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{
"cells": [
{
"cell_type": "markdown",
"id": "59f50187-f73f-471b-b668-9126e6f48501",
"metadata": {},
"source": [
"# Learning high-resolution data from low-resolution\n",
"\n",
"This is an example notebook showing how to use the `pes_to_spec` infrastructure in this package.\n",
"\n",
"We start by importing some modules. The key module here is called `pes_to_spec`."
]
},
{
"cell_type": "code",
"id": "d44af0b6-9c00-4e70-b49b-d74ed562e92f",
"metadata": {},
"outputs": [],
"source": [
"import sys\n",
"# add the pes_to_spec main directory\n",
"# (change this depending on where you started the notebook if needed, or comment it out if you have done pip install in pes_to_spec)\n",
"sys.path.append('..')\n",
"\n",
"# you meay need to do pip install matplotlib seaborn extra_data for this notebook, additionally\n",
"# for this notebook the following packages are needed:\n",
"# pip install \"numpy>=1.21\" \"scipy>=1.6\" \"scikit-learn>=1.2.0\" torch torchbnn matplotlib seaborn extra_data"
]
},
{
"cell_type": "code",
"id": "da002d3e-c0da-419b-922b-0ab5c6deece8",
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"from matplotlib.gridspec import GridSpec \n",
"import seaborn as sns\n",
"\n",
"import lmfit\n",
"import scipy\n",
"from extra_data import open_run, by_id\n",
"from itertools import product\n",
"from pes_to_spec.model import Model, matching_ids\n",
"\n",
"from typing import Any, Dict"
]
},
{
"cell_type": "markdown",
"id": "494a729c-dff4-4501-b828-fba2aaae5a23",
"metadata": {},
"source": [
"# Input data\n",
"\n",
"Read data from two runs. One shall be used for training the model. The second one is used for testing it.\n",
"Note that the data in the training run must be large enough, compared to the number of model parameters.\n",
"\n",
"Only the SPEC, PES and XGM data is used for training, while only the PES and XGM data is needed for testing.\n",
"However, more data is collected here to validate the results."
]
},
{
"cell_type": "code",
"id": "4a301f2a-dedb-46e4-b096-fc9c6cf5b23a",
"metadata": {},
"outputs": [],
"source": [
"run = open_run(proposal=900331, run=69)\n",
"run_test = open_run(proposal=900331, run=70)\n",
"\n",
"# useful names to avoid repeating it all over the notebook, in case they ever change\n",
"spec_name = \"SA3_XTD10_SPECT/MDL/SPECTROMETER_SCS_NAVITAR:output\"\n",
"pes_name = \"SA3_XTD10_PES/ADC/1:network\"\n",
"xgm_name = \"SA3_XTD10_XGM/XGM/DOOCS:output\"\n",
"\n",
"pres_name = \"SA3_XTD10_PES/GAUGE/G30310F\"\n",
"volt_name = \"SA3_XTD10_PES/MDL/DAQ_MPOD\"\n",
"\n",
"# PES channels\n",
"channels = [f\"channel_{i}_{l}\" for i, l in product([1, 3, 4], [\"A\", \"B\", \"C\", \"D\"])]\n",
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"\n",
"def get_gas(run) -> str:\n",
" \"\"\"Get gas in chamber for logging.\"\"\"\n",
" gas_sources = [\n",
" \"SA3_XTD10_PES/DCTRL/V30300S_NITROGEN\",\n",
" \"SA3_XTD10_PES/DCTRL/V30310S_NEON\",\n",
" \"SA3_XTD10_PES/DCTRL/V30320S_KRYPTON\",\n",
" \"SA3_XTD10_PES/DCTRL/V30330S_XENON\",\n",
" ]\n",
" gas_active = list()\n",
" for gas in gas_sources:\n",
" # check if this gas source is interlocked\n",
" if gas in run.all_sources and run[gas, \"interlock.AActionState.value\"].ndarray().sum() == 0:\n",
" # it is not, so this gas was used\n",
" gas_active += [gas.split(\"/\")[-1].split(\"_\")[-1]]\n",
" gas = \"_\".join(gas_active)\n",
" return gas\n",
"\n",
"def get_tids(run, need_spec:bool=True) -> np.ndarray:\n",
" \"\"\"Get which train IDs contain all necessary inputs for training.\"\"\"\n",
" spec_tid = run[spec_name, \"data.trainId\"].ndarray()\n",
" pes_tid = run[pes_name, \"digitizers.trainId\"].ndarray()\n",
" xgm_tid = run[xgm_name, \"data.trainId\"].ndarray()\n",
"\n",
" # match tids to be sure we have all inputs:\n",
" tids = matching_ids(spec_tid, pes_tid, xgm_tid)\n",
" return tids\n",
"\n",
"def get_data(run, tids) -> Dict[str, Any]:\n",
" \"\"\"Get all relevant data.\"\"\"\n",
" data = dict()\n",
" data[\"int\"] = run[xgm_name, \"data.intensitySa3TD\"].select_trains(by_id[tids]).ndarray()[:, 0][:, np.newaxis]\n",
" data[\"pressure\"] = run[pres_name, \"value\"].select_trains(by_id[tids]).ndarray()\n",
" data[\"voltage\"] = run[volt_name, \"u212.value\"].select_trains(by_id[tids]).ndarray()\n",
" data[\"energy\"] = run[spec_name, \"data.photonEnergy\"].select_trains(by_id[tids]).ndarray()\n",
" data[\"spec\"] = run[spec_name, \"data.intensityDistribution\"].select_trains(by_id[tids]).ndarray()\n",
" data[\"pes\"] = {ch: run[pes_name,\n",
" f\"digitizers.{ch}.raw.samples\"].select_trains(by_id[tids]).ndarray()\n",
" for ch in channels}\n",
" data[\"gas\"] = get_gas(run)\n",
" return data\n"
]
},
{
"cell_type": "code",
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"id": "210c0550-1abb-43a0-99a5-7c35d2766be0",
"metadata": {},
"outputs": [],
"source": [
"\n",
"# get the matched train IDs\n",
"tids = get_tids(run)\n",
"\n",
"# we don't need the spec for testing in reality,\n",
"# but it is nice to plot it in the test run too,\n",
"# to check that this works during validation\n",
"test_tids = get_tids(run_test, need_spec=True)\n",
"\n",
"# get the data\n",
"data = get_data(run, tids)\n",
"data_test = get_data(run_test, test_tids)\n"
]
},
{
"cell_type": "markdown",
"id": "017865a1-057f-48c7-8bef-a6e40490de2c",
"metadata": {},
"source": [
"Now the `data` and `data_test` dictionaries contain the necessary information about the training and test runs.\n",
"The code above also selected only entries with train IDs on which at least SPEC, PES and XGM were present.\n",
"\n",
"Note that for training, it is assumed that only one pulse is present. For testing there is no such requirement.\n",
"\n",
"First output some general information about the conditions of the measurement device."
]
},
{
"cell_type": "code",
"id": "956105a6-d37e-453c-bfeb-2b1c876ee3f2",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Gas in training: NEON\n",
"Gas in testing: NEON\n"
]
}
],
"source": [
"print(f\"Gas in training: {data['gas']}\")\n",
"print(f\"Gas in testing: {data_test['gas']}\")"
]
},
{
"cell_type": "code",
"id": "4654f205-edc6-45f7-97bd-0d088c38edb0",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Voltage in training: -116.00 +/- 0.01\n",
"Voltage in testing: -116.00 +/- 0.01\n"
]
}
],
"source": [
"print(f\"Voltage in training: {np.mean(data['voltage']):0.2f} +/- {np.std(data['voltage']):0.2f}\")\n",
"print(f\"Voltage in testing: {np.mean(data_test['voltage']):0.2f} +/- {np.std(data_test['voltage']):0.2f}\")"
]
},
{
"cell_type": "code",
"id": "fa662544-3caa-4404-bb61-fa41add82642",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Pressure in training: 1.29e-06 +/- 3.95e-08\n",
"Pressure in testing: 1.29e-06 +/- 3.91e-08\n"
]
}
],
"source": [
"print(f\"Pressure in training: {np.mean(data['pressure']):0.2e} +/- {np.std(data['pressure']):0.2e}\")\n",
"print(f\"Pressure in testing: {np.mean(data_test['pressure']):0.2e} +/- {np.std(data_test['pressure']):0.2e}\")"
]
},
{
"cell_type": "markdown",
"id": "5962e483-60da-4c70-bb09-dce5fc9745e0",
"metadata": {},
"source": [
"Now we will actually train the model. We do that by creating a `Model` object (from `pes_to_spec`) and calling the `fit` function.\n",
"The `fit` function requires the PES intensity, the SPEC intensity, the energy axis from SPEC (stored as a reference only), as well as the energy measured in the XGM (which has better resolution than the integral of the PES)."
]
},
{
"cell_type": "code",
"id": "a0adb57b-7496-4781-9511-ac2a8d05658d",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Checking data quality in high-resolution data.\n",
"Fitting PCA on low-resolution data.\n",
"Using 1000 comp. for PES PCA (asked for 1000, out of 7201, in 7165 samples).\n",
"Fitting PCA on high-resolution data.\n",
"Fitting outlier detection\n",
"Fitting model.\n",
"Calculate PCA unc. on high-resolution data.\n",
"Calculate transfer function\n",
"Calculate PCA on channel_1_A\n",
"Calculate PCA on channel_1_B\n",
"Calculate PCA on channel_1_C\n",
"Calculate PCA on channel_1_D\n",
"Calculate PCA on channel_3_A\n",
"Calculate PCA on channel_3_B\n",
"Calculate PCA on channel_3_C\n",
"Calculate PCA on channel_3_D\n",
"Calculate PCA on channel_4_A\n",
"Calculate PCA on channel_4_B\n",
"Calculate PCA on channel_4_C\n",
"Calculate PCA on channel_4_D\n",
"End of fit.\n"
]
}
],
"source": [
"# this is the main object holding all\n",
"# information needed for training and prediction\n",
"# the default parameters should be sufficient in most times\n",
"model = Model(channels=channels,\n",
" high_res_sigma=0.0,\n",
" )\n",
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"\n",
"# this trains the model\n",
"# the first parameter is expected to be a dictionary with the channel name as a key\n",
"model.fit(data['pes'],\n",
" data['spec'],\n",
" data['energy'],\n",
" pulse_energy=data['int'])\n",
"\n",
"# save it for later usage:\n",
"model.save(\"model.joblib\")\n",
"\n",
"# load a model (you can start from here if working on an existing model)\n",
"model = Model.load(\"model.joblib\")\n",
"\n",
"# and use it to map a low-resolution spectrum to a high-resolution one\n",
"# as before, the low_resolution_raw_data refers to a dictionary mapping the channel name\n",
"# in the format \"channel_[1-4]_[A-D]\" to the 2D numpy array with shape (number_of_train_IDs, features)\n",
"# all names and shapes must match the format in training, except for the number_of_train_IDs, which may vary\n",
"pred = model.predict(data['pes'], pulse_energy=data['int'])\n"
]
},
{
"cell_type": "markdown",
"id": "e0286ae3-1a59-468f-ae40-c3ed94b7b301",
"metadata": {},
"source": [
"Now we can try it in the test dataset:"
]
},
{
"cell_type": "markdown",
"id": "ffc06362-3479-4cb9-b102-b438a83d2950",
"metadata": {},
"source": [
"We can predict it in the training data itself, but this is a bit biased, since we used the same information to fit the model."
]
},
{
"cell_type": "code",
"id": "917156f3-9476-48e0-9121-5f75f185045f",
"metadata": {},
"outputs": [],
"source": [
"pred = model.predict(data_test['pes'], pulse_energy=data_test['int'])\n",
"\n",
"# add the references in this array in the same array format, so we can plot them later\n",
"pred[\"energy\"] = model.get_energy_values()\n",
"\n",
"pred['spec'] = data_test['spec'][:, np.newaxis, :]"
]
},
{
"cell_type": "markdown",
"id": "77866435-1cb6-40ac-a9a5-8f2ae37017b7",
"metadata": {},
"source": [
"Let's try to predict in the independent run in the test dataset. The performance of the model varies a lot if the beam intensity is very different from the training one. To ensure we take a train ID to visualize that is relatively high intensity, we sort the train IDs by XGM intensity and then choose the highest intensity one.\n",
"One could try other train IDs.\n",
"\n",
"For train IDs with close to zero beam intensity, there is a relatively larger error, since the training data did not contain any of those samples and the signal-to-noise ratio is relatively high."
]
},
{
"cell_type": "code",
"id": "ed62606a-4ea7-4e0a-8b61-73e682cacf04",
"metadata": {},
"outputs": [],
"source": [
"# choose train ID of the test dataset by XGM intensity\n",
