNSykpKRcuXIiOjmaVrV1cXNySJUvs7e3//PNPPEoaBl6NAoAoNDQ0cCsTQkJCtLogER08eHD9+vUscmlbvHjx5MmTq6qquPPrwQDQCF+moqLi7t27LS0trIPwUk1Nzc2/dXpaE4AhBQUFPX782M3NbeHChVqXqqur3d3dXVxcmATTwm2FamJiEhER0enRb9Dt8GpUp/Dw8IsXL44ePTotLS09PR2nsb+qGzdunD17loiUSuW5c+dKS0tZJwLxKikpcXV1bW1tvXbt2vjx41nH+WcrVqz48ccf58yZk5CQwDqL8KER6uTu7n706NFhw4bNmzdvy5Ytb7/9NutEfPXdd9/V19fjiDVgaPbs2cnJyStXroyNjWWdpUuqqqqGDBlSX1+fmpo6ffp01nEEDq9GdVq+fPnixYvDw8NNTEzGjRvHOg5fqdVqboYC6yAgXunp6cnJyba2tl999RXrLF1lb2+/fft2Itq0aRP2V9M3NEKd5HK5r69vQ0NDaWlpWVkZ6zh8lZSU5O7ubmdnxzoIiFRbW1tAQAAR7dy5s1+/fqzjvIJNmzY5OzvL5fLvv/+edRaBw6vRzjU1NQ0cOLCqqoqIQkNDGxsbtVYdQRd5enrGxsbKZDLWQUCkDh06tHHjRplMlp+f36NHD9ZxXk1CQsK8efN69+5dVFSEp0n9wYiwcxYWFk5OTpGRkWlpaUlJSVOmTGGdiJdycnJef/11dEFgpa6u7ssvvySiiIgI3nVBIpo7d+60adPq6uq++OIL1lmEDCNCnRQKRWJiYl1d3ZQpU4YPH846Di/l5+dbW1u/+eabrIOASPn5+UVHR0+dOrX9wFvekcvlo0aNUqvVt27dwhkUeoJGCADCJJgWIoB2buTwahQAhCkgIKCtrW3t2rW87oJEFBQUZGdnd+nSJRx5rScYEQKAACUmJs6dO1cw00x4PeXH+GFECABC09LS8vnnn9PfYynWcboBN64tKSmJiopinUWAMCIEAKE5cODA9u3bhw8fnpeXZ2ZmxjpO90hPT3/vvfdsbW0LCwv5tSDS+GFECACC0n5uQ0REhGC6IBF5eXnNmjWroaFh586drLMIDUaEACAoAt6umndbh/MFRoQAIBy5ubnHjx83NzcPCQlhnaX7yWSyDRs2qFSqjRs3YgzTjdAIAUAg1Gq1v7+/SqUKCAhwdnZmHUcvdu3a1a9fv6ysrPj4eNZZhAOvRgFAIOLi4pYsWWJvb19YWCjgA0S541wGDBhQUFBgbW3NOo4QYEQIAEKgVCp37NhBRPv37xdwFySiFStWjB8/vry8PDQ0lHUWgcCIEACEYPfu3UFBQWPHjs3OzjYxEfgjflZWlru7u4WFxf379wcOHMg6Du8J/HYBADF4+PBheHi4RCKJjIwUfBckIjc3twULFiiVym3btrHOIgTCv2MAQPC2bNmiUCgWLVrk4eHBOouBhIaGWltbx8fHX7lyhXUW3pPivFkA4LsnT57cvXv3p59+EvbXQU09e/ZsbW0lIl9f3zfeeIN1HH7DN0IAEIK2tjZTU1PWKQxKhL+ynuDVKADwg5ubm5eXV8f6Dz/84OTklJGRYfhIbJmami5btszJyam5uVnr0uXLl52cnGJiYpgE4x08TQAAP5SVlXW6bK6urq6kpKSxsdHwkZirqKgoKSlRqVRa9cbGxpKSktraWiapeAcjQgAAEDU0QgAAEDU0QgAAEDV8IwQA3lAoFElJSVpFuVzOJIzxSElJMTc316zcvHmTVRg+QiMEAN6orKycM2cO6xRGZ/78+awj8BsaIQDwhqOjY2pqqlYxNjY2IiKCSR4jkZub26NHD83KlStX1qxZwyoP76ARAgBvmJmZDR8+XKuIfVVcXFwsLS01K2VlZazC8BEmywAAgKihEQIAgKihEQIAgKihEQIAgKjh9AkA4Ie7d++amJi4urpq1auqqioqKmQymXjOYGpXXFxcX18/ZswYiUSiWa+vry8uLnZwcLC3t2eVjUfQCAEAQNTwahQAAEQNjRAAjFFFRUVxcTH3s1KpTE1NzczMxBusrlOpVNevX28/qrCwsPD8+fOVlZVsUxknNEIAMC5qtfqTTz7x9vYODg4momfPnk2dOjU7O/vYsWNLly5lnY4f/vjjj/fff9/Hx6e8vJyIDh8+7Ofnl5+f7+npia1ZO8I3QgAwOgqF4vr16ydPnjxy5IharX706JGDg4NCoRg6dOjDhw9Zp+MBpVJpYWExY8aMb7/9ViaTPXz40N7e3tzcPCQkRCKRBAYGsg5oXDAiBACjY2Vl1f6zRCJxcHCIiYn58MMPw8PDGabiEUtLS815pI6OjtzxFKWlpYMHD2aXy0ihEQIAD7i5uXl7ex85coR1EB775Zdf5HI5ju/oCI0QAIxdc3PzqFGj1q1bV1hY+OLFC9ZxeCk+Pj4sLCwxMVEqlbLOYnSke/fuZZ0BAOB/JCQkJCQk3L59u7W1deDAgTNnznz27NmpU6esrKwwX6Yrnj59evjw4UuXLjU2NtrZ2cXExOzevXvRokW3b9+ur693dnZmHdC4YLIMABidO3fuPHnyhIh69Ojh4eFRW1t7/fp1W1tbNzc3DGi6orGxMSsri/vZxcXl8ePHz58/5/7Yv3//jkdZiRwaIQAAiBq+EQIAgKihEQIAgKihEQIAgKihEQIAgKihEQIAgKihEQIAgKihEQIAgKihEQIAgKihEQIAgKihEQIAgKihEQIAgKihEQIAgKj9B17N0rJLIAu2AAAAtnpUWHRyZGtpdFBLTCByZGtpdCAyMDIyLjA5LjMAAHice79v7T0GIOBlgABGIOaB4gZGNgcNIM3MQizNyACiGRmJpbkZGDOYGFgTGNgYGJkSGNkZmJgzmJg4gCYlMHMysLBmMLFwMbByM4gwsjGwsjAzMYrPgroRDICOPGC/etUqFQjXwf6h27L9UPZ+BPvA/gn9U1SRxO2R1MPYDkBzoGoOAMXVDsD0ItgOB5DUgMQdkNSD2WIAeEowBObG7QEAAAEeelRYdE1PTCByZGtpdCAyMDIyLjA5LjMAAHicjZPNboMwDMfvPIVfgMhOwkeOBapumgpSy/YAlTjswmmnPn3tVtRhrSKSIBLzi/+OHS7TfJ3mKQNpp+7r9w+ezXYZ2zExQgjw4xAxO4JMoNkfPntox12zWNrhux/PQFYGSl+zu3E4LhaCFsgUKA3Q4P/Jwlnm0FR3EHIyNgR09RvQMZhvIr2QW7SLlcuExzIOMsFV8AHuVe+Fq5l7BphbU4Qaq/INGBjMN5GEgm4RJ1o5Tfm0cZwJcN93q+I/rkMz9J1eB8+P1arLstTaEvdKC+iZcFom3gm1FsPzBq85J14GTa3sLjSBd+U4TfxVlUmEWYCiM3Ng/IrPFp9E1stPwfPsBklppcPiwSVEAAAArXpUWHRTTUlMRVMgcmRraXQgMjAyMi4wOS4zAAB4nG2OPQrDMAxGr9IxAVtIjh3b6gUyle4hQwkdi0vJmMPXMaWyS5dPejz0M0/LSms3T0v/Jynnae80gVPagIsBgzprBJ+ZwMSINvOhMdehZLGt/I62rlGfJWXHz4HmfK9uW3pcX+nJCEd7SdsdApOAYSMw8CAQ2QpYdgKEPAo59pUiDkLIUWBkqp4gpuoLv78B3OteYT5+y/cAAADtelRYdHJka2l0UEtMMSByZGtpdCAyMDIyLjA5LjMAAHice79v7T0GIOBlgABGIBaA4gZGNgYFIM3CyKABkmLkANPMTDA+jGZzAIuzEEuj68dFc4Ocwwx0AAMDEwMrWwYTK1cCKzcDG3sCGw8DO0cGEzsvAwdnAgcfAxOnAidXBhMXP4MIIxsrFycHO5v4Kah3wADoH4f9QVmVDiBOoTLzAc2tUxwgUgf2P3RTA7MP9/LYB2V5QsUbVB66LYOpsWeAAwd7oPh+KHs/gn1gP5IakLg9knoY2wGh5gBQXO0AkhugbAcg3aCKpAasRwwAU+Q1F510MG0AAAFlelRYdE1PTDEgcmRraXQgMjAyMi4wOS4zAAB4nI1Uy26EMAy88xX+ASI7L8hxgVVbVQtSu+19Dz2sVLWH9v/VGAoOYjcicMBmMnY8A5ev68/350cBvF665+svLEt3Rcxj5g4hwLtBxOIE/ADN8eGph/Z8aOZMO7z151cgzzfytcYezsNpzhC0UKKqXGREMKoOFZEFVDgu2arhEUqt0AY0NVjl7R2ggQFKUm5Ka+VCjZW/AbSREZWbCI2ie4RuxP1nnaLgyekbOB+PstSV1xtcFXHLiWOnOowdbIH1ajY5ZGDkntqEK84MJVHaZg7IyphtyS3QRODSZJlRhliach+U1Sl3lfdp+Qzlse9WFp1M2wx9J6blS4s1ObRiQA6d2IxDI26y0SmVmIZDIjEHcazFAzbmahGaGGISPW3EhEQ1ijHZRB3Lu1IViBMuGTZxz+lILRNLU/wlj9Q+nVE6EY7nX0B8Lv4A4BXRSLBAT9EAAADgelRYdFNNSUxFUzEgcmRraXQgMjAyMi4wOS4zAAB4nG2Ouw7CMAxFf4URpMTyzaNN3JGFCdgRA0KMqAgx8vGkERAXsVg+vnZyDpvjGeflYXNcfepuXZta1BylLp5LUDTWUcyJkxmY+oIglzMHM0whm8FPpUaz5Htl55n9E3pKuQcKOuIwrQXqQp0wxTLwqezgPTGRkDvE91vld1sd7I+fndmvzOkxXvf38SZMU7sdHxeCEzTqxDWI4tUeJChiiYqidI2y9A2cpAYsWR0JlIYXKI0gUB5JoEVKqEx6gTbxzxfPpoBhczlbPwAAAABJRU5ErkJggg==\n", "text/plain": [ "" ] @@ -570,7 +570,7 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", 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\n", + "image/png": 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F7CnBAYD92WUKDDoAVHC4g+SlAYAmJb3TamGJWkcGgxEXF5eVlWVlZdXMsYmREJIl5hsaGiwsLPhzio4aNap///7y8vJYBYWDhYVFaFpFeodJ3jm1XueiZw3tOG+4dutOzjE/HH379p0yZcrNmzfJryQAJCQkKCgoZGZmYiH8WRndT1WCQeNoGkBOGBTFV/ecMO9M1Amr/n205MnX5JWzXqeW+yeUvkopS8ytAQDg1EN5GhQnQUF0cPG7QG7DOi1lALDrojpMQQYAnlfW+VfUAQDwuHRFpc6dOwNAS5NlipEQkpw/f37r1q1WVlb8zjIdOrSjoJafGzaHt/Ri7NOYYiabwyMAADxjiv9wTfXbNbynZpt8oDE/Cnv37r1//z7/nerq6mHDhgUEBIhqSBhBI8GgyUsxKjUNAD5FE3pGF/onlNgv6qelLO2fUOqfUJJRVAcAwGFCcRIUJUBRPJSmAo+DWuDRaLpy0pqSjAxWw5ft0xgSUt1bGZIhjkI4c+bMrVu3enl5sdnstmTEwLSOlQ5xXtFFdb67wfQAulMTcatO22j0fm7KyXGKMuL4mRQTbty4gRI/SUtL37p1a+DAgeRTenp6UVFRgwYNEt3oMIJl/fWE2noudOgJUvJQUwC1xTx5jbp67ibnRAAADgtKkqH4HRQnQlECKX5Ao4NyV9DQA61BoGU4ofqVZemz0OqcRo3TZeU6bNgO9FZ6vYjjpNOrVy89Pb3ExMTg4GCRFLwmCCI5OVlXVxc9zM7O7tChg4KCQm5u7qNHj+h0+uDBgxMSEpSUlBYuXPjVFq5fv66pqWlhYRESEhIUFLRz504hDr9NxH6sehxZWMfmQlnav3drCnis6oo6zkmvzH3zmuXujPkRQQarhoYGLS2tqqoqsrQFAEyfPl1048IInKwS5r03eRweATQ6qOtBXjgUJ4D0SChJgYJoKE7k3/kBjQ6qvUFDDzQGgJYRSCkAgJaK9Jh+qn36DJPwYp+WDGawmOi145XkxmqqKUydpWzZ+kAscRRCAEBleN3d3UUihA0NDSi5Inpoa2u7atUqc3PzTp06bdy4cf/+/XV1dRkZGT179vxWC3PmzHFzc6uvrw8NDaU2/bGgeRhWwOZ8PXSVyebefp2HhfCnR1JSsm/fvqGhoSEhIciJBvOzklFU559Q8iql3Cum6FM9XnYNyCgDAERegTd/A/G5SC9dAtR1QVMfOhqAuh5IygKAtor06H6q5vpqJn1V9TopfHLhmOpQ+9y7/KZDQ8Z7ALpMX90OqzbImYxvyzjFVwiPHDni7u5+5syZduUgc/PmzdmzZ/v6+u7YsePkyZNNv9jDw0NOTu7Nmzf19fU/SmKOtMI6DpcHANBQB347Pt2tzoMuJgBQVIljCsWC8ePHh4aGBgYGYiEUCSwWi8FgSEpKVldXKyoqNv8HiXoWp6SIoahEV/p6LDxBwLvc6oB3pUHJZUHJZUVVbACABiaUpkBBNBTEQHk6EAQwpKG+CmgMUO0NWkagoQea+iApDwA9NOTG9lcdr6c6Tle1h8bXnQbkJ0yVnzC1xW/724ipEA4fPrxz584fP36MiYkRybEEi8Vyd3dH1+TWMCkpqbq6+uPHj7Nnz3Z1dW0iG7ifn19BQcGyZcskJCR69er1o6ggAOh0kKHTgccDkJSDicc/3Q09hf5XlvuRdreYVjNu3Dh7e/uXL1+KeiDiCJfL3bRp05w5c5hMZnp6+vDhw8eNG/fdn2rIyiw+aseKCqVJSBGcBolOndW37iH3YWjn559QGpBYWlLNBgBgVUBRwieHl4osgM92ILokaPSFklQAAAsHUNQGABoN5g3XmqivPtFA/VviJ1DEVAhpNNr06dMdHBzc3d1FIoQcDocsI1VVVYUudHV1yYPDvn37NvHjCxYsIK8nTpwomDF+haqqKjk5OQkJiZKSkg4dOrQiJ4jFII0rL7JqWNwvn5KSoM0c0pGKYWLaO6NHj5aQkIiIiGjpjgTTdpydnS0tLTkcDorobc4ymhUXlf8/68iSikFyUkRDAwAkJ6eobV1bPHnVdfVJL96VltZ8Fr/SFChOhIIYKEvnEz8JUOkOWkagNQg09IAhBS/tIC8CylJBUVuCTpthqHxillq3bl1ra2tTU1Obnv0EgZgKIQDMmjULCeG+ffuE37uCgsKOHZ8Mg81PfyBaamtr3d3dIyMj16xZ8+7du6SkJDs7u5Y2YqqnZtBFKTKzks3g89elSwCNLsmg/zGr9QnpMT8QCgoKgwcPfvv27Zs3b0RSB+3hw4fz589H1+Hh4Zqamt26dUP3mUzm3Llz79y506lTp5/Shae2tjYkJERRUVFSUrK4uNjc3Lzp1xP1rIItv3Dq6tanF4YadEE3LxZUzlblDH16PUGLVlpZ8cnbk1/8JKShQ69P3p5I/ADoNBoPZbfWNIC8CCiMh27jpCUZk9U/3rsXPHXq1Fu3bs2ZM0f4Qih2KdZIzMzMFBUVY2JiMjMzRT2WVlJeXm5jY4OKTB04cIDD4Xz/Z9qAvLy8vr4+g8EYMGDA5MmTZWVlW9fOk+1D+mjJyy+6/elxXTGo9gHtIXOHd9TpgLPMiAvIHBcYGCiS3tevX09eOzs7h4aGAgBBECNGjEhJSWGxWNbW1mFhYd/6cW9v7xs3bgBAXl4euaL9Udi4ceOWLVusrKy4XK6CgsLHjx+bfn2NnxfBZn95v5ZHzHyXmeRlC6+OQIoHlKWBhAxoDwbDZTDxOMx3hYnHwWhlz4HGNhN7O68z/HjWbOfMnrJSdBoNQPPf2oTrJnRdu2KRnJwci8WqrKz09vauq6sTwPtuCvEVQmlp6cmTJwOAp6en0Dqtq6vbtWtXfX39ypUryZuTJ0/u0qVLK1pbsWLFoEGDQkJCAgIC0tPTjx8//v2faQOVlZXIFpqTk3Pw4EGUOrIVqClIRR8efdKqfydVGQAAv9/h7TkoS43MqKJwtJh2jmiF8KvQaDQJCQk6nS4hIZGYmGhgYPCtV06dOrWsrAwAnJ2dNTU1hThGalBSUtLS0po8ebKlpeV3t1/MiDe8uloAqOcRj0pr0L/s+gZ5Oq2eR8jS6aBlBAZWYHYI5t8F0wMwYGHPAUNtzHs6rzPMPmeWfmq8w2r9ZWM6dVGTObSgX8zhMcN6qoBaH5CUhaocYJZlFNVduHBBS0ure/fu6urqsrKytbW1Qvk1/Iv4mkYBYNasWQ8fPnR3d9+4caMQumtoaFiwYIGXl1dGRgZ/Wo3ly5e3orWysrLY2NjHjx8DgISExKFDhyZMmLBr1y7KhvsFysrKVVVV69ev53A4kydPLi4u7tGjR+uakmTQ1k7oNrJPB6Ndr6DTCEh9AjmhiWr9UvNr+2rLf//nMT8+o0ePZjAY4eHhtbW18vLC/qNzudyDBw+i64iIiDFjxqCb7u7uDAYjJyfnxo0b37UZvn37tqioKCcnJy8vT0dHR+CDphT+X3t9fT2DweAvpOzg4GBiYqKvr+/q6iqT/mEAAAAQAJzPZftQ0INLXy05acXh/Q4BQE9NOXN9dXN9NVM9NXXFbyYq6astv2tmrzmnKkBdF/KjoPhdYHLHP9aOAoKQkpLasGFDQ0ODhoaGYN70NxFrIbSwsJCUlAwMDCwvLxd0ijWCIGxsbLy8vNTV1Sk5lczNze3cuTMZ+6Gjo5Ofn9/2Zptm9OjR6IKSwo2GXZV6aMhldh6JhBAMlz+JLtqm3UpxbTVFRUUEQXTs2BEAOBzO+/fvkcuSr6/v+/fvhw8fHhoaWlVVtWHDBv78tJg2oqysbGRkFBkZGRoaKvxwXhqNNmzYMHQdHh6OLhgMxrp169D1+fPnm/jxt2/fEgTRu3fvU6dO+fn5/XAqCADdu3cvLi5G15s2bZo8efLcuXPRw5iYmPLy8sLCQgUFhYKCAlkFZX0paWDXy9BpC9Q/eTaF1dQDQGcpibyO3Vw3DTIboKam0NwsXWP7q9JpNJ6mPuRHQWF8WdcxUhp9DLooAoCKSosrFFKC+JpGAUBFRWXMmDEcDufp06eC7mvr1q1OTk5KSko+Pj6ka2hb0NbWzsvLIx8WFhZqaWm1vdmvQhAEv9W+rq6Ox+M18frmM2OwJmjqg5QCVH6E6lz3iEJKmm0RN2/edHZ2Rtfl5eWkD8XEiROLiorodHpWVlavXr3alRHv50CE1lE6nT75M604mBg+fPj27dvRwkiYbtvC4cKFCwwGIyMj4++//5aRkclTVqfRvx5sTZeVH7T5V8sR2s1XQQBQVZDU76LwqTZhUTwAtKgkkyAQayEEgFmzZgEACumrrKyMjIwUhA/nnj17Tp8+LSUl9fDhwyFDhlDSprq6eq9eve7duwcABEHY29svWrSIkpa/JD09nX/NPnHixLS0tCZe33xmDekIdAnQGQYAkBMW8r68uOorx/Iigclk2tjYvH37dvPmzdXV1YJbZ4gtIhHC5ORkYXbXbuHxePGfQeedJI6OjnPnzh06dOjZs2fnz58/ZtJk1d920WVlV2kqka+ZqCLXXUleZtBQBfNpreh9nK4aqPYBCRmozAZWRWArivRSilibRgFg1qxZmzdv9vb2Juuk3759m9rIwgsXLhw8eJDBYLi4uFC7eHR2dv7111/Pnz/P4XBGjhwp0ANCATG2v6qaglRp55HwIQByQrm6c71iilaM7SzkYSQkJPzzzz/AF9MJAOHh4UlJSbNnz46IiFBSUho1apSQR/XTM3bsWAaDERYWxmQyW+2E3CJcXFyWL19++PBh/rRNixcvbt0qp6io6PDhw4mJidra2ps3b/6xMoY3NDRcvHgRXScmJjZ6tlevT4FMqqqqZmZmAECXk193bB/QgGhooElITZWWVpq1QG3rHmhVZq5x/VXP+UqAen8oiIGihMBkTYJoXUvUIO5C2K1bt4EDB8bGxr58+XLKlClqamrGxsYUtn/79u1NmzbRaLQrV67MmzePwpYBQEdH59GjR+TDTZs2eXh4REdHC+K8s66uLi4ujrymqlkJBm2KofqdiiHAkIKSRGBVuEcWCl8IKyoqkBM5v7va+PHjx48fDwAzZ84U8njEBBUVFQMDg5iYmLCwMPSrFigODg4bNmzg8XhsNnvZsmXkfRMTk1a0xmazzczM7Ozs7O3t4+PjkR+cQAPg3r9/HxQUJCUlNXDgwKCgoHHjxvGX72gp0tLSly5dQtdr16797usVLeYpTLJgRoZx8nPpikqyQ0YyVNVa3fvY/qo0GhCaBkgIi6tGJ+XV6HVSaHWDbUTcTaPwX+uom5sbekgJT548WblyJY/HO378+KpVq6hqlp+EhISFCxdu3boVABITE7Oysry8vATRUWFhocNnCgoKKGx51pCOICELmgZAEJAX/iyupI79lbwzAmX06NFbtmzZsmVLc2YEDIUg6yjKtRYSEvLgwQMBWUovXry4fv16giBOnjz5xx9/tL1BHx8fAwMDS0tLGRmZYcOGbdmyxcHBoe3NNkGfPn00NTXLy8udnJxkZGT4nTyFA01KWm7UWKW5ixUmTm+LCgKAhpKUXicF4KtN+DJRlNZRLISfhPD+/fuPHz+eNGkSVR+vly9fLliwgMPh7N27d9u2bZS0+SUEQbi6ut65c4fL5fIrOuX06NHjwmeaKIvRCqYaaspI0qHzSACAnNA6Ntc/oYTC9jHtGf5jQmNj4759+3K51C+Djh49+r///Q8Azpw5g1aNbScjI4N//9e/f/+MjAxKWv4WTCZz6tSpRUVFCgoKa9asefjwYVta4w8OMTAwEP4R+DhdNVDvBxLSUJEF9VWByaL0l8FCCFlZWVJSUlwud86cOfr6+jo6OgsWLDhz5kxkZGTzfSM9PDwcHBzKy8vRw9jY2Dlz5rBYrPXr1+/fv19gYwcDA4OePXsWFRWFhYXNmjWLRqP5+PiQ550/BAoyDFM9Neg8EoAG+VHAqRea7+izZ8/u3LkzZMiQoUOHojtycnL8RjOMoBk3bhydTg8NDUUfWh8fH1NTU2q7OHr06CdXvjIAACAASURBVM6dOxkMxrVr1yiMGFZTUyO/7wBQWlqKag4LjqKiomvXrpmZma1cufL8+fOtzmiBuHv3LgCUlZWdPHmyoKCA2iOh5jCuvyrQJUCtHwABRe9eJpYSX6/PJhQI8cbX1xcVqTczMzMzM1NQ+I+RWllZecqUKfv37/f396+qqmqindWrVzs6OtbW1hIEkZqaioLSUBIjQb+FzZs3A8Dvv/9OEISRkREAPH36lNou8vLytm/fTj7csWNHbm4uhe1f9s8CKy9Q6wsAMHaP5jo/DpdHYftf5c2bN/Ly8nQ6PSgoSNB9YZoAJXAJCgoqKSl5+PAhhS3zeDy0/2MwGDdv3qSwZYIgcnJyevXqVVZWRhAEh8OZOnWqm5sbtV0IgaKiIhqNJisry2KxhNx1YWU9bYkXGCwBAOg3C6y8knKrhTwGErEWwtDQUKR8GzduRHc4HE5CQoKDg4O1tbWenh6/KDIYDD09PWtrawcHh4SEhEZN7dq1KzEx8f79+9nZ2Sh778SJE+vr64XwLgICAgCgT58+BEGgLNhr166lvJc7d+788ccfcXFxlLdMEERuGZO2xAsMlwEA9JwIVl6vUsoE0RFJfHw8CgKztrbm8QQuupRQUFCwf/9+Lpfr5ubm4+ODbvJ4vPT0dHK9lZ2dzeFwCIKorKwU2UBbiJmZmZSUlKam5qZNm/z8/Kj61vB4vE2bNgGAlJTUP//8Q0mbjbh//76+vv7ixYuHDBmya9cuQXQhBAYMGAAAwcHBwu+6//ZAmGAPANChB1h5XfbPEv4YEC0WwtLSUoIgqquryc8rh8OpqKggX4CuP378GBISIhwlaB1fToVeXl4XLlyIiYlBUwlBEHl5eR4eHra2tiYmJo2KlWhpaVlYWNjb2wcHBzOZTG9v73PnzqWnp6Ng+ZEjR9bU1AjnjXA4HGSTSUpKioqKAoCOHTtSvhNFafhdXFyobZZk+J7XMP0iAIC0Iix+ssMlSUAdEQSRlZWFYqhnzpzZ0NAguI6opaGh4dSpUxwOp6Gh4cSJE+hmTU0NAFy6dAk9NDQ0LCoqKisrk5KSGjJkiK2tbXBwsBDMEq3m5MmTNBpNRubfZOvKysrz58+/fv16fn5+q5vlcDjIPU1aWtrd3Z3CATeCy+V+/Pixvr4+MTFx3759rq6uguhl0aJFycnJ6Prhw4cHDx6ksPENGzYAALVtNpN11+Jh0WNgSAGNBvPuLT4fLfwxIFomhG/fvv3tt99qampsbW2joqLQzaioKBqNFhAQgB7q6OgQBHHkyBEAkJSUHDJkyKZNm1xdXQsLCykdeZtIT09HWZH4p8LZs2ejr6KCgoKJiYmtra2HhwcSfoIgamtrAwMDDx8+bGFhoab2H48pWVnZMWPGbN68uX///gBgYGCADCZCAx1r2dvbEwSB8n+GhoZS2D6Px0Na++HDBwqb5efQ4zSw8gJFHQCAicd6bwkQUEeFhYX9+vUDAFNTUyaTKaBeBAQSQoIg+IWwR48eBgYGBQUFxGchfPHiBb+0dOzYccWKFffv3y8vLxfl6L/A3t4eAGg02unTp4ODg21tbYcMGULjiybT09OztbX18/Njs9lNtPPq1auTJ09GRESghxwOx9raGgDk5eX9/PwE+hb27dtnampaW1t769YtALCwsBBEL6ampqQxxsnJadu2bRQ2jvIeT5w4kcI2m8ndkFyw8vpUiWLsHull3o4vPtY3iGDd1uIdIfoGPn/+nF8IjYyMhgwZgj6sSAgvXLigp6dH+2+EZN++fZcvX+7g4BAfHy/CVeq3psJbt24tXbq0kUskg8EYOHDg+vXrb968mZaWRr44PT3d2dl506ZNQ4YModM/+Rxpa2ujnJ9CfkcoGHzUqFEEQSB3AGoNNSjeFv1lBURCdjVYeUH/OQAAunMFdGBQUVGBop6HDRvW9KFvO6SiomLPnj2vXr0KDAz8888/0YF0TU2Nnp6ei4vLkiVLiM9CSBBEXV2dn5/fpk2bkKGe/DAPGTLEzs4uIiJC5AZhZMZnMBjXr1/nv5+Zmeng4GBpaamk9G8eE1VVVUtLSwcHh68eTm/duvXq1atI8+rr61HOTGVl5devXwv6XQwePBgAnj9/np2djTol7UkUYmpqiixVHA7nxo0b1AphYWEhjUaTk5MTvgHv07d+7F4w3g7zXMDKS26FT+8tL3PKhL1CbZkQVlRU7N69u7Ky8vbt2+QpRVRU1OzZs3fu3Il2JPzTZVVVlZ+fn52dnYWFRaNsqoqKiuSuS5gL1eZMhfn5+R4eHnZ2dubm5vwra7S4trCwsLOz8/PzI0W0tLTU09Nz6tSpolpY1dTUyMjI0On0/Px8f39/tJSmsP2rV68CgKWlJYVtfknfbS/B/CgAgKIOWHkdcU/7/s+0hLq6OlRkoE+fPmj/9BOAhJDH45mamj5//tzQ0LCgoGDdunV3794ljRnp6ekODg4WFhb85n1NTU1ra2tXV1f+cw3hwOPxtmzZAv/1YfHw8OjZsyc6JkRLaiaT6efnh7aJ5LDpdPqXJt9Dhw7V1NTs27ePxWKh7AcdOnQICwsTwntBb2Tv3r0EQaA1dGRkJOW9mJqaDhkyZOzYsWPHju3Xrx+1QkgQBDJlhYSEUNts0/B4xJA/XtGsvGD6Jeg6BtR1oYsxTDkrYf2077aXbI5QF2otE8KsrCw/P7/s7Gw/Pz9ytYWEsKampn///llZWTo6Ou/fvx81atS2bdsePXpEbo++64diY2Pj7Oz8pR8KhbRiKmQymcHBwUePHp05c2aj4iAyMjImJiY7duxAS9GCggI6nS4rKyu000F+0Bmeo6Mjm81GmWVSUlKoahwdt5w6dYqqBr/KttuJsPgJSCsBAFhcHrmXyuU8m82eNm0aAHTu3FlwBl7hg4SQIIh3794NHjxYX1/fx8eHf/+HNAPt/2pra9E2kT/NNIPBMDExsbe3F842kcfjIaNFIx+WX3/9lX//t2jRImdnZ/I8BWm5paUlv1+3urq6paWls7Pzhw8frl27lpqaimLjNDU1Y2NjBf1GEKgO2tixYwmCQEVG//77b8p7EahplCAIVHPjyJEj1DbbNO6RBQqrfGHeXVDQhimnwcoLpp0HBS2Y7aSw0udGYLYwB0OB1ygSQoIg/vnnn4ULF+ro6Fy/fp1fMLS1tS0tLdExAGnrz87Ovn///m+//TZs2LBGMew7d+5s+6i+hJKpMDc319XVtZFFdOnSpejZESNGAIBIvKivXLkCn48orKysAIA8Rmo7aMEo6CV2YFIpWHlBd1MAAJ2hvZZerKiuo6RlLpeLMpKrq6snJQnQDUf4kEJIEIStrS2NRktISDhx4sSECRNQXBBCR0dn9erVDx8+RN6kPB4vOjr68OHDqCgg+bLu3btfvHhRcKPlcDhILWRlZUmTEqKhoYE8JuSfDchjQnSWX1tb++TJk/Xr1/MXApOQkDA2Nu7Tpw8AdOrUifQrEQJlZWV0Ol1aWrqurs7JyQkAZs2aRXkvghZCFFM4ZcoUapttmoXnosDKC4ZtQEchn/4NtAbDZWDlNeGQMDb0JFQKIUEQ06ZNU1VVraqq8vb23rt3r7m5uaKiIv/HWklJadKkSfv27Xv27Bnp4V1bWxscHHz69GlLS0t1dfV79+61fVSNIKdCDQ0NqqbC8vLyp0+f7tmzx8PDA905fPgwAKxYsYKS9lsE2o9KS0tXVVWh0+8xY8ZQ0nJJSQmKNBL0EQKHy9Nc6yOvoyevrE5OcMjZytnZOSur9a7Vv/32G/rskf4UPw0sFmvLli3ouqamZsmSJW/fvm20/+vatSv//q/RMWF1dbWHh4eNjU2nTp0A4Ny5cwIaKofDWbp0KTTDhyUjI+Nb+z8HBwfSyNTI5Ivqm79//15A4/8WhoaGABAQEIDS1Xbo0IFyB4jFixfze40eOnSI2vZRQTcFBYWmnZKoZdieV2DlBXrzYdiGf4XQeAf0ngJWXr23vhTaSAhKhDAjI+PYsWPo+v379ytWrPDy8srJyUF3kEXU2dnZxsam6cg89LXk8XiC8GgXzlT47t07AFBTUxOJUz4qj/Do0aPq6mppaWkGg4H8JtrIkydPSOOPQOFwODNnzUZT3uLFi/X19ck9N6J3797W1taXLl2Ki4tr/lyDEkvKysq+fCnUr5ZIKC4uptPpGhoayGZIei+np6efPn3a3Nycf5vYsWNHdEyIlqRcLvft27cC8u6ur6+fM2cOtNCHBbn82NraIpsEv5bzm3yrqqpQ6ZWRI0cKYvBNg+aWffv2EQSBXJNiYmKo7SI8PNzR0ZHaLBaNQOnihHOwijDZHwJWXjD4FzBY8q8QDl4D+ovBystos/uZM2eOHTsWGBh4+fLlxMREgQ6G+oB6ZCsAAG1tbTLSjtxM5ObmPnz4cMuWLSNHjpSUlOSf5rS0tKZMmTJnzpwrV65Qm+Zg9+7dQpsK0edJJMlKkDP68uXLCYKYPHkyANy4caPtzaIpRtDxwjwe75dffkETZXT0p3Ciqqqq4OBge3t7CwuLRiU1+ENcmghWOXfuHJo6BRRS3d4IDQ3l3/9JSEiMGzfO3t6eNKyVl5ffv39/xYoVKPkRQkZGZtKkSfv373/27JkgRlVbW4s+kG3xYUlKSvqqyRedbFVUVDAYDCkpKeRMK0xQBRhTU1OCIJYvXw4Ap0+fprYLFOqHtFZArFmzBgCOHj0quC74iciokF/pA1ZeMOMKKHWB+a5g5QULHkKHnjD1rOxy74Nu7wmCsLOz8/T0vHz5sqAdeagXwrS0tClTpigrKzeatszMzPbs2fP06VPSR5TNZkdERCCLqKamJv/rSZtY2wMQhTwVovzalBvxv0p1dTWLxeJyuWipmJSUBACqqqpkpTHSZN0WUFpk0vwrILZv3w4AcnJyZIaLP//888GDB+QquPlJf0iPj1u3btHpdBqNdu3aNYEOvr3R9P6PdBNNSEiwt7c3NzfnP6SXl5e3sbFxdXWtrqYmfKWmpgZVdabKh6WRydfOzg7dR97gL168aHsXLaK0tJROp8vIyDCZTOQeMWfOHGq7QP6ob9++pbZZfm7fvg0A06ZNE1wXJMHJZUqrfWH2zU/6Z2ILHXqB9hDo0AtGbgYrL+VffEuqWJcvX05NTUUf1z179gh0SAJMsYYi7ZBFtFFAYc+ePb+ctpKTk3fv3j1o0CB1dfVGNrE+ffosW7asFQGI5FTYKFZJcAQFBQFAr169BN1RTU3Nzp07IyMjX7x48c8//zg5OREEgeIjX758mZubi2KDmr9AzsrK+vXXXwmCuHTpkqura11dHUEQbDZbTk6ORqNRYmX9FocOHQIASUlJMkvqhw8fyL/+t0wLrq6umzdvHjFiRCPTgra29rx581avXo3m95MnTwpu5O2cmpoadP7XufO/JR4lJCRIN1H0suLi4tu3bxsYGPD/JuXk5KZPn37hwoWMjIxWD6CiogJlc9bS0hKEQ3hsbGx29if3QpR0l9RFYUKmS01PT0eLUQqPCVEUr4aGhkBjr9ExoZKSkiDiIPkJSCxRWOUDs26Aog6o9wfLh5+Mogv+ASsvCeunCqt8gpJLk5OTr1275u7unpKScvny5bZ8CJuDkHKNFhQUkJF5jUpRKysrm5ubo8g8NPkSzQ5AbDqBi7u7u/CnQg6Hg6Is3r17J+i+Xrx4gYKW/vzzT7SR2rFjBwAg74lhw4YBQIvySyFH0/379zs6OqIoybCwMADo37+/QN4AQRAEcfnyZQCg0+n8TlL5+fn79u2bPHkyf1Q1ACgoKEyYMGHv3r3e3t7kzobftECGuDAYDGlpaUEvJH8gyP0fv9r16NED7f9QTC2bzX716pW9vb2JiQn/YrRnz542NjYeHh4tOrMoKysbPnw4AHTr1k0IPixubm4AMH78eEF39CUo9uPAgQMEQaB9KoVZeU+cOAEAy5Yto6rBb4Gq0oeHhwuuC6/oIpnl3jDDEeQ0AADU+sL8+zIrfORW+oCVl/xKH8szUemFwjZuEyJJus1isV6/fn3ixIk5c+Y0KoKloqLy5aqndQGIZJYp4a8QkYP44cOHBd3RnTt3vL29w8LCoqOjUZDfq1evAKB79+47d+5Ev9t58+Y1v0EkhCwWKzg42NfXlyCIU6dOAcCqVasE9BYePXrEYDBoNJqDgwN5Mzk5+eLFi56engEBAWfPnvX09GypaeHGjRvI2C7oDFs/IiUlJS4uLkuXLuWPi5WVlf3rr7/4X1ZUVOTq6mptbc1/OisnJ2dubn769OmPHz823UthYSGqn96jRw9BL+cRyEQpLS0t/Mx5Dx48AABzc3OCIJBnLIXOt8iwfPfuXaoa/BarV68GSsOuGuERWSi93BssLoOsKgCAxgCwfKC9wf9djsiKTpCIvvoEGZlnYmLSnLQsOTk5KABx+PDhjWxiOjo68+fPP3Xq1K1bt9A+csOGDUJ4C41AMbYjRowQdEcorUF5eXlwcDDyU+VyuUj/+AXjq3vuLyktLT137lxYWJirq+vly5fR2n/BggUA4OjoKKDxI8f3RpG8yP5pZ2dXX19fU1ND+iQTBFFQUPDo0aNt27YZGxs3SoPesWPH2bNnHz9+HHksIw8pZOzFfBUulxsREUHu//jXIvxwOJyIiAg7O7tGiUDJRDBfxtXk5+ejmgb9+/cXqK9jI5D0BgYGCq1HBCpmhLKUOTo6AsD8+fMpaZn0AC8pKaGkwSa4efMmAMyYMUMQjd8NyZWwfgpTz4K0MgBARwNY8LDbphdpBSLY/32J6IWQn5aap79qEwMAZBFdsmSJSDKa1tXVoXM18vRCmKDjCklJyVGjRn2555aSkho5cuSWLVsePnyYl5f33dbQ2VJLfZd5PB4S3crKSicnJzKWhh+yBtamTZu+fPb27dvR0dENDQ0nT54sLi7+ai8NDQ3or29tbc3vKolsO8io26lTJ5En1fwhKCwsbE7lpsLCQmdnZ0tLS35vOHl5eQsLCwcHB/SH/vDhQ+/evQHAyMhIoEfLX4Jy1uzfv1+YnSKQser169epqakAoKGhQckHD7mkjh49uu1NfRcUBymIdKmOLz7SlzyFKadBWhEAQGcYLHTru+1ldml7yXrfLoQQ6RlBEElJSa12k+XxeElJSdevX1+1apW8vDwAoMD58vLyBw8eCHk2RAkPL1++LMxOic8R/fxeJ8R/99xf+pWQSX++XNcjj5VWBAiXlpYePnw4Nzf34MGDBQUFX0ZVkjWwli1b9uWfBoVM+Pv7Ozk5XblyhUzv/i3q6+szMzPfv3/v6OhoY2ODgoJ5PB5ScUHkfvyZqKysZLPZXC43LS2t+V+T+vp6f3//rVu38kf40Wg0Q0NDVJtlxIgRQq7BQhDEw4cPAcDMzEwIfSEDLIvFQrKBIhxQqDtKTUCJlwCKaqA8gv5boDjI69evU6iFl/yz6EuegvkxkJQDAOg0AhY91t0RmCv0zNpN0C6EMC4ubvfu3SwWa/fu3deuXWu1j/WZM2cOHjxYWFjIn2MM/WmFPBteu3YNAKZOnSrMTp2cnGg0Gp1Ob+I4obq6mozMQ1LEv65vVHzKxcUFAKZPn96Kwbi4uOTm5qLj20bFwdPT07W1tQFg1qxZlGQe8PDwcHBw8PPzO378OL9zEMqgiBIiY75KRUXF5s2bU1JSjh079vTp0zNnzrSiEbJeBEoj1bVrV11dXZFUBi4uLhZOvfU3b9789ttv9fX1169fR+Z3lM5p0qRJBEEsXrwYAC5cuND2jpCpg4ysFSgZGRlqamood13T5T6az7En6WDlBRPsQUIWAKDbOFjsMWh3cHFV+ypV2y6EkCCIEydO5ObmOjg4REdHtzpX57Zt2+7fv3/v3j3+HGMimQ0LCwuR16LQav24ubmhpJHND+blcDixsbEXL178svgUnU43MDDQ19eHzz6oLaKuru6XX35xdnY+c+bMmTNnvLy8yKcKCwtRzgEzMzMKnRpcXFzi4+NTU1P5hdDb2xsADA0Nqerlp8TDwyMlJQUFa7fas6ywsPDatWs5OTnIfxIVKvnw4YOjo6OQaxoIrd46Wme/e/cOOSKgYkYoSxlyhF6wYEHzW6utrS0oKGAymREREWQ2wZiYGGS2EYJBKyEhAS1Pe/bsib6h5FQwfPjwffv2vX37tqWWIXuPNLDygvH7gCEFANB7Ciz2HPrnq9Jq4SVyaybtQgjT0tKWLVuWlJS0devWvXv3tvpoLS8v7/r164GBgfw5xkQ1G5qYmADAgwcPhNCXv78/8hxpi6sqf4gLWXxKTk4O/lt8qgl3m6/C/x2uqKgwMjICqssBuri4ODo6VlRUBAYG8vv1sFgsFH0hHK/FHxEej+fo6BgcHHzw4MG3b98eP368de3s37+/rq7uwIEDKMugtrY28dlQv379ekqH/B3+97//geDrrTOZzAMHDpSXl8fHx+/cuRNZ45GV+M2bNy9evAAATU3N5gvYkydPTp48+fbt20ePHpF+Mai8+erVqwX1Nj4TFRWFfCzGjx+PEik0M91rE+x5kAJWXjB6F9AlAAD6TIfFnmP2v6msE0H6ye/SLoSQxWKVlZUxmUw2m92WDEmenp4uLi7owzdlyhRk7BbVbHjs2DHgK0whOEivk40bN1LVJpPJ9Pf3R7kIGhWfkpaWNjY23rZtm5ubW4uq+tXW1o4ePRoA+vbtS21Cy4SEhIiIiMLCwri4uEYHipaWlgDQOoufOIAcRxMTE+vr69+8edNqS3VQUJCjo+P27dt5PB5K3paSkvL69WugujTmd3F1dQXBlwVNSkpydXXNyMh4+fIlabdcu3YtACA3IuRea29v38xiqzwe7+TJk+np6VevXiU/rmPHjgWAhw8fCuptEARBEOHh4ehYd/r06chIk5iYGBoaivZ/zUz3+t/3Qmy++Q6svMDkd6AxAAD05oOV1/gDodVMwUbrt5p2IYSC4NKlS/C5JIpIZsP3798DgIqKClotFhYWpqenU+6ORXqdWFtbU2s/ef78OQAMHTqU+EbxKQTpbhMREdGE5YTNZqPCxV26dGlLHYmWghJHCcd7QpyJjY09d+6cv78/8fnrduXKFTabLS8vT6PRhFkGWYT11huVn0NISEiMHz/+6NGj8fHxTfxscnLyli1bYmNj/f39kddrRUWFpKSkpKSkQCsnBwUFoX2CpaUlWXoCKbqamlrT5T4QmpqaKMM7knwuj7f6ShxYecHwjYCCbfTmg5XX9OPhTLYIfPibyU8rhCjHmKysbG1trahmQ11dXQB4/vw5QRD79+8na6o1kxcvXsTFxRUVFTk4OHy1xFp6erqOjg4AzJw5k/J6FwcOHICvxTa0IhE2l8tduHAhAGhoaAizVhxBEOXl5ZKSkhISEkIIw8Igzp8/DwBLliwhCAJVyhXOAQEJ+t4J+WyyvLwc5XLS0dGJi4tLTU39brkPfvLy8tLT09lsdkpKChIk5OiAcnkLiICAAGRMWrx4Mf8E8tdff/E7DTAYDGNj44MHD0ZGRn63wtf6LbukZ5yDYRsAaAAAA63BysvyTJSQK863lJ9WCAm+HGOimg137txJasn169cPHTrUorCqqKioGzdufPjwgcvlfpkqrLCwEGUWNTU1FUQqDbSBa7o2JIfDiY6OPn/+/JIlS/gLpaJvhaGh4YYNG27evImceJWUlEQSyYASc9y6dUv4XYsn8fHx8PmYEC2nhJzWQPj11svKytBs061bt7S0NP6nmpnu9UtWrFgBAPzZJKjFy8sLuQKsWbOGtOWwWCwnJ6fQ0FCCIF6/fr1p06ZG+z+ywhdp8o2JiTly5MjYsWP/U1+dxgCgwdD1YOW1+Hx0Q/tWQeLnFsKDBw/C5/RgwpwNyU/Vmzdv0HeDx+OFhIQ4Ojq2qFBZSUkJqqP07NkztK0kqaioQLn2qfU6IeHxeMji+t1MWvzk5+d7eHjY2tqamJiQ7jYAoKKiIi0tLZLSVARBnD17FqjL9IH5LjweD+W3S0tLCw4OBgB9fX1hDkDI9dYLCgpQFot+/fo17ejXdLpX/nIfPB4P+XC2IlN5fHx8fn4+h8Nxc3P7VhFyd3d3JG/r16/nP1IpLCysqqr6888/CYI4duwYcp5qZoVnUvJVVFRkZOTo3UaDldcaxzjuj5DR4mcWQrQy1dTU5HA4QpsNKysrhw8fjgSMTHh2/vz5t2/ffjcwvBE3b960sbFxc3Nbvny5p6cneb+urm7MmDEA0KdPHwGdviQkJABAp06dWt1CXV1dUFCQvb390KFDgS/hXHR09KtXrygaZrPIyspCfu3CT0EptsydOxcArl69Wl9fj7IsCaje71cRZr31jx8/9unTBwB0dXWbH3JXWlrq6upqY2OD1A4hKytrbm5ub2+fmJgYHh4OAF26dGnpeBoaGm7cuOHj43Px4sXU1NSv+h66uLig3Zutre2XzyYkJNy6dcvFxeXMmTNr165tdPDfnApft27dAoBBxhO23Er8EUSQIH5uISQIAn1GX716JZzZkJQoXV3d+vr64uJiTU1NFNEP38gQ3VLYbPa0adMAoHPnzh8+fKB2/CRXrlwBgIULF7a9Kf6QSpSF1cTEpO3NtggUs8GfbQcjUM6cOQMA1tbWBEGYmZkBgJALI/PXWz9z5oyDg4MgQvE+fPiAKjYMGjToW4kAm4bL5YaGhu7Zs2fo0KH8bmjq6uoAMGPGjFa4/MTHx/v4+Nja2t66devLwjtXrlxBHX1VBWNjY+fMmXP79u3i4uL09PT9+/d/6/f2VZMvikZFCxFFRUXKHRcEx08uhFu2bAGAHTt2EIKfDdls9vTp09FGKjMzs6qqCh0bdOrUadSoUfymQmRqnzlzJqqx16LIPOSJoKmpmZKSIqA3Qnw+n6DKzxZFTSDjj4yMDJ1Ob04cEoXY2dkBwNq1a4XZqTiDIsHRhmb//v3wjYyyggNlJkMHbL/9OMSuywAAIABJREFU9ltLIxC4XC4SgCbWzcnJyUgDhg4dijIxtZHi4mL+ch/IjYUs99F8X+vz58/v27fv9u3bly5dapTl8eLFiygmisISE3V1dZGRkX/99ZexsTFZwkkIFZ2o5ScXwpcvXwJA7969CQHPhjweD4mHuro6isqaNGkSAPTq1QvltubPEE3uERESEhJkManMzMxGLe/Zs4c8XXN2do6Pj9++fbugvU7Qgpqqz/Hx48fhsxsh2s5evXqVkpa/RV5e3qVLl/z9/TMzMy9cuIDchoWToQNDEASPx0OhaSjSDoSV0YK0haK/OMoOyOPxUOKh5jeyd+/e58+fv3jx4sqVK1+t6Z2YmIgctseMGUP5IX1hYSGdTmcwGOjokWTgwIG2trYvX75s5k6rkWX46NGjAECj0SgMJLt9+/Yff/yRkJDg6Oi4ceNGcmCCruhEOT+5EHI4HGRkSExMjIqKEtxsiLaeioqK4eHhHA5n/vz5AKCjo/OtKP7mJ8Lu16+foaEhWpmuWrXKx8eH8sE3gkzYSNURC39IpYODAwis1AtJeXk5l8vdu3fv3r17eTzenj17kFMrspVhhMCsWbMA4MaNGywWC5kBBO2z/e7du969e6MaTKR1DtVFOnfuXItsjFFRUcg9LTMz88tFW1RUFJpVTE1N+T1cqAKpOEpb2pxyH83B3t4eABgMxrVr16gdrbOzc2pqalpaGn8GHIFWdBIEP7kQEgSxfPly+OxLLaDZcO/evQAgJSXl6+vL4/HQakhZWbmZqXIrKyt9fX3t7OwmTZrUqCD7oUOH+vXrt2/fPhQ+IRwhdHd3B6orfZMhlQUFBXQ6XVZWtqamhsL2v+Tp06cBAQHo92ZnZ4dyYO7evVugnWJI/v77bwBYsWIFQRDjxo0DgMePHwuuOzI9Csrw+fHjR2VlZU1NzQ4dOrQieXRQUJCHh0dSUpK9vX0jqQsPD0cO1WQeFspZsmQJAKBS2yQcDic4ONjW1rZRVUg9PT1bW1s/P78mlq1//vknUkFnZ2dqh+rh4eHi4lJbW5uUlHT48GEypkJwFZ0ExM8vhKig18iRI4nP5cqonQ3RoR2DwUBRw9u3b0eW/dal/SUTYVtbW/fs2fP58+f9+vWrqKjQ19dPTk4WjhDa2toCwB9//EFhm/whlSNGjACAVqdWbw6vXr1av379gwcPwsLCzp075+np6efnBwADBgwQXKcYfpABpkePHsTnlWIrsrc3k7dv3yJxsrCwYDKZ6enpaMnLv4tqUfLox48fu7u7v3nzxtXVlf+AICgoCFXYWLBggYBcUrlcLgo+acIJoFG5DwSZCIa/ziiPx/vtt9/QMl0Q/kqPHz92dXXNysp68eJFI2/wHj16wI9TBO3nF8La2lo5OTk6nZ6Xl+fv7w+UJj+8ffs2OnxG9hMUuSglJUWhS06/fv2YTOazZ88mTJggHCFEjq/8ARtthz+kEuViRnsFocFms5EPgkCdjDAkXC4X/cI/fPiA0vUNHjxYEB0FBgbyi1MjHxaUFeyryaOdnZ1bVDHxxYsXqBErKyvB+UOGhIQAQM+ePZvzYiaTiRKBorLApOSjRKCBgYErV64EAGlpaf6qLMIB+Uz8/fffQu63dfz8QkgQhIWFBXxOfkjhbOjh4YHCcdCZMMpuSqfT79+/3/bGSZAQEgRhaWnZvXt3QQshm81GgV+UOMKRcLlcFDIVExODChSoqakJx7uaNM6gBDdmS231dgTaXI13DsrJLGpZJQ1Mi5gxYwYAODs719XVSUtL0+n0Zmagbj7e3t6ysrKkODXhw9JE8mj+qPBvwWQyUbldGxubllYjahF79uyBVjnZoqRukyZN4ndQZzAY8vLyjdJxCIcbN27A52zP7R+xEEJHR0f47ELGX7O3Lbx+/VpeXh4AUBaGf/75h8Fg0Gg0BwcHCkbMBymE2dnZCgoKghbC0NBQANDV1aW8ZeTRjhIKI69UIeSacXNzMzQ0ROdDKHmjrEY3mHIGFnuAlRdYeWlv8Lc4Hm7vkRacXFbf0H6TAv+InDhxAj6ndkIhNE+ePKGw/SdPnqBJH4lTZGRkM31YvpU8GkWFI7W+fPmympoaig6Mi4ubPHlyVFTU7t27Be14PGTIEADw9vZudQsowm/dunXKysp0Op2/xqQwvaYzMzMBQFVVVaDrBqoQCyFE7sgopvvhw4djx451dXVtS4OxsbEqKioAsG7dOoIg/Pz80DfK3t6eoiH/S0JCAvlJ8vDwoHxN3YiTJ08CwC+//EJ5y56enqR9DJ2kbtu2jfJe+CH/LufOnSMI4sqVK/Ly8p+qo0lIg4Ye9JsJo3fBvLtIFOVX+pjsC7G9m+wRWVhS/XUnw7iPVWd9Mu090u6G5LbPymrtB5QepVevXsRnf43t27dT1fj9+/eRu/X//vc/Ho9HHhO2yIelqqrq0aNHa9asQbs9hKSkpLm5+cWLFwcPHow8IZEQUjXyJsjPz0cO2y2t+vlVUCU4FDB24MCBzp07U3ve8V1QnFiL8kqKip9cCK9evfrXX3/V1NSMGjUKKEpv8f79e1Rxbe7cuRwOhywHKOiQYXR09+bNG4H2ggI/KPexJgiCxWKhs5zMzEyUghJNkQIiLCwM/V1QxmfSjg3q/UDx31kPAABooNwVek2GkVvAwgGJIlh59dwcYH0xxuF5VkJ2NY9HlFazJx4Jk1vpI7Pcm7bkqeJqX/mVPo4vWpCLVdzgcDhovZiVlYWclVBVr7Zz584d/iRhjY4JW9cmf/IwY2Pjy5cvHzp0aNy4ccHBwUITQlTIycLCgpLWwsLCAKBfv34EQezatQsAfv/9d0pabibLli2DH6Qa6E8uhLm5uevWrWOxWCiMZvbs2W0/l0JNTZ06tb6+niwHuGzZMkGbHZDL665duwTaC1oaC6hY0rx589D+jMvlosVEK3IKN4eEhATkTL906VIul/v69Ws5OTkAmLh44yeds3QFs0NgYAVaRsCQ+o8sSsqBlhEYWIHZIVjohl6v/Iuv/Eof+uS/YfQuUilBd578Sp/L/sIrr/ijwOVykR8Kyp9w+/bt2trajh07zpkzp+2GMgcHB5QkDCX0anRM2PbBl5WVJSYmIiGMjY01MjKKiooSjhCiUo4XLlygpLWGhgYUjpWbm+vj4wN8KX+Fw7Vr19CGQZidto6fXAirq6uvXr2anp7u7OysqKgoISEhLy9P1sxrdYTvzZs3a2tr09PTkQPIrFmzhOD3QbnL65ewWKxVq1aNHDlSQKKOwmzNzc0JgkD+bIcOHaK8l48fP6I0+ahMY0xMDNqXrF+/ns3hvU2vOO2dueBsVKf/Pf+kZ4s9YPpFGL4RepiBvOZ/94oMUO4KvafAqK0w6zqM2gb9Z/8rhNLKyKD6LTuq2PLkyZOzZ8++fPkSZTNZs2YNVS2fP3+eRqPRaDSURdPT0/PLWkKUgISQIIgtW7bY2NgIQQgbGhrQBzU9PZ2qNqdMmQIA9+7dq66uRqXoBFGs5lukpaX9KMeEP7MQ8ni8u3fvXrt2LTw8HK2MNDQ0+Gc5Op0+YMCANWvWODk5tdSPlMVioYzeEyZMYLFYAnoL/DQ0NAguAODGjRtkIYuYmBgBZWQtLS2VkJCQlJQsKytDCbiHDx9ObRdFRUWoTOP48eOZTCZpx168ePGX38bcMqZHZKHt3WSTfSFSy7w/yducWzB6F/SbCRp6nw4UERKyMGrL14TQ92oANpD+h7q6ul9//TUtLQ1Z53r06EFJvXhkjKHRaGfPniX4jgk3bNhA+dKNFMKqqqquXbsKQQhRLjpqV7pHjhxBS0Dic/yuEOKv+EFL0ubntxMVP7MQIhpNhRUVFX5+fnZ2dubm5siiQqKsrGxubm5nZ+fn5/flYXVaWhoZF1FTU+Pm5ubm5iaITINNgFJOoCJh1DJhwoTY2Fh0ffPmTcHFPo8fPx4AXFxc6urqUJxG0yXcWgRZptHQ0LC8vDwnJwcFVk+cOPG7i5UaFic4uey0d6blmSj1tX6fLagPwewQGCwB7cHQZRSM2gbymqA9+NM/aUX0ss23EskyQy2qvfyzEhYW9uLFC1dX1zdv3igoKKioqLQieXQj0OaSwWA4OTkRXxwTUo6np6eLiwu6fvz48c6dOwXRCz8okQW1HmQofheJ6++//y6Es5VGoCkLeau1Z35yIczOzm5iKmxRIux79+4xGAxfX1/ULNrKCDmJs6urKwCMHj2a8paFJoSnTp2CzwWeZs6cCQCNEuS3mkZlGouLi1Fet5EjR7YinVt6Ya1zUI7N1Xi9HYG0JZ+3gKO2Qd8ZsOCfT/+klcHKi7H06Zqjjzw9PQ8dOnTkyBEPDw9HR0dK3tGPS05OTkBAABmBjlKlkLQ0eTQiJSWlc+fON2/eJPiOCQWkgvy8ePFiy5YtcXFxgu4Ipdj29/ensM2GhgZFRUUajVZQUODl5QUAo0aNorD974IKurX/stg/sxC2dCpMT0+/efPm+vXrBw4cyGAw+L+6vXr1unPnzqJFiwYOHMhkMkkhFDJkGSPK6/FOmDBh2rRpVlZWVlZWo0aNEpwQougiRUVFFouFztKnTp3a9mYblWmsqqpCBYENDAxalD3kq1TWNRjtDIYlXl89I1Rc5eufUHLr1q1r165duHBh48aNz549a/s7+tFp5MPSdPLoZloFUJnZCxcuoGNC4RQ3sLGxAYCjR48KtJfs7GxUMJXycxZUBufBgwdVVVXoYELQaX75SU1NRWdS7bzwy08rhG2cCqurq4ODg+3t7S0sLFRVVY2MjO7du/f777/v379/z549ohJC4vPp91dLw7SFCRMm+Pj4fPjw4cOHD6dOnRKcEBKfV76+vr78NXsJgggPD6+qqnr+/Lmrq2tSUlLzG+RyuYsWLYJv18BqO69SyuRW+nwphIylT/V2BMXHJ6Slpe39f3v3HdfUvT4O/MkiCYS9URAVB6Age5uoOAuC63pr6+hV26qtHfbWWuvsddY62v7ctXUPXAxFFJSwQUABFZA9wggbQnZyfn8cTPm6lZAA+bxf/pGEk3M+AXOecz7jeTZuXL9+fUlJyfbt25Vy0P5LMYflxTws75o8ms/nK8qsi8Xi0tJSfX19AoGgrKmVb3T27FkAmDFjRq8eBa/KMmvWLKXvedu2bfBsERF+SlTxhRqe6+fx48eqPOi7GpiBsPupsOc1YKVSaU1NDR4IBQKBo6PjvXv31BUI8URuSk9cpLKuUexZEqmVK1diGDZ79uxly5bV1NS0trbu2LGjsLCwvb09JiYGL6bzlr7++msA0NPTy8zMfJsaWO9nd2QJfcEVmH1WEQi1Qo+bfX6nlNvJ5/Pv3bvX1tbW1NQUFxenlNXQ/dfbz2F5afJoIyOj7smj161bZ2Njg9/EREZGfvbZZ0lJSfgwoWqopt56aGgoABw9elTpe05KSgKAMWPGYM8SWSg3n/4bffjhhwBw4Lc/JNK+e1M4AAOhVCrF16sp91SIB0IMw6Kjoz09PdUVCDkcDp57QnGZrBSqDISZmZn4X+e5s2RERAQ+IRYvLP6W1q9fDwA0Gi0+Pl5RA8vAwKA38lnEPmp0/ymJ/PFNrUXRuv+59fmfeWjhxHPebw6LQCC4devW6tWr7ezsFBGRSCTOnDlz3bp1+Hon7Fkg7LW2vxLeqt6rt44v+FPuxDEFsViso6NDIBDq6+sjIyN7aZLBiwe9fPky/vjw4cMA4MoM0vnkVtAv94/EVVQ390r5qp4YaIGw906FikCIYdicOXPUFQgxDPPw8AAA5aaTz83NVYwc1NfXK3El04vkcrm1tTUA3LhxQ/GiVCpdu3btyZMny8vL8YJWb+P333/HZxLi37o1a9YAgLa29nMVYZRLLJWj5GovpZQ5LIpEoDQabfHixevWrTt58uS4cePy8vLUFQjxU0pvzNZWrCqpr68PCwvrpfV2kyZNAoArV660traSSCQtLS3lXkY/RygU4vnW8ZwyT548AQAyVRv81sLcC3hvisN/2WvPF9zJaxD3jdvEgRYIe+9UWFxcnJaWhj+uqanp1XJ6r4cXe8JzGfdHMpnMwcEBr5VjaWkZFBS0c+fOhISE2tratrY2kUj0lqeD06dP4zWw8IRwW7duBQAtLa2eJCxG3ttzS917jsfj1dTUrFu37uzZs/Hx8QEBAeoKhHgiCGVlPuvO1NRU8XjZsmW9VLsY/2rgOSBdXV0BoPfqUXR2duLDUoaGhunp6XK5fPXq1SQSqWtJLoEIRnbgMBcm74YFUbDghvGnd+YdyD4SV1HToorV2K8yoAKhhpwK8/Ly8Cnpyqr+rJiopprMACtWrAAAOp3efQIhPoeQxWKtX78+KirqjUWgFAkN9u/fj2HYwYMH8VtD5dbAQt6SYqm70hNL4oEQw7CPP/74448/VksgrKqqgt6pt66aQJiQkAAAzs7OGIZ98803ALBx48beOBCPx5s4cSIAmJub5+TkSKXSroKINDrYTQOLcf8nQwXdCIZPAf91MO8SLLhB/Oim2/qktecLEguaZS8bWuYJpbGPGv9iV51P4eRUtCt3FurACYQqPhWKxeK3T3KvdHgMUMpdb3V19V9//bVx48a0tLSzZ8/+8ccfPd/na/z44494FIyPj8cwDM9+9+mnnzo4OHSfQwgAw4YNW7hw4f79+19VK47D4eBTBxU1sHpjrgHyRps2bcK/er2Rq10RCGtraw0NDdUSCLFn9dazs7OVu1tjY+OCZ+bNm9dLgVAkEuHJKxobG/GMTkwmU+lHaWlpwWsbWFpaPn78WCqVLly4EL/AXbH9DOnjm7DgBvzrKkzcBqNmgk63JF/4beLYBTDtAH6baL4idsnhnIupNS2dYgzDmjrESw7n0BZH6y+9rfufGL2lMbpLY8xXxJ6IV1pGpwESCM+dO4f3kqnsVPjrr7/2xpjBWzpy5Mjvv/+uyGbSQ3FxcT///DO+2/Pnzytlny+lGNJ7aRmQtra2VyX90dPTUyT9eW54Q1FrqbdXeiEvksvl3377Lf43xZe6K93Zs2cTExMVj5Ve7/Mt4Xc2Sq+3zmAwlj4zatSoXgqEGIZNmDABAK5fv97U1ITXpFPu3Obm5mZPT08AGDJkSHFxsUgkmj17Nn4bnZycjGFYh0AakVX/6fE8K0WO35l/gtunz98m0gxg6ETwXwfzwvBsFWPXJtDn/k1y/Q+M+wS8v4V5lxXTtnU+ifnw92yl3BoOhEAolUrxxFoqOxXeu3cvOTlZNet5XyokJETRjXnkyJGrV6++9644HE5dXd3atWsLCgqampq6l/FUrlOnTuEXK2+zCFIikTx69OjIkSMLFy7EcwMpdE/6c+XKFbw8sgoyjCDPkcvleEUULS0tpRQ468v+/vtv6IVlS6rpGsUwbPPmzQCAzwZ3dnYGALxLRinq6urwxcEjR46sqqoSCoV40ih8mPC5jWVyeUZJ6+YrTz03JBM/wm8TrwBzI9hNA22TbreJJDB1gHGfwLQDwNwIVu4w4Wdw+hhMHf5ZyLvghs4ntw7cKuv5R+gHgbCkpOTFVRBisfjJkyeK8hFNTU0HDx5UWZPOnDlz5MiRTz75RGVHfI61tbXigu6nn346dOjQe+9KLBZfv369uLi4qanp8uXLbxycez/h4eH4rPr3m0nB4XAuXbq0evVqPz8/La1/qibhN45OE+befdyg9DYjr4dHQTqdPrCH5HG9VG9dZYHw3r17AODi4oJh2OrVq+FZEaueq6mpcXBwAAB7e3sOh8Pj8QIDA/FJDIoVWa/S2CG6lFbz6fE8i5WxsOAGLIiCab+B00IwGQ0E4j8dp37fw/CpsOAG/Ps60AxgwQ0Y9wm4r4CRQbDghvGnSsgP0NcDoVgsjoqK+vrrr7vnm7h+/bqjo+PSpUsDAwPnzJmjrvXL5eXlajkuptRAqAJ3797F84wo5Xazvb39zp07y79aN8jBm0CmApEM8y4vPdrrqSA1Snh4+C+//PJc37tMJisuLlasdUtLS7OwsLhz547KWtXQ0NBLszzeRm/UW583b57i8d69e1NSUnq4Q7lcfujQodTU1ISEhCNHjih6X4RCIZ6dsbm5+cqVKwAwYcKEHh4Lw7Dy8vLhw4fjIbahoaG1tdXX1xcfJuxearSysvLgwYNXr15NT09/aUogmVyeWdq6M6LYb3NKV2rfORfA73uwnQCDvYC5CfQGg900MLID989hwQ2gG4H/DzDrNCy4QV8SXdvjGad9PRBiGCaTybqnQmhubra1tVXk+P/yyy937typpqapjbW1ta+vr7+/v7+/v7W1dV8OhA8ePMCrrOGpZJTlUGwFLLgBxiMBAMZvMPv8jlTWJxYkDQwCgSAjIyMqKkrxSlZWlrOz86JFi+bMmePn51ddXY1hmIqvQXfu3KnGL3t/qbdeVlaGr8TNzc3tfpkyfvx4AIiIiGhqavr11197PvEHT4MOAO7u7k1NTc3NzXilJ3yYsPuWQqFQLpdv3rxZJpO9cUSJ2ya6lFaz8OBDg2W3u7pAmZtgsA9MOwCszaA3GGafAbqRoneUuji6qUP8+n2+ERH6NoFAsHr16vHjx4vFYvyVzMxMd3d3RWXBhQsX4hVrNU1sbGxiYmJiYuLixYvV3ZZXKioqmjZtWmtr64IFC/CZMsoy09WMQAAY7A0AUJ3GbRenFbcqcf8aTiwWx8XF4QvCcP/5z3+OHz9+8uTJy5cvL1myBK8Z9Nycpl6VkZFRXl6elZVVW1ursoN2x2QyAYDNZqvl6G9PMfv61q1b+FJ6AOC0CAfbewIAm802MjL69ttv8XkV7y0/P3/ChAnV1dUBAQFxcXFSqZTFYqWnp48cOTIxMRG/TVSgUqnh4eGTJ0/G8y28nqme1jwvy1MrnBuPBJ5dOU6LTAAAoOqBkR1YeYCOGbTXdN9+kCHNiEHpyWcBgL4eCMlk8rfffmtnZ6f4Dba3t+O1XXC6urrt7e1qah3yOtXV1ZMnT66vrw8KCvr777/f5jvw9qwMaR7DDLoCIScNMHl4Vr0S96/hduzYoauri1cYB4D6+nqhUIinbAaAjz76KC4uTsVN8vT0PHTo0KJFiywtLVV8aNyUKVP27duHzzrpuTNnzvz+++9lZWVHjx7duHGjUvaJi4uLy8vL4/P59vb2eFCsbBKwfk67VKIPAKdOnTpx4kQPLyYePHgwfvz4mpoaFot18+ZNPp8/ceLE3Nxce3v7e/fu4XmjuktLS0tMTKyurs7JyamtrS0sLHybo5CIhAV+VpMcTbTIRGgtgyeXIeMPEPPAeIRiG20q6bdFDj35LF16eEepejk5OR4eHoqnly5dWrhwoRrboxbDhw9XrGLcvHlzH1w/x+VyR48eDQA+Pj5KKfvy22+/RUdHJyQkHD16FF9Ytu16MSy4AbpWAACTd9t9c6/nR0FeqrCw0M3NTfFULpfr6+ursT1qsWDBgqKiIvzx5cuX3ykj7ou4XO6GDRva2trkcjmeFKKXlHI7h351D/51GczGAPGf6nKvKvfxRvfv3zcyMgKADz74QCAQVFRU4LlYXVxceqMqdbtA4v7dTfrUnTBxG0zbDx9GwoIbELib+NFN7SXKmTKK9f2u0Rc5OTkZGxvv3Lmzrq4uPT19y5YtePEBjVJcXIxPPwGATZs2LV++XL3teU57e/v06dMLCgqcnJxu3LiBr3DoodDQ0Pb2drFYXFRUxOfzASDEzRwAYJAXAEB1WnE9v6CG1/MDIS8aMmRIdXW1UCjEnxYWFj63pkUTcDgcxQBNe3t7Y2NjT/amo6Ojp6fX2toaGRnZvQtauQpqeAFb0spqGuDuBuA+Ai09ivNHvhOm6ejoPHnyZNeuXZMnT7awsPjwww9Pnz7d0NDwxh0mJiZOmjSpubl53rx5165dq62tZbFYxcXF7u7ud+7cUYxYKZEujZy+e9rvPyywc/bTMh2lr01l0MhUq7FTnUyTN/usnmqrlKOQ37xJ33Pt2rX9+/evWrXK1NT01KlTePY8pI8QCAQzZ87Mysqys7OLiYkxNDRU4s7v37//008/7dq1CwAcBzNGWOgUDfaGgmtQnQYuS8Oz6kdbMd64E+RdUanURYsWffHFF5s2bers7Fy5cuX333+v7kb1YxiG4b0apqamIpEIrx+udA8r2qfszGhobIZ7G6DpKeiYwaTtFKPBm751m2hvkJaWFhUVFRsbm5WVdeHChQsXLhCJRBcXl8DAwKCgIF9f35eOZeTm5ra3ty9cuPCvv/4qKiqaNGlSTU1NQEBAVFSUnp5eb3wKACASCEtZ1ktZ1rWNbY0CIo1CNNMBfV0lXF4rEDAMU+LuVOzWrVthYWGbN29+sVdaE8hksrCwMEtLS0dHx9u3by9YsEDdLQIAmDNnztWrVwcPHpyUlITPOFeKs2fPcrncmTNnxsXFjRkzBp+lveZs/t4bJXD1IxC1Q9BhHzenlM0+yjoi0h2GYSdOnLhz5w6dTp83b96MGTPU3SJVY7FYHR0d+PwgLpc7a9Ys/IKsb8oqa5u6835TIxfu/gStZaA7CCZt1zE0j1jjPtHRuPuWpaWlsbGxsbGx0dHRPF5Xn4qJicmECROCgoKCg4Ofu5aNiYmZPHlybm7ulClTGhoaWCzW3Llzm5ub16xZc/DgQSKR+M033zyXLlEpEhISSktLBQKBmZlZcXGxr69vQECA0vaulA5WdcGz+PR2esw+q6Ojo66u7ocffqirq1NXVfSWlhYOh6N4+ujRo6ioKGtra9UUpGbnN8GCGzAsEABg3BLiRzfVm8NeE5w9e3b58uVqTLSrLkwmU/G/+sSJE4qibH1QYkGz3tIYmHUa9IcAAOhZw6zTBstupzxtfs27+Hz+nTt31q5di4/u40gkkpub26ZNm7qn/M3MzDQ2NgaAGTNmCAQCuVx+7Nix6OjoK1euhIWF5eb21qLe6upqfKlYVFRUamqqEvfc/8aZElJQAAAgAElEQVQIuwsJCQGA8PBwdTdEPRgMRm1tLZlMNjc3755vRZVu3bq1Y8cOxVMWi/XBBx8UFRXhySZ6m99IQ1M9LcUiCjmGRT3gquC4muyXX345duxYenq6uhuCvBw7v3n67oz2xhq48z20VYDhcJi829DEPOYHD58RrxunoNPpgYGBO3fuzM/Pf/LkyZ49eyZOnEgikbKysrZs2eLu7m5tbb18+fJt27ZNnDixqakJzxJOo9Hy8/OJRKKNjY1IJBKJRHjuX6UrKio6efLkRx99dPfu3fr6+nHjxilx5/07EAYFBZHJ5Pj4+NZWTVxD1tjYmJKSQiKRSktLHz58eP/+fXW3qEsvfRNeRCISPhhnBhauQKZCUyEImnu+iELWyG2/cq7pwM7WU0dFhU+U0s6BpL8sp1M6ExMTCqVrvZq2tnbvDYn1RHROw/Td93kNlXDne+DVgtEImLTN3MyUvcHbc7jB2+/H3t5+zZo1cXFxLS0td+7cWb16tbW1NYfDOX78+M8//4wPE54/f55CoUil0rCwMF1dXTMzs87OTpFINHLkyN74aK2trSNGjHjy5IlEItHV1a2srFTizvv3GCEAsFgsNpt9/vz5f//73+puiya6cOHC6dOnFdNWly5d2tTUpMoGXM+sn7UvC9hbgZMOnl9S7Wc0HA7Upb3XLDAMaz66v+3kUSAS5AIBgUwBMpnm4mmx83ciQ/f+/fuWlpb6+vrp6elDhgzBK2FpoGvXrs2ePXvixImqX0qIvF5kNnfeb9mipgqI+xEEzWDmCMzNlqaGsT96OQxSwiSynJycqKgoPEFMfX19b8wRVZf+fUcIGt87qkpCobCtrQ1/XFPzT3KHtra24mdUf101xclEW4uk6B0VSeR38t5zXnvTH7+0njq2sZgjFwgAAJNKzlVzHybEc5b/u7W5iUAg7N+/PysrSyqV4gMkmonJZBKJxNTUVJFIpO62IP+4kFoze3+WiFsEd9aCoBnMxwJryxBL48SNPkqJggDg7Oy8fv16f39/DMOSk5OVss8+ot8HwlmzZgFAdHS0YokP0kuioqL27NlTVVVVUFDQfYaqi4vLd8+QSKTX7KE3aGuRJo0xhkGeQCBC3UOQCN6vd1RSXdF2/i+5UBDdwle8mMUT1QuEkqpy4r0YmUxGJpOtra3r6upOnjypvE/QzxgZGTk6OgoEgr7TFa9K5eXlp06dam5uTkxMDAsLEwgE6m4RAMDxe1Uf/b8cacNTuLseRG1g5QGsrSOtTZI2+Qw311busQZk33i/D4S2trZjxoxpa2vr1T+MSCSSSqUCgaClpUUxw1jTMJnMmpoaPT29K1eu4PXG+ogQN3OgGYDxKJBLoC476gFXKsOioqL27dtXWFi4ffv2AwcOvH4PfLEs869zUqn0pT/FBPzysycqKytFIhGPx9PT09Pwqy4WiwUA8fHxam6HOsjlci8vr+vXr6enp48fP16V2VZf5XBc5Wd/PpLXP4K4H0HUATb+MP4nexuje+u9BhvRlH44PBAOsL9+vw+EABAaGgrK6B0Vi8Vnz55VPI2Ojq6vrweA+vr6kydP/vjjj0VFRbdv396+fXsPD9RPFRQUeHl5ZWdn6+nppaWl5efnA8DIkSPxLwZOLTlugl3NSUSCone0mSdJetocFBQkl8srKyutrKyysrJe+saGdvGpRE7wnkyjT+9kxmWKJRIAaJPJ//20Dv+X2N51va/X3MBisbZv3+7s7Ozr66vhy8kH5D3BWxo2bNitW7csLS1ZLNaFCxcyMzNV34aaFuH+W2VLj+auOZO/7GjeihOP5HW5EL8JJHywZYHf9y7DjBI2eFsZKj8KAoCPjw+NRsvNzVXxbIBeNRACIT5MiNe07Ml++Hz+li1bFE8PHz5cWloKAObm5kuXLqXT6U5OTnQ6feHChT1scD9lZWXl4eExYcKEL7/8cs+ePXg6DFdX17lz5yq2UctVgpmelredAVj7AgBw7gMmC8/s6h2dPHkyk8k0Nzfvvn0pl3/gVrn/llSLlXGLD+dEpRWJnt75f4/T3XIqq8VSfRLxwkgL/F+A3rPrfRrN1NQUv/y3sLDojfXC/cj48eMJBEJKSooG3hnfvn2bRCKJRKLCwsKWlpbenjDS3t6OL3RTZHT79UaZ3bfsHy8UnmBX74su/5NdBYBB7imQCsBuGvh85z7cKHadl4lub62notFoHh4ecrl8IA0T9ssUa89xc3MbPHhwdXV1dna2m5ub0vff2dm5d+9ePP4VFhb2qV5BVepeWsXGxkaNLXlRiJt58tMW0BsM7dXAfXwqiWYHRRQKpaSkJD4+/rPPPpNj2IPy9shs7sW02q6UpK0VwEmD6jRoKgLACgGIBMjrfNkEEBJR28NXxZ+oLzM1NXVwcHj8+HFmZiae4kdzTJkypfdSgz6nvr5+7dq1Bw4c2Ldvn52dnYGBQQV17KYrTwUiMZTchqYijKoLthPBYAiM3wilt8F+dsAo46j/uuvRe/fEzmKxEhMT2Wz2gDkZDoQ7QgKBEBwcDMroHW1sbPzvM0+edK0ha25uHj169KNHjyQSyZw5c3raXKQXzPKwAIDuvaPf36OdanCT6VgYOX+wLY5vviLO/afkLVcKCx49hLyzEPUZ3FwJOaeg6SmQKGAxjui2PMzFaaqh7os7J1K0DJd9qdoP1Ncpq3d06dKliqnIly5dCgsLwx+Hh4fv2rXr4cOH27Ztu3fvXg+P0k+Zm5v7+/sDAIVCSU5O1jexWHu+oFMkg+TdwKsD+1lg7gxJ26G5BKi6YD+HZW9y83uP3o6CMBD7xgfCHSEAhISEHDp0KDw8fOvWre/0RgzDMjMzw8PDY2Njr1+/zmAwFKFOkTvD2tpakct02LBhSmw2oizFdZ1EAsgH+8CTy1CdCm6f8kWy7LK20d+xMQxAJoa6B8DJgOp0ELZ0vYeqC1YeMMgLLN2AQqdpkWJsgx3iNoSPHQIgxzf5ydpYm04z3bibMgT93f8PJpN58OBBNpu9bt26nuwnNTVV0b9aXl6uSPQ8efLk27dvGxoazpw5U12VeNVOIpE0NjbW19d3dHR8+eWXO49dJRI8ob0aOjjgvw4AQM8anBZC/hXw+55KJt5a60GlqOLexsfHR0tL6+HDh62trQYG77BOv88aIIFw4sSJBgYGubm5ZWVlQ4cOfeP2MpksNTU1LCzs6tWr1dXV+ItsNptGo3l7e+NPlVs2Aek9Ehm26FCOHAMwHgU0Q+jkQmsZGAzFhO1Qcx84GVCTBdJn09wZ5jDICwZ5gflYIJCMGVozxpkGu5pNdzZj0Eiyr/xNDu/jxUTI2tsIWlRLd2+TL9dqjeyV4gD9GovFIhAIycnJEolEkW9Fidra2j799NP79++PGjVK6TvvL6RSqbu7e2dn59dff11QUGDjPZcXUQrt1aBv+89GhsPg8SUAkMjlZJKKhq61tbU9PDySk5OTk5M/+OAD1Ry0Vw2QQEihUKZMmXLp0qWIiIivvvrqVZt1dnbevXs3LCwsIiJC0SFjZmY2derUefPmeXl5qaq9iNLIMexQbEW7QAoAQCDAIE8oiYEHf4JECE0FgM+fIhDAZDQM9obB3qBnDQDDzLSDXMyCXc1Y9sbdTx8kA0OTH7aa/PBu/QoayMzMbPTo0fn5+dnZ2e/xxWlsbExISMDHF4ODg8lkMgBwOJxVq1bhG5SWluLLVS9cuCCTyZTb+P4CT/6JP7awsEgJLyERCVIKHWTCfzaSCkFLGwBIBCJRhXO4mExmcnIym81GgbBvCQkJuXTpUnh4+IuBsLKy8tatW5GRkbdv31b0wzg4OAQHBwcFBfn5+eGTACUSyf79+xXv+u9//9tLSfOQnhNK5LGPGiOy6iOyufVtIsAwaCkBTjrUZgORDLUPAABIWmDqAIM8wdoftI2JBIKXnUGIm1mImzkqW9hzTCYzPz8/Pj7+LQMhl8tNT09PTk6OjY198OCBXC4/ceIEAERGRuJzL3fv3q3Y2M/Pz8/PDwA++eST3ml+/+M70oBOJXUY2kFzCYg7QEsXAKAyCcydAWCsta4q5zIzmczt27cPmNWEAycQfvDBB1paWgkJCY2NjSYmJgDw+PHjqKioyMjIlJQUfGUFiUTy8/MLDg4ODQ19scuFQqF0r7KGD1MjfUozTxL3uDEymxueVd8ukIJMDHUPoToNOOkgVCReJwAAeH4Jtkwg0wGAZW8c6m4+z8uil1ZWaSYmk3n48GE2m7127dpXbVNbW8tmsxMSEthstmL2GQDQ6XQfHx9NzlT3HpijjS0MqDwhA3NZCrHrwNIFOhtA0gkBP+pQSRtn26myMb6+vhQK5cGDB+3t7X0z//g7GTiBUF9ff/z48bGxsQcOHJBIJNeuXXv69Cn+I21t7YkTJwYHB4eEhDy3pAzpO0SPc0X5eZhcRh3lSHNyhW7Xt+UNgtt5DZHZ3JjcBokMA3EH1OUAJx2qU0HybPBPxwwsXWGQJ5Tchuo0wGR4FLQ10b73E+r0Vj586mBSUpJUKsX7NnF1dXWJiYlJSUnJycnZ2dmK1b3a2touLi7+/v6BgYH+/v40Gg0AfvjhB7U0vj8iECD8GzefzSk82wCZpSu0VQJVD6h6QKb7jjQMcVPpmY3BYLi5uaWlpSUnJ0+fPl2Vh+4NAyQQdnR0REdH48WY/ve//+EvWlpa4sFv4sSJ+LcO6ZskleV1//1cUlMNchlgQCCRiMYmlrsPFWlbh6XXRj3gZpW1AQB01kN1GnAygPsI5M/SoenbwCBPGOQJpg5d94IiHlSnQXUajPiASiZu+xfq3+4VlpaWI0eOfPr06YMHDwYPHpyUlBQbG5uUlJSfn68Ifjo6Oj4+Pn5+fv7+/gEBAS/W58rJyVHMtfnuu+9U+gH6IftBjAfb/T89/ujuY5Dr28Ctr0DYBnMvOFur4Z6MxWKlpaWx2WwUCNWspqYmIiIiPDz83r17eC58fPr1mjVr5syZ4+HhoZiNjfRZ0loOZ1Hojeo6OyrZjkYBgCqxNL2aO+PjWbNs/ltMtYS2CqhMAk4GNBd3vYdABFMHsPEHa1/QNgWAUZY6DR2STpFUJJHDYE8gkKA+F8Sd1hamC/ys1PjpBrDy8nJLS8unT59Onz69e7YtPT09f39/JpPJZDLd3Ny63yy+qPuMU/RtfRtDTbXvrPP8+XrRxrAiwDCQdEJLKbtADVPcmUzmzp07B8Zqwn4ZCEtLSyMjI8PCwhSDf0Qi0cXFZcaMGVevXs3Pz2exWGgKaH/B3fajjM9LaOOT9el4IKwTS2NbOwdpkUckbyhuFwP/2UmWogNWbjDYB6zcgKKjRSZOcDAOcTOf6WY2yJDGF8v+d73k91vlPNAFUwfg5kFtVoPBRLFUrkVGZ1jlqKmpwWe7xMbG4gkIdXV1m5qaGAyGt7d3YGCgn5+fp6enllZv5fdCcNOdzDaGFYH5WODVAjfvQcWIdoFUBUvpu/P39yeTyZmZmR0dHbq6L8lE0Y/0m0Aol8sfPHgQGRl58eLFgoIC/EU6ne7n5xcUFDR79uwzZ864urpSKJTNmzeHh4d3n/aC9Fny9jZhVhq8bH58eFNHdBMPAIBmAJauYOMPlm5AJOtQSRMcjOd5WYa4metr//MfWFuLtP1fI9fMsLVYGScd7A3cPKhObRsyPqGgOXCMico+0cDz9OlTfLZLfHy8YtEtABgZGbm7u9++fZvBYDQ1NaHgp0outnr62uQ2szFQchu4edLRs5Kftkx3VmmlXAaD4erqmpGRkZqaqrK0c72kTwTC9PT04cOH41M96+rqOByOImWoQCCIjY2NioqKiIioq6vDXzQ2Np4xY0ZwcPC0adMUVyIzZ86srq4OCQnBA+GhQ4dQT0vfJ64sI2hRMbEYAI7WtV1v6gSAVplMl0ScZcwwoNKyRyzIGjQFgGBjTJ/mbBrkYjbVyeQ1d3jGDC2/kYbsdh/IPgY1WSCXhmfVo0DYXWdnZ35+vru7O/40PT3d2dn5uUH00tJSfLZLTExMRUWF4nU9PT1PT8/AwEAHB4eIiIhjx47Z2dmVlJTk5eX1Rppf5FVIRILfSMObjU4AAPWPAJOz85tVHAgBgMlktrW18fn8N2/at/WJQPjrr7+uXLkSL3KWlZUVFha2Z8+emzdvRkVFRUdHK+r/DR06NDg4ODg4mMVivTjwUFtby+Vyp06dOnTo0LKysvv376Pe0b4pKipKIpHMmjUrIiJCXF3p9mxixb9NdPFqD7l80eUmnieDZq9P3203OjDQLsjFzG+k4VsukwpxM2fnN4PBEGitAG7etfuM3xY5ana5iP+joqLiu+++U6wA++KLLy5fvjxkyJDS0lJ8tkt8fHxVVZVie1NTUy8vL3y2p4uLi+L6Ep+VzWQyS0pK2Gw2CoQqxrQ3vvnQFHTMobMeWsvZ+UYqboBMJnNxcVGs/rx7966Dg4OFhYWKm6EUfSIQvmj+/Pl3794FACKR6O3tHRISEhISgtf9eRUzMzP8njI4OPi3334LDw9HgbAPksvlY8aMOXbsmJWVVUNDw8xZczrOHsR/pEcmmlJIAKBP6jrV6pLkZ/Z9RNR9txlxs9wtvj2TD4O8obUCqtM4Fi4PKtpcbfWV+0EGkvz8fE9PTy6Xq3jFwsKC+YyDg8Nr3stkMk+cOMFms7/99tvebynyD+ZoIwAA8zFQWg/1uZllwzuEUl2a6k7pIpHohx9++PDDD/Gnhw4dWrVqFQqEPXLp0iW8eipe7nXevHk0Gi0kJGTmzJlv+Zt1cnLCH4SEhPz222/Xr1/X2Aq6fRl+PyGTycrLy+Vy+fbdu39a8EnbuRMvbkmgUnWnh75rFAQAW1P6mMG6j5q84fFFqE4F98/Ds7goEHZXW1t79OhR/HFDQ4ONjU1LS4uFhUVAQAC+1MHV1fX1NRdra2spFEpCQsKECRMAICEhQSaTkUgkVbQeAQAAt6H6ujRyh9lYKI0D7iPp6NDUotYpY3s2CiCXy3kdBBqdoGEjvqoOhI8ePaJQKKNGjSooKBAKhePGjcNft7Kywsvdtba2VlVVff75559//vn7HWL8+PGGhob5+flPnz5FOdL6GqlUGh0dTSQSfXx8/vzzTwsLC6PPvhE9zFoplOrJnmW/09b6YZglZchw4+82vt9RQtzNH1WPAG0T4DdCc2l4pv6WOSOU9yH6jZs3b5aXl8+dOzcuLq6hoWH58uV4bWEikYg/wB/r6OiUlpYOHjz47fdsaWm5evVq/LGtrW15eXleXp7i64yoAJlE8B1pGIMPE3IfAYax85veOxBK6+sa92/jx98BwDCZnDrSwfibH+lub+hUa2tr27BhA/748ePH73fovkCl00lEIhGXyz1y5EhOTk5qamr3JC/+/v6hoaGhoaGK4g/vjUwm41NGIyIiergrROnIZPKKFSu2b99uY2OzZcuWtWvXEshky8NnnFavMTQ2IWpRCTSarr7huGUrBp+8RqRrv99RQtzMAQgwyBMAoDo1p7K9rKHfj+e/BzxNYEVFRWlpaUBAQExMDP66ubn5wmfwPGfvFAWfM/Cq0/UXTHsjYJiDjimIO6CtnJ3f/H77ERcVVM2b8iAqAhOLMLEYZNKynOziVYvbLp0GgNu3bycnJ8tksgsXLhQWFnZ/I41Gm/SMmZmZEj6Smqg0EFKp1ICAAAaDkZOT09LScvDgwV46UEhICCijTi+iGgQS2XDx57ZxWTY3U4ZEJNjGPzRasaYnnTPuQ/UHG9G66vRy0gAgMpv7hvcMRNra2iYmJg0NDUQikUQi9VIZBxQI1aVrmNBsLAAA91FGSWun6J3/xJhEUvvlEnkn7+MCjuLF/bWtGY2tTfu3dzzKsbOzi4yMvHLliqOj45kzZ7q/l0qlsp7BM6f3UyoNhB0dHcuWLXNxcbG3t8cwTPG1/OSTT0aM6Oq5cnR0/Oijj3p4oOnTp9NotJSUlPr6+h7uClElkqERyVgJXycCAYJdzcDcGSja0FIGvPrwLE38n3D9+nUOh+Po6GhhYREXFzd16lQAMDMzW7p0qWKbzz//vIe1VfH53mw2Wy6X96y9yLvxGG6gQyV1BcL6PIkMSytufdObntd+5aysvRUAe/FHmFjEP3VYKBSKRCJvb+/Y2NiioqKeN7sPUukYIZVK3bRpEwAMGTJk6NCh+vpd8xe6p6qztbW1tbXt4YEYDAaLxbp169bNmzd7r4zLgwcPTExMLC0ts7KyHB0dGQxU2acPCXEzPxRbCZZuUJkInLSEAouWTomhjvJLyPZlc+fOxR90/xaYmJgsXLhQ8bR7UHw/Q4cOtbGxqaysfPz48dixY3u4N+TtUUgEnxGGsY1jAAC4jwAwdn7TJMc31/SQcCoF6cnCh/cFmWnS+tquFzHsdmvXCEKNWAoAgGEtmensoU4AQKVSGQzG+PHjFTuhUql79+5VPP3iiy9Gjx6trI+mYiq9I9TS0ho2bNiwYcNIJJKJiUlvFLZWUFbvqFwuX7NmjeLpsWPH8Goyzc3NnZ2dBw4cKCoq0tPT27VrVw8PhCjXREcTA21KV+9odZpUht182KDuRg1YeO/ogKlO15fFx8fv2rWrubl527Zte/bsYdobga4VQdsERG0BNbdrE5KxZyVXnyPhVLZfPc/d+G3FDJ/KmcyGbT9Whl++UVjyc3VzcH5Nk1Qmw6BaLMX/8WVdN4hUmXTFihX79u0zNzdfvnz5ypUrFTskkUhz5sxRPGUymf23ts+Azb0yc+ZMAoFw+/btzs7OnuxHLpefP39e8TQ+Pp7D4QCAkZHRuHHjjIyM7O3t8/LyBg0a1NMWI0pFIRGmOJnAIA8gkoH7CEQdmtk7qhpomFBl8HQiHA7H0NDw6dOnPkN1FrbET6eJAWB86cUvUn8pm+TWdrXrlIUHv/q1q8onueLBryL88u2npb9wWkILarxyq1aVck9y2/MF4vs8EY1I+I+ZHv7Pjt51l0IZPERtH1WF+so6QqWzsrLy8PDIyMiIjY3F7w6Vq66u7rvvvvv4449zcnL09fUV6U+RviPEzfxSWi2YjYG6h1CbeS1T99eb+ktZ1gbamtVBqgJ4IExISMAw7PULEBGlGDt2LJlMLigoGHP3r+8bIsIZlJvN8KCD94kJHeND0+7NbWePy5qb5O1tANAokeXyRVk8UXKH4DFfrBgMpBEJjtpa7jo0Xz2am85LCtUR6Nr68xe9sTFisXjRokVbt25NTU2VSqU972xXvQEbCAEgJCQkIyMjPDz8vQOhUCgkk8lNTU0TJ07EX3ny5MmSJUsAQE9Pb+vWrQQCwdbW1tDQcNKkScpqNqIsH4wzI5EIshFBYOkGZmOkMlh/6enP14ovf+WKso8ql52dnbW1dVVV1ZMnTxwdHd97P+3t7YcPH/7+++/xpwcPHgwJCRk0aBCPx/vzzz+lUmlgYGB6enpdXd3Gje+5xrS/S09Pl8vlxcXFycnJc7w9+Ad30OUiTwYNANI7hBgAAQCTiGuKijJ5wsxOURZP2D340YkEh2fBz12HRiUSAICob4B18uab/DPLwU+Xbq2rTR1hrxs05yWN+L+OHj06Y8YMmUw2d+7c06dP98an7m0DPBCuX78+IiLiXXNeNDY24plOb926lZuba2xsrFiAtXjxYvyBtrb2sGHD8Mc2NjbKbTmiFFfu1wKGQW0mOM7HyxaKylJFgsbQvVjKZl8nm/5dOKavCQgIOHfuHJvN7kkg5PF4586dUwTCa9eu+fr6Dho0iMFgfPXVV5s3b3Z2djY2Nk5JSVFSq/sfLy8vLy8vTCYdzHdt3L1ZLBACwDAaxYxC4kpkx+rbyoSS+zxRuUiieIs2kejGoHoxaJ66NCdtLTKBAACUQTZ0Lz+aszvdw4dsbtl26dR/D+zCSAQQi4FMCbWi6bCmmG3YCW8qXSCTyaqrq/l8PoPB6L9nwoEcCB0dHUeMGFFUVJSamoqvLH69wsLC69evR0REpKWl4RPBiURieno6dCsfirp9+guhRP7N6XyZHKC9Cp7lrAFRG3Q28EWyVX8/Stzoo9YGDjRMJhMPhN3nUyhRTEyMn58fAISFha1atao3DtHHYRKJ6EmOICtdmJUuzM2S8/kAwJXIsnjC5A6hQI5RCITdnBZ8YzqR4KpDc2NQ3RhUTwaNQiAAkag1fCTdzZvu5kVz9SIZ/J9avvr/WqT7wWx+aoKUU0XUN6B7+FIGWb9Nq0gk0s6dO6uqqnR1de/cuUMkEkUiEZVKVfrH71UDORACQHBw8N69e8PDw18VCBVlDsPCwvDpoABAo9H8/f2DgoLmzp1rbm7+zTffqLDJiHIkFTa/6qIFA8goaVVxhuIBTzFx9P2GCSUSSV5enoWFRVlZ2cyZM/EXc3Jy8AccDicxMdHGxgZP2j5wah/K5fyUeH5qgqy1lWI7THdKEGXIsO4/x2RS8dN8QXqy4OF9YXaGvJMHAFUiaTpPmN4hzOAJOfg6h2fMKaT/mOl76FId6VQSAYBI1LIdThvnTvfyp3v4kPRfV8ieqMNgBL5nGVdra2sACA0Nfb+3qx0Bw16yjnLASEhIYDKZdnZ2z60DFQqFSUlJkZGRly9frqmpwV80MjKaNGlSUFBQaGiont4/uZ7Ly8sVSxvr6+v19PQUeRqRPusvdvXqk094IinEfg9SIZCoAADCVrD2g3FLdOnkzJ/9RlrqqLuZA8qgQYNqamry8/NlMllCQkJISIiVldVrtpdKpTk5OXjtp8TExPb29tzc3A8//DAhIQHfYPbs2fv27RuoKUyldTU1Xy6R1dfI+XzAMAKFAkSS3uwPjb/6QVxcKEhP5mckCR9mYSIhAFSJpJmdwiyeKKldUN0t+OmQiOO0qb56NHMK6bvyRjMKKWWsNQAAkagzabrZ+u3vkbZeAw3wK2I/Pz8TE5Pi4uL8/Hx7e/vm5ua4uLjIyMjr1693dHTg29ja2k6ZMlnTbEEAAA/MSURBVCUoKGjatGkvXdrYfYF//10oo2lMdLVIpGf3Jb7fg95gAICS29BRAwBiidxYF80dVbKAgICLFy+y2eyqqqotW7b8/PPPmzdvfm4boVCYkZERHx/PZrPT0tK613S1t7evq6sjkUiGhl03Li+WHR0w5Hx+9Sdz08sr8zsFC031AACTSL4sq/nj0sm2i6dALoNuwS+xXcB5WfDz06U7aGvhg3gYwE5OC1ciKxNJhlIpRC2qyTc/oSj4lgbs/zMciUQKCgr6+++/v/vuOx6Ph6eOBQACgeDu7o6XeVLUb0IGkvGjjcTSV2b8GmpKN2YMlO61PoPJZOKB0M7OjkQiKXqbJBJJbm5ubGxsbGxscnKyQCBQvGXYsGGBgYF+fn4TJkywtrZWdM8MeK2njsjbmhvF4mLhP7Na0jqEtUJxXKsggyfM4AkbJP8kDjUmkzwYNC9dqheDNoKuRQAgkMlUR2ciQ1eQmYKJxAQADwYtuqUzo0M4lEoxWfc/snm/LA2oFgM5ED5+/DgsLCwxMZFCody8eRMASCSSn5/fvHnz5syZ05N0+0jfp69N/mb60AO3yjqJFFAMWRFIQCRra5EOLH7/mY3Iq+BJR+Pj4+fMmfPrr7+amZnt2rUL7/kUCoWKzfDgFxgY+GKmZjMzs4sXLyqeHj58+PWdq/1XR9QVTCR68fWHnaJNVU34Y2MyyVmH6sag+unSHbW1CAAEEklrpEPXbE9XTyJDFwAEGckNOzZIKsu98EDIE8430aVY99cJnGox0AKhSCS6d+9eeHh4RESE4uqSQCAQCIRDhw41NjbS6XRHR0cUBTXB1rkjSrmdkbCrU9TVrUQcPommRdoxf1RP65ciL2NtbW1oaFhbW7t9+/a8vDyJpOteh0gkuri4MJlMFosVEBBgZGT0qj2QyeRRo0YpnuI1SgcGiUQiFAp1dXUFAgGBQJA1diX8u9PKLxR0/aJEcsyDQZtppOPBoHkyaMNpFHgu+Ll5EXWez2lM9/SzuXa3enGo5/37AJDaIQQAQWYazdlddR+vnxsggbClpSU2NjYyMjIiIqKtrQ1/0cbGZtq0aUFBQUeOHLlx4waBQJBIJD/++OOWLVsCAwPV22BEBUhEwvkvXKJzGvZFlz2q6qCQiAGjDdcGDx9rjVYQKk1nZ2dqampSUlJycnJiYqJIJNLX18/OziaRSA4ODv7+/oGBgZMmTXpN8NMQ165dy83NXbx48cGDBzEM+4JOJ0jEADDZQHuLdVeabI/cKmMyaa+tKRCJ1FGOdC8/bU9/2jg3AvUlaV+eo+0dMOJRjjGZxJXIKkVSela64dIvevcjDSD9OxBWVlbeunUrMjLy9u3b4mepZh0cHIKDg4OCgvz8/PBp3Fwu98aNG+Hh4U5OThwOB30nNcp0Z9PpzqZyufzSpUtmZpiYKzsanWVsbNw9XzDyTtrb2xMTE9lsdkJCQlZWllTadcNNJpPxavUsFis8PLz71GtkwoQJMTExpqamTU1NVCpVx3s8P+7GS7ckUGkWe49pe7956XN3dFdvAvzhzqDGtPLTecIhOVmYRELozcIGA0nfDYRXr151dHTE+0nKy8tTUlIWLFiA/+jx48dRUVGRkZEpKSn4gDw++BccHDxr1qyRI0c+t6vg4GASiRQXF7dnz56EhIQVK1ao+LMgaicQCFgs1m+//bZ9+/aRI0deuHBB3S3qQzo7O+Vyua6uLgBgGMblcl+cHc3j8dLS0vABv4yMDEW3J4lEcnNz8/Pz8/f3nzx5cnV19dixY58+fYqi4HOKi4v9/f2TkpLc3NwkEomEGUBMjAXoeG4zApmiNWL0u0ZBAKA5uxIoFE8GLaaVn94hnCcUiPLzaE6uSmr+ANd3A2FUVJSWlhYeCCsqKq5evWptbX3p0qWIiIjKykp8G11d3WnTpoWEhMyYMUMx5fpFZmZmXl5eKSkpT548UURTRKPo6OiUlJTgPQTnz5//17/+pe4W9SHHjx/v6Oj46aefAKCtrc3f3x9fd9vR0ZGenv5i8COTyW5ubvhsz/HjxysKiwKAvr6+mZlZTU1NSUnJQBrh6zljY+PRo0f7+PhkZmZSKBQbZ+fOn/dP2/D1FCMxvlgCAFLdR5DNzS33HX+P/RNodKqDk2d7KgCk84QAIMxOR4HwLfXdQPiiY8eO4RldTU1Np02bNm/evClTprxlLp+QkJCUlJTw8HDUIaaZmpub8fnDGIYRicQe1mQf8MLCwrZt25aXl6coOk+lUsePH89isZhMpre3t7a29kvfSCAQ/P39r169Gh8fjwJhd4qeKnf3rjksOhOm2JyPbjl2gJ+aiAn4JDMLo9D5Bv9eTNB6z/xkdFev0TlZBmRirVhaLZZqZ6UbLEG9X2+lTwfCPXv2nDt3DgC4XK6BgcHixYsHDx4cGhrq4eHxrjmcZs2atXbt2hs3bkil0gG8Shd5FSMjI0WCymXLlqm3MX1QZWVlUlISAPB4PACQyWQ5OTlkMtnFxQVf6uDn5/eWCZWYTObVq1fZbHZ/LMejYhTrIWZb9755u7dDc/Mi/HXQg0G708pP7xDaPLyPSaUEdLp7C+r/Hcnl8osXL5qYmEyePLmlpSUuLm7u3Ln4j5YsWYKXN0pPT79w4cKkSZPeu9rRiBEjRo0aVVhYmJSUhK92QhANFBkZ2dHRMX/+/MuXLxsbGyumTxcUFERFRQGASCQCgKlTpyYkJHh6er5H9mRUrV5daM7uBDLZk0G708rP4Ann8PniwsdUR2d1t6sfUH+F+s7OzsDAQDy74NGjR7snBTUxMbG2tra2tn5u1e37wasShoeH93xXCNIfyeXycePGcbnc/Pz8srKy7sWMpkyZsnPnzp07d27atAkADA0NAwIC3q+GgJOTk7GxcVVVVVlZmdKajrwForY2dfQYr2e1CQFAkJmm7kb1D+oPhPgKU0tLy/j4eIFAUFlZ2T0DkxLhgfD69eu9sXME6fuIRKK+vj6fz6fT6UQisb29Hc84qFz4MCEAsNlspe8ceT2am9dobS19ErFaLOWIpYKsdHW3qH9QfyAsLy9ftWoVmUweN27cokWLrKys8MzX69atw8uPAYCLi8v//ve/Hh7I29vbwsKivLw8Nze3p41GkH5IIBB88cUXQ4cO7ezsFAgE2tra+Fj7qFGjxowZg2+jpaUVHBzcwwPhvaMoEKoe3c2LCODGoAFABk8ofHhfMSUVeY0BXobpOcuXLz9+/PjWrVs3bNig7rYgyID14MEDV1fXoUOHlpaWqrstmkXO7yxnOR+vadnBaZ5nzNgxxGTw6Qiqw1h1t6uvU/8doSqhYUIEUQFnZ2cDA4OysrKKigp1t0WzELV1tEY5eOpS4dlqQn5aorob1Q9oViCcPHmyrq5udnZ2VVWVutuCIAMWkUgMCAgAAEWJXURl6K5eDnSqLolIIRA6ZfLmg3vqvl4qra9Vd7v6NM0KhFQqNTAwEMOwyMhIdbcFQQYyNEyoHhgmevSwViI9N8IixmGQDokok8vvxcRUz58uqUCTeF9JswIhoN5RBFEJtJpQLTqirggLH8e18u+28/FX+HJsXRlXxuuoXfMpaNKMkHeicYEwKCiITCbfu3evtbVV3W1BkAHLxcXFwMCgpKREkRkYUYGW439gL11+hsll3DphTpbKW9Q/aFwgNDY29vX1lUgkMTEx6m4LggxYJBLJ19cXABIT0WQNFcHEYkltNQAGADmd4jMNHWcaOi418hQ/FT56qNYG9l0aFwgBYOXKlTt27PD29lZ3QxBkIEPDhKqGyQEIAAQAoBEJBmSiAZmoT352kpfLoRfyJwwMmhgI4+PjP/744yFDhgBARETEsWPH1N0iBBmA8KS+KBCqDIFKIxl0VaMbRacEGeoEGepMNeiqE0Kg0bTsRqmvdX2aJgbC3NxcRRa3uro6tNQJQXqDq6vr/PnzV61apajlhPQ2/fmLibSXFwkh0mh0r3eu96shNDEQIgiiAhcuXPjss89Wr15NJBKrq6vxdN5IrzJY9CnZZqi7oa6vblc4pBEJn5rrEeh0sx1/oJJMr6Khv5cFCxbQaDQAqKurmz9/vrqbgyADUF5enqKofXt7O1pKoQIECmXQn2HUnT/xYm8SyFoAci0MPhpkbf6//TRnN3W3ru/S0EB47ty5ESNGAMDRo0fR9G4EQQYMora22da9xl+vF+Y9wIQCio0tdfQYeMdK5ppGQwMhgiAqsGPHjj///BMABAIBg8FQd3M0CMnIWIcZePHixYYitr9Idu/ePUNDwyVLlqi7XX2UJo4RkkgkwrPrIyKRSCRq4i8BQVRg3bp1SUlJSUlJp0+fVndbNE5NTY1IJBKJRBEREatXr0Z1kl9DE+8IuycCXrZsmRpbgiAI0kvwRJLZ2dkuLi48Hg/N3X0NTQyECIIgA96KFSsAYMyYMU5OThcvXgwNDVV3i/ouzSrM251UKr148aKVldXo0aOvX7/u5ubm6emp7kYhyMDR2dlJIpHw6dkymayjo8PAwEDdjUKQl9DcQNja2iqXy/ft2+fn50ckElkslpaWlrobhSAIgqia5s4TMTAwaG1ttbGxcXNz09HR2b59u7pbhCADkEQiSU1N5fF4nZ2dqampaKQK6YM0NxAWFRV98803JBKJw+Hk5eXZ2tqqu0UIMgA9fvyYTqf/+uuve/fuJRKJR44cUXeLEOR5mts1iiCIahQWFqalpREIBKlUWlFRsWXLFnW3CEH+D829I0QQRAXS0tL27Nnj5OTk4eFBp9N9fHzU3SIEeR66I0QQpBc1NTW1tbXR6XQdHR0+n29hYaHuFiHI81AgRBAEQTQa6hpFEARBNBoKhAiCIIhGQ4EQQRAE0WgoECIIgiAaDQVCBEEQRKOhQIggCIJoNBQIEQRBEI2GAiGCIAii0VAgRBAEQTQaCoQIgiCIRkOBEEEQBNFoKBAiCIIgGg0FQgRBEESjoUCIIAiCaDQUCBEEQRCNhgIhgiAIotFQIEQQBEE0GgqECIIgiEZDgRBBEATRaCgQIgiCIBoNBUIEQRBEo6FAiCAIgmg0FAgRBEEQjYYCIYIgCKLRUCBEEARBNBoKhAiCIIhGQ4EQQRAE0WgoECIIgiAaDQVCBEEQRKOhQIggCIJoNBQIEQRBEI2GAiGCIAii0VAgRBAEQTQaCoQIgiCIRkOBEEEQBNFoKBAiCIIgGg0FQgRBEESjoUCIIAiCaDQUCBEEQRCNhgIhgiAIotFQIEQQBEE0GgqECIIgiEZDgRBBEATRaCgQIgiCIBoNBUIEQRBEo6FAiCAIgmg0FAgRBEEQjYYCIYIgCKLRUCBEEARBNBoKhAiCIIhGQ4EQQRAE0WgoECIIgiAaDQVCBEEQRKOhQIggCIJoNBQIEQRBEI2GAiGCIAii0f4/Jhyq7pPSGlUAAAKOelRYdHJka2l0UEtMIHJka2l0IDIwMjIuMDkuMwAAeJx7v2/tPQYg4GWAAEYg1gRiLSBuYGRjUADSLFCKg0EDSDEzsTmAaRZ2CM0M46PT7Axo8mA+E1SciRkuD6ER5kNtxWEsAWlGsCmMjIOF5gaFqTgDgwQDgyQDIxMDoxQDozTQ9wrMnBlMzCwJLKwZTKxsCaw8CmzsGUxsMgzsHArsnAkcsgwccgycXApc3BrMPLwKPPIMvHwaTLz8DPwCDPwKDPyKDAJiCQKCGUyCQgmCSgxCwgxCIhlMwsoMwioMwqoMIqIJImoMomIZTKLqDGIaDCJMbMwsrGzsnGyCQiKiYgLi3xghsQ0GmsZvew6oajcfAHGmSs4+ID1PC8z+5rrywPXTc/eD2O+XdBzov8K+D8TmWW98YEPaOzD7z80n+43ylexB7ENH+Q78CWZxALGnJOQc6FwsCWaviWk5sDO6FMwOvDjtwLnSZWD1u+YdPfBC5CKYrZzz5QDTlt9gdtLEZfv+JM6yA7E79hvaH8ncDBb/sqPBTijXFGzOFi4uh1WnOsDibUvTHeTDbcBs1f+NDvM7jcBu7juzweHV42aI+3/sc1BdJQvxY+5Fh61X+2xBbGPHww67Yw+A9Z4qnuJw9c8rMNtMye5A+99MsHrvk7sOtIdPBLMXNdYesGXfCGav+XriwP1vbmD2z6qoAxGufGB2Ovvs/fP3eIHd6VnufkDv0VwwW1Rr84HFoa1g9utLH2wfXp8AdptjlLyDOoMSWNyv7KX989UnwWFrvcfJQVTsGVgN+9U3DomBjGDzb8hMdHjBZAVmB6qedQhJqgSz62UZHP8cawPrtXr02KFhuhrYzAz3NodZZoFgthgAd9rDOs3t2tIAAAOYelRYdE1PTCByZGtpdCAyMDIyLjA5LjMAAHicfVbLbiQ3DLz7K/QDI/AlSjr6sVgvAo+BxMk/5J7/xxbVtroXS2TsQ4tTTRaLDw0Pl3njhxKfP1/++Pe/sj/y8gA7/c//nLP8o0T08FbioTx9+/7jXp4/Hp++LM/vf98//irGxQTvUOFfsY8f729fFi7P5WbVSGdr8YQAxL1QpfUpO44cyGE+upabVPc2A/AbUst7ubUqY/ZJ5cZVVPrQBGmHT+nEauVGlVXcsuit3CPmoN7aDKR5c8mQHj659oHgSL+6WqOZAHsABeRoCNKr5N3FE+AIoFafCpEANCKnDDiDpCEJV5aidah2yfJmOhLvrNS4cEVBrWda8iqQV+4tyDHSiXwypET0DgDSwbFOd1POkBpIqipNZQCgxE0yiTgKBAmnKb5nZM6oegZsqDlVaWxgjDqp9SEZMMojlZtPUngkHz5Sj1EeiM4TnvB919bJMmCUB33rEtW5BdtunMaeQLbaxGZffUmKts/qI7SQ0/sYI5rEXBtnqkvUx2sz++zGiJ7RFFlAxnBRSNlIhqcedeXjOmMmGFK2YVkZxcprcEMyQ8fq0OYY3wzaAtrgq1OX6LxmPDXTHU32Gr6mM2mPl9AkwSSB9oA6xnfOMeMlpjF4ZNBxeDVWNPIaO5GZdgga7XUtDWZ2RQuwdwxmtmfocMrRlA7NMKC5phiDlX+bqGcHEm/k3aQCJPYQ5u3YXazcRpaSKpDojAGfhoeGmW+Z+Bp1Qh3Rn6htzN3UMVOaUSatZNJ1riU2e7PUZ1SpxzxicYScJNRmKlIUCfXGQHZe7UJu6eLUqJGjRDSoha4DGyRHRokGzF2IIyNxFc2QRosno4PcIZKTzJkCedHk6YLJV3gcs2Wyf7u//HKBHVfa0/v95bzS4k/Oe2sd7byd1rmdd9A6+3nT4FT0vE7iKP28NdZ5nJcDw+X2PuEPHq+bPgw7GrYjTmVHw6IyxNjuOegh5o7P8XovOx4Hv44puWxUBovzjP4OVtcFyeCl1z24DPtngSzRgtxlrWlw5VPGpSOMO1XRw6InxkJtpL/TlWAblp1wXGKL805Z+mHRnbOMT8sZK3RFZrIxR+Xw6lZGQ9pIdkuhi/PAXF2GliN/uU6nLcvOXVcHxIxdpu2wbM7aPy1n9AE/kE22Ghoyh5CbjwVnCHlmYav30APnIARnSGubT3T8tb/j/PVzD88PPwGf2+eJW2t42wAAAoh6VFh0U01JTEVTIHJka2l0IDIwMjIuMDkuMwAAeJxlkr1uWzEMhV+lQBcHuBH4J1KU0SlLJqd70KEIOrYpiox5+B7JRc2iy7VIk4efDvX8+OWFX07Pj1/uXk4P+3d/Hk6fnu4u17z8eDk9/fPf3+Ofvo8XfC+3jFzz/6nuBOP74f0UjYU1DmlOepy9MXHwwa2TDD/O1lyTFYmhfdhKcLrIoU18JCq0OaeNg1poD0KJNO6eaCEfPlZsI8fAjK4saIGYaMpxT01TRx5nap6mspqM2GNlpLMxo2uoBWRWtXSVNUqJu+w+5uh53HNjZRzOOMWIRI2rdVoZKFB0X+PMuwvU761JEO6FHKu4rVxvMjK2mKjskdaG+YgDGu5b3tGYiSKFVWMIUtoMFvoqEpG89hlp9gMHyiTZFBkEB1EOCN7zhgbFKurGqVsqneEh/sNNWbdUsFKHM0q0ObEkXGftxBV3RCZwAgqj2+2q46maMM+I9jDsTV0ZexuqccVmdvfDGkrXrtdQ7CmRwR+xmUUJwoJVhut2VxhtkMGWYj8QcrHlo2cYy3oQZBK2bIyMDj7oimWsDCl8WZn0wCRMMNcOwFiLXzEJLaPhCda+nU/al3LYTIOXgXh4vjPd0I5d0xquGDUaUeyXJa6Ch313fH17/f751+vPSW0dL69v3/DsJt8i4Sklkqm3yGjaLWKfvUR9etGUGSXSOUqlziyRTS4wLJMLjfLkgtMnV5zJBUcnFxzIFByoFBzxyYVHYnIBoimFR3RKdcemVHv6lAKUU6o/UKoGwdiCFFMK0phSiSBUiJSmFiSfWonG1ELEMbUQqU2tSCiuSDm1Lq1PrVvDoAKlUC5QgrdQH9F4/w1K3nKDCKtQHwAAAsZ6VFh0cmRraXRQS0wxIHJka2l0IDIwMjIuMDkuMwAAeJzNkl1IFFEUx8/M7M7sZ7vrfrqmjZnramVqZGnq3qWMAkG2Hgp7aPwgtigxoQw0Wg3KNAmzD4hCxYdAdsOnoCL3piVFX0ovRl+kgeGDpvUShTZ7ZivwxdcuXP6/c+6Z/z33MLODkQ8gLyMoi5F3trw3yDvE8CDKqoqLBryycCxPUFWCotyfeKkKsOQcYzaeZ7m/54r+84/fusRumeM0TDPLfMVgyDD/m+pjk08CWAmQDAwLTAowq+QZiZw2yHIqSaUOsmpeUhtEXgiyvAiCRhS0kiYVNKtBqxN1ei9nMIqGNDCu8LJGE5jMYFoDpnQwuySzJchaEkSLTUqwgtUD1gywesFmD7K2TLA7wO6UnK4g60wUXVmQ6Ab3WnCvA/d6sLE8p1Lzgpa32OxOl9mZxyq/CK7sEeEKDX1tprEgcLGHNg54kN85wnRm8VI0xsdD5+mF2fcPYqx+nE9dJTzmj+ZMRl9zKl+My25p6HzVL+ScyRraMuYiMd4x30gF9gTyonyXuSWMNfPpwzRt6BPy521zNKlZjzVdE/2DlcnPi2M85/f5nk10Yk2+fqRkOtWDNZLBQOpKCzBfujNILrfdLIlxxd7TpHpB6bk9q5CETQZ8S29BgOSSQ8jdTyOkoeQJ1vDSKOG2Km/5cWqBZN6vQ24af0RWnKtF/7Z7V8n1M0rPk7VTpKlGeeO+qbdkqGo/9qP9WEanx46gv7ZnmOpOdiAXyXMbah1A7sp4RTujxcgpM9X0YK8J+ef3s9FvDwn6NMiz9e+6gWx3hqkm0IrcWfmmKK87gr0dDnsI6dBh/vb4hE/s34z5woQvvr6MEHpaN10jSbm7ke/I86nY0oecFbhLaLkfeTGvnQTqN6LPC5/aP36sHtlRHiGRPduRXx4YJX3xfhy/AaS3zV2mm8gkAAAD6npUWHRNT0wxIHJka2l0IDIwMjIuMDkuMwAAeJx9VstuJDcMvPsr9AMjiA+J1HFtL+Ig2DGQOPmHXIP8P1JUz6h7ASJjH1qcaqpYfA0Zsd7opcTn9/ff/v637A+/v8De/ud/zln+ktbay48SD+X1+y+/3svb17fXp+Xt88/71x9FR1HDO63Qz9hvX58/nhYqb+WmdXQjOMZTG/jz0mpbn7Lv4UD22sh09nLjOpp25gQp5TOQYmazlRtVUlfpCVKP23mOKVRurVJzGZYge7nHnd67WQ9kZxXPfI7wSdVAk+L7Tn24JEALIFeSzkSF6jQyawnQAyi125AxCldxM89IziApdQI2uEhF1G6aAKlt2ZkH7hY1JCBDrgSN2qaLM5AgYZJdThy3W+0yOwAIzKb1TCGSQLYqIjZWwHOOkSIjQa2aGsGMRMo0SQPqyDk8+vCmuLvP7pxpSZEecHPnxc1YDLYEGOmROngqNIDrZmNmdUm+gNOnNXtUm3iKnCDJdTZ3NMZtsdCeaclt+eQxSTlSOlS7ZkXEkZ9eVZoj5bi9A9qygDgaaKAcCTl/VrCknSbl9Z9idXZGpQV0apOeOtXllDp4xvdoI9PUZwdQK/LIRo+CG6nHAZGssjD7OLrCKdXI1tUegwM9X5XR7alEXj5CQ25EKD08WWtC2eTgGVDEQ8JobnSIcsu94v/jOTsAwLzpNMbMihOjZXlVk+4UiZ9D86CED659kLYZUFZlzoQSCSjVhsLoBAEQnqf5RMd8PIaXN0YW3GVqOjl7INfodNQ1ouuo6xQ5gISZR8cjaKA+lClDGpBrWrNJtB5itxQYeYrByYxHVB3mIWo5Q86FdJKGqoyl0TQPXSNLKDvGppirCFxZM+U1knT0e9zesQoaDhkyctSrTaLu4bNx01QklXW76MQkROjesDJSYGTIK4quoeFQoO6UDhvt626lPjHncPXABE3Difysbh/YlkfKPYvm+/39p4V8rOjXz/v7uaLjj889vI5+btt1nudOjbNcNie+LXKux3XcPwnsOG/3Hi8XPVeahsfr5gpDv+wnnMq4bCHFHXbZNRR37vspXrey6VPwg+YnIBh62fGgTzVYXQc+gZdsjpjry7BJ8RItyF3GtARXOmVcOsK43bAcFtnBYLpyEKbTc7ANy4kJusF5h8x2WOS0+MOyo+bQFZHx9nxkDq+eiV2Jx6tbCglxYeEdlyzOExFfhg2FAHK+1Q+LXofHYTn9hMgQibc+Epxh0dMSMkM23qnS4ByWraFGOYRsOy4NncOyFdPgDCH19LM446fVVkwXZ5A6PY+H5fRsD8v2HL107Zw4P38Y4/nlP4c1HzojuEZRAAACw3pUWHRTTUlMRVMxIHJka2l0IDIwMjIuMDkuMwAAeJxlkrtuXEcMhl/FgJsVcHYwvA9nkSZqXMnpDReB4DJRYLj0w+fnWUumk2IPlpchP/7kpw+fn+n58vvXh+fLx8fLpw+fH85P/Z4v929zP15++/jwdPfz378++d/rx/dP+D799PD5pf/0eff9YiOSyI+rjMlT87jZUJkLHhrmqnTcfBhR8HGdw1hF1nGLkcZrlSt1inllkZHKAY9Q6JnEwpwHjQxaXCkrKPOQoRxotYbwmgt2rIqjNVmFp4e/NvY8dKwleHfTETQ5UFE0IsohmrkOHmvKsuMmwzkBPYfMcERuPAjFHZ5gCYwKj08A4BG5LlShEcGsNU0AXY8b/mgQ+pBkSNmyfE3FG0srVuSKSFTdmelu56vJbmKrxIMoWnlVXJORZ2S+UOwKWrN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\n", + "image/png": 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\n", 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\n", + "image/png": 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\n", 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\n", + "image/png": 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\n", 