{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Integration with Python\n", "\n", "This cookbook explains you how to perform azimuthal integration using the Python interpreter.\n", "It is divided in two parts, the first part uses purely Python while the second uses some advanced features of the Jupyter notebook.\n", "\n", "We will re-use the same files as in the other tutorials.\n", "\n", "## Performing azimuthal integration with pyFAI of a bunch of images\n", "\n", "To be able to perform the azimuthal integration of some images, one needs:\n", "\n", "* The diffraction images themselves, in this example they are stored as EDF files\n", "* The geometry of the experimental setup as obtained from the calibration and stored as a PONI-file\n", "* other files like flat-field, dark current images or detector distortion file (spline-file).\n", "\n", "Image file: http://www.silx.org/pub/pyFAI/cookbook/calibration/Eiger4M_Al2O3_13.45keV.edf\n", "\n", "Geometry file: http://www.silx.org/pub/pyFAI/cookbook/calibration/alpha-Al2O3.poni\n", "\n", "The calibration has been performed in the previous cookbook.\n", "\n", "### Basic usage of pyFAI\n", "To perform azimuthal averaging, one can use pyFAI to load the geometry and FabIO to read the image:" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "image_file: /tmp/pyFAI_testdata_jerome/Eiger4M_Al2O3_13.45keV.edf\n", "poni_file: /tmp/pyFAI_testdata_jerome/alpha-Al2O3.poni\n" ] }, { "data": { "text/plain": [ "['alpha-Al2O3.poni',\n", " 'pyFAI-calib_notebook.png',\n", " 'integration_with_scripts.ipynb',\n", " 'integration_with_python.ipynb',\n", " 'integration_with_the_gui.rst',\n", " '.ipynb_checkpoints',\n", " 'calib-gui',\n", " 'pyFAI-integrate.png',\n", " 'Eiger4M_Al2O3_13.45keV.edf',\n", " 'calibration_with_jupyter.ipynb',\n", " 'index.rst',\n", " 'calib-cli',\n", " 'jupyter.poni']" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# This cell is just to download the files to perform the analysis:\n", "import time\n", "import shutil, os\n", "from silx.resources import ExternalResources\n", "\n", "t0 = time.perf_counter()\n", "os.environ[\"PYOPENCL_COMPILER_OUTPUT\"] = \"0\"\n", "downloader = ExternalResources(\"pyFAI\", \"http://www.silx.org/pub/pyFAI/cookbook/calibration/\", \"PYFAI_DATA\")\n", "image_file = downloader.getfile(\"Eiger4M_Al2O3_13.45keV.edf\")\n", "poni_file = downloader.getfile(\"alpha-Al2O3.poni\")\n", "\n", "print(\"image_file:\", image_file)\n", "print(\"poni_file:\", poni_file)\n", "\n", "# Copy all files locally\n", "shutil.copy(poni_file, \".\")\n", "shutil.copy(image_file, \".\")\n", "# clean-up files from previous run:\n", "for result in ('integrated.edf', \"integrated.dat\", \"Eiger4M_Al2O3_13.45keV.dat\"):\n", " if os.path.exists(result):\n", " os.unlink(result)\n", "\n", "os.listdir()" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "pyFAI version: 2023.9.0-dev0\n", "Image: \n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/jerome/.venv/py311/lib/python3.11/site-packages/pyopencl/cache.py:495: CompilerWarning: Non-empty compiler output encountered. Set the environment variable PYOPENCL_COMPILER_OUTPUT=1 to see more.\n", " _create_built_program_from_source_cached(\n", "/home/jerome/.venv/py311/lib/python3.11/site-packages/pyopencl/cache.py:499: CompilerWarning: Non-empty compiler output encountered. Set the environment variable PYOPENCL_COMPILER_OUTPUT=1 to see more.\n", " prg.build(options_bytes, devices)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Integrator: \n", " Detector Eiger 4M\t PixelSize= 7.500e-05, 7.500e-05 m\n", "Wavelength= 9.218156e-11 m\n", "SampleDetDist= 1.625467e-01 m\tPONI= 9.636511e-02, 8.600623e-02 m\trot1=0.002605 rot2=0.000641 rot3=0.000000 rad\n", "DirectBeamDist= 162.547 mm\tCenter: x=1141.104, y=1286.257 pix\tTilt= 0.154° tiltPlanRotation= 166.178° 𝛌= 0.922Å\n" ] } ], "source": [ "import pyFAI, fabio\n", "print(\"pyFAI version:\", pyFAI.version)\n", "img = fabio.open(\"Eiger4M_Al2O3_13.45keV.edf\")\n", "print(\"Image:\", img)\n", "\n", "ai = pyFAI.load(\"alpha-Al2O3.poni\")\n", "print(\"\\nIntegrator: \\n\", ai)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Azimuthal averaging using pyFAI\n", "\n", "\n", "One needs first to retrieve the image as a numpy array. This allows to use other libraries than FabIO for image reading, for example HDF5 files.\n", "\n", "This shows how to perform the azimuthal integration of one image over 1000 bins:" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "img_array: (2167, 2070) float32\n" ] } ], "source": [ "img_array = img.data\n", "print(\"img_array:\", type(img_array), img_array.shape, img_array.dtype)\n", "mask = img_array>4e9\n", "\n", "res = ai.integrate1d_ng(img_array, \n", " 1000, \n", " mask=mask,\n", " unit=\"2th_deg\", \n", " filename=\"integrated.dat\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "*Note:* There are 2 mandatory parameters for this method: the 2D-numpy array with the image and the number of bins. In addition, we specified the name of the file where to save the data and the unit for performing the integration.\n", "\n", "There are many other options of `integrate1d`. The difference between the `legacy` and the `ng` flavor is mostly on the way error is propagated:" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Help on method integrate1d_ng in module pyFAI.azimuthalIntegrator:\n", "\n", "integrate1d_ng(data, npt, filename=None, correctSolidAngle=True, variance=None, error_model=None, radial_range=None, azimuth_range=None, mask=None, dummy=None, delta_dummy=None, polarization_factor=None, dark=None, flat=None, method='csr', unit=q_nm^-1, safe=True, normalization_factor=1.0, metadata=None) method of pyFAI.azimuthalIntegrator.AzimuthalIntegrator instance\n", " Calculate the azimuthal integration (1d) of a 2D image.\n", " \n", " Multi algorithm implementation (tries to be bullet proof), suitable for SAXS, WAXS, ... and much more\n", " Takes extra care of normalization and performs proper variance propagation.\n", " \n", " :param ndarray data: 2D array from the Detector/CCD camera\n", " :param int npt: number of points in the output pattern\n", " :param str filename: output filename in 2/3 column ascii format\n", " :param bool correctSolidAngle: correct for solid angle of each pixel if True\n", " :param ndarray variance: array containing the variance of the data.\n", " :param str error_model: When the variance is unknown, an error model can be given: \"poisson\" (variance = I), \"azimuthal\" (variance = (I-)^2)\n", " :param radial_range: The lower and upper range of the radial unit. If not provided, range is simply (min, max). Values outside the range are ignored.\n", " :type radial_range: (float, float), optional\n", " :param azimuth_range: The lower and upper range of the azimuthal angle in degree. If not provided, range is simply (min, max). Values outside the range are ignored.\n", " :type azimuth_range: (float, float), optional\n", " :param ndarray mask: array with 0 for valid pixels, all other are masked (static mask)\n", " :param float dummy: value for dead/masked pixels (dynamic mask)\n", " :param float delta_dummy: precision for dummy value\n", " :param float polarization_factor: polarization factor between -1 (vertical) and +1 (horizontal).\n", " 0 for circular polarization or random,\n", " None for no correction,\n", " True for using the former correction\n", " :param ndarray dark: dark noise image\n", " :param ndarray flat: flat field image\n", " :param IntegrationMethod method: IntegrationMethod instance or 3-tuple with (splitting, algorithm, implementation)\n", " :param Unit unit: Output units, can be \"q_nm^-1\" (default), \"2th_deg\", \"r_mm\" for now.\n", " :param bool safe: Perform some extra checks to ensure LUT/CSR is still valid. False is faster.\n", " :param float normalization_factor: Value of a normalization monitor\n", " :param metadata: JSON serializable object containing the metadata, usually a dictionary.\n", " :return: Integrate1dResult namedtuple with (q,I,sigma) +extra informations in it.