"test_intensity = np.argsort(data_test['int'][:,0])\n",
"example_tid = test_intensity[-1]"
]
},
{
"cell_type": "markdown",
"id": "f931e9e0-84a7-4e4f-bfad-588bfe77267c",
"metadata": {},
"source": [
"Now we can actually plot it."
]
},
{
"cell_type": "code",
"id": "fd42984c-554c-4c69-bf8a-119eeb0cca62",
"metadata": {},
"outputs": [],
"source": [
"def plot(data):\n",
" \"\"\"Plot prediction and expectation.\"\"\"\n",
" fig = plt.figure(figsize=(12, 8))\n",
" gs = GridSpec(1, 1)\n",
" ax = fig.add_subplot(gs[0, 0])\n",
" ax.plot(data[\"energy\"], data[\"spec\"], c='b', lw=3, label=\"High-res. measurement\")\n",
" ax.plot(data[\"energy\"], data[\"expected\"], c='r', ls='--', lw=3, label=\"High-res. prediction\")\n",
" ax.fill_between(data[\"energy\"], data[\"expected\"] - data[\"total_unc\"], data[\"expected\"] + data[\"total_unc\"], facecolor='gold', alpha=0.5, label=\"68% unc.\")\n",
" ax.legend(frameon=False, borderaxespad=0, loc='upper left')\n",
" ax.spines['top'].set_visible(False)\n",
" ax.spines['right'].set_visible(False)\n",
" Y = np.amax(data[\"spec\"])\n",
" ax.set(\n",
" xlabel=\"Photon energy [eV]\",\n",
" ylabel=\"Intensity\",\n",
" ylim=(0, 1.3*Y))\n",
" plt.show()"
]
},
{
"cell_type": "code",
"id": "bbbf77b5-f914-4b47-8ab6-fd3a89d0f983",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 1200x800 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# select the correct train ID for the data to plot\n",
"# except for the energy axis, which is always the same\n",
"plot({k: v[example_tid, 0, :] if k != \"energy\" else v\n",
" for k, v in pred.items()\n",
" if k in [\"expected\", \"total_unc\", \"spec\", \"energy\"]})"
]
},
{
"cell_type": "markdown",
"id": "eca3a06a-d613-4206-b128-6d81031de1d1",
"metadata": {},
"source": [
"## Resolution assessment using the autocorrelation\n",
"\n",
"We establish the resolution of the virtual spectrometer using the autocorrelation function, which estimates which level of detail can be observed in the test dataset.\n",
"\n",
"The autocorrelation function cannot assess which effect are physically relevant and which are simply noise. Therefore this method can only provide a rough estimate of the resolution. It is not expected to be very precise, but it can be used for a quick assessment.\n"
]
},
{
"cell_type": "code",
"id": "1491550c-6940-425e-a557-f2cd381d287b",
"metadata": {},
"outputs": [],
"source": [
"def fwhm(x: np.ndarray, y: np.ndarray) -> float:\n",
" \"\"\"Return the full width at half maximum of x.\"\"\"\n",
" # half maximum\n",
" half_max = np.amax(y)*0.5\n",
" # signum(y - half_max) is zero before and after the half maximum,\n",
" # and it is 1 in the range above the half maximum\n",
" # The difference will be +/- 1 only at the transitions\n",
" d = np.diff(np.sign(y - half_max))\n",
" left_idx = np.where(d > 0)[0][0]\n",
" right_idx = np.where(d < 0)[-1][-1]\n",
" return x[right_idx] - x[left_idx]\n",
"\n",
"def autocorrelation(x: np.ndarray, y: np.ndarray) -> np.ndarray:\n",
" \"\"\"Given the energy axis in x and the intensity in y, calculate the auto-correlation function.\"\"\"\n",
" mean_y = np.mean(y, keepdims=True, axis=0)\n",
" e = x - np.mean(x)\n",
" Rxx = np.mean(np.fft.fftshift(np.fft.ifft(np.absolute(np.fft.fft(y - mean_y))**2), axes=(-1,)), axis=(0,1))\n",
" Rxx /= np.amax(Rxx)\n",
" return Rxx\n"
]
},
{
"cell_type": "code",
"id": "45fc52a4-5716-42b9-adbd-a307d16c0c34",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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MTERKSgpOnDiBm2++uc1jXBVEaZydOc8bo0ePxsGDB70+b+rUqbj//vsxZMgQ2fqeSy65BKtWrYIgCN1aH+XiWhf47LPP4vPPP+/0ed2Z2jdmzBgYjUZs3LjR3c3wyJEjyM/Pd0+v64irMnjs2DH8+OOPePLJJwF4qkDS6h4gJsCuBLulIUOGnPd33XXNuXPn4s0330SfPn0QHByMX/7yl62O279/P1JTUxEXF3fe5+FLqiZS3377Lf785z9j586dKCwsxEcffXTevQ9ycnKwcOFCHDhwAGlpaXj00Udx2223KRIvERER+U94eDhuuOEGLF68GFartct/3zMyMvDtt9/ixhtvhMlkQlxcHC699FKcO3cOzz77LH75y19iw4YN+Pzzz2GxWNznDRgwAP/4xz8wduxYWK1WLFq0yKsK0A8//ICNGzfiiiuuQEJCAn744QecO3cO2dnZ7mPsdjvmzZuHRx99FHl5eVi6dCnmz58PvV6PiIgIPPjgg1iwYAGcTicuvvhiVFVV4bvvvoPFYsGcOXMwf/58vPTSS7jxxhuxePFiREZGYtu2bRg3bhwGDRqEjIwM/O9//8ORI0cQGxvb4XS6RYsWYenSpcjKysKoUaOwZs0a7Nmzp91OeJ21bNky3H///YiMjMTMmTNhs9nw448/oqKiAgsXLkRCQgJCQkKwYcMGpKamwmw2IzIy8rzneWPGjBmtmlR0hmud1OrVq7Fq1Sr341OmTHGvi7rnnnu8vm5bnnzySSxatKjT1Sige1P7IiMjMW/ePCxcuBAxMTGwWCy47777MGHCBFx00UXu4wYPHozly5fjmmuuASA2fYmPj0ffvn2xb98+PPDAA7j66qtxxRVXuI/v378/fvvb3+K5555DbGwsPv74Y3cDmLZ05nfdZe7cuXjiiSfw+9//HjfddFOb/5/cvHmzOx5FCSr67LPPhD/84Q/Chx9+KAAQPvroow6PP3HihBAaGiosXLhQOHjwoPDSSy8JBoNB2LBhQ6fvWVVVJQAQqqqquhk9ERER+dr3338vABCuvPLKVj9bunSpMHLkSPf3c+bMEX7xi1+0Om7r1q3CiBEjBJPJJEjf6rz66qtCWlqaEBYWJtx6663CU089JaSnp7t/vmvXLmHs2LGC2WwWBgwYILz33ntCenq68Je//MV9TEfvVw4ePCjMmDFDiI+PF0wmkzBw4EDhpZdeahXvkiVLhNjYWCE8PFy48847hYaGBvcxTqdTWLFihTBo0CDBaDQK8fHxwowZM4RvvvnGfczevXuFK664QggNDRUiIiKEyZMnC7m5uYIgCEJJSYkwffp0ITw8XAAgbNq0STh58qQAQNi9e7csXofDITz++ONCnz59BKPRKIwcOVL4/PPP3T9v67xNmzYJAISKigr3Y2vWrBEiIyNl13777beFUaNGCcHBwUJ0dLRwySWXCB9++KH756tWrRLS0tIEvV4vTJkypVPntfc82lJWViaYzWbh8OHDHcbZlvT0dAGAUFhYKHs8IyNDACAUFBTIHm/v97Dla9XWayf10UcfCf5+a15fXy/cc889QnR0tBAaGipcc801rZ4nAGHNmjXu71988UUhNTVVMBqNQt++fYVHH31UsNlssnOOHj0qXHvttUJCQoIQGhoqjBgxQvj73//eYSyd+V13ueKKKwQAwvbt29t8TpGRkcLWrVu9eCU8upMb6AShxYRhleh0uvNWpB5++GF8+umn2L9/v/uxG2+8EZWVlbJ9DzpitVoRGRmJqqoq2adQRETUewiCgFNldcjd9h8YK3NhHn87RmUmITiIu4KQ/9x2222orKzExx9/rHYovcKiRYtgtVrbXFNDPcerr76Kjz76CF988UWXzu9ObhBQa6S2bt0q630PiKXb3/3ud+2eY7PZZD3lrVarv8IjIiKNEwQBb245ibe25iGrcivWBj8LAPjvkc0YrfsdLs9OwmM/H6JacwYi8p0//OEPeOWVV+B0Olut36Gew2g04qWXXlLl3gH1W1VUVORuu+mSmJgIq9Xa7sK75cuXIzIy0v3VsssKERH1DrW2Jtzz9i788dNDsJUX4Hmjp1XxbMM2XOn4Gv/ZW4CrXt6Cvacr1QuUiHwiKioKv//975lE9XB33HEHBg0apMq9e/xv1uLFi1FVVeX+On36tNohERGRwvLL6nDNK9/h8/1F0MOJvwS/glhdteyYJ4xrkaU7i8KqBvy/17fiw13tty4m6oq1a9dyWh9RDxJQiVRSUhKKi4tljxUXF8NisbTbVcdkMsFisci+iIio97A2NOKW1T/gaLG4b8n9pk8xSd+8z0pEMjD8egBACOx4M+wVmGCHvcmJhe/uxdeHi9u7LBER9XIBlUhNmDABGzdulD325Zdfdqr3PRER9T6CIOCRD37CqTJxn5NhcTrcH/RR8091wLWrgNkvAvFie+qMppNYnuXZe2bhu3txtrL9PVuIiKj3UjWRqqmpwZ49e9wb4p08eRJ79uxBfn4+AHFa3q233uo+/q677sKJEyfw0EMP4fDhw3jllVfw7rvvYsGCBWqET0REGvfW93n4bF8RAMBiDsJbkyqgdzSIPxwzB8icDASHAlf91X3ONeadmD5EXI9bWdeI+f/aBXtT25tKEhFR76VqIvXjjz9i9OjRGD16NABg4cKFGD16NJYsWQIAKCwsdCdVAJCZmYlPP/0UX375JUaOHInnn38eb7zxBmbMmKFK/EREpF0/nanEU58dcn//5/83ErGn/+c5YNgvPePUC4FIsRmR7uQ3eO7nGUiNFqeM786vxLMbDisSMxERBQ7N7COlFO4jRUTU8zmdAn6x8jvsO1sFALjj4kw8OiMTeLYf0FgHhMYC/3cUMEh2AdmwGNj2iji+5m/YGzMDv3ztezQ6BOh0wKf3TcaQFP7dICLqSbqTGwTUGikiIqLO+O9PBe4kanBSBB6aORjI3SQmUQAw6Ep5EgUA2bM940P/wci0KCycLrbUFQTgGValiIhIgokUERH1KLYmB/78vyPu7xdfmY3gID1w6L+eg6RJk0vaeCA0Thwf3wjYazF3Ugb6RIlT/L49eg6bj53zZ+ikgry8POh0Ovd6bepZNm7ciOzsbDgcDrVDIT+x2+3IyMjAjz/+qPi9mUgREVGP8o+tp3CmQuy0N3lAHKYMjAccjcCRz8QDgiOAzCmtT9QbgME/E8dN9cDxjTAbDVg0w7PR49OfHYbT2atmxCuqqKgIDzzwAPr37w+z2YzExERMmjQJr776Kurq6rp9/dtuuw1XX3217LG0tDQUFhZi2LBh3b6+lmktYVQqnoceegiPPvooDAYDAHEvL51O1+rrjTfewOHDh6HT6bBt2zbZNS666CKYzWY0NDS4H2toaIDZbMabb74JoO3fLQDIycmBTqdDZWWl7Pvo6GjZ9QBgx44d7nj8qaGhAffeey9iY2MRHh6O6667rtX2Qi3ddtttrV6zmTNnyo7JyMhodcwzzzzT5Tiff/75Nl8nAKirq4PFYsFf//pXBAcH48EHH8TDDz/c5Xt1FRMpIiLqMarqGvHS18cBADod8MisweIP8rYADZXieOAVgNHc9gWyr/KMD38CALhqZAqG9RHnzR8qtOLjPWf9EXqvd+LECYwePRpffPEFnn76aezevRtbt27FQw89hE8++QRfffVVu+c2NjZ2+b4GgwFJSUkICgo6/8GkSe39+2/ZsgW5ubm47rrrZI9bLBYUFhbKvm6++WYMHjwYSUlJyMnJcR9bXV2NXbt2IT4+XpZgbd26FTabDZdddlmXYo6IiMBHH30ke+zNN99E3759u3Q9byxYsAD//e9/8d577+Gbb75BQUEBrr322vOeN3PmTNlr9s4777Q65oknnpAdc99993U5zltuuQW1tbX48MMPW/3s/fffh91ux69//WsAwM0334wtW7bgwIEDXb5fVzCRIiKiHmP1dydRVS++qbpmdB8MTYkUf9CcFAFoe1qfS+YlgKl5sfGRDUCTHXq9Dr+fle0+ZMVXx+BgVcrn7rnnHgQFBeHHH3/E9ddfj+zsbPTr1w+/+MUv8Omnn2L2bM+/m06nw6uvvoqrrroKYWFheOqpp+BwODBv3jxkZmYiJCQEgwYNwosvvug+5/HHH8dbb72Ff//73+5Py3NyclpVRlwVg40bN2Ls2LEIDQ3FxIkTceTIEVm8f/zjH5GQkICIiAjccccdeOSRRzBq1Kh2n19FRQVuvvlmxMfHIyQkBAMGDMCaNWsAeKoz69atw8SJE2E2mzFs2DB88803smvs378fs2bNQnh4OBITE3HLLbegtLTU/XOn04lnn30W/fv3h8lkQt++ffHUU08BEDsfA8Do0aOh0+lw6aWXus954oknkJqaCpPJhFGjRmHDhg3ua7pie/fddzF58mSEhITgwgsvxNGjR7Fjxw6MHTsW4eHhmDVrFs6dk099feONN5CdnQ2z2YzBgwfjlVdecf+svXjOd54rnvXr12PKlCkwm814++2323zN161bh+nTp8Nsln9wotPpkJSUJPsKCRGn8E6dOlWWSG3ZsgUDBw7E7NmzZY/n5OQgPT3d/Ty8NWfOHKxevdr9fX19PdatW4c5c+Z06XqdVVVVhTfffBMvvPACLrvsMowZMwZr1qzB999/36oS15LJZJK9ZtHR0a2OiYiIkB0TFhbW4TUrKytxxx13ID4+HhaLBZdddhn27t0LAEhISMDs2bNlr5PL6tWrcfXVVyMmJgYAEB0djUmTJmHdunWdfSl8Q+hlqqqqBABCVVWV2qEQEZEPNTQ2CWOe/EJIf/gTod/iT4X8slrPD1eMEISlFkF4Il4QGqo7vtD788Rjl1oEIe8798O/WrVVSH/4EyH94U+E/+0v9NOz6J1KS0sFnU4nLF++vFPHAxASEhKE1atXC7m5ucKpU6cEu90uLFmyRNixY4dw4sQJ4Z///KcQGhoqrF+/XhAEQaiurhauv/56YebMmUJhYaFQWFgo2Gw24eTJkwIAYffu3YIgCMKmTZsEAML48eOFnJwc4cCBA8LkyZOFiRMnuu//z3/+UzCbzcLq1auFI0eOCMuWLRMsFoswcuTIdmO+9957hVGjRgk7duwQTp48KXz55ZfCf/7zH0EQBHcMqampwvvvvy8cPHhQuOOOO4SIiAihtLRUEARBqKioEOLj44XFixcLhw4dEnbt2iVMnz5dmDp1qvseDz30kBAdHS2sXbtWOH78uLB582Zh1apVgiAIwvbt2wUAwldffSUUFhYKZWVlgiAIwgsvvCBYLBbhnXfeEQ4fPiw89NBDgtFoFI4ePSqLbfDgwcKGDRuEgwcPChdddJEwZswY4dJLLxW2bNki7Nq1S+jfv79w1113yV6j5ORk4YMPPhBOnDghfPDBB0JMTIywdu3aDuM533mueDIyMtzHFBQUtPmajxgxQnjmmWdkj61Zs0aIjIxs99/pb3/7mxAWFiY0NjYKgiAIixYtEu69915h3bp1wiWXXOI+bvLkycJtt93m/n7OnDnCL37xi1bXc/0+VVRUyL4/cuSIYDKZhFOnTgmCIAj/+Mc/hJEjRwofffSRcL635zNnzhTCwsLa/RoyZEi7527cuFEWj0vfvn2FF154od3z5syZI0RGRgrx8fHCwIEDhbvuusv9u+mSnp4uJCYmCjExMcKoUaOEZ5991v06tmfatGnC7NmzhR07dghHjx4V/u///k+IjY11/z58+umngk6nE/Ly8tzn5ObmCjqdTvjiiy9k13r44YeFKVOmdHi/tnQnN2Adm4iIeoT/7i1EaY0dADBzaBLSYkLFH9SVAxV54jhlFGAK7/hC6ZOAfe+J44LdQPpEAMAdF/fDd8fLAABrvsvDFUOTfPwM/Oj1KUBNifL3DU8AfvvNeQ87fvw4BEHAoEGDZI/HxcW510fce++9+NOf/uT+2a9+9SvMnTtXdvyyZcvc48zMTGzduhXvvvsurr/+eoSHhyMkJAQ2mw1JSef/t3vqqacwZYq4lu6RRx7Bz372M/e6mJdeegnz5s1z33/JkiX44osvUFNT0+718vPzMXr0aIwdOxaAuJ6kpfnz57unob366qvYsGED3nzzTTz00EN4+eWXMXr0aDz99NPu41evXo20tDQcPXoUycnJePHFF/Hyyy+7qxpZWVm4+OKLAQDx8fEAgNjYWNnzf+655/Dwww/jxhtvBAD86U9/wqZNm7BixQqsXLnSfdyDDz7o3rfzgQcewE033YSNGzdi0qRJAIB58+Zh7dq17uOXLl2K559/3j1lLDMzEwcPHsTrr7+OOXPmtBvP+c5z+d3vfnfe6WinTp1CSkpKq8erqqoQHu7570B4eDiKisSNu6dOnYra2lrs2LEDEyZMQE5ODhYtWoSLL74Yc+bMQUNDAwRBwPbt23HHHXfIrvvJJ5/Irgug3SYXCQkJmDVrFtauXYslS5Zg9erVuP322zt8Pi5vvPEG6uvr2/250Whs92dFRUUIDg5GVFSU7PHExET3a9CWmTNn4tprr0VmZiZyc3Px+9//HrNmzcLWrVvd68/uv/9+XHDBBYiJicH333+PxYsXo7CwEC+88EKb19yyZQu2b9+OkpISmEwmAOLv48cff4z3338fv/nNbzBjxgykpKRgzZo1ePzxxwGI69zS0tJw+eWXy66XkpKCU6dOtfsc/IGJFBERBTxBELB6y0n397dfnOH5YeFezzhl9PkvJj2mYI97OGVgPPrFheFEaS22nijDoUIrspMDZF+pmhKgukDtKLy2fft2OJ1O3HzzzbDZbLKfuRISqZUrV2L16tXIz89HfX097HZ7h9PtOjJixAj3ODk5GQBQUlKCvn374siRI7jnnntkx48bNw5ff/11u9e7++67cd1112HXrl244oorcPXVV2PixImyYyZMmOAeBwUFYezYsTh0SNxUeu/evdi0aVOrN+oAkJubi8rKSthstlZvLjtitVpRUFDgToZcJk2a5J5e5SJ9PRITEwEAw4cPlz1WUiIm67W1tcjNzcW8efNw5513uo9pampCZGRku/F4c15b//4t1dfXt5rWB4jTz3bt2uX+Xq/3rHTp378/UlNTkZOTg6FDh2L37t2YMmUKEhIS0LdvX2zduhWCIMBms2Hq1Kmy606dOhWvvvqq7LEffvjBvY6npdtvvx0PPPAAfv3rX2Pr1q147733sHnz5vM+rz59+pz3GF9zJdqA+O8+YsQIZGVlIScnx/07t3DhQvcxI0aMQHBwMH77299i+fLl7kRJau/evaipqUFsbKzs8fr6euTm5gIQ1zDOmTMHa9euxdKlSyEIAt566y3MnTtX9u8GACEhIT5pSuMNJlJERBTwtp8sx8FCKwBgRGokLugrmbtfsNszTh51/oslDAEMwYDDLjtXr9fhtkkZWPJvcTHzmu9O4tlfjvRF+P4XnqDp+/bv3x86na7VOqR+/foBgHv9ilTLtRfr1q3Dgw8+iOeffx4TJkxAREQE/vznP+OHH37oUujST/VdXdScTmeXrgUAs2bNwqlTp/DZZ5/hyy+/xOWXX457770Xzz33XKfOr6mpwezZs2VVOZfk5GScOHGiy7F1RluvR8vHXK+PqzK3atUqjB8/XnYdV/WiLd6cd761N