NEUP_MaxProfit_V20.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-06-24 15:42:52.872701 start\n",
      "2022-06-24 15:42:52.900078 Profiles complete\n",
      "Building Model\n",
      "Params & Vars established\n",
      "Model Built\n",
      "Solving...\n",
      ">>Solver status is ok and solver termination condition is optimal\n",
      "-5303586.404923634\n",
      "iMSR was chosen for operation\n",
      "1.0 modules used\n",
      "2022-06-24 15:42:54.374339 end\n",
      "0:00:01.501638\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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13jv59NNP2bJlC6eeeioPPfQQN954o9d4/vvf/9LU1MTLL7/Mhg0bWLZsGU888URQf2MgiHAiRB3BzLKDasjeMiiWaKUN1ajPpKNwIoQXB5CGcu73l1hUKrd0zoJjM+p+COZ5dBiVCSDCY+AcQJULpqEyTkSIOpYmYB/WzDgBdQ+tMjuICKcVZQ57VoD7e2Y7iHASOCWoUsxAyuLiUaWfIiJ2ZKbBx/vAgGMcOXKE22+/nQMHDgBwxx13UFBQwCuvvMKLL75IW1sbp512Gtddd12n+7///vtMnjzZLZoAjB49mmeffRaAnTt3cuedd+J0OklMTGTx4sW0trbyi1/8ggEDBlBVVcUJJ5zAPffc02WcZ5xxBu+++y7z5s2jvLwcu93uzmqZOnUqX3zxBccffzyffvopDzzwAL/4xS/42c9+xtq1a5k2bRoA+/fv56c//Sl79+6loKCAe+65hz/96U80NjYyduxYCgq6Xnh73bp17mONGTOGb7/91odP2HikVEeIOhwoxXBEgPsXIaaYwbIzPp4GVCdLhBPzKEFdg0AHiJJ9FTzBCrlSz24MerbJcJRo0mhiLFbFqksR64gpZvBUx8fTRPDPI2kXgkMv35S2ObL59NNPO5TqPP300x1+/+STTzJ58mRWrFjB4sWLWbRoEfv27eOpp57ihRde4PXXX+fIkSPU1XVeQFpdXY3dbnf/fM0111BcXMyZZ57Jrl27uP/++ykuLmbFihVcccUVPPjggwBs27aNJUuW8Morr/Dhhx+yd2/XPfCzzz6bf/7znwC88cYbnHPOOe7fnXTSSaxbt46amhqSk5PJzc1F0zT279/P559/zsknnwxAbW0t9913Hy+//DKffPIJBw8e5KqrruLss8/mtNNO6/azrK2tJS0tzf1zbGwsLS0t3e5nNGHNOGlubua2225j+/btNDU1cc011zBgwACuvvpqhgwZAsCll17K7NmzWblyJS+99BJxcXFcc801nHLKKeEMVejBlKA6x/EB7u85UDnJkIiij/KEBKBjqc4e06KJXhwENwtTBLyCGmQmGRFQFOJAdY4DMWKEjl4zpxoSUXSi+5sMB75CleskmxeOJdE/Iyuaw0LHtnmimYFEMFsSlbNJoMJJJpCFCLnBUkLgWT+g2oV/oLLEEgyJKPL5wIRzdleqU1ZWxqeffuoWJQ4fPkxVVRUjR44kKUn1qm677Tav+w8YMKBD5oVeujJ37lxaWlooKyvjj3/8I08//TSaphEfr0Y+drvdLUJkZ2fjdDq7/DsGDlRy+c6dO/nyyy+54YYb3L8rKChg27Zt/O9//3NnhJx88sl89tlnNDU1kZ2dTXV1Nbm5ufTu3RuAzMxMGhoaujzn0aSlpXUQkNra2oiLC3/hTFjP+MYbb5CRkcEDDzzAgQMHuOCCC7j22mu5/PLLWbBggXu7vXv3smLFCl577TWcTifz5s1j6tSpJCTI7S8ETzBGjNBxRkWEk8DY6uqcFQJ9UalvknESXupsNqoJ/l7QUKndow2JKvooQZkkpwS4v5hiGoOncAKqXCfTpFisiv4ZWTXjxFNEFOEkMLa4+tmBepyAZDsEy+GYGHYRfNvcCpQHeRwhtAwbNoxzzz2Xc845h3379vHKK69gt9vZsmULTU1NJCQkcP3113P77bfTv3//Y/Y/7bTTeOqpp9iwYQNjxowBoKKigl27dmGz2Rg2bBgLFixg3LhxlJeX8/nnnwN49RzpitmzZ7Ns2TLGjh3bYX+bzeYuL7r33nsBmD59Or/5zW+YOHFih+2OJiYmhra2Np/OP27cON5//31mz57Nhg0byM8PdLopOMIqnJx55pmcccYZ7p9jY2P59ttv2bp1K6tWrSIvL4/bbruNr7/+mrFjx5KQkEBCQgJ2u52SkhJGj/beNXc6nTgc1n5UNzY2Wj7Gns6RxkY2aRpT9+3D0U1qmjdagYSCAj46cICT9kieRCCUZWeT0dJCzaZN1AAZI0dSduQIjl27ut1XMIZSVyOWXl2N48iRgI6RkJgIw4bx3+pq4gM8RjTT2NjI+sZGclpacFRVBXycIUOG8EVbG47KSgOjix4aGxv5avdu6N+f1J07YeBAvi4vp65JFkn3ZH1GBgwcSO2mTTgMTpE2on/UAsQVFvLRvn1MDLB9j3Y29etHVksLOzdtcgtl/jJgwAD+k56OI8iVPaIVR4xyUUitqsJRWxvQMZKSkmDoUP5TXQ3SNpuGXqrjyVNPPeX+/9VXX83tt9/OypUrqa2t5Wc/+xl9+/bl//7v//jxj3+MzWbjlFNO6VQ0AUhNTeWJJ57gt7/9LQ8++CAtLS3ExcWxePFiBg8ezM0338yiRYtwOp00NjZy++23B/y3nHnmmSxZsoS///3vx/xu6tSpPPLII4wYoUwQRo8ezZYtW1i4cGGXx8zPz+eJJ55g1KhRfO973+ty21mzZrFmzRouueQSNE1j6dKlAf8twWDTNE0L90lra2u55pprmDt3Lk1NTRQUFHD88cfzxBNPcPjwYQoLCykrK+OXv/wlAL/61a84//zzOekk7/P7DoeDoiJr66qREGNP5+3ycs4ePpxngMuCOM73gFzEJDZQJtTXk5SSwkeun49HlSq8bmJM0cb927dzy+DBfEfgM1INQCpwt+uf4B8bHQ5OLCriauChII7zE+C/KH8HwX8cDgdPFxXxJPAicB7wBTDe3LAsx93AYlT6v9Gzbkb1j0ahjDH/HvSRopMx9fX0SUnh/SCO8TtgIar8NrvrTYVOWLJjB3cMGsQmAvfiq0VlIv4aCHyobF18eV7ImCtwiouLWbRokXtVnXBQXV3NjTfeyMqVK8N2Tm909d0Je3HQzp07ufbaa5k3bx7nnHMOhw8fplevXoBSkxYvXsyECRM61DHV1dWRnp4e7lCFHsgWD2+NYCgCPg86muhlS0ICczx+zkY8TsLNlsTEoEySQXlADEXSsgPF0yQ5GIqAZ1GmmL2Cjio62Yny7kh1/dy5FV90sxP1rLbycoyFgDlrLUQ+Gqpd+HGQx/EsmRLhxH+2JCaSgGpbAyUNNbknbXPPYNGiRZSXlx/z/lNPPeX2QjGam2++mWXLlrmzSEJJRUWFO1nCjL/VH8La/tXU1LBgwQLuuusupkyZAsAVV1zBnXfeyejRo/nkk08YNWoUo0eP5ne/+x1Op5OmpibKy8tNq2USehZbPLw1gqEIWImacRcDQf/YBxyIi+sgXmWjDBmF8LElIYERBG6SrCP17IFTbpCQ67kEqHg7BMZOlHeH7jUjwsmx6J+RlSlCTDEDZRdwJDbWkIklUM+j6UEeKxrZkpBAPhAb5HGKEJPensKiRYvCer4VK1aE9Xx5eXmWyDTxhbAKJ08++SSHDx/m8ccf5/HHHwfglltuYenSpcTHx5OVlcXixYtJS0ujuLiYefPmoWkaCxcuJNE14BWEYNiSkMBgVApjMBTSborZdVWecDSdLb+ajZjDhpstiYmGfHcLUWUirQTf0Ys2tga5goWOp2G1CCeBsQtVMqhnnNSbGItV2YX1hZNC1LNoE6psR/AdfZAd7MRSLkqAFEE9MLYkJjLJgOMUAn8G2lAG/IIgBE9YhZM77riDO+6445j3X3rppWPemzt3LnPnzg1HWEIUsSUx0RCHcc8ZFRFO/EPvTHl2zrKBA0AzwWdACN3TDFQmJHCxAccqApxABTDMgONFE1sSEtzLdwaDvry6zC4Gzk5gFlKq0xU7UeKSlfFsm0U48Y/OJjUCIQYoQISTQGgEquPjmW/AsYpQz7FqwG7A8QRBEBFSiCI01EDFCOEkH7AhHYNAKAGS2trI83ivn+t1nwnxRCPlQIvNZsi94FnPLviHUUJuHMoQU65BYDTabBxCSnW6og3YjfUzTgpcryIi+k8JkNLaymADjiUlnIGxCWgzuG2We0EQjEOEEyFq2A7Ux8YGnYYKkISYYgaKAxjS1NTh4aMbyEm5TngwamYRRDgJBqOEXFDXQa5BYOyNU8m3Yg7rnRrUcr9WF07EFDNwHMCwpiZsBhyrCKhE7iN/6SwjN1A8SzgFQTAGK5ujC4KhGDlY1I8jSr7/OIDjnE7wcMcW4SS86PdCQZdb+UYm6vrJveAfNRxrkhwMYooZODUu4WQg4nHijZ2uV6sLJyBtc6A4gPFOJyQHb3mvP9dKgXFBHy16KAFsmka+LXj5qh/QB7kXzGLZsmVs3LiRvXv30tjYSG5uLn369OGjjz5i1KiOhYTLly+nubmZRYsWsWfPHmw2G2lpaSxatIg+ffp4PcemTZt44IEHaGhooL6+nhkzZnDdddexdu1aXnrpJR5++OFQ/5md8t577/HYY48RFxfHnDlzepT1hggnQtRglPGZjphi+k89ygvj7KamDu+LcBJeSoABzc2kxxvjKCNp2f5j5MwiqGvQCmwGjjPomNHCXg/hJBZIRGbKj2aX63WAqVH4RhHwNGKK6Q9HUFm5PzyqbQ4Uz0xEEU58xwEMbm4mJSF4+duGZCKayS233ALA66+/zpYtW7jpppuorq5m165dna5a89JLL5GVlcWyZcsAJaY89thjnXqDAhw+fJgbb7yRRx55hCFDhtDa2srPf/5zXnrpJYYNM89xrrm5mfvuu49XX32V5ORkLr30Uk455RSys3vG4uQinAhRgwNIb21lQKwxModuirkNZc4odE8ZymtmmNPZ4X0RTsKLA9c1MFA4WYm6tkakeUcDupBrZKkOuDK6DDpmtOBZqgPK50SEk45EUsZJIWKK6S/682iYQcLJSJQIKYN2/3AAQ5uawADhBFT78pYhR+oBbDA492aMUdMeisGDB/Pqq68ybtw4Jk6cSHFxMZqmed1+1apVTJo0iSFDhgAQGxvL/fffT3x8POvXr6eiooIrr7yS/fv3c8opp7gzUR599FEAGhsb3dtfc801ZGRkMH36dCZNmsQ999xDamoqmZmZJCYmsmzZMlasWMFbb72FzWZj9uzZXHbZZZ3GVV5ejt1up3fv3gCMHz+eL774grPOOsvQz8ssRDgRooYS1GDRlpLS7ba+4DlQEeHEN/RO1PCjOmeZqAG3CCehR0PdC+cZ1EEGdS8cQF2/ft1sKygcKJNke4wxc+LiNRM4NXFxxNIu4KYipTpHE2kZJ6CecyKc+Ib+3Dh6UiNQElD9IikT8Z1WVGnTxU4npKUZcswi4C/AfqCvIUcUgmXz5s0UFxe7fx41ahS33HILM2fOpKmpiVdffZVbb72V/Px87rjjDgoKOi+q3rNnD7m5uR3eS01Ndf/f6XTy+OOP09raysyZM7nuuuvcpT39+/fnySef5N133+Wcc85h7969vPbaayQkJHDBBRfwm9/8hpEjR/Lwww+ze/duNm/ezDvvvMMLL7yAzWZj/vz5nHzyyZ1mttTW1pKent4hptra2mA/NssgwokQNTiAyU1NYJBw4tk5O9uQI/Z8HKjU6byjBu2xqEZdhJPQsx2oxbiZRehoQifCiW+4TZI9vH6CIRU1SJSBiv/sjYujP+1lHalIxsnR7ATSafeAsTKez6PTzQwkgihBDQhyDRbURcj1nUrUcsRGts2eK+ucZNhRIxSDM0QCZcSIEZ2W6qxfv54pU6Zw+umn09rayj/+8Q9uvfVWXn/99U6PM2jQIL777rsO71VVVbFrl5K5R44cSYIrcynOlVXZv39/lixZQkpKCrt372bcOFVIl5OT4952z549jBw5ElDZIu+88w5lZWXs2LGD+fPnA3Do0CEqKys7FU7S0tKoq2tvQevq6joIKZGOlH8KUcFB1IzZcINmU0AN9PshHQN/cADDgIRO0g+zEeEkHBg9swgdRUTBNxwY+zwC8ZoJlJq4uA6ZFFKqcyy7iIwyHVBtSR/kXvAHB6q8xpjiTUURanndFgOP2ZMJZdss94L1efvtt3n66acBVXZTUFDgFjM645RTTuF///sflZWVgPIWWbZsGWVlZQDYOjEYvuOOO1i6dCnLli2jX79+7lKgGI/M1wEDBrB582YAvvrqKwCGDRvGiBEjePbZZ1mxYgUXXngh+fn5ncY1fPhwKioqOHjwIE1NTXzxxReMHTvW34/DskjGiRAVGF2/q1OIDBb9oQTvng4inIQH971gYOcsBzUTLZ0z39BNks8JwfPof4gppr/sjYvDc95MSnWOZSeRI5zYkJV1/CUU3kiFQDNQjjEruPV09O/r0aXMwTAEZXYt94J1OLpUB2Dp0qXccMMNLF68mPPOO4/k5GRSUlJYsmSJ1+OkpaWxbNky7rjjDjRNo66ujlNOOYV58+axdu3aTvc577zzmDt3Lr169SIrK4s9e/Ycs83dd9/NbbfdRkpKCvHx8fTv35/CwkKmTJnCpZdeSlNTE6NHj6Z///6dniM+Pp5bbrmFK664Ak3TmDNnjtdtIxGvwslf//rXgA96+eWXB7yvIISCUCj5IKaY/tCCMof1Zg+VjQy8w4EDyACyWlsNO2YMqmMs1883Sl2voXge1SOmmP6yNy6OqR4/p6I8e4R2dgITzA7CD4qAN80OIkLQxY05Bh/XMxNRhJPucQBZQIaBbXMs0jabzYUXXuj+f05ODl9++aXXbe+//36/jn388cfz7LPPHvP+pEmTmDRpkvvnNWvWAHDrrbdy6623HrP9ypUr3f//5ptvePLJJ+nbty8PP/ww8a5FBK688kquvPJKn+I69dRTOfXUU/36WyIFr8KJvxdPx2aziXAiWA4HyqxscHOzocctQkwxfWUr0ETXGScfhi+cqMWBmgk0WugrQq6fr4QyAw7UNRbhxDdagf2xsceU6lSbFI9V2UlkGMPqFAF/RkwxfWEzamLDqBW+dDz9Nc4z+Ng9ka4ycoOhEPgiBMcVwsOiRYsoLy8/5v2nnnqKJIM80jzJzMxkwYIFpKSkkJ6e7l4e+WhWrVrF8uXLj3n/sssuY9asWYbHZRW6LNVZuXIlo0eP9vlgGzZs4NJLLw06KEEwmhIgH+Nr08QU03f0waI3e65+wD7UQMaYBaOFzijBe9ZPMBQBz6OMZ41ZD6DnopskDzFYOPF8Hp1h6JF7LnuANputQxmKlOp05AjK8yVSSnWgo4g4tasNBXc2gtGD9t6o74xkO/iGA7iw2638pwh4BWU8a/wwWwg1ixYtCuv5zjzzTM4888xutzvttNM47bTTwhCRtfBaBn3aaae512D2lYyMjB6bmiNENvosu9HIEqC+013nLBtV8rQ/POFEJQdRJo+hmtWC9jIUwTsleDdJDgYxxfQffZndo4UTMYdtp7PPyOqIKabv6J9RKMppxGvGN/aiJo5C0TYXofpWZSE4tiBEG16Fk8cee4y8vDxA1URVVVV1ut2WLVu45pprABgyZAiPPfZYCMIUhMBpBLYQmgYpFzHF9JUSVKp3hpffZ7texSA2dIRqZtHzmHIvdI+D0FwDMcX0n52uV09RQFbV6Uhnn5HVyUPNrsu90D0lqL5MKDIF9ZW+jJWIex7691TaZkGwNl4rF7777jv3MkV///vfmTRpEocPHz5mu/fff5+PP/44dBEKQpBsQq0yEaqBSiHSIPlCd1k/unCyB+Pd/QWF/j3VVzswkhGoEisZqHRNdybJwSKmmP7RmSigl+qI6bciEoWTWFR5rrTN3RMqbw1Qbc1h1HdoUIjO0RPwnNQwukxwJOo5JveCIASPV+HkL3/5C2+99ZZ7HejOXHh1YeX8888PTXSCYAChVPL1464O0bF7ChrqOnTlgCQZJ6GnBGWSPBTj03YTUOKJdM66ZhtdmyQHSyFiiukPuijguVhiKuqZ1Qgkhz0i66F/RpFkDgvqHut8UU5BR2+bF4To+J4r64hw4h0HKtMtF+PLXZNRbb5MaghC8HgVTu6++27mzp2Lpmn85Cc/4a677mLEiBEdtomJiaFXr16MHDky5IEKQqA4UGp7PlARguMXAs8hpphdsRvlr9HVYFE31xXhJHQ4UHXsoTLfleyr7glluZTnccUU0zd2Ab1aW0mKbb8rUlyvdYhwAko4SSDyhLhCYCXQgFxHb2xH9V1C4QEHHZ9H4oDoHb1t9uqfECTSNoefZcuWsXHjRvbu3UtjYyO5ubn06dOHjz76iFGjRnXYdvny5TQ3N7No0SL27NmDzWYjLS2NRYsW0adPn06P/9lnn3HDDTcwYsQINE2jpaWFyy67jNmzZ+NwOFi1ahU/+9nPOt331FNP5Z///CeJiYmG/909Ha/Cyd69e5k4cSIAzz77LKNGjSI1NTVsgQmCUThQ9c4p3W0YIHrHoBQYH6JzRDq+DBYzXa8inIQOBzAuhMcvAt5BlQHFh/A8kYxnudTOrjYMEM8ZXhFOumcnkN3SAh7Cid7TkZV1FLtQ2SaRVrbkaYr5PZNjsSqhFnIHAr2QQXt3hPp5XQSsQlYtDCe33HILAK+//jpbtmzhpptuorq6ml27drFixYpjtn/ppZfIyspyL/+7fPlyHnvsMe644w6v55g8eTIPP/wwAHV1dRQXFzN06FCKioooKgrVXR3deBU3Z8+ezfTp07n55pvZsWMHR44cCWdcgmAYoazfBVlZxxc8B4veiEetCCLCSWhoBLYS+nuhGWXGLHSOg65NkoNFN8WU55Fv7ASyWlo6vKcLJ2IQq9hJZPmb6OjtjZQoeCfUwon4wHVPHSobOlRZP7iO7USVikYrM2fOZPny5e6fly9fzsyZM4PexigGDx7MmjVreO+996itraW4uNgtvvhCamoqF198Me+++y6fffYZCxcuBJSAM2/ePObMmcM777zj3n7RokUUFxdTXFzMoUOHqK2t5ec//zkLFizgggsu4IUXXgCguLiY66+/nvnz51NfX8/111/PJZdcwsKFCzn55JMBKC0tdR/ruuuu69GagdeMk4ceeogvv/ySL7/8krfeeou2tjby8vKYMmUKU6ZMYdKkSX4vVywI4aYNlQlySgjPIaaY3VOCKmMa3M122YhwEip0k+RQds4807JDsbRlTyDUQq6YYvrHTuD4o4QTz1IdQWWcDDM7iADIR0wxu8OBEnH7dbNdMBQB/wnh8SMd3dMklO2CZ9s8PITnEbpn8+bNFBcXu38eNWoUt9xyCzNnzqSpqYlXX32VW2+9lfz8fO644w4KCnzvTWVmZrJx40b3z7W1tXz22We89tprAKxZs8b9uzlz5jBhwgRuueUW1qxZQ15eHj/4wQ84/fTT2b17N8XFxcybNw+Ac845h1mzZvHMM8+Qk5PDH/7wB8rLyzn77LMBuPPOO1m6dCkjRozglVde4emnn3YLNz0Nr8LJ7NmzmT17NgD19fVs2LCBL7/8knXr1vGPf/yDxsZGCgsLmTJlCpMnT2batGlhC1oQfKUCVd8cygYpAdUQSefMO/ryq92leotwEjp8yfoJFr15FxGxczTUdfhRiM8jppi+oaFEgZleMk6kVEexk8gs+xJTzO7RhdxQlmEVAc8AhwCZbj0W/fsZ6owT/Vxnh/A8VuaDDz7o8PP8+fOZP39+0Nv4y4gRIzot1Vm/fj1Tpkzh9NNPp7W1lX/84x/ceuutvP766z4fe8eOHQwY0G7jnZaWxp133smdd95JbW0t5557rvt3xx9/PABZWVk0NjaSlZXFM888w7///W/S0tJo8WgXhw4dCkB5eTnTp08HYPjw4fTt29f9/j333ANAc3Oze/ueiFfhxJOUlBROOukkTjrpJADa2tr46KOPeOWVV3jmmWf4y1/+gsMhw0bBeoSjQQLVMZDOmXd8NYbLBjaHOJZopQTVOQ5lJkhv1MoJ0hp0zi7U4CEczyMxxeyeBlQJW9/W1g7vS6lOOxqwD8gyO5AAKUKeR13hAGaH+ByevkuTQnyuSMSB8k0I5TIbmaj+lfRTrcvbb79NamoqCxcuJDY2loKCAhISEnzev7a2lldeeYXf//737N2rpiD37NnDxo0beeyxx3A6ncyYMYPzzjsPwL1qrs5f/vIXxowZw7x58/j0009Zvbp9vVB92/z8fNavX8/3v/99KisrOXDgAKCElfvvv59Bgwaxbt069/l7Ij4JJwD79+/n448/5pNPPuGLL76gsrKS1NRUpk2b5hZUBMFqhLp+V0c3xWzBj5sqSjiCcu735Rr0Az4JbThRS6hNknVERPROOJ9HYorZPU7Xa2JbW4f3pVSnnWbUdynJ7EACpBD4L2KK2RkHUCvehfp55OkDJ8LJsZSgspZDvb6JiIjW4OhSHYClS5dyww03sHjxYs477zySk5NJSUlhyZIlXR7r008/pbi4mJiYGFpbW7nuuusYNmyYW7jIzs5m7969nH/++aSkpLBgwQLi4jofpZxyyiksWrSIN998k4yMDGJjY2lqauqwzUUXXcQtt9zCj370IwYNGuRelWfRokXcfPPNtLomIbqLO5LxOsZramri888/Z82aNXz88ceUlZURExPDmDFjOPfccznppJMYPXo0sbHSFAnWpQQ1Uxbq2TLdFLMc8XY4Gn0Q7UvnLBuoQXlxhGpZvmgl1N4aOoXAs6jBVqStwhFqwpUB5zlQEeHEO7pwEq9pHd6XUp123OKSqVEEThHqb6ggMn1aQkm4hNxhKPN3EdQ7x0Ho2wRoz0SUtjm8XHjhhe7/5+Tk8OWXX3rd9v777/f5uJMmTeKTTzqfapw0aRKTJimZ8t577z3m9++99577/zfddJP7/+++++4x23qWFX333XdcdNFFnHzyyWzbto3169cDquyns/KjnohX4eTEE0+kubmZgoICJk+ezMKFC5k4cSLJyZL4K0QO4WyQQHUMRDjpiD/eGtmomcGDQN9QBRSFhMMkWacIlWW0g+7NgKMNB5BO6D+XfJTwKAOVrulOOJGME9DnG31PGLcWnqaYIpx0xJ9JjWCIQ5WhSLbDsbSgjNt/EIZzFaGyjPYSWjNgwVgWLVpEeXn5Me8/9dRTJCWFNxcwNzeXG2+8kUcffZSWlhbuuuuusJ7fCngVTpxOJ7169SI3N5fc3FzsdruIJkLEUQJcEIbzeM7wnheG80USJagHjS9O7tmu1z2IcGIk4TBJ1vEUEUU46Ygu5IZ6ti8JZYopA5Wu8SacSKlOOz0h4wTUvRCOwWkk4UBd1yFhOFcR8HUYzhNpbEWJk+HKBgV13UU4iRwWLVpkdghusrOzoyazxBtes+HXrFnDnXfeSWpqKk899RSzZ89m5syZ3Hbbbbz11lvs27cvnHEKgt/UuP6Fo0HqhRokykDlWByo2aZ4H7bVhZOeaytlDuGaWfQ8h9wLx1JCeDLgcJ1HrkHXuLMppFTHK7pwEqkZJ31Qg0S5F47FgcpOC0fBfSGqlNnZ3YZRRrjKpTzPEU2ZiNpRz3ZB6I7uvjNeM04yMzM555xzOOeccwC11JBuDrto0SLq6urIz8/npJNOYsqUKe7liQTBKoSzQQIZqHjDH28NEU5CQzjvhQEoIVHuhY4cxneTZCMoQkwxu8MtChzVUYpFzcRLxkm7uBSpGScghtXeKAHGh+lcRaiS0c3AqDCdMxLwp5Q5WHJRonC0tM1JSUns27ePzMzMY1aQEYTO0DSNffv2dVkC5fMCIMOHD2f48OEUFxfT2trK+vXreemll1ixYgXLly/3aTni5uZmbrvtNrZv305TUxPXXHMNI0aM4JZbbsFmszFy5EjuvvtuYmJiWLlyJS+99BJxcXFcc801nHJKOKrzhZ5EOBskUB2DZxDjLU+aUR0lX8ulMl2vks9mLA6UQXJmdxsagA1x7++MUtdrOIVcJ7AN38rkohFvwgmoch3JOIn8Uh1Q94KYYnakEVUm8uMwnc8z20GEk3ZKgIFA7zCcy0Z0TfDl5ORQXV3do5fGFYwnKSmJnJwcr7/3WTjZuXMn69evZ8OGDWzYsIGSkhLa2to44YQT3M693fHGG2+QkZHBAw88wIEDB7jgggsoLCzkhhtuYNKkSdx1112sWrWKMWPGsGLFCl577TWcTifz5s1j6tSpfq1nLQglqM6vPUznE1PMY9mMMj/zdbCoews0hCacqCVcK+roFAH/CuP5IgEzhFz9vCKcdI6eTXG0xwmomVnJOOkZwoluirkH6G9yLFahDJUBEq7nkW6aHy2Ddl8J1wIGOoXA/8J4PjOJj49n6NChZoch9DC8Cie6SKK/7t27l9jYWEaNGsXkyZO5/vrrGT9+vF+GsWeeeSZnnHGG++fY2Fg2btzIxIkTAZg+fTpr1qwhJiaGsWPHkpCQQEJCAna7nZKSEkaPHh3EnypEG3r9briWtfUcqIhwovDXW0NPjmsMQSzRjAOYE8bzFQLLgUOEZyYtEvDHJNkI9M54CXB2mM4ZaXSVcZKKZJxA5K+qAx3vBRFOFOEWclNRk1hSMtWOhroO4cr6AdUXex6oBdLCeF5B6Cl4FU4uvfRSYmNjKSoq4pxzzmHSpElMmDCBlJQUb7t0S2qqslyrra3l+uuv54YbbuD+++93156lpqZy5MgRamtrSU9P77BfbW1tl8d2Op0+lQuZSWNjo+Vj7El8PXw4YxsacOzY4X4vlNcgNi4ORo7k/V27GHzgQEjOEWmszsyEfv2gpASHx+DE23VoBSgqonLvXhw1NeELtAezPzaWffn59N21C4fH9zKU90JaWhrk5