{ "cells": [ { "cell_type": "markdown", "id": "03058ac5", "metadata": {}, "source": [ "# Multifluid+Association\n", "\n", "Version 0.22 of teqp adds a new sort of hybrid model -- a combination of multifluid model plus an association model. Conceptually the pure fluid EOS consider the self-association implicitly, so you may want to consider the association model to only allow cross-assocation, which be enforced by supplying the \"self_assocation_mask\" logical mask in the \"options\" of the model. See below" ] }, { "cell_type": "code", "execution_count": 1, "id": "ae2f6b60", "metadata": { "execution": { "iopub.execute_input": "2024-12-12T18:09:47.762836Z", "iopub.status.busy": "2024-12-12T18:09:47.762435Z", "iopub.status.idle": "2024-12-12T18:09:48.265831Z", "shell.execute_reply": "2024-12-12T18:09:48.265299Z" } }, "outputs": [], "source": [ "import teqp, json, numpy as np\n", "import matplotlib.pyplot as plt\n", "import pandas \n", "\n", "BIP = [{\n", " \"Name1\": \"Water\",\n", " \"Name2\": \"Ammonia\",\n", " \"betaT\": 1.0,\n", " \"gammaT\": 1.0,\n", " \"betaV\": 1.0,\n", " \"gammaV\": 1.0,\n", " \"F\": 0.0\n", "}]\n", "jmf = {\n", " \"components\": [\"Water\", \"Ammonia\"],\n", " \"root\": teqp.get_datapath(),\n", " \"BIP\": BIP, \n", "}\n", "\n", "jassoc = {\n", " \"kind\": \"Dufal\",\n", " \"model\": {\n", " \"sigma / m\": [3.0555e-10, 3.3309e-10],\n", " \"epsilon / J/mol\": [3475.445374388054, 323.70*8.3124462618],\n", " \"lambda_r\": [35.823, 36.832],\n", " \n", " # Note the scaling factors of 0.2 on the bonding energy to yield more reasonable behavior\n", " \"epsilon_HB / J/mol\": [0.2*13303.140189045183, 0.2*1105.0*8.314462618], \n", " \n", " \"K_HB / m^3\": [496.66e-30, 560.73e-30],\n", " \"kmat\": [[0.0,0.0],[0.0, 0.0]],\n", " \"Delta_rule\": \"Dufal\",\n", " \"molecule_sites\": [[\"e\",\"e\",\"H\",\"H\"],[\"e\",\"H\",\"H\",\"H\"]],\n", " \"options\": {\"self_association_mask\": [False, False]}\n", " }\n", "}\n", " \n", "j = {\n", " 'kind': 'multifluid-association',\n", " 'model': {\n", " 'multifluid': jmf,\n", " 'association': jassoc\n", " }\n", "}\n", "\n", "model = teqp.make_model(j)" ] }, { "cell_type": "code", "execution_count": 2, "id": "469bdad6", "metadata": { "execution": { "iopub.execute_input": "2024-12-12T18:09:48.268024Z", "iopub.status.busy": "2024-12-12T18:09:48.267636Z", "iopub.status.idle": "2024-12-12T18:09:48.269977Z", "shell.execute_reply": "2024-12-12T18:09:48.269590Z" } }, "outputs": [], "source": [ "# model.get_assoc_calcs(300, 300, np.array([0.5, 0.5]))" ] }, { "cell_type": "code", "execution_count": 3, "id": "3d9a26b9", "metadata": { "execution": { "iopub.execute_input": "2024-12-12T18:09:48.271582Z", "iopub.status.busy": "2024-12-12T18:09:48.271317Z", "iopub.status.idle": "2024-12-12T18:09:48.525981Z", "shell.execute_reply": "2024-12-12T18:09:48.525523Z" } }, "outputs": [], "source": [ "T = 293.15 # K\n", "pure = teqp.build_multifluid_model([\"Ammonia\"], teqp.get_datapath())\n", "anc = pure.build_ancillaries()\n", "j = model.trace_VLE_isotherm_binary(T, np.array([0, anc.rhoL(T)]), np.array([0, anc.rhoV(T)]))\n", "df = pandas.DataFrame(j)" ] }, { "cell_type": "code", "execution_count": 4, "id": "e9760392", "metadata": { "execution": { "iopub.execute_input": "2024-12-12T18:09:48.527716Z", "iopub.status.busy": "2024-12-12T18:09:48.527368Z", "iopub.status.idle": "2024-12-12T18:09:48.791303Z", "shell.execute_reply": "2024-12-12T18:09:48.790776Z" } }, "outputs": [ { "data": { 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i27ZtOH36dJXzhYaGGqddGDduHCZPnozBgwfj9OnT9X75qrIZM2bgjTfewKZNm6r0KapOdHQ0rl27hvfffx/e3t6YMWOG2Woj82LQIYvSp08f/P777zh27Jixf45Br169MGvWLFy9ehU7d+5EQECA8X/y58+fx6BBg9C6dWt88sknCA4Ohkajwbp16/Dpp5/WqCVDr9djyJAheOmll6p9vmXLliaPq/tH2dBf6Ha/OGvCcI7q+nBU1rFjR2zcuNFk34svvgg/Pz/MmjXLZP/tOnOePXsW+/fvB4BqWxqWLl1aJejc7H+7N9t/YxC409fXRl3O+eOPP2LChAkYM2YMZs2aBR8fH6hUKsyZMwfnz583HleTn/2RI0dw7733ol27dli5ciXs7Kr/J7imP/uMjAwEBAQYH1+8eBGjRo0yOaZ79+4mQefy5ctVQuTN5OTkYPjw4cjOzsaOHTtM3ssgMDAQO3fuxNmzZ5GcnIwWLVrAz88PAQEBVf7OVOfBBx/EggULsH37dgwdOrRGddWFg4MDvLy8kJmZWaPj7ezs8Msvv2DYsGF48cUX4e7ujokTJ5qtPjIfBh2yKIYWmp07dyI2NhbTp083PhcZGQmtVoutW7di7969GDFihPG533//HSUlJVi7dq3J/9qrGxlzs/8lh4eHIz8/H4MHD65z/YYJyRISEup8DoOEhAQolcrb/rLw8PCoUrOHhwf8/f1r/VmWLl0KtVqNH374oUoo2LlzJ+bNm4fExMQqLSNyaNKkCRwdHattNTh16hSUSmWNf6HfysqVK9GsWTOsWrXK5M/O7NmzTY4LCQmBg4PDTX/258+fx7Bhw+Dj44N169ZVGZlVmeEcbdq0uWVtrq6uJpdl/fz8TMIXYHqpMDU1Fb/99hvWrFlzy/MCQHFxMUaNGoUzZ85g06ZNiIiIuOXxLVq0MIbj+Ph4JCUlYcKECbd9H8Nlq5pcXr4TeXl5SE9Pv2mLbXXs7e2xdu1aDBgwAJMmTYK7u7txtBw1HuyjQxala9eusLe3x9KlS3H16lWTFh2tVosuXbogJiYGBQUFJpetDL+UK//PPCcnB4sWLaryHk5OTlVmpAWAhx9+GLt378bff/9d5bns7GyUl5fftv7AwEAEBwfXaAjv7cTFxaFt27a3veRWn5YuXYq+ffti7NixePDBB002Q+vQTz/91GD13IpKpcLdd9+N3377zWTYekpKCpYtW4Y+ffrA1dW1Xt4HMP2ztXfvXuzevdvkOLVaja5du1b7s09OTsbdd98NpVKJv//++7a/bOPi4uDm5mZy2a06bdq0MRlddt999+Grr77CsmXLcOnSJfz000/45ptvoNPp8Pfff2PAgAHo06cPBg0adMvz6nQ6jB07Frt378aKFSvQs2fPWx5fmV6vx0svvQRHR0c888wzxv03u1y2cOFCKBQKk9Fq6enpOHXqVJVL0DVRXFxsMjLP4J133oEQAsOGDavV+VxdXbF+/Xo0b94c48aNw+bNm2tdE8mLLTpkUTQaDbp164YdO3ZAq9UiMjLS5PlevXrh448/BmDaP+fuu++GRqPBqFGj8PTTTyM/Px8LFiyAj48PkpKSTM4RGRmJ+fPn491330Xz5s3h4+ODgQMHYtasWVi7di1GjhyJCRMmIDIyEgUFBTh27BhWrlyJixcv3vZSAiD1i1i9ejWEECYtAJcuXcIPP/wAAMZfhu+++y4Aqd/C+PHjjceWlZVh27ZtmDJlSm2+vjuyd+9enDt37qbDrQMDA9GlSxcsXboUL7/8coPVdSvvvvsuNm7ciD59+mDKlCmws7PD119/jZKSEmM/lTs1cuRIrFq1Cvfddx/uueceJCQk4KuvvkJERESV4cqjR4/G66+/jtzcXJOQNWzYMFy4cAEvvfQSdu7caexwD0h9ZoYMGWJyno0bN2LUqFG37aMzcuRIfPzxx0hKSoK/vz+eeeYZbNq0Cf/6178ASJfTZs2ahTfffBP33nsvnnzySXz00Ue3/cwvvvgi1q5di1GjRiEzMxM//vijyfOPPfaY8f4LL7yA4uJidOrUCWVlZVi2bBn27duHJUuWmLT8GToDDxs2DCEhIcjMzMSvv/6K/fv3Y9q0aca+eIC0pMR//vMfbNmyxWQOoi+++ALZ2dm4du0aAKkl98qVKwCkqQPc3NyQnJyMzp07Y9y4ccYW1r///hvr1q3DsGHDMHr06Nt+/hs1adIEGzduRO/evTFmzBhs3rwZ3bt3r/V5SCYyjfYiuinDMORevXpVeW7VqlUCgHBxcaky5HPt2rWiQ4cOwt7eXoSFhYn//ve/4rvvvqsypDk5OVncc889wsXFRQAwGUqcl5cnXn31VdG8eXOh0WiEt7e36NWrl/joo49EaWmpEOL68PIPP/yw2voPHjwoAIgdO3aY7N+yZYvJsNrK243D3f/66y8BQJw9e7YW39x1dRlePm3aNAHAZKj2jd566y0BQBw5ckQIUf1U+Tf7fgyff8WKFSZ1tm3btsr7hIaGinvuuafK/ure7+DBg2Lo0KHC2dlZODo6igEDBlRZfsMwvHz//v3V1lR55uIbh5fr9Xrx/vvvi9DQUKHVakXnzp3FH3/8IaKjo43Dtg0MQ/N/+OGHKnXX9Gd/8uRJAUBs2rSpyuevTr9+/cR9991nMmw+Pj5exMbGioKCApGVlSX27dsnCgoKanQ+wzlvVXNlixYtEh07dhROTk7CxcVFDBo0SPzzzz9VzrlhwwYxcuRIERAQINRqtXBxcRG9e/cWixYtqjKL+OzZs6v8XIQQxiHf1W2Gv+NZWVniscceE82bNxeOjo5Cq9WKtm3bivfff9/4d/h2qvtzJoT0s/H29haenp7i+PHjNToXyU8hRD0MDyEiE4MGDUJAQICxBae2xowZc9NFTsmyPfnkkzhz5gx27NhRp9dPnz4d27dvR1xc3G1bdACpA3m3bt3wwAMPYP78+VVmNAakfjAbN27EvffeW6eaiBozBh0iM9i7dy/69u2Ls2fPmqxgXhMnT55E+/btcfjwYbRr185MFZK5JCYmomXLlti8ebPJCuY1kZGRgdDQUPzyyy8mne1vZ+/evbj33nvh5OSE5557Dv369YOPjw/S09Pxzz//YN68eVCpVDh69OgtO0ETWSMGHSIiK5CWloa3334bS5cuNRni7u3tjaeeegqvvPJKg3ZsJ7IUDDpERFZEp9Ph9OnTSE9Ph5eXF1q3bs2ZfcmmMegQERGR1eI8OkRERGS1GHSIiIjIatn0hIF6vR7Xrl2Di4tLjYZxEhERkfyEEMjLy0NAQACUylu32dh00Ll27Vq9rIVDREREDe/y5csICgq65TE2HXRcXFwASF9UfayJQ0REROaXm5uL4OBg4+/xW2n0QSc7OxuDBw9GeXk5ysvL8cILL2DSpEk1eq3hcpWrqyuDDhERUSNTk24njT7ouLi4YPv27XB0dERBQQHatWuH+++/H15eXnKXRkRERDJr9KOuVCoVHB0dAQAlJSUQQoBTAxERERFgAUFn+/btGDVqFAICAqBQKLBmzZoqx8TExCAsLAz29vaIiorCvn37TJ7Pzs5Gx44dERQUhFmzZsHb27uBqiciIiJLJnvQKSgoQMeOHRETE1Pt88uXL8fMmTMxe/ZsHDx4EB07dsTQoUORmppqPMbd3R1HjhxBQkICli1bhpSUlIYqn4iIiCyY7EFn+PDhePfdd3HfffdV+/wnn3yCSZMmYeLEiYiIiMBXX30FR0dHfPfdd1WO9fX1RceOHbFjx45qz1VSUoLc3FyTjYiIiKyX7EHnVkpLSxEXF4fBgwcb9ymVSgwePBi7d+8GAKSkpCAvLw8AkJOTg+3bt6NVq1bVnm/OnDlwc3MzbpxDh4iIyLpZdNBJT0+HTqeDr6+vyX5fX18kJycDAC5duoS+ffuiY8eO6Nu3L6ZNm4b27dtXe75XX30VOTk5xu3y5ctm/wxEREQkn0Y/vLx79+44fPhwjY7VarXQarXmLYiIiIgshkW36Hh7e0OlUlXpXJySkgI/Pz+ZqiIiIqLGwqKDjkajQWRkJDZv3mzcp9frsXnzZvTs2VPGyoiIiKgxkP3SVX5+Ps6dO2d8nJCQgMOHD8PT0xMhISGYOXMmoqOj0bVrV3Tv3h2fffYZCgoKMHHiRBmrJiIiosZA9qBz4MABDBgwwPh45syZAIDo6GgsXrwYY8eORVpaGt58800kJyejU6dOWL9+fZUOyrURExODmJgY6HS6O66fiIiILJdC2PB6Cbm5uXBzc0NOTk69L+qpKytBQZkero4O9XpeIiIiW1eb39+yt+hYI315OdLfbwuhL8cA5XwEe7sizMsRIV5OCPV0RKiXI0I8HdHERVujlVeJiIiobhh0zKAoLxO+Ig1QAMqiDBy+rMfhy9lVjnNQqxDi6YhgT0cEezog2MP0vpOWPx4iIqI7wd+k5qBSG+8uejQCiQp/XMooxKWMAlzKKERiZiGScopQVKbD6ZQ8nE7Jq/Y0nk4aBHs4IMjDEUEeDhWbIwI9HBDo7sAgREREdBv8TWkOlS5HtfBUoV2Qf5VDSsv1uJZdhEuZhbicKYWfy5mFuJxViMuZRcgpKkNmQSkyC0px5EpOtW/j4ag2hp5Ad0cEuNsjyMMBAe7S5uWk4aUxIiKyaQw65lZWWO1ujZ0SYd5OCPN2qvb53OIyKfhkFuFqdhGuZBXiSlYRrmQV4WpWIXKLy5FVWIaswjIcv1r94qRaO2VF6LGHv1tFAHKzh3+lW2e2ChERkRWzyd9yDTm8XFFeXKfXudqr0TbADW0D3Kp9Pq+4TApAmUW4llOEq1lSILqaLd1Pyy9BSbkeCekFSEgvuOn7uNjbIcDNAf7u9vB3kwKRn9v1+/5u9rxERkREjRaHl5theHlBXjacPg4FAJQ+/BM0ESPq7dw1VVquR0puMa5mF+FadhGScopxJasISTlFSMouRlJOEXKLy2t0Lhd7O5PgYwhCvq7Xg5GrvR0vkxERUYPg8HJLUl4ky9tq7JQVI7gcb3pMfkk5knOKcK0i+CTlFEshKLcYyRWP84rLK7Z8nEnJv+m5HNQqYwjyc7OHn6t9xWMH+LlK+7ycNFAqGYaIiKjhMOiYW3mJ3BXclLPWDs19XNDcx+WmxxjCUFJOsTEIJVcKQsm5xcguLENRmQ4X0gtw4RaXydQqBXxc7K8HIldD65AD/Ny08HNzgI+LFmqVRS/BRkREjQiDjpkpLDjo1ERNwlBxmQ7JFUEoJVe6rRyEknOKkZZfgjKdMPYjuhmlAmjiIoUef9frl8n8Kl0683W1h8aOYYiIiG6PQcfcdKVyV2B29mrVLUeQAUCZTo+0vJKKEGTaKmQIRym5xSjTCaTkliAltwRHbvGe3s7aitFk9hWjyQwdqqVRZj4u9lDxMhkRkc1j0DE3mfroWBq1Smmc3+dm9HqBjILSitah65fLKrcOJeUUo7Rcj/T8EqTnl+DoTeYYUikVxn5C/u4Vcw15OCDQ3d4455CLvbra1xIRkfWwyaDToKuXN/JLVw1JqVSgiYsWTVy0aB9U/bB6IQQyC0qRlFNsHE12zdChOvt6INLpK10mu5RV7blc7e0Q6OFYEX6kEGaYgDHY05ETLhIRWQEOLzfz8PLy3i/Cbsib9XZuuj2dXiAtr6QiAEnD6aVJF6XHV7Olmadvx1GjQpDH9TXIgjykACQ9dmCLEBGRTDi83JLYQB8dS6NSKozD3LuEeFR7TH5JuRR6sopwpWKuIcOki1eyCpGSW4LCUh3OpNx8WL27o9oYekI8nRDm5YhQLyeEeTvC18WeQ+mJiCwAg4656XjpyhI5a+3Q0tcFLX2rH01WXKbDtewiXM4qMq5BdiWzqGItskJkFZYhu7AM2YU5OHa1aj8hrZ0SoYbgY7x1QqiXIwLcHdhRmoiogTDomFs5W3QaI3u1Cs2aOKNZE+dqn88vKa9Yi6zQGIYuVqxOfzmzECXl+pu2BqlVCgR7OiLMywnNvJ0Q7uOM5j7OCG/iDE8njbk/GhGRTWHQMQMHRxcUtn8Mjsd+hErJ+V6skbPWDm38XdHGv+q14TKdtDL9xYxCXMoowMX0ituMAlzOLEKpTo8LaQW4kFaAf254rYejGuFNpNAT7uNkvB/k4QA7TqRIRFRrDDpmoFSp4OjTDACg0LNFx9aoVUqEejkh1MsJQBOT53R6gaScIlzKKJRmkk7Lx/m0ApxPzcfV7CJkFZbhwKUsHLhhpJhGpUSYt6Mx+LTwdUYrPxc083bm5IlERLfAoGMuKq10y+HlVIlKqUCQhyOCPBzRu7m3yXNFpTpcSL8efM5XhKALafk3vRRmp1QgvIkUelr5uaB1xW2guwOHxhMRgUHHfOwYdKh2HDQqtA1wQ9sA0zmE9BVzAhmCz7nUfJxNycPp5DzklZTjdEoeTqfkofJU0i5aO7SsHH58XdDazxVujhwST0S2xSaDToNMGGgIOhxeTndIqVQYV6Lv3+r6fiEEruUU43RyLk4lS8HndHIezqflI6+kHHGXshB3wyUwfzd7tPJzQbsAN7QLdEOHIDf4u9mz9YeIrBYnDDTDhIEAgCPLgdWTgWb9gcd/q99zE91CabkeF9LzcTo5zyQA3WwxVS8njTH0tAt0Q/tAhh8ismycMNASGC9dsUWHGpbGTonWfq5o7eeK0ZX25xaX4UxyHk4m5eLY1Rwcu5qLMyl5yCgoxbYzadh2Js14rLezxhh6DCHIz5Xhh4gaHwYdczFeumIfHbIMrvZqdA3zRNcwT+O+4jIdTibl4vhVaeLDo1dycDY1H+n5pdh6Og1bT1cffjoGuaNLqAfn/SEii8egYy7sjEyNgL1ahc4hHuhcaakMQ/g5djUHx65IAehm4adZEyd0DfVA11BPRIZ5oJm3E1t9iMiiMOiYi529dMugQ43MzcJPfEXLz9ErOTiUmFUx9F3afjlwBQDg6aRBlxAPdA3zQNdQD7QLdIO9WiXXRyEiYtAxG7bokBWxV6vQJcTDZJHUrIJSxFVMbhh3KRNHruQgs6AUm06mYNPJFADSRIftAl3RNcwTkaFS+PFy1sr1MYjIBjHomIuxRadY3jqIzMTDSYPBEb4YHOELACgp1+H41VwcvJSFA5cyEXcpC+n5pTiYmI2DidnG1zX1djKGnu5NPdGUl7uIyIwYdMyFQYdsjNZOhchQD0SGemASmkEIgUsZhcYWnwMXs3A2NR8J6QVISC/AyjjpcleAmz16N/dGnxbe6BXujSYubPEhovrDoGMuxktXDDpkmxQKBcK8nRDm7YQHI4MAANmFpTiYmIUDF6VLXocTs3Etpxgr4q5gRUXwae3ngj7NvdG7hTe6h3nCSct/poio7vgviLnYOUi3ulJArwOU7JBJ5O6owcDWvhjYWrrcVVSqw/6LmYg9l44dZ9MRnyTN8nwqOQ/f7kyAWqVA5xAPKfg090bHIDeu4k5EtWKTMyNXXgLizJkz5pkZuSQfmBMo3X/tGqBxqt/zE1mhjPwS7DqfYQw+N87m7KK1Q49wL2PwCW/C/j1Etqg2MyPbZNAxMOsSEHod8HbFxGyzLgBOXvV7fiIrZ+jjs/NcOmLPpWPX+QzkFJWZHONv6N/T3Bu9mnvBx8VepmqJqCFxCQhLoFQBSjWgLwPKq19jiIhurnIfn8d6hEKnFzhxLQc7z6Vj59l0HLiYhaScYqyMu2Ls2Nwx2B13R/hiSIQvWvg4s7WHiNiiY7YWHQCYEwyU5ALTDgJe4fV/fiIbVlSqw4FLmcYWn+NXc02eD/NyxJAIXwyJ8ENkqAdUSoYeImvBFh1LoXaQgk5ZodyVEFkdB40KfVs0Qd8WTQAAqbnF2HQyFRvjkxF7LgMXMwqxYEcCFuxIgJeTBgNb+2BIhC/6tmgCBw0HBxDZCgYdczLMpVPGIeZE5ubjao9Ho0LwaFQI8kvKsf1MGjbGp2DzyRRkFJQah7Dbq5Xo26IJhkT4YlBrH87UTGTlGHTMSe0o3bJFh6hBOWvtMKK9P0a090eZTo/9CZnYEJ+CjfEpuJpdhI0V95UKoGuoJ+5uK/XrCfXi6Egia8M+Oubso/NNf+DaIeDRX4CWQ+v//ERUK0IIxCflYmN8CjacSEF8kmm/npa+zrg7wg9DInzRPtANSvbrIbJI7KNjKQyTBrJFh8giKBQKtA1wQ9sAN0wf3BJXsgqxKT4FG+JTsDchE2dS8nEm5Ry+2HIOvq5aDInwxcgOAege5snQQ9RIMeiYk9oQdDi8nMgSBXk4YkLvppjQuylyCsuw5XQqNsQnY9vpNKTkluDHPYn4cU8iAtzsMapTAMZ0CkQbfzO0/hKR2TDomJOGfXSIGgs3RzXGdA7EmM6BKC7TYfeFDPx1LAl/HUvGtZxifL3tAr7edgGtfF1wb6cAjO4UgCAPR7nLJqLbYB8dc/bRWTUZOLocGPIO0Pv5+j8/EZldcZkOW06lYs3hq9hyKg2lOr3xuW5hHhjdKRD3tPeHh5NGxiqJbAv76NxG5bWuzIqjrogaPXu1CsPb+2N4e3/kFJVh/fEkrDl0DXsSMrD/Yhb2X8zCW2tPoH+rJri3UyCGtPHlPD1EFoQtOuZs0fn7dWD3F0Cv54G736n/8xORbJJyivD7kWtYc+iayegtJ40KQ9v6YXTnQPQO9+Jq60RmwBYdS2Fs0WFnZCJr4+/mgMl3hWPyXeE4m5KHNYev4rfD13AlqwirDl3FqkNX4e2sxaiO/hjXPQQtfV3kLpnIJjHomJOhM3Jpgbx1EJFZtfB1wayhrfF/d7dC3KUsrDl8FX8eTUJ6fgkWxV7EotiLiAz1wKPdQ3BPB3/Yq3lpi6ihMOiYk8ZZui1j0CGyBQqFAl3DPNE1zBOzR7XF9jNp+OXAZWw6mYq4S1mIu5SF//x+Avd3CcK47iFo5cdWHiJzY9AxJ03FdPJs0SGyOWqVEoPa+GJQG1+k5hZjRdwV/LQvEVeyirB410Us3nURXULc8WhUKO5p788OzERmwqBjTgw6RARpwdGpA5rj2X7h2HEuHT/tTcTGkyk4mJiNg4nZUitP50CMiwpBaz9OSEhUnxh0zMkQdEry5a2DiCyCUqlAv5ZN0K9lE2Mrz8/7E3E5swhLdl/Ckt2X0CXEHeO6h2BkhwC28hDVAw4vN+fw8sQ9wHdDAY+mwAuH6//8RNTo6fUCO8+l46d9idgYn4JyvfRPsou9HVt5iG6Cw8stBS9dEdFtKJUK3NWyCe5q2QSpecVYGXcFP++7jMTMQpNWnif6NMWwtn6cl4eoltiiY84WncwEYF4naT6d15Pq//xEZJX0eoHY81Irz4YT11t5At0dMKFXGMZ2D4arvVrmKonkU5vf3ww65gw6BRnAh82k+29mAkpebyei2knNK8bSPYn4cc8lZBSUApBmX364WzAm9mqKEC8uLEq2h0GnhswedMpLgHd9pPsvXwIc3Ov/PYjIJhSX6fDb4atYuDMBZ1KkAQ5KBTAkwhdP9mmGbmEeUCgUMldJ1DDYR8dS2GkBlQbQlQIleQw6RFRn9moVxnYLwcNdg7HjbDoW7kzAtjNp+PtECv4+kYIOQW54sk9TjGjvDzX78RAZsUXHnC06ADC3GVCYATy7G/CNMM97EJFNOpuSh+9iE7Dq4FWUlOsBAH6u9ojuFYZHu4fAzZH9eMg61eb3N2O/uWkrpngv5Vw6RFS/Wvi6YM79HbDrlYGYOaQlvJ21SM4txn/Xn0KPOZvxzh/xSM0tlrtMIlkx6JibIegU58pbBxFZLS9nLZ4f1AKxrwzARw91RGs/FxSV6bBwZwL6zN2CN9Ycx9XsIrnLJJKFTQadmJgYREREoFu3buZ/M62bdFuSY/73IiKbprVT4cHIIPz1Ql8seaI7uoZ6oLRcjx/2XEK/uVvw8sqjuJjOeb3ItrCPjrn76Pz0KHD6T2Dkp0DXJ8zzHkRE1RBCYM+FTHz+z1nsOp8BQBqpNbpTIKYOCEdzH66eTo0TR11ZEvuKHwAvXRFRA1MoFOgZ7oWe4V6Iu5SJL/45hy2n07D60FWsOXwVw9v5YeqA5mgb4CZ3qURmY5OXrhqUveHSFYMOEcknMtQTiyZ2x+/P9cHQtr4QAlh3LBn3zNuJp5bsx+HL2XKXSGQWDDrmpjW06LCPDhHJr32QG74e3xV/T78LozoGQKEANp1MxZiYWIxfuBf7EjLlLpGoXjHomJuhRYdBh4gsSCs/F3w+rjM2zeyHByODoFIqsONsOh7+ejeiv9uH41f5bxZZBwYdc2PQISILFt7EGR891BFb/68/xnUPgZ1SgW1n0jDy8514btlBJHCUFjVyDDrm5uAh3RZlyVsHEdEtBHs6Ys797bFpZj+M7hQAAPjjaBIGf7INr646huQcTjxIjRODjrkZ1rcqypazCiKiGgnzdsL/HumMdc/3xcDWPtDpBX7al4h+H27BnHUnkV1YKneJRLXCoGNubNEhokYoIsAV303ohhXP9ETXUA+UlOvx9fYL6Dt3C7745ywKSsrlLpGoRhh0zM3eXbotzgZsd25GImqkuoV5YsUzPfHdhK5o7eeCvOJyfLThDPp9uAVLdl1EacViokSWikHH3AwtOvpyoCRP3lqIiOpAoVBgYGtfrHu+L/73SCeEeDoiPb8Us9eewJBPt2HDiWTY8CT7ZOEYdMxN7QDY2Uv3efmKiBoxpVKB0Z0CsWlmP7wzph2auGhxKaMQk3+Iw2ML9+JUMidGJcvDoGNuCgXg6CXdL8yQtxYionqgsVNifI9QbPm//pjSPxwaOyViz2VgxP924N9rjiGzgB2WyXIw6DQEB0/ptogzjhKR9XDW2uGlYa2xeWY/DG/nB70AftyTiP4fbsHCnQko07H/DsmPQachOFYEnUIGHSKyPsGejpj/WCR+mtQDbfxdkVtcjnf+iMfQz7Zjy6lUucsjG8eg0xB46YqIbEDPcC/8Ma0P5tzfHl5OGlxIK8DExfsR/d0+nEvlYAySB4NOQ2DQISIboVIqMK57CLbM6o/JdzWDWiUtKTH0sx14a+0J5BaXyV0i2RgGnYbAoENENsbVXo3XRrTBhhn9MLiNL3R6gcW7LmLwx9vw59EkDkenBsOg0xCcvKXbgjR56yAiamBNvZ3wbXRX/PBkdzT1dkJqXgmmLjuIiYv343JmodzlkQ1g0GkITk2k23wGHSKyTX1bNMFfL/TFC4NaQKNSYuvpNAz5dBvmbz3P0VlkVgw6DcHZR7pliw4R2TB7tQozhrTEX9P7okczTxSX6fHf9acwct5OxF3ihKpkHgw6DcHQosOgQ0SE8CbO+GlSD3z0UEd4OKpxOiUPD8zfhddWH0NOITsrU/2yyaATExODiIgIdOvWrWHe0NBHpyQXKCtumPckIrJgCoUCD0YGYfOL/fFQZBAAYNneRAz6ZCt+O3yVnZWp3iiEDf9pys3NhZubG3JycuDq6mq+NxICeKcJoC8Dph8H3IPN915ERI3QngsZeH31MZxPKwAA9G3hjffGtEeIl6PMlZElqs3vb5ts0WlwCkWly1ecJZSI6EY9mnlh3Qt98eKQltDYKbHjbDqG/W87fth9EXq9zf5/nOoBg05DcfGVbvNS5K2DiMhCae1UmDaoBf6efheimnqisFSHN347gfHf7cWVLA5Fp7ph0GkoLv7SbX6yvHUQEVm4pt5O+GlSD8weFQF7tbQy+rDPduDnfYnsu0O1xqDTUJzZokNEVFNKpQITezfFXy/cha6hHsgvKccrq44hetF+JOUUyV0eNSIMOg3F0KKTlyRvHUREjUhTbycsf7on/n1PG2jslNh+Jg13f7odKw5cZusO1QiDTkMx9NHJZ4sOEVFtqJQKPNW3GdY93xedgt2RV1yOWSuP4qklB5Cayyk76NYYdBqKoUUn95q8dRARNVLNfZyx8pmeeHlYa2hUSmw+lYqhn23H5pP8DyTdHINOQ+GlKyKiO2anUuLZ/uH44/k+aBvgiqzCMjy55ADeWnsCxWU6ucsjC8Sg01DcpJk/UZAGlJfIWwsRUSPX0tcFq6b0wpN9mgIAFu+6iDExsTibkidzZWRpGHQaioMHYGcv3c+9Km8tRERWQGunwhsjI7B4Yjd4O2twKjkPo77YiWV7OQydrmPQaSgKBeAaKN1nPx0ionrTv5UP1r3QF31beKO4TI/XVh/Dsz8eRHZhqdylkQVg0GlIbhVBJ4ctOkRE9cnHxR5LJnbHayNaQ61SYP2JZAz/3w7svZAhd2kkMwadhmRs0bkibx1ERFZIqVRg8l3hWPVsb4R5OSIppxjjFuzBJxvPQMf1smwWg05DMgSdHAYdIiJzaR/khj+e74sHugRBL4B5m88i+rt9yMjnQBBbxKDTkNxDpNvsy/LWQURk5Zy1dvj44Y74bGwnOKhV2HkuHaM+34nDl7PlLo0aGINOQzIGnUvy1kFEZCPGdA7Eb8/1RjNvJ1zLKcbDX+3G0r2XOCrLhjDoNCSPUOk2OxHgXzIiogbR0tcFvz3XG0Pb+qJUp8frq49j1sqjnGDQRjDoNCTXIAAKoLwYyE+VuxoiIpvhYq/GV49F4pXhraFUACvjruD+L3chMaNQ7tLIzBh0GpKd5nqH5OxEeWshIrIxCoUCz/QLx49PRsHLSYP4pFyM+mIntpzifzytGYNOQ2M/HSIiWfVq7o3fp/VBp2B35BSV4Ykl+/HFP2fZb8dKMeg0NI8w6TYzQdYyiIhsWYC7A5Y/3QOP9QiBEMBHG85g5i9H2G/HCjHoNDSvZtJt5nl56yAisnFaOxXeHdMe745pB5VSgdWHruJf3+5FOufbsSoMOg3NM1y6zbwgbx1ERAQAeKxHKJZM7A4XezvEXcrCmJhYnE7mKujWgkGnoXlWtOhksEWHiMhS9GnhjdVTeiPUyxFXsorwwPxd7KRsJRh0GppXRYtOYTpQnCNvLUREZNTcxxlrpvRGj2aeyC8px5NL9uO7nQnspNzIMeg0NK0L4OQj3eflKyIii+LhpMH3T0RhbNdg6AXw9h/xeH3NcZTp9HKXRnXEoCMHXr4iIrJYGjslPnigPV4f0QYKBbBsbyKeWLwf+SXlcpdGdcCgIwfv5tJtxjl56yAiomopFApMuqsZvn28Kxw1Kuw4m45HF+zhCuiNEIOOHLxbSbdpp+Stg4iIbmlQG18sm9QDnk4aHL2Sgwe/2o3LmVw2ojFh0JFDk9bSbdppeesgIqLb6hTsjhXP9ESguwMS0gvwwPxdOJmUK3dZVEMMOnJo0lK6zTgH6HjNl4jI0oU3ccaqKb3Q2s8FqXklePjr3dhzIUPusqgGGn3QuXz5Mvr374+IiAh06NABK1askLuk23MLAewcAF0pkHVR7mqIiKgGfF3tsfzpnuge5om84nI8/t0+rD+eLHdZdBuNPujY2dnhs88+Q3x8PDZs2IDp06ejoKBA7rJuTakEvFtI99N5+YqIqLFwc1Dj+ye7Y0iEL0rL9ZiyNA7L9ibKXRbdQqMPOv7+/ujUqRMAwM/PD97e3sjMzJS3qJow9NNJjZe3DiIiqhV7tQrz/9UF47pLc+28tvoYFsVyoWZLJXvQ2b59O0aNGoWAgAAoFAqsWbOmyjExMTEICwuDvb09oqKisG/fvmrPFRcXB51Oh+DgYDNXXQ98I6TblBPy1kFERLVmp1Li/fva4+l+0rxo//k9Hgu2cxJYSyR70CkoKEDHjh0RExNT7fPLly/HzJkzMXv2bBw8eBAdO3bE0KFDkZpqugZJZmYmHn/8cXzzzTcNUfad820v3SYfl7cOIiKqE4VCgVeGtca0gdLcaO+tO4kvt3J+NEujEBa0iIdCocDq1asxZswY476oqCh069YNX3zxBQBAr9cjODgY06ZNwyuvvAIAKCkpwZAhQzBp0iSMHz/+pucvKSlBScn1yZ5yc3MRHByMnJwcuLq6mudD3UxeMvBxK0ChBF69CmgcG/b9iYio3vxv01l8uukMAGDmkJZ4flALmSuybrm5uXBzc6vR72/ZW3RupbS0FHFxcRg8eLBxn1KpxODBg7F7924AgBACEyZMwMCBA28ZcgBgzpw5cHNzM26yXuJy9gUcvQGhB9JOylcHERHdsRcGt8CsodJksJ9sPINPNp7hYqAWwqKDTnp6OnQ6HXx9fU32+/r6IjlZGtIXGxuL5cuXY82aNejUqRM6deqEY8eOVXu+V199FTk5Ocbt8uXLZv8MN6VQAH7tpPu8fEVE1OhNHdAcr42QBprM23wWH/59mmHHAtjJXcCd6tOnD/T6mq0qq9VqodVqzVxRLfi2Ay5sBVIYdIiIrMHku8KhUirxzh/x+HLreZTrBV4d3hoKhULu0myWRbfoeHt7Q6VSISUlxWR/SkoK/Pz8ZKqqHvkZOiRX3wJFRESNz5N9muLt0W0BAN9sv4BPNp6RuSLbZtFBR6PRIDIyEps3bzbu0+v12Lx5M3r27CljZfXEv5N0m3SES0EQEVmRx3uG4Z2KsPP5P+fwzfbzMldku2S/dJWfn49z564Px0tISMDhw4fh6emJkJAQzJw5E9HR0ejatSu6d++Ozz77DAUFBZg4caKMVdcT7xaAxhkozZdmSPZtK3dFRERUT8b3DENeSTnmrj+N99edgrNWjUejQuQuy+bIHnQOHDiAAQMGGB/PnDkTABAdHY3Fixdj7NixSEtLw5tvvonk5GR06tQJ69evr9JBuTZiYmIQExMDnU53x/XfEaVKatW5tBO4epBBh4jIykzp3xx5xeWYv/U8Xl9zDE5aFUZ3CpS7LJtiUfPoNLTajMM3mw3/BnZ9DnR9Ahj5qTw1EBGR2Qgh8MZvx/HjnkTYKRX4enwkBrWp+3/WyYrm0bEJAV2k26sH5a2DiIjMQqFQ4O1722FMpwCU6wWeXXoQu89nyF2WzWDQkVtgRdBJOQGUFctbCxERmYVSqcCHD3U0rnr+1JL9OHolW+6ybAKDjtzcQ6UZkvVlQPJRuashIiIzUauU+HxcZ/Ru7oWCUh2eWHwAV7IK5S7L6jHoyE2hAEJ6SPcTd8tbCxERmZW9WoWvHotEaz8XpOeX4InF+5FTVCZ3WVaNQccSGIPOHnnrICIis3OxV+O7Cd3g66rFmZR8TFkah9Lyms3wT7Vnk0EnJiYGERER6Natm9ylSEIqJj9M3APUcDkLIiJqvALcHbAwuhscNSrEnsvA66uPcV0sM7HJoDN16lTEx8dj//79cpci8esA2DkARZlAxlm5qyEiogbQLtANMY92gVIBrIi7gi/+OXf7F1Gt2WTQsTh2GiCoq3Sf/XSIiGzGgNY++M+90mSxH288gzWHrspckfVh0LEUlS9fERGRzRjfMwyT+jYFALy08ij2XuAcO/WJQcdSGDokX9wJ8DotEZFNeXV4Gwxv54dSnR5Tlh7EtewiuUuyGgw6liKkB6BUAzmXgcwLcldDREQNSKlU4JOHOyHC3xUZBaV49sc4FJfJvB6jlWDQsRQaJyA4Srp/YauspRARUcNz0Kjw9fhIuDuqceRKDmb/doIjseoBg44ladZfumXQISKyScGejpj3SGcoFcDyA5exbF+i3CU1ejYZdCxuHh0DQ9BJ2A7o2WRJRGSL7mrZBP83tBUA4K21JxB3KUvmiho3mww6FjePjkFAZ0DrChRnA0lH5K6GiIhk8my/cAxv54cyncCUpXFIzeOiz3Vlk0HHYqnsgLC+0v0LW+SthYiIZKNQSKudt/BxRkpuCaYuPYhyHWfOrwsGHUsTPkC6PbdZ3jqIiEhWzlo7fD0+Ei5aO+y/mIV5mzlzfl3Y1eVFBQUF2LZtGxITE1FaWmry3PPPP18vhdmslkOBdf8nzZBcmAk4espdERERyaRZE2e8d397PP/TIXyx5Rx6N/dGVDMvuctqVGoddA4dOoQRI0agsLAQBQUF8PT0RHp6OhwdHeHj48Ogc6fcQwCftkDqCeDsRqDjWLkrIiIiGd3bMQDbz6RhZdwVTF9+GH+90Bfujhq5y2o0an3pasaMGRg1ahSysrLg4OCAPXv24NKlS4iMjMRHH31kjhptT6vh0u2Zv+Stg4iILMJ/7m2Lpt5OSMopxiu/cqXz2qh10Dl8+DBefPFFKJVKqFQqlJSUIDg4GHPnzsVrr71mjhptjyHonNsMlJfe+lgiIrJ6Tlo7zHukM9QqBdafSMZP+y7LXVKjUeugo1aroVRKL/Px8UFiojSZkZubGy5fbhxfvMXOo2MQ0AVw8gFKcoFLsXJXQ0REFqB9kBtmVcyv8/YfJ3A2JU/mihqHWgedzp07G+ef6devH958800sXboU06dPR7t27eq9QHOw2Hl0DJRKqVMyAJxZL28tRERkMZ7q0wx9W3ijuEyP538+zPWwaqDWQef999+Hv78/AOC9996Dh4cHnn32WaSlpeGbb76p9wJtluHy1al1XM2ciIgASIt/fvxwR3g5aXAyKRefbjwjd0kWr1ZBRwgBNzc3+Pj4oLy8HD4+Pli/fj1yc3MRFxeHjh07mqtO2xM+ENA4AzmJwNU4uashIiIL4eNij/8+0AEAsGDHBRy5nC1vQRauxkEnISEBHTp0QOvWrdGhQweEh4fjwIED5qzNtqkdrrfqHP9V3lqIiMiiDI7wxehOAdALYNbKIygp5yWsm6lx0Jk1axbKy8vx448/YuXKlQgKCsLTTz9tztqo3QPS7fFVXOSTiIhMzB7VFl5OGpxJyUfMlvNyl2Oxajxh4M6dO7Fy5Ur06dMHANCjRw8EBQWhoKAATk5OZivQpoUPBOzdgPxkaabksD5yV0RERBbC00mDt0e3w9RlB/HllnMY1tYPEQGucpdlcWrcopOamooWLVoYH/v7+8PBwQGpqalmKYwA2GmB1qOk+8dXyVsLERFZnBHt/TCsrR/K9QIv/XqEC39Wo8ZBR6FQID8/H7m5ucZNqVQiLy/PZB/Vs3b3S7fxawBduaylEBGRZVEoFHh7TFu4Oahx/GouvtlxQe6SLE6Ng44QAi1btoSHh4dxy8/PR+fOneHh4QF3d3d4eHiYs1bb1LQf4OgFFGYA5/+RuxoiIrIwPi72eHNkBADgs01nkZhRKHNFlqXGfXS2bNlizjroZlR2QPuHgb3zgUM/AC3vlrsiIiKyMPd3CcTqQ1ex81w63v4jHt9Gd5W7JIuhEPW4MlhRUREcHBzq63RmExMTg5iYGOh0Opw5cwY5OTlwdbXgDlwpJ4D5vQClHTDzFODcRO6KiIjIwpxLzcOwz3agXC+waGI3DGjlI3dJZpObmws3N7ca/f6u9czIzz//fLX7CwoKMGLEiNqeThYWvwTEjXzbAoGRgL4cOPKT3NUQEZEFau7jgom9wwAAb/8ez7l1KtQ66Pz555+YPXu2yb6CggIMGzYM5eXsLGs2XR6Xbg9+zyUhiIioWs8PaoEmLlokpBdg4c4EucuxCLUOOhs2bMCCBQvw2WefAQDy8vIwZMgQKBQKrF/PBSjNpt0DgNoJyDgLJO6RuxoiIrJALvZqvDKsNQDgi3/OITmnWOaK5FfroBMeHo7169fjnXfewbx583D33XdDo9Hgr7/+4sSB5qR1AdrdJ90/+L28tRARkcW6r3MgIkM9UFiqw/vrTspdjuxqHXQAoEOHDvjjjz/w2muvwdHRkSGnoXSJlm5PrAaKc+SthYiILJJSqcB/7m0LhQJYe+Qa9lzIkLskWdVoeHnnzp2hUCiq7Ndqtbh27Rp69+5t3Hfw4MH6q45MBXUDmrQB0k4Ch5YCPafIXREREVmgdoFuGNc9BMv2JuK9P09i7XO9q/09bgtqFHTGjBlj5jKoRhQKIGoy8McMaV6dqKcBpUruqoiIyALNHNISaw5dxbGrOfjreDJGtPeXuyRZ1Os8Oo1NbcbhW4zSQuDTtkBRJvDw90DEaLkrIiIiC/XJxjOYt/ksmjVxwobpd8FOVaceKxbHrPPokMw0jkC3J6X7u76QtxYiIrJok/o2hYejGhfSCvDrwStylyMLBp3GqNskQKUBruwDLu+TuxoiIrJQLvZqTB3QHIC0DlZxme1NIsig0xi5+ErrXwHA7hh5ayEiIov2WI9QBLjZIymnGD/sviR3OQ3OJoNOTEwMIiIi0K1bN7lLqTvDiKuTa4Es2/uDS0RENWOvVmH64JYAgC+3nkNucZnMFTWsGgedvn374qOPPsKZM2fMWU+DaHRrXVXHty3QbAAg9MCuz+WuhoiILNj9XQIR3sQJWYVlWLTzotzlNKgaB51JkyZh9+7diIyMRJs2bfDyyy8jNjYWNjxoS359Zki3B5cA2ZflrYWIiCyWnUqJFypadRbvSkBhqe2sTVnjoPP444/j119/RXp6Oj7++GNkZ2fjoYcegp+fH5544gmsWbMGRUVF5qyVbtSsHxDWF9CVAts/lLsaIiKyYCPa+SHUyxFZhWX4aZ/t/Oe41n10tFotRowYga+//hrXrl3D2rVr4e/vjzfeeANeXl4YOXIkYmNjzVErVWfA69Lt4aVAJleqJSKi6tmplHj6rnAAwLc7LqC0XC9zRQ3jjjsjR0VF4b333sOxY8dw7NgxDBo0CElJSfVRG9VEaE8gfCCgLwe2zZW7GiIismAPRAbCx0WLpJxirDl8Ve5yGkS9jroKDw/HjBkz8OCDD9bnael2Bvxbuj36M5B+Vt5aiIjIYmntVHiqb1MAwFfbzkOnt/5+tjY5vNzqBEUCLYdJI7C2fiB3NUREZMEejQqFq70dLqQVYMOJZLnLMTsGHWsx4DXp9vivQEq8vLUQEZHFctbaIbpXGABg/rbzVj96mkHHWvh3BNrcC0AAW96TuxoiIrJgE3qFwV6txNErOYg9lyF3OWZ1R0FHCGH1SbBRGfAaoFACp/4AErbLXQ0REVkoL2ctHukWAgBYFGvdI3brFHQWLlyIdu3awd7eHvb29mjXrh2+/fbb+q6NasunDRA5Ubr/18uAznYmhCIiotp5vGcoAOCf06m4nFkoczXmU+ug8+abb+KFF17AqFGjsGLFCqxYsQKjRo3CjBkz8Oabb5qjRqqNgf8GHDyA1HhgP8MnERFVr1kTZ/Rt4Q0hgKV7E+Uux2wUopbXnpo0aYJ58+Zh3LhxJvt/+uknTJs2Denp6fVaoDnl5ubCzc0NOTk5cHV1lbuc+rN/IfDnTEDrBkyLA5ybyF0RERFZoA0nkjH5hzh4OKqx+9VBsFer5C6pRmrz+7vWLTplZWXo2rVrlf2RkZEoL+elEosQOQHw6wCU5ACb/yN3NUREZKEGtfFFoLsDsgrL8OdR65zst9ZBZ/z48Zg/f36V/d988w3+9a9/1UtRdIeUKmBExdpXh34ErsbJWw8REVkklVKBR6OkTsnf77kkczXmYVeXFy1cuBAbNmxAjx49AAB79+5FYmIiHn/8ccycOdN43CeffFI/VVLthfQAOowFji4H1s0CntwEKDmbABERmRrbLRj/23QWRy5n4+iVbHQIcpe7pHpV66Bz/PhxdOnSBQBw/vx5AIC3tze8vb1x/Phx43EKhaKeSqx/MTExiImJgU6nk7sU8xryNnDqT6lF58BCoPskuSsiIiIL4+2sxYj2flhz+BqW7U20uqBT687I1sRqOyNXtvdr4K+XALUj8MxOwCtc7oqIiMjC7D6fgXEL9sBFa4f9/x5s8Z2SzdoZmRqZbpOAsL5AWSGw5llAb+WtWEREVGtRTT0R6O6AvJJybIxPkbucesWgY+2USmDMl4DGBbi8F9j1udwVERGRhVEqFbivcyAAYPWhqzJXU78YdGyBewgwvGJV8y3vASkn5K2HiIgszn1dpKCz7Uwa0vJKZK6m/jDo2IpO/wJaDgd0pcDqp4HyUrkrIiIiCxLexBkdg92h0wusPXJN7nLqDYOOrVAogFH/Axw8geRjwPYP5a6IiIgszAMVrTqrDl6RuZL6w6BjS1x8gZEVcxvt+JgTCRIRkYmRHQKgVilw4louTifnyV1OvWDQsTVt7wPaPQgIHbD6GaCsSO6KiIjIQng6adC/lQ8AYNUh62jVYdCxRSM+BJz9gPQzwPpX5K6GiIgsiOHy1W+HrkGvb/xT7THo2CJHT2nIORRA3GLg4PdyV0RERBZiQGsfOGvtkJxbjMNXsuUu544x6Niq5oOAga9L9/98EbjC/jpERARo7VQY0Fq6fPX3iWSZq7lzDDq2rM+LQOuR0pDzX8YD+WlyV0RERBZgWFs/AMDfx5PR2FeKYtCxZUolMGY+4N0SyL0KrJgA6MrlroqIiGTWv1UTaOyUuJhRiDMp+XKXc0cYdGydvSswdqm0RMSlncDGN+WuiIiIZOaktcNdLbwBAOuPN+7LVww6BDRpCdw3X7q/JwY4ukLeeoiISHZ3Gy5fNfJ+Ogw6JGkzCuj7onR/7TRp9mQiIrJZg9v4QqVUID4pF4kZhXKXU2cMOnTdgNeB8EFAeRGwbCyQfVnuioiISCaeThpENfUEAGyIb7ytOgw6dJ1SBTzwLeDdSuqc/MN9QEGG3FUREZFMBlYMM99+Nl3mSuqOQYdMOXoC41cBrkFAxllg6YNAiXWsd0JERLVzV8smAIC9FzJQXKaTuZq6YdChqtyCgPGrpZXOrx0Elj8GlJfIXRURETWwFj7O8HO1R0m5HvsSMuUup04YdKh6TVoCj60E1E7Aha3AqsmAvnGmeSIiqhuFQoF+Fa062840zkllGXTo5gIjgUeWAioNEL9GWiqikc+QSUREtWO4fLWdQafxiImJQUREBLp16yZ3KZYvfABw/wJIC4AuAra8J3dFRETUgPo094ZSAZxNzce17CK5y6k1mww6U6dORXx8PPbv3y93KY1D2zHAyE+k+9s/BPZ8JWs5RETUcNwc1egY7A4A2HG28bXq2GTQoTro+gQw4N/S/fUvA0eWy1sPERE1mLtaGC5fNb5h5gw6VHN3/R8Q9Yx0f80zQNwSeeshIqIGYeinE3s+HXp94+qryaBDNadQAEPnAJETAKEHfn8e2PmZ3FUREZGZdQhyg4NahezCMpxNbVyrmTPoUO0olcDIz4A+M6THm2YDG2dzNBYRkRVTq5ToEuoOANh3sXHNp8OgQ7WnUACD3wIG/0d6HPsZ8Md0zrNDRGTFuoVJ617tb2QTBzLoUN31mQ6MmgcolEDcYuDXJ4HyUrmrIiIiM+hescDnvoRMiEbUis+gQ3cmMhp4cBGgVAMnVgM/PQKUFshdFRER1bPOwR5QqxRIzi3GlazGM58Ogw7dubZjgEeXA2pH4PxmadXzoiy5qyIionrkoFGhXaAbADSqda8YdKh+NB8EPP4bYO8GXN4LLLoHyEuRuyoiIqpH3Q39dBpRh2QGHao/wd2BiX8Bzr5A6gngu6FA1kW5qyIionpi6JDcmEZeMehQ/fJtCzzxN+AeCmQlAAsGAhe2yV0VERHVg65hHlAogAtpBUjPL5G7nBph0KH659kUeHID4N8RKMwAfhgDxM7jXDtERI2cu6MGzbydAADHruTIXE3NMOiQebj4SS07HR+VZlHe+AawYgJQkid3ZUREdAc6BLkDAI4y6JDNUzsAY74E7vlYGn4evwb4djCQfk7uyoiIqI7aV4y8OnY1W95CaohBh8xLoQC6PQVM+BNw9gPSTgELBgCn/pS7MiIiqoMOQVLQYYsOUWUhUcDT24GQXkBJLvDzo8A/73LZCCKiRiYiwBVKBZCaV4KU3GK5y7ktBh1qOC6+QPRaIOpZ6fH2D4GlDwGFjWeYIhGRrXPU2KGFjwuAxtGqw6BDDUulBoZ/ANz/LWDnIM2k/E1/IOmo3JUREVENta+4fHXsSra8hdQAgw7Jo8NDwFObAI8wIPsSsHAIcORnuasiIqIa6FgRdI6wRYfoFvzaAZO3Ai3uBsqLgdVPA3/MAEry5a6MiIhuoX3FEPNjV3MsfiVzBh2Sl4MHMG450O8V6fGB74D5vYCE7fLWRUREN9XazwUqpQKZBaVItvAOyQw6JD+lEhjwKjB+DeAWLF3KWjIK+PNFtu4QEVkge7UKTStmSD6dbNkTwTLokOUIHwA8uwvo+oT0eP+3wPyeXCuLiMgCtfKVRl6dSWHQIao5e1dg5KfA478BbiFAdiLw/b0VfXcs+y8TEZEtaVkRdE4nW3bLO4MOWaZm/YEpu6RZlQGp786XvYDzW2Qti4iIJK38nAGwRYeo7rQu0jpZ0b8D7iFATqK0EvrvLwDFuXJXR0Rk01r5uQIAzqbmQae33JFXDDpk+ZreBTy7G+g2SXoctxj4sidwbrOsZRER2bIQT0do7ZQoLtPjcmah3OXcFIMONQ5aZ+Cej4DoP6RJBnOvAD/eD6ydBhRb/oRVRETWRqVUoIWvdPnqtAVfvmLQocalaV9pZFb3p6XHB7+XWnfObpK3LiIiG2TokHzGgoeYM+hQ46NxAkbMBSasAzyaArlXgaUPAKsmAzlX5K6OiMhmtParGHnFFh0iMwjrDTwbW7EaugI4uhz4PBLY/A6HohMRNQDDKubnUi13iLlVBJ377rsPHh4eePDBB+UuhRqaxklaDX3SZiCkl7Rm1o6PgHmdgf0LAV253BUSEVmtsIrZkS9mFEBvoSOvrCLovPDCC/j+++/lLoPkFBgJTFwHjF0KeIYDBWnAnzOldbPO/A1Y+KJzRESNUZCHA+yUChSX6ZGSZ5lrXllF0Onfvz9cXFzkLoPkplAAbUYCU/YAw+cCDp5A+mlg2cPA96OBpKNyV0hEZFXUKiWCPR0BAAnpBTJXUz3Zg8727dsxatQoBAQEQKFQYM2aNVWOiYmJQVhYGOzt7REVFYV9+/Y1fKHUeNhpgKingecPAb2eB1QaIGEb8PVdwJopQM5VuSskIrIaYV5S0LmYbplz6cgedAoKCtCxY0fExMRU+/zy5csxc+ZMzJ49GwcPHkTHjh0xdOhQpKamNnCl1Og4uAN3vwM8dwBo9wAAARxeKnVY/udddlgmIqoHlfvpWCLZg87w4cPx7rvv4r777qv2+U8++QSTJk3CxIkTERERga+++gqOjo747rvvav1eJSUlyM3NNdnIBniEAg9+Bzy1GQjuAZQXAds/BOZ1AQ4sYodlIqI70LQi6PDSVR2UlpYiLi4OgwcPNu5TKpUYPHgwdu/eXevzzZkzB25ubsYtODi4PsslSxfUFXhiPfDwD4BnM6AgFfhjOvBVH+DsRnZYJiKqgzAvBp06S09Ph06ng6+vr8l+X19fJCcnGx8PHjwYDz30ENatW4egoKCbhqBXX30VOTk5xu3y5ctmrZ8skEIBRNwLTNkLDPsv4OABpJ0Elj4oLRiafEzuComIGhVDi05iRqFFLu5pJ3cB9WHTpppN/6/VaqHVas1cDTUKdhqgxzNAx7HAjo+BvV8DF7YCX/UFOj0K9JkJeDeXu0oiIosX4O4AjUqJUp0e17KLjKOwLIVFt+h4e3tDpVIhJSXFZH9KSgr8/PxkqoqsioMHcPe7wNR9QNv7Yeyw/EVXYPl44Eqc3BUSEVk0lVKBYE8HAMClDMsbeWXRQUej0SAyMhKbN2827tPr9di8eTN69uwpY2VkdTybAg8tkjostxwGQAAn1wLfDgQWj5QWDWUfHiKiagV6SK04V7MtL+jIfukqPz8f586dMz5OSEjA4cOH4enpiZCQEMycORPR0dHo2rUrunfvjs8++wwFBQWYOHGijFWT1QrqCjy6HEiJB3Z9Dhz7Bbi4Q9p82wG9X5BaflSy/9UhIrIYge5Si87VrCKZK6lK9n+tDxw4gAEDBhgfz5w5EwAQHR2NxYsXY+zYsUhLS8Obb76J5ORkdOrUCevXr6/SQbk2YmJiEBMTA51Od8f1k5XyjQDumw8MfB3Y/SUQtxhIOQ6smiQtGtrrOaDzY9JaW0RENi7IQwo6V7ItL+gohLDd9vjc3Fy4ubkhJycHrq6ucpdDlqwwEziwENjzFVCYLu1z8AS6T5Y2Jy956yMiktFvh6/ihZ8PI6qpJ5Y/bf6uJbX5/W3RfXSILIajJ3DXLGDGceCejwGPMKAoE9j2AfBpW2DdS0DWJbmrJCKShfHSlQW26DDoENWG2gHo9hQw7SDw4CLAv6M00/K+r4F5nYFfn+JcPERkcwIrLl0l5xRb3Fw6DDpEdaFUAe3uByZvA8avAZoNAIQOOLZCmmn5h/uBhO0cqUVENsHHxR52SgXK9QIpucVyl2OCQYfoTigUQPgA4PE1Uuhpez+gUALnNwNLRgELBgLxvwF6dnwnIuulUirg724PwPIuXzHoENWXgE7SXDzTDkqXt+zsgWsHgV8eB77oJi0gWmZZ/9MhIqovljrE3CaDTkxMDCIiItCtWze5SyFr5NlU6rA8/bjUgdneHcg8Ly0g+klrqeMy+/EQkZUJdDdMGmhZQYfDyzm8nMytJB84+D2w50sgp9JCsv6dgC7jgXYPAg7uclVHRFQvPt5wGp//cw7je4TinTHtzPpeHF5OZEm0zkDPKcALR4B/rQQiRgNKNZB0GPjzReDjVsCqyUDCDnZeJqJGy8dV6qNjaZ2RZZ8ZmchmKFVAiyHSVpAOHF0OHPwBSDsp3T+6HPBoCnT+F9DpX4BrgNwVExHVmI+LFgCQklcicyWm2KJDJAcnb6DnVGDKbmkh0S7RgMYFyEoA/nlXmoRw6UNA/FqgvFTuaomIbsu3okUnjS06RGSkUEgLiQZ1BYbNAU6sAQ79ACTuBs5ukDZHb6DjI0CXx4EmreSumIioWr6uUotOal4J9HoBpVIhc0UStugQWQqNk3TZ6on1wHNxQO/pgLOvtLbW7i+AmO7At0Okjs0leXJXS0RkwttZC4UCKNcLZBZaTks0gw6RJfJuDgz5DzDjBPDIT0CrEYBCBVzZB6ydBnzUClgzFUjcww7MRGQR1ColvJwq+ulY0OUrmww6nEeHGg2VGmg9Ahj3EzAzHhj8FuDVHCgrAA7/CHw3VGrpif0fkJ8qd7VEZOOMl69yLadDMufR4Tw61NgIIfXhOfQjcGI1UFYo7VfaAS2HAZ3HA80HAyp2wSOihjVx0T5sOZ2GD+5vj0e6h5jtfWrz+5v/EhI1NgoFENpL2oZ9AJxYJQ1Tv3oAOPWHtDn7SfP1RNwLhPSUhrYTEZmZYeRVqgUNMWfQIWrM7F2ByAnSlnpSCjxHfwbyk4F9X0ubozfQ+h4p9ITdBdhp5K6aiKyUYS6d1DzL6aPDoENkLXzaAMPel/rxnN8MnPwdOPWnNGrr4BJps3cDWg6XQk/4QEDtIHfVRGRFvJyloJNZYDmjrhh0iKyNnQZoNVzadGXAxR1S6Dn5B1CQKrX4HP0ZUDtJszRH3Au0uBvQushdORE1cp5OUotxej6DDhE1BJVaarkJHwiM+Ai4vLci9PwuLTAav0baVFrpmIh7pYDk4CF35UTUCHk5S0EnI599dIiooSlV1zsxD30fuHYIOLlWWmYi8zxw5i9pU9oBYX2l0NN6JODsI3flRNRIePPSlWWIiYlBTEwMdDqd3KUQyUOhAAK7SNug2VJHZkPoST0BXNgibX/MlEZtRdwLtBkFuAXJXTkRWTDDpauswjKU6/SwU8k/XR/n0eE8OkSmMs5fDz3XDpo+F9ClIvTcC3iFy1MfEVksnV6g+evrIASw7/VB8HGxN8v7cB4dIqo7r3Cgzwxpy74szcsTv1aapPDaQWnb9Bbg0/Z66PFpI7USEZFNUykV8HTUIKOgFJkFpWYLOrXBoENEN+ceDPR4VtryU6+Hnos7pEtcqSeArXMAz/DroSegM0MPkQ3zdJKCToaFjLxi0CGimnH2Abo+IW2FmcCZ9VLoOf+P1Jl556fS5hYsjdwKHwiE9eGwdSIb4+WswdlUIN1CRl4x6BBR7Tl6Ap0elbaSPODsBin0nN0oDVvf9420Ke2AoG5S6Gk2QGrt4RpcRFbNsIK5pYy84r84RHRntC5AuwekrawIOL8FOLdJaunJSpD69iTuBra8B2jdgKZ9K+b2GQB4NpO7eiKqZ+6OagBAdmGZzJVIGHSIqP6oHYDWI6QNALIuSsHn/D9AwjagOOf6wqMA4B56PfQ0vYsTFRJZAUPQySli0CEia+cRBnSdKG16HXDtsBR6LmyRZmnOvgTELZI2hVIavh4+QLrMFdSNC5ASNULuDtLf2+xCXroiIluiVAFBkdLWbxZQkg9cipWCz/ktQPpp4OoBadv+IaBxljozNxsghR/vlhzNRdQIuBkuXbFFh4hsmtYZaDlU2gAg56rU0nN+C3Bhq7Tq+pn10gYAroHXQ0+z/oCTt1yVE9EteDgaWnQYdGTDJSCILJBbIND5MWnT64GUYxWhZwtwaTeQexU4/KO0AYBfh+uXuUJ6Amr5JyYjosqdkS3j0hWXgOASEESWr6wIuLTreotPynHT5+3spcVKDS0+vu14mYtIJmdT8jDk0+1wd1Tj8Jt3m+U9uAQEEVkXtQPQfJC0AdIszRe2Xh/RlZ9c0dfnH2AjACcf6fKWocXH1V/G4olsi1ulUVd6vYBSKe9/Ohh0iKjxcfYBOjwsbUIAaaeuh55LsUBBKnDsF2kDgCatpRaf4Chp8whjiw+RmRhGXQkB5BWXG4OPXBh0iKhxUyikRUV92gA9pwDlJcDlfdeHsV87LAWhtFPAge+k1zj7AsHdK4JPD8C/A2CnlfVjEFkLjZ0SjhoVCkt1yC4qZdAhIqpXdlpp9uWmfQHMltblurhTmrfn8l4p+OSnACd/lzYAUGmBwC4V4aeHdMtRXUR15mJvh8JSHfKKy+UuhUGHiKyco6e0snrEvdLjsiIp7FzeI7X8XN4LFGZcX6oC/5OO82p+/VJXcJQ0j49SKdenIGpUXO3VSMktQW6x/EPMGXSIyLaoHYDQntIGSB0JMs5XBJ+9UvhJOwVknJO2w0ul4+zdK13uipJagDROsn0MIkvmYi/Fi9witugQEclLoQC8m0tb58ekfYWZwJUD11t9rhwAirOlVdrPbpCOUdoBfu1NW33cAmX7GESWxMVe6peTxxYdIiIL5OgJtLxb2gBAVwYkH7vezydxL5B3Dbh2SNr2fiUd5xZs2s/Htx2g4j+zZHtcHQxBhy06RESWT6WWLlUFdgF6PCtd7sq5cj34XN4rBaGcy9J2/FfpdWonaW2v4B5Si09QV8DBXdaPQtQQDJeuGHSIiBojhQJwD5a29g9K+0rypQVJL+8DEvcAV/YDJblAwnZpk14oDYM3XOoKiQI8mnJOH7I6xj46vHQlD651RUT1TusszcbcrL/0WK+XOjUb+vkk7gGyEoDUeGmLWyQd59TkevAJ6gb4tQO0LnJ9CqJ64WpBfXS41hXXuiKihpKfatrPJ+kwoLtx4UOFNLTdvwPg31Ha/DpI/YaIGokfdl/EG7+dwPB2fpj/WGS9n59rXRERWSJnH6DNKGkDgLJiKexUDj65V4GMs9Jm6OsDAO4hlYJPxa2Lrxyfgui2nLRSvMgvYR8dIiLbpbYHQnpIW++KfflpQPIRIKnSlnURyE6UNsNszgDg7Hc9/Bg2tyD2+SHZMegQEVH1nJsAzQdLm0FRtjSqq3L4ST8jrdp+Nhk4+/f1Yx08TS97+XeSOjxzVmdqQC4VQaeAQYeIiG7Lwb3S+l0VSvKBlBPXg0/yESD1JFCUCVzYKm0GGpfr4cev4ta7Jef4IbNxMgYd+Qf98E85EVFjpHWWhqeHRF3fV14ijeiq3PKTfBwozQMuxUqbgZ29NKFh5ctePm24ijvVC0PQsYRRVww6RETWwk4LBHSWNgNdmXSZK+lopfBzFCitmPfn6oHrxyrVgE/r65e8/DsCvm25phfVmrOhRadUByEEFDL2G2PQISKyZiq1FFZ82wKdxkn79Hog84I0yiu5UgAqypL6AiUfAw79KB2rUAJeLUxbfvzac4ZnuiUnrQoAoNMLlJTrYa9WyVYLgw4Rka1RKq8vZGqY2VkIafmKpBtGfOWnAOmnpe3YL9fP4dFU6vfj01a65OUTAXg2BZTy/UIjy+GkuR4v8kvKGXSIiEhmCoU0V497yPV5fgAgL7nSZa/D0v2cRGmW56wEIP6368eqtECTVhXBpyL8+LSRFjvlkHebolQq4KhRobBUh4KScng7y9f3i0GHiIhuzsVP2gwruQNAYWZFX59j0jIXqfFA6imgvEi6FJZ81PQcGhep748h/DRpLd06+zAAWTFHjR0KS3UoLJV35BWDDhER1Y6jJxA+QNoM9Dog+5I0xD01vuL2JJB+Vhr1dWW/tFXm4Hm91cfYAtQacPBo2M9DZuGokS5XMegQEVHjp1QBns2krfU91/fryoCM85XCT8Vt5gVpzp9LO6WtMhd/00tfPm2kViCO/mpUrgcdeScNZNAhIiLzUVUMWfdpbbq/rEga9n5jC1DOZSAvSdrO/2P6Go8w00tfPm0A7xac+8dCsUWHiIhsl9rh+nD1yopzgLTT14OPIQQVpEprfmVdBE6vu368QiWt9u7TGvBuJXWG9m4hDYnXODbkJ6IbGCYNZIsOERGRgb0bENxd2iorSDcNP2mngJR4oCTn+vD3G7mFAE1aSstdGLYmrQAn74b5LDbOQc0WHdnExMQgJiYGOp38a3AQEVENOHlXXe9LCOkSl2HUV/oZaUs7LfX/yUmUtnObTM/l4FkRegwBqKIVyD2E8wDVI8OlqyIGnYY3depUTJ06Fbm5uXBzc5O7HCIiqguFAnANkLbKq70DQEFGRUvPGSCtIgClnwayL0sh6PIeaavMzl66DGa49OXdQnrs1Rywd224z2UlHBh0iIiIzMTJC3DqBYT2Mt1fWghknDNt/Uk/K+0rLwZSjkvbjZx9K8JPRfAxBCH3UK4CfxMO6oo+OmUMOkRERA1D4ygtXeHfwXS/YR4gQ+tPxrmKQHRW6gidnyJtNw6FV9pJy2FUbv3xag54hUvhyIYnRHTQKAGwRYeIiEh+lecBajXM9LmibCDzPJB+Dsg4W9ECdL6iFahI2pdxtuo5Nc7S+QzBxzP8+n1Hzwb5WHIydEZm0CEiIrJkDu5AYKS0VabXA7lXK4LO+YoAVHE/5zJQml/9khiANPuzZ/j1AGQIWV7NrGZmaMNCnsXlDDpERESNj1IJuAdLW/hA0+fKS6Q5fwwtP5nnK+6fB/KuAUVZwNUD0nYjQwjybFYRhJpd3xw8Gs3lMHZGJiIislZ2FSu5N2lV9bnSAmkJjIzz0m3meSAzQXqcn3zrEKR1AzybVgSfiluPptJ9Zz8pfFkIe7uKoMPOyERERDZE4wT4tZe2G5XkA1kJFQGochhKkFqCSnKApMPSdiM7e2mZDEPwqXzrHtzgS2UYLl2VlOkb9H1vxKBDRERkKbTONw9BZUXS5TBD8Mm8cD0UZV+WhsennZK2KhSAW1BFEAqVwo8hFHmESZ2j6/mSmL1aal1iHx0iIiK6PbXD9dXcb6QrkzpAZyZUhJ+EilBUcVtWID2fcxm4uKPq67Wu0pxAHqEVASis4nGYNGO02r7W5Ro7I/PSFREREd0Rlfp6h+UbCQEUpFUKPglA1qXri6TmXQNKcoGUY9JWHWe/661BhkBkuHUNrHbpjOtBh5euiIiIyFwUCsDZR9puXCwVAMqKpckSK4efrIsV+y5Kw+Tzk6XtxmUzAGnSRLcgqeWnUghyL/eCD7JQUqo27+e7DQYdIiIiW6a2v/kIMSGAwkwg++L1IGQIRdmXpL5B+rLr4aiScAD77IE3yp8FcLfZP8bNMOgQERFR9RSKinXDvKpOmAhIS2fkJQHZiZXCj3S/POsSkHMVqSrfhq+7EgYdIiIiqhulSrps5RZUZQFVOwDQleFryDvBIYMOERERmYdK3v45AGA5UygSERER1TMGHSIiIrJaDDpERERktRh0iIiIyGox6BAREZHVYtAhIiIiq2WTQScmJgYRERHo1q2b3KUQERGRGSmEEELuIuSSm5sLNzc35OTkwNXVVe5yiIiIqAZq8/vbJlt0iIiIyDYw6BAREZHVYtAhIiIiq8WgQ0RERFaLQYeIiIislk2vXm4YcJabmytzJURERFRTht/bNRk4btNBJy8vDwAQHBwscyVERERUW3l5eXBzc7vlMTY9j45er8e1a9fg4uIChUJRr+fOzc1FcHAwLl++zDl6zIjfc8Pg99xw+F03DH7PDcNc37MQAnl5eQgICIBSeeteODbdoqNUKhEUFGTW93B1deVfogbA77lh8HtuOPyuGwa/54Zhju/5di05BuyMTERERFaLQYeIiIisFoOOmWi1WsyePRtarVbuUqwav+eGwe+54fC7bhj8nhuGJXzPNt0ZmYiIiKwbW3SIiIjIajHoEBERkdVi0CEiIiKrxaBDREREVotB5w7ExMQgLCwM9vb2iIqKwr59+255/IoVK9C6dWvY29ujffv2WLduXQNV2rjV5ntesGAB+vbtCw8PD3h4eGDw4MG3/bmQpLZ/ng1+/vlnKBQKjBkzxrwFWonafs/Z2dmYOnUq/P39odVq0bJlS/7bUUO1/a4/++wztGrVCg4ODggODsaMGTNQXFzcQNU2Ptu3b8eoUaMQEBAAhUKBNWvW3PY1W7duRZcuXaDVatG8eXMsXrzY7HVCUJ38/PPPQqPRiO+++06cOHFCTJo0Sbi7u4uUlJRqj4+NjRUqlUrMnTtXxMfHi3//+99CrVaLY8eONXDljUttv+dHH31UxMTEiEOHDomTJ0+KCRMmCDc3N3HlypUGrrxxqe33bJCQkCACAwNF3759xejRoxum2Eastt9zSUmJ6Nq1qxgxYoTYuXOnSEhIEFu3bhWHDx9u4Mobn9p+10uXLhVarVYsXbpUJCQkiL///lv4+/uLGTNmNHDljce6devE66+