{ "cells": [ { "cell_type": "markdown", "id": "01406ab8", "metadata": {}, "source": [ "# Detector model interface\n", "\n", "In addition to the neutrino data itself, the IceCube collaboration provides some information about the detector that can be useful to construct simple simulations and fits. For example, the effective area is needed to connect between incident neutrino fluxes and expected number of events in the detector.\n", "\n", "\n", "`icecube_tools` also provides an quick interface to loading and working with such information. This is a work in progress and only certain datasets are currently implemented, such as the ones demonstrated below." ] }, { "cell_type": "code", "execution_count": 1, "id": "518791b3", "metadata": { "execution": { "iopub.execute_input": "2024-03-22T09:55:57.355826Z", "iopub.status.busy": "2024-03-22T09:55:57.355300Z", "iopub.status.idle": "2024-03-22T09:55:58.944405Z", "shell.execute_reply": "2024-03-22T09:55:58.943637Z" } }, "outputs": [], "source": [ "import numpy as np\n", "from matplotlib import pyplot as plt\n", "from matplotlib.colors import LogNorm\n", "\n", "from icecube_tools.utils.data import IceCubeData\n", "from icecube_tools.detector.detector import IceCube, TimeDependentIceCube" ] }, { "cell_type": "markdown", "id": "5bb3f706", "metadata": {}, "source": [ "The `IceCubeData` class can be used for a quick check of the available datasets on the IceCube website." ] }, { "cell_type": "code", "execution_count": 2, "id": "0b39c313", "metadata": { "execution": { "iopub.execute_input": "2024-03-22T09:55:58.947519Z", "iopub.status.busy": "2024-03-22T09:55:58.947289Z", "iopub.status.idle": "2024-03-22T09:55:58.972978Z", "shell.execute_reply": "2024-03-22T09:55:58.972335Z" } }, "outputs": [ { "data": { "text/plain": [ "['2021_03-all_data_releases.zip',\n", " '20080911_AMANDA_7_Year_Data.zip',\n", " '20090521_IceCube-22_Solar_WIMP_Data.zip',\n", " '20110905_IceCube-40_String_Data.zip',\n", " '20131121_Search_for_contained_neutrino_events_at_energies_above_30_TeV_in_2_years_of_data.zip',\n", " '20150127_IceCube_Oscillations%20_3_years_muon_neutrino_disappearance_data.zip',\n", " '20150219_Search_for_contained_neutrino_events_at_energies_greater_than_1_TeV_in_2_years_of_data.zip',\n", " '20150619_IceCube-59%20_Search_for_point_sources_using_muon_events.zip',\n", " '20150820_Astrophysical_muon_neutrino_flux_in_the_northern_sky_with_2_years_of_IceCube_data.zip',\n", " '20151021_Observation_of_Astrophysical_Neutrinos_in_Four_Years_of_IceCube_Data.zip',\n", " '20160105_The_79-string_IceCube_search_for_dark_matter.zip',\n", " '20160624_Search_for_sterile_neutrinos_with_one_year_of_IceCube_data.zip',\n", " '20161101_Search_for_point_sources_with_first_year_of_IC86_data.zip',\n", " '20161115_A_combined_maximum-likelihood_analysis_of_the_astrophysical_neutrino_flux.zip',\n", " '20180213_Measurement_of_atmospheric_neutrino_oscillations_with_three_years_of_data_from_the_full_sky.zip',\n", " '20180712_IceCube_catalog_of_alert_events_up_through_IceCube-170922A.zip',\n", " '20180712_IceCube_data_from_2008_to_2017_related_to_analysis_of_TXS_0506+056.zip',\n", " '20181018_All-sky_point-source_IceCube_data%20_years_2010-2012.zip',\n", " '20190515_Three-year_high-statistics_neutrino_oscillation_samples.zip',\n", " '20190904_Bayesian_posterior_for_IceCube_7-year_point-source_search_with_neutrino-count_statistics.zip',\n", " '20200227_All-sky_point-source_IceCube_data%20_years_2012-2015.zip',\n", " '20200421_IceCube_Upgrade_Neutrino_Monte_Carlo_Simulation.zip',\n", " '20200514_South_Pole_ice_temperature.zip',\n", " '20210126_PS-IC40-IC86_VII.zip',\n", " '20210310_IceCube_data_for_the_first_Glashow_resonance_candidate.zip',\n", " '20211217_HESE-7-5-year-data.zip',\n", " '20220201_Density_of_GeV_muons_in_air_showers_measured_with_IceTop.zip',\n", " '20220902_Evidence_for_neutrino_emission_from_the_nearby_active_galaxy_NGC_1068_data.zip',\n", " '20220913_Evidence_for_neutrino_emission_from_the_nearby_active_galaxy_NGC_1068_data.zip',\n", " '20221028_Observation_of_High-Energy_Neutrinos_from_the_Galactic_Plane.zip',\n", " '20221208_Observation_of_High-Energy_Neutrinos_from_the_Galactic_Plane.zip',\n", " '20230424_Observation_of_High-Energy_Neutrinos_from_the_Galactic_Plane.zip',\n", " 'ic22-solar-wimp-histograms.zip']" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "my_data = IceCubeData()\n", "my_data.datasets" ] }, { "cell_type": "markdown", "id": "f5dc4bff", "metadata": {}, "source": [ "## Effective area, angular resolution and energy resolution of 10 year data\n", "\n", "We can now use the date string to identify certain datasets. Let's say we want to use the effective area and angular resolution from the `20210126` dataset. If you don't already have the dataset downloaded, `icecube_tools` will do this for you automatically. This 10 year data release provides more detailed IRF data.