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wvJx7u4yTUMX7nDZJoYKBFZg6gkE3uLMSakoBgMLJsxk7n6H37Lu0gqDMWaP1cPbs2Tlz5owcOfLOnTstqAaXy83JyenQocO7d+/at29f54K6srKSwWCoqqrm5+e3atWq+ZX8KmKC6Lr28bu0dNGzUzBgIwDAxzgoSmX3/vHh5v69LRrR6wfTPBAEIRAIGAwGAIhEIoIgVFRkV8wZGRmRkZFoj/j06VPJ7AhHR0dTU1NTU1N9ff01a9b0798/JiamuV8AhiCK9nhU3ggk+AJCJKSo0EGFpukykaprcMPnLKUor0JE3C7lvOcJUrh8dAaTSrHXYA3RYnFNBx0wmVlNYQAAhK6G4lRoP1LV3GHVIHWHnh1cXFxa8nUpDLwjrIOxY8eqqKjcv3+/rKxMunmb4ggICOjWrZuVlRUAHDhwYO7cuVpaWi9fvkxMTIyMjDQ0NIyOjq6z5ffZs2ft7OwYDIanp+eZM2eaQdXG8uBVyceSGlGt5VY1X7wjOC1olW1LKIWpDwqFgqwgAHzJn2lhYWFhYTFr1iwAqKysjIuLe/LkSUxMTL9+/V6/fj1//vzZs2dPnTpV+pTCwsKIiIgJEyY0LekG03BKz52ovHF5TnLWactWAMAT8Fe+LTxSff45p+ZwbmkuXySd/GmnwRqixRqpzVbX0GQ7DlYbPFK/usOmoEyqoKbGuJ+wOJUu5p1Ufem+wR+o361zGxvCOtDT07O3t3/06FFoaGjzlEw8e/ZMV1cXGcLw8HBXV1ctLa3evXvHxMR06NAhOTn5S7kJjo6OAoHA19fXyMhIOtnh2yE+s7xaIAYAKH8PiT4AAJWfQNOYAOJpGs4/+h7Q0NCQxNcBYMaMGTQabc+ePb/99pv00/z9/SdOnHj58uXp06e3hJrKAiEUlp06JK7mFgk/jyIgCCgWiBZnFNwv/zyirjVDpZ0qfZq++ihtNkNbl+04SG3YGHb/ARQGAwBWAfw4pF2QV2B8ZvXfAOwPj/tpG9ckJjB79WmxV6VgsCGsm3Hjxj169Cg4OLjZageLiopycnIAAJUzAsCxY8dUVVVFIpGJicmXLFxycjKKZd6/f7+2/+pbQIVK+ZxZpmYIFsMAAPKTgVsEADQajht9t/Tv39/b27uy8v9dmMeOHRsSEoLD7YqGn/4WKFQAEBJwtbgKAIQEAQAOGsx3NfwOTIa9BrOHmmoBW3PytGmag0Ywe/er3f9MV53+4wynQZc23WPS02oET6t4ehGh2BAqHePGjVuzZs2tW7f4fL7ETaRQAgMDY2NjASAjIwMdWbp06VfPQr4pAGhgOnuTQe+DSCQSCoUoybCBDOisS6dSeACgwgQNIwCAyo/ALVKhUoZY6X/lZMx/kLFjx6IxLzt37vT29pYcFwqF6urqEydObDnVlAOxCCgUAKAAaKt8togAMNNA80dDTbppO7UhI9UGDGVa20K9GUwqbY1VO3YZkVuSlld+t4w7ICJUb/V3O4QHG8K6ad++fdeuXV++fPno0aNhw4Y1wxUXL16MLiQZMvztIBAIgoODc3NzLSwsoqOjd+1qxHSxPhZavdtrR1cW8lX/yYuhMYChpkojfp9oqRB1MS2Kq6srAJSWls6dOzc1NXXNmjUoZbRTp06dOnVqae3+22RlZbFYLJQW9/LlS3NzczU1NZnnMNpZEgI+ANAoMESLDQA8MXEqv5xGAaqauunV8PrtnzRqg0eOeP7iWF753TLO7x9z+G9fMzp2IfUFfSv8hwfzKppx48YBAOrKrVDk71mzf//+MWPGTJkyZfr06SUl5A+DptPpHTp0EIvFLi4uTZgbF7TKxsbKQt1pJYUgoPIjvfAlS7Otj0GChSGbdFUx3wja2trPnz9PTU1NSEhoaV2+Hy5duvTw4UP0ePfu3ZmZmbWfQ2GyNCdMp0hVdn4+zmJpz1vWcCsIAGqDR3ZlM9oyVPIFomQur6rxM+v/K2BD+EWQIQwKClJohcnGjRsnTZrk5ubWq1cvdMTDw0NXV7fhEqKjo2NiYm7evBkYGDhp0qTNmzeTriSPx0tOTmYymatWrdLV1ZXkyjcQbTY92sM+cIXNbNV3PdIDBUn+TkmeNvFXCJGQdFUx3wgUCuWHH34AgKCgoGa76K1bt9ADsVh89+5d9PjSpUsHDx709vbes2dPRUVF7bOqqqoWLFggEolWrVqVmJjYbNoqDr2fNzB79r7S87PHRZVKOW9toTZwuI77/EbJYVh2Ypi1G6bFBoC7ZVzO92sIgcB8AbFYbGxsDAAJCQkKusS+ffsAQEVF5fHjx00WsmPHjvPnz6PHAoGgZ8+eJGlHPmWXfK52bgMAbCplu6neneNHW0oTLpcbGhrK5XLDw8M/ffrUUmp839y7dw8ArKysmu2KvXr1Qg+qq6vt7e3R44yMDA8Pj717965ataq8vLzOE0+cOFFdXe3j4+Pt7d08qjaNXbt29evXb9KkSZMmTTI1NU1OTv7iU8XiqvDbH1fO/TBjTN4vizhPHjXtikUHdvh1aA0A7VTpaTbm/OzMpsn5xsE7wi9CoVBQ9aiCvKO+vr6//PILhUI5ceKEPPOeKJR/dUX4llt4qA0a8YLDb01X4YoJY4ZK/K2bzXDRBw8e+Pv7o8dnz56Njo4GgMLCQj09PS8vLx6Ph7pLY0hn4MCB+vr6r169Sk1NbSkdCIKIjY2lUqk0Gs3ExKSgoKD2c4RCYUpKSnx8PJARp1A0q1evRj19Bg4cWN/zKBS1Ic5tPE8b+91stdeL3X9A0y6nNnhkH3WmjgotkydIrxFwHoU1Tc43DjaE9aG4MOH169fnzp1LEMRff/01Z84ceUQ5OTnduHEDPQ4JCSF98Pf8+Z/dKaWlpevWrZNHlFhbV8/EVJ9OA4AzBRXaH+qIcJBOYWHh+/fv0eOsrCw0YMjU1LSoqMjMzKy4uDgvL68Z1FBCVFRU0Gjra9euNc8VuVwumq02Y8YMdIRCoUybNm3z5s2rVq36+eefLS3ryM9SUVE5ePCgo6PjrFmz5s6dq1ANq6qqOBxOcXExn8//0nO8vb2jo6OvXr2alJSkUGUaArN7L4aB4RAtFgCElXE59++2tEYKARvC+hg8eLCmpuaLFy8yMzPfvn179uzZU6dOyS82IiJi2rRpQqHQw8Nj9erVckpzcHDo3r376NGj3dzcfH19t2zZIhaLq6ur5dcTIemzzOfz5bwzGQyG++zZv5voAMCbav5IqOGnNcdeITs7OzIyMjIyUmIRX716VVhYaGFhQaPRcH234pgwYQI0Y5iQzWYHBgYGBgZKfAAg5SP5FpwlBw4cCA0NjYmJ2bt375eek52dzefz7ezsPnz4IPMnKysrU9PPHXrt7Oyao+8Vlao2cNgI7c9hwprkBFFRHbvq/zrYENaHqqrqyJEjAeDGjRsdO3ZUUVGR/0fzxYsXEydOrKmpWbJkyZYtW8hQEzZs2HDr1q2zZ89evnw5NDTUyMioURUO9cPj8ZAVefr0qfzS1AYO76XGbE1XKRCIXnL5nAfNscDMzc1FXTE/ffqEjlhZWc2cObNXr17Tp0/v0+e7LRNucUaOHKmmpvb06VPULOJbRiAQbN68efLkyePHj/fz81PEJc6fP29sbFxWVta3b18jI6M6nyMSiRYvXoxKimszduzY/v37A8CuXbv+/vtvRShZG7VBIxw1WGo0ajKXl8mnVD0Mb57rNifYEH4FiXeUIAixWFy7aqdRvHv3ztnZuayszM3N7fDhwyTpCI8ePZo8efLZs2cBQE9PLy8vj0R3Lp/PR1aElIQ6RscuDCOTodosALhXzuU8aI6Qg729/cqVK1euXNmvX79muBxGAovFGjFiBEEQ169fV+iFdu7c+ebNG4lHVEVFpbE9oTw9Pdls9uXLlwMDAy9evKiIXuHDhw/v1auXlpbWtm3bDA0N63wOlUpNSkqaNGnS27dv69nCxsfHJyUlNUNxFwCw+jowtHTa6pkTVJqz1lSDqxqjdse9y+M0w6WbjxZN1fkPUFBQQKPRWCyWi4vL7t27Hz9+XF1d3TRRHz58MDMzAwAXFxc+n0+iksgRNHjwYIIgampqUBerjIwMUoRLMvHy8vKcnZ3lF1i4x8PbshUAdGYx0mzbCfI+yi/zS1RUVAQEBOzcuRP9d8uWLWhNg2k2UC7SsGHDFHeJP//8EwDatm3L5XKbLGTgwIHFxcXo8dWrVzdv3kySdgoB7VmHDh3aDNcSisQO83yZY0/AuDMwbA+MPUGbeUtjXmhCZt0puP9FsCGsD7FYjHqYSTcVU1FRsbKyWrBggY+PT2ZmZgNFFRYWdu7cGQDs7OyqqqrI1bOsrIzBYNBotKKiIoIgpkyZAgAHDx4kRTjphpD7NOpNLzNNGhUAHnQzLgs4J7/MOjly5IiZmVl8fHxhYSE6kp+fX1FRoaDLYeqktLSUwWDQ6XSJmSEXHx8fCoVCpVIvXbokj5y+fftyOBz0+NatW7/88gsZ2ikKdMurqKhIvtuK4/CdTLVZITB0F7TqAbYLwdIZLJ0pbiHtVkaIxYq+eDPxbRnC5ORkedZ0pLNmzRoAYLPZwcHBAQEBK1ascHBwkBki06ZNmylTpnh6ej5+/PhL+7zy8nJbW1sA6NGjR0lJiSJURe3ZfH19iX9Wi0OGDCFFcnh4+Js3bwiCEAqFWVlZ8gsUCwWZg3uN1lEDgE3Guh+XuMsvszbnzp2jUCgUCsXPz08R8jENB305z50jf8UTHByMes17enrKKWrJkiW3b99Gj9etWxcQECC3dv/n9evXW7ZsQY9v3rxJylsxYsQIAPDx8ZFfVP1YrnoAbiGg3wUm+oNbCLiFgJkTjDygMTf0ybtSRV+9efiGDKFIJNLR0WnafksRbN26FQAYDMadO3ekj1dWVj5+/HjXrl0uLi4yLWDU1dUdHBzWrVt3/fp1yfqXy+Wiih9LS0vF1W6jiOPkyZMJgigtLaXT6SoqKmiDKCfz5s0DgEOHDskvSkL+pp/3mxsAQH8NZnpfS1EFyT6W69evo9/Hv/76i1zJmCZw9OhRAJg4cSJBEElJSYGBgUKhUH6xDx48YDKZAODh4SG/tNzc3MGDB//yyy+zZ8+eOXOmSCQSk7ffSUhImDdvHnrs6+u7f/9++WV6eXkBwPjx4+UXVT9099vgFgLaZp+toFsI2PwE/Vaqzwn1fvhB0VdvHprJEKampv75558vX77cs2cPOpKfn9+pU6fs7GyCIC5dunThwoW8vDxra2uZQaBmZmYoryQhIYGUm6eBHDt2DABoNJr0wrC8vBwN7JYgFAoTExOPHj06c+ZMmfkPVCq1e/fuCxcutLe3BwAjIyOF2vXs7GwKhaKuro5CmGg4HCmbIeTRjY2NlV+UhMqwW8+tTekUCo0C8T1MK0NvkCg8KioK5TRt2rSJRLGYJpObm0uhUNhsNofDOXjw4F9//ZWfny+nzKSkJB0dHQBYtGgRKUoSBCEWi9PT05FuW7duNTExefbsGSmSExIShg8ffu3atWvXrq1atYoUQ5iXl0elUlksFumhFhk05t4BtxDQNge3m58NYTdXcNqkOe/u5djvpCtTM2WNduzYUV9f38rKSlL4IhKJVFVVURVdRUVFRUVFq1atXrx4UVZWJr3fys7OPn/+/PLly21sbLS0tBwdHX/77bcbN24UFxcrTtsLFy4sW7aMQqF4eXmheBti6dKl2traEh3KyspoNFqPHj2WLFni6+ubnp6el5d3/fr1LVu2DBs2jMFgJCcnHz9+/P379xoaGnfv3jU3N1eczqampj179qyqqrp//z6Q1wqgtLQ0NTWVxWL17NmTBC3/gW0/UJPF6qvOFBHwoILLeUha7mhSUtKYMWM4HM6iRYu2bdtGlliMPLRt27Zv375cLjcsLEwkErVv315S0Nk0MjIyRowYUVpaOn78+CNHjpClZ2ho6LZt27hcLgDk5+d/+PCBxFYAfD4f/dCRVePbqlWrvn37VldXS7qqKgJRcaFd1RsqEGDYHdLvAqqCCHAAACAASURBVABwi+FjPLSyFohETp0b0RX5m6Z57G1WVtaiRYuePn26cOFC5K/7+PHj5MmTV65cee3atZMnTx4/fjw3N1dmvyUSiZKSkry8vNzd3du3by+ttrq6ukAgUISqd+/eRakxu3fvlvmTk5OTtA4qKiq9evVatmyZv79/7eBZcXGxl5fX4sWLAcDY2FgRqsrg4eEBAAsWLCBqbRCbDOpZ4+TkRJKO/+fj8tlbTHQBwFmbneHUXUxGGu27d+/QhJqJEyc2p/8A81VQYeugQYOuX78eGBgoz9eyoKCgY8eOADBkyJCamhoSlUQVFwcOHCAIIiwsDAC6detGimRFuEYJgti9ezcAuLsrJMpOEISoqvLD9DFXevdmu14F12vQ3Q1MHKDdEBhzTG3OnZ/PvVLQdZufFosRIkNYVlbWr1+/Q4cOHT9+3NLSUjrGVjupJC8vLygoaM2aNfb29qg4iXSePHmCvGq//fbbl9S+fv36unXrHBwcZObTtmnTxsXFZdeuXY8fP66pqfnzzz8TExNFIlHr1q0BoL72uCTx/PlzAGjVqhVaT6A93K1bt+SRuWHDhnreDXkov+z/qJsxBYBNpb7sacaN+Urb8Y8fP/r6+kZFRR04cKDOzM+cnBy05x42bBi5v48Y+fHw8JB4g7S0tIYNG7Zly5Y6b/P6KS8vR3Na+vTpQ3oC8MWLFyXLPj6fjzIAUlNT5ZesIEOIZpdqa2vzeDxSBErD51QdHtRvh6meOo3qYGik4XqZ7RoEbiF099vsOXd+9EoUir6XnNEWN4QEQfj5+XXo0OHQoUMynkMajWZtbb1kyRI/Pz+ySuLqRxJ1+PHHHxsSJ+dwOA8ePNixY4eLi4uenp608uPHj1+9evW+ffsyMzNR98Lt27c3w0to164dAMTExBAEgdrWLFy4UB6BKM3n+vXrJCn4fwQFee9s2xnQaeoqND11tUljRnt6esbHx8t4BSSIxWI+n3/48OHg4ODaEabCwkIrKysA6N+/v6JDJpjGcuHCBSqVSqFQ+vTpg9aF0rd5jx49Fi5c6O3t/fr16/rvOx6PhxJQLS0t8/LySNezsrKSyWRSqVSU1Obu7g4AkrQGeRAIBNevX09MTBQKhdXV1SR+Rbt27QoAYWFhZAn8jEj46dfF97saq9OoANCJxbhjbXns4LVVvq+3B71LzP7eapBa3hASBDFkyJDjx48TBPHp06cv7bdat24t2W/J6e6rk7S0NHSLjh8/vmlO1/T0dB8fnxUrVtja2u7evfvq1atHjx7lcDgoUNenTx/Sda7N8uXLAWD9+vUEQaCBqG3atGly8hufz2ez2RQKpaCggFQ1P7N8nAvyckt/0JqamiNHjvTw8Lh79670kr+qqurvv/+uqKiobQgrKip69+4NAN27d1dQsdp/CC6X6+/vn56e3tKKfCY8PBzdyxKLkpubK7nNmf+eH6upqSnxCcnkPAuFwkmTJoGC885Ql/BTp04RBHH16lUAsLOzI0UySqZLTEwkRZqETZs2AcCSJUvIFCoWF/yx7mkPk1Z0GgDYqKsubKX14dQRMi/xjdFihlAkEkmWRTU1NbUT/blc7qNHj/7888+xY8fq6+tL3y1MJtPR0XHt2rUhISGk/Ebn5uaivdTQoUNJ96pxuVw1NTUKhfLhg8JTjWUmwKFNdpMTPlFz0U6dOpGn4P/ZsWMHANDp9MOHDycmJnp4ePz0009oVye9XbCysnJ3dz9+/Pjdu3cvXbr09OnTq1evRkdHS+TweDxUUNW+ffuPHxXYpOab4kv7ZoIgHj16VNsDWVraMvVeT58+RQud5cuX1/kEPp8fHx/v6enp7u6O7kFpLCws3N3dPT094+LiUBmPlpbWixcvFKfwyZMnAWDMmDEEQXA4HDabTaVSc3Nz5RSbn58PABoaGqSHrlFP/LZt25JY7FF0aPcLa9MubAYAmDBUzli2erhhNVnCv02+lTpCe3t7mRibzBOk91tU6udk1wEDBoBUSXs9jrV6KC0ttba2BoC+fftWVlaS9IL+xfjx4wHg2LFjihBOEAQq5OfxeHw+H3l3UWBj2bJlALBhw4amifX09ASAOXPmkKkrQRAEgfoFU6nUXbt2nTt3LjQ0NDk5+cSJE8S/vQIy2wXkFdiyZUtYWBjyCgiFwsmTJ6Mfgubxn7csN27c8Pf3v3HjxubNm5HfIigoqE2bNmVlZQRBzJ8/PzExMSYm5ocffpBpZYkSu5YsWXLu3Lm3b982j7Zv375FasycOVP6Z3r79u1Hjx5NSEio7Xp5//59QEDAqlWr7O3tZXxCdDqdxWJJr4EUQWFhIY1GU1VVRSN8Ufa1l5eXnGJR9ilZPS5kkA6IyE/ZJZ/XvcwcNVkAYKZKj+luUrB9PSmSv2W+CUMoySiRoKamNmjQoI0bN968ebO2s6ukpOTmzZsbN26cM2eOTBdsbW3t0aNHb9u27f79+w2xahwOBw3w69q1q+K8at7e3gAwcuRIRQh///792rVr09LS0LhONzc3+KeQHGW+de3atSFyfH19nz175uXlFRoaio5MnToVAE6ePEmuwlevXqXRaBQK5fjx4yKR6NChQ9XV1efOnYuIiJB5JvIK7Nq160teAZQ3oaur2wy5SN8CVVVV/v7+BEHs3bsXmZagoKBhw4b9/PPPxD+GUPLktLQ0Pz+/ZcuW2draovYCEvT19V1cXLZv3x4ZGakgVXNyciSddaUNnkAgkNyzbDbbwcFhxYoVPj4+tfOueTzekydPPD09XV1ddXV16XT61KlTFaStNGh5jRq2oUb28qfm/fbbbwCwceNGMhSUZeXKlQCwbt06+UVV3gpKtbUYpaMGAIZ02oNuxp9W/SQWKiQ//5vimzCECLTnW7BggZWVlUzbdYmHJD4+XiwWi0QidAOHhoZmZWWlpKQcP37c3d29TscaalKTkpJS+4o8Hs/Z2RkATExM3r9/r7iXVlRUpKKiQqfTFeShQnspPz+/27dvX7p0Ce2VCYKQbBAbsglIT0+/d+/eiRMnJE2bjI2NAeDly5ckqhoWFoZW+n/++SdBECdPnjx16lRISAgad1W/VyA3Nxc1upN4Bdq2bctkMh8//krG6XfD/fv3N2zYsGHDBi8vL9SMMCgoaM+ePePHj09ISECGMDQ0tPaSTtoDKZ2VNnbsWEXoWVRU9KXcperq6qNHj86aNQuVQEjTrl07Nze3Q4cOPX36VNKtMCAgYN26dREREQDQoUMHRWgrw/79+wHA1dWVIIji4mJ058rZGXHQoEEAcOMGmY0jJDx48AAATE1N5WxcxY15nNavwzR9DQDQoFFvdGmbO99VzFOKBOxvyBBKU1BQEBwcvG7dOkdHRxaLJbOYTUlJ2blzZ3R09MOHD2Wq/aTj8PWXNwiFQrTjMTQ0RL00FYr0MpNc8vPzV6xYERER4e/vHxwcXFlZqaqqSqPR/vjjj5UrV7Zt29bZ2fnUqVN1LgWkefLkCZo+g5oiZmdnA4COjk4TvM1fIiYm5ksRo23btiGbLYHNZg8cOHDDhg1f8gpcu3aNxWJRKBSFLmK+cZAhzMjIGDJkyE8//XT79m3Jt13SArd2bn1WVtb58+dXrlypiE6VEi9Lt27d6rcfhYWFN2/e3LRp09ChQzU0NKQ/fRaL5ejoGBQUxOfzfXx8hEKhgYEBALx6pfDatczMTAqFoqGhQVaTJqFQqK6urrikszdv3mhoaKDEdelfuUYlFVYnP89wsFrcWgsAmFTKhY6t309zJr334TfLN2oIpREIBJI9n7m5OfppPnHixO3bt1++fFlPRU5VVVVERMT27dvHjBkj0xSUzWajQc/a2toKjb1L+OuvvwDAzc2tGa61aNEi1NdNJiHTyMhoypQpBw4ciI2Nrd0f/PXr1ykpKR8+fEA5mefPn4d/sgZIITk5GX0Ks2bNqjOwLxaLX758efLkydmzZ3fq1ElacwqF0qVLl7lz5545c+b169eSU9AA9KNHj5Kl5H8OZAgJgti2bVubNm0CAgIGDBjAZrOl3z11dfXBgwdv2LDh+vXrCvotlsDn80eNGgWN97IIhcKUlBSZPAB/f//AwECUSTdnzhwA2LFjh8J0/z/SNbioi++kSZOaLA3lb1taWpKn4P+RuKDbtm0rs5hgMpkODg5r1qy5fPlyTk5OPUJ4aakZg3puNdEDABoFjlkYZv8wUFik2K/KN8V/wBDKkJ+fX1VVdeDAgYSEhFOnTj148KCBJ0qn26DOhxoaGpJ+84omLS0NALS0tBRR+ioDajpFo9H8/f0fP37s6ek5ZcoUmRgbnU63tbVdsWJFQEBAnT+OqBKjUeWP58+ff/Dggbe3d1BQkMyf0tPT27RpAwDjxo1rYHVKeXl5WFgY6lcn7RVo166d5DlkhXD+u4SEhKB+6DU1NT179kT7folRqTPQIL1ZJDdHWiwW//jjjwBgYGAgp5eltLT09u3bBQUFEg3RaN/evXuToelXQA3358+fTxBETk6OpFFq06ShxsWK6P9SVlaGEv369euHXNB1JhVKPndJrpn0kB9B3qes0fYH2hlQASgAf5rpZ43sx89RLi/Lf88QyklBQcHJkycTEhJQnsXFixclf1J0ITYqfb13755Cr+Lr64uKl0+fPi3zp3puEukoLPKForlR9+/fb/ilc3Jybt26tWDBgoMHD0o7VPPz81FAaPDgwU2rAeXz+TExMQcOHEBt+STHFR18/c+RlJRU27bl5+dfv359w4YNgwYNknESsNlsJyenXbt2BQUFyT8aZdWqVQCgoaERHx8vp6ja1NTUaGhoUCgU1KlfoSQlJQGAoaEhqnbo06cPADR5pDOaaUq634LL5To6OsKXE/0qKiokfZtlOn78f8jP2bPhLoN8OrRiUCgAsN5IN2NgD97b17Wlfd8onSE8c+ZMVlbWzp079+3bBwDTp08nCEIgEAwdOpTNZqOcaQWB2pV9qaCKFBo+fqioqOjGjRvr1693cnKS8aTp6uo6OztTqVQajdaoKQF37tw5cOCAh4fHH3/8ITF4ZWVlyNGkiJ5YxD+9b6QXNEpLTU2Nqqqq9CCz2oFhmc0iWg8hRzrIEWEiCOKPP/4AAAaDIck6Jh1UKkPuRDAJUVFRp06dEovFS5YsSU9PR0s3lIe1c+dOAJg9e/ZXhdTU1Gzbtq2iouLvv/9+9OgROohEkTXIAsHn81Htv7GxsfTK4M6dO+fOnavdFg7FHc6cOTN//vxu3brJrIPZNCoALGillW7XuTrhKVlKiriciuuBBTs2FB3YyX0aRZZYRaB0hpDH4926devAgQMyvkrFJbNIiI2NBQBTU1MSS1+liYiIQIV3mzdvbtSJ0lFYSVEzynGXzrz9arOSioqKsrIyHo8ncSJxOBy0aO3YsaP8k3fqBOX4oQWNkpOZmdm9e3eZQWZt2rSZMGHC3r17Hz9+XHvqdVFRUUhIyM6dO4cNG6apqSl9IpPJtLe3X716dWBg4Fd7QRw/fhwAqFQqufNsZfD390d+BQXJP3HixKVLl86fP//27dtffvkFAFavXk0QRGpqKgDo6ek1xKsfEBDw7NmzX3/99cqVKwRBFBUVIc8qiUMCxGLx7NmzAUBfXx8ldUscMC4uLujj09TUlLRTqL1fRENVUdBER0dHV1vbiMV817t9VThpoaKalMTMwT0zHKzSbMzTbC0yHK1yf5oq4nyj7Q+VzhDm5eVduHABeUGl2/Q1QzKLWCxGBQkJCQmkC3/x4gVqarx48WI5RWVnZ6PhU4aGhjL1ZyYmJq6urgcPHoyLi/vqjS2dN6E4d1Z6ejo0V/D1P4H0bxzKtKzDIebjk5KSUlxcfOjQocLCQonXrgkRJoIgrl27hr4nCtqrSSgrK2MwGDQarbCwkHThb968WbVq1b179zw8PO7duxcVFQUA5ubmZ8+e/emnnzp06ODu7h4REVF7MSHDkSNH4uLi9uzZs2vXLoIgbt68CQADBw4kUVU0vU5DQ+PSpUt+fn4XL17cuXMn6lLp5eU1YcIEFI+XQKPRunfvvmDBAm9v71evXqGF+M2bN48cORITE3Pw4EH0rmZdIc2tIiwuzBjQ/U0vs9VtdZy11Zy11bwtDdP7dcxdoqhBGXKidIZQmo0bNwLAsmXLiOZKZlm4cCEAoPqEqKgosppZS8YPTZ8+nZRqB+R1QSuGL4UZJNXQAQEBtX+YRCKRq6srABgYGEjneSqCbt26AcDdu3cVepX/KJLiSwcHBwaDIfn4tLW1xWIxqkBF/8ogHWGSybSSNqje3t7ID9E8beVRvam3tzfpkjkcTklJiVgsFggEIpFIJBLZ2Nigr5Z0rpbMYqK2d+fZs2cZGRnv379H7X5QL1BSqt0RqDchg8G4c+cOQRAPHz4MCwurPew3KyvrwoULK1eu7Nevn/TnDgA6OjqjRo06duzY4cOHeTze6dOn0btaO6ugyRTu257er+Oatjpr2uqk2Zgn9TTto84MtTLKcLSqef0t9r5QakOIemkaGRmhbzMqAVZoMsutW7cAoGfPngRB7N+/v+FFUQEBAQ8fPrx69aqnp6fMnz58+IBKpIcPH05KEqBYLEZ1DrXT37/a9OD48ePo1wF1u9DU1CQ3NFInaEGzdOlSRV/ov05lZWVERMTOnTt/+OGHmTNnEv+YwDoNoQySzaKDgwOdTpdeDwHAihUrFK49QRD/NOf74YcfmuFaaMAng8G4cOFCnYsJaEBDVzQuo3YeddPw8fGhUChUKhUFcR49erRy5crQ0NDDhw8fPnz4S2dJt1OQdB0ZNGgQWun6+PicOHECAFxcXEhRkiCI7AlD0mzMrdVUk3uapdmYp9mY7zHTX2ekk963Q+m5r3/Zmh+lNoQSXyX6sW6GZBaU+QYAGRkZhYWFe/fubWCqal5eHto+ymTBFBYWdunSBUgdP5SSkoLWB/U/DTU9WLt2raOjo0xTUPQaWSzWw4cPSVGpfmQWNJgGwuVyAwICYmNjAwICGlWoXlFRce/evW3btjk7OzOZTDqdrog0qDrJy8ujUqlMJlPRV0QWl0ajBQYGSh9HPhLkeUZuGAnSPeLRclAkEmlpaQGA/Bm5BEEEBwcjF/TBgwflkfPhw4fAwMBz587Fx8eXlpZyOJz8/HzUYZWsdzVrlF2ajXlnFuOtjTkyhEcsDJa30U6zbVfs1aBZjPzszE8/z0u365Rm2y5rtH2p/xlCpMBR20ptCAmCWLRoEQD8/vvvhMKSWcLCwpycnCTxapT5tnbt2uDgYNQ3siE8efLk6NGjnp6eFy5ckDSwlx4/JGcLKGnQ8nDatGkNP0Wm6QHaC/7xxx9kqVQ/MgsaTHNiZ2cHACgxRCQSPX78uCH7S3lAOa6XL19W3CWCgoJQO9yvvpb09HQ/P7/ly5f37t1beqMMAHp6ek5OTgDQunVr+dPRJalwW7dulVNUnaCkNrJynT4um5VmYz5WR+1KpzbIEM411Py7vWHGgG6Vd29+9fSalBfvHLpc6WKMzk2wNg3tZfFx+WyCvC5XMii7IUQtqaytrQnFJLNER0ej9EuJB3/q1KmoJb+amtqXRq/V5uPHj9nZ2ZmZmenp6SgKKD1+iJT1pgTUwqO2D7bhoGJ86RlpWVlZtXvZkIj0ggbTnOzZswcAkKO1sLBQRUVFMrpBQezduxcAZsyYoSD59+/fR90Zd+7c2agT6xwphXaEUFepbsNJSkoiKxXuS6ByMrKyBTlPHmU4dAnratRHnflLWx13A00XHbW3NuaZA63F1V/JNhILBVkj+j63Nu2nwUSGMLBTm5kGGhkOVuVXzpOiXm2U3RDW1NSgrHE0xEc6mUV+ajcV8/LyAgAqlYosrgQqldqtW7cFCxacPXu2gVNykChFjB9C7c3i4uKaLCEuLk7aVzlt2jQAUFx5GfHvBQ2mOXn37h0AaGtroywztAdSaFlnRkYGKCyvTZJ9LX/IOTs7e/DgwQBgbm4uE1nU1tYeOXLkli1bbt++/dVeEJKZ4a6uriQ2/pUhMzOTxHe1uLh4evfOf5nrv+plFty5zf2uxmm25hn2XbixX592wo2NzBjQrbYhTLMxfz9xqPy61YmyG0KCIFCpAHK7SyezyEntpmLS44eIf8J+qIWYTIxNEoGXTlUvKiqaOXMmCgTevXs3PDzcw8Pjq620G0thYSGFQmGxWPJs4CR7a9RhRCFDtP+NzIIG05xIZ+0eOHCgsX71JtC9e3fJ0kosFpMVHZdkX7u5uZFiclD8/unTp9KxA5khOdKbxcePH8vcd5KuTEOGDCF9ZrgMqFsbSkaVk5CQEADoo85MszF/2dPMSZO1umfnBjasKQ/0Tbfv/NzaVFeF5qzNdtZm99dgIkOYbt9Fft3qBBtCws/PD/6ZmSmdzCKPzNpNxSTjh1B1kQw1NTVRUVF79+4dP368TASewWDY2dmtXr36wYMHRkZGv/76K0EQXl5eJOY6SxMcHAwAgwYNklPO4sWL4Z/SfpkNooKQXtBgFM3Dhw/v3Llz9+7d48ePr1u3TuK1y8rKolAo6urqTeul10B+//13AFi0aBFBEDt27Gh4Tpa3t3d2dnZgYGDtiUi5ubkovD1s2DBSdkWlpaUor6e2tI8fP0oWwTLTddTV1SVVSenp6agTZJ8+fRQ0M1walCW7cOFC+UVt3rwZABa00kqzMffr0BoA2rVrd+HCBdTHvH4qQ298aUeYOUxRnWaxISRKS0vpdLqKigoK1Mnfxql2UzHJ+KEGZpnXHrwHAEFBQa6uruPGjUtKSlKcIUQTRJs81F7CnTt3AKBHjx5ErQ2igpBe0GCagRMnTuzbty8jIwM192ndujXaRaHf7pCQEMVd+vnz5wDQqlUrgUAwb968P/74o4FrrPj4eFTnJ1P1KLln+/btS5bJQbeAg4ND/U/j8/mxsbEHDx50c3OTRBal6dy5syIaCNQmMTERvavy74ZR0cgxC8M0G/P1RroAYGdn5+npiZKq6kdYWpJu17m2IUzv17Fgh7y/S1+CWvt9Vza0tbWdnJyEQiH64o4bNw4A0MaoCXC5XBcXlxcvXnTs2PHmzZsaGhopKSmjR4+uqqqaNWuWp6dnQ4S0bdt2ypQpBw8eRPnNd+7c2bJlC3JA/fXXX6tWrSIIomnqfZXo6GgAQKmA8jBkyBBtbe2kpCQ03W3s2LEgx7vaEFxcXBgMxsOHD4uLixV3FQyCy+VyuVyBQPDu3Ts7O7t27drl5eWhOhY0GysoKIjcK2ZlZQUEBKDHPXv2tLCwyM/Pj4uLa9++PZPJlKlqrR8mkyndiK66unrs2LEvXrywsrK6ffu2TF/yJvPkyRNowK1Ep9P79u27YsUKf3//jIwMScTExcVFTU2NSqXu3LlTpqGBgujRo0f79u3z8/NjYmLkkSMWi+OePgWAnmqqAJDA4QHApEmTJk6cmJ6eLhQK6z+dpq2jM28ZQ4XuqPF5r6yrQrVWZ1LZbN1Fq+VRrB6wIQT4t/EbM2aMiopKTEwMl8ttrByBQDB58uTIyEgTE5OwsDBDQ8P09PQRI0aUlJSMGzfu9OnTjbpdEZqamiNHjvTw8ECeVUtLS0dHRzSGnnQEAkF8fDyFQunfv7+couh0OmpXgd5VOZcX9RAdHX369OnXr19funSpW7duIpEoaOe2iqCL/PS3IBaTfjkMIisrq2vXrj/99JOWllb//v3R54uMHzKE165dE4lEZF2uoKBgxIgR06dPv3LlCjrSs2dPFRWVUaNGPX/+nMFgREZG8vn8r8opLCwsKyt7+/Yt+nICgEgkmjFjxuPHj42NjW/fvi0zuFQekDlp7K3EYDDi4+Pnz58/a9YsOzs7sViM1qYAUFpaev78eRLf1dqMHz8e5F7EvHr1qryiwpihYkinAcBzDg8AdHR0CgoKhg8fLtO1sU505i1la6pzxeJlGYUvuXwzNnNa715GZ4NounpfPbeJKGin+d8iOztbOrARHh7etPxvVITbqlUrlPkpHeiWP2Ty/v17V1dXgiBqamo6duyoCNcounW7dCEnIo1G+6JwI4/HU0QyS2xsbHh4OMobKi0tRT/BtnraEV2N02zMMxy75i6eUfz3gaqH95Rn1naL8PDhQ5CaPYu+9pLxC3JSUVGBhoL16NED5VhGR0ezWCyZVEx1dfVBgwatX78+ODi44R3e0dAMfX19chsBisViHR0dAKh/Im6dnDx5kiCIuLi4M2fOAED79u3RcdQbOSIigkQ9ZYiMjIR/j/xsAp/71OiopdmYR3c3AQBtLa1GuVuFxUVpNuYdmHQAuNKpTcGuxk0RaALYEH5GeiZ1kxGLxRs3bkRliNJRB1L6NVRXVyclJaHHjx49SkxMlF+mDCjYM2/ePFKkybRInjp1KshXnijDy5cv9fT0GAwGmr0eGRk5duxYTQ0NtOnWp9OGaLFXtNH26dDqZU+ztN7t308eXrB9fcWNK7w02SE1BEGk53OO3M3acvmtf1RuZbUCe1h8l4hEIpTij9KYf/31VwBAPnw5qV0vm5KSgvZt7u7uycnJ9cwfloyUks63LCgoOHLkCIopPnny5Pnz5xMmTIiNjZVfVWlevnwJACYmJo09kcfjbdq0KTc39++//5a8q+jGR/F76XmcpCMSiVCuu+SnpgnMnTsXADYZ66bZmB9uZwAAzs7OjZJQFRG61USPAkCjUJa21mqGEU7KbggrKir8/PwCAwO3bNkCJGVMEQoeP4RS5nbs2EGuWOKfxMtTp06RJRDFzM+dO0eQPUPn/fv3pqamADBw4MA7d+7k5eXt3LkTBX66shm6Kv8aRaRKpdiqq85vpfV3e8PYHiZpNuaZI/p+XDm31Nur+nmcqIa3yvcVa/Yd1o+3wS1EY26o1k+ht14UkKKn8jBv3jwA2LZtG0EQyJtnbm4uZ56wSCRC30lJveyHDx/Q5/7DDz/IzD8pKCi4cePGxo0bhWWIPQAAIABJREFUhwwZgnK/JbBYLEdHx19++eXKlSvR0dEaGhq+vr4EQfz5559Xr16VR8Mvcfr0aQCYOnVqY08UCATp6eklJSXIKTV//nz4p5sMan1lZmam0OzrBQsWgHz9a1B9COopM9dQEwA8PDwaJeHRbz//ZqQDAG0ZKlP1NQSKd+couyEkCCIqKurEiRMJCQkAoKWl5evrm5mZKY9ARY8funbtGtpoki4Z5XaS6CM6fPgwAEyaNImQ2iB+tY3OVykoKEBV//b29mj2YXh4OIqhbprpmjGgG/LJHG5n8KOhZjc2QyYSrqtC7cZWnW2guay19inLVsvH/MyeEQwWQ8EtBNxCYLwPdHNlz77zLLOMhLdAaUDzhmxsbAipPOHnz5/LIxO1KNLS0kJyCgsLO3fujFY/9ccaJPOHa4+U8vf3nzNnjoODQ0lJieIMITJgMhMhmgCqbO7VqxdBEGKx2MTEBBTcSlDOWmpUNMKgUF71MkuzMbdRU4XGN9O4P9l5tI4aAIzQZm/q2blpmjQKZTeEJSUlN2/e3L9/v4+Pj4qKiiRhrHXr1k0b1S0SiVAjFQMDgzdv3ihCZy6Xy2azKRTKV8elNgqxWPzgwYPdu3eTuN7MycmRDr4OHToUANBivMmUl5fb2NgAgLW1NYoYxcbGog8OTdQiREJeWmrFjSsF29e/nzw8zbZdgrXpactWy9po22kw2f8es6dKpVDb2MDgbaDb/rMhHHsSzJwoM0IGbntCxnugLMj0NJC/6R0aKsJisVCskcPhoAzMJnTWLSkpuXXr1u+//z5q1Kj4+PhFixaFhIQsXLhQcYYQ5Xg/eSLvV0jmXV26dCkAbNq0iQwd64bP56PeOmkNa3ElA8q9t1FTTbMxf9PLjEmlUCiURn1eYqEgw9FqoCYLAA63M1BcyYQ0ym4ICYIoKSm5cuUKymUaO3Zs7cF7LBZrwIABqCnoVwt6iouLra2tJWtYBYGS9Ly8vEiRFh8fLylw9PX1JUssQrqq7NChQwAwefLkJkvjcrmoiZelpWVeXh5BECkpKejzmjlzZp0BeWFxYdXDe8V/H8hdPCO9f6fUXuZHLQzd9DWsWIw2jH8S2BzXg6YxDNkBQ3aA3WowcwK3ELr7bTzNolFIh4FDQ0OR0WqaqKNHjwIAjUZDlWc8Hg/leVpYWMjZWTcpKQlV4k+ePNnV1VURhrCiogINcyClFwya63ngwAGCIMLCwgCga9eu8ouVRubGQVf06N8ze6xT/uZVZefPVD+PE39tEDcCVeXPa6WZZmN+pVMbALCysmqUMjWvk9/ZmGurUAHgcTfjiuuBXz9HbrAhJO7fv486nEk7susf1T1lyhTp5rkTJkxAETsejzdq1KjS0lJ5GnU2BJRO1tgQ9JeIjY2VBEfPnDlD7pxxdGMsWLCAqJWd21j4fP6YMWMAwMjICLmvJZHC2hGjOhELBTWvkop8TyWs/ClrtP2T7iZ/9BvJtF8Dky6AWivosxT6LIUeM5EhpM4IEYqwJWwEFy5cgH+msfP5/N69e//6668N+VxkuHTpEpVKpVAoZ8+eJQhCJBIhE2toaJiaWkeiU6OQGMLs7Gx1dXVFGEJkrvr370+KNFQr5eTkRBAEn89HiULkeptWrFgxe/ZsySd16tQpANBWoc000Nhnrh/e1SjNxjzD0SpnzuTCPR6VYSHC4i9GN5ydnQFgWwfzVBuLTca60Pjku7KAc3etjADAkE5LszHnZ6bJ9doahrIbwqdPn6K4ej2dMIuLi2/evLlhw4aBAweiMaQSdHR0fv311+HDhw8fPvzdu3c8Hs/R0bEZ1C4qKkJLTlLa/MfGxtrb2+/fv3///v2urq7kGkJJExBJ2xFNTc0mzPcQi8WzZ88GAH19fTQ8TxIpHDRoUNMsq6Ag78WVENaMG+AWIuMaBbcQ8xX3myBTmamoqEC16gUFcqUapaenW1pa7t27F/1XMuSZlLEwBQUFknaa165dS04mf2A6KskgJWmWIIjKykomk0mlUtFW2N3dHQB2795NinDin+Q7FouFMkUrKyttbW1p/179a9CoDhqsFW20T7Q3fGZtitLN8tYu+bxZ/Kc/avKHio4TPajtRzDHnWK6Bpm37QIAqBqk4eRvXrXbTB8AnLXVMgb2UNzoJWmU2hC+ffsWNfacMWNGA8tcUBAeNc9F/ZCQIYyLixszZkyzGUKC1PlhsbGx48ePj4yMjIyM3LBhA7mGkCAI9EaheEl6ejryF6Wnp5eWloaFhd2/3yB7s2rVKgDQ0NBAu+2ysjKZSGGTcdz6hO5+S8YQqs2983c4+YlO3zEFBQWvX79GaWLyF7lKVngonZvJZJI+5Pmvv/7S0dEhMUdawujRowEADZEnBeQIQapevXqVxO3msWPHpF3QfD4fbelMDQyOWhj+YqQzVIttQP9XDjaNAl1YjOn6GnvM9EOtjN7ZmGc4WOXMmXzXY6/arJsUtxD44RQ4bYKxJxjDdxn2d3+V+q5RKmWPGzhdXwMA1hvpflw+m5SX+VWUt7NMTk7O8OHD8/PzXVxcvL29ZfyfX4JGo3Xt2nX69Olz587NyMh4//79ihUrAMDKyqpLly6BgYEK1vr/kNurpVWrVg4ODg4ODpaWlqQIlEa6v5qFhQVK74yLi3v16pWtrW1ycvJXJWzZsuXAgQMMBuPy5cu9e/dGPbESEhI6dOgQGhqKYvtNJmBFr9baTOZQj8//VzNUs1sytpfhgsGm8ohVNi5evBgZGYl6GqAfa3lAGSJeXl5bt26l0Wh+fn4oNkwiWlpapaWlpLeCIwgCtZqTvz2TBOmudSNHjlRTU4uNjc3JyZFT7MWLF5ctW4bm4UycOJEgiJ9++unOnTsGBgZL584u6tmvaw/rTppq0d1NorubnGhvuLCVlq26qgqF8rqaf6Gocm120chXub0S309JTP/jVtj0sI8cThVR/BZiDoKwBrIf8w26V3Vyu5jciAZPovJSQc77FxweAPRUU2V27yXna2wozWNvvzUKCgpQHradnV0TZrh4eXkdPXpU8t/hw4dzOJzKyko7O7tm2xHKzIGTh7i4uOXLl6PH586dO3bsmNza/Yvw8HCo1bDm5cuXUVFRGRkZ9+7dq//0I0eOAACNRgsMDCSkIoXGxsZZWVmkaFhZLfS4/LbzLw9bLQ5z8IgOjCVz0LEyEB4evmPHjrVr16amplKpVHV1dSsrK3d39+PHj6ekpDQtD1kytkwRmzaCIPLz81F8gZR+FxJEItH9+/f37dtHokyZiceoEZqc96mk4kjiZf35558BQFNT89mzZyUlJSdPnhSLxXv37uXnZFfcuFK4x+PDjLFpvS1Seppd6thmvZFuf3WmFu3f+weNttD/Z7Ca8tm54hYCbiGGboGFezwqblzh57z/qlacR+HJPc1oFFChUFJ6mnGekNOc6KsooyEsLy+XdGxqbB42QRDv3r3buHGjdOn9hAkT0NTAy5cvjxo1ikxd6wUVrn7VkLQ4fD4fLfAXL15848YNVEd4+fLlCxcu3Lt376u/krt376ZSqd7e3kRdkULMt8OtW7cGDBiAVi3SP48GBgYuLi7bt28PDw9voNUpKChQU1ODL4wtIwukLSnxBYIg4uLiPhfwEISPj4/0Wll+0IYYuVt9fHwAYPjw4U2W9vTp039VHP2T1MZgMO7evVtaWjpr1qw7d+6sXLny9OnT0kttEaeq+nlcqbfXx5VzXw+yPmZheL5DqzmGmj0NjGn6naD9CJgcACZ2YNgVnDbD9Jtg3B+6u52ybC2JLEoaWYhrZOP6osqKj8vn+HZoDQDWaqpptu1E5c1Uy6t0hpDL5Q4cOBAALC0t5cnDJr0nUxNYv349AEg2c98s169fp9FohoaGkl9GySRSSeZt/UgyGiSLVoVOdMI0AaFQOHHiRAAwMjJKS0uLj4/39PR0d3c3MzOTNoo0Gk16syj96Z8+fRoFgEUi0bFjx27fvi3/OLD6QT0Fp0+fToo0hWZfo4nHqNswmhxHp9ObsI4nCOLt27foZpRUHKEmyRKnSwNJS0vz/dsrJyjg5eZfFtgP03C9/P+N4ER/0GkHQ/+U/uhNVFXG66pvMta92LH1m15m6X3+1fWQE/0wY0C3tN4Wa9rqAMCPhpoZA6xqG0sFoVyGUPpelbN9zLcA6pFtamoqTwl8cnKyn59fYmKiv79/dHQ0ieohoqKiUKrtjBkzfvvtNycnJ5lJpHp6ei4uLjt27Hjw4AFqE/Ml0KR7tGglXU+MPIjFYtRiTU9Pr/ZOPTMz8/z58ytWrOjbty+dTpf+9HV1dUeNGrV169bIyMiRI0c6OjryeDyBQGBvb98MaqMZYRoaGqQU/MXGxvbv33/v3r179+6dOnUquYbw6tWrFAqFxWIdPXq0tLTU2dl5+PDhbxtf856Tk4OWJi4uLqheIigoCLmgT5w40ShRISEhAQEBycnJ58+f//CpkD37DriFwLgzMOkiTL0K+l1gjBdlwAaz9gN6qzOZ1H91glWjUe00mEtaa51obxjXwyTNxjzNth2aPjhEiw0AB9oZpPVpn7f2i8n85KJEhlAsFs+ZMwd51V6+fNnS6pCASCQaMGDA2rVr5Rxt4enp+ezZs3v37qGmoCSSmJiIMlnQ+HKEQCD4X3v3GRfF1TUA/Mz2nV1g6b2IDREQQVHEhlQFBYwYTYxpapom8UmeVBNNnvRuNImJUWNQYySRbgEEFBQ7CoIU6dLrsruzfeb9MMprFCPgUvf+f/mwLjt3zhqXs3Pn3nPuWnl71+XCunXr9u7de1efiu47hX/99Zdug0QeHl1lG8fx06cfUB9ZpVJ1Xyze+X9/xYoVISEhW7du/fjjjwctEVK3q+0fPXq0H8dmZ2e3t7cnJibSLSbOnTsXFRWVm5ubm5u7adMmna++puurRUVFNTc39693bmtrK30/ZebMmfTaiIyMDPpO4SeffPKQ4b296yT/0YMQ8i2MDQaHOTDnHXgsRbAqMfupZ8pnTiiZ6njU1fYjB9NIE+E4HvuudnSmLOZCY8E6S6O94y1NWUwAyHKzu+HldGPGBFl2JjXwhS30KBHm5+fz+XwDA4Pz588PdSy6QZLkG2+8QT+urq7etm1bPwaprq6urKz86aefWltbv/jiCx2GV1ZWRu9OWbp0qUZz334OVVVV+/fvX79+/dSpU++6t2Rvb79y5crvv//+o48+ondYD0TzKeQh0QWD2Gx2P5q31NTUHDp0aOPGjYcOHQoJCRGLxfPnzy8tLR20RPjBBx/A7YIPfVJfX79x48bLly+XlZXR61YGdGq0tbWVLmEfEhKSnZ3djxFkMpmfnx8AuLm5tbW1URR15coV+nvqSy+99PARhoeHYxjGmbsJX3GYE/YDHv69+brU0yXtFEWRckKed6HzwO6m9zZWhfnd8HI6627/81iL562MfIQ8LuPuLq1sDHvO0mjHWItcd/sbXk4Vc9172U+NVCq1Ukk/gh9VibC0tPSuLbcymSw3N/fy5cv0L+JTp06dOHFiiKLTPa1W6+3tTT/Oz89fvXp1PwZpa2tLSUmRyWRHjhzRYaOMmzdvOjk5AUBgYGDv552kUml2dvZnn302d+5cen0Njf64/i94Hqnpc5kSZEDFxMRgGNZdBeZhhISESCSSS5cuLV26dNASYX5+PgBYWlr+y3e1Hm3atGnz5s1paWklJSU7duygKOrixYvdPZJiYmLoJ3WlpqYm5qvP6cnkvoZKowunOTk51dXVUXd8T33sscf6d315J5IkzUxNAeDwlEmf+a+cbD0OALb/0HOxRnVzo/Rkeuv3n918eln5zIkFno7PWRkFi3BfA56IxWD+s52WBZu5wAj/r63xwQlW3f3Umt7bKP77gPJGSffFovzKhdrHwm5MH1c+Y1xFgFf7bzsobR/+lkZVIrx+/fqXX37ZXSiovLx89uzZX3755XvvvRcYGEgv7BxNtFqtm5tbcXFxcXFxYmJi/xLhQGhpaZk0adKdMzB9pdFofv75548++igqJNjf3oYBwGdg16c6EhdRIexhJCkpiS7S+/BtFqjbiZCiqA0bNgxaIqRutxHOycnp64ElJSUlJSUxMTH9OLav5PmXK/xczfg8Loczffr0N998Mz4+vk/L/QoLC6dOnUrXZqurq+v+nvrw+68oiiotLYXbRdFueDk5cFkAcOXKlQceSKrVH7z68vdeLr+Ns/zA3nSJiQAAHjEVvmQlmmXAE/xzewaPgXkLuc9aGr5tZ/Kxg1m+p8OPkxzK165ofPOlgmnj/pxgTZ/9tLv9Mc+xDa8+2/s51VGVCNvb23/++efuP65Zs6a7m/Pnn3+u8+1xQ47uovnuu+++++6769atGyaJsKura9q0adCvLgF3+uWXX7799tu8rIztzhYT+BwA+G2cZctXH+owVORh5Obm0jsc3ntPNw3EU1JSVCoVRVGdnZ3x8fE6GbM3Xn/9dQB47bXXBu2MfaW8UVIxf8o7diYAIGSz7swNd/YffuBaAXpV3Z09w+lvHg+P3tERIsJveDmd87AHAAGO977MLHEup3yWyw0vp8k4BwD+mGB1w8vphrfTDS+nzMl2XzqZ9dhPTchkzDXkB4rwgxOsjk6yXWgsoBPhz2MtXrYWlc92lRxN6GUAoyoRfvrpp3/++Wf3v4aZM2d2b1rKzMzUyTz4sKKTqVHdurOfeH19fb/HuX79ekxMTEFBQWxsbPHy0BetjABglblBVfgg1StA/l1BQYGxsXH/7q4NNzk5OfSc4VAH0jPVzZrKYJ+vncwZABjAB/amB8P8N2/eHB4eThfg/v9ba2y2t7f3yy+/fO9as24EQdC7J11dXek7hTrxwgsvAMCbtsZ0HoK+t+Bu2vz6NU9HFoYxMSjwdCyfOaFyoS9x7rT47wNN722sWRZ0w8vp6hSHgxOs/mtrPNuQj//zzuLnjmZ3JcIbXk43n4zq5dn/8eVipHvrrbfu/KOxsXFHRwddU7u9vf2ufzSIzmm12scffzw1NdXGxiYtLc3a2rrfQ7m4uNClf9zc3Do66gPzr/7YKE7rJDbX31TdKOGMm6i7qJE+q62tXbRoUUdHR0REBN0vSbcIgkhISJg+ffpAFPy7l6+vr6Oj4+TJk7u6uu68Mz0caDvaGzY8lV5e/UZ1Cwnwjp3JE2NsbL/54dEJk+gXVFRU5OTkXLp06fTp03l5eZcuXbp06RL9I2tra29v79mzZ/v5+Xl7e/P5fPoTmp2dbWdnd/ToUR3+SqS3cnkKuACQJ1MCAN08svdYVjYFhFJDUZNxDp+BGT+7XrRqDcbj831mGS5dCQDathZFYb7d9YI5Vy+6ncyulxFmbOaxDpmSosrkahec82195/qKFgBo0Wj8DPgAoG7odRW6vuf+EWPPnj0vvviiVquVSqX+/v50bfVRpntfuVQq1WFn+X7o3kkmEol024tRWVJU5uVkxWYBQLyLTfvO/iyORXSlu+mHv7//Q+7buZ9r165t27ZNJ51Vemnr1q30A7FYvHfv3kE777/TSiW1j4X/PdGaz8AA4EUro3JfF3nefVu8dXR0HD16dPPmzSEhIUZGRnf+nudyub6+vh4eHgBgZmam298VUqmUxWKyMKzA0/GGl9NMAx4AJCYm9mmQ+hef+K+tMQCsMjeoXhrw7y8mNWrljRL6YrEq2OeGl1PGZLt7rwhrokN6efbRnAhJkty5c2d0dPSKFSvQFuyBRt9owXF8IBYOVC+e87i5AQCstxbVrlqs8/GR3mtvb/fz8/Pw8HjIph//QqVStbW16bZc57+bOnUq/eDmzZvh4eGDdt5/QapUdS+sOupqS7eojTIR3vAZJ8vpQ2uwe5uqWltb92avZ19lZmYCgDvOveHlVDLVCWcwAIDum91bJFkxf0qQCAeAr53Mmja/3vtD1Q115b4T702EFX4u7Xt622N8NHefwDBszZo1hw4devTRR3fv3l1dXT3UEQ2U4uLigwcP1tbWHjx4cEgC2L9//1dffcXhcOLi4ui9SrqFzwkINMIBIL2TUF6/pmlq1PkpkG5ZWVkxMTEURXU/c+bMmVdfffU///nP+fPnjY2NU1NT09PTH7Lpx79oaWlJSUmhOzoNGpIk6bUkg3nS+yK1zZtercjOeuZGU6eGDDDCP3Mys3j3E9zPvzdHJycnf/311x0dHWq1euvWrRcvXuzo6Dhy5IhEIiEIwszMTLfB0vOi43hsAMiRyAmSHOvsTO/N6CVVRRnZJe5f0wmWlY1o1VojgWCu4a2SVXYclqchzjS1FK18qpeDjOZE2O3AgQMHDx7UVceiYcjOzq69vV0sFkskksE5Y2tra25uLv24rKzMzc0tOjp637599EoZnRPMD5ppwBMyGcVy1U2lWnYqbSDOMipRFJWQkJCfn5+dnX3kyJHeHOLl5VVYWKhSqeg/nj59+sMPP3zttdc2bNjwxhtvXLp0Ccdxc3PzgYvZxsbmiSeeoHfgDI7m5uYVK1asWLGCXvQxxCiq5ZNNtceSnrrR1KDSzDDgfT/G3GLjOwZLons5QHh4OJPJ9PT0JMlbLZAMDQ0XLlxI925LTEzUbbx0IpSTlIqi9jR3AYDvrFl9GkFRkHdTpWlRa41ZTAcum+fh1afDTV58bcxL/3nU1oIhNGAYGLqIDEKCgmz3xmFcXi9H0ItEqNvWfcMQhmGurq4tLS2DdsaKiorff/+dfpyTk5OdnX3o0KHo6N5+UPuK5+XDNRLR3/jSOwkiCyXC3tJqtePGjcvOzp40aVJJSUlvDpHJZC4uLh0dHfQfd+/evWXLFnt7+zFjxmzatOm3334bwHCHiIWFxaFDhw4dOkSXnx5a7T990/DXgadvNJUr1C58zk/OFlbPviRatbb3I/z0009mZmb19fU1NTUKhaL7+Tv7GurQubNnAWASn7Orqatdo4W+92JUFuTRS2ymCrhMXMAZO76vMRivXud04qL1d7ssP/7OIT7LZttvTJFx7w/Xi0QYFhbGZrNPnTrV/dkeZaqqqurr652dnZlMZmVl5VCHo3sYk4X7+d+aHRUT8ou5pKSrH+NI01Jqo4MrfMZV+E6oW7dCWZiv60iHHSaTaWJiIpfLW1pa6D3UD9TW1mZra2tlZUX/sbm5uXuay9raurm5eYBCRQDg5u+/1O/8fl1F8zVC5cBl7RlnabvkEZOXXu/TIGFhYbNmzeLz+U8//fSdZQsXLlyI4/jZs2cbGhp0FXBFRUVjUxOfgY3hsdZZGkq1JPQ9ESoK8q7cToTcyVOAwXzgIffCeHze1Om4nz/Lwqqvx+pFIhSJRHPmzNFoNL2cGhpxJk+e/Nhjjzk6Oj7zzDN3lbEeOKmpqdHR0dHR0T/++OMgnE4wP8jfiM/GsAtSRYdCReSe6usIbd98fPWdjTdLiimtllKpr53OKXl2uSzj2EBEO3yoVKqsrKyZM2eeOHFCo9FoNJoHHuLm5hYUFNT9x/Hjx1+/fp1+XFhYSC8ZHWXoxiYAYGxsvH79+qEKo+towtfvvPViRcs5iQJnMHaPs3QKCDbf/AVgd1fj/HcODg7Ozs5mZmbOzs53tvvAcTwwMJAkSR3Ojt4suGorxH0N+IuMBRItVavU8Hk8enlqL5EyqaryRp709g3CPs6L6sSo2kf4LyIiIjIyMhISEh5//PF+D0JRUNlCqLXUWAucxezbP80RLTMzMz8//4UXXvj7779NTExCQkIAIDg4+KeffgKAPXv2yGSygY4BnzXfkM+fLuSekShOdhF2J9OEweG9P1x++bz48P5jjW2GTMZSUyEA7GnuWkGSBptfc/CewTTqwyzKyMLlcleuXAkAs2fP7t8IGzZsWLVqlVQq1Wq127Zt+/vvv3Ua4LBAd2cDABzH6X/eg0apJi9VisVyzbiWEuaHbxTJlNldci4Dm2PImzRrtuXnP2BMXf6WjoqKSkxMjI+Pf+655x5+NNmJo/ZfvHPSxVKpIQGgXaP1NuAZT5t5V7Otf6coyEtvlxbJVQwMbqo0fVopoyt6cUUIABERERiGHT169M4Z8z75+USN6XNpU97Onr7ptPG6tA8Ol2nJ4bHAbOD5+PhUVVWVlZXxeLz29vYhiYGB4/zpvoEiHADSOuVETialVvfyWEql7PjhK0oh7+FnJCVLG53zBHdqaWkpLi6mH5eXl9fV1fX+2DFjxsTHx5MkyWQy6W9CAxPjsHD16tWamppjx45ptdpBON2vmbXmz6ct+vLiyu8veexuW2W+plJFcjBsvZVR5KTxVl/9jHG4uj3j4sWLWSzWiRMnOjs7H3IodVV58/v/EcuItytbXqtqWVPedEGqODjBZreNsE/j1OScLJGrNRQlYjLrVBqum+dDBtYP+pIIHR0dp0yZIpVK6S0vffX2nyX/+eVUR+VlqUIrUWikDaWfx2Ss2Jan8ziHp/z8fDs7u5KSEqlUWlNTAwDW1tbdC0Td3d29vAZjNkMwLyjQCMcATnXJ5V1disvn//31ZFenNP1I8/v/qQqc1n75/A25GgD2tUjWV7Ssr2g53SUHAFIhV5WXDkLwQ+vSpUsxMTH04/j4+JMnT/bpcHNz8xUrVly/fn38+PHx8fEDEOCwIJPJUlJSCgoKSkpKlErlQJ/ux7TqV2OKJJIu8dV48cX9itbSC0IX+aId+yY5vOztsTYpjWFo9OBR+sjU1HTOnDlqtfro0aMPOVTH7h9ItWprQ6efIX+7s8XPzpbpYuKaTKGurlRcvdj7cQpO51QoVQBgx2FxjUSMvixy0RV9mRoFgIiIiCtXriQkJPR1f1JRnfT741VE202oOw8W7gAALYVyJvdYvtOxqy2hUwZwHfkw4eHhMWbMGCsrK5IkCYIAAHt7e7pNKADQJbYHAT43wIbLnoRzighVrkRhcTKVP6OHPYuahjoi95TsVPrxY8cqZYp6teaSVFkiV1tzmCvNDFaZG9BTo+8QM2uLAAAgAElEQVTWtAEAsFgM49F8iaNDRkZGMpksLi7u0UcfHepYBsRvv/2G43hDQ4POd9rdq5NQ//dAMSGXQ+b74LoMzFyh6JBG2tTqNOfM+OWR219img7UL5aoqKjMzMy4uDh6zrzf5JfOgZY8J1G8bWsCAEwMFhkLciVydyOB4upl3pTe/VqgqOkdTbtJCgDCjAVRgQuwPt4Q1Ql9uSKE25so6MI/vT9K29629/cMlbKHJQZShXZXVq9r2Y1kAoGAXkPIYDCEwr7Ne+gQy9yS6+rhZWoJAK+oHN3/rnv6s4Qvvt1eUlICFKW8XtD+0ze1KxZWh88+//5/t/4V993N1v/dbP+1qStPplSQpJDJUN7zvx5jsQVzA4bi3Qy2w4cPL1u2bNmyZd37XvoqKioKw7CUlJR+318Y5l566aUnnnhi1qxZ7e3tBQUFA3qu1PxWFgODm2fB2gvsfMF4DPhsgJJEBcZOtAtk2zkO3KmXLl36kPeJbiFJAKDg/5fyMADTUkCRJKV98JosAJCfP10dPkcr6QQALgObb8Qf79u3DYi6okdXhFOnTnVycqqqqrpw4YKPj8+/v1hVXkpkZ8hOnVDkX7poFKlRcoAngrpzIGsGAJA2wITFAFDZQgxC5Ei3FJeo2OZ5UPsfWUupzHrGvsRMrsim7Ohrm1jtqqaGIkKVISYyxMQ14tZmcBaGueNcV5w904C/0BjP7lLwbn9op+AcY1wgmBfIdXEbsvcziJYuXfrxxx8DwNdff92/ERwdHT09PfPy8jIyMhYtWqTT6IYLU1NTU1NTV1fXgT5RfadCqdEC0QoCi1tPsXHQqgCgdYC/Ztja2np7e1+8eDE9PT08vA8rzu7CdfXQtDS545xciZwucn2qS77CTMjg8XvzmZJlHKt9d2NWc3uQCN/ubFGv0tSpNF1/HTBa9gTG4fQ7qv7Ro0QIAOHh4du3b09ISOg5EZJaRX6eLPuELDP1TGFRmlgu15JnpfJKxbeAYTD7HbCdAV5rAABKk+gjnMz5gxi+viuul/6n1EJpag5CS5A2QddNjftKTVvpvga2vO16WoekWX1rgYMRkzHLkO8p4EwX8ByNDJrHufo9+hjXzSv403cVRfkYkwkAK7g8YfhSszc+GNL3NMJERUXl5eXFxcWFhobu2LFj+fLlgzCLOCrZiHhcFlMpsIDOqltPqWTA4gGAueGAp4GAgICLFy/++OOPVVVVkZGRdnZ2/RhE9NTz8nPZG22M36lpTWiXdWi0rnzudEM+w0iE93TP4k6knGje8l8ZQexrkdAlRutUmuOdxIym+s4Du42fer4/7+oh6FcijIiIoBMh/dWYRhKEPPek7GSaLDtTKe44J1GkiYkMsbxBdevqns9kqmxnarWqu0YTcllPz+3PPyCkf76JK1ZpKAAAGx8oTYLW65DzGTRfU2hV+wEAwI7DChThAUa4j5DLEZngs+fjcwPxWfMYuIAewWbnQXVVuaKoAONw+VOnDdxtmOHGzs7O29ubfuzu7n5Xa4Lei4qKev/99xMTE3fs2OHi4tLV1YUSYf8Ee5hpSArsZkBxPBg7g9AKimLBJZLPxp7wsx3os9O77M+fP7948WJb236ejuc+VbTuFfjlu1/HsWQaLY+BsThcBh+33rr7gTvi5edyenyeVCgkcQdRIhxY8+bNMzY2LiwsLC0tHWthTuRkEtkniNNZMpksVyI/0kFkiIku7a3qfHwGw9+Ib8pivG1n8o393P2McXL+7eVMZpN4XM4CN9OwqRb3PRmiI93T1Ke6QjQaJjTkQXsZYBi03toPAEYOrqY277OqvIU8jq09PidAMDeQN21Gjxuw2E5j2U5jB/UNDANubm5ubm4AsHfv3m3btm3ZsqXf40yYMKG0tPTMmTM6DK+5uTkzM9PNzc3Z2Xn37t0vvfSSDgcfnkQ4+8vHXN74o1jm/yFUn4SuWpgYwTIZa85QvB7uPKCnrq+vt7a2FolEbW1tLS0tD7M4xXj1c3wP745d25il1xk8Pj5ngfEz65kmpg88UNNYT29/Kpar6CaCHVrtJD4HALTtg1cqspt+JUI2m71w4cIDBw7se/yRJ4FoU6lPdcmPdhDZXXL17WUU9lzWAiPc35DvJuAa2dnjsxcYB4T+4uXjnn7z/b9K6VdprCe+Gjrmg0f6XBAP6a3/n6Y+rqquvEYo0zvl1fJcEP9zA5z7YzA2mIGbzSEvB803EMwN4E5yH6KIR4a6urpLly7FxcX1++ZQRETEl19+GRcX5+/vX1dX5+zct9/ara2tTz31VHJyMgCcOnUqOTn5iy++MDExMTExKSoqOnPmTHed6FHvxSBHLpuxMaYIxi7QNl6VX/zRwtg23l0o4EYN6HltbGzWr19fWFi4Y8eOhy9QzPOcZr1tb1+PYppZApsNIHfhc7Y7mwPABanieCcBAAzREKziHoWJUKPRpKenh4aG9vjTiIiIAwcO7M4vjmMxCgkVnf2YGMww4AUa4YFGuD2XxXEej88NEMwJ4E3x7l4R9XKI00tBjqUNMi1FTbAScFh6tOBW50iZVJJyWH7pPIPDwWfNFQSGYWw2AFAKOXH+DJF9QpaVpmprzZMpjnYSxzuIRjU9Td0JTC5YTQGH2VB3HmpygMUH3FzAY0S98ryJh77Mcz6MyMjId999NyEh4eeff2ax+vPxj4qKohPh119/3Y+LCYqiusu8kSRJ71uXSCSzZs3atm0bRVHV1dVSqXQIFycPpmfn26/ys8385ufT19M+ai/HpNUGRnaq8lLO2AkDfeqnnnrqjz/+4HJ1vGG/l3DfORjZQ8kCjMczCH9k8OMZhYlQLpdfu3btrkSo1Wpzc3OTk5MPHz6MYVijWlun0vAY2CwD/gIjfoARbs7l8NynCoMWCRaEsiytexyZycAm2erF53NAKQryGjc8TapVdKkXaeZx5vYvRSufuXH6ZG5mpjcHi2npKparL0oVktvT1KYs5hxD/jxT4w89P+1kGVMYAJMDNTlQd5btEjHBUhjsjrJgr7i6uk6cOLGkpOT06dPz5s3rxwgzZsywtrauqqq6evWqp2d/ioBUVlZ+8cUXAFBRUSEQCACAzWanpKSsXr3axsamuLhYT7Igjctm+EfOHXfoi1/ZrDqV5jqhMs08PgiJ0Nraet++ffTEQF5eHkmS3XeRBwFDIDR780P5J+8tNb11/96Ww5pnYsCysDJ+Uge13/pqFCZCAwOD7rUAUqn02LFjiYmJKSkp3bXBOByOSqV6ysLwvzbGPBznT/cVBoYJ5gUyDAyHLmp9oW1va3jpyb9qGhgYRJoIAeDL8rogow7zz7ekdhLx7bLrcpX29jT1OB47wAj3N+Lfuvk3N3Cuu9OSZEWXVCm1mUYxOdBSNL6r6LDPxKHYgztSRUZGfv7553Fxcf1LhAwGIyIiYseOHXFxcb1MhHd+DY2NjTU1NfX39wcAIyOj0tJSABAKhcuXL6df7OLi0o+oRjTuxMkcW/uA2rb9LZJUMTE1M9V4zYaBPmltbe3x48fpRHjx4kWtVjuYiRAADBYvc8QFj3z2PimXA4Nhy1FOmBtg/u6nGG8IluKPwkQol8ubm5s/+eSTK1euHDlypLsetLOzc3h4+OLFi0tKStavX99kZu30006+90ysL/VhkYck/vM3Sq1SUBTj9tZ2QksVyVXvlbTRf8QwcMM5VmzWJnsTO87d09RmABUBZOK5m+mfbjtqbFXTWrP62lfc3OUQGTlkb2mkiYqK+vzzz+Pj47/99tv+LZSIioqiE+EHH/zb5hO5XJ6enh4XF5eUlNTa2ko/mZGRYWhoOH36dACQyWR0IkTweUHBxaX7WySpncSrxdfUdbVsW/uhDmrACQIWChaEqmsqSTnBtnNkCA2GKpKRlwjVarVcLjc0NAQAuVxOURSO4/SPKioqkpKSYmNjc3Nz6VvuDAbD29s7PDx8+fLl3ZtkXV1dN2zYcLKqFjy8URYcZETuKUqlBIALUiWdCksVqlBj3ILNdOax7Tispy0MixQarymenstX9DhNzWExlvk5zB7fYVsgex8gvZNYfiaLUil1Xp54tPLx8bGzs6uurs7Ly+tfkVh/f39jY+OCgoLS0tIJE+6exJPJZBkZGbGxsQkJCV1dt9pG0l9Do6OjXVxccnJuLZ1nsVg8Xm97iI9uAv+QGft3iViMUrmqUqk2O5VutPLpfoyjrq6QpCZrblaz7J0MgsLZjv/WlO3YsWN0M+3Kyso1a9b0M/SHhGFsx4FdJdsbIy8RXrhw4cCBA9u3bweAffv2qdXqGTNmJCUlHTp0qLtrGo/Hmz17Nv3Bs7GxuWsEGxubadOmXbhwIT09fcmSJYP9BvTc7ZYdJiyGPYcFAAZMBhOwHHd7JpPFnzVXGBgW2otpanxeUOCJ1M3QliORy6Qy+YVc3G/+QMc+OmAYtmTJkh9//DEuLq5/iZDNZi9atGj//v2JiYmvv36rZ2xra+uRI0diY2NTU1NVqlubbl1dXaOjoxcvXnzntFt3I6fZs2f3uznUKMP39Oaams03bItvl6Z3Eq6Zx/uRCNu2fyk+sBu0Wkqjxtjszj0/ip5YZ/L8xvu9PjQ0dNu2bQCwc+fOwem2MWyN+KWPCoVi1qxZH3zwwfXr101MTKKjo/fu3dvc3JyWlvbKK6/cmwVpdN3RhISEwQ0WAZ73DIzFAoCxPPZsQ/5sQ741mwUATC7PfPPn1t/tMghf2pubtYK5gRZcjjvOVZDUGYlcdjJtwEMfRaKiogAgLi7u4UeoqqraunVrUFCQtbX1k08+mZycrNVq/fz8Pvvss7KyssLCwi1btgzyzacRicEUzA2ka6ykdhLyvIvajr71OxP/tV98cM/aohpKowYASq1eW1gj3rdTkvDngAQ8uoy8K0IAOHXqFN1FuqioKDo6evXq1YaGhhEREX5+fnTFhAeKiIjYtGkT/aHt5SGITogee0by94G7n8WAaWgoDArr/ThME1Oe+9TA+vZ8QpneSQSfTDd/+6O+NvLWW/PmzTMxMSksLCwpKelfx3kbGxs2m33mzJkxY25NvvH5/LCwsMjIyMWLF5uaPnhLNXIXgX/w3MMH+QzsqkzZrFRZnEo3iFje24NJsmP7F5Rc3qT+/2rXTWoNKZe3ff+5wZLl9340nJ2dly1bRj+eNWuW/mzf7NFwT4QXL14sLS1duHBhSkqKr6/v2LFjAWD69OnvvfceAOzfvx8Adu7c2ddh3dzcxo8fX1ZWdvbsWT+/B5TFQ3SIZWlt+eVPq954gVJrKLUKADaPt2Xw+dY/7uvrTT7B/KCgc7nf1HekiQlVS7OyKJ87ecrARD3asNnssLCwmJiYhISEN954o5dHkSSZl5eXlJT0xx9/0Itc+CwmRZJBY52WbdoSFRVlYDBkix1GAb7PbIGBgZ8BP11MpIsJp8zjvUmEpJyQX8iVxP+plUgAgKQgoV32jxcoFZqGOpbN3cUgra2tra1v3YCfPHmyjt7ESDXcp0ZdXV1NTU1/+OEHpVLZvSiGz+dbWlpaWlo+zAePXjeMZkcHH+471/5whtHqdbypPvzps0xe2OiQcJLj3OcyPQL/kPE8thOX3akh82QKWRaaHX2AysrKAwcO0M13ej87qlQqjxw5snbtWmtr62nTpn3wwQelpaWWQnymkCfXaKcJed+ZslctewRlwYeEcTj4rHnB3bOj53JIQna/F2s7OyTJh0tfXH3Kd3LK2lUf/HHoufKmDDEBAHwGRv93a1iMQWl61RRJnw33RNjW1tbY2GhpablkyZJDhw4BAI7j3Xf+TExM+j0JQ98mfJjbJEi/scwtTV98zfbXP2127BetWssQ9GcDNdveke00dqqACwD7W6XHDx0crX3ydOXw4cNBQUH05ygkJEQgEJw7d+7mzZ57ahIEkZSUtHr1aktLy7CwsF9//bW5uXnMmDEvv/xydnZ26aH9250tmBiclSg65QrizKnBfSujk2B+sL8R3v232vX3AfhnB011Xa34jz11z0RXBU27+vYrr/95+PnS+pVlTTsaxSfEREoHwcAgWITT/906hqJYtqg3wAMM96nR69ev4zg+d+7cY8eO0f2UPT09u7fxPvJI/4vxzJ4928zM7MaNG8XFxXq4h3d0wOcFzrx0Na5dekWmnFpZSdbVwlhUAPa+MAxjsVj03SAcx4OCguLj45OSkl544YXu17S1taWkpMTGxqalpSmVSvrJexd/klKJMZ87Xcg7K1GcFMttM48LAxcO/jsaZcqMLTKlyrE8dqlc/b/a9q+2f9H5xx7Lj75j4LgsM1WWeVxVXlqr1JwQE0c7ZZdvb0BiYmDDYT1pbrjYRLC2vOnOARk8vtHjz/RYfR6503D/CwoODqYfdN+T1xUmk7lo0aLff/89ISGhf4lQLpcnJSXRFTFqa2vLysoWLFig2yCRfyecHzztlx/4DEatUq0kKVVuFo4S4f0tWrQoKSmJ3joGAFFRUfHx8XFxcS+88EJ1dXV8fHxycnJWVhZdC5TBYPj5+S1evHjp0qXjx9/9t8oQGvCn+QbXt52VKNLERGROBqVWo125D2mymck1NnM8j10qVxfLVaDRaJsa6p9bASRVplAf7ZCldMjKFWr6xXwG5mvAH8dnu/I5Coz5RPACbUvzIaEAlLfmRf70dOb7zDJe98rQvaGRg9Jj9H4mX1/f/h3e0tISFBREP87Kynr11Vd1FxrSK1q1+jv3sT5CHgBssBLdfDZ6qCMaSTo6OlgsFpPJdHf//5YdPB4vPDycngj998PFf+0/7W6PAeAMRqGno+zMycEJexSLDZ3ztIXRz2MtMQA2hl2d4nhwgtWTFoaW7P9f2S5iMSJNhNvGmBd4OlbM82h891Vp+hEtIaMoiiJJ6Ymj9RueqlkWVP/y09LM1KF+QyPGcL8iHFChoaE4jp87d66xsdHKymqow0H6jMFiPbY8moj583xp5U61eXzX4rnfnX97metkOz2q2txX3Ys/Dx48qNFoDA0NCwoKcBxfsGBBdHR0ZGQkXbbpgQT+wZafveeGcwsI5WmJ3DwrFfedO9DBj1aUQi7LzvBqqZtqZywnKQcuu1qp9i2oIW4XoLDlsIJEeLAI9xbwOMbGuN98QWAY7jv3H1fhGCZYECpY0HPjHeRf6HUipD/8ycnJSUlJa9eu7f2BjY2NKSkpYWFh+fn59ERTa2tr/yrxIw/p6sTAj91cmSaXFPaza9n4H2dvxl1q+OX5aY8PfJvvkUWlUmVkZMTFxSUmJjY2NtJPCgSCrq6u5cuXx8TEcDicPg3INDHjuU8Nru8oIJRpnURQxnHzNz8ExnBffzesaDs7iJxMaXqK/FxOp1xxpkt+QixP7SQIkjRgMiRakm6PulCEdzedFgaF8Ty80N+zbul1IgSAiIiI5OTkhISE3iRCupZp932Uw4cPe3h4xMbGAsDJkyfj4+MHPl7kH7rkmuhUUtbVAB1V4BwMAGTFCQJjrtvF8xkrGm8lGOoAhx5d+To2NjYxMVEsFtNPOjk5LVmyZPHixR0dHcuXL+/q6uprFqQJ/EOCz539mt7K2daiuHaF59Gfmm36RtNQJ8tKlWWfUFw826BQpnUSaZ3EOalCSwEAMABc+ZwiucqQyUh3tWViGDBZNtt282fMGerARy19T4RLlix57rnn0tPTJRJJjxuhSJLMzc1NSEhISEjorpTP4/FCQ0NRveAh9+fZBrW2h4oYKg257XjV90+O/m3CBEHQ+2spilIoFHz+rRY27e3tycnJycnJdzZgcXV1Xbx4cXh4uJ+fH913oqKiAgCuXr3av7ML/IPHfvfJWB67XKG+JFOaZh7vXyIkck9JDv+hbqhlmlkaLokW+IeM6CJBlErVGfOLOHY/2daMGRgKAxeZvPg6U2SsKr0uzTwuy0pVlV7vcfGnt5C7UCRYaCywZDNDiurKFeoLUuVMIz7P3RNlwQGl74nQwsJixowZubm5aWlpS5cu7X5eoVDk5OTQvSwaGhroJ01MTAICAsLDw+n7KHK5XCqV0j9ydnZG9bsHX25Zp0ypBQBougoXfgAAEFeD0wKNljpT1jm0sQ2O2bNnX758GQBaWlpWr179yy+/HDt2LCkp6fjx42q1Gu5owLJy5cp7q6mNGTPGyMiooaGhqanJ0tKyr2dn2zlyxk0Mqu8obxSndhJ+mcdNX3m7b0OQZNN7G8Un05lyAgAACrvOnzWYesD6u10jdA0qpVTUPbOsoOi6M6ZlYRgl7jxzIMb9SDzT1KyqomJbQ6cthxXbJmlQ3SpyTbcHX2iMBxjhRjw+YCRoSUqrDRLh9N+qrxFu8eE3Q/umRj19T4QAEBERQV/zLV26tL29/cSJE0lJSfHx8RKJhH6Bk5NTcHBweHh4aGgo+44PJ5/P716Jbm9vb28/+vuHDTcCLgPDgAIAs0ng8QQAQEU6/SM+Rx9LyD755JNZWVkAwGKx/Pz8oqOjly9f3l1J614Yhnl4eGRnZ1+5ciUkJKQfZxT4hwRdzd/RKE7rJN6trVaVl/apu3pHzC+yk2krrlTsn2DFwTAAeOTqjSSMbNv6qdnr7/cjniHX/tO36sryTysavnIyM2UxAeCdyqaPHDRxpTfj26WS2xMYQiYWIhIEi3A/Az5uaIjP9hf4h+Cz5lMqZds3/5MeSwwW4Tsaxcc7Ze/Zm1D3LzGD6ARKhBAREfHWW2/99ddflZWVZ86coduRYBg2bdq0yMjIJUuW3Lm4vEc1NTWNjY2TJk06d+7cvHnz2CPzm+xIFOJhvvfUTQkAMDnANQQAYHEBAOcwIrwshja2wSEWi9966y0AIAgCAB5//HELC4uoqKhFixb1cvGnp6dndnb21atX+50IPXZ+b81h1ak0RYTKNPN4HxIhRXXu+oGSy+96mlQoug7/YbrhDYw70u4+UFTX4f2k8u4KRzldipiWLgDAGQwfIdeWy3Lhc1aNc7i1+HPmHKz7Hi2OW3z4jeixZ8nHw605rAaV5hqhNM08zhmPin4MIL1OhIWFhbGxscnJyfQfs7OzmUymt7f3E0888cgjj9jZ9bYukUwmu3LlCr0l68yZM/PmzRuwkJF/WORpPtZSUNjMV+O3K+1xjTAGw4DHfi7QYUhD0z2CII4cOeLj43Pt2jVLS0u6yItQKKTXebW3t7/33ntr1qzpa4fVKVOmwEPcJuROdOXY2gfUtu1rkaR2El4Zx43XbHjgUaryUiI7Q3riKCmT0itEvm/opNdBqkgKADA2W1V5g+vi1r+ohgopEVO3+z+8Xd1GX+N2asiFxricJINF+AQ+R2Brz58TwPPzN5jpd7+aLxyXyRxbh8Da9piWrtROwjvzONoXP6D0LhEqlcqMjIyEhISkpKT6+nr6SS6XSxBEWFiYn5+fi4uLiYlJ77MgADg4OKhUKi8vr9jYWHNz84EJHOkBA8PS3vZZ+DmjxHKCTKGlKBBOmG8iZKe+5WPAG23/ttVq9fjx47OysqZNm5aWlkYnQiaTSbdk6XfNa3rbz7Vr1/odGD4vKKi4dF+LJLVTtrGkUF1Xy7bt6TYBSSpLCruy0uL3/jZG2l4iVx/vlF0nVI+ZGwDAEmMBC8MAIFN86wIRY4y8yW2Mh1O3+xl96mhKT40uKa4fw2W/ZmdqsCjKMGoFb4p3b5YCCfyDg4tL6UT4Wul1dV0N23a0fbcbPkbbL4v76ejoSE9PT0pKunMRuYODQ2hoaHh4uIGBgb+/f2FhYUBAQGBg4J9//tmnq7qdO3daWVnl5OTIZDLUg3SQmRlwzv9vVtb1tqzrbWoNNWOcKMzTgsUcwWsO7wfHcZFIJJPJTE1Nu8uL29re2i7JZDL7VxSCy+VevHjRw8MDAAoKCh54I+BeAv+QGft3GbGYZQr1l7xp0w9kR70QbSK8dYOA0mqUBVekaSnS9COqlqZsiSK5XZYlJrpu3y073aUAACcem75+utU1gSTZfW9IMuQwDofnNkVx9fK9P2IKDMzf/6z3ZT8F/iE+Mb8as5jlCnW5Qm2amSpa1bdrfaT3MOqf1c1HIoqiWltb6UsxlUpFEIRIJKJ/VF1dffz48aSkpNTUVJVKRT957yJykiRtbW0bGxt/+OEHBoMRFhaGVr4gw01zc3NaWpqbm1tJSYmpqWlAQIBOhl20aNHu3bvpJOrl5UWvQe0bkvwyYMnbhZXaliKW55O8yVEkm/dR1Ji1/DJZ5nEiJ1MukZyWyDPE8nQx0aq+tVpSyGSM47FfsDKaZ8hfX9GyzdmcjWEA8OyNpj3uY0TrXjFevU4nb3CQqUqv1z2z7IfKhifMDYRMBgB8Udfx1lhb802fCBdG9GEgkqwKnfna5euH26Sv2xi/Ghpguyt2oILWe6PhilAqlS5btuzkyZMAcPny5d9//33NmjUJCQmJiYlXrlyhX8NisQICApYsWRIREeHo6HjXCHTy27VrV3t7+6ZNmwb7DSBIL1hYWDz++ONw+67e8HG+smuL3fPazjhoKdLcPCd1XQ4q7aaDRZ11B8xrs06I5WliQnb7+s+OyxrDZQeLcCs2C0xMI6OXk9Kun9OPgEZLadQYk7XHzclgSbTxE32o9DSscCZMst7224Y3XyQVCqAoCsPeGo+bvbGlb1kQABgMwdyA4Irqw23SNDHxfP5lbVsL0xTdeRkQoyER3mvlypX05ne6iNrixYsjIyMtLP5tGWFERMSuXbsSEhJQIkT0zbp167hcLgC0trb24/D1ewvlLWUgbQQmF1pLoKMSOiuImpwPGy4DeWvlyDgee5GxYJGxYByPzZ3oKpgfLPAP6V4JafzMesnxRHV1JdvGThgcPtJXSPKmTnc4mqvIu6CurWaamvOn+TJuNxXvE4F/yOy//+AzsHyZskGhMj91wjBqhc6jRWB0TI1KJJKJEyfS+9mbm5utrKwcHR2rqqoiIiL8/f3pT/gDyeVyc3NzgiBqamr6tMo3DRkAAApRSURBVFIGQUa0h5waVSg1hmtS1SQAqYHU16D9BgAGQAEAYEx3Q8NHDBlBItySy+ZOnIzPWWAQuoTt6DwQb2T0oVSqqkDvZZfLLsuUj5gKI2fNXJWYNtRBjU4j74rwypUrly9fjo6Orqura2xsnD9/PgCMGTPmo48+AoBLly4lJCS8+eabfR2Wz+f32KcUQRAAoDQaZeFVbXsr22EMvVOQlBPSowlV+2Iw1lNQcQKK40DWAiwuaNVgMx1sZxhYT/lv5+EFzrgwaJEwKIxpphc7O3UI43Aknj4eJTWXZcrsLrlv0TVSJmUIUGcV3Rt5iXDixIlKpfLq1atFRUUYhtGJkMVimZmZAYCRkVG/R46IiIiPj09ISECJENEfPj4+3VVz58zpuaClNO1I6yfvUCQJGAZaDdPMkj/Drywx7kRdY3aXXCVJB60aAMDQDiT1gGHg+xpwhGom+G/fa2tnNmjvZfS56TBuEp/DAGhRa8tlciInUxiyeKiDGoVGXi8PsVhM73nSarXFxcUAwGQyuztoCwSCfi/4XLx4MYvFysjI6OzUizKVCAIAW7Zs6V5lvXXr1ntfIE070rzl9R9Ka0iphJR0tXdJf7+cn79vzy/ltf+rbU/rJECrxgysYN77EL4DLD2A1EL9BRZGznE1s0NZ8OGEvvjKMmvT6QY8CsCczeyKjSG7xEMd1CjE3LJly1DH0DcXL17UarVubm4BAQGOjo5mZmZsNru74LWlpeX9vtU+EI7j6enplZWVXl5ekyeP/sYFCPJAlEbTsPZRkpD+72b7SjMDAGjTaPe1SPY0S052yQEDdwF3mZlRk9089biFamCAWg71FxkUZWnneWyTnxDv1R165H4wNlt++TxRWy3RUv5GfIeOls6De0iZDJ8+a0Q36BhuRt7UKD0XSnNx0fHqsoiIiOzs7ISEhOXLl+t2ZAQZWk1NTZmZmSwWKy8vb+PGjfSthAdSFl6hKC0AkBR0akgAoDfCP29ldI1QPmth5GhsKAyN+OrxtbvKGNv+Lqix9ZZfxBgNF+LKNhuWWIPZ/IF8T6MfpVSoSousOay3bI2nCLgUwKG61vn7do2TSczf/miooxs9Rt7U6ICKjIwEgJSUlO7d9wgyOlhaWopEIqVSOWPGDLqyfG9oOzoAMABo12i/qu/4qr5jZ5MYAJaZCrfYm3q8+Ipjymnzdz/hOY15Kcix8BPfgrpPpwg4Gq0mTyyWZR4fwPejHzoPxZByoliuuqm6tRHlvFTZKZVJkv9WV1cMbWyjCUqE/zB27NjJkyeLxeJTp04NdSwIokv19fUEQUgkkmvXrpWUlPTyKLa9I2hJADBjMz9yMP3IwfQ1G2P6R0wDQ5MXX2cYirpfzDQx5XlMDTLCASC1k5CdTAOytxkX6ZHsWCKlkAOAWEs2q7XNaq2CpAAASFJ2Kn2IgxtFRt7U6ECLiIgoLCxMSEgIDAwc6lgQRGdsbGwWLlzI5/M7Ozt73ymMM3YC09KSrK6863mMxzOI7GFzt8A/JPRc7lf1HRliQtXWqsjP43lOe9jQ9ZhW3EE/SO8kbsjVAFCmUAEApVKRHR1DGdnogq4I7xYREQEAiYmJo6DUAILcic/nA4BIJBIIBL0/yuqLnxi44M3b6z9NWMx1duZsG3uT51+998XCBaFOXPZYHrtTQ56XKtHs6ENiO42lHzxiKnzf3uR9exN3nAsADD7OdkJ1CXQGJcK7TZ8+3cbGRiqV1tbWDnUsCDL0OGMn2P2REhgagrE5GJstMDCY9/Qa29/jMR7/3hezbOw4EyYFi27Njkozjg16vKOKaMWTDH5P5dkwEMwPHvRwRq3RUGJN5w4cOLB8+XIWi6XRaC5cuODr6zvUESHIMECSpJx4YGWTjl+2nvz286jiBks2M8fd3uGPI5wJkwYnwFGpecsbRclxXJXSgs0EgCK52slQ6PzpVoF/yFCHNnqgK8IefPXVVywWCwAIgnj77beHOhwEGR4YjN7U9xL4h7jhXBsOq0mtLSCUUjQ7+nAsNn/uvekjGwcHwDCMyfSaMXP8rwdRFtQttFgGQRBd4ox3UVjbOd9oqldpfmoUp373/U/P9XA3EektDDOIfNQg8tGhjmM0Q4mwB11dXdHR0QCg0WiGOhYEGXmsAxbOvVqU0yUvlauXKKXqm9Vsu7ubgCLI8IESYQ8MDQ1jY2MBoKurq7t4G4IgvaTw8rFkM3EGo1qpduSxZVmpolUjtdEuog/QPUIEQXTMds6C0LFOC41xADgllosP/q64dmWog0KQ+0KJsAf0VkIA4HA4CxcuHNpgEGTkYTCMps9YbCxYZir0FnI1DTfrn3+sfu0KUtI11JEhSA/Q9gkEQXSMJGQ1i/yO1NazMCzACAeAXU3iOSaG7t5edrv/Rm0TkOEGXRHel1arbWpq0mq1FRUVXV3omyyC9Jb4r32kStWqJlvVt2qN1qo0UpVKdaNUfv7M0MaGIPdCi2XuKy8vr7i4mMViqVQqNEGKIL1HZKZSSjkAlCvUZyQKAGhUaQGAkhNEbhZ/ht8Qx4cg/4SuCO9r2rRpGo1m6tSpOI6npKQMdTgIMmJQcoJ+0KYhq5TqKqVaQpIAABRFyoihjAxBeoKuCO/r8OHDLS0tHR0dcrncxsZmqMNBkBGD6zZFWV4KAD5C7qNmBgBQKlcBAIOPcyd7DHFwCHIPlAjva+nSpfSDmTNnDm0kCDKyGK18RnI0oYcfMFnCoLBBDwdBHgCtGkUQRPfEh/+o+uIDUCqFDACAdgoz4PMct/3G9/IZ6tAQ5G4oESIIMiCURQUdu7Yrrl3B2Gx8xmzjZ9ezbOyGOigE6QFKhAiCIIheQ6tGEQRBEL2GEiGCIAii11AiRBAEQfQaSoQIgiCIXkOJEEEQBNFrKBEiCIIgeg0lQgRBEESvoUSIIAiC6DWUCBEEQRC9hhIhgiAIotdQIkQQBEH0GkqECIIgiF5DiRBBEATRaygRIgiCIHoNJUIEQRBEr6FEiCAIgug1lAgRBEEQvYYSIYIgCKLXUCJEEARB9BpKhAiCIIheQ4kQQRAE0WsoESIIgiB6DSVCBEEQRK+hRIggCILoNZQIEQRBEL2GEiGCIAii11AiRBAEQfQaSoQIgiCIXkOJEEEQBNFrKBEiCIIgeg0lQgRBEESvoUSIIAiC6DWUCBEEQRC9hhIhgiAIotdQIkQQBEH0GkqECIIgiF5DiRBBEATRaygRIgiCIHoNJUIEQRBEr6FEiCAIgug1lAgRBEEQvYYSIYIgCKLXUCJEEARB9BpKhAiCIIheQ4kQQRAE0WsoESIIgiB6DSVCBEEQRK+hRIggCILoNZQIEQRBEL2GEiGCIAii11AiRBAEQfQaSoQIgiCIXkOJEEEQBNFrKBEiCIIgeg0lQgRBEESvoUSIIAiC6DWUCBEEQRC9hhIhgiAIotdQIkQQBEH0GkqECIIgiF5DiRBBEATRaygRIgiCIHoNJUIEQRBEr6FEiCAIgug1lAgRBEEQvYYSIYIgCKLXUCJEEARB9Nr/AVj68LQn5gsIAAACjXpUWHRyZGtpdFBLTCByZGtpdCAyMDIyLjAzLjEAAHice79v7T0GIOBlQABNINYC4gZGNgYFIM0CpTgYNIAUMxObA5hmYYfQzDA+Os3OgCYP5jNBxZmY4fIQGmE+1FYcxhKQZgSbwsg4WGhuBkYGBnEGBgkGBkkGRiYGRikGRmmg7xWYOTOYmFkSWFgzmFjZElh5FNjYM5jYZBjYORTYORM4ZBk45Bg4uRS4uDWYeXgVeOQZePk0mHj5GfgFGPgVGPgVGQTEEgQEM5gEhRIElRiEhBmERDKYhJUZhFUYhFUZREQTRNQYRMUymETVGcQ0GESY2JhZWNnYOdkEhURExQTEvwGdxQiPcuO3PQdUtZsPgDhTJWcfkJ6nBWZ/c1154PrpuftB7PdLOg70X2HfB2LzrDc+sCHtHZj95+aT/Ub5SvYg9qGjfAf+BLM4gNhTEnIOdC6WBLPXxLQc2BldCmYHXpx24FzpMrD6XfOOHnghchHMVs75coBpy28wO2nisn1/EmfZgdgd+w3tj2RuBot/2dFgJ5RrCjZnCxeXw6pTHWDxtqXpDvLhNmC26v9Gh/mdRmA3953Z4PDqcTPE/T/2OaiukoX4Mfeiw9arfbYgtrHjYYfdsQfAek8VT3G4+ucVmG2mZHeg/W8mWL33yV0H2sMngtmLGmsP2LJvBLPXfD1x4P43NzD7Z1XUgQhXPjA7nX32/vl7vMDu9Cx3P6D3aC6YLaq1+cDi0FYw+/WlD7YPr08Au80xSt5BnUEJLO5X9tL++eqT4LC13uPkICr2DKyG/eobh8RARrD5N2QmOrxgsgKzA1XPOoQkVYLZ9bIMjn+OtYH1Wj167NAwXQ1sZoZ7m8Mss0AwWwwAc/XDOYy8IC8AAAOYelRYdE1PTCByZGtpdCAyMDIyLjAzLjEAAHicfVbLbiQ3DLz7K/QDI/AlSjr6sVgvAo+BxMk/5J7/xxbVtroXS2TsQ4tTTRaLDw0Pl3njhxKfP1/++Pe/sj/y8gA7/c//nLP8o0T08FbioTx9+/7jXp4/Hp++LM/vf98//irGxQTvUOFfsY8f729fFi7P5WbVSGdr8YQAxL1QpfUpO44cyGE+upabVPc2A/AbUst7ubUqY/ZJ5cZVVPrQBGmHT+nEauVGlVXcsuit3CPmoN7aDKR5c8mQHj659oHgSL+6WqOZAHsABeRoCNKr5N3FE+AIoFafCpEANCKnDDiDpCEJV5aidah2yfJmOhLvrNS4cEVBrWda8iqQV+4tyDHSiXwypET0DgDSwbFOd1POkBpIqipNZQCgxE0yiTgKBAmnKb5nZM6oegZsqDlVaWxgjDqp9SEZMMojlZtPUngkHz5Sj1EeiM4TnvB919bJMmCUB33rEtW5BdtunMaeQLbaxGZffUmKts/qI7SQ0/sYI5rEXBtnqkvUx2sz++zGiJ7RFFlAxnBRSNlIhqcedeXjOmMmGFK2YVkZxcprcEMyQ8fq0OYY3wzaAtrgq1OX6LxmPDXTHU32Gr6mM2mPl9AkwSSB9oA6xnfOMeMlpjF4ZNBxeDVWNPIaO5GZdgga7XUtDWZ2RQuwdwxmtmfocMrRlA7NMKC5phiDlX+bqGcHEm/k3aQCJPYQ5u3YXazcRpaSKpDojAGfhoeGmW+Z+Bp1Qh3Rn6htzN3UMVOaUSatZNJ1riU2e7PUZ1SpxzxicYScJNRmKlIUCfXGQHZe7UJu6eLUqJGjRDSoha4DGyRHRokGzF2IIyNxFc2QRosno4PcIZKTzJkCedHk6YLJV3gcs2Wyf7u//HKBHVfa0/v95bzS4k/Oe2sd7byd1rmdd9A6+3nT4FT0vE7iKP28NdZ5nJcDw+X2PuEPHq+bPgw7GrYjTmVHw6IyxNjuOegh5o7P8XovOx4Hv44puWxUBovzjP4OVtcFyeCl1z24DPtngSzRgtxlrWlw5VPGpSOMO1XRw6InxkJtpL/TlWAblp1wXGKL805Z+mHRnbOMT8sZK3RFZrIxR+Xw6lZGQ9pIdkuhi/PAXF2GliN/uU6nLcvOXVcHxIxdpu2wbM7aPy1n9AE/kE22Ghoyh5CbjwVnCHlmYav30APnIARnSGubT3T8tb/j/PVzD88PPwGf2+eJoQGDrAAAAoh6VFh0U01JTEVTIHJka2l0IDIwMjIuMDMuMQAAeJxlkr1uWzEMhV+lQBcHuBH4J1KU0SlLJqd70KEIOrYpiox5+B7JRc2iy7VIk4efDvX8+OWFX07Pj1/uXk4P+3d/Hk6fnu4u17z8eDk9/fPf3+Ofvo8XfC+3jFzz/6nuBOP74f0UjYU1DmlOepy9MXHwwa2TDD/O1lyTFYmhfdhKcLrIoU18JCq0OaeNg1poD0KJNO6eaCEfPlZsI8fAjK4saIGYaMpxT01TRx5nap6mspqM2GNlpLMxo2uoBWRWtXSVNUqJu+w+5uh53HNjZRzOOMWIRI2rdVoZKFB0X+PMuwvU761JEO6FHKu4rVxvMjK2mKjskdaG+YgDGu5b3tGYiSKFVWMIUtoMFvoqEpG89hlp9gMHyiTZFBkEB1EOCN7zhgbFKurGqVsqneEh/sNNWbdUsFKHM0q0ObEkXGftxBV3RCZwAgqj2+2q46maMM+I9jDsTV0ZexuqccVmdvfDGkrXrtdQ7CmRwR+xmUUJwoJVhut2VxhtkMGWYj8QcrHlo2cYy3oQZBK2bIyMDj7oimWsDCl8WZn0wCRMMNcOwFiLXzEJLaPhCda+nU/al3LYTIOXgXh4vjPd0I5d0xquGDUaUeyXJa6Ch313fH17/f751+vPSW0dL69v3/DsJt8i4Sklkqm3yGjaLWKfvUR9etGUGSXSOUqlziyRTS4wLJMLjfLkgtMnV5zJBUcnFxzIFByoFBzxyYVHYnIBoimFR3RKdcemVHv6lAKUU6o/UKoGwdiCFFMK0phSiSBUiJSmFiSfWonG1ELEMbUQqU2tSCiuSDm1Lq1PrVvDoAKlUC5QgrdQH9F4/w1K3nKD7XLvigAAAzJ6VFh0cmRraXRQS0wxIHJka2l0IDIwMjIuMDMuMQAAeJzVkllIlFEUx893v5lvxpm0GZ0ZbbbGXDIrlylbHpruYNRDaZqGLYgjBg2RIWZR0aI1rVYklu2bS0opUqgVbrdIwbRsgcxMUaSoFyUpA7Nl5qhJvkSPXbj8f+d/L/97Dtz+urIucC53GF/znXuBc2dyApicKvpTpBDkFJ4IFFUkGVF+rJ6oEphwjjUZ9Qn/+3xEx/NHX50Q95djBdrC2K1/C+Ow5Lj/VeXAAUwF1zC+wBHgeOCmAfED4g8kwDmvSSSzE5HYJhbsRJDYBHeTRGonkkCQupmkMpvbdHALApncJJ8UxLt7mNxngMfkIOKhAIUSFMGgmAlKvU3paSeeXiZPtc1LBapZoJoNqhBQa+xEHQoab9DobN4+UuI9xbm1oA0DbThozaDT24nOYNLPAYMRjHPBGAHGeaAigkgsSKQywVOt0emV3lcIuKYY/YpZkocstT2RuYrKyNMs8h1FvtZ0kKXOO4ncXJvPVlc/rndxyZr9bDDic52LuwcC2c3OFehHxLbUy4cvWVzcoOeZObprMfodNtbQxlMX21r3sMSkJOTaF6fYBj4D7wzermeylALkkJw+lvyoBfl6vlC36MyRGhenZiYvPpCdjb4jqcOyNsoXc44bNTQhfgn6w2+30mi/Xuwh136E3ju+G3vrEWKodkiMs0z6tpneyUoemWvZXVpRXIx3+krbaNfTlTiX11LeurFKQP/ZnGGqev0D2ZIuta6LTMO3yiu1Vs3Otei3m5tovPrySM9xF2jp9kbkb2c/0rxkNfb53tBBQz6mIVe8qmEDTcewh7NZz9irWIIc1dzPzj3Zhfzy5wrm88kPOSHnKmv5fh65RB7GynfsQ1Zf3cIWDlUj/yx01It/RGC+TryI+etykT80ljE3RQayEFNoORh+H3ue3xpMD730QL87laObiszoZ2xV0s5tezFzlbWQHj6xBPniVwctclxB7nnQQNPXhyI7jqqtVXoV8pueqdZbmQacfXmA2dpryMPM7PZs2vfQgm+1p4mspX2HkLXryij/JQ75eUELTVlwA1nzCxg3/7LOE3WmAAAEnXpUWHRNT0wxIHJka2l0IDIwMjIuMDMuMQAAeJx9V9tuHTcMfPdX6AfOgndJj7EdtEWRY6Bx+w957/+jQ62tPQGYOjaw4s6K5AxJKTzC5cZPLX/+ev3zx79t/8jrE+z0P79zzvaPEtHTt5YP7fnrb3/c28v7l+dPy8vb3/f37829eeAbavwz9sv727dPC7eXdouDQo2j3fSwCJ347KD107YfSaQdoSrhiSTWObVA6onkQSMkn8yMzQqktbd284OGR4x248OHDpMC6eee1OEee9JhPWav9ox2bzc5ApviPZCDSLwXyJ57wucMkYH3ShFMBXAkUA6yEcqNj67RgwvgTKAezqp9NnwRROYFEG7uiZzhfUgD7ZN4lkg+M3fkIAmYrtyrdHgp5MecZMicD+lhUeXDmt77Ye4CNpMCcFlJyZZIOljZBuUTcUQvoSkR2E7VA3vSLzMKqA4BZVgoSHIhtariOAWSQ82DU+keMbkqDk6B9BhMGhmamYbMCpgC2SEBaiKzmdmL1ZZCCFIPcTI8pvhTdVZkCi/nnWSILvVF3arqkBTIjy7dkQ8qT2kOqiiSbKFAnSG+xbqQTB0V0tr3NvAeSo9EOpKT0rsjow59WPEI76iTGRVLkgIN7NTHzJ3g2aV0ngLNA7IoGixbzSYIq5BjJSSjG5AoTenCVRXJqdAAAPmgMrHfrKpdaaUjExRmFakajSpv5eV6oCdghlBhopXkMP6++odYaGbPoYvZyz01oYFG94GayvJAxZWdrpZQ9Bo4ir56nkefFZ3qCUXRkXIWCFoj2Mui00ioZf0aNMLTYBEtY+0JlaM7dQyTDGByLwsUFbYC6NmalFxALymbWGdCeeU9HPyPX/Wc0ekfc8sQCybZ7BSV9ohpCWBKmHGrodV6uWdKhYENnbLnGB0fQ8oTJpXCaRBjoIzkAEdRzkSzBSSwCN+oZRwvQ6vKM19IVHB0WXx2n/Xxlio5hqfkUZVHJhQo29hSJByZOZBjCesas6pSS43i4J4qLl1xCns1Pi0lmhifgVMzkZjh4uXhSgs5p4QlN6i6upg9FWJGRsa0hghjmo2KJZeVkXbCsQq60KZhJTAVwgTr3bE9hjME0tK5LTYNLpGaIh0eZcW5L4p0Go615H+ajqrgvt5ff7oMndej57f763U9yn9y3YFyqXut51qv+8xa23Vrwcdtv4613G/7+davawa+RWtft4m17g+XhmUYD3cDMNb2BjgcLX3u+x5GbBr2jpgqhiiuHR3r3q4NM0KEtUNEiRnimg8nLWOtlyGDxN/jwWlp2DHI4jDnwMM5eFouzCISvxfTluQCuMmTJBMJ8E5X4rTY9VVPiQDcGUsGnJads2TEyIt31qfOyHT7Uv6wbCI0qUX2sjNXPS18WZb8uPc9TnRblqtiVg0kKw/zmZMk23np+LDsLHR+WHYWljSDSLlqb1Uqwtx5WfIMamVnYRlzWi4M1km2bO9o/NOyvduKGZbL+4oZ/OzcbZwWfxw/p2Vn6ivmRw59xYx6ehwbnBL5ZsyzNpYmD4OAUyLfubt/WK7Oiw/Lziub/bG1c/35vyY8P/0HMJmCd5ZXNvgAAANGelRYdFNNSUxFUzEgcmRraXQgMjAyMi4wMy4xAAB4nGWTu24kRwxFf8WAk1mgVeCzqsiBIyUbSQYcLjYwBIdrGcaG+/G+7LYxhBxoVMXi4/CS/eXz1zd+u73cvnz++qn+3m7PP7/g9/X5NN3+/fff84frn298hT7ffnntj5X09YPz2/Vy+618P/hfL/+LYPz+9OP2xIN0ShwytunU4/4kg2zjxGPpXBMWHc6qGz40iey0xPS1Dx0WJGdQuJodNjwWVZAPU3KFy2a1DQvexJceVL681ukUQbZQS9a0ClvD3EVg8YhlZxgtVTmeaNial80HbZ/rAL5v3ZfbhBdMOog14qQUUuYDdD7ZyzTRgpqUl815pQpigZMN1cV+Ojn7loorcezCCvUze/C+uuFNe1acmfGFEHtalGmzyCXmclp8xgWvS7slG72i8l4mcXpN9LOqxU0EkWArAaaUWkpzMiw0WBlK4kA850Kzd7wKBdzgbzYBDdMusAkJ6RwOdKuCivk5BEGHMtSgCHKvOYOlLHPu2gJRpK47oQGtWpB8K0rVICEHgszOlbnbkCnrxEbjNTU4iZNd0oVqlGWR7FNxF3Wr3LJkXlouD1gcnUlcs1tcWkJdrIGWlFiWGeWDhp2rUaXYEGQOXhFnrYWBz7LgUDMhTF4CK3vfOBlahclBi0HfFwTiWiiu7UNZOLkstI8+dLsgLKBH6JlpW3gtfUC0SavKYQziZUF5LBE0YheDhRmd2AnJwI1CEky5hiHo+qwuEfUtqRrtctiGvYBM02plALyWO74kwSS0Gjfkcnhgl7dWhIYf0CysOsSyITXXF+PkddeFtUeF2DT9+HT8/v39269/v/+VNOr48v79j6E7+XFbKY/LTn1cIq0FRfrjZpTzcWPK1W6cu90ko8Uld5DkBsLJjUTh2lgoucPAt9GoJDccRd7Gg8fGo57cgHSmdCKo0ZA8pSHBtRGxpjQk45SGxJbSFUKmLhFSNSaTlC6SpjYmXqldJsyoQXGkNiqh1E5lqX1sntqobKZ2oTi1U6FuoxJJa1SiaY1KLK1Riad1qpnWqXZap8KiNSqntE610hqVwLnPj9IblUt6n5+mNyq39E610xuV849/ALSF8o8Q0UcpAAAAAElFTkSuQmCC\n", + "image/png": 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\n", 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\n", + "image/png": 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\n", 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\n", 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\n", 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\n", + "image/png": 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\n", 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\n", + "image/png": 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\n", + "image/png": 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\n", 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\n", + "image/png": 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\n", 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\n", + "image/png": 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\n", 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\n", + "image/png": 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NDSUlJcKPM3/+/CNHjsTExEyfPl10eW4GBgZ5eXmqqqo5OTlt7lE1ZcoUVE928uTJ3bt3MxgMSm3EfKeIc36JogxCEhsbO2bMmIiICG9vbzs7O3JzlXIUFRUJgtDX1xfmUbB7924AyM7Onjt37o+SK/B9QWAa4ejoCAAnT54U6VV4PJ6zs7ORkVFKSgpSnCIIgsVisVisVo0TFBS0YMECdFxRUWFiYsLlcqk1FfH69evLly8/efJk8+bNQUFBQo62ZMkSAPjrr78osQ3znYOmlaqqqvX19SK9UGJiorq6+ubNm8vKysiT/MctZOTIkSkpKej4yJEjR44cocxEPng83rVr19zc3J4/f3727Nnc3FxhRisoKGAwGLKyspWVlVRZ2BJYbO6V0A8zT8dNOf7m0ENmQYVo/8SiADvCJrh+/ToAjBo1SqRXWbVqFQAoKyvHxMQIM46Li8uFCxfIlyNGjMjJyRHaOpGDYvuWlpbtbQhBEER6evqNGzeKi4s9PT2TkpLa25yfE9R94unTp6K7BJPJ1NXVBYBp06YJOR00MjIij1+9ejV79myhrRMHQ4YMAQBfX1+xXfFdflXHlSFKi57AnECYEyi34LHSoqAHbz6LzQBKwFujTTBx4kRpaemwsDBK9i2bZNeuXWfOnJGRkblz546VlZUwQ8nJydXV1ZEvWSyWvLy80AaKnDFjxrRLeZmdnR15PHbsWBT+UVNTYzAY169fNzc3F3/pt4Qg6t1RJDz9+fPnUaNGeXt70+lCPdz4P15XV6egoCC0geJAzCkO1XXckfuiP6W+qo77txK6Li++OtZnzpm3CbmV4rGBErAjbAI1NbVhw4ZxOBwUxK6srCwrK+NwOFSNf/bs2b/++ovBYHh7ewsvPTxq1Ki7d+8i89LS0ng8HuXau97e3rdu3ULHN27cIMv/haG9yss+fvxIfCkSJY+1tLQYDIaJiUlCQkJxcbE47ZEc+It0ORxOWVkZElGihPLy8okTJ2ZnZ1tbW/v5+Qnf6nLw4MHkN9PHx4cU4qcQMuUbAFDRlPCgwqTAwMAmtago5+Lz3EoWh2ioB/aXGCqnHtjVrAbe5hupYjCAKrAjbBr+iVVqauqpU6eoyp3x9vZes2YNjUZzd3efPn268AOamZnNmTNn1KhR06ZNW7Vq1ZUrVwCgtrZW+JFJSkpKyKaMJSUlVC2U20t8KywsLDQ0NDQ0lPwtRUREFBcXd+3alcvlOjk5idkeCQEV6ebn58fFxVVWVsbGxh46dIiSkWtra+3t7d++fdu7d+9Hjx4pKSkJP+aRI0c8PDymTJkyduxYNTW1GTNmNDQ0UOtdMjIymjwWBmNj4169epWXl4eHh1MyYPP4xRbUsrkAADVFUJgEhUlQkQMABEGEpZaJwQCqwHWETePg4LB27dqgoKC6ujpra+uYmJju3bsLP+zDhw8XLlzI4/GOHDmyePFi4QdELF26dOnSpWw2W0ZGJiMjw9zcnMFgvHnzhqrxASA3NxepbHz48KFz586UjNku5WUA8Pr1a3RAOsIhQ4ag4IqJiYnYzJA0aDTapEmTzp075+fn99dff+no6Pzyyy/CD8tms6dNm/by5cvOnTs/evRIQ0ND+DEBQEdH58GDB2w2W1pamkaj7du379ixYxcvXqRk8org8Xikcg2FLtbBweHdu3d+fn6jRo2iasyvUVX3ZZ+srhRK0gEAKvNBVhkA2A0/gPQxCV4RNo2BgUG/fv2qq6ufP3+enJyM4vxCEhkZOXv2bA6Hs2PHjo0bNwo/IMm7d+9Wr1599uxZADAwMMjMzKQ89paYmOjv7+/v79/mUqfGtEt5GQBs3Lhx06ZNmzZtwm0mxQz/HsDz589tbW2FHBCt4IOCgrS0tIKDgw0MDCiw8guXLl1ycHBAGheysrLl5eXU7l40NDT4f4HFYlE1LPlLJkSvE9lXgUUHAgBAwwR6TYNe06CzDfqvzpo/QKYCCXaEX2XkyJEAsHr16oCAACkpqfr6emFGS0xMnDhxYk1NzbJly0jlJ6r4+PHjmTNnLl26BACysrJ2dnbEl7RMqrC3t9+zZ8+ePXsmTJhA4bDi3B0Vw3MB0zwjRoxQUFBISkpatGgRKkgVZjSCIJYvX+7r66uqqvrkyRPKV/O3bt0KCgoKDAwEgF9//RUAAgICKFy6ycrK7vmCiooKVcNaW1vr6enl5OQkJCRQNWaT1P+TMD3ksCyviaZ1irJSG37pKtKrUwt2hE1TXFwcEBCgqKiYmZm5devWIUOGKCkpWVlZrV279tq1a629gZlM5tixY8vLy6dOnXrmzBnKrR02bFiHDh1SUlJQLW17xd7aAHq+iKEHZG1trZ2d3a1bt06dOkWj0dDJkydP4op+cXL9+nUWi9WhQ4crV64sXLjQ0NCwY8eOkyZNOnToUERERGvnmlu2bLl48aK8vPzDhw/brPDQDPz3kZhjb8JAp9NR6o1InwDszPSPqxZ+zElUC1ohp6QBXb9sw2p0lzcZO7Cb6ooxbRTJah/arXDjO6ampsbGxgYAevbs6ebm9vvvv/fp00cgG7tbt25OTk5ubm4JCQkcDqeZ0fLy8gwNDQHAzs6OLJynnDlz5gDA0aNHCYIoKSmRkpKSlpZuQx1xk7x79+7t27foODU1NTU1lZJhEWZmZgDw5MkTCscUoL6+fvz48QBgZGTUWr0CDFU8ePBASkoKAFavXr1//357e3uBeJ68vPzQoUO3bNni7+9fVFTU/Giurq4AIC0tHRgYKCKD8/LyaDSakpIS+s5s2bIFANasWUPV+Ldv3yaLHVEyLVWgVay5uTmFY/LT8Cn/dP/u0zSUZOg0ABjRqVtnx2vS8wLlFgR1+O3p3nsZDRyeiC4tItrZEc6YMcPU1HTp0qWenp6ZmZntawyivr5+3LhxyNWh9HpEVVVVeHi4i4uLvb19hw4d+G9gJSUlW1vbzZs3+/v7l5SU8I9WVFSExOAHDRpUXV0tOrNRecPQoUPRy+HDhwPAjRs3KBkc1VH17dtXFLIgqAfFihUrKB8ZweVykfqilpbWu3fvRHSV74f09HRVVVU7O7tdu3YFBwfX1ta2t0UEQRAhISFycnLQSEsoMzPT09NzzZo1lpaWAnNNIyMjJycnV1fXN2/eCFTHX716lUaj0el0qr7hXwMp7yNfGxUVBQBdunTh8ah5yh86dEhVVfXUqVOUjMZPXV2dsrIyAGRnZ1M+OKekOOfXUZ7ddWRoNACwUpJNtzAs9/GoZDUUtoumDI9X9ej+h9m/ZNn2yh7R7/OmZexsZmvHEJUjfPDgQVhYWFBQUHBwMDqTn5+/Zs0a9CRNSkq6d+8eQRAC+YedO3eeNWvWyZMnY2JiGhoaRGRbM3A4HKSvpq2tnZaW1szbkpOTPT09ly5dKqBPz2AwTE1NnZyc3N3do6OjUbG8mZlZaWmpSC2vqqqSk5Oj0+mfP38mCOL48eMAMGvWLEoGf/nyJQD06dOHktEEiImJAYBOnTpR9XwRYM2aNQCgoqISGxsrivHFRn5+/rFjxwoLCw8dOkSe9PDwuH//Pjo+cuRITU0NauxMIiMjM3jw4A0bNty9e5d/YidOoqOj0XN55cqVzbytoqIiODh4165d9vb2qqqq/D+FsrIycu3+/v7Xr19HK0tXV1dRW75nzx4AWLp0KUEQXC5XT08PAMjdESFBdYReXl6UjCbAtGnTAOD06dPUDsutqvwwe2JI704qUnQAGKkqf9xQq9Tj7Dc/WJ+VUXhgx4dZv+Qt+LXkwklOOUXPQx7v8+ZVrwb0SO7fhWlhyLQwfNWvS8rgnjURz1s1jKgcYVVVlaenJ0EQbm5u6ExycrK6uvrevXsJgggMDNyyZQtBENXV1eHh4a6uro6OjgL7JAoKCra2tmvWrPH19f3mPgkl8Hg8VNKgqqoaHx/P/19z587duXPno0ePysvLG38wPz//9u3b69evHzRoUGOd+27dun369EkM9qPdPw8PD4JqaccjR46QjwPK4fF4+vr6APDmzRvKB9+6dSvacwsNDaV8cPFz9uxZgu+eIgjCycnJ2Ni4oKCAIIhhw4ahzfD8/HxfX98ml1l6enqOjo6urq7h4eFsNlsMNqelpWlrawPAvHnz+Bd2QUFB8+fPP3/+fFJSUmM5NDab/fr1a1dX1xkzZnTq1ElgAwYA0JNE1Lx9+xYAdHR0kIW//fYbAOzZs0f4kUnhi6ysLOFHa8y1a9cAAOXNUQWvjpW32PGlWWd9GSkAGKgkF9fP4OPBnd/8YPnt65k2PTMHdEO+KnNQj6xhfVlJ8d/84DepuO2VZWvqqKHka6KHBp+hqXyjh27WkN6ckuKWjyNWRzh37tyhQ4dmZGSQjpAfLpebnJx84cKFBQsWCPSYpdFopqamq1evFpG1CFTSoKCgEB4ezn9eQMwe7di4u7snJyc3XsTU1taGhYW5uLhMnjxZUVGRRqMhzyQGzp07BwAODg7oJYXSjiil5erVq8IP1STLly8HgJ07v31HtYpTp04BgLS0dEBAALUjtxdNOkJXV1cnJyeCzxHyU1FRERQUtGvXLqRpJ7DMGjNmTEhIiOgMzs3NRSUNkyZNEtjjWbduHb9vI4MLTe6d5OTk+Pj4rF69unfv3vLy8h07dhSdzQKgrmFRUVEEQQQEBACAhYWF8MOmpqYiFyv8UE1CJgpQtRfFa2j4uNo5pm/nbnLSANBfUTapf5eCneuJb23k1MZGZ9n0WqKjetlYB/mqpTqqF7ppZw/vx60QNokhe/wgpoVhY0eYOcik1MPt25//gqiyRkNDQ2tqaoKDgzkcDtnNi06nHz16FG1VAcDJkyfJwDj63969e//2229Xr15NS0sj90ns7Ozk5ORSUlIorGBrzIEDB44ePSotLX3nzh1UW02irKx8//79jRs32tjYyMrKZmVleXl5oQwaXV3dKVOmHDlyJCIiAgl+ysvLd+vWTV5efu/evZs2bSII4tWrV6Izm5/JkyfTaLSnT5+iOnEKc0dRdAQlEImC1ppaVVWFUsObSd/18vJau3YtUvAh27H+0BQXF3O53NDQUA6Hg/4iCDs7OxaL9fz5cwCoqKiYM2fO6dOnY2NjkeqeiorKuHHjdu/e/fTp07KyMv4t/erq6uDgYNFpcRUVFY0ZMyY3N3fEiBG+vr5oP5Nk6dKlp0+fnjNnjoGBQXV19cuXLw8dOjR58mQtLS1zc/NVq1Z5e3u/f/8evdnAwIDBYFhbW79580ZWVvbjx49ZWVkiMlsA/gxMOzs7JJBLGtZmIiMjAUDgUUMh6urqQ4cObWhoQDqRwsLjFf65oSgsZBGzMLOuwURe5lI3Hc2RY7R2HYYvOdhfo/T0IV4di0cQZPkSDwAACHZ9xR0fYYwi6ljc0n8FEX2Kqo5+LDv6sSypph4NXhcX3ZqxxEVycjKat/7++++///77li1bUF86BLlj0zgwThBEfX19VFRURESEiGxDayk6nX7z5s3m31lXV/fy5csjR45MmTJFR0eH/zdJBmNOnDhx/vz5Fy9exMfHA9++ihgYMGAAfKmlRfopwsfemEwmAGhqaooohkcQBJvNRjGh48ePtyS8HxUV5e3t7e/v/8cffzT5BjJB8fjx4xTb+p3h5OSUnJycm5s7ePBgW1vb27dvk19IRUXF5oMLHz9+vHv3bkVFhSgMq6ioQJkm/fr1+2b2cl5enq+v79q1a62trQWCCx07dpw+fbqfn9/ChQvd3NxYLBZKfTpx4oQozG5MSEgIAPTq1Qu9nDp1KgCcOXNGyGHRLitK8xYRJ06cAID+/fu/ePGipqZGmKGKjux5Z95liIo8ABjISkWadc5fOptX36Ic+MyBPZgWhou0VZbqqB431DpuqDVSVf5CN22mhWHeIkdhrOLV1zEHGKMV4TFDzcBeHQN7dRyvpnCjhy7TwvDj2iUtH6odHGFJSYmuru6WLVsePXq0ffv24cOHKyoq8n/11dTUJkyYsHfv3mfPnlVVVYnasHv37twnTZEAACAASURBVDEYDLR0aO1nGwdj1NTUbt68GRUVdfjwYeLLvsqrV69EYHgT7Nu3DwAWLVpEUBd7Q8EGcsdVFKSnpysrK5OZU7q6uvb29i4uLuHh4V+rdjh79mxZWRn/JiHJs2fPUIIiJbGc7xzkCAmCOHz4sJSUVGpqqru7+4IFCwQUAVFwYcmSJVeuXKG2+qVJamtrhw0bBgDGxsYoe6vlsNnsN2/eoLwBUj7+4MGDu3bt8vT0zMjI8PHxAYARI0aIyHgBGhoaUJY4yp7z9PQEAKQXLwwoyQ7tuIqIpUuXkpvhUlJSZH5+a6OSJWeOpJkbjldTBABtacaLPvofnCZza1qaA585wIh0hCe6ap3oqjVKVeFfRzhvcut/rP/Bra3JHNSz6a1R217lN1oRyhGfI8zPzyejZf7+/ihrFIGSMN3d3Z2cnJpMwkR/P3TDU0twcDASqj948KCQQ6FgzLVr13g83osXL9BEG+0Db926lQpjvw3aPdbW1kaljZTE3pYtWwYA/JmK1JKTk4NcoJmZ2cSJE5usLdu8ebOfn19hYSH6yKlTp44fP56QkLBhwwaBiojXr1+3JEHxp8HDwyM/P58gCDabvXbtWv6Jf3l5ORlcEOjMpaKiQtZXCLlWaAybzUZ70fr6+kKm7/N4vHfv3nl4eKSkpOTl5aHOneXl5TIyMgwGQzw5dARBzJ07FwBQb15KYm9lZWV0Ol1WVlZ0hcUHDhwAAGlpaQcHByQ+zP8FMDAwmD179qlTp968edN8fn75jasZFoaOGkoAoMKgP+zVMWfKCE5JK37zOVNHIUd4qdu/McIlOqrIEb4fP4hb3calDq+h4ePqhTF9Oyf179LYEWaP7NeqkdutjvDFixdPnjxpclvmw4cPN2/eXLNmjZWVlUBcoVOnTqtWrTpx4kR0dLTwOW+vXr1CGWgUFskKILCvIgbQUgBtIyMNz759+wozYN++fQFAIIGIKgoLC3v27AkAgwcPJussm6kta34LnUxQnDt3rti2o78fOBzOpUuXmszhqq+vj4yMPHbs2NSpU1ENAIm0tPTAgQN37drl6+uLfKow8Hi8hQsXAoCmpibZ4Z1yUFMklI4nBnx9fQFgyJAh6OWIESMAwMfHp80DooJ3ckDKaRzrQfn5qAxaYK7Jv4VeXPyfTMvKgHtMy65LdFQBQJ5Ou9VD7/0vgxo+5rXKmEr/O1m2po0d4X4DjUizzh/mTuKUlnx7FAG43NxNy++a6JkqyPSSl4ky65xuYYgGTx3Ygzmsb90/ia0ar90cIfoq81fdNXkD19TUkPUVSCIZfQvRDWxpaYn+fuRaoeUkJSWpq6sDwPz580UX/RLYVxEFLBYLtR+7c+cOk8ncsGEDAKA8nfr6eqRh2MLNEB6PV11dXVZWlpmZieaJFRUVDAZDWlpaFHXZFRUVlpaWyFV/bX5dWVnZktqysrIyMkHR3t6+XYpQ2524uLgmfzON30lu6dva2srIyEhJSZHhCT09PXJfug21N+vXr0cGoNWbiEA6hVOnThXdJVJSUlJSUt69e/fgwYOKigpUpIvqoFDsbebMmS0cCsV3srOzUZUL8UVE4msRbiFpSawHzTVRzhTtv9kuZFZ87P07zAHd1ndUAwBpGs3DWCfbzpL9vvXFHjze521rXlr1iO9ngHxVeB/9fQaaAKAhzfDr2fGYpemHhNaUUvB4hfu37TXQ6ConDQAdpOhRZp2z7SxzJg/LnTG25PShNnjWdnOEf/7556BBg2RkZPj/Brq6ulOnTj127FhkZGTjm5DH46WkpHh7ezs7O/fs2ZP/70ej0Xr27Ons7Hz58uWUlJRvOrbMzEw0L3ZwcBD1Q5N/X0UUFBUVHT58uLS0NCcn5/Dhw6GhoQBgbGyM/nfGjBnQ4tLj4ODgM2fOnDlzJjg4GG3aPHnyBAAGDRpEudm1tbVI/sbY2Ji/zpLJZJ47dy4xMbHxkq6hoSEmJubkyZMzZ85E4U8SKSkpNOEYPnz4d6KlIn4SEhKa/M1YWlquXr36xo0bubm5jT9VWVkZEhKyZ8+ecePGCUg/KykpjR49+s8//3z8+HGTFbQC7Ny5EwBkZGREKphHfBE/U1RUFJ1gHpPJdHd3f//+vbu7e3Z2NuoYdfnyZeJLka6KikpLJgpFRUVLlizhcrmbN28mgztI0J9aWTUEGetxcXHhP4+SywQWfIiCgoIHDx5s2rTJ1tYWBddJVOTlAIBBgzNGWllD+9SltG6Z9T94vEr/OzlTR2UO6JY5sEfWMLPYfgZdZKUAQJFBv99Tb3OPzvXMli4VSk4fDujVsYecNABoSzPWd1RrVcyySdpZYo0/MI42tQRuYLTgKygoQN/ImpqaCxcuPHv2jODTobCzs1NQUPjP3+9LCMTf37/xDVxQUIDqFEeNGiUG5UmBfRVRgHJG4uPjz5w5w+Fw0NIZTQhQLV3L585ubm6hoaHHjh1DS9hdu3YBwP/93/9RazCHw0HZd506dRIIIyENSWhBbVl+fr6/v//mzZvRskZPT09dXV1EOZA/FgK/Gf5bg3/BV1dXd//+/devX7948eLChQto4dKStUKTmzdolcZgMG7fvi2GnxGlpIquQpTD4bi7u7NYLFdX14yMjPPnzwPA5MmTCYIoLCxE4sMtTHVxc3Orr6+/evUqynRtaGhAEZnWphF9k2ZiPaQoQTPCdcg29EB2cnLq0qULAKgrKTlqKGcO7lkbG02Vnbz6+sTl82dpKutKSwGACoM+V1M5a0R/VmLcNz+LYpa/qisCgByddryr1m/GnWuElkz6vkS3U1NTr1y58ttvv/Xu3Zs/OKShocHhcNzc3Hx9fTMzMwUmOwRBNDQ0kOk2KFGThD/dJisrq7y8vH///gAwYMCAyspKMfxQAuJnlFNcXHzw4MHQ0NCrV6/6+/sTBLFgwQIAmDlzJkqW0dDQmDhxIvnsa2aoqKioJUuWPH78+PDhwyi3EO1g37lzh0KDeTyes7MzMuzhw4ceHh4eHh6XL1/evn07QRBPnjyZM2cOugn5/4j9+vVbuXLl9evXm9zmLSsrQ6uZ9PR0Ck39Caiurn727NnevXsnTJggoJF7+PDhkpISHx8fFxeX3Nxcb29vgc9+/vzZ39+frOXl/6y2tra9vT0pZ3r9+nU6nU6j0S5duiSenwuJn/32228iGh9F1B4/fnzq1KmSkpL8/HwajSYvL//rr79OmTKFwWD06NEDPVXev3/fzDjV1dVLliwJDw+/fPkyKuZBHbO7d+9OrcH8sR5/f/9t27ZFRkaiKWx9ff3WrVuHDRsmsGBQV1efOHHivn37+Osr3r17d+nSJSaT6e3tjSpVpgy0qg79m1preZyGgl0bLxlrD1OVBwBZOs3DWCdriGntq+ZyEVDMcrGOCvwvZjm44ZOwgW3ie3OE/JSWlgYGBu7YsWPkyJFoO97Nzc3Pzy8lJaWxIxTg/fv33t7eq1atapwuhVIKTU1Nm9wlEBH84mdiIDQ01NnZGeUZoRkiiaKi4ogRI7Zv3x4QECCgD94YLpeLwnKt1ahEM82vrbZRFFNBQeHly5cEQWRmZt68eZPL5Qr8WZFw3bp16wYOHChQW6anpzdt2rTjx4/zqz7OmjULJKBwUBh4PF5ycvLFixcXLlxoYmISERFRVlbm4+Nz8ODBjIyM5qc7dXV1ERERX6ugFX/VpoD4mRjYuXMnavakpqYm8FTp2rXrvHnzzp49+/bt2+Z70RBfBI8WLFjQqquTP2aTc9nGsR43N7fG9UVsNjs6OhrtwAkI10lJSVlZWa1Zs8bT0/PTp0979uy5ffv2zp07+ftvUAyPV3xsb5q54UxNZQCQptFOdtXKHNSj+u9HTb69+kVw5oBu6/T4YpajLdjvqWnV8P06QgE+f/586dKld+/eeXl5tUo9WSBdSk1NTU5OTszrBgHxM1ETFRWFch9Q2cY3N7vQPknjzS70rOnatWurrv78+fMTJ05cv379xo0bjZOYdu/ejR6dQUFBBEEkJCQsX748Pj5++fLlpHh0Y5qsLRNYENy4cQMAhg8f3iprJZxnz55du3YtLi7Oy8urVakx/BW0NBpNR0dHpJv/TcIvfiZquFwummmhHiZt60WDQOO0qmq5vLx80aJF1dXVO3fufP78ucD/No71nD59+vjx42lpaWvWrGkmE/hrW+i7d+9OSkq6cePGsWPHUNL448ePW25tqyi7ci7DwnCpjioAMGiw10CDadWt8sEtgbfVxkRmDjLZ01kDve10V62soX3q3iVRZcYP4wiFJCoq6vTp058+fUKhBf5nbmFhoeiyRhHkvopIOzEhEhMT0Z25YMGCxj8Xf2D1m7VlZ8+eBYB58+a11gY3N7c//vjj3LlzAlMWNCCDwfD19UVnWCxWaWlpVVVVaWlpy7snoi30JUuW8KcbiL+8TMLhcrkXL1709fV9+PAh/Hevj8vliuGvsHbtWgBorFosums12cNEoBcN/1yzyax4lNuclNS6h/iFCxeSkpI2b9587Ngx/vNNxnoqKipKS0tra2tLS0tbWKpYVVUVEhLy119/jRgxwtPTMyEh4e7du0FBQX/++ScALFu2rFXWtoryW55MK6NNnTqgX9zvOqpMy65lXhfIN9QlJ2QN6X2iqxYdgAaw30CD2pglITmO8NChQ8nJyadPn0Z1pgsXLkTnZ8yYwWAwRJrqjbC2tgbR5Inxw2QydXV1AWDKlCnf3KJBwnXHjx+fNm1ak7VlqMLvwIEDrbIhJSVlzZo1rq6uFy5c+Pvv/4UWvL29URjp4sWLbfnZWsDYsWNBlOLgGH4KCgrOnDnzf//3f2R+FoorR0VF6enpUdv3oEmePXsGAD179hT1hbZt2wYt7mGChOs2bNgwePDgxlnxKPtUSUmpVbnNbDZ7/fr1kZGRW7du3bVrF3m+pqYGqZX26NGDrM2gltjYWADo2LGjSFcLVY/uZ1obH+6iyaABAMzXUkm3MMyZPCx77IBsO6uswSZXjf9tf/hHpw6Z1sY14RQrxUuKIywtLfX29vbx8fnnn38AQENDA+2kr1ixAgB27NghagP2798PAM7OzqK7RH5+PtosGj16dBsUKwRqy9Cti1KxW1VbVlNTU1payuVy+Ze/T58+RWMi5TkRgVacv/76q+gugeEnNjYWJSg6OTnBF/mhsrIyaWlpCvsefA0Oh4Nqw0UqGodCegwG4+7du639bJNZ8Siri8yK9/T0zMnJaX4cHo9XWlpaXV3NYrHICS6bzUZutXPnzt8cQRgMDQ0BIDqayhVYY2rCQjIH9zxnpC1LpwHAFHWlNPN/WwzeMdGTp9MAYJmuKtOya6U/9WnJkuIIk5KSbt68iZwf2k8PCwsjvtTJmZmZidqA5ORk+JL+Korxy8rKkIj5wIEDhRdoraysRFUfUlJSTdaWNdOdsTGRkZEoZrlt2zYhDWseVF6moKBAuWwYpklu3rwZFxdHEMSdO3cAwMbGBp1HdXKN01ApZ968eSKdXV27do1Go1HVTC0tLQ1tWmhrawtIJnXv3r2Z7oyN4XK5KKVTS0tL1OKxq1atEsPNS6CGTcPMvLrrKjLoADBaVeGf/l0Ce3VUZdABYKqGUoaFYdl1kaQlS4oj5Of//u//4EttHJvNVlNTA4DMTGqyj5oBiZ8hrbL4+HgKRctqampsbW0BoE+fPt/MBW0h6NGGxIXbXFtGEERCQgKKWf7++++UGNY8SKrm4cOHYrgWhoSsEULaCKgYdMaMGaK+Luq2YWtri2wIDg7+8OEDVYP7+flRng2L+sP8/ffflZWVZLoNegTxzzWbr6Dl8Xi///47fCVmSTnBwcEA0Lt3b1FfiCCIupTETOvu93rqdZBiAEA/RVlNKQYAjFFTSDPv8mHeJBFdVxIdYVhYGAB069YNvRRbVxdUNrBx40aCIFxdXVtek1BYWLhv376Kigp3d/fG+zP19fWoPMPAwKBJ6RBhrOUPSCCaqS1TVVUl021QCCQjIwPFLH/99VcRLYUF+OuvvwBgyZJWdGDBUMKECRMAANURonZ9SkpKohOVRvA74NDQUFIRqSU8e/bswYMHf//998mTJxunN5M9THbv3k2VtbW1tSifS6CC+ZtdB8i5Jnr/li1boMUxS+Fhs9loLiueZPss215MC8PAXh01pP6tURmoJPfOvAvTwjB3urBNP76GJDpCDoeDUvD/+ecfgiDE1tWFX/wsMjJyy5YtLb9pUWtygiAEdoE4HI6joyPabKF2h2TQoEEA0LxWVvPdGQcMGIByKMaNG9cG1cq2gRr2amtrS6Dodvvi7u4OAJMm/TtnRxv1oku7JyHFzwoKCp48edKqToHotgoLCxOowSB7mKxYsYJCU1Hz5G82uG++OyPKe5eWlhbntsfs2bMBQCBhVURkDe3NtDB809fAUFYK7T4ZyUlHmOkzLQxzfh0lootKoiMkCAIJ5KN8SJGm3Z8/f57sJ8wvfubj43PkyJGWJ2KdPXuWx+NduXKFyWSSJ3k8HmrvqaqqikI1VFFXVycrK0un01sYBUQ07s5oZGRkaGgohqaS/KCMocjISHFeFPP582c6nU7WCIku7b6kpGTTpk3kJJIUP0tNTfXw8GiVzOnZs2eZTKZA/C89PR3N6ubMmUPtdAplzK1atarlH2lcQYvC9qdOnaLQsG9y8+ZNABg2bJjIr8TlZttZJfXvYqkkCwBGstI95GQAoJOMVHDvTlnDzBryKdv65kdCHeH9+/cBYODAgeiliLq6eHl50el0RUVFsqZ16NChcnJyHTp0QL2EwsPDW9JMqqqqysvL6++///by8uK/z//44w+0Q4ISfygkPDwchOvfVFFRcfjwYeCT/xYbqORr8+bNYr4uBu0ioFajIkq7r66uRldZvnw5OpOWloYEuFvbiyYmJsbLy8vf39/X15fMuvzw4QOS97O3txe+0ZsAqE1jm/s3oe6MSN3mypUrlJr2DSorK2VlZcVQpFt8bO878y7DVOQBQE2KvkJX9Xw3bX0ZKQDQkGL49+yYPWZAffq7bw/USiTUEdbW1iooKNBoNBRaF0VXF39/f7StQfaduHPnDoPBENR3V1EZO3bs7t27nz592irtU5TVLS0t/ehR04pEwuDi4sL/rGkbAuVlOTk5c+fOnT59OkU2fhVUXka5liPmmxw8eBC+iIfxeDzUb/n169dUjV9fXy9QMMBisVBfNoEkTBMTE2dn50uXLv3zzz8t98RVVVUmJiYAMHToUMp7mPB4PHQ7NK9N+k3c3NwAYMqUKeilh4eHtbU1f8GuiBg3bpyoHXDJ+RNp5oYTOigit/fEtNOmTh1SzLs4aanYKssBgAKdfq27btaIfqy3b6i9tIQ6QoIgJk+eDADnz58nRNDVhSwYQELSBEGEhISgmrw9e/bw61A0ExgXuIFzcnLIaHlaWlpcXFyPHj1u3LhBicECODg4AMC1a9eEHGf+/PnwpSMMKi+TkpISQ3kZvwPGiI2UlBTgK9JduXIl/y0gJFwuF/UUIwsGOBzOtGnT0LozMTGxhX0rBYYNDQ1Fe7lcLjc6OtrFxaWZ7pjCgH45HTt2FHIcJFNF1ght374dAFauXEmFjc0h4IApp9z3WobFv7qjygz6PRO9DQba9wb0emjb19Wi510TvYkdFAFAhkY7a6SdZWtaE0llopDkOsLLly8DwC+//IJeUtjVhSwYIAMk0dHRSPy6cXjg06dPpNyfwGJRR0eHFPhnsVjHjx/X0NBA6enr1q0LCQkRUUoej8dDxb/Cl5TcvXsXAAYPHoxejho1CgCuX78utI3fgL++GyNOUJEuymZ8+vQpAPTp00f4YXk83tKlS1E4HBUM8Hi8RYsWIb+Lst5IWtiLBk0rO3bsuG7dOoIgWCzWgAEDiK+oWgvPpUuXAMDR0VH4oVANhp+fH0EQr1+/BoBOnTqJRydSREW6VY/uM62MkOKoHJ12o4du2ljr6MeP4uPji4uLk5OSik/sz7AwXKitAgAMGrh00cwc2L3qKWVNuCTXERYUFDAYDFlZWbQhSVVXF7JgYOrUqahgIDk5GelfzJs3r/nYe21tbVhYmIuLy+TJk/mlpdHdfuzYsSVLlsyZM4f44giFNPVrpKWlITcs/FDV1dX85WUnT56k6lnQPAL13RixsXHjRgDYsGEDQWna/ebNm+G/4XBUDUz2MGmG3NxcHx+f1atXW1paorpAEhcXFwsLi0mTJsXFxZGOUEQgt01Jmda+ffsAYNGiRQRB8Hg81IpZDAWFVlZWAIB6vVFITVhIprXxpk4dAECKRrvUTSd7lHl9puB3puzKOaaFIXobDWCbvjrTqlvFPWq2xCTXERIEgYrQUR9RStLu8/LykByRnZ0dmlfm5uYijd1Jkyah/aKWw5+EaWdnd/z48WvXrs2aNSs4OFikjvDKlStAXcQUJQggfdH379+LsKsLHwL13RixgdKsyCJdStLu0fxJWlqa3LBB01ayh0nLQb1oUBKmpqbmkydPLCws0tLShgwZUlNTI1JHiJR7KREqS0pKQg8rNNVetmwZAPz555/Cj9w8e/fuBYDFixdTOCYrITbL1vRwF00aAB3AtatW1pDedf8kNvnmitvXmVZGf3ZWRwHhf+W5r5wT3gyJdoQorZHsriBk2n1RURGK+ZEiZ4WFhSj2PmLECCEf/Q0NDcgR5ufnDxgwYOXKlaJzhKgk4+jRo5SMduHCBQCwt7dHL0VaXtbQ0IDa9ubl5aHY/sULF775KQyFcLlcVHuANh6FT7v39PREImdkmgYKVvH3MGkbPB6Pw+Ggqr7t27efPn1adI6wuLgY9Z+hqqAWyVSh0qzHjx8DQL9+/SgZuTEsFqumpqa6ujokJAQ54IZ6anaP69PfZY3od85IG2lt79BXzxzYvSaquRz4qiD/TGvjY4aadBoAwKQOin8ZaPgumUcItzMs0Y4wIyMDANTU1FCe9MaNG8eMGdM2R1hTU2NjYwMAZmZmSOSsvLwcxR379evX8gZDzYAcITrQ0NAQnSPs3bu3MBMCAVB5GbkFLbryMh6PFxER4eXldfXq1XPnzqFkqPHm/bLHDPi4dlHZlXOs+BgeRTcwphnQHuC+ffsIgigvLzcyMlq9enXbhnrw4AHazCR3FCnvYYIcYU1NjaWlpegcob+/P1DaLHP9+vUAsGnTJoIg6urqkCAwmgVSC4/H8/T09PPz27t378GDB1Ftyd1BvXOnjynct7Xy4V12Xhv1vtm577PHDPDqrovaSmzo2IFp1a0q+Ns58LXREVlDTM8baUvRaABgrSS3VEe14M8NPE7rttz4kWhHSBBEr169AEB4p5Kdnd21a9du3boh4bTa2tqhQ4cCQPfu3T9//kyFpYSbm9utW7cIgmhoaBg4cCDltYOIsrIy5LcoTBkQQ3kZQRBbtmxZtmzZ1atXL1y4kJ6evmPHDjqdLk2nL9ZWce+m/bpvZ6aFYaa18Ye5k4oO764KDuSUFFNrAAbh5+cHAJQ4lUOHDgHAX3/9hV6KoocJ6Zz8/f1HjRKVcMnWrVvhS6NsSnjx4gXwFemifFpXV1eqxkfk5eWZm5v7+/v7+fldvnx5x44dkyZNAoDuctJ/dlZ/0LMjahCRPdaab67ZoiVvQ8Gn9/ZD7proKdDpAOCkpcK07Fpx/2YLDatLfvvPsH6uhlqoVYWBrFRy/y5ZQ/tkDjDKHNDtw+wJNWGte6RLriNkMpnV1dVItQ+1khGS/Px8NCNjs9koMKavry9kzVCTJCQk7N69u5nG08Lw6NEj+KJiTBX85WXEl64uFJaXEQRx9OhRtF3222+/RUZG7t+/397eHgAUv8gVAoC2NOOXDoo79NUf9OyYbmGIbuDPf6wo9/GoS0kk+GLDDRzepecfbHZHGq17PvDPl+f/fs/miDYl7+egtrY2PT29trZWUVGRLNIVElKYKSoqCpUkUehOSGpqanx9fVExlSgYNmwYUKoFzy9TRRCEt7c3AIwcOZKq8QmCKCoqQusEc3PzvXv3hoSEbNu2zcDAQFnhfw295ek0SyXZBdoqp7tqxaC5pk2vPOfp/841y/5ThZKYWzn1RKzO8mDNpU9Hzjp7y2aEoaw0kG0lrrUuihH/OOCYWbeLxtqKDLoUjXbeSCe6b2fUtinKrPNr6+4lbq2ITEuoI6yqqgoLC9uzZ09UVBQAdOnShaoFCo/HQ/ptmpqa6DtKOajI79w5CkLEjdmxYwcA/PHHHxSO+e7dOwBQV1cXRXkZwdcrhwwjnTt3DgDodPrvOqordFUHKsuhfmYkKgz6CFX59R3VrnfXje9nkNDPIGtI75S5DsWnXD7/HWyxJVTRyQ/mBKJ/ivP9zDaHldVQrDPy8xETE+Pp6fn69WvKv6KJiYkoAXXBggWiqBMQ+IpSC5vNRvId1MqyLFiwAAAOHjxI8OlEFhdTs89RWVmJckTNzMxQVSUZ6+mhpbFDX32qhhJyYyQ0AGM56Rmayoe6aD417fSmr0GaeRfmxKHpm1ZU3PXxuv9GYWEQfV4gzPKDWX60OQHysx8sG/zrbE3lNPMuJW5tyUhg5+XmOAx/YtrJzUh7u776PgMN5Ah36qvv6ayRadOLFdfS2baEOkKCICIjI48dO8blcjt06ECj0aZOndpaHYomWbduHQCoqKi8eUOx9gGJh4cHAIwfP14Ug6McE7SNSSEoaejFixcEpeVlRFO9cu7evctgMGg0mtuB/Z83r3z/yyCmhWGaueFj0077DDSmqCt1lv1PDj2dBupS9EFKck5ayqv01H6ZtFd2th+o6JOOEFT0Zec/nngkhhKDf2IaGhq2b9/OZDJPnz4NADo6OqhvpZAxciaTiUqSpkyZIroeJvxfUWqJi4sDABMTE2qHvXfvHgAMGjQIvRw9ejQAeHl5CT9yfX096pvYZKwn/fzJPOfpmYNMmBaG8f0MPLvrrNFTs1WWl/vvQkXJ5QAAIABJREFUXFOOTusiK23fQdFeXfGelbn87Psw8z4YjoSOA0B/MOgPhum35Gc/uGszovBA2+fEDYWfs2xNmRaGjR0h07Lrx7WLWjiOhDrCsrKyy5cvnzlzBi0m1NXVyb8fvw5FqySniS/LKRkZmadPn4rIcoIgiouLUQVkRUUFVWOy2Wz0jWez2REREWlpaVSNjNi0aRMArF+/nqC0vIzslbNnzx50Jjg4GCn4IDkbREPh5+rQv4tPueQ5T88c2J1pYRjeR9+1q9YCbRUzBVnGf+5foMlrwKANAo4Q5gQqOAcxP+N+v83x/PnzEydOvH792srKSqBtwjf7Vn6N/Px8lM49evRokTZ14v+KUkVBQQFKF//06ROSxaBw8JqaGgUFBTqdju5cpLkovIQhh8OZPn06AHTs2LGZWA+P01CXklju41Gwc/37ibZMC8N35l1um+ht01cfp6agLc3g/+vTaXRan1nQ3xn6L/z3nhq4Fno60GYHjl58lRBO2Tx7lDlyhNZKcnM0ledoKlsqye7prMG0MHw/flALB5FQR4jw9/dHi4n169e7urrOmDGjU6dO/H8/KSkpKyurNWvW3Lx5s8mAR3V1NZkPXVlZ6erqKi0tfefOHVFbPmTIEAAQMn2cn7S0tMmTJ6PjjIwMstqBKiIiIgDA0NAQvaSkvKxxr5xXr14hBZ9mgr7c2hpWfEy5j8fnP1agW+iMkfZ0DaVf1BT7K8rKMqQAAIZuA0UdGLDi33+KWjAnUHnxk+sRIonL/kyQ3TE7dep08eLFjRs32traoqkJiba2toODw6FDh8LDwxv7Bi6XS05AUT1M//79yZIk0SHwFaUEZ2dnMvt68eLFFPbiRqBA+IULFwiCyMnJEb5Il8fjLV68GABUVVXj4+OJFsd6Ggo/VwUHFh3enec8PdPaOKyP/mhVhZW6qj3lZQxkpWh0BlgtBz0LmHzpX0foeAc6GMGcQI3fhF0zZI8biBzhpk4dwvvoh/fRX6un9q8jtB/SwkEk1xG+fPkSxd537NjBfz4/P5/UPEMpaiR6enr29vYuLi7h4eFocuro6EgWnltbW7NYLDF0uicI4siRIwAwd+5cqgYUtSPkcrlodyspKYmgorwsPT0d6cDNnTsXaSAkJiailf38+fNbvuZg5+VUPrz7ee+W3OljMiy7nhr+q8KEk+B4B5T0YMSef/8p6cCcQKVFQdfC89pssCTA4XAE5EARDQ0NqJeQk5MT0pfgn2uirhGenp5otcFkMul0OvIfISEh69atKy0tFbU+LdHoK0oJonaEFy9eBICJEyeil6gxRWBgYJsHRMJACgoKpKltiPVwa2pY8TFlV859XLsoa0R/poWh/hR3cLwNuv3BweNfRzjzPqh2gTmBqkta0TarSQq2r2MOMGpia3SAceHelragkVBHmJCQoKamBt8qaKuqqnr27NnevXsnTJiA3k+iqKi4YsUKR0fH8ePHI80h5AjFY79ABaTwpKWl9ejRY+fOnTt37ly1ahXljpBoVF4mIyPTq1evttUX8/fKQdkNTCZTT08PABwcHNqc78CtrvrnSZi8UwC5Hcq/NaroHPRPnlgbK/5YtLw7JpPJvHbt2rJly8zMzAS6RhgZGd27d2/IkCG2trYNDQ3IEYrtR0CLIfQVpQRnZ2dnZ2d0W5mbm1PuCAsKCviLdHfv3i0nJ9fmVoVIuU1GRobUu6Ag1sPl1DPTpmx+SJsTCKbTYdCGf++pEXvAyA7mBA7eJWy9Mvt9VpZtr5366ge7aCJHuLuzxj4DjSzbXuy83BYOIomOMCMjA4lfkHKgLSQzM5PsGkGj0datW+fo6BgdHW1hYVFdXS1OR0gQBFKxoar9Slpa2vDhw+Pi4uLi4vz8/EThCAXKy3Jz//2OJiQklJeX+/r6tnBLubCwEKlVDR48GPUNIMNIo0aNEv5PYHcgWmb+YwFHKO30yGY37vTbHG3rjllVVRUeHu7i4mJvb48ix5GRkY6OjgcPHjx69KiYHSEqe6ewrN7Z2fnq1avotpoyZQrljpAgCKTjcffuXYIgSktL0QZybW1tQkLC27dvfX19yZY1zUMmWqNiZeJLczoGgyF8rCcyvVTBOQim3QDt3mA6HfrMBs2e4OCh5PzkXgwFIojVL4KzbHuh8D/TwjBzYI8sW9Oal63Ie5I4R9hYDrRtFBYW5ufnOzo6MpnMCxcubNu2TcyOEJXotlmzQwBRb40SBNFkeVldXd2RI0eys7NLS0tbIupWUVFhaWkJAGSvnPLy8v79+wOAtbU1JWGkwor6rmufy033JB2h3HTPzqtCPpZhVZqvQsqBCrMvx+FwkpKSmEymo6NjfX29lZWVl5eXOB0htRWQhOi3RokvmgPz58/nP5mamnr06NHa2tro6Gj+Vt5f4969eyjR2t3dHZ1BTcVpNNrly5cpsXPzjXeKzkEw2x9+OQ3jT8IsP8VFQXPd3lIyOEEQDZ8/lZx0yVvwa96CX0vOHG4obJ2MiWQ5QlIOdNCgQWgxISTIEXK53OHDh+vr64vTEb569QoADAwMKCmrEoMjJAhiypQpAODm5sZ/MjAw8P3791lZWd8UIK2trR0+fDgAGBsbIzXtmpoalDfUu3dvqiqoCIKoruPsupPede1zhYVBBqufbfdNrWRRX17203D16lUajUan0ynpjokcIUEQISEh3bt3F6cjJL7yFW0zYnCE6enp0FQFJPoRzpw5883nQ+NEazKLkCrBYYR3RL7BmhBpp0cyTo91lv999ul7EXeOagUS5Aj5S0SRHKjwIEdIEERsbCyDwRCnI+TxeCjHFSV3CQmXyyVbcvN4PMrbcyNQBaSJiUlgYCBZW3b48GF/f//bt29/845NT0/X09Mjc7jZbLZAv3KM+Ll//76AHKiQkI6QIIhZs2aJ2RGi1ivjxo2jZDQWi0UGX/iPqcXY2BilvicmJqLEsfT09K1bt+bm5rakZRIKBG7cuBG9fP78OSpJElE7i0pWQ0XtdzetlBRH2LhElBI+f/5Mpqvk5uaKujemAKhV6e7du8V5UWFYu3Yt2noiMyNaW1uWmZmJcri5XO7MmTMbJyhixElISAhaTOzdu5eqMdlsNinPW1NTQ+FCvyUUFxdLSUlJS0tTIpQvBvLy8jQ1NTt37ozuKSUlJVtb282bN/v7+7c81TYgIADdgG/fvkVZgcuXLxel1d8dEuEIG5eI/hwEBgYCgLm5uZDjfPz48caNG58+fbp37x4l/dKaBMmBSktLT5s2zdbWFs06SbS0tCZPnoxKU765HiX7lauoqIihHymmSaKjo1HV5qpVq9rbFipBEipkzkib8fPzQ02DHz9+XFhYSIVpgpByoD179pw1axbKfiBhMBj9+vVbsWKFl5dXS5576enpKItwxowZt27d4nK5Dx48kJD76+d3hI1LREVEXl6ep6en6MZvDIvFUlZWtrCwqKkRSvGksLDw1atXPj4+GRkZ/IIsFNJYDpS/tgzVQvDXlpmami5dutTT0zM7O7vxaGS/8tDQUFFYi/kmycnJGhoaADBv3jxhell/Ey8vLzLBWDwcPXpURUWlzUUICDabnZGRceDAgezs7C1btiQkJFBlHkljOVCCID59+kSWQQvMNXV1de3t7Xft2tWkxg1ZkjRmzJiioqKjR48WFBQcOHCAVC78ufn5HeHx48dR2V9UVJSoL3T27FmRXkIANptNpic0NDQIo23/8uXL69ev5+bmIg1faiG7yjVzU+Xn5/v6+q5Zs8bW1lZAnUtPT8/R0dHV1TU8PLy+vr5xv3KMmGGxWKgufvLkyaJQqSZJTEzcvXu3SOevjYmIiMjIyEDHcXFxbW4gk5eXt3///g0bNuzdu7clsbpW0ZJYD5vNRnNNR0dHLS0t/ntKWloa6Rj4+voWFBSQK0syi/Ds2bNpaWkXL14ke2D93Pz8jrC8vNzOzq4lOcTCEBcXd/DgwSVLlohOFLgxFRUVZM1TdXW1paVl28aJi4u7cuVKdHT05cuXUUEShTSWA/0mFRUVT5482bVr19ixY1HHURKk4k+n06+67KfWTkyruH///vjx40WdHbZ37959+/bdv39fpFcRYOvWreRdsGPHjtu3b7dhkLq6Og8PD7S/mpqaSm07trbFelJTU69cubJkyRJUBs1/W6G4INlCvLKy8uDBg+Hh4eSP8NPzwzvC58+fu7i4vH37v3qUqKioCRMmTJgwwcHBgUK1pJYg6kWnAFQ5QtFByoGuXLmybSN8+vRp7ty5p0+ftra21lJXp9Fo6rIyEzsofpg9gVpTMST19fXu7u779/9vqsFms7du3Tp+/Pjx48f/+eefIl0FCpCTkyOi1ptfgxJHKDrIWI+amlqb18qVlZXBwcG7du2yt7dXVVXV0NBQVFTMy5NcBcH/9KP5ERkxYkRVVRXaeQOA8vLypUuXPn36VFdXl8lkTp069c2bNwKSoaIDtWIXJ6jmDwC4XK6YL/1NkG1VVVVz585F0vhtQFdX18bGZvr06Xl5ecM5NXIKsDevtEKWUZ+W0pD/QbpTZ2ptxgCAjIzM4sWLjx07Rp5xdXWl0WiPHz8GgPXr1587d2716tXiMUZAm1Q8HDhwAJX6pKen9+vXT/wGNMMff/xx+fJlBQWFgIAAJCXRBlCPHTs7OwDgcDi9evViMpnp6ekCXQckB/q33/J9ExgYmJqaipKdAODly5cjR45E4rnGxsa9evVKSkpqVwNFS/fu3QMCAgICAu7cudPetvyHDx8+jBkzprCw0N7e/urVqwKSki2nuro6MzMzPj5eV1dXraepg7oSgwavquoqubza8BBqbcYgWCzW5s2b+Wd1Dx8+RO2UAWD16tUPHz5sJ9PExLZt29BthUp0vh/27dt39OhRGRmZu3fv2traUjKmlJTUtGnTAACJIEomP7wjtLS0nDZtGtnnpaqqCqV0I5SUlCorK9vJNMmlqKho7NixOTk5gwcPvnnzJrlebwNKSkrHjh0bN27cunXrNrkcUZOiWyrKcQgirJJV8yKYQpsxJAwGY8WKFfypvJWVlWiLGwCUlZXxPdUunD9/fufOnXQ63cvLC/W6ogoHBwcAePDgAUEQFA77A/HDO0JdXV0jIyPyLjU1NUX9oBEJCQlIoPmnhMFgIFEJAKDT6eRx+1JZWTl+/PjU1NS+ffsGBgaiXleUIN25i7RhtzFqCgDwd3ltXVw0r7KCqsExJDIyMkZGRvyOsHfv3rGxseg4Nja2T58+7WSaONDV1VVVVW183L7cu3dv1apVNBrt3LlzqNcVhQwcOFBPTy8nJycxMZHakX8Y2jtIST3Ozs7r16+/f//+okWLtm7d2t7mSBa1tbXDhg0DAGNjY1IfhEKKTx160UcfAJQZ9HfmXaoeiTWfUGJJTk4eOHDgtWvXrly5Ym1tnZ6e3t4WSRakHOihQ4dEdAnUQqvlqd0/GT/8irAxHh4eDg4OZWVlv//++86dO9vbHAmCy+XOmzcvLCysU6dOwcHBZOCWQhRHjNGXkeohL1PF5UVX1eHdUfHQu3dvf39/GRkZBQWFhw8fCggg/H97dxrW1LX2DfzOSAaGMCmiIqKIUFERFRS0lEnFhFAr1tPavlptazmnet6jdjitfarPsb2srdricaitin1ttblUQphBUECCKCpWUBTKICrznBGSvB+2TS1aK5gBsu/fp33thJ07F2z+e6+19lrIqIqKimJiYlQq1bp164i1royBaB0lbzehuZPYWGpra2fOnDllyhRzF2IKly9fTkhISEtL27hxo6GW6h2EM2fO0Gg0JycnYjpQo9BoqiNnx7nYAcAKZ5tfg320KlwdyXR27Njh6Oh49OhRcxdidL29vUeOHNm3b19KSsq2bdtMPOXpw4iZ5VevXm3UqYyVSiXRwfTYuZwsngXeERJGjRpVVVV1/fp1YpkSy+bn59fa2mpjYzN58mSdCbu78/PzExMTie3z588T6+umpqYSs1QYBZXKnRcazuMAQFaHXCOXKy4VGeuz0CNYLFZraysZ7hvodLq/v39jY2NUVJS7u7tcLjfZR2/atEl/Fr///vvHjx//7LPPDhw40O8peMOysrKKiIgAAGIGY7Kx2CBkMBjElZTFD/UGgFu3bkVHR1+9elUul5vyjK2pqSkvLye2a2try8rKlixZMmvWLKN+KDckwpdj5cKgN/ZqyuVq2blnah3VymXqWzf6mhoMVZ5li46OplAoGRkZSqXS3LUYl1qtBgCdTpeVlUWn04knskwjNTVVq9US22lpaWw2+8MPP6TRaMb+XDK3jlpsEAKZfq+jRo26c+fOypUrvb297969a8qP1mg0arVarVab7Il+9uxgGocbxmMDQHanXHY2CwZ1E9zX1HB//Rs1oX5333y5LiakNnq+vCjf0MVaGjc3t+nTp/f09OTk5Ji7FuNiMplKpXLVqlW2trYODg4mfmKkt7eXOK1M2cDD5/PpdPrZs2fb29tN9qFDhCUHYVRUlJWVVWFhYXNzs7lrMS4bG5uQkBAulxseHv7cc8+Z8qOPHz++fPny5cuXx8fHm+YTKUwmO3BeuN2Dhyg0LU2q8gGP+e5raqhfHiVJS1eq1Nqebp1KlVleUft/3+pJs/zLpmdEnuvLmTNnjhs3LiAgIDw8nFhqw2ReeeUV4rSqq6sz2Yc6ODgEBwf39vamp6eb7EOHCEsOQmtr65CQEI1GQ85Wb8Pq7u4+cuTI/v371Wr18uXLW1paiP2vvvrqqVOnTp069c9//tNkxXCfjwi0YVnTqDcU6np13yDGjjb97wdaWfd399t7fmuD+r6pq1Mma972obarw9D1WhR9EOqb79CglZSUHDlyJDs7e+PGjRcvXtTvF4lExGll4gG65LnK6ceSgxCM/HtVqVQ6na69vZ0MLQk2NjaTJk2Sy+WHDx+OiIgwZYvNo7jzQ5l0+nxbNgBkd8gH2k2ounVDcaFA19f3mNcolJ6cDIMUaammT58+fvz4xsbGh/9xG4pOpyM65/oe+9uxOP7+/s3NzQwGw83NbSgM64uJiQGAtLQ04rdAHpYfhBQKJTMz81mGkJw5c6a+vp7YzsnJIRoramtrRSJRfHx8dnb2w6O8LFV7e7tGo5HJZCwWq7y8vKamBgCee+45/YyUPj4+c+fONU0xVFsea/qsB62jnXJ11a3eupq//Cl11a22A7vrV0TX/y2qQ9Xb0acFgA01LW9VNb1V1VShUAOAVi7vrak0cvnDHjHP+7NcXxJLaRLbvb29x44dI7ZPnjz58ccfJyQknDx5Ur/TgpWWls6dO7esrMzX15c4pwDgjTfe0M/Nu3LlShMMk9Fzd3f39fXt6uo6e/asyT50SDDfkxsmQizi/CwLY65duzY3N5fYjouLy87OJrbr6+uPHDkik8n0q65bttu3b3d3d+t0up6eHlMuu/hYHce+vzLNjUGh0Chwaapb+w8HH/s2rUopLypo/uLTrOBpX7k7H5gwgm/PncFl0SmUja72s6xZF6aOrZzhXjnDPcCGJfUdWzXbs+37PSb+LsNOdnY2APj4+Az6CB0dHQEBAcQ2sdK6/qXt27cnJia+9957ZFh4WaVSlZeXa7XaqqoquVxu7nJ0Op3u448/BoC4uDhzF2JSFn5HCEZrHa2oqNiyZcv06dNPnTpFtCdYvIkTJxITmnO5XFNepT4WNyTShkZ9ztZWowNhn+/z313alpC757/7iPt1TWd7d/KphvfiasJm3I1bkX/owOnKmq/utb9d1ZTcLrssU1IAWvseN8yVQecEhZj4uww78+fPt7e3Ly8vN3hr3scff+zo6Hjnzp158+bpm2EsGJPJ9Pb2plAoHh4ebDbb3OUAPPQPU2fprVwPG/brEf6lmJiYzZs3SyQSjUYz0H/fbW1txLDpf//738SwsevXry9ZsgQA3N3dt2/fzmAwJk+erF/7ApkM3XXMd5NeLaM0QcnBuz2dd7njKvb+NGJKWGXee++P4iqkeX29vVdkqrQOeUa7vKH3QYcTg0KZa8Pi0Kjb3BxtadSP6lrpvz2k7GHFYLJYnIB5Vl4mHXY7HDEYjKioqGPHjiUlJW3cuHFAP6vT6a5everh4VFRUSEQCOCPS2lu3LhRp9PZ2toqFIqHl5FBJuPv779169bIyEhzF2JSln9HOGXKFE9Pz6ampqKip52CpLa29ttvvxUIBC4uLh999BEAfPbZZxKJRCKRREVFEe+xsrKyt7e3trbGFDSLk8UN39ADe8cEAVDgXgloNapxkXdvXzt6QytKStlQ2TDz2p3ltxoSmroaevsc6bSFPO6qEbaZPq4vOduuD5vv8cFWh7hNn08azeNwAIBCo23zGusaNH/kZ1+b+5sNDwNtaNFoNAUFBevXrx83btyMGTPKysq8vLyIc+rEiRP6t/F4PHt7exqNhiloLsQsGQEBARQKpa6uzuArXQxNln9HCAB8Pn/Xrl1isfgJS1nqdLqSkhKxWJyUlKRfi4TBYJBt9NRwsfHHG3ItFbjOYO8B7VXQcgMUbdr7Je2ddeu1D+7/JrIYYXacF+zY/tYsKovNnjWHOz98XkgkzeHBM2G2wqU92anqygqaozNnXihryiDX+yahRYsWsViswsLCxsbGJ8yu3tnZmZaWlpiYmJ6e3tn5YM2sMWPGmHjaB/T0NBpNT0/Po9uWjRRBKBQKd+3adfr06S+++KLfSxqNRiqVikSi06dP37lzh9jJ4XBCQ0NjY2Ojo6N5PN6uXbt4PB7x0qRJk+zt7U1aPXqIViFXXJTezci81xQMFBq0VwGDAwBQm/fgHRTaCEf3t9hd4TzOGCadZu/Amfs8N3wxJ3AehcnsdzSa0wi75StN+gUsBfGQbnp6empq6qpVq/q92tTUlJ6eLhKJMjMz9ZeSHh4efD4/NjY2KChILpfn5uYS+4lZPU1aPXqixsZGYmbKxsZGc9diIhQy9IhqNBoXF5eWlpYbN24Q6/TKZLKcnByRSJSUlKS/UB0xYsSCBQtiY2MjIyOxwXNI6Wu4Jy88J8vLVhTl96l7c5T0d2FqX30RdN8DAKDSQauB0bNgdACMCeSrq/5LyebMC7WOWMya5g/GnKqYzPbv3//OO+8IhUL9xOtlZWXJyckSiaSwsJD4x0Kj0QIDAwUCQUxMjJeXl1nrRU+lsrLypZde+vvf/w4Ara2t+fn5qamp5i7K6EhxR0ij0aKioo4ePXrs2DFPT88nXKgadX539FiaznZ11W2qFYs5yZvCYDz8krrqljw/R5Z3RnmtpKdPk9elyO6Un+1UdGm0AJUAAGwHGB0A9UWgbIcpy8HRi0XTvbBkkduruA6l0UVHR8fFxWVmZmZnZ2dnZ58+fVo/iJTNZoeFhQkEAqFQaIxlKZFRubm5vfXWWwBQXV2dn0+KCXhJEYSlpaVE7P3nP/8h9tBotJCQkOjoaKFQ6OHhYdbqyEvT0d78vx/IC89SrFig04FGw1vzLm/FauWVi23ZaX3nc9X3795X9xX3KNPa5QVdCvVvrRdjreh2LlNueb6sdvYFCgWodLglgfoicPSiM+ivCaaa93uRQU9PT2FhoYODQ2trK7F8DwC4uLgQ4RcWFsZiscxbIUJPz2KbRonOv+TkZP2FKnG3Fx4evnTpUrxQNTutrKf+5UW5ldUeDIorkw4ABV2K8VzWGA6rrkuW36WQdiuvypUNag3xB0oF8OEwQ+04i+25E1iMPnvn5W7/uqWxlfcBNFyFnI/Azo296OuEl5xjl/7pkCj0jJqbm9PS0pKTk1NTU2UyGQAwmUwrK6tVq1YJBIKQkBA6nRTX1hZMoVDcvn176tSpAKBUKisqKqZNm2buoozO0v5qu7u7MzIyxGJxSkqKfgpQFxeXsLCw27dvFxcXL1u2bM2aNeYtEgFA296vNG3NWW1di3hcIgizO+WzNdqE++3ZnfI61e9P/oXYscPsOKF2bAc6jTHOgxsSwX0+guXrd0EL2yW/xiffanP20TCtobPuq4rPQu++CIBBaGBlZWVJSUlisfjixYvEXNtUKnXWrFlBQUG7d+/m8Xg7d+40+xwLyCDYbDaRggDAYrHIkIJgMUFIXKiKRKKsrCyVSkXsJDr/BAIBh8MpKioKCQkpLi4Wi8UYhENBT8op7W+/KT06hZLQ3KXRAZdGmcBiOtNpHiz6prFObK/nOPNCufPDrLx99W9mUmHzixM/ihh5MyLwQ2tdUhvcaaqS5WY4rv/QtF/FMmm12itXrkgkkp9//vnGjRvEThaLFRwczOfz+Xz+gQMH3n///ZSUlNu3b0ul0uDgYPMWjAyroqLi5s2bfD4/JSVl/Pjxvr6+f/0zw9bwCMKioiIKhRIQEAAAxcXFGo1mzpw5APDrr79KJBKRSCSVSvUXqv7+/nw+f9myZT4+PvojXL16NTo6+u23387Kyuru7raxsTHXd0EAoFOrNXIZsf3lvXZ7OhUAbit6I3icT8Y4erEZPmwrLpvFmO7PDQ61jeTTnf+0HZtqbcPz8wtvbE5qk2V3ylffqe2tqWK4TzDRNxnOvv766/Xr1/fbVigU2dnZycnJSUlJDQ0NxKuOjo5RUVECgWDhwoX6c4eYxVcgEOzcuVMsFmMQWhgWi9XU1PTtt9+6urpa/ECK4RGEly5d0gdhSUmJXC4/efKkWCyurHywUACbzY6IiBAKhQKBwNnZud+PFxUVXbp06ZVXXgkICJBKpVlZWcQ0achcKAwGhUbXaTQAsNHVPtCGBQCf3mkFgFedbegjXRziNnFfiKRyn2p6Ee7zkSHSfCsqpaRH2dKrcTybxVuJQfjX9u/frw/C/fv3T506dffu3VlZWQqFgtg5ceLEmJiY6OjouXPn9mv51Gq1eXl5TCZTKBTu3LkzMTFxx44dpv4CyJhGjhypUChaWlp8fX337t27adMmc1dkRMMjCPthMBinTp2qrq5+7IXqowLPE0n4AAAQR0lEQVQDA4nVgoRCoVQqFYvFGIRmRqFwAoPlBbmPvkLlcB3/tdk6POrpD8Z9IZKz49MAa1Zel+Jsl2LMuSzeyrWGq5UsGhoakpKSAMDHxyc2NlYgEDzhOXcqlbpnzx4A0Gg0Tk5OlZWVN27c8Pb2Nl25yMjy8vIoFEpsbGxubi5x92/Bhseo0T179hw4cMDd3R0Aamtr16xZM27cOAcHh0cvVJ/s5s2b3t7eDg4OjY2NOLzNvHrraupfXZxQ2zDTmuXNZgLA/2vu9rfj+k2bOvqoGKgDmwW3/lXB4QLpJ3Wt4Xac/Z4u7ulFNMf+DQOoH2LxOWJbKpVWVVWdOHFCIBCMGjVqQMdZuXJlQkLC559//sEHHxihTISMbsgFoUKhIBbkHDlyZFdX19KlS62srPbs2UOhUIjJDvbt29fb27tu3brBHX/y5MkVFRVnz559/vnnDVk3GjjlL1ca/vWmTqXSymUUGo3CYFr5THP5ch/V1m6gh2o/+M3N+C+DfrljRaVcnOrm9j/bbWNeNkbNw1RhYeGVK1d8fHxaWlrq6uo2bNgAAN7e3vpRMA9vD9Tp06eXLFkSGBgolUoNVjFCJjTkVp9gs9lr1qxpb2+XSCQymay3t9ewx4+OjgYjLE+IBoHl6zcuVTpi606Ht9Y7/OM912+Pu3774yBSEAC4z0eMYNB8OVZKra6wWyE7m2nwaoe1uXPnzps3T6VSzZkzZ9KkSYY9+IIFCzgcTnFx8f379w17ZIRMY8gFYV9f3+7du6OiosaOHevi4lJRUQEAEyZMmDDhwfAHDw+PiRMnDvr4xPIxp0+fNki16BlRGAxuSIT9W+t5r71p5TP48dnMSd6M0WMnshkA8GNztyg1rb6q0nBlDns3b97Mzc0NCQkRi8X6pcT0M8L02x4oDocTFham1WqJmZoRGnaGXBB2dnaOHj26trZ29erVPB6P6K5ftGjRwoULiTcsWLBAfyYPwpw5c1xcXGpqan755RfDVPwQhULR1tYGAM3NzQY/OHoyq6AX+PZcALguV8vVvfQb18xd0RDS1dXl6upaV1e3YMECfbf6N998o3/Dw9uDMNDlCQdEv2YTeRZDQCY25ILQ0dExNjY2KirK1dV1/vz5Bj8+lUpdvHgxPNtJ297efvjwYWK7s7Pz0KFDxHZ2dvaXX34plUrz8/NPnjz57NWip2cbumAym8mj01r7NNXKXurF8+auaAiZPXt2bGzspEmTnqU15QkEAgGNRjtz5kx3d/egD5KYmFhdXU1si8XiqqoqACgpKbly5coPP/xw7ty5LVu2GKZchP5oyAWhCTz71WtnZ+fPP/+s39YvsS0QCGxtbQMCAmpra11cXJ69VPT06FNnFPRSpnKYANCj0crycnSaPnMXRRYjRowICAhQqVSZmYPvnU1PT6+rqyO2MzIyamtrAcDf35/D4YwdO/bChQsTJkzQaDSGqRihh5AxCMPDw7lcbklJSX19vWGPvHr1ap1Ol5eXZ2dn19raatiDoydjWLH+z9Ila13sAKCoR6nt6rj/zmvK0kvmrossjNQ6KpFIjhw5otPpZsyYUVdXp3/YHyEDGnKPT5jGiy++mJiYuHfv3nfeeWdAPyiXy8+fP+/p6Tl79uzZs2cDgFKppNFoGRkZxqkUDUDHD9817tp2vLU7zJY9iknXAUVnxXJ4+XXH9fh8m9Hdvn170qRJg3tIt7KyUqVSxcfHX7t2zcHBAQCuX79+6NCh0NBQ4xSL0B+Q8Y4QBn712traevTo0WXLlrm4uERGRlZXV/v7+ycnJycnJ+s7CJF56ZSKjqP7yxWqqz2qUUw6AJTLVRsr6rpEP/Sk4dMyRufp6enl5dXW1lZQUPCUP1JWVvbpp5/OnDnT09Pz008/BYDPP/+cOK2eZUAcQgNF0tlVBAIBnU7Pycnp6Ojg8Xh/9rbKysrExMSkpKTCwkKic4JCocyaNauzs9OExaKn0p2epFM+pt1Mq5C3/neH9SKh6Usim5iYmO3bt4vF4pCQkD97j1KpzMnJEYvFEolE/9yhvb09cSOIkFmQNAgdHR3nzp2bl5eXkZHx8sv9pyApKysTiUTJycklJSXEHjqdHhQUFBsb+9JLL40ZM6a5ubm8vJx4icPhGGN0Kxoo+YUCrVwOALWqXlFrDwDcUz8YLKNpadTKep5yCm80aEKhcPv27YmJibt27er3Unt7e3Z2tkQiEYvFXV1dxE43N7eFCxfy+fwFCxYwmcyDBw/qZ8yfMWPGiBEjTFo9IjGS9hECwM6dOzds2PC3v/3txx9/BAClUllQUCCRSE6ePKl/bsne3j48PJzP5wuFQju7wcx4gkymYdNaWU7GNblq+932lc62AHBH3Xddrtrp7kxhMt0zLlBt//TWHxmEVqsdPXp0Q0NDaWkpsbhrbW1tRkaGRCLJzMxUq9XE23x8fAQCAZ/PDwoKolAoZi0ZIQDS3hECgFAo3LBhQ0pKyk8//ZSWlvaEC1Xz1omeEnv6LPn5cyBXjWLQI3gcACiTq6/LVQBA4VpjCpoAlUrl8/nffffdwYMHx4wZI5FICgsLiUttGo0WFBQkEAiWLFni6elp7koR+gOSBmFNTU1ycjKXy+3p6XnllVeInX5+fkKhMDo62s/Pz7zloUGw5i9pO9C/RQ4AqCwO7/W3TV8P2ajV6nPnzhHdfsQKTQBga2u7cOFCoVAYFRX1hM54hMyLXE2jZWVlycnJD1+oUqnU0aNHv/766xwOx8/Pb9GiReauEQ2eXJpXu2Ftu0w2kk4FAJVW10FneswNdtl1EKgDWK4LPT25XH7mzBmRSCSRSDo6OvT7X3vtNScnp1GjRv3jH/9gs9lmrBChv2T5d4QajUYqlYpEotOnT9+5c4fYyeFwQkND/f39t2zZotVq7ezsNmzY8OWXX2IQDmucOfMn/ihp++8OxUWptldt4zp27Io1NtGxgB1RhtbU1JSeni4SibKyslQqFbHTw8ODz+dfvXo1Ly8vMDCwp6cnODj44sWLOJoMDXEWG4SPvVB1dnZeuHBhbGxsZGSklZWVTqf7/vvv6+vrZTJZVVWVVqs1b83o2THcJ4zcsd/cVVisR9tU9J1/QqFw8uTJAJCQkJCXlycWi4OCgqqqqgIDA81dNUJ/RTdsbd68+dHtxsbGhIQEPp9vZWWl/44eHh7r1q3Lz8/XarX9DrJ27VoA+OSTT9LT0zs7O01XPUJDT05OTm5uLrGdm5ubk5Oj0+n6+vry8/Pff/99Ly8v/TnFZrP5fP6BAwfu37/f7yAtLS10Op3BYJSWlkqlUhN/BYQGYRj3EfZbX3vLli1ffPHF5cuXdb9dqAYHB0dHR8fExHh4ePzZQdLT0xctWjRt2rSrV6+aqG6Ehqr4+HgajRYXFwcAe/fuVSgUpaWlKSkpxOJiADBy5Eji5i8sLOwJPX8hISHnzp376aefli9fbqLSEXoGltM02tXVVVJSwmazw8LCBAJBdHT006z/8MILL9ja2paWllZXV48fP94EdSI0XDCZzAsXLrS1tRGdfwKBICQk5GnmERUKhefOnROLxRiEaFgYxneEzs7O+u6HX375paSkRCqVPvlC9bGWLVsmEom+/vrrdevWGaFMhIaN+Pj4gwcPjhs3DgBqa2vffPPNKVOmuLq6Ptwo+jRqamrGjx9vZ2fX1NSET+KioW94BKFYLL58+fKGDRuysrI6OzvfeOMNeKRpVL89UMeOHVuxYkVoaOiZM2cMVjFCQ5tSqTx69KhcLudyuTweb8mSJTQarV/TqEajeffddwd3fF9f3+vXr2dmZkZERBi0cIQMb3isPiEUCl1dXe/evavT6fRjtQ1l8eLFDAYjLy8PVxBE5MFisd588021Ws3hcJqbm+VyuWGPb6TlCREyhuERhCdOnLCxsamrq2ttbb127Rqx8+Huh2fpiuDxePPnz+/r60tLS3vWQhEaJpRK5datW8PDw728vBgMxr179wDA19d3ypQpxBumTJni6+s76OMTQZiYmDgs2pwQyQ2PptHr16+rVKrJkydzudzm5mb9FPWGEh8fv27duqVLl4pEIsMeGaGhSS6X37hxg8lkjhgxoqOjY6C9gH9Jp9O5ubnV19dfunTJ39/fsAdHyLCGRxAaW11dnbu7O5GyLBbL3OUgZAni4uL27du3efPmrVu3mrsWhJ5keDSNGpubm9u0adN6enpyc3PNXQtCFgK7CdFwgUH4AJ60CBlWaGgoj8e7du1adXW1uWtB6EkwCB8ggjApKQnbihEyCAaDERkZCQBJSUnmrgWhJ8EgfMDPz8/d3f3+/fsXL140dy0IWQhsaEHDAgbh7wQCAQDgY/UIGUpUVBSDwbhw4YJMJjN3LQj9KRw1+rvi4mIKhTJr1iwAaGpqsra25nA45i4KoWFMp9OdOHEiKirK1tZWp9PV1dUR87chNKTgHeHvjh492tXVRWxv27atoKDAvPUgNNzJZLKvvvrK1tYWAJRK5YsvvmjuihB6DAxChBBCpGY5yzAZxKFDh3JycgBAKpUuXrzY3OUgNOzdvXv3o48+AoC+vj5z14LQ42EQ/kFISMjs2bMBoKamxty1IGQJHBwcli1bBgAqlSorK8vc5SD0GBiEf+Dh4TFt2jQAcHJyMnctCFkCNptNnFMKhcLctSD0eNhHiBBCiNTw8YnfqdVqGo1Go9H6bSOEBk2hULDZ7Ee3ERo6MAgRQgiRGvYR9qdQKI4fP+7k5EShUBQKxdKlSykUirmLQmh4O3/+fGlp6dKlS1NTU318fIghaQgNEdhH2B+LxVqxYsXNmzfLysrodLxQQMgAZs2aNXv27MOHD9+7dw9bR9FQg0HYH4VCSU1NDQ0NXb9+PZVKvXnzprkrQmjY6+joKC4u9vb2joqKSktLM3c5CP0B3vH019zc3NjYyGAw2traFAqFl5eXuStCaNiTSqVOTk4+Pj75+fkRERHmLgehP8DBMgghhEgNm0YRQgiRGgYhQgghUsMgRAghRGoYhAghhEgNgxAhhBCpYRAihBAiNQxChBBCpIZBiBBCiNQwCBFCCJEaBiFCCCFSwyBECCFEahiECCGESA2DECGEEKlhECKEECI1DEKEEEKkhkGIEEKI1DAIEUIIkRoGIUIIIVLDIEQIIURqGIQIIYRIDYMQIYQQqWEQIoQQIjUMQoQQQqSGQYgQQojUMAgRQgiRGgYhQgghUsMgRAghRGoYhAghhEgNgxAhhBCpYRAihBAiNQxChBBCpIZBiBBCiNQwCBFCCJEaBiFCCCFSwyBECCFEahiECCGESA2DECGEEKlhECKEECI1DEKEEEKkhkGIEEKI1DAIEUIIkRoGIUIIIVLDIEQIIURqGIQIIYRIDYMQIYQQqWEQIoQQIjUMQoQQQqSGQYgQQojUMAgRQgiRGgYhQgghUsMgRAghRGoYhAghhEgNgxAhhBCpYRAihBAiNQxChBBCpIZBiBBCiNQwCBFCCJEaBiFCCCFSwyBECCFEahiECCGESA2DECGEEKlhECKEECI1DEKEEEKkhkGIEEKI1DAIEUIIkRoGIUIIIVL7/zc4cmpjWzotAAACjXpUWHRyZGtpdFBLTCByZGtpdCAyMDIyLjAzLjEAAHice79v7T0GIOBlQABNINYC4gZGNgYFIM0CpTgYNIAUMxObA5hmYYfQzDA+Os3OgCYP5jNBxZmY4fIQGmE+1FYcxhKQZgSbwsg4WGhuBkYGBnEGBgkGBkkGRiYGRikGRmmg7xWYOTOYmFkSWFgzmFjZElh5FNjYM5jYZBjYORTYORM4ZBk45Bg4uRS4uDWYeXgVeOQZePk0mHj5GfgFGPgVGPgVGQTEEgQEM5gEhRIElRiEhBmERDKYhJUZhFUYhFUZREQTRNQYRMUymETVGcQ0GESY2JhZWNnYOdkEhURExQTEvwGdxQiPcuO3PQdUtZsPgDhTJWcfkJ6nBWZ/c1154PrpuftB7PdLOg70X2HfB2LzrDc+sCHtHZj95+aT/Ub5SvYg9qGjfAf+BLM4gNhTEnIOdC6WBLPXxLQc2BldCmYHXpx24FzpMrD6XfOOHnghchHMVs75coBpy28wO2nisn1/EmfZgdgd+w3tj2RuBot/2dFgJ5RrCjZnCxeXw6pTHWDxtqXpDvLhNmC26v9Gh/mdRmA3953Z4PDqcTPE/T/2OaiukoX4Mfeiw9arfbYgtrHjYYfdsQfAek8VT3G4+ucVmG2mZHeg/W8mWL33yV0H2sMngtmLGmsP2LJvBLPXfD1x4P43NzD7Z1XUgQhXPjA7nX32/vl7vMDu9Cx3P6D3aC6YLaq1+cDi0FYw+/WlD7YPr08Au80xSt5BnUEJLO5X9tL++eqT4LC13uPkICr2DKyG/eobh8RARrD5N2QmOrxgsgKzA1XPOoQkVYLZ9bIMjn+OtYH1Wj167NAwXQ1sZoZ7m8Mss0AwWwwAc/XDOYy8IC8AAAOYelRYdE1PTCByZGtpdCAyMDIyLjAzLjEAAHicfVbLbiQ3DLz7K/QDI/AlSjr6sVgvAo+BxMk/5J7/xxbVtroXS2TsQ4tTTRaLDw0Pl3njhxKfP1/++Pe/sj/y8gA7/c//nLP8o0T08FbioTx9+/7jXp4/Hp++LM/vf98//irGxQTvUOFfsY8f729fFi7P5WbVSGdr8YQAxL1QpfUpO44cyGE+upabVPc2A/AbUst7ubUqY/ZJ5cZVVPrQBGmHT+nEauVGlVXcsuit3CPmoN7aDKR5c8mQHj659oHgSL+6WqOZAHsABeRoCNKr5N3FE+AIoFafCpEANCKnDDiDpCEJV5aidah2yfJmOhLvrNS4cEVBrWda8iqQV+4tyDHSiXwypET0DgDSwbFOd1POkBpIqipNZQCgxE0yiTgKBAmnKb5nZM6oegZsqDlVaWxgjDqp9SEZMMojlZtPUngkHz5Sj1EeiM4TnvB919bJMmCUB33rEtW5BdtunMaeQLbaxGZffUmKts/qI7SQ0/sYI5rEXBtnqkvUx2sz++zGiJ7RFFlAxnBRSNlIhqcedeXjOmMmGFK2YVkZxcprcEMyQ8fq0OYY3wzaAtrgq1OX6LxmPDXTHU32Gr6mM2mPl9AkwSSB9oA6xnfOMeMlpjF4ZNBxeDVWNPIaO5GZdgga7XUtDWZ2RQuwdwxmtmfocMrRlA7NMKC5phiDlX+bqGcHEm/k3aQCJPYQ5u3YXazcRpaSKpDojAGfhoeGmW+Z+Bp1Qh3Rn6htzN3UMVOaUSatZNJ1riU2e7PUZ1SpxzxicYScJNRmKlIUCfXGQHZe7UJu6eLUqJGjRDSoha4DGyRHRokGzF2IIyNxFc2QRosno4PcIZKTzJkCedHk6YLJV3gcs2Wyf7u//HKBHVfa0/v95bzS4k/Oe2sd7byd1rmdd9A6+3nT4FT0vE7iKP28NdZ5nJcDw+X2PuEPHq+bPgw7GrYjTmVHw6IyxNjuOegh5o7P8XovOx4Hv44puWxUBovzjP4OVtcFyeCl1z24DPtngSzRgtxlrWlw5VPGpSOMO1XRw6InxkJtpL/TlWAblp1wXGKL805Z+mHRnbOMT8sZK3RFZrIxR+Xw6lZGQ9pIdkuhi/PAXF2GliN/uU6nLcvOXVcHxIxdpu2wbM7aPy1n9AE/kE22Ghoyh5CbjwVnCHlmYav30APnIARnSGubT3T8tb/j/PVzD88PPwGf2+eJoQGDrAAAAoh6VFh0U01JTEVTIHJka2l0IDIwMjIuMDMuMQAAeJxlkr1uWzEMhV+lQBcHuBH4J1KU0SlLJqd70KEIOrYpiox5+B7JRc2iy7VIk4efDvX8+OWFX07Pj1/uXk4P+3d/Hk6fnu4u17z8eDk9/fPf3+Ofvo8XfC+3jFzz/6nuBOP74f0UjYU1DmlOepy9MXHwwa2TDD/O1lyTFYmhfdhKcLrIoU18JCq0OaeNg1poD0KJNO6eaCEfPlZsI8fAjK4saIGYaMpxT01TRx5nap6mspqM2GNlpLMxo2uoBWRWtXSVNUqJu+w+5uh53HNjZRzOOMWIRI2rdVoZKFB0X+PMuwvU761JEO6FHKu4rVxvMjK2mKjskdaG+YgDGu5b3tGYiSKFVWMIUtoMFvoqEpG89hlp9gMHyiTZFBkEB1EOCN7zhgbFKurGqVsqneEh/sNNWbdUsFKHM0q0ObEkXGftxBV3RCZwAgqj2+2q46maMM+I9jDsTV0ZexuqccVmdvfDGkrXrtdQ7CmRwR+xmUUJwoJVhut2VxhtkMGWYj8QcrHlo2cYy3oQZBK2bIyMDj7oimWsDCl8WZn0wCRMMNcOwFiLXzEJLaPhCda+nU/al3LYTIOXgXh4vjPd0I5d0xquGDUaUeyXJa6Ch313fH17/f751+vPSW0dL69v3/DsJt8i4Sklkqm3yGjaLWKfvUR9etGUGSXSOUqlziyRTS4wLJMLjfLkgtMnV5zJBUcnFxzIFByoFBzxyYVHYnIBoimFR3RKdcemVHv6lAKUU6o/UKoGwdiCFFMK0phSiSBUiJSmFiSfWonG1ELEMbUQqU2tSCiuSDm1Lq1PrVvDoAKlUC5QgrdQH9F4/w1K3nKD7XLvigAAAp16VFh0cmRraXRQS0wxIHJka2l0IDIwMjIuMDMuMQAAeJzNkX9IU1EUx8+7b3tvm5v7vWVmvqbTpRCk5B+Z7i4wqSCQKMMKVn/kE7T+CAuCSLH8AWEJWqCo0MrIjCwNymS7VEaaBEYi/VOk0Q/D9A+zokntna0Ewf+7cPl+zveee955584Gb72ByDLA0sqI7MzIruIEkCKqiokGPBHhiUBRVWJU+b/xchVh2TnGJOYT/t95VJfqx766QtmVdNl1DkOO+180DjiAVQAJAKuBI8AlArcmMgWJ18qEV/lVapmoBb9aLwmiTIQkEDWSqPVr1oImGbQ6SRfn4fUGSS+BId5DDEYwmsC4DowuMNn9JrNMzBa/OQUsVplYUsFq81sdks0uE5sb7GngcIIzHZwecK4HGxF4lVoQtYLZYrXZTU5CQGku9vrZM+eZO7OGKUFLQhtL7MhAXii4wSaet4cUnr1azy68EoMK629ns94jX5HDr9+Hso65vAo/Gopn4d0qqnCzv5w1BBKQew6cZQ/2VyIXjV1iLyq7MH+gY4h9so0hp5TPM9L/C/lwU1cwfKg1X+H60Ebvk7I+9OfvV+VbKjZhnX6djnaP1KNfe62UJhdvQXb/rqadDVnYc+NoL52eqkFuqRij98Yb8xTO9j2mDw8yzB853kzHw9PIZ5LAF35ai/9Fjpp8Bu9e9HNc+axusQxnsnN4gNUVNyFfqT7N8sQ7yD3fnrG3C9uQf54qYfsK4pFLxbZQ5+AO7Hn7yUK2YbId2Z7RxwJ7ziF/eTmX927iIvbpK0mm6eBCf9eJz96PN4exn9zBrdTu+BB9ix9B6u5OwvqbJ6do1eU0zJcLa2lrThFypsblu5uai3f7ry/SQGAmOqug5Bud+47s+AOSp8XyCW6pvwAAA616VFh0TU9MMSByZGtpdCAyMDIyLjAzLjEAAHicfVbLbiQ3DLz7K/QDI4hPSUc/FutF4DGQOPmH3PP/2KLaVvdiiYx9aHGq+agiqaHhIjd6KPH58+WPf/8r+8MvD7C3//mfc5Z/pLX28FbioTx9+/7jXp4/Hp++LM/vf98//irKRQXvtEK/Yh8/3t++LFSey02rNplm8YQAjXppta1P2XH4QA710aXcuLrbDMBvSCnv5WaVx+yzlRtVFu5DEqQePrk3Ei23VknYNYtu5R4xR+tmM5Dq5pwhPXxS7QPBGd+7qLWZAHsAGcm1wSivNu/OngBHAKX6FJAEoLbmLQPOSFJRhAtxkTpEOmd1UzsK7yTNqFCFoNozLmkJ5JW6RXKEcqKeDMkRvQOAcnCs012FMqQEslVhEx4ASCPjjCIKgUDhVMH3hMoJqmdAg+atspEiY+gk2gdnwJCHK5nPJvDYfPhIPYY8IJ0mPOH7LtabZsCQB33rHOrcItuulMaeQFo11tlXXzZB22f6cOjj1VQ/eyx8ZsGZFpAwMi0IssbDM3WYV5YuMzqdQJANzcThmJ4Bc+dGUQ67sKQDGeLM2hgEBkNDPJ9HtvIanY6qh4zVyuaY8wzqATX46kggWtSUpmQCgZjX8DWdGvLDS+imKC6BjoA65nzOMeMlamPQyKDz8Kok6Pg1n8wzbSX8v67tQkQu6BXyjgnOkHQ4pehehwyY5Fwm4aN+m2OMDiTeyNtOBEgsLAzmseRIyEZWEih5ja0x4FPxYFgOlpEvoRNaA42MLogBnTJmmmbIJLUpd5lr281umvoMlQwidRQUdKqLUeozROqVoKE70vTGc2Z9JyERmJnOGFJBg45pWeEaAhE4Yp/zWO+Du2U+lVbwyd459uEwN8tWrPLhs2H/Y+lgy6FTJUN+u7/8ctMdd9/T+/3lvPvij88Lbh3tvMbW2c/Lap37eSXhVOS8d+LI47xe1nmetwjBpZ53hYbH65UQhh0daxSnsqNjoyli7OgU6SHmjk/xei87frCDs1xXLyGLnRDE08jqukkJecn+uYCFuQwnRYu0SO6yACVypROzeIRxl8pyWGTnitXFkTDtcjmyDcsuOG67lfMumfthkdPz+LScfoJXVMY7n0M5vLqZkaA2it1UyMp5YK4u461RP1/HmMIiOx/MqwYlvNmQ1QRR8WX+NEi65BM5B21nPkEzaJOdjwbPoI13pbp6DxJfJ+Gw7Ogqn5YdK2bg2vFx/vqliOeHnyjN8okel5whAAACmHpUWHRTTUlMRVMxIHJka2l0IDIwMjIuMDMuMQAAeJxlkr1uHTkMhV9lgW2ugbHAP5EiL7Zyk8rZPkgRGCk3DoKUefg9kgszSDHCiCKPPh7q04fPL/xy+/h0+/Th88NZ9vdy62s7fLr98/Hh+S0u334v/EPj6e9nrM/vETkrY/3r122O9Fh5Pcow18nXfY4plnE98iAlzevuYxoOLxrkGabzuq9BFHI90hBX0bjuOUh8OZKWOoqYBkvsDF4SExkxUhxFPNb06SeFzCcCqUG2L2LiYAQmCbRQwsJQl+Gk192Ga7JuBZ3LdoDTRS4FxkoU6HBOW6AInUdTBk/woBnQrb23lWtBcirLphhLNE8vmgor7jQ8TWUXGbHHjshkY0bVUgvI7GyZKvsqJZ5y6phj5naOlfFzx1+sSOS42qQdgQIFuocAendYdH+0IUHoa5ul4rZjc8jKOGKicq60scxX7Fm5H3lHYSaSFM6tJQjpMDjmO0lE8q3OMMd54YcySQ5FBsFBpAOCz30LQ4idNI1Tj1Q6w0OcoVPWIxWstEemRIcTM0M7eyau6BGRwN/aY053e9PxVDwkWE90LsPc1JUxt6Uab9jM7n4Z3oLu0e9LMadEBAdxmEUJwoJRhutxVxhlkMGU0DPeA7nY9nE/VJb9IMgkbNsYGXNeD9eXn6///fvj9XvR2L/Prz+/DvXi9x2vkrbL0vedSFnbac2mkuXvO6OKtuNarY4qW10UNxjh4kajq7jjALXx8CxuQKrFnciKGxLjtDExThsUS3GnkpJGNUsalJU0Ji3pFpU0Ii5pQBIlDUjgdAOikm4SLmk8Mku7S17agDCkbhKVdpO4tCFFaUNapY1IUdmIFJUNCXd2oixrRBxlfW7z1/8ZJXq9E3wmfAAAAABJRU5ErkJggg==\n", + "image/png": 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\n", 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\n", + "image/png": 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\n", + "image/png": 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\n", 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\n", + "image/png": 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GRkZGcrlc8vGqqqrm5uZIFP39/X18fEJDQ9XV1YcMGYKTzloYYU115QPXqoCnBJcr36e/+pwlzLYGAJBRXOMbU/gwqsAvtrCuIg+yQiE7DPLjgBAAANDooNUTDCygvSWEnYX8WLDcDJ1GqgiK7KfIDRlsER0dTRDE9OnTW/ntSQ0shOJYW1v7+PhcuXJl+fLlreVDbm4ujUbT1tZOTEw0MjKqdzWdl5enp6dXV1eXl5fXvn37lneyYeIyKwftDq6OcoY2XcDAAgDg+S7miB0DexqE7B3c2t5hJAJ19w0ODkap14mJiehrpFevXhMmTGjbtu3Hjx/t7e2xEEoOl8t1dHQcM2ZMTEyMhYVFwwc0/NzsrOUzeOWlDG4tAAgZTILBCJq283xFl6j0cij/BNnhkP3mc+QTABhyoN0LDMyh4wiagjrQgCAAkh9CxEXoMJQ2bPv1VSZy2cGampopKSl0On3x4sXff7O05oHjVOJMnToVADw9PVvm5Zydncl5bLt37xYKhQCQkZFx48YNNze3jIyMepPXMzIyHBwcAODOnTseHh4t42qTOPwwlVsnFLuTLyDiMipjM2R05tlPA5PJ7N2798qVK2/cuJGQkJCTk+Ph4bFly5YlS5YAwOjRo0tKSsLCwkSfEhsbe/36ddF9JKYxFBcXl5WVAUDv3r1zcnIaeihB5Kxfkp6VvSnx8+CBBwVlLtlFzOs7P9y3g4cr4dFvEH0NCt+BnBJ0GAqDN8J0F80pRxevWPNgx5i0UyNHGWnKMenQzhKABjmRBJ+nCpzRo0enpKRUVVVVVVWRvWF/PnCyjDhTp05dvXr106dPq6qqWiDjPyYmxsDAALWFe/r06e7du+l0evfu3ePi4hQVFT9+/FhvG8YOHTro6+unpqYmJiaSme7fFWGp5UIUa0jxhdy3AAAVn6/Pt5/KTTs0c+YZ5jtET09v2rRp06ZNA4CNGzcCwPHjx1HJBPmY58+f9+3b9+nTpzY2Nq3m6A+Iqqrq+vXrL1++PHHixIYfWRMVxs/PA+F/Iny1QuHy91l8IhMAQF4V9AdAh6HQtn97bWWrPjpiyZ/Pdlok5nBMtwXxNbpCyQfIe/s8uT2rIm3x4sXp6ekEQfzEB71YCMXR1dU1NzcPDQ198uSJra1tC7xiSkoKqjev/neI15UrV8zMzJSUlLhcrqam5pdPycvLy83NZTKZf/7554sXL1rAyaYiz/w32NBxOLTtBwBQng4AdDoosH7aywmDMDAwmDBhwqVLl8h7lixZ4uLi0q9fv1b06keExWL5+fktXrz47du3DZev1CbFA48HAAnVvANZJQCQyq0bq6Zoq6FUp6DxoPfvoNmji66STV+dmRZth3RvU68xI33lkUYaT2MHQ8kHyHz9MG7kmeXWAIBW6j8xWAjrYerUqaGhoZ6eni0jhGFhYUVFRQBQWlqK7tm8eXPDT9HT09u1axe6LdUT7JycHB0dndra2tra2ia1kJ/cX/dDflUtALAUQV4NAIDGBIA6PjGsRxvpOItpfRYvXtyhQwcAWLdunWg7oezs7O7duw8aNKj1XPshUVBQQIc1qAGIGMnJyZGRkWPGjAkODtbOydOn0wGgG5u1WlcNAHzKqgDgUEethDY9+qycPGtQ216NSP60HaD7NMQSYm5Admh6AScmo6JPB1WK39X3Bz4jrAf0yfP29ubz+S3wcvPmzdu2bdu2bdsMDAya8fTCwkJpjGMEgJqamuTk5PPnz799+/bSpUt1dXWNf+6mSZ2U5Bk0RW2Q+zcKqt6BxqAvHdlOv83Ped6OAYA+ffq0adOmsrKyc+fOGzZsIA8Fe/fuXe9XOaYxBAUFJSUlodvOzs5k6CgqKopOpysoKHTu3DmGR6AtoxyNpsViaLEYyqimRV5h+JIZe3/p1hgVBIBpA3Rpau1BtR3UVkLBu+bNrP/hwEJYD0ZGRt27dy8uLg4JCZHeq/D5fNEWNs0gPT197Nix+/fvnzFjxv79+6lyjITNZsvLyyspKVlYWLDZ7CYdlWsqywXuHtzefLKCgQkQBCQ/hMo8gsZaPLQ5Yo/5sVBRUdHX1+dwOP7+/q3oRm5ubmZmJp/Pj42N/dpROkpVCwoKSkxMbFnvmkBgYCDpnouLC/m9YWNjM2LECE9PT319fa4CW3HoKAW2fGeFz9epJouhJcdkqqiqNqX/tX4bBXNDdWg3GAAgK8QjHAuhDDNlyhSQZu4oQRCrVq0aOXLk8uXLzc3N0Z2urq7MpjTo27Fjh52d3dmzZ728vGJjY2NiYqh1srKyMi0tTVNT88yZM1paWuQ6tJH0MlBOPTHSaZUpnU6DD48h+w0UxD9825T515gfFnSs0GIpzffv34+Pj0e37ezsUIwkLy/vxo0bHh4e2dnZaASbGCUlJW5ubgDw8uVLNI7tu6Wuro7L5XK5XJRYjigqKnr16pWtrW1ISMi4ceN0DpzoPGrcH13a0uXkaQzmqLbak02M9B3d6IqKTXot2wG60N4SACAzJCajPK2gaRf+jwgWwvqRdhHFhg0bHB0d379/X1VVpfjvxxQdrjSexMRE1CgVAEaMGBEREUGtkyoqKvPnz7e1td20adOCBQsUm3g5AQCTQZs1qO2Q7hr/zr8ObcVIS2RkpLe3d3l5+bVr176RiY6RGCSEXl5eLXO+8Pbt2+zsbHTbz88PhfFRoa28vHx6erq8fD1tHDQ0NNq1awcAkydPvnr1agv42WwuXLiwatWqVatWxcX9/7TbTp06zZo1S1lZedq0aaampjQ5ed0jFwycHmj8vl1j3Ra9oxc63HvGMmjatwoATB+oB5rdQFEbqougJFUWoqNYCOvH0tJSV1c3NTU1ISGBcuP79u07c+aMnJycm5ubJHl0TCaT/JZpmWKP5jF1gO6/QhgSn1XxIU+igHBjcHBwQCt9AFi2bFlWVhYAtGvXLjo62tPTc8yYMc7OztL2QcYxMjLq2bNncXFxUFBQy7xiVlbW+/fv379/X1NTg+6JiIho3769jo6OlpZWvc1Rq6uri4uL4+LiYmNjv/OM1vXr1zs5OTk5OfXp06fhR8oZdlebs1h94f/YFkOB3pxv+G56Sr0MVKCdBQBAZohHRF4zjPxYYCGsHzqdjvpEo01hXFxcZGSkaFCi2djb2+/du5fBYNy6dWvChAmSmLKyskIZ6mVlZZ6enqNHj5bcPRLSWmFh4Zw5cyQxNa2/Lmh2A0UtqC6GkjRv6UdHa2pqamtr0e3Kykp0PqSgoKCurj5x4sTo6OifuDT4+wFVFrZYdPTFixcuLi4uLi75+Z93MJMnT16+fPmgQYNmzpxJxk5EUVRU3LJli4mJycKFC8eNGyc93/Lz81++fJmUlHTv3r0mJZ21FrYD9f49JnwdklyWV1bb2h5JFyyEX0U0Oqqmpubv79+MMTRiuLi4rFu3jkajXbx48cuW/01l586d+fn5c+bM+fXXX0+cOKGmplZSUiKhTRIOh4NuCIXCpp4OimGoq9jLQAUMzAEAsl57RrZEpMXHx+fYsWPHjh1LTk5G9yQmJrZr145Op1dXV69YsaIFfJBxUHT0/v37LdPHceHChfv27du3b1+nTp2a+lwejxcfH//p0ycp+AUA4ObmlpKSoqOjo6GhQXaSajwzZswgK0/mzZunpvbVERBUYTtAF3RNQF4VyjOE5Zk//dE+riP8KuPGjVNSUgoPD8/KymrTpo2hoaGEQujv77906VKhUHjkyBFKvojl5eX3799fWlpaUVERHBw8ceLEJUuWnD59WnLLAMDlcg8dOgQiiigJU/vrvosYBB98IDs06P3Cokqelop0J7YYGRmNHDkSAHx9fdE95FeJhBtcTCMZOHBg+/btMzMzo6Ki+vfvL6VXIQhCwvKh1NTU5cuXjxkzJjc3l8PhODk5Sb7kFSU+Pr6qqio1NXXq1KmpqanoY9kkunXrhm7069fv7du3AwcO7N69O4Uefkn/zmqddVQ+6g+Ej88gK/TcE6OxxpqdtZucJfCjgHeEX4XNZo8dO5YgCGdn57i4OAlbQ71+/drW1pbH4+3YseOb9fKNx83NTVdXd9u2bYaGhhUVFQ8ePKBq9c1isaysrKysrMaMGSO5tan9dUHXFFiKUPpRUJHnE10ouc2G6dy584ABAwYMGNCmDa7fbx1oNBrKvpZqdHTbtm0jR46cO3eupaUlusfBwYHNZjfewr59+w4fPvzXX3/Z29urqqpSXvJhbGy8devWpUuXhoWFqaqqkpHbZmBiYgItFW0eb6IFHYaAnhmo6MdmVPbe/GrJxVgen4Ljoe8QLIQNoaGhoaGhsW3bthkzZixevPj06dNBQUGNXH4GBARERkai27GxsdbW1mhC6cGDByn0cODAgXV1dY8fPzYzMzMwMMjIyKCqiILBYJiZmZmZmfXu3Vtya+aG6gaaKp97rWW/kV50tKKiIjw8XEVFhUwd0tTUbFJRCoZCpF1Ecfz48SNHjkRGRhYUFKiofG7d0LVrV3pTkkQSEhLIDauFhUVsbCz1jgKYmppaW1vPmjVLkvluLVaUIiSI5++KwcACetpCTjiE29fkJ7u9yV1ykeIare8ELIRf5f79+zdu3CgpKVFSUsrNzXVzc9uwYcOwYcM0NTXHjh27Z88eX1/f8vLyep+blpYWEBCAxvmmpKRMmDChtLQUzTul1slOnToZGxuXl5e/evVKNLtHcsg2NwwGA42mkwQaDWz66ZBFFH6xhV/OppAcLpc7derUkSNHtm/fnmyPd/Hixea17MFIzogRI7S0tN69e/f+/XvKjd+6dWvLli00Gs3BwaEZ8UYSNTU1NOEBAEpKSqgNIaBJfuj227dvJbQ2YcIEJSWlsLAwlAgtPTzC83PLaiE3EtL8od8K6D0bIi9VF2d6RhYkZFFwVvK9gYWwfp4/fz5//nyBQLB//34Oh5Oamurk5LRy5cpevXpVVVU9e/Zs//79VlZW6urqhoaGixYtunz5ckJCAvmJLysrMzY2dnV1zc7OHjduXF5e3tixY2/fvi2N9u1kUg+1tY8rV648dOjQu3fvtLS00MgnCZnaXxf0zYHOhIL4qoqyZ/FFktsURSAQLFiw4OXLl+ifQq1xTPNgMpmTJk0CgAcPHlBr2dvbG524Hz9+fPHixZKYmjNnzsGDB/l8fm5u7u3bt6mdj7FmzRpU4yQUClevXi2hNTabPX78eIIgvLy8qPDuq9wLy+Nw+fDxBRjPBZYSKGpC98mQEcgXEj7RP2HiTAsJYUFBQWFhYXl5ObmQyc3NJYua0Wrx/v37ly9fjo+Pp6RKQRLCw8OnTZvG5XLXrFnz119/lZWVrV279tOnT7Nnz37z5k1ubq6Xl9eePXvGjh2roKCQlpZ28+bNVatWGRsbt2nTZty4cXv37i0uLi4tLTU1NR0/fnx6erqFhYWHh0e9Jb2Sg/TvwYMHo0ePVlFRefv2bXp6uuRmnZ2dd+zYERAQILkpxOhemiqqaqDdGwgB5EZQGx0lCGLlypX37t3T1NT09/fv3LkzhcYxkiAazbty5cqRI0ckL7EPDQ2dM2cOn8/ftWvXH3/8IaG1//3vfyYmJosWLdq1a9fly5fpdHpaWpqENqVHy0RHizg8AAAeB+T+7VAqrwo8Tp1AmBgfc/369ZycnIsXLz569EiqbrQcRIvw9OlTT0/PmJiYK1euoHv+/vtvIyMjLpdLEISlpSVBEKNGjUIuqaioDBkyZOvWrV5eXiUlJS3jIcn79+91dHQAYMGCBQKBgCAIHx8f8s/FYDB69eq1cuVKJyen+Ph4LpcbFBR09OjRadOmicUP5eTkUNMmExMTqb4LoVCIumNERET88ssvAHD27FnJzSItQYEdqphxKhL6rwIA6DBUd/VTgVBIleVNmzYBgKKiYnBwMFU2MZRQXV2tpKREo9EyMzMPHTq0cePG2tpaSQzGxcWhQSgrV66kykmCINLT08PDw+/cucNgMGbNmkWVWUtLy1mzZs2ePXv27NkWFhaSG3cljXgAACAASURBVCwtLZWTk2OxWMXFxZJb+xrzzkfDvEdg9AsM3w3zHsG8R2C2BAb9obzM1+lVVmBgoI+Pz/Hjxzdv3iw9H1qSFhLCgoICT09PgiBEhXD58uV79+4l/hVCBweHuXPnirUZYzAYffv2Xbt2rbOzMxoOKVUyMjKQA5MnT66rq0N3FhcXu7q6rl+/fsCAAWJpFwYGBjNnzjx58uSbN294PF52dvbdu3fXr1/fv39/Op2up6enqqqak5MjbbdRyOWvv/66ceMGAKBkV0nIy8tDKxI+n0+Jh4ibgVkw9ToADZhsmO0RkkzN+mDfvn1o5eHr60uJQQy1oE2Mvb39yZMnDx8+XFhY2GxTGRkZ7du3B4CpU6eSV6jkPH/+HAAGDBiQmZlJo9GUlZVramoosWxpacnj8QiCEAgElAghQRBojseNGzcosfYl8ZmVaiv8YN4jsL0JOibQdzmYzAP9ATDHU2WZb1Bo5PHjxysrKyMjI48ePSolH1qYFhJCf39/BweH8PBw8g/3999/+/r6Tpw4MTk52dLSMjk5+dChQ69evaqurs7JyfHy8tq6deuQIUPEwol6eno2NjZ2dnaBgYFUfVJJCgoKevToAQCWlpZVVVX1Pqaqqurly5cHDx60trYWm8+nqKg4fPjw7du3e3l5odAo2iO+ffuWWj+/BJXKmZqaFhcXM5lMFosl4R7U3d0dAMaNG0eVh4jSKh5roQ+06QIAMHLf1ttJktu0t7dHC6a7d+9Kbg0jDY4dOwYAampq8+bNO3PmTFhYGNKGb1JbW+vv7x8SEoJ+LCwsRFfoyJEjqb38uVyuiooKjUb79OkTyiD19vamxLI0hBDl3Nna2lJiTYzM4poO657DoD9gwkmY9whme8CEE2B1hjbPW3Gp7/2wvPT09IiIiKKioqioKAk3998PLSSEX4KEMCEhYdq0aZaWlmfOnEFywmQyydjjx48fq6qqAgIC/vnnHxsbG7FZ7Ww2287Ojip/ysvLUbPBPn36lJaWNvJZKImG3AKSvh0/fpwgiJUrVwIA2vVKFR6Ph4agpqWlofQ5FxcXSQxu3LgRAPbs2UORg//P6IOhYDIPAEDXtPevt4SSRUddXFzodDqNRrt8+TJVHmKoJTc3t3PnzmL1DCwWq3///qh/Zlpa2teeKxAISkpKzp07RxBERUUFkihTU9PGX6GNZ8aMGQBw5syZv//+GwCWL19OidmTJ0+iExaU10OJzezsbBqNpqioyOFwKDFIUlhR2/PPABi2A2h0YLLB9iZz4WPafB/GAp9Bu4OpCuF8h7SyEBIEsWXLFj09vcDAwN9++61Pnz5ieZUdO3acN2/e2bNno6Ki+Hy+mPBcu3aNEmeqq6uHDx8OAF27ds3Ly2uekaKioocPH27fvn348OFhYWEEQaCT5L59+1LiZMPMmjULAE6dOnXy5EkAmD17tiTWUBMWPz8/qtwjOfX4o0JPKznFzz2iVFRUUC2Kl5dXU7/dnjx5ggIGR44codxPDCWUl5f37dsXRR3DwsLqXTUCQNu2bb8W6QkODo6JiamtrR0/fjwAGBoa5ubmSsNV1Id91KhRqM++lpYWJaFXZ2dnMzMzyhdqFhYWAODh4UGhzapa/uA9ITDGDhhyAAB9Fqmt8Iv+VE7hS3y3tJoQPnr0CNUbcDicP//888CBAw8ePMjPz6+srAwMDLSzs7OxsRGLPSopKZFJNEVFRcXFxRUVFZJ7wuPxUAWegYHBx48fJTdIguItAECt2XohL2OUMqqsrIwSkZoBl8uVl5en0+llZWXUOkkQhN3ho2jfP2LECJTjQ8JkMtEu4fbt2xkZGQ3bef36NSqZ37ZtG+VOYijhy/Xl7t27PTw8cnNzKyoqyMtcLNIjGhOKj48PDAwUCASoMa++vn4D20cJKSsrk5OTYzAYZAA2ICBAcrO//vqrNNZqdnZ2ALB48WKqDNbWCSbYhcGkcyCnBADQzZq9xDcw6afdAorRakIoCjlIDK0NZ86ceerUqYiIiLq6utjY2AsXLixcuFCsMoxOpysrK5uYmFy/fj05ObnZLy0UCpcsWYIWgO/evaPwTSHIeAvllsUgL+OioiJTU1MAaHbmSGBgIAD06dOHWg8Jgrhx4waNRqPRaI6OjgRB1NXVZWdnk+fBcnL/6T4quksQE/XY2FhU9bx48WIJg6s/Fo8fP46MjAwMDLxx4wZ642FhYeS5+9mzZzkczv79+0WFpLX+Pnw+H+Uwk+vL1NTUev+5VVVVCQkJV69eXbFihbGxsdhmUV9fH/XVVFdXj4mJkarPaBrMtWvXtm7dCgAbNmyQ3KaZmRkABAYGSm5KFNRKXkNDg5Jtq0AonHUmCqZcAQV1AICOI1gLHj16WyC55R+F70IIc3Jydu3aNXLkSLGJeurq6lZWVvv373/27FllZWVeXh5ZwCf2pammpoYibP7+/tXV1Y1/aVSEpKKiEh4eLo23dvPmTQAYM2aMNIz7+fm9e/cuIiLizp07hEgu2V9//QUAq1evboyR0tLSvLy8jIwMf39/dFGhxWYjn954Hjx4gHJuT5w4QRBEdXW1oqIieVCUnp7O4XACAwNPnTo1c+ZMselxioqKQ4YMWb9+/d27d8PDw1GTqmnTplGYN/hDUFpa6u7uzufz79y5g/ZYt2/f1tPTe/bsGUEQNjY2xcXFEydOFP3T6erqTpky5dChQy9fvqT8SOlrCIXC5cuXi6lXdnb2X3/9NXbsWFVVVVEPFRQUhgwZsnHjRjc3t6ysLNGYECpAUlVVVVFRQe9Rqly8eBEApkyZEhoaCgAdO3aUcBnB4XBQ8trXku8koVevXgBAyZ/l9xsJYHsTlHUBAPTMaHMeXAvIlNzsD8R3IYQkfD4/Pj7+0qVLCxcuRP9mErECvvLycgcHhzVr1tRbwDd48OCNGzfeu3ev4eOE3bt3o8dL4zAMQVUaZ73k5OR4enqWlJSg9jdnz54FgF9++QWNqtfX12/MZfz69eubN29evXr13bt36PGoQp/a5Oznz58rKCgAwL59+9A94eHhYmv/Ll26LFiwwN7ePiYmpq6uLj4+3sHBYcmSJShOJfpJAIBx48b9NBlrjQcJYUxMDCpGIgji9u3bBw8eHDp0KJfLRUIoup4QuzTQRbRw4cJLly5JdbOIdlSKiopBQUH1PuBrWWbw72YRLWqrqqqSkpK6du0KAD4+PlLyliQvL49OpysoKJSXl6O4fVRUlCQGyaoMqjwUZefOnQCwdu1aCe3scU+GmXc/53JrdodZ7scfSSv+/N3yfQmhGJmZmXfu3Pn9998HDhwoVsCnr68vGskUK+ATu65QrDUwMFA0aRulIDMYDDc3N6m+C9QowNnZmXLLqDqzuLj45s2bGRkZnz59QiVQp0+fRt33X79+/U0jXC735s2b6enpDg4OiYmJQqEQ9RNITU2lys+wsDB0VrpmzRp0D4/Hu3fvXnJy8uXLl3/99VcbGxuxBo/KysrkeXBxcXFhYSGKoA4bNkxZWZlOp7fAset3SFBQ0MOHD1++fHn37l1UT3379u3z5887OjoeOHDAxsYmODgYlRih/KOysrKUlJRbt26tXbt2wIABYuOINTU1ra2tDxw40JjPSeNBCzIWi9VI6SotLfX19d27d+/EiRNR8jPJvHnziH/rRP/3v/9R6OTXQCMs3N3dydpcSaz9888/ALBu3Tqq3BPl6dOn6Eoh082aUWJ/4eknmO0BOr0BAFT0YbrzzrvvpeHtd853LYSiVFVVicbNGog2lJeX+/r67t69e+zYsWRDeoSqqur48eP37Nmzbds2lHZPFvhLj1OnTgEAhb0qSAICAjw8PGJjY728vNDqHmXokdPUyPSTu3fvFhTUH/EPCgpCUzUcHBw4HA5qd6erq0uVk8nJyUhZUe9WdGd5eXlVVdW5c+dyc3NRcjyfz4+Ojj537tz8+fPFGqQxGAxTU9PVq1c/ffqUIAgU+kOnjBgkhEKhcPz48f369UNVlSRMJrNfv35r1qy5efPmhw8feDxeRETEqVOnFi5cKDq9duHChVT5c+vWLXRlNS+jWygUvnv3ztHREbU9Q58NNA5CR0eH2vYO9XL06FH0WX3y5AkAGBsbS2INTaGSxiKYw+GQ8zVFrxQTE5OVK1deu3aNDPA0gEd4HmO+N7S3BABQ1IKp1xZdiKYkTHDv3j1nZ+egoCAXF5eW7w7WDH4YIRRFKBQ2ckOAYq1kv2xSIdCIZ6rKehpG8jTOxoOS9JhMZu/evY2MjMTmi/bo0WPJkiUODg4JCQlfu0iuXbsGANOnT2/kK6K9HUEQoaGhz58/F/ttRkZGx44dAcDGxkbsPM/d3T05OTk5Odnd3f1Ls6ihK0qiQTFV+Lci88KFCwAwderURnr4c4OEkCCIuLg4FotVWFhIRke+7EdBHqWj5oXp6em3b9/+/fff0X9Qcry9vVHkhvIrC+XLUJ5y8iWoy6iamhqHw0FRivfvm79DQqFpCoMrCB6PZ2VlBQDt27cPDw//8kpBiPaqLCoqEjPyLL5IfrEPGI4HAJBXAZuLU45F1PEpi5afPHnSz8/vxo0b38wA/x748YTQ398/OTk5NjbW2dm5qadEOTk5V69eXbNmDYPBYDKZpaWlHz58sLOze/PmjZS8RUiYxtlIUFMCBoNBfq+Vl5f7+/vv2bPHxsYGab/oRVJvAd///vc/ADh27FgjX5TL5Z45c6a2tnbt2rVxcXGivyooKOjZsycADB48WCxNIy0tzd7ePjQ09MKFC3fv3m2gz0h8fPyzZ8/8/f1Xr16NDmxQNTGbzZZGAsIPR3FxMbnXf/funYmJiehROofDCQgIsLOzmzZtmtgYPLTPXrVq1ebNm318fMjNerMJDQ1FyW47d+6U+G2J8+effwLAxo0bKbf8JWj4rZ+f34IFC0CCyoeUlBS0kaXWPaFQiKZtaGtrJyUlEQRx9OjR69evJyUlie74xXIsAKBLly4LFy5ECflvPpQoL/MFo+kAAEx5GH9s5IHQGp6knwGSkydP3rlz5927d48fP378+DFVZqXHjyeE6enp3t7eJ0+e/PDhQ1PbIDk5Obm5uXl7e5Pndjt27AAqDpwbBmXlUJ6HKYpYZcKXiCYifRl7JL89UWIC2dSqMVy5cqWqqur48eOi8kl26jE1NZUkNpKZmXnhwoWcnBwUJUMMHDgQAMiEEQyCw+GIZVPr6OhMmTLln3/+efHiRWVlpWixCrl7QGGDhncP3yQ+Ph5V/UqpoCU4OBgAOnXqRLllgiB8fX1R0rizs3N2dja6Wn/99dd79+6hZVxjjMTHxwcHB5eXlzs7O6McvVu3bkkjdEEmukdERBAEwePxFBUV0b9SVVVV9GQ9MzPTzc1t48aNlpaWX2wWVeU1OwEA0Jkw6oDJ1lelVY1qetcY6rIz411vvfP25FZW/ihn+T+eEObm5np7ex8/fjwuLu7JkydNem5oaOitW7fOnTtHntu9efMGAAwMDKRaboVG1TcyjbMZeHp6ilYmNIavFfAhNZ00aVK9BXxfwufzr169Gh0d7eXl9fLlS3QnJZ16SD+dnJwIkXbtBEGgJljLli2TxPJPSeNTRqurq4OCgg4ePNilSxexb0k6nd67d+/ly5dfuXIlPj7+m5tFshH2lClTpFTQIhAI0JhrasehICoqKu7evRsTE3P06NH4+Hg0PldXV7eyslJRUZFOp2dnZ3/TSHV19ZUrV06cOOHq6ooafaxduxYADh06RKGrBw4cgP8munM4nGPHjv3yyy9i06fpdLqxsfGKFSuuXr2akJBQW1srlpBPp9OZCsrQd7nhHy9yS6k5teEXF2avmps6uGfaCNO04cZpw0wqfZrT++bVq1d1dXVeXl6UtExpDD+eEL548cLT0/Pjx48eHh5Njed8/Pjx6tWrGRkZ5LldTU0NypOOjIyUksMEQQiFQvRNgVqvUQtZmdDspqYcDufFixcHDhxAOy1RRAv4Gjk0gM/nT58+HSjq1HP16tW7d++mpqba29uT30fx8fEAoKmp2QLZEz80qampt27dWrduXQMpo+gMDC2MUJEuSjkW2T38/2bxy89AYWEhCoBT3ghbjFWrVoF0+t/m5uY6OTk5OzsfPHgQxfG6dOmCVlroQGHXrl2N+aQhISwqKkKLNtQZ9cWLF1T5eenSJSRgZH95Ho/n5OR08+ZNV1fX06dPJyQkkKvbhv+JOTk5o0ePBoABv2xKzafmiEFYU51uMyzVvNv1rrpr9NR3ttN426dD6hCjSp8HTbLj4+OzY8cOV1fXlJSU06dPU+LbN/nxhJAqyHM71ANp9+7dUn25NWvWgBSOT8jKhN9++01ya7t27QIAFCMVSy9CiB4z1LsKEQqFS5cuBQAtLS3UQk9KdOvWDVoke+KnQfQASTQ2/mW/SlTEWW85L/y3HqmoqEiqjbBFIUesUG45OjoadVMrKytDh9ko/Cj64Uf9HdGKMD8//0sj8fHxN2/ezMnJuXnzZkVFRXV1NYvFYjAYlZWVlDhJ9qMQa1PF4XAuXLhw9uzZTZs2ica0G/gn9u/fnyAINzc3ABg6dCgl7hEEUXL1fJplz0MdtWZpqTzrbXDFULefknxi345pw0yEjU7m4PF4q1ev/vXXXz99+uTi4nLgwAGq3GsY2RVCsv3K48ePQTodxUQRzcY+c+ZMI7vlcrlcFBj09vb+stgrOTkZhb9EKxMkAS0SRX0j022+3CioqqqS6TZkV1Jpd+ohQfMx/vzzT6m+yk/Mp0+fXF1dN2zYUO/XuijZ2dn379/fvHnzsGHDxD4DKKgueQC8MfB4PJTGmZKSIu3XQp8uBoMxYsSI+fPni/V3RG95wYIF586di4yMrDca/OrVK2hiw/1r1669ffvW19d3165dYu2xXrx4gaI+YsJQVlY2f/58X1/f8+fPnzt3roGlZ3Z29r179/7888+hQ4du2rSJIIjKyko2m02n06lqYp4xc3xKv059lOTjzDqm9OuU0q/TMh1Vx666acNNqiOaVqgaFBSEyqMbE5SmBNkVQrL9Sk1NDer5JL1+voTIpKTk5OSXL1/+888/jQl/19XVXbhwQSgUlpWVnTp1SvRXmZmZZGVCI6e7NQyfz1dWVgaAr10YPB4vNDT05MmTM2bM0NfXF/1eYDKZAwcOHDx4MAAoKCi0QDesgIAA9H0k7RfCiCK2z2Cz2UwmE50NZ2ZmSlui5s2bB01JaW4ezs7OqBRSNO+srKyMTMAWa/4gOlKKPAs4fPgwNDE/LikpCUU4xEZVxMTEoK8OSaI+AQEBhw8fTkhIcHV1Jf+AkydPBgCqJmN8HDcgpV8nI7YcUsGUfp12tNM40lErbZhx5ZNGZTVy38Xmblv7adrIzDlWxZdPCzjUbKYbg+wKIXluFx4ejnrbSyke/fTpUyRUc+bMAYATJ074+/sfOXKkkT1R0XnD8+fPRTM5G6hMaDYoo6dbt26NfPyX6TYqKipsNhs1PpU2fD4f9SOVRqt0WSAlJWXPnj3Z2dlbt25tthF04Rw7duzatWs0Go3Cwvx6QdG8IUOGSO8l/P390Ye5gVJI1PzB3t5+0aJFYv3/AKBTp05z585FJy9Navtw7969K1euhIWFiSYEpaSkoLqXuXPnShj1uXz5ckVFxcOHD3NyctA9jo6OAGBlZSWJWZLMBVNS+nWyUFEINWmPhHCmpvLdHm3ThhnXxH17OHmZ6/VYix4JfT+LaMTAbmnjzOuyW6jlqewKIUEQv/32GwDs2rULJTqPHj2a8pdwc3NjMBiTJk3i8/l2dnYMBkNeXr7xE/gEAsHNmzfDw8OdnJzIWpHy8nLyYIbCrg2oDLF5g10qKyufPn2K8vqCgoJevHhhY2Mj7ZkbqJqK2qw8mQKtsSRprkQq04cPHwBAXV1dqg1gyWge+VVOLWQp5I4dOxr/LNFqXdHJcUwmk2zt5OTk9M2y+oqKivLyctE87ezsbHSaO2bMGAnbcVy6dOnQoUOFhYWiPYSLiopQJ2RKzncrPFzThvRy6qY7QpV90VBns0GbieqKKf06pU8wJ74l4dzEuFRLo436bS4a6iAhHK7KjjDrmDFrouSONQaZFkI/Pz8AMDExKS0tZbFYTCaT2m5AT58+Ra09Dh48+OHDB11dXbHcE3SdrFu3zsXF5dOnT400a2NjAwDdu3f/5ulOk5g7dy4AXLx4sdkW0AHh5s2b7969C5Sew9fL/fv3AWDQoEFSfZWflaysrM2bNycmJm7evLnZyb2iymRsbAwA/v7+lLopDormXbp0iXLLZCnkokWLml3mJBAI4uLi0PwWBoMh1ve4Xbt2M2fOPHHiRHBw8DeFraysDI1wMjc3pyrj5ktGjhwJALdv36bAloB/c9yw6z3a+/UyONlZ+0Y3veT+nVKHGNVEfTtVPnf7upT+ncWF0LRD6tDe3Hjpzt5CyLQQip7boTyRW7duUWX8zZs36Mht7dq15Mpu9OjRHz9+JCOKYh2wvjaBLzU1lexf7OvrGxISYmZmRnmlaocOHQBArDtMk3j58iU6t6usrFRQUKDwHJ6kpqbG3d09Nzc3KirK09MTlXlJaX+AaQykMqHsM0qylxsARfMmTqR4o5CZmYk+/5SUQrq6ugKAtbW16PxhscliYvOHxSxUV1cPGzYMHVVQu94V4/Tp00BdJ+RuJuYAcL6bwa/6mvO01Z5NGMaNb1Td5yfbkSn9Om3Ub2OhojBVQ3mqhrIOixFh2iF1SK9yD1dKfGsYmRZCQuTcDn0gZs6cSYlZcnW5cOHC4uLiPn361LuyQwV8f//9t7W1tWhQBQAUFRVHjBixY8eOhw8f3rp1q02bNuhqmTJlSlFREeWF+Wg2sqqqqiTnEKLndqgX4tWrVyl0kiCI0tJSLpd7/vz5EydOuLu7oxGM0tgfYBogNjbWx8cnLy/P0dERDViYOHGitLtGIESjeU+fPr1161Yjy1u9vLzS09Nfv3794IF4WRtZCjlixAhKSiE3bNgAX2R4Ev8dPoWmiZGQM0P8/f0rKirIStz09HTJ/WmAzMxMNLJG8jdeV1fHlFcEoDF+ucVU0oGmdD/InGuNhPB8F52kvh2T+nYcpsqOMO2QNsy40u+hhI41BlkXwtu3b6MLID09naoPxKdPn8hGG+Xl5UOGDAGA3r17f7Nzleh1QgZRaTTa1atXt2zZMmHCBKFQiIRQQg+/5M6dO5QstMlzO3LGKSXuiYLCyKdOnXrw4AGKQU2aNInyV8E0gFAovHr16tmzZ3k83sGDB0llQmGP0NBQqb46iua5uLj8/fffTk5OqNPYN0lISAgICMjPz9+zZ49odXxVVRXKdjYxMaHqZMTCwgIA0LCUr4FOFg8cOGBtba2pqSkqikgjNTU1WyYRDCUcNLVd5ZeglRCa5QQATHl24/fWxQ5nUi17fhkaTRvck1/cqIWOhPwnhC2DWFlZycnJBQUFKSkpmZqacjgcFN9rNgUFBePHj8/MzBw1atTNmzdnz54dHBzcvn37x48fi33cv6RLly6LFi06ffp0REREQUGBl5fXtm3bFi5cqKys3KFDh6FDh16/fl0S3xrg9evXAIC+ESQBDfX19PScPHkyjUZDs1Up8O9fPn78WFNTU1BQYGFhQaPRli5dymAwnj17VllZSeGrYBoGrdIUFRWLiopUVFSGDh1aV1f3+PFjNHXIw8NDGi+am5tbWloKALa2tgDw4MGDnj17VldXc7ncxjwdNdaRk5PT1tYmP5M8Hm/69OmvX7/u0qXLkydPxOoimkdtbW10dDSdTv+yT5MoqAx3165d3t7eRUVFojNDAEBeXn779u1GRkYFBQXR0dGSe9UA6O8p+X/N/2UQAICWERS9BwDDXn3Fhsg2gPrcpXQllU7yTG3m541yHyV5OTZbdc5ihoZWw8+lhhYQ2++ccePGAYCTkxPZbLfZpsrKytBEQDMzs6KiolmzZoFIk/hmc+fOnXPnznG53KFDh44cOVIaO0J00Ta8hm0MVVVV5Lmdubk5AHwZiaIWNEl136lrIckltXWUtc/HNEBSUtLdu3dLS0sfPnxYWVlJHiugRWTjK3AaT0lJiYmJibGxcU5OTnJyMgreTJ069dChQy9fvmxMBdGTJ098fHzCw8PJ43aBQEBeoZLMWhIjJCQEAExMTJptYc+ePQCwfPnyV69e0el0KQ24J0lISAAALS0tCQ9HR1nPBAAY+Bv0ngUAK9ZuatLTKx95RPfpMESFvaGtekq/TqkW3YtOHfpmuilVYCEkzp07BwDTp0+X8JBD9Hw7NzcXzTNSU1NDw4MkAQkhQRB+fn40Go1yIayurpaTk2MwGJS0uEVJrZcvXz548CAALF26VHKbX3Lo0CF0aIoql9W7D4E5D5gLffrvDFrvlHA3NCe/XIp5/BhRyHMmDoejra0NANR21yOvrF69ehUUFKAdjOgZm1g/8UZev8eOHUNX6Nu3365yazzHjx8HgJUrVzbbAqlMHA5HRUWFRqM1PqW8eaBqSNRnrtm00esAAGB1FnRNAOCBp1eTnl7icMapmy4AmCnJpw7sWpfbQj1lEFgIiaysLBqNpqSkVF1d3alTp6FDh35tmHsD8Hi8SZMmAUC7du3S09O3bNkCAGw2+9WrV5J7mJqaSuaVOTg4UN7aGC3km9QOqgEcHBwAwNrammyNTflEgiNHjgCAnp4eh8NxdHRUUVEFGh1oDNDoCj2mwOCNMPUazHvU9renM09HnXr8MSKtTFDfl2NqfpVzcPbtkOxPRY1qboD5Guic6eHDh8uWLQOAv//+myrLfD4fKR+6slasWIHUy9/f/2vzh0Wb/xUXF5Omamtrjx07hrpbREVFhYeH29rakiNTqAI1GWhSKf2XkMqErEm7JHfr1q0AsGHDhmZbKCoqAhoNmAow5wEw2UCjNfVbNGfd0g1t1QFgqY5q1mLb+hWe9AAAIABJREFUZnvSPGRaCD98+ODq6hodHY0m53l7ezdPYwQCAco+1dbWTkxMRJEiFov16NEjah2ePHkynU6XMND6JSjxb82aNZRYy8/Pp9Pp8vLyFRUVaLA4JasBEicnJzQr6vr16yEhIagCGtga8N8aTVBuC51Hw8DfYNI5mPtQZZnfkL0hW28neUXml3B4lTV825MR7CW+qsufqC73Yy95PO/c2+paPMuimaDBWMuXL3/48CH829ZZckR7uL9792779u1ofSnWbL2qqurVq1dHjhyxtbVFXR1EN4smJiYrV650dHRMSkpSVVU9fPgwQRD29vaipeUUghLlJMxzIZXJxcUFAEaNGkWVe/USGhoKAB07dmx2xq+nlxcAgK4JWJ0FgDZ6HZr2fKHw4+i+I9XYAHCms3bR8RbqtU0i00JIEISjo+OrV6/27t0LAJaWlmFhYc3YvlRWVg4bNkxVVTUyMvL69es0Go1Op1NTo/pfFi5cCADoSqYQFMx0dnamyiA6t3N3d9+0aRMAoCa/lODh4YFO4E+ePBkXF4dqTgaP/4Wx4BHMcodxR8BsKRiYg5zyf0SRyQbtXtBrBozYDTNcGQt8lJf50q1Ow9DtMO8RzHsEln/KT7todZj6IVkyglg0Dyjq3Is+P4qKiiEhIegIozHry3rnD6Nd2qRJkyZMmJCeni4lIURlSOrq6hK2QyOVqbS0FB1bNLJKpHkIhUI0ja7Z5zgr1m4CAOg9Cwb+BgCjrJtWh8ZLT/3Qr1MbJgMAXhm3a2RvUgqRaSFMSUl58+bNtWvXFi9ejCrroVkT+AiCqK6ujoqKEv2alobD7u7uSLCpNfvs2bOdO3dmZlLW1g+d2y1cuBD14Dc0NKTE7LNnz1AQ7MCBA6mpqWjtP3Xq1Lq6uvLqOr/Ywj3uyeMPvVFd7gdzH4LVWRiwGjqNAqX/zKcFGh3UO0M3axi8CUwXfBbCXjNgjJ3yMt/AJCpbC8kUYtE8yTv3Hjp0CCmfr69vvY2wG0NNTU1wcPDx48dnzpyZkJBgbW0dHR1ta2srJSFEPeckL0MSVaaJEycCwLVr16hw8KugfpPbd+7iC5qzKezR1xIAYPhu6DwGAOyONe2/X/HQ3b+3AQDosBgp/TrV5bV0iwyZFkKBQJCUlIS2g3JychMnThRroUuj0YyMjJYtW3b16tXExMRvxg3QAYn0ZmhR3rFl8uTJ6EZ0dPSuXbsosUkQRHJyMgBoaGhwuVyqsidEO/Xk5+ejoOvo0aO/jGbzBcL4zEqnV1krr8T12hxAm/8Ipt+CEbuh1wzQ7gUMuc9yOGQbdJsEE0/BxFPQeQyMsaPP99nmSnHYWXYQi+aNHDlSEms3btxAkRVXV1eyEbaEcycqKiqsra0Jgvj999+nTp0qDSFEW1hKpgcjZfrrr7/QPF5plOQSBFFRUYGaoPr7+wOAfqceSkt9h+wNaVLGGZ/PZykoAQBMdwYVA2j6nPOCf3Ye6agFABPVFdMnWDTnnUiGTAshQRDnz59HBwnk0OfGTOBD5XEEQXh6elpYWKDj9/nz5+fn59+/f1+qDltbWwOAg4MDJdbMzc3RjTdv3lB1RohAS4oXL14sWbIEAP755x9JrMXHx6MqzAULFpSUlKAejAMHDmxMmmteGdcrMn+Pe/LYf94oLH4Msz1g7BGw+B1G7Ia2/aDvcui7HHRNYYwdzHs0Y+89NAXtxo0bvr6+kvgsa5DRvPLycnl5eQaD0YykM4SXlxeKrJw6dSo0NBQtgLZv3y6hh6QQlpeX6+vrS0MIURUgJZ8cpEzGxsZ5eXkMBkNBQYGSpG5RuFzu2LFjdXV13759W1tbq6ioyGDJg2YP6DEFhm6H6c6iGWeBSSVcXv3x3s+Vjsp68IsrAI0pp9DUA6bMuZPmaakAwDaDNrmbmzC7iipkWgjJeMvXdKWBCXwsFsvc3Pzy5cuDBg06cuQIQRDTpk1r9pXfeC5fvgwANjY2lFgzNDQ8f/78+fPnt2zZQq0QorzZDRs2oEJdC4vmr/IyMjJQH8jJkyeXl5cPHToUmttzvI4vjEgrm3YigrHAB0bsFguNKi31vfw8w87OLiEhYe3atQ0M4sF8yZfRPBcXl2bYefnyJTrY27t3r2irQsk7t9XV1ZFZNoGBgY1vANZIeDwem82m0WiUdKjh8Xjovb9//x7pq7u7u+RmScgySh0dnffv36O2cHSWgui3HLA1wMAczJbCuCMw5wHr3/Ikp1dZHws+J1oHJpW0W3oHjOeC2VIYuRcAuhg37WIXVFelDjQ0YssBgGt3vdIb1MxHbBKyK4RPnjxB8RYkY41BbAKfqqrqnTt3Tp8+PXbs2PT09JYRwry8PDInU3JrvXv3DggICAgIuHTpErVCGBwcDACdOnWqrq5WVFSk0WiZmZm1tbUJCQmZmZn+/v6NHOJaUFCANpcjR46sqKhALUzbt28vSWVVQXmt8jLfL4VQbYVfZExiSEjI9evXr1y5IuEuVqYoKSl58uQJGc2LiIiIiWnO0IDY2FjU3uXXX38lG2FPnjyZ2gqcEydOyMnJUX6E8ebNGwAwMjKiyiBKjjty5AgqeZw/fz5VlgmCWL9+PYpyRUVFoZJfOTk52vCdMPogmMyrJ+OMzhQpT3KEeY86rn8+5uAbuYU+MO8RjD0M/VfB6H/A2l5vtn2TErCrw0PizDoyaMCk0eLMOtZEh1P4NhuJjAohmXbf7HhLZWVlVFSUm5vbmTNnoqKiZsyY0TJCSBDEoEGDAICSGKz0QqMCgQANFI2Njf3999+3bduWm5ublpZ2+vRpLpcbExPz+PHjbxopLy9HlS19+vQpLi6ePXs2UNGphyCIR28LlBZ6MmbdRULInOWqvMjreUIRh8N58+aNUChMSkqifHTGT4y9vb2DgwOK5vXs2bPZuoW+nWfNmpWfn09tI2xRvL29AaBfv37UmkV1U8uWLaPKIBo0Nnjw4I8fPwKAmpoaVeMeURctNpsdEBCA6pHodPpN59sPo/J33n0/+mCoyjI/mPsQJp0D87XQZQyotgP4b3mSkg50HA79V8KEEzD2MLSzgHFHYMhWmPeIvfjxgfsfGu9MieP5W930AMBUUT7VvKuQS/G/uzHIohCSq84lS5ZIGG9BQkgQxLp16/T19VtGCFE2XfMm6Ioxbtw4dCMyMnLLli2SGxRl+fLl8EXqEBoDe+nSpW/ml1dXVw8fPhz+7dSzatUqtIBt6jn810jO5Sy+ENP59xddNrxY4RBLhnowTSUlJWX//v3r1q3LzMxUVFTU0NBgsVjkQNomzQsTCAT29valpaWUN8IWhcvlqqqqAkU1HgRBFBYW8ni82tra0NDQZ8+eUWKTIIiqqiolJSU6nZ6dnY1G3lNy+mhvb48SI+7du+fp6YmOY8WyfMUyzujzfWCm2/+XJ8mr/L8iqujD0G3QZRzM8fwcX5n3iLnAx+Zo+B73ZP+4wm/uDnP/WPGnfhsAWKStmrlQKjlB30TmhDAlJQXtVGxtbUWb0DcPT0/Py5cvEwRRVlZmaGgo1Vofknfv3gGAhoYG5R1bqAUVe2hpae3Zs8fPz6+8vDw9Pf3UqVOFhYViBdFfwuPxUFoQ6ieybds2cgHbMs5jmkpISAhqtKuuri42gLpTp05z5849ffp0aGgoyixrGBcXFxqNZmhoKL1NOToeO3XqFCXWFi9eTOZFDxw4kBKbiGnTpgHAhQsXUAPSVatWSWjw/v37DAaDRqNduXLlxYsX6Dh23759DT+ruJL36G3BbvfkcYfeqK3wg7neYG0PFuvBcDz0ng1zPMF4Lmh0hX4rYKYbKGqBgTmYzIPRB2G2h9yixxa7g9c7JbgEZ4svNwWCSl+vVIvuY9QUAeBEJ+3CI3slfIPNQ7aEMCsrC02KGTNmzDcnRH/PoGMzyrtDUUhdXZ21tbVo+/kmNYREuTaoU8+ZM2dQdpLkk2IwUqK2thY1r+/atWteXh6Zem1jYyM2aFN0s4gS9xGTJk1C+7OAgAB3d/dr16418hS5eZDz1yixJj0hdHJyAoDx48ejzExdXV1JSvXJStzDhw/Hxsai4unVq/8/S9PHx+fo0aNJSUm3bt1qoCFUan6V06useefeMuY/IneBMNcLtIxg6Hbxk0WUhjpkCzpZ1PvtKdoseodmfFoyI83SKKVfJy0WAwBeGrfL/eN/zX53kiBDQlhUVNSrVy8AsLCwEBuQ+8OxefNmAPjjjz+abSErK+vOnTuhoaEPHjxoXnZfAwiFQlRSqaGhcerUqT/++GPQoEEoNYlET09v+vTpx48fDwkJ+fLkIz8/f8SIEeHh4WRDNWkXFGOaDZ/PnzFjBgDo6+t/GQgVCARxcXEODg7Lli3r1asXnf6f0W/t2rWbOXPm06dPDQ0Np0+fThDE3bt3T5w4IW2fKyoqFBQUJKnxEGXx4sVWVlazZ8+ePXt2x44dJTdIUlpaymKxWCxWSUlJly5dACA4OLh5psLCwshKXDIwNmfOHDFlPXHihFAojI6O/tosmoKCAn9/f4FA8OSJf9tlbjDvEYw/DiP3wZhD0MYQZrnDlKsweCP0mALaveD/2rvvgKbu7QHg52aSsMIKG1GGgChThiAu3CCiUlurtra1u67Wp9aO92p/rZ3a9lm1WtuqrQMrW2WIguytbJAd9ggkZI/7++Mqz1qrENn5fv6KSe7NiUBO7necQ/prJyYNvXsXi/M++iggrMTT7q67dfIMCwAwoJDvulvX+Dr0J4/BtiV1SYQCgYCo++Xs7PxgHd5hlJOTMywbaQcjOzt706ZN8fHxT3OSqKio6OhoDocz7GEPVMZ68I9WKpXm5eUdPnw4LCyMzWY/+NdBoVCIq4SLFy8+uCkiMjKSuKYchU9GRDVKpXKg0cpgNiTweLxbt24dPHgwKCjI0PBeq7nTp08HBATs3bv30qVLo5MIcRwnViD//PPPT3+qYbwiJEaD6+rqBoasFi5cCABnz57dtWsXqLpbv6qqivije/7551tbWwfqUTw0MPbxxx9/++23aWlpW7duHSj0/5CamprU1NT09PTy8vJt+w+SF34CQcfAezu4b4XgE7TNV4xfT3r5xJ1Ze1PJG69A2CVY9Bm4bAZzL6DrPPhXT8awGUzaRiPt5420ASBQl0m05K1b7CnMzVAIBCq8TZWpSyLMzc3V0dGZNm1ac/OIdPeQSCQ//fTTsPxRDUZiYuJAadCvvvpKhbotjY2NBQUFP/74Y3p6+ieffDKMsf3nP/8BABqN9viJ/YqKil9++WXr1q0zZsx46CrB3t7+hRde2LVr1yAnMJAx9E+FsAdDqVSWlZWdOnWKw+EEBAT09/f7+vr+8ssvo5MIh3FL7nAlwoaGhgMHDtTX1x89evTHH38k7vzhhx8AYO3atTU1NTk5OSqs7+NwOFOmTAGAlStXdnd3E1O5s2fPVm1gTCKRHDlyRCKRVFVVETOXoGkE6y7CtEDMd8eaQ/nd/HvTwHyRPLm069OI6uCvc41eT4QNcRB6Bvz3wfRVmgbTaA9MJFMxjE7C/LQZ20xZP9mw812s7nraND67vOOz/byYS9L6GvxR71opl4mK8viJV8QVJY98wuBNnkR4+/btmJiYB+9pamo6c+ZMeHg48fPOz88fuVmH7u7uixcvvvHGG09ZbHeQzp8/P1Bu6q233srOzh7qGYjPIKlU2tzcPIyLXQcWpA1U6hkMHo83MKU0UPSVRCKRyRRL37D8ut7hCg8ZXgOFsJ9++jYgIADH8T///NPZ2Xl0EmF7ezuZTB6WLbnffffdQKne3bt3P82pTp48WVlZ+fnnnw9U+fjk90wMwxgMhgoVJHAc53K5xIpTHx+fzs7OgZXYqp0Nx/E///zz9OnTxDzi9p3vAQBMCYAFnwDAFMfHdR1p7hFdzGrZ9lup378zXJ45kelhf87eZLe5ng75L9+DAYCMgSODtsFQ+8sphglO5tXu1rVznTlb1nV9f7A/JUnRx8VxXJCRUrfIvXbuzNr5LjX+M+pXBYgrHn0JOxiTJxF2dnaeO3duYNizqKho/vz5MTExv/3225w5cyb6pOBDzp8//+KLL0ZERERERKxYsUKFRDgS/vjjj8dX6hkMmUyWlZNrtXTHvYGUhZ9+cHHYuocjw0jlQtiPRCRCHMdXrlw5aiPhRJWiIX1pG1Gtra0HDhzIzs4+ffp0dHQ0juNHEurhmcskhq4R2+Sh5WaD+c4tEAiIqjTOzs4dHR1r164FAHNz8/r6+mEJeNma5wEA3LfCzA0A8Pwr7wzyQL5AXOU/8667dYnrFAqGkTG4PsP8Jxv2a8a6Hlp0Oukvq461yCQPLfprxro/2bDzZlnddbeuW+J9d7bNdlMWMZp628Vqv4V+rf8MaUOdam9k8iRC4vpv4J+bN2/OybnXVefbb78lNjlMGufPn9+4ceO5c+fOnTu3ZMmS8ZAIExISiAVpg6/U8xj+/8mAGesBAOyDZ+4ZznaGyLAYrkLYAwYuy8Ri8bDvoP8nRDf5DRs2jM7LDdW5jGbSc9FgNhsAMJoWhUJ9MD3o6+uvWLHik08+SUhI6Ovre+QZ6uvrp06dam1tzeFwiH29LBbr5MmTDQ0Nzc3N1dVD2Pb+SGxLOwCApd+CqTsAnLsQ/vjn5+bmHj16tKqq6ptvvkk6+EmNt/05exMAcGLSiJR213NazWybEtcpF+xN95nrL2MxjankB981CcBOgxpmoPWLrfEsJp04KnuW5QJdxt3Z01reeVG1N/LXJT0TWXZ2Np1O7+3tJcbWGhoa7OzsiIfs7OwyMzPHNLrh5+rqSnQDTktLG+tYICsrKzQ0VCKR7N27l1jR+pRCPIzTMnyg9AJwMoqbeHfbhbbGzKc/LTIscnJyQkNDpVLpvn37iIVRT4/oYggAD7WbH1Fr1qx599134+LipFLpQ6uaB6+4uPjo0aM7d+7MyckxNTUl1rY8vfg7nS8cu63M/gFacoGugy/+6pmlHrt8IS0tLT8/Py0tra6u7sqVK1euXCGeP23aND8/Pw8PD39/fzc3N2LefcqUKenp6QKB4Pvvv//555+ZTGZUVNS0adMiIiJWrlyZkpJia2urcoQ8Hq+TUwMkKuhNhe4qAJjr5/v4Qzw9PVtaWqqrq/l8fquNzXSrqYWcHABw06QDiUTW1jb+8qiG62xpdblFUd788mJxUZ6sualDpigRSkqE0vx+Sb5AXC2WVYtlxjSKEvBasQwA+hRKAACFUpSbDjj+cI/uQZg8iZC46h9gbm7e0NBAJMX6+nqibTQyEoqLi1esWCEQCF544QWi2b0KiD9XfX396urqhQsXrvY02W1gB0xDEHZBT21sodOOZVOHN2xENaWlpcuXL+/v79+0aRNRo3J4Xbt2jc/nr1q1ahQyorW19Y4dO9zc3HAcV+0MCoUiPj7e0dHRxsbm1q1bKmfTh+TW9q37rkCaexJqE4FCh3kfL/Bx+fk1Nw0qycPDg3hOS0tLfn5+eno6kRpra2tra2vPnDkDANra2rNmzfL39/fz8/P19b127dqXX35JpVIvXboUEBCQlZXl4uIybdq0GzduPE2QmZlZOK4EA3vgt4G0X9vAxNzc/PGHREVFpaambtmyJTs7u7e3V8HrLRJIAMBNk870DTD5+hhGowMA3XEm3XEmcYi8s92kvGR6efHK23nionypWFQqlBYKJF5aGkm9whiuAABEyvs/PrnixyNHPL28RCLR3bt3161bp6urO6g3o9qF5PiXkZGxbNmy/Pz8xMREHx8fLpc71hENJ4VCMVAWRyaTPX1hfpUNbEhavXr1U1bqOXny5NmzZ/ft21dZWYnjuOPuFLBbAQDg/Nz8A1nDFC/yVGQyGTHQEhIS8vSFmR4pMjLyl19+6e0dpRVSISEhxI27d+++++67Qz1cKBQmJibu27fv9u3bv//+e1xc3NOHVNXaz349EVw2AwCQKLDgwKy9qVzB4yryiMXijIyMQ4cOPfPMMw996ccwjEKhkEiks2fP4jjO5/OPHTuWlZVVVlb222+/PU2c77y3HwDAIRS8twPAnMAhVEdTKBSyFs5dd2s2lQwASTPMebFPLp6slMskdyv7zv9613PaXXfrh4dG3a3rAmd//vnniYmJhw4dampqGny/jslzRfgQX1/fr776KjY2lslkXr58eWAt4uTw4H6DB6u3jLKWlpbFixe3tbUtWrTo/PnzZDL5ycf8A6FQKBQKeTxeWFjYnTt37O3tQzyMy/N8oPoKNGfdqtzUxZcaag/P121EZRQK5dSpU19//fVT/rgfY+rUqXw+v6GhgVjrONJaWlqIG1KptLOzc6iHMxiMwMDAwMBAABiWgJu54sWf53Tcvgq3zwCGwZz3bFz9E/Z6sZjUxxxFp9N9fX19fX2Jbkqtra15eXnE9WJqaiqTyfTw8Hj++ecBQEtLiyjbCwCOjo5PE+rNWxkAAIYO0FYIAIHz/Qd/LIlEEt4p4EjlHTIFi0KaQqdqzHR74lEYmUKzsafZ2ItLb/cnxj38qAZD87kXV3j6X716lUKhFBcXP/EKdcCkTYQA4Ozs7OzsvHv3bhcXlxs3bsyYMWOsIxpmSqUyPDycQqGYm5u3tLSsWbNmlAM4evRoXV2dj49PVFTUUw5kdXV1zZkzx8nJqbi4mHgjIR7GB41nAZUJ3DoFr+1KUefmuYP9tUZU1tHRcfPmTaVSScxAA8CFCxd++eUXXV1dsVj8/fff+/v7E4stR4iNjQ2LxSIaMI0CPp9PbChsa2sbnVd8jO5+6eLPcxqKbkLWdwA4uL9q5rI4cZ+Xse7Q/rhMTU2Dg4ODg4MBICUlZf78+bW1tcMbKo7j1aWFAACG06HkDwBYtnBovxXi4kJiXNRdU4Oiq0e1nDL4Yw33fCIpL/5WogBQAACLTPrY1lzD1cPwhVdbS8u2bt1Ko9FqampcXFwGecLJnAgJXV1dnZ2d0dHRky8RkkgkZ2fnq1evrl279ueffx6FVywqKqqurg4LCwOA8+fPh4WFaWlpbd26lehp9TSsrKyIzz4vLy/iHi8bXVN9rVZTd2hMg+bsqHwXlAhVUFFRkZeX5+DgUFFRQSaTn3vuucc/n81ms1isvr4+4p+tra1HjhxJTk6mUCh5eXk7d+4kegONHE1Nzaf/dRo8CoVCLBghPjpH7XX/TihVrPomv7wwE9IPAq6AWRt13dbG7facavRUy8Tmzp1rampaV1dXXFw8c+bM4Yq2srJS3N8LDH2gakFfE4lCI/bpD57kfiJ01aTTZ7kNaYULSVPL4vdYrbM/82MvKfv6aEZst/Uv6IQ8AyTSwHX54LMgADy8k3HyCQkJAYCoqKixDmT4KRSK9vZ2mUzW1NRENKod6VdsbW0tKSkhbhcXF7e3t+/Zs+ehqsrDhYRhwe7GYOEDAMDJir/TKZYpVTuVrKFWcDNBfDsPl8uHM8SJwMHBgc/nk0ikZ599tqur64nPb2lpITbjEv/Mz88PCAgght89PT2H/cJizDEYjIULFy5cuNDb23sMw5Ap8HWHCzJyCiH1U1BIwW4lw31jzHuerlN0nnzwY5FIJOLSMCIiYjgivefeuKiRE3RXAq60tJ1B1IEaJFwqlVSVFd5fKTOYcdGHYDS63ktvWl1Otr6eb3n+qk7os0BSPZ1N/kS4dOlSJpOZk5PD4XDGOpZhRmyw3bZtG5PJfPHFF0fnRdva2goLCwsLC9vb20f6tUI8jMHMC0gU6CgR8Hqvlzz5c/whsuZGzvNBTRuCOv69u3XbloZAT2Jqgcvl9vb2CoXC2tpaiUQyArGPF2VlZYaGhjU1Ndu3byeKzj+emZnZc889R3QpAgAqlapQKEY4xrFE1KEGADKZzGAwxiQGHIetJ4qvphdD8gcg7QdLX7LXG7+/5TJ3+vB8xSR6OQ1vIoxLSAEAPZYF1lUOAES38MGTlBdLJJJyoZQEMJNJUyERDq/JPzRKzGZHR0fHxcUNzBJPGsSKTU1NTQMDg2E/OY/HE4lExsbG/f39QqGQKNpbU1OTkJAAAKNwcbBohoG2ji6f7QxtRdCaF5XvtNKN/eTD7lNwe5o3r/6zrtlbk2ZKowDAj219b/17N4ZhpRo6xCYqMzMzU1PTEXsHY8/Jycna2lpDQyMwMFCF5S2zZ8/++OOPiQ6xSUlJQxpumhCSk5OJG/b29seOHRuTGHadLfstqRRufARiLhi7YH57ftrqEuppMlznDwwMZLFYRUVFtbW1RAuLp6JU9Bw7vOzOdbIh63l51uGW7gyAoMUBQzqHuLiwRCiV4rgDg6ZFpdBnjPHv1eS/IoThGB3t5EmPX2989/eKQ1fr6jqFwxfauFZeXn78+HEA+OOPP6Kjo4k7/fz89uzZs2fPHqKH+IiiU0lLZhkOjI5GF3QocVwul/f398vl8ideknKPH1IIBLl8Ua/i3phqUq8QF4s6P3vf2spSIpF4eXk1NDRkZGSM9BsZW0wmk0Qi6enpEW3Zh0RfX////u//iGvEs2fPEqVYkKeE4/BnTtvSgzkO76VMfzfl8LV6yPgG+M1gYA/zPvxy48yX5lkM48tRqdRly5bBMM0Qte/b1vvHzzNpmDeT3CISfGGh+5O9hZ+kb0gnkRQXFvbfGxel2diTNLWePrCnoRaJMCgoiEwmJycn8/l8FQ4/m948dceNXWfLv71S+/6Fyhl7bv3ncjUApKam3rlzJzs7+8KFC01NTcMd9diztbWVSCRJSUkCgaC1tXVMYgjxMAZzHwAMWvLbe/jZd3tTU1MjIiKOHj0aFRVVXl7+yKO6+NLTt5o5sbEgkwJAq1TeKJE3SuQyHAcAXK7IjIo0MzPj8XgSiWQYviOPe4cPHy4sLCRuD3UUfdGiRdHR0UePHvX3909KShr+4MYBHMd37NjR2Nj4wQcfiESikTg/AMjlcgBQKPHV3+a/ePx2QnFXZQuvqk0AADD7TTA7Qrm1AAAgAElEQVSbDfP/s3eN83srh/8XMjQ0FIZjdFSUky5Iv1na3be3ocuMRsEBttd1ejNIcPI7RS938OcRFxcU3l8pM+bjoqAOQ6MAwGazfXx80tPT4+PjiQ6ig5dS3vPazyXCtB9g1iaga4tlSkg7+BV8YKZHXzndNiIi4q233mpsbBwoEDWZ5Obmmpqazpkzx8PDIzU1FQACAgIGVnXu3LmTyRzxsmcrXdkUHbZcbypwa6H9TlS+4ydr/C9cuODo6Hj79u2mpqYH90LVdghjCjrCs1szq3uVPM7OBo4JGQAgsVdYSJUAAJ+4NCRhQV4eDG9/GOLSsomLw+EMfAssLy/HcRwbYhmq3NzcrVu3uri4DGyrmEwuXrzo6upKLMNWKlVck/VPenp6Pvnkk48++uj48eM0Gk1sE3K9tEvQWARlF4GuAxIezNoEBvYw/9+b/M0/e2b68L46YcWKFRoaGunp6W1tbcR8imr40ZdwsehUB2+Pub4zkwYAXTJFDFewQVtLmJasHbT2iWcAAHlnu7y9rQglwtEXEhKSnp4eFRU11ET47u/lQokChJ2A318yIGgXSOR7z1W6v25A9Lfs7++fZBv2CcRwCgAwmUxiePnBpe0DXVVHlL4W1d9e/6aFD3BrgZN1PitgsUlHY2Oji4sLk8mcO3euEscL63kxBR0Xs1vLOXzg1kBzNjTnQM/dQwCODNosTfpmto4jgwYA6TwxAOAyGdXaZhSCH1dSUlI6OjoAoKenR4XDFy1axGKxbt++PTzzTOOMkZFRcnLy9OnTc3JyLC0tiY4Nw0VfX3/mzJkaGhpaWlp1dXWnKmoFIgkUnoDAL4DCAEkf3PgYlh3WoJJOvTpr6GUyB0VLS2vRokVxcXGxsbGvvPKKyueRtTQBjrdI5Vb0e7ljCp1SJJDgYrG8bVCDRkpeb/d3B1ul8jaZXIdMmqpB1ZjlrnI8w0VdEmFoaOi//vWv2NhYmUxGpT6uRgNBIFEkl3ZfzG7Jr7s/9t14C6hMAAClHACkMllEXJK3q2N7e/uiRYtGMHS1t8qDfTPDB4r/AE5mQ4dg5S/kt5assbazq5cbv32mOqawo5PbD21F0JwNnCwQ9947jKZtaTT1FY2unL6/jIdjFArd0YViPJkXyIwEKpW6fPnyc+fORUZGEq3SJxNiBwUAjMTMt0AgqKysbGhoMDExoTD15Gk4CNpByxQoDAAAui7QNEHSp6CwRDKFNnmkPpNDQ0Pj4uIiIiKeJhGSTczgdr4pjdwsleswaADAkcpNaRSga1CMnryQTVJ6u+WtzUqhUI9C/tnWuEeuIGEkGPFtX0+mLonQ1taW2FOclpa2YMGCf3paB0967XZneHZrQnGXVMCFljxoTAPjWQAANG2gERdDGABgJMrGl990MBvjOV51cLuBD3o2oGkMgnboqZYY2H93rf67a/UKMQ/abt/Lf7L7K5g02WDqDuZeYOpBZms5dJ72qEw3VMqIB7+yMyXr6Rt/dnjM3szYmTdvHtGU9auvvlLtDKGhoefOnYuIiJh8iXBEaWpqfvnllwDg6OjY2it5N+0GkCjE9+l7lHIgUQAHMmlkrgcBACAkJOT111+/fv06j8dTYdkUQWfFauGt65uNdL5o5u40ZXHlypgewSlbY0yJM/3/8XOVoOTzWt964UpTK0cif8VYd54O4wCnZxpdTH3nBcvIm9jYlYoE9UmEALB69eqDBw9GRUX9PRGWcvpjC9tjCjoyqrk4tx44WcDJgp67QHxXkfCArgMmrqDBAgAo/gMAcABb49Grf6G2ypv7w3NaAQDMvaAqBjhZoKGraC2E5mxoLfjfp4muFZh7gbkXGDk5mWsHuxsHubH97PUwWNB/Lar3zEkZp4Gko+MRGKT38lsk7afdpKyeli9frqGhkZGR8ZTzTOrMlEXXYVBFEjaIuCDsBKYR8DgAGFA1pxgymbQRKd9KMDQ0nDNnTmpq6tWrV9evX6/aSZj+CzVmeboW5XxIJt3oEzLJpBM2xrqamqxX3yEbGD3+WH7sn7hMguP/uwLEccABV/T1CW9d11ywVLWQhoUaJcKQkJCDBw9GRkYeOnQIwzC5Ak+t6IkuaI/Kb6/vEEBXOTTnACcLePf33ZNpYOIK5t5g4Q25R/53IoysSSe/t3IqhTyCX98QACjl9G8/UyqSKAFw0LUEAKi4DKUX7j1MooCJG1j4gIU3WYvtY8sKdmevmW1iZ/LgFxRMa/lqreWrRz/4ceXLL78kkUg4jufn569Zs2aoK2UIWlpagYGBsbGxMTExW7duHa7YhEJhS0uLra2tXC5vaWkZtSqjY+XTZ+x3nC4T+O6EgpOAkQAw8N1JIWOfrx+RZTIPCg0NJRZdq5wIAcD00Inubw/YRofbsQ1BqQRcqf/OXt2wjU88UJSfpRSLASCVJ+IplABQKJCs0tdUigSS4qKxTYTYKNTlGieUSqWFhUVra+t355JLBUZR+e3tPXxoK7yX/wbmlujaYDYbzL3BzEOfpbNohqFMgcff6ZDKcYUSxzCMSSOvcjc686briI5jqC2JTJlc1h2V3x5T0NHSI4SucmhMg6YMEHYBAJDIgFHAxAWs/MHcR0dXd5mLUYiH8QpXo8eX50cAQKFQmJqadnZ2lpaWDqbKzN+dOnXq5ZdfXrFiRVzcw7X/B4PD4SQnJ2/evBkAbty4oaGh4evrW1xcnJqaunbt2qSkpJ6enm3btqlw5oll19ny48mNIokCl4ugKRMkfVquYX0nF5NGaKnMfQ0NDVOnTtXU1Ozs7BxSRbQH1dfXKxSKqSbGLdkZcow8xT8AG8SqCwBo2/u2IDHuKldQIpSuN9QGgB9ae5830nbVYui9/Kb+G8PT4Vk1anRFSCKRgoKCTpw4sf3AUdC1hOYcaMkH+f09Q1omYO4F5t5gPNPWRDvEw3iVh7GfvR6R7W438k7d5JS39FsZMDb5m89zHJHqmupAwe2Rd7RRzS1JWn/ZcMIVyJJKumIKOqLy23l9fdCaB02Z0Jr/v8k/phGQqcBvAdfN4LSORMKe8Tb97fVZNIpa7IUdFmQyOTg4+NSpUxEREaolwpCQkNdee03leabu7u5bt24RibCsrExbW9vX19fR0fHKlSs1NTVlZWXDvnVhfPp2o+NGf7N9FyoTMssg81ugaPTbrcy+2+trpzeirztlyhRXV9fCwsKMjAxicZAKeDxeYmLiu+++G1tVS6VSX14w2KWCmv4LROk3gCtgUUjEolNtMgkAMAaD4Tni1TkeT40SYUVFhUwmAwAoC793F4aBoQNY+IKFD+hYOJlrhXmbBruzPaY+3NTYxUrnu82qfHAgA6Q1VR0fvyutqcbodFws1vD0YX/0RTNJN/5OZ0xBR0Jxl7SvHVrzHzP5B41pkHYQWnLAaR2dQjoQZo+y4FCFhoYSiXD//v0qHG5gYODn55eSkqLyPBOfzyeK83V3dxO7b7Ozs1kslq2t7e7du2/evKnCOScid2vdc2+5GZd2y/WnQU8NtN+OyHMY6UQIAB9++OH06dOJr0H19fXW1tZDPYOZmZment6FCxeIBqKDP1BrSXDP8cPaHVz9+w3l2VQyg0alTbNDiXBkKZXKwsLCmJiY8PDwsrIyAMAwDMdxMJoBVn5gNZeuYzh3un6QG3udt4m5nopjBcgTSWuqml9cc5HTsU5fE5dKREo89mq8f37REqv3uwU90JwLzdnQWX5vdRJGAiMnMPcGCx8yy9LBTLO6TShTKHEzTyBRobMcJH2bFs60NR7x7fyTz+LFi7W1tfPz8+vq6qZOnarCGUJDQ1NSUoY0zyQWi5OSkhISErZs2VJZWXny5EkAKCgoID6F/fz8BvbtEQVQ1IS+FjXAQT/59hzoqYGmzIjceV8+5zDSL/rrr78OlFRdv359dnb2UM/w008/2dnZrVmzhijXNfgDMRrN/MSFBW9tlne04SIhRiK9Oc2MamNv9v2pIfVgGgmTMxGKRKLExMTo6OiYmBhiEzEAGBgYzJ07t62tLSsri2m/YOUzLwW5sVd7GuswJud/wrjS8fF7SrHojw7eOn1NABAolFHd/EqRVFn0MogF955EYYCZB5h7g/lsTW3W0llGIR7GK92MDLRoebV9/zpXcbO8GzeeCa0F0JwrU0y27pKjg06nL1u2LDw8PCYmRrXZuNWrV+/cuTMuLk4sFj9+nkkoFF6/fj08PDwqKoq4dPDw8PD09Pzss88A4MiRI485Vk2EepokZ/jCnTPAybzb1l/C4TtbjPcaVe+///7A7aGOr1JMzKzCE4RZt8TFhRiZouE2m+Exlt2vBkyqHNDT03P9+vWYmJjIyMiBglLW1tZLliwJCgpycHC4evUqi8XKysqaw6i8uG3s6/qoCQW3R1pTBUoFABBTQMTISJtUxhULgK4DZp5g7g1mnoZ6OstdjILd2ctd2Foa/1tK7jlNN3m/97M/FF6o8oHWAuBkReevkCtwtHBXBaGhoeHh4REREaolwilTpri5uRUUFCQnJ69YseLvT2hvb4+Ojo6IiEhOTh5oceXh4REaGjq5G32oIHS28TbWFFzHAngc6CyPyHVQLRHiYpH4doFSwKfZ2FOnPKHuzxtvvEF8g+nv71cl6Kcgl8tfeuml48ePnyssMTE0WTs+siBMxES4a9eub7/9FgDq6uri4uLefvvturq66Ojo2NjYlJSUe7OAAE5OTmFhYcHBwe7u7gOLxVNTU4OCgigUSkrKTS6Xq6c34iPyCAAoerowKhWXSkRK5bt1nQAgwXEAeIWt85yhzga3o5YmOkFu7GB39nxHg8fkthAP4ws3fCH3KLQVdPfyM+9yh6thm1oJCgqi0+m3bt3q7Ow0MnrC3q9HCg0NLSgoiIiIeDARNjQ0REZGxsbG3rx5k6guTSaT/fz8goOD16xZY2dnBwAikYioSggAYWFhKrSFmmTM9TQ8p+rmWvhA2SXgZETk+X4YajvUk/SFn+357nOMQsZxDBQyqp2DyRc/UtgmHR0dSqWSy+V2dXXNnTt34PlHjx4lvpGMfi/is2fPurm5KZXKyspK+Xjqkj3xEmFaWhpxo6+v786dO35+fgNtdGg02tKlS0NCQlatWmVubv7QgQ0NDf39/Uql0t/f/+bNm9euXXvuuedGNXR10t3dnZiYiON4T0+PpZGhs1QKAAwS6dBUIwDokineb+y2Z9BkGlpFXy90Mh9UgZ4VrmyatqHUwA66q6CtKCrPASVCFWhray9YsODatWuxsbFbtmxR4QyhoaEffvhhZGTksWPHKioqiJ2FGRkZxF4sDQ2N+fPnBwUFrV+//qF99wwGY2B1BtHeEgmdbZKb4wtll6Aps7C+r65TONVoCJPffRdPd3538Fh92xsmugBwVyy7m5YR9MJqy0tJFy5c0NfX7+vre+SF+5jQ0dHJycnJz8+fPn26UDiO+tlNvEV3IpEoLS0tLS2tqKgIAKZMmaKpqRkUFPTbb791dHRcu3btjTfe+HsWJJ65fft2Q0PDp29PiDyRgYEB8UlXXl5eVltHcZv99xJKGJ1uuPbZQWZBANBlUuY56t9rT9icFZn/hH6EyD95yqY8jo6OlpaWXV1dVlZWzs7Oe/fuTU9P19HR2bBhw8WLFzs7OxMTE7dv346qzwzG2tkmYDgdmIYgaAduXdRQfquVQmHPd1/IRMIU3r1tYO0yeUm/WNHXd3LbGxKJpLGxceHChWfOnBkYow4ICGAwGMTtJUuWDO97eaI1a9YcOnQoICBgy5YtO3fuHOVXf4xxnQiVSmVxcbFCoejp6SkpKSHuFIvFRUVFRUVFlZWVAPDdd99xudyYmJjNmzfr6j687eGRVq9eDQBXr16VSqUjF7yaa29vb2trw3HcxsYGANgfHiRp6+yecq9hhQ6F9JqlIdXMQv+Nof0x3G9PCNCcU9PWX9Y82pMck0NISAiZTE5ISBhSh06FQpGWlrZ9+3ZLS8umpiaGpk5LSwuFqbtp06bo6Oj29vbff/89LCxMSwsV4B0Ce1NNBzNtMPcGAGjKiMgdbCKU1lR1frZfKZUAgFiprBHLasSyVqkCAHCJeHlf2/bt2+fOnVtXV2dpaUmn04mj3n333YFWOQcOHBj2t/NERCRMJpMypsVFHzKOQvm7xsZGDMPOnDlTV1e3ceO9Ej56enpvv/02ABQVFf34448qTHJYW1vPnDmzuLj45s2bo/+dSE0YGxtv2LDhwXssL8Yv+fbT/uR4XC7TYDAXhoTpv/EuiTG0LRCrPY3f0ZuCa5sBvwW6KqLyHQd/QYkMMDY2HnyHTj6ff/Xq1cjIyCtXrvT13W/GwjAQCbpBQ18e8tsn3yyyNmKMeNCTV6in8ecFvlAdB5zMtMpNbb0SExb90U9VKsR3CgW3rguSr4ka6rL7xQm9wteNdXvkyjiuAAAaJXITGhkAFP18KpXq7+//Ty/a0dHBYDCqqqrIZLKrq+vIvLMJY1xfEVpbW9fV1Xl7e3d2dp49e3YYz4xGR0cfWd+Q/enhaRnl026VTE25Y7jrw6FmQQAw19Nwm6IL5l4AAJysIY0jIQ8iRkcjIyP/6Qnd3d2nT59+5plnTE1N169ff+7cub6+Picnpz179rz69i4I2AdMQxD3QG99ZH7bKAY+SSgUCi6XCwBdXV1BrvrAngk0LeitZ3eXXMuuf+jJuFQiyk7v+uo/9ct8a7asu/zdt3vS8v2KOS9Ut//eyU/uE5rRKNtMWdtMWaEG9wrt0qbZPf7VDx06lJ+fr6enl5CQMDJvcSIZ11eExcXF5eXl1tbW3t7eA9fRsbGxxI0ZM2YQzU1UEBIS8umnn0ZFRf33v/9VrQAxojKM/lSFC0I82AU5PlARCZzMnJreZq4YVUJQwdq1a997773Y2FipVEqj0Qbub2xsvHbtWkxMTHx8PLEGm0QieXh4BAUFbdiwgVjzWVVVde3jhEZzL6i+Ak0ZEbmeO5apsjdfneXl5eXn57u5ueXl5YlE4un6M5h6hoXt/RtKvprzwU+c32ewP/icYmouTL8huBEvzEjp4fXf4AkTeoVpPJHofmUWOw3qEpamp5ZGDPcvC09IDAZr86uPefWffvrJxMSkvb3d3t7e2dl5BN/nBKFGRbcfhOO4lZUVh8PJy8vz8PBQ4Qy1tbVmZmbEdpzS0tIZM2YAwKVLl6RS6YwZMwoLCwMDAy0sLIY5bgTgdiPPdW8qXH4eJDwIOn5sx7LXFk3yfgUjxMXF5c6dO/Hx8UuWLKmtrSUKMA0s/qRQKN7e3mFhYcRF4YMHnj179vT1ysQGOtz4EFjWpJU/Nv934T+O5iH/4NixY9OmTTM0NEy9kex3NTqj/u6OmlZ3TfrF6aYAAGQyhmHdYkkKT3SVK7zFE8nuf1bbalBX6Gmu1NO0YdLpDjMkdTWN/UIrMgCAUKkUUOl2z71osPP9f35lEAgETU1NfD6/p6dn8eLFJNK4HhocBeP6inDkYBi2atWqH3/8MSoqSrVE+O9//3v//v3Tp08HgC1btuTk5ADAunXrDh8+vGHDhpiYGLQSZ4S4WOlMZWvVmc+G2uvAyYzKd0OJUDWrV6++c+fOnj173n777erqauJObW3tFStWhIaGLl++/J/Kam/cuHH6nL7E/SnEaJ6Sx4kr6nh5vuUoxj7h/frrr3K53M7OLi4uTq+qVE/QsUibxiBhRQJJh0whUeLX+3hXewUF/RIi+5Ex8NCiL2dpLtfTNNGg0Z3dtBav0Fq8kmzIVvRxdX8+Iki+hovF+tY2NlteZ/o9oUeupqamg8OIl3ObSHB1FR8fDwAzZ85U7fBNmzZVVFQQt2fPnk3c+OKLLyIiIkpKSqKiopKSkoYnUORvtv1WCnM/AAAwdCBtjPvuWh1fJB/roCYMuVx+69atbdu2sdnsgRkHAwMDYvGnWCwezEmUStzynetgvQAAwO3llV/ljnTYk5W4oqQ2YOZdd+tqd+vZWhoAYET9X50BBglbxmJ+Y21Y4GJVM8exZftLvJg/FXzeWEc92ajpFSEALFiwgMViFRcX19bWTpv2hKJED+ro6CBKE+3cuZMon9/Z2Uk8tG7dOgzDLCwsmEymahWNkcGY52jwvakbOISChbdSCf86V/mfy9Xxe7w8pw1q/4x6EggE165di4iIiIuL6+29132TGBM7f/58RUWFjo5OcHDwIM+GYRDiYfzfIl+ovwFNGUkl63giOSrbO1hKhfh2geBGvOBmgpjTVCgQ3+gTXesVNkhkWmRSp0zBopDm6zAX6TLm6TK0DQw15y3WXLCE4eWPPTCbiwwj9f3FpVKpS5cuvXDhQnR09I4dO574fGIShaggtW7dOiqVeujQIWJo1MvLi3jOQEJFWXBEfR51F0Q9oJQBeyYASKoSJBhp8UGo+Xa+vhZqz/sXPT09sbGxsbGxV69eHagt6eTkFBwcHBQUtH///tTU1N7e3oCAgDt37gzpzKGeJv+94gkUOnRXSHhd1253PuODSok+Di6VigtzBalJ/UlXBB3taXxRYq/wep+wV36vC6MxjdIplZMxSHQy16OQMQrF6N/faC9dCSR1r0U30tQ3EQJASEjIhQsXoqKi/ikRKpXKnJycyMjI6Ojo8vJy4s6BranImEir5Fa29gOuANn9psoKGWCYRKo8dr3h/ZAhl2qc0ORyOTG8ieO4UqkcqN7Z1NR09erVRy7+fPbZZwfmh1xcXFJTUzs7O7lc7sA+60Ga56hvqKfdZeIKnGxozo7Ic1YlESoV/Qmx/PgYZW8P3WGmTtjGx6/7H+ekNVU9R74SF+TgSgXNzkn/zXcZHt64WCTMyRAkxQlSknr7+tJ5out9osQ+oUBxL/9Z0ikLdZnLWUwPLY1N1W1ZfPEtnniVgRbF0lp7+aqxfUdqQq0T4YoVK2g02q1bt7q6ugwNDQfuF4vFaWlpMTExly5damlpIe7U09MLDAwMCgpavXq1jo7OL7/8MvDBoVZN1MZcZjVXTHyD7muE8ssAAN2VYOoukimSSrvVLRHOmTOHWKhVWlp66NCh/fv3P7T4k6h8HRYWFhYWZmZm9tDhLi4uAFBWVvbHH38M9aXJJGylK/u323OAkw1NGXGFwWKZUoM6hPWHSkF/62vPCetrSCIRAIhL73CjLrK37xPMDUxPT3dycmppaVEoFMuWLRtqbGNCmJlat/PVdoFwCo0MAI25Wa2vbTCeaiPjNPQIRTf/YfHnCj1NWyYdo9NBJsPl8iUsZhZfnNArWGWgZfJ/343pG1Ijap0IdXV1AwICkpKSrl69umnTpoEuTgPt0wBgypQpS5cuDQoKWrp06YPbrR6sVrxv377RDl2NYRhgOAYAoMECtjMAgPRenTC0IXTZsmXE+k8Gg7Fo0aKwsLBVq1Y95lKPKClClO1VQehs49+SvAEjQ/sdPq/3Rln3cpchVHrq+Pg9SU3VujsNlx1MAaCQJ4zjCj764aCxvSOdTq+trY2MjFy1amJcEikF/e173q7p45/v4h+wMgCAK1yBBgnjteUl9gpvCyTE1R8ZgznaGktYmoEshimTwfD01VywVHP+YpKWdveRr3nhZ5ayNA809aTyxGKFUikaR2WpJze1ToQAEBISkpSU9PXXX586derWrVsKhQIAMAxzd3cPCQkJCQkhvjIj48ccOz0alSQFALoOGNgDAHBrAYBBIy+dafj4Yyefrq6u9957DwC6u7tJJNILL7xQU1MTGhq6ePHix3fNJTg7O1Op1KqqKqFQyGQOudDPkpmGmjosAXsGtN+BlryIXMfBJ0J5R5sg/Sb8bZeRUiyu/uFrv69+PHv2rK2tbU1NDY7j47/qheBGAo4r/37/ty29chynkzA/bcZCXUagLtNIS5PhNUcrcKXmvECS9v82qBju+kB/6zYI9HRm0ouFkgy+yORGvIar5yi+CfWlvomwtLQ0PDz88uXLJBKpuLgYx3Eymezr67t+/fo1a9ZYWqJNUePUHHu9mZbaeT0UGe1+lVEKHcNIDBrp1Um9oZDP58fHx3t6etbX1wPA/PnzAYDFYhEz3JWVlX/88cf+/fuHdE46ne7g4FBcXFxSUjKw5mvwGDTy0llGl4vnQPsd4GRG5S85+pIzmfS4pKVQ4pnVveHZra0J8bvlGAugTSb/jNMDAJ0yhQGVDDjOqK/OycnZsmVLW1ubTCYb/1kQAGQNNbhQCADpfNH2uk4AqJPIXmbr7jBlTdWgBugwNFl6mnMXMAMCmXPmk/7hOwdJW0fDw3sJp6tYKEnoFS5Jvmawc2g/UEQ16pUIpVLpzZs3IyMjY2JiOBwOcSeZTFYqlf/617/IZLKGhsZbb701rsqiI38X857H4s8Vd/Ut+sUKHEDLIZBBIyfs9WIxJ/OSURKJZGZmlpub6+7ufunSJSIRUigUooDRwI6IoXJxcSktLa2pqVEhEQJAqKfx5ZQ5kHccWvI7uP2Z1b3+0x/R71okVSSVdMcWdkTmtXVw+6GtkF0bd7Wl6oQN24BCfslYBwBKhdJMvhgAaFTq8uXLAeCfdvSPE8Tnxtdff3337t3ovDuraTQQSvy0GcTQ6K8dPAB43USXamVtsPMD5px5f+9E9ndaC5cuSbnxTQs3qU8oaW6SVpXT7B1H/J2oPbX4xBcIBMnJyeHh4dHR0QPl89ls9tKlS8PCwnJzcw8cOMDlcp2dnQ0MDHp7ex9cOIOMQwZatLxP/a4UdSYUd4mkcj97/Wd8TJm0yb/E3NbWtrCwUE9PT6m8Nwrn5ORE3Hiw5+3giUSiF1544dixY5qams3NzXK5fMqUKUM6Q5A7m6ZjJNW3hZ5qaCv8INzk2EvODmb3Lta5AllSSVdMQUdkXju/rwc4WcDJgrZCUEg7AAAgv19CxTATKgUAWikKAMDIFKbv3H98vfEkPDzc0NAQx/Hk5GQtR2eozPvbU3ASU9Nw14fMuQsHeU7NBUttvvjYRoNaI5bl9UHtdyYAAA4cSURBVEsMb8Tro0Q48iZDIuzt7SWWAyiVyv7+/oFvkQPlgxMSEgYKng3soPLz8yOGXKysrA4cOBAdHe3h4cHlclEWnBBIGBbkxg5yY1++fLmjrKNEw/369evLli1zc3Mb69BGikwmS05ODgoKKigo8Pb2Ju789ddfiRs2NjYffvjhUM/Z2tp68uTJwMBAALh+/TqPxyN6nA0ei0mdZaWdZ+kLPdXQlJla4ePxQXqYl8kCJ4NLOW0JxV3SvnZozYfmbGgtAKUcAADDQN8WzL226Cg3iwtqJfcWZjNJmBmNgtHp+q8+eV/veCAQCBoaGvLy8kgkUn5d/ZrQZ43Cf/eRyIhHHRk0LQ0NurML0/8JBc8eRDYw0pjhspjTXdPWl9AnnHsjXv+1ifG/MaFNhkS4ZMkSYgV5U1PTe++998EHH0RFRUVFRRUUFBBPoFAoCxYsWLVqVUhIyN+3uru4uEydOrWurs7FxcXHx2e0o0eejpub25kzZ7y8vIqKiiZxFgQAFov17LPPAsBQL9pG1M288tsp0aBjAQDQnIUr5UIJ+bdbzb9dzYfmbGhMg85yABwAACOBkRNY+YPVXA0dw8UzDee6s1mZJ7+KCwccxyVSR0N9J1OqyaGTFLOJUa3+pZdeWrVqlaGh4ezZs1evXm1oYEDSZRn/dhyjUDAAHxpde/lqw73/gSHOcWouWLo4K+tYW188V/hBdYWM00C1GEc/8UlpMiTCh7z22mvZ2dlwfwV5cHBwSEiIsbHxYw4JDg7+/vvvo6KiUCKccAoLCzEM6+zsfPyPGPknmZmZRH6tr68faH89eD9lS5Qm7tCcBzoWwONA3Q0QtEPDLeA13XsGhQ7GLmDlDxY+TE2dhTMMwrxNV3sa36vHtuAT2eaXhRkpSkE/zWoqc+5CjDaRClYMDCARN/Rf3a63+VVJRSmukNPtHEk6qtT801y4bNb3B01plFapvFQoNbyZwNq4dTiDRv5mMrRhmj59+ty5cwGgv79foVAsWrSosLAwJCRk4cKFg1lBDgDJycmLFi1ydHQsKysb4WCRYaZUKlETGZXV1ta+//7758+fB4DTp08PDI0WFRUZGBhgGFZRUTFv3jwq9dGrkCQypdHm8/zbf8LUQMg7Ch3F/3uMrgsW3mDhCyauhiyt5S5GYd6mS2Ya0oey415tNT2z9P2bmWc7+W+a6L6/fLH5qfCxjmiSm2BXhHfv3s3JyfHz8zM1NT1+/Pg777wDADo6OocPHwaApqamjz766PXXXx/qaQMCAgwMDMrLy6uqqojWo8hEgbLgsMNxnEQinT9/Xk9Pz93dnUKh4BKx4NYNOaeBpKXN9F9AMTHr7pceSWg4ktjAFwhBKYek90AqBIwEAGCzBMy9wNTDVF/zeT/zUE9jHzsWaSJsgRg/NBcsWVJ052wnP6FXuKu4QNHVQTZkj3VQk9kES4TW1tYsFosonziwcA7DMC0tLQBQYUcwgUKhLF++/OzZs1FRUbt37x62cBFkfNPT0xsoEDhz5kyJRAIAGIbp6uqSyeSVK1feuHFDXlnG/ukbXCFXigQYTQP/5kDurJCtsgX97TVQfhkaUu8tgTFyAh4HJDywXQ76tnQqqfAzf2PdiTTOOX5oLljqfeIHPQr5rlhWK5QYpV7XWfPcWAc1mU2wb9M8Hi8iImL16tWtra0VFRXEXoiB/U8MBmPWrFmqnTkkJAQAoqKihitUBBn/9PT01q9fT9x2c3Mj5siVSmVGRoaNjU19fb2wpVnrv/93pKqhrocLShwXi/ZVN4tSzrGiX4crb0NdMgCA9QJY/gMs/gqmBAAANGWQMGztbBOUBVVGd3DWsLCar8MAgKQ+Ye/p46KCnLEOajKbYHOEFRUVxcXFzs7Ojo6OdXV1w9jtqL+/38jISCqVNjc3m5iYDNdpEWT8w3G8v79fW1tbJBIxGIwHH2rf86YgOX5ffecmI21HBg0A1la2+mszjrT1AkUDbJaAQ6i2gZlYrlAoQdlSCMn7QdcKW3ns7qF509gqjtAgANDxwc5rF8+l9IlW62s5MWkYg6nh5GJy+ASJqTnWoU1CE+yK0MHBISwszNHREYa755+WltaCBQuUSuWVK1eG8bQIMv7l5+f//PPPd+7cef/99x96SJSTgSuVAJDFFyf2ChN7hQKFcpOR9jYLtsuyT6Yt33n49fltRxeVfTlvs785GM8Eug70NeK8pqSSrrF4K5OEks8TpCZ1yZSztTScmDQA+LSaU1+Q0/avt8Y6tMlpgiXCEYVGRxH15O7uzuVyDQwMnJ2dH3oIV8iJGzIcl+C4BMdxAEMq+RVzw/+scaj6Zt72ZdZMGtnWmPnLa7OmsrXBbDYAACcrIq99lN/FZNIXfhaXy/kKJf9+z8JOmUIiloiLcqRV5WMb26SEEuH/BAcHYxiWmJgoEAjGOhYEGT05OTnGxsZ8Pr++vr6iouLBh2h29+p7zdVhBOlpBulpapFJAKBJUgSt8X+ovnaIBxssfQEAOJnXS7p6hbJRegOTjjD9Ji4RA0CeQPJnd/+f3f1NUjkAgBIXFeaOcXCTEUqE/2NmZjZ79myRSJSUlDTWsSDI6PHx8XnzzTcdHBwOHDgw0LyeoP/qDuyvs4YAgNHpWguXkXUfLq692tMETD2AogFdlTJ+59WizpGNexJT3rsQ1yZjRlSyEZVMJ/af4EpQKsYysEkKJcK/QKOjCPIghref3ivbPrUzd9C8twT0oqsNzd7JcP9nf3+y/3Q9Iz0tMHUDwKE5G42Oqozh4YNRaQAwXYMWoMMI0GGwqWQAwChUupOKC+ORx0CJ8C9CQkL09fV1dVUpjIQgk5Lei69b/fKn7ur1Gs6uzIBFZh8etDh1icR4xIpQMgkLdmODxRwAgKbMq7c7xLJH9KpFnkj32RcxKpWMAfn+2DMVw0hkMs16moaLx5iGNjlNsO0TI62lpSUrK2vNmjUAkJmZyWAwXF1dxzooBJkwYgs7gj9LgcsbAADW/B7z/vwgN1QSRRWi/Ky2HVtBIVdKxACAMTUpRsbmJ86TDYzGOrRJCF0R/kVbW1tCQgJxOzc3t6SkZGzjQZCJZbGzobaOLrCdQSmHltyI3LaxjmiiYnj4WMWk6r2+gzl3kebiIPb7n1pejEdZcISgRPgwmUzG5/P5fD5RbgpBkMGjU0nLXIzAwhcAoCkjKr9DrkBjTiois/RYm18zPXwyf+6yy528K/Hx58+fH2itigwjlAgflpmZuWvXrl27dkVERIx1LAgy8YR6GoOFLwAGrfndvfz0Ku5YRzSxdXV1iUQimUx269YtLpfL5/PHOqJJCCXChwUEBJw4ceLEiRNEkzYEQYZkhSubpmMEBnYgl0BbYUQeGh19Kl1dXUqlsrq62szMTCKR9Pb2jnVEk9AE6z6BIMg4p8ukLJxhUJ5n19Bd9ULlkZf++2droZ/B27tp0+zGOrQJycHBwcHBISQkhE6n/70YLDIs0KrRv+DxeI2NjUShqfr6ehqNZmZmNtZBIcgEE//xV6I/fxRIRT7aDACok8jNdDSnHvqZ4e031qEhyCOgRIggyHCS1lQ1bQq51NIlUeLPG2kDwL6GrhfZOo5GhlOuZT5yAyKCjC00NPpoUVFRbDabQqHU19cHBwdraGiMdUQIMjHw/jyHyR9VZRRXCFOStJatGvWIEOQJUCJ8BBzHAwMDf/31VxqNZmRkRKVSxzoiBJkwpNVluEIBAOHd/Jx+MQDcEUpeZOsoBUJZY91YR4cgj4AS4SNgGJaenu7l5eXg4FBcXJybm0t07kYQ5IlI+vc2fYcZaA8MjQIARqeT/lanG0HGA7R94hEkEklbW1tHR0djY2NHR4eXl9dYR4QgE4b2smCM+aiJQAyY/gtGPRwEeTK0WAZBkGGF4y2vPpeVnSWTSDy1NAAgukfga8iy2/iSwfa9Yx0cgjwCSoQIggwzXCLu/PzD/vgYEp0GOCjlcr1X3tZ78Q3AsCcfjCCjDiVCBEFGhJLXK6muLK2rt5k7r6q+gc/n+/n5of3gyDiE5ggRBBkRJB0WeaYb2cb+1z/OOTo6VlVVkcnksQ4KQR4BJUIEQUYKjUbDMIzBYDAYDF1dXRqNNtYRIcgjoKFRBEFGikgkioiIYLFYJiYmU6dO1dND2yeQ8QglQgRBEEStoaFRBEEQRK2hRIggCIKoNZQIEQRBELWGEiGCIAii1lAiRBAEQdQaSoQIgiCIWkOJEEEQBFFrKBEiCIIgag0lQgRBEEStoUSIIAiCqDWUCBEEQRC1hhIhgiAIotZQIkQQBEHUGkqECIIgiFpDiRBBEARRaygRIgiCIGoNJUIEQRBEraFEiCAIgqg1lAgRBEEQtYYSIYIgCKLWUCJEEARB1BpKhAiCIIhaQ4kQQRAEUWsoESIIgiBqDSVCBEEQRK2hRIggCIKoNZQIEQRBELWGEiGCIAii1lAiRBAEQdQaSoQIgiCIWkOJEEEQBFFrKBEiCIIgag0lQgRBEEStoUSIIAiCqDWUCBEEQRC1hhIhgiAIotZQIkQQBEHUGkqECIIgiFpDiRBBEARRaygRIgiCIGoNJUIEQRBEraFEiCAIgqg1lAgRBEEQtYYSIYIgCKLWUCJEEARB1BpKhAiCIIhaQ4kQQRAEUWsoESIIgiBqDSVCBEEQRK2hRIggCIKoNZQIEQRBELWGEiGCIAii1lAiRBAEQdQaSoQIgiCIWkOJEEEQBFFrKBEiCIIgag0lQgRBEEStoUSIIAiCqDWUCBEEQRC1hhIhgiAIotZQIkQQBEHUGkqECIIgiFpDiRBBEARRaygRIgiCIGoNJUIEQRBErf0/C8R/Xxp84wcAAAKOelRYdHJka2l0UEtMIHJka2l0IDIwMjIuMDkuMwAAeJx7v2/tPQYg4GWAAEYg1gRiLSBuYGRjUADSLFCKg0EDSDEzsTmAaRZ2CM0M46PT7Axo8mA+E1SciRkuD6ER5kNtxWEsAWlGsCmMjIOF5gaFqTgDgwQDgyQDIxMDoxQDozTQ9wrMnBlMzCwJLKwZTKxsCaw8CmzsGUxsMgzsHArsnAkcsgwccgycXApc3BrMPLwKPPIMvHwaTLz8DPwCDPwKDPyKDAJiCQKCGUyCQgmCSgxCwgxCIhlMwsoMwioMwqoMIqIJImoMomIZTKLqDGIaDCJMbMwsrGzsnGyCQiKiYgLi3xghsQ0GmsZvew6oajcfAHGmSs4+ID1PC8z+5rrywPXTc/eD2O+XdBzov8K+D8TmWW98YEPaOzD7z80n+43ylexB7ENH+Q78CWZxALGnJOQc6FwsCWaviWk5sDO6FMwOvDjtwLnSZWD1u+YdPfBC5CKYrZzz5QDTlt9gdtLEZfv+JM6yA7E79hvaH8ncDBb/sqPBTijXFGzOFi4uh1WnOsDibUvTHeTDbcBs1f+NDvM7jcBu7juzweHV42aI+3/sc1BdJQvxY+5Fh61X+2xBbGPHww67Yw+A9Z4qnuJw9c8rMNtMye5A+99MsHrvk7sOtIdPBLMXNdYesGXfCGav+XriwP1vbmD2z6qoAxGufGB2Ovvs/fP3eIHd6VnufkDv0VwwW1Rr84HFoa1g9utLH2wfXp8AdptjlLyDOoMSWNyv7KX989UnwWFrvcfJQVTsGVgN+9U3DomBjGDzb8hMdHjBZAVmB6qedQhJqgSz62UZHP8cawPrtXr02KFhuhrYzAz3NodZZoFgthgAd9rDOs3t2tIAAAOYelRYdE1PTCByZGtpdCAyMDIyLjA5LjMAAHicfVbLbiQ3DLz7K/QDI/AlSjr6sVgvAo+BxMk/5J7/xxbVtroXS2TsQ4tTTRaLDw0Pl3njhxKfP1/++Pe/sj/y8gA7/c//nLP8o0T08FbioTx9+/7jXp4/Hp++LM/vf98//irGxQTvUOFfsY8f729fFi7P5WbVSGdr8YQAxL1QpfUpO44cyGE+upabVPc2A/AbUst7ubUqY/ZJ5cZVVPrQBGmHT+nEauVGlVXcsuit3CPmoN7aDKR5c8mQHj659oHgSL+6WqOZAHsABeRoCNKr5N3FE+AIoFafCpEANCKnDDiDpCEJV5aidah2yfJmOhLvrNS4cEVBrWda8iqQV+4tyDHSiXwypET0DgDSwbFOd1POkBpIqipNZQCgxE0yiTgKBAmnKb5nZM6oegZsqDlVaWxgjDqp9SEZMMojlZtPUngkHz5Sj1EeiM4TnvB919bJMmCUB33rEtW5BdtunMaeQLbaxGZffUmKts/qI7SQ0/sYI5rEXBtnqkvUx2sz++zGiJ7RFFlAxnBRSNlIhqcedeXjOmMmGFK2YVkZxcprcEMyQ8fq0OYY3wzaAtrgq1OX6LxmPDXTHU32Gr6mM2mPl9AkwSSB9oA6xnfOMeMlpjF4ZNBxeDVWNPIaO5GZdgga7XUtDWZ2RQuwdwxmtmfocMrRlA7NMKC5phiDlX+bqGcHEm/k3aQCJPYQ5u3YXazcRpaSKpDojAGfhoeGmW+Z+Bp1Qh3Rn6htzN3UMVOaUSatZNJ1riU2e7PUZ1SpxzxicYScJNRmKlIUCfXGQHZe7UJu6eLUqJGjRDSoha4DGyRHRokGzF2IIyNxFc2QRosno4PcIZKTzJkCedHk6YLJV3gcs2Wyf7u//HKBHVfa0/v95bzS4k/Oe2sd7byd1rmdd9A6+3nT4FT0vE7iKP28NdZ5nJcDw+X2PuEPHq+bPgw7GrYjTmVHw6IyxNjuOegh5o7P8XovOx4Hv44puWxUBovzjP4OVtcFyeCl1z24DPtngSzRgtxlrWlw5VPGpSOMO1XRw6InxkJtpL/TlWAblp1wXGKL805Z+mHRnbOMT8sZK3RFZrIxR+Xw6lZGQ9pIdkuhi/PAXF2GliN/uU6nLcvOXVcHxIxdpu2wbM7aPy1n9AE/kE22Ghoyh5CbjwVnCHlmYav30APnIARnSGubT3T8tb/j/PVzD88PPwGf2+eJW2t42wAAAoh6VFh0U01JTEVTIHJka2l0IDIwMjIuMDkuMwAAeJxlkr1uWzEMhV+lQBcHuBH4J1KU0SlLJqd70KEIOrYpiox5+B7JRc2iy7VIk4efDvX8+OWFX07Pj1/uXk4P+3d/Hk6fnu4u17z8eDk9/fPf3+Ofvo8XfC+3jFzz/6nuBOP74f0UjYU1DmlOepy9MXHwwa2TDD/O1lyTFYmhfdhKcLrIoU18JCq0OaeNg1poD0KJNO6eaCEfPlZsI8fAjK4saIGYaMpxT01TRx5nap6mspqM2GNlpLMxo2uoBWRWtXSVNUqJu+w+5uh53HNjZRzOOMWIRI2rdVoZKFB0X+PMuwvU761JEO6FHKu4rVxvMjK2mKjskdaG+YgDGu5b3tGYiSKFVWMIUtoMFvoqEpG89hlp9gMHyiTZFBkEB1EOCN7zhgbFKurGqVsqneEh/sNNWbdUsFKHM0q0ObEkXGftxBV3RCZwAgqj2+2q46maMM+I9jDsTV0ZexuqccVmdvfDGkrXrtdQ7CmRwR+xmUUJwoJVhut2VxhtkMGWYj8QcrHlo2cYy3oQZBK2bIyMDj7oimWsDCl8WZn0wCRMMNcOwFiLXzEJLaPhCda+nU/al3LYTIOXgXh4vjPd0I5d0xquGDUaUeyXJa6Ch313fH17/f751+vPSW0dL69v3/DsJt8i4Sklkqm3yGjaLWKfvUR9etGUGSXSOUqlziyRTS4wLJMLjfLkgtMnV5zJBUcnFxzIFByoFBzxyYVHYnIBoimFR3RKdcemVHv6lAKUU6o/UKoGwdiCFFMK0phSiSBUiJSmFiSfWonG1ELEMbUQqU2tSCiuSDm1Lq1PrVvDoAKlUC5QgrdQH9F4/w1K3nKDCKtQHwAAAzR6VFh0cmRraXRQS0wxIHJka2l0IDIwMjIuMDkuMwAAeJzVkllIlFEUx893v5lvxhm1GZ0Za7amXLLNZcqlB6c7GPXQpi2Yhjhi0BAZohYVLVqjWVoUle27pZQSRVrhdosULMtK0MwURYp8MYoyKNNmzmiRL9FjFw7/3/nfw7nn8H0f6iq6wXm8wH04Z0Q5I9oZOZwAJqeK/hQpBDuFJwJFFUncyo/l41UC4+4xJ6M+4X/du/V3/9FXx7X7y7UCbWGs6t+acZhy3P+qctf3mwyuZaYAR4DjgZsKxB9IAJBA574mkcxORGKbWLATQWITvEwSqZ1IgkDqYZLKbB7TwCMYZHKT3DOY9/I2eU0H7wnBxFsBCiUoZoBiJij1NqWPnfj4mnzUNl8VqGaBajaoQkCtsRN1KGj8QKOz+U2UEr9JztCCNgy04aA1g05vJzqDST8HDEYwzgVjBBgjQUUEkViQSGWCj1qj0yv9zhP3X4gnKlfykKV3JDNXUhl7jMW+pcgXm/JYeuQh5Ce1l9nq6sf1Li5bs4cNRnyuc3HPpyB2vWsJ+hHxzfXyobMWFzfoeWZe1j0f/U4ba2jnqYttLTtZckoKcu3Lw2wdn401g7fqmSztCnLIkQGW+qgZ+dJloS7meEGNi9NzUufvLSxE35HSaUlcOgX7FBk1NGHVAvSH3mTRZf59OMNRewG9V7QDZ+sV4qj2mxh38fy+kd7OTXXvteguvVNaijUD5e20+9ly3Mt3IW9dXyWg/3zOEFW9Gka2ZEqtSbEZ+NbNSq1Vsy0R/Q5zE12lPueeeeVpWr6lEfn7iX5anKrGOd8ZOmlIfwbynbYa9qnpAM5wIvc5a4snyEuffGAnn25Hbh1ZwiZ+9EdOOHKBNf84hVwmD2M3t+5GVl/YxOZ9q0YeKXHUi4cjsL9OHMMCdEeR3zdWMA9FNrIQV2LJC7+PM0e1zKD5rd7o96RzdMNVM/rZWUratXkX9lxhLaH7Di5APvPVQa86ziP3PmigmWtDkR371dYqvQr5de9k640cA+6+ONBs7TMUY8/CjkI68NCCb3VkiKzlA/nI2qQKyn9ZifziSjNNi76GrPkJHVz/s8+Ww4cAAASdelRYdE1PTDEgcmRraXQgMjAyMi4wOS4zAAB4nH1X224dNwx891foB86Cd0mPsR20RZFjoHH7D3nv/6NDra09AZg6NrDizorkDEkpPMLlxk8tf/56/fPHv23/yOsT7PQ/v3PO9o8S0dO3lg/t+etvf9zby/uX50/Ly9vf9/fvzb154Btq/DP2y/vbt08Lt5d2i4NCjaPd9LAInfjsoPXTth9JpB2hKuGJJNY5tUDqieRBIySfzIzNCqS1t3bzg4ZHjHbjw4cOkwLp557U4R570mE9Zq/2jHZvNzkCm+I9kINIvBfInnvC5wyRgfdKEUwFcCRQDrIRyo2PrtGDC+BMoB7Oqn02fBFE5gUQbu6JnOF9SAPtk3iWSD4zd+QgCZiu3Kt0eCnkx5xkyJwP6WFR5cOa3vth7gI2kwJwWUnJlkg6WNkG5RNxRC+hKRHYTtUDe9IvMwqoDgFlWChIciG1quI4BZJDzYNT6R4xuSoOToH0GEwaGZqZhswKmALZIQFqIrOZ2YvVlkIIUg9xMjym+FN1VmQKL+edZIgu9UXdquqQFMiPLt2RDypPaQ6qKJJsoUCdIb7FupBMHRXS2vc28B5Kj0Q6kpPSuyOjDn1Y8QjvqJMZFUuSAg3s1MfMneDZpXSeAs0DsigaLFvNJgirkGMlJKMbkChN6cJVFcmp0AAA+aAysd+sql1ppSMTFGYVqRqNKm/l5XqgJ2CGUGGileQw/r76h1hoZs+hi9nLPTWhgUb3gZrK8kDFlZ2ullD0GjiKvnqeR58VneoJRdGRchYIWiPYy6LTSKhl/Ro0wtNgES1j7QmVozt1DJMMYHIvCxQVtgLo2ZqUXEAvKZtYZ0J55T0c/I9f9ZzR6R9zyxALJtnsFJX2iGkJYEqYcauh1Xq5Z0qFgQ2dsucYHR9DyhMmlcJpEGOgjOQAR1HORLMFJLAI36hlHC9Dq8ozX0hUcHRZfHaf9fGWKjmGp+RRlUcmFCjb2FIkHJk5kGMJ6xqzqlJLjeLgniouXXEKezU+LSWaGJ+BUzORmOHi5eFKCzmnhCU3qLq6mD0VYkZGxrSGCGOajYoll5WRdsKxCrrQpmElMBXCBOvdsT2GMwTS0rktNg0ukZoiHR5lxbkvinQajrXkf5qOquC+3l9/ugyd16Pnt/vrdT3Kf3LdgXKpe63nWq/7zFrbdWvBx22/jrXcb/v51q9rBr5Fa1+3ibXuD5eGZRgPdwMw1vYGOBwtfe77HkZsGvaOmCqGKK4dHeverg0zQoS1Q0SJGeKaDyctY62XIYPE3+PBaWnYMcjiMOfAwzl4Wi7MIhK/F9OW5AK4yZMkEwnwTlfitNj1VU+JANwZSwaclp2zZMTIi3fWp87IdPtS/rBsIjSpRfayM1c9LXxZlvy49z1OdFuWq2JWDSQrD/OZkyTbeen4sOwsdH5YdhaWNINIuWpvVSrC3HlZ8gxqZWdhGXNaLgzWSbZs72j807K924oZlsv7ihn87NxtnBZ/HD+nZWfqK+ZHDn3FjHp6HBucEvlmzLM2liYPg4BTIt+5u39Yrs6LD8vOK5v9sbVz/fm/Jjw//QcwmYJ353BbPAAAA0Z6VFh0U01JTEVTMSByZGtpdCAyMDIyLjA5LjMAAHicZZO7biRHDEV/xYCTWaBV4LOqyIEjJRtJBhwuNjAEh2sZxob78b7stjGEHGhUxeLj8JL95fPXN367vdy+fP76qf7ebs8/v+D39fk03f7999/zh+ufb3yFPt9+ee2PlfT1g/Pb9XL7rXw/+F8v/4tg/P704/bEg3RKHDK26dTj/iSDbOPEY+lcExYdzqobPjSJ7LTE9LUPHRYkZ1C4mh02PBZVkA9TcoXLZrUNC97Elx5UvrzW6RRBtlBL1rQKW8PcRWDxiGVnGC1VOZ5o2JqXzQdtn+sAvm/dl9uEF0w6iDXipBRS5gN0PtnLNNGCmpSXzXmlCmKBkw3VxX46OfuWiitx7MIK9TN78L664U17VpyZ8YUQe1qUabPIJeZyWnzGBa9LuyUbvaLyXiZxek30s6rFTQSRYCsBppRaSnMyLDRYGUriQDznQrN3vAoF3OBvNgEN0y6wCQnpHA50q4KK+TkEQYcy1KAIcq85g6Usc+7aAlGkrjuhAa1akHwrStUgIQeCzM6VuduQKevERuM1NTiJk13ShWqUZZHsU3EXdavcsmReWi4PWBydSVyzW1xaQl2sgZaUWJY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\n", + "image/png": 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\n", 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\n", + "image/png": 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\n", "text/plain": [ "" ] @@ -802,23 +802,23 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 18, "id": "5ebec1d5", "metadata": {}, "outputs": [], "source": [ - "from openfe.setup import Network\n", + "from openfe import LigandNetwork\n", "\n", "# load a new network from this graphml representation\n", "with open('network_store.graphml', 'r') as file:\n", " network_data = file.read()\n", " \n", - "new_network = Network.from_graphml(network_data)" + "new_network = LigandNetwork.from_graphml(network_data)" ] }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 19, "id": "05b277f8", "metadata": {}, "outputs": [ @@ -827,8 +827,8 @@ "output_type": "stream", "text": [ "edge 0 molecule 1: benzene\n", - "edge 0 molecule 2: benzonitrile\n", - "edge 0 mapping: {0: 2, 1: 3, 2: 4, 3: 5, 4: 6, 5: 7, 6: 8, 7: 9, 8: 10, 9: 11, 11: 12}\n" + "edge 0 molecule 2: phenol\n", + "edge 0 mapping: {0: 0, 1: 1, 2: 2, 3: 3, 4: 4, 5: 5, 6: 6, 7: 7, 8: 8, 9: 9, 11: 11}\n" ] } ], @@ -853,7 +853,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 20, "id": "eecd0fea", "metadata": {}, "outputs": [ @@ -868,6 +868,7 @@ "\r\n", "Options:\r\n", " --version Show the version and exit.