{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Fitting interaction parameters of binary systems" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Sometimes Equations of State needs an extra help to better predict behaviours.\n", "\n", "Let's see how we can optimize binary interaction parameters of a Cubic Equation,\n", "using equilibria data." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Data structure\n", "For all the default approachs of parameter optimization the datapoints must\n", "be in the format of a `pandas` DataFrame, with the following columns:\n", "\n", "- `kind`: Which kind of point is defined `[\"bubble\", \"dew\", \"liquid-liquid\", \"PT\"]`.\n", "- `T`: Temperature (in Kelvin).\n", "- `P`: Pressure (in bar).\n", "- `x1`: Mole fraction of component 1 (lightest component) in heavy phase. \n", "- `y1`: Mole fraction of component 1 (lightest component) in light phase.\n", "\n", "In the following cell we import the datapoints from a `.csv` file" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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kindTPx1y1
0bubble303.1520.240.23850.9721
1bubble303.1530.360.36980.9768
2bubble303.1539.550.50630.9815
3bubble303.1550.950.70780.9842
4bubble303.1557.830.84300.9855
5bubble303.1564.680.94100.9884
6bubble303.1567.460.96560.9909
7bubble315.1520.840.21680.9560
8bubble315.1530.400.33220.9660
9bubble315.1540.520.44460.9748
10bubble315.1550.660.58090.9753
11bubble315.1560.290.71800.9753
12bubble315.1569.110.84570.9764
13bubble315.1572.890.89280.9773
14bubble315.1575.940.92520.9777
15bubble315.1577.500.94240.9768
16bubble315.1578.660.95200.9756
\n", "
" ], "text/plain": [ " kind T P x1 y1\n", "0 bubble 303.15 20.24 0.2385 0.9721\n", "1 bubble 303.15 30.36 0.3698 0.9768\n", "2 bubble 303.15 39.55 0.5063 0.9815\n", "3 bubble 303.15 50.95 0.7078 0.9842\n", "4 bubble 303.15 57.83 0.8430 0.9855\n", "5 bubble 303.15 64.68 0.9410 0.9884\n", "6 bubble 303.15 67.46 0.9656 0.9909\n", "7 bubble 315.15 20.84 0.2168 0.9560\n", "8 bubble 315.15 30.40 0.3322 0.9660\n", "9 bubble 315.15 40.52 0.4446 0.9748\n", "10 bubble 315.15 50.66 0.5809 0.9753\n", "11 bubble 315.15 60.29 0.7180 0.9753\n", "12 bubble 315.15 69.11 0.8457 0.9764\n", "13 bubble 315.15 72.89 0.8928 0.9773\n", "14 bubble 315.15 75.94 0.9252 0.9777\n", "15 bubble 315.15 77.50 0.9424 0.9768\n", "16 bubble 315.15 78.66 0.9520 0.9756" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import pandas as pd\n", "df = pd.read_csv('./data/CO2_C6.csv')\n", "df" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Default model prediction\n", "\n", "Before starting to fit BIPs, let's see how the model predicts without BIPs.\n", "\n", "First, let's define the model" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "import yaeos\n", "\n", "Tc = [304.1, 504.0]\n", "Pc = [73.75, 30.12]\n", "w = [0.4, 0.299]\n", "\n", "model = yaeos.PengRobinson76(Tc, Pc, w)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Claculation of Pxy diagrams\n", "Now that the model is defined, lets calculate Pxy diagrams for each temperature.\n", "\n", "First, we find the unique values of temperature to iterate over them later." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([303.15, 315.15])" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Ts = df[\"T\"].unique()\n", "Ts" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "\n", "z0 = [0, 1]\n", "zi = [1, 0]\n", "\n", "for T in Ts:\n", "\n", " # Let's use a mask to only get the data for a specific temperature\n", " mask = df[\"T\"] == T\n", " px = model.phase_envelope_px(z0=z0, zi=zi, temperature=T, a0=0.001, ns0=len(z0)+3)\n", "\n", " plt.plot(px.main_phases_compositions[:, 0, 0], px[\"P\"], label=\"x\")\n", " plt.plot(px.reference_phase_compositions[:, 0], px[\"P\"], label=\"y\")\n", "\n", " plt.scatter(df[mask][\"x1\"], df[mask][\"P\"], label=\"data Bubble\")\n", " plt.scatter(df[mask][\"y1\"], df[mask][\"P\"], label=\"data Dew\")\n", "\n", " plt.title(f\"Pxy diagram at T = {T} K\")\n", " plt.ylabel(\"P [bar]\")\n", " plt.xlabel(r\"$x_1, y_1$\")\n", " plt.xlim(0, 1)\n", " plt.legend()\n", " plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here we can see that the PengRobinson EoS present a negative deviation with\n", "respect to the experimental points. Let's se how we can improve these\n", "predictions." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Definition of the optimization problem\n", "\n", "Now that we already have our data points defined. We need to define the\n", "optimization problem.\n", "\n", "The `yaeos.fitting` package includes the `BinaryFitter` object, this object\n", "encapsulates all the logic that is needed to fit binary systems." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001b[31mInit signature:\u001b[39m BinaryFitter(model_setter, model_setter_args, data, verbose=\u001b[38;5;28;01mFalse\u001b[39;00m)\n", "\u001b[31mDocstring:\u001b[39m \n", "BinaryFitter class.\n", "\n", "This class is used to fit binary interaction parameters to experimental\n", "data. The objective function is defined as the sum of the squared errors\n", "between the experimental data and the model predictions.\n", "\n", "Parameters\n", "----------\n", "model_setter : callable\n", " A function that returns a model object. The function should take the\n", " optimization parameters as the first argument and any other arguments\n", " as the following arguments.\n", "model_setter_args : tuple\n", " A tuple with the arguments to pass to the model_setter function.\n", "data : pandas.DataFrame\n", " A DataFrame with the experimental data.