\n", "\n", "We restrict our examples to the Northern hemisphere.\n", "\n", "The simpler, earlier versions are explained afterwards.\n", "\n", "The format of the effective area has not changed, though.\n", "\n", "\n", "For this latest data set, we have different detector configurations available, as they changed through time (the detector was expanded). We can invoke a chosen configuration throught the second argument in `EffectiveArea.from_dataset()` for this particular data set only." ] }, { "cell_type": "code", "execution_count": 3, "id": "07a40eef", "metadata": { "execution": { "iopub.execute_input": "2024-03-22T09:55:58.975580Z", "iopub.status.busy": "2024-03-22T09:55:58.975187Z", "iopub.status.idle": "2024-03-22T09:55:58.978254Z", "shell.execute_reply": "2024-03-22T09:55:58.977727Z" } }, "outputs": [], "source": [ "from icecube_tools.detector.r2021 import R2021IRF\n", "from icecube_tools.detector.effective_area import EffectiveArea" ] }, { "cell_type": "code", "execution_count": 4, "id": "fe41e231", "metadata": { "execution": { "iopub.execute_input": "2024-03-22T09:55:58.980562Z", "iopub.status.busy": "2024-03-22T09:55:58.980185Z", "iopub.status.idle": "2024-03-22T09:56:10.743081Z", "shell.execute_reply": "2024-03-22T09:56:10.742403Z" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "\r", "20210126_PS-IC40-IC86_VII.zip: 0%| | 0/39848048 [00:00" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots()\n", "h = ax.pcolor(\n", " my_aeff.true_energy_bins, my_aeff.cos_zenith_bins, my_aeff.values.T, norm=LogNorm()\n", ")\n", "cbar = fig.colorbar(h)\n", "ax.set_xscale(\"log\")\n", "ax.set_xlim(1e2, 1e9)\n", "ax.set_xlabel(\"True energy [GeV]\")\n", "ax.set_ylabel(\"cos(zenith)\")\n", "cbar.set_label(\"Aeff [m$^2$]\")" ] }, { "cell_type": "markdown", "id": "ea8e92f3", "metadata": {}, "source": [ "### Energy resolution\n", "\n", "Angular resolution depends on the energy resolution. The [paper](https://arxiv.org/pdf/2101.09836.pdf) accompaying the data release explains the dependency: For each bin of true energy and declination a certain amount of events is simulated. These are sorted first into bins of reconstructed energy. These are then reconstructed in terms of `PSF`(the kinematic angle between the incoming neutrino and the outgoing muon after a collision) and actual angular error. Data is given as fractional counts in the bin $(E_\\mathrm{reco}, \\mathrm{PSF}, \\mathrm{ang\\_err})$ of all counts in bin (E_\\mathrm{true}, \\delta). This is nothing but a histogram, corresponding to a probability of finding an event with given true energy and true declination: $p(E_\\mathrm{reco}, \\mathrm{PSF}, \\mathrm{ang\\_err} \\vert E_\\mathrm{true}, \\delta)$.\n", "\n", "We find the energy resolution, i.e. $p(E_\\mathrm{reco} \\vert E_\\mathrm{true}, \\delta)$, by summing over (marginalising over) all entries of $\\mathrm{PSF}, \\mathrm{ang\\_err}$ for the reconstructed energy we are interested in.\n", "\n", "The `R2021IRF()`class is able to do so:" ] }, { "cell_type": "code", "execution_count": 6, "id": "af088825", "metadata": { "execution": { "iopub.execute_input": "2024-03-22T09:56:11.448114Z", "iopub.status.busy": "2024-03-22T09:56:11.447867Z", "iopub.status.idle": "2024-03-22T09:56:12.498407Z", "shell.execute_reply": "2024-03-22T09:56:12.497754Z" } }, "outputs": [], "source": [ "irf = R2021IRF.from_period(\"IC86_II\")" ] }, { "cell_type": "code", "execution_count": 7, "id": "d4ad5656", "metadata": { "execution": { "iopub.execute_input": "2024-03-22T09:56:12.501209Z", "iopub.status.busy": "2024-03-22T09:56:12.500973Z", "iopub.status.idle": "2024-03-22T09:56:12.695708Z", "shell.execute_reply": "2024-03-22T09:56:12.695080Z" } }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots()\n", "idx = [0, 3, 6, 9, 12]\n", "# plotting Ereco for different true energy bins, the declination bin here is always from +10 to +90 degrees.