vLPrVv5XqPIYABrc3KwJySwecsW93uhbhMyR47kk9paHDt3huwckcxm1/dU6+Q6xAwdyu7mZhzV1SZFZw3KUlIgL49dFRU46kMnJYXyXohztc3v7dxJv4MHQ3KOSON/WVnYsrLQSkt9apuNwJ6by/rYWBzbtoXk+JHG3rg4Do8cSZ8wts3p6emQk8O7W7cyStpmQfAbr8LJk08+yYQJE1QH2EB27tzJtddey7x58zjnnHN44IEH3L+rq6ujV69epKWlUVdX1+F9TyGlMxITEykqCmcyuv84HA7Lx9hTqEOVzFydkEBR7/Y571Beg0LU7PrBAQMoGjAgJOeINPYBOcCEwo7zWl1dh3ggLTubouzsTn8v+MeHrtcZR30vQ3kv6A1L49ChYS0RsjLVwPegw2ce6jbheGBXRgZFGRkhO0cks9H1mpaQQNGwYR1+lwnEJCVFfZu9zfWan5cX0ns5lPdCASrj4dDAgRQNHBiSc0QaNahliMf60TYHy3jgKaCgqChsmcBWRpezTwlj26xLZJHeNssktGAWXoWT7OxsKioqfD7QqFHd2z3V1NSwYMEC7rrrLqZMmQLAcccdx2effcakSZP48MMPmTx5MqNHj+Z3v/sdTqeTpqYmysvLyc/P9zkWQShzvYazdlRMMY8lEG+NZGTJQiMJ51LEOkNRqf1yLyiaUUtxhrNcCtQ1fxkxxfRGd6U6B8MZjEXpCaU6MSjxRJ5H7YTb9wrX+eqBKiAvzOe2IuFe+RFgBGrVMCmZEoTA8CqczJkzp8PyTfq6xkcv6aRpGjabzSf178knn+Tw4cM8/vjjPP744wDcfvvt/PrXv+ahhx5i2LBhnHHGGcTGxlJcXMy8efPQNI2FCxeSmBjJ1mRCuDGjQQIl1Lwb5nNaFQ3VOC/wc78kxOPESBwo093cMJ4zDhiJDFR0/DVJNopCxBSzK3ThpDNz2BTU8tHRTk8whwV1733Y7VbRQRtqla/Twnxez5V1RDhR7WM6alWdcJGA8tmStlkQAsOrcJKcnExDQwOFhYWcddZZjBkzJuh1sO+44w7uuOOOY95/7rnnjnlv7ty5zJ07N6jzCdFLCWqWaWSYz1uEMsU8CGSE+dxWoxplQBZIxomsqmMcDtRsa7hTo4uAr8J8TqtilpDrOVAR4eRY3NkUYg7rFXdWjqlRBI+YYrZTgZqcMOt55ADO6GrDKEHP+gl3NmARknEiCIHiVTj59NNPWb16Ne+++y5//OMfSU9P54wzzuCss85i7Nix4YxREPzGAQwj/LNknu79k8N8bqsRqGt/EiKcGEkJMNWE8xYCr6MGXpE+Wx0s+r1Q0OVWxuM5UJkR5nNHAu6Mk7a2Y34nyxErdHEp0u9hvR0qRXltRDNmCblZQF8k20HHAXzfhPMWAu+gSkjjTTi/IEQyXoWTxMRETj/9dE4//XScTierV6/mX//6F1dccYVbRDnzzDMZN25cOOMVBJ8wo34XOg5Uol04CdRbIwnxODGKOtTs4hUmnLsIlRK+CWVSGs2UoEqlwj3TnYMSAGSg0jndeZyIcNKzSnVA3QsinCjC6QEH7T5wku0Ah1ALGJjVT20GtgLiHikI/uFVOPHEU0Rpamriww8/5F//+hdXX301ycnJnHHGGdx2222hjlUQfKIFZQ57lgnn1k0xpWOgOmd9gH5+7ielOsZR6no1o3PmmX0V7cKJA3OugQ11HUQ46Rw9m8Kbx0kdYqzbU4QT3RRT7gX1TM5GrRwVbgqBN0w4r9XQ2+Zwi1fQUUQU4UQQ/MPvsveEhAROO+00Lr74Ys4++2z27dvHihUrQhGbIATEVlSH2IyBiphituNAdQr8HXSIOaxx6AKeGZ0zvSwl2u+FNtR1MOMagMzwdoUT9XzqbAYpFSWaRHv2W09YVQdU/COQewHME3JxnXcvsM+k81sFs8qlQNpmQQgGnzJOAFpbW/nkk0/497//zapVqzhw4AAnnHACCxcu5PTTTw9ljILgF2Ysv+pJEbDBpHNbiRLgBwHsl4R0qozCgTkmyaAGnnnIQKUalblg5vPoOcQUszN0/53OxN1U12sd6pkUrfSUjBOQ7CtQYqAD+KFJ59cFZAdwskkxWIESlL/IMBPO3RsYhNwLghAIXQonelnOv//9bz744ANqa2sZO3Ys/+///T9OP/10BgwYEK44BcFnzFTyQUwxQS2BupvAZtnFHNY4SlBLD5r1PSxCOmdmZv14nrcEmGBSDFalCe+ZFCmu1zrMKWmwCk5UiUus2YEYQBFiirkX2I+5GXCgnkfRLJw4UBlQZn0PpW0WhMDwKpz8/Oc/58MPP6S5uZkTTzyRhQsXMmvWLLKyssIZnyD4jQMYiFLVzUBMMYMzn0tGSnWMQi+XMotCYDXqfgj3cshWwWwh13OgIsJJR7oSt/WMk2hfkrgrcSnS0E0xtxD+Fa6sgtkZuXmoyZFoz0QsAUaZeP4i4BnEw0kQ/MWrcPKvf/2LmJgYRo8eTWJiIqtXr2b16tWdbmuz2XjiiSdCFqQg+IPZg0XPgUq0CifBdM7E48QYdJPkQMqljKIIlT1UheowRyOBmiQbxQhUQy+zi8fixLso4FmqE830pMxJT1PMaBVOzBZyY1GffTQ/j5qAzcBFJsZQCBxBrewz2MQ4BCHS8CqcnHjiie7/19VFe9dBiBQ01KD9RybGIMZb6m9PRK0y5C+ScWIMW1Gzq2Z1kKHjQCVahRPdGNasWb14VLlWtM/wdkYT3kUBz1KdaKYrcSnS8GybzzcxDjNxoL7bOSbGUAisNfH8ZrMZaMUabXMJIpwIgj94FU5kpRwhEtkFHMLcBikFNUiMZuGkBLXMXSB18eJxYgxmzyxCRyPAM02Mw0wcwNkmxyD17J3jS6lOtAsnXYlLkUYv1CAxmkVEPSPXzNLJImAlqp1PNjEOswimlNkoPCc1TjMxDkGINLw+O2fOnElJiX/Ni8PhYObMmcHGJAgBY4UGCWQJ0GDKpSTjxBiscC9ko4w1o3XQvh/Yg7niFajvwGZUBpLQTlfCiZ5xEu0eJz2pVAdkZZ0SzH8eFaGyg8tMjsMszDYMBxiA8gGM5ntBEALBa8bJrl27KCkpobW11eeDlZWVsXv3bkMCE4RAsMIsu37+aDXFbESViQRaLpWESmNtwY/10oVjMNskWSeaRUQrdJBBTDG90ZXxqWScKHqacFIELCc6TTFrgUrMfx55ZiJ+z8xATMIB2Gl/xpiBDRERBSEQuhyX3HrrrX4dTNM0bLZoa4oEK1ECpKPWqDcT3RSzEhhibihhZxNKMApUvEpyvTagrqUQGFaYWQTVOfu72UGYhNkrWOh4DlREOGlHMk66pyetqgPqXqglOk0xS12vZgsn+agJpWgdtDswv00AFcO/zA5CECIMr8LJs88+G844BMEQ9BIRs+U7z4HKEBPjMINgs3504aQREU4CRUNdh2KzA0F9D54GaoBoW8xeN0keYnIcns+j802Mw2o48T7rKxknip6YcQLqXog24cQqQm4Syjg+GjMR21B/9zSzA0G1C8tRvoBmZ6YKQqTgVTiZOHFiOOMQBENwAN83Owg6OpafZWYgJuBACVf5Ae7vmXEiBMZO4DDmzyxCx0G7FTqL4cRB4CbJRiKmmJ3ThFoqujNkVR1FTxVOSrBGXyGcOFDPopFmB0L0GlZXo7LYzBavoKOIONnMQAQhgog2+wWhB3MYlX5rhcFilutfNHYM9CybQN3y9f3EIDZwrDKzCB0HKtGGVVKyIXoHKl3RlSgQhypRkVKdnlWqMwAlJEbjvVCCWprcCtezEGUO67uLYs/ACqbtOnoM0dg2C0KgiHAi9BisNFiE6DXeCtZbw7NURwgMq5gkgzLBSyL6Ome6SbIVrgG0P480swOxEE66HkSmIhknPS3jxEb0iojBrHZnNEWo79Y2k+MIN1Zqm4ehnn/ReC8IQqCIcCL0GKzUIEF0ds5aUQZ0wVwDPeNESnUCRzdJHmh2IKjU8AKi714oQ4kUVnoe6aaYgqKJrkWBFEQ46U5cikSicaWvFpRxu5WeRxB97UIJ0BfINjsQVFbdSKLvXhCEYPBJONmxQ7pagvUpAeJRKroVKAT2AXvNDiSMVKBm2oOZ1RKPk+DRS0TMNknWicaBipVSsiF6Bypd0V02hWScdC8uRSJFKB+oQ2YHEka2oJYkt4pw4ul9FU1YZQEDnWjNjBaEQOlyOWKdH/3oRyxcuJBzzz031PEIBvFnYKnZQYSZLahOQbzZgbjQOyjj6Hkzdt7Y4no1IuPEGWQsOjcAbxp0rEhhC3CZ2UF4UAS8BOThY6PTAzhEcCbJRqPfk7MIrbh8HvBQCI9vJL4IJ+H2OPkHcBNq9Y1QkYi6H0f7sG1PK9WB9nuhL+aveBUu9IkIqwgnfYD+wK+BJ02OJZxsAa4wOwgPioDXUd43Or8B5pgTjiBYHp/6sM3NzfTq1SvUsQgG8ifUTNFMk+MIJycB55gdhAczgJ+iTGujhZNQndETgziGkR4nTuCPqFKREww4XqQwFbjW7CA8+BGqlr3Z5DjCzWgCN0k2mv7A3UB5CM/xEfA2kSOcdGd8akapzl+BzcCPQ3iOF4BXiF7h5BTgGuCI2YGEmSxgrNlBeHAf8J7ZQYSZk4GrzQ7Cg8tQK/20eLzXz6RYBCES8Ek4ue6661i0aBHz589n2LBhZGZmHrPNqFGjDA9OCIwjwDrgZmCJybFEMynAY2YHEYEYWaqzFiXALALON+B4QmAMB/5idhBRjg11H4SSy4msgZAvGSfhLOdoA/4HLEBljYaKUmC1j9v2tFV1ANKAx80OQuBy1z/BPEaixFpBEHzDJ+Hk7rvvBmDZsmUA2Gzt1XmapmGz2XA4pErOKnyMMumcYXYgghAARi5HvBo1YJxuwLEEQeiaJCLHm6gNNcvanXASToe3b4H9hL7tngH8AXWtusuI6okZJ4IgCIIQCD4JJ88++2yo4xAMZDVqJYuTzA5EEALAyIyT1agSnb4GHEsQhK6JJOGkyfVqpVIdPQskHMLJg8BndF/OK8KJIAiCICh8Ek4mTpzo/n9LSwsHDhygT58+xMVFi81fZLEa5TGRZnYgghAARmWcNKOyr6xkxCYIPZlkjMkUCwe6+bSVzGFXowyU80J8npNRmXir6Vo4aUVl5ohwIgiCIAg+LkcMsGHDBubPn8/YsWOZMWMGpaWl3HTTTTz88MOhjE/wk3rgc6RMR4hcjDKH/QJ1P8i9IAjhIRlV/tLS3YYWwFfhJFwZJxrwIeF5XmUAY+je50T/jHqax4kgCIIgBIJPwsknn3zCj3+sPN4XLlyIpmkA5Ofn89RTT/HXv4q1kFX4BDXTLoNFIVLRO+nBpvzrgwLxNxGE8GDkilihRi/V6Uo40Ut1tNCHgwPYS/ja7pmo/kJXy777Ii4JgiAIQrTgk3Dy4IMPMnv2bJYvX85ll13mFk6uuuoqrr76al5++eWQBin4zgeoizrV5DgEIVBsGOOVsBo4DsgOOiJBEHxBL7OLBJ8TX7IpUlGiSTiEoHD5m+jMQP1dn3exjS/ikiAIgiBECz4JJ5s2beLcc88FOq6oAzBp0iR27Ain77zQFR8C44BeZgciCEGQTNczod3RAnyEZF4JQjgxckWsUONrqQ6Ex+dkNTAYGBaGc4HyOdHP6w0p1REEQRCEdnwSTjIzM9m8eXOnvysvLyczM9PnE3711VcUFxcDsHHjRqZNm0ZxcTHFxcW88847AKxcuZILL7yQuXPn8v777/t87GinEeWSL4NFIdJJIrjB13qgFrkXBCGcGLkiVqjxdVUdCL3PiYYSMGagMu7CQSZqxbEPuthGSnUEQRAEoR2flsU5//zz+f3vf096ejrTpyvHgNbWVj7++GMeffRR5syZ49PJnnrqKd544w2Sk9W81Hfffcfll1/OggUL3Nvs3buXFStW8Nprr+F0Opk3bx5Tp04lIUHmPLrjM1RHRwaLQqSTTHCDrw9cr+JvIgjhQzJOAmMTsIvwt90zgL+gRKTOeli+iEuCIAiCEC34JJz87Gc/Y9euXdx+++3uUp1LLrkETdOYNWsW119/vU8ns9vtPPLII/zqV78C4Ntvv2Xr1q2sWrWKvLw8brvtNr7++mvGjh1LQkICCQkJ2O12SkpKGD16dJfHdjqdOBwOn+Iwi8bGxpDG+GpWFrasLLLLynC0tYXsPJFMqK+B4BvdXQfbsGHscTpxbN8e0PHfzslhSEICB7ds4WCAMfZ05F4wn552DfampoLdzndbtxLfaG35pCw5GYYMYVdFBQO9XIf9aWmQm8u3W7eihfDveSUjAwYOJKe8HEdTU7fbG8Xw9HTqc3J4bds2xjQcK1WXJCXB0KHsqarCUVsb0lh62r0Qqch1MB+5BoJgXXwSTmJjY7nvvvu48sorWbt2LYcOHSI9PZ3x48dTWFjo88nOOOMMqqur3T+PHj2aH/7whxx//PE88cQTPPbYYxQWFpKenu7eJjU1lVofGuzExESKiop8jsUMHA5HSGP8DvgeMLmgIGTniHRCfQ0E3+juOvQG4hMTKerlv1tPK7ABuBjkWneB3Avm09OuwS7X64ChQ7H6X6VLsvl5eSTV13d6HfRt+oX47ykD+gNnDR8etlIdUOU6C4GKIUO4tJPfH3S9jsjNDfn17Gn3QqQi18F85Bp0jwhLgln4JJwsXLiQGTNmMG3aNC69tLPmNTBmzZpFL9fAaNasWSxevJgJEyZQV9deUVxXV9dBSBE6xwl8DPw/swMRBANIJvB0/6+BQ0jJmiCEm0gs1eluVR0IbamOGf4mOv1QK4+tBm7p5Peyqo4gCIIgtOOTOWx9fT333nsv06ZNY86cOfzhD3/gq6++ci9LHChXXHEFX3/9NQCffPIJo0aNYvTo0axbtw6n08mRI0coLy8nPz8/qPNEA5+jOqsyWBR6AsGYw4Z7WU9BEBSRZA7rj8dJKM1htwDVmPe8moFagaylk9/JqjqCIAiC0I5PGSd//OMfaWlpYd26daxZs4bVq1fz5JNP0qtXL04++WRmzpzJ2Wef7ffJFy1axOLFi4mPjycrK4vFixeTlpZGcXEx8+bNQ9M0Fi5cSGKizHd0hz5YnGZqFIJgDEnAgQD3XQ0MRy3tKQhC+IikjBPPbApvU0DhWFXHbKF3BvAEaiWyE4/6nayqIwiCIAjt+CScAMTFxTFp0iQmTZrEjTfeyMcff8wf/vAH3nrrLd5++22fhZOcnBxWrlwJwKhRo3jppZeO2Wbu3LnMnTvX19AElKfDCCDL5DgEwQiSCHzWej1wkoGxCILgG5GYcZLg8f+jCUfGyQYgDVUyYwaTXa+dCSdSqiMIgiAI7fgknDQ1NfH111+zbt061q1bx/r166mtrSU3N5cLL7yQiRMnhjpOoRvqgD5mByEIBhGMx4ncC4JgDnrGSSQJJ4l0L5yE0uOkDmWGHW5/E50MjziORkp1BEEQBKEdn4ST8ePH09LSwogRI5g4cSLnnXceJ554Iv369Qt1fIKP1NOeViwIkU4wHidyLwiCOegZJ5FUqtOVKBCOUp162gUaM9D/xs7EISnVEQRBEIR2fBJOpk2bxrp166ioqKB3796kpaWRkZFBWloaKSkyRLEC9UiZjtBzCDTjREPNdid3t6EgCIYTqRkn3ohDCSuhzDgxW+iNB2Lp/G/0RVwSBEEQhGjBJ+Hk8ccfB6CkpITPPvuMtWvX8vLLL1NbW0thYSEnnngiN998c0gDFbqmHhksCj2HQD1OGlHiiZkzuIIQreiD8J4inIASNUKdcWL29FMKnV8zyTgRBEEQhHZ8NocFKCwspLCwkIsvvpjPP/+cF198kffee4+NGzeKcGIyZqf7CoKR6KU6Gv7V/uuzpmYPRAQhWgnG2Dmc+JpN0Qs4EsI4rCKcSKmOIAiCIHSNz+aw69ev57PPPuPTTz/lm2++wWazMW7cOH75y18yffr0UMcpdIMVOl+CYBR69lQT/nXaRTgRBHMJxtg5nDhRGTLdCbPhEE4yQ3h8X/AmnMiqOoIgCILQjk/CyYknnkhTUxODBg1i2rRpXHnllUyePFn8TSyECCdCT8JzWdNAhBMpWxMEc4gk4cSXZ0s6cDiEcVghW7S7jBO/UpMFQRAEoYfiU3t44403Mm3aNIYNGxbqeIQA0DjW40T7+mN4e7k5AZnJlLOwzbzA7CgA0Brr4Y93QX0ou90WJL0P/L/F2OIDtxQMdHWOzjJOtH+/COveDziWiMRmg/OvwlY43uxIANB2bIXnH4SWZrNDCS/Djsd28fVmRwGApmnw7DLYsTWk50n6f4tp2FWB9o+nQ3oen4lPhPm3Ycsa2OFtX7PZegEHQhGXi6MnPbTyb+CVx0BrC+FZO5Iy/zbq+vaHxI6Ssy4umbVUcqjQGmrhT3dDfShziSxI70y46l5scfFmRwKA9u5zsP5Ds8MIL7YYuOD/YSsYa3YkAGjV5fDCb6G1pf3NH8zHNvok84ISBAvjk3Dyk5/8hMbGRp5//nnWrl1LbW0tGRkZjB8/nvPPP18yT0ymGWjlqFmrVx+Dtf+FozqLPZqDNbDpa7CIcMJXa+CNpyF7MFikoxJydm5TrzPOhxOmBHwYz4wTf+i0VOfPi6FmBwwcEnA8Ecfe7YANbrGGcMJ/V8K7z0O/HIiNkvnrusOw6hW08/8PW6IFcqAO7IFn74eMLEhOC9lpko8cpKG+DjauDdk5fKb+CBzaByNGw/n/1+FXTnxbLSYdqAhFbC6OyRZ96xn4z0thfV6l7K6mISYO+ud0eN/fUsmIYf2H8MafISYG+tvNjiY8NDXCvl1wyhwommB2NIqn71Vx9eprdiThY0+16g8WPGJ2JIr/vAj/fhEG5LW/N/UH5sUjCBbHpx7svn37KC4uZtu2bRQUFJCZmUl5eTn//Oc/eeaZZ3jhhRfIzDS7Sjd66XSwWLUJJs3Cds9zJkRkDtrT98Arj6K1tmCzwuCsslS9/vFDbL2j4/7Qdm6DH4+Byk1BCSf6MDPYjBOt7rASTa64C9u8GwOOJ9LQfnkeVJaZHUY7lWUwMA/bc1+ZHUnY0N5/HX69AKo3w/ATzA4HKlzPo9uexjZ+ZshOkww05hVge25DyM7hK5qmwXl5nd4LvpbqhMPjpIOsVlkKRROwPfrfEJ61IylffkiN54yzC1/FpYijcpN6/dtWbGm9zY0lTGhVm2H+BPUcsIBwoh05qMTcq+61TFZeONAWzrZW21xRBoOGYXt2ndmRCEJEEOPLRvfffz+NjY28+eab/O1vf+Ppp5/m73//O2+++SYtLS08+OCDoY5T6IJjBostzbB9C+TmmxWSOdjzVSlAiFPRfaZqE/TOjBrRBIB+uZCQ1C4aBYhhpTpVm9WrfWRQ8UQcuflQtUkNHK1AZWl0Po9AdUytgN5ZD/G9oK+IZQVsNhvkjuz0edSEb6JAL0LncdKG+qyOmfSwh/deSYmLo55jnxW+iksRR2UpZA6IGtEEgEFDVKZD1SazI1G4n0fR1i4UQGWpddrmqk2QF2XXQBCCwCfhZPXq1dxwww0MHz68w/vDhw/n5z//OR988EEoYhN85JjB4vYtql4x2h6G7oFKcIN2w6goi7pOgS02FnJHBN05CzbjxD2Dqw+Y7AVBxRNx2PNVmULNTrMjQWtrUwJWbrSJVyNUKUCVVYSTUkhJh6xBIT1NMhZbjtieb0jGSSgcR/TPyT3pUXtIlVOE+XmVEp9IXWzsMYM5X8WliKOyLOqeR7bYOBg8POhJDcNwt83R1UfCPhKOHFSl5SajtbaojMho6x8JQhD4JJwA9OrVy+v7DQ2W6iZFHfqn7x4s6h31aHsY6h0hy8yolEbfNQD3jEowBOtx4vb7qdykPDUGDQ0qnohDF02tcC/srlR17HnRdS/YEpJU3bhV0rIrN0HuSJWFEUKSsKBwsm+XEiU88Ec4Aag1Oi46mfQwaRY+OSmFhvhEJdp40BMzTjRNU59zVLbN+dbKgItP7OitEQ3o3zsrCOo7tqks7SgTEQUhGHwSTk444QSeffZZWltbO7zf0tLC8uXLOeEEC9RvRzHHdL70hjF3hAnRmIcttZeaTbVAxol2sAYO74++EhFQnbNdlWjOwIdPhpnDVpbC4GGWWUUgbOhlMVaYXdT9BKJtZhHUdbDA8whwDRZDfw0sKZzAMSKir8an6a7XUPicHJ1xYtYsfEpqOvWJyceIfD1ROGH/bmXcHG0ZuaC+Vzu3oTU5u9821FSWQc5wlaUaTViphFMXb6JsUkMQgsEnB82bbrqJSy65hFmzZnHaaaeRlZVFTU0Nq1atoqamhmeeeSbUcQpdcKyvQxlkD8aWku5ljx6MfaQ1ZngrozTrB1THQNPUQGXE6IAOoWdP+du9O2YgYoJfgCXIHACpvazROYvWlGxQg7MvP0BrbTV1gOA2SQ7DNUjGOh4nQPsz+ChTTF+NT/WMk8PAYINDO7a0cBPEJ8DA8M7Cp6T1pj4xGa2yFNu4Ge73e+SqOvrzKNo8l0Dd/22tsGMLDCkyN5bKMsgfY24MZpA9GJJSrTGpEaWTrIIQDD5lnBQWFvLiiy8yatQo3nrrLf7whz/w9ttvM2rUKF588UXGjrXGeuTRSqfpvtGaeme3iCmmLpxE66wWtGcaBECwGSdJeJgkR6F4ZbPZXPeCBYSTaDRJ1rHnQ7NTlSuZSVX4sn4s53HixRTT12yKUGacHFtaWAo5I8K+KlxKchqtsXE0V2/p8H6PXFWnwpxyKEvgbpvNbRe0pkbYVRGV18AWE6OECktM8OkmyRlmRyIIEYPPrXNRURGPPGKRdceFDnjOWqn63U1w5jwzQzIPe0G7KWZ2aE0Qu6SyDJJSIDvHvBjMIme4MsUMYkYlGHPYZJQirOkmyVHYOQPU3/3Fe2ZHoQYq0Ti7Cx0Nq8302akMX0q21Up1bLFxaJ2YYvrrcRKKlXU6nfQIMEsvGFJdvjd1uys6fCZOjloquSdQVeYySR5odiThR59QM3vQXrUZ2tqiuF0ogG8+NjsKJSZH6ySrIARIlxknJSUlLFq0iKuuuorFixdTUlISrrgEP+gwa1WzAxpqo3KWHWj3FDG7Y1BZpmYOY3z2X+4xGGGKGUzGidl+AZYht3NTzLBTWRqdmVfg4TVjgedRbBwMHBLyU+kZJxZZbFNhzz8mA86f5YgBQnEXeQonWpMTdm4zZSCjiyMNe7Z3eL9nluqUhcUk2YrYklOhX05Q2aCGoGd/RWu7kJcPe6rRGkJhOe0bUW2SLAhB4HVU98knn3DRRRexcuVKNm7cyIsvvsicOXP4+9//HsbwBF/oMGsVzWmo0N4ImF0/WhV9SxF3ILfzJUB9JZiMk3bhRC9PiNIZlTzzB+3tJsnReS/YevWBjGzzhZOKUhg8PCwmycko0aQ55Gfyg7x82LEVrbnJ/ZaVSnWSAbaXq1l4E4wa9Wdmff2RDkJrjyzVqYxS3yudvOBXvQuaylKw2SAnSr013CtAbjYvhmg2SRaEIPAqnDz++OMUFhby3nvvsWbNGj755BOmTp3Kb3/723DGJ/hAB+HEPcsepSpy3/7KFNPEGRWtsR52V0V55ywfqsvRjlqJy1f0jJPghJNSZZKcnBZQDBGPW0Q0cdAezSbJOnnBiYiGULUpbAJioPduSLEXKFPM7eXut/w1hw25x4n+HTEh48QtnCSldPiu9rRVdcJpkmxZckcqH7i2NvNiqCiDAXZsiT2uEMw3LNE2R7FJsiAEgVfhpKSkhGuvvZYBAwYA0Lt3b2666SZqamrYsWNH2AIUuqfDrFVlGaT1hj7ZJkZkHm5TTDNnVKo2qVVlorlzFqQpZhwQS+AeJ0DYll+1LAPz1AodlhBOojTrB1THtKrMNMNqrbkprCbJgWaLhRRdjPBYZcrf5YhD7nGiL1ttpnBy1JLEvpYzRQz6DH80twv2Amish73bu982VFRFeYnI4GEQE2tu2xzt2emCECBehZO6ujr69u3b4b28vDw0TePw4VB0IYRAqQficTn9umoWo7F+141rRsU0wriChWXxNMUMkEBMJvWME7dJchQP2G2xcdCJKWZYqSxVJsn9cs2LwWzs+XDkIBzYa875d2xV2RZheh4F6k8UUtyp8f5nUyS6/oVcOKnaBP1zsSWldLFHaNDP2JCS1qHt7GkZJ1HvewWml3Bqra1KwIria2CLT1Bm4aZO8EWxSbIgBIFX4aStre2YwXdcnFqEpzXA9HshNNTjuZxhWVQPFgE1k2GmKWZFqVpVZvBwc85vBfTZpCAErECWNXWX6uzdDo110T2rBa7sKzMzTjZFrUmyG92zwqylocM8WLSicGJLToX+uQGXoaQT2lIdd5mtSc8rd8bJoGEdBnM9TzhxmSSbucKV2ZjtA7e7CpoapUTEnt8hAy7suDJyo3qSVRACIIp7sz0H9yz7kYNwYI8MFvUZFbOyTirLYOAQbAk9qsvpF7b0DOjTL+iMk4A9TiolDRVQf//ObWrFDjOoLDXF7NJS2M2d4Q23SbIlS3VAtYuu55GGfyvG9CJ0wokNSGhrM3UWvl04GdphMNcjhZMwmSRblt6ZkN7HvEG7LthEuylpXgHs2ILWYpKNdkWZLEUsCAEQ19Uv33rrLdatW+f+Wc9CefPNN1m7dq37fZvNxvz580MWpNA1xwwWo/1hqAtHFaVQNCH85492bw2dILMdRDgxAHu+WqljxxYYUhTWU2sNdWp2cfZlYT2v5cgeDEmpQYmIQRFmk2T30rZhOZsf2EfCNx+jtbXR7MqA8kc4CVWpTgpg21MFzgbzhZOBeUpobW7CFp/Q8zxOKstgSKHZUZiKzWZDyyswMQNO76dGeducOxJamk1ZglyrOwz7dsqkhiAEQJfCybPPPtvp+8uXL+/wswgn5uI2xHQr+VH+MDTRFFNrbVErN0w6Peznthz2fHj/NTRNCygdNNBSHfcKFWm9VdZLNOP2mikLu3DizviK8g6yzWZDs5vouxRmIdeKpTqAhylmNc7+dsC/Up1QCiftWUHmCid12YPdqw+1DSmimZ6TceI2SZ5+ntmhmI89Hz5+x5xzV5ZBRha23n2737Ynk+cxwRfuyU7x4ROEgPEqnJSUlIQzDiEIGtAHi5uUYDAgz+SIzMUWG4c2eLg5Myo7K6C5SRokUJ9B7SFlitnXfwEjGHNYMUl2kTNCvZpxL+ids2hPyQZ1L3z9cdhP6zZJPuvHYTundTNO2kXEJpdw4ms2RS