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SIRIUS Beamline
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tutorial_xrr_refnx
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2dd3153a
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2dd3153a
authored
2 months ago
by
Arnaud HEMMERLE
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Add first version nb
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4fd4d1d4
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xrr_analysis_refnx.ipynb
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2dd3153a
{
"cells": [
{
"cell_type": "markdown",
"id": "cdcdc51f",
"metadata": {},
"source": [
"# Import data"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "98e73446",
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"\n",
"from refnx.dataset import Data1D\n",
"from refnx.util import refplot\n",
"\n",
"data_init = np.loadtxt('SIRIUS_2024_09_20_4446-4530_XRR.dat')\n",
"qz_exp = data_init[:,0]/10. #A^-1\n",
"R_exp = data_init[:,1]\n",
"R_err_exp = data_init[:,2]\n",
"\n",
"data = Data1D(data=(qz_exp, R_exp, R_err_exp))\n",
"\n",
"#refplot(data)"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "b7dbf4ea",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.errorbar(qz_exp, R_exp, R_err_exp, marker = 'x', linestyle = '')\n",
"plt.yscale('log')\n",
"plt.xlabel(r'$q_z$ (A$^{—1}$)')\n",
"plt.ylabel(r'R')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"id": "e3a440cd",
"metadata": {},
"source": [
"# Creating model"
]
},
{
"cell_type": "markdown",
"id": "d081153a",
"metadata": {},
"source": [
"## Define SLD"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "bca2c9cf",
"metadata": {},
"outputs": [],
"source": [
"from refnx.reflect import SLD, Slab, ReflectModel\n",
"\n",
"# SLD = r_e*rho_el\n",
"r_el = 2.81794e-5 # in A\n",
"\n",
"# SLD in refnx should be in 1e-6 A^-2\n",
"SLD_water = 0.334*r_el*1e6\n",
"SLD_helium = 0.\n",
"\n",
"water = SLD(SLD_water, name = 'water')\n",
"helium = SLD(SLD_helium, name = 'helium')"
]
},
{
"cell_type": "markdown",
"id": "f25844f4",
"metadata": {},
"source": [
"## Define slabs"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "85f3f29a",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# first number is thickness, second number is roughness (with respect to top layer)\n",
"# lengths in A\n",
"\n",
"# Semi-infinite bulk : thickness = 0\n",
"water_bulk = water(0,1)\n",
"\n",
"helium_atm = helium(0,0)\n",
"\n",
"# Constructed from top to bottom\n",
"structure = helium_atm | water_bulk\n",
"\n",
"\n",
"fig, ax1 = plt.subplots()\n",
"\n",
"# First y-axis (SLD)\n",
"ax1.plot(*structure.sld_profile())\n",
"ax1.set_xlabel('Distance / $\\AA$')\n",
"ax1.set_ylabel('SLD / $10^{-6} \\AA^{-2}$', color='b')\n",
"ax1.tick_params(axis='y', labelcolor='b')\n",
"\n",
"# Define transformation functions\n",
"def sld_to_ed(sld): # sld in 10^-6 Å⁻²\n",
" return (sld * 1e-6) / r_el # e⁻/ų\n",
"\n",
"def ed_to_sld(ed): # e⁻/ų\n",
" return (ed * r_el) * 1e6 # back to 10^-6 Å⁻²\n",
"\n",
"\n",
"# Add secondary y-axis\n",
"ax2 = ax1.secondary_yaxis('right', functions=(sld_to_ed, ed_to_sld))\n",
"ax2.set_ylabel('Electron Density / e$^- \\ \\AA^{-3}$', color='r')\n",
"ax2.tick_params(axis='y', labelcolor='r')\n",
"\n",
"plt.title('SLD and Electron Density Profile')\n",
"plt.tight_layout()\n",
"plt.show()\n",
"\n"
]
},
{
"cell_type": "markdown",
"id": "5a2c4843",
"metadata": {},
"source": [
"## Plot initial guess"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "3ad6f334",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"model = ReflectModel(structure, bkg=0)\n",
"\n",
"plt.errorbar(qz_exp, R_exp, R_err_exp, marker = 'x', linestyle = '')\n",
"plt.plot(qz_exp, model(qz_exp), 'k-')\n",
"\n",
"plt.xlabel(r'$q_z$ (A$^{—1}$)')\n",
"plt.ylabel(r'R')\n",
"plt.yscale('log')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"id": "30d14073",
"metadata": {},
"source": [
"# Fit"
]
},
{
"cell_type": "markdown",
"id": "4914e6dc",
"metadata": {},
"source": [
"## Define fit parameters"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "d973e8ea",
"metadata": {},
"outputs": [],
"source": [
"water_bulk.rough.setp(bounds=(0.5, 7), vary=True)"
]
},
{
"cell_type": "markdown",
"id": "f59599a3",
"metadata": {},
"source": [
