diff --git a/docs/jupyter_execute/notebooks/localization.ipynb b/docs/jupyter_execute/notebooks/localization.ipynb index ff346f63..d18946ce 100644 --- a/docs/jupyter_execute/notebooks/localization.ipynb +++ b/docs/jupyter_execute/notebooks/localization.ipynb @@ -609,13 +609,8 @@ "name": "stdout", "output_type": "stream", "text": [ -<<<<<<< HEAD "Unlocalized CCSD: -169.48050963491698\n", "Diff of complete virtual space energies: -2.7119256174046313e-08\n" -======= - "Unlocalized CCSD: -169.48051043912199\n", - "Diff of complete virtual space energies: -2.5353074306622148e-08\n" ->>>>>>> 2061f52c9c68d258f384a4c72344409cbc59dd27 ] } ], @@ -668,11 +663,7 @@ { "data": { "text/plain": [ -<<<<<<< HEAD - "" -======= - "" ->>>>>>> 2061f52c9c68d258f384a4c72344409cbc59dd27 + "" ] }, "execution_count": 12, @@ -695,11 +686,7 @@ "\n", "plt.semilogy(np.arange(5, 33), np.abs(localized[1][::-1][:-1]))\n", "plt.semilogy(np.arange(5, 33), np.abs(unlocalized[1][::-1][:-1]))\n", -<<<<<<< HEAD "plt.vlines(cl_driver.huzinaga[\"cl\"].shells[0], ymin=1e-5, ymax=1e-1, color=\"k\", linestyle=\"--\")\n", -======= - "plt.vlines(cl_driver.mu[\"cl\"].shells[0], ymin=1e-5, ymax=1e-1, color=\"k\", linestyle=\"--\")\n", ->>>>>>> 2061f52c9c68d258f384a4c72344409cbc59dd27 "plt.hlines(1.5e-3, xmin=5, xmax=33, color=\"red\", linestyle=\"--\", linewidth=0.5)\n", "\n", "plt.title(\"Formamide (C=O act) 6-31G\")\n", @@ -729,16 +716,10 @@ "metadata": {}, "source": [ "\n", -<<<<<<< HEAD "\n", "Whereas Concentric Localization gives a procedure to iteratively localize the entire virtual space after the embedding procedure is complete, the Projected Atomic Orbitals method is used to define the contribution of virtual orbitals to the projector used in embedding.\n", "\n", "### Defining the Environment Projector\n", -======= - "Whereas Concentric Localization gives a procedure to iteratively localize the entire virtual space, the Projected Atomic Orbitals method includes the use of cutoff parameters to reduce the size of the virtual space. \n", - "\n", - "### Theory\n", ->>>>>>> 2061f52c9c68d258f384a4c72344409cbc59dd27 "\n", "Define the projector of the occupied orbitals\n", "\n", @@ -752,19 +733,11 @@ "\n", "We project these orbitals into the basis of the active subsystem\n", "\n", -<<<<<<< HEAD "$$ {C}_{pao}^A = P_A{C}_{pao}$$\n", "\n", "We can then truncate the size of the virtual space by setting a cutoff $\\nu$ in the norm of the overlap between \n", "\n", "$$ N_i = \\sum_{\\mu}^{act AOs} (C^A_{PAO})_{i\\mu} (SC^A_{PAO})_{i\\mu}$$\n", -======= - "$$ \\bar{C}_{pao}^A = P_A\\bar{C}_{pao}$$\n", - "\n", - "We can then truncate the size of the virtual space by setting a cutoff in the norm of the overlap between \n", - "\n", - "$$ N_i = \\sum_{\\mu}^{act AOs} (C_{PAO})_{i\\mu} (SC_{PAO})_{i\\mu}$$\n", ->>>>>>> 2061f52c9c68d258f384a4c72344409cbc59dd27 "\n", "The remaining orbitals are renormalized forming ${C'}_{PAO}$\n", "\n", @@ -772,21 +745,15 @@ "\n", "$$S_{PAO} = ({C'}_{PAO})^{T}S {C'}_{PAO}$$\n", "\n", -<<<<<<< HEAD "we restrict to only the orbitals with an eigenvalue $|S_{PAO}|$ above some parameter $\\sigma$. This prevents linear dependence between the orbitals.\n", "\n", "Finally we have the reduced form of the Projected Atomic Orbitals\n", "$$\\bar{C}_{PAO} = \\set{C_{PAO} s.t. |S_{PAO}>\\sigma}$$\n", -======= - "we restrict to only the orbitals with an eighenvalue above some parameter.\n", - "\n", ->>>>>>> 2061f52c9c68d258f384a4c72344409cbc59dd27 "\n", "### Cutoffs\n", "\n", "In the supplementary material to the paper above, they suggest using \n", "\n", -<<<<<<< HEAD "- Norm Cutoff $\\nu$: 0.05\n", "- Overlap Cutoff $\\sigma$: 1e-5\n", "\n", @@ -809,12 +776,6 @@ "metadata": {}, "source": [ "Let's try running the same embedding as above but with PAOs." -======= - "- Norm Cutoff : 0.05\n", - "- Overlap Cutoff : 1e-5\n", - "\n", - "so these are the default values nbed uses." ->>>>>>> 2061f52c9c68d258f384a4c72344409cbc59dd27 ] }, { @@ -823,7 +784,6 @@ "metadata": {}, "outputs": [ { -<<<<<<< HEAD "ename": "NotImplementedError", "evalue": "PAO not yet fully implemented.", "output_type": "error", @@ -834,14 +794,6 @@ "\u001b[36mFile \u001b[39m\u001b[32m~/Code/Nbed/nbed/embed.py:79\u001b[39m, in \u001b[36mnbed\u001b[39m\u001b[34m(config, **config_kwargs)\u001b[39m\n\u001b[32m 76\u001b[39m config = NbedConfig(**config_kwargs)\n\u001b[32m 78\u001b[39m driver = NbedDriver(config)\n\u001b[32m---> \u001b[39m\u001b[32m79\u001b[39m \u001b[43mdriver\u001b[49m\u001b[43m.\u001b[49m\u001b[43membed\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 80\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m driver\n", "\u001b[36mFile \u001b[39m\u001b[32m~/Code/Nbed/nbed/driver.py:820\u001b[39m, in \u001b[36mNbedDriver.embed\u001b[39m\u001b[34m(self, init_huzinaga_rhf_with_mu, n_mo_overwrite)\u001b[39m\n\u001b[32m 813\u001b[39m \u001b[38;5;250m\u001b[39m\u001b[33;03m\"\"\"Run embedded scf calculation.\u001b[39;00m\n\u001b[32m 814\u001b[39m \n\u001b[32m 815\u001b[39m \u001b[33;03mArgs:\u001b[39;00m\n\u001b[32m 816\u001b[39m \u001b[33;03m init_huzinaga_rhf_with_mu (bool): Will run mu-shift projector even when input projector='huzinaga'.\u001b[39;00m\n\u001b[32m 817\u001b[39m \u001b[33;03m n_mo_overwrite (tuple[int, int]): Enforces a specific number of MOs are included in the active region. Used for ACE-of-SPADE reaction path localization.\u001b[39;00m\n\u001b[32m 818\u001b[39m \u001b[33;03m\"\"\"\u001b[39;00m\n\u001b[32m 819\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m.config.virtual_localization \u001b[38;5;129;01mis\u001b[39;00m VirtualLocalizerTypes.PROJECTED_AO:\n\u001b[32m--> \u001b[39m\u001b[32m820\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mNotImplementedError\u001b[39;00m(\u001b[33m\"\u001b[39m\u001b[33mPAO not yet fully implemented.\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m 822\u001b[39m logger.debug(\u001b[33m\"\u001b[39m\u001b[33mEmbedding molecule.