\n", " The DataFrame should have the following columns:\n", " - kind: str, the kind of data point (bubble, dew, liquid-liquid, PT,\n", " critical)\n", " - x1: float, the mole fraction of component 1\n", " - y1: float, the mole fraction of component 1\n", " - T: float, the temperature in K\n", " - P: float, the pressure in bar\n", "verbose : bool, optional\n", " If True, print the objective function value and the optimization\n", "\u001b[31mFile:\u001b[39m ~/docs/programming/python/virtualenvs/thermo/lib/python3.13/site-packages/yaeos/fitting/core.py\n", "\u001b[31mType:\u001b[39m type\n", "\u001b[31mSubclasses:\u001b[39m " ] } ], "source": [ "import yaeos\n", "from yaeos.fitting import BinaryFitter\n", "\n", "BinaryFitter?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Model setter function\n", "\n", "Looking at the object's documentation we can see that we need to define a\n", "function `model_setter` that takes the vector of fitting parameters and extra\n", "required arguments." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def fit_kij(x, model):\n", " \"\"\"Fit kij function.\n", "\n", " Args:\n", " x (list): kij value.\n", " model (yaeos.CubicEoS): CEOS model that will be fitted\n", " \"\"\"\n", " kij = x[0]\n", "\n", " kij_matrix = [\n", " [0, kij], \n", " [kij, 0]\n", " ]\n", "\n", " lij = [\n", " [0, 0],\n", " [0, 0]\n", " ]\n", "\n", " mixrule = yaeos.QMR(kij=kij_matrix, lij=lij)\n", "\n", " model.set_mixrule(mixrule)\n", " \n", " return model" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1.7427277515958346 [0.]\n", "1.7367297572626244 [0.00025]\n", "1.7307370080649485 [0.0005]\n", "1.72474952538773 [0.00075]\n", "1.712790445469462 [0.00125]\n", "1.700852689778661 [0.00175]\n", "1.6770418462495278 [0.00275]\n", "1.6533183947480148 [0.00375]\n", "1.6061393533646415 [0.00575]\n", "1.559327111499111 [0.00775]\n", "1.4668506368944916 [0.01175]\n", "1.3759871697248645 [0.01575]\n", "1.199517124958797 [0.02375]\n", "1.030807090584202 [0.03175]\n", "0.7207125515768745 [0.04775]\n", "0.45494890252076453 [0.06375]\n", "0.1633296153190301 [0.09575]\n", "0.22052696455206036 [0.12775]\n", "0.22052696455206036 [0.12775]\n", "0.09792206009347366 [0.11175]\n", "0.22052696455206036 [0.12775]\n", "0.11825669217983152 [0.10375]\n", "0.10598288555962829 [0.11975]\n", "0.09823429765712599 [0.11575]\n", "0.10459879763851507 [0.10775]\n", "0.09712360613884066 [0.11375]\n", "0.09823429765712599 [0.11575]\n", "0.09730049165208345 [0.11275]\n", "0.05599850855641941 [0.11475]\n", "0.09823429765712599 [0.11575]\n", "0.09823429765712599 [0.11575]\n", "0.05537507866049399 [0.11425]\n", "0.09712360613884066 [0.11375]\n", "0.05567258588798029 [0.1145]\n", "0.05510588012730981 [0.114]\n", "0.09712360613884066 [0.11375]\n", "0.09712360613884066 [0.11375]\n", "0.05523694758807889 [0.114125]\n", "0.054981862883314914 [0.113875]\n", "0.09712360613884066 [0.11375]\n", "0.09712360613884066 [0.11375]\n", "0.05504299106424188 [0.1139375]\n" ] } ], "source": [ "problem = BinaryFitter(\n", " model_setter=fit_kij,\n", " model_setter_args=(model,),\n", " data=df,\n", " verbose=True\n", ")\n", "\n", "x0 = [0.0]\n", "\n", "problem.fit(x0, bounds=None)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Optimization result\n", "Now that we have fitted the parameter, let's see the solution. For this, we\n", "can see the `solution` property of the problem.\n", "Where we can see that the optimization terminated succesfully with a `kij` value\n", "of around `0.113`" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/plain": [ " message: Optimization terminated successfully.\n", " success: True\n", " status: 0\n", " fun: 0.054981862883314914\n", " x: [ 1.139e-01]\n", " nit: 21\n", " nfev: 42\n", " final_simplex: (array([[ 1.139e-01],\n", " [ 1.139e-01]]), array([ 5.498e-02, 5.504e-02]))" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "problem.solution" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Obtain the fitted model\n", "Now that the problem has been optimized, let's redefine our model with the\n", "solution. For this, we use the `_get_model` method inside of the `BinaryFitter`\n", "object. This just uses the function that we have provided originally (`fit_kij`)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "model = problem._get_model(problem.solution.x, model)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Make predictions\n", "Let's repeat the calculation of Pxy diagrams that we have done earlier" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "image/png": 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168fBgweZMGECdevWPeP71Lp1a3799VfeeustIiIiiImJOeu8qbJQlnN6unbtSs2aNWnXrh0hISEkJSUxefJkDhw4wFdffVWs7quvvsoVV1xBx44duf/++9m3bx9vvvkm1113XbGdmx944AEyMjK46qqrqFGjBikpKUyfPp0tW7bw5ptv4uPjY607fPhwPvvsMxITE6lVq5a1vChx2bhxI2BJrpYsWQLAc889B8Dq1avp27cvffv2pW7dupw4cYLvvvuOv/76i/vvv59WrVqV+vtRr149fvrpJzp16kRCQgJLliwp8/lT4uBsuHJMpMKca0fmU+3du9fw9/c3unfvfsZjN910k+Ht7W3s2rWrWHlubq5RrVo1w9/fv9gS4wv5999/jY4dOxoeHh5GjRo1jJdfftmYNGnSBZesm81mY9SoUUZ0dLTh7u5utGzZ0pg3b54xYMCAM5ZIHzp0yOjXr5/h6+tr+Pv7GwMHDjT++usvAyi2hHzAgAGGt7f3WePctGmTER8fb/j4+BjVq1c37rvvPmPdunVnXfZ+tjbOtWNvdHS00a1btwt+nyZNmmTUq1fPcHd3N2JjY43Jkydbl4yfasuWLcZVV11leHp6GkCJl6/bekfm9957z2jfvr1RvXp1w8XFxQgODja6d+9+1qXphmEYf/75p3HFFVcYHh4eRnBwsDFo0CAjIyOjWJ0vv/zSiI+PN0JDQw0XFxejWrVqRnx8vDFnzpwz2ivaauD0XcA5z1L8Irt27TJ69+5t1KpVy/Dw8DC8vLyM1q1bGx9++KFhNpsv+LWfbUfmU79OT09PIyYmxti/f/8F2xIpKZNhXGA2oYicU0FBAREREXTv3v2M5bv2aPbs2dx0000sWbKEK6+80tbhiIhUKB1DIXIJZs+ezaFDh4pN9LUXp+/dUlhYyPjx4/Hz87uooQcRkcpOc3pELsKKFSv4999/efnll2nZsiUdO3a0dUhnGDJkCCdOnCAuLo7c3FxmzZrF0qVLGTVqVKk2zhMRqSqU9IhchA8++IBp06bRokWLYod32pPOnTvz5ptvMm/ePHJycqhbty7jx49n8ODBtg5NRMQmNKdHREREHILm9IiIiIhDUNIjIiIiDqHKz+kxm80cOHAAX19fmx5SKCIiIiVnnDyKJSIi4qwHQ1+MKp/0HDhwwHoAnoiIiFQue/fute6Qf6mqfNJTdEbN3r178fPzs3E0IiIiUhIZGRlERkZaP8fLQpVPeoqGtPz8/JT0iIiIVDJlOTVFE5lFRETEISjpEREREYegpEdEREQcQpWf01NShYWF5Ofn2zoMsQNubm5ltjxSRETsh8MnPYZhkJKSQlpamq1DETvh5ORETEwMbm5utg5FRETKkMMnPUUJT0hICF5eXtrA0MEVbWaZnJxMVFSUfh5ERKoQh056CgsLrQlPUFCQrcMROxEcHMyBAwcoKCjA1dXV1uGIiEgZceiJC0VzeLy8vGwcidiTomGtwsJCG0ciIiJlyaGTniIawpBT6edBRKRqUtIjIiIiDkFJj4iIiDgEJT0iIiLiEJT0iIiIiN3Z+PfvZd6mkp5K6NChQ4SFhTFq1Chr2dKlS3Fzc2PhwoU2jExEROTSpezfQ+Nf7yzzdh16n57TGYbBiXzbLFP2dHUu8aqh4OBgPv30U3r27Ml1111HgwYNuPPOOxk8eDDXXHNNOUcqIiJSvpb9Ppfy+DRT0nOKE/mFNHrhJ5u89qaXEvByK/nbcf3113Pfffdx++2306ZNG7y9vRk9enQ5RigiIlL+0o/nk7P9j3JpW8NbldjYsWMpKChg5syZTJ8+HXd3d1uHJCIickmmLN1NM2NrubStnp5TeLo6s+mlBJu9dmnt3LmTAwcOYDab2b17N02bNi2HyERERCpGVm4BX/61hUGmvRwvh/aV9JzCZDKVaojJlvLy8rjjjju49dZbadCgAffeey/r168nJCTE1qGJiIhclOnL91AjZzsu7mYM72Ags0zbt+nwVq1atTCZTGdcBg0aBEBOTg6DBg0iKCgIHx8fevXqRWpqqi1DthvPPvss6enpjBs3jqeeeor69etz99132zosERGRi5KRk8+Hi3fSwmknAKbwVmX+GjZNev7++2+Sk5Otl19++QWA3r17AzB06FC+//57Zs6cyeLFizlw4AA333yzLUO2C4sWLeKdd97h888/x8/PDycnJz7//HP+/PNPPvjgA1uHJyIiUmofL97FseP5XOm5x1IQ0bzMX8OmYznBwcHF7o8ZM4Y6derQsWNH0tPTmTRpEl988QWdO3cGYPLkyTRs2JDly5dz+eWX2yJku9CpUyfrCfFFatWqRXp6uo0iEhERuXgHM3L4ZMkuAC533w0FQHiLMn8du1m9lZeXx7Rp07j77rsxmUysWrWK/Px84uPjrXViY2OJiopi2bJl52wnNzeXjIyMYhcRERGxX2//up2cfDNX1HTDK3uvpTCsSZm/jt0kPbNnzyYtLY2BAwcCkJKSgpubGwEBAcXqhYaGkpKScs52Ro8ejb+/v/USGRlZjlGLiIjIpdiaksnX/1gSnWfbnkxLfMLAK7DMX8tukp5JkybRtWtXIiIiLqmd4cOHk56ebr3s3bu3jCIUERGRsmQYBs/P2UCh2SChcSiNXfZbHghpWC6vZxfrs/fs2cOvv/7KrFmzrGVhYWHk5eWRlpZWrLcnNTWVsLCwc7bl7u6uTfpEREQqgbnrDrAy8Sgerk48f0MjWPGt5YGQRuXyenbR0zN58mRCQkLo1q2btax169a4uroWO0Bz69atJCUlERcXZ4swRUREpIxk5uTz6vzNAAzqVJea1bzg4CbLg1W1p8dsNjN58mQGDBiAi8t/4fj7+3PPPfcwbNgwAgMD8fPzY8iQIcTFxTn0yi0REZGq4K1ftnEwM5daQV7cd1VtS+FBSxJUZZOeX3/9laSkpLNurPf222/j5OREr169yM3NJSEhgffff98GUYqIiEhZ+Wf3UaYs3Q3AyBub4OHqDCfSIOvkBsTBDSDXKPPXtXnSc91112EYZ//CPDw8mDBhAhMmTKjgqERERKQ85OQX8uQ3/2IYcEvrmnSsf3LPvmOJlmvvEHD3hdyy33LG5kmPiIiIVB2FZoOViUc5mJlDiK8HbWMCcXYyWR9/+5dt7DqcTYivO893awTmQtizFLbMs1SoVqvcYlPSIyIiImViwYZkRn6/ieT0HGtZuL8HI7o3okuTcFYnHWPin5adl0fd1BT/3T/Cgqcg48B/jaSuh01zoWanMo/PLlZviYiISOW2YEMyD05bXSzhAUhJz+HBaav5bvU+Hp2xFrMBPVtEEM8K+Lp/8YQHIP+EpXzLD2Ueo3p6RERE5JIUmg1Gfr+Js83QNQAT8Mx36zmRb6ZGgCcjuzeEj/qdfPQcfh1R5nGqp6cSmjp1KkFBQeTm5hYr79mzJ3feeaeNohIREUe1MvHoGT08pzKAE/lmTCYY17cF/gf/PrOH5/RnZCaXeZxKek5lGJCXbZvLOVawnU3v3r0pLCxk7ty51rKDBw8yf/78sy79FxERKU8HM8+d8JyqS+MwWkcH/rc0vYJpeOtU+cdh1KWd/XXRnjkAbt4lqurp6Um/fv2YPHkyvXv3BmDatGlERUXRqVOncgxSRETkTCG+HiWqd0e7aMsNn9ByjObc1NNTSd133338/PPP7N9vOZxtypQpDBw4EJPJdIFnioiIlK22MYGE+3twvk+gEF93Lq8TZLkTfQX4RcA5n2EC3/AyjlI9PcW5ell6XGz12qXQsmVLmjdvztSpU7nuuuvYuHEj8+fPL6fgREREzs3ZycSI7o14cNpqTJx9evJLNzb+b78eJ2fo8pplldYZzzhZJ34kPNu3TONU0nMqk6nEQ0z24N577+Wdd95h//79xMfHExkZaeuQRETEQXVpEs4Hd7Q6Y58ePw8XXr+lGV2anNZz06gH9Jl65j49fhHQZYz26ZHi+vXrx759+5g4caImMIuIiM11aRLOJ/3b4OnqDEDH+sGsfv7aMxOeIo16wKMboNGNlvuNb4ZH11vKy4GSnkrM39+fXr164ePjQ8+ePW0djoiIOLiU9BzunfoPJ/ILubx2IBP7t8HF+QKphpMzmE7WqXmZ5X45UdJTye3fv5/bb78dd3d3W4ciIiIOLCu3gLun/E1yeg51gr356I42uLmUMM3IOLknj1/ZT14+leb0VFLHjh1j0aJFLFq0iPfff9/W4YiIiAPLKzAzaPpqNiVnUN3HjSl3tcXfy7XkDWSenNPjW77bxijpqaRatmzJsWPHeO2112jQoIGtwxERkSrqQqemF5oNhn61lsXbDuHh6sQnAy4jMrCEK5KLTljPPLlZoVdQOXwF/1HSU0nt3r3b1iGIiEgVd6FT081mg6e//Zf565NxdTbx0Z1taBEZULLGN809c+XWZ92g6xuayCwiIiIV50Knpv+4/gAvzdvEzFX7cDLB+L4t6Vg/uGSNb5p79hPWM1Ms5Zvmnv15l0g9PSIiIlLMhU5NB3h85r9k5xUC8MYtzc+9LP105kJLD8/5Tlhf8DTc/VdpQi4R9fSIiIhIMRc6NR2wJjyv9GxCr9Y1S974nqUXPmE9Yz8krSh5myWkpEdERESKKemp6bddFskdl0eXrvGSnrCefbB07ZaAkh4REREppqSnpt/YokbpGy/pCeveIaVv+wKU9IiIiEgxJTk1Pdzfsny91EpywrpfDYhqV/q2L0BJTxXRqVMnHn30UVuHcUmmTJlCQEDAeeu8+OKLtGjR4rx1Bg4cqGM5REQuQdGp6ediAkZ0b1Rsv54SKzph3drS6S1jOXC0HI6jUNLjoBYtWoTJZCItLe2S2xo4cCAmk8l6CQoKokuXLvz777+XHqiIiNhElybhvNjjzMQm3N+DD+5oVfLVWmdTdML66cdO+EVYystpnx4tWS8DF9qt0hF06dKFyZMnA5CSksJzzz3HDTfcQFJSko0jExGRi7F052He+XU7hWaDEF937ruqNk0i/MvuM65RD4jtBpOuhf2r4MpH4ZoXdOCoPVuwIZn2r/1G34nLeWTGWvpOXE77135jwYbkcnvN7Oxs+vfvj4+PD+Hh4bz55ptn1Pn8889p06YNvr6+hIWF0a9fPw4etMyE3717N1dffTUA1apVw2QyMXDgQMvXs2AB7du3JyAggKCgIG644QZ27tx5wZjc3d0JCwsjLCyMFi1a8PTTT7N3714OHToEnL1nae3atZhMpjN2l549ezb16tXDw8ODhIQE9u7de8brffTRR0RGRuLl5UWfPn1IT08/Z2xms5nRo0cTExODp6cnzZs355tvvrng1yQi4ogMw+DzZbu5c9JKjh3Pp3lNf+Y/3IH7OtQmrk5Q2f5T7+QMTifP6KrRulwTHlDSc0kutFtleSU+TzzxBIsXL2bOnDn8/PPPLFq0iNWrVxerk5+fz8svv8y6deuYPXs2u3fvtiY2kZGRfPvttwBs3bqV5ORk3n33XcCSUA0bNox//vmHhQsX4uTkxE033YTZbC5xfFlZWUybNo26desSFFS6c1SOHz/Oq6++ytSpU/nrr79IS0vjtttuK1Znx44dfP3113z//fcsWLCANWvW8NBDD52zzdGjRzN16lQ+/PBDNm7cyNChQ7njjjtYvHhxqWITEanq8grMPDt7A8/P2Uih2aBniwi+eiCOYF/38nvR3EzLtbtv+b3GSRreukgX2q3SBIz8fhPXNgor06w4KyuLSZMmMW3aNK655hoAPvvsM2rWLL4x1N133229Xbt2bcaNG8dll11GVlYWPj4+BAZaZtyHhIQUmzzcq1evYu18+umnBAcHs2nTJpo0aXLOuObNm4ePjw9gSZzCw8OZN28eTk6ly6vz8/N57733aNeunfVra9iwIStXrqRt27YA5OTkMHXqVGrUsCyVHD9+PN26dePNN98kLCysWHu5ubmMGjWKX3/9lbi4OOv3Y8mSJXz00Ud07NixVPGJiFRVBzNzGPzFGlYmHsVkgqe6xPLAVbUxmcp5ukZuhuXa3a98Xwf19Fy0C+1WaQDJ6TmsTDxapq+7c+dO8vLyrEkBQGBg4Bknra9atYru3bsTFRWFr6+v9cP9QnNstm/fTt++falduzZ+fn7UqlWrRM+7+uqrWbt2LWvXrmXlypUkJCTQtWtX9uzZU6qvz8XFhcsuu8x6PzY2loCAADZv3mwti4qKsiY8AHFxcZjNZrZu3XpGezt27OD48eNce+21+Pj4WC9Tp04t0bCdiIgj+GvHYa5/dwkrE4/i4+7CpAFt+F/HOuWf8MApSY96euxWSXerLGm9spSdnU1CQgIJCQlMnz6d4OBgkpKSSEhIIC8v77zP7d69O9HR0UycOJGIiAjMZjNNmjS54PO8vb2pW7eu9f4nn3yCv78/EydO5JVXXrH2+BjGf31j+fn5l/BVlkxWVhYA8+fPL5YogWUekoiIIys0G4z/bTvvLtyOYUCDUF8m3N6KuiE+FROAYfw3vOVR/j09SnouUkl3qyxpvZKqU6cOrq6urFixgqioKACOHTvGtm3brL05W7Zs4ciRI4wZM4bIyEgA/vnnn2LtuLm5AVBYWGgtO3LkCFu3bmXixIl06NABgCVLllxUnCaTCScnJ06cOAFAcLDl5N3k5GSqVasGWCYyn66goIB//vnHOpS1detW0tLSaNiwobVOUlISBw4cICIiAoDly5fj5OR0Rm8XQKNGjXB3dycpKUlDWSIipziUmcujX63hrx1HAMuREiO6N8bTrXwnExeTfxyMk3NG1dNjv4p2q0xJzznrvB4TEHaxu1Weh4+PD/fccw9PPPEEQUFBhISE8OyzzxabOxMVFYWbmxvjx4/nf//7Hxs2bODll18u1k50dDQmk4l58+Zx/fXX4+npSbVq1QgKCuLjjz8mPDycpKQknn766RLFlZubS0pKCmBJwt577z2ysrLo3r07AHXr1iUyMpIXX3yRV199lW3btp111ZmrqytDhgxh3LhxuLi4MHjwYC6//HJrEgTg4eHBgAEDGDt2LBkZGTz88MP06dPnjPk8AL6+vjz++OMMHToUs9lM+/btSU9P56+//sLPz48BAwaU6OsTEalKftmUyvBZ/3I4Kw8vN2devakJN7UsxaGhZaWol8fkBK5e5f5ymtNzkU7drfIc+0le/G6VF/DGG2/QoUMHunfvTnx8PO3bt6d169bWx4ODg5kyZQozZ86kUaNGjBkzhrFjxxZro0aNGowcOZKnn36a0NBQBg8ejJOTEzNmzGDVqlU0adKEoUOH8sYbb5QopgULFhAeHk54eDjt2rXj77//ZubMmXTq1AmwJDNffvklW7ZsoVmzZrz22mu88sorZ7Tj5eXFU089Rb9+/bjyyivx8fHhq6++Klanbt263HzzzVx//fVcd911NGvWjPfff/+csb388ss8//zzjB49moYNG9KlSxfmz59PTExMib42EZGqIjMnnye/Wcd9U//hcFYesWG+zB3c3jYJD/yX9Lj5QgXMHzIZp06yqIIyMjLw9/