{

“cells”: [

{

“cell_type”: “markdown”, “metadata”: {}, “source”: [

“# Instrument synergy”

]

}, {

“cell_type”: “markdown”, “metadata”: {}, “source”: [

“The purpose of this notebook is to prototype support for multiple instruments operating on the same signal incident at Earth. The instruments may have different wavebands and the data may or may not be phase resolved for each instrument.”

]

}, {

“cell_type”: “markdown”, “metadata”: {}, “source”: [

“Some extra data files not available on the GitHub repository are needed to run this tutorial. They can be found in the [Zenodo](https://zenodo.org/record/7094144) and should be placed in the directory examples/examples_modeling_tutorial/model_data/ of your local xpsi directory. These are the required files: n”, “- nicer_v1.01_arf.txtn”, “- nicer_v1.01_rmf_matrix.txtn”, “- nicer_v1.01_rmf_energymap.txt”

]

}, {

“cell_type”: “code”, “metadata”: {

“ExecuteTime”: {

“end_time”: “2026-03-06T11:21:23.417644Z”, “start_time”: “2026-03-06T11:21:22.921441Z”

}

}, “source”: [

“%matplotlib inlinen”, “n”, “import osn”, “import numpy as npn”, “import mathn”, “import timen”, “n”, “from matplotlib import pyplot as pltn”, “from matplotlib import rcParamsn”, “from matplotlib.ticker import MultipleLocator, AutoLocator, AutoMinorLocatorn”, “from matplotlib import gridspecn”, “from matplotlib import cmn”, “rcParams[‘text.usetex’] = Falsen”, “rcParams[‘font.size’] = 14.0n”, “n”, “import xpsin”, “n”, “from xpsi.global_imports import _c, _G, _dpr, gravradius, _csq, _km, _2pin”, “from xpsi.utilities import PlottingLibrary as XpsiPlot”

], “outputs”: [

{

“name”: “stdout”, “output_type”: “stream”, “text”: [

“/=============================================\n”, “| X-PSI: X-ray Pulse Simulation and Inference |\n", "|———————————————|\n", "| Version: 3.2.1 |\n", "|———————————————|\n", "| https://xpsi-group.github.io/xpsi |n”, “\=============================================/n”, “n”, “Imported emcee version: 3.1.6n”, “Imported PyMultiNest.n”, “Check your UltraNest installation.n”, “Check your installation of UltraNest if using the UltranestSamplern”, “Warning: Cannot import torch and test SBI_wrapper.n”, “Imported GetDist version: 1.7.5n”, “Imported nestcheck version: 0.2.1n”

]

}

], “execution_count”: 1

}, {

“cell_type”: “code”, “metadata”: {

“ExecuteTime”: {

“end_time”: “2026-03-06T11:21:23.426927Z”, “start_time”: “2026-03-06T11:21:23.423020Z”

}

}, “source”: [

“class Telescope(object):n”, “ pass”

], “outputs”: [], “execution_count”: 2

}, {

“cell_type”: “code”, “metadata”: {

“ExecuteTime”: {

“end_time”: “2026-03-06T11:21:23.430643Z”, “start_time”: “2026-03-06T11:21:23.427198Z”

}

}, “source”: [

“NICER = Telescope()n”, “XMM = Telescope() # fabricated toy that we’ll just pretend is XMM as a placeholder!”

], “outputs”: [], “execution_count”: 3

}, {

“cell_type”: “markdown”, “metadata”: {}, “source”: [

“## Likelihood”

]

}, {

“cell_type”: “markdown”, “metadata”: {}, “source”: [

“Let us load a synthetic data set that we generated in advance, and know the fictitious exposure time for.”

]

}, {

“cell_type”: “code”, “metadata”: {

“ExecuteTime”: {

“end_time”: “2026-03-06T11:21:23.435434Z”, “start_time”: “2026-03-06T11:21:23.430881Z”

}

}, “source”: [

“obs_settings = dict(counts=np.loadtxt(‘../../examples/examples_modeling_tutorial/model_data/example_synthetic_realisation.dat’, dtype=np.double),n”, “ channels=np.arange(20, 201),n”, “ phases=np.linspace(0.0, 1.0, 33),n”, “ first=0,last=180,n”, “ exposure_time=984307.6661)n”, “n”, “NICER.data = xpsi.Data(**obs_settings)”

], “outputs”: [

{

“name”: “stdout”, “output_type”: “stream”, “text”: [

“Setting channels for event data…n”, “Channels set.n”

]

}

], “execution_count”: 4

}, {

“cell_type”: “code”, “metadata”: {

“ExecuteTime”: {

“end_time”: “2026-03-06T11:21:23.445518Z”, “start_time”: “2026-03-06T11:21:23.437921Z”

}

}, “source”: [

“obs_settings = dict(counts=np.loadtxt(‘../../examples/examples_modeling_tutorial/model_data/XMM_realisation.dat’, dtype=np.double).reshape(-1,1),n”, “ channels=np.arange(20, 201),n”, “ phases=np.array([0.0, 1.0]),n”, “ first=0,last=180,n”, “ exposure_time=1818434.247359)n”, “n”, “XMM.data = xpsi.Data(**obs_settings)”

], “outputs”: [

{

“name”: “stdout”, “output_type”: “stream”, “text”: [

“Setting channels for event data…n”, “Channels set.n”

]

}

], “execution_count”: 5

}, {

“cell_type”: “markdown”, “metadata”: {}, “source”: [

“Now for the data:”

]

}, {

“cell_type”: “code”, “metadata”: {

“ExecuteTime”: {

“end_time”: “2026-03-06T11:21:23.745096Z”, “start_time”: “2026-03-06T11:21:23.450736Z”

}

}, “source”: [

“fig, axs = NICER.data.plot(dpi=130, num_rot=1)”

], “outputs”: [

{

“data”: {

“text/plain”: [

“<Figure size 832x624 with 5 Axes>”

], “image/png”: 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”

}, “metadata”: {}, “output_type”: “display_data”, “jetTransient”: {

“display_id”: null

}

}

], “execution_count”: 6

}, {

“cell_type”: “code”, “metadata”: {

“ExecuteTime”: {

“end_time”: “2026-03-06T11:21:23.897642Z”, “start_time”: “2026-03-06T11:21:23.758686Z”

}

}, “source”: [

“print(f"XMM data is a spectrum. The array shape is : {np.shape(XMM.data.counts)}")n”, “print(f"NICER data is a pulse profile. They array shape is : {np.shape(NICER.data.counts)}") n”, “n”, “data_to_plot = [XMM.data.counts,n”, “ np.sum(NICER.data.counts, axis=1)] # Summing the NICER pulse profile over phase-bin to make spectrumn”, “label_to_plot = [‘XMM’, ‘NICER’]n”, “n”, “XpsiPlot.plot_spectrum(all_data=data_to_plot, n”, “ all_labels=label_to_plot)”

], “outputs”: [

{

“name”: “stdout”, “output_type”: “stream”, “text”: [

“XMM data is a spectrum. The array shape is : (181, 1)n”, “NICER data is a pulse profile. They array shape is : (181, 32)n”

]

}, {

“data”: {

“text/plain”: [

“<Figure size 1000x1000 with 1 Axes>”

], “image/png”: 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”

}, “metadata”: {}, “output_type”: “display_data”, “jetTransient”: {

“display_id”: null

}

}

], “execution_count”: 7

}, {

“cell_type”: “markdown”, “metadata”: {}, “source”: [

“### Instrument”

]

}, {

“cell_type”: “markdown”, “metadata”: {}, “source”: [

“We require a model instrument object to transform incident specific flux signals into a form which enters directly in the sampling distribution of the data.”

]

}, {

“cell_type”: “code”, “metadata”: {

“ExecuteTime”: {

“end_time”: “2026-03-06T11:21:23.910121Z”, “start_time”: “2026-03-06T11:21:23.900606Z”

}

}, “source”: [

“class CustomInstrument(xpsi.Instrument):n”, “ """ A model of the NICER telescope response. """n”, “ n”, “ def __call__(self, signal, *args):n”, “ """ Overwrite base just to show it is possible.n”, “ n”, “ We loaded only a submatrix of the total instrument responsen”, “ matrix into memory, so here we can simplify the method in then”, “ base class.n”, “ n”, “ """n”, “n”, “ matrix = self.construct_matrix()n”, “n”, “ self._folded_signal = np.dot(matrix, signal)n”, “ n”, “ return self._folded_signaln”, “n”, “ @classmethodn”, “ def from_response_files(cls, ARF, RMF, channel_edges, max_input, min_input=0,n”, “ translate_edges=None, scaling=None, inst_name=None):n”, “ """ Constructor which converts response files into numpy.ndarray`s.n", "        :param str ARF: Path to ARF which is compatible withn", "                                :func:`numpy.loadtxt.n”, “ :param str RMF: Path to RMF which is compatible withn”, “ numpy.loadtxt().n”, “ :param str channel_edges: Path to edges which is compatible withn”, “ numpy.loadtxt().n”, “ :param float translate_edges: Optional translation of the energy edges (in keV).n”, “ :param float scaling: Optional Scaling to scale the ARF.n”, “ n”, “ """n”, “n”, “ if min_input != 0:n”, “ min_input = int(min_input)n”, “n”, “ max_input = int(max_input)n”, “n”, “ try:n”, “ ARF = np.loadtxt(ARF, dtype=np.double, skiprows=3)n”, “ RMF = np.loadtxt(RMF, dtype=np.double)n”, “ channel_edges = np.loadtxt(channel_edges, dtype=np.double, skiprows=3)[:,1:]n”, “ except:n”, “ print(‘A file could not be loaded.’)n”, “ raisen”, “ n”, “ if scaling is not None:n”, “ ARF[:,3] *= scalingn”, “ n”, “ matrix = np.ascontiguousarray(RMF[min_input:max_input,20:201].T, dtype=np.double)n”, “n”, “ edges = np.zeros(ARF[min_input:max_input,3].shape[0]+1, dtype=np.double)n”, “n”, “ edges[0] = ARF[min_input,1]; edges[1:] = ARF[min_input:max_input,2]n”, “n”, “ for i in range(matrix.shape[0]):n”, “ matrix[i,:] *= ARF[min_input:max_input,3]n”, “n”, “ channels = np.arange(20, 201)n”, “ n”, “ if translate_edges is not None:n”, “ edges += translate_edgesn”, “n”, “ Inst = cls(matrix, edges, channels, channel_edges[20:202,-2])n”, “ Inst.name = inst_namen”, “ return Inst”

], “outputs”: [], “execution_count”: 8

}, {

“cell_type”: “markdown”, “metadata”: {}, “source”: [

“Let’s construct an instance of the first instrument called NICER. This is made from RMF and ARF in ASCII (text) file format, but a new loader from FITS file is available (see "Modelling" tutorial).”

]

}, {

“cell_type”: “code”, “metadata”: {

“ExecuteTime”: {

“end_time”: “2026-03-06T11:21:24.593723Z”, “start_time”: “2026-03-06T11:21:23.910406Z”

}

}, “source”: [

“NICER.instrument = CustomInstrument.from_response_files(ARF = ‘../../examples/examples_modeling_tutorial/model_data/nicer_v1.01_arf.txt’,n”, “ RMF = ‘../../examples/examples_modeling_tutorial/model_data/nicer_v1.01_rmf_matrix.txt’,n”, “ channel_edges = ‘../../examples/examples_modeling_tutorial/model_data/nicer_v1.01_rmf_energymap.txt’,n”, “ max_input = 500,n”, “ min_input = 0,n”, “ inst_name=’NICER XTI’)”

], “outputs”: [

{

“name”: “stdout”, “output_type”: “stream”, “text”: [

“Setting channels for loaded instrument response (sub)matrix…n”, “Channels set.n”, “An empty subspace was created. This is normal behavior - no parameters were supplied.n”

]

}

], “execution_count”: 9

}, {

“cell_type”: “markdown”, “metadata”: {}, “source”: [

“For the second instrument, called XMM, we use the some response files (those of NICER) with two additional arbitrary modifications (for the sake of the example):n”, “- add a scaling=0.5 which reduces the sensitivity by half (see CustomInstrument class above).n”, “- add a translate_edges=0.1 which shifts the sensitivity matrix by 0.1 keV.”

]

}, {

“cell_type”: “code”, “metadata”: {

“ExecuteTime”: {

“end_time”: “2026-03-06T11:21:25.291765Z”, “start_time”: “2026-03-06T11:21:24.605263Z”

}

}, “source”: [

“XMM.instrument = CustomInstrument.from_response_files(ARF = ‘../../examples/examples_modeling_tutorial/model_data/nicer_v1.01_arf.txt’,n”, “ RMF = ‘../../examples/examples_modeling_tutorial/model_data/nicer_v1.01_rmf_matrix.txt’,n”, “ channel_edges = ‘../../examples/examples_modeling_tutorial/model_data/nicer_v1.01_rmf_energymap.txt’,n”, “ max_input = 500,n”, “ min_input = 0,n”, “ translate_edges = 0.1,n”, “ scaling = 0.5,n”, “ inst_name=’XMM’)”

], “outputs”: [

{

“name”: “stdout”, “output_type”: “stream”, “text”: [

“Setting channels for loaded instrument response (sub)matrix…n”, “Channels set.n”, “An empty subspace was created. This is normal behavior - no parameters were supplied.n”

]

}

], “execution_count”: 10

}, {

“cell_type”: “markdown”, “metadata”: {}, “source”: [

“The NICER v1.01 response matrix:”

]

}, {

“cell_type”: “code”, “metadata”: {

“ExecuteTime”: {

“end_time”: “2026-03-06T11:21:25.426619Z”, “start_time”: “2026-03-06T11:21:25.299609Z”

}

}, “source”: [

“energy_inputs = np.arange(1,len( NICER.instrument.energy_edges)+1)-0.5n”, “channels = NICER.instrument.channel_edges * 100 - 0.5n”, “n”, “rmf_plot = XpsiPlot.plot_rmf(matrix=NICER.instrument.matrix,n”, “ x=energy_inputs,n”, “ y=channels,n”, “ xlabel=’Energy interval’, n”, “ ylabel=’Channels’n”, “ )n”, “n”, “XpsiPlot.veneer((25, 100), (10, 50), rmf_plot)”

], “outputs”: [

{

“data”: {

“text/plain”: [

“<Figure size 1400x700 with 2 Axes>”