\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m 823\u001b[39m \u001b[38;5;28mself\u001b[39m.e_nuc = \u001b[38;5;28mself\u001b[39m._global_ks.energy_nuc()\n", "\u001b[31mNotImplementedError\u001b[39m: PAO not yet fully implemented." -======= - "name": "stdout", - "output_type": "stream", - "text": [ - "Number of Molecular Orbitals: 27\n", - "Embedded CCSD Energy: -169.35216765732525\n", - "Error from complete virtual space: 0.1283427817967322\n" ->>>>>>> 2061f52c9c68d258f384a4c72344409cbc59dd27 ] } ], @@ -853,11 +805,7 @@ " n_active_atoms=2,\n", " basis=\"6-31g\",\n", " xc_functional=\"b3lyp\",\n", -<<<<<<< HEAD " projector=\"huzinaga\",\n", -======= - " projector=\"mu\",\n", ->>>>>>> 2061f52c9c68d258f384a4c72344409cbc59dd27 " localization=\"spade\",\n", " virtual_localization=\"pao\",\n", " convergence=1e-6,\n", @@ -868,15 +816,9 @@ " norm_cutoff=0.05,\n", " overlap_cutoff=1e-5,\n", ")\n", -<<<<<<< HEAD "print(\"Number of Molecular Orbitals:\", pao_driver.huzinaga[\"scf\"].mo_coeff.shape[-1]+1) #zero-indexing!\n", "print(\"Embedded CCSD Energy:\", pao_driver.huzinaga[\"e_ccsd\"])\n", "print(\"Error from complete virtual space:\", pao_driver.huzinaga[\"e_ccsd\"] - unlocalized[0])" -======= - "print(\"Number of Molecular Orbitals:\", pao_driver.mu[\"scf\"].mo_coeff.shape[-1]+1) #zero-indexing!\n", - "print(\"Embedded CCSD Energy:\", pao_driver.mu[\"e_ccsd\"])\n", - "print(\"Error from complete virtual space:\", pao_driver.mu[\"e_ccsd\"] - unlocalized[0])" ->>>>>>> 2061f52c9c68d258f384a4c72344409cbc59dd27 ] }, { @@ -894,11 +836,7 @@ { "data": { "text/plain": [ -<<<<<<< HEAD "" -======= - "" ->>>>>>> 2061f52c9c68d258f384a4c72344409cbc59dd27 ] }, "execution_count": 14, @@ -907,11 +845,7 @@ }, { "data": { -<<<<<<< HEAD "image/png": 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IiYiIiNtSICQiIiJuy+UDIQ4Axkn5ONQ9J7/kuBsiIiIibpEszUHHOAopB/Y6c+aMGT5+3759KZ7qgPP9cFh5TvqXmlmeRURExHEY3nA0ec6fx8E23XZkaY6ZYR03gzMjc74njnab0kCIQVBKJm8UERGR7NkylNyI49k+EOLQ65y2gPPksHZn1qxZCWZ8/vTTT80+rPHhkOsc0j+xSfl4DM5vlJrAhjVB1gvJIfFFREQk+7t27Zq531vv404bCHHeGQY3nLeoa9euCZ6fMWMGhgwZgsmTJ6Nhw4ZmwkJOdsgJEa1z7BBrgTjB4ldffZWq17c2hzEIUiAkIiLiXO6U1uJUOUI8mfg1Qgx+6tevj08++cSW08MI8NlnnzUTI1JERISZQLB///547LHHkn0N7sslfkTJSQcVCImIiDgH3r854fWd7t9O3Wvs1q1bprmLM01bMSGK65wRmhjnPf7442YW5jsFQcTZsHnhrIvyg0RERFyXUwdCFy5cMDk/hQoVirOd68wXolWrVpnmM046yJ5jXLZv357kMYcNG2aiR+vC3CARERFxTdk+Ryi9mjZtaprLUsrPz88sqcHjs3ZKRNLG19c32e6tIiKZxakDIXaF9/LyMuME2eM6u8qnB3uicWGNU3IYAB0+fDhVwZaIxMUgqEyZMiYgEhHJSk4dCPGPJgdIXLJkiS2BmgEJ1wcNGpSuYw8cONAs1mSrxDD/iF36GYwxl0jfaEVSzzpoKf8vlSxZUgOXikiWyvaBUGhoKA4cOGBbZ+3L1q1bkS9fPvNHk13n+/Tpg3r16pmxg9h9nl3u+/btm+k1QlFRUQgLCzOjVgYEBKTr9UTcWYECBUwwxP9TPj4+ji6OiLiRbN99/u+//8Y999yTYDuDnylTppjH7DpvHVCRydCTJk0y3eozu/tdeHi4CcxKly6NHDlyZMjribijmzdv4siRI6Z5zN/f39HFERE36j6f7QMhR0tJIKQ/3iLpo/9LIpLR3GIcoczEZjHOWM/BGkVExHVEX7+OyNtDrMTH7Xxe3IcCoSQwUXrXrl3YsGGDo4siGYiDa8afq86ZvPXWW6b5NyvPh83TTGC+cuVKpr6OSFZgkHP8f/1x9LHeiDx9Os5zXOd2Pq9gyH0oEHJTzKfiNCRly5Y14yax11vHjh1NjztnwxwtJsmnxMSJE225Za4QSKX3fOJr0aIFnn/++Tjb7rrrLtOjK6nekyLOJObGDURduoTI48dxtHcfWzBkgiCuHz9unud+4h4UCDmwacxR1bNMSuWwA0uXLjVJ5hxpe8GCBSYpnTVhroi9/9hNmzfzPHnywFVkxflwmAqOy6Vu7eIKfAoXRqkfvodPiRK2YChs8xZbEMTt5vl0jkUnzkOBkIOaxhxZPTtgwABzU1u/fj26deuGihUrolq1amYogrVr19r2O3bsGDp16oSgoCCTaPbwww/HGbzS2kzz448/mloZ3pQfeeQRXLcrM4OPDz/8EOXLlzc1Txzy4L333rM9zylMeFzezDkkAl+PgVr8GpixY8eiSJEiyJ8/v3lvIiMjbTUYR48exQsvvGDOyXqzZi0Jjzl37lwT0PK1eT7xa3TuVL7U+ueff8wwDjwWy8uJf9klPKWv98orr5j3g8MxsLbuzTfftJ1rYuzPh9fNeg3sF14junjxInr06IFixYqZ49eoUQPTpk2LcyyWn7VM1t/lMRNrGvv111/NZ4bnwPf+o48+ilMubhs1ahT69euHnDlzmvP88ssv03xdRTKST5EicYOhnj3jBkFFiji6iJKFFAi5WfXspUuXTO0Pg4nAwMAEz1trF3jDZlDC/XlzXLx4MQ4dOoTu3bvH2f/gwYNmHrd58+aZhfu+//77ceZu4zpv6Awsf/rpJ9vccLzBt2nTxtwoV6xYYeaFY9DVtm3bOFOWLFu2zLwOf37//fcmyLE2B/32228oXrw43nnnHdN8w8WKYzx98MEH+Prrr7Fz504ULFgwwfkmV77UOnnyJNq3b29qEbdt24bPP/8c33zzDUaOHJni1+O14LnxOQYkX331FcaPH5+i12fzpvUacNmyZYsJHJs1a2brmcWawPnz52PHjh148sknzUTEDIiJr9e4cWP079/fdozEJh3mRMcMXhn0sjaRATHPJ34THYMjju/FcjD4fuaZZ7B37940XVuRjMZgp+gHH8TZxnUFQW6I3eclaVevXuXwAuZnfDdv3rTs2rXL/EyLW6dOWfa3us+yq1Jl8/PGps1x1vl8Rlu3bp05n99++y3Z/RYtWmTx8vKyHDt2zLZt586d5nfXr19v1keMGGEJCAiwXLt2zbbPSy+9ZGnYsKF5zO1+fn6Wr776KtHX+PHHHy2VKlWyxMTE2LZFRERYcuTIYVm4cKFZ79Onj6VUqVKWqKgo2z4PPfSQpXv37rZ1Pj9+/Pg4x/7uu+9MWbdu3RpnO4/XqVOnFJUvMfa/H99rr72W4Hw+/fRTS1BQkCU6OjpNrzdmzBhL3bp1beu85iEhIXcsDz+TfB/uv/9+89pJ6dChg+XFF1+0rTdv3twyePDgOPssW7bMXMvLly+b9Z49e1ruu+++OPvwfa9atWqc9+TRRx+1rfOaFCxY0PL5558nWo70/l8SSc/fX+uSWX93Jfvdv+2pRsiBOUKOqJ5N6bBRu3fvNrUB9jUCvB6sMeJz9k0grMWwYnPQuXPnbMeIiIhAy5YtE30N1ppw1HD+PmuCuLB5jDUXrAGyYhMMpzFJ7DXulNtSs2bNZM8xufKlFo/HGhX7XJomTZqY0dFPnDiRotebMWOG+R3m5PB6vPHGG6ZJL7XYJMUmStY4Wad+YZ7Uu+++a5rEeJ15/IULF6b6+DwPltEe1/fv3x9nJHb7a89rwnNKyfsmktnsa97N39uffoqTMxQ/XUFcmwIhB3efz+rq2QoVKpib0p49ezLkePGnQ+CxrRPQ3mm0bQYIbKrhlCn2y759+9CzZ88UvUZy+PrJJfhm9Wjgd3q9NWvWoFevXqZ5jc2MbFJ6/fXX4zQTpgSb4hjgMD/KPkhlYjybv5iHxGZGXms2Tab2+CmV1vdNJDOxI0r8xOiAOrUTJFAn1ZFFXI8CIQfjN49Tr7wSZxvXM+sbCWsCePNjjRfnZIvPmhBbpUoVk8jMxYqBIZ9nzVBKgy7e/JPqkl+nTh1Ti8DcHSYP2y+p6arNmp/k5oRLa/lSi9eMwYx9rRvznhiMMI/pTq+3evVqlCpVygQ/zK3h/kwETw0mMTNf6ueff0a5cuXiPMeyMO/r0UcfRUhIiEnGZtCZ2mvJ8+Sx4h+bSd72NXci2ZFnYCC88+VLUPNuX0PP57mfuAcFQm5YPWudTJa9m3jjZDDC5g7O0camHWrVqpVpQmENxebNm01Cbe/evdG8eXNzk04JTpXA2oeXX34ZP/zwg2nuYq80JhATjx0cHGxuzkyW5hQL7KH03HPPmaaklGLz3PLly02y8oULF1L8e3cqX1I4XHv8WiwGjEwI5k+Oz8Qatzlz5mDEiBGmNx6bp+70egx82Ew1ffp08xzfj1mzZqX4fJgAzfeIr8HmRI4VxYUJ79bjM+mdARff76eeeipOL0DrtVy3bp3pLcZrmVgNzosvvmiCOTazMZBiAjvn+xs6dGiKyyriKF45c6LE11+h1I8/JKh5N8HQjz+Y57mfuAcFQm5YPcuaAAY3HDeIN7Xq1avjvvvuMzc39nSyNmPwRp43b17T64iBEX+POSypwd5EfI3hw4ebmgT2OrPmibALNwMYdq3u2rWref6JJ54wOULJzQsTH2tAeONmDQhnMc+o8iWFwVrt2rXjLG+//bbplv7HH3+YoJE1Lk8//bQ5H+b5pOT1HnjgATMMwKBBg8ywBAxYuH9Kbdy40fSUY9MY86isC68tsRyshWONILvUM2cn/uCQDGZYq8NaP17LxPKHeAzWODFg42eH58L3gN3vRZwBg5ykxgnidgVB7kWTriZTa2KtOeG33oyedNU6jhC7yMdPjLbWFLF6Vt9MxB1o0lURyWiafd4JZp9nMMRxghL7ZsKaILZRKwgSd6BASEQcFQh5Z/grS4oxyEkq0NHw7iIiIplPOUIiIiLithQIiYiIiNtSICQiIiJuS4GQA6fYEBEREcdSIOTgKTZERETEcRQIiYiIiNtSICQiIiJuS4GQiIiIuC0FQm6Ic0JxLjEuPj4+ZjRfTgTK0X2zEif4ZBk4Z1V8nDSUz02ZMiXOds6/1b59ezMHGkcg5sSw48aNS9Ps8yIiIgqE3FTbtm1x+vRpHDp0COPHj8cXX3xhZkrPaiVKlMB3330XZxtnZOes6YGBgXG2cyb25s2bo3jx4li2bJmZ4X3w4MFmktFHHnkEmi1GRERSS4GQm/Lz8zOzjzMQ4QzknF1+8eLFtucvXryIHj16mBnVOUs8a16mTZtme37evHnIkyePrSZm69atpgbn1Vdfte3zv//9D48++miy5ejVqxf++ecfHD9+3Lbt22+/Ndu9vf+bAebGjRvo37+/maH9yy+/NLOzs0aJr/H999/jl19+MTOi061bt8wM7px5nbVGpUqVwujRozPoyomIiCtRIJSB4wixRiLsVpRDlvTUhuzYscM0Ofn6+tq2sZmsbt26mD9/vnn+ySefxGOPPYb169eb5++++25cv34dW7ZsMesMZoKDg/H333/bjsFtLVq0SPa1CxUqhDZt2phghsLCwjBjxgz069cvzn6LFi0ywdnQoUMTHKNjx46oWLGiLVCbNGkS5s6dawKjvXv3YurUqSZoEhERiU+TriYzjhAX6+y1KXEzMhpVhy+EI+