\r\n", + " --log PATH logging configuration file\r\n", " -h, --help Show this message and exit.\r\n", "\r\n", "Setup Commands:\r\n", @@ -885,7 +886,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 21, "id": "363f9691", "metadata": {}, "outputs": [ @@ -907,7 +908,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 22, "id": "3b0dc398", "metadata": {}, "outputs": [ @@ -921,12 +922,12 @@ }, { "data": { - "image/png": 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\n", + "image/png": 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\n", "text/plain": [ "" ] }, - "execution_count": 20, + "execution_count": 22, "metadata": {}, "output_type": "execute_result" } @@ -1051,13 +1052,13 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 23, "id": "0b6c9ec3", "metadata": {}, "outputs": [], "source": [ "# First let's define the Protein and Solvent Components which we will be using\n", - "from openfe.setup import SolventComponent, ProteinComponent\n", + "from openfe import SolventComponent, ProteinComponent\n", "from openff.units import unit\n", "\n", "protein = ProteinComponent.from_pdb_file('inputs/181L_mod_capped_protonated.pdb')\n", @@ -1070,13 +1071,13 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 24, "id": "8c6d6504", "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", "text/plain": [ "" ] @@ -1094,13 +1095,13 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 26, "id": "23b778d6", "metadata": {}, "outputs": [], "source": [ "# Let's create the four ChemicalSystems\n", - "from openfe.setup import ChemicalSystem\n", + "from openfe import ChemicalSystem\n", "\n", "benzene_complex = ChemicalSystem({'ligand': benz_to_phenol.componentA,\n", " 'solvent': solvent,\n", @@ -1157,7 +1158,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 27, "id": "7d9fe95a", "metadata": {}, "outputs": [], @@ -1177,7 +1178,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 28, "id": "2cf07be4", "metadata": {}, "outputs": [ @@ -1197,7 +1198,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 29, "id": "43d9a59a", "metadata": {}, "outputs": [ @@ -1218,7 +1219,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 32, "id": "511f57fd", "metadata": {}, "outputs": [ @@ -1232,12 +1233,12 @@ ], "source": [ "# A complete set of settings is created via the RelativeLigandTransformSettings class\n", - "from openfe.protocols.openmm_rbfe import RelativeLigandTransformSettings\n", + "from openfe.protocols.openmm_rbfe import RelativeLigandProtocolSettings\n", "from pprint import pp\n", "\n", "# There are non-optional settings which need to be set\n", "# we set them here\n", - "settings = RelativeLigandTransformSettings(\n", + "settings = RelativeLigandProtocolSettings(\n", " system_settings=SystemSettings(\n", " constraints='HBonds',\n", " ),\n", @@ -1271,19 +1272,19 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 33, "id": "2da2945b", "metadata": {}, "outputs": [], "source": [ - "from openfe.protocols.openmm_rbfe import RelativeLigandTransform\n", + "from openfe.protocols.openmm_rbfe import RelativeLigandProtocol\n", "\n", - "rbfe_settings = RelativeLigandTransform.default_settings()" + "rbfe_settings = RelativeLigandProtocol.default_settings()" ] }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 34, "id": "85b38150", "metadata": {}, "outputs": [ @@ -1322,13 +1323,13 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 36, "id": "d1829ab6", "metadata": {}, "outputs": [], "source": [ "# Create RBFE Protocol class\n", - "rbfe_transform = RelativeLigandTransform(\n", + "rbfe_transform = RelativeLigandProtocol(\n", " settings=rbfe_settings\n", ")" ] @@ -1354,7 +1355,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 37, "id": "b3237be8", "metadata": {}, "outputs": [], @@ -1382,19 +1383,19 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 38, "id": "dd35cb04", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "[RelativeLigandTransformUnit(benzene phenol repeat 2 generation 0),\n", - " RelativeLigandTransformUnit(benzene phenol repeat 1 generation 0),\n", - " RelativeLigandTransformUnit(benzene phenol repeat 0 generation 0)]" + "[RelativeLigandProtocolUnit(benzene phenol repeat 2 generation 0),\n", + " RelativeLigandProtocolUnit(benzene phenol repeat 1 generation 0),\n", + " RelativeLigandProtocolUnit(benzene phenol repeat 0 generation 0)]" ] }, - "execution_count": 32, + "execution_count": 38, "metadata": {}, "output_type": "execute_result" } @@ -1420,7 +1421,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 39, "id": "981cde0c", "metadata": {}, "outputs": [ @@ -1444,10 +1445,10 @@ "WARNING:openmmtools.multistate.multistatesampler:Warning: The openmmtools.multistate API is experimental and may change in future releases\n", "DEBUG:openmmtools.multistate.multistatesampler:CUDA devices available: (['0', ' NVIDIA GeForce GTX 1660 Ti'],)\n", "DEBUG:mpiplus.mpiplus:Cannot find MPI environment. MPI disabled.\n", - "DEBUG:mpiplus.mpiplus:Single node: executing >\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n" + "DEBUG:mpiplus.mpiplus:Single node: executing >\n", + "DEBUG:mpiplus.mpiplus:Single node: executing \n", + "DEBUG:mpiplus.mpiplus:Single node: executing \n", + "DEBUG:mpiplus.mpiplus:Single node: executing \n" ] }, { @@ -1469,21 +1470,21 @@ "output_type": "stream", "text": [ "DEBUG:openmmtools.multistate.multistatereporter:Serialized state thermodynamic_states/0 is 1009455B | 985.796KB | 0.963MB\n", - "DEBUG:openmmtools.utils:Storing thermodynamic states took 1.240s\n", + "DEBUG:openmmtools.utils:Storing thermodynamic states took 1.208s\n", "DEBUG:openmmtools.multistate.multistatesampler:Storing general ReplicaExchange options...\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.065s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.349s\n" + "DEBUG:mpiplus.mpiplus:Single node: executing \n", + "DEBUG:mpiplus.mpiplus:Single node: executing \n", + "DEBUG:openmmtools.utils:Storing sampler states took 0.111s\n", + "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.411s\n" ] }, { "data": { "text/plain": [ - "{}" + "{'debug': {'sampler': }}" ] }, - "execution_count": 33, + "execution_count": 39, "metadata": {}, "output_type": "execute_result" } @@ -1497,7 +1498,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 40, "id": "77accb06", "metadata": {}, "outputs": [ @@ -1519,17 +1520,17 @@ "WARNING:openmmtools.multistate.multistatereporter:Warning: The openmmtools.multistate API is experimental and may change in future releases\n", "WARNING:openmmtools.multistate.multistatesampler:Warning: The openmmtools.multistate API is experimental and may change in future releases\n", "DEBUG:openmmtools.multistate.multistatesampler:CUDA devices available: (['0', ' NVIDIA GeForce GTX 1660 Ti'],)\n", - "DEBUG:mpiplus.mpiplus:Single node: executing >\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", + "DEBUG:mpiplus.mpiplus:Single node: executing >\n", + "DEBUG:mpiplus.mpiplus:Single node: executing \n", + "DEBUG:mpiplus.mpiplus:Single node: executing \n", + "DEBUG:mpiplus.mpiplus:Single node: executing \n", "DEBUG:openmmtools.multistate.multistatereporter:Serialized state thermodynamic_states/0 is 56528B | 55.203KB | 0.054MB\n", "DEBUG:openmmtools.utils:Storing thermodynamic states took 0.071s\n", "DEBUG:openmmtools.multistate.multistatesampler:Storing general ReplicaExchange options...\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.012s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.038s\n" + "DEBUG:mpiplus.mpiplus:Single node: executing \n", + "DEBUG:mpiplus.mpiplus:Single node: executing \n", + "DEBUG:openmmtools.utils:Storing sampler states took 0.018s\n", + "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.051s\n" ] }, { @@ -1549,10 +1550,10 @@ { "data": { "text/plain": [ - "{}" + "{'debug': {'sampler': }}" ] }, - "execution_count": 34, + "execution_count": 40, "metadata": {}, "output_type": "execute_result" } @@ -1580,3034 +1581,24 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 42, "id": "9abc4787", "metadata": {}, "outputs": [], "source": [ - "from gufe.protocols import execute as execute_DAG" + "from gufe.protocols import execute_DAG" ] }, { "cell_type": "code", - "execution_count": 36, + "execution_count": null, "id": "106ec948", "metadata": { "tags": [ "nbval-skip" ] }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:openfe.protocols.openmm_rbfe.equil_rbfe_methods:creating hybrid system\n", - "INFO:openmmforcefields.generators.template_generators:Requested to generate parameters for residue \n", - "INFO:openmmforcefields.generators.template_generators:Generating a residue template for [H]c1c(c(c(c(c1[H])[H])[H])[H])[H] using openff-2.0.0.offxml\n", - "DEBUG:openmmforcefields.generators.template_generators:Generating parameters...\n", - "DEBUG:mpiplus.mpiplus:Single node: executing >\n", - "INFO:openmmforcefields.generators.template_generators:Requested to generate parameters for residue \n", - "INFO:openmmforcefields.generators.template_generators:Generating a residue template for [H]c1c(c(c(c(c1[H])[H])O[H])[H])[H] using openff-2.0.0.offxml\n", - "DEBUG:openmmforcefields.generators.template_generators:Generating parameters...\n", - "INFO:openfe.protocols.openmm_rbfe._rbfe_utils.relative:creating hybrid system\n", - "INFO:openfe.protocols.openmm_rbfe._rbfe_utils.relative:setting force field terms\n", - "INFO:openfe.protocols.openmm_rbfe._rbfe_utils.relative:adding forces\n", - "INFO:openfe.protocols.openmm_rbfe._rbfe_utils.relative:DONE\n", - "WARNING:openmmtools.multistate.multistatereporter:Warning: The openmmtools.multistate API is experimental and may change in future releases\n", - "WARNING:openmmtools.multistate.multistatesampler:Warning: The openmmtools.multistate API is experimental and may change in future releases\n", - "DEBUG:openmmtools.multistate.multistatesampler:CUDA devices available: (['0', ' NVIDIA GeForce GTX 1660 Ti'],)\n", - "DEBUG:mpiplus.mpiplus:Single node: executing >\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Please cite the following:\n", - "\n", - " Friedrichs MS, Eastman P, Vaidyanathan V, Houston M, LeGrand S, Beberg AL, Ensign DL, Bruns CM, and Pande VS. Accelerating molecular dynamic simulations on graphics processing unit. J. Comput. Chem. 30:864, 2009. DOI: 10.1002/jcc.21209\n", - " Eastman P and Pande VS. OpenMM: A hardware-independent framework for molecular simulations. Comput. Sci. Eng. 12:34, 2010. DOI: 10.1109/MCSE.2010.27\n", - " Eastman P and Pande VS. Efficient nonbonded interactions for molecular dynamics on a graphics processing unit. J. Comput. Chem. 31:1268, 2010. DOI: 10.1002/jcc.21413\n", - " Eastman P and Pande VS. Constant constraint matrix approximation: A robust, parallelizable constraint method for molecular simulations. J. Chem. Theor. Comput. 6:434, 2010. DOI: 10.1021/ct900463w\n", - " Chodera JD and Shirts MR. Replica exchange and expanded ensemble simulations as Gibbs multistate: Simple improvements for enhanced mixing. J. Chem. Phys., 135:194110, 2011. DOI:10.1063/1.3660669\n", - " \n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "DEBUG:openmmtools.multistate.multistatereporter:Serialized state thermodynamic_states/0 is 1009455B | 985.796KB | 0.963MB\n", - "DEBUG:openmmtools.utils:Storing thermodynamic states took 1.191s\n", - "DEBUG:openmmtools.multistate.multistatesampler:Storing general ReplicaExchange options...\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.065s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.351s\n", - "INFO:openfe.protocols.openmm_rbfe.equil_rbfe_methods:minimizing systems\n", - "DEBUG:openmmtools.multistate.multistatesampler:Minimizing all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _minimize_replica serially.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 1/11: initial energy -127025.055kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Using FIRE: tolerance 1.0 kJ/(nm mol) max_iterations 10000\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 1/11: final energy -242814.674kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 2/11: initial energy -127026.826kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Using FIRE: tolerance 1.0 kJ/(nm mol) max_iterations 10000\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 2/11: final energy -238665.537kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 3/11: initial energy -127027.510kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Using FIRE: tolerance 1.0 kJ/(nm mol) max_iterations 10000\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 3/11: final energy -242045.497kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 4/11: initial energy -127025.411kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Using FIRE: tolerance 1.0 kJ/(nm mol) max_iterations 10000\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 4/11: final energy -239630.635kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 5/11: initial energy -127011.315kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Using FIRE: tolerance 1.0 kJ/(nm mol) max_iterations 10000\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 5/11: final energy -237904.062kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 6/11: initial energy -126875.169kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Using FIRE: tolerance 1.0 kJ/(nm mol) max_iterations 10000\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 6/11: final energy -242243.678kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 7/11: initial energy -126878.209kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Using FIRE: tolerance 1.0 kJ/(nm mol) max_iterations 10000\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 7/11: final energy -235929.772kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 8/11: initial energy -126878.342kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Using FIRE: tolerance 1.0 kJ/(nm mol) max_iterations 10000\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 8/11: final energy -240671.943kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 9/11: initial energy -126876.761kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Using FIRE: tolerance 1.0 kJ/(nm mol) max_iterations 10000\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 9/11: final energy -241571.384kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 10/11: initial energy -126874.699kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Using FIRE: tolerance 1.0 kJ/(nm mol) max_iterations 10000\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 10/11: final energy -239972.204kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 11/11: initial energy -126872.203kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Using FIRE: tolerance 1.0 kJ/(nm mol) max_iterations 10000\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 11/11: final energy -239023.773kT\n", - "DEBUG:mpiplus.mpiplus:Single node: executing >\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.105s\n", - "DEBUG:openmmtools.utils:Minimizing all replicas took 73.360s\n", - "INFO:openfe.protocols.openmm_rbfe.equil_rbfe_methods:equilibrating systems\n", - "DEBUG:openmmtools.multistate.multistatesampler:Equilibration iteration 1/5\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 29.910s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.235s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:numba.core.byteflow:bytecode dump:\n", - "> 0\tNOP(arg=None, lineno=321)\n", - " 2\tLOAD_GLOBAL(arg=0, lineno=321)\n", - " 4\tLOAD_FAST(arg=0, lineno=321)\n", - " 6\tCALL_FUNCTION(arg=1, lineno=321)\n", - " 8\tGET_ITER(arg=None, lineno=321)\n", - "> 10\tFOR_ITER(arg=234, lineno=321)\n", - " 12\tSTORE_FAST(arg=6, lineno=321)\n", - " 14\tLOAD_GLOBAL(arg=1, lineno=324)\n", - " 16\tLOAD_ATTR(arg=2, lineno=324)\n", - " 18\tLOAD_METHOD(arg=3, lineno=324)\n", - " 20\tLOAD_FAST(arg=1, lineno=324)\n", - " 22\tCALL_METHOD(arg=1, lineno=324)\n", - " 24\tSTORE_FAST(arg=7, lineno=324)\n", - " 26\tLOAD_GLOBAL(arg=1, lineno=325)\n", - " 28\tLOAD_ATTR(arg=2, lineno=325)\n", - " 30\tLOAD_METHOD(arg=3, lineno=325)\n", - " 32\tLOAD_FAST(arg=1, lineno=325)\n", - " 34\tCALL_METHOD(arg=1, lineno=325)\n", - " 36\tSTORE_FAST(arg=8, lineno=325)\n", - " 38\tLOAD_FAST(arg=2, lineno=328)\n", - " 40\tLOAD_FAST(arg=7, lineno=328)\n", - " 42\tBINARY_SUBSCR(arg=None, lineno=328)\n", - " 44\tSTORE_FAST(arg=9, lineno=328)\n", - " 46\tLOAD_FAST(arg=2, lineno=329)\n", - " 48\tLOAD_FAST(arg=8, lineno=329)\n", - " 50\tBINARY_SUBSCR(arg=None, lineno=329)\n", - " 52\tSTORE_FAST(arg=10, lineno=329)\n", - " 54\tLOAD_FAST(arg=3, lineno=332)\n", - " 56\tLOAD_FAST(arg=7, lineno=332)\n", - " 58\tLOAD_FAST(arg=10, lineno=332)\n", - " 60\tBUILD_TUPLE(arg=2, lineno=332)\n", - " 62\tBINARY_SUBSCR(arg=None, lineno=332)\n", - " 64\tSTORE_FAST(arg=11, lineno=332)\n", - " 66\tLOAD_FAST(arg=3, lineno=333)\n", - " 68\tLOAD_FAST(arg=8, lineno=333)\n", - " 70\tLOAD_FAST(arg=9, lineno=333)\n", - " 72\tBUILD_TUPLE(arg=2, lineno=333)\n", - " 74\tBINARY_SUBSCR(arg=None, lineno=333)\n", - " 76\tSTORE_FAST(arg=12, lineno=333)\n", - " 78\tLOAD_FAST(arg=3, lineno=334)\n", - " 80\tLOAD_FAST(arg=7, lineno=334)\n", - " 82\tLOAD_FAST(arg=9, lineno=334)\n", - " 84\tBUILD_TUPLE(arg=2, lineno=334)\n", - " 86\tBINARY_SUBSCR(arg=None, lineno=334)\n", - " 88\tSTORE_FAST(arg=13, lineno=334)\n", - " 90\tLOAD_FAST(arg=3, lineno=335)\n", - " 92\tLOAD_FAST(arg=8, lineno=335)\n", - " 94\tLOAD_FAST(arg=10, lineno=335)\n", - " 96\tBUILD_TUPLE(arg=2, lineno=335)\n", - " 98\tBINARY_SUBSCR(arg=None, lineno=335)\n", - " 100\tSTORE_FAST(arg=14, lineno=335)\n", - " 102\tLOAD_FAST(arg=11, lineno=336)\n", - " 104\tLOAD_FAST(arg=12, lineno=336)\n", - " 106\tBINARY_ADD(arg=None, lineno=336)\n", - " 108\tUNARY_NEGATIVE(arg=None, lineno=336)\n", - " 110\tLOAD_FAST(arg=13, lineno=336)\n", - " 112\tBINARY_ADD(arg=None, lineno=336)\n", - " 114\tLOAD_FAST(arg=14, lineno=336)\n", - " 116\tBINARY_ADD(arg=None, lineno=336)\n", - " 118\tSTORE_FAST(arg=15, lineno=336)\n", - " 120\tLOAD_FAST(arg=5, lineno=339)\n", - " 122\tLOAD_FAST(arg=9, lineno=339)\n", - " 124\tLOAD_FAST(arg=10, lineno=339)\n", - " 126\tBUILD_TUPLE(arg=2, lineno=339)\n", - " 128\tDUP_TOP_TWO(arg=None, lineno=339)\n", - " 130\tBINARY_SUBSCR(arg=None, lineno=339)\n", - " 132\tLOAD_CONST(arg=1, lineno=339)\n", - " 134\tINPLACE_ADD(arg=None, lineno=339)\n", - " 136\tROT_THREE(arg=None, lineno=339)\n", - " 138\tSTORE_SUBSCR(arg=None, lineno=339)\n", - " 140\tLOAD_FAST(arg=5, lineno=340)\n", - " 142\tLOAD_FAST(arg=10, lineno=340)\n", - " 144\tLOAD_FAST(arg=9, lineno=340)\n", - " 146\tBUILD_TUPLE(arg=2, lineno=340)\n", - " 148\tDUP_TOP_TWO(arg=None, lineno=340)\n", - " 150\tBINARY_SUBSCR(arg=None, lineno=340)\n", - " 152\tLOAD_CONST(arg=1, lineno=340)\n", - " 154\tINPLACE_ADD(arg=None, lineno=340)\n", - " 156\tROT_THREE(arg=None, lineno=340)\n", - " 158\tSTORE_SUBSCR(arg=None, lineno=340)\n", - " 160\tLOAD_FAST(arg=15, lineno=343)\n", - " 162\tLOAD_CONST(arg=2, lineno=343)\n", - " 164\tCOMPARE_OP(arg=5, lineno=343)\n", - " 166\tPOP_JUMP_IF_TRUE(arg=188, lineno=343)\n", - " 168\tLOAD_GLOBAL(arg=1, lineno=343)\n", - " 170\tLOAD_ATTR(arg=2, lineno=343)\n", - " 172\tLOAD_METHOD(arg=4, lineno=343)\n", - " 174\tCALL_METHOD(arg=0, lineno=343)\n", - " 176\tLOAD_GLOBAL(arg=1, lineno=343)\n", - " 178\tLOAD_METHOD(arg=5, lineno=343)\n", - " 180\tLOAD_FAST(arg=15, lineno=343)\n", - " 182\tCALL_METHOD(arg=1, lineno=343)\n", - " 184\tCOMPARE_OP(arg=0, lineno=343)\n", - " 186\tPOP_JUMP_IF_FALSE(arg=10, lineno=343)\n", - "> 188\tLOAD_FAST(arg=10, lineno=345)\n", - " 190\tLOAD_FAST(arg=2, lineno=345)\n", - " 192\tLOAD_FAST(arg=7, lineno=345)\n", - " 194\tSTORE_SUBSCR(arg=None, lineno=345)\n", - " 196\tLOAD_FAST(arg=9, lineno=346)\n", - " 198\tLOAD_FAST(arg=2, lineno=346)\n", - " 200\tLOAD_FAST(arg=8, lineno=346)\n", - " 202\tSTORE_SUBSCR(arg=None, lineno=346)\n", - " 204\tLOAD_FAST(arg=4, lineno=348)\n", - " 206\tLOAD_FAST(arg=9, lineno=348)\n", - " 208\tLOAD_FAST(arg=10, lineno=348)\n", - " 210\tBUILD_TUPLE(arg=2, lineno=348)\n", - " 212\tDUP_TOP_TWO(arg=None, lineno=348)\n", - " 214\tBINARY_SUBSCR(arg=None, lineno=348)\n", - " 216\tLOAD_CONST(arg=1, lineno=348)\n", - " 218\tINPLACE_ADD(arg=None, lineno=348)\n", - " 220\tROT_THREE(arg=None, lineno=348)\n", - " 222\tSTORE_SUBSCR(arg=None, lineno=348)\n", - " 224\tLOAD_FAST(arg=4, lineno=349)\n", - " 226\tLOAD_FAST(arg=10, lineno=349)\n", - " 228\tLOAD_FAST(arg=9, lineno=349)\n", - " 230\tBUILD_TUPLE(arg=2, lineno=349)\n", - " 232\tDUP_TOP_TWO(arg=None, lineno=349)\n", - " 234\tBINARY_SUBSCR(arg=None, lineno=349)\n", - " 236\tLOAD_CONST(arg=1, lineno=349)\n", - " 238\tINPLACE_ADD(arg=None, lineno=349)\n", - " 240\tROT_THREE(arg=None, lineno=349)\n", - " 242\tSTORE_SUBSCR(arg=None, lineno=349)\n", - " 244\tJUMP_ABSOLUTE(arg=10, lineno=349)\n", - "> 246\tLOAD_CONST(arg=3, lineno=349)\n", - " 248\tRETURN_VALUE(arg=None, lineno=349)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "DEBUG:numba.core.byteflow:pending: deque([State(pc_initial=0 nstack_initial=0)])\n", - "DEBUG:numba.core.byteflow:stack: []\n", - "DEBUG:numba.core.byteflow:dispatch pc=0, inst=NOP(arg=None, lineno=321)\n", - "DEBUG:numba.core.byteflow:stack []\n", - "DEBUG:numba.core.byteflow:dispatch pc=2, inst=LOAD_GLOBAL(arg=0, lineno=321)\n", - "DEBUG:numba.core.byteflow:stack []\n", - "DEBUG:numba.core.byteflow:dispatch pc=4, inst=LOAD_FAST(arg=0, lineno=321)\n", - "DEBUG:numba.core.byteflow:stack ['$2load_global.0']\n", - "DEBUG:numba.core.byteflow:dispatch pc=6, inst=CALL_FUNCTION(arg=1, lineno=321)\n", - "DEBUG:numba.core.byteflow:stack ['$2load_global.0', '$nswap_attempts4.1']\n", - "DEBUG:numba.core.byteflow:dispatch pc=8, inst=GET_ITER(arg=None, lineno=321)\n", - "DEBUG:numba.core.byteflow:stack ['$6call_function.2']\n", - "DEBUG:numba.core.byteflow:end state. edges=[Edge(pc=10, stack=('$8get_iter.3',), blockstack=(), npush=0)]\n", - "DEBUG:numba.core.byteflow:pending: deque([State(pc_initial=10 nstack_initial=1)])\n", - "DEBUG:numba.core.byteflow:stack: ['$phi10.0']\n", - "DEBUG:numba.core.byteflow:dispatch pc=10, inst=FOR_ITER(arg=234, lineno=321)\n", - "DEBUG:numba.core.byteflow:stack ['$phi10.0']\n", - "DEBUG:numba.core.byteflow:end state. edges=[Edge(pc=246, stack=(), blockstack=(), npush=0), Edge(pc=12, stack=('$phi10.0', '$10for_iter.2'), blockstack=(), npush=0)]\n", - "DEBUG:numba.core.byteflow:pending: deque([State(pc_initial=246 nstack_initial=0), State(pc_initial=12 nstack_initial=2)])\n", - "DEBUG:numba.core.byteflow:stack: []\n", - "DEBUG:numba.core.byteflow:dispatch pc=246, inst=LOAD_CONST(arg=3, lineno=349)\n", - "DEBUG:numba.core.byteflow:stack []\n", - "DEBUG:numba.core.byteflow:dispatch pc=248, inst=RETURN_VALUE(arg=None, lineno=349)\n", - "DEBUG:numba.core.byteflow:stack ['$const246.0']\n", - "DEBUG:numba.core.byteflow:end state. edges=[]\n", - "DEBUG:numba.core.byteflow:pending: deque([State(pc_initial=12 nstack_initial=2)])\n", - "DEBUG:numba.core.byteflow:stack: ['$phi12.0', '$phi12.1']\n", - "DEBUG:numba.core.byteflow:dispatch pc=12, inst=STORE_FAST(arg=6, lineno=321)\n", - "DEBUG:numba.core.byteflow:stack ['$phi12.0', '$phi12.1']\n", - "DEBUG:numba.core.byteflow:dispatch pc=14, inst=LOAD_GLOBAL(arg=1, lineno=324)\n", - "DEBUG:numba.core.byteflow:stack ['$phi12.0']\n", - "DEBUG:numba.core.byteflow:dispatch pc=16, inst=LOAD_ATTR(arg=2, lineno=324)\n", - "DEBUG:numba.core.byteflow:stack ['$phi12.0', '$14load_global.2']\n", - "DEBUG:numba.core.byteflow:dispatch pc=18, inst=LOAD_METHOD(arg=3, lineno=324)\n", - "DEBUG:numba.core.byteflow:stack ['$phi12.0', '$16load_attr.3']\n", - "DEBUG:numba.core.byteflow:dispatch pc=20, inst=LOAD_FAST(arg=1, lineno=324)\n", - "DEBUG:numba.core.byteflow:stack ['$phi12.0', '$18load_method.4']\n", - "DEBUG:numba.core.byteflow:dispatch pc=22, inst=CALL_METHOD(arg=1, lineno=324)\n", - "DEBUG:numba.core.byteflow:stack ['$phi12.0', '$18load_method.4', '$n_replicas20.5']\n", - "DEBUG:numba.core.byteflow:dispatch pc=24, inst=STORE_FAST(arg=7, lineno=324)\n", - "DEBUG:numba.core.byteflow:stack ['$phi12.0', '$22call_method.6']\n", - "DEBUG:numba.core.byteflow:dispatch pc=26, inst=LOAD_GLOBAL(arg=1, lineno=325)\n", - "DEBUG:numba.core.byteflow:stack ['$phi12.0']\n", - "DEBUG:numba.core.byteflow:dispatch pc=28, inst=LOAD_ATTR(arg=2, lineno=325)\n", - "DEBUG:numba.core.byteflow:stack ['$phi12.0', '$26load_global.7']\n", - "DEBUG:numba.core.byteflow:dispatch pc=30, inst=LOAD_METHOD(arg=3, lineno=325)\n", - "DEBUG:numba.core.byteflow:stack ['$phi12.0', '$28load_attr.8']\n", - "DEBUG:numba.core.byteflow:dispatch pc=32, inst=LOAD_FAST(arg=1, lineno=325)\n", - "DEBUG:numba.core.byteflow:stack ['$phi12.0', '$30load_method.9']\n", - "DEBUG:numba.core.byteflow:dispatch pc=34, inst=CALL_METHOD(arg=1, lineno=325)\n", - "DEBUG:numba.core.byteflow:stack ['$phi12.0', '$30load_method.9', '$n_replicas32.10']\n", - "DEBUG:numba.core.byteflow:dispatch pc=36, inst=STORE_FAST(arg=8, lineno=325)\n", - "DEBUG:numba.core.byteflow:stack ['$phi12.0', '$34call_method.11']\n", - "DEBUG:numba.core.byteflow:dispatch pc=38, inst=LOAD_FAST(arg=2, lineno=328)\n", - "DEBUG:numba.core.byteflow:stack ['$phi12.0']\n", - "DEBUG:numba.core.byteflow:dispatch pc=40, inst=LOAD_FAST(arg=7, lineno=328)\n", - "DEBUG:numba.core.byteflow:stack ['$phi12.0', '$_replica_thermodynamic_states38.12']\n", - "DEBUG:numba.core.byteflow:dispatch pc=42, inst=BINARY_SUBSCR(arg=None, lineno=328)\n", - "DEBUG:numba.core.byteflow:stack ['$phi12.0', '$_replica_thermodynamic_states38.12', '$replica_i40.13']\n", - "DEBUG:numba.core.byteflow:dispatch pc=44, inst=STORE_FAST(arg=9, lineno=328)\n", - "DEBUG:numba.core.byteflow:stack ['$phi12.0', '$42binary_subscr.14']\n", - "DEBUG:numba.core.byteflow:dispatch pc=46, inst=LOAD_FAST(arg=2, lineno=329)\n", - "DEBUG:numba.core.byteflow:stack ['$phi12.0']\n", - "DEBUG:numba.core.byteflow:dispatch pc=48, inst=LOAD_FAST(arg=8, lineno=329)\n", - "DEBUG:numba.core.byteflow:stack ['$phi12.0', '$_replica_thermodynamic_states46.15']\n", - "DEBUG:numba.core.byteflow:dispatch pc=50, inst=BINARY_SUBSCR(arg=None, lineno=329)\n", - "DEBUG:numba.core.byteflow:stack ['$phi12.0', '$_replica_thermodynamic_states46.15', '$replica_j48.16']\n", - "DEBUG:numba.core.byteflow:dispatch pc=52, inst=STORE_FAST(arg=10, lineno=329)\n", - "DEBUG:numba.core.byteflow:stack ['$phi12.0', '$50binary_subscr.17']\n", - "DEBUG:numba.core.byteflow:dispatch pc=54, inst=LOAD_FAST(arg=3, lineno=332)\n", - "DEBUG:numba.core.byteflow:stack ['$phi12.0']\n", - "DEBUG:numba.core.byteflow:dispatch pc=56, inst=LOAD_FAST(arg=7, lineno=332)\n", - "DEBUG:numba.core.byteflow:stack ['$phi12.0', '$_energy_thermodynamic_states54.18']\n", - "DEBUG:numba.core.byteflow:dispatch pc=58, inst=LOAD_FAST(arg=10, lineno=332)\n", - "DEBUG:numba.core.byteflow:stack ['$phi12.0', '$_energy_thermodynamic_states54.18', '$replica_i56.19']\n", - "DEBUG:numba.core.byteflow:dispatch pc=60, inst=BUILD_TUPLE(arg=2, lineno=332)\n", - "DEBUG:numba.core.byteflow:stack ['$phi12.0', '$_energy_thermodynamic_states54.18', '$replica_i56.19', '$thermodynamic_state_j58.20']\n", - "DEBUG:numba.core.byteflow:dispatch pc=62, inst=BINARY_SUBSCR(arg=None, lineno=332)\n", - "DEBUG:numba.core.byteflow:stack ['$phi12.0', '$_energy_thermodynamic_states54.18', '$60build_tuple.21']\n", - "DEBUG:numba.core.byteflow:dispatch pc=64, inst=STORE_FAST(arg=11, lineno=332)\n", - "DEBUG:numba.core.byteflow:stack ['$phi12.0', '$62binary_subscr.22']\n", - "DEBUG:numba.core.byteflow:dispatch pc=66, inst=LOAD_FAST(arg=3, lineno=333)\n", - "DEBUG:numba.core.byteflow:stack ['$phi12.0']\n", - "DEBUG:numba.core.byteflow:dispatch pc=68, inst=LOAD_FAST(arg=8, lineno=333)\n", - "DEBUG:numba.core.byteflow:stack ['$phi12.0', '$_energy_thermodynamic_states66.23']\n", - "DEBUG:numba.core.byteflow:dispatch pc=70, inst=LOAD_FAST(arg=9, lineno=333)\n", - "DEBUG:numba.core.byteflow:stack ['$phi12.0', '$_energy_thermodynamic_states66.23', '$replica_j68.24']\n", - "DEBUG:numba.core.byteflow:dispatch pc=72, inst=BUILD_TUPLE(arg=2, lineno=333)\n", - "DEBUG:numba.core.byteflow:stack ['$phi12.0', '$_energy_thermodynamic_states66.23', '$replica_j68.24', '$thermodynamic_state_i70.25']\n", - "DEBUG:numba.core.byteflow:dispatch pc=74, inst=BINARY_SUBSCR(arg=None, lineno=333)\n", - "DEBUG:numba.core.byteflow:stack ['$phi12.0', '$_energy_thermodynamic_states66.23', '$72build_tuple.26']\n", - "DEBUG:numba.core.byteflow:dispatch pc=76, inst=STORE_FAST(arg=12, lineno=333)\n", - "DEBUG:numba.core.byteflow:stack ['$phi12.0', '$74binary_subscr.27']\n", - "DEBUG:numba.core.byteflow:dispatch pc=78, inst=LOAD_FAST(arg=3, lineno=334)\n", - "DEBUG:numba.core.byteflow:stack ['$phi12.0']\n", - "DEBUG:numba.core.byteflow:dispatch pc=80, inst=LOAD_FAST(arg=7, lineno=334)\n", - "DEBUG:numba.core.byteflow:stack ['$phi12.0', '$_energy_thermodynamic_states78.28']\n", - "DEBUG:numba.core.byteflow:dispatch pc=82, inst=LOAD_FAST(arg=9, lineno=334)\n", - "DEBUG:numba.core.byteflow:stack ['$phi12.0', '$_energy_thermodynamic_states78.28', '$replica_i80.29']\n", - "DEBUG:numba.core.byteflow:dispatch pc=84, inst=BUILD_TUPLE(arg=2, lineno=334)\n", - "DEBUG:numba.core.byteflow:stack ['$phi12.0', '$_energy_thermodynamic_states78.28', '$replica_i80.29', '$thermodynamic_state_i82.30']\n", - "DEBUG:numba.core.byteflow:dispatch pc=86, inst=BINARY_SUBSCR(arg=None, lineno=334)\n", - "DEBUG:numba.core.byteflow:stack ['$phi12.0', '$_energy_thermodynamic_states78.28', '$84build_tuple.31']\n", - "DEBUG:numba.core.byteflow:dispatch pc=88, inst=STORE_FAST(arg=13, lineno=334)\n", - "DEBUG:numba.core.byteflow:stack ['$phi12.0', '$86binary_subscr.32']\n", - "DEBUG:numba.core.byteflow:dispatch pc=90, inst=LOAD_FAST(arg=3, lineno=335)\n", - "DEBUG:numba.core.byteflow:stack ['$phi12.0']\n", - "DEBUG:numba.core.byteflow:dispatch pc=92, inst=LOAD_FAST(arg=8, lineno=335)\n", - "DEBUG:numba.core.byteflow:stack ['$phi12.0', '$_energy_thermodynamic_states90.33']\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "DEBUG:numba.core.byteflow:dispatch pc=94, inst=LOAD_FAST(arg=10, lineno=335)\n", - "DEBUG:numba.core.byteflow:stack ['$phi12.0', '$_energy_thermodynamic_states90.33', '$replica_j92.34']\n", - "DEBUG:numba.core.byteflow:dispatch pc=96, inst=BUILD_TUPLE(arg=2, lineno=335)\n", - "DEBUG:numba.core.byteflow:stack ['$phi12.0', '$_energy_thermodynamic_states90.33', '$replica_j92.34', '$thermodynamic_state_j94.35']\n", - "DEBUG:numba.core.byteflow:dispatch pc=98, inst=BINARY_SUBSCR(arg=None, lineno=335)\n", - "DEBUG:numba.core.byteflow:stack ['$phi12.0', '$_energy_thermodynamic_states90.33', '$96build_tuple.36']\n", - "DEBUG:numba.core.byteflow:dispatch pc=100, inst=STORE_FAST(arg=14, lineno=335)\n", - "DEBUG:numba.core.byteflow:stack ['$phi12.0', '$98binary_subscr.37']\n", - "DEBUG:numba.core.byteflow:dispatch pc=102, inst=LOAD_FAST(arg=11, lineno=336)\n", - "DEBUG:numba.core.byteflow:stack ['$phi12.0']\n", - "DEBUG:numba.core.byteflow:dispatch pc=104, inst=LOAD_FAST(arg=12, lineno=336)\n", - "DEBUG:numba.core.byteflow:stack ['$phi12.0', '$energy_ij102.38']\n", - "DEBUG:numba.core.byteflow:dispatch pc=106, inst=BINARY_ADD(arg=None, lineno=336)\n", - "DEBUG:numba.core.byteflow:stack ['$phi12.0', '$energy_ij102.38', '$energy_ji104.39']\n", - "DEBUG:numba.core.byteflow:dispatch pc=108, inst=UNARY_NEGATIVE(arg=None, lineno=336)\n", - "DEBUG:numba.core.byteflow:stack ['$phi12.0', '$106binary_add.40']\n", - "DEBUG:numba.core.byteflow:dispatch pc=110, inst=LOAD_FAST(arg=13, lineno=336)\n", - "DEBUG:numba.core.byteflow:stack ['$phi12.0', '$108unary_negative.41']\n", - "DEBUG:numba.core.byteflow:dispatch pc=112, inst=BINARY_ADD(arg=None, lineno=336)\n", - "DEBUG:numba.core.byteflow:stack ['$phi12.0', '$108unary_negative.41', '$energy_ii110.42']\n", - "DEBUG:numba.core.byteflow:dispatch pc=114, inst=LOAD_FAST(arg=14, lineno=336)\n", - "DEBUG:numba.core.byteflow:stack ['$phi12.0', '$112binary_add.43']\n", - "DEBUG:numba.core.byteflow:dispatch pc=116, inst=BINARY_ADD(arg=None, lineno=336)\n", - "DEBUG:numba.core.byteflow:stack ['$phi12.0', '$112binary_add.43', '$energy_jj114.44']\n", - "DEBUG:numba.core.byteflow:dispatch pc=118, inst=STORE_FAST(arg=15, lineno=336)\n", - "DEBUG:numba.core.byteflow:stack ['$phi12.0', '$116binary_add.45']\n", - "DEBUG:numba.core.byteflow:dispatch pc=120, inst=LOAD_FAST(arg=5, lineno=339)\n", - "DEBUG:numba.core.byteflow:stack ['$phi12.0']\n", - "DEBUG:numba.core.byteflow:dispatch pc=122, inst=LOAD_FAST(arg=9, lineno=339)\n", - "DEBUG:numba.core.byteflow:stack ['$phi12.0', '$_n_proposed_matrix120.46']\n", - "DEBUG:numba.core.byteflow:dispatch pc=124, inst=LOAD_FAST(arg=10, lineno=339)\n", - "DEBUG:numba.core.byteflow:stack ['$phi12.0', '$_n_proposed_matrix120.46', '$thermodynamic_state_i122.47']\n", - "DEBUG:numba.core.byteflow:dispatch pc=126, inst=BUILD_TUPLE(arg=2, lineno=339)\n", - "DEBUG:numba.core.byteflow:stack ['$phi12.0', '$_n_proposed_matrix120.46', '$thermodynamic_state_i122.47', '$thermodynamic_state_j124.48']\n", - "DEBUG:numba.core.byteflow:dispatch pc=128, inst=DUP_TOP_TWO(arg=None, lineno=339)\n", - "DEBUG:numba.core.byteflow:stack ['$phi12.0', '$_n_proposed_matrix120.46', '$126build_tuple.49']\n", - "DEBUG:numba.core.byteflow:dispatch pc=130, inst=BINARY_SUBSCR(arg=None, lineno=339)\n", - "DEBUG:numba.core.byteflow:stack ['$phi12.0', '$_n_proposed_matrix120.46', '$126build_tuple.49', '$128dup_top_two.50', '$128dup_top_two.51']\n", - "DEBUG:numba.core.byteflow:dispatch pc=132, inst=LOAD_CONST(arg=1, lineno=339)\n", - "DEBUG:numba.core.byteflow:stack ['$phi12.0', '$_n_proposed_matrix120.46', '$126build_tuple.49', '$130binary_subscr.52']\n", - "DEBUG:numba.core.byteflow:dispatch pc=134, inst=INPLACE_ADD(arg=None, lineno=339)\n", - "DEBUG:numba.core.byteflow:stack ['$phi12.0', '$_n_proposed_matrix120.46', '$126build_tuple.49', '$130binary_subscr.52', '$const132.53']\n", - "DEBUG:numba.core.byteflow:dispatch pc=136, inst=ROT_THREE(arg=None, lineno=339)\n", - "DEBUG:numba.core.byteflow:stack ['$phi12.0', '$_n_proposed_matrix120.46', '$126build_tuple.49', '$134inplace_add.54']\n", - "DEBUG:numba.core.byteflow:dispatch pc=138, inst=STORE_SUBSCR(arg=None, lineno=339)\n", - "DEBUG:numba.core.byteflow:stack ['$phi12.0', '$134inplace_add.54', '$_n_proposed_matrix120.46', '$126build_tuple.49']\n", - "DEBUG:numba.core.byteflow:dispatch pc=140, inst=LOAD_FAST(arg=5, lineno=340)\n", - "DEBUG:numba.core.byteflow:stack ['$phi12.0']\n", - "DEBUG:numba.core.byteflow:dispatch pc=142, inst=LOAD_FAST(arg=10, lineno=340)\n", - "DEBUG:numba.core.byteflow:stack ['$phi12.0', '$_n_proposed_matrix140.55']\n", - "DEBUG:numba.core.byteflow:dispatch pc=144, inst=LOAD_FAST(arg=9, lineno=340)\n", - "DEBUG:numba.core.byteflow:stack ['$phi12.0', '$_n_proposed_matrix140.55', '$thermodynamic_state_j142.56']\n", - "DEBUG:numba.core.byteflow:dispatch pc=146, inst=BUILD_TUPLE(arg=2, lineno=340)\n", - "DEBUG:numba.core.byteflow:stack ['$phi12.0', '$_n_proposed_matrix140.55', '$thermodynamic_state_j142.56', '$thermodynamic_state_i144.57']\n", - "DEBUG:numba.core.byteflow:dispatch pc=148, inst=DUP_TOP_TWO(arg=None, lineno=340)\n", - "DEBUG:numba.core.byteflow:stack ['$phi12.0', '$_n_proposed_matrix140.55', '$146build_tuple.58']\n", - "DEBUG:numba.core.byteflow:dispatch pc=150, inst=BINARY_SUBSCR(arg=None, lineno=340)\n", - "DEBUG:numba.core.byteflow:stack ['$phi12.0', '$_n_proposed_matrix140.55', '$146build_tuple.58', '$148dup_top_two.59', '$148dup_top_two.60']\n", - "DEBUG:numba.core.byteflow:dispatch pc=152, inst=LOAD_CONST(arg=1, lineno=340)\n", - "DEBUG:numba.core.byteflow:stack ['$phi12.0', '$_n_proposed_matrix140.55', '$146build_tuple.58', '$150binary_subscr.61']\n", - "DEBUG:numba.core.byteflow:dispatch pc=154, inst=INPLACE_ADD(arg=None, lineno=340)\n", - "DEBUG:numba.core.byteflow:stack ['$phi12.0', '$_n_proposed_matrix140.55', '$146build_tuple.58', '$150binary_subscr.61', '$const152.62']\n", - "DEBUG:numba.core.byteflow:dispatch pc=156, inst=ROT_THREE(arg=None, lineno=340)\n", - "DEBUG:numba.core.byteflow:stack ['$phi12.0', '$_n_proposed_matrix140.55', '$146build_tuple.58', '$154inplace_add.63']\n", - "DEBUG:numba.core.byteflow:dispatch pc=158, inst=STORE_SUBSCR(arg=None, lineno=340)\n", - "DEBUG:numba.core.byteflow:stack ['$phi12.0', '$154inplace_add.63', '$_n_proposed_matrix140.55', '$146build_tuple.58']\n", - "DEBUG:numba.core.byteflow:dispatch pc=160, inst=LOAD_FAST(arg=15, lineno=343)\n", - "DEBUG:numba.core.byteflow:stack ['$phi12.0']\n", - "DEBUG:numba.core.byteflow:dispatch pc=162, inst=LOAD_CONST(arg=2, lineno=343)\n", - "DEBUG:numba.core.byteflow:stack ['$phi12.0', '$log_p_accept160.64']\n", - "DEBUG:numba.core.byteflow:dispatch pc=164, inst=COMPARE_OP(arg=5, lineno=343)\n", - "DEBUG:numba.core.byteflow:stack ['$phi12.0', '$log_p_accept160.64', '$const162.65']\n", - "DEBUG:numba.core.byteflow:dispatch pc=166, inst=POP_JUMP_IF_TRUE(arg=188, lineno=343)\n", - "DEBUG:numba.core.byteflow:stack ['$phi12.0', '$164compare_op.66']\n", - "DEBUG:numba.core.byteflow:end state. edges=[Edge(pc=168, stack=('$phi12.0',), blockstack=(), npush=0), Edge(pc=188, stack=('$phi12.0',), blockstack=(), npush=0)]\n", - "DEBUG:numba.core.byteflow:pending: deque([State(pc_initial=168 nstack_initial=1), State(pc_initial=188 nstack_initial=1)])\n", - "DEBUG:numba.core.byteflow:stack: ['$phi168.0']\n", - "DEBUG:numba.core.byteflow:dispatch pc=168, inst=LOAD_GLOBAL(arg=1, lineno=343)\n", - "DEBUG:numba.core.byteflow:stack ['$phi168.0']\n", - "DEBUG:numba.core.byteflow:dispatch pc=170, inst=LOAD_ATTR(arg=2, lineno=343)\n", - "DEBUG:numba.core.byteflow:stack ['$phi168.0', '$168load_global.1']\n", - "DEBUG:numba.core.byteflow:dispatch pc=172, inst=LOAD_METHOD(arg=4, lineno=343)\n", - "DEBUG:numba.core.byteflow:stack ['$phi168.0', '$170load_attr.2']\n", - "DEBUG:numba.core.byteflow:dispatch pc=174, inst=CALL_METHOD(arg=0, lineno=343)\n", - "DEBUG:numba.core.byteflow:stack ['$phi168.0', '$172load_method.3']\n", - "DEBUG:numba.core.byteflow:dispatch pc=176, inst=LOAD_GLOBAL(arg=1, lineno=343)\n", - "DEBUG:numba.core.byteflow:stack ['$phi168.0', '$174call_method.4']\n", - "DEBUG:numba.core.byteflow:dispatch pc=178, inst=LOAD_METHOD(arg=5, lineno=343)\n", - "DEBUG:numba.core.byteflow:stack ['$phi168.0', '$174call_method.4', '$176load_global.5']\n", - "DEBUG:numba.core.byteflow:dispatch pc=180, inst=LOAD_FAST(arg=15, lineno=343)\n", - "DEBUG:numba.core.byteflow:stack ['$phi168.0', '$174call_method.4', '$178load_method.6']\n", - "DEBUG:numba.core.byteflow:dispatch pc=182, inst=CALL_METHOD(arg=1, lineno=343)\n", - "DEBUG:numba.core.byteflow:stack ['$phi168.0', '$174call_method.4', '$178load_method.6', '$log_p_accept180.7']\n", - "DEBUG:numba.core.byteflow:dispatch pc=184, inst=COMPARE_OP(arg=0, lineno=343)\n", - "DEBUG:numba.core.byteflow:stack ['$phi168.0', '$174call_method.4', '$182call_method.8']\n", - "DEBUG:numba.core.byteflow:dispatch pc=186, inst=POP_JUMP_IF_FALSE(arg=10, lineno=343)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "DEBUG:numba.core.byteflow:stack ['$phi168.0', '$184compare_op.9']\n", - "DEBUG:numba.core.byteflow:end state. edges=[Edge(pc=188, stack=('$phi168.0',), blockstack=(), npush=0), Edge(pc=10, stack=('$phi168.0',), blockstack=(), npush=0)]\n", - "DEBUG:numba.core.byteflow:pending: deque([State(pc_initial=188 nstack_initial=1), State(pc_initial=188 nstack_initial=1), State(pc_initial=10 nstack_initial=1)])\n", - "DEBUG:numba.core.byteflow:stack: ['$phi188.0']\n", - "DEBUG:numba.core.byteflow:dispatch pc=188, inst=LOAD_FAST(arg=10, lineno=345)\n", - "DEBUG:numba.core.byteflow:stack ['$phi188.0']\n", - "DEBUG:numba.core.byteflow:dispatch pc=190, inst=LOAD_FAST(arg=2, lineno=345)\n", - "DEBUG:numba.core.byteflow:stack ['$phi188.0', '$thermodynamic_state_j188.1']\n", - "DEBUG:numba.core.byteflow:dispatch pc=192, inst=LOAD_FAST(arg=7, lineno=345)\n", - "DEBUG:numba.core.byteflow:stack ['$phi188.0', '$thermodynamic_state_j188.1', '$_replica_thermodynamic_states190.2']\n", - "DEBUG:numba.core.byteflow:dispatch pc=194, inst=STORE_SUBSCR(arg=None, lineno=345)\n", - "DEBUG:numba.core.byteflow:stack ['$phi188.0', '$thermodynamic_state_j188.1', '$_replica_thermodynamic_states190.2', '$replica_i192.3']\n", - "DEBUG:numba.core.byteflow:dispatch pc=196, inst=LOAD_FAST(arg=9, lineno=346)\n", - "DEBUG:numba.core.byteflow:stack ['$phi188.0']\n", - "DEBUG:numba.core.byteflow:dispatch pc=198, inst=LOAD_FAST(arg=2, lineno=346)\n", - "DEBUG:numba.core.byteflow:stack ['$phi188.0', '$thermodynamic_state_i196.4']\n", - "DEBUG:numba.core.byteflow:dispatch pc=200, inst=LOAD_FAST(arg=8, lineno=346)\n", - "DEBUG:numba.core.byteflow:stack ['$phi188.0', '$thermodynamic_state_i196.4', '$_replica_thermodynamic_states198.5']\n", - "DEBUG:numba.core.byteflow:dispatch pc=202, inst=STORE_SUBSCR(arg=None, lineno=346)\n", - "DEBUG:numba.core.byteflow:stack ['$phi188.0', '$thermodynamic_state_i196.4', '$_replica_thermodynamic_states198.5', '$replica_j200.6']\n", - "DEBUG:numba.core.byteflow:dispatch pc=204, inst=LOAD_FAST(arg=4, lineno=348)\n", - "DEBUG:numba.core.byteflow:stack ['$phi188.0']\n", - "DEBUG:numba.core.byteflow:dispatch pc=206, inst=LOAD_FAST(arg=9, lineno=348)\n", - "DEBUG:numba.core.byteflow:stack ['$phi188.0', '$_n_accepted_matrix204.7']\n", - "DEBUG:numba.core.byteflow:dispatch pc=208, inst=LOAD_FAST(arg=10, lineno=348)\n", - "DEBUG:numba.core.byteflow:stack ['$phi188.0', '$_n_accepted_matrix204.7', '$thermodynamic_state_i206.8']\n", - "DEBUG:numba.core.byteflow:dispatch pc=210, inst=BUILD_TUPLE(arg=2, lineno=348)\n", - "DEBUG:numba.core.byteflow:stack ['$phi188.0', '$_n_accepted_matrix204.7', '$thermodynamic_state_i206.8', '$thermodynamic_state_j208.9']\n", - "DEBUG:numba.core.byteflow:dispatch pc=212, inst=DUP_TOP_TWO(arg=None, lineno=348)\n", - "DEBUG:numba.core.byteflow:stack ['$phi188.0', '$_n_accepted_matrix204.7', '$210build_tuple.10']\n", - "DEBUG:numba.core.byteflow:dispatch pc=214, inst=BINARY_SUBSCR(arg=None, lineno=348)\n", - "DEBUG:numba.core.byteflow:stack ['$phi188.0', '$_n_accepted_matrix204.7', '$210build_tuple.10', '$212dup_top_two.11', '$212dup_top_two.12']\n", - "DEBUG:numba.core.byteflow:dispatch pc=216, inst=LOAD_CONST(arg=1, lineno=348)\n", - "DEBUG:numba.core.byteflow:stack ['$phi188.0', '$_n_accepted_matrix204.7', '$210build_tuple.10', '$214binary_subscr.13']\n", - "DEBUG:numba.core.byteflow:dispatch pc=218, inst=INPLACE_ADD(arg=None, lineno=348)\n", - "DEBUG:numba.core.byteflow:stack ['$phi188.0', '$_n_accepted_matrix204.7', '$210build_tuple.10', '$214binary_subscr.13', '$const216.14']\n", - "DEBUG:numba.core.byteflow:dispatch pc=220, inst=ROT_THREE(arg=None, lineno=348)\n", - "DEBUG:numba.core.byteflow:stack ['$phi188.0', '$_n_accepted_matrix204.7', '$210build_tuple.10', '$218inplace_add.15']\n", - "DEBUG:numba.core.byteflow:dispatch pc=222, inst=STORE_SUBSCR(arg=None, lineno=348)\n", - "DEBUG:numba.core.byteflow:stack ['$phi188.0', '$218inplace_add.15', '$_n_accepted_matrix204.7', '$210build_tuple.10']\n", - "DEBUG:numba.core.byteflow:dispatch pc=224, inst=LOAD_FAST(arg=4, lineno=349)\n", - "DEBUG:numba.core.byteflow:stack ['$phi188.0']\n", - "DEBUG:numba.core.byteflow:dispatch pc=226, inst=LOAD_FAST(arg=10, lineno=349)\n", - "DEBUG:numba.core.byteflow:stack ['$phi188.0', '$_n_accepted_matrix224.16']\n", - "DEBUG:numba.core.byteflow:dispatch pc=228, inst=LOAD_FAST(arg=9, lineno=349)\n", - "DEBUG:numba.core.byteflow:stack ['$phi188.0', '$_n_accepted_matrix224.16', '$thermodynamic_state_j226.17']\n", - "DEBUG:numba.core.byteflow:dispatch pc=230, inst=BUILD_TUPLE(arg=2, lineno=349)\n", - "DEBUG:numba.core.byteflow:stack ['$phi188.0', '$_n_accepted_matrix224.16', '$thermodynamic_state_j226.17', '$thermodynamic_state_i228.18']\n", - "DEBUG:numba.core.byteflow:dispatch pc=232, inst=DUP_TOP_TWO(arg=None, lineno=349)\n", - "DEBUG:numba.core.byteflow:stack ['$phi188.0', '$_n_accepted_matrix224.16', '$230build_tuple.19']\n", - "DEBUG:numba.core.byteflow:dispatch pc=234, inst=BINARY_SUBSCR(arg=None, lineno=349)\n", - "DEBUG:numba.core.byteflow:stack ['$phi188.0', '$_n_accepted_matrix224.16', '$230build_tuple.19', '$232dup_top_two.20', '$232dup_top_two.21']\n", - "DEBUG:numba.core.byteflow:dispatch pc=236, inst=LOAD_CONST(arg=1, lineno=349)\n", - "DEBUG:numba.core.byteflow:stack ['$phi188.0', '$_n_accepted_matrix224.16', '$230build_tuple.19', '$234binary_subscr.22']\n", - "DEBUG:numba.core.byteflow:dispatch pc=238, inst=INPLACE_ADD(arg=None, lineno=349)\n", - "DEBUG:numba.core.byteflow:stack ['$phi188.0', '$_n_accepted_matrix224.16', '$230build_tuple.19', '$234binary_subscr.22', '$const236.23']\n", - "DEBUG:numba.core.byteflow:dispatch pc=240, inst=ROT_THREE(arg=None, lineno=349)\n", - "DEBUG:numba.core.byteflow:stack ['$phi188.0', '$_n_accepted_matrix224.16', '$230build_tuple.19', '$238inplace_add.24']\n", - "DEBUG:numba.core.byteflow:dispatch pc=242, inst=STORE_SUBSCR(arg=None, lineno=349)\n", - "DEBUG:numba.core.byteflow:stack ['$phi188.0', '$238inplace_add.24', '$_n_accepted_matrix224.16', '$230build_tuple.19']\n", - "DEBUG:numba.core.byteflow:dispatch pc=244, inst=JUMP_ABSOLUTE(arg=10, lineno=349)\n", - "DEBUG:numba.core.byteflow:stack ['$phi188.0']\n", - "DEBUG:numba.core.byteflow:end state. edges=[Edge(pc=10, stack=('$phi188.0',), blockstack=(), npush=0)]\n", - "DEBUG:numba.core.byteflow:pending: deque([State(pc_initial=188 nstack_initial=1), State(pc_initial=10 nstack_initial=1), State(pc_initial=10 nstack_initial=1)])\n", - "DEBUG:numba.core.byteflow:pending: deque([State(pc_initial=10 nstack_initial=1), State(pc_initial=10 nstack_initial=1)])\n", - "DEBUG:numba.core.byteflow:pending: deque([State(pc_initial=10 nstack_initial=1)])\n", - "DEBUG:numba.core.byteflow:-------------------------Prune PHIs-------------------------\n", - "DEBUG:numba.core.byteflow:Used_phis: defaultdict(,\n", - " {State(pc_initial=0 nstack_initial=0): set(),\n", - " State(pc_initial=10 nstack_initial=1): {'$phi10.0'},\n", - " State(pc_initial=12 nstack_initial=2): {'$phi12.1'},\n", - " State(pc_initial=168 nstack_initial=1): set(),\n", - " State(pc_initial=188 nstack_initial=1): set(),\n", - " State(pc_initial=246 nstack_initial=0): set()})\n", - "DEBUG:numba.core.byteflow:defmap: {'$phi10.0': State(pc_initial=0 nstack_initial=0),\n", - " '$phi12.1': State(pc_initial=10 nstack_initial=1)}\n", - "DEBUG:numba.core.byteflow:phismap: defaultdict(,\n", - " {'$phi10.0': {('$8get_iter.3',\n", - " State(pc_initial=0 nstack_initial=0)),\n", - " ('$phi168.0', State(pc_initial=168 nstack_initial=1)),\n", - " ('$phi188.0',\n", - " State(pc_initial=188 nstack_initial=1))},\n", - " '$phi12.0': {('$phi10.0', State(pc_initial=10 nstack_initial=1))},\n", - " '$phi12.1': {('$10for_iter.2',\n", - " State(pc_initial=10 nstack_initial=1))},\n", - " '$phi168.0': {('$phi12.0', State(pc_initial=12 nstack_initial=2))},\n", - " '$phi188.0': {('$phi12.0', State(pc_initial=12 nstack_initial=2)),\n", - " ('$phi168.0',\n", - " State(pc_initial=168 nstack_initial=1))}})\n", - "DEBUG:numba.core.byteflow:changing phismap: defaultdict(,\n", - " {'$phi10.0': {('$8get_iter.3',\n", - " State(pc_initial=0 nstack_initial=0)),\n", - " ('$phi12.0', State(pc_initial=12 nstack_initial=2))},\n", - " '$phi12.0': {('$8get_iter.3',\n", - " State(pc_initial=0 nstack_initial=0)),\n", - " ('$phi12.0', State(pc_initial=12 nstack_initial=2))},\n", - " '$phi12.1': {('$10for_iter.2',\n", - " State(pc_initial=10 nstack_initial=1))},\n", - " '$phi168.0': {('$8get_iter.3',\n", - " State(pc_initial=0 nstack_initial=0))},\n", - " '$phi188.0': {('$8get_iter.3',\n", - " State(pc_initial=0 nstack_initial=0))}})\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "DEBUG:numba.core.byteflow:changing phismap: defaultdict(,\n", - " {'$phi10.0': {('$8get_iter.3',\n", - " State(pc_initial=0 nstack_initial=0))},\n", - " '$phi12.0': {('$8get_iter.3',\n", - " State(pc_initial=0 nstack_initial=0))},\n", - " '$phi12.1': {('$10for_iter.2',\n", - " State(pc_initial=10 nstack_initial=1))},\n", - " '$phi168.0': {('$8get_iter.3',\n", - " State(pc_initial=0 nstack_initial=0))},\n", - " '$phi188.0': {('$8get_iter.3',\n", - " State(pc_initial=0 nstack_initial=0))}})\n", - "DEBUG:numba.core.byteflow:changing phismap: defaultdict(,\n", - " {'$phi10.0': {('$8get_iter.3',\n", - " State(pc_initial=0 nstack_initial=0))},\n", - " '$phi12.0': {('$8get_iter.3',\n", - " State(pc_initial=0 nstack_initial=0))},\n", - " '$phi12.1': {('$10for_iter.2',\n", - " State(pc_initial=10 nstack_initial=1))},\n", - " '$phi168.0': {('$8get_iter.3',\n", - " State(pc_initial=0 nstack_initial=0))},\n", - " '$phi188.0': {('$8get_iter.3',\n", - " State(pc_initial=0 nstack_initial=0))}})\n", - "DEBUG:numba.core.byteflow:keep phismap: {'$phi10.0': {('$8get_iter.3', State(pc_initial=0 nstack_initial=0))},\n", - " '$phi12.1': {('$10for_iter.2', State(pc_initial=10 nstack_initial=1))}}\n", - "DEBUG:numba.core.byteflow:new_out: defaultdict(,\n", - " {State(pc_initial=0 nstack_initial=0): {'$phi10.0': '$8get_iter.3'},\n", - " State(pc_initial=10 nstack_initial=1): {'$phi12.1': '$10for_iter.2'}})\n", - "DEBUG:numba.core.byteflow:----------------------DONE Prune PHIs-----------------------\n", - "DEBUG:numba.core.byteflow:block_infos State(pc_initial=0 nstack_initial=0):\n", - "AdaptBlockInfo(insts=((0, {}), (2, {'res': '$2load_global.0'}), (4, {'res': '$nswap_attempts4.1'}), (6, {'func': '$2load_global.0', 'args': ['$nswap_attempts4.1'], 'res': '$6call_function.2'}), (8, {'value': '$6call_function.2', 'res': '$8get_iter.3'})), outgoing_phis={'$phi10.0': '$8get_iter.3'}, blockstack=(), active_try_block=None, outgoing_edgepushed={10: ('$8get_iter.3',)})\n", - "DEBUG:numba.core.byteflow:block_infos State(pc_initial=10 nstack_initial=1):\n", - "AdaptBlockInfo(insts=((10, {'iterator': '$phi10.0', 'pair': '$10for_iter.1', 'indval': '$10for_iter.2', 'pred': '$10for_iter.3'}),), outgoing_phis={'$phi12.1': '$10for_iter.2'}, blockstack=(), active_try_block=None, outgoing_edgepushed={246: (), 12: ('$phi10.0', '$10for_iter.2')})\n", - "DEBUG:numba.core.byteflow:block_infos State(pc_initial=12 nstack_initial=2):\n", - "AdaptBlockInfo(insts=((12, {'value': '$phi12.1'}), (14, {'res': '$14load_global.2'}), (16, {'item': '$14load_global.2', 'res': '$16load_attr.3'}), (18, {'item': '$16load_attr.3', 'res': '$18load_method.4'}), (20, {'res': '$n_replicas20.5'}), (22, {'func': '$18load_method.4', 'args': ['$n_replicas20.5'], 'res': '$22call_method.6'}), (24, {'value': '$22call_method.6'}), (26, {'res': '$26load_global.7'}), (28, {'item': '$26load_global.7', 'res': '$28load_attr.8'}), (30, {'item': '$28load_attr.8', 'res': '$30load_method.9'}), (32, {'res': '$n_replicas32.10'}), (34, {'func': '$30load_method.9', 'args': ['$n_replicas32.10'], 'res': '$34call_method.11'}), (36, {'value': '$34call_method.11'}), (38, {'res': '$_replica_thermodynamic_states38.12'}), (40, {'res': '$replica_i40.13'}), (42, {'index': '$replica_i40.13', 'target': '$_replica_thermodynamic_states38.12', 'res': '$42binary_subscr.14'}), (44, {'value': '$42binary_subscr.14'}), (46, {'res': '$_replica_thermodynamic_states46.15'}), (48, {'res': '$replica_j48.16'}), (50, {'index': '$replica_j48.16', 'target': '$_replica_thermodynamic_states46.15', 'res': '$50binary_subscr.17'}), (52, {'value': '$50binary_subscr.17'}), (54, {'res': '$_energy_thermodynamic_states54.18'}), (56, {'res': '$replica_i56.19'}), (58, {'res': '$thermodynamic_state_j58.20'}), (60, {'items': ['$replica_i56.19', '$thermodynamic_state_j58.20'], 'res': '$60build_tuple.21'}), (62, {'index': '$60build_tuple.21', 'target': '$_energy_thermodynamic_states54.18', 'res': '$62binary_subscr.22'}), (64, {'value': '$62binary_subscr.22'}), (66, {'res': '$_energy_thermodynamic_states66.23'}), (68, {'res': '$replica_j68.24'}), (70, {'res': '$thermodynamic_state_i70.25'}), (72, {'items': ['$replica_j68.24', '$thermodynamic_state_i70.25'], 'res': '$72build_tuple.26'}), (74, {'index': '$72build_tuple.26', 'target': '$_energy_thermodynamic_states66.23', 'res': '$74binary_subscr.27'}), (76, {'value': '$74binary_subscr.27'}), (78, {'res': '$_energy_thermodynamic_states78.28'}), (80, {'res': '$replica_i80.29'}), (82, {'res': '$thermodynamic_state_i82.30'}), (84, {'items': ['$replica_i80.29', '$thermodynamic_state_i82.30'], 'res': '$84build_tuple.31'}), (86, {'index': '$84build_tuple.31', 'target': '$_energy_thermodynamic_states78.28', 'res': '$86binary_subscr.32'}), (88, {'value': '$86binary_subscr.32'}), (90, {'res': '$_energy_thermodynamic_states90.33'}), (92, {'res': '$replica_j92.34'}), (94, {'res': '$thermodynamic_state_j94.35'}), (96, {'items': ['$replica_j92.34', '$thermodynamic_state_j94.35'], 'res': '$96build_tuple.36'}), (98, {'index': '$96build_tuple.36', 'target': '$_energy_thermodynamic_states90.33', 'res': '$98binary_subscr.37'}), (100, {'value': '$98binary_subscr.37'}), (102, {'res': '$energy_ij102.38'}), (104, {'res': '$energy_ji104.39'}), (106, {'lhs': '$energy_ij102.38', 'rhs': '$energy_ji104.39', 'res': '$106binary_add.40'}), (108, {'value': '$106binary_add.40', 'res': '$108unary_negative.41'}), (110, {'res': '$energy_ii110.42'}), (112, {'lhs': '$108unary_negative.41', 'rhs': '$energy_ii110.42', 'res': '$112binary_add.43'}), (114, {'res': '$energy_jj114.44'}), (116, {'lhs': '$112binary_add.43', 'rhs': '$energy_jj114.44', 'res': '$116binary_add.45'}), (118, {'value': '$116binary_add.45'}), (120, {'res': '$_n_proposed_matrix120.46'}), (122, {'res': '$thermodynamic_state_i122.47'}), (124, {'res': '$thermodynamic_state_j124.48'}), (126, {'items': ['$thermodynamic_state_i122.47', '$thermodynamic_state_j124.48'], 'res': '$126build_tuple.49'}), (128, {'orig': ['$_n_proposed_matrix120.46', '$126build_tuple.49'], 'duped': ['$128dup_top_two.50', '$128dup_top_two.51']}), (130, {'index': '$128dup_top_two.51', 'target': '$128dup_top_two.50', 'res': '$130binary_subscr.52'}), (132, {'res': '$const132.53'}), (134, {'lhs': '$130binary_subscr.52', 'rhs': '$const132.53', 'res': '$134inplace_add.54'}), (138, {'target': '$_n_proposed_matrix120.46', 'index': '$126build_tuple.49', 'value': '$134inplace_add.54'}), (140, {'res': '$_n_proposed_matrix140.55'}), (142, {'res': '$thermodynamic_state_j142.56'}), (144, {'res': '$thermodynamic_state_i144.57'}), (146, {'items': ['$thermodynamic_state_j142.56', '$thermodynamic_state_i144.57'], 'res': '$146build_tuple.58'}), (148, {'orig': ['$_n_proposed_matrix140.55', '$146build_tuple.58'], 'duped': ['$148dup_top_two.59', '$148dup_top_two.60']}), (150, {'index': '$148dup_top_two.60', 'target': '$148dup_top_two.59', 'res': '$150binary_subscr.61'}), (152, {'res': '$const152.62'}), (154, {'lhs': '$150binary_subscr.61', 'rhs': '$const152.62', 'res': '$154inplace_add.63'}), (158, {'target': '$_n_proposed_matrix140.55', 'index': '$146build_tuple.58', 'value': '$154inplace_add.63'}), (160, {'res': '$log_p_accept160.64'}), (162, {'res': '$const162.65'}), (164, {'lhs': '$log_p_accept160.64', 'rhs': '$const162.65', 'res': '$164compare_op.66'}), (166, {'pred': '$164compare_op.66'})), outgoing_phis={}, blockstack=(), active_try_block=None, outgoing_edgepushed={168: ('$phi12.0',), 188: ('$phi12.0',)})\n", - "DEBUG:numba.core.byteflow:block_infos State(pc_initial=168 nstack_initial=1):\n", - "AdaptBlockInfo(insts=((168, {'res': '$168load_global.1'}), (170, {'item': '$168load_global.1', 'res': '$170load_attr.2'}), (172, {'item': '$170load_attr.2', 'res': '$172load_method.3'}), (174, {'func': '$172load_method.3', 'args': [], 'res': '$174call_method.4'}), (176, {'res': '$176load_global.5'}), (178, {'item': '$176load_global.5', 'res': '$178load_method.6'}), (180, {'res': '$log_p_accept180.7'}), (182, {'func': '$178load_method.6', 'args': ['$log_p_accept180.7'], 'res': '$182call_method.8'}), (184, {'lhs': '$174call_method.4', 'rhs': '$182call_method.8', 'res': '$184compare_op.9'}), (186, {'pred': '$184compare_op.9'})), outgoing_phis={}, blockstack=(), active_try_block=None, outgoing_edgepushed={188: ('$phi168.0',), 10: ('$phi168.0',)})\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "DEBUG:numba.core.byteflow:block_infos State(pc_initial=188 nstack_initial=1):\n", - "AdaptBlockInfo(insts=((188, {'res': '$thermodynamic_state_j188.1'}), (190, {'res': '$_replica_thermodynamic_states190.2'}), (192, {'res': '$replica_i192.3'}), (194, {'target': '$_replica_thermodynamic_states190.2', 'index': '$replica_i192.3', 'value': '$thermodynamic_state_j188.1'}), (196, {'res': '$thermodynamic_state_i196.4'}), (198, {'res': '$_replica_thermodynamic_states198.5'}), (200, {'res': '$replica_j200.6'}), (202, {'target': '$_replica_thermodynamic_states198.5', 'index': '$replica_j200.6', 'value': '$thermodynamic_state_i196.4'}), (204, {'res': '$_n_accepted_matrix204.7'}), (206, {'res': '$thermodynamic_state_i206.8'}), (208, {'res': '$thermodynamic_state_j208.9'}), (210, {'items': ['$thermodynamic_state_i206.8', '$thermodynamic_state_j208.9'], 'res': '$210build_tuple.10'}), (212, {'orig': ['$_n_accepted_matrix204.7', '$210build_tuple.10'], 'duped': ['$212dup_top_two.11', '$212dup_top_two.12']}), (214, {'index': '$212dup_top_two.12', 'target': '$212dup_top_two.11', 'res': '$214binary_subscr.13'}), (216, {'res': '$const216.14'}), (218, {'lhs': '$214binary_subscr.13', 'rhs': '$const216.14', 'res': '$218inplace_add.15'}), (222, {'target': '$_n_accepted_matrix204.7', 'index': '$210build_tuple.10', 'value': '$218inplace_add.15'}), (224, {'res': '$_n_accepted_matrix224.16'}), (226, {'res': '$thermodynamic_state_j226.17'}), (228, {'res': '$thermodynamic_state_i228.18'}), (230, {'items': ['$thermodynamic_state_j226.17', '$thermodynamic_state_i228.18'], 'res': '$230build_tuple.19'}), (232, {'orig': ['$_n_accepted_matrix224.16', '$230build_tuple.19'], 'duped': ['$232dup_top_two.20', '$232dup_top_two.21']}), (234, {'index': '$232dup_top_two.21', 'target': '$232dup_top_two.20', 'res': '$234binary_subscr.22'}), (236, {'res': '$const236.23'}), (238, {'lhs': '$234binary_subscr.22', 'rhs': '$const236.23', 'res': '$238inplace_add.24'}), (242, {'target': '$_n_accepted_matrix224.16', 'index': '$230build_tuple.19', 'value': '$238inplace_add.24'}), (244, {})), outgoing_phis={}, blockstack=(), active_try_block=None, outgoing_edgepushed={10: ('$phi188.0',)})\n", - "DEBUG:numba.core.byteflow:block_infos State(pc_initial=246 nstack_initial=0):\n", - "AdaptBlockInfo(insts=((246, {'res': '$const246.0'}), (248, {'retval': '$const246.0', 'castval': '$248return_value.1'})), outgoing_phis={}, blockstack=(), active_try_block=None, outgoing_edgepushed={})\n", - "DEBUG:numba.core.interpreter:label 0:\n", - " nswap_attempts = arg(0, name=nswap_attempts) ['nswap_attempts']\n", - " n_replicas = arg(1, name=n_replicas) ['n_replicas']\n", - " _replica_thermodynamic_states = arg(2, name=_replica_thermodynamic_states) ['_replica_thermodynamic_states']\n", - " _energy_thermodynamic_states = arg(3, name=_energy_thermodynamic_states) ['_energy_thermodynamic_states']\n", - " _n_accepted_matrix = arg(4, name=_n_accepted_matrix) ['_n_accepted_matrix']\n", - " _n_proposed_matrix = arg(5, name=_n_proposed_matrix) ['_n_proposed_matrix']\n", - " $2load_global.0 = global(range: ) ['$2load_global.0']\n", - " $6call_function.2 = call $2load_global.0(nswap_attempts, func=$2load_global.0, args=[Var(nswap_attempts, replicaexchange.py:321)], kws=(), vararg=None) ['$2load_global.0', '$6call_function.2', 'nswap_attempts']\n", - " $8get_iter.3 = getiter(value=$6call_function.2) ['$6call_function.2', '$8get_iter.3']\n", - " $phi10.0 = $8get_iter.3 ['$8get_iter.3', '$phi10.0']\n", - " jump 10 []\n", - "label 10:\n", - " $10for_iter.1 = iternext(value=$phi10.0) ['$10for_iter.1', '$phi10.0']\n", - " $10for_iter.2 = pair_first(value=$10for_iter.1) ['$10for_iter.1', '$10for_iter.2']\n", - " $10for_iter.3 = pair_second(value=$10for_iter.1) ['$10for_iter.1', '$10for_iter.3']\n", - " $phi12.1 = $10for_iter.2 ['$10for_iter.2', '$phi12.1']\n", - " branch $10for_iter.3, 12, 246 ['$10for_iter.3']\n", - "label 12:\n", - " swap_attempt = $phi12.1 ['$phi12.1', 'swap_attempt']\n", - " $14load_global.2 = global(np: ) ['$14load_global.2']\n", - " $16load_attr.3 = getattr(value=$14load_global.2, attr=random) ['$14load_global.2', '$16load_attr.3']\n", - " $18load_method.4 = getattr(value=$16load_attr.3, attr=randint) ['$16load_attr.3', '$18load_method.4']\n", - " replica_i = call $18load_method.4(n_replicas, func=$18load_method.4, args=[Var(n_replicas, replicaexchange.py:321)], kws=(), vararg=None) ['$18load_method.4', 'n_replicas', 'replica_i']\n", - " $26load_global.7 = global(np: ) ['$26load_global.7']\n", - " $28load_attr.8 = getattr(value=$26load_global.7, attr=random) ['$26load_global.7', '$28load_attr.8']\n", - " $30load_method.9 = getattr(value=$28load_attr.8, attr=randint) ['$28load_attr.8', '$30load_method.9']\n", - " replica_j = call $30load_method.9(n_replicas, func=$30load_method.9, args=[Var(n_replicas, replicaexchange.py:321)], kws=(), vararg=None) ['$30load_method.9', 'n_replicas', 'replica_j']\n", - " thermodynamic_state_i = getitem(value=_replica_thermodynamic_states, index=replica_i, fn=) ['_replica_thermodynamic_states', 'replica_i', 'thermodynamic_state_i']\n", - " thermodynamic_state_j = getitem(value=_replica_thermodynamic_states, index=replica_j, fn=) ['_replica_thermodynamic_states', 'replica_j', 'thermodynamic_state_j']\n", - " $60build_tuple.21 = build_tuple(items=[Var(replica_i, replicaexchange.py:324), Var(thermodynamic_state_j, replicaexchange.py:329)]) ['$60build_tuple.21', 'replica_i', 'thermodynamic_state_j']\n", - " energy_ij = getitem(value=_energy_thermodynamic_states, index=$60build_tuple.21, fn=) ['$60build_tuple.21', '_energy_thermodynamic_states', 