], “image/png”: 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5UrTYTFsKB813wqWpCUuJTCliTVmKlsSs75qMJ7/UJEvlJV2qKOyQJAX9Jl9ymrDUW67UHDaTlRITltraC9TdYa4rkSv5ExOWEqlSm3uuJMmWLHnCiSlLiUzJ2iYSpUSa1N0tdadNWZIcJyy55krpnPKh/k5Z2s93+3yMAzweAOPZZ5/VBx98oIsvvlgTJ0484O+PHj1aktTR0ZHtS8spFmUAAAAAABhmYtavXJ9joPTlAb9uXnvtNUlSTU1Nti5pQJAvAQAAAACAQfPxxx/r6aef1ujRozV9+vRe93vttdcUiUR6vL9s2TJt2LBBn/zkJ3XiiSfm8lKzjjtlAAAAcGhwmrDkkitJSiVL/nzzS5Lyra3HJE9xv9k3VlKgSGkiW7LnSqEyj/XafDVaIkVKrYyn1CQ+eYVmW2ZNWCovMhOWRhV0KJhvpiwFvFHbz5CeLPWWKzlNWOqRKyWzpVSy5JYrSbInS265kmRPlhxypXTxWCxruZL52CU3IlfCMNatuLpzPBK7P8dfsWKF1q9fL0l66623ku+tW7dOkjRjxgzNmDHD9p2HHnpIkUhEs2bNkj/x710HCxYs0Lvvvqva2lpNmDBBnZ2d2rhxo/70pz+poqJCP//5z+Wx/t08XLAoAwAAAAAAsmL9+vVavXq17b0NGzZow4YNkkxelLko09d0aebMmXr88cf18ssva8+ePZKkSZMm6Tvf+Y6++93vavz48Vn6KQYOizIAAAAAAAwz3XHzK9fnOFCrVq3SqlWrDug7f/nLX/q037x58/r9zJmhikUZAAAAjExOuVLa+568PPdcKX1bEFCsyHwWC1iTi6zEJmblS+HyfIWsKUuREnuuFC432/RkyV9ipiuVWLlSScBsS/PNNug3yVKxLyyfx2RA0bg5fme3uYaWRKoULnDNlST7hKUeuVJHIltKJUs9cqVQIluykqT0ZKm3XCmR81iv5ZAmuU5a6i0d6u+EpWznSgdwXABwwqIMAAAAAADDzEibvnSoYvoSAAAAAADAIOBOGQAAAIwcacmSY64k2ZIlt1xJUjJZ6i72K1psPosWmu97rYctdPvN685Kr8JB8/WIlS1FglbiVG7Sn/RkqazATFcqD5hMqSDP7OPzWAmRtzv5s7RGTZqUmLDUGCqUZJ+w5JYrSalkydcu+a1MKT1XkuwTlmzTlST3CUuZuVIiAXKYtOSaK6V/17ZPP3OlbB+jj8cDBkJMHnUrt5OGYjk+PliUyZqGhgY1NDTY3quvrx+kqwEAAAAAAEMdizJZsnz5ci1evHiwLwMAAAAAcAiIxXt/FnY2z4HcYlEmS+bPn6/p06fb3quvr9fMmTMH6YoAAAAOAU4TllxyJUlSvvkjsCcQcM2VJClSYj6PlOYpFDTH6TbFkLymRFLMOkTX6FSu1F1qZUAlJkmqLOuQJFUUmm3QH1KJz0xZKrSypUjcHD8aM9fZEQ1Y23y1hq1sqdNkS04TlnrLlTInLPk6YvJ1JLIlkyIlJyylJ0tuuZJkT5ZcciUpY8JSZq6UkP6//vaTBvUpNyJXAjAMsCiTJdXV1aqurh7sywAAAAAAHAK6B+CZMrk+Ppi+BAAAAAAAMCi4UwYAAADDh1OulPa+Jy/PPVeSbMlSZq6UmLAULjXfDZV5rddSuNx8rds6jM8MT1LcOk3osG55g6ZpKioy27IiM1mpqrhVkhTMN68D3qi81pSlaNyeK7VYE5YSyVJrV8AxV5LsE5Z6y5USE5byOq1tV1R5nYlMycqWnCYsueVKkj1ZysiVElwnLfWWEDlkQgebKx3QcQ7geMBg4k6ZkYE7ZQAAAAAAAAYBd8oAAAAAADDMxOIexeK5vZMl18cHizIAAAAY6tKSJcdcSbInS4lcKd8+WclpwpJbriSlkqVIeUyxMmtSkd/kPF3NfttlBsZ0qKLYNE2lAZMpBf1mwtIov2mLfF7z3WgsT+3JXMmMc9rbVWRed5rXHZ1WUtXud8yVJHuy1FuulJywZCVKnnA0OWUpmSuFTcZkS5bcciXJMVlyzZXSj2Hbp5+5Ui/f7fex+ng8AMgmFmUAAAAAABhmeKbMyMAzZQAAAAAAAAYBd8oAAABgaHGZsJTMlaz3PD7rj7PpyVJmrlRgMqCYNWEpUppKlsJBc5yQtU3PlSQpVm7ynuKyTlUFWyRJ/jyT+Pw9MMrs022ut7qsRRUFJl8qyDM5kM+asBSz/ra51UqVWiIFag6Z3zd1FUqS2trN9UbazHV6283Pmt/mVX6b9WO6TFjqNVdKTFiKWGlSJGqmK0lS1Hov8To9WepDriRlJEuZuVJCekK0n0TINTciVwIwwrAoAwAAAADAMNMtr7pzHL905/TokMiXAAAAAAAABgV3ygAAAGBwueRKkmzJkmOuJEkBaxJSYYFrriTJliy55UqSNK6sWZI0qaRREwv2mX2sFKkraq6hxcqQKgo6VeQLm32sMbId3eYaEhOWWsImUWrsLFJ7h5UrtZvr87Sa4/nbzM+dniw55UqSfcJSj1wplEiSMhKlaHfqPSs9iidfpyVLfcmVHD63HSfTgU5aOpjUqR/HA4aT+ACMxI4zEjvnuFMGAAAAAABgEHCnDAAAAAAAwwwjsUcGFmUAAAAw8NKSpR65UuK1lS2lJ0tOuZJkn7DklitJaROWyuKKVZjUJ5ErJSYsTS41qdLkwj2SpIn+PRrjM5/t7S6VJL1TMM4cr9tcn9cTV0fUXEdbxNpauVJnxFxTerKUyJXyE7mSNVnJl5ywZLb+9lgyV/J1mMwor9Ns05OlHrlS2PxsyQwpPVmysp9ktuQyYWlAc6VevtPn7x7g8QBgsLEoAwAAAADAMNMd96o7nuPpS/1YB8WB4ZkyAAAAAAAAg4A7ZQAAAJB7LhOW3HIlKWPCUsDkP7Eia5uYsFRi9o2U+hQqs+dKkTLz9XC5+SvfWLnJeorKOlVd1nuuJEmH5zdKksq9ncr3mAymK27O6fWY44Wj5rr3dRWq08qWWrvM9XV1mtfdndbP0mb2zW/z9syVktuYbevriMnXYdKkHhOWIlZ+FIm650pSKlmKp/Kl5D4JVppkS5accqW049m/75wK9XfCErkS0LuYPIrl+D6LmLhVJte4UwYAAAAAAGAQcKcMAAAAAADDDNOXRgYWZQAAAJB9LrmS7XVennuuJNmSJadcSVIyWQoHPQq75EqSbMmSW64kScUekwt1y6P2uLmuvdESSVJT2Ex+au4olCS1h/ypXKnd7JvXkZis1HPCUmK6Un6HlSt1mOtNTFbyWdu8jkgqV0pMWIpY25iVH0W7e+RKyclKmQlQPN5jylK2JyylPup74rTf7/W+c9/3BYAhhkUZAAAAAACGmYGZvsQzZXKNZ8oAAAAAAAAMAu6UAQAAQHakJUtuuZJkn7DklitJ9glLTrmSJFuy5JYrSfYJS1W+ZknSqDzTFaXnSpLUHjfna4oV6sNIhSTpzfYJkqSGlqC5liZznaG4J5krBVrNNjlZqUMZr2O26UqSlBcyuVFiwpK3K23SkpUvKZrIlnpOWOqRK2X+DbfDRCXXbClbudJ+vrPf7/bzeMBIZ6Yv5faZL7k+PrhTBgAAAAAAYFBwpwwAAAAAAMNMTF515/g+i5h4pkyusSgDAACA/nGZsOSWK0kZE5asXCleaE0uSuRKpfnW1kqWgh6Fy3rmSpJ9wpJbriTJliwVeKwJR9b/8EjPlSRpV7RckrQtPEpbO8ZIkt7ZVyVJatlbbH6kPeY6vd2SLzFRKTFlycqW/Bmpkq8zprzORKZkZUvhxGQl89oTtpKlSMRMV5J6TFhKJEbx7u7ecyWHB3XGe/usH9OSXLOj/qROB3gcABjOWJTJkoaGBjU0NNjeq6+vH6SrAQAAAAAAQx2LMlmyfPlyLV68eLAvAwAAAABwCGAk9sjAokyWzJ8/X9OnT7e9V19fr5kzZw7SFQEAAORAHycseaxsSX6T9vQ2YSle6HfNlSTZkiWnXEmyT1hyy5UkJZMlSeqKm+t0ypUk6b2Ow8zr9grtbDbNVKuVLfk+Ntdb+LH1j6FbyrfypR65UmLCUlcqWfJaE5U8iWyp20p0EqlSJG3ikpUbuU5Y6iVJ6jFhyWGfHtlSf7Ojg0mdDuA4ADBSsCiTJdXV1aqurh7sywAAAAAAHAJi8irGg36HPUZiAwAAAAAADALulAEAAEDvnCYsOeRKkmzJkluuJKUmLEVL8hUuNd8LlZnj9piwVGYlSxURx1xJsk9YOjy/UZJU7jX7uk1YcsuVJKWSpaZCeZrNzxRoNNfpN5eggr3muHkRKT+ZLVnntCYr5XXYUyVPpFuKJKYtWZlSIu1J5EaJVCnancqW+jBhyTFXcvpu+jlTX+75tf0lR9masNSH4wFI6Y571B335PwcyC3ulAEAAAAAABgE3CkDAAAAAMAw0y2vunN8n0U3z5TJORZlAAAAkOKUK6W97/F6euZKPvNHSo81aUl+v1RYIMk5V5JkS5bCwYxcqTyVK0n2CUtOuZIkW7JU7Inafob0XEmyT1hyy5UkpZKlJq/8ZoiT8lvNNi9srrNwr0mLvJG4fB3WdKVOa5vIlaxJS8lkqbs7NWUpkRtlpkjdaROXulMTo2z7JF6mJ0v7G2GbnhT1kgodzISl/X6/H8cDgJGKRRkAAAAAAIaZWNyrWDzH05f2t8iLg8YzZQAAAAAAAAYBd8oAAAAc6tKSJcdcSbInS065kiQVWBOWigK95krhoDluKG3CUjJXKrdypfLeJyw55UqSbMlSV9yca2+sSJJ6nbD0XluldrUEJTnnSpJsyVKgxWQ2iQlLsv7R+JutyUqRmLydmVOWrOsKh822Oy1VSiRJ3b0kRMnPu91zpeSbmZOZ+p4gkSsBww/PlBkZuFMGAAAAAABgEHCnDAAAAAAAw0xMUnfck/NzILdYlAEAADjUOE1YcsuVJHuy5JArSfYJS265kmRPlhK50tigGWtUY+VKRxV9LMk+YWmUt8Oc2mOfRpSeLLnlSpJsyVJvuZK/xdq2xqzX3fK1mxQpL2TO3R0w58xrCZl/Nt3d8oSsXCmRLUXTpi2lb+PxVJ7UW/qTyJHicedcKX0ft+Mk9+15jF6zI5IlABgQLMoAAAAAADDMxORVLMdPJMn18cEzZQAAAAAAAAYFd8oAAACMdE65Utr7Hq/HPVeSbMlSMlcqsnKl0p4TltxyJUkqLOuSJFWVtbjmStKBT1hyy5Uk+4Qlt1xJUjJZ8rVH5OkwE5Q8VkrkKTb/HLwdJl9SLCZFzM+naEauZL2Op2c8sVSe5Mg6TzwW632fzGOl6yUZcsyOsp0rHcBxAfRPd9yr7niOpy/l+PjgThkAAAAAAIBBwZ0yAAAAAAAMMzF5FFOupy/l9vhgUQYAAGBkSkuWHHMlyZ4sueRKkhQvNK+7iwOKlph9IlauFCrLmLAUTMuVKnrmSpJ9wlJfciXJJEtOuZKUmrC0rb1CO5tNM+WUK0n2CUtuuZKkVLIUCksh8/tESuTxWv+Mw2nvRzKmLVn7xjNem384+0l74g55k1tCdCC50n6+s9/v9eN4AICeyJcAAAAAAAAGAXfKAAAAAAAwzPCg35GBRRkAAICRwGnCkluuJNmTJZdcSZItWQoFzXHDVq4UypywVBFxzZUk+4Qlt1xJsk9YcsqVJNmSJbdcSbInS/lt5px5HQ65kiR1WZOVIhHFE2mS9c/TU1hgfWa9H49LUev3VvqTnLaUSJDSk6X9TFSKxxwmNaU+dPnefrKjbCVLpEoAcNBYlAEAAAAAYJjpllfdOX4iSa6PD54pAwAAAAAAMCi4UwYAAGA4y0yU0l+75UqSLVlyy5Uk2ZKlHrlSuUl2CstNhlRV1pLMlSYX7jXbwG5J7hOWnHIlyT5h6b22SvNZS1CS84Qlf5P147UmtvYJS8lkqT0sT6eVLTnkSpJSyVI0mpyg5LH+OSb2SSZLSpuyFMuYnJR4P028r9OXYvH9pkKO2VEf8iJyJWD4isU9isVzPBK7H8d/+OGH9dJLL+mNN97QW2+9pXA4rJUrV2rOnDk99q2rq9PixYsdjxMIBNTV1eX42S9/+Uvdc889euedd+T3+3X66adryZIlOuWUUw74egcbizIAAAAAACArFi5cqG3btmn06NGqrq7Wtm3b9vud2bNnq6amxvaez+e8XHH77bfrlltu0cSJE3Xdddepra1Njz76qM4880ytWbNG5557bhZ+ioHDogwAAAAAAMNMbACeKRPrx/FXrFihKVOmaNKkSbrzzjt188037/c7c+bM6dNiyubNm7Vo0SIdffTReu2111RWZm7d/Pa3v61TTz1V8+bN07vvvtvrgs5QNHyudIhraGhQQ0OD7b36+vpBuhoAADCiebzOuZJkT5Yyc6V8a5uYsFRktrEivyKlJl9yypUk+4Qlp1xJki1Z6kuuJJlkyS1XkmRLlpxyJUm2ZCnQbDKb/LaeuZKkVLLUGZLCVrbkkCtJGTmSle8kgh9P1Pqs22FKUkau5Jgq7Wf6UiqBSn3XNTfqT+LUz2MBQG8uvPDCnB175cqVikajuuWWW5ILMpJ03HHHadasWXrggQf0wgsv6KKLLsrZNWQbizJZsnz58l5bOAAAAAAAsikW9yoWz/GdMjk+fsJLL72k1157TXl5eTr22GN14YUXKhAI9Nhv3bp1kuS46HLxxRfrgQce0IsvvsiizKFo/vz5mj59uu29+vp6zZw5c5CuCAAAAACAg9fS0mJ7HQgEHBdN+uvWW2+1va6urtbq1as1bdo02/ubN29WSUmJqqqqehxjypQpyX2GExZlsqS6ulrV1dWDfRkAAGAk8XgzXqamYCQnKvU2YSnfZ5uuJNlzJSk1YSlc6lOozMqVguYc4YwJS90VJucpKOtSdXmzJLlOWHLLlST7hCW3XElKTVjyNuUr0NwzV5LsyZJrriTZkiXXXCn9tZRMejyJ/7tYn8UTqU8snsqWMtMkp1RpfzlRIpdishIAB93yqFu5nb6UOP6ECRNs7y9atEh1dXUHffypU6dq9erVqq2t1dixY/XBBx/o0Ucf1e23367p06frlVde0Yknnpjcv7m5WWPGjHE8VjAYTO4znLAoAwAAAAAAerVjx47kooekrN0lM2PGDNvro446SgsXLtTYsWP19a9/XT/60Y/0q1/9KivnGqoGJhADAAAAAABZk3imTK5/SeYulPRf2UyXnMyePVs+n08bNmywvV9WVtbrnTCJxCr9AcDDAXfKAAAADBUuuZKUlih5vCZPklwnLMULTaYUKzZ/eE7PlSQpbE1YCpV5XHMlSZpgTViaHNzrmCtJzhOWnHIlyT5hKZEr7Ww2F9HenMiVzHWmJ0t+67EGgRYrV2q1Jiy1p5Ilt1xJsk9Ycs2VJMeEyJPMlaxt+vQlK1NynLaUdh6nc/R8O77ffXrdf/87931fABhgfr9fpaWl6ujosL0/ZcoUbdy4Ubt27erxXJnEs2QSz5YZLrhTBgAAAACAYaZbqefK5O7X4Ni8ebMaGxtVU1Nje7+2tlaS9Nxzz/X4zpo1a2z7DBcsygAAAAAAgAHV2tqqP//5zz3eb2xs1Fe/+lVJ0tVXX237bO7cufL5fLrttttsGdM777yjhx56SEceeaTOP//83F54lpEvAQAADKa0ZCmZK1nvZb5WnvV+fn4qV8qcsJSWLLnlSpJ9wlK03CQ9heX2XGlSqUmTjiz6WJJJlqp85g/Co/LaJTlPWHLLlSTZkiW3XEmyJ0tOuZKUMWHJJVeS7BOW+pIrZYpnpk5pqVIyW8qctpQ4nkM2lDhe4v/ePc7t8p0+IVUCRqT0Z77k8hwHasWKFVq/fr0k6a233kq+t27dOknm4b4zZszQ3r17deKJJ+qUU07RCSecoDFjxujDDz/UM888o71792ratGm68cYbbcc++uijVVdXp4ULF+pTn/qUrrjiCrW3t+uRRx5RJBLRT3/6U/l8w2uZY3hdLQAAAAAAGLLWr1+v1atX297bsGFD8qG9NTU1mjFjhiorK3X99dfrlVde0dNPP62mpiYVFxfrhBNO0MyZMzVv3jzlJZ6lluaWW25RTU2N7rnnHt1///3y+/0644wztGTJEn3mM58ZkJ8xm1iUAQAAAAAAWbFq1SqtWrVqv/sFg0H95Cc/6dc5rrnmGl1zzTX9+u5Qw6IMAADAYHBIlJK/t/5mMDltKfE6PVlyyJWk1ISlSIlPoTIrVwr2zJUkKZqYsFTe1WuuNCmwR5I0zmfeH5XX7pgrSfYJS265kmSfsBRosnIl6/EAbhOWErmSt8NKlLqsbVqy5JYr2V5LfcqV0vcz8mzHs6VKid/3I0Fyy5bIlQBk6o571Z3jfCnXxwcP+gUAAAAAABgU3CkDAAAAAMAwE5dHMXlyfg7kFosyAAAAA6GXiUrpyVIyV7ImRyRzpcQkibRkKZkrlZp8KTlhqTQ1YalHrmRNWCqoMBOWDi9rlSRNLt2nyUUmU5ocMNlSeq4k2ScsOeVKkn3CkluuJCmVLDVJfnMZjrmSJHuy5JArSfYJS665ktS3ZMktAYrbJyzF06Yv9ZYt9ftc+/tuH48BABiaWJQBAAAAAGCY4ZkyIwP/hAEAAAAAAAYBd8oAAADkSnqi5JArSbIlS7bpSlIqVwqYP7LFCs3r7pJ8hcvMvskJS2XWhKWg+Wq4LJ6arpSRK00qsSYsFZtUaXLg4x65UoHHJD95MulMerKUmStt7RhjXrdVSDLJkluuJNmTpWSu1GZSpLwOs3WcsOSQK0n2CUtZz5V6kziPbfqS/ThuE5V6v5QsXyeAESkW9ygWz+0zX3J9fHCnDAAAAAAAwKDgThkAAAAAAIaZbnnVneP7LHJ9fLAoAwAAkD1uE5YcciVJ9mQpkSsVWZlSkcmYYgXmO+GgNWEpmJfMlUKmGEpNWCozOU+gPKTDy1sk9cyVJvr3SpIOz98nSarM61Cxx0qHrFypPW7O1RQzGVIiWXovdJje6xwtyZ4rSWkTlhrzFWg219cjV2q2Jiy1pZIl11xJsidLDrmS7bXUt1wpY9/+SE5dcjhPX7KlPl1fL98FAIwMLMoAAAAAADDM8EyZkYF7kQAAAAAAAAYBd8oAAAD0V2aulHg7kSglPs/3OedKki1Z6i4270VLzD7hUnOcaGHPCUuhSitXqjQ5T1FFpySputRMT5pU2qgjivZIMtOVJHuuJCmZLEmp6UqJXOnDiEmTtodNqpRIlt5rrdSuFjPiqb0pMWHJXG96shRoMdfnT2ytCUu+diuTajdpkrcz0nuuFLauz0qT4t3dzrmSNGDJUvo5PImpS2nHS56/l3P0OVlyOQYASFJMXsVyfJ9Fro8P7pQBAAAAAAAYFNwpAwAAAADAMNMd96g7x898yfXxwaIMAADAgXFIlhxzJcmeLGXmSoXmdXeJeR0tyU9OV0plSmYbLTBfDZebbbQ8Kv+oLknShPJmSdJRZSZVqg6Y15MCezQh30xZcsqVpFSytLe7WLuiZoKSU64kSQ3NJlnqaC5M5koFTeb68s0pbcmSa64k2ZMlt1xJGtQJS64S15V+Df3JlsiUAOCQxaIMAAAAAADDDNOXRgaeKQMAAAAAADAIuFMmSxoaGtTQ0GB7r76+fpCuBgAAZI3bhCW3XEmS8q1tYUEqVyq2cqVSa8JSWrIUsnKlsBl8pEiZyVpiBWbrKw9JkqrK2nVkucmVjin+SJJ0VIHZjsprlSSVeztV5LXSIZl0pj1uztnUbaYmpSdLWzoOM79vMydPz5Uk2ZIlp1xJsk9Ycs2VJHuy1JdcSZLiscHNlXo7j8P59nudJEsADlI87lUsntv7LOI5Pj5YlMma5cuXa/HixYN9GQAAAAAAYJhgUSZL5s+fr+nTp9veq6+v18yZMwfpigAAAAAAwFDGokyWVFdXq7q6erAvAwAAHCyPN+OlSYocJywlcqVEvpQ5YanIbLuLA6lcqdQcJ1TmtbapZClcbiUtlSbxCZaZqUmF+VFJ0uGlphuaXLxXxxbulCQd4d8tSarKazPnUiqz6oqb60rkSjujJk3aFkpMWBolSXq/tVIftZRKcs6VJMlvJUv+lrhjriRlTFhyy5Uke7LkkiuZzRBKltLO5XRdvV4ruRKALOuWx/bv/FydA7lFIAYAAAAAADAIuFMGAAAAAIBhJhbP/cjqvtyoiIPDogwAAEBastRbrqS8RLZksh6PP981V5JkS5acciXJnixVVpoEqaasUZI0sWifObXH/Kl4QmCv2ebv1eG+JvO1PDORKTFhaW/MnHtfd5F2RcslOedKkrTLmrDU2VyQypWae+ZKkhRoNtv8tm7nXEmSOrusHypifkmKR0161WPCUuJ1+p/4h2qutJ9rcL3eoXCdAIAhi0UZAAAAAACGmdgAjMTO9fHBM2UAAAAAAAAGBXfKAACAQ4/bhCW3XEmyJUs9cqUSK1cK2icshcs8CpWbr4UrrJylwqQ/laNSydKxJbskSUcXmu04n8mYmmJFkqRR1oSlYk9YRV6TB3VZf4vZ1F0gSfp7ZIwk6b3QYdrWaTIlp1xJsk9Y8rdYP56VKQVaUrmSJOW3mvN5O8PydlgTlbpC1g8VsW3j0WjfciXzRtpv95MtDbEUyPF6h9g1Ahi5YvIoluPpSLk+PrhTBgAAAAAAYFBwpwwAADg0ODzMN/Fe8qG+eV7XO2MkKV5oXseKA4qUmt9HSu13xoTKPdbWfDVcEUveGVNR2S5JmlxuHuI7pWS3JOkThTs1xW/dIZPXKUkq9prj/T3SZftRuuL5aooWSpL2dpdKkj4Im7th3mkbJ0na1lqhj1rMZ053xkj2h/km74xpNQ/mzW8zW2+n9TDf9LtjnO6Mkex3x/ThzhjHz132HfKG2/UCGNa64x5153j6Uq6PD+6UAQAAAAAAGBTcKQMAAAAAwDDD9KWRgUUZAAAwMjk9zNchV5IyHubrkitJsiVLrrmSZEuWnHIlSclkaYKvU5Vec+xCj5Udxc2DdLvi5vr2dhdLknZFy7U7Yh7au7PLnPS9NpMvfdBoXnc2FcrbZP6oV9DcM1eSZEuWkrlSu5UrdfX+MF/HXCn9dXqSRK4EAECvWJQBAAAAAGCYicmjWI6f+cL0pdzjXiQAAAAAAIBBwJ0yAABgZHDKldLe9+TlOedKki1ZcsuVpLQJS2UehcvN10KVJmOJl5vEp3JUmyRpUlmjJOmY0o8ccyVJPZIlKZUt7ew2x3sndIQk6f2u0ZKkHZ0Vamg3+dLu1hJJUkeLmcbk3ZuasORvMccLNJtkyN9irjO/rdvappIlt1xJsk9Ycs2VzBtpv3XJlTL2BQD0XVyenN/JEudOmZzjThkAAAAAAIBBwJ0yAAAAAAAMM7H4ADxTJsfHB4syAABgOEtLlhxzJcmWLPXIlQJmGy8yqVKsOKBoidknHDR/TErPlSQlk6VwRUyxCpP2lFe2S1JywtIxpR9JSk1YOiJ/tyb4zD6H5fXMlSQpFI+oMWbSoR1Rcz2JbOmlximSpO2tFZKkj1tKFG4x+3ibMyYsNVk/WnPcNVeSMiYsueRKkn3CkluuZF6OoAlLAADkEPkSAAAAAADAIOBOGQAAAAAAhplY3KtYPLf3WeT6+GBRBgAADCdOE5ZcciUpbcJSfr5UYJKf9FxJUipZKvWlcqXynrmSJFuylMiVppTsliQdW9hgXicnLKWSJadcSZItWdoamSBJqu8cJ0n6S2u1JOmtnWYbaiqQJPma81TQYuVKreZ4fmvCUqA5Zr3fLV+bOYe3w9q6TFhyy5WkjCSJXAkAgKxgUQYAAAAAgGGGB/2ODEPyXqSHH35Y8+fP1ymnnKJAICCPx6NVq1Y57ltXVyePx+P4q6CgoNdz/PKXv9Spp56q4uJiVVRU6NJLL9Uf//jHHP1EAAAAAAAAdkPyTpmFCxdq27ZtGj16tKqrq7Vt27b9fmf27NmqqamxvefzOf94t99+u2655RZNnDhR1113ndra2vToo4/qzDPP1Jo1a3Tuuedm4acAAAAHzSlXSnvfk5fnnitJtmQpVmQmH0VLzTZcmjFhqdzjmitJ0qSyRklmwpJbriTZJyw55UqSksnS37qq9Ne2sea9ptGSpI/3mu97G8y+RYkJSy2Sv9UkQ4ltfmvU2qaSJbdcSbJPWHLNlcwbab91yZUy9gUA5EZMHsWU4ztlcnx8DNFFmRUrVmjKlCmaNGmS7rzzTt188837/c6cOXP6tJiyefNmLVq0SEcffbRee+01lZWVSZK+/e1v69RTT9W8efP07rvv9rqgAwAAAAAAkA1DMl+68MILNWnSpJwce+XKlYpGo7rllluSCzKSdNxxx2nWrFnaunWrXnjhhZycGwAAAACAbEg8UybXv5BbI+Z2kJdeekmvvfaa8vLydOyxx+rCCy9UIBDosd+6deskSRdddFGPzy6++GI98MADevHFFx0/BwAAAyAtWXLMlSRbsuRJ3N0aMMlQMlcqNK+TE5ZK/T1ypXCZOX6o3Hw1XBlTrNykPWVWrlRTnsqVJPuEpUSuVOk1qVSRt1iS5LX+3qszbrKhfbGwdkbNs+7ScyVJqrcmLL3XXKm9TSWSpO595tr9+8zPW7jb+hFbEslSTPltJjPytZsEydtuUiVvRyJVCrvmSpJ9wpJbrmRe7idZcvgOAABwN2IWZW699Vbb6+rqaq1evVrTpk2zvb9582aVlJSoqqqqxzGmTJmS3MdNQ0ODGhoa9ntN9fX1+90HAAAAAIADxfSlkWHYL8pMnTpVq1evVm1trcaOHasPPvhAjz76qG6//XZNnz5dr7zyik488cTk/s3NzRozZozjsYLBYHIfN8uXL9fixYuz90MAAAAAAIBDzrBflJkxY4bt9VFHHaWFCxdq7Nix+vrXv64f/ehH+tWvfpXVc86fP1/Tp0/f73719fWaOXNmVs8NAMCI4zRhySVXkmRPlnrLlYqtCUtlqWSpR65UYZKcWIVJf8oq211zJUm2