x6pw0CfFP+drJGJygoCFFRUYiIiICnpyc++eQT2/OsCbIPOp599lksXLjQBBcNGjQw14S1Mgx86tWrZ36+8MILePvttxEaGmpm+z1w4IBpyroTBj0vvvgiXn/9dVOzU65cOXNse/v27TM/q1SpkugxKleubNvn2LFjqFChApo2bWpqqVgjJCIikhjVCLmpe+65xzRnrVu3Dn369EHfvn3RrVs32/Ns8nr33XdNk1i+fPlM0MRAiEGGFYMcBkCsjVqxYgW6du1qApWVK1ea2qCiRYuagOROOnToYIKn5cuXm2ax+LVB9lJS88VkcJ5bpUqV8Nxzz5naJBERkcSoRigD5fDxMjUzjnrt1GAicvny5c1jBh8hISH45ptv8MQTT5htY8aMwcSJEzFhwgQTDHH/559/3uTfWLHZi7+7bds20/uMtTLcxuDo8uXLKaoNIuYCsdmNydoMzJgUHR+bvmj37t246667EjzP7WzKpDp16uDw4cP4888/8ddff+Hhhx82OVCsbRIREbGnGqEMxGYYNk85YuFrpxWbxV577TW88cYbuHnzptm2atUqdOrUySQ7M0gqW7asrenJyponxF5n1qDHGghxuVN+kD3WArEWia/JrvHxtW7d2tRMffTRRwmeYz7Q/v37TXK3Va5cudC9e3d89dVXJufo119/xaVLl1J1XURExPUpEBLjoYcegpeXl0kSJzZpsRcZk6hZ2/LUU0/h7NmzcX6HAUvNmjVNMrI16GnWrBk2b95sgqaU1ggRm9QuXLiQoCu9FWuk2MV/zpw5JnH733//xZEjR0wtFpvCHnzwQVPzQxxXiInT7F7PcsycOdP0kGMvNxEREXsKhMTWPMUu5x9++KHpqs7aITYxsUcXgxwGEuxmHx+DHeYTWQMh1tqwiYr7M0cnNfLnz48cOXIk+TyDHY4fxDwl1kbx+KyNYpI1B2W01orlzJnTnAeTuNnrjwHTH3/8YWq+RERE7HlYNApdsqy9xtgLis0t9tjFnLkoHJmZ49WISNro/5KIZOX9256+IouIiIjbcotAqEuXLiafhU0rIiIiIm4VCHE+qh9++MHRxRAREZFsxi0CISbyMoFWRERExKkCIY42zLmkOEoxewXNnj07wT7s8s25pJhk2bBhQ9t8WCIiIiJOHQixKzcH9LOObxMfB8sbMmSIGZWY49dwX3b5PnfuXJaXVURERJxLtp9io127dmZJCgfP69+/v5kriyZPnmxmTOfUD6+++mqqX48TkHKx734nIiIirinb1wglh/Nebdq0ycwjZcVB87i+Zs2aNB1z9OjRZtwB61KiRIkMLLGIiIhkJ04dCHFKBo5qXKhQoTjbuX7mzBnbOgMjTiHB0YWLFy+ebJA0bNgwM/iSdTl+/HimnoOIiIg4jlMHQinFGcjPnz+PsLAwnDhxAo0bN05yXz8/PzMC5Y8//ohGjRqhZcuWWVpWV8apLpjwvnXr1nQdh4nxEyZMsK0nlUQvIiLi0oFQcHCwmSg0/mSgXOdcV+kxcOBA7Nq1Cxs2bIArYo3Zs88+a2aVZ/DHJkD2zluyZEmSAcedcIqEnj17mh5+7MHH2jfOJs/JT0VERLIjpw6EfH19Ubdu3Tg375iYGLOeXK2Pu2PNDK/b0qVLMWbMGGzfvh0LFizAPffcYwLAtIiMjMR9991nmhN/++037N271/Toq1GjBq5cuZLh5yAiIuIWvcZCQ0Nx4MCBOLUObFrhLOclS5Y0Xef79OljZhpv0KCBqcFgl3trL7K0Ynd9LsxBcjUDBgwwzUkcbykwMNC2vVq1aujXr1+ajrlz504cPHjQBKGlSpUy2/izSZMmCfY9dOgQXnjhBaxbtw4VKlQwPf3sA9eVK1eaXK2NGzeaWj9OkcIkdvuyioiIuEWNEG+GtWvXNgsx8OHj4cOHm/Xu3btj7NixZr1WrVomSGLtRvwE6ixpGrNYgFs3HLPwtVPg0qVL5vrw/BILLPLkyYO0KFCggOmx98svv9wxeHz99dcxdOhQ815VrFgRPXr0QFRUlHmOwVTbtm3RrVs3/Pvvv6ZWiYHRoEGD0lQuERERp64R4vQYljvc5HmTzOgbZZpqhCLDgFFF4RCvnQJ871xjwto1Xs/KlStn6MsXK1YMkyZNwssvv4y3337b1NCxqa1Xr14mD8keg6AOHTqYx9yXNVEsF8vEmh/+zvPPP2+eZ40Rj9u8eXN8/vnnJvdIRETEbWqEHMVVk6XvFFSm95oxCXvq1KmmqWvmzJkmyFm8eHGc/WrWrGl7XKRIEfPTOhL4tm3bMGXKFAQFBdkWjhTO3C82i4qIiLhVjZBT8QmIrZlx1GunAGtYmB+UWT25OLkte59xGTlypAli+JOJ1Lai+vjYHrMsxEDHmhP21FNP4bnnnktwbOaEiYiIZCQFQhmJN/UUNE85EpPMGZyw2Y/BRvw8IfbwSmueUHwMctjctXr16hT/Tp06dUxNXPny5TOkDCIiIslR01gSGChUrVoV9evXh6ux5j6xl92vv/6K/fv3Y/fu3SYXJ/6wAydPnjRJzfbL5cuXExyT2zlmEJOlGcgw5+ebb74xc75xe0q98sorJnBizhePybLNmTNHydIiIpIpVCOUTL4LF066yjnHXAmTlzdv3oz33nsPL774Ik6fPm16fXFsISYk22OPPC72OOr2o48+GmcbB0/kAIxMfraOIG1dZ1f5lGL+0D///GN6lt19990mp6lcuXKmd6CIiEhG87BkZvasC7AGQhwokFNv2AsPDzcJvGXKlFFvJpF00P8lEcnK+7c9NY25YdOYiIiIxFIg5Gbd50VEROQ/CoRERETEbSkQEhEREbelQEhERETclgKhJChZWkRExPUpEEqCkqVFRERcnwIhERERcVsKhERERMRtKRASERERt6VAyE0tX74cHTt2RNGiRc28YLNnz77j70RERJg5wEqVKgU/Pz8zlxgnVU2vpF7/8ccfR+fOndN9fBERkaRo0tVkeo1ZZ2l3RTdu3EBISAj69euHrl27puh3Hn74YZw9e9bMKl++fHkzWWtMTEyml1VERCSzqEbITXuNtWvXDiNHjkSXLl1StP+CBQvMrPB//PEHWrVqZWqDGjdujCZNmiSowRk1ahQKFSqEPHny4J133kFUVBReeukl5MuXz8xS/91336WpzCxD06ZNzXHz58+P+++/HwcPHkzTsUREREiBUCbVtiS1cJbtlO578+bNFO2bFebOnYt69erhww8/RLFixVCxYkUMHTo0QRmXLl2KU6dOmaa3cePGYcSIESZgyZs3L9atW4enn34aTz31FE6cOJHqMvBchwwZgo0bN2LJkiXw9PQ0gZxqpUREJK3UNJYJgoKCknyuffv2mD9/vm29YMGCCAsLS3Tf5s2b4++//7atsxbmwoULCfazWCzpLvOdHDp0CCtXroS/vz9mzZplyjFgwABcvHgxTg0Pa30mTZpkgpRKlSqZwInn99prr5nnhw0bhvfff98c65FHHrH9Xo8ePeDl5ZUgJ6lDhw629W7dusV5nvlJBQoUMDV31atXz8SzFxERV6VASFKEtS5Map46dSpy585ttrHG58EHH8Rnn32GHDlymG3VqlUzQZAVm8jsgxQGO2zWOnfuXJzjjx8/3jS52XvllVfi5Gjt378fw4cPNzVLDMSsNUHHjh1TICQiImmiQCgThIaGJvlc/FqP+AGBPfuAgo4cOQJHKVKkiGkSswZBVKVKFVMbxWauChUqmG0+Pj5xfo/BU2Lb4jdnFS5c2CRg28uZMyeuXLliW2cvN/ZY++qrr0xvNx6DAdCtW7cy9FxFRMR9KBDKBIGBgQ7fN6MxKXrmzJkmyLM2/e3bt88Ea0yAzmxsgtu7d68Jgu6++26zjc1rIiIi6aFkaTfFgGbr1q1mocOHD5vHbGay5vL07t3btn/Pnj1Nk1bfvn1NTg6TodkTjN3vrc1imYnJ1nz9L7/8EgcOHDBJ2UycFhERSQ8FQm6KPa9q165tFmJQwcfMwSGOEWQNioi1QIsXLzZNVew91qtXL9NUxcTorMCap+nTp2PTpk2mOeyFF17AmDFjsuS1RUTEdXlY0tDlKDIyEmfOnDG9gdhrhz2FXHlARTYBXb16Fbly5YqzD7vCsyalTJkypjeViKSN/i+JSEa7du2ayWtN7P6dphqh69ev4/PPPzddunlAduVmsiwDISaw9u/f36UGH3T1ARVFREQkhYEQu0kz8OF4MezizHmhmE/CmpI1a9aYQfM4enDr1q3Rtm1b081ZRERExCV6jbFWhMmxHCMmMQ0aNDBJs5MnTzbB0ooVK2zdqUVEREScOhCaNm1aig7GGck5hYKIiIiIS/caYxfmhQsX2uaayoppHkREREQcGghxYDvmCXHSTc6bxW7W9MQTT+DFF1/M0MKJiIiIZKtAiOO3eHt7mzFmAgICbNu7d++OBQsWZHT5RERERLJPILRo0SJ88MEHCaZVYHL00aNHkR3NmzfPzITOMn799deOLo6IiIg461xjN27ciFMTZHXp0iWTLJ3dsFs/R01etmyZGVipbt266NKli5muQURERNxbqmuEOOHlDz/8kGAm8Q8//BD33HMPspv169ebbv+cOZ3TRLRr187UaomIiIikOhBiwMOJLxlQ3Lp1Cy+//LKZ+4njDLHJLKPxuJzTqmjRoibo4mCO8XEqDA74yKH5GzZsaIIfq1OnTpkgyIqPT548CXf3+OOPm+vJxdfXF+XLl8c777xjatCs2rRpAy8vryRH1169erVJmOeEqLz2NWrUMINvcloSERERlwyEGPRwROkmTZqgU6dOpqmsa9eu2LJlC8qVK5fhBeTxQ0JCTLCTmBkzZpimL45uvXnzZrMvb+Dnzp3L8LK4Go4Czl5/HAmcPf7eeust20SmTIZnoDNo0CB8++23CX531qxZZroV5oqx2XHPnj0YPHgwRo4ciUceeUTDKYiIiHOwOBEWd9asWXG2NWjQwDJw4EDbenR0tKVo0aKW0aNHm/VVq1ZZOnfubHt+8ODBlqlTpyb5GuHh4ZarV6/aluPHj5vX5eP4bt68adm1a5f56Wz69Olj6dSpU5xt9913n6VRo0bm8VtvvWV55JFHLLt377bkzp3bEhYWZtsvNDTUkj9/fkvXrl0THHfu3Lnmek2fPj0LzkJchTP/XxKR7In37aTu3/bSNKAip9B49NFHcdddd9mamX788UesXLkSWYlNc5s2bTLjGll5enqadc6BZp3+Y8eOHaacoaGh+PPPP02NUVJGjx5tkqqtS4kSJeAucuTIYa4pY05OlcL3uHLlyqbZ7JdffrHtxxwrjic1dOjQBMdgMybHmErpaOQiIiKOlOpA6NdffzWBBG+abIqKiIgw2znN/ahRo5CVLly4YPJRChUqFGc718+cOWMec8yjjz76yCRy16pVyzQBJddjbNiwYeZcrMvx48fh6hj4/PXXX2ak8Hvvvdc8DgsLswWMDIi++eYb2/5sGqUqVaokejwGT9Z9REREXKr7PHNAOLlq7969MX36dNt25gzxuezogQceMEtKcAiAdA8DwNoQa43I1KnAa68BHGOpenVg0CDAOh9b//5AZCQwZUrsOnNxmHC+dy9QvjzwxhvMao597rHHAA5b8MUXseuffRb7eNs2oEeP2CUN4yuxJ11kZKTp+dezZ0+TJ/S///3PDJDJIJJ69OiBl156CQcPHoyTB6Y8IBERcbtAaO/evWjWrFmC7WxGunLlCrJScHCw6dV09uzZONu5Xrhw4XQdm8nZXNLUAyp+YPLxx3Gfnzs37nrXrv89vp2snOS+HTr89/jdd5EerCX7/PPPTa8x9spj4MPxoJgIzeCIz1nxOjBp+r333jNNX7R7927TPBoft1etWjVdZRMREcmWTWMMMDjhanzMDypbtiyyEm/gHCBxyZIltm2s2eB648aN03XsgQMHYteuXUl2HXcFgYGBJv+nZMmSttqfqVOnmp5g27Ztw9atW20LmxenTJliAqLWrVsjX758Zlt8c+fONb3QWIskIiLicjVC/fv3N92kWTvAMWg4Tg8Tk5k4++abb2Z4AZngbB94HT582NyYeSPmDZxd5/v06YN69eqZxOgJEyaYLvd9+/Z1XI2QE2Mu0IMPPmiGSbDHpHHmT3E+uQ4dOuCLL74w3eSffPJJ08U+V65cJgBlExp//+GHH3bYOYiIiKRYarujxcTEWEaOHGkJDAy0eHh4mMXf39/yxhtvWDLDsmXLTPe3+Au7f1t9/PHHlpIlS1p8fX1Nd/q1a9dmSfc7V+s+v3HjRnOu69evT/R32rVrZ+nSpYttffny5ZY2bdpYcuXKZa59tWrVLGPHjrVERUVlevnFtTjz/yURce7u8x78J6VBE2tHVq1ahZo1a5r5xlhTwxob5oMw6dYVXbt2zeQ/sQcZaz3shYeHmxqqMmXKmJGVRSRt9H9JRLLy/p3mpjEmJjM/hMmwefLkcemEWHdtGhMREXEnaZpi49ChQ3B17pAsLSIi4u5SHQhxrCAmRnMMGs5Txaon+0VERETEZXuNcbZx4gCF7DVmxVQjrrtKU5KaxkRERFxfqgMhzjTuDtg0xsWabJUcjbAskj76PyQiThMIsVcHx5Sxrw2y/iFzh3m54iePEycq5dxrIpI2/D9k/39KRCRbB0LMDSpYsGCc7Zyagc+5U1MSR2PmMALnz5+Hj4+PmfleRFKHo8Hz/xD/L1lHOBcRySqp/qtjzQWKj+MJudL4HynJEeJ1KFKkiBn/5CgnVRWRNOGXCI4Un9jfFhGRzJTiARU5lQVNnDjRTLPBb29WDBbWrVtnqrU54KK7DcjEb7TWqn0RSdu8gapRFZFsPaDili1bzE/GTdu3bzd/uKz4OCQkxHSrd0f8A+5KtWEiIiLuwju1vcU4memkSZOQM2fOzCyXiIiISKZLVV10ZGQkfvzxR+XDiIiIiPsFQuwZxYRGd+gZxkRpzqVWv359RxdFREREMkmqZp+nb775Br/99pupGcqXLx9cXUqTrURERMTFZ5+nTz75BAcOHEDRokVRqlQpBAYGxnl+8+bNaSuxiIiISBZLdSDUuXPnzCmJiIiISHZvGnM3ahoTERFx3fu3RjATERERt5XqQIg9xsaOHYsGDRqgcOHCJmHafnEV6jUmIiLi+lIdCL399tsYN24cunfvbqqbOPVG165dzejKb731FlzFwIEDsWvXLmzYsMHRRREREZHsEghNnToVX331FV588UUzU3SPHj3w9ddfY/jw4Vi7dm3mlFJEREQkOwRCZ86cQY0aNczjoKAgUytE999/P+bPn5/xJRQRERHJLoFQ8eLFcfr0afO4XLlyWLRokXnMJiQ/P7+ML6GIiIhIdgmEunTpgiVLlpjHzz77LN58801UqFABvXv3Rr9+/TKjjCIiIiLZcxyhNWvWmIXBUMeOHeFqNI6QiIiI88m0KTbia9y4sVlEREREnE2KA6G5c+emaL8HHngArjKOEBeOmyQiIiJu3jTGcYLi/KKHB+L/Kre5WuCgpjERERHnk+FTbMTExMRZAgICzCz09ttcLQgSERER16a5xkRERMRtKRASERERt5XuXmOSNmunvgPPC3vSdQwPvoFenvAxi8ftn57wtj222+bpYfZPeBCP9JUgTwmgQGUguBKQrwzg5ZOO44mIiDhJIMTEaC6SNgHH/0bN8E1wKZ4+QP5yQIFKsYFRgdtL/gqAj7+jSyciIpL2QChv3rxxAp/Q0FDUrl07QW+yS5cupfSQbu1WjR5Yc6FRuo7BTnu3omIQERWDW9ExiIiMtq1bl1tR0Wa/JI6QYIuXhwdyB/ggb4Av8vJnoK/tcU5/H3jax74xUcDlI8D5PcD5fUDkjduP49V0eXgCeUrF1hwVqPhfDRIf++VM1zUQERHJkkBowoQJ6Xohiateh/5Z8joc4iA8MgbXwyNxPSIK18OjEMolIhKXwyJx9GIYjly4gcMXbuDIxRsmeMI19jtMeCxfb0+Uzh+A0vkDUSY4EKWDA5Envw8Cqnkj0McDuSLPIff1Qwi4dgD+Vw7A+9J+eDAoCr8CXD4cu+z7M+5BPbxim9NYm+TlHfvT0/u/x9bnPJPYzycH4BsE+AYmsQQl/tgnMPY4IiLi1tI9xYYz4Pxof//9N1q2bIlffvklVb/rTuMIxcRYcOZauAmKTGB0Ozg6dOEGjl8KQ2R06j8q/j4eKOkTiso+p1HR4xTKepxAqZjjKB51DLmjL8OhvPwA/9xAQD4gR77bP/MCAfnjbuO69bF/HgVQIiJOIKX37xQFQtzFmfOBGARdv34d33//vQKhNIqKjsGpK+E4dCH0doAUhqMXbyA0Igo3IqIRdisKYbf4Mxo3bkUl0xz3n5wIQwDC4eMRDS9EwxvR8DE/o5DLFygQ4IkCAV7IH+CJ/Dk8kc/fA3n9PZHXH8jt54FcPhb4WG4Bt0KBWzeAW2F2j61LIuuWdI53ZYKn28GRX5BdzZV3wlqrRNft9uNj/t9i82H8hbVgtvUk9onzvFcSv+sV9/ftn+Pre/sB3v7/LQr0RMQFZOhcY9WqVcPw4cPRtWtX+Pr6Jrnf/v37MW7cOJQqVQqvvvoqsosWLVqYYEjSjr3TSuYPMAsqpaw5zhoc3bAGSRGxj2/e3nYjIgoXQ2/h7LVwUxN17loEjl0LN/siArHLHSqNCub0M010ZYNjm+vKlAxE2QKBKJEvAH7eXokVDohm8HQDiLgOhF8Fbl4Cwi7999P+sf22iKuxx+DvcMEhuCQTHPnfDpByxP5kE2Si6/7xgiuv249vB1/Wx3GetwvQvHyBQlWBYnWVLyYiDpGiQOjjjz/GK6+8ggEDBuC+++5DvXr1ULRoUfj7++Py5cvYtWsXVq5ciZ07d2LQoEF45plnUlyA5cuXY8yYMdi0aRNOnz6NWbNmoXPnznH24Zxf3OfMmTMICQkx5WnQoEHqz1ayBGsPc/h6mSV/Gn6ftUwMjs7eDo5iH0fg7HWu3358LdzkM527HmGW9YfjJukzqbt43oDY4IiBUoFAW25T0Tw54BXgF9vUlRrRUcDNy3bB0UUgMgyIjgRiIm//jLJbj0piu/16VGzSekx0bJBmiUndwt8zvx//ueh4+yXxPH8/Kjw2OLRimUxNWiiyDAOjgtWAEvWB4g2AEg2AfGXTObyDiEgGBULMrdm4caMJdmbMmIGpU6fi6NGjuHnzJoKDg03vsd69e6NXr16md1lq3LhxwwQ3/fr1MzVO8fH1hgwZgsmTJ6Nhw4YmabtNmzbYu3cvChYsaPapVasWoqJ4Q4lr0aJFJmAT5xLk542gAkEoVyAo2VqnqzcjcexSmMlnOnQ+Nq/JujCY4nNc/tl3PtGkbwZFtUvmxQMhRU1wdEdsMgoqELu4GgZK0RFA5E0gKgKIuv0z/jqDpsjw2J/WxQRxdoEZg6zEtsV5/vZzDLZObwWuHAPObo9dNn4bW6aA4NiAqHj92J9F6wC+AY6+UiLiYrJVsjRrEuLXCDH4qV+/Pj755BOzzjnNSpQogWeffTZVzW9sGuMx7pQjFBERYRb7Nka+nrvnCDkTfqTPh0bg8Pn/kr35mAESe8lxqAF7rHRoVCY/utQphnbVC5thAiSLXT8DHF8PHF8HnNgAnNoSt5bK2mRXqHpsUFSiYWyAlKekao1EJPNzhBzl1q1bpsls2LBhtm0ct6hVq1ZYs2ZNprzm6NGj8fbbb2fKsSXrAuqCOf3N0rBs3Ma56BgLTl25aYKjA+dCsWjnGaw7fAlrDl00y/A5O9C6amETFN1dPtjkRkkWyFkYqPpA7EKsfTr97+3AiAHSeuD66djaIy7rv4zdL6hQbNK6yVOyTx63Txa35jBZE8bjJZUzz4k1TfGHWvAJiDfsQrx93HwIBtbmBwUF2caVCwwMhLNxhXOQ9MvW/4svXLhgZrQvVKhQnO1c37Mn5dNTMHDatm2b+dAXL14cM2fOROPGjRPdl0EXm+Li1wiJa/Dy9DCJ1FyaVyyAJ5qWwYnLYZiz9RR+23wCB8/fwNxtp8wSHORnms261imGakVzOXXPSafDRGzmC3EhVlxfPfFfjREDozP/AqFnYxdHDsHAoIi9B31z3v4ZlMR6vMdMDufP3MWUKC7iQNk6EMoof/31V4r39fPzMwsTtLkwEBPXxqTqgfeUx4AW5bD95FX8tvmkCYQuhEbg21WHzVKhYJCpJepcq1jK8okkY3ncnteOS40HY7dxuIRzu2J7ANqSwJlwHj8/KZlEc+7L2qcEwyzEG3KBSfH2wzGYJHdWMUYAN7mkY0R91lAVrQWUvhsoczdQsnFscCUiWSJb5wixaSwgIMDk9djnDfXp0wdXrlzBnDlzMr1MGkfIPUVGx2D5vvP4bctJLN511kxdQsonEiPKOn7V7cAogj+v3/4ZmsJ1/rx2eyiGeLlQxerFBkUMjpgTxeEKshlXaFZyhXOQLM4RYs+sn376yfTait9clRk4ZlHdunWxZMkSWyDEZGmus5t+ZlKNkHvz8fJEyyqFzHItPBJ/bj+NXzefNN307fOJ2HT2WvsqyBOQ9Pha4oK8fQHv26ONp9eV48CRFcDhFbE/rx4Hjq+NXZaPiR1riUMKWAOj4vVimw5FxDE1Qqyh2b17txk0MSMwCj9w4IB5zG74HJDxnnvuQb58+VCyZEnTfZ41QF988YUZO4jd53/++WeTI5QVwZhqhMQepxqZs/WkqSlil30qlMsPHz4YYnKORNKFf445kbF9YMQkcXsc1LKENTBqBhSrEztSeRZzhdoUVzgHyaIpNuKP0vzCCy+gU6dOyAjs1s7AJz4GP1OmTDGP2e3dOqAixwyaNGmS6VafFRQISWL432bDkct49bd/bQFR78alMKxdFTOQpEiG4J/niweBI8v/C4xuxB0Xy0z30vBpoEH/2LnysghbCBYuXGges5XA29v5Uk5d4RzEAYEQa2PYs4rBEJut4kfQNWvWhCuwbxrbt2+fAiFJFKcLef/P3fh+zVGzzqk+xnWvhVol8ji6aOKK+Of6/N7bNUbLgSMr/0vUZg+0en2BRgOBXEUcXVIR1w2EOI5PgoN4eNgmZnW1nBrVCElKrNh/Hi/N/NfMmcYu+uyF9uy95U2ukUim4TQuu2YDK8cDZ3fEbmNOUUgPoMlgIH85R5dQxPUCIU6tkZyMyh3KLhQISUpdDYvEm3N2mK73VLN4box7uBbKF0x6qhCRDME/4/sXAyvHAcduDzbLASOrdgKavgAUCcnwl4yMjDTTLRGnV/Lxcb4elK5wDuKAQMjdKBCS1GIg9Mas7bgWHgU/b08Ma1cZvRuXhidnghXJbEfXxNYQ7Y/NfTHKt4oNiEo1ybApSVwh0dgVzkEcFAgdPHjQ9N5i7zGqWrUqBg8ejHLlXKcaVjlCkh5nrobjpV+2YcX+C2a9aflgjHmoJorkzn7jwYiLOrMDWDUB2PFr7ACSxG74dw8BKrSJnZLEzYMIVzgHcUAgxAz7Bx54wPTeatKkidm2atUqM4XF77//jvvuuw+uRDVCklb8r/Xj2qMY9cduhEfGIJe/N97tXB2dahVzdNHEnVw6DKyeBGyZGjsSNhWsCjR5HqjeLc3zpblCEOEK5yAOCIQ41g+7Gb7//vtxtnMm+EWLFmHz5s1wJQqEJL0Ong/FkBlbse1E7AjC99csgpGdq2sQRsla188Caz8DNnwTO8o15SkZ28uMAVFQAbcLIlzhHMQBgZC/vz+2b9+OChUqxNnO5iN2nQ8PD4crUNOYZPSUHZ8uO4CPlx5AdIxFgzCK49y8Amz8Blj7+X9jEjGxmvlDTK6u8gCQs5BbBBGucA7igECIM7Fz9OeHHnoowfhCQ4cOxbFjx+BKVCMkGWnb8St44eettkEYOUVHpcI5USDIDwVy/rfkC/RV13vJXJE3gS3/B2z9CThlX5PvAZS667+gKIkxiVwhiHCFc5AsnmuM+vfvjyeffBKHDh3CXXfdZcsR+uCDDzBkyJDUHk7ErYSUyIP5z95tG4TRdLXflvi+DIasAVJwkG+cQCnYGjgF+SFvgK96pEnqcSJXjkbN5fJRYPdcYOds4ORG4Oiq2OXPV4ASDYFqnWODotzKbxPXk+oaIe7OHmMfffQRTp2KHS+laNGieOmll/Dcc8+ZQRVdiWqEJLNwAtdle8/hwvUInA+NwPnrEbgQyuWWaT5LKQ7gaAuU7GuWzGP/OAFUoK+Xy/0flQzGSWAZFO2aAxxfF/c59jpjTVHVTogKKoJZs2aZzV26dHHaKTac/Rwki5vG4s8+f/16bMJdzpw54aoUCElWi4mx4HLYrTjBEX9aFwZK5nFoBC7duJWqY+fw8YoTKJXMH4BB95ZHLn8NJCeJuHoS2P177OjVx9byq/B/zxWrC1TtHBsY5XWtgXTFNWRajlBGzz6fXSlZWpwlCfuiLTAKjxMwMVC6cP2/gCo0IirRYzzWqJTp1i+SrGun/wuKjq62C4o8gBoPAS1e1ZQe4j6zzz///PPo3Lkz3IFqhMRVhN2Kuh0YxQZMB86FYuyifWB60YLnm6FiIdet2ZUMdv0sorbPwqypXwHndqNLFW94e/sAtR8Fmr3sNLlEahpzbZp9PoMoEBJX9vSPm7Bg5xncXSEYP/RroPwhSVuPq686IfDEstgnvPyA+v+LndIjlWMTZTX1GnNtmn0+gygQEld29OIN3DduOW5Fx+C7x+vjnsoFHV0kcRIJgogL/wJL343tbUY+gUCjZ4C7ngVy5EF2pEDItWVa9/nDhw+nt2wikk2Uyh+Ivk1K44vlh/Du/F1oWiFY4xdJ2pRqDDw+Hzi4NDYgOrUFWDEW2PAV0GQw0PBpwFeBhmQ/qfqLFxkZiXvvvRdhYWEmWTqxRUScy8B7yyN/oK8Z5HHq2qOOLo44Mzatlm8J9F8GdP8/oEAVIPwqsOQdYGJI7GjWka4x+4C4aSDk4+PjMlNo3Al7jFWtWhX169d3dFFEMhW7zg9pXdE8Hv/XflwJS12XfJFEA6IqHYFnVgFdvwLylomdzmPBq8DHdYFN3wPRkY4upYiR6jrwgQMHmlGkmW3vynieu3btwoYNGxxdFJFM171eCVQunBNXb0Zi4pL9ji6OuApPL6Dmw8CgDcD9E4CcRYFrJ4DfnwM+bQBs/4UDZzm6lOLmUp0szS6GS5YsMQlmNWrUSJBc9ttvv8GVKFla3MXK/Rfw6Dfr4O3pgYUvNEO5ArFJpCIZlmjMZrGN3wIrPgLCLsRuK1gNaPc+UKYZspqSpV1bpiVL58mTB926dUtv+UQkm2GidKsqBfHX7nMYNX83vnlczcKSNF9fX3z33Xe2xyni4w80HgDU6Q2s+xxY9TFwbifwfUeg5iNA65FZ2uU+TecgLifVNULuRjVC4k4OnQ9F6/HLERVjMeMKNauYvceBESd38zKw9D1gw9exI1X75wZavQ3U6cOxWhxdOnGT+3eKP2nnzp1L9nnmDK1fvz51pRSRbKVsgSD0blzaPB45fxeiopW/IZkoR16gw1ig/xKgcM3YHmbznge+bQ2c2e7o0ombSHEgVKRIkTjBEPODjh8/blu/ePEiGjdunPElFJEsNbhlBeQJ8MG+s6GYvuG//+Mi8b/8zp8/3yzp7jzDCVzZ5b7tB4BvTuDEBuCL5sDC14GIUDjFOYjrB0LxW9COHDlixhVKbh8RcT65A3zwQqvY7vTjFu8zPclE4ouIiMD9999vFj5ONy9voNHTwKD1sbPaW6KBNZ/E9i7bPY83GGT7cxCnlKGNsJqnSMQ19GxYEuULBuHSjVv4dNkBRxdH3EmuosDD3wO9fgHylAKunQRm9AKm9QCuHHN06cQFKRstCRpQUdwZp9l4vUMV8/i7VYdx5MINRxdJ3E2F+4ABa4G7hwKePsC+P4FPGwIrJ2gwRnFMIMTanuvXr5ssbGZgc53jLnDdurgSDago7u6eSgXRvGIBREZbMOqP3Y4ujrgj3wCg5ZuxI1SXagpEhgF/jQAm3w0cXePo0ok75ghVrFgRefPmRb58+UwQVLt2bbPOpVKlSplbUhHJcm90qAIvTw8s2nUWqw/eHgBPJKsVqAQ8Pg/oPBkIyA+c3w181xaYMxC4cdHRpRMnl+IBFZctW5a5JRGRbKdCoZzo1bAkflhzFO/O2415zzY1gZFIlmMOaq0eQMU2wF9vAZu/B7b8H7Dnj9h8ouJ1HV1CcfVAqHnz5plbEhHJlp5vVRGzt5zE7tPX8Mum4+hev6SjiyTuLCAf8MAkoFZPYPYA4NJBYNdsBUKSZqmeYkNE3Eu+QF8817ICRs7fjTEL96F9jSLI6e/j6GKJg3FKik8++cT2OMuVbARU7gCsnhQ7KrUznoNkCwqEROSOONr01HXHcPjCDXz290G80rayo4skDubj42M6lTgzVzgHST91nxeRO/L19sRr7WO703+z8jCOXwpzdJFERDKEywdCnAakRYsWZkygmjVrYubMmY4ukohT4sz0Tcrnx62oGLz/5x5HF0ccLDo6Gn///bdZ+NgZucI5iAMCoe+++w5hYc7zbdDb2xsTJkwwYwItWrQIzz//PG7c0OBwIqnFscPe6FAV7DQ2f/tpbDhyydFFEgcKDw/HPffcYxY+dkaucA7igEDo1VdfReHChfHEE09g9erVyO44WWytWrXMY5Y7ODgYly7pD7hIWlQpksvWa+yd33chJkbzC4qImwVCJ0+exPfff48LFy6YJqfKlSvjgw8+wJkzZ9JUgOXLl6Njx44oWrSo+cY5e/bsRKe7KF26NPz9/dGwYUOsX78+Ta+1adMmU/1ZokSJNP2+iAAvtq6IID9vbD95FbO2nHR0cUREsjYQYlNTly5dMGfOHJN/079/f0ydOhUlS5bEAw88YLbHxMSk+HhspgoJCTHBTmJmzJiBIUOGYMSIEdi8ebPZt02bNjh37pxtH9b4VK9ePcFy6tQp2z6sBerduze+/PLL1J6yiNgJDvLDoHvLm8cfLtyDGxFRji6SiIhjus8XKlQITZs2xb59+8yyfft29OnTx0y5wVwi1hjdSbt27cySlHHjxplgq2/fvmZ98uTJmD9/Pr799lvTTEdbt25N9jUiIiLQuXNns/9dd911x325WLnaHGoiGaFvk9L4ad0xHLsUholL9tt6lImIuEWvsbNnz2Ls2LGoVq2aCXYYLMybNw+HDx82TWcPP/ywCYjS69atW6Y5q1WrVv8V2NPTrK9ZsybFc6Q9/vjjuPfee/HYY4/dcf/Ro0cjd+7ctkXNaCIJ+Xl7YUTHqubxVysOYdNR5d2JiJsEQsznYXAwZcoUU1PDwGfatGm2YCUwMBAvvviiaTZLL+YhMaeHNU/2uJ7SnKRVq1aZ5jXmHrEJjQtrrpIybNgwXL161bZkxHmIuKKWVQqhW53isFiAF3/ehrBbaiITETdoGitYsCD++ecfNG7cOMl9ChQoYGqHsgM23aUmZ8nPz88szFniorElRJI2vGNVMyv9kYth+ODPPXi7U3VHF0mycFTmDz/80PbYoRiNO/s5iPMEQt98880d92Hvr1KlSiG92NXdy8vLNMXZ4zq7wmcmDrvOhc1+bCITkYRy5/DBB91qove36/H9mqNoXa0wmpQPdnSxJAtwbq6XXnrJ8TPSO/s5iPMFQpMmcYK7xIMfdm8vX748mjVrZgKY9OKHtG7duliyZIlJdibW7nB90KBB6T6+iKRfs4oF8Gijkvi/tcfw8i//YsHzd2tSVhFx3UBo/PjxOH/+vBldmr3D6PLlywgICEBQUJDp1l62bFksW7YsRYnGoaGhOHDggG2dTWrsBZYvXz7TJZ9d55l4Xa9ePTRo0MCMEs0u99ZeZJlFTWMiKTesXRUs33fB9CIbOW83PniwpqOLJJmMfxs5pAnVqVMnQ778ZjVXOAfJAJZU+umnnywtWrSwHDhwwLZt//79lnvvvdcyffp0y/Hjxy1NmjSxdOvWLUXHW7ZsGRt3Eyx9+vSx7fPxxx9bSpYsafH19bU0aNDAsnbtWktWuXr1qikPf4pI0tYevGAp/eo8S6lX5lmW7D7j6OJIJgsNDbX9veZjh1j0psUyIpfFsuA15z0Hcfj924P/pCZwKleuHH799VfbtBVWW7ZsQbdu3XDo0CEz9QYfnz59Gs7OmiPEHmS5cuVydHFEsrWR83bh65WHUSCnHxa/0Ax5AnwdXSTJJKyZZyuAtWafPYaz3OLhwKqJQONBQJv3nPMcxOH371R3n2dwExWVsJsst1m7tHO6jOvXr8OZsVmMM9bXr1/f0UURcRpD21RCuQKBOH89AiPm7nR0cURE7ijVgRBn6X3qqadMDZAVHz/zzDNm0ELiOD1lypSBM2OPMc5Yv2HDBkcXRcRp+Pt44aOHa5kZ6udsPYU/tjt/rbCIuDbPtHSfZyIze3NZx9xhIjO3WbvWs6rxo48+yozyikg2V6tEHgxoETsX2Ruzd5jaIRERl+g1xnQiTnsxd+5cHDt2DHv37jXbK1WqZBb7WiNnp15jImn3XMsK+Gv3Wew5cx2vz9qOLx6ra4bYEBFx6hohBkIcJ+jEiRMm8OFs81zsgyBXoaYxkbTz9fbEuIdrwcfLA4t2ncWsLScdXSQRkfTXCHHC0woVKuDixYvmp4hIUqoWzYXBLStg7KJ9JnG6cbn8KJI7h6OLJRmEU1KMGDHC9tgZucI5SPqluvv877//buZm+fzzz1G9uuvPK6Tu8yJpFxUdg26fr8a2E1fNCNTf962vJjLJNt3nxbVlWvf53r17Y/369QgJCUGOHDlMkrT94irUfV4k/by9PE0vMj9vTyzfdx7T1h93dJHEpSioFgdMscEpLtyBJl0VyRjlCwbhpTaVMHL+boycvwt3VwhGiXwBji6WpBPnfdy9e7d5XKVKFZM64Wxc4RzEAYEQ5/0SEUmNfk3KYNHOs1h/5BKGztyGaf0bwZODDYnTunnzpi09wllHZXaFc5D0S1P4e/DgQbzxxhvo0aOHmWSV/vzzT+zcqZFkRSQhBj1jHqqJAF8vrDt8CVNWH3F0kURE0hYI/fPPP6hRowbWrVuH3377zUTRtG3bNlv2vYhIfKXyB+K19lXM4w8W7MHB87F/O0REnCoQevXVVzFy5EgsXrwYvr7/TajI6TXWrl0LV6FkaZGM16thSZMjFBEVgxd/3mZ6lYmIOFUgxHnEunTpkmB7wYIFceHCBbgKDagokvHYdf6DbjWR088bW49fwZcrDjm6SCLi5lIdCOXJk8fMQB8fJ14tVqxYRpVLRFxU0Tw5MOKBaubx+MX7sO34FUcXSUTcWKoDoUceeQSvvPIKzpw5Y77dsfvhqlWrMHToUDPGkIjInXSrUwytqhRCZLQFD01eg0+XHVAzmYg4R/f5UaNGmWajEiVKmAlJmUfDnz179jQ9yURE7oRfosY+VBNDft6GpXvOYczCvViw44zpWVa5sEZwdwackoJfgK2PnZErnIM4YIoNK84+v2PHDtNrrHbt2i4795im2BDJPPzzwwlZ3/59F67ejDSTtD57bwU806IcfLw0uJ3cweIRwKoJmmJD0nX/TnWNkFXJkiXN4qrYa4wLa7tEJPNqhrrWKY6m5YPx2qwd+Gv3WYxbvA8Ld57BmAdDzMStIneUtu/zImmrEWJgMGXKFCxZssQMpsgcIXtLly6FK1GNkEjW4J+iudtOmZnqr4RFwtvTA4PuLY8BLcrD11u1Q9kN//azZYD4pdgh01NYa4QaDQTajnLOcxDnqxEaPHiwCYQ6dOhghibXTNIikhH4t6RTrWJoXC4/hs/eiQU7z2DCX/tN7tDYh0JQvZjm/Mtu01OUKVPGsdNTpPP+ky3OQRwu1YHQ9OnT8fPPP6N9+/aZUyIRcWsFc/rj80frYP720xg+Zyf2nLmOTp+uwoAW5UwNkZ+3l6OLKCIuJNX1gBxNunz58plTGhGR27VD99csikUvNEOHGkUQHWPBx0sP4IGPV+HfExp3SEQcGAi9+OKLmDhxomnPFxHJTMFBfvi0Vx181qsO8gf6Yu/Z6+jy2WqMWbgHEVHqyCAiDmgaW7lyJZYtW2Zmm69WrVqCsRc4EauISEZqX6MIGpXNbxKpf992Cp8uO4hFO8+a3KGQEnkcXTwRcadAiFNsJDbXmIhIZsoX6IuPe9Q2TWVvzN6B/edC0eWzVXihVUWTO6SOGyKSJYHQd999B3egcYREsqe21QujYZl8ePv3nZi99RQ+WrwPhy7cwPvdaiiRWkQyLxDimEGcYT4pUVFR2Lx5Mxo0aABXwGlEuFjHIRCR7CNvoC8mPFIbDcvmx5uzd5jRqU9cDsMXj9UzNUeS+by9vTFgwADbY2fkCucgWTigopeXl5l13hoM1ahRA3/88YeZc4zOnj2LokWLulwNigZUFMneVu6/gGembsL18CiUyh+Ab/rUR/mCQY4ulmSFv94CVo5P84CK4tpSev9Oca+x+PHSkSNHEBkZmew+IiKZrWmFYMwacBdK5MuBoxfD0PWzVVh94IKjiyUiTiJDxxNXsqKIOEL5gjkxe0AT1C2VF9fCo9D72/X4ecNxRxfLpfGL7/nz583i+C/BFhc4B3EUTawiIi4hf5Afpv6vIR4IKYqoGAte/vVfvP/nHsTE6AaXGcLCwkyqBBc+dkaucA6Sft6pqe25fv06/P39TeTMdc7NwjY4sv6UVJg2LXahqVOB114Djh4FqlcHBg0Cnn469rn+/QE2Q06ZErv+7bfABx8Ae/cCHOX7jTeAxx+Pfe6xx4CAAOCLL2LXP/ss9vG2bQDzucaMAR55JPa5hx8GihQBJk6MXR8/PrY869cDzAXj71mHSujUCahUCfjww9j1998H5s8HVqwA2Pb6f/8HdO3KrHmgTRuASfPvvhu771tvAatWAYsXA35+wMyZQM+enNwHaN4caN0aeP312H1ffRXYsQOYNy92fe5coG9f4OJFoHFj4MEHOapn7HNDhgCcMPGXX2LXf/4ZeOEF4ORJoHZt4IknYq8jMSHy6tXY60w//giMGAEcOgRUqRJ7LF5n6tfvv+tMX30FjBsH7N4NlC0LvP127HWmXr0AJtPzOtMnnwDffANs2QIUKxZ7TXmdiWUvWTL2WPTRR7FlX7MGyJ+fXTKBBx6Ife7++2M/B7zO9N57wKJFwD//AEFBwE8/AQ89BEREAPfdBzRpEnud6c03Y9/DhQuZAcrBvYBHH+V/UuDuu4EOHWKvM738cuznaM6c2PVZs4CnnmLviNj3sEeP2GtKgwcDp0/HXmeaPh146SXg+HEgJCT2924nnprHvLHwOhM/uyNHAgcOxH6OXnnlv+vMzy7HI+N1psmTY68jPwelSgGjRsVeZ2J5eK34PE2aBPzwA7Bxo/ks+3/yCSZOG4HB50MxOW9NbD6+Azten49qRXPDa+yY2PPkZzFvXuD774HOnTnzJsApg/iZ4XWmd94B/v6bs0gDOXIAM2YA3btzcirg3nuBFi2A4cNj9+Vnl+/3H38AnLRz9mygTx/g8uXY94X/d3idaehQ4ODB2OtMv/4a+xnlda1XD+jdG3juudjnuJ2f+2z6N8I3Xz7zTZpn4sf9+P8/q/9GnN8LtAEwbj7w2c5U/43w6NEDtz/58OJr8/+T/kYgy/9G8BhcsnuyNGfltW/6sgZD8deVLC0i2cGsLSfwyi/bcSs6BiHFc+Or3vVQMJe/o4vlMm7cuIEg3nAdOWHpX28DK8cBjQYAbUc75zmI88w+z9GkRUScRZfaxVEsTwCe+nEjtp24is6frsK3feujcmF9oRGRNARCzVk96YSuXLmCVq1amXGOuAwePBj9rdWbIuLSGpTJh1kDmqDflA1m0MUHP1+Dj3vWxj2Vkh4TTUTci8snS+fMmRPLly/H1q1bsW7dOowaNQoX2e4uIm6hdHCgCYYal82P0IgoPDFlA35Yc8TRxRKRbMLlAyEOBBnAxEAwZyzC5DKpm6SIe8kd4IPv+zXAQ3WLg53Ihs/Zibfm7kS0epSJuD2HB0KsrenYsaMZlZrJ1rPZ4yIezvlVunRp02OtYcOGWM9s91Q2j4WEhKB48eJ46aWXEBwcnIFnICLOwNfbEx8+WBMvt61k1qesPoL+P2w0tUSSepySok+fPmZx1ukpXOEcJP0c/s4za59BSr9+/dCVXSvjmTFjBoYMGYLJkyebIGjChAlo06YN9u7da5vuo1atWib/J75FixaZACtPnjzYtm2bmQaEr/Hggw+iUKFCWXJ+IpJ98MvWgBblUTp/IF6YsRVL95zDvWP/xpPNyqJHg5II9HP4n0Sn4efnhynW7vpOyhXOQbKw+7w9/grzbPhHJT/HNsggPN6sWbPQmWN73Mbgp379+vjk9rghMTExZn6zZ599Fq9axzlIBU6wd++995pgKDFsPuNi3/2Or6fu8yKuZevxKxg4dTNOXrlp1vME+ODxu0qbJU+AJm51CunsPi+uLcPnGqMzZ86gd+/eyJs3r6lRYY0MH7M2h7UtGe3WrVvYtGmT6fVlK7Cnp1lfwwGmUoDl4kCQxIvBprhKHPQrCaNHjzYXzrpYJ5UVEddSq0QeLBvaAh92q4kywYG4EhaJCX/tR5P3l2LUH7tx7lq4o4uYrfELMWv0uThr3qUrnIOkn3dqIqu77rrLDDrVt29fVK5c2Xxwdu3ahWnTpmHlypXYvHmzbXCqjHDhwgUzQGP8Ziyu79mzJ0XHOHr0KJ588klbkjRrkmrUqJHk/sOGDTNNcfFrhETENfOGHq5fAt3qFsefO07j02UHsfv0NXy5/JDJIWJy9VPNyqFk/tgOF/IfTkmRbQYjTGMQk63OQbJ/IDRx4kTTA2vnzp0oUKBAnOfeeOMNNGnSBJMmTcJrHAI+G2nQoIHpOp+aNmMuTNDm4mojZYtIQl6eHri/ZlF0qFEEf+89j0+WHcCmo5cxdd0xTN9w3Mxf9kyLcqhYKKejiyoiGSzFTWPz5883QU78IIjYRMaalN9//z1DC8feXQy+4je7cb1w4cLITAMHDjS1XRs2bMjU1xGR7IN5ivdULohfnm6MGU82QrOKBUwX+1lbTqL1+OV48oeNJrdIsgm7aZ5EMj0Q2rdvn2kaSwqfY0+ujOTr64u6detiyZIltm1MluZ6Y06ul4lYG1S1alWTqC0i7hcQNSybHz/0a4DfBzVFu+qFzT130a6zZqqOXl+vxeoDF5RXIuJuOULshp4UPpeWGejZLnuAM1LfdvjwYdOUlS9fPpQsWdLk63CMh3r16plmLnafZ2Ib85Qyu0aIizXrXETcU43iufH5o3Vx4Nx1fP73IczeehKrDlw0CxOun2tZ3kzZYT8JtYi4YCDEbz7ssZUU/hFIy7ejjRs34p577rGtWxOVGfxwfIfu3bvj/PnzGD58uOm1xjGDFixYoHGARCRLlS+YEx89HILnW1XAVysOmdwhNpP1m7IR9UvnxcttK6N+6XyOLqaIZNY4QgyCWDOS1LceHoa1J66SXGyfLM1mQY0jJCL2zl0Px9crDuP71UcQERVjtrWsXBAvta3kFjPcs2be4T2ulrwDrPgIaPgM0O595zwHcfg4QimuEfruu+/gTtQ0JiLJKZjTH6+1r4J+Tcpg4pL9+HnjcSzZcw5L955D51rFMOS+iiiRz3W73bMji3VgWj52Rq5wDuKgkaXdSUojShFxb4fOh+Kjxfsw/9/TZt3HywO9GpbCwHvKo0BOP0cXzzWls0ZIXFumjCwdX3h4OL7//nt89tln2L9/f3oOJSLi1MoWCMKnPetg7qAmuLtCMCKjLWZQxuZjlmHcor24Hh7p6CKKSHoCISYxc1Rm++kv2IW9f//+Znyh2rVrp3jaC2eg7vMikhY1i+fBj080xNT/NURI8dwIuxWNSUsPoNmHy/D1ikMIj3SNPEoRtwuEOJP7fffdZ1ufOnWqmb6CNUGXL1/GQw89hJEjR8JVaEBFEUmPJuWDMXtgE0x+tA7KFgjE5bBIjJy/28x2z3yiqOjYBGtnxURjdp7hwsfOyBXOQbIwEDp27JipIbEPjJhkVqpUKfMhGjx4MLZs2ZIBRRIRcQ3829i2ehEser6Zmdy1SG5/nLoajpd/+RdtJ67Agh1nNChjhtA1lLRLca8xdp+3/w+7du1avPnmm3EGVGTNkKvQXGMiklG8vWInd32gVlH8uOYoPv37AA6cC8XT/7cJhXP5o2gefxTO7W96ohXKxcd+KJTTHwXNY38E+aX4T7WIpFKK/3dVqVLFzCXGXCFOvMoaIvuBENlM5kqDHKr7vIhkNH8fL/RvVhbdG5TAV8sPmXGIzlwLN0tyAn29TIAUu/jZPfZH9WK5UCq/u45/o9G8JQsDoZdffhmPPPKImXyVgVD79u1RpkwZ2/N//PGHmQJDRESSl8vfBy+2roT/3V0WB8+H4uzVcJy9Fo6z1yNiH1/neuzj6xFRuHErGocu3DBLfF6eHmbMomeal4OnpwIDkUwLhLp06WKCnXnz5qF169ZxepBRQEAABgwYkOoCiIi4q9w5fFCnZN5k97kREYVz1yNw5mq4Gc2aAdOZqxEmWDp2MQzbT17FmIV7sfbQRYzvXgvBQRqzSCQ1UtXw3LJlS7MkZsSIEal6YRERubNAP2+U4RKcsPmLeZvsgTZi7k6s2H8B7SeuwMRHaqNxufwOKauIS/caYzf5Hj16JDrDPEdt7NmzJw4dOgRXoXGERMQZeqV1r18ScwY2RYWCQabmqNfXazHhr32IjsncnlSckoIpElycdXoKVzgHycIpNp588knTM+zDDz9M9PlXXnnFBEmff/45XImm2BARZxB2Kwoj5uzEzE0nzPpd5fJjQvdapueZy1ryLrBiLNDwaaDdB44ujbj6FBv//POPGTQxKQ8//DCWLl2a+pKKiEi6Bfh6Y8xDIRj3cAgCfL2w+uBFtJ+0Aiv2n3d00URcZ0DFggULJvl8cHAwjh8/nlHlEhGRNOhapzjmDmqKyoVz4kLoLfT+dj3GLtzr9CNZizg8EGL10sGDB5N8/sCBA2o6EhHJBsoXDDLTe/RsWBJMfvhk2QH0/GodTl+9mWGvwSkpAgMDzeKs01O4wjlIFgZCzZo1w8cff5zk85MmTcLdd9+dAUUSEZGMGLxxVJca+LhHbTMy9fojl0yvsqV7zmbYa4SFhZnFmbnCOUgWBULDhg3Dn3/+aeYXW79+vUk+4rJu3Tp069YNCxcuNPu4CvUaExFX0DGkKOY929SMQM2JX/tN2YhRf+xGpJrKRFIXCNWuXRu//PILli9fjsaNGyNfvnxmueuuu7BixQr8/PPPqFOnDlyFZp8XEVdROjgQvz5zFx6/q7RZ/3L5ITw0eQ2OX3KRmhBNXCtZNaDi/fffb+YUW7BggckJYs/7ihUrmpGmObK0iIhkT37eXnjrgWpoVDY/Xv5lG7Yev4IOk1bgwwdD0LZ6YTglD00pIumX6imNc+TIYabbEBER58Ogp1rRXBg0bQu2Hb+Cp/9vE+6tXBDD2lVGhUI5HV08kezbNMYxgpgzk9TI0tWqVTNNZCIikr2VyBeAmU81xlPNy8Lb0wNL95xDmwnL8dqs7Th/PcLRxRPJnoHQhAkT0L9//0S7yLNr/VNPPYVx48ZldPlERCQT+Hp7Yli7Klj0QjO0rloInJHjp3XH0GLMMnyydD9u3opO9vc9PT3RvHlzs/CxM3KFc5AsnGKjVKlSJjeoSpUqiT6/Z88ekyvEgRddiabYEBF3sO7QRbz3x278e+KqWS+S2x9DW1dCl9rF4OmZTXNxlo4Elo8BGjwFtE98+idxX9cyeoqNs2fPwsfHJ8nnvb29cf68hnIXEXFGDcvmx+wBTTDxkVoolicHTl8Nx4szt6HjJyux+uAFRxdPJNOkOBAqVqwYduzYkeTz//77L4oUKZJR5RIRkSzGmp9OtYphyYvN8Urbysjp542dp66ZUan/9/0GHDgX6ugiijguEGrfvj3efPNNhIeHJ3ju5s2bGDFihOle7yo0oKKIuPOo1M+0KIe/X2qB3o1LwcvTA3/tjk2ofnP2DlwIjTBTUhQoUMAszjo9hSucg2RhjhCbxjhgopeXFwYNGoRKlSrZcoMYNERHR2Pz5s0oVKgQXIlyhETE3R08H4rRf+zBX7tjp+fglB1PNCqCIe1DzHpoaKiZr8vZcoQY/AQFBTn2HMTh9+8UjyPEAGf16tV45plnzFQa1vjJw8MDbdq0McGQqwVBIiIClCsQhK/71MOag0yo3oUdJ69h/OL9tudT+H1axPkHVGTPsT/++AOXL1+2jSxdoUIF5M2bN/NKKCIi2ULjcvkxd2BTzNl2Eu/P3Yrjt7dPXXsUT7Wq5uDSiWTRyNLEwEe5MyIi7plQ3aV2cTQrkwvBb8VuG7NwH5pUKY7qxXI7qFSqkZK00whSIiKSpoRqq1vRMRj002aERkRlcSmy6fhG4lQUCImISLoUye2HIxfD8Mas7coXEqejQEhERFKNU1LUq1fPLB91r2262M/eegozN52AM56DpthwX27zzoeFhZlk76FDhzq6KCIiTi9HjhzYsGGDWZpWLoYh91U024fP2YH9Z6/D2c6Bj8U9uU0g9N5776FRo0aOLoaIiEt6pnk5NC0fjPBI5gttQXhk8pO2imQXbhEI7d+/3wz82K5dO0cXRUTEZXuTjeseguAgX+w9ex1v/77L0UUScY5AaPny5ejYsSOKFi1qBmecPXt2gn04WGPp0qXh7++Phg0bYv369al6DTaHjR49OgNLLSLi3phuwL/LXPiYCub0x/juteDhAUxbfwzz/j0FZzsHcT8OD4Q4xHlISIgJdhIzY8YMDBkyxMxlxik8uC9Hsj537pxtn1q1aqF69eoJllOnTmHOnDmoWLGiWUREJGOwd9jRo0fNYt9T7O4KBUwzGQ37dTuOXcy+AUZS5yDuJU0DKmYkNlcl12Q1btw49O/fH3379jXrkydPxvz58/Htt9/i1VdfNdu2bt2a5O+vXbsW06dPx8yZM81cMpGRkWbOkeHDhye6f0REhFns5yoREZGUY+L0usOXsOnoZQyathm/PH0XfL0d/r1bJFHZ+pN569YtbNq0Ca1atbJtYxdHrq9ZsyZFx2CT2PHjx3HkyBGMHTvWBFVJBUHW/TlJm3UpUaJEhpyLiIi78PbyxKQetZE7hw/+PXEVHy7Y4+giiThnIHThwgUzq338yVy5fubMmUx5TU4oy5lqrQuDKBERSZ1ieXJg7EOxs9N/vfIwlu6JnbleJLvJ1oFQRnv88cdNrVBy/Pz8TNPZjz/+aLrbt2zZMsvKJyLiSu6rWgiP31XaPH7x5204ffVm5ryQ8nvEVQOh4OBgeHl54ezZuN8kuF64cOFMfe2BAwdi165dZqAtERFJm2HtK6N6sVy4HBaJwdO3Iio6JuMOzu5pIq4cCPn6+qJu3bpYsmSJbVtMTIxZb9y4sUPLJiLizjjcSdWqVc3Cx0nx8/bCxz3qINDXC+sPX8KkpQfgbOcgrs3hvcbYk+vAgf/+Yxw+fNj0AsuXLx9Klixpus736dPHzAXToEEDTJgwwXS5t/Yiyyzszs+FOUoiIhJXQEAAdu7cmaJ9ywQHYlTXGqZG6OOl+9GobD7cVS4YznQO4rocXiO0ceNG1K5d2yzEwIePrT27unfvbvJ6uM7xghgkLViwIEECdUZT05iISMbpVKsYHq5X3KTzPD99Ky6E/jdMiYhb1wi1aNHijgNZDRo0yCwiIuK83nqgGjYfu4ID50JN8vR3j9c3U3OIuHWNUHbFZjG2G9evX9/RRRERyXY4JUW1atXMktLpKQJ8vfFpzzrw8/bEP/vO46sVh+Bs5yCuR4FQEtQ0JiKSNNbk828kl9RMT1GpcE6M6FjNPB6zcC82H7sMZzsHcS0KhEREJEv1aFACHWoWQVSMBc9N24KrNyMdXSRxYwqEkqCmMRGRzMGu6qO71kDJfAE4cfkmpqw64ugiiRtTIJQENY2JiGSeXP4+eK5lBfN47raTapoSh1EgJCIiDtGmWiEzK/3B8zew6/Q1RxdH3JQCIRERcYic/j64t1JB83jutlPpOJJqkyTtFAglQTlCIiLJ5/mUKlXKLOmZnuKBWkXNz3nbTqehecwjW5yDODeHD6iYnXOEuFy7dg25c+d2dHFERLIVTk9x5Ej6k5zvrVzQzEN28spN05W+bql8cLZzEOemGiEREXEYfx8vtK5W2DyeuzU9zWMiaaNASEREHOqBkNjmsfnbTyMqOsbRxRE3o0BIRERS7ebNmyaHkgsfp0fTCsHIG+CDC6G3sObQRTjjOYjzUiCUBCVLi4gkLSYmBhs3bjQLH6eHj5cn2tUokuXNYxl5DuK8FAglQQMqiohkffPYgp1nEBEV7ejiiBtRICQiIg7XoHQ+FM7lj+vhUfhn73lHF0fciAIhERFxOE9PD9xfs0gGDK4okjoKhEREJFvoeLt57K/dZ3EjIsrRxRE3oUBIRESyhZrFc6NU/gCER8aYYEgkKygQSoJ6jYmIJC84ONgsGYXTXFiTplPVeywdM9dn9DmI81EglAT1GhMRSVpgYCDOnz9vFj7OKNZAaPn+87gSdiv5ndM5P1hmnYM4FwVCIiKSbVQolBOVC+dEZLQFC3accXRxxA0oEBIRkWzFOiO9eo9JVlAgJCIiqcYpKVq0aGGWjJ6eomPN2ECI022cuxYOZzwHcR7eji6AiIg4H05J8c8//9geZ6QS+QJQp2QebD52BfP+PY1+TcvA2c5BnIdqhEREJNuOKaTmMclsCoRERCTb6VCzCDw9gK3Hr+D4pTBHF0dcmAKhJGgcIRERxymY0x+Ny+U3j1UrJJlJgVASNI6QiIhjWccU+l2BkGQiBUIiIpItta1WBD5eHthz5jr2nb3u6OKIi1IgJCIiaRIQEGCWzJI7wAfNKxZM/ZQb2egcJPtTICQiIqnGKSlu3LhhlsycnsJ+cEVLknOKWbL1OUj2pkBIRESyrVZVCiKHjxeOXQrDthNX4z2bvrnGREiBkIiIZFsBvt5oVbWQeaykackMCoRERCTVwsPD0aFDB7PwcVb0Hpv37ylEx6StGczR5yDZl6bYEBGRVIuOjsYff/xhe5yZmlUMRi5/b5y9FoH1hy/ZxhdypnOQ7MstaoRKly6NmjVrolatWrjnnnscXRwREUkFP28vtKtexDzW4IqS0dwiEKLVq1dj69atWLZsmaOLIiIiaew99ueO07gVpQlSJeO4TSAkIiLOq1HZ/AgO8sOVsEisPHDe0cURF+LwQGj58uXo2LEjihYtCg8PD8yePTvReb/YvOXv74+GDRti/fr1qXoNHrd58+Zm3rCpU6dmYOlFRCQreHl64P6asc1jv2877ejiiAtxeLI0B7IKCQlBv3790LVr1wTPz5gxA0OGDMHkyZNNEDRhwgS0adMGe/fuRcGCsSOOMvcnKioqwe8uWrTIBFgrV65EsWLFcPr0abRq1Qo1atQwOUMiIuI8OoYUxZTVR7Bo5xncvBWNHI4ukLgEhwdC7dq1M0tSxo0bh/79+6Nv375mnQHR/Pnz8e233+LVV18125j7kxwGQVSkSBG0b98emzdvTjIQioiIMIvVtWvX0nReIiKSseqUzIPieXPgxOWbWLrnHDo4ukDiEhzeNJacW7duYdOmTaYWx8rT09Osr1mzJsU1Ttevx07WFxoaiqVLl6JatWpJ7j969Gjkzp3btpQoUSIDzkRExLVwSgpOecElq6anYJoDa4Vo7raTTnkOkv1k60DowoULZmyHQoViRxW14vqZM2dSdIyzZ8+iadOmpvmtUaNG6N27t8kVSsqwYcNw9epV23L8+PF0n4eIiGTs4IrL9p5HeNTtsX+SnINMxAmaxjJb2bJlsW3bthTv7+fnZxYmaHPRIFsiItlH5cI5UaFgEPafC8WBczdQ3dEFEqeXrWuEgoOD4eXlZWp17HG9cOHCmfraAwcOxK5du7Bhw4ZMfR0REUld85i1Vmjv2di0BxGXDYR8fX1Rt25dLFmyxLYtJibGrDdu3DhTX5u1QVWrVk22GU1ERLKeNU/o2MUwRxdFXIDDm8aYwHzgwAHb+uHDh00vsHz58qFkyZKm63yfPn1Qr149NGjQwHSfZwK0tRdZZtYIcWGvMSZNi4hI9lA6OBA1i+dGzGnlBokLBEIbN26MM/8XAx9i8DNlyhR0794d58+fx/Dhw02CNMcMWrBgQYIEahERcR9sHruqcRXFFQKhFi1amK6LyRk0aJBZspKSpUVEsq/7axbFTwtjH9+4FQ11fheXzBFyJCVLi4hkX4Vz+6NYntixpY9duuHo4ogTUyAkIiJOqXDu2EDo5i3NRi9pp0BIRESckoejCyAuQYFQEtR9XkRExPUpEEqCcoRERERcnwIhERFxah7QeEKSdgqEkqCmMRGR7E3hj2QEBUJJUNOYiIiI61MgJCIiIm5LgZCIiIi4LQVCIiIi4rYUCCVBydIiIiKuT4FQEpQsLSIi4voUCImIiIjbUiAkIiIibkuBkIiIiLgtBUIiIiLitrwdXYDs3GuMS1RUlFm/du2ao4skIiJ2btyMwLUIC0L5U3+jJR7rZ8JiSX4yFg/LnfZwcydOnECJEiUcXQwRERFJg+PHj6N48eJJPq9A6A5iYmJw6tQp5MyZEx4eHhkaqTLA4huUK1euDDuuO9E1TB9dv/TTNUwfXb/00zVMGsOb69evo2jRovD0TDoTSE1jd8CLl1wkmV784OrDmz66humj65d+uobpo+uXfrqGicudOzfuRMnSIiIi4rYUCImIiIjbUiDkIH5+fhgxYoT5KWmja5g+un7pp2uYPrp+6adrmH5KlhYRERG3pRohERERcVsKhERERMRtKRASERERt6VASERERNyWAqEs9tZbb5kRqu2XypUrO7pY2dry5cvRsWNHMzoor9fs2bPjPM98/+HDh6NIkSLIkSMHWrVqhf379zusvM52/R5//PEEn8m2bds6rLzZzejRo1G/fn0zunzBggXRuXNn7N27N84+4eHhGDhwIPLnz4+goCB069YNZ8+edViZnfEatmjRIsHn8Omnn3ZYmbOTzz//HDVr1rQNmti4cWP8+eeftuf1+UsfBUIOUK1aNZw+fdq2rFy50tFFytZu3LiBkJAQMwluYj788ENMmjQJkydPxrp16xAYGIg2bdqYPw5y5+tHDHzsP5PTpk3L0jJmZ//884+5yaxduxaLFy9GZGQkWrduba6r1QsvvIDff/8dM2fONPtzWp6uXbs6tNzOdg2pf//+cT6H/L8tMLMbvP/++9i0aRM2btyIe++9F506dcLOnTvN8/r8pRO7z0vWGTFihCUkJMTRxXBa/MjOmjXLth4TE2MpXLiwZcyYMbZtV65csfj5+VmmTZvmoFI6z/WjPn36WDp16uSwMjmbc+fOmev4zz//2D5vPj4+lpkzZ9r22b17t9lnzZo1Diyp81xDat68uWXw4MEOLZczyZs3r+Xrr7/W5y8DqEbIAdhsw2aKsmXLolevXjh27Jiji+S0Dh8+jDNnzpjmMPu5ZRo2bIg1a9Y4tGzO5O+//zZNFpUqVcIzzzyDixcvOrpI2dbVq1fNz3z58pmf/JbOGg77zyCbu0uWLKnPYAqvodXUqVMRHByM6tWrY9iwYQgLC3NQCbOv6OhoTJ8+3dSmsYlMn7/006SrWYw36ClTppgbDqt+3377bdx9993YsWOHaT+X1GEQRIUKFYqznevW5yR5bBZjNXqZMmVw8OBBvPbaa2jXrp35I+rl5eXo4mUrMTExeP7559GkSRNzsyZ+znx9fZEnT544++ozmPJrSD179kSpUqXMl8R///0Xr7zyiskj+u233xxa3uxi+/btJvBhkz/zgGbNmoWqVati69at+vylkwKhLMYbjBWT3xgY8T//zz//jCeeeMKhZRP39Mgjj9ge16hRw3wuy5UrZ2qJWrZs6dCyZTfMc+GXFuX1Zfw1fPLJJ+N8Dtn5gZ8/Buf8PLo7fnlm0MPatF9++QV9+vQx+UCSfmoaczBG8RUrVsSBAwccXRSnVLhwYfMzfg8Jrlufk9Rhky2bJ/SZjGvQoEGYN28eli1bZpJXrfg5u3XrFq5cuRJnf30GU34NE8MviaTPYSzW+pQvXx5169Y1vfDYAWLixIn6/GUABUIOFhoaar7x8NuPpB6bc/iffcmSJbZt165dM73HWI0sqXfixAmTI6TPZCzmmPMGzqaIpUuXms+cPd6YfHx84nwG2aTD3D99BlN2DRPD2g/S5zDpJsaIiAh9/jKAmsay2NChQ82YLmwOYxdHzhrMPIwePXo4umjZOli0/1bIBGn+kWSiJRMCmW8wcuRIVKhQwfyBffPNN02eAccqkeSvHxfmqXHcEQaUDMpffvll882TQxBIbFPOTz/9hDlz5pg8PmveBZPyOW4Vf7JZe8iQIeZ6cpyXZ5991tyEGjVq5OjiO8U15OeOz7dv396MhcMcIXYJb9asmWmqdXdMHGdaBf/eXb9+3VwrNl0vXLhQn7+MkBFdzyTlunfvbilSpIjF19fXUqxYMbN+4MABRxcrW1u2bJnpChp/Ybdvaxf6N99801KoUCHTbb5ly5aWvXv3OrrYTnH9wsLCLK1bt7YUKFDAdMEtVaqUpX///pYzZ844utjZRmLXjst3331n2+fmzZuWAQMGmC7NAQEBli5dulhOnz7t0HI70zU8duyYpVmzZpZ8+fKZ/8Ply5e3vPTSS5arV686uujZQr9+/cz/Td43+H+Vf+MWLVpke16fv/Tx4D8ZElGJiIiIOBnlCImIiIjbUiAkIiIibkuBkIiIiLgtBUIiIiLithQIiYiIiNtSICQiIiJuS4GQiIiIuC0FQiKSYkeOHIGHh4dt+oPsYM+ePWYEXX9/f9SqVQvuhu/H7Nmz03WMKVOmJJi93FFlEclqCoREnMjjjz9ubjbvv/9+nO28+XC7O+I0NYGBgWZ+Jfv5lhK7bk8//XSi0z/wOe5j7/jx4+jXr5+ZroUTXnJanMGDB5t52NKK0yLwteJPkCkijqNASMTJsObjgw8+wOXLl+EqOHt2WnGeqqZNm5pAhfNUJaVEiRKYPn06bt68adsWHh5u5m3iHE72Dh06hHr16mH//v2YNm2amatt8uTJJtDiHE6XLl1Kc3lFJHtRICTiZFq1amUmSB09enSS+7z11lsJmokmTJiA0qVL29ZZA8KJaUeNGoVChQqZppF33nkHUVFReOmll8wEjsWLF8d3332XaHPUXXfdZYKy6tWr459//onz/I4dO8wkkUFBQebYjz32GC5cuGB7vkWLFmY2ck6YGxwcnOQEr5xhm2ViOfz8/Mw5LViwwPY8a1c2bdpk9uFjnndS6tSpY4Kh3377zbaNjxkE1a5dO0EtEWuBFi1ahObNm5t9eD5//fUXTp48iddff92272effWYm/OW14Lk++OCDSG2TFCfPrFKlirlebdu2xenTp+Ps