4BY0ayoqGj1uF6vR//+/ds979JLL8WmTZswYsQIDBgwAAkJ4u/xlClTsGnTJgiCgP79+yMtLa1VTC2ve+bMmXbvM2vWLPzmN7/BvHnzMHv27FYJRUfndZRwpaent9t0ISkpCXa7HZWVlbKqVHFxcacqtS79+vVDXFwcjh8/3m7yPn78eDQ1NSEvL69VtRkQ/72Tk5Nla89cpLHdfvvtWL58Ob7++ms4nU6cPn26VTUaAMrLy92VTqUwkSIiooC35rs89/j2SZny9sGFezzjzlSkgoKBxKFiElV2HLBVA6YIAMB1F6Tiz/87guqGJny8pwAPzxyM2PDWn7RqTiem16kpNjYW06dPx8svv4z77ruvU2+SW/ruu+8wceJEWaXI9am2S3BwsE/2Exo0aBB27NiBW2+91f3Yjh07zntefHw85syZgzlz5mDy5MlYtGiRLJHatm0bLrnkEgBiFWbnzp2YP38+AOCCCy7ABx98gIyMjDY7DA4YMAAhISHYuHFjqylngPjcAflUM4vFgpSUFHz33XfuaYyA+FqOGzfuvM+nPYmJiUhJScGJEydw8803t3lMW/F05jxvjB49GgcPHvT6vKlTp+L+++/HkCFDZE0wLrnkEqxatQqCILSqRnVFUFAQbr31Vjz77LP4/PPPO31ed6b2jRkzBkajERs3bnRPIz1y5Ajy8/NlFdHzOXPmDMrKytzV2rbs2bMHer3enYi2dMEFF6CoqAhBQUFtTnV1ycrKwpQpU7B69WoIgoBp06YhPT291XH79+/H6NGd+G+8DzGRIiKigHamog5fHBTn9idEmHDl8BZ/2F1VJWMYEDegcxdNHtV8ngAU/gRkiFOfwkxBuPHCNKzafBL2Jif+9UM+7ru8k9ekDr3yyiuYNGkSxo4di8cffxwjRoyAXq/Hjh07cPjwYYwZM6bD8wcMGIC///3v+N///ofMzEz84x//wI4dO2Rd1TIyMvC///0PR44cQWxsbIfTzDpy33334c4778TYsWMxceJErF+/Hj/99JO7gtaWJUuWYMyYMRg6dChsNhs++eQTZGdny45ZuXIlBgwYgOzsbPzlL39BRUWFe93Mvffei1WrVuGmm27CQw89hJiYGBw/fhzr1q3DG2+8AbPZjIcffhgPPfQQgoODMWnSJJw7dw4HDhzAvHnzkJCQgJCQEGzYsAGpqakwm82IjIzEokWLsHTpUmRlZWHUqFFYs2YN9uzZ024nvM5atmwZ7r//fkRGRmLmzJmw2Wz48ccfUVFRgYULF7Ybz/nO88aMGTPw1ltveR27a53U6tWrsWrVKvfjU6ZMcSepLad2dtWTTz6JRYsWdboaBXRval9kZCTmzZuHhQsXIiYmBhaLBffddx8mTJiAiy66yH3c4MGDsXz5clxzzTWoqanBsmXLcN111yEpKQm5ubl46KGH0L9/f/e6ua1bt+KHH37A1KlTERERga1bt2LBggX49a9/3WZ3PwCYNm0aJkyYgKuvvhrPPvssBg4ciIKCAnz66ae45pprZNM3pdM9pWvxpDZv3ownn3yyy69Nl3jdniLAsWsfEVHPsuLLo+5uei9+dVT+w9oyTwe+N2d0/qI/rvWc991Lsh/ll9UKGY+I95v8p68Fp9Ppg2dBgiAIBQUFwvz584XMzEzBaDQK4eHhwrhx44Q///nPQm2tpwsjAOGjjz6SndvQ0CDcdtttQmRkpBAVFSXcfffdwiOPPCLrpFdSUiJMnz5dCA8PFwAImzZtardrn7Sr2e7duwUAwsmTJ92PPfHEE0JcXJwQHh4u3H777cL9998vXHTRRe0+tyeffFLIzs4WQkJChJiYGOEXv/iFcOLECUEQPJ3o/vWvfwnjxo0TgoODhSFDhghff/217BpHjx4VrrnmGiEqKkoICQkRBg8eLPzud79z/w46HA7hj3/8o5Ceni4YjUahb9++wtNPP+0+f9WqVUJaWpqg1+vd3c0cDofw+OOPC3369BGMRqMwcuRI4fPPP3ef0/L1ae81aqsb3ttvvy2MGjVKCA4OFqKjo4VLLrlE+PDDDzuM53zntRVPe8rKygSz2SwcPny4wzjbkp6eLgAQCgvlHTozMjIEAK06BXrbta9l1zyXznTt6676+nrhnnvuEaKjo4XQ0FDhmmuuafU8AQhr1qwRBEEQ6urqhCuuuEKIj48XjEajkJ6eLtx5551CUVGR+/idO3cK48ePFyIjIwWz2SxkZ2cLTz/9tNDQ0NBhLFarVbjvvvuElJQUwWg0CmlpacLNN98s5Ofny46rq6sTIiMjhZiYmDav+f333wtRUVFCXV2d169Hd3IDnSAIvWozDKvVisjISFRVVcFiCZBFwkRE1CZBEHDpczk4VVYHnQ747uHLkBIlWU9zfCPwz+bOXuPvBmY907kLF+4FXhenWGH4/wOue0P241ve/AGbj4n797x/1wSMzYjp7lOhADd9+nQkJSXhH//4h9fn5uXlITMzE7t37+5ycwxq26JFi2C1WvH666+rHQr50Q033ICRI0fi97//vdfndic34Ia8REQUsHblV+JUmdilaUK/WHkSBbRYHzWq8xeOzxYbTgCyzn0u14z2TK35cPfZzl+XeoS6ujq88MILOHDgAA4fPoylS5fiq6++8vtmquS9P/zhD0hPT+9WoxDSNrvdjuHDh2PBggWK35uJFBERBawPd3k6Yl17QWrrA6RJUGcaTbgEBQOJw8Rx2TGgwSr78YyhSQgNFruIfbK3AA2N3W9gQIFDp9Phs88+wyWXXIIxY8bgv//9Lz744ANMmzZN7dCohaioKPz+979v1Sqbeo7g4GA8+uijbXb39Dc2myAiooBka3Lgk58KAQAhRgNmDmujda8rkTKGAbHttztuU8oooKB5r5min4CMi90/CjMFYebQJHy4+yysDU3YdLgEs1o2uaAeKyQkBF999ZXPrpeRkYFettKCqEdgek5ERAFp0+ESVNU3AgBmDE1EuKnFZ4O1ZUBVvjhOHgno29+/pk2yjXl3t/qxtAL2wS5O7yMi6m2YSBERUUCSJi9tTusrlCQ/3qyPcpFu3tvGOqkJWbFItIh7SOUcKUFZjc37exARUcBiIkVERAGnss6OnCMlAMS9oyb1j2t9UFfXR7kkZAOG5s12pU0rmhn0Olzd3HSiySng032F3t+DiIgCFhMpIiIKOF8dKkGjQ1xTctXIFBj0utYHFf3kGUurS51lMAJJroYTxwFbTatDrh7l6d73+b4i7+9BREQBi4kUEREFnP8d8CQtbTaZAIDSY+L/6o1ATL+u3Sg+2zMuO97qx4OTIpAeGwoA2J5XjvJae9fuQ0REAYeJFBERBZQ6exO+PXoOABAfYcIFfaNbH+R0AGW54jg2CzB0sUlt3ADP2JWYSeh0OswcKiZyDqeArw4Vd+0+REQUcJhIERFRQPnmyDnYmsTNNacPSYS+rWl9lfmAo7n5gzQZ8lbcQM+4rHUiBQBXDPVUxL44wOl9RES9BRMpIiIKKBuk0/qGnmdaHwDEdieRklakjrZ5yOi0KCREiE0pvj1WihpbU9fvR0REAYOJFBERBQx7kxNfHxK79UWYg3BRv9i2D5RWj6RVJW9FZwD65mmBpa3XSAGAXq/DFUMT3fF9c+Rc1+9HREQBg4kUEREFjO9zS1HdXPGZlp2I4KB2/oxJq0fdmdpnMALRmeK47BjgdLZ52Myhye7xBk7vIyLqFZhIERFRwPjfAU8zhxnNVaA2yab29e/eTV0VraYGoOp0m4eM7xeDyBAjAGDT4RLYmhzduycREWkeEykiIgoITqeALw+KiZTZqMclA+PbP9iVSIUlACFR3btxnCQRa6fhhNGgx+XZCQCAGlsTvs8t6949iYhI85hIERFRQDhQYEVpjdiJ7+L+cQgNbqeleX0lUCuuo+rW+igX6TXaaIHucsUQT4WM66SIiHo+JlJERBQQNh0pcY8vHZTQ/oHSjXO7sz7KfY3OJVKT+schqLkVuzRWIiLqmZhIERFRQMiRJVIdTevzUaMJF+kaq3ZaoANAhNmIMeni5sCnyupwsrS2+/cmIiLNYiJFRESaV1lnx57TlQCA/gnhSI0Obf/gUh+1PncJjQFC48RxWdst0F2klbIcVqWIiHo0JlJERKR53x4rhVMQx5d21GQCkFeNutuxz8VV2aouBBqs7R4mrZTlcJ0UEVGPxkSKiIg0L6ez66MAT9XIYAKi+vomAOkUwQ6qUoOTIpBkMQMAtp0oQ0Mj26ATEfVUTKSIiEjTnE4B3x4VqzuhwQZcmBnd/sGOJqAsVxzHZgF6g2+CiJUkUh00nNDpdJjSXDGzNTmx9QTboBMR9VRMpIiISNPEtud2AMDErDiYgjpIjipPAc5GceyLRhMuss597TecAICpgz3T+9gGnYio52IiRUREmtbpbn2A7xtNuK8lndrXfkUKkLdBZ8MJIqKei4kUERFp2jdHPVWd8yZS0iQn1ocVqah0QG8Uxx1M7QPkbdDzyupwqoxt0ImIeiImUkREpFm1tiZ32/N+cWEdtz0HgIpTnnFMpu8CMQQB0eniuDIfEIQOD79E0lnwu+NcJ0VE1BMxkSIiIs3afrIcTc19zyf2jz3/CZX5nnFUum+DcXUAtNcA9RUdHjoxyxPrd7mlvo2DiIg0gYkUERFp1veSJGRSVtz5T3AlUkFmIPw8bdK9JW2lXnmq/eMADO8TiQhTEABga24ZnM6OK1hERBR4mEgREZFmuabF6XTARf3OU5ESBE8iFZkmnuRLskQqv/3jAAQZ9BjfHG95rR2Hi6p9GwsREamOiRQREWlSea0dBwutAIAhyRZEhwV3fEJdOdDY3NjBVxvxSkmnCp4nkQLk0/u+5/Q+IqIeh4kUERFp0tZcT5OGSf07M61PMt3OL4lU5ytSgDzm73PZcIKIqKdhIkVERJokbdIgre60S9ZoQv1EamBiOOLCTQCAH06UodHh9H1MRESkGiZSRESkSa6KlNGgw7jMmPOf4O9EKiwBMJha36sdOp3OnQDW2h346Uyl72MiIiLVMJEiIiLNKaisx8lScb3T6LRohAYHnf8kfydSej0Qmeq513n2kgKASZKW7dxPioioZ2EiRUREmvPdccm0vs7sHwX4P5GSXrcTe0kBwERJy3bpcyIiosDHRIqIiDRn6wlP9WZiZ/aPAjyJlMEkTsPzBy/2kgKAtJhQpMWEAAB251eiodHhn7iIiEhxTKSIiEhztp8sBwCYgvQYmRZ5/hOke0hFpYnT8PzBy4YTAHBRplhRszuc2HO60g9BERGRGphIERGRppytrMeZinoAwAV9o2EKMpz/JH/vIeXi5V5SANwb8wKeBJGIiAIfEykiItKU7Sc90/o61a0PAKoUWB/V8tqVpzt1ynjJc2AiRUTUczCRIiIiTZEmG+M7m0gp0Wii5bU7WZFKjQ5BcqQZALDzVAX3kyIi6iGYSBERkab80JxIGQ06jO4b3bmTZIlUevvHdVd4ImAIbn3PDuh0nn2w6hsd2He2yl/RERGRgphIERGRZpyrtuHEOXGt04jUKIQEd2J9FKBcRUqvByLTPPfsxF5SADA+k+ukiIh6GiZSRESkGdIko9ProwDlEinp9e3VndpLCpA/FyZSREQ9AxMpIiLSjC41mgCU2UPKpQvrpLLiwxAbJk4J3JFXDoezc5UsIiLSLiZSRESkGa71UXodMDa9k+ujpHtIRab6bw8pl6g0z7gL66SqG5pwuMjqj8iIiEhBTKSIiEgTKuvsOFJcDQAYmhKJCLOxcyfWVwD2GnHs72l9QJf2kgLYBp2IqKdhIkVERJqwI6/C3bvBu2l9pzxjRRIp76f2AcA4ScOJH04wkSIiCnRMpIiISBN+POVJLi7M8CaRkmyMK5125y9dTKQGJ0UgwhwEAPjxVAWETnb8IyIibWIiRUREmrAzz9MBb0xn10cBQNUZzzhSgUQqPBHQiwkRrGc6PlZCr9fhguZ9sUprbMgvr/NHdEREpBAmUkREpDpbkwM/NW9UmxEbivgIU+dPtp71jC19fBxZG/QGICK5+d4FXp0qbaDxY17nWqcTEZE2MZEiIiLV7T9rhb3JCQC4wJtqFCBPpCIVSKQAT8JWVwY01nf6NGml7cdTTKSIiAIZEykiIlLdTsn6qLHpXqyPAoAqhStSgDxh86IqNapvFAx6HQBgFxMpIqKAxkSKiIhUJ53mNjajixWpsHggyIspgd0hTdiqOr9OKjQ4CEOSLQCAoyXVqKpv9HVkRESkECZSRESkKkEQsCtfTKQs5iD0jw/v/MmOJqC6UBwrVY0CxI1/XaRTCzvBNb1PEOB+3kREFHiYSBERkapOldWhtMYOQFwfpW+e+tYpNUWAIK6tkiU3/iarSHUtkQI4vY+IKJAxkSIiIlXtlCQTY71tNKHG+iigxRqpzk/tA+RTF9m5j4gocDGRIiIiVUm713nfsU+6h5SCiZRFUv3ysiKVHBmCPlEhAIA9pyvR6HD6MjIiIlIIEykiIlKVq2OfQa/DqLQo705WqyIVFgcYmhtbeLlGCvAkjPWNDhwurPZlZEREpBAmUkREpJqq+kYcLa4BAAxNsSA0OMi7C8j2kFJwjZROB1hSxLGXFSmgxca8ktbvREQUOJhIERGRavacrnSPL+jr5bQ+QN56XMmKFOBJ3GxVgM27qpKs4UR+pQ+DIiIipTCRIiIi1ezO78b6KEBSkdIBEUm+CaqzXBUpwOuq1OCkCJiN4p/gPafZcIKIKBAxkSIiItXsllRjRnu7PgrwJDARSYDB6JOYOs3S9c59QQY9hveJBACcLq9HWY3Nl5EREZECmEgREZEqnE7BPbUvLtyE1OgQ7y7QZANqS8Sx0tP6AHmXwC6sk5I21pBOcSQiosDARIqIiFRxsqwWVfWNAIDRfaOg03mxES8AWAs8YyVbn7tIW6B3oXPfqDTPVEYmUkREgYeJFBERqUI2ra9vlPcXkCYvFgU79rl0tyIlec5MpIiIAg8TKSIiUoW00cTotK507JO2PlejItX1NVIAkBJpRnyEuBfVntOVcDoFX0VGREQKYCJFRESqcFWk9DpgRGqk9xewqtj6HABCogFjqDjuQkVKp/NsQFzd0IQTpbU+DI6IiPyNiRQRESmuzt6Ew0VWAMCgJAvCTF5uxAu0qEipMLVPp/MkcNazgOB9RUnacGIvp/cREQUUJlJERKS4n85UwTWTrUvro4AWa6RUqEgBnimFjXVAvff7QbFzHxFR4GIiRUREiuv2/lGApyKlDwLCE7odU5d0s3PfiNRIuJoVMpEiIgosTKSIiEhxskYTfbvQaALwrJGKSAH0Bh9E1QXd7NwXYTaif3w4AOBQoRUNjQ5fRUZERH7GRIqIiBQlCAJ2N1dfLOYg9IsL8/4idslUOjU69rl0s3Mf4Jne1+QUcKCgygdBERGREphIERGRogqqGnCu2gYAGJkWBb3ey414AflmvJYUH0XWBdIkThqTF6T7SUmnPBIRkbYxkSIiIkX9JFkLNKqr66OqNZJIRUjubS3s0iXYcIKIKDAxkSIiIkXtPeOZvjYiNaprF5FVpNSc2pfsGVd3rSI1KDECIUZxjRcTKSKiwMFEioiIFPXTmUr3eGRXNuIF5IlURHL7x/mbOQoIChHHXaxIBRn0GN5HfB3OVNSjtMbmo+CIiMifVE+kVq5ciYyMDJjNZowfPx7bt2/v8PgVK1Zg0KBBCAkJQVpaGhYsWICGhgaFoiUiou5wOgXsa65IJVnMSLCYu3ahaknSoubUPp3OU5Wq7loiBcjXSe3hOikiooCgaiK1fv16LFy4EEuXLsWuXbswcuRIzJgxAyUlJW0e/69//QuPPPIIli5dikOHDuHNN9/E+vXr8fvf/17hyImIqCtOlNai2tYEQNxDqcu0UpECPOukbFbAVtOlS4yUTHHcK6nYERGRdqmaSL3wwgu48847MXfuXAwZMgSvvfYaQkNDsXr16jaP//777zFp0iT86le/QkZGBq644grcdNNN561iERGRNsim9XW10QQgqf7ogIik7oTUfbJ1Ul1sOCGtSHGdFBFRQFAtkbLb7di5cyemTZvmCUavx7Rp07B169Y2z5k4cSJ27tzpTpxOnDiBzz77DFdeeWW797HZbLBarbIvIiJSx0+yRhM+qEiFJwAGYzej6iZpRayLLdBTIs2IjzABEBMpp1PwRWRERORHqiVSpaWlcDgcSExMlD2emJiIoqKiNs/51a9+hSeeeAIXX3wxjEYjsrKycOmll3Y4tW/58uWIjIx0f6Wlpfn0eRARUedJp62N6BPVtYs4moCaYnGs9rQ+QL5Gq4sVKZ1O526DXt3QhBOltT4IjIiI/En1ZhPeyMnJwdNPP41XXnkFu3btwocffohPP/0UTz75ZLvnLF68GFVVVe6v06dPKxgxERG5NDqcOFggzgrIiA1FZGgXK0m1JYDgFMdqNppw8UFFCuB+UkREgSZIrRvHxcXBYDCguLhY9nhxcTGSktqe7/7YY4/hlltuwR133AEAGD58OGpra/Gb3/wGf/jDH6DXt84LTSYTTCaT758AERF55UhRNWxNYgLU5f2jAHmbcS0kUj6oSAHAaFkiVYFfjkntRlBERORvqlWkgoODMWbMGGzcuNH9mNPpxMaNGzFhwoQ2z6mrq2uVLBkM4iaGgsD55EREWua79VFnPWMtTO3zUUVqeGokdDpxzIoUEZH2qVaRAoCFCxdizpw5GDt2LMaNG4cVK1agtrYWc+fOBQDceuut6NOnD5YvXw4AmD17Nl544QWMHj0a48ePx/Hjx/HYY49h9uzZ7oSKiIi0yfcd+6CNilREEgAdAKFbFakIsxEDEsJxtLgGhwur0dDogNnIv21ERFqlaiJ1ww034Ny5c1iyZAmKioowatQobNiwwd2AIj8/X1aBevTRR6HT6fDoo4/i7NmziI+Px+zZs/HUU0+p9RSIiKiT9jZXpPQ6YGiKpesX0tIeUoDYNTAsXly7Ze16IgWI66SOFtegySlg/9kqjM2I8VGQRETka6omUgAwf/58zJ8/v82f5eTkyL4PCgrC0qVLsXTpUgUiIyIiX6m3O3C0uBoAMDAxAqHB3fjzI6tI9elmZD5iSRYTqZpiwOkA9F2rJI1IjcK7P54BAOxjIkVEpGkB1bWPiIgC08HCKjia90bq1vooQF6RsmigIgUAEc1TDAUHUFPS5ctIX5t9Z6s6OJKIiNTGRIqIiPxu72lpo4mo7l3MlUgFRwCmiO5dy1ekCV111xtODEqKgNEgdpzYd4aJFBGRljGRIiIiv5M1muhOIiVIGjpopRoFeCpSQLfWSZmCDBiUJCaHuedqUGtr6m5kRETkJ0ykiIjI71ytz4MNenei0CUNVUBjnTjWQsc+F1lFqnsNJ4b3Eaf3OQXgYKG1W9ciIiL/YSJFRER+VVXfiBOltQCA7BQLgoO68adH1rFPQ4mUj/aSAoDhfaLcY07vIyLSLiZSRETkV/slTRNGdrfRRLUGG00A8uqYjypSABtOEBFpGRMpIiLyq72S9VHdbzQhSVK0sIeUiw8rUgOTwhFsEP88S9eWERGRtjCRIiIiv/rptC8rUhrcQwoAzJGAMVQcd7MiZQoyYHCyuI7sRGktathwgohIk5hIERGRX7mqKmHBBvSLD+/exbS4hxQA6HSeqlQ3uva5DGue3icIwAFO7yMi0iQmUkRE5Dfnqm0oqGoAICYHBr2uexfUarMJwLNOyl4N2Kq7dakRXCdFRKR5TKSIiMhvZPtHpUV1/4KuZhP6ICAsvvvX8yXZOqnuVaWGMZEiItI8JlJEROQ3eyXtu0d0d30U4ElQwpMAvcb+hMn2kupmw4nECHebeCZSRETapLG/QkRE1JPIKlLd7djXZAPqSsWxltZHuUinGnazIhUcpEd288bFJ87VorqhsVvXIyIi32MiRUREfiEIgnsPqahQI1KjQ7p3wZpiz1hLrc9dfFiRAoDhkgregQJrt69HRES+xUSKiIj8oqTahtIaOwBgWEokdLpuNpqoLvKMtZhI+bAiBQAj+kS5x/vOcHofEZHWMJEiIiK/kL75lzZP6DKttj53sUgSqW7uJQXIX7OfuE6KiEhzmEgREZFf7C+QJlKW7l9Q6xWp8ERA1/xn1dr9qX0DEsNham44sZ+JFBGR5jCRIiIiv5C++R+W4oOKlHTdkRYTKUMQEJYgjn1QkTIa9MhOFhPQk6W1sLLhBBGRpjCRIiIiv9h/VmyQEGEOQnpsaPcvqPWKFOCZclhTDDiaun05act4VqWIiLSFiRQREfncuWobiqwNAIChKZbuN5oA5FWeiKTuX88fXA0nBCdQW9Lty8k25mXDCSIiTWEiRUREPiddHzXcF40mAE8nvOBwwOyDNVf+IG2C4YN1UtKKFDfmJSLSFiZSRETkc/t93bEP8Ezt02o1CpBPOfRBItU/Phxmo/inmokUEZG2MJEiIiKfk3fs80EiZasG7NXiWKvrowCft0APMugxpLnhxKmyOlTVseEEEZFWMJEiIiKfczWaCAs2IDM2rPsXDIRGE4DPK1IAMCI1yj2WJqhERKQuJlJERORT5bV2nK2sBwAMSbFAr+8ljSYAn1ekgBYNJzi9j4hIM5hIERGRTx3w9bQ+wNNoApAnK1rjl4oUO/cREWkREykiIvKpfb7eiBcInIqU2SJ2FQR8VpHKig9HiNEAAPjpbKVPrklERN3HRIqIiHzqQPP6KAAYnuqPRErDFSnAU5WyFgKC0O3LGfQ6DEkRG06cLq9nwwkiIo1gIkVERD7lqkiZjXr0i/NBowkgcCpSgGcvqcZawGbt+NhOGpri2TfrQCGn9xERaQETKSIi8pmqukbkl9cBAIYkWxBk8NGfGVnXPo0nUtKKmdVHDSckUyQPFvgmOSMiou5hIkVERD7jl0YTgCchCY0Fgky+u64/WCQNJ6p903BiiKQitZ+d+4iINIGJFBER+YxsI15fNZoQBM/UPi3vIeXih4rUwMQIGA1iG/kDrEgREWkCEykiIvKZfZJGEz6rSNWVAc7mBguBkEj5oSIVHKTHwMQIAEDuuRrU2x0+uS4REXUdEykiIvKZA83TzoKD9BiQGO6biwZSownALxUpwFPhcwrAoSJWpYiI1MZEioiIfKK6oREnSmsBANlJETD6pdFEoFWkfJdIDe