\n", "for i in idx:\n", "\n", " x = np.linspace(*irf.reco_energy[i, 2].support(), num=1000)\n", " ax.plot(x, irf.reco_energy[i, 2].pdf(x), label=irf.true_energy_bins[i])\n", "ax.legend()" ] }, { "cell_type": "markdown", "id": "c39da669", "metadata": {}, "source": [ "This should look like the Fig.4, left panel, of the mentioned paper, the y-axis is only scaled by a constant factor, corresponding to a properly normalised distribution. On this topic, it should be mentioned that the quantities distributed according to these histograms are the logarithms of reconstructed energy, PSF and angular uncertainty! Accordingly, logarithmic quantities are drawn as samples and only exponentiated for calculations and final data products." ] }, { "cell_type": "markdown", "id": "4d563338", "metadata": {}, "source": [ "### Etrue vs. Ereco\n", "\n", "Below, a colormap of the conditional probability $P(E_\\mathrm{reco} \\vert E_\\mathrm{true})$ is shown. It is normalised for each Etrue bin." ] }, { "cell_type": "code", "execution_count": 8, "id": "7f760a8e", "metadata": { "execution": { "iopub.execute_input": "2024-03-22T09:56:12.698408Z", "iopub.status.busy": "2024-03-22T09:56:12.698019Z", "iopub.status.idle": "2024-03-22T09:56:12.701096Z", "shell.execute_reply": "2024-03-22T09:56:12.700526Z" } }, "outputs": [], "source": [ "etrue = irf.true_energy_bins\n", "ereco = np.linspace(1, 8, num=100)" ] }, { "cell_type": "code", "execution_count": 9, "id": "344d9676", "metadata": { "execution": { "iopub.execute_input": "2024-03-22T09:56:12.703445Z", "iopub.status.busy": "2024-03-22T09:56:12.703066Z", "iopub.status.idle": "2024-03-22T09:56:12.708630Z", "shell.execute_reply": "2024-03-22T09:56:12.708151Z" } }, "outputs": [], "source": [ "vals = np.zeros((etrue.size - 1, ereco.size - 1))\n", "for c, et in enumerate(etrue[:-1]):\n", " vals[c, :] = irf.reco_energy[c, 2].pdf(ereco[:-1])" ] }, { "cell_type": "code", "execution_count": 10, "id": "4b6868d7", "metadata": { "execution": { "iopub.execute_input": "2024-03-22T09:56:12.710915Z", "iopub.status.busy": "2024-03-22T09:56:12.710534Z", "iopub.status.idle": "2024-03-22T09:56:13.726677Z", "shell.execute_reply": "2024-03-22T09:56:13.726012Z" } }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots()\n", "h = ax.pcolor(np.power(10, etrue), np.power(10, ereco), vals.T, norm=LogNorm())\n", "cbar = fig.colorbar(h)\n", "ax.set_xlim(1e2, 1e9)\n", "ax.set_ylim(1e1, 1e8)\n", "ax.set_xscale(\"log\")\n", "ax.set_yscale(\"log\")\n", "ax.set_xlabel(\"True energy [GeV]\")\n", "ax.set_ylabel(\"Reconstructed energy [GeV]\")\n", "cbar.set_label(\"P(Ereco|Etrue)\")" ] }, { "cell_type": "markdown", "id": "3de3a9e4", "metadata": {}, "source": [ "### Angular resolution\n", "\n", "Now that we have the reconstructed energy for an event with some $E_\\mathrm{true}, \\delta$, we can proceed in finding the angular resolution.\n", "\n", "First, from the given \"history\" of the event, we sample the matching distribution/histogram of $\\mathrm{PSF}$, again by marginalising over the uninteresting quantities, in this case only $\\mathrm{ang\\_err}$. We thus sample a value of $\\mathrm{PSF}$, to whom a distribution of $\\mathrm{ang\\_err}$ belongs, which we subsequently sample. This is now to be understood as a cone of a given angular radius, within which the true arrival direction lies with a probability of 50%.\n", "\n", "For both steps, the histograms are created by `R2021IRF()` when instructed to do so: we pass a tuple of vectors (ra, dec) in radians and a vector of $\\log_{10}(E_\\mathrm{true})$ to the method `sample()`. Returned are sampled ra, dec (both in radians), angular error (68%, in degrees) and reconstructed energy in GeV." ] }, { "cell_type": "code", "execution_count": 11, "id": "1ec19c54", "metadata": { "execution": { "iopub.execute_input": "2024-03-22T09:56:13.729279Z", "iopub.status.busy": "2024-03-22T09:56:13.729078Z", "iopub.status.idle": "2024-03-22T09:56:13.801869Z", "shell.execute_reply": "2024-03-22T09:56:13.801233Z" } }, "outputs": [ { "data": { "text/plain": [ "(array([3.15403423, 3.13035278, 3.14113431, 3.10152851]),\n", " array([0.80411628, 0.80839959, 0.77166036, 0.78062339]),\n", " array([0.81314284, 1.18484801, 0.8403489 , 1.56147217]),\n", " array([ 638.4537496 , 1580.39481862, 985.08856794, 837.29002682]))" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "irf.sample((np.full(4, np.pi), np.full(4, np.pi / 4)), np.full(4, 2))" ] }, { "cell_type": "markdown", "id": "40bb9216", "metadata": {}, "source": [ "If you are interested in the actual distributions, they are accessible through the attributes `R2021IRF().reco_energy` (2d numpy array storing `scipy.stats.rv_histogram` instances) and `R2021IRF().maginal_pdf_psf` (a modified ditionary class instance, indexed by a chain of keys):" ] }, { "cell_type": "code", "execution_count": 12, "id": "58af7d5e", "metadata": { "execution": { "iopub.execute_input": "2024-03-22T09:56:13.804390Z", "iopub.status.busy": "2024-03-22T09:56:13.804169Z", "iopub.status.idle": "2024-03-22T09:56:13.808313Z", "shell.execute_reply": "2024-03-22T09:56:13.807650Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[-3.13065092 -2.86582289 -2.60084567 -2.33592241 -2.07109231 -1.80631897\n", " -1.54136215 -1.27646224 -1.01153023 -0.74666199 -0.48174935 -0.21688286\n", " 0.04805317 0.31281183 0.57772152 0.84267163 1.10754913 1.37235958\n", " 1.63728955 1.90222053 2.1670218 ]\n", "\n" ] } ], "source": [ "etrue_bin = 0\n", "dec_bin = 2\n", "ereco_bin = 10\n", "print(irf.marginal_pdf_psf(etrue_bin, dec_bin, ereco_bin, \"bins\"))\n", "print(irf.marginal_pdf_psf(etrue_bin, dec_bin, ereco_bin, \"pdf\"))" ] }, { "cell_type": "markdown", "id": "69943002", "metadata": {}, "source": [ "The same works for `marginal_pdf_angerr`. The entries are only created once the `sample()` method needs to. See the class defintion of `ddict` in `icecube_tools.utils.data` for more details." ] }, { "cell_type": "markdown", "id": "16ee6f8c", "metadata": {}, "source": [ "### Mean angular uncertainty\n", "\n", "We can find the mean angular uncertainty by sampling a large amount of events for different true energies, assuming that the average is sensibly defined as the average of logarithmic quantities." ] }, { "cell_type": "code", "execution_count": 13, "id": "3ab19338", "metadata": { "execution": { "iopub.execute_input": "2024-03-22T09:56:13.810718Z", "iopub.status.busy": "2024-03-22T09:56:13.810397Z", "iopub.status.idle": "2024-03-22T09:56:40.831071Z", "shell.execute_reply": "2024-03-22T09:56:40.830518Z" } }, "outputs": [ { "data": { "text/plain": [ "Text(0, 0.5, 'mean angular uncertainty [degrees]')" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "num = 10000\n", "loge = irf.true_energy_bins\n", "mean_uncertainty = np.zeros(loge.shape)\n", "for c, e in enumerate(loge[:-1]):\n", " _, _, samples, _ = irf.sample(\n", " (np.full(num, np.pi), np.full(num, np.pi / 4)), np.full(num, e)\n", " )\n", " mean_uncertainty[c] = np.power(10, np.average(np.log10(samples)))\n", "mean_uncertainty[-1] = mean_uncertainty[-2]\n", "plt.step(loge, mean_uncertainty, where=\"post\")\n", "plt.xlabel(\"$\\log E_\\mathrm{true} / \\mathrm{GeV}$\")\n", "plt.ylabel(\"mean angular uncertainty [degrees]\")" ] }, { "cell_type": "markdown", "id": "8d1ec0ca", "metadata": {}, "source": [ "## Constructing a detector\n", "\n", "A detector used for e.g. simulations can be constructed from angular/energy uncertainties and an effective area:" ] }, { "cell_type": "code", "execution_count": 14, "id": "4d9b9e44", "metadata": { "execution": { "iopub.execute_input": "2024-03-22T09:56:40.833746Z", "iopub.status.busy": "2024-03-22T09:56:40.833252Z", "iopub.status.idle": "2024-03-22T09:56:40.836322Z", "shell.execute_reply": "2024-03-22T09:56:40.835661Z" } }, "outputs": [], "source": [ "detector = IceCube(my_aeff, irf, irf, \"IC86_II\")" ] }, { "cell_type": "markdown", "id": "19a82046", "metadata": {}, "source": [ "`irf = R2021IRF()` is used both as spatial and energy resolution, because it encompasses both types of information and inherits from both classes." ] }, { "cell_type": "markdown", "id": "9e72c3b9", "metadata": {}, "source": [ "## Time dependent detector\n", "\n", "We can construct a \"meta-detector\" spanning multiple data periods through the class `TimeDependentIceCube` from strings defining the data periods. Alternatively, a" ] }, { "cell_type": "code", "execution_count": 15, "id": "884d3faa", "metadata": { "execution": { "iopub.execute_input": "2024-03-22T09:56:40.838861Z", "iopub.status.busy": "2024-03-22T09:56:40.838512Z", "iopub.status.idle": "2024-03-22T09:56:41.907182Z", "shell.execute_reply": "2024-03-22T09:56:41.906466Z" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/icecube_tools/detector/r2021.py:89: RuntimeWarning: divide by zero encountered in log10\n", " self.dataset[:, 6:-1] = np.log10(self.dataset[:, 6:-1])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Empty true energy bins at: [(0, 0), (1, 0)]\n" ] }, { "data": { "text/plain": [ "{'IC86_I': ,\n", " 'IC86_II': }" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tic = TimeDependentIceCube.from_periods(\"IC86_I\", \"IC86_II\")\n", "tic.detectors" ] }, { "cell_type": "markdown", "id": "eeec93c3", "metadata": {}, "source": [ "Available periods are" ] }, { "cell_type": "code", "execution_count": 16, "id": "2f376088", "metadata": { "execution": { "iopub.execute_input": "2024-03-22T09:56:41.909804Z", "iopub.status.busy": "2024-03-22T09:56:41.909440Z", "iopub.status.idle": "2024-03-22T09:56:41.913583Z", "shell.execute_reply": "2024-03-22T09:56:41.912997Z" } }, "outputs": [ { "data": { "text/plain": [ "['IC40', 'IC59', 'IC79', 'IC86_I', 'IC86_II']" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "TimeDependentIceCube._available_periods" ] }, { "cell_type": "markdown", "id": "2f68e400", "metadata": {}, "source": [ "# Effective area, angular resolution