cnPT0dP7/i44U5OTkkJiYSExODh8fFDUMt2JDMyO83FZvUHO7vwYjujS5tt0qxmbL4uRARsTcrdh3hsZnr2HfsBCYT3H9VbYZdWx93lwoczjrdgbXwcUfwjYDHNhd76Hyf3xdLw1uXqEuTcK5tFObwOzKLiIh9Op5XwNu/bOOTJYkYBkQGevJm7xZlPv3iohSc7DBwqZiFJUp6yoCzk4m4OqXbhE9ERKS8Ld52iOdmr2fvUcuiktsui+S5Gxrh424nH//5lrhw9ayQl7P5nJ79+/dzxx13EBQUhKenJ02bNi220sgwDF544QXCw8Px9PQkPj6e7du32zBiERER+3YkK5dHZ6xhwKcr2Xv0BDUCPJk88DLG9GpmPwkPnNLTUzFTCWz6lR87dowrr7ySq6++mh9//JHg4GC2b99uXdIM8PrrrzNu3Dg+++wzYmJieP7550lISGDTpk2abyEiInIKwzD4dvV+Xpm/ibTj+TiZYOAVMTx2XX287SnZKVKU9FRQT49NvwOvvfYakZGR1tO5gWIragzD4J133uG5557jxhtvBGDq1KmEhoYye/bsM85kEhERcVQb9qcz8vuN/L37GAANw/0Yc3NTmkcG2Daw88mv2J4emw5vzZ07lzZt2tC7d29CQkJo2bIlEydOtD6emJhISkoK8fHx1jJ/f3/atWvHsmXLbBGyiIiIXTmanccz362n+3tL+Hv3MTxdnXm6ayxzB19p3wkPQEHFzumxaU/Prl27+OCDDxg2bBjPPPMMf//9Nw8//DBubm4MGDDAutldaGhoseeFhoZaHztdbm4uubm51vsZGRnl9wWIiIjYSEGhmekrknjz561k5BQA0KN5BMOvjyXcv2KSiEuW70Crt8xmM23atGHUqFEAtGzZkg0bNvDhhx9e9E65o0ePZuTIkWUZpoiIiN0wDIM/th9m1PzNbE21bO7XMNyPF7s3ol3tSraS2DqR2QFWb4WHh9OoUaNiZQ0bNrSe6F10rEBqamqxOqmpqWc9cgBg+PDhpKenWy979+4th8hFREQq3vp96dwxaQUDPl3J1tRMArxceaVnE+YNaV/5Eh44ZSKzA6zeuvLKK9m6dWuxsm3bthEdHQ1YJjWHhYWxcOFCWrRoAViGq1asWMGDDz541jbd3d0d8vTsTp060aJFC9555x1bhyIiImVs79HjvPHTVuauOwCAm7MTd8ZFM/jqulTzdrNxdJegaJ8eR5jIPHToUJYvX86oUaPYsWMHX3zxBR9//DGDBg0CLCd1P/roo7zyyivMnTuX9evX079/fyIiIujZs6ctQ6/0Fi1ahMlkIi0t7ZLbGjhwICaTCZPJhKurK6GhoVx77bV8+umnmM3mSw9WRMRBHc3OY+T3G+n85iJrwtOzRQQLH+vI8zc0qtwJD0BhnuXaEeb0XHbZZXz33XcMHz6cl156iZiYGN555x1uv/12a50nn3yS7Oxs7r//ftLS0mjfvj0LFiywrz16zIWwZylkpYJPKERfAU42PMvEBrp06cLkyZMpLCwkNTWVBQsW8Mgjj/DNN98wd+5cXFzscH8IERE7lXY8j4l/7mLKX7vJzisEoEO96jzVJZYmNfxtHF0ZKjr+01QxfTA235H5hhtuYP369eTk5LB582buu+++Yo+bTCZeeuklUlJSyMnJ4ddff6V+/fo2ivYsNs2Fd5rAZzfAt/dYrt9pYikvJ9nZ2fTv3x8fHx/Cw8N58803z6jz+eef06ZNG3x9fQkLC6Nfv34cPHgQgN27d3P11VcDUK1aNUwmEwMHDgQsp6W3b9+egIAAgoKCuOGGG9i5c+cFY3J3dycsLIwaNWrQqlUrnnnmGebMmcOPP/7IlClTrPXS0tK49957CQ4Oxs/Pj86dO7Nu3ToA0tPTcXZ2tu7IbTabCQwM5PLLL7c+f9q0aURGRl7U901EpKIVmg2W7TzCnLX7WbbzCIXm85/xnX48nzd/3kr7135nwu87yc4rpEkNPz6/py2f39OuaiU85kJI32e5nbbXcr+c2TzpqdQ2zYWv+0PGgeLlGcmW8nJKfJ544gkWL17MnDlz+Pnnn1m0aBGrV68uVic/P5+XX36ZdevWMXv2bHbv3m1NbCIjI/n2228B2Lp1K8nJybz77ruAJaEaNmwY//zzDwsXLsTJyYmbbrrpooapOnfuTPPmzZk1a5a1rHfv3hw8eJAff/yRVatW0apVK6655hqOHj2Kv78/LVq0YNGiRQCsX78ek8nEmjVryMrKAmDx4sV07Nix1LGIiFS0BRuSaf/ab/SduJxHZqyl78TltH/tNxZsSD6jbvqJfN7+ZRvtX/uN8b/tICu3gIbhfnx8Z2u+H9yeDvWCbfAVlKOiDoOt8y33/51R7h0GoANHL565EBY8BZwtazcAEyx4GmK7lelQV1ZWFpMmTWLatGlcc801AHz22WfUrFmzWL27777bert27dqMGzeOyy67jKysLHx8fAgMtJyuGxISQkBAgLVur169irXz6aefEhwczKZNm2jSpEmp442NjeXff/8FYMmSJaxcuZKDBw9aJ5uPHTuW2bNn880333D//ffTqVMnFi1axOOPP86iRYu49tpr2bJlC0uWLKFLly4sWrSIJ598stRxiIhUpAUbknlw2uozPiFS0nN4cNpqPrijFV2ahJOZk8/kv3bzyZ+7rHvtNAj1Zei19biuURhOTqaKD768FXUYnP7dKeow6DMVGvUol5dW0nOx9iw9s4enGAMy9lvqxXQos5fduXMneXl5tGvXzloWGBhIgwYNitVbtWoVL774IuvWrePYsWPWnpqkpKQztgk41fbt23nhhRdYsWIFhw8fLva8i0l6DMPAZLL80q5bt46srCyCgoovqzxx4oR1CK1jx45MmjSJwsJCFi9ezHXXXUdYWBiLFi2iWbNm7Nixg06dOpU6DhGRilJoNhj5/abz/UvMC3M28u++dKYt32NNduqF+PBofH26NqmiyQ6UrsOgHCjpuVhZqReuU5p6ZSg7O5uEhAQSEhKYPn06wcHBJCUlkZCQQF5e3nmf2717d6Kjo5k4cSIRERGYzWaaNGlyweedy+bNm63nqWVlZREeHm4dvjpVUW/TVVddRWZmJqtXr+aPP/5g1KhRhIWFMWbMGJo3b05ERAT16tW7qFhERCrCysSjJKfnnPNxAziYmcv7iyz/7NUJ9uaR+Pp0axqOc1VNdoqUpsMgqHmZv7ySnovlE3rhOqWpV0J16tTB1dWVFStWEBUVBVhOq9+2bZt1rsuWLVs4cuQIY8aMsU76LZocXMTNzbLMsbDwv4ljR44cYevWrUycOJEOHSy9U0uWLLnoWH/77TfWr1/P0KFDAWjVqhUpKSm4uLhQq1atsz4nICCAZs2a8d577+Hq6kpsbCwhISHceuutzJs3T/N5RMTuHcw8d8JzqshAT569vmHVHcY6m9J0GJTDXouayHyxoq8AvwgsHZVnYwK/GpZ6ZcjHx4d77rmHJ554gt9++40NGzYwcOBAnJz+eyujoqJwc3Nj/Pjx7Nq1i7lz5/Lyyy8XDz86GpPJxLx58zh06BBZWVlUq1aNoKAgPv74Y3bs2MFvv/3GsGHDShRXbm4uKSkp7N+/n9WrVzNq1ChuvPFGbrjhBvr37w9AfHw8cXFx9OzZk59//pndu3ezdOlSnn322WJJWadOnZg+fbo1wQkMDKRhw4Z89dVXSnpExO6F+JZsS5XXbm5GlybhjpPwgM06DIoo6blYTs7Q5bWTd07/gT15v8uYctmv54033qBDhw50796d+Ph42rdvT+vWra2PBwcHM2XKFGbOnEmjRo0YM2YMY8eOLdZGjRo1GDlyJE8//TShoaEMHjwYJycnZsyYwapVq2jSpAlDhw7ljTfeKFFMCxYsIDw8nFq1atGlSxd+//13xo0bx5w5c3B2tnwPTCYTP/zwA1dddRV33XUX9evX57bbbmPPnj3FDpXt2LEjhYWFxebudOrU6YwyERF7dFmtagR6u57zcRMQ7u9ROY+NuFQ26jCwtm4Yxvk3DajkMjIy8Pf3Jz09HT8/v2KP5eTkkJiYSExMzMVvdrhprmVS1qljlH41LAlPOc0+l/JVJj8XIuJwcgsKmbv2AJOWJLIlJfOsdYo+6otWbzkk6+otKD6h+eR35+TqrfN9fl8szem5VI16WGaZO/iOzCIijupIVi7TVyQxddkeDmflAuDp6ky72oFsPJDBocxca90wfw9GdG/kuAkPWD43+0w9S4dBRLl3GCjpKQtOzmW6LF1EROzf9tRMPv0rkVmr95NbYNneI8zPg4FX1qLvZVH4e7lSaDZYmXiUg5k5hPh60DYmsOqv0CqJog6D6bfAzt+g9d3QbWy5dxgo6RERESkhwzD4c/thJi1JZPG2Q9byZjX9uad9DNc3DcfV+b/pss5OJuLqOODcnZJwcgbvkztNB9WukBESJT0iIiIXkJNfyJy1+5m0JJFtqZZjcUwmuK5RKPd2qE2b6GrWjVilFKzTiivme6ekB0vmLlJEPw8iUuRwVi6fL9vDtOV7OJJt2aTV282Z3m0iuevKWkQHeds4wsquYk9Zd+ikx9XVsqTw+PHjeHp62jgasRdFu08XLbUXEcezNSWTSUt2MXvtAfJOzteJ8LfM17n1sij8Pc+9JF1KwTh5mHUF9ZI5dNLj7OxMQEAABw8eBMDLy0vdkw7ObDZz6NAhvLy8cHFx6F8PEYdjGAaLtx1i0pJE/tx+2FrePDKAe9vH0KVJWLH5OlIGNLxVscLCwgCsiY+Ik5MTUVFRSoBFHEROfiHfrbHM19lx0DJfx8kECY3DuLdDDK2iNF+n/Gh4q0KZTCbCw8MJCQkhPz/f1uGIHXBzcyt2rIeIVE0HM3OYtmwP01YkcfTkfB0fdxf6nJyvExnoZeMIHYCGt2zD2dlZczhERBzA5uQMJi1JZO7aA+QVWj50awR4cteVtehzWSR+HpqvU2EqeOGIkh4REanyzGbLfJ1Pluzirx1HrOWtogK4p31tEhqH4qL5OhXP2tOj4S0REZFLkpNfyKzV+5m0ZBc7D2UDlvk6XZuEc3f7GFpHV7NxhAJoeEtERORinWu+zm2XRTLgCs3XsRuGJjKLiIhclC0pGUz6M5E5p83Xubt9DH3a1MRX83XsS9Hwlpasi4iIXJhhGPyx/TCf/Lmr2P46raICuLdDba5rpPk69quop0dJj4iIyDkVnYf1yZ+JbD9lfx3N16lENLwlIiJyboezcpm2fA+fL/vvPCwfdxduvSySgZqvU7loeEtERORM21MzmbQkkVlr9lvPw9L+OpWdhrdEREQAy3ydFYlH+WjxTn7fesha3jwygPs6xNClcZjm61RmGt4SERFHV2g2+HljCh/+sYt1e9MAnYdVJWl4S0REHFXRZoIT/9xF4mHLZoLuLk70aRPJvR1iiA7ytnGEUrY0vCUiIg4m/UQ+05bvYfJfuzmclQuAv6crA+Ki6X9FLar7uNs4QikXGt4SERFHkZx+gk+XJPLFiiSy8woBy+Tke9rHcOtlkXi762OqSrMOb1UM/TSJiEiF256ayYeLdzFn7X4KzJb/9mPDfHmgY21uaBaBqyYnOxYNb4mISGVUaDZYmXiUg5k5hPh60DYmEGcny4faqj3HeP/3HSzcctBa//LagTzQsQ6d6gdrcrKjMBfCnqWQdfDCdcuQkh4RESkzCzYkM/L7TSSn51jLwvzc6ds2ihWJR1m68whg+ce+S+MwHuhYhxaRATaKVmxi01xY8BRkHPivbP5j4OQKjXqU60ubDKNoFlHVlJGRgb+/P+np6fj5+dk6HBGRKmvBhmQenLaa832ouDiZuLlVDf7XsQ61g30qLDaxE5vmwtf94aw/JSboM9Wa+JTH57d6ekRE5JIVmg1Gfr/pvAmPl5