"## Fit the data"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "f0138907",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"56813.855182359344: : 4it [00:00, 5.03it/s]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"________________________________________________________________________________\n",
"Objective - 140218423146320\n",
"Dataset = <None>, 85 points\n",
"datapoints = 85\n",
"chi2 = 114299.55496834047\n",
"Weighted = True\n",
"Transform = Transform('logY')\n",
"________________________________________________________________________________\n",
"Parameters: '' \n",
"________________________________________________________________________________\n",
"Parameters: 'instrument parameters'\n",
"<Parameter: 'scale' , value=1 (fixed) , bounds=[-inf, inf]>\n",
"<Parameter: 'bkg' , value=0 (fixed) , bounds=[-inf, inf]>\n",
"<Parameter:'dq - resolution', value=5 (fixed) , bounds=[-inf, inf]>\n",
"<Parameter: 'q_offset' , value=0 (fixed) , bounds=[-inf, inf]>\n",
"________________________________________________________________________________\n",
"Parameters: 'Structure - ' \n",
"________________________________________________________________________________\n",
"Parameters: 'helium' \n",
"<Parameter:'helium - thick', value=0 (fixed) , bounds=[-inf, inf]>\n",
"________________________________________________________________________________\n",
"Parameters: 'helium' \n",
"<Parameter:'helium - sld' , value=0 (fixed) , bounds=[-inf, inf]>\n",
"<Parameter:'helium - isld', value=0 (fixed) , bounds=[-inf, inf]>\n",
"<Parameter:'helium - rough', value=0 (fixed) , bounds=[-inf, inf]>\n",
"<Parameter:'helium - volfrac solvent', value=0 (fixed) , bounds=[0.0, 1.0]>\n",
"________________________________________________________________________________\n",
"Parameters: 'water' \n",
"<Parameter:'water - thick', value=0 (fixed) , bounds=[-inf, inf]>\n",
"________________________________________________________________________________\n",
"Parameters: 'water' \n",
"<Parameter: 'water - sld' , value=9.41192 (fixed) , bounds=[-inf, inf]>\n",
"<Parameter:'water - isld' , value=0 (fixed) , bounds=[-inf, inf]>\n",
"<Parameter:'water - rough', value=3.39189 +/- 0.0124, bounds=[0.5, 7.0]>\n",
"<Parameter:'water - volfrac solvent', value=0 (fixed) , bounds=[0.0, 1.0]>\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"\n"
]
}
],
"source": [
"from refnx.analysis import Transform, CurveFitter, Objective, Model, Parameter\n",
"\n",
"objective = Objective(model, data, transform=Transform(\"logY\"))\n",
"\n",
"fitter = CurveFitter(objective)\n",
"fitter.fit(\"differential_evolution\")\n",
"\n",
"print(objective)"
]
},
{
"cell_type": "markdown",
"id": "44f8d495",
"metadata": {},
"source": [
"## Plot the results"
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "b340cc4f",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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WWm/NuwziFLfB1q2c/OhrS+oSERHJjhRqsjF3b83VweY0BXmLVwC4+OIrTPxcJ3iLiIiAQk22FxkJU6emfOxT+rKPMEpxmL19RxEXZ01tIiIi2YlCjRdo1CjlaE0iAQxhBAAvGe9w8LcjFlUmIiKSfSjUeIHUpqGm04k1NKAA56g4YYh1xYmIiGQTCjVeIjISDhwwz4Qy96+x8RyfABD83STmv7zG0vpERESsplDjRUJCYOTIK/vXrKc+X/IUACVGPEfcQddNPkFERMR3KdR4oav3rxnCCM4QSH3Wc+zNcdYWJiIiYiGFGi909f41xynGa7wBQLlxg5k86piFlYmIiFhHocYL/btx+HOeYQN1KMhpjBcHsm6dtfWJiIhYQaHGS129f40TP3ozBhc2HuMbXm3wA9HR1tYnIiKS1RRqvNjV+9espz6f8SwAo41e9O95XpvyiYhIjqJQ48X+PQ31Mm9zkFDKsY+hrmGsXm1tfSIiIllJocbLRUbCmjVmsDlHAXoxFoD+fMAHndZoGkpERHIMhRofUL/+lRGbxdzHVzyOAxdfGk/yQs+LmoYSEZEcQaHGR1zdOPwCH3GYElTmT6Jcr7FwobW1iYiIZAWFGh/ibhw+TUF6YG7EN4BRTO8Tw5NPWlubiIiIpynU+BB347DNBotozTi6Y8fgax5nwaS/tH+NiIj4NIUaHxMZCa++av5zPz7kTyoSwiHG0YNVvxjWFiciIuJBCjU+6MEHzT8vkI9HmcolctGOObQ9/oW1hYmIiHiQQo0Pql8fnnjC/OdN1GEw/wdA2Ef9Yft2CysTERHxHIUaHzVxIqxdCx98AI+u6QctW8LFi1zq0IUVS/7WMm8REfE5CjU+rH596NcP6jeww6RJXCxQhNzbt7Dl3kGUKYM25hMREZ+iUJNDxCUVp+O5CQA8zyd0dE2lVy80YiMiIj5DoSaH2LULFhqteYuXARhPd6o4t7J7t8WFiYiIZBKFmhyiQgVzY76hvM5S7iEfF5hNOyoUOW11aSIiIpnCa0LNQw89ROnSpQkICKBEiRI8/vjjHD582OqyvEbyxnwOB12YwgFKU4HdFOrXjZifXJqGEhERr+c1oaZ58+bMmDGDP//8k1mzZrFnzx7at29vdVleJTIS9u+HmTHBBCycRZKfP3l+XMCPd49Q47CIiHg9m2EYXrnN7Pz582nbti2JiYnkypUrTe9JSEggKCiIM2fOEBgY6OEKs7e4OBheOppxRndc2GjDPL53PMj+/eaojoiISHaR1t/fXjNSc7WTJ08yefJkGjVqdMNAk5iYSEJCQopLTLt2wXgjkrH0xI7BVB7lducWNQ6LiIjX8qpQ89///pd8+fJRuHBhDh48yLx58274+hEjRhAUFJR8hYaGZlGl2Z+7cbgvn/ITEeTnPAt4kIqBR60uTUREJEMsDTVRUVHYbLYbXuvXr09+/UsvvcSmTZtYunQpDoeDbt26caPZsyFDhnDmzJnkKzY2Nit+LK/gbhw2HLloz0z+pCKliaVknzbE7bpITIz2sBEREe9iaU9NfHw88fHxN3xNWFgYAQEB1zweFxdHaGgov/76Kw0bNkzT91NPzbXi4mD3bqjs2EXxtnfCyZPMoCOdmYrNbueLL8wGYxEREauk9fe3XxbWdI3g4GCCg4Mz9F53FktMTMzMknKckBB3Y3AFjo+ZzW0d76EjM9hJBV5zvUmvXtCqlZqHRUQk+/OKnpq1a9fy6aefsnnzZg4cOEBMTAxdunShfPnyaR6lkZvbFtyUXowF4FXe4hk+w+lEzcMiIuIVvCLU5MmTh9mzZ9OiRQsqVarE008/TbVq1VixYgX+/v5Wl+czKlSAr+xP8RqvA/AJz9HR/i3h4RYXJiIikgZeu09NRqin5uaio6FXT4OPXH15ls9x+uXGseR7iIiwujQREcmhfHqfGvGcyEjYf8DG7T9+zIXW7XEkXcLVpi3rvtik1VAiIpKtKdTINUJCoFkLB3lnfcPhys2xnztLaK/7iCi9W0cpiIhItqVQI9cVd8Kf2/+cwyZqUZxj/GhE8HbP/RqxERGRbEmhRq5r1y44bQTRiiX8QWVKE8sPrghiVyvViIhI9qNQI9flPkrhBEVpwU/spjzl2Ee9wS04vPGodh0WEZFsRaFGrst9lILDAUcoyT32ZZwtXIZce3dysu7ddIiIp0wZ1GcjIiLZgpZ0y025j1IIDwfH/j24mvyHUhxmMzW5hx845SjC/v3adVhERDxDS7ol04SEQLNm5p87LpcngmUcpRi12EIMzSnsPKZdh0VExHIKNZIuFSrAbnslmrKCQ5SkGttYTjNuu3BYPTYiImIphRpJF3efzR6HGWwOEkoVdpCvdVO6RcSqx0ZERCyjnhrJEHefTcHT+wh8OIKy7GcvZbmbHznoKKceGxERyTTqqRGPcvfZnAwqS1NWJC/3XkVjqji3qsdGRESynEKN3JIKFeCQvTT/4We2Uo0SHOVn/kPV078SF4f6bEREJMso1MgtcffYHHeU5D/8zGoaUpDTFOp0Nz1LLyYiAvXZiIhIllCokVsWGQn798OcmIKU/vMH/m52L36XLjLPeJDOTMXlgl69NGIjIiKepVAjmcLdY1OqYj7WDJnHVDqTiySm0oWBjMTpNNRnIyIiHqVQI5kuvGpuutm+4SOeB2Akg/jU9hz5ApzqsREREY9RqJFMFxICY8Y5eNHxEf0ZhQsbzxqfcaRhO1pHXFCPjYiIeIT2qRGPce9lE7JmJiFDHiOARH7jDh5iPn85imkvGxERSRPtUyOWc/fZxDZoTwt+4i8K0YC1rOUOqjr/px4bERHJVAo14nEVKsAae2MaspqdVKAMB1lFY6rtW6C9bEREJNNkeqiZOXNmZn+keDn3XjZ7HRW5kzUsI4ICnKNwZBs+Lj2SiAhDfTYiInLL0h1qkpKS2LZtGzt37kzx+Lx586hZsyZdu3bNtOLEd7j3spkdU4iKexdz7rFe2AyDd41BTOApcrn+1l42IiJyS9IVarZv307FihWpUaMGVapUoV27dhw7doymTZvyxBNPcM8997BbjRJyHe4em5CyuVj31Gie42Oc2HmSSfzMfyjujFOfjYiIZFi6Qs3gwYMpW7Ys8+bNo2PHjsydO5cmTZrQokULYmNjee+99wgNDfVUreJDKlS08bn9Oe5lMX9RiDtYxwbqUmznSvXYiIhIhqRrSXfx4sVZtGgRderU4fTp0xQqVIixY8fSo0cPT9aYabSkO3uJjjaPTwh17mMubanJ/7iMH/34kDG2Z/hinI3ISKurFBERq3lkSffx48cpVaoUALfddht58+aladOmt1ap5FjuPpsJMWVJWvErU3mUXCTxGX2ZaHSjf8/zGrEREZE0S1eosdls2O1X3mK328mVK1emFyU5h7vPJsGZjy5M5kXeIwkHj/MNv7oacHjZDi37FhGRNEnX9JPdbicoKAibzQbA6dOnCQwMTBF0AE6ePJm5VWYSTT9lX3FxUKYMuFzQhJ+ZTidKcJRL/vl54tJ4phmdsNvNpeGakhIRyVnS+vvbLz0fOmHChFsuTCQ17r1sevWClc7/UM++idVhj1J673Km0plG/MJA13v06uVPq1Y6XkFERK6ls58kW3GfFxUeDrt3JLHqnmG8wtsAbKQ2nZnGWzMqEhxs7lSscCMi4vt89uynxMREatWqhc1mY/PmzVaXI5kseS+bEAiv7MdQ+1vcz3ecIJg6bGIjdVjY6WsiItAuxCIikkKGQk3BggUpVKjQNVfhwoUpVaoUTZs29dhU1aBBgyhZsqRHPluyF/eU1FLH/dRiMzE0Iz/nmWR0YyJPkNd1VrsQi4hIsgyFmqFDh2K322ndujXDhw8nKiqK1q1bY7fbefbZZ6lYsSJ9+vRh3LhxmVrs999/z9KlS3nvvfcy9XMl+3Iv+54cU4r4qT/yGq/jxM4TfMUmalPX+Ru7d6MVUiIikr5GYbdffvmFN998k969e6d4fOzYsSxdupRZs2ZRo0YNPv7440zbmO/YsWP06NGDuXPnkjdv3jS9JzExkcTExOSvExISMqUWyVohIeYVF+egs/01Vria8g2PEc4eVtGYNSOGUe6HIVw2/LRCSkQkB8vQSM2SJUu4++67r3m8RYsWLFmyBID777+fvXv33lp1/zAMgyeffJLevXtTr169NL9vxIgRBAUFJV86wsG7uaejfnX8h5psYbqtE344uWvpUJYZzQhjHy4XmpISEcmhMhRqChUqxIIFC655fMGCBRQqVAiA8+fPU6BAgRt+TlRUFDab7YbX+vXr+eSTT0hISGDIkCHpqnPIkCGcOXMm+YqNjU3X+yX7cU9HzYkpSOMDU9k++CsSKMBdrGILNXmKL3E6DR2MKSKSA2VoSfe4cePo06cP999/P3fccQc2m421a9eyaNEixowZQ2RkJO+//z5r165l+vTp1/2c+Ph44uPjb/i9wsLC6Ny5MwsWLEje9A/A6XTicDjo2rUrkyZNSlPdWtLte+LioGnpfUwyHucuVgGwkAeos2EcrqLF2bVLS79FRLxdWn9/Z3ifmlWrVvHpp5/y559/YhgGlStX5rnnnqNRo0YZLvp6Dh48mKIf5vDhw7Rq1YqZM2fSoEEDQtL4G0uhxjdFR0Ofnk76ud7nDV7Dn0v8na8QT14YzXSjo/psRES8nMdDjZX2799P2bJl2bRpE7Vq1Urz+xRqfJd7077Kl7dSqH83cm/bDMB0OtKXTznlKMLq1XDunEZuRES8jUeOSbia0+lk7ty5/PHHH9hsNqpWrcpDDz2Ew+HI6EeKZJh7hRRUZ/mo3/i51Zu8zNt0YgYRLKOv81PubNARl2HTyI2IiI/K0EjN7t27uf/++zl06BCVKlXCMAx27txJaGgo3333HeXLl/dErbdMIzU5g/twzFquDUzgKWqwFYDZPMwzfM4xiuNwmA3HGrEREcn+PHpMwvPPP0/58uWJjY1l48aNbNq0iYMHD1K2bFmef/75DBctkhncS7+3OOpSj/UMZxiX8aMdc9hOVZ5gYvIKKW3aJyLiOzI0UpMvXz7WrFlD9erVUzy+ZcsWGjduzLlz5zKtwMykkZqcxd1nky8f9GywhWjjKeqwCYCfaMHe/46l98jyuFxoSkpEJBvz6EiNv78/Z8+evebxc+fOkTt37ox8pEimcx+OWb8+9B1Xk4b2tbzEu1wkgBb8RNd3qjPANRIHSdq0T0TEB2Qo1DzwwAP07NmT3377DcMwMAyDNWvW0Lt3bx566KHMrlHklkVGwp4DfrSOeYnTP2/lVO0I8nKRkQxiHfWpz1qcTli9WtNRIiLeKkOh5uOPP6Z8+fI0bNiQgIAAAgICaNSoEeHh4Xz44YeZXKJI5nCP3JRoEs75eT/ytG0CJylIbTazhjv5jGfp2ekMERFmo3F0tNUVi4hIetzSPjW7d+/mjz/+wDAMqlatSnh4eGbWlunUUyNXi46GV3qe4B3XQJ7gKwCOUJz+fMB0OuFw2LRCSkQkG8j0zfcGDBiQ5m8+atSoNL82KynUyL+5m4mNZTGUfKM3ldgJwI+0oC+fMiamMuHh6LgFERELZfrme5s2bUrT664+n0kku3Nv2hcX3pwKb/6Pl4x3GMII7uYn/kcNNr0+gKrLX+WskV8rpEREsjmvPCYhozRSIzcSHW2ugCrt3MtH9ONBzJPoYwnhRd7nWzpoSkpExAIeXdIt4osiI81dhr+MKUft2Pn878357KUsocQxg07E0Jyqzv9phZSISDalUCNyFfcKqZAQKPTEg1S3bWMYUVwkgGasYBO1Od6xL49EnNQKKRGRbEahRuQ6QkLg43F5eMsxjMrsMKefcPEsn7GLCvR2fcYzPZOIi9NxCyIi2YFCjcgNuKekJsWUwZg+g+YsYyvVKMxJPqMvG101WdxvMWXKoP1tREQspkZhkTRyn/5tcyXRnfG8wWsUIR6ARdzHi7zPDqroBHARkUymRmGRTOY+/RuHH2PpTWX7LlbUe5FL5OJ+vmcr1fmcPhRyHlczsYiIBRRqRNLBPR0VEwNbDtxG+TnvUd22jTm0xQ8nfRjDbsLZ3OltWkdc0HSUiEgW0vSTyC1y72/TyPkz7/Mi9VkPmPvbDOV1Jtu7sfeAA9DOxCIiGaHpJ5Es4h69eT3mP+yb+htdmMwBShNKHBN4mg2uWix5/jvKlDbUTCwi4kEaqRHJRO5m4lyuv+nLp7zM2xTiFADLacp/eYe1NMDhgNWr4dw5jdyIiNyMRmpELOBuJk5yBPA+A6lo38Py+i/xN/40YwW/cSezaEdF53buvFPLwEVEMpNGakQ8wH36d3i4+XXj0rFEGUPpxlc4cOHEzld0I4ooDlJGy8BFRG5AIzUiFrr6uIWQEBg6LpQejglUZytzeBgHLp5iIjupyMc8R7DzKLt3a2diEZFboVAjkgXczcSfx1QlZO1sGtrW8BMR+HOJ5/iUvZQj7/D/Urv0X5qSEhHJIE0/iVjAvQz8P85lvM0r3MkaABIowCgG8CH9OOe4Tc3EIiKk/fe3Qo2IRZL7bsobnPxmEc6XX6U2mwE4xW28z4t8anueM0YgdrvZgBwZaW3NIiJWUKhJhUKNZFdxcRBW2kVbYzbDGcbtbAfgLwrxHgP5lL5cdBRg/37z9drET0RyEjUKi3iRkBAYO87OXEd7avA/utimsoNKFOYkI3iZfZRlkPNtxryboBPBRUSuQyM1ItmIe0oqXz5o1MBJZ2MKr/EGFdkFmCM3H9Cfj3meswRqEz8RyRE0UiPihdxLwevXhzHjHEx1PE5VttPN9jXHC1akMCd5k9c4QBmGEUUB5ylt4ici8g+N1IhkYyk28XM6GVx2Oq8Yb1CFHYC5WupT+vIB/YmnSPImfqC+GxHxHRqpEfEBKTbxK+Og+bgu1LT/Tkem8z+qE8hZXmYE+wnjfQZQzHmIjz5CfTcikiN5TagJCwvDZrOluAYPHmx1WSJZKjIS9h5w8ExMRxLXbOZh21zWU5d8XGAAH7CXclR4rxdlXHsBcLnM/XDi4rRbsYj4Pq8JNQCvv/46R44cSb5effVVq0sSyXLJfTcN7Dwwrg132tdxL9+zkib4c4mefMEuKvA1j1GNrTid8NFHULq0OXpTurRGb0TEN3lVqClQoADFixdPvvLnz291SSKWioyE/QdsDI65l7KxP3N85s8s5l4cuHiMyWylBgt5gNXvrcLdPWcY0KOHRmxExPd4Vah55513KFy4MLVq1eKtt97i0qVLVpckYrmr+26KPtKEQ+O/p759AzPogAsbrfmOX7iLn2nCAyzAhgvDMJeCi4j4Ej+rC0irF154gTp16lCwYEHWrl3LkCFD2LdvH+PHj7/uexITE0lMTEz+OiEhIStKFbFUZCS0alWH3btncNyxi/PDRxLy0ySa8AtN+IXtVGEkL2G/3AXwB8xRG62WEhFvZ+mS7qioKIYPH37D16xbt4569epd8/isWbNo37498fHxFC5cOF2fryXdkpPExUGD0MO8wIf0YixBmOHeWbQEjhf78VWeXjzVLwiXC50xJSLZklec/RQfH098fPwNXxMWFkZAQMA1jx86dIiQkBDWrFlDgwYNUn1vaiM1oaGhCjWS47hPBc/nPENv2xcMDfqQfKcPA+ZeN2PpxUe8wCFCkve60YiNiGQXXhFqbsXChQt58MEHOXDgAKVLl07Te7T5nuRkV2/kF1L0EkyZwvmokeQ7YB6eeRk/ptGZUQzgg5jaNGt25X2amhIRK/lUqFm9ejVr1qyhefPmBAUFsW7dOvr370+9evWYN29emj9HoUYkpbiDLvqEfc9A412a8nPy4383ak7AkAFEH7mfnr3tmpoSEUv5VKjZuHEjzzzzDDt27CAxMZEyZcrQuXNnBg0aRN68edP8OQo1ItdyT03Vcq7nRdsoOtlmYHc5AfiDynxAf77mcf4mj6amRMQSPhVqMotCjUjqUkxNuQ7CJ5+QNPoL/M6bTcUnCGYMvRlNH6bElEyemnK/V9NTIuJJOvtJRNLs6r1uKF0aRo7k2LpY+ts+YD9lKEI8r/Em+wmj/sePwfr1gDnKo3OmRCS7UKgRkVSVqhJItXH9qGTfzSPM5BfuIjeXyTdnMtSvT+Idd7Gkx0xsriQg5TlTIiJWUKgRkeuKjIQ9B/x4LuYRwmJXwrp18NhjkCsX/utWMcPowB7K8xLvUpCTOJ3mNJaIiBXUUyMi6Xf4MAnvjCbx4zEUwdxr6gJ5mGLryoNLnqPYPTUA9duISOZQT42IeE7JkgR+9AbffX6Q7rZoNlGLvFykuzGeYi1rQrNm/PTMLMqVTlK/jYhkGY3UiMgtiYuD3bsMqp5aRdFpH8Ps2eA0l4QfJJTR9GE83TnlKKLl4CKSIVrSnQqFGpEsEBfH/sFjyDv5C4pyAoBEcjODjtz+2bPU6dMAbDb3SzU9JSI3peknEbFGSAh+//cmYbaDdGMSa6mPP5d4nG+o82xDqFcPvvySiaMvajm4iGQqjdSIiEe4dyp2OqGBfR1f3fkZFTdMg38OmT1JQb7kaUbTh72U127FInJdGqkREUtFRpohJSYGZh6oT8VVE835pnfe4WLxMApxioG8zx7CWUwrHnDOZc+fScnvj4sz36t9b0QkrTRSIyJZLu6Ak2fKfk8f4zNasQQ75r+GkoqXwq93D6bm78Fjg0rqIE0RAdQonCqFGpHswz09Vdq5l962L3guXzR5zpl73iThYB5tGE0flhGB3WHX1JRIDqbpJxHJ1tzTU1/GlKPLwf8jT3wcTJ7M6epN8MPJI8zmR+5hJxUZ4HyXA+uOJ79XU1MikhqN1IhIthIXB/eX/p2exhge52uCME8KN3LlwtauHYtCe/Hg+81wGTZNTYnkEBqpERGvFBICL4yrRj/Hp5TkMN1t0Rwvewe2y5dh+nTufy+C7UZlBvA+BV3xOkRTRJIp1IhItuOemvouJh9RB5+m6N7fYNMmDj3YmwQKUImdvM9ADlOSb5ydiZ/+k3lM+D80PSWSM2n6SUS8RlwcVC19jk7GVHryBfVZf+XJcuWge3em+D/F4y8V18opER+i1U+pUKgR8X5Xb+pXx76Zr5uOo+qGbyDB7L1JwsECHmQcPVhCK2wOh1ZOiXg5hZpUKNSI+Ia4ONi9G8LD/wkr58/Dt99y5r1xBG37Nfl1sYQwkSe5Z8rT3Plo2eT36rwpEe+iUJMKhRoR3xYXB/eV3kakMY7H+ZrCnLzyZIsWxJTvTutxbbloBGhqSsSLaPWTiOQ4ISHQb9ztDHR8SEkO86htGoeq3mM++dNPNP/iUeKMknzE81RzbUl15ZSajEW8l0KNiPgU98qpJTH+jDzYiVLblsK+fezrNpRYQijEKZ7nE7ZQi9+cdbkw8jM4dQow+3V0criI99L0k4jkCHFxULa0k7uNpTzNl7RhHrm5bD7p78+Fe9vRdv7T/GhEYPzz33s6OVwke9D0k4jIVUJCYMw4Bz847qMj3xJqP8yazh9C9eqQmEjeeVNZatzDPsoSxTDC2IfTaTYku2lqSiR700iNiOQo16ycMgzYuJFzH39J0leTuY0zya9dTjNqjHqSQj3bEz0tHz17ov1vRCyg1U+pUKgRkRuZOPoiPz47h27GRO7mR+yY/3p05cvPxPMd+ZKnWEVjwKapKZEspFCTCoUaEbkZ90hOpTwHKfHDVzBxIuzZk/z8LsKZxBN8RTe+iilNs2Yp36s9cEQyn0JNKhRqRCTdDIPjs3/hu/YT6MAM8nMeABc2LjVuTkCvJ6FdO01PiXiQGoVFRDKDzUbRR5rgGv8lpexH6cYklhGBHYOAVcugWzdcxYpj7/40d7lWYMOFy8U1e+CoyVjE8xRqRETSIDISth3Iz9Mx3agY+5PZUPPGG1C+PPbz53iKCaygGXsoz3CGEubcnbxySvvfiGQNTT+JiNwKw+D43F+Z324iHZlOIGeTn0qs14gLj3Qj/OWOnDQKJj+uJmOR9NH0k4hIVrDZKPpwY2zjx1HKfpRHmcJi7sVls+O//lcKDunNIaME0+lIaxbix2XtfyPiIV4Var777jsaNGhAnjx5CA4Opl27dlaXJCICmNNTfxzIS6+YR6kW+z32Q3EwciSXK1UjgEQ68i0LeZBDlOJj2/NUOb8eDENTUyKZyGumn2bNmkWPHj14++23iYiIwDAMtm7dSvv27dP8GZp+EpEsZxjMidrCwTcm0dmYQjGOJz91Obwyw/Z04xujK7GUBlKfmtJSccnpfGpJd1JSEmFhYQwfPpzIW1gjqVAjIlaJi4M9fyZx+6GlBH//NcydC3//DZjLw1fQlK95nFk8wryYoOT9b6Kj0VJxyfF8qqdm48aNHDp0CLvdTu