and energy resolution of earlier releases\n", "\n", "Repeating the prodecure for the `20181018` dataset." ] }, { "cell_type": "code", "execution_count": 17, "id": "bf0b047f", "metadata": { "execution": { "iopub.execute_input": "2024-03-22T09:56:41.915937Z", "iopub.status.busy": "2024-03-22T09:56:41.915576Z", "iopub.status.idle": "2024-03-22T09:56:41.918562Z", "shell.execute_reply": "2024-03-22T09:56:41.918002Z" } }, "outputs": [], "source": [ "from icecube_tools.detector.effective_area import EffectiveArea\n", "from icecube_tools.detector.energy_resolution import EnergyResolution\n", "from icecube_tools.detector.angular_resolution import AngularResolution" ] }, { "cell_type": "code", "execution_count": 18, "id": "e72a19a8", "metadata": { "execution": { "iopub.execute_input": "2024-03-22T09:56:41.921030Z", "iopub.status.busy": "2024-03-22T09:56:41.920584Z", "iopub.status.idle": "2024-03-22T09:56:41.971656Z", "shell.execute_reply": "2024-03-22T09:56:41.971006Z" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/icecube_tools/detector/effective_area.py:379: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", " output = pd.read_csv(\n", "/opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/icecube_tools/detector/angular_resolution.py:111: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", " output = pd.read_csv(\n" ] } ], "source": [ "my_aeff = EffectiveArea.from_dataset(\"20181018\")\n", "my_angres = AngularResolution.from_dataset(\"20181018\")" ] }, { "cell_type": "code", "execution_count": 19, "id": "57256b80", "metadata": { "execution": { "iopub.execute_input": "2024-03-22T09:56:41.973804Z", "iopub.status.busy": "2024-03-22T09:56:41.973605Z", "iopub.status.idle": "2024-03-22T09:56:43.627292Z", "shell.execute_reply": "2024-03-22T09:56:43.626568Z" } }, "outputs": [ { "data": { "image/png": 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9u7QJclG2XFvU0IT9vOvv+OMhH6MC4GeAZbhjAZZh+MjYWeQ4kDizG4ZhFBN+VBEebliAZRgFJtzImz2PvJZXBrJipKBoAJ3swcejdd6xpnI+V6ljFJVik+RX5dlo7kqCyqtiqFwfpYhwU0y+RkrU5JUoTwwH0rTyrnA5OS65SEr1kcaagdwfD1Vh59KoWcH2LW0hHPas7ndUNIxmyhSrZuzIHKxMNuCkvKo1gM7xwSp2LMAyjHbAjEMBe0MZhtF1sCPCzsW+DwzDR5I9cv8wevk7vH3O2NneY8amfm7NlBuJZx8zQQUg29ywikGZYiOqCNn+lGbhUhmoFJF8r9YBt+oxP7yPwo7eXS55XEqVYvfRtfKOKjaOjZCdvrzliy9/NS5IKlW7Rw5WUN7n3NfYhx0RumMBlmG0wbgKbrEAh0AKAEbN44oXs3UwDMPoatgRoTsWYBlGOwg3iZyShHe8+Qh9BcmS4pkvVUjkYMX78HE/0jyoUCKqCFVVdoDcdVVFmExxBSXm0ARawSoD/WiVo3LBYg7dtl39uPKdq27TtfLOBaXasNt0PZJiypvKtVKPE3s8XKonAZXX5n0dpEQOVyHI+pCD5Vr1axzAAizDaIMXH8nNCHQ/TPGK7s3SdWjFoSAjfLDE6RJKdnrH4kfwufG+fJx+rqq2OqXii4scM8ZK+CLRSO7HWYpoSBy3kS/tlDA2VQGWi/E+S2ZXRJVJaAFbr/C+g/k//jrgzD25XN1vfdzp0oYn92DWNRmd3Xf2OOuej/5jOVidiwVYhtEBjLuMBFP2uWUYRpFgOVjuWIBlGD7ClCoaXGHfEdyLK3NTyE79mqhabODzE728Y6kysThpyLwPB/PQhGiF0tOrOqgjB5fjG9WQWXimOh0RuuBqeMosGfxIZnclTI/mck8AB0SbG1l8wNdmj57rUSVLRtfWErkXFCTE16OyaWD3nTbV9iF5PlfS2WDeSfXpf94ty8FyxwIsw+hijLyOBFMuXlWGYRjYd2yZ75FkpoBH1d0dC7AMo8DkqlK1RSYqfiHEp1Spd0ylBak8LnqdLXLBpE0DW1cZfKZzbwjs2s6GKV4lIhHdKedIqnE5L+ELfqhdypbAD2NMlzwpNVfmAmW9X2MuOV8AUELmuyo/7HHq7Bwso3OxAMswOokx/yaS3HtZcpZhGPljSe6diwVYhtHFiOzN4qWHWqteo6/gOVjCG5J5LyIpmkurNejnqhJKmsVVecyrEqmPa2U0Gg4xZcDtQz+qZDqCUqWiojl0vqj8JOW1wRQeWbXIfDIAIOtdQ9kSKCXHF9WHNq52y59iVgiupqkJooIpRc/FXLZ75GDZEWF7sQDLMNrBhIu/LX+37rHrW/379K/y4Eid+gHePoX2RjUMozOxKkJ37HPbMDqJRO8AXvkPr5M7awLd1E8s4oN6r/K7siFy5crGAGlAGhQeT3QJolT5BVOlwkKRUuoYy+NSuWAuOVEu+Un75js8pg4VeREHlQ9QzY0LZ6KpjqqCJKfJtRJRVQYWO/uS3PNs9gxr9txeLMAyjE6E5mH1tJwHwzDyJ+NDL0KrImw/FmAZRjs49BiwLZhKBegkd9aUOV2i19+00Lv+id/0qmBK+JDVhXHvL9JRlesj1CDisRUI8DXSwlk965D7pFQphnRyd1hDqUlJYb1fGko4rJ17pWREqXEOnmMqV0dVEdKqOefKu9wbNevHg1Xqqfw1/nw55Smpx4ONkdvryBpCy8HqXIouwFq+fDmWLFmC6upqjBw5EsuWLcO4cePo3IkTJ+I3v/mNZ/ycc87BqlWrAACXXXYZHnnkkVa/nzJlClavXu3/5o1uw5lf5sFRSjhdHpq07ienfIPkeBGbBsMwDKPjKKoA64knnkBlZSWqqqowfvx4LF26FFOmTMGmTZswYMAAz/yf/exnSCQOXDHu2rULI0eOxAUXXNBq3tSpU/Hwww+3/DsWU37QhlF4onXesVQPPlelmrB0mtL3+dyGwXw8WUauXMXtZVO5XyWHRfNmP5zVlWKTIFVlZWHREFgcibD9KUd57dNF8rhE7pNSg2Ihh0bSytEcbj5RuaJUJj/6KqqqPpY7lm+DY8Cf5tesmjHVgUduGQTNaLQTKaoA67777sOcOXMwa9YsAEBVVRVWrVqFFStWYP78+Z75ffu27mL7+OOPo6yszBNgxWIxDBo0qHAbN7odsvlyKvcG0ekS/iUQbvR+oEX38LlqH6xKvXGg2Ij4/A01eX+R6s2/dAIR/uWXJUeEGXE0p74UlX2DyxrMpqGQrXJUoOGa0M5glgz6WE0cidHE8MIEXW1BLSdEUrZqZsCec3W/XZLZVXK4S+BVQh7TjLpKKQDpbMCp8bhaw2gfRRNgJRIJbNiwAQsWLGgZCwaDmDx5MtatW5fTGg899BAuuugi9OjRWg544YUXMGDAABxxxBH4zGc+g29+85s48sgj5TrxeBzxeLzl33V1RHIwjH+iehGih31wGYZRHJhNgztFE2Dt3LkT6XQaAwe2vgwfOHAg3njjjQ/9+xdffBGvvvoqHnrooVbjU6dOxYwZMzB8+HC89dZb+PrXv45p06Zh3bp1CIW4PLB48WLcdttt7b8zRtETSuavfkQasnm30Tm9kntsuQgl6Si/L0wFywqlSuSt03GlSEm1hShe6phMrcHGXRLAXWFNnffdpve+BH04ilLHQLJRM2uQ7Jjkztf14ygwd9UNcFOUXO6ji6HovvkdZyCaK2kfqgjT/3wczKbBnaIJsPLloYcewmmnneZJiL/