From 08fb4541b1495d5aa15cbe060c0e0f7d9a54ea79 Mon Sep 17 00:00:00 2001 From: Yeongdo Park Date: Mon, 14 Nov 2022 15:49:49 +0900 Subject: [PATCH] initial commit --- CokeOvenServiceSimulator.ipynb | 2682 +++++++++++++++++ ...‰ ν›„ μ΅œμ € μ—΄λŸ‰ 곡급 μ‹œκ°„ ( μ •μˆ˜ μ‹œκ°„ ).ipynb | 221 ++ 2 files changed, 2903 insertions(+) create mode 100644 CokeOvenServiceSimulator.ipynb create mode 100644 감급 ν›„ μ΅œμ € μ—΄λŸ‰ 곡급 μ‹œκ°„ ( μ •μˆ˜ μ‹œκ°„ ).ipynb diff --git a/CokeOvenServiceSimulator.ipynb b/CokeOvenServiceSimulator.ipynb new file mode 100644 index 0000000..509a6b5 --- /dev/null +++ b/CokeOvenServiceSimulator.ipynb @@ -0,0 +1,2682 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "8aed3e97", + "metadata": {}, + "outputs": [], + "source": [ + "from matplotlib import pyplot as plt\n", + "%matplotlib widget" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "37a3a13f", + "metadata": {}, + "outputs": [], + "source": [ + "class Battery:\n", + " \n", + " def __init__ (self, name, size, heat_program, charge_program, T_combustion_0):\n", + " self.name = name\n", + " self.size = size\n", + " self.heat_program = heat_program\n", + " self.charge_program = charge_program\n", + " self.t = 0\n", + " self.t_last = 0\n", + " self.processing = []\n", + " self.product = []\n", + " self.T0 = T_combustion_0\n", + "\n", + " normal_period = self.charge_program.period(-1)\n", + " \n", + " self.t = - normal_period * self.size\n", + " self.t_last = self.t\n", + " \n", + " for i in range(self.size * 2):\n", + " \"\"\" Fill battety with normal charge\"\"\"\n", + " self.update(normal_period / 2.)\n", + " \n", + " def bake (self, dt):\n", + " dQ = self.dQ(dt)\n", + " for cc in self.processing:\n", + " cc.bake(dQ)\n", + " \n", + " def push_and_charge (self, coke_charge):\n", + " if len(self.processing) >= self.size:\n", + " self.push()\n", + " self.charge(coke_charge)\n", + " \n", + " def push (self):\n", + " coke = self.processing.pop(0)\n", + " coke.end_baking(self.t)\n", + " self.product.append(coke)\n", + " \n", + " def charge (self, coke_charge):\n", + " self.processing.append(coke_charge)\n", + " \n", + " def dQ (self, dt):\n", + " return self.heat_program.dQ(self.t, self.t+dt)\n", + "\n", + " def update (self, dt):\n", + " dQ = self.heat_program.dQ(self.t, self.t+dt)\n", + " self.bake(dt)\n", + " if self.t+dt > self.charge_program.period(self.t) + self.t_last :\n", + " print(\"Push timing within this time step.\", \n", + " self.t+dt - self.t_last, \"since last P/C. \", \n", + " \"P/C interval = \", self.charge_program.period(self.t))\n", + " \n", + " if self.t_last + self.charge_program.period(self.t) > self.t:\n", + " self.t_last += self.charge_program.period(self.t)\n", + " else:\n", + " self.t_last = self.t\n", + " \n", + " self.push_and_charge(CokeCharge(self.t_last))\n", + " self.t += dt\n", + " \n", + "\n", + "class CokeCharge:\n", + " \n", + " def __init__ (self, t_charge):\n", + " self.t_charge = t_charge\n", + " self.t_push = None\n", + " self.Q = 0\n", + "\n", + " def bake (self, dQ):\n", + " self.Q += dQ\n", + " \n", + " def end_baking (self, t):\n", + " self.t_push = t\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "070094b4", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "\n", + "sample_program = np.array('''\\\n", + "-3\t81\n", + "0\t81\n", + "0\t69.61621622\n", + "3.5\t69.61621622\n", + "3.5\t58.83157895\n", + "7\t58.83157895\n", + "7\t48.6\n", + "10.5\t48.6\n", + "10.5\t42.039\n", + "14.5\t42.039\n", + "14.5\t38.88\n", + "24.5\t38.88\n", + "24.5\t42.039\n", + "27.75\t42.039\n", + "27.75\t48.6\n", + "30.35\t48.6\n", + "30.35\t58.83157895\n", + "32.43\t58.83157895\n", + "32.43\t69.61621622\n", + "34.094\t69.61621622\n", + "34.094\t81\n", + "36.46688136\t81\n", + "36.46688136\t72.9\n", + "39.46688136\t72.9\n", + "39.46688136\t67.23\n", + "42.46688136\t67.23\n", + "42.46688136\t62.37\n", + "47.46688136\t62.37\n", + "47.46688136\t67.23\n", + "50.46688136\t67.23\n", + "50.46688136\t72.9\n", + "53.46688136\t72.9\n", + "53.46688136\t81\n", + "56.46688136\t81\n", + "'''.split(), dtype=np.double).reshape((-1,2))" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "9713bb3a", + "metadata": {}, + "outputs": [], + "source": [ + "example_program = np.array([\n", + "-600 , 0,\n", + "-360 , 0,\n", + "-360 , -6.8,\n", + "-280 , -6.8,\n", + "-280 , -6.8 -6.63,\n", + "-180 , -6.8 -6.63,\n", + "-180 , -6.8 -6.63 -6.8,\n", + "-120 , -6.8 -6.63 -6.8,\n", + "-120 , -6.8 -6.63 -6.8 -6.35,\n", + "-40 , -6.8 -6.63 -6.8 -6.35,\n", + "-40 , -6.8 -6.63 -6.8 -6.35 -6.18,\n", + "9*60 + 80 , -6.8 -6.63 -6.8 -6.35 -6.18,\n", + "9*60 + 80 , -6.8 -6.63 -6.8 -6.35,\n", + "9*60 + 240 , -6.8 -6.63 -6.8 -6.35,\n", + "9*60 + 240 , -6.8 -6.63 -6.8,\n", + "9*60 + 480 , -6.8 -6.63 -6.8,\n", + "9*60 + 480 , -6.8 -6.63,\n", + "9*60 + 720 , -6.8 -6.63,\n", + "9*60 + 720 , -6.8,\n", + "9*60 + 960 , -6.8,\n", + "9*60 + 960 , 0,\n", + "9*60 + 1200 , 0,\n", + "(15+13)*60 + -300 -240 , 0,\n", + "(15+13)*60 + -240 , 0,\n", + "(15+13)*60 + -240 , - 5.46,\n", + "(15+13)*60 + -180 , - 5.46,\n", + "(15+13)*60 + -180 , - 5.46 - 5.25,\n", + "(15+13)*60 + -120 , - 5.46 - 5.25,\n", + "(15+13)*60 + -120 , - 5.46 - 5.25 - 5.14,\n", + "(15+13)*60 + -40 , - 5.46 - 5.25 - 5.14,\n", + "(15+13)*60 + -40 , - 5.46 - 5.25 - 5.14 - 4.31,\n", + "(15+13)*60 + 3*60 + 40 , - 5.46 - 5.25 - 5.14 - 4.31,\n", + "(15+13)*60 + 3*60 + 40 , - 5.46 - 5.25 - 5.14,\n", + "(15+13)*60 + 3*60 + 180 , - 5.46 - 5.25 - 5.14,\n", + "(15+13)*60 + 3*60 + 180 , - 5.46 - 5.25,\n", + "(15+13)*60 + 3*60 + 280 , - 5.46 - 5.25,\n", + "(15+13)*60 + 3*60 + 280 , - 5.46,\n", + "(15+13)*60 + 3*60 + 360 , - 5.46,\n", + "(15+13)*60 + 3*60 + 360 , 0,\n", + "(15+13)*60 + 3*60 + 360 + 300 , 0,\n", + "]).reshape((-1, 2)).T\n", + "\n", + "example_program[0] /= 60.\n", + "example_program[0] += 9.\n", + "example_program[1] += 73." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "bc714fba", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "31df4c517e764ddf820efaa37ae44ead", + "version_major": 2, + "version_minor": 0 + }, + "image/png": 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", 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service_load, aux_start, aux_time, aux_load):\n", + " self.xp = np.array([service_start, service_start, service_start+service_time, service_start+service_time, \n", + " aux_start, aux_start, aux_start+aux_time, aux_start+aux_time, ])\n", + " self.fp = np.array([normal_load, service_load, service_load, normal_load,\n", + " normal_load, aux_load, aux_load, normal_load])\n", + " self.f = lambda x: np.interp(x, self.xp, self.fp)\n", + " \n", + " def to_charge (self, t0, t1):\n", + " self.f(t0)\n", + " return np.trapz(self.f(x), x)\n", + " \n", + " def period (self, t):\n", + " return 24 / self.f(t)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "69ab56fc", + "metadata": {}, + "outputs": [], + "source": [ + "sample_schedule = HeatSchedule(*sample_program.T)\n", + "sample_schedule2 = HeatSchedule(*example_program)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "6d6eac92", + "metadata": {}, + "outputs": [], + "source": [ + "sample_charging = ChargeSchedule( 81, 9, 9, 1e-12, 24+13, 2, 1e-12 )" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "e742dad8", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "a96ccf235bd149df92dee4920c267e94", + "version_major": 2, + "version_minor": 0 + }, + "image/png": 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", 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P/C interval = 0.2962962962962963\n", + "Push timing within this time step. 0.4444444444444464 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within this time step. 0.4444444444444464 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within this time step. 0.4444444444444464 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within this time step. 0.4444444444444464 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within this time step. 0.4444444444444464 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within this time step. 0.4444444444444464 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within this time step. 0.4444444444444464 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within this time step. 0.2962962962962976 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within this time step. 0.2962962962962994 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within this time step. 0.29629629629630116 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within this time step. 0.29629629629630294 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within this time step. 0.2962962962963047 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within this time step. 0.2962962962963065 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within this time step. 0.29629629629630827 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within this time step. 0.29629629629631005 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within this time step. 0.2962962962963118 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within this time step. 0.2962962962963136 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within this time step. 0.2962962962963154 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within this time step. 0.29629629629631715 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within this time step. 0.29629629629631893 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within this time step. 0.2962962962963207 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within this time step. 0.2962962962963225 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within this time step. 0.29629629629632426 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within this time step. 0.29629629629632603 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within this time step. 0.2962962962963278 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within this time step. 0.2962962962963296 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within this time step. 0.29629629629633136 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within this time step. 0.29629629629633314 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within this time step. 0.2962962962963349 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within this time step. 0.2962962962963367 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within this time step. 0.29629629629633847 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within this time step. 0.29629629629634024 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within this time step. 0.296296296296342 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within this time step. 0.2962962962963438 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within this time step. 0.2962962962963447 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within this time step. 0.2962962962963447 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within this time step. 0.2962962962963447 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within this time step. 0.2962962962963447 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within this time step. 0.2962962962963447 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within this time step. 0.2962962962963447 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within this time step. 0.2962962962963447 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within this time step. 0.2962962962963447 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within this time step. 0.2962962962963447 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within this time step. 0.2962962962963447 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within this time step. 0.2962962962963447 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within this time step. 0.2962962962963447 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within this time step. 0.2962962962963447 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within this time step. 0.2962962962963447 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within this time step. 0.2962962962963447 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within this time step. 0.29629629629634424 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within this time step. 0.2962962962963438 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within this time step. 0.29629629629634335 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within this time step. 0.2962962962963429 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within this time step. 0.29629629629634247 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within this time step. 0.296296296296342 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within this time step. 0.296296296296342 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within this time step. 0.296296296296342 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within this time step. 0.296296296296342 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within this time step. 0.296296296296342 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within this time step. 0.296296296296342 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within this time step. 0.296296296296342 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within this time step. 0.296296296296342 since last P/C. P/C interval = 0.2962962962962963\n", + "1395.1111111111131 -19.259259259259256\n", + "1373.4814814814833 -18.96296296296296\n", + "1351.8518518518536 -18.66666666666666\n", + "1330.2222222222238 -18.370370370370363\n", + "1308.592592592594 -18.074074074074066\n", + "1286.9629629629642 -17.777777777777768\n", + "1265.3333333333344 -17.48148148148147\n", + "1243.7037037037046 -17.185185185185173\n", + "1222.0740740740748 -16.888888888888875\n", + "1200.444444444445 -16.592592592592577\n", + "1178.8148148148152 -16.29629629629628\n", + "1168.0000000000005 -15.999999999999984\n", + "1146.3703703703707 -15.703703703703688\n", + "1124.7407407407409 -15.407407407407392\n", + "1103.111111111111 -15.111111111111097\n", + "1081.4814814814813 -14.8148148148148\n", + "1059.8518518518517 -14.518518518518505\n", + "1038.222222222222 -14.222222222222209\n", + "1016.5925925925923 -13.925925925925913\n", + "994.9629629629626 -13.629629629629617\n", + "973.3333333333329 -13.333333333333321\n", + "951.7037037037032 -13.037037037037026\n", + "930.0740740740736 -12.74074074074073\n", + "908.4444444444439 -12.444444444444434\n", + "886.8148148148142 -12.148148148148138\n", + "865.1851851851845 -11.851851851851842\n", + "843.5555555555549 -11.555555555555546\n", + "821.9259259259252 -11.25925925925925\n", + "800.2962962962955 -10.962962962962955\n", + "778.6666666666658 -10.666666666666659\n", + "757.0370370370362 -10.370370370370363\n", + "735.4074074074065 -10.074074074074067\n", + "713.7777777777768 -9.777777777777771\n", + "692.1481481481471 -9.481481481481476\n", + "670.5185185185175 -9.18518518518518\n", + "648.8888888888878 -8.888888888888884\n", + "627.2592592592582 -8.592592592592588\n", + "605.6296296296287 -8.296296296296292\n", + "583.9999999999991 -7.9999999999999964\n", + "562.3703703703696 -7.703703703703701\n", + "540.7407407407402 -7.407407407407405\n", + "519.1111111111107 -7.111111111111109\n", + "497.48148148148124 -6.814814814814813\n", + "475.8518518518517 -6.518518518518517\n", + "454.2222222222221 -6.222222222222221\n", + "432.59259259259255 -5.925925925925926\n", + "410.962962962963 -5.62962962962963\n", + "389.33333333333337 -5.333333333333334\n", + "367.7037037037037 -5.037037037037038\n", + "346.074074074074 -4.740740740740742\n", + "324.44444444444434 -4.444444444444446\n", + "302.81481481481467 -4.148148148148151\n", + "281.185185185185 -3.8518518518518543\n", + "259.5555555555553 -3.555555555555558\n", + "237.92592592592572 -3.2592592592592617\n", + "216.29629629629613 -2.9629629629629655\n", + "194.66666666666654 -2.666666666666669\n", + "173.03703703703695 -2.370370370370373\n", + "151.4074074074074 -2.0740740740740766\n", + "129.77777777777777 -1.7777777777777803\n", + "108.14814814814812 -1.481481481481484\n", + "86.51851851851849 -1.1851851851851878\n", + "64.88888888888887 -0.8888888888888915\n", + "43.25925925925925 -0.5925925925925952\n", + "21.629629629629626 -0.29629629629629894\n", + "0 -2.6645352591003757e-15\n" + ] + } + ], + "source": [ + "bat3A = Battery(\"3A\", 66, sample_schedule2, sample_charging, 1220) \n", + "for cc in bat3A.processing:\n", + " print (cc.Q, cc.t_charge)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "99d40063", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Push timing within this time step. 0.30000000000004573 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within this time step. 0.30370370370374977 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within this time step. 0.3074074074074544 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within this time step. 0.31111111111115775 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within this time step. 0.2981481481481938 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within this time step. 0.30185185185189645 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within this time step. 0.3055555555555993 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within this time step. 0.309259259259302 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within this time step. 0.296296296296338 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within this time step. 0.3000000000000407 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within this time step. 0.30370370370374333 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within this time step. 0.307407407407446 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within this time step. 0.31111111111114864 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within this time step. 0.29814814814818513 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within this time step. 0.3018518518518878 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within this time step. 0.3055555555555909 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within this time step. 0.309259259259294 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within this time step. 0.2962962962963305 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within this time step. 0.3000000000000336 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within this time step. 0.30370370370373667 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within this time step. 0.30740740740743977 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within this time step. 0.31111111111114287 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within this time step. 0.29814814814817936 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within this time step. 0.30185185185188246 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within this time step. 0.30555555555558556 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within this time step. 0.30925925925928865 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within this time step. 0.29629629629632603 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within this time step. 0.3000000000000451 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within this time step. 0.3037037037037642 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within this time step. 0.3074074074074833 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within this time step. 9.127777777778093 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within this time step. 0.29999999999998295 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within this time step. 0.3037037037036683 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within this time step. 0.3074074074073536 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within this time step. 0.31111111111103895 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within this time step. 0.29814814814805857 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within this time step. 0.3018518518517439 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within this time step. 0.30555555555542924 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within this time step. 0.30925925925911457 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within this time step. 0.3129629629627999 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within this time step. 0.2999999999998195 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within this time step. 0.30370370370350486 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within this time step. 0.3074074074071902 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within this time step. 0.3111111111108755 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within this time step. 0.29814814814789514 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within this time step. 0.3018518518515805 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within this time step. 0.3055555555552658 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within this time step. 0.30925925925895115 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within this time step. 0.3129629629626365 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within this time step. 0.2999999999996561 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within this time step. 0.30370370370334143 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within this time step. 0.30740740740702677 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within this time step. 0.3111111111107121 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within this time step. 0.2981481481477317 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within this time step. 0.30185185185141705 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within this time step. 0.3055555555551024 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within this time step. 0.3092592592587877 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within this time step. 0.31296296296247306 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within this time step. 0.2999999999994927 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within this time step. 0.303703703703178 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within this time step. 0.30740740740686334 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within this time step. 0.3111111111105487 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within this time step. 0.2981481481475683 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within this time step. 0.3018518518512536 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within this time step. 0.30555555555493896 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within this time step. 0.3092592592586243 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within this time step. 0.31296296296230963 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within this time step. 0.29999999999932925 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within this time step. 0.3037037037030146 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within this time step. 0.3074074074066999 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within this time step. 0.31111111111038525 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within this time step. 0.29814814814740487 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within this time step. 0.3018518518510902 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within this time step. 0.30555555555477554 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within this time step. 0.3092592592584609 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within this time step. 0.3129629629621462 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within this time step. 0.2999999999991658 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within this time step. 0.30370370370285116 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within this time step. 0.3074074074065365 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within this time step. 0.3111111111102218 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within this time step. 0.29814814814724144 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within this time step. 0.3018518518509268 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within this time step. 0.3055555555546121 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within this time step. 0.30925925925829745 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within this time step. 0.3129629629619828 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within this time step. 0.2999999999990024 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within this time step. 0.30370370370268773 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within this time step. 0.30740740740637307 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within this time step. 0.3111111111100584 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within this time step. 0.298148148147078 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within this time step. 0.30185185185076335 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within this time step. 0.3055555555544487 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within this time step. 0.309259259258134 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within this time step. 0.31296296296181936 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within this time step. 0.299999999998839 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within this time step. 2.0703703703690906 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within this time step. 0.29999999999998295 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within this time step. 0.3037037037036683 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within this time step. 0.3074074074073536 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within this time step. 0.31111111111103895 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within this time step. 0.29814814814805857 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within this time step. 0.3018518518517439 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within this time step. 0.30555555555542924 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within this time step. 0.30925925925911457 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within this time step. 0.3129629629627999 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within this time step. 0.2999999999998195 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within this time step. 0.30370370370350486 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within this time step. 0.3074074074071902 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within this time step. 0.3111111111108755 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within this time step. 0.29814814814789514 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within this time step. 0.3018518518515805 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within this time step. 0.3055555555552658 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within this time step. 0.30925925925895115 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within this time step. 0.3129629629626365 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within this time step. 0.2999999999996561 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within this time step. 0.30370370370334143 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within this time step. 0.30740740740702677 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within this time step. 0.3111111111107121 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within this time step. 0.2981481481477317 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within this time step. 0.30185185185141705 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within this time step. 0.3055555555551024 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within this time step. 0.3092592592587877 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within this time step. 0.31296296296247306 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within this time step. 0.2999999999994927 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within this time step. 0.303703703703178 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within this time step. 0.30740740740686334 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within this time step. 0.3111111111105487 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within this time step. 0.2981481481475683 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within this time step. 0.3018518518512536 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within this time step. 0.30555555555493896 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within this time step. 0.3092592592586243 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within this time step. 0.31296296296230963 since last P/C. 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P/C interval = 0.2962962962962963\n" + ] + } + ], + "source": [ + "t = 0\n", + "dt = 1 / 60. # 1 min = 1/60 hour\n", + "# meanQ = [np.mean([cc.Q for cc in bat3A.processing])]\n", + "meanQ = []\n", + "for it in range (5000):\n", + " t += dt\n", + " bat3A.update(dt)\n", + " meanQ.append(np.mean([cc.Q for cc in bat3A.processing]))" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "a86acbef", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-1155.5555555555554 1417.0111111111137\n", + "-1137.7777777777776 1417.2814814814844\n", + "-1119.9999999999995 1417.5518518518552\n", + "-1102.2222222222217 1417.822222222226\n", + "-1084.444444444444 1416.87592592593\n", + "-1066.666666666666 1417.1462962963008\n", + "-1048.8888888888882 1417.4166666666715\n", + "-1031.1111111111104 1417.6870370370423\n", + "-1013.3333333333325 1416.7407407407463\n", + "-995.5555555555546 1417.011111111117\n", + "-977.7777777777768 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1347.3413555554814\n", + "1542.2222222222426 1353.4103555554852\n", + "1560.0000000000205 1358.1512444443774\n", + "1577.7777777777983 1363.173077777714\n", + "1595.5555555555761 1367.2020777777175\n", + "1613.333333333354 1371.2310777777193\n", + "1631.1111111111318 1374.043411111052\n", + "1648.8888888889098 1379.0652444443847\n", + "1666.6666666666877 1383.094244444384\n", + "1684.4444444444655 1387.123244444383\n", + "1702.2222222222433 1391.1522444443822\n", + "1720.0000000000211 1393.964577777716\n", + "1737.777777777799 1398.9864111110503\n", + "1755.5555555555768 1403.0154111110514\n", + "1773.3333333333549 1407.0444111110526\n", + "1791.1111111111327 1409.856744444387\n", + "1808.8888888889105 1413.8859194443871\n", + "1826.6666666666883 1415.925919444387\n", + "1844.4444444444662 1417.965919444387\n", + "1862.222222222244 1420.0059194443884\n", + "1880.0000000000218 1420.8292527777228\n", + "1897.7777777777997 1423.9725861110571\n", + "1915.5555555555775 1426.0125861110585\n", + "1933.3333333333555 1428.0525861110598\n", + "1951.1111111111334 1428.8759194443942\n", + "1968.8888888889112 1432.019252777729\n", + "1986.666666666689 1434.0592527777278\n", + "2004.4444444444669 1436.0992527777266\n", + "2022.2222222222447 1438.1392527777257\n", + "2040.0000000000225 1439.0266666666134\n", + "2057.7777777778006 1440.2433333332788\n", + "2075.5555555555784 1440.2433333332774\n", + "2093.333333333356 1440.243333333277\n", + "2111.111111111134 1439.0266666666103\n", + "2128.888888888912 1440.243333333277\n", + "2146.6666666666897 1440.243333333277\n", + "2164.4444444444675 1440.243333333277\n", + "2182.2222222222454 1440.2433333332765\n", + "2200.000000000023 1439.0266666666098\n", + "2217.777777777801 1440.2433333332765\n", + "2340.999999999947 1332.103066666607\n", + "2358.7777777777246 1339.0317333332735\n", + "2376.5555555555025 1343.8630666666065\n", + "2394.3333333332803 1349.9110666666063\n", + "2412.111111111058 1355.9590666666058\n", + "2429.888888888836 1362.8877333332725\n", + "2447.6666666666138 1368.3622638888285\n", + "2465.4444444443916 1371.9005972221623\n", + "2483.2222222221694 1376.655597222163\n", + "2500.9999999999472 1381.4105972221635\n", + "2518.777777777725 1387.1180972221641\n", + "2536.555555555503 1390.6564305554975\n", + "2554.3333333332807 1395.4114305554974\n", + "2572.1111111110586 1400.1664305554973\n", + "2589.888888888837 1405.0186916666084\n", + "2607.6666666666147 1408.231691666608\n", + "2625.4444444443925 1410.2280249999412\n", + "2643.2222222221703 1413.4410249999412\n", + "2660.999999999948 1416.6540249999412\n", + "2678.777777777726 1420.9051916666085\n", + "2696.555555555504 1421.415483333275\n", + "2714.3333333332816 1423.0534833332752\n", + "2732.1111111110595 1424.691483333275\n", + "2749.8888888888373 1427.4551499999418\n", + "2767.666666666615 1428.3666666666086\n", + "2785.444444444393 1427.1499999999419\n", + "2803.222222222171 1427.1499999999419\n", + "2820.9999999999486 1427.1499999999419\n", + "2838.7777777777264 1428.3666666666086\n", + "2856.5555555555047 1427.1499999999419\n", + "2874.3333333332826 1427.1499999999419\n", + "2892.1111111110604 1427.1499999999419\n", + "2909.888888888838 1428.3666666666086\n", + "2927.666666666616 1428.3666666666086\n", + "2945.444444444394 1427.1499999999419\n", + "2963.2222222221717 1427.1499999999419\n", + "2980.9999999999495 1427.1499999999419\n", + "2998.7777777777274 1428.3666666666086\n", + "3016.555555555505 1427.149999999942\n", + "3034.333333333283 1427.149999999942\n", + "3052.111111111061 1427.149999999942\n", + "3069.8888888888387 1428.3666666666088\n", + "3087.6666666666165 1428.3666666666088\n", + "3105.4444444443943 1427.149999999942\n", + "3123.222222222172 1427.149999999942\n", + "3140.99999999995 1427.149999999942\n", + "3158.7777777777283 1428.3666666666088\n", + "3176.555555555506 1427.149999999942\n", + "3194.333333333284 1427.149999999942\n", + "3212.1111111110617 1427.149999999942\n", + "3229.8888888888396 1428.3666666666088\n", + "3247.6666666666174 1428.3666666666088\n", + "3265.4444444443952 1427.149999999942\n", + "3283.222222222173 1427.149999999942\n", + "3300.999999999951 1427.149999999942\n", + "3318.7777777777287 1428.3666666666088\n", + "3336.5555555555065 1427.149999999942\n", + "3354.3333333332844 1427.149999999942\n", + "3372.111111111062 1427.149999999942\n", + "3389.88888888884 1428.3666666666088\n", + "3407.666666666618 1428.3666666666088\n", + "3425.444444444396 1427.1499999999419\n", + "3443.222222222174 1427.1499999999419\n", + "3460.999999999952 1427.1499999999419\n", + "3478.7777777777296 1428.3666666666086\n", + "3496.5555555555075 1427.1499999999419\n", + "3514.3333333332853 1427.1499999999419\n", + "3532.111111111063 1427.1499999999419\n", + "3549.888888888841 1428.3666666666086\n", + "3567.6666666666188 1428.3666666666086\n", + "3585.4444444443966 1427.1499999999419\n", + "3603.2222222221744 1427.1499999999419\n", + "3620.9999999999523 1427.1499999999419\n", + "3638.77777777773 1428.3666666666088\n", + "3656.555555555508 1427.149999999942\n", + "3674.3333333332857 1427.149999999942\n", + "3692.1111111110636 1427.149999999942\n", + "3709.8888888888414 1428.3666666666088\n", + "3727.6666666666197 1428.3666666666088\n", + "3745.4444444443975 1427.1499999999419\n", + "3763.2222222221753 1427.1499999999419\n", + "3780.999999999953 1427.1499999999419\n", + "3798.777777777731 1428.3666666666086\n", + "3816.555555555509 1427.1499999999419\n" + ] + } + ], + "source": [ + "for cc in bat3A.product:\n", + " print(cc.t_charge * 60, cc.Q)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "1179dfeb", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[-1. , 3. , 3. , 4.33333333, 4.33333333,\n", + " 6. , 6. , 7. , 7. , 8.33333333,\n", + " 8.33333333, 19.33333333, 19.33333333, 22. , 22. ,\n", + " 26. , 26. , 30. , 30. , 34. ,\n", + " 34. , 38. , 28. , 33. , 33. ,\n", + " 34. , 34. , 35. , 35. , 36.33333333,\n", + " 36.33333333, 40.66666667, 40.66666667, 43. , 43. ,\n", + " 44.66666667, 44.66666667, 46. , 46. , 51. ],\n", + " [73. , 73. , 66.2 , 66.2 , 59.57 ,\n", + " 59.57 , 52.77 , 52.77 , 46.42 , 46.42 ,\n", + " 40.24 , 40.24 , 46.42 , 46.42 , 52.77 ,\n", + " 52.77 , 59.57 , 59.57 , 66.2 , 66.2 ,\n", + " 73. , 73. , 73. , 73. , 67.54 ,\n", + " 67.54 , 62.29 , 62.29 , 57.15 , 57.15 ,\n", + " 52.84 , 52.84 , 57.15 , 57.15 , 62.29 ,\n", + " 62.29 , 67.54 , 67.54 , 73. , 73. ]])" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "example_program" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "3ed23b12", + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "ba49f91237814f169a176fff3296fb64", + "version_major": 2, + "version_minor": 0 + }, + "image/png": 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+ "text/html": [ + "\n", + "