9gZwhCan9emTvp4TaHzVSrKFJZRpNLaO0pwgnbtyhRKNozckF9Bgdr0A7tD7+AUbVJrgF4tM0mCOp69mO0l/ULQgCIx0kPoEPGSc4IbLGxJkdkAcwy3hLX/naCNKFLRtXY+0NH4USuQWemmGGj0mWSPGhY+M9tNez5sKcaraE2vOfVTZLDOCC3rseJ7jVT6n6umG0O24DedpdB70xsvTNDcJbu0a9ZfUa2+k9lGU2u93pMqY4uHkd7Ri60fwZhNojVNE0N2mXAji2tN2QONKeEs7IM4uJh0JDwn1sQIpywCydfffUVxcXFAFRUVHDppZcyb9487r77btra2gBYuXIlF154IXPnzuX9998Pd4gRh3uwWLVJBos69nzYuTX8ppgV4q3hRjejDHDQ7m/GSStKaEl2NrhMkuUaAC4R0YTOWUUZDBwa1SbJbtyD9jDPLprge6WX6lhNOLFlZEGvvlC5yS2c+JNxEtJSnYoyU0va4lCfRUN8glto9Vdcsjzue2GEuXFYAbOeR4f2wZEDkoWok2fSqne6SXJsl24NgiB0QliFk6eeeoo77rgDp1M1yffddx833HADL7zwApqmsWrVKvbu3cuKFSt46aWX+POf/8xDDz1EU1NTN0eObuqBlNZWZTIlg0WFboq5vTy8560qg8wBajYh2tFNMQOc1fLXHFYXWZIP7FX/kXtBYc+H6s1oLmE6bFRtkqXRddzZV2HuJJuQkm3ZUh1wGVaXurMpzM44aZ/0KDN9MJkC1IFbaPVXXLI8FWXQLydsJsmWpl8uJCSFf0li/XxR7nvlxp4PVZtUJk44kYxcQQiYsAondrudRx55xP3zxo0bmThxIgDTp0/n448/5uuvv2bs2LEkJCSQnp6O3W4Xv5VuqAeS648ooUBSIBVmzvBK/S6gTDGxjwy4ZMpf4aTe9Zq6z+VGICnZCg9TzHChtbZA9WZ5HukMGgoxseEXTqo2QXoG9MkO2yktW6oD7pW+/M2m6IV6vrQYHE49kNLcpGbiTW43UnCJXS6h1ekSWntOxkmpDBZd2GJjVeZN2IVcvVxKrgOgBKT6I7BvV9hOqTU3wY6tci8IQoD4lKfV1tZGTEzwGssZZ5xBdXV7591ztY3U1FSOHDlCbW0t6enp7m1SU1Opre2+LtzpdOJwOIKOMZQ0NjaGJMYjI0ei7awEYGtrHE6Lfw7hwNbURr7NRs26j9jXr72BCNU1AEDTGLnVweETz2C3XAMABvYeQMqmLyk/6vPw5To0DRzI4dRUHJs3+3Su7fHxMGIEbVtKaYtLoOxAHRyS65CsJZIHVH30X+qOm+J+P5T3QvyeKoa3NLMzPp1Dci8AMDR7ME0bv2C7x+cR0ucRYHesh+xcKsM4+VBns0FhIdt278axf3/YzusLfRN70+/QPsq++xqOG82eqioctbXdXoeGPn1gwAC+KC2lt4GZWweGDqWXK0OuypZCnYn3Stzw4exsaGBnfC8GNtZTtmEtjJtMzfbtOA6HolCpIyG9F9rayK8o5eBJ57JHnkcADOo9gKTyjWwJoG0OlH5ffUpGQhJlNUdgv1yHFJKwA5Uf/Yf6ghPd74fyGiTs3MKwtlZ2xKVxWO4FQfAbn4STadOmce6553LeeedRWFho2Mk9xZi6ujp69epFWloadXV1Hd73FFK8kZiYSFFRmJfb9BOHwxGSGJ1AdnMj2GwMnX46tsTkbveJBrT+uWQ3HKCfx2ceqmsAoO3bBY119PneJPpa/LsYLrQTJsDn71KYl4Mtpf0+9uU69Ef5lvh6vfRk1361+4jJHUHRqOMDC7qHoQ3Mht9BrtaALVz3woGtAAycNINBci8AoA0/nsTqzR0+81BeAwCtphomnxHWtlHPyujdvz9F/fuH7by+oB2eBn97hAGJqu8xPDeXIrq/DnouyMCCAoxcs64VyGxrBiD35NOwmbgiXgYQn5DAwEkz4KX7GeCq0Rk2eDBFgweH/PwhfR7troKmRvqOmUymPI8AV9u8fhWFQ/OwJaW43w/pdaitgbwCikaNCsnxIw2tXx94BOw0hq9t3quyfgZNnsng/Mi9F6w+US70XHxKIykuLuZ///sfF1xwAeeccw5PP/00u3fvDvrkxx13HJ999hkAH374IRMmTGD06NGsW7cOp9PJkSNHKC8vJz9fUsq8oeFK992/C/rnimjiSW5+eGt43SvqSHmCGz0dtMq3rBFPAvY42blNynQ88DTFDBv6uSQduJ28Ati+Ba2lOSyn0w4fgIN7w34vxLn+WdbjBHDu3QH453ECxvuc1AMph/ZBUorynTCRFFc8+mfUtLsK6CEeJ5Vi2n4MufmgaeH1gauQcqkO9O0Pqb3CuwKkmCQLQlD4JJxcffXVvPXWW7z++uucfPLJrFixglNOOYX58+fz97//vUOGiD/cfPPNPPLII1x88cU0NzdzxhlnkJ2dTXFxMfPmzeMnP/kJCxcuJDGxx1TZGo4TJZ4k790hA/ajsedDVRhNMWWweCxBLEmcjH+DL93jJGVXhVyDo7EXhF9EFJPkjtjzobVFmXiHgyrzBov+3rtho78dEpJwunyQ/PE4AeNX1qkHUg7sgZwR2Awohw4GXTjRhVZnjX+fkaVxDxalXXCjPxfCNGjXGmphT7W0zR4oH7j89md1OKjcJCbJghAEfq1FVVRURFFREb/61a/48ssvefjhh7n11lu55557mDVrFpdddhnHH991enxOTg4rV64EYOjQoTz33HPHbDN37lzmzp3rT2hRi9sQc3eVNEhHY88HZwPsqYJwpEBXlkJKOmQNDP25IoXBwyA2LqBshySgGZXOHuvD9m7hpKFOOshHYx8Ja94O3/nEtf9Y3AOV0vAYgbpX1An/dfB3KfFwYYuJQcsdQZPLjNGf5YghRMJJzU5L3CvJwCH9B3u+3+KSpaksC7tJsuXJHQExMeEbtOtZpxb4rlsKez588V74zicmyYIQFH5PcXz++ecsWrSI66+/nvXr1zN16lQWLlzIwYMHmTt3Ln/9619DEafgBfdgsf6wZJwcTZhnVKjcBLkj3YbHAtji4tWKIgFmnADuFTC6w30vNDaIa//R2PPh0D60Q/tCfipN01yrS8k16IAullSFqWSqsgziE1WWRZhJxqKr6gDYC3AeqgHMLdVpAZqA5AN7LDGQcZfqANjzadq/B+ghpTpVZWAvkLbZA1tCkppQCtfKOu5yKemndsBeAPt2odUe6n7bINE0TQlYFnjeCEKk4pNwsnHjRu6//35mzpxJcXExX375JQsWLOCDDz7g6aef5rLLLuNPf/oT5557Lo8//nioYxY8kMFiF7j9NcLYMZAG6Vjs+QFnnIDvM9fue6GpAXKkfrcD7pKpMNwL+3dD3WF5Hh2FLbUXZA1qzwQJNZWbIGe4Wno0zFi2VAdUNkWdkkDMzDjRP5/UxnpLtBsdhJPckTiblGTdIzJOKspMX+7ZkuTmh/F5VKaWZB88LDznixTsru9lONrmPdXQWGeJ540gRCo+lerMmTOHvn37cvbZZ3P++edz3HHHdbpdYWEhBw8eNDI+oRvcg0Vng8zwHoUtIwutV9+wNEha3WGo2SENUmfYC+DTf6G1NKsMFB/RM058nbnW74Xk3n3FJPloPDtnJ0zpettgEZNk79jzwzjDWwoFY8NzrqPw19g5rNjzadpTDZjrcdI+6WEd4cTtVpdXgLNsPRD5GSdmmSRHBHn58OUHaK2toRdYK0th0FBs8ZH+jTIYz0mN407settgEZNkQQgan4STJ554gunTpxPbzYN1/vz5zJ8/34i4BB9xDxbjE7D17mtqLJbEXhCegUqVGMN6xT5SmWLu2OrX56NnnPgrnKRmhn7pzIjDZYoZltnFCumcecU+Ev79EpqmhbRsQGtqhF0VMOvikJ2jKyyfcfLVR4D/pTohEU6aGi0xC5+CxzWz59MUpwa4EZ9xYqJJsuWx50OzE3ZXqpLaUFK5Sa5BZwzMg/iE8PRTpVxKEILGq3DimTkyduxYjhzpuro3IyPDqJgEP3APFntlmhqHZQmXKabeIMms1rF4zqj40XHSc0Z8HYDVtbVBTAwpA8Lv6WB1dFPM8IiIZcokOXNA6M8VadgLoP4I1OyE7EGhO0/VZrXUqElZiJYWTnKG40xQsqyvokAsSlgw0uPELZwkpym/CZPRS3U0gH65OJNTgR4gnFRIBpxX9M+kojSkwonW2qKWPZ5yZsjOEanYYuPQBg8Pz6p3lWWQ3gcyskJ/LkHooXgVTiZPnuzXjJjD4TAkIME/6jQNbDaSZZDSOfZ8eOdZtEP7sPUOobhUWaZWjxk4JHTniFQCrOH1O+Ok7jAxKekkiL9J59gLwPF56M/jEsjEiLETdOGwsizEwokrA84kn5kkjBUZjMSWkERTn36Af2UovQiNx0myRQYxKSjRxAkkxcbi7Kcy9yJeOHGbJOeaHYn18HwehVLU2LEVWpol48Qb9nwo/yb053GtqCNtsyAEjlfhZOnSpXJzRQD19UcgtRcpoeyERzKe2Q6h9HaoKIXBw/3y8IgWbCnpaNmD/Z5R8Vs4qT1IclwcNruYAHaKfSR88DpaYz22pJTQnaeiDMbPDN3xIxn3QKU0tJ9RZSnYbKaZJFva4wRwupaM91c4MVIMqmttgdg4UjKssUSu/kSoR12/piwlnES8I0XlJsgdYYpJstWxpWeg9ekX+kxE8dboGvtI+OhNtCYntoQQSpWVZTDlrNAdXxCiAK/CyYUXXuj+/29+8xtmz57N8ccfH5agBN+p378bUnuRbMKSkxFBuEwxqzbBkMLQHT/SyR0ZcMaJz6vq1B0hNSlFUrK9YS9Q5RvVm2HE6JCcQqs9BPt2SgfZG337Q2qvgFaZ8ouKMhhgN80k2dKlOoCzb3/iWpqJscWAjwPqdAz2ODmwF7IGkuISccxGF07qgL6AM7M/AAnOBohks20TTZIjArv/bbPf6MeXlY06x14AbW2qnGlo54tvBIt2aD8crJG2WRCCxKfliF9++WUOHzayyyAYRf2hfQCkDhpibiBWpb9ddfpCaIqpNTfB9i0yYO+KPLUksaZpPu/ir8dJg7OelCanmCR7w53tEMJBe/XmjucSOmCz2VyG1SGuZ68qM/V5lIS1hZOmjCwSmpuUga6PGF2qU1+zE4CUfjkGHjVwdOFEv27Ovi7hpCrEIl8I0ZwN6hrL88g7rueRP22z31SWQeZAbGm9Q3eOSEYXlELZNotJsiAYgk/CyZgxY/jPf/5Da2trqOMR/KT+8AEAUrKkVKczbDExkDuiveY/FGzfAm2tMpvSFfYCaKhVSzb7iN+lOk1NpGhtfocWNeQMh5iY0A7a3UaM0jnzSl5olyTWWluVOayJ1yAZi5fq9M4ksbnJr+tgdKlO/YE9ACRbxMzas1QHoKl3lsrKCXV2VCipLldZdjKp4Z3ckVB7CFzfx5BQWdae/Ssci1s4CWXbLMKJIBiBT8sRDx48mFdeeYV33nmHvLw8+vbtOKNrs9l44oknQhKg0DX19aorlxLjkwYWndgL4Lu1oTu+3tjJijre0TsGFWWQ7dtywf4IJ5qmUd/aTAriy+QNW0IS2oC80KZlV5ZBXLyYJHdFbj68+zxa7cHQHH93FTQ1mraiDkRAqU56HxJqD/pliml4qc5hlS2altrLwKMGztHCibNXHxKbGsOz2keocHtryKDdK3kePnCuLCMj0TRNTVx935yl0SMBW3IqWv/c0LbNVbpJsjWEWkGIVHwabW/dupWxY8eSn59PYmIidXV1Hf7V1taGOk7BC/UNtSQ1N/l2IaOV3JGwuwqtsb77bQNBz2bJldVcvOLunPneCferVOfQPhpi40iO9UkLjl7soc12oLIMBg0Tk+SuCHXJlFvINU840c1hQ5j8HxRNCYkktrb49TwyvFTnyCGgXbAwm2MyTmLjSGhtDc8S5qHCZJPkiEB/HoUqK3ffLqg7LJkO3WEvCH3bLCbJghA0Po0yVqxYEeo4hADQGuqob20huU1KqLokz8MUkxAM6CrKoF8OtuQ044/dU+jTz2WK6XvHwK9SncpS6hOTyQilI31PwF4A6z5Q5RyhoLIMhhaF5tg9Bc+VdfLGG3989yy7eRlwySjRpAlrLmfbBCRq+CVeGZlxomka9Q2ubFGDjhksR3ucqM+oLcKFE3NNkiOC7MGQlBo6Hzj9+2OikBsR2EfC12vQ2kJUblxZBgXjQnNsQYgifBJOduzo3pdg0CDx2Ag7VZtoSEwm1arTelbBPVApg4GjjD9+ZanMpnSDzWZD83NGxa+Mk4oy6kdNZJB0kLsmLx+anX6ZYvqK1twEO7bCjPMNP3aPYmAexCcowTVUwklGNrZefYw/to943rtWFE6cQKLL78dXU8xeQLO+b7AB7N9NvS2G2LY24i1SZqtfM3epDq6/s7ocrbU1MmeqK801SY4EVNscwpV19KwuE0sHIwJ7ATgbYG+14Yd2myTPknIpQQgWn4STU089Va0G0AUOh8OQgAQ/cAknKbHW6HhZlsEuU8yKUsOFE62tTaW4Hj/Z0OP2SOwj4fNVPm+uD058zjgZP5MUEU66Ru+8VpRCnyHGHltMkn3CFhuHNnh46LwjLGDE6K+xc7hxAgmxcS5TzL0+7aM7kRwGsoMNoLKM+qQUkrU2bBYptD3G4wRIjIltF1oHDzMpssDQWluVOez4U8wOxfrY8+Hrj0Nz7MpNKts0c0Bojt9T0CffKsog3TcfOJ8Rk2RBMAyfhJNHH330mPfq6+v54osvWLVqFffff7/hgQk+UFlKfcF4UmLFT6ArbAmJaAOHqAGF0frG3u3QWC/GsL5gL4B/veCzKWYMkICPg6+qTdSnpJPcjcAb9eR51LMbLZyISbLv2POh/BvDD6tpmroOMy4w/Nj+oMuXVhZOEuMT1A+VpZCY1e0+hgsnicmkWuh5dYzHCS5xCZTQGmHCidskWbJBu8deAP9didYQAr/CylLIHdnt5GvU41nCOcpg4cRdvin3giAEi0/Cyfe///1O3z/33HPJysri+eef5+STTzY0MMEHKsqoP3EWyRZJ9bU09vzQmJ/px5QGqXs8Z1Ri0n3aRTeZ7JaKUuqTUkgNNLYowZaWgda3v+qcjZ5l7MHFJNl37Pnw0ZvYmpuMPe7BGjhy0PTnkV9ldibgBBITXHkxlWUw0nfhxJAliStLaRg1iRSbddpuXTipc712EJeqNgFnhT+oYNCFXGmbu0fPUKvcRHu+mEFUlknWjw/YMrLQevVV12DUqcYeXDdJlrZZEIIm6FZ78uTJfPrpp0bEIvhLZRn16RkyWPSF3HxlDtvaYuxxK6R+12f0TIQq/3xOjh58NQCe9mlaQx3sqaY+PtEyRouWxp6vxCujEZNk37HnQ1sb8XurjD1ulTVmFvWhl1WFkyYgIT4BktN8LpnSpV5DDGIrN1GfkU2KhWbhO/U4iY1Txt6RuCSxBUySIwb9MzLY50SrPaRW1ZFr4Bv2gtDca5WbYEAetgSDRTFBiEKCFk7eeecd0tN9mz0WjENrbYHt5dSn9pLBoi/k5UNzE/H7dhp73KpNkJ4BfYJO3u75DLC3m2L6yNEZJy2omdGbPDeq2kRzbBzNsbFyL/iCPV91znw0xfQZMUn2HdfnlLjbYJPeCmsIJxFRqmOzqZl2H1fWMTbjpIz63n3dn5MViEE9bzuU6kDohNZQYwGT5Ihh8DCIjTPeINadkSu+Vz4RKpPeylIRrwTBIHwq1TnnnHOOeU/TNGpqajh06BALFy40PDChG3Zsg5Zm6pNTLdX5siyujJDEXduMPa6rQZL63e6xxcah5YzwK+PkaOFkq+v1j8BD+puVZTS4TGFFOPEBez7UHSb2yH7DDuk2ST5himHH7NHkjgSbjYRQPI+SUtQSoyZi9YwTJx6iwIaPfNrHqIwTre4w1OygPs162aIpdMw46QPqM3r/NTRNi6x2zgImyZGCLS4ebdBQ9fw4ycAD6xm5Mmj3jdyRcGgfsT76wPmC1toKVZthgsHlP4IQpfgknIwaNarTBjMtLY3p06czbdo0wwMTusGVzlefkCSDRV9wmWImhGKGd8qZxh6zJ2PPh81f+7z50aU637leMz03qiyj3lUeIiKiD3QQEQ3yptJNkiXjxCdsSSlo/XONF06qNikjRpN9r6yecdKEa9Wu3Hz4z8vENNZ1s0dHc9igqNoMQH1KesfnmAU4Wjhxi0u1h+DAHujb37TY/MEqJskRhT0/NBkncfFqCXahe1zlzKqfatAkxO5KtTKWrHYnCIbgk3CybNmyUMch+Isrvbg+Lk4Giz6gm2Im7N5m2DG1wwfg4F4ZLPqDPR/+9wa2ZqdPmx+dcaIvet7ByrGylHpXp8BqM7iWRBcRjRy0i2u//+Tmk7hzm7HHrCiFE4ycMg6MSMg4SQQPQb0SmNDlPrpwcijYk+uTHonJ5AZ7LINJof2aucUlT/+LCBFOrGKSHFHYC+DTfxnrA1dZCoOHY4uTlR99wh7CtllWuxMEQ/BJOAFobGzktddeY+3atdTW1pKRkcH48eM5//zzSUmRnIewU1kKmQOot8XIYNFXckeSuMvAjBOLGDFGFC5TzIQ9VcCYbjf3lnHSQXap3ET9WJX1Jk8iH8gaBMlpxpatVUpKtt/Y80nY8D+0tjZDMkS0hlrYU22J8oSIWFUH3N9XXwT1VMCGAR4nlZsgNo76+ATLPa+OzjhRn5G+4koZjImQ7GJZGt1/8vKhtYWEvdXACcYcs3ITDD3OmGNFA/3tkJBkrPeVmCQLgqH41Fvbt28fF154IUuWLKGyshKbzUZ5eTm//vWvueCCC9i3b1+o44xoNE1D+9UFZL673LiDVpbRlldAIzJY9Jm8AhJ2b1NpvEYg9bv+Y/evZMpbxsle16vbJNlVfiL3QvfYXKaYhpatVbpMkjO6X9ZVcJFXQEyzE/YYtLKOqwTECkKunnFi5VKdBIBBQyE2zqcZXhvK5yR44UTNwtfbbJbLFu1UOMke7NfqQ5ZAHyxKeYLv5Opt8zZDDqc1N8GOrZZ4HkUKtpgYyB1hbMZJRakySU7PMO6YghDF+CSc3H///TQ2NvLmm2/yt7/9jaeffpq///3vvPnmm7S0tPDggw+GOs6Ixmazwf7dJG/71pDjaZoGVZtoGDIKkMGiz9jziW2ohf27jTleZRkkJEF/qyVcW5icEcoU08fOWTLtgy8NKHH9fx+uJYldJskNg4a6txd8wJ5vsHBSCrn5kWUeaTb6oM6oFUssNLMYKRkntrh4GDzM5+yrXhjgceIyLa3HeqWFnsKJLi7Z/Fx9yBJUlkFSKvTLMTuSyMGVWZRgVFbu9nJoaxXhxF/sBYaWlFNZ5i5JFAQheHwSTlavXs0NN9zA8OHDO7w/fPhwfv7zn/PBBx+EIraehZEDlX27oO4w9UMLARks+oxnrbYRVJZBznBssbHGHC8KsCWlQP9cn0umPDNOqoBa4DiUaLIP2v0CBtgBERF9Jjef+IN70OoNWVxVDaqkg+wfehmBUTP5lWUQE6uWFjUZKwsnGh7ZFAC5vmdf9SK4jBN9Fl6zF1CP9Z5XKYBuk9vhMwqFcWgoqSyD3BEi5PqBLSUdsgaRaNSgXXyvAsOeT/z+XWjO4J+eapK1zBJiuiD0FHwurO7Vq5fX9xsarNg9shj2fOJrdqA1GZC87PLWqHc1SFabtbIsnrXaRlApDVJA2At8TkVNon3wpZfpTHe97oV2k2TX8qtWG4hYFn0GyoBZZLdJsvgJ+IWtdyYtaRlq5QkjqCyFQUOxxScYc7wgsLI5bCtKPHF/SvYCEvZWo7U0d7tvOkFmnOzYCq0tNOcV0or1nlee5rAdxaV82LvdOKE11EjbHBj2fOMyTqRcKjDs+dhcWeVBc2CvMkmWayAIhuGTcHLCCSfw7LPP0tra2uH9lpYWli9fzgknGGQk1ZOx52PT2qC6PPhjuVK76werDCCrdb4sS9YgWpNS2k1dg0BzNsCuCksYMUYc9nwS9lSgtbV1u6lnqY5uDDvD9aqEk1LIHEhDspIP5V7wEX1QYcC9ICbJgdPUf4iBpTrWyfqJQQkTVvQ40Y2l3aJAXj62tlYlanRDsBkn7gy5IdbMFk1GlepoePjAgKFCa6hxmyRLeYL/uLyvDPGBqyyDfjnYkmVqzy/sBt5rbtN2uRcEwSh8WlXnpptu4pJLLmHWrFmcdtppZGVlUVNTw6pVq6ipqeGZZ54JdZyRj/7gqtoEw0YFd6zKMkhJp95lxCiDRd+w2Ww09c8j2YiBSnU5aJrMagWCPZ+Y5ibYXQkDh3S5qWepjgO1DLHu0V8D7vpdvS5f7gUfGTQULSYWmxH3gpgkB0xT/zxSvv1f0MfRTZKZcqYBURnD0cbOVkEXTtyiQK4+UCntdoDRC9gRzMkr9UkPVU5ltSGl7nHSghJP2kt1PITWwnFmhOY7ukmyzLL7j72AWGc91OxQpsDBUFkmWYiBkDMCzRaDzYgSTj1rRYQTQTAMn4STwsJCXnzxRR5//HHeeustDh8+TO/evRk/fjxXX301o0YFJwScf/75pKenA5CTk8PVV1/NLbfcgs1mY+TIkdx9993EGLBco6nkjECz2bBVGPEwLAN7Pg2u+l0ZLPpOU/8hJG/5KvgDSf1u4LhnVMp8Ek4aUJ14B1AEZLt+t0dPZ/3+xW7hxGozuFbFFhePMzuHRCM6Z5VlEJ8oJskB4ByQBx//A+3QPmy9MwM/0I6t0NJsqefR0UuJW4Um12u7KOBh0nty1/sGXarjmoVvtGiGnO5xcoy45Fp9yLDsqFAiQm7geGY7BCGcaG1tqm0efZJBgUUPtoREmrIGkWBESXlFqZgkC4LB+CScABQVFfHII48YHoDTqZroFStWuN+7+uqrueGGG5g0aRJ33XUXq1atYtasWYafO5zYklJo6jvAoIdhGYyfKYPFAHD2z4O1/0SrP6LM0AKlqgxsNsgZ3v22Qkc8hZNJp3e5aTLKCLYZVapzESrrBGBPfS3UHQZ7e8aJ1WZwrUzTgCEkGlFHXblJGTGKSbLfNPUfov5TURrcIEMfLFpohtfqGSe6cGJLSacpox/xPpStBb2qTmUZ5I60bIZcCurz0a+b+zOKi0cbPCwyliS2kElyxGH3yL4aPzPw4+ythsZ6Ea8CpKl/njFjhapNYB8pJsmCYCBehZONGzf6daBAs05KSkpoaGhgwYIFtLS0cOONN7Jx40YmTpwIwPTp01mzZk23wonT6cThcHS5jdkMyrbTVvY124KIM6ahlvx9O9mTlEFZdTXk5LB7yxYcTmf3Owsk9h0EwLYP/01j3nHdbO2dQd98QVLfgWzZss2gyKKLEam9qf16LbuO7/peONS3L/Tvz3ubN7N/xAgyd+1i84ED9MrPZ+vu7QBUkkRlTQ1xmZlsLinp8nhCOxlZOaR98xEl336jZpMDZFj5tzTai9hh8eevFWnrMxCAnZ+t5lB8n4CP03fdGvoBZfVttFnkOsQOG8ZupxPH9u1mh9KBbQkJMHw4Ndu34zisZJDB2bm0lH5NRTefnTM7myOZmXxXUoLfQ5G2NvK3lXLwpHPYuHUrDB1KTWUljrq67vcNE7Wu5+0XmzfDiBHs37kTx8GDAAzuM5CEzd+yNUTfr8bGRkP6cIM3riMhazBbNxvgJxdtaBojk1I5/NVn7C6c3v32Xkj97hNygQotgQaLPI8iiT5ZOaSUrKVs47dKBAyQ4eUbqR8xhp1yDQTBMLz2lufMmeOTSqlpGjabLeAGLykpiSuuuIIf/vCHbNu2jf/7v/9zHxMgNTWVI0e6t2NLTEykqKgooBjCxb5Bw+i15msKCwqwBVh6pJWsA6DfhJPJzFHpd8cPG4Z1ErStTblrNZchMU3Ygvi+aAd3wojjLf+dsyp1A4aScXgvfbr5/PJcr4dHjABg5oABFA0YQH+gMSEeAPvJ3yclK4sUkOvhB9vXDsfW1kphegK2ADMVNGcD7NtBwuwf01s+e79xtLVBYjIDmw8zKJjn0T8OQOYACsadaGB0wdELSEhMpMjLinxmoVvcDx08mKLBqhxh36BhpK/9J4WFhV32e4ahMuDyior8zm7TdldBUwN9x0ym/9ChABTY7Vjprhnies10PW+HDBxI0UAl7mmjJsC3aygcOQJbXLzh53Y4HIY8v7UD0jYHQ/2AofQ5spe+wTyPvnsfgLxpp2PLyOpma+FodgweQUxLM4UZKdgCzJzSGmrhwG56n3AiGT3wXrD6RLnQc/EqnDz77LMdfm5tbeXyyy/nnnvuYair0TeCoUOHkpeXh81mY+jQoWRkZHTIdqmrq/O6FHKk4eyfB84Gn0wxveKu3xVDzEBoys5Rs+tBpEFqra3KHHb8KQZGFl34aoqpl6Gtd73qzX82sFdrg9RekDmAeqRkzV/cZSJVQZj4VW8Wk+RgiImB3BHBr6BQZZ0VdXSs6nFyzKo6qLI1Gmph7/Yu/QD04s4jBFAWqJfFeZTqWK20UO9LHHS9dljY2j4SWluUn47Fvms6Wksz7NgCU2ebHUrE4hyQR3LZF8EdpKIM0vuIaBIgzgFD1H8qSgMvOdNNki16rwpCpOJVONFLZXT0pYiPP/74oM1gPXn11VcpKytj0aJF7N69m9raWqZOncpnn33GpEmT+PDDD5k8ebJh5zOTJv1h6IMpplcqyyAuHgYNFY+TQIiNUw1RMPWju6ugqVEapCBwm2IerOmyc5Xkev0SNWjRhzTZwJbYeMhV9bv1WG8QYnWcA1z5PD6YYnqlUlz7g8ZeAN+tDXh3TdPU82zWJQYGFTwRJZzoImLlpi6FE30K5zAwwN8TdzLpYbW2WxdODrhePT8jtzjqw+pDprFzmzJJlhV1Aqap/xD49G202oPY0jICO0hlqaX8liKNpv6utrmyDE46K7CDiEmyIIQE05equeiiizhy5AiXXnopCxcuZOnSpdx+++088sgjXHzxxTQ3N3PGGWeYHaYhON2dsyAG7ZWlMHg4ttg4y85aWR57fvDXQD+OEBAdRMQu0AcWX6KyTfQk+mxgb3KKu4Ncj2Re+YuWmKIGicEYPlaWKpPk3BHGBRZt5I6E3VVojfXdb9sZ+3ZB/RHLPY/0FbGshr6qjmc2hVtE7OZe0IWTQ4GcuGoTpPWGPv0smy16dMZJB+FEv8eDzY4KJXp7IoP2gGly3wtBXGcLZsBFEm0p6dC3v8oGDRTdJHmQcRUCgiD4sapOqEhISOC