'energy_ij']\n", - " $72build_tuple.26 = build_tuple(items=[Var(replica_j, replicaexchange.py:325), Var(thermodynamic_state_i, replicaexchange.py:328)]) ['$72build_tuple.26', 'replica_j', 'thermodynamic_state_i']\n", - " energy_ji = getitem(value=_energy_thermodynamic_states, index=$72build_tuple.26, fn=) ['$72build_tuple.26', '_energy_thermodynamic_states', 'energy_ji']\n", - " $84build_tuple.31 = build_tuple(items=[Var(replica_i, replicaexchange.py:324), Var(thermodynamic_state_i, replicaexchange.py:328)]) ['$84build_tuple.31', 'replica_i', 'thermodynamic_state_i']\n", - " energy_ii = getitem(value=_energy_thermodynamic_states, index=$84build_tuple.31, fn=) ['$84build_tuple.31', '_energy_thermodynamic_states', 'energy_ii']\n", - " $96build_tuple.36 = build_tuple(items=[Var(replica_j, replicaexchange.py:325), Var(thermodynamic_state_j, replicaexchange.py:329)]) ['$96build_tuple.36', 'replica_j', 'thermodynamic_state_j']\n", - " energy_jj = getitem(value=_energy_thermodynamic_states, index=$96build_tuple.36, fn=) ['$96build_tuple.36', '_energy_thermodynamic_states', 'energy_jj']\n", - " $106binary_add.40 = energy_ij + energy_ji ['$106binary_add.40', 'energy_ij', 'energy_ji']\n", - " $108unary_negative.41 = unary(fn=, value=$106binary_add.40) ['$106binary_add.40', '$108unary_negative.41']\n", - " $112binary_add.43 = $108unary_negative.41 + energy_ii ['$108unary_negative.41', '$112binary_add.43', 'energy_ii']\n", - " log_p_accept = $112binary_add.43 + energy_jj ['$112binary_add.43', 'energy_jj', 'log_p_accept']\n", - " $126build_tuple.49 = build_tuple(items=[Var(thermodynamic_state_i, replicaexchange.py:328), Var(thermodynamic_state_j, replicaexchange.py:329)]) ['$126build_tuple.49', 'thermodynamic_state_i', 'thermodynamic_state_j']\n", - " $130binary_subscr.52 = getitem(value=_n_proposed_matrix, index=$126build_tuple.49, fn=) ['$126build_tuple.49', '$130binary_subscr.52', '_n_proposed_matrix']\n", - " $const132.53 = const(int, 1) ['$const132.53']\n", - " $134inplace_add.54 = inplace_binop(fn=, immutable_fn=, lhs=$130binary_subscr.52, rhs=$const132.53, static_lhs=Undefined, static_rhs=Undefined) ['$130binary_subscr.52', '$134inplace_add.54', '$const132.53']\n", - " _n_proposed_matrix[$126build_tuple.49] = $134inplace_add.54 ['$126build_tuple.49', '$134inplace_add.54', '_n_proposed_matrix']\n", - " $146build_tuple.58 = build_tuple(items=[Var(thermodynamic_state_j, replicaexchange.py:329), Var(thermodynamic_state_i, replicaexchange.py:328)]) ['$146build_tuple.58', 'thermodynamic_state_i', 'thermodynamic_state_j']\n", - " $150binary_subscr.61 = getitem(value=_n_proposed_matrix, index=$146build_tuple.58, fn=) ['$146build_tuple.58', '$150binary_subscr.61', '_n_proposed_matrix']\n", - " $const152.62 = const(int, 1) ['$const152.62']\n", - " $154inplace_add.63 = inplace_binop(fn=, immutable_fn=, lhs=$150binary_subscr.61, rhs=$const152.62, static_lhs=Undefined, static_rhs=Undefined) ['$150binary_subscr.61', '$154inplace_add.63', '$const152.62']\n", - " _n_proposed_matrix[$146build_tuple.58] = $154inplace_add.63 ['$146build_tuple.58', '$154inplace_add.63', '_n_proposed_matrix']\n", - " $const162.65 = const(float, 0.0) ['$const162.65']\n", - " $164compare_op.66 = log_p_accept >= $const162.65 ['$164compare_op.66', '$const162.65', 'log_p_accept']\n", - " bool166 = global(bool: ) ['bool166']\n", - " $166pred = call bool166($164compare_op.66, func=bool166, args=(Var($164compare_op.66, replicaexchange.py:343),), kws=(), vararg=None) ['$164compare_op.66', '$166pred', 'bool166']\n", - " branch $166pred, 188, 168 ['$166pred']\n", - "label 168:\n", - " $168load_global.1 = global(np: ) ['$168load_global.1']\n", - " $170load_attr.2 = getattr(value=$168load_global.1, attr=random) ['$168load_global.1', '$170load_attr.2']\n", - " $172load_method.3 = getattr(value=$170load_attr.2, attr=rand) ['$170load_attr.2', '$172load_method.3']\n", - " $174call_method.4 = call $172load_method.3(func=$172load_method.3, args=[], kws=(), vararg=None) ['$172load_method.3', '$174call_method.4']\n", - " $176load_global.5 = global(np: ) ['$176load_global.5']\n", - " $178load_method.6 = getattr(value=$176load_global.5, attr=exp) ['$176load_global.5', '$178load_method.6']\n", - " $182call_method.8 = call $178load_method.6(log_p_accept, func=$178load_method.6, args=[Var(log_p_accept, replicaexchange.py:336)], kws=(), vararg=None) ['$178load_method.6', '$182call_method.8', 'log_p_accept']\n", - " $184compare_op.9 = $174call_method.4 < $182call_method.8 ['$174call_method.4', '$182call_method.8', '$184compare_op.9']\n", - " bool186 = global(bool: ) ['bool186']\n", - " $186pred = call bool186($184compare_op.9, func=bool186, args=(Var($184compare_op.9, replicaexchange.py:343),), kws=(), vararg=None) ['$184compare_op.9', '$186pred', 'bool186']\n", - " branch $186pred, 188, 10 ['$186pred']\n", - "label 188:\n", - " _replica_thermodynamic_states[replica_i] = thermodynamic_state_j ['_replica_thermodynamic_states', 'replica_i', 'thermodynamic_state_j']\n", - " _replica_thermodynamic_states[replica_j] = thermodynamic_state_i ['_replica_thermodynamic_states', 'replica_j', 'thermodynamic_state_i']\n", - " $210build_tuple.10 = build_tuple(items=[Var(thermodynamic_state_i, replicaexchange.py:328), Var(thermodynamic_state_j, replicaexchange.py:329)]) ['$210build_tuple.10', 'thermodynamic_state_i', 'thermodynamic_state_j']\n", - " $214binary_subscr.13 = getitem(value=_n_accepted_matrix, index=$210build_tuple.10, fn=) ['$210build_tuple.10', '$214binary_subscr.13', '_n_accepted_matrix']\n", - " $const216.14 = const(int, 1) ['$const216.14']\n", - " $218inplace_add.15 = inplace_binop(fn=, immutable_fn=, lhs=$214binary_subscr.13, rhs=$const216.14, static_lhs=Undefined, static_rhs=Undefined) ['$214binary_subscr.13', '$218inplace_add.15', '$const216.14']\n", - " _n_accepted_matrix[$210build_tuple.10] = $218inplace_add.15 ['$210build_tuple.10', '$218inplace_add.15', '_n_accepted_matrix']\n", - " $230build_tuple.19 = build_tuple(items=[Var(thermodynamic_state_j, replicaexchange.py:329), Var(thermodynamic_state_i, replicaexchange.py:328)]) ['$230build_tuple.19', 'thermodynamic_state_i', 'thermodynamic_state_j']\n", - " $234binary_subscr.22 = getitem(value=_n_accepted_matrix, index=$230build_tuple.19, fn=) ['$230build_tuple.19', '$234binary_subscr.22', '_n_accepted_matrix']\n", - " $const236.23 = const(int, 1) ['$const236.23']\n", - " $238inplace_add.24 = inplace_binop(fn=, immutable_fn=, lhs=$234binary_subscr.22, rhs=$const236.23, static_lhs=Undefined, static_rhs=Undefined) ['$234binary_subscr.22', '$238inplace_add.24', '$const236.23']\n", - " _n_accepted_matrix[$230build_tuple.19] = $238inplace_add.24 ['$230build_tuple.19', '$238inplace_add.24', '_n_accepted_matrix']\n", - " jump 10 []\n", - "label 246:\n", - " $const246.0 = const(NoneType, None) ['$const246.0']\n", - " $248return_value.1 = cast(value=$const246.0) ['$248return_value.1', '$const246.0']\n", - " return $248return_value.1 ['$248return_value.1']\n", - "\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "DEBUG:numba.core.ssa:==== SSA block analysis pass on 0\n", - "DEBUG:numba.core.ssa:Running \n", - "DEBUG:numba.core.ssa:on stmt: nswap_attempts = arg(0, name=nswap_attempts)\n", - "DEBUG:numba.core.ssa:on stmt: n_replicas = arg(1, name=n_replicas)\n", - "DEBUG:numba.core.ssa:on stmt: _replica_thermodynamic_states = arg(2, name=_replica_thermodynamic_states)\n", - "DEBUG:numba.core.ssa:on stmt: _energy_thermodynamic_states = arg(3, name=_energy_thermodynamic_states)\n", - "DEBUG:numba.core.ssa:on stmt: _n_accepted_matrix = arg(4, name=_n_accepted_matrix)\n", - "DEBUG:numba.core.ssa:on stmt: _n_proposed_matrix = arg(5, name=_n_proposed_matrix)\n", - "DEBUG:numba.core.ssa:on stmt: $2load_global.0 = global(range: )\n", - "DEBUG:numba.core.ssa:on stmt: $6call_function.2 = call $2load_global.0(nswap_attempts, func=$2load_global.0, args=[Var(nswap_attempts, replicaexchange.py:321)], kws=(), vararg=None)\n", - "DEBUG:numba.core.ssa:on stmt: $8get_iter.3 = getiter(value=$6call_function.2)\n", - "DEBUG:numba.core.ssa:on stmt: $phi10.0 = $8get_iter.3\n", - "DEBUG:numba.core.ssa:on stmt: jump 10\n", - "DEBUG:numba.core.ssa:==== SSA block analysis pass on 10\n", - "DEBUG:numba.core.ssa:Running \n", - "DEBUG:numba.core.ssa:on stmt: $10for_iter.1 = iternext(value=$phi10.0)\n", - "DEBUG:numba.core.ssa:on stmt: $10for_iter.2 = pair_first(value=$10for_iter.1)\n", - "DEBUG:numba.core.ssa:on stmt: $10for_iter.3 = pair_second(value=$10for_iter.1)\n", - "DEBUG:numba.core.ssa:on stmt: $phi12.1 = $10for_iter.2\n", - "DEBUG:numba.core.ssa:on stmt: branch $10for_iter.3, 12, 246\n", - "DEBUG:numba.core.ssa:==== SSA block analysis pass on 12\n", - "DEBUG:numba.core.ssa:Running \n", - "DEBUG:numba.core.ssa:on stmt: swap_attempt = $phi12.1\n", - "DEBUG:numba.core.ssa:on stmt: $14load_global.2 = global(np: )\n", - "DEBUG:numba.core.ssa:on stmt: $16load_attr.3 = getattr(value=$14load_global.2, attr=random)\n", - "DEBUG:numba.core.ssa:on stmt: $18load_method.4 = getattr(value=$16load_attr.3, attr=randint)\n", - "DEBUG:numba.core.ssa:on stmt: replica_i = call $18load_method.4(n_replicas, func=$18load_method.4, args=[Var(n_replicas, replicaexchange.py:321)], kws=(), vararg=None)\n", - "DEBUG:numba.core.ssa:on stmt: $26load_global.7 = global(np: )\n", - "DEBUG:numba.core.ssa:on stmt: $28load_attr.8 = getattr(value=$26load_global.7, attr=random)\n", - "DEBUG:numba.core.ssa:on stmt: $30load_method.9 = getattr(value=$28load_attr.8, attr=randint)\n", - "DEBUG:numba.core.ssa:on stmt: replica_j = call $30load_method.9(n_replicas, func=$30load_method.9, args=[Var(n_replicas, replicaexchange.py:321)], kws=(), vararg=None)\n", - "DEBUG:numba.core.ssa:on stmt: thermodynamic_state_i = getitem(value=_replica_thermodynamic_states, index=replica_i, fn=)\n", - "DEBUG:numba.core.ssa:on stmt: thermodynamic_state_j = getitem(value=_replica_thermodynamic_states, index=replica_j, fn=)\n", - "DEBUG:numba.core.ssa:on stmt: $60build_tuple.21 = build_tuple(items=[Var(replica_i, replicaexchange.py:324), Var(thermodynamic_state_j, replicaexchange.py:329)])\n", - "DEBUG:numba.core.ssa:on stmt: energy_ij = getitem(value=_energy_thermodynamic_states, index=$60build_tuple.21, fn=)\n", - "DEBUG:numba.core.ssa:on stmt: $72build_tuple.26 = build_tuple(items=[Var(replica_j, replicaexchange.py:325), Var(thermodynamic_state_i, replicaexchange.py:328)])\n", - "DEBUG:numba.core.ssa:on stmt: energy_ji = getitem(value=_energy_thermodynamic_states, index=$72build_tuple.26, fn=)\n", - "DEBUG:numba.core.ssa:on stmt: $84build_tuple.31 = build_tuple(items=[Var(replica_i, replicaexchange.py:324), Var(thermodynamic_state_i, replicaexchange.py:328)])\n", - "DEBUG:numba.core.ssa:on stmt: energy_ii = getitem(value=_energy_thermodynamic_states, index=$84build_tuple.31, fn=)\n", - "DEBUG:numba.core.ssa:on stmt: $96build_tuple.36 = build_tuple(items=[Var(replica_j, replicaexchange.py:325), Var(thermodynamic_state_j, replicaexchange.py:329)])\n", - "DEBUG:numba.core.ssa:on stmt: energy_jj = getitem(value=_energy_thermodynamic_states, index=$96build_tuple.36, fn=)\n", - "DEBUG:numba.core.ssa:on stmt: $106binary_add.40 = energy_ij + energy_ji\n", - "DEBUG:numba.core.ssa:on stmt: $108unary_negative.41 = unary(fn=, value=$106binary_add.40)\n", - "DEBUG:numba.core.ssa:on stmt: $112binary_add.43 = $108unary_negative.41 + energy_ii\n", - "DEBUG:numba.core.ssa:on stmt: log_p_accept = $112binary_add.43 + energy_jj\n", - "DEBUG:numba.core.ssa:on stmt: $126build_tuple.49 = build_tuple(items=[Var(thermodynamic_state_i, replicaexchange.py:328), Var(thermodynamic_state_j, replicaexchange.py:329)])\n", - "DEBUG:numba.core.ssa:on stmt: $130binary_subscr.52 = getitem(value=_n_proposed_matrix, index=$126build_tuple.49, fn=)\n", - "DEBUG:numba.core.ssa:on stmt: $const132.53 = const(int, 1)\n", - "DEBUG:numba.core.ssa:on stmt: $134inplace_add.54 = inplace_binop(fn=, immutable_fn=, lhs=$130binary_subscr.52, rhs=$const132.53, static_lhs=Undefined, static_rhs=Undefined)\n", - "DEBUG:numba.core.ssa:on stmt: _n_proposed_matrix[$126build_tuple.49] = $134inplace_add.54\n", - "DEBUG:numba.core.ssa:on stmt: $146build_tuple.58 = build_tuple(items=[Var(thermodynamic_state_j, replicaexchange.py:329), Var(thermodynamic_state_i, replicaexchange.py:328)])\n", - "DEBUG:numba.core.ssa:on stmt: $150binary_subscr.61 = getitem(value=_n_proposed_matrix, index=$146build_tuple.58, fn=)\n", - "DEBUG:numba.core.ssa:on stmt: $const152.62 = const(int, 1)\n", - "DEBUG:numba.core.ssa:on stmt: $154inplace_add.63 = inplace_binop(fn=, immutable_fn=, lhs=$150binary_subscr.61, rhs=$const152.62, static_lhs=Undefined, static_rhs=Undefined)\n", - "DEBUG:numba.core.ssa:on stmt: _n_proposed_matrix[$146build_tuple.58] = $154inplace_add.63\n", - "DEBUG:numba.core.ssa:on stmt: $const162.65 = const(float, 0.0)\n", - "DEBUG:numba.core.ssa:on stmt: $164compare_op.66 = log_p_accept >= $const162.65\n", - "DEBUG:numba.core.ssa:on stmt: bool166 = global(bool: )\n", - "DEBUG:numba.core.ssa:on stmt: $166pred = call bool166($164compare_op.66, func=bool166, args=(Var($164compare_op.66, replicaexchange.py:343),), kws=(), vararg=None)\n", - "DEBUG:numba.core.ssa:on stmt: branch $166pred, 188, 168\n", - "DEBUG:numba.core.ssa:==== SSA block analysis pass on 168\n", - "DEBUG:numba.core.ssa:Running \n", - "DEBUG:numba.core.ssa:on stmt: $168load_global.1 = global(np: )\n", - "DEBUG:numba.core.ssa:on stmt: $170load_attr.2 = getattr(value=$168load_global.1, attr=random)\n", - "DEBUG:numba.core.ssa:on stmt: $172load_method.3 = getattr(value=$170load_attr.2, attr=rand)\n", - "DEBUG:numba.core.ssa:on stmt: $174call_method.4 = call $172load_method.3(func=$172load_method.3, args=[], kws=(), vararg=None)\n", - "DEBUG:numba.core.ssa:on stmt: $176load_global.5 = global(np: )\n", - "DEBUG:numba.core.ssa:on stmt: $178load_method.6 = getattr(value=$176load_global.5, attr=exp)\n", - "DEBUG:numba.core.ssa:on stmt: $182call_method.8 = call $178load_method.6(log_p_accept, func=$178load_method.6, args=[Var(log_p_accept, replicaexchange.py:336)], kws=(), vararg=None)\n", - "DEBUG:numba.core.ssa:on stmt: $184compare_op.9 = $174call_method.4 < $182call_method.8\n", - "DEBUG:numba.core.ssa:on stmt: bool186 = global(bool: )\n", - "DEBUG:numba.core.ssa:on stmt: $186pred = call bool186($184compare_op.9, func=bool186, args=(Var($184compare_op.9, replicaexchange.py:343),), kws=(), vararg=None)\n", - "DEBUG:numba.core.ssa:on stmt: branch $186pred, 188, 247\n", - "DEBUG:numba.core.ssa:==== SSA block analysis pass on 188\n", - "DEBUG:numba.core.ssa:Running \n", - "DEBUG:numba.core.ssa:on stmt: _replica_thermodynamic_states[replica_i] = thermodynamic_state_j\n", - "DEBUG:numba.core.ssa:on stmt: _replica_thermodynamic_states[replica_j] = thermodynamic_state_i\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "DEBUG:numba.core.ssa:on stmt: $210build_tuple.10 = build_tuple(items=[Var(thermodynamic_state_i, replicaexchange.py:328), Var(thermodynamic_state_j, replicaexchange.py:329)])\n", - "DEBUG:numba.core.ssa:on stmt: $214binary_subscr.13 = getitem(value=_n_accepted_matrix, index=$210build_tuple.10, fn=)\n", - "DEBUG:numba.core.ssa:on stmt: $const216.14 = const(int, 1)\n", - "DEBUG:numba.core.ssa:on stmt: $218inplace_add.15 = inplace_binop(fn=, immutable_fn=, lhs=$214binary_subscr.13, rhs=$const216.14, static_lhs=Undefined, static_rhs=Undefined)\n", - "DEBUG:numba.core.ssa:on stmt: _n_accepted_matrix[$210build_tuple.10] = $218inplace_add.15\n", - "DEBUG:numba.core.ssa:on stmt: $230build_tuple.19 = build_tuple(items=[Var(thermodynamic_state_j, replicaexchange.py:329), Var(thermodynamic_state_i, replicaexchange.py:328)])\n", - "DEBUG:numba.core.ssa:on stmt: $234binary_subscr.22 = getitem(value=_n_accepted_matrix, index=$230build_tuple.19, fn=)\n", - "DEBUG:numba.core.ssa:on stmt: $const236.23 = const(int, 1)\n", - "DEBUG:numba.core.ssa:on stmt: $238inplace_add.24 = inplace_binop(fn=, immutable_fn=, lhs=$234binary_subscr.22, rhs=$const236.23, static_lhs=Undefined, static_rhs=Undefined)\n", - "DEBUG:numba.core.ssa:on stmt: _n_accepted_matrix[$230build_tuple.19] = $238inplace_add.24\n", - "DEBUG:numba.core.ssa:on stmt: jump 247\n", - "DEBUG:numba.core.ssa:==== SSA block analysis pass on 246\n", - "DEBUG:numba.core.ssa:Running \n", - "DEBUG:numba.core.ssa:on stmt: $const246.0 = const(NoneType, None)\n", - "DEBUG:numba.core.ssa:on stmt: $248return_value.1 = cast(value=$const246.0)\n", - "DEBUG:numba.core.ssa:on stmt: return $248return_value.1\n", - "DEBUG:numba.core.ssa:==== SSA block analysis pass on 247\n", - "DEBUG:numba.core.ssa:Running \n", - "DEBUG:numba.core.ssa:on stmt: jump 10\n", - "DEBUG:numba.core.ssa:defs defaultdict(,\n", - " {'$106binary_add.40': [],\n", - " '$108unary_negative.41': [],\n", - " '$10for_iter.1': [],\n", - " '$10for_iter.2': [],\n", - " '$10for_iter.3': [],\n", - " '$112binary_add.43': [],\n", - " '$126build_tuple.49': [],\n", - " '$130binary_subscr.52': [],\n", - " '$134inplace_add.54': [],\n", - " '$146build_tuple.58': [],\n", - " '$14load_global.2': [],\n", - " '$150binary_subscr.61': [],\n", - " '$154inplace_add.63': [],\n", - " '$164compare_op.66': [],\n", - " '$166pred': [],\n", - " '$168load_global.1': [],\n", - " '$16load_attr.3': [],\n", - " '$170load_attr.2': [],\n", - " '$172load_method.3': [],\n", - " '$174call_method.4': [],\n", - " '$176load_global.5': [],\n", - " '$178load_method.6': [],\n", - " '$182call_method.8': [],\n", - " '$184compare_op.9': [],\n", - " '$186pred': [],\n", - " '$18load_method.4': [],\n", - " '$210build_tuple.10': [],\n", - " '$214binary_subscr.13': [],\n", - " '$218inplace_add.15': [],\n", - " '$230build_tuple.19': [],\n", - " '$234binary_subscr.22': [],\n", - " '$238inplace_add.24': [],\n", - " '$248return_value.1': [],\n", - " '$26load_global.7': [],\n", - " '$28load_attr.8': [],\n", - " '$2load_global.0': [],\n", - " '$30load_method.9': [],\n", - " '$60build_tuple.21': [],\n", - " '$6call_function.2': [],\n", - " '$72build_tuple.26': [],\n", - " '$84build_tuple.31': [],\n", - " '$8get_iter.3': [],\n", - " '$96build_tuple.36': [],\n", - " '$const132.53': [],\n", - " '$const152.62': [],\n", - " '$const162.65': [],\n", - " '$const216.14': [],\n", - " '$const236.23': [],\n", - " '$const246.0': [],\n", - " '$phi10.0': [],\n", - " '$phi12.1': [],\n", - " '_energy_thermodynamic_states': [],\n", - " '_n_accepted_matrix': [],\n", - " '_n_proposed_matrix': [],\n", - " '_replica_thermodynamic_states': [],\n", - " 'bool166': [],\n", - " 'bool186': [],\n", - " 'energy_ii': [],\n", - " 'energy_ij': [],\n", - " 'energy_ji': [],\n", - " 'energy_jj': [],\n", - " 'log_p_accept': [],\n", - " 'n_replicas': [],\n", - " 'nswap_attempts': [],\n", - " 'replica_i': [],\n", - " 'replica_j': [],\n", - " 'swap_attempt': [],\n", - " 'thermodynamic_state_i': [],\n", - " 'thermodynamic_state_j': []})\n", - "DEBUG:numba.core.ssa:SSA violators set()\n", - "DEBUG:numba.core.byteflow:bytecode dump:\n", - "> 0\tNOP(arg=None, lineno=1319)\n", - " 2\tLOAD_GLOBAL(arg=0, lineno=1319)\n", - " 4\tLOAD_ATTR(arg=1, lineno=1319)\n", - " 6\tLOAD_METHOD(arg=1, lineno=1319)\n", - " 8\tCALL_METHOD(arg=0, lineno=1319)\n", - " 10\tRETURN_VALUE(arg=None, lineno=1319)\n", - "DEBUG:numba.core.byteflow:pending: deque([State(pc_initial=0 nstack_initial=0)])\n", - "DEBUG:numba.core.byteflow:stack: []\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "DEBUG:numba.core.byteflow:dispatch pc=0, inst=NOP(arg=None, lineno=1319)\n", - "DEBUG:numba.core.byteflow:stack []\n", - "DEBUG:numba.core.byteflow:dispatch pc=2, inst=LOAD_GLOBAL(arg=0, lineno=1319)\n", - "DEBUG:numba.core.byteflow:stack []\n", - "DEBUG:numba.core.byteflow:dispatch pc=4, inst=LOAD_ATTR(arg=1, lineno=1319)\n", - "DEBUG:numba.core.byteflow:stack ['$2load_global.0']\n", - "DEBUG:numba.core.byteflow:dispatch pc=6, inst=LOAD_METHOD(arg=1, lineno=1319)\n", - "DEBUG:numba.core.byteflow:stack ['$4load_attr.1']\n", - "DEBUG:numba.core.byteflow:dispatch pc=8, inst=CALL_METHOD(arg=0, lineno=1319)\n", - "DEBUG:numba.core.byteflow:stack ['$6load_method.2']\n", - "DEBUG:numba.core.byteflow:dispatch pc=10, inst=RETURN_VALUE(arg=None, lineno=1319)\n", - "DEBUG:numba.core.byteflow:stack ['$8call_method.3']\n", - "DEBUG:numba.core.byteflow:end state. edges=[]\n", - "DEBUG:numba.core.byteflow:-------------------------Prune PHIs-------------------------\n", - "DEBUG:numba.core.byteflow:Used_phis: defaultdict(, {State(pc_initial=0 nstack_initial=0): set()})\n", - "DEBUG:numba.core.byteflow:defmap: {}\n", - "DEBUG:numba.core.byteflow:phismap: defaultdict(, {})\n", - "DEBUG:numba.core.byteflow:changing phismap: defaultdict(, {})\n", - "DEBUG:numba.core.byteflow:keep phismap: {}\n", - "DEBUG:numba.core.byteflow:new_out: defaultdict(, {})\n", - "DEBUG:numba.core.byteflow:----------------------DONE Prune PHIs-----------------------\n", - "DEBUG:numba.core.byteflow:block_infos State(pc_initial=0 nstack_initial=0):\n", - "AdaptBlockInfo(insts=((0, {}), (2, {'res': '$2load_global.0'}), (4, {'item': '$2load_global.0', 'res': '$4load_attr.1'}), (6, {'item': '$4load_attr.1', 'res': '$6load_method.2'}), (8, {'func': '$6load_method.2', 'args': [], 'res': '$8call_method.3'}), (10, {'retval': '$8call_method.3', 'castval': '$10return_value.4'})), outgoing_phis={}, blockstack=(), active_try_block=None, outgoing_edgepushed={})\n", - "DEBUG:numba.core.interpreter:label 0:\n", - " size = arg(0, name=size) ['size']\n", - " $2load_global.0 = global(np: ) ['$2load_global.0']\n", - " $4load_attr.1 = getattr(value=$2load_global.0, attr=random) ['$2load_global.0', '$4load_attr.1']\n", - " $6load_method.2 = getattr(value=$4load_attr.1, attr=random) ['$4load_attr.1', '$6load_method.2']\n", - " $8call_method.3 = call $6load_method.2(func=$6load_method.2, args=[], kws=(), vararg=None) ['$6load_method.2', '$8call_method.3']\n", - " $10return_value.4 = cast(value=$8call_method.3) ['$10return_value.4', '$8call_method.3']\n", - " return $10return_value.4 ['$10return_value.4']\n", - "\n", - "DEBUG:numba.core.ssa:==== SSA block analysis pass on 0\n", - "DEBUG:numba.core.ssa:Running \n", - "DEBUG:numba.core.ssa:on stmt: size = arg(0, name=size)\n", - "DEBUG:numba.core.ssa:on stmt: $2load_global.0 = global(np: )\n", - "DEBUG:numba.core.ssa:on stmt: $4load_attr.1 = getattr(value=$2load_global.0, attr=random)\n", - "DEBUG:numba.core.ssa:on stmt: $6load_method.2 = getattr(value=$4load_attr.1, attr=random)\n", - "DEBUG:numba.core.ssa:on stmt: $8call_method.3 = call $6load_method.2(func=$6load_method.2, args=[], kws=(), vararg=None)\n", - "DEBUG:numba.core.ssa:on stmt: $10return_value.4 = cast(value=$8call_method.3)\n", - "DEBUG:numba.core.ssa:on stmt: return $10return_value.4\n", - "DEBUG:numba.core.ssa:defs defaultdict(,\n", - " {'$10return_value.4': [],\n", - " '$2load_global.0': [],\n", - " '$4load_attr.1': [],\n", - " '$6load_method.2': [],\n", - " '$8call_method.3': [],\n", - " 'size': []})\n", - "DEBUG:numba.core.ssa:SSA violators set()\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.453s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 1122/2662 attempted swaps (42.1%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 30.600s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion (of equilibration only) in 0:02:02.404464, at Mon Oct 31 10:17:38 2022 (consuming total wall clock time 0:02:33.005580).\n", - "DEBUG:openmmtools.multistate.multistatesampler:Equilibration iteration 2/5\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 28.298s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.241s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 988/2662 attempted swaps (37.1%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 28.543s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion (of equilibration only) in 0:01:28.716848, at Mon Oct 31 10:17:33 2022 (consuming total wall clock time 0:02:27.861413).\n", - "DEBUG:openmmtools.multistate.multistatesampler:Equilibration iteration 3/5\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 28.157s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.239s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 902/2662 attempted swaps (33.9%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 28.400s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion (of equilibration only) in 0:00:58.364157, at Mon Oct 31 10:17:31 2022 (consuming total wall clock time 0:02:25.910391).\n", - "DEBUG:openmmtools.multistate.multistatesampler:Equilibration iteration 4/5\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 28.507s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.250s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 936/2662 attempted swaps (35.2%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 28.761s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion (of equilibration only) in 0:00:29.077179, at Mon Oct 31 10:17:30 2022 (consuming total wall clock time 0:02:25.385897).\n", - "DEBUG:openmmtools.multistate.multistatesampler:Equilibration iteration 5/5\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 29.016s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.275s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 1200/2662 attempted swaps (45.1%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 29.295s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion (of equilibration only) in 0:00:00, at Mon Oct 31 10:17:30 2022 (consuming total wall clock time 0:02:25.606120).\n", - "DEBUG:openmmtools.utils:Equilibration Iteration took 29.295s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing >\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.117s\n", - "INFO:openfe.protocols.openmm_rbfe.equil_rbfe_methods:running production phase\n", - "DEBUG:mpiplus.mpiplus:Single node: executing >\n", - "DEBUG:openmmtools.multistate.multistatesampler:Storing general ReplicaExchange options...\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.274s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing >\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 1/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 1244/2662 attempted swaps (46.7%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 29.701s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.261s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 1 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.119s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.868s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 30.837s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:12:20.117363, at 2022-Oct-31-10:30:22 (consuming total wall clock time 0:12:50.955586).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 2/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 1260/2662 attempted swaps (47.3%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 29.441s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.265s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 2 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.098s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.344s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 30.056s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:11:40.331653, at 2022-Oct-31-10:30:12 (consuming total wall clock time 0:12:41.230057).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 3/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 770/2662 attempted swaps (28.9%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 29.594s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.289s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 3 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.110s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.348s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 30.235s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:11:08.343457, at 2022-Oct-31-10:30:10 (consuming total wall clock time 0:12:39.481202).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 4/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 868/2662 attempted swaps (32.6%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 29.018s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.281s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 4 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.096s\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.344s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 29.648s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:10:34.141374, at 2022-Oct-31-10:30:06 (consuming total wall clock time 0:12:34.930207).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 5/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 820/2662 attempted swaps (30.8%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 29.187s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.300s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 5 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.105s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.349s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 29.844s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:10:02.551180, at 2022-Oct-31-10:30:04 (consuming total wall clock time 0:12:33.188975).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 6/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 854/2662 attempted swaps (32.1%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 29.775s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.300s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 6 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.102s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.347s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 30.428s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:09:33.390690, at 2022-Oct-31-10:30:05 (consuming total wall clock time 0:12:34.461434).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 7/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 800/2662 attempted swaps (30.1%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 31.237s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.297s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 7 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.096s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.332s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 31.871s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:09:07.571104, at 2022-Oct-31-10:30:11 (consuming total wall clock time 0:12:40.515422).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 8/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 1036/2662 attempted swaps (38.9%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 30.173s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.343s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 8 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.132s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.371s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 30.892s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:08:38.158439, at 2022-Oct-31-10:30:13 (consuming total wall clock time 0:12:41.997704).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 9/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 1198/2662 attempted swaps (45.0%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 32.059s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.304s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 9 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.098s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.342s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 32.710s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:08:11.649440, at 2022-Oct-31-10:30:19 (consuming total wall clock time 0:12:48.202251).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 10/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 1188/2662 attempted swaps (44.6%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 32.208s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.295s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 10 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.099s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.344s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 32.853s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:07:44.112668, at 2022-Oct-31-10:30:24 (consuming total wall clock time 0:12:53.521113).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 11/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 1006/2662 attempted swaps (37.8%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 29.541s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.356s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 11 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.105s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.350s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 30.253s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:07:12.301529, at 2022-Oct-31-10:30:23 (consuming total wall clock time 0:12:51.967015).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 12/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 1024/2662 attempted swaps (38.5%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 29.361s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.346s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 12 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.097s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.333s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 30.044s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:06:40.521211, at 2022-Oct-31-10:30:21 (consuming total wall clock time 0:12:50.233098).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 13/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 922/2662 attempted swaps (34.6%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 29.026s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.350s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 13 not on the Checkpoint Interval of 50. Sampler State not written.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "DEBUG:openmmtools.utils:Storing sampler states took 0.099s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.337s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 29.717s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:06:08.706383, at 2022-Oct-31-10:30:19 (consuming total wall clock time 0:12:48.138298).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 14/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 972/2662 attempted swaps (36.5%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 30.121s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.387s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 14 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.134s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.373s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 30.886s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:05:38.109140, at 2022-Oct-31-10:30:19 (consuming total wall clock time 0:12:48.429864).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 15/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 788/2662 attempted swaps (29.6%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 31.233s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.375s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 15 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.106s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.344s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 31.960s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:05:08.190162, at 2022-Oct-31-10:30:21 (consuming total wall clock time 0:12:50.475404).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 16/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 1084/2662 attempted swaps (40.7%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 32.443s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.398s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 16 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.109s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.354s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 33.201s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:04:38.712590, at 2022-Oct-31-10:30:25 (consuming total wall clock time 0:12:54.201640).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 17/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 1030/2662 attempted swaps (38.7%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 33.534s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.407s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 17 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.109s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.359s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 34.306s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:04:09.317511, at 2022-Oct-31-10:30:30 (consuming total wall clock time 0:12:59.117222).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 18/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 1016/2662 attempted swaps (38.2%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 33.504s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.382s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 18 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.102s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.377s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 34.268s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:03:39.360919, at 2022-Oct-31-10:30:34 (consuming total wall clock time 0:13:03.431853).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 19/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 770/2662 attempted swaps (28.9%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 29.105s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.407s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 19 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.109s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.364s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 29.882s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:03:07.564919, at 2022-Oct-31-10:30:32 (consuming total wall clock time 0:13:01.520494).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 20/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 1052/2662 attempted swaps (39.5%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 29.470s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.389s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 20 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.121s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.363s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 30.228s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:02:36.046702, at 2022-Oct-31-10:30:31 (consuming total wall clock time 0:13:00.233509).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 21/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 894/2662 attempted swaps (33.6%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 30.314s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.397s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 21 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.101s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.338s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 31.055s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:02:04.808719, at 2022-Oct-31-10:30:31 (consuming total wall clock time 0:13:00.054491).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 22/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 882/2662 attempted swaps (33.1%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 31.366s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.378s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 22 not on the Checkpoint Interval of 50. Sampler State not written.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "DEBUG:openmmtools.utils:Storing sampler states took 0.114s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.364s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 32.115s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:01:33.731434, at 2022-Oct-31-10:30:32 (consuming total wall clock time 0:13:01.095285).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 23/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 968/2662 attempted swaps (36.4%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 32.082s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.411s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 23 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.103s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.361s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 32.861s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:01:02.628591, at 2022-Oct-31-10:30:34 (consuming total wall clock time 0:13:02.857389).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 24/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 954/2662 attempted swaps (35.8%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 33.673s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.446s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 24 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.130s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.407s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 34.532s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:00:31.448530, at 2022-Oct-31-10:30:37 (consuming total wall clock time 0:13:06.213248).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 25/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 1130/2662 attempted swaps (42.4%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 34.339s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.412s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 25 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.126s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.375s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 35.130s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:00:00, at 2022-Oct-31-10:30:41 (consuming total wall clock time 0:13:09.897467).\n", - "INFO:openfe.protocols.openmm_rbfe.equil_rbfe_methods:creating hybrid system\n", - "INFO:openmmforcefields.generators.template_generators:Requested to generate parameters for residue \n", - "INFO:openmmforcefields.generators.template_generators:Generating a residue template for [H]c1c(c(c(c(c1[H])[H])[H])[H])[H] using openff-2.0.0.offxml\n", - "DEBUG:openmmforcefields.generators.template_generators:Generating parameters...\n", - "DEBUG:mpiplus.mpiplus:Single node: executing >\n", - "INFO:openmmforcefields.generators.template_generators:Requested to generate parameters for residue \n", - "INFO:openmmforcefields.generators.template_generators:Generating a residue template for [H]c1c(c(c(c(c1[H])[H])O[H])[H])[H] using openff-2.0.0.offxml\n", - "DEBUG:openmmforcefields.generators.template_generators:Generating parameters...\n", - "INFO:openfe.protocols.openmm_rbfe._rbfe_utils.relative:creating hybrid system\n", - "INFO:openfe.protocols.openmm_rbfe._rbfe_utils.relative:setting force field terms\n", - "INFO:openfe.protocols.openmm_rbfe._rbfe_utils.relative:adding forces\n", - "INFO:openfe.protocols.openmm_rbfe._rbfe_utils.relative:DONE\n", - "WARNING:openmmtools.multistate.multistatereporter:Warning: The openmmtools.multistate API is experimental and may change in future releases\n", - "WARNING:openmmtools.multistate.multistatesampler:Warning: The openmmtools.multistate API is experimental and may change in future releases\n", - "DEBUG:openmmtools.multistate.multistatesampler:CUDA devices available: (['0', ' NVIDIA GeForce GTX 1660 Ti'],)\n", - "DEBUG:mpiplus.mpiplus:Single node: executing >\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Please cite the following:\n", - "\n", - " Friedrichs MS, Eastman P, Vaidyanathan V, Houston M, LeGrand S, Beberg AL, Ensign DL, Bruns CM, and Pande VS. Accelerating molecular dynamic simulations on graphics processing unit. J. Comput. Chem. 30:864, 2009. DOI: 10.1002/jcc.21209\n", - " Eastman P and Pande VS. OpenMM: A hardware-independent framework for molecular simulations. Comput. Sci. Eng. 12:34, 2010. DOI: 10.1109/MCSE.2010.27\n", - " Eastman P and Pande VS. Efficient nonbonded interactions for molecular dynamics on a graphics processing unit. J. Comput. Chem. 31:1268, 2010. DOI: 10.1002/jcc.21413\n", - " Eastman P and Pande VS. Constant constraint matrix approximation: A robust, parallelizable constraint method for molecular simulations. J. Chem. Theor. Comput. 6:434, 2010. DOI: 10.1021/ct900463w\n", - " Chodera JD and Shirts MR. Replica exchange and expanded ensemble simulations as Gibbs multistate: Simple improvements for enhanced mixing. J. Chem. Phys., 135:194110, 2011. DOI:10.1063/1.3660669\n", - " \n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "DEBUG:openmmtools.multistate.multistatereporter:Serialized state thermodynamic_states/0 is 1009455B | 985.796KB | 0.963MB\n", - "DEBUG:openmmtools.utils:Storing thermodynamic states took 1.361s\n", - "DEBUG:openmmtools.multistate.multistatesampler:Storing general ReplicaExchange options...\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.079s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.396s\n", - "INFO:openfe.protocols.openmm_rbfe.equil_rbfe_methods:minimizing systems\n", - "DEBUG:openmmtools.multistate.multistatesampler:Minimizing all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _minimize_replica serially.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 1/11: initial energy -129037.204kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Using FIRE: tolerance 1.0 kJ/(nm mol) max_iterations 10000\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 1/11: final energy -238293.969kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 2/11: initial energy -129038.772kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Using FIRE: tolerance 1.0 kJ/(nm mol) max_iterations 10000\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 2/11: final energy -239609.215kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 3/11: initial energy -129039.323kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Using FIRE: tolerance 1.0 kJ/(nm mol) max_iterations 10000\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 3/11: final energy -241611.937kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 4/11: initial energy -129037.018kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Using FIRE: tolerance 1.0 kJ/(nm mol) max_iterations 10000\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 4/11: final energy -240282.562kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 5/11: initial energy -129022.761kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Using FIRE: tolerance 1.0 kJ/(nm mol) max_iterations 10000\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 5/11: final energy -237167.602kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 6/11: initial energy -128886.417kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Using FIRE: tolerance 1.0 kJ/(nm mol) max_iterations 10000\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 6/11: final energy -239368.944kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 7/11: initial energy -128889.181kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Using FIRE: tolerance 1.0 kJ/(nm mol) max_iterations 10000\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 7/11: final energy -239002.343kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 8/11: initial energy -128888.967kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Using FIRE: tolerance 1.0 kJ/(nm mol) max_iterations 10000\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 8/11: final energy -237957.209kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 9/11: initial energy -128887.065kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Using FIRE: tolerance 1.0 kJ/(nm mol) max_iterations 10000\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 9/11: final energy -241928.833kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 10/11: initial energy -128884.619kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Using FIRE: tolerance 1.0 kJ/(nm mol) max_iterations 10000\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 10/11: final energy -241536.823kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 11/11: initial energy -128881.828kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Using FIRE: tolerance 1.0 kJ/(nm mol) max_iterations 10000\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 11/11: final energy -239345.683kT\n", - "DEBUG:mpiplus.mpiplus:Single node: executing >\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.109s\n", - "DEBUG:openmmtools.utils:Minimizing all replicas took 74.139s\n", - "INFO:openfe.protocols.openmm_rbfe.equil_rbfe_methods:equilibrating systems\n", - "DEBUG:openmmtools.multistate.multistatesampler:Equilibration iteration 1/5\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 29.823s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.247s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 742/2662 attempted swaps (27.9%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 30.074s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion (of equilibration only) in 0:02:00.300407, at Mon Oct 31 10:34:59 2022 (consuming total wall clock time 0:02:30.375509).\n", - "DEBUG:openmmtools.multistate.multistatesampler:Equilibration iteration 2/5\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 28.731s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.246s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 964/2662 attempted swaps (36.2%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 28.980s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion (of equilibration only) in 0:01:28.585587, at Mon Oct 31 10:34:56 2022 (consuming total wall clock time 0:02:27.642645).\n", - "DEBUG:openmmtools.multistate.multistatesampler:Equilibration iteration 3/5\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 28.988s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.284s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 930/2662 attempted swaps (34.9%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 29.277s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion (of equilibration only) in 0:00:58.890074, at Mon Oct 31 10:34:56 2022 (consuming total wall clock time 0:02:27.225184).\n", - "DEBUG:openmmtools.multistate.multistatesampler:Equilibration iteration 4/5\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 28.942s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.281s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 928/2662 attempted swaps (34.9%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 29.228s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion (of equilibration only) in 0:00:29.391095, at Mon Oct 31 10:34:55 2022 (consuming total wall clock time 0:02:26.955475).\n", - "DEBUG:openmmtools.multistate.multistatesampler:Equilibration iteration 5/5\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 29.646s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.254s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 1114/2662 attempted swaps (41.8%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 29.904s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion (of equilibration only) in 0:00:00, at Mon Oct 31 10:34:56 2022 (consuming total wall clock time 0:02:27.470073).\n", - "DEBUG:openmmtools.utils:Equilibration Iteration took 29.904s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing >\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.116s\n", - "INFO:openfe.protocols.openmm_rbfe.equil_rbfe_methods:running production phase\n", - "DEBUG:mpiplus.mpiplus:Single node: executing >\n", - "DEBUG:openmmtools.multistate.multistatesampler:Storing general ReplicaExchange options...\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.252s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing >\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 1/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 1140/2662 attempted swaps (42.8%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 29.924s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.259s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 1 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.095s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.791s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 30.980s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:12:23.555986, at 2022-Oct-31-10:47:51 (consuming total wall clock time 0:12:54.537486).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 2/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 1092/2662 attempted swaps (41.0%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 29.973s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.266s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 2 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.115s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.359s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 30.603s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:11:48.270064, at 2022-Oct-31-10:47:46 (consuming total wall clock time 0:12:49.858766).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 3/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 1064/2662 attempted swaps (40.0%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 29.819s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.282s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 3 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.103s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.364s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 30.472s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:11:15.152214, at 2022-Oct-31-10:47:43 (consuming total wall clock time 0:12:47.218425).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 4/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 810/2662 attempted swaps (30.4%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 28.573s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.288s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 4 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.112s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.358s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 29.225s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:10:36.794207, at 2022-Oct-31-10:47:34 (consuming total wall clock time 0:12:38.088341).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 5/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 1016/2662 attempted swaps (38.2%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 29.107s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.300s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 5 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.099s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.337s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 29.750s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:10:04.190229, at 2022-Oct-31-10:47:31 (consuming total wall clock time 0:12:35.237787).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 6/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 880/2662 attempted swaps (33.1%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 29.682s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.278s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 6 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.101s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.338s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 30.304s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:09:34.287104, at 2022-Oct-31-10:47:32 (consuming total wall clock time 0:12:35.640926).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 7/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 1114/2662 attempted swaps (41.8%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 29.934s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.303s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 7 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.110s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.347s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 30.589s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:09:05.004802, at 2022-Oct-31-10:47:33 (consuming total wall clock time 0:12:36.951113).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 8/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 934/2662 attempted swaps (35.1%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 31.240s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.288s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 8 not on the Checkpoint Interval of 50. Sampler State not written.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "DEBUG:openmmtools.utils:Storing sampler states took 0.100s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.337s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 31.870s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:08:38.116521, at 2022-Oct-31-10:47:38 (consuming total wall clock time 0:12:41.936060).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 9/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 1144/2662 attempted swaps (43.0%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 31.217s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.319s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 9 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.121s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.371s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 31.911s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:08:10.193385, at 2022-Oct-31-10:47:42 (consuming total wall clock time 0:12:45.927164).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 10/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 964/2662 attempted swaps (36.2%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 32.190s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.308s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 10 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.143s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.398s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 32.904s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:07:42.962999, at 2022-Oct-31-10:47:48 (consuming total wall clock time 0:12:51.604998).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 11/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 1060/2662 attempted swaps (39.8%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 30.259s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.383s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 11 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.110s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.352s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 30.999s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:07:12.274450, at 2022-Oct-31-10:47:48 (consuming total wall clock time 0:12:51.918660).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 12/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 920/2662 attempted swaps (34.6%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 33.351s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.469s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 12 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.136s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.401s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 34.227s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:06:45.030835, at 2022-Oct-31-10:47:55 (consuming total wall clock time 0:12:58.905452).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 13/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 896/2662 attempted swaps (33.7%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 34.008s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.422s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 13 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.129s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.385s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 34.821s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:06:17.263622, at 2022-Oct-31-10:48:02 (consuming total wall clock time 0:13:05.965878).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 14/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 1016/2662 attempted swaps (38.2%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 34.755s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.431s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 14 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.111s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.377s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 35.570s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:05:49.073752, at 2022-Oct-31-10:48:10 (consuming total wall clock time 0:13:13.349435).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 15/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 1144/2662 attempted swaps (43.0%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 36.061s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.477s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 15 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.152s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.419s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 36.964s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:05:20.829376, at 2022-Oct-31-10:48:18 (consuming total wall clock time 0:13:22.073439).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 16/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 952/2662 attempted swaps (35.8%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 36.738s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.451s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 16 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.131s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.400s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 37.594s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:04:51.847957, at 2022-Oct-31-10:48:27 (consuming total wall clock time 0:13:30.688768).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 17/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 1134/2662 attempted swaps (42.6%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 35.856s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.367s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 17 not on the Checkpoint Interval of 50. Sampler State not written.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "DEBUG:openmmtools.utils:Storing sampler states took 0.098s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.338s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 36.566s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:04:21.369283, at 2022-Oct-31-10:48:33 (consuming total wall clock time 0:13:36.779010).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 18/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 1246/2662 attempted swaps (46.8%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 32.877s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.393s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 18 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.133s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.400s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 33.677s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:03:49.090699, at 2022-Oct-31-10:48:34 (consuming total wall clock time 0:13:38.181069).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 19/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 868/2662 attempted swaps (32.6%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 30.031s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.386s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 19 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.115s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.358s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 30.781s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:03:15.750344, at 2022-Oct-31-10:48:32 (consuming total wall clock time 0:13:35.626435).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 20/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 1050/2662 attempted swaps (39.4%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 31.781s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.486s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 20 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.147s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.409s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 32.681s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:02:43.140284, at 2022-Oct-31-10:48:32 (consuming total wall clock time 0:13:35.701418).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 21/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 964/2662 attempted swaps (36.2%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 34.024s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.487s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 21 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.131s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.408s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 34.928s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:02:10.951461, at 2022-Oct-31-10:48:35 (consuming total wall clock time 0:13:38.446632).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 22/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 918/2662 attempted swaps (34.5%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 34.571s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.461s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 22 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.151s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.428s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 35.467s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:01:38.586509, at 2022-Oct-31-10:48:38 (consuming total wall clock time 0:13:41.554242).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 23/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 872/2662 attempted swaps (32.8%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 35.828s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.481s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 23 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.140s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.409s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 36.726s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:01:06.060658, at 2022-Oct-31-10:48:42 (consuming total wall clock time 0:13:45.758221).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 24/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 1426/2662 attempted swaps (53.6%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 35.639s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.401s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 24 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.109s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.354s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 36.399s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:00:33.170872, at 2022-Oct-31-10:48:46 (consuming total wall clock time 0:13:49.271806).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 25/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 898/2662 attempted swaps (33.7%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 34.606s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.398s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 25 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.133s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.386s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 35.395s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:00:00, at 2022-Oct-31-10:48:48 (consuming total wall clock time 0:13:51.498585).\n", - "INFO:openfe.protocols.openmm_rbfe.equil_rbfe_methods:creating hybrid system\n", - "INFO:openmmforcefields.generators.template_generators:Requested to generate parameters for residue \n", - "INFO:openmmforcefields.generators.template_generators:Generating a residue template for [H]c1c(c(c(c(c1[H])[H])[H])[H])[H] using openff-2.0.0.offxml\n", - "DEBUG:openmmforcefields.generators.template_generators:Generating parameters...\n", - "DEBUG:mpiplus.mpiplus:Single node: executing >\n", - "INFO:openmmforcefields.generators.template_generators:Requested to generate parameters for residue \n", - "INFO:openmmforcefields.generators.template_generators:Generating a residue template for [H]c1c(c(c(c(c1[H])[H])O[H])[H])[H] using openff-2.0.0.offxml\n", - "DEBUG:openmmforcefields.generators.template_generators:Generating parameters...\n", - "INFO:openfe.protocols.openmm_rbfe._rbfe_utils.relative:creating hybrid system\n", - "INFO:openfe.protocols.openmm_rbfe._rbfe_utils.relative:setting force field terms\n", - "INFO:openfe.protocols.openmm_rbfe._rbfe_utils.relative:adding forces\n", - "INFO:openfe.protocols.openmm_rbfe._rbfe_utils.relative:DONE\n", - "WARNING:openmmtools.multistate.multistatereporter:Warning: The openmmtools.multistate API is experimental and may change in future releases\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:openmmtools.multistate.multistatesampler:Warning: The openmmtools.multistate API is experimental and may change in future releases\n", - "DEBUG:openmmtools.multistate.multistatesampler:CUDA devices available: (['0', ' NVIDIA GeForce GTX 1660 Ti'],)\n", - "DEBUG:mpiplus.mpiplus:Single node: executing >\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Please cite the following:\n", - "\n", - " Friedrichs MS, Eastman P, Vaidyanathan V, Houston M, LeGrand S, Beberg AL, Ensign DL, Bruns CM, and Pande VS. Accelerating molecular dynamic simulations on graphics processing unit. J. Comput. Chem. 30:864, 2009. DOI: 10.1002/jcc.21209\n", - " Eastman P and Pande VS. OpenMM: A hardware-independent framework for molecular simulations. Comput. Sci. Eng. 12:34, 2010. DOI: 10.1109/MCSE.2010.27\n", - " Eastman P and Pande VS. Efficient nonbonded interactions for molecular dynamics on a graphics processing unit. J. Comput. Chem. 31:1268, 2010. DOI: 10.1002/jcc.21413\n", - " Eastman P and Pande VS. Constant constraint matrix approximation: A robust, parallelizable constraint method for molecular simulations. J. Chem. Theor. Comput. 6:434, 2010. DOI: 10.1021/ct900463w\n", - " Chodera JD and Shirts MR. Replica exchange and expanded ensemble simulations as Gibbs multistate: Simple improvements for enhanced mixing. J. Chem. Phys., 135:194110, 2011. DOI:10.1063/1.3660669\n", - " \n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "DEBUG:openmmtools.multistate.multistatereporter:Serialized state thermodynamic_states/0 is 1009455B | 985.796KB | 0.963MB\n", - "DEBUG:openmmtools.utils:Storing thermodynamic states took 1.321s\n", - "DEBUG:openmmtools.multistate.multistatesampler:Storing general ReplicaExchange options...\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.066s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.354s\n", - "INFO:openfe.protocols.openmm_rbfe.equil_rbfe_methods:minimizing systems\n", - "DEBUG:openmmtools.multistate.multistatesampler:Minimizing all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _minimize_replica serially.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 1/11: initial energy -129420.641kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Using FIRE: tolerance 1.0 kJ/(nm mol) max_iterations 10000\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 1/11: final energy -238737.438kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 2/11: initial energy -129422.041kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Using FIRE: tolerance 1.0 kJ/(nm mol) max_iterations 10000\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 2/11: final energy -236313.696kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 3/11: initial energy -129422.381kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Using FIRE: tolerance 1.0 kJ/(nm mol) max_iterations 10000\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 3/11: final energy -240792.306kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 4/11: initial energy -129419.944kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Using FIRE: tolerance 1.0 kJ/(nm mol) max_iterations 10000\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 4/11: final energy -241420.879kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 5/11: initial energy -129405.481kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Using FIRE: tolerance 1.0 kJ/(nm mol) max_iterations 10000\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 5/11: final energy -238163.440kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 6/11: initial energy -129269.003kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Using FIRE: tolerance 1.0 kJ/(nm mol) max_iterations 10000\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 6/11: final energy -241650.614kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 7/11: initial energy -129271.904kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Using FIRE: tolerance 1.0 kJ/(nm mol) max_iterations 10000\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 7/11: final energy -238343.709kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 8/11: initial energy -129271.858kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Using FIRE: tolerance 1.0 kJ/(nm mol) max_iterations 10000\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 8/11: final energy -240350.716kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 9/11: initial energy -129270.134kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Using FIRE: tolerance 1.0 kJ/(nm mol) max_iterations 10000\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 9/11: final energy -236440.634kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 10/11: initial energy -129267.866kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Using FIRE: tolerance 1.0 kJ/(nm mol) max_iterations 10000\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 10/11: final energy -236416.758kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 11/11: initial energy -129265.222kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Using FIRE: tolerance 1.0 kJ/(nm mol) max_iterations 10000\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 11/11: final energy -240265.322kT\n", - "DEBUG:mpiplus.mpiplus:Single node: executing >\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.129s\n", - "DEBUG:openmmtools.utils:Minimizing all replicas took 73.786s\n", - "INFO:openfe.protocols.openmm_rbfe.equil_rbfe_methods:equilibrating systems\n", - "DEBUG:openmmtools.multistate.multistatesampler:Equilibration iteration 1/5\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 28.672s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.240s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 1020/2662 attempted swaps (38.3%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 28.914s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion (of equilibration only) in 0:01:55.658964, at Mon Oct 31 10:53:00 2022 (consuming total wall clock time 0:02:24.573705).\n", - "DEBUG:openmmtools.multistate.multistatesampler:Equilibration iteration 2/5\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 27.663s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.234s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 1066/2662 attempted swaps (40.0%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 27.900s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion (of equilibration only) in 0:01:25.223919, at Mon Oct 31 10:52:58 2022 (consuming total wall clock time 0:02:22.039865).\n", - "DEBUG:openmmtools.multistate.multistatesampler:Equilibration iteration 3/5\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 27.817s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.241s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 940/2662 attempted swaps (35.3%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 28.062s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion (of equilibration only) in 0:00:56.586066, at Mon Oct 31 10:52:57 2022 (consuming total wall clock time 0:02:21.465166).\n", - "DEBUG:openmmtools.multistate.multistatesampler:Equilibration iteration 4/5\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 28.049s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.247s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 818/2662 attempted swaps (30.7%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 28.299s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion (of equilibration only) in 0:00:28.294930, at Mon Oct 31 10:52:57 2022 (consuming total wall clock time 0:02:21.474648).\n", - "DEBUG:openmmtools.multistate.multistatesampler:Equilibration iteration 5/5\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 28.349s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.250s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 874/2662 attempted swaps (32.8%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 28.603s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion (of equilibration only) in 0:00:00, at Mon Oct 31 10:52:58 2022 (consuming total wall clock time 0:02:21.783662).\n", - "DEBUG:openmmtools.utils:Equilibration Iteration took 28.603s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing >\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.108s\n", - "INFO:openfe.protocols.openmm_rbfe.equil_rbfe_methods:running production phase\n", - "DEBUG:mpiplus.mpiplus:Single node: executing >\n", - "DEBUG:openmmtools.multistate.multistatesampler:Storing general ReplicaExchange options...\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.247s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing >\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 1/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 928/2662 attempted swaps (34.9%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 28.715s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.256s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 1 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.100s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.793s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 29.769s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:11:54.484674, at 2022-Oct-31-11:05:22 (consuming total wall clock time 0:12:24.254869).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 2/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 920/2662 attempted swaps (34.6%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 29.134s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.270s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 2 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.096s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.377s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 29.786s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:11:24.935325, at 2022-Oct-31-11:05:22 (consuming total wall clock time 0:12:24.494918).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 3/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 1074/2662 attempted swaps (40.3%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 29.479s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.271s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 3 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.098s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.335s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 30.090s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:10:57.450383, at 2022-Oct-31-11:05:25 (consuming total wall clock time 0:12:27.102708).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 4/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 872/2662 attempted swaps (32.8%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 27.853s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.275s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 4 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.099s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.337s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 28.470s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:10:20.157654, at 2022-Oct-31-11:05:16 (consuming total wall clock time 0:12:18.282922).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 5/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 1172/2662 attempted swaps (44.0%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 28.240s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.284s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 5 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.097s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.335s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 28.865s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:09:47.967608, at 2022-Oct-31-11:05:13 (consuming total wall clock time 0:12:14.959511).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 6/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 1026/2662 attempted swaps (38.5%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 28.682s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.280s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 6 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.097s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.334s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 29.302s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:09:18.272103, at 2022-Oct-31-11:05:12 (consuming total wall clock time 0:12:14.568556).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 7/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 798/2662 attempted swaps (30.0%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 29.607s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.286s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 7 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.098s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.335s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 30.233s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:08:51.082524, at 2022-Oct-31-11:05:16 (consuming total wall clock time 0:12:17.614616).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 8/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 920/2662 attempted swaps (34.6%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 29.827s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.294s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 8 not on the Checkpoint Interval of 50. Sampler State not written.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "DEBUG:openmmtools.utils:Storing sampler states took 0.098s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.334s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 30.460s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:08:23.616140, at 2022-Oct-31-11:05:18 (consuming total wall clock time 0:12:20.611971).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 9/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 1104/2662 attempted swaps (41.5%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 30.530s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.298s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 9 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.102s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.339s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 31.172s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:07:56.749683, at 2022-Oct-31-11:05:23 (consuming total wall clock time 0:12:24.921379).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 10/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 1090/2662 attempted swaps (40.9%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 31.244s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.300s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 10 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.098s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.335s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 31.886s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:07:30.091637, at 2022-Oct-31-11:05:28 (consuming total wall clock time 0:12:30.152729).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 11/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 1006/2662 attempted swaps (37.8%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 28.830s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.350s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 11 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.098s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.334s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 29.519s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:06:59.470625, at 2022-Oct-31-11:05:27 (consuming total wall clock time 0:12:29.054688).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 12/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 1046/2662 attempted swaps (39.3%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 28.247s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.350s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 12 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.098s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.334s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 28.937s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:06:28.400861, at 2022-Oct-31-11:05:25 (consuming total wall clock time 0:12:26.924733).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 13/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 894/2662 attempted swaps (33.6%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 28.944s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.357s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 13 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.097s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.335s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 29.643s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:05:58.310768, at 2022-Oct-31-11:05:24 (consuming total wall clock time 0:12:26.480767).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 14/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 1204/2662 attempted swaps (45.2%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 29.501s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.360s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 14 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.099s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.340s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 30.209s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:05:28.730022, at 2022-Oct-31-11:05:25 (consuming total wall clock time 0:12:27.113686).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 15/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 878/2662 attempted swaps (33.0%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 30.267s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.360s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 15 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.097s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.335s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 30.969s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:04:59.570649, at 2022-Oct-31-11:05:27 (consuming total wall clock time 0:12:28.926621).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 16/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 1006/2662 attempted swaps (37.8%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 31.166s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.366s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 16 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.101s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.346s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 31.883s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:04:30.698523, at 2022-Oct-31-11:05:30 (consuming total wall clock time 0:12:31.940341).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 17/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 958/2662 attempted swaps (36.0%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 31.949s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.365s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 17 not on the Checkpoint Interval of 50. Sampler State not written.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "DEBUG:openmmtools.utils:Storing sampler states took 0.098s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.337s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 32.656s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:04:01.835720, at 2022-Oct-31-11:05:34 (consuming total wall clock time 0:12:35.736626).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 18/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 1042/2662 attempted swaps (39.1%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 32.435s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.368s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 18 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.098s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.335s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 33.144s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:03:32.741394, at 2022-Oct-31-11:05:38 (consuming total wall clock time 0:12:39.790692).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 19/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 936/2662 attempted swaps (35.2%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 27.801s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.373s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 19 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.103s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.341s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 28.522s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:03:01.760289, at 2022-Oct-31-11:05:35 (consuming total wall clock time 0:12:37.334536).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 20/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 1136/2662 attempted swaps (42.7%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 28.524s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.376s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 20 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.099s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.339s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 29.245s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:02:31.205747, at 2022-Oct-31-11:05:34 (consuming total wall clock time 0:12:36.028734).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 21/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 844/2662 attempted swaps (31.7%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 29.434s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.377s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 21 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.104s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.341s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 30.158s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:02:00.949469, at 2022-Oct-31-11:05:34 (consuming total wall clock time 0:12:35.934183).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 22/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 792/2662 attempted swaps (29.8%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 30.198s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.377s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 22 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.099s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.335s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 30.915s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:01:30.805081, at 2022-Oct-31-11:05:35 (consuming total wall clock time 0:12:36.709008).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 23/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 850/2662 attempted swaps (31.9%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 31.106s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.379s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 23 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.095s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.331s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 31.820s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:01:00.671990, at 2022-Oct-31-11:05:36 (consuming total wall clock time 0:12:38.399870).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 24/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 1042/2662 attempted swaps (39.1%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 32.051s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.383s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 24 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.098s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.335s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 32.774s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:00:30.437691, at 2022-Oct-31-11:05:39 (consuming total wall clock time 0:12:40.942265).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 25/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 968/2662 attempted swaps (36.4%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 33.134s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.386s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 25 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.099s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.336s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 33.863s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:00:00, at 2022-Oct-31-11:05:42 (consuming total wall clock time 0:12:44.370978).\n" - ] - } - ], + "outputs": [], "source": [ "# Finally we can run the simulations\n", "\n", @@ -4617,2196 +1608,14 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": null, "id": "820aaf86", "metadata": { "tags": [ "nbval-skip" ] }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:openfe.protocols.openmm_rbfe.equil_rbfe_methods:creating hybrid system\n", - "INFO:openmmforcefields.generators.template_generators:Requested to generate parameters for residue \n", - "INFO:openmmforcefields.generators.template_generators:Generating a residue template for [H]c1c(c(c(c(c1[H])[H])[H])[H])[H] using openff-2.0.0.offxml\n", - "DEBUG:openmmforcefields.generators.template_generators:Generating parameters...\n", - "INFO:openmmforcefields.generators.template_generators:Requested to generate parameters for residue \n", - "INFO:openmmforcefields.generators.template_generators:Generating a residue template for [H]c1c(c(c(c(c1[H])[H])O[H])[H])[H] using openff-2.0.0.offxml\n", - "DEBUG:openmmforcefields.generators.template_generators:Generating parameters...\n", - "INFO:openfe.protocols.openmm_rbfe._rbfe_utils.relative:creating hybrid system\n", - "INFO:openfe.protocols.openmm_rbfe._rbfe_utils.relative:setting force field terms\n", - "INFO:openfe.protocols.openmm_rbfe._rbfe_utils.relative:adding forces\n", - "INFO:openfe.protocols.openmm_rbfe._rbfe_utils.relative:DONE\n", - "WARNING:openmmtools.multistate.multistatereporter:Warning: The openmmtools.multistate API is experimental and may change in future releases\n", - "WARNING:openmmtools.multistate.multistatesampler:Warning: The openmmtools.multistate API is experimental and may change in future releases\n", - "DEBUG:openmmtools.multistate.multistatesampler:CUDA devices available: (['0', ' NVIDIA GeForce GTX 1660 Ti'],)\n", - "DEBUG:mpiplus.mpiplus:Single node: executing >\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Serialized state thermodynamic_states/0 is 56528B | 55.203KB | 0.054MB\n", - "DEBUG:openmmtools.utils:Storing thermodynamic states took 0.071s\n", - "DEBUG:openmmtools.multistate.multistatesampler:Storing general ReplicaExchange options...\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.012s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.032s\n", - "INFO:openfe.protocols.openmm_rbfe.equil_rbfe_methods:minimizing systems\n", - "DEBUG:openmmtools.multistate.multistatesampler:Minimizing all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _minimize_replica serially.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Please cite the following:\n", - "\n", - " Friedrichs MS, Eastman P, Vaidyanathan V, Houston M, LeGrand S, Beberg AL, Ensign DL, Bruns CM, and Pande VS. Accelerating molecular dynamic simulations on graphics processing unit. J. Comput. Chem. 30:864, 2009. DOI: 10.1002/jcc.21209\n", - " Eastman P and Pande VS. OpenMM: A hardware-independent framework for molecular simulations. Comput. Sci. Eng. 12:34, 2010. DOI: 10.1109/MCSE.2010.27\n", - " Eastman P and Pande VS. Efficient nonbonded interactions for molecular dynamics on a graphics processing unit. J. Comput. Chem. 31:1268, 2010. DOI: 10.1002/jcc.21413\n", - " Eastman P and Pande VS. Constant constraint matrix approximation: A robust, parallelizable constraint method for molecular simulations. J. Chem. Theor. Comput. 6:434, 2010. DOI: 10.1021/ct900463w\n", - " Chodera JD and Shirts MR. Replica exchange and expanded ensemble simulations as Gibbs multistate: Simple improvements for enhanced mixing. J. Chem. Phys., 135:194110, 2011. DOI:10.1063/1.3660669\n", - " \n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "DEBUG:openmmtools.multistate.multistatesampler:Replica 1/11: initial energy 7824.310kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Using FIRE: tolerance 1.0 kJ/(nm mol) max_iterations 10000\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 1/11: final energy -14389.463kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 2/11: initial energy 7823.331kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Using FIRE: tolerance 1.0 kJ/(nm mol) max_iterations 10000\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 2/11: final energy -14274.754kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 3/11: initial energy 7822.354kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Using FIRE: tolerance 1.0 kJ/(nm mol) max_iterations 10000\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 3/11: final energy -14353.540kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 4/11: initial energy 7821.401kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Using FIRE: tolerance 1.0 kJ/(nm mol) max_iterations 10000\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 4/11: final energy -14523.337kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 5/11: initial energy 7820.472kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Using FIRE: tolerance 1.0 kJ/(nm mol) max_iterations 10000\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 5/11: final energy -14414.472kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 6/11: initial energy 7819.567kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Using FIRE: tolerance 1.0 kJ/(nm mol) max_iterations 10000\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 6/11: final energy -14315.365kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 7/11: initial energy 7820.909kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Using FIRE: tolerance 1.0 kJ/(nm mol) max_iterations 10000\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 7/11: final energy -14663.298kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 8/11: initial energy 7822.511kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Using FIRE: tolerance 1.0 kJ/(nm mol) max_iterations 10000\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 8/11: final energy -14264.755kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 9/11: initial energy 7824.371kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Using FIRE: tolerance 1.0 kJ/(nm mol) max_iterations 10000\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 9/11: final energy -14323.100kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 10/11: initial energy 7826.491kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Using FIRE: tolerance 1.0 kJ/(nm mol) max_iterations 10000\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 10/11: final energy -14498.850kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 11/11: initial energy 7828.869kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Using FIRE: tolerance 1.0 kJ/(nm mol) max_iterations 10000\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 11/11: final energy -14303.902kT\n", - "DEBUG:mpiplus.mpiplus:Single node: executing >\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.011s\n", - "DEBUG:openmmtools.utils:Minimizing all replicas took 14.654s\n", - "INFO:openfe.protocols.openmm_rbfe.equil_rbfe_methods:equilibrating systems\n", - "DEBUG:openmmtools.multistate.multistatesampler:Equilibration iteration 1/5\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 4.394s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.077s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 968/2662 attempted swaps (36.4%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 4.475s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion (of equilibration only) in 0:00:17.900908, at Mon Oct 31 11:06:22 2022 (consuming total wall clock time 0:00:22.376136).\n", - "DEBUG:openmmtools.multistate.multistatesampler:Equilibration iteration 2/5\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 4.191s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.067s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 944/2662 attempted swaps (35.5%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 4.262s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion (of equilibration only) in 0:00:13.108336, at Mon Oct 31 11:06:22 2022 (consuming total wall clock time 0:00:21.847226).\n", - "DEBUG:openmmtools.multistate.multistatesampler:Equilibration iteration 3/5\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 4.210s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.069s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 992/2662 attempted swaps (37.3%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 4.282s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion (of equilibration only) in 0:00:08.681947, at Mon Oct 31 11:06:22 2022 (consuming total wall clock time 0:00:21.704866).\n", - "DEBUG:openmmtools.multistate.multistatesampler:Equilibration iteration 4/5\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 4.218s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.066s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 944/2662 attempted swaps (35.5%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 4.288s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion (of equilibration only) in 0:00:04.327921, at Mon Oct 31 11:06:22 2022 (consuming total wall clock time 0:00:21.639607).\n", - "DEBUG:openmmtools.multistate.multistatesampler:Equilibration iteration 5/5\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 4.229s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.068s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 730/2662 attempted swaps (27.4%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 4.301s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion (of equilibration only) in 0:00:00, at Mon Oct 31 11:06:22 2022 (consuming total wall clock time 0:00:21.613586).\n", - "DEBUG:openmmtools.utils:Equilibration Iteration took 4.301s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing >\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.015s\n", - "INFO:openfe.protocols.openmm_rbfe.equil_rbfe_methods:running production phase\n", - "DEBUG:mpiplus.mpiplus:Single node: executing >\n", - "DEBUG:openmmtools.multistate.multistatesampler:Storing general ReplicaExchange options...\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.070s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing >\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 1/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 702/2662 attempted swaps (26.4%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 4.247s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.070s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 1 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.011s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.061s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 4.383s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:01:45.238146, at 2022-Oct-31-11:08:11 (consuming total wall clock time 0:01:49.623069).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 2/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 1068/2662 attempted swaps (40.1%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 4.246s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.076s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 2 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.011s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.032s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 4.360s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:01:40.624775, at 2022-Oct-31-11:08:11 (consuming total wall clock time 0:01:49.374756).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 3/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 846/2662 attempted swaps (31.8%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 4.234s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.069s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 3 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.010s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.031s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 4.341s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:01:36.025339, at 2022-Oct-31-11:08:11 (consuming total wall clock time 0:01:49.119703).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 4/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 730/2662 attempted swaps (27.4%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 4.175s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.070s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 4 not on the Checkpoint Interval of 50. Sampler State not written.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "DEBUG:openmmtools.utils:Storing sampler states took 0.009s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.032s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 4.284s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:01:31.256525, at 2022-Oct-31-11:08:10 (consuming total wall clock time 0:01:48.638720).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 5/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 876/2662 attempted swaps (32.9%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 4.182s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.067s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 5 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.009s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.030s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 4.284s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:01:26.683064, at 2022-Oct-31-11:08:10 (consuming total wall clock time 0:01:48.353831).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 6/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 1026/2662 attempted swaps (38.5%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 4.189s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.076s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 6 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.013s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.035s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 4.306s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:01:22.271992, at 2022-Oct-31-11:08:10 (consuming total wall clock time 0:01:48.252621).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 7/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 760/2662 attempted swaps (28.5%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 4.206s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.073s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 7 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.008s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.029s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 4.313s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:01:17.905780, at 2022-Oct-31-11:08:10 (consuming total wall clock time 0:01:48.202472).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 8/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 932/2662 attempted swaps (35.0%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 4.217s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.072s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 8 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.013s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.035s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 4.330s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:01:13.585235, at 2022-Oct-31-11:08:10 (consuming total wall clock time 0:01:48.213581).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 9/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 752/2662 attempted swaps (28.2%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 4.223s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.068s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 9 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.010s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.033s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 4.329s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:01:09.262337, at 2022-Oct-31-11:08:10 (consuming total wall clock time 0:01:48.222402).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 10/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 918/2662 attempted swaps (34.5%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 4.222s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.072s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 10 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.011s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.033s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 4.333s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:01:04.943289, at 2022-Oct-31-11:08:10 (consuming total wall clock time 0:01:48.238814).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 11/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 1204/2662 attempted swaps (45.2%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 4.182s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.068s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 11 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.011s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.032s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 4.290s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:01:00.569341, at 2022-Oct-31-11:08:10 (consuming total wall clock time 0:01:48.159538).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 12/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 828/2662 attempted swaps (31.1%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 4.180s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.070s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 12 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.011s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.033s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 4.289s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:00:56.206394, at 2022-Oct-31-11:08:10 (consuming total wall clock time 0:01:48.089220).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 13/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 856/2662 attempted swaps (32.2%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 4.202s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.076s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 13 not on the Checkpoint Interval of 50. Sampler State not written.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "DEBUG:openmmtools.utils:Storing sampler states took 0.011s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.032s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 4.317s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:00:51.879772, at 2022-Oct-31-11:08:10 (consuming total wall clock time 0:01:48.082858).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 14/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 906/2662 attempted swaps (34.0%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 4.206s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.071s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 14 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.011s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.032s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 4.315s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:00:47.553063, at 2022-Oct-31-11:08:10 (consuming total wall clock time 0:01:48.075144).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 15/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 784/2662 attempted swaps (29.5%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 4.205s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.072s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 15 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.009s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.029s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 4.313s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:00:43.225600, at 2022-Oct-31-11:08:10 (consuming total wall clock time 0:01:48.064000).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 16/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 892/2662 attempted swaps (33.5%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 4.206s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.072s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 16 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.009s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.033s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 4.316s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:00:38.901404, at 2022-Oct-31-11:08:10 (consuming total wall clock time 0:01:48.059455).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 17/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 748/2662 attempted swaps (28.1%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 4.218s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.069s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 17 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.011s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.034s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 4.327s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:00:34.582530, at 2022-Oct-31-11:08:10 (consuming total wall clock time 0:01:48.070407).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 18/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 800/2662 attempted swaps (30.1%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 4.239s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.070s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 18 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.010s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.033s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 4.349s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:00:30.271705, at 2022-Oct-31-11:08:10 (consuming total wall clock time 0:01:48.113233).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 19/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 978/2662 attempted swaps (36.7%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 4.171s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.068s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 19 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.009s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.032s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 4.277s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:00:25.933670, at 2022-Oct-31-11:08:10 (consuming total wall clock time 0:01:48.056958).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 20/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 818/2662 attempted swaps (30.7%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 4.177s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.070s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 20 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.010s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.031s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 4.284s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:00:21.602324, at 2022-Oct-31-11:08:10 (consuming total wall clock time 0:01:48.011621).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 21/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 716/2662 attempted swaps (26.9%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 4.193s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.068s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 21 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.011s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.032s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 4.302s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:00:17.278990, at 2022-Oct-31-11:08:10 (consuming total wall clock time 0:01:47.993688).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 22/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 980/2662 attempted swaps (36.8%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 4.214s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.073s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 22 not on the Checkpoint Interval of 50. Sampler State not written.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "DEBUG:openmmtools.utils:Storing sampler states took 0.009s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.032s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 4.326s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:00:12.960597, at 2022-Oct-31-11:08:10 (consuming total wall clock time 0:01:48.004972).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 23/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 690/2662 attempted swaps (25.9%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 4.187s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.071s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 23 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.012s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.034s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 4.299s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:00:08.638979, at 2022-Oct-31-11:08:10 (consuming total wall clock time 0:01:47.987241).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 24/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 998/2662 attempted swaps (37.5%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 4.238s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.068s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 24 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.009s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.033s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 4.343s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:00:04.320666, at 2022-Oct-31-11:08:10 (consuming total wall clock time 0:01:48.016645).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 25/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 752/2662 attempted swaps (28.2%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 4.249s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.077s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 25 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.016s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.038s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 4.370s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:00:00, at 2022-Oct-31-11:08:10 (consuming total wall clock time 0:01:48.069395).\n", - "INFO:openfe.protocols.openmm_rbfe.equil_rbfe_methods:creating hybrid system\n", - "INFO:openmmforcefields.generators.template_generators:Requested to generate parameters for residue \n", - "INFO:openmmforcefields.generators.template_generators:Generating a residue template for [H]c1c(c(c(c(c1[H])[H])[H])[H])[H] using openff-2.0.0.offxml\n", - "DEBUG:openmmforcefields.generators.template_generators:Generating parameters...\n", - "INFO:openmmforcefields.generators.template_generators:Requested to generate parameters for residue \n", - "INFO:openmmforcefields.generators.template_generators:Generating a residue template for [H]c1c(c(c(c(c1[H])[H])O[H])[H])[H] using openff-2.0.0.offxml\n", - "DEBUG:openmmforcefields.generators.template_generators:Generating parameters...\n", - "INFO:openfe.protocols.openmm_rbfe._rbfe_utils.relative:creating hybrid system\n", - "INFO:openfe.protocols.openmm_rbfe._rbfe_utils.relative:setting force field terms\n", - "INFO:openfe.protocols.openmm_rbfe._rbfe_utils.relative:adding forces\n", - "INFO:openfe.protocols.openmm_rbfe._rbfe_utils.relative:DONE\n", - "WARNING:openmmtools.multistate.multistatereporter:Warning: The openmmtools.multistate API is experimental and may change in future releases\n", - "WARNING:openmmtools.multistate.multistatesampler:Warning: The openmmtools.multistate API is experimental and may change in future releases\n", - "DEBUG:openmmtools.multistate.multistatesampler:CUDA devices available: (['0', ' NVIDIA GeForce GTX 1660 Ti'],)\n", - "DEBUG:mpiplus.mpiplus:Single node: executing >\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Serialized state thermodynamic_states/0 is 56528B | 55.203KB | 0.054MB\n", - "DEBUG:openmmtools.utils:Storing thermodynamic states took 0.070s\n", - "DEBUG:openmmtools.multistate.multistatesampler:Storing general ReplicaExchange options...\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.013s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.036s\n", - "INFO:openfe.protocols.openmm_rbfe.equil_rbfe_methods:minimizing systems\n", - "DEBUG:openmmtools.multistate.multistatesampler:Minimizing all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _minimize_replica serially.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Please cite the following:\n", - "\n", - " Friedrichs MS, Eastman P, Vaidyanathan V, Houston M, LeGrand S, Beberg AL, Ensign DL, Bruns CM, and Pande VS. Accelerating molecular dynamic simulations on graphics processing unit. J. Comput. Chem. 30:864, 2009. DOI: 10.1002/jcc.21209\n", - " Eastman P and Pande VS. OpenMM: A hardware-independent framework for molecular simulations. Comput. Sci. Eng. 12:34, 2010. DOI: 10.1109/MCSE.2010.27\n", - " Eastman P and Pande VS. Efficient nonbonded interactions for molecular dynamics on a graphics processing unit. J. Comput. Chem. 31:1268, 2010. DOI: 10.1002/jcc.21413\n", - " Eastman P and Pande VS. Constant constraint matrix approximation: A robust, parallelizable constraint method for molecular simulations. J. Chem. Theor. Comput. 6:434, 2010. DOI: 10.1021/ct900463w\n", - " Chodera JD and Shirts MR. Replica exchange and expanded ensemble simulations as Gibbs multistate: Simple improvements for enhanced mixing. J. Chem. Phys., 135:194110, 2011. DOI:10.1063/1.3660669\n", - " \n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "DEBUG:openmmtools.multistate.multistatesampler:Replica 1/11: initial energy 3413.675kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Using FIRE: tolerance 1.0 kJ/(nm mol) max_iterations 10000\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 1/11: final energy -14514.682kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 2/11: initial energy 3412.134kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Using FIRE: tolerance 1.0 kJ/(nm mol) max_iterations 10000\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 2/11: final energy -14529.871kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 3/11: initial energy 3410.614kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Using FIRE: tolerance 1.0 kJ/(nm mol) max_iterations 10000\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 3/11: final energy -14481.227kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 4/11: initial energy 3409.119kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Using FIRE: tolerance 1.0 kJ/(nm mol) max_iterations 10000\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 4/11: final energy -14462.353kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 5/11: initial energy 3407.647kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Using FIRE: tolerance 1.0 kJ/(nm mol) max_iterations 10000\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 5/11: final energy -14494.012kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 6/11: initial energy 3406.199kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Using FIRE: tolerance 1.0 kJ/(nm mol) max_iterations 10000\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 6/11: final energy -14626.501kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 7/11: initial energy 3408.334kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Using FIRE: tolerance 1.0 kJ/(nm mol) max_iterations 10000\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 7/11: final energy -14035.892kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 8/11: initial energy 3410.728kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Using FIRE: tolerance 1.0 kJ/(nm mol) max_iterations 10000\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 8/11: final energy -14664.470kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 9/11: initial energy 3413.381kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Using FIRE: tolerance 1.0 kJ/(nm mol) max_iterations 10000\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 9/11: final energy -14531.540kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 10/11: initial energy 3416.293kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Using FIRE: tolerance 1.0 kJ/(nm mol) max_iterations 10000\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 10/11: final energy -14553.051kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 11/11: initial energy 3419.464kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Using FIRE: tolerance 1.0 kJ/(nm mol) max_iterations 10000\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 11/11: final energy -14551.805kT\n", - "DEBUG:mpiplus.mpiplus:Single node: executing >\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.009s\n", - "DEBUG:openmmtools.utils:Minimizing all replicas took 14.634s\n", - "INFO:openfe.protocols.openmm_rbfe.equil_rbfe_methods:equilibrating systems\n", - "DEBUG:openmmtools.multistate.multistatesampler:Equilibration iteration 1/5\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 4.592s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.071s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 968/2662 attempted swaps (36.4%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 4.666s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion (of equilibration only) in 0:00:18.667766, at Mon Oct 31 11:08:51 2022 (consuming total wall clock time 0:00:23.334707).\n", - "DEBUG:openmmtools.multistate.multistatesampler:Equilibration iteration 2/5\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 4.380s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.071s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 946/2662 attempted swaps (35.5%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 4.455s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion (of equilibration only) in 0:00:13.685684, at Mon Oct 31 11:08:50 2022 (consuming total wall clock time 0:00:22.809473).\n", - "DEBUG:openmmtools.multistate.multistatesampler:Equilibration iteration 3/5\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 4.393s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.069s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 876/2662 attempted swaps (32.9%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 4.467s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion (of equilibration only) in 0:00:09.062137, at Mon Oct 31 11:08:50 2022 (consuming total wall clock time 0:00:22.655342).\n", - "DEBUG:openmmtools.multistate.multistatesampler:Equilibration iteration 4/5\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 4.399s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.068s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 984/2662 attempted swaps (37.0%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 4.469s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion (of equilibration only) in 0:00:04.516267, at Mon Oct 31 11:08:50 2022 (consuming total wall clock time 0:00:22.581333).