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\n", + " " + ], + "text/plain": [ + "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(1, 1, figsize=(11, 7), sharex=True)\n", + "ax.plot(*np.array([(cc.t_push, cc.Q) for cc in bat3A.product]).T, '.')\n", + "ax.set_ylim(ymin=0)\n", + "\n", + "ax.plot(np.arange(0, 5000) * dt, meanQ)\n", + "\n", + "twin = ax.twinx()\n", + "twin.plot(sample_time, np.interp(sample_time, *example_program), '-')\n", + "twin.set_ylim(ymin=0)\n", + "twin.grid()\n", + "\n", + "for x in [9, 9+9, 24+13, 24+13+3]:\n", + " ax.axvline(x, linestyle=\":\")\n", + "\n", + "# ax2.plot(sample_time, sample_charging.period(sample_time))" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "4df78f65", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1425.6" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "72 * 24*66/80" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "2065d0c6", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "\n", + "T104 = np.array('''\\\n", + "6.469635511825288 97.62584917278241\n", + "16.728957359665642 99.54879021268471\n", + "18.569715003482443 103.65207384901169\n", + "20.23392722854227 114.40742124017106\n", + "21.935674341607154 161.14094501700924\n", + "22.981977601017448 195.1785155500827\n", + "24.15864522242518 235.88581465069137\n", + "25.640236342576202 287.9364470509861\n", + "26.945866244561714 343.30806423935337\n", + "28.33778257391458 406.679318037288\n", + "29.817737932409315 468.0569715445107\n", + "31.561459857974853 525.452471559652\n", + "32.95804979205987 562.1750936235078\n", + "34.530951464665264 593.578083519235\n", + "35.973605615391534 617.645129054075\n", + "37.811559096369066 637.7375917308494\n", + "40.00038345261282 657.184026971646\n", + "41.97057073748064 673.2867655223238\n", + "43.634899802658765 683.3758971201314\n", + "47.226156098902706 706.2341647571851\n", + "51.91258403403752 734.4852473578494\n", + "55.9423997143347 756.7025346482691\n", + "59.841383672161214 774.9149565445614\n", + "65.36295556290182 791.2221022136539\n", + "72.37558262337606 805.6164010215316\n", + "77.7223616971342 818.5823735451772\n", + "83.15823136109901 823.5588036379428\n", + "86.27058922244535 827.0690542763996\n", + "89.99431300281623 844.605166200653\n", + "93.27632271593967 880.7700848920435\n", + "97.5625440358545 940.9768136715566\n", + "'''.split(), dtype=np.double).reshape((-1, 2))" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "315b9960", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "\n", + "T105 = np.array('''\\\n", + "6.294258494251331 97.61575499409514\n", + "19.70919825724002 106.38254918388611\n", + "32.511720540139464 107.11942422804805\n", + "36.45022566798224 149.9843540230354\n", + "37.762982817184295 164.7168078169325\n", + "38.89966190810257 183.43646219225434\n", + "41.784269168845285 235.5678480220463\n", + "45.14555490223785 319.7053509241225\n", + "47.54530488204948 386.4657252162683\n", + "49.4653385461354 438.54159306328097\n", + "52.87070221415806 521.3491879233252\n", + "54.530007154249105 560.0855986352677\n", + "57.50171872318727 615.553110521163\n", + "60.12571410005344 653.678823422533\n", + "64.32646687342904 701.2224050390148\n", + "68.5287385683426 740.1051813419208\n", + "72.20780021349182 762.3022802749662\n", + "76.85155229541621 783.8886813974389\n", + "81.67184979609755 798.823018765079\n", + "84.03850481239965 804.2890165241714\n", + "85.9656657237021 815.7257209767135\n", + "87.71628121624755 833.8144891840884\n", + "89.90487189225468 854.5933560115893\n", + "91.56756519577655 874.0095087163243\n", + "93.75580535142879 896.7870229238808\n", + "95.59364199228798 917.545701394007\n", + "97.60662197048458 939.6469056295243\n", + "'''.split(), dtype=np.double).reshape((-1, 2))\n", + "\n", + "progress = np.linspace(0, 100, 1001)\n", + "mean = (np.interp(progress, *T104.T) + np.interp(progress, *T105.T))/2" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "85cbc85b", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "0c31665b58574d3796702f44e72b881e", + "version_major": 2, + "version_minor": 0 + }, + "image/png": 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", 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duration, ):\n", + " \n", + " if not (table.shape[1] == 3):\n", + " return None\n", + " \n", + " dQ = table.T[0]\n", + " \n", + " step_times = np.concatenate((start + table.T[1], start + duration + table[::-1, 2]))\n", + " \n", + " step0 = np.cumsum(dQ) - dQ\n", + " step1 = np.cumsum(dQ)\n", + " \n", + " print(step0)\n", + " print(step1)\n", + " \n", + " steps_decreasing = list(zip(-step0, -step1))\n", + " steps_increasing = list(zip(-step1[::-1], -step0[::-1]))\n", + " \n", + " steps = steps_decreasing + steps_increasing\n", + " \n", + " return np.array([ (step_times[i], s0 + heat_base, step_times[i], s1 + heat_base)\n", + " for i, (s0, s1) in enumerate(steps) ]).reshape((-1, 2))\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "d79e655e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ 0. 8.4 16.38 23.83 28.13]\n", + "[ 8.4 16.38 23.83 28.13 29.9 ]\n" + ] + } + ], + "source": [ + "curve1 = heat_table_to_curve(table1, 81, 8*60, 12*60)" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "1a04c477", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(5, 3)" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.array('''\\\n", + "7.30 \t1set\t\t1Set\t-340\t380\n", + "5.64 \t2Set\t\t2Set\t-180\t220\n", + "4.14 \t3set\t\t3Set\t-20\t60\n", + "'''.split()).reshape((5,-1)).shape" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "51040443", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ 0. 7.3 12.94]\n", + "[ 7.3 12.94 17.08]\n" + ] + } + ], + "source": [ + "table2 = np.array('''\\\n", + "7.30 \t1set\t\t1Set\t-340\t380\n", + "5.64 \t2Set\t\t2Set\t-180\t220\n", + "4.14 \t3set\t\t3Set\t-20\t60\n", + "'''.split()).reshape((3,-1))[:,[0,-2,-1]].astype(np.double)\n", + "\n", + "curve2 = heat_table_to_curve(table2, 81, (24+13)*60, 4.5*60)" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "37535ff4", + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "09cfa23944d24b61ba8492dd35b64c00", + "version_major": 2, + "version_minor": 0 + }, + "image/png": 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linestyle='--')\n" + ] + }, + { + "data": { + "text/plain": [ + "(1120.0, 1320.0)" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "6e0522b1e0e54a028e98779e53faa3fc", + "version_major": 2, + "version_minor": 0 + }, + "image/png": 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KRkanBx/oXWg1Mjo9eCej04MP9E67RkYXrd69vtRl36qOd51pk2aNyyumXl8auf0uUPlF+vgD2l5H1XxQefxJUFdXh5EjR2LRokVej5988slYtGgRtm3bhu+++w4DBgxAbm4uDh8+7IiZM2cOli9fjvfeew/fffcdamtrcdFFF8FmszliZs6ciaKiIuTn5yM/Px9FRUW49tprO5V7xBDuVepIaCktLbWv1FhcbC+oqhICsG9b8SzTEuNHU9dodVvBVrZei8UiVqxYISwWS4fybaMLhEYmPy0aP/nRu861SSkf6F1YfQjlWIo27+hD5zSubxCoa7SG3Ls6U7zz/IePqeNdkHxoT1N3+JjTC1N85Pa7IHkXKT64jSmP66iad6qNv5KSEs2ruAMQy5cvbzemqqpKABBr1qwRQghx/PhxYTKZxHvvveeIOXDggIiJiRH5+flCCOF469SGDRscMevXrxcAxM6dOzucZ6TBJ+iEEEIIIYQQQgKKxWLBK6+8gvT0dIwcORIAsGnTJlitVuTm5jrisrOzMWzYMKxbtw4AsH79eqSnp7t9LH7ChAlIT093xEQz/A46IYQQQgghhOiYmpoax+uYgbavau4IK1euxFVXXQWz2YysrCysXr0aGRkZAICKigrExcWha9eubppevXqhoqLCEdOzZ8829fbs2dMRE83wCbpeMRqd29xc5763Mi0xMhpfefmrRxWNjE4PPtC70GpkdHrwTkanBx/onXaNjC5avZs8uXO5yegiwQfPmMmTI7ffBSq/SB9/QNvrqJoPio6/nJwcpKenO36eeOIJ3zn54ZxzzkFRURHWrVuHqVOn4oorrsChQ4fa1QghYDAYHPuu/+8rJlrhE3S9kpRk3yYnA6tWuR/zLNMS40/ja/VkmXpU0cjotGj85Ufvgp+fyhoZnR68k9Hp4T7EPqRdI6OLVu8++cS5inRykjreeYsJtsb1PvDJJ+2v4q5yvwtUfpE+/oC211E1HxQdf8XFxejdu7djX+vTc/upknHSSSfhpJNOwoQJEzBo0CAsWbIE8+bNQ2ZmJiwWCyorK92eoh86dAinn346ACAzMxMHDx5sU+/hw4fRq1cvzXlFCnyCrldaV3BsbATy8uzbVjzLtMTIaLwhU48qGhmdHnygd6HVyOj04J2MTg8+0DvtGhldtHr3+EKXfYs63nWmTZo1LitaP74wcvtdoPKL9PEHtL2Oqvmg6PhLTU1FWlqa46czE3RPhBBobDnvqaeeCpPJhNWrVzuOl5eXY/v27Y4J+sSJE1FVVYWCggJHzA8//ICqqipHTFQT5kXqSIjhKu7qrkDKVdx1uIotvQurD1zFnX0oKD5IaLiKe2j7EFdxj/7xx1XcQ7eKe01NjdiyZYvYsmWLACCeeeYZsWXLFrF3715RW1sr5s2bJ9avXy9+++03sWnTJnHzzTeL+Ph4sX37dkcdf/zjH0WfPn3EmjVrxObNm8W5554rRo4cKZqamhwxU6dOFSNGjBDr168X69evF8OHDxcXXXSRVI6RDj/iTgghhBBCCCHEL4WFhTjnnHMc+/fccw8A4Prrr8dLL72EnTt3YunSpThy5Ai6d++O0047Df/9738xdOhQh+bZZ59FbGwsrrjiCtTX12Py5Ml4/fXXYXT5fvzbb7+NO++807Ha+/Tp032+ez3a4ASdEEIIIYQQQohfJk2aBCGEz+PLli3zW0dCQgKef/55PP/88z5junXrhrfeektTjpEOv4OuV0wm5/bmm5373sq0xMhofOXlrx5VNDI6PfhA70KrkdHpwTsZnR58oHfaNTK6aPXuuus7l5uMLhJ88Iy57vrI7XeByi/Sxx/Q9jqq5oPK44+oQbg/Y09Ci+M76JLfMwkWPr/76Qev3wEKkk5VDb0LvUb1/FTWqJxfKMdStHkXSo3q+WnReP0OehDO40vn2ve9nV9l7wKtac8LFfILt0b1/Lx+B93PmAq3d6qNv45+B50EHz5B1ysNDfZtfT1wyy32bSueZVpiZDTekKlHFY2MTg8+0LvQamR0evBORqcHH+iddo2MLlq9m32Hy36DOt51pk2aNQ3Ostl3RG6/C1R+kT7+gLbXUTUfVB5/RAk4QdcrVqtzu2SJc99bmZYYGY2vvPzVo4pGRqcHH+hdaDUyOj14J6PTgw/0TrtGRhet3r2xtHO5yegiwQfPmDeWRm6/C1R+kT7+gLbXUTUfVB5/RAk4QSeEEEIIIYQQQhSAE3RCCCGEEEIIIUQBOEHXK/Hxzu38+c59b2VaYmQ0vvLyV48qGhmdHnygd6HVyOj04J2MTg8+0DvtGhldtHo374HO5SajiwQfPGPmPRC5/S5Q+UX6+APaXkfVfFB5/BE1CPcqdSS0cBV3dVcgVXHlaa26aNOonp/KGpXz4yrukaFRPT+u4h7ZGq7irs65uIp7aPPjKu7qwifoesVstm/r6oApU+zbVjzLtMTIaLwhU48qGhmdHnygd6HVyOj04J2MTg8+0DvtGhldtHp3ySUu+2Z1vOtMmzRrzM6ySy6J3H4XqPwiffwBba+jaj6oPP6IEnCCrldsNuf2yy+d+97KtMTIaHzl5a8eVTQyOj34QO9Cq5HR6cE7GZ0efKB32jUyumj17quvOpebjC4SfPCM+eqryO13gcov0scf0PY6quaDyuOPKAEn6IQQQgghhBBCiAJwgk4IIYQQQgghhCgAJ+h6JSHBuX31Vee+tzItMTIaX3n5q0cVjYxODz7Qu9BqZHR68E5Gpwcf6J12jYwuWr1b9K/O5SajiwQfPGMW/Sty+12g8ov08Qe0vY6q+aDy+CNqEO5V6kho4Sru6q5AquLK01p10aZRPT+VNSrnx1XcI0Ojen5cxT2yNVzFXZ1zcRX30ObHVdzVhU/Q9UrrCo61tcDQofZtK55lWmJkNN6QqUcVjYxODz7Qu9BqZHR68E5Gpwcf6J12jYwuWr0bO9Zlv04d7zrTJs0alxWtx46N3H4XqPwiffwBba+jaj6oPP6IEnCCrleam53b4mLnvrcyLTEyGl95+atHFY2MTg8+0LvQamR0evBORqcHH+iddo2MLlq927mzc7nJ6CLBB8+YnTsjt98FKr9IH39A2+uomg8qjz+iBJygE0IIIYQQQgghCsAJOiGEEEIIIYQQogCcoOuVxET7NikJyM+3b1vxLNMSI6Pxhkw9qmhkdHrwgd6FViOj04N3Mjo9+EDvtGtkdNHq3YoVLvuJ6njXmTZp1iQ6y1asiNx+F6j8In38AW2vo2o+qDz+iBLEhjsBEiZiY53bKVPaHnMt0xLjT2Np8p2Xv3pU0cjotGj85Ufvgp+fyhoZnR68k9Hp4T7EPqRdI6OLVu/OOw/4dpX288joIsGHKVPc7wPnnef891GwcpPRRYp3ndHI6LT64HkdVfNB5fFHlIBP0PVKTY19W10NpKXZt614lmmJkdF4Q6YeVTQyOj34QO9Cq5HR6cE7GZ0efKB32jUyumj1LjPLZb9GHe860ybNmhpnWWZW5Pa7QOUX6eMPaHsdVfNB5fFHlCAm3AkQBaip8V+mJUZGI5OPyhoZnR58oHeh1cjo9OCdjE4PPtA77RoZXTR6V6uwd95iQqXx9CVY55HRRZp3Kvng7Tqq5IPq44+EHU7QCSGEEEIIIYQQBeAEnRBCCCGEEEIIUQBO0PVK6wqOycnA9u32bSueZVpiZDTekKlHFY2MTg8+0LvQamR0evBORqcHH+iddo2MLlq9K9josp+kjnedaZNmjcuK1gUbI7ffBSq/SB9/QNvrqJoPKo8/ogScoOuVmBjntm9f5763Mi0xMhpfefmrRxWNjE4PPtC70GpkdHrwTkanBx/onXaNjC5avevTp3O5yegiwQfPmD59IrffBSq/SB9/QNvrqJoPKo8/ogS8Qnqltta+rakB0tPdF43wLNMSI6Pxhkw9qmhkdHrwgd6FViOj04N3Mjo9+EDvtGtkdNHqXbbLKtI1tep415k2adbUOsuysyK33wUqv0gff0Db66iaDyqPP6IEnKATQgghhBBCCCEKwAk6IYQQQgghhBC/fPvtt7j44ouRnZ0Ng8GAFStWOI5ZrVbMnTsXw4cPR3JyMrKzs3HdddehrKzMEfPbb7/BYDB4/fnwww8dcQMGDGhz/P777w9lU8NGbLgTIIQQQggh4cFstQGmeMBiAyxNAACrtQmNNsBsaYJJGOzHPGI8ywKicYlJFAKGMPhBogMhBOo19DtHfw2ABvA+lsymhDA4Ejjq6uowcuRI3Hjjjfj973/vdsxsNmPz5s14+OGHMXLkSFRWVmLOnDmYPn06CgsLAQB9+/ZFeXm5m+6VV17B008/jWnTprmVP/roo5g1a5ZjPyUlJUitUgtO0PVKawdPTQWqquzbVjzLtMT401ht3vOSqUcVjYxOi8ZffvROzTbRu9BqZHR6uA+xD2nXyOii1buycuAf6wAAY//5A3DPx459J7G4r+Br5663mDZlAdC0xIx9Zwc+/ONEGILtXXyis6ysPHL7XaDyi/TxB0AcKMNl7+zApr2VzkLJfucWExCNF92f33ZpT4pS3skwbdq0NhPpVtLT07F69Wq3sueffx7jxo3Dvn370K9fPxiNRmRmZrrFLF++HFdeeWWbCXhqamqbWD3Aj7jrleZm57a01LnvrUxLjIzGV17+6lFFI6PTgw/0LrQaGZ0evJPR6cEHeqddI6OLUu8SKw5gbP8uvmMUoHBvJeqtttD2h/37I7ffBSq/SB9/AOr3lrpPzhVlbP+uSDQalPGupqYG1dXVjp/GxkYNrWpLVVUVDAYDunTp4vX4pk2bUFRUhJtvvrnNsaeeegrdu3fHqFGj8Pjjj8NisQQkJ9XhE3S9Yjbbt3V1wLBh9r+opaV5L9MS40+TkNQ2J9lzq6LxVdZZjb/86J2abaJ3kedDNIwl9iH1fIgA7wzDh+PD48dRn5gMVLesKl1WDqTZn6xZrVasWvUlpkzJhclk8hrjWRYQDQDz0eP2p/rt+RlI7w4fc5aNOw04cigy+12g8ov08QcAv/sdcOc7AIDCh85DUoPZb79r018l+qo/DdD+WErs3gWGmhplvMvJyXHbnz9/PvLy8rznJElDQwPuv/9+zJw5E2k+2rdkyRIMGTIEp59+ulv5XXfdhTFjxqBr164oKCjAvHnzUFJSgsWLF3cqp0iAE3RCCCGEEJ1hMBiQFBcLxBkBa6N9G2f/Z6HVIBBvBJLiYmEyeY/xLAuIBgBMxjC4QaKVpDgjkpol+p1nf5Xpq340gJ+xZDCEyRXvFBcXo3fv3o79+Pj4TtVntVpx1VVXobm5GS+88ILXmPr6erzzzjt4+OGH2xy7++67Hf8/YsQIdO3aFZdddpnjqXo0wwk6IYQQQgghhOiY1NRUn0+5O4rVasUVV1yBkpISfP311z7r/eijj2A2m3Hdddf5rXPChAkAgD179nCCTnSAt8UiPMu0xMhoZPJRWSOj04MP9C60GhmdHryT0enBB3qnXSOjo3e+Y4LlgxZNINqUEgXesQ85F0LuiMZbjMoaGV2gxl8HaZ2c7969G9988027k+klS5Zg+vTp6NGjh996t2zZAgDIysoKWK6qwgm6XmkdoGlpQHW1+zHPMi0x/jStr77wRKYeVTQyOi0af/nRu+Dnp7JGRqcH72R0ergPsQ9p18jo6J3vmKD54DGBCHabXO8DFeXOj+MH+jz+yjqrCVR+0dCHfv0FeOyrzuWnskZGp9U7CWpra7Fnzx7HfklJCYqKitCtWzdkZ2fjsssuw+bNm7Fy5UrYbDZUVFQAALp164a4uDiHbs+ePfj222/x+eeftznH+vXrsWHDBpxzzjlIT0/Hxo0bcffdd2P69Ono169fp9ugOlzFXa80NTm3q1Y5972VaYmR0fjKy189qmhkdHrwgd6FViOj04N3Mjo9+EDvtGtkdPROe5s644O/eoLVpjVrIt879iHgm2/81xOMNkWDdxIUFhZi9OjRGD16NADgnnvuwejRo/HII49g//79+PTTT7F//36MGjUKWVlZjp9169xfP/faa6+hd+/eyM3NbXOO+Ph4vP/++5g0aRJycnLwyCOPYNasWXj33Xc7lXukwAm6Xqmvt2/NZmDqVOeq7t7KtMTIaLwhU48qGhmdHnygd6HVyOj04J2MTg8+0DvtGhkdvdPeJs0+1PuvJ6BtcjnfpZdGuHfsQwCAK6/yX08w2hQN3kkwadIkCCHa/Lz++usYMGCA12NCCEyaNMmtnoULF6K0tBQxMW2no2PGjMGGDRtw/Phx1NfXY+fOncjLy0NSko+3r0QZnKATQgghhBBCCCEKwAk6IYQQQgghhBCiAJyg65XWj5PExAA5Oc59b2VaYmQ0vvLyV48qGhmdHnygd6HVyOj04J2MTg8+0DvtGhkdvdPeps744K+eYLXplFMi3zv2IWDwyf7rCUabosE7ogRcxV2vJCfbtykpwI4d7sc8y7TE+NP4Wj1Zph5VNDI6LRp/+dG74OenskZGpwfvZHR6uA+xD2nXyOjone+YoPmQrEHTiTa53gcKC9tfxV1579iHAAD//a/7Ku4q+6Cad0QJ+CcUvWK12rcWC7B4sX3bimeZlhgZjTdk6lFFI6PTgw/0LrQaGZ0evJPR6cEHeqddI6Ojd9rbpNkHq/96Atoml/O9vjTCvWMfAgC89Y7/eoLRpmjwjigBJ+h6paHBuZ01y7nvrUxLjIzGV17+6lFFI6PTgw/0LrQaGZ0evJPR6cEHeqddI6Ojd9rb1Bkf/NUTrDb96Y7I9459CLjnbv/1BKNN0eAdUQJO0AkhhBBCCCGEEAXgBF0hBgwYAIPB0ObnjjvuAAAIIZCXl4fs7GwkJiZi0qRJ2MHvkhBCCCGEEEJIVMAJukJs3LgR5eXljp/Vq1cDAC6//HIAwNNPP41nnnkGixYtwsaNG5GZmYnzzz8fNTU1HT+Z0ejc5uY6972VaYmR0fjKy189qmhkdHrwgd6FViOj04N3Mjo9+EDvtGtkdPROe5s644O/eoLVpsmTI9879iHgnEn+6wlGm6LBO6IEXMVdIXr06OG2/+STT+LEE0/E2WefDSEEnnvuOTz44IOYMWMGAGDp0qXo1asX3nnnHdx2220dO1lSkn2bnAysWuV+zLNMS4w/ja/Vk2XqUUUjo9Oi8ZcfvQt+fiprZHR68E5Gp4f7EPuQdo2Mjt75jgmaD0kaNJ1ok+t94JNP2l/FXXnv2IcAAO+/776Ku8o+qOYdUQI+QVcUi8WCt956CzfddBMMBgNKSkpQUVGB3NxcR0x8fDzOPvtsrFu3zmc9jY2NqK6udvy0Pm1vMpthtVphrauD9dFH7Vur1XuZlhgJTStWa1OHzm3XWDuWr6cuEBqZ/LRoJPKjd51rkzI+0Luw+xDKsRRt3tGHzmick0J659kms7s3Qe93Lud74skI907PfchlTP3jmRD3oUj3zvm7kKiBQQghwp0EacsHH3yAmTNnYt++fcjOzsa6detwxhln4MCBA8jOznbE3Xrrrdi7dy9W+fiLWF5eHhYsWNCmfPHixcjIyAha/v5otAH3Fdj/Sv30uCbE89M20tA7QgIDxxIJF+x7vgm1N7wW0YHrdXzs1CY8tInXVJYjR47glltuQWlpKfr06RPudAgACKIkubm54qKLLnLsf//99wKAKCsrc4u75ZZbxJQpU3zW09DQIKqqqhw/xcXFAoAo+fFHYbFYhOXoUWFJTLRvLRbvZVpi/GiO15pF/7krRf+5K8W+Q5XieK3Z/lN+SBxP7WLfetk/XFkt3v94hThcWS2t8arrpKaxsdFrO+vq6sSKFStEXV2dtHdtNH78dPXucGW19DXp6HlaY/y2SYsmTN4p5wO9C6sPoRxL0ead3nxoPHLE5+8cv79fvJTtO1Tp6HvHa81R7V1H+9Dx8kPu3gS537mdL7VLRHun5z50uLLacR3LuvYKaR+KdO927dolAIjS0tKgzWtIx+B30BVk7969WLNmDZYtW+Yoy8zMBABUVFQgKyvLUX7o0CH06tXLZ13x8fGIj4937FdXVwMAYmNjYTKZgNhYoL7evjWZ0HLQvUxLjB+NqbUMwFn/+7170rPfAp4r8L2PWKDg2w5qvOg6oRnbvys+/ONEGLy1E4DJZJL311PjLcaHdyZTrJRGy3lcNe22SYtGxocgeKecD/QurD6EYyxFi3d68kHU1+Oyd4uxaX+1s1J/v5e8/T7x+jum9VyxUemdlj5kio11L7dZg9rv3M7X0LIfod7puw+5X0e3uoLchyLfO/fzkvDD76AryL///W/07NkTF154oaNs4MCByMzMdKzsDti/p7527VqcfvrpHT9J62A0mYCbb3a/KXiWaYnxo0k0GXFqvy4dz1sRCvdWot5q895OV2S8k9FFm0ZGR+98x9A7OZ0efKB32jUyOpMJ9Tff6j45DyCn9uuCRJPRey5R4J3mPuSvnmC16brrI9879iFg5jX+6wlGm6LBO6IG4X6ET9yx2WyiX79+Yu7cuW2OPfnkkyI9PV0sW7ZMbNu2TVx99dUiKytLVFdXS9dfWlqqzMdYGhsbxfsfrxDHa82irtEq9XO81txhjVadN83hmgbHx6bqGq1t2mSxWMSKFSuExWKR9qGjmrpGq9tHt4J1ns7ook2jen4qa1TOL5RjKdq8C6Um3Pm59pPDNQ0B/f3S2NgY1PYE0odQaVz9DsXv2fbOF2neBUOjen6tGrevLNXUh/3faqE+V2c0JSUlyswNiB0+QVeMNWvWYN++fbjpppvaHLvvvvswZ84czJ49G2PHjsWBAwfw5ZdfIjU1teMnamiwb+vrgVtusW9b8SzTEiOhMRgMiDcCSXGxzh+bFUmz/2jfetuPi9WkaaPTqplzp7uP3trp77g/jayfkayR0dE73zH0Tk6nBx/onXaNjK6+Hph9h2M3Kc7o//eSt98nPjQGgyH8PijXhxr81xPQNrmcb/YdEe4d+xAAYM49/usJRpuiwTuiBJygK0Zubi6EEDj55JPbHDMYDMjLy0N5eTkaGhqwdu1aDBs2TNuJWl+pYLUCS5Y4972VaYmR0fjKy1894dS8sbT9evydR8U2hUMjo6N3vmPonZxODz7QO+0aGR3v+9rb1Bkf/NUTrDa9sTTyvWMfAt552389wWhTNHhHlIATdEIIIYQQQgghRAE4QSeEEEIIIYQQQhSAE3S90vrqtfh4YP585763Mi0xMhpfefmrJ5yaeQ+0X4+/86jYpnBoZHT0zncMvZPT6cEHeqddI6PjfV97mzrjg796gtWmeQ9EvnfsQ8C9f/VfTzDaFA3eETUI9yp1JLSotIp7JK6iGerVZb3BVdxDr1E9P5U1KufHVdwjQxPu/FS472vVqJ6fCn5zFXd1zsVV3MOj4Sru6sEn6HrFbLZv6+qAKVPs21Y8y7TEyGi8IVNPODWXXNJ+Pf7Oo2KbwqGR0dE73zH0Tk6nBx/onXaNjI73fe1t0uyD2X89AW2Ty/kuuSTCvWMfAgBceaX/eoLRpmjwjigBJ+h6xWZzbr/80rnvrUxLjIzGV17+6gmn5quv2q/H33lUbFM4NDI6euc7ht7J6fTgA73TrpHR8b6vvU2d8cFfPcFq01dfRb537EPAN//xX08w2hQN3hEl4ASdEEIIIYQQQghRAE7QCSGEEEIIIYQQBeAEXa8kJDi3r77q3PdWpiVGRuMrL3/1hFOz6F/t1+PvPCq2KRwaGR298x1D7+R0evCB3mnXyOh439feps744K+eYLVp0b8i3zv2IeCZZ/3XE4w2RYN3RA3CvUodCS1cxT2yVpf1BldxD71G9fxU1qicH1dxjwxNuPNT4b6vVaN6fir4zVXc1TkXV3EPj6ajq7ivXbtWXHTRRSIrK0sAEMuXL3er87777hPDhg0TSUlJIisrS1x77bXiwIEDbnWcffbZAoDbz5VXXukWc+zYMfGHP/xBpKWlibS0NPGHP/xBVFZWSrcvkuETdL3SuoJjbS0wdKh924pnmZYYGY03ZOoJp2bs2Pbr8XceFdsUDo2Mjt75jqF3cjo9+EDvtGtkdLzva2+TZh+cK0ybLTaYj1XBPGKUfWtpgtnShEYbHP9vtjS1jemQptp5vvET3TRtdC71CiEU9C60/UHU1Pj0uz3v2ivTrnEufFY/abJ/P8PsnbLjT4K6ujqMHDkSixYtanPMbDZj8+bNePjhh7F582YsW7YMu3btwvTp09vEzpo1C+Xl5Y6fl19+2e34zJkzUVRUhPz8fOTn56OoqAjXXnttp3KPFGLDnQAJE83Nzm1xsXPfW5mWGBmNr7z81RNOzc6dwAXt1OPvPCq2KRwaGR298x1D7+R0evCB3mnXyOh439feps740MLYx9bY/+eCx4Gnv3MJisV9BV+76zxjZDXPrXeeb0qeh8aLrqXesf274sOZQ2FQzbsQ9QdRXIzLlhZh0/6WP3C08Rvw5Z0b/q6TpAYFax17Z533QPtt9NEmjj85pk2bhmnTpnk9lp6ejtWrV7uVPf/88xg3bhz27duHfv36OcqTkpKQmZnptZ6ffvoJ+fn52LBhA8aPHw8AePXVVzFx4kT8/PPPGDx4cKfaoDp8gk4IIYQQQpQg0RSDsft3hDsNvxTurUS9tXMTnUim3hTvnJwrytj+XZFoMoY7Dd1TVVUFg8GALl26uJW//fbbyMjIwNChQ3HvvfeipqbGcWz9+vVIT093TM4BYMKECUhPT8e6detClXrY4BN0QgghhBCiBAaDAR++PRf1h48BaalAdQ2QnQWUlQNpqbBarVi16ktMmZILk8lkF3nEdERj/m0/xv6/HwAAhf9vJpL2/WavA2irq66Buf9AjP3z22FwRl0K7xqPpP59nP7Du3du1wjwf506oDnrnMmY+JT9KXrrdUzs3gUGgyEclkQkNTU1qK52/tElPj4e8fHxnaqzoaEB999/P2bOnIm0tDRH+TXXXIOBAwciMzMT27dvx7x587B161bH0/eKigr07NmzTX09e/ZERUVFp3KKBDhB1yuJifZtUhKQn2/ftuJZpiVGRuMNmXrCqVmxAvjW5l7Wni4YPvj6g73q3nXUh2jtQ/ROHR+iYSyxD2nXyOhUue+rppHRdcIHQ34+krqkArGxQJdU4LNP7NvYWFgNAvFGICkuFiZTyz9jPWI6pklxnvqj953nBdrquqQCH77n7A9Jicp5F45/DyWlpyDJ1X/48M4jxu916pDG+aTccR1bJ+eqeafo+MvJyXHbnz9/PvLy8nzn5Qer1YqrrroKzc3NeOGFF9yOzZo1y/H/w4YNw6BBgzB27Fhs3rwZY8aMAQCvf1wRQujijy6coOuV1htdbCwwZUrbY65lWmJkNL7y8ldPODXnnQd8u0peFwwfLE0d12g5TzA1Mrpo7UP0Th0fomEssQ9p18joVLnvq6aR0UWKD673gfPOc/77yJfGtT/o2TuVffC8jqp5p+j4Ky4uRu/evR37nXl6brVaccUVV6CkpARff/2129Nzb4wZMwYmkwm7d+/GmDFjkJmZiYMHD7aJO3z4MHr16qU5r0iB30HXK63f86iuBtLS7NtWPMu0xMhovCFTTzg1mVnt1+PvPCq2KRwaGR298x1D7+R0evCB3mnXyOh439fepojxwfm9V2Rm+de49ofqGv16p7IPntdRNe8UHX+pqalIS0tz/GidoLdOznfv3o01a9age/fufjU7duyA1WpFVpa9X02cOBFVVVUoKChwxPzwww+oqqrC6aefrimvSIJP0Ilzst5emZYYGY1MPippajW0KRp9CIRGRkfvfMfQOzmdHnygd9o1Mjre933HRJsP3q61J54xevVOZR+0jFlvMSprZHRa+5AfamtrsWfPHsd+SUkJioqK0K1bN2RnZ+Oyyy7D5s2bsXLlSthsNsd3xrt164a4uDj88ssvePvtt3HBBRcgIyMDxcXF+Mtf/oLRo0fjjDPOAAAMGTIEU6dOxaxZsxyvX7v11ltx0UUXRf0K7gAn6IQQQgghhBBCJCgsLMQ555zj2L/nnnsAANdffz3y8vLw6aefAgBGjRrlpvvmm28wadIkxMXF4auvvsI///lP1NbWom/fvrjwwgsxf/58GI3OtQTefvtt3HnnncjNzQUATJ8+3eu716MRTtAJIYQQQgghhPhl0qRJEEL4PN7eMQDo27cv1q5d224MYH/i/tZbb3U4v2ggJtwJkDDRuoJjcjKwfbt924pnmZYYGY03ZOoJp6ZgY/v1+DuPim0Kh0ZGR+98x9A7OZ0efKB32jUyOt73tbcpYnxwWdG6YKN/jWt/SE7Sr3cq++B5HVXzTuXxR5SAE3S9EhPj3Pbt69z3VqYlRkbjKy9/9YRT06dP+/X4O4+KbQqHRkZH73zH0Ds5nR58oHfaNTI63ve1tynSfADs19qfxrU/6Nk7lX3wvI6qeafy+CNKwCukV2pr7duaGiA93X3RCM8yLTEyGm/I1BNOTXZW27L2dNHqQ2c1Mjp65zuG3snp9OADvdOukdHxvq+9TRHjQ62zLDvLv8a1P9TU6tc7lX3wvI6qeafy+CNKwAk6IYQQQgghhBCiAJygE0IIIYQQQgghCsAJOiGEEEIIIYQQogCcoOuVlBT7NjUVqKqyb1vxLNMSI6Pxhkw94dSUlbdfj7/zqNimcGhkdPTOdwy9k9PpwQd6p10jo+N9X3ubIsaHFGdZWbl/jWt/SE3Rr3cq++B5HVXzTuXxR5SAE3S90tzs3JaWOve9lWmJkdH4ystfPeHU7N/ffj3+zqNim8KhkdHRO98x9E5Opwcf6J12jYyO933tbYo0HwD7tfance0PevZOZR88r6Nq3qk8/ogScIKuV8xm+7auDhg2zL5txbNMS4yMxhsy9YRTM+609uvxdx4V2xQOjYyO3vmOoXdyOj34QO+0a2R0vO9rb1PE+GB2lo07zb/GtT/UmfXrnco+eF5H1bxTefwRJeAEnRBCCCGEEEIIUQBO0AkhhBBCCCGEEAXgBJ14XyzCs0xLjIxGJh+VNCka2hSNPgRCI6Ojd75j6J2cTg8+0DvtGhkd7/u+Y6LNB2/X2hPPGL16p7IPWsastxiVNTI6rX2IhJ3YcCdAwkTrAE1LA6qr3Y95lmmJkdF4Q6aecGoqyoFHVsnrguGDpanjGi3nCaZGRhetfYjeqeNDNIwl9iHtGhmdKvd91TQyukjxwfU+UFEOxLXzT2PP/pCWql/vVPbB8zqq5p3K448oAZ+g65WmJud21SrnvrcyLTEyGl95+asnnJo1a9qvx995VGxTODQyOnrnO4beyen04AO9066R0fG+r71NkeYDYL/W/jSu/UHP3qnsg+d1VM07lccfUQJO0PVKfb19azYDU6c6V3X3VqYlRkbjDZl6wqm59NL26/F3HhXbFA6NjI7e+Y6hd3I6PfhA77RrZHS872tvU8T4UO8su/RS/xrX/mCu1693KvvgeR1V807l8UeUgBN0QgghhBBCCCFEAThBJ4QQQgghhBBCFIATdL0SE+Pc5uQ4972VaYmR0fjKy1894dScckr79fg7j4ptCodGRkfvfMfQOzmdHnygd9o1Mjre97W3KdJ8AOzX2p/GtT/o2TuVffC8jqp5p/L4I0rAVdz1SnKyfZuSAuzY4X7Ms0xLjIzGGzL1hFNTWOi+mq8/XTB88LXytOreddSHaO1D9E4dH6JhLLEPadfI6FS576umkdFFig+u94HCwvZXcffsDynJ+vVOZR88r6Nq3qk8/ogS8E8oesVqtW8tFmDxYvu2Fc8yLTEyGm/I1BNOzetL26/H33lUbFM4NDI6euc7ht7J6fTgA73TrpHR8b6vvU0R44PVWfb6Uv8a1/5gserXO5V98LyOqnmn8vgjSsAJul5paHBuZ81y7nsr0xIjo/GVl796wqn50x3t1+PvPCq2KRwaGR298x1D7+R0evCB3mnXyOh439fepkjzAbBfa38a1/6gZ+9U9sHzOqrmncrjjygBJ+iEEEIIIYQQQogC8DvohBBCCCGEdBCz1QaY4gGLDa3fZbdam9BoA8yWJpiEwX7MI8azLCAaLzGB0Phqk9mUEEqrCdEVnKDrFaPRuc3Nde57K9MSI6PxlZe/esKpmTy5/Xr8nUfFNoVDI6Ojd75j6J2cTg8+0DvtGhkd7/va2xRpPgD2a+1P49Ifxv7zB+Cej4F/rPMIjMV9BV87d73FtCkLgMZbTEA0XnR/ftv5/6r1Ic/rqHK/U807ogScoOuVpCT7NjkZWLXK/ZhnmZYYGY03ZOoJp+aTT9xX8/WnC4YPvlaeVt27jvoQrX2I3qnjQzSMJfYh7RoZnSr3fdU0MrpI8cH1PvDJJ+2v4p6cjMTPV2LsS+tRuLey/fp1wtj+XZHYJVWtPuR5HVXsd53RyOi0ekeUgN9B1yutKzg2NgJ5efZtK55lWmJkNN6QqSecmscXtl+Pv/Oo2KZwaGR09M53DL2T0+nBB3qnXSOj431fe5sixgeXFa0fX+hXY1iwAB/eOAbFj05B8YPnoDh+k3376BQUPzoFWx8+F0+Pa8LWh8/1GeNZFhCNN10ANP7y+/CPE2GwWNTqQ57XUcl+p+j4I2ogiK4oLS0VAERpcbG9oKpKCMC+bcWzTEuMhMZisYgVK1YIi8UiXa8WjVedRk2dKV70n7tS9J+7UtQ1Wv3nFwQf6hqtjhyO15ojxrsO+xClfYjeqeNDKMdStHmnJx9UuO9Hqneq+NCepu7wMef1NcXTuwjtQ8drzT6vI71rX1NSUmKfG5SWCqIGfIJOCCGEEEIIIYQoACfohBBCCCGEEEL88u233+Liiy9GdnY2DAYDVqxY4ThmtVoxd+5cDB8+HMnJycjOzsZ1112HsrIyR8yxY8fw5z//GYMHD0ZSUhL69euHO++8E1VVVW7nGTBgAAwGg9vP/fffH6pmhhVO0PWKyeTc3nyzc99bmZYYGY2vvPzVE07Ndde3X4+/86jYpnBoZHT0zncMvZPT6cEHeqddI6PjfV97myLNB8B+reld5Pchz+uosg+qeSdBXV0dRo4ciUWLFrU5ZjabsXnzZjz88MPYvHkzli1bhl27dmH69OmOmLKyMpSVleEf//gHtm3bhtdffx35+fm4+eab29T36KOPory83PHz0EMPdSr3iCHcn7EnocXxHXQFvmfi9fs8QdAE8lyu31mta7QG7Tzt4fN7swE+T2d00aZRPT+VNSrnF8qxFG3ehVIT7vxUuO9r1aienwqa9q6vCvmFW6N6fl6/g+5lnIYjt1CeqzOaznwHHYBYvnx5uzEFBQUCgNi7d6/PmA8++EDExcUJq9V57fr37y+effbZDucUDfAJul5paLBv6+uBW26xb1vxLNMSI6Pxhkw94dTMvqP9evydR8U2hUMjo6N3vmPonZxODz7QO+0aGR3v+9rbFDE+NDjLZt9B76KhD3leR5V9UMi7mpoaVFdXO34aA7Tae1VVFQwGA7p06dJuTFpaGmJj3V9z+NRTT6F79+4YNWoUHn/8cVgsFh81RBecoOsVq9W5XbLEue+tTEuMjMZXXv7qCafmjaXt1+PvPCq2KRwaGR298x1D7+R0evCB3mnXyOh439fepkjzAbBfa3oX+X3I8zqq7INC3uXk5CA9Pd3x88QTT/jOSZKGhgbcf//9mDlzJtLS0rzGHD16FH/7299w2223uZXfddddeO+99/DNN9/gT3/6E5577jnMnj270zlFArH+QwghhBBCCCGERCvFxcXo3bu3Yz8+Pr5T9VmtVlx11VVobm7GCy+84DWmuroaF154IXJycjB//ny3Y3fffbfj/0eMGIGuXbvisssuczxVj2Y4QSeEEEIIIYQQHZOamurzKXdHsVqtuOKKK1BSUoKvv/7aa701NTWYOnUqUlJSsHz5cpj8LF43YcIEAMCePXuifoLOj7grxoEDB/CHP/wB3bt3R1JSEkaNGoVNmzY5jgshkJeXh+zsbCQmJmLSpEnYsWNHx0/U+lex+Hhg/nznvrcyLTEyGl95+asnnJp5D7Rfj7/zqNimcGhkdPTOdwy9k9PpwQd6p10jo+N9X3ubIs0HwH6t6V3k9yHP66iyD6p5FwBaJ+e7d+/GmjVrvE6mq6urkZubi7i4OHz66adISEjwW++WLVsAAFlZWQHPWTnCvUodcXLs2DHRv39/ccMNN4gffvhBlJSUiDVr1og9e/Y4Yp588kmRmpoqPv74Y7Ft2zZx5ZVXiqysLFFdXS11Dq7iHvmr+XIV99BrVM9PZY3K+XEV98jQhDs/Fe77WjWq56eChqu4q3MuruIeHk1HV3GvqakRW7ZsEVu2bBEAxDPPPCO2bNki9u7dK6xWq5g+fbro06ePKCoqEuXl5Y6fxsZGIYQQ1dXVYvz48WL48OFiz549bjFNTU1CCCHWrVvnqPfXX38V77//vsjOzhbTp0+Xbl8kwyfoCvHUU0+hb9+++Pe//41x48ZhwIABmDx5Mk488UQA9qfnzz33HB588EHMmDEDw4YNw9KlS2E2m/HOO+907GRms31bVwdMmWLftuJZpiVGRuMNmXrCqbnkkvbr8XceFdsUDo2Mjt75jqF3cjo9+EDvtGtkdLzva29TxPhgdpZdcgm9i4Y+5HkdVfZBNe8kKCwsxOjRozF69GgAwD333IPRo0fjkUcewf79+/Hpp59i//79GDVqFLKyshw/69atAwBs2rQJP/zwA7Zt24aTTjrJLaa0tBQAEB8fj/fffx+TJk1CTk4OHnnkEcyaNQvvvvtup3KPFPgddIX49NNPMWXKFFx++eVYu3YtevfujdmzZ2PWrFkAgJKSElRUVCA3N9ehiY+Px9lnn41169a1Wf2wXWw25/bLL5373sq0xMhofOXlr55war76Chg5272sPV0wfDAFuE3h0MjoorUP0Tt1fIiGscQ+pF0jo1Plvq+aRkYXaT4A9mtN7yK/D3leR5V9UM07CSZNmgQhhM/j7R2T0QPAmDFjsGHDBk35RQOcoCvEr7/+ihdffBH33HMPHnjgARQUFODOO+9EfHw8rrvuOlRUVAAAevXq5abr1asX9u7d67XOxsZGt/cY1tTUAACamppgtVqBpiYgMdG+bX3tgmeZlhgJjbWl3Or6ugc/9WjRuMZbO9BGbxprQqLjtFarFVZ/+QXBB9c4q7VJuk3h9q7DPkRpH6J36vgQyrEUbd7pyQcV7vuB9s6WlIzCX4/ikO04ehptGJuUDGMU96H2NNamJuf1TWjZV6DfqeJd5Pjg+zrSuw74QJTAIPz9CYOEjLi4OIwdO9bxERAAuPPOO7Fx40asX78e69atwxlnnIGysjK3BRJmzZqF0tJS5Ofnt6kzLy8PCxYsaFO+ePFiZGRkBKchUUyjDbivwP53rafHNSHeqM8cCIkGOJaIDNHWT7YeNWDZbzE4bjE4yrrECcwY0IyR3fX3T8Jou756hddRO0eOHMEtt9yC0tJS9OnTJ9zpEICLxKlEv379xM033+xW9sILL4js7GwhhBC//PKLACA2b97sFjN9+nRx3XXXea2zoaFBVFVVOX6Ki4sFAFHy88/CYrEIS22tsCxebN9aLN7LtMRIaOrq6sSKFStEXV2ddL1aNF51GjXHX1nitqiU3/yC4IPrQiiHK6sjxrsO+xClfYjeqeNDKMdStHmnJx9UuO8HyrvPtpSKAXNXiv5zP3O0qf/clWLA3M/EgLkrxWdbSqOyD7WnOX6synl9X1miTL9TxbtI8eFwZbXP60jv2tfs2rVLmQWkiR1O0BXi6quvFmeeeaZb2Zw5c8TEiROFEEI0NzeLzMxM8dRTTzmONzY2ivT0dPHSSy9JnYOruEf+ar5cxT30GtXzU1mjcn5cxT0yNOHOT4X7vlaNq66+oVFMWLjGbWLuPklfKSYsXCOabM1KtynQGq7irs65OqPhKu6hW8WdBB+u4q4Qd999NzZs2ICFCxdiz549eOedd/DKK6/gjjvuAAAYDAbMmTMHCxcuxPLly7F9+3bccMMNSEpKwsyZMzt2stYVHGtrgaFD7dtWPMu0xMhovCFTTzg1Y8e2X4+/86jYpnBoZHT0zncMvZPT6cEHeqddI6OLovt+4d5KlFc1+KxKACivakBBybHAtElRH9pqXFa0HjtWnX4XEd4p6oPndVTZB9W8I0oQG+4EiJPTTjsNy5cvx7x58/Doo49i4MCBeO6553DNNdc4Yu677z7U19dj9uzZqKysxPjx4/Hll18iNTW1YydrbnZui4ud+97KtMTIaHzl5a+ecGp27gQuaKcef+dRsU3h0Mjo6J3vGHonp9ODD/ROu0ZGF0X3/UM1jW3KvHGopgFAWufbpKgP7eayc6c6/S6SvFPNB8/rqLIPqnlHlIATdMW46KKLcNFFF/k8bjAYkJeXh7y8vNAlRbxittgAiw0wxdu3liZYrU1otAFmSxNMwtDmOAD/Gi8xrvvmmCa3HEwm7/V6Ow+XhCSERANCCNS3c78DEJj7scUGsykhjC0NHCaj3Icme6ZGR3sJISRS4QSdEI2MfWyN/X/u+Rj4xzqXI7G4r+Br526b4xIabzFe6pn41NoOaQamGnHBBZylE0IiFyEELnvzR2ySuEcG5H7857cDlXpYOXdwD8TFxsDS5P3pmQFAZnoC+nRNxBNf/IyT+OYlQggJC/wOul5JbHmva1ISkJ9v37biWaYlxo/G1izwQ8kxbDpiwA8lx2BrFvL1eBJCTeKnKzC2XxffMYpTUmNAvdXmLAiX3zI6LZpwtonehVYjo9ODDzr0rt5qw6b91b7rCxJj+3dFoskYUd412ZrR3PL7NS42Bk/8zzAA9sm4K6378y/OwTOrd+G1dXvxzi8u/0SMsj7kHuN8zz1WrOD4i4b7kOd1VNkH1bwjSsAn6HolNta5nTKl7THXMi0x7Wjyt5djwWfFLYvVGPHG7kJkpSdg/sU5mDosy3893toSIo1h6lR8KIT7JNcFq9WKVau+xJQpuTCZTO3Xp1FjtjRh7GNfAQDWzz0b6cn+P45otticT/xdCaF3fvtQIDSByk9ljYxOD97J6PxpLE1tNYHKT2WNjE71PtRC4UPnISnO+wuPA30/TjQZYTAYIsa7o7WN+NM7W3D6CV3Rr+Xw70/ti+T4WJffwXYyXX4HJ8fH4qfyakztUek4bosxIiY3195+2fwU8cGvxvU+cN55zn8fBSs3GV2keNcZjYxOqw+e11FlH1TzjigBn6DrlZoa+7a6GkhLs29b8SzTEuNDkz9qMm5/a3OblWQrqhpw+1ubkV/wq/96PJE5dwA1hpoaJMXF2n8azEjK6GbftpTFG9Hucb8abzEe+60kxRklNd7/ARtq79rtQ4HQhLNN9C60GhmdHnzQq3ct+LpHBux+7HLcMTmNEO/W7jqM9b8excv//Q21Lh9Xn9ovGd89OQPvXjMc/7xqFN69Zji+e3IGpvZLBgCcNagHPp09AX1TnJo3vtmJK6//X2z7+YB8mxTxwb+mxlmWmcXxFw33Ic/rqLIPqnlHlIBP0Ilzst5emZYYj31bs8CC06+Ft29AC9g/Yrdgza84v7YObtNJb+fWkm8wNDI6LRpvMdGmkdHRO98x9E5Opwcf9OqdvzpkY4LhgwLezRjTB6XH6nH+kAzsLvzWLcRYXY2J/bs4/7Hu8Q921yflVlszXt6wHxXZQ/BjeS2GD+5Afgr40CFNLcdfSDUyOi0ab9dRZR9U8o4oQUy4EyD6oaC0CuVpPXweFwDKqxtR0Gdo6JIihBBCooDGJhteWFeKRqPz2ctd5w3CoJ4p7aj8YzLGYNn1o/DHDR/iylGZjvJ9lfWojUtsR0kIIUQLnKCTkHGo1iIXl9I1yJkQQggh0cWtb2zC0//5DQsm3xbwurPT4nH/2qWIjbE/WRdC4M5PfsakWa/gh31VAT8fIYToGU7Q9UrrCo7JycD27fZtK55lWmK8aHpmpEml1vP5/23/3J7I5BsMjYxOiyacbaJ36mtkdHrwTkanBx/06p1D287vskC0KYK8u+nMgeiWZMKUe28I+jU6WN2I4402mLt0w8A+3eXOo/Fcwde4rGhdsJHjLxruQ57XUWUfVPOOKAG/g65XYmKc2759nfveyrTEeNGMOyEDWalxqKixeP0eeus7WMeOOan9c3tri798g6GR0WnR+MvP5n0F+YBrtOQmq5HRBcO7aNDI6PTgnYzOn0a1cRFJ3oW7Ta5areeR0SnsnRACR+qs6NESc/bJPfDtfecgxVIf9GuUmZ6AL+f8DsW/VKBnuvNj7usOGjDoYC1y+nRVvw95i+nTh+MvGu5DntdRZR9U844oAa+QXqmttW9raoD0dPdFIzzLtMR40RjrajH/jfkAfL+D9fLhPfA/dy/Fvn2HfJ/bE5l8g6GR0WnRhLNN9E59jYxOD97J6PTgg169c2jb+V0WiDYp6l1dYxP+9O4WzHjhexzv1dsRk2KpD1kfiquvw6ghfRxlvxyuw4e/xuDiF9aj5Eid+n3IEVPrLMvO4viLhvuQ53VU2QfVvCNKwAk6CSlTd63HizOGIDPd/d3dmekJ+NfMMfhi51FszzwJr20sC1OGhBBCiNpYbc34cf9xlFc3orB3TrjTAQDEx8ZgWDeBSSdnYGAGP0JLCCFa4UfcSciZekoGzh87EOv3HMKX//0BuWeNx8STesIYY8DobrH4f7cswP33PRbuNAkhhBAl6ZIUh5f/MBbmyiqMfaIg3OkAAPp0TcTNg5sxOXeko6w+Nh7XvrEV1555AqaPzG7z6TlCCCFt4RN0EhaMMQaMH9gNp2YIjB/YDcaWlWGz0uLxxKp/ISHW2TXfHTkFVQ1N4UqVEEIICSu2ZoFn1+zBT8edU9yc7DSM7Zsexqy8E+/y+/vNMRegcH81ns7/GY1NzWHMihBCIgc+QdcrKS3vRU1NBaqq7NtWPMu0xMhovOER89GuKsyb+me8+saP+Pyus2CU0Gg5jyaNjC5APrjtW30sbBVojZbcZDUyumB4Fw0aGZ0evJPR+dOoNi4iybtwt8mhbed3WSDapIh3b677DS+s/RVJxhjcbLaiR7rJr0bLeaQ1kj5c+/kSWLYewUk9U5BgMgKxds1BYUKvYObXEU28y7vcy8qV8U7p8ae6D57XUWUfVPOOKAGfoOuV5mbntrTUue+tTEuMjMZXXi4xg3smo3eyEZeMzLL/cpfQaDmPJo2MLkA+RJ1GRkfvfMfQOzmdHnzQq3euWq3nkdEp4t3V4/th/MCu+P3AZnRJMklptJxHWiPpQ2JFGf509gmYOizLUfafDT/jzKe/wdP5O4OXn9Y27d+vjHdKjz/VffC8jir7oJp3RAk4QdcrZrN9W1cHDBtm37biWaYlRkbjDY+Y4V1i8fnjl+HP4zIdIQ02+wI5HcrXz3k0aWR0AfIh6jQyOnrnO4beyen04INevXNo2/ldFog2hdG7zbvKHfvxsUa8eeNYjO0h2tVEQh/6+u+vwWoTsPj4yLutphbrp12NTwpKsP6Xo7DV1Aa5TWZn2bjTlPZOmfGnug+e11ExH2zDR2B98QF8UnQA64sPwDZ8hDreESXgR9yJ8qQ31gEG+/fumpsFlu6KwceHNuFf15yKHqnxYc6OEEIICRxCCDz62Q78+/vf8OSIXFzVUm4wRMcSawvWvIRznp6LMYN7O8rKqxrw86FjaLI149FPt6N85hPAJz8DALJS4zD/5ImYGq6ECQkg+TuPYMEfl6D87W2Osqw/LsH8nUcwdVxaGDMjKsEJOokofjlSh1+qDUBtFQ5WN3CCTgghJOKwNQvsrjLgsx/LkdUlGeMGdnOssWIwGJCRYv/ddji5a/iSDBIGAOec2A1IMsFqtQIAnlm9Gyu2lnuNr6ix4PZLH8CLnMCQCCd/ezluX/YTRGqGW3lFanfcvuwnvJiU6Pw6CNE1nKAT74tFeJZpiZHRyOTjsj+oZwr+MsKG3kNGYVjvdCmNlvNIa2R0QfAhKjQyOnrnO4beyen04INevfNXh2xMMHxoR5O/vRx5n+5ARbURKLY/RctKT8AjkwdgWkvM7WefiImZCRjzwtXtn8fbuSKsDwkB9Erz/cd2AcAAgQVrfsX5YwfKn0tLm1IiyzspncoaGZ0WjbfrGGYfbM0CCz4rhgAcnwptRRhiYACw4LNinJ+T6U0e2j5Ewg4n6HqldYCmpQHV1e7HPMu0xMhovCFRT69EIDfHuf5raVMsXnt7PeYmJiMhgOfxq5HRBcMHi49XzgVaoyU3WY2MLoh9KKI1Mjo9eCej86dRbVxEknfBzE9G44ht53eZv/PI6AKsyd9ejtvf2gyPb5KjvKoBty/biRe++xkXpKUhBsCYIX3U8TuI3hkMwFmDMvDyf3/zWZUwxKC8uhEFJccwtp+Xp+idaZPrfaCiHIhr55/GinkXlPwisA+1wfM6KuBD4d5KlFc1+KxKwH4f8NrHQ+kdUQIuEqdXmpqc21WrnPveyrTEyGh85eWvHheamwX+9PYm/Pv73zB/xbagnUdzm0LkQ8RpZHT0zncMvZPT6cEHvXrnqtV6HhldADW2/Hws+HRHm8m5Kw98sBk2i1V7myK0Dx2qafR9brc4HxOcQLVpzZqI8y7g+UVoH3LD8zqG2Yc6K7DnkNzCbF77eCi9k+Dbb7/FxRdfjOzsbBgMBqxYscJxzGq1Yu7cuRg+fDiSk5ORnZ2N6667DmVlZW51NDY24s9//jMyMjKQnJyM6dOnY//+/W4xlZWVuPbaa5Geno709HRce+21OH78eKdyjxQ4Qdcr9fX2rdkMTJ3qXNXdW5mWGBmNN2TqcSEmxoB7f9cPJx4txZ0TsoN2Hs1tCpEPEaeR0dE73zH0Tk6nBx/06p1D287vskC0KYCaglvvQ3l1+xPR401Awc/l2tsUoX2op+R6MrsP1uDP723FL54PATvVpnpn2aWXRpx3Ac8vQvuQG57XMYw+vLFhHx7ZZMT3vxz1XY8LPVMT/J9HJj+t3klQV1eHkSNHYtGiRV5Oa8bmzZvx8MMPY/PmzVi2bBl27dqF6dOnu8XNmTMHy5cvx3vvvYfvvvsOtbW1uOiii2Cz2RwxM2fORFFREfLz85Gfn4+ioiJce+21nco9UuBH3EnEc9bArvhyyR0w/u9MR9lPPQbgFCEQHWveEkIIiXQOpcgt+Hao1hLkTNRjbP+uyEpPQEVVg9dPGBhEMzLTE/HDr8ewcW8lYvrytztRk0PJXZHY2ITWb3oPzEhCkzCgut6CzPQEHPTVxwFkpidg3MBuaLZ17gl3sJk2bRqmTZvm9Vh6ejpWr17tVvb8889j3Lhx2LdvH/r164eqqiosWbIEb775Js477zwAwFtvvYW+fftizZo1mDJlCn766Sfk5+djw4YNGD9+PADg1VdfxcSJE/Hzzz9j8ODBwW1kmOETdBIVGIXzfao/ltfgkuuewexlP6HBamtHRQghhISGnrWVcnEpcUHORD2MMQbMvzgHANr8Yd3Q8t/5552ABZcMw7Xj+2K8y/vgt+yrxI3vb8fXJ4wNVbqEeOWJr0swcfbr+PjHg46yM07ojnuHN+HNm05DXmsfd/k3q+v+/ItzYIyJvj8+VVVVwWAwoEuXLgCATZs2wWq1Ijc31xGTnZ2NYcOGYd26dQCA9evXIz093TE5B4AJEyYgPT3dERPNcIKuV2JinNucHOe+tzItMTIaX3n5q8eP5tdjDRAxMbAKIM7oQxeA80jpwuiD0hoZHb3zHUPv5HR68EGv3rlqtZ5HRhdAzbjUZmSl+p58GwBkmSsxrn9X7W2K4D40dVgWXvzDGGR6eJSZFo8XC9/E1JyeyMlOwyMXDUEXl0/Ev1uwD9/8UomVEy7qfJtOOSUivQtofhHchxx4XscgtWl/tQW2nKGOmOz0BNhijNh+0Pl985gYA/qm2F+fOHVYFl68LAeZDdVu9WTWV+HFy3J8v2ItRN7V1NSgurra8dPYKLc2RHs0NDTg/vvvx8yZM5GWZl/8rqKiAnFxceja1f1TRb169UJFRYUjpmfPnm3q69mzpyMmmuFH3PVKcrJ9m5IC7NjhfsyzTEuMjMYbMvX40Vw68UQM6NMdJ/RIRoyvv0QG4DxSumD44Gvl6UBrtOQmq5HRhbEPKa2R0enBOxmdP41q4yKSvAtmfjIaR2w7v8v8nUdGFyBNSYMBA3dsx3wfq7i3/qaaf+tkGFtXplfJ7xB5N3VYFs7PyURByTEcqmlAz1T7R36ND5znU3P7pJPQNTkOU4aebq8fwCERi7vvXoLLd1fhklHJ7efneh8oLGx/FXeFvQtYfhHehwC0vY5BaNOf392ClT+W4d/Lvsakln73PxNPwMScbJzcy/erzKaOHYjzxwxo28fbe3IeIu9ycnLc9ufPn4+8vDzf5/CD1WrFVVddhebmZrzwwgt+44UQMLi8gs5gaOuJZ0y0IvHnaRKVWFtWibVYgMWL7dtWPMu0xMhovCFTj4Rm1KqPkBbj/CfQ81//gs/2xsDWLAJ3HhldmH1QViOjo3e+Y+idnE4PPujVO4e2nd9lgWhTJzVWWzPmLduG3GfXomjRUkw9ubv9KbHHe78z0xPw4lUjMHXD/+m+DxmbrJj4zXJcktMDE0/sDmOTtV3NwIxkzJt8IsZ8+bEj5uOCvfh+z1G8sa7E9z/mHblYnWWvL41o73gfasHzOgagTZ6vSOueHAchgKJPvnbEpMUInPzZ+37PY3xtCSb2TcUlo3pjYt9UGF9booR3xcXFqKqqcvzMmzfPd05+sFqtuOKKK1BSUoLVq1c7np4DQGZmJiwWCyor3b/2c+jQIfTq1csRc/DgQXhy+PBhR0xUI4iuKC0tFQBEaXGxvaCqSgjAvm3Fs0xLjITGYrGIFStWCIvFIl2vFs2uimox4P6Vov/clWLNjjLpfNucS0ubguBDXaNV9J9rb8/xWnPQNNI+RJB3WjVB84HehdUHreOC3oXXB9frVnf4mHo+uBxvbm4Wt79VKAbMXSmWnDrdoalvaBR/X/qJ+Lhwr1i354hosjWzD0m2SUZzoPSQePaMq8XnP/zi0Hy0bIW4bskG8e4Pe0WDtcmhqTt8zNmfTPG69y5S+9DxWrPP69gZHyzHKsWVL68TA+5fKV5916kpO24Wv/1WERXelZSU2OcGpaWiowAQy5cvdyuzWCzi0ksvFUOHDhWHDh1qozl+/LgwmUzi/fffd5SVlZWJmJgYkZ+fL4QQori4WAAQP/zwgyNmw4YNAoDYuXNnh/OMNPgRdxL1DOqVimcuG47Pvt+K3w3KCHc6hBBCopxmGBAD+0c0n/z9CPxheA+c/tSnjuPGGAMGpQtcMCILJpMpfIlGKdlp8Zjz/bvAKS85ynZUGrB21xHsKKvB70/tE8bsiOocrG5A6zNakzEG8bFGGACU1Dg/jZGVnggYrF710U5tbS327Nnj2C8pKUFRURG6deuG7OxsXHbZZdi8eTNWrlwJm83m+M54t27dEBcXh/T0dNx88834y1/+gu7du6Nbt2649957MXz4cMeq7kOGDMHUqVMxa9YsvPzyywCAW2+9FRdddFHUr+AO8DvoRCdcNCILMfu3OPYbrDb85+SJmBrGnAghhEQX9RYb5v/fLnQ9+3q0fjg0LcGE0wd0CWdaBMAJaQL3TRmExDgTTC4LyD7wxe4wZkVU4nBSF9z02hb8eqweBXeOR+vqBQ9fNATxxiHY/N3XYc1PFQoLC3HOOec49u+55x4AwPXXX4+8vDx8+qn9j5GjRo1y033zzTeYNGkSAODZZ59FbGwsrrjiCtTX12Py5Ml4/fXXYTQaHfFvv/027rzzTsdq79OnT/f67vVohBN0vdI6AIxGIDfXue+tTEuMjMZXXv7q6aRGCIEHVv2CZf/zIO5Yuxd/vXi4tvPI5KewD2HVyOjone8Yeien04MPevXOVav1PDK6Dmo2/HoUH2w9COP4GZhZZUH/NP8an/WqrJHRKdaHUk3AlWcOdH5iwWjEzulXY8WOw856Jk+md9HQhzyvow+NEAKHaxrRIzUeMBqRceY41FhssNiasbWiDqe3aE7qmQyr1cvT8mj0ToJJkyZBCM+lLp20d6yVhIQEPP/883j++ed9xnTr1g1vvfWWphwjHU7Q9UpSkn2bnAysWuV+zLNMS4yMxhsy9QRAM6BXOkzGQzgjJ0v7eWR0wfDB18rTgdZoyU1WI6NTvA/RuzBrZHT+NKqNi0jyLpj5yWgcse38LvN3HhldBzXnnNITd00ehPEDu6F/3wwpjc8YlTUyOtX7UHIyst9fioc2luKx//vJXvbJJ0BcLP6+aieS42MxY1RWG43ti/yWFbiP21fg/iK//RW4o9Q7lfuQbfkKrD9Q7bxGA7vB6KEpqwOmPb8OAsBX95wNQ3IyDKtW4Z+lx9G3WxK6Jcfp0juiBlzFXa+0ruDY2Ajk5dm3rXiWaYmR0XhDpp5OagwGA+48sx++iS3C6X1SHZqG+Qs6dh6Z/BT2IawaGR298x1D7+R0evBBr945tO38LvN3HhmdH43Z0oT/Xb0bjTbn4bt/1x+nv7VIXe/YhxwxaU8+jpmjMp1ljy9E1fFaLP5vCZ7O/xn7K+vdJPlbSnHmA8tx9asbcNd7Rbj61Q0484HlyN9SKp+bTH4R4J3Kfei8vJXu1+jJr/D5Q/9EZWWNI6Zbgn1l9rLj9fjtqNlR78ieifbJuU69I4oQ3jXqSKjR4yruXnUeMUfKDouzbn1VLFldLJqbm6U0UvkFwQeu4q5mH6J3kecDV3GPzD6k0iruf1i8QfSfu1Jc/o9PI8K7gGkC4F0ofWhP47mKe/2RY+L9jfvEPe8XicbGRodu7kdb7XH3feaI7z93pRhw36diwNyV4ottZVHpnVR+CvQh11XcPa9Ra9nFz/3HTfPfnytEVb1cm6LZu86s4k6CA5+gEwLgwx8PYl/XLLy1uRz1Vpt/ASGEEF1gM8Rg/d7j+KToANb/chS2Zuf3K/90zknISk/A+J7NYcyQBJIEkxFXjO2L/71ipOMd6tamZnxQ2PKU3OO96sJg/6f0gs+K3foGCS1u3hu8fOXAYMC28lpU1zu/Sz5+YDekJfAtCkQ9+B10QgDcNqEPEh64H2d88AqS4jgsCCGEAPk7j2DBH5eg/O1tjrKMZBMea3kLyPgTumP1nDPx1Zf54UuSBJ2Nvx1De3NvAfvHpQtKjmFsvzTfgSRobN533G+MALCjrJrXiCgPn6DrldZVTE0m4OabnfveyrTEyGh85eWvniBoDHFxuGF0LwzK7uII+ebnw9hRafCpkTpXhPkQMo2Mjt75jqF3cjo9+KBX71y1Ws/jR5e/vRy3L/sJ5akZbuVH6qz446UPIH9PJQAgPtbjn1Kqe8c+5Dvmuuu9ao6a5d53faimAc3NAhbXD+LpwTsF+tCRWrnvVR+qaZA7l468I+rBR4V6JSHBvk1MBBYvdj/mWaYlRkbjDZl6QqDZd9SMez7chrrGGIzffQSTc7K0tSkYPvhaeTrQGi25yWpkdBHeh4KmkdHpwTsZnT+NauMikrwLZn4yGkdsO7/L/J2nHZ2tWWDBZ8UQgM+Pyy5YtQfnj+rXqfO0G6OyRkYXKT643gde+Bfg5VN0PVPj26/LEZeArfur8GChEYXNO/DUZaOi27vOaGR0EprGpmYcrgcGpMhfI6lz6cA7oi58gq5XGlr+glhfD9xyi33bimeZlhgZjTdk6gmBpld6PKaPzMTAVGDCwG6OGNstt2D9T2XO7yLWmds/V4T7EDSNjI7e+Y6hd3I6PfigV+8c2nZ+l3WiTYV7K1Fe5eNJWwutH2nuzHna1amskdFFjA8u13n2HV41Y/t3RVZ6Any9TM0AICvd/jqvb3cfgaXZgAZrs9t5CnaWo6F1jZuo8S68fWhHWRWm/r/v8erPRgzv3f7H1g1CICstHuNa/00XjDZFkHdEbThB1ytWq3O7ZIlz31uZlhgZja+8/NUTAk18rBELLs7B7UNsiGv5+OIX28owIeFsXL10i/PVHc99j/z/Fvs+V4T7EDSNjI7e+Y6hd3I6PfigV+9ctVrP047uUE0nPi6runfsQ75j3ljqVWOMMWD+xTkAAINwXxCwdX/+xTkwxhhw57kn4p7hTbj1rAGO8xx+92NctXQzTntsDY6bLdHnXZj6UN9uSahrbEJ9E9z+oNbmGrVs5593gu931uvMO6I2nKAT0g5xRvvW/l3EnTic3NXteEWNBbdf+gDydx4JQ3aEEEKCQUc+0kz0wdRhWXhxxhBk1hx1K8+sOYoXZwzB1GFZAACDwYD+KcDJvVIdMfu6ZqJXShxO6JGMLklxjvKPtx3E1zsPwtLEtwDIUHrMjDcKyxz7aQkmLL52DB4abcPAjGRHec9a90+2ZKbF48UVCzH1FPf1JAhRFX4HnRA/2JoF8j4rtu94vl4FgAECC9b8ivPHDgx9coQQQgKGEPalusf274rMtARUVHv/mLtBNCMzPRHjBnZDs83HWgYk6ph6SgbOf+lmFGwtwSGbET2NNowbORDGJyvb1Z16YCe+/9M4HI1x/kGnyRCDJ74qwRGzFa9dNybYqUc8R2obcf6za9FgbcaIrJMxqqV8RJ907P/RPXbN4tuxvXCn/RqlJmBc91gYH1wf6pQJ0Qwn6HolPt65nT/fue+tTEuMjMZXXv7qCbGmcG8lKtr5LqIwxKC8utH761WiyIeAamR09M53DL2T0+nBB71656rVeh6XMhEXh6Xfl+CHX49iSqr9I81503Nw+1ubAQgIl28fGwDAYMD8aYNhjDGg2SZ/HiW8Yx/yHTPvAb8a4yMPY+LJvexxjY3AIw9LnScmIQE9XPIzP5yHi4ZnYv1vxzHhhG5Yvdt+6IONpdhUcgRXPfw0RkeKdyHoQxnx8bhweDbKKuuQdMuN7Z7LeP/9zmsE2K+Tqj6oNv6IGgiiK0pLSwUAUVpaGu5UhMViEStWrBAWiyWoms6e6+PCvaL/3JV+f1Zs2R+SNtU1Wh3nPF5rDppGa37RqFE9P5U1KuendVzQu9BqPHWu162u0RqQc/12pFYMeuBz0X/uSvG31z5xaL7YViYmLFzjdq+fsHCN+GJbWafaxD6kjqa9/hSO/Ga88L3oP3eleO27Xx3Hm2zNwtpkC0tuoTxXq6axsVF8/dNB8fsXvheVdY2O4/WWJtHc3OxVc7zWLHVfCGV7QnmuzmhKSkqUmRsQO/wOul4xm+3bujpgyhT7thXPMi0xMhpvyNQTYk2nvosYRT4EVCOjo3e+Y+idnE4PPujVO4e2nd9lHWhT/wTgoYuG4JELT8GwrsJxeOrANHz3n7/j3WtH4p9XjcK7sybguz+Nx9S/3BC53rEPecSYnWWXXBJ27+7NHYw/nJqNaU/8xXH8v7sPY9zja/D3G/IU8y44PjQL4Kn8nSjcW4mX7njCcTzBZITBbPZ/Ls/rqLIPqo0/ogT8iLtesdmc2y+/dO57K9MSI6PxlZe/ekKsaX29SkVVA0SbowCEcLxepc13EYPhgylEGi25yWpkdFHUhwKqkdHpwTsZnT+NauMikrwLd5tctRrOY7Y04R+rduGmURno01J23cQBsFqt+Pzz7W4a45erMLFvGpDW8hWm6urI9o59yHfMV1+F3buJJ3bHxB4m4IrljuNf/XQIx8xWVJcddNPsrQVszcJ5K4vgPnSkthGpJvtXSYwxBjx0YQ6+3b4ft199JfDPu9uvxxPP66iyD6qNP6IEfIJOiB/afb1Ky0/rqzt2H6rF1qO+3pRKCCFEBR5cvh2vfV+Cv3y2y/sfXglRiPkX5+DNq4fhus3/5yjbd8yMZ7bFYtL/fut8v3qEsviH/fjd099g5bYKR9mZgzLwwOQTkN7Ip71Ef3CCTogEPl+v4vLqjnqLDXe9vxWv7TLi3Y2lYcqUEEKIP+acNwgn9kjGnWf0Bf+kSlQn1hiDswZ2xaCjzn9b/HqkDklGgRMykpFgMjrKPx56LgpLq9DcHDl/empsaobZYsPqnw6FOxVClIAfcdcrCQnO7auvOve9lWmJkdH4ystfPWHSTB3VB+ePbkLB707FoXqb/dUdvVNg7HkTkJAAY4wBvxuUgYOVtTh/SM+o9SEgGhkdvfMdQ+/kdHrwQa/euWolNAeO12P73mpMaYnpnxaH1XefjZgma+B9UN079iHfMYv+FTHeTTq5B/421obTzhrmCGkwmvDIxXNQ9+aPWDY7BWMyk5X0+4dfj6JHajxOSLeX3fy7EzGwT3ecN7g7vvjiQId88IrndVS536k2/ogahHuVOhJauIp78FfEfOcjd80vh2oCeh6u4h56jer5qaxROT+u4h4ZGk9dR1dx/7msUgybny9OfvBzsauiOuD5qaxRPT8VNKqt4t4ZzaHqBnH3e1tE7jNrhc3mXO38jfW/ib99tkPs2H8s7H3opf/sEf3nrhQ3/rsgoOfhKu5cxT2a4Efc9UrrCo61tcDQofZtK55lWmJkNN6QqUcVjY+yJJfPpWwsPoDz//41HvpwC2ytHzdTuU1h9q7TmnC2id6FViOj04MPevXOoW3nd1kLA7onYXS/rhjSKxnxF10QfB9U9459yCPG5TvOY8dGtHc9DFY88+g1yL9lNGJiDA7NW9+XYPF3Jdh2oMoRa2sWEEKE3O/zB6QiPjYGWekJsFZVB6cPeV5HJfudmn2IqEFsuBMgYaK52bktLnbueyvTEiOj8ZWXv3pU0Ujotuyvgi3GiLrGJsQY5DRhbZNC3umiD9E77RoZnR580Kt3rlovmu0HqnBid/vHOA0GA56/ajSSGutguvO74PugunfsQ75jdu6MCu8MQjj2RXEx7vldP3z+SxXOO6Unviu3H/p8WzmeXrUT14/Jwi1B8rupGfj3ur2IiYnBLSMzgOJinNA1AevuPxfdU+Ltb0QIhg+e11HlfqdaHyJKwAk6IUHk1gl9MPTWmRhV+A0MBvsMvVkIrs5ICCFB4oX/7ME/Vv2MO889CQNbytKTTEAT77xEfxgATBmcgSmnnQCr1eooX7WjAqXH6nG0zuIoE0Jg16E6nNyi6yw/HTdg8Q8/I9FkxPQT09CyOo99ck4I8Qkn6IQEmTP2bgXinCusPrr6VzROuQPzrTZwmQ5CCOkYNpfVqQv2VeGsbl1gdDmemZaAZmFfGG6Ar3fdE6Jznr5sBKYNy0JOmvMPVz+V1+CCxZtxyo3P4/+ahdu48sRmiEHB3uM4ZKtBT6MN4wwxMAKot9iQ2PJvnmFdBXJzeuLcU3qhexIHIyGycIKuVxIT7dukJCA/375txbNMS4yMxhsy9aiikdF5HN9zqBZLN5VBjJqGi45YcEZ3Dfn5+mRSoDW+CJN3Ic1PZY2MTg/eyej8aVQbF5HkXZjatGrHQfzt852O/Rs+2IHMVb/gr+edhN+3aP5ndCr6d0/CiOxUfP753tD7oKh3AdXI6CLGh0Rn2YoVuvEuKS4WF47IApqaHDG7fjmI+NgY9M85AcaUZEfsJ0VlOGJ2avNLarDg4U9R/vY2R1nPhz7FgLe34XhDEz6/8ywAgMEA/OvqUTCZTG7nCboPntdRyX6naB8iSsAJul6JjXVup0xpe8y1TEuMjMZXXv7qUUUjo/M4flLPFLxx0zj8uL8KZwzupS0/S1NoNFpyk9XI6PTQh+iddo2Mzp9GtXERSd4FMz8fmq1HDfj3+q3wfLtzRVUD/vLxdsRdPQoXx8bCAODU/t3cPs6ruU1R4l3ANTK6SPHB9T5w3nnOfx8FKzcZXZi8u3R0b5yX0wvHzRaHD1X1VsxbsQNWWyzOObsOJcfqcfu7RW3G4WELcGjvcQDAhl+PYfyA9M63SasPntdRxX7XGY2MTqt3RAn4hSyFyMvLg8FgcPvJzMx0HBdCIC8vD9nZ2UhMTMSkSZOwY8cObSerqbFvq6uBtDT7thXPMi0xMhpvyNSjikZG5+X4Wb3iccclYxxlNQ1WfLY3BvUWW/jbpLh3SreJ3oVWI6PTgw868c7WLLDst5g2kwJX8pashe14le+AUPmgmHdB0cjoIsaHGmdZZpbuvUtpNKNP/0zHfpXZirNOykCfZIH+3ZOw4LNir+OwtSwjJQ4TT/Ty8cBQ+uB5HZXsd4r2IaIEMeFOgLgzdOhQlJeXO362bXN+fOjpp5/GM888g0WLFmHjxo3IzMzE+eefj5qamnZqlMCb3rNMS4yMRiYflTUyOj+ahz/5CWvKYnDn+1sDn5/KGhmdHvoQvdOukdHpwQcdeFe4txLHLe0vW3U0KR0Fpe1M0H3lEgwfFPIuaBoZXaT5UEvvPPf7dU/Cy38Yjb8Mt6FwbyXKqxrarepIrQUFJcfkziOTnxaNt+uocr9TrQ+RsMMJumLExsYiMzPT8dOjRw8A9qfnzz33HB588EHMmDEDw4YNw9KlS2E2m/HOO++EOWvSGa48rTe6xgncMemEcKdCCCFKcqimUS6u1hLkTAjRJzGGDozDmvYn8YSQ9uF30BVj9+7dyM7ORnx8PMaPH4+FCxfihBNOQElJCSoqKpCbm+uIjY+Px9lnn41169bhtttu81pfY2MjGhudN9TWp+1NTU327+c1NdkXjGtqAlq/r+dZpiVGQtP6/UC37wn6qUeLxjXe2oE2+tXItElCM7ZvGh4abcPQzGSHrvhAFQakdkGSl/xc2261Nkm1SYsmEryL+D5E78Lqg9ZxQe9C54OwWGAwGNAtsb31pJ10TzS2OUfIfVDEO/YhOY21qclRZE1o2ad3XtskPQ6TYsPgg+/rqIJ3EdOHJPj222/x97//HZs2bUJ5eTmWL1+OSy+91HF82bJlePnll7Fp0yYcPXoUW7ZswahRoxzHf/vtNwwcOLBtxQA++OADXH755QCAAQMGYO/evW7H586diyeffLJD+UYiBiFEe1/pIiHkiy++gNlsxsknn4yDBw/isccew86dO7Fjxw78/PPPOOOMM3DgwAFkZ2c7NLfeeiv27t2LVatWea0zLy8PCxYsaFO+ePFiZGRkBK0tRDtVFuDprUYkm4A/DrGhm8frQhttwH0F9r+tPT2uCfESvy+1aAiJdjgu1EUI4PuDBnxbEYM/DrGhSxywYLMRxy2A9zc0C3SJA+aPsSEmEC9wJrqB9wF5moW645DXUTtHjhzBLbfcgtLSUvTp08dv/BdffIHvv/8eY8aMwe9///s2E/Q333wTJSUlyM7OxqxZs9pM0G02Gw4fPuxW5yuvvIKnn34aFRUVSElJAWCfoN98882YNWuWIy4lJcVxPJrhE3SFmDZtmuP/hw8fjokTJ+LEE0/E0qVLMWHCBACAweB+xxNCtClzZd68ebjnnnsc+wcOHEBOTg4mn3suevfpY/9XUG0tkJJifx+GvVL3Mi0xEhqr1YrVq1fj/PPPt7+CQ6JeLRoAbXWB0Mi0SYNm24EqJO8qQrdEIy6/cALi42LdNGarDfcVfA0AOPfcc5GenODXBy2aSPQu4voQvQurD1rHBb0LjQ8ffPQzDtYfQ1nSSbjy3IHYdmwN/r3L/q9u1ycLhpb/PnbByZgypn/4fVDAO/YheY05LtFxH5hy+ulI6pZO73y0aUru+TANOIY/v7fV9zicMRJThvYKuQ/nnnsuUPCt1+uogncq96Hf/e536AjTpk1zm7N4cu211wKwPyn3htFodFsEGwCWL1+OK6+8ss3kOzU1tU2sHuB30BUmOTkZw4cPx+7dux2ds6Kiwi3m0KFD6NXL9+u64uPjkZaW5vhJTU0FAMQ2NMBkMsHU0ABT9+72rcnkvUxLjIym5QbnOC5ZjxZNG10gNDL5adCMGZCBz28ahVcevRopNqtDY+uZ6dC0YjLFSrdJiybSvIu4PkTvwu6D1nFB7wLrQ+lJw/HEZztgQ4xDc9fpfbBg+lDcnTsYJpMJI7sLPH/VSPRMc/9YUWZ6Al6cMQQXTTxZDR84/pT0oT2N4z7Qry+989Omi0b1wYszhiCz2v0JaGb1Yfs4HNUnTD44nzm2uY6KeKdyHwLsX4Otrq52/Lh+RTaYbNq0CUVFRbj55pvbHHvqqafQvXt3jBo1Co8//jgsFn2sM8In6ArT2NiIn376CWeddRYGDhyIzMxMrF69GqNHjwYAWCwWrF27Fk899VSYMyWBpntyHLpXHXTsL9t2EItufB7/OlSH/v2SwpgZIYQElmYhcMPlC7BvYxlO7NMN1wzpCgAY3y8d49PSADi/IzllaC+cfUovDM/7EgDw+hVDcdao/jDKrL5NCAkIU0/JwPkv3YyCrSU4ZDOip9GGcSMHwvhkZbhTI50gJyfHbX/+/PnIy8sL+nmXLFmCIUOG4PTTT3crv+uuuzBmzBh07doVBQUFmDdvHkpKSrB48eKg5xRuOEFXiHvvvRcXX3wx+vXrh0OHDuGxxx5DdXU1rr/+ehgMBsyZMwcLFy7EoEGDMGjQICxcuBBJSUmYOXNmuFMnQaTJ1ox/rSvFr937YPWuo7ilX49wp0QIIZppFsB/dx/BOUMyYTAYEGMw4MZNn+L72+dhaHa6X73R5cut4/qlu+0TQkKDUTRjYv8uzvdqi+Zwp0Q6SXFxMXr37u3Yj4+Pbyc6MNTX1+Odd97Bww8/3ObY3Xff7fj/ESNGoGvXrrjsssscT9WjGU7QFWL//v24+uqrceTIEfTo0QMTJkzAhg0b0L9/fwDAfffdh/r6esyePRuVlZUYP348vvzyS8fH1kl0EmuMwYfXjsTrl9+JO+5/AaH5wBEhhAQeW7PA01uNKN+wGe/dOgETTrD/I+uGTZ/hxsvfcv5jnxBCSEhJTU1FWsunlkLFRx99BLPZjOuuu85vbOt6XHv27OEEnYSO9957r93jBoMBeXl5gfm4SesiDKmpQFWVfduKZ5mWGBmNN2TqUUUjowuQD916dcM9//eifd9qc4T9VF6DCScl+m+TiybgPijuXdRpZHR68E5G50+j2riIJO86kJ/58DEktewbYww4IU2gHnE4WN3giDF0tE2O87XzuywQbeL4096mSPEhPtFZVlZO76KhD3leR5V9UM27ELNkyRJMnz4dPXr4/3Toli1bAABZWVnBTivscJE4vdLc7NyWljr3vZVpiZHR+MrLXz2qaGR0QfZh1ltbUN1g7ZCm3mKD2dJk/2mwwPzrXvvW237LT6MNgdX4KGtPI4RQuz/oqN8poZHR6cGHEHsn9u3zPWY9xm1VvQV//XArxj63Hr8crHbEnJfdjC/uPB3n5/TSeE+xuecZBh903+9kdJHmAwDs30/voqEPeV5HlX1QzTsJamtrUVRUhKKiIgBASUkJioqKsG/fPgDAsWPHUFRUhOLiYgDAzz//jKKiojYLXe/ZswfffvstbrnlljbnWL9+PZ599lkUFRWhpKQEH3zwAW677TZMnz4d/fr161T+kQCfoOsVs9m+rasDhg2z/0Wt9WMtnmVaYmQ03pCpRxWNTJuC4UOCc5G4uyefiLQEU4c0E55a6+WE+/zsxzpeQxNYjWeZb83Y/l3x4cyhMKjaH6K936mmCYQPCT4WXIwkH0LonaitxWVPfoFNffa6BHobs4DnWJ/83HdumgVbAnQfqjMDifFqjSWOv8jy4fAxZ9m404Ajh+hdpPchz+uosg+qeSdBYWEhzjnnHMd+6+ucr7/+erz++uv49NNPceONNzqOX3XVVQDaLjr32muvoXfv3sjNzW1zjvj4eLz//vtYsGABGhsb0b9/f8yaNQv33Xef5rwjCU7QCYlgZozOdvx/6fEGVGaehBFe4hJNRpzarws27TsestwCTeHeStRbm8E17AkJD7WNNmzqk+M/MESM3b8DiaYzw50GIYToikmTJtk/1eiDG264ATfccIPfehYuXIiFCxd6PTZmzBhs2LBBa4oRDyfohEQwBoMBAGBpasaflv+E4j/8Hf/86TAuGJ/WJu7dW07DipVfYMqUXMc7L1FdA2Rn2b+vlZbadh/21xutWvWlUxcIjZdz+9KY9+7H2H/+EBpDCSFeyd9ejvkrtruV9UqNR24vM+69+nwgxogpz/0XFVUNWLD6RVy+6k2v94c24xzQfE9J7HERDC/8MdRWEEIIIUGFE3TifbEIzzItMTIamXxU1sjoQuCDxdaMzNR4/GZtxMjsVK8ag8GAeCOQFBcLk6ll6McZgYQ4+zYutu0+AKtBuOsCofFybp8akzGo3kWkRkbH8add4y0m2jQyupbj+dvLcftbm+H5vORgTSPerInB+N1HcdGoPrhr8iCUH6rChf8uQpKP+0ObcQ5ov6eo3If00O9kdJHmQwq9C6lGRqdF4+06quyDSt4RJeAEXa+0DlBvr7TxLNMSI6Pxhkw9qmhkdMHwwdLUJjwlPhYv3TgepcdGoHf3JIfmeMURdEmKg61Z4IeSY9h0xIDuJccw8aSe9ncHR4J3ru1NS1W3P6joXUc0gcovknzwMpYClp/KGhldy3Fbs8CCzza2mZy78vgXOzFtRG9cPa6fveDg/s7lp6APQdd4i1FZI6OLFB9c7wMV5c4/IgcrNxldpHjXGY2MTqsPntdRZR9U844oAVdx1ytNTc7tqlXOfW9lWmJkNL7y8lePKhoZXQh9MNhs6Ff4X0fM9n3HcMbjq3Hv+1twxlNf4w+vFeKN3Ub84bVCnPnU18jfXk7vIlEjo9ODdzI6PfgQZO/WbC9DeVWD77phQHlVIwpKjoW3TSr1IT30OxldpPkAAGvW0Lto6EOe11FlH1TzjigBJ+h6pb7evjWbgalTnau6eyvTEiOj8YZMPapoZHRh9OHTTftQZwM+2lKGCo9/YFdUNeD2tzYjf/O+CPCu3qWsXt3+oKR3HH9R70MAvTtYD7y5YR++233EcfzosVrf9bpwqKYhvG1SqQ/pod/J6CLGB5ffMZdeSu+ioQ95XkeVfVDNO6IEnKATEqXcN2kA0utrvB5r/bjqgjW/wmbgbYAQvVFe3YjPB58Bq63ZUbbpSAwe/b+dWLbF+RH1gd0TperrmZoQ8BwJIYQQPcJ/mRMSpWzcX42qRN+LgQjY/5Fe0Gdo6JIihIScBqsN+9N6OPaFEJi6eDNmXzoPOw/VOcpPShM4e1AGxvTr6igb1zcdWekJMPisXSArPR7jBnYLTvKEEEKIzuAEXa/ExDi3OTnOfW9lWmJkNL7y8lePKhoZXRh9OFQn9x2jQ4OH07tI0sjo9OCdjE4PPnjsCyFga3Yu6bam+CCG/e96zLnyEUeMwWDA2L5pGFp1ALVWZ+zJ6QKLrxuDP0zo76jXGGvE/Ivt7z73nKS37j847RT7opMK+SClkdGx32lvU6T5AACnnELvoqEPeV5HlX1QzTuiBFzFXa8kJ9u3KSnAjh3uxzzLtMTIaLwhU48qGhldMHzwtfK0h6Znj/T2z9HCkfl/w6HmWPQMRG6yGhld63HX9qYkq9sfVPSuI5pA5RdJPkiOpWC2ybZtOwpKjuHQnir0TE3AuG3bnZPdDp7nbyuLsWzzfiy4eIjj8Ak9ktHULHDwpBw0JyU7/ir/6o0TEHPzRKnzTB2Wghf/MAbzP92Bg9WNjpDM9HhM62XGlKG9Ou1DxPahQGm8xaiskdFFig+u94HCwvZXcad3gdPI6LT64HkdVfZBNe+IEvBPKHrFarVvLRZg8WL7thXPMi0xMhpvyNSjikZGF0YfxvVOQZbR5vOjqQYAmWnxeOHz7Tjr6W+waW+lot5ZXcqs6vYHJb3j+FPZh/yiUpz58Ge4+tUNuOu9Ilz96gac+fBnyC8qbfc8R156DXM/2IKrXlkP0djoOI+lqRmVZiuKSqsc4QMzkvHdPWfi266/IqbJOZZimvyMJY/8pw7Lwpo/ne44/PqNp+Gbe36Hkd1FuzqV/A5ZH1K83yntXVja5PI75vWl9C4a+pDndVTZB9W8I0rACbpeaWhwbmfNcu57K9MSI6PxlZe/elTRyOjC6IPR0oj5Hz0JwPdHU/80sQ/6l+5CRrIJw3unO+oQ9fXwiQ68U1ojo9ODdzI6hX3I316O29/7EeVN7r+GK5oMuP29H+2vQQSw7UAVVu6Lwf9tq3DUm/zn2fi4qBwbfj2GskNVjvNcf3p/rLjjDNxz/iBHfQaDAX0SDDDc2nnvjBbn0/NxA7t5f9LPPqR0vwuYRkYXaT4AwJ/uoHfR0Ic8r6PKPqjmHVECfsSdkChm6q71eHHGECz4qsTtXcaZ6QmYf3EOpvZLxjXn/hWHDxxGXGzLd1gBXP/+Dgzp0xW3TzoRyaZ2Pm5LCOkwtmaBBZ8V29+mYHAfX8IQAwOABZ8V4/ycTGz8rRKrD8RA/FiOS8f0BQAkNjXiwckD0SerG7ommhzak3raF4W0Wq0ghBBCSGTCCTohUc7UUzJw/tiBWL/nEL787w/IPWs8Jp7U0/70q7oaBgA9U+Ic8Zt6D8G3v1Ziw74q3HTmQCSbjOFLnpAopKDkmNsfzDwRAMqrGlBQcgzjB3bD+B7NmDYs0y3mxtN6A2lpQHV1kLMlhBBCSCjhBF2vGI3ObW6uc99bmZYYGY2vvPzVo4pGRqeID8YYA8YP7IajPwmMd/1oqhfNqUP74rWrhuO3Wht6pSU4nsat+ekQJpzYA1115p1yGhmdHryT0Snig+fK6mt3HfJdjwuHahpwwdCemHlSMy4YmRW43GR0vjT+YB9Spt8FVSOjizQfAGDyZHoXDX3I8zqq7INq3hEl4ARdryQl2bfJycCqVe7HPMu0xMhovCFTjyoaGV0wfPC18nSAfDCsWoVzPcKONQIL39+K+Fgjvrz7d8gOpXeu7U1OUrc/RHu/U00jo/OnCfJYwqpVeG7NLrxbsA93nXsSWt6dgZF9u7RfVws9UxOCk5uMzqsmqf3zBCq/SOpDgdJ4i1FZI6OLFB9c7wOffNL+Ku70LnAaGZ1WHzyvo8o+qOYdUQIuEqdXWldwbGwE8vLs21Y8y7TEyGi8IVOPKhoZXZT4UGcFTuyRguG905GdGOOIaW4WIfDOZbXRRkvEeRdwjYwuSvpdp9sUQh/MNWb886FXMOv1AvvT8hZNY4MVB6sbsWlfpSM2NycTvVLj233DQlZ6AsYN7BaQ3ALnncTKv+xDHH+d0YSlTS79+vGF9C4a+pDndVTZB9W8I2ogiK4oLS0VAERpcbG9oKpKCMC+bcWzTEuMhMZisYgVK1YIi8UiXa8WjVddIDQy+QXBh7pGq+g/d6XoP3elOF5rDpl3DQ2N4lhtoyOm/milOP+Z/4hnV/4ozLHxQfOu7vAxR3vrDh+Lrj4UQf1OOe8C4IPWsXTweK1YsOQTsXr7AYfEWnlc5Mz5QPSfu1IUl1U5NL/8Wi7W/3JEVNXWu+X2xbYyMWDuSjHgvk8dOfRv2R8wd6X4YluZct65jcVGK/sQx1/E+NCexq1fmzr4u0wH3kWKD8drzT6vI71rX1NSUmKfG5SWCqIGfIJOCJEiJsaArsnOxeRWFh/GroO1eL+oAjHCFsbMCAkeQgjsOWJGncn5cfNVOw7htV1GvPrdb46y2BgDZm/4EH+bciJ6pMY7yk/onoQJJ3RHYpz7d/6mDsvCizOGILPmqFt5Zs1RvDhjCKYOywpOgwghhBCiNPwOOiFEEzOG90RCWjIM9fWIf8j5Hb6PNu3H5MHdw5gZIdoRQrh99PyKl9dj42+VeKX/SOS2lI3t3wVZSQLDstPctHds+BA4dTGQEg9U+/8I4dRTMnD+SzejYGsJDtmM6Gm0YdzIgTA+WelXSwghhJDohBN0vWIyObc33+zc91amJUZG4ysvf/WoopHRRbEPMXFxuGhEOlBf79Cs/+Uo7v1wK3qmxuOvQzrpQxR7x36nng/7K+tx61tFOFpnwZrbxzliTuqZgh/3V6FiysUOzUk9U3D/SBsumDq4020y3nQjJp7UA0hMtI+lm25U2zt/6LgPdUoTqDZFunfhbtN119O7aOhDntdRZR9U846oQbg/Y09Ci+M76Ap8z8Tr93mCoAnluUKh8fm92SDk1lHdf3cdFuf84xsx7+MiN02TrVnzeVzbW9do1ZxbZzShPFe0aVTMb/0vR8RDy7eJDzbuc/StA0erxcD77f9fdtw5ro7VNopGqy0s7QnluWQ0nmNRrz5Ekkb1/FTQqPA7RmWN6vl5/Q66x3UMV26hPFdnNPwOunrwO+h6paHBvq2vB265xb5txbNMS4yMxhsy9aiikdHpwQeX/TMHZWD13Wfjr7knO8Irqhpw1pNf4eU/PwVbnVneB8fxBpeyhojwIagaGZ0e+p2ETpjNKL7tHrz57W40WG0OzeZnl+DNDXux+qeDjtjk+Fj8a+YYrLnnbGSahKPerslxiLM2qutDSPtQg/dYFdqk0lji+NOuCUubXPr17DvoXTT0Ic/rqLIPqnlHlIATdL1itTq3S5Y4972VaYmR0fjKy189qmhkdHrwwWPfGGNASrzz2zNv/7AXZdWN+OoYENNE79jvAqTxUtZgteFIg7vmOtMYPPz5Lmw7UOUo+90HL+Om07IxY3Rvt1NMG56Fk3qmwNDUFDk+hLoP+UMvPnD8RZ8PAPDGUnoXDX3I8zqq7INq3hEl4HfQCSFB5c7Jg9A30YATrv4rDIbbAQCWpmZ8sLkcl8XGIcGPnhBfCJcF3dbuOoxblm5EZoIR17WUGQwGTCopxMHpl7vphh36FcPOPxHmhKSQ5ksIIYQQ4g8+QSeEBBWTMQZXjMzE2AM/Oco+KCzFQ/l7cPnMpyCECGN2JBJ58T+/4Nz//Q9W7DjsKDu5VwqsNoEaq/0PQK384/N/4s2rh+O0Ad3CkSohhBBCSIfgBF2vxMc7t/PnO/e9lWmJkdH4ystfPapoZHR68EGDpkuSCb3TEzCjbxwMCc5n6Lbmthqbyfnu9YIKM2yPqNkm9rvAaxptwL/X7cXd7xeh2RTniDlW14hfD9eh4ECtoywrPRH/+ctZyBtjQ1xsTFT5EHCNjM6Xxh968UEH46/TbYo0HwBg3gP0Lhr6kOd1VNkH1bwjahDuVepIaOEq7uquohkNq7h3RNNotbmtjr1+90Ex8uHPxJvrfnWUfbGtTIx7fLWjvf3nrhQTFq4RX2wrC2pugdJRI6+rabCKtT8fEuv2HHFoPlq2Qgx+6HPRf+5KsftgtSN2Z3m1WFNcISrrGjuVXyjHUjT0O67iHnka1fNTQcNV3NU5F1dxD4+Gq7irB5+g6xVzy2radXXAlCn2bSueZVpiZDTekKlHFY2MTg8+aPQurrEecRdOc5S99UMpjlsM+HG/fTGv/MLfcPubm3CwutGtqorj9bj9rc3I316uVpvY7zqkqbhoBhqOVzv2l900D9e9VoAX/rPHERYbA9wwsT/mTj0Fac1WR72DM1MxeUgvdBHWiPchsvqQ2XusCm1SaSzpod/J6CLGB5d+fckl9C4a+pDndVTZB9W8k+Dbb7/FxRdfjOzsbBgMBqxYscLt+LJlyzBlyhRkZGTAYDCgqKioTR2TJk2CwWBw+7nqqqvcYiorK3HttdciPT0d6enpuPbaa3H8+PFO5R4pcIKuV2w25/bLL5373sq0xMhofOXlrx5VNDI6PfgQIO+enDEMlw204fazT4CtWWDBqt3w9u10YbAvC7bgs2LYmr1ERJIPOu1317xZhAnDbsb6X485NGNXf4y+XRLQv7v7wm1/OX8Qbp90InomxUadDxHZh/yhFx/03u9kdJHmAwB89RW9i4Y+5HkdVfZBNe8kqKurw8iRI7Fo0SKfx8844ww8+eST7dYza9YslJeXO35efvllt+MzZ85EUVER8vPzkZ+fj6KiIlx77bWdyj1S4CruhBAliI+NwVmZAn26JqKg5BjKayyAweA1VgAor2pA4d7K0CZJOsS+o2bkfbYDR2sbcVNfZ3l2Wjximm0oOVaPc1rKcg6X4L+zTwPS0sKSKyGEEEL8M23aNEybNs3n8dZJ9G+//dZuPUlJScjMzPR67KeffkJ+fj42bNiA8ePHAwBeffVVTJw4ET///DMGDx6sLfkIgU/QCSHKcaimwX8QgEM1jf6DSEjYfKAaT36xE6t+PuIoS0804ZufD2Hr/ipUW5yx957dH1v/eRVuGtc7DJkSQgghJNy8/fbbyMjIwNChQ3HvvfeipqbGcWz9+vVIT093TM4BYMKECUhPT8e6devCkW5I4RN0vdK6cnZCAvDqq859b2VaYmQ0vvLyV48qGhmdHnwIgnc9U+Xejt4zNR5HW/5/dfFBZHdJwJBuCYiJFB8isN8JAZRWmlG0vwYXjchGXEvMd6W1eGntL5g+PBNTWjTpcSY8/fsRODEjEb8Vfe+oo1ePdOBf/y+ifYhojYzOl8YfevFB7/1ORhdpPgDAon/Ru2joQ57XUWUfFPKupqYG1dXVjv34+HjEB2nF92uuuQYDBw5EZmYmtm/fjnnz5mHr1q1YvXo1AKCiogI9e/Zso+vZsycqKiqCkpNShHuVOhJauIq7uqtoqrjytFZdZzVNtmYxYeEaMcBl9XbPn5ELVon6hkaxYsUK0dDQKEYuWCX6z10pCn875qiz0WoTzc3Nnc4tEG2KVI21ySYOVtU7NMuXrxCjWrzevNfp9ea9x8Tcj7Y6VtgPVX6d0XAV945puIp75GlUz08FDVdxV+dcXMU9PJrWVdw9f+bPn++3DgBi+fLlXo+11rtlyxa/9RQWFgoAYtOmTUIIIR5//HFx8sknt4k76aSTxBNPPOG3vkiHH3HXK60rONbWAkOH2reteJZpiZHReEOmHlU0Mjo9+BAE74wxBsw//wRACHh+C93QUvbkjOEwxtiPVjVYMbpvF/RKi8fIrrGOel9a+wsmLlyDNy68RU0fFO93//1xH0bOXYFblxY4QgwGYPzAbhjdrwsam5odmtHdTHjy9yMwdUBq1PkQdRoZnVeNxMq/uvCB/U5KFzE+uPTrsWPpXTT0Ic/rqLIPCnlXXFyMqqoqx8+8efN85xRgxowZA5PJhN27dwMAMjMzcfDgwTZxhw8fRq9evUKWV7jgBF2vNDc7t8XFzn1vZVpiZDS+8vJXjyoaGZ0efAiSd1NP7o4XVyxEz5Q4t/LMmiN4ccYQTB2W5SjrmhSHf984Duvvn4xYCEe9BSXHUFFjQUxZmeM8NQ1WvP9LDP5vWwWEEOH1QaF+t+S7Ekxfsgmf27o6YgZ0SUBdbDxKKxtgtTl1/+/KEVg++wxMOKF71PkQsPxU1sjofGn8oRcf9N7vZHSR5gMA7NxJ76KhD3leR5V9UMi71NRUpKWlOX6C9fF2b+zYsQNWqxVZWfZ/202cOBFVVVUoKHA+IPjhhx9QVVWF008/PWR5hYvYcCdACCG+mLprPc649VQMf2Y9AOD1K4birLEnwvik99XbY2Lcn7cvvn4sNhXvx6BF6x1lBSWVWHcoBuVf7cGlY5xLi//UYwB6NzRF/SLi9VYbPinYh237j+M0lz/Rlh4z48fyWmzoOxwXtJT1SY9H/pI7MGj3VhiNMbA221/NYvCxuj4hhBBCopva2lrs2bPHsV9SUoKioiJ069YN/fr1w7Fjx7Bv3z6UlZUBAH7++WcA9qfimZmZ+OWXX/D222/jggsuQEZGBoqLi/GXv/wFo0ePxhlnnAEAGDJkCKZOnYpZs2Y5Xr9266234qKLLor6FdwBPkEnhCiO0WXSPa5fOoxC4ileCwkmI84Y2BU96447yvp0TcSkrGb8fkxvt9g//s8DGPXsevzw61FEC41Nzdi0txJb9jn/oGEwGPDIJzvwdkEpjjQ4Yy87tQ/+eclgzN7woVvsKUf2ul0DQgghhOiXwsJCjB49GqNHjwYA3HPPPRg9ejQeeeQRAMCnn36K0aNH48ILLwQAXHXVVRg9ejReeuklAEBcXBy++uorTJkyBYMHD8add96J3NxcrFmzBkaj0XGet99+G8OHD0dubi5yc3MxYsQIvPnmmyFubXjgE3S9kpho3yYlAfn59m0rnmVaYmQ03pCpRxWNjC4YPvian0atd4kuZYmdzm9wZir+Z0AzLvjdQIekOsaEmOxsGBoNyMlOA2INQH4+3is+huVbd+Dqcf1w4bCeHTqPltykNO3oquutSDHEIL7l+Nvbj+LRz3fi3FN64rU/jAby85GQloKrx/VFUlwMTDW7HdphvdMxrFcy8NHbUdiHfGiiYSyF1LtE77EqtEmlezh//2nXhKVNLv16xQp6Fw19yPM6quyDat5JMGnSJOdXBL1www034IYbbvB5vG/fvli7dq3f83Tr1g1vvfWWlhQjHk7Q9UpsrHM7ZUrbY65lWmJkNL7y8lePKhoZXTB8sDR1XKPlPMHUyOhaj7u2N0j5paUk4pv5F+JIbSNSE0z2wilTsPatTfih5BjOGpThiLXamvH6+l8x8cTuGJqb6/y4d5i9e/mnGMzZ8A3euGkczhrUA5gyBWP3H0e35Dh0S45z0yy4ZBisVis+/3x3+/VGSx/yVRYNYynU3vlDLz6E+r6vmkZGFyk+uN4Hzjuv/X5O7wKnkdFp9cHzOqrsg2reESXgR9z1Sk2NfVtdDaSl2beteJZpiZHReEOmHlU0Mjo9+BB072pcymqCml9Gc6Pb/v1n9cHj/zPMbUG6H/dX4fHPf8J1izdApHdx1Hv84FEIifPY0tOxfts+fFJ0AOu37YMtPb1D7Sk9Zsaf3yjAtdc84aZLirW/o/znihqHZniqAZseOg//uHyk2v2B4y+0GhmdV02N91gV2qRSH9JDv5PRRYwPLv06M4veRUMf8ryOKvugmndECfgEnTgn6+2VaYmR0cjko7JGRqcHH6LUu/5dE9G/v/11HlarFYB9IbrJp/RE93gDYmqcv+Suf38Hyq9dhEX7qjBumPeV5vJ3HsGCa/6J8re3Ocqyrvkn5u88gqnj2mp2lFXhm62lGJYxCJNaypLijPis+DDQdwQqzVZ0bZFd0LcZz998DrK6pth/+dbU2J/uuy7opnJ/iNI+pKxGRidTr4wuGn1gv5PTRZoPtfQupBoZnRaNt+uosg8qeUeUgBN0QkjEYLbaAFM8YLEBliZYrU1otAFmSxNMomUianGP8dzXogHg0A3ulYLnZ44GqmtgbolpqGvErsN1qE/tjoxkE8wtmq+LK/D2nhiYfjoIa7MBc5b9BJGa4damitTuuH3ZT3g6xoS0RBN+d2JXx7H87RV4fu1ezMiZ5Jigd0+JR975J+Dkm65C8lznd7i6JwAZKaF7JQoJHEII1Pvpd47+2oG+6lMD+B8XXjRmqy0s/hBCCCF6ghN0QkjEMPafPwD3fAz8Y51LaSzuK/jaPdAzJiAaLzovMee+vMlDE4OCd7Y6dz1eUSYMMTAAuO+jHyEArLh9guPYmSdl4Nfy4/jdZ5vdNDec1hvY9yNgjAGJbASAy978EZv2t3waQ0u/09hX/Y4Lr/USQgghJJhwgq5XWldwTE4Gtm+3b1vxLNMSI6Pxhkw9qmhkdMHwweZj5cwo9S6xSyrG9u+Kwr3e330eDbRe0T5dE1DdYHWUjz+hO8b37wKMXRT+fqeaRkbnT6PAWKrf8iM2vbfPd4yCjO3fBYkmI5qavCyyp7c+FChNoNoU6d6FpU0uK1oXbKR30dCHPK+jyj6o5h1RAk7Q9UpMjHPbt69z31uZlhgZja+8/NWjikZGFwwfbD4+Zhql3hmMRnz4x4mot9rsq6DV1AKpKYDBAKvVilWrvsSUKbkwmVpWX/eICYQGQFtdBzTNvUfg/uXFvv1p4a9TTsHEE7rj852B8Y7jz49GhbHUpw8A+wS98MHJSGqsD1i/86kB/I+LdjSJ3dKdby8Ip3cq9KFAaQLVpkj3Ltxt6tOH3kVDH/K8jir7oJp3RAl4hfRKba19W1MDpKe7LxrhWaYlRkbjDZl6VNHI6PTgQwi8MxgMSIqLRVJjPZJ6dLNv42KRFBeLeCMc/+81JkCaNroOaPp0lXvnaM/UhIB7J63xFqOyRkYXCT5kO98QEOh+51MjMy7a0Rhaf3+E2ztV+1Ak9DtVvQtLm1z6c3YWvYuGPuR5HVX2QTXviBJwgk4IIUFmbP+uyEpPgI9njjAAyEpPwLiB3UKZFiGEEEIIUQxO0AkhJMgYYwyYf3EOAMAgmt2Ote7PvzgHxhhfU3hCCCGEEKIHOEEnhJAQMHVYFl6cMQQ9a4+5lWfWHMWLM4Zg6rAsH0pCCCGEEKIXuEicXklJsW9TU4GqKvu2Fc8yLTEyGm/I1KOKRkYXDB98vYuY3oW3TRKaqacNxBkDumD4M+sBAK/fMBZnZSbAmJ4mfx6Zc0Whd0HxQYWxVFbufJVZakrkeBfM/CKpDwVKE6g2Rbp34WhTfKKzrKyc3kVDH/K8jir7oJp3RAn4BF2vNDc7t6Wlzn1vZVpiZDS+8vJXjyoaGZ0efKB3HdIYyw44dsf17wLjgf30Ts99aP/+0Ocmo4sE71T1gd5p14S7Tfs7eD+md9o1MjqtPnheR5V9UM07ogScoOsVs9m+rasDhg2zb1vxLNMSI6Pxhkw9qmhkdHrwgd51TDPuNJd9M73TqpHRRYIPne0PevZOVR/onXZNWNpkdpaNO43eRUMf8ryOKvugmndECThBV5QnnngCBoMBc+bMcZQJIZCXl4fs7GwkJiZi0qRJ2LFjR/iSJIQQQgghhBASMDhBV5CNGzfilVdewYgRI9zKn376aTzzzDNYtGgRNm7ciMzMTJx//vmo4fsMCSGEEEIIISTi4QRdMWpra3HNNdfg1VdfRdeuXR3lQgg899xzePDBBzFjxgwMGzYMS5cuhdlsxjvvvNO5k3pbLMKzTEuMjEYmH5U1Mjo9+EDv5DUp9C5gGhmd6j4Eoj/o1TuVfaB32jXeYkKl8RyPwTqPjC7SvFPJB2/XUWUfVPKOKAFXcVeMO+64AxdeeCHOO+88PPbYY47ykpISVFRUIDc311EWHx+Ps88+G+vWrcNtt93WsRO1DtC0NKC62v2YZ5mWGBmNN2TqUUUjowuGD5amjmu0nCeYGhldtPahinLgkVUt+6n0TqtGRudPo8JY6mx/4PjTrpHR0TvfMdHig+t9oKIciGvnn8b0LnAaGZ1WHzyvo8o+qOYdUQI+QVeI9957D5s2bcITTzzR5lhFRQUAoFevXm7lvXr1chzzRmNjI6qrqx0/rR+Hb2pogNVqhbW+Htb8fPvWavVepiVGRmO1AoDzuGQ9WjRtdIHQyOQXJB9asVqb6F2k9aHVq53Xr76B3oW5D4VyLHnVdLY/cPyp5wO9U9IH35oG5xhcvZreRWwfcv6hpc11pHd+NUQtDEIIEe4kCFBaWoqxY8fiyy+/xMiRIwEAkyZNwqhRo/Dcc89h3bp1OOOMM1BWVoasrCyHbtasWSgtLUV+fr7XevPy8rBgwYI25YsXL0ZGRkZwGkOCSqMNuK/A/pfhp8c1Id4Y5oRIh+D1UwcVroUKORCiZzgGowNeR+0cOXIEt9xyC0pLS9GnT59wp0MAQBAlWL58uQAgjEaj4weAMBgMwmg0ij179ggAYvPmzW666dOni+uuu85nvQ0NDaKqqsrxU1xcLACIkh9/FBaLRViOHhWWxET71mLxXqYlRkJTV1cnVqxYIerq6qTr1aLxqguERia/IPhwvNYs+s9dKfrPXSkOV1bTuwjrQ8dTuziu3/HyQ/QujH0olGPJl6bT/YHjTz0f6J1yPrSnOV5+yDkGU7vQuwjtQ4crq31eR3rXvmbXrl0CgCgtLQ32dIdIwu+gK8LkyZOxbds2t7Ibb7wRp5xyCubOnYsTTjgBmZmZWL16NUaPHg0AsFgsWLt2LZ566imf9cbHxyM+Pt6xX93y3ZPY2FiYTCYgNhaor7dvTSa0HHQv0xIjo2nBZDLZc5E9t0aNmy4QGpk2BcEHk5smlt5pbFO4fDA11DvLY2Nhondh60PhGEuemk73B44/9Xygd8r64E1jio11Hm9o2ad3EdiH2rmO9M6vhqhFrP8QEgpSU1MxbNgwt7Lk5GR0797dUT5nzhwsXLgQgwYNwqBBg7Bw4UIkJSVh5syZHT9hTIxzm5Pj3PdWpiVGRuMrL3/1qKKR0enBB3rXMc0pp3SuPTK6aPUuGn3obH/Qs3eq+kDvtGvC3aZTTqF30dCHPK+jyj6o5h1RAk7QI4j77rsP9fX1mD17NiorKzF+/Hh8+eWXSNXyyoTkZPs2JQXYscP9mGeZlhgZjTdk6lFFI6MLhg++Vp6md4HLL5iawkLnqt0pyfROq0ZG50+jwljqbH/g+NOukdHRO98x0eKD632gsLD9VdzpXeA0MjqtPnheR5V9UM07ogT8E4rC/Oc//8Fzzz3n2DcYDMjLy0N5eTkaGhqwdu3aNk/dpWldsdFiARYvtm9b8SzTEiOj8YZMPapoZHR68IHedUzz+lLHrrm2AeZXlsBcWw+zpcnx02iDc7+2vm2Ml7JOa7zFBEjjNz8tmgD54LgWFlvQffCqee0Nl/5h5fgLpUZGR++0tylifHBZwfr1pfQuGvqQ53VU2QfVvCNKwFXcdcb+/fvRt29flBYXo8+QIfb3IaanA1VV9vcjAm3LtMRIaKxWKz7//HNccMEFzu+/+KlHiwZAW10gNDJtCoIP5oQk5LQ8cdv68LlIT06kdxHUh8wZPZFzz8eaxi+JborvPR1JPbpx/EX6fYjeKedDexrz4WPI+cc6+xh85vdIOnKI3kVgH5p0Xi5G/u1rr9eR3rWvGTlyJAYOHMhV3BWCT9AJISSEJFobMbZPWrjTIIoxtn9XJJr4K5kQQgjRO/wOOiGEhBADgA+vHYH6xGSgugbIzgLKyoE0+1oSVqsVq1Z9iSlTclv+Ct42xrMsIBovMYHQSLUpTD6YExIx9rGvAADr556N9OSEoPngT5PYvQsMNTUB72+EEEIIiSz453q9YjQ6t7m5zn1vZVpiZDS+8vJXjyoaGZ0efKB3HdYYYmORFBeLpMQ4JJ1ztn0bF+v4iTfCue8txktZpzXeYgKk8ZtfGH1oJSnOGHQf2tMYDAaOv2i5D9E77Zpwt2nyZHoXDX3I8zqq7INq3knw7bff4uKLL0Z2djYMBgNWrFjhdnzZsmWYMmUKMjIyYDAYUFRU5Hb82LFj+POf/4zBgwcjKSkJ/fr1w5133omqqiq3uAEDBsBgMLj93H///Z3KPVLgE3S9kpRk3yYnA6tWuR/zLNMSI6Pxhkw9qmhkdMHwwdfK0/QucPmprJHR6cE7GZ0/TTSMJfYh7RoZHb3zHRMtPrjeBz75pP1V3Old4DQyOq0+eF5HlX1QzTsJ6urqMHLkSNx44434/e9/7/X4GWecgcsvvxyzZs1qc7ysrAxlZWX4xz/+gZycHOzduxd//OMfUVZWho8++sgt9tFHH3WrIyUlpdP5RwJ8gq5XWldwbGwE8vLs21Y8y7TEyGi8IVOPKhoZnR58oHeh1cjo9OCdjE4PPtA77RoZHb3T3qaI8cFlRevHF9K7aOhDntdRZR9U806CadOm4bHHHsOMGTO8Hr/22mvxyCOP4LzzzvN6fNiwYfj4449x8cUX48QTT8S5556Lxx9/HJ999hmamtz/cJ6amorMzEzHj14m6BBEV5SWlgoAorS42F5QVSUEYN+24lmmJUZCY7FYxIoVK4TFYpGuV4vGqy4QGpn8guBDXaNV9J+7UvSfu1IcrzXTu0juQ/QurD6EcixFm3f0gd5Fkg/taeoOH3PcB+pM8fQuQvvQ8Vqzz+tI79rXlJSUCACiuLhYVFVVOX4aGhqEPwCI5cuXez3WWu+WLVv81vPqq6+KjIwMt7L+/fuLzMxM0a1bNzFy5Ejx2GOPicbGRr91RQP8iDshhBBCCCGE6JicnBy3/fnz5yMvLy/o5z169Cj+9re/4bbbbnMrv+uuuzBmzBh07doVBQUFmDdvHkpKSrB48eKg5xRuOEEnhBBCCCGEEB1TXFyM3r17O/bj4+ODfs7q6mpceOGFyMnJwfz5892O3X333Y7/HzFiBLp27YrLLrsMTz31FLp37x703MIJJ+h6pfU1PyYTcPPNzn1vZVpiZDS+8vJXjyoaGZ0efKB3odXI6PTgnYxODz7QO+0aGR29096mSPMBAK67nt5FQx/yvI4q+6CQd6mpqUhLS/OdR4CpqanB1KlTkZKSguXLl8PUngcAJkyYAADYs2dP1E/Q+R10neH4DnppabhT8f59niBoQnmuUGh8fm82CLlp1UWbRvX8VNaonF8ox1K0eRdKjer5qaxRPT8VNK73gbpGq3L5hVujen5ev4PucR3DlVsoz9UZTet3xbXMDaDxO+hVVVViwoQJ4uyzzxZ1dXVS5/rss88EALF3794O5xlpcBV3vdLQYN/W1wO33GLftuJZpiVGRuMNmXpU0cjo9OADvQutRkanB+9kdHrwgd5p18jo6J32NkWMDw3Ostl30Lto6EOe11FlH1TzToLa2loUFRU53m9eUlKCoqIi7Nu3D4D9PedFRUUoLi4GAPz8888oKipCRUUFAPuT89zcXNTV1WHJkiWorq5GRUUFKioqYLPZAADr16/Hs88+i6KiIpSUlOCDDz7AbbfdhunTp6Nfv36dyj8iCPdfCEho4Sru6q6iKdsmruIeRX2I3oXVB67izj4UFB/onXI+tKfhKu7R0Ye4invnV3GXfYL+zTffCABtfq6//nohhBD//ve/vR6fP39+u3oAoqSkRAghxKZNm8T48eNFenq6SEhIEIMHDxbz58+Xftoe6fA76IQQQgghhBBC/DJp0iQIIXwev+GGG3DDDTdo1gPAmDFjsGHDBq0pRjycoOuM5uZmAEB5RQWQmgrU1NgPHDgAVFfb/9+zTEuMhKapqQlHjhzBgQMHEBsbK1WvFg2AtrpAaGTaFAQf6hMS0VR9pGX3AGqSEuhdpPYhehdWH0I5lqLNO/pA7yLJh/Y09WUHnPeB5mYk0ruOt0kRH3xdR3rXvqa8vByAc45Awo9B+PsTBokqNm7ciHHjxoU7DUIIIYQQQogiFBQU4LTTTgt3GgScoOuOpqYmnHTSSfj2228RE2NfI3DSpEn4z3/+4xbnWaYlxp+mpqYGOTk5KC4uRmpqqlQ9WjS+dIHQyOSnig/0Tk0f6J0+fKB3+vKB3qnlA73Tlw/0rmOa7du3w2w2Y/To0c5PB5CwwqugM2JjY5GUlOS2AmJcXBz69OnjFudZpiXGn6a65SM3vXv3dnvvYnv1aNH40gVCI5OfKj7QOzV9oHf68IHe6csHeqeWD/ROXz7Qu45p+vbtG9L3nxP/xIQ7ARJ67rjjjnb3AxUjo9GanyoaGZ0efKB3odXI6PTgnYxODz7QO+0aGR298x1DH+hdZzQyOnrnOyZYPhAFCOcS8kTfVFVVCQCiyvUVEEHQhPJc0aZRPT/6oL5G9fxU1qieH31QX6N6fiprVM+PPqivUT0/rW0iwef/t3f3QVGcdxzAn703DgEBQeFO3i4VA46kWKTk0OoZpWiDgKlGkpoTGzRGnGBj2lRsRaOhJKZ5sw5GR60xLTGJSmosJk2IAUdNBIrQiBFFFAQUX0blHeHbP5jbYdk9Xi7mbof8PjM34+3u955nl/O3++zd7dIn6MRhnJycWEZGBnNycvpBM/Zsa7hl5N4/2g7yz8i9f3LOyL1/tB3kn5F7/+SckXv/aDvIPyP3/tm6TuSHRxeJI4QQQgghhBBCZIA+QSeEEEIIIYQQQmSABuiEEEIIIYQQQogM0ACdEEIIIYQQQgiRARqgE0IIIYQQQgghMkADdELuM7ruIiGEEEIIIcQWKkd3gPx41NbWsuzsbHb8+HHW0NDAOI5jPj4+LDo6mi1fvpz5+/s7uov3hZOTEzt9+jQLDQ11dFfspr6+nmVnZ7Njx46x+vp6plQqmcFgYImJiSw5OZkplUpHd5HIXHNzM/vnP/8pqg9TpkxhTzzxBHNxcRnS6129epW98847bN26daJ5tbW1zMPDg7m6ugqmd3Z2shMnTrBp06YJpt+4cYOVlZWxn/70p2zUqFHs+vXrbOfOnay9vZ0tWLBg0P/XH3jgAfbpp5+y4ODgQS3f2dnJDh8+zCorK5lOp2Pz5s0TbYfa2lqm1WqZt7c3Y4yxwsJCtm3bNnb58mUWGBjIUlNTmdFoFGT++te/svnz57PAwMBB9cPi0KFDrKioiM2ePZsZjUaWn5/PXnvtNdbd3c0ee+wxtmzZMlGmtbWV5eTkSNaGmTNnDql98uMk59rAmGPqw3CoDYxRfSDEKgffh538SBQWFsLV1RWhoaFIS0tDZmYmXn75ZaSlpWHChAlwc3PDsWPHhvy6ly9fxpIlS0TTW1paUFhYiG+//VY0r7W1FXv27BFNP3PmDHbt2oWKigoAQEVFBZYvX44lS5bgiy++EC3/u9/9TvKhUChgNpv55/25efMm3njjDaxYsQIbN27E5cuXRcuUlJSgqqqKf753715ER0fDz88PU6ZMQU5OjuRrr1y5EgUFBf22L+Xtt9+G2WzGvn37AADvvvsuQkND8eCDD2LNmjXo7OwULH/q1Cm4u7sjPDwcRqMRCoUCTz31FBYuXAgPDw8YjUbcuXNHsq2mpiZs374dycnJmD17NubMmYPk5GTs2LEDTU1NQ+57Q0MDNmzYIDmvpqYGd+/eFU3v6OjAV199JZp+/fp15Ofn48aNGwCAxsZGZGVlYcOGDThz5syg+2QwGHDu3LlBLdvR0YGDBw/i1Vdfxd69e61ug5qaGjQ2NvLPCwoK8OSTT2Lq1Kn4zW9+g+PHj4syr732Gqqrqwfdb4t//etfWLduHf+aX3zxBebMmYPY2Fi88847kpmWlhbs3LkTS5YswezZs/Hoo49i5cqV+PzzzyWX//bbb6HX6+Hh4YGEhAQsW7YMS5cuRUJCAjw8PDB27FjJ/8v9KS0thUKhEEyrq6tDZGQkFAoFlEolzGaz4D3R0NAgynz99ddwd3cHx3Hw9PREUVERDAYDgoODMW7cODg7O6O4uFiQeeuttyQfSqUSa9as4Z/3ZTQacevWLQDAtWvXEBYWBo1Gg+DgYGi1WgQEBKC2tlaU+fe//w0AyM3NhUKhQHx8PF588UXMmzcParUahw4dEmQ4joNSqcSsWbPw/vvvo729fcDtmZ2dDZVKhYiICIwcORLvvfce3NzckJKSgmeeeQbOzs548803BZnKykoEBgbCy8sLOp0OHMfh0UcfRVRUFJRKJRYsWCCqJxZUG8R9odogr9oA2K8+DLfaANheH+xZG4DhUx9sqQ3EcWiATuxi8uTJWLVqldX5q1atwuTJk4f8ulI72u+++w6BgYHgOA4KhQLTp09HXV0dP19qR5uXlweNRoNRo0ZBq9UiLy8Po0ePxqxZszBz5kyoVCrRIJ3jOISHh8NkMgkeHMchMjISJpMJM2bMEGR0Oh2uX78OAKiqqoKvry98fX0RExMDPz8/uLu78ycILCZNmoT8/HwAwI4dO+Ds7IznnnsO2dnZWLVqFVxdXbFz507RtrGsf3BwMLKyslBfXz/g9nzppZfg5uaGX//61/D19UVWVha8vLywadMmZGZmYvTo0Vi3bp0gM2XKFKxfv55/vnfvXkRFRQHoOQERHh6O5557TtSWnA+85HzQZcnJ9cDLloMuk8mEpKQkyT61t7fjiSeegMlkEkw/ffp0v499+/aJ/q5msxkPP/wwTp06hf/85z+YPHkyIiIicPPmTQA97wWO4wSZWbNmISUlBXfu3MHmzZvh5+eHlJQUfv7TTz+NxMRE0bb28/NDUFCQ4MFxHMaOHYugoCAYDAbRunIch6tXrwIAli5divDwcP7/7fXr1xEdHY3f/va3goybmxsuXrwIAIiKikJWVpZg/pYtWzBp0iRRO7t370ZCQgLUajW8vLyQlpaG8vJyUZ8sQkNDsX37dgBAfn4+tFottm7dys/fvXs3QkNDBZk5c+bgmWeeQVdXFwDgL3/5C+bMmQMAOHfuHIKCgpCRkSFqi2oD1QYLOdcGwH71YbjVBsC2+mCv2gAMv/pgS20gjkMDdGIXWq0WZ8+etTq/oqICWq1WNP3jjz/u9/HGG2+ICmRiYiLi4uLQ2NiIyspKzJ07FwaDAZcuXQIgXVSNRiPWrl0LAMjJyYGnpyfS09P5+enp6YiJiRFkMjMzYTAYRAN3lUpldQfReyeblJQEk8mE5uZmAEBbWxvi4uIwf/58QWbEiBF83ydNmiT6ZOIf//gHJkyYINnW559/jrS0NHh7e0OtViM+Ph6HDh3id4h9PfDAA9i/fz+Anp2WUqnEe++9x88/cOAAxo0bJ8g4OzvjwoUL/POuri6o1Wo0NDQAAD777DPo9XpRW3I+8JLzQRcg7wMvWw66nJ2d+z2oKi8vh7Ozs2h9FAoFOI4TPSzT+74X9Ho9vv76a/55W1sbEhISEB4ejhs3bkjWBk9PT/5Tj46ODigUCsFrlJSUYOzYsYLMsmXLEB4eLvq0pL/aYFkny/th/Pjx+OSTTwTzv/zySwQFBQmmubu74/Tp0wCAMWPG8P+2OH/+PEaMGGG1natXr+KVV15BSEgIFAoFIiMjsX37dtG3Xpydnfk6BABqtVrw3rl48aKonREjRgg+AWpvb4dareZPUubm5orWB6DaYMlQbZB3bQDsVx+GW20AbKsP9qoNwPCrD7bUBuI4NEAndmEwGLBr1y6r83ft2mX1EyVrO9reO9zexowZg7KyMsG0FStWICAgABcuXJDc0Y4cORKVlZUAegaYKpVKcJazvLwcPj4+ov598803GD9+PFavXo2Ojg4Ag9/JSg3uT548CT8/P8E0Ly8vFBUV8etWWloqmH/+/HnRAUrftjo6OrBv3z7ExsZCqVRCr9cjPT2dX2cLqR3t//73P/55dXW1aEcbGBgo+HlCXV0dOI5DS0sLgJ6ds9TJFzkfeMn5oAuQ94GXLQdder0eubm5ovW0OHjwoOgkj7e3N3bu3Inq6mrJx+HDh0V/VxcXF9FXBjs7O5GYmIiHHnoIZWVlkhnLQQ0AuLq6Ck5IXbp0SfL9ffDgQfj7+2PLli38tMEM0K9duwag5+/ad9nq6mo4OTkJpsXHx+OPf/wjACA2Nlb0ScuOHTsQHBwsasfyXuitoKAAixcvhouLC1xcXATz/Pz8+J/MXLlyBRzH4fDhw/z8o0ePimqXXq8X1NFbt26B4zj+fVZVVSVaH4Bqg2V9qDbIuzZYcvaoD8OtNgC21Qd71QZL/4ZTfbClNhDHoQE6sYutW7dCo9EgNTUVubm5OHHiBE6ePInc3FykpqbCyckJ2dnZopxer8fBgwetvu5///tfUYF0c3OT/J3PypUr+R1JfwN0QLyTra6ultzJAsDdu3dhNpsRFhaGsrIyqNXqQe1k9Xq9YPAL9BzU9N0hLVq0CE8//TQAYMGCBfjTn/4kmJ+ZmYmwsDDJtqR2tJcuXUJGRgYCAwNF28FgMCAvLw9AzycaCoUCH3zwAT//8OHDoqKflpaGiRMnIi8vD/n5+ZgxY4bgDPaRI0fwk5/8RNQPOR94yfmgC5D3gZctB10ZGRlwd3fH5s2bUVpaivr6ejQ0NKC0tBSbN2+Gp6en6DeCsbGx2Lhxo2h9LEpLS0WfboSFheGjjz4SLWt5PwQEBIjeCyEhIYITaZ988gl/8gmQPqlmUVtbi0ceeQSzZ89GfX39oAbov/rVrzBv3jx4enryX0e0OHHihOhE4ZkzZ+Dl5QWz2YyNGzfC1dUVixYtwssvvwyz2QwnJyfs3r1bkFEoFJLvBYvbt2/zn45apKamIjg4GJs2bcLPf/5zLF68GCEhIcjLy8ORI0cQFhYm+sRm8eLFmD59OioqKlBVVYWFCxcKPqE5evQo/P39Re1TbaDaYCHn2gDYrz4Mt9oA2FYf7FUbgOFXH2ypDcRxaIBO7Ob9999HVFQUVCoVf/ZSpVIhKiqKvyBZX3PnzsWf//xnq68ptaONjIzEu+++K7l8amoqPDw8REX1oYce4gemQM9Z2N6/hSssLJT8hL+3nJwc+Pj4QKFQ9FtUw8LCMGnSJLi6uuLAgQOC+V999ZXo7OqVK1cQFBSEadOm4fnnn4ezszOmTp2KpUuXYtq0adBoNIIDo95t9bej7e7uxmeffSaYtnbtWowePRopKSkwGAxYs2YNAgICkJ2djW3btsHf31904bu7d+/i8ccf5/+u0dHRgovaffrpp4JBvoWcD7zkfNAFyPvAy9ZBWVZWFv+7VIVCwX+yodPp8Morr4iWP3DgAPbu3Wt1fW7evIm///3vgml/+MMf8Mtf/lJy+c7OTsTHx4veC+vXr7d6IUag5+cvjz32mNX53d3dyMzMhK+vL5RKZb8D9OTkZMGj7/+bF154AbGxsaLc+fPnkZSUBDc3N762qtVqREdHS57gHKg2SGlqakJKSgomTpyI5cuXo6OjA5s3b4ZGowHHcTCZTKLXvHr1Kh5++GH+bxoUFISSkhJ+/ocffoi3335b1BbVBqoNvcm1NgD2qw/DrTYAttUHe9UGYPjVB1tqA3EcGqATu+vo6EBdXR3q6ur4r4VbU1BQIBg499XU1ISjR48KpmVmZvK/a5Py7LPPiopxdna26CtCvaWnp/OfYvenpqYGubm5Vq8kun79esHjyJEjgvkvvPACkpKSRLlbt27hxRdfxIQJE6DVaqHRaBAYGIgnn3wSp06dkmwrKCiI/+rgYN27dw+bNm1CXFwc//uknJwc+Pv7w8vLC8nJyVbXrbW1VfJKp/2Ry4FX3/eD3A+6APkeeNk6KLOoqqrC8ePHcfz4ccGJnvuhs7MTt2/ftjr/3r17Q76adXNzM9ra2gZcrqioCG+++Sb/+0VbNDU1obW11er87u5uNDQ0DKq23i+tra1W79Jgce7cOdFJz4HIpTY46oTN960NCxcuHNa1ofenkoPV3d1tdZ612mDJDLU2WHJDqQ+vv/76kOuDpZ2h1IbBXATQWjtD0dzcPGBtAMT1YaC27FEbgOFZH4Z63EAchwMAR9/qjRDy43bx4kXW0NDAGGPM19eXGQyG+/ba9+7dYy0tLWzkyJGS87u6ulhtbe2Q7vva0tLClEolc3Jy6ne54uJiduzYMWY2m5mnp+eQ+s1Yz/1/lUol02q1VpcBwK5du8a6u7uZt7c3U6vVQ25nqNra2lhnZydzc3OzukxlZSVrb29nISEhTKVS/eB9IsMT1QZpP/baoNFo2OnTpwd9j3F7ZuTePzlnhpL7IWsDY46tDyUlJaywsPAHqw+OqA1kaOioiRDicAaDQbRzrampYRkZGWzXrl2Dfh2pjEqlsrqDZYyxuro6tmHDhiG1c+PGjUH1LSIigkVERFjt20Bu3rw5YIbjOObj4yOYdr+2nTVarZZptdp+M8HBwUNqp7W1lRUXF7NRo0axCRMmCOa1tbWxDz74gJnN5mGdkXv/7LkdKioq2MmTJ1l0dDQzGo3s7Nmz7NVXX2Xt7e1s0aJF7JFHHrGaMRqNLCQkhJ09e5a99dZbVjMqlYpduXKF7d+/f9AZW9qRyri4uLCzZ8+y1atXD5iJjo5mDz744KDasZZ76aWXfrB1Gkr/emdUKtWAmeeff15yHbu6ulhWVhbz8vJijDH2+uuv2z0j9/7JOfN9chYGg4F5eHiwPXv2sMrKSqbX65nZbGb+/v6Sy1vcunWLz+h0OrZ48WLJTN9jh7655OTkAQfng22rb6agoIBVVlayrVu3DjozlHY4jmMajWbI247YkUM/vyeEECus3ZuUMvJp635lvvvuOwQGBvJfWZw+fTrq6ur4+VJXyx1uGbn3z57bIS8vDxqNBqNGjYJWq0VeXh5Gjx6NWbNmYebMmVCpVKI7YFBG/v2zJcNxHMLDw2EymQQPjuMQGRkJk8mEGTNmOCQj9/7JOWNrTqfT8T/dq6qqgk6ng6+vL2JiYuDn5wd3d3dUVFT0m/H19R0wY2tuuGWI49AAnRDiELbc454y8u+fLZnExETExcWhsbERlZWVmDt3LgwGA38bJ6mB3HDLyL1/9twORqMRa9euBdBzDQxPT0+kp6fz89PT0xETE0OZPhm598+WTGZmpuQtSfu7gJa9MnLvn5wztuZ6Xy8hKSkJJpMJzc3NAHpugxYXF4f58+d/74w925JzhjgODdAJIQ7R371Je9+jlDLiAYyc+2dLZsyYMSgrKxNMW7FiBQICAnDhwgXJgdxwy8i9f/bcDr1ve9nV1QWVSiW4PVd5ebnoCsWUkX//bF2nb775BuPHj8fq1av5ix8ONPizV0bu/ZNzxpZc70Gm1OBe6irptmTs2ZacM8RxFI7+ij0h5MdJp9Ox/fv3s+7ubslHSUkJZSQycu+fLZnW1lbRxaK2bt3K4uPj2fTp09m5c+eGfUbu/bPnduhNoVAwrVbLPDw8+Glubm7s9u3blOknI/f+DSUTGRnJiouLWWNjI4uIiGDl5eWM4zirr23PjNz7J+eMrTnL/Pb2dtH1V3x8fFhjY+N9ydizLTlniGPQAJ0Q4hARERFWB6CM9exI0OcmE5SRf/9syYSEhLCioiLRslu2bGEJCQksPj5eNG+4ZeTeP3tuh6CgIHb+/Hn++YkTJ1hAQAD/vKamhul0Osr0yci9f7auE2OMubq6sj179rD09HQWExPDurq6JJdzREbu/ZNzxpbczJkz2c9+9jN2584d0Um+y5cvM29v7/uSsWdbcs4Qx6CruBNCHOL3v/89a25utjp/3Lhx7Msvv6RMn4zc+2dLZt68eSwnJ4c99dRTouX/9re/se7ubrZt27ZhnZF7/+y5HZ599lnBQfrEiRMF8/Py8kRX+6aM/Ptn6zr1lpSUxKZOncqKi4sHfXsre2Xk3j85Zwaby8jIEDwfMWKE4PmhQ4fYL37xi++dsWdbcs4Qx6H7oBNCCCGEEEIIITJAX3EnhBBCCCGEEEJkgAbohBBCCCGEEEKIDNAAnRBCCCGEEEIIkQEaoBNCCCGEEEIIITJAA3RCCCGEEEIIIUQGaIBOCCGEEEIIIYTIAA3QCSGEEEIIIYQQGaABOiGEEEIIIYQQIgM0QCeEEEIIIYQQQmSABuiEEEIIIYQQQogM0ACdEEIIIYQQQgiRARqgE0IIIYQQQgghMkADdEIIIYQQQgghRAZogE4IIYQQQgghhMgADdAJIYQQQgghhBAZ+D8hNTsP8vo/WAAAAABJRU5ErkJggg==", 