3v/3tMe8/99xzJkQTWtrSekNGVpCD9k0wVBUs1KMGkold7iAcg70APnkXraU5sFpt91LE0jkLlCZPEbGL1UT0jJMa4Ace72c1NVKT3gctrwAbIpwETO7I9hKCQKgsgwF52BKSut9W6Jy8AlXuVLUJRn7P//3dg0VrDVSSiYxVdQBa0/sqUaObe8GzVMdvKkvBXuDOkAPrZpwcdL16iku2lHS07MHWXllHvxck4yRg3GUilaVwnP+eSdqh/XCwRq5BsOSODG7576oyGDwMW3xC99sKguAzpmecRB25IwMWTrTmJlVfnKs6yLqvgyw05id6rfb2LYHtX1kGGdnYegW+Cka009ynPySldHsveA7HPe3Nsg/spSUunoND1cpIIpwEiD0fKjepco9AqCwTATFY9JnZQAUsfSCbK8KJL+jCSYfhhM2mvscVvmWcBCScVJS5r7VVs0W7LNWBoPovYaGiFLIGYUvtGb54ZtDcdyDEJwQ+aJeMXGPIK4DK0sDbZo/njSAIxuG3cCLrgQeJvSDwjsf2LdDW2qHzJYPFANAHeoGmQVaWWW52N+KIiYGcEYELJ7urANibVwgg5rCBYs9Xppg1O/zeVWttVQZ0dplZDIrBw9X90M2g3SuVm9wmyVbC6qU6x4gCPpRwenqc+IN2+AAc3OsuIbF6xokunBwzV50XpNAaairL5HkULLFxrrY5iOcRiKAeLLkjoe4wHNjj965aSzNsLxfhRBBCgNdSnauvvrrT95cuXUpaWlqH92w2G0888YSxkfVU7CPh8P5uTTE7RW/IPDpfVut4RQR6xyoAU0xlxFgKMy4wPq5oI68Avv20y008v9/Hefw/e3s5jD6Jvf0GUwBiDhsoeseqogyyB/u37+5KaHZK5yxIbAmJaAOHBC6ou8w6rTapYfWMk06Fk3efQztyEFt6Rqf7BiqcuEV6D0+meNc/K9GlxwmowbAutPr7vAgxml7udrq1TJIjEns+bP46sH0rSyEhCfrnGhtTtKELTxWlyu/EH3ZsVVnV0jYLguF4zTipq6s75t+JJ56IzWY75v3a2tpwxhzZeDrT+4vHcoYgg8VAsSWnqU5fINfgYA0cOSgNkhHoppgNdV438cw48bQ4y976HQA1sUr7FRExQPTvcSD3QkVHIVcIAnt+EKU6ZZb0E4ikVXUAj3vBu4AVsMfJUaalVs0W9alUB4LzXggVNTtdJsnyPAoaez7s3IbWFID0WbUJckdgi401Pq5oQs9qDqRdEB8+QQgZXjNOVqxYEc44ogc926FyE4ye6t++FWXQLwdbspJLrNr5iggCXVmnUgaLhqE36tWbvZpiegonnt2w7E1qNmyv62e5FwKkTz+fTDE7xS3kiogYNPYC+OI9tNYWbLG+e7ZrtYfUqjoW7CAnoTJONKzlw9XZqjpARxFx1MRO901CdZr8zjipLIP4ROhvB6wr9OoxeRVO8jwmfiacEp6gfMU9WLSeiBhx2POhrQ2qy2HYKP/2rSyFgvGhiSuayBoEyWmBTWq4TZJltTtBMBoxhw03/XIhKYXWqjLqwa9/mislW0cGi0EQqCmmuPYbh1tE9C5gdTa40JqbyC5dD4Be/WvVgYjVsfloitkpFaVikmwU9pHQ3AQ7K/zbTxevLOi5pN+Pzi63Cj9eM04G5Clxo4vnkQ1IA3bjX9vdVtlxFr4Ba2aLdrWqDhCc0Bpq3Kak1hMRI44ADas1ZwPsqrTk8yjSUG3zyMCWha4Uk2RBCBUinIQZW0wMWs4Ijp9zLang17+rzr/qGOFEBosBklcAjXWwd7t/+1WUQlKq5eq7IxLdFLOLGRX9+91h/nd7OUnOetJamtmLmtGWsrUgsAe4UkbVJukgG0WgJZwV1h0s6tliVivX8Sac2GJjIWd4t/dCH+Av+Nd2n3fRTyNi0kO/Zt4yToISWkONbpLsrx+EcCw5I9RKU/5e5+rNaml1Cz6PIpKAM6M3STm5IIQI33OCBcPYOWoipf0GcxFwoo/73KZpfHDCFDUr6aIeGBiKAKOBXI9sh345vu+n1+/GiOYYLO2mmN5nVHoBbwEneb7p6khkt7WxF5V634Y1ByIRgb0A3n2+S1PMoxGTZIPxLOE8qetNO1BZppYOHZgXkrCCQRc9G1Big1XwWqoDarCx6asu9/8z8Lkf5/tnWysfFI6j7es17nJDqwonNlRcXkt1QH1XP/tP2GLyGYuaJEcitqQUtP65/g/ade8bGbQbg70A/vMyWv0RbCnp3W+PmCQLQqgR4cQEvj1OzZ9f42zg1ETfckbqdmxl8cAh1A/Z7Z5Zt2rnKyJwz/CWwYRTfd+vsgxOmBKamKIRH2ZUfnD0G67t+8XFsZf2pT3lXggQT1NML94OxyAmyYZiS8tA69vf/4yTqjIYPNwvX5RwoWcvWG1lHSdKIOj0E7Pnw//eQGtqxJaQ1NkWnOL65yu9a3bxQb/BVOSPZZjrPStni6bQ/kztXFzyX2gNC5VlcOJpZkfRc8jN9/95VFmqskhzhocmpmjDU1AvHOfbPjU7lEmyeI8JQkiQaXMT+G5IEQCjtm/xeZ9RVZvQYmIoySt0v9eADBYDpk82pGf4NaOiNdTCnmoZLBqJvQC2l6O1tvi+T2UZ9M8lOyZWhBMjCGRlHTFJNp7cAEqmKsos+zzyzDixEk6UINBpXoKnKaZBjKpWGXXfDWlvu61cWuj5HO0846T71YfCjVZ7EPbvlhIRI7HnQ3U5Wlub7/tUlsGAPK+io+AngZRw6hm8UkYrCCFBhBMT2Ng/h6yDNfRzLanqC6NcZpgbPWZ4JOMkcNy12v50/qo2q1eLDlQikkBMMStUSnY2dBBOrDqDa3l8MMU8BjFJNp489Tzy1bBaa3LCzm2WXUXEqh4nTXgRBCAkooC77fbwxbJy263H1WVWDqhsJ6ugDxYtei9EJHkF4GyA3ZW+71NpXSE3Ihk0FGLj/DOI1e9LEREFISRYL783CvgutTejvvkYlv4f2tL/82mfkbFxxP/oF3wXF+9+z8rpvhGBPR/+uQLttAw/95MGyTD0DKqfjMev9Y3mXHOMcGLVGVyrY4uNRcsdASsfQVv5iO87ikmyseQVQN1h+H4f/+4Fiz6P9LbJiqU6XoWT3JGq1GDx5WiLLzfkfBnAoDN+xHd9+7nfs3LbrQsnXj+jAXmQkAQP/AztgZ8Zdt5C8O973xkWvRciEj2b8Mdj/LsuUi5lGLa4eLTBw+HFh9BefMj3HdN6i0myIIQIEU7CjAZsjInhR5kDofhXPu8XD+S3NLPRJZy0oGbOZLAYBJfcANmDlAu8r2RkS3mCkRSMg58uVX4ZvhITA6dfSjbqHtjtetuqM7gRwXW/gS9X+7fPiNFikmwkp/5QCSceBuDdkpgMJ50VupiCwMqlOt5EAVtiMtqtfzK8DOW4uDg2evwcCRknXj+j2Fi0W/8IWzZ62SIwampqyMrKCvwAffvD4GHdbyf4RtGJcM0SqD3k+z4xsXDGvNDFFI1c/wB89ZF/++SPEZNkQQgRIpyEme3AYeD4nOHY5t/m176jaHfz1zujVu18RQK2nOHwk1vNDiOqscXEwJyfBrRvtutVL/KReyFwbKOnwuipZocR1dh69YEf3WR2GIZhVXPYJryYnrqwnXqR4eccBTyFWv0rhgjPOAFs08+D6ecZet4ah4PsoiJDjykEji02Fi661uwwoh7b2OkwdrrZYQiC4EKmC8OMPkczKoB9jwe2AnWIr4MgHC2cyL0gCNYhEjNOQsUoVJu9DZV1GgnmsF2JS4IgCIIQjYhwEmaCEU70fRyIcCIIRwsnVh2ICEI0YtWME7OEE1DtvxMlnlg1Q86XjBNBEARBiEZEOAkzG4F+QCCVvMd5HEMMMYVoRzJOBMG6WDXjpLtSnVDQWdstwokgCIIgRBYinISZjbR3ovxlBKrD9x3W73wJQqgRjxNBsC6ScdJOBjCYjsKJVYVeKdURBEEQhM4R4SSMaCjRI5AyHVBOvgVExqyVIISaVNT3v9L1s9wLgmAdrJpx4sQcUWAUkZEtql83yTgRBEEQhI6IcBJGdsXFcYTAhRM4tvMlg0Uhmsn2+L9VZ3AFIRrRB95WE06aMEcUGIXyJ6t1/WzVtluPK9bUKARBEATBeohwEkY2J6ruWrDCyTZgr+tnGSwK0YwunCQga6sLgpWIQQkUUqqjGIX6LL5z/Wx14aTV1CgEQRAEwXqIcBJGjBJOAL5wvVo13VcQwoEunFh1ECII0Uwy1ss4MVM4Afjc9WrVZ5YIJ4IgCILQOSKchJHNiYn0BzKDOMbRwolVO1+CEA5EOBEE65KE9TJOzFhVB9pN4XXhxKrZoiKcCIIgCELniHASRjYnJgaVbQIwHDVbZvVZK0EIB7pwYtVBiCBEM5Jx0k4vIJf2ttuq2aJ6n6LF1CgEQRAEwXqIcBImNKA8ISFo4SQWKKS9UyMDRiGaye5+E0EQTCIZ62WcmCWcgMoY1dtuq056SMaJIAiCIHSOCCdhohKoj40NWjiB9nKdWMxJORYEq6ALJ3WmRiEIQmckYb2ME7NKdaCjv5lVJz104aTN1CgEQRAEwXqIcBImNrpejRBO9FrpVsBmwPEEIVIR4UQQrItknHQkkoQTyTgRBEEQhI6IcBImdOHkuC638g0jxBdB6AmIcCII1sVqHidtqFIZK2ScSKmOIAiCIEQWcWYH4I22tjYWLVpEaWkpCQkJ/PrXvyYvL8/ssAJmI5DV0kLfuOA/chFOBEGhCyeSVi4I1iMJ2Gd2EB40uV7Nyjgp8vh/rEkxdIeYwwqCIAhC51g24+S///0vTU1NvPzyy/ziF79g2bJlZocUFBuBEU6nIccaZshRBCHyEXNYQbAuVluO2GzhJN2k8/qD/tlIxokgCIIgdMSywsm6deuYNm0aAGPGjOHbb781OaLA0VDpyq3TZ7L83vtgQwlsKGH5vfcxc8JE989sKGHmhIndbnPahIncfO99vB/kcaJxG/uBBkvFE63b6Nch2OOkbyjhfdc/K/xdkbTNh8+9bKl4onEb/T6wSjxGbzP23vtImDCRtRtK3P/GT5jIXffe5/75rnvvY3yYtnnw3vv43oSJXHxUzB8+93LYPh+rt90/njCR9zeU8FqY47EfaDD9b5dtpI9khW2u+lGxqfEIguAdy5bq1NbWkpaW5v45NjaWlpYW4ryUujidThwOR7jC85tXgKva2nA6ndTV1wMq5ta2NvfPAK0+btO/uZkRTid1ra1BHSfatmlzvVolnmjdps1ju2COU19fT15cHKltbZb4uyJpG03TLBVPNG6j3wdWicfobVKbm4nXNBJb2/MXbJpGXFub+724tjZsYdwmXtNIbmigTtPcMWuaFrbPZ2BzM0ObmqhrabHc9dK/kznNzfRtaaHO9YwIRzxt8gy3xDbSRzJ/GzyeR2bEY+WxlCCYjU3TXL0Hi3Hffffxve99j9mzZwMwffp0PvzwQ6/bOxwOioqKvP7eCkRCjD0duQbWQK6D+cg1MB+5BtZAroP5yDWwBnIdzEeuQffIZySYhWVLdcaNG+cWSjZs2EB+fr7JEQmCIAiCIAiCIAiCEG1YtlRn1qxZrFmzhksuuQRN01i6dKnZIQmCIAiCIAiCIAiCEGVYVjiJiYnh3nvvNTsMQRAEQRAEQRAEQRCiGMuW6giCIAiCIAiCIAiCIJiNCCeCIAiCIAiCIAiCIAheEOFEEARBEARBEARBEATBCyKcCIIgCIIgCIIgCIIgeEGEE0EQBEEQBEEQBEEQBC/YNE3TzA7CCDZs2EBiYqLZYQiCIAiCIAiCIAghwOl0MmbMGLPDEKKQHiOcCIIgCIIgCIIgCIIgGI2U6giCIAiCIAiCIAiCIHhBhBNBEARBEARBEARBEAQviHAiCIIgCIIgCIIgCILgBRFOBEEQBEEQBEEQBEEQvCDCiSAIgiAIgiAIgiAIghdEOBEEQRAEQRAEQRAEQfCCCCchpq2tjbvuuouLL76Y4uJiKioqzA4pamhubuaXv/wl8+bN46KLLmLVqlXu37355ptcfPHFJkYXXezbt48ZM2ZQXl6Ow+Fg7ty5XHrppdx66620tbWZHV6P5/XXX6e4uJji4mLmzp3LCSecgMPhYN68eRQXF3PFFVdQU1Njdpg9mqamJn7xi18wd+5cFixYwLZt29i3bx/XXHMNP/rRj7jkkkuorKw0O8wey1dffUVxcTGA1+/+ypUrufDCC5k7dy7vv/++meH2WDyvg05n7XFbWxtXXnklL774YjjDiwo8r4G3Z9Cf//xnLrzwQubMmcN//vMfM8PtsXheh40bN3LRRRcxb948Fi9e7O4XLV++nB/+8If88Ic/5NFHHzUzXEEQgDizA+jp/Pe//6WpqYmXX36ZDRs2sGzZMp544gmzw4oK3njjDTIyMnjggQc4cOAAF1xwAaeddhoOh4NXX30VTdPMDjEqaG5u5q677iIpKQmARx99lGuvvZYZM2bwi1/8gg8++IBTTz3V5Ch7NhdeeCEXXnghAPfccw9z5sxhyZIl3HnnnRQVFfHSSy/x1FNPceutt5ocac9l5cqVpKSksHLlSrZs2cLixYvJzs7mnHPOYfbs2Xz66ads2bIFu91udqg9jqeeeoo33niD5ORkgE6/+1deeSUrVqzgtddew+l0Mm/ePKZOnUpCQoLJ0fccjr4OgNf2+He/+x2HDh0Kd4g9nqOvwQMPPHDMMygjI4MVK1bw73//m4aGBs4//3xmzZplcuQ9i6Ovw5133skdd9zBuHHjePjhh3nzzTcZN24cb7zxBq+88go2m4158+bx/e9/n8LCQpOjF4ToRTJOQsy6deuYNm0aAGPGjOHbb781OaLo4cwzz+TnP/+5++fY2FgOHDjAgw8+yG233WZiZNHF/fffzyWXXEK/fv0AKCoq4uDBg2iaRl1dHXFxot+Gi2+++YbNmzdz8cUX89BDD1FUVARAa2sriYmJJkfXs9m8eTPTp08HYNiwYZSXl/Pll1+ye/du5s+fz5tvvsnEiRNNjrJnYrfbeeSRR9w/d/bd//rrrxk7diwJCQmkp6djt9spKSkxK+QeydHXwVt7/O6772Kz2dz3i2AcR1+Dzp5BycnJDBo0iIaGBhoaGrDZbCZG3DM5+jrs3r2bcePGATBu3DjWrVvHgAEDePrpp4mNjSUmJoaWlhZppwXBZEQ4CTG1tbWkpaW5f46NjaWlpcXEiKKH1NRU0tLSqK2t5frrr+fnP/85t99+O7fddhupqalmhxcVvP766/Tt29ctHgIMGTKEJUuWcNZZZ7Fv3z4mTZpkYoTRxR//+EeuvfZaALeQ9eWXX/Lcc88xf/58EyPr+RQVFfH++++jaRobNmxg9+7dbN++nV69erF8+XIGDhzIU089ZXaYPZIzzjijg0Db2Xe/traW9PR09zapqanU1taGPdaejOd1aG1t7bQ9Lisr46233uow6SEYx9H3grdn0MCBA/nBD37ABRdcwGWXXWZWuD2Wo69Dbm4ua9euBeD999+noaGB+Ph4+vbti6Zp3H///Rx33HEMHTrUrJAFQUBKdUJOWloadXV17p/b2tpkhj2M7Ny5k2uvvZZ58+YxZMgQKioqWLRoEU6nk82bN7NkyRJuv/12s8Pssbz22mvYbDY++eQTHA4HN998MyUlJfztb39j5MiRPP/88yxbtoy7777b7FB7PIcPH2bLli1MnjzZ/d4777zDE088wZ/+9Cf69u1rYnQ9nzlz5lBeXs5ll13GuHHjGDVqFDt37nSXqZ166qk8/PDDJkcZPRz93T+6ra6rq+sgpAjGsnHjxk7b4/j4eHbv3s1PfvITtm/fTnx8PIMHD5bskxCRkZFxzDPoww8/ZM+ePW5fuCuuuIJx48YxevRoM0Pt0SxdupQlS5bw9NNPc8IJJ7hLBJ1Op1tclH6SIJiPjOBDzLhx43j//feZPXs2GzZsID8/3+yQooaamhoWLFjAXXfdxZQpUwB4++23AaiurubGG28U0STEPP/88+7/FxcXs2jRIq699lp3Fla/fv348ssvzQovqvj888856aST3D//4x//4OWXX2bFihVkZGSYF1iU8M033zB+/Hhuu+02vvnmGyorKxk0aBCrV6/m/PPP5/PPP2fEiBFmhxkVdPbdHz16NL/73e9wOp00NTVRXl4u7XUIGT16dLft8SOPPEJWVpaIJiFk/PjxxzyDevfuTVJSEgkJCdhsNtLT0zl8+LDZofZoVq9ezdKlS+nfvz+LFy9m+vTpaJrGT3/6UyZNmsRVV11ldoiCICDCSciZNWsWa9as4ZJLLkHTNJYuXWp2SFHDk08+yeHDh3n88cd5/PHHAWXIpZuUCubw61//moULFxIXF0d8fDyLFy82O6SoYOvWreTk5AAqTX7JkiUMHDiQ6667DoATTzyR66+/3swQezR5eXn8/ve/5y9/+Qvp6eksWbKElpYW7rjjDl566SXS0tL47W9/a3aYPZ6uvvvFxcXMmzcPTdNYuHCh+AkIPZ6bb775mGdQ7969+fjjj5k7dy4xMTGMGzeOqVOnmh1qjyYvL4+rrrqK5ORkJk2axIwZM/jPf/7D2rVraWpq4n//+x8AN954I2PHjjU5WkGIXmyaLC0iCIIgCIIgCIIgCILQKWIOKwiCIAiCIAiCIAiC4AURTgRBEARBEARBEARBELwgwokgCIIgCIIgCIIgCIIXRDgRBEEQBEEQBEEQBEHwgggngiAIgiAIgiAIgiAIXhDhRBAEQRAEQRAEQRAEwQsinAiCIAiCIAiCIAiCIHjh/wPff70edJTCggAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1152x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Adding in the hourly profiles. will do this using the naics code\n",
    "\n",
    "## V19 Making the script more able to handle the per facility format. Demands in particular are now not a csv which is brough in, but a single hourly value. 06/13/2022\n",
    "\n",
    "## V18 Dropping the Avoided cost of ff in the objective function. Add in more post process for comparing and analizing revenue breakdown 06/09/2022\n",
    "\n",
    "## V17 More cleanup\n",
    "\n",
    "## V16 Cleaning up some constraintes based upon the writing. Making the total charge and discharge for the TES for TES scale not for the modules.\n",
    "\n",
    "## V15 Making TES paramters optimization -- Lied, did not do that. Just updated some things\n",
    "\n",
    "## V14 Removing Fast solve as it was far slower. And inputing TES without optimization\n",
    "\n",
    "## V13 This is a short-cut for resolving results more quickly, by brute force\n",
    "\n",
    "## V12 Adding the fast solve functionality with solely a float value number of modules option. \n",
    "# Adding this possibility on top of current layout to provide layered effect.\n",
    "# Can run fast, choose best 3, then run full analysis.\n",
    "\n",
    "## V11 Adding in thermal penalties for coolant to working fluid transfer. 01/21/22\n",
    "# This is primarily done in the Generator spreadsheet 'sites.csv'\n",
    "# The Temperature coefficient is 0.75 for all systems currently and are adjusted to occur prior to importing.\n",
    "# Energy loss to the heat transfer are adjusted to the maximum thermal capacity BUT the electric capacity already accounts for this in the efficiceny therefore it is only applied to the pTCap value.\n",
    "\n",
    "## V10 Fixing the MSL for thermal baseline issue.\n",
    "# This means that the vTGen is now the total thermal generation from the 'boiler' aspect of the cycle.\n",
    "# vGen remains unchanged\n",
    "# The thermal demand for the facility is satisfied by (vTGen-vGen/pEff)\n",
    "# This allows for the system to have a more specific link between the thermal requirements of the nuclear reactor itself which has a distinct ramping and range\n",
    "# and for the electricicty to behave a certain way without tryign to directly couple them in vTGenabovemin which would provide complications if the \n",
    "# vTGen were simply the heat to industry.\n",
    "# new value is put into the results vHeatGen which is the MWht for the supplied industrial heating.\n",
    "# this opens the question is the pTVOC is solely the thermal cycle and if the pVOC is just to operate the turbine and generate electricty outside of the thermal behavior.\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import cplex\n",
    "import os\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import math\n",
    "\n",
    "import plotly.graph_objects as go\n",
    "import plotly.io as pio\n",
    "import plotly.express as px\n",
    "\n",
    "import pyomo.environ as pe\n",
    "import pyomo.opt\n",
    "from pyomo.opt import SolverStatus\n",
    "\n",
    "from datetime import datetime\n",
    "s = datetime.now()\n",
    "print(s,'start')\n",
    "\n",
    "\n",
    "import Facility_Processing_2015 as FP\n",
    "\n",
    "class EconomicAssessment_OneGen:\n",
    "    def __init__(self):\n",
    "        \n",
    "        self.Generator_IDs = None             # Names of the egenrators being evaluated\n",
    "        \n",
    "        #### sets ####\n",
    "        self.G = None                         # Generator indexes [0,1,2....]\n",
    "        self.T = None                         # Timestep indexes [0,1,2....8759]\n",
    "        self.M = None #list(range(6))              # Maximum number of modules for any generator\n",
    "        #self.YR = None                        # Number of years\n",
    "        \n",
    "        #### paramteres ####\n",
    "        \n",
    "        # Environmental paramters/System\n",
    "        self.pLMP = None                      # LMP electric $MWhe [t] or [t,yr]\n",
    "        #self.pTLMP = None                     # LMP heat $/MWht [g,t] or [t,yr]\n",
    "        self.pTempdemanded = None             # Minimum temperature which the reactor must supply for the given TLMP single value\n",