\n", "\n" ] } ], "source": [ "help(ai.integrate1d_ng)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The result file contains the integrated data with some headers as shown:" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "# == pyFAI calibration ==\n", "# Distance Sample to Detector: 0.16254673947902704 m\n", "# PONI: 9.637e-02, 8.601e-02 m\n", "# Rotations: 0.002605 0.000641 0.000000 rad\n", "#\n", "# == Fit2d calibration ==\n", "# Distance Sample-beamCenter: 162.547 mm\n", "# Center: x=1141.104, y=1286.257 pix\n", "# Tilt: 0.154 deg TiltPlanRot: 166.178 deg\n", "#\n", "# Detector Eiger 4M\t PixelSize= 7.500e-05, 7.500e-05 m\n", "# Detector has a mask: True\n", "# Detector has a dark current: False\n", "# detector has a flat field: False\n", "#\n", "# Wavelength: 9.218156017338309e-11 m\n", "# Mask applied: user provided\n", "# Dark current applied: False\n", "# Flat field applied: False\n", "# Polarization factor: None\n", "# Normalization factor: 1.0\n", "# --> integrated.dat\n", "# 2th_deg I\n", "1.919453e-02 1.069850e+02\n", "5.758360e-02 1.369700e+02\n", "9.597267e-02 1.055188e+03\n", "1.343617e-01 7.239056e+03\n", "1.727508e-01 0.000000e+00\n", "2.111399e-01 2.046833e+04\n", "2.495289e-01 1.767070e+04\n", "2.879180e-01 1.170921e+04\n", "3.263071e-01 7.144741e+03\n", "3.646961e-01 4.495773e+03\n", "4.030852e-01 2.918518e+03\n", "4.414743e-01 1.955721e+03\n", "4.798634e-01 1.355407e+03\n", "5.182524e-01 9.685624e+02\n", "5.566415e-01 7.158431e+02\n", "5.950306e-01 5.435185e+02\n", "6.334196e-01 4.240502e+02\n", "6.718087e-01 3.373719e+02\n", "7.101978e-01 2.713134e+02\n", "7.485868e-01 2.221988e+02\n", "7.869759e-01 1.840064e+02\n", "8.253650e-01 1.547038e+02\n", "8.637540e-01 1.308537e+02\n", "9.021431e-01 1.118037e+02\n", "9.405322e-01 9.608986e+01\n", "9.789212e-01 8.361961e+01\n", "1.017310e+00 7.308742e+01\n" ] } ], "source": [ "with open(\"integrated.dat\") as f:\n", " for i in range(50):\n", " print(f.readline().strip())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Azimuthal regrouping using pyFAI\n", "\n", "This option is similar to the integration but performs N integrations on various azimuthal angle (chi) sections of the space. It is also named \"caking\" in Fit2D.\n", "\n", "The azimuthal regrouping of an image over 500 radial bins in 360 angular steps (of 1 degree) can be performed like this:\n" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "res2 = ai.integrate2d_ng(img_array, \n", " 500, 360, \n", " unit=\"r_mm\", \n", " filename=\"integrated.edf\")" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{\n", " \"EDF_DataBlockID\": \"0.Image.Psd\",\n", " \"EDF_BinarySize\": \"720000\",\n", " \"EDF_HeaderSize\": \"1536\",\n", " \"ByteOrder\": \"LowByteFirst\",\n", " \"DataType\": \"FloatValue\",\n", " \"Dim_1\": \"500\",\n", " \"Dim_2\": \"360\",\n", " \"Image\": \"0\",\n", " \"HeaderID\": \"EH:000000:000000:000000\",\n", " \"Size\": \"720000\",\n", " \"Engine\": \"Detector Eiger 4M PixelSize= 7.500e-05, 7.500e-05 m Wavelength= 9.218156e-11 m SampleDetDist= 1.625467e-01 m PONI= 9.636511e-02, 8.600623e-02 m rot1=0.002605 rot2=0.000641 rot3=0.000000 rad DirectBeamDist= 162.547 mm Center: x=1141.104, y=1286.257 pix Tilt= 0.154 tiltPlanRotation= 166.178 = 0.922\",\n", " \"detector\": \"Eiger 4M\",\n", " \"pixel1\": \"7.5e-05\",\n", " \"pixel2\": \"7.5e-05\",\n", " \"max_shape\": \"(2167, 2070)\",\n", " \"dist\": \"0.16254673947902704\",\n", " \"poni1\": \"0.09636511239091199\",\n", " \"poni2\": \"0.08600622810318177\",\n", " \"rot1\": \"0.0026048269580961157\",\n", " \"rot2\": \"0.0006408875619633374\",\n", " \"rot3\": \"7.381054962294179e-11\",\n", " \"wavelength\": \"9.218156017338309e-11\",\n", " \"r_mm_min\": \"0.1289601535655313\",\n", " \"r_mm_max\": \"128.83119341196578\",\n", " \"chi_min\": \"-179.4870352534758\",\n", " \"chi_max\": \"179.50004446145505\",\n", " \"has_mask_applied\": \"from detector\",\n", " \"has_dark_correction\": \"False\",\n", " \"has_flat_correction\": \"False\",\n", " \"polarization_factor\": \"None\",\n", " \"normalization_factor\": \"1.0\"\n", "}\n", "cake: (360, 500) float32\n" ] } ], "source": [ "cake = fabio.open(\"integrated.edf\")\n", "print(cake.header)\n", "print(\"cake:\", type(cake.data), cake.data.shape, cake.data.dtype)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "From this, it is trivial to perform a loop and integrate many images. \n", "\n", "*Attention:* The AzimuthalIntegrator object (called `ai` here) is rather large and costly to initialize. The best practice is to create it once and to use it many times, like this:" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "import glob, os\n", "\n", "all_images = glob.glob(\"Eiger4M_*.edf\")\n", "ai = pyFAI.load(\"alpha-Al2O3.poni\")\n", "\n", "for one_image in all_images:\n", " fimg = fabio.open(one_image)\n", " dest = os.path.splitext(one_image)[0] + \".dat\"\n", " ai.integrate1d_ng(fimg.data, \n", " 1000, \n", " unit=\"2th_deg\", \n", " filename=dest)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Using some advanced features of Jupyter Notebooks\n", "\n", "Jupyter notebooks offer some advanced visualization features, especially when used with *matplotlib* and *pyFAI*.\n", "Unfortunately, the example shown hereafter will not work properly in normal Python scipts.\n", "\n", "### Initialization of the notebook for matplotlib integration:\n" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "from pyFAI.gui import jupyter" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Visualization of different types of results previously calculated" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ax = jupyter.display(img.data, label=img.filename)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ax = jupyter.plot1d(res)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAkwAAAGyCAYAAAAWFJs3AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8pXeV/AAAACXBIWXMAAA9hAAAPYQGoP6dpAACoiUlEQVR4nOydd5wURfr/P93TPbO7swEGWBYki6KeoJgQ9QynP9HzVEynp64BRMIoIBgABUUOxeyJqygGPE7lzu+pp56iHMFwIoZjVaIgIHEB2Zxmprvr98cww4YJHarTTL1fr3nBzvRUPf08VTVPVz31FEcIIWAwGAwGg8FgJIW3WwAGg8FgMBgMp8McJgaDwWAwGIw0MIeJwWAwGAwGIw3MYWIwGAwGg8FIA3OYGAwGg8FgMNLAHCYGg8FgMBiMNDCHicFgMBgMBiMNgt0CZAKKomD37t0oKCgAx3F2i8NgMBgMBkMFhBDU1dWhe/fu4PnUc0jMYaLA7t270bNnT7vFYDAYDAaDoYMdO3agR48eKa9hDhMFCgoKAADr1k9AQYEP/xm2HdUhH2olAY2SBw0Sh4gCRBSgQW793b9XzbdBYkY2Mrp4FIpEgl8aOdbuADza70aIvIKvDuQY1seIzqNwWJ6Mn+o8lur26o6jNNV392G3oMgroybswaO7XjJRMgbDLRAAJP47ngrmMFEgtgxXUOBDYaEPeR4vQh4vwooAmfdA4jnw4MAh6jS1+bbV4jJcQuhpEfzRxRCH7aJSno/3gecI/l71HFi7A3weLwSO4O9VL8GoPiKKDxwUeDnecFla0Cp7B6+AAoFDSBY1fY/ByGyIqnAaFvRNG0UCAHCcdUf0lQaClnzHDBLJEV7eD5FFeZq+k4ns+vw4NC1p52HrtneeQFAgyLbqz0ltVSYcIgqdIdAvADme9raiAU2dRRQOYYUHjRNEI4vyEHlFiNeXqE4n2dstuFlnbpZdDcxhMglCDnmroslaXlhZlvD9VA0x2XesJpEc0me/ov6/nZPK7xTZzabfP79F4eN72r2fafaOYbW9PRQfahokuuW1hKa9JYWDQknM+v92RvXKPvH6EtXpJHu7Bda/nQtzmCjCcR5wnpx277dfhotidkPRUr5VXr6aevLur0XHudtd1ZGcQLrBim/ztxVoqcdqe9dGBIQUOstSZs4wJWNhZVnSmZ22xK6huYm349zt6DJ/c7x8tbLEYP1bG24dz2Nkgr2Zw0QTTgAhUtrLYg2HdqNOVV66uqxqzOnqMXIP2YYWXS2sLEO9xCF0cAmK2RsoEGSqS5T1EQ+VcpKRSM7YzI7a/k0IoCC6HEmTmBxOtrfbYOO58+AIobGand3U1taiqKgIO3bejcJCHz48fTtqIyKqwyIa4rvkOISV6NR9SzLB62a4gzHFQQS8BDsaOdbuAMzpOxpej4JJm43tavv2nP+HY8//L/hTijG19Pd4Yvdzmr5fM6k78n6zF8gVIV7bbEiWdMzqMwYFgoyaiAf3/zKPWrmxHz/WrhjugwBQUFNTg8LCwpRXshkmmkhNAACeI/EYppbuaFtnKR1meeBO8+yz5T7TYbYevLx5cTZacIq9QwqHJsn4ELirOoC6Ld3Bbd6BiIYlvpi8ldu7IfRzB0g/i4ZlaVluIpokHs0yj5BMd+hPNbvkFHvbTbboIZPvkzlMNFFC8V1yMTgO8UHUrzGJQ8sBiEZjcepTIM0lypZlGL1PrfIYld8KezshEb1T7J0vKMgXDyVG02vvS1f/HdP/NQzPPDoSVWH134/J2/f/vsPYedcgd3qdpvqTyZNKDx6OgOMIBN46x9kp9k5VlhnXt4WN59qx096JYA4TTbhDMQyxtAKEAKLKwamlQRPFoLR8X4/xndax2qJHvrb6aFtGKp3Slkfr9XbY20kL8Hbbm+NIqwHQqL09HEn5UOSE/i0TDoRwCFMKdm+JGXE1rH/Tk8dq3G7vRLAYJgrEY5i23Y6iziX499Cf4jFM9REPGuVoDFOip0+9jSoW6On0TmMm2aIHWvc5ufs4dPAq+KnW40p90bb37D6jIfAE92x50XBZY4qD6OOXsLZGoPIkbFa7ntFrLDp4JTRIHkzfRi+GCaA/48H6d3Zhnx5YDJPtkAS7UNItyan1mGONKZs7F6BdD231a/WauN76adnbxwNeXtG8NOwUaNtbAZewn+qhQQLkNo+edts7GYRwmmKt1KJml5zW8lr+m45s799uxw32Zg4TTfho0CbfIrCW49Qnrsz2DmM2bfWbSN+0Ol2yLeDp6jcTBdHEhdlCOn3HAuDNGmjttncicjwKvLwCH29tzigryPb+nW1Yae8YzGGiCZ/40T1Z4spEOGEnQCai9WlPrx20LkvYYW+tuzWTEfmbD5EFzpyuUqNXD0fgPZhs0qi9o4kr00c3sP5tDqx/ZxdW2bstzhztXArHeUAUY3lU2FOJOZgd4Gl1PXrgQXmXXIMEFXlabUGtXnkN16arR82RI3b370yNWGX9O7uwyw7MYaKN4tBfEEbWo4DuD6Y4Wk5/kYORCdcu7kgvDRIQoZw92wwUcNSzfJtJ7c5rkbPxS3jP3Wa3KAwGW5IzA8VFAxKDka3cdf/LGP/kP7F3xAAq5dkxe6N1qSEscwgph47IcTqcFAInRRCe60HzN4MduwTMyA7c0WtcBu+AbMoMRlsiSnSGgXGQPB8AgMvAAOhkcBwgcOpzw9lNQZ9/Qhy2C1A4QJGj06QMhk0wh4nByBJEhxyN4hjqmqFsqsO+nd0MF+UXAI9NvqiWWSZJOXiupewux9k7QULOkB8gjmAhDwz7YPObFCFEBmDuieUMhl5EjjCHqQVPzh4JH08w0eDhuzEUaD/+iAZaAlorwxwAD+oldzlMDIYTYDNMtFEi7d5Sm4eJwbACtyauNAsaW44bpGgME62UDWrRuvuni09BR6+ETj53B+wzGHbAfsppk2CftZY8TG6C5RhxF9FdYZzuH/VMs7ekcFBAb8uxZMNmD6024Q7GLwkqZhozzd6M1DB7p4c5TCbQdpcc7RkmpzRsp+QYMVsf6cq3u361GE0rkIn2TrREqcfeCyvLcP8v86joKFH59dM6Ifx84uV+rfqQCafKuctEe+sp3+76rYLZOz3MYaIIx1kTvxQ7oJBx6MBGvd9V81668tWm5NdrM1r25hENTHbzkhxteyfKSaTX3pO7j0t4mvrPl5+MplkFCP9FneIT2VsM1IHrlJPwWi36yPEoyPHIrtklx/p3duEEe6eCOUy04az7Ncr2Tmb0/hN1HFpPWWaUbfR+Q0o0uaLVcTa0oG3ve7e9gEmUAr79AnBYXqSVMxqrL79DLYTiRnBdtI0NLe/Xd2cY4h8bDMsZC/x3Q/A/69/ZhZPtHYM5TLQRctvlYaJ9llzMC3fKFKpdxHSgVmd6MXPK3kp7i7y64zucCm17T+kxDlN6jNP83WSf5/AK/EJ7BXd9ZSPE0TLEa5tt799hhUOz7EGD5PzdvKx/ZxdOsXcqOELcfbrQZ599hsceewzfffcd9uzZg3feeQfDhw+Pf37TTTfhtddea/WdYcOGYfHixfG/Kysrcfvtt+P9998Hz/O44oor8Je//AX5+fmqZKitrUVRURF27pqKggIBi8/4BVUhH2oiAhokDxokDg0Ht/G2fbrP9k7CsI7J3cehUFSwp8mDeftYu3u6/yh4OILbN71kuKzSQBBHFsr4qdbj6D49s/cYFIgyqkICZu14nmrZRpZTnERpIAi/ANZHsgYCQEFNTQ0KCwtTXun6GaaGhgYcd9xxKCtL3rgvuOAC7NmzJ/568803W31+3XXXYe3atViyZAk++OADfPbZZ7j11lupyul1vaYZbkfkAYEjrl2So02zzKNR5qkshfiF6AyT0xE4Ah6A10P/OVmLs9Q024/mObmour0XdTmM4heAvvnt08MwGK7/Gb/wwgvx5z//GZdddlnSa3w+H0pKSuKvjh07xj9bv349Fi9ejJdeeglDhgzBGWecgblz52LRokXYvXt3wvJCoRBqa2tbvawg29e405Fp+qF9P5l2NIpR/cgEIISjMivSIAHNBs9ny7T2mwphAA/PUTnw9088xibCKv3EHiimJViupUk22VsPTtSP6x0mNaxYsQLFxcUYMGAAxo4diwMHDsQ/W7lyJTp06ICTTjop/t55550HnuexatWqhOU9/PDDKCoqir969ux