ZMktV5KUlixNVH3nOEnS36wJS+81V0qSLVnyN1q5UpP141l/T1S4x2RGyWSpLaK8DnMOT8hKlBwmLLnlSlJGkkSuBADDCnfKjAzDflGmN7Nnz9b/9//9f9qwYYPt/bKysl7vhGlpaUnu46a6ulrV1dXZuVAAAAAAAHBIGpLTl7LB7/ertLRUHR0dtvenTJmitrY27dq1q8d3Es+SSTxbBgAAAACAoYjpSyPDiL1TZvPmzWpsbLQ9T0aSamtrtXHjRj333HOaNWuW7bM1a9Yk9wEAADnilCulve/Jy3PPlSRbspSZK0WC9glLofJUsuSUK0n2CUuJXOlIvxl5NMHXKkkanTFhSZJCcZMKZeZKfwtPMtsuc2ft39rGJnOlPY0mV4o1WhOW0pIlf4v1Y7aYLMjfbDIjf5M5fjJZ6gxLIStXiliJUsaEpXgkIsWtBMkpVzJvpP22D7lSxncAAMDBGdZ3yrS2turPf/5zj/cbGxv11a9+VZJ09dVX2z6bO3eufD6fbrvtNlvG9M477+ihhx7SkUceqfPPPz+3Fw4AAAAAwEGIS4rJk9NffVyux0EYknfKrFixQuvXr5ckvfXWW8n31q1bJ8k83HfGjBnau3evTjzxRJ1yyik64YQTNGbMGH344Yd65plntHfvXk2bNk033nij7dhHH3206urqtHDhQn3qU5/SFVdcofb2dj3yyCOKRCL66U9/Kp9vSP5jAQAAAAAAI8iQXH1Yv369Vq9ebXtvw4YNyYf21tTUaMaMGaqsrNT111+vV155RU8//bSamppUXFysE044QTNnztS8efOUl5jakOaWW25RTU2N7rnnHt1///3y+/0644wztGTJEn3mM58ZkJ8RAIBDSlqy5JgrSalkKS9PHr+VKTnkSpJ9wpJbriTZk6XMXGlK6ceSpE8WfijJJEtuuZJkT5Z2RAslSVsdciVJtmQpM1fyN5njpSdL/pbElCWTIOW1mUTJa22TyVIoLEWtJMllwpJbrmReMmEJAIYrpi+NDENyUWbVqlVatWrVfvcLBoP6yU9+0q9zXHPNNbrmmmv69V0AAAAAAICDNSQXZQAAAAAAQO+4U2ZkYFEGAABkn8uEJadcSZI9WbJ+H7cypVihlSuV9pywFCrLyJUqE7mSyYzKKtskSRPLmnRM0ExUOrZwpyRpiv8jSfYJS265kiRbspTIld5trZIkvd9cIUna22RNWNpnrtvf5HXMlaTUhKX89qjy2s05vNaUJXV2mW2XyZbiEXMt6o6lfu82YYlcCQAwwB5++GG99NJLeuONN/TWW28pHA5r5cqVmjNnjm2/SCSip556Sk8//bReffVVbd++XV6vV5/85Cc1e/ZszZ8/v8fjSN5//31Nnjy513M/8sgj+qd/+qdc/Fg5w6IMAAAAAADIioULF2rbtm0aPXq0qqurtW3bNsf9tm7dqiuuuEKlpaU6//zzNX36dDU3N+vpp5/W9ddfr2effVZPPvmkPJ6ed+uceOKJmjFjRo/3jz/++Gz/ODnHogwAAAAAAMPMUM2XVqxYoSlTpmjSpEm68847dfPNNzvuV1paqvvuu0+zZ89WUVFR8v1///d/17nnnqunn35ajz32mL70pS/1+O7UqVNVV1d3wNc2FLEoAwAADp5LriTZJyz1yJX8ZsqRAokJS4HkdKXuEvNZuMxskxOW0pKl5HSlSpPzBCvMhKVJ1oSl9GTJKVeS7BOW3HIlSXq3c5wkM2EpmSs1WrlSo5UrNZrr9DdbP1qzFGg2WVB+aypXkmRPlrqsbClsbUNmm5iwZEuWnHIl80bab/uQK2V8BwCAg3HhhRf2ab/DDz9c3/jGN3q8X1xcrJtuuklf/vKX9eKLLzouyowkLMoAAAAAADDMDNU7ZbIhP9/8pYnP57xksXPnTt1///1qamrSuHHjdMEFF2j8+PEDeYlZw6IMAAAAAADoVUtLi+11IBBQwLrDNRd+9rOfSZIuuugix8+ff/55Pf/888nXPp9P3/72t/Vv//Zv8nq9jt8ZqliUAQAA/ZOWLPXIlRKvE6mS9TddHr/fMVeSZEuWwsnpSub7ofKMCUtpyZJbriTZJyw55UqSPVlyy5Uk2ZKlzFwp0GSOl5ywlJYs5beZcyRzpU4rRUpPlqw8KR5OTFmyEiVry4QlAEBCPO5RPMd3siSOP2HCBNv7ixYtytkzXf7rv/5LzzzzjM4//3xdeumlts+Kioq0aNEiXXbZZTriiCPU1dWlV155Rd///ve1bNky+f1+3XHHHTm5rlxhUQYAAAAAAPRqx44dCgaDyde5ukvmf/7nf/TNb35TkyZN0sMPP9zj8zFjxtgWg0pLS/WP//iP+sxnPqPjjz9ey5Yt04IFC1RRUZGT68uF4XVfDwAAAAAAUEyeAfklScFg0PYrF4sya9as0eWXX66xY8fqhRdeUHV1dZ+/W1VVpUsvvVThcFivv/561q8tl7hTBgAA9I3LhCXXXEmyJUtOuZIkW7KUmK4ULjdfC1X2PmHJLVeSDnzCkluuJNknLLnlSpJsyZJjriTZk6XMXMlKkZKvHbIjciUAwHD37LPP6rLLLtPo0aO1du1aHXHEEQd8jNGjR0uSOjo6sn15OcWiDAAAAAAAw8xImb707LPPasaMGaqsrNTatWt11FFH9es4r732miSppqYmi1eXe+RLAAAAAABgwCUWZCoqKrR27VpNmTLFdf/XXntNEesO03TLli3Thg0b9MlPflInnnhiri43J7hTBgAAOHPJlWyv8/JccyUpNWEpXuRXtNTs02PCUlqyFKrIyJUqe5+w1JdcSTLJklOuJNknLGXmSvF95trzm3pOWHLLlaS0CUudXVJiopK1jUej5nV6spRIkazcKJ7xOoFkCQAwkNOXDsSKFSu0fv16SdJbb72VfG/dunWSpBkzZmjGjBl69913NWPGDIVCIZ177rl65JFHehyrpqZGc+bMSb5esGCB3n33XdXW1mrChAnq7OzUxo0b9ac//UkVFRX6+c9/Lo8n93f3ZBOLMgAAAAAAICvWr1+v1atX297bsGGDNmzYIMkstMyYMUO7du1SKBSSJD366KOOx6qtrbUtysycOVOPP/64Xn75Ze3Zs0eSNGnSJH3nO9/Rd7/7XY0fPz4HP1FusSgDAAAAAMAwM1SfKbNq1SqtWrVqv/ude+65isf7cOdnmnnz5mnevHkHfE1DGYsyAAAgJS1ZcsuVpNSEJeX75EmMxnTIlSQpWmIlS2U+hYPm+13lGROW0pKl0gozOWGilSsdbeVKxxV9KMk+YcktV5LsE5acciVJ+ntzpSSpsbE4mSv5G83P7W+WbRtoMVmQv6VbvvaMXKnDmqjUZf7mLz1ZcsyVJFuyRK4EAMDA6c+Up0w33HCDvv3tb/f7+yzKAAAAAAAwzAzVZ8oMJ++//77KyspUXl7er+9v375dTU1NB3UNLMoAAAAAAIBD0o033qhbb721X9/1eg9+oDWLMgAAHMpcJiy55UqSUsmSP18qLJAkxYrNe4lcKeIwYSlUbr4WqrRPWEpPlo5OTldqMNuA2bpNWHLKlST7hCWnXEmSLVnqkStZE5b81oSl9GTJLVeS7BOWnHIls+mZLJErAQD2Jz4Az5QZ6XfKDAUsymRJQ0ODGhoabO/V19cP0tUAAAAAAAA3mzdvVmVl5aB9X2JRJmuWL1+uxYsXD/ZlAAAAAACAPjjyyCMH9fsSizJZM3/+fE2fPt32Xn19vWbOnDlIVwQAQC/6OGHJNVeSbMmSW64kKZkshSvi6k7kSpXOE5aOLWzQlMAuSVKNr0WSc64kmWTJLVeS7BOW9u0rMV9sNNebnLDUZP1oLe65kpQxYcklV5IyJiy55Eq2992QLAEALHFJBzhRul/nQG6xKJMl1dXVqq6uHuzLAAAAAAAA/RQOh/XEE0/o9ddfV1NTk7oTf8GSxuPx6MEHH8zK+ViUAQAAAABgmInJI49y+yDeWI6PP9Rs27ZN06ZN09atWxV3uQ2JRRkAAHDg0hMlh1xJsk9Y8uRbmVKBlS0lcqUik/5ES837kaBPoaCVK5X3zJUkJZOl4opOTawwudKxwY/M1pqwlJ4sueVKkvRxt0mIdnYXanN4siTpb51VkqR328z2/eYKSbIlS065kmRPlvJbzTm8nQ65kmRLllxzJcmeLPUnV0rt3Pd9AQBAv9x4443asmWLrr32Wn3lK1/R+PHj5fPldtmERRkAAAAAAIaZeNyT85HVh9pI7BdeeEEXXHCBVq9ePWDn9O5/FwAAAAAAgJEtFovppJNOGtBzcqcMAAAjVWailP7aIVeSZE+WMnOl5IQls0/6hKVwuflaqLJnriQpmSwdXbpbnyzaKSmVK03wtUqSDvOa45WkJUtOuZIkW7LklitJ9glLTrmSpFSy1B6Wt6v3XEmyT1hyzZXSXtve6wtyJQDAfsTiHnlyfCdL7BC7U+b0009XfX39gJ6TO2UAAAAAAMAh784779TatWv12GOPDdg5uVMGAAAAAIBhJh43v3J9jkPJ008/rfPOO09XXXWVamtrddJJJ6msrKzHfh6PRz/84Q+zck4WZQAAGCncciUplSzl5Ul+kyC5TVhyy5UkpZKliri6K03SU1zZIcmeK0nSMUUmVTo2sLNHrlToMefMs663IxbWvphJhTJzpfrOceZ12xhJJllK5kr7rFypyT5hKdBs/kTpb4k75kqS7MmSS64kZUxYcsmVbO/3BckSAACDqq6uLvn7devWad26dY77sSgDAAAAAMAhjOlL2bd27doBPyeLMgAAAAAA4JBXW1s74OdkUQYAgOEsPVFyy5Uke7KUyJWsbazYbNOTpXDQfK+rvGeuJCmZLBVVdGhCRZMk6djgR5JSudIUv9nW5DdLMsmSU64kSfu6TS60I1qsv0fGS+qZK73XVClJamy0JjTt87vmSmZrrjO/LeqcK0m2ZMk1V5LsyRK5EgBgkHCnzMjAogwAAAAAAIDl5Zdf1qpVq7Rp0yY1NzcrGAzqpJNO0qxZs3TWWWdl9VwsygAAAAAAMMzE4h55cnwnS+wQvFPmu9/9ru6++27FrdFTXq9XsVhMb7zxhh588EF95zvf0bJly7J2PhZlAAAYTtwmLLnlSpIUMGmSCgt65ErR0p4TlkLlZvdQZc9cSVIyWTq69GN9oninJOdcSbJPWHLKlSTZkqUeudI+K1dqzJiw1CgFrEwpkS3lt6ZyJSljwpJDriTZJyy55kppr23v9QW5EgAAQ9pDDz2kZcuW6dhjj9WiRYt07rnnauzYsdq9e7fWrVunxYsX695779XUqVM1a9asrJzTm5WjAAAAAACAAROPD8yvQ8n999+vCRMm6NVXX9VVV12lsWPHSpLGjBmjK6+8Uhs3btT48eN13333Ze2cLMoAAAAAAIBD3ttvv63LL79cpaWljp8Hg0F98Ytf1DvvvJO1c5IvAQAwlLnlSpI9WXLJlSQpVmRed5cEFAn2zJUkpZKliriiVq5UXNkzV5JkS5Ym+kyuNDrPXF+JlSslhOLmWPu6I465kiT9tdX8bdS25grXXEmyJ0uuuZJkT5YcciUpY8KSS65ke78vSJYAADli7mTJ9fSlnB5+SIrv54f2eLL7z5w7ZQAAAAAAwCHv+OOP1+OPP662tjbHz1tbW/X444/ruOOOy9o5WZQBAAAAAACHvOuuu04ffPCBTj/9dD3++OPas2ePJGnPnj167LHHdMYZZ+iDDz7QN77xjaydk3wJAIChJjNRUtpEpcwJSz7zn3JPwO+aK0myJUtOuZKkZLJUVNGpI6xc6djgbknSMUUNZhsw2/RkqbdcaU/M5EIfWsnS5vBE/a2rSpI9V5LsE5bcciXJPmHJNVdK28ajUedcSbInS+RKAIBhIB73DEC+dGiNxJ49e7Y2bdqke++9V1deeaWk1EhsyaRN3/rWtzR79uysnZNFGQAAAAAAAEl33323Lr/8cq1cuVKbNm1SS0uLgsGgTjrpJM2ePVtnn312Vs/HogwAAAAAAMNM3PqV63McSrZv367y8nKdddZZOuussxz3aW1tVWNjoyZOnJiVc7IoAwDAYHPIlSQrUeotV7ImLanApEkqLOg9Vwqa44fKrW2ZFKrsmStJSiZLU4If65NF1nSlwC5JUo3PfJaYsFTkMefxyqPOuEmE9sXM8dJzJUl6t7PavG4b45grSZK/0UqWmlKZUiJbSuZKreY83o7ENiSFrWzJKVeS7MmSU66U9tr2Xl+QKwEAMGJMnjxZixYt0q233trrPvfdd59+8IMfqDuRQh8kFmUAAAAAABhmeKZM9u1vHHZf9zkQTF8CAAAAAADogw8++EClpaVZOx53ymRJQ0ODGhoabO/V19cP0tUAAIY8jzftt+ZvoRwnLPWWKwXMNmalSt0lAUVKzL6hcvP9UFkiWzJfCVsTliKVURVV9syVJNmSJbdcSZItWdoRNX842RoeI8meK0nSe02VkqTmxiLHXEmyJ0uuuZIkhaxtJOqeK0m2ZMkpV0rXp3SJZAkAMBTwUJmsWLJkie31unXrHPfr7u7WBx98oEcffVSnnXZa1s7PokyWLF++XIsXLx7sywAAAAAAAH1UV1eX/L3H49G6det6XZiRpHHjxumuu+7K2vlZlMmS+fPna/r06bb36uvrNXPmzEG6IgAAAADAiDUAz5TRIfBMmbVr10oyz4o5//zzNWfOHM2ePbvHfnl5eaqsrNSxxx4rrzd7T4JhUSZLqqurVV1dPdiXAQAYqjzejJeeVK6UmL6Ub/1nOT1ZysyViqxcqdQkQJFSa8JSeZ5rriRJhRVdkqTJlY2OuZJkn7DklitJsiVLiVzpr61jJSk5YanJmrDkaTLX6W/0KmBOIX8iV2o2OZC/1eRGvraIe64k2ZMll1zJbHpPlsiVAAA4tNXW1iZ/v2jRIp133nk655xzBuz8LMoAAAAAADDMxOPmV67PcShZtGjRgJ+TRRkAAAAAAADLyy+/rFWrVmnTpk1qbm5WMBjUSSedpFmzZumss87K6rlYlAEAIBccciVJtmTJMVeSJL9Jk1RY0GuuFC7rOWEpVOmcK02yJiwdU7bbbIt26diAyZYm+JolSWPzzPUlkiVJCsXNcZxyJUm2ZCkzV/I2musMNHmtrfWjNccdcyVJymsPm38OnWH3XEmyJ0vkSgCAQ1B8AJ4pk/Nn1gxB3/3ud3X33Xcrbt0m5PV6FYvF9MYbb+jBBx/Ud77zHS1btixr58ve02kAAAAAAACGqYceekjLli3TMccco0ceeUQNDQ2KRqPatWuXHn30UR177LG699579dBDD2XtnCzKAAAAAAAw3MQ9A/PrEHL//fdrwoQJevXVV3XVVVdp7FgzwGDMmDG68sortXHjRo0fP1733Xdf1s5JvgQAQLakJUtOuZJkn7DklCtJ9glLkRIrVyrvmStJUshUQ4pURlVY2SkplSslJix9orhBkmzJ0mHWZZV4/LYfIT1ZSuRKm8PmDyTvdo4zr1sPk2SfsJTIlQoazfX5m60fzWHCkmOuJNmTJbdcSbIlS265knmbZAkAAOzf22+/ra997WsqLS11/DwYDOqLX/yiVqxYkbVzsigDAAAAAMAww/Sl3Ijv54f2eLJ79xD5EgAAAAAAOOQdf/zxevzxx9XW1ub4eWtrqx5//HEdd9xxWTsnd8oAANBfDhOW3HIlKWPCkkOuJMmWLLnlSpJUkJYsZeZKUwK7JEk1vkZJckyWErnSxzGTBe2MlkgyyVJmrvR+kzl5c2LCUlMqWUrkSoEm87dL/pbeJyz1yJVC1uuolShFo665ktkwYQkAAGTXddddp7lz5+r0009XXV2damtrNXr0aO3Zs0fr1q3T4sWL9cEHH2jJkiVZOyeLMgAAAAAADDdx61euz3EImT17tjZt2qR7771XV155paTUSGzJpE3f+ta3NHv27Kydk0UZAAAAAAAASXfffbcuv/xyrVy5Ups2bVJLS4uCwaBOOukkzZ49W2effXZWz8eiDAAAfeWQK6W/78nLk/Ks31sTlXpMWCowqVK8KKDuEvP7cNDKlcp6TlhK5EpRK1cKjDK50oRy0wsdU7ZbkkmWesuVijzmP/de61FyHfGI9ll/45OeK0n2CUtuuZIk+ZvM8QPNcddcScqYsOSUK0n2ZCnbuVIv3wUAYLiKxz2K53hkda6PP1SdddZZOuusswbkXDzoFwAAAAAAYBBwpwwAAAAAAMPRIfbMl5GIRRkAANykJUuOuZKUSpby81O5UsCkSem5kqRkshQpzVeovGeuJNmTJX9llyRpckWTJGlKcI+k1ISlYwp2SpIm+JpUlWf+ZOaUK0nSvpjZ7oiW6u/hMZJSudJfW8zr7c3mIpr3FcvbaJKrgibzc6fnSmZrcqD8tm75Wq1cqcMhV5JsyZJjriTZkiW3XMm8zYQlAACQfeFwWE888YRef/11NTU1qTvx55Q0Ho9HDz74YFbOx6IMAAAAAADDDM+Uyb5t27Zp2rRp2rp1q+Lx3v8CKJuLMv1+psw777yjhx56SC0tLcn3Ojs79Y1vfEOHH364pkyZop/+9KdZuUgAAAAAAIBcuvHGG7VlyxbNnDlTa9eu1ebNm/Xee+/1+PX3v/89a+fs950yt912m9atW6drr702+d4PfvADLV++XCUlJdqzZ4+uu+46HXHEEbrggguycrEAAOScy4Qlp1xJypiwVFggyTlXkqRw0Byjq8KrUJn5WtjKlSKjTNaTSJZqypt1dNnHklK50lGJCUv5+yTJliy55UqSbMmSU64kyZYs9ZorZU5Y6gi75kqSfcKSU65kNgc5YYlcCQBwKIkr98+UOcSeWfPCCy/oggsu0OrVqwfsnP2+U+a1117TeeedJ4/H/GE1EonoZz/7mU499VTt3r1b7733ng477DDdfffdWbtYAAAAAACAXIjFYjrppJMG9Jz9XpT56KOPNHHixOTrV199Va2trbruuutUUFCgcePG6Qtf+ILefPPNrFwoAAAAAABI8AzQr0PH6aefrvr6+gE9Z7/zpby8PIUStyhLeumll+TxeHTeeecl3xs1apT27NlzcFcIAEAuueRKkn3CkmOuJNmSpe5i814kaLbJXKncPmEpXCFFKq1caVQqV5Kko8rMfzuPLW7QMQGTLSVypcO8JtEp9ebbrrsr3t0jV9ocqjLbLrNNJEvbmirU0lgkKS1XarQmLJlLUKA57porSRkTllxyJSljwlK2cqVevgsAANAfd955p84++2w99thjuuKKKwbknP1elKmpqdHatWuTrx977DFNnjxZkyZNSr734YcfatSoUQd3hQAAAAAAwI5nyhy0JUuW9HjvvPPO01VXXaXa2lqddNJJKisr67GPx+PRD3/4w6xcQ78XZa699lp973vf02c/+1n5/X5t2rRJP/jBD2z7/O///q+mTJly0BcJAAAAAACQTXV1db1+tm7dOq1bt87xsyGxKPPNb35Tr732mh577DHF43FdcskltkWZ119/Xe+8844WL16clQsFACBr0pIlt1xJypiwlMiVCsxEJacJS+Ey85/WRK4Utv5yJVRptslkqbJLEytaJCk5YelYa8JSerLUM1eycqi4yYH2xcznO6KlyVzp3c5qSdKW1sMkmVxJkloaExOW8nvmSk3WhKWWVLLkmitJtmTJNVeS7MlSL8kRE5YAADgAQ/ROmYcfflgvvfSS3njjDb311lsKh8NauXKl5syZ47h/S0uL6urq9Pjjj2vXrl2qqqrS5Zdfrrq6OgWDQcfv/PKXv9Q999yjd955R36/X6effrqWLFmiU0455YCuNb3+GSz9XpQJBAL6v//3/6qlpUUej0elpaW2zydPnqw//elPqqmpOdhrBAAAAAAAw8DChQu1bds2jR49WtXV1dq2bVuv+7a3t6u2tlabNm3StGnTdPXVV+vNN9/U3XffrbVr12r9+vUqLi62fef222/XLbfcookTJ+q6665TW1ubHn30UZ155plas2aNzj333D5fa21tbX9/zKzp9/SlhGAw2GNBRpJGjx6tE0880bG/AgAAAAAAByHuGZhfB2jFihV6//339fHHH+u6665z3Xfp0qXatGmTFixYoOeee0533nmnnnnmGd16663atGmTli5datt/8+bNWrRokY4++mj9+c9/1r//+79r+fLlevnll+Xz+TRv3jxFE3fvHqRoNKq//OUv2rhxo/7yl79k7biZ+n2nDOwaGhrU0NBge2+gR2kBAHrhMmHJNVeSbMmSU64kSeGg+c9pqNybnK4UMsWQIpUm4/GP6pQkW7KUyJWmBD6SJNX49kqSxuaZ7xR5fMrzmOvriJs/COzrNvcRf9htn7D0bme1a64kpSYsBZokf3PPXElKm7DUHkrlSuFEttT7hCXXXCntdTpyJQAARp4LL7ywT/vF43GtWLFCJSUluvXWW22f3XzzzfqP//gPPfjgg6qrq5PHY/4Ms3LlSkWjUd1yyy22G0COO+44zZo1Sw888IBeeOEFXXTRRf2+/o8//lg/+MEP9Mgjj6izszP5fmFhob785S/rtttu02GHHdbv42fq86LMEUcc0a8TeDwebd26tV/fHU6WL1/O83MAAAAAAAMiHje/cn2OXNm8ebN27typiy++uEeiVFBQoHPOOUdPPvmktmzZkhwglHjwrtOiy8UXX6wHHnhAL774Yr8XZT788EOdeeaZ2r59uw477DCdc845Gjt2rD766CO98cYbWrFihZ5//nmtX79ehx9+eL/OkanPizKxWCy5OnUg4rn+/5IhYv78+Zo+fbrtvfr6es2cOXOQrggAAAAAgIPX0tJiex0IBBQIBA7qmJs3b5akXic2J97fvHmz7fclJSWqqqpy3b+/FixYoO3bt2vx4sX63ve+p4KCguRnXV1dWrp0qerq6vT9739fDz/8cL/Pk67PizLvv/9+Vk44UlVXV6u6unqwLwMAILnmSpJ9wpJbriSlTVgqDigS7Jkrma35SqgilSvlV3ZJkiZWmvFGRwb3SJI+UbxLknRMQUMyVzrMypVKPOa4eR5zno54VPui5i83tkfNLbp/D4+RlJqw9LcW83pHc3kqV9pn5UpNqVxJsidLTrmSJHuy5JIrSRkTlvqQK9k+d0OyBADAkDJhwgTb60WLFrmOk+6L5mbzZ6TenkObmLyU2C/x+zFjxvR5/wP17LPP6pJLLnEcd11QUKBbb71VL7/8sp555pl+nyMTz5QBAAAAAGC4GcCR2Dt27LCNpz7Yu2SGqnA4rE9/+tOu+5x88snasGFD1s6ZlUWZtrY2/e1vf1N7e7vOPvvsbBwSAAAAAAAMAcFg0LYokw2JO2R6u7MlkUyl30lTVlZ2QPsfqJNPPlnvvvuu6z7vvvuuTj755H6fI9NBLcq8//77+s53vqPf/va3yWfOJMZEbdiwQV/72td03333HdCccAAA+iUtWXLLlaSMCUsuuZIkRdMmLHVl5Epha8JSOJEsjerSeGu60pSyjyWlcqWjAmZbk2+Spaq8aDJXSvznOCRznPRkyS1XkmRLlpLTlaw/qyRzpWZrwlJbKllyzJUkW7LkmitJ9mSJXAkAgIHVz5HVB3yOHNnfM2CcnjkzZcoUbdy4Ubt27erxXJn9PaOmL/71X/9V06ZN06pVqzRnzpwen//sZz/Tb3/7Wz3//PP9Pkemfi/KbN++XZ/97Ge1d+9efeELX9CuXbu0cePG5OennXaa9uzZo0ceeYRFGQAAAAAAkDRlyhSNGzdOGzZsUHt7u20CU1dXl/7whz9o3LhxOuqoo5Lv19bWauPGjXruuec0a9Ys2/HWrFmT3Ke/1q5dq/POO09f/epXtXTpUp155pkaM2aMdu/erQ0bNuivf/2rLrroIr3wwgt64YUXkt/zeDyOz6Hpi34vyixatEiNjY168cUXdcYZZ2jx4sW2RRmfz6ezzz47q60VAAAAAACQPHHzK9fnyNmxPR7NmzdPS5Ys0ZIlS3TXXXclP7vjjjvU2Niob33rW7Yp0HPnztX/+T//R7fddpu+8IUvJFOld955Rw899JCOPPJInX/++f2+pvSHF7/77ruOKdOaNWuSC0DpP8uAL8qsWbNGl112mc4444xe95k4caJt9QgAgKxxmbDUa67ks/6zF7CSpcICxQvN77tLzMjD9FxJUjJZCpeZ6UqSFB5lZUDWhKXDK1olSUeV7dGxJQ2SpGMCZpvIlQ7LM1OOSq1kyStfMlf62KqCPoyaVntz2NyO+9eOKm1pPUyStL3JnLy1sch8v9GasJSWLLnlSlLGhCWHXEmyT1hyzZXSXqcjVwIA4NC2YsUKrV+/XpL01ltvJd9bt26dJGnGjBmaMWOGJDOC+qmnntLSpUv1pz/9SSeffLLefPNNPfPMM5o6daoWLFhgO/bRRx+turo6LVy4UJ/61Kd0xRVXqL29XY888ogikYh++tOfyufr/1Na1q5d2+/v9le/r3bfvn2qqanZ736hUKi/pwAAAAAAAE4GcPrSgVi/fr1Wr15te2/Dhg3Jiqampia5KFNcXKx169Zp8eLFeuyxx7Ru3TpVVVXpxhtv1KJFi2xJU8Itt9yimpoa3XPPPbr//vvl9/t1xhlnaMmSJfrMZz5z4Bec5mDSp/7q96LM2LFjtWXLFtd93n77bU2cOLG/pwAAAAAAAMPIqlWrtGrVqj7vX1ZWpmXLlmnZsmV9/s4111yja665ph9XN/T0e1Fm2rRp+vnPf663335bxx9/fI/PX3rpJf3+97/XDTfccDDXBwBASh8nLCV+70lMVgo4T1iKFQcUCZrPErlSqMwcN1RubdMmLOVVmrs/D680E5aOKtsjSbZkqfdcyeRGHXGTB+2LebQjajroreGxkkyuJEl/azUTlj5oKu89V2qyfjQrWfK3xORvNcdO5EreDpMmOU1YikfMPuq2UiKnCUt9yJVsn7shWQIAILuG+fQlGN797+Js4cKFKiws1FlnnaXbb789edfMM888ox/+8Ie65JJLNHr0aH3ve9/L2sUCAAAAAACMFP2+U6ampkZr1qzRP/3TP2nhwoXyeDyKx+P6/Oc/r3g8rokTJ+qxxx5TdXV1Nq8XAAAAAAAM0WfKDDcrV67U5z//eR122GGDcv7+P5ZY0mmnnabNmzfr6aef1quvvqp9+/YpGAzqtNNO0xe+8AX5E7eNAwDQH04TlhK5UiJfsp6wb0uWXHIlSYqUJpKlPIWs6UrJbCktV5KkvFEmWaquaE3LlXZJko4usE9YqsoLqchjriPP+k9sV9wcZ1/MHH9HtFySSZacciUpbcLSvnwVNPWeK0myJUs9cqUu62H7VqpkS5acciXJniyRKwEAgBHuq1/9qvLy8vTZz35WM2bM0PTp0zVlypQBO/9BLcpIks/n02WXXabLLrssG9cDAAAAAAD2hztlsuLll1/Wk08+qaeeekrf+973tGDBAh199NG67LLLNH36dH32s5/N6fn7/UwZAAAAAACA4eyzn/2s7rjjDr3zzjvavHmz7rrrLh122GFaunSpzjzzTFVXV2v+/Pn67W9/q1AolPXzH9SdMuFwWE888YRef/11NTU1qTtxC3Qaj8ejBx988GBOAwA41GQmSumvE5lSIltymLAULzS/j5UUSLLnSpIUKrOSpXJPz1yp0qQ/1daEpSPKTJp0TPFHOiaZK5mMaWxelySp1JtKljqsXGlvzJxjp5UrbQ6ZVOmvHeZZa1vaRmt7ozl5e1OhJJMrSVKhNWHJ3+yeK0kZE5bcciXJniw55Uppr9ORKwEAMARxp0zWHXnkkfrud7+r7373u9q7d6+eeuopPfnkk/rFL36hFStWqKioSBdddJFmzJihz33uc6qsrDzoc/Z7UWbbtm2aNm2atm7dqni89/9LsSgDAAAAAACGk1GjRmnu3LmaO3euurq69Nxzz+nJJ5/U//zP/+jXv/618vLydOaZZ2rdunUHdZ5+L8rceOON2rJli6699lp95Stf0fjx4+XzHfQjagAAAAAAwP7EPeZXrs8BFRQUaPr06Zo+fbri8bg2btyoJ554Qk899dRBH7vfqygvvPCCLrjgAq1evfqgLwIAcIhzy5UkW7LklCtJSiVLxQFFk7mSNQEpY8JS2EqWQpWx5HSlqopWSdKR5SZNOqb4I7NNS5YOS+ZK5nheWTmUle7sjXm1I2puY/17yExUSuRKf2s1YxYTE5baGwvlbeyZK0n2ZMk1V5JsyZJrriTZkiW3XMm8TbIEAACQyePx6IwzztAZZ5yhpUuXHvTx+v2g31gsppNOOumgLwAAAAAAAOBQ1O9FmdNPP1319fXZvBYAAAAAANAHnvjA/DqUbN++XS0tLa77tLa2avv27Vk7Z7/zpTvvvFNnn322HnvsMV1xxRVZuyAAwCEiPVFyyZWktAlL/nwpYOVKxWYbs7bpyVJiulJXuT1XCleY3MZjJUtjKtpSuVKJyZWOLtglSarJ/1iSVJXXKckkS4lcKTFhaV+3ub4Pu02ytDlUpS0dYyU550qSlJeYsNTkkb/JXFegxfyJJ9Bsri+/LZUsueVKUsaEJZdcyWyYsAQAANCbyZMnq66uTj/84Q973ee+++7TD37wA8fp0/3R70WZp59+Wuedd56uuuoq1dbW6qSTTlJZWVmP/Twej+sPBAAAAAAADhAjsbMuHo+7TpdO7JNN/V6UqaurS/5+3bp1vY6BYlEGAAAAAACMBB988IFKS0uzdrx+L8qsXbs2axcBADgEuE1YysiVlG9lS1aqlEyWCgOKlVq5UrGVK5WZfUOJCUvlHoUyc6VKk/wcVtkmSZpctk+SdHTJRzo6bbqSJFX5OiRJpYmSyrrOrngsmSttj5o06e/hxISlKknS1tbR2tFYLknqaOqZK0lKJUvNcQVaeuZKUsaEJbdcSbInS+RKAAAAB2TJkiW2173dcNLd3a0PPvhAjz76qE477bSsnb/fizK1tbVZuwgAAAAAAICBll4BeTwe1xJIksaNG6e77rora+fv96IMAACu3O6Mkex3x7jcGSPZH+abvDPGephvqMIcL1RuvhquiEmV5k6T0ZWtkqSaskZJ0rGl5iG+6XfHJO6MKbYuKzGWsMvqhZMP842O0paQuSNmc6e5Q2Zzi7lj5sMm80y1jsYi5TWa/QsbrTtjmq0fqdl6mG/i7piWqHwd1p0x7eZuGE+XdRdM2t0xrnfGSLa7Y9zujDFvc3cMAAAjhUe5n47kye3hh4REBRSPx3X++edrzpw5mj17do/98vLyVFlZqWOPPVZeb78HWfdw0Isyu3bt0htvvKGmpqZenz48a9asgz3NkNfQ0KCGhgbbe4wMBwAAAABg6EqvgBYtWqTzzjtP55xzzoCdv9+LMl1dXfra176mRx55pNenD8fjcXk8nkNiUWb58uVavHjxYF8GAAAAAOBQEPeYX7k+xyFk0aJFA37Ofi/KfP/739cvfvELHX300br66qs1fvx4+XyHbg01f/58TZ8+3fZefX29Zs6cOUhXBACDID1RcsuVJHuy5JIrSVIkaPbtKvcqVG7lShkP800kS5WVbToi7UG+knR0ocmWkg/zzTMP/C31OuRKMfNg3h3RSknS30OJh/mO1dbW0ZKkD6yH+XYmH+ZrJUtNHtdcSZItWXLKlaSMh/m65Epmk/YXIxnpEbkSAADA0NbvVZRf/epX+uQnP6k33nhDgcQzAA5h1dXVqq6uHuzLAAAAAAAcCuLWr1yf4xATDof1xBNP6PXXX+/1MS0ej0cPPvhgVs7X70WZpqYmffnLX2ZBBgAAAAAADHvbtm3TtGnTtHXr1l4f0yINkUWZT3ziE/roo4+ychEAgGHKbcJSIleytvKbLMiTb7YqsJKlokLFi0ymlMiVwmVmn+SEpbRkKVRp5TYVJleqqGyXJE0uN8nSlJLdOrrAypX8H0tKz5XMd/OsWQJd8bj2xsx17IyWS5I2h8ZKkrZ02Ccs7WwqU0ejyZV8jalcSZICTdaP2BLvmSu1m+v0dpitLVlyypUke7Lkliulve/4WW9IlgAAGP64UybrbrzxRm3ZskXXXnutvvKVrwzIY1oO6pky8+bN05YtW3TUUUdl85oAAAAAAAAG1AsvvKALLrhAq1evHrBz9ntRpqqqSpdccolOPfVU3XDDDTrppJNUVlbmuO9AjpMCAAAAAGCk88TNr1yf41ASi8V00kknDeg5+70oc+6558rj8Sgej6uurk4eT++jspwejAMAGKY83lSulHgrkSilJ0u95EoqLJBkn7AUtqYrhcrzrK2VK5Wbr4StZClWEVG5Q64kpSYsHeHfrcPyzD6lHvPfnzzrcrussY5NMZNJ7YhU6O9hkyn9rb1KkrS1dZQkkytJsiVLxY3m+5kTlvwt5jz5rd09c6VOs1XI2qYnS065kmRLltxyJfOSCUsAAADZcPrpp6u+vn5Az9nvRZlbb73VdSEGAAAAAADkCM+Uybo777xTZ599th577DFdccUVA3LOfi/K1NXVZfEyAAAAAAAABs/TTz+t8847T1dddZVqa2t7fUyLx+PRD3/4w6ycM7ePEQYADH/WRKXUS49zriTZk6XecqWSxIQlK1kqy7NNV5KkcIX5a5mYNWGpfJTJkSaVNWpKqZmoNKUglStJUpWvVZJJljJzpY+i5twfWhOWtoRMqrSlY4w2t4yWJDVYuVJnk7le3z5zfcVNqWTJKVeS7BOWXHMlyZYsOeVKZpP211LkSgAAwAl3ymRd+s0n69at07p16xz3Y1EGAAAAAAAgi9auXTvg5zyoRZkdO3boRz/6kX73u99p586dCofDPfbxeDyKRqMHcxoAAAAAAICcqq2tHfBz9ntR5u9//7tOO+00NTY26rjjjlMoFNKkSZNUUFCgrVu3KhqN6sQTT1R5eXkWLxcAkHMOuZJkn7CU/L3P/GfEEzBJUnqyFCsy70VLzXuRYCpXkqSutGQpmStVmsX9MmvC0sSyJknSUVaydGxhg2r85vdVeVau5DUL/4mrDsU92tttEqQPo6aHei9kTVjqGCtJ2tpiJiw1NJWps9HKlRozciVzagVaUsmSU64kZUxYcsmVpIwJS065knkj7bd9vG+YZAkAgEMKI7FHhn4vyixevFjNzc36/e9/r9raWnm9Xs2dO1e33nqrdu7cqW984xuqr6/X888/n83rBQAAAAAAyLolS5b0ab8h8UyZ3/3ud7r00kttt/fE42YZbdy4cfp//+//6YQTTtAtt9yi5cuXH/yVAgAAAAAAI+4xv3J9jkPI/qZMezwexePxobEos2fPHh177LGpA/l86ujoSL4OBAKaNm2annjiiYO6QADAAEhLlnrkStZnnnzrPxk+XypXSmwdJixFyswkpsxcKVxuvhKqTCRLEQUrrOlK5Y2SUrnSlMKPJCktWWpJ5kp51jiArri5vr3dhZKkndEK/T18mCTpb+0mV3ovkSs1B8130pKl4sbUdCXJniuZrXW+johzriTZkyW3XEliwhIAAMAQ1duDfpubm/W///u/+vGPf6wLL7xQ119/fdbO2e9FmdGjR6u9vd32+v3337cf3OdTU1NTf08BAAAAAACcMBI769we9Dt9+nRdc801+vSnP63LL788a+f07n8XZ1OmTNHWrVuTr0899VStWbNGf//73yVJH3/8sR577DEdeeSRB3+VAAAAAAAAg2jKlCm67LLLdOedd2btmP2+U+Yf/uEfVFdXp6amJpWXl+uGG27Q008/rU996lP6xCc+oS1btqilpWW/TRYAYBA4TFhyy5UkyeM3OZIKAo65kiRFgqlkKZSYrlRuvpaeK0lSaYVJXieWN/bIlSb590iSqnwtkqSgx5p25EnlSk3WhKWd1oSlLSGTKm3pOKxnrrTPpE35jeZnTE5YapYCzea6AlaulN+aypWkjAlLTrmSZEuW+pQrpb3v+JkTciUAAJCG6UuDY8yYMfrrX/+ateP1+06Zb3zjG1q3bp3yrD/En3vuuXr00Uc1adIkvf322xo7dqx+/OMf62tf+1rWLhYAAAAAAGAwhEIhPfvssyovL8/aMft9p0wwGNRpp51me+9LX/qSvvSlLx30RQEAAAAAABc8UybrHnroIcf3o9GoPvzwQz366KN699139a1vfStr5+z3ogwAYBhxyJXS3/fk5bnnSlIqWSryK1pq3kvPlczWSpYqpHCF+a94t5UrFVd0SpImVlgTlkpMonRU0UeqSeRKeWYEUqnXZEKJCUth68bOfdEifWjlSu+FMiYstVZKknY1BRWypis55UqSPVlyzZUkW7LklCtJGROW+pArOX7uhGQJAABgwMyZM0ceT88x4PG4+XObx+PRVVddNTSeKZMQjUb117/+VU1NTepO/KE0wznnnHOwpwEAAAAAAAkD8EyZQ+1OmZUrVzq+7/V6VVFRoU9/+tMaN25cVs/Z70WZeDyuW2+9Vf/xH/+h1tZW1317W6zpzcMPP6yXXnpJb7zxht566y2Fw2GtXLlSc+bMcdw/8UDhxx9/XLt27VJVVZUuv/xy1dXVKRgMOn7nl7/8pe655x6988478vv9Ov3007VkyRKdcsopB3StAAAAAABg+Js9e/aAn7PfizL/+q//qttuu03l5eWaNWuWxo8fL58vOzXUwoULtW3bNo0ePVrV1dXatm1br/u2t7ertrZWmzZt0rRp03T11VfrzTff1N133621a9dq/fr1Ki4utn3n9ttv1y233KKJEyfquuuuU1tbmx599FGdeeaZWrNmjc4999ys/BwAMKjSkiWnXEmyT1hK5kp+M0kpmSuVmFSpuySVLCVypS5rwlK43HwllEyWoiquNNOVxpc3SZKOLDWJ0pTC3ZKkSX4zcanK15zMlfwyuU5X3Bx/V6xIkvRhJJEsjdHWDpMtbbUmLO1uKjXXYCVLvkafis0pFbByJX9LLxOW2iLydvWeK0kZE5acciXJOVkiVwIAALnEM2VGhH6vovzsZz/TpEmT9Mc//lGjRo3K5jVpxYoVmjJliiZNmqQ777xTN998c6/7Ll26VJs2bdKCBQt01113Jd9ftGiRlixZoqVLl2rx4sXJ9zdv3qxFixbp6KOP1muvvaaysjJJ0re//W2deuqpmjdvnt59992sLTABAAAAAIDh4+WXX9aqVau0adMmNTc3KxgM6qSTTtKsWbN01llnZfVc/R6J/dFHH2nGjBlZX5CRpAsvvFCTJk3a737xeFwrVqxQSUmJbr31VttnN998syoqKvTggw8mH8ojmUYsGo3qlltuSS7ISNJxxx2nWbNmaevWrXrhhRey98MAAAAAAJBt8QH6dYj57ne/q7PPPlsrVqzQH//4R23dulVvvPGGVqxYodraWt10001ZPV+/bweZPHmyWlpasnktB2zz5s3auXOnLr744h6JUkFBgc455xw9+eST2rJli6ZMmSJJWrdunSTpoosu6nG8iy++WA888IBefPFFx88BYEhzmrCUkSspz3ptJUq2ZCk5XcnKlYK9T1gKlZuvhSpTuZIkFVWYZOnw8uZkrnRUkcmVjvCbbZWv54SlZK7UbVKknRkTlra2m+17rZWOuZIkFTWZawo0u+dKkuTtDJmfvyvinitJ9mTJLVdKe9/xMyfkSgAAAEPGQw89pGXLlunYY4/VokWLdO6552rs2LHavXu31q1bp8WLF+vee+/V1KlTNWvWrKycs9+LMt/85je1ePFi7d69W2PGjMnKxRyozZs3S1JywSVT4v3Nmzfbfl9SUqKqqirX/d00NDSooaFhv9dXX1+/330AAAAAADhQngGYvpTz6U5DzP33368JEybo1VdfVWlpafL9MWPG6Morr9Qll1yiE044Qffdd9/AL8ps377d9vrzn/+81q1bpzPOOEO33nqrTjrpJFsOlG7ixIkHd5W9aG42f9va23kTk5cS+yV+39siktP+TpYvX257Tg0AAAAAABje3n77bX3ta1+zLcikCwaD+uIXv6gVK1Zk7Zx9XpSpqamRx+Pp8X48HtfcuXN7/Z7H41E0Gu3f1Q1R8+fP1/Tp0/e7X319vWbOnDkAVwTgkOSUK6W978nLS+VK+SZB8gSsyUqZE5aKAuouNe9FSq1cqTyRK5ljhExRpFBFXNFkrtQpSRpXbhazjyzdK8kkS5MDVq6UZz4Lek0y5LX+yqUrbv4T1NRd2CNX2txuFs/fb6mUlJqwFGkMJHOlxIQlv1XSBprNcf2t3e65kpRKlsJhxRP/jXLKlSRbsuSWK5mXJEsAAADDWfozaZ04rYscjD4vysyaNSvrJz9YiTtkeruzJfHMm/Q7acrKyg5ofyfV1dWqrq4+4OsFAAAAAABD0/HHH6/HH39c//qv/6qSkpIen7e2turxxx/Xcccdl7Vz9nlRZtWqVVk7abbs7xkwTs+cmTJlijZu3Khdu3b1eK7M/p5RAwAAAAAARqbrrrtOc+fO1emnn666ujrV1tZq9OjR2rNnT/JBvx988IGWLFmStXMe8IN+b7vtNrW3t2vx4sXKt26HzxQOh7V48WKVlpbqX/7lXw76InszZcoUjRs3Ths2bFB7e7ttAlNXV5f+8Ic/aNy4cTrqqKOS79fW1mrjxo167rnnejyYZ82aNcl9AGBISkuWHHMlyZYsJacrBcwkJRWYbazYZEvpyVLYmq7UVW7lSuXmK+EKcwtnxEqWCiu6NDGRKwVNrnRkcsLSx5LMhKVSb5e5HGuWYljm+PuiRZKkDyMmTXovdJj+3jFaknOuJMmWLPmtmx0D1oQlf+aEpfaIa64kyZ4sueRKZpN2Cyu5EgAAGCoGYmT1Ifag39mzZ2vTpk269957deWVV0qSvF6vYjHrz4XxuL71rW9p9uzZWTund/+7pPzud7/TrbfeqlGjRvW6ICNJfr9fo0aN0i233KIXXnjhoC+yNx6PR/PmzVNbW1uPlao77rhDjY2Nmjdvni27mjt3rnw+n2677TZbxvTOO+/ooYce0pFHHqnzzz8/Z9cMAAAAAACGprvvvlt/+MMfNGfOHE2dOlU1NTWaOnWq5s6dqxdffFH33ntvVs93QHfKPPTQQ6qoqNA3v/nN/e57/fXX64477tDKlSsPeJFjxYoVWr9+vSTprbfeSr63bt06SdKMGTM0Y8YMSdKCBQv01FNPaenSpfrTn/6kk08+WW+++aaeeeYZTZ06VQsWLLAd++ijj1ZdXZ0WLlyoT33qU7riiivU3t6uRx55RJFIRD/96U/l8/V7UjgAAAAAADnHSOzcOeuss3TWWWcNyLkOaPXh5Zdf1oUXXqhA4jZ4F4FAQBdeeKFefvnlA76o9evXa/Xq1bb3NmzYoA0bNkgyk6ASizLFxcXJtuuxxx7TunXrVFVVpRtvvFGLFi2yJU0Jt9xyi2pqanTPPffo/vvvl9/v1xlnnKElS5boM5/5zAFfLwDkjMuEJadcSVIqWfL7k9OV4kXm39vdJWYbDpp9wmkTlhK5UmLKUiJXKqg0E5bGl5mHoU8O7tVRRSZTmhxI5EpNkqRSa8JSnmKKxM2xd8XMv4d3RqwJS11mwlJ6svRxk3mQWrTRXG9+o/lusXVDY3qy5JQrSRkTltxyJcmeLLnlSmnvO37mhFwJAAAAfXRAizI7d+7UEUcc0ef9J0+erCeffPKAL2rVqlUH9GDhsrIyLVu2TMuWLevzd6655hpdc801B3xtAAAAAAAMCYfonSy5smHDBj3++ONasGBBj8FAkrRr1y4tXbpUV155pT772c9m5ZwH9EwZr9erSCTS5/0jkYi83gM6BQAAAAAAwIBbtmyZnn76accFGUmqqqrSb37zG919991ZO+cB3Skzbtw4vf32233e/+2339bhhx9+wBcFAIcsl1xJsk9YcsyVJFuylMiVIqVmn1BarmRem6+EKqSolSv5K83UpEnlVq5Uuk+SdGQyWdqtKp/piYLWhKV8j8mBuuLmPyu7uoP6KFomKZUrbWkfI0na3mpOmp4sJXOlJutHMadWoNmkQP5Ws81vjTrnSpItWXLNlSRbsuSWK5mXLn8FRaoEAAAGyxCcvrRq1SrNnTvXdZ/zzz9fv//97yVJdXV1Wrx4seN+gUBAXV1dB3YBB+n111/XBRdc4LrPOeeco+effz5r5zygRZmzzz5bDz/8sN5//33V1NS47vv+++/rhRde6DF2GgAAAAAAjDxTp07VokWLHD977LHH9M477+jiiy/u8dns2bN7rDEMxgCe3bt37/fGkqqqKu3evTtr5zygn/L666/XypUrdcUVV+jZZ5/V6NGjHffbu3evvvSlLykajeob3/hGVi4UAAAAAAAYQ3H60tSpUzV16tQe74fDYf3kJz+Rz+fT7Nmze3w+Z84cnXvuuf27yCwqLy/X9u3bXffZtm2bSkpKsnbOA1qU+fSnP60bbrhB99xzjz75yU/quuuu03nnnafx48dLkj788EP9/ve/13/913/p448/1k033aRPf/rTWbtYABiR0pIlt1xJypiwlMiVCkyilDlhKRL0Kxw03++qsHIlUxQpnDFhyV/ZpfHlrZLScqXi3iYsdSnfY7Kdrpj5z8ju7lJJaROWQofpvXazcL+txbyXyJW6G8315TelkqVAYspSS89cSbJPWHLKlaSMCUsuuZLZpP0Jg1wJAAAgp379619r7969mjFjhsaOHTvYl9Or008/Xb/+9a+1Y8cOTZgwocfn27dv1xNPPKHzzz8/a+c84PuB/v3f/10FBQX6t3/7N91222267bbbbJ/H43Hl5eXp5ptv1o9+9KOsXSgAAAAAALAMwWfK9ObBBx+UJM2bN8/x85deekmvvfaa8vLydOyxx+rCCy9UIBDIzskPwE033aSnnnpKZ555pn70ox9p2rRpqq6uVkNDg5577jktXLhQnZ2d+ud//uesnfOAF2U8Ho9uv/12ffWrX9XKlSv18ssva9euXZJMW3XmmWdqzpw5OvLII7N2kQAAAAAAYHC0tLTYXgcCgT4vmmzbtk2///3vdfjhh+uSSy5x3OfWW2+1va6urtbq1as1bdq0/l1wP5199tn68Y9/rBtuuCH5wGKPx6N43KxOeb1e3XvvvTrnnHOyds5+PznnyCOP5E4YADgYiUQpLVlyzZUkW7LUI1eyJiyFy8y/2rvKvQpbuVIomSuZnCffmrB0uJUs1QT36YiiPZLScqX8JklSqcfs609OWMrXx91FkqQPrVzp/S6TKqUnS3ubi831JXIla8JSgZUqpSdLbrmSlDZhqSskRczvXScsueVKae87fpaJZAkAAAxBA/lMmcyUZ9GiRaqrq+vTMVauXKlYLKa5c+cqL/FnXcvUqVO1evVq1dbWauzYsfrggw/06KOP6vbbb9f06dP1yiuv6MQTT8zGj9Jn119/vWpra3X//ffr9ddfV1NTk8rLy3Xqqafquuuu0/HHH5/V8w3844wBAAAAAMCwsWPHDgWDweTrvt4lE4vFtHLlSnk8Hn3lK1/p8fmMGTNsr4866igtXLhQY8eO1de//nX96Ec/0q9+9auDuvb+OP744/Wf//mfA3Iu7/53AQAAAAAAQ0p8gH5JCgaDtl99XZR5/vnntX37dp1//vmaPHlyn3+02bNny+fzacOGDX3+znDFnTIAMFAcciXba58vmS95EplSwNoWFkhKm7BUHFAkaOVKQfOv8lC5NWGp3HwlXC6FrVzJZ+VK1eVtkkyuJCmVLBV8rLE+0xMFvWbffI/Jg7ri5jwfR1MTlt4PHSZJeq99lCRpR4s56d7khCV/crpSQZP1o1gpcmLCUsDa+tqi8rWZCUqersQ2LVeS7MmSU64k2ZIlt1zJvGTCEgAAQK7t7wG/vfH7/SotLVVHR0cuLmtI4U4ZAAAAAACQVXv37tWTTz6pyspKXXbZZQf03c2bN6uxsVE1NTW5ubghhEUZAAAAAACGmwHMl/rj5z//ucLhsGbOnOmYO7W2turPf/5zj/cbGxv11a9+VZJ09dVX9/8ChgnyJQDIJY+391wpkSr5zL+KPX5/KlcqMP/hSk1YMvlStDSVLHU55EpSKlnKqwypysqVJgUbJUmTi02uNDGwV5J0eL55v9TbqXylpitJ0u7uVK4kSTtCJlV6r32Utjebk+1rTuVKkuRPm7CUmK6UyJQSE5Z8rSZF8lkTljxdYXk6TbakkLV1mbDklCuZTdqfGsiVAAAABtX+0qW9e/fqxBNP1CmnnKITTjhBY8aM0YcffqhnnnlGe/fu1bRp03TjjTfm7PpisZj+8pe/qLKyUuPGjbN9FolEtHHjxqyOvu4NizIAAAAAAAwzAzkS+0C99tprevvtt3XqqafqhBNOcNynsrJS119/vV555RU9/fTTampqUnFxsU444QTNnDlT8+bN6zFCO1u2bdumSy+9VPX19fJ4PPrc5z6nlStXatQo85eQ+/bt03nnnafuxF8G5hCLMgAAAAAAIGtOPfVUxePuKzrBYFA/+clPBuiK7BYsWKDx48drzZo1ampq0j//8z/rzDPP1AsvvJC8a2Z/158tLMoAQDY5TFhyzZUkW7KUyJVixWYbKTWfhcusCUtl5lihco9CpipSuCKRK5n0Z0xaspSZK43NN01Rudc8yd7vSSRLPn3cHZSUypW2hyolSe9bE5a2WxOWGpuKFUvkStaEpcImcy1+a8JSoCXmmitJsidLLrmSlDFhySlXMm+k/XY//xElWQIAAMPdQT7zpc/nGIFefPFFrVmzRuPHj9f48eP17LPP6utf/7rOPvtsrV27VoFAQB6PZ0CuhQf9AgAAAACAQ0ZHR4ft4cMej0c//elPddFFF+mcc87R1q1bB+xaWJQBAAAAAGC4GeLTl4ayY445Rn/84x97vH///ffr0ksv1ec///kBuxbyJQA4GA65ku11Xl7PXMlvphup0ExUihea92MlBalcKWiyoFBiwlIiW0omSzF5K0KSpMMqTK40saxJkjS52KRKEwJ7VeWzcqW8dkmS1/ova0Tm+B9HTbL0UaRM73eZTCmRK33QWiZJamwsNtfXlEqW/E3Wj5KWK0n2CUuuuZJkT5bcciWJCUsAAADImi9+8Yv65S9/qZkzZ/b47L777lM8Htfy5csH5FpYlMmShoYGNTQ02N6rr68fpKsBAAAAAIxkQ3n60lB388036+abb+718/vvv1/333//gFwLizJZsnz5ci1evHiwLwMAAAAAAAwTLMpkyfz58zV9+nTbe/X19Y63QwEY5tITJYdcSUpNWFK+T57EQ8SsbbzYPmEpmkyWfOrKyJXCabmSJKnCpD+VFe2aEGySJNVYuVJNQcaEpbx25cl8Lxw319PUXSRJ+ihq0qRtnaMlSe+3V+pDK1dqajL7xBrN9fmbzDX5zWHlb3HOlST7hCXXXEmyJUt9ypXS3nf8rJf9AAAAhiRP+iNevQf+/BamL+VMLBbTBx98oA8//FAR68+wmc4555ysnItFmSyprq5WdXX1YF8GAAAAAADoh3g8rjvvvFN333239u7d67pvd+IvFQ8SizIAAAAAAAwzPFMm+26++WYtXbpUY8aM0dy5c1VdXS2fL7fLJizKAMD+uE1YcsiVJNmSpXihlSuVZuRKpWbf5ISlck9qulK5PVcqq+iQpLRkaZ8mFOyTJFXnN0qSgnld5hI8Jg+KxH36OJErRUyalJiwtKPdnGhHS7kkqbmpSPHkdCUrVzKnSk1YslIlf3O3fO3mHMlcqcOasBRKS5ZcciUpY8JSH3Ilx89d9gUAABhybMkShppVq1bpmGOO0euvv66SkpIBOSf/HwEAAAAAwHATH6Bfh5C2tjZ97nOfG7AFGYlFGQAAAAAAAE2dOlU7d+4c0HOSLwFApozbShPZksdKlZLJUl6e5M83v8+csFSYmrCUzJXKrFypLJUrSUolSxUxxctN8lNW0S5JGh80I48mlZhUKZEsjfW1qDzP7JPvMRlQJG6ua2+0XJL0UTSobZ32XCkxYanZmrCkRnNt+U1e23QlKW3CUos5fnqy5JgrSfZkyS1XkpyTJXIlAAAwkuQyV2L6UtYtXLhQl19+uf73f/9Xn/70pwfknCzKAAAAAACAQ94ll1yi1atX6x/+4R80ffp0nXjiiQoGg