\n", - "DEBUG:openmmtools.multistate.multistatesampler:Equilibration iteration 5/5\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 4.402s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.069s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 938/2662 attempted swaps (35.2%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 4.475s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion (of equilibration only) in 0:00:00, at Mon Oct 31 11:08:50 2022 (consuming total wall clock time 0:00:22.541381).\n", - "DEBUG:openmmtools.utils:Equilibration Iteration took 4.475s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing >\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.014s\n", - "INFO:openfe.protocols.openmm_rbfe.equil_rbfe_methods:running production phase\n", - "DEBUG:mpiplus.mpiplus:Single node: executing >\n", - "DEBUG:openmmtools.multistate.multistatesampler:Storing general ReplicaExchange options...\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.071s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing >\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 1/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 942/2662 attempted swaps (35.4%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 4.398s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.070s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 1 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.010s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.061s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 4.534s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:01:48.865986, at 2022-Oct-31-11:10:44 (consuming total wall clock time 0:01:53.402069).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 2/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 1020/2662 attempted swaps (38.3%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 4.413s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.069s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 2 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.011s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.033s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 4.522s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:01:44.221012, at 2022-Oct-31-11:10:43 (consuming total wall clock time 0:01:53.283709).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 3/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 970/2662 attempted swaps (36.4%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 4.396s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.074s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 3 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.011s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.034s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 4.511s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:01:39.570930, at 2022-Oct-31-11:10:43 (consuming total wall clock time 0:01:53.148785).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 4/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 1120/2662 attempted swaps (42.1%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 4.359s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.067s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 4 not on the Checkpoint Interval of 50. Sampler State not written.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "DEBUG:openmmtools.utils:Storing sampler states took 0.016s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.037s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 4.469s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:01:34.760526, at 2022-Oct-31-11:10:43 (consuming total wall clock time 0:01:52.810150).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 5/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 754/2662 attempted swaps (28.3%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 4.396s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.070s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 5 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.014s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.038s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 4.509s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:01:30.247866, at 2022-Oct-31-11:10:43 (consuming total wall clock time 0:01:52.809832).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 6/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 948/2662 attempted swaps (35.6%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 4.398s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.068s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 6 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.012s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.032s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 4.505s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:01:25.722874, at 2022-Oct-31-11:10:43 (consuming total wall clock time 0:01:52.793256).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 7/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 814/2662 attempted swaps (30.6%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 4.402s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.070s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 7 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.012s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.033s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 4.510s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:01:21.215447, at 2022-Oct-31-11:10:43 (consuming total wall clock time 0:01:52.799232).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 8/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 756/2662 attempted swaps (28.4%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 4.402s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.068s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 8 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.009s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.032s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 4.507s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:01:16.699054, at 2022-Oct-31-11:10:43 (consuming total wall clock time 0:01:52.792727).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 9/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 852/2662 attempted swaps (32.0%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 4.435s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.069s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 9 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.018s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.047s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 4.557s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:01:12.273432, at 2022-Oct-31-11:10:43 (consuming total wall clock time 0:01:52.927238).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 10/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 758/2662 attempted swaps (28.5%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 4.438s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.076s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 10 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.011s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.033s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 4.553s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:01:07.816299, at 2022-Oct-31-11:10:43 (consuming total wall clock time 0:01:53.027165).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 11/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 864/2662 attempted swaps (32.5%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 4.384s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.076s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 11 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.009s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.032s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 4.498s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:01:03.272323, at 2022-Oct-31-11:10:43 (consuming total wall clock time 0:01:52.986291).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 12/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 970/2662 attempted swaps (36.4%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 4.363s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.072s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 12 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.011s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.033s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 4.473s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:00:58.705965, at 2022-Oct-31-11:10:43 (consuming total wall clock time 0:01:52.896087).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 13/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 1010/2662 attempted swaps (37.9%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 4.393s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.069s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 13 not on the Checkpoint Interval of 50. Sampler State not written.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "DEBUG:openmmtools.utils:Storing sampler states took 0.009s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.033s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 4.502s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:00:54.180836, at 2022-Oct-31-11:10:43 (consuming total wall clock time 0:01:52.876743).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 14/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 876/2662 attempted swaps (32.9%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 4.383s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.067s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 14 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.009s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.031s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 4.487s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:00:49.647110, at 2022-Oct-31-11:10:43 (consuming total wall clock time 0:01:52.834342).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 15/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 902/2662 attempted swaps (33.9%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 4.416s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.073s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 15 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.011s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.040s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 4.535s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:00:45.150720, at 2022-Oct-31-11:10:43 (consuming total wall clock time 0:01:52.876801).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 16/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 776/2662 attempted swaps (29.2%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 4.415s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.069s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 16 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.010s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.032s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 4.521s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:00:40.640720, at 2022-Oct-31-11:10:43 (consuming total wall clock time 0:01:52.890889).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 17/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 1136/2662 attempted swaps (42.7%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 4.415s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.070s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 17 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.009s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.032s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 4.522s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:00:36.129538, at 2022-Oct-31-11:10:43 (consuming total wall clock time 0:01:52.904806).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 18/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 704/2662 attempted swaps (26.4%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 4.423s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.069s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 18 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.010s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.031s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 4.530s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:00:31.620421, at 2022-Oct-31-11:10:43 (consuming total wall clock time 0:01:52.930076).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 19/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 804/2662 attempted swaps (30.2%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 4.383s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.069s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 19 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.010s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.031s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 4.490s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:00:27.095713, at 2022-Oct-31-11:10:43 (consuming total wall clock time 0:01:52.898805).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 20/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 780/2662 attempted swaps (29.3%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 4.396s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.069s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 20 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.009s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.032s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 4.503s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:00:22.577864, at 2022-Oct-31-11:10:43 (consuming total wall clock time 0:01:52.889318).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 21/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 862/2662 attempted swaps (32.4%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 4.393s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.070s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 21 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.009s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.031s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 4.500s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:00:18.060003, at 2022-Oct-31-11:10:43 (consuming total wall clock time 0:01:52.875019).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 22/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 1230/2662 attempted swaps (46.2%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 4.404s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.074s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 22 not on the Checkpoint Interval of 50. Sampler State not written.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "DEBUG:openmmtools.utils:Storing sampler states took 0.009s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.031s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 4.515s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:00:13.545481, at 2022-Oct-31-11:10:43 (consuming total wall clock time 0:01:52.879006).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 23/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 966/2662 attempted swaps (36.3%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 4.409s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.069s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 23 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.009s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.033s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 4.516s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:00:09.030708, at 2022-Oct-31-11:10:43 (consuming total wall clock time 0:01:52.883854).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 24/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 702/2662 attempted swaps (26.4%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 4.434s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.070s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 24 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.015s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.037s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 4.547s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:00:04.516873, at 2022-Oct-31-11:10:43 (consuming total wall clock time 0:01:52.921835).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 25/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 878/2662 attempted swaps (33.0%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 4.416s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.072s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 25 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.012s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.034s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 4.529s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:00:00, at 2022-Oct-31-11:10:43 (consuming total wall clock time 0:01:52.939261).\n", - "INFO:openfe.protocols.openmm_rbfe.equil_rbfe_methods:creating hybrid system\n", - "INFO:openmmforcefields.generators.template_generators:Requested to generate parameters for residue \n", - "INFO:openmmforcefields.generators.template_generators:Generating a residue template for [H]c1c(c(c(c(c1[H])[H])[H])[H])[H] using openff-2.0.0.offxml\n", - "DEBUG:openmmforcefields.generators.template_generators:Generating parameters...\n", - "DEBUG:mpiplus.mpiplus:Single node: executing >\n", - "DEBUG:mpiplus.mpiplus:Single node: executing >\n", - "DEBUG:mpiplus.mpiplus:Single node: executing >\n", - "INFO:openmmforcefields.generators.template_generators:Requested to generate parameters for residue \n", - "INFO:openmmforcefields.generators.template_generators:Generating a residue template for [H]c1c(c(c(c(c1[H])[H])O[H])[H])[H] using openff-2.0.0.offxml\n", - "DEBUG:openmmforcefields.generators.template_generators:Generating parameters...\n", - "INFO:openfe.protocols.openmm_rbfe._rbfe_utils.relative:creating hybrid system\n", - "INFO:openfe.protocols.openmm_rbfe._rbfe_utils.relative:setting force field terms\n", - "INFO:openfe.protocols.openmm_rbfe._rbfe_utils.relative:adding forces\n", - "INFO:openfe.protocols.openmm_rbfe._rbfe_utils.relative:DONE\n", - "WARNING:openmmtools.multistate.multistatereporter:Warning: The openmmtools.multistate API is experimental and may change in future releases\n", - "WARNING:openmmtools.multistate.multistatesampler:Warning: The openmmtools.multistate API is experimental and may change in future releases\n", - "DEBUG:openmmtools.multistate.multistatesampler:CUDA devices available: (['0', ' NVIDIA GeForce GTX 1660 Ti'],)\n", - "DEBUG:mpiplus.mpiplus:Single node: executing >\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Serialized state thermodynamic_states/0 is 56528B | 55.203KB | 0.054MB\n", - "DEBUG:openmmtools.utils:Storing thermodynamic states took 0.072s\n", - "DEBUG:openmmtools.multistate.multistatesampler:Storing general ReplicaExchange options...\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.011s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.033s\n", - "INFO:openfe.protocols.openmm_rbfe.equil_rbfe_methods:minimizing systems\n", - "DEBUG:openmmtools.multistate.multistatesampler:Minimizing all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _minimize_replica serially.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Please cite the following:\n", - "\n", - " Friedrichs MS, Eastman P, Vaidyanathan V, Houston M, LeGrand S, Beberg AL, Ensign DL, Bruns CM, and Pande VS. Accelerating molecular dynamic simulations on graphics processing unit. J. Comput. Chem. 30:864, 2009. DOI: 10.1002/jcc.21209\n", - " Eastman P and Pande VS. OpenMM: A hardware-independent framework for molecular simulations. Comput. Sci. Eng. 12:34, 2010. DOI: 10.1109/MCSE.2010.27\n", - " Eastman P and Pande VS. Efficient nonbonded interactions for molecular dynamics on a graphics processing unit. J. Comput. Chem. 31:1268, 2010. DOI: 10.1002/jcc.21413\n", - " Eastman P and Pande VS. Constant constraint matrix approximation: A robust, parallelizable constraint method for molecular simulations. J. Chem. Theor. Comput. 6:434, 2010. DOI: 10.1021/ct900463w\n", - " Chodera JD and Shirts MR. Replica exchange and expanded ensemble simulations as Gibbs multistate: Simple improvements for enhanced mixing. J. Chem. Phys., 135:194110, 2011. DOI:10.1063/1.3660669\n", - " \n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "DEBUG:openmmtools.multistate.multistatesampler:Replica 1/11: initial energy 3341.917kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Using FIRE: tolerance 1.0 kJ/(nm mol) max_iterations 10000\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 1/11: final energy -14544.019kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 2/11: initial energy 3340.845kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Using FIRE: tolerance 1.0 kJ/(nm mol) max_iterations 10000\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 2/11: final energy -14324.677kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 3/11: initial energy 3339.796kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Using FIRE: tolerance 1.0 kJ/(nm mol) max_iterations 10000\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 3/11: final energy -14552.917kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 4/11: initial energy 3338.770kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Using FIRE: tolerance 1.0 kJ/(nm mol) max_iterations 10000\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 4/11: final energy -14421.202kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 5/11: initial energy 3337.768kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Using FIRE: tolerance 1.0 kJ/(nm mol) max_iterations 10000\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 5/11: final energy -14128.653kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 6/11: initial energy 3336.790kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Using FIRE: tolerance 1.0 kJ/(nm mol) max_iterations 10000\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 6/11: final energy -14613.027kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 7/11: initial energy 3337.466kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Using FIRE: tolerance 1.0 kJ/(nm mol) max_iterations 10000\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 7/11: final energy -14745.327kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 8/11: initial energy 3338.401kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Using FIRE: tolerance 1.0 kJ/(nm mol) max_iterations 10000\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 8/11: final energy -14557.050kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 9/11: initial energy 3339.595kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Using FIRE: tolerance 1.0 kJ/(nm mol) max_iterations 10000\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 9/11: final energy -14555.424kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 10/11: initial energy 3341.048kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Using FIRE: tolerance 1.0 kJ/(nm mol) max_iterations 10000\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 10/11: final energy -14588.659kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 11/11: initial energy 3342.760kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Using FIRE: tolerance 1.0 kJ/(nm mol) max_iterations 10000\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 11/11: final energy -14251.469kT\n", - "DEBUG:mpiplus.mpiplus:Single node: executing >\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.010s\n", - "DEBUG:openmmtools.utils:Minimizing all replicas took 14.785s\n", - "INFO:openfe.protocols.openmm_rbfe.equil_rbfe_methods:equilibrating systems\n", - "DEBUG:openmmtools.multistate.multistatesampler:Equilibration iteration 1/5\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 4.315s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.069s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 990/2662 attempted swaps (37.2%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 4.387s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion (of equilibration only) in 0:00:17.548892, at Mon Oct 31 11:11:25 2022 (consuming total wall clock time 0:00:21.936115).\n", - "DEBUG:openmmtools.multistate.multistatesampler:Equilibration iteration 2/5\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 4.146s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.070s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 834/2662 attempted swaps (31.3%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 4.219s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion (of equilibration only) in 0:00:12.911737, at Mon Oct 31 11:11:24 2022 (consuming total wall clock time 0:00:21.519562).\n", - "DEBUG:openmmtools.multistate.multistatesampler:Equilibration iteration 3/5\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 4.101s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.068s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 742/2662 attempted swaps (27.9%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 4.173s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion (of equilibration only) in 0:00:08.521378, at Mon Oct 31 11:11:24 2022 (consuming total wall clock time 0:00:21.303444).\n", - "DEBUG:openmmtools.multistate.multistatesampler:Equilibration iteration 4/5\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 4.186s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.074s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 848/2662 attempted swaps (31.9%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 4.263s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion (of equilibration only) in 0:00:04.262147, at Mon Oct 31 11:11:24 2022 (consuming total wall clock time 0:00:21.310734).\n", - "DEBUG:openmmtools.multistate.multistatesampler:Equilibration iteration 5/5\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 4.210s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.069s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 938/2662 attempted swaps (35.2%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 4.282s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion (of equilibration only) in 0:00:00, at Mon Oct 31 11:11:24 2022 (consuming total wall clock time 0:00:21.331745).\n", - "DEBUG:openmmtools.utils:Equilibration Iteration took 4.282s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing >\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.015s\n", - "INFO:openfe.protocols.openmm_rbfe.equil_rbfe_methods:running production phase\n", - "DEBUG:mpiplus.mpiplus:Single node: executing >\n", - "DEBUG:openmmtools.multistate.multistatesampler:Storing general ReplicaExchange options...\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.068s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing >\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 1/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 918/2662 attempted swaps (34.5%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 4.232s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.076s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 1 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.014s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.064s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 4.379s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:01:45.134583, at 2022-Oct-31-11:13:14 (consuming total wall clock time 0:01:49.515190).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 2/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 740/2662 attempted swaps (27.8%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 4.222s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.071s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 2 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.009s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.036s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 4.334s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:01:40.262573, at 2022-Oct-31-11:13:13 (consuming total wall clock time 0:01:48.981058).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 3/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 1016/2662 attempted swaps (38.2%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 4.230s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.069s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 3 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.010s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.033s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 4.337s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:01:35.773705, at 2022-Oct-31-11:13:13 (consuming total wall clock time 0:01:48.833756).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 4/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 1020/2662 attempted swaps (38.3%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 4.188s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.068s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 4 not on the Checkpoint Interval of 50. Sampler State not written.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "DEBUG:openmmtools.utils:Storing sampler states took 0.014s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.042s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 4.304s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:01:31.182551, at 2022-Oct-31-11:13:13 (consuming total wall clock time 0:01:48.550656).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 5/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 904/2662 attempted swaps (34.0%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 4.198s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.073s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 5 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.012s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.033s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 4.311s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:01:26.731079, at 2022-Oct-31-11:13:13 (consuming total wall clock time 0:01:48.413849).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 6/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 920/2662 attempted swaps (34.6%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 4.211s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.069s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 6 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.008s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.032s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 4.317s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:01:22.342558, at 2022-Oct-31-11:13:13 (consuming total wall clock time 0:01:48.345471).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 7/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 758/2662 attempted swaps (28.5%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 4.206s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.068s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 7 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.012s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.035s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 4.314s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:01:17.964530, at 2022-Oct-31-11:13:13 (consuming total wall clock time 0:01:48.284069).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 8/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 946/2662 attempted swaps (35.5%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 4.215s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.073s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 8 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.011s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.033s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 4.327s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:01:13.628557, at 2022-Oct-31-11:13:13 (consuming total wall clock time 0:01:48.277290).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 9/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 776/2662 attempted swaps (29.2%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 4.210s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.070s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 9 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.009s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.033s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 4.319s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:01:09.282271, at 2022-Oct-31-11:13:13 (consuming total wall clock time 0:01:48.253549).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 10/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 774/2662 attempted swaps (29.1%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 4.226s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.069s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 10 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.017s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.038s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 4.339s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:01:04.970477, at 2022-Oct-31-11:13:13 (consuming total wall clock time 0:01:48.284128).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 11/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 1036/2662 attempted swaps (38.9%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 4.189s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.071s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 11 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.012s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.035s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 4.300s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:01:00.603835, at 2022-Oct-31-11:13:12 (consuming total wall clock time 0:01:48.221134).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 12/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 974/2662 attempted swaps (36.6%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 4.184s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.068s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 12 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.008s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.029s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 4.286s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:00:56.231997, at 2022-Oct-31-11:13:12 (consuming total wall clock time 0:01:48.138455).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 13/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 898/2662 attempted swaps (33.7%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 4.193s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.072s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 13 not on the Checkpoint Interval of 50. Sampler State not written.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "DEBUG:openmmtools.utils:Storing sampler states took 0.012s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.034s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 4.306s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:00:51.890028, at 2022-Oct-31-11:13:12 (consuming total wall clock time 0:01:48.104225).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 14/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 828/2662 attempted swaps (31.1%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 4.174s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.068s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 14 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.009s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.034s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 4.281s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:00:47.534636, at 2022-Oct-31-11:13:12 (consuming total wall clock time 0:01:48.033263).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 15/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 892/2662 attempted swaps (33.5%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 4.186s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.072s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 15 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.012s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.033s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 4.295s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:00:43.198154, at 2022-Oct-31-11:13:12 (consuming total wall clock time 0:01:47.995385).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 16/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 974/2662 attempted swaps (36.6%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 4.217s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.070s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 16 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.009s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.033s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 4.326s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:00:38.883441, at 2022-Oct-31-11:13:12 (consuming total wall clock time 0:01:48.009557).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 17/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 694/2662 attempted swaps (26.1%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 4.243s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.069s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 17 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.011s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.038s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 4.355s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:00:34.581120, at 2022-Oct-31-11:13:12 (consuming total wall clock time 0:01:48.066000).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 18/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 1054/2662 attempted swaps (39.6%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 4.243s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.071s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 18 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.015s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.038s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 4.356s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:00:30.272418, at 2022-Oct-31-11:13:12 (consuming total wall clock time 0:01:48.115779).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 19/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 722/2662 attempted swaps (27.1%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 4.183s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.072s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 19 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.014s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.035s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 4.296s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:00:25.939698, at 2022-Oct-31-11:13:12 (consuming total wall clock time 0:01:48.082074).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 20/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 1084/2662 attempted swaps (40.7%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 4.191s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.072s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 20 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.011s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.033s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 4.302s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:00:21.611965, at 2022-Oct-31-11:13:12 (consuming total wall clock time 0:01:48.059826).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 21/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 804/2662 attempted swaps (30.2%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 4.209s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.071s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 21 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.011s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.032s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 4.317s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:00:17.289123, at 2022-Oct-31-11:13:12 (consuming total wall clock time 0:01:48.057016).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 22/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 814/2662 attempted swaps (30.6%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 4.205s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.075s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 22 not on the Checkpoint Interval of 50. Sampler State not written.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "DEBUG:openmmtools.utils:Storing sampler states took 0.011s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.034s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 4.319s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:00:12.966980, at 2022-Oct-31-11:13:12 (consuming total wall clock time 0:01:48.058165).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 23/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 1018/2662 attempted swaps (38.2%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 4.218s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.072s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 23 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.011s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.035s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 4.331s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:00:08.645710, at 2022-Oct-31-11:13:12 (consuming total wall clock time 0:01:48.071380).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 24/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 900/2662 attempted swaps (33.8%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 4.231s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.070s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 24 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.011s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.034s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 4.341s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:00:04.323758, at 2022-Oct-31-11:13:12 (consuming total wall clock time 0:01:48.093939).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 25/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 648/2662 attempted swaps (24.3%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 4.249s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.069s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 25 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.008s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.029s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 4.352s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:00:00, at 2022-Oct-31-11:13:12 (consuming total wall clock time 0:01:48.125912).\n" - ] - } - ], + "outputs": [], "source": [ "# Next the ligand transformation\n", "solvent_dag_results = execute_DAG(solvent_dag, shared='./solvent')" @@ -6834,341 +1643,14 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": null, "id": "fd1332db", "metadata": { "tags": [ "nbval-skip" ] }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:openmmtools.multistate.multistatereporter:Warning: The openmmtools.multistate API is experimental and may change in future releases\n", - "WARNING:openmmtools.multistate.multistateanalyzer:Warning: The openmmtools.multistate API is experimental and may change in future releases\n", - "DEBUG:openmmtools.multistate.multistatereporter:analysis_particle_indices != on-file analysis_particle_indices!Using on file analysis indices of [ 0 1 2 ... 37553 37554 37555]\n", - "WARNING:openmmtools.multistate.multistatereporter:Warning: The openmmtools.multistate API is experimental and may change in future releases\n", - "WARNING:openmmtools.multistate.multistateanalyzer:Warning: The openmmtools.multistate API is experimental and may change in future releases\n", - "DEBUG:openmmtools.multistate.multistatereporter:analysis_particle_indices != on-file analysis_particle_indices!Using on file analysis indices of [ 0 1 2 ... 37553 37554 37555]\n", - "WARNING:openmmtools.multistate.multistatereporter:Warning: The openmmtools.multistate API is experimental and may change in future releases\n", - "WARNING:openmmtools.multistate.multistateanalyzer:Warning: The openmmtools.multistate API is experimental and may change in future releases\n", - "DEBUG:openmmtools.multistate.multistatereporter:analysis_particle_indices != on-file analysis_particle_indices!Using on file analysis indices of [ 0 1 2 ... 37553 37554 37555]\n", - "WARNING:openmmtools.multistate.multistatereporter:Warning: The openmmtools.multistate API is experimental and may change in future releases\n", - "WARNING:openmmtools.multistate.multistateanalyzer:Warning: The openmmtools.multistate API is experimental and may change in future releases\n", - "DEBUG:openmmtools.multistate.multistatereporter:analysis_particle_indices != on-file analysis_particle_indices!Using on file analysis indices of [ 0 1 2 ... 2209 2210 2211]\n", - "WARNING:openmmtools.multistate.multistatereporter:Warning: The openmmtools.multistate API is experimental and may change in future releases\n", - "WARNING:openmmtools.multistate.multistateanalyzer:Warning: The openmmtools.multistate API is experimental and may change in future releases\n", - "DEBUG:openmmtools.multistate.multistatereporter:analysis_particle_indices != on-file analysis_particle_indices!Using on file analysis indices of [ 0 1 2 ... 2209 2210 2211]\n", - "WARNING:openmmtools.multistate.multistatereporter:Warning: The openmmtools.multistate API is experimental and may change in future releases\n", - "WARNING:openmmtools.multistate.multistateanalyzer:Warning: The openmmtools.multistate API is experimental and may change in future releases\n", - "DEBUG:openmmtools.multistate.multistatereporter:analysis_particle_indices != on-file analysis_particle_indices!Using on file analysis indices of [ 0 1 2 ... 2209 2210 2211]\n", - "DEBUG:openmmtools.multistate.multistateanalyzer:Checking if we need to unbias the restraint...\n", - "DEBUG:openmmtools.multistate.multistateanalyzer:Trying to get radially symmetric restraint data...\n", - "DEBUG:openmmtools.multistate.multistateanalyzer:Retrieving end thermodynamic states...\n", - "DEBUG:openmmtools.multistate.multistateanalyzer:Isolating restraint force...\n", - "DEBUG:openmmtools.multistate.multistateanalyzer:No force of type could be found. The restraint will not be unbiased.\n", - "DEBUG:openmmtools.multistate.multistateanalyzer:Reading energies...\n", - "DEBUG:openmmtools.multistate.multistatereporter:read_replica_thermodynamic_states: iteration = [ 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23\n", - " 24 25]\n", - "DEBUG:openmmtools.multistate.multistateanalyzer:Done.\n", - "DEBUG:openmmtools.multistate.multistateanalyzer:Assembling effective timeseries...\n", - "DEBUG:openmmtools.multistate.multistateanalyzer:Done.\n", - "DEBUG:openmmtools.multistate.multistateanalyzer:Could not find t0: Online Analysis information was never written!\n", - "DEBUG:openmmtools.multistate.multistateanalyzer:Equilibration data:\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: number of iterations discarded to equilibration : 1\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: statistical inefficiency of production region : 1.0\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: effective number of uncorrelated samples : 26.0\n", - "DEBUG:openmmtools.multistate.multistateanalyzer:Assembling uncorrelated energies...\n", - "DEBUG:openmmtools.multistate.multistateanalyzer:Done.\n", - "DEBUG:openmmtools.multistate.multistateanalyzer:Computing free energy differences...\n", - "DEBUG:openmmtools.multistate.multistateanalyzer:Done.\n", - "DEBUG:openmmtools.multistate.multistateanalyzer:Computing covariance matrix...\n", - "DEBUG:openmmtools.multistate.multistateanalyzer:Deltaf_ij:\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: 0.000 -1.386 -2.275 -2.813 -3.799 -5.230 -2.853 -0.406 2.055 4.377 6.284\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: 1.386 0.000 -0.889 -1.428 -2.413 -3.844 -1.467 0.980 3.440 5.762 7.670\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: 2.275 0.889 0.000 -0.538 -1.523 -2.954 -0.578 1.869 4.330 6.652 8.559\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: 2.813 1.428 0.538 0.000 -0.985 -2.416 -0.040 2.407 4.868 7.190 9.098\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: 3.799 2.413 1.523 0.985 0.000 -1.431 0.945 3.392 5.853 8.175 10.083\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: 5.230 3.844 2.954 2.416 1.431 0.000 2.376 4.823 7.284 9.606 11.514\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: 2.853 1.467 0.578 0.040 -0.945 -2.376 0.000 2.447 4.908 7.230 9.137\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: 0.406 -0.980 -1.869 -2.407 -3.392 -4.823 -2.447 0.000 2.461 4.783 6.690\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: -2.055 -3.440 -4.330 -4.868 -5.853 -7.284 -4.908 -2.461 0.000 2.322 4.229\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: -4.377 -5.762 -6.652 -7.190 -8.175 -9.606 -7.230 -4.783 -2.322 0.000 1.908\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: -6.284 -7.670 -8.559 -9.098 -10.083 -11.514 -9.137 -6.690 -4.229 -1.908 0.000\n", - "DEBUG:openmmtools.multistate.multistateanalyzer:dDeltaf_ij:\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: 0.000 0.025 0.072 0.147 0.196 0.204 0.204 0.208 0.221 0.250 0.310\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: 0.025 0.000 0.051 0.132 0.184 0.193 0.193 0.198 0.211 0.241 0.304\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: 0.072 0.051 0.000 0.089 0.149 0.159 0.159 0.165 0.181 0.216 0.284\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: 0.147 0.132 0.089 0.000 0.072 0.085 0.086 0.099 0.125 0.172 0.252\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: 0.196 0.184 0.149 0.072 0.000 0.020 0.035 0.064 0.102 0.157 0.243\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: 0.204 0.193 0.159 0.085 0.020 0.000 0.034 0.066 0.105 0.160 0.244\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: 0.204 0.193 0.159 0.086 0.035 0.034 0.000 0.035 0.078 0.139 0.231\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: 0.208 0.198 0.165 0.099 0.064 0.066 0.035 0.000 0.045 0.114 0.213\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: 0.221 0.211 0.181 0.125 0.102 0.105 0.078 0.045 0.000 0.074 0.183\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: 0.250 0.241 0.216 0.172 0.157 0.160 0.139 0.114 0.074 0.000 0.117\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: 0.310 0.304 0.284 0.252 0.243 0.244 0.231 0.213 0.183 0.117 0.000\n", - "DEBUG:openmmtools.multistate.multistateanalyzer:Checking if we need to unbias the restraint...\n", - "DEBUG:openmmtools.multistate.multistateanalyzer:Trying to get radially symmetric restraint data...\n", - "DEBUG:openmmtools.multistate.multistateanalyzer:Retrieving end thermodynamic states...\n", - "DEBUG:openmmtools.multistate.multistateanalyzer:Isolating restraint force...\n", - "DEBUG:openmmtools.multistate.multistateanalyzer:No force of type could be found. The restraint will not be unbiased.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "DEBUG:openmmtools.multistate.multistateanalyzer:Reading energies...\n", - "DEBUG:openmmtools.multistate.multistatereporter:read_replica_thermodynamic_states: iteration = [ 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23\n", - " 24 25]\n", - "DEBUG:openmmtools.multistate.multistateanalyzer:Done.\n", - "DEBUG:openmmtools.multistate.multistateanalyzer:Assembling effective timeseries...\n", - "DEBUG:openmmtools.multistate.multistateanalyzer:Done.\n", - "DEBUG:openmmtools.multistate.multistateanalyzer:Could not find t0: Online Analysis information was never written!\n", - "DEBUG:openmmtools.multistate.multistateanalyzer:Equilibration data:\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: number of iterations discarded to equilibration : 4\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: statistical inefficiency of production region : 1.493607521057129\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: effective number of uncorrelated samples : 15.398958206176758\n", - "DEBUG:openmmtools.multistate.multistateanalyzer:Assembling uncorrelated energies...\n", - "DEBUG:openmmtools.multistate.multistateanalyzer:Done.\n", - "DEBUG:openmmtools.multistate.multistateanalyzer:Computing free energy differences...\n", - "DEBUG:openmmtools.multistate.multistateanalyzer:Done.\n", - "DEBUG:openmmtools.multistate.multistateanalyzer:Computing covariance matrix...\n", - "DEBUG:openmmtools.multistate.multistateanalyzer:Deltaf_ij:\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: 0.000 -1.450 -2.499 -3.342 -4.577 -6.057 -3.615 -1.122 1.418 3.995 6.463\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: 1.450 0.000 -1.049 -1.892 -3.127 -4.607 -2.165 0.328 2.868 5.445 7.913\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: 2.499 1.049 0.000 -0.843 -2.078 -3.559 -1.116 1.377 3.917 6.494 8.962\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: 3.342 1.892 0.843 0.000 -1.235 -2.715 -0.273 2.220 4.760 7.337 9.805\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: 4.577 3.127 2.078 1.235 0.000 -1.481 0.962 3.455 5.995 8.572 11.040\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: 6.057 4.607 3.559 2.715 1.481 0.000 2.442 4.936 7.476 10.052 12.521\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: 3.615 2.165 1.116 0.273 -0.962 -2.442 0.000 2.493 5.033 7.610 10.078\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: 1.122 -0.328 -1.377 -2.220 -3.455 -4.936 -2.493 0.000 2.540 5.117 7.585\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: -1.418 -2.868 -3.917 -4.760 -5.995 -7.476 -5.033 -2.540 0.000 2.577 5.045\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: -3.995 -5.445 -6.494 -7.337 -8.572 -10.052 -7.610 -5.117 -2.577 0.000 2.468\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: -6.463 -7.913 -8.962 -9.805 -11.040 -12.521 -10.078 -7.585 -5.045 -2.468 0.000\n", - "DEBUG:openmmtools.multistate.multistateanalyzer:dDeltaf_ij:\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: 0.000 0.027 0.084 0.162 0.200 0.211 0.211 0.219 0.234 0.254 0.305\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: 0.027 0.000 0.061 0.145 0.186 0.197 0.197 0.207 0.222 0.244 0.297\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: 0.084 0.061 0.000 0.091 0.139 0.152 0.153 0.166 0.186 0.212 0.271\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: 0.162 0.145 0.091 0.000 0.057 0.074 0.080 0.105 0.136 0.172 0.241\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: 0.200 0.186 0.139 0.057 0.000 0.023 0.047 0.087 0.125 0.165 0.237\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: 0.211 0.197 0.152 0.074 0.023 0.000 0.046 0.088 0.128 0.167 0.239\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: 0.211 0.197 0.153 0.080 0.047 0.046 0.000 0.045 0.088 0.133 0.215\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: 0.219 0.207 0.166 0.105 0.087 0.088 0.045 0.000 0.046 0.096 0.189\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: 0.234 0.222 0.186 0.136 0.125 0.128 0.088 0.046 0.000 0.054 0.160\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: 0.254 0.244 0.212 0.172 0.165 0.167 0.133 0.096 0.054 0.000 0.121\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: 0.305 0.297 0.271 0.241 0.237 0.239 0.215 0.189 0.160 0.121 0.000\n", - "DEBUG:openmmtools.multistate.multistateanalyzer:Checking if we need to unbias the restraint...\n", - "DEBUG:openmmtools.multistate.multistateanalyzer:Trying to get radially symmetric restraint data...\n", - "DEBUG:openmmtools.multistate.multistateanalyzer:Retrieving end thermodynamic states...\n", - "DEBUG:openmmtools.multistate.multistateanalyzer:Isolating restraint force...\n", - "DEBUG:openmmtools.multistate.multistateanalyzer:No force of type could be found. The restraint will not be unbiased.\n", - "DEBUG:openmmtools.multistate.multistateanalyzer:Reading energies...\n", - "DEBUG:openmmtools.multistate.multistatereporter:read_replica_thermodynamic_states: iteration = [ 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23\n", - " 24 25]\n", - "DEBUG:openmmtools.multistate.multistateanalyzer:Done.\n", - "DEBUG:openmmtools.multistate.multistateanalyzer:Assembling effective timeseries...\n", - "DEBUG:openmmtools.multistate.multistateanalyzer:Done.\n", - "DEBUG:openmmtools.multistate.multistateanalyzer:Could not find t0: Online Analysis information was never written!\n", - "DEBUG:openmmtools.multistate.multistateanalyzer:Equilibration data:\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: number of iterations discarded to equilibration : 10\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: statistical inefficiency of production region : 1.0\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: effective number of uncorrelated samples : 17.0\n", - "DEBUG:openmmtools.multistate.multistateanalyzer:Assembling uncorrelated energies...\n", - "DEBUG:openmmtools.multistate.multistateanalyzer:Done.\n", - "DEBUG:openmmtools.multistate.multistateanalyzer:Computing free energy differences...\n", - "DEBUG:openmmtools.multistate.multistateanalyzer:Done.\n", - "DEBUG:openmmtools.multistate.multistateanalyzer:Computing covariance matrix...\n", - "DEBUG:openmmtools.multistate.multistateanalyzer:Deltaf_ij:\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: 0.000 -1.388 -2.261 -2.829 -3.735 -5.113 -2.754 -0.320 2.162 4.601 6.760\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: 1.388 0.000 -0.873 -1.441 -2.347 -3.725 -1.366 1.068 3.549 5.989 8.148\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: 2.261 0.873 0.000 -0.568 -1.474 -2.852 -0.493 1.941 4.423 6.862 9.021\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: 2.829 1.441 0.568 0.000 -0.906 -2.284 0.075 2.509 4.991 7.430 9.589\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: 3.735 2.347 1.474 0.906 0.000 -1.378 0.981 3.415 5.896 8.336 10.495\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: 5.113 3.725 2.852 2.284 1.378 0.000 2.359 4.793 7.274 9.714 11.873\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: 2.754 1.366 0.493 -0.075 -0.981 -2.359 0.000 2.434 4.915 7.355 9.514\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: 0.320 -1.068 -1.941 -2.509 -3.415 -4.793 -2.434 0.000 2.482 4.921 7.080\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: -2.162 -3.549 -4.423 -4.991 -5.896 -7.274 -4.915 -2.482 0.000 2.439 4.598\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: -4.601 -5.989 -6.862 -7.430 -8.336 -9.714 -7.355 -4.921 -2.439 0.000 2.159\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: -6.760 -8.148 -9.021 -9.589 -10.495 -11.873 -9.514 -7.080 -4.598 -2.159 0.000\n", - "DEBUG:openmmtools.multistate.multistateanalyzer:dDeltaf_ij:\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: 0.000 0.032 0.097 0.178 0.231 0.244 0.242 0.248 0.262 0.290 0.356\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: 0.032 0.000 0.072 0.160 0.218 0.232 0.230 0.237 0.251 0.280 0.348\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: 0.097 0.072 0.000 0.099 0.174 0.190 0.188 0.197 0.214 0.248 0.323\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "DEBUG:openmmtools.multistate.multistateanalyzer: 0.178 0.160 0.099 0.000 0.098 0.117 0.117 0.131 0.157 0.201 0.289\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: 0.231 0.218 0.174 0.098 0.000 0.029 0.043 0.080 0.120 0.175 0.272\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: 0.244 0.232 0.190 0.117 0.029 0.000 0.044 0.084 0.125 0.179 0.274\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: 0.242 0.230 0.188 0.117 0.043 0.044 0.000 0.042 0.088 0.150 0.255\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: 0.248 0.237 0.197 0.131 0.080 0.084 0.042 0.000 0.048 0.117 0.233\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: 0.262 0.251 0.214 0.157 0.120 0.125 0.088 0.048 0.000 0.074 0.201\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: 0.290 0.280 0.248 0.201 0.175 0.179 0.150 0.117 0.074 0.000 0.137\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: 0.356 0.348 0.323 0.289 0.272 0.274 0.255 0.233 0.201 0.137 0.000\n", - "DEBUG:openmmtools.multistate.multistateanalyzer:Checking if we need to unbias the restraint...\n", - "DEBUG:openmmtools.multistate.multistateanalyzer:Trying to get radially symmetric restraint data...\n", - "DEBUG:openmmtools.multistate.multistateanalyzer:Retrieving end thermodynamic states...\n", - "DEBUG:openmmtools.multistate.multistateanalyzer:Isolating restraint force...\n", - "DEBUG:openmmtools.multistate.multistateanalyzer:No force of type could be found. The restraint will not be unbiased.\n", - "DEBUG:openmmtools.multistate.multistateanalyzer:Reading energies...\n", - "DEBUG:openmmtools.multistate.multistatereporter:read_replica_thermodynamic_states: iteration = [ 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23\n", - " 24 25]\n", - "DEBUG:openmmtools.multistate.multistateanalyzer:Done.\n", - "DEBUG:openmmtools.multistate.multistateanalyzer:Assembling effective timeseries...\n", - "DEBUG:openmmtools.multistate.multistateanalyzer:Done.\n", - "DEBUG:openmmtools.multistate.multistateanalyzer:Could not find t0: Online Analysis information was never written!\n", - "DEBUG:openmmtools.multistate.multistateanalyzer:Equilibration data:\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: number of iterations discarded to equilibration : 7\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: statistical inefficiency of production region : 1.2979750633239746\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: effective number of uncorrelated samples : 15.408616065979004\n", - "DEBUG:openmmtools.multistate.multistateanalyzer:Assembling uncorrelated energies...\n", - "DEBUG:openmmtools.multistate.multistateanalyzer:Done.\n", - "DEBUG:openmmtools.multistate.multistateanalyzer:Computing free energy differences...\n", - "DEBUG:openmmtools.multistate.multistateanalyzer:Done.\n", - "DEBUG:openmmtools.multistate.multistateanalyzer:Computing covariance matrix...\n", - "DEBUG:openmmtools.multistate.multistateanalyzer:Deltaf_ij:\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: 0.000 -0.749 -1.257 -1.839 -2.717 -3.646 -1.597 0.206 1.642 2.413 2.144\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: 0.749 0.000 -0.508 -1.090 -1.968 -2.897 -0.848 0.955 2.391 3.162 2.893\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: 1.257 0.508 0.000 -0.582 -1.460 -2.389 -0.340 1.463 2.899 3.670 3.400\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: 1.839 1.090 0.582 0.000 -0.878 -1.807 0.242 2.045 3.481 4.252 3.982\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: 2.717 1.968 1.460 0.878 0.000 -0.929 1.120 2.923 4.360 5.130 4.861\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: 3.646 2.897 2.389 1.807 0.929 0.000 2.049 3.852 5.288 6.059 5.790\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: 1.597 0.848 0.340 -0.242 -1.120 -2.049 0.000 1.803 3.239 4.010 3.741\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: -0.206 -0.955 -1.463 -2.045 -2.923 -3.852 -1.803 0.000 1.436 2.207 1.938\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: -1.642 -2.391 -2.899 -3.481 -4.360 -5.288 -3.239 -1.436 0.000 0.770 0.501\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: -2.413 -3.162 -3.670 -4.252 -5.130 -6.059 -4.010 -2.207 -0.770 0.000 -0.269\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: -2.144 -2.893 -3.400 -3.982 -4.861 -5.790 -3.741 -1.938 -0.501 0.269 0.000\n", - "DEBUG:openmmtools.multistate.multistateanalyzer:dDeltaf_ij:\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: 0.000 0.036 0.101 0.165 0.181 0.191 0.201 0.231 0.288 0.381 0.496\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: 0.036 0.000 0.068 0.134 0.151 0.162 0.174 0.209 0.271 0.369 0.486\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: 0.101 0.068 0.000 0.069 0.090 0.104 0.123 0.170 0.244 0.350 0.473\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: 0.165 0.134 0.069 0.000 0.025 0.046 0.083 0.148 0.231 0.343 0.467\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: 0.181 0.151 0.090 0.025 0.000 0.022 0.074 0.144 0.229 0.342 0.466\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: 0.191 0.162 0.104 0.046 0.022 0.000 0.071 0.143 0.229 0.342 0.466\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: 0.201 0.174 0.123 0.083 0.074 0.071 0.000 0.081 0.183 0.310 0.444\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: 0.231 0.209 0.170 0.148 0.144 0.143 0.081 0.000 0.113 0.260 0.409\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: 0.288 0.271 0.244 0.231 0.229 0.229 0.183 0.113 0.000 0.166 0.342\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: 0.381 0.369 0.350 0.343 0.342 0.342 0.310 0.260 0.166 0.000 0.214\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: 0.496 0.486 0.473 0.467 0.466 0.466 0.444 0.409 0.342 0.214 0.000\n", - "DEBUG:openmmtools.multistate.multistateanalyzer:Checking if we need to unbias the restraint...