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4\n", + " t30 = t40 * 0.8\n", + " t20 = t30 * 0.8\n", + " t10 = t20 * 0.8\n", + " t00 = self.b / 5.9\n", + "\n", + " tt1 = 3\n", + " tt2 = 3\n", + " tt3 = 5\n", + " tt20 = 3\n", + " tt10 = 3\n", + "\n", + " q1 = q0 - 1 * (q0 - q5) / 3.7 \n", + " q2 = q0 - 2 * (q0 - q5) / 3.8\n", + " q3 = q0 - 3 * (q0 - q5) / 3.9\n", + " q4 = q0 - 3.7 * (q0 - q5) / 4\n", + "\n", + " q40 = q4\n", + " q30 = q3\n", + " q20 = q2\n", + " q10 = q1\n", + " q00 = q0\n", + " \n", + " qq1 = q00 * 0.9\n", + " qq2 = q00 * 0.83\n", + " qq3 = q00 * 0.77\n", + " qq20 = qq2\n", + " qq10 = qq1\n", + " \n", + " T1 = T0 - (q0 - q1) * t1 * 3 / (0.4 * 4.18) * 0.33\n", + " T2 = T0 - (q0 - q2) * t2 * 3 / (0.4 * 4.18) * 0.33\n", + " T3 = T0 - (q0 - q3) * t3 * 3 / (0.4 * 4.18) * 0.3\n", + " T4 = T0 - (q0 - q4) * t4 * 3 / (0.4 * 4.18) * 0.25\n", + " \n", + " T5 = T4 + 2 * t5\n", + " \n", + " T40 = T5 + (q40 - q5 ) * t40 * 3 / (0.4 * 4.18) * 0.33\n", + " T30 = T40 + (q30 - q40) * t30 * 3 / (0.4 * 4.18) * 0.33\n", + " T20 = T30 + (q20 - q30) * t20 * 3 / (0.4 * 4.18) * 0.33\n", + " T10 = T20 + (q10 - q20) * t10 * 3 / (0.4 * 4.18) * 0.33\n", + " T00 = T10 + (q00 - q10) * t00 * 3 / (0.4 * 4.18) * 0.33\n", + " \n", + " TT1 = T00 + (qq1 - q00) * tt1 * 3 / (0.4 * 4.18) * 0.33\n", + " TT2 = TT1 + (qq2 - qq1) * tt2 * 3 / (0.4 * 4.18) * 0.33\n", + " TT3 = TT2 + (qq3 - qq2) * tt3 * 3 / (0.4 * 4.18) * 0.33\n", + " \n", + " TT20 = TT3 + (qq20 - qq3) * tt20 * 3 / (0.4 * 4.18) * 0.33\n", + " TT10 = TT20 + (qq10 - qq20) * tt10 * 3 / (0.4 * 4.18) * 0.33\n", + " \n", + " self.step_sizes = [0, t1, t2, t3, t4, t5, t40, t30, t20, t10, t00, tt1, tt2, tt3, tt20, tt10]\n", + " self.step_times = np.cumsum([0, t1, t2, t3, t4, t5, t40, t30, t20, t10, t00, tt1, tt2, tt3, tt20, tt10])\n", + " self.step_qs = [q0, q1, q2, q3, q4, q5, q40, q30, q20, q10, q00, qq1, qq2, qq3, qq20, qq10, q0]\n", + " \n", + " \n", + " \n", + " self.step_temperatures = [T0, T1 , T2 , T3 , T4 , T5 , T40, T30, T20, T10, T00, TT1, TT2, TT3, TT2, TT1,]\n", + " \n", + " self.step_q_from_to = list(zip(self.step_qs[:-1], self.step_qs[1:]))\n", + " \n", + " for pair in zip(self.step_times, self.step_q_from_to):\n", + " print(pair)\n", + " \n", + " self.program = list(zip(self.step_times, self.step_q_from_to))\n", + " \n", + " self.drying_time = 24. / load\n", + " \n", + " self.aux_duration = (q0 * self.step_times[9] - (np.array(self.step_sizes)[:10] * np.array(self.step_qs)[:10]).sum()) / 2 / qq3\n", + "\n", + " def lowest_heat_duration (self):\n", + "\n", + " t = self.duration\n", + " t5 = t\n", + "\n", + " if t > 14 :\n", + " t5 = t - 6\n", + " elif t > 12 :\n", + " t5 = t - 5\n", + " elif t > 10 :\n", + " t5 = t - 4\n", + " elif t > 8 :\n", + " t5 = t - 3\n", + " elif t > 7 :\n", + " t5 = t - 2\n", + " elif t > 6 :\n", + " t5 = t - 1\n", + " elif t < 5 :\n", + " t5 = t + 1\n", + "\n", + " return t5\n", + "\n", + "duration = 16\n", + "start = 6\n", + "load = 81 / 66\n", + "T0 = 1212\n", + "q0 = 81\n", + "\n", + "cosp = CokeOvenServiceProgram(duration, start, load, T0, q0)\n", + "\n", + "from functools import reduce\n", + "program_arr = np.array(reduce(lambda x, y: x+y,[ [(t, q0), (t, q1)] for t, (q0, q1) in cosp.program]))\n", + "\n", + "import matplotlib.ticker \n", + "\n", + "plt.figure(figsize=(10, 4))\n", + "plt.plot(program_arr.T[0] * 60, program_arr.T[1])\n", + "ax = plt.gca()\n", + "ax.xaxis.set_major_locator(matplotlib.ticker.MultipleLocator(60))\n", + "ax.xaxis.set_minor_locator(matplotlib.ticker.MultipleLocator(20))\n", + "plt.xticks(np.arange(0, 60)*60, np.arange(0, 60), rotation=90)\n", + "# plt.xticks(rotation=90)\n", + "# plt.minorticks_on()\n", + "plt.grid()\n", + "plt.grid(b=True, which='minor', color='r', linestyle='--')\n", + "\n", + "plt.axvline((start)*60)\n", + "plt.axvline((start+duration)*60)\n", + "\n", + "plt.axvline((start+duration+cosp.drying_time)*60)\n", + "plt.axvline((start+duration+cosp.drying_time+cosp.aux_duration)*60)\n", + "\n", + "twin = plt.gca().twinx()\n", + "twin.plot(cosp.step_times * 60, cosp.step_temperatures, 'o:')\n", + "twin.set_ylim(1120, 1320)" + ] + }, + { + "cell_type": "markdown", + "id": "b56ace82", + "metadata": {}, + "source": [ + "# μ˜¨λ„ 계산 κ°œμ„  - 배터리 μ—΄μš©λŸ‰ 0.07 Gcal / Rev / 'C" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "35f7f4af", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(0.0, (81, 69.61621621621622))\n", + "(3.5, (69.61621621621622, 58.83157894736841))\n", + "(7.0, (58.83157894736841, 48.599999999999994))\n", + "(10.5, (48.599999999999994, 42.038999999999994))\n", + "(14.5, (42.038999999999994, 38.879999999999995))\n", + "(24.5, (38.879999999999995, 42.038999999999994))\n", + "(27.75, (42.038999999999994, 48.599999999999994))\n", + "(30.35, (48.599999999999994, 58.83157894736841))\n", + "(32.43, (58.83157894736841, 69.61621621621622))\n", + "(34.094, (69.61621621621622, 81))\n", + "(36.4668813559322, (81, 72.9))\n", + "(39.4668813559322, (72.9, 67.22999999999999))\n", + "(42.4668813559322, (67.22999999999999, 62.370000000000005))\n", + "(47.4668813559322, (62.370000000000005, 67.22999999999999))\n", + "(50.4668813559322, (67.22999999999999, 72.9))\n", + "(53.4668813559322, (72.9, 81))\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\RIST\\AppData\\Local\\Temp\\ipykernel_16724\\1371208751.py:132: MatplotlibDeprecationWarning: The 'b' parameter of grid() has been renamed 'visible' since Matplotlib 3.5; support for the old name will be dropped two minor releases later.\n", + " plt.grid(b=True, which='minor', color='r', linestyle='--')\n" + ] + }, + { + "data": { + "text/plain": [ + "(1120.0, 1320.0)" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "4af505b882a34797a4fbb34822959ed8", + "version_major": 2, + "version_minor": 0 + }, + "image/png": 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0Ijo9+EDvQqsR0enBOxGdHnygd9o1Irpo9e7N5S7nVnm8a02fNGtcvmLqzeWRO+4ClV+kzz+g+X2UzQeZ558ANTU1GDx4MJYuXap6/aKLLsLSpUuxa9cufPPNN+jevTtycnJw6tQpR8xDDz2EVatWYcWKFfjmm29QXV2Na665BjabzREzY8YMFBYWIi8vD3l5eSgsLMTMmTNblXvEEO5d6khoKSkpse/UWFRkL6ioUBTAfmzCs0xLjB9NTb3VbQdb0XotFouyevVqxWKxtCjfZrpAaETy06Lxkx+9a12fpPKB3oXVh1DOpWjzjj60TuP6DQI19daQe1djine2f+qsPN4FyQdfmppTZ51emOIjd9wFybtI8cFtTnncR9m8k23+FRcXa97FHYCyatUqnzEVFRUKAGXDhg2KoijKuXPnFJPJpKxYscIRc+zYMSUmJkbJy8tTFEVxfOvU1q1bHTFbtmxRACh79+5tcZ6RBl9BJ4QQQgghhBASUCwWC1577TWkp6dj8ODBAIDt27fDarUiJyfHEZednY0BAwZg8+bNAIAtW7YgPT3d7W3xo0ePRnp6uiMmmuFn0AkhhBBCCCFEx1RVVTm+jhlo/lXNLWHNmjW4+eabYTabkZWVhfXr1yMjIwMAUFZWhri4OLRt29ZN07FjR5SVlTliOnTo0KzeDh06OGKiGb6CrleMRucxJ8d5rlamJUZE4y0vf/XIohHR6cEHehdajYhOD96J6PTgA73TrhHRRat3Eya0LjcRXST44BkzYULkjrtA5Rfp8w9ofh9l80HS+devXz+kp6c7fp555hnvOfnh8ssvR2FhITZv3ozJkyfjpptuwsmTJ31qFEWBwWBwnLv+v7eYaIWvoOuVpCT7MTkZWLvW/ZpnmZYYfxpvuyeL1COLRkSnReMvP3oX/Pxk1ojo9OCdiE4PzyGOIe0aEV20evfJJ85dpJOT5PFOLSbYGtfnwCef+N7FXeZxF6j8In3+Ac3vo2w+SDr/ioqK0KlTJ8e51lfP7U0lo1evXujVqxdGjx6N3r17Y9myZZg/fz4yMzNhsVhQXl7u9ir6yZMnMXbsWABAZmYmTpw40azeU6dOoWPHjprzihT4CrpeadrBsb4eyM21H5vwLNMSI6JRQ6QeWTQiOj34QO9CqxHR6cE7EZ0efKB32jUiumj1btFil3OLPN61pk+aNS47Wi9aHLnjLlD5Rfr8A5rfR9l8kHT+paamIi0tzfHTmgW6J4qioP58u5dccglMJhPWr1/vuF5aWordu3c7FuhjxoxBRUUF8vPzHTHffvstKioqHDFRTZg3qSMhhru4y7sDKXdx1+EutvQurD5wF3eOoaD4IKDhLu6hHUPcxT365x93cQ/dLu5VVVXKzp07lZ07dyoAlBdeeEHZuXOncvjwYaW6ulqZP3++smXLFuXQoUPK9u3blTvvvFOJj49Xdu/e7ajjnnvuUTp37qxs2LBB2bFjh3LFFVcogwcPVhoaGhwxkydPVgYNGqRs2bJF2bJlizJw4EDlmmuuEcox0uFb3AkhhBBCCCGE+KWgoACXX3654/yRRx4BANx+++145ZVXsHfvXixfvhynT59G+/btMWLECHz99dfo37+/Q/OXv/wFsbGxuOmmm1BbW4sJEybgzTffhNHl8/HvvPMOHnjgAcdu79OmTfP63evRBhfohBBCCCGEEEL8Mn78eCiK4vX6ypUr/daRkJCAF198ES+++KLXmHbt2uHtt9/WlGOkw8+g6xWTyXm8807nuVqZlhgRjbe8/NUji0ZEpwcf6F1oNSI6PXgnotODD/ROu0ZEF63e3XZ763IT0UWCD54xt90eueMuUPlF+vwDmt9H2XyQef4ROQj3e+xJaHF8Bl3wcybBwutnP/2g+hmgIOlk1dC70Gtkz09mjcz5hXIuRZt3odTInp8Wjepn0IPQjjed69hXa19m7wKt8eWFDPmFWyN7fqqfQfczp8LtnWzzr6WfQSfBh6+g65W6OvuxthaYNct+bMKzTEuMiEYNkXpk0Yjo9OADvQutRkSnB+9EdHrwgd5p14jootW7Ofe5nNfJ411r+qRZU+csm3Nf5I67QOUX6fMPaH4fZfNB5vlHpIALdL1itTqPy5Y5z9XKtMSIaLzl5a8eWTQiOj34QO9CqxHR6cE7EZ0efKB32jUiumj17l/LW5ebiC4SfPCM+dfyyB13gcov0ucf0Pw+yuaDzPOPSAEX6IQQQgghhBBCiARwgU4IIYQQQgghhEgAF+h6JT7eeVywwHmuVqYlRkTjLS9/9ciiEdHpwQd6F1qNiE4P3ono9OADvdOuEdFFq3fzf9e63ER0keCDZ8z830XuuAtUfpE+/4Dm91E2H2Sef0QOwr1LHQkt3MVd3h1IZdx5Wqsu2jSy5yezRub8uIt7ZGhkz4+7uEe2hru4y9MWd3EPbX7cxV1e+Aq6XjGb7ceaGmDSJPuxCc8yLTEiGjVE6pFFI6LTgw/0LrQaEZ0evBPR6cEHeqddI6KLVu+uvdbl3CyPd63pk2aN2Vl27bWRO+4ClV+kzz+g+X2UzQeZ5x+RAi7Q9YrN5jyuW+c8VyvTEiOi8ZaXv3pk0Yjo9OADvQutRkSnB+9EdHrwgd5p14jootW7L75oXW4iukjwwTPmiy8id9wFKr9In39A8/somw8yzz8iBVygE0IIIYQQQgghEsAFOiGEEEIIIYQQIgFcoOuVhATn8fXXnedqZVpiRDTe8vJXjywaEZ0efKB3odWI6PTgnYhODz7QO+0aEV20erf0763LTUQXCT54xiz9e+SOu0DlF+nzD2h+H2XzQeb5R+Qg3LvUkdDCXdzl3YFUxp2nteqiTSN7fjJrZM6Pu7hHhkb2/LiLe2RruIu7PG1xF/fQ5sdd3OWFr6DrlaYdHKurgf797ccmPMu0xIho1BCpRxaNiE4PPtC70GpEdHrwTkSnBx/onXaNiC5avRs+3OW8Rh7vWtMnzRqXHa2HD4/ccReo/CJ9/gHN76NsPsg8/4gUcIGuVxobnceiIue5WpmWGBGNt7z81SOLRkSnBx/oXWg1Ijo9eCei04MP9E67RkQXrd7t3du63ER0keCDZ8zevZE77gKVX6TPP6D5fZTNB5nnH5ECLtAJIYQQQgghhBAJ4AKdEEIIIYQQQgiRAC7Q9Upiov2YlATk5dmPTXiWaYkR0aghUo8sGhGdHnygd6HViOj04J2ITg8+0DvtGhFdtHq3erXLeaI83rWmT5o1ic6y1asjd9wFKr9In39A8/somw8yzz8iBbHhToCEidhY53HSpObXXMu0xPjTWBq85+WvHlk0IjotGn/50bvg5yezRkSnB+9EdHp4DnEMadeI6KLVu4kTgU1rtbcjoosEHyZNcn8OTJzo/PdRsHIT0UWKd63RiOi0+uB5H2XzQeb5R6SAr6Drlaoq+7GyEkhLsx+b8CzTEiOiUUOkHlk0Ijo9+EDvQqsR0enBOxGdHnygd9o1Irpo9S4zy+W8Sh7vWtMnzZoqZ1lmVuSOu0DlF+nzD2h+H2XzQeb5R6QgJtwJEAmoqvJfpiVGRCOSj8waEZ0efKB3odWI6PTgnYhODz7QO+0aEV00elctsXdqMaHSePoSrHZEdJHmnUw+qN1HmXyQff6RsMMFOiGEEEIIIYQQIgFcoBNCCCGEEEIIIRLABbpeadrBMTkZ2L3bfmzCs0xLjIhGDZF6ZNGI6PTgA70LrUZEpwfvRHR68IHeadeI6KLVu/xtLudJ8njXmj5p1rjsaJ2/LXLHXaDyi/T5BzS/j7L5IPP8I1LABbpeiYlxHrt0cZ6rlWmJEdF4y8tfPbJoRHR68IHehVYjotODdyI6PfhA77RrRHTR6l3nzq3LTUQXCT54xnTuHLnjLlD5Rfr8A5rfR9l8kHn+ESngHdIr1dX2Y1UVkJ7uvmmEZ5mWGBGNGiL1yKIR0enBB3oXWo2ITg/eiej04AO9064R0UWrd9kuu0hXVcvjXWv6pFlT7SzLzorccReo/CJ9/gHN76NsPsg8/4gUcIFOCCGEEEIIIYRIABfohBBCCCGEEEL8smnTJkydOhXZ2dkwGAxYvXq145rVasW8efMwcOBAJCcnIzs7G7fddhuOHz/uiDl06BAMBoPqz4cffuiI6969e7Prjz32WCi7GjZiw50AIYQQQggJD2arDTDFAxYbYGkAAFitDai3AWZLA0yKwX7NI8azLCAal5hERYEhDH6Q6EBRFNRqGHeO8RoADaA+l8ymhDA4EjhqamowePBg/OpXv8INN9zgds1sNmPHjh144oknMHjwYJSXl+Ohhx7CtGnTUFBQAADo0qULSktL3XSvvfYann/+eUyZMsWt/KmnnsLs2bMd5ykpKUHqlVxwga5XmgZ4aipQUWE/NuFZpiXGn8ZqU89LpB5ZNCI6LRp/+dE7OftE70KrEdHp4TnEMaRdI6KLVu+OlwJ/2gwAGP7Xb4FHPnacO4nF3PwvnadqMc3KAqA5HzP83T348J4xMATbu/hEZ9nx0sgdd4HKL9LnHwDl2HFMf3cPth8udxYKjju3mIBoVHT3v+PSnxSpvBNhypQpzRbSTaSnp2P9+vVuZS+++CJGjhyJI0eOoGvXrjAajcjMzHSLWbVqFX7xi180W4CnpqY2i9UDfIu7XmlsdB5LSpznamVaYkQ03vLyV48sGhGdHnygd6HViOj04J2ITg8+0DvtGhFdlHqXWHYMw7u18R4jAQWHy1FrtYV2PBw9GrnjLlD5Rfr8A1B7uMR9cS4pw7u1RaLRII13VVVVqKysdPzU19dr6FVzKioqYDAY0KZNG9Xr27dvR2FhIe68885m15577jm0b98eQ4YMwaJFi2CxWAKSk+zwFXS9YjbbjzU1wIAB9r+opaWpl2mJ8adJSGqek2jbsmi8lbVW4y8/eidnn+hd5PkQDXOJY0g+HyLAO8PAgfjw3DnUJiYDled3lT5eCqTZX1mzWq1Yu3YdJk3KgclkUo3xLAuIBoD5zDn7q/q+/Aykd6fOOstGjgBOn4zMcReo/CJ9/gHAz38OPPAuAKDg9xORVGf2O+6ajVeBsepPA/ieS4nt28BQVSWNd/369XM7X7BgAXJzc9VzEqSurg6PPfYYZsyYgTQv/Vu2bBn69u2LsWPHupU/+OCDGDZsGNq2bYv8/HzMnz8fxcXFeOONN1qVUyTABTohhBBCiM4wGAxIiosF4oyAtd5+jLP/s9BqUBBvBJLiYmEyqcd4lgVEAwAmYxjcINFKUpwRSY0C485zvIqMVT8awM9cMhjC5Io6RUVF6NSpk+M8Pj6+VfVZrVbcfPPNaGxsxEsvvaQaU1tbi3fffRdPPPFEs2sPP/yw4/8HDRqEtm3bYvr06Y5X1aMZLtAJIYQQQgghRMekpqZ6fZW7pVitVtx0000oLi7Gl19+6bXejz76CGazGbfddpvfOkePHg0AOHjwIBfoRAeobRbhWaYlRkQjko/MGhGdHnygd6HViOj04J2ITg8+0DvtGhEdvfMeEywftGgC0aeUKPCOY8i5EXJLNGoxMmtEdIGafy2kaXF+4MABfPXVVz4X08uWLcO0adNwwQUX+K13586dAICsrKyA5SorXKDrlaYJmpYGVFa6X/Ms0xLjT9P01ReeiNQji0ZEp0XjLz96F/z8ZNaI6PTgnYhOD88hjiHtGhEdvfMeEzQfPBYQwe6T63OgrNT5dvxAt+OvrLWaQOUXDWPopx+Bp79oXX4ya0R0Wr0ToLq6GgcPHnScFxcXo7CwEO3atUN2djamT5+OHTt2YM2aNbDZbCgrKwMAtGvXDnFxcQ7dwYMHsWnTJvznP/9p1saWLVuwdetWXH755UhPT8e2bdvw8MMPY9q0aejatWur+yA73MVdrzQ0OI9r1zrP1cq0xIhovOXlrx5ZNCI6PfhA70KrEdHpwTsRnR58oHfaNSI6eqe9T63xwV89werThg2R7x3HEPDVV/7rCUafosE7AQoKCjB06FAMHToUAPDII49g6NChePLJJ3H06FF8+umnOHr0KIYMGYKsrCzHz+bN7l8/949//AOdOnVCTk5Oszbi4+Px/vvvY/z48ejXrx+efPJJzJ49G++9916rco8UuEDXK7W19qPZDEye7NzVXa1MS4yIRg2RemTRiOj04AO9C61GRKcH70R0evCB3mnXiOjonfY+afah1n89Ae2TS3vXXRfh3nEMAQB+cbP/eoLRp2jwToDx48dDUZRmP2+++Sa6d++uek1RFIwfP96tnsWLF6OkpAQxMc2Xo8OGDcPWrVtx7tw51NbWYu/evcjNzUVSkpdvX4kyuEAnhBBCCCGEEEIkgAt0QgghhBBCCCFEArhA1ytNbyeJiQH69XOeq5VpiRHReMvLXz2yaER0evCB3oVWI6LTg3ciOj34QO+0a0R09E57n1rjg796gtWnPn0i3zuOIeDii/zXE4w+RYN3RAq4i7teSU62H1NSgD173K95lmmJ8afxtnuySD2yaER0WjT+8qN3wc9PZo2ITg/eiej08BziGNKuEdHRO+8xQfMhWYOmFX1yfQ4UFPjexV167ziGAABff+2+i7vMPsjmHZEC/glFr1it9qPFArzxhv3YhGeZlhgRjRoi9ciiEdHpwQd6F1qNiE4P3ono9OADvdOuEdHRO+190uyD1X89Ae2TS3tvLo9w7ziGAABvv+u/nmD0KRq8I1LABbpeqatzHmfPdp6rlWmJEdF4y8tfPbJoRHR68IHehVYjotODdyI6PfhA77RrRHT0TnufWuODv3qC1aff3Bf53nEMAY887L+eYPQpGrwjUsAFOiGEEEIIIYQQIgFcoEtE9+7dYTAYmv3cd999AABFUZCbm4vs7GwkJiZi/Pjx2MPPkhBCCCGEEEJIVMAFukRs27YNpaWljp/169cDAG688UYAwPPPP48XXngBS5cuxbZt25CZmYkrr7wSVVVVLW/MaHQec3Kc52plWmJENN7y8lePLBoRnR58oHeh1Yjo9OCdiE4PPtA77RoRHb3T3qfW+OCvnmD1acKEyPeOYwi4fLz/eoLRp2jwjkgBd3GXiAsuuMDt/Nlnn0XPnj1x2WWXQVEULFmyBI8//jiuv/56AMDy5cvRsWNHvPvuu7j77rtb1lhSkv2YnAysXet+zbNMS4w/jbfdk0XqkUUjotOi8ZcfvQt+fjJrRHR68E5Ep4fnEMeQdo2Ijt55jwmaD0kaNK3ok+tz4JNPfO/iLr13HEMAgPffd9/FXWYfZPOOSAFfQZcUi8WCt99+G7/+9a9hMBhQXFyMsrIy5OTkOGLi4+Nx2WWXYfPmzV7rqa+vR2VlpeOn6dX2BrMZVqsV1poaWJ96yn60WtXLtMQIaJqwWhta1LZdY21Zvp66QGhE8tOiEciP3rWuT9L4QO/C7kMo51K0eUcfWqNxLgrpnWefzO7eBH3cubT3zLMR7p2ex5DLnPrTCyEeQ5HunfN3IZEDg6IoSriTIM354IMPMGPGDBw5cgTZ2dnYvHkzxo0bh2PHjiE7O9sRd9ddd+Hw4cNY6+UvYrm5uVi4cGGz8jfeeAMZGRlBy98f9TZgbr79r9TPj2xAPN9tIwy9IyQwcC6RcMGx551Qe8N7ER243senL2nA77fznopy+vRpzJo1CyUlJejcuXO40yEAoBApycnJUa655hrH+f/+9z8FgHL8+HG3uFmzZimTJk3yWk9dXZ1SUVHh+CkqKlIAKMXff69YLBbFcuaMYklMtB8tFvUyLTF+NOeqzUq3eWuUbvPWKEdOlivnqs32n9KTyrnUNvajyvmp8krl/Y9XK6fKK4U1qrpWaurr61X7WVNTo6xevVqpqakR9q6Zxo+frt6dKq8Uvictbacpxm+ftGjC5J10PtC7sPoQyrkUbd7pzYf606e9/s7x+/tFpezIyXLH2DtXbY5q71o6hs6VnnT3Jsjjzq291DYR7Z2ex9Cp8krHfTzetmNIx1Cke7d//34FgFJSUhK0dQ1pGfwMuoQcPnwYGzZswMqVKx1lmZmZAICysjJkZWU5yk+ePImOHTt6rSs+Ph7x8fGO88rKSgBAbGwsTCYTEBsL1NbajyYTzl90L9MS40djaioD8LM//8896TlvA0vyvZ8jFsjf1EKNiq4VmuHd2uLDe8bAoNZPACaTSdxfT41ajBfvTKZYIY2Wdlw1PvukRSPiQxC8k84HehdWH8Ixl6LFOz35oNTWYvp7Rdh+tNJZqb/fS2q/T1R/xzS1FRuV3mkZQ6bYWPdymzWo486tvbrz5xHqnb7HkPt9dKsryGMo8r1zb5eEH34GXUL++c9/okOHDrj66qsdZT169EBmZqZjZ3fA/jn1jRs3YuzYsS1vpGkymkzAnXe6PxQ8y7TE+NEkmoy4pGubluctCQWHy1Frtan30xUR70R00aYR0dE77zH0TkynBx/onXaNiM5kQu2dd7kvzgPIJV3bINFkVM8lCrzTPIb81ROsPt12e+R7xzEEzLjVfz3B6FM0eEfkINwv4RN3bDab0rVrV2XevHnNrj377LNKenq6snLlSmXXrl3KLbfcomRlZSmVlZXC9ZeUlEjzNpb6+nrl/Y9XK+eqzUpNvVXo51y1ucUarTo1zamqOsfbpmrqrc36ZLFYlNWrVysWi0XYh5Zqauqtbm/dClY7rdFFm0b2/GTWyJxfKOdStHkXSk2483MdJ6eq6gL6+6W+vj6o/QmkD6HSuPodit+zvtqLNO+CoZE9vyaN20eWqmrD/m+1ULfVGk1xcbE0awNih6+gS8aGDRtw5MgR/PrXv252be7cuXjooYcwZ84cDB8+HMeOHcO6deuQmpra8obq6uzH2lpg1iz7sQnPMi0xAhqDwYB4I5AUF+v8sVmRNOce+1HtPC5Wk6aZTqvmoQfcfVTrp7/r/jSifkayRkRH77zH0DsxnR58oHfaNSK62lpgzn2O06Q4o//fS2q/T7xoDAZD+H2QbgzV+a8noH1yaW/OfRHuHccQAOChR/zXE4w+RYN3RAq4QJeMnJwcKIqCiy66qNk1g8GA3NxclJaWoq6uDhs3bsSAAQO0NdT0lQpWK7BsmfNcrUxLjIjGW17+6gmn5l/Lfdfjrx0Z+xQOjYiO3nmPoXdiOj34QO+0a0R0fO5r71NrfPBXT7D69K/lke8dxxDw7jv+6wlGn6LBOyIFXKATQgghhBBCCCESwAU6IYQQQgghhBAiAVyg65Wmr16LjwcWLHCeq5VpiRHReMvLXz3h1Mz/ne96/LUjY5/CoRHR0TvvMfROTKcHH+iddo2Ijs997X1qjQ/+6glWn+b/LvK94xgCHv0///UEo0/R4B2Rg3DvUkdCi0y7uEfiLpqh3l1WDe7iHnqN7PnJrJE5P+7iHhmacOcnw3Nfq0b2/GTwm7u4y9MWd3EPj4a7uMsHX0HXK2az/VhTA0yaZD824VmmJUZEo4ZIPeHUXHut73r8tSNjn8KhEdHRO+8x9E5Mpwcf6J12jYiOz33tfdLsg9l/PQHtk0t7114b4d5xDAEAfvEL//UEo0/R4B2RAi7Q9YrN5jyuW+c8VyvTEiOi8ZaXv3rCqfniC9/1+GtHxj6FQyOio3feY+idmE4PPtA77RoRHZ/72vvUGh/81ROsPn3xReR7xzEEfPVf//UEo0/R4B2RAi7QCSGEEEIIIYQQCeACnRBCCCGEEEIIkQAu0PVKQoLz+PrrznO1Mi0xIhpvefmrJ5yapX/3XY+/dmTsUzg0Ijp65z2G3onp9OADvdOuEdHxua+9T63xwV89werT0r9HvnccQ8ALf/FfTzD6FA3eETkI9y51JLRwF/fI2l1WDe7iHnqN7PnJrJE5P+7iHhmacOcnw3Nfq0b2/GTwm7u4y9MWd3EPj6alu7hv3LhRueaaa5SsrCwFgLJq1Sq3OufOnasMGDBASUpKUrKyspSZM2cqx44dc6vjsssuUwC4/fziF79wizl79qzyy1/+UklLS1PS0tKUX/7yl0p5eblw/yIZvoKuV5p2cKyuBvr3tx+b8CzTEiOiUUOknnBqhg/3XY+/dmTsUzg0Ijp65z2G3onp9OADvdOuEdHxua+9T5p9cO4wbbbYYD5bAfOgIfajpQFmSwPqbXD8v9nS0DymRZpKZ3ujxrhpmulc6lUURULvQjselKoqr3778s5XmXaNc+Oz2vET/PsZZu+knX8C1NTUYPDgwVi6dGmza2azGTt27MATTzyBHTt2YOXKldi/fz+mTZvWLHb27NkoLS11/Lz66qtu12fMmIHCwkLk5eUhLy8PhYWFmDlzZqtyjxRiw50ACRONjc5jUZHzXK1MS4yIxlte/uoJp2bvXuAqH/X4a0fGPoVDI6Kjd95j6J2YTg8+0DvtGhEdn/va+9QaH84z/OkN9v+5ahHw/DcuQbGYm/+lu84zRlSzZIuzvUm5HhoV3fl6h3driw9n9IdBNu9CNB6UoiJMX16I7UfP/4Gjmd+AN+/c8HefBDXI3+g4+9nE3/nuo5c+cf6JMWXKFEyZMkX1Wnp6OtavX+9W9uKLL2LkyJE4cuQIunbt6ihPSkpCZmamaj0//PAD8vLysHXrVowaNQoA8Prrr2PMmDHYt28fLr744lb1QXb4CjohhBBCCJGCRFMMhh/dE+40/FJwuBy11tYtdCKZWlO8c3EuKcO7tUWiyRjuNHRPRUUFDAYD2rRp41b+zjvvICMjA/3798ejjz6Kqqoqx7UtW7YgPT3dsTgHgNGjRyM9PR2bN28OVephg6+gE0IIIYQQKTAYDPjwnXmoPXUWSEsFKquA7CzgeCmQlgqr1Yq1a9dh0qQcmEwmu8gjpiUa86GjGP63bwEABX+bgaQjh+x1AM11lVUwd+uB4fe/EwZn5KXgwVFI6tbZ6T/UvXO7R4D/+9QCzc8un4Axz9lfRW+6j4nt28BgMITDkoikqqoKlZXOP7rEx8cjPj6+VXXW1dXhsccew4wZM5CWluYov/XWW9GjRw9kZmZi9+7dmD9/Pr777jvHq+9lZWXo0KFDs/o6dOiAsrKyVuUUCXCBrlcSE+3HpCQgL89+bMKzTEuMiEYNkXrCqVm9Gthkcy/zpQuGD97+YC+7dy31IVrHEL2Tx4domEscQ9o1IjpZnvuyaUR0rfDBkJeHpDapQGws0CYV+OwT+zE2FlaDgngjkBQXC5Pp/D9jPWJapklxNv3R+852gea6NqnAhyuc4yEpUTrvwvHvoaT0FCS5+g8v3nnE+L1PLdI4Xyl33Memxbls3kk6//r16+d2vmDBAuTm5nrPyw9WqxU333wzGhsb8dJLL7ldmz17tuP/BwwYgN69e2P48OHYsWMHhg0bBgCqf1xRFEUXf3ThAl2vND3oYmOBSZOaX3Mt0xIjovGWl796wqmZOBHYtFZcFwwfLA0t12hpJ5gaEV20jiF6J48P0TCXOIa0a0R0sjz3ZdOI6CLFB9fnwMSJzn8fedO4jgc9eyezD573UTbvJJ1/RUVF6NSpk+O8Na+eW61W3HTTTSguLsaXX37p9uq5GsOGDYPJZMKBAwcwbNgwZGZm4sSJE83iTp06hY4dO2rOK1LgZ9D1StPnPCorgbQ0+7EJzzItMSIaNUTqCacmM8t3Pf7akbFP4dCI6Oid9xh6J6bTgw/0TrtGRMfnvvY+RYwPzs+9IjPLv8Z1PFRW6dc7mX3wvI+yeSfp/EtNTUVaWprjR+sCvWlxfuDAAWzYsAHt27f3q9mzZw+sViuysuzjasyYMaioqEB+fr4j5ttvv0VFRQXGjh2rKa9Igq+gE+di3VeZlhgRjUg+MmmqNfQpGn0IhEZER++8x9A7MZ0efKB32jUiOj73vcdEmw9q99oTzxi9eiezD1rmrFqMzBoRndYx5Ifq6mocPHjQcV5cXIzCwkK0a9cO2dnZmD59Onbs2IE1a9bAZrM5PjPerl07xMXF4ccff8Q777yDq666ChkZGSgqKsJvf/tbDB06FOPGjQMA9O3bF5MnT8bs2bMdX79211134Zprron6HdwBLtAJIYQQQgghhAhQUFCAyy+/3HH+yCOPAABuv/125Obm4tNPPwUADBkyxE331VdfYfz48YiLi8MXX3yBv/71r6iurkaXLl1w9dVXY8GCBTAanXsJvPPOO3jggQeQk5MDAJg2bZrqd69HI1ygE0IIIYQQQgjxy/jx46Eoitfrvq4BQJcuXbBx40afMYD9Ffe33367xflFAzHhToCEiaYdHJOTgd277ccmPMu0xIho1BCpJ5ya/G2+6/HXjox9CodGREfvvMfQOzGdHnygd9o1Ijo+97X3KWJ8cNnROn+bf43reEhO0q93MvvgeR9l807m+UekgAt0vRIT4zx26eI8VyvTEiOi8ZaXv3rCqenc2Xc9/tqRsU/h0Ijo6J33GHonptODD/ROu0ZEx+e+9j5Fmg+A/V7707iOBz17J7MPnvdRNu9knn9ECniH9Ep1tf1YVQWkp7tvGuFZpiVGRKOGSD3h1GRnNS/zpYtWH1qrEdHRO+8x9E5Mpwcf6J12jYiOz33tfYoYH6qdZdlZ/jWu46GqWr/eyeyD532UzTuZ5x+RAi7QCSGEEEIIIYQQCeACnRBCCCGEEEIIkQAu0AkhhBBCCCGEEAngAl2vpKTYj6mpQEWF/diEZ5mWGBGNGiL1hFNzvNR3Pf7akbFP4dCI6Oid9xh6J6bTgw/0TrtGRMfnvvY+RYwPKc6y46X+Na7jITVFv97J7IPnfZTNO5nnH5ECLtD1SmOj81hS4jxXK9MSI6Lxlpe/esKpOXrUdz3+2pGxT+HQiOjonfcYeiem04MP9E67RkTH5772PkWaD4D9XvvTuI4HPXsnsw+e91E272Sef0QKuEDXK2az/VhTAwwYYD824VmmJUZEo4ZIPeHUjBzhux5/7cjYp3BoRHT0znsMvRPT6cEHeqddI6Ljc197nyLGB7OzbOQI/xrX8VBj1q93MvvgeR9l807m+UekgAt0QgghhBBCCCFEArhAJ4QQQgghhBBCJIALdKK+WYRnmZYYEY1IPjJpUjT0KRp9CIRGREfvvMfQOzGdHnygd9o1Ijo+973HRJsPavfaE88YvXonsw9a5qxajMwaEZ3WMUTCTmy4EyBhommCpqUBlZXu1zzLtMSIaNQQqSecmrJS4Mm14rpg+GBpaLlGSzvB1IjoonUM0Tt5fIiGucQxpF0jopPluS+bRkQXKT64PgfKSoE4H/809hwPaan69U5mHzzvo2zeyTz/iBTwFXS90tDgPK5d6zxXK9MSI6Lxlpe/esKp2bDBdz3+2pGxT+HQiOjonfcYeiem04MP9E67RkTH5772PkWaD4D9XvvTuI4HPXsnsw+e91E272Sef0QKuEDXK7W19qPZDEye7NzVXa1MS4yIRg2ResKpue463/X4a0fGPoVDI6Kjd95j6J2YTg8+0DvtGhEdn/va+xQxPtQ6y667zr/GdTyYa/Xrncw+eN5H2byTef4RKeACnRBCCCGEEEIIkQAu0AkhhBBCCCGEEAngAl2vxMQ4j/36Oc/VyrTEiGi85eWvnnBq+vTxXY+/dmTsUzg0Ijp65z2G3onp9OADvdOuEdHxua+9T5HmA2C/1/40ruNBz97J7IPnfZTNO5nnH5EC7uKuV5KT7ceUFGDPHvdrnmVaYkQ0aojUE05NQYH7br7+dMHwwdvO07J711IfonUM0Tt5fIiGucQxpF0jopPluS+bRkQXKT64PgcKCnzv4u45HlKS9eudzD543kfZvJN5/hEp4J9Q9IrVaj9aLMAbb9iPTXiWaYkR0aghUk84NW8u912Pv3Zk7FM4NCI6euc9ht6J6fTgA73TrhHR8bmvvU8R44PVWfbmcv8a1/FgserXO5l98LyPsnkn8/wjUsAFul6pq3MeZ892nquVaYkR0XjLy1894dT85j7f9fhrR8Y+hUMjoqN33mPonZhODz7QO+0aER2f+9r7FGk+APZ77U/jOh707J3MPnjeR9m8k3n+ESngAp0QQgghhBBCCJEAfgadEEIIIYSQFmK22gBTPGCxoemz7FZrA+ptgNnSAJNisF/ziPEsC4hGJSYQGm99MpsSQmk1IbqCC3S9YjQ6jzk5znO1Mi0xIhpvefmrJ5yaCRN81+OvHRn7FA6NiI7eeY+hd2I6PfhA77RrRHR87mvvU6T5ANjvtT+Ny3gY/tdvgUc+Bv602SMwFnPzv3SeqsU0KwuARi0mIBoV3f3vOP9ftjHkeR9lHneyeUekgAt0vZKUZD8mJwNr17pf8yzTEiOiUUOknnBqPvnEfTdff7pg+OBt52nZvWupD9E6huidPD5Ew1ziGNKuEdHJ8tyXTSOiixQfXJ8Dn3ziexf35GQk/mcNhr+yBQWHy33XrxOGd2uLxDapco0hz/so47hrjUZEp9U7IgX8DLpeadrBsb4eyM21H5vwLNMSI6JRQ6SecGoWLfZdj792ZOxTODQiOnrnPYbeien04AO9064R0fG5r71PEeODy47Wixb71RgWLsSHvxqGoqcmoejxy1EUv91+fGoSip6ahO+euALPj2zAd09c4TXGsywgGjVdADT+8vvwnjEwWCxyjSHP+yjluJN0/hE5UIiuKCkpUQAoJUVF9oKKCkUB7McmPMu0xAhoLBaLsnr1asVisQjXq0WjqtOoqTHFK93mrVG6zVuj1NRb/ecXBB9q6q2OHM5VmyPGuxb7EKVjiN7J40Mo51K0eacnH2R47keqd7L44EtTc+qs8/6a4uldhI6hc9Vmr/eR3vnWFBcX29cGJSUKkQO+gk4IIYQQQgghhEgAF+iEEEIIIYQQQvyyadMmTJ06FdnZ2TAYDFi9erXjmtVqxbx58zBw4EAkJycjOzsbt912G44fP+6IOXv2LO6//35cfPHFSEpKQteuXfHAAw+goqLCrZ3u3bvDYDC4/Tz22GOh6mZY4QJdr5hMzuOddzrP1cq0xIhovOXlr55wam673Xc9/tqRsU/h0Ijo6J33GHonptODD/ROu0ZEx+e+9j5Fmg+A/V7Tu8gfQ573UWYfZPNOgJqaGgwePBhLly5tds1sNmPHjh144oknsGPHDqxcuRL79+/HtGnTHDHHjx/H8ePH8ac//Qm7du3Cm2++iby8PNx5553N6nvqqadQWlrq+Pn973/fqtwjhnC/x56EFsdn0CX4nInq53mCoAlkW66fWa2ptwatHV94/dxsgNtpjS7aNLLnJ7NG5vxCOZeizbtQasKdnwzPfa0a2fOTQePr/sqQX7g1suen+hl0lXkajtxC2VZrNK35DDoAZdWqVT5j8vPzFQDK4cOHvcZ88MEHSlxcnGK1Ou9dt27dlL/85S8tzika4CvoeqWuzn6srQVmzbIfm/As0xIjolFDpJ5waubc57sef+3I2KdwaER09M57DL0T0+nBB3qnXSOi43Nfe58ixoc6Z9mc++hdNIwhz/sosw8SeVdVVYXKykrHT32AdnuvqKiAwWBAmzZtfMakpaUhNtb9aw6fe+45tG/fHkOGDMGiRYtgsVi81BBdcIGuV6xW53HZMue5WpmWGBGNt7z81RNOzb+W+67HXzsy9ikcGhEdvfMeQ+/EdHrwgd5p14jo+NzX3qdI8wGw32t6F/ljyPM+yuyDRN7169cP6enpjp9nnnnGe06C1NXV4bHHHsOMGTOQlpamGnPmzBn84Q9/wN133+1W/uCDD2LFihX46quv8Jvf/AZLlizBnDlzWp1TJBDrP4QQQgghhBBCSLRSVFSETp06Oc7j4+NbVZ/VasXNN9+MxsZGvPTSS6oxlZWVuPrqq9GvXz8sWLDA7drDDz/s+P9Bgwahbdu2mD59uuNV9WiGC3RCCCGEEEII0TGpqaleX+VuKVarFTfddBOKi4vx5ZdfqtZbVVWFyZMnIyUlBatWrYLJz+Z1o0ePBgAcPHgw6hfofIu7ZBw7dgy//OUv0b59eyQlJWHIkCHYvn2747qiKMjNzUV2djYSExMxfvx47Nmzp+UNNf1VLD4eWLDAea5WpiVGROMtL3/1hFMz/3e+6/HXjox9CodGREfvvMfQOzGdHnygd9o1Ijo+97X3KdJ8AOz3mt5F/hjyvI8y+yCbdwGgaXF+4MABbNiwQXUxXVlZiZycHMTFxeHTTz9FQkKC33p37twJAMjKygp4ztIR7l3qiJOzZ88q3bp1U+644w7l22+/VYqLi5UNGzYoBw8edMQ8++yzSmpqqvLxxx8ru3btUn7xi18oWVlZSmVlpVAb3MU98nfz5S7uodfInp/MGpnz4y7ukaEJd34yPPe1amTPTwYNd3GXpy3u4h4eTUt3ca+qqlJ27typ7Ny5UwGgvPDCC8rOnTuVw4cPK1arVZk2bZrSuXNnpbCwUCktLXX81NfXK4qiKJWVlcqoUaOUgQMHKgcPHnSLaWhoUBRFUTZv3uyo96efflLef/99JTs7W5k2bZpw/yIZvoIuEc899xy6dOmCf/7znxg5ciS6d++OCRMmoGfPngDsr54vWbIEjz/+OK6//noMGDAAy5cvh9lsxrvvvtuyxsxm+7GmBpg0yX5swrNMS4yIRg2ResKpufZa3/X4a0fGPoVDI6Kjd95j6J2YTg8+0DvtGhEdn/va+xQxPpidZddeS++iYQx53keZfZDNOwEKCgowdOhQDB06FADwyCOPYOjQoXjyySdx9OhRfPrppzh69CiGDBmCrKwsx8/mzZsBANu3b8e3336LXbt2oVevXm4xJSUlAID4+Hi8//77GD9+PPr164cnn3wSs2fPxnvvvdeq3CMFfgZdIj799FNMmjQJN954IzZu3IhOnTphzpw5mD17NgCguLgYZWVlyMnJcWji4+Nx2WWXYfPmzc12P/SJzeY8rlvnPFcr0xIjovGWl796wqn54gtg8Bz3Ml+6YPhgCnCfwqER0UXrGKJ38vgQDXOJY0i7RkQny3NfNo2ILtJ8AOz3mt5F/hjyvI8y+yCbdwKMHz8eiqJ4ve7rmogeAIYNG4atW7dqyi8a4AJdIn766Se8/PLLeOSRR/C73/0O+fn5eOCBBxAfH4/bbrsNZWVlAICOHTu66Tp27IjDhw+r1llfX+/2PYZVVVUAgIaGBlitVqChAUhMtB+bvnbBs0xLjIDGer7c6vp1D37q0aJxjbe2oI9qGmtCoqNZq9UKq7/8guCDa5zV2iDcp3B712IfonQM0Tt5fAjlXIo27/TkgwzP/Uj1ThYffGmsDQ3O+5tw/pzeReAY8n4f6V0LfCBSYFD8/QmDhIy4uDgMHz7c8RYQAHjggQewbds2bNmyBZs3b8a4ceNw/Phxtw0SZs+ejZKSEuTl5TWrMzc3FwsXLmxW/sYbbyAjIyM4HYli6m3A3Hz737WeH9mAeKM+cyAkGuBcIiJwnEQ3vL/RAe+jdk6fPo1Zs2ahpKQEnTt3Dnc6BOAmcTLRtWtX5c4773Qre+mll5Ts7GxFURTlxx9/VAAoO3bscIuZNm2actttt6nWWVdXp1RUVDh+ioqKFABK8b59isViUSzV1YrljTfsR4tFvUxLjICmpqZGWb16tVJTUyNcrxaNqk6j5txry9w2lfKbXxB8cN0I5VR5ZcR412IfonQM0Tt5fAjlXIo27/TkgwzP/Uj1ThYffGnOna1w3t/XltG7CB1Dp8orvd5Heudbs3//fmk2kCZ2uECXiFtuuUW59NJL3coeeughZcyYMYqiKEpjY6OSmZmpPPfcc47r9fX1Snp6uvLKK68ItcFd3CN/N1/u4h56jez5yayROT/u4h4ZmnDnJ8NzX6tG9vxk0HAXd3naao2Gu7iHbhd3Eny4i7tEPPzww9i6dSsWL16MgwcP4t1338Vrr72G++67DwBgMBjw0EMPYfHixVi1ahV2796NO+64A0lJSZgxY0bLGmvawbG6Gujf335swrNMS4yIRg2ResKpGT7cdz3+2pGxT+HQiOjonfcYeiem04MP9E67RkTH5772PkWMDy47Wg8fTu+iYQx53keZfZDNOyIFseFOgDgZMWIEVq1ahfnz5+Opp55Cjx49sGTJEtx6662OmLlz56K2thZz5sxBeXk5Ro0ahXXr1iE1NbVljTU2Oo9FRc5ztTItMSIab3n5qyecmr17gat81OOvHRn7FA6NiI7eeY+hd2I6PfhA77RrRHR87mvvU6T5ANjvNb2L/DHkeR9l9kE274gUcIEuGddccw2uueYar9cNBgNyc3ORm5sbuqSIKmaLDbDYAFO8/WhpgNXagHobYLY0wKQYml0H4F+jEuN6bo5pcMvBZFKvV60dbglJCIkGFEVBrY/nHYDAPI8tNphNCWHsKSGEEL3BBTohGhn+9Ab7/zzyMfCnzS5XYjE3/0vnabPrAhq1GJV6xjy3sUWaHqlGXHUVV+mEkMhFURRMf+t7bBd4RgbkeXz/O4FKnRBCCPELP4OuVxLPf69rUhKQl2c/NuFZpiXGj8bWqODb4rPYftqAb4vPwtaoiNfjSQg1iZ+uxvCubbzHSE5xlQG1VpuzIFx+i+i0aMLZJ3oXWo2ITg8+6NC7WqsN249Weq8vSAzv1haJJmNEe9cUY/v8c2wpq8MnhcewpawOts8/19UYco9xfs89Vq/m/IuG55DnfZTZB9m8I1LAV9D1Smys8zhpUvNrrmVaYnxo8naXYuFnRSitqANgxL8OFCArPQELpvbD5AFZ/utR60uINIbJk/Ghorgvcl2wWq1Yu3YdJk3Kgclk8l2fRo3Z0oDhT38BANgy7zKkJ/t/+6XZYnO+4u9KCL3zO4YCoQlUfjJrRHR68E5E509jaWiuCVR+MmtEdLKPofMU/H4ikuLUv/A40M/jRJMRBoMh4r3L23sKC3eaUPrfbY6yrPQELOh8yv47WKQdkbYk98ER4/ocmDjR+e+jYOUmoosU71qjEdFp9cHzPsrsg2zeESngK+h6parKfqysBNLS7McmPMu0xHjR5A2ZgHvf3nF+ce6krKIO9769A3n5P/mvxxORtgOoMVRVISku1v5TZ0ZSRjv78XxZvBE+r/vVqMV4nDeRFGcU1Kj/AzbU3vkcQ4HQhLNP9C60GhGdHnzQq3fn8faMDNjz2OW6wWCIeO/ydpfafwefq3UrLztntv8O3l0auD5J7IN7TJWzLDOL8y8ankOe91FmH2TzjkhBTLgTIBJQVeW/TEuMx7mtUcHCsTOh9gnoprKFG36CzfUrT7y13dJcgqUR0WnRqMVEm0ZER++8x9A7MZ0efNCrd/7qEI0Jhg8SemdrVLDwsyL779umPzacRzHY/zm48LMi50fO/LUjkp+EPviMqeb8C6lGRKdFo3YfZfZBJu+IFHCBTkJGfkkFStMu8HpdAVBaWY/8zv1DlxQhhBCiAwoOlzd795orCoDSijrkF58NXVKEEEKawQU6CRknqy1icSltg5wJIYQQoi9OVtULxnlfxBNCCAk+XKDrlaYdHJOTgd277ccmPMu0xKhoOmSkCaXW4cU/+27bE5F8g6ER0WnRhLNP9E5+jYhOD96J6PTgg169c2h9/C4LRJ+iyLsOqfHe63GLU9l4NIp8aK5x2dE6fxvnXzQ8hzzvo8w+yOYdkQIu0PVKTIzz2KWL81ytTEuMimbkhRnISo2D+yffnBhg30l22NBevttW64u/fIOhEdFp0YSzT/ROfo2ITg/eiej04INevXPVam1HRBdF3g3v1hZZ6QlefwcDQGyMAcYYlYgo8sFnLp07c/5Fw3PI8z7K7INs3hEp4B3SK9XV9mNVFZCe7r5phGeZlhgVjbGmGgv+tQAAmv0Doen8xoEX4Jq57+LAT2Xe2/ZEJN9gaER0WjTh7BO9k18jotODdyI6PfigV+8cWh+/ywLRpyjy7qMdx3BJN/tHyAxKo9u1pvOGRgW7j1UEpk+S+tBcU+0sy87i/IuG55DnfZTZB9m8I1LABToJKZP3b8HL1/dFZrr7W+gy0xPw0q3DsOHAWRzI6IZ/bffyVS+EEEIIaREna4EFn/2ANd+X4q7RnZBZdcbtembVGfzpmovwyJUX4fax3R3l9VZbiDMlhBAS6z+EkMAyuU8GrhzeA1sOnsS6r79Fzs9GYUyvDjDGGDAyw4QXZz6Ox+Y+F+40CSGEkKigQyLwxNV9UFRahccu746511+C/O+KcdJmRAejDSMH94Dx2XL7dyQDaLQBjQpwx/Lt6NY+BU9c1hltwtsFQgjRDVygk7BgjDFgVI92OPODglE92jk+89Y+OQ65X7wGxP7REbts+DRMrbagg9gec4QQQgjx4NaRXRAbGwtDVRWMSiPGdGtjX5BXVgIeb3kHgOIqYPuRc/ihtAoPj8niAp0QQkIE3+KuV1JS7MfUVKCiwn5swrNMS4yIRg2PmI8PVOAPE+7C1DcLUVVnFdJoaUeTRkQXIB+iTiOio3feY+idmE4PPujVO4fWx++yQPQpgr2rrLPij2v3or7Bufg2GAzC7fRMA96fNRLP3DAInTtf4Iipa3rbe4T44F+T4iw7Xsr5Fw3PIc/7KLMPsnlHpIALdL3S2Og8lpQ4z9XKtMSIaLzl5RIzrHM6Lm5jwowRXZCaYBLSaGlHk0ZEFyAfok4joqN33mPonZhODz7o1TtXrdZ2RHQR6p1is+GB93bi71/9iLkf79LcztCubTBtcLYj5mBZJS597ku8l38Eis0mvQ8tHg9Hj3L+RcNzyPM+yuyDbN4RKeACXa+YzfZjTQ0wYID92IRnmZYYEY0aHjE9EoHVT0zD/SMznak3ANX1DS3L1087mjQiugD5EHUaER298x5D78R0evBBr945tD5+lwWiTxHqncFsxqxLL0THtHjc9bMeAWvnza9/xOlqC9YXnYgIH8Q0ZmfZyBGcf9HwHPK8jzL7IJt3RAr4GXQiPYkN9YDB/hn1xkYFbx2IwRuHvsWrM4ejV4cUP2pCCCFEf1zaOwMb/+9yGNGIw4WBqXNhTk/0yG6LKQMyYTDYP3ZmtTXCYOMrcoQQEij4CjqJKMoq63DMbMDR8lrUN9jCnQ4hhBAiDV8ePIvTSemO8wSTMaD1G2MMuPPSHshuk+go+/vmElz/8mbsLeN3KxNCSCDgK+hEfbMIzzItMSIakXxczrPbJOL/BtmQ2XcY+menC2m0tCOsEdEFwYeo0Ijo6J33GHonptODD3r1zl8dojHB8CEM3m3+8TTu/qgIWbe9gI/8ffNJgMaQuW0G3t5eitNmK346pfK2WdnHkGdMCudfSDUiOi0atfsosw8yeUekgAt0vdI0QZu+YsUVzzItMSIaNQTqSTUBl110geO8pCEWf1j6JZ42xKFDANvxqxHRBcMHS0NoNFpyE9WI6II4hiJaI6LTg3ciOn8a2eZFJHkXzPxENI5YH7/L/LUjoosg7zrUVSOzTQIGDhiCjMz2QWvHtSzp7Cn8p7IOq3Yew5QBHfF5if1SndWGBNnHUFOM63OgrBSI8/FPY86/wGlEdFp98LyPMvsgm3dECvgWd73S0OA8rl3rPFcr0xIjovGWl796PJj74XdYV3QCv/v4+6C2o6lPIfQhojQiOnrnPYbeien04INevXPVam1HRBdB3vXqkIJVd43Cn9ucREyjj4+ABXgMdUiKxd2X9bR/hRuAeqsNU1/8Bk+u+h7V/86TdwypxWzYwPkXDc8hz/sosw+yeSfApk2bMHXqVGRnZ8NgMGD16tWOa1arFfPmzcPAgQORnJyM7Oxs3HbbbTh+/LhbHfX19bj//vuRkZGB5ORkTJs2DUePHnWLKS8vx8yZM5Geno709HTMnDkT586da1XukQIX6HqlttZ+NJuByZOdu7qrlWmJEdGoIVKPB4tyemD04e+xcEL3oLajqU8h9CGiNCI6euc9ht6J6fTgg169c2h9/C4LRJ8k9858rgoHZ8xyxGTE2JBw9ZSwjqGNB07jwMlq/Gd3GWw3TJd3DDliap1l113H+RcNzyHP+yizD7J5J0BNTQ0GDx6MpUuXqjRrxo4dO/DEE09gx44dWLlyJfbv349p06a5xT300ENYtWoVVqxYgW+++QbV1dW45pprYLM5/7g4Y8YMFBYWIi8vD3l5eSgsLMTMmTNblXukwLe4k4jnwvZJWLHid8Cr9znK/tdtMIZYbEgOY16EEEJIsGiwNeL+1XuRP/NPePXQOYwd5OtD56Ejp19HvH3nKFirq5H+pPNz6XVWGxLCmBchJDBMmTIFU6ZMUb2Wnp6O9evXu5W9+OKLGDlyJI4cOYKuXbuioqICy5Ytw1tvvYWJEycCAN5++2106dIFGzZswKRJk/DDDz8gLy8PW7duxahRowAAr7/+OsaMGYN9+/bh4osvDm4nwwxfQSdRx67SKvxq+gJc92YhzlTXhzsdQgghJOCYrTZU1jXAYjQhwSTXP+cu7Z2By3u2c5znF5/Fpc99iU/3nAxjVoSQcFBRUQGDwYA2bdoAALZv3w6r1YqcnBxHTHZ2NgYMGIDNmzcDALZs2YL09HTH4hwARo8ejfT0dEdMNCPXE52EjpgY57FfP+e5WpmWGBGNt7z81eNHY1UMaGutRbd2iWibFBe0doR0YfRBao2Ijt55j6F3Yjo9+KBX71y1WtsR0UnsXVqCCW/9cjDe/fZ1DOvSJmjtaO6Ty/V/fFOM09UW/O9whTxjSC2mTx/pvBPWBCo/SceQsAZofh9l9kEi76qqqlBZWen4qa9v/YtcdXV1eOyxxzBjxgykpdnf5VNWVoa4uDi0bdvWLbZjx44oKytzxHTo0KFZfR06dHDERDN8i7teST7/5u+UFGDPHvdrnmVaYkQ0aojU40czrE821jx9A+JiYxATY9+0xtaowKYEth0hXTB88LbzdKA1WnIT1YjowjiGpNaI6PTgnYjOn0a2eRFJ3gUzPxGNI9bH7zJ/7YjoJPTubI0FHduYAAAJbdJwyTefB6Udv/m3wIe/3TIU//hfMW4Z2RW42V5WZ7XB0Kj41GnOryUa1+dAQYHvXdw5/wKnEdFp9cHzPsrsg0Te9evXz+18wYIFyM3N9Z2bD6xWK26++WY0NjbipZde8huvKIpjs0kAbv/vLSZaEfjzNIlKrFb70WIB3njDfmzCs0xLjIhGDZF6BDQXvP8W0o3OX/xLv/oRLxXF4HTTW94D0Y6ILsw+SKsR0dE77zH0TkynBx/06p1D6+N3WSD6JJl3P1UCl7/wNd799oiwRks7AemTy/W42Bjcc1lP++/l82VPrSnCL/9ZgJO13nWh6ZPVWfbmcum8E9YEKj9Jx5CwBmh+H2X2QSLvioqKUFFR4fiZP3++95z8YLVacdNNN6G4uBjr1693vHoOAJmZmbBYLCgvL3fTnDx5Eh07dnTEnDhxolm9p06dcsREM1yg65W6Oudx9mznuVqZlhgRjbe8/NXTQs2Z6nq8ueUIDlbG4Nvi8sC1I6KTyAepNCI6euc9ht6J6fTgg169c9VqbUdEJ5l3hWdjYLbY8OXek1AUJWLH0KnTlfhk5zFsO1SOCov7q2E2cy22PPU3fLL9CLb8eAY2c23o+vSb+6T3Tor5J7sPnvdRMh9sd92NLfvK8EnhMWzZVwbbXXdL4V1qairS0tIcP/Hx8d5z8kHT4vzAgQPYsGED2rdv73b9kksugclkcttMrrS0FLt378bYsWMBAGPGjEFFRQXy8/MdMd9++y0qKiocMdEM3+JOop72KfH46O5R+PvqTbh6YGa40yGEEEI08f+6NWLiiAG4YXiXiH6b5wUpcch76OfYUFSKdmd2O8o/KTyGZ/9dhNIZzwCf7AMAZKXGYcFFYzA5XMkSEkDy9p7GwnuWofSdXY6yrHuWYcHe05g8Uo5vYvBHdXU1Dh486DgvLi5GYWEh2rVrh+zsbEyfPh07duzAmjVrYLPZHJ8Zb9euHeLi4pCeno4777wTv/3tb9G+fXu0a9cOjz76KAYOHOjY1b1v376YPHkyZs+ejVdffRUAcNddd+Gaa66J+h3cAb6CTnRCzwuSMbGT8y3vtVYb/vizmaix2HyoCCGEkPDS4PI5bYMBuHlEZyT5+px0hNClXRJ+Oaqr4/zjHUfx4IpClFa5v/22rMqCe6/7HfL2ng51ioQElLzdpbh35Q8oTc1wKy9LbY97V/6AvN2lYcqsZRQUFGDo0KEYOnQoAOCRRx7B0KFD8eSTT+Lo0aP49NNPcfToUQwZMgRZWVmOH9fd1//yl7/guuuuw0033YRx48