    "        self.pTEnergyDemand = None            # Thermal energy required per hour in MWt [t]\n",
    "        \n",
    "        # Engineering parameters for generators\n",
    "        self.pOnoroffinitial = None           # initial state, default, 1 = On [g]\n",
    "        self.pStartupfixedcost = None         # $/MW [g]\n",
    "        self.pVOC = None                      # Variable O&M ($/MWhe) [g]\n",
    "        self.pTVOC = None                     # Variable O&M ($/MWht) [g]\n",
    "        self.pCap = None                      # Maximum Electric capacity MWe == pTCap*ThermalEff [g]\n",
    "        self.pTCap = None                     # Maximum Thermal capacity MWt [g]\n",
    "        self.pThermaleff = None               # Thermal efficiency [g]\n",
    "        self.pThermaTransfereff = None        # V11 Thermal transsfer efficicnecy between coolant and workign fluid.\n",
    "        self.pMSL = None                      # Minimum stable thermal load (MWt) [g]\n",
    "        self.pMSLTurb = None                  # Minimum stable trubine load minumum core + turbine power [g]\n",
    "        self.pMDT = None                      # Minimum down time (hours) [g]\n",
    "        self.pGenabovemininitial = None       # Initial generation above the min (MWe) [g]\n",
    "        self.pRamprate = None                 # Maximum ramp (up and down) (MW/hr) [g]\n",
    "        \n",
    "        self.pCAPEX = None                    # Capital costs of the generator $/MWe [g]\n",
    "        self.pFOPEX = None                    # Fixed O&M costs of the generator $/MWe [g]\n",
    "        self.pModulesize = None               # Module size in MWe [g]\n",
    "        self.pOutlettemp = None               # SMR outlet temperature [g]\n",
    "        self.pWorkingTemp = None              # SMR working fluid temp \n",
    "        self.pMaxMod = None                   # maximum number of modules [g]\n",
    "        self.FastMod = None                   # V12 Fast mdouel helper       \n",
    "        \n",
    "        self.pIR = 0.07                       # Discount/ Interest Rate 7% (OMB Circular)\n",
    "        self.pLifetime = 30                   # Lifetime, years\n",
    "        \n",
    "        # V14 Additions MV 3/1/2022\n",
    "        ## TES Parameters ####\n",
    "        self.pTESCapacity = None              # Storage thermal capacity (MWh-thermal)\n",
    "        self.pTESChargeEff = None             # Charging Efficiency (%) \n",
    "        self.pTESDischargeEff = None          # Discharging Efficicency (%) \n",
    "        self.pTESLossRate = None              # Rate at which the stored thermal energy is lost (%/hr)\n",
    "        self.pTESPowerCap = None              # Power in or out maximum (MWt)\n",
    "        \n",
    "        ## TES Variables #### \n",
    "        self.vTESSOC = None                   # TES State of Charge (MWht)\n",
    "        self.vTESSOC_Charge = None            # TES Charge amount (MWht)\n",
    "        self.vTESSOC_Discharge = None         # TES Discharge amount (MWht)\n",
    "        # V15\n",
    "        self.vTESCapacity = None              # DV of the total storage (MWht)\n",
    "        self.vTESPowerCap = None              # DV of CHarge/Discharge Power (MWt)\n",
    "        \n",
    "        #### variables ####\n",
    "        \n",
    "        # Float variables\n",
    "        self.vGen = None                      # Electric generation (MWhe) [g,t] --> [t]\n",
    "        self.vTGen = None                     # Heat generation (MWht) [g,t] --> [t]\n",
    "        self.vGenabovemin = None              # Electric generation above the MSLTurb (MWhe) [m,t]\n",
    "        self.vTGenabovemin = None             # Thermal Generation above MSL (MWht) [m,t]\n",
    "        \n",
    "        # Binary variables\n",
    "        self.vTurnon = None                   # Does the generator turn on (1) or not (0) [m,t] --> [t]\n",
    "        self.vTurnoff = None                  # Gnerator turns off(1) or not (0) at time t [m,t] --> [t]\n",
    "        self.vOnoroff = None                  # Generator state at time t [m,t] --> [t]\n",
    "        self.vEOnoroff = None                 # Electricity generation on (1) or off (0) [m,t] --> [t]\n",
    "        self.vTOnoroff = None                 # Heat generation on (1) or off (0) [m,t] --> [t]\n",
    "        \n",
    "        # results/objective variables  --These are not explicit model components and ar calculated from other variable values\n",
    "        self.rEProfit = None                  # Profits from vGen*LMP [m,t]\n",
    "        self.rTProfit = None                  # Profits from vTgen*TLMP [m,t]\n",
    "        self.rProfit = None                   # Hourly profits ($) [m,t]\n",
    "        \n",
    "        self.vTotalProfits = None             # Total Profits ($) [-] single value\n",
    "        self.rFeasible = None                 # is this configurations feasible     \n",
    "        \n",
    "        self.vGenerator = None                # Index of the generator chosen for the\n",
    "        \n",
    "        #V18 \n",
    "        self.rACC = None                      # Annualized capital cost\n",
    "        self.rFOMC = None                     # Fixed cost\n",
    "        self.rVC = None                       # Variable cost\n",
    "        self.rFC = None                       # Fuel cost\n",
    "        self.rEP = None                       # Electricity profit\n",
    "        self.rAVC = None                      # Avoided cost of natural gas\n",
    "        self.rSUC = None                      # Startup costs\n",
    "        self.rTES = None                      # TES CAPEX\n",
    "        self.RampCost = 0.001\n",
    "        \n",
    "        \n",
    "    def BuildModel(self):\n",
    "        pTS = []\n",
    "        for i in range(len(self.G)):\n",
    "            if self.pOutlettemp[i] >= self.pTempdemanded:\n",
    "                pTS.append(1)\n",
    "            else:\n",
    "                pTS.append(0)\n",
    "            \n",
    "        self.pThermalSat = pTS\n",
    "        \n",
    "        print('Building Model')\n",
    "        model = pe.ConcreteModel()\n",
    "        \n",
    "        #### SETS ####     \n",
    "        \n",
    "        model.G = pe.Set(initialize = self.G, doc = \"Generator IDs\")\n",
    "        model.T = pe.Set(initialize = self.T, doc = \"Timestep\")\n",
    "        model.M = pe.Set(initialize = self.M, doc = \"Modules\")\n",
    "        #model.YR = pe.Set(initialize = self.YR, doc = 'Years of model runs-- CURRENTLY NOT USED')\n",
    "        \n",
    "        \n",
    "        #### PARAMETERS ####\n",
    "        \n",
    "        model.pLMP = pe.Param(model.T,initialize = {self.T[i]: self.pLMP[i] for i in range(len(self.T))} , doc = \"LMP Electric $/MWhe\")\n",
    "        #model.pTLMP = pe.Param(model.G, model.T,initialize = self.pTLMP, doc = \"LMP Thermal $/MWht\")\n",
    "        model.pTEnergyDemand = pe.Param(model.T,initialize = {self.T[i]: self.pTEnergyDemand[i] for i in range(len(self.T))} , doc = \"Energy demand MWht\")\n",
    "        \n",
    "        model.pOnoroffinitial = pe.Param(model.G,initialize = {self.G[i]: self.pOnoroffinitial[i] for i in range(len(self.G))}, doc = \"Inital state, 1 on, 0 off\")\n",
    "        model.pStartupfixedcost = pe.Param(model.G,initialize = {self.G[i]: self.pStartupfixedcost[i] for i in range(len(self.G))}, doc = \"Startup costs, $500 default\")\n",
    "        #model.pVOC = pe.Param(model.G,initialize = {self.G[i]: self.pVOC[i] for i in range(len(self.G))}, doc = \"Variable O&M Electric $/MWhe\")\n",
    "        model.pTVOC = pe.Param(model.G,initialize = {self.G[i]: self.pTVOC[i] for i in range(len(self.G))}, doc = \"Variable O&M Thermal $/MWhe\")\n",
    "        model.pCap = pe.Param(model.G,initialize = {self.G[i]: self.pCap[i] for i in range(len(self.G))} , doc = \"Maximum Electric Capacity MWe\")\n",
    "        model.pTCap = pe.Param(model.G,initialize = {self.G[i]:self.pTCap[i] for i in range(len(self.G))}, doc = \"Maximum Thermal Capacity MWt\")\n",
    "        model.pThermaleff = pe.Param(model.G,initialize = {self.G[i]: self.pThermaleff[i] for i in range(len(self.G))}, doc = \"Thermal efficiency (MWe/MWt)\")\n",
    "        model.pThermalTransfereff = pe.Param(model.G,initialize = {self.G[i]: self.pThermaTransfereff[i] for i in range(len(self.G))}, doc = \"Thermal transfer efficiency (MW -coolant/MWt- working fluid)\")\n",
    "        model.pMSL = pe.Param(model.G,initialize = {self.G[i]: self.pMSL[i] for i in range(len(self.G))}, doc = \"Minimum Stable Load Thermal MWt\")\n",
    "        model.pMSLTurb = pe.Param(model.G,initialize = {self.G[i]: self.pMSLTurb[i] for i in range(len(self.G))}, doc = \"Minimum Stable Load of Turbine MWe\")\n",
    "        model.pMDT = pe.Param(model.G,initialize = {self.G[i]: self.pMDT[i] for i in range(len(self.G))} , doc = \"Mimimum Down time (hours)\")\n",
    "        model.pGenabovemininitial = pe.Param(model.G,initialize = {self.G[i]: self.pGenabovemininitial[i] for i in range(len(self.G))}, doc = \"Initial Generation above the Minimun MWe\")\n",
    "        model.pRamprate = pe.Param(model.G,initialize = {self.G[i]: self.pRamprate[i] for i in range(len(self.G))}, doc = \"Ramp rate up and down MW/hr\")        \n",
    "        model.pFOPEX = pe.Param(model.G,initialize = {self.G[i]: self.pFOPEX[i] for i in range(len(self.G))}, doc = \"FOPEX pparamter $/MWe\")\n",
    "        model.pCAPEX = pe.Param(model.G,initialize = {self.G[i]: self.pCAPEX[i] for i in range(len(self.G))}, doc = \"CAPEX values$/MWe\")\n",
    "        model.pWorkingTemp = pe.Param(model.G,initialize = {self.G[i]: self.pWorkingTemp[i] for i in range(len(self.G))}, doc = \"working temperature which the reactor can supply in Degrees C\")\n",
    "        model.pThermalSat  = pe.Param(model.G,initialize = {self.G[i]: self.pThermalSat[i] for i in range(len(self.G))}, doc = \"SMR can supply the temperature\")\n",
    "        model.pMaxMod = pe.Param(model.G, initialize = {self.G[i]: self.pMaxMod[i] for i in range(len(self.G))}, doc = \"maximum number of modules\")\n",
    "        \n",
    "        # V14 Additions MV 3/1/2022\n",
    "        model.pTESCapacity = pe.Param(model.G,initialize = {self.G[i]: self.pTESCapacity[i] for i in range(len(self.G))}, doc = \"TES max storage\")\n",
    "        model.pTESChargeEff = pe.Param(model.G,initialize = {self.G[i]: self.pTESChargeEff[i] for i in range(len(self.G))}, doc = \"TES Charge Efficiency\")\n",
    "        model.pTESDischargeEff = pe.Param(model.G,initialize = {self.G[i]: self.pTESDischargeEff[i] for i in range(len(self.G))}, doc = \"TES Discharge Efficiency\")\n",
    "        model.pTESLossRate = pe.Param(model.G,initialize = {self.G[i]: self.pTESLossRate[i] for i in range(len(self.G))}, doc = \"TES Loss Rate if just sitting\")\n",
    "        model.pTESPowerCap = pe.Param(model.G,initialize = {self.G[i]: self.pTESPowerCap[i] for i in range(len(self.G))}, doc = \"Maximum power in or out of stroage\")\n",
    "        \n",
    "        #### VARIABLES ####\n",
    "        model.vGenerator = pe.Var(model.G,within=pe.Binary,doc = \"Choice of generator\")\n",
    "        model.vModuleS = pe.Var(model.G,model.M,within = pe.Binary, doc = \"choice of modules for full optimization\")\n",
    "        \n",
    "        model.vGen = pe.Var(model.G,model.M,model.T,within=pe.NonNegativeReals, doc = \"Electric generation (MWhe)\")\n",
    "        model.vTGen = pe.Var(model.G,model.M,model.T,within=pe.NonNegativeReals, doc = \" Heat generation (MWht) \")\n",
    "        model.vRGen = pe.Var(model.G,model.M,model.T,within=pe.NonNegativeReals, doc = \" Total Reactor generation (MWht) \")\n",
    "        \n",
    "        model.vGenabovemin = pe.Var(model.G,model.M,model.T,within=pe.NonNegativeReals, doc = \"Electric generation above the MSLTurb (MWhe)\")\n",
    "        model.vRGenabovemin = pe.Var(model.G,model.M,model.T,within=pe.NonNegativeReals, doc = \"Thermal Generation above MSL (MWht)\")\n",
    "        \n",
    "        model.vTurnon = pe.Var(model.G,model.M,model.T,within=pe.Binary, doc = \"Does the generator turn on (1) or not (0)\")\n",
    "        model.vTurnoff = pe.Var(model.G, model.M,model.T,within=pe.Binary, doc = \"Generator turns off(1) or not (0) at time t\")\n",
    "        model.vOnoroff = pe.Var(model.G,model.M,model.T,within=pe.Binary, doc = \"Generator state at time t\")\n",
    "        model.vEOnoroff = pe.Var(model.G,model.M,model.T,within=pe.Binary, doc = \"Electricity generation on (1) or off (0)\")\n",
    "        model.vTOnoroff = pe.Var(model.G,model.M,model.T,within=pe.Binary, doc = \"Heat generation on (1) or off (0)\")\n",
    "        \n",
    "        # V14 Additions MV 3/1/2022\n",
    "        model.vTESSOC = pe.Var(model.G,model.T,within=pe.NonNegativeReals, doc = \"TES State of Charge\")\n",
    "        model.vTESSOC_Charge = pe.Var(model.G,model.M,model.T,within=pe.NonNegativeReals, doc = \"TES SOC charge\")\n",
    "        model.vTESSOC_Discharge = pe.Var(model.G,model.T,within=pe.NonNegativeReals, doc = \"TES SOC charge\")\n",