3atSlRogT33Xcf27Ztu+H7EhMTSUhISHGJiFghJASatvAjuNv9MHUqHDvGyZHRxNAMOwbNWc6XRHKU4tzxfidYsIC4fZeTAw2Q6lJxEbnCK0LN3r17AYiKiuLVV19l4cKFFCxYkKZNm3Ly5Mnrvm/EiBEEBQUlX6GhoVlVsojIjQUGUmjg0+wdH0MY+xnC22ynCnn4m7wLZ8BDD1G0dkk+cvXlDn6Df45sUJOxyPVZGmqioqKw2Ww3vNavX4/rn/9MeeWVV3jkkUeoW7cuEyZMwGaz8e23317384cMGcKZM2eSr9jY2Kz60URE0iQyEn6JLUOrmCEEHtwGGzdC//5QrBi5z8TTl8/4jTvZSUWGEUUl+y7Cw833RkdD6dJmk3Hp0moyFrG0pyY+Pp74+PgbviYsLIzVq1cTERHBypUrueuuu5Kfa9CgAXfffTdvvfVWmr6fempExGskJcGyZewe9jUl1swmHxeuPHfHHZxu3ZUqUZ04ahRLflhNxuKr0vr72y8La7pGcHAwwcHBN31d3bp18ff3588//0wONZcvX2b//v2UKVPG02WKiGQ9Pz9o2ZLwli059OdoDnw1j7KrJ5Pn56Wwdi23rV1LLAP4gXuYTFfm0pbzzvzs3n0lvERHQ48eYBhgs8G4cWoyFt/mFaufAPr168fMmTP58ssvKVOmDCNHjmTBggXs2LGDggUL3vwD0EiNiPiAY8dg+nQuTZhM7s1rkx8+T17m29pwz4SuBHdpSdyxXJQpc6XJGLSTsXgvn1rSDebIzJAhQ/j666+5ePEiDRo04MMPP+T2229P82co1IiIL/n2rZ3seG0yjxqTCWfPlScKF+ZQ4450nN+V1TTEuKp9MiaG5OXiN5qaGjUKEhIgMBAGDPD8zyJyIz4XajKDQo2I+Jq4ONi9y6DKuXUU+3EyTJsGx69s8LePMKbyKFPowg5HteSRmptNTQUGwtmzUKCAGW5ErKRQkwqFGhHxef80GDN5MpemzyZ34rnkp06Wqk6hvl040rQzIXeFXXdqKjoaune/8tz48erFEWsp1KRCoUZEcpQLF/hr0kKMyVMovHYRtsuXk5/6hcZMoQvf0oF4igDm1FR4OJnSi6PpK8lMCjWpUKgRkRzr1CmYNQumTMFYvhzbP//qT8LBD9zDdNujvLWtLTuPBhIRce3br+7FSYuQEDh0CEqV0saAcut86pgEERG5RQULmnNKy5Zhi43lt47vs4E6+OHkPhYz0XiCUnWKUfPNDrRlDv78neLt+fJZVLdIOijUiIjkNKVK0WD6AIrFbuC3r/7kzIAoqFQJ/v6bQstmMod2HKMYX/IULVmCgyTOn7e6aJGb0/STiIiYy6A2b+bsF1M5PWYqoVyZMzpBMHke70D+7p3hrrvM48JvokQJOHoUiheHI0c8WbjkBJp+EhGRtLPZoHZtCox+l6VfHKAJP/M5fThOEYoQT/6vR0PTpuYhUy++COvXm0EoFdHRZqAB80+dSSVZRSM1IiJyjQIF4Nw5CMqXxOnZy2DqVJg9O+WmNeHh0Lmzef2zEWpcnFZPSebT6qdUKNSIiKRNqquX/v4bFi82N/ibPx8uXrzyhurVoXNnVpfpTKPHyl3zeVo9JbfCKw60FBGR7GnAgCsjJckCAqBtW/M6dw4WLDBHcBYvhq1bYetWGvIKa7iDaXRmBh05TCkcDnNQR8TTNFIjIiK35tQpc2pq2jRzN+N/5p5c2FhJE3J17USjUe2haNE0f6QajeVqmn5KhUKNiIiHHT0KM2eydsBU7rj865XH7XaIiDD7bx5+GAoVuu5H3OoxDerH8T0KNalQqBERyRohIWA/dJAegTN4reJ0c7WUm58ftGxpBpw2bVLMcWVGo7H6cXyPlnSLiIilYinN2AIDYd062L0b3noLatQwD91ctAi6dTOnpNq1g+nT4fx5du1KGWgAnE7z7WnldKb8U3IOhRoREfG88uXh5ZdhyxbYvh2GDTN3MU5MhDlzzFGbokVp8EFn2tlSHtOQnkZj7ZGTs2n6SUREMl2a+loMA/73P3OUZto02Lcv+akECjCXtsygI4+MaclTvXLf9HtqjxzfpZ6aVCjUiIhkU4Zh9t1Mn25eVzfD3Hab2VzcsSO0aAG5cqX6ETEx6IRxC3kyDCrUpEKhRkTEC7hctC22moj46XSyf0sx19ErzxUubPbgdOxoJhW/K9utZdZIjUJNxnjy702NwiIi4p3sdv4zpDEnh33MlHfiYPly6NMHihSBv/6CcePgnnugZEnz8eXLwekkJAS++CLlR40dm75AA2o09mYaqREREe+QlAQrVsCMGTBrlhlw3IoXh/btoVMnSrZvxJFj9gxt3Here+RAzu3JyQ4jNQo1IiLifS5fNncvnjHD3M349Onkp47YSzHN1YGYIp2Yf6yBeQJ5Gmj66tZkh1Cj6ScREfE+uXJBq1bm0MqxY7BwobnvTWAgJVyH6M+HzD/REMLCYOBAWLvWbEa+gczYI+dWjRoFUVHmn1aw+vvfKo3UiIiI7/j7b1i61FxBNX++efCmW5kyZoNxx45Qt+41IzjZYaTmVkc7bnXqy8rab0QjNSIikvMEBMBDD8HkyXD8uLmx36OPQr58cOAAjBwJ9eubmwEOHgwbNyaP4PhCo/GoUTB8uPeOtNwqhRoREfFNefJA27YwZYoZcGbNgk6dIG9ec6O/d94xR2wqVDB3O968mcinDYoXN99evHj6m4S1o7G1FGpERMT35c1r7m8zbZoZcGbMMFdL5ckDe/bAiBFQuzZUqsTg869Sgy047OnrzoiLg549Uz7Wq1fOaha2mkKNiIjkLPnyQYcO8O23ZsCZNs0MPAEBsGsXL5x9iy3UYsXxyvDqq+ZRDmloP80OjcY5nUKNiIjkXPnzm1NSs2aZAWfqVHZXa0uSw5/ySTvNk8Vr1oTKleG1124YcCpUAPu/fqum5zBOuXUKNSIiIgAFCkDnzoRvnYPfyeNms3HbtuDvDzt3wptvmgGnShUz4GzdmiLgZEajsXYzvjUKNSIiIv8WGAhdupirp47/E3DatDEDzp9/mgGnRg0z4AwdmhxwIiPJcKOxmoxvnUKNiIjIjbgDzty5ZsD55hsz4OTObQacN94wA07VqjB0KFVdvwMGDkfav4WajDOHV4Sa5cuXY7PZUr3WrVtndXkiIpJTBAZC165mwDlxwgw4Dz1kBpwdO+CNN/jpeHW2U5UXE4bB77+ryTgLeUWoadSoEUeOHElxde/enbCwMOrVq2d1eSIikhO5A868eeYIztdfw0MPkUhuqrCD/mdfh+rVzRGcYTcOOGoyzhxeEWpy585N8eLFk6/ChQszf/58nn76aWxpPKhMRETEY4KC4LHHYN48apU4zmN8zdKAB6+M4Lx+VcAZOvSagOMLuxlnB14Rav5t/vz5xMfH8+STT1pdioiISAo9BgYRPuwxfn9rfooRnKunqFILOLfSZAxqNAYvPdDy/vvvB2DRokU3fF1iYiKJiYnJXyckJBAaGqoDLUVEJOudOQMLFpib/i1eDJcuXXmucmXo0IEWYzuy7PjtlCplS1eTsC8cxnkjXnGgZVRU1HUbgN3X+vXrU7wnLi6OJUuWEJmGCDtixAiCgoKSr9DQUE/9KCIiIjd21RTV9UZw3E3GAxOu3QfnRtRobLJ0pCY+Pp74+PgbviYsLIyAgIDkr9944w0++eQTDh06RK5cuW74Xo3UiIhItnfVCE7i/MX4c9UITqVK5pEOHTqYU1bX6SPVSI3Jq6afDMOgfPnytGvXjvfeey/d70/rX4qIiIgVKpdMoO6RBTwe8C33Govhqv8wp2LFKwGnRo1rAk50NHTvfuXr8ePT35fj7aHGqxqFly1bxr59+9I09SQiIuJtztkDmUJXuheee2Un46uPanjrLahVyxzBeeUV2Lw5eYrqVhuNwftXT3lVqImOjqZRo0ZUqVLF6lJEREQ8K7WjGtwBZ9cuePttqF3bHMF5+WXYtAmH3Qw46dnN2M0XVk951fTTrdL0k4iIZGejRkFCgplnBgy4zovOnoWFC81VVN9/D3//nfzUPkc405ztWVGkA4uP1b5uD86/ZUZPTnaYflKoERER8VZnz8J335kBZ9GiFAGH8uWhfXuzB6dOnRsGnJgYiIhI/fFmzdJWSokS5ghP8eJw5Ej6foyb8cmeGhEREblKgQLQuTPMmgUnTtCn4DRm8ggXbXlgzx545x2oV888b+G//4UNG1JdJn6rxzRkl6krjdSIiIj4CPcUUIUS59j54SJzBOe77+DixSsvKlv2yiqqunWTR3Ayunoqs5aT34hGakRERHKoC/b80LGjGWpOnIAZM8wQkzcv7NsH774L9etDuXIwaBCsW0fk00aGVk9lp43/FGpERER8Wb58ZqCZMcNcRfXtt2bgyZvXHE4ZORLuuAPKlWPYuZeoz9rkVVRpkZ1OGFeoERERySny5TObh6dPN0dwZs6ETp3Mx/fvp/e591hLA1YfKwsDB8Jvv930qIbMOmE8M6inRkRExEdkeFn1hQuweDHzHptBi4sLyc/5K8+VLn1lFVWDBtddRaXVTyIiImK9vHmhXTueLTSNIpygR6FZ5qqqfPng4EFzA52GDc2O4AEDYM2aa0Zw3Bv+ZWTjv8ziZ923FhERkcw0YMCVzfsy6m/y8H2edjC1nblqavFisw9nwQKIjYUPPjCv0NCUIzjZYJxE008iIiIC3GT66uJFWLLEDDjz58O5cyneOO5Ueyac70BsyTuJPZS5AUfTTyIiIpJ58uQxz56aPNlsMp471zybqkABiIujx/kP+ZXGdLkwzrISFWpEREQkfQICoE0bM+AcPw5z5zI7T1dOE8SPAQ9aVpZ6akRERCTj/gk4zxdqw7FDlynmyGVZKRqpERERkUyRhHWBBhRqRERExEco1IiIiIhPUKgRERERn6BQIyIiIj5BoUZERER8gkKNiIiI+ASFGhEREfEJCjUiIiLiE7SjsIiIiACZc8q3lRRqREREBDBDjTfT9JOIiIj4BIUaERER8QkKNSIiIuITFGpERETEJyjUiIiIiE9QqBERERGfoFAjIiIiPkGhRkRERHyC14SanTt30qZNG4KDgwkMDKRx48bExMRYXZaIiIhkE16zo3Dr1q2pWLEiy5YtI0+ePHz44Yc88MAD7Nmzh+LFi1tdnoiISI6WHY5YsBmGYVj37dMmPj6eIkWK8PPPP9OkSRMAzp49S2BgID/++CMtWrRI0+ckJCQQFBTEmTNnCPTWgy1ERERymLT+/vaK6afChQtTpUoVvvrqK86fP09SUhJjx46lWLFi1K1b97rvS0xMJCEhIcUlIiIivskrpp9sNhs//PADbdq0oUCBAtjtdooVK8bixYu57bbbrvu+ESNGMHz48KwrVERERCxj6UhNVFQUNpvthtf69esxDINnnnmGokWLsnLlStauXUubNm144IEHOHLkyHU/f8iQIZw5cyb5io2NzcKfTkRERLKSpT018fHxxMfH3/A1YWFhrFq1ipYtW3Lq1KkUc2kVKlQgMjKSwYMHp+n7qadGRETE+6T197el00/BwcEEBwff9HUXLlwAwG5PObBkt9txuVweqU1ERES8i1c0Cjds2JCCBQvyxBNPsGXLFnbu3MlLL73Evn37aN26tdXliYiISDbgFaEmODiYxYsXc+7cOSIiIqhXrx6//PIL8+bNo2bNmlaXJyIiItmAV+xTk1nUUyMiIuJ9fGqfGhEREZGbUagRERERn6BQIyIiIj5BoUZERER8gkKNiIiI+ASFGhEREfEJXnGgZWZxr17Xad0iIiLew/17+2a70OSoUHP27FkAQkNDLa5ERERE0uvs2bMEBQVd9/kctfmey+Xi8OHDFChQAJvNRkJCAqGhocTGxmozvmxM98k76D55B90n76D7lJJhGJw9e5aSJUtecw7k1XLUSI3dbickJOSaxwMDA/U/Gi+g++QddJ+8g+6Td9B9uuJGIzRuahQWERERn6BQIyIiIj4hR4caf39/hg0bhr+/v9WlyA3oPnkH3SfvoPvkHXSfMiZHNQqLiIiI78rRIzUiIiLiOxRqRERExCco1IiIiIhP8PlQ8/nnn1O2bFkCAgKoW7cuK1euvOHrV6xYQd26dQkICKBcuXKMGTMmiyrN2dJzn44cOUKXLl2oVKkSdrudfv36ZV2hOVx67tPs2bO55557KFKkCIGBgTRs2JAlS5ZkYbU5V3ru0y+//ELjxo0pXLgwefLkoXLlynzwwQdZWG3Old7fT26rVq3Cz8+PWrVqebZAb2T4sGnTphm5cuUyxo0bZ2zfvt144YUXjHz58hkHDhxI9fV79+418ubNa7zwwgvG9u3bjXHjxhm5cuUyZs6cmcWV5yzpvU/79u0znn/+eWPSpElGrVq1jBdeeCFrC86h0nufXnjhBeOdd94x1q5da+zcudMYMmSIkStXLmPjxo1ZXHnOkt77tHHjRmPKlCnG77//buzbt8/4+uuvjbx58xpjx47N4spzlvTeJ7fTp08b5cqVM1q2bGnUrFkza4r1Ij4dau644w6jd+/eKR6rXLmyMXjw4FRfP2jQIKNy5copHuvVq5dx5513eqxGSf99ulrTpk0VarLIrdwnt6pVqxrDhw/P7NLkKplxnx5++GHjsccey+zS5CoZvU+dOnUyXn31VWPYsGEKNanw2emnS5cusWHDBlq2bJni8ZYtW/Lrr7+m+p7Vq1df8/pWrVqxfv16Ll++7LFac7KM3CfJeplxn1wuF2fPnqVQoUKeKFHInPu0adMmfv31V5o2beqJEoWM36cJEyawZ88ehg0b5ukSvZbPnv0UHx+P0+mkWLFiKR4vVqwYR48eTfU9R48eTfX1SUlJxMfHU6JECY/Vm1Nl5D5J1suM+/T+++9z/vx5Onbs6IkShVu7TyEhIZw4cYKkpCSioqLo3r27J0vN0TJyn3bt2sXgwYNZuXIlfn4++6v7lvn834zNZkvxtWEY1zx2s9en9rhkrvTeJ7FGRu/T1KlTiYqKYt68eRQtWtRT5ck/MnKfVq5cyblz51izZg2DBw8mPDycRx991JNl5nhpvU9Op5MuXbowfPhwKlasmFXleSWfDTXBwcE4HI5rUu/x48evScduxYsXT/X1fn5+FC5c2GO15mQZuU+S9W7lPk2fPp3IyEi+/fZb7r77bk+WmePdyn0qW7YsANWrV+fYsWNERUUp1HhIeu/T2bNnWb9+PZs2baJv376AOZ1rGAZ+fn4sXbqUiIiILKk9u/PZnprcuXNTt25dfvjhhxSP//DDDzRq1CjV9zRs2PCa1y9dupR69eqRK1cuj9Wak2XkPknWy+h9mjp1Kk8++SRTpkyhdevWni4zx8us/z8ZhkFiYmJmlyf/SO99CgwMZOvWrWzevDn56t27N5UqVWLz5s00aNAgq0rP/qzrUfY895K56OhoY/v27Ua/fv2MfPnyGfv37zcMwzAGDx5sPP7448mvdy/p7t+/v7F9+3YjOjpaS7qzQHrvk2EYxqZNm4xNmzYZdevWNbp06WJs2rTJ2LZtmxXl5xjpvU9Tpkwx/Pz8jM8++8w4cuRI8nX69GmrfoQcIb336dNPPzXmz59v7Ny509i5c6fx5ZdfGoGBgcYrr7xi1Y+QI2Tk33tX0+qn1Pl0qDEMw/jss8+MMmXKGLlz5zbq1KljrFixIvm5J554wmjatGmK1y9fvtyoXbu2kTt3biMsLMwYPXp0FlecM6X3PgHXXGXKlMnaonOg9Nynpk2bpnqfnnjiiawvPIdJz336+OOPjdtvv93ImzevERgYaNSuXdv4/PPPDafTaUHlOUt6/713NYWa1OmUbhEREfEJPttTIyIiIjmLQo2IiIj4BIUaERER8QkKNSIiIuITFGpERETEJyjUiIiIiE9QqBERERGfoFAjIiIiPkGhRkRERHyCQo2I+IyFCxdSrlw56tevz86dOzP0GQ8//DAFCxakffv2mVydiHiajkkQEZ9RsWJFPv/8c7Zt28bq1auZNm1auj8jJiaGc+fOMWnSJGbOnOmBKkXEUzRSIyJe46+//qJo0aLs378/1eeDg4MJDw+nXLlyBAUFZeh7NG/enAIFCqT6XPv27Rk1alSGPldEPM/P6gJERNJqxIgRPPjgg4SFhaX6/FNPPUX58uUpUqQI27Zty/TvP3ToUJo3b0737t0JDAzM9M8XkVujkRoR8QoXL14kOjqa7t27p/p8UlISH330EYMGDeLs2bMULFgw02uoUaMGYWFhTJ48OdM/W0RunUKNiHjcjh07aN68OQEBAVSsWJHFixdjt9vZsGFDmj/j+++/x8/Pj4YNG6b6/JgxYyhXrhzPPvssFy5cYNeuXSmenzx5Mu3bt7/hlZbRnYceeoipU6emuW4RyTqafhIRj/rzzz+544476NWrF+PHj+f333+nW7du2O12br/99jR/zs8//0y9evVSfe7UqVO88cYbLF++nJCQEIKCgti8eTOVKlVKfk3Xrl3p2rXrLf88d9xxByNGjCAxMRF/f/9b/jwRyTwaqRERj3r++ed55JFHGDlyJOXLl6dNmzY0b96cSpUqERAQkObP2b9/PyVLlkz1uaFDh/Lwww9TpUoVAKpWrcqWLVsyVG+rVq3o0KEDixYtIiQkhHXr1qV4vlSpUiQmJnL06NEMfb6IeI5GakTEY2JjY1m6dOk1ASN37tzUrFkzXZ918eLFVEPQ9u3b+eabb/jjjz+SH6tWrRqbN2/OUM1Lliy54fN58uQB4MKFCxn6fBHxHIUaEfGYjRs3kitXLqpWrZri8a1bt9KlS5d0fVZwcDCnTp265vH+/ftz+vRpQkJCkh9zuVyUKFEiY0XfxMmTJwEoUqSIRz5fRDJO008i4jF2ux2n00lSUlLyY0uWLGHLli3JIzVNmjShVq1a1KpVC4fDcd1po9q1a7N9+/YUjy1cuJANGzawadMmNm/enHxFR0dz+PBhTpw4kek/0++//05ISAjBwcGZ/tkicms0UiMiHlO3bl1y5crFkCFDeO6559i0aRODBg0CSA41K1euBODVV18lIiLiutNSrVq1YsiQIZw6dYqCBQty+fJlXnzxRV566SVq1aqV4rXuPWS2bNnC3Xffnak/08qVK2nZsmWmfqaIZA6N1IiIx5QsWZLx48czc+ZMatSowbRp0+jduzdFixalePHiya8bNWoUhw4d4v3337/uZ1WvXp169eoxY8YMAD755BNOnz5N3759r3ltaGgoefPmzXBfzfX8/fffzJkzhx49emTq54pI5tDZTyKSpQYNGsSWLVuSG3InTJjAwoULmTFjBg6H44bvXbRoEQMHDuT333/Hbs/6/yb77LPPmDdvHkuXLs3y7y0iN6fpJxHJUv/73/+Sp5jmzJnDtGnTmD9//k0DDcD999/Prl27OHToEKGhoZ4u9Rq5cuXik08+yfLvKyJpo5EaEclSpUqV4t1336Vr164ULFiQIkWKkDdvXsA82+m+++6zuEIR8VYKNSIiIuIT1CgsIiIiPkGhRkRERHyCQo2IiIj4BIUaERER8QkKNSIiIuITFGpERETEJyjUiIiIiE9QqBERERGfoFAjIiIiPkGhRkRERHyCQo2IiIj4BIUaERER8QkKNSIiIuIT/h/XxPA48W6E8gAAAABJRU5ErkJggg==\n",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"objective.plot()\n",
"plt.xlabel(r'$q_z$ ($\\AA^{—1}$)')\n",
"plt.ylabel(\"logR\")\n",
"plt.show()\n",
"\n",
"fig, ax1 = plt.subplots()\n",
"\n",
"# First y-axis (SLD)\n",
"ax1.plot(*structure.sld_profile())\n",
"ax1.set_xlabel('Distance / $\\AA$')\n",
"ax1.set_ylabel('SLD / $10^{-6} \\AA^{-2}$', color='b')\n",
"ax1.tick_params(axis='y', labelcolor='b')\n",
"\n",
"# Define transformation functions\n",
"def sld_to_ed(sld): # sld in 10^-6 Å⁻²\n",
" return (sld * 1e-6) / r_el # e⁻/ų\n",
"\n",
"def ed_to_sld(ed): # e⁻/ų\n",
" return (ed * r_el) * 1e6 # back to 10^-6 Å⁻²\n",
"\n",
"\n",
"# Add secondary y-axis\n",
"ax2 = ax1.secondary_yaxis('right', functions=(sld_to_ed, ed_to_sld))\n",
"ax2.set_ylabel('Electron Density / e$^- \\ \\AA^{-3}$', color='r')\n",
"ax2.tick_params(axis='y', labelcolor='r')\n",
"\n",
"plt.title('SLD and Electron Density Profile')\n",
"plt.tight_layout()\n",
"plt.show()\n"
]
},
{
"cell_type": "markdown",
"id": "bdfd1460",
"metadata": {},
"source": [
"# Statistics (for later use)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "c6b0fc85",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████| 100/100 [00:23<00:00, 4.24it/s]\n"
]
},
{
"data": {
"text/plain": [
"[MCMCResult(name='water - rough', param=Parameter(value=3.3920854514375325, name='water - rough', vary=True, bounds=Interval(lb=0.5, ub=7.0), constraint=None), stderr=0.01276232987084347, chain=array([[3.39184015, 3.39438269, 3.41649244, ..., 3.39849706, 3.41172303,\n",
" 3.39824873],\n",
" [3.39176784, 3.39438269, 3.40740042, ..., 3.39664135, 3.41172303,\n",
" 3.39816917],\n",
" [3.38919273, 3.38919182, 3.40740042, ..., 3.39664135, 3.40834449,\n",
" 3.40034635],\n",
" ...,\n",
" [3.39544814, 3.38676058, 3.39825959, ..., 3.39790449, 3.39701153,\n",
" 3.39476713],\n",
" [3.39365428, 3.38676058, 3.39731078, ..., 3.39797642, 3.39647369,\n",
" 3.39699511],\n",
" [3.39364197, 3.39110629, 3.39731078, ..., 3.39819214, 3.40205574,\n",
" 3.39701509]]), median=3.3920854514375325)]"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"fitter.sample(100, pool=-1)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "a7fa301e",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"objective.plot(samples=100);"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "89b2d45c",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.2"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
%% Cell type:markdown id:cdcdc51f tags:
# Import data
%% Cell type:code id:98e73446 tags:
```
python
import
numpy
as
np
import
matplotlib.pyplot
as
plt
from
refnx.dataset
import
Data1D
from
refnx.util
import
refplot
data_init
=
np
.
loadtxt
(
'
SIRIUS_2024_09_20_4446-4530_XRR.dat
'
)
qz_exp
=
data_init
[:,
0
]
/
10.
#A^-1
R_exp
=
data_init
[:,
1
]
R_err_exp
=
data_init
[:,
2
]
data
=
Data1D
(
data
=
(
qz_exp
,
R_exp
,
R_err_exp
))
#refplot(data)
```
%% Cell type:code id:b7dbf4ea tags:
```
python
plt
.
errorbar
(
qz_exp
,
R_exp
,
R_err_exp
,
marker
=
'
x
'
,
linestyle
=
''
)
plt
.
yscale
(
'
log
'
)
plt
.
xlabel
(
r
'
$q_z$ (A$^{—1}$)
'
)
plt
.
ylabel
(
r
'
R
'
)
plt
.
show
()
```
%% Output
%% Cell type:markdown id:e3a440cd tags:
# Creating model
%% Cell type:markdown id:d081153a tags:
## Define SLD
%% Cell type:code id:bca2c9cf tags:
```
python
from
refnx.reflect
import
SLD
,
Slab
,
ReflectModel
# SLD = r_e*rho_el
r_el
=
2.81794e-5
# in A
# SLD in refnx should be in 1e-6 A^-2
SLD_water
=
0.334
*
r_el
*
1e6
SLD_helium
=
0.