YhKSkJn332GYxGoyPmnXfewcCBA5GTk4OcnBwMGjQIb731Vsj7Gw4i/wlPtNE0AYxGICfHea5WpiVGROMtL3/1BECTu/4nvD/2F9jxURHevXssDFraEWlLch/CphHR0TvvMfROTKcHH/TqnatWazsiujB715iTg/tX70VW+1TMzeklpInEMWRrVLDwU/XNqxQABgALN/yEK4f3CG6fJkyIOO8Cnl+EjiE3PO+jBD7YGhUs/KwICmD/S5sLiiHGPsY/K8KV/VTe6RlK7wQYP368/SM2XvB1rYmEhAS8+OKLePHFF73GtGvXDm+//bamHCMdLtD1SlKS/ZicDKxd637Ns0xLjIhGDZF6AqCZProHNhWfw/1XXmx/m6CWdkTaCoYP3naeDrRGS26iGhGd5GOI3oVZI6Lzp5FtXkSSd8HMT0TjiPXxu8xfOyK6MHu39e/v4PM3vkWc8Sz+3xCVf7hHyRgqOFyOyjov8xGAYjCgtLIe+cVnMbyrytuAW9Mn1+fAJ5/43sVdQu8Cnl+EjiE3PO+jBD4UHC5HaYX3z5krAEor6tTHeCi9I1LAt7jrlaYdHOvrgdxc+7EJzzItMSIaNUTqCYBmRFYyvqr7BmM7pzo0JU8uRoPZcztZH+2ItCW5D2HTiOjonfcYeiem04MPevXOofXxu8xfOyK6MHs39u2l+Nv0AfjjjYPQJzNVSBOJY+hklcC9BnCyyssCp1V9cnlL/aLFEeddwPOL0DHkhud9lMCHVo3xUHpHpIALdL3i+jBZuLD5BHYt0xIjovGWl796AqRJeCrXEXPmbBVuOp2Nmcu340y1j4ddS/sUAT6ERSOio3feY+idmE4PPujVO1et1nZEdBJ4N+2itrh2SKcWaSJtDHVIFdstukNqgr0az+1jAtWnZwQW6JJ5F/D8InQMueF5H8PkQ3V9A97cchjbThlaPMZ9tiOSn1bviBRwgU4IgH2nzKhISMGJKvv3tRJCCCHh4PvSKtz3zg5dbWI6vFtbZKUnwNu+9AalEVlp8RjZox32HK/EEwVGPLd2v9BnXQkJF58WHsei/+zDf0piMKRzuu8xDiArPQEje7QLZYpEUrgSIQTA2O5t8Om/HsarN/RFaoIp3OkQQgjRIdYYI+5btRf/3lWKP288FO50QoYxxoAFU/sBQLMFjOH8fxdMvBDGGAP+vasM9Y0GnKisi+ivmiPRx76TNdjTwbmR4fXDOuGSrm0wsVMjDAaXMa40uumazhdM7QdjDMc04QJdv5hMzuOddzrP1cq0xIhovOXlr54gaXpdNwm9s9s4Qjb8cBJvHYiBuWkDGS19ikAfQqIR0dE77zH0TkynBx/06p2rVms7IroQe2f61R342w398bPeGXhkQi95/A6BD5MHZOHlXw5DZpr7W4Ez0+LxclU+Jg/MAgD8X05v3NPXhnsvu9ARU24Fbn/4DXxZXGF/VV1rn267PSK9C2h+ETyGHHjexxD06e2thzHpjR1YfOsTDk2CyYgVs0diXEcFcbEx9jF+0wBkNrp/zjyzsQ4v3zQAkwdkBc4Hrd4ROVCIrigpKVEAKCUlJeFORbFYLMrq1asVi8USVI0WXXWdVRmycK3Sbd4a5cUN+xzlDbZGZfPB08rqnUeVzQdPKw22xlbn11JNTb1V6TZvjdJt3hrlXLU5aBqt+UWjRvb8ZNbInJ/WeUHvQqvx1Lnet5p6a9jz06KpratX/rj8E+XjgsOqv0sC1Y5WXTg1Wn7P/m3DfqXbvDXK1X/bpDQ2NvfSV26+xlOkeRcMjez5NWnOVZuFnguBys3SYFPOmZ36krM1Su/f/UeZ8/Z2pd5q89mWDP+WdNUUFxdLszYgdvgKul6pO//Xu9paYNYs+7EJzzItMSIaNUTqCYEmOT4WS28ZjMHtGvHrcd0AAHk7DuPSeR/hlte34sEVhbjl9a249NkvkDfnSe9tRbgPQdOI6Oid9xh6J6bTgw969c6h9fG7LBB9CoImb3cpxv95E5YWGfHIh7twy+tbMXjhWrzzzUGOIQDG+jqMeWYerr24Hcb0bA9jfZ1fzfQBGbjbvB+/GdfV/rb32lo0zJqNN77ch/Iai7rIkYvLq5lz7oto7/gcOo/nfQxwn/J2l+Jnz32FP/9njyOmc9skbH1kLP7+1UuIs/rYhK22Fsa7ZmNMdhKuHdIJY7KTYLxrtjzeESngAl2vWK3O47JlznO1Mi0xIhpvefmrJ0Sakd3b4dcXN8JkjEHe7lLc+8FulMYkulVTVlmPe1NHIm9XqVg7Ye6TNBoRHb3zHkPvxHR68EGv3rlqtbYjoguwJm9XKe59ewfKKt3/AV9db8Pja/bh0//t5xjSkF9WohHzX3wEk3u1dWjWfvMDnl53ENe8+A1sjSqbyanV+6/luvMuKseQ530McJ/SEkwoq6zDxoNn0PCPfzpi2pkMke8dkQIu0Anxg61RwcLPiqAAgMeGNMr5/y7c8JP6PwAIIYQQADZDDBau/xG+flM8c/mv+bskQKTWm9G/YzKmX9LZbeOt/SeqwpgViTR+OFmDB1fsxLv5JY6yMT3b46Vbh2Hd7EsQ67HhGyGBgAt0QvxQcLgcpRV1Xq8rhhiUVtYjv/hsCLMihBASSeR37o/SKi9vtz5PadoFyC+pCFFG0c3PD+3Eml8PxX2X93KU7S2rQs5fNuGmV7aggX8IIQIUlFTgk8LjeOObQ2gaMgaDAVcNzEI8v5aXBAmOLL0SH+88LljgPFcr0xIjovGWl796Qqw5WeXjs0RucSqL+CjyIaAaER298x5D78R0evBBr965arW2I6ILoObk7bP89wfAyXpFe584htzODQkJiHNZRO06Vok4YwwuSItHbGJC83rn/47eRcMY8ryPgvnVP5mLD/acxvbDZx2a6cO74uYRXfDizYPR7BvQotE7Igfh3qWOhBbu4t7ytr7eV+bYGdTXz+aDp6XdeZq7uLdOI3t+Mmtkzo+7uEeGxlMXqbu4bz54Wvh3SaByC3afIlFzoqJWOXKmxnHtWHmNw/vymvqw5yebRvb8ArmL+zP/+UHpNm+NMnPZtwHJTauOu7gTvoKuV8xm+7GmBpg0yX5swrNMS4yIRg2RekKsGd6tLbLSE+D5h1MHioILUuIwskc7/+1I0qewa0R09M57DL0T0+nBB71659D6+F0WiD4FUDNyzq3ISo33+rvEACDLUoWRHeK194ljyK+mQ1oCurRLcsR8tu2w45rphv9H76JhDF17rZDm8FkzTlbWOWJufekJdG6TgJ/1yoBSXa1P74gUcIGuV2w253HdOue5WpmWGBGNt7z81RNijTHGgAVT+wEADF42A/lDTk+3TWi8tiNJn8KuEdHRO+8x9E5Mpwcf9Oqdq1ZrOyK6AGqM69ZiwZUXAkCzRXrT+YJ//w3Gpt8zMvkdjWPofMyMwR0dRYYvvgBsNtgaFcxfuQvfFp+ForhrbOs3YMtPZ/BJ4TFs+ekMbOs36NY7WceQ7cuvnPfoxzOwWRuaadYfMyBnyTd4eeOPjnq7rPkIm+4djtk/vxCGxkZdekfkgAt0QgSYPCALL1/fF5lVZ9zKs9Li8crqxZjcJwMAcPBkNV4qisHxc7XhSJMQQojETO6TgZd/OQwd09w/A5qZnoCXr++Lyfu3hCkz/aL2x/X1RWV4L/8IfvPed7C6/F0+b+9pXHrPMtzyzi48uKIQt7yzC5feswx5e0+HMGPij4mzXnbeo9e34tK/5+M/F42D1ea8mV2SgUYFOFFZB8XlrzAxBq/vlyQkZMSGOwFCIoXJfTJw5St3Iv+7Ypy0GdEhNQEj28fC+LjzH1RPfFqEfRUxeCZvP16ZOTyM2RJCCJGJ/Rld8cWWEsye2Bfje7fH0vfzcGH/Ichqk4yRPdrBWM2v/5KFvllpuHVUV3RIiUOceS8AIG93Ke5Z+QOQmuEWW5baHveu/AEvJyVi8oCscKRLPDiR0t7tvLTKgjnXPYZb1v6IZ26+BABwcbqCvAfGoU92mzBkSIhv+Aq6XklIcB5ff915rlamJUZE4y0vf/WEUWN87VWMuTgT1w7phDE928OYlOime/6GAejfthFPXt0nqn1otUZER++8x9A7MZ0efNCrd65are2I6AKkscXFY+6cJXjuq0N4Lm8vjDEG9E5XMHVQlv13SYyBY0iGPi39O5CQgG7tk7Ho/w3EnPH2jyTYGhX8fvVue4zHK6yKwf5P6YWfFal/h70evJNgDLl5r/YquMGAj3adcMQZDEDPC5ID26cI9Y7IB19B1ysmk/0YFwfM8vjqF88yLTEiGjVE6pFFo1LWpW0S7urTiAtS4x3XPx46GUMrLLjwgrjA5GdpCI1GS26iGhGdHsYQvdOuEdH508g2LyLJu2DmJ6JxxPr4XeavHRFdgDQx8XH45bQROPPFftx56YViOpn8jsYx1BTj+hy443Ygrvk/jQsOl+N0tffvsFcAlFbUIb/4LIZ3TfOdm0h+keJdazQiuhZodhw557t9AFabon6P1OrVkXdEPvgKul5p2sGxuhro399+bMKzTEuMiEYNkXpk0Qjotu89hrnv78A1f/sah8/48FyWPknknS7GEL3TrhHR6cEHvXrn0LbiuSqiC5DGUFOD6bdNwlf3jEBmupdXrziGwtQnlx2thw9X1Zysqvdej1tcHQDgWA3w3/2nUGG2Rrl38oyh09Utu0d+29KRd0Q+uEDXK42NzmNRkfNcrUxLjIjGW17+6pFFI6DrnBqHESV7MLF3e3RtlyR/nyTyThdjiN5p14jo9OCDXr1z1WptR0TXSo2iKPaNqc6XxULlLdDe6pXJ72gcQ2oxe/eqajqkxjcrU6NDqv2PL1tOxmD2Wzvxlw37He0oNhtKzprtG5JFm3dhGkOffXccM/+xDSdqgYyUlt2joPQpgrwjcsMFOiFBpGNqPN55//d47ureMJz/TFR9QyMOtO8S5swIIYQEm5U7jmHa0v/h+1JuABfJDO/WFlnpCb6/wz49ASN7tAMApMQq6N4+CSO6t3PEHDlXh589/xV+/sev1D+rTlrMqp3HsLW4HN+ejMGwrm18xhqURmSlxTvuESEywwU6IUHGqDQi0WR0nD/3VTGuvuOv+PD7E2HMihBCSDCxNSr4+1cH8UNpJb4uPhfudEgrMMYYsGBqPwD2hZ4rTecLpvZzfGXb5C4K1j90Ka4amOmIO3DajNgYAzqkJrh9tdv97+3E7W8W4BD/huMVq60Rb205hBtf2YzqeueeAb8a1x0PXN4Tl2Y2unna7B6d/++CiReqfq0eIbLBBbpeSUy0H5OSgLw8+7EJzzItMSIaNUTqkUUjovO43mBrxKFKKyyxcWjbJlldE84+Seyd9H2id6HViOj04INevXNoffwuC0SfWqExpiTjg3vGYM74nrjr8t7yeMcx5BGT6CxbvdqrZvKALLx8yxBkJrj/0zkzIQYv3zJE9SvWDAaDo52Jg7tgV+4kLPnFEEdZY0IiNu47ic0/nnXbeHznGQtyX/w3/nvU5fPxUnoXmjEUm5KMf/7vELYdKse/f6x0aH7W+wLcf0VPtPN4d3uHeI97lJ6AlwcaMXlY1+D2SULvWjz/iBTEhjsBEiZiY53HSZOaX3Mt0xIjovGWl796ZNGI6Dyuxxpj8MbtI/Bt8VmM6en8ns4Gz48D+crP287TgdYI9ilU3oU0P5k1Ijo9eCei86eRbV5EknfBzE9E4xqrtR0RXSs1GSmxmDu5j71cFu84htxjXJ8DEyc6x5QKkwd3wpUDs5FffBYnq+rQIdX+tnafr8q65JIIoEvTfjSTJsGgKPjgnjHY+uNppJ3c5ZD89+BZvHlUwbnvyzC+X5ajnk86DkCf07Xo3SHFb1uq5zJpvJTVNQAvb/wJBUcqsPxXI2CYNAkGAL+5ohfOma3IGZgNJHf32dSG3+Vg97FK8XsUqD5Fw/wjUsBX0CUiNzcXBoPB7Scz0/n2KEVRkJubi+zsbCQmJmL8+PHYs2ePtsaqzr+XqrISSEuzH5vwLNMSI6JRQ6QeWTQiOpXrMdVVGDO0h6Osqq4Bz39vxOvfFKOx6XNpkeRDCL2Tuk/0LrQaEZ0efNCrdw6tj99lgeiTBk3VqbP4rtcQeb3jGPKIcXlveWaWX42xTTrGXGDCtUM6YcwFJhjbpGv2zlBVhT6Zabh1ZBcYY5yXR2cm4Pbv12LShemOsvKyM3hwRSEmLdmEcrPFUX6muh51VlsAfJBjDBkMwKubirFp/ynk7znquH79sM749aU90NZW57ctY3a28x71bA9jdZW8Psg2/4gUxPgPIaGkf//+KC0tdfzs2uX8i+rzzz+PF154AUuXLsW2bduQmZmJK6+8ElVVrfzgkpres0xLjIhGJB+ZNSI6P5pPvjuOE7UGvL21BNWuf8mPJB/C5J2wLto0Ijo9eCei04MPevXOXx2iMQH24bmvDuH/Xf8UXtt6tPX5yawR0ck+hjxjquXwbky3Nlj4+YuY0ifDUVZea8XYQ99hYGYK2rvsWP7c2v0YlLsO73x7OHD5hUhzoqoezwy7AY+u2e8oizcCD1zRE3+6cTAGZqZoG0Nq91FiH6SbfyTs8C3ukhEbG+v2qnkTiqJgyZIlePzxx3H99dcDAJYvX46OHTvi3Xffxd133x3qVEmAuHVkF+wt2oPrJwxCWoIp3OkQQgjRSGOjArPFhsYYIwZkenkbMiEauLB9Et59/3Eor55zKz90xgyLrRGd2zo/V7y7rBoPvL4Dl/dogydCnGdLMFsb8ero6YjZdQK/rahFRpJ9WfLrcd1hMpn4Si/RLVygS8aBAweQnZ2N+Ph4jBo1CosXL8aFF16I4uJilJWVIScnxxEbHx+Pyy67DJs3b/a6QK+vr0d9fb3jvOnV9oaGBlitVqChwb5hXEMDYLXi/EX3Mi0xAhrr+fKmo0i9WjSu8dYW9NGvRqRPApqGhgaM7ahgYFayQ/fNwdMoGH8HfmOxItYjP9e+W60NQn3SookE7yJ+DNG7sPqgdV7Qu/D6YFWcnyW1Nnj/XRYuH567qifufOyXuGjuFq/1cAyF1gdfGmtDg6PImnD+XGbvbDY3zdt3DEVZdQM6pMbDWmcGEhPx7eFy/HSqBp1TTLC61POntftQdtyAoeU1yGqbHNIx9OOJKvzzqgfR7quf8MhV/QEAndNMuGfbSgxaPB/p8TGt9M77feT8a4EPAmzatAl//OMfsX37dpSWlmLVqlW47rrrHNdXrlyJV199Fdu3b8eZM2ewc+dODBkyxHH90KFD6NGjh2rdH3zwAW688UYAQPfu3XH48GG36/PmzcOzzz7bonwjEYOiKPwyRkn4/PPPYTabcdFFF+HEiRN4+umnsXfvXuzZswf79u3DuHHjcOzYMWRnZzs0d911Fw4fPoy1a9eq1pmbm4uFCxc2K3/jjTeQkZGhoiDhpq4BeLrQiCqrAdd2s+GKbPcpWm8D5ubb/7b2/MgGxBvVanFHi4aQaIfzIjLhfSOBJBrHk7kB+KnKgLgY4KJ0+78hGhqBeflGNCgGPD6kAR3Ob15/3AxU1BvQPVVBYgtetmtUgB8rDai0AmkmoGeaAl/7sBWVG/DqXiOSYhX84RIbYgP8IdtovI+h4vTp05g1axZKSkrQuXNnv/Gff/45/ve//2HYsGG44YYbmi3Q33rrLRQXFyM7OxuzZ89utkC32Ww4deqUW52vvfYann/+eZSVlSElJQWAfYF+5513Yvbs2Y64lJQUx/Vohq+gS8SUKVMc/z9w4ECMGTMGPXv2xPLlyzF69GgA57+ywwVFUZqVuTJ//nw88sgjjvNjx46hX79+mHDFFejUuTOgKEB1NZCSAsd3fHiWaYkR0FitVqxfvx5XXnml/a1MAvVq0QBorguERqRPGn0wdTuOd7YcxtO3jUBCXKybxmy1YW7+lwCAK664AunJCX590KKJVO8iagzRu7D6oHVe0Lvw+mBVDI77NiknB0nxsWH34Z+r1+NEYnc8cuVFSI4zSuudXseQL405LtE5nsaORVK79Kj0rsZiw4Mph/BV4UHMmDoRcXFxAIDF/9mLf353BDNGdMbCq/oBigKlqgqltlhktUmEQaWttXvK8My/96KsyrlZXWZqHH5/dR9M6p+JnUfO4R//O4T0ulIs+OVEmEwmTLI1ouaTXZg8pDNGdG+HmBiB+9QCH6644gogf5PqfeT88635+c9/jpYwZcoUtzWLJzNnzgRgf6VcDaPR2OzjvKtWrcIvfvGLZovv1NRU1Y/+RjvcJE5ikpOTMXDgQBw4cMAxOMvKytxiTp48iY4dO3qtIz4+HmlpaY6f1NRUAEBsXR1MJhNMdXUwtW9vP5pM6mVaYkQ0TYvRpuuC9WjRNNMFQiOSn0Yfrr+oLT564DKk2qwOzeeX/j8oNWbnLwYAJlOscJ+0aCLRu4gaQ/Qu7D5onRf0Lrw+OO6bBD4YjbFY8aMR//r2KP684UfpvdPrGPKlcYynrl2i1rs2KYm4Z3wv3H5RI+Li4hya9nEGdD97HKOykx2akxf2xWUvfIMJS76BIcbo1tYX+07j/hXfo6zS+fFJADhRWYf7V3yPL/adxu7SauQVncQ3ZTGOdhIs9Xj65hG4NCsJ8fFxQfDB+Zpjs/vI+edXA9g/BltZWen4cf2IbDDZvn07CgsLceeddza79txzz6F9+/YYMmQIFi1aBIvFolJD9MEFusTU19fjhx9+QFZWFnr06IHMzEysX7/ecd1isWDjxo0YO3ZsGLMkwcL1fRGrd5/EA9Pm4sa3vofV5vml6YQQQsJFTIwBV3dtRL+sVNw/oVe40yGkRfxmXFf89/W7MK3fBY6yAxndYDQA7ZLiEOvy/W//99H3eGBFIRTA+arseRSDPW7hZ0WYNjgbM0d1wYxetlB0gQSIfv36IT093fHzzDPPhKTdZcuWoW/fvs3WMw8++CBWrFiBr776Cr/5zW+wZMkSzJkzJyQ5hRu+xV0iHn30UUydOhVdu3bFyZMn8fTTT6OyshK33347DAYDHnroISxevBi9e/dG7969sXjxYiQlJWHGjBnhTp0EmdR4I9rUVmL8hV1hMvLvaoQQIhN92yh45JbR9rcNV+rjFR4SXbh+XPLynwqw67djccoQ5yhTFOCLfadgafD+IoECoLSiDgdOVuPJa/riP/8pDmbKJMAUFRWhU6dOjvP4+Hgf0YGhtrYW7777Lp54ovn3DTz88MOO/x80aBDatm2L6dOnO15Vj2a4QJeIo0eP4pZbbsHp06dxwQUXYPTo0di6dSu6desGAJg7dy5qa2sxZ84clJeXY9SoUVi3bp3jbeskepnQuz3W/uM3yFj4E1zfcFRrtSE9bFkRQoh+URQFtZYGmM6va3ztB0NIpJEUZ0S3tGS3srt+diH+vP6AX+3JqjoAaUHKjASL1NRUpKWF9r599NFHMJvNuO222/zGNu3HdfDgQS7QSehYsWKFz+sGgwG5ubnIzc1tfWNNmzCkpgIVFfZjE55lWmJENGqI1COLRkQXQB86Hiu2n1udbxm7dVkBXvnlJejdMdV3n1w0AfchAryLKo2ITg/eiej8aWSbF5HkXbj75ND6+F0WiD750Hyw9xz+9uV2/GFa39a3oxYjs0ZEFyk+xCc6y46X0jsvGoMBGNpF7CWBDqkJgemTVh8876OM407WMRRili1bhmnTpuGCCy7wG7tz504AQFZWVrDTCjt8r6xeaWx0HktKnOdqZVpiRDTe8vJXjywaEV2QfSg5W4uGRqVFmlqLDWZLg/2nzgLzT4ftR7Xz8z/1NgRW46XMl0ZRFLnHg47GnRQaEZ0efAixd8qRI97nrODzQYvGXWdzzzNMPry19TCOnavF/pPVrW9HLUZmjYgu0nwAgKNH6Z0PzfBubZGVnuC2R44rBgBZ6QkY2aNdYPqk1QfP+yiBdwHViOi0eidAdXU1CgsLUVhYCAAoLi5GYWEhjhw5AgA4e/YsCgsLUVRUBADYt28fCgsLm210ffDgQWzatAmzZs1q1saWLVvwl7/8BYWFhSguLsYHH3yAu+++G9OmTUPXrl1blX8kwFfQ9YrZbD/W1AADBtj/otb0thbPMi0xIho1ROqRRSPSp2D4kJDkCPvzjQPQN8tZrzJgAAx+NKOf26jS4BE/57GOr6EJrMazzLtmeLe2+HBGfxhkHQ/RPu5k0wTCB5d5IUWfJPdOqa7G9Gc/x/bOh10C1eYs4Pv5oEXjRVdjBhLjQz6GDAMH4oNTZ/FOUTl+ObIT1q0tal07ajEya1rhnXR9OnXWWTZyBHD6JL3zojHGGLBgaj/c+/YOGJRGx8ZwAGBQGgFDDBZM7QdjjAGNNj/tBNMHz/sogXcB1QTTOwEKCgpw+eWXO86bvs759ttvx5tvvolPP/0Uv/rVrxzXb775ZgDAggUL3N4F/I9//AOdOnVCTk5Oszbi4+Px/vvvY+HChaivr0e3bt0we/ZszJ07V3PekQQX6IREMD/vneH4/5JzdZhz21+wqLQKgzwevIkmIy7p2gbbj5wLcYaBo+BwOWqtjfCypCKEBJlaayO2d+4X7jQcDD+6B4mmS8PWflKcEbN/fiGsVmvYciAk1EwekIWXr++LhW9uQmma823JmVVnsOCOn2PygOh/+7HeGT9+vP1djV644447cMcdd/itZ/HixVi8eLHqtWHDhmHr1q1aU4x4uEAnJEp49sti7Mrqjac3/IT3L8p2ewuawWDAe7NGYPWazzFpUo7jOy9RWQVkZ9k/r5WW2vwcgNVqxdq165y6QGhU2vamMR8+iuF//TaEThJC/FHw+4kwGRrd5yzg9/nQbJ4LaAD1Z0riBdfA8NI9Ie13hdmKHT+exeX+QwmJWib3ycCVr9yJ/O+KcdJmRAejDSMH94Dx2fJwp0ZIVMAFOlHfLMKzTEuMiEYkH5k1IroQ+bB4Si8krP4YD8151Lmb8HmNrVFB/qFy7C43IPtYJcb06gBjjAGIMwIJcfZjXGzzcwBWg4J4I5AUFwuTSSVGiwZoVuZVYzIG3buI04joOP+0a9Riok0jovNxPSnOCJMhxn3OAn6fD83muYAG8PJMCZEPtrQ05B8+h5O2KqzecQxf7T+FORNnwecbLfUw7kR0keZDCr0T1RhTkjGmWxv7W6UrK4GUZL+akPmgdh8l8k76MUTCDhfoeqVpgjY9WF3xLNMSI6JRQ6QeWTQiumD4YGlQlaR3bI8/r37eTbPyv0U4uu0E3svfhtKKOgBG/OtAAbLSE7Bgaj/7W9Fk9861v2mp8o4HGb1riSZQ+UWSD17mUkT5EFLvBP5hF2U+5B2pwcLHVqL0nV1u5Unz53r/DCfnn3ZNoPJricb1OVBW6vwjcrByE9FFinet0YjotPrgeR9l9kE274gUxPgPIVFJQ4PzuHat81ytTEuMiMZbXv7qkUUjogujD3uPlmPuh9/hhfX7zy/OnZRV1OHet3cg77tj9C7SNCI6PXgnotODD6H2zh9R5EPe7lLc+/aOZs9PAPjzuv3252cA2glonyTxTrMm3H3asIHeRcMY8ryPMvsgm3dECrhA1yu1tfaj2QxMnuzc1V2tTEuMiEYNkXpk0YjowuhDdpwCU33zf1gCQNPWHgv//QNsU66S3Ltal7JaeceDlN5x/kW9DyH1rlY9VoY+BdgHW6OChZ8Vwes2SEqj/fnZqBKhh3EnoosYH1zG9XXX0btoGEOe91FmH2TzjkgBF+iERCl7TlSjNi7B63UFQGllPfI79w9dUoQQEgEUHC5XfeW8CcUQY39+Fp8NYVaEEEL0ABfohEQpJ6stYnEpbYOcCSGERBYnq+oF47wv4gkhhBAtcIGuV2JinMd+/ZznamVaYkQ03vLyV48sGhFdGH3okOr91XNXOmSk0btI0ojo9OCdiE4PPoTaO39EiQ8dUuN9dNI1TuU5q4dxJ6KLNB8AoE8fehcNY8jzPsrsg2zeESngLu56Jfn812GkpAB79rhf8yzTEiOiUUOkHlk0Irpg+OBt52kPzch+nZCVvg9lFXWqn6M0AMhMT0DXTz5AQ2K8/WEgo3eu/U1Jlnc8yOhdSzSByi+SfBCcS1L7EFLvBL5GKUp8GN6tLbLSE/w+P0f2aIdGm8c44vzTrglUfi3RuD4HCgp87+JO7wKnEdFp9cHzPsrsg2zeESngn1D0itVqP1oswBtv2I9NeJZpiRHRqCFSjywaEV0YfTA2WLEgsRSA/R+TrjSdz7+yF371l7W48eXNOFpultQ7q0uZVd7xIKV3nH9R70NIvbOqx8rQpwD78I/Nh3DvZT0BAAaPJbr9+algwZSLYIzxfLq2rJ2A90kC71qlCUufXMb1m8vpXTSMIc/7KLMPsnlHpIALdL1SV+c8zp7tPFcr0xIjovGWl796ZNGI6MLsw+SHZ+Ll6/siM939bZiZ6Ql4+ZfD0D01FqWV9SgpNyPRZKR3kaAR0enBOxGdHnwItXf+iAIfvj1pwPNrD+AvG/bjT9dchMzK027VZKbF4+VVizH5wvRWtROUPkXCGJK5T7+5j95FwxjyvI8y+yCbd0QK+BZ3QqKcyX0ycOXwHthy8CTWff0tcn42CmN6dbC/8lNZibx/3I/SLdvRPiUeqLRvjFTX0IiEMOdNCCHhoG8bBYM6pyGnXyZuGNQR1/38TuR/V4yTNiM6pCZgZPtYGB/fEu40CSGERClcoBOiA4wxBozq0Q5nflAwqkc7t7dldqo6hU5dnK8EFXTqizkvbcOz0wfhij4dw5EuIYSEjbQ44N07RyI5IQ6oqoJRacSYbm2AtDR7QGVlWPMjhBAS3fAt7nrFaHQec3Kc52plWmJENN7y8lePLBoRXQT68MrUe3Gy2oJ/f18WvHZEdBHoHcddCDUiOj34EGrv/BGhPpyus2HbIed3msfHxsBgMHDctaZPkeYDAEyYQO+iYQx53keZfZDNOyIFfAVdryQl2Y/JycDate7XPMu0xIho1BCpRxaNiC4YPnjbeTpAPiz92xy8uvEn/PrS7sFrR0TXdN21v8lJ8o6HaB93smlEdP40QZ5L0mpEdKqaJN/tBCq/EPtQt+Y/uOv1rdh1rAJ/nj5QSMP5J6CLFB9cnwOffOJ7F3d6FziNiE6rD573UWYfZPOOSAFfQdcrTTs41tcDubn2YxOeZVpiRDRqiNQji0ZEF4E+JCz6Ax78WVekJpgcYSuLY/DiVz+iwdYYBu9cdhutt0jtHcddCDUiOj34EFLvBHb+jUQfnn4aWalxSDQZcXHHVDGN3sediC5ifHAZ14sW07toGEOe91FmH2TzjsiBQnRFSUmJAkApKSqyF1RUKApgPzbhWaYlRkBjsViU1atXKxaLRbheLRpVXSA0IvkFwYeaeqvSbd4apdu8Ncq5anPQvdt56LSjvW3FZ0LuXc2ps472a06dja4xFEHjTjrvAuBDKOdSNHjnNhfrrVHlg+3cOaX4VDXnnwbvIsEHXxq3cW2Kp3cROobOVZu93kd651tTXFxsXxuUlChEDvgWd0KIT/pnp2FmLxtSOvXC8O7tuEESISQqKDlrRpfz/wqKMRjQPSMZVqvAd70TQgghQYRvcSeE+GX4BQoemdjbcV4Rn4z5/zmA09V8mxQhJPIoOHQWE17YiOf/ewiNMPgXEEIIISGCC3S9YjI5j3fe6TxXK9MSI6Lxlpe/emTRiOii1Ien7nke7xWW4e63tkOJjaV3odSI6PTgnYhODz6E2jt/RIgPBYfLYWloxMGztcCdvw7sGNLDuBPRRZoPAHDb7fQuGsaQ532U2QfZvCNyEO732JPQ4vgMugSfM1H9PE8QNKFsKxQar5+bDUJu3nRFxyuUKUs2KdsPnw1YW940rv2tqbcGrZ1g6KiRO79QzqVo8M5zLka6D2t3l4btmRLp3kWTRobfMTJrZM9P9TPoHvcxXLmFsq3WaPgZdPngK+h6pa7OfqytBWbNsh+b8CzTEiOiUUOkHlk0Iroo9aHvE49gzexLMKxrW4fmmz3HUFhyTqwdkbYc1+tcyuqk8oHjLowaEZ0efAipd3XqsTL0SUDTUFODRpeYnP6ZSLJZAz+G9DDuRHQR44PLuJ5zH72LhjHkeR9l9kE274gUcIGuV5o2wrFagWXLnOdqZVpiRDTe8vJXjywaEV0U+xDT0OA4P/Xex3jw4z244eXN+PrAKf/tiLQVxd5x3NGHsGpEdN40/pDUB0VR8OSavbjvTAfUmuu81+OvnWBpNPQprBoRXaT5AAD/Wk7vomEMed5HmX2QzTsiBVygE0JaTZzNirHd26B3hxSM7NEu3OkQQogbP56qxkffn0DeRWOw81hVuNMhhBBCvMIFOiGk1aTX1+DF6/rgg3vGID7WCABQFODrg6ehKEqYsyOE6J1eHVLxzoyB+MO6lzG2e5twp0MIIYR4hQt0vRIf7zwuWOA8VyvTEiOi8ZaXv3pk0Yjo9OCDy3lagnNn0ILTBvx6+Q7c+/YOKHFx9C5QGhGdHrwT0enBh1B75w+JfRjRqwN+ee3I4I8hPYw7EV2k+QAA839H76JhDHneR5l9kM07Igfh3qWOhBbu4i7vLpoy7jytVWexWJTfvvKJ0ut3/1b+tmF/q9qRYYfdULYVbRqZ8+Mu7i3TRNou7iWnK5Vrnv1UOXqmKqjtyKyRPT8ZNDL8jpFZI3t+3MW99Rru4i4ffAVdr5jN9mNNDTBpkv3YhGeZlhgRjRoi9ciiEdHpwQcvmp9lKvjsvrG4d3xPR8yZk+UwWxrE2nJcN7uUmSPOh4BrRHR6GHciOj34EFLvzOqxMvRJRTN/1R7sKo/BvJW7ffQpCGNID+NORBcxPriM62uvpXfRMIY876PMPsjmnQCbNm3C1KlTkZ2dDYPBgNWrV7tdX7lyJSZNmoSMjAwYDAYUFhY2q2P8+PEwGAxuPzfffLNbTHl5OWbOnIn09HSkp6dj5syZOHfuXKtyjxS4QNcrNpvzuG6d81ytTEuMiMZbXv7qkUUjotODDz40PS9IRqwxBrDZoKxbh4dX/4Cr/vo1dh+roHdaNSI6PXgnotODD6H2zh8S+bBgal/0TFXw1LS+vvsU6DGkh3Enoos0HwDgiy/oXTSMIc/7KLMPsnknQE1NDQYPHoylS5d6vT5u3Dg8++yzPuuZPXs2SktLHT+vvvqq2/UZM2agsLAQeXl5yMvLQ2FhIWbOnNmq3COF2HAnQAjRB6WpGThwqgZnaxuQYIoBwM3jCCHBo1u7JNzf34YubZPCnQohhEQNU6ZMwZQpU7xeb1pEHzp0yGc9SUlJyMzMVL32ww8/IC8vD1u3bsWoUaMAAK+//jrGjBmDffv24eKLL9aWfITAV9AJISEhu+o08mYNw+u3DUevDqmO8rqGxjBmRQiJJj7ZcxJ7jlc4zg2GMCZDCCHEK++88w4yMjLQv39/PProo6iqcn4F5pYtW5Cenu5YnAPA6NGjkZ6ejs2bN4cj3ZDCV9D1SkKC8/j6685ztTItMSIab3n5q0cWjYhODz60QJPeNhU/7xjnKDv80j8w/aVtePjKizF9aKaqht5x3Gnukx58CLV3/gijD/l/exO//Ww/4mIP4tPfXIpubeN9a0Ta4rjT3qdI8wEAlv6d3kXDGPK8jzL7IJF3VVVVqKysdJzHx8cjPkg7vt96663o0aMHMjMzsXv3bsyfPx/fffcd1q9fDwAoKytDhw4dmuk6dOiAsrKyoOQkFeHepY6EFu7iLu8umjLuPK1VJ6p56rM9Srd5a5QbX9ms1NbVcxf3KNTInB93cW+ZRvZd3M+ZLcqM17co97+7Q2lsbJTKu3BpZM9PBo0Mv2Nk1sieH3dxb72maRd3z58FCxb4rQOAsmrVKtVrTfXu3LnTbz0FBQUKAGX79u2KoijKokWLlIsuuqhZXK9evZRnnnnGb32RDt/irleadnCsrgb697cfm/As0xIjolFDpB5ZNCI6PfjQCu8en3cTfn9lT7xw02AYY+zvRVUUxUPjsttodY28feK4C61GRKcHH0LqncDOv2H0IX34ELx5Yz88P30QDGrvbQ/VGNLDuBPRRYwPLuN6+HB6Fw1jyPM+yuyDRN4VFRWhoqLC8TN//nzvOQWYYcOGwWQy4cCBAwCAzMxMnDhxolncqVOn0LFjx5DlFS64QNcrjY3OY1GR81ytTEuMiMZbXv7qkUUjotODD63wLqZoD2aNyEZnl02c3txyBPNX7kJNnRUoKoKtwbnbaP6hcth+2CtnnzjuQqsR0enBh1B7548Q9MnWqGBL8Vl8omRg08Ez2HzwtENjMgAJJmPL+hToMaSHcSeiizQfAGBvCH6/iOgizTvZfPC8jzL7IJF3qampSEtLc/wE6+3tauzZswdWqxVZWVkAgDFjxqCiogL5+fmOmG+//RYVFRUYO3ZsyPIKF7HhToAQQpqosgJ/Xn8A9Q2NGN4xAckXjcGC17Y7rt/xwR5k3bMMC/aexuSRaWHMlBASDtbuOYFFn+9DaUUdMG0usGI3AODmIR3xbJhzI4QQPVBdXY2DBw86zouLi1FYWIh27dqha9euOHv2LI4cOYLjx48DAPbt2wfA/qp4ZmYmfvzxR7zzzju46qqrkJGRgaKiIvz2t7/F0KFDMW7cOABA3759MXnyZMyePdvx9Wt33XUXrrnmmqjfwR3gK+iEEIlINQGvzxyKW0Z2RZIpBvde9zucqLa4xZSltse9K39A3u7SMGVJCAkH350x4P4V39kX5x6sKDyBvIvGhCErQgjRFwUFBRg6dCiGDh0KAHjkkUcwdOhQPPnkkwCATz/9FEOHDsXVV18NALj55psxdOhQvPLKKwCAuLg4fPHFF5g0aRIuvvhiPPDAA8jJycGGDRtgNDrfAfXOO+9g4MCByMnJQU5ODgYNGoS33norxL0ND3wFXa8kJtqPSUlAXp792IRnmZYYEY0aIvXIohHRBcMHb++CihLvxlzYHuN6d8Slz34BReVzpIohBgYACz8rwvjeP5OnTxJ41ypNOPsULh+iYS6F1LtE720EuU+2RgUrD8VA8VKFAcDCmx/HlQmJ8PIG99DNJc4/7Zqw9MllXK9eTe+iYQx53keZfZDNOwHGjx/v3C9IhTvuuAN33HGH1+tdunTBxo0b/bbTrl07vP3221pSjHi4QNcrsbHO46RJza+5lmmJEdF4y8tfPbJoRHTB8MHS0HKNlnaCqfGjyy8+i9LKeq/VKQBKK+pQcLg8OPnJrBHRcf6JaaJhLoXaO38EqU8Fh8txzuL9S80VAKX1QH5JJcb0bC/Wjkh+nH/eY6LFB9fnwMSJvsc5vQucRkSn1QfP+yizD7J5R6SAb3HXK1VV9mNlJZCWZj824VmmJUZEo4ZIPbJoRHR68CEI3p2sav4WVjW+OXgGjZ5/xI0kHzjutGtEdHrwIaTeVXlvI8h9Olnl/Q92rpw8qfJHO2/tiOTHcae9TxHjg8u4zsyid9Ewhjzvo8w+yOYdkYKYcCdAJKBK5R9dnmVaYkQ0IvnIrBHR6cGHAHvXITXBf30A/rn5MCxqb1OOJB847rRrRHR68CGU3okQhD51SBXbTbhDSlzL2vFWFg6NWozMGhFdpPlQTe9CqhHRadGo3UeZfZDJOyIFfIs7IUQ6RvZoh6zUOJRV1kExqP8dMSnOiOuGZMGAQzBbGmBSDHh140+4OM2IS+MSEWux2d+6aLEBpnj70dIAq7UB9TY4NACaxTQ7B5rrWqlJVBR4f8Mu0ROKoqA2ROPO8XZef/NCRWO22lTzDxallfV4es0O/Gpsdwzv1hZt4hRUWAyqn0M3AMisPIWRXdJDmiMhhBASaLhAJ4RIhzHGgAVX9sS9HxfBAKj+g9xsseHd/KN4F7GYm/+l+8WHPwT+tNl5/sjH7udqGs+YZhoVXSs0w7u1xYcz+nORrnMUANPf+h7bj55/y2GQx50b/uaFar2h46XNJfj396U4fKYGK+8eheu7N+Kf+43NnglNc2jBF6/BGHN9GDIlhBBCAgff4q5XmnZwTE4Gdu+2H5vwLNMSI6JRQ6QeWTQiOj34ECTvJl/SDS+P74COaWJvbY00Cg6XozYugeNOq0ZEFwE+1O50WZxHCMO7tUGiycte6a30rrFRcZw/PLkvrujTAc/dMAgGgwGD2yt48ebByEx3/whMZnoCXp4xBJNXvi7HGIqAccf55xrjsqN1/jZ6Fw1jyPM+yuyDbN4RKeAr6HolJsZ57NLFea5WpiVGROMtL3/1yKIR0QXDB5uXt5lGoXeTx/XBlTkpyD90FkfLa3FBShwuaWeCMT0VMBhgtVqxdu06TJqUA5PJZNcpClBVDaSmwGJTMP5P/8U5sxUv3zoUl13cwa8GBkPzc6C5TqPGfLYCw//6bdC94/wT0Mgwlzp3BnAEAFDw+AQk1dcGZdx5xniWtUST2C4dBpWvQGyNDyfbdsTiD79HSkIsnr52ANClC9qlJuAfd4xw9AkAJvXviCmDOiG/+AxOnjyHDh3aYGSP9jAaAFSlyjGXOP+0a8Ldp86d6V00jCHP+yizD7J5R6SAd0ivVFfbj1VVQHq6+6YRnmVaYkQ0aojUI4tGRKcHH4LsnbGmGmN6ZuDG4V0wPjsRqR3bI6m+FklxsUiKi0W8EY7/T4qLtV+7oB2S6mthjDFg5tBMDDm+F1d2SXbE7K8wIG/PCRhgaKZRPVdrS6umW2eXPlZz3GnViOgiwYfsLMdpUMedZ4xKmajG0PT7I4A+/Dj6CqwuPI4V+SU4fuy0T40xxoAxF8Th2rG9MeaCOBhjDHKNoUgYd5x/LjEu4zk7i95FwxjyvI8y+yCbd0QKuEAnhEQtqQkm/Pay7lj91qOIjXG+4pd3NAbzVu7Be/lHwpgdIfqmss7q+P8xJbvw2593w8o5Y5EdpR9rIYQQQkTgAp0QoisabI0Y3K4RPS9IxnVDOznKd2ZdhLe2H3dbNBBCAk9tA/DYqt248oWNqHKZb/df2hWDOrcJX2KEEEKIBHCBTgjRFbHGGOR0VvD5/WPRLtn5ncn/GH4tnlj7I/60dl8YsyMk+omNAQoOncOJynr8d9+pcKdDCCGESAUX6HolJcV+TE0FKirsxyY8y7TEiGjUEKlHFo2ITg8+RKh3bptcpaZi5MO/Ru8OKbhpeBeH5mhDLN74+iecrbEEzofjpS7nKRHpnRQaEV0k+NDa8RAh3h2sNUBR7F+OZooBnr2+P1bOGYupg7OjbwxFwriT1buw9CnFWXa8lN5FwxjyvI8y+yCbd0QKuEDXK42NzmNJifNcrUxLjIjGW17+6pFFI6LTgw9R4t3MDjase2AcBnRKd2g+2HYET//7Bzz0fmHA2sHRo63rj4hOD+NORBcJPrR2PESAdws+LsSVSzZhXdEJR9jwbm0xrGvbwPVJJh8iYdzJ6l24+3T0KL2LhjHkeR9l9kE274gUcIGuV8xm+7GmBhgwwH5swrNMS4yIRg2RemTRiOj04EMUeWfwmBe90mIxsFM6brzEufN6ndWGl/57EGVlZ7W1M3KEy7k5arzjGNKoae14iADvUv75BhQFKCw5F7w+yeRDJIw7Wb0LS5/MzrKRI+hdNIwhz/sosw+yeUekgAt0SXnmmWdgMBjw0EMPOcoURUFubi6ys7ORmJiI8ePHY8+ePeFLkpAoZ1r/Dvjs/ktxzSDnV2Gt3XMCz+ftw81vfw8ljLkRIiu7O/bE8cp6x/lvtnyAD2cOwrzJfcKYFSGEEBIZcIEuIdu2bcNrr72GQYMGuZU///zzeOGFF7B06VJs27YNmZmZuPLKK1HF7zMkJKi4fl69XUocRnRvixsGdkRTqaIoeCP/GI6kdwxPgoRIwvKC45h22wv4w/ofHWWJDfUY0SU9jFkRQgghkQMX6JJRXV2NW2+9Fa+//jratm3rKFcUBUuWLMHjjz+O66+/HgMGDMDy5cthNpvx7rvvtq5Rtc0iPMu0xIhoRPKRWSOi04MPOvLuZ70y8OE9Y3HfuC6OmJ0l5/D0hp8w6c6/w2yx+WzGlprm+P/8IxWwpaX5iPaRSwR6F3CNiE52H1Iixztbo4ItF4/EJ3tOYsuPZ2BrbP4eklFd02EAYDLGwNLQqD0/iX0ImkYtRmaNiC7SfPCcj8FqR0QXad7J5IPafZTZB5m8I1IQG+4EiDv33Xcfrr76akycOBFPP/20o7y4uBhlZWXIyclxlMXHx+Oyyy7D5s2bcffdd7esoaYJmpYGVFa6X/Ms0xIjolFDpB5ZNCK6YPhgaWi5Rks7wdSI6CQfQzHp6Y4YY8U5/Kx3BjqkJiApw/mHtY92HEOly8cb847UYMFv3wXOv/33jg/2IOuxlVhwpAaTB3hZqEehd9KMIRnmUlkp8OTa8+ep0nqXt7sUCz8rQul1TwKf2L+KMDMtHqPbGhCz5wSmDrHv09CnVxa+nDcB3dona88vksZQoDRqMTJrRHSR4oPrc6CsFIjz8U9jehc4jYhOqw+e91FmH2TzjkgBX0GXiBUrVmD79u145plnml0rKysDAHTs6P4W2o4dOzquqVFfX4/KykrHT9Pb4Rvq6mC1WmGtrYU1L89+tFrVy7TEiGisVgBwXhesR4ummS4QGpH8guRDE1ZrA72TYAz1y0zGP2YMwqLU4w5NaXk1nvikCM98F4sfT1RiTeFR3Pv2Dpxw+WwuAJRV1OHet3dgTeFRXXoX7jEUyrmkqlm/3plDbZ2U3jWN3dKKOvexW1mP1Ydj8OhHu3DsbLVDl73ta12NIc6/yPDBu8Y5rq3r19O7iB1Dzj+0NLuP9M6vhsiFQWn6YlISVkpKSjB8+HCsW7cOgwcPBgCMHz8eQ4YMwZIlS7B582aMGzcOx48fR1aWc8Oq2bNno6SkBHl5ear15ubmYuHChc3K33jjDWRkZASnMySo1NuAufn2vww/P7IB8cYwJ0RUOVkLfHo4BjUNBtzf34aFO4w4ZwEAg0q0gjZxwIJhNsSoXSZBQYa5JEMOvmhU4HfsmgzA40NtaBsf4uQICQCyz0EiBu+jdk6fPo1Zs2ahpKQEnTt39i8gwUchUrBq1SoFgGI0Gh0/ABSDwaAYjUbl4MGDCgBlx44dbrpp06Ypt912m9d66+rqlIqKCsdPUVGRAkAp/v57xWKxKJYzZxRLYqL9aLGol2mJEdDU1NQoq1evVmpqaoTr1aJR1QVCI5JfEHw4V21Wus1bo3Sbt0Y5VV5J7yQfQx+vWq18teeo4575+nnpy/1u7Rw5XalUlJ3UrXfBHkOhnEveNOdS2zhyOFfa/F6H27uvd/4kNHa/3lemyzHE+RcZPvjSnCs96ZyDqW3oXYSOoVPllV7vI73zrdm/f78CQCkpKQn2cocIws+gS8KECROwa9cut7Jf/epX6NOnD+bNm4cLL7wQmZmZWL9+PYYOHQoAsFgs2LhxI5577jmv9cbHxyM+3vmyRuX5z57ExsbCZDIBsbFAba39aDLh/EX3Mi0xIprzmEwmey6ibWvUuOkCoRHpUxB8MLlpYumdxj6FygejAThb63vjuCa2Fpdj1qXd7TpjLC7701doVID8mAR0OF/vmu+P4/OdJbi85xhMd2lr3zkDOp8wY2CXdoiLEu98akT65EcTjrnkqTHV1TqvxcbCJIF3lfWN+GbfWRi6j4AiOHbPmBtalF+0jKFofnZFow9qGlNsrPN63flzeheBY8jHfaR3fjVELmL9h5BQkJqaigEDBriVJScno3379o7yhx56CIsXL0bv3r3Ru3dvLF68GElJSZgxY0bLG4yJcR779XOeq5VpiRHReMvLXz2yaER0evCB3vnVdEgVe+/vsK5tHP9fXd8AY4wBjTYFbbp3dtS761gF/v3DaXTsO8JRZmtU8PIPMXjph2+R//gEdDify9s7y/CvHTtx7ZBOuG9EpiO/5ZsPIc4IxLisvSwNjYg1GBAjmXdeNSK6SBhDffq4n4fYOxsM2He8Eu2TnO8H3XaoHA9+sg8Xj5+J3NQE7/W60KEpTsL551UjoovWcSerd+HuU58+9C4axpDnfZTZB9m8I1LABXoEMXfuXNTW1mLOnDkoLy/HqFGjsG7dOqRq+cqE5PM77KakAHv2uF/zLNMSI6JRQ6QeWTQiumD44G3naXoXuPwCrBnerS2y0hNQVlEHtU0/DAAy0xPwmyt6o9Fmv79piSbsf3oKqusbEPfs1Y7YSf0zkZmWgL5Zo+1tAqi12pCdBDSaEtAmMQ6ITQD27EHJ5z9g/4lqnK2xOPKzNSrI/WwjFAX4wyXOHJZ9U4w/rt2LGYtW4Onz9SIlBU8ufh8Jm0pw72U9kRJn/wzyqap61FjrcEFqPNpE8hiSYS4VFDh3cU9JDrp3VlsjTMYYR9mv/5GPjftPYdG1/XD+rmN497YY1DkdI7r3wPA+2chK3+d17AIKstITMLJHu4DkF1KNiE7nzy6fMdHig+tzoKDA9y7u9C5wGhGdVh8876PMPsjmHZEC/glFYv773/9iyZIljnODwYDc3FyUlpairq4OGzdubPaquzBNOzZaLMAbb9iPTXiWaYkR0aghUo8sGhGdHnygd341xhgDFkztBwAweCxzms4fm9IH9Q02mC0NqLcBZksDaq02GBsaYH5tGczVtTBbGtAnMxW/GJyJQRtWO8piDMCDA2zIe2AsGhob7eWvLcONgzKx7PbhuHZItqOsoqIGVw/Mwrie7WA02NsxWxpwqqoOjQpg/OEHR71VlWa8teUQXtv0E6rqrY7c3i84iiv/sgkLPt3jqNdcXYtbXtuKma9vxaGX/umoY9exc1i18xiKq5xtuWpUz8//NPngNUalrKWaJswWW4vyc2unNZp//MtlTFmDNlZPvvIPXPvi1xi5aIP9+8vP6wZkpiA5zojKOqcXGSnx+PSukXiibDNMjQ0uY9edpvPHp/SBsWl3Qwnnn1eNiE7nz65W9SlifHDZwfrN5fQuGsaQ532U2QfZvCNSwF3cdcbRo0fRpUsXlBQVoXPfvvbvQ0xPByoq7N+PCDQv0xIjoLFarfjPf/6Dq666yvn5Fz/1aNEAaK4LhEakT0HwwZyQhH7nX3H77okrkJ6cSO8iZAzl5f+EBcu/xonUDE3zl0Q3RY+ORdIF7Vo97r7ZfwJ/+ywfOSP6YtbPewGVlbC1aYvBv/83qi02fP7gz9A3GUB6OmpOnUV823QojTaf7eTtLsXCT3ajtMr5D7us9HhM6WjG/JlTImL+hew5FIXPrkj3wZfGfOos+v1ps30OvnADkk6fpHcROIbGT8zB4D98qXof6Z1vzeDBg9GjRw/u4i4RfIs7IYSEiMl9MjDx5V/jquc3YP9pc7jTIRLRIyMZ3x2vxEhDDFry7UDbj1Zi28gbcJPZinbn/w12+IwZ+adigKKT9gU6AKPSiFdv6ItuXS9ApzaJQFUVACA5zggYY2Bt9L0Z3OQBWbiycxLyB/0MJ99fiQ4d2mJo51SszftcS3cJIYQQ4gUu0AkhJITEKo1YO3sYahOTgcoqIDsLOF4KpNn3krBarVi7dh0mTco5/1fw5jGeZQHRqMQEQqPWp8aKShztNxTlX36NAb2zYKyuBrKz8PX/ivDlkWoM7dIGU/pfgLVr12HilVdizHMbUd/QiG9e/hXa/bgPSEvFa5t+wpINB/D/dn+BRW8+4chv1OIvUGczYM3943BhvAJkZ+HTDd/jtW3HcUWfDnhkVJYjv7f2nMEzn+8DAGyZdxnSkxNgaWiEsboaxs7ZAfVBTbO+4BAWv/M/nEjNQPHpGtzy7m5k3bMMC/aexuSRac3GzjmzBUfLquH6wab5/zmA/Zf/Cj1KKjApsz0AYEzPdsjp1IgZ4y9004/r0RZIS9I8do0xBowp2QX07+B49YUQQgghgYWfQdcrRqPzmJPjPFcr0xIjovGWl796ZNGI6PTgA71rscYQG4ukuFgkJcYh6fLL7Me4WMdPvBHOc7UYlbJWa9RiAqTx1KUkxaPPsD4Yc2F7pCaYHJpJ/TriuRsG4eaRXR2a1AQT9v5hMvbMuxSdRg911Duxb0csnNwL/y+hylGWEGvEBQlApzYJyG6T5Kj3TG0DfjpVgwqz1S2/pV/96Lg1SXFGJMXF4t+7SjHohS147I7Fbn1acuvv8MI3R1BZ2+DIrdbaiCNnzai12Frs3ab9p/DQp/twIqW92xApS83AvSt/wOe7SmG1NTrKdxw5hyFPrcfdHxe5jbOJF2dg0rmDSE92flNAl7ZJuLprI8b1bO99bOp4/gXdB3qnXRPuPk2YQO+iYQx53keZfZDNOwE2bdqEqVOnIjs7GwaDAatXr3a7vnLlSkyaNAkZGRkwGAwoLCx0u3727Fncf//9uPjii5GUlISuXbvigQceQEVFhVtc9+7dYTAY3H4ee+yxVuUeKfAVdL2SdP5VlORkYO1a92ueZVpiRDRqiNQji0ZEFwwfvO08Te8Cl5/MGhFdlHlnMBiQ3DbdLWZAp3QM6JQOjH/dURYTY8Cjg2y46qqfn3+VOg5YuxY3VNRhaM8OaJsc52hLURRc0acDPik87tbWObMVCoD4Kyc4v+0iORnvdR2Fqq1Hccu4no6vJFtXdAJPfvoDruzXEa/fNtyR3+x/FaC+oREL31mJHufrOHzGjB2nDehyrAKDu7bHws+K7NsDGty3XlPOn9/37g48NqUPfjWmKwDg4o4piI0xID4uFlWfrEFqgv1V+LlTBwJTB7bMbwHPo20MeY0Jhg/0TrsmUPm1ROP6O/WTT3zv4k7vAqcR0Wn1wfM+yuyDbN4JUFNTg8GDB+NXv/oVbrjhBtXr48aNw4033ojZs2c3u378+HEcP34cf/rTn9CvXz8cPnwY99xzD44fP46PPvrILfapp55yqyOl6Vtmohy+gq5XmnZwrK8HcnPtxyY8y7TEiGjUEKlHFo2ITg8+0LvQakR0evBORHf+emaCAWN7ZaBvVpqjzGCx4Jnrmy9s7/75hSj4v59j3u7PHPUqdXW4z1aMu8d1c/tO+0YFaJcch4yUOLdctv54Bpv2n0Lj31501PG/H89g+QEjXvrvT8gvPovSijrvfT1f94aiE47z5PhYbH/iSnz5mzFIfXYRx5DMc4neadeEpU8uO1ovWkzvomEMed5HmX2QzTsBpkyZgqeffhrXX3+96vWZM2fiySefxMSJE1WvDxgwAB9//DGmTp2Knj174oorrsCiRYvw2WefoaHB/UWo1NRUZGZmOn70skCHQnRFSUmJAkApKSqyF1RUKApgPzbhWaYlRkBjsViU1atXKxaLRbheLRpVXSA0IvkFwYeaeqvSbd4apdu8Ncq5ajO9i+QxRO/C6kOw5tIXP5QpH369X6kxxTs0n+4sUSYs+kz5U16RsnrnUUe7vn4+LDgirXciPgRKI5SfTD7QO+l88KWpOXXWMedc5yy9iywfzlWbvd5HeudbU1xcrABQioqKlIqKCsdPXV2d4g8AyqpVq1SvNdW7c+dOv/W8/vrrSkZGhltZt27dlMzMTKVdu3bK4MGDlaefflqpr6/3W1c0wLe4E0IIIQHkij4dgcpEwOp8lWJy/45oPGzDVVf0QsGRSqF6OrXRvqEbIYQQ0hL69evndr5gwQLk5uYGvd0zZ87gD3/4A+6++2638gcffBDDhg1D27ZtkZ+fj/nz56O4uBhvvPFG0HMKN1ygE0IIISFkZI92yEpPQFlFnf1z6B4YAGSmJ2Bkj3ZotHnZc4IQQggJIEVFRejUqZPjPD4+3kd0YKisrMTVV1+Nfv36YcGCBW7XHn74Ycf/Dxo0CG3btsX06dPx3HPPoX379p5VRRX8DLpeafpqIJMJuPNO57lamZYYEY23vPzVI4tGRKcHH+hdaDUiOj14J6KT1AdjjAELptpfqTAo7kv0pvMFU/vBGGNoptW7dwHViOjonfY+RZoPAHDb7fQuGsaQ532U2QeJvEtNTUVaWprjJ9gL9KqqKkyePBkpKSlYtWoVTL48ADB69GgAwMGDB4OalxSE+z32JLQ4PoNeUhLuVNQ/zxMETSjbCoXG6+dmg5CbVl20aWTPT2aNzPmFci6paT7fdVwZvXiD2+fORy/eoHy+63jA24pkjez5yayRPT8ZNK7PgZp6q3T5hVsje36qn0H3uI/hyi2UbbVG0/RZcS1rA2j8DHpFRYUyevRo5bLLLlNqamqE2vrss88UAMrhw4dbnGekwVfQ9Urd+R2Ea2uBWbPsxyY8y7TEiGjUEKlHFo2ITg8+0LvQakR0evBORCe5D5N7tsE3P76H924fir/ePATv3T4U3/z4Hib3bBPc3ER0knsntQ/0TrsmLH1y+UaFOffRu2gYQ573UWYfZPNOgOrqahQWFjq+37y4uBiFhYU4cuQIAPv3nBcWFqKoqAgAsG/fPhQWFqKsrAyA/ZXznJwc1NTUYNmyZaisrERZWRnKyspgs9kAAFu2bMFf/vIXFBYWori4GB988AHuvvtuTJs2DV27dm1V/hFBuP9CQEILd3GXdxdN0T5xF/coGkP0Lqw+hHIuRZt39IHeRZIPvjTcxT06xhB3cW/9Lu6ir6B/9dVXCoBmP7fffruiKIryz3/+U/X6ggULfOoBKMXFxYqiKMr27duVUaNGKenp6UpCQoJy8cUXKwsWLBB+tT3S4SZxhBBCCCGEEEL8Mn78eCge+6e4cscdd+COO+7QrAeAYcOGYevWrVpTjHi4QNcZjY2NAIDSsjIgNRWoqrJfOHYMqDz/1T+eZVpiBDQNDQ04ffo0jh07htjYWKF6tWgANNcFQiPSpyD4UJuQiIbK0+dPj6EqKYHeReoYondh9SGUcynavKMP9C6SfPClqT1+zPkcaGxEIr1reZ8k8cHbfaR3vjWlpaUAnGsEEn4Mir8/YZCoYtu2bRg5cmS40yCEEEIIIYRIQn5+PkaMGBHuNAi4QNcd/7+9e4+K4rz/B/7M7gLLTUBQLnLbVAxwJIUiJaDVNUrRBgFTrSQ1KzZojHiCjWlTsRWNhpKY5mY9GD1qjWmJaVVaYzFpQgx41ESgCIkYUURBQPFyVO4I798f/Ha+DLMLy1Z3nwOf1zl7jjsz751nHtbPzDO7O3P//n02ceJEVlRUxBSKvmsEarVaduzYMclyA6eZs8xQmXv37rHQ0FB29uxZ5uzsbNLrmJMxlnsQGVPax0s/UN/x2Q/Ud6OjH6jvRlc/UN/x1Q/Ud6OrH6jvhpf59ttvWVtbG4uIiPi/bwcQq6K/wiijUqmYg4OD5AqItra2zNfXV7LcwGnmLDNU5u7//8rNhAkT2JgxY0x6HXMyxnIPImNK+3jpB+o7PvuB+m509AP13ejqB+o7vvqB+m509QP13fAyfn5+km0i1qewdgOI5aWnpw/6/EEtY0rG3PbxkjElNxr6gfrOshlTcqOh70zJjYZ+oL4zP2NKjvrO+DLUD9R3/0vGlBz1nfFlHlY/EA5Y8xLyZHS7c+cOGGO40/8WEA8hY8l1jbQM7+2jfuA/w3v7eM7w3j7qB/4zvLeP5wzv7aN+4D/De/vM3Sby8NEn6MRq7OzsWFZWFrOzs3uoGUuua6RleG8f9QP/Gd7bx3OG9/ZRP/Cf4b19PGd4bx/1A/8Z3ttn7jaRh48uEkcIIYQQQgghhHCAPkEnhBBCCCGEEEI4QAN0QgghhBBCCCGEAzRAJ4QQQgghhBBCOEADdEIIIYQQQgghhAM0QCfkAaPrLhJCCCGEEELMobJ2A8joUV9fz3Jzc9mJEydYU1MTEwSBeXp6stjYWLZixQrm5+dn7SY+EHZ2duzMmTMsJCTE2k2xmMbGRpabm8uOHz/OGhsbmVKpZBqNhiUnJ7PU1FSmVCqt3UTCudbWVva3v/1NVh+mTp3Knn76aebo6Dis17t27Rp7//332fr162Xz6uvrmaurK3NycpJM7+7uZidPnmTTp0+XTL958yarqKhgP/zhD9nYsWPZjRs32K5du1hnZydbuHChyf/XH3nkEfbpp5+yoKAgk5bv7u5mR44cYdXV1czb25vNnz9f1g/19fVMrVYzDw8PxhhjxcXFbPv27ezKlSssICCApaens5iYGEnmT3/6E1uwYAELCAgwqR16hw8fZiUlJWzOnDksJiaGFRYWsjfffJP19vayp556ii1fvlyWaW9vZ3l5eQZrw6xZs4a1fjI68VwbGLNOfRgJtYExqg+EGGXl+7CTUaK4uBhOTk4ICQlBRkYGsrOz8dprryEjIwOhoaFwdnbG8ePHh/26V65cwdKlS2XT29raUFxcjO+++042r729HXv37pVNP3v2LHbv3o2qqioAQFVVFVasWIGlS5fiiy++kC3/61//2uBDoVBAp9OJzwdz69YtvP3221i5ciU2bdqEK1euyJYpKytDTU2N+Hzfvn2IjY2Fr68vpk6diry8PIOvvWrVKhQVFQ26fkPee+896HQ67N+/HwDwwQcfICQkBI8++ijWrl2L7u5uyfKnT5+Gi4sLwsPDERMTA4VCgWeffRaLFi2Cq6srYmJicPfuXYPramlpwY4dO5Camoo5c+Zg7ty5SE1Nxc6dO9HS0jLstjc1NWHjxo0G59XV1eHevXuy6V1dXfjqq69k02/cuIHCwkLcvHkTANDc3IycnBxs3LgRZ8+eNblNGo0G58+fN2nZrq4uHDp0CG+88Qb27dtntA/q6urQ3NwsPi8qKsIzzzyDadOm4Ze//CVOnDghy7z55puora01ud16//rXv7B+/XrxNb/44gvMnTsX8fHxeP/99w1m2trasGvXLixduhRz5szBk08+iVWrVuHzzz83uPx3330HHx8fuLq6IikpCcuXL8eyZcuQlJQEV1dXTJgwweD/5cGUl5dDoVBIpjU0NCAqKgoKhQJKpRI6nU7ynmhqapJlvv76a7i4uEAQBLi5uaGkpAQajQZBQUGYOHEi7O3tUVpaKsm8++67Bh9KpRJr164Vnw8UExOD27dvAwCuX7+OsLAw2NraIigoCGq1Gv7+/qivr5dl/v3vfwMA8vPzoVAokJiYiFdeeQXz58+HjY0NDh8+LMkIggClUonZs2fjo48+Qmdn55D9mZubC5VKhcjISIwZMwYffvghnJ2dkZaWhueffx729vZ45513JJnq6moEBATA3d0d3t7eEAQBTz75JKKjo6FUKrFw4UJZPdGj2iBvC9UGvmoDYLn6MNJqA2B+fbBkbQBGTn0wpzYQ66EBOrGIKVOmYPXq1Ubnr169GlOmTBn26xra0X7//fcICAiAIAhQKBSYMWMGGhoaxPmGdrQFBQWwtbXF2LFjoVarUVBQgHHjxmH27NmYNWsWVCqVbJAuCALCw8Oh1WolD0EQEBUVBa1Wi5kzZ0oy3t7euHHjBgCgpqYGXl5e8PLyQlxcHHx9feHi4iKeINCLiIhAYWEhAGDnzp2wt7fHiy++iNzcXKxevRpOTk7YtWuXrG/02x8UFIScnBw0NjYO2Z+vvvoqnJ2d8fOf/xxeXl7IycmBu7s7Nm/ejOzsbIwbNw7r16+XZKZOnYoNGzaIz/ft24fo6GgAfScgwsPD8eKLL8rWxfOBF88HXfocrwde5hx0abVapKSkGGxTZ2cnnn76aWi1Wsn0M2fODPrYv3+/7O+q0+nw+OOP4/Tp0/jPf/6DKVOmIDIyErdu3QLQ914QBEGSmT17NtLS0nD37l1s2bIFvr6+SEtLE+c/99xzSE5OlvW1r68vAgMDJQ9BEDBhwgQEBgZCo9HItlUQBFy7dg0AsGzZMoSHh4v/b2/cuIHY2Fj86le/kmScnZ1x6dIlAEB0dDRycnIk87du3YqIiAjZevbs2YOkpCTY2NjA3d0dGRkZqKyslLVJLyQkBDt27AAAFBYWQq1WY9u2beL8PXv2ICQkRJKZO3cunn/+efT09AAA/vjHP2Lu3LkAgPPnzyMwMBBZWVmydVFtoNqgx3NtACxXH0ZabQDMqw+Wqg3AyKsP5tQGYj00QCcWoVarce7cOaPzq6qqoFarZdP/+c9/Dvp4++23ZQUyOTkZCQkJaG5uRnV1NebNmweNRoPLly8DMFxUY2JisG7dOgBAXl4e3NzckJmZKc7PzMxEXFycJJOdnQ2NRiMbuKtUKqM7iP472ZSUFGi1WrS2tgIAOjo6kJCQgAULFkgyDg4OYtsjIiJkn0z89a9/RWhoqMF1ff7558jIyICHhwdsbGyQmJiIw4cPizvEgR555BEcOHAAQN9OS6lU4sMPPxTnHzx4EBMnTpRk7O3tcfHiRfF5T08PbGxs0NTUBAD47LPP4OPjI1sXzwdePB90AXwfeJlz0GVvbz/oQVVlZSXs7e1l26NQKCAIguyhnz7wveDj44Ovv/5afN7R0YGkpCSEh4fj5s2bBmuDm5ub+KlHV1cXFAqF5DXKysowYcIESWb58uUIDw+XfVoyWG3Qb5P+/TBp0iR88sknkvlffvklAgMDJdNcXFxw5swZAMD48ePFf+tduHABDg4ORtdz7do1vP766wgODoZCoUBUVBR27Ngh+9aLvb29WIcAwMbGRvLeuXTpkmw9Dg4Okk+AOjs7YWNjI56kzM/Pl20PQLVBn6HawHdtACxXH0ZabQDMqw+Wqg3AyKsP5tQGYj00QCcWodFosHv3bqPzd+/ebfQTJWM72v473P7Gjx+PiooKybSVK1fC398fFy9eNLijHTNmDKqrqwH0DTBVKpXkLGdlZSU8PT1l7fvmm28wadIkrFmzBl1dXQBM38kaGtyfOnUKvr6+kmnu7u4oKSkRt628vFwy/8KFC7IDlIHr6urqwv79+xEfHw+lUgkfHx9kZmaK26xnaEf77bffis9ra2tlO9qAgADJzxMaGhogCALa2toA9O2cDZ184fnAi+eDLoDvAy9zDrp8fHyQn58v2069Q4cOyU7yeHh4YNeuXaitrTX4OHLkiOzv6ujoKPvKYHd3N5KTk/HYY4+hoqLCYEZ/UAMATk5OkhNSly9fNvj+PnToEPz8/LB161ZxmikD9OvXrwPo+7sOXLa2thZ2dnaSaYmJifjd734HAIiPj5d90rJz504EBQXJ1qN/L/RXVFSEJUuWwNHREY6OjpJ5vr6+4k9mrl69CkEQcOTIEXH+sWPHZLXLx8dHUkdv374NQRDE91lNTY1sewCqDfrtodrAd23Q5yxRH0ZabQDMqw+Wqg369o2k+mBObSDWQwN0YhHbtm2Dra0t0tPTkZ+fj5MnT+LUqVPIz89Heno67OzskJubK8v5+Pjg0KFDRl/3v//9r6xAOjs7G/ydz6pVq8QdyWADdEC+k62trTW4kwWAe/fuQafTISwsDBUVFbCxsTFpJ+vj4yMZ/AJ9BzUDd0iLFy/Gc889BwBYuHAhfv/730vmZ2dnIywszOC6DO1oL1++jKysLAQEBMj6QaPRoKCgAEDfJxoKhQIff/yxOP/IkSOyop+RkYHJkyejoKAAhYWFmDlzpuQM9tGjR/GDH/xA1g6eD7x4PugC+D7wMuegKysrCy4uLtiyZQvKy8vR2NiIpqYmlJeXY8uWLXBzc5P9RjA+Ph6bNm2SbY9eeXm57NONsLAw/OMf/5Atq38/+Pv7y94LwcHBkhNpn3zyiXjyCTB8Uk2vvr4eTzzxBObMmYPGxkaTBug/+9nPMH/+fLi5uYlfR9Q7efKk7ETh2bNn4e7uDp1Oh02bNsHJyQmLFy/Ga6+9Bp1OBzs7O+zZs0eSUSgUBt8Lenfu3BE/HdVLT09HUFAQNm/ejB//+MdYsmQJgoODUVBQgKNHjyIsLEz2ic2SJUswY8YMVFVVoaamBosWLZJ8QnPs2DH4+fnJ1k+1gWqDHs+1AbBcfRhptQEwrz5YqjYAI68+mFMbiPXQAJ1YzEcffYTo6GioVCrx7KVKpUJ0dLR4QbKB5s2bhz/84Q9GX9PQjjYqKgoffPCBweXT09Ph6uoqK6qPPfaYODAF+s7C9v8tXHFxscFP+PvLy8uDp6cnFArFoEU1LCwMERERcHJywsGDByXzv/rqK9nZ1atXryIwMBDTp0/HSy+9BHt7e0ybNg3Lli3D9OnTYWtrKzkw6r+uwXa0vb29+OyzzyTT1q1bh3HjxiEtLQ0ajQZr166Fv78/cnNzsX37dvj5+ckufHfv3j384he/EP+usbGxkovaffrpp5JBvh7PB148H3QBfB94mTsoy8nJEX+XqlAoxE82vL298frrr8uWP3jwIPbt22d0e27duoW//OUvkmm//e1v8dOf/tTg8t3d3UhMTJS9FzZs2GD0QoxA389fnnrqKaPze3t7kZ2dDS8vLyiVykEH6KmpqZLHwP83L7/8MuLj42W5CxcuICUlBc7OzmJttbGxQWxsrMETnEPVBkNaWlqQlpaGyZMnY8WKFejq6sKWLVtga2sLQRCg1Wplr3nt2jU8/vjj4t80MDAQZWVl4vy///3veO+992TrotpAtaE/XmsDYLn6MNJqA2BefbBUbQBGXn0wpzYQ66EBOrG4rq4uNDQ0oKGhQfxauDFFRUWSgfNALS0tOHbsmGRadna2+Ls2Q1544QVZMc7NzZV9Rai/zMxM8VPswdTV1SE/P9/olUQ3bNggeRw9elQy/+WXX0ZKSoosd/v2bbzyyisIDQ2FWq2Gra0tAgIC8Mwzz+D06dMG1xUYGCh+ddBU9+/fx+bNm5GQkCD+PikvLw9+fn5wd3dHamqq0W1rb283eKXTwfBy4DXw/cD7QRfA74GXuYMyvZqaGpw4cQInTpyQnOh5ELq7u3Hnzh2j8+/fvz/sq1m3traio6NjyOVKSkrwzjvviL9fNEdLSwva29uNzu/t7UVTU5NJtfVBaW9vN3qXBr3z58/LTnoOhZfaYK0TNv9rbVi0aNGIrg39P5U0VW9vr9F5xmqDPjPc2qDPDac+vPXWW8OuD/r1DKc2mHIRQGPrGY7W1tYhawMgrw9DrcsStQEYmfVhuMcNxHoEALD2rd4IIaPbpUuXWFNTE2OMMS8vL6bRaB7Ya9+/f5+1tbWxMWPGGJzf09PD6uvrh3Xf17a2NqZUKpmdnd2gy5WWlrLjx48znU7H3NzchtVuxvru/6tUKplarTa6DAB2/fp11tvbyzw8PJiNjc2w1zNcHR0drLu7mzk7Oxtdprq6mnV2drLg4GCmUqkeepvIyES1wbDRXhtsbW3ZmTNnTL7HuCUzvLeP58xwcg+zNjBm3fpQVlbGiouLH1p9sEZtIMNDR02EEKvTaDSynWtdXR3Lyspiu3fvNvl1DGVUKpXRHSxjjDU0NLCNGzcOaz03b940qW2RkZEsMjLSaNuGcuvWrSEzgiAwT09PybQH1XfGqNVqplarB80EBQUNaz3t7e2stLSUjR07loWGhkrmdXR0sI8//pjpdLoRneG9fZbsh6qqKnbq1CkWGxvLYmJi2Llz59gbb7zBOjs72eLFi9kTTzxhNBMTE8OCg4PZuXPn2Lvvvms0o1Kp2NWrV9mBAwdMzpizHkMZR0dHdu7cObZmzZohM7GxsezRRx81aT3Gcq+++upD26bhtK9/RqVSDZl56aWXDG5jT08Py8nJYe7u7owxxt566y2LZ3hvH8+Z/yWnp9FomKurK9u7dy+rrq5mPj4+TKfTMT8/P4PL692+fVvMeHt7syVLlhjMDDx2GJhLTU0dcnBu6roGZoqKilh1dTXbtm2byZnhrEcQBGZrazvsviMWZNXP7wkhxAhj9yalDD/relCZ77//HgEBAeJXFmfMmIGGhgZxvqGr5Y60DO/ts2Q/FBQUwNbWFmPHjoVarUZBQQHGjRuH2bNnY9asWVCpVLI7YFCG//aZkxEEAeHh4dBqtZKHIAiIioqCVqvFzJkzrZLhvX08Z8zNeXt7iz/dq6mpgbe3N7y8vBAXFwdfX1+4uLigqqpq0IyXl9eQGXNzIy1DrIcG6IQQqzDnHveU4b995mSSk5ORkJCA5uZmVFdXY968edBoNOJtnAwN5EZahvf2WbIfYmJisG7dOgB918Bwc3NDZmamOD8zMxNxcXGUGZDhvX3mZLKzsw3eknSwC2hZKsN7+3jOmJvrf72ElJQUaLVatLa2Aui7DVpCQgIWLFjwP2csuS6eM8R6aIBOCLGKwe5N2v8epZSRD2B4bp85mfHjx6OiokIybeXKlfD398fFixcNDuRGWob39lmyH/rf9rKnpwcqlUpye67KykrZFYopw3/7zN2mb775BpMmTcKaNWvEix8ONfizVIb39vGcMSfXf5BpaHBv6Crp5mQsuS6eM8R6FNb+ij0hZHTy9vZmBw4cYL29vQYfZWVllDGQ4b195mTa29tlF4vatm0bS0xMZDNmzGDnz58f8Rne22fJfuhPoVAwtVrNXF1dxWnOzs7szp07lBkkw3v7hpOJiopipaWlrLm5mUVGRrLKykomCILR17Zkhvf28ZwxN6ef39nZKbv+iqenJ2tubn4gGUuui+cMsQ4aoBNCrCIyMtLoAJSxvh0JBtxkgjL8t8+cTHBwMCspKZEtu3XrVpaUlMQSExNl80Zahvf2WbIfAgMD2YULF8TnJ0+eZP7+/uLzuro65u3tTZkBGd7bZ+42McaYk5MT27t3L8vMzGRxcXGsp6fH4HLWyPDePp4z5uRmzZrFfvSjH7G7d+/KTvJduXKFeXh4PJCMJdfFc4ZYB13FnRBiFb/5zW9Ya2ur0fkTJ05kX375JWUGZHhvnzmZ+fPns7y8PPbss8/Klv/zn//Ment72fbt20d0hvf2WbIfXnjhBclB+uTJkyXzCwoKZFf7pgz/7TN3m/pLSUlh06ZNY6WlpSbf3spSGd7bx3PG1FxWVpbkuYODg+T54cOH2U9+8pP/OWPJdfGcIdZD90EnhBBCCCGEEEI4QF9xJ4QQQgghhBBCOEADdEIIIYQQQgghhAM0QCeEEEIIIYQQQjhAA3RCCCGEEEIIIYQDNEAnhBBCCCGEEEI4QAN0QgghhBBCCCGEAzRAJ4QQQgghhBBCOEADdEIIIYQQQgghhAM0QCeEEEIIIYQQQjhAA3RCCCGEEEIIIYQDNEAnhBBCCCGEEEI4QAN0QgghhBBCCCGEAzRAJ4QQQgghhBBCOEADdEIIIYQQQgghhAM0QCeEEEIIIYQQQjjw/wB5eG+JsJa/GAAAAABJRU5ErkJggg==", 