    "        \n",
    "        # V15 \n",
    "        #model.vTESCapacity = pe.Var(model.G,within=pe.NonNegativeReals, doc = \"DV of TES Capacity\")\n",
    "        #model.vTESPowerCap = pe.Var(model.G,within=pe.NonNegativeReals, doc = \"DV of TES Power Charge/Discharge\")\n",
    "        \n",
    "        print('Params & Vars established')\n",
    "        \n",
    "        \n",
    "        #### OBJECTIVES ####\n",
    "        \n",
    "        def ERevenues(model):\n",
    "            return sum(sum(sum(model.vGen[g,m,t]*model.pLMP[t]for g in model.G) for m in model.M) for t in model.T)\n",
    "        \n",
    "        #def TRevenues(model):\n",
    "        #    return sum(sum((sum(model.vTGen[g,m,t] for m in model.M)+ model.vTESSOC_Discharge[g,t]*model.pTESDischargeEff[g])*model.pTLMP[g,t] for g in model.G) for t in model.T)\n",
    "        \n",
    "        def RampCost(model):\n",
    "            return sum(sum(sum((((model.vRGenabovemin[g,m,model.T.prevw(t)])-(model.vRGenabovemin[g,m,t]))**2)*self.RampCost for t in model.T) for m in model.M) for g in model.G)\n",
    "        \n",
    "        def Costs(model):\n",
    "            return (sum(model.pStartupfixedcost[g]*sum(sum(model.vTurnon[g,m,t] for t in model.T) for m in model.M) for g in model.G) \n",
    "                    + sum(sum(sum((model.vRGen[g,m,t]*model.pTVOC[g]) for g in model.G) for m in model.M) for t in model.T) \n",
    "                    + sum(sum(model.pCap[g]*(model.pCAPEX[g]*model.vModuleS[g,m]*(self.pIR/(1-((1+self.pIR)**(-1*self.pLifetime))))+model.pFOPEX[g]*model.vModuleS[g,m]) for m in model.M) for g in model.G)*(len(self.T)/8760)\n",
    "                    + sum(model.vGenerator[g]*model.pTESCapacity[g]*self.pTESCapex*(self.pIR/(1-((1+self.pIR)**(-1*self.pLifetime))))*(len(self.T)/8760) for g in model.G))\n",
    "        \n",
    "        \n",
    "        def Obj_Profit(model):\n",
    "            return ERevenues(model)-Costs(model)-RampCost(model) # +TRevenues(model)\n",
    "        model.Obj_Profit = pe.Objective(rule = Obj_Profit, sense=pe.maximize, doc = \"Maximize the profits by balancing thermal and electric generation\")\n",
    "                \n",
    "\n",
    "        #### CONSTRAINTS ####\n",
    "        \n",
    "        model.GeneratorChooser = pe.Constraint(expr = sum(model.vGenerator[g] for g in model.G)<=1)\n",
    "        model.ModChooserS = pe.Constraint(expr = sum(sum(model.vModuleS[g,m] for g in model.G)for m in model.M)>=1)\n",
    "\n",
    "        def ThermalEquality(model,t):\n",
    "            return sum(sum(model.vTGen[g,m,t] for m in model.M) + model.vTESSOC_Discharge[g,t]*model.pTESDischargeEff[g] for g in model.G) == model.pTEnergyDemand[t]\n",
    "        model.ThermalEquality = pe.Constraint(model.T, rule = ThermalEquality, doc = \"Limits the thermal output (and therefore the 'revenue from heat')\")\n",
    "        \n",
    "        def ModuleLimit(model,g):\n",
    "            return sum(model.vModuleS[g,m] for m in model.M) <= model.pMaxMod[g]\n",
    "        model.ModuleLimit = pe.Constraint(model.G,rule = ModuleLimit, doc = \"limits the number of available modules\")\n",
    "        \n",
    "        def ModGenS(model,g,m):\n",
    "            return model.vModuleS[g,m] <= model.vGenerator[g]\n",
    "        model.ModGenS = pe.Constraint(model.G,model.M,rule = ModGenS, doc = \"cannot build modules for the non-optimal gens Slow\")\n",
    "        \n",
    "        def ModStatus(model,g,m,t):\n",
    "            return model.vOnoroff[g,m,t] <= model.vModuleS[g,m]\n",
    "        model.ModStatus = pe.Constraint(model.G,model.M, model.T, rule = ModStatus, doc = \"The status of the plant must be the previous state plus the turnon/off\")\n",
    "        \n",
    "        def ECapacityLimit(model,g,m,t):\n",
    "            return model.vGen[g,m,t] <= model.pCap[g]\n",
    "        model.ECapacityLimit = pe.Constraint(model.G,model.M,model.T,rule = ECapacityLimit, doc = \"The sum of the electric generation must not surpass maximum generation\")\n",
    "        \n",
    "        def RGenEquality(model,g,m,t):\n",
    "            return (model.vTGen[g,m,t] + model.vGen[g,m,t]/model.pThermaleff[g] + \n",
    "                    model.vTESSOC_Charge[g,m,t]/model.pTESChargeEff[g]) == model.vRGen[g,m,t]*model.pThermalTransfereff[g] # Updated V14 MV 03012022\n",
    "        model.RGenEquality = pe.Constraint(model.G,model.M,model.T,rule = RGenEquality, doc = \"electricity and thermal must equal ttoal\")\n",
    "        \n",
    "        def EStatus(model,g,m,t): \n",
    "            return model.vEOnoroff[g,m,t] <= model.vOnoroff[g,m,t]\n",
    "        model.Estatus = pe.Constraint(model.G,model.M,model.T, rule = EStatus, doc = \"The electric generation cannot be on when the overall status is off\")\n",
    "        \n",
    "        def Estatus2(model,g,m,t):\n",
    "            return model.vGen[g,m,t] <= model.pCap[g]*model.vEOnoroff[g,m,t]\n",
    "        model.Estatus2 = pe.Constraint(model.G,model.M,model.T,rule = Estatus2, doc = \"The sum of the electric generation must not surpass maximum generation\")\n",
    "        \n",
    "        def RStatus(model,g,m,t):\n",
    "            return model.vRGen[g,m,t]*model.pThermaleff[g]<= model.pCap[g]*model.vOnoroff[g,m,t]\n",
    "        model.RStatus = pe.Constraint(model.G,model.M,model.T,rule = RStatus, doc = \"The sum of the electric and thermal generation must not surpass maximum generation\")\n",
    "        \n",
    "        def TAllow(model,g,m,t):\n",
    "            return model.vOnoroff[g,m,t] <= model.pThermalSat[g]\n",
    "        model.TAllow = pe.Constraint(model.G,model.M,model.T, rule = TAllow, doc = \"Must satisfty heat constraint\")\n",
    "        \n",
    "        def RGenAbove(model,g,m,t):\n",
    "            return model.vRGenabovemin[g,m,t]  == ((model.vRGen[g,m,t])-(((model.pMSL[g])/model.pThermaleff[g])*model.vOnoroff[g,m,t]))\n",
    "        model.RGenAbove = pe.Constraint(model.G,model.M,model.T, rule = RGenAbove, doc = \"This defines the generation above the minimum stable load\")\n",
    "        \n",
    "        def EGenAbove(model,g,m,t):\n",
    "            return model.vGenabovemin[g,m,t]  == (model.vGen[g,m,t]-(model.pMSLTurb[g]*model.vEOnoroff[g,m,t]))\n",
    "        model.EGenAbove = pe.Constraint(model.G,model.M,model.T, rule = EGenAbove, doc = \"This defines the generation above the minimum stable load\")\n",
    "        \n",
    "        def RRamprateUp(model,g,m,t):\n",
    "            if t == model.T[1]: \n",
    "                return pe.Constraint.Feasible # V11C THis means that we can start iur reactor at any output.\n",
    "            return (model.vRGenabovemin[g,m,model.T.prev(t)])-(model.vRGenabovemin[g,m,t]) >= -1*model.pRamprate[g]/(model.pThermaleff[g])\n",
    "        model.RRamprateUp = pe.Constraint(model.G,model.M,model.T, rule = RRamprateUp, doc = \"The hourly change must not exceed the ramprate between time steps\")\n",
    "        \n",
    "        def RRamprateDn(model,g,m,t):\n",
    "            if t == model.T[1]:\n",
    "                return pe.Constraint.Feasible # V11C THis means that we can start iur reactor at any output.\n",
    "            return (model.vRGenabovemin[g,m,model.T.prev(t)]-model.vRGenabovemin[g,m,t])*(model.pThermaleff[g]) <= model.pRamprate[g]\n",
    "        model.RRamprateDn = pe.Constraint(model.G,model.M,model.T, rule = RRamprateDn, doc = \"The hourly change must not exceed the ramprate between time steps\")\n",
    "                \n",
    "        def Status(model,g,m,t):\n",
    "            if t == model.T[1]:\n",
    "                return model.vOnoroff[g,m,t] == (model.pOnoroffinitial[g]+ model.vTurnon[g,m,t] - model.vTurnoff[g,m,t])\n",
    "            return model.vOnoroff[g,m,t] == (model.vOnoroff[g,m,model.T.prev(t)] + model.vTurnon[g,m,t] - model.vTurnoff[g,m,t])\n",
    "        model.Status = pe.Constraint(model.G,model.M, model.T, rule = Status, doc = \"The status of the plant must be the previous state plus the turnon/off\")\n",
    "        \n",
    "        def SingleGenStatus(model,g,m,t):\n",
    "            return model.vOnoroff[g,m,t] <= model.vGenerator[g]\n",
    "        model.SingleGenStatus = pe.Constraint(model.G,model.M, model.T, rule = SingleGenStatus, doc = \"The status of the plant must be the previous state plus the turnon/off\")\n",
    "        '''\n",
    "        def TurnOn(model,g,m,t):\n",
    "            if t == model.T[1]:\n",
    "                return model.vTurnon[g,m,t] == (model.vOnoroff[g,m,t]-model.pOnoroffinitial[g] + model.vTurnoff[g,m,t])\n",
    "            return model.vTurnon[g,m,t] == (model.vOnoroff[g,m,t]-model.vOnoroff[g,m,model.T.prev(t)] + model.vTurnoff[g,m,t])\n",
    "        model.TurnOn = pe.Constraint(model.G,model.M,model.T, rule = TurnOn, doc = \"Ensure turn on variable goes from off to on states\")\n",
    "        \n",
    "        def TurnOff(model,g,m,t):\n",
    "            if t == model.T[1]:\n",
    "                return model.vTurnoff[g,m,t] == (model.pOnoroffinitial[g] - model.vOnoroff[g,m,t] + model.vTurnon[g,m,t])\n",
    "            return model.vTurnoff[g,m,t] == model.vOnoroff[g,m,model.T.prev(t)] - model.vOnoroff[g,m,t] + model.vTurnon[g,m,t]\n",
    "        model.TurnOff = pe.Constraint(model.G,model.M,model.T, rule = TurnOff, doc = \"Ensure turn off variable goes from on to off states\")\n",
    "        '''\n",
    "        def MinDownTime(model,g,m,t):\n",
    "            if t == model.T[1]:\n",
    "                return pe.Constraint.Feasible\n",
    "            if t <= model.pMDT[g]:\n",
    "                return sum((1-model.vOnoroff[g,m,model.T.prev(t,td+1)]) for td in range(t)) >= t*model.vTurnon[g,m,t]\n",
    "            return sum((1-model.vOnoroff[g,m,model.T.prev(t,td+1)]) for td in range(model.pMDT[g])) >= model.pMDT[g]*model.vTurnon[g,m,t]\n",
    "        model.MinDownTime = pe.Constraint(model.G,model.M,model.T, rule = MinDownTime, doc = \"We must enforce minimum down time\")\n",
    "        \n",
    "        # V14 Updates\n",
    "        \n",
    "        def TES_SOC(model,g,t):\n",
    "            if t == model.T[1]:\n",
    "                return model.vTESSOC[g,t] == model.pTESCapacity[g]/2\n",
    "            return model.vTESSOC[g,t] == (model.vTESSOC[g,model.T.prev(t)]*(1-model.pTESLossRate[g]) \n",
    "                                          - model.vTESSOC_Discharge[g,model.T.prev(t)]\n",
    "                                          + sum(model.vTESSOC_Charge[g,m,model.T.prev(t)] for m in model.M))\n",
    "        model.TES_SOC = pe.Constraint(model.G,model.T, rule = TES_SOC, doc = \"SOC is equal to the in/out plus the previous step with losses.\")\n",
    "        \n",
    "        def TES_SOC_Max(model,g,t):\n",
    "            return model.vTESSOC[g,t] <= model.pTESCapacity[g]\n",
    "        model.TES_SOC_Max = pe.Constraint(model.G,model.T, rule = TES_SOC_Max, doc = \"SOC has to be below the capacity limit\")\n",
    "        \n",
    "        def TES_SOC_Power(model,g,t):\n",
    "            return model.vTESSOC_Discharge[g,t] <= model.pTESPowerCap[g]\n",
    "        model.TES_SOC_Power = pe.Constraint(model.G, model.T, rule = TES_SOC_Power, doc = \"TES cannot surpass the power out max\")\n",
    "        \n",
    "        def TES_SOC_Power2(model,g,t):\n",
    "            return sum(model.vTESSOC_Charge[g,m,t]for m in model.M) <= model.pTESPowerCap[g]\n",
    "        model.TES_SOC_Power2 = pe.Constraint(model.G, model.T, rule = TES_SOC_Power2, doc = \"TES cannot surpass the power out max\")\n",
    "        \n",
    "        self.model = model\n",
    "        print('Model Built')\n",
    "        \n",
    "        \n",
    "    def SolveModel(self, solver='cplex'):\n",
    "        self.BuildModel()\n",
    "        print('Solving...')\n",
    "        \n",
    "        opt = pyomo.opt.SolverFactory(solver,tee = True) #iterlim = 1,\n",
    "        opt.options.mipgap = 0.1\n",
    "\n",
    "        results = opt.solve(self.model, tee = False, logfile=\"CPLEX_test_log.log\")\n",
    "        \n",
    "        print('>>Solver status is {} and solver termination condition is {}'.format(results.solver.status,results.solver.termination_condition))\n",
    "        if results.solver.termination_condition =='optimal':\n",
    "            print(pe.value(self.model.Obj_Profit))\n",
    "            self.vTotalProfits = pe.value(self.model.Obj_Profit)\n",
    "            self.rFeasible = True\n",
    "            self.ResultData()\n",
    "            self.ResultOutput()\n",
    "            \n",
    "        else:\n",
    "            self.rFeasible = False\n",
    "            self.vTotalProfits= None\n",
    "            self.rACC = 0\n",
    "            self.rFOMC = 0\n",
    "            self.rVC = 0\n",
    "            self.rFC = 0\n",
    "            self.rEP = 0\n",
    "            self.rAVC = 0\n",
    "            self.rSUC = 0\n",
    "            self.rTES = 0\n",
    "            \n",
    "    def ResultData(self):\n",