477zpo1KyvnZFEGAAAAAIBhxmP9yvU5DiWhUEhPPvmk9uzZowcffFCS5PHY/ynE43F5PB4WZQAgq9JuLXXNlaRUspSfLxVYuVJRoSSTK0lSd4nZJ1yW3zNXKjeHCVea/CZmJUvBig4dXmblSsWJXMlMVhqbb94vyzNTmPyeboWtXGlf1DyILDFhKZksdZRrZ4tZ2W9J5EpNqVxJki1ZcsuVpIwJS065kmRLlvqUK6W97/hZJpIlAAAwlDFdaVi76aab9Itf/EKf+tSndMUVVzASGwAAAAAAYCD86le/0sknn6yNGzfmfDEmgUUZAAAAAACGGx70m3VdXV0677zzBmxBRmJRBsChym3CkluuJCWTJRUWJHOl5ISlYGLCkvluqNyTypUqzH/VYpUm+Sku65QkjUskSyWNmpiYrmTlSuVWruT1mGwnEjfH3xctSeZK27sqJUk72s2JGtKSJU+TuWa/Q64kSf7WVLKU32YSpLwOh1xJsiVLTrmSlDFhqQ+5kuPnLvsCAAAMOSRLI8bJJ5+sLVu2DOg5+f8eAAAAAACGGU98YH4dSm6//XY9++yz+s1vfjNg5+ROGQAAAAAAcMh7/vnnde655+oLX/iCzjvvPE2dOtVxJLbH49EPf/jDrJyTRRkAhw63CUvWZ558n2Q1pJ6ASZKU2CYmLBWZ19HSgCIZuVKXNWEpXG6+EqqIK1ZhcqCicpMrVZeZdmhiiZmsNL6gybzvb1Kp1+zj95gMKDFhqbXbTFjaHTH/Ufigq0Lb2iokSbusXKm12VxfIlkKNHldcyVJtmTJLVeSMiYsOeVKknOyRK4EAABGkqGSK/FMmayrq6tL/v6FF17QCy+84LgfizIAAAAAAABZtHbt2gE/J4syAAAAAAAMR4fYnSy5VltbO+DnZFEGwMjlNGEpkSklJizlW/8aTCRL/nzbdCVJqQlLJSZbigRNHhQqy1PIypUyJyx1V5jUp7C8U2ODrZKkCSVNkqSJhWbC0hirKUpMWMr3dCti5Uq7olauFDZp0s6QNWnJSpZ2t5aqtcnKlZpTuZIk+c1p5G+VAs0mB8pvtXKldpcJS265kmRPltxypbT3HT/LRLIEAACGuqGSLGHE4f+zAAAAAAAYZpi+lH0bNmzQTTfdpF27djl+vmvXLt1000165ZVXsnZOFmUAAAAAAMAhb9myZXr66adVVVXl+HlVVZV+85vf6O67787aOcmXAIwcTrlS2vuevDznXEmyJUvp05Uke65ktqkJS6Fy89cHUStXKqjokiQdXpZKlg5PTlcyo5BK88yEpYDHZEKRuLmWfdESfWRNV2roMrnSB+1mm5iw1G5NWPI2+RRozsiVrAlLgZZUspTMldqtTKnTypYcJiy55kqSLVlyy5XMSyYsAQCAYayfuVLiz5/7zbezgelLWff666/rggsucN3nnHPO0fPPP5+1c3KnDAAAAAAAOOTt3r1bhx9+uOs+VVVV2r17d9bOyZ0yAAAAAAAMMwPxzJdD7Zky5eXl2r59u+s+27ZtU0lJSdbOyaIMgOEt7dZSp1xJkpRnvfb7U7mS3yRKyQlLJSZV6i4J9JorJScsWclSpKJbAStXqgq2SZLGl5hEaUJRoyRpTH6LytKmK0lKTlj6qNukSYkJSw1dZclc6aOWUklSR0sqV5LsE5b8ppCy5UqSbMmSW64kZUxYcsmVzCbtv8rkSgAAYCQ5iOlKyT+DaoCyJeTM6aefrl//+tfasWOHJkyY0OPz7du364knntD555+ftXOSLwEAAAAAMNzEB+jXIeSmm25SR0eHzjzzTD300ENqaGiQJDU0NGj16tU688wz1dnZqX/+53/O2jm5UwYAAAAAABzyzj77bP34xz/WDTfcoLlz50qSPB6P4nGzOuX1enXvvffqnHPOydo5WZTJkoaGhuQqWkJ9ff0gXQ0wgjlNWOotV8o3GVIyWQoEUrlSkZUrlZqMKVJqJUvleQqVme+HTUmkUIXZJiYs5ZeHJEnjytqSudK4wiZJ0hirKUokSwXeiMLWdKXmaJEkaXdywpLZftiWSpY6W8z1eZvM9QSaze2wyQlLiWSpOab8tp65kpQxYcktV5LsyZJbrpT2vuNnDvsAAAAMaQc5YSmh38lS+p+b+vFnKJ4pkxvXX3+9amtrdf/99+v1119XU1OTysvLdeqpp+q6667T8ccfn9XzsSiTJcuXL9fixYsH+zIAAAAAAMBBOP744/Wf//mfA3IuFmWyZP78+Zo+fbrtvfr6es2cOXOQrggAAAAAAAxlLMpkSXV1taqrqwf7MoCRxylXSnvfk5fXe66UMWEpXhRQtzVlKWxNWAqXJyYsea2tFC43X4tUmLTHZ01YGhM0SdLhpSZZqi5sVpW/RZJU5rNPWIrFzXV+FCnTnogZmbez0xy4ocNMVtrdbLZdLeaavE35KrBypXxzCgVazD2jfmubniw55kqSLVlyzZXMhZptPOaaK5mXDvevkisBAIDhYihMWMrmn50G4kG8Izxf+vKXv6wrrrhCX/ziFwfl+xLTlwAAAAAAwCHo0Ucf1dtvvz1o35e4UwYAAAAAgOGHO2WyYtOmTXrooYcG7fwsygAYmhJ5klOuJNmSJbdcSVIyWYqU5iscNN/vKrdypXLzlfRkKa/c5D+jytolSeNKTKJ0eFGTJOkwf5skkyylciVzvD3WhKW9kWJJJllK5Eoft5iMKdRsTVhqNv8KTiRL/uZUppTctloJVbtJlPI6rFSpM+KYK0kZE5ZcciWzSfsvbV9yJYf9AAAAhqQs5UpSliYsYUh64okn9OSTTx7w9xJjsg8WizIAAAAAAAwzjMQ+eCtXrjzoY0ydOvWgvs+iDAAAAAAAOOTMnj17sC+BRRkAQ0h6spSZL/nMv66SE5as1yoImF9yzpUkKRw0+4bKvQqVma8lcqWwNWHJayVLleUdGlvSKkk6vMiMQBqdlitJUoHH5EGReF6PXOmjrqAkaVe7SZb2tBancqWmjFzJVFG2ZMktV5IyJiw55UqSPVlyy5XS3nf8zGEfAACAIa2fydKwzJV4psyIwPQlAAAAAACAQcCdMgAAAAAADDOeeFyeLD1s1u0cyC0WZQAMnt4mLHk9kjVlKZEpeRKTlQLWNi1Z6i4xeVA0I1dKTFgKW8lSqFyKlFu3lFq5Ulm5SZLGlJhEaVxRi8YGTFdU5uuUpOSEpW6Z69wTMVOU9kaKk7lSQ5vZ7mszOVOo2Vyfr8mngpbUdCUplSsFrG1+WypZcs2VJHuy5JQrSbZkyS1XMi8d/kNLrgQAAIaLLE1Y6neuZL7c/+/ikMeiDAAAAAAAww3PlBkReKYMAAAAAADAIOBOGQADy+N1zpUkW7LklitJUqw4MWHJr3DQfC9k5UqhoDleuMJ8NWwlS/HyiErLTJI0OpErFZtUKTFhqSK/I5Urxc3xmqOFkqR9iQlLnWay0kftpWpqM58lc6Vm86/VwrQJS265kmSfsOSaK0m2ZMkpVzKbtL/S6Euu5LAfAADAkJSlXEkaJhOWXHji5leuz4Hc4k4ZAAAAAACAQcCdMgAAAAAADDc8U2ZEYFEGQG45TFhyypUkSfnWpKVAQApYuVKxPVeKlpqcKTFhKVTmVajcHC9Ubg6TmLAUKzfJT1HQSpZK21Vd1CpJGhWwciWfmb6U700lS23dZprT3rCZpLTbypV2t5upS03WhKVwc0C+ZvMzFKXlSmZr/gvmb4275kpSxoQll1xJypiw5JQrmTfSfkuuBAAAhrl+JktZy5XMl/v/XcAFizIAAAAAAAwzPFNmZOCZMgAAAAAAAIOAO2UAZFfG7aWJ20Y9VqqkvLzU7/355jMrVUomS4WBnrlSWSpXMq9TyVK43Czhx8pN+lMQDEmSRpW2S5Kqiq1kyd+u8nwrV7ImLCU0RkyStC9crD0hM2UpkSs1t5oJS1FrwlJeIllq8TjmSpKU32ryI19H1D1XkuzJkluuJPV/whK33AIAgOEiSxOWRnyuxDNlRgTulAEAAAAAABgELMoAAAAAAAAMAvIlAAcv7RZTp1zJ9tqfL0++yZZUYGVLhWbaUXqylJquZL6XmLAULjNfSSRL0fKoAmUmV6osMWnSYUVmstLoArMN+rokSQFvNHmdjVGTKzVHTJq0u9OkSns7itXcYj7rbjHXmZiwVNiSMWGpNS5/i7m1NTlhqcOcI6/dpEmerqh7riTZkqX95kp9mayU2sH9cwAAgKEgS7mSdBDJ0jD8cxMP+h0ZuFMGAAAAAABgEHCnDAAAAAAAww0P+h0RWJQBcOAcJiwl8yTrM0++9a8Xn9l6AmaKkgJ+qcgkQ7Ei81601GRLkbRkKVRmz5VCVq7UXWZlQlayNKa0Q6OLzZSl0QGzLc2350qxuDlWU6QomSvt6TKJ0p52M2mp1ZqwFGvxy9difoaiZvO9fDO8KW3CUipZym8z5/B2hq2tSZMUtrahsGuuJGVMWHLJlWzvOxmGt90CAIBDVD+TpazlSubL/f8uelVTU6Nt27Y5fjZ//nw98MADtvdaWlpUV1enxx9/XLt27VJVVZUuv/xy1dXVKRgMDsQlDyoWZQAAAAAAGIaG6jNfysrKdMMNN/R4/5RTTrG9bm9vV21trTZt2qRp06bp6quv1ptvvqm7775ba9eu1fr161VcXDxAVz04WJQBAAAAAABZU15errq6uv3ut3TpUm3atEkLFizQXXfdlXx/0aJFWrJkiZYuXarFixfn8EoHH4syWdLQ0KCGhgbbe/X19YN0NUCWOeRK6e978vJ65kr+XiYsFfnTciWzTzhoTViykqVQmRQuN1+LWrlSXpnJg8pLOyVJhxWbyUqVgU6V5Zv3ErlStxK5kkmSWqzt3s4i7esw2VJbq3U9LSah8rWYayhokfLTpiuZrX3CUnqy5JgrSfZkyS1XkuzJErkSAAAYibI0YYlcKU08bn7l+hw5O3RcK1asUElJiW699VbbZzfffLP+4z/+Qw8++KDq6urk8Xh6Ocrwx6JMlixfvnzEr+ABAAAAALA/oVBIq1ev1ocffqiKigqdccYZOvHEE237bN68WTt37tTFF1/cI1EqKCjQOeecoyeffFJbtmzRlClTBvLyBxSLMlkyf/58TZ8+3fZefX29Zs6cOUhXBAAAAAAYqTzx3D9TJnH8lpYW2/uBQECBQKDX7+3atUtz5syxvXfJJZfo5z//uUaPHi3JLMpI6nXBJfH+5s2bWZTB/lVXV6u6unqwLwPInrRbTJ1yJUlSnvXa70/lSn5rylIyVzL/su4uNe9HSvMVLkvkSl5ra76SmLQUKe+WJ2jyn2DQpEmVRR2SpFGFZlviM9OXCvPCisXNcVK5kjn3XmvCUmO72Xa0BRRvMdeZnitJqQlLgZZ4j1zJ1+4yYckpV5JsyZJrrpT22vZeb0babbcAAGBkylKuJB1EssSfm7JmwoQJtteLFi3q9ZkxX/nKV1RbW6vjjjtOgUBAf/nLX7R48WI988wzmj59ujZs2CCPx6Pm5mZJ5qHAThKTlxL7jVQsygAAAAAAMNzErV+5PoekHTt22MZTu90lk/l8mNNOO02/+c1vVFtbq/Xr1+u3v/2tPve5z+Xkcoej/i9fAgAAAACAES8YDNp+uS3KOPF6vZo7d64kacOGDZJSd8j0didMIpnq7U6akYI7ZQAYThOWesuV8k0CZEuWrFwpnsiVSsw2nJiwlEiWgt5kppTMlcqsnKfMJEBFpV2qKLaypQKTKwX9XeZU1oSlhOZIoVrC5txNIZMvNbabbYc1YUmt5l91+S15aZOVEtueE5ZccyXJliw55UpSxoQll1zJ9r79zZ7vAQAADGX9TJayliuZL/f/u8OMJ2Z+5foc2ZJ4lkxHh/nzffozY5zs75kzIwV3ygAAAAAAgJx69dVXJUk1NTWSzGLLuHHjtGHDBrW3t9v27erq0h/+8AeNGzdORx111EBf6oBiUQYAAAAAgOEmPkC/DsBf/vIXNTU19Xh//fr1WrZsmQKBgL74xS9Kkjwej+bNm6e2tjYtWbLEtv8dd9yhxsZGzZs3Tx6Pp8fxRhLyJeBQl0iUnCYsueVKklRgEqX/v707j5KyuvM//nlq7b2BH5ElCj2CxiUeCSJq0LTEKB51kKMJxgQGIiBmZrJgErJK02rMQM4hOTEz0YkJEohGk7glgWiciAguMRpJUHTQiAviuARseq3t/v6496mlq7rphi6qq3m/zqlz+7n1LLf6PHQq1/t5vqYqmo4rxWtdXKnO/nnxKyxlR5b8uFKq3kZ/orW2klJ9lY0oDa9sV13Y9kWCuXGlVldZqSVur/deZ6X2urhSZ6sb1z47hnCLvXYkq8JSXlypLb/CUjqu1JlbWalghaVCcSUpN7LUl7hS1jEAAABl5QBiS9mRJeJKQ8edd96plStX6pxzzlFDQ4Oi0ai2bdumBx54QIFAQDfddJPGjRuX3n/p0qW67777tHLlSv3lL3/RKaecoq1bt2rDhg2aNGmSli5dWsJPc2gwKQMAAAAAQJnxjH0V+xr9MX36dG3fvl1PP/20Hn74YXV2dmrUqFG67LLLtGTJEk2dOjVn/+rqam3cuFHNzc361a9+pY0bN2r06NFasmSJmpqaVF1dPYCfZnBiUgYAAAAAABy0xsZGNTY29uuY+vp6rVq1SqtWrSrSqAY3JmWAw5EXyI8r+dsh+2fBCwbl+TGlqGv9CkuVdjtZY7cTtWHFal11pXRcyXOtXGun2ZN1CUVqbSxoWLWNK9VX2EpLdREbWarIiiy1uphSa9xes6XTxZc6bNvVGpX3nh1zZJ+9tl9hKdwq15+JLIVb/biSjSIF2l3blRVZ6i2uJOVGlgrFlbK2c/q6Y7ktAAAoRwdYZSnbAUeW+P6EIYZJGQAAAAAAyo0x9lXsa6ComJQBDgcFHuab/jloV7h4/gqZ7NUxWQ/ylaRUtW0TNe5hvrWZh/mmV8bU2cMzK2PsqpJgrV11Ul/dqbpKu0Km1q2MiQTsPgEXWm2NR9Tmr4zpsitiWtvttWPuYb7ePrc6piWg8D57zUi6tecJt7qH+Watjim4MkbKWR3T68oYKWd1TKGVMXazwP+A8V92AABAuRqA1TEHjO9QGMKYlAEAAAAAoMwMxgf9ov9KON0JAAAAAABw+GKlDDAUdVte6keVPBdVUjCY+Tlio0he1MaD5FpTGc3ElWptZChe5+JKdfb8XS6yFK/NPNA34eJKnosrVVXbiFJdpW2rI12KBF1cSXbqvT1hx9DuIkutXRG1ubhS3MWVgq12vJEW9zDfrMhSpDXzIF9JCrX18jDfAnElqdvDfHuJK9mGh/kCAIAhjrjS4Gfcq9jXQFGxUgYAAAAAAKAEWCkDAAAAAECZ4ZkyQwOTMsBQkV1RKatPkrywq6zkR5ZCIXlRV2XJVVhSpa1ylKmwFMmKK9njsuNKUqbSUrwuJdXa+E+k2saDalxcqSpit6NB+37AM+qI27hSp4st7eu01+zssGNKtIUUdNWVKvbZa3avsBT2I0utKYVb7bmD7e4aflzJjyp1ZUWVuseV4q515f5MMtmnuFJOf25nfh8AAMBgR1wJKAkmZQAAAAAAKDfGpP/DYlGvgaLimTIAAAAAAAAlwEoZoFz50SQ/slQovhRysaWIjQWlI0vRSLrKUqrGtknXxutspKirLqCYX2UpHVPyWxfrqXFVjqpjqqywsaDqqI0MRUIuSuQe2e5HlToTIbV32nF0ddi+VJttA67CUsU+LxNXanXtPnuecKu9ZthVWAq2JRTo6CGuFHPbLqpkEol0XCldWcmf/c+KKBFXAgAAhwUiS2WNZ8oMDayUAQAAAAAAKAFWygAAAAAAUG6MexX7GigqJmWAclEgrpSzHQplqiv5h7jYkl9ZyW9NZUTJavtevM62MVdhqXOYPW+8VorVy/1s/xonXYWlYJVtK1xkqTIaVyRo40CeW+PY6SosdSXseTu63HXaw1K7/dMTbLXXqmh1FZZa7PXCbVKktee4kiQF2200yeuK9x5XknIjSwXiSrbJjywVjCt12wcAAKBsEFcCBh0mZQAAAAAAKDM8U2Zo4JkyAAAAAAAAJcBKGWAwy66o1D2u5KJKXnaFpYiNDCnllodWVkqSTK1t/QpLieqw4rX2+K56V2Gp3p43O7KUrLHn8aptPKiiyrbRsI0DhUM2UhSQUSzpzhe344nFbBt3FZbUZt8PtQYU8uNKrsJSuM22kdZUug252FKwo0BcyV5I7kK9x5Wk3MhSL3GlnP7czvw+AACAwayUUSWJ70+HQspkIvnFvAaKikmZAbJ7927t3r07p2/79u0lGg0AAAAAABjsmJQZIDfffLOam5tLPQwAAAAAwOGA6ktDApMyA2Tx4sWaOXNmTt/27ds1Z86cEo0IZanbMlMv4GUqKnWLKynsYktRG0lSZYVM1FY48hI2tpOqtdWWYiNsfMmPLMVqAorX2AhRlx9XqvNbF/mpTipUaSNCkUhuXMlzCaq4q6wUi4cUd3GlZKcbb5vdDrXZzxRuVbr1f/bjSmHXhlyFpVB7XIEOe+10XKmzy7+oa22/SSZ7jiu57ZxIEnElAAAwFJU6riTxHQo4AEzKDJAxY8ZozJgxpR4GAAAAAAAoE0zKAAAAAABQZjwdgpLYxT09xKQMUFoF4kqSciJL6Z9dZaV0XMm1ptK2qeqoktV2n0CXje3E622cqeN99p96V509f6JKitfY08Rr7V/yZK2rduQqLIUrEgoF7RLUgPtrn3AVlvzYUrzTnjfVGVSg3faFu8WVQq6yUnZkKdzmx5ZcZSVXYSknstRbXCmr3ySTvceVbEfh/gL7AAAAlIVSxpX43gQMGCZlAAAAAAAoN8Zk/uNkMa+BohoET4MCAAAAAAA4/LBSBjjUspaa+nElv8+PKnmuspJCIXmuopIqMlWWJBtXkqRErX0/VhdSvNpFh1w8qGuYPV/HSHudmKu0lKwwSlbaWW9T7SoeVdo4UMhVWgp4RsmkPV9XwrbJmD2f6bDj8yNL0TYvE1NKx5WM27ZtJF1hKZGJK7XH7OftdBWWYnZb8UQmruTHl/xoUoEKS8SVAADAYYHIErJ45hA8U4aFMkXHShkAAAAAAIASYKUMAAAAAADlxrhXsa+BomJSBii2HiosyQtkVVly8aVIxLW2ipIqovlxpRq7T7zO7tNVb88Rq/MUr3KH7bHX6Bpm2473yR3rlp2GU1LEVVaKJN1w7HbKRZbiiYBScTe+DtsGOvx4lD1vdoWlTEzJxZbaXVwpXWEp7s4RLxxXknIjS73ElaRu0aS+xJWy9gMAACgLxJWAIY9JGQAAAAAAyoxnjLwiV0cq9vnBM2UAAAAAAABKgpUyQLH4FZV6qLCkYEBe2EaQ0nGlaLcKS1VRJV11pXit3Sfm4kpd9a4yUp09JF4vJSr9mWxXbWmY3UoMcxGgymTeMP24UspVWJLbVmdAQRdXCrm4Ul6FJRdZCrcahdtcFad222bHlaRuFZYKxZWk3MhSb3El21G4v8A+AAAAg94BRpXS3zW72W+sO/+AA7o+SijlXsW+BoqKlTIAAAAAAAAlwEoZAAAAAADKDM+UGRqYlAEGkhfIjyv52yH7zy0nsuSqLanCxpZMtY0tJav9CksRxepy40oxF1eK1ds2Xu+qJtUl5IXsz/FWe55EtfsjGnWxJc9tJwJSwo0raVsvbttgp2s7vB7jSn6FpVA6spRQsN1GkAKdtqKS1+UiSX6FpZiLLyUSheNKUk5kibgSAAAY0g6islKhyFK/40r2oAMeA4CBwaQMAAAAAADlxrhXsa+BouKZMgAAAAAAACXAShngYBSosJT+2VVZ8vzYkh9VimYiS6bKxpZS1baNu0pL2ZGlWL2rpORXWaqz09WJOhcdqrGxoNqqLqWM3bcrYuNLCth9TSo3qhToCCrgx5Rirq/L7hJqt224Pau6Ult+XElSTmQpp7qSlKmwlLBtdmQpHVdKuiWzhaJJfYkrZe0HAAAwaA1wZaVs/Yot8b1paDEmE/8v5jVQVKyUAQAAAAAAKAEmZQAAAAAAAEqA+BLQH92WnvpLSj0XVVIwmI4rKexiS1EbTVLUr7CUiSwl0lWW7L5+haWuOs/1Z2JLflzJc3GlyirbVlXYuFBlOK62mIs/uZWpnh9NarPjC7gKS6E2Lx1TCnbaNtTht35kKaVwmz2RH1cKdHSrsJQdWeolriQpN7LUUzSJCksAAGAo6EdcqS8Rpe76XWmJ709DkmcyxVWLeQ0UFytlAAAAAAAASmBITMo0NDTI87yCr6uuuipv/5aWFl199dUaP368otGoxo8fr6uvvlotLS0lGD0AAAAAAP3kP+i32C8U1ZCJL9XX1+uLX/xiXv+UKVNyttva2tTY2KhnnnlG5557ri6//HJt3bpV3/ve9/TQQw9p8+bNqq6uPkSjRlnIWn6aXWVJysSWPBdVUiQsLxy2P1e42FKlrYTkV1hK1GQiS12uylLMxZVitXLvubY2JVNrY0AhF1eqrLBtRdi2QVdhKZ4MqqPLXtuPJAW77HlNh5fTH27NqqjU0a1tdxWWOpIKtttreF2u7aXCUq9xJSk3mtRtCS1xJQAAUJb6GFM6kIhStn7HlexBB3VNAIfGkJmUGTZsmJYvX77f/VauXKlnnnlGS5cu1YoVK9L9TU1Nuvbaa7Vy5Uo1NzcXcaQAAAAAABwcL2Vfxb4GimtIxJf6yhijW265RTU1NVq2bFnOe1//+tc1fPhw/eQnP5FhiRYAAAAAACiyIbNSpqurS2vWrNGuXbs0fPhwffjDH9bJJ5+cs8+OHTv0xhtvaMaMGXkRpYqKCn3kIx/RvffeqxdffFHHHHPMoRw+BiM/ohTw8uJKCrrtSMS1WZElP65UZd9L1tjYUrzO7hOrdRWWagOKu7hSrN628Vo7IZiqdpWWqhKKVtrIUDTsYkxBO12dNPY8nV12TF3xkLres9eqd49HSs9su3nGkIsvRfalFG63bwY7XNuZcNu29Tpj8rpcJKl7XClmx5SOKCUSMil3sUJxpaztnL6esNwWAAAMVn2ILB1sXMnXr9gS358OP4fimS8sWCi6ITMp8+abb2r+/Pk5feeff77Wrl2rkSNHSrKTMpJ6nHDx+3fs2NHrpMzu3bu1e/fu/Y5p+/btfRk6AAAAAAA4DA2JSZkrrrhCjY2NOvHEExWNRvXcc8+publZGzZs0MyZM7VlyxZ5nqf33ntPkn0ocCF1dfbpqv5+Pbn55pt57gwAAAAAoHSM0ivii3oNFNWQmJTp/nyY0047Tb/97W/V2NiozZs3a/369brwwgsH7HqLFy/WzJkz97vf9u3bNWfOnAG7Loqk2xLU7stNvWBQCoUyP0vy/MpKLr7kR5ZMVTQTV6r140ouXlQfcP1y/VKixv6VS9S45aYuthSM2phQOJxUOOSqGRk7Lr/CUjxuz5vodP+MO0KK7LF9Ff+w5w0kbOvHmEIuqhTel0zHlALpykoutuSiSYrHe40rScqNLPUSV8rpL4TltgAAYLDqJa40UDGl7voUW+L7EzAkDIlJmUICgYA+85nPaPPmzdqyZYsuvPDC9AqZnlbCtLTYB3H0tJLGN2bMGI0ZM2ZgBwwAAAAAQB95xsgr8jNfin1+DPHqS/6zZNrb2yXlPjOmkP09cwYAAAAAAGCgDNmVMpL0xBNPSJIaGhok2cmWsWPHasuWLWpra8upwNTZ2alNmzZp7NixmjhxYimGi0MpaxlqetlpdrWl7O1IWAq7+FLUxZay4kqSlKr2I0sRxbvFlWK1nnvPHhp3t12iJqVUlY0DBaK2Dbk24CosGUldMXvtRMyeN+Vatds22G6vE27zFNnrhvcPd56YPY/nYkx+haVAezwdW/KjScqOLUlSIinjYkpysaV0fKlbRMmkTN/jSiy1BQAAg9l+qiv1Glnq7dj9fAcisoR+o/rSkFD2K2Wee+457d27N69/8+bNWrVqlaLRqC655BJJkud5WrhwoVpbW3Xttdfm7P+d73xHe/bs0cKFC+V5xcmGAgAAAAAA+Mp+pcydd96plStX6pxzzlFDQ4Oi0ai2bdumBx54QIFAQDfddJPGjRuX3n/p0qW67777tHLlSv3lL3/RKaecoq1bt2rDhg2aNGmSli5dWsJPAwAAAABAHxhJxV48xUKZoiv7SZnp06dr+/btevrpp/Xwww+rs7NTo0aN0mWXXaYlS5Zo6tSpOftXV1dr48aNam5u1q9+9Stt3LhRo0eP1pIlS9TU1JQTacIQVCCi5FdUUrBbFaawrXLkVUSlitzYkh9XStTa6kuxupBrg+m4UsyPK9XY1q+0lKp0fzkrkgqGbRzIC9r3/NWBCRdZSsYDMjE7roCLK4U67Haoze4bbnVtmxR9z5478m6XPSbuV0uy/enIUmcsK6bkIkoJF1HKiiz1GlfK2s7p645ltgAAYLA7kApL+4k55ejl+xAVKoHDW9lPyjQ2NqqxsbFfx9TX12vVqlVatWpVkUYFAAAAAEDxUH1paCj7Z8oAAAAAAACUo7JfKQP0qqeKSv52KJSJKUXCuce6flVVKlXtx5b8uJJ9L1ZvI0WxWr/SUqbKUqLazionqlw0qcJFlcKuIlLQyBg7jpSLKCnpxtVlzxvoDCjYbvtC7W7IfmzJtZE2e/5wa1KRFhs9Cu11OyeSuW08U2EpE1Nyy2ILVVjqJa6U05/bmd8HAAAwmBQ7ruTrb2yJ71HoD6NDUH2pf7vv2rVLv/zlL7V+/Xo9//zzevPNNzVixAhNmzZNS5cu1WmnnZaz//Lly9Xc3FzwXNFoVJ2dnQc68rLBpAwAAAAAADhoN954o1asWKEJEybo3HPP1RFHHKEdO3bonnvu0T333KPbb79ds2fPzjtu3rx5amhoyOkLhQ6P6YrD41MCAAAAAICimjp1qjZt2qSzzjorp/+RRx7ROeeco89+9rO6+OKLFY1Gc96fP3++zj777EM40sGDSRkMPVlLS/3lp5kKS7b13KyrF4lIURtJSldY8peSRm1EKVlfma6yFK+xx3XV22t01dnzZyJLmdhSqsItP41k4krZTDwgk3Bxqi7bBrvs+YIdmciSH1sKp2NLqW6tjRsF2xMKttqqS9rndk51iyb5kaV4IhNT8pc8Fqqw1D2+1BOW2gIAgMGuv5GlA4kr+Qp8N+L7FAacMYcgvtS/819yySUF+8866yxNnz5dDzzwgP72t79pypQpAzG6IYFJGQAAAAAAUFRh98zOQrGkRx55RH/6058UDAZ13HHH6WMf+1jeapqhikkZAAAAAADKTUpSD8+lHtBrSGppacnpjkaj/Zo0efXVV/Xggw9q9OjROumkk/LeX7ZsWc72mDFjtGbNGp177rn9H3OZYVIG5a/b0lIv4GXiSn61pbC71V2FJb/ikiqiUqVfWcmPL9m/PCkXX4r9v2imulK1a+vsrt0rLSUrjEzURX5Cts//O2niLkqVcBGqmKdQp4srudavrBTqsG24zSjsV1dycaWQH1fqsFGkQIetqBTojEmdNr5kOtwJ/MpK7jPlRJb8SFP3pbR9iSyxvBYAAAx2h6rCUrYeviNRaQnl7qijjsrZbmpq0vLly/t0bDwe19y5c9XV1aWVK1cq6P9/NUmTJk3SmjVr1NjYqFGjRun111/XL37xC91www2aOXOmHn/8cZ188skD+VEGHSZlAAAAAAAoM54x8or8TBn//K+99prq6urS/X1dJZNKpXTFFVdo06ZNWrRokebOnZvz/qxZs3K2J06cqG9961saNWqUrrzySl1//fX65S9/eXAfYpA7yKlgAAAAAAAwlNXV1eW8+jIpY4zRokWLtG7dOs2ZM0c33XRTn683b948hUIhbdmy5WCGXRZYKYPy5UeT/OWn/nYwKAXdz5GIa11cyW2nI0s1USVr7B8Uv7JSMGaXkiYr7LK6jv8XVKzGXiNRZQ9Px5aqXKWliG1NODNT7SXcuFwbjNsxBfyoUkemslJ2XCmnbU8p1GZjSt3jSl6XbeW3sZgUtz8bF2PqS2UlH8tqAQBAWeshetRjVKmXY/qsP3Gl/RwD9NsgrL7kS6VSWrhwoVavXq3LL79ct956qwKBvv97i0Qiqq2tVXt7+wFdv5ywUgYAAAAAAAyI7AmZyy67TGvXrs15jkxf7NixQ3v27FFDQ0NxBjmIMCkDAAAAAEC58VfKFPvVD6lUSgsWLNDq1av1iU98QuvWretxQmbfvn3661//mte/Z88eLViwQJJ0+eWX9//3UmaIL6G8eIH8uJK/7erde+FwflypwkaUTJVt/chSoiasWK39IxGrs+cLt7pKSlF73q5hnuIutpSs8NtucSV/SAlPMnYjEM+trBTsdMPMiix1jyuF2pM5bbAjnokrdWbFlCQpnnCtiyzFE1LC9fmtY7rFl3L6umNJLQAAKBcF4kdFjStlK/Cdie9XONxde+21uvXWW1VTU6Njjz1W119/fd4+s2bN0qRJk/Tuu+/q5JNP1pQpU3TSSSfpiCOO0K5du7Rhwwa9++67Ovfcc7VkyZISfIpDi0kZAAAAAADKzSB8pszOnTslSa2trfr2t79dcJ+GhgZNmjRJI0aM0L/927/p8ccf129+8xvt3btX1dXVOumkkzRnzhwtXLiw37GncsSkDAAAAAAAOGi33nqrbr311j7tW1dXpx/+8IfFHVAZYFIGg1u3JaZewLPVlSTJtZ4fW/KjSpGw5Eq0mWrbplybqLb7xP3IUm1QsVq7xDVeYw9PhTzX2u14dSa25PcZf1Vs0o8qufElPAVduijoCiAFXVwp7Coshdr9yFJKofaU63MVljq7VVjqjGfiSjF3ERdNMvHcqJJJJtNVlkwyqUKosAQAAMpaf+JKAxlVkogrYfBJKf0YhaJeA0XFg34BAAAAAABKgJUyAAAAAACUGc8YeUV+pkyxzw8mZQbM7t27tXv37py+7du3l2g0ZaxAXCm73wsG5YXdbevHlqIutuS3lRXpuFLSjyvV2H1jdS62VGPPF6+18SRJSrgKS34UyV8KmApnjcelgoIp+6bnlvN5LkkU7MrElkLp2FImrmT7M5GlQIeLK3XaiFLBCksutmT8ikoumpSOKGVHltxS2R6X0vpYUgsAAMpBDxGkgpGlQxBXst29fM/iOxaAfmJSZoDcfPPNam5uLvUwAAAAAACHg0FYfQn9x6TMAFm8eLFmzpyZ07d9+3bNmTOnRCMCAAAAAACDGZMyA2TMmDEaM2ZMqYdRvvx4UoG4kiQp6LbDYXkRlyeKZOJKkmSqMpGlRI3dJ17jV1myx8dqXKUlP7KUXVkpameB/epLflTJS+XHlPxtP+rkV1wKdRiFOt3P6ZhS0rWuwpKLLHmdsUxcqcuPK/kVllxEKZHIjyv52/7S2axlsnnLaVlCCwAAys0AV1jyj91vvNvuVKCLuBKA4mFSBgAAAACAcpMyklfkeFFfJjNxUCiJDQAAAAAAUAKslEHpZC039WNK6aWp/rZfYSmSVWGpwsaUTKXtS1W62JIfWaoNZaorVbu2xh6eqMxtU1GjlDu1CdhZYM9VVgq4qFKoPSuu5FJGmdiSPSbU6SostacU7MhUV5KkgIsoeV2uzY4sFYorSbmRpV7iSjn9Bd4DAAAY9AYortTTMf2NH1HFEmWDB/0OCayUAQAAAAAAKAFWygAAAAAAUHYOwUoZsVKm2JiUQfH1sMw0u9JSOr4UdrekH1uKZsWWJKmyQqkq+3Oy2raJ6m6Vlmo8xavtuf2YUtJvbdJJKVfAyQSyqizF7TF+9aSgiySlurxMTKnLtkG/7Ux1axOZuFKnq7LkV1bqVmHJJBJS3O6TqazklsO6ZbEmZXqPK2XtCwAAUBb2992wD/v2dsyBVEuiwhKAUmFSBgAAAACAcsMzZYYEnikDAAAAAABQAqyUQXFkV1bqvqzUvedHlhQMyAvbPJEXcbkiv9pS90pL1VElquw+iXRcyVVYqrJtokpKVNjDk6417k43blh+NSUv6aWrLHmujexzUaV2fydPwZiLJ7nKSoGYjRsFO/0KS35UKS65KkvpykrZcSUpN7JUKK5UaDsbS2gBAEC5KWKFpQOplkRcCUNCyqjoz3zZ378vHDRWygAAAAAAAJQAK2UAAAAAACg3JlX8lV2sHCs6JmUwMLotM/UCXiae1J1faclVWFI4lI4v+XElVdrcUarS9mdXWkq4mFK82o8tdau0FJFSEX8grnWr7oKuAJJfcSkQlwKuL9hl2+he+2aozbaeMQp02Z8DXS6mFPPjSi6SFPMrKyUycSUXU5KrrFSowhJxJQAAMCQNUIWlniJOVFgCMFQwKQMAAAAAQLmh+tKQwDNlAAAAAAAASoCVMjg4fiUlf2lpdmWloP+ebU3KLgstWGmpe5WlKtsm/UpL1fZWjVcGFK+210pWuNYlnlL+3RzIxJPSrVuRGnAFkfyKS8EuKdTpV1uybfQfNooU6Ii5c5h0XEmxeG7rR5LctkkmpURWdSUp/cTy9LYvO77UHUtoAQBAORmguFJPxxFXAgqg+tKQwEoZAAAAAACAEmClDAAAAAAA5YZnygwJTMqg/7KWnPpLS9OVlrIqK3kRVwLJX37q4jte1OWNIpmKS6kKP7bkx5Vc1aVKv8KSbRMVnpJRez4/rmT85FRWZMkzuX1BlzYKdpmcNtRpFOywy1dDHXbn4J52e2xXLDNuv5KSH01K+JWVUjmfzSSTmZ+7VVby5SylZeksAAAoV/2pmtSHyFK2XqNHdocBOQYASo1JGQAAAAAAyo3RIVgpU9zTg2fKAAAAAAAAlAQrZbB/3ZabegEvt8qSJC/sbqWQbb1opqJS+riEyxJFbVTJ+G1lOF1lKVlpj0+42FIy6tqIXQJrglnn86NJbiWqX2HJS0qBhIspuQRSJrZkdwp2+m1CwQ4bRfK6bMbJa2m1B8X9CkupTOUkP76USqXfcx2uMbk/F8LSWQAAUI76E1fqwzH9QoUlAEMUkzIAAAAAAJQbHvQ7JBBfAgAAAAAAKAFWyqBnfkTJX46ave3HlMI2duT5lZT8iksVUZkqF19yUR+vy0V/Kuy+6YpLFUElK+z5/LhSKpQbV/KrKQUSkpc0OX1+bMmPLAViRsG4+zkdV3KVlbpcBajOhHs/LsVcTMm1ps1WX8qJKvkzxN0rK/l6iyyxdBYAAJSj/USPCkaWBiKu1Mt3J+JKQJZUSlKR7/sU/66KjZUyAAAAAAAAJcBKGQAAAAAAyg3PlBkSmJQZILt379bu3btz+rZv316i0RygQlWWsvpzKi1lV1mS0hWVVFkhSUpVRZSstn2BuF3yFvC6JEkmauNLJmLPlwoH03El+Y0fV3LJIi+R+WPguWWrAVcQKRNbSqXbgIspBfyKSjG/wpJt05WVYvFMTCnu2i47Tj+qJGUtlaWyEgAAOBwUiCH1WGGph/37rMD3p15jSj0cAwDliEmZAXLzzTerubm51MMAAAAAABwOWCkzJDApM0AWL16smTNn5vRt375dc+bMKdGIAAAAAADAYMakzAAZM2aMxowZU+phHDgvULjKkiT5sSU/shSJZOJKrtqSqYy61kaTktURJars/n7lIy9u21TYns8EM0tg/UiSXKoomMidkfUrLHkpk44yBRJ+XMmd348oxZNZMaVucSXXGhdZUjIl47/nZoH9GFP2stieqi0BAAAMCb3Ej3qNLe3n2DxUVgIGTspIKvJKlv1FCXHQqL4EAAAAAABQAqyUAQAAAACgzBiTkinyKrJinx9MyiA7qtStypKCbjtsI0lexLbKii+ZStdW2DZZaW+pZEVQyQp3fNItefPs0lfPf1iU6w8oJS9T6Mjto5xjvaT9Y+AlUukYlF8dyXPxpZyokh9PStj3MnElt+1XVkqZzM9O923byR8jAAAwBB1MbMnXz+9JVFYCgAwmZQAAAAAAKDfGFP+ZL1RfKjqeKQMAAAAAAFACrJQ53HRboppdccmPLXlhd1v41Zb8bVdpSdGIjB9fitj3UhFXUSlkz28CXnpW1a+SJNd6Kdd6WUti/RnYdJUl90MqE1uy50hmoknx3EhSTlTJjyn553HxJ385bHrZrElRWQkAABxe+lAtqc/Rpf3Yb1TJ7jQg1wIOO+YQVF9ipUzRsVIGAAAAAACgBFgpAwAAAABAuUmlJK/IK81YyVZ0TMocLnqJLUm24lKPsSVXfSnThiQXU1Kg22Irt7rNRpbse4HOpGtj7pru2unIksn62Y8b5UaS0v2JRKY6UjJ33+yKSoViSjnbOWPmDw0AADgM9CG2dCD6FFHK7FyUMQBAuSK+BAAAAAAAUAKslAEAAAAAoNzwoN8hgUmZoa4PsSVJUjAg+dWX/D4XY0pHlNLHenlxIy9u3wu65avG8xR0uwdbO+0PHa5NR4qy2mRuTCkniiTlRpSyY09S3yoq+VgyCwAADjcHEFvqVyQp98ADOw4ADlNMygAAAAAAUGZMKiVT5Af9GiZai45nygAAAAAAAJQAK2WGsqylqnmxJX/btV4gkKmK5L/Xnb+MNZGU50eIksYdnyiwv7tke4f9oc226dnW7BhT99hSbxGlbrO1VFQCAAAoAb5vAaXFM2WGBFbKAAAAAAAAlAArZQAAAAAAKDcpI3mslCl3TMoMRX2JLfn7eF5uK+VVPErz/0EmEpnYk+cV3idlMj93dtmujo5e9u1WQSm9Ty9RJZbMAgAA7J//nakvVZj4fgUAhxSTMgAAAAAAlBtjlH6QZ1GvgWLimTIAAAAAAAAlwEqZoaQvS1K7M1kVkFxcKT0X6pavekk3+9pTVSYpHXnKqazkzm1iMdvnn79bRaWc4RSqpNTDvgAAAOgHvk8BQ4pJGZkiP1PGsFKm6FgpAwAAAAAAUAKslAEAAAAAoNyYlIr/TBlW2BUbK2WGMK+3uJFJ2VfKSCkjk0ymX0ok7CsWl2JxmVjMvjq77KujM++V6uhQqqMjt9/fP56QiSeUcq/0tVIm8/Kv7Y+r+wsAAAAAUBaefPJJXXDBBRo+fLiqq6s1depU3XbbbaUe1qDEShkAAAAAAMrMYH2mzMaNGzVjxgxFIhF98pOfVH19ve666y59+tOf1s6dO/WNb3yjCCMtX0zKDGEmZTKrZdxqE5PKXRzlBdwqlGTWcft7YHDWypW+PJi3twf7AgAAAACGhkQioYULF8rzPG3atEkf+tCHJElNTU0644wz1NTUpE984hM65phjSjzSwYP40gDZvXu3nn766ZzX9u3bSz0sAAAAAMBQ1NOjHwb61Q9//OMf9dJLL+lTn/pUekJGkmpra3XNNdcokUho9erVA/2bKGuslBkgN998s5qbmwu+16aWrDrTxZQ/x5a/mq3bSplUgefOeL08i0bKlNFWL8vZslfKmPw+AAAAAIDVphZJUkdHR5+PSShe9P+fmVBcktTS0pLTH41GFY1G8/bfuHGjJOm8887Le8/ve/jhhwd4lOWNSZkBsnjxYs2cOTOnb/369brmmmv0rJ48NIMo9A8yWaAPAAAAADDo7Ny5U9OmTet1n0gkotGjR2vzm+sPyZhqamp01FFH5fQ1NTVp+fLlefvu2LFDkgrGk4YPH66RI0em94HFpMwAGTNmjMaMGZPTN27cOP3TP/2TGhoaVFlZWaKR9d327ds1Z84crVu3Tscff3yph9Nn5TjuchyzxLgPpXIcs1Se4y7HMUvlOe5yHLPEuA+lchyzVJ7jLscxS+U57nIcs8S4DyV/zNddd51mzJix3/0rKir08ssvKxaLHYLR2XSE1y1NUWiVjCS99957kqT6+vqC79fV1en1118f2AGWOSZlimjkyJH69Kc/Xeph9Nvxxx+vyZMnl3oY/VaO4y7HMUuM+1AqxzFL5TnuchyzVJ7jLscxS4z7UCrHMUvlOe5yHLNUnuMuxzFLjPtQuuCCCzRy5Mg+7VtRUaGKiooijwiHAg/6BQAAAAAAB81fIeOvmOmupaWlx1U0hysmZQAAAAAAwEHznyVT6Lkxe/bs0TvvvEM57G6YlAEAAAAAAAetsbFRkvTAAw/kvef3+fvAYlIGaWPGjFFTU1PeA4sHu3IcdzmOuZyV4++7HMcsle+4y1E5/q7LccxS+Y67HJXr77ocx12OYy5X5fq7Ztw4EOecc46OPvpo3XbbbXrmmWfS/fv27dN1112nUCik+fPnl2x8g5FnjClyZXMAQ8XTTz+tU045RU899VTZPTgN6An3NYYq7m0MRdzXGIqG2n390EMPacaMGYpGo7r88stVV1enu+66Sy+//LKuv/56ffOb3yz1EAcVqi8BAAAAAIABMX36dG3evFlNTU268847FYvFdOKJJ+q6664ry+rExcakDAAAAAAAGDBTp07Vhg0bSj2MssAzZQAAAAAAAEqASRkAfcaD0zAUcV9jqOLexlDEfY2hiPv68MaDfgEAAAAAAEqAlTIAAAAAAAAlwKQMAAAAAABACTApAwAAAAAAUAJMygAAAAAAAJQAkzIAAAAAAAAlwKQMcJhZt26dFi9erClTpigajcrzPN1666097t/S0qKrr75a48ePVzQa1fjx43X11VerpaWlx2Nuu+02TZ06VdXV1Ro+fLguuOAC/fnPfy7CpwGkXbt26fvf/77OO+88jRs3TpFIRKNHj9all16qJ554ouAx3NcY7Pbu3avPf/7zOuOMMzR69GhFo1G9//3v10c/+lH9+te/VqHimdzXKEcrV66U53nyPE+PP/54wX24t1EOGhoa0vdy99dVV12Vtz/3NXyUxAYOMw0NDXrllVc0cuRIVVdX65VXXtHq1as1f/78vH3b2tp05pln6plnntG5556ryZMna+vWrfr973+vSZMmafPmzaqurs455oYbbtA3v/lNjRs3Th//+MfV2tqqX/ziF+rs7NT999+vs88++9B8UBw2vva1r2nFihWaMGGCGhsbdcQRR2jHjh265557ZIzR7bffrtmzZ6f3575GOXjxxRc1adIknX766Zo4caJGjBiht956S7/5zW/01ltvadGiRfrv//7v9P7c1yhH27dv14c+9CGFQiG1tbXpscce0+mnn56zD/c2ykVDQ4P27t2rL37xi3nvTZkyRRdddFF6m/saOQyAw8of/vAHs3PnTmOMMd/5zneMJLN69eqC+y5btsxIMkuXLi3Yv2zZspz+//3f/zWhUMgce+yxZu/even+bdu2maqqKjNhwgQTj8cH9gPhsPfrX//abNq0Ka9/06ZNJhwOmxEjRpjOzs50P/c1ykEikSh4X7W0tJgTTjjBSDLbtm1L93Nfo9wkEglz6qmnmqlTp5o5c+YYSeaxxx7L2497G+Vi/PjxZvz48X3al/sa2ZiUAQ5jvU3KpFIpM3bsWFNTU2NaW1tz3uvo6DDDhw8373//+00qlUr3f/3rXzeSzJo1a/LOd9VVVxlJ5v777x/wzwH05LzzzjOSzJNPPmmM4b7G0LBkyRIjydxzzz3GGO5rlKdvf/vbJhKJmG3btpl58+YVnJTh3kY56eukDPc1uuOZMgAK2rFjh9544w1NmzYtb/lkRUWFPvKRj2jXrl168cUX0/0bN26UJJ133nl555sxY4Yk6eGHHy7eoIFuwuGwJCkUCknivkb56+zs1B//+Ed5nqcTTjhBEvc1ys+2bdvU3Nysb33rWzrxxBN73I97G+Wmq6tLa9as0Q033KAf/ehH2rp1a94+3NfoLlTqAQAYnHbs2CFJOuaYYwq+7/fv2LEj5+eamhqNHj261/2BQ+HVV1/Vgw8+qNGjR+ukk06SxH2N8rN37159//vfVyqV0ltvvaX169frtddeU1NTU979x32NcpBIJDR//nwdf/zx+trXvtbrvtzbKDdvvvlm3nMazz//fK1du1YjR46UxH2NfEzKACjovffekyTV19cXfL+uri5nP//nI444os/7A8USj8c1d+5cdXV1aeXKlQoGg5K4r1F+9u7dq+bm5vR2OBzWd7/7XX3pS19K93Ffo5zccMMN2rp1q5544on0asaecG+jnFxxxRVqbGzUiSeeqGg0queee07Nzc3asGGDZs6cqS1btsjzPO5r5CG+BAAYUlKplK644gpt2rRJixYt0ty5c0s9JOCANTQ0yBijRCKhl19+Wddee62++c1v6tJLL1UikSj18IB+2bp1q66//np9+ctf1uTJk0s9HGBALVu2TI2NjRo5cqRqa2t12mmn6be//a3OPPNMPfbYY1q/fn2ph4hBikkZAAX5s/c9zbq3tLTk7Of/3J/9gYFmjNGiRYu0bt06zZkzRzfddFPO+9zXKFfBYFANDQ362te+puuvv1533323fvzjH0vivkb5mDdvniZMmKDly5f3aX/ubZS7QCCgz3zmM5KkLVu2SOK+Rj4mZQAUtL98aqE87DHHHKPW1la9+eabfdofGEipVEoLFizQT3/6U11++eW69dZbFQjk/s8c9zWGAv9Bj/6DH7mvUS62bt2q559/XhUVFfI8L/1as2aNJOmMM86Q53m65557JHFvY2jwnyXT3t4uifsa+ZiUAVDQMccco7Fjx2rLli1qa2vLea+zs1ObNm3S2LFjNXHixHR/Y2OjJOmBBx7IO9/999+fsw8wkFKplBYuXKjVq1frsssu09q1a9PPkcnGfY2h4I033pCUqSrGfY1ysWDBgoIv//9Mzpw5UwsWLFBDQ4Mk7m0MDU888YQkcV+jZ6WuyQ2gdL7zne8YSWb16tUF31+2bJmRZJYuXVqwf9myZTn9L7zwggmFQubYY481e/fuTfdv27bNVFVVmQkTJph4PD7gnwOHt2QyaebPn28kmU984hP7vce4r1EO/vKXv+Tcb753333XTJo0yUgya9euTfdzX6OczZs3z0gyjz32WN573NsoB88++6zZs2dPXv8jjzxiKioqTDQaNa+88kq6n/sa2TxjjCnFZBCA0rjlllu0efNmSdLf/vY3Pf3005o2bVp6Nn7WrFmaNWuWJKmtrU1nnnmmnnnmGZ177rk65ZRTtHXrVm3YsEGTJk3S5s2bVV1dnXP+b3/72/rWt76lcePG6eMf/7ja2tp0++23q6OjQ/fff7+mT59+SD8vhr7ly5erublZNTU1+sIXvpBePZBt1qxZmjRpkiTua5SHL37xi7rllls0ffp0jR8/XtXV1XrllVf0u9/9Tq2trbr00kt15513piN63NcoZ/Pnz9eaNWv02GOP6fTTT895j3sb5WD58uVauXKlzjnnHDU0NCgajWrbtm164IEHFAgEdNNNN2nhwoXp/bmvkaPUs0IADi3/v0b19GpqasrZf+/evWbJkiXmqKOOMuFw2Bx11FFmyZIlBf8Lrm/dunVmypQpprKy0tTX15vzzz/f/OlPfyryJ8Phan/3tAqsBuO+xmD3yCOPmPnz55vjjjvO1NXVmVAoZI444ghz/vnnm9tuu82kUqm8Y7ivUa56WyljDPc2Br+NGzea2bNnm4kTJ5ra2loTDofNkUceaT75yU+aJ554ouAx3NfwsVIGAAAAAACgBHjQLwAAAAAAQAkwKQMAAAAAAFACTMoAAAAAAACUAJMyAAAAAAAAJcCkDAAAAAAAQAkwKQMAAAAAAFACTMoAAAAAAACUAJMyAAAAAAAAJcCkDAAAAAAAQAkwKQMAAEqioaFBDQ0NpR7GoOJ5ns4+++xSDwMAABwiTMoAADCAdu7cKc/zen1NmjSp1MMcMjZu3CjP87R8+fJSDwUAAKDfQqUeAAAAQ9GECRM0Z86cgu+NHj36EI9mcPqf//mfUg8BAACgpJiUAQCgCCZOnMjqjf2YMGFCqYcAAABQUsSXAAAoMf85Im+//bauuOIKHXHEEaqsrNTpp5+ujRs3Fjxm3759ampq0oknnqjKykoNGzZM559/vjZv3py379lnny3P89TV1aVly5Zp4sSJCofDOZNGd911l6ZMmaLKykqNGjVKixYt0p49e/Ke+zJv3jx5nqcnn3yy4LiWLl0qz/N099137/dzF3qmzPLly+V5njZu3Kg777xTkydPVmVlpcaMGaPPf/7z6ujoyNl3+vTpkqTm5uaciNjOnTvT+8ViMa1atUqTJ09WdXW1amtrddZZZ+m+++7LG9P8+fPleZ7+/ve/63vf+55OPPFERaNRzZ8/X9dee608z9PatWsLfp6f//zn8jxP1113Xbrv7rvv1uWXX66JEyeqqqpK9fX1Ouuss/TrX/96v78fAAAw9LFSBgCAQWDv3r2aNm2a6urq9OlPf1pvvfWW7rjjDs2YMUNPPfWUPvjBD6b3/cc//qGPfOQjevbZZ3XWWWdpxowZeu+993Tvvfdq+vTp+uUvf6lZs2blXeOSSy7R1q1bNWPGDI0YMUJHH320JOmnP/2pFixYoGHDhulf/uVfVF9fr/Xr1+vcc89VPB5XOBxOn2Px4sX62c9+ph//+Mc69dRTc84fj8f1s5/9TKNHj9Y///M/H9Tv4z//8z+1YcMGXXzxxTr77LP1+9//XjfeeKPeffdd/fznP5dkJ5t27typNWvWqLGxMecBucOGDZMkdXV16fzzz9fGjRv1oQ99SAsWLFA8Htfvfvc7XXzxxbrxxhv17//+73nX/9znPqfHH39cF154oS666CKNGjVKs2bNUlNTk9atW6e5c+fmHbNu3Tp5npcTW/v617+uSCSiM888U2PGjNHbb7+t++67Tx//+Mf1gx/8QJ/73OcO6vcEAADKnAEAAAPm5ZdfNpLMhAkTTFNTU8HXhg0bco6RZCSZf/3XfzXJZDLdf8sttxhJZvHixTn7f+pTnzKSzE9/+tOc/jfffNMcddRR5n3ve5/p6OhI9zc2NhpJZtKkSebdd9/NOWbPnj2mpqbG1NbWmpdeeindH4/Hzcc+9jEjyYwfPz7nmA9+8IOmtrbWtLa25vTfddddRpL56le/2qff1fjx4/PO3dTUZCSZ+vp68/zzz6f729vbzbHHHms8zzO7du1K9z/00ENGkmlqaip4jW984xtGklm+fLlJpVLp/paWFjNlyhQTiURyzjdv3jwjyRx55JHmlVdeyTvftGnTTDAYNLt3787p/7//+z8TCoXMmWeemdOf/Tv17du3z5x00kmmvr7etLW15bwnyTQ2Nhb8LAAAYOghvgQAQBG89NJLam5uLvj6/e9/n7d/dXW1VqxYoUAg8z/N8+bNUygUyokKvfPOO7rjjjt0zjnn6DOf+UzOOUaNGqWvfOUrevvtt/Xggw/mXaO5uVkjRozI6bv33nvV2tqqhQsXplfOSFIoFMqJ4WS78sortW/fPt1xxx05/bfccos8z9PChQt7+c30zRe+8AV94AMfSG9XVlbq8ssvlzFGTz31VJ/OkUql9KMf/UgTJ07UsmXL5Hle+r3a2lotW7ZMsVhMd911V96xX/nKVzRu3Li8/jlz5iiZTOr222/P6b/99tuVSCTyHu6c/Tv11dTUaP78+Xrvvfd6jIEBAIDDA/ElAACKYMaMGQUnX3pyzDHHqKamJqcvFApp1KhR2rt3b7rvySefVDKZVGdnZ8EHCe/YsUOS9Pzzz+uiiy7KeW/q1Kl5+2/dulWS9OEPfzjvvalTpyoUyv+qMHfuXH31q1/VLbfcoiuuuEKStGvXLt1///1qbGzUxIkTe/+wfTB58uS8viOPPFKScn4fvXnhhRe0Z88ejR07Vs3NzXnvv/3225Ls76q7Qr8rSbrsssv0hS98QevWrdOSJUvS/WvXrlUkEtHs2bNz9n/rrbf0H//xH9qwYYNeeeWVnGfiSNIbb7zRp88CAACGJiZlAAAYBOrr6wv2h0IhJZPJ9PY//vEPSdKWLVu0ZcuWHs/X1taW1zdq1Ki8vpaWFknS+973vrz3AoGARo4cmdc/bNgwzZ49W2vWrNFzzz2nE044QatXr1YymdSiRYt6HFN/FPp9+BNE2b+P3vi/q2effVbPPvtsj/v19XclScOHD9eFF16ou+++W88//7yOO+44vfD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}, “metadata”: {}, “output_type”: “display_data”, “jetTransient”: {