ooota/v+0007D6aefjuOOOw4vvPACJk2aRNdasGABKisPfDHW1dVh6NChhdm4UXScMbd10NNWn+YzvuINkFJlZKL47DYNzDAMRSYbdK7s9K5hOVjtpWgCrH79+iEUCqGmpqbVeE1NzYfmTzU0NODxxx/H7bff/qG3M2LECPTr1w+bN2+WAVYsFrNE+MOcTFgkU5M+gskeAfzfcq+VAguuXEkLk9BQk3dMXegHSJ4UAKR6eD9Ys41c1c2Wi/wdBzVINXuOhrwbDznaEjBclSoXRUPl7zDjT7VnPxo4u+Da1JmpQS5tdVxRSe7UtsJRFWT7VsqitD0gT2OavGbYmNE9KZq23tFoFKNHj8aaNWtaxjKZDNasWYMJEya0+bc/+clPEI/H8eUvf/lDb+cf//gHdu3ahcGDRWmVYRiGYRQB+48I8/0x2kfRKFgAUFlZiYqKCowZMwbjxo3D0qVL0dDQ0FJVOHPmTBx11FFYvHhxq7976KGHMH36dE/ien19PW677Tacf/75GDRoEN566y3ceOONOP744zFlypQOu19G8fGnR/P3tVKCAbtwzohzRrVGog+ZKwqokj3FFTX5XJVmpUo5IuMy90m01WFqlWv+lItapeYyVcqlFQ3gZtPgUnHoR/6PUmbKQnE6zu6Lq0lovnPbuk0XmHqnFD31WHe04pgLGeRfBdj1MsuKh6IKsC688ELs2LEDCxcuRHV1NUaNGoXVq1e3JL6/8847CAZbv+k3bdqE3/3ud3juuec864VCIbzyyit45JFHUFtbiyFDhuDss8/GHXfcYUeAhm+MvkIcBdqFoWEYBcQfHyz7oGovRRVgAcC8efMwb948+rsXXnjBM3bSSSchK5L0SktL8Ytf/MLP7RlGzgQywIbve3OzTrveG5CpDiuJXnrtQ1FNncNNIp+sj3eRbIm4nhXqU5A0cFbX0yqvirbQEf5fLm1u/ECpFi75WkqBUcnJflQdMpVOKTZJ1rtJzNceVh2r7jCDT0DngrHnyw9VsLONRo3OxUJTwzAMw+iG7O9FmO9PR1JbW4sxY8Zg1KhROPXUU/Hggw926O37SdEpWIZRbATSblesTDAQF+SINPDxNDnhzor+hCp1JJDyqhGyYltUIrL5IaJqAVr1cXFyLyRsf6qpcyzIE95YvpVSW5Sy5ZLro9Ux7/MV8sHzQ92eUrb88M3irvSF8xaTzwtRvGhD7A50cs8gICtaXdboSHr16oW1a9eirKwMDQ0NOPXUUzFjxow2zb+7KhZgGUYX4693e48NR17H87hE/jHYRWemXNyg+o5jn6vKUFQETSz5XSWRy2Al4g1WZCK6Q0KvnOvwfcL64LnepjIlLWRvRrZvpVRExLEaCzRcg8VCoSrflNUDPe5UR7TSlI4Feuy1a2njbREKhVBWts8MMB6PI5vNyjSfro4FWIbRiYy53NtGJxskX4rMfNQwDKMN/Djic/37tWvXYsmSJdiwYQO2b9+Op556CtOnT281Z/ny5ViyZAmqq6sxcuRILFu2rJUJeG1tLT796U/jzTffxJIlS9CvX7+87kNnYQGWYRSYlx7ilg4suFIosSUuOldkia0DMx8FgGRvoQaVkivtpNiICADZcaBSVUqkXUFh2tywBtCAtk1gRyXqWFMdq7h8VekEeu9j6oexpvMa5D66HvkxhUehnttcj+Zccdmb2kcy6/2KTXZg82R/WuW4/X1DQwNGjhyJ2bNnY8aMGZ7fP/HEE6isrERVVRXGjx+PpUuXYsqUKdi0aRMGDBgAAOjTpw9efvll1NTUYMaMGfjCF77gaZNXDFiAZRg+8rF/9x7lRZrEl46o6jMMw+hq1NXVtfq36mgybdo0TJs2Ta5z3333Yc6cOS3+lVVVVVi1ahVWrFiB+fPnt5o7cOBAjBw5Er/97W/xhS98wYd70bFYgGUYnUS8XKgcREgQOdMIpoCNy7w5W6d8nfQ4VH1alejAxkWyfTadfz6TC8qOIeygXKRUjk2Wr6EUL0apD61hXHBPlGdqi3hyHQjJBHCRd0cUHpWjFEHuj2mCrAsAJYHc11BJ7moNtm8/1MZ8yGQDTnmJag0Ann67ixYtwq233uq0ViKRwIYNG7BgwYKWsWAwiMmTJ2PdunUA9rW/KysrQ69evbBnzx6sXbsWV111VV73obOwAMswipyP3OoNpsx/xTCMjA9HhPsDzW3btqF37wNXae0x4965cyfS6bTnuG/gwIF44403AABvv/02rrjiipbk9quvvhqnnXZaHveg87AAyzB8JNxMGiQHgBcfyb21zmmVpGJQvFOTPfl4mlkyqByWOL/CzZSQNjfCaDTgIFWpgiCVt8SMRjOsEABalYoS1cBF7VKoPSelSaj3zktrA5lnluPmxO0BwljTQe3aN58oNo6PKZuvKv1cAoWoY/4UQ1ULHq5VgJMmTUIoFMLcuXMxd+7cgt3OuHHjsHHjxoKt35FYgGUYHcCYf+MJ7SoB3jAMI18y2aDsCOCyBgCsX7++lYLVHvr164dQKISamppW4zU1NRg0aFBea3dFLMAyDB9hSpUKrhR/uS+39jmAbvZM02mEGajohKJzswjBMFc//DAJZWu4KDNtzXeZ66Jc+NHmRlYRsjY3Qm1R+TeZAPPjUkqaui+sQlT4TzmYc7r6f9E1HKsZXdriuAQsrDVPqgNzsNIISJNXlzUAYOzYsXkrWNFoFKNHj8aaNWtarBsymQzWrFkjW+AVMxZgGUY7kEGT42eZagTNehQahmG40BkKVn19PTZv3tzy7y1btmDjxo3o27cvjjnmGFRWVqKiogJjxozBuHHjsHTpUjQ0NLRUFXYnLMAyjAKjhI9MxCEaU1NVPhN5Z6siuFRPkVdFFK9AWMx1+AwPC9d3lxYwyTSveHOp9FO4qh8usGo6rcY5qFLiCVCqlFMzalHC6lINp9Q/llcVdGwl4+J55eptlS8sj07l1nV1clWwXnrpJZx11lkt/66s3KfqV1RUYOXKlbjwwguxY8cOLFy4ENXV1Rg1ahRWr15dlD5XH4YFWIbRBTn9q4coW/lX0BuGcZiRhu4D6bIGkLuCNXHixA9tbTNv3rxueSR4KBZgGUYbTLj423Q801tLNn+uan28dzqrCgQQbuR/nyIVgKqps6oiDDPXdqV2hfh9SffwKgPK7yqdELk3YdYwOvfG0ACv9ouEcndbVyhVxSlfyzXXh+YtiUpEEVUz5UjlYBUSdvTkS5Nl1T/RKX+NPx5x1uJArC3VvyKqIvTziNBwxwIswygwYREcAcCfv+fNtTr1Rm9AFqjnfx/bzcebSEGOSmbPRHiQkA15xwNREZSIY79AkKyh7CLIXEC0qHFMcndBt+Ehc0VAFxZfzuzLKhZyO7Zi9122kRHBADMV9aO9jFJLIg7WC673xQUVNPkRtHZGkNuR+JHkfrhhAZZhdCKj5xB164iO34dhGN0PP5s9+2HTcLhhAZZhtMG6x67Pew2mUgEiuALAuqwkewKv/Id3nY8u4GtEa71jzUfy/WXDQsEiylZAHBEG+MkLPQ5UCpaLpYNUmYQK5lr6nyuuthDseEklPaukf6aU6GM1tQ+H4zZ5H3NXbJRJqItypAIFZoWgcLFjkHsGVxxZ2x+2N5dijnzJIuB0bK7WMNqHBViGUWBGXc2DINe8dZrL5d6twjAMw+gALMAyjE5CJahH93qvcBO9RXJ5CV+DJcqr9CR1QR1IkdsMuV3NuqhSrCUOAJSEHRQKJxVMHJ04KDN+JDz7kdSt7rdSL3irHLf74keSu4vlhIuy5Ue+lro9plQBHW8BkQt+HhFaDpY7FmAZho9QA9Iyk9gNw+h4MtmADOBd1gAsB6s9WIBlGAUmUs/VhTRppgwA8XLSZFnkOCnBILLXO6bWYE2dAWjfBDZVCAZMWVE2DaFg7qpD2GGuXqPjWpa0hc7XEq8bB3NOlSPGFB7XNjeFsivwo1GzS3sfwK0CsCsqVUbXxAIswzAMw+iGpBGUyfouaxjtwwIsw/ARJorIVB9xIRytAzY82Lpi8KPzeaK8Uv9TJL9L+V3JC/JSB8NN0UKHzhWVfn4gqwuJkqP9rpTqk3+Vo0tBluv+GMzvSuFSjQeotj9uClGhqjv9yMHyQ6FjVYsulYz54ucRoeVguWMBlmF0QTytcqxa0DAMRzIIIpOnArX/7y0Hyx0LsAyjDcbOIknr/2T9w5WesVQpcR1PCuUom8VLP/Cu4QmuoFWwUFxuz7s3UbWocrPYbapmzyqviqlVKtdKrcHyrVz9p1zwY42ID2141BejSzNkFyVHurD70P5GVjmyZsjifqv74o8DPctr697O7EbhsQDLMPyEfI+kY44SPZkebuZT48L1nfU5TDPbBQDZkty/SLJJ/uUXiPFzRoc8eUTDuScPp0TEGXY4inIxA1X4YfWgUF/wfgQ8LChRhqIuye/68ci9RY3CjzZICj/a3LAjZBYkZwp4Pw4lnQ1II1uXNYz2YQGWYbQT6sTu6B6qGkEbhmHki585WIY7FmAZRhuwY0BAt7kJN/Gr0xcfIUeBIrhKR8mguOhVgkHzkaTNTUYoWCL5nRGICIVIJK4HqFdp7sdFajzqaLHAVCmliEgVhqgcrlYP7L5ExJGpy7FapICWEy6KnrIwkMn25CkvCfBke3UU6HKUp1577D66qlpsH8xyIu2DAmkUBxZgGUYHMPLa/NUuwzAMF7LZYBsXDLmvAVgVYXuwAMsw2kGylxhHAK/c19pigQZXAMINfI1wk3dMNWouq+bjDUeRJsuOF84soT2gLBYcThFc8rIA1/wplyTy3C0dAJ24zveRf5K7UlCYWuViKOoXLsqWui8uKpGLeahLo+xCwpLnOzQHCwFZvOCyBmBVhO3BAizD8BlP5aHoI2gYhmF0XyzAMox2cKhKtR9m6xCty9Jcro9dJRLcyQVupJ5PTYlmz4y0MA4NJEVuFql+DIbcrr4j4dxls7BT9V7+Jpza6sEl58hNEWFVc8qOQdkVRBxsGlz24Qo9ehLXEq55VfT2xOLs8QuJx0i6krOXguN96YpksvknqWc6TnDrdliAZRjtQAVHKq1q/Ezip9XDlC3DMApHxoccrHz//nDGAizD8JFUGal42yua9rbhzv7y0tYK2Slf5wGd6m4SInlcGVadCF2JCJKDlUkLM8po7jlRIR9a5cgcLIf8FqWIuLTEUfkt6iFl1XSuahLNRRJKkKvCxlD+WHyu2+01E5fbsmCCznV5nNQ+pCpIqv3U60BVRLL9sX2YgenhQ9GFpsuXL8ewYcNQUlKC8ePH48UXX5RzV65ciUAg0OqnpKT1mUo2m8XChQsxePBglJaWYvLkyXjzzTcLfTcMwzAMo6BkEPDlx2gfRaVgPfHEE6isrERVVRXGjx+PpUuXYsqUKdi0aRMGDBhA/6Z3797YtGlTy78Dhxjz3HPPPbj//vvxyCOPYPjw4bjlllswZcoUvPbaa55gzDD28+cHeA7WqHlepSneO4CN3/XOP/VruZuMZsU7NSXa3CT6Eh8sZZQuvK1AfLMipVwyC4ir/TRZIyRuT61REvJu3LWdDZuvcr78yM1SuKhjLmqQUnfU8Q5bWSlH+jZzd97Xa7CcNKGSulQcqnY7DsqWyuNyUdI6vYrQRyd3s2lwp6gCrPvuuw9z5szBrFmzAABVVVVYtWoVVqxYgfnz59O/CQQCGDRoEP1dNpvF0qVLcfPNN+Pzn/88AODRRx/FwIED8fTTT+Oiiy4qzB0xDjs+9u8kmGqj4vmMuYfMFzYNhmEYCj9zsMymwZ2iCbASiQQ2bNiABQsWtIwFg0FMnjwZ69atk39XX1+PY489FplMBh/72Mdw11134aMf/SgAYMuWLaiursbkyZNb5peXl2P8+PFYt26dDLDi8Tji8QNdduvq6vK9e0Y3gSlVNLgCZJVShPhjlewCGsl1QrSWr5Ekn4PpEnHlrPoLlhEX6rToAehQLeha1cT6Dqqeg063KbaRFF9IMYf+fS64qnGs8s4lL0iNqzViItGPqTPq9pSixCryVGWhqrbMNfcJ0CqkH/5YzLWdqaFhH6pAjeKgaAKsnTt3Ip1OY+DAga3GBw4ciDfeeIP+zUknnYQVK1bg9NNPx549e3Dvvffi4x//OP7617/i6KOPRnV1dcsah665/3eMxYsX47bbbsvzHhmHC3/+Hj9OVLA2PPXH8Lmq2TOLEYKi2XOmjH/gZ0kwFSrhx0KsJQ4AhEPeL66sCLCUkSRDtpHxIV9EJYZzW4JCGnkWznLCj4bWNDHcMbmcPV9dpcGBSkYvpHmr32TgQy9Cy8FqN0UTYLWHCRMmYMKECS3//vjHP46TTz4Z3/ve93DHHXe0e90FCxagsvKAr1FdXR2GDh2a116N7gELjlQbtlQZHxdpVYZhGE5kfUhSz1qA1W6KJsDq168fQqEQampqWo3X1NTIHKtDiUQiOOOMM7B582YAaPm7mpoaDB48uNWao0aNkuvEYjHEYm3U2BtGHrCEdnXRrJLfI/XeD8V4P6FUCfPQILNpUA2jHU65wiFxbOXQsFipOzJx3YcWOtEgOTIVc8PSRiJ/9cPFpkHm37DkcqVUKS8QtjehVLHjM8DNVkPBVDO1D61KmZum4T9FE2BFo1GMHj0aa9aswfTp0wEAmUwGa9aswbx583JaI51O4y9/+QvOOeccAMDw4cMxaNAgrFmzpiWgqqurw5/+9CdcddVVhbgbRjeBKVWGYRhdiUzWhyPCPP/+cKZoAiwAqKysREVFBcaMGYNx48Zh6dKlaGhoaKkqnDlzJo466igsXrwYAHD77bfjzDPPxPHHH4/a2losWbIEb7/9Ni6//HIA+yoMr7vuOnzzm9/ECSec0GLTMGTIkJYgzjBcCKa9V8KpiNsHVFM/71hGHDNGRH1FvB+zaVCJUkKVYqai4vxS5WCxfCuVg+WHeagL6osjLC0PiG2FVI7EffTBZJUldUeIutYWTK1SeVlaBSN7g0iIl42ac1ctXRLoXS0kXJRFmUBPXqdMDe3InCZzcu9ciirAuvDCC7Fjxw4sXLgQ1dXVGDVqFFavXt2SpP7OO+8gGDzwYvjggw8wZ84cVFdX44gjjsDo0aPxhz/8AaecckrLnBtvvBENDQ244oorUFtbi09+8pNYvXq1eWAZhmEYRgezbds2XHrppXj//fcRDodxyy234IILLujsbbWLQDbrkj1hMOrq6lBeXo49e/aYT4jhYexs0ofwn6xf4W0CzRh5LT+SbBpIh5Hs5VAlJqoIwz1I2TmpCgSAaJQrBmVRr3klqywEgJIwVz/KyHhJ2E2hKAl512A5VQAQc1CDlIIVI+aoAFdKlDKjFD1WxabXUC10iArm2LLHj9Y1LvfFj9tTj0eUrK1ULZVPRpU0YsnQsDeN80a+WdDvi/3fSZ9/bjYiPUSPrBxJNiTw32ev6LDvt+3bt7fkQVdXV2P06NH429/+hh49ehT8tv2mqBQsw+jqjL7CGwi1JbAz5/dUqXdeVyldNwyjePCj1U1H2zQMHjy4pehs0KBB6NevH3bv3m0BlmEYXjLhADZ83+uFxYIrhWoMrYvESO5HiSpFzP0DVLWzUbDcm1KiJgGOlX5iz1FRicjmq8oxlXPC1A8/2ue4wnJ9dI6TMtYszMGFi1IFFC6/TuVg+VEt6NqG53Bj7dq1WLJkCTZs2IDt27fjqaee8uQ0L1++HEuWLEF1dTVGjhyJZcuWYdy4cZ61NmzYgHQ6XbQ2SBZgGUY7kFWEIlY5rdI731QpwzAKiZ9VhId2LFF2RQ0NDRg5ciRmz56NGTNmeH6fa0/h3bt3Y+bMmXjwwQfz2n9nYgGWYfhIgFQRxsuFc3kU+Mt9XmXrI7d5gzFVFBVq5uPpMnKlLvyuAsTvCgCybNjxs5opXrJ6L6hapHjny4pDlWPj4LGlYPlWah+qwW6YVBEqRSRC2sgA/PFQ7Wx05R15TOlMN2XGValyqfZTjynLn9IqU8e2qWGPR0c6wfsZYB2qIi1atAi33nqrZ/60adMwbdo0uV4uPYXj8TimT5+O+fPn4+Mf/3he++9MLMAyjE7k9K8SJaxPh2/DMIxuiJ8B1rZt21olubfHbDuXnsLZbBaXXXYZPvOZz+DSSy/Na++djQVYhuEj2WAAmUO8oqJ7+dxkTz4eavSOpUniOwCogjfqeSVc2CG8mTKkF2FQKA7anZ1UiTmqScydXSkiKn8qQ+Q41/59fF3+mLL7Dbi6jufeb9HFsX3fPnLPRdL7I8qRoxs8a+Csq/fyV5+UCsZys1Rz6RLh9dXde/b17t077yrCXHoK//73v8cTTzyB008/HU8//TQA4Ic//CFOO+20vG67M7AAyzDawYYHeQPnM76Se+J6uImPJ8q9YyExNyk+77Jhh2Re9aUT8wZTKqBQ5qEu+NHORpmEsrWTIhiIOQRYLntWqGM8dR/9sFhwQa3dVZK6WUCsgjEVYPnRwqiQz0F78VPBGjt2LEKhEObOnYu5c+f6sT3KJz/5SWQyXeO1lS8WYBlGO5BJ7tap2TCMLkIW+Str+y8h1q9fn7eC5UdP4WLCAizD8JFg0qtuMW8sYF9/Xmbf8LGrvPPrVZWyEFACCfKhKmwaAsL4k7V1UUeByr4hRI7KkqLvTzQr1Ce2Dx+aOquSfT/WULDjNnbUBrgpIqrptEuTEz8UKZXALY8OHVva5It6TJl5qDoaPVzxQ8Hyo6dwMWEBlmH4zMf+vXWA1Nb1IwumDMMw/MDPI8JcFaz6+nps3ry55d9btmzBxo0b0bdvXxxzzDEf2lO4O2EBlmG0A5WDdWhwBexLcfrz93JTqvbz5wdaz2fWDQCQ7CUSoSPecZkHrXLfibKVyfBFwsI8NE3m94pxbwltvZC71YOLcaVOlFeJ6yypm89V7XaYguKq+hSqzN8lmd0Vlz1rZTH/+60S13Ntc+MKKyYolMkrozNysF566SWcddZZLf+urNzXDqyiogIrV6780J7C3QkLsAyjC+LpPdi3c/ZhGIYB5K5gTZw4ER/W4njevHnd8kjwUCzAMgw/cbhYVO1v2AWu60VolpiKBpKiAjDKx5lNQ2mJt3lzW7AcrHiaf+yUCNfUFFHBVMNoldDromypBs4uKNUgSfz7lbojTTuZeaWjKsJyn1xbwLioUi5VetJKQRqykvviYMewb+38zVS7In4qWIY7FmAZRjsYc/l9/Bdh+zAyDKNrUIw2Dd0JC7AMw08chATlbdV8pHcs1UMsrC6myYdqNiYqAMUSrDLQD78rpQCkRM4RNRp1rN5zyd9RFXlhcpuulYhMKVHqmksDZ3X/dKsc79pKxXFRtvxo3uxHDpb07lL5bg7