    "        self.vGen = np.array([[[pe.value(self.model.vGen[g,m,t]) for g in self.G] for m in self.M] for t in self.T])\n",
    "        self.vTGen = np.array([[[pe.value(self.model.vTGen[g,m,t]) for g in self.G] for m in self.M] for t in self.T])\n",
    "        self.vRGen = np.array([[[pe.value(self.model.vRGen[g,m,t]) for g in self.G] for m in self.M] for t in self.T])\n",
    "        \n",
    "        self.vTESSOC_Discharge = np.array([[pe.value(self.model.vTESSOC_Discharge[g,t]) for g in self.G] for t in self.T])\n",
    "        self.vTESSOC_Charge = np.array([[[pe.value(self.model.vTESSOC_Charge[g,m,t]) for g in self.G] for m in self.M] for t in self.T])\n",
    "        self.vTESSOC = np.array([[pe.value(self.model.vTESSOC[g,t]) for g in self.G] for t in self.T])\n",
    "        \n",
    "        # Binary variables\n",
    "        self.vTurnon = np.array([[[pe.value(self.model.vTurnon[g,m,t]) for g in self.G] for m in self.M] for t in self.T])\n",
    "        self.vTurnoff = np.array([[[pe.value(self.model.vTurnoff[g,m,t]) for g in self.G] for m in self.M] for t in self.T])\n",
    "        self.vOnoroff = np.array([[[pe.value(self.model.vOnoroff[g,m,t]) for g in self.G] for m in self.M] for t in self.T])\n",
    "        self.vEOnoroff = np.array([[[pe.value(self.model.vEOnoroff[g,m,t]) for g in self.G] for m in self.M] for t in self.T])\n",
    "        #self.vTOnoroff = np.array([[[pe.value(self.model.vTOnoroff[g,m,t]) for g in self.G] for m in self.M] for t in self.T])\n",
    "        self.vGenabovemin = np.array([[[pe.value(self.model.vGenabovemin[g,m,t]) for g in self.G] for m in self.M] for t in self.T])\n",
    "        self.vRGenabovemin = np.array([[[pe.value(self.model.vRGenabovemin[g,m,t]) for g in self.G] for m in self.M] for t in self.T])\n",
    "        \n",
    "        self.vTotalProfits = pe.value(self.model.Obj_Profit)\n",
    "        self.vGenerator = [pe.value(self.model.vGenerator[g]) for g in self.G]\n",
    "        self.vModuleS = np.array([[pe.value(self.model.vModuleS[g,m]) for g in self.G] for m in self.M])\n",
    "        \n",
    "    def ResultOutput(self):\n",
    "        self.vRGen_PD = pd.DataFrame(self.vRGen[:,:,self.vGenerator.index(1)])\n",
    "        self.vGen_PD = pd.DataFrame(self.vGen[:,:,self.vGenerator.index(1)])\n",
    "        self.vTGen_PD = pd.DataFrame(self.vTGen[:,:,self.vGenerator.index(1)])\n",
    "        self.vTurnon_PD = pd.DataFrame(self.vTurnon[:,:,self.vGenerator.index(1)])\n",
    "        \n",
    "        self.vTESSOC_Discharge = pd.DataFrame(self.vTESSOC_Discharge[:,self.vGenerator.index(1)])\n",
    "        self.vTESSOC_Charge = pd.DataFrame(self.vTESSOC_Charge[:,:,self.vGenerator.index(1)])\n",
    "        \n",
    "        self.rHeatDelivered = self.vTGen_PD  - (self.vGen_PD/self.pThermaleff[self.vGenerator.index(1)])\n",
    "        \n",
    "        self.rERevenue = self.vGen_PD.multiply(self.pLMP, axis = 0)\n",
    "\n",
    "        print(self.Generator_IDs[self.vGenerator.index(1)],\"was chosen for operation\")\n",
    "        print(sum(sum(self.vModuleS)),\"modules used\")\n",
    "        \n",
    "        self.Output = pd.DataFrame()\n",
    "        m = [i for i, x in enumerate(self.vModuleS[:,0].tolist()) if x == 1]\n",
    "        for x in m:\n",
    "            self.Output['Reactor_Gen [MWht]_'+str(x)] = self.vRGen_PD[x]\n",
    "            self.Output['Thermal_Gen [MWht]_'+str(x)] = self.vTGen_PD[x]\n",
    "            self.Output['Elec_Gen [MWhe]_'+str(x)] = self.vGen_PD[x]\n",
    "            self.Output['Elec_Gen [MWht]_'+str(x)] = self.Output['Elec_Gen [MWhe]_'+str(x)]/float(self.pThermaleff[0])\n",
    "            self.Output['TES_Charge_'+str(x)] = self.vTESSOC_Charge[x]\n",
    "        self.Output['TES_Disharge'] = self.vTESSOC_Discharge[0]\n",
    "        self.Output['SOC'] = self.vTESSOC[:,self.vGenerator.index(1)]\n",
    "        self.Output\n",
    "        \n",
    "        self.rACC = float(sum(self.pCap)*sum(self.pCAPEX)*sum(self.vModuleS)*(self.pIR/(1-((1+self.pIR)**(-1*self.pLifetime))))*(len(self.T)/8760))\n",
    "        self.rFOMC = float(sum(self.pCap)*sum(self.pFOPEX)*sum(self.vModuleS)*(len(self.T)/8760))\n",
    "        self.rVC = float(self.vRGen_PD.values.sum()*self.pVOC.values.sum()*self.pThermaleff[self.vGenerator.index(1)])\n",
    "        self.rFC = float(self.vRGen_PD.values.sum()*self.FuelCost.values.sum()*sum(self.pThermaleff))\n",
    "        self.rEP = float(self.rERevenue.values.sum())\n",
    "        self.rAVC = float((self.vTGen_PD*self.NGprice).values.sum())\n",
    "        self.rSUC = float(sum(self.pStartupfixedcost)* sum(sum(sum(self.vTurnon))))\n",
    "        self.rTES = float(sum(self.pTESCapacity)*self.pTESCapex*(self.pIR/(1-((1+self.pIR)**(-1*self.pLifetime))))*(len(self.T)/8760))\n",
    "        \n",
    "        \n",
    "def ParamsVarsPD(base,sites,lmps, dem, TES, naics, EP, heatLMP = 4, minTemp = 500, MaxMod = 2, hrProfiles = None):\n",
    "    # Environmental paramters/System\n",
    "    base.G = list(range(len(sites['Sites'])))\n",
    "    base.T = list(range(len(lmps['Settlement Point Price'])))\n",
    "    base.M = list(range(MaxMod)) #will need to be fixed eventually!!!!!!!!!!!!!!!!!!!!!!\n",
    "    base.Generator_IDs = sites['Sites'].tolist()\n",
    "\n",
    "    base.FastMod = {(base.G[i],base.M[j]): np.ones([len(base.M),len(base.G)],dtype = int) for i in range(len(base.G)) for j in range(len(base.M))}\n",
    "\n",
    "    base.NGprice = heatLMP\n",
    "    HLMP = pd.DataFrame()\n",
    "    for x in base.G:\n",
    "        HLMP[x] = [heatLMP]*len(base.T)\n",
    "    base.pTLMP_PD = HLMP\n",
    "    HLMP = np.array(HLMP)\n",
    "    HLMP_Dict ={(base.G[i],base.T[j]):HLMP[j,i] for i in range(len(base.G)) for j in range(len(base.T))}\n",
    "    base.pTLMP = HLMP_Dict\n",
    "    \n",
    "         \n",
    "    base.pLMP = (lmps['Settlement Point Price']).tolist()\n",
    "    base.pTempdemanded = minTemp\n",
    "    \n",
    "    base.pTEnergyDemand = [dem]*len(base.pLMP)\n",
    "    \n",
    "    if naics != None:\n",
    "        ###########################################################3333\n",
    "        TotalHeat = dem*8760\n",
    "        TotalHours = sum(hrProfiles)\n",
    "        CorrectiveFactor = TotalHeat/TotalHours\n",
    "        NormalizedHour = [hrProfiles[i] * CorrectiveFactor for i in range(len(hrProfiles))]\n",
    "        base.pTEnergyDemand = NormalizedHour\n",
    "    \n",
    "    # Engineering parameters for generators\n",
    "    base.pCAPEX = (sites['CAPEX $/kWe']*1000).tolist()   \n",
    "    base.pFOPEX = (sites['FOPEX $/kWe']*1000).tolist()   \n",
    "    base.pOutlettemp = sites['Outlet Temp (C)'].tolist()\n",
    "    base.pWorkingTemp = sites['Working Fluid Temp (C)'].tolist()\n",
    "    base.pOnoroffinitial = [0]*len(base.G) #sites['Onoroffinitial'].tolist()    \n",
    "    base.pStartupfixedcost = sites['Startupfixedcost in $'].tolist()\n",
    "    \n",
    "    \n",
    "    \n",
    "    base.pCap = sites['Power in MWe'].tolist() \n",
    "    base.pTCap = (sites['Power in MWt']).tolist() \n",
    "    base.pThermaleff = (sites['Thermal Efficiency']).tolist() \n",
    "    base.pThermaTransfereff = sites['Thermal Transfer Efficiency'].tolist() #V11\n",
    "    base.pMSL = sites['MSL in MWe'].tolist()\n",
    "    base.pMSLTurb = sites['MSL_turb in MWe'].tolist()\n",
    "    base.pMaxMod = [MaxMod]*len(base.G) # THis can and likely will need to be changed, but for now it will work nicely.\n",
    "    \n",
    "    #base.pVOC = sites['Effective VOC in $/MWh-e'].tolist() # V18  \n",
    "    #base.pTVOC = sites['Effective HVOC in $/MWh-t'].tolist() # V18\n",
    "    \n",
    "    base.pVOC = sites['VOM in $/MWh-e']\n",
    "    base.FuelCost = sites['FC in $/MWh-e']\n",
    "    \n",
    "    base.pTVOC = ((base.pVOC+base.FuelCost)*base.pThermaleff).tolist()\n",
    "    \n",
    "    base.pMDT = sites['MDT in hours'].tolist()\n",
    "    diff = [base.pCap[i] - base.pMSL[i] for i in range(len(base.pMSL))]\n",
    "    base.pGenabovemininitial = [diff[i]/1.2 for i in range(len(diff))] # This value is currently not being used as it was too often limiting in the conditions. \n",
    "    base.pRamprate = sites['Ramp Rate (MW/hr)'].tolist()\n",
    "    \n",
    "    CapValue = TES\n",
    "    base.pTESCapacity = [CapValue]*len(base.G)\n",
    "    base.pTESChargeEff = [0.85]*len(base.G)\n",
    "    base.pTESDischargeEff = [0.85]*len(base.G)\n",
    "    base.pTESLossRate = [0.01]*len(base.G)\n",
    "    base.pTESPowerCap = [CapValue/EP]*len(base.G)\n",
    "    base.pTESCapex = 20000 # $/MWh-th generally from table 3 of https://aip.scitation.org/doi/pdf/10.1063/1.4984433\n",
    "    \n",
    "    base.YR = None\n",
    "        \n",
    "def ImportData(sites , lmps , hrcount = 8760):\n",
    "    Sites_pd = pd.read_csv(sites)\n",
    "    LMP_pd = pd.read_csv(lmps)\n",
    "    \n",
    "    LMP_pd = LMP_pd.iloc[:hrcount]    \n",
    "    return Sites_pd, LMP_pd\n",
    "\n",
    "def NGTempCostCurve(Temp,NG_Cost = 3.0, AHF_Coeffs = [0,-0.00038,0.90556]):\n",
    "    HHV = 54 # MJ/kg\n",
    "    Density = 0.68 # kg/m3\n",
    "    cfTom3 = 35.31 # Unit conversion\n",
    "    AHF = AHF_Coeffs[0]*(Temp^2) + AHF_Coeffs[1]*(Temp) + AHF_Coeffs[2] # avaialble Heat fraction - Deep Patel Equation\n",
    "    \n",
    "    HHV = HHV*Density*(1/cfTom3)*(1/1000000)*(1000) # returns  TJ/thousand cf \n",
    "    Cost = NG_Cost*(1/HHV)*(1/AHF)*(1/277.778) # returns the Cost in $/MWh\n",
    "    return Cost\n",
    "\n",
    "def QuickGraphOutputs(base,name,hr = 72):\n",
    "    labels = A.Output.columns.tolist()\n",
    "    labels_2 = []\n",
    "    for i in labels:\n",
    "        if 'Reactor' in i:\n",
    "            pass\n",
    "        elif 'SOC' in i:\n",
    "            pass\n",
    "        elif 'MWhe' in i:\n",
    "            pass\n",
    "        else:\n",
    "            labels_2.append(i)\n",
    "    OG = A.Output[labels_2]\n",
    "\n",
    "    t = 0\n",
    "    td = hr\n",
    "    dashes = []\n",
    "    colors = ['xkcd:orangered','xkcd:cyan','pink']\n",
    "    pal = []\n",
    "    l = len(OG.columns.tolist())\n",
    "    d1 = 3\n",
    "    d2 = 0\n",
    "    while len(dashes)<l-1:\n",
    "        dashes+=[(d1, d2),(d1, d2),(d1, d2)]\n",
    "        pal+=colors\n",
    "        d2+=2\n",
    "    dashes+=[(1,2)]\n",
    "    pal+=['xkcd:black']\n",
    "    sns.set_style(\"whitegrid\")\n",
    "    ticks_24 = list(range(0,td+1,24))\n",
    "\n",
    "    fig = plt.figure()\n",
    "    plt.figure(figsize = (16,4))\n",
    "    g = sns.lineplot(data=OG.loc[t:td,:], palette=pal, dashes=dashes)\n",
    "    g.set_ylabel('Hourly Power [MWt]', fontsize=16)\n",
    "    #g.lines[0].set_linestyle(\"--\")\n",
    "    plt.xticks(ticks_24)\n",
    "    plt.legend(bbox_to_anchor=(1.01, 1), loc='upper left', borderaxespad=0)\n",
    "    g.figure.savefig(str(name)+'_Reactors.png')\n",
    "\n",
    "    fig2 = plt.figure()\n",
    "    plt.figure(figsize = (16,4))\n",
    "    g2 = sns.lineplot(data=A.pLMP[t:td+1])\n",
    "    g2.set_ylabel('LMP [$/MWh]', fontsize=16)\n",
    "    g2.set_xlabel('Hour', fontsize=16)\n",
    "    plt.xticks(ticks_24)\n",
    "    g2.figure.savefig(str(name)+'_LMPS.png')\n",
    "    \n",
    "def WaterfallPlot(A,name, gtitle):\n",
    "    fig = go.Figure(go.Waterfall(\n",
    "        orientation = \"v\",\n",
    "        measure = [\"relative\", \"relative\", \"relative\", \"relative\", \"relative\", \"relative\",\"relative\", \"total\", \"relative\"],\n",
    "        x = [\"Annualized Capital Cost\", \"Fixed Cost\", \"Variable Cost\", \"Fuel Cost\", \"Start-Up Cost\", \"TES Annualized Capital\", \"Electric Wholesale Revenues\", \"Net Revenue\", \"Natural Gas Cost\"],\n",
    "        textposition = \"outside\",\n",
    "        y = [-1*A.rACC,-1*A.rFOMC,-1*A.rVC,-1*A.rFC,-1*A.rSUC,-1*A.rTES,A.rEP, (A.rEP)-((A.rACC+A.rFOMC)+A.rVC+A.rFC+A.rSUC),A.rAVC],\n",
    "        connector = {\"line\":{\"color\":\"rgb(63, 63, 63)\"}},\n",
    "    ))\n",
    "\n",
    "    fig.update_layout(\n",
    "            title = gtitle,\n",
    "            showlegend = True\n",
    "    )\n",
    "\n",
    "    fig.write_html(name+\".html\")\n",
    "    fig.write_image(name+\".png\")\n",
    "\n",
    "\n",
    "def main(Temp = 300, NGCost = 4, hrcount = 8760, dem = 100, lmps = 'lmps.csv',sites = 'sites_3.csv', genInd = None, TES = 0, MaxMod = 2, naics = None, year = 2019, EP = 10):\n",
    "    if isinstance(lmps,(str)):\n",
    "        S,L= ImportData(lmps = lmps, sites = sites, hrcount= hrcount)\n",
    "    else:\n",
    "        S,L = sites,lmps\n",
    "    \n",
    "    Assessment = EconomicAssessment_OneGen()\n",
    "    heatPrice = NGTempCostCurve(Temp,NG_Cost = NGCost)\n",
    "    if genInd == None:\n",
    "        pass\n",
    "    else:\n",
    "        S = S.loc[S.index == genInd]\n",
    "    \n",
    "    if naics == None:\n",
    "        hrProfiles = [1]*8760\n",
    "    else:\n",
    "        hrProfiles = FP.QuickProfile(year, naics)\n",
    "    s = datetime.now()\n",
    "    print(s,'Profiles complete')\n",