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4\n", + " t30 = t40 * 0.8\n", + " t20 = t30 * 0.8\n", + " t10 = t20 * 0.8\n", + " t00 = self.b / 5.9\n", + "\n", + " tt1 = 3\n", + " tt2 = 3\n", + " tt3 = 5\n", + " tt20 = 3\n", + " tt10 = 3\n", + "\n", + " q1 = q0 - 1 * (q0 - q5) / 3.7 \n", + " q2 = q0 - 2 * (q0 - q5) / 3.8\n", + " q3 = q0 - 3 * (q0 - q5) / 3.9\n", + " q4 = q0 - 3.7 * (q0 - q5) / 4\n", + "\n", + " q40 = q4\n", + " q30 = q3\n", + " q20 = q2\n", + " q10 = q1\n", + " q00 = q0\n", + " \n", + " qq1 = q00 * 0.9\n", + " qq2 = q00 * 0.83\n", + " qq3 = q00 * 0.77\n", + " qq20 = qq2\n", + " qq10 = qq1\n", + " \n", + " T1 = T0 - (q0 - q1) / (0.07 * 4.184) * 0.6\n", + " T2 = T0 - (q0 - q2) / (0.07 * 4.184) * 0.6\n", + " T3 = T0 - (q0 - q3) / (0.07 * 4.184) * 0.6 \n", + " T4 = T0 - (q0 - q4) / (0.07 * 4.184) * 0.6\n", + " \n", + " T5 = T0 - (q0 - q5) / (0.07 * 4.184) * 0.6\n", + " \n", + " T40 = T0 - (q0 - q4) / (0.07 * 4.184) * 0.6\n", + " T30 = T0 - (q0 - q30) / (0.07 * 4.184) * 0.6\n", + " T20 = T0 - (q0 - q20) / (0.07 * 4.184) * 0.6\n", + " T10 = T0 - (q0 - q10) / (0.07 * 4.184) * 0.6\n", + " T00 = T0 - (q0 - q00) / (0.07 * 4.184) * 0.6\n", + " \n", + " TT1 = T0 - (q0 - qq1) / (0.07 * 4.184) * 0.6\n", + " TT2 = T0 - (q0 - qq2) / (0.07 * 4.184) * 0.6\n", + " TT3 = T0 - (q0 - qq3) / (0.07 * 4.184) * 0.6\n", + " \n", + " TT20 = T0 - (q0 - qq20) / (0.07 * 4.184) * 0.6\n", + " TT10 = T0 - (q0 - qq10) / (0.07 * 4.184) * 0.6\n", + " \n", + " self.step_sizes = [0, t1, t2, t3, t4, t5, t40, t30, t20, t10, t00, tt1, tt2, tt3, tt20, tt10]\n", + " self.step_times = np.cumsum([0, t1, t2, t3, t4, t5, t40, t30, t20, t10, t00, tt1, tt2, tt3, tt20, tt10])\n", + " self.step_qs = [q0, q1, q2, q3, q4, q5, q40, q30, q20, q10, q00, qq1, qq2, qq3, qq20, qq10, q0]\n", + " \n", + " self.step_temperatures = [T0, T1 , T2 , T3 , T4 , T5 , T40, T30, T20, T10, T00, TT1, TT2, TT3, TT2, TT1,]\n", + " \n", + " self.step_q_from_to = list(zip(self.step_qs[:-1], self.step_qs[1:]))\n", + " \n", + " for pair in zip(self.step_times, self.step_q_from_to):\n", + " print(pair)\n", + " \n", + " self.program = list(zip(self.step_times, self.step_q_from_to))\n", + " \n", + " self.drying_time = 24. / load\n", + " \n", + " self.aux_duration = (q0 * self.step_times[9] - (np.array(self.step_sizes)[:10] * np.array(self.step_qs)[:10]).sum()) / 2 / qq3\n", + "\n", + " def lowest_heat_duration (self):\n", + "\n", + " t = self.duration\n", + " t5 = t\n", + "\n", + " if t > 14 :\n", + " t5 = t - 6\n", + " elif t > 12 :\n", + " t5 = t - 5\n", + " elif t > 10 :\n", + " t5 = t - 4\n", + " elif t > 8 :\n", + " t5 = t - 3\n", + " elif t > 7 :\n", + " t5 = t - 2\n", + " elif t > 6 :\n", + " t5 = t - 1\n", + " elif t < 5 :\n", + " t5 = t + 1\n", + "\n", + " return t5\n", + "\n", + "duration = 16\n", + "start = 6\n", + "load = 81 / 66\n", + "T0 = 1212\n", + "q0 = 81\n", + "\n", + "cosp = CokeOvenServiceProgram(duration, start, load, T0, q0)\n", + "\n", + "from functools import reduce\n", + "program_arr = np.array(reduce(lambda x, y: x+y,[ [(t, q0), (t, q1)] for t, (q0, q1) in cosp.program]))\n", + "\n", + "import matplotlib.ticker \n", + "\n", + "plt.figure(figsize=(10, 4))\n", + "plt.plot(program_arr.T[0] * 60, program_arr.T[1])\n", + "ax = plt.gca()\n", + "ax.xaxis.set_major_locator(matplotlib.ticker.MultipleLocator(60))\n", + "ax.xaxis.set_minor_locator(matplotlib.ticker.MultipleLocator(20))\n", + "plt.xticks(np.arange(0, 60)*60, np.arange(0, 60), rotation=90)\n", + "# plt.xticks(rotation=90)\n", + "# plt.minorticks_on()\n", + "plt.grid()\n", + "plt.grid(b=True, which='minor', color='r', linestyle='--')\n", + "\n", + "plt.axvline((start)*60)\n", + "plt.axvline((start+duration)*60)\n", + "\n", + "plt.axvline((start+duration+cosp.drying_time)*60)\n", + "plt.axvline((start+duration+cosp.drying_time+cosp.aux_duration)*60)\n", + "\n", + "twin = plt.gca().twinx()\n", + "twin.plot(cosp.step_times * 60, cosp.step_temperatures, 'o:')\n", + "twin.set_ylim(1120, 1320)" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "01cba2bf", + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "160.4006440677966" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.sum(np.array(cosp.step_sizes) * 60 / 20)" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "417a4faf", + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "159.0" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.sum(np.round(np.array(cosp.step_sizes) * 60 / 20))" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "id": "4ac4926b", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 0., 10., 10., 10., 12., 30., 10., 8., 6., 5., 7., 9., 9.,\n", + " 15., 9., 9.])" + ] + }, + "execution_count": 39, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.round(np.array(cosp.step_sizes) * 60 / 20)" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "id": "58fb00cb", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 0.00000000e+00, 0.00000000e+00, 0.00000000e+00],\n", + " [ 1.05000000e+01, 1.00000000e+01, 5.00000000e-01],\n", + " [ 1.05000000e+01, 1.00000000e+01, 5.00000000e-01],\n", + " [ 1.05000000e+01, 1.00000000e+01, 5.00000000e-01],\n", + " [ 1.20000000e+01, 1.20000000e+01, 0.00000000e+00],\n", + " [ 3.00000000e+01, 3.00000000e+01, 0.00000000e+00],\n", + " [ 9.75000000e+00, 1.00000000e+01, -2.50000000e-01],\n", + " [ 7.80000000e+00, 8.00000000e+00, -2.00000000e-01],\n", + " [ 6.24000000e+00, 6.00000000e+00, 2.40000000e-01],\n", + " [ 4.99200000e+00, 5.00000000e+00, -8.00000000e-03],\n", + " [ 7.11864407e+00, 7.00000000e+00, 1.18644068e-01],\n", + " [ 9.00000000e+00, 9.00000000e+00, 0.00000000e+00],\n", + " [ 9.00000000e+00, 9.00000000e+00, 0.00000000e+00],\n", + " [ 1.50000000e+01, 1.50000000e+01, 0.00000000e+00],\n", + " [ 9.00000000e+00, 9.00000000e+00, 0.00000000e+00],\n", + " [ 9.00000000e+00, 9.00000000e+00, 0.00000000e+00]])" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.array(list(zip(np.array(cosp.step_sizes) * 60 / 20,\n", + "np.round(np.array(cosp.step_sizes) * 60 / 20),\n", + "np.array(cosp.step_sizes) * 60 / 20 - np.round(np.array(cosp.step_sizes) * 60 / 20))))" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "id": "2d1a860e", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\RIST\\AppData\\Local\\Temp\\ipykernel_16724\\1001734892.py:1: RuntimeWarning: invalid value encountered in true_divide\n", + " 100 * (np.array(cosp.step_sizes) * 60 / 20 - np.round(np.array(cosp.step_sizes) * 60 / 20)) / (np.array(cosp.step_sizes) * 60 / 20)\n" + ] + }, + { + "data": { + "text/plain": [ + "array([ nan, 4.76190476, 4.76190476, 4.76190476, 0. ,\n", + " 0. , -2.56410256, -2.56410256, 3.84615385, -0.16025641,\n", + " 1.66666667, 0. , 0. , 0. , 0. ,\n", + " 0. ])" + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "100 * (np.array(cosp.step_sizes) * 60 / 20 - np.round(np.array(cosp.step_sizes) * 60 / 20)) / (np.array(cosp.step_sizes) * 60 / 20)" + ] + }, + { + "cell_type": "markdown", + "id": "51b40602", + "metadata": {}, + "source": [ + "# μ—΄λŸ‰ 증감 μ‹œμ μ„ λ¦¬λ²„μŠ€ 간격과 μ •λ ¬" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "id": "0fa97f83", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(0.0, (81.0, 69.61621621621622))\n", + "(3.3333333333333335, (69.61621621621622, 58.83157894736841))\n", + "(6.666666666666667, (58.83157894736841, 48.599999999999994))\n", + "(10.0, (48.599999999999994, 42.038999999999994))\n", + "(14.0, (42.038999999999994, 38.879999999999995))\n", + "(24.0, (38.879999999999995, 42.038999999999994))\n", + "(27.333333333333332, (42.038999999999994, 48.599999999999994))\n", + "(30.0, (48.599999999999994, 58.83157894736841))\n", + "(32.0, (58.83157894736841, 69.61621621621622))\n", + "(33.666666666666664, (69.61621621621622, 81.0))\n", + "(36.0, (81.0, 72.9))\n", + "(39.0, (72.9, 67.22999999999999))\n", + "(42.0, (67.22999999999999, 62.370000000000005))\n", + "(47.0, (62.370000000000005, 67.22999999999999))\n", + "(50.0, (67.22999999999999, 72.9))\n", + "(53.0, (72.9, 81.0))\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\RIST\\AppData\\Local\\Temp\\ipykernel_16724\\4051397401.py:170: MatplotlibDeprecationWarning: The 'b' parameter of grid() has been renamed 'visible' since Matplotlib 3.5; support for the old name will be dropped two minor releases later.\n", + " plt.grid(b=True, which='minor', color='r', linestyle='--')\n" + ] + }, + { + "data": { + "text/plain": [ + "(1120.0, 1320.0)" + ] + }, + "execution_count": 48, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "560d90bd0ae24e6d9d801a0d850dbffd", + "version_major": 2, + "version_minor": 0 + }, + "image/png": 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\n", + " \n", + "
\n", + " " + ], + "text/plain": [ + "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "class CokeOvenServiceProgram:\n", + " \"\"\" as implemented in excel sheet provided \"\"\"\n", + " def __init__ (self, duration, start, load, T0, q0):\n", + " self.start = start\n", + " self.end = start + duration\n", + " self.duration = duration\n", + " self.load = load\n", + " self.T0 = T0\n", + " self.q0 = q0\n", + " \n", + " self.a = 0.6 - (self.duration - 4) * 0.01 # μ΅œμ†Œ μ—΄λŸ‰ / μ΅œλŒ€ μ—΄λŸ‰ λΉ„μœ¨\n", + " self.b = 6 + self.duration / 2 # 감급 μ‹œμž‘μ—μ„œ 감급 μ™„λ£ŒκΉŒμ§€ μ‹œκ°„, μ •μˆ˜ -6 μ—μ„œ 감급 μ‹œμž‘ μ •μˆ˜ 쀑간 μ‹œκ°μ— 감급 μ™„λ£Œ\n", + "\n", + " t5 = self.lowest_heat_duration()\n", + " q5 = self.q0 * self.a\n", + "\n", + " t1 = self.b / 4\n", + " t2 = self.b / 4\n", + " t3 = self.b / 4\n", + " t4 = self.b / 3.5\n", + "\n", + " t40 = 13 / 4\n", + " t30 = t40 * 0.8\n", + " t20 = t30 * 0.8\n", + " t10 = t20 * 0.8\n", + " t00 = self.b / 5.9\n", + "\n", + " tt1 = 3\n", + " tt2 = 3\n", + " tt3 = 5\n", + " tt20 = 3\n", + " tt10 = 3\n", + "\n", + " q1 = q0 - 1 * (q0 - q5) / 3.7 \n", + " q2 = q0 - 2 * (q0 - q5) / 3.8\n", + " q3 = q0 - 3 * (q0 - q5) / 3.9\n", + " q4 = q0 - 3.7 * (q0 - q5) / 4\n", + "\n", + " q40 = q4\n", + " q30 = q3\n", + " q20 = q2\n", + " q10 = q1\n", + " q00 = q0\n", + " \n", + " qq1 = q00 * 0.9\n", + " qq2 = q00 * 0.83\n", + " qq3 = q00 * 0.77\n", + " qq20 = qq2\n", + " qq10 = qq1\n", + " \n", + " T1 = T0 - (q0 - q1) / (0.07 * 4.184) * 0.6\n", + " T2 = T0 - (q0 - q2) / (0.07 * 4.184) * 0.6\n", + " T3 = T0 - (q0 - q3) / (0.07 * 4.184) * 0.6 \n", + " T4 = T0 - (q0 - q4) / (0.07 * 4.184) * 0.6\n", + " \n", + " T5 = T0 - (q0 - q5) / (0.07 * 4.184) * 0.6\n", + " \n", + " T40 = T0 - (q0 - q4) / (0.07 * 4.184) * 0.6\n", + " T30 = T0 - (q0 - q30) / (0.07 * 4.184) * 0.6\n", + " T20 = T0 - (q0 - q20) / (0.07 * 4.184) * 0.6\n", + " T10 = T0 - (q0 - q10) / (0.07 * 4.184) * 0.6\n", + " T00 = T0 - (q0 - q00) / (0.07 * 4.184) * 0.6\n", + " \n", + " TT1 = T0 - (q0 - qq1) / (0.07 * 4.184) * 0.6\n", + " TT2 = T0 - (q0 - qq2) / (0.07 * 4.184) * 0.6\n", + " TT3 = T0 - (q0 - qq3) / (0.07 * 4.184) * 0.6\n", + " \n", + " TT20 = T0 - (q0 - qq20) / (0.07 * 4.184) * 0.6\n", + " TT10 = T0 - (q0 - qq10) / (0.07 * 4.184) * 0.6\n", + " \n", + " self.step_sizes = [0, t1, t2, t3, t4, t5, t40, t30, t20, t10, t00, tt1, tt2, tt3, tt20, tt10]\n", + " \n", + " self.step_sizes = np.round(np.array(self.step_sizes) * 60 / 20) / 3\n", + " \n", + " self.step_times = np.cumsum([0, t1, t2, t3, t4, t5, t40, t30, t20, t10, t00, tt1, tt2, tt3, tt20, tt10])\n", + " self.step_times = np.cumsum(self.step_sizes)\n", + " \n", + " self.step_qs = np.array([q0, q1, q2, q3, q4, q5, q40, q30, q20, q10, q00, qq1, qq2, qq3, qq20, qq10, q0])\n", + " \n", + " self.step_temperatures = [T0, T1 , T2 , T3 , T4 , T5 , T40, T30, T20, T10, T00, TT1, TT2, TT3, TT2, TT1,]\n", + " \n", + " if False: # use_old_temperature:\n", + " self.step_temperatures = self.calculate_temperatues_old()\n", + " \n", + " self.step_q_from_to = list(zip(self.step_qs[:-1], self.step_qs[1:]))\n", + " \n", + " for pair in zip(self.step_times, self.step_q_from_to):\n", + " print(pair)\n", + " \n", + " self.program = list(zip(self.step_times, self.step_q_from_to))\n", + " \n", + " self.drying_time = 24. / load\n", + " \n", + " self.aux_duration = (q0 * self.step_times[9] - (self.step_sizes.ravel()[:10] * self.step_qs[:10]).sum()) / 2 / qq3\n", + "\n", + " def lowest_heat_duration (self):\n", + "\n", + " t = self.duration\n", + " t5 = t\n", + "\n", + " if t > 14 :\n", + " t5 = t - 6\n", + " elif t > 12 :\n", + " t5 = t - 5\n", + " elif t > 10 :\n", + " t5 = t - 4\n", + " elif t > 8 :\n", + " t5 = t - 3\n", + " elif t > 7 :\n", + " t5 = t - 2\n", + " elif t > 6 :\n", + " t5 = t - 1\n", + " elif t < 5 :\n", + " t5 = t + 1\n", + "\n", + " return t5\n", + " \n", + " def calculate_temperatues_old (self):\n", + " t0, t1, t2, t3, t4, t5, t40, t30, t20, t10, t00, tt1, tt2, tt3, tt20, tt10 = self.step_sizes\n", + " q0, q1, q2, q3, q4, q5, q40, q30, q20, q10, q00, qq1, qq2, qq3, qq20, qq10, qq00 = self.step_qs\n", + " \n", + " T0 = self.T0\n", + " \n", + " T1 = T0 - (q0 - q1) * t1 * 3 / (0.4 * 4.18) * 0.33\n", + " T2 = T0 - (q0 - q2) * t2 * 3 / (0.4 * 4.18) * 0.33\n", + " T3 = T0 - (q0 - q3) * t3 * 3 / (0.4 * 4.18) * 0.3\n", + " T4 = T0 - (q0 - q4) * t4 * 3 / (0.4 * 4.18) * 0.25\n", + " \n", + " T5 = T4 + 2 * t5\n", + " \n", + " T40 = T5 + (q40 - q5 ) * t40 * 3 / (0.4 * 4.18) * 0.33\n", + " T30 = T40 + (q30 - q40) * t30 * 3 / (0.4 * 4.18) * 0.33\n", + " T20 = T30 + (q20 - q30) * t20 * 3 / (0.4 * 4.18) * 0.33\n", + " T10 = T20 + (q10 - q20) * t10 * 3 / (0.4 * 4.18) * 0.33\n", + " T00 = T10 + (q00 - q10) * t00 * 3 / (0.4 * 4.18) * 0.33\n", + " \n", + " TT1 = T00 + (qq1 - q00) * tt1 * 3 / (0.4 * 4.18) * 0.33\n", + " TT2 = TT1 + (qq2 - qq1) * tt2 * 3 / (0.4 * 4.18) * 0.33\n", + " TT3 = TT2 + (qq3 - qq2) * tt3 * 3 / (0.4 * 4.18) * 0.33\n", + " \n", + " TT20 = TT3 + (qq20 - qq3) * tt20 * 3 / (0.4 * 4.18) * 0.33\n", + " TT10 = TT20 + (qq10 - qq20) * tt10 * 3 / (0.4 * 4.18) * 0.33\n", + " \n", + " return [T0, T1 , T2 , T3 , T4 , T5 , T40, T30, T20, T10, T00, TT1, TT2, TT3, TT2, TT1,]\n", + "\n", + "duration = 16\n", + "start = 6\n", + "load = 81 / 66\n", + "T0 = 1212\n", + "q0 = 81\n", + "\n", + "cosp = CokeOvenServiceProgram(duration, start, load, T0, q0)\n", + "cosp.step_temperatures = cosp.calculate_temperatues_old()\n", + "\n", + "from functools import reduce\n", + "\n", + "program_arr = np.array(reduce(lambda x, y: x+y,[ [(t, q0), (t, q1)] for t, (q0, q1) in cosp.program]))\n", + "\n", + "import matplotlib.ticker \n", + "\n", + "plt.figure(figsize=(10, 4))\n", + "plt.plot(program_arr.T[0] * 60, program_arr.T[1])\n", + "ax = plt.gca()\n", + "ax.xaxis.set_major_locator(matplotlib.ticker.MultipleLocator(60))\n", + "ax.xaxis.set_minor_locator(matplotlib.ticker.MultipleLocator(20))\n", + "plt.xticks(np.arange(0, 60)*60, np.arange(0, 60), rotation=90)\n", + "# plt.xticks(rotation=90)\n", + "# plt.minorticks_on()\n", + "plt.grid()\n", + "plt.grid(b=True, which='minor', color='r', linestyle='--')\n", + "\n", + "plt.axvline((start)*60)\n", + "plt.axvline((start+duration)*60)\n", + "\n", + "plt.axvline((start+duration+cosp.drying_time)*60)\n", + "plt.axvline((start+duration+cosp.drying_time+cosp.aux_duration)*60)\n", + "\n", + "twin = plt.gca().twinx()\n", + "twin.plot(cosp.step_times * 60, cosp.step_temperatures, 'o:')\n", + "twin.set_ylim(1120, 1320)" + ] + }, + { + "cell_type": "markdown", + "id": "889c6940", + "metadata": {}, + "source": [ + "# 좔가감산 감급 없이" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "id": "07c3818c", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(0.0, (81.0, 70.4918918918919))\n", + "(3.0, (70.4918918918919, 60.53684210526316))\n", + "(6.0, (60.53684210526316, 51.0923076923077))\n", + "(9.0, (51.0923076923077, 45.036))\n", + "(12.333333333333334, (45.036, 42.120000000000005))\n", + "(20.333333333333336, (42.120000000000005, 45.036))\n", + "(27.000000000000004, (45.036, 51.0923076923077))\n", + "(35.0, (51.0923076923077, 60.53684210526316))\n", + "(45.0, (60.53684210526316, 70.4918918918919))\n", + "(57.666666666666664, (70.4918918918919, 81.0))\n", + "(59.666666666666664, (81.0, 72.9))\n", + "(62.666666666666664, (72.9, 67.22999999999999))\n", + "(65.66666666666666, (67.22999999999999, 62.370000000000005))\n", + "(70.66666666666666, (62.370000000000005, 67.22999999999999))\n", + "(73.66666666666666, (67.22999999999999, 72.9))\n", + "(76.66666666666666, (72.9, 81.0))\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\RIST\\AppData\\Local\\Temp\\ipykernel_16724\\3299767158.py:161: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n", + " plt.figure(figsize=(10, 4))\n", + "C:\\Users\\RIST\\AppData\\Local\\Temp\\ipykernel_16724\\3299767158.py:170: MatplotlibDeprecationWarning: The 'b' parameter of grid() has been renamed 'visible' since Matplotlib 3.5; support for the old name will be dropped two minor releases later.\n", + " plt.grid(b=True, which='minor', color='r', linestyle='--')\n" + ] + }, + { + "data": { + "text/plain": [ + "(-60.0, 3540.0)" + ] + }, + "execution_count": 54, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "6be7e273c1e440f795c00e9ed06b1073", + "version_major": 2, + "version_minor": 0 + }, + "image/png": 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l4r5v377Izc3FrFmz8PrrrwNw3tI7efLkiJ/BHeAFulRcc801ePrpp9G9e3f0798fP/zwA1588UXcfffdAJz3ecyePRvz589Hr1690KtXL8yfPx+JiYmYNm1acAdr/I+AmBigXz/Xuq+2pm5rHX/96ajnaJUBtRxZ6kxHX7kJxZGlznT0lZtQnQsv1L7OdPSXG57X9Ou0tD2cGfDD5s2bMW7cOGX94YcfBgDceeedeOuttwLax0svvYTY2FjccsstqK2txfjx4/HWW2/BaHTdSP/ee+/hoYceUibHvvbaa/1+93qkwAt0iXjllVfw+OOP4/7778eJEyeQlZWF++67D0888YTS59FHH0VtbS3uv/9+nD17FsOHD8eKFSuC/w70pCTnMjkZ2LXLc5t7m9uMsV63t9TW0nZ//fXseKuZLGMLpH+kObLUuS0cZk0uR5Y609FXboJx3F/zmzcDcS38GSfrcxNpjh5y46stkL/xZKkznZa3JycF7gQythYYO3YsRBBzjB88eLBZW3x8PF555RW88sorPr0OHTpgyZIloQxR94T2XyekTUhJScHLL7+MQ4cOoba2FgcOHMBTTz2FuLg4pY/BYEBeXh5KSkpQV1eHNWvWYMCAAcEfrPGrFmw2YNEi57IRb23uhMPx15+Oeo5WGVDLkaXOdPSVm1AcWepMR1+5Ccpx+4qmt97Wvs50dJIbntciwmlpu/u5wZ8TyNiIpvACPVqpq3MtZ81yrftqa+q21vHXn456jlYZUMuRpc509JWbUBxZ6kxHX7kJ1fnVA9rXmY7+csPzmn6dlraHMwNEc3iBTgghhBBCCCGESAAv0AkhhBBCCCGEEAngBXq00jhLotEI5OS41n21NXVb6/jrT0c9R6sMqOXIUmc6+spNKI4sdaajr9yE6owfr32d6egvNzyv6ddpaXs4M0A0h7O4RyuJic5lUhKwfLnnNvc2bzN8+nO80XS7v/56dnzNrC3D2ALpH2mOLHVuC4dZk8uRpc509JWbYBz31/x//tPyLO6yPjeR5ughN77aAvkbT5Y602l5e1Ji4E4gYyOawnfQo5XGmRutViAvz7lsxFubO+Fw/PWno56jVQbUcmSpMx195SYUR5Y609FXboJy3GZdfnq+9nWmo5Pc8LwWEU5L260+ZmQPJQNEewSJKoqLiwUAUVxY6GwoLxcCcC4bcWurttpF9pxlInvOMlFWVROQI4QQNptNLF26VNhsNu+Ol31EiuO1ZgEcp9kx1HLC8XzK7OgkN6E4zJpkjk5yE6qjWW5CcfSUmyCc6pNnlNd8tcmsi9yE4jBr6jgB/Y2no9yE4ug5a+7PX/XJM4E9fi9tRUVFzmuD4mJB5IDvoBNCCCGEEEIIIRLAC3RCCCGEEEIIIUQCeIEerZhMruU997jWfbU1dVvr+OtPRz1Hqwyo5chSZzr6yk0ojix1pqOv3ITq3HGn9nWmo7/c8LymX6el7eHMANEerT9jT9RFuQc9wPtMfN7j6gev99pEicOa0VHLYdboqOnIOq5ocjzuObXapRpbOB1ZxxVpDn+HyDuuQBxv54NQjsN70OWD76BHK3V1zmVtLTBzpnPZiLc2d8Lh+OtPRz1Hqwyo5chSZzr6yk0ojix1pqOv3ATl1Lna7n9A+zrT0UlueF6LCKel7e7nBn9OIGMjmsIL9GjFbnctFy92rftqa+q21vHXn456jlYZUMuRpc509JWbUBxZ6kxHX7kJ1Xnnbe3rTEd/ueF5Tb9OS9vDmQGiObxAJ4QQQgghhBBCJIAX6IQQQgghhBBCiATwAj1aMZtdy7lzXeu+2pq6rXX89aejnqNVBtRyZKkzHX3lJhRHljrT0VduQnUe+4P2daajv9zwvKZfp6Xt4cwA0R6tZ6kj6sJZ3DmzNp3IcZg1Omo6so4rmhzO4k4nnA5/h8g7rkAczuIeufAd9Gilpsa5rK4GJk50Lhvx1uZOOBx//emo52iVAbUcWepMR1+5CcWRpc509JWboJwaV9t112lfZzo6yQ3PaxHhtLTd/dzgzwlkbERTeIEerdTXu5YrVrjWfbU1dVvr+OtPRz1Hqwyo5chSZzr6yk0ojix1pqOv3ITqrF6tfZ3p6C83PK/p12lpezgzQDSHF+iEEEIIIYQQQogE8AKdEEIIIYQQQgiRAF6gRyvx8a7lwoWudV9tTd3WOv7601HP0SoDajmy1JmOvnITiiNLnenoKzehOq/+Q/s609Ffbnhe06/T0vZwZoBoj9az1BF14SzunFmbTuQ4zBodNR1ZxxVNDmdxpxNOh79D5B1XIA5ncY9c+A56tNI4c2NVFdC/v3PZiLc2d8Lh+OtPRz1Hqwyo5chSZzr6yk0ojix1pqOv3ATluM26PHSo9nWmo5Pc8LwWEU5L26t8zMgeSgaI5vACPVppaHAtCwtd677amrqtdfz1p6Oeo1UG1HJkqTMdfeUmFEeWOtPRV25CdX78Ufs609Ffbnhe06/T0vZwZoBoDi/QJaJHjx4wGAzNfh544AEAwIwZM5ptGzFihMajJoQQQgghhBASDmK1HgBxsWnTJtS7fSfhzp07ceWVV2LKlClKW25uLt58801lPS4uTtUxEkIIIYQQQghpG3iBLhGdOnXyWH/22Wdx/vnnY8yYMUqb2WxGRkZG6w+WkOBcJiYC+fnOZSPubd4+/eLP8UbT7f7669nx9YkhGcYWSP9Ic2Spc1s4zJpcjix1pqOv3ATlJLjali7Vvs50dJIbH04gf+PJUmc6LW93Pzf4cwIZG9EUXqBLis1mw5IlS/Dwww/DYDAo7d988w3S09PRrl07jBkzBk8//TTS09N97sdqtcJqtSrrlZWVAAAHALvd7my84gpACKBx3a3NbncoTXa7IyAHdrvSz+5ju7f1SHF81szPcbweQy0nHM+nzI4OcsOsRYijg9yE6miam1AcPeUmQMduc3vNjxkDuw5yE4rDrKnjBPw3nk5yE4qj56x5PH9CwN7K3BB5MAghhNaDIM3517/+hWnTpuHw4cPIysoCAHz00UdITk5GdnY2ioqK8Pjjj8PhcGDLli0wm81e95OXl4d58+Y1a1+0aBHS0tL8jsNaDzy60fn/OM8Pc8BsbMWDihJYM6IWzBoh0QVf8yScME/6JlzP36lTpzBz5kwUFxeja9euYRwhCRmtv+eNeCcnJ0dMnjy5xT7Hjh0TJpNJfPzxxz771NXVifLycuWnsLBQABBF27cLm80mbKdPC1tCgnNpszVrK6uqUb5j8eTZioAcm80mqqurxdKlS0V1dbV3x8s+IsXxWrMAjtPsGGo54Xg+ZXZ0khtmLQIcneQmVEez3DBryvaykhOu761OaaeL3DBr8joB/Y2no9xEW9bcn7+ykhMh52bv3r38HnTJ4EfcJeTQoUNYtWoVPvnkkxb7ZWZmIjs7G/v27fPZx2w2e7y7XlFRAQCIjY2FyWQCYmOB2lrn0mTCuY1Km6mxDYDJFJgDD8fk3fHRPxIcrzXzViN/NVPLCcfzKbOjk9wwaxHg6CQ3rXVUzw2zpmw3xca6ttWdW9dJbkJxmLW2dQL6G0+HuYmWrJmE6xZYU7PnM/DcmJrUjmgPv2ZNQt58802kp6fj6quvbrHf6dOnUVxcjMzMTJVGRgghhBBCCCGkreAFumQ0NDTgzTffxJ133olYt/8pr6qqwiOPPIJ169bh4MGD+Oabb3DNNdcgLS0NN9xwQ/AHapy5MSkJ2LnTuWzEW5s74XD89aejnqNVBtRyZKkzHX3lJhRHljrT0VdugnLcZl3euEn7OtPRSW54XosIp6XtST5mZA8lA0RzYv13IWqyatUqHD58GHfffbdHu9FoxI4dO/DOO++grKwMmZmZGDduHD766COkpKQEf6CYGNeyWzfXetM2t+9l97q9pbaWHH/99ex4q5ksYwukf6Q5stS5LRxmTS5HljrT0VduQnW6dtW+znT0lxt/v0NkrTOdlreHMzdEc/jMSEZOTg6EEOjdu7dHe0JCApYvX44TJ07AZrPh0KFDeOutt9CtW7fQDlRV5VxWVgIWi3PZiLc2d8Lh+OtPRz1Hqwyo5chSZzr6yk0ojix1pqOv3ATlVLnasjK1rzMdneSG57WIcFra7n5u8OcEMjaiKbxAJ4QQQgghhBBCJIAX6IQQQgghhBBCiATwAp0QQgghhBBCCJEAXqBHK8nJzmVKClBe7lw24q3NnXA4/vrTUc/RKgNqObLUmY6+chOKI0ud6egrN0E5ya62YyXa15mOTnLD81pEOC1tdz83+HMCGVsLfPvtt7jmmmuQlZUFg8GApUuXemzPy8vDhRdeiKSkJLRv3x4TJkzAhg0bPPqMHTsWBoPB42fq1Kkefc6ePYvp06fDYrHAYrFg+vTpKCsrC2nMeoMX6NFKQ4NrWVzsWvfV1tRtreOvPx31HK0yoJYjS53p6Cs3oTiy1JmOvnITqnPkiPZ1pqO/3PC8pl+npe1BZqD6p0NYu/+k77G1QHV1NQYNGoRXX33V6/bevXvj1VdfxY4dO/D999+jR48eyMnJwcmTnsebNWsWSkpKlJ/XX3/dY/u0adNQUFCA/Px85Ofno6CgANOnTw9pzHqDF+jRSk2Nc1ldDQwY4Fw24q3NnXA4/vrTUc/RKgNqObLUmY6+chOKI0ud6egrN0E5Na62YZdqX2c6OskNz2sR4bS03f3c4Mc5euw0fvbPHbj77S04WWn1PT4fTJo0CU899RRuvPFGr9unTZuGCRMm4LzzzkP//v3x4osvoqKiAtu3b/fol5iYiIyMDOXHYrEo23bv3o38/HwsWrQII0eOxMiRI7Fw4UIsW7YMe/bsCXrMeoMX6IQQQgghhBASoQghlH9npZrRrawUnVPicLSstk2Pa7PZ8MYbb8BisWDQoEEe29577z2kpaWhf//+eOSRR1Dp9rVv69atg8ViwfDhw5W2ESNGwGKxYO3atW06ZhmI1XoAhBBCCCGEEELCS42tHotGTcWqtwrw8a9+BpMxBgaDAa9/Oh9pfz+A2HYWHDxYBgCorKxERUWF4prNZpjN5pCOu2zZMkydOhU1NTXIzMzEypUrkZaWpmy/7bbb0LNnT2RkZGDnzp147LHHsG3bNqxcuRIAUFpaivT09Gb7TU9PR2lpaUhj0hN8B534mGzCz8QR4XACmZyCjjqOVhlQy5GlznT0lZtQHFnqTEdfuQnFSZakznT0lZtQHFnqTMdje32D613xjYfLPdbdefuSa7C9pAr5O10XthkGG2JjDB79+vXrp0zIZrFY8Mwzz/gfvw/GjRuHgoICrF27Frm5ubjllltw4sQJZfusWbMwYcIEDBgwAFOnTsW///1vrFq1Clu3blX6GAyGZvsVQnhtjzT4Dnq00vgCT00F3P63rFmbzdHc9ed4o+l2f/317HirmSxjC6R/pDmy1LktHGZNLkeWOtPRV26Ccdxf86UlQFwLf8bJ+txEmqOH3PhqC+RvPFnqTMdje/7OEsz9bJeyaca/diFz+QHMHn8Btp8w4Kpz7Ylp7fHHGZcj1hiDqwZmtnicwsJCdOnSRVkP9d1zAEhKSsIFF1yACy64ACNGjECvXr2wePFiPPbYY177X3zxxTCZTNi3bx8uvvhiZGRk4Pjx4836nTx5Ep07dw55XHqB76BHKw6Ha7l8uWvdV1tTt7WOv/501HO0yoBajix1pqOv3ITiyFJnOvrKTajOqlXa15mO/nLD85p+nXPb87cdxS+XbMXxCs8J3krL6zDnk51474AR24+UK86NJ3fh2v7pMDa+Y+7jOCkpKUhNTVV+WnOB3hQhBKxW3xPS7dq1C3a7HZmZzv9EGDlyJMrLy7Fx40alz4YNG1BeXo5Ro0aFbVyywgv0aKX23KQQNTVAbq5rVndfbe6Ew/HXn456jlYZUMuRpc509JWbUBxZ6kxHX7kJynGb0On667WvMx2d5IbntYhwampQP+kqzFtWCG8fZm9sMxoErPZ63/sMZGwtUFVVhYKCAhQUFAAAioqKUFBQgMOHD6O6uhp/+MMfsH79ehw6dAhbt27FzJkzceTIEUyZMgUAcODAATz55JPYvHkzDh48iC+++AJTpkzBkCFDMHr0aABA3759kZubi1mzZmH9+vVYv349Zs2ahcmTJ6NPnz4hjVtP8CPuhBBCCCGEECI5G7v2R0mlrcU+9cKAFr59vdVs3rwZ48aNU9YffvhhAMCdd96J1157DT/++CPefvttnDp1Ch07dsSll16K7777Dv379wcAxMXFYfXq1fjb3/6GqqoqdOvWDVdffTXmzp0Lo9Go7Pe9997DQw89hJycHADAtdde6/O71yMNXqATQgghhBBCiOScSG4fWL8Qvt88UMaOHevxtW1N+eSTT1r0u3XrhjVr1vg9TocOHbBkyZKgxxcJ8CPu0UpMjGvZr59r3VdbU7e1jr/+dNRztMqAWo4sdaajr9yE4shSZzr6yk2ozoUXal9nOvrLDc9runS2HDqL33+xDx07tfO9PzfSU8y+9xnI2Iim8B30aCUpyblMTgZ27fLc5t7mbYZPf443mm7311/Pjq+ZtWUYWyD9I82Rpc5t4TBrcjmy1JmOvnITjOP+mt+8ueVZ3GV9biLN0UNufLUF8jeeLHWOYsdqr8esdzbjTLUNw1/6BzKX70FpeZ3X+9ANACxxAkOz2/veZyBjI5rC/zqJVux259JmAxYtci4b8dbmTjgcf/3pqOdolQG1HFnqTEdfuQnFkaXOdPSVm6Acu6vtrbe1rzMdneSG5zU9OY7aOvxv/ymlm9lkxL2Xn4efX5yFwetXYu6k3gCcF+PuNK7f2KPBNWN7KBkgmsML9Gilrs61nDXLte6rranbWsdffzrqOVplQC1HljrT0VduQnFkqTMdfeUmVOdXD2hfZzr6yw3Pa1I7db+4H1e+sha3LdqAXccqlK6/GHM+nss9Hz0fuBu551mw4PaLkZ7q+TVoGZZ4vDJ1EAZ1FL6PEejYiKbwI+6EEEIIIYQQogH2+gaYzv07vt6OizKTUW6tx9GyWp9O7oBMjL4gDQPzVgAA3rqlP342OBsN9Q58cUiFQZM2he+gE0IIIYQQQoiKWB0NeGbMDIx6dSPOVLs+bv6n8efhf3OuQE6/zi36ysfYAQzrbvFYJ/qGF+jRSuP3DBqNQE6Oa91XW1O3tY6//nTUc7TKgFqOLHWmo6/chOLIUmc6+spNqM748drXmY7+csPzmjROXFwsvr/ocpystmPpD0cVp5MlAQlxXryW9hnODBDN4Ufco5XEROcyKQlYvtxzm3ubtxk+/TneaLrdX389O75m1pZhbIH0jzRHljq3hcOsyeXIUmc6+spNMI77a/4//2l5FndZn5tIc/SQG19tgfyNJ0udI8AprgLmLduNvGsHINYYA0NyMv740DWotddjXJ90IMYQ/POpbEsM3Ank8RBN4Tvo0UrjzI1WK5CX51w24q3NnXA4/vrTUc/RKgNqObLUmY6+chOKI0ud6egrN0E5brMuPz1f+zrT0UlueF7T2rE6GrBgtxFLNhTjy52lSp9RS17F+PPaISbGENrzqWzzMSN7KBkg2iNIVFFcXCwAiOLCQmdDebkQgHPZiFtbtdUusucsE9lzlomyqpqAHCGEsNlsYunSpcJms3l3vOwjUhyvNQvgOM2OoZYTjudTZkcnuQnFYdYkc3SSm1AdzXITiqOn3AThVJ88o7zmq01mXeQmFIdZU8cJ6G88HeUmFKetclNfViY2dunn4dz/6n/Eg+9tEXtLK0I7TpPt7s9f9ckzgT1+L21FRUXOa4PiYkHkgB9xJwFTa6uHyeQAbPWAyexcNn48qkmb3e6AtR6osTlgEobmjpd9hOoI4WvEhBBCCCHhQwjR8t8qQPB/E2nk1MS4PuJe4+tvvDD+vSaj06y/l7oG69hqbLj5jS3Yf/vz+ORIBS68IBF2uwNjMgUmTuwLk8mEGm/jD/L5rLHVt1HKidbwAl0ievTogUOHmn83wv33349//OMfEEJg3rx5eOONN3D27FkMHz4c//jHP9C/f39VxjfiuTWulYc/Bl5Y69mhWVssHt34le/t3vYRgtMzxYirruJVOiGEEELaDiEEpi7ahK2H/fyt4rVNZgcY2dLfeGH6e01ep0n/cDkAbnxnG4Bt4TuO18dLIg3egy4RmzZtQklJifKzcuVKAMCUKVMAAM8//zxefPFFvPrqq9i0aRMyMjJw5ZVXorKyMviDmUyu5T33uNabtCWYjLike7tWPrK2pajSgFr7uf9FbPp4vD2+pkS74ycDundkqTMdfeUmFEeWOtPRV25Cde64U/s6R5lTa6/H1sNlvl1CNGCo/TQSEs3eN4ZyviHao/FH7EkL/PrXvxbnn3++aGhoEA0NDSIjI0M8++yzyva6ujphsVjEa6+9FvA+lXvQg7jPxGq1io8+XirKqmpEtdUe0E9ZVU2bOycr67zff+sHr/cOhdHxeV+wxuOiE3kOs0ZHTUfWcUWT43HPqdUu1djC6cg6Lvf6l5yplOpvotD+jqpVHs+xCHg8oTitPUZVnU3kvLRGZM9ZJhZ8vU+Tx9LQ0NCq1wHvQZcPvoMuKTabDUuWLMHdd98Ng8GAoqIilJaWIicnR+ljNpsxZswYrF3r+6MuVqsVFRUVyk/ju+2OqirY7XbYKyth/8UvnEu73Wubw+GA2QiYDML5Y62F6aFfOZcttLXoeNtH0E6D8jjtdof3x+Pt8dnt5xzvjzccTrNxBXgcj2Oo5QSQAV07GmWAWZMoA8xa2BxNcsOsuW2vcr3mH/q1bnITSVlrJDacfxNp6DTStn+vye149PfjFJ2owIsr98EU43TiYoA5E3vhlZv6YsaHL4TtOME8n45f/jIs5xsiDwYhOMWWjPzrX//CtGnTcPjwYWRlZWHt2rUYPXo0jh49iqysLKXfvffei0OHDmG5j+8zzMvLw7x585q1L1q0CGlpaW02fjWw1gOPbnROo/D8MOd/IsiArOMikQezRkh0wde8tkRa/SPt8bQ1jgbgiS1GVDsMuKt3PQZ3jIxLqFOnTmHmzJkoLi5G165dtR4OAfgRd1nJyckRkydPVtb/97//CQDi2LFjHv1mzpwpJk6c6HM/dXV1ory8XPkpLCwUAETR9u3CZrMJ2+nTwpaQ4FzabF7bqqurxdKlS0V1dXX4HC/7CNYpq6pRPpp18mxFmx0nWMfruAI4TrNjqOVolQEdZU1Wh1mTzNFJbkJ1NMsNs6ZsLys54bqtJaWdLnITSVkL6O8OCXPjy5H17yhZsmY9dUps+umkh/PX/N1i1lsbxD/ei5zz2t69e/kRd8ngLO4ScujQIaxatQqffPKJ0paRkQEAKC0tRWZmptJ+4sQJdO7c2ee+zGYzzGbXxBEVFRUAgNjYWJhMJiA2FqitdS4bJ4vw1gbAZDKFz/HRPxhH+WoLACaTj8cThuME65g8Hr/beoDHUY6hlqNVBnSUNVkdZk0yRye5aa2jem6YNWW7KTbWta3u3LpOchOKI1vWvJ5zdZAbX05Aj0eHuQlH1qw2O254ZwcKj1cjf+bFuPCc85ucPnA4HPjii5KIOa+ZmtSOaA/vQZeQN998E+np6bj66quVtp49eyIjI0OZ2R1w3qe+Zs0ajBo1KviDNF60m83A3LmudV9tTd3WOv7601HP0SoDajmy1JmOvnITiiNLnenoKzehOo/9Qfs609Ffbnhe8+k0NAilj/lPf0TPtCQkxRmxt8ymOAaDoZmn2uNRKwNEe7R+C594Ul9fL7p37y7mzJnTbNuzzz4rLBaL+OSTT8SOHTvErbfeKjIzM0VFRUXA+w9lFnebTf7ZVGWawVrWcdGJPIdZo6OmI+u4osnhLO7aOpF2zo20xxOKY7PZxL8/WSpeXvGjuPz5r0RZtcs7erbGY13NcanpcBZ3+eA76JKxatUqHD58GHfffXezbY8++ihmz56N+++/H0OHDsXRo0exYsUKpKSkBH+gmhrnsroamDjRuWzEW5s74XD89aejnqNVBtRyZKkzHX3lJhRHljrT0VdugnJqXG3XXad9nenoJDc8r7XkGA3Af3eU4tDpGvy/LcVKnyxTAyyJJnkej1oZIJoT678LUZOcnBwIHxPrGwwG5OXlIS8vr/UHqq93LVescK37amvqttbx11/PjknisQXSP9IcWercFg6zJpcjS53p6Cs3oTqrV2tf52hzvJ1z9ZabYB+PXp6bIJyfNu3Ev1cfwCOTBwIADAbg0Ym9UG0XuHpgJlBdJefjUSs3RHN4gU4IIYQQQgiJeKyOBtw4/QWUrS3GReelY3yfjgCAMb07cbI0Ig38iDshhBBCCCEk4hBCYFd6T2XdHBuD6T98gQm9OqBr+wQNR0aIb/gOerQSH+9aLlzoWvfV1tRtreOvPx31HK0yoJYjS53p6Cs3oTiy1JmOvnITqvPqP7SvMx395SaKzmtWRz1ueWc7tt31ClZUONA71ek8PH0MDNMvAeLiYLfbdfN4VMsA0R6tZ6kj6sJZ3DmzNp3IcZg1Omo6so4rmhzO4q6tE2nn3Eh7PEIIYbVaPZz73tks+vzpC/Hp1iOajktmh7O4ywc/4h6tNM7cWFUF9O/vXDbirc2dcDj++tNRz9EqA2o5stSZjr5yE4ojS53p6Cs3QTlusy4PHap9nenoJDeRf16zORrwxrcHkPv3tah1uJQ/jsvG/5bNxfW9LLp6PAH1byuHaA4v0KOVhgbXsrDQte6rranbWsdffzrqOVplQC1HljrT0VduQnFkqTMdfeUmVOfHH7WvMx395SZCz2uxMQb8e8sR/HSqGutPGBSlW2ocOhZs0t3jCah/WzlEc2K1HgAhhBBCCCGEBErxmRp8vO4QHoIBMQBiYgx4dOKFOFlZi7hj27QeHiGtghfohBBCCCGEEF1gdTTg2le/x9kaO/pfcCmuPNc+oV9n2O12fFHKC3Sib/gR92gl4dxXSyQmAvn5zmUj3trcCYfjrz8d9RytMqCWI0ud6egrN6E4stSZjr5yE5Tj9rVQS5dqX2c6OsmN/s9r+45XKn3MqcmYOqw7fnZBR3SaP0/zsanmqJUBojl8Bz1aiY11LSdObL6taZu/7cE6/vrr2bE5gnfUGlsg/SPNkaXObeEwa3I5stSZjr5yE4zj/pqfMMH1uzyQ48jy3OjZ8XbO1UNufLUF8ng0fm4cDcCtizZiy+EyrPzNGFxwrs8jOX1gjDE066/m2FR31MoN0Ry+gx6tVFY6lxUVQGqqc9mItzZ3wuH4609HPUerDKjlyFJnOvrKTSiOLHWmo6/cBOVUutoyMrWvMx2d5EZf5zUhhNItNgZol2CCKSYG2/aVKH2MMQZ5MqCWo1YGiObwHXTiulj31xZux19/Ouo5WmVALUeWOtPRV25CcWSpMx195SYUp0qSOtPRV25CcVSqs726Bu9tOoqPdp7Cv+4bgXijs/2xSX0w/0Yz0mGTNwNqOWplgGhKjNYDIIQQQgghhEQ3xoYGvLu1BLtLKvDRpmKlvXuHRKSnxms4MkLUhe+gE0IIIYQQQlSlJKUjPllbjF/m9HN+VRoEfje2B07WGzHlkq4A+D3dJDrhBXq00jhzY1ISsHOnc9mItzZ3wuH4669np14E72j5eLTKgFqOLHVuC4dZk8uRpc509JWboBy3WZc3btK+ztHmeDvn6iI3PpxAHk8b1dlqTsBVv3kXZ785iAuz0zC+dxqwcydyL8wGjM7PttvtTS7QZciAlo5auSGawwv0aCUmxrXs1s217qutqdtax19/PTv19fKOLZD+kebIUue2cJg1uRxZ6kxHX7kJ1enaVfs6R5vj7Zyrt9wE+3jCWOeTta5/m+NiccuQLBQcr0G7xDj9ZEBLR63cEM3hMxOtVFU5l5WVgMXiOVmEtzZ3wuH4609HPUerDKjlyFJnOvrKTSiOLHWmo6/cBOVUudqyMrWvMx2d5Ebb85rN0YA739yMpwuMKDpVrfT53Y0X46Nb++OS7PbyPJ8yO2plgGgOL9AJIYQQQgghbUJcbAziTUYYDMCWw2VKe6zgPeaEeIMX6IQQQgghhJBW46hvwAc/lOC66X9FldWhtD82qTceH1KPmy/uouHoCNEHvEAnhBBCCCGEtBqDwYCFG45iW1YffLTtuNLeo2MSOpg1HBgJG99++y2uueYaZGVlwWAwYOnSpR7b8/LycOGFFyIpKQnt27fHhAkTsGHDBo8+VqsVDz74INLS0pCUlIRrr70WR44c8ehz9uxZTJ8+HRaLBRaLBdOnT0dZWVkbPzo54AV6tJKc7FympADl5c5lI97a3AmH468/HfUcrTKgliNLnenoKzehOLLUmY6+chOUk+xqO1aifZ3p6CQ3bXdeO1lpxaKCkxBlZUBKCowxBvw290I8PuE8TL3sAu/HCOE4dHxsbwvHD9XV1Rg0aBBeffVVr9t79+6NV199FTt27MD333+PHj16ICcnBydPnlT6zJ49G59++ik+/PBDfP/996iqqsLkyZNR7zZx4bRp01BQUID8/Hzk5+ejoKAA06dPD2nMeoMX6NFKQ4NrWVzsWvfV1tRtreOvPx31HK0yoJYjS53p6Cs3oTiy1JmOvnITqnPkiPZ1pqO/3ITxvGZzNCD35W/x1Bc/4psNe5U+V/fvjHsyG5BkauEyQ9bnU2ZHrQz4YdKkSXjqqadw4403et0+bdo0TJgwAeeddx769++PF198ERUVFdi+fTsAoLy8HIsXL8Zf//pXTJgwAUOGDMGSJUuwY8cOrFq1CgCwe/du5OfnY9GiRRg5ciRGjhyJhQsXYtmyZdizZ09I49YTvECPVmpqnMvqamDAAOeyEW9t7oTD8defjnqOVhlQy5GlznT0lZtQHFnqTEdfuQnKqXG1DbtU+zrT0UluwndeO1rm+q60uNgY3HhxFwzOSkHSPXfJ99xEmtPGGaisrERFRYXyY7VafY89QGw2G9544w1YLBYMGjQIALBlyxbY7Xbk5OQo/bKysjBgwACsXbsWALBu3TpYLBYMHz5c6TNixAhYLBalTyTDC3RCCCGEEEKIT2z1DZjx5kZc/vzXOHTa9R9Fv5t4IT69cxCGHdml4ehIOOjXr59yv7fFYsEzzzwT8r6WLVuG5ORkxMfH46WXXsLKlSuRlpYGACgtLUVcXBzat2/v4XTu3BmlpaVKn/T09Gb7TU9PV/pEMrFaD4AQQgghhBAiL3HGGBgACCGwoegMGmdDiIuNAQwGLYdGwkRhYSG6dHHNsm82hz6r37hx41BQUIBTp05h4cKFuOWWW7BhwwavF92NCCFgcMuSwUuumvaJVPgOumQcPXoUt99+Ozp27IjExEQMHjwYW7ZsUbbPmDEDBoPB42fEiBGtO6i3SSL8TRwRDieQySnoqONolQG1HFnqTEdfuQnFkaXOdPSVm1CcZEnqTEdfuQnAcaRa8PGO47jltXWosdUr/f94dT98/chY3DK0q/99yvLcRJrThhlISUlBamqq8tOaC/SkpCRccMEFGDFiBBYvXozY2FgsXrwYAJCRkQGbzYazZ896OCdOnEDnzp2VPsePH2+235MnTyp9Ihm+gy4RZ8+exejRozFu3Dh8+eWXSE9Px4EDB9CuXTuPfrm5uXjzzTeV9bi4uOAP1vjCTE0FKio8t3lr87c9WMdffz07NkfwjlpjC6R/pDmy1LktHGZNLkeWOtPRV26Ccdxf86UlQFwLf8bJ+tzo2fF2ztVDbny1+Xg8hrNn8fe/foNDp2vw4Y9luPtc/wtSnV3sdnvLx9HL86k3R63ctAFCCOWe9ksuuQQmkwkrV67ELbfcAgAoKSnBzp078fzzzwMARo4cifLycmzcuBHDhg0DAGzYsAHl5eUYNWqUqmPXAr6DLhHPPfccunXrhjfffBPDhg1Djx49MH78eJx//vke/cxmMzIyMpSfDh06BH8wh8O1XL7cte6rranbWsdffzrqOVplQC1HljrT0VduQnFkqTMdfeUmVGfVKu3rTEd/ufHhfLjpCIQQgMMB48oVePiKCzAn90LcPDhDjjrTUS83fqiqqkJBQQEKCgoAAEVFRSgoKMDhw4dRXV2NP/zhD1i/fj0OHTqErVu3YubMmThy5AimTJkCALBYLLjnnnvw29/+FqtXr8YPP/yA22+/HQMHDsSECRMAAH379kVubi5mzZqF9evXY/369Zg1axYmT56MPn36hDRuPcELdIn47LPPMHToUEyZMgXp6ekYMmQIFi5c2KzfN998g/T0dPTu3RuzZs3CiRMnfO7TarV6zMhYWVkJAHBUVsJut8NeUQH7DTc4l3a7zzYALW4P2vG2jxCcRux2R5seJ1in2biCeDzux1TF0SoDOsuarA6zJpGjo9yE6miSG2bNbXul6zU/9Vbd5CaSstbsnKuL3Ph2Gnkmfy++3l2qbL+qZxJmju6OBFutrnITSVlTOzeBsnnzZgwZMgRDhgwBADz88MMYMmQInnjiCRiNRvz444+46aab0Lt3b0yePBknT57Ed999h/79+yv7eOmll3D99dfjlltuwejRo5GYmIjPP/8cRqNR6fPee+9h4MCByMnJQU5ODi666CK8++67QY1VrxiEEELrQRAn8fHxAJxBnzJlCjZu3IjZs2fj9ddfxx133AEA+Oijj5CcnIzs7GwUFRXh8ccfh8PhwJYtW7zeK5KXl4d58+Y1a1+0aJEym6JesdYDj250frzv+WEOmI1+BJWQdVwk8mDWCIku+JrXlkipf7kNsMR5Pp6sRIEbezSgl4WXBdHGqVOnMHPmTBQXF6Nr165aD4cAgCDSYDKZxMiRIz3aHnzwQTFixAifzrFjx4TJZBIff/yx1+11dXWivLxc+SksLBQARNH27cJmswnb6dPClpDgXNpsXtuqq6vF0qVLRXV1dfgcL/sI1imrqhHZc5aJ7DnLxMmzFW12nGAdr+MK4DjNjqGWo1UGdJQ1WR1mTTJHJ7kJ1dEsN8yasr2s5ITymi9LaaeL3ERS1gL6u0PC3DRur6qpEzMX/U+c/8hSsf/AUY/Hc+JMuVQZiPasqZmbvXv3CgCiuLg4rNc1JHQ4SZxEZGZmol+/fh5tffv2xccff9yik52djX379nndbjabPd5Zr6hwTgoRGxcHk8kExMUBPXs6lyaTs5O3NgAmkyl8jo/+wTgmYXBzYtvsOME6Jo/HH+taD/A4yjHUcrTKgI6yJqvDrEnm6CQ3rXVUzw2zpmw3xcW5tvXIdq7rJDehOLJlzes5Vwe5adxuMgG2BsBhjMX64irc0DVdceLi5MxAtGZNzdyYmtSOaA8v0CVi9OjR2LNnj0fb3r17kZ2d7dM5ffo0iouLkZmZGdzBkpKcy+RkYNcuz23e2vxtD9bx11/Pjq+ZtWUYWyD9I82Rpc5t4TBrcjmy1JmOvnITjOP+mt+8GS3O4i7rc6Nnx9s5V9Lc1DcIfHGgAh/+ZjEWxsUj8Vz7H68diMcNwAXpKagJ5PHo5bmJBketrBHN4SRxEvGb3/wG69evx/z587F//368//77eOONN/DAAw8AcM6a+Mgjj2DdunU4ePAgvvnmG1xzzTVIS0vDDTfcENzBGieEsNmARYucy0a8tbkTDsdffzrqOVplQC1HljrT0VduQnFkqTMdfeUmKMdtMqe33ta+znSkzY0QAs/n/4j/7T+Nf60/qLT3bm/GBZ99xPOaHh21ska0R+vP2BNPPv/8czFgwABhNpvFhRdeKN544w1lW01NjcjJyRGdOnUSJpNJdO/eXdx5553i8OHDAe+/uLjYeZ9JYaGzobxcCMC5bKRJm81mE0uXLhU2my18jpd9BOtUW+2ue/GqatrsOME6XscVwHGaHUMtR6sMqOVokAG1HGZNMkcnuQnV0Sw3oTh6yk0QTvXJM8prvtpk1kVuQnFkzVpAf3cE8XyG0ymvtYkla38Sn37qcj7+fq94edRUcbb0lFdH1r+j1HRkzVpAjyVMTlFREe9Blwx+xF0yJk+ejMmTJ3vdlpCQgOXLl6s8IkIIIYQQIiu2+gZM+OsanKi04v6+Blx9rv3GgZ2BtR8CCa9rOj5CSHDwI+6EEEIIIYToiDPVro8nxxljcNXATPRKTwK/JI0Q/cML9GjFaHQtc3Jc677amrqtdfz1p6Oeo1UG1HJkqTMdfeUmFEeWOtPRV25CdcaP177OdFTPgA0G/Or9rRgxfzWKK22KMyf3Qix7YBQubCda3ifPa/p11Moa0Rx+xD1aSUx0LpOSgKYfm/fW5m97sI6//np2fM2sLcPYAukfaY4sdW4Lh1mTy5GlznT0lZtgHPfX/H/+0/Is7rI+N3p2vJ1zVc5AHICyGjts9Q34prga0885CQDs9obgjhPI49HLcxMNjlpZI5rDd9CjlcaZG61WIC/PuWzEW5s74XD89aejnqNVBtRyZKkzHX3lJhRHljrT0VdugnLcZl1+er72dabTphloEMDKwhO4682NqKuqUbb/4aq++OKhn2H6kAye16LJUet8Q7RH61nqiLpwFnfOrK31LMR6zJqsDrMmmaOT3ITqcLZj7R3O4h5ds7h//MlSMeqZVSJ7zjLx9urdYT+OrH9HqenImrVQns9QHc7iLh/8iDshhBBCCCEaU22Kx/Idx3HDqBQAgDEGeHDc+Sguq8OkC9M0Hh0hRC14gU4IIYQQQoiG2OobMH7Wayj9fC8yMtrj0u4WAMBNF3eByWQCKio0HiEhRC14D3q0YjK5lvfc41r31dbUba3jrz8d9RytMqCWI0ud6egrN6E4stSZjr5yE6pzx53a15lOq57P8npXU1y8GTmx5ejRIQFWR4NPh+e1KHbUygDRHq0/Y0/URbkHPYj7TLzeNyOB4/P+W43HJuu46ESew6zRUdORdVzR5Li/5qutdqnGFk5H1nGF65xrtdeL33z0g+jzpy/EkbOu/VTW2YWjvkF3j0fPjqzjUtPhPejywXfQo5W6OueythaYOdO5bMRbmzvhcPz1p6Oeo1UG1HJkqTMdfeUmFEeWOtPRV26Ccupcbfc/oH2d6YT0fMbZrShd/wPq7A1YVXhccZIf+AWM1jqvDs9rdFTLANEcXqBHK3a7a7l4sWvdV1tTt7WOv/501HO0yoBajix1pqOv3ITiyFJnOvrKTajOO29rX2c6fp9PIQS+2XsSi/fEwGqvV7b/4f2n8Z8Zg3HnqB5hOU6bObLUmY56GSCawwt0QgghhBBC2gBHg8Dcz3Zj+5kYfFJwTGkfcPwABmWlaDgyQois8AKdEEIIIYSQMFBnr8eywpMQ59ZNxhjcP/Y8jMtswLg+nTQdGyFEH/Br1qIVs9m1nDvXte6rranbWsdffzrqOVplQC1HljrT0VduQnFkqTMdfeUmVOexP2hf5yhz6huE0m3TwbMY1zceRrftNkcDxv91DY6W1aLT43/B8HP7+PnQrkg5sR0ZqfG+xyFr1nTy3ESFo1YGiPZoPUsdURfO4s6ZtelEjsOs0VHTkXVc0eRwFnftnC93HBPDnl6p1D97zjIxYv4q8cnWIx79/vDJdjHqmdViVWGp1I9HCP4OkXlcajqcxV0++BH3aKWmxrmsrgYmTnQuG/HW5k44HH/96ajnaJUBtRxZ6kxHX7kJxZGlznT0lZugnBpX23XXaV/nKHHyb7kfv1yyFccrrB5dS8rr8JuPCvDBtIeV/nMmXYhv7r8U42ffIVFueF6LCEetDBDN4QV6tFJf71quWOFa99XW1G2t468/HfUcrTKgliNLnenoKzehOLLUmY6+chOqs3q19nWOAqfe7sC8tGEQvg08kz4c9XYHACA13gQThLy54XlNv45aGSCawwt0QgghhBBCvLCxuBwlqZ1a7FMRn4yNxeUqjYgQEunwAp0QQgghhBAvnKiyhbUfIYT4gxfo0Up8vGu5cKFr3VdbU7e1jr/+dNRztMqAWo4sdaajr9yE4shSZzr6yk2ozqv/0L7OEeqULFiMf/5wAkIIpHdI9t3XDY9+MueG5zX9OmplgGiP1rPUEXXhLO6cWZtO5DjMGh01HVnHFU0OZ3Fve6faahf9Hv9SZM9ZJjYVnRaO+gYxYv4q0cNt9nb3nx7nZnN31DdI+Xhagr9D5B2Xmg5ncZcPvoMerTTO3FhVBfTv71w24q3NnXA4/vrTUc/RKgNqObLUmY6+chOKI0ud6egrN0E5brMuDx2qfZ0jwGkQwM6jFcp6oq0O1+xbixHZFhhjDDDWVGPuqjcAAIYmu29cn7vqDRhr3J4b6XLD81pEOGplgGgOL9CjlYYG17Kw0LXuq62p21rHX3866jlaZUAtR5Y609FXbkJxZKkzHX3lJlTnxx+1r7POnbIaO57+wYgpb2zA8Yo6pc+f/zUfH952EYZ0bw80NCD3q39hwY19kZ5q9th9hiUeC27si9yv/qWf3PC8pl9HrQwQzYnVegCEhINaWz1MJgdgqwdMZufS5mUdgN3ugLUeqLE5YBKGsDs1MQ4tS0EIIaSNEEK0/LsAaNbm9/dHEE6NzfW1SDWx6v5uU9Np1t9LjUJ1rPHJMJ9bj4s1INkE2AxGbD9ShtEXpDn7GmNhb3Kcy89rj89+dRmGz18NAFg0fQjG9c2EsaqyLSNHCIlCeIFOIoIRz61xrTz8MfDCWt/rAIBYPLrxqzZ3REtfnEoIIUQ3CCEwddEmbD3s53eB17YwOX/foKwOfeh9zX63qeM06R8u59cfNnEMAByY9c6WoB7PpT3awxjT9EPvhBDSevgRd8k4evQobr/9dnTs2BGJiYkYPHgwtmxx/dIQQiAvLw9ZWVlISEjA2LFjsWvXruAPlJDgXCYmAvn5zmUj3trcCYfjr38AToLJiEu6t/P/WDWk1u56t6MtahAWR6sMqOXIUmc6+spNKI4sdabTJhmotddj6+Ey32MkUUPPFIEEk9G5oqdzVCiOLK9pOuplgGgO30GXiLNnz2L06NEYN24cvvzyS6Snp+PAgQNo166d0uf555/Hiy++iLfeegu9e/fGU089hSuvvBJ79uxBSkpK4AeLjXUtJ05svq1pm7/twTr++gfgGAwGfDDzUixd9iUmTsyByWRqeX/nsNvtWL58RZs5p6ts+NnzXwf9eLyihqNVBtRyZKlzWzg2H7dTyDC2QPpHmiNLnem0TQbcXm/r54xBalJgX1EUzt85NTYHhj7l/Ij15j+NR2JcrF9HrbGF02ntMezCgE9/OIqC4nL85eaLlD7f7DmB9JR49MtKbdVxvl65AgbDuXfP9XSOatrm7XeIrK9pOurlhmgO30GXiOeeew7dunXDm2++iWHDhqFHjx4YP348zj//fADOd89ffvll/PGPf8SNN96IAQMG4O2330ZNTQ3ef//94A5Wee6eqYoKIDXVuWzEW5s74XD89Q/QMRgMMBuBxLhY509dDRLTOjiX3tbP/bSlkxDn42XVRjVotaNVBtRyZKkzHX3lJhRHljrTafMMJMQZff++8NLW4u+PoJxaZQyJ3bup+rtNbcejf5BOgwD+kr8X/91egh/7D1P6XJWdjKEX9Wj1cQzun2zX0zkqFEeW1zQd9TJANIfvoEvEZ599hokTJ2LKlClYs2YNunTpgvvvvx+zZs0CABQVFaG0tBQ5OTmKYzabMWbMGKxduxb33Xdfs31arVZYrVZlvfLchbnD4YDdbgccDteP3Y5zGz3a7Ofa7T62h+R42UekOHZDjFJvh90R8HGaHUMtR6sMMGutz5rb825n1rR3dJKbUB3NciNJ1ry+3lTOmt3hcI2hcV3y3ITiBJuBHQdPYceRMrQ757SPN+Hen/VAmkng/P8rctVJJ1mTKtM6yo0aWYu085q7Q+TBIASnsZKF+Hjnx+UefvhhTJkyBRs3bsTs2bPx+uuv44477sDatWsxevRoHD16FFlZWYp377334tChQ1i+fHmzfebl5WHevHnN2hctWoS0tLS2ezBRTKUN+NMW5/99PXWJAylxGg+IRCzWeuDRjc6sPT/MAbNR4wEREsHI8HqTYQyycawGeG5bLIwGgbyL65HK37kBwzwRADh16hRmzpyJ4uJidO3aVevhEAAQRBpMJpMYOXKkR9uDDz4oRowYIYQQ4n//+58AII4dO+bRZ+bMmWLixIle91lXVyfKy8uVn8LCQgFAFG3fLmw2m7CdPi1sCQnOpc3mta26ulosXbpUVFdXh8/xso9IcY6dqRTZc5aJ7DnLxOHjZ33WyG/N1HK0ygCz1mqnrKpGydrJsxXMmtaOTnITqqNZbiTJmtfXm8pZKys5oYyhLKWdLnIT7qydLTkhfjh4ysOZsuB/4sH3Nol/fhQZWZMq0zrKDc9roTl79+4VAERxcXHbXOCQoOFH3CUiMzMT/fr182jr27cvPv74YwBARkYGAKC0tBSZmZlKnxMnTqBz585e92k2m2E2m5X1inP3m8SmpjonRLFYgE2bnEvjuf869dYGwGQyhc/x0T8SHFOt66NCsaZY18QzAR5HOYZajlYZYNZan7V64dafWdPc0UluWuuonhtJsub19aZy1kyWVNe2b7+FSUe5CcVpmoGd+d9j2mtbkRBnxHe/HYO4c84H945EQ70DX3xxNCKyJlWmdZibcGRNSqeNchPoBIlEPWL8dyFqMXr0aOzZs8ejbe/evcjOzgYA9OzZExkZGVi5cqWy3WazYc2aNRg1alRwB4uJcS27dXOt+2pr6rbW8defjnqOVhlQy5GlznT0lZtQHFnqTEdfuQnV6dpV+zqr4FRbHUqfXgPOg9lkRILJiKMVVsXx+X3kzBrPa5HkqJUBojl8ZiTiN7/5DdavX4/58+dj//79eP/99/HGG2/ggQceAOCcsXz27NmYP38+Pv30U+zcuRMzZsxAYmIipk2bFtzBqqqcy8pK5/+gNc7q7qvNnXA4/vrTUc/RKgNqObLUmY6+chOKI0ud6egrN0E5Va62rEzt69yGzrEa4NZFG3HXm5uUPuaO7fH/bhuIr347Fj3NDXI8Hl3khue1iHDUygDRHF6gS8Sll16KTz/9FB988AEGDBiAP//5z3j55Zdx2223KX0effRRzJ49G/fffz+GDh2Ko0ePYsWKFcF9BzohhBBCiMQkxQLbjpRj6+GzKD5To7T36JCAGF/vmBNC2pxvv/0W11xzDbKysmAwGLB06VJlm91ux5w5czBw4EAkJSUhKysLd9xxB44dO+axj7Fjx8JgMHj8TJ061aPP2bNnMX36dFgsFlgsFkyfPh1lZWUqPELt4T3okjF58mRMnjzZ53aDwYC8vDzk5eWpNyhCCCGEkDbiRJUNi8fMQMPqn/DHGwYDACxxwAs3DcTw8zshwxLP72wmRBKqq6sxaNAg3HXXXbjppps8ttXU1GDr1q14/PHHMWjQIJw9exazZ8/Gtddei82bN3v0nTVrFp588kllPSEhwWP7tGnTcOTIEeTn5wNwfmvV9OnT8fnnn7fRI5MHXqATQgghhBDNOHy2Fq+PuBlxm4/hF1f2RarZ+QHPqwZmcAIrQiRj0qRJmDRpktdtFovFY64sAHjllVcwbNgwHD58GN27d1faExMTlQmwm7J7927k5+dj/fr1GD58OABg4cKFGDlyJPbs2YM+ffqE6dHICT/iHq0kJzuXKSlAeblz2Yi3NnfC4fjrT0c9R6sMqOXIUmc6+spNKI4sdaajr9wE5SS72o6VaF/nEJz6BoHlh6uxbN1+pc8lfbvgjksy8X+3XYz2iT6+yFzSx6OP3PC8FhGOWhkIM+Xl5TAYDGjXrp1H+3vvvYe0tDT0798fjzzyCCrd7olft24dLBaLcnEOACNGjIDFYsHatWtVGbeW8AI9WmlocC2Li13rvtqauq11/PWno56jVQbUcmSpMx195SYUR5Y609FXbkJ1jhzRvs4hOMu2H8N9S7Zi/op9cNidM7QbhMCT/cyY0KeT7/vLJX08ussNz2v6ddo4A5WVlaioqFB+rFar77EHSF1dHX7/+99j2rRpSE1NVdpvu+02fPDBB/jmm2/w+OOP4+OPP8aNN96obC8tLUV6enqz/aWnp6O0tLTV45IdXqBHKzXnJlyprgYGDHAuG/HW5k44HH/96ajnaJUBtRxZ6kxHX7kJxZGlznT0lZugHNdkaRh2qfZ1DsApKa/F3uOud8Ym9s/A+R0TcN3qD2GtqNJ0bGFxdJEbntciwmnjDPTr10+ZkM1iseCZZ57xPfYAsNvtmDp1KhoaGvB///d/HttmzZqFCRMmYMCAAZg6dSr+/e9/Y9WqVdi6davSx2Bo/p91Qgiv7ZEG70EnhBBCCCFh5z8FR/Hbf23DJdntMO3crabxJiNW3nsJYh4dD8T9XdsBEkIUCgsL0aVLF2XdbDaHvC+73Y5bbrkFRUVF+OqrrzzePffGxRdfDJPJhH379uHiiy9GRkYGjh8/3qzfyZMn0blz55DHpRf4DjohhBBCCGk19Q0ClVaHsj6sZwcAgBCArd7VLyYK3gEjRG+kpKQgNTVV+Qn1Ar3x4nzfvn1YtWoVOnbs6NfZtWsX7HY7MjMzAQAjR45EeXk5Nm7cqPTZsGEDysvLMWrUqJDGpSf4DjrxPkmEv4kjwuEEMjkFHXUcrTKgliNLnenoKzehOLLUmY6+chOKkyxJnc/1+Xr/GeSt3oLR3VMx/5yTaUnAN78bi87JJnzxxReaja3NHT3lJhRHljrTUS8DLVBVVYX9+/cr60VFRSgoKECHDh2QlZWFm2++GVu3bsWyZctQX1+v3DPeoUMHxMXF4cCBA3jvvfdw1VVXIS0tDYWFhfjtb3+LIUOGYPTo0QCAvn37Ijc3F7NmzcLrr78OwPk1a5MnT474GdwBXqBHL40vzNTU5t8t6q3N3/ZgHX/99exU1ck7tkD6R5ojS53bwrE5gnfUGlsg/SPNkaXOdNomA95eb2pnzX0MpSVAXAt/xqn83CT+dBqHTtegzl4P25kyxMU6P6TZtX0i7Ha7pmNrU0dP56imbYFkWpY601EvN37YvHkzxo0bp6w//PDDAIA777wTeXl5+OyzzwAAgwcP9vC+/vprjB07FnFxcVi9ejX+9re/oaqqCt26dcPVV1+NuXPnwmg0Kv3fe+89PPTQQ8jJyQEAXHvttXj11VdDHree4EfcoxWHw7Vcvty17qutqdtax19/Ouo5WmVALUeWOtPRV25CcWSpMx195SZUZ9Uqzep8tBp4+P9tx9trDyp9hnVLxd+mDsbXsy9D3OqV2mdALUdvueF5Tb+OWhnww9ixYyGEaPbz1ltvoUePHl63CSEwduxYAEC3bt2wZs0anD59GlarFfv378ff/vY3dOjQweM4HTp0wJIlS5RZ5ZcsWdLsq9oiFV6gRyu1tc5lTQ2Qm+ua1d1XmzvhcPz1p6Oeo1UG1HJkqTMdfeUmFEeWOtPRV26Ccmpdbddfr1mdD1UZ8Pn2Uiz87ifUV1UDubkw1NbiusFdkOiwyZEBtRxd5IbntYhw1MoA0Rx+xJ0QQgghhHil2hSPf28+hj497BhxnnOyp6FpAujQFdNH9oDR1/eWE0IICQleoBNCCCGEEK+8OvLnWLDiAC67oFy5QI8zAk9d0w8mk6lV97ISQghpDj/iHq3ExLiW/fq51n21NXVb6/jrT0c9R6sMqOXIUmc6+spNKI4sdaajr9yE6lx4YZvUecfIK3G00qas31a9D+d3TMTE/p0hhAjbcSLG0VtueF7Tr6NWBojm8B30aCUpyblMTgZ27fLc5q3N3/ZgHX/99ez4msVdhrEF0j/SHFnq3BaOr1ncZRhbIP0jzZGlznTaJgPeXm9qZ819DJs3tzyLewh1fu77I1hw+a9xx+ZSPNklDUhORtfN/8MqIWDw9d3lsmZALUdP56imbYFkWpY601EvN0Rz+F8n0UrjV57YbMCiRc5lI97a3AmH468/HfUcrTKgliNLnenoKzehOLLUmY6+chOU4/Z1ZW+93eqaVVsdsNW7lJ/1aIdYCDjsDg/H0PRr0oI8TkQ7usgNz2sR4aiVAaI9gkQVxcXFAoAoLix0NpSXCwE4l400abPZbGLp0qXCZrOFz/Gyj0hxTlbWiuw5y0T2nGXi2JlKnzXyWzO1HK0yoJajk9yE4lRb7UrWyqpqfNaIWVPJ0UluQnU0y00oThtkwOvrTeWsVZ88o4yh2mRu1fP5z+9/EgPn5ouHF/xHcRrKysTxpPbMWpSc1wLKtI7OUaE4zJoQRUVFzmuD4mJB5IDvoBNCCCGERBmxxhhU1Dmw66zro+sGgwHp1Wc1HBUhhBDeg04IIYQQEsF8/eMJLFi9Bw/0vBhjzrXdfHFXdEqMRe1PmzUdGyGEEE/4Dnq0YjS6ljk5rnVfbU3d1jr++tNRz9EqA2o5stSZjr5yE4ojS53p6Cs3oTrjxwdcs2/3ncTG4gq8NfEuxUmIM2J833R4fI25rM+nzI7ecsPzmn4dtTJANIfvoEcriYnOZVISsHy55zZvbf62B+v4669nx9cs7jKMLZD+kebIUue2cHzN4i7D2ALpH2mOLHWm0zYZ8PZ6Uztr7mP4z3+8zuJeUl6H9zcdwLRh3dH93D7vGtUT8SYj7hw5HkiKD3xssjyfMjt6Okc1bQsk07LUmY56uSGaw3fQo5XGmRutViAvz7lsxFubO+Fw/PWno56jVQbUcmSpMx195SYUR5Y609FXboJy3GZdfnq+1/5PfFaI19YcwD+/3a/ss3vHRMwZ1xMZLz2r/XMTaY4ucsPzWkQ4amWAaI/Ws9QRdeEs7pzFXdbZZNvM0UluQnE4i7tkjk5yE6oT7bMdyziLu+NsmVi+s0RUW+2K81VhibjltbXiq61FUuQmFCfas6aWw1ncmTUhOIu7jPAj7oQQQgghOuSuf+3Ctz+dxZ+v64+pQ7sAAC67oCPG9c0AKio0Hh0hhJBQ4EfcCSGEEEJ0wMkqm8f6Fee3hyXBBEeD0GhEhBBCwg3fQY9WTCbX8p57XOu+2pq6rXX89dezUy/x2ALpH2mOLHWmo6/chOLIUmc6+spNgM4fHnoF/3pji6vtjjsx9dJuuOVnvZAYFwu73d7ycWR5biLNkTw3PK9FkKNWBoj2aP0Ze+Ji7ty5AoDHT+fOnZXtd955Z7Ptw4cPD+oYyj3oQdxn4vW+GTo+8XkPusbjohN5js970DUeF53IdGQdl1qO2q+3Tz/1dJ5YukM5fvacZaLaag/LcWR0ZB1XpDn8HSLvuNR0eA+6fPAj7pLRv39/lJSUKD87duzw2J6bm+ux/YsvvgjtQHXnvgqsthaYOdO5bMRbmzvhcPz1p6Oeo1UG1HJkqTMdfeUmFEeWOtPRV26atAkhsGTDYTyzzYgjZ12P977/vo7/N+Nil3P/A9rXmY40uWkzR5Y601EvA0RzeIEuGbGxscjIyFB+OnXq5LHdbDZ7bO/QoUNoB2r8KJzdDixe7Fr31dbUba3jrz8d9RytMqCWI0ud6egrN6E4stSZjr5y06TNYDBg5e4TOF5rwAebipXtWQtfRf80t+8wf+dt7etMR5rctJkjS53pqJcBojm8B10y9u3bh6ysLJjNZgwfPhzz58/Heeedp2z/5ptvkJ6ejnbt2mHMmDF4+umnkZ6e7nN/VqsVVrfvOaysrAQAOBwO2O12wOEAEhKcy8YXapO2xvva7D62h+R42UekOHaD6/+9HHZHwMdpdgy1HK0ywKy1Pmtuz7udWdPe0UluQnU0y40kWfP6egvDcXYercCHa3/Cn5ItiD/Xdt/o7shqOIl7R3f3OI7d4XCNIf7cuuS5YdbkdQLKtI5yw6y1ziHyYBBCcOpPSfjyyy9RU1OD3r174/jx43jqqafw448/YteuXejYsSM++ugjJCcnIzs7G0VFRXj88cfhcDiwZcsWmM1mr/vMy8vDvHnzmrUvWrQIaWlpbf2QopJKG/CnLc7/+3rqEgdS4jQeEIlYrPXAoxudWXt+mANmo8YDIiSCaYvXW4MA/vyDEWesBvz8vHqM6tzyn2R8zZNwwjwRADh16hRmzpyJ4uJidO3aVevhEICTxMlMVVWV6Ny5s/jrX//qdfuxY8eEyWQSH3/8sc991NXVifLycuWnsLBQABBFe/YIm80mbFVVwjZvnnNps3ltq66uFkuXLhXV1dXhc7zsI1KcY2cqlUlXDh8/67NGfmumlqNVBpi1VjtlVTVK1k6erWDWtHZ0kptQHc1yI0nWvL7egjxOWVWN+H/rDghrnmv7P7/bLx58d6PYPvd5v2MrO1PumtQr78+6yA2zJq8TUKZ1lBtmLTRn7969nCROMniBLjkTJkwQv/jFL3xuv+CCC8Szzz4b8P44iztncacTOQ5n4KWjpiPruNRyWvt6q6qpE8OfXiWy5ywT6w6cCmls7mPgLO50Wuvwd4i841LT4Szu8sFJ4iTGarVi9+7dyMzM9Lr99OnTKC4u9rm9RWpqnMvqamDiROeyEW9t7oTD8defjnqOVhlQy5GlznT0lZtQHFnqTEea3BSfqVGa42JjML5vOnp0SEDtnMdCPI5rf7juOu3rTEearLWZI0ud6aiXAaI5vECXiEceeQRr1qxBUVERNmzYgJtvvhkVFRW48847UVVVhUceeQTr1q3DwYMH8c033+Caa65BWloabrjhhuAPVl/vWq5Y4Vr31dbUba3jrz8d9RytMqCWI0ud6egrN6E4stSZjua5qf1qDX7+9g8Y+8I3KCmvUzY9dlVffHXfJRj374WtP87q1drXmY7mWeN5LYoctTJANIezuEvEkSNHcOutt+LUqVPo1KkTRowYgfXr1yM7Oxu1tbXYsWMH3nnnHZSVlSEzMxPjxo3DRx99hJSUFK2HTgghhEQ09Q2uCdw2HTyLcX3j4T6nVkODQEyMAQCQ4LAixgAYAGw+dFbpl2yOBawG1cZMCCFEf/ACXSI+/PBDn9sSEhKwfPlyFUdDCCGEEADI//EU5q76SVmf+e4PyLTsxtzxPfEzUzxe+boIK/afxRcP/QyN31Q+L+d8pKa1R8dEI7448oM2AyeEEKI7+BH3aCU+3rVcuNC17qutqdtax19/Ouo5WmVALUeWOtPRV25CcWSpM52wZiD/pXfxy09243iF1aN7aXkdfvnJbnzz3EJ8XngKP52sRv7OUmU/vbt1RIbFy7HCNbZX/6F9neno6xwViiNLnemolwGiPVrPUkfUJdhZ3B31DeK7PaXi8UX/Ed/tKRWO+oaAPJlnq+Qs7nQixeEMvHTUdGQdV1s6jvoGMWL+KuV11vSnx5xlYsT8VeLzgqNi+c4Sr78jwzk2zuJOJ5wOf4fIOy41Hc7iLh98Bz1aaZy5saoK6N/fuWzkXFv+5iJc9txXuP2fm/HOPiNu/+dmXPbcV8jfXOTT8Whzp+l2f/3pqOe0kIGIcGSpMx195SYUR5Y60wlbBjYWHvWY5K0pAkBJeR06GhuQ0z8DxhhDG4/NbdbloUO1rzMdfZ2jQnFkqTMd9TJANIcX6NFKQ4NrWVjoWj/Xlu+w4Jf/Lmz2h0lpeR1++e9C5DsszZxm+2l6PPft/vrTUc/xkYGIcWSpMx195SYUR5Y60wnL87nzaDke/3Kv7zG5caLS7XelWvn88Uft60xHX+eoUBxZ6kxHvQwQzeEFOmlGfYPAvPH3QnjZ1tg2b/y9HjPaEkIIIXqlss6O/B9PobBTT6Ut2RyL/adrA/LTk+PaamiEEEKiDF6gk2ZsLC5HSWonn9sFgJLUTthYXK7eoAghhJAwIYTnfzA/8+WP+MUnu/HRoBylrUdaEp696gJ0qjoDX1+MZgCQWXESw7pZ2m6whBBCogpeoEcrCQnOZWIikJ/vXJ7jhC2w72j16OdlPx403e6vPx31HG/bI8mRpc509JWbUBxZ6kzH5/MpvvwSj60owohnVuNYmevd8TG9O+G8tERkTLnOw5k66nz8eUQaADS7SG9cnzuqM4zJSS0fO4CxBeYkuNqWLtW+znT0dY4KxZGlznTUywDRnFitB0A0IjbWtZw40WNTervAXrCL1h5EvcGAawdlIdbLfpodz327v/66dhwSjy2A/pHmyFLntnBszJpUjix1pgPExkLk5GB3SSUOn6lB7oAMIDYWhtxc7FuwFscrrFiz7xQa3/fO6dcZE/tneN1P7u2TsGBnCeZ+tsvjq9YyLPGYe00/5A7IbHms/h5LMI77a37CBNfv8kCOI8tzE2mOns5RTdu8/Q6Rtc501MsN0Ry+gx6tVFY6lxUVQGqqc3mOYR1jkVl1usWP9FnqqrDjaAXmf/EjHA3C6348aLrdX3866jnetkeSI0ud6egrN6E4stSZDlBRgYJeF+Oqv3+HR/+9DY76BsX59agueOfuYbhxcJbS3WAwtJiB3O5JWPXwGKV50fQh+H7OFcjtnqRy1ipdbRmZ2teZjr7OUaE4stSZjnoZIJoTo/UAiARUVnqsGmMMmLvyNQC+P9KXt3IBHh3bA7/N6Y14k1HZz9f7z8Be72NWyCbHabZORzvH2/ZIcmSpMx195SYUR5Y6R5mzfNdx/G2nEQvW/KS0XfTTdmSlmnFpjw44W2NXnJ/1bI/Le3eCufF3l79xnGszxrh+I17ao71rXausVenjuYkKR0/nqFAcWepMR70MEE3hBTrxSu7edVhwY19kWOI92jMs8VhwY1/cULgG94/qhluHdVe2bc3qg7v+tQs5L30Lq4Nf3UAIIST8lNfYsazwJCrjXPdjl9Xa8VOlAV/tOam0GUUDvn/gUiyecSk6pZi1GCohhBASNLwHnfgk98I0XDm0J9btP4EV321Azs+GY+QF6TD6+F/7k0ntkZZkwqU92sMc6/q/HyGEz4/LE0IIIcFw02trsf9EFV7rMRi559rG9emEKT3rcf8NF3n0jTHwtw8hhBB9wQv0aKVx5sakJGDnTueyEbc2Y4wBw3t2wOndAsN7dnB+pM+HM/HTRbj8vF6odXvz/FSVFbct/h9mjOyO27bvgLHR8baPpjTtoxen1i7v2ALpH2mOLHVuC6deBO9o+Xj0lJtQHFnqHCHO8Yo6vLS6EAdPV+Pfs4bBcK7PZRekwQCg/qWXFSc9xYzLMgS6tk/wvc/WZsDb6031rLlN4rpxk66ez4h19HSOatoWSKZlqTMd9XJDNIcfcY9WYmJcy27dXOu+2pq6PpyEeBM6JMUpze9tKMZPp6rx8Q/HENO9W8vH9XccOm3jhDkD0jmy1JmOvnITiiNLnXXoNHTtih0lldh+pEzplmyOxefbjmHLobMoOlOrOH+6ui9W/uZyXH3ZhfJlQE2na1dpn8+ocvSWG57X9OuolQE/fPvtt7jmmmuQlZUFg8GApUuXKtvsdjvmzJmDgQMHIikpCVlZWbjjjjtw7Ngxj31YrVY8+OCDSEtLQ1JSEq699locOXLEo8/Zs2cxffp0WCwWWCwWTJ8+HWVlZSGNWW+E9swQ/VNV5VxWVgIWi+dkEd7a3AnC+cXlPfHn6/pjzuXdYWjXDqishL2+Ae+t2YvajuktT1LRdJ/+xkUnNKeNM6C5I0ud6egrN6E4stRZh87iK+/CNa/+D39fvU/plmSOxRPX9MP7M4ejq6lecWKNMfJmoM2dKldbVqa0z2dUObrIDc9rEeGolQE/VFdXY9CgQXj11VebbaupqcHWrVvx+OOPY+vWrfjkk0+wd+9eXHvttR79Zs+ejU8//RQffvghvv/+e1RVVWHy5Mmor69X+kybNg0FBQXIz89Hfn4+CgoKMH369JDGrDf4EXfSpphNRkwf2cPjqxz+U3AMf8zfj3env4AveX86IYREFW9uOorPb/8LHisux6X9UwEAIw9tQ1KcESnxJo++t4/Idv6DXwdECCFSMGnSJEyaNMnrNovFgpUrV3q0vfLKKxg2bBgOHz6M7t27o7y8HIsXL8a7776LCRMmAACWLFmCbt26YdWqVZg4cSJ2796N/Px8rF+/HsOHDwcALFy4ECNHjsSePXvQp0+ftn2QGsN30InqJMUZ0dVixg27vnZ+9+w5rPb6FixCCCF640y1DYVnPf8btuBYJbZ26YuvD5xV2vqf+Ak//GYEXvr5YJVHSAghpC0pLy+HwWBAu3btAABbtmyB3W5HTk6O0icrKwsDBgzA2rVrAQDr1q2DxWJRLs4BYMSIEbBYLEqfSIbvoBPVmTQwExO6JqD+8c+Vtm3FZZjx5kZc3smAqzQcGyGEkPBwusqKkc99AyFicHe1DZ3bOd8dv3VwBkb89QmMe/Bdpa8BQJyR7xkQQohWVFZWosLt00pmsxlmc+u+orKurg6///3vMW3aNKSmOj8xVVpairi4OLRv396jb+fOnVFaWqr0SU9Pb7a/9PR0pU8kw9+G0UpysnOZkgKUlzuXjXhrcycMjqmdBfFnTinrH24qxtkaO47VGHw6fo9BJzRHowyo5shSZzr6yk0ojix11sDZUdGA2R/+gGe+P6o4HZPN6N05BRmJQGlFnaKMGNANt377L2RkpbXd2PSUm6CcZFfbsRKpMhC1ji5yw/NaRDhtnIF+/fopE7JZLBY888wzvsceAHa7HVOnTkVDQwP+7//+z29/IYTHJ2sNXr4ms2mfSIUX6NFKQ4NrWVzsWvfV1tRtrdNk/c/X9ccLNw1ATheXf6ayDn//7w6UV1sDO0YAx1HbqbPVo8bmcP7U2VDz0yHn0tu6zQFrPVz929ARooXHEkgN9OToMDcR6+gpN6E4YaqZEMLzdR3Aa7rZuaCNHGs9UFlnx7oDp3DodLXSp7SsBksLjmHpD0dRfeCg4rw142L8ZkA9enRMVPdc6O3xttqplydrAHDkiPavaTr6OkeF4shSZzptnoHCwkKUl5crP4899pjvsfvBbrfjlltuQVFREVauXKm8ew4AGRkZsNlsOHv2rIdz4sQJdO7cWelz/PjxZvs9efKk0ieS4Ufco5WaGueyuhoYMMD5P2mNLx5vbe6Ew2myHmuMwXWDs/DFsQJFWfj1XizYVob1ZZvx/i8va+bUNwhsKDqDLacM6Fh0BiMvSIfRz3G8Em4nxvU1c+Ne+t6LfLiF9Vg8uvGrNnd6phhx1VXC+2Px1eaOnhwtMqCWE58YvKPl49FTbkJxwlAzIQSmLtqErYe9va5bOg8A3s8F4XZigY1fN3tYjX2OV1rR/6NiAMV+juH/OOFxmvYPh+OG2lk7ecbVNuxS4NQJuc9R0eDo6RzVtM3b7xBZ60ynzXOTkpLicSEdKo0X5/v27cPXX3+Njh07emy/5JJLYDKZsHLlStxyyy0AgJKSEuzcuRPPP/88AGDkyJEoLy/Hxo0bMWzYMADAhg0bUF5ejlGjRrV6jLLDC3QiLYOyUtD75CHcdbNrpkiHIQZVtXasP1yCeZ8XoqS8DoAR7+zbjExLPOaO74lc7YYMAEgwGTUegX+KKg2otdcjLs5/X0KIetTa67H1cJnWwyAt0DNF6OI8TwghbUFVVRX279+vrBcVFaGgoAAdOnRAVlYWbr75ZmzduhXLli1DfX29cs94hw4dEBcXB4vFgnvuuQe//e1v0bFjR3To0AGPPPIIBg4cqMzq3rdvX+Tm5mLWrFl4/fXXAQD33nsvJk+eHPEzuAO8QCcSk9snDTn//BUML7o+ArOs7+WY88pGWB3NP8pTWl6HX36yGwt6j9T0It393ph1c8bAkhTvXKmodH5v7bESIDWl2brdbsfy5SswcWIOTCZTmzg18YkY+tQqlStCCAmF9XPGIDUp3u95AEDzc0EIzumS0zg+aiz6bVsLpKZAlFdg3LxlOJHcEQvvuATDsi1YvnwFLr9iPBLMcTAZY6Q4r3l1vDzecDlfr1wRFfdAEkKINzZv3oxx48Yp6w8//DAA4M4770ReXh4+++wzAMDgwYM9vK+//hpjx44FALz00kuIjY3FLbfcgtraWowfPx5vvfUWjEbXf36+9957eOihh5TZ3q+99lqv370eifACnXifWKKlSS3C5fjrDyAmJRlw+0NoRd/RXi/OAUDAORPwvAn34coGAeUlHsBxQhlbIE5inBGJcedeZnFGID7OuYyLbbZuNwiYjUBiXCxMprZyfLzro1UG1HI0zAAdP/0jzQljzRIazx9+zgMAmp8LgnS+O3Aad7+1GRdOehD5jX3MsXhoy1LEPf8sLsnugMQ4A8xGoGOS2XXhLMV5zYvj5fGGy2l2ba5V1pIleU3T0dc5KhRHljrTUS8DLTB27FgIj8mMPGlpWyPx8fF45ZVX8Morr/js06FDByxZsiSkMeodXqBHK40vzNRUwO0rFXy2+dserOOvvw9n+nOz8cXCDT4VAaAkJQ0bTzswsl3ox2mVY3Ooc5xwjU2rDKjlaFFntRyZsxZI/0hzZKmzH+erH4/jk61HkdvP9RU2g7u1R4wBiB00EHUJSYg/59y+7hOlj91ul/LxBNQ/Uhz313xpies/EgI5jizPTaQ5esiNr7ZA/iaQpc501MsN0ZwYrQdAXOTl5cFgMHj8ZGRkKNuFEMjLy0NWVhYSEhIwduxY7Nq1K7SDORyu5fLlrnVfbU3d1jr++vtwTvxvU0AP70RZTauOE1WOVhlQy5GlznT0lZtQHFnq7NbHYbVhy6GzEHa74mw+eBbLtpdgeaFrhtwOSXHY8tg4LOtdg3iDkPbxMGtu21et0r7OdPSXmwg4r0Wto1YGiObwAl0y+vfvj5KSEuVnx44dyrbnn38eL774Il599VVs2rQJGRkZuPLKK1FZWRn8gWprncuaGiA31zWru682d8Lh+Ovvw0n/05yAHl5Cgx0bi85AVFeHdJyocrTKgFqOLHWmo6/chOLIUudzfRpyJ+FnL6zBTQvWovDgScW5amAmfjXuAtw5MttDaQ+H1I+HWcsFampdbddfr32d6egkN5FzXotqR60MEM3hBbpkxMbGIiMjQ/np1KkTAOe75y+//DL++Mc/4sYbb8SAAQPw9ttvo6amBu+//77Go1aPYUd2ITMlDr6m5zEAyKw4ie0llbjl9XX4Y/5+NYdHCCGaUFJeh2WHYzBv2W6lLQYC/Tonw5JgwpGyOqV9QBcLHpnYB4O6WrQYKiGEEEJagPegS8a+ffuQlZUFs9mM4cOHY/78+TjvvPNQVFSE0tJSZSZDADCbzRgzZgzWrl2L++67z+v+rFYrrFarst74brvD4XDeT+hwAAkJzmXj/YVN2hrvO7T72B6S42UfATnxZvxxfA88uHQvDHDec95I40X7H797C9uvHIS42BiM7J4K+7l9OOqszv4N9W0ztnPr7vdp2u2OgI/T7Bht4Hgdm1YZkD1rOnBkzppUGdBR1uqsrvqu238KYy/sDKMX50THTNSfrUZmQgIAoLLGipVHY2AqPYJHruyFpHPO07nnwZLWDrE11cq5MNSxaZYbZk3Zbnc4lJLY48+tS3yOYtbkdgL6m0BHuWHWWucQeTCIQKbaI6rw5ZdfoqamBr1798bx48fx1FNP4ccff8SuXbuwZ88ejB49GkePHkVWVpbi3HvvvTh06BCWL1/udZ95eXmYN29es/ZFixYhLS2tzR5LW7PttAGfHIxBmc31Xnq7OIEbezRgUEdnpCtsQLIJiDnXZfNJA5YdjkFutwaMSG+72FvrgUc3Ov/v6/lhDpgl+rpcmcdGgofPZ2Sx7bQBHx+MQXkL5zUA+LLYgPwjRozJaMCNPZ3faiEE8PHBGGQnCwzqIHx+YQPRN3zNk3DCPBEAOHXqFGbOnIni4mJ07dpV6+EQ8B10qZg0aZLy74EDB2LkyJE4//zz8fbbb2PEiBEA0Oy7V4UQLX4f62OPPaZ8PyEAHD16FP369cP48ePRpUuXgMZlt9uxcuVKXHnlla6v1tHYuQrAow0C6w+cxFfrtuCKkZdgxPmdYIzxXYuP3tqMs7Yz6NTtAsC6r83GVmNz4NGNXwEArrjiCtf3oIfxGKE6Mo+NTvAOn8/IcZbvOo43121D0/86LLMZ8M+9Rvz52n6YeqnzD6eYXcex/KNtSE7LwlVXXaQcx9DGj0e2mkWj4/6anzgxx/U1nhKMLZyOrOOKNIe/Q+Qdl5rOwYMHA+pH1IP3oEtMUlISBg4ciH379imzuZeWlnr0OXHiBDp37uxzH2azGampqcpPyrmvV4sVAiaTCSYhYHr7befSZPLZBqDF7UE73vYRpBNvjsPoXum4JE1gdK90xJvjWnT+OWMYnrp+AO4Y1UM5zp4TNXj2ix9xavG7YR1bIyZTbFA18DhGGznNxqZVBnSUNZkdmbMmTQYkz1rMW2/jqf/+2Ozi3J0Xlu1ETH0DTCYTruzVEZuyj+MftwxUPZ+a5IZZc9vuyoTpvfd1cY5i1uR2mv0OkSgDzJq6uSHywAt0ibFardi9ezcyMzPRs2dPZGRkYOXKlcp2m82GNWvWYNSoUcHvvK7OtZw1y7Xuq62p21rHX/82cOJNRtw+IhuWBNeJaMGaA1i87jCeX7ZL07Fp6miVAbUcWepMR1+5CcUJsGYrn1uIeZ/vwnf7TgJ1ddj457+jtNLq2wFQ3hCDjfuOAwDi6+1I++U92j+fMjt6yk2ozq8e0L7OdPSXmzY6r9GJoKwRzeEFukQ88sgjWLNmDYqKirBhwwbcfPPNqKiowJ133gmDwYDZs2dj/vz5+PTTT7Fz507MmDEDiYmJmDZtmtZD1y0/H9oNI7pbcO/GT5S2szV2FGT21nBUhBA9Uml1oNycpKyfqrLijjc345kCI9yne/m258V4c9MxrDtwGgBwIrl9QPs/UWUL74AJIYQQIh28B10ijhw5gltvvRWnTp1Cp06dMGLECKxfvx7Z2c7vqn300UdRW1uL+++/H2fPnsXw4cOxYsUK5WPrJHgu790Jl2eYgQcOKm1vbT6Gv93xIu5Yvh9PTrlYu8ERQqSjXgBbD5fhWIUV15+Xonx7xNP/LcTC74rwq2E34JFzbSnxsVhfdAZCGHC62obMuDgAwJiftiLhl/dh5PkdAQDpVWcDOnZ6clyYHw0hhBBCZIMX6BLx4YcftrjdYDAgLy8PeXl5rT+Y0eha5uS41n21NXVb6/jrr6FTZW9ArKjH8B6ud7XqGwQahG9H5sejyvMpsyNLnenoKzcANhypxOoZ8zBg9ylcOzwVAFDfAPx84UYAwOUPj0LHc06nFDMA4GTvgco+zLFGvDzlIhzYtRUp8SblOBPOb4cJOb2ApCSguhrD+mYhM8WM0kqr1/vQDQAybJUY1qNDYI/FW59oc3SWtZCc8eO1rzMd/eUmWEeWOtNRLwNEc3iBHq0kJjqXSUlA069o89bmb3uwjr/+GjqP3zAI94zrjc6p8Upb/q7jePoHI+q7luDmod1bPo7N9T21Mjwev2PTKgNqORJnrU2eT1nGFkh/jZw6ez3Kq10fF69vELj7o104NOH3+BQmNP7X3A8n6/BG50twXVE5rh3ubIszAoO7WZBgikVNrBkdz+3z1mHdceuw7kiJv9rjsFcNzMAXxYA5Nsb72JKSYFyej7k7S/DLJVthADwu0hvfoZ979xgYU5IDe/w+jhNVjiRZC7vj/pr/z3+AuBb+jJP1uYk0Rw+58dUWyN8EstSZjnq5IZrDe9CjFdu5P06tViAvz7lsxFubO+Fw/PXX2Ml6+TkY7a4/4P+1+QhOWQ04fKZG87GF3dEqA2o5stSZjuoZ2P7TSXy27RhqbA6l7c01+3Dh4/mY/+UepbsxxoDdJeU4eLoGh0rLlPZLs5Ixw3gcOb07euz+/907HB/cOwLdkozKOFLiTUgxNIRcs9xeHbDg9ouRnmr26J5hiceCn1+E3H+/Lt/zKbOjp3NUUI7bPARPz9e+znR0kptWOLLUmY56GSDaI0hUUVxcLACI4sJCZ0N5uRCAc9lIkzabzSaWLl0qbDZb+Bwv+5DZKauqEb9Z8B9xvKxK6VPYqYd4dmmBOF5R6+FU1NpE9pxlInvOMrFy51HhqG8I6DjNxtUGTrXVroytrKomfM+nzI7OshaM4/X5DOA4amStzZ5PL23bDp0W9/z9M/Hmd/s9tl/y5HKRPWeZ2F5cprQtW79fZM9ZJm59Y63HcVZu/kms7TZQVJ08E/rYwpABr+cPCbIWtgyo5ejpHBWEU33yjJKPapNZ+nNUqA6zpo4T0N8EOspNKA6zJkRRUZHz2qC4WBA54EfcCQmAxLhYjMkUaJ/omqRpwYib8dm6IyipbcDLV50PAMj/8RTmrvpJ6TPz3R+QadmNudf0Q273pGb7JUSv1DcIbDxUhhN9L0f6oTIM65+CcN/NZnM0oKLahjS3tt/+axu2HTqNv3fqiX7n2n46VY1VR2Nw1liKGZedr/S9uEsqymwC9UKg8cPi487vgB8evxJJJuDLL79U+k7o1REo3uH8DLuGGGMMyr8v7dHeY50QQgghkQ8v0AkJkWsLv8WRK6/BrMvPAwDk9x6JX36yu9kkT6Xldfjlkq1YcGNf5Ko/TELCTv7OEsz7vBAl5XXAtY8C7+1ApmUf5o7vGVLG96Rl48DukxjaLw7t450XyCsLT+BXHxZgaNdU/Mutb9GpKuw/XYuDHbKUC/R+mSm4rHMDcodkeez3jZv7AanOyd1QUQEASIwzIjEpDna7PYSREkIIIYS0LbxAj1ZMJtfynntc677amrqtdfz114EzYexATJh5KZCQgPrqGsy79jdeZ2BufO9u3qqfcOU998Ao2+PRKgNqObLUOUKc5buO48EPt3n/j6hPdmPBLx9Hro/jFFc58P8e/Avi1h3FryamKseZc8efUfDpj1iQmIgJFzrfL09PNaNBACeq7R7j+G1OHzTUWTHw9EClrWdaEqac14Crhnb1PXZmLbocPZ2jQnXuuFP7OtPRX254XtOvo1YGiPZo/Rl7oi7KPehB3Gfi9b4ZOh6s3X9KuY+rpZ+1+09p+lh83rMc5uPQUcdR+/n85NOlYvjTK33mu8ecZWLE/FXCUd8gnvlit5j44jfi6Tf/oxxnyyHn/bMj5q/y2PfjS3eI6179XqwqLFXGVlVTJ45X1IqGhoY2ezwyOjK/RmWtWTQ57vmottqlGls4HVnHFWmOzOcbtRxZx6Wmw3vQ5YOzuEcrdXXOZW0tMHOmc9mItzZ3wuH4668z58TpCt993Tjx11fkezxaZUAtR5Y6R4Cz84wBpRW+Z30VAErK67BxTwmOltXix+NVOO52yPOSjbi1rggzLu0CIYRynCc/fxlL774Y4/t2VvrGxcYgPSUehro6Zo0Oz2vK9jpX2/0PaF9nOjrJDc9rEeGolQGiObxAj1Ya77+024HFi13rvtqauq11/PXXmZOeENjEUunLPgHsdggh8Pv/FGLpugOw1rbwNRdqPB6tMqCWI3FuZHVqbfX4avdxfLD5iIez8mhgE5adKKvBXaN7YOH0IbgkzfVh+HaxwDN/exD3DcuCwWAIbGzMGp1QHD3lJlTnnbe1rzMd/eWG5zX9OmplgGgO70EnJAwM62ZBZsVJlKZ28nofugFA55Q4DDuyCwCw42g5Piwoxec593tMqlXfoMZoCXGx6eAZ/G//KVycZsbl59oq6uy4+1+7EJNzP250NMB8rr1zAnC42v8+05PjcHH39rDbk/HF/rYaOSGEEEJI5MF30AkJA8YYA+aufgNA45c5uWhcz7vyfBiF8wq8Y7IZvxrVDbcXfIn4WNfL8I0fY3DLGxtQUFzW9oMmUUVdbBzmr/4JM9/eDIfb/wSt2n0cL6/ah1X7Titt6SlmDOmSgkl716LaVq+033p+AzJSzc0y3ogBQGbFSQzrZmmjR0EIIYQQEtnwAj1aMZtdy7lzXeu+2pq6rXX89dehk3trDhb8/CKkp3p6GZZ4LLj9YuQO6qI4Xdol4JGJffDYmO7KPqqsDuyrMOCH4nK0SzAp+z34+HzsPGN13rPbFo9Hqwyo5Uiem3A5yj3dAL48UIapj72Pv607qjhxf3gM724twardx1F81nXf2YieHTHlkq4Ydl6asl+DwYBP7x2OfwyOR4f2yUpfYwzwp6suBOD7P6LmdiyHMSE+sMcSSA305OgwNxHr6Ck3oTqP/UH7OtPRX254XtOvo1YGiPZoO0cdURvO4t72TkWtTZkVdeXOo8JR738W6sZjvPXRUvH/Nh70aH9i6Q6RPWeZ+PPnu1o1LiE4Y6veHZujXpypsirrVXWurO06clppf3/DIZE9Z5m4Y/EGD///vt4v3ll3UJScrWrVuL7ccUyMmL/KYwb3EfNXiS93HAv4sQRynGh0ZH6NylqzaHI4izudcDoyn2/UcmQdl5oOZ3GXD76DHq3U1DiX1dXAxInOZSPe2twJh+Ovv44dY4zrvcVLe7R3rQdwnHZm4LrBWR7Hafjsc8THxmDk+R0Vp7TkDOb8ezvW7D3Z+sejVQbUcnSSG3es9c55CqqtDqXPp+sOoO/j+fjdh1sVR5loDUDRqRrl35dlJeKvh1bg92OzPY7zy0szMH1ENjomxbXq8eQOyMT3c67AB9MH4W8H8/HB9EH4fs4VyO2ZKmcG1HJ0mLWIdfSUm6Ac1+sc112nfZ3p6CQ3PK9FhKNWBojm8AI9Wqmvdy1XrHCt+2pr6rbW8defjtLnz//8A374zQhc3ruT4qzYfQIfbS7GP775yaO7vb6hxePUN7g+Br3p4FnnulYZUMuR7Pl071NVY8W63SVYs++UR7cXthtx42sb8MPhMsXpnGSCo0HgSHmd1+MM79le+Xe31Djc9OHf0Tctoc0ejzHGgJHdUnHdR69iZLdU539EyZoBtRyJsxZ1jp5yE6qzerX2daajv9zwvKZfR60MEM3hLO6E6IAEk9F5A/A5hnRJwR0js9EnPQk4cRoA4KhvwOhnv0K/9ES8kGhBWpN95P94CnNXuS7oZ777AzItuzF3fE+PmeRJ27D2wCnsPHAC4zt0wfnn2rYcrcSd055Br1U/YczgbKVveoJAvdGMyjo7AOe73Rd3ScHa31+BTIMN+G3z/ZuM/P9WQgghhBC9wwt0QnTIwMwUDOzTBXa7HV98sR0A8ENxGU5UWuFw1KN9baXSd9uxSuwYlIvHP9nd7CvgSsvr8MtPdmNB75G8SA8TxyuteG/DXtTZ6/G7Ky9Q2t/49id8s+ckkroNVC7QL+iYgO5nS3Be7wEQQigfWb+rdwOunTwWJpMJqKgAAMSbjMhKTQAq+L2lhBBCCCGRCi/Qo5X4eNdy4ULXuq+2pm5rHX/96QTtDM1uj5W/uRyHT1TA2OV1pc/jKw5ge+6vvO5ewDn79rwpv8eVcWYYfR3X37hkdtrwufnghSVY8f9245Zh3TGpT0dg4ULUxpjw99X7YI6NwW/Hn690v+yCNCSbYtAlc5qy3y6d2+HbSwHcfjHgdj95bIzncWTLWsiOnnITiiNLnenoKzehOq/+Q/s609Ffbnhe06+jVgaI9mg9Sx1RF87i3vaOTLOiWu31YtLL33rMtu3rZ+3+U+JkZZ1oaGg+67yMdW5Lp6GhQdTaHMr6iYo6cfOC/4lLnvhcWK2uWdQbZ9if/99Cpc1R3yDm/HubeH3NflFZXRs1WaMTHkfm51TWmkWTw1nc6YTTkfl8o5Yj67jUdDiLu3zwpsVopXHmxqoqoH9/57IRb23uhMPx159OWJy42BjcN7yL775ulBw/i+HzV+PCx/Nx6vgZZR87j5ZjZeEJnK5rYVyBjE3CrNkcDdh/ohKislLp84+v96P/E/l49ee/UxxLgglbD5fhlNWA0gqrst/JL8zBU5N64YaLuyjHMdZU49mbLsK9l58Ps8noeUCd5KbNHD3lJhRHljrT0VdugnLcZl0eOlT7OtPRSW54XosIR60MEM3hBXq00tDgWhYWutZ9tTV1W+v4608nbE56Uqzvvm6YYgAhBASADvFGZR//3nIE939QgO+Pu04XDkc9Hjz/ajy3+gDq7PWBjS3I3NQ3CKwrOoP/iDSsKzrjmoE+hKxV7/sJ246UY3dJhesxNACDn1qNCS9+i9NVVsVJMBlRY2/AARGv7CMuNgZ///lF+O1Ah+sryhoacOn3X+D2IRm4MCNV6gxI42h1vlHLkaXOdPSVm1CdH3/Uvs509Jcbntf066iVAaI5vEAnJMIZ1s2CzIqTMPjYbgCQWXESV13YCXuemoSvHxmLGLf7oTMt8bioSyoyE11TzJVUWPF5vzFYvPEo4txmD3963N0Y/Y+NWLL+kNJW3yCw5dBZnKyyNZukzhfLdx3HZc99hVvf24FfX/sobn1vBy577ivk7yzx6647VIZ3h1yFSqtDaVsy5Cpc91YB/vH1fqUtNgbonBqPpDgjShrfFQdwzaAsrLr3Evz9s+c99pvTrzO6Jzsv1gkhhBBCCGkLAntrjRCiW4wxBsxd/QZ+ecMfYQA8LpIbL8Pnrn4DxpgbYTTGoEs7z5nC7xtzPu4e1R1ffPGF0pYUZ8SfVi9Edd6fERPjupgvap+Fo+VWCOE6SmlFHW5asBZxRgN2G2KUiejyd5biaOlZXJaWjT5uY9p22oA3123zPuP8kq1YcGNf5AL46XQNVvxwEilxMUh26/fwZ3tQknM/+p2swSWdOgAALjhdjE5JJiSbPU95n/5iBNJSE2CodM163ynFjE4iEWjg94MSQgghhBB14QV6tJKQ4FwmJgL5+c5lI97a3AmH46+/nh1fnxjScGy5f5+LBRkDMPe/u3Hc7d3iDEs85l51IXLHzQ3q+eyQZsHMp34JjO/r4Tw7YzQO9b8EXTqmKM3ltXZ0aZeAeFMMjF9+oezz461HsLLwOJ58dgH6nGs7WlaLt/fFeH2nvbFt3uoiXPnll9hbUY9nv/wRA7ukYmZ3V7/LenXC2UPHEJvoyvgVrz6JTROuAGI9T3ntEk3OrzaTITehOBJmrc3PHTI7stSZjr5yE5ST4GpbulT7OtPRSW58ON5+h8haZzrq5YZoDi/Qo5XGC5XYWGDixObbmrb52x6s46+/nh2bI3hHhbHlAhjdJx0D81YAABZNH4JxfTNhjDEAg7q0vA9/xzjXlnZNLtLOrdrtznfh+2Wm4n+/v8J5D7nbu+0jz+sIk9GAfqN7KnlcUXgc9cLXh/GdlJTXYWOvEejXLgHXDMpCv4xkoOKMsv0vtwwGMNhjXIbc3Bb3qfVzE7IjadYC7h9pjix1pqOv3ATjuL/mJ0xo9p+OLe5Tlucm0hw95MZXm7ffIbLWmY56uSGaw5spJeWZZ56BwWDA7NmzlbYZM2bAYDB4/IwYMSK0AzR+pLeiAkhNdS4b8dbmTjgcf/3ptIljdLtAvrRHe+e6ShkwVlV6bL/7sp74v2t6YehFPZS2+KaznvvgxJTb0N3kwCu3DsHMy3oE/Ph9QkcdR6vzjVqOLHWmo6/cBOW4bsdBRqb2daajk9zwvBYRjloZIJrDd9AlZNOmTXjjjTdw0UUXNduWm5uLN998U1mPi4tr/QHd7r9tsS3cjr/+dNRztMpAk7bz0pJa9s+RfupYcMeRpc50pMlamzmy1JmOvnITilMlSZ3p6Cs3oTiy1JmOehkgmsILdMmoqqrCbbfdhoULF+Kpp55qtt1sNiMjI0ODkZFIo9ZWD5PJAdjqAZPZuWz8uFuTNrvdAWs9UGNzwCQMbeb0y0yBJU6gwmbweh+6AUBGqhnDjuxSo0TED0II1Lo/pxrlRhrHyz6CdWpsnJyQEEIIiWZ4gS4ZDzzwAK6++mpMmDDB6wX6N998g/T0dLRr1w5jxozB008/jfT0dJ/7s1qtsFpdk4JVnvsfM4fD4bxH2OFwThjncADn7hlu2tZ4L7Hdx/aQHC/7iBRH6QPnH+eBHqfZMdrAce834rk1Lufhj4EX1sKDZm2xeHTjVyo43u9Bb2z94xXZaIg3o4FZ0zRrQghMfWcbtjZ9/jTLjSSOt32E5Lg9pxJkTa1zVNgcrX63tbFjd7juGbbHn1uX+BzFrMnteP0dIkkGmDX1c0PkwSDcvw+JaMqHH36Ip556Cps3b0Z8fDzGjh2LwYMH4+WXXwYAfPTRR0hOTkZ2djaKiorw+OOPw+FwYMuWLTCbzV73mZeXh3nz5jVrX7RoEdLS0rwYpLVY64FHNzr/7+v5YQ6YA7utWhWEAP62y4iiypYnYpORdnECN/ZowKCOPGU1omXW3I9Nwk/PFIFf96+HQX8vVdKGyPz7hegP5okAwKlTpzBz5kwUFxeja9eufvt/++23+Mtf/oItW7agpKQEn376Ka6//npl+yeffILXX38dW7ZswenTp/HDDz9g8ODBHvsYO3Ys1qxZ49H285//HB9++KGyfvbsWTz00EP47LPPAADXXnstXnnlFbRr1y7kx6oX+NeVJBQXF+PXv/41VqxYgfj4eK99fv7znyv/HjBgAIYOHYrs7Gz897//xY033ujVeeyxx/Dwww8r60ePHkW/fv0w/oor0KVrV+cVW1UVkJwM5S/BJm12ux0rV67ElVdeCZPJFB7Hyz4ixamx1yvvll1xxRWwJMV7rZHfmrWRc+WVNvx3+SpcccUVMJlind9fpmw/F5QmbXa7A1999ZWqjrUeGP2XbwEAi24fgst6pTkntWPWpMhajc2hHHv9o2OQYDZKkRtNHS/7CNX5/puvkJMjT9bUPEeFxdHqd1sbOzVxCcrrbuKoUUjsYJH6HMWsye14/R0iSQaYNfVyc/DgQQRDdXU1Bg0ahLvuugs33XST1+2jR4/GlClTMGvWLJ/7mTVrFp588kllPaHxK6DPMW3aNBw5cgT5+fkAgHvvvRfTp0/H559/HtR49Qgv0CVhy5YtOHHiBC655BKlrb6+Ht9++y1effVVWK1WGI2e/7WZmZmJ7Oxs7Nu3z+d+zWazx7vrFedmbIytq3O+cCsqgI4dgfJy54yOzk7N2wCYTKbwOT76R4Jjik906x/rOhEHeBzlGG3omI2AJSne9Vgy05s/n25tdrtddcfu9nVrozISEG+OY9YkyprJ7flJbbAjMSlZitxo6njZR6iOwSBX1tQ+R7Xa0ep3Wxs7ppNnXNu6d4Pp1Ampz1HMmtyO198hkmWAWWv73CjjCZBJkyZh0qRJPrdPnz4dAPxe+CcmJvqcV2v37t3Iz8/H+vXrMXz4cADAwoULMXLkSOzZswd9+vQJasx6I0brARAn48ePx44dO1BQUKD8DB06FLfddhsKCgqaXZwDwOnTp1FcXIzMzEwNRkwIIYQQQgiJBCorK1FRUaH8uM9h1Ra89957SEtLQ//+/fHII48o82QBwLp162CxWJSLcwAYMWIELBYL1q5tPmdLpMF30CUhJSUFAwYM8GhLSkpCx44dMWDAAFRVVSEvLw833XQTMjMzcfDgQfzhD39AWloabrjhBo1GTQghhBBCCNE7/fr181ifO3cu8vLy2uRYt912G3r27ImMjAzs3LkTjz32GLZt24aVK1cCAEpLS71Ogp2eno7S0tI2GZNM8AJdJxiNRuzYsQPvvPMOysrKkJmZiXHjxuGjjz5CSkqK1sMjhBBCCCGE6JTCwkJ06dJFWfc1AXU4cL83fcCAAejVqxeGDh2KrVu34uKLLwYAGLzMkiqE8NoeafACXWK++eYb5d8JCQlYvnx5+HaenOxcpqQ470Fxv8j31uZOOBx//fXs2H18j7EMYwukv0yO0i859OPIUue2cLTOmuK2cD5p6RiR5uglN9Hg6Ck3wThmt0mUjpVoX2c6+siNL8fb7xBZ60ynzXOTkpKC1Cb38qvFxRdfDJPJhH379uHiiy9GRkYGjh8/3qzfyZMn0blzZw1GqC68Bz1aaWhwLYuLXeu+2pq6rXX89aejnqNVBgJ9PI39Qj2OLHWORMfdDcSROWs8r0WWo6fchOocOaJ9nenoLzc8r+nXUSsDGrBr1y7Y7XZlXq2RI0eivLwcGzduVPps2LAB5eXlGDVqlFbDVA1eoEcrNTXOZXU1MGCAc9mItzZ3wuH4609HPUerDAT6eACguib048hS50h0FLeF80lLx4g0R6bnJtodPeUmKMftXDjsUu3rTEcnueF5LSIctTLgh6qqKmVCawAoKipCQUEBDh8+DAA4c+YMCgoKUFhYCADYs2cPCgoKlHvHDxw4gCeffBKbN2/GwYMH8cUXX2DKlCkYMmQIRo8eDQDo27cvcnNzMWvWLKxfvx7r16/HrFmzMHny5IifwR3gBTohhBBCCCGEkADYvHkzhgwZgiFDhgAAHn74YQwZMgRPPPEEAOCzzz7DkCFDcPXVVwMApk6diiFDhuC1114DAMTFxWH16tWYOHEi+vTpg4ceegg5OTlYtWqVx7dWvffeexg4cCBycnKQk5ODiy66CO+++67Kj1YbeA86IYQQQgghhBC/jB07FkIIn9tnzJiBGTNm+NzerVs3rFmzxu9xOnTogCVLloQyRN3Dd9CJ94klWprUIlyOv/501HO0ykAgTqD7aWm7LHWORMffPgLZHkmOTM9NtDt6yk0oTrIkdaajr9yE4shSZzra/u1FVIPvoEcrjS/M1FSgosJzm7c2f9uDdfz117Njc8g7tkD6y+Qo/dx+kTBr8mRN6dvC+aSlY0Sao5fcRIOjp9wE47i/5ktLgLgW/oyT9bmJNEcPufHV5u13iKx1pqP+315EM/gOerTicLiWy5e71n21NXVb6/jrT0c9R6sMBPp4GvuFehxZ6hyJjrsbiCNz1nheiyxHT7kJ1Vm1Svs609Ffbnhe06+jVgaI5vACPVqprXUua2qA3FzXrO6+2twJh+OvPx31HK0yEOjjAYCa2tCPI0udI9FR3BbOJy0dI9IcmZ6baHf0lJugHLdz4fXXa19nOjrJDc9rEeGolQGiObxAJ4QQQgghhBBCJIAX6IQQQgghhBBCiATwAj1aiYlxLfv1c637amvqttbx15+Oeo5WGQj08TT2C/U4stQ5Eh13NxBH5qzxvBZZjp5yE6pz4YXa15mO/nLD85p+HbUyQDSHs7hHK0lJzmVyMrBrl+c2b23+tgfr+OuvZ8fXzNoyjC2Q/jI5Sr+k0I8jS53bwtE6a0rfFs4nLR0j0hy95CYaHD3lJhjH/TW/eXPLs7jL+txEmqOH3Phq8/Y7RNY601H/by+iGfyvk2jFbncubTZg0SLnshFvbe6Ew/HXn456jlYZCPTxAKipqkONzeH8qapFzRuLnctzbdZ6+N7upX8kOUqNbPVBHcfjGCE59W7PYwvnEy1yw/MaHT3lJijH7mp7623t60xHJ7nheS0iHLUyQDTHIIQQWg+CqMeRI0fQrVs3FBcWomvfvs7vQbRYgPJy5/ciAs3a7HY7vvjiC1x11VUwmUzhcbzsI1KcmvhE9HtiOQBg2+NXwJKU4LVGfmumlqNVBvw4dmFQ6kjkpvCRUUhMay9FbjR1dHKOCtXR7BwViqOn3ATh1Jw8g34vrHW+7l68CYmnTkifG2ZNXsfr3yuSZIBZUy83Bw8eRM+ePVFcXIyuXbuCaA/fQSeESEmCyYihXVO1Hgbxw9Aju5Bg4q8SQgghhJBwwHvQCSFSYjAY8P+mX4TaTp2BYyVAaopzQ0UlkJWptNntdixfvgITJ+ac+99iz+3N1oGIcWriEzD0qdUAgHVzxsCSFO+1Rn5r1gonodNkGP7vFyokghBCCCEk8uEFerRiNLqWOTmudV9tTd3WOv7601HP0SoDATiG2FgkjhsDJMS5JkNKiAPc2uwGAbMRSIyLhckU22x7s3UgchyT6xSeGGdEoo8a+a1Za5wIyRrPaxHm6Ck3oTrjx2tfZzr6yw3Pa/p11MoA0RxeoEcriYnOZVISsLzJfb7e2vxtD9bx11/Pjq+ZtWUYWyD9I82Rpc5t4TBrcjmy1JmOvnITjOP+mv/Pf1qexV3W5ybSHD3kxlebt98hstaZjnq5IZrDGwejlcaZG61WIC/PuWzEW5s74XD89aejnqNVBtRyZKkzHX3lJhRHljrT0VdugnLcZl1+er72daajk9zwvBYRjloZINojSFRRXFwsAIjiwkJnQ3m5EIBz2UiTNpvNJpYuXSpsNlv4HC/7iBSn2moX2XOWiew5y0RZVY3PGvmtmVqOVhlQy9FJbkJxmDXJHJ3kJlRHs9yE4ugpN0E41SfPKK/5apNZF7kJxWHW1HG8/g6RJANqOcyaEEVFRc5rg+JiQeSA76ATQgghhBBCCCESwAt0QgghhBBCCCFEAniBHq00fk2SyQTcc49r3VdbU7e1jr/+dNRztMqAWo4sdaajr9yE4shSZzr6yk2ozh13al9nOvrLDc9r+nXUygDRHq0/Y0/URbkHPYj7TLzeN0PHJz7vC9Z4XHQiz2HW6KjpyDquaHLcX/PVVrtUYwunI+u4Is3h7xB5x6Wmw3vQ5YPvoEcrdXXOZW0tMHOmc9mItzZ3wuH4609HPUerDKjlyFJnOvrKTSiOLHWmo6/cBOXUudruf0D7OtPRSW54XosIR60MEM3hBXq0Yre7losXu9Z9tTV1W+v4609HPUerDKjlyFJnOvrKTSiOLHWmo6/chOq887b2daajv9zwvKZfR60MEM3hBTohhBBCCCGEECIBsVoPgKhLQ0MDAKCktBRISQEqK50bjh4FKiqc/27S5nA4cOrUKRw9ehSxsbHhcbzsI1Kc2vgEOCpOnVs9isrEeK818lsztRytMqCWo5PcMGsR4OgkN6E6muUmFEdPuQnCqT121PWab2hAgg5yE4rDrKnjeP0dIkkG1HKYtQqUlJQAcF0jEO0xCCGE1oMg6rFp0yYMGzZM62EQQgghhBBCJOH777/H6NGjtR4GAS/Qow6Hw4ELLrgA3377LWJinHc4jB07Ft98841HP/e2yspK9OvXD4WFhUhJSQmb03Q9khxv/WV2tMqAWo5echOKw6zJ5eglN6E4zJpcjl5yE4rDrMnl6CU3oTjMGlBeXo4BAwbg9OnT6NChA4j28CPuUUZsbCwSExPRvXt3pS0uLg5du3b16OfeVnHuIzFdunRBampq2Jym65HkeOsvs6NVBtRy9JKbUBxmTS5HL7kJxWHW5HL0kptQHGZNLkcvuQnFYdag9FM+ek80J0brARD1eeCBB1pc99UWbsdffzrqOVplQC1HljrT0VduQnFkqTMdfeUmFEeWOtPRV25CcWSpMx31MkA0RtuvYSd6oLy8XAAQ5eXldAJ0ZB0XnchzZB0XnchzZB0XnchzZB0XnchzZB2X7A5pW/gOOvGL2WzG3LlzYTab6QToyDouOpHnyDouOpHnyDouOpHnyDouOpHnyDou2R3StnCSOEIIIYQQQgghRAL4DjohhBBCCCGEECIBvEAnhBBCCCGEEEIkgBfohBBCCCGEEEKIBPACnRBCCCGEEEIIkQBeoJOog/MiEkIIIYQQQmQkVusBEPk4cuQIFixYgLVr16K0tBQGgwGdO3fGqFGj8Itf/ALdunXTeoitwmw2Y9u2bejbt6/WQ1GFkpISLFiwAN9//z1KSkpgNBrRs2dPXH/99ZgxYwaMRqPWQyQSUl1djffff7/ZeWD06NG49dZbkZSUFNT+jh8/jtdffx1PPPFEs21HjhxBu3btkJyc7NFut9uxbt06XH755R7