szPz16lY6JcFTmQksPz/l+ShY8DbHdwMm5XELQTDEREblkKxOPFhvSOpvjeYXsO3aigiISu7NnafEhrTMYkLHfUq+c2DTpefHFFzGZTMUusbGx1sdzcnIYNGgQQUFB+Pj40KtXL1JTU20YsYiInC63oJDZa/eXqO7BzPMnRlKFZZXw87uk9S6CzYe3GjduzK+//mq97+LyX0hDhw5l/vz5zJw5E39/fwYPHszNN9/MX3/9ZYtQRUTkFLkFhcz8Zx/v/76DAxfo5SkS4utRzlGJ3fIJLdt6F8HmSY+LiwthYWFnlKenpzNp0iS++OILOnfuDMDkyZNp2LAhy5cv5/LLL6/oUEVEBMgrMDNz1V4m/PZfshPq586JvEIycgrO+hwTEOZvWb4uDir6CvCLgIxkzjmR2S/CUq+c2HxOz/bt24mIiKB27drcfvvtJCUlAbBq1Sry8/OJj4+31o2NjSUqKoply5ads73c3FwyMjKKXURE5NLlFZj5cmUSV49dxLPfbeBAeg6hfu6M7NGYxU9czeu3NMPEmUdHFt0f0b2Rdb8ecUBOztDltZN3zvFz0GVMuU1iBhsnPe3atWPKlCksWLCADz74gMTERDp06EBmZiYpKSm4ubkREBBQ7DmhoaGkpKScs83Ro0fj7+9vvURGRpbzVyEiUrXlF5qZsTKJzm8uYvis9exPO0GIrzsjujdi8RNXM+CKWni4OtOlSTgf3NGKMP/iQ1hh/h58cEcrujQJt9FXIHajUQ/LsnS/034WPIOKLVcvL3a1T09aWhrR0dG89dZbeHp6ctddd5Gbm1usTtu2bbn66qt57bXXztpGbm5usedkZGQQGRmpfXpEREopv9DMd6v3M/737ew9all1FezrzoMd69CvXRQermf/j/x8OzKLAP/tyDznIUhLgn5fQ/2EYlWq/D49AQEB1K9fnx07dnDttdeSl5dHWlpasd6e1NTUs84BKuLu7o67u07jFRG5WAWFZr5bs5/xv+0g6ehxAKr7uPO/jrW54/LocyY7RZydTMTVCaqIUKWycnKGmA7g4X/yfsWkI3aV9GRlZbFz507uvPNOWrdujaurKwsXLqRXr14AbN26laSkJOLi4mwcqYhI1WM2GyzYmMKbP29l56FsAKr7uPG/jnW4vV00nm7lN9dCHFTRYJMjHDj6+OOP0717d6Kjozlw4AAjRozA2dmZvn374u/vzz333MOwYcMIDAzEz8+PIUOGEBcXp5VbIiJlyDAMFm87xNift7Jhv2XxR4CXKw92rMOdcdF4udnV/8dSlVhn2DhA0rNv3z769u3LkSNHCA4Opn379ixfvpzg4GAA3n77bZycnOjVqxe5ubkkJCTw/vvv2zJkEZEq5e/dR3ljwVZW7j4KgLebM/d2qM29HWLw9XC1cXRS9RX19FTMuiq7mshcHnTgqIjImTbsT2fsz1tZtPUQAG4uTgyIi+bBTnUJ9HazcXTiMCa0g0NbYMD3EHNVsYeq/ERmEREpXzsOZvH2L9uYvz4ZsJx63ueySIZ0rku4v6eNoxOH40jDWyIiUjFSM3J4+5dtfP3PXsyGZd7ojc0jeDS+PrWqe9s6PHFYFTu8paRHRKQKy8jJ56PFO5m0JJGcfDMA1zYK5bHr6hMbpiF/sTHD8jPpEKu3RESkfOQVmJm+Yg/jf9vB0ew8ANpEV2P49bG0jtb5V2InNLwlIiIXyzAM5v2bzBs/bbVuLFg72Junu8RybaNQTBX0H7VIiVh7ejS8JSIipbBs5xHG/LiZdfvSAcuREUPj69OnTU1cnG1+vrTIWTjQ5oQiInLptqZkMubHzfx+cvm5t5szD3Ssw70dYrSxoNg3QxOZRUSkBA5m5PDmz9uYucqyIsvFyUS/dlE8fE09qvvoDEKpBDSnR0REzicnv5BJSxKZ8PsOjucVAnB90zCeSIglRsvPpVIp6umpmFdT0iMiUkkYhsH3/ybz2o9b2J92AoAWkQE8f0MjWkdXs3F0IhdBw1siInK6NUnHeHneJlYnpQEQ4e/BU11j6d4sAicnrciSSqpo9ZaGt0RE5EDaCV5bsIU5aw8A4OnqzIOd6nBfh9p4ujnbODqRS6XVWyIiDi87t4CPFu/koz92kVtgxmSCXq1q8kRCA0L9PGwdnkjZ0PCWiIjjMpsNvl29jzd+2srBzFwA2sYE8sINjWhSw9/G0YmUMQ1viYg4plV7jvHi3I2s32/ZXDAq0Itnro8loXGYdlKWKkrDWyIiDuVgZg5jftzCrNX7AfB1d2Fw57oMvLIW7i6atyNVmIa3REQcQ36hmc+W7uadX7eTlVsAQJ82NXmyS6w2FxTHoOEtEZGqb8n2w7z4/UZ2HMwCoHlNf0be2IQWkQG2DUykQml4S0Skytp37Divzt/MjxtSAAjyduPJLg3o3TpS++2I49HwlohI1ZOTX8hHi3fxweId5OSbcXYycefl0Qy9tj7+nq62Dk/ENnT2lohI1WEYBr9sSuXl+ZvYe9RydES7mEBG3tiY2DA/G0cnYmsa3hIRqRJ2Hspi5Peb+GPbIQDC/Dx4tltDbmgWriXoIvDfRGYlPSIildPxvALG/7aDT/7cRX6hgZuzE/d2iGHQ1XXxdtefXRErDW+JiFRev2xK5cW5G62noF/dIJgXujcmprq3jSMTsUca3hIRqXT2HTvOi3M38evmVABqBHjyYo/GXNso1MaRidgx6/BWxWzCqaRHROQS5BWYmbQkkXcXbiMn34yLk4n7rqrNkM518XLTn1iR87ImPVqyLiJi15bvOsLzszew/eQGg+1iAnmlZxPqhfraODKRSkITmUVE7NvhrFxG/bDZelZWkLcbz1zfkJtb1dCqLJHSUE+PiIh9MpsNvvw7idcXbCX9RD4mE/RrG8WTCbH4e2mDQZFS047MIiL2Z3NyBs98t541SWkANI7w45WeTWgZVc22gYlUVobBf6u3lPSIiNhcTn4h7y7czsQ/dlFgNvBxd+Gx6+pz5+XRuDhXzB9qkSrJukcPSnpERGxtyfbDPDt7PXuOHAegS+MwXuzRmDB/DxtHJlIFFM3nAU1kFhGxlSNZubw6fzOz1lgmKof5efDSjY25rnGYjSMTqUKKJT3q6RERqVCGYfDt6v28On8Tx45bJioPiKvF4wkN8NHxESJl69SkR8dQiIhUnMTD2Tz73XqW7jwCQGyYL2N6NaNFZIBtAxOpqtTTIyJSsfIKzEz8cxfvLtxOXoEZdxcnhl5bn3vax+Cqicoi5cdek55WrVqVqlGTycTcuXOpUaNGiZ8zZswYhg8fziOPPMI777wDQE5ODo899hgzZswgNzeXhIQE3n//fUJDdZaNiFy6VXuO8cys9WxNzQSgQ73qvNKzCdFBOhxUpPzZ6eqttWvX8thjj+Hj43PBuoZhMGbMGHJzc0scxN9//81HH31Es2bNipUPHTqU+fPnM3PmTPz9/Rk8eDA333wzf/31V4nbFhE5XXZuAW/8tJXPlu3GMCDQ240XbmjEjS0itKOySEWx154egCeeeIKQkJAS1X3zzTdLHEBWVha33347EydO5JVXXrGWp6enM2nSJL744gs6d+4MwOTJk2nYsCHLly/n8ssvL/FriEjVVmg2WJl4lIOZOYT4etA2JhBnp7MnL39sO8TwWevZn3YCgF6tavJct4ZU83aryJBFHJe5EPYshaO7/iuzp6QnMTGR4ODgEje6adMmIiIiSlR30KBBdOvWjfj4+GJJz6pVq8jPzyc+Pt5aFhsbS1RUFMuWLTtn0pObm1uslykjI6PEcYtI5bNgQzIjv99EcnqOtSzc34MR3RvRpUm4tSzteB6vzN/MN6v2AVCzmiejbmrKVfVL/rdNRC7Rprmw4CnIOFC8fPM8aNKz3F++RKlVdHQ0JpOJgoICXnrpJfbt23fe+pGRkTg7O1+w3RkzZrB69WpGjx59xmMpKSm4ubkREBBQrDw0NJSUlJRztjl69Gj8/f2tl8jIyAvGISKV04INyTw4bXWxhAcgJT2HB6etZsGGZAB+XJ9M/Ft/8M2qfZhMMPCKWvz06FVKeEQq0qa58HX/MxMegG8GWB4vZ6XqT3JxceGNN96goKDgkl947969PPLII0yfPh0Pj7Lb3XT48OGkp6dbL3v37i2ztkXEfhSaDUZ+v+nUqZBWRWUvzNnI/z5fxYPTV3M4K5c6wd588784XuzRGG/tuyNSccyFlh6es/7GAphgwdOWeuWo1INonTt3ZvHixZf8wqtWreLgwYO0atUKFxcXXFxcWLx4MePGjcPFxYXQ0FDy8vJIS0sr9rzU1FTCws69K6q7uzt+fn7FLiJS9axMPHpGD8+pDOBgZi4LNqbg4mRi8NV1mf9wB1pHB1ZckCJisWfp2Xt4rAzI2G+pV45K/a9O165defrpp1m/fj2tW7fG27v40s4ePXqUqJ1rrrmG9evXFyu76667iI2N5amnniIyMhJXV1cWLlxIr169ANi6dStJSUnExcWVNmwRqWIOZp474TlVzWqefHRnaxpH+JdzRCJyTlmpZVvvIpU66XnooYcAeOutt854zGQyUVhYsq4pX19fmjRpUqzM29uboKAga/k999zDsGHDCAwMxM/PjyFDhhAXF6eVWyJCiG/JhsXH3NxMCY+IrfmUcH+9kta7SKVOesxm84UrlZG3334bJycnevXqVWxzQhGRtjGBhPt7kJKec85ZAuH+HsTVCarQuETkLKKvAL8IyEjm7PN6TJbHo68o1zBMhmGc6+9FlZCRkYG/vz/p6ema3yNSxfyw/gAPTV9z1sdMwAd3tCq2bF1EbKho9RZwZuJjgj5TodF/U2TK4/P7opYvZGdns3jxYpKSksjLyyv22MMPP1wmgYmInM/uw9lM+WvPWR872z49ImJjjXpYEpsz9uk5M+EpL6Xu6VmzZg3XX389x48fJzs7m8DAQA4fPoyXlxchISHs2rXrwo1UIPX0iFQtZrPB5KW7eeOnLeTkm/F2c+bprg2pE+zNoazcC+7ILCI2VrQjc8q/8NMz4OYDz+w/o5pd9PQMHTqU7t278+GHH+Lv78/y5ctxdXXljjvu4JFHHimToEREzibxcDZPfrOOv3cfA+DKukGMubkZkYFeNo5MRErMyRliOoBvuCXpMV14M+OyUuqkZ+3atXz00Uc4OTnh7OxMbm4utWvX5vXXX2fAgAHcfPPN5RGniDiwQrPBlNN6d57t1oi+bSN1QKhIZVV04GgF/g6XOulxdXXFycmyp2FISAhJSUk0bNgQf39/7X4sImVu16EsnvzmX/7ZY+ndaV+3OmN6NaVmNfXuiFRq1qSnYg4bhYtIelq2bMnff/9NvXr16NixIy+88AKHDx/m888/P2PfHRGRi1VoNpj8VyJv/LSV3AIzPu4uPNutIbddpt4dkSqhMvT0jBo1iszMTABeffVV+vfvz4MPPki9evX49NNPyzxAEXE8SUeO8/jMdazcfRSADvWqM6ZXM2oEeNo4MhEpM5Whp6dNmzbW2yEhISxYsKBMAxIRx2UYBl+sTOLV+Zs5nleIt5szz93QSL07IlWRcfIEB6eKO/z3ol/p4MGDbN26FYDY2FiCg4PLLCgRcTwp6Tk8+e2//LHtEADtYgIZ27u5VmaJVFXmAsu1PSc9mZmZPPTQQ8yYMcN6zpazszO33norEyZMwN9fZ9yISMkZhsHstfsZMWcjGTkFuLs48VSXWAZeUQsn7bUjUnWZi3p6Km7JeqkH0u69915WrFjBvHnzSEtLIy0tjXnz5vHPP//wwAMPlEeMIlJFHcnK5cFpqxn61ToycgpoHhnA/Ic7cHf7GCU8IlVdZejpmTdvHj/99BPt27e3liUkJDBx4kS6dOlSpsGJSNX108YUnpm1niPZebg6m3jkmnr8r2MdXJwrblKjiNhQZUh