“display_id”: null

}

}

], “execution_count”: 11

}, {

“cell_type”: “markdown”, “metadata”: {}, “source”: [

“Summed over channel subset $[20,200]$:”

]

}, {

“cell_type”: “code”, “metadata”: {

“ExecuteTime”: {

“end_time”: “2026-03-06T11:21:25.499859Z”, “start_time”: “2026-03-06T11:21:25.429108Z”

}

}, “source”: [

“arfplot = XpsiPlot.plot_arf([XMM.instrument, NICER.instrument])n”, “XpsiPlot.veneer((0.1, 0.5), (100, 500), arfplot)”

], “outputs”: [

{

“data”: {

“text/plain”: [

“<Figure size 700x700 with 1 Axes>”

], “image/png”: 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”

}, “metadata”: {}, “output_type”: “display_data”, “jetTransient”: {

“display_id”: null

}

}

], “execution_count”: 12

}, {

“cell_type”: “markdown”, “metadata”: {}, “source”: [

“### Pile-up”

]

}, {

“cell_type”: “markdown”, “metadata”: {}, “source”: [

“Some X-ray detectors (e.g., CCD) may suffer from pile-up, which happens when more than one photon is incident on the same pixel of the detectors within one read-out time (or frame time). CCD can take several seconds (e.g., up to 3.2 sec for Chandra ACIS-I) to completely read all pixels. During this time, bright X-ray sources may cause two or more X-ray photons to fall on the same pixels. The detectors will therefore account for a single event with energy equal to the sum of the energies of the incident photons during that readout time. This distorts the X-ray spectrum to higher energies. For a given readout time, the brighter the X-ray source, the stronger the effects of pile-up.n”, “n”, “Pile-up can be mitigated by reducing the CCD read-out time with a faster mode, for ex., by restricting the readout area to a sub-portion of the CCD. For Chandra, the readout time can be reduced to 0.4 sec by selecting 1/8 of the CCD to be used.n”, “n”, “Nonetheless, if the pile-up effects are still presents (e.g., 1 or 2 % of photons are piled-up), a correction to the spectral model might be necessary to limit biases in the inferred spectral parameters. To do so, a statistical accounting of piled-up photons can account for these effects. Mathematically, this is means that pile-up correction function (see [Davis 2001](https://ui.adsabs.harvard.edu/abs/2001ApJ…562..575D/abstract) ) needs to be convolved first to ARF * spectral_model and the result to RMF. To perform this efficiently for inferrence in X-PSI, this product of convolutions is done in Fourier space.n”, “n”, “In practice, the InstrumentPileup class will do all this in X-PSI. It works as the Instrument class, but it includes two extra parameter: n”, “- grade_migration, which gives the probability that an event with two or more photons was not rejected, i.e., that it corresponds to a piled-up event.n”, “- psf_fraction, which corresponds to the fraction of the telescope point spread fonction in which pile-up is consider (fixed by default at 95%, and can be fitted).n”, “n”, “So grade_migration is the only parameter of the pile-up model that really needs to be fitted. Two other (fixed) parameters, frame_time and frac_expo, are read directly from the header of the data file (this requires to load the data from a FITS file).”

]

}, {

“cell_type”: “markdown”, “metadata”: {}, “source”: [

“### Signal”

]

}, {

“cell_type”: “code”, “metadata”: {

“ExecuteTime”: {

“end_time”: “2026-03-06T11:21:25.527164Z”, “start_time”: “2026-03-06T11:21:25.507088Z”

}

}, “source”: [

“from xpsi.likelihoods.default_background_marginalisation import eval_marginal_likelihoodn”, “from xpsi.likelihoods.default_background_marginalisation import precomputationn”, “n”, “class CustomSignal(xpsi.Signal):n”, “ """ A custom calculation of the logarithm of the likelihood.n”, “ We extend the xpsi.Signal.Signal class to make it callable.n”, “ We overwrite the body of the __call__ method. The docstring for then”, “ abstract method is copied.n”, “ """n”, “n”, “ def __init__(self, workspace_intervals = 1000, epsabs = 0, epsrel = 1.0e-8,n”, “ epsilon = 1.0e-3, sigmas = 10.0, support = None, *args, **kwargs):n”, “ """ Perform precomputation. """n”, “n”, “ super(CustomSignal, self).__init__(*args, **kwargs)n”, “n”, “ try:n”, “ self._precomp = precomputation(self._data.counts.astype(np.int32))n”, “ except AttributeError:n”, “ print(‘Warning: No data… can synthesise data but cannot evaluate a ‘n”, “ ‘likelihood function.’)n”, “ else:n”, “ self._workspace_intervals = workspace_intervalsn”, “ self._epsabs = epsabsn”, “ self._epsrel = epsreln”, “ self._epsilon = epsilonn”, “ self._sigmas = sigmasn”, “ if support is not None:n”, “ self._support = supportn”, “ else:n”, “ self._support = -1.0 * np.ones((self._data.counts.shape[0],2))n”, “ self._support[:,0] = 0.0n”, “ n”, “ @propertyn”, “ def support(self):n”, “ return self._supportn”, “n”, “ @support.settern”, “ def support(self, obj):n”, “ self._support = objn”, “n”, “ def __call__(self, **kwargs):n”, “ self.loglikelihood, self.expected_counts, self.background_signal, self.background_given_support = \n”, “ eval_marginal_likelihood(self._data.exposure_time,n”, “ self._data.phases,n”, “ self._data.counts,n”, “ self._signals,n”, “ self._phases,n”, “ self._shifts,n”, “ self._precomp,n”, “ self._support,n”, “ self._workspace_intervals,n”, “ self._epsabs,n”, “ self._epsrel,n”, “ self._epsilon,n”, “ self._sigmas,n”, “ kwargs.get(‘llzero’))”

], “outputs”: [], “execution_count”: 13

}, {

“cell_type”: “markdown”, “metadata”: {}, “source”: [

“Note that if we need an additional overall phase shift parameter for additional instruments whose recorded data are phase resolved, then it could be passed to the subclass above for the signal associated with a given telescope.”

]

}, {

“cell_type”: “code”, “metadata”: {

“ExecuteTime”: {

“end_time”: “2026-03-06T11:21:25.535259Z”, “start_time”: “2026-03-06T11:21:25.529992Z”

}

}, “source”: [

“NICER.signal = CustomSignal(data = NICER.data,n”, “ instrument = NICER.instrument,n”, “ background = None,n”, “ interstellar = None,n”, “ workspace_intervals = 1000,n”, “ epsrel = 1.0e-8,n”, “ epsilon = 1.0e-3,n”, “ sigmas = 10.0)”

], “outputs”: [

{

“name”: “stdout”, “output_type”: “stream”, “text”: [

“Creating parameter:n”, “ > Named "phase_shift" with fixed value 0.000e+00.n”, “ > The phase shift for the signal, a periodic parameter [cycles].n”

]

}

], “execution_count”: 14

}, {

“cell_type”: “code”, “metadata”: {

“ExecuteTime”: {

“end_time”: “2026-03-06T11:21:25.557692Z”, “start_time”: “2026-03-06T11:21:25.542537Z”

}

}, “source”: [

“XMM.signal = CustomSignal(data = XMM.data,n”, “ instrument = XMM.instrument,n”, “ background = None,n”, “ interstellar = None,n”, “ support = None,n”, “ workspace_intervals = 1000,n”, “ epsrel = 1.0e-8,n”, “ epsilon = 1.0e-3,n”, “ sigmas = 10.0)”

], “outputs”: [

{

“name”: “stdout”, “output_type”: “stream”, “text”: [

“Creating parameter:n”, “ > Named "phase_shift" with fixed value 0.000e+00.n”, “ > The phase shift for the signal, a periodic parameter [cycles].n”

]

}

], “execution_count”: 15

}, {

“cell_type”: “markdown”, “metadata”: {}, “source”: [

“### Star”

]

}, {

“cell_type”: “code”, “metadata”: {

“ExecuteTime”: {

“end_time”: “2026-03-06T11:21:25.571379Z”, “start_time”: “2026-03-06T11:21:25.558123Z”

}

}, “source”: [

“bounds = dict(distance = (None, None), # (Earth) distancen”, “ mass = (None, None), # massn”, “ radius = (None, None), # equatorial radiusn”, “ cos_inclination = (None, None)) # (Earth) inclination to rotation axisn”, “n”, “values = dict(frequency=300.0 ) # spin frequencyn”, “n”, “spacetime = xpsi.Spacetime(bounds=bounds, values=values)”

], “outputs”: [

{

“name”: “stdout”, “output_type”: “stream”, “text”: [

“Creating parameter:n”, “ > Named "frequency" with fixed value 3.000e+02.n”, “ > Spin frequency [Hz].n”, “Creating parameter:n”, “ > Named "mass" with bounds [1.000e-03, 3.000e+00].n”, “ > Gravitational mass [solar masses].n”, “Creating parameter:n”, “ > Named "radius" with bounds [1.000e+00, 2.000e+01].n”, “ > Coordinate equatorial radius [km].n”, “Creating parameter:n”, “ > Named "distance" with bounds [1.000e-02, 3.000e+01].n”, “ > Earth distance [kpc].n”, “Creating parameter:n”, “ > Named "cos_inclination" with bounds [-1.000e+00, 1.000e+00].n”, “ > Cosine of Earth inclination to rotation axis.n”

]

}

], “execution_count”: 16

}, {

“cell_type”: “code”, “metadata”: {

“ExecuteTime”: {

“end_time”: “2026-03-06T11:21:25.586288Z”, “start_time”: “2026-03-06T11:21:25.571704Z”

“execution_count”: null, “outputs”: [

{

“name”: “stdout”, “output_type”: “stream”, “text”: [

“Creating parameter:n”, “ > Named "super_colatitude" with bounds [0.000e+00, 3.142e+00].n”, “ > The colatitude of the centre of the superseding region [radians].n”, “Creating parameter:n”, “ > Named "super_radius" with bounds [0.000e+00, 1.571e+00].n”, “ > The angular radius of the (circular) superseding region [radians].n”, “Creating parameter:n”, “ > Named "phase_shift" with bounds [-5.000e-01, 5.000e-01].n”, “ > The phase of the hot region, a periodic parameter [cycles].n”, “Creating parameter:n”, “ > Named "super_temperature" with bounds [3.000e+00, 7.600e+00].n”, “ > log10(superseding region effective temperature [K]).n”

]

}

}, “source”: [

“bounds = dict(super_colatitude = (None, None),n”, “ super_radius = (None, None),n”, “ phase_shift = (-0.5, 0.5),n”, “ super_temperature = (None, None))n”, “n”, “# a simple circular, simply-connected spotn”, “primary = xpsi.HotRegion(bounds=bounds,n”, “ values={}, # no initial values and no derived/fixedn”, “ symmetry=True,n”, “ omit=False,n”, “ cede=False,n”, “ concentric=False,n”, “ sqrt_num_cells=32,n”, “ min_sqrt_num_cells=10,n”, “ max_sqrt_num_cells=64,n”, “ num_leaves=100,n”, “ num_rays=200,n”, “ prefix=’p’) # unique prefix needed because >1 instance”

]

}, {

“cell_type”: “code”, “execution_count”: null, “metadata”: {}, “outputs”: [

{

“name”: “stdout”, “output_type”: “stream”, “text”: [

“Creating parameter:n”, “ > Named "super_colatitude" with bounds [0.000e+00, 3.142e+00].n”, “ > The colatitude of the centre of the superseding region [radians].n”, “Creating parameter:n”, “ > Named "super_radius" with bounds [0.000e+00, 1.571e+00].n”, “ > The angular radius of the (circular) superseding region [radians].n”, “Creating parameter:n”, “ > Named "phase_shift" with bounds [-5.000e-01, 5.000e-01].n”, “ > The phase of the hot region, a periodic parameter [cycles].n”, “Creating parameter:n”, “ > Named "super_temperature" with bounds [3.000e+00, 7.600e+00].n”, “ > log10(superseding region effective temperature [K]).n”

]

}

], “execution_count”: 17

}, {

“cell_type”: “code”, “metadata”: {

“ExecuteTime”: {

“end_time”: “2026-03-06T11:21:25.601217Z”, “start_time”: “2026-03-06T11:21:25.592233Z”

}

}, “source”: [

“class derive(xpsi.Derive):n”, “ def __init__(self):n”, “ """n”, “ We can pass a reference to the primary here insteadn”, “ and store it as an attribute if there is risk ofn”, “ the global variable changing.n”, “ n”, “ This callable can for this simple case also ben”, “ achieved merely with a function instead of a magicn”, “ method associated with a class.n”, “ """n”, “ passn”, “n”, “ def __call__(self, boundto, caller = None):n”, “ # one way to get the required referencen”, “ global primary # unnecessary, but for clarityn”, “ return primary[‘super_temperature’] - 0.2n”, “ n”, “bounds[‘super_temperature’] = None # declare fixed/derived variablen”, “n”, “secondary = xpsi.HotRegion(bounds=bounds, # can otherwise use same boundsn”, “ values={‘super_temperature’: derive()}, # create a callable valuen”, “ symmetry=True,n”, “ omit=False,n”, “ cede=False,n”, “ concentric=False,n”, “ sqrt_num_cells=32,n”, “ min_sqrt_num_cells=10,n”, “ max_sqrt_num_cells=100,n”, “ num_leaves=100,n”, “ num_rays=200,n”, “ is_antiphased=True,n”, “ prefix=’s’) # unique prefix needed because >1 instance”

], “outputs”: [

{

“name”: “stdout”, “output_type”: “stream”, “text”: [

“Creating parameter:n”, “ > Named "super_colatitude" with bounds [0.000e+00, 3.142e+00].n”, “ > The colatitude of the centre of the superseding region [radians].n”, “Creating parameter:n”, “ > Named "super_radius" with bounds [0.000e+00, 1.571e+00].n”, “ > The angular radius of the (circular) superseding region [radians].n”, “Creating parameter:n”, “ > Named "phase_shift" with bounds [-5.000e-01, 5.000e-01].n”, “ > The phase of the hot region, a periodic parameter [cycles].n”, “Creating parameter:n”, “ > Named "super_temperature" that is derived from ulterior variables.n”, “ > log10(superseding region effective temperature [K]).n”

]

}

], “execution_count”: 18

}, {

“cell_type”: “code”, “metadata”: {

“ExecuteTime”: {

“end_time”: “2026-03-06T11:21:25.614389Z”, “start_time”: “2026-03-06T11:21:25.607388Z”

}

}, “source”: [

“from xpsi import HotRegionsn”, “n”, “hot = HotRegions((primary, secondary))”

], “outputs”: [], “execution_count”: 19

}, {

“cell_type”: “code”, “metadata”: {

“ExecuteTime”: {

“end_time”: “2026-03-06T11:21:25.618740Z”, “start_time”: “2026-03-06T11:21:25.614608Z”

}

}, “source”: [

“class CustomPhotosphere(xpsi.Photosphere):n”, “ """ Implement method for imaging."""n”, “ n”, “ def _global_variables(self):n”, “ n”, “ return np.array([self[‘p__super_colatitude’],n”, “ self[‘p__phase_shift’] * _2pi,n”, “ self[‘p__super_radius’],n”, “ self[‘p__super_temperature’],n”, “ self[‘s__super_colatitude’],n”, “ (self[‘s__phase_shift’] + 0.5) * _2pi,n”, “ self[‘s__super_radius’],n”, “ self.hot.objects[1][‘s__super_temperature’]])”

], “outputs”: [], “execution_count”: 20

}, {

“cell_type”: “code”, “metadata”: {

“ExecuteTime”: {

“end_time”: “2026-03-06T11:21:25.623248Z”, “start_time”: “2026-03-06T11:21:25.618923Z”

}

}, “source”: [

“photosphere = CustomPhotosphere(hot = hot, elsewhere = None,n”, “ values=dict(mode_frequency = spacetime[‘frequency’]))”

], “outputs”: [

{

“name”: “stdout”, “output_type”: “stream”, “text”: [

“Creating parameter:n”, “ > Named "mode_frequency" with fixed value 3.000e+02.n”, “ > Coordinate frequency of the mode of radiative asymmetry in then”, “photosphere that is assumed to generate the pulsed signal [Hz].n”

]

}

], “execution_count”: 21

}, {

“cell_type”: “code”, “metadata”: {

“ExecuteTime”: {

“end_time”: “2026-03-06T11:21:25.635392Z”, “start_time”: “2026-03-06T11:21:25.629553Z”

}

}, “source”: [

“star = xpsi.Star(spacetime = spacetime, photospheres = photosphere)”

], “outputs”: [], “execution_count”: 22

}, {

“cell_type”: “code”, “metadata”: {

“ExecuteTime”: {

“end_time”: “2026-03-06T11:21:25.642436Z”, “start_time”: “2026-03-06T11:21:25.635575Z”

}

}, “source”: [

“likelihood = xpsi.Likelihood(star = star, signals = [NICER.signal, XMM.signal],n”, “ threads = 1, externally_updated = False)”

], “outputs”: [], “execution_count”: 23

}, {

“cell_type”: “markdown”, “metadata”: {}, “source”: [

“The instrument wavebands exhibit a high degree of overlap. The energies at which we compute incident specific flux signals are based on the union of wavebands, distributed logarithmically:”

]

}, {

“cell_type”: “code”, “metadata”: {

“ExecuteTime”: {

“end_time”: “2026-03-06T11:21:25.694473Z”, “start_time”: “2026-03-06T11:21:25.644339Z”

}

}, “source”: [

“fig = plt.figure(figsize=(8,8))n”, “plt.plot(XMM.signal.energies, ‘kx’)n”, “ax = plt.gca()n”, “ax.set_xlabel(‘Index’)n”, “_ = ax.set_ylabel(‘Energy [keV]’)”

], “outputs”: [

{

“data”: {

“text/plain”: [

“<Figure size 800x800 with 1 Axes>”

], “image/png”: 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”

}, “metadata”: {}, “output_type”: “display_data”, “jetTransient”: {

“display_id”: null

}

}

], “execution_count”: 24

}, {

“cell_type”: “markdown”, “metadata”: {}, “source”: [

“This is a simple, non-adaptive protocol to ensure that signals are not computed at very nearby energies for multiple telescopes, resulting in overhead.”

]

}, {

“cell_type”: “markdown”, “metadata”: {}, “source”: [

“Let’s call the likelihood object with the true model parameter values that we injected to generate the synthetic data rendered above, omitting background parameters:”

]

}, {

“cell_type”: “code”, “metadata”: {

“ExecuteTime”: {

“end_time”: “2026-03-06T11:21:26.355128Z”, “start_time”: “2026-03-06T11:21:25.701146Z”

}

}, “source”: [

“p = [1.4,n”, “ 12.5,n”, “ 0.2,n”, “ math.cos(1.25),n”, “ 0.0,n”, “ 1.0,n”, “ 0.075,n”, “ 6.2,n”, “ 0.025,n”, “ math.pi - 1.0,n”, “ 0.2]n”, “n”, “t = time.time()n”, “ll = likelihood(p, force=True) # force if you want to clear parameter value cachesn”, “n”, “print(‘ll = %.8f; time = %.3f’ % (ll, time.time() - t))”

], “outputs”: [

{

“name”: “stdout”, “output_type”: “stream”, “text”: [

“ll = -30055.76414850; time = 0.628n”

]

}

], “execution_count”: 25

}, {

“cell_type”: “code”, “metadata”: {

“ExecuteTime”: {

“end_time”: “2026-03-06T11:21:26.432464Z”, “start_time”: “2026-03-06T11:21:26.385080Z”

}

}, “source”: [

“NICER.signal.loglikelihood # check NICER ll ~ -26713.602 ?”