water
=
SLD
(
SLD_water
,
name
=
'
water
'
)
helium
=
SLD
(
SLD_helium
,
name
=
'
helium
'
)
```
%% Cell type:markdown id:f25844f4 tags:
## Define slabs
%% Cell type:code id:85f3f29a tags:
```
python
# first number is thickness, second number is roughness (with respect to top layer)
# lengths in A
# Semi-infinite bulk : thickness = 0
water_bulk
=
water
(
0
,
1
)
helium_atm
=
helium
(
0
,
0
)
# Constructed from top to bottom
structure
=
helium_atm
|
water_bulk
fig
,
ax1
=
plt
.
subplots
()
# First y-axis (SLD)
ax1
.
plot
(
*
structure
.
sld_profile
())
ax1
.
set_xlabel
(
'
Distance / $\AA$
'
)
ax1
.
set_ylabel
(
'
SLD / $10^{-6} \AA^{-2}$
'
,
color
=
'
b
'
)
ax1
.
tick_params
(
axis
=
'
y
'
,
labelcolor
=
'
b
'
)
# Define transformation functions
def
sld_to_ed
(
sld
):
# sld in 10^-6 Å⁻²
return
(
sld
*
1e-6
)
/
r_el
# e⁻/ų
def
ed_to_sld
(
ed
):
# e⁻/ų
return
(
ed
*
r_el
)
*
1e6
# back to 10^-6 Å⁻²
# Add secondary y-axis
ax2
=
ax1
.
secondary_yaxis
(
'
right
'
,
functions
=
(
sld_to_ed
,
ed_to_sld
))
ax2
.
set_ylabel
(
'
Electron Density / e$^- \ \AA^{-3}$
'
,
color
=
'
r
'
)
ax2
.
tick_params
(
axis
=
'
y
'
,
labelcolor
=
'
r
'
)
plt
.
title
(
'
SLD and Electron Density Profile
'
)
plt
.
tight_layout
()
plt
.
show
()
```
%% Output
%% Cell type:markdown id:5a2c4843 tags:
## Plot initial guess
%% Cell type:code id:3ad6f334 tags:
```
python
model
=
ReflectModel
(
structure
,
bkg
=
0
)
plt
.
errorbar
(
qz_exp
,
R_exp
,
R_err_exp
,
marker
=
'
x
'
,
linestyle
=
''
)
plt
.
plot
(
qz_exp
,
model
(
qz_exp
),
'
k-
'
)
plt
.
xlabel
(
r
'
$q_z$ (A$^{—1}$)
'
)
plt
.
ylabel
(
r
'
R
'
)
plt
.
yscale
(
'
log
'
)
plt
.
show
()
```
%% Output
%% Cell type:markdown id:30d14073 tags:
# Fit
%% Cell type:markdown id:4914e6dc tags:
## Define fit parameters
%% Cell type:code id:d973e8ea tags:
```
python
water_bulk
.
rough
.
setp
(
bounds
=
(
0.5
,
7
),
vary
=
True
)
```
%% Cell type:markdown id:f59599a3 tags:
## Fit the data
%% Cell type:code id:f0138907 tags:
```
python
from
refnx.analysis
import
Transform
,
CurveFitter
,
Objective
,
Model
,
Parameter
objective
=
Objective
(
model
,
data
,
transform
=
Transform
(
"
logY
"
))
fitter
=
CurveFitter
(
objective
)
fitter
.
fit
(
"
differential_evolution
"
)
print
(
objective
)
```
%% Output
56813.855182359344: : 4it [00:00, 5.03it/s]
________________________________________________________________________________
Objective - 140218423146320
Dataset = <None>, 85 points
datapoints = 85
chi2 = 114299.55496834047
Weighted = True
Transform = Transform('logY')
________________________________________________________________________________
Parameters: ''
________________________________________________________________________________
Parameters: 'instrument parameters'
<Parameter: 'scale' , value=1 (fixed) , bounds=[-inf, inf]>
<Parameter: 'bkg' , value=0 (fixed) , bounds=[-inf, inf]>
<Parameter:'dq - resolution', value=5 (fixed) , bounds=[-inf, inf]>
<Parameter: 'q_offset' , value=0 (fixed) , bounds=[-inf, inf]>
________________________________________________________________________________
Parameters: 'Structure - '
________________________________________________________________________________
Parameters: 'helium'
<Parameter:'helium - thick', value=0 (fixed) , bounds=[-inf, inf]>
________________________________________________________________________________
Parameters: 'helium'
<Parameter:'helium - sld' , value=0 (fixed) , bounds=[-inf, inf]>
<Parameter:'helium - isld', value=0 (fixed) , bounds=[-inf, inf]>
<Parameter:'helium - rough', value=0 (fixed) , bounds=[-inf, inf]>
<Parameter:'helium - volfrac solvent', value=0 (fixed) , bounds=[0.0, 1.0]>
________________________________________________________________________________
Parameters: 'water'
<Parameter:'water - thick', value=0 (fixed) , bounds=[-inf, inf]>
________________________________________________________________________________
Parameters: 'water'
<Parameter: 'water - sld' , value=9.41192 (fixed) , bounds=[-inf, inf]>
<Parameter:'water - isld' , value=0 (fixed) , bounds=[-inf, inf]>
<Parameter:'water - rough', value=3.39189 +/- 0.0124, bounds=[0.5, 7.0]>
<Parameter:'water - volfrac solvent', value=0 (fixed) , bounds=[0.0, 1.0]>
%% Cell type:markdown id:44f8d495 tags:
## Plot the results
%% Cell type:code id:b340cc4f tags:
```
python
objective
.
plot
()
plt
.
xlabel
(
r
'
$q_z$ ($\AA^{—1}$)
'
)
plt
.
ylabel
(
"
logR
"
)
plt
.
show
()
fig
,
ax1
=
plt
.
subplots
()
# First y-axis (SLD)
ax1
.
plot
(
*
structure
.
sld_profile
())
ax1
.
set_xlabel
(
'
Distance / $\AA$
'
)
ax1
.
set_ylabel
(
'
SLD / $10^{-6} \AA^{-2}$
'
,
color
=
'
b
'
)
ax1
.
tick_params
(
axis
=
'
y
'
,
labelcolor
=
'
b
'
)
# Define transformation functions
def
sld_to_ed
(
sld
):
# sld in 10^-6 Å⁻²
return
(
sld
*
1e-6
)
/
r_el
# e⁻/ų
def
ed_to_sld
(
ed
):
# e⁻/ų
return
(
ed
*
r_el
)
*
1e6
# back to 10^-6 Å⁻²
# Add secondary y-axis
ax2
=
ax1
.
secondary_yaxis
(
'
right
'
,
functions
=
(
sld_to_ed
,
ed_to_sld
))
ax2
.
set_ylabel
(
'
Electron Density / e$^- \ \AA^{-3}$
'
,
color
=
'
r
'
)
ax2
.
tick_params
(
axis
=
'
y
'
,
labelcolor
=
'
r
'
)
plt
.
title
(
'
SLD and Electron Density Profile
'
)
plt
.
tight_layout
()
plt
.
show
()
```
%% Output
%% Cell type:markdown id:bdfd1460 tags:
# Statistics (for later use)
%% Cell type:code id:c6b0fc85 tags:
```
python
fitter
.
sample
(
100
,
pool
=-
1
)
```
%% Output
100%|██████████| 100/100 [00:23<00:00, 4.24it/s]
[MCMCResult(name='water - rough', param=Parameter(value=3.3920854514375325, name='water - rough', vary=True, bounds=Interval(lb=0.5, ub=7.0), constraint=None), stderr=0.01276232987084347, chain=array([[3.39184015, 3.39438269, 3.41649244, ..., 3.39849706, 3.41172303,
3.39824873],
[3.39176784, 3.39438269, 3.40740042, ..., 3.39664135, 3.41172303,
3.39816917],
[3.38919273, 3.38919182, 3.40740042, ..., 3.39664135, 3.40834449,
3.40034635],
...,
[3.39544814, 3.38676058, 3.39825959, ..., 3.39790449, 3.39701153,
3.39476713],
[3.39365428, 3.38676058, 3.39731078, ..., 3.39797642, 3.39647369,
3.39699511],
[3.39364197, 3.39110629, 3.39731078, ..., 3.39819214, 3.40205574,
3.39701509]]), median=3.3920854514375325)]
%% Cell type:code id:a7fa301e tags:
```
python
objective
.
plot
(
samples
=
100
);
```
%% Output
%% Cell type:code id:89b2d45c tags:
```
python
```
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