\n", - "DEBUG:openmmtools.multistate.multistateanalyzer:Trying to get radially symmetric restraint data...\n", - "DEBUG:openmmtools.multistate.multistateanalyzer:Retrieving end thermodynamic states...\n", - "DEBUG:openmmtools.multistate.multistateanalyzer:Isolating restraint force...\n", - "DEBUG:openmmtools.multistate.multistateanalyzer:No force of type could be found. The restraint will not be unbiased.\n", - "DEBUG:openmmtools.multistate.multistateanalyzer:Reading energies...\n", - "DEBUG:openmmtools.multistate.multistatereporter:read_replica_thermodynamic_states: iteration = [ 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23\n", - " 24 25]\n", - "DEBUG:openmmtools.multistate.multistateanalyzer:Done.\n", - "DEBUG:openmmtools.multistate.multistateanalyzer:Assembling effective timeseries...\n", - "DEBUG:openmmtools.multistate.multistateanalyzer:Done.\n", - "DEBUG:openmmtools.multistate.multistateanalyzer:Could not find t0: Online Analysis information was never written!\n", - "DEBUG:openmmtools.multistate.multistateanalyzer:Equilibration data:\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: number of iterations discarded to equilibration : 8\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: statistical inefficiency of production region : 1.0\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: effective number of uncorrelated samples : 19.0\n", - "DEBUG:openmmtools.multistate.multistateanalyzer:Assembling uncorrelated energies...\n", - "DEBUG:openmmtools.multistate.multistateanalyzer:Done.\n", - "DEBUG:openmmtools.multistate.multistateanalyzer:Computing free energy differences...\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Complex dG: 3.8525873030019717 kcal/mol, err 0.11626215732671775 kcal/mol\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "DEBUG:openmmtools.multistate.multistateanalyzer:Done.\n", - "DEBUG:openmmtools.multistate.multistateanalyzer:Computing covariance matrix...\n", - "DEBUG:openmmtools.multistate.multistateanalyzer:Deltaf_ij:\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: 0.000 -0.730 -1.169 -1.637 -2.461 -3.356 -1.398 0.361 1.743 2.320 1.616\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: 0.730 0.000 -0.438 -0.907 -1.730 -2.626 -0.668 1.092 2.474 3.050 2.346\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: 1.169 0.438 0.000 -0.468 -1.292 -2.187 -0.230 1.530 2.912 3.489 2.784\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: 1.637 0.907 0.468 0.000 -0.824 -1.719 0.238 1.998 3.380 3.957 3.252\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: 2.461 1.730 1.292 0.824 0.000 -0.895 1.062 2.822 4.204 4.780 4.076\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: 3.356 2.626 2.187 1.719 0.895 0.000 1.957 3.717 5.099 5.676 4.971\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: 1.398 0.668 0.230 -0.238 -1.062 -1.957 0.000 1.760 3.142 3.718 3.014\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: -0.361 -1.092 -1.530 -1.998 -2.822 -3.717 -1.760 0.000 1.382 1.958 1.254\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: -1.743 -2.474 -2.912 -3.380 -4.204 -5.099 -3.142 -1.382 0.000 0.577 -0.128\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: -2.320 -3.050 -3.489 -3.957 -4.780 -5.676 -3.718 -1.958 -0.577 0.000 -0.704\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: -1.616 -2.346 -2.784 -3.252 -4.076 -4.971 -3.014 -1.254 0.128 0.704 0.000\n", - "DEBUG:openmmtools.multistate.multistateanalyzer:dDeltaf_ij:\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: 0.000 0.033 0.093 0.159 0.177 0.186 0.195 0.223 0.282 0.382 0.492\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: 0.033 0.000 0.063 0.134 0.154 0.163 0.173 0.205 0.268 0.371 0.484\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: 0.093 0.063 0.000 0.077 0.100 0.111 0.126 0.167 0.240 0.352 0.470\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: 0.159 0.134 0.077 0.000 0.027 0.044 0.076 0.135 0.220 0.339 0.460\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: 0.177 0.154 0.100 0.027 0.000 0.020 0.065 0.130 0.217 0.337 0.459\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: 0.186 0.163 0.111 0.044 0.020 0.000 0.061 0.127 0.216 0.336 0.458\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: 0.195 0.173 0.126 0.076 0.065 0.061 0.000 0.074 0.177 0.310 0.440\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: 0.223 0.205 0.167 0.135 0.130 0.127 0.074 0.000 0.113 0.265 0.408\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: 0.282 0.268 0.240 0.220 0.217 0.216 0.177 0.113 0.000 0.174 0.344\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: 0.382 0.371 0.352 0.339 0.337 0.336 0.310 0.265 0.174 0.000 0.205\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: 0.492 0.484 0.470 0.460 0.459 0.458 0.440 0.408 0.344 0.205 0.000\n", - "DEBUG:openmmtools.multistate.multistateanalyzer:Checking if we need to unbias the restraint...\n", - "DEBUG:openmmtools.multistate.multistateanalyzer:Trying to get radially symmetric restraint data...\n", - "DEBUG:openmmtools.multistate.multistateanalyzer:Retrieving end thermodynamic states...\n", - "DEBUG:openmmtools.multistate.multistateanalyzer:Isolating restraint force...\n", - "DEBUG:openmmtools.multistate.multistateanalyzer:No force of type could be found. The restraint will not be unbiased.\n", - "DEBUG:openmmtools.multistate.multistateanalyzer:Reading energies...\n", - "DEBUG:openmmtools.multistate.multistatereporter:read_replica_thermodynamic_states: iteration = [ 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23\n", - " 24 25]\n", - "DEBUG:openmmtools.multistate.multistateanalyzer:Done.\n", - "DEBUG:openmmtools.multistate.multistateanalyzer:Assembling effective timeseries...\n", - "DEBUG:openmmtools.multistate.multistateanalyzer:Done.\n", - "DEBUG:openmmtools.multistate.multistateanalyzer:Could not find t0: Online Analysis information was never written!\n", - "DEBUG:openmmtools.multistate.multistateanalyzer:Equilibration data:\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: number of iterations discarded to equilibration : 4\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: statistical inefficiency of production region : 1.2276790142059326\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: effective number of uncorrelated samples : 18.734539031982422\n", - "DEBUG:openmmtools.multistate.multistateanalyzer:Assembling uncorrelated energies...\n", - "DEBUG:openmmtools.multistate.multistateanalyzer:Done.\n", - "DEBUG:openmmtools.multistate.multistateanalyzer:Computing free energy differences...\n", - "DEBUG:openmmtools.multistate.multistateanalyzer:Done.\n", - "DEBUG:openmmtools.multistate.multistateanalyzer:Computing covariance matrix...\n", - "DEBUG:openmmtools.multistate.multistateanalyzer:Deltaf_ij:\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: 0.000 -0.717 -1.100 -1.525 -2.384 -3.327 -1.298 0.551 1.951 2.492 2.054\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: 0.717 0.000 -0.383 -0.808 -1.666 -2.610 -0.580 1.269 2.669 3.209 2.771\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: 1.100 0.383 0.000 -0.425 -1.283 -2.227 -0.198 1.651 3.052 3.592 3.154\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: 1.525 0.808 0.425 0.000 -0.858 -1.802 0.228 2.077 3.477 4.017 3.579\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: 2.384 1.666 1.283 0.858 0.000 -0.943 1.086 2.935 4.335 4.875 4.437\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: 3.327 2.610 2.227 1.802 0.943 0.000 2.029 3.878 5.278 5.819 5.381\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: 1.298 0.580 0.198 -0.228 -1.086 -2.029 0.000 1.849 3.249 3.790 3.351\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: -0.551 -1.269 -1.651 -2.077 -2.935 -3.878 -1.849 0.000 1.400 1.941 1.502\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: -1.951 -2.669 -3.052 -3.477 -4.335 -5.278 -3.249 -1.400 0.000 0.540 0.102\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: -2.492 -3.209 -3.592 -4.017 -4.875 -5.819 -3.790 -1.941 -0.540 0.000 -0.438\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: -2.054 -2.771 -3.154 -3.579 -4.437 -5.381 -3.351 -1.502 -0.102 0.438 0.000\n", - "DEBUG:openmmtools.multistate.multistateanalyzer:dDeltaf_ij:\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: 0.000 0.038 0.100 0.175 0.196 0.207 0.210 0.234 0.295 0.390 0.476\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: 0.038 0.000 0.068 0.149 0.172 0.183 0.187 0.214 0.280 0.379 0.466\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: 0.100 0.068 0.000 0.088 0.114 0.128 0.135 0.171 0.250 0.357 0.449\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: 0.175 0.149 0.088 0.000 0.031 0.051 0.072 0.130 0.226 0.342 0.437\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: 0.196 0.172 0.114 0.031 0.000 0.022 0.059 0.125 0.224 0.341 0.436\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: 0.207 0.183 0.128 0.051 0.022 0.000 0.059 0.127 0.225 0.342 0.437\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: 0.210 0.187 0.135 0.072 0.059 0.059 0.000 0.075 0.188 0.317 0.418\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: 0.234 0.214 0.171 0.130 0.125 0.127 0.075 0.000 0.126 0.271 0.383\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: 0.295 0.280 0.250 0.226 0.224 0.225 0.188 0.126 0.000 0.162 0.302\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: 0.390 0.379 0.357 0.342 0.341 0.342 0.317 0.271 0.162 0.000 0.176\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: 0.476 0.466 0.449 0.437 0.436 0.437 0.418 0.383 0.302 0.176 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Solvent dG: 1.147969963054243 kcal/mol, err 0.13666481503768743 kcal/mol\n" - ] - } - ], + "outputs": [], "source": [ "# Get the complex and solvent results\n", "complex_results = rbfe_transform.gather([complex_dag_results])\n", diff --git a/openmm-rbfe/Untitled.ipynb b/openmm-rbfe/Untitled.ipynb deleted file mode 100644 index 80ee091..0000000 --- a/openmm-rbfe/Untitled.ipynb +++ /dev/null @@ -1,33 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "id": "6e5c6147", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python openFE", - "language": "python", - "name": "openfe" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.13" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/setup/Lomap_OpenFE_Comparison.ipynb b/setup/Lomap_OpenFE_Comparison.ipynb index a038051..0f4ce17 100644 --- a/setup/Lomap_OpenFE_Comparison.ipynb +++ b/setup/Lomap_OpenFE_Comparison.ipynb @@ -14,7 +14,7 @@ }, { "cell_type": "code", - "execution_count": 49, + "execution_count": 1, "id": "52e2f9e8", "metadata": {}, "outputs": [ @@ -71,7 +71,7 @@ }, { "cell_type": "code", - "execution_count": 60, + "execution_count": 2, "id": "d06e63f0", "metadata": {}, "outputs": [], @@ -94,7 +94,7 @@ " m = Chem.MolFromMolFile(fname, removeHs=False)\n", " # OpenFE lightly wraps rdkit molecules\n", " # to make them hashable and immutable\n", - " smallmols.append(openfe.setup.SmallMoleculeComponent.from_rdkit(m))" + " smallmols.append(openfe.SmallMoleculeComponent.from_rdkit(m))" ] }, { @@ -112,7 +112,7 @@ }, { "cell_type": "code", - "execution_count": 58, + "execution_count": 3, "id": "7551f09d", "metadata": {}, "outputs": [ @@ -120,12 +120,12 @@ "name": "stdout", "output_type": "stream", "text": [ - "Score is 0.7981034820053445\n" + "Score is 0.6321205588285577\n" ] }, { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "" ] @@ -136,8 +136,8 @@ ], "source": [ "# LomapAtomMapper wraps the lomap.mcs.MCS object\n", - "mapper = openfe.setup.LomapAtomMapper()\n", - "scorer = openfe.setup.lomap_scorers.default_lomap_score\n", + "mapper = openfe.LomapAtomMapper()\n", + "scorer = openfe.lomap_scorers.default_lomap_score\n", "\n", "molA = smallmols[0]\n", "molB = smallmols[1]\n", @@ -163,7 +163,7 @@ }, { "cell_type": "code", - "execution_count": 55, + "execution_count": 4, "id": "a79d3ebe", "metadata": {}, "outputs": [], @@ -200,36 +200,36 @@ }, { "cell_type": "code", - "execution_count": 56, + "execution_count": 5, "id": "c8d438da", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([[0. , 0.20189652, 0.90483742, 0.74081822, 0.81873075,\n", + "array([[0. , 0.36787944, 0.90483742, 0.74081822, 0.81873075,\n", " 0.77880078, 0.52204578, 0.22313016, 0.54881164, 0.54881164],\n", - " [0.20189652, 0. , 0.22313016, 0.27253179, 0.24659696,\n", + " [0.36787944, 0. , 0.22313016, 0.27253179, 0.24659696,\n", " 0.23457029, 0.67032005, 0.27253179, 0.67032005, 0.67032005],\n", " [0.90483742, 0.22313016, 0. , 0.81873075, 0.90483742,\n", " 0.86070798, 0.4965853 , 0.24659696, 0.60653066, 0.4965853 ],\n", " [0.74081822, 0.27253179, 0.81873075, 0. , 0.74081822,\n", - " 0.70468809, 0.27253179, 0.20189652, 0.4965853 , 0.27253179],\n", + " 0.70468809, 0.4965853 , 0.20189652, 0.4965853 , 0.4965853 ],\n", " [0.81873075, 0.24659696, 0.90483742, 0.74081822, 0. ,\n", " 0.95122942, 0.30119421, 0.27253179, 0.67032005, 0.30119421],\n", " [0.77880078, 0.23457029, 0.86070798, 0.70468809, 0.95122942,\n", - " 0. , 0.52204578, 0.25924026, 0.63762815, 0.52204578],\n", - " [0.52204578, 0.67032005, 0.4965853 , 0.27253179, 0.30119421,\n", - " 0.52204578, 0. , 0.33287108, 0.81873075, 0.95122942],\n", + " 0. , 0.2865048 , 0.25924026, 0.63762815, 0.52204578],\n", + " [0.52204578, 0.67032005, 0.4965853 , 0.4965853 , 0.30119421,\n", + " 0.2865048 , 0. , 0.33287108, 0.81873075, 0.95122942],\n", " [0.22313016, 0.27253179, 0.24659696, 0.20189652, 0.27253179,\n", " 0.25924026, 0.33287108, 0. , 0.40656966, 0.33287108],\n", " [0.54881164, 0.67032005, 0.60653066, 0.4965853 , 0.67032005,\n", " 0.63762815, 0.81873075, 0.40656966, 0. , 0.81873075],\n", - " [0.54881164, 0.67032005, 0.4965853 , 0.27253179, 0.30119421,\n", + " [0.54881164, 0.67032005, 0.4965853 , 0.4965853 , 0.30119421,\n", " 0.52204578, 0.95122942, 0.33287108, 0.81873075, 0. ]])" ] }, - "execution_count": 56, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -240,17 +240,17 @@ }, { "cell_type": "code", - "execution_count": 57, + "execution_count": 6, "id": "b0a90b3e", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "True" + "False" ] }, - "execution_count": 57, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } diff --git a/setup/openFE_AtomMappers.ipynb b/setup/openFE_AtomMappers.ipynb index 7d65f62..9afe623 100644 --- a/setup/openFE_AtomMappers.ipynb +++ b/setup/openFE_AtomMappers.ipynb @@ -66,22 +66,7 @@ "execution_count": 2, "id": "5741762f-4200-4c20-898e-86535edcc70f", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/riesbenj/Programs/miniconda3/envs/openfe/lib/python3.9/site-packages/numpy/core/getlimits.py:499: UserWarning: The value of the smallest subnormal for type is zero.\n", - " setattr(self, word, getattr(machar, word).flat[0])\n", - "/home/riesbenj/Programs/miniconda3/envs/openfe/lib/python3.9/site-packages/numpy/core/getlimits.py:89: UserWarning: The value of the smallest subnormal for type is zero.\n", - " return self._float_to_str(self.smallest_subnormal)\n", - "/home/riesbenj/Programs/miniconda3/envs/openfe/lib/python3.9/site-packages/numpy/core/getlimits.py:499: UserWarning: The value of the smallest subnormal for type is zero.\n", - " setattr(self, word, getattr(machar, word).flat[0])\n", - "/home/riesbenj/Programs/miniconda3/envs/openfe/lib/python3.9/site-packages/numpy/core/getlimits.py:89: UserWarning: The value of the smallest subnormal for type is zero.\n", - " return self._float_to_str(self.smallest_subnormal)\n" - ] - } - ], + "outputs": [], "source": [ "import openfe\n", "import os\n", @@ -101,7 +86,7 @@ " m = Chem.MolFromMolFile(fname, removeHs=False)\n", " # OpenFE lightly wraps rdkit molecules\n", " # to make them hashable and immutable\n", - " smallmols.append(openfe.setup.SmallMoleculeComponent.from_rdkit(m))" + " smallmols.append(openfe.SmallMoleculeComponent.from_rdkit(m))" ] }, { @@ -115,7 +100,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 3, "id": "12ded123-49f3-4b55-9426-9da5caa3debc", "metadata": {}, "outputs": [ @@ -123,12 +108,12 @@ "name": "stdout", "output_type": "stream", "text": [ - "Score is 0.7981034820053445\n" + "Score is 0.6321205588285577\n" ] }, { "data": { - "image/png": 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\n", + "image/png": 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\n", "text/plain": [ "" ] @@ -139,8 +124,8 @@ ], "source": [ "# LomapAtomMapper wraps the lomap.mcs.MCS object\n", - "mapper = openfe.setup.LomapAtomMapper()\n", - "scorer = openfe.setup.lomap_scorers.default_lomap_score\n", + "mapper = openfe.LomapAtomMapper()\n", + "scorer = openfe.lomap_scorers.default_lomap_score\n", "\n", "molA = smallmols[0]\n", "molB = smallmols[1]\n", @@ -164,7 +149,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 4, "id": "0af3a289-7710-4c9a-aade-cebc196bbd75", "metadata": {}, "outputs": [ @@ -180,12 +165,12 @@ "name": "stdout", "output_type": "stream", "text": [ - "Score is 1.0\n" + "Score is 0.7534030360583935\n" ] }, { "data": { - "image/png": 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\n", + "image/png": 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\n", "text/plain": [ "" ] @@ -196,7 +181,7 @@ ], "source": [ "# LomapAtomMapper wraps the lomap.mcs.MCS object\n", - "mapper = openfe.setup.PersesAtomMapper()\n", + "mapper = openfe.PersesAtomMapper()\n", "#scorer = openfe.setup.lomap_scorers.default_lomap_score\n", "\n", "molA = smallmols[0]\n", @@ -211,79 +196,6 @@ "perses_mapping" ] }, - { - "cell_type": "code", - "execution_count": 25, - "id": "d751e147-c264-49d8-9fe2-68ee5aab59c2", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{10: 0, 24: 13, 25: 14, 26: 15}" - ] - }, - "execution_count": 25, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "mapper._atom_mappings.new_to_old_atom_map" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "id": "2568d3eb-e884-4b9d-bf24-c284a492dae9", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "14" - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "len(lomap_mapping.molA_to_molB)" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "id": "b92ed8ee-5d06-4256-8e1f-9a155fbca702", - "metadata": {}, - "outputs": [], - "source": [ - "generic_mapping = openfe.setup.LigandAtomMapping(molA, molB, mapper._atom_mappings.new_to_old_atom_map)" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "id": "5e3cf703-4fd9-42ff-9496-837aa69f36bc", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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nvpnJmwpvMh6jhwuWEHGkjNAFyuMa+rCLskdb7Da7lCVFve9Ok0okgtOBinpXa+y1y6WPNxbatUTx9be99KeMY568XGlUraqPrRRz4axQkq2T7HRpbGdc7JNaEgDAw7FXLylaVbPtH8iDQInwX+BqTcTyjXnjBseeP705Tj8g05jhZIZez/+mfhhsWgOiGDxpekX7EHj26iXXH795blw7aqev0e63w3Rw9wzC4qpmAuVy7N3pr0TonRctPR8YAyDbdQTcl7MIeFAI77B7H8+MCYqSFf9CPnmhuKCB6GGJkDAf9oj4AX93tBciIYJKjnfGofi6BwMMOH/POnJ3a8drCFwvLd4vqCotqysKaOnW4wolwn+HqzURKzYbJwytdzJ9SwP9gGvG407m7esFa2PDYPNagaFDP/jk/hMp/4ovKqDSkqmkQ1RGimC3eZ9sp1YQGHxbYD9qp1lBVOJYsOTupl3P7SyfvaqHVLyDsLj8d6Wcg6Hu8bx10xrx3rcJZbENceWjtbweeVjSmDq8xex9/Hygqpa8+G1nZX7xn4Do8Ugiov79m6uKrEFjXEEKDA0ALVXy10M03uevMh7vA0wml9WJ9Vt8SGVCifCecFKpX7zOOHFY3ROpWxro3/TmwhsF38SGwY4tIIqhH855kFwoMjRz9hSVkUynp7CXz3ufFAAuUGyKnTlqp0+7WK7klhiJY7QgDso0elfrAAAm/Y8iOJVF4OlTGQJAhpOFknpvPl/Fqus/1LZtw70SIaZQ+PFgSMRXtL3fYK9fuc9tQrJdx/8s9lTZVE89Y1o4575fIvjrfGyksqFEeD84qYxY/E3exGF1jh/7/omIAdeMJ5zM0Bv538SGw86tIAih0z6911Sh+8Y1KukwlZHMnD4uuotnXYo8/HEnk+JgDtuoAk/xXBCBQVOlLEFDdtIqYuXSIdfzL9PuvteMm+P0oVJCdDMiz1XkhIfyYS6dFxz2S5TbwvFRMkm0TCIJ00tr1fFtL0RAoH7Byrz3hq+uHx5SZhy8sE5IbblEmdjNj2cxIr6i/t8rlg0rudw79/oCaVTtqoznXxFaHchkwHn+9bMYSQZNmIbJ5FUcFVI1am5lmQcnMnTepOF0Rkqum3vzmjHHzTVTyjfEhesIXNv7jdDpn5XmQq4wn05LotKS6PTk0rkgRhBPuphjdibFQV+k3KX/3dEySaKWTNAoOmlJHXE3m5o4fmBm/jXaHauQborTh0kJVffnwj9b4sPdew/Csv5r87L5a/Jtc3MtrwWrP68donnh1bBZ8yujL/bqxYKPJ3tuXRc93FE7/WmOuaNGMSsmWPtKv9Dpn1VGj0gV4/Lzcof04WwWKHPeJCfCXovzhUCVRK6otfOgJDLajxGaly+wrFt21uWW41hD8u7wVAQgSKXm5b4h7830Y3hIpaquidDhcFgsllq1agHAsWPHOnToUKnFcEWGznt3OJ2eYnBzb2Yas1muqVK2ob4+QIJrevXR9HyJOnGMTk9hr1woPfI3m+VSHPRhG53ioFnh7sxna5Wik1aRoCGbKkv+2DCAv/4QTBw/KDP/Ku2up5BujtOHSQllYjf9/BVVOX1kGD2AzkgZcj0/yU5/VTf0hUBV2OwvNf97ufJ6dOzbXTBz4hkX++rVvDpy6cEmUZKIqNp7/XZCMuJbAkVZvlnq+GmbQNOixw0ijLxRcMhG/V9M8JuhGvXT/wv/okpL+5blyc3OfvVpjmV6XTbcZD3fxIYnakmMIEQMk9d/Imjc+8qOXfwVG1IFqmUi5Hl+7dq1eXl5s2bNOnTo0Jw5c37++We1Wl2pnYput/H90VTSYYObG5BpvMNyTZSyb+vrAyR3B3MWjk9zMCkO5k87necuXmCGAzQumflsp1bISm4rSsL1ZHwXZXxnSURU/tRxvN1adi+BmeMHZeZfod115dLNDfThUkKZ0FW/YEXVTM6IbjbrqZZuhm599g4jiMeaxYRKidr7UiXh+krslOdudWvtcTg6nL9j5YQjTaJj5JJaPx6u+ElPyCOFNxWali9w7N7+m5Uac7NAR+CHmkQHSPDI1d+RbTr4JSTj+2Nch/Z/W2D/JMdcSy75tXGUXCLRL/6GbN0Bkyv8EhJSlarlhnqCIN5++20AoCjq8OHDzzzzTBV0islk+nlfKzs9GSmTbInT15ZLL1LuQdeNJg9/gXIvybP2vmLocC57fFbhd0WOPDcXIiWeC1TNqRWc3CxmV8PIKVGBCRpSriDJDgnB46dGb95T+5djYR/NVT/TS9GsVa1dR0ImTVe0aEuEhkmjYqRRMUESYksDfTOlPIv1vHnNaPRwVMoR4+RR91lg6UPM2ZMiy5xxsbQgxpGyUCkhrRNbqVkQADBCQraNJzCIV5MAkOygAYBKPVqpnSJVjwgODZk4jQgIfCZAmaglbbywJM8KAKaFs/2yrZ4+keo6tN/GC0uNVgCYHh0kwzBd37eUHZ9EWbCGqJaJsNTPP//MsuzRo0fT0tKqoDtMJtd/uUb1ZI8ImWRLg/A6cuklyp14Iaf3FcOSPOsFyi3Dsc5aclp00L5GkWnNYpbWDe0bogmTEtKoWtpX+um/Wlv3jzORX28OeGuUvFGzsitOMZlM26e/OPurfjbcOmdZrV1HNL366Aj827jw5kr5LdbT96oxx81RKX8Y3x1R9rgM37p9+/bvv/zy0/gRN8e/BXc3NSqgqs4HJjskAkBnrQIAku00ANDpqMDjYwjX6gJHTQKAGdFBBAZbiuxXaTd79ZJ91/aqDkXgixZ+AgBfGSxWTuioUXTXKXFtQJWdOYM8CqprIlyzZo3FYmnfvv2CBQtGjhyZmJhYNf1iUmn43K9VnbvrpZL19cNlGCbH8Wi5pG+IZnVs2MnmtdbXDx8apm1AyojAIHWP50Onf1Z7X2qtPX+GTv9M1aX7/Sc2P/vss7S0tO7du585dz7s43maF17VEviGuPAWKnlOyTodKvVo5eVCFUsr5s/cvXOHyLqhpMSot8wNERBYGT3+jbLjkwCQqCUBIMXBcKJIH08tXXOLVCWTyTR79uwJEybYbLaxY8euXr2a5305XNP16S+La1hfIe0bouFF+DTHDADmrxcIJQUWqob9x+/c1y5fZzzbihwEBjOigwAgeNwUXBvgw16OHz/+5ptvchy3bNmy2bNnHz3qg3mO77777oMPPsjJyVm9evX06dPT09Mr2GBmZubIkSOzsrIAYPfu3R9//HHFg0xPT+/fvz8ALF26dPr06UlJj+h1bXVNhKNHj168eHHdOnXYi+e6cU5m5xYq5UjlDZXKwqRS7esDASDHzblFMVpG/NEkek6t4G46pUIqkTdqFjhiQvTmPXV+Ox4+d7n2lX4PPqO4aNGi3r17FxUVde3a9fjJk2Efz9O+OkBL4Bvqh7dUyXPdXP9rxmyWo9KS8sa/Jdx7V1b5iBxHvT9K67Do8eLtjGddLIFBO7UCACyb1nhys33b4z9JY2pLo2pFyST1FFInL5x1uQWaYs6jAo9+EBQUNG3atJYtW2ZkZABAs2bNfLweDSdCJn8EAJMiAgMk+DEHc8hG8RazZW3VLZkRHHbzyi8BYE6OmRfhzRDtE6RM1qCR9uU3fNtRu3bt4uLiRFHMzMycOXPm1q0+OGS0b9++BEFER0ePGDECAFq3bl3BBuPi4rp06ULTtM1mu3TpEsf5oIxOhw4d6tWrBwCjRo2aMmXKgQMHKt5mZaiuiRAAmHOnbvd+yjCqf9FXn5mXzc+fNuFW9zbWbRug8pf/0CfTACC1TMkVWYPGEUvW1fnzXPTmPUEjJ8obNStHKTKZTLZ9+/aXX37ZarU+88wz6RkZoR/M1r0+SEPg6+uHt1LJvWtW77AcfTI97x0f50LHnh/4osIdBfaXgtQAkOFkPKLYVCn3VoYU3ax5cVXsZCA7dgaAzsWDQhoA6NRH9Cry8YZhmNPpvHz58pNPPjl58uTk5OTTp0/7tguybUdVt54BEny8PgAAPs0xu0XR9t16z+2bvu3oXsyrF/MW8+9WKtlO6wj8nYgAAAiZ/BHglbUE/amnnlq8eLFv2zQYDHq9XiqV+qrBadOmNWjQICsry3uosk9IpdIVK1YMGTLEVw36VnVNhFTKH4bRb/528YrD4RAZWnSzgtNxNN90c/HnhXMqvRZJSe2xu3VBg0ZNUiZ0rfhZ21Kp9IcffujXr5/Van322WfT0tND3p+le2OQhsA3xuk7aBTeXHib9TCnj+eNHyxQroq/HIGm6PQU88ovBZoKkhBhUgL+eoMQAIDnXUmHRV9cJN6fMr4zACRoFABw1E4DgG/PgfPk3HYdOeA88DN79WIVXDNVX7du3erTp0+LFi0uXLiwd+/emzdvhoeH+7yX4InTMJl8QKimASm7w3LfFthFjiv68lOfd/RPnls37Ns3ekRxbq4FACZFBgZIcHWP58g28T7v6/bt25cuXTpy5IhCoeA4rlevXhVvMz09/fLly5cuXdqwYcOgQYMq3qDZbM7IyDhy5MiYMWPq1KmjUCgqPgdw69aty5cvHzlypH///hiGmUyP6Eky1XP7hMV858UnBcoZfz57S5w+VlF8KfTspdwvaoe0CQkMnbVA3eM5H/ZI07TZbI6KisrMzJRyHn7Qiy6Pp825bBHEk81rqaXSuodP4Rqtr7rjeX7w4MFbtmzR6XT79+/vGB9ftGC27bsNtCAOv5Gf5mBCpcSmOH19hVTRom3Esg24UvXQfQg8e/USnZ5CZSQzpzJET3FBjQIP793++IeNcovix9HB/UOLiy5iSmXMd79Ko2J89TL/PS6X81a3VpTb0+bsHR7E9Ga1AmSSOgcyKn4Ahfva5fxZk7nbWUAQACKIGKYkQz+co3qqKlYdVzsej8c7IFCr1S6XS6FQKBSVsoTSvGy+Zf3XxxzMoEyjisB/bxwVJiUilqxXJjxVGd2VyntnCJXyx0qjbYHBUl8h3dsoUipXxOz4vTJ+w91ut8vlkkgk3vdbrdYH7xUul8vtdpMkKYoiSZIVb5DjOIfDgWFYQEAAANA0XfFmS1+4d6JVJpOpVA//ZlX5qmWJNdv2b0X+3yshAYBAU+alc32YCAVB2LhxY05OTq9evQ4ePFh0/sxLNJvJuDlRbKOWqwlc0bgZrtGuXbvW4/H06dMnLKyipyUQBPHtt99iGLZ58+ann3567969T03+CHActq5bExs+4kZ+qoMZkGncFKePO3sib9zgiKUbcJVaFMWrV696L+Xu1TJvKqRSk6ikQ1TGMcFu9T5JCUKag0myM0l22nsaVKmFBkszlayZUg4AmCBilVm1wAtXqeVNWopnT7RWy9McTJqT7hmgojNS1D1frEizdHpK3nvDTxRZmyllpVs5zxaanvjgncgR7wQOHZOWlnb06NGwsLC4uLhdu3bNn18pNXSqEYkoSlL/dCUdpgqNkrAISZduYrfnKqOkQ8DQsY69OztBfjed8rCN+spg+bx2SNGXc2I6JFZeNSUq+TCV8keRh/cW/p4eHSTBsIBBwyvpOg8rNIq/7HKePS5ynKJRc/a5F+VPNKlQi6KIXzjNHdxnyrqOawLc7Tupn3upgivaJBJJYODdFnySXLG8HOGXnxznT4MoKpo2lz73MsQ2qHizPlctE6Hr8AGRvd92Oq7AyFvMRGCQT7rDcXzEiBEfffRRcnJy//79L8+/cYZiz7lYKJkX9Zainj9//rVr11q1alXxRAgABEFs2LABx/GNGzf26tXr559/7vreTFxBwrrlq2PDR90oSHHQAzONG+uHNzh7MvvVpyWRUYvSTj8RE71AkKze9XPZExv+teo3AGQynsM26pj9LwfhKnG8lUreVUd20ykX5Vn2mF2DMvPX1w9vqZILLFOOAzfKQdmxM3P2RKKG9KbnngEqKi2pIomQt5iNU0aLND0kM/9A48iIksOexmUVro3FlOuWkq3a1a9fPz4+fuzYsX379t2/f7+PXkp1xZw/nf/eCIGmS+feqaRDRV9+FvHVanmTFvRsI6YAACAASURBVL7tC1cqg8ZNKfh48ozooGQ7vdPk7BeqaX7rhn37Rl3/ob7ty6t09nW+weLkhacDlJ21pCRMHzB4tO87EwTTsnm2778FnvfOu9Cnj1t/2KRK7Bo2e2H59ilyhfn57w5nb90UaRpEAQDo9CTz8nkhU2drXnjVx/GXl8hzpvn/Z9+zA3jOe0uFOZVh3fat+uleodM/xXx3R9MnquU9Qt5UVPp4QKYx8XyO96N0NINJpXxRgc/7DQkJyc/Pzzl9MpDAy24tINt1zM3NvXbtmkajadOmja+6Iwhi3bp1gwcPdrlcvXr1Onz4cNDYyYFvjyNxbFVsWKKWLPLwAzPzr9JursDInDkZQTn+OHVad+38nf8l0MePsVcuWDesMIzsn/VUC8OYAdYNK9nL500cv9/imnHH1Ol89nOXcufnWlIcNA9iU6VsZLju27jwUy2iv40LfytMW0sumV879KUglYMX3rqef9rFgigaRg/gCvN99QLvhezYBUrWy/xpowGAOvZnRe7nWbetE+5diECgmaJFn4WEhJw7d65hw4aVNAFYjbBXLuSNfvPn67ePGO/+oR3MK/zlxi3DqP7uMqfs+ormf68omrasJZcMDtMKAJ9km8WSlSw+7wsAvOtxLlLun0xOKYZNjQoEgKDxUyvjwK/CL2batm+clWmg2JJdQDw/94Yh+/DveWMHgyDc97v/heCw5w566dTpMxtv53mzIABctzlW3jIWzf3Isaf8GzFXrVpVdkP2pk2bjhw5Uu7WCmZMsuzZ8dH1XM5TvLBA5Ln/y8wt+HV3/uSR5W62klTLRIhrNKWPN8fpk5tFez/qyIuvMkSex7U6H/b4/fffu93u+Pj43/bsvp6d04CUXaPdJI61VMkxmVzRos3BgwcB4KmnnvLh2i0AIAhi/fr1o0ePpijqhRdeOHjwYNCY9wKHjVfg2OrYsG46pbdC9xXaDQAXKPbDqKBsJ83brYbRA3LefMG0dB59IpVm3SkOen6upfcVQ3xJ7ZsCDx8mJXoHqZfWDc1oXqu09o0Eu/srQWAwr3boS0FqJy8MyjSmOxjP7ZuGkf24AqMPX+M/KRo3w7UBjZWyYAlh9HA3GQ9vKnTfuPaw7YgMTaenmJbOtW1aA577bUZ0X7m4f8+euXPnNmnSJCsrKysr6/z58/f5+seZKBqnjhVo5riTPe+6e/VwjnKfdDICRRunjvP9IiMMC578EWDYOL0uTEqcdrG/WFylext8izebLGuXiQBzcswCwNAwbR25VNGslea5l3zeF3PmhGPfLpGmtxbZmTI57weT00ZTzJUL9l3fPWybRQvn8FbrLYo5ZKNLn8xzc/stlEDTRXNn8eai+3z7fezfv//KlSul/zx8+PDZs2fL1xSVfJhKOszS9JZCh1CmkvLWIgdF0/TJdOfvv5Sv5UpSLadGlR272AzZ9znSGleqJOERPuyxb9++ffv2BYBJiR0KDny31+ISAdqoFTIMU7Rog8kVhw8fBoBu3br5sFMvDMOWL1+OYdjXX3/94osv7t69++nR7wKOW1YvXl4vdNzNwkM26s1rxg1x4a1V8k2F9ie8hfNF0Vv1O8XBHLXTLr74j/Dfq37juLxRU2V8F3mrdta1y9hrF0uPjiMwmFc7BAf4yewcdiN/TWx4/O0sw8h+kSu3VWLFNZxQtu/kPLgvQavYY3Yl2el6CimVdlRW/4kH+W5P7h3q6CFX0qGyB2BZON57vmOfq3lEyQRvQcm1KiaTxYYGvfvuuxiGKRSKd99999G8pV8F6FMZvMX89zLwd4m8qYA5d0rRwmczH17FqWjfrokRAdPumL7IsXTXKWHHFvuP38lq1dG++qb21QEVmU/7aNJEx4nURMrSEjwAsMfsOu5kQqTEKL2uNA377tUUs3y7UqTpe31WpCnLhpXaV/o/eIMCTTl/23OfOouiKNh3bQ8cOubhAvU1y/oVAn3PzV0CTVnWLVc//b+qDOn+qmUi1PUfat+9XbxHIsQVZMCQSpjrBwAA+vgx+OvWArJ9JwDwJsLu3btXRqcYhi1btgzH8WXLlr3wwgs//PDDCyMneu5kOX/ds6xe6PibhQdt1ODM/A1x4fEaRZqDmXHHVLbqNwDUV0i765R/q/pNBIWQrduTHRKVnbtJQotXxqviOzsP7bdtW+fOvAq8gGvUUFTwRe0QDIMfTc7hN/JXx4Z3vHMrd9hrkSu3+nBlQVpamsPhSExM3LZtW+/evcmOXZwH9yVqyD1mV7KDHhympVOTAgYMv9e38xYzfTKNTk+mko+UDlhZQTxRcgDWZdqd3qwWAOx8IqL0HmHnCzneByLniW3UpHStQUSEL6+iqhfm7AmRKX7vznFzaY7iIhW5LOetLy+6WebsSZ8nQgAIGj/V9cdvr4bAd0XOcxS7Jt/2TkQACLz71g3T8gW27RsjV39X+ov6UKy7t9/5fuOUyIDPs4taxgQzgrjQYAGAyZGBGgL3Tsz6+tUAADBnT5ZeUpx0serS89pKhtR8vlGgXA++6tt95SImlXtXSNg4ofSn450TAgCRZem0o+VOhFeuXCmdDjUajS1alPN+cNm1COkORlLyniOW/G+4b2SCwFfefs2HVS0ToTS6dtC4983L5g8L0waVOfxhQKgmQkZIIqIC+g6upK7pE6kAcMxBw90bhJ2uXr2ak5MTFhbWtGnTSuoXw7AlS5ZgGLZ06dJXX331hx9+SIyMxjBMCrC0JBcOuGZkBLF0/iVMSnTWkt4jD4NKzrzF5ApFyzbK9olkhwR5w6b/chWMYeoez6t7PD916tTmzZu/+eab9l3fF346bW7tEBLHthQ6RtzIXxkblmDIMYzsF7lqqzSqlk9eoEQi+eOPPxo2bFhUVFRYWBjb8UkA6KwlMYA0B+MWRUhLznvnraBR78kbN/N+i8hz7PkzrqRDfzsA6xrtTnYwSXb6uJNhyhyAlcncc3YU1wVUTQ25R59gt0NJKbWLlJsXi7dUX6bd3l940eMRnJVSBU0Sptf1H2ZZu2RGTOAbV42r8219gtVRMgkAiDTF5eYYRvSN2f7bw44L6fRky7xZCUrpjgK79ypwZb7N4OaaKGWvBKsxqSRo3JTKeDkAUHpJAQA/m12l16Cl57KBRCK4nA+eCAWXo/RxvofbaSr+6RSWGRUIDsffv+3BcMbcIyfSs/bu8v7zSm5e9+blekMTxbJlEX8yu/C7nyl5hIHAshXfeO0r1TIRAkBAvyGEWj1i3izR7RY57hrt3lzoqCWX6KUSsm18JV1oeLJvc4acXDeXzXJaAm9MynGlSt6o2aHVqwGgW7duWGUuqsQwzFuTYunSpW+88Ub6pFEqUQQAKYYtqxf67KVcWhA5ENqVzHw2UcpKo5FG1SI7JCg7d1fGJz7IQU6HDh2aN28eQRA8z3v36hZ+Om1WTDAAbCl0jLxRsLJeWCLkGkb0i1y1VRrtg+PF27Ztu2vXrpiYGL1eDwCScD2u1YXabU+Qsiu0+5STjdcoqGNH6ZMZ2lf6SvRRdFoSfSqj9I3GygmpJfPAhv8aCv8NRpIBgx65u/f+IomKxuQKb7XCZwOU4yKKS24uyrN6J9gxBSnRR1VS76KHBRxrrVI8H6j6xeJakGv5qm5o8ad4zpN9+1a3lg+70pK3WkEUnLyY7+FeCFLlublv8m0YwIzoIBwAxEr8myUCgzmjwft4VkxQYMn16B/2kgTJc0TgQ2yQlYRHimJxzmtAyubXCfE+TrbTCw3Fu6Gk0eWcpxEcjj4Sro+8eN51KiGUs+4rhhEaHV+yO+uL2sHSkj+9ny3Fi5AxifTRyYJQfRMhAGheeE3VtadlzRLr5rVmTtha5GhIyoaF66jjqZXUY9mCMvEaBYEB2TYek0gq7wbh33hzoVQqbdCgQW2dwvTHfu8kiQTDGEEs8PC7GkaWOe8XUzRpoXnpdWXCUw97x7R79+7z5s17//33hwwZwvP8kCFDMAwrmPPhrJhgDGBzoWPEjYJl9UK7gcEwol/kqm3SGB/kwrKsm9aIlAsAErXkFdqd7KDjNQoQRZGhbVvXe7+GF+Ey7U5x0Idt1GknWzoUDpYQ7TWKBI2iq04ZLi25JJJIMEF4I0StIu5OIbwcpAokSVm9Bro3fFCY4/Gg7PSkadF9a+mJorLTk5XU+6dLlmI0XVcu+TA68LCN+tni6h+q8Va79XYtUBSUq7Lg84FKHFPhAO9kFdKC+GKQqrhZHHcd3KfrVymlv1Tdetp/2Czea6GWd4XBw+yVlMU2wKUyHu5ZTwpXqlTdn3/YOH1O2aWbc/8eEO6x2xvDVI/YQcfVOBECAK7WBI2dbPthcxtRVOL4Vdpd4OHDbt3gDDmSyGifd1ecCO3eEqPFOwgFQfjzzz+h0m4Q/g2GYQsXLgQAT262acnn3idvMJ58Dx8sIZoo7+53xmQy/eJ15Z7xmzJlCoZhU6ZMefvtt2maHjNmDKYgC2ZO+jgmGAdsY6F97M3CZfVCu0OeYWTfyFXfVTAX7ty5s1atWsePH2cYJu2PP7psXOzde5SoUazNtyXZ6cmRxS+kdBFQsp12lCwCIjBoo5J30yn/NhSW1X9CGd+F7NiZbNnWsn7FzE1rREHwvjFhBPFebKSyQ2LYJ4swonr/IfiQNKqWKrGb6+ihf/0sJperuz0r0UdWRtciQxc5XK2UMlYU9VLJ0DDtcqNtQKZR4ruJFhEAB5Dj2JSSXyfRzTLnz+j6+aqHvwgcPNKx67t7JUJMrgh+54OHaxHHg8ZNMS2cA5Z/y4UYhgcEqp72fyIMHDHBdWg/eP49EWJyeeDod6s4pPur9n//mExOtmonpiV10MiP2OgUB/1ykJrKSNH29nH9eBBF+mSaCJDmLLODsH3CmTNnioqKateu7S2yXmWkUTHq7s85f9srclzp4p2SNwwRU5DaPm9W8L7X5MmTcRx/7733xo0bJ4ri2LFjAcMKZkycGROEY7ChwD7uZuHSeqE9wJj79muRK7fI6sWVu68+ffp4H7SMDLesXeosee9op1aQOHaJcu+3uk442UNWKqfMzGeMXJKgIRM0ii5asnSoRwQEkm07kh0S/zYUDhr9rrb3G/ZffmRPZ4gej+yJJpqeL8mbNC93zI+r0I/mugf3HkIzRJlzl14LVgsSiTS6dsi0yqq9LgiCBMc0BH6VdgOADMcwAF4E3qe7NZQ4LoIoLZNcRfGhN/M9ICIkLPyLZcapYzfH6bVlpiJWx4ZFaDRBYyaXozqBtndf5mR6xwO/lJaWBIDmKvmntUMItTZiyfpyX9VN69lN+vve0n+ODNfVebJz+ZqSRtUK/Wge/3/vb22gL3sdsykuPFCtCnn//2SPWH2Zap8IAYDs2JlKS0rUkEdsdLKdfjlITace9XkidGde4S3m64yn0MOHSYlYhZQICpbVizu0YAEA9OjRw7fdPYjQ6Z9RSYd4h8O7eKeTtrgkEk6q5E1bPPTF5r959913lUrlmDFjxo8fLwjC+PHjAcMKpk+YER2kwLGVRtv4m4VL6oY+DYWGkf0jV24p3++3QFPsudNURjJ19JD7Zqb3SV6E8xSbZKelGMaCOP5moff5QAmRoFEkasnOWvLuzCdOyJ9oTHZIUHXurmje+l5Hf0giooKGjS9HhDUKrlJHbdqDjx7InD+1sdC+udAxOFT7ZqhG0bJt5PKNmMIHlbf+FaFUOTEi2+2R41iRh1+TbxcBVsWGJWh82eP4rMIjNmqhwfJ57RAAwGRyRcnyq8qgTOgatfr79kP7iB7Pa1fzbLzwwxMRrVRy/eeLVE89W54WMSzsk6/woODALetOOtkP7xS1Uck/rx3SLCosevOeimwbaxgeapMSCwyW36zU5MjAZwKU+pDyF/hVP9MrOlyPDesLAv+/ywaPKO5rFNlOrYhc9A3ZrlO5m60kj0MiVHZ80vTVZ97TXI/aGQGAykjx+dpcqsy8aEfvvGi7ToBhVXaD8J8wmUwEjBcho8wgFVOqAoaNDxw4zFcvf9SoURiGjR49esKECYIgTJgwATC8YPo7kyMDMYAVRtv4rMLFdUOfhSLDqP6RK7c+aC4UBPbqxX9W/S708CeczCEb/YedsnLFl+o4QIiEGBymTdQqmijlpVnOuwiI7JCojO+MqzX/1g1SHjipVHV7ljl/yswJNxmPmeMBQNWtZ+VlQa+v332nYOt6OQYf3C5y8kIPnbK77q9LKjD8oTf8iXzZXZEzo4NSSgu5KeUg8OoelbuhTd64mSRM78nNvsVyFo73DnCVnZ4qf4sYpu7xP9uWdbQg3GQ8kTIJAChatvXJ5ukCD3+T8ZTedKgIRYu2uFoj2K03GA8niiIAJpU+glkQHo9EKIttIAnXx+Ybo2WSHDd3mXI3ARtz8ZyiWSsf9kJnHIO7Ry8Vz4u63e7k5GQMw7p27erDvh4Qc/Gc4LBfolkrJ8TIJdEyiSQkrPb+Yz5fNDty5Egcx0eNGjVp0iRRFCdOnIhhS/KnvfNeZCAO2HKj9Z2sgvm1Q18EyB32mjKhq2A2gZxUtu2gfu4lIiikbFO8qZA+fZw6etCVdKS06jctiKdKNvxdoO7eUPHOfNaTSz7LtWAYjNLrvG+BGCHR9H4t8K0xlXEnGPEjIiBQjsFFyv2jt/hZdJm5fRyThOpr/XT4YVeNuo4cKJg5SSjZ2F5LLhkUpl2bb5uTbf7+iQgMwzH5f6+jRh5vj0MiBACyfaLj5x0JWvL7IkeSnW6ilNFpST5MhCLPMWeO8yIcdxYvGQUAsl2ntPR0p9PZpEkTv2zB9u7uL3ssItnpyUraOjJ8+HAMw0aOHDlp0iSKoqZNm6ZfuMo4ZfSkyAAcg6V51im3C0UQXwJw7t/t/RYmPcn89cKgCR9oXniVOZVOpSZRqUc9t26UtpnNcods1BEbXbbqd2ntmx46ZeldkPWFjjw3d5V2NyRlAICRypDJsyrjJATEj3izybp5bWnxs+Fh2rolRRMxuZzQBRzo1ptasbIcLbfs+Wqt/TtENwOCCADj9bo9ZucpF/uLxdUrEMyrFoV+OMeXrwSpbh6TRKiM7+z4eUdnreL7Ikeygx6l11GpSYHD3/FV++yFM4LLeYFi7bxQRy6NkkkkEVHSqJhDa76Bqlov+k/eQWrqX8p/V+K0w7Bhw3AcHz58+PTp0wVBmDFjhn7BSuPk0RMiAjCAJXnW928XiQC9g9TerxdYBgBM82ebFnwi8sUrXEwcn+FgUhzMERuV7ylei0Fg0FQpS9CQnbSK9mqF9B9zXwkaxQ6TM9lONyRlmFQaOhVlwceQ+esFgsP+l+JnAIATRFCQtnffgMGjZjdokJubW46W586d23Ht96blC5kTqSLPqQD+Usjtx23a3m/IG1XinULkEfeYJEIyvjPgeCcNSWBw0slSgoBdOC04HRW/byQytGX9CuvmtfDXymrKDolQUlnNLzcIRTfLnDvlEcWTTgYD6KBWAADZ1veHa5c1dOhQhUIxaNCgmTNnCoLw0Ucf6ReuMr438p2IADmOzc+1TL1dJIjwSrD6bpwCz4viaRfrXdN7kXKX3q8JlRJt1YpuOrKrVhlQUiEIVyoVbeKVHZ9kL51zHtrnLdWYqCV3mJzJDmZYuE4Sqlc//3Klvkyk6rFXL9l3//C34mfq514Mm/FF6Y3JYcOGWa3WcjTetm1beaNmkcs2AID7Rmbu4Jf+VsitaMHsqLXbq+aUMeQR9JgkQiIgUP5EE+3l882U8jMuNs3BdNPh9PFjqq7lWpdVgiswGoa+xllM3gomZSur4boAiqIyMjIIgujSxQ+bQ5kzJ0SWOe1iaUF8gpSFSglZ3fqSsEorhF2if//+OI4PHDjw448/FkXx448/1s//Om/C2yPDdRjAvFzLB7eLRIA+wer/rPr9lw1/9eKUXbor2ycqWrUrHe1pXnzNsnweffZUoobEAY47GVoQscKHK8+IVAumhbNB4Ffl2wxurrFS9kqwGlcqgydMK7s8Z9asWRXvSBYbF/DWaPOKL2fEBL1xNa+4kNuZE85Dv/rwNG+kenlMEiEAKDt2YS+f76Ilz7jYFAfTTaek0pIqkghFnjOMGeApyPOWXmQF8ZSLxQA6aBQAkL5+tRCgZ1m2ffv2ZY91rjL0cW/V07+X/64Cffv2xTBswIABs2bNoml69oi3HRLp7/mW7jqlIMICg+WD20Vzcy3mkvqHGEBjpayLlkzUkG3Ucundqt/BZOsOf6v6XRbZpgO5bmd2v+cDrl1uopSfp9gTTqYzjtEn0lRd/DMdjVQG5++/0CfT89zc2nwbBjAzOggHCBgypnwltv9TwKAR9t3bWxtyyhZyM335iSrxqcpeFos8mh6fREjGd7asW56gIZfkWZPsJae5VoBz327eaCwtQHzKxTKC2IiUBUmIMy52Rb5tZeqhffv2iT4/m+3BeMvcpJYt/90+ocp6f+ONN0RRHDhw4Ny5c2srZMdzLaOCSTmGjdLrct3cfgtl5vh/LXWGyeSKVm3vV/X7H5Txnd3XLidqFd5thZ21JJ12FCXCx4bIMqYlXwDA57mW0uJn0qiYgAHDKqlHTCYPnvBh/tSxfynkBnnWLd8Evj2ukjpFHmXV8mDef6Vo0QZXqVuq5DoCv8l4ctwcZ8jx3LlVjqYEmqLTU8wrFgr03TpGqcWb1hUA0FIlb6iQYafTe3bv9vzzfihoJFAu9vJ5ShDOutwEBu3UCsAJsnWHqoyhb9++27Zt69KlS9+BgzKd9A6T87CNBgA1gVt5/vUQdVrzmKV1Q/uGaMKlBCaTa1/pFz53eZ1DJyO/3hzw1ih5o2YPeEtGGX/3wPriS5y0pMp8ZUiVsm5czRlyTjrZ/RaXAsfeiwwEgOCJ0x6kOny5qXs8T7aJ10slw8N1APBJjlkAsKz7urRGNlKjPD6JEJNIyDYdCKx4b0NK8Tvm0Qf9fkFgL5+3blhpGDPgVteWhjEDuPw8ACj08Pstrsm3itYV2CUYFl62Qq5MzhlyfP5CHgR9PFXk+QwHy4liM6VcQ+Dyho1xra6Kw3j11Vf/+OOPwNj6tQK0Q8O05ykWSq4Y/heoKs1yuEIRPPHD0OmfqXs8X457e4pW7XBS2VolVxN4JuPJc3Oe21me3GxfvhLET7gCo/XbVQLAnByzCDAyXBclk5BtO6q69azsrkMmfwQ4MSJcFyWTXKLcO01OkaHNXy+o7H6RR9DjkwgBgIzvAgCJxUMHBgDo/xo68KZC58F9BR+9m9W9Tc6AF01L59LpKRTrPmKjP8kxP3Mpt+P57PFZhbvMTkYQeVFcarSedrFXabdTEPYYLeCnYs0l52B4y39X9bxoWd6Tp4a+N3mDmR4SprXzwmXaLcWw1qq7u54xpUrz4mvl70ImU7RqJ8Ew78rYFAcDAHR6coVjR/zPtPgLgaZ+KHKcp9gImWRYuA5wIuS9mVXQtaxBI23v1xU4NjkqEAAW5FocvODYt4s5c6IKekceKY9VIlTGd4aSObRjDpoXxeuHfv+wz0srlywp+2Uiy9DpKaalc3MGvHjr2Q75U8c6fvlJsFuzWW5DgX1wZn6bs3eG38j/tsB+k/GQOJagIadEBe5rFPVSkNrBC4MyjVZOmBkd9KJOBhL/nLDs3UqfWmYrvdJPidCr+/h3J7zwXKRMkupgeBFaq+UkjgEAhuO4UhWxeH0Fzx4j4zsDQKJWAQDJaHb0ccGcO+U8sMfFC4vyrADwYVQgiWPaV/rJGjSqmgCCxkzGNdoXAlXt1AoTx68w2kAUixbMBqGyynAjj6bHZ7EMAEhr1yWCQ6NNhXXk0lus5xzl/tXqmiC//M2X6Vn1akXGxdGpSVRaEnP2ROkByg+1v3tu7RAM4Cezc9iN/DWx4fEahWFEv6hV26q40BdvNrlvZlo54SrtluNYS5Uck0rlzVtXZQz/JImIgr8OUjEJIW/aKuzj+dJadSrYuLJjF1PJJU6ygxYA6IwUkefQ8UnVmCAULZgNorjEaC308K1V8ucCVbhWF1SFB/QQgUGBw8abvvp0ZnRQ7yuG9QX210LUdS+fd+zbpen1SpWFgfjdY/U+Qp/K4K1mAOisJW8VepLsNCuIJO8JErhrH4wX5MUv9qH2dwMAgAiAAQCBwRe1Q3AMdpqcw2/kr4oN72TIyR3RN3LVNmlUOU+FLs/LzEgBUUx10AJAG5VcgWOK5m38ftxzySrWu4PU8M+Xq7pVaB9nKVm9OEl4RJ38vBi5JJvlLlJsM7CzF84qWrTxSfvIA8rPzz9w4MCgQYNSU1PT09PfeuutgICA8jXl2LuTvXj2NuvZVODAAWbGBGEAQSMmVPDssIel6/uW/cdtjW/f7BOs/sHk/CLHsio2zLT4c1XXZ3CV+r+/H3ksPEZTowJfMG28d7dD55I5tKZK+c9m1yXaXVtGZLPcd0WO8VmFrc9l971mXJVvu0C5FSUzn7saRh5rFrO0bujLQeoAmUTeqFngkDH6pevJtvE4effvwZsL3wzV0II48kZ+ioPm8nINI/t5cu9U2Qv9lxKjVbWD8F64/DxP9m1v3XoVgTdTynBSqezsy4I7xbOjmrs3gNHsaNXLy8s7deqU2+3euHHj4MGD58wpZ4lOgXKZli8AgDk5ZrcovhaiaaaUS+vEal8b6NN4/xsmkYS8NwMAJkcFagj8kI06aqd5c5F1Q3mKmiLV1OMzInTs3y3YbN7HHTWkDMPOUuwCdehvVpdbFLtfzDX89UDXbjplNx3ZTq2Q3Xd/t6rjk64jB6zb1ruvXQaOx9VqzlQ0KyYYALYUOkbeKFhZLywRcg0j+kWu2iqNrtAp7Q+obCKsghKjDxRSmcU7HdQKCYYpWrfHpNL/+r6HoIzv7Ni9PVFLbityJNnpMXodnZYEIyf6sIvHgEBRmERSeYVYW7ZsqVari4qKatWqFRgYyLJsORoRPR7zqq/4ooIUB33ERqsIfGJEAACEvDsD4jTtdwAAIABJREFUk/jhHUmZ0FWZ8BSk/DFar5uXa/k0x9ypUaR102pVt57yRk2rPp6H55+tzP4lOB0YSfrq5kj1ToS8zUIfT6XTk6nUo1xecTVeThQvUGyIlDC4ue4Xc8rOfCZqyEQtmahVBEseeH83hqm69Sy7mNv56+78me/NignGAdtUaB9xo2BZvdBuYDCM6Be5aps0xve50GQyBQcHO53Om4cOBO/e5snNznNzt1mPhsCbKuW4UlWOc659qyQRVmJuJjskAI4naBQSDDvlYpy8ABfOCnYrri3n1NzjhCvMt6xe5Pxtn8jQIAqEPlLX9y3d64Mqklf+OcnBlWxZCQ0NvXXrVkFBgUr1EJthRI/Htn2j7ftvOWMu8AIvip/mWADgHX1AqJRQdu6mTHiq3NFWUMiUWdkZTw8J0/5Q5LzBeLYWOQaFanMGvoRJJIo2HYLHvFeRPzGRZQSH429P8jZrRermcPnGMv/CAIDLM4AoVqRc6j+3JF09dapl12fxexxz/Z9Onz4tejjfbupy37hm/nohlZYEPAeCIK0bF/j2WPXT/6tgndhqmAgFnr16qfhA1xNpYknll9LKlil22s4LAKDEMVoQ26jl3XTKv1W2vHuga6cnH3Znm7rnS4DhBTMnfRQThAFsLLSPvVm4rF5od8jLffu1yFVbZXXrV/xVUhR17NixgwcPHjx48ObNmwUFBSuHDrSdSI3AxGcDlN6U00GtIDAggkP9ch1dFn0iDQDSyyZCX8/WErpAeaOmcPFcC5XspJNNdzLddTh9PFXVvabXh6RPphsnDhM9bOnhxlxutvnrhY6fvotc8335brmZvvrUsXv73560/7g13ZCviahz4MCBUaNG7dixY8aMGQ/YIG8xG0a84TEYRIby3nTfWOi4RrtrySUDwzSYVBoyaXo54vQVaUxt3euDrFu+mRodOOpGwWKD9YVAVaAERI+bTk82nDkR8Pa4wKFjytEyl2/MHfY677D97fmc15+NWLFZ3rA8I07XwX0FH/99SZH72kXDyDcjlnxTnipxAp8/bQKd9pctSRcp9/LPv1gfFho5eEQ5gvzmm2+USuWum3mz9D671Wrfvb1o3sciy0JJPS/39SuFs6c6f9kZvmB1RaagHolEKDI0lZ7iyc7CZHJ54+aKJi3+md49uXfo9BQ6PZlKTxYcdu+TDl5IdTDJdvqonc4pM/MZp5A2VMp+NrsiZZLvG5SeFIgpGjfTvNxX2elJiT6yIgGrn30BMKxgxsSZMUE4BhsK7ONuFi6pG/o0FBpG9o9cuUVWL64czQqCcPr06d9+++233347duyYu2Rpa0BAwLnlXyXeuvip1dklIgD+OvbijAbH7u81L71RkVdUEe6s61yBMYv15Lq5QAnRgJQRukB5nO9XwCs7dmEvnkvQkCedbJKd7u4tJ1uzE6EnN9s4cejZIsvHd8w/Niz+Vb/FeoZfzDzY0m0cPzjq213wkFf01m0b7Du3lqbVUqLH0+jE0cQJCbpevQCgVasHPu9TEAxjB7rv3AbO2yZm5YTlRisAzIgOkmGY5oVXpbXrPVSQPhc4cqLtx209ADprySQ7vTjP6r0DAqIoMLRl3TJpRJT6uZceqk3R4zGM6scXGOAfhRh5hy1v1ICYHw8RQcEP1SZ78Vz+x5PFf0xKizzHXDhdMHNS+PyHvrtZ9OWnrqTDIveXn3gTpaw5KTWv+DIgtr4y8aHv958+fXrp0qXXP37/Dsv991c/APpEqmnerB8NRakOZl7t4uO+D9moH24UrBKFwk+nh82aV+7G/Z0IRdG6ea1l5SIgCNHjBhzDCAmuCwz/dJGieWuBpthzp6mMZOroIffNTO938CJcpt0pDvqYnclwMp6S37AACd5RQyZoFF20ZKRMIgAcszMGN5fFerzHe2IyuX7RWiI41CeBq5/pBRhWMH3CjOggBY6tNNreySpcXDf0mdJcGNvgAZsqKCj4888/Dx48uHfvXoOhuMITQRBt2rTp0aNHjx49OrdtY+iV4OE84yMC/rTT9RTStOJ6byQAiB530cJP1M++6K96wd5jEY/Zi8t/4wBku/iHffN9EMr4Lpa1y7poySV51mTvepnUB64c9JgyfTlHYFleBGeZrW+8CA5BED0cc+n8jY5PYA85a/TPFHj3UzRV9MVM04LZ8FBNCoLIC2VvZX1psFg5IUFDdtMpATDebHqoCCuDyLLg8QDAjOig/102bCty9A3ReA+CBgCRpvNnTir4vykP16gglE5Z/aM/4B22W8+2x4iH24ssctw/0yoAAGAiyziPHHB2iHuon7goisDdM1eJLJ038W1M8tCDLeZO0cX0X4ucroCQMoeyeTw34x/0jfHvkXAeEMEtiBR/9+V7RJESBJFhnL/vDRg0vHwjEPB7Iiz4//buM76pqg0A+HNHknuz03S3lF32hhZoAWXI5kXBBYgIKntPwYGKyLKyl6igMkQFBQRkCNiWjbJXGd0jbZM062bde98PaUNlSZuE0ub8f/3QhPScc+l4cs94nrnTM//Yc7FQ7zoiBgBGljudrenyzquC6jUdqXfcP0b5DvasyXq4iDlqsOidxb/zpQ/8tZVSRKlvPw7QXkbt1pmTDEzNIAFGCuT/G+itKOgi7dYbMFwzZ8K0cBUOsLokFnaHguxRg8LXbnlMLGQYJjk52TXz+ffff7szd4eGhnbr1q1v375du3Z1FbVwZKXrVyxy2u3faIrMHN9XJbltdeQ52EABUYdy/3RilpOJkude8OLVPbnSp/t9unlH1LQFLpU14UFJ4qk2R4bNWS0ny3r2BNW6nS+6e9ZkZ2evW7cuPT192bJlLMsOGDBg/erV+PGj8Ig/tTyAk+fh0VHtSbA8DwAcgKP0H1+H3ZM2b1kdPxYaCQzmRKpOGK0XLbbEDT8c/fRLkbgijwCZj/yRaLatS9d8Xzd0UKDsu3zDvEztt3VKLePxPNg9unDXWwEHz9/7z2TZR337noQTeADgSjfI88B59B3neAAAFvg7VoeB5Q7pmZ4qcTkufHCAeH1WYZCAUJQcRSsepAf/h495c807HKa9v6a06fjBBx8cOHBg7dq1d+/eXbhw4RO2XJGB0PTHbtPhfbd0hs8ytfsbRriezHWw76cXdlaI7bdTGI7/22w9brDed+CvmoiMk9FxMipOTsuJkoKuMjndqq3t1jW2IJ+3WgEgXk7v1pkTDdY3guS4WKye4v28TdKuPXHRutwZo6eEqzDAVuXqJ9zVLKoe9D+A7FGDwtdsFtapV/r1V65c2b9//4EDBxITExmGKW5EKn3uuee6d+/+wgsvREdHA4DrVrjwdBJzKtl27RIA4ADDgxWXLbZ9OvMunZnEsEgh6Q77PGOxp9yomEDIscy5UxzAKVOp4xxtfJLmBiNIunVb7ujBdjL6rtVR4GSricjssUNFjZoFz13i+bH9Z1x4ePjHH3+8dOnSgoKCb775pn///rbsDJoUuv6y5DvY99IKXK80sjwA3GDsfa55IYU0BrAiR78ipzwVcR8FB3g5UBZNCwGguUTEkQxeoIEK/Q7qL/1zTmtoIREBwIQw5Q6t6YLZ1uCfNK93FHfJmwmKMYDjRqt3x4kBzE67d48+JdWj1j5ML26q6XlPz5h1Voi7KOjLFpv7Rz3LvSLGOq3XLsWOmx4TEwMAo0aNevIFbKjYQFi4ajHPWB76TxfMtsXZur9NNnvJOx0ZgbeTUfFyuoOMrlZyNB5wQlSvIR0bJ46Jd+3X51mnfuMa/bfrOMbcUU5jACeMjJ3nRTYr8D5JmyTu0Dl08Zrc6aMnhytxDFbk6Gek5QPw/wPIeufV8NXfixo0cb947Nixx44VF4dq2LCh686vQ4cOIpEIOM5244p+41rL6STr36fd01PZdmeSgUk0Wk8YGfetsBjHLphtP+QbhwTJACpy+7Tt+lXOoL9useucbLiQjBKRZHCooHpNH3XHFumLWO6dYHlTSXFpApPNfv7kyRaD+4Sv2ypq2OTxX17Z3bhxw2q1Zmdnp6SkZGVlBRFYW6z4N0SC471Uxdu+ch3sWbMVByA9LrnO8TwHgGOYF2e6OddsXMmv9o5CU9+QAKvd/uWiRe+++265T+iX244dO4KCgn45epzE4JLFnmFzigmM44Hzxn9gaa5L9mKbPADL8xgA4b02WZ7nvf0d99aFCzAAgDAh6f5RP2uy/WO2uj7HPPg7WGGBkC3M5/I17ofuGOW6FCGOnTRacXh4qjNCHSRuGy/u2FUc0/6+3fMYQapGjFe9NSat33NBOZl1aeFNxn7ebIvBMOv5s3RsvC+uRRzfOXTJutxpoyaGKSkcW5ylm5FWwAG8CJA1cpAk/nlOrwOhiG7V5uXevWrUqPHCCy9069YtKCgIAFhtAZN42HAqyfzXYbag+D/kP2+F8xzsZ5najzMKnTw/LFjO8xxgFVkW0XWCMM7H6b9NB/bYr12+ZLYlZOvdG0NuWR1TUwsOiYU5E9+qvjuxChdWvXTp0uTJk1955ZXw8PC1a9fu2LGjSevW/A8rXf8qJjD3+sJtqwMAomnh9RaeHuZZmqNfmaMfH6oYH+a1+HTb6uh9LXtnoemNIHkjsVDnZKsJiFEfzt2ydWtubm5CQoK3OnoSOp1u5MiRhYWFuz55P8JaaEvLDRUSczO0Fo57XkF/VdublYHbXMzQOdnjTSJPGK13rI5OCrqp2KNSU0kGZtitvDg5vbpW0Pf5Robjx4YqBJ7FmxlpBTsKTZ9HqQeovbbbs/4/aU6ev9Q8ysOxAcCPBUY1Sbh/1M0c5wqEGEmKGre4efPmtWvXdu3axXHctWvXUlJS6tZ9olXDCguEzoJ8EArBbgOAVJvz+cvF0wWueeT6tHBVreBYKeVOdYZRNNWsZfGBvwb/9cYfJySduhRt2xQvo24y9kQDEyOlLCf+8lEgBABx3HOhX6zLnTpyZIgCABZn6WalFfA8vARg+mO36zXM6aRegA8eM0Xx0ovMuVMF3yUyJxPdm4AAIMPmPFxkOVLEnDFZ3bfCNI61lFDt5VQXhbjUoiDQOPZBeuG8TK2V40eFKvQb11LN29BPfbXs3wuErjQ3vgqEhcsXcFbm4f/G8xzDGHf/In95iI96r3BNmjQ5dOiQ++GIESMAILdTN/OffwA84mA7hmG0uGw7HXjgTPrHvrfGcJm8TMe2eIeDt5rdbdamBG8Eyb7VGOZlardGh44PU0nin58xeOSP27evWLFixIgRjRo1KsOAPTN37tyCgoLnn3++57jJabu3TQlXXWPsPxcaSQx7LyLgXy/FMFwmhzJtE7LbOSvz4IxNvJyuTQmSDNZmCjlGl+2tG2c2AXv/3hYcsKFB8t1aUwrjaBSkLtsGHJ7nHjjdURqG45hUXqZBPiANAHCZEsfLHwg5k+FxydAJUtbrxYDqNX/55RfXE/3793/yxp9eIDx27NiqVau2b9+ekJCQl5fXvW1MzZKtSjVEpHuN8JbVMTQlFwPorhQDgLBWXXHHLuKYeKpFmzLlyxC37Vi0bVMHOf2NxpBoYKaGqywnE8u2T7mMxO07hSasz506cmQI4AALs3Sz0go4gIElb6x4m40HKFz2uXb5Qr7kR1nrZE89kPX7UbfCpb0aKMMxbE5awZJsHQcwJhRyJgwP+/Ir3wX7B/EOh/XCOSfPnzHZAIorQfooGDtzslid9nGDYSymA7urcCB8qMDJ7zPJxwSYRV2qEIoAw4JIAhMIqYZNwr/+qaxnjYt2bNUmzOMetmyB0eKgmXNlfctYVIvnM98aYL9+2T3hPz5M+ZvWfNZk/UNv7qGUYBJJs2bNRowYsX79+smTJx84cKBs7ZfXtWvX1qxZQxDE0qVLMRwDggSn49MMLcvDiBBZrVLvOzGaDvkkoaxVEnnWmfl6b3vanfu2ZQox7M8ia60AeY0DJ8uaEcJ+81rm8AE88693hBSO2Tj+poMb+uKAiPnLH/W1j1K4YlHRjxvva9MFp+jQhK/oWI/e3WJCITgcNQ6eFnqQ88h64Wz2mKE0blaVSgQtwnAVSeAULes7wJMVmaeXa7RTp06unSBvv/12eHg4J1Ngj98khhOy/q9U++mAevxMOjaurFmjqNZtMaEwRkpROHbVYi90svZbN9xzjz4ibtcxbMVGnBa/E6KYFaHiAN5LK9ic/++kEhzndDrOmqyLs3T9r2fHXswYfzd/W4Exz8EGCYieKsniGoGnm0b9Wj98eoQqTkaLxBJxfOfA6XPlL75237zfy2rpZ9UDcYCEbN3KHD1vs+ZMGmFJ/NOn11ia9eLfHGM5b7ZZOK4OJQgWEILqtciQUF/05dTkuA/MXmPs8ZcyXR/v3r73PXXVUvYrZEho2Jrvm4YEbWsc5X4ySkT+3qqOqG690GXflCPjhuKl1xVD3n7IfSQpUA0bXeYoCAAYFr78W2HtaHdqeDmBTQpXAsBnmTqG4417dtpv3Zg3b55SqTx48ODevXvL3EW5TJkyxeFwvPvuu02bNtWuTuBtzF6d+bTJqiaJsaEl8QnDMIoOGDejHLWCMYIMX7tZUK36fd+Fw0WW5iFqTZd+5ciLJIxuELpgJSb615xqvoOdmV7Yp12seFIZdoi4qcdOk3Xr+2BeDkwgCJz1iYdR0FuoZq2DP1rUPyJwXq1g95PPK+gVjaqLO3RWT//Ik8YrIOm2SCSKjIzMzMxUvjUGe3TNBFwoVA0fV+5ecFpMNWkpwrHWUopznUDnectJn1dzpVvFhi5ZC4C9HaKYHRnAA8zNKPwh3wgA7qzfrUpl/RY9NOs3iQsiohSvDQtf/UONP/8JW/a14rU3g97/PHz1d3RMnOt3ABMKMQH5slr6Rc0gAoOlOfoVOXrebs+dPsp87NB/DdM7SmdW83VZRFwic0+MNKCFSU0iXR/raweXeo0/lgugGjev9usR+ctDicAgwDCMIIS1o9VTPojYtBOXlXNGK2DUZPnAwfc9qXh1qOrtcv5K4nJF5Pe/qad+IKwdDQQBgL2qljWghTl257caA3BswZJPgoKCPvjgAwCYOHFi+bKYlsnu3bv379+vUqk++eQT+50Uw44tVo5flKUDgCnhStd2dFwik3buHrlpp/K1YeXrhQgIrLZtP6H4V6Kx9jKqzqKVH35RztVQcXznkPkrSj8jxLEx7dvUmD2vnGlmcTzoo4XiuOfvezpw0mxZ34HlG6QvSF/oE7ltn7R7P0KuAABMKKCatgyZvyJkwUoPk44+vanRM2fOHD9+/Oeff75z547ZbG7ZsqWybx/L4f2R1r+HBt/7dVWT+DshCpyiAybN9rC2Ed2uA3PuZLyMSjIwSQamr0piOZn4FMqMkZFRGCXirdbhwXIMYH6m9uOMwuU5eq2zeOYTA2goFnaQ0fFyupVU9JCs3/HPk8EPua+imrUOX/OD+6HtxtWcMUP6AmAAU1Pzl+XorRw/PUKVN3NMyIJVkue6ef3SWJYFAIvFkpeexh/cQ27fBO7SS3KfZFZzE9Ss/fhyqZiAdFWo8EOEUhU4ZU7gFG9mKSNDwv7zmbLBCfmLr8lffA0AtOuW6tYvmxMZMCQld02uvn+AJPzMcfPRg+PHj1+/fv2NGzdWr149efJkj7p7LLvdPm3aNAD46KOPAgMDcz6awrPsV3lFmXZnA1o4UC3DRFTUjsMepqBywUgSl8hY/b3zJwEkUatjp7IepS/tvnw0CgIPr18vtJVHVcnI0Pu/v4QH2VB9RBBZPfgT72+nenqBsE2bNn/+WTxr53Q6SZIEgLA135OfzQ49tBc4zlUsN0AuH6GQB836VNrrRQ97FLfrqF25uIOcXpCl+8vA8ADMiWPAcb5IelIaZzZhpIAHKwC8FSzPczh/LjRpnWwAScTKqDgZ9bxCHCIoyfpNEKLGLSQduzwy6/ejieo1DF+zOXv04D4AOMDk1Px1eUUAMD1ClTdrbMiCld46WZibm3vgwIE9e/YcPnz4559/PvPHPtGen2qR0JAAhuMvmG0EBm2kFGCYqHFzr/T4IIwg5QOHFP30HRgevl/G9QIf9Y54l2rYKOPun9tCVg+leL/ekpCtX1IjsDBhXrX2nRISEnr37j137tzXX389NNQn0+wAsHz58ps3b9avX3/MmDHmI39YTibmOdj1eUUA8EG1AAID1VujvRIFkUqhYnaNkiWT0ZhQFPzxF6oR481HD9hSruEUTTVr7a2SmKJ6jYgAdbS2MFhAaBxsCmOPBp3t5tXyJbp9cmRwGF8qe4IIw/VO7kW1dFH1wPNmm8bBBgsITCiU9RlQvqzfpQmjG4Rv2J49clAvAAxgcmrBurwiDmBmhCpv5tiQz1eUY2HDxWw2HzlyxJX49MaNG+7nT/x52PTTDwKHXU0JQEa5Nrg2k4gUBA6AFSz4IDThq3JfzuMFjJpkPnYg3MT0Vt2bUQ8UEP0DJBgtVo2e+jTLIyOewESUetyMvDkTZ0UGHDEwv2lNrwVKW2elF239ttebI3v27Llv3765c+euXeuTooAajcZVSTEhIYEEyFm+AAAWZmkZju+lksRIKTIkTPnGO77oGnk2PROFeQVRNZRD3w359MugOfNlfV7y2koPhtEx8RhAvPxeNVfG99VcCVUAGR7hfug6Y9dLKbFxvInlTCyXxLCqMdOC5syXdu3lSRR0EdasE7F+KxEY3FMlWVoziMSwr/KKPs/S8k5n7sxxpv2/lam1K1euLFy4sFu3bmq1um/fvitWrLhx44ZEIunatevSpUvv3LkzgjW+HSgdGiQ7XGSBf2dWA56znDnuOlDhCxhFR2zc0Si23ds1w9y3ztVk0gk1w9QT31MOHu6jfhFfkPboR7eMiRSSI4LlPMC8TC0HoNuwgi3QLF++XCgUfvXVV+fOnfNF13PmzCkqKurTp0/Pnj31P3zlSE/9x2zbrTVTODYjQgUA6slzqvCBVORBz0Qg9B1xu3tlzZOMDABYTjyNsuaBk+fgFA0AZpa7ZLETGLSRiigc6yCnDSxXTS5RDBjkxe4ENWpHrN9KBoX0UIrX1AoWYtjXeYb5mVrg2LwPphr3/vr4L9doND/99NPIkSMjIyMbN248a9asQ4cOOZ3OVq1azZw58+DBg1qt9uDBgxPHjwsz6syH9v6er1+XV9RMIoKSMF8cCAF4xmL4abMXL+0+hEIVvn5r+IpNilfeFMfGizt2Cxg7LWp3osLPTk1UDeppHwKOjw5VhgnJyxb7b1oTZzEXrv6iTp06Y8eO5Thu4sSJ/MMTTJffP//88+233wqFwiVLlrDaAv23aziAeZlaHuDtEEWkkKSatZZ27eXdTpFnXEVXn/AxcbuOgGEd5BQOcNpktXI8duEsZ7HgPk7vK47vTLfraD7yx2mTzcnzLSQiKYEDwB69pYZcErd+s+c3gvcRVK8Vvm5r9qhBz0Pu6lrBY+9qvtEYrBz/cZRa89E04HlZ73+tuVqt1qSkpIdm/e7QoUOfPn369OkTEBAArgJYe37RlSqA1UslMbHcCaN1dnrBDcZOYlgLcXEgBB6s1y5699IeRDVvTTVvzTDMzp07o6OjG1L0r1u2REdHt27d2tddI14kqtdI1ncg/LZ9SrhyemrBwixdN4UYdv+sGDBo7ty5W7duTU5O/uWXXwYO9ObGxUmTJrEsO3ny5Hr16mk+msaZTTsKTRfMthABMTJEATgeOO1DD6u8IpVOFb8jJNRBwtrRKpJoKBbaOP6syco7HNa/Tz6Frl3H6Vw3TK6jBfk8ZCkCHUNG3iY8yqv0KILqNcPXbSVDQp9T0KtrBYtwbEuB8cP0Qp5jNXOnG/f8UvrFa9as6dat28KFC8+dO0fTdNeuXRcsWHD27NmcnJzt27cPeXkgnXKtcMXCzCH90vt1yv9stunQXqfRcNliX5dX9GZKXpuLGaPvaLYXmAgMc/L8nPQC1v3O/bF7O73o6tWrHTt2XLly5fXr17t06bJ69eqn0y/iRepxM3CprH+AtJVUVOBg1+UVAccVLPlELpPNnTsXAKZMmWKxPDwjcTn8+OOPf/31V3Bw8Pvvv2+7ftm4d6eZ5RKydQAwIyKAxjH5/16p8hlrkQdV8TtCABC362i/dSNeTl+22BONTLyctpxILEedybJyVekrPlogowCg8WfL2vbs59NOBVE1Ijb8lDXy9U6QuaZW8Og7mq0FRg7g0yi15uMZ1vPnAMd4m1VYp17X5k1btmz5wgsvvPDCC3FxcUKh8FFZv3PsziSjNdHAHC+V9du1TTReToUJyM8ytbt1ZgcPS2sGkhhWzpNMZdeqVSuHwyGVSlu2bLllyxa53MMsUEgFIALUquFjC5cv+CBS/dL17A15hoFqafWLf5v+2P3OO++4lgkTEhLKVEngURiGmTVrFgDMmzdPIZdnTfkYOG51bpHGwbaQiPoFSHCxJGD0/ZXfEX9Q9QMh3baD/vuvOsjotblFSQYrRIDF9/tlWG2B/e4tvZO7ydhFONZcIsKEQknnp1EmiQyPjFi/LXvk6x0hY12t4FF3ND8WGHmAeVFqw86trtdgQpEEx/b17Rs0ey6r0zH7ftWdTGROJbNFOtcLrBx/7okLYEXTwmG3cvfrzZPu8ktrBkF6qn7TOuWbI319pUajMSEhYc6cORkZGa+//npycjLP82UtQotUOMWg4YbffmycdrdfgPRXrWlxlm5lreDC5Z+LO3VdunRpx44dP//886FDh0ZFRf13W4+1aNGi1NTU5s2bDx8+3LjvN+v5sxk258Z8AwbwfmQABqAaOdG7JUuRyqKKT40CAN0yBqPollKRGMdvMPZch9ORetuRleHTTplTScDzx40MB9BGSolwjGraChNR//2V3kCGRYSv3yaIiIqX09/UCRHj+PYC45y0gnslPuw23mo17d2R2qVVWu/2mk9mmg7sYYt0GTbnRo3hzZS8VhfT30zJc+W+oUpy3+xvGHGkUeS8KHVPlcQdBQFg0o6EAAAgAElEQVSgsVi4qU6oksT36y2j72jsPF+4fIFuw4qHjs2LkpKSCIL47rvvjEbjl19+2b17dxQFKyNMIFBPnA0AMyNUUgLfr7ckGRhnXq7+u/Xx8fEDBgywWCxz5niaKyArK2vx4sUAsHTpUtxh165aDADzs7Q2jn9JLW0mEQmqVVe88qbnl4NURlX/jhATiugWbfgTf7WVUUeLLJfM9lAlmfFqd+Wg4arhY320Sdpy5gSU5B5rK/Vh0fZHIUPDw9dvzR45KCYz7es6wSNuaX4qNFk5fkmNIKIkWPAsy1vM7qzfR4uYXEdxauBHZf0mQ8PFbTvgcqVh+ybebudLlgMbiYXf1Qkdeiv3SBEz5o5mVc1g7ZoE4DjVuxN9d409e/bs2bOn6/OGDRv6riPE1ySduorbdQw68de7IYqEbN1nmdo9DcL1m9bJ+w5MSEjYu3fv5s2bR44cGR9f/oTyM2bMMJvNr7zySqdOnbRrvnDmZp8wWg/qLWIcnxquAoDAaR8+tVl95FlT9QMhABCBQQDwXqRyQXV1AEkAAM8w2s3fGPftivjmJ9IHaYRcZ+mKd8r4OPfYo7hiYcYr3dsAfFMneMRtzW6dmQf4okYgAFxjHH8WWf4ssly12N13ioECoo2UipNRnRXiYHfum9IFsEpy38j7v1q4fKHl+FGMIHinE3iuoRi+rxs6NCXvaBEz5o5mda1g7bqlnM2qHj/zKV84Uhmpp37AvNbz7RD5L4WmFKtjW4FpcBBW8MUn4e8vmDx58s2bNz2cGu3fv/+5c+cWzp9vvXJB/8PXLA/zMrUAMCZUESwg6Ni4p7BvAHlmVf1AaL91w3RgzxmTdW6G9vcGxTmTbjD2EZcyk1s4csa9WW3r74CXP+nfgxyZac7szGy7M93mlBN4I1qEiyWihk292MUTIkPCcKWKMxlbS6kNtYPfvq3ZozP/Y7ZpnSzD3at3GCOl4uV0Rzldu1TdGUFElLhDZ0nHrg8tgCWoXjP0i7XAsc78fEwkwggie+wbDa5c/K5uyJspeccMzKg7mjW1gvUb1wLPqyfMenrXjFROwpp15AMHF23bND1CNe6O5sscXW+VBI4eNCcdHUHTsr4DApSK/27l0frWqdEurol9cM9sjuNZdmuB8QZjryYi3wqWYwQROPVDb10IUhlV/UBYsHhu6YRnpfFOpyM7w3Rwr7R7Xy/26Novmmy0AkCsjCIwoFrFPlji5OngS6rKtZFS39YJGZaSa+F4huNd2146K+g4GS0qqZZJqALoVm0fk/X7fjjhLroUvur7nHFvNrh8fmt06BspuYkGZtQdzdpawfpN64Dj1JNm++b6kKojYORk0/5dPQDi5XSSgVmVq58TGQBOB2d0GH7ZYtr3W8SG7YIatcvRsvarZUWb1nKM1fWwiOWW5egBYHZEgAjH5K++Kawd7c0rQSqbKr5ZhrOYrRfOwaOTU/AWi+G37d7t1DUverJUyhXfFSf6T0RAoPtzJYFbOJ7j+RNNqh1pFPl2iLy6SCDCMVGjZurxMyN/2FXj4NmQhavkL73+RFHw33CZPGzVd1STFrUpwfd1Q4MFRJKBGX4rz8Jx+u+/KvxyvlcvC6mCcLlC8ca7ADAnMoDA4Lt8w02m+C0sb7Oxel3WO6+zej1vt5fpw/j7Tv2mde4oCADLsvU6J9tORnVTijGhqNy1pZAqo4rfEToy0jAR5ToSl2V3jr5TXMTVxN479O1Ive3NLnmeOXsSAE6WqtL3lHfKlCZ5vocj/S5vs0HJocYOcjpIQNh5fnO+UUUSE1o2ivzuP3KwPSFcKgtbuSln/LDaF//+oW7okJTc0ybriFuar+sEww9fAc+pJ89BOTuQx2DzsgHD6lKCV9WyLQXGzzJ1m+qWLOHzPKvNT+3SooxN8gD/+pG7bXVsKTASGMyJDLBwnNPusOl1YoXKOxeAVE5V/I4QI0n37aCr6rTr47VA2b0XeVAV7EH2WzdYbcEtqyPPwQYKiNqUgAhQC+vU82IXZaJ4dShGFK/8laS5oQBgk8YwKEiGCQTeXcDDpbKw1d/TrdrWogSbo0NDBMQZk3X4LY2Z5fSbv85f8MFj7s6RZwdnMpr27wKAEAHRWCx0VQ0z7d/FmU0+7df0xy7XT8iUcJWSxJONzC6tSedkPfjg7nvm4wytk+dfD5TVp4ULMnX79Oa0nV6eE7qP9fxZR14OADSkhY3FQhLDAMB0eH/5W+Q4V6IoGYE3FgtriEgAsJ476chI83y0kUKysVioIr0ZGlq1atWqVSvcx/XvPFHF7wgF1Wq4M6RQONZYXLzpQ+B+j4gB1cCb21gsxUXbGQCIk1EYAN26XQXeBhFKVcjny3ImjeB4OG2yAUB7OW3n+dtWh8PouEzJpN16e7dHnBaHLvs6d9LbNc+e2BwdOuRm3lmTdfjtvG9qh8DPm4Hjgt6b5+uSkIgnOIM+Y3C/G2lpJMe+Hih7PVAGAKk2B3v+H25wn8jvd5W78P1/9GuxsGaz63Mlib8ToliSpZuSWuDdXmQELsaxiWFKALDzvNnuwFJvebeL0syH9+V9MOUvrbG9jHLf3R43Wlt+MjM8K6M8s7Iclzt9dPpfh7MstmYS0a/1wwFA7+TuZufxg/pEfL1dGN2gfEO9mJVDOdgJYcoJYUoAuMHYbbl5nhxL4jhu//79PXv2PHHihOuZ/fv3d+7cWfjsHVOp4n+PMKFQ2qUHkIJHvoAWy1/xZuEC1wKhaxKyrWtetOIWCF0IlRp4uGqx65xshJCMFJJCnFhUP2rmp59OTFjmix5xWhy27Gu6TfsaIsG2eqGRQvKcyfbWrTwTyxl2bM2fP+ep5SNFykEzdzqbn7c5V79ba3Y/+avWvE1T5MzLzf9kho/6xQi89IRBkZPjAUQ4piRxb30oSNzO804ezBwPAAuqB/ZRSb6/eM1HV+TMy9F8NJW3Wd+5nWcstRwz8W5+rtGk37jaev5sWdvUb/+OOZV0qtCQkH2v5P1li+2TDC1nMedMHO5+619WSw79lVSq6vU3GsOe5BPla8rFarX27t3bUWo8/fr10+v1j/mSilLF7wgBQD15jiXpqMRoqSm6Fw4pHKtDCzARJY6Jo1u381ZfPOu0/n2a5eF0qSp9T/8E4X1cN6knSs2L0jHtwr7cgIko3xWbwSg6bOmGnCnvRJ5K3hwdOiQl92+zbUhK7sY6obBzG1ekp5q3dmoLSHUQ3aa9sG59nw0EKRtH2l3LySTe8YiN1nab+fgxR1a6IMLThGcPwkQUERDI5ucBQIbNuSnfgAFsqRvaTCKycbx7b7OHpqUW/Ko1LcjULq8VvD63yIThb7zR0ystP0i/aS3vdD7qXznGWrhiUcTXZZmY5Vj9uqUc86gs5DxnMpgO/i7r1b9sA/V7VT8QEuqg8K+2wZghq1Ry3lr8fqe6SLCpSU26ZUzI/OVe7Mt25SJnNl1hbEUsFyUiI4UkGRruiz8ZZeI6zuFKc9NeTgOArPeAp5DvDaPosC835Ex+J+JU0vd1Q4fczL1ssY+4nfd93RD4c7/56EGeYzGhCHBc1KBxyGfL3ScxkAr0BJl4MeZEomDgYF/0Lv/fy7qvVwHPu5KfDVBLm0lEq3OLJDjWTkZFi0WEXIlJylbCzJmXAyzrfjgzQnWoyLJfbzluYEaFKjBSUH3EKG9fRzHz0YOPvT/jbVfO8w4HJnjklNV9bCk3+FLX8iDOYjEd2osCYVlV/UAIAMK69aN+O1a0daNp705nbg4mIIX1GykGjZB07OLdjopDjqHUftHY8ieF8grebrNeOOfg+bMmK+bO9+a9m+DHw0RU2NKvc2eMjkz8c2t06JCUvGhKcJVxUBg0Fotcw7tksTmOH28zqHfk1t/LcWwD8S5nThZvKz5psEFj2FZQvDvGxHEvq6UAwFsZpybHZ907gedLJz+zcfwFsy1GSilFImGtupFbf8eIsv3VYv4+nTN+mPtNcJCAKJ3IjQCet1of30K5sXqt+/Pe17Lxkv2rRc6SYEaQrF775Mmt2PxcwIrXs04brfGXMl2f23k+Qlj838LmZJZvtLhc8fndtGU6m+uhjrG1VSjL11RptWvfO/rpKO+0ra/5RSAEAFwsUY0YqxoxFgD0ej1FUQ6H486dO1KpNDg42PP2OYulaOu3um9WAcBJ0715UXFFz4taL5zjbdbzZhvD8dG0MEhACGrUfpr3XphQGLp4bd7MMWHHDv1cL1RBEvMztXICdwVCADhSxBhYrqWUyps9IWKDb/fvIf+JUCgxUsA7HQDwdrB8XFjxn8KlOXozywEAJhDgco+SvDyKMy9Hv23TfcnPGI4PExKDqwWv1NtWrt1S1igIAHTLmOC5i/M/ns47nMXXVZLI7cdC06BAWeGKhSGf+yRHPEaLXSeXAOD3BuEqsniDepuLJUn/nU5cInvo1z4ULpNjULyMGiOjNtYpjqBJBuaLkiVDXF7O6EWGhH25evSwYcNcD9966y0yJKx8TZV2+/Zt9+6YZ3CbjEsV3yzzoPT09JkzZ545c+bbb7+9cuVKYWGh523ab9/M6N9J9/Uq11vpRdUDv6gRGCejAUDUuLnn7XuCOXMC/l0W8emf7scEgpAFq+iWMSqSwOERxydYp+3aZfutG093aMj9qJZtMNFjC0eTQrpFjC+6Lkz4lLcypZOfAYBETIeFhf9QvfHLK9cTAerytSzt1rvaz4dk/V8lg0OBJIUYNj1CBQAJ2Tq9kzMd2MOcO+XNKylBN2/jvoF7KDIsAheLn7xBUb1G3COWb10wESWO7/TkDSIufhcIo6KiXnzxRQDAcTwtLS0lJcXDBlltYfbbr17Jznn7aqrrmWAB0VpKjb6jAQzL//wDD9v3EFN8nKPU5p02T2letDRMKBR37Ao44TrdbOV497mukqynGHCca7RIBaKatiKCgh954AfHBeERokbNvN6v9fxZ0+H97uRn70UEiHBM8fpbNZOvLbpw/fNtP3Xu0tWT9snQ8KD3Pq2+70TNg2cJpaqHUhwvp/VOblWuHgAKv/gEuMetvZWP8q3ROPXIdxU4LXbNUT05jKJlfQY85p0KRuCyvq+UqU0E/DAQuo0dO3bcuHFHjhzxsJ3C5Qs4i9nq5FKt97aH2Xn+jtUBPG+7cM6SfNTDLsqNs5ht1y4xHH/BbHPVlAccp1rGVsxgdFr335pftaY3b+W5Pn7VFq9C8XYbW+jlQ2NImWFY6KI1OE3HyemW0ns7qlpLRO3kFE7RIQtXeb9TjitY8gnwvDv52QtKMS5Xqt4Z7/WucLlC9e4k+HciN9uNq4ZdP3u9L6pxc9lLgzCafidEQZU6Ozs0SKaSiEXNWsn6DChrm+pJswl1cG0p3Ut571YyUkS+qJZgFB0053NCWc4sOS+99FKTJk3cD3v16tW6devyNeUiEAimT59OlMpYMm3aNEkZ9zo9Hf6yRuhmMBhOnz5NkqRGo0lJSYmL82iekGedpgN7XAsPD8UxlqLt34njnvOkl3Jjzp7knc7TJquD55tJRHICF9VrVO7fEw8RgUGYUOhKgP5aoGxCyeLT8hy9wbX4JBQR6sDHNYE8FcLa0eEbtveY8i5rLOJcJ9wx6BgahCuVYQlfCWvW8XqPhp3bbNculU5+BgDqsVMJ32Q+U7w82LBjS91bN0onctOuWizt2guXlmHF7kkETp5DyBUzvlnDA+daL8QEggk1QmXd+wW+92k5Um3gYknkd78KZoxucP0yb7W6aoLWVCprBaiCP1wo6Vr+I1FDhw4t/fDll18ud1MuAoFg0aJFpZ+ZP/8ZzTnsd4FQLpd/+KHXSq44szIxgnBN7elYdnlO8Xq1znnv8KztxlVvdVdWpU/3t6/oQ41063ZAkAC2R/w7DwRegUlZkdJE9RpV23XMkvinJfmIMy+XDAkXxz0n7vB8Ofaq/CfObNKuWwoAn2VqnTw/OEhWnxYKa9WVvfia1/sqhhOB0z7MHjV4Srhqr96cbGSOFDHPQ6Fuwwrvl0nBMNXb42X/e8V8YI/1/FmeZYX1G8u69xFUr1XuJglVQPhXP1ovnDMf+cOeepuQSKk27aXdeuMSqRcH7lf8LhB6F886+ZIt0QIMCyvZwSzESq03lMwH2mw2lmXFYjHHcXq9PiAgwNfDY0rle2tX0em/hdENRHUb2K6ef+i/YiQpqtsQnax/dmAECW3iqFbtZDKZ0+ksKCiQ+CAKAoBu3VK2MP9wkeUvA6Mg8ElhSgBQT/3AF0HXjW7TXvJcNzh6cGyo8rNM7bxMbZw8vGjbRvmLr3kSoh6FDAoh/vdqyOARTqczLy8vICLC8zapZq2cteuppVKWZfPz8+XeiIJ2u53jOJ7nc3JyKIoKDw/3vE2TySSVSnme12g0ISHer4LuFf67RugVgohq4Ci+xZHi+Mtqqeuju+re9L3r9yo/P3/x4sXjxo27ffv2hAkTdu7caS5Jq7h48eLffvvN/fq1a9du3rzZ87Gx2kL77Zt6J3fdYhfhWEuJCBMIqOYeTfp7KGTBClwi76mWPaeg3U92klM91TJcIg9Z4JMt7Ej55Obmrly5csKECbdu3Zo0adKBAwcYhvnvLysjR0Za0fbvHDy/IFMHABPClCqSkHTuIW7bwet93Uc95X1MKBwaJKtLCdJsju81Rt7hKPjyM1/0dfLkyX79+gHA5MmTDx06tGTJEs/b3Lhx44wZMywWy4QJEw4ePGgwGDxsUKvVzpkzZ+fOnfv379+1a9ft214oy3Pq1Kl+/frxPL927dqffvpp3bp1nrfpCygQegQTiuiYuMekkMZpibz/awAQFBT0/vvvN2/e/PDhwyKRSKfTkSWlei9dupSWVpw2Xrsm4eLBP1KuXPZ8bMyZ48DzJ00MB9BSIqJwjGrSAqfLsFfb68iQsMgte9rHxjQPVLmyaWACQYuggPaxMZFbf/fKoSXEW0JDQ2fOnNmwYcPDhw/TNJ2Xl+eL6gEFi+fyDsdGjeGuzVGbEgx2VUQZP9PrHT1IEBGlGDScwLD3qwUAwPJcfb6DtST+aTl+zOt9tW3btl27dgAgEAiuX79e+ox5uQ0bNiwgICAxMZEgiOzsbNLj0t8BAQFvvvkmAJhMJrvdrtFoPB9kbGysax/GkCFDbt26FR39jBZARoHQU4FTP8AftZuZIIiwMGmPvq5HZ8+exTAsLCysU6dOTZo0OXTo0H0vd+Zk6TassCT9qdu0LnNIv4LFH5sO7S134ZviedHSaW4qOv03AJCh4ZHf7gjfsD1g7HTF4BEBY6eHb9ge+e0OFAWfQUlJSQqFIigoqEuXLg0aNPjrr7+82z5zKsmSfLTQya7OLQKAOZEBJIYp33hXEFXDux09imrEOCIwOE5GP6egzSUnNwoT5j0mQaiHWJYdP358YuJ/5rF7UjiOd+jQITY2dv9+D+o6/dsbb7wxY8YMLw4SAGQy2YwZMw4fPuzFNr0IrRF6SlC9VsjCVS1mjj0op6EkwXxNkeB0TDSpUoev+t611HHlypU5c+Z07ty5evXqP/zwA4ZhM2bcy+J/4sQJmqat588YC4wpjL21lLJdu2S7dqlo20aMIITRDenYOHFMPNUy5snTEj54lL7C03+7ieo1EtVrlJaWtn379ok1665fv57n+erVq/fo0aOih4YUu3z58urVq5977rmGDRtu2rQJAGbN8mbpSt7pzF/4EQAsydIZWa6zQtxRThMBgcphvsr8+SBcLFGPnab5eMYHkQHHDdnbC4yvBkqb3L1l+Ol7xetvebGjGzduaDSaXbt2BQUFbdmyJSbGCxkJ9u3bl5eXp1arjxw5wvP81KlTPWyQZdk9e/bodDqxWHz9+nWv3L3dvHkzNzd3165daWlper2+U6dn9LA/xqNCqd7gyEzTLl9gTjyCEQTPsjgtlr88RPXWaIyi//Nrhw4dWlhYGBsbazq8z55y/ZiB6SinG4mFF8y2djK6pUTkzruP02JR0xbimHg6Nk5Ur9FjpmSdOVlpfeLzHGzcpQwJgf/dtJpALIk8eHb3vn0vvPBCdnb2gQMHBgwYEBZWkfdhqampK1eudK2X7Nu3TyqVdujg85Uh5Bmh/2FD4ZefXbXY+1/PJjBsb8PwmiJB8MdfyPq89FTHwXGZw16yXbkwP1P7jcbQSiraFh1GyBVRO49U1EEj5OlDd4TeIYisHrJoDXCsM1+DCUWEqmw7Qrt37z5h/PjUpD1smFLPcgDwq9a8T2denVtEYNCAFsbJ6PZyKobnuVPJzKlkWAGEUkW3bkc1b001by1q0KR0a9ZL/xQu+QQAkg0MAMRKKQLDqJYxd9LTT58+3aZNm5ycnMGDB8+dO3fZMp/UI3xCNWrUEJXMKh89enThwoUVOBjkaWJ1Wt2GFQDwaaaWAxgRLK8pEogaNKmAsgk4Hjjtw6zhAyeEKXfrzOdMtn06cy8A7ZqEoPc+fdqDQSoICoSestlsCQkJUql04MCBS5cujYqKGju2bGmTXOx3UtjCfPfDwYGyMAFxwmi9wdgvW+yXLfZ1eUUyAo+RUu1lVDsZVRd0pkN7TYf2AgARGEw3b03HxtOxcbqvVpoO7nHl2j9eal5UWK9BvXr1atSoAQCdOnW6fPlyrVre3yZeJjabjWEYk8l0586dli1bVuxgkKeE4xw5WYUrFnJGwx6d+YzJqiaJMaEKwLDAaR8+ZpLDd6imLaXd+8H+3yaGKd9PL/w8S/e8Qgw7topj46hWsT461I88U1Ag9JRIJBozZsy6des2b95cvXr1ckcXQbUa4eu2MGeOEyvWYAZdWxnVVkYBgM7J/mO2nTPZko3MZYv9cJHlcJEFANQkESOj4mRUnIyuVqBxB0UMuzfdffJeIOQNv2xVDnnH9fyZM2eSk5MnTpzo+eV7Ij09vV27dufPn1er1QMGlDnXFFK5cEaDdvUXxt0/8xznSk+/X2cGgKnhShmBy3r1r8CzPeqJsyzHDr6shs35xltWx99ma5yMzps1DnCCDI9Qj5sh6YxWr6sytEboBUVFRevWrWMYZuDAgdu2bRs9enSZDqLqdDqBQCCVSt2tgc0quHHFcjqZOXPcfusGlHyP8h3sWZM12Wj9y8Bk2+9tbAsWEK2kVJyMek5BhwqK39zctjq6X81Sk8TJptUwAEwgNMV3PVa9vkqlslgsABAWFta3b1/v/BcgyGM5MtOyh79s1Osp1uHOKmbh+L8MzAtKMUHRUb8cIkO9cHy73HRfLdeu/fIaYycwqCUSkCXJz+w8T9BiVc9+QXM+L0dGNKRSQIHQCxYvXnzkyJFJkybt27fP4XAkJCR4sewWq9My504yp5Ks58/a79yrlZFhcyYbmWSj9YSR0ZfK6FZNRMbJ6DgZlWVnF2Rp+6gkS2sGFf8bQYZ9uYFu0w57VquCIVUS73Ckv9TZmZvV+nzajnrhUaLi92qdr2QuqxnURCxSvDYscPpHFTxIm/Vu19a8xTw9taCRWDgsWO56/tNMrYLAJ9YKCxgzXTnIm1tJkWcHCoSVCVugYc6fdZ2+cuYVVwlnebjK2E4YrceN1nMma0lVI8AAeIDn5fTwEEXpracYRVPNWj7J1lME8QrDL1sKvvyMZyytL6Y/GAibikVUTFz4mh8qdpDOnKz0F5/nHY6HBsIJYUqcFtc4dPZJ9oEjlQ5aI6xMiMBgadde0q69AMCRlc6cSmZOJVlOJzfBipqIRe+GKFiev8Y4ko1MssF6wsiQGHbEwBwxMPdtPeVLbT0VNWlBN29Dx8bdt/UUQbzF8Nt2nrE86l95AOs/p3m7DRM+tiCwj5mPHviPN4UEzpw5Ie7Q+WmNCHl6UCCsrAQRUYKXouQvvQ4ca7txlTmVrP9+Peh1jcXCxmJhnIzuf51REHh/teS44XFbT9nEPy2Jf8IKINRBdIs2dGy8uH2nil2tQaoYR1aG+/OZaQXuyQmNozgfPSYUOnOyfJHt+snZb91wlUkCgK0FxqOG4sSqd6yOgWopAPA2uz3tDgqEVRIKhJUfTogaNBE1aEIEBucv/JC3mKGk4kQXpfi9iACIeOTW0wCSiHVvPS3Md289FURE0bFxdGy8OKY9LldW7PUhlR1WKg3mmFCFu0jL8Ft5rk94jgfySVMm+Ujpsu/dlOL+AcWb19bkFhW/AMcxAVpcr5pQIKw6pD366b5e4ciwAM+7Mqu1kxVXGFeRRGeFuLNCPB1U92093acz79OZ4b6tp1npjh3phh1bAccNUXWmX73764ql4nYdcYnUZDL16NEjKSmpIi8VqVREDRpbEv90fV5dJHCvEZIlezAxAEGYFyoTeULUuAW++xfOYgaAQJKoQxUHZiVZPF/Kk6SofqMKGx/iSygQVh0YSYYt/Sb9pS4Onj9rsmIAbaUUAACGYUKRrHd/4HjmzPGgrIyeKklPlQT+vfVU42DdQdG99bS9nDbevHomJTtv5lhMKFS9MwF7aXBycnLFXilSuShefdN67iRnecQyoUAk6zuwwjdtSZ7rVrDg/ce8AJfKqCYtntp4kKcJBcIqxZmfC8CfN9sYjo+mhUECAjBM1Kh50IyPRI2albwmz3rhHHMqyZJ8pFpe7msi2WuBMvfW0xNG61mTNcPm3GYzbiswEhj8FF2cj5S328ngMPbRvSPIQ4nbdaRbtbWcPt5YfG/3MgA0pEUSkiTlsoAxniaM9hwulgROn5u/4MPqIjJYQLifjxSSUgLHKDrk0y8rPFojPoICYZXCnD4O/644Ie//WtD780u/hgwKecKtp8cNVo3DGSAg7Dy/UWMAgIDzV2yXb1bAhSGVXMjCVXkzxnxHEK65R5dVjaJIZUDY2s24TF6BY3OT9R3IGo3jVy0Ch4Nni9/yDY8IxEgy+OMldKvYih0e4jsoEFYpxTUISy0QiuMeV/fkwa2nltNJ1n/ONsawxmLhyBAFy0OOwwkALA+EKoCjxazVWrqFc+fOJSYmDho06Ny5c4EDdAcAAAbASURBVBcvXuzZs2fTpk19eIVIZeN0OouKitRqdfCXG3THDtl3bLHdvMo7HILIKHnfgbL/vfpMpXdQDnpLEvec/of1lhOJnNlMKJSS57rZuv9P0qARAGg0muDg4IoeI+J96EB91cFZzKnPN7fYHS0vpHPAn2kaJReQNQ6dLWvWYJ512m9eKw6Kf5/OMDP9rmX/3SxK8fIbgbM+0ev1KpXK/WOTlJTUsGHDzz//XCKRzJ071/tXhVRmLMsuXLjQaDR27Njx9OnTAQEB48ePr+hBlc3XX3/NsqxGo+F5Pjw8/O7du/PmzavoQSFehqa8qw7ruVO803naZHXwfGOxSE7gonqNypE7HyNIUYMmymGjwlf/UOPQuaD3F2BCobB29EPr+sbHx1+4cKFFixZ169ZduHDhtm3bvHEpSBVBEMTs2bN79OhhNptHjhxZ0cMpjxEjRrzxxhtGozEnJ2fEiBE6na6iR4R4HwqEVYfldDJ4uyQ9LpWJY+MwEVVt+x8PTcB/4MCB1NTUQYMGtWrVasSIEefPn/ewR6SKSU9PP3LkyIsvvljRAyknhmEWLFgwefJklmUBgOO4//wSpNJBgbDqKFkgZACgnYwGADomzvNmo6KiUlNT3Q8VCoVWq3U/vHr1qkaj2blzZ0ZGxo8//jh79mzPe0SqDIPBMGXKlNDQ0PPnz+/YsePWrVsFBQUVPaiymTVrlkQiOX36dLdu3ebPn9+hQ4eKHhHifWiNsIpgtYWpL7TRO9iYi+kCHDvXNIoWCWscOY/T4ooeGoIgyDMN3RFWEczZE8DzJ00MB9BSIqJwjGrSAkVBBEGQ/4QCYRVRPC9qcC0Qem1eFEEQpMpDgbCKKH2Uvp2XdsogCIL4AxQIqwJnbrYjKz3Pwd61OSQE3lgsxGmxqGGzih4XgiBIJYACYVXAnEoCgGQDAwCxUorEMKplDCao4Lo2CIIglQIKhFWB5fRxKMms5q0ThAiCIH4CBcLKj+eZsycA4GTpBcI2aKcMgiDIE0GBsNKz30lhCzR3rI5ch1NNEtG0kFCoRHXrV/S4EARBKgcUCCs95swJALhosQFAOxmFAdBt2qLCaQiCIE8IlWGq9JgzyQDQP0DaTkYzHAdoXhRBEKQs0H1D5RYdHX3m2DHX5yECYmpqQbKRQUfpEQRBnhwKhJUba7WyZqP7IccDrgwQRNWouBEhCIJUMigQVm68zXrfM6J6jSpkJAiCIJUUWiOs3DCKXoaLZUUWTq/jOS7D7hDVa1jRg0IQBKlMUCCs3DCBYOi0GfXr1+cddnvK9ZHvfyiq37iiB4UgCFKZoEBY6TVp0qR169YAAB07ydduwBXKih4RgiBIZYLWCBEEQRC/hgJh5VanTh2apt0Pa9asKZVKK3A8CIIglQ7G83xFjwFBEARBKgy6I0QQBEH8GgqElRXLsgUFBa7PrVarzWar2PEgCIJUUigQVkocxy1evHjZsmX79+93Op2jRo3auHFjRQ8KQRCkUkJrhJXY8ePHU1NTCwoKGjRocOfOnZEjR1b0iBAEQSofdEdYWWVmZv7xxx/dunXbvXv3n3/+eerUqYoeEYIgSKWEDtRXSmazedKkSV27dr179+7Bgwdzc3OPHz9e0YNCEASplNDUKIIgCOLX0NQogiAI4tdQIEQQBEH8GgqECIIgiF9DgRBBEATxaygQIgiCIH4NBUIEQRDEr6FAiCAIgvg1FAgRBEEQv4YCIYIgCOLXUCBEEARB/BoKhAiCIIhfQ4EQQRAE8WsoECIIgiB+DQVCBEEQxK+hQIggCIL4NRQIEQRBEL+GAiGCIAji11AgRBAEQfwaCoQIgiCIX0OBEEEQBPFrKBAiCIIgfg0FQgRBEMSvoUCIIAiC+DUUCBEEQRC/hgIhgiAI4tdQIEQQBEH8GgqECIIgiF9DgRBBEATxaygQIgiCIH4NBUIEQRDEr6FAiCAIgvg1FAgRBEEQv4YCIYIgCOLXUCBEEARB/BoKhAiCIIhfQ4EQQRAE8WsoECIIgiB+DQVCBEEQxK+hQIggCIL4NRQIEQRBEL+GAiGCIAji11AgRBAEQfwaCoQIgiCIX0OBEEEQBPFrKBAiCIIgfg0FQgRBEMSvoUCIIAiC+DUUCBEEQRC/hgIhgiAI4tdQIEQQBEH8GgqECIIgiF9DgRBBEATxaygQIgiCIH4NBUIEQRDEr6FAiCAIgvg1FAgRBEEQv4YCIYIgCOLXUCBEEARB/BoKhAiCIIhfQ4EQQRAE8WsoECIIgiB+DQVCBEEQxK+hQIggCIL4NRQIEQRBEL+GAiGCIAji1/4PXvsktvm3G/EAAAG9elRYdHJka2l0UEtMIHJka2l0IDIwMjIuMDMuMwAAeJx7v2/tPQYg4GWAAEYglgZiGSBuYGRTUADSLGwJGkCKmXo01FgIxagAEmVkpBfNzcqWwMKawcTGnsHEzpHAwZnBxMyRwMmVwMiVwcTIlMDEDBRgSQAGBQsPAxs3OGz4GBj4GZgEGFgFGdiFGDiFGbhEGLhFGbjFGLjFGXgkGHgkGXikGESY2BiBZnFysbEwc7CzsYr7MUJCFAyk+WXbDtx3TNwL4mis0j9ws3W9HYjtvPra/mPfTPeD2JujcvZV2C/YB2LnWYfZb3+zDix+SU7IIYWpAsx2jNJy2CTzHay3hPuMPduSI/YgtlOMuF3jjolgNnd19v61XjwOIPbOPPEDV1c+BYvLlDY7JKcmgtlJs1/ZdUeZHACxuVK3HAi89hxs74frkw7cFd0EVhNzpOjAb80DYHsFJzIfeJjIBVYfURzmwJbPCGYHN/2xNzKzB9vl28u1/45eJpjtcDj6QLizGZgtvnejg/m8xWAzS0pyHfxfioDF/+pPcCi9JQA2f97/Rbax8zvAZprnf7H/NdUHzOZxrtsvIy8DZosBAEThetWsx7OKAAAChnpUWHRNT0wgcmRraXQgMjAyMi4wMy4zAAB4nH1Vy27cMAy871foHCCC+JBIHpNs0C2K7AJtmn/ovf+PDu0mVlCh9gKW6RHFxwz37g7XqeT1/fzt1+/ycfH5BHv7zy8iypu01k4vJRfl8fnL12t5en14fLc83X5eX38UtsKOPbg/Yx9eby/vFipP5V4rBTW1ct9q6xqhBYvtOvZyIrmaqg3Cd1F28gVQEkh1mFGjdImnhS2QmshWWZpTz5UQh68O70C26jJcNZ3LGB2F+Bc4AOTKLiTb2THcfCyAtgHHIOvpR52H0gLnwFHtoQQrFjK4r/zFFiGpOQlwJIhUFjhqe9JByHQgBApufVVHor3iyJUkfVooa18hszdoYvO2ffd8Lg+XLUrtzmTpGx1svEqHtFzKfa9DTEQzYO3sbdVFtO6SHOruPrJGEmgjr5AjkYJEhKVnH3sjsGgFtYQy6NiNI1c0mHnFDZDwUqRKB8vatge4tnQaQFINY0tx1HAbvqoTtzy91a7E2CR1qKivSs+0p6QihpxRUTAAVVggGcheeweUEQUbKLcqPcuWD1TDaDv6z8N06TF7hLqDPiBQhju6yzKfbBHMFt0je8UjtC9VPvYStRipNAE9IbUl0vYaBZoJqqLuKiFLUT5fz5+Gzj6GHm/X8zGGFNK1Y9YoJD+OiaLQqx9jQyHLOGaDQn00jYAExCF0TdERHYpWHHC8x/Yuk0A1T5xkmP76JDbCzZOmCDGQTNIhnEo8CSR3kE462Ax9YvtmGBOnCUGQTdQlZEY+UZSQCsXERMrgeWYcZfhME7UoE2CeOLRbZOLKbtGJE5Rpcp96v1s+Amb/a7G56XOL8/39Xwrr0x+5E0xIVZ6PYwAAAcN6VFh0U01JTEVTIHJka2l0IDIwMjIuMDMuMwAAeJx1kTtvWzEMhf9KRweQBfH98NglU9E9yFAYHQsXRcb8+FJazCWDfS/Jw6NP5769vt/hfvl+eXt9fzl/+3c/b3f8avClHur57fNypclEFgOns7mO2xUnqQINmBaMcjrGbDrWJEYHrxZPCFgyrmsu4QjeOplKRrSbLOjLjlDcyxcmBWBUh8qXsFQwZQFuN5hqBnvPYFnYOXOJWKlwgiJitdZEWg7nUCov53Fb00mdebsVtu4OiyPY3izWRbtVpuJRJ/BEPbAwY4XWHtVNVOX4R4EB7E2mKHec6ATnQqF+4qFJUm5bsxCPptI64bCjMoxbZbO8sik2XywFIFOkYq4Q0Bxtu9RFK4PKGtV478iS8q+TRMVLW4QSZVcPUtRDbGh7J9zU97WgPtn5UkA7g3MFqFy0RBC49LSEASEGTWXiGC/j18fjz89/j7+55n798fj4PXElPCtYia1KehYruU0opVWc2ipJa/7pbaQZz4oSGggndBBMaCRY08aCktBoUBMaTg07jiU0npJ2IEhoRAiJPRtMbExIiY3JEhsSeGJDqqoRRWInis//I1b4nsM0O0QAAAEqelRYdHJka2l0UEtMMSByZGtpdCAyMDIyLjAzLjMAAHicxZC9TsMwFIWPncT5Kf+0DdAlS6uKR2Bxpg4M3bJnjNhhZmDgPVAZUrHBWvslWBiQ+gS8AnYcY6tiYONK1vl85Ht97K/NyydU7cMUUWvSr3vCikJp+LvUcyXB39W0kUJvCfkvHYRRQyNWh2mdpA2Nk5rFDVXvJhQ0UDkRZ8AA2NOfQg5ADkGPQI8RnCA4RTQEGyEZI82RnSE7R3aBIWFhmsQsypfE/GFXk3XblrPX/ElvqurW45HHFd883E01r9tL4fw3sV08C+NfScc38nF5PfV87p3veSXdzI+fu3QeQHKPhc1m/Z6FzbnD3GZ2fsfc5t8uZtLm3OHSO1Oa3nfp3ruSbn7H3fzxNyh5cYwoLYYUAAABvXpUWHRNT0wxIHJka2l0IDIwMjIuMDMuMwAAeJyNlc9ugzAMxu88Rc6ViGLnHzmWUm3TVCpt3d5h972/ZoNoHKlzCRzA/dVO6u9zDwdaneH1Mb3//Jr7wqmjuFPuUor59s657mL4wYznl7fZnG7HcYucrl/z7dNg5tvx1bLH2/WyRcCcTLR+WaZ31i3L1IcNRAK9HZ6DnkDckzEQ6LaMCheJozyJV1YzJibBBlhJsFiK88MDMjOJtjwnByb9Vl0pXpqUSkZwzTY1krvTRwtPiwOaV5P2tAc8kfeOg43/gkGCvUZGIu/a0MAkQTVlJhJ3FR8kqYGFQLenODpJaiAQKKSJNpbB5fQI5QZVHamob1CNDE19jeQW9WmPiZCbVEWnHT83pPaTnuepGTvrIBqv81QHUSCnpzpuAtk516HCn4KYHYEMB2JGBHJqqYMgkMWH6nagC6ul6cX4alzg/MKey16ECYG/AsJrHAIUlloCXlhnCQThEC4JUThhCSQheN4FZKHrJTAI/fK+oAiZcgClHIGOjiBEB3R4RCEtoPOiFwoCPjEGoRTgM2MUglgjSTR+jWTZX9lNft/+kui5+wMwqEdJQlLlGwAAASx6VFh0U01JTEVTMSByZGtpdCAyMDIyLjAzLjMAAHichZC7asQwFER/JaUXbKG5eti6KdNsFdIvWwSTMjiELffjI8tYGicLEVjM3OeRL+frjLl76S7n66lc6zd3fFPyX1k6kO+nezc4E/MZ+0FMSJOd+uesEkoIRlKyPof2Kpt1MKg6PojDBHLDZveZf0e2vTD+V40tNYeiGsorrZnceg56W1htcWJq3S6HQ6IYt/WsVbs8xLeW0GbF1hB4UDjsqO8aHjys/vZT/35bPt++ly+1ZpWvy+3DCBTNRZVmRnXNwKqnLq+BXNBILurY3KQTpURTc0nBIE5BJF5BKDnHLJOCYJAHMY2CaBAVhINRQUD58QQEr0JECCpEZFWICFBhJFFhJKdCSHkQ/yB7/wEu0OSHosQwagAAAABJRU5ErkJggg==\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "generic_mapping" - ] - }, { "cell_type": "code", "execution_count": null, @@ -295,9 +207,9 @@ ], "metadata": { "kernelspec": { - "display_name": "Python openFE", + "display_name": "Python 3 (ipykernel)", "language": "python", - "name": "openfe" + "name": "python3" }, "language_info": { "codemirror_mode": {