9+2336JatWrmGnA2dl67lNY4sRmT29isaf+6PKeAgAB06dIl0VouTpDKa8bzKlu2rJmcl58NKwaJnBSVz1etWhWLFy9O8XmLZCcKhEScjJeXlwlePv74Y5w4cSJdx1q6dClOnTqF5cuXY9y4caaZ6f7770fevHmxbt0605T01FNPJXgdBkovvvgitmzZYmpIOnbsaLuZ8iZ87733muBi48aNJnA5e/YsHn744TjH+P77702wsWrVKlPbkpiJEyfio48+wtixY82M5AyYHnjgAXMTJgYMDBBYFj4eOnRosufLpi77wI4BRt++fePsw9oeBiYDBgwwM6PbYwDaq1cvzJgxgxNWm/N77rnnTCDGpjmeK4OD1AgLCzPn9+OPP5r34dixY3HO4/PPPzeB2ZNPPont27dj7ty5KF++PNKK7ytnK2cwxSDpnnvuwciRI+Pss2LFCvTu3ds0Be7atQtffPGFCZ7ee+89W4DatWtX8/7xeHz/XnnllTSXScSh0jlpq4hkIc4Y36lTJ/O4UaNGZlZqmjVrlpnN22rEiBGWkJCQOL87fvx4M4O1/bG4Hh0dbdtWqVIly913321bj4qKsgQGBlqmTZtm1g8fPmxe5/3337ftExkZaSlevLjlgw8+MOvvvvuumdHe3vHjx83v7d2716w3b97cUrt27Tueb9GiRS3vvfdenG3169c3M21b8Tx5vim5bufOnTOzmx85csQs/v7+lvPnz5vnuA+tXbvWlJXXNDHjxo0zz589e9by66+/WnLlymW5du2aJSWWLVtmfvfy5ctmnbOvc/3AgQO2fT799FNLoUKF4lyD119/Pclj2pc1/vFpy5YtZhvfO+rRo4elffv2cY7RvXt3S+7cuW3rnN181KhRcfb58ccfLUWKFDGPFy5caPH29racPHnS9vyff/6Z7HUTya68HRuGiUhaMU+INS93qgVJDmtTPD3/qxhm0w6buuxrn5h3c+7cuTi/x1ogK29vb5NPs3v3brO+bds2LFu2zDTzJJbPU7FiRfO4bt26yZbt2rVrpraqSZMmcbZzna+RFgUKFECHDh1M7QZjCD5m01xiYmOM5N13330mN4lNR2zS4sKmJjY5pRT3LVeunG2dTV/W682fvAYtW7ZERuH7xDLGfz/tmxx5fVlTZ60BoujoaJNTxRosHoPNjEwktz+GiDNSICTipNgEw6aiYcOGJejxxOAm/o08MjIywTF8fHzirDOXJLFtbApJqdDQUNNUxkAtPt7krdjTyxHYPGbNsfn0008TPM9mJ55zYgEDcTubDhlUcb/Nmzeb3BzmEw0fPtzkKW3YsCHF3dETu97W9y5+09ydWINa+/c+sfc9Je8hc4LY/BUfc4JEXIlyhEScGLvR//7771izZk2c7bxJnzlzJs4NMSPH/lm7dq3tMRNombDMZF9igu3OnTtNYjaDCvslNcFPrly5TI0DaybscZ3JuWnFWhv2UmOAkFiSNmvAWNPDJGj7HmbEazp16lR0797dNlwBa8SYwP7hhx+aPCYmJTP3KiPkzJnTXMekhgWIj+872Sdbx3/f+T4xryep99P6HjLnKf77x4XBFo/B4QXsXyf+MUSchQIhESdWo0YNk7w7adKkONvZK+v8+fPm5szmKNZ8/Pnnnxn2ujzerFmzTO8xJvKyKz9rWojrTDju0aOHqRnh6zP5mEnJbF5JDSZls2aJycm8Mb/66qvmxs4k3rRicx9rdZgEzMeJ+eSTTxAREWECJSYw86bPpiMGSMWKFbM1Gc2bN89ce5bp6NGj+OGHH0ztWaVKlZBRWMPEhHG+DpPEWQPFRPnEMFBhkxV/h/vOnz/f/K49JnfzXJigzX14rvbNYsSaLZ4La4UY1PJ6ceiBN954wzzPwI9NnH369DHNaEyutu9JJ+JMFAiJODn2WIrfdMVv7KzRYMASEhKC9evXpyuXKLGaKC489sqVK01PJmuujbUWh0FP69atTbDGbvJsKrLPR0oJ3rSHDBlieoXxOLxh87XYXT09WNvEJSk8PnuEMfeHvd2Yw8NeW+xhxdo3Di1APCd2wWeuFq85e09x3CHmXmUUBhsc+oDvJ4/LXn3WXnOJNbPx9Rmg1qxZ0wSR8XuEcRTur776yvTI4/vHJj1rgGPFAJBBHp+rX7+++Z3x48ebfCji+8hAmDVmDRo0wP/+9784+UQizsSDGdOOLoSIiIiII6hGSERERNyWAiERERFxWwqERERExG0pEBIRERG3pUBIRERE3JYCIREREXFbCoRERETEbSkQEhEREbelQEhERETclgIhERERcVsKhERERMRtKRASERERuKv/B6BDdfY0lCsbAAAAAElFTkSuQmCC", -======= - "image/png": 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", ->>>>>>> 2061f52c9c68d258f384a4c72344409cbc59dd27 "text/plain": [ "
" ] @@ -923,19 +857,11 @@ "source": [ "from matplotlib import pyplot as plt\n", "\n", -<<<<<<< HEAD "plt.scatter(x=[pao_driver.huzinaga[\"scf\"].mo_coeff.shape[-1]], y=[np.abs(pao_driver.huzinaga[\"e_ccsd\"]-unlocalized[0])], marker=\"x\", color=\"tab:red\")\n", "plt.semilogy(np.arange(5, 33), np.abs(localized[1][::-1][:-1]))\n", "plt.semilogy(np.arange(5, 33), np.abs(unlocalized[1][::-1][:-1]))\n", "plt.vlines(cl_driver.huzinaga[\"cl\"].shells[0], ymin=1e-5, ymax=1e-1, color=\"k\", linestyle=\"--\")\n", "plt.hlines(1.5e-3, xmin=5, xmax=33, color=\"red\", linestyle=\"--\", linewidth=0.5)\n", -======= - "plt.semilogy(np.arange(5, 33), np.abs(localized[1][::-1][:-1]))\n", - "plt.semilogy(np.arange(5, 33), np.abs(unlocalized[1][::-1][:-1]))\n", - "plt.vlines(cl_driver.mu[\"cl\"].shells[0], ymin=1e-5, ymax=1e-1, color=\"k\", linestyle=\"--\")\n", - "plt.hlines(1.5e-3, xmin=5, xmax=33, color=\"red\", linestyle=\"--\", linewidth=0.5)\n", - "plt.scatter(x=[pao_driver.mu[\"scf\"].mo_coeff.shape[-1]], y=[pao_driver.mu[\"e_ccsd\"]-unlocalized[0]], marker=\"x\", color=\"tab:red\")\n", ->>>>>>> 2061f52c9c68d258f384a4c72344409cbc59dd27 "\n", "plt.title(\"Formamide (C=O act) 6-31G\")\n", "plt.ylabel(\"CCSD Energy Error (Hartree)\")\n", diff --git a/nbed/localizers/system.py b/nbed/localizers/system.py index 2f5abbe3..20b5c194 100644 --- a/nbed/localizers/system.py +++ b/nbed/localizers/system.py @@ -9,15 +9,14 @@ class LocalizedSystem: """Required data from localized system. - Args: - active_mo_inds (np.array): 1D array of active occupied MO indices. - enviro_mo_inds (np.array): 1D array of environment occupied MO indices. - c_active (np.array): C matrix of localized occupied active MOs (columns define MOs). - c_enviro (np.array): C matrix of localized occupied ennironment MOs. - c_loc_occ (np.array): C matrix of localized occupied MOs. - c_loc_virt (np.array | None): C matrix of localized virual MOs. - dm_active (np.array): active system density matrix. - dm_enviro (np.array): environment system density matrix. + active_mo_inds (np.array): 1D array of active occupied MO indices + enviro_mo_inds (np.array): 1D array of environment occupied MO indices + c_active (np.array): C matrix of localized occupied active MOs (columns define MOs) + c_enviro (np.array): C matrix of localized occupied ennironment MOs + c_loc_occ (np.array): C matrix of localized occupied MOs + c_loc_virt (np.array | None): C matrix of localized virual MOs. + dm_active (np.array): active system density matrix + dm_enviro (np.array): environment system density matrix """ active_mo_inds: NDArray diff --git a/nbed/localizers/virtual/projected_atomic.py b/nbed/localizers/virtual/projected_atomic.py index 4d695c4f..3c615503 100644 --- a/nbed/localizers/virtual/projected_atomic.py +++ b/nbed/localizers/virtual/projected_atomic.py @@ -5,7 +5,6 @@ import numpy as np from numpy.typing import NDArray from pyscf.lib import StreamObject -from scipy.linalg import fractional_matrix_power from nbed.localizers.virtual.base import VirtualLocalizer @@ -106,9 +105,7 @@ def _localize_virtual_spin_pao( # Take the columns of C matrix (MOs) truncated_paos = pao_projector[:, np.abs(pao_norms) > norm_cutoff] - s_half = fractional_matrix_power(ao_overlap, 0.5) - - renormalized_paos = s_half @ truncated_paos + renormalized_paos = truncated_paos renormalized_paos = renormalized_paos / np.sqrt( np.einsum("ij,ij->j", renormalized_paos, renormalized_paos) ) @@ -121,7 +118,7 @@ def _localize_virtual_spin_pao( logger.debug(f"{eigvecs.shape=}") # How to transform the truncated paos? - final_paos = renormalized_paos[:, eigvals > overlap_cutoff] + final_paos = renormalized_paos[:, np.abs(eigvals) > overlap_cutoff] logger.debug(f"{final_paos.shape=}") if (n_paos := final_paos.shape[-1]) == 0: