{ "cells": [ { "cell_type": "markdown", "id": "168e1167", "metadata": {}, "source": [ "## Ideal Curves\n", "\n", "Ideal curves, sometimes known as characteristic curves or Brown's curves in the literature, are a test of the extrapolation behavior of an EOS. These curves are defined as level set functions of a derivative, so some sort of tracing method is needed to obtain the curve. One possible method is that employed in CoolProp where a polar tracing method locks onto the curve and integrates it until termination is requested.\n", "\n", "Ideal Curve:\n", "\n", "$$ Z=1 $$\n", "\n", "Boyle Curve:\n", "\n", "$$\n", "\\left.\\frac{\\partial Z}{\\partial v}\\right|_{T} = 0\n", "$$\n", "\n", "Joule-Inversion:\n", "\n", "$$\n", "\\left.\\frac{\\partial Z}{\\partial T}\\right|_{v} = 0\n", "$$\n", "\n", "Joule-Thomson:\n", "\n", "$$\n", "\\left.\\frac{\\partial Z}{\\partial T}\\right|_{p} = 0\n", "$$" ] }, { "cell_type": "code", "execution_count": 1, "id": "56f76494", "metadata": { "execution": { "iopub.execute_input": "2025-10-15T23:05:56.924936Z", "iopub.status.busy": "2025-10-15T23:05:56.924820Z", "iopub.status.idle": "2025-10-15T23:05:57.991221Z", "shell.execute_reply": "2025-10-15T23:05:57.990749Z" } }, "outputs": [], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import CoolProp, scipy.optimize\n", "CP = CoolProp.CoolProp\n", "import teqp" ] }, { "cell_type": "code", "execution_count": 2, "id": "a446343e", "metadata": { "execution": { "iopub.execute_input": "2025-10-15T23:05:57.992873Z", "iopub.status.busy": "2025-10-15T23:05:57.992657Z", "iopub.status.idle": "2025-10-15T23:05:58.046349Z", "shell.execute_reply": "2025-10-15T23:05:58.045949Z" } }, "outputs": [], "source": [ "# Some helper classes\n", "\n", "class teqpAbstractStateShim(object):\n", " \"\"\"\n", " A shim class that exposes a CoolProp-compatible interface\n", " so that the tracing can use either teqp or CoolProp\n", " \"\"\"\n", " def __init__(self, j):\n", " \"\"\"\n", " \"\"\"\n", " self.model = teqp.make_model(j)\n", " self.z = np.array([1.0])\n", " self.R = self.gas_constant()\n", "\n", " def update(self, pair, in1, in2, guess=None):\n", " if pair == CP.PT_INPUTS:\n", " self.p_ = in1\n", " self.T_ = in2\n", " # Assume to be ideal gas\n", " if not guess:\n", " rho_guess = self.p_/(self.R*self.T_)\n", " rho = rho_guess\n", " else:\n", " rho = guess\n", " for i in range(10):\n", " # Iterate for density a few times\n", " Ar0n = self.model.get_Ar02n(self.T_, rho, self.z)\n", " Ar01 = Ar0n[1]; Ar02 = Ar0n[2]\n", " pEOS = rho*self.R*self.T_*(1+Ar01)\n", " dpdrho = self.R*self.T_*(1 + 2*Ar01 + Ar02)\n", " res = (pEOS-self.p_)/self.p_\n", " dresdrho = dpdrho/self.p_\n", " change = -res/dresdrho\n", " if abs(change/rho-1) < 1e-10 or abs(res) < 1e-12:\n", " break\n", " rho += change\n", " self.rhomolar_ = rho\n", " else:\n", " raise ValueError(\"????\")\n", " \n", " def update_with_guesses(self, pair, val1, val2, guesses):\n", " return self.update(pair, val1, val2, guesses.rhomolar)\n", " \n", " def keyed_output(self, key):\n", " if key == CP.iT:\n", " return self.T_\n", " elif key == CoolProp.iZ:\n", " return self.p_/(self.rhomolar_*self.R*self.T_)\n", " elif key == CoolProp.iT_triple:\n", " return 80\n", " elif key == CoolProp.iP_critical:\n", " return 6e6\n", " else:\n", " raise KeyError(key)\n", "\n", " def gas_constant(self, ):\n", " return self.model.get_R(self.z)\n", " \n", " def p(self):\n", " return self.p_\n", " \n", " def T(self):\n", " return self.T_\n", " \n", " def rhomolar(self):\n", " return self.rhomolar_\n", " \n", " def first_partial_deriv(self, k1, k2, k3):\n", " keys = (k1, k2, k3)\n", " if keys == (CoolProp.iDmolar, CoolProp.iT, CoolProp.iP):\n", " return -self.first_partial_deriv(CP.iP, CP.iT, CP.iDmolar)/self.first_partial_deriv(CP.iP, CP.iDmolar, CP.iT)\n", " elif keys == (CoolProp.iP, CoolProp.iDmolar, CoolProp.iT):\n", " Ar0n = self.model.get_Ar02n(self.T_, self.rhomolar_, self.z)\n", " Ar01 = Ar0n[1]; Ar02 = Ar0n[2]\n", " dpdrho_T = self.R*self.T_*(1 + 2*Ar01 + Ar02)\n", " return dpdrho_T\n", " elif keys == (CoolProp.iP, CoolProp.iT, CoolProp.iDmolar):\n", " Ar01 = self.model.get_Ar01(self.T_, self.rhomolar_, self.z)\n", " Ar11 = self.model.get_Ar11(self.T_, self.rhomolar_, self.z)\n", " dpdT_rho = self.R*self.rhomolar_*(1 + Ar01 - Ar11)\n", " return dpdT_rho\n", " else:\n", " raise KeyError(keys)\n", "\n", "# This approach was taken from CoolProp\n", "class AbstractCurveTracer(object):\n", "\n", " def __init__(self, *, AS, p0, T0):\n", " \"\"\"\n", " p0 : Initial pressure [Pa]\n", " \n", " \"\"\"\n", " self.P = [p0]\n", " self.T = []\n", " self.RHO = []\n", " self.AS = AS\n", "\n", " # Solve for Temperature for first point\n", " T_ = scipy.optimize.newton(self.objective_T, T0, args = (p0, -1))\n", " print(T_)\n", "\n", " self.T.append(T_)\n", "\n", " def objective_T(self, T, p, rho_guess):\n", " \"\"\" Base class function \"\"\"\n", " if rho_guess < 0:\n", " self.AS.update(CoolProp.PT_INPUTS, p, T)\n", " else:\n", " guesses = CoolProp.CoolProp.PyGuessesStructure()\n", " guesses.rhomolar = rho_guess\n", " self.AS.update_with_guesses(CoolProp.PT_INPUTS, p, T, guesses)\n", " return self.objective()\n", "\n", " def TPcoords(self, t, lnT, lnp, rlnT = 0.1, rlnp = 0.1):\n", " return np.exp(lnT + rlnT*np.cos(t)), np.exp(lnp + rlnp*np.sin(t))\n", "\n", " def obj_circle(self, t, lnT, lnp):\n", " T2, P2 = self.TPcoords(t, lnT, lnp)\n", " if len(self.RHO) > 0:\n", " guesses = CoolProp.CoolProp.PyGuessesStructure()\n", " guesses.rhomolar = self.RHO[-1]\n", " self.AS.update_with_guesses(CoolProp.PT_INPUTS, P2, T2, guesses)\n", " else:\n", " self.AS.update(CoolProp.PT_INPUTS, P2, T2)\n", " r = self.objective()\n", " return r\n", "\n", " def trace(self):\n", " t = self.starting_direction()\n", " for i in range(1000):\n", " try:\n", " lnT = np.log(self.T[-1])\n", " lnp = np.log(self.P[-1])\n", " t = scipy.optimize.brentq(self.obj_circle, t-np.pi/2, t+np.pi/2, args = (lnT, lnp))\n", " T2, P2 = self.TPcoords(t, lnT, lnp)\n", " self.T.append(T2)\n", " self.P.append(P2)\n", " self.RHO.append(self.AS.rhomolar())\n", " if self.T[-1] < self.AS.keyed_output(CoolProp.iT_triple) or self.P[-1] > 1000*self.AS.keyed_output(CoolProp.iP_critical):\n", " break\n", " except ValueError as VE:\n", " print(VE)\n", " break\n", "\n", " return self.T, self.P\n", "\n", "class IdealCurveTracer(AbstractCurveTracer):\n", " def __init__(self, *args, **kwargs):\n", " AbstractCurveTracer.__init__(self, *args, **kwargs)\n", "\n", " def objective(self):\n", " \"\"\" Z = 1 \"\"\"\n", " return self.AS.keyed_output(CoolProp.iZ) - 1\n", "\n", " def starting_direction(self):\n", " \"\"\" Start searching directly up ( or calculate as orthogonal to gradient ) \"\"\"\n", " return np.pi/2.0\n", "\n", "class BoyleCurveTracer(AbstractCurveTracer):\n", " def __init__(self, *args, **kwargs):\n", " AbstractCurveTracer.__init__(self, *args, **kwargs)\n", "\n", " def objective(self):\n", " \"\"\" dZ/dv|T = 0 \"\"\"\n", " r = (self.AS.p() - self.AS.rhomolar()*self.AS.first_partial_deriv(CoolProp.iP, CoolProp.iDmolar, CoolProp.iT))/(self.AS.gas_constant()*self.AS.T())\n", " #print self.AS.T(), self.AS.p(), r\n", " return r\n", "\n", " def starting_direction(self):\n", " \"\"\" Start searching directly up \"\"\"\n", " return np.pi/2.0\n", "\n", "class JouleInversionCurveTracer(AbstractCurveTracer):\n", " def __init__(self, *args, **kwargs):\n", " AbstractCurveTracer.__init__(self, *args, **kwargs)\n", "\n", " def objective(self):\n", " \"\"\" dZ/dT|v = 0 \"\"\"\n", " r = (self.AS.gas_constant()*self.AS.T()*1/self.AS.rhomolar()*self.AS.first_partial_deriv(CoolProp.iP, CoolProp.iT, CoolProp.iDmolar)-self.AS.p()*self.AS.gas_constant()/self.AS.rhomolar())/(self.AS.gas_constant()*self.AS.T())**2\n", " #print self.AS.T(), self.AS.p(), r\n", " return r\n", "\n", " def starting_direction(self):\n", " \"\"\" Start searching directly up \"\"\"\n", " return np.pi/2.0\n", "\n", "class JouleThomsonCurveTracer(AbstractCurveTracer):\n", " def __init__(self, *args, **kwargs):\n", " AbstractCurveTracer.__init__(self, *args, **kwargs)\n", "\n", " def objective(self):\n", " \"\"\" dZ/dT|p = 0 \"\"\"\n", " dvdT__constp = -self.AS.first_partial_deriv(CoolProp.iDmolar, CoolProp.iT, CoolProp.iP)/self.AS.rhomolar()**2\n", " r = self.AS.p()/(self.AS.gas_constant()*self.AS.T()**2)*(self.AS.T()*dvdT__constp - 1/self.AS.rhomolar())\n", " #print self.AS.T(), self.AS.p(), r\n", " return r\n", "\n", " def starting_direction(self):\n", " \"\"\" Start searching directly up \"\"\"\n", " return np.pi/2.0" ] }, { "cell_type": "code", "execution_count": 3, "id": "bb419a93", "metadata": { "execution": { "iopub.execute_input": "2025-10-15T23:05:58.047550Z", "iopub.status.busy": "2025-10-15T23:05:58.047375Z", "iopub.status.idle": "2025-10-15T23:06:00.853438Z", "shell.execute_reply": "2025-10-15T23:06:00.852903Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "---- HEOS ----\n", "Ideal Curve\n", "871.9188660407749\n", "Saturation Curve\n", "Boyle Curve\n", "871.3231023164184\n", "solver_rho_Tp was unable to find a solution for T= 367.236, p=4.23194e+06, with guess value 7148.15 with error: The molar density of -534.989081 mol/m3 is below the minimum of 0.000000 mol/m3\n", "Joule Inversion Curve\n", "5203.0347638747335\n", "Joule-Thomson Curve\n", "1607.8131112921665\n", "f(a) and f(b) must have different signs\n", "---- SAFT-VR-Mie ----\n", "Ideal Curve\n", "869.1678675951697\n", "Boyle Curve\n", "868.5717695687255\n", "The function value at x=2.844988966902614 is NaN; solver cannot continue.\n", "Joule Inversion Curve\n", "7532.324134784955\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Joule-Thomson Curve\n", "1639.8924107978214\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "---- PC-SAFT ----\n", "Ideal Curve\n", "937.290324467432\n", "Boyle Curve\n", "936.5094002217145\n", "The function value at x=2.884887861538658 is NaN; solver cannot continue.\n", "Joule Inversion Curve\n", "Failed to converge after 50 iterations, value is 38232770.52660468.\n", "Joule-Thomson Curve\n", "1797.0614181657859\n" ] }, { "data": { "image/png": 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xxTWHO7tF+CpH2HjVlWEmicQRyErSf/H5F+KPiQTk+GNFa2ZauZFINq4ZIhbvxsUTJac+hptr4O5+Y+Pa3HjWhhYvQfMXin5dSZGQQqeI2FToHHsTTr6ftxBWSXCvLroEe/iLxbOWcexRCzz1Y3c/2wiXgshMEJ6Z+OPiAytB/9rpKwh62Hhc0kX4s3He89Wu+rLwzY1Cps4I6T6WSJwVRSdC2fHHRG5eTv5NZmzB53nWNoqemiHi86QowicrWbS8iNoAt9YLD7MpnRZA03Djuk4rZnAWJ5dIkgeZo1MIdlEZ2bVK4SLHvbqoPupZx6QaaYD52KOW9UJF1iBmm6hnkZMgnHzJ8nHxx83XKzcQiYaVG4FPazH2bS2moNqbeJNIJLZDpRYTBLwbQeBosU1RxGfNvQPGpOPYw8aJBiDSAK4uFQuIoocBQyBgqMjhcfGyfD/XysZQFV+IL2W3NogO8LcjRV85U+7ugS19xTXrPyRsLMoUekmJkB4dW3p0bqwR9Ru86hpLqZsunnVKnpxraxRFxM5TrkPNXub7/g4T//wF4RkAjR6Dtu9YzUSJROLkaDMh9hDc3i6WO7vzD3mp3UX5iIChEDjKPDRe2D1yfxE9PAPOfmZc13hAneFQ/2EhljTuJXo7zoYMXRURm+foOAKKTrhqY49A3BGI/Ue8ZsYJj9SYO/knBmu8jDMjcjw0Pq2KniQokUgkZYUuW3yO3d4O0X+LL2QW6/6ooGYfCJoAgfeLXMbicPJDOP+15URqV18Rsm/0OFRrX4I34TxIoVNEpNApISnX4OJ3Ignv3v6C++aMvCr6MeUQe1g06qvaXuTWOHJDR4lEUnHJToHoLRC1Dm6uExNDcqN2FeGtoEeg7siih9EVRYTwr/wC15ZZrird4mVo/1Hp3oMDI4VOEZFCpxB0WWIWg0cNUZsih/gT8Feb/M/zqAXVOggx0+TpopWZl0gkEntFUURy840/hDhJOpf3GM8A8XnX+AnxmVlUdFlCUF1ZAtdXGmeWhm2E2gPKxHxHRAqdIiKFjgmKIqaz39un99TsMybrtXkL2swxHqvTwvKqYuaAh7+oFlqtkyioVa29eYl1iUQicSQURYTnLy+Ba0tFErMpaneoPx6az4CqbYt37axEkQwdtR56rzAvg3FjDZyLEFPVaw90+vo8UugUgux1pSf5CsRsgZjtIh6dX/fh2oMgbL35tphtULlh/hVHJRKJxNHRaSFmK5z/P7ixmjwNf+uNE5MqqjQt/b1MJ3JUaQGtXhOCSu2cE6il0CkiTuXRUZS8gmT/43Dx+/zPqdxIeGv874NGU6xrn0QikVRkki8LwXPhO/PegCoNNJwMredApSI2Cc5NZjxs6Ji3HEflxtD6dZEY7WRlNqTQKSIOLXQURZRJv71deGzu7IQhx81nCFz+Cfbq+0JpPEXpcr/uQtxU71K8OLNEIpFIRBLzhUVw8gPzHl0aLwh+D5o+W7JJGDqtaDlxZp6YCm9KpQbCcxT0sNNUfZdCp4g4nNBJuQa3NkH0JiFw0m+b7+/zh5gZkENqFFxeDDVDRY5NRSo8KJFIJPZMVjKc/QJOf2I+M7VaJ+j6nSitUVJitsOJd0TYzJSq7aHv38Wf8l4BkUKniDiE0FEUUZvm1nrReC4/XCpD+3nQ5Mnys00ikUicnYxYOPaGqJ2Tk8OjcoF2H4qE5dLkON7ZDcffEV9uQRQ17LvFKfImpdApIhVS6GQlgau3+bYNncQMKVNcq0CN3uAfKvq2VG3vtElrEolEYnPu7IEDUyHhlHFb4P3Q7YfSt4CI3gL/vAxdF0K1jub7dNkO+dkvhU4RqRBCR9GJ3iw3/xRLdioMP2+u2P99HU59CNW7iWmHtQcK96gsxieRSCT2gzYDjr8Np+Yat3k3FbNaKzcs3bUtTTiJ3goHp0GXb4W3x4GQQqeI2K3Q0Wnh7m64thyur4C0KPP9Q0+DT3PjevodkXEvu+FKJBKJ/XPjTzERJGd2lmdtCNsk2uCUFdoM+Kutsbhhs+cgeG7F7aGYi6I+v50jNbuioOggJhIOhsMfdeHvEDj3VS6Ro4LqXUUfKVM8akiRI5FIJBWFusNh8GFRDwdE0cG/+8C9g2V3j4x75jNnz34hpqjnTnNwcJzWo2OXBQMzE2BVLVGJ2BS1G9QaAIGjRedcT3/b2CeRSCSSsiX9LkQOgVi9wHGvDv13Q5VmZXN9RQdnv4Sjr4IuQ2xTu0HHL6DxkxU6aVmGroqIzUJXGbEQ9w/Uus98+65xcO03UUI8YDAEPiCUf2kT1SQSiURin2QlwfbhoiQIiL6C/feUbY/AhFOwZ6JoXZFDg/9A56/Bxavs7lOOSKFTRMpd6Nw7KCpnXl0qVPXoaPN46d0DkHwR6gzLO7NKIpFIJI5JVqJIV4g7KtardYb+O0HjXnb30GbC0Vfg7OfGbVXbQ8jaCtl4Webo2BvXV8KGzrCxC1xaLMJTWYlwc435cX5dIOghKXIkEonEmXCtAqHroVKQWI89CP/MLNt7aNyg42fQcxm4VBLbEk5A0vmyvY+dIYVOeZF4DmIPGdddfaDZ83nrHUgkEonEOfGsBb1XitQFgHMLxJfksqb+gzDwgGgb0fV78A8p+3vYETJ0VV6hq/TbYiaVTxtoGi46zlbQuKhEIpFIrMiFRXDgCTH28Iehp6zT0iE7tUI/h2Toyt7wqAnDzsCgQ9Do0Qr9xyWRSCQSK9LocagzQozTY+DIDOvcx9Jz6OwCiNponfvZCCl0ypPKDSv0VD6JRCKRlAMqlZgN5eoj1i//D+7ut/59z38Dh5+BnaNFuwoHQQodiUQikUjsDa8AaPuecf3IDNHiwVooirETujYNIodC/HHr3a8ckUJHIpFIJBJ7pMmTUEXf6ufuHtHr0FqoVND9J6jVX6xnxQuxkxZjvXuWEw4hdD777DNatWpFy5YtefbZZ3Hy/GqJRCKROAJqV2j3oXH9xLvW9epo3MWsr2qdxXrqddg5RvTMqsBUeKFz584dFixYwOHDhzl+/DiHDx9m3759tjZLIpFIJJLSU2cE+AaLcewhiN5s3fu5VoaQ1eBZR6zf3QOHn7PuPa1MhRc6ANnZ2aSnp5OVlUVWVhY1a9a0tUkSiUQikZQelQpav25cP/ul9e/pWVuIHY2HWL/wLVz73fr3tRI2Fzo7duxg+PDhBAQEoFKp+OOPP/IcExERQVBQEB4eHnTt2pUDBw4Y9tWoUYOZM2dSr149AgIC6NevH40aNSrHdyCRSCQSiRWpOxq86olx1DpIumD9e1brCJ0WGNf3Pw7JV6x/Xytgc6GTkpJCcHAwERERFvcvW7aMGTNmMGfOHI4cOUJwcDADBw7k9u3bAMTFxbF27VquXLnCzZs32bNnDzt27CjPtyCRSCQSifVQu0DTp43rF38on/s2fBTqP2RcTzxdPvctY2wudAYPHsx7773H6NGjLe6fP38+U6dOZcqUKbRs2ZJvvvkGLy8vfvhB/KL//vtvGjduTLVq1fD09GTo0KEF5uhkZGSQmJhotkgkEolEYtc0mAwqjRhf/h/otNa/Z049n4ZTYOhJCBhs/XtaAZsLnYLIzMzk8OHD9OvXz7BNrVbTr18/9u7dC0BgYCB79uwhPT0drVZLZGQkzZo1y/eac+fOxcfHx7AEBgZa/X1IJBKJRFIqPP2hzjAxTouC29vK575uPtDtB/CqWz73swJ2LXTu3r2LVqvF39/fbLu/vz/R0dEAdOvWjSFDhtC+fXvatm1Lo0aNGDFiRL7XnDVrFgkJCYbl+vXrVn0PEolEIpGUCUGPGMdXl9nOjgqGi60NKAvef/993n///SId6+7ujru7u5UtkkgkEomkjAkYAi6VIDtFdDXv/LXI3ylPMuPh+FsinFU1uHzvXULs2qPj5+eHRqMhJsa8MmNMTAy1atUq1bUjIiJo2bIlnTt3LtV1JBKJRCIpF1y8hNgByIwVNW7Kk/jjsLYZnP0C/n2tfO9dCuxa6Li5udGxY0e2bNli2KbT6diyZQvdu3cv1bXDw8M5deoUBw8eLK2ZEolEIpGUD3WGG8fWbAlhCe8moNbX1on6C27vKt/7lxCbC53k5GSOHj3K0aNHAbh8+TJHjx7l2rVrAMyYMYNFixbxv//9j9OnT/PUU0+RkpLClClTSnVf6dGRSCQSSYUjYAigEuNbG8v33hoPaPOWcf1k0VJGbI1KsXFjqMjISMLCwvJsnzRpEosXLwZgwYIFfPLJJ0RHR9OuXTu+/PJLunbtWib3T0xMxMfHh4SEBKpUqVIm15RIJBKJxGps6AKx+mjE6ChRybi80GXDn00g5YpYH3QYqnUov/ubUNTnt82Fjq2RQkcikUgkFYp/Zxu9KT2WQNDD5Xv/89/AwafEuMEk6L64fO+vp6jPb5uHriQSiUQikRQD/1Dj+LYNOgE0mARuVcX42jLIiC1/G4qB0wodmaMjkUgkkgqJX3dQ6aeV20LouHgKsQOgTReVmu0YGbqSoSuJRCKRVDRM83QeiAM33/K9f+JZWNtcjL2bwrAzomVEeZogQ1cSiUQikTgofiYlVu7uL//7V2kG/vqJRMkXIf5Y+dtQRJxW6MjQlUQikUgqLH4mM49jD9nGhtZvQtfvYUyMXVdJlqErGbqSSCoUmUCK/tUFcANc9YvGhnZJJOWKaeio7ijos8qm5tiCoj6/HaLXlUQiqXgkAzeBq/rXKOCG/vWeflsW8AHQCIjRn7MQ2F2E67sBjQFvIOfb3A0gCSGIPIGa+v2++rEvUMlk8QZqAbX1S3Wc2A0usS+8m4BLZchOhrh/bG2NXSOFjkQisRrZwDmEgAgw2b4cGFvEa0wq4b0zgVMF7I8HbhXzmhqgBlAH8X5q68ctgFZAE4RnSSKxOio1+LaBu3sh5SpkJoCbj21tUpRyT0guCk4rdCIiIoiIiECr1draFInEIcgCDgEHgX/1yxGEN6UW8AtwEiE+yrkVISCK5pc2Tq8FovXLYQv7XYBmQGuE8GkFBAMNMRTtl0jKDt9gIXQAEk5AjZ62sSMmEq7+CvcOwaBDdid2ZI6OzNGRSEqEggghfaN/vQVklNG1NYAPwnsSiBAM/kAVRDgpCxF6qowQEZVNliwgTm9Lbf22HP4B1gB3EaEtH+A2Ilx2DTihP89L/74SEKLmCLC+FO+nDnAf0E//GlDw4RJJ0TgXAYemi3GXb6HxE7axY+tAiN4kxsPOiBlZ5YDM0ZFIJFZFBTxI8cM/OdTC6PloAdRDCIAASpcL44EQQ5Zor18KIhPx3kxDUJEIEXS9CPe35Dm6CfyoXwCaIwTPcIT4kUnUkhLh09I4TigoUGtlavUzCp3ov8tN6BQVKXQkEkmBxAJzgU361yFAOrAN4WEpTOh4Ap2BdhjDOS2BqtYxt9S4WdgWivD4pCIEz1GT5V/9dhDeo3lAb0SY7gQiZHch1/XO6JcIhOCbADyGEHwSSZGp0tw4TjxrOztq9xf/DADRm6FpuO1ssYAMXcnQlUSSBx3wN/AtItSTrd/eEzE7aRNiirclauiP66Vf2mNZPDgKWkTeUSRC+JkmTytAEEIkgfBS+SFCZzoL1woGntZfw90q1kocCkWB330gOwkqNYCRl2xkhw5W1oKMO+BaBe6/B2rr+1Fk6KoQZDKyRJKX68AS4AfgvIX9lqZ1uwF9MYZhmuBcibcaoI1+yc0VRA5QDjqTdR+EaDT9Of8LPAm8C8xCeHmk4JHki0olppnHHYHUq6DNBI0Nvlao1KJK8rXfICtRJEZXbVf+duSD05aECA8P59SpUxw8eNDWpkgkNucwwvNSD/GAtSRyTKkBTAZWImrerEd4IpriXCKnMBoAd4BliOn0Xib7EjD+nDWIuj053ADCEcnU3RHeIqd2vUvyx7uReFV0Ypq5rTBrSbHPdnZYwGk9OhKJQxAbC8eOwfnz4OkJrq7g4wOJiZCQABEREBMD2dlQty6kpUF6OmRlQXw8pKWhKAqvr1/P0UGDDJdtdfw42/r2RadWc6R9e5Y88ghajYaOFy/S5/x5Ov3zD2qNBipXhnbtICAA6tQRS87Y29vuppnagsqIpO0HEbk8GxB5O6sxhgQ9gWMI0fguIlwIYvbYPiAM+A7h4ZFIzKjcyDhOvgRVmtjGDr9uxvG9/dBkmm3ssIAUOhKJvZKSAjdvwqJFsHUrXLkiRERiIiQlQWoqZGaKOH1RuHvX4mYV8NT//R8bBw0yFPzSaLXU0B8/eONGBm/cmP91d+2yvN3FRQieBg2gXj1o21aIonbtoHr1otnsYHgBY/TLLYR4+RYYhfD+NEAIoCPAM5jXG3oCkcD8NuaeIYmTUynIOE61oUenantQu4EuU3p0JBKnRqcT4iQ6Gg4dEt6YU6dg0ybhbcmpLOrhAcnJec+PjS3V7RMrV2bcsmV4paWx4oEHDNuHrF/PyJUrWT1mDABuWVncqVkTv9u3Sx6Kys6Gq1fFkpuqVSE4GHr1EsKnWzchipyI2sAbiFBh7sTuBogZW00QFZzvIPJ75iE8QqsQrTCCEAJIfpA7MaZCJ/mKrawAjbvIy7l3ABLP2EelZj3y/0MiKWuysoTX5epVER66dAm2b4c1a8T2omBJ5FhCrRbCyM1NhJH8/GDkSPHqo/+QSUmBtDQiW7Zk8JAhpLuIf/vJJ05wLDOTTFdXslxdSa5cmQCEJ+GJzp2pFhMDq1YJcZWaKpaEBOFl+vdfuH3bEP7i5ZeFUImKgmXLhPepIOLiIDJSLDm0aQMDB8LQodCzpwjDOQEuiMRkUz4DEvULQCdEaCsTIYA6AGmI0NcPwH5kLR6npVI94zi1KJWerEjjJ6HuSPBpI7w7doKcXi6nl0tKgqKIh/z27bB3L5w4AQcOCFGgszRxuJjUrAnNm4u8mipVRP6NViu2NW0qXmvXFkKnENKA14DPCzimFjAbeJwSzPJJTQWNBtz1Z2ZlwU8/wRtvCOFTEtzchOAZPhzGjhUizon4G3gRIW5y8EUkLN/MdeyriPpGEiclKxl+15fIrBkC/SJtak55UtTnt9MKHdPp5efOnZNCR2IZrVaEmc6dE8uXX8LFi5BRimYHKpXwtlSvLrwgDRtCo0bQoYNY/P3LLIn3EDARkdthieqIB+XTWCnvIzkZjh+Ho0eFF+jQITHOKesQGAj/+Q9s3iyEoiW8vWHCBJg2TYS7nIRsYAHwJqLjeg5BiGnrObwBvFNuVknskt99ISsBKjeGEYXNmXQcpNApItKjIwHEA/nvv2H9erHcuSNyTLKzCz/XEu7u4iE+YoQQMQ0aQFAQ1K8PXtZPJc0ERiA8A5YqRWmA6YjE1nKPoqekwM6d4udduza8+KLYvnYtTJ0qhGV+1KwJjz0Gb70lvD5OQBTwHKLjew7VETO0cjAVOxuBxYgkZ/mJ5iSsbQmJp0HjBQ8mO81sRyl0iogUOk5GaqrwHvz2G2zbZsynycws/rU8PYX3JSgIWrWC9u0hJEQIGxt+0NxF9JCKyWd/b4SnoG25WVQMjh0rmtemWTP46CMhJJ3gQ10B/ocQpzmJy56IsGQOC4CHEb/7KEQ/rS3IBqJOwZZ+ELNFjB+It20ScFYiJF2ElCsQONqqt5KVkSXOTUwMLFkCGzeKZNmsLJE8e/ly0adjg0iI7dgRuncXuTG1aolp0g0a2O0D9kcsi5zKwHxEHo59Wg40bgy//CJ+dxs2GENcuTl7FkaNgh494JNPhMj09CxXU8sTFaJAY09gPGL6+eeITuvP6o95FhHiyum7VQtR2FHiBHjWMo7TY2wrdHaMgphtYvxAHLj52s4WPVLoSCo2qamixsw//4ik4NOnRegjPb141/HwEN6Zxo3F6333wf33G2cuVSCmIKYhmzbb7IXwCDS0iUXFwMsLHnpILHfuCNHz+ef5z+Las0fM0PL2hvHj4d13xe/PQWkC7ERUpH5Evy0K+BAx/fx9YAXwBSJ85Rzz1iR4mAqdaKjS1Ha2VG5kFDrJF6FaR9vZokcKHUnFQFHg1i0xu+nYMbEsXy68NUWlUiVo2VKImdRUke8xaBAMG1bh8z20iLybS8BgjCJHhcjDeY0KOP24Rg147jkID4fffxehqn//Ffu6dBFFE0+fFutJSaKw4okTQvw4MF4YRQ4IcXMV+BVIBmYiqinnzgTL+RuROCAeNY3j9Nv5H1cemFZqTpJCRyKxTHo6HD4MS5fCH3+IkFN2dvGmbbu4iITg2rWhb1948EEICyvSdOyKxufAf4EvEW0Gcj7mqiJaDQyyfFrFwcVFeHjGjxehyDffFJ6e+vVhwQJ46SVj0viBA/Df/8KUKba1uRxRAwMRQgfgOPASEGFyTCYwGugPPF+exknKBzOhk192XjnhbSJ0ki/azg4TpNCR2Jbjx4Wg2bBBeFlu3RJF6YqCSiXqt6jVIhm4Sxchavr3F5V3nYDvgRf0476I8AVAC2AdosKuw6BSCQ/cwIHG/Kjnn4cBA4RX7vJlkdPz6KOiSOM774hEc0UBX19bWm5VLiPyrkz5P2Ac0AeRyPwf4C/9Uk2/LnEg3E2ysTIst3opNypLoSNxVpKTjdO2T54UtWiOHhVJw0XF01PMshkyRFTRbd7coRNQCyMW0QAyhxyR0xv4A/FAc0hyJ4HXrJm3rtF778H160LkbN0Kv/4q2k04IA2AD4CXc21/DOHd8UDMwEI/do6vAE6Gu59xnHEv/+PKg9yhKzvAaYWOacFASRmTmipyaLZuFZWDd+4sXi6NSiU8Mjn1Z0aMEFVyHfhbeXHJQIQicneRGoUIYXiUt0G25ORJyy0z/vc/43jIEJHQXM0x5d9M4BpiinkOFxBJya8AcxB/M4MRXh6Jg+Fu0iTX1h4dNx9wqwqZcWKKuR0g6+jIOjqlIy4OjhyBuXOFhyYxUUzlLgrVq0Pr1iInx91dVAV++GFRR6WCJwdbm2mIgnCmjAaW4aQzbS5fFrPk/vnH8v7Fi2HSpHI1qbzRAsMQTT9zqIQQPLUsniFxGDJiYYVe7NQeBGHrbWvPujaQcALU7jAuzWqlOGQdHUnZkpkpCuytWyf6F928KaZxF9a80RS1WiQHh4aKGTQBAXZbi8ZeyZlCnFvkjACW4qQiB4Tnb/t20Rdr48a8+0ta4boCoQF+RjT8vKbfloLog/VFPufcBmrms09SgXD1QcyxVCAz1tbWgGeAEDq6DMiKFx4eGyKFjiQv8fFiptOZM6K79K5dIjyQmlrYmUY8PcXDp3NnkSzasyfUqyeFTSl5BVEjx5QeCJHj9D4wb2/48094/HH48UfzfU8/LfK6unQR6ydOiOrKDtYhvTrwO+JvIico/zUif6eOyXEKouv5c4h6Ow+Un4kSa6DWgGsV0e8qM97W1oCXvh63qy+k37W50JGhK2cPXSkK3LghpnP/8ovoN1TUfBo3N+jUSYScAgKEsNFoRBXhOnWkqCljDgJdEQ+pHJoCexAPOIkerVY0AV22zHx7/fpilt+JE0J833+/mIrugH+nbwDvmaw/i7lXZytwn35cDTgLmKSzSioiqxuInBj3GnC/jWvpZMaB2gNcrDtZRIauJHlRFFi5UhRfO3RIVP29eLHo07lBfAMODBQPiiefhHbtrGauxJyzmIucysAa7F/k6BQdiRmJxKXFEZceR1xaHEmZSdSsVJMGvg3wr+yPWlWG9Y00GuHRSUqCv/4ybr96VUxHX7tWJC//73/QtSs89VTZ3dtOeANRIfkM4m/me0TTz5w632EIL85yYCh23BJEUnRc9b/drGJ8nlsLG3twciOFjiOiKOJD/fBhIUz27xchqGPHLM9OyQ93d9HfqUcP6NdPhKFk+MlmtEP8w+Zkm/wENLOZNebcSrrFzms72Xl1J2fvnTUImrj0OOLT49Ep+Rd7dNe4E+QbZFga+DYgyDeIZn7NaOvftmQiyM1N1GcKDYVx40RNnZQU+OEHUXDwnXdEPZ4JE0r+pu0YN2AtYtr594hcne+BGfr9KuAr4ClE/SWJA+Cq92joMkGbDhqnmntZIFLoOAq3b8PBg8JT8+mn4ttsUVCpRIG9Tp1E88p27YSHp3JlMb3bASsJV0QygYkYRc7ziKnktkBRFC7GXWTn1Z3svLaTHVd3cDGu5PUyMrQZnL13lrP3zubZV8e7DqOaj2J089H0qd8HV00xcmq8vUWlZI1GjJ9+WmxfsUJ4evr1c7gcHVMaIqadf69f/wFRXDLna0ot5GwshyLHowOig7gUOgak0KmIXL0KL78sPDZZWSJJ+G4xaieoVFC3riig1revmHbrwB/4FRkFSEPkVxzVb2uFmElT3txOuc1P//7Ed0e+48y9M6W+Xlv/tgRWCeRK/BVO3TmFQt50wZtJN4k4GEHEwQiqelRleLPhPNjyQQY3GVw0T49G393pySdFPs7BgyKx/vJlp/ibb45o6LoLOAkcAjrb1CKJ1XA1yVHJSjJvC1HeZMbBqU/EDDDfNtA03Ha2IJOR7TcZOSf8tGuXmBobGyu6c+/dK6Z2F4ZGI5IyQUzp7tJF9Hrq2lXUqXHiisIViTWIbuRJQBair9FBxBTi8kCr07Lx4kYWHVnEmrNrLIag3DRudKnThdTMVI5EHynytWd0m8GnAz8FoPGXjYvlFWrr35Y3+rzBmBZjih7a2rtXhGFB/E9cuiS61oP4X/nnH+HZdDC+x9gioiNC7FhiH6LDfQTi70xSwTgwDS7oC08MOgLV2tvOlvTbsNJfjAOGQeifVrmN0yQjnz17lnHjxpmt//rrr4waNcp2RpUERRHemWPHxLfOlSvh77+Lfn5goGhc2amTyKUJChKViTt1cpq+T46GgghRmVbFeJ7yETk6RcfS40uZuXkmt5JvFXjszRk38fPy45PdnxiEzmu9XmNap2l4uXqhVbRcibvCD0d/QKto6Vy7M30b9sXH3ehqv7/F/Zy6e4r9N/YTnx5Plq7gopPHYo4x9vextKzRkumdpzOt0zRUheWOff65cXzrluh2/swzYibW1Kmi4OXx49CkScHXqWDcZzI+jOXaOS9hLFvQFxhbDnZJyhiXysZxdort7ADzZGQ7qOvjUB6d5ORkgoKCuHr1KpUqVSrSOTbz6Oh08O23sHAhnDsnQkmXLhWvsJmrq6gNEhICI0eKV4nDEIuofZKuX68OXEHMtrIWiqKw7vw6ZmycwfnY8wUe+3qv1xnRfATta7XHVePK5bjLXI6/TMOqDalbpS4u6pJ9j8rIzuDE7RMcvnWYfTf28evxX0nXphv2q1DlCXNV86zGouGLGNNiTP4XnjsXXnvNuJ7zP/faazBP/5gfPNh8ppaD0ARRIRngUYx5OzlsxNjlvj+wqZzskpQhx9+G42+JcehfEDDYpubwu4/IFarSDIaVPtRtiaI+vx1K6Pzyyy+sXr2aZbnrZxSA1YVOUpJokfDvv+LbY1aW6MB84ICYBVIQKpVwrefUtWnQAHr3FnVqunYV7ROcIM/AWfkLMfU3h/8Ck614vwuxF3h8zeNsv7o9zz5vN29GNBuBm8aNC7EXGNhoIFPaTyHAO8CKFgnSstJYdWYV/z36X7Zc2sK4VuOY1G4Sb29/m3039hmO83Dx4OuhXzMpeJJl7056uphFeP26cduyZaKPWosWImH544/FuoPxDWKGFYAvQkSb/oQUYBJi2vlDOFmvNEfh9Dz45yUx7vU71LNxGchyqOtT5Oe3YmO2b9+uDBs2TKldu7YCKKtWrcpzzIIFC5T69esr7u7uSpcuXZT9+/dbvNbIkSOVFStWFOv+CQkJCqAkJCSUxPzCGThQUURgSlGuXlWU119XFJXKuC33Mnq0onz+uaLs3q0oKSmKcvy4omzapChxcdaxT2K3tFUUBf0SpCiK1kr3ydZmK29sfUPxfM9T4S3MlqZfNlVWnFqhZGmzrHT34nE1/qpy7u45RVEURafTKU/8+YSieVtjZvPDKx5W0rPSLV/g66/N/9969RLbz51TlCz7eI/WIFVRFLVi/HvabVtzJNbg3P8pyhLEcvG/trZGUf7qIGz5RaMoOp1VblHU57fNc85SUlIIDg4mIiLC4v5ly5YxY8YM5syZw5EjRwgODmbgwIHcvm2uEBMTE9mzZw9DhgwpD7OLTmeTOQ4HD0KlSuIj1hS1WlQXfuYZkUfw3HMiadLLS3ht+veXnbudjAPAMZP1eVgnQXTH1R1U/7g67+54l7Rs4Tms5ik6fDfwbcDXw75mTIsxJQ5DlTX1fOrRpLrIoVGpVHw77FvmhMwxO+aX478w4OcBfLrnU/6z6j/cSblj3DllighZ5bBrlwgdN2kCLvbxHq2BJ9DWZP0jWxkisR4aL+M4uxjteqyFu/gcQdFCdhHLnVgJm/9nDx48mMGD848lzp8/n6lTpzJlyhQAvvnmG9atW8cPP/zAq6++ajhu9erVDBgwAA+Pgp2uGRkZZGRkGNYTExNL+Q4KoX9/uHNHCJ6uXaFaNfFB2707dOsmlvbt5SwoiYFoRE+rHGohOpOXNXO2zeGdHe+YbQvvHM67Ye+y98ZeBjUeVLYVi63EgagDebbtuLqDXdd2oVN0/HX+L/Y+tlcIJHd3CA+HWbOMBy9eDB98UH4G24jHgen68XrETD5v25kjKWtcTPJStXYgdNyqGccZ98ynv5czdv0plpmZyeHDh+nXr59hm1qtpl+/fuzdu9fs2N9++81s9lV+zJ07Fx8fH8MSGBhY5nab0acPfPMNPPaYEDihoSJH4LffYMYM4bmRIkdiwgdApMn6u5TtP2pGdgY9v+9pJnI0Kg0f9P2ABUMWUNWzKkOaDKkQIgdg1bhVzO49G1WuRgY5U+HDGoTRuFpj446pU43TykH0eDP1skZFwVtvienmDoRpmnYWQuxY4gqiT9Z/rG2QpGzRmDxH7MGjYzbzKt5mZoCdC527d++i1Wrx9/c32+7v7090dLRhPSEhgQMHDjBw4MBCrzlr1iwSEhIMy3XTxMTyQLZPkBRAOuYzYiohkkTLkk92f8KeG3vMtu2asotZvWflc4Z946J24d2+77LlP1uo7pm389fV+KuGsBwA1auLhp6GA66KCQMAW7aI5p9vvw1ffIEjURsw/Vq3Ip/jhiN6Zf0ElLzetaTcMRU62iI2ZrYmdjTd3a6FTlHx8fEhJiYGNze3Qo91d3enSpUqZotEYi9kYR5PngKU1bw6RVF4O/Jt3oh8w7DNVe3KigdX0C2wWxndxXaENQhj16O7CKxi7qU9GHWQJ9c+iaL32txNvcu9R3JNQ1+5Urx27y7an4DwuhanN1wFwPSr4D6wUItatBrJYbN1zZGUJfYmdHxbi2KB9caBm69NTbFroePn54dGoyEmJsZse0xMDLVqla5LS0REBC1btqRzZ1kQXWI/5M4wm12G1/5o90e8tf0tw/prPV/j2gvXCq49U8Fo7tec3Y/upll183anPx/7mW8OfUNsWiz9f+pP2OW3uDPTpGv5unXi1csLXnkF5syBEyeMosdBCDUZT8Zy1/KHgAXAZWCa9U2SlBUupkInPf/jyouGk0VF5F5LheixIXYtdNzc3OjYsSNbtmwxbNPpdGzZsoXu3buX6trh4eGcOnWKgwcPltZMiaTM2ATkpMcPA/wLOLY4LD66mFlbjKGpzwZ+xvv93qdWZcdr6xjoE8jGRzbmqfHz3IbnuH/Z/RyNPsrx28d5oNVJlE4dxc5//zW2Vnn1VZGj07Bh+RpeDvQxGe/M55hAIBwIsro1kjJFbfI1yR48OnaEzYVOcnIyR48e5ejRowBcvnyZo0ePcu3aNQBmzJjBokWL+N///sfp06d56qmnSElJMczCKinSoyOxR34wGT9ZRtfcenkrj6953LA+9765PN/t+TK6un1S37c+6yesZ2LbiUzrKPwSWbos4tLjqFulLnW86/DloC9RDTYpR7F1q42sLT8CEdW2QbSDyNu5TFJhMe1Wrs3I/zgnxOaVkSMjIwkLC8uzfdKkSSxevBiABQsW8MknnxAdHU27du348ssv6dq1a5nc326bekqcjt2IvkQZCE/ODUpf/+FG4g06fNuBO6milsz0ztP5cvCXhfeFciDSs9Np9007zt47C8CsXrMI7xxOnSp1YPt2MRMSYNIkMdXcwRmJaBYLoi1EIxvaIilD0qJhVW0xrjsS+vxhU3PKg6I+v23u0QkNDc0p1mm2LDb5wJk+fTpXr14lIyOD/fv3l5nIkUjshUxEM8Wc72GjKL3IydJmMfb3sQaRM6jxID4f9LlTiRwQrSG+H/G9Yfr5/x38Pzxc9N9+q1c3FgrM3UQ3NhZ+/VW0hXAgTJvCjgHW5nPcOWARovaOw/QJcmQ07saxPeTo3NkDa5rAqgA4ads6VTYXOrZChq4k9sQ+hNjJoX0ZXPP9ne8bekEF+QaxZMwSNGpNGVy54tGzXk9D0nVCRgLv7nhX7Ni9G7KzWdUcYuJviq7mOXTuDA8/DG+84VCzr0yFzjHyb+D5DPAEEIFITJbYOWoToaPLzP+4ckOB5AuQdksUDLQhTit0ZDKyxJ5wz7U+vpTXOxx1mPd2vAeIYoC/PfCbobWDMxKTHMPGixsN618f/JqopChS+3TnyWEwZjy8MAgwLUQ6YIB4zcwUIS4HIbeI3p3Pcb1MxvklLkvsCFOhYw85OqaVmrNt+0XBaYWORGJP3DQZVwd8SnGtbF02j//5OFpFC8DsPrPpXMe5PZf+lf0Z3dzYSCNTl8mnez4lJbAWK1qJkNavbWD3oVXGk8aNEzOwNm0CC3mEFZU6mLd+OI4xZGrKSERPrN2UXnhLygG1BnKqmeuybGsLyIKBEonEnF9NxqUrnADfHPqGo9FHAQj2D+b13q+X8oqOwbth7+Juksfw9aGvcXVx45OoVvimwbd/QvfIC8YTQkNh7lzRr87LK+8FKygqwLTKUBZC7OSmLfAy0IO8HkeJnZLj1dFJj44pTit0ZI6OxJ4wbchQeMe2/LmXeo/ZW41lBv9v6P/hqimr2soVm/q+9Xm689OG9bTsNBYfXczkFg9z/it44jCoT5y0oYXlR7Nc64dtYoWkzFHr/9ftwaNjR9PdnVboyBwdib2QBJikwDKqFNf6cNeHJGQkADC53WR6BPYoxdUcjxe7v4iL2jifbcGBBdC5M345PRCTksCkj56jIoWOg2IQOnbg0VGbtGSycXK00wodicRe6I9x+q4PUNKmAzcTb/LVga8AMaX63bB3y8A6x6JOlTpMCja2Sb0Yd5GD/tnmB504Yb6ekgI7d8KBA+VgYfmQW+gU5Me6BfyFFEMVghxxYQ8eHbNZYNKjI5E4LQrwr8l6q1Jca96eeWToXcTTO0+nbpW6pTHNYZneZbrZ+neXV0LVqgAkucGnhxfQ+7+9ydZlw6VLUKUK9OkDH35oC3OtQm6hczaf4/YDAcBQ4BurWiQpE1R2FLpSaTB0U9NKj45NkDk6EnsgHvPGiqElvM7d1LssPLIQEN6cl3q+VDrDHJh2tdrROcD4f//bqd/QNROP/imjYGb6anZd28Xac2shKAgq6ZMqDzuOT6NJrvV7+iU3pq0YLSUsS+yMnNCVYg9CR2XiYbKt0Clt8dUKS3h4OOHh4YYS0hKJLagKuAE5LfgGlfA6X+7/ktQskWgytcNUalaqWQbWOS6Pd3icg1EiPy8hI4Gz9wXTYt8+HjsCK1qKY/bf2M+o5qNE0UBFgXbtxKsDVJb2QpQxuIcIlX6E5YdBJWCK/th25WWcpOTYUzIyQKevABV41LCpGU4rdCQSeyAGSNCPuwMlbW7i59EQd+qSrYrmhW4vlo1xDsy4VuOITo5mTuQcACJ6aFhQtSoDL8bx5pEqPPT9fpr7NRcHf+OYQZtAhNBJQzSQza9m9g/5bJfYIfYmdBpPtbUFgBOHriQSe8A0GNID4d0pLoqisOd4M/zTIqiR9hF/H9eWkXWOi4+HDy90ewFX/YNh/YUNKI0aolbg7T+TaF6loY0ttD6B+lct4PjzzJwEld53ocjPAFOk0JFIbIjp/J6S9rdadvA6+y7FokJDQ592PNy1XlmY5vB4u3vTu35vAC7FXeJcc717XVHg6lUbWlY+BJqMr9vMCkmZkiN07MWjYyc4rdCRycgSW5MNLDBZL8mMq5jEdN7/67Rhfe6YNlRylxHpojKokTEramugyTTz/ISOAzX3NBU6J4BThRyvBRKtZ46kLDDUiFJA0dnUFABSrkHCKYg7ZlMznFboyIKBEltzDfNv0sWtNKEoCm/8cYKkdPGAHtOhDn2a2jbpr6KRlJlkGAf/bmxvmXzpDD/9+xOjl41m3p55MHAgVK8ObdrYwkyrYCp0pgJ98jkuGjH7qhLwuLWNkpQOlcmXHHsIX20fDutawaaSZh+WDfKrn0RiIy7kWo8r5vnrT0Sz6VQMANUrufHG0JZlYpczka0zenH+bJhJD/0vJe78Mf5zaxEAUUlRzLynhdhYiI+HrCxwrfhtNerkWr8HpCJmZJlSHTgN6IDL5WCXpBSoTFLKddnG5GRbkVM0UJth0xmLTuvRkUhsTdNc67mLuBVEfGomb642Zvi8PbIVVSuVJJXZuenboK9hvKuO8Rtw4IU7tKkpvDeX4i6R2rQB1KsnCgcmJOS5TkXEku/vpoVtroi/zZbkrb8jsTPUdubR0eR8Jik2tUd6dCQSG3HFZKwCipNC/N6609xNFkW4+rXwZ2ib2mVomfPQvW53NCoNWkXLaT+THdeuMW/APLzdvOlSpwsaldoh6ueY4mdh23Usi5nC8nckdoKpR0fJzv+48sKsDUSmuRArR6TQkUhshGkYwIf865jkZuf5Oyw/fAMAb3cX3hvVGpWDPYTLi0pulWhUrRHn7p3jXiWI9wDfdOD2bQY0GmBr86xKNQvbbpS7FZIyxUzo2IFHx6yxZwZ5A6PlZIZN7iqRSDhvMq5VxHNSM7N5bZWxGP+sIS2o5eNRpnY5G33qGdNwD+QkrsQVN2Oq4uEK+ObaJqeZV3DsWehobdfY02mFjpxeLrE1m0zGQUU857PN57geKxpGdG1QjfGdAws5Q1IYfeobhc6RnAhgairo7GB6rpXJnadzxyZWSMoMuxM69pEz5LShK9nrSmJL0jCvipw7MdkSx27E8/0uEfByc1Ezd0wb1GoZsiotHWp3MIwNQkdRIDaWe14qIq9EsufqTkbvukevG2qoXdthOpn7Ye5ZvJvPcaeBhYjQ1v3AeCvbJSkhZkLHDoS6nQgvpxU6EoktuZVrvbCGA9laHa+uOI5OEevP3deEhjUqW8M0p6OZXzNc1C5k67I5btoL9e5dtulOMPb3sQB4HdTQa7NW1NJxIKFjSn5CJxr4XD9uiBQ6dovKJEhjDx4dOxFeThu6kkhsiWeu9bqFHP/D7sucuiXq0jav5c0TfRy/F1N54aJ2oY63SM45Xx3Sc77+3btH5wBjaPtQfX1Nklu5ZWrFpahCxzTEJcNbdoydeFCM2IfwkkJHIrEBpv/y9YDmBRx7PTaV+ZvPAWKG89wxbXDVyH/dsuTZrs8C0PwOvNtbv/HuXer51OP9vu/z50N/8t9JK+HkSTh3znaGljGmQudn4O98jmsM7AcuAl9a2yhJybE3odPxcxh5FUbdgEr1bWaGDF1JJDbA1CcwlPz7XCmKwhurT5CeJdy+k7oH0b5eVWub53Q81v4xXtz0Iif9wS9NvzE2FpVKxWu9XxPrRUmkqmBUyTX2zec4D6CL1a2RlB5TD4od5Oh42EdLGvm1UCKxAdEm44Kmlq87fovIsyJYUKuKBy8OcMCnrR3g4+FDzUoiQedcdf3G2FjbGVROmAqdpHyPklQY7C1Hx06QQkcisQFRJuP8ahonpmfxzp/GmrRvjWiFt0fF77FkrzSq2giAW96Q5oLoa+XgeJuMZWdyB8BU6KDYzAx7Q4auJBIb8LnJOL9aoZ9uPMvtJFFkq1+Lmgxs5W9ts5yaym6VCYyHBnGQ5AaeV68a9sWnx3P6wl6Uy5fpkVULgoOhUSPbGVtGmHp0NiMae/4H887mOZwDriKSkR9EPjzsEnvz6MRsg7ijwpagieBpm88wp/1bjYiIICIiAq3WDv4YJE5HvMn4E2BCrv3HbyTw4z7xoPV01fDWiFayzYOV2XN9Dym+oFVDzVTguqgTHJ8eT9WPRF5U6GXY9j8gIgKeftpmtpYVpkJnpX7pgWWh8wLwl348AMu9siQ2xiwZ2Q48OtdWwPkIMa4ZajOh47Shq/DwcE6dOsXBgwdtbYrECTH9hpG7Go5Wp/D6H8cNn1PP92tC3aq26RHjTNSoJBIno6pAhgZIFMEcXw9fqnuKxJ3zOfk7DtIiwtvCtvxydUzLqjpG/3ZHxPSRbgfJyHZSR8dpPToSiS2ph7GBYu5vxr8cuMaxG+JR0tS/Mo/2alCepjktdavU5Ur8FQCu+0DjRGPWyiNtHyHj3m0anYxCea8/qtBQm9hY1lSxsC0/oTMKMc28oNlZEhtjb6ErO7FHCh2JxAaYzucx7SJ9NzmDTzacMay/N0rWzCkvGvg2YNe1XQDc9IbGqamGfZ8P+txGVlmX4gidB/WLxI4xExZ2ELqyk7o+8hNUIrEB8SZjX5PxR+vPkJieDcCYDnXo0sBUBkmsSbPqzQzjG1WAtLT8D3YQLIWu5OyrioxJHp891NGRQkcicV5MHyY5uQ+Hr8by+2ER0PL2cGHW4Bblbpcz07haY8P4ljeQmWk7Y8qJ3K1IQNbTqdiYTliQHp0cpNCRSMqZs0CqyboPIgH5zdUnDdtmDmhGDW/38jbNqQnwDjCMowoQOoo9hATKCFfMH41QNKHjOD8BB0NlZ5WRpdCRSJyTM7nWfYBfD1zjZJSxaeeErvXK3S5np1ZlY43qmEqgy84y5DlcT7hO+2/bU3OWC4+O84DWrW1lZpmiIq9XJz+h8zeiuGUlwDF6tzsi9ubRsQ/hJZORJZJy5naudZeMLOZtOmtYf2dka1xkAnK541/ZWOMjpjJkqcE9MxPc3fF29+Zo9FHwgOueWoeZXg6ij5WphzG/HB0NxtYlMo/HTlHZm9CxD4+OFDoSSTmTu1P5pkM3iE/NAmBUuwCZgGwjvN288dC4U/NeBh5ZkOIK7ikp4O6Oj7sPvh6+eMen4adxg8aNC79gBcFD/6pB9C3NryVJVaABwgMk28raKXbiQTHgUROqtBB2uVSymRkOIXQuX77Mo48+SkxMDBqNhn379lGpku1+qBJJQaSbjAelZbFj63lUgJebhldlArLNUKlUhHeZzqd7P+WaL5ysCb31M69UKhWxL8c6ZHXqnNBVVeBUAce1Ay5Z3RpJ6bAzj06TaWKxMQ4hdCZPnsx7771H7969iY2Nxd1dJnFK7JcUk/Gt47dQpQhvTnhYY2r5eFg+SVIu5FRABrjrhdkUc0cUOWD06KQXeJSkYmA6vdwOhI6dUOETAU6ePImrqyu9e/cGoFq1ari4OIR+kzgopkLnWrRI/axXzYvHZAVkm1PN0xg2jPPEKWrp5Agdx3+nToDsXm4RmwudHTt2MHz4cAICAlCpVPzxxx95jomIiCAoKAgPDw+6du3KgQMHDPvOnz9P5cqVGT58OB06dOCDDz4oR+slkuJzwWSs0oo4+mtDWuDhqrF8gqTcMBU6sU4mdLRAti0NkZQBdlYw0E6wudBJSUkhODiYiIgIi/uXLVvGjBkzmDNnDkeOHCE4OJiBAwdy+7aYu5Kdnc3OnTv5v//7P/bu3cvmzZvZvHlzeb4FiaRYbDEZp9fzpWuj6gxsZZuuvhJzDkYdJDga2kRDy9tARoZh3+5ru3ltwWienNWao0+PgZs3bWdoGWI6vXwwMC6f47TAi8DTyOnldou9zbq6/gdsHSCWmG02M8PmMZ7BgwczePDgfPfPnz+fqVOnMmXKFAC++eYb1q1bxw8//MCrr75KnTp16NSpE4GBgQAMGTKEo0eP0r9/f4vXy8jIIMPkwysxUU6UlJQvCVod6KePp3YM5I0AH4fN/6hoxCTH8K++nI5Gh5lH52DUQebe+wM8IGTdSdpFvw516tjEzrLENCvsbyA/ya0GPkM8PjsDr1rZLklJsDOhk3oNovWOh4aTbWaGzT06BZGZmcnhw4fp16+fYZtaraZfv37s3bsXgM6dO3P79m3i4uLQ6XTs2LGDFi3yn7kyd+5cfHx8DEuOQJJIyovMaKO4VukUWte21FpRYgtqVKphGKe4YebRMU1UjvUE0h0jfTd3+nt+wToVMp/H/rG3ZGT7+AJnc49OQdy9exetVou/v/l3DH9/f86cEfVlXVxc+OCDD+jTpw+KojBgwACGDRuW7zVnzZrFjBkzDOuJiYlS7EjKjQu3k4i6Gg91fAHwso/PAYkePy8/wzjJHTDpYN6vYT+29P+ZqilagoY2hBbtyt9AK5C7MnJB8m0rom2ETwHHSOwFexA6JthQeNm10CkqhYW/THF3d8fd3Z2IiAgiIiLQam1XrVHifHzw1xm0rYytBrxkyMqu8K9k/FKV6A4kGRsi1PauTe0eE2xglXXJXYwjE5GPYyk1vpv1zZGUBpU9e3RsZ49dh678/PzQaDTExMSYbY+JiaFWrVr5nFU0wsPDOXXqFAcPHizVdSSSorLn4l22nrmN4mZ8hFjqHi2xHaazrpLccYpZV5a+7WZY2CapCNiHsLA37FrouLm50bFjR7ZsMc5T0el0bNmyhe7du9vQMomkeOh0CnP/EuFWU6HjZSuDJBap4m7Ml0p0B1JS8j/YQXC1sM3x5Z2jYmdCx05mgdk8dJWcnMyFC8bKIpcvX+bo0aNUq1aNevXqMWPGDCZNmkSnTp3o0qULn3/+OSkpKYZZWCVFhq4k5cmfx6I4fjMBgKyGxqRW2ajEvvB29zaMk3MlIyuKwtmL+0m4fBqXLB0dgwc5xKwrSw+B/OrpnAYSgCygt9UskpQYew6FO3OOzqFDhwgLCzOs5yQKT5o0icWLFzNu3Dju3LnDm2++SXR0NO3atWPDhg15EpSLS3h4OOHh4SQmJuLjI1PrJNYjI1vLJxuN3clVrjJ0Za9UcjVKzyQ38oSuWi3pgQ6FjlFw6PICCA8vZwvLHksPgax8jn0U2Kcfa7HzkIBTYs85OrbD5kInNDQUpZBfyPTp05k+fXo5WSSRlC2/7L/GjTjxwOzdxI9VapXhQSK7stkXld0qG8aprqCkpxs+qlUqFVXUXsTrUoQIysy0iY1lTXE8Oq65jnEre3MkpcI+hIWBihy6unbtGlevXiU1NZUaNWrQqlWrCtdIU4auJOVBUnoWX201hmZfGdScIxi7RFes/xrHx9fDl44JlagUm4JaB5kuKrPf0aMNRpN14hh1vH2gQweb2VmWmD4ERgONAO98jn0A6IDlvB6JvWEHHh3fttDsBSF4fFrazIwiC50rV67w9ddfs3TpUm7cuGHmhXFzc6N379488cQT3H///ajV9u/QlKErSXmwaMclYlPEN//hwQG0ruNj+LbsC6yzlWESi3i7e1MzqCXrfcRszK+z1GZC59NHfrKNYVbE9CHwODCkgGOftbItklJiJx4UAzV6isXGFEmRPPvsswQHB3P58mXee+89Tp06RUJCApmZmURHR/PXX3/Rq1cv3nzzTdq2bSunbEskwN3kDL7bdRkAF7WKmQOaAqJOCUi3v73ipTFmTqVmO/78I1OhI5t6VnTsLHRlJxTJo1OpUiUuXbpE9erV8+yrWbMmffv2pW/fvsyZM4cNGzZw/fp1OnfuXObGSiQVif/bdpHUTBEafahLPepXF4muUujYN54uJkJH6xhtHgoid96NxEGwi2Rk+6BIQmfu3LlFvuCgQYNKbEx5InN0JNbkZnwaP++7CoCHq5pn+jYGQAfE6o+RRdnsE09XD1BApUB6tuMLHdOHQBZiNpUKOaOqQmJvoSswF1w2mv7utH/LsjKyxJp8teU8mVodAJN7NKBmFdEOMQNjL6E44FebWCcpiL/OrwcV1EoBt0vXzPbNWjaVZi+6EfiyC9dmP2MjC8sWU6EzXr++N59jXwYaA/WBK9Y1S1Ii7EzoXFgEv6rFcum/NjOjRLOuli9fzm+//ca1a9fIzDXF8siRI2VimERSUblyN4XfD98AwNvdhWkhDQ37TL042cAO4KFytU5SKPpvnekukKU1/3yLSb3DuSqiOEDy7Zg8p1ZEijO9/DZwUT92fF9XRcSec3QqUK+rL7/8kilTpuDv788///xDly5dqF69OpcuXSpyY02JxJH5cst5tDrxTz21T0N8vYzZOLlbPshpuvaHmyI+FtNcIDOX0PHx8ME3XUXtFDVaTw9bmFfmFKdgYBWgOlC6ToMS58E+ChgW26Pzf//3fyxcuJCHHnqIxYsX8/LLL9OwYUPefPNNYmNjC7+AnSBzdCTW4MLtJFYdvQmAr5crU3oGme3P/X1LCh37w03ftzvDBbK15o/8zx76H5899D9bmGU1iuPR+VK/SCoAMhnZQLE9OteuXaNHjx4AeHp6kpSUBMDEiRP59deKk3Egc3Qk1uCzv88bPl+e7NMIbw9zKZO7lq6ceWV/5AgdRQWZOP4XoeJ4dCR2jr31urKT5OhiC51atWoZPDf16tVj3z7R+eTy5cuFtnKQSByZs9FJrDt2CwC/ym5M6lE/zzG5hY706Ngfbirjoz9N5xhtHgrC0t+gnGbuCNjb87gCCZ2+ffuyZs0aAKZMmcILL7xA//79GTduHKNHjy5zAyWSisKXW84bxtNCGuHllve7ckKudSl07A9ToZPuBL4N6dFxJOzMo2Mn9hQ7R2fhwoXodGLabHh4ONWrV2fPnj2MGDGCJ598sswNlEgqAmejk1h3PMeb486Ernm9OQCXcq1LoWN/uKqM3eXTcoWu/rm8h3U/vUG6LpPRdfvT8fE3y9u8Mqc4OTo7gG36/f8BmljLKEkZYA8enQqYjLxv3z7+/PNPMjMzue+++xg0aBDjx49n/Pjx1rLPashkZElZYu7NaYinm8bicTJHx/4x9ehkq8w/Hw7fOMgbylZQQdDOeIcQOpbc+vl5dCKBt/Tj7kihY3/YhwfFMhUgdLV8+XJ69uzJF198wXfffcfQoUOZN2+eNW2zKjIZWVJWnIspmjcHoHKu9RIVspJYFVe10c+Wgc5sn4ebsUBAhuIYAR5Lj8b83plrEY6R2An2kDNrJ8nRRf6cnTt3LlOnTiUiIgKNRsPcuXP54IMPmDlzpjXtk0jsngVbLxjGT/bJ35sD4G8yrgs0sp5ZkhIyvM393N70MQoQ52de+Si0SX/WXpyDm8ad5ve1tY2BZYzpo+g/wASgZT7HjgPaI74ht7OuWZKSYCfCwkCt/hC6QYx98vursj5FFjpnz55l2bJlaDTiQ/zFF1/kzTff5Pbt29SsWdNqBkok9sylO8msPRYFQLVKbkzoVq/A400DIX2BodYzTVJCgpp04fBJMR5xzfwjsm61IOo+/Fb5G2VFTN36jYEBBRzbUL9IJEXCq45YbEyRQ1epqalUqVLFsO7m5oaHhwfJyclWMUwiqQj8X+RF9EWQeaxXA4szrUwxFTr5+30ktsTV1d0wdoY6OqY+AF2+R0kqHnYQurITipUi8N1331G5sjHLIDs7m8WLF+Pn52fY9uyzz5addRKJHXM9NpVV/4gqyD6ervyne/65OTlIoWP/uGqMKeLZTvCwMP226/jvVuKMFFno1KtXj0WLFpltq1WrFj/99JNhXaVSSaEjcRoW7rhk6Gk1uUdQnirIljhqMj5tHbMkpeTezfN0uCk8HZqUNLN9GdkZRJ3cR1Z2Bj6VquHfvJNtjCxDTD0654ENQHMgyMKxCcBdhOfHH9H7SiLJl9QoiD8OKODTAioV/mXQGhRZ6Fy5csWKZpQ/cnq5pDTcTkpn2aHrAFRy0+TpaZUfSSbjg8AK4P6yNk5SKv4+8jtH9GkFI66bh+b/if6H7n+EAvD8pZp89r+K38Hc1KPzi375ArD0lfVHk+0/IxKXJfaEnSUjx2yFvRPFuNMCaBpuEzOKXRnZUZDTyyWl4ftdl8nMFhkNE7rVN+tQXhCmORCZQErZmyYpJS4m08uzVebBHNOp55kqx8hosfRozO+dqYtwjMResLNApA2nuxdJ6CxdurTIF7x+/Tq7d+8usUESib2TkJrFkn3XAHDTqHm8V4Min9sx17rM07E/TIWONpfQ8fPyY1xyEI8kNqBLve7lbZpVsPQQyO+R1BThxZkIFP2vXlJ+2JlHB/to6lmk0NXXX3/N22+/zZQpUxg+fDgtWrQw25+QkMDu3bv5+eef2bx5M99//71VjJVI7IGf9l0hOUMUyR/bqS41q3gU+dzcgVKndanaMa4aE6GT68O5vm99ln5yubxNsirF8ej01y8SSUWiSEJn+/btrFmzhq+++opZs2ZRqVIl/P398fDwIC4ujujoaPz8/Jg8eTInTpzA39+/8ItKJBWQ9Cwti/dcAUCtgif6FK+qSG6hIz069oer2qQFhBMo0eJ4dCQVCLurjGznHh2AESNGMGLECO7evcuuXbu4evUqaWlp+Pn50b59e9q3b49a7QSfChKnZsWRG9xNFh2rhrSpTf3qlYp1vvTo2D8uJkInt0fHESmOR0ciKR4VsKkngJ+fH6NGjbKCKRKJfaPVKSzaYew/Pi2k+A0cLuZal0LH/jBNONY5gdCRHh2JoyM/ZyWSIrLxZDRX7qUC0KuxH63r+BT7GudzrcvQlf2hMfXo5PqEjE2LpdfManR7oTIzpzctZ8usQ3E8On8hupZ3A1ZazSJJibG3XlcVKRlZInF2FEXhWxNvzpMhJev4I0NX9o+L2ig/c8+60ik6dnvHAeB3/Ua52mUtLD0a83sk3QH26ccVv4KQo2Nvfjk7n17uiERERNCyZUs6d+5sa1MkFYBDV+P493o8AC1qV6FXY7+CT8iH3P17nfYf0I4xzdHJ/dGsURlFkGJvX55LiKW/wfw8OrIvlqRYqCpojo6jEB4eTnh4OImJifj4FD8EIXEuTHNzpvZugKqELuJqJuNRQOtSWSWxBhPGvssL8xaCCq77m5cO8PXwJXt2Fmq1psR/A/aG6buYDnxVwLET9YtjvHOJ1an3INQfZ2srSid0FL1Cc5R/eInEEpfvprD5tHDU+1dxZ1jbgBJfyzR01QuwTecXSUG4urgZnuS5vRYqlQqNxrG+HxbHqyg/6e0dO/sN2Yk2KJHn/Pvvv6d169Z4eHjg4eFB69at+e6778raNonELvhh12WD13Vyjwa4uZQ84GT64JRhK/tErTL+ZnLn6DgiMhwlcXSK/dXkzTffZP78+TzzzDN07y5KoO/du5cXXniBa9eu8c4775S5kRKJrYhPzWT5YZF06uWm4eEu9Up1vXSTcVapriSxFqZ5OM4gdEwFt+O/W4kzUmyh8/XXX7No0SIeeughw7YRI0bQtm1bnnnmGSl0JA7F0oPXScsSAacHOwXi4+VayBkFY9oFbjvwDOBZqitKypoTRzYQclkkGwek5JWjX0dMRpuaQk2P6jz4zDc2sLBsMfXo7AJeAPoBQy0cewnYgPD89AA6WN06SYUm7hhcWgwoUHcE+IfZxIxiC52srCw6deqUZ3vHjh3Jzs4uE6MkEnsgS6vjf/p2DyoVTOoRVOprmoYG/gIOAb1LfVVJWXLp+jG26ztWTjieuyAAhN/5H4oKOl924UEqvtAx9egc1y+eWBY6R4Fw/fhjpNCxa+yhBUTSeTj7mRh7BthM6BQ7TWDixIl8/fXXebYvXLiQCRMmlIlREok9sPFkNLcSRLDpvuY1aeBXvHYPlsg9KV3m6dgfGnXBU8g1+ueH1j7yLEtNcQoGynwee8fO/ijtJBm5RNMHvv/+ezZt2kS3bt0A2L9/P9euXeM///kPM2bMMBw3f/78srFSIrEBP+wydql+tGeDMrlm7unk9vExIDHFRWPaAiIv/63/HGRkUL1eNQt77QtFUcjIyCj0uNyz/ypjnk+WQzCwFCHQm+dzjMSG6DzBTf/b1LpCuo1/Q9kuRnt0nqW2x93dvUSzvIstdE6cOEGHDsJhefGi6Nzj5+eHn58fJ06cMBwnp5xLKjL/Xo/nyLV4AJrX8qZ7o+plct3czmT5X2J/mHl0LOx/5NHPy82W0hIVFUViYmKBxyiQJwBXBbhs4ViAtibj/I6R2IjsYAjS/zZT/eCyjX9D2XWM9uh8S21PlSpVqFOnTrHPK7bQ2bZtW7FvYm2CgoKoUqUKarWaqlWr2qWNkopFTm4OwJSeQWUm3KXQsX9Mp5dX5OrH2dnZJCYmUr16dapUqZLvcelAZq5t1YHa1jROYh3S70KaXqhXqgtuvjY1h8wESNF/6nnWBI+aJb5UYmIi9+7dw9/fHxeX4kkXh6l8tWfPHipXrmxrMyQOwN3kDNYeuwWAr5crI9sV/xtEfthBeqCkENQmmVO6Ci50ALy9vfHw8CjkaHNcgOKdIbELFFdjVVJ3N3C38W9RnWaso+HuCsX8OzRFURTu3btHdna28wodiaSsWHrgGplakZ0xrnMgHq5l12P8cK71CvwcdVjU6oJTxJNjo9FmZ6JCRZWageVkVckpiTcyP0GuIPKWFESejkymlxRM2X3ClcarbvO/0x07djB8+HACAgJQqVT88ccfeY6JiIggKCgIDw8PunbtyoEDB8z2q1QqQkJC6Ny5M0uWLCknyyWOSLZWx8/7rgGgVsEjXcu2ScPdXOtS6NgfKpWpRyfvI7/evDr4fl2fjh8ElaNV9kEi8A9imvkt25oiKRTpP87B5kInJSWF4OBgIiIiLO5ftmwZM2bMYM6cORw5coTg4GAGDhzI7du3Dcfs2rWLw4cPs2bNGj744AOOHTtWXuZLHIxNp2KITtRPKW/hT2A1rzK9vvzosX9Mc3Qs7tfLU8UJf5v5CfMrV67wwAMP5Nn+wAMPcOXKlWLdw1Kdtps3bzJmzBhCQkLo2bMnP/74Y7GuWRwWL15MkyZNWLNmTR57kpOTCQ0NBeCtt96iTZs2hIaGEhoayvbt2wH4/fff6dmzJyEhIUybNo10/UyjpUuX0q1bN/r06cPo0aMB+OWXXwgKCmLt2rVlY7z85mQRm4euBg8ezODBg/PdP3/+fKZOncqUKVMA+Oabb1i3bh0//PADr776KoAhC7t27doMGTKEI0eO0LZtW4vXy8jIMJtuWdiMBIlzYZqEPKl7UJlfPxQxPTcH+blkf/hVq0uHKFApoLKQjRymq0diXCJ1dKWvq2QPmL5DV0SxwPwyKVwQM7IA3K1pVC4eeeQR3n77bfr06YNOp2Pnzp2FnqPT6QoNQ+bHc889x4gRIwo9bu7cuQwbNsywfubMGT799FO2bt2Kl5cXc+fO5b333uO9997jww8/5MCBA7i5uREXFwfAww8/zLlz50pkY4VA5QKu+r8YtZvNzLC50CmIzMxMDh8+zKxZswzb1Go1/fr1Y+/evYDwCOl0Ory9vUlOTmbr1q08+OCD+V5z7ty5vP3221a3XVLxOB+TxP7LsQA0rFGJno3LZkq5KabZPh9gPlVXYh906DqKs3+qSHFVSPfMm5/1+4eXbGBVKenUCaKjLe5yw/h3qKbgh4IX0LRWLTh0KN9j/v77b15++WWCgoK4dUsEuNLT03n88ceJioqicuXK/Pzzz1SuXJkBAwaQlZWFm5sbK1assDg77Pr16+h0Ovr06SNsVKsJCQnhypUrzJw5k+XLl5OcnMywYcOIjIwkNDSULl268M8//9CsWTMmTpxI165d2bJlC5s3b2bu3Lk8++yznDhxAo1Gw+LFi6lbt27hP8Mi8Pvvv/Pkk0/i5SU8wS+88ALt2rXjvffeIy0tjT179tC7d2+qVq1aJveze1wrg2tTW1th30Ln7t27aLVa/P39zbb7+/tz5swZAGJiYgxuQK1Wy9SpU+ncuXO+15w1a5ZZUcPExEQCA+0/oVBifZbsv2YYP9K1vlVqQZkGO2pSvt+KJUXn9MT9qAA3VweZexQdDTdvWtylQoidsmL27Nn8/fffVKpUiaZNxUPuu+++o2/fvjz66KMsW7aMhQsXMnPmTNasWYOXlxefffYZy5YtY+rUqXmuFxUVRUBAQLFsGDhwIB9//DG7du1i6dKldO3alWXLlvH000+zbt06QxmS/fv38+GHH7JgwYJ8r5WQkGAIV2m1WjQao/idNWsW8+bNA+Dbb78lKiqKLl26GPZ7eHiQmSkm7y9ZsoS5c+cyZcoUJk+ezJw5c4r1noqEmx+46kWUuuwmUVR07FroFIWGDRvy77//Fvl4d3d33N3diYiIICIiAq02by8bifORmpnNCn2Xcg9XNfd3LJtveLkxFToybGW/BDbL/8tShaRWrXK7llarpVo1UTU6J4Xg1KlTHDx4kB9//JGsrCx69+5NcnIyTz75JDdu3CA2NtZijg9AQEAANy2INNMvIkquvk45X3Z79uzJSy+9RGZmJqdOnaJdu3Zs2rSJVatWsWPHDhRFKfSLro+PD5GRkQAGz1EOuUNXtWvXJioqyrCenp6Om5uQkZ06dWLFihVkZmYyaNAgzpw5Q/PmzQu8d7FRazD3G0vAzoWOn58fGo2GmJgYs+0xMTHUKuU/bnh4OOHh4SQmJuLj41Oqa0kqPmuORpGUIeqOjAgOwMezdF3K8+OGyfieVe4gkViggFBTWaPRaIiLi8PLy4vjx48D0Lx5c7p3787EiRMB0Rx6zZo1NGjQgCVLlvDpp5+SlJRk8XqBgYFoNBp27txJ7969URSFnTt3EhwcbBBAub/s5uTmqFQqevbsydtvv02/fv0Mtjz44IO88cYbBlvKirFjxzJlyhTGjRtn8FTdf//9AJw/f54mTZrg5uaGj49PHnEmsR42n3VVEG5ubnTs2JEtW7YYtul0OrZs2UL37t1taJnEkVAUhZ/2XTWsP9KtbKeUm3LCZPwncMdqd5JIbMM777zDfffdx0MPPUS9evUAeOKJJ9i8eTN9+/alb9++hl6J69evZ+jQoZw8ebLAa/7888/Mnz+fkJAQevXqxdWrV/Hx8aF9+/b07t2bDRs25Hvu+PHj+eijjxg/fjwAw4cP5969e4SFhdG3b99SzeCaNWuWYdbVmjVraNGiBc8//zz9+vWjT58+XLp0idmzZwMwc+ZMevToQa9evWjevDktWrQo8X0lxUSxMUlJSco///yj/PPPPwqgzJ8/X/nnn3+Uq1evKoqiKEuXLlXc3d2VxYsXK6dOnVKeeOIJxdfXV4mOji7VfRcsWKC0aNFCadq0qQIoCQkJZfF2JBWQf67FKfVfWavUf2WtMvyrnVa91yhFUTBZjlj1bhJnJi0tTTl16pSSlpZma1MqFL///rvSoUMHZfXq1Va/15IlS5Tg4GBly5YtVr9XRcfS33NCQkKRnt8qRbGt/ywyMpKwsLA82ydNmsTixYsBWLBgAZ988gnR0dG0a9eOL7/8kq5du5bJ/XNCVwkJCQX2g5E4Lq8sP8ayQ9cB+Oj+NozrXM9q95oFfGiyfgRob7W7SZyZ9PR0Ll++TIMGDYrdAkIisTcs/T0X9flt8xyd0NDQQmOV06dPZ/r06eVkkcSZSErP4s9jInmwsrsLw4OLN7ujuOROcZYJyRKJRGJd7DpHx5pERETQsmXLAqeiSxyfNf9GkZopZt6NbBeAl5t1tb9MP5RIJJLyxWmFTnh4uGHKo8R5+fWAsXbOQ12sF7LKIbfQkR4diUQisS5OK3QkkuM3EjhxU7QAaVvXh9Z1rF9mIHfVJil0JI5AUlISw4cPJzQ0lO7du7N+/fp8j124cGGp7/fHH38Y+h1u2LCBVatWlfhaCQkJTJo0iT59+tCzZ08+/vjjUtsnsS+cVujI0JXk14NGb854KyYgm7KxXO4ikZQvP/74I4MGDSIyMpI9e/YUWP6jOEJHp9NZ3G4qdAYNGmSojl8SnnnmGQYMGMCOHTvYvXu3xaaiuVH0EyclFQOnFToydOXcpGVqWXNUJCF7uWkY0c66Scg5yNCVxBHx9PRk3759xMTEoFKp8PX1BUTTypzaN9euXWPVqlWcPXuW0NBQfvnlFyZPnsyJE6K61MyZM4mMjCQyMpLhw4czevRoFi9ezCeffEJoaCgdOnRg8+bNXL58mQ0bNjBlyhRefvllFi9ebGjhMH/+fLp3706vXr04cuQIAB06dGD69Ol07dqVjz76yMxurVbL3r17mTBhgmFb3759AfOu5TnjyZMnEx4ezoABA5g3bx7Lli0D4OLFizz00EMAfPDBB4SEhNCnTx9DwUSJbbH5rCuJxBb8dfwWyfpKyMPbBlDZvXz+FXzL5S4SiTnDv9rFnaSMMrlWDW93/nyml9m2iRMnEhUVxcCBA/H09GTx4sU0a9aM7777Di8vL1atWsW3337L+++/T7NmzQwtFTZt2mTxHgkJCWzfvh2VSkVqaiovvfQSt2/fZuzYsWzfvp1BgwYxc+ZMWrdubShDEh0dzR9//MHu3bu5du0aU6dOZfPmzcTHx/PSSy9Rt25dgoODeeWVVwz3uXv3Ln5+fsV6/x06dCAiIoLr16/z/PPPM27cOJYtW8a4ceM4ceIEZ8+eZfv27URFRfHUU0+xevXqYl1fUvZIoSNxSn7T180BeLCzdfpaWaILsEw/ro1s6ikpH+4kZRCdmG6167u6ujJ79mxmz57N5s2bmTNnDkuWLOHll1/m2LFjpKWl0bp16zzn5devqlOnToZ9P/30E0uWLEGtVhu6oVviypUrBAcHo1arCQoKIj4+HoCqVatSv76odp67npCfnx93794t9P2Z2paT7hAYGEhCQgKJiYls3LiRF198kdWrV7Nnzx5DE1DTBqAS2yGFjsTpuHI3hf2XYwFoWKMSHepVLbd7m4auvgCaltudJc5MDe+yk9SWrnX16lVq166Nm5sbNWvWRFEUjh49Snx8PDt27GDFihX8+eefgLm4qVq1Kjdu3KB169YcO3aM4cOHA8ZeVQBfffUV//77L3fv3qVXL+FJcnV1zdOQOSgoiKNHj6LT6bh27ZohfGZ6v9xoNBq6d+/OkiVLDOGryMhIQkNDSU9PR6vVcvPmTeLi4gznmNo2atQoPvroIxo2bIi7uzvNmzcnJCSE7777DijbPlqSkuO0Qkd2L3defj9s9OaM6xRY4AdhWSO7l0tsQe5QU1lz/Phxxo0bh4eHB4qiEBERQYMGDbh69Sr9+/c369IdFhbGyJEjmTJlCpMnT2bixIksWrQILy8vi9fu1asXvXr1olu3blSuXBmAwYMHG3pK1alTB4BatWoxcuRIevTogVqt5quvviqS7V999RXPPPMM3377LVqtllGjRhEaGsqECRPo3r07ffr0MYim3IwdO5Z69eoZwlNt27alSZMmhISEoFar6d+/P6+99lpRf4wSK2HzFhC2RraAcC60OoUeH24hJjEDjVrF3ll9qeldfuXxPwFe1o+XA/eX250lzoZsASFxJErTAsJpZ11JnJMd5+8QkyiSMsOa1SxXkQNwzGR8Nd+jJBKJRFJWSKEjcSqWH75hGD/YqfySkHMwTaX8Grie34ESiUQiKROk0JE4DQmpWWw+FQNA9UpuhDWvaVN7LgApNrVAIpFIHB+nFTqyMrLzsfZ4FJnZotLqiHYBuGrK/8+/R7nfUSKRSJwbpxU6sjKy87HCJGx1f4fyD1sBeOZalzOvJBKJxLo4rdCROBeX7iRz5Fo8AM38vWkVYB8z7KTQkTgCV65coUaNGoSGhtK5c2eWLl1arPMjIyOZOXNmkY69efMmY8aMISQkhJ49e/Ljjz+WxOQisXjxYpo0acKaNWsA87YQycnJhsKAb731Fm3atCE0NJTQ0FC2b98OwO+//07Pnj0JCQlh2rRppKeLoo1Lly6lW7du9OnTx9Cn65dffiEoKIi1a9da7f04K05bR0fiXKw8ctMwvr9jnXKtnSOROAMhISEsX76c9PR0evbsyfjx461yn0ceeYS3336bPn36oNPp2LlzZ6Hn6HQ6s0J/xeG5555jxIgRhR43d+5chg0bZlg/c+YMn376KVu3bsXLy4u5c+fy3nvv8d577/Hhhx9y4MAB3NzcDMUIH374Yc6dO1ciGyUFIz06EodHp1NY9Y8QOmoVjGpXx2a25C42L+WWxNFITU01FP9LTExkxIgRhISEMH78eDIzM3nuuefYs2cPIHpdvf7662bnb9iwgd69e9OjRw9+/fVXs33Xr19Hp9PRp08fQFQpDgkJ4cqVKzzwwAOAuaclNDSUl19+mYEDBzJ9+nT2798PwJYtW3j11VdRFIVnnnmGsLAw+vXrx40bNygrfv/9d5588knDz+KFF15g+fLlAKSlpbFnzx60Wi1Vq5ZfZXZnRXp0JA7P/sux3IxPA6BP0xrUrGK74mlHbXZniTPTaWEnopOjy+RatSrX4tATh/Js3759O6GhoZw/f57Zs2cDsHDhQoYMGcK0adN49913Wbp0KZMmTeK7776jR48eLFmyhFmzZhEdLWxTFIV3332Xbdu2odFo6NOnDw8++KChZ1RUVBQBAQHFsnfgwIF8/PHH7Nq1i6VLl9K1a1eWLVvG008/zbp166hatSrbtm1j//79fPjhh4ZO6JZISEgwiCitVmvWy2rWrFnMmzcPgG+//ZaoqCi6dOli2O/h4UFmZiYAS5YsYe7cuYbq0HPmzCnWe5IUD6cVOrIFhPOw+qgxbDW6ve28OZaQHh1JeRCdHM3NpJuFH1gKckJXWVlZhIWFMXz4cC5cuMDUqVMB0Qxz9+7d/Oc//+HUqVMkJCRw/fp1mjdvbhA6d+7c4dy5cwwYMACA+Ph47ty5Q61atQAICAjg5s287yO/5qA59wXo2bMnL730EpmZmZw6dYp27dqxadMmVq1axY4dO1AUhcDAwALfo4+Pj6HzenJyslmoKnfoqnbt2kRFRRnW09PTcXNzA0Suz4oVK8jMzGTQoEGcOXPGrE2GpGxxWqETHh5OeHi4oYS0xDFJz9Ky7rgo01fJTcOAlrVsak9DYKvJuhQ6kvKgVuWy+7sv7Fqurq64u7sTGxtL48aNOXDgAB07duTgwYM0adIEgGHDhjFt2jRGjhxpdq6fnx/Nmzdn06ZNuLm5kZWVhaurq2F/YGAgGo2GnTt30rt3bxRFYefOnQQHBxsE0L///mt2zZzcHJVKRc+ePXn77bfp168fAM2bN+fBBx/kjTfeAMq2CefYsWOZMmUK48aNw8vLi88++4z77xdNX86fP0+TJk1wc3PDx8cnjziTlC1OK3QkzkHk2dskpWcDMLB1LTzdNIWcYV0amoyHAvYx90vi6FgKNZU1OaGr9PR0unTpQtu2balfvz4TJkxg6dKl+Pv788orrwAwYcIEZs+ezRdffGF2DbVazezZs+nfvz9qtZoaNWrw22+/mR3z888/M336dGbPnk12djbTpk2jT58+tG/fnt69exMSEpKvjePHj6dbt26cOHECgOHDh7N161bCwsJQqVRMmDCBxx57rETv3zR0NWPGDEaMGGFoPOri4kKzZs348ssvAZg5cyZ37txBrVbTu3dvWrRoUaJ7SoqGbOopm3o6NE/+dIiNJ0U15J8e60LvJjVsas9cIKeX8Wqg8LkcEknJsOemntHR0UybNo0//vjD1qYUyPLly5k7dy5z5swp0syr0vDLL7/w8ccfM3/+fPr27WvVe1VEStPUU3p0JA5LQmoW287cAaCGtzs9GvnZ2CKJRLJ7925eeuklg/fDnnnggQcMs7mszcMPP8zDDz9cLvdyNqTQkTgsf524RaZW3/IhOACN2vYZMU7tPpVIEEnBOdPLJZLyQNbRkTgsf/xjf7OttpuMT9jMColEInEepNCROCS3EtI4cCUWgEY1KtlNy4dMk/EbwBUb2SGRSCTOghQ6Eodk3bFb5KTZjwi2n5YPXiZjnc2skEgkEudBCh2JQ/Lnv8ZCXcODa9vQEnN62toAiUQicTKcVuhERETQsmVLQ9VMieNw9V4K/95IAKB1nSo0rFHZxhZJJI6Pab+polCcjuWmXcOtyZNPPlmq8xcuXEjPnj3p06cPY8aM4d69e2VkWV6aNGnCmDFjANFl3bR1xcyZMw0VnL29vQ1d1adMmQJAXFwc48ePJyQkhN69exuOTUpKYvjw4YSGhtK9e3fWr18PwIABA8rtd2ANnHbWlayM7LiYeXPaFq8vjkQicXzy62b+7bfflvia27ZtY/Xq1Wzbtg03NzfOnDlDRkZGiW0pDB8fH1auXFnocc2aNTMImRymT5/OmDFjePDBB7l9+zZ9+/Zlx44d/PrrrwwaNIjw8HAURSEhQXxh3LRpU4UWOk7r0ZE4Ln/+e8swHtrWfsJWEonN2NAJVtUtm2VDwQ+8bdu20a1bN7p168aPP/4IwOTJkw3ViE29DQbzCuhYbspbb73FxIkTGTJkCCEhIaSlpTFv3jyWLVsGwMWLF3nooYcA+OCDDwgJCaFPnz4cP34cgA4dOvDcc88xceJEVq9eTZcuXQgLC+Prr78GjJ6j48eP06tXL3r27MncuXPzvbcpP//8MzNnzjT0s2revDkBAQEW33tkZCTDhw9n9OjRfPLJJ2Y9svr160dCQgKHDh0iLCyM3r17l2nNIa1Wy/79+3nwwQcBqFmzJqNHj2bdunV4enqyb98+YmJiUKlU+Pr6ltl9bYnTenQkjsnZ6CTOxiQB0LF+VepW9SrkjPLljK0NkDgnadGQZt2mnjnMmjWLtWvX4uPjQ/fu3Rk7dmyBxxfWsTw3TZo04aeffuKVV15h8+bNjBs3jueff55x48axbNkyxo0bx4kTJzh79izbt28nKiqKp556itWrVxMXF8czzzxD48aNmThxIosXL6Zly5bodOZTA1577TUWLVpE8+bNGThwoEE85b63abXk4nZWT0hIYPv27ahUKiIjI7l37x5paWlUqVIFHx8fXn31VVauXEnVqlUZPnw4EydOxN/fP9/rffHFFyxfvhwQgi9HPJ09e9bQcb179+48//zz1KhhXiG+bt26REVFMWPGDKKiohg4cCCenp4sXryYZs2aFfk92StS6EgcirXHjGGrEcH2F7a6bmsDJM6JZxk2sy3kWlqtFj8/UYW8cePGREVFFdhdvLCO5blp3749IBp8xsXFERgYSEJCAomJiWzcuJEXX3yR1atXs2fPHsMDPkc0Va1alcaNGwPwxhtvMG/ePNLS0ggPD6dbt26Ge0RHRxv6T3Xo0IGLFy9avLcpOZ3VcwuD/N57p06dDPvuv/9+VqxYQUpKisHTcuzYMUaPHg2InJrr168XKHSee+45pk+fDmCW+5Q7dKXVarlz547ZuTdu3KBp06a4uroye/ZsZs+ezebNm5kzZw5Lly7N954VBSl0JA6DoiisPSbCVmoVDG5j207llpCVkSU2YZD1m3rmoFaruXv3Lj4+Ppw/f56AgACqVq3KjRs3aN26NceOHWP48OGG4wvrWJ4bS8Jh1KhRfPTRRzRs2BB3d3eaN29OSEgI3333HWDsSm6aCxMYGMjChQuJiorikUceYevWrYZ9/v7+nD59mubNm3PkyBGmTZvGzp07CxRsEyZM4NNPP6VXr164ublx7tw5vL29833vprbcf//9PPTQQ2RnZ7N69WoAgoODWb58OT4+Pmi12hLl8VhCo9HQpUsXfvvtN0OOzsqVK9mxYwdXr16ldu3auLm5UbNmTYfpqi6FjsRhOH0rict3UwDo2qA6Nb3tq5EhQBfMqyNLJI6CoihoNBo++OADhg4dikqlYvr06Xh6ejJ58mQmTpzIokWL8PIyDycXpWN5YYwdO5Z69eoZRELbtm1p0qQJISEhqNVq+vfvz2uvvWZ2zttvv83evXvJzMzkmWeeMdv3/vvv8/jjj6MoCkOHDiUoKKhQG/r27cuFCxcICwtDo9Hg5+fHokWLCnzvOVStWhV3d3eqVatGpUqVAPjwww8ZM2YMOp0Od3d3Vq1ahaenZ7F+LmAeuqpWrRorV67kq6++4qmnnmLBggXodDq++uorqlevzt69exk3bhweHh4oikJERESx72ePyO7lsnu5wzBv41kWbLsAwLujWjOxW30bW5SX94HZ+vEbwKuYFxGUSMqK8u5evn37dlatWsXnn39u9XtJICQkhOrVqxdp5lVpGTBgAB4eHqxZs8bq98oP2b1c4vQoisJfx0XYSqWCga3yj2XbC12QIkfiGGzYsIE333yTxYsX29oUp2H79vLzDW/atKnc7mUNpNCROARnopO4pA9bdQmqZpdhK4nEURk0aBCDBg2ytRkSiUUcpo5Oamoq9evXL3KlTYljkePNAfuunZNgMk61mRUSiUTiPDiM0Hn//ffNpgdKnAdFUVhnErYa1Nr+ZlvlcMBkvAFIy+9AiUQikZQJDiF0zp8/z5kzZxg8eLCtTZHYgLMxSVy6I8JWnStQ2Oq/wG1bGyGRSCQOjs2Fzo4dOxg+fDgBAQGoVCr++OOPPMdEREQQFBSEh4cHXbt25cCBA2b7Z86caSjTLXE+/jpmErZqY79hK4BAWxsgkVgJazT1vHz5sqEhZU5zygEDBuRpYmkP5BQoDAkJoW/fvhw6ZL3aRZMnT6Zz585cu3Ytz8997dq1vPXWWwCEhobSu3dvw88wISEBnU7H7Nmz6d27N7169eLLL780nDtz5kx69uxJr169ePfddwF4+eWX8fX1JTk52Wrvx9rYPBk5JSWF4OBgHn30UUMnVlOWLVvGjBkz+Oabb+jatSuff/45AwcO5OzZs9SsWZPVq1fTtGlTmjZtyp49e2zwDiS2Zv2JaMD+w1YAzW1tgERSgWjQoIGhqm+nTp0MY3ub3RUbG8tTTz3Fhg0bqF27NgkJCYZqygVR0oaeAP/973+pV68eV65cKfC49evXU7lyZcP6okWLiI2NZefOnWRnZzNy5EhatmxJ7dq1uXr1Krt37wYwVH7++OOP8zgXKho29+gMHjyY9957z1DqOjfz589n6tSpTJkyhZYtW/LNN9/g5eXFDz/8AMC+fftYunQpQUFBzJw5k0WLFvHOO+/ke7+MjAwSExPNFknF5eKdZM7fFt80OtSrin+VihG2kkgcFWs29cx9n+HDh9O5c2du3RJe3fnz59O9e3d69erFkSNHANHCITw8nPbt2xMREcHEiRMNVYcBpkyZYvB6XLlyhcTEREaMGEFISAjjx48nMzOTyMhIBg0axOjRowkODja8lxzWrVvHqFGjqF1beJR9fHzo0KGDmefJ1NPSsmVLpkyZwowZM+jVqxeZmZmAaEuxYcMG7t69y6hRo+jbty8TJkxAq9UW51dQIEuXLuWll14CwMXFhRkzZvDrr7/i4eHB+fPnOX36NCCKGDoKNhc6BZGZmcnhw4fp16+fYZtaraZfv37s3bsXgLlz53L9+nWuXLnCvHnzmDp1Km+++Wa+15w7dy4+Pj6GJTBQBhMqMhtPRhvGg1rZtzcHZAsIiW3oBNQto6Xg3uXGpp47d+7kyy+/zNPlOzc5TT23bNnCzp07WbBgQZEe7D4+Pvz55588+uij/P7770RHR/PHH3+we/dufv75Z1555RVA9M569dVX2blzJ6+++iqffvop27dvZ8GCBWRlZXH27Fl27NhBZGQk9erVY+HChQwZMoTt27fTqlUrQ6+nrKwsVq1axYcffmj4op1DcRt63rhxg/nz5/P5558zaNAg1q9fD8DWrVvp168fH374Ic8++yxbt26lbdu2rFq1qsDrbd++3RCemjVrltm+wYMHExoaashhzW1rTkPPRo0a8eqrr/L000/TtGlTQ5VpR8DmoauCuHv3LlqtNk8jM39/f86cKVkf6FmzZjFjxgzDemJiohQ7FZiNJ4xCZ2AFEDoSiS2IBsqnd3nZNfX8z3/+Q2ZmJl999RVt2rTJcx/TBpuHDx/mypUrBAcHo1arCQoKIj4+HhCeiZzP+KZNm1KzZk1AVNp1dXUlPDyciRMnUr16dd5//30uXLjA1KlTAejcuTO7d++mXr16tGvXznA/Sw09z58/n8fG/N5348aNDR6T8ePH8+abb1KvXj3atGmDi4sLp06dYv/+/bzzzjukpaUxceLEgn7khISEGDxUa9euNcsPyh26ql27NlFRUTRo0AAQoitH+IwfP57x48cTHR3Nfffdx8iRIwu8b0XBroVOcZk8eXKhx7i7u+Pu7k5ERAQRERFl6hKUlC9R8Wn8e0NUpmlRuwr1qtt/neHdtjZA4pSU5VeAwq5VVk09C6vGm1tEBAUFcfToUXQ6HdeuXcPX1zfPcaZjEKLswQcfZMKECXzwwQesXLmSxo0bc+DAATp27MjBgwdp0qSJxfuZMnToUMLCwnjqqaeoXbs2iYmJXLhwgapVqxpCQf/++6/ZzyiHxo0bc+vWLRYvXsz48eMBaN68OaNHj6Z3796AsSlpWTB+/HjmzZtHREQE2dnZzJ8/n+eff57Y2FgURaF69er4+voW2Fi1omHXQsfPzw+NRkNMTIzZ9piYGGrVKt2/bnh4OOHh4YZeGZKKx6YKFrYCSLG1ARKnpDx6l9uyqSdArVq1GDlyJD169ECtVvPVV18Vek5SUhIjR45EpVKhUqlYsmQJlSpVYsKECSxduhR/f39eeeWVQie6VKtWja+//pqHHnrI8HP45JNP6NevH/PmzWPIkCHUqVOHOnXqWDx/6NChfPnll3z22WcAvP7660ydOpU5c+YAIiG4U6fCgoaWGTx4MBqNBoAff/yRxx57jNmzZ9OrVy8URWHs2LH079+fy5cvM2nSJBRFITs7m9dff71E97NH7Kqpp0qlYtWqVYwaNcqwrWvXrnTp0sXwR6vT6ahXrx7Tp0/n1VdfLfU9ZVPPisv4hXvZdykWgI3P96FZLW8bW1Q4fYFtJutXAPtrPSpxBGRTT8dl5syZ7N27l19//ZV69epZ9V4vv/wya9as4ciRI/l2Xy8PKnRTz+TkZC5cuGBYv3z5MkePHqVatWrUq1ePGTNmMGnSJDp16kSXLl34/PPPSUlJYcqUKaW6rwxdVWzuJWdw4LIQOQ38KtHUv3IhZ9gHuYWOROIIyKae5cu8efPK7V4ff/wxH3/8cbndzxrYXOgcOnSIsLAww3pOovCkSZNYvHgx48aN486dO7z55ptER0fTrl07NmzYkCdBubjI0FXFZsvp2+j0vsgBrfzzxN4rAn8A1v0uJpGUD7Kpp8SesbnQCQ0NzZPYlZvp06czffr0crJIUhHYUAHzc3LjAlQ8eSaRSCQVC7uuo2NNIiIiaNmyJZ07d7a1KZJikpqZza4LdwGo6e1OcF1f2xokkUgkErvFaYVOeHg4p06d4uDBg7Y2RVJMdp6/S2a2DoB+Lf1RqyuOX+SSyVg29JRIJBLr47RCR1Jx2XzKWG6gf8vS5WqVN6YlxRYDcfkcJ5FIJJKywWmFjgxdVUy0OoWtZ4QvxMtNQ/eG1W1sUcnZAchOaxJHIL/O5Q888EChTSdzY6lezM2bNxkzZgwhISH07NnT0EPLGixevJgmTZqwZs0awHJHb4CzZ8+iVqu5evWqYZtpt/CXX36ZkSNHEhoaiq+vr2H7qVOnUBSFNm3aGHpcAbzzzjv89NNPvPXWW7Rp04bQ0FD69+9vqPBsikql4r///a9hvVWrVoZO8E8++WS+7+3UqVO0a9eu0K7xjobTCh0ZuqqY/HMtjtgU8eHQp0kNPFw1NraoeLS3tQESSQXkkUce4fnnn2f79u3s3LmT+vULrz6l0+lKfL/nnnuOESNGcPLkSUNH7127dplNivn999+ZNm2aofVCDuvXrycyMpKPP/6Y1atXExkZSbt27QzbW7ZsiUql4r777uPvv/82nPfnn38aWi7MnTuXyMhIQkJCWLJkSR77WrRowYYNGwAhXipVqmTY9+233+b7vlq2bOmUdY6cVuhIKiYVOWwFUMPWBkicl/nzoW7dwpcRI/KeO2KE2Dd/fqG3+fvvv+nQoQNjxozh5k3RYSs9PZ1HHnmEvn37MmLECBITE9HpdPTr14+QkBD69+9PYqJl/+b169fR6XT06dMHEJWUQ0JCzLxIycnJhIaGAhi8KQMHDmT69Ons378fgC1btvDqq6+iKArPPPMMYWFh9OvXjxs3buT7Xgrq6B0ZGcknn3zC5s2bC/2ZWGLs2LEGkXTu3Dlq166dp+hdfHy8xVnJXl5euLq6kpiYyPLly828aTkeMWt2QK9oSKEjqVBsPi2EjloFYc1r2tgaiaQCkZgIN28Wvty5k/fcO3fEvnzEiCmzZ8/m77//5tdffyUqKgqA7777jr59+7J161YmTJjAwoULUavVrFmzhu3btzNkyBCWLVtm8XrF7QwOMHDgQDZv3sz48eMN3ceXLVvG+PHjWbduHVWrVmXbtm28//77fPjhh/leJ7+O3ufOnaNJkyZUqlSJunXrcu3aNcM5Od3Cf/nllwJt7NGjB4cOHSI7O5vff/+dsWPHGvbNmjWL1q1bs3Hjxnwbeg4bNoy1a9dy4MABiykYxe2A7sjYvI6OrZCVkSseF+8kc+mO6BbVqX41qlVys7FFEkkFokoVyKfXkhk1LPgda9QQ5xahTY5Wq6VatWoAtG3bFsCQJvDjjz+SlZVF7969SU5O5sknn+TGjRvExsZazPEB0Rk8xzNkSkFNNnMe/D179uSll14iMzPTkJ+yadMmVq1axY4dO1AUxdDZPD8sdfT+/fffOXz4MIMGDSI2Npbly5cbit3m7hZuyqlTp3j66afRaDRs2bKFsLAwtm7dyp9//snGjRsNx82dO5fBgwczZswYbty4wbp161i4cCEdO3bk008/BUR/rIEDB9K1a1eLBVOL2wHdkXFaoSMrI1c8/q7gYSuANFsbIHFeZswQS0nQJ+YWBY1GQ1xcHF5eXhw/fhwQ3bi7d+9ueNhmZWWxZs0aGjRowJIlS/j0009JSkqyeL3AwEA0Gg07d+6kd+/eKIrCzp07CQ4ONggg087gYOwOrlKp6NmzJ2+//Tb9+vUz2PLggw/yxhtvGGzJj/w6em/atIm9e/ei0WjIzs5m6NChBqFTEC1btiQyMtKwPnbsWObMmUONGjXyPIc0Gg2zZ8/m7bff5rfffuPhhx822+/t7c3gwYMZNmwYCQkJee5lzQ7oFQ0ZupJUGP4+bRQ6/Sqo0NlpawMkEivzzjvvcN999/HQQw8ZGk4+8cQTbN68mb59+9K3b182bdpEt27dWL9+PUOHDuXkyZMFXvPnn39m/vz5hISE0KtXL65evYqPjw/t27end+/ehsRcS4wfP56PPvqI8ePHAzB8+HDu3btHWFgYffv2LXAGV0JCgkEshIWF8frrr3P+/Hl8fX0NHcFdXFxwc3MzC18VlZ49e3LmzBmzsJUpnTt35ubNm/nmEb3xxhu0b295isPrr7/OZ599ZviZ5xaDzoRddS+3BbJ7ecUgLiWTju9tRqdAoxqV2PJiqK1NKhG9gV0m61eQ3csl1qG8u5c7CsuXL2fu3LnMmTOHEZYSsyswp06d4tFHH2XEiBG89tprtjanWFTo7uUSSVHYcf6OoYln3wqchFwxu3JJJM7DAw88kG++UEWnZcuW7Nu3z9ZmlDsydCWpEOQUCYSKPduqjcm4LeBuK0MkEonESXBaoSMrI1cctDqF7efElFdvdxc6B1WzsUVlw4dID49EIpFYG6cVOrIycsXhn2txxKeKGQN9mtbAVeO0f7YSiUQiKSbyiSGxexwlbCWROCpXrlyhRo0ahIaG0rlzZ0ORvqISGRlZ5P5Lsu+VObLvVeFIoSOxe3KEjkoFoc0qdhOFAybjyzazQiIpe0JCQoiMjGTnzp188sknVruP7Htljux7VThS6EjsmpvxaZyJFoXE2tb1xa9yxU7fjTMZvwdE2coQicRKpKam4uXlBYjpvyNGjCAkJITx48eTmZnJc889x549ewBReO/11183O3/Dhg307t2bHj168Ouvv5rtk32vZN+rkiCFjsSu2WYSturbzLHCVrcA561VKilv5u+dT935dQtdRvyat3bMiF9HUHd+Xebvzb+p5/bt2wkNDaVNmzaGKr4LFy5kyJAhbN++nVatWrF06VImTZrEzz//DMCSJUvMWhMoisK7777Lli1b2LlzJwsWLDB7KMu+V7LvVUlwWqEjZ11VDEyFzn0tKr7Q6W9rAyROS2JGIjeTbha63EnN29TzTuodbibdJDEj/6aeOaGrK1eusGTJEm7cuMGFCxcMn7GdO3fm/PnzdOjQgVOnTpGQkMD169dp3ry58T537nDu3DkGDBjAfffdR3x8PHdMmoyWtu/Vvn37zPpenTp1ilWrVhk8P5ZyYEwZP34827ZtY8eOHYaCe6Z9r44dO2YWvsoJUeVu3wAizBQaGsp9992HSqUy63tlWqhw7ty5/PvvvzRs2JAbN27wyy+/EBoayosvvmg4ZujQoSxYsIAmTZrk2/dqzpw5hIaGsnLlSqKjowt8n46G0xYMlL2u7J/0LC27L94FoKa3O60CKn7laqf9ZiGxOVXcq1DHu/CmnjW88ubB1fCqQR3vOlRxL/x/0NXVFXd3d2JjY2ncuDEHDhygY8eOHDx4kCZNmgDCAzFt2jRDHkoOfn5+NG/enE2bNuHm5kZWVpahvxTIvley71XJcFqhI7F/DlyOJT1LJBGGNqth8ZuKRCIpGjO6z2BG95I19VzzUOFNPXNCV+np6XTp0oW2bdtSv359JkyYwNKlS/H39+eVV14BYMKECcyePZsvvvjC7BpqtZrZs2fTv39/1Go1NWrU4LfffjM75ueff2b69OnMnj2b7Oxspk2bRp8+fQx9r0JCQvK1cfz48XTr1o0TJ04Aou/V1q1bCQsLQ6VSMWHCBB577DGL5yYkJDBp0iQURSE7O9tqfa/mzp1rcb9p36u6devm2Z8j1kzFUw6vv/46U6dOZc6cOQB8/PHHhvwdZ0D2upK9ruyWd9ee4vtdYm7S/03owJA2tW1sUel5B5hjsn4F2etKYh3suddVdHQ006ZN448//rC1KQUi+17ZD7LXlcQhyamGrFGr6NnYz8bWlA3F/54nkTgWu3fv5qWXXmLevHm2NqVQZN8rx0AKHYldciMulQu3kwFoH+iLj6drIWdUDE7b2gCJxMb07NnTML1cIikPZG6kxC7Zce6uYRzStGIXCZRIJBKJ7ZBCR2KXbD9nnFYeUsGrIZvSytYGSCQSiZMhhY7E7sjS6th94R4A1Sq50TrAcab/m07unQL42sgOiUQicRacVujIgoH2y5GrcSRnZAPQp4kfarVjTit/EHAcCSdxdkzbMBSF4jTyLK+p0AU1wSwKCxcupGfPnvTp04cxY8Zw7969MrIsL02aNGHMmDEAHDt2jD59+hASEkKPHj3Miio+/PDDPProo4b1yMhIAgMDCQ0NJTQ0lPfff5/Q0FDatWtn2P7yyy8DsGjRIj7++GPDuWlpaQQHBwOidk9oaCidOnVi8eLFeex76623aNCggWH9t99+Q6VSkZyczIYNGwqszvzyyy/j6+tLcnJyyX44uXDaZGRZMNB+yZltBY4VtpJIJPaBTqczFBI0paAmmIWxbds2Vq9ezbZt23Bzc+PMmTNkZGSU2JbC8PHxYeXKlQC8++67fP3117Rq1Yq0tDRDzbH09HTu3r2LVqs1K744btw4s1lvr7/+OpGRkaxdu9Zs++jRoxkxYoRB+Pz1118MGTIEgGbNmhEZGUlaWhqtW7dm8uTJeWz08/Pj0KFDdOrUiT///NMgkgYNGlTge/v44485cOBAgccUB6f16Ejsl8izRqHTu4ljCR1tPmOJxFHYtm0b3bp1o1u3bvz4448ATJ482VCkb+bMmXmK2hXUyNOUt956i4kTJzJkyBBCQkJIS0tj3rx5LFu2DICLFy/y0EMPAfDBBx8QEhJCnz59OH78OAAdOnTgueeeY+LEiaxevZouXboQFhbG119/DRg9R8ePH6dXr1707NnTUMDP0r1N+fnnn5k5cyZubm6AqEYcEBBg8b1HRkYyfPhwRo8ezSeffMKwYcMM1+nXrx8JCQkcOnSIsLAwevfuXehUfE9PT/7++29SUlLw9PQ01JlZv349Q4cOZeDAgWzZsqXAa1jCz8+PypUrGwogLl++3KwPF0BSUhKZmZkWz3/ggQdYsWIFaWlpZGRk4OvrC8DixYtZsGCBYZzzu9+6dWuxbSwKUuhI7IrbiemcuiX66bSuU6XCdyvPjelHzQGk2JGUI6fnw6q6hS/bLRTG2z5C7Dudf1PPHGbNmsXatWvZuXMnX375ZR5BkJvCGnnmpkmTJvz1119069aNzZs3M27cOEP15GXLljFu3DhOnDjB2bNn2b59O0uXLmX27NkAxMXF8cwzz7BkyRKWL1/O4sWL2bZtW56Q1WuvvcaiRYvYtWsX27Zt48qVKxbvbUpxG44mJCSwcuVKXnnlFbRaLffu3ePGjRtUqVIFHx8fXn31VVauXMnOnTvZvn07MTEx+V7rk08+4fTp0wQHBzNu3DhSUlIAWLFiBWPHjmX8+PFmPbiWLVtGaGgoAwYMKNTOBx54gOXLl5Oens7p06fp0KEDAGfPniUkJITGjRvz9ttvWzy3VatWnDx5kvXr1zNw4MA8++/du8fSpUvZsWMHmzdv5p133inUnpLgtKEriX2y47zzTCt/B3gMqGdrQyTOQVYipOVtiJmH9EAL2+6Ic7Pyb+qZg1arxc9PFPhs3LgxUVFRBTbdNG3kCRgaedaqVcvi9du3bw+IvldxcXEEBgaSkJBAYmIiGzdu5MUXX2T16tXs2bOH0NBQAEOLhqpVq9K4cWNAtEyYN28eaWlphIeH061bN8M9oqOjadGiBSC8QBcvXrR4b1NyGo42a9bMbHt+771Tp06Gfffffz8rVqwgJSWFBx98EBB5N6NHjwaEQLt+/Tr+/v4Wfyb+/v588803AMyePZuffvqJyZMns3PnTkN+zoULF8jOFrmPuUNXuXn55Zc5cOAAjz32GKNHj+b++++nUaNGZiGnZs2asX37dvbs2cOCBQt49NFHGTBgAJmZmXz11VeG49q0acOHH37IX3/9xU8//WR2n4sXL3Ly5EnCwsIAzBq4liVS6Ejsil3njX/ofRwsbAVQ1dYGSJwX1yrgWXhTTzws/N951BDnuhbeJketVnP37l18fHw4f/48AQEBVK1alRs3btC6dWuOHTvG8OHDDccX1sgzN5aEw6hRo/joo49o2LAh7u7uNG/enJCQEL777jvA2MTSNBcmMDCQhQsXEhUVxSOPPGIWNvH39+f06dM0b96cI0eOMG3aNHbu3FmgYJswYQKffvopvXr1ws3NjXPnzuHt7Z3veze15f777+ehhx4iOzub1atXAxAcHMzy5cvx8fFBq9UWmMdz/vx5Q8PUGjVqoCgKGzZs4JlnnjEkfH/88cds2bIFd/fCveSmCcgA7u7u/H979x4WdZ33f/w5DAfBEyKKeEDcPK0poBwUBWdAFJTFU1YU4ele7zQkrx9rRXeW1d6Zm8WyKK3pfbXercfVxNt0JSsDTDGVstxoTQ3wkJCIHMTEhPn9MfKV4exhZmDm/biu73UN8z19ZnSYN5/v5/t5/eUvf2HVqlUNth07diwrVqwgNzeX/fv3K89/+OGHADz11FMASvFb129+8xu8vLzYs2cPKpXKaGGjUuiINkOn0/HF7dvKnezVjPSwvLLAD9hr7kYI6/TbBP1yLzQth3rqdDrUajUrVqwgMjISlUrF4sWLcXR0ZO7cucTGxrJ+/XqcnJwM9mtNkGdLHn30UTw8PJQiwcvLi0GDBqHRaLCxsWHixIkN8pxee+01srOzuXnzJvHx8Qbr3njjDX7/+9+j0+mIjIzE09OzxTaEhoZy5swZQkJCUKvVuLq6sn79+mZfe61u3brh4OCAi4sLHTt2BGDlypXMnDmTmpoaHBwcSEtLw9HRsdH9t27dyp49e3B0dMTZ2ZmNGzfy9NNPG9zVFhISwtq1a4mNjW3xtdQ3a9Ys/vSnP+Hr69vo+meeeYaUlBSlV6mu3/72t7zxxhuN7ufq6kp0dDQajQa1Ws2IESNISUm56/a1REI9JdSzzfh3YTkRyQcBCB3ak/fnWt6t/68Cda9mFyCXroRxmDrUMzMzk7S0NJKTk41+LgEajYbu3bsrd15Zkueff57du3fz1VdfKcWhhHoKi/BFnfE5QRYS4imENUhPT+eVV15pdD4VYRyZmZnmboLRvPXWWw0un90PKXREm3GwTqETPEgKHSHai4iIiBbnRhHCXNr97eWlpaX4+fnh4+PD8OHDWb9+vbmbJO5B1a1qvszTj89x6+LAwJ6dzNwi4/jG3A0QQggr0+57dDp37kxWVhZOTk5UVlYyfPhwZs6cSffu3c3dNHEXcgqucuPXGgCCBvYwuLvBkjQ9E4YQQghjaPc9Omq1WhmsVFVVhU6na3Dbn2j7Dp2pMz5nkBSpQgghHgyzFzpZWVlERUXRu3dvVCoVu3btarBNamoqnp6edOjQgdGjRzfIwCgtLcXb25u+ffvy3HPPNXq/vmjb6g5EHmfBA5HHm7sBQhiJMUI98/LylPDJ2hDJSZMmGUQItBW1ExRqNBpCQ0M5fvy40c41d+5c/P39OXfuHBUVFURFRaHVagkMDGTfvn3Kdu+99x4PPfSQwb6172PdQM/67y/o5+aJjIw02HfChAmcO3cOrVarxDbExcU1aF9GRgY2Njbk5eUB+gkYbW1t2bNnD4WFhSxfvrzJ17Z582Y8PT3Zs2fPPb8/9Zn90lVlZSXe3t7Mnz9fSWKta9u2bSQkJLB27VpGjx5NcnIy4eHhnDp1ip49ewLg7OzMN998Q1FRETNnzmTWrFlNziAp2p7S6zf59mIZAEN7daZnZ+PfCmsudV/Zu0Djc78KIQAGDBig5GL5+fkpj9va3V0lJSUsWrSI9PR03N3dKSsrU2ZTbs69BnoC/O1vf8PDw4PU1FQiIiKIi4tDp9NRVlambPPRRx+h0WjIyclR5sCpDeOs9dJLLwGG7y/o4y4uX75MaWkpzs7O/Pzzz1RVVeHhoZ8QY9++fXTq1ImwsDC+++47Hn74YYP2+fr68uGHH7J06VJ27typREf06tWrycgI0Ket//DDD/f0njTF7D06kydP5r//+7+Vqa7rS0pKYsGCBcybN49hw4axdu1anJyceP/99xts6+bmhre3NwcPHmzyfFVVVZSXlxsswrwOn71C7dVGa7qt/DeAvbkbIcQDZsxQz/rniYqKwt/fn0uXLgH674vAwECCgoL46quvAH2EQ1xcHCNHjiQ1NZXY2Fhl1mGAefPmERwcjFarJT8/n/LycqZOnYpGoyE6OpqbN2+SkZFBREQEM2bMwNvbW3kttfbu3cv06dNxd3cH9Mnio0aNMuh52rNnD6+++ioAw4YNY968eSQkJBAUFKSEYr788sukp6dTXFzM9OnTCQ0NJSYmptnsL0dHR44cOUJRUREqlUoJzrx8+TKOjo4sXLiQ7du3t+o9rW/atGns3q2fLDItLa1BZ8StW7e4du1ao8NFgoOD+eKLLwD49NNPCQsLAwx7/u4muPR+mL3Qac7NmzfJyclR3iDQz6IZFhZGdnY2AEVFRVRUVAD6kLSsrKwGWSN1vfnmm3Tt2lVZ+vVrJNdFmNQXBuNzLLvQkdFjwlySgL6tWBqJ9GTq7XUtR3oaP9SzVteuXfnoo4+YP38+27dvp7CwkF27dnHo0CE2btzICy+8AOiHNiQmJnLw4EESExN55513yMzMZM2aNfz666+cOnWKrKwsMjIy8PDwYN26dUyZMoXMzEwefvhhtm7dCuhjJNLS0li5cmWDP7TvNtDzwoULJCUlkZycTEREhHK56cCBA4SFhbFy5UqeffZZDhw4gJeXF2lpaU0eKzY2liFDhhAeHk5gYCCnTp0C9IXJI488QkBAgFL0gT6Ms/ZyVW5ubrPtfPTRR5Uohw8//NDg0uTkyZPx9PRk8ODBDB8+vMG+NjY2uLu78/XXX9OlSxcl1b2uuwkuvR9tutApLi6murq6wWUoNzc3CgsLASgoKCA4OBhvb2+Cg4OJj49nxIgRTR7zxRdfpKysTFnOnz9v1NcgWlY7ENlebcPoAZY9EPnnOo8rzNYKYY3KgYutWBqLVbx8e11r+r9rQz3t7OzuOtRzwoQJSqjnpEmT0Gq1nDx5stHz1A/YzM/Px9vbGxsbGzw9PSktLQX08Qr9+vWjU6dODB48mJ49e+Ls7MyNGzews7MjLi6O2NhYlixZwvXr1zlz5gz+/vpZ2f39/Tl9+jQAPj4+BuerqzbQs76mXvfAgQPp1k0fcRMdHc22bdv4+uuvGTFiBLa2tuTm5rJ8+XK0Wi07d+5Uvu8aY2dnx7Jlyzhx4gSvv/66Mv5l165drF+/noiICH788Uel2Km9dJWRkcGwYcMaHG/z5s1otVr+8Ic/MHjwYAoLC8nLy+P69evKZSvQX7r65ptvOHv2LDdu3OD5559Hq9UaBHfOnDmT+fPnM3369EbbXhtcqtVqOXfunNG+j80+Rud+BQQEcOLEiVZv7+Dg0KpQM2EaF65ep+DKdQBGejjjaK82c4uMq+48Oh8CEYBlzhgk2pouQCsiPWksSrfH7X1bE5LzoEI96wZENqZ+EeHp6cmJEyeoqanh3LlzyiWcutvVn7aiurqaxx57jJiYGFasWMHOnTsZOHAgR48exdfXl2PHjilhmc0Va5GRkYSEhLBo0SLc3d0pLy/nzJkzdOvWje+//x6Ab7658+mvOy5n4MCBXLp0iQ0bNhAdHQ3A0KFDmTFjBsHBwQDNhl0WFBTg7u6Ovb09PXv2RKfTUVxcjL29Pf/85z8BOHr0KNu3b1fGyTTnySef5Mknn1R+joqK4ve//32jw0u6d+/O9OnTef/99w1mMq69NBkSEoKXlxcRERGNfk/fTXDp/WjThY6rqytqtbpBd1ZRURG9et3fMM7U1FRSU1Nb1UUqjCf77BXl8diHLPuyVX1bgT8hhY4wjYTby71oOdLTvKGeoB/kOm3aNMaOHYuNjQ2rV69ucZ+KigqmTZuGSqVCpVKxadMmOnbsSExMDFu3bsXNzY0XXniBw4cPN3scFxcX/vrXv/LEE08o78OqVasICwvj7bffZsqUKfTp04c+fRovNSMjI0lJSeHPf/4zoB8gvGDBAqV35q233sLPz6/RfU+ePMnjjz9Ohw4d0Ol0pKamkpaWhkajUbbx9fVl0aJFvPnmmy2+J/U9+uijvPzyy42OiwWYP38+EydOZNGiRQ0KSVtbW/73f/+3yWPfTXDp/WhToZ4qlYq0tDSDbq7Ro0cTEBCg/KetqanBw8ODxYsXk5iYeN/nlFBP80rYdoKdX+u7fLcvDMTf08XMLTKu2cDf6/wsoZ7CWCTU03ItXbqU7OxstmzZYnA5yRJs3ryZt956i6SkJEJDQ5Xn23Wo57Vr1zhz5ozyc15eHidOnMDFxQUPDw8SEhKYM2cOfn5+BAQEkJycTGVlJfPmzbuv80qPjvnpdDoO3+7RcbRT493X2bwNMoEB5m6AEEYgoZ6mZcw7lMyt/qWzB8HshU7t7WW1EhL0natz5sxhw4YNPP7441y+fJlXXnmFwsJCfHx8SE9Pv+95cuLi4oiLi1MqQmF6+VeuU1h+AwD/AS7Y27bpsfFCiCZIqKdoy8xe6Gi12hYjGxYvXszixYtN1CJhKofP3rmtPPA3ln23lRBCCPOQP6GF2Rw2GIgshY4QQogHz2oLndTUVIYNG6bMlyBMS6fTceR2odO5gy0P97aOgeAHzN0AIYSwMlZb6MTFxZGbm8uxY8fM3RSr9EPRNa5U6qc9Hz3ABVu1dfxXlKHvQghhWtbx7SLaHIPxOVY0f47MmSMsUVPJ5bNmzSI/P/+ujtXYfDEXL15k5syZaDQaxo0bp2RoGcOGDRsYNGiQkvE0aNAgtFotvr6+ylwyZWVlzJkzh/HjxzNu3DiDyfJqLV26lHHjxhEUFMQf//hH5flTp05hY2NDQUGB8lxtGrhWq+X5559n2rRpaLVanJ2dlecPHz7MmDFj7iohXuiZfTCyucjt5eZlreNzAoFPbj/uhvylIURrPPXUU7z22muMHz+empqaZoOba91PMviSJUuYOlWf+tW1a1cyMjKorKzk4YcfJjY2lvj4eMLDw4mJiQH0GVV1fffddxQUFHDo0CEAg8iI7du3s3DhQnbs2MEf/vAH5fnaNPC6tFote/bsUZ7funUrS5cuvafXZM2s9vesXLoyn+oaHV/+qC90XDraM8Sts5lbZB5b0QclCmESSUnQt69+qZceTl7enXXx8Q33nTpVvy6p5VjPTz/9lFGjRjFz5kwl/+nGjRs89dRThIaGMnXqVMrLy6mpqSEsLAyNRsPEiRMpL288Sev8+fPU1NQwfvx4QD+TskajMehFunbtGlqtFkDpFQkPD2fx4sV8+eWXAHz22WckJiai0+mIj48nJCSEsLAwLly40Io3Dzp27Ii7uzslJSVkZ2crRQ5gMLEdQIcOHTh9+rQS/1CbawX6eIRVq1bxySefIEzDagsdYT65P5VTfuMWAGN+44KNjaqFPYQQ9628HC5e1C9VVYbrqqvvrKsXWAnA5cv6dU0UI3UtW7aMTz/9lC1btvDTTz8B8D//8z+EhoZy4MABYmJiWLduHTY2NuzevZvMzEymTJnCtm3bGj3e3SaDA4SHh/PJJ58QHR2tpI9v27aN6Oho9u7dS7du3fj888954403WLlyZauOefnyZX7+WR/L6+ra/OX2hx56iMTERJ555hkGDx7M//3f/wHwww8/MGjQIDp27Ejfvn05d+6css/kyZPRarVs3rz5rl6raJnVXroS5pP9o3WOzxHCrLp0gdqspfrBxmr1nXV1eh8UPXro17ciJqe6uhoXF32Ui5eXF4DSe/7BBx/w66+/EhwczLVr13j66ae5cOECJSUlTY49udtkcEC5m3bcuHE899xz3Lx5k9zcXHx8fNi/fz9paWlkZWWh0+no169fs6+nrKxM6S1as2YNrq6uFBcXN9juwIEDvP766/Tp04dNmzYRHR1NdHQ0hYWFTJgwgWnTprF9+3ZycnKIiIigpKSEHTt2KJPkNnbpSjwYVlvoyBgd8/nyxxLlceBvLDvbqr7v6zxu+KtbCCNKSNAvjRkwAJq7hLO7NbGeemq1mqtXr+Lk5MTJkycBfRp3YGAgsbGxgD6Ne/fu3QwYMIBNmzbxzjvvUFFR0ejx+vXrh1qt5uDBgwQHB6PT6Th48CDe3t5KAVQ3GRzupIOrVCrGjRvHa6+9RlhYmNKWxx57jJdffllpS3Nqx+jUFRgYyKZNm5TLVxkZGYSGhiqXsEpKStDpdHTv3h1nZ2fs7OwA2L9/P9nZ2ajVam7dukVkZKRS6AjjsdpLVzJGxzyqa3QczdcXOq6d7Hmoh3X9BVP3qyQFuNLUhkK0U6+//joTJkzgiSeeUAIn//M//5NPPvlEKQb279/PmDFj2LdvH5GRkXz33XfNHnPjxo0kJSWh0WgICgqioKCArl27MnLkSIKDg0lPT29y3+joaP70pz8RHR0NQFRUFFeuXCEkJITQ0NB7uoNr9erVfPzxx8pdV/W/R8rKypgxYwbBwcGEhITw0ksvcfr0aZydnVGr1YA+2dve3t7g8pUwjjaVXm4Okl5uWv+6WMbvVn8BwJQRvXg3xtfMLTKtsUB2nZ/PAc13nAtxb0ydXm4pduzYwZtvvsny5cuVO6/agpKSEqZOnYqXlxfvvvuuuZtjcu06vVxYl6N5dy5bBXha12UrgNEYFjpCiLZl1qxZbXKuGhcXF7744gtzN6NdstpLV8I8vsy7c7FmtBUGeUqfoRBCmJbVFjqSdWV6NTU6pUenq6Od1c6fI4QQwnSsttCRwcimd+byNa5e19/h4O8p8+cIIYQwPqstdITp1c6GDPqJAq1RI1OxCSGEMCIpdITJHKkzEHn0AOsbnwMg/YfCEuXn59OjRw+0Wi3+/v7KbMStlZGR0eoMJwn4lIDPuyV3XQmT0OnujM/p5GDLb91lfI4QlkSj0bBjxw5u3LjBuHHjlHlrHjQJ+JSAz7slPTrCJPKKK7lcoc/X8fPshq3aOv/r9Td3A4QwsuvXr+Pk5ATo5zmZOnUqGo2G6Ohobt68yZIlSzh8+DCgnyn4pZdeMtg/PT2d4OBgxo4dy5YtWwzWScCnuBfW+W0jTO5LuWwFwJA6j8MAJ3M1RFidpOwk+ib1pW9SXzLyMwzW5V3NU9bF/7NhevnULVPpm9SXpOym08szMzPRarWMGDGCJ598EoB169YxZcoUMjMzefjhh9m6dStz5sxh48aNAGzatEmJhQB9z+8f//hHPvvsMw4ePMiaNWsMYnok4FPcC6stdOT2ctMymChwgHUORK7vOcB6Sz5hauVV5VysuMjFiotU3TJML6/WVSvrrt5oOGT+8vXLXKy4SHlV0+nlGo2GjIwM8vPz2bRpExcuXODMmTPK71h/f39Onz7NqFGjyM3NpaysjPPnzzN06NA757l8mR9++IFJkyYxYcIESktLuXz5srL+fgM+jxw5YhDwmZubS1pamtLzU1pa2sw7