56EPFvMd3NQN720aW7m+nQEPuTFgOaAlte4s8wIM4YpecE+wtUN4pZjQ+zIr+3SzzaJZ5NEh0h36tcUAjbr0B4vAqeCdIqr4PWNu/RZ4gVzB3xpCN56lx4nie8Q7TBRdcgL/+9a9YunQpHnnkEXz66ae48MILIcvRTLcVFRUoLi5u9R1BEBAIBFBRUZGwzKlTp6Kmpib+2rFjR/QDDTvkzPjRShRDkO5vp6BGbqcODnah1d4RxZ7kiolwgr3HXf0Obj5nBf41+Goq5fEmq5ZG/44QDrINUatOsLcWzHAqtcLGc+eR8Q7TNddcg0suuQQDBw7E8OHD8cEHH+Cbb77BihUrdJfp8/lQWFjY6hWHT+4JtYwdMSOOxEjnsarxGqnHqYODXejRR+xHndkbyBkioeOJm3FUjx1UyjP7QFuj/TsazKwgx0MgUk4rEJPNyfbWQp5HgdfmXFVsPHceGe8wtaVfv37o3LkzNm/eDAAoKSnBvn37Wl0jSRIqKytRUlKivYIUS3JmLYXQ2GJpVeOlMbC68cmEJnrvP3ZK/bx91m1hdrK9n5l8FZ59agQGvP8llfLyRdm0mWOj3225bZ0zMQeTk+2tBZFXbEvuycZz55J1DtPOnTtx4MABdOvWDQAwdOhQVFdX47vvvotfs2zZMiiKgiFDhmgrPME5clZgpHOY2bEOjOuDvSMGUK/XjU8mNNF7//kCQY5HsWWAcqK9m2UOdREPtV1ygkkTTDR1xyO6S44zefnQifbWQqPkMX3GMBlu1J0bZdaD6x2m+vp6lJeXo7y8HACwdetWlJeXY/v27aivr8ddd92Fr776Ctu2bcPSpUtx6aWXon///hg2bBgA4Oijj8YFF1yAUaNG4euvv8Z///tf3HbbbbjmmmvQvXt3TbIQOUT79qhglwdfcBWPwBXVmr/npicOJ5JMf/UShwbJeXmC7LK3wEcdSFo0y/ZkUVerv9JAEBcd8RPOP3K9rZm+zbD3t+f8P2y6dAj+feKVVMprVng02uQwpcNt46Pb5E2F6x2mb7/9FoMHD8bgwYMBAJMmTcLgwYMxY8YMeDwe/PDDD7jkkktw5JFHYuTIkTjxxBPx+eefw+fzxct4/fXXcdRRR+Hcc8/F73//e5xxxhl48cUX9QmkOC9/h10Bdt5ztkC8aK+m72RK8js7SWbvZyrK8NDO52yQKDl22psH4OMJtbQCtHfdpSMWl6S2fy+sLMNv/vQ5jrjhfxBscpjMsvdx1y5Dn5t/wpnnf0qlvAJBTpi13Qm4KWA608ZzB2wwNsbZZ5+NVMnKP/7447RlBAIBvPHGGzTFchRuarSxwcAt8joRN+nPTnvXSR74eIVK/R29QAdvBH5BpCRdemK60yL/8nmXQPRIaJbteVY2zd69SiB9W41337sYwF8NFxdSODj1DAzWv+3D9Q4TIz1ua6xuk9dpuE1/dsnrO5iZm0b9VWFgX7PPliU5tfKXBoJYtU+GX5Ah60gvse2Pg3HY8PVAWIJ4k/4bNcPe+/4awOatJ6B0rXFnyS9EZx4bnLkiF3dC+vgVzPjLG2j83Wh4/X2QK5qbaFMvbhuPUsEcJgYjS4idJbe5znlxTHZBM/iZ9lZ9NWh5gl9YWYYZ+WMhK5yuGKbP1w7E70QJsuQBUI7IojygMQxxhP1n7Rz2+o8AfqRWXo5Hho93bsTKwsoyoBIIvn8UOh14AijKsVukrMC5LYKhGj3r2U5ZA3ez7HahV2c+HlSDnPXgJHtLhNM105KIjl7Aa5Juk91/quW4RN+JtoHodnk991269q847PUf0evv5QCA6qXd8eunR7SSRa3sKeuxuX97ecDLR/NV2YGW+y95dQPEUTJGjLnJPIE04EZ7a4E5TC4h8oIHoc+1bdFP1RCdMsOQSo5k8jtFdrvQa++QAoQNnndmFCfZu0iUkUfJyakKA1UhcybsU91/ss8Svb+wsgxensDLK1QC1LvM34xur62Pl52szmQ4uX83yR5Uhz221M3Gc+fCHCYb0dRQinyQO3QxrXwnZoZ1U0dyAukGq7yDGZ79ArM3EE3gKPD0ZoWs3iWnp3+HFQ6jpryG6Y++ZopMTra3WsIK4OGI5fZMBxvP7Yc5TDagJztq8/d+iN99lfKaVOWlq8vMxhxe2geR97uoqieZnJGPuiHybkc0P8rW6mNotbfAE4g8QYNkfSbgZBhps0ZpkjxokujMInT0RmeszMRo/45d07imGM3f+6nLpiaWyk57q8XLA82yh9pyrV6cOp5rqccN9tYCR1LtyWeoora2FkVFRdixfRIK83ksPnsfqkI+1EQENEgeNEhc9FgKpf0ZcpngdacjPNcDpUlEzt3NussIPSmChETUbemOLvM3U5Que5jVZwz8gozVld6saHfpmNVnDHgQ3LvtBcNlTe4+Dn38YXx9wOdY3ZYGgjiqUIJfUHAgJGDWjueplg1kxng2pjiIPn4Ja2uEjLgfRjoIAAU1NTWtz4VNAJthMpmW7qjWLcdmeeBWe/be2+WUzpIaeXyTIsiZ2qjJWXLbE4zZ9lYIQAhn2pmGWuWxu1yRI1QPWI1onJGwWg9m/vgni2FKJY9RzCp33r4ybGug7yy5TQ96yeT7ZA6TyXBc9NBTQPvhuy07LI3G4tSnQBoHOMZoWYbR+9Qqj1H5zba3pHCQiHbHnTZOsXehKMHTwmEyYu+IwqFJ0jacGrV37Dtt/02lB4lEZ7tpH/uhJiDZbnunKqst8/ap23moBTaea8fO8TwRzGEyGUKg+tTrlgZta9y2jVCP8Z3WsdqiR750PxapdEpbHq3XW21vmXAgNsdltMRuezdInvjDjB557LZ37Dtt/02HAkA0YeQ3I66G9W968liN2+2dCBbDRIFUMUz1EQ8a5ehTXVW4/Xf1NqpMSzmvh2zRA637nNZjHPyCjA217ozNoG3vh/qOhkw4TN82z3BZ40uC6JcfxncU4sO03GdpIIiXZ88D160QqK2HeEMk5fWRv/mA/DwomxvguzPBgOQgWP/OLuzTA4thciTpluTUesxanygzFa16aKtfq9fE9dZPy95+QYZfsDdxpRFo25uQaFwXDfISHNRqlb3lX/OA/bVQfk1/rfKrAuyvRfhAkaqy7YT17+zCDfZmM0wUYLvkMgdaTzdOfFqc3nMscgUF69nuHwDA0/1HoUHiqcy4Te4+DoflRRy9AzG2Sy5XULCvWcScnc/ZLZLlZHL/ZrRHnZ3YDJOjaBknkQ4n7ATIRLQ+7em1g9ZATKvtrecMMTeiRq8t+6VRe0cUTlU/p21vreXd/eAruP3Rf2DGLa9TlcNuWP/OLqyyd1vYDBMF2AwTww1M7zkWfkFh+WUOMrvPaCigE8M0pjiInnkSlZxOWtA60xF+3gPOz6PpxwAKH9tromQMhltQP8Nkc0aW7EDkgYh7Q0cYGUKEcJpzBWUyuYKCsExnkt3LAzke4vilmsceuQW5goJdjSKA7FuSYzCMwJbkGAxGVqN2uv6z0/+AyMseRF5of5yK2tQhdlIaCILj2KDPYOiF9R0LYLNLDCcQUbTF02U6PKIH8KqdESKEAxSAJOnPIYVz9OwSANw1/WUE//w68jzOd/AYDKfBluRow4t2S8BgJCSicCBEvYOQDUgaHMizvnwf+DLxZyJvT0C9ZlvyAHjO9oNlGQw3wmaYGIwsIV8gyPEobOfOQRokHnVS++U1PTRKLnJCeB7Tx75mtxQMhutgDhODkQWUBoLwcAQCz2aYYuSLMgoEmZoD2UwpgNxUcryALwd8rrOzfDMYTsQFPZzhVNhMhXtYWFkG7uAEiNEcNJnCuDtfxaRRf8OkEzYYLkvkCXy89bN3Wut7bGop5t4xHNMeH0W9bIa7YfZOD3OYXIhTGrZTZirM1ke68u2uXy0iTyAYWDXKOHsX+eApbEZebpOm8lN9TiuLtFk0STyaJHXDfsbZW2f5dtdvFcze6WEOE02U1Adf0iJ2QCHD2BEFiXSY6L105Sf6XG3ZaqBl7yaJR7PskjibJNC099z7rsNjT9yCAe+3juTWY++IwkFqE8Nkt70TlTtrx/O4d9sLeGK3O3Iwsf6dXTjB3qlgDhNteOs2HmZ7JzN6/4k6Dq2nLDPKttLeoce9CD3trB2ftO1Nc/DLOxhQn6o+rai5X6fMCpgB69/ZhZPtHYM5TDThRUCR2r0tatCymkYT88IzebBUQ0wHanWmFzOn7K20dyzoWw18CQe+xFkOE217J0uPpsfe1WEO9ZH0O+5Y/1YP69/ZhVPsnQp2lhwF4mfJbbsdhX6CxefWtjtLrkGKTtezs+QYdlAaCOKwPIICQcaGWnaWHACUHTkSNREB07YaP/9tTHEQ3XJl3P+L8XPpGAyGlag/S47NMFmEl2maYSMLK8sQUYCQwjNnqQU86CyFsP7NYGQ+rJu7iGxf405HpumH9v3keQj8FPMO2Y3R+xD46KIcDQeySFRQIMqGysgUu5hFpukn0+6HNk7UD3OYaGNi0Leagb1tI0v3t1OgIXemzZzQtHdpIIgI4TQdBWImTrB3kyQgREkfMuEQNpi4kvXv1LD+zextNyyGiQLxGKadd6MwN4zF51S2imEKKxwaJQ5h5VAMk1+I/p9mozCyJdMNZPr9aUWrPmb0Ggu/IGNNtegKPZpt77IjR6I6LODebcZjmMaXBNElR8b0bebFMGV6+8/0+9NKpuvDOffHYphsgeM8AJd8hqllwHfb4G8aGGl8Vj2pGKnHGZ3LOWjRR2kgeHDzgSf+txVki72LRAVePtm+Ozqw/p1dMHs7D+Yw0YZPvhXbb9JqHY0tllY13lg9dm0LzQT03P/CyjJMu2Qx7rz6bUu3MDvZ3hygOs1COhpl8w7fZf07u2D2di5sSY4CsSW5nbumoiA3gsVn70u7JBfDjV42w51E3u0IUtQR46++EPP2sXZXduRIVIZF/FTrMdwPp/QYhwJBprK8x2AwrIQtyTFa4CYPHjAu785rB6HunmJsueIkShK5i2T6mzX+Gvxl5HmOc5bsap8RhYdC6XExYu5qXEqyrX9nO27Tn9vkTQVzmLIAN51VZDQQ8F+Dr0bx6CaQCcPQc8SOtNd/97vzdNflVJLZ28cr8HoUR7UFOwM/QwqHJpleXqq2Z8lZRTb1bwazt50wh4k2CWKY7Hz6BNzVaI0OBr9/4D8InzIK3j3r8f3jg9JeP+AFAeG56Y+0cBOJ7F0aCKIy7MGBkPOOO7Fj8C8NBJEvyCigmJeKtyljQzb1bwazt52wGCYKxNMKbJ+Ewg5+LD7jl3ZHo0QUFsNkBeHnPWi++Grk/rgc4oV70l5f3/QoxG9fRtM/69Hh6V0WSGgfkb+KQFEhxEsP2C2KI3ik362oj3gwa8fzhsua3H0cAl4Ww8RguA/1MUzmZVnMRpQIuBRpBRjm88vik9Ar9BZq1vdKe23dtivg+2g2Gj7Lx3crTwaQuQ5TaSCIPf/+L3z+JgDMYQIAkSfgnJHHk8FguAC2JGcBosla1jPl6ZRpUtqyH/GvVfBNjKDzCz+nLcf30bvY/3YPdHh6F85d9a5mOexCr+2e++9peGnZ2XSF0YhT2mppIIjbn3oXE37/MZXyRB5Q4Jy0Apnav7MBN+vMzbKrwfUO02effYaLL74Y3bt3B8dxePfdd1t9TgjBjBkz0K1bN+Tm5uK8887Dpk2bWl1TWVmJ6667DoWFhejQoQNGjhyJ+vp67cLwIojcDMXi4M9ky3qpGqJTlgJTyZFMflqye8fK6P63tVTKshK99o4oQLPM2zpA2WnvtmU2f8Fh07ojqZTHA6YlrmT9O7tg9nYurneYGhoacNxxx6GsLLHSH330UTzzzDOYN28eVq1aBb/fj2HDhqG5uTl+zXXXXYe1a9diyZIl+OCDD/DZZ5/h1ltv1S4MLwCROvAcAccdCg1LFvRtdkPRmgnaCrTU46aO5ATSDVZ+gcAvKPG/rcCp9i4NBPH6u3/A0u29qZRHKwGmFlj/zi6Yve3H9Q7ThRdeiD//+c+47LLL2n1GCMHTTz+N++67D5deeikGDRqEv/71r9i9e3d8Jmr9+vVYvHgxXnrpJQwZMgRnnHEG5s6di0WLFmH37t2aZOF4H8AL4EFAUswy0ciOmohU5aWry+rMsMkwcg/ZhlZdSQoH+eDvOrM30Ch74kfFGKU6zKMmbG78Iuvf2QWzt/PIqF1yHMfhnXfewfDhwwEAW7ZsweGHH47Vq1fj+OOPj1931lln4fjjj8df/vIXvPLKK5g8eTKqqqrin0uShJycHLz11lsJHbFQKIRQKBT/u7a2Fj179sSuipnI99bhk7P34kAoh+2SYziK0H+PhtyhGKN/e2zWt7vSQBCndm7G3mYvHtxufJfctB7j4OEIlR13DAbDSlimbwBARUUFAKBr166t3u/atWv8s4qKChQXF7f6XBAEBAKB+DVtefjhh1FUVBR/9ezZEwBAFAngxVbBny3dUa0H7prlgTvNs8+W+0yH2XrgN22CuOkH250lJ9g7GtNl7/DnBD1YQbbcZzqyRQ+ZfJ8Z7TCZxdSpU1FTUxN/7djRIqM0H52Wj8UwcRwQUaIOlNbDd1v+sNFoLFYfzKgWmkuULcswep9a5TEqv9n2njVjFJ6cdJXhco3iJHtLyqGHGyP2FnSkKGD9Wzusfx+C2Vt/WXrJaIeppKQEALB3795W7+/duzf+WUlJCfbt29fqc0mSUFlZGb+mLT6fD4WFha1ebYnFMBESzfeihpYGbWvcto1Qj/Gd1rHaoke+tvpIlOE60f/NkEfr9VbbOzbb6YQnNcBee5cGgvBwBJ4WmzP02HvbHwcj8kYOps9/D/uaUw+nrH+z/t22LGbv1jjJ3gkhGQQA8s4778T/VhSFlJSUkMcffzz+Xk1NDfH5fOTNN98khBCybt06AoB8++238Ws+/vhjwnEc2bVrl6p6a2pqCACyc9dUUnNgInlrUCmZN2AUeaTfWDK9521kcvfbyfiS8aQ00P4FeDS/Yt/T+/1MeWWLHmjd57Qet5OH+o5zrb5o2/uJw8eSmb2Dhsqour0vCX1+DIn8Ncex9+nWV7boIVvu07l64AkAUlNTk/a33vUzTPX19SgvL0d5eTkAYOvWrSgvL8f27dvBcRwmTpyIP//5z3jvvffw448/4oYbbkD37t3jgeFHH300LrjgAowaNQpff/01/vvf/+K2227DNddcg+7du1OVNd2SnFqPOeYpO/0Jw2y06qGtfq2eadFbPy17C3zrGRW3QdveHo6onv1NRlNVAfi9uyHeENFcfzJY/47C+nd24QZ7u36X3IoVK3DOOee0e//GG2/EggULQAjB/fffjxdffBHV1dU444wz8Nxzz+HIIw8lrKusrMRtt92G999/HzzP44orrsAzzzyD/Px8VTLEzpLbuWsqCnKasPicSnaWnEspDdA52JJWOTR5qO9oFAgyvj7gc5xsdlB25EjUSx6sqRapxEcwnTqfTO7fjPaosxNB1uySO/vss0EIafdasGABgGiqgQcffBAVFRVobm7Gf/7zn1bOEgAEAgG88cYbqKurQ01NDV555RXVzlI7DJ4l55T4kkxD69Oe3qcXrYGYVtq7WeYRtnlnmFVYFdDrZHtnE0b7t9Z6mL3txSp7t8X1M0xOIDbDtGPn3SjMU7D47H1shonhOCZ3H4dCUcHmOg9rdwD+csQtaJZ53LPlRbtFYTAYtpFFM0wMBkM9ssXnHDoZmXCt0gowGAxGKpjDRBGOo3PMAoNhBiIP+Ew6INaNEMJBAceWTRgMhiqYw8RgZAkipz25Im0ifxUR/ou5Z66pJbZjkC1PMhgMNTCHicHIAkoDQXBc1GmyFZ4/mPbEfkReAQ9nyMJgMJwPc5gYjCwgNoti94KceH0I3ttlm6WI4uEIBIc4bwwGw/kwh8kCRKZlhgNokDjUR9gOuRjcwcSVLIaJwWCogf2U04ZI7d6K2P1YbxLsh8Z9CAYchEyzd7PsQZPEMwcyCZlmb0ZqmL3TwxwmF+KUhu2UHxqz9ZGufLvr1wJvIOg70+zdLPMIJUjkmUn2NkKm2Vtv+XbXbxXM3ulhDhNNOMFwpm81LKwsc0wnsxsjRxQk0mGi99KVn+hztWWrgZa9fTzgdXlaAZr2FpI4j5li70yA9e/swgn2TgVzmGgj5Mb/G1OuWTFM2d7JjN5/oo5D6ynLjLKN3q/Ak6ROghugbW+BV6gdRpzoYG277e12WP/OLpxs7xjMYaKNciiGKfYsryWGSU2jiXnhTplCtYuYDtTqTC9mTtlbaW9CgIiLM1vTtneyTN967N3Fp2BKj3FU5GL9Owrr39mFU+ydCnaWHAViZ8ntqpiJfG9du7PkwgqH6nB0YGZnyTHsoDQQRM88Ar8gY0OtwNodomfJ1Uc8uHfbC4bLmtVnDAoEGRM3z6cgGYPBsA52lpwtELk56WdepmmGjSysLIvnHWLOUtSBFDkCD2d8KaA0EERE4dAgsU7OYGQyrIdThBAZkJtNyx6c7Wvc6TBTP3bonnadkYNLUJnSjozcx8LKMvAcgQLjs7wLK8tACOAxuNqZKXYxi0zTT6bdD22cqB/mMNFEkQBegAJz4kTUDOxtG1m6v50CDbnNnDmxY1aGpr2dZncn2Pumm9/EhOvewldnXmCoHADwCwpEg1nDWf9OTabNjDJ7p8aJ9mYxTBSIxTDt2HY7CgMd8OHp21EbEVEdFuMxTI0Sh7ByKIbJL0T/T7NRGNmS6QYy/f60olUf03uORa6gYH2NO2KYzLZ35OPDgLpa7H+vJ7r/dZ2hsqb0GAeRI5i143lK0rUn09t/pt+fVjJdH865PxbDZA+8CI4TwHMEhLSfZWoZ8N02+JsGRhqfVU8qRpdRGIfQoo+Y3mPb6Jm9gbnB3+PBO6437CzFMPtcOta/swtmb+fBHCaa8AJIgqNRYiTK1UIDGlssrWq8sXrs2haaCei5/4WVZeC4aIc3cwvzr6MPx54bj25VL+BMe3s4Ao7S6nlEiaYpMAPWv7MLZm/nwpbkKBBbktu5ayoKCgR8csY2VIZ9KZfkYrjRy2a4k8ndxyHPQ7Ctwbzz0yJvFwGNzRCvD5lSPi1KA0Gc0imEXU1ezNn5nOHypvUYh1xBwfRt8yhIx2AwrIMtydmH1AQFXMIlObtwkwc/rcc4jCl2nrxu0mEyWfM8BPmibKqTLl5eo9lZsku3MuGoDIClgSByBcW2Y2fc1DYB98nrNNymP7fJmwrmMGUBbjqraEcjh3n7nDfr5qaZwFT2JsRZaQXsDPwsFCPIFYw7OQsry9Ak8agJm3+OZLL6nWTTVDgn0Ne9MHvbB3OYaMIJ4MSC+J9OUa6bGq2bBgOnksjepYEgIoRDyGFHo9hl74WVZaiNiGiilGxSwaGjkKyG9e/sgtnbPpzym54ZEAngok+ZXItDPbWcJWcGbulcMdwmr9NIdghlREHCs9PsRou9w0v7IPJGDmrv7GaoztJAEA0Sj3qJjj4iCiByxDbnz024TV6n4Tb9uU3eVNgzh5zhxDJ9x/wkkbffaWJkN6WBIPyCggJBxsJK83IFmU3DuwoaDhyJLb/0AvCBobJyBQVFIp2OGfDKCCnmBdMzGAz70T3DFIlEsGPHDmzcuBGVlZU0ZXIthMgAkSwP+tbzVOuUaVI3y24Xeu+fEPuWjWIYtXfHudvR440fcOZ/jTlLQHTwo5UKoFnm4TMp6NvNfcTNstuFm3XmZtnVoMlhqqurw/PPP4+zzjoLhYWF6NOnD44++mh06dIFvXv3xqhRo/DNN9+YJavzkZri/+U4Ytl6Z7Kn2lQN0SlPwqnkSCa/U2S3C732lh1wlpyT7O3lFSpB3wDg9dDL6dQW1r+zC2Zv56L6N/3JJ59Enz598Oqrr+K8887Du+++i/Lycvz0009YuXIl7r//fkiShPPPPx8XXHABNm3aZKbcjoUQSfXhu2Y3FD2ZoM1GSz1u6khOIN1gFQ1M5uJ/W4GT7R0hHLWg7waJp1aWWlj/zi6Yve1HdQ//5ptv8Nlnn+Hrr7/G9OnTMWzYMAwcOBD9+/fHKaecghEjRuDVV19FRUUFhg8fjs8//9xMuR1NusN3aWRHTUSq8tLVZXVm2GQYuYdsQ6uuIgoQlqNtk9k7Ovh5ODp5e3nAtBmmGKx/ZxfM3s6DZfqmQNvDdxef8Quqw17UhkXUsUzfDIcwufs4+AWCrfUsODmW6Xtfs0jlwNxpPcYBAB6ikDWcwWBYiQmZviORCBYsWIDHHnsM//73v6EodoePOptY0HdLd1TrgbtmeeBO8+yz5T7TYZUe7HaWnGLvXEGiFsNU5JXQOSei6TtO0YPZZMt9piNb9JDJ96naYbrmmmvw7bffIjc3Fx988AFOOOEEbNy40UzZ3AcvAkprr6jlNL3Ww3db/rDRaCxWH8yoFppLlC3LMHqfWuUxKr/Z9vbxMG0nlxacYu8mSUCzfGgINGLvmrCAuohH0/dZ/9YO69+HYPbWX5ZeVDtMW7ZswbPPPovbbrsNzz//PBYsWIBRo0YZFiCTUdB6hikdLQ2abFYg9r4e4zutY7VFj3xt9ZEow3Wi/5shj9brrbZ3zFVywpMaYL+9FbRO5GnE3h6OgE8Tw8T6N+vfbcti9m6Nk+ydCNUxTGeeeSZeeeUV9O/fP/7ewIED8eOPPxoWwu3EY5i2T0JhoQ+Lz9yFqpAPNREB9REPIiQaw1QVbv9dvY0qlnLe6Z3GTLJFD7Tuc1qPcfALMjbUCrrKibyRAyhE8+G6tKBp79JAECd1CmF7gxdP7DYedzSn72h4OIK7trxouCwt95nJbZ/17+zCPj2YEMP07LPP4tJLL8Vdd92FV199FXfffTd69+5tVNLMQ4nGMSTKw5RuSU6txxxrTNncuQDtemirX6tnWvTW7xR7k/0SlF/tW9KjbW+BI/ALdPa8NMo8wm2OnbHC3na3CTNh/Tu7cIO9Ne2SC4VCePfdd7F+/Xp069YNpaWlyMvLM1M+V9Bqhimfx+Kz97Fdci6F1tONE58Wp/QYh1yPgs11HstkiyzKA+pCEEfJltSnltJAEGcUN+LXZq/uGbeWTOsxDh6OUNlxpwUt7Wxy93Ho4FXQJPHY0cg5rn1aQSb3b0Z71NnJhBkmAPD5fLj66qvxwAMPYPTo0cxZSoSQa+jrTokvyTS0Pu3ptYPWQEwr7c0jOqtiKTUhKHXWn/GtRq+ywqNZ5uPLAHrrKQ0E4fOom3mjbW+tP9oiR+AXnOW80oD17+zCKnu3RfUM0/bt29GrVy/VBe/atQuHHXaYbsHcRMsZpqKORfjo9J/ZDBPDcUzvORa5goL1NcZnVNxOaSCI07s0YW+zF/f/Ms9weTN6jQUhsHyGSQtTeoxDcU4EhHAorxKzvg0wGFFMmGE6+eSTMXr06JRnxdXU1GD+/Pk49thj8c9//lO1uAwGw3wihKN22GwmwKnY2aYWSXGHbmWFg8RSFTMYulCdGWjdunWYPXs2/t//+3/IycnBiSeeiO7duyMnJwdVVVVYt24d1q5dixNOOAGPPvoofv/735sptzPhNSZaYjAsRmE/lnEkhd75bwLvfMVGlEOpJRgMhnZUjxadOnXCk08+iT179uDZZ5/FEUccgV9//TV+yO51112H7777DitXrsxOZ6kFag/fZTCsxJYYJgfDQd1ZcpF/dULoSTHlNT5eoXYunZnw0Bi4ymAw4mieEsnNzcWVV16JK6+80gx5qPPAAw9g5syZrd4bMGAANmzYAABobm7G5MmTsWjRIoRCIQwbNgzPPfccunbtqrvOdIfvMhhWUxoIwsMp8HAsbi4Gr9bB4XlVXoYdS3Jad2vRWoJkMLKRrHjY+M1vfoM9e/bEX1988UX8szvuuAPvv/8+3nrrLXz66afYvXs3Lr/8chulZTDos7CyDDLhIBO2cydGSOHRKKf3IMSL98M3MfU5cc0y3ypruBMRs2K0ZzDMIyuCbgRBQElJSbv3a2pq8PLLL+ONN97A7373OwDAq6++iqOPPhpfffUVTj311ITlhUIhhEKHsh3X1ta2+pwtyTGcRtRJirZLNsMUReAIVSfC6fFBEQWQyKGDwWnC8hIxsoGseObYtGkTunfvjn79+uG6667D9u3bAQDfffcdIpEIzjvvvPi1Rx11FHr16oWVK1cmLe/hhx9GUVFR/NWzZ8+U9UecPpLqhM1UuIeFlWUQeAIvTwznoGG0ZtOlQzB92ot4ILjA8rq1OimxWUY1aLF3WznCZR40P8zy9LkJ1r/Tk/EO05AhQ7BgwQIsXrwYzz//PLZu3Yrf/va3qKurQ0VFBbxeLzp06NDqO127dkVFRUXSMqdOnYqampr4a8eOHSlloD0V7pSG7ZQnSrP1ka58u+tXi6RwCCv6Mzxnmr05RBM5ai2/7ee5/iZwRSL4Qjpn7JnZnjwcAadygkmLvdvKzBUJ4POdrw815dtdv1VkWv82o3xdP+X/+c9/dFdoNRdeeCGuuuoqDBo0CMOGDcOHH36I6upq/OMf/9Bdps/nQ2FhYauXlRjJTJxpGFkKSKTDRO+lKz/R52rLVgMte4eUaNyOm6Fp72RB31rt3eONHzD97hGYOnt0yvrUYlb/FtXFruuirU7E60Pw3m48ozjr39mFE+ydCl3956KLLsKkSZMQDod1V2wXHTp0wJFHHonNmzejpKQE4XAY1dXVra7Zu3dvwpgnp5Htnczo/SfqOLSesswo2+j9+gWCAkF2bbuhaW+/EE1cSWtnW6KZKivsrVUnsegAp8wmpIL17+zCyfaOocth+uyzz/DBBx/gpJNOwpo1axJes2fPHlxxxRWGhDOD+vp6/Pzzz+jWrRtOPPFEiKKIpUuXxj/fuHEjtm/fjqFDhxqqR29gpdpBcmFlmSsGPTOJ6cCMHxYt3zWz7Ng1Wu396+jD0fxwXqv8QbElObfiZHt7PQR5nvTBQbTtrXUM4BFdlqP942zGj72T7U2r7Ng1bDx3jr1TofosubbU19dj/PjxePPNNzF79mxMmjQJAKAoCjZs2ICnnnoK//rXv7Bv3z7dwtHgzjvvxMUXX4zevXtj9+7duP/++1FeXo5169ahS5cuGDt2LD788EMsWLAAhYWFuP322wEAX375peo64mfJ7bwbRUV+fHz6ZlSGfa3OkqsOR3+o2FlyDCtonFkIsW8TkJcD8co6lAaC6JuvwC/IWFPNzhErDQRxUqcQtjd48cTu5wyXN7tPdDnu3m0vGC5LLVoPgp3ecyw6HzxL7rtKL9U2wHbJMdyL+rPkdKcVyM/PxxNPPIG8vDzcddddePPNN6EoCtatW4dQKITevXvj4Ycf1ls8NXbu3Ik//elPOHDgALp06YIzzjgDX331Fbp06QIAeOqpp8DzPK644opWiStp0HJznJcHwhm6W47hPPLuj6W6iOYPWlhZhil54+CRefbDZgINksfxaQVkwpmWUiD2r9Vtq3lOLirX9EP3v601VE64zIPIeb/D3mlV6PfPbylJx8g0dC3JvfTSS+jVqxc6d+6MBQsW4JRTToEgCFi9ejVuueUWVFZWYuvWrRg5ciRteTWzaNEi7N69G6FQCDt37sSiRYtw+OGHxz/PyclBWVkZKisr0dDQgLffftux8UvZvsadjkzTD+37iSjRZblM0ZPR+xA4Ah+lKGgaZ8lpvR+tyzi5ggKRp+/WxeQw21lKpB/PkV4U9dljvPCjeoLvdxk6991lvCyVZEo/NAsn6kfXcDFt2jRcdNFFWLduHerq6rBy5UqsXLkSTzzxBF566SVMmjQJjY2NtGV1HbR3pKgZkNo2snR/OwUacmfazAlNe5cGghB5IMfjjHkQJ9hboOg86AtsaI1We+8f1R/10zph+9XHJ/y8LU0SD4lw4FQcCVN7ZzfU3tkt7XVqMcve4uU18P+50phwALznbkOuOA6Fj1NwvlTCxvPUOHE81xXD9Mc//hFz585NeN7ajz/+iOuuuw6NjY14/fXXMWTIECqCOhk1MUyNEoewciiGyS9E/8/iCNST6fenFS36KA0E0cevIFdQsL5GcIUezbR3aSCI07s0YVejD7N2PG+4vFl9xqBB4jFnJ53l/ES01Ud4rgdctzxE1vDIe6Am7fcndx+HgFdGSOHx4PbU9xz5qwjwPMTr0+dS0hpLlaocN7RLq8h0fTjn/tTHMOmaBPnHP/6R9HDagQMH4ptvvsEf/vAHnHnmmXqKz1haBny3Df6mgZHGZ9WTipF6nNG5nIOeLM+xxyNmbyCi8iw5NfCIpmwwk7b68N4uQ7yyTpWzFJthzBUUFInpU0uIN0RUOUsxuWKymWXv6omH4efLT9Zdthth47nzMCWPmc/nw9NPP40PPvjAjOJdi9+kk/tobLG0qvHSGFidOg1tFXruf2FlWTxxpZVbmJ1sb1o5mAAgx0PgMSljQ6r7V7slPmYHQjhIJh51aZa9/WfW47CTjQV2uwU2njsX3WkFGIfQsyQXw41eNsOdTO4+Dnkegm0NbKfcmOIgju/YhHU1uXimwrgupvUYh3qJo1KWFrQsa0zrMS6eVqC8im5qCecsrzAYWjF5SY7hLtzkwQPuk9dpJNPfE7ufw6wdzzvuh80OezdI0dk2kdLutnxRRr5g/bOnlmM1FAARhUOzrP88wURotR/r38Zwm/7cJm8qmMOUBbjprCL2pGocZm91+HiFysHYCyvLELExg7pae3t5Ah9P4KXgJOqF9W/jsP5tH8xhygLc1GjdNBg4FWbv9PiFaNA3DUoDQfBc9MgRKykNBOMvNfb28Qq8vAKRp380ilpY/zYO69/2oXvE+Pzzz3H99ddj6NCh2LUrmuxr4cKF+OKLL6gJ5zZ4wW+3CAlxS+eK4TZ5nUay09Wn9xwbP8LDSdhh7wYpGvTdKBmfGYrNMEVMyKKdrt6W/6ajTvKgKizgQIhuWgmtmwhY/zaG2/TnNnlTocth+uc//4lhw4YhNzcXq1evRigU3X5aU1ODhx56iKqADEYiwsv7IfKyx24xXAXNXWGZQK4goYOXTvJKSaHjfGlFU6Zvj4JCUUZHn5RRT/0MhlXocpj+/Oc/Y968eZg/fz5E8dBp6Keffjr+97//URPOlXAm5Q5IgZ7Bz4oBM7y8HyL/Tpyvy4gcpYEgwktqsH/5UXpFczVGbGfW1ne1OKmtygqPBkpOjs+jmBb0TUtntREetREPqijPMGmVw4zvZBJu1pmbZVeDLodp48aNCZNSFhUVobq62qhMroUoJmSjVEGywS9VQ7RiwJQ++xXNX6R+gk8lRzL5F1aWIf+hA4YP3HQreu0dUoBG2d6wRb32po1fiG4mphH0DUTPpaNxnlwiaPVv38HElfkqElfSwin2dhNOHc/VkOn21jVclJSUYPPmze3e/+KLL9CvXz/DQmULZjcULeWbMYDm3V+Lgof3667HTR3JCaQbrPI8BD5eiQcKW4FV9t5xzXH44oyLVF/fIAESpRimGJLFO+X09O+IwiGsmJeHi/Vv87B7PDdaTybYW5fDNGrUKEyYMAGrVq0Cx3HYvXs3Xn/9ddx5550YO3YsbRkzDhrZURNhJCOw1Zlhk2HkHrINPbriDv6mZ4q9I68ICC/pha5jG3HylZ+olssvAEViBJ18dI4zCSk8Qiafa2y0fws8oZJ3Kl09qWD9Wz1sPHceujJ9E0Lw0EMP4eGHH0ZjYyOA6HEod955J2bNmkVdSKcTy/S9c/d0FBbm4OPTNrBM3wzHMavPGOR5FOpZnu2k7p5i+LpXAl6Cuh97otNz21R9b0xxEL8pCmFTnY9Kdu7pPcciQjhTD981yrQe41DklRBWeEzfNs9ucRgMh2Bypm+O43DvvfeisrISa9aswVdffYX9+/dnpbPUFo5vHfTd0h3VeuCuWR640zz7bLnPdJithwaJR6Ns/7EoNO+z4JF98E6Q4B0rY+Ii9UtyQDRvEq0ZlxyPArFNHqY9Nx6NyAvJd3Ia1UOy76cqV+CiBwVbCevfUbJFD5l8n6odpkmTJrV7TZkyBS+99BIWLVqEGTNmxN9nHIJrEdag9fDdlj9sNBqL1QczqoXmEmXLMozep9VHPphp79JAELMefBFTnnzTcLlGcYK9GyQgrPCtMnQbsTeXIHwpp7AB6JiT9PtG7d32ftP171hiTQX2xFqx/s3Gc63Yae9EqHaYVq9erepVXl5uWKhMQsuCZ0uDtjVu20ZIY4B1Gnrka6uPZD8ibf9vhjxar7fS3gsryyDtygXZ0aC5HLOw2947GsVWy3FG7M2jfZ6rmr2doGyPqJJNr73V/qCUBoLo6JOQ65Gxq9Ge/GV229uoPE7u307E7fZOhK4YJkZrWsYwFXXIx+JT18RjmGojHkQO7sapCrf/rt5GFZs1cHqnMZNs0QOt+5zcfRz8AsGD25+nKJ11ONnec4+4BXWSB9O2vmC4LDPuszQQxGF50XPk3GJ/J9ubJtlyn+mwTw8mxzAxkkBSBymlW5JT6zFrPRIhU9Gqh7b6tXpNXG/9tOyd5yHI8SiOiAXQA017lwaCeKTfrRhTTEcX+0MiDoRaz9xYYW8tZc7Z+ZxrnCWA9e9sww321jXDlCxOieM45OTkoH///rj00ksRCAQMC+gG4jNMu6aisCgfH5+2AdVhH6rZLjnXoebphtY1VjOlxzjkehRsrvM4TjarKQ0EMahDpN2ynJHysl2nboCWnZi93YE6O5k8w7R69Wq8/PLLePHFF/Hpp5/i008/xfz58/Hyyy9j6dKlmDRpEvr3749169bpKd7dHJxl0puSxa1P/05H69NequvVZLPV+5RkNnyWHCenRq8eniCsqL8+VT098wjGl6Qvg7a9m2b7EV7SC83fnUS1XLdBs3+rqcep/TtbsMrebdE1w/T000/j888/x6uvvhr3yGpqanDLLbfgjDPOwKhRo3DttdeiqakJH3/8MRVBnUyrGabCHHx8+maWh4nhONwew0ST0kAQJ3UK4WdKeZge6XcrGiSP5bqtn9YJvlMUkI6d4T1rk6V1MxiZgfoZJl0nxT722GNYsmRJq8KLiorwwAMP4Pzzz8eECRMwY8YMnH/++XqKdzecAI5jcfQM5yHyh7aWM6LT67TyMBHCadoRa4TIvzpB7t4LOSevRv5DBw6+W2VN5QxGFqNrSa6mpgb79u1r9/7+/ftRW1sLAOjQoQPC4QTbwrIM9gNlPZH3u6B5Tq7dYjiSbFmSU4NMuFZ5mPRSGggiQjhEiEXKjYTBSZH01zEYDKrocpguvfRSjBgxAu+88w527tyJnTt34p133sHIkSMxfPhwAMDXX3+NI488kqasroEcHDh5rn1uFoZFML23I6IgoYMQnutBZIGAplkFNkhlL2EK578trCyLZg236OFoxK03wDd0jSV1MRiMQ+haknvhhRdwxx134JprroEkRYNyBEHAjTfeiKeeegoAcNRRR+Gll16iJ6lbaJFaQCHAhD98hIKreHD1dUCoGaSoI0ZefbGNAmY+4sX77RbBcZQGghCTHb7KI+rd8yafHuswIgoHLw9E/ubDrveOQZ9/rNZdlpX5WVjcI4NhD7ocpvz8fMyfPx9PPfUUtmzZAgDo168f8vPz49ccf/zxVAR0FZwAIje3esvXoQ5S56PB59WCC4cgd+hik3CMbGZhZRmm5Y0DD9Juq603KAOQATh7mSeyQEBkmx95D9RQKS+sRDdiwCtAEGRDZSmAdUtyDAbDFnQ5TDHy8/MxaNAgWrK4n4OzS7Ggb54DeK8E4s0Fws2AFAERvHZKyMhSSgNBKAcPXXXtDEWeD7yPnlOX4yHw8oD4xwYAP+gupzQQBCESItk1QcdgZB26Z5KXLl2KadOm4ZZbbsGIESNavbIVQmSAP+SDKgSQQ17wzfXgmhvA11ZDqG4fLO9WWI4R97Cwsize2Y3moLEL8Y8NyJnaSKUsvwAIvEJll9zCyjJIlALInYTd9mZYC7N3enQ5TDNnzsT555+PpUuX4tdff0VVVVWrVzbDcUI86BsAnnzjCsy8aDDuuuJ3uP3Gy3HLRWcZrsMpDdspMxVm6yNd+XbXrxYFgEQ43XbLJHs3SNHNGYmcHD32zvEo6JJjbFlPbf1WkUn2NlK+3fVbBbN3enQ5TPPmzcOCBQuwatUqvPvuu3jnnXdavbIWpXVWSrO2cMcOKGQYO6IgkQ4TvZeu/ESfqy1bDczeh6Bl745ewC9IyBPazzDpsbfAATxal8XsbRzWv7MLJ9g7FbocpnA4jNNOO013pZkMORjHxCO6JGcm2d7JjN5/oo5D6ynLjLKN3q+XJ8gX3Hv4Lk17Vx1MEUcr7iikcJDaBH3bbW+3w/p3duFke8fQ5TDdcssteOONN6gKkhHITUCkrlWmb615mNQ0mpgX7pQpVLuI6UCtzvRi5pS9lfaWFA7NsnvjbIzYOzzXg13XDUR4rgfbrz4eANAgJd7zosfe9REPDoQ8muVKdg3r36x/ZxtOsXcqdJ0lN2HCBPz1r3/FoEGDMGjQIIii2OrzJ598UrdAbiR2ltyOrWNRWOTHknMqcCCUg/qIiJpI9Cy56nD0h4qdJcewg9JAEIflEfgFBT/VerKu3UX+1QmNn/qQd1YIDctyMeXNSzGwQzM21uZQOUtuZu8xOBDyUCmLwWBYiclnyf3www/xPEtr1rTOOMtx7n2CNYxYAPCeeNB3yyU5L08nqzCDoYeFlWWYnDMOYVl/0LebES89eObawWe50gAQUvj40pwRSgNBCFzieCgGg5E56FqSW758edLXsmXLaMvoGjju0JQ8j+iuJJpk+xp3OjJNP7Tvp1BUUCjKGaMno/dx24RXcP+Z3xiWY2FlGRokDxolYw+LmWIXs8g0/WTa/dDGifqxMqN/5sO1yMEE+spVMzPQtpGl+9sp0JDbzJmT0kDQct3RtHdpIIiQzDsmG7Xd9vYLAAb1Q5d+O3WX0ZJcQUG+QLDmgjPRODP1tH4yWP9OTabNjDJ7p8aJ9tYVwxRj3bp12L59O8Lh1vPal1xyiWHB3EQshmnnrqkoLMzBx6dvRmXY1yqGqVGKHsMQi2HyC9H/02wURrZkugE77y/WwZ2kX636mNJjHHI9Cu7/ZZ6JUtHDTHuPKQ7iiVvfxLovTsLJKz4xXN4j/W5FfcSDOy79EAX/r/7QEiBFWP/OLjJdH865P/UxTLomQbZs2YLjjjsOxx57LC666CIMHz4cw4cPx2WXXYbLLrtMT5EZA2mxS04hgKfFjrmWAd9tg79pYKTxWfWkYqQeOzuXmbtYPjrpCuy8dhDqpmo7Z1CLPKWBYKst9Nluby8PeId40O+ozVTKa5A82B/iEXj2F1OcJYD172yD2dt56HKYJkyYgL59+2Lfvn3Iy8vD2rVr8dlnn+Gkk07CihUrKItoHWVlZejTpw9ycnIwZMgQfP3117rKIUmWPfyGTu5LDo0tllY13lg9dm0LdSLH9t+M4iHrkHO2unMG9dz/wsoyiPyhZKrZbu+wAsyffAkeeu8CKuVJJh6Lwvp3dsHs7Vx0Lcl17twZy5Ytw6BBg1BUVISvv/4aAwYMwLJlyzB58mSsXr3aDFlN5e9//ztuuOEGzJs3D0OGDMHTTz+Nt956Cxs3bkRxcXHK77ZckivwA5+cuROVYR9qwyIaZR5NMt9uSS6GG71shjuZ0mMc/IKC6dtSL8ntuOY4eAQJ3f+21iLJrGdMcRCP3fwWfvjyJJz++YeGy5vRayyqwzxLK8BguA6Tl+RkWUZBQQGAqPO0e/duAEDv3r2xceNGPUXazpNPPolRo0bh5ptvxjHHHIN58+YhLy8Pr7zyiu4yeU574kozcJMHD7hPXqeRTH9eniCHT793s2T4JnS5nE4wtBrssLeXB4Rr+mLwuf81XFZpIIg8TzTo2w7c1l/cJq/TcJv+3CZvKnQ5TMceeyy+//57AMCQIUPw6KOP4r///S8efPBB9OvXj6qAVhAOh/Hdd9/hvPPOi7/H8zzOO+88rFy5st31oVAItbW1rV7JaBnDZBduOqvIOYGA7iWZvbfW86hoTr8uLF7TCPHyGjNEa4dd9q4KA48OH4zRz1xvuKyFlWVolHmEbMqzxvp3dsHsbR+6HKb77rsPihIdHR588EFs3boVv/3tb/Hhhx/imWeeoSqgFfz666+QZRldu3Zt9X7Xrl1RUVHR7vqHH34YRUVF8VfPnj111WtVQ3JTo3XTYOBUktnbrBg6I9hpb5qHY0sKhwaDeZj0wvp3dsHsbR+6htBhw4bF/9+/f39s2LABlZWV6NixY1Zk+p46dSomTZoU/7u2tjah06QQZyzJuaVzxXCbvE7DbfqzS16R4uxvdCbZnr7O7J1duE1/bpM3FdSeOQOBAK2iLKdz587weDzYu3dvq/f37t2LkpKSdtf7fD74fL7EhfEOfIxnZD2lgSACXoJ8UbZbFEfQ0Qt08EZQnJP+wFw1CDyBl6UBZjAyGtbFAXi9Xpx44olYunRp/D1FUbB06VIMHTpUdTlEDgFK+wRLEZNjG/RMeTplmtTNsttFovsPl3kQebcjIvOTOwAhBWiW7e3yTrK3wCuIUEoHEJJ5086KdJLOtOJm2e3CzTpzs+xqYA7TQSZNmoT58+fjtddew/r16zF27Fg0NDTg5ptvplJ+opQCtEg25ZmqITplmjSVHMnkd4rsdpHo/n9dNQBN//WicmX/pN/xCwR+wd6z5Jxi72h/FKg5OX5BRgevORs8WP/OLpi9nQtzmA5y9dVX4/HHH8eMGTNw/PHHo7y8HIsXL24XCJ6SFstxTlCs1kzQVqClHjd1JLvp/td1KHxsL7q+kjitR2kgCEIOJVVl9qbbR3M8BAWCtcudrH9nF8ze9uOE33XHcNttt+GXX35BKBTCqlWrMGTIEO2FKBFwHIGC6AG8LdOCxnYpmXXMRqrGm65hW50ZNhlG7iHb0KOr2IQKszfgFyRqs0ISMX+5k/Xv7ILZ23mozvTdcldYOp588kndArmRVpm+c5rwyTkHUBn2oTosoiYsIKQgvuW45bJcJnjcDPcwvedY5AoK1tcIWd/2SgNBnNIphE11PirZuaf0GIdGiWOZvhkM12FCpu/Vq1erepWXlxsU3t1w3o7x/xPCJT18Vw1meeBO8+yz5T7TYbYeGmUOzTJvu7PkFHvXSXR2yAFArkfRHA/lFD2YTbbcZzqyRQ+ZfJ+qHably5erei1btsxMeV2B0iYfS2wnjtbEgS1/2Gg0FqsPZowRXtILkX91Svo5jQMcY7Qsw+h9apXHqPxm2zsW9G03TrB3Ry8Q8EYg8oceaIzYW1I4zWkFMqV/p8MJ9k5XlhnXt4XZWzt22jsRhhbd161bh8WLF+O9996Lv95//33DQrkZQqSD/x5ymloOyqloadC2xm3bCPWeWG8H3I7dIDur016nR762+mhbRiqd0pZH6/VW2zuscAgrvCOe1AD77a0QrlVaASP2zlFxllym9m+12G1vo/I4vX87DbfbOxGqY5hasmXLFlx22WX48ccfwXEcYkXEsnzLsv1PsVYSj2HaPR0FeRI+OXMnDoRyUBkW0STxaJSjxya0XZLTa8BYanw3pcg3g2zRA637nNFrLAoEGT9Ui67UF017jykOYnCgEWur86jEHc3pOxr7Qx48sfs5Xd+PfNQNzb/5LQp6/SNr2nU6skUP2XKf6bBPDybEMLVkwoQJ6Nu3L/bt24e8vDysXbsWn332GU466SSsWLFCT5GZgxJJ+lHbJbm2HrJajznWmLK5cwHa9aBX37Sw296SwiGscK5tNzTtHfAS+HhF9exvOvaHPNjX3HopXpO99/8Kcf8WAKx/x2D9O7twg711zTB17twZy5Ytw6BBg1BUVISvv/4aAwYMwLJlyzB58mSsXr3aDFkdS6tdcrkRLD57H6pC0V1ytREP2yXnImg93TjxaXFKj3EoEGRsqGW75MYUB3FUYRgbar2Yt8859o687IFyZH/4fps4nxbDGJncvxntUWcnk2eYZFlGQUEBgKjztHv3bgBA7969sXFj9nZ0IocAIRdAND6C4wg8HEFEab8cByT2iJ0SX5JpaH3a02sHrYGYVtrbyxN47D8L2hLS6XXevjJM3Dw/7iwZsXdpIIijiyRM6zHOsFxKDQ++tlqXLNkM69/ZhVX2bouuGabf/va3mDx5MoYPH45rr70WVVVVuO+++/Diiy/iu+++w5o1a6gI5xZiM0w7tt2Ook6d8dHpP+NAcw5qIwIaUsQwAWyWiWEdU3qMQ65Hwf2/zLNbFNspDQRxbIcIttaLVGaYHut3K2ojHsza8TwF6RgMhnWon2HSuNE9yn333YeGhgYAwIMPPog//OEP+O1vf4tOnTrh73//u54iMwMiAZwulTIYphNRAC+fJVNMafALQAdvBH6BTn+VCYcIYbplMDIZXaPFsGHD4v/v378/NmzYgMrKSnTs2DG+Uy5rOZhWgOcIOK715J1fMO8AXgYjHSIfTXHB4i+ieDgCH4XTTEoDQciEdWwGI9OhdvhRIBBgzhKbXWI4mIgSTaLKnKXog0tIjm7IMArTJ4ORHej+hV+6dCmWLl2Kffv2QVFajzqvvPKKYcFciZAbT1ypEK5V8koGw05KA0E2u9kCvwBEFL5V4kqnUXdPMXL6HYDSwMM3KXm6EgaDYQ26HKaZM2fiwQcfxEknnYRu3bqxmaUWcJwAhXDg2yzHeXloPmuKwaAFmwVpjZcHvLyCJ+a8gNnri/HtZ6finJXv6S6vWeYRodC/66d1gq9/DcQREnhRAkQOnMgGDgbDCehymObNm4cFCxagtLSUtjyuh/PktHKWZDbLxHAI40uCKBIVtpOrJXk+eHJD8ArGpt/kNses6IX3RYA8LwAJ/j9XGi6PwWDQQ1cMUzgcxmmnnUZblowkk58NWY4Rd/HY3S9ixgMvGs5BkwmElaiTM3n8Tch7oAanf/6hofJyBQV5ac6SU0Pe/bUQr2k0XA4NMsnejPQwe6dHl8N0yy234I033qAtS8YiUgutj+KUhu2UZR6z9ZGufLvrVwtfpABFebq/z+yd/HOFACJH55gV1r9b40R7W1m/VTB7p0f1T/mkSZPir1AohCeffBJnnXUWbr/99lafTZo0SbcwmQBRWk/t0xpEWxI7oJBh7IgCtZnW05Wf6HOaWdxp2fv++0fj0TuvNVyOndCy95Pj/oYx9y/EPed+1u46PfaWFA6Ncuqz5NSixd6ZPg6w/p1dOMHeqVAdw9T2fLjjjz8eANpl9c7mAHCO8yR4D9FEoiaQ7fl0jA4yiXRHS59mlE3D3grc2z9p2lvoUg/SrSf8XejECXk9pF3iSivsraWOaT3GweeiTO+sf2cXTrZ3DNUzTMuXL1f1WrZsGVUB3QQhcjxxJYB2iSvVoKbRxDpWNncuAHEdqNWZXsycsrfS3qGDeZjcCk17T509GlxdDSINuZq/m+hzXuVTkVrZ55b+Cy/NeFFVmWrrLc6JoGtO2DWzGax/ZxdOsXcqdJ0lt337dvTs2TPhbNL27dvRq1cv3QK5kfhZcjvvRlGRH/8+bQtqwl7URAQ0yzyaZB7V4aiu2ubCyfZOwrCG6A8mgV8g2FrPG253zQ/ngUge5E6voyShtYwpDuLoohB+rvPhmQrjffCJw29FdZjeWXLNc3Lh6S9AvJKefmf2HoMCUcatf3wbYudaEMmDnLubqZXPYLgT9WfJ6QpH7tu3L/bv39/u/QMHDqBv3756iswIYktyidIKeCkHfjMYWlhYWYaIwkGilOnbM0CEcDRwYFwf48LZgJcHcjwylZ1tAFAT8bSLYTJCzpQmqs4SAITkaKJOX+9q8P3y4eknUi2fwch0dP2ME0ISzi7V19cjJyfHsFCZgGJC/qVUU4mNDxQh8nL7GKpswi1LDWqhfT95AkGuoFAplxR1BOnYCfn9dlOQTB9G7oN2Etk8j2J4N6zZ7ffuK97Dbdf+H6q+6QdxeBV1h8xsWP/OLpyoH01dPLYLjuM4TJ8+vdXOuAkTJuDqq6+OB4Mzosr1UNwll2pmQDxGgnzsb9o1snR/OwUacmfa8qaa+1Grt9i/CqXm6D13G7znboPvzrCu79ttby8f7Zu0Jn4bZR6NkrGHJJr2ToT/mD3wHtuMoqN+0SegAey2txMx2952kqn21hTDdM455wAAPv30UwwdOhRerzf+mdfrRZ8+fXDnnXfiiCOOoC+pg4nFMO3cNRUFBQIWn/ELqkI+1EUE1EkehBUOjRKHsHIohskvRP9Pq1FEFgggvXvBe84WKuU5kWzfRdIWrfqY0Wss/IKMNdWiK/Ropr3HFAdxfMcmbKrLwRO7nzNc3kN9R6M24sGcncbLSkYifajVUWkgiFcemQcU+BDZICLvgRqzxNQN69+tyXR9OOf+1McwaToaZfny5QCAm2++Gc888wwKCgp0i5gtRGeY2gd80z4IVbxJAqDfWbKq8Rqpxxmdyzlo0UdpIIiwQuA7uEuO2Rtolj3Udg1Gj0ahUlRSjOrj/pmjUeSVUB/xADDneBwn29ttGNEH69/moGtGuk+fPnjqqafw4IMPJnwxEuPXdXJfemhssbSq8cbqsWtbaCag5/4XVpbFO7uVW5idbO8cjwyRp7NGGZbpnCWXiFT3n86OLfu3wBOIPMGYM7/A3hEDsOqsYVTlbCmPE+3tFth47lx0pRUYPHhwq78jkQi2bt0KQRBw+OGH43//+x81Ad1AqiW5xoNpBdouycVwo5edjVg9EJnB5O7jUCgq2FzncfV90KA0EMTgQBg/1Xoxb5/xhINHF0Vnbh4ycUmubZ0x1NpyVp8xKBBkBKe9CuR50fRtEQof32OWiAyGSzA5rcDq1atbvdasWYM9e/bg3HPPxR133KGnSIaJuMmDB5wrr1ucjHT6c9p92GVvD0eopfsgBBAozVapITZLqOVYjVjA/6MPjoR4TSM1Z6k0ENRkQ6f2b7fgNv25Td5UUMsOVFhYiJkzZ2L69Om0inQ1HEfAOySpspvOKnJOIGBrnChTMpLZO6JwCMm8o9qCVnu/d8IfqdTrF6KDH40luYWVZQgpPBoM7pJryRvHXo+/HVuqun41NpUJB5lwkCinPNGyxOvU/u0m2HhuH1TTKdbU1KCmxnm7LyxFoRzNTQE3NVo3DQZOJdluKpEn8HDEUW1Bq70vnLkcoSfpJFwUeYVK3FFpIAgfbzwPU4zGB4pw1bT/w9V3vqm6fjU29XBR+wsmHAiuFta/jcPGc/vQFYb8zDPPtPqbEII9e/Zg4cKFuPDCC6kIxqCHWzpXDLfJ6zSSHUI5viSIiAMzzmux966FPVBRcSKAxYbq9PLATX/9FtK/duGZhwwVhYWVZZjuH2s4D1OMlR+dg57/q4AkCQC+UFV/OkoDQQicpOt8S9qw/m0Mt+nPbfKmQpfD9NRTT7X6m+d5dOnSBTfeeCOmTp1KRTAGg0GP0kAQJbkS/IJstyiG6POP1VTKCStA5O0KrF15Mow6XwDd+KVzV71LrawYCyvLMKtwDPVyY5gx61GzfyxyNn8O39A1VMtlMPSi63lz69atrV4///wzvvrqKzz00EMsN1MLaGVVToeeKU+nTJO6WXa70Hv/zTKPsExvimlMsbZgX8A59vbyQM4ZBEcc8xOV8vI8CrrkmOOMJrv/VMHWid5vkng0SR6EFPrTjMmcJSP2zvvoJcgf70h4TdPqIYi8YlKeFptxSh/Rg5tlV4MDJ+gzB6uCvvUMVk6ZJk0lRzL5nSK7Xei1d1jh0Egx6HvePu35nJxk7+YvOGxadySVshplHg2SOcNpqvtP9lmy97mDcUw00TvOpLO3eEMkaUZy/tP/oWZVbw1Sugc2njsXXXmYAKC5uRk//PAD9u3bB0VpneL2kksuoSKcW2iVh8kPLD5zF2rDXtRGRNRLyfMwOamhuCEzLEM/pYEginMI/ALB1vroD7sRO0T+4QckGeK1zWnrdaK9xxQH8ZuiEDbV+fBMhXH5nu4/CgdCAmbtMCeDtlFKA0EclkdQKMpolnk8uN15mb4Z9GDjuRZMzsO0ePFi9OrVC6eeeiouueQSDB8+PP667LLL9BSZVdDIjpqIVOU5JTeP2szEWj/LRrTqSuQPbaM3au/wDz40/5h++d2p9m6QgAjhqGX6DinRLftmYrR/+wUFfkFGnse8M1ycam83wsZz56FrhumII47A+eefjxkzZqBr165myOUq2s4wfXLmTlSHfagOiyzTN8MxTOkxDrkefZm+I2/kADyP8Dof/A9WmSShNUT+1Qn1//HjtY+GYUu9l8oM06w+Y1Ad5nH/te8gZ5gIJVCMnBO/pSAtPSIveICuhUA4DPGPDXaL40im9RgHAKgMc4YzwDPcgskzTHv37sWkSZOYs5SGtjFMWg/cNcsDd5pnny33mQ6z9dAoRRNX6nHSI5u8kH7mUPtLCTV5aKM64/XPtTiwI3oftGaYYhs86vd1BLdrNzxbN6b9jtV6aNrcGfJPTZA2mVKtZnmcWK6HI8gVFOqHowPu0oMRMvk+dTlMV155JVasWEFZlMxH6+G7LX/YaDQWp56HRnOJUs8ZW2rKMuP6tpht71jiSj3k3V+L3Hsb0O219YblstvevkkRPP7lycgVWv8q0rD3L9t6o6G8K5pW5aX9vtX9+y9vXYZ5L5biqZeuN1yXFuy2t5qyYnAcUCDICcdqp/dvp+Ame2tFl8P07LPP4u2338ZNN92EJ554As8880yrFwPQEyXQ0qBtjdu2EeoxvtM6Vlv0yNdWH4kyXCf6vxnyaL3eanv7eCDHozjiSQ2w194NEtAse1pl+jZib5En8PHA0M8+Qoend6Hwsb1JZU8km1Z7x1IKaPlB2dXoQUWziIomutvx1bYnN/RvkScQeSXhGYNO799Oww321gzRwUsvvUQEQSD5+fmkd+/epE+fPvFX37599RRpGr179yaILlLGXw8//HCra77//ntyxhlnEJ/PR3r06EEeeeQRTXXU1NQQAGTnrqmkpnoqeWtQKXnhqFvIE4ePJTN7B8mUHreT8SXjSWmg/QvwaH7Fvqf3+5nyyhY90LrPKT1uJzN7B12rL5r2Lg2MJ5GPepDIKz6y9cpTDJc3u884Mq3H7Zbfp1ttmeoVWVRAmufk29K/Z/UJkrlHjCZjiq2rM1vGMefqgScASE1NTdrfel0zTPfeey9mzpyJmpoabNu2rVUSyy1bthj34ijz4IMPYs+ePfHX7bffHv+strYW559/Pnr37o3vvvsOjz32GB544AG8+OKL1OVItySn9UnN6U8YZqNVD231a/VMi976adrbKQdC64Gmvf0CQAQRyPfBl5M6NYIaYjvQ1NafCi33qaVNRF7wILIoD+G/0E/4SLMvkf0hhA8U2tK/Y7FoiWaYtNZfe1fXaPqNNLDxPIobxnNdDlM4HMbVV18NnndH3suCggKUlJTEX37/oUb8+uuvIxwO45VXXsFvfvMbXHPNNRg/fjyefPLJpOWFQiHU1ta2erVFj2ayvcOYTVv9JtI3rU6XqBw19ZtJRAHCsos9Jo2k0ze3Zw/I3mbU1RQZqqc0EERI4dpl0Lbb3m2Rq7wgVSFI1enjq/RAq+94b5cTLmmmg1b/5lXE+anp3+HafJADxp3xtowvCWJ2n9GY3We0Y5bX7cDK8TyGLo/nxhtvxN///neqgpjJnDlz0KlTJwwePBiPPfYYJOlQsOfKlStx5plnwuv1xt8bNmwYNm7ciKqqqoTlPfzwwygqKoq/evbs2e4avZlOsrkDmInWp3u9dtAaiGmlvUWe7plnTiadXhskYO6DN+HO2WMw4P0vDds7rPCQlPTOqJ39e3bZTZj7+Eg89sq11MteWKk963vjzEJU3tabSv3p9BpZlIfIojw8evGytNdHFB7hJAO4lv7d+YWfMfJec87v83BAjodo3kiUKVg1nrdFl7plWcajjz6Kjz/+GIMGDYIoiq0+TzU7YzXjx4/HCSecgEAggC+//BJTp07Fnj174jJWVFSgb9++rb4TS5dQUVGBjh07titz6tSpmDRpUvzv2tradk6T3rk3u59CMxWzA7itrkcPPOhto3c6avTaso8atfeswjGqHpLs7t8KgekJNtUi9m0CnxumUlZavTaEAI5DpNmX8npJ4RBSuKRpBZzQv8NKLFFq+lQ14bkecH4O4ggT8iTYiF120OUw/fjjjxg8eDAAYM2a1idJc5z5nXHKlCl45JFHUl6zfv16HHXUUa0cm0GDBsHr9WL06NF4+OGH4fP5dNXv8/l0f5eRWfx0yanoO+pniBfvt1uUtChwzo+l3fgFIFeQkCeI6S/OEDgO8PAEPt68TN+aaFKgNOYBoL9s1RZxZCy+rDzldRHCqZoptBuZqMssT0IeQDDnUOhsRJfDtHz5ctpyaGLy5Mm46aabUl7Tr1+/hO8PGTIEkiRh27ZtGDBgAEpKSrB3b+v18tjfJSXGk/QxMhtJEgDZPU9vSnZMMKlixAN/x68f9cac11q/H1mUh/WvnoxBH3+qqpzSQBA8JIiUD7WljYcj4BFdznEC4mgZQOLDde0iogAKHKKgFPh4BdG5idRrGb476czgMaK4cgW0S5cu6NKli67vlpeXg+d5FBcXAwCGDh2Ke++9F5FIJL60uGTJEgwYMCDhcpxaHPIMxzCZYz78AvjQbikYuvD74e9SjcUnX4ELvvnnofcVBUTDTNzCyjLMKhyDiMNn72KzEk6X007yPOTgWXseu0VJCcdBdxJaWmiJ5wo9LaL5TzfBW7EGucetNFs001DtME2aNAmzZs2C3+9vtcyVCKfEMK1cuRKrVq3COeecg4KCAqxcuRJ33HEHrr/++rgzdO2112LmzJkYOXIk7rnnHqxZswZ/+ctf8NRTT+mrlI+q1B37BxnZQnQWhEDgiO1xNI6hoQGK1LHdrijx2mYAKzQVFXHBMo6kcJAIm2VMh4czFkx9YFwfFJ6yE+JN5s08y4SD9lNg6bKwskx1MDXHEYAXQHhnO6LpUN0sVq9ejUgkEv+/G/D5fFi0aBEeeOABhEIh9O3bF3fccUcrh6+oqAiffPIJgsEgTjzxRHTu3BkzZszArbfeaqPkDAZdFlaWYVpe9GDR0kCQOU0AXpzxJ2yp9+GJ3c8ZLktSOEQsnlbWasfYkpye72YLjTIHv2zscdcjSoBXAGCOw9QgRdODWBEvnAotO8+8EyRgwvMmSmMNqh2mlnFLdscwqeWEE07AV199lfa6QYMG4fPPP6daN1uSYziJaK4gAo/Ow3czjQYJ4BBNtUCDHY2cLXrV4vh4PQRej4II0S5reK4HRObhmxjRI6arMLrU1eEvO3V9r25qF+T0q4Q4Kn2QdqPMUWu7etEyw5Qp6FL57373O8ycObPd+1VVVfjd735nWKhMITb1bfWTp1VkW2dxMwsry+J5mIzmHMoURj3xHu76w2IqZdnhLGnNfSRy0R1yPpWpJVram/Nz4P3JB7JMahsK0m/XNwMhvxHwq9u1KfJRez4/ZhFCT4tomlVguP5MPM+ONrocphUrVuDZZ5/F8OHD0dDQEH8/HA7j00/V7SzJdHgcOoaC9pOAUwYnp3QWs/Wh5iBUO+vXgpGjUTLJ3h29gNIhALGgod1neu1tZpZ4GoQUrt2Bw6lodbjwCEnVzIcZWN2/JIVrFcNkVf259zYcjJ9Lj8gRCDyBUNgIvpDAU9hkWI5M6t9mla/7p/w///kPKioqcOqpp2Lbtm26BcgkCLFmQMnGqdBkGInFSKRDNUceqPlcbdlqoGVvL0/gdUoOHp3QsndVGHh55Bl46M0rWl0TelrEy39/H1uuOClpOYnqH18SxGF5rWdu7LZ3W2TCHczFRb1o07C6fxeKCvyCkvRzrWWrwYi9Rz06AuIIKRojlAE4YTxPhW6HqVu3bvj0008xcOBAnHzyyVixYoVuITIJjrMuU0O2O01G7z9Rx6H1lGVG2UbvN8ejHNwy7U5o2zus8GiUWs+28MUeSCW9kN9RW36gLjlyu8HUbnu3ZcrI1xEc85opeZjMmJ2wo3+HZD5lpm8jZadD7f36hWhfznFxX06Ek8fzGLocplh0vs/nwxtvvIEJEybgggsuwHPPGd9t4mY4rv2WSa2ZldU0mpgX7pQpVLuI6UCtzvRi5pKclfYmDjoWQw9W2Fu8thmjzzoOxS9tUl32tj8OxpQHXlIVLGxn/xa7N4DvzsPrUVzxsGVH/07kgjixfyvgEFYyK3mNU8bzVHCEaM/mwPM8Kioq4skfAeCf//wnbrzxRjQ1NUGWsysVe21tLYqKirBj590oKvLj49M3ozrsQ21ERL3Eo0nmUR2O/lC1fXLJdqeHYQ2lgSD6+BXkizLWVItZ3+5KA0EcXSRhe4OAefuM6aLq9l7wD+fxwI2/x0M7rXto1HrQ8/SeY9HBK6NZ5nDvtheoy5IJbWpMcRC9/HTahVmUBoIoziEQeWCXTbszMwsCQEFNTQ0KCwtTXqlr/Wjr1q3tMm1fccUVOOqoo1yTcsAqYk/0Xh5JT8BmMMxmYWUZpvvHgujYUp6J+IXo9nEaP4rjX78Y/d+TLU8lotWOMuGgEHOO/siUNuXlYXtCSDX4MmtyyTXoUnvv3r1bJc2qq6vDiy++iJtvvhkTJkygJpwrMTGGyQ3T6HaSafqhfT+NcvQk9kzRk5H7mLevDPdseZGKHLScBT33o+U7Ho7AwxPwcIFHkAAr2m1YAUIKb0lagXT3s/XKE1F7V1c0zmw96+EXokuHleHMfvhx4jhlyE/97LPPcOONN6Jbt254/PHH8bvf/U5VoshsguZ5P2o6R9tGlu5vp0BD7kwbPGjauzQQRJ6HOCZQNNPsnS8oKDB4Krwee2v5PHqcBgeOs34ccIu9vbzxo1HUku5+Oh++A7lHH4D4m0O/IaWBIObtK8Ocnc9h3j7n7pZ2i721ojmGqaKiAgsWLMDLL7+M2tpa/PGPf8S8efPw/fff45hjjjFLTkcTi2HauXs6Cgtz8PFpG+IxTDURD8IKh0aJQ1g5FMPkF6L/p9koMiWOIBmZfn9a0aqP6T3Hwi8oWFsjuEKPbrL3nL6j0SjzeHC7ecc/JNKHFh1N6zEOHb0Swgr9GKZUHBjTDx5fGPX7A+jxxg9Jr3OCvSd3H4fD8iLYVu/FMxX2yuIEfZiJc+5PfQyTphmmiy++GAMGDMAPP/yAp59+Grt378bcuXONSJpRcHz7x5KWM0wtp3nNmPI10viselIxUo8zOpdz0KKP0kAQHBc9BHPWeV+i6vZeqJ54GH665FQTJcwee0cIB8nkA3hp9G8FgGTiTslE9i48+Rf4T96Pzsf/lPK7TrB3g8ShSeIdEW/KxnPnoclh+uijjzBy5EjMnDkTF110ETwed588bAaJnKYYRqd5lw+9BFuvPBF7bjy61fs0tlha1Xhj9di1LTQT0HuEQYPEoT7iQbdT1iL/rCr4z6xHz5PXmiBh63oBZ9q7NBCkVnZE4RAy6Uc2lYzp+m3L/q0AIIQzNbVEInuLI2WI14eQc7e6LNZ2928zHUo1sPHcuWhymL744gvU1dXhxBNPxJAhQ/Dss8/i119/NUs2RhuOO/F7HHZOOToP29rqfSOdwy4v340yOwW99+/jownvbnloFMjeRqA5BE/nRsrSJcZp9i4NBDG0c3O77Nx6IcS8nUup7j/dj03LH7QCQUaBKCHglaj+SCVyPJ1mby3IJH3iSjNxo+7cKLMeNHXxU089FfPnz8eePXswevRoLFq0CN27d4eiKFiyZAnq6urMkpMBIP+IXcCx/RAZmPzYhkS4yYMH3Cev01DzI+oNyhCvbYZ3rP050+yy96Unf42hXQ4YLieaFycCv8GgbyP1p2NhZRkEnsDDESgAJp+4Dmsv/C2V+rUmXXR6/6a5UccMnK6/trhN3lToeiby+/0YMWIEvvjiC/z444+YPHky5syZg+LiYlxyySW0ZWQcxDtBgvesTcgb+IWm77np7DnnBAK6l1T2FnjiqLZgl739AtDl1macc+5nhstaWFmGfc0i6iTrQxS0ZEfe1yygOiyiQfLgN9d9jiNv/I6KDFqWNt3Qv82ORTMKG8/tw/Ak8oABA/Doo49i586dePPNN2nIlBEoABSHPKi4qdG6aTBwKsl2UwHO+zGwy94NErDhoe54aNFlhssqDQSR41Eg2jQzobZ/+wWCXEFGnkfB8ucuwRd/Oc8C6Vrjhv7t9ViTVkAvbDy3D11HozBaE0srsKtiJgr8SHg0Stu0AjHc0vAZ7mdaj3HwCzI21LojrYCZlAaCODEQxhZK28dn9h4DSeEwawedtAKNDxRB7N8MKArEGyJUyoylFYgQDtO2WpdWwE2MKQ6iW66MPU0exx6NwqCNSWkFGKkhigRwAjiHr4Ezso/SQBAejkDgWdsEoktywafexf3DP6JSHs9FUwvQYu2KIdj74QBUfEgvt12RV0KhN4I8E5KXumEWIfJuR+wf1T/lNQ0SEJKtyfTNcB/MYaIJkcDxAgjh4ktyVpwOb/aRCmbiZtntQu/9R0j0hHM7Z5ecYu8GCWj4jw9rfvgNlfLCMt1+fvKKT9DjjR/Qc9H31HRWExZQHxHRLJsz7Ceq0yn2BoC6/xTixx9T29svAPmibOuSnJN0phU3y64G5jCZCM9Zs+Mi2Q+gkfwtVqFny7RTZLcLvfbmAfCwN+jbKfb2C8BbH/8/fLy9J5XycjwKck06doZW/1YQTVzKcfTbQLKdck6xNwAEnv0Fv/vqXymvaZCA+ojHkWkF2HhuP8xhMomYYlPNMJndUJy41VdLPW7qSE4gnb5CyqGT6rPd3g0S0Cx7qCabtDqgXmv/9gvRwHSRJ9SPZGp5ZqFanNq/80X7U20kgo3n9sMcJooQOQQit85mm2iGiUZ21ESkKk9tgjuzUZuZWOtnZhBe2qddVnUnoUVXpYFgq8SKzN6AyCvUkk02SB40Ul6Wa0tbfYTnehD5v4KEn7WlpR0I4ajqtuXskpPtnYzIojz8MOys+N/1EWecYMHGc+fBHCaa8AIIkeJB38nSCpjVUFI1Xrd49466h3U7ULGru7V1akCLrhZWlsHDEdu2vifDTns3yx7US3ScHIEnEE0eTdvqo+GnwyCtT/xZIhokHmGFh0ScmRHatjGqLoSm5pz4n4WijI5ee0RpiSN1pZFMuIeWMIeJIhznAcdFowUVRGOYWi7JaV0XN/MMLSfh1Pv03i5j8H+WUpImPWbrQSYcZBt/LNvKY3e5IYpLaHkeBV6NOxCN6mH86xcjd3r70xWSlStyJD7j7aTZbTPq1ELTxmJsq+oc/zukcKYcvut0PdAik++TOUwmQFo4SS2X5LTuvGj5w0ajsVh9MKNaaC5RtizD6H1qlceo/GbbO6QAIcX+Lu8Ue4ttHBwj9taTUsDq/s0dFNHqFuAUeycrq/DxPbj6h9fj74eS7CJ0ev92Ck63txHsHz0zFD2KbWnQtsZt2wj1GN9pHasteuRrq49kGa7b/t8MebReb7W9/QJBgSA74kkNsN/eObyCTr5DAb5G7O05GEydCtr2bptFOZ38DRKHZplHWOGojwVqyrPb3lrkSbQa4PT+7TTcZG+1MIeJIoTIQIslOUDbklxLgyYzbmyQzLSU83poqYdU+kr0fydgtb2JRXnBzIK2vcMKn3Q2QStNEp82F5Pd/dvHA96DS4duGDtY/84u3GBv5jDRRJGiySs5Ah7tg77TLcmp7TBqd6RkOlr1kGjnmJXorZ+WvXM8CnJMyhVkBbTtHVI4akHfHId2WdStsLeWNuHzKPDx5rQBM/qSHntH3shB6EkR268+XrdMPo+iK+jb7v7tdtwwnjOHiTKEHJpG4jWOxdneYcwm0c6xttDqdInKUVO/mYQUHhGHHb5rJun0nedR0i6jqaE0EESB0D53jxX21tJeJYVDROFNyfbuhLFrYWUZSFUEUnU+Gur9uvt3nkdBvqB+ebVl/an+ZtDFyvE8BnOYTIAYWPbI9mlZs9D6tKfXDloDMa22t3vnl7ShRq88dygVgFF7h1U6onb275bLsS3lCD0pIjzXGbmH9BK7H29Qhv/BKhz97/8mvE5N/25WeFSGE9vT6f07W7BqPG8Lc5hMRuvRKOypxByMBnBr7aB66zETSeFcHcOkBTV65XBoANRrh4WVh5I2qplRtrN/KwAiCgepzZDEiQo4B+QdMgLNfheWOczblz6Gxgy5GOqwyw7MYTKRZIkrGe7DL2TG0yLr8IcgoDfjpoBz/HJnRImmP2h7hItcnQe5yuUeEyU6egGvhw3cjMSw8ZPBUIGdh3HSJFuW5NRAK/N5aSCIPI8Cf4I4JjeQO70OOVOaDJWRCQ8TQDQ3l9Oy4TOcA3OYKBPL9A1oD/pmOJeWSy9uJaS4O60AbbQulyfDLe1C5GPZvs0pP1OcJgYjGcxhMhG2JMdwEiIP8KB7Ur1bWVhZhrDC68rQnYiaiAdhB2RRTwWP6EMcZ9IMSia0q4gSPT6IwUiEs3u421AidkvAYCSFRzRfEJsJiKIQLqsGwJCCdgHftMgEZwkAwg45PojhTFjLoAmR4nmYWKwIw2kILD4jTmkgCL7FYbRGkRTO8TPKsaB0NugzGPpgfYcmntz4f1sqNpKh3hObqXAXxOAPeqbZW6IYz+XhiC3xYVpmdkSeQOAAj8pknVrs7fa2EXm/C34YdhYaJGD6nFfR+OMZdotkOW63oRUwh8lEeC4aZCtS1rJTGrZTpuHN1ke68u2uXy2NModGA2enZZq9hSSzS3rszXH0gsjNak9z5szH+Cf/idvve1XV9VrsbWbbsKJ/KR0CyMs9uFOwqAhyUXdL63cCmda/zSifOUwmwHEECswL+mYHNR4i1UGNar6r5r105atNya/XZrTsLfLJnQS3QNPeyeaD9Ng7kbNkt73b0aUzlOJuQKeO9Ms2Cav6t++3G9H/3a/hF4DHbhuOe0/p0upzvWWrgY3nh3DCeJ4KVztMs2fPxmmnnYa8vDx06NAh4TXbt2/HRRddhLy8PBQXF+Ouu+6CJLVOqrNixQqccMIJ8Pl86N+/PxYsWEBFPrPTCmR7JzN6/4k6Dq2nLDPKNnq/EYXuMpTZRBYIaJpVEP871f1H3i5C4wNFKctre9J5hNDLfB6SeYTazN7Zbe921NSA/3UvyJYqx8wmpMKO/t0gRZ3fdGfJObF/ux0nj+cxXO0whcNhXHXVVRg7dmzCz2VZxkUXXYRwOIwvv/wSr732GhYsWIAZM2bEr9m6dSsuuuginHPOOSgvL8fEiRNxyy234OOPP9YlE8fnmH6WXMwLd8OgZyYxHajVmV7MXJKz0t5um2EiffvCc2pHhMui55ylsndo4FCIg4D9o/oDUGczvyDpmhlK9LlfkFUtydG2t5a2d/+dN+GZiZfhyadHUv1xLg0EMaY4SP0H367+HVZ41EuJz90zWnaya9h47pzxPBUcIUZDQe1nwYIFmDhxIqqrq1u9/9FHH+EPf/gDdu/eja5duwIA5s2bh3vuuQf79++H1+vFPffcg3//+99Ys2ZN/HvXXHMNqqursXjxYlX119bWoqioCDt23o2iDkX4+LQNqA77UB0W0SjzaJJ5VB88zLFtxuhs7yQM82mY0RFCh3r8+albkC/K2FovJj0ry0mE/ns05A7FEDevgXjpgZTX1hy4HTkb/gPp4wr4H6xKW/aY4iCO79iETXU5eGL3c4Zlnd1nNCTC4f5f5hkuSy1aD4Kd0mMcOnolyITD+hqB6thjZCnFSZQGgvhNkYTqiAdzdhpvFww3ED0kqaamBoWFhSmvFFJ+6nJWrlyJgQMHxp0lABg2bBjGjh2LtWvXYvDgwVi5ciXOO++8Vt8bNmwYJk6cmLTcUCiEUCgU/7u2thYAwHGHTvxWcCjoGwC8fDTHB4NhNd4+dUCgAI0yB4HnXXPMi+/09QDWq7q2qNNcTWU3SPQ2ZJQGgvBwkuWpRLQ6KCJH4OMJFNB/Rs4EZymGqHIXISP7cPWSXDoqKipaOUsA4n9XVFSkvKa2thZNTYnPV3r44YdRVFQUf/Xs2fPQh0SKZ9KlHfSd7Wvc6XCLfvaP6o/IfA/23XJEyuto3Y84QoI4PDrrkklHoxjVT01EiM/8GmFhZRmVTR5mt1+ZcFAAxx8SnAyr+nezbI1+3DJe2YUT9eM4h2nKlCngOC7la8OGDbbKOHXqVNTU1MRfO3bsiH8WS1wZg9ZWY0DdU1zbRpbub6dAQ24nPOWW/79z0PxoDqonHpb0msIB26EcezQKj9yR9BqAvr1FHvDxiedBau/qioYZ1u2ecoK98zwKOnjp9M8cjwKfwZkJs/t3SIkd/UHfIdAa9+Xk/p0vyigUzT9ImY3nqXHCeN4Wxy3JTZ48GTfddFPKa/r166eqrJKSEnz99det3tu7d2/8s9i/sfdaXlNYWIjc3FwkwufzwefztXufEBkcFw36jp3blGxw8gvt45mMkiiOIN3fTkGN3HbGSTQ+UASi8CASj/yHksfT9Dz8F/AnlsDfeWfSa3x3hgGsSfq5WrTa28sTCEl+1HMH1wA57du0WTjF3rSSylpxBpnR/h0N+o+eJ2g1TrG3GgjhHDELx8Zz5+G4GaYuXbrgqKOOSvnyer2qyho6dCh+/PFH7Nu3L/7ekiVLUFhYiGOOOSZ+zdKlS1t9b8mSJRg6dKh24RUJnCcn+t+Db7WcYWrpIJkRR2Kk8Vn1pGKkHjs7l3BWJ3iHeOEbArwz+Jqk13WatwXec7dBHGF+oJAWfZQGgmiQODRInvjfLRGvbYZ4eQ1V+RLVowWz7a0A1JLKNkge01M20OjfHEdUZ/rWU4eT7a0Ws2bhtMLGc+fhOIdJC9u3b0d5eTm2b98OWZZRXl6O8vJy1NfXAwDOP/98HHPMMSgtLcX333+Pjz/+GPfddx+CwWB8hmjMmDHYsmUL7r77bmzYsAHPPfcc/vGPf+COO+7QLhDfesKubUyD36T5PBpbLK1qvLF67NoWqpemQRehcakH4qUHcNnqRZbX3xI997+wsgy+g0tyVm5hdrK9RY5Qm2Ey82gUmv2bEM5Q2pNkxNqUk+2tBZqhFFph47lzcXVagZtuugmvvfZau/eXL1+Os88+GwDwyy+/YOzYsVixYgX8fj9uvPFGzJkzB4JwyHtZsWIF7rjjDqxbtw49evTA9OnT0y4LtiSWVmDnrqko6tgBHw/9EQdCOahtkVagUeIQVlhaAbfixunjtszqMwYFgowt9V48U+HuezHKmOIgBgcasa/Zh59qPYaf5o8qlKCAw/Rt1qUV0Mrk7uNQkiOB54DyKtH17dkMSgNBHNshgvqIB7N2PG+3OAxLUJ9WwNUzTAsWLAAhpN0r5iwBQO/evfHhhx+isbER+/fvx+OPP97KWQKAs88+G6tXr0YoFMLPP/+syVlqC1GiHlEshskJuMmDB5wpr5t+XJLprzrMozIsOM5ZssPeDVJ0tiUsc1Rsy9kUGwSo15/II75Lzoz2rFYOJ/bvljTLPCIOWJJLhtP11xa3yZsKVztMjoRIIAe37zoFN51VlAkzOXaTzN6xQFYntQVbA/llD5VltIWVZaiPeNBs4GBjPcRihtT270bpUGyOGW1A7a4vp/fvzr4IOnqdm6yMjef2wRwmmnBCq7QCZh2+qxU3NVo3DQZOJZG9SwNBlORK6JoTRkd1eyYswU575/AKBJ4Yrr80EITPo4CzeFKi5VESWvo3Z2N8jhv6t8ArEJw7wcTGcxtxdQyTU4jFMO2qmIn8PAmfnLENlWEf6iMi6iUWw2Q2zXNy4ekuIfxTAfx/rrRbHMcyvedY+AUFaykfi+FWIm/kADyPfe8fjsNe/9FQWTN7j4HAEdy77QVK0qlDy49nyximyT+/aLJk7qQ0EMQLn6+Bp/YAbrnoLNZPsoIsiWFyKi0zfTthe2qmwxeFga7FELvUWlrvxotPQ3hpH0vr1EtpIIhGmUPIpPgVt1EaCGLvB0di3/uH47MfjjNcXkjmLV+SA7Q9cDVIHMJKNMeQk576I/9XgPBfnJES0C8AeKscoX8eYP2E0Q7mMJlAy227VmxP1TP4OWXApCH73k+PRdN/JOz63PgPnxbW7uiF0CeNltYJ6LddoaigQJQxptg+2zulrS6sLEOPN37AI0vPxJ9+/Jvh8nweBX7BnMjFZPefKu9Rovc7eAnyRRkFIv34nGSyqLFd7X+6tOq7do5N8/aVYe5fr8bc/7vENhmc0kf04GbZ1cCW5CgQTyuwezoK8hFfkrM7rYCb1roTYZX84eX9EFpcj4JH9qW/2MGk09eUHuNQIMjYUOvMJTk3t9cpPcaBB/CQhSfca9XXmOIg+vgleDiCH6rtTytghr3Dcz3gOgqo/bIbOj23TZdMQzs3I0I4fHvAZ7uOWuLm/gE4WX62JGcfivqnN7Mbj9ZM0FagpR6rOpeyag/2rFd33I6TSaUvu86gcqK9zcAvKPAL5p8/1hK9/dvDE+pJdGPl223vyP5CKL8qaKpO/cOXCtmk5J5GYeO5/TCHyQZoZEdNRKry0tVlRmOu33QBmr4fiqrbe6mux8g96CVnShOOfO8rU8o2Ey26susMKifa2wyaJPOHUhr928wdcmpmEMy29751/VD9Qz9s+aVX+osTsLCyDLURDxossGc6nDae66knU/p3DLYkR4FWS3J5Ej45c6cjluTsJvJ2EdAcwtZFx7vSIck0pvUYh3xRxnq2S446s/uMhl9QMHHzfLtFScqY4iD65kcgmJDp2+ojOczkkX63ghAOU7Zau+NRC2OKg/ALBE/stm4JOHNhS3L2QA4dvpsIrQfumuWBW+XZi5fXQLy2GYc9kofmbwZbLo/bnmDM1kNIiS432P2j5gR7lwaCeKjvaMzoNZZK3RKJ7kDUAo38T1rLjS010V6SS3U+IQ17R/6vAE2z/dTLTYSkcJCJtSsAWungJeieK2F2n9G2buJIhBP6t1kwh4kihMgA13okaplWQOsg1XIAotFY7HoK5KQwoCSP76C5RNmyDKP3qVUeo/KbbW8fHz2+ozQQRPgvAiKv2LOV2yn29gsyfPyhnW1G7B2Sec1xL0btnSg5aaL3YzRIgGzDegINeyvbQmjc0dWS/u3hgNwEOx6d1L/9goJ8MYJOvoihcszAKf07VVl6YQ6TyWhZ8Gxp0GQxKHqCK9uWYZTIv7uiZv9Y1OxX93QublkPz9bNaa/TI19bfST7EWn7fzVolUfr9Vbbu17i4rmCuI4c0KUjIi97NJdLCzvtvbCyDDyiP4565Wl5vYcjaeOD7O7ffiHqCOR4ZHgpj/xq5DVi71seGoVO87ZY0r9FnkBMYEsn9e8GiUeDJKBRtq//psPt43kiWAwTBWIxTDt23o2iIj8+Pn1zPIapNuJBhHBolDhUhdt/V2+j0nMkAi0iL3sQOWUoACBv4BeW1x/Dbj1YBa37HF8SRL5AsKORw/wpL8NTFIH0aw5y722gKK150LZ35O0iyBsj2LBsCI5fstxQWeNLguieK1GJe6m8rTcKhjUi3H8gxIptGHnFhSmXu9TqYkxxEL8pCsHrkfFTba7j41/s6t+z+oyByBHLMuLruc/J3cehe64EjiPYVu913IHaerBvPGcxTI4k3ZKcWo851pjschLEkTLyBn5hq7MEaNeDXVvrjdZPy94iTyDw0eejnLubIY6WXeMsAfTtTfbWI3ygCDUN+YZle6airN1yl157N1UVAvurIO7bAW7/XsOytSSs8AjLnvhBzE7Grv7tF2Tk6kgRYWX/jigcJBKNSQunyZdaGkie4NRJuGE8Zw6TyWg5kNNtsyTh5f0QXu6e/EVqttbT6nSJyrFra38MN/xI0iSdvuc/dRPm/t8leGmDvi3oLSkNBLG3ufUTkV57H/b6jxBHSPCetQniHxuotZMGCWiWuajTRDkpuRN+lGn174iSPg+T3f07XyDIF2TkixH4hdSLRKkC8ltit/20YuV4HoM5TA7DTY2Wq6kCV1Nltxiq0Pq0p9cOWgPrrbR3nkAg8tmxAq9GrxLh4g80NOytZlnE7v4t8MSU45rU/iibBc3+3SB5kp4L6JT+rQBQKCbYNBI7ZwdWjedtccaJh4w4bpplEoe7w1kCzA/gtroevfDIDodJjV7DMm84zYLT7W0VdscR0rQDIckTfDrF3tG8flGnzo6ZYzUzbGZilx2Yw0QRjmu/YyH6NJddSyEMZxJRAIW1xTgKgLBMRx/jS4LokiPjp1qPo52i6I8s/UOC7b7n5jm58JREUPftYQg8+4uhsrwe62di6+4pRk7/AyDNgPd2dfFTHBedLcxLsyRnBgsry+LJMwGgQcqOcYUtyVGGEPongTMYtMi2OKZ0aIkxTEWeEP3xsttxSAcPAoGDLWkFzITIPCATEIneNnvayT1ToUQEkPDB+1BJbDmOhin1BJ7HEjH7ssiLYDNMDEaWIPLImhgmNeR56M20RBRtOddoodVB8/IEXp7+DBNg77Lcod2eWwyXFZY55PDWPlgUPbn74P/UzS6FFUAiQIRwJswXqqdB4gAbZrjsIot8QwYjuxE5834s3YiHi8620EDkEU/ZkI3YHfSdbbScIaT5I65npjCicJqP/XIrbIaJwcgiaO2qyQSaFR6NGpZAMgGZcJAJx2YaU8BxgMUTTJoReZI0I7laNg8/BXmF9ZBCXvT6ezlzeFWQXaMFg5HFRAhny1liToXHobP1jNIoca3Ojcw27I5hokHkZQ8Wn3wFfLwCgVeox3nRhgfAc0T3klz34zei0wmbUHz8TygNBDGmWF8uLZEnlsZ72YnDm4T7kQmHSIaugmTCIMlQT6bZ28MReDg6O7zCCmxxmLTaRMtQlGn2Tovfh1wxDIlw4AHqyT1p0iBxaJR5hBT9P+F8Thi8XwGfFz5YpvYyvHw06NvLA+G5HjTN9uuWxw0wh8kCxAzbkRLDKVO4ZusjXfl2168Wo2kFMs3ejTKfcMZNj739AqGW40rL/WmxycLKMhASe4hL3w60lm0WVvWvui+64KuKbvFlS6vr14ORB/KcKU0QR8nwTpDiMWhabV4Vjh7qHVYAroiHp7BZnzAtsFLfpYEgfv3wcNXfZQ6TC4kdUMgwtjMnkQ71JGRTm5Jfr81o2TuicNTyDtkFTXuPGf4Bzuqxs911euzt4wFPG9Xabe9EhBTeVaklrOzfgWd/wT1bXkSDxKFB4uMzLk7t3x4uGsOk9YG87p5iNM/JRehpEc2P5mDfLUcA0H68TWkgCL8QPabFywPiDRHVOaRSlWnleO4XAKlTN9V1MIfJxWS702T0/hN1TFpPymaUbfR+8wSCHI/i2ngD2vbOO60OJ/1hGcr/3zmGygWAUIKnfCvsrVUnhESX5arCOoWyELv6d0Th0u54tLt/N0iApERnwrT+iHu7VMNTHAHfmYens4ScjrVx50cPlWGOSnuyw97z9pWhZIj6Q+SZw+Qw1A6SbBvvoa3MZvywaPmumWXHrqFpb7duAaZtb9KlBJ6SCDp0rNb03USfx57yabQVLfbW2iZqIjxqI/SSO8Yw4+HNzv4ttZiF++rMCxB6WqRWdqJrHjj7azw36i00P5qjumyOi8XhaVsK9t0ZhjhCgnh9COIICYWP7QWgb1ygOZY4ZTxPBUeIHenWMova2loUFRVh566pKCgQ8MkZ21AZ9qE2LKJO8iCscKgORztg2waW7U4Pwzqm9BiHAkHGjkYB8/axdvd0/1EIKRzu2fKi4bImdx+H7rkSyqtEx/bp0kAQvymSkCvI2NPkxZydz9ktkiOZ0mMcOnkl/FwvYt6+Mmy6dAj63LgB4uU1ptX56+jDUXjsNnCdRIjXpo8DGlMcRC+/hCJRQmVYxPRt8wzLoGc5rDQQREdvNEDevWMKAaCgpqYGhYWFKa9kM0wm0nKW3ulbVBmZT6PEoVnmXTyw0cXDRXPZ0JgdKRQV5HiMxW9YgUzs2c3nJholDpEWOjriX6tMdZYAoPMLP8N7u6zKWQKiD94ejkDgFUO5mNLWM6Mj6qZ2SfhZbBkvm3J6sZ9xF5HtMUvpyDT9mHU/maIno/cRObhbjMaM0J4mD6rDxoLDzLbLwsqyaFJGU2sxD6vabViJOiMdvOY6Akbvp1nm0SQJaJDoWbStTN7+9cg5oirhtQsry9Ag4WCQPDURksriBNzad1wBbeWqGdjbNrJ0fzsFGnKbvRSidReJUWjbW+QJtcNmjeIEe9M8GgUwlrIBsKZ/x2JerB74nWBvtXgPnrlodv48o/YWD54L6PXQcewSySPeEIE4IrE31FIWv9B+u36yaxP9rVYeu2ExTBRQE8PUeDBXRcwT9wvR/9NsFHYefmkFmX5/WtGqD7fFMJlt77IjR6JZ9mDyz8ZimFadNQzHnvU1hEA9fBMjlKRrj1F9lAaCGNQhghyPgl2UY5ho2Mop/Xt8SRD98sPY1yziIRvjvNLpY3af0SgUZVRHBCoxTGrqpIlT7M1imBxKy2lLM6YwjQ6mVmCkHmd0LufA7G2MkMIjRCEnUb8jf4Z3iBcYMsBUvdKwd7PCI6zw1GdPYrI52d5aUJA4VYSVpNJHaSCYckYzmR2qJx6GunuKddWppR41OMneamEOk4WYlf+GxhZLqxovjYHVqcuKVqH3/mOZvuftsy4lhZPtHVG057BJRJf5mzF55JV4+rrTKJTWHlr9e2FlGSIKB8nENQUn21sLHo4gj9JSl1bU3j+PQ+fIxb6Tbjz3n92InDPprUNnir3VwpbkKKBnSS6GG71shjuZ1mMc8kUZ62sE1u4APNR3NAgBNtQa18eY4iACXmL5Eo7WZY3pPccix6Ngb7OAZypYG0hELP3C/pAHT+x2buqF2X1Go6NXQp3koZIaI3thS3KMFrjJgwfcJ6/TSKY/BdFMz07DLnt7OHpB8F4eyBdlW+5FbZ2lgSA4DvDyxJSt4FrkcDIdvUCOx7kn78b0Fzq4vNos847WqZNl0wpzmLIAN50955xAQPeSzN5GD981A7vtTQidWd6wYr0z2nL5xQm7jtTu+nJ6/64KA5yOM9qsImbvB7c/j4mb5+P+X+Y5VqdusLcWHNok3Akhcov/O+eHyU2N1k3OnVNJZW/FYTNMdtpbJtEzw2jVb7Uz2jJ+RG3/9nDRuBe7DuB1Q/9eWFmGCZtecmwmdDae24dLj+F0LhwnHPzXOb9MbulcMdwmr9NIpr+IwrU6I8sp2GXvPI+CEKXElQDgF2QA9M9pS4da+UsDQeR5IvALMjr56D4ra9k84vT+PaY4iD5+CbubzIvzCs/1AMf0hPfcbZq/63T9tcVt8qaCOUwmoIBz1AwTg9GSTBrAjBAs+xBKYUeM/Wc3FD6+x3B51WHrg+m11LewsgyzCseAl4HaCF2HKZPalF8gKBAl5JtwSHGMbUtORpefdptWPsMcXL0kN3v2bJx22mnIy8tDhw4dEl7DcVy716JFi1pds2LFCpxwwgnw+Xzo378/FixYYEguHsTSGSY9U55OmSZ1s+x2oVdnHbwKOnhljCm2T39OsnfTEgVkxRZs/WGA4bJi2aHNgJbOSgPB+NhEOz4nWRZ8J9lbCxGFh5Gw79DTIpq/GYy6LZeidue17T4/8r2v0HHu9oTfdavOAHfLrgZXO0zhcBhXXXUVxo4dm/K6V199FXv27Im/hg8fHv9s69atuOiii3DOOeegvLwcEydOxC233IKPP/7YZOnpkezpLlVDdMoTYbrkbFq/kw3otXdY4dAsm3Puk1qcZO9X3v0DNi4/Bcu3HGG4rLASfVAyY/Cn1b8XVpaZNvsdy/OkRo4YTu3fNOK79n99NLhP1yJnxb+Ru+Kfmr7LxnPn4mqHaebMmbjjjjswcODAlNd16NABJSUl8VdOTk78s3nz5qFv37544okncPTRR+O2227DlVdeiaeeeippeaFQCLW1ta1eenDCrpUYTsz87KaO5ATS6Su2S67tuU9m4mR7N8o8Pto0ABM3zzdclpcHcixOdKinf4sHT7g3a+B3sr3VElaibaNR0u849XjjB/juDEMcIUG8PkRFLjae24+rHSa1BINBdO7cGaeccgpeeeUVtMzVuXLlSpx33nmtrh82bBhWrlyZtLyHH34YRUVF8VfPnj01yUMjO2oiUpWXri6rMz8nw8g9ZBt6dMWDUD/DMBVOtreP4hJaWDF/55nR/l0aCCKkcAjLvOFjPyJv5CDyyqEQ2NiSnJPtrYUcj2LaEqta2HjuPDI+6PvBBx/E7373O+Tl5eGTTz7BuHHjUF9fj/HjxwMAKioq0LVr11bf6dq1K2pra9HU1ITc3Nx2ZU6dOhWTJk2K/11bW4uePXuC430Al16lZjWUVI3XLd59JtyDVejRleSwzQh22rtZ5qimAvCanOyQhq4EjsDDExjdJBdelwclIgDYp6l+N/RvLx91fu1KvRDDDbpKRybcQ0scN8M0ZcqUhIHaLV8bNmxQXd706dNx+umnY/Dgwbjnnntw991347HHHjMko8/nQ2FhYasXAHC8AKI0t4oTaJnMTmvsiFmOldM8+2y5z3SYrQcfH31yNutMQ63y2F2uRDjwoDOL0CBp3yVntR4WVpahWY5mhjY6w+T/cyUKHtlnSB6jmFVu+KBuqsJ0y3WbHvSSyffpOIdp8uTJWL9+fcpXv379dJc/ZMgQ7Ny5E6FQdF25pKQEe/fubXXN3r17UVhYmHB2KS1SU/y/CtDq6AWtP1QtB18ajcXqg3bVQnOJsmUZRu+zZVlqZDMqP7O3dozYu+2SnFZ52l6v9TgNO+zNcdHklVbiFHunKqsl8/aVYWu92K581r/V4TZ7a8FxDlOXLl1w1FFHpXx5vV7d5ZeXl6Njx47w+XwAgKFDh2Lp0qWtrlmyZAmGDh1q6D6AqHK1HJeQ6ge6bSPUY3ynday26JGvrT5SDXJaddayLDMS8lltbwVwVOJKu+2d41FQKB7Kzq9VnrbX82nqNMPeWn9QRJ5A4GB4SU4Pdttbizzz9mnb8ZcINp67x95q4Qhx4nGc6ti+fTsqKyvx3nvv4bHHHsPnn38OAOjfvz/y8/Px/vvvY+/evTj11FORk5ODJUuW4M4778Sdd96JmTNnAoimFTj22GMRDAYxYsQILFu2DOPHj8e///1vDBs2TJUctbW1KCoqwq6KmcjPacYnZ+7EgVAO6iICaiMeRAiHRolLOMWrt1HFgjid3mnMJFv0QOs+x5dEczFtreddqS/a9n6k363w8YTKLrnSQBD9C2RsrvNQeRJWe59aZxke6Xcr8gUZlWER07fNQ+RlDyDyEG+IaJIx8nI0qWNkRz7yHqjR9F21sP6dXdinBwJAQU1NTTy8JhmOm2HSwowZMzB48GDcf//9qK+vx+DBgzF48GB8++23AABRFFFWVoahQ4fi+OOPxwsvvIAnn3wS999/f7yMvn374t///jeWLFmC4447Dk888QReeukl1c5SMhIlrky3JKfWY441pmzuXIB2PbTVr9Vr4nrrp2XvPCF6Sr3dMUx6oW3vZplHo0xnCPQL7RNXWmHvZPmPkjHpnpdx631/jcduyfu9UH5V/fU48n4v5P1ehA4Uaf+ySlj/zi7cYG9XzzA5hUQzTJVhH2rDIuokD8JKdIYprLQP/M72TuI0aD3dOPFpcXrPscgXZWytFxMuOWQbj/S7FSJPsLrSS2VW6KhCCRtqrT8eRS2lgSDm3/0K+NwIZj0+CrN2PG+3SJaTyf2b0R51dsqSGSanQRQJ4I09vjthJ0AmovVpT68dtC6RWGnvjj4JAW8EfiHzn5HU6lU+GNNl1N5mXU+ThZVlePDZm/H40yMRcVh6CaOw/p1dWGXvtrAZJgrEZph27p6OgnzgkzO2sRkmhuOY1WcMCgQZG2q9bIYJwBOH3wqJAPdsedFwWbET7tfWOHeGCYjOMvoFBftDHjyx+zm7xWEwHID6GSaXRjM4G5rJ8Bj2Efp8AGpeU1D80ia7RaHC9G3z7BbBUXg4Ak8WdlUzDt9lMLIB1m1MghDO0GnXDPvhwiHIkcx5ppjSYxxm9RljtxiOQT7YR2lM1zdIgOyiuXo28DMY2mH9hjIcJ4AHAccRplyX4z13G7q9tt5uMTQRelpE5I2chJ/xiM6qsLiKKLE+6uQlNLOwOnklg5EJsN90mhBJ1VlyDIZZcBwB+MTdul7i0CS5MweTWdByHPwCXLG8JxxMXGkGzBFnZDrMYaIIITI4XoifJceW5Bhqqbj5qOjs0N98hsrxTpAgXtOY8DORJ+A49sMWI6JwCCt0hsAGKZpJ3ek5rngucY44BoORHuYwUYTjPHaLwHApgjcCPlcBcvQf+6MGD0fYDNNBwgqPeomn5kASov2A7UyCtStGpsMcJsoQpfWI6eEIIhk61cRmKujR+YWfIY6WIV5ZZ1odEYWDpHCW5RxyOtO3zcP9v8yj8kO/sLIMIUqzVVbAqViWyzR7M1LD7J0e9/Rwl0CkegCHpr1lwlHfwuuUhu2UJ0qz9ZGufLvr14KaH8pkZJq9J3cfh/El7cvSa+8Htz9PLYu0WUQULp6sMx2ZZm+95dtdv1Uwe6eHOUwmwIOAmJhJN3ZAIcPYEQWJdJjovXTlJ/pcbdlqoGXvfIEgz+Pu6U6a9u7kk9ElR253nV57T+kxLmV9ajGzf0sKhwjh4JR0xfXTOqF64mHYcsVJCT9n/Tu7cIK9U8EcJspwnpx2iSvNWpLL9k5m9P4TdRxaT1lmlG30fu3YSh7+i4Cm2X4qZdG2NyGAQkklHb1AJ6/USka77Z2IHY0c1tcIjjlHznNVf/B3noXD7mxu9xnr39mFk+0dgzlMJhJTrpYlOTWNJuaFO2UK1S5iOlCrM72YOWVvtb2tdpq4bj4I3UJUylJr7/BcD/bdckTa8vyCAh/fXh967B1WoOp8Ntr21tr2DssjOLJQTrgUaSWRv/nQ+OMZCHcfBE/nkxDuNqDdNax/ZxdOsXcq2FlyFIidJbdj590o6lCEfw/9CbUREdVhEQ0Hz5KrDkcHU3aWnPtx60nlU3qMQ4EgY0ejkNFnyUXe7Yjw/wD/g1Upr3uk362QFA73bnvBcJ3jS4Lo7Y+gvEp0dNuY1WcM/IKMqpC9s0yhzwegeeClEPZ8C//Ry2yTg8FgZ8nZCZHAc+1jmLx89CmUwbATjsv8re/i8NSOUgyBAwhHxwEOK0C95OwJ+9JAEDwk8DAW/E8D3283AnjUXiEYDI04u4e7ECK3X4unRbavcafDTP3YoXsz6syk+WQaMV25gkxtRog36ISY3cYWVpbBw0Xv28cnf3prmNERzXNyUTe1i6nyaCXTxr9Mux/aOFE/zGEyAR7m/CrpiWlI97dToCG3mUshLcu2asmFpr1LA0F4eYIcD3FENmon2DukcGiW6Q2BRgPIrejfDRKPRplHg5Q8ya73mBA8v8lHztHVaeVRixPs7TTYeJ4aJ9qbxTBRoGUMU2GhDx+evr1dDFOjxCGsHFoO8QvR/9NsFG6NrVFLpt+fVrTqY3xJEEWigm0N7jhPzmx7197VFb6uVdj0n1Nx7OLPDJVVGgjiyEIZP9V6TJOZhj5m9xkNv6CgMizgwe2JY5gi86POlDiqfcoFM2H9uzWZrg/n3J/6GCY2w2QCbWOYYmkFWsaOmBFHYqTxWfWkYqQeZ3Qu56BFH6WBIDp4FeSLMvyCdfaum9oFzY/m6FreMdveuUMagSF90fOYzYbLKs4h6CCaGxxmtH+XBoLgVJwlJ46SdTtLrH/Tg43nzoM5TBYQSytg1lIIjS2WVi8z2bUtNBPQc/8LK8sgKdFDoefts24Lc86ZAnDO0dF/dWKWvR+afB0eLz0bRU/uNlzWvuboQb5m9HFa/XthZRlkwkE2Oaluy3r1wPo3G8+dCluSo0DbJbnFZ/yCqpAPNREh6ZJcDDd62Qx3EksrsKFWYO0O0bQCAgcqqQDGlwRxTFETyqtyHZ2yYVafMcjzKNgfEjBn53N2i8NgOAC2JGcLHOcBFOft2XaTBw+4T16nkUx/EQWOPCDWLnsLKpan1FIVBvaHvLakbNCiPx4EHo7AmyBhp1Ww/m0Mt+nPbfKmwnmjpwuJTdL1OGwOijo8gpqwjCY5jGYljFD8FUJYCSFMWr+i3q25r4WVz+LqjrdYUpfR19Udb8HCymdtl8PNr2T2FvgQfJ4my9qd0+3drIQRVsJU6hf5EDy8PbrV0r8lhCCTZnj4pqyzd6a82HhuxgtQs9jGluQosHPnTvTs2dNuMRgMBoPBYOhgx44d6NGjR8prmMNEAUVRsHv3bhQUFICzO4WuS6itrUXPnj2xY8eOtOvGjChMZ9phOtMO05l2mM604xSdEUJQV1eH7t27g+dTL7o5IIWd++F5Pq1nykhMYWEhG2A0wnSmHaYz7TCdaYfpTDtO0FlRUZGq61gME4PBYDAYDEYamMPEYDAYDAaDkQbmMDFswefz4f7774fP57NbFNfAdKYdpjPtMJ1ph+lMO27UGQv6ZjAYDAaDwUgDm2FiMBgMBoPBSANzmBgMBoPBYDDSwBwmBoPBYDAYjDQwh4nBYDAYDAYjDcxhYpjGww8/jJNPPhkFBQUoLi7G8OHDsXHjxlbXNDc3IxgMolOnTsjPz8cVV1yBvXv32iSx85gzZw44jsPEiRPj7zGdtWfXrl24/vrr0alTJ+Tm5mLgwIH49ttv458TQjBjxgx069YNubm5OO+887Bp0yYbJbYXWZYxffp09O3bF7m5uTj88MMxa9asVudpMZ0Bn332GS6++GJ0794dHMfh3XffbfW5Gh1VVlbiuuuuQ2FhITp06ICRI0eivr7ewruwllQ6i0QiuOeeezBw4ED4/X50794dN9xwA3bv3t2qDKfqjDlMDNP49NNPEQwG8dVXX2HJkiWIRCI4//zz0dDQEL/mjjvuwPvvv4+33noLn376KXbv3o3LL7/cRqmdwzfffIMXXngBgwYNavU+01lrqqqqcPrpp0MURXz00UdYt24dnnjiCXTs2DF+zaOPPopnnnkG8+bNw6pVq+D3+zFs2DA0NzfbKLl9PPLII3j++efx7LPPYv369XjkkUfw6KOPYu7cufFrmM6AhoYGHHfccSgrK0v4uRodXXfddVi7di2WLFmCDz74AJ999hluvfVWq27BclLprLGxEf/73/8wffp0/O9//8Pbb7+NjRs34pJLLml1nWN1RhgMi9i3bx8BQD799FNCCCHV1dVEFEXy1ltvxa9Zv349AUBWrlxpl5iOoK6ujhxxxBFkyZIl5KyzziITJkwghDCdJeKee+4hZ5xxRtLPFUUhJSUl5LHHHou/V11dTXw+H3nzzTetENFxXHTRRWTEiBGt3rv88svJddddRwhhOksEAPLOO+/E/1ajo3Xr1hEA5Jtvvolf89FHHxGO48iuXbssk90u2uosEV9//TUBQH755RdCiLN1xmaYGJZRU1MDAAgEAgCA7777DpFIBOedd178mqOOOgq9evXCypUrbZHRKQSDQVx00UWtdAMwnSXivffew0knnYSrrroKxcXFGDx4MObPnx//fOvWraioqGils6KiIgwZMiRrdXbaaadh6dKl+OmnnwAA33//Pb744gtceOGFAJjO1KBGRytXrkSHDh1w0kknxa8577zzwPM8Vq1aZbnMTqSmpgYcx6FDhw4AnK0zdvguwxIURcHEiRNx+umn49hjjwUAVFRUwOv1xjtKjK5du6KiosIGKZ3BokWL8L///Q/ffPNNu8+YztqzZcsWPP/885g0aRKmTZuGb775BuPHj4fX68WNN94Y10vXrl1bfS+bdTZlyhTU1tbiqKOOgsfjgSzLmD17Nq677joAYDpTgRodVVRUoLi4uNXngiAgEAgwPSIaj3nPPffgT3/6U/wAXifrjDlMDEsIBoNYs2YNvvjiC7tFcTQ7duzAhAkTsGTJEuTk5NgtjitQFAUnnXQSHnroIQDA4MGDsWbNGsybNw833nijzdI5k3/84x94/fXX8cYbb+A3v/kNysvLMXHiRHTv3p3pjGEJkUgEf/zjH0EIwfPPP2+3OKpgS3IM07ntttvwwQcfYPny5ejRo0f8/ZKSEoTDYVRXV7e6fu/evSgpKbFYSmfw3XffYd++fTjhhBMgCAIEQcCnn36KZ555BoIgoGvXrkxnbejWrRuOOeaYVu8dffTR2L59OwDE9dJ2J2E26+yuu+7ClClTcM0112DgwIEoLS3FHXfcgYcffhgA05ka1OiopKQE+/bta/W5JEmorKzMaj3GnKVffvkFS5Ysic8uAc7WGXOYGKZBCMFtt92Gd955B8uWLUPfvn1bfX7iiSdCFEUsXbo0/t7GjRuxfft2DB061GpxHcG5556LH3/8EeXl5fHXSSedhOuuuy7+f6az1px++unt0lX89NNP6N27NwCgb9++KCkpaaWz2tparFq1Kmt11tjYCJ5vPfx7PB4oigKA6UwNanQ0dOhQVFdX47vvvotfs2zZMiiKgiFDhlgusxOIOUubNm3Cf/7zH3Tq1KnV547Wma0h54yMZuzYsaSoqIisWLGC7NmzJ/5qbGyMXzNmzBjSq1cvsmzZMvLtt9+SoUOHkqFDh9ootfNouUuOEKaztnz99ddEEAQye/ZssmnTJvL666+TvLw88re//S1+zZw5c0iHDh3Iv/71L/LDDz+QSy+9lPTt25c0NTXZKLl93HjjjeSwww4jH3zwAdm6dSt5++23SefOncndd98dv4bpLLpbdfXq1WT16tUEAHnyySfJ6tWr4zu61OjoggsuIIMHDyarVq0iX3zxBTniiCPIn/70J7tuyXRS6SwcDpNLLrmE9OjRg5SXl7f6XQiFQvEynKoz5jAxTANAwterr74av6apqYmMGzeOdOzYkeTl5ZHLLruM7Nmzxz6hHUhbh4nprD3vv/8+OfbYY4nP5yNHHXUUefHFF1t9rigKmT59OunatSvx+Xzk3HPPJRs3brRJWvupra0lEyZMIL169SI5OTmkX79+5N577231o8V0Rsjy5csTjmE33ngjIUSdjg4cOED+9Kc/kfz8fFJYWEhuvvlmUldXZ8PdWEMqnW3dujXp78Ly5cvjZThVZxwhLVK7MhgMBoPBYDDawWKYGAwGg8FgMNLAHCYGg8FgMBiMNDCHicFgMBgMBiMNzGFiMBgMBoPBSANzmBgMBoPBYDDSwBwmBoPBYDAYjDQwh4nBYDAYDAYjDcxhYjAYDAaDwUgDc5gYDAaDwWAw0sAcJgaDwWAwGIw0MIeJwWC4mrPPPhsTJ05s938ncODAARQXF2Pbtm2W133NNdfgiSeesLxeBiNTYQ4Tg8EwnZtuugkcx4HjOIiiiL59++Luu+9Gc3Mz1XrefvttzJo1i2qZRpg9ezYuvfRS9OnTx/K677vvPsyePRs1NTWW181gZCLMYWIwGJZwwQUXYM+ePdiyZQueeuopvPDCC7j//vup1hEIBFBQUEC1TDVIktTuvcbGRrz88ssYOXKk5fIAwLHHHovDDz8cf/vb32ypn8HINJjDxGAwLMHn86GkpAQ9e/bE8OHDcd5552HJkiXxzxcvXowzzjgDHTp0QKdOnfCHP/wBP//8c6syGhoacMMNNyA/Px/dunVrt+TUdkmuT58+ePrpp1tdc/zxx+OBBx6I//1///d/GDhwIHJzc9GpUyecd955aGhoSHof27ZtA8dx+Mc//oHf/va38Pl8eO+999pd9+GHH8Ln8+HUU09t991//vOfOPPMM5Gbm4uTTz4Z27dvx+eff45TTz0VeXl5OPfcc1FdXW34exdffDEWLVqU9F4YDIZ6mMPEYDAsZ82aNfjyyy/h9Xrj7zU0NGDSpEn49ttvsXTpUvA8j8suuwyKosSvueuuu/Dpp5/iX//6Fz755BOsWLEC//vf/3TLsWfPHvzpT3/CiBEjsH79eqxYsQKXX345CCFJv/P9998DAB577DHMmDEDa9euxbnnntvuus8//xwnnnhiwu8+//zzeOihh/Dll19i7969uP766zFnzhw8++yzWL58Ob7//nu8+uqrhr93yimn4Ouvv0YoFNKtIwaDEUWwWwAGg5EdfPDBB8jPz4ckSQiFQuB5Hs8++2z88yuuuKLV9a+88gq6dOmCdevW4dhjj0V9fT1efvll/O1vf4s7KK+99hp69OihW6Y9e/ZAkiRcfvnl6N27NwBg4MCBKb9TXl4Ov9+Pt956K2Vs0i+//ILu3bu3+24gEMDf//53dOrUCQBw1lln4YsvvsDatWuRl5cHADj55JNRUVFh+Hvdu3dHOBxGRUVF/P4YDIY+2AwTg8GwhHPOOQfl5eVYtWoVbrzxRtx8882tnKRNmzbhT3/6E/r164fCwsK4M7J9+3YAwM8//4xwOIwhQ4bEvxMIBDBgwADdMh133HE499xzMXDgQFx11VWYP38+qqqqUn7n+++/xyWXXJI2kLupqQk5OTntvnvZZZfFnR4gen9XX3113OmJvde3b1/D38vNzQUQjadiMBjGYA4Tg8GwBL/fj/79++O4447DK6+8glWrVuHll1+Of37xxRejsrIS8+fPx6pVq7Bq1SoAQDgc1l0nz/PtltcikUj8/x6PB0uWLMFHH32EY445BnPnzsWAAQOwdevWpGWWl5fj7LPPTlt3586d2zlf5eXlrRw+IOoMtYxzam5uxsaNG3HccccZ/l5lZSUAoEuXLmnlZTAYqWEOE4PBsBye5zFt2jTcd999aGpqwoEDB7Bx40bcd999OPfcc3H00Ue3czYOP/xwiKIYd6QAoKqqCj/99FPSerp06YI9e/bE/66trW3nDHEch9NPPx0zZ87E6tWr4fV68c477yQsr7a2Ftu2bcPgwYPT3uPgwYOxbt26lN/dunUrampqWr33448/ghASXxrU+z0gGivWo0cPdO7cOa28DAYjNcxhYjAYtnDVVVfB4/GgrKwMHTt2RKdOnfDiiy9i8+bNWLZsGSZNmtTq+vz8fIwcORJ33XUXli1bhjVr1uD/t3O3KqpFYRjHnzNqE+/AtEERtJgVQUTxC7wDLTaLwSCYDYrJ4AVYxKAWi1GToOBGTTbB5AVomxMOI3PQmQVzGA8M/19d+11rpc3D2u/apVJJLy8fv8bi8bj6/b4Wi4W2262KxaIcDsdtfLlcqtlsarVa6Xg8ajQa6Xw+KxAIPJzPtm05HA5jn5MkpVIp7ff7W/B7qw0Gg7dn3nqT3vcXbTYbWZYlt9v9T3XSn8bzZDJp3CsAMwITgP/C6XSqUqmo1WrpcrloMBhovV4rGAyqWq2q3W7f1bTbbUWjUeXzeSUSCUUikbubaO/V63XFYjHlcjlls1kVCgVZlnUb93g8ms/nymQy8vl8ajQa6nQ6SqfTD+ezbVt+v/+uN+mRUCikcDis4XD4Ya1t23enVbZt//VZ7at11+tVk8lE5XLZuFcAZr9eP7s/CwD4sul0qlqtpt1u9+lJ2Hfo9Xoaj8eazWZPXRf4qfitAAB8k2w2q8PhoNPpJK/X+9S1XS6Xut3uU9cEfjJOmAAAAAzoYQIAADAgMAEAABgQmAAAAAwITAAAAAYEJgAAAAMCEwAAgAGBCQAAwIDABAAAYEBgAgAAMPgNiNdtbbGaBQEAAAAASUVORK5CYII=", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ax = jupyter.plot2d(res2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Conclusion\n", "\n", "This cookbook explains the basic usage of pyFAI as a Python library for azimuthal integration and simple visualization in the Jupyter notebook." ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Total execution time: 6.610 s\n" ] } ], "source": [ "print(f\"Total execution time: {time.perf_counter() - t0 :6.3f} s\")" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.11.2" } }, "nbformat": 4, "nbformat_minor": 4 }