    "    ParamsVarsPD(Assessment,S,L,dem, TES, naics, EP, heatLMP = heatPrice, minTemp = Temp, MaxMod =MaxMod, hrProfiles = hrProfiles)\n",
    "    Assessment.SolveModel( solver='cplex')\n",
    "    return Assessment\n",
    "\n",
    "A = main(hrcount = int(8760/10),sites = 'Base4_MSR.csv',lmps = 'SPP_SOUTH_2021.csv',NGCost = 4, MaxMod=1, TES = 0, naics = 325311, EP=1)\n",
    "e = datetime.now()\n",
    "print(e,'end')\n",
    "d = e-s\n",
    "print(d)\n",
    "QuickGraphOutputs(A,'v20_testing',hr = 200)\n",
    "WaterfallPlot(A,'v20_621_testing', 'SPP 2021, 100 MWht, 300 C')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
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       "    <tr>\n",
       "      <th>871</th>\n",
       "      <td>190.40085</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>872</th>\n",
       "      <td>300.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>873</th>\n",
       "      <td>300.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>874</th>\n",
       "      <td>300.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>875</th>\n",
       "      <td>300.00000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>876 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             0\n",
       "0      0.00000\n",
       "1      0.00000\n",
       "2      0.00000\n",
       "3      0.00000\n",
       "4    240.00000\n",
       "..         ...\n",
       "871  190.40085\n",
       "872  300.00000\n",
       "873  300.00000\n",
       "874  300.00000\n",
       "875  300.00000\n",
       "\n",
       "[876 rows x 1 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A.vRGen_PD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-06-24 15:34:01.034654 Profiles complete\n",
      "Building Model\n",
      "Params & Vars established\n",
      "Model Built\n",
      "Solving...\n",
      ">>Solver status is ok and solver termination condition is optimal\n",
      "-5300676.419366688\n",
      "iMSR was chosen for operation\n",
      "1.0 modules used\n"
     ]
    }
   ],
   "source": [
    "A = main(hrcount = int(8760/10),sites = 'Base4_MSR.csv',lmps = 'SPP_SOUTH_2021.csv',NGCost = 4, MaxMod=1, TES = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "thermFac = pd.read_csv('Thermal_Demands.csv')\n",
    "\n",
    "WithinRange = thermFac.loc[thermFac['Temp_degC']<850]\n",
    "WithinRange\n",
    "SPP_Facilities = WithinRange.loc[(WithinRange['STATE']=='NE')|(WithinRange['STATE']=='KS')|(WithinRange['STATE']=='OK')|(WithinRange['STATE']=='SD')]\n",
    "SPP_Facilities\n",
    "ERCOT_Facilities = WithinRange.loc[(WithinRange['STATE']=='TX')]\n",
    "ERCOT_Facilities\n",
    "SPP_Facilities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def PerFacilityRuns(facilities,time,region,year,outDir, TESval = 0):\n",
    "\n",
    "    try: \n",
    "        os.mkdir(outDir) \n",
    "    except OSError as error: \n",
    "        print('Directory Already Exists, Overwriting')\n",
    "\n",
    "    if region =='ERCOT':\n",
    "        if year == 2020:\n",
    "            LMPs = ['ERCOT_2020_HubAVG.csv']\n",
    "        if year == 2021:\n",
    "            print('Will fail until new data is entered')\n",
    "            LMPs = ['ERCOT_2021_HubAVG.csv']\n",
    "        if year == 'all':\n",
    "            print('Will fail until new data is entered')\n",
    "            LMPs = ['ERCOT_2020_HubAVG.csv','ERCOT_2021_HubAVG.csv']\n",
    "\n",
    "    if region =='SPP':\n",
    "        if year == 2020:\n",
    "            print('Will fail until new data is entered')\n",
    "            LMPs = ['SPP_SOUTH_2020.csv']\n",
    "        if year == 2021:\n",
    "            LMPs = ['SPP_SOUTH_2021.csv']\n",
    "        if year == 'all':\n",
    "            print('Will fail until new data is entered')\n",
    "            LMPs = ['ERCOT_2020_HubAVG.csv','ERCOT_2021_HubAVG.csv']\n",
    "\n",
    "    pdf2 = pd.DataFrame()\n",
    "    pdfBestPer = pd.DataFrame()\n",
    "    for LMP_Val in LMPs:\n",
    "        T = facilities['Temp_degC'].tolist()\n",
    "        \n",
    "        facID = facilities['FACILITY_ID'].tolist()\n",
    "\n",
    "        Ds = facilities['Thermal MWh/hr'].tolist()\n",
    "\n",
    "        S,L = ImportData( lmps = LMP_Val, sites = 'Base4.csv', hrcount= time)\n",
    "        j = 0\n",
    "        \n",
    "        while j < len(T):\n",
    "            print(\"####### \",str(j),\"of \",str(len(T)),' ##########')\n",
    "            G = []\n",
    "            P = []\n",
    "            LMP_List = []\n",
    "            ACC = []\n",
    "            FOMC = []\n",
    "            VC = []\n",
    "            FC = []\n",
    "            SUC = []\n",
    "            rTES = []\n",
    "            EP = []\n",
    "            AVC = []\n",
    "            t = T[j]\n",
    "            D = Ds[j]\n",
    "            N = []\n",
    "            GI = 0\n",
    "            while GI < len(S.index):\n",
    "                A = main(hrcount = time,sites = S, lmps = L,dem = D, genInd = GI, Temp = t, TES = TESval)\n",
    "                LMP_List.append(LMP_Val)\n",
    "                G.append(S.loc[S.index==GI]['Sites'].tolist()[0])\n",
    "                P.append(A.vTotalProfits)\n",
    "                if A.vTotalProfits != None:\n",
    "                    A.Output.to_csv(outDir+'/'+str(facID[j])+'_'+str(S.loc[S.index==GI]['Sites'].tolist()[0])+'.csv') \n",
    "                    N.append(sum(sum(A.vModuleS)))\n",
    "                else:\n",
    "                    N.append(0)\n",
    "                GI+=1\n",
    "                ACC.append(-1*A.rACC)\n",
    "                FOMC.append(-1*A.rFOMC)\n",
    "                VC.append(-1*A.rVC)\n",
    "                FC.append(-1*A.rFC)\n",
    "                SUC.append(-1*A.rSUC)\n",
    "                rTES.append(-1*A.rTES)\n",
    "                EP.append(A.rEP)\n",
    "                #AVC.append(A.rAVC)\n",
    "                #print(A.rAVC)\n",
    "                \n",
    "            df = {'LMPType':LMP_List,'Facility ID':[facID[j]]*len(S.index),'Temperature Req':[t]*len(S.index),'Generator':G,'Modules':N,\n",
    "                  'Facility Demand':[D]*len(S.index),'Profit':P,\"Annualized Capital Cost\":ACC, \n",
    "                  \"Fixed Cost\":FOMC, \"Variable Cost\":VC, \"Fuel Cost\":FC, \"Start-Up Cost\":SUC, \"TES Annualized Capital\":rTES, \n",
    "                  \"Electric Wholesale Revenues\":EP,} #  \"Natural Gas Cost\":AVC}\n",
    "            j+=1\n",
    "            pdf = pd.DataFrame(df)\n",
    "            \n",
    "            prof_best = pdf['Profit'].max()\n",
    "            \n",
    "            pdfBestPer = pd.concat((pdfBestPer,pdf.loc[pdf['Profit']==prof_best]))\n",
    "            \n",
    "            # Need to make the lowest cost option only returned value\n",
    "            \n",
    "            \n",
    "            pdf2 = pd.concat((pdf2,pdf))\n",
    "    pdfBestPer.to_csv(outDir+'/BestPer.csv')   \n",
    "    pdf2.to_csv(outDir+'/PerFacilityTest.csv')\n",
    "    return pdfBestPer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "SPP100 = PerFacilityRuns(SPP_Facilities,100,'SPP',2021,'SPP100', TESval = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "SPP100 = pd.read_csv('SPP100/BestPer.csv')\n",
    "SPP100\n",
    "\n",
    "SPP100['NatGCost_multiplier'] = NGTempCostCurve(SPP100['Temperature Req'],NG_Cost = 1.0, AHF_Coeffs = [0,-0.00038,0.90556]) # find the $/MWh-th of Natural gas from the $/mmbtu so 1 $/mmbtu = This output $/MWhth\n",
    "SPP100['NG_100Cost_1USD'] = SPP100['NatGCost_multiplier']*SPP100['Facility Demand']*100\n",
    "SPP100['NGBE_Cost'] = -1*SPP100['Profit']/SPP100['NG_100Cost_1USD']\n",
    "SPP100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Type = ['iPWR','iMSR','HTGR','Microreactor']\n",
    "Size = [1000,500,100,50,10]\n",
    "SupCurve = pd.DataFrame()\n",
    "for s in Size:\n",
    "    sub_df_s = SPP100.loc[SPP100['Facility Demand']<=s]\n",
    "    SupCurve_s = pd.DataFrame()\n",
    "    for t in Type:\n",
    "        subdf_t = sub_df_s.loc[sub_df_s['Generator']==t]\n",
    "        NGBEs = subdf_t['NGBE_Cost'].unique()\n",
    "        SupCurve_t = pd.DataFrame()\n",
    "        DemC = []\n",
    "        NumC = []\n",
    "        for i in NGBEs:\n",
    "            subdf = subdf_t.loc[subdf_t['NGBE_Cost']<= i]\n",
    "            DemC.append(subdf['Facility Demand'].sum())\n",
    "            NumC.append(len(subdf.index))\n",
    "        SupCurve_t['Natural Gas Cost'] = NGBEs\n",
    "        SupCurve_t['Facility Demand'] = DemC\n",
    "        SupCurve_t['Facility Count'] = NumC\n",
    "        SupCurve_t['Reactor Type'] = t\n",
    "        SupCurve_t['Demand Size'] = s\n",
    "        SupCurve = pd.concat((SupCurve,SupCurve_t))\n",
    "    SupCurve = pd.concat((SupCurve_s,SupCurve))\n",
    "SupCurve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.set(rc = {'figure.figsize':(15,3)})\n",
    "sns.set_style(\"whitegrid\")\n",
    "sc = sns.lineplot(SupCurve['Natural Gas Cost'],SupCurve['Facility Demand'], drawstyle='steps-pre',style = SupCurve['Reactor Type'], hue = SupCurve['Demand Size'])#, hue = All_models50['Region'], style = All_models50['Region'])\n",
    "sc.set(ylabel='Facility Demand [MWh-th/hr]')\n",
    "sc.set(xlabel='Natural Gas Cost [$/mmbtu]')\n",
    "sc.set(xlim=(5,40))\n",
    "plt.show()\n",
    "sc.figure.savefig('exampleSupplyCurve.png', dpi = 700)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.set(rc = {'figure.figsize':(15,3)})\n",
    "sns.set_style(\"whitegrid\")\n",
    "sc = sns.lineplot(SupCurve['Natural Gas Cost'],SupCurve['Facility Count'], drawstyle='steps-pre',style = SupCurve['Reactor Type'])#, hue = All_models50['Region'], style = All_models50['Region'])\n",
    "sc.set(ylabel='Facility Count')\n",
    "sc.set(xlim=(0,40))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "SPP10 = pd.read_csv('PerFacilityTest.csv')\n",
    "#SPP10 = pd.read_csv('BestPer.csv')\n",
    "#print(SPP10)\n",
    "\n",
    "SPP10['NatGCost_multiplier'] = NGTempCostCurve(SPP10['Temperature Req'],NG_Cost = 1.0, AHF_Coeffs = [0,-0.00038,0.90556]) # find the $/MWh-th of Natural gas from the $/mmbtu so 1 $/mmbtu = This output $/MWhth\n",
    "SPP10['NG_100Cost_1USD'] = SPP10['NatGCost_multiplier']*SPP10['Facility Demand']*8760\n",
    "SPP10['NGBE_Cost'] = -1*SPP10['Profit']/SPP10['NG_100Cost_1USD']\n",
    "SPP10\n",
    "\n",
    "Type = ['iPWR','iMSR','HTGR','Microreactor']\n",
    "SupCurve = pd.DataFrame()\n",
    "for t in Type:\n",
    "    subdf_t = SPP10.loc[SPP10['Generator']==t]\n",
    "    NGBEs = subdf_t['NGBE_Cost'].unique()\n",
    "    SupCurve_t = pd.DataFrame()\n",
    "    DemC = []\n",
    "    NumC = []\n",
    "    for i in NGBEs:\n",
    "        subdf = subdf_t.loc[subdf_t['NGBE_Cost']<= i]\n",
    "        DemC.append(subdf['Facility Demand'].sum())\n",
    "        NumC.append(len(subdf.index))\n",
    "    SupCurve_t['Natural Gas Cost'] = NGBEs\n",
    "    SupCurve_t['Facility Demand'] = DemC\n",
    "    SupCurve_t['Facility Count'] = NumC\n",
    "    SupCurve_t['Reactor Type'] = t\n",
    "    SupCurve = pd.concat((SupCurve,SupCurve_t))\n",
    "SupCurve_t\n",
    "\n",
    "sns.set(rc = {'figure.figsize':(15,3)})\n",
    "sns.set_style(\"whitegrid\")\n",
    "sc = sns.lineplot(SupCurve['Natural Gas Cost'],SupCurve['Facility Demand'], drawstyle='steps-post',style = SupCurve['Reactor Type'])#, hue = All_models50['Region'], style = All_models50['Region'])\n",
    "sc.set(ylabel='Facility Demand [MWh-th/hr]')\n",
    "sc.set(xlabel='Natural Gas Cost [$/mmbtu]')\n",
    "sc.set(xlim=(0,50))\n",
    "plt.show()\n",
    "plt.tight_layout()\n",
    "#sc.figure.savefig('exampleSupplyCurve_best.png', dpi = 700, bbox_inches = \"tight\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.set(rc = {'figure.figsize':(15,3)})\n",
    "sns.set_style(\"whitegrid\")\n",
    "sc = sns.lineplot(SupCurve['Natural Gas Cost'],SupCurve['Facility Count'], drawstyle='steps-post',style = SupCurve['Reactor Type'])#, hue = All_models50['Region'], style = All_models50['Region'])\n",
    "sc.set(ylabel='Facility Count')\n",
    "sc.set(xlim=(0,50))\n",
    "plt.show()\n",
    "#sc.figure.savefig('exampleSupplyCurve_Num_best.png', dpi = 700, bbox_inches = \"tight\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "facIDs = SPP10['Facility ID'].unique().tolist()\n",
    "inds = []\n",
    "for i in SPP10['Facility ID'].tolist():\n",
    "    inds.append(facIDs.index(i))\n",
    "SPP10['Indexer'] = inds\n",
    "sns.set_style(\"whitegrid\")\n",
    "sns.scatterplot(data=SPP10, x=\"Indexer\", y=\"NGBE_Cost\", style= \"Generator\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "SPP10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import altair as alt\n",
    "\n",
    "alt.Chart(source).mark_circle(size=60).encode(\n",
    "    x='Horsepower',\n",
    "    y='Miles_per_Gallon',\n",
    "    color='Origin',\n",
    "    tooltip=['Name', 'Origin', 'Horsepower', 'Miles_per_Gallon']\n",
    ").interactive()"
   ]
  }
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