6goKCzjqE5e/vT7Vq1cokKBGputJP5DNy7kZmrbFsOx8b5subfZrTOEJD4yIOpWh4qwJ3ZC71v1TPPfccw4YNIyUlxVqWkpLCE088wfPPP1+mwYlI1fLHtkMkvP0Hs9bsx8kED3Wqw5zBVyrhEXFENpjTU6KenpYtWxZbLrp9+3aioqKIiooCICkpCXd3dw4dOqR5PSJyhuzcAkb/uJlpy5MAiKnuzdjezWkdrd5hEYdlr8NbPXv2LOcwRKSqWpl4lMdnriPp6HEABl5Riye7NMDLreL+0ImIHbLXpGfEiBHlHYeIVDG5BYW89fM2Pv5zF4YBEf4evNG7OVfWrW7r0ETEHthr0iMiUhpbUjJ4dMZatqRYjqzp3bomz3dvhJ+Hq40jExG7YU167Gwic2BgIIcPHy5xo1FRUezZs+eigxKRyslsNvjkz130GP8XW1IyCfR24+M7W/NG7+ZKeESkOLOdHkORlpbGjz/+WOLdlo8cOWLdrVlEHMOBtBM8PnMdS3ceAaBzbAiv9WpGsK+7jSMTEbtkz2dvDRgwoDzjEJFKbM7a/Tw/ewMZOQV4ujrz3A0N6dc2SoeEisi52eucHrPZXN5xiEgllH48n+fnbGDuugMANI8M4O0+zakd7GPjyETE7tlgTo8mMovIRVm64zCPzVxHcnoOzk4mhnSuy6Cr6+KqYyREpCTsdXNCEZEiOfmFvPHTViYtSQSgVpAXb9/agpZR2mhQRErBXoe3REQANh3IYOhXa9maalmK3q9dFM91a6iNBkWk9JT0iIg9Kjy5FP3Nn7eRV2imuo8br/VqxjUNQ20dmohUVkp6RMTe7Dt2nGFfr2Nl4lEA4huGMqZXU6r7aCm6iFwCe57TYzabeeONN5g7dy55eXlcc801jBgxAk9Pz/KMT0RsxDAMvluznxFzNpKZW4CXmzMv3NCIWy+L1FJ0Ebl09ro5IcCrr77Kiy++SHx8PJ6enrz77rscPHiQTz/9tDzjExEbSDuex7PfbWD++mQAWkUF8PatLYgO8rZxZCJSZdjz8NbUqVN5//33eeCBBwD49ddf6datG5988glOTlqiKlJV/LXjMMO+XktqRi4uTiYeuaYeD3aqg4uWootIWbLnpCcpKYnrr7/eej8+Ph6TycSBAweoWbNmuQQnIhUnr8DMmz9vtZ6KXru6N+/c1oJmNQNsHZqIVEX2vDlhQUEBHh4excpcXV3Jz88v86BEpGLtOJjFIzPWsPFABgB920bx/A1aii4i5ahoTo/JDpMewzAYOHAg7u7/rdjIycnhf//7H97e/43zz5o1q2wjFJFyYxgGX67cy0vzNpKTbybAy5UxNzejS5MwW4cmIlWdPQ9vne3A0TvuuKNMgxGRinM0O4+nv/2XnzelAnBl3SDe6tOCUD+PCzxTRKQMFOZarl0qbvuLEic9kydPLs84RKQUCs0GKxOPcjAzhxBfD9rGBOLsVPJl5Eu2WyYrH8zMxdXZxJMJsdzTPganUrQhIlJi5kLYsxSyUsEnFKKvgMKT02Oc3SosDA3Yi1QyCzYkM/L7TSSn51jLwv09GNG9EV2ahJ/3ubkFhYz9aSsT/7Scm1Un2Jt3b2tJkxr+5RqziDiwTXNhwVOQceC/Mr8ICIi23FbSIyJns2BDMg9OW41xWnlKeg4PTlvNB3e0Omfis+NgJg9/uZZNyZbJyre3i+K5bo3wdKu4SYQi4mA2zYWv+8Ppf7Uykv9LgpxdKywcm2688cEHH9CsWTP8/Pzw8/MjLi6OH3/80fp4Tk4OgwYNIigoCB8fH3r16kVqaqoNIxaxnUKzwcjvN52R8MB/f05Gfr+JQnPxGoZhMG35Hm4Yv4RNyRlU83Ll4ztb8+pNTZXwiEj5MRdaenjO+1cLx0l6atasyZgxY1i1ahX//PMPnTt35sYbb2Tjxo0ADB06lO+//56ZM2eyePFiDhw4wM0332zLkEVsZmXi0WJDWqczgOT0HOsZWWCZrHzf1FU8N3sDOflmOtSrzoJHr+K6xlqdJSLlbM/S4kNa53JsT/nHcpJNh7e6d+9e7P6rr77KBx98wPLly6lZsyaTJk3iiy++oHPnzoBlMnXDhg1Zvnw5l19+uS1CFrGZg5nnTnjOVu/P7YcY9vU6DmXm4ubsxJNdGnD3lZqsLCIVJKuEIzP5x8s3jlPYzZyewsJCZs6cSXZ2NnFxcaxatYr8/Hzi4+OtdWJjY4mKimLZsmXnTHpyc3PJzc213s/IyCj32EUqQohvyZaSV/Ny45V5m/hkiWWyct0QH969rQWNIzRZWUQqkE9oyep5BZVvHKewedKzfv164uLiyMnJwcfHh++++45GjRqxdu1a3NzcCAgIKFY/NDSUlJSUc7Y3evRoRo4cWc5Ri1S8tjGBhPt7kJKec9YRchMQ5OPG6B83szk5E4A7Lo/i2es1WVlEbCD6CssqrYxkzj6v56SwZhUWks1PEGzQoAFr165lxYoVPPjggwwYMIBNmzZddHvDhw8nPT3detm7d28ZRitiO85OJkZ0bwRYEpzTGUD6iXw2J2cS6O3GJ/3b8EpPTVYWERtxcoYur528c/pfrVPuu1bchqg2T3rc3NyoW7curVu3ZvTo0TRv3px3332XsLAw8vLySEtLK1Y/NTWVsLBzT8J0d3e3rgYruohUFV2ahPPBHa0I8y/+R8LDxfKrnF9oWCYrP9KB+EYl7FoWESkvjXpAn6ngd9pWGn4R4Hvys9yR9+kxm83k5ubSunVrXF1dWbhwIb169QJg69atJCUlERcXZ+MoRWynS5Nwrm0UxsrEoyzbdZjpy5M4kp2Hq7OJJxIacG/72pqsLCL2o1EPiO125o7M41pYHneUpGf48OF07dqVqKgoMjMz+eKLL1i0aBE//fQT/v7+3HPPPQwbNozAwED8/PwYMmQIcXFxWrklgmUJ+3u/7cBsQK0gL8b1bUmzmgG2DktE5ExOzhDToXhZQZ7l2lGSnoMHD9K/f3+Sk5Px9/enWbNm/PTTT1x77bUAvP322zg5OdGrVy9yc3NJSEjg/ffft2XIIjZ3IO0Ej3611rofz80ta/BSzyb4uNtdx62IyLkVVnzSYzIM4zxTqiu/jIwM/P39SU9P1/weqfQWbEjhqW//Jf1EPt5uzrxyUxNualnT1mGJiJTeqJqQlwkPr4HA2mc8XB6f3/rXUKQSyMkv5JX5m5i2PAmAZjX9GXdbS2pV97ZxZCIiF6nw5J56jjK8JSIXtjUlkyFfrmZbahYAD3SszWPXNsDNxeaLL0VELo5h2GR4S0mPiJ0yDINpK5J4Zd4mcgvMVPdx560+zbmqfrCtQxMRuTSF+f/dVtIj4tjSjufx5Df/8vMmy9k1HesH82af5lT3cbdxZCIiZaColweU9Ig4shW7jvDoV2tJTs/B1dnEU11idVCoiFQtpyY9LhX3z5ySHhE7UVBoZtxvO3jvt+2YDYip7s34vi1pUkMHhYpIFVOU9JicLHv4VBAlPSJ2YN+x4zw6Yy3/7DkGwC2tazKyR2O8tfeOiFRF+Scs1y6eFfqy+osqYmM/rE/m6W//JSOnAB93F169qQk3tqhh67BERMpPUdLjqqRHxCGcyCvkpXmb+HKlZe+d5pEBjL+tJVFBXjaOTESknBUo6RFxGJuTMxjy5Rp2HMzCZIL/dazDsGvr4+qsvXdExAGop0ek6jMMg8+X7+GV+ZvJKzAT7OvO231a0L5edVuHJiJScfJzLNcuHhX6skp6RCpI2vE8nvjmX345uffO1Q2CGdu7OUHae0dEHE3+ccu1a8UO5yvpEakAq/YcZcgXaziQnoObsxNPd43lritrYTJp7x0RcUAFJ3t6XNXTI1JlmM0GH/2xi7E/b6XQbFAryIv3+rXS3jsi4tjU0yNStRzOymXY1+v4Y9shAHo0j2DUzU3x0d47IuLoNKdHpOpYuvMwj8xYy6HMXDxcnRjZozF92kRqOEtEBNTTI1IVFJoNxi3czrjftmMYUC/Eh/f6taJBmK+tQxMRsR+a0yNSuaVm5PDIjDUs33UUgD5tavJij8Z4uenXTESkGGtPj/bpEal0Fm09yLCv13E0Ow8vN2devakJN7WsaeuwRETsk3VOj5IekUojv9DMmz9v48PFOwFoFO7He/1aUjvYx8aRiYjYMeuOzBreEqkU9h07zsNfrmF1UhoA/eOieeb6hni4Ots2MBERe2c9e0sTmUXs3s8bU3jim39JP5GPr4cLr/dqRtem4bYOS0Skcijq6dGSdRH7lVtQyOgftjBl6W7AcjL6e31bEhmok9FFREpMS9ZF7Nvuw9kM/nI1G/ZnAHBfhxieSIjFzUUno4uIlEpuluXavWLnPyrpESmBuesO8Mys9WTlFhDg5cqbvZtzTcNQW4clIlI55Z1MetyU9IjYjZz8QkZ+v4kvVyYBcFmtaozr25Jw/4pdZikiUqWop0fEvuw4mMmg6WvYmpqJyQSDr67LI9fUw8VZw1kiIpckL9Ny7Vaxu9Ur6RE5i29W7eP52Rs4kV9IdR933rm1Be3rVbd1WCIilZ9hqKdHxB5k5xbw/OwNzFqzH4Ar6wbx9q0tCPGt2GWVIiJVVkEOGIWW25rTI2Ibmw5kMPjL1ew6lI2TCYZdW58HO9XF2Ukno4uIlJnczP9uK+kRqViGYTB9RRIvzdtEXoGZMD8PxvVtSduYQFuHJiJS9RQlPa7e4FSxcySV9IhDy8jJZ/i365m/PhmAzrEhjO3dnEBvNxtHJiJSRRUtV3ev2EnMoKRHHNi6vWkM/nI1e4+ewMXJxFNdYrmnfQxOGs4SESk/NprEDEp6xAEZhsGkJYm8tmAL+YUGNat5Mr5vS1pGVbN1aCIiVZ+NNiYEJT1SCRWaDVYmHuVgZg4hvh60jQks8WTjY9l5PPHNOn7dfBCArk3CGNOrGf6eruUZsoiIYzIXwp6lkJUKPqEQfcV/c3o0vCVyfgs2JDPy+00kp+dYy8L9PRjRvRFdmpz/lPN/dh9lyJdrSE7Pwc3ZiedvaMgdl0djMmk4S0SkzG2aCwuegowD/5X5RUC9LpbbNujpsenWsqNHj+ayyy7D19eXkJAQevbsydatW4vVycnJYdCgQQQFBeHj40OvXr1ITU21UcRiSws2JPPgtNXFEh6AlPQcHpy2mgUbks/6PLPZYMLvO7j14+Ukp+cQU92b7wZdwZ1xtZTwiIiUh01z4ev+xRMegIxkWPWp5baHX4WHZdOkZ/HixQwaNIjly5fzyy+/kJ+fz3XXXUd2dra1ztChQ/n++++ZOXMmixcv5sCBA9x88802jFpsodBsMPL7TRhneayobOT3myg0F69xKDOXAZNX8sZPWyk0G/RsEcH3Q9rTOMK/3GMWEXFI5kJLD895/2IDHhX/d9imw1sLFiwodn/KlCmEhISwatUqrrrqKtLT05k0aRJffPEFnTt3BmDy5Mk0bNiQ5cuXc/nll9sibLGBlYlHz+jhOZUBJKfnsDLxKHF1ggBYuuMwj3y1lkOZuXi4OvHSjU3o3bqmendERMrTnqVn9vCczambFFYQu5rTk56eDkBgoGVTuFWrVpGfn098fLy1TmxsLFFRUSxbtuysSU9ubi65ubnW+xkZGeUctVSEg5nnTnhOr1dQaGbcwu2M/30HhgH1Q314r18r6odW/KQ5ERGHk2W/U1DsJukxm808+uijXHnllTRp0gSAlJQU3NzcCAgIKFY3NDSUlJSUs7YzevRoRo4cWd7hSgUr6dlXLk5O9PtkBSsTjwJw22WRjOjeGE835/IMT0REiviElqye7/kXn5QHm87pOdWgQYPYsGEDM2bMuKR2hg8fTnp6uvWyd+/eMopQbKltTCDh/h6ca2DKBFTzcuX52RtYmXgUbzdn3r2tBWN6NVPCIyJSkaKvsKzSOudf7JNqXlYh4ZzKLpKewYMHM2/ePH7//Xdq1qxpLQ8LCyMvL4+0tLRi9VNTUwkLCztrW+7u7vj5+RW7SOXn7GRiRPdGwNl/jQzg2PF8jh7Po3GEH/Me7sCNLWpUaIwiIgI4OUOX107eOf0v9in3vYIqKiIrmyY9hmEwePBgvvvuO3777TdiYmKKPd66dWtcXV1ZuHChtWzr1q0kJSURFxdX0eGKjXVpEs4Hd7QizL/4UJer83+/RAOvqMWsh64gprp3RYcnIiJFGvWAPlPB77QhLL8IcPWy3PYMqPCwTIZhnG1NWYV46KGH+OKLL5gzZw4NGjSwlvv7++Pp6QnAgw8+yA8//MCUKVPw8/NjyJAhACxdurREr5GRkYG/vz/p6enq9akiinZk/mVzCl+t3Et2XiF+Hi68fkuzC25QKCIiFej0HZkj28ErIYABj28Hn5BzPrU8Pr9tOpH5gw8+AKBTp07FyidPnszAgQMBePvtt3FycqJXr17k5uaSkJDA+++/X8GRij0pMJv5aWMKU5buBqBFZADj+7YkMtDLtoGJiEhxTs4Q0+G/+8ePYt2rxyOgwsOxadJTkk4mDw8PJkyYwIQJEyogIrF3iYezGfLlajbst2xF8MBVtXk8oQGuznYxPU1ERM4nJ81y7eoNLm4V/vJ2s2Rd5ELmrN3PM7PWk51XSDUvV97q04KrY8/dNSoiInbm+DHLtWc1m7y8kh6xeyfyChn5/UZm/G3ZfqBtTCDv3taCcH9PG0cmIiKlkn3Icu1d3SYvr6RH7Nr21EwGfbGabalZmEwwpHM9Hu5cFxcNZ4mIVD5FSc95JjCXJyU9YpcMw2Dmqn28MGcDOflmgn3deefWFlxZ1zb/HYiISBnIPmi59g62ycsr6RG7k5VbwPOzN/Ddmv0AdKhXnbf6tCDY193GkYmIyCXJPmy5VtIjAhsPpDPkizXsOpyNs5OJYdfW58GOdXBy0snoIiKVnnVOj5IecWCGYTBtRRIvz9tEXoGZcH8PxvVtyWW1Am0dmoiIlJUsDW+Jg0s/kc/T3/7LjxtSALgmNoSxvZtTzbvi93AQEZFyVDS85aOkRxzQ2r1pDP5iNfuOncDV2cRTXWK5p30MJpOGs0REqhxNZBZHZBgGk5YkMubHLRSYDSIDPRnftxUtIgNsHZqIiJSHgrz/enp8bXNOopIeqXDHsvN4fOY6Fm6xZPzXNw1jTK9m+Hm42jgyEREpN1kpgAHO7uAVZJMQlPRIhfp791Ee/nINyek5uLk48cINjbi9XZSGs0REqrp0yzYk+EWAjf7mK+mRCmE2G3yweCdv/bKNQrNB7erevNevFY0i/GwdmoiIVISMoqSnhs1CUNIj5e5QZi7Dvl7Ln9stY7k3tazBKz2b4O2uHz8REYeRccBy7RdhsxD0qSPlasn2wzz61VoOZ+Xi6erMSzc25pbWNTWcJSLiaJT0SFVVUGjm3YXbee/3HRgGNAj15b1+LakX6mvr0ERExBYy9lmuNbwlVUly+gke+XItK3cfBaBv20heuKExnm7ONo5MRERsRj09UtX8tiWVx75ex7Hj+fi4uzDq5qb0aG67H3AREbET6Sd7evzV0yOVXF6BmbE/b+XjP3YB0KSGH+/1bUWt6t42jkxERGwuNwuyUi23q8XYLAwlPXLJ9h49zpAv17B2bxoAA6+oxfDrY3F30XCWiIgAx3Zbrj0DwTPAZmEo6ZFLsmBDMk988y+ZOQX4ebjwRu/mJDQOs3VYIiJiT45aRgEItF0vDyjpkYuUk1/I6B8289myPQC0jApgfN+W1KzmZePIRETE7hxLtFwH1rZpGEp6pNR2Hcpi8Bdr2JScAcADHWvz+HUNcHV2snFkIiJil4p6emw4nweU9EgpzVm7n2dmrSc7r5BAbzfe7NOcqxuE2DosERGxZ0fV0yOVyIm8Ql6cu5Gv/tkLQLuYQMb1bUmon4eNIxMREbt3ZIflWkmP2LttqZkMmr6a7QezMJng4c71ePiaejg76SgJERG5gBNp/x02GhJr01CU9Mg5GYbBzH/28cLcDeTkmwn2defdW1twRd3qtg5NREQqi0NbLNd+NcHD36ahKOmRs8rKLeDZ79YzZ61l2/AO9arz9q0tqO7jbuPIRESkUjm4yXId0tC2caCkR85iw/50hny5hsTD2Tg7mXjsuvr876o6OGk4S0RESuvgZsu1kh6xJ4Zh8PnyPbwybzN5hWYi/D0Y17clbWoF2jo0ERGprKxJTyPbxoGSHjkp/UQ+T33zLws2pgAQ3zCUsb2bEeDlZuPIRESk0jIMSN1gua2eHrEHa5KOMeTLNew7dgJXZxPDuzbkritrYTJpOEtERC7BsUQ4cQyc3dTTI7ZlNhtMWpLIawu2UGA2iAr04r1+LWlWM8DWoYmISFWwf7XlOqwpuNh+5EBJj4M6mp3HY1+v5fethwDo1iyc0Tc3xc/D1caRiYhIlbF/leW6RmvbxnGSkh4HtHzXER6ZsYbUjFzcXZx4oXsj+rWN0nCWiIiULSU9YiuFZoP3ftvBuwu3YTagTrA3E25vRWyYn61DExGRqqYgF5LXWW5HtLJtLCfZ9FjsP/74g+7duxMREYHJZGL27NnFHjcMgxdeeIHw8HA8PT2Jj49n+/bttgm2kjuYkcMdn6zg7V8tCc8trWvy/ZD2SnhERKR87F8FBTngHQzV69k6GsDGSU92djbNmzdnwoQJZ3389ddfZ9y4cXz44YesWLECb29vEhISyMnJqeBIK7fF2w7R9d0/WbbrCF5uzrzVpzljezfHy00dfSIiUk4S/7Rc12oPdjJ9wqafel27dqVr165nfcwwDN555x2ee+45brzxRgCmTp1KaGgos2fP5rbbbqvIUCul/EIzb/68jQ8X7wQgNsyXCbe3ok6wj40jExGRKm93UdLTwbZxnMJu/9VPTEwkJSWF+Ph4a5m/vz/t2rVj2bJl50x6cnNzyc3Ntd7PyMgo91jt0b5jx3n4yzWsTkoD4M7Lo3m2W0M8XJ1tG5iIiFR9+Sdg70rLbSU9F5aSYtkZODQ0tFh5aGio9bGzGT16NCNHjizX2OzdTxtTeGLmOjJyCvD1cOH1Xs3o2jTc1mGJiIij2LUYCnMtJ6vbyXwesPGcnvIwfPhw0tPTrZe9e/faOqQKk1tQyItzN/LA56vIyCmgeWQAPzzcQQmPiIhUrK0/WK4bdLWb+Txgxz09YWFhAKSmphIe/t+HdmpqKi1atDjn89zd3XF3dy/v8OxO4uFshny5mg37LcN5919Vm8eva4CbS5XLa0VExJ6ZzbBtgeV2g7PP27UVu/1EjImJISwsjIULF1rLMjIyWLFiBXFxcTaMzP7MWbufG8b9yYb9GVTzcmXywMt45vqGSnhERKTiHVgDWang5mtZuWVHbNrTk5WVxY4dO6z3ExMTWbt2LYGBgURFRfHoo4/yyiuvUK9ePWJiYnj++eeJiIigZ8+etgvajpzIswxnffWPZQivbUwg425rSZi/h40jExERh7VxluW6Xjy42NfIi02Tnn/++Yerr77aen/YsGEADBgwgClTpvDkk0+SnZ3N/fffT1paGu3bt2fBggV4eOhDfVtqJoOmr2b7wSxMJhjSuR4Pd66Li7N6d0RExEYKC+Dfry23m/axbSxnYTIMw7B1EOUpIyMDf39/0tPT8fOr/LsPG4bBV3/v5cXvN5KTbybE1513bmvBFXWq2zo0ERFxdNt+gi/6gFd1eGwLOF/8Idbl8flttxOZ5UyZOfk8890Gvl93AICr6gfzVp/mVPexr+5DERFxUGu/sFw37X1JCU95UdJTSazfl87gL1ez58hxnJ1MPJHQgPs71MbJyX6WAoqIiANL3w9b5llut+hn21jOQUmPnTMMgylLdzPqh83kFxrUCPBkXN+WtI6uZuvQRERE/rPyIzAXQPSVEN7M1tGclZIeO5Z2PI8nvvmXXzalApDQOJTXezXH38v+ugxFRMSB5WbCP1Mst+MG2zSU81HSY6dW7TnKkC/WcCA9BzdnJ57t1pD+cdGY7GhnSxEREQBWTYHcdAiqC/W72Dqac1LSY2fMZoMP/9jJmz9vo9BsUCvIi/f6taJJDX9bhyYiInKmnHT4803L7SsfBSf73TpFSY8dOZSZy7Cv1/Ln9sMA3NgigldvaoqPu94mERGxU0vegRPHoHoDaN7X1tGclz5N7cTSHYd55Ku1HMrMxcPViZd6NKF3m5oazhIREft1bA8s/8ByO34EONt3WmHf0TmAgkIz4xZuZ/zvOzAMqB/qw3v9WlE/1NfWoYmIiJybYcD8YVBwAqLbQ4PrbR3RBSnpsaHk9BM88uVaVu4+CkDftpG8cENjPN2cbRyZiIjIBfz7Nez4FZzdoPs7UAlGJpT02MhvW1J57Ot1HDuej4+7C6NubkqP5hG2DktEROTCju6C+Y9Zbl/1JFSvZ9t4SkhJTwXLKzDz+oItfLIkEYAmNfx4r28ralX3tnFkIiIiJZCfA9/cDXmZEHUFtB9q64hKTElPBdp79DiDv1jNun3pANx1ZS2e7hqLu4uGs0REpBIwDJgzCA6sAY8A6DXR7icvn6ryRFrJfb/uAM/MWk9mbgH+nq68cUszrmscZuuwRERESu73V2HDN+DkAn2mgn9NW0dUKkp6ytmJvEJGfr+RGX/vBaB1dDXG9W1JjQBPG0cmIiJSCn+MhT/esNy+4W2o3dG28VwEJT3laEtKBoO/WMOOg1mYTDCoU10eja+Hi7P97lYpIiJSjGFYEp7fX7Hc7/wctOpv25gukpKecmAYBtNWJPHKvE3kFpgJ8XXnnVtbcEXd6rYOTUREpOQK8+GHxy1na4El4bnqCZuGdCmU9JSx9OP5PPXtvyzYmALA1Q2CGdu7OUE+7jaOTEREpBSyD8O398CuRYAJEkZB3EO2juqSKOkpQ//sPsojM9ayP+0Ers4mnuoSy91XxuDkZP8bNomIiFjt/B2+ewCyUsHVG26ZBA262jqqS6akpwwUmg0+WLSDt3/dbj0ZfXzfVjStqZPRRUSkEslJh4Uvw9+fAAYEx8Itn0JoY1tHViaU9Fyi1IwcHp2xlmW7jgDQs0UEr+hkdBERqUzMZtg4C356FrIs0zNofZdlSMvNy7axlSF9Ml+C37ak8vjMfzmanYeXmzMv3diEXq1q6GR0ERGpHAwDdv4GC1+C5LWWsqC60O2tSrkk/UKU9FyE04+SaBTux/h+LakT7GPjyERERErAXAhbf4Sl42HvckuZmy9c+Qhc+TC4VM3FN0p6Smn34WyGfLmG9fstR0kMvKIWw6/XURIiIlIJHD9qOR195cdwdKelzNkNLrsPOgwD76q9tYqSnlL4bs0+nvtuA9l5hQR4ufLGLc25tlGorcMSERE5t8J82LUY1k6HLfOgMM9S7hEAbe6GtveDX7hNQ6woSnpKIDu3gBfmbOTb1fsAaBsTyLu3tSDcX0dJiIiIHco/AYl/wKY5sGU+5KT991hYM8uOys37grtjTctQ0nMBG/an8/CXa9h1OBsnEzx8TT2GdK6Hs/beERERe2EYkLrRMil552+wZykU5v73uHcINO4JLe+A8OY2C9PWlPScg2EYTFm6m9E/bCGv0EyYnwfv3taCdrWDbB2aiIg4uoJcSP4X9q2EvSshaZllI8FT+dWA2G7Q6EaIigMnzT1V0nMWR7PzePKbdfy6+SAA8Q1DeeOWZlTzdrNxZCIi4nDyc+DQFktPTuoG2Pc3JK/7b25OEVcvqNUe6nS2XKrXB22hUoySntMs33WER2esJSUjBzdnJ57t1pD+cdHae0dERMpX/gk4uguO7IDD208mORst943CM+t7VYfItlDzsv+uq+hS87KipOekgkIz43/bwfjftmM2oHawN+P7tqRxhI6SEBGRMmAYlkM80/dC+j5IS7IsGz+yA47sgox9536uZzUIbWI5DiKipSXJqRajnpxSUtIDHEg7waMz1rJy91EAbmldk5E9GuOtoyRERKQk8nMg+yBkHbTMrclKhcxUSyKTfsqlIOf87Xj4W3ZEDqwDoY1OJjpNwDdMCU4ZcPhP9Z83pvDkt/+SdjwfbzdnXr2pKT1b1rB1WCIiYiuGAfnH4cQxy+X40ZO3j1puZx8+mdicTHCyD1oO6iwRkyWB8Y8E/5oQWNuS5ATVsSQ6XoFKbsqRwyY9OfmFjP5hM58t2wNA0xr+jO/bklrVvW0cmYiIXBKzGfKzITcTcjIs17lF15nF7+ek/5fcnJrgnLrcu6Sc3cEnFHxCTl4Hg19NS3ITcDLJ8Y0AFy2KsRWHTHp2HMxiyJdr2JycAcB9HWJ4IiEWNxcnG0cmIuJgzIWW3pK845belfzjkJd98vq4JXkp9thpZda62cWTGoxLj83J1dLz4lkNPIuuq1mSGe+QU5Kbk4mOh796aeycwyU9i7cd4n+fr+JEfiGB3m682ac5VzcIsXVYIiKOKesgvNWwfNp2cgF3P3D3PeXaFzxOue3u+19C43VKYuMZCG7eSmKqGIdLehqG+eLl5kzLqADevrUFoX4etg5JRMRxuXmBydmSYLh6We67eoOr53+33bxOPnZandPLPfyKJzku7kpapBiTYRhl0AdYviZMmMAbb7xBSkoKzZs3Z/z48bRt27ZEz83IyMDf35/09HT8/PwASDycTVSgl46SEBGxtaKPICUncpqzfX5fKrufxPLVV18xbNgwRowYwerVq2nevDkJCQkcPHjwotuMqe6thEdExB6YTEp4pMLYfdLz1ltvcd9993HXXXfRqFEjPvzwQ7y8vPj0009tHZqIiIhUInad9OTl5bFq1Sri4+OtZU5OTsTHx7Ns2bKzPic3N5eMjIxiFxERERG7TnoOHz5MYWEhoaGhxcpDQ0NJSUk563NGjx6Nv7+/9RIZGVkRoYqIiIids+uk52IMHz6c9PR062Xv3r22DklERETsgF0vWa9evTrOzs6kpqYWK09NTSUsLOysz3F3d8fdXafMioiISHF23dPj5uZG69atWbhwobXMbDazcOFC4uLibBiZiIiIVDZ23dMDMGzYMAYMGECbNm1o27Yt77zzDtnZ2dx11122Dk1EREQqEbtPem699VYOHTrECy+8QEpKCi1atGDBggVnTG4WEREROZ9KsSPzpSiPHR1FRESkfDnkjswiIiIiZUFJj4iIiDgEJT0iIiLiEJT0iIiIiEOw+9Vbl6ponrbO4BIREak8ij63y3K9VZVPeo4cOQKgM7hEREQqoSNHjuDv718mbVX5pCcwMBCApKSkMvumycXJyMggMjKSvXv3avsAG9N7YV/0ftgPvRf2Iz09naioKOvneFmo8kmPk5Nl2pK/v79+gO2En5+f3gs7offCvuj9sB96L+xH0ed4mbRVZi2JiIiI2DElPSIiIuIQqnzS4+7uzogRI3B3d7d1KA5P74X90HthX/R+2A+9F/ajPN6LKn/2loiIiAg4QE+PiIiICCjpEREREQehpEdEREQcgpIeERERcQhVIumZMGECtWrVwsPDg3bt2rFy5crz1p85cyaxsbF4eHjQtGlTfvjhhwqKtOorzXsxceJEOnToQLVq1ahWrRrx8fEXfO+k5Er7e1FkxowZmEwmevbsWb4BOpDSvhdpaWkMGjSI8PBw3N3dqV+/vv5OlaHSvh/vvPMODRo0wNPTk8jISIYOHUpOTk4FRVs1/fHHH3Tv3p2IiAhMJhOzZ8++4HMWLVpEq1atcHd3p27dukyZMqX0L2xUcjNmzDDc3NyMTz/91Ni4caNx3333GQEBAUZqaupZ6//111+Gs7Oz8frrrxubNm0ynnvuOcPV1dVYv359BUde9ZT2vejXr58xYcIEY82aNcbmzZuNgQMHGv7+/sa+ffsqOPKqp7TvRZHExESjRo0aRocOHYwbb7yxYoKt4kr7XuTm5hpt2rQxrr/+emPJkiVGYmKisWjRImPt2rUVHHnVVNr3Y/r06Ya7u7sxffp0IzEx0fjpp5+M8PBwY+jQoRUcedXyww8/GM8++6wxa9YsAzC+++6789bftWuX4eXlZQwbNszYtGmTMX78eMPZ2dlYsGBBqV630ic9bdu2NQYNGmS9X1hYaERERBijR48+a/0+ffoY3bp1K1bWrl0744EHHijXOB1Bad+L0xUUFBi+vr7GZ599Vl4hOoyLeS8KCgqMK664wvjkk0+MAQMGKOkpI6V9Lz744AOjdu3aRl5eXkWF6FBK+34MGjTI6Ny5c7GyYcOGGVdeeWW5xulISpL0PPnkk0bjxo2Lld16661GQkJCqV6rUg9v5eXlsWrVKuLj461lTk5OxMfHs2zZsrM+Z9myZcXqAyQkJJyzvpTMxbwXpzt+/Dj5+fllericI7rY9+Kll14iJCSEe+65pyLCdAgX817MnTuXuLg4Bg0aRGhoKE2aNGHUqFEUFhZWVNhV1sW8H1dccQWrVq2yDoHt2rWLH374geuvv75CYhaLsvrsrtQHjh4+fJjCwkJCQ0OLlYeGhrJly5azPiclJeWs9VNSUsotTkdwMe/F6Z566ikiIiLO+MGW0rmY92LJkiVMmjSJtWvXVkCEjuNi3otdu3bx22+/cfvtt/PDDz+wY8cOHnroIfLz8xkxYkRFhF1lXcz70a9fPw4fPkz79u0xDIOCggL+97//8cwzz1REyHLSuT67MzIyOHHiBJ6eniVqp1L39EjVMWbMGGbMmMF3332Hh4eHrcNxKJmZmdx5551MnDiR6tWr2zoch2c2mwkJCeHjjz+mdevW3HrrrTz77LN8+OGHtg7NIS1atIhRo0bx/vvvs3r1ambNmsX8+fN5+eWXbR2aXIRK3dNTvXp1nJ2dSU1NLVaemppKWFjYWZ8TFhZWqvpSMhfzXhQZO3YsY8aM4ddff6VZs2blGaZDKO17sXPnTnbv3k337t2tZWazGQAXFxe2bt1KnTp1yjfoKupifi/Cw8NxdXXF2dnZWtawYUNSUlLIy8vDzc2tXGOuyi7m/Xj++ee58847uffeewFo2rQp2dnZ3H///Tz77LM4OanvoCKc67Pbz8+vxL08UMl7etzc3GjdujULFy60lpnNZhYuXEhcXNxZnxMXF1esPsAvv/xyzvpSMhfzXgC8/vrrvPzyyyxYsIA2bdpURKhVXmnfi9jYWNavX8/atWutlx49enD11Vezdu1aIiMjKzL8KuVifi+uvPJKduzYYU08AbZt20Z4eLgSnkt0Me/H8ePHz0hsihJSQ0dXVpgy++wu3Rxr+zNjxgzD3d3dmDJlirFp0ybj/vvvNwICAoyUlBTDMAzjzjvvNJ5++mlr/b/++stwcXExxo4da2zevNkYMWKElqyXkdK+F2PGjDHc3NyMb775xkhOTrZeMjMzbfUlVBmlfS9Op9VbZae070VSUpLh6+trDB482Ni6dasxb948IyQkxHjllVds9SVUKaV9P0aMGGH4+voaX375pbFr1y7j559/NurUqWP06dPHVl9ClZCZmWmsWbPGWLNmjQEYb731lrFmzRpjz549hmEYxtNPP23ceeed1vpFS9afeOIJY/PmzcaECRMcc8m6YRjG+PHjjaioKMPNzc1o27atsXz5cutjHTt2NAYMGFCs/tdff23Ur1/fcHNzMxo3bmzMnz+/giOuukrzXkRHRxvAGZcRI0ZUfOBVUGl/L06lpKdslfa9WLp0qdGuXTvD3d3dqF27tvHqq68aBQUFFRx11VWa9yM/P9948cUXjTp16hgeHh5GZGSk8dBDDxnHjh2r+MCrkN9///2sf/+LvvcDBgwwOnbseMZzWrRoYbi5uRm1a9c2Jk+eXOrXNRmG+udERESk6qvUc3pERERESkpJj4iIiDgEJT0iIiLiEJT0iIiIiENQ0iMiIiIOQUmPiIiIOAQlPSIiIuIQlPSIiIiIQ1DSIyIiIg5BSY+I2MyXX36Jp6cnycnJ1rK77rqLZs2akZ6eftHt1qxZk/fff79Y2dKlS/Hy8mLPnj0X3a6IVG5KekTEZm677Tbq16/PqFGjABgxYgS//vorP/74I/7+/hfdbrt27fj777+t9w3D4NFHH2Xo0KFER0dfctwiUjkp6RERmzGZTLz66qtMnDiRV199lfHjx7NgwQJq1KgBwE033US1atW45ZZbStXu5ZdfXizp+fzzz9m7dy/Dhw+/pHZFpHLTgaMiYnOtWrVi48aN/Pzzz3Ts2NFavmjRIjIzM/nss8/45ptvStzen3/+SadOnUhPT8dkMtGgQQNGjhzJPffcc0ntikjlpp4eEbGpBQsWsGXLFgoLCwkNDS32WKdOnfD19S11m61bt8bJyYnVq1fz2muvERwczF133XXJ7YpI5aakR0RsZvXq1fTp04dJkyZxzTXX8Pzzz5dJu15eXjRt2pRvv/2WsWPH8vbbb+PkpD93Io7OxdYBiIhj2r17N926deOZZ56hb9++1K5dm7i4OFavXk2rVq0uuf3LL7+c8ePHc+ONN9KpU6dLD1hEKj396yMiFe7o0aN06dKFG2+8kaeffhqwrLjq2rUrzzzzTInamDJlCiaT6ZyPN2/eHFdXV954440yiVlEKj/19IhIhQsMDGTLli1nlM+fP7/EbSQmJhab9Hy6GTNmMHjwYOrWrXtRMYpI1aOkR0TsVnx8POvWrSM7O5uaNWsyc+ZM4uLiAPjxxx957733itU3m80cOnSISZMmsX37dubMmVPqdkWk6tKSdRGpMhYtWkTnzp2JjY1l8uTJtGvXztYhiYgdUdIjIiIiDkETmUVERMQhKOkRERERh6CkR0RERByCkh4RERFxCEp6RERExCEo6RERERGHoKRHREREHIKSHhEREXEISnpERETEISjpEREREYegpEdEREQcwv8B6xCbWvtgOA4AAAAASUVORK5CYII=", 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "for T in Ts:\n", "\n", " # Let's use a mask to only get the data for a specific temperature\n", " mask = df[\"T\"] == T\n", " px = model.phase_envelope_px(z0=z0, zi=zi, temperature=T, ds0=1e-5, max_points=500, a0=0.01)\n", "\n", " plt.plot(px.main_phases_compositions[:, 0, 0], px[\"P\"], label=\"x\")\n", " plt.plot(px.reference_phase_compositions[:, 0], px[\"P\"], label=\"y\")\n", "\n", " plt.scatter(df[mask][\"x1\"], df[mask][\"P\"], label=\"data Bubble\")\n", " plt.scatter(df[mask][\"y1\"], df[mask][\"P\"], label=\"data Dew\")\n", "\n", " plt.title(f\"Pxy diagram at T = {T} K\")\n", " plt.ylabel(\"P [bar]\")\n", " plt.xlabel(r\"$x_1, y_1$\")\n", " plt.xlim(0, 1)\n", " plt.legend()\n", " plt.show()" ] } ], "metadata": { "kernelspec": { "display_name": "thermo", "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": "3.13.3" } }, "nbformat": 4, "nbformat_minor": 2 }