/LlatWiUAiNWrV9/y+AsXLghHR0cxc+ZMER8fLz7//HOhUqnE+vXrzVong04dde/eXUydOtX4WKfTiYCAADFnzpxqj3/44YfFPffcY7IvKipKPP3002ats7Gr7fd8o/LycuHi4iKWLFlirhKtQl2+5/LyctGrVy/x7bffiujoaAadGqjt9zx//nzRrFkzUVpa2lAlWo3aftdTp04VAwcONNk3c+ZM0bt3b7PWaS1qEnReeukl0bZtW5N9Y8eOFUOHDjVjZULw0lUdlJaWIi4uDoMHDzbuUyqVGDx4MHbv3l3ta3bv3m1yPAAMHTr0psdT3b7nGxUWFqKsrAyenp7mKrPRq+v3/Pbbb8PHxwdPPvlkQ5TZ6NXle167di169uyJqVOnwtfXF+3atcP7778PnU7XUGU3SnX5rnv16oW4uDjj5a0LFy5g3bp1GDFiRIPUbAvk+j1o04t61lV6ejp0Oh18fX1N9vv6+uLUqVPVviY5Obna45OTk81WZ2NXl+/5Ri+//DICAgKq/OWi6+ryPe/cuRMLFy7E4cOHG6BC61CX7/nChQv4559/8K9//Qvr1q3DuXPnMGXKFJSVlWH27NkNUXajVJfv+tFHH0V6ejr69OkDIQTKy8vxzDPP4LXXXmuIkm3CzX4P5ubmoqioCA4ODmZ5X7bokNX64IMP8PPPP2P16tWwt7eXuxyrkZeXh/Hjx2PBggXw9vaWuxyrptfr4ePjg2+++QaRkZEYO3YsXn/9dXz11Vdyl2Z1tm7divfffx9ffvklDh48iFWrVuHPP//EO++8I3dpdIfYolMH3t7eUKlUSElJMdmfkpICPz+/al/j5+dXq+Opbt+zwUcffYQPPvgAmzZtQocOHcxZZqNX2+/5/PnzuHjxIkaNGmXcp9frAQB2dnY4ffo0wsPDzVt0I1SXP8/+/v5Qq9VQqVTGfW3atEFycjJKS0uh0WjMWnNjVZfv+o033sD48ePx1FNPAQDat2+PgoICTJ48Ga+//jqUSrYL3Kmb/R50dXU1W2sOwBadOtFoNIiMjMTmzZuN+/R6PTZv3oyePXtW+5qePXuaHA8AGzduvOnxVLfvGQDmzp2Ld955B+vXr0fXrl0botRGrbbfc+vWrXHs2DEcPnzYuN17770YMGAADh8+jODg4IYsv9Goy5/n3r1749y5c8YgCQBnzpyBv78/Q84t1OW7LiwsrBJmDAFTcEnIeiHb70GzdnW2Yj///LPQarVi8eLFIj4+XkyePFm4u7uL5ORkIYQQ48ePF6+88orx+NjYWGFnZyc++ugjcfLkSTF79mwOL6+B2n7PH3zwgdBoNGLlypUiKSnJuOXl5cn1ERqF2n7PN+Koq5qp7fecmJgoXFxcxHPPPSdOnz4t/vjjD+Hj4yPeffdduT5Co1Hb73r27NnCxcVF/PTTT+LChQtiw4YNIjw8XDz88MNyfQSLl5eXJw4dOiQOHTokAIhPPvlEHDp0SFy6dEkIIcQrr7wixo8fbzzeMLx81qxZ4uTJkyImJobDyy3d559/LkJCQoRGoxHdu3cXe/bsMT7Xr18/ER0dbXL8L7/8Ilq2bCk0Go1o27at+PPPPxu44sapNt9zaGioAFBlmz17dsMX3sjU9s9zZQw6NVfb73nXrl0iKipKaLVa0axZM/Hee++J8vLyBq66carNd11WVibeeustER4eLuzt7UVwcLCYMmWKyMrKavjCG4ktW7ZU+++t4XuNjo4W/fr1q/KaTp06CY1GI5o1ayYWLVpk9joVQrBNjoiIiKwT++gQERGR1WLQISIiIqvFoENERERWi0GHiIiIrBaDDhEREVktBh0iIiKyWgw6REREZLUYdIiIiMhqMegQERGR1WLQISK6Qf/+/TF9+vQ7OocQApMnT4anpycUCgUOHz5cL7URUe0w6BCRRZs4cSL+/e9/y11Gra1fvx6LFy/GH3/8gaSkJLRr107ukohskp3cBRAR3YxOp8Mff/yBP//8U+5Sau38+fPw9/dHr169bnpMaWkpNBpNA1ZFZHvYokNkg3766Sc4ODggKSnJuG/ixIno0KEDcnJy6nzeoKAgfPnllyb7du3aBUdHR1y6dKnW59u1axfUajW6detW7fP9+/fHtGnTMH36dHh4eMDX1xcLFixAQUEBJk6cCBcXFzRv3hx//fWXyetKSkrw/PPPw8fHB/b29ujTpw/2799/0zr0ej3mzJmDpk2bwsHBAR07dsTKlStvevyECRMwbdo0JCYmQqFQICwszFjvc889h+nTp8Pb2xtDhw7F+vXr0adPH7i7u8PLywsjR47E+fPnq7z/3Llz0bx5c2i1WoSEhOC9996r4bdIZNsYdIhs0COPPIKWLVvi/fffBwDMnj0bmzZtwl9//QU3N7c6nzcqKsokMAghMH36dMyYMQOhoaG1Pt/atWsxatQoKBSKmx6zZMkSeHt7Y9++fZg2bRqeffZZPPTQQ+jVqxcOHjyIu+++G+PHj0dhYaHxNS+99BJ+/fVXLFmyBAcPHkTz5s0xdOhQZGZmVvsec+bMwffff4+vvvoKJ06cwIwZM/DYY49h27Zt1R7/v//9D2+//TaCgoKQlJRk8p0sWbIEGo0GsbGx+Oqrr1BQUICZM2fiwIED2Lx5M5RKJe677z7o9Xrja1599VV88MEHeOONNxAfH49ly5bB19e3tl8nkW0SRGSTfv/9d6HVasW7774rPDw8xPHjx43PjRkzRri7u4sHHnigVuecO3euaNu2rfHxkiVLhJ+fn8jLy6vTeVu0aCH++OOPmz7fr18/0adPH+Pj8vJy4eTkJMaPH2/cl5SUJACI3bt3CyGEyM/PF2q1WixdutR4TGlpqQgICBBz5841nveFF14QQghRXFwsHB0dxa5du0ze+8knnxTjxo27aW2ffvqpCA0NrVJv586db/mZ09LSBABx7NgxIYQQubm5QqvVigULFtzydURUPbboENmokSNHIiIiAm+//TZWr16Ntm3bGp974YUX8P3339f6nD169MDJkyeRn5+PgoICvPbaa3j33Xfh7Oxc6/OePHkS165dw6BBg255XIcOHYz3VSoVvLy80L59e+M+Q8tHamoqAKnvTFlZGXr37m08Rq1Wo3v37jh58mSV8587dw6FhYUYMmQInJ2djdv3339f5RJTTURGRpo8Pnv2LMaNG4dmzZrB1dXVeJkrMTERgPQ9lJSU3PZ7IKLqsTMykY1av349Tp06BZ1OV+UySP/+/bF169ZanzMyMhJKpRIHDx7Epk2b0KRJE0ycOLFO5127di2GDBkCe3v7Wx6nVqtNHisUCpN9hstelS8F1UZ+fj4A4M8//0RgYKDJc1qtttbnc3JyMnk8atQohIaGYsGCBQgICIBer0e7du1QWloKAHBwcKhT3UQkYYsOkQ06ePAgHn74YSxcuBCDBg3CG2+8US/ndXR0RPv27fHrr7/io48+wqeffgqlsm7/zPz2228YPXp0vdRVWXh4uLGPjEFZWRn279+PiIiIKsdHRERAq9UiMTERzZs3N9mCg4PvqJaMjAycPn0a//73vzFo0CC0adMGWVlZJse0aNECDg4O2Lx58x29F5GtYosOkY25ePEi7rnnHrz22mvGSyY9e/bEwYMH0aVLlzs+f48ePfD5559j9OjR6N+/f53OkZqaigMHDmDt2rV3XM+NnJyc8Oyzz2LWrFnw9PRESEgI5s6di8LCQjz55JNVjndxccH//d//YcaMGdDr9ejTpw9ycnIQGxsLV1dXREdH17kWDw8PeHl54ZtvvoG/vz8SExPxyiuvmBxjb2+Pl19+GS+99BI0Gg169+6NtLQ0nDhxwljvF198gdWrVzMMEVWDQYfIhmRmZmLYsGEYPXq08RdqVFQUhg8fjtdeew3r16+/7TkWL16MiRMnQghR7fMdO3aEWq3Ghx9+WOc6f//9d3Tv3h3e3t51PsetfPDBB9Dr9Rg/fjzy8vLQtWtX/P333/Dw8Kj2+HfeeQdNmjTBnDlzcOHCBbi7u6NLly547bXX7qgOpVKJn3/+Gc8//zzatWuHVq1aYd68eVUC4htvvAE7Ozu8+eabuHbtGvz9/fHMM88Yn09PT69TfyEiW6AQN/vXiohs2tatW/HFF19UmS9m9uzZ2LZt20372gwYMABdunTBxx9/XKvzVnbvvfeiT58+eOmll+pcPxERwBYdIqrG4MGDceTIERQUFCAoKAgrVqxAz549AQB//fUXvvjiC5Pj9Xo90tLSsHDhQpw9exa//fZbrc9bWZ8+fTBu3Lj6/2BEZHPYokNEd2zr1q0YOHAgWrdujUWLFiEqKkrukoiIADDoEBERkRXj8HIiIiKyWgw6REREZLUYdIiIiMhqMegQERGR1WLQISIiIqvFoENERERWi0GHiIiIrBaDDhEREVktBh0iIiKyWgw6REREZLUYdIiIiMhq/T8tSHekJ/77gAAAAABJRU5ErkJggg==", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(df['xL_0 / mole frac.'], df['pL / Pa']/1e3)\n", "plt.plot(df['xV_0 / mole frac.'], df['pL / Pa']/1e3)\n", "plt.yscale('log')\n", "plt.gca().set(xlabel='$x_1,y_1$ / mole frac.', ylabel='p / kPa')\n", "plt.title(f'Water(1) + Ammonia(2) @ {T} K')\n", "plt.show()" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.11.0" } }, "nbformat": 4, "nbformat_minor": 5 }