0j6dzHdVJERKpjIkVERD4h7SY31KeNJiTT4ywBkEiFJQC65j+vVt9M7QOAoZI1Z/vPsiJFRKQ2JlJEROQT+yWJlM8aTQCBV5EyBAHhieLYhxWp7OQI6MR+E9xLiohIA5hIERGRT0jbcg/3ZUVK2rAhEBIpwBNnTQngaPTJJUODg5AVL647O1JUDXuT0yfXJSKirmEiRUREPuFKpIwGHQYm+ajRBBB4FSkAsLgaTghATbHPLjuseT+pRoeAYyXVPrsuERF5j4kUERF1W529CbnnagCIex6Zggy+u7hrepzOAITF+e66/iRN+KSJYDdJ10kd4DopIiJVMZEiIqJuO1RohVMQxz5dHwV4WohHJAF6HyZo/iRt0+6vzn0FXCdFRKQmJlJERNRtB6SNJiRv9rvN0QjUnhPHgbCHlIvFP3tJDU2WdO4rYEWKiEhNTKSIiKjbpK3Ph/iyIlVTDKC51BUo66MAv1WkIkONSIsJASBWAR2uMiARESmOiRQREXXbwUIxkdLpgMFJEb67cCA2mgCACElFyoeJFOCpStXZHTjZvAEyEREpj4kUERF1S5PDicNFYge5jNgwhJmCfHdxaRISSFP7/FSRAuRTJ7lOiohIPUykiIioW06U1rr3NBqS7MP1UYB8fZF03ZHWmSMBY6g49uEaKaBF5z6ukyIiUg0TKSIi6hb5+igfJ1KBWpHS6Tzx+rD9OSDv3CfdBJmIiJTFRIqIiLrFtT4K8ENFSpZIBVBFCvCs6bJVAXbfrWVKiDAjPsIEQKxICQIbThARqYGJFBERdQsrUu3w06a8ADCs+XWuqm/EmYp6n16biIg6h4kUERF1mSAIONRckYoNC0ZCc6XEZ1wJSFCIuO4okPix4QTXSRERqY+JFBERdVlJtQ1ltXYAYjVKp9P59gauRg2WZHHdUSDx06a8ADv3ERFpARMpIiLqMtm0Pl+vj7LXiuuLgMDaQ8pFoYoUG04QEamDiRQREXWZrNGEz9dHBehmvC5+3JQ3NToEFrO4Xxen9hERqYOJFBERdZlfK1KB3GgC8GtFSqfTuatSJdU2lFQ3+PT6RER0fkykiIioy1wVKVOQHplxYb69eMBXpCSJlI/XSAEt10mxKkVEpDQmUkRE1CU1tibklYn7Iw1OikCQwcd/UqwFnrElABMpYwgQEi2OfVyRAlp07uM6KSIixTGRIiKiLjlSZIVrL1ifr48CAr8iBXjiri4CfLxxrrQiJV2rRkREymAiRUREXeLX9VEAUC2pSAV6IuWwAfUVPr10Zlw4zEbxz/hBTu0jIlIcEykiIuoSv3bsA1pUpAKw2QQgTwB9PL3PoNdhUJL4uueV1aHG1uTT6xMRUceYSBERUZe4qiA6Hdxv6H3KlXiYo8T1RoFIurbLDw0npJXAw5zeR0SkKCZSRETktSaHE4eLqgEAGbFhCDcF+fYGguBJPCwpHR+rZX5sgQ7IK4FcJ0VEpCwmUkRE5LWTpbWwNTkB+Gl9VH2FuK4ICNxpfYBfp/YB8tf+wFkmUkRESmIiRUREXvP/+ijpZryBXJHybyKVnRwBnU4csyJFRKQsJlJEROQ1/3fskyZSPaUiVdT+cV0UGhzk3gj5SHE1Gh1On9+DiIjaxkSKiIi8xo59nRSeAOia/9RKNxj2IVcia29y4sS5Wr/cg4iIWmMiRUREXhEEwV2Rig0LRkKEyfc3kXa4C+RmE3oDEJ4ojv1QkQJaNpyo8ss9iIioNSZSRETklZJqG8pq7QDEN/E61yIdX5JtxhvAFSnAM72vtgRw+H6vJ+nUSm7MS0SkHCZSRETkFemb9Wx/rI8C5BWpQG42AXgSKcEpJlM+xhboRETqYCJFREReka2P8lci5apI6QziOqNAJq2o+WFT3oQIM+LCxemVBwqsEATB5/cgIqLWmEgREZFX/N5oAvA0ZohIEtcZBTKLf1ugA8DQ5n+HyrpGFFY1+OUeREQkx0SKiIi8cqh5al9wkB79mltv+1STHag9J46l7cMDlZ/3kgJaTO/jOikiIkUwkSIiok6rtTXhZJnYYntwUgSCDH74M1Ij6W4XyB37XJRIpJK5ToqISGlMpIiIqNMOF1XDtQTHb+ujpPst9bhESoEW6KxIEREpgokUERF1mqLro4CeMbVPukbKT5vyZsSGIcQoriVjRYqISBlMpIiIqNOk1Q7/deyTbsbbxz/3UJI5Cggyi2M/VaQMeh0GJ0cAAPLL62BtaPTLfYiIyIOJFBERdZq02jFYkal9PaAipdN5WqBX+6ciBcgT28OF1X67DxERiZhIERFRpzQ5nDjcnEhlxIYi3BTknxv1tDVSgGdT4YYqwF7nl1sMTYl0jw8UVPnlHkRE5MFEioiIOiWvrBa2JicAP66PAnreGilAvilvDRtOEBH1BEykiIioUw4osT4K8Ex/C4kGjCH+u4+SpJU1q39aoA9KjIBeJ47ZcIKIyP+YSBERUaco0rHP6fQ0ZOgJjSZcpBUpP+0lFRJsQL/4cADAseIa2Jurh0RE5B9MpIiIqFPkHfsiOziyG+rKAIddHPeUaX2AIpvyAp5Kod3hRO65Gr/dh4iImEgREVEnCILgTqRiwoKRaDH550bVPaxjn4sCm/ICXCdFRKQkJlJERHRe56ptKKsVK0VDki3Q6XT+uZGsY18Pndrnp015AWCoNJHiOikiIr9iIkVEROd1QIn1UUDP7NgHKFaRypY0AWELdCIi/2IiRURE53VQsY59kvVDPakiFRwKmJvXlflxjVRcuMk97fJggRWCIPjtXkREvR0TKSIiOi9FOvYBLab29aCKFODZlLe6EPBjguNKdK0NTThbWe+3+xAR9XZMpIiI6LwONSdSwUF69IsL89+NeurUPsCzTqqpAWio9Ntt2HCCiEgZTKSIiKhDdfYmnCytBQAMTopAkMGPfzpc096CQsQNeXsSBTblBeSt6dlwgojIf5hIERFRhw4XVbtnovl1fRTgqUhZkgF/dQZUiwKb8gKsSBERKYWJFBERdUjWaMKf66Ns1YCt+V4RKR0fG4gU2pQ3PSYUYcEGAKxIERH5k+qJ1MqVK5GRkQGz2Yzx48dj+/btHR5fWVmJe++9F8nJyTCZTBg4cCA+++wzhaIlIup9pG/Gs/1ZkZJOd7MwkeoqvV7n/nc6U1GPqvpGv92LiKg3UzWRWr9+PRYuXIilS5di165dGDlyJGbMmIGSkpI2j7fb7Zg+fTry8vLw/vvv48iRI1i1ahX69OlBLXKJiDRGWpEanBThvxtV9+COfYA8kfLjGimA0/uIiJQQpObNX3jhBdx5552YO3cuAOC1117Dp59+itWrV+ORRx5pdfzq1atRXl6O77//HkajEQCQkZGhZMhERL2KwyngcJH4Rjw9NhQRZqP/bmbtoXtIuViU2ZQXkK9lO1hoxYSsWL/ej4ioN1KtImW327Fz505MmzbNE4xej2nTpmHr1q1tnvOf//wHEyZMwL333ovExEQMGzYMTz/9NBwOR7v3sdlssFqtsi8iIuqck6W1aGh0AlCi0cRZz7intT4HgLAEQNf8Z1daffMDVqSIiPxPtUSqtLQUDocDiYmJsscTExNRVNT2J3UnTpzA+++/D4fDgc8++wyPPfYYnn/+efzxj39s9z7Lly9HZGSk+ystLc2nz4OIqCeTbcTr70SquoevkTIEAeHNf/P8PLVvYGIEDHqx6yEbThAR+YfqzSa84XQ6kZCQgL/97W8YM2YMbrjhBvzhD3/Aa6+91u45ixcvRlVVlfvr9OnTCkZMRBTYFOvYB8g34+2JiRTgeV41xYDDf00gzEYDsuLFjZOPl1TD3uT0272IiHor1RKpuLg4GAwGFBcXyx4vLi5GUlJSm+ckJydj4MCBMBgM7seys7NRVFQEu93e5jkmkwkWi0X2RUREnSOrSCmVSOn04jS4nsidIAp+Xyc1NEXcmLfRIeBYSbVf70VE1BuplkgFBwdjzJgx2Lhxo/sxp9OJjRs3YsKECW2eM2nSJBw/fhxOp+eTtaNHjyI5ORnBwcF+j5mIqLdxVaSiQ41Ispj9ezPX1L7wRHEaXE8kbaJh9fM6qWSukyIi8idVp/YtXLgQq1atwltvvYVDhw7h7rvvRm1trbuL36233orFixe7j7/77rtRXl6OBx54AEePHsWnn36Kp59+Gvfee69aT4GIqMcqqW5AaY0NgFiN0ul0/ruZoxGoad76oqdO6wPkz03aXMMPpBXEA0ykiIh8TtWP/G644QacO3cOS5YsQVFREUaNGoUNGza4G1Dk5+dDr/fkemlpafjf//6HBQsWYMSIEejTpw8eeOABPPzww2o9BSKiHku2PsrvjSaKAAjiuCd27HNRsCKV3aIFOhER+Zbqcyfmz5+P+fPnt/mznJycVo9NmDAB27Zt83NURESk6Pqo6h6+h5SLrCLl30QqJiwYyZFmFFY14FCBFYIg+LeqSETUywRU1z4iIlKOvCIV6d+bSae5WXpyRUq5qX2Ap5JYbWvCmYp6v9+PiKg3YSJFRERtclWkgoP06NfcSttvpPsqRfTgNVLSaYt+rkgBLTbm5fQ+IiKfYiJFRESt1NmbcLK0FgAwKDECRoOf/1zIKlI9OJEKMgGhceJYiUSKnfuIiPyGiRQREbVyuKgaQnPvB783mgBarJHqwYkU4Hl+1YWA0+HXW7HhBBGR/zCRIiKiVg4p2WgCaDG1rwevkQI8zTQEh6flu5/0jQlFWLC4iT0rUkREvsVEioiIWpE1mlAkkWqe2meOAoJD/X8/NSnYuU+v17mrUmcr61FV3+jX+xER9SZMpIiIqBXpNLDBSRH+vZkgeKb29fRpfYDinfuk0/sOcXofEZHPMJEiIiIZh1PA4cJqAEB6bCgizEb/3rCuDHDYxXGvSKQk+2RJ14b5iaxzH6f3ERH5DBMpIiKSySurRX2j2ARBkUYT0ultPX19FKDaXlIAK1JERL7ERIqIiGTkG/GyY5/PSStSCrRAH5QUAb1OHLNzHxGR7zCRIiIimYOKd+zrJXtIuViU3ZTXbDSgX3w4AOBYcQ3sTU6/35OIqDdgIkVERDLKd+yTtj7vBYlUcJjYnRBQZGof4Kks2h1O5J6rUeSeREQ9HRMpIiKScVWkokONSLKY/X/DaklVxtIL1kgBnsqbtQDunY/9SJoQc50UEZFvMJEiIiK3kuoGnKu2ARDbZut0Ov/fVDq9Tbp+qCdzJVIOu9i10M+kLdDZuY+IyDeYSBERkduh5rbngEKNJgDP1D6DCQiJVuaealOxcx8bThAR+QYTKSIiclN8fRTgqUhZUgAlKmBaoHDnvvgIE+IjTADEqX2CAtMJiYh6OiZSRETkpnjHPnstYKsSx72hY5+LwhUpwFOVqqhrRJG1QZF7EhH1ZEykiIjI7WCBmNQEG/TIam6Z7Veyjn29pNEE0CKR8n9FCuA6KSIiX2MiRUREAIA6exNOlNYCAAYmhcNoUOBPhPWMZ9yrKlKSqX1VClWkUphIERH5EhMpIiICABwpqnZ34las0USVJJGKTFPmnlogS6TOtH+cD0n/TQ8VMZEiIuouJlJERASgxfooVRKpVGXuqQVmC2COFMdVpxW5ZWZcGMxG8c8+K1JERN3HRIqIiAC07NgXqcxNpUlEb0qkAE8FzloAOB1+v51Br8OgJDFBziurQ42tye/3JCLqyZhIERERALEttsvg5AhlbiqtSEX1oql9gCdxdDYCNSWK3FJaaTzM/aSIiLqFiRQREcHhFHC4SNyMt29MKCxmozI3diVSweGAOUqZe2qFdE2YUuukJA0nDjGRIiLqliBvT6itrcUzzzyDjRs3oqSkBE6nU/bzEydO+Cw4IiJSxqmyWtTZxelliq2PEgRPAhGZ2ns243WRTmWsOg2kXej3Ww6RVBoPMpEiIuoWrxOpO+64A9988w1uueUWJCcnQ9fb/vAREfVAim/ECwB1ZUBT88awvW19FNAikVKmIjUoyQKdTsxh2XCCiKh7vE6kPv/8c3z66aeYNGmSP+IhIiIVyBpNKNaxrxc3mgBaTO1TpnNfuCkIGbFhOFlai8NF1WhyOBGkxH5hREQ9kNf/9YyOjkZMTIw/YiEiIpWoUpHqra3PXVSoSAGeRNnW5EReWa1i9yUi6mm8TqSefPJJLFmyBHV1df6Ih4iIVOCqSEWFGpEcaVbmppXSilQv69gHABFJgM4gjhWqSAFAtmSd1AFO7yMi6jKvp/Y9//zzyM3NRWJiIjIyMmA0yjs77dq1y2fBERGR/52rtqGk2gZArFYotva1t1ek9AbA0geoyle2IiWpOB4stOIXo/oodm8iop7E60Tq6quv9kMYRESkFmkbbMXWRwEt1kj1wooUIO6dVZUP1FcAthrAFO73Ww5J9my2fKiw2u/3IyLqqbxOpJYuXeqPOIiISCWqrI8CJFUYHWBJUe6+WiKtxFnPAvGD/H7LRIsJ0aFGVNQ1snMfEVE3eJ1IuezcuROHDh0CAAwdOhSjR4/2WVBERKQcWcc+NRKpiGTAoNAGwFrTci8pBRIpnU6HISkWfHe8DKU1NpRUNyAhQqF1cUREPYjXiVRJSQluvPFG5OTkICoqCgBQWVmJqVOnYt26dYiPj/d1jERE5EeuilSwQY+seP9PLQMANDYAtSXiuDeuj3JRsXPfd8fLAIjT+5hIERF5z+uufffddx+qq6tx4MABlJeXo7y8HPv374fVasX999/vjxiJiMhP6u0OnDhXAwAYmBQOo1J7ClnPesa9OpGSrA2rVK5zn6zhBKf3ERF1idcVqQ0bNuCrr75Cdna2+7EhQ4Zg5cqVuOKKK3waHBER+deR4mo4BXGsbKOJXt6xz0WlilR2srxzHxERec/rjx6dTmerlucAYDQa4XQ6fRIUEREpQ1qNyFYtkeqlHfsAsf25i4KJVFZ8OIKbq48HC6oUuy8RUU/idSJ12WWX4YEHHkBBQYH7sbNnz2LBggW4/PLLfRocERH518FCz5toVqRUYLYA5uZ25Apuyms06DEwSVwPd7K0FvV2h2L3JiLqKbxOpF5++WVYrVZkZGQgKysLWVlZyMzMhNVqxUsvveSPGImIyE9kFSlFO/ble8ZRvbgiBQCRfcX/tRYATuUSmuwk8d/bKYhTPImIyDter5FKS0vDrl278NVXX+Hw4cMAgOzsbEybNs3nwRERkf84nQIOF4lvoNNiQmAxK9iCnBUpj8hUoHgf4GwEakoAS7Iitx2SYgF2iuODBVaMSotS5L5ERD1Fl/aR0ul0mD59OqZPn+7reIiISCGnyutQ1zylS9FpfYAnkQoOB8xRyt5ba1o2nFAqkZI1nOA6KSIib3UqkfrrX/+K3/zmNzCbzfjrX//a4bFsgU5EFBhkG/EmRyp3Y0HwJFKRqYBOp9y9tajlprxpFypyW+lUzkOFnNpHROStTiVSf/nLX3DzzTfDbDbjL3/5S7vH6XQ6JlJERAFC1mhCyfVRdWVAU4M47u3T+oDWiZRCLGYjUqNDcKaiHocKrXA6Bej1vTypJSLyQqcSqZMnT7Y5JiKiwCWrSCnaaEKSLDCRkrd/V7AFOiBO7ztTUY86uwOnyuuQGRem6P2JiAKZ1137nnjiCdTV1bV6vL6+Hk888YRPgiIiIv9zbcQaGWJESqRZuRuz0YScSpvyAvIE+hA35iUi8orXidSyZctQU1PT6vG6ujosW7bMJ0EREZF/ldbYUGy1ARCrEjol1ylxM165iCRA3zxBRMGpfYB8E2ZphZKIiM7P60RKEIQ2/+Du3bsXMTExPgmKiIj8S1p9UHRaH8BEqiW9AbCkiGMVpva5HGRFiojIK51ufx4dHQ2dTgedToeBAwfKkimHw4GamhrcddddfgmSiIh8S96xT+FEqlKyGS+n9oki08TXpb4CsNUApnBFbpsaHYIIcxCqG5pYkSIi8lKnE6kVK1ZAEATcfvvtWLZsGSIjPa1yg4ODkZGRgQkTJvglSCIi8q2DmqhI6TyVmN5OmlBazwLxgxS5rU6nw5BkC344WY4iawPKa+2ICQtW5N5ERIGu04nUnDlzAACZmZmYOHEijEaj34IiIiL/clUfgg16ZMUrU/1wcyVSEcmAgX9LALRuga5QIgWI66R+OFkOQJzyOal/nGL3JiIKZJ1OpFymTJniHjc0NMBut8t+brEo/MkmERF5paHRgdxzYtOgAYnhCA7yerls1zU2ALUl4pjT+jykr0Wlsg0npBXJgwVMpIiIOsvrv551dXWYP38+EhISEBYWhujoaNkXERFp25GiajgFcaz4+ijrWc+YiZSHyntJubDhBBFR53mdSC1atAhff/01Xn31VZhMJrzxxhtYtmwZUlJS8Pe//90fMRIRkQ9pY30UmEhJqbiX1IDEcATpxQZS3EuKiKjzvE6k/vvf/+KVV17Bddddh6CgIEyePBmPPvoonn76abz99tv+iJGIiHxI1Y590iQhqq+y99YyFRMpU5AB/RPEdXLHS2rQ0OhQ9P5ERIHK60SqvLwc/fr1AyCuhyovFxeoXnzxxfj22299Gx0REfmctCKVzYqUNpgiAHOUOFZ4U17Ak1A3OQUcL6lR/P5ERIHI60SqX79+OHnyJABg8ODBePfddwGIlaqoqCifBkdERL7ldAru6VtpMSGwmBXumidNEphIybnWSVkLAKeyVSFZwwlO7yMi6hSvE6m5c+di7969AIBHHnkEK1euhNlsxoIFC7Bo0SKfB0hERL5zqrwOdXbxTbri0/oAVqQ64no9nI1ATYmit85OlnfuIyKi8/O6/fmCBQvc42nTpuHw4cPYuXMn+vfvjxEjRvg0OCIi8i35+qjIDo70E1dFKjjcM5WNRC3XSVmSFbt1Njv3ERF5zetEqqX09HSkp6f7IhYiIvKzg4VV7rHiHfsEwVORikwFdDpl7691skQqH0i7ULFbx4QFIznSjMKqBhwqtEIQBOj470NE1KFOJVJ//etfO33B+++/v8vBEBGRfx0qrHaPs5MjlL15XRnQ1CCOOa2vNRU79wHiVM/CqgZUNzThTEU90mJCFY+BiCiQdCqR+stf/tKpi+l0OiZSREQa5praZzEHoU9UiLI3Z6OJjqm4KS8gTu/beFhcm3Ww0MpEiojoPDqVSLm69BERUeAqq7GhyCpWhIakWJSfuiVrNJHW/nG9ldoVqRR5w4kZQ5MUj4GIKJB43bXPxW6348iRI2hqavJlPERE5CfSaX3qNJpgItWhiCRA3/z5pop7SQFwt8gnIqL2eZ1I1dXVYd68eQgNDcXQoUORn58PALjvvvvwzDPP+DxAIiLyDVUbTQBsfX4+egNgSRHHKlSk+saEIizYAICd+4iIOsPrRGrx4sXYu3cvcnJyYDab3Y9PmzYN69ev92lwRETkO/LW52okUlwjdV6uSl19BWCrUfTWer0Og5t/L85U1KOqvlHR+xMRBRqvE6mPP/4YL7/8Mi6++GLZ/PqhQ4ciNzfXp8EREZHvuKoMRoMO/RPClQ/AXWXReSovJCdNMK1nFb89p/cREXWe14nUuXPnkJCQ0Orx2tpa7jlBRKRRDY0O5J6rBQAMSIhAcFCXl8h2XaU4FRwRyYDBqPz9A4E0kXK9XgqSTvlkIkVE1DGv/5KOHTsWn376qft7V/L0xhtvYMKECb6LjIiIfOZocTUcTgGASuuj7LVA7TlxHM1N3NsVJXltKvIUv312srxzHxERta9T7c+lnn76acyaNQsHDx5EU1MTXnzxRRw8eBDff/89vvnmG3/ESERE3aT6+ihpdSWKiVS7pElm5SnFbz8oMQJ6HeAU2HCCiOh8vK5IXXzxxdi7dy+ampowfPhwfPHFF0hISMDWrVsxZswYf8RIRETdJH1TrEpFqkKSFLAi1T5ZRUr5RCok2IB+8eL6uWPFNWh0OBWPgYgoUHhVkWpsbMRvf/tbPPbYY1i1apW/YiIiIh+TVqSyValISZICVqTaF5kK6PSA4FSlIgWIvx/HS2pgdziRe64Gg5NU+H0hIgoAXlWkjEYjPvjgA3/FQkREfuB0Cu7GAanRIYgMUaHRg3S9DytS7TMYAUtzwwkVKlKAfOon10kREbXP66l9V199NT7++GM/hEJERP6QX16HWrsDgErrowB5UsCKVMdciWZDJdBQ1eGh/iCd+slEioiofV43mxgwYACeeOIJfPfddxgzZgzCwsJkP7///vt9FhwREXWf6uujAM80Nb2Re0idT1Q6gM3iuOIUkDxC0dvL9pIqYiJFRNQerxOpN998E1FRUdi5cyd27twp+5lOp2MiRUSkMap37BMET0UqMhXQG5SPIZC07NyncCIVH2FCXLgJpTU2HCywQhAE7hNJRNQGrxIpQRCQk5ODhIQEhISE+CsmIiLyIWlFSpVGE/UVgL1aHHN91Pmp3LkPECuX3x49h4q6RhRZG5Acyb/5REQtebVGShAEDBgwAGfOnPFXPERE5GMHCsR1NpEhRqRGq/CGWNpoguujzk/lvaQANpwgIuoMrxIpvV6PAQMGoKyszF/xEBGRD5XW2FBstQEQ3xyrMkWrkntIeUVWkcpTJYTs5Aj3+BA35iUiapPXXfueeeYZLFq0CPv37/dHPERE5EMHJNWEoWo1mmDHPu+EJwIGkzhWaWqf9HflIBMpIqI2ed1s4tZbb0VdXR1GjhyJ4ODgVmulysvLfRYcERF1j2taH6CBjn0AEJ2hTgyBRK8HovoCZceAynyxWYfClcTMuHCYjXo0NDo5tY+IqB1eJ1IrVqzwQxhEROQPB2UVqUh1gmBFynvR6WIi1VQP1JQAEYmK3t6g12FQkgV7T1fiVHkdamxNCDd5/ZaBiKhH8/q/inPmzPFHHERE5AeuRMoUpEdWfNh5jvYTV0XKGAqExakTQ6CJatFwQuFECgCGJEdg7+lKCAJwpMiKMekxisdARKRlXfp4yeFw4OOPP8ahQ4cAAEOHDsVVV10Fg4F7gxARaUWtrQkny2oBAIOTIhBk8HpZbPc5neL0NEBMDrgfUedEt2iBnjZO8RBadu5jIkVEJOd1InX8+HFceeWVOHv2LAYNGgQAWL58OdLS0vDpp58iKyvL50ESEZH3DhVaIQjieIha0/pqigCHXRyzY1/nySpSeaqEMIQNJ4iIOuT1x5P3338/srKycPr0aezatQu7du1Cfn4+MjMzcf/99/sjRiIi6gJtdOzL84zZaKLzpK+VSp37BiVZ3AXEg4XVqsRARKRlXlekvvnmG2zbtg0xMZ4Sf2xsLJ555hlMmjTJp8EREVHXaaJjHxtNdI0GNuUNNwUhPSYUeWV1OFxoRZPDqc70UCIijfL6v4gmkwnV1a0/maqpqUFwcLBPgiIiou5zVaT0OiA7SQutz5lIdVpINGBqno6pUkUK8CTgtiYn8prX2xERkcjrROrnP/85fvOb3+CHH36AIAgQBAHbtm3DXXfdhauuusofMRIRkZfsTU4cK64BAPSLD0dIsErNgFiR6rrovuL/Vp0BHE2qhCBtOHGA+0kREcl4nUj99a9/RVZWFiZMmACz2Qyz2YxJkyahf//+ePHFF/0RIxEReel4SQ3sDicAFddHAS3WSDGR8oprnZTgAKxnVAkhW5JIHeI6KSIiGa8TqaioKPz73//G0aNH8f777+P999/HkSNH8NFHHyEysmtdoVauXImMjAyYzWaMHz8e27dv79R569atg06nw9VXX92l+xIR9VTS9VGqJlLlJ8T/DYsHTBHqxRGIojM9Y9frqDB27iMial+Xtynv378/+vfv3+0A1q9fj4ULF+K1117D+PHjsWLFCsyYMQNHjhxBQkJCu+fl5eXhwQcfxOTJk7sdAxFRTyPv2KdS63N7rdj+HABi+qkTQyCTvmblJwEVdhdJspgRHWpERV0jDkqScyIi6kJF6rrrrsOf/vSnVo8/++yz+H//7/95HcALL7yAO++8E3PnzsWQIUPw2muvITQ0FKtXr273HIfDgZtvvhnLli1Dv37840xE1NJBSSIlXeeiKNm0vsx2D6N2xKhfkdLpdO6qVGmNHSXWBlXiICLSIq8TqW+//RZXXnllq8dnzZqFb7/91qtr2e127Ny5E9OmTfMEpNdj2rRp2Lp1a7vnPfHEE0hISMC8efPOew+bzQar1Sr7IiLqyZxOwT0NKyXSjOgwlTqqSt/8syLlvZYVKZVIK5psOEFE5OF1ItVem3Oj0eh1klJaWgqHw4HExETZ44mJiSgqKmrznC1btuDNN9/EqlWrOnWP5cuXIzIy0v2VlpbmVYxERIEmv7wONTaxy9sQtab1AfI3/0ykvGfpAxia/95WqJlIeSqa+89yeh8RkYvXidTw4cOxfv36Vo+vW7cOQ4YM8UlQ7amursYtt9yCVatWIS4urlPnLF68GFVVVe6v06dP+zVGIiK1SZsCaKLRBCCfpkadozd4WsaXnwQEQZUwWJEiImqb180mHnvsMVx77bXIzc3FZZddBgDYuHEj3nnnHbz33nteXSsuLg4GgwHFxcWyx4uLi5GUlNTq+NzcXOTl5WH27Nnux5xOsb1vUFAQjhw5gqws+Wpck8kEk8nkVVxERIFMMx37KliR6raYfkDZMaCpHqguAizJioeQGReGEKMB9Y0OHChkRYqIyMXritTs2bPx8ccf4/jx47jnnnvwf//3fzhz5gy++uorr9uQBwcHY8yYMdi4caP7MafTiY0bN2LChAmtjh88eDD27duHPXv2uL+uuuoqTJ06FXv27OG0PSIitOjY10fNqX3NFSlzJBASrV4cgUy2TkqdhhMGvQ7ZyWLr+tPl9aiqb1QlDiIirelS+/Of/exn+NnPfuaTABYuXIg5c+Zg7NixGDduHFasWIHa2lrMnTsXAHDrrbeiT58+WL58OcxmM4YNGyY7PyoqCgBaPU5E1Fu5EqmoUCNSIs3qBNFkB6qaN5GNzgR0OnXiCHQtO/dlTFIljKEpkdiVXwlA7Ag5IStWlTiIiLSky/tI7dy5E4cOHQIADB06FKNHj+7SdW644QacO3cOS5YsQVFREUaNGoUNGza4G1Dk5+dDr/e6cEZE1CuVVDfgXLUNgNj2XKdWAlOZDwji1GtO6+sG6WunkYYTBwqqmEgREaELiVRJSQluvPFG5OTkuKtBlZWVmDp1KtatW4f4+Hivg5g/fz7mz5/f5s9ycnI6PHft2rVe34+IqKeSb8SrlUYTTKS6TANT+wB5w4mDbDhBRASgC2uk7rvvPlRXV+PAgQMoLy9HeXk59u/fD6vVivvvv98fMRIRUScdlCVSKq6PkjWaYMe+LotMA3TNf6pV3EtqYFI4gvRidXN/ARtOEBEBXUikNmzYgFdeeQXZ2dnux4YMGYKVK1fi888/92lwRETkHc107GNFyjeCgsVkClC1BbopyIABiWLDidxztWhodKgSBxGRlnidSDmdThiNxlaPG41GdytyIiJSh6siZTbq0S8+XL1AuBmv77heP1sVUFeuWhiuxNzhFHC4qFq1OIiItMLrROqyyy7DAw88gIKCAvdjZ8+exYIFC3D55Zf7NDgiIuq86oZG5JXVAQAGJ1lg0KvYKc9VkTKGAuGJ6sXRE0inRmqo4QQRUW/ndSL18ssvw2q1IiMjA1lZWcjKykJmZiasViteeuklf8RIRESdcKjQUyVQdVqf0wFU5Iljtj7vPg02nDjAhhNERN537UtLS8OuXbvw1Vdf4fDhwwCA7OxsTJs2zefBERFR50mrBEPUTKSsZwFn86atbDTRfdHSvaTUq0i5NuUFmEgREQFdSKT+/ve/44YbbsD06dMxffp09+N2ux3r1q3Drbfe6tMAiYiocw5opWOfrNEEE6lu00hFKsJsREZsKPLK6nC40IomhxNBBu7zSES9l9f/BZw7dy6qqlrPja6ursbcuXN9EhQREXnPlUgZ9DoMToo4z9F+xEYTvhWd4RmrmEgBngTd1uTEidJaVWMhIlKb14mUIAjQtTHf/cyZM4iMVPETUCKiXszW5MCxYnGNVFZ8GMxGg3rBlB33jKNZkeq24FAgIkUcl+eqGsrQPp4po/vPsuEEEfVunZ7aN3r0aOh0Ouh0Olx++eUICvKc6nA4cPLkScycOdMvQRIRUceOFdegySnuMaTqtD5AnkjFDVAvjp4krj9QXQDUlYkt0ENjVAmjZcOJay9QJQwiIk3odCJ19dVXAwD27NmDGTNmIDzcsz9JcHAwMjIycN111/k8QCIiOr+DsvVRKjaaAIDSY+L/GiWVFOqeuIHAyW/FcekxoO94VcJgC3QiIo9OJ1JLly4FAGRkZOCGG26A2Wz2W1BEROQdWce+ZBUTqSa7p/V5bBagZzMCn4gb6BmXHlUtkYoLNyHRYkKx1YaDBdZ2p/sTEfUGXv+FmzNnDpMoIiKNkXbsU7X1eUUeIDjEcSyn9fmMdIpk6VH14oBnep+1oQlnKupVjYWISE1eJ1J6vR4Gg6HdLyIiUpbTKeBQoZhI9YkKQVRosHrBlB3zjKVVFOoeWUXqWPvHKYDT+4iIRF7vI/Xhhx/KyviNjY3YvXs33nrrLSxbtsynwRER0fnlldWi1i5WgdRfHyWplrDRhO9EpADGMKCxVgMVKWkiZcXMYckqRkNEpB6vEylX0wmpX/7ylxg6dCjWr1+PefPm+SIuIiLqJM1sxAsApZKOfbH91Yujp9Hrxc59hXvF6ZNNNiDIpEooLTv3ERH1Vj5bBXzRRRdh48aNvrocERF10sFCDXXsk07tYyLlW67pfYJDvumxwlKjQ2Axi5/Dci8pIurNfJJI1dfX469//Sv69Onji8sREZEXZBWpPmpP7WtOpCJSAFN4x8eSd2K10XBCp9O5q1Il1Tacq7apFgsRkZq8ntoXHR0tWyMlCAKqq6sREhKCt99+26fBERFRxwRBwMHmBf/RoUYkWVTsqlpXDtSXi+M4VqN8TlOd+yzYeqIMgNhw4tJBCarGQ0SkBq8TqRUrVsi+1+v1iI+Px/jx43H27FlfxUVERJ1QUm1DaY0dgLh2RdU9faTd5Nj63Pe01Lmvj7zhBBMpIuqNvE6k5syZI/u+uroa77zzDpYuXYoff/wRDofDZ8EREVHHpGtUVF8fxY59/hWbBUAHQJCvRVPBMFnDCa6TIqLeqctrpL799lvMmTMHycnJeO655zB16lRs27bNl7EREdF57JMkUsP6qNyxr4wVKb8yhgBRfcVx6TFAEFQLpV98OEKM4t6R+9hwgoh6Ka8qUkVFRVi7di3efPNNWK1WXH/99bDZbPj4448xZMgQf8VIRETtkFakhqudSElbn3ONlH/EDQQqTwE2K1BTDEQkqRKGQa/DkBQLdp6qwOnyelTW2dXdCJqISAWdrkjNnj0bgwYNwk8//YQVK1agoKAAL730kj9jIyKi83BVAyLMQUiPDVU3GFdFKsgMRKapG0tPJVsnpW7DCWnivv8s95Miot6n04nU559/jnnz5mHZsmX42c9+BoPB4M+4iIjoPEqqG1BsFVtPD1O70YSjybO3UUwWoOffCL/QUOc+6VRSTu8jot6o04nUli1bUF1djTFjxmD8+PF4+eWXUVpa6s/YiIioAwckVYDhqSpP66s8BTgbxTGn9fmPhjr3ySpSbDhBRL1QpxOpiy66CKtWrUJhYSF++9vfYt26dUhJSYHT6cSXX36J6upqf8ZJREQt7NNqxz42mvAfDU3ty4oPg9kovo3Yz4oUEfVCXnftCwsLw+23344tW7Zg3759+L//+z8888wzSEhIwFVXXeWPGImIqA37NNVoQtr6fGD7x1H3hMUB5uZ/a5UrUkEGPbKTxQT+VFkdquobVY2HiEhpXW5/DgCDBg3Cs88+izNnzuCdd97xVUxERNQJripAuCkIGbFh6gZzTpJIxTOR8hudzpOoVp0G7LWqhiNN4A+wKkVEvUy3EikXg8GAq6++Gv/5z398cTkiIjqP0hobCqsaAIjT+vR6FRtNAJzapyRpxa/sePvHKYANJ4ioN/NJIkVERMrS1LQ+QQBKj4hjSypgClc3np5O1rlPOw0nmEgRUW/DRIqIKABJp1ENUzuRqikBGprj4bQ+/9NQ574BCeEwBYlvJQ4UcC8pIupdmEgREQWgfVpKpGSNJgapF0dvoaHOfdKGEydLa2FtYMMJIuo9mEgREQWg/c17SIUFG9AvTuVGE65pfYB82hn5R3QGoA8SxypXpICWDSdYlSKi3oOJFBFRgCmvteNsZT0AYGhKpPqNJmQd+1iR8juDEYjpJ47LjgFOp6rhDOvj2cOM+0kRUW/CRIqIKMBoalofwKl9anBN72tqENugq4id+4iot2IiRUQUYPbLEilLB0cqxJVImaPEDWPJ/zTUuW9gYgSCmxtO7C9gIkVEvQcTKSKiALNfS63PbdWA9aw4jh8kbhhL/qehhhNGgx7ZSREAxIYTNbYmVeMhIlIKEykiogDjmj4VGmxAv3iV92ySTetj63PFSDc9VjmRAjzT+wRB3pqfiKgnYyJFRBRAKmrtOFMhNpoYkmyBQe1GE9JpZWw0oZy4/p6xxjr3cZ0UEfUWTKSIiAKIdA2KJhpNnJO2PmdFSjEh0UBYgjjWUEUKYOc+Iuo9mEgREQWQ/ZJ9elRfHwVwap+aXK93bQlQX6lqKAMTIxBscDWc4F5SRNQ7MJEiIgog+7Xa+jzIDET1VTeW3kbaua/suHpxAAgO0mNQc8OJ3HM1qGXDCSLqBZhIEREFENf6E7NRj6z4MHWDcTQC5SfEcewAQG9QN57eRkOd+wB5w4mDhaxKEVHPx0SKiChAVNU1Ir+8DoDYaCLIoPJ/wstyAWdz5UFaHSFlSBOpc4fVi6OZrOHEGa6TIqKej4kUEVGAkDaa0MT6qJIDnnHCEPXi6K0SBnvGxQfVi6PZcDacIKJehokUEVGA2Ke19VHSN++JTKQUZ+kDmJp/D0rUT6QGJoXDaBDb8bMFOhH1BkykiIgChOYaTUjfvLMipTydzpPAWs8C9RWqhmMKMmBgoqfhRJ2dDSeIqGdjIkVEFCBcn/KbgvQYkBCucjQAipun9gWHA1Hp6sbSWyUO9YxLDqkXRzPX9D6nABxkG3Qi6uGYSBERBYDKOjtOlYmNJoamaKDRhK0aqDwljhOyAT3/nKhCWgksPtD+cQoZkRrlHu9lwwki6uH4l4+IKAD8JHlTKn2zqpoSSZc4TutTj6wipf46qRGpnimne09XqhcIEZECmEgREQUA6ZvSkWlaWB8lqX5I38yTshKyPWMNdO4blBQBU5D41uKnM5XqBkNE5GdMpIiIAoB0mtRILVSkitn6XBPMkUBkmjguOSjuhqsio0GPoSkWAEBeWR2q6hpVjYeIyJ+YSBERBQDXp/sR5iBkxIapGwzQovU5K1KqciWyNitQdVrdWCCfevrT2UrV4iAi8jcmUkREGldU1YCSahsAcQ2KXq9TNyBB8EztC08CQmPUjae3k+7hpYHpfdKpp1wnRUQ9GRMpIiKN2yN5M6qJRhPVRZ49i7gRr/oSpA0n2LmPiEgpTKSIiDROumhfE+ujSrg+SlM0VpHKjA1DhCkIABtOEFHPxkSKiEjjpK3PNdGxT7Y+aph6cZAodgCgN4pjDbRA1+t1GNH8e1pstaGoqkHliIiI/IOJFBGRhgmC4P5UPz7ChCSLWd2AAPmbdU7tU19QMBA3UByXHgWa7OrGg5bT+ypVi4OIyJ+YSBERaVheWR2sDU0AgJGpkdDpVG40AXhan+sMQNwgdWMhkSuhdTYBZcfUjQXi76oLp/cRUU/FRIqISMNkG/FqYX2UoxE4d0Qcx2YBRg1UyEi+Vq1ov3pxNJO1QGfDCSLqoZhIERFpmHRa1Ii0KNXicDt3BHCIrdiRNFzdWMgjaYRnXPSTenE0S440Iy7cBED8MEBQeaNgIiJ/YCJFRKRh0k/zR/TRQKMJ6Zv05JHqxUFyyZJEqnCvenE00+l0GNXccMLa0IS8sjqVIyIi8j0mUkREGtXocOJAgZhI9Y0JRXRYsMoRQf4mXVoFIXWFJwARyeK46Cdx02SVyaf3VaoWBxGRvzCRIiLSqKPF1WhodAIARmphWh8AFLIipVmuf4+GKqDylLqxABghaTix9zTXSRFRz8NEiohIo2T7R6VqYFqf0+mZ2heZBoTGqBsPySVpa3ofK1JE1NMxkSIi0ijpm88RWujYV3ESsNeIY1ajtEf6b6KBRComLBhpMSEAgP0FVWhyOFWOiIjIt5hIERFplGs6lF4HDOtjUTkaAIV7PGOuj9IeWcMJ9Tv3AZ6W/Q2NThwtrlE3GCIiH2MiRUSkQQ2NDhwprgYADEyMQGhwkMoRgeujtC4yDQiJFscaqEgB8r3POL2PiHoaJlJERBp0oKAKDqfYeW2EFtZHAfI358msSGmOTuepFNaWANVF6saDFg0nuDEvEfUwTKSIiDRI2uVME+ujBMHTaCIs3tNqm7RFY+ukhvWJhF4njveerlQ1FiIiX2MiRUSkQdJpUCO1kEhZzwJ1ZeI4aYRY/SDtkSVS6q+TCjMFoX9COADgSHE1GhodKkdEROQ7TKSIiDTI1fo8OEiPQUkRKkcDro8KFLJEao9qYUi5PghwOAUcKLCqGwwRkQ8xkSIi0piq+kacKK0FAAxJtiA4SAP/qeb6qMAQkwUYw8SxBipSADBCspk0p/cRUU+igb/OREQktU9rG/ECnvVRACtSWqbXA0nDxXFVPlBXrm48kP8Os3MfEfUkTKSIiDRmr9Y24hUE4MyP4tgUCURlqBoOnYc00T27S704mg1OsiDYIL7dYOc+IupJmEgREWnM7vwK93h03yj1AnGpzBfbaQNA6hix6kHalXqhZ3xmh3pxNAsO0mNo84bSJ0trUVFrVzkiIiLf4F9DIiINEQQBu/IrAQBRoUZkxoWpGxAgfzOeOk69OKhz0qSJ1Hb14pAYnRbtHu8+XdHBkUREgYOJFBGRhuSX16G8+RP70WlR0GmhzfhpyZtx6Zt00qaodCAsQRyf2Qk4nerGA+CC9Cj3eHfzBwVERIGOiRQRkYbskk3ri+7gSAVJqxp9xqoXB3WOTgekNVcObVVA6VF144H8d1n6O05EFMiYSBERaYj00/oLtJBINdYDRfvEcdwgICRK1XCok1IlCa8GpvelRJqRaDEBAPaeroLDKagcERFR9zGRIiLSEFcipdMBI9M00Pq8YA/gbBLHnNYXOKRr2U6rn0jpdDr3OqkaWxOOl9SoHBERUfcxkSIi0oh6uwOHCq0AgIEJEYgwG1WOCPJqBhtNBI6U0YDOII5dretVJl0nxel9RNQTMJEiItKIfWer0NQ85UkTbc+BFh37WJEKGMGhQNIwcXzuMNCg/v5N0nVSu5lIEVEPwESKiEgjdmlt/yhBAE43J1ImCxA/WN14yDvuCqKgiarU8D6RCNKLXSh3sXMfEfUATKSIiDRC+im9JhpNVJ0GaorEcR9uxBtw0iRTMTWwMa/ZaMCQFHFj3uMlNaiqb1Q5IiKi7uFfRSIiDZBuxBthDkJWfLi6AQEt9o/i+qiAI52KqYFEChD3RnPZc7pStTiIiHyBiRQRkQacrazHuWobAGBUWhT0eg1sxCudDsb1UYEnOgMIjRPHZ3ZoZGNerpMiop6DiRQRkQZI14xociPeVG7EG3CkG/M2VAFlx9SNB3C3QAe4ToqIAh8TKSIiDZCvj4pSLxCXxgag8CdxHDcQCNFIckfe0dj0vrSYEMSFBwMA9uRXwMmNeYkogGkikVq5ciUyMjJgNpsxfvx4bN/e/uaBq1atwuTJkxEdHY3o6GhMmzatw+OJiALBbmlFKk0DSUvhHsDZ3AyA0/oCl/TfTiMb845q/v22NjThRGmtyhEREXWd6onU+vXrsXDhQixduhS7du3CyJEjMWPGDJSUlLR5fE5ODm666SZs2rQJW7duRVpaGq644gqcPXtW4ciJiHyjodGBAwXiPj9Z8WGIDNXCRrzcP6pH6HOBZGNe9StSADfmJaKeQ/VE6oUXXsCdd96JuXPnYsiQIXjttdcQGhqK1atXt3n822+/jXvuuQejRo3C4MGD8cYbb8DpdGLjxo0KR05E5BsHCqxodLg24tVANQpgx76eIjgMSBwqjksOAQ1WdeOBvOK6m+ukiCiAqZpI2e127Ny5E9OmTXM/ptfrMW3aNGzdurVT16irq0NjYyNiYmLa/LnNZoPVapV9ERFpieb2jxIET/UiOIIb8Qa6NMnGvGd3qhoKAIxMi4SrKSU79xFRIFM1kSotLYXD4UBiYqLs8cTERBQVFXXqGg8//DBSUlJkyZjU8uXLERkZ6f5KS0vrdtxERL4kWx+lhUYTVWeA6kJx3OcCQG9QNx7qHo01nAgNDsLgJHFj3iPF1aixNakcERFR16g+ta87nnnmGaxbtw4fffQRzGZzm8csXrwYVVVV7q/Tp08rHCURUcdc60TCgg0YmBihcjSQv9nmtL7Ap7GGE4DnAwNBAPZyY14iClCqJlJxcXEwGAwoLi6WPV5cXIykpKQOz33uuefwzDPP4IsvvsCIESPaPc5kMsFisci+iIi0orCqHoVVDQCAkWlRMGhiI15powkmUgEvph8QGiuOz+wQsxeVSaewcnofEQUqVROp4OBgjBkzRtYowtU4YsKECe2e9+yzz+LJJ5/Ehg0bMHYsN4kkosCluWl9gLxqwY14A59O56lKNVQCZcdVDQeQ/65zY14iClSqT+1buHAhVq1ahbfeeguHDh3C3XffjdraWsydOxcAcOutt2Lx4sXu4//0pz/hsccew+rVq5GRkYGioiIUFRWhpqZGradARNRlmms00dgAFO4Vx7H9gdC2G/lQgNHY9L7MuDBENbf5351fAUEDVTIiIm+pnkjdcMMNeO6557BkyRKMGjUKe/bswYYNG9wNKPLz81FYWOg+/tVXX4Xdbscvf/lLJCcnu7+ee+45tZ4CEVGXST+NH5UWpVocbkU/STbi5bS+HkO61k0DDSd0Oh1GN/++V9Q1Iq+sTt2AiIi6IEjtAABg/vz5mD9/fps/y8nJkX2fl5fn/4CIiBRgb3Ji31lxI96M2FDEhptUjghA/jbPmNP6eo6UCwCdHhCc8n9jFV3QNxqbjpwDAOw6VYHMuDCVIyIi8o7qFSkiot5qf0EV7E1OABraiPfkt55xxsXqxUG+ZQoHkkeJ43OHgJpzqoYDABeke37nd7LhBBEFICZSREQq2XGy3D2+MEMDa5EcjUB+82bo4YlA3EB14yHfypzsGedtVi+OZqMkXSql/18gIgoUTKSIiFSyI8/z5nFcpgYqUgW7AXtz456MyWK3N+o5Mi/xjKWVR5WEmYIwLEXckuRYSQ0qau0qR0RE5B0mUkREKnA6Bfx4SpzOFB1qRFZ8uMoRQf7mWlq9oJ4h7SJA37w0WgMVKQAYK6nEuv7/QEQUKJhIERGp4Pi5GlTWid3xxmbEQKeF6o/0zbW0ekE9gykc6NPcQKTsOGAtUDceyKe0Siu0RESBgIkUEZEKtkvWhIzTwvqoJpunm1tkGhCdqW485B+y6X3qV6UuzPBMaWUiRUSBhokUEZEKfpS8aRyboYH1UWd+BJoaxDHXR/Vc0imbGlgnFRtuQr94se35vjNVqLc7VI6IiKjzmEgREalgR564HsRs1GNYn0iVo0GL9VGc1tdjpY4DDM37leWpn0gBnopsk1PA7tNcJ0VEgYOJFBGRws5W1uNsZT0AYHRaNIwGDfynWLY+io0meiyjGUgbJ44r84GKPFXDAVo0nMhjIkVEgUMDf72JiHoX6bS+CzM1sD7KXgec3i6OY/oBkanqxkP+lTnFM9bA9L5xbDhBRAGKiRQRkcI012gifyvgFDsIIoPVqB5PWnE8kaNaGC5pMSFItIjTDXedqkCTw6lyREREncNEiohIYa7pSwa9DqP7RqkbDAAc/8ozzrpMvThIGX3GACZxI1zkfg041W3woNPp3NP7au0OHCqsVjUeIqLOYiJFRKSgyjo7jhSLbxSHplgQZgpSOSIAx74Q/1dnAPpdqmoopACD0fPvXF8BnN2lajiAvDK7ndP7iChAMJEiIlKQdDH92HQNTOsrPyluzgoAaeOBkChVwyGFDJjuGR//Ur04mkm3APiRiRQRBQgmUkRECtpxSrI+KlMD+0dJp/UNmKZeHKSs/pJ/62PqJ1KDkyyIaK7O7sgrhyAIKkdERHR+TKSIiBS046R0I14NVKSkb6L7T2//OOpZLClA4jBxXLALqDmnajgGvQ5jmqtSpTV25JXVqRoPEVFnMJEiIlJIQ6MD+85WAQD6xYUhLtykbkCNDZ721+FJQNJwdeMhZUmrUrkb1Yuj2YXSNugnOb2PiLSPiRQRkUL2nK5Eo0OcsnShFqpRp74DmsSNgdF/GqDTqRsPKUu6TkoD0/suZMMJIgowTKSIiBQi/ZRdExvxcn1U75Y2XtIGfaPqbdBHpEYi2CC+LWHDCSIKBEykiIgUIv2U/cIMDTSakLU9n6puLKQ8gxHoN0Uca6ANutlowIjUSABAXlkdSqobVI2HiOh8mEgRESmgyeHErlNi6/OECBP6xoSqG1DpMUnb83Fse95bSRuMHPlMvTiaSSu10q0CiIi0iIkUEZECDhdVo9YuTp26MCMGOrXXIx342DMe/DPVwiCVDZoF6JrfChz8GFC57bi0UrudDSeISOOYSBERKWCH1qb1HfzYMx7yC9XCIJWFJwDpk8Rx+QmgaJ+q4YxJj3H3PNnBdVJEpHFMpIiIFCBLpNRuNFF6HCjeL477jAGi+qobD6lLmkhLE2wVRIYYMSgxAgBwqNCK6oZGVeMhIuoIEykiIj8TBAHbToiJVIQ5CIOTLOoGdPAjz3jI1aqFQRqRfZVnet+Bj1Wf3je++YMGp8B1UkSkbUykiIj87GhxDcpr7QDEN4kGvdrro/7tGXNaH0UkSqb35XqqlSq5qF+se7z1RJmKkRARdYyJFBGRn22TvBmUvklURVkuUNy8DiblAiA6Xd14SBukCbW0EYkKxkv+P7KNiRQRaRgTKSIiP9uaq6FE6oBkWt/Qq1ULgzQm+yoAzZVSlbv3xYQFY3CSuE5q/9kqWLlOiog0iokUEZEfOZ0CfjgpJlIWcxCyk9VeH/WxZ8xpfeQind5XdhwoPqBqOK4PHJwCsINt0IlIo5hIERH50ZHialTUiZ+oj+8Xq+76qLJcT3vrlNFAdIZ6sZD2SCuUKnfvk62TyuX0PiLSJiZSRER+pKn1UdI3x0OvUS0M0ijp9D6Vu/eNz/TsJ7XtJBMpItImJlJERH4k/TR9gtqJlLSJAKf1UUsRiUD6RHFcdgwoOahaKNFhwe5tAg4UWFFVx3VSRKQ9TKSIiPxEXB8lru+ICjW6F9CroiwXKPpJHHNaH7VHuq+YtDGJCi7qJ+4nJQjA9jyukyIi7WEiRUTkJ4eKrKiqb14flRkDvZrro2RNJq5WKwrSuiHamd43geukiEjjmEgREfnJthOeT9FVXx8lndbHtufUnogkzUzvG58Z614nxY15iUiLmEgREfnJ98dL3eMJWSomUuUnPNP6kkdxWh91TDa972O1okBkqBFDmrcLOFRoRXmtXbVYiIjawkSKiMgPGh1O9/qouPBgDEpUcX0Uq1HkjSHa2Zx3Uv8495jT+4hIa5hIERH5wU9nKlFjawIATMyKg06n0vooQQB+etfzPbv10flEJAF9J4jj0qNA4R7VQpEmUlskFV4iIi1gIkVE5AffHfd8ej6pv4rT+s78CJw7JI7TxgMx/dSLhQLHyBs8411/Vy2MCzOiYTSIH0J8n8tEioi0hYkUEZEfSD89l36qrrjdkjfBF9yqXhwUWIZdBxjDxPG+9wF7nSphhAYH4YK+0QCAU2V1OF2uThxERG1hIkVE5GN19ibszq8AAKTHhiI1OlSdQGw1wP4PxXFwONueU+eZIoCh14hjmxU49B/VQrlY8kEEq1JEpCVMpIiIfGz7yXI0OsQF+hOzVKxGHfgIsNeI42HXAaZw9WKhwCOtYKo4vW+ibJ0UG04QkXYwkSIi8rHvJd3FLlZ1Wt8/PGNO6yNvpY0D4gaK41PfAaXHVQljZGokwk1BAMQtBZxO9boIEhFJMZEiIvKxLcc0sH/UuSPA6R/EccIQoM8YdeKgwKXTyRNwaWKuoCCDHhf1iwEAlNXacaS4WpU4iIhaYiJFRORD5bV2HCy0AgCGplgQExasTiA/rvGMR98ivikm8taIGwG9WA3CnreBJpsqYUgbtnzHNuhEpBFMpIiIfEi6GF61bn0NVmD3P8VxUAgw8kZ14qDAFx4PZM8Wx7XngP0fqBIG95MiIi1iIkVE5EPfHj3nHqu2Pmr3PwF78/SnkTcCoTHqxEE9w0X3eMZbXxE3eVbYgIRwJFpMAIAfTpTD1uRQPAYiopaYSBER+YggCPj2qPhpuSlIj3GZKiQwTgfww2ue78ffpXwM1LOkjQP6jBXHxfuAvC2Kh6DT6TB5QDwAoL7RgR/zKhSPgYioJSZSREQ+cqykBkXWBgDA+H6xMBsNygdx5HOg8pQ4zrocSBisfAzU81x0t2e87VVVQpg8wFPhlVZ+iYjUwkSKiMhHpG/uLhmg0rQ+6Ztc6ZQsou4Y8gsgIkUcH/kMKD+heAiTB8S7e6Z8w0SKiDSAiRQRkY98K2l7fsnAeOUDKNgNnGqedhU3COh/ufIxUM9kMALj7mz+RlClKhUTFozhfSIBAIeLqlHSXP0lIlILEykiIh9oaHTghxPiRrxJFjMGJIQrH0TOM57xRXex5Tn51pjbAGOoON75FlB1VvEQLhng+YBi8zF27yMidTGRIiLyge0ny2FrcgIALhkYB53SScyZH4GjG8SxJRUYdbOy96eeLzQGuPAOceywAZufVzwEaaX322Oc3kdE6mIiRUTkA7L1UWpM69v0lGd8yYNAkEn5GKjnm/Q7ILi52rrr70BlvqK3H903CuEmcYPgzcdK4XQq34qdiMiFiRQRkQ+4Ph3X61TYP+rUViD3a3EclQ6M/rWy96feIyzW01Lf2Qh886yitzca9JiQFQsAKK+140CBVdH7ExFJMZEiIuqmwqp6HC2uAQCMSI1CVGiwsgFIq1FTHhYbAxD5y8T5gEls+oA9/wLKchW9Paf3EZFWMJEiIuqmnCMqTus78Q2Qt1kcx/YHRtyg7P2p9wmJBibcK44Fh+JVqUsl/x/bdLhE0XsTEUkxkSIi6ibpm7nLBicod2NBAL7+o+f7KY8AhiDl7k+910V3iwkVAOx7Fzh3VLFbp8WEIis+DACwK78ClXV2xe5NRCTFRIqIqBtsTQ5sOS62YY4NC8aI5n1uFHH8K+DMdnEcPxgYdq1y96bezWwBJt4vjgUnkLNc0du7PrBwCtycl4jUw0SKiKgbtp8sR53dAQCYMigeer1Cbc8FQb426tLFgN6gzL2JAGDcb4DQ5sYqBz4Eig8oduupgzyVX07vIyK1MJEiIuqGTYc9n4YrOq3vyGdAwW5xnDgcyL5KuXsTAYApHLh4gef7TU8rduuxGTHuNujfHD0HB9ugE5EKmEgREXXDpiPip+EGvQ6TByjUaMLpAL6WVKOm/h7Q8z/npIIL5wHhSeL48CfA2Z2K3DY4SI/JA8RqWEVdI/acrlTkvkREUvzLS0TURSdLa3GytBYAMCY9GpEhCrUd/3E1UNI8jSrlAmDQLGXuS9SSMUTcANrls0WA06nIrTm9j4jUxkSKiKiLVOnWV3MO2Pik5/uZzwA6hdZlEbVlzG1isxNArEjt/ocit710kKQN+hEmUkSkPCZSRERdJH3zJv103K++ehywVYnjkb8C+o5X5r5E7TEYgSv/7Pn+q8eBunK/3zbBYsawPhYAwIECK4qtDX6/JxGRFBMpIqIuqLE14YcT4pvFPlEhGJgY7v+bnt4O7PmnODZFAtOX+f+eRJ2ReQn+f3v3HR9VlfYB/Dd9kslMeu8hFOm9l+CCIIqyirgWBEQXFV3b+qqr++q+u667orsuwqKuu+DaUFAEKyDSBKR3pCSBVNL7JJl63z/OZJIxlEyYEpLf9/OZz8zcuXPvGZjMvc89z3kO+jjK7zdUuM5v5kXXtriA8T3T+4jIxxhIERG1w7bTpTDbxFiQa3tFQebt9DqrCfjisebnE38HBPmwSiDR5Vz3J0AlJsrFvv8Aubu9vstrr4l2Pt54otjr+yMiaomBFBFRO7Q8aZvcO/oSa3rI1r82F5iI7gcMu8/7+yRyR3A8kPGM44kEfP4AYDZ6dZf944MRpdcAAH7ILIPRZPXq/oiIWmIgRUTkJovN7kwj0muUGJkW7t0d5u8Dfvi7eCxXAb9cBiiU3t0nUXuMfAhIGCYeV2QD33k3/VQul2GS40KG2WrH9jOll3kHEZHnMJAiInLT3nMVqG6wAAAm9IyEWunFn1JLA7DmAUBylJSe8DQQ0897+yO6EgolMGMZoNSK53veArK3enWXLXuENzC9j4h8iIEUEZGbfJbWJ0nAN08D5WfE87jBwNjHvbc/Ik+I6A5MerH5+ecPAnXeKwQxuls4dGoFAFFwwmrzzTxWREQMpIiI3CBJkjOQUsplyPBm2fM9bwMH3hWPFRrgl28ypY+uDsMXACnjxOOaAmDlXaJgihdolApMcMwpVVVvwb6cSq/sh4jo5xhIERG54WRRLfIrGwAAI9PCERyg8s6Osr4Hvn22+fnNS4DInt7ZF5GnyeXAre8A+jjxPH8P8OXjopfVC1r2DLN6HxH5CgMpIiI3+CStrzwLWDUXkGzi+djHgf6zvLMvIm/RxwB3fAgoA8TzQx8Au5Z6ZVcTe0ZBIRdTEGw8UQzJSwEbEVFLDKSIiNyw4USR8/EkbwRSlkZg1RygsVo873E9cO3/en4/RL4QNwiY8c/m5xv/1yvzS4UEqjE8JQwAkFtRj1PFtR7fBxHRzzGQIiJqo7yKehwrqAEA9I03ID4kwPM7Wf87oOioeBzeHbj1XyJNiuhq1fcWYOwT4rFkA1bPA+orPL6bKX2aL2x8c7ToEmsSEXkGj85ERG307bHmk7Np/WI9v4NjnwL7/i0eK7XArHcBjd7z+yHytYnPAcljxOOaAmDNAsDu2ep6U/s2/022/FslIvIWBlJERG309bHzzsfX9/VwIFV0FFj3m+bn0xYB0X08uw8if1EogVv/DQRGiOdnNgBb/+rRXcQEazE4KQQAcKq4FpkldR7dPhHRzzGQIiJqg8KqBhzMrQIA9IrRIzVC57mNV5wF3r8VMDtO/Pr/Chg023PbJ+oIDLHALW8DEEUhsPUvwN5/e3QXLXuKv21x4YOIyBsYSBERtYHX0vrqSoD3bwHqHNUA44cCN/4NkMk8tw+ijiL9F8B1f2p+/tWTwPHPPbb5qX1jnI+/YXofEXkZAykiojb4psXV7Wn9Yi6xphuM5SKIqsgWzyN6AnetAtQe7O0i6mhGPwyMedTxRAI+ux84vcEjm04IDcSAhGAAwPHCGuSUGz2yXSKiC2EgRUR0GSU1jdiXUwkA6B4VhPQoDxSAqC0CVtzQXKHPEA/M/gwIDLvybRN1dJP+AAy8Szy2mYGVdwIn1npk09e36DFmrxQReRMDKSKiy1h/vAhN83te39cDvVFVecDy64HSn8RzfSww+3MgOOHKt010NZDJgOmLgT6/FM/tFmDVPODwx1e86ZZ/o98c5TgpIvIeBlJERJfxxZHmk7GpV1qtL+t74F8Tm9P5gpOAeV8DkT2ubLtEV5umSn4D7hTPJRuw5tfAht8DNku7N5scrkPvWAMA4HB+NXLL6z3RWiKiVhhIERFdQmFVA/acFZOHdovU4ZrYdqb12azA938C3rsFMJaKZWHdgHu/AcLSPNRaoquMXAHcvBQYOr952c7FwPJpoue2nW4c0HzB44sjhVfSQiKii2IgRUR0CV+2OAm7eWA8ZO2ppmeuBz6+C9i2CIAjRzB9MjB/I9P5iORy4IbXgCkvA3KVWJa/B3g7Ayg82K5NTu8f53y87hADKSLyDgZSRESXsLbFSdhNA+IuseZFNFQC7/0SOP2teC5TiIH2d34C6MI91Eqiq5xMBox6CJi/HghJEsvqy4AV04HsrW5vLjEsEEOSQwGIyXlPFtV4srVERAAYSBERXVRmSR2OF4oTsP4JwUhxdxLeyhyRopT3o3iu1gP3fA6MfUxchSciV/FDgAXbgKTR4rm5FvhgJnB0tdubannhg71SROQNPJITEV3EusNX0Bt1dDXw5lig5IR4rosE5n0FpI73YAuJOqGAUDEVQI/rxXObGfh0PrB2IWBu+7xQ0/rFQiEXqbjrDhdCaiq9SUTkIR0ikFq6dClSUlKg1WoxYsQI7Nmz55Lrr1q1Cr169YJWq0W/fv3w9ddf+6ilRNRVSJKELxyBlEwGTG9rINVQCax5UJz4mRzpRKGpwL3rgdgBXmotUSejCgBufx8YNLt52cH3gbfGA3l727SJSL0Go7uJ9Nn8ygYcyK3yQkOJqCvzeyD18ccf44knnsALL7yAAwcOYMCAAZgyZQpKSkouuP7OnTtxxx13YP78+Th48CBmzJiBGTNm4NixYz5uORF1ZkcLqnG2TFz9HpkajmiD9tJvkCQxB84bQ4HDHzYv7zdLpCqFd/Nia4k6IYUSuOkNYMYyQOVIqy3PBP49GfjycXHR4jJuHhjvfLzuUIG3WkpEXZRM8nNf94gRIzBs2DAsWbIEAGC325GYmIhHHnkEzzzzTKv1b7/9dhiNRnz55ZfOZSNHjsTAgQPx5ptvXnZ/NTU1CA4ORnV1NQwGg+c+CBF1Kn/44jiW7zgHAHj5ln64Y3jShVe020QhiR2Lm8dCAWI81A2vAQNu935jiTq78izg0/uAwgPNy3SRwIgHgKH3AoFhF3xbbaMFQ/70HcxWO8J0avz47C+gVvr9GjIRdSBXEhv49dfEbDZj//79mDRpknOZXC7HpEmTsGvXrgu+Z9euXS7rA8CUKVMuur7JZEJNTY3LjYjoUsxWu7Nan1opx7QLTcIrScDBD4A3hgAr73QNoq65CXh4D4MoIk8J7yamC5jyMqAOEsuMpcD3fwT+1hv46kmgvqLV2/RaFSb3jgYAVBjN2HLqwtkuRETt4ddAqqysDDabDdHR0S7Lo6OjUVRUdMH3FBUVubX+yy+/jODgYOctMTHRM40nok5ry6kSVBjNAIDrekcjOFDlukJZJvDudGDtQ0Dl2eblET1EWfPb3wMM7SiVTkQXp1CKEukL9wB9bgHgmNPN2gDsfQdYMgw4skpc5Ghh5pDmudo+PZDvwwYTUWfX6fu3n332WVRXVztveXntnymdiLqG1fubT7ZubXEShprzwIbngWWjgXPbm5enZQB3rQYe2g30mOK7hhJ1RcHxwG3Lgd8cBEY82NxDVV8GfHYf8N+bgLPbnQHVuPQIROo1AIBNP5WgvM7kr5YTUSfj10AqIiICCoUCxcXFLsuLi4sRExNzwffExMS4tb5Go4HBYHC5ERFdTHmdCd+fFOk/UXoNxqVHAOePAOseAf7RH9j5BmBznIiFJAF3fwrcsxboPplzQxH5UlgqcP1fgIf3iXTaJme3Ae/eKIpSHPsMSsmCWwaJohNWu+QyrQER0ZVQ+nPnarUaQ4YMwaZNmzBjxgwAotjEpk2b8PDDD1/wPaNGjcKmTZvw2GOPOZdt3LgRo0aNcm/nXz4J6NSAqQ6oLwcaqgBdOBCSDIQkioHiSg2g1Dbf261AQ4WoFKQMED/ioalikKtSI5YpWvyT2m1iXbsV0EXxJIvoKrD2UCGsdju6yQrxdFwRlO/8ETh/yHUlhQYYsQDIeAZQuzlJLxF5liFWpNOe/Ar49lmgKkcsz98LrJ4HBIZjQfdbcVgehYP2dKzen495Y1L922YiapvGGsBcJ+aXUwU0L7fbxUVNS4OYX64qV6Ta1xYBGoM4N1cHiXWsJsDaKO4tDUBdMVB5DqjOE8dztL+Txa+BFAA88cQTmDNnDoYOHYrhw4fj9ddfh9FoxLx58wAA99xzD+Lj4/Hyyy8DAB599FFMmDABr732Gm644QasXLkS+/btw9tvv+3ejo9+DGhknv44gFwpAiq5XPznw5GrrdAAocmAPkaUcVUHAnKVmKBGkkTAZSwFGqvFOiFJgD5WBGgKlVhXoRaBmrlepDDUlwPaECA0RQR12lCxXVWg+PKoA0V7TLXiS6gMAIIiPf+ZiTqL84eRtO1F7NPsQ4SsBsj52esaAzDsPlEpTB99oS0Qkb/0ugHoPgU4vgb44e9AyXGxvL4cYYffxko1YJYUOFjWHQVbH0T82LvE8ZWIWrM0iKBErRPnlAqVCFgs9eK+6VadD1RkAzX5Yr3ACCAgRJzb2syA3QLYHLeGChHwVOWJ819dJKCLEOe3kgRIdrF9S704L67MEe9pogoU7bA0NmeGeIKp/QXM/V7+HACWLFmCRYsWoaioCAMHDsTixYsxYsQIAEBGRgZSUlKwYsUK5/qrVq3C888/j3PnzqF79+545ZVXMG3atDbty1ni8Bk9DC0DKZkCkGye/FgdU2AEEHWNCLx0kc23wHARvcvkoidNqQHC03mQoc7PXA9kbRKD1bO3XHid2IHAkDlA35mAlunBRB2eJIkUvwP/BX5aJ07ofs6QAIz4tShcEcJCVNTJSRJQUyAu2ssU4nzPXCeeG0sBY5m4rykESn4SvTuS3d+t9i6ZHJDsqDFJCP5LbbvKn3eIQMqXnIFU1j4YggJFpB0YLqLchkrR1VdT6OgCbGzuCrQ2ApCJYCMgVPTyVJwVKQSmWvG6pVFUD7I0inS+gFDH3BYysV7lOcd2LkImF9G8qYOUaFdqgbhB4iQyNBkIThQ9ZSGJoidM5oUePSJvkyRx9ezsNiDzOyBzk/i7baFGCkBN1DAkDJwEdLsWiOnnp8YS0RWrrwBOr4cpazvOH9mEFNkFqvzGDgB6XA+kTQDihwJKte/bSeQJVpPoJarKFalrVXlAyQkgfx9Qd+EK1z6ndExwf6lzYsgAQ7zIugoIEefo9eXi/FoZAKi0juE3WvHYEC+G2xjiRE9ZQ6W4//kwHaVadCqEJgP6OAASaopyEJzQnYFUW/h1Ql5Jcu0WtdvgTP3TBouATq4Q47aq88UX3mb9WdeoWXwZdJFAQJj4UlWeFX8wplpxdd1idNzXi/dogkSA1lgFFJ8AjB6YR0OtF1fm5QqRthjVC0gaDSSNBCK6Axr9le+DyBPKzgA/fSHuq/OA8kyg9vwFV82VovG2dRq+kk/E1uemwaBljyxRZ/LYRwdQeOR7/Fr5JSYpDl54JVUgENnTceEwCUidICpzMkODOgKbRRzLCg4AOTvFWEBTjTintDaKXqUrpdQCkb1E9pLVJM4vbRYxZEStax6iogoUw1DCUsXFdmuj6NlqrBJDS+RKx7AUlbhpDKIWgS5C7MdUK9or2QHIxAV6VUDzEBWFb0YgXUlswECqKzKWiRNJZ1euozu3oQLiiywXfwQF+0UvWnsEhIloPyRZ3OsiRVeyXCH+6BKHi7FgRJ7WWAMUHgDy9gI/rQWKjl56fV0k0HMatipGYN52A+yQ4/ahifjrzP6+aS8R+cyesxWY9dYuAMDNCUb8o18OcPLL1gVlfi4wXFQGTBkLJAwTARazMsjTLA1A4SExvs9mEQGG2SjGClXliPuagvYNRdEYgPghQFgaAEkEXuogUWit5VAPXYQIiuQKT3+6DouBlBsYSLmprhQoOyW6hqtygWrHIMHqfNHjZbc297K5KyQJiOot7oMTgaAocbDSGMR2bSbRfRt1jejWJQJEz27JTyJYMhvFgaehEig7DZSeEml7uMTPmjYYiBsMpI4Xt7hBgFyBm5f8gMP51QCAtQvHYEBiiE8+DhH5jiRJmPz3bcgsqQMAbHx8PLpH68Ux7ew2ccvZIY5zl/odCQwHIq8BInsAwQniWKXUiCvziSPF1XoiQAREZadF4QaFSmTx2Ewio8hYJgKjqlxx7Co6JjKQ3CFTiKEkcqXYflB08zCMpiEZoali3DurR18QAyk3MJDyArtd5N/m7AQKD7peNbnUgcgdwUlARLrIa9VFiNRBVYD4QTLXNefCBicC4d1EL5jGILqgm3JxZTLRW1FbJNIbNQbxQ2NIuHA+vNUsPkN1vvgchniRe9tUfrOpwoxkF93ZVXniszdWi6s6+liR/mi3icCw6WYzA9UFIiWzplC0Qx8tPhskx3ot3mMsE9utyhM/koY4kder1FygzY2ONheIH+OQJEc3eqTjB1zd3NUuk4teyLpi0b0enCh+aEOTRbd6U+5xU9e63SbGGjRUOIqThHvuiqzdLoL0upLmCkGQxIGmvsKR750DlGeJ71l9Wdu3HTcY6DdTpOeEJF2wWMSxgmrc+MYPAIA+cQZ8+chYyHi1mahTWr7jLP7wxQkAwNzRKXjxpj6tV2r6/S86Chz/DDj1bauxlBclV4msi+g+zVOq6KIc5Zh14lhlqhO/yRHdXUs6XylLgzhWqALE777KcfxrWSraahLHwLIz4uTdbhMXMoOiRNt/XmnNWVr6nEgh08eKSZEDQgH87HdSsjUHB/XljpP6ZLG+QuNI91I0p32Z60SGTF2pOK6HpYoxMeqg5nWa1rdbgNpisb5kb8540YaI45lMLo5JMpn4THUl4hjbWOloc4K4kPZzkiSycqrygNpC0Y6gGPHv0VRZuemisdkoziEqssTxqGk8fFOVOmujWKe+XNyq88QFvgsVO2kPbYgjMEoWqXfJo4GE4WIIB7UbAyk3MJDyIavZMdAxR8zTJdnFj07pSSBvj8jvbeuByescP74t7y96VUgGjwWIVwO5UgRUlnrXCj7aEJEioNa1DtKsjSL4qSkUB8HgBHFTO37sJXvz3A/15eJAYzFeeVuVAWJsQ8JQMWA8eZQ4KF/Gs58dxUd7cgEAL/2yL+4akXzlbSGiDqm63oLhf/4OJqsdBq0Se56bBK3qMmlMpjpxEadgnxi0X3xMXIS6UjK56C0ISXRMW6ITv6OACGJqz4vf0voKMX2JIV5cTLRbmwMdm9kxbiZfHHNbHp9UuuYMjy7jEsdoedM4t6YLodLF1/WHiJ5A4jCRgqcxiO+HUtPcs8TsHK9gIOUGBlIdiN0uropV5YpbUze3qVacmCs1oqep6Jg4aJnr/N1i6gg0BiB5DJAyRlw1VGrEyUd4uiOv273UhdpGC0b+eROMZht0agV2PzcJQRq/T7FHRF705CeH8emBfADAopn9cdvQdpQ/b6gESk+LHnJrowi2Cg+I9MCKbA+3mK5KMjkQ0QOI7it62+xW0SMoV4qAODBCTCgdkiyC5KYeRPKpK4kNeLZA/iOXi4IT+hiRBnEpkiQKYBjLxUHLXNecoqAOEl3rSrUIyMozRWqb2Sh6Oaym5qtOap3o4tdFihS8qlzRa2K3iteb1pMkcYJuiBc9KTKZWK+m0NFFL2uRSiAXvS6GeNHdHhAq0gRqi5qDQrlSXAlrSlEIihY/qoZ4sU5dkbji2DLloemxxuAo3JEi2llTILZ9oR4zuUr8KBvixXubyu431rimatgtIvUhIFT8W6iDgKpzIlWhZfl/S4tpAJRakeoQECbaW5YpJuC7GJlc/FtLdtHei171k4leo6je4t/a2iBOSACRChMYLv69nMVLUj1ayWfVvnwYzWLg7s2D4hlEEXUBd41McgZSK3aew8whCe6n8waEAkkjXJcNmSPua4tF+nZljuglairdbDaK31tNkPjtL/lJ9MhfKjtDEwwEhopUtUuNR9YYxAWlsDRxnDKWiv3KVa6lopUa0bMR1k2sr9SIbRtLxO+1XOWaZaDUit/m0BSRGtfUS3axi5uB4SIFPTDc8e9wThwzmtLWbRbHY4voMdNHi9THxurmKsRWU/NxqinNXSYXxwJ9jDhGN12ENRvR3MNkb86c0EWJlEJtsDgGNf0/NGWeyOTNjwPCRI+PIc6RblgkLuwCjnUVjrRznTguhaeLfz+VVmyzoVKsowoQt4AwR6AUzmqPnVyXPWOoNJqhC5KgkHMcxFVBJnPkIYcCSL/4enGDfNYkv9FHt33dwDDv/pvYHAfDptSSpjSTpmCxKeCxmkXuubVFnnhT6VR1kN/mbLHZJazYec75fN7oFL+0g4h8a1BiCAYkBONwfjWOF9Zgz9kKjEgL99wO9NHiljTy8uva7Y5pS4wtpkaBOO4FRTWP65EkccLeWN0c6ChUroGPL8Z2ag0ihbpN6waLghxEnVSXDaTGvbIZck0g9BolDAEqBP/8FqiCQatEcIDqgq8bAlRQKVj9hLo4hVLcLjdYWqlu01glX9v0UzFyK8QV3nHdI0T1LiLq9GQyGe4dm4pHVx4CAPxnx1nPBlLukMtFgYPLzb8okzl66cN80y6iq4zNLqGu0YqaRgtqG62obbSgzmRFrWNZVb0F1Q3N9zUNFlQ1mFFRWdXufXbZQKpJrcmKWpMVBVXuFz3QqRUIDlAhSKtEkEYJnUbcuzzWNj1WIEijgk6jcK4TqFYiQKWARiWHRilnlTAiH1u+45zz8b1jUv3XECLyuev7xuLPhp9QXGPCxhPFyKuoR2IYy5YT+YLVZkej1Y5Gi63Fze68b2ixvN5sQ60jOKpxBEi1LvficVOavrvspvZXVeyygdSY9HA0QIOaBhGVVjdYYLW7V3fDaLaJ/7TqK2+PTAYEqBTQqhSOe3mLx47lagW0SjkC1ApHAOZYTyle1yjljnXl0Cibt9H0XON4rlHKoVYwcKOu7URhDXZllwMA0iJ0mNAj0s8tIiJfUivlmD0yGa9uOA27BLy78xyev7G3v5tF5BeSJMFktaPBbEOjtTmoaQpoTC7Pmx+bLDZnQCTee+HgqOl9Yn0bLLaOUetOq5IjSK1GXjvf32UDqbdmD3WpzCFJEurNNmdQ1TLAuuDzRqvzcV2jFQ2W9kXBzfsH6s0i6vYFmQyOAMw16NIo5Y4ATQRtGsd9U0AWoFZC7+hpa+pxa/lc7+h1UzLtkTq45TvOOh/PG5MCOcdLEnU5dwxPwhvfZ8JktePjvXl4bHIPFpyhDk2SJDRa7C3OR0X6WoPjHLLebHWeTzY4HjeYbTC2eFxvFoFNg7lFoGS1X37nHVCgWmR66bVK6LUq6LVKGBz3LZfptSqEBKgQEug6TEerUoiqfX9s3/75a+Egk8mgc6TkxYW4Pzme1WYXPVQmK+ocN6PJirrGFo9NVtSZbKgzWWA0iS97U/elyRGtOyN9x5fb3V6ytpIkOPcHuDmLdhsEqBTQa5UIDVQjJFCFMJ0aoTo1QgNVCA1UIzRQjTCdGpF6DaIMGoTrNCz8QT5TVN2ItYcKAQB6rRK3DE7wc4uIyB/CgzSYMTAeH+/LQ63JipV7cnHfuDR/N4u6CEmSYDTbUFprQlmdCaW14lZeZ0LVBS/oW1HTYIHZdvUEPQq5zJlN1fLCfXPGlbhoH9AiyypA7ZppFahWtAiImgOlII3S7xfuGUh5iFIhR3CAHMEBni1zabHZL9A12vIqgh0mq6PL1dqi+7VFt6zJ2ryNVutaXbfvqSsSTUFaSW3bJgFUyGWI0msQZdAiWq9BtEGLmGAtog1aJIUFIiksEFF6DXsNyCPe2Z7tPBDdPTIZOl6BJuqy7huXio/3icSet7dlY/aoZGiUl5mgl6gNGsw2FFTVI6+iAfmV9civbEB+ZQOKahqdQdOVZjS1h1rRPExEBDjyVkNLtC0CHZGlpECAuvmxRiV3HX7SYt2Wr3X2wmw8e+jgVAo5VAo59D6ao60pR9bkCLoaWwRdjRY76s2OnjVHT1ttY4vn5ubldY7Ux8p6c5uCM5tdwvnqRpyvbrzoOmqlHImhAUgKC0RKhA7pUUHoHqVHelQQwnT+KZ9NV59Koxkf7skFAGiUchaZIOriukfrMaVPNNYfL0ZJrQmf7i/AnSOS/N0suko0Wmw4W2ZEZkkdMkvqkF1mRF5FPfIr61FW1/4iBj/XVGVaVJJWNqenaVWOjCoFAtRKBDp6cAI1SgQ6gqVAtQKBaiUCNQoEqjj8wpMYSJELmUzmvLoAeKZ3rcFsQ0W9GZVGMyrrzaist6DSaEaF0YySWhNKahpRVNOI4hoTyo0mMSfuBZitdmSVGpFVagROlbq8FqZTIz0qCL1jDegTZ0CfuGB0jw7q9FdCyH0rdp5zjkW8fVgiIvUaP7eIiPztoYx0rD9eDAB4a1sWZg1N4MkmuZAkccH3eGENjheK+cdOF9cir6Ie7RmFERKoQmSQBpF6xy1Ig4gW9yEtptzRa/2fwkYXxkCKvC5ArUC8OgDxbRh7ZrHZUVprQrEjsCqoakBeRT3yKuqR67hdqIerwmjGnrMV2HO2wrlMrZCjZ4wefeODMSQ5FEOTQ5EcHshqhV1YncnqnIBXKZfh1+M5FoKIgAGJIRibHoEfMsuQU16Pr46ex80D4/3dLPKj6gYLDuRWYv+5ShzOr8LxwhpUGNvWwySTAdF6LRJCA5AQGoDEsEDH40AkhgYiOljD9NFOgoEUdSgqhRxxIQEXLfghSRJKak3IKqlDZmmdsys9s6Su1Xgss82OowXVOFpQjY8cqVwRQWoMTgrF0JRQjEgNR9/4YBa56EI+3J2D6gZRXOXmgfFICOWcMUQkPDSxG37ILAMALNuShen94zgutws5X92AXVnl2HuuEvtzKnCmpO6iGTJNAtUKdIsMQnqUuDU9TgwLYKDURTCQoquKTCZDtEEUoRidHuHyWlW92dnlfqxA3GeXGV1+CMvqzNhwohgbTogUjuAAFUZ3C8eY9AiM6x6BpDD2WHVWRpMVb23NBiCuFj6Ywd4oImo2Ki0cAxNDcCivCieLavHt8SJM6xfr72aRl9Q0WrA7uwI/nCnFD5llYtjAJYTr1OgTH4w+cQb0jRP3SWGBDLa7OAZS1GmEBKoxJj0CY1oEWEaTFYfzq3AgpxL7cipxIKcSNY1W5+vVDRZ8c6wI3xwrAgAkhAbgF72iMLl3DIanhkGtZE5yZ7F8x1mUO9IybugXi/QovZ9bREQdiUwmw6OTumPe8r0AgNc2nMKUPjHMWuhEskrrsPFEMb47UYyDeVWwXWRwk0IuQ+9YA4YkhzpvscFaXmilVmSSdLmOy86lpqYGwcHBqK6udpmQl7oGu11CZmkd9pytwM6sMuzILHemev2cXqtERs8oTO4djYyekTBoPVvannynut6Csa98j9pGK+QyYOMTE9AtMsjfzSKiDkaSJMx6axf2nqsEALx62wDMHMJ55q5WNruEg7mV2HiiGBt/Kkb2RXqdFHIZBiaGYEx6BEamhWFAQginxehCriQ2YCBFXZrNLuF4YTW2nynDD2fKsC+nAhZb6z8JtUKOjJ6RuGlgHH7RKxoBauY+X01e+fYk/rklCwAwa2gCXpk5wM8tIqKOand2OW5/+0cAIkvh+yczmJ1wFZEkCUcLqrH2UCG+OFx40fks0yJ1GN890hk86XmxtMtiIOUGBlJ0KbWNFmw9XYqNJ4qx+WSJSxpgE51agev6xOCmgXEYmx7BEusdXGmtCeNf2YwGiw0qhQybf5vBIhNEdEn3/GcPtp0W02z88eY+mD0qxb8NosvKLKnDusOFWHeoAOfK61u9LpcBQ5PDMLl3NCb1jkZqhM4PraSOiIGUGxhIUVtZbHbsPVuBDSeK8dXR8yi9wFWtSL0Gtw5OwO3DEvmj3EH9/vNjeO/HHADA3NEpePGmPn5uERF1dEfzqzF9yQ8AgIggDbY8lYEgpnp1OEaTFV8dOY+Ve3NxILeq1esqhQwTekRiSp8YXNsrCuFBnDeQWmMg5QYGUtQeNruEH7PLse5QIb4+dh61F+ipGpEahl8NT8T1fWMdExqTv50sqsG0f2yHXRJlarc8lYEovdbfzSKiq8BDH+zH10dFIaIHM7rh6am9/NwiAkTq3uH8aqzck4svDhfC6JhgvYlMJiow3jQgDlP7xiAkUO2nltLVgoGUGxhI0ZUyWW3YeqoUaw4WYOOJYlh/VvXHoFVi1tBEzBmdgsQwppD5iyRJuPvfu7EjsxwA8NSUnlg4Md3PrSKiq0VueT0m/W0rzDY71Ao5Nj4xHsnhzDzwl0aLDWsPFWD5jnM4WVTb6vVeMXrMHJKA6QPiEG3gBTNqOwZSbmAgRZ5UVmfCZwfysXJvXqtqQHIZMOmaaMwbk4qRaWEsm+pjG44X4dfv7QcgBox/98QE9hQSkVtaFqqZ0icab80e6ucWdT3nqxvw3q4cfLQnF5X1rlV2dWoFbhoYj18NS0T/hGAeZ6ldGEi5gYEUeYMkSdiXU4mP9uTiyyPnYbbaXV7vFaPHvWNScfOgOM527gMmqw3X/X0bchwDjpfdNRjXc2JNInJTncmKia9ucY6R/fC+Ea0mgyfvOJRXhX9tz8a3x4pazfc0MDEEd45Iwg39YlmmnK4YAyk3MJAibyuvM+GjPbl478ccFNe4FqiIMWhx37hU3DE8iT/+XvSP787g79+dBgCMTAvDR/eP5JVKImqX1fvz8dtVhwEA3SJ1+PrRcbwg5iWSJGH7mTIs25KFXdnlLq+pFDLc0C8Wc8ekYmBiiH8aSJ0SAyk3MJAiX7HY7PjmWBFW7DjbqppQcIAKc0anYO7oFITpOBDWk84U12La4u2w2CQo5DJ8+chYXBPLv3Uiah+7XcIty3biUF4VAOA3v+iOJyb38G+jOhmbXcK3x4qwbGsmjhXUuLwWEaTGnSOScfeIJERx7BN5AQMpNzCQIn84mFuJZVuysOFEscvyAJUCd41IwgMZ3RDBsqxXzG6XcNtbu7A/pxIAK20RkWecLKrBjYt/gNUuQaWQ4ctHxqFnjN7fzbrqWW12rD1UiCWbM3G2zHWccWqEDg9MSMOMQfHsASSvYiDlBgZS5E+ZJbVYtiUbaw8VuFT7C1ApMGd0ChaMT0Moe6ja7b+7zuF/1x4HIA7C3zw6jgUmiMgjXttwCm98nwlAjNH59MHRUMiZMtweNruEdYcLsHhT6wCqb7wBD2WkY0qfGP77kk8wkHIDAynqCAqqGvCvbdn4aE8uTC0KU+jUCtw7NhX3jU1DcKDKjy28+uRV1GPq69ucc4qs/PVIjEwL93OriKizaLTYMG3xdmeF1uemXYP7x6f5uVVXF5tdwpdHCvGPTWdaVbodmRaGhRPTMTY9gmNayacYSLmBgRR1JCU1jfjnlix8uDsXZltzQKXXKnHf2DTcOzYFei0Dqsux2OyY9dYuHHSMRbtjeCJevqW/fxtFRJ3O3nMVuO3NXQBE8YM1D41B3/hgP7eq45MkCRtOFOPV9adwpqTO5bWRaWF4fFIPjOCFL/ITBlJuYCBFHdH56gYs+T4Tn+zLg8XW/CcZEqjC/ePSMG9MCgLVrPJ3Ma+uP4Ulm0XKTVJYIL76zVgGoETkFX/++ie8vS0bAJAWocMXj4xlFdZL2J1djr9+e7JV0aXhqSKAGtWNART5FwMpNzCQoo4sr6IeS77PxOoD+S7zZkQEafCbX6TjV8OSoFbK/djCjmdnVhnuemc3JAlQymVY/eBolsYlIq8xW+24ddlOHC2oBgDMGpqAV2YO8HOrOp4ThTV4Zf1JbDlV6rJ8SHIonpwsAiim8FFHwEDKDQyk6GpwrsyIxd+fwecHC9ByHsLEsAA8ObknbhoQBzkH4aK4phE3LfnBOV/X/0ztiYcy0v3cKiLq7M6WGXHD4u2od4zJfO22Abh1SIKfW9Ux5FXU47UNp7D2cCFanmH2iA7CU1N6YdI1UQygqENhIOUGBlJ0NcksqcVrG07jm2NFLst7xejx1JSeuLZX1z0gNVpsuP3tH3HYMbfL6G7heH/+CAaYROQTLSfqVSvkWLlgJAYnhfq5Vf5TVmfCku8z8cHuHJcU9bhgLR6f3AO3DE5gFT7qkBhIuYGBFF2NDudVYdH6U/ghs8xl+ZDkUPzPlJ5dbpCuJEl44pPDWHOwAAAQHxKAtQ+P4VxcRORTz39+FO//mAsAiNRrsO7hMYgNDvBzq3yrttGCf20/i3e2Zzt76AAgNFCFhRPTcffIZE5DQR0aAyk3MJCiq9mOzDK88u1JHM6vdlme0TMST03piT5xXaN61NLNmVi0/hQAMQfX6gdHdZnPTkQdh8Vmx+x/78aP2RUAgH7xwfjo1yMR1AWKT5isNnzwYy6Wbs5EudHsXB6gUuC+cam4f3waDCz6Q1cBBlJuYCBFVztJkrD+eDFe3XAKmT8rIzt9QByenNwDKRE6P7XO+z7YnYPn1hxzPl9212Bc3y/Wjy0ioq6swmjGzUt/QF5FAwBgVFo4ls8b1ml7YWx2CZ8dyMfr351BQVWDc7lSLsOdI5Lw8LXpiNJr/dhCIvcwkHIDAynqLC51MJs1LBGP/qI7og2d62C25mA+nvjksHMA81NTemLhRBaXICL/Ol1ci9ve3IXqBgsA4Be9ovDm7CFQKTpPldWufhGPOi8GUm5gIEWdTVN6xZLNmahokV6hUcoxd0wKHpzQDSGBaj+20DO+PFKIR1cecpaFXzAhDc9M7dVli20QUcdyMLcSd7+zG0bHOKFp/WLw99sHQqO8+numdmaW4a/rTzmL+zSZ2DMSv+1CaeXUOTGQcgMDKeqs6kxW/Hv7WfxrezbqTFbncr1WiQcmdLuqJ/V9d+c5vPjFcWdP1OyRyfi/m/swiCKiDmVXVjnmLt8Dk9UOABiTHo437x5y1U4QfiivCq9tOIXtZ1joiDovBlJuYCBFnV2F0Yx/bs7Ef3/MgdlxMAeuzkl9JUnCqxtOYenmLOey24cm4uVb+rHMORF1SFtOleCB9/ej0SJ+f/vEGbB83rCratzQ3nMVWLzpTKsAqme0mHrjF5wLijoRBlJuYCBFXUVhVQP+8d0ZrNqf5zKpb1ywFvPHpeFXwxKh68CVparrLfjt6sPYeKLYuWzhxG747XU9eQAnog5tf04l5r+7F1X1YsxUjEGLN+4chGEpYX5u2cVJkoSdWeVYvOkMdp+tcHktITQAT17XAzcNiOdcUNTpMJByAwMp6moyS+rwt42n8PVR10l9DVolZo9KxpzRKR3uSumR/Co89MEB5FeKIhoyGfDCjb0xd0yqn1tGRNQ2mSW1uOffe1BY3QgAUMhl+J8pPXH/uLQO1aNusdmx8UQx3tmejQO5VS6vJYYF4KGMdNw6OOGqyWQgchcDKTcwkKKu6kh+FV7/7gy+P1nislytkOOWwfGYOyYFvWL8+zfRYLZh8fdn8K9t2bA6utFCAlX426wBuLZXtF/bRkTkrpLaRvzmo4POeaYAYHhqGF6a0Rfdo/V+bBlQVmfCyj25eP/HXBTVNLq8lhahw0MT03HzwLhOVXmQ6EIYSLmBgRR1daeLa/H2tmysPVQAi831z39ocijuHJGE6/vGIkDtu0pTTWV1//TVCWcvFAAMTgrBG3cORnxIgM/aQkTkSVabHf/YdAZLNmc6C+Yo5TLcPz4ND0zohuAA3xWikCQJh/Kq8N6POfjy8HmYbXaX13tG67Hw2nTc0C+WKXzUZTCQcgMDKSKhqLoRy3ecxYe7c1HbosofAOjUCkztG4sZg+IwMi3ca1ck7XYJ3x4vwhvfZ+Kn8zXO5WqFHA9mdMPD16bzaigRdQo7MsvwuzVHkVNe71ym1ygxd0wK7h2TilCd96apyC6tw5dHzuPzgwXILjO6vCaTAZOuicacUSkYkx7OMajU5TCQcgMDKSJXNY0WrDlQgA925+B0cV2r1w1aJSb2isK1vaIwKi0cUR6Y5DertA6fHcjHmgMFzvEDTcakh+OPN/dFWmTQFe+HiKgjabTY8M/NmVi2NcslI0CjlGNy72jcOjgB47pHQHmFF5AaLTYczK3CtjOl2HiiuNUEugAQHKDCr4Yl4u6RyUgMC7yi/RFdzRhIuYGBFNGFSZKEfTmVWL0vH18fPd+ql6pJt0gdBiWFonesAdfEGpAUHohoveaiB/6aRgvOlRlxprgOe85WYFd2OXIr6lutNyAhGI9c251ldYmo08str8c/t2Ri9f5853jQJkEaJYanhmFUWjh6xxmQGqFDjEF7wQIVkiShqt6CgqoGnC6uxYnCGhwtqMbBvCqX6S+ayGTAyNRw/HJwPKb3j/NpCjdRR8VAyg0MpIgur9Fiw6afSrD+eBE2nyy5aFDVRC4DwoM0CFApoFWJgKqu0YpakxW1jRd/r0Iuw7juEZg3JhXju0cwgCKiLqWgqgHvbM/G2kOFqDCaL7qeWilHcIAKeo0SaqUcJqsdJosNFfVm53xVFyOTAUOSQjG5dzSmD4hDHMecErlgIOUGBlJE7jFb7dh7rgI7s8qwK6scR/KrW11BdYdaIcegpBBM7h2NmwbGdbjS60REvma22rH1dCm+OFyInVllKKu7eFDVFolhARiVFo6RaeEY3yMSEUEaD7WUqPNhIOUGBlJEV6bRYnOmkJwqrkVhVQPOVzeitNaERosNJqsdkgQEaZXQa5SICNIgLVKHtEgd+sQFY0hyKLQqppMQEV2IJEnILKnDvpxKZJfWIbvUiPzKBtSZrKhptMBstUOjlEOrUsAQoEJssBbxIQFIDtehT5xIuY7UM3AiaqsriQ2UXmoTEXVSWpUC/RNC0D8hxN9NISLqdGQyGbpH6/0+zxQRXR7rChMREREREbmJgRQREREREZGbGEgRERERERG5iYEUERERERGRmxhIERERERERuYmBFBERERERkZsYSBEREREREbmJgRQREREREZGbGEgRERERERG5iYEUERERERGRmxhIERERERERuYmBFBERERERkZsYSBEREREREbmJgRQREREREZGbGEgRERERERG5iYEUERERERGRmxhIERERERERuYmBFBERERERkZsYSBEREREREbmJgRQREREREZGbGEgRERERERG5iYEUERERERGRmxhIERERERERuYmBFBERERERkZsYSBEREREREblJ6e8G+JokSQCAmpoaP7eEiIiIiIj8qSkmaIoR3NHlAqny8nIAQGJiop9bQkREREREHUF5eTmCg4Pdek+XC6TCwsIAALm5uW7/Y1HnUFNTg8TEROTl5cFgMPi7OeQn/B4QvwPE7wDxO0DV1dVISkpyxgju6HKBlFwuhoUFBwfzD6aLMxgM/A4QvwfE7wDxO0D8DpAzRnDrPV5oBxERERERUafGQIqIiIiIiMhNXS6Q0mg0eOGFF6DRaPzdFPITfgcI4PeA+B0gfgeI3wG6su+ATGpPrT8iIiIiIqIurMv1SBEREREREV0pBlJERERERERuYiBFRERERETkJgZSREREREREburygdRNN92EpKQkaLVaxMbGYvbs2SgsLPR3s8hHzp07h/nz5yM1NRUBAQHo1q0bXnjhBZjNZn83jXzopZdewujRoxEYGIiQkBB/N4d8YOnSpUhJSYFWq8WIESOwZ88efzeJfGjbtm2YPn064uLiIJPJ8Pnnn/u7SeRjL7/8MoYNGwa9Xo+oqCjMmDEDp06d8nezyIeWLVuG/v37OydjHjVqFL755hu3ttHlA6mJEyfik08+walTp/Dpp58iKysLM2fO9HezyEdOnjwJu92Ot956C8ePH8ff//53vPnmm/jd737n76aRD5nNZtx222148MEH/d0U8oGPP/4YTzzxBF544QUcOHAAAwYMwJQpU1BSUuLvppGPGI1GDBgwAEuXLvV3U8hPtm7dioULF+LHH3/Exo0bYbFYcN1118FoNPq7aeQjCQkJ+Mtf/oL9+/dj3759uPbaa3HzzTfj+PHjbd4Gy5//zLp16zBjxgyYTCaoVCp/N4f8YNGiRVi2bBmys7P93RTysRUrVuCxxx5DVVWVv5tCXjRixAgMGzYMS5YsAQDY7XYkJibikUcewTPPPOPn1pGvyWQyrFmzBjNmzPB3U8iPSktLERUVha1bt2L8+PH+bg75SVhYGBYtWoT58+e3af0u3yPVUkVFBT744AOMHj2aQVQXVl1djbCwMH83g4i8wGw2Y//+/Zg0aZJzmVwux6RJk7Br1y4/toyI/Km6uhoAePzvomw2G1auXAmj0YhRo0a1+X0MpAA8/fTT0Ol0CA8PR25uLtauXevvJpGfZGZm4o033sCCBQv83RQi8oKysjLYbDZER0e7LI+OjkZRUZGfWkVE/mS32/HYY49hzJgx6Nu3r7+bQz509OhRBAUFQaPR4IEHHsCaNWvQu3fvNr+/UwZSzzzzDGQy2SVvJ0+edK7/1FNP4eDBg9iwYQMUCgXuueceMOPx6ubudwAACgoKMHXqVNx22224//77/dRy8pT2fAeIiKjrWbhwIY4dO4aVK1f6uynkYz179sShQ4ewe/duPPjgg5gzZw5OnDjR5vd3yjFSpaWlKC8vv+Q6aWlpUKvVrZbn5+cjMTERO3fudKtrjzoWd78DhYWFyMjIwMiRI7FixQrI5Z3yGkOX0p7fAY6R6vzMZjMCAwOxevVqlzExc+bMQVVVFTMSuiCOkeraHn74Yaxduxbbtm1Damqqv5tDfjZp0iR069YNb731VpvWV3q5PX4RGRmJyMjIdr3XbrcDAEwmkyebRD7mznegoKAAEydOxJAhQ7B8+XIGUZ3ElfwOUOelVqsxZMgQbNq0yXnibLfbsWnTJjz88MP+bRwR+YwkSXjkkUewZs0abNmyhUEUARDHA3digE4ZSLXV7t27sXfvXowdOxahoaHIysrC73//e3Tr1o29UV1EQUEBMjIykJycjFdffRWlpaXO12JiYvzYMvKl3NxcVFRUIDc3FzabDYcOHQIApKenIygoyL+NI4974oknMGfOHAwdOhTDhw/H66+/DqPRiHnz5vm7aeQjdXV1yMzMdD4/e/YsDh06hLCwMCQlJfmxZeQrCxcuxIcffoi1a9dCr9c7x0gGBwcjICDAz60jX3j22Wdx/fXXIykpCbW1tfjwww+xZcsWrF+/vs3b6JSpfW119OhRPProozh8+DCMRiNiY2MxdepUPP/884iPj/d388gHVqxYcdGTpy78p9HlzJ07F++++26r5Zs3b0ZGRobvG0Ret2TJEixatAhFRUUYOHAgFi9ejBEjRvi7WeQjW7ZswcSJE1stnzNnDlasWOH7BpHPyWSyCy5fvnw55s6d69vGkF/Mnz8fmzZtwvnz5xEcHIz+/fvj6aefxuTJk9u8jS4dSBEREREREbUHB4MQERERERG5iYEUERERERGRmxhIERERERERuYmBFBERERERkZsYSBEREREREbmJgRQREREREZGbGEgRERERERG5iYEUERGRGzIyMiCTySCTyXDo0CGPbvvcuXPObQ8cONCj2yYiIs9iIEVERF4zd+5cZ2DQ8jZ16lR/N+2K3H///Th//jz69u3bpvWnT59+0c+8fft2yGQyHDlyBImJiTh//jyefPJJTzaXiIi8QOnvBhARUec2depULF++3GWZRqPx6j7NZjPUarXXth8YGIiYmJg2rz9//nzceuutyM/PR0JCgstry5cvx9ChQ9G/f38AQExMDIKCgjzaXiIi8jz2SBERkVdpNBrExMS43EJDQ52vy2QyvPPOO/jlL3+JwMBAdO/eHevWrXPZxrFjx3D99dcjKCgI0dHRmD17NsrKypyvZ2Rk4OGHH8Zjjz2GiIgITJkyBQCwbt06dO/eHVqtFhMnTsS7774LmUyGqqoqGI1GGAwGrF692mVfn3/+OXQ6HWpra936nJdq44033ojIyEisWLHC5T11dXVYtWoV5s+f79a+iIjI/xhIERGR3/3hD3/ArFmzcOTIEUybNg133XUXKioqAABVVVW49tprMWjQIOzbtw/ffvstiouLMWvWLJdtvPvuu1Cr1dixYwfefPNNnD17FjNnzsSMGTNw+PBhLFiwAM8995xzfZ1Oh1/96letesuWL1+OmTNnQq/Xt7n9l2ujUqnEPffcgxUrVkCSJOf7Vq1aBZvNhjvuuMPtfzMiIvIziYiIyEvmzJkjKRQKSafTudxeeukl5zoApOeff975vK6uTgIgffPNN5IkSdIf//hH6brrrnPZbl5engRAOnXqlCRJkjRhwgRp0KBBLus8/fTTUt++fV2WPffccxIAqbKyUpIkSdq9e7ekUCikwsJCSZIkqbi4WFIqldKWLVsu+pkmTJggPfrooy7L2tLGn376SQIgbd682bnOuHHjpLvvvrvVPl544QVpwIABF20DERH5H8dIERGRV02cOBHLli1zWRYWFubyvGl8ECB6igwGA0pKSgAAhw8fxubNmy84bigrKws9evQAAAwZMsTltVOnTmHYsGEuy4YPH97qeZ8+ffDuu+/imWeewfvvv4/k5GSMHz/erc/Yljb26tULo0ePxn/+8x9kZGQgMzMT27dvx//93/+5tS8iIuoYGEgREZFX6XQ6pKenX3IdlUrl8lwmk8FutwMQ44imT5+Ov/71r63eFxsb67Kf9rjvvvuwdOlSPPPMM1i+fDnmzZsHmUzm1jba2sb58+fjkUcewdKlS7F8+XJ069YNEyZMaFe7iYjIvzhGioiIOrTBgwfj+PHjSElJQXp6usvtUsFTz549sW/fPpdle/fubbXe3XffjZycHCxevBgnTpzAnDlzvNbGWbNmQS6X48MPP8R///tf3HvvvW4HbURE1DEwkCIiIq8ymUwoKipyubWsuHc5CxcuREVFBe644w7s3bsXWVlZWL9+PebNmwebzXbR9y1YsAAnT57E008/jdOnT+OTTz5xVs1rGbyEhobilltuwVNPPYXrrruuVXlyT7YxKCgIt99+O5599lmcP38ec+fOdXtfRETUMTCQIiIir/r2228RGxvrchs7dmyb3x8XF4cdO3bAZrPhuuuuQ79+/fDYY48hJCQEcvnFD2OpqalYvXo1PvvsM/Tv3x/Lli1zVu37+TxW8+fPh9lsxr333tuuz+hOG+fPn4/KykpMmTIFcXFx7dofERH5n0ySWtRhJSIi6sReeuklvPnmm8jLy3NZ/t577+Hxxx9HYWHhZSfyzcjIwMCBA/H66697rZ0vvvgiPv/8cxw6dMhr+yAioivDHikiIuq0/vnPf2Lv3r3Izs7Ge++9h0WLFrmMgaqvr0dWVhb+8pe/YMGCBZcNolpuNygoCEePHvVoe3NzcxEUFIQ///nPHt0uERF5HnukiIio03r88cfx8ccfo6KiAklJSZg9ezaeffZZKJWiaO2LL76Il156CePHj8fatWsvWL785woKCtDQ0AAASEpKanPw1RZWqxXnzp0DINIPExMTPbZtIiLyLAZSREREREREbmJqHxERERERkZsYSBEREREREbmJgRQREREREZGbGEgRERERERG5iYEUERERERGRmxhIERERERERuYmBFBERERERkZsYSBEREREREbmJgRQREREREZGb/h8devSEJu0Q6gAAAABJRU5ErkJggg==",
"text/plain": [
"<Figure size 1000x800 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig = plt.figure(figsize=(10, 8))\n",
"R = dict()\n",
"res = dict()\n",
"for instr, title in {\"expected\": \"Virtual spectrometer\",\n",
" \"spec\":\"Grating spectometer\",\n",
" #\"pes\": \"PES\",\n",
" }.items():\n",
" e = pred[\"energy\"] - np.mean(pred[\"energy\"])\n",
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" R[instr] = autocorrelation(pred[\"energy\"], pred[instr])\n",
" res[instr] = fwhm(e, R[instr])\n",
" plt.plot(e, R[instr], lw=2, label=f\"{title} (FWHM = {res[instr]:.2f} eV)\")\n",
"\n",
"plt.legend(frameon=False)\n",
"plt.xlabel(\"Energy [eV]\")\n",
"plt.ylabel(\"Autocorrelation\")\n",
"plt.xlim((-3, 3))\n",
"plt.ylim((None, 1.05))"
]
},
{
"cell_type": "markdown",
"id": "ffd654f9-8175-45da-9ae1-09c184a7e520",
"metadata": {},
"source": [
"## Resolution assessment using deconvolution\n",
"\n",
"Here we attempt to establish the resolution of the virtual spectrometer using a deconvolution-based method. The idea here is that the virtual spectrometer can be seen as a *linear* device that somehow *worsens* the resolution of the grating spectrometer. Within the context of linear systems theory any such device can be modelled mathematically as a block that applies a convolution between a function $g$ and the grating spectrometer data.\n",
"\n",
"That is, if the grating spectrometer data is $y$ and the virtual spectrometer result is $\\hat{y}$, then we assume that there is a function $g$ such that:\n",
"\n",
"$\\hat{y} = y \\ast g + \\epsilon$,\n",
"\n",
"where $\\epsilon$ is zero-mean Gaussian noise.\n",
"\n",
"Under such an approach, one can calculate the function $g$ exactly, by performing a deconvolution between $\\hat{y}$ and $y$."
]
},
{
"cell_type": "code",
"id": "7ed071e5-4f60-4195-830a-73ab8e5c2577",
"metadata": {},
"outputs": [],
"source": [
"def deconv(y: np.ndarray, yhat: np.ndarray) -> np.ndarray:\n",
" \"\"\"Given the grating spectrometer data and the virtual spectrometer data,\n",
" calculate the deconvolution between them.\n",
" \"\"\"\n",
" # subtract the mean spectra to remove the FEL bandwidth\n",
" yhat_s = yhat - np.mean(yhat, keepdims=True, axis=(0, 1))\n",
" y_s = y - np.mean(y, keepdims=True, axis=(0, 1))\n",
" # Fourier transforms\n",
" Yhat = np.fft.fft(yhat_s)\n",
" Y = np.fft.fft(y_s)\n",
" # spectral power of the assumed \"true\" signal (the grating spectrometer data)\n",
" Syy = np.mean(np.absolute(Y)**2, axis=(0, 1))\n",
" Syh = np.mean(Y*np.conj(Yhat), axis=(0, 1))\n",
" # approximate transfer function as the ratio of power spectrum densities\n",
" H = Syh/Syy\n",
" return np.fft.fftshift(np.fft.ifft(H))"
]
},
{
"cell_type": "code",
"id": "a1c5137f-fe6b-4930-aff5-90026ad5f3c3",
"metadata": {},
"outputs": [],
"source": [
"# centered energy axis\n",
"e = pred[\"energy\"] - np.mean(pred[\"energy\"])\n",
"# impulse response\n",
"g = deconv(pred[\"spec\"], pred[\"expected\"])"
]
},
{
"cell_type": "code",
"id": "75d3ddc3-a4b6-4869-bc1c-f08320089845",
"metadata": {},
"outputs": [],
"source": [
"def fit_gaussian(x: np.ndarray, y: np.ndarray):\n",
" \"\"\"Fit Gaussian.\"\"\"\n",
" def gaussian(x, amp, cen, wid):\n",
" return amp * np.exp(-0.5 * (x-cen)**2 / (wid**2))\n",
" gmodel = lmfit.Model(gaussian)\n",
" result = gmodel.fit(y, x=x, cen=0.0, amp=1.0, wid=1.0)\n",
" return result.best_values[\"wid\"]*2.355, result"
]
},
{
"cell_type": "code",
"id": "f9bbd13c-d972-4af1-a6c1-0fe12509317a",
"metadata": {},
"outputs": [],
"source": [
"width, result = fit_gaussian(e, np.absolute(g))"
]
},
{
"cell_type": "code",
"id": "26641ed6-47cd-418d-ab3c-e63c83962387",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.legend.Legend at 0x2b5378c4e700>"
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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",
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"text/plain": [
"<Figure size 1000x800 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(10, 8))\n",
"plt.plot(e, np.absolute(g), lw=2, label=\"Virtual spectrometer\")\n",
"plt.plot(e, result.best_fit, lw=2, label=f\"Gaussian fit, FWHM = {width:.2f} eV\")\n",
"plt.xlim(-3, 3)\n",
"plt.xlabel(\"Energy [eV]\")\n",
"plt.ylabel(\"Impulse response [a.u.]\")\n",
"plt.legend(frameon=False)"
]
},
{
"cell_type": "markdown",
"id": "00a1bdb9-b52a-4f8b-8c03-30407f486595",
"metadata": {},
"source": [
"Note that this response function does *not* tell us the resolution of the virtual spectrometer. It tells us how we can smear the grating spectrometer data to transform that data into the virtual spectrometer. That is, this is how much worse we do with the virtual spectrometer, relative to the grating spectrometer.\n",
"\n",
"As a result, if we approximate the response functions with Gaussians and assume that the previous autocorrelation function gives us an estimate of the grating spectrometer resolution, we can guess the total resolution as:\n",
"\n",
"$\\sigma_{total} = \\sqrt{\\sigma_{grating}^2 + \\sigma_{VIRT}^2}$\n",
"\n",
"The same relation is applies for the FWHM. This relation assumes independence between the two systems and assumes we can approximate the response functions as Gaussians."
]
},
{
"cell_type": "code",
"id": "2f466226-fbad-4f64-86d3-9b5d0685678b",
"metadata": {},
"outputs": [],
"source": [
"total_resolution = np.sqrt(width**2 + res[\"spec\"]**2)"
]
},
{
"cell_type": "code",
"id": "051d329e-3527-4ce1-abfe-6da441ce2e4b",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"total_resolution"
]
},
{
"cell_type": "markdown",
"id": "2c85f6fd-c2a5-419f-ae61-8fe77289cf7d",
"metadata": {},
"source": [
"The previously obtained resolution of the virtual spectrometer using the autocorrelation method was:"
]
},
{
"cell_type": "code",
"id": "e8d3b2f8-09ba-4e9f-9b54-b48867dadc89",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"res[\"expected\"]"
]
},
{
"cell_type": "markdown",
"id": "efac989d-34e1-4378-abe1-b215bf58c05c",
"metadata": {},
"source": [
"Notice, however, that the response function is not Gaussian and therefore, one could use the full function. to actually simulate the virtual spectrometer.\n",
"\n",
"Furthermore, this ignores the uncertainty effect, which could be seen as an extra noise level added on top of the virtual spectrometer."
]
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{
"cell_type": "markdown",
"id": "dd1f6722-472f-488d-a0eb-e362732fd364",
"metadata": {},
"source": [
"## Validation: compare grating spectrometer and simulated virtual spectrometer\n",
"\n",
"To check that the resolution estimate is correct, we take an example grating spectrometer pulse and smear it by the impulse response function $g$ above. If it is correct, we should get a similar result as the virtual spectrometer itself.\n"
]
},
{
"cell_type": "code",
"execution_count": 38,
"id": "36e25952-b295-43d1-bffc-d62dc1cd0a95",
"metadata": {},
"outputs": [],
"source": [
"# smearing\n",
"y_simul = scipy.signal.fftconvolve(pred[\"spec\"], np.absolute(g)*np.ones_like(pred[\"spec\"]), mode=\"same\", axes=-1)"
]
},
{
"cell_type": "code",
"execution_count": 43,
"id": "e25f2828-fb32-4747-b7ea-24752c390625",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.legend.Legend at 0x2b5378ec7d90>"
]
},
"execution_count": 43,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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",
"text/plain": [
"<Figure size 1000x800 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig = plt.figure(figsize=(10, 8))\n",
"plt.plot(pred[\"energy\"], y_simul[example_tid,0], c='b', lw=3, label=\"Smeared grating spec.\")\n",
"plt.plot(pred[\"energy\"], pred[\"expected\"][example_tid,0], c='r', ls='--', lw=3, label=\"Virtual spectrometer\")\n",
"plt.fill_between(pred[\"energy\"],\n",
" pred[\"expected\"][example_tid, 0] - pred[\"total_unc\"][example_tid,0],\n",
" pred[\"expected\"][example_tid,0] + pred[\"total_unc\"][example_tid,0],\n",
" facecolor='gold', alpha=0.5, label=\"68% unc.\")\n",
"plt.xlabel(\"Energy [eV]\")\n",
"plt.ylabel(\"Intensity [a.u.]\")\n",
"plt.legend(frameon=False)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f8f25400-9d57-43f4-bd19-7d0d316f9a27",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "pes_to_spec",
"language": "python",
"name": "pes_to_spec"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.16"
},
"widgets": {
"application/vnd.jupyter.widget-state+json": {
"state": {},
"version_major": 2,
"version_minor": 0
}
}
},
"nbformat": 4,
"nbformat_minor": 5
}