eCfg89FHH20x4FOr1So9PdHR0Xz++edkZWXxX//1XwAGAZ/ffvstO3bsUPbft28fGRkZSsEo7o/VjtGJi4sjLi5OmUJaGI9Op1PuuHK0UzOij7zfQphaF4cu9OmsTyh3sDVML1er1Mq6bh0appf3cOpBn8596OLQ8pSXdnZ2ODg4UFJSwsCBAzl69Ci+vr4cO3aMQYMGAfC73/2OhQsXMm3aNIN9XV1dGTp0KPv378fe3p5ff/1VCcQECfgU98ZqCx1hOheu/sJPZTcAGNXfGXtbq+1IFMJsEgITSAhs/It0QLcBXEhoenzK7idaTi+vvXR148YNAgIC8PLyon///sTExLB161bc3Nx44YUXAIiJiWHZsmX85S9/MTiGjY0Ny5YtY+LEidjY2NCjRw/+8Y9/GGyzceNGFi9ezLJly7h16xYLFy5k/PjxSsCnRqNpso3R0dGMGTOGf/3rX4A+4PPAgQOEhISgUqmIiYnhP/7jP1p8rXWtXr2a+Ph43nvvPaqrq5k+fboyRgju3JWl0+m4deuWBHyagYR6Sqin0e386gIJ/9D/lfX/wgazJGyQmVtkPqHA57cfpwANR0MI8WC05VDPwsJCFi5cyK5du8zdlGZJwGfbIaGeok07XnDnmr+/Z8NucWvyS53HLwAzgT5maosQ5nDo0CGee+453n77bXM3pUUS8GkZpNARRnc8Xz8QWW2jwsfD2byNMbO6IyN+AWrM1RAhzGTcuHHK7eVCmIIMlhBGVXr9Jj8UXQNgeO8uONlbd23d9OgBIYQQxiCFjjCqr87duWzl219uKxdCCGFaUugIozqWL+NzhBBCmI/VFjoyYaBp1I7PAfCVQkcIi1U3hqE1WhPkmZeXh1arRavV0rlzZ7RaLZMmTWLDhg2sWbPmfpv8QB0+fBitVotGoyE0NJTjx48b7Vxz587F39+fc+fOGQSqBgQEcOyYPjr4+++/Jzw8HI1Gw/jx40lPTzc4RkVFBVFRUWi1WgIDA9m3b5+y7r333uOhhx4y2L72/ddqtbzxxhuN/rscOHCAoUOHtrl/G6sdMCETBhpf1a1qvrlQBkD/7k707Ny2bnE1hzPmboAQ7ciAAQOUCfr8/PyUxxs2bDBbmxpTUlLCokWLSE9Px93dnbKyMs6ePdvifvcTNvq3v/0NDw8P8vPzlUDVL7/8kpdeeom9e/fyxBNPsHXrVoYOHUpVVRU5OTkG+3/wwQdEREQQFxeHTqejrKxMWffRRx+h0WjIycnB19cXgCFDhhhMllibUVb33wUgMTGRa9eu3dNrMhar7dERxvevi2XcvKW/r8hPxucA8KO5GyCEkX3++eeMGTOGMWPG8MEHHwD6HojaSfqWLl3aYHbh5oI8mztPVFQU/v7+XLp0CYCkpCQCAwMJCgriq6++AmDUqFHExcUxcuRIUlNTiY2NxdvbW4lcmDdvHsHBwWi1WvLz8xsNIs3IyCAiIoIZM2bg7e2tvJZae/fuZfr06bi7uwP6GZRHjRpl0PO0Z88eXn31VQCGDRvGvHnzSEhIICgoiJs3bwLw8ssvk56eTnFxMdOnTyc0NJSYmBiDvK/m+Pj4cP78eY4cOYK3t7cSr+Hg4MDYsWMNtnV0dOTIkSMUFRWhUqlwdnYG9DEcjo6OLFy4kO3bt7fqvG2dFDrCaGR8jhBtRxLQ9/aSUW9dXp11jU1iOfX2uqYjPe948cUX2bNnDwcPHiQlJYVffvml2e1bCvJsSteuXfnoo4+YP38+27dvp7CwkF27dnHo0CE2btyozMJcWlpKYmIiBw8eJDExkXfeeYfMzEzWrFnDr7/+yqlTp8jKyiIjIwMPD49Gg0hBHw+RlpbGypUref/99w3acrdhoxcuXCApKYnk5GQiIiKUy0YHDhwgLCyMlStX8uyzz3LgwAG8vLxIS0tr1XEzMzMZOnRoq9oTGxvLkCFDCA8PJzAwkFOnTgGQlpbGI488QkBAgFIsApw6dUq5XJWbm9vq19oWSKEjjOZ4nULHTwodAHzN3QBhtcqBi7eXqnrrquusaxjpCZdvr2s60rPOsaqrcXV1xc7OjoEDB/LTTz81G7rZVJDnpEmT0Gq1nDx5stHzjBw5EtDnX129epX8/Hy8vb2xsbHB09NTCejs1q0b/fr1o1OnTgwePJiePXvi7OzMjRs3sLOzIy4ujtjYWJYsWcL169cbDSIFfW9J3fPVdbdhowMHDqRbN/3vxOjoaLZt28bXX3/NiBEjsLW1JTc3l+XLl6PVatm5cyeFhYXNvue18RspKSmsWrWqyfYkJSWh1WpZtWoVdnZ2LFu2jBMnTvD666+zfPlyAHbt2sX69euJiIjgxx9/VIqd2ktXGRkZDBs2rNn2tDVWO0ZHGFdNjY6cAv1A5G5OdjzUo5OZW9Q21E0rfwGQC3rCVLpwZxZuh3rr1HXWNfYnSY/b61sTkmNjY0NxcTFdu3bl9OnT9O7dm27dunHhwgWGDx/Ot99+S1RUlLJ9U0Ge+/fvb/Y89YsIT09PTpw4QU1NDefOnVMuxdTdru5j0Bdljz32GDExMaxYsYKdO3c2GUTaXLEWGRlJSEgIixYtwt3dnfLycs6cOUO3bt34/vvvAcOw0brjcgYOHMilS5fYsGED0dHRgD5sdMaMGQQHBwMth43WjtGp1b9/f+Lj4/n3v//N0KFDuXnzJjk5OSQkJCjBoQUFBbi7u2Nvb0/Pnj3R6XQUFxdjb2/PP//5TwCOHj3K9u3bGTVqVLPnb+uk0BFG8WPxNa5e1384ffu7NPgFI/S5Vx3N3QhhNRJuL40ZADQd6QktR3rqv/zVajUrVqwgMjISlUrF4sWLcXR0ZO7cucTGxrJ+/XqcnJwM9mtNkGdr9OrVi2nTpjF27FhsbGxYvXp1i/tUVFQwbdo0VCoVKpWKTZs20bFjxwZBpC3N5Ozi4sJf//pXnnjiCeV9WLVqFWFhYbz99ttMmTKFPn360KdP44EvkZGRpKSk8Oc//xnQD/RdsGCB0svy1ltv4efn1+r3ws7Oji1btrBkyRJu3LhBTU0Ny5YtM9jm5MmTPP7443To0AGdTkdqaippaWkGoai+vr4sWrSIN998s9Xnbosk1FNCPY1i69FzJO7UdzknTh7KQs1DLexhHZYDr99+/DEwyYxtEZbN1KGemZmZpKWlkZycbPRzWbulS5eSnZ3Nli1b8PDwMHdzFAcOHOD555/n2WefZfbs2Q/02BLqKdocGYjcuMo6j+uPkxCivUpPT+eVV15pc7d9W6q2Gohq7PmD7pUUOsIojt8en2Nva8PwPjJPUa1DdR4fAMIBezO1RYgHJSIigoiICHM3Q4hGtfu7rs6fP49Wq2XYsGF4eXlZzH3/7dnliioKrlwHwLtvVxxs1WZuUduUDPxs7kYIi2floxOEhbif/8ftvkfH1taW5ORkfHx8KCwsxNfXlylTptCxowzzNJe6QZ6j+stlq7paP9OGEPfH1lb/672iokJuBhDtXkVFBXDn//XdaPeFjru7uzIbZa9evXB1daWkpEQKHTP6+lyp8niUhxQ6dQ0Hdpq7EcIq2Nra0qVLF65cucKVK1fM3Rwh7luXLl3aZ6GTlZXFqlWryMnJ4dKlS6SlpTF9+nSDbVJTU1m1ahWFhYV4e3uzevVqAgICGhwrJyeH6upq+vXrZ6LWi8bU7dEZ6eFsvoYIYeV69+6Nq6urXL4S7Z5KpcLe/t5GNJq90KmsrMTb25v58+czc+bMBuu3bdtGQkICa9euZfTo0SQnJxMeHs6pU6fo2bOnsl1JSQmzZ89m/fr1pmy+qOfX6hq+vVAKQN9ujhLkKYQZqVQqHBzqTw8ohHUxe6EzefJkJk+e3OT6pKQkFixYwLx58wBYu3Yte/fu5f333ycxMRGAqqoqpk+fTmJiYoPgsvqqqqqoqrpzY29tYmt5eWsmNxctyf2pjOu3k2uHu3aR97We+reUV9C6afWFEEIYqv1+abHHUteGALq0tDTl56qqKp1arTZ4TqfT6WbPnq2bOnWqTqfT6WpqanTR0dG65cuXt+ocy5cv1wGyyCKLLLLIIosFLOfPn2/2e9/sPTrNKS4uprq6Gjc3N4Pn3dzc+Pe//w3AoUOH2LZtG15eXuzatQuAv//974wYMaLRY7744otK1gdATU0NJSUlRERE3PNER/7+/hw7dsyo+7Rm+/Lycvr168f58+dllufb7uXfxlRM3TZjne9BHfd+jiOfwbZLPoPGP581fAYb21an01FRUdFiUnubLnRaIygoiJqamlZv7+Dg0OCatbOzs3KHwr1Qq9V3ve/d7nM323fp0kV+yd52L/82pmLqthnrfA/quPdzHPkMtl3yGTT++azhM9jUtl27dm1x3zY9YaCrqytqtZqioiKD54uKiujVq9cDPVdcXJxJ973bfe6nfdasLb9vpm6bsc73oI4rn0HL1JbfN/kMPrjjGPszeD9ta1OhniqVqsHt5aNHjyYgIEBJoq2pqcHDw4PFixcrg5GFngSUCmFe8hkUou0x+6Wra9eucebMGeXnvLw8Tpw4gYuLCx4eHiQkJDBnzhz8/PwICAggOTmZyspK5S4scYeDgwPLly+X20mFMBP5DArR9pi9RycjI4OQkJAGz8+ZM0dJwl2zZo0yYaCPjw8pKSmMHj3axC0VQgghRHtj9kJHCCGEEMJY2vRgZCGEEEKI+yGFjhBCCCEslhQ6QgghhLBYUugIIYQQwmJJoWMFzp8/j1arZdiwYXh5ebF9+3ZzN0kIq1JaWoqfnx8+Pj4MHz6c9evXm7tJQlgNuevKCly6dImioiJ8fHwoLCzE19eXH374gY4dO5q7aUJYherqaqqqqnBycqKyspLhw4dz/Phxunfvbu6mCWHxzD5hoDA+d3d33N3dAejVqxeurq6UlJRIoSOEiajVapycnACoqqpCp9Mhf2MKYRpy6aodyMrKIioqit69e6NSqZSU9rpSU1Px9PSkQ4cOjB49mqNHjzZ6rJycHKqrq+nXr5+RWy2E5XgQn8HS0lK8vb3p27cvzz33HK6uriZqvRDWTQqddqCyshJvb29SU1MbXb9t2zYSEhJYvnw5X331Fd7e3oSHh/Pzzz8bbFdSUsLs2bNZt26dKZothMV4EJ9BZ2dnvvnmG/Ly8ti8eXODsGIhhHHIGJ12pqngU39/f9asWQPog0/79etHfHy8EnxaVVXFxIkTWbBgAbGxseZouhAW4V4/g3U988wzhIaGMmvWLFM1WwirJT067dzNmzfJyckhLCxMec7GxoawsDCys7MB0Ol0zJ07l9DQUClyhHjAWvMZLCoqoqKiAoCysjKysrIYMmSIWdorhLWRQqedKy4uprq6Gjc3N4Pn3dzcKCwsBODQoUNs27aNXbt24ePjg4+PDydPnjRHc4WwOK35DBYUFBAcHIy3tzfBwcHEx8czYsQIczRXCKsjd11ZgaCgIGpqaszdDCGsVkBAACdOnDB3M4SwStKj0865urqiVqsbDGwsKiqiV69eZmqVENZDPoNCtG1S6LRz9vb2+Pr68tlnnynP1dTU8NlnnxEYGGjGlglhHeQzKETbJpeu2oFr165x5swZ5ee8vDxOnDiBi4sLHh4eJCQkMGfOHPz8/AgICCA5OZnKykrmzZtnxlYLYTnkMyhE+yW3l7cDGRkZhISENHh+zpw5bNiwAYA1a9awatUqCgsL8fHxISUlhdGjR5u4pUJYJvkMCtF+SaEjhBBCCIslY3SEEEIIYbGk0BFCCCGExZJCRwghhBAWSwodIYQQQlgsKXSEEEIIYbGk0BFCCCGExZJCRwghhBAWSwodIYQQQlgsKXSEEEIIYbGk0BFCWKRTp07Rq1cvKioqWr1Peno6Pj4+1NTUGLFlQghTkkJHCNFmqVSqZpdXX321yX1ffPFF4uPj6dy5M6DPq1KpVJSWlirb/PTTT4wYMYLx48dTVlZGREQEdnZ2bNq0ycivTAhhKlLoCCHarEuXLilLcnIyXbp0MXhu6dKlje537tw59uzZw9y5c5s89tmzZwkKCqJ///58/PHHdO3aFYC5c+eSkpJijJcjhDADKXSEEG1Wr169lKVr166oVCqD5zp16tTofv/4xz/w9vamT58+ja7/9ttvCQoKIjAwkF27duHo6Kisi4qK4vjx45w9e9Yor0kIYVpS6AghLM7Bgwfx8/NrdN3hw4fRaDQ88sgjbNy4EVtbW4P1Hh4euLm5cfDgQVM0VQhhZFLoCCEsTkFBAb1792503YwZM4iKimLNmjWoVKpGt+nduzcFBQXGbKIQwkSk0BFCWJxffvmFDh06NLpu2rRppKWlNdtj4+joyPXr143VPCGECUmhI4SwOK6urly9erXRde+99x7R0dFMnjyZrKysRrcpKSmhR48exmyiEMJEbFveRAgh2peRI0eSm5vb6DqVSsW6deuwsbFhypQp7N27F41Go6y/ceMGZ8+eZeTIkaZqrhDCiKRHRwhhccLDw8nOzqa6urrR9SqVirVr1zJ79mymTJlCRkaGsu7IkSM4ODgQGBhootYKIYxJCh0hhMWZPHkytra2fPrpp01uo1KpSE1NZd68eURGRvL5558DsGXLFmJiYnBycjJVc4UQRqTS6XQ6czdCCCEetNTUVHbv3s3HH3/c6n2Ki4sZMmQIx48fZ8CAAUZsnRDCVGSMjhDCIj399NOUlpZSUVGhxEC0JD8/n3fffVeKHCEsiPToCCGEEMJiyRgdIYQQQlgsKXSEEEIIYbGk0BFCCCGExZJCRwghhBAWSwodIYQQQlgsKXSEEEIIYbGk0BFCCCGExZJCRwghhBAWSwodIYQQQlis/w+YXSs0lZKYKwAAAABJRU5ErkJggg==", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# And here is a block of code that actually does the calculations with the tracer,\n", "# with three different models for propane\n", "\n", "backend = 'HEOS'\n", "fluid = 'Propane'\n", "ASCP = CP.AbstractState(backend, fluid)\n", "\n", "ASteqpSAFTVRMie = teqpAbstractStateShim({'kind': 'SAFT-VR-Mie', 'model': {'names': [fluid]} })\n", "ASteqpPCSAFT = teqpAbstractStateShim({'kind': 'PCSAFT', 'model': {'names': [fluid]} })\n", "\n", "for AS,modelabbrv in [\n", " (ASCP,'HEOS'),\n", " (ASteqpSAFTVRMie,'SAFT-VR-Mie'),\n", " (ASteqpPCSAFT,'PC-SAFT')\n", " ]:\n", " print(f'---- {modelabbrv} ----')\n", "\n", " kwargs = dict(lw = 2)\n", " for klass, label, p0, T0, color in [\n", " (IdealCurveTracer, 'Ideal Curve', 1e5, 900, 'r'),\n", " (BoyleCurveTracer, 'Boyle Curve', 1e5, 800, 'g'),\n", " (JouleInversionCurveTracer, 'Joule Inversion Curve', 1e5, 1800, 'orange'),\n", " (JouleThomsonCurveTracer, 'Joule-Thomson Curve', 1e5, 1800, 'cyan')\n", " ]:\n", " try:\n", " print(label)\n", " tracer = klass(AS=AS, p0=p0, T0=T0)\n", " x,y = tracer.trace()\n", " if modelabbrv == 'HEOS':\n", " style = '-'\n", " elif modelabbrv == 'PC-SAFT':\n", " style = ':'\n", " else:\n", " style = '--'\n", " plt.plot(x, y, style, label=f'{label} [{modelabbrv}]', color=color, **kwargs)\n", "\n", " if modelabbrv == 'HEOS' and label == 'Ideal Curve':\n", " print('Saturation Curve')\n", " Tt = tracer.AS.keyed_output(CoolProp.iT_triple)\n", " Tc = tracer.AS.keyed_output(CoolProp.iT_critical)\n", " Ts = np.linspace(Tt, Tc - 1.e-6)\n", " ps = CoolProp.CoolProp.PropsSI('P','T',Ts,'Q',0,backend + '::' + fluid)\n", " plt.plot(Ts, ps, '-', label = 'Saturation Curve', **kwargs)\n", " \n", " except BaseException as BE:\n", " print(BE)\n", " pass \n", "\n", "plt.yscale('log')\n", "plt.xscale('log')\n", "plt.xlabel('T (K)')\n", "plt.ylabel('p (Pa)')\n", "plt.ylim(100, 1e9)\n", "plt.legend(loc='best', fontsize=6)\n", "plt.savefig('ideal_curves.pdf')\n", "plt.show()" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.11.14" } }, "nbformat": 4, "nbformat_minor": 5 }