], “outputs”: [

{

“data”: {

“text/plain”: [

“-27446.92311279029”

]

}, “execution_count”: 26, “metadata”: {}, “output_type”: “execute_result”

}

], “execution_count”: 26

}, {

“cell_type”: “code”, “metadata”: {

“ExecuteTime”: {

“end_time”: “2026-03-06T11:21:26.452794Z”, “start_time”: “2026-03-06T11:21:26.432752Z”

}

}, “source”: [

“XMM.signal.loglikelihood # check XMM ll ~ -2608.841 ?”

], “outputs”: [

{

“data”: {

“text/plain”: [

“-2608.841035710167”

]

}, “execution_count”: 27, “metadata”: {}, “output_type”: “execute_result”

}

], “execution_count”: 27

}, {

“cell_type”: “markdown”, “metadata”: {}, “source”: [

“Let’s fabricate some rough prior information as the constrained support of the background parameters for XMM:”

]

}, {

“cell_type”: “code”, “metadata”: {

“ExecuteTime”: {

“end_time”: “2026-03-06T11:21:26.460520Z”, “start_time”: “2026-03-06T11:21:26.454076Z”

}

}, “source”: [

“support = np.zeros((181, 2))n”, “support[:,0] = XMM.signal.background_signal - 5.0 * np.sqrt(XMM.signal.background_signal)n”, “support[:,1] = XMM.signal.background_signal + 5.0 * np.sqrt(XMM.signal.background_signal)n”, “support /= XMM.data.exposure_time”

], “outputs”: [], “execution_count”: 28

}, {

“cell_type”: “code”, “metadata”: {

“ExecuteTime”: {

“end_time”: “2026-03-06T11:21:26.466628Z”, “start_time”: “2026-03-06T11:21:26.460877Z”

}

}, “source”: [

“XMM.signal.support = support”

], “outputs”: [], “execution_count”: 29

}, {

“cell_type”: “code”, “metadata”: {

“ExecuteTime”: {

“end_time”: “2026-03-06T11:21:27.099109Z”, “start_time”: “2026-03-06T11:21:26.466957Z”

}

}, “source”: [

“ll = likelihood(p, force=True)”

], “outputs”: [], “execution_count”: 30

}, {

“cell_type”: “markdown”, “metadata”: {}, “source”: [

“Let’s confirm that the XMM background-marginalised likelihood did indeed change:”

]

}, {

“cell_type”: “code”, “metadata”: {

“ExecuteTime”: {

“end_time”: “2026-03-06T11:21:27.156379Z”, “start_time”: “2026-03-06T11:21:27.122863Z”

}

}, “source”: [

“XMM.signal.loglikelihood”

], “outputs”: [

{

“data”: {

“text/plain”: [

“-2610.121609530978”

]

}, “execution_count”: 31, “metadata”: {}, “output_type”: “execute_result”

}

], “execution_count”: 31

}, {

“cell_type”: “markdown”, “metadata”: {}, “source”: [

“The background-marginalised likelihood function has the following form. Subscripts N denote NICER, whilst subscripts X denote XMM.n”, “n”, “$$n”, “\begin{aligned}n”, “p(d_{\rm X}, d_{\rm N}, \{b_{\rm X}\} \,|\, s)n”, “ \propton”, “ &n”, “ \underbrace{\mathop{\int}_{\{0\}}^{\{\mathcal{U}_{\rm N}\}}n”, “ p(d_{\rm N} \,|\, s, \{\mathbb{E}[b_{\rm N}]\}, \texttt{NICER})n”, “ d\{\mathbb{E}[b_{\rm N}]\}}_{\rm exp( \texttt{NICER.signal.loglikelihood} )}\\n”, “ &n”, “ \times\underbrace{\mathop{\int}_{\{0\}}^{\{\mathcal{U}_{X}\}}n”, “ p(d_{\rm X} \,|\, s, \{\mathbb{E}[b_{\rm X}]\}, \texttt{XMM})n”, “ p(\{\mathbb{E}[b_{\rm X}]\} \,|\, \{b_{\rm X}\})d\{\mathbb{E}[b_{\rm X}]\}}_{\rm exp( \texttt{XMM.signal.loglikelihood} )}.n”, “\end{aligned}n”, “$$n”, “n”, “The term $p(\{\mathbb{E}[b_{\rm X}]\} \,|\, \{b_{\rm X}\})$ truncates the integral over XMM channel-by-channel background count rate variables to an interval $[a,b]$ in each channel, where $a,b\in\mathbb{R}^{+}$. This is the joint prior support of the variables $\{\mathbb{E}[b_{\rm X}]\}$ rendered in the spectral plot below. The form of the prior density $p(\{\mathbb{E}[b_{\rm X}]\} \,|\, \{b_{\rm X}\})$ is flat on this interval for each channel. This is a simplifying approximation to the probability of the background data $\{b_{\rm X}\}$ given the variables $\{\mathbb{E}[b_{\rm X}]\}$.”

]

}, {

“cell_type”: “code”, “metadata”: {

“ExecuteTime”: {

“end_time”: “2026-03-06T11:21:27.333320Z”, “start_time”: “2026-03-06T11:21:27.157743Z”

}

}, “source”: [

“support_data = [support[:,0]*XMM.data.exposure_time, support[:,1]*XMM.data.exposure_time ]n”, “n”, “data_to_plot = [XMM.signal.background_signal,n”, “ XMM.signal.expected_counts,n”, “ XMM.data.counts,n”, “ support_datan”, “ ]n”, “label_to_plot = [‘MCL background’, n”, “ ‘MCL counts given support’,n”, “ ‘XMM data’,n”, “ ‘support’n”, “ ]n”, “n”, “XpsiPlot.plot_spectrum(data_to_plot, all_labels=label_to_plot, thickness=[2,5,2,14])”

], “outputs”: [

{

“data”: {

“text/plain”: [

“<Figure size 1000x1000 with 1 Axes>”

], “image/png”: 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”

}, “metadata”: {}, “output_type”: “display_data”, “jetTransient”: {

“display_id”: null

}

}

], “execution_count”: 32

}, {

“cell_type”: “markdown”, “metadata”: {}, “source”: [

“The spectrum labelled MCL counts given support means the expected signal from the pulsar, plus the background count vector that maximises the conditional likelihood function given that pulsar signal, subject to the background vector existing in the prior support. The spectrum labelled MCL background, on the other hand, is the background vector that maximises the conditional likelihood function, but not subject to the prior support.”

]

}, {

“cell_type”: “code”, “metadata”: {

“ExecuteTime”: {

“end_time”: “2026-03-06T11:21:27.357166Z”, “start_time”: “2026-03-06T11:21:27.341125Z”

}

}, “source”: [

“likelihood[‘p__super_temperature’] = 6.1”

], “outputs”: [], “execution_count”: 33

}, {

“cell_type”: “code”, “metadata”: {

“ExecuteTime”: {

“end_time”: “2026-03-06T11:21:27.363697Z”, “start_time”: “2026-03-06T11:21:27.357410Z”

}

}, “source”: [

“likelihood.externally_updated = True”

], “outputs”: [], “execution_count”: 34

}, {

“cell_type”: “code”, “metadata”: {

“ExecuteTime”: {

“end_time”: “2026-03-06T11:21:28.015249Z”, “start_time”: “2026-03-06T11:21:27.363899Z”

}

}, “source”: [

“likelihood()”

], “outputs”: [

{

“data”: {

“text/plain”: [

“-388650.0256658284”

]

}, “execution_count”: 35, “metadata”: {}, “output_type”: “execute_result”

}

], “execution_count”: 35

}, {

“cell_type”: “code”, “metadata”: {

“ExecuteTime”: {

“end_time”: “2026-03-06T11:21:28.062847Z”, “start_time”: “2026-03-06T11:21:28.037246Z”

}

}, “source”: [

“XMM.signal.loglikelihood”

], “outputs”: [

{

“data”: {

“text/plain”: [

“-321117.0621271163”

]

}, “execution_count”: 36, “metadata”: {}, “output_type”: “execute_result”

}

], “execution_count”: 36

}, {

“cell_type”: “code”, “metadata”: {

“ExecuteTime”: {

“end_time”: “2026-03-06T11:21:28.231278Z”, “start_time”: “2026-03-06T11:21:28.064146Z”

}

}, “source”: [

“support_data = [support[:,0]*XMM.data.exposure_time, support[:,1]*XMM.data.exposure_time ]n”, “n”, “data_to_plot = [XMM.signal.background_signal,n”, “ XMM.signal.expected_counts,n”, “ XMM.data.counts,n”, “ support_datan”, “ ]n”, “label_to_plot = [‘MCL background’, n”, “ ‘MCL counts given support’,n”, “ ‘XMM data’,n”, “ ‘support’n”, “ ]n”, “n”, “XpsiPlot.plot_spectrum(data_to_plot, all_labels=label_to_plot, thickness=[2,5,2,14])”

], “outputs”: [

{

“data”: {

“text/plain”: [

“<Figure size 1000x1000 with 1 Axes>”

], “image/png”: 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”

}, “metadata”: {}, “output_type”: “display_data”, “jetTransient”: {

“display_id”: null

}

}

], “execution_count”: 37

}, {

“cell_type”: “markdown”, “metadata”: {}, “source”: [

“## Synthesis”

]

}, {

“cell_type”: “markdown”, “metadata”: {}, “source”: [

“In this notebook thus far we have not generated sythetic data. However, we did condition on synthetic data. Below we outline how that data was generated.”

]

}, {

“cell_type”: “markdown”, “metadata”: {}, “source”: [

“### Background”

]

}, {

“cell_type”: “markdown”, “metadata”: {}, “source”: [

“The background radiation field incident on the model instrument for the purpose of generating synthetic data was a time-invariant powerlaw spectrum, and was transformed into a count-rate in each output channel using the response matrix for synthetic data generation. We would reproduce this background here by writing a custom subclass as follows.”

]

}, {

“cell_type”: “code”, “metadata”: {

“ExecuteTime”: {

“end_time”: “2026-03-06T11:21:28.257183Z”, “start_time”: “2026-03-06T11:21:28.238905Z”

}

}, “source”: [

“class CustomBackground(xpsi.Background):n”, “ """ The background injected to generate synthetic data. """n”, “n”, “ def __init__(self, bounds=None, value=None):n”, “ n”, “ # first the parameters that are fundemental to this classn”, “ doc = """n”, “ Powerlaw spectral index.n”, “ """n”, “ index = xpsi.Parameter(‘powerlaw_index’,n”, “ strict_bounds = (-3.0, -1.01),n”, “ bounds = bounds,n”, “ doc = doc,n”, “ symbol = r’$\Gamma$’,n”, “ value = value,n”, “ permit_prepend = False) # because to be shared by multiple objectsn”, “ n”, “ super(CustomBackground, self).__init__(index)n”, “n”, “ def __call__(self, energy_edges, phases):n”, “ """ Evaluate the incident background field. """n”, “ n”, “ G = self[‘powerlaw_index’]n”, “n”, “ temp = np.zeros((energy_edges.shape[0] - 1, phases.shape[0]))n”, “n”, “ temp[:,0] = (energy_edges[1:]**(G + 1.0) - energy_edges[:-1]**(G + 1.0)) / (G + 1.0)n”, “n”, “ for i in range(phases.shape[0]):n”, “ temp[:,i] = temp[:,0]n”, “n”, “ self._incident_background = temp”

], “outputs”: [], “execution_count”: 38

}, {

“cell_type”: “code”, “metadata”: {

“ExecuteTime”: {

“end_time”: “2026-03-06T11:21:28.265897Z”, “start_time”: “2026-03-06T11:21:28.258395Z”

}

}, “source”: [

“background = CustomBackground(bounds=(None, None)) # use strict bounds, but do not fix/derive”

], “outputs”: [

{

“name”: “stdout”, “output_type”: “stream”, “text”: [

“Creating parameter:n”, “ > Named "powerlaw_index" with bounds [-3.000e+00, -1.010e+00].n”, “ > Powerlaw spectral index.n”

]

}

], “execution_count”: 39

}, {

“cell_type”: “markdown”, “metadata”: {}, “source”: [

“We will use this same background signal, albeit with different normalisations, for both telescopes. This is simply to generate finite background contributions.”

]

}, {

“cell_type”: “markdown”, “metadata”: {}, “source”: [

“### Data format”

]

}, {

“cell_type”: “markdown”, “metadata”: {}, “source”: [

“We are also in need of a simpler data object.”

]

}, {

“cell_type”: “code”, “metadata”: {

“ExecuteTime”: {

“end_time”: “2026-03-06T11:21:28.276714Z”, “start_time”: “2026-03-06T11:21:28.272318Z”

}

}, “source”: [

“class SynthesiseData(xpsi.Data):n”, “ """ Custom data container to enable synthesis. """n”, “ n”, “ def __init__(self, channels, phases, first, last):n”, “ self.channels = channelsn”, “ self._phases = phasesn”, “ n”, “ try:n”, “ self._first = int(first)n”, “ self._last = int(last)n”, “ except TypeError:n”, “ raise TypeError(‘The first and last channels must be integers.’)n”, “ if self._first >= self._last:n”, “ raise ValueError(‘The first channel number must be lower than the ‘n”, “ ‘the last channel number.’)n”, “ “

], “outputs”: [], “execution_count”: 40

}, {

“cell_type”: “markdown”, “metadata”: {}, “source”: [

“Instantiate:”

]

}, {

“cell_type”: “code”, “metadata”: {

“ExecuteTime”: {

“end_time”: “2026-03-06T11:21:28.288739Z”, “start_time”: “2026-03-06T11:21:28.283633Z”

}

}, “source”: [

“NICER.synth = SynthesiseData(np.arange(20,201), np.linspace(0.0, 1.0, 33), 0, 180)”

], “outputs”: [

{

“name”: “stdout”, “output_type”: “stream”, “text”: [

“Setting channels for event data…n”, “Channels set.n”

]

}

], “execution_count”: 41

}, {

“cell_type”: “code”, “metadata”: {

“ExecuteTime”: {

“end_time”: “2026-03-06T11:21:28.300565Z”, “start_time”: “2026-03-06T11:21:28.295357Z”

}

}, “source”: [

“XMM.synth = SynthesiseData(np.arange(20,201), np.array([0.0,1.0]), 0, 180)”

], “outputs”: [

{

“name”: “stdout”, “output_type”: “stream”, “text”: [

“Setting channels for event data…n”, “Channels set.n”

]

}

], “execution_count”: 42

}, {

“cell_type”: “markdown”, “metadata”: {}, “source”: [

“### Custom method”

]

}, {

“cell_type”: “code”, “metadata”: {

“ExecuteTime”: {

“end_time”: “2026-03-06T11:21:28.311780Z”, “start_time”: “2026-03-06T11:21:28.306411Z”

}

}, “source”: [

“from xpsi.tools.synthesise import synthesise_given_total_count_number as _synthesise”

], “outputs”: [], “execution_count”: 43

}, {

“cell_type”: “markdown”, “metadata”: {}, “source”: [

“We now need to instantiate, and reconfigure the likelihood object:”

]

}, {

“cell_type”: “code”, “metadata”: {

“ExecuteTime”: {

“end_time”: “2026-03-06T11:21:28.319848Z”, “start_time”: “2026-03-06T11:21:28.314169Z”

}

}, “source”: [

“NICER.signal = CustomSignal(data = NICER.synth,n”, “ instrument = NICER.instrument,n”, “ background = background,n”, “ interstellar = None,n”, “ workspace_intervals = 1000,n”, “ epsrel = 1.0e-8,n”, “ epsilon = 1.0e-3,n”, “ sigmas = 10.0,n”, “ prefix=’NICER’)n”, “n”, “XMM.signal = CustomSignal(data = XMM.synth,n”, “ instrument = XMM.instrument,n”, “ background = background,n”, “ interstellar = None,n”, “ workspace_intervals = 1000,n”, “ epsrel = 1.0e-8,n”, “ epsilon = 1.0e-3,n”, “ sigmas = 10.0,n”, “ prefix=’XMM’)n”, “n”, “for h in hot.objects:n”, “ h.set_phases(num_leaves = 100)n”, “ n”, “likelihood = xpsi.Likelihood(star = star, signals = [NICER.signal, XMM.signal], threads=1)”

], “outputs”: [

{

“name”: “stdout”, “output_type”: “stream”, “text”: [

“Creating parameter:n”, “ > Named "phase_shift" with fixed value 0.000e+00.n”, “ > The phase shift for the signal, a periodic parameter [cycles].n”, “Warning: No data… can synthesise data but cannot evaluate a likelihood function.n”, “Creating parameter:n”, “ > Named "phase_shift" with fixed value 0.000e+00.n”, “ > The phase shift for the signal, a periodic parameter [cycles].n”, “Warning: No data… can synthesise data but cannot evaluate a likelihood function.n”

]

}

], “execution_count”: 44

}, {

“cell_type”: “markdown”, “metadata”: {}, “source”: [

“### Synthesise”

]

}, {

“cell_type”: “markdown”, “metadata”: {}, “source”: “We proceed to synthesise.”

}, {

“cell_type”: “code”, “metadata”: {

“ExecuteTime”: {

“end_time”: “2026-03-06T11:21:28.336942Z”, “start_time”: “2026-03-06T11:21:28.325430Z”

}

}, “source”: [

“likelihood”

], “outputs”: [

{

“data”: {

“text/plain”: [

“Free parametersn”, “—————n”, “mass: Gravitational mass [solar masses].n”, “radius: Coordinate equatorial radius [km].n”, “distance: Earth distance [kpc].n”, “cos_inclination: Cosine of Earth inclination to rotation axis.n”, “p__phase_shift: The phase of the hot region, a periodic parameter [cycles].n”, “p__super_colatitude: The colatitude of the centre of the superseding region [radians].n”, “p__super_radius: The angular radius of the (circular) superseding region [radians].n”, “p__super_temperature: log10(superseding region effective temperature [K]).n”, “s__phase_shift: The phase of the hot region, a periodic parameter [cycles].n”, “s__super_colatitude: The colatitude of the centre of the superseding region [radians].n”, “s__super_radius: The angular radius of the (circular) superseding region [radians].n”, “powerlaw_index: Powerlaw spectral index.”

]

}, “execution_count”: 45, “metadata”: {}, “output_type”: “execute_result”

}

], “execution_count”: 45

}, {

“cell_type”: “code”, “metadata”: {

“ExecuteTime”: {

“end_time”: “2026-03-06T11:21:28.993666Z”, “start_time”: “2026-03-06T11:21:28.337294Z”

}

}, “source”: [

“p = [1.4,n”, “ 12.5,n”, “ 0.2,n”, “ math.cos(1.25),n”, “ 0.0,n”, “ 1.0,n”, “ 0.075,n”, “ 6.2,n”, “ 0.025,n”, “ math.pi - 1.0,n”, “ 0.2,n”, “ -2.0]n”, “n”, “NICER_kwargs = dict(expected_source_counts = 2.0e6,n”, “ expected_background_counts = 2.0e6,n”, “ name = ‘new_NICER’,n”, “ directory = ‘../../examples/examples_fast/Data’)n”, “n”, “XMM_kwargs = dict(expected_source_counts = 1.0e6,n”, “ expected_background_counts = 5.0e5,n”, “ name = ‘new_XMM’,n”, “ directory = ‘../../examples/examples_fast/Data’)n”, “n”, “likelihood.synthesise(p,n”, “ force = True,n”, “ NICER = NICER_kwargs,n”, “ XMM = XMM_kwargs)”

], “outputs”: [

{

“name”: “stdout”, “output_type”: “stream”, “text”: [

“Using background from signal._background.n”, “Exposure time: 984282.620969 [s]n”, “Background normalisation: 6.05224909e-04n”, “Using background from signal._background.n”, “Exposure time: 1818398.718453 [s]n”, “Background normalisation: 2.21412640e-04n”

]

}

], “execution_count”: 46

}, {

“cell_type”: “markdown”, “metadata”: {}, “source”: [

“Notice that the normalisations, with units photons/s/cm^2/keV, are different because we require so many background counts. This detail is unimportant for this notebook, wherein we simply want some finite background contributions.”

]

}, {

“cell_type”: “code”, “metadata”: {

“ExecuteTime”: {

“end_time”: “2026-03-06T11:21:29.165669Z”, “start_time”: “2026-03-06T11:21:29.021195Z”

}

}, “source”: [

“XpsiPlot.plot_2d_pulse(pulse=np.loadtxt(‘../../examples/examples_fast/Data/new_NICER_realisation.dat’, dtype=np.double),n”, “ x=NICER.data.phases,n”, “ y=NICER.data.channels,n”, “ rotations=1,n”, “ ylabel=’Channel’,n”, “ cbar_label=’Counts’,)”

], “outputs”: [

{

“data”: {

“text/plain”: [

“<Figure size 700x700 with 2 Axes>”

], “image/png”: 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”

}, “metadata”: {}, “output_type”: “display_data”, “jetTransient”: {

“display_id”: null

}

}

], “execution_count”: 47

}, {

“cell_type”: “markdown”, “metadata”: {}, “source”: [

“Check we have generated the same count numbers, given the same seed and resolution settings:”

]

}, {

“cell_type”: “code”, “metadata”: {

“ExecuteTime”: {

“end_time”: “2026-03-06T11:21:29.242879Z”, “start_time”: “2026-03-06T11:21:29.167205Z”

}

}, “source”: [

“diff = XMM.data.counts - np.loadtxt(‘../../examples/examples_fast/Data/new_XMM_realisation.dat’, dtype=np.double).reshape(-1,1)n”, “(diff != 0.0).any()”

], “outputs”: [

{

“data”: {

“text/plain”: [

“True”

]

}, “execution_count”: 48, “metadata”: {}, “output_type”: “execute_result”

}

], “execution_count”: 48

}, {

“cell_type”: “code”, “metadata”: {

“ExecuteTime”: {

“end_time”: “2026-03-06T11:21:29.249792Z”, “start_time”: “2026-03-06T11:21:29.244074Z”

}

}, “source”: [

“diff = NICER.data.counts - np.loadtxt(‘../../examples/examples_fast/Data/new_NICER_realisation.dat’, dtype=np.double)n”, “(diff != 0.0).any()”

], “outputs”: [

{

“data”: {

“text/plain”: [

“True”

]

}, “execution_count”: 49, “metadata”: {}, “output_type”: “execute_result”

}

], “execution_count”: 49

}, {

“cell_type”: “markdown”, “metadata”: {}, “source”: “Some differences may exist between the newly created synthetic data and the synthetic data found in the GitHub examples (especially at the lowest energy channels). This is because the example data were produced using significantly newer NICER response files.”

}, {

“metadata”: {

“ExecuteTime”: {

“end_time”: “2026-03-06T11:20:47.698609Z”, “start_time”: “2026-03-06T11:20:47.688059Z”

}

}, “cell_type”: “markdown”, “source”: “### Saving synthetic data to FITS file (event or PHA)”

}, {

“metadata”: {

“ExecuteTime”: {

“end_time”: “2026-03-06T11:21:30.954596Z”, “start_time”: “2026-03-06T11:21:29.256031Z”

}

}, “cell_type”: “code”, “source”: [

“NICER_kwargs = dict(expected_source_counts = 2.0e6,n”, “ expected_background_counts = 2.0e6,n”, “ format=’FITS’,n”, “ instrument_name = ‘PN_MOS’,n”, “ name = ‘new_NICER’,n”, “ directory = ‘../../examples/examples_fast/Data’)n”, “n”, “XMM_kwargs = dict(expected_source_counts = 1.0e6,n”, “ expected_background_counts = 5.0e5,n”, “ format=’FITS’,n”, “ instrument_name = ‘XMM_MOS’,n”, “ name = ‘new_XMM’,n”, “ directory = ‘../../examples/examples_fast/Data’)n”, “n”, “likelihood.synthesise(p,n”, “ force = True,n”, “ NICER = NICER_kwargs,n”, “ XMM = XMM_kwargs,n”, “ )”

], “outputs”: [

{

“name”: “stdout”, “output_type”: “stream”, “text”: [

“Using background from signal._background.n”, “Exposure time: 984282.620969 [s]n”, “Background normalisation: 6.05224909e-04n”, “Using background from signal._background.n”, “Exposure time: 1818398.718453 [s]n”, “Background normalisation: 2.21412640e-04n”

]

}

], “execution_count”: 50

}, {

“metadata”: {

“ExecuteTime”: {

“end_time”: “2026-03-06T11:21:31.148963Z”, “start_time”: “2026-03-06T11:21:30.984916Z”

}

}, “cell_type”: “code”, “source”: “!ls ../../examples/examples_fast/Data/*evt ../../examples/examples_fast/Data/*pha”, “outputs”: [

{

“name”: “stdout”, “output_type”: “stream”, “text”: [

“../../examples/examples_fast/Data/new_NICER.evtrn”, “../../examples/examples_fast/Data/new_NICER_bkg.evtrn”, “../../examples/examples_fast/Data/new_NICER_bkg_realisation.evtrn”, “../../examples/examples_fast/Data/new_NICER_realisation.evtrn”, “../../examples/examples_fast/Data/new_XMM.pharn”, “../../examples/examples_fast/Data/new_XMM_bkg.pharn”, “../../examples/examples_fast/Data/new_XMM_bkg_realisation.pharn”, “../../examples/examples_fast/Data/new_XMM_realisation.pharn”

]

}

], “execution_count”: 51

}

], “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”: “3.11.11”

}

}, “nbformat”: 4, “nbformat_minor”: 4

}