tp0+fxvbt2zFo0CB06NABp06dwuLFi2G1WjFlypSAX9vnnXceli9fjl69evnta7fb8d///hf79u1DZmYmbrjhhmY1OHLkCOLj45GWlgYA+O677/Daa6/h8OHDyM7OxgMPPICRI0d6OH/9619x8803Izs7O6AxN/L5559j8+bNyM3NxciRI/HVV1/hhRdeQENDA2688Ubce++9zZza2lp88MEHXs8F48ePD+r4JDrgecATngcIIaqg8fewE8n47rvvRHJysujbt6/49a9/LebPny+efvpp8etf/1r069dPpKSkiO+//z6ofR4+fFjcddddzdpramrEd999J3bt2tVsW21trXj77bebtRcWFop//vOfYvfu3UIIIXbv3i1+8YtfiLvuukusXr3ao+9vfvMbrz8xMTHijjvuUNb9cebMGfHSSy+J+++/X/z5z38Whw8f9ti+detW8dNPPynr7777rhg1apTo2rWrGD16tPjggw+a7fNXv/qV+Pbbb/0euyl///vfxR133CE++ugjIYQQ77zzjujbt6/o06ePeOyxx4Tdbvfov2nTJmGxWMTgwYPFyJEjRUxMjJg+fbr4+c9/Ltq1aydGjhwpKioqvB6rqqpKvPHGG2LGjBkiNzdXTJo0ScyYMUMsXLhQVFVVBT320tJSMW/evGbtxcXForKyslm7zWYTa9asadZ+6tQp8dVXX4nTp08LIYQ4efKkePbZZ8W8efNEYWFhwOPp2bOn2Lt3b0B9bTab+PTTT8Xzzz8v3n33Xa+Pv7i4WJw8eVJZ//bbb8W0adPEZZddJm677Taxdu3aZs4LL7wgDh48GPCYG/nss8/EE088oexz9erVYtKkSWLixIni9ddf9+rU1NSIxYsXi7vuukvk5uaKq6++WvzqV78Sq1atatZ3165dIisrS7Rr105cd9114t577xWzZs0S1113nWjXrp3o0qWL19dtSxQUFIiYmBiPtmPHjolLL71UxMTECKPRKO644w6PLJSWljZzNmzYICwWizAYDKJ9+/Zi8+bNomfPnqJXr17iggsuEAkJCWLLli0ezt/+9jevP0ajUTz22GPKujsjR44UZ8+eFUIIceLECTFw4EARFxcnevXqJeLj40X37t3FkSNHmjlffPGFEEKIpUuXipiYGHHttdeKOXPmiBtuuEGYTCbx+eefezgGg0EYjUYxYcIE8eGHHwqr1eq3lgsWLBCxsbHikksuEampqWLJkiUiJSVFzJw5U9x3330iISFBvPzyyx7Ovn37RHZ2tujYsaPIzMwUBoNBXH311WL48OHCaDSKKVOmNDt/qHUOEILnAZ4HeB6Q9TwgROSfC3ge8H0eINrAC3TiwdChQ8Xs2bN9bp89e7YYOnRoUPv09gt5z549Ijs7WxgMBhETEyPGjBkjjh07pmz39gv5yy+/FHFxcaJDhw4iPj5efPnll6JTp05iwoQJYvz48SI2NtbjIt1gMIjBgweLsWPHevwYDAZx6aWXirFjx4px48Y1G29mZqY4deqUEEKIn376SWRkZIiMjAxx5ZVXiq5duwqLxaL8B4EQQgwZMkR89dVXQgghFi5cKBISEsRDDz0kFixYIGbPni2Sk5PF4sWLPY7R+Lh79eolnn32WVFSUuK3jk8++aRISUkRN910k8jIyBDPPvus6Nixo3jqqafE/PnzRadOncQTTzzh4YwePVrk5eUp6++++64YPny4EML5Hw+DBw8WDz30ULNjqfGHGf8ok/ePsrFjx4qpU6d6HY/VahW33nqrGDt2rEf7tm3bWvz56KOPmj2fd9xxhxgxYoTYtGmTWLlypRg6dKi45JJLxJkzZ4QQzgwYDAYPZ8KECWLmzJmioqJC/OUvfxFdu3YVM2fOVLbfc8894vrrr29W565du4oePXp4/BgMBtGlSxfRo0cP8f/bu//YJur/D+Dvdm3X/QLGBtvqflUBB2FkuEzcgjDAOTFjgEFZDG5EQYWZkAD+Kpqh4ARJ8AcuEIwg/sgUBWaADIkBBAOIbI7NuMnGGAJjMIEgjP1ifX7+WHrf3u7arRPq0e/zkTSh975n31d6e92979o7q9WqyFy8eBEAMH/+fCQlJUl/p3///TfS0tLw7LPPyjIhISE4ffo0AGDcuHFYtWqVrH3dunUYO3asop/Nmzdj+vTpMBqNCAsLw6JFi1BVVaX4v3cYOXIkNm7cCADYt28fzGYzioqKpPbNmzdj5MiRsszUqVPxwgsvoKurCwDw7rvvYurUqQCAkydPIj4+HgUFBdL8HJyxDrAOsA4AvlULWAf6f5CGvIsDdJIxm82oqalx2V5dXQ2z2Syb9v3337t9vP/++4qiOmPGDGRlZaG5uRm1tbWYNm0arFYrzpw5A0C9EKempmLZsmUAgOLiYoSGhsJms0ntNpsNGRkZ0vPCwkJYrVbFmXWDweB2Y+K8Qc7JyUF6ejpaWloAAG1tbcjKysKsWbOk+QMDA6XlHjt2rOKI5VdffYVRo0Yp+vjxxx+xaNEihIeHw2g0Ijs7Gzt37pQ2nD3de++92LZtG4DujZufnx++/PJLqX379u0YNmyYLBMQEIBTp05Jz7u6umA0GtHU1AQA2Lt3LywWi6Ivb+yYcadMuztlAQEBbv9GqqqqEBAQoHgver0eOp1O8XBM7/k3bbFY8Msvv0jP29raMH36dCQlJeHy5cuqdSA0NFQ6K9LR0QG9Xi97jfLyctxzzz2yzPPPP4+kpCTF2RR3tcB5HRgxYgR27dola9+/fz/i4+Nl0wYOHIgTJ04AAIYOHSr926Gurg6BgYEu+7l48SJWr16NhIQE6PV6pKSkYOPGjYpvuQQEBEg1BwCMRqNsnTl9+rSin8DAQNkZovb2dhiNRulgZElJiez9cHDGOsA6wDoA+FYtYB3o30Ea8j4O0EnGarVi06ZNLts3bdqkWrxcbZCdN8zOhg4disrKStm0hQsXIjY2FqdOnVLdIA8YMAC1tbUAugeaBoNBdmS0qqoKERERssyxY8cwYsQILFmyBB0dHQA8G6CrDfCPHj2K6Oho6XlYWBiOHz8uva+KigrZ/HV1dao7MY4+Ojo68M033yAzMxN+fn6wWCyw2WzSe3VQ2xj//vvv0vOGhgbFxjguLk72k4TGxkbodDrcvHkTQPcGvOcBF0dfd3rHjDtl2t0ps1gsKCkpUf2/AYAdO3YoDuyEh4fj008/RUNDg+pj9+7dis8zKChI8bXCzs5OzJgxA2PGjEFlZaVqxrHjAwDBwcGyg1BnzpxRXad37NiBmJgYrFu3TprW2zpw6dIlAN2fZ8/5Ghoa4O/vL5uWnZ2N1157DQCQmZmpOBPzySefYPjw4Yp+HOuAs4MHDyIvLw9BQUEICgqStUVHR0s/kTl//jx0Oh12794ttR84cEBWo4Duz9S5Xl69ehU6nU5av+rr62Xvh4Mz1gHWAdYBwLdqAetA/w7SkPdxgE4yRUVFMJlMyM/PR0lJCY4cOYKjR4+ipKQE+fn58Pf3x/r162UZi8WCHTt2uHzN3377TVFUQ0JCVH8b9NJLL0kbHXcDdEC5QW5oaFDdIF+/fh25ublITExEZWUljEZjrwN0xwbZYrHIBsFAd8Fz3oDNmTMHzz33HADgySefxBtvvCGbv7CwEImJiYo+1DbGZ86cQUFBAeLi4hTv32q1orS0FED3UU69Xo+tW7dK7bt371YU1UWLFmH06NEoLS3Fvn37MGnSJNmR7j179uC+++5TLIc3dsy4U6bdnbKCggIMHDgQa9asQUVFBS5cuICmpiZUVFRgzZo1CA0NVfx+MDMzEytWrFC8F4eKigrF2Y/ExER89913inkd60FsbKxiHUhISJAdNNu1a5d0wAlQHkBzdu7cOUyePBmPPfYYLly40Os68Pjjj2PmzJkIDQ2Vvq7ocOTIEcUBwT/++ANhYWHIzc3FihUrEBwcjDlz5uCdd95Bbm4u/P39sXnzZllGr9errgMO165dk86QOOTn52P48OFYuXIlHnzwQeTl5SEhIQGlpaXYs2cPEhMTFWd08vLyMHHiRFRXV6O+vh6zZ8+WncE5cOAAYmJipOccnLEOsA6wDgC+VwtYBzw/SEPexwE6KXz99dcYN24cDAaDdMTTYDBg3Lhx0sXJnE2bNg1vvvmmy9dT2yCnpKTg888/V50/Pz8fgwYNUhTiMWPGSANUoPuorfPvZA4dOqQ4u++suLgYERER0Ov1vQ7QExMTMXbsWAQHB2P79u2y9p9++kl2RPb8+fOIj4/HhAkTsHjxYgQEBGD8+PGYP38+JkyYAJPJJCuYjj7cbYztdjv27t0rm7Zs2TIMGTIE8+bNg9Vqxeuvv47Y2FisX78eGzZsQExMjOKid9evX8dTTz0lfZZpaWmyC9r98MMPskG+gzd2zLhTpu2dslWrVkm/T9Pr9dJZj6ioKKxevVqxrNu3b8cXX3zh8r1cuXIFn332mWzaK6+8gkcffVR1/s7OTmRnZyvWgeXLl6teeNHBZrPhiSeecNlut9tRWFiIyMhI+Pn5uVwH5s6dK3v0/DtZunQpMjMzFbm6ujrk5OQgJCREqp9GoxFpaWmqBzJ7qwVqbty4gXnz5mH06NF48cUX0dHRgTVr1sBkMkGn0yE9PV3xmhcvXsRDDz0kfZ7x8fEoLy+X2r/99lt89NFH0nMOzlgHANaB/+91APDNWsA64FkdIO/jAJ1c6ujoQGNjIxobG6Wvh6s5ePCgbODc040bN3DgwAHZtMLCQuk3L2oWLFigKN7r169XfLXImc1mk85ku3L27FmUlJS4vero8uXLZY89e/bI2pcuXYqcnBzZtKtXr+LVV1/FqFGjYDabYTKZEBcXh6effhq//vqroo/4+Hjp60R9devWLaxcuRJZWVnS75mKi4sRExODsLAwzJ071+X7am1tVb0qqjt3esesLztlPdcB7pR5b6fMob6+HocPH8bhw4dlB3duh87OTly7ds1l+61btzy+qm1LSwva2tp6ne/48eP44IMPpN83eurGjRtobW112W6329HU1NRr/bydWltbXd6VweHkyZOKg5uucHDW/zowe/Zsn60Dzmco+8put7tsc1UHHBlP6oAj40kdWLt2rcd1wNGPJ3WgLxf9ctWPJ1paWjyuA73144u1gPsD/1cHnM+ou6sD5D06APivb/VGRNp1+vRp0dTUJIQQIjIyUlit1tvyurdu3RI3b94UAwYMUG3v6uoS586d8+i+sDdv3hR+fn7C39/f7XxlZWXi559/Frm5uSI0NNSj5Rai+97Afn5+wmw2q7YDEJcuXRJ2u12Eh4cLo9HocR+eamtrE52dnSIkJMTlPLW1taK9vV0kJCQIg8Fwx5eJfMOdqgFCsA7cbt6oAyaTSZw4caLP9xlnxncyvlgLysvLxaFDh1gHSFP4iRCRW1arVbERPnv2rCgoKBCbNm3q8+v0zBgMBpcbYiGEaGxsFG+99ZZHfVy+fLlPy5WcnCySk5NVl6svrly54jaj0+lERESEbNrt+D9zx2w2C7PZ7DYzfPjwPvfT2toqysrKxODBg8WoUaNk87e1tYmtW7eK3Nxc2XRmfCtTXV0tjh49KtLS0kRqaqqoqakR7733nmhvbxdz5swRkydPFj05MqmpqSIhIUHU1NSIDz/80GXGYDCI8+fPi23btvU5059+1DJBQUGipqZGLFmypNdMWlqauP/++z3qxznz9ttv37H3099lMxgMbjOLFy9W5IXoHiitWrVKhIWFCSGEWLt2LTM+nHFmtVrFoEGDxJYtW0Rtba2wWCwiNzdXxMTEqM7vcPXqVSkTFRUl8vLyFJme+wQ9M3Pnzu11cN6XfnrOf/DgQVFbWyuKiop6nb8/feh0OmEyme7I/5m7jMViEXl5eSI6Olp1Xsf+QH/6IS/4T8/fE9FdSe0eprc7440+mFHP/Pnnn4iLi5O+Ajdx4kQ0NjZK7WpX02XGtzKlpaUwmUwYPHgwzGYzSktLMWTIEDzyyCOYMmUKDAaD4g4XzPhWRqfTISkpCenp6bKHTqdDSkoK0tPTMWnSJFkfzPheJioqSvpJXn19PaKiohAZGYmMjAxER0dj4MCBqK6udpuJjIzUREary6X1DHkfB+hEpNCfe9t7mvFGH8z0LzNjxgxkZWWhubkZtbW1mDZtGqxWq3Q7F7UBIDO+lUlNTcWyZcsAdF/rIjQ0FDabTWq32WzIyMiQ9cGMb2UKCwtVbzXq7oJazPhexvm30Tk5OUhPT0dLSwuA7luhZWVlYdasWXdFRqvLpfUMeR8H6ESk4O4eps73Mv03GW/0wUz/MkOHDkVlZaXsNRYuXIjY2FicOnVKdQDIjG9lnG9r2dXVBYPBILuQUFVVleLKxcz4XubYsWMYMWIElixZIl3s0N1gjhnfyzgP6NQG92pXStdqRqvLpfUMeZ/+v/6KPRFpT1RUlNi2bZuw2+2qj/Ly8n+d8UYfzPQv09raqrhoTFFRkcjOzhYTJ04UJ0+eVPTBjO9lHPR6vTCbzWLQoEHStJCQEHHt2jVmfDyTkpIiysrKRHNzs0hOThZVVVVCp9O5fF1mfDPjaG9vb1dcXyUiIkI0NzffNRmtLpfWM+RdHKATkUJycrLqQM9Bp9MJ9LgBhKcZb/TBTP8yCQkJ4vjx44r51q1bJ6ZPny6ys7MVbcz4ViY+Pl7U1dVJz48cOSJiY2Ol52fPnhVRUVHM+HhGCCGCg4PFli1bhM1mExkZGaKrq0sxDzO+nZkyZYp44IEHxD///KM4mPfXX3+J8PDwuyaj1eXSeoa8i1dxJyKFl19+WbS0tLhsHzZsmNi/f/+/ynijD2b6l5k5c6YoLi4WzzzzjGLejz/+WNjtdrFhwwbZdGZ8K7NgwQLZTvvo0aNlmdLSUsVVwpnxvYyznJwcMX78eFFWVtbnW10xc/dnCgoKZM8DAwNlz3fu3CkefvjhuyKj1eXSeoa8j/dBJyIiIiIiItIAfsWdiIiIiIiISAM4QCciIiIiIiLSAA7QiYiIiIiIiDSAA3QiIiIiIiIiDeAAnYiIiIiIiEgDOEAnIiIiIiIi0gAO0ImIiIiIiIg0gAN0IiIiIiIiIg3gAJ2IiIiIiIhIAzhAJyIiIiIiItIADtCJiIiIiIiINIADdCIiIiIiIiIN4ACdiIiIiIiISAM4QCciIiIiIiLSAA7QiYiIiIiIiDTgfx7N3kSOGiWrAAAAAElFTkSuQmCC", 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\n", + " " + ], + "text/plain": [ + "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "class CokeOvenServiceProgram:\n", + " \"\"\" as implemented in excel sheet provided \"\"\"\n", + " def __init__ (self, duration, start, load, T0, q0):\n", + " self.start = start\n", + " self.end = start + duration\n", + " self.duration = duration\n", + " self.load = load\n", + " self.T0 = T0\n", + " self.q0 = q0\n", + " \n", + " self.a = 0.6 - (self.duration - 4) * 0.01 # μ΅œμ†Œ μ—΄λŸ‰ / μ΅œλŒ€ μ—΄λŸ‰ λΉ„μœ¨\n", + " self.b = 6 + self.duration / 2 # 감급 μ‹œμž‘μ—μ„œ 감급 μ™„λ£ŒκΉŒμ§€ μ‹œκ°„, μ •μˆ˜ -6 μ—μ„œ 감급 μ‹œμž‘ μ •μˆ˜ 쀑간 μ‹œκ°μ— 감급 μ™„λ£Œ\n", + "\n", + " t5 = self.lowest_heat_duration()\n", + " q5 = self.q0 * self.a\n", + "\n", + " t1 = self.b / 4\n", + " t2 = self.b / 4\n", + " t3 = self.b / 4\n", + " t4 = self.b / 3.5\n", + "\n", + " t40 = 13 / 4 * 2\n", + " t30 = t40 / 0.8\n", + " t20 = t30 / 0.8\n", + " t10 = t20 / 0.8\n", + " t00 = self.b / 5.9\n", + "\n", + " tt1 = 3\n", + " tt2 = 3\n", + " tt3 = 5\n", + " tt20 = 3\n", + " tt10 = 3\n", + "\n", + " q1 = q0 - 1 * (q0 - q5) / 3.7 \n", + " q2 = q0 - 2 * (q0 - q5) / 3.8\n", + " q3 = q0 - 3 * (q0 - q5) / 3.9\n", + " q4 = q0 - 3.7 * (q0 - q5) / 4\n", + "\n", + " q40 = q4\n", + " q30 = q3\n", + " q20 = q2\n", + " q10 = q1\n", + " q00 = q0\n", + " \n", + " qq1 = q00 * 0.9\n", + " qq2 = q00 * 0.83\n", + " qq3 = q00 * 0.77\n", + " qq20 = qq2\n", + " qq10 = qq1\n", + " \n", + " T1 = T0 - (q0 - q1) / (0.07 * 4.184) * 0.6\n", + " T2 = T0 - (q0 - q2) / (0.07 * 4.184) * 0.6\n", + " T3 = T0 - (q0 - q3) / (0.07 * 4.184) * 0.6 \n", + " T4 = T0 - (q0 - q4) / (0.07 * 4.184) * 0.6\n", + " \n", + " T5 = T0 - (q0 - q5) / (0.07 * 4.184) * 0.6\n", + " \n", + " T40 = T0 - (q0 - q4) / (0.07 * 4.184) * 0.6\n", + " T30 = T0 - (q0 - q30) / (0.07 * 4.184) * 0.6\n", + " T20 = T0 - (q0 - q20) / (0.07 * 4.184) * 0.6\n", + " T10 = T0 - (q0 - q10) / (0.07 * 4.184) * 0.6\n", + " T00 = T0 - (q0 - q00) / (0.07 * 4.184) * 0.6\n", + " \n", + " TT1 = T0 - (q0 - qq1) / (0.07 * 4.184) * 0.6\n", + " TT2 = T0 - (q0 - qq2) / (0.07 * 4.184) * 0.6\n", + " TT3 = T0 - (q0 - qq3) / (0.07 * 4.184) * 0.6\n", + " \n", + " TT20 = T0 - (q0 - qq20) / (0.07 * 4.184) * 0.6\n", + " TT10 = T0 - (q0 - qq10) / (0.07 * 4.184) * 0.6\n", + " \n", + " self.step_sizes = [0, t1, t2, t3, t4, t5, t40, t30, t20, t10, t00, tt1, tt2, tt3, tt20, tt10]\n", + " \n", + " self.step_sizes = np.round(np.array(self.step_sizes) * 60 / 20) / 3\n", + " \n", + " self.step_times = np.cumsum([0, t1, t2, t3, t4, t5, t40, t30, t20, t10, t00, tt1, tt2, tt3, tt20, tt10])\n", + " self.step_times = np.cumsum(self.step_sizes)\n", + " \n", + " self.step_qs = np.array([q0, q1, q2, q3, q4, q5, q40, q30, q20, q10, q00, qq1, qq2, qq3, qq20, qq10, q0])\n", + " \n", + " self.step_temperatures = [T0, T1 , T2 , T3 , T4 , T5 , T40, T30, T20, T10, T00, TT1, TT2, TT3, TT2, TT1,]\n", + " \n", + " if False: # use_old_temperature:\n", + " self.step_temperatures = self.calculate_temperatues_old()\n", + " \n", + " self.step_q_from_to = list(zip(self.step_qs[:-1], self.step_qs[1:]))\n", + " \n", + " for pair in zip(self.step_times, self.step_q_from_to):\n", + " print(pair)\n", + " \n", + " self.program = list(zip(self.step_times, self.step_q_from_to))\n", + " \n", + " self.drying_time = 24. / load\n", + " \n", + " self.aux_duration = (q0 * self.step_times[9] - (self.step_sizes.ravel()[:10] * self.step_qs[:10]).sum()) / 2 / qq3\n", + "\n", + " def lowest_heat_duration (self):\n", + "\n", + " t = self.duration\n", + " t5 = t\n", + "\n", + " if t > 14 :\n", + " t5 = t - 6\n", + " elif t > 12 :\n", + " t5 = t - 5\n", + " elif t > 10 :\n", + " t5 = t - 4\n", + " elif t > 8 :\n", + " t5 = t - 3\n", + " elif t > 7 :\n", + " t5 = t - 2\n", + " elif t > 6 :\n", + " t5 = t - 1\n", + " elif t < 5 :\n", + " t5 = t + 1\n", + "\n", + " return t5\n", + " \n", + " def calculate_temperatues_old (self):\n", + " t0, t1, t2, t3, t4, t5, t40, t30, t20, t10, t00, tt1, tt2, tt3, tt20, tt10 = self.step_sizes\n", + " q0, q1, q2, q3, q4, q5, q40, q30, q20, q10, q00, qq1, qq2, qq3, qq20, qq10, qq00 = self.step_qs\n", + " \n", + " T0 = self.T0\n", + " \n", + " T1 = T0 - (q0 - q1) * t1 * 3 / (0.4 * 4.18) * 0.33\n", + " T2 = T0 - (q0 - q2) * t2 * 3 / (0.4 * 4.18) * 0.33\n", + " T3 = T0 - (q0 - q3) * t3 * 3 / (0.4 * 4.18) * 0.3\n", + " T4 = T0 - (q0 - q4) * t4 * 3 / (0.4 * 4.18) * 0.25\n", + " \n", + " T5 = T4 + 2 * t5\n", + " \n", + " T40 = T5 + (q40 - q5 ) * t40 * 3 / (0.4 * 4.18) * 0.33\n", + " T30 = T40 + (q30 - q40) * t30 * 3 / (0.4 * 4.18) * 0.33\n", + " T20 = T30 + (q20 - q30) * t20 * 3 / (0.4 * 4.18) * 0.33\n", + " T10 = T20 + (q10 - q20) * t10 * 3 / (0.4 * 4.18) * 0.33\n", + " T00 = T10 + (q00 - q10) * t00 * 3 / (0.4 * 4.18) * 0.33\n", + " \n", + " TT1 = T00 + (qq1 - q00) * tt1 * 3 / (0.4 * 4.18) * 0.33\n", + " TT2 = TT1 + (qq2 - qq1) * tt2 * 3 / (0.4 * 4.18) * 0.33\n", + " TT3 = TT2 + (qq3 - qq2) * tt3 * 3 / (0.4 * 4.18) * 0.33\n", + " \n", + " TT20 = TT3 + (qq20 - qq3) * tt20 * 3 / (0.4 * 4.18) * 0.33\n", + " TT10 = TT20 + (qq10 - qq20) * tt10 * 3 / (0.4 * 4.18) * 0.33\n", + " \n", + " return [T0, T1 , T2 , T3 , T4 , T5 , T40, T30, T20, T10, T00, TT1, TT2, TT3, TT2, TT1,]\n", + "\n", + "duration = 12\n", + "start = 6\n", + "load = 81 / 66\n", + "T0 = 1212\n", + "q0 = 81\n", + "\n", + "cosp = CokeOvenServiceProgram(duration, start, load, T0, q0)\n", + "cosp.step_temperatures = cosp.calculate_temperatues_old()\n", + "\n", + "from functools import reduce\n", + "\n", + "program_arr = np.array(reduce(lambda x, y: x+y,[ [(t, q0), (t, q1)] for t, (q0, q1) in cosp.program]))\n", + "\n", + "import matplotlib.ticker \n", + "\n", + "plt.figure(figsize=(10, 4))\n", + "plt.plot(program_arr.T[0] * 60, program_arr.T[1])\n", + "ax = plt.gca()\n", + "ax.xaxis.set_major_locator(matplotlib.ticker.MultipleLocator(60))\n", + "ax.xaxis.set_minor_locator(matplotlib.ticker.MultipleLocator(20))\n", + "plt.xticks(np.arange(0, 60)*60, np.arange(0, 60), rotation=90)\n", + "# plt.xticks(rotation=90)\n", + "# plt.minorticks_on()\n", + "plt.grid()\n", + "plt.grid(b=True, which='minor', color='r', linestyle='--')\n", + "\n", + "plt.axvline((start)*60)\n", + "plt.axvline((start+duration)*60)\n", + "\n", + "plt.axvline((start+duration+cosp.drying_time)*60)\n", + "plt.axvline((start+duration+cosp.drying_time+cosp.aux_duration)*60)\n", + "\n", + "twin = plt.gca().twinx()\n", + "twin.plot(cosp.step_times * 60, cosp.step_temperatures, 'o:')\n", + "twin.set_ylim(1120, 1420)\n", + "\n", + "twin.set_xlim(-60, 59*60)" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "id": "5fb855c8", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([1. , 0.87027027, 0.74736842, 0.63076923, 0.556 ,\n", + " 0.52 , 0.556 , 0.63076923, 0.74736842, 0.87027027,\n", + " 1. , 0.9 , 0.83 , 0.77 , 0.83 ,\n", + " 0.9 , 1. ])" + ] + }, + "execution_count": 44, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cosp.step_qs / cosp.step_qs[0]" + ] + } + ], + "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": "3.9.12" + }, + "toc": { + "base_numbering": 1, + "nav_menu": {}, + "number_sections": true, + "sideBar": true, + "skip_h1_title": false, + "title_cell": "Table of Contents", + "title_sidebar": "Contents", + "toc_cell": false, + "toc_position": { + "height": "calc(100% - 180px)", + "left": "10px", + "top": "150px", + "width": "384px" + }, + "toc_section_display": true, + "toc_window_display": true + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/감급 ν›„ μ΅œμ € μ—΄λŸ‰ 곡급 μ‹œκ°„ ( μ •μˆ˜ μ‹œκ°„ ).ipynb b/감급 ν›„ μ΅œμ € μ—΄λŸ‰ 곡급 μ‹œκ°„ ( μ •μˆ˜ μ‹œκ°„ ).ipynb new file mode 100644 index 0000000..d0036eb --- /dev/null +++ b/감급 ν›„ μ΅œμ € μ—΄λŸ‰ 곡급 μ‹œκ°„ ( μ •μˆ˜ μ‹œκ°„ ).ipynb @@ -0,0 +1,221 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "45a4ee2a", + "metadata": {}, + "outputs": [], + "source": [ + "from matplotlib import pyplot as plt\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "929bc20d", + "metadata": {}, + "outputs": [], + "source": [ + "def lowest_heat_time (t):\n", + " t5 = t\n", + " if t <= 4 :\n", + " t5 = t + 1\n", + " elif t <= 6 :\n", + " t5 = t - 0\n", + " elif t <= 8 :\n", + " t5 = t - 1\n", + " elif t <= 10 :\n", + " t5 = t - 3\n", + " elif t <= 12 :\n", + " t5 = t - 4\n", + " elif t <= 14 :\n", + " t5 = t - 5\n", + " elif t >= 16 :\n", + " t5 = t - 6\n", + " \n", + " return t5" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "70e13998", + "metadata": {}, + "outputs": [], + "source": [ + "def lowest_heat_time (t):\n", + " t5 = t\n", + "\n", + " if t > 14 :\n", + " t5 = t - 6\n", + " elif t > 12 :\n", + " t5 = t - 5\n", + " elif t > 10 :\n", + " t5 = t - 4\n", + " elif t > 8 :\n", + " t5 = t - 3\n", + " elif t > 7 :\n", + " t5 = t - 2\n", + " elif t > 6 :\n", + " t5 = t - 1\n", + " elif t < 5 :\n", + " t5 = t + 1\n", + " \n", + " return t5" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "77a65776", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "c55961eb", + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "([,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ],\n", + " [Text(0, 0, ''),\n", + " Text(0, 0, ''),\n", + " Text(0, 0, ''),\n", + " Text(0, 0, ''),\n", + " Text(0, 0, ''),\n", + " Text(0, 0, ''),\n", + " Text(0, 0, ''),\n", + " Text(0, 0, ''),\n", + " Text(0, 0, ''),\n", + " Text(0, 0, ''),\n", + " Text(0, 0, ''),\n", + " Text(0, 0, ''),\n", + " Text(0, 0, ''),\n", + " Text(0, 0, ''),\n", + " Text(0, 0, ''),\n", + " Text(0, 0, ''),\n", + " Text(0, 0, ''),\n", + " Text(0, 0, ''),\n", + " Text(0, 0, ''),\n", + " Text(0, 0, '')])" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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9msQpUyk9fz6XPTwo3bsXpXr3xq2k6TSYnw/9zEf7Z1LHvSRftf+aACnshUaKuxCiwHISE0mcMYOkuaaTFNNbNKfu++/jFhQEmI5ln7ZsIFMS1tK8fHPGtxiPt5u3PVN2elLchRBWMyQlcf6bWVz44Qd0VhYBjz5C0MsvE3XkyJXCbjAaGLPyVX5KWEvXgLt5t9VE3F3c7Zy585PiLoSwmCEllQv/+44Ls77FmJqKf+fOBA0cgEflyqYnHDkCQJYhixHrRrAsbhUvlG/Lq60+Q7nI10gUBSnuolhbtCOOcUsOEZeUTtimlQzrEG7RiTuOlEeBYqyfaOrIePXx5MfXmk4eajrkyl3GjAwuzpnL+ZkzMVy8iF/bNgQNHIRXeA1TDP1fjJTLpxky/yE2q0yGNhxKj3t6IIqOFHdRbOW2qM3tZJjbohYo0gJvizwKHCO31W632abbx9dec1tnZZE0fz6JU6eRc/YsvpGRBA0ZjHedOjeNcclwiV7/9OAIGXxc6gEeksJe5KS4i2Lrdi1qi7K42yKPAsfIPc3/l57ULFEHNu+CJ/+HLhdB8rSPSPxpOdmnE/BuUJ+w157Gp1UXCAiDrDSI3Qhla5liPDKVk788x5dlg0gig8m1+tKs0cD8vhXChmTxSxRb8TfpYHi7+x05D1vEyK7UmMvhHQk5sxodUpdLB1M59vBjnJ74A66emgozZ1Bpyqf47HwDji43DUpJgB8eg+NrADjgYuS50r6kG9P4Ori9FHY7kpm7KLZCA71v2qI2NLBoD9GzRR4FjZFwKZZ+S3pjSI7ji/QHME4/SOaF1/C8qxphowZSosujKP9ykJMJvZZCqSqmgSVCzbersuX0FgZteocSSvGxsTb37lkENR+RvjB2IjN3UWzZos2to+RRkBjHTqzjuV8747U/nhfnaNJ/i8XoGUxoi2yqTByK/9OvmAo7gJsnVLwf/Mqabrt7QcX7WZq4g37L+lIuM53vm3xM1l0vS7teO5PiLootW7S5dZQ8rI2x+9xu3vtpKK/8ZGT0XCOlLsOWxyOotnQFASNmoRJ25rntnw7+xNA1Q7nHsxSzm40npObDpgekXa9dybKMKNZs0ebWUfKwNMbGRWOI+fpHRhw1oEoFUvbNl3nb+xdcjJn0cHfPs9Wu1pqpu6YydddUWpRvwbgW424861Ta9dqNFHchipnMY8fY9f5rBG46RA0vhe/AvpR/oQ8uPj7c/28Cc/fPJT0n/bbtAQxGAx9v/pifD//Mw9UeZnST0XLWqYORZRkhiomsU6eIHzGS6C5dcNt+iI2tSlH979+o2H8ILj4+AESGRpJDDtvObLtlnExDJsPWDuPnwz/Tq3YvPoj8QAq7A5KZuxBOLvvMGRKnTiXp1/kYMPB3Q0jq1pp3O3+Oh6vHNc+NCI7AXbkTFRdF07CmN8S6nHWZwasG82/CvwxrOIzn73m+qF6GsJAUdyGcVM6FC5yfMZOLc+eiDQYONQllwr1xtC9XnQ8enIyri+sNY7zcvLjL8y6i4qNueCwxPZF+y/oRnRTNmGZj6FK1S1G8DGElKe5COBmVlsbZSZO4+N3/MGZk4NulE1ManOevzK28Uqsn/SJeu23zrpreNVl4cSHxKfGE+oUCEHsplr7L+nI+4zxftvmSyLDIono5wkpS3IVwEsbUVC788CNlpk/jfFo6JTp1xOuFbgzZNoA9mTm888A7PBn+ZJ5xannXYuHFhUTFR9GtRjf2n9/Py8tfxqiNfNP+G+oE1ckzhrA/Ke5C3OGMmZkkzZtH4oyZGM6fJ7tOHWq+/x5JFUvSe1lfTrrkML7aU7TLR2EHCHYLJsQ3hA1xG6hQogKDVw4mwDOA6e2mUyWgSiG/GmErUtyFuEPp7GySFiwkcepUchIS8HngAYK+/IItycnEe52mz1/9Sc1JZ3qHb7gv5L58x1VKERkayV/H/mLNqTVU8q/EtLbTCPYNLsRXI2ytQIdCKqVeVUrtU0rtVUrNVUp52Sox4fwW7YgjcuxKei5OJXLsShbtiLNLjIIqcA7rJ954iv7xtab7b0IbDCT//jvRD3YhYfRo3IODqfh6FyqNfgGf+vWJSTvE86sHYUg7z+zgthYV9lxNw5qSYcigTpk6zO44Wwr7HcjqmbtSKgwYBNTSWqcrpX4GngJm2yg34cQcooe5Ddgkh9w+6I9/jYsh+79e6l2/MLXU9TAdg67TLnB5zQbOfTWNrKPReIZXp/xXk/Br3Q51fC383IO1LYcwOfE7ynn4My3+NBVatLPqdbWu2JrJrSbTOLQxXm4yZ7sTFfQkJjfAWynlBvgA8QVPSRQHt+s/XpQxCsomOVRpDl2/hB8ep97Ot/77koxfe8PqMWitSVm7lpg2jYh7dRgYNWETPqNKvTWUcNuJUgoqNOJ310wGHZhJJaMH/4tPoMIT31l96r+LcqFVxVZS2O9gSmtt/WClBgMfAenAUq31Mzd5Th+gD0BwcHDEvHnzrN4eQEpKCn5+fnYb70wx7JlDz8Wpt3xsdkffIouRy56vIzojmoMZB3kj9l+CLuwgptKTxFR5htC4v0g744FauQuPo9EQ4E1Wh6ZcbPEwuCjC4v7ikn8NLvuHsyJpGYuSf6eewYdpJw9xrmI3Yqrc8OOYL46wbzpKDEfIAaBVq1bbtNYNLRlTkGWZksDDQBUgCfhFKfWs1vqHq5+ntZ4BzABo2LChLmhzpoI2eLJFgyhniWHPHMI2rbxp//GwQO98x7NFjFz2eh3Lo8Yw5eQvZBmz6ZGTTmqlJ6l8bgXB5ZpybsUFDBs24Fa2LGVGjyLw8cdRHlefUdoaozYyYesEFp34nQ5BDfh47zriK3aj8rkVVG75nFUzd0fYNx0lhiPkYK2CLMu0BY5rrc9prbOBBUAT26QlnJ29e5jbSkFy+Hn/j7x+5EeqG0BpzfoG3Tjl3oKTBxoRM2QsGXt3U3b4cKotXULJ7t2vK+yQbczm7fVv893+7+ge2oJP967Do9ts04xdeqkXewU5FDIWeEAp5YNpWaYNsNUmWQmnl/th47glh4hLSics0JthHcIt7mFe0BgFZU0O2mhk2p7pTNk5hebB9zHeqwavH15J0PS1lNr5G2l+fgQ935WSjUrj2rbnTWOkZacxdM1Q1sWtY2D9gbyUnIrqNts0Uz+x+tpe6tJyt1iyurhrrTcrpX4FtgM5wA7Myy9C5Ic9epgXBktyMORkMWb+I/yUcZKu1bryVqU+XJw2g/4L48hy1SS3b0Oj9z7ENTDwljGSMpLov7I/exP3MrrxaJ6o8cTNnyi91Iu1Ap3EpLUeDYy2US5COLVMQyYj141kWcZJ+hmr8NgqL078/BAKMD7WgYGhy3ii0l00vk1hP51ymr7L+xJ3OY4JLSfQpmKbIstf3FnkDFUhikDK5dMMXjec/Se2MykmgtAlu0nKOU7gY49R5uV+EFwGw7zmHMg4cMsYRy8epd/yfqRlpzG93XQahlh08IQoZqS4C1HIElPPMmROJ2puyeK17R64ZPxLiYe6EDRgAB4VK1553v3l7md73Ha01qZj16+y4+wO+q/oj5erF992/JbwUkX7Jd7iziPFXYhCFHv2CPM+6sHgtZn4pUOJds0JGjQQz+rVb3huZFgkK2JXcDz5OFUDq165f/XJ1QxdM5QQ3xCmt5tOmF/RfoG3uDNJcReiEBizsjj05Tskz/mDh1I0xvvrUXnY23jXvueWYyJDTT3So+KjrhT3hUcW8t7G96hZqiZT2k6hlFepIslf3PnkO1SFsCGdk0PSr79yoF1rmPE750qC+9Sx3PPd3NsWdoBQv1DKupUlKj4KrTXf7PmGURtG0SikEd90+EYKu7CIzNyFsAFtNOL1778cGzOWrBMnOF5OsaZXKK89N55y5ernO04t71psTNjI2C1jmXNwDp0qd+Kjph/h7ipfQC0sI8W9GFq0I+6/k242rbTqxB9bxLC79RNNHRmvPhb8+FrTiT9Nh+QrhF73OSknXTk3bwUBhw+TXM6PLx93IadOCF88uoAAzwCLUqrpXZPVl1cz5+Acnq75NMMbDcdFyR/YwnJS3IsZZ2m1axO5rXa7zTbdzm21m3v7NrTWpEZt4Ny4pWQcjsE9tCyrn2rA1Mq7aJGexaf1h+JtYWEHqO5ZnXpB9WhZoSW9ave64agZIfJLinsxc7sWtfktzLaI4RByT9Gf250GniGw5aLp9vrP4cAf0Hmc6Xmzu0DIvdDxYwDS3mvBuX81aUfP4RZajuBWHkypFcdP/hd4ND2HUe2m4nZXW6tS8nDx4PvO39vm9YliTf7eK2bib9LB8Hb3F1YMR3E5tB4EVMD/8lFo2NtU8EPqQOm7/ntSubpQuirpe/cR+1IfTsw9S2ZiGsFvv02Fv37jw+Zl+Mnfm95JybxX/VmrC7sQtiQz92ImNND7pi1qQwO9izSGvRmNBib+O47vDs7hh8w0/Cs+QaWt30CVZtDu/Wuem1m1J+cmf8HlZZ/hGhBA2aGvU/KZZ0h1zeGVVYP5N+ssb1zOpJl/B9S2WVBVeroI+5OZezHjLK12CyLbmM07P3Xk24M/YkSzst7DHK/63A1tcrNOnCDujTc41vVhUjdsoEz//lRbvozSL77IeZ3CC4tfYMeZbYxNzuC5B7+WVrvCocjMvZhxlla71rrSKjcrgQGUYoN/SaJST1K3BFfW4LP3rCVx1nKSFixAubtTqtcLlH7xRdxKlgQg9lIsfZb14ULGBb4MaUdks67Salc4HCnuxZCztNq1VNLF4wxYPYQ9l2MY1XgU3Wp0w2X3TCbvmMwln0vkJCaSOHc9SfN+QWtNyaeeonTfPriXLXslxr7z+3hl+SsYtZFv2n9DnaA6N25IWu0KByDFXRQLCakJ9P3jSU4Z0/ms6Rja3vUQAE3CmvDNhkkw/0eODn0XnZVFwCMPE/TKK7iHXfuXyKbTmxi8cjABngFMbzedKgFV7PFShMgXKe7C6R1LOkafZX1IdXVhWt2h3Gcu7IaUVILmrearmUZ8MnZTonNnygwcgGeVG4v24pjFvLnuTSr5V2Ja22kE+wYX9csQwiJS3IVT27l3DgO2j8fdK4DZnf5HeKlwjBkZXJwzl/MzZ2K4eJHEuiF8fV8qP70+7qZng849OJcxm8dQv2x9JreebPFZp0LYgxR34bTWnlrL69s/pWyOgWktJlPerwoX584lceo0cs6exbdJE4KGDOao7wkOrn+TAxcOcE/p/5p7aa35cueXzNg9g5YVWjKu+Ti83Lzs+IqEyD8p7sIp/X7oF0Zt/ojwUuF81fhj3NbuIfqr18g+dQrvBg0IHT8O30aNAGicHgrAhrgNV4q7wWjgw80f8uvhX3n0rkcZ1XgUbi7y4yLuHLK3Cqfz7T/9mHA2ivtL1+dj/RjJzw0i69gxvGrVImTGdHybNbumZ0sZ7zKU9yhPVHwUL937EpmGTIavHc6K2BW8VOclBtYfKD1exB1HirtwGkZtZMLWCXx3Zj0vxfjz4IJkzh8aicdd1QibNIkS7dvdskjf7XU3q86uIiE1gRHrRrDtzDZGNBrBM3c/U8SvQgjbKNAZqkqpQKXUr0qpg0qpA0qpxrZKTNzaoh1xRI5dSc/FqUSOXcmiHXH5H7x+4o1nTx5fa7r/Do5hNGTy1j+92fr3t0yd40m7eRfRaemEfvoJVX/7Df8O7W87+77b+25ydA7d/ujGrnO7+LT5p1LYxR2toO0HJgGLtdY1gbrArb+6XdhEbrvd3N4uue12813gc9vc5hbW3Da3YQ3yn4SDxUg7uoxlm9+l/qTNjJ5rJDjNk5D33qPa338R0LUrytU1z1BVPKvg6+5LliGLKW2m0KlKp/znIYQDsnpZRinlDzQHegJorbOALNukJW6lwO12c0+P/6UntXzDYd1WePoX0/17F8DBP+GJWabn7v4Fji6Dx2aYbu+cCzHr4JEpV1rlRniUhS3Jptvnj8K+RdBlgun5W2ZC4hHo/Knp9qapkHTS1Dq3SnOo9TD82I3KYV1hywoIfxCOrf7v7M4148CYDa3eNN1eNQZcXNHNh5Fz5gyZv88hKS6cnf0H8fwJyPHSBPd5ksD+b+Hi6WnR++qm3JjWdhr+nv5UDaia9wAhHJzSWls3UKl6wAxgP6ZZ+zZgsNY69brn9QH6AAQHB0fMmzevIPmSkpKCn5+f3cbbO0bPxam3fGx2R998x6l8/Ecqn/iZbLcSbGjyHdrFlbBTfxAav5h/G30FQIXYhQSfWc3W+yYBUPHErwSd28C2hqbiXXfHm5RM3kdMpSeJqfIMVY79QEDyfnbWN/U9rxo9G7+UY+yua+qyWO3o1/ikxbPn3lEAVD88nZIXd+KTHk9MpSfxyErCLSeF/fcMByD84CRUVhZHA57E7VQcZff8jiExm6yLCpfU/96HswHgdVc6ZZu35kT48/l+D652p+8XtozhCDk4SgxHyAGgVatW27TWDS0ZU5Di3hDYBERqrTcrpSYBl7TW79xqTMOGDfXWrVut2l6ugvYysUUvFHvFSM9Jp834NcQnGW54LCzQm6gRrfOMcXTbN5SJ3ULgkWXEBLWh8rkVpll3PnuhaK355/g/NMzKoezvQ6yKcTnrMrP2zuIJ74qE/fE6MUFtqHR2BTmtPicjLYDMQ4fJPHSQjEOHyTp+HIxGAJS3N57Vq+MVHs6liqX5LOkXoktm8snli5QJbGVxHle7k/cLW8dwhBwcJYYj5ACglLK4uBfkaJlTwCmt9Wbz7V+BEQWIJ25jb+JeXln+ChVr3sPFbQ9fszST33a78w/P5/29E+mQmsanj80k5pQrlVs+999Xy+VRFLON2by74V1+j/6dR9Ozeb/bbGJOGC2KkZieyMB/+pJ6+BBBJ3No7deJ5PWnOHK6FIZv/9t93END8axZkxLt2+EVXhPP8Bp4VKyIcnVlx9kd9F/RHy8/F6aevkD4o9+y2sI8hHB2Vhd3rXWCUuqkUipca30IaINpiUbY2Ib4DQxZNYT0nHTSc7by/iNvMHHZ8Xy329VGIzN3TuGLPdPxVK5E+ZfGULUlnFqX7xa16TnpDF0zlLWn1lLG1ZuogEB05WZwYs0tY2ityTlzhoyDB8k8dJgLe7cTt3MDbydm46IBFEneGyEkmBIdO+MZ5IqXXxqe3d7GtUSJm+ax+uRqhq4ZSjnfckwLbETYAy2k3a4QN1HQ49wHAj8qpTyAY8ALBU9JXO3vY3/zVtRbVA2oSvea3Xlv43uUL5dA1IjW+fpzz6iNjJ3/CHPTjtOlckealG/Om+vfZN/5ff89KY8WtcmZyfRf0Z/d53bzzgPv4KpceXfju0QnRf+3nXKNyEwpReb8+WQcPETmIdPFkJx85TmJgS4kBLsR1PlxooOMTLr8O7N7/UbCjmjq5uPP1oVHFvLexveoWaomU9pOoZRXqRufJO12hQAKWNy11jsBi9aBRP79eOBHxm4ZS0RwBJNbT8ZNufHx5o/ZEL+BxqF5n1KQZcjirfVvsTjtOD187+K1pmO4lJ2CQhEVF8Xd3J1njITUBPot60fs5Vg+a/kZbSu25fSxPdQ/auTYF+MIOpxK9KfjyIqJuXZtvEZ1SnTogGd4DY4FGRl26kvc/QOY1m4aVQOq4pN0jITf/mDDmU0EEXTbHLTWzNo7i4nbJ9K4XGMmtpqIj7tPvt5DIYorOUPVAWmt+WLHF8zcM5PWFVrzSfNPrjSsalC2Aevj1vN6w9dvGyM1JYEhKwayKekgr0W8xgu1TX9UBboGUrtMbaLio7jb5/bFPTrhAB/91I8apy7znkczApd+z+HD72BMTmYkAGsxlC6NR716+HfsgGd4TbzCa+BesSLKxXQKxZKYJYxcN5JKpa9tlVsloArlfMsRFRfFI+qRW+Zg1EbGbx3P9/u/p1OVTnwU+RHuru75eh+FKM6kuDuYHGMOH2z6gAVHFvBEjSd4+/63cXX57yScyLBIJmybwJnUM7eMcT79PK8seoxDOZf4qNGbdK319DWPNwltwsw9M0kLSwPMa+MJCWQcOkTmwUNkHDrIpf270bHxDDMfTKV81qOrV8e/Qwc8a4azwLiN2emrGFVtFLVbt79pHvMOzuPjzR9Tr2w9vmj9xTWtcpVSNAltwpKYJTxU7qGbjs82ZPPOhnf469hfPHP3M7xx3xs3bckrhLiRFHcHkpGTwbC1w1h9cjV97+1L/3r9bzhlvkloEyZsm8CG+A2UpOQNMU5dPkXfZX05SxaT7+lH8+sKuzEjg2aXy3F4Zw5q3becmD2PjMOHMV61Nm4IKcNO/4ucbxVI1w4DCasXiXuFCldm4wC14iqTvHwpRzKP0J5ri7vWmim7pjBt1zRalm/Jpy0+xdvN+4ZcI8MimX9kPjGZMTc8lpadxmurXyMqPorBDQbTu3Zvad4lhAWkuDuI5MxkBq0cxI6zO3jz/jfpXrP7TZ9Xo2QNgryDiIqPogtdrnns0OE/6Lf5PbLcPJnZbib3GEO4vGoVmYcOk3HIdMRKVkwMHkYjLwNZHvvRd9fBv2NHPMNr4FWzJqvcjvLWzo+pUbIWU9pOoYx3mZvm0SC4AZ6unhxMP3jN/QajgY82f8Qvh3/Js1Xu/eXux1W5ciD92q4VFzIu0H95f/Zf2M/7Td7n0eqP5vNdFELkkuLuAM6knqHf8n6cuHSCcS3G0aFyh1s+N3c5Y9XJVXQO6QyYZuM7Nv3GT398xJOnc2iVUx418WWOXjUbdy9fHs+a4aZCXjOcCUnzWZm5jzXPzr0yI/5u33eM3zqe+0PuZ2Krifh53PqsOi83LxoGN+TA2f8Kc6YhkxFrR7A8djm9a/dmcIPBt51t+3v4c2/QvRxI+i9GfEo8fZf15XTqaSa2nEiriq3yfgOFEDeQ4m5nx5KP0W9ZP5Izk5nadir3l7v/ls/VWpNz+jRtTwXisioJj8uTif50PJknYvAxatNxqN5eeNXwwrNjR7xqhuMZHo5njRq4Xnf6c+3Dl5i/cQPRSdFUDazK59s+Z/a+2bSv1J4xzcbg4eqRZ+6RYZFExUcRnxJPCY8SDFo5iK1ntvLGfW/wXK3n8vX6m4Q2YcrZKVzIuEBieiIvL3uZ9Jx0prebTkRwRL5iCCFuJMW9KK2fyPq0igzfHkhcUjoh276FoGl4uhiZ1Wn2NV/xZkxPJ/Po0SsnAGUePGhaG790iWCgO3C51EkSK/iztDEQ5s/Lz35N6bvuuWZt/FYiQyMBWHNqDd/u+5bfo3/nqfCnGNFoxDUf4OYnxm/Rv7HixAqik6IZ22wsD1Z9MN9vSdOwpny18yum75rOH8f+wNvVm9mdZlOjZI18xxBC3EiKexFan1aRWlGDqJg9iARfd9JL/Y+QzAz6+7xIxV1nSTy87soJQFknTvx33LiPD141auDfqRNeZVzxjJnNKxHh7M9JJF0n0iw9m89ajMK7Rp1851LOrxwh7iF8seMLDNrAgHoD6HNvH4s+tKwSUIWSriWZsnMK3m7efNXmK5qENbHoPbm71N34uvgy5+AcKvtXZnq76YT6hVoUQwhxIynuRWj49kAqZg+iR+pU7jvhTc3VmpCz7nhnf80p83PcK1TAs1II/nVK49nySbxq18XdJREVGwX3vQTuXrClHI02j2Wbvzdd07N5t90M3KtZvjZd27s2Ky+vZHTj0TxR4wmLxyulaODbgB1ZO/iy9ZfUCcr/L5dcri6uNPFrwmXfy3zS/JObn3UqhLCYFPciFJ+Uzv3xW6m0xYty7kYy/H1YGVaPUoGXearMKjxHRuEaFg7/fg1/vQ7NPwO/srDpL1j6NtR/1lTcs1LoeSGRWmleNG84ABcrCjtA58DOvNb2NSqUqGD1a+oa2JXxLcYX6Muju5bsWuCueUKIa0lxLyJGg4EhsZ/Tfnsc0dU0J+rW5Sn3KCZm1yS+RF16vTYL3M392Os/D/f+33+3G/Y2Ffbco1dC7sXHqySVy7bFZdssqGpdPxV35V6gwg7golwKVNiFEIVDfiqLQHZWBkt6tqT99mSO3GNkbo0ebDTWZX12BF+5T2Z/xGTwvKoLopuH6XKz28fXwoIX4cnvLG63K4QoPuRc7kKWevkCy55pR7XtyRxv5kfJlz4kNuABAGL9G7I/cjJNfWLzHzBu+7WF/Oo2t0IIYSYz90J0MXYP//Z+hkonsznV90E6vzoegKiOBfh2lqZDbrxP2twKIa4jxb2QxB/fw+HnuxNywcCFIV1o12+cvVMSQhQjsixTCI7uWMXxp7rjn2wg693eNJPCLoQoYjJzt7E9CyeQ/v5MXF0U3tPHc3fj/J+tKYQQtiIzdxvavHAahlEzyfSEclM+ksIuhLAbmbnbyKovXqXM1MWcLedFndnzKFsh3N4pCSGKMSnuNrDk7W5U/HUvJ6r50OT7v/AvFWLvlIQQxZwU9wIwGo3880Z3qv65l2P3+NJ29hI8S5S2d1pCCFHwNXellKtSaodS6k9bJHSnyEpL5u9nGlP1z91Et65Oh3kbpLALIRyGLWbug4EDgL8NYjmuq3qxJ54/x/uftOLe4+kcbxFA5y8X4ZKPHupCCFFUClSRlFLlgQeBr22TjuPK7cVe7cIKxm4dQ+2YdI5HZuP/+BtS2IUQDqegM/eJwBtAiTyed8cbvj2Q+1K70mvDfEolK+JbZfF9YF9itwcS1d7e2QkhxLWU1tq6gUp1ATprrV9RSrUEhmqtu9zkeX2APgDBwcER8+bNsz5bICUlBT+/W39xc2GNH71oKx9GzcInU6NaJbOuRHs+z+kGwOyOvkWWhy1jOEIOjhLDEXJwlBiOkIOjxHCEHABatWq1TWvd0KJBWmurLsAY4BQQAyQAacAPtxsTERGhC2rVqlVFPn7nH1/pzXXv1hvq3623vFZJT3yrh04cFaafGvmJbjJmRZHlYesYjpCDo8RwhBwcJYYj5OAoMRwhB621BrZqC2u01csyWuuRwEiAq2buz1obz1Ft+t/7eH06lww/RanmqXzk1p+NOfewyVjL1Iu9wWR7pyiEEDeQ49xvY9WM0QR9/jNnQty4d0AHDpVoQuz2QEhKN/Vib2BhL3YhhCgiNinuWuvVwGpbxHIUi9/oQqXfozlRI4AmsxfhXyqEICCqfQF6sQshRBGRmft1DIYcFr/6OFWXRnOstg9tv1+Op3fBPgwRQoiiJgdoXyUrI5V/enem6tLDRLesRIc5UVLYhRB3JCnuZinnT7Li8QeotukkMU82pvOUv3Hz8LJ3WkIIYRVZlgEuJJxgW4/HqBibQ9xTten07ix7pySEEAVS7GfucbuWs6fbQ5Q9lUbS271p++4v9k5JCCEKrFjP3I9s/pNzA4bhlwWGCW8R2cHpDtMXQhRTxXbmvmvFzyT3fQMXrfAd9xoNpLALIZxI8Zi5X9WuNy4pnV4LutN15U7S/F2oPGs2FWveZ+8MhRDCpopFcc9t11sxexD3ntvLYxt2ciZIkzNgoBR2IYRTKhbFffj2QCpk9efpuJlU3+JGTAXN/PqPceBYDTrYOzkhhCgExaK4J1xM5qXjv1J9txvHqhs4XKcRy42RqKR0e6cmhBCFwuk/UM1KT2PEwc+4f3caR+vkcOTeB3jefTWNXfYRGuht7/SEEKJQOPXMPeVsDOv6dKfpoSSO3GdgTvlebDLcy2ZdW9r1CiGcmtMW9/Pxx9jxzENUTDAS360GpZr14KS06xVCFBNOWdxPHdlBdK8eBF00kjSgNW36fwVIu14hRPHhdMX98PJvSRw5Hr8sjXHC20S2f8beKQkhRJFzquK+c9lccoZ9inIDvxkTCL+/k71TEkIIu3Cao2U2/DwZ9er7pPm5UX76eCnsQohizSlm7is/eIqyc3aRUN6Het/9TJnQavZOSQgh7OqOLu5Go5ElH/al8pxdnKjiStPvF+FXpoK90xJCCLu7Y4u7ITuLf159gmrLj3CsURjtpi7Aw9ff3mkJIYRDuCOLe1Z6Gkufa0a1vWlEt61Bp0nzcXW9I1+KEEIUCqsrolKqAvA/IAQwAjO01pNsldg1rmrZe/78Gd4d04K6J9I40aoEnSctwMXVtVA2K4QQd6qCTHdzgNe11tuVUiWAbUqpZVrr/TbK7Yrclr1105/kwS3LqHw2h5hm2ZR4dIQUdiGEuAmri7vW+jRw2nz9slLqABAG2Ly4D98eSI3Up+kZ9QeBl+FM60z+F9CP2O2BRLW39daEEOLOp7TWBQ+iVGVgLVBba33pusf6AH0AgoODI+bNm2dx/J6LU/EwpvHxng+4KzyRNX7t+TynGwCzO/paFCslJQU/Pz+Lc3DGGI6Qg6PEcIQcHCWGI+TgKDEcIQeAVq1abdNaN7RokNa6QBfAD9gGPJbXcyMiIrQ1moxZoZ8a+YlOHBWmJ77VQyeOCtNPjfxENxmzwuJYq1atsioHZ4zhCDk4SgxHyMFRYjhCDo4SwxFy0FprYKu2sDYX6BATpZQ7MB/4UWu9oCCxbueTBknUippM/+xBbDTewyZjLWnZK4QQt2F1+wGllAK+AQ5orSfYLqUbNfWJZX/kZGL9TX+VxPo3ZH+ktOwVQohbKcjMPRJ4DtijlNppvu9NrfXfBc7qek2H0BRp2SuEEPlVkKNl1gPKhrkIIYSwEafpCimEEOI/UtyFEMIJSXEXQggnJMVdCCGckE3OUM33xpQ6B5woYJgyQKIdxztTDEfIwVFiOEIOjhLDEXJwlBiOkANAuNa6hEUjLD3ryd4XrDhTy5bjnSmGI+TgKDEcIQdHieEIOThKDEfIwdoYsiwjhBBOSIq7EEI4oTuxuM+w83hniuEIOThKDEfIwVFiOEIOjhLDEXKwKkaRfqAqhBCiaNyJM3chhBB5kOIuhBBO6I4p7kqpWUqps0qpvVaOr6CUWqWUOqCU2qeUGmxFDC+l1Bal1C5zjPeszMVVKbVDKfWnleNjlFJ7lFI7lVJbrYwRqJT6VSl10PyeNLZwfLh5+7mXS0qpIRbGeNX8Pu5VSs1VSnlZ9CJMMQabx+/L7/Zvti8ppUoppZYppY6Y/y1pRYxu5jyMSqk8vzXnFjHGmf9PdiulFiqlAi0c/4F57E6l1FKlVKilOVz12FCllFZKlbHidbyrlIq7av/obE0eSqmBSqlD5vf1Uwtz+Omq7cdc1b3Wkhj1lFKbcn/WlFKNrIhRVym10fwz+4dSyv82429apyzdP4E75zh3oDnQANhr5fhyQAPz9RLAYaCWhTEU4Ge+7g5sBh6wIpfXgDnAn1a+lhigTAHfz++AF83XPYDAAsRyBRKAShaMCQOOA97m2z8DPS3cbm1gL+CDqcPpcqC6NfsS8Ckwwnx9BPCJFTHuBsKB1UBDK/NoD7iZr39yuzxuMd7/quuDgGmW5mC+vwKwBNNJh7fd126Rx7vAUAv+L28Wo5X5/9TTfLuspa/jqsc/A0ZZkcNSoJP5emdgtRUx/gVamK/3Aj64zfib1ilL90+t76Dj3LXWa4ELBRh/Wmu93Xz9MpD7hd6WxNBa6xTzTXfzxaJPpJVS5YEHga8tGWdL5plDc0xftoLWOktrnVSAkG2AaK21pWcfuwHeSik3TAU63sLxdwObtNZpWuscYA3waF6DbrEvPYzpFx7mfx+xNIbW+oDW+lD+Ur9ljKXm1wKwCShv4firv8PYlzz2z9v8XH0OvJHX+Dxi5NstYrwMjNVaZ5qfc9aaHJRSCngSmGtFDhrInWkHkMc+eosY4Zi+YxpgGfD4bcbfqk5ZtH/CHbQsY0vK9IXe9THNvC0d62r+8+4ssExrbWmMiZh+aIyWbvsqGliqlNqmTF9AbqmqwDngW/Py0NdKKcu+afxaT5HHD871tNZxwHggFjgNJGutl1q43b1Ac6VUaaWUD6aZVQULY+QK1lqfNud2GihrZRxb6gX8Y+kgpdRHSqmTwDPAKCvGdwXitNa7LB17nQHmJaJZ+VpGuFENoJlSarNSao1S6j4r82gGnNFaH7Fi7BBgnPn9HA+MtCLGXqCr+Xo38rmPXlenLN4/i11xV0r5Yfre1yHXzXLyRWtt0FrXwzSjaqSUqm3BtrsAZ7XW2yzd7nUitdYNgE5Af6VUcwvHu2H603Gq1ro+kIrpTz2LKaU8MO24v1g4riSm2UgVIBTwVUo9a0kMrfUBTEsXy4DFwC4g57aD7hBKqbcwvZYfLR2rtX5La13BPHaAhdv1Ad7Cil8K15kKVAPqYfrl/ZkVMdyAksADwDDgZ/Ms3FLdsXDycZWXgVfN7+ermP/atVAvTD+n2zAttWTlNaCgdQqKWXFXNvxCb/MyxmqgowXDIoGuSqkYYB7QWin1gxXbjjf/exZYCNz2Q56bOAWcuuqvjl8xFXtrdAK2a63PWDiuLXBca31Oa50NLACaWLpxrfU3WusGWuvmmP4ctmZ2BnBGKVUOwPzvLZcACptSqgfQBXhGmxdZrTSH2ywB3EI1TL9wd5n30/LAdqVUiCVBtNZnzBMhIzATy/dRMO2nC8zLoVsw/bV72w93r2de8nsM+MmK7QP0wLRvgmkCY/Hr0Fof1Fq311pHYPolE32759+iTlm8fxab4m7+jV+gL/RWSgXlHr2glPLGVKAO5ne81nqk1rq81roypqWMlVpri2arSilfpVSJ3OuYPoCz6AgirXUCcFIpFW6+qw2w35IYV7F2VhQLPKCU8jH/37TBtL5oEaVUWfO/FTH9EFs7Q/sd0w8y5n9/szJOgSilOgLDga5a6zQrxle/6mZXLNg/AbTWe7TWZbXWlc376SlMH/AlWJhHuatuPoqF+6jZIqC1OV4NTB/8W9pdsS1wUGt9yortg2mNvYX5emusmDxctY+6AG8D027z3FvVKcv3z7w+cXWUC6Yf2tNANqYdrreF45tiWqveDew0XzpbGONeYIc5xl7y+PQ9j1gtseJoGUzr5bvMl33AW1Zuvx6w1fxaFgElrYjhA5wHAqzM4T1MxWcv8D3moyIsjLEO0y+mXUAba/cloDSwAtMP7wqglBUxHjVfzwTOAEusiHEUOHnVPnrLo11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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "t = np.arange (1, 21)\n", + "t_cont = np.linspace(1, 21, 100)\n", + "\n", + "t5 = t + 1\n", + "\n", + "offsets = [1, 0, -1, -3, -4, -5, -6]\n", + "\n", + "for ll, offset in zip(range(4, 17, 2), offsets):\n", + " t5[t>ll] = t[t>ll] + offset\n", + "\n", + "plt.plot(t, t5, 'o')\n", + "plt.grid()\n", + "\n", + "plt.xlim(0, 20)\n", + "\n", + "\n", + "\n", + "plt.plot(t, list(map(lowest_heat_time, t)), ':x')\n", + "plt.plot(t_cont, list(map(lowest_heat_time, t_cont)),)\n", + "\n", + "plt.plot([1, 4, 9, 16, 20], [2, 5, 6, 10, 14])\n", + "\n", + "plt.xticks(np.arange (1, 21))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "78d8c049", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "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": "3.9.12" + }, + "toc": { + "base_numbering": 1, + "nav_menu": { + "height": "338px", + "width": "228px" + }, + "number_sections": true, + "sideBar": true, + "skip_h1_title": false, + "title_cell": "Table of Contents", + "title_sidebar": "Contents", + "toc_cell": false, + "toc_position": {}, + "toc_section_display": true, + "toc_window_display": false + } + }, + "nbformat": 4, + "nbformat_minor": 5 +}