Vnep5Yd5bTLkrVIPrQuOHTcPhhgVYhtEBnHaDN0ndLLMMwygk2Wwg74siPy6qDlcswDKMdqAupkOZLNavqGw1xoIrAEiIi0HVFscFtj913RyMiBwbkefEcPkQVuqOasjs0qhZqQMuflUqn4kdtahqQReSWe4LViLay7is4eLCrpSqkkCcjrPH2jU/iT0vrn5cLs+tVp9yfx6LKgcLgbyNRrt7C6BCYgGWYRSYMOktCOgAi30nBplxKIBUT5HsXeZdJEAS3wE4HWsqQ9Gw6L2n5jNYMjvAAzI/+tKFHXsisv6C0tJB7I9ZPbgeGbEAUB2fqR6FLEgoCQirDXlUmfvjp+wRQg5WCPIIkzwHQRET+BEcqQCcHQey12myAwM0y8HqXCzAMgyfOf2rrRUrri0YhmEUD5aD5Y4FWIbRDg49BtzPocEVAIQSSqEQ9ghRMhbjawRSfI1A2jseEEnuIXFEyNQn1rYGcEs6dzEUVeNKfZJrENXBxVAUcFNK/GgCrfDDcNOFQtkxAK72CLkrR1oxE9YXDsedfrQl6igsB6tzsQDLMAzDMLoh5oPVuViAZRg+8sp/eFvifHS+aIkjRA7mt5kmqhYAZEQOFk2bSfLDykBJ7gm+rsqMUoNc1laNnfkahVN3XBs754pKIi7xIc9MNVN2eR5lo2aHJHyXx86P51Ddb18aWhepzUK+WA6WOxZgGYZhGEY3xM8jQsvBcscCLMMoMKrJcla8+1w+D1UOVpblVYkqwkxG5HH5cDLgR/4GO6JwrbxjKhEzDm0L1/yiXFEVhx1NZ9gP8LY/XcMGoTsoVVkfjggtB6v9WIBlGO3gY1fxYz8aTJUXdi+GYRhG18MCLMMoMLE9wP8t9+ZmnXojD9KSvfK/TeZ5FXAwDgW4sqWuhllTZ4ArR65VhH4QdTAE9aMi0gWZ4yRNQnP3wXJp2eO6Dxc6WpVyqRYEfPLH6oIGpFkA2TxfosWv43UeFmAZRgcw8loSTMU6fh+GYRw+ZBBAwCcnd0tyd8cCLMNoByqvCvCqVTS4glaqUqWkybJwK1UXzSxvIpsSjtyx3NUdl6pANV+pKkodU95bLqQy3gfQjzY3ruoOU1AiYh/ab4mMqxekw/5cFRiWk+baqNmXqr4CubOrnDt1e1xZJEpyAZXaQmJJ7u5YgGUYhmEY3RAzGu1cLMAyjDYYO/s+/osS/aFzxtzWilXzQD4v1MTH0zHiwi6EiFRv4cIeJb0IxZVzQLmzk3HXykKXCkDVz9AFVwXFbY3c3eD9UMdUFZtLDpZcu4vkC7EG06rPpFSUnNzgc3dn98OZnb0+OjJgyWQDCJjRaKdhAZZh+InDZ1G8L/8CzUbIsur7upR/GWWT3q8H1SpHfeBHwrkfL6ngjbe5UY2J1RGhd75rcjmbX0hTUmUe6nKk6BQUOh61udhcKNNO1o4mitzNRwG+b3V7sum0D2nYLGj1Iwjt7CPCbNaHJPfiPNHsEnTF9kmGYRiGYRhFjSlYhtEGyTKuRCR78Pmse0goweeG4nztFFk7pVriKJPQcP7He8p6gaHa2bAr+FSGX9eVhHNv6yLVLgc1ww+LBZfm0gpXQ0umbLnYMQDCPkPsQzVO9sW+weE5kMnlZFypXS6oNdQ+2HPAHv+OtKywHKzOxQIswzAMw+iGWIDVuViAZRhtsPG7XoNQQDu5A8CfH2j9N6d8Xdg0OFQ8Z6IiQV20v2H2gAGhSJVEed5MKk1ybMRcF1QOllRQyAe8HwnxSgVzUWZcjVe7SlschlRmHJLL/bBjSIoeUio3K0k8TFxMWgFlOaGew9xzCEPdKDYxHyx3LMAyDJ/xOLTbu8wwjE7AzypC88Fyp+iS3JcvX45hw4ahpKQE48ePx4svvijnPvjgg/h//+//4YgjjsARRxyByZMne+ZfdtllCAQCrX6mTp1a6LthFDmZEP9JEPPQYIr/pMsyOf84E/D+hMMZ+pNKB+lPIADPTzCQpT+hYIb+hMlP5p8NaD0/4D/s9iLBNP1RhJD1/MSCKfqjiATS3h+xj2Q2lPOP0+0F0shkg56fEDL0RxFE1vMjH7tAlv6kEfT8KNjcNIIIIpPzj7qPLvdFrU3vN3nNtJUvx56rzmZ/FWG+P0b7KKpr6yeeeAKVlZWoqqrC+PHjsXTpUkyZMgWbNm3CgAEDPPNfeOEFfOlLX8LHP/5xlJSU4O6778bZZ5+Nv/71rzjqqKNa5k2dOhUPP/xwy79jMeth0p0Zd5nwtgLw4srKVv8ePUccBdpLxDAMw2iDogqw7rvvPsyZMwezZs0CAFRVVWHVqlVYsWIF5s+f75n/ox/9qNW/f/CDH+CnP/0p1qxZg5kzZ7aMx2IxDBo0KOd9xONxxOPxln/X1dW53hWjyEmV8vF0iXcsQ3ytACCQ5NJ9JkYuGWPCUFQ0cA5GvOPKfycs1oiGcjcrdWl/o1rfqEpERloce4QdWvko9agsKMo+HVAqClM1XJUOlYvEKKShaKEq5KIO9w/g91EpTaoisiSQf24hg+VrqRyuQrBPgco3yd2nzRyGFM0RYSKRwIYNGzB58uSWsWAwiMmTJ2PdunU5rdHY2IhkMom+ffu2Gn/hhRcwYMAAnHTSSbjqqquwa9euNtdZvHgxysvLW36GDh3qfocMwzAMo4DsryLM98doH0WjYO3cuRPpdBoDB7buOzJw4EC88cYbOa1x0003YciQIa2CtKlTp2LGjBkYPnw43nrrLXz961/HtGnTsG7dOoRC/Ap3wYIFqKw8cJRUV1dnQVYRQfr+SjY8yKsIT7mZHx1G93jHmvqLfYjKwCyrDEyJDzmhjoWIKiVb5YhxduWq8pxUNV2UzHetAGTKlot/kkKt4YeypRo4u7izZ4QPk2vDbQZTfVxb0fjRfojvI/e5gFujZgVrlePqT8agDvE+rGsUB0UTYOXLt771LTz++ON44YUXUFJy4Bznoosuavn/0047DaeffjqOO+44vPDCC5g0aRJdKxaLWZ6WQUmSJHcVCwSESWiWRDY06ILuzMP6Cyqj0ZA4smPjynwxLAMKF+PP3L8U1ZewNP50CCgU7L67HsGxoCnC3Gnhz3Gby/Gja9BKjwil8aroAcgeD7FnaR5KnwPVVschSBP3JQqxP/JuzHWsUGTBDFvc1zDaR9EEWP369UMoFEJNTU2r8Zqamg/Nn7r33nvxrW99C7/85S9x+umntzl3xIgR6NevHzZv3iwDLOPw4YyviCT3vnzYMAyjq1CsRqPnnXdei8jx5JNPdvjt+0XRBFjRaBSjR4/GmjVrMH36dABAJpPBmjVrMG/ePPl399xzD+6880784he/wJgxYz70dv7xj39g165dGDx4sF9bN7oY2WD+HxjymJFcOKvPp0xUtL8h40yRAoAgaYkD8A/FMElabwt2dOjSJqSQKLVLHVW6iAaFvC9s3+pI0kUdU3OV6uOSKK9gtgwhoe74kWwvjT/FbRZqHwqWvB4lihkbKxhFKmFde+21mD17Nh555JGOv3EfKZoACwAqKytRUVGBMWPGYNy4cVi6dCkaGhpaqgpnzpyJo446CosXLwYA3H333Vi4cCEee+wxDBs2DNXV1QCAnj17omfPnqivr8dtt92G888/H4MGDcJbb72FG2+8EccffzymTJnSaffT6Dw8tgwix8kwDMMoDBMnTsQLL7zQ2dvIm6IKsC688ELs2LEDCxcuRHV1NUaNGoXVq1e3JL6/8847CAYPXFU98MADSCQS+MIXvtBqnUWLFuHWW29FKBTCK6+8gkceeQS1tbUYMmQIzj77bNxxxx2WY9WNUYnrzPMqRWwXAN7UGQDSxL4hXep4CUhUmECEX6VnxcUwbZAsmiyHQ7nnT+kcJ2G94JDQrtZgSd0qAVzZNKj5DJX7RC0W2jAm5WsXRg5wTQyncx2T2aPk8XDNa+N5XLnnScl1Hddgrz2Va6Uba5OcxU7OwYIfVYCOf7927VosWbIEGzZswPbt2/HUU0+1nDrtZ/ny5ViyZAmqq6sxcuRILFu2DOPGjctvn12QogqwAGDevHnySPDQiHfr1q1trlVaWopf/OIXPu3MMAzDMLoOfjix7//7Q/0eVbFXQ0MDRo4cidmzZ2PGjBme37sahhczRRdgGUahCJBPolQPt6s3KpSoJUQVIZjqIz4kQyKPK0jWUHYMqvKLzVc5TimhjmVIvpuLoagrSqEoDeVusSAVCrJvaaWgKuHIi0GZXPrRakVWzfmQZ8bMXiNCsZTqWIEUPRelat/8wrwmu5NNw6FWRPtPgg5l2rRpmDZtmlzH1TC8mLEAy+gWjKvg7W9efKTSMzZqHq8MtDeDYRjdCT+rCLdt29aq2XN70mj2G4YvWLCgZczVMLyYsO8Uw/gnWWIUpUQEJQAkyTsq3UNcHZfwxdnaAeGDFRRtbuhcVYnooGaonKoSkcflkoOllC2mcrh4ZgG8RUrYUSFi1X6ubXX8UKXYGjLnyAcfMqXu+KGC8dtz9ONiHmfOnl4O+V0Oc1nBsg9FzLmTDTjnUNE1AEyaNAmhUAhz587F3Llz27VUrobhkydPxssvv4yGhgYcffTR+MlPfoIJEya0/z50EhZgGZ3O+Jle9SncqL9Af//kDTmvfXqlV60qmv5QhmEYXYT169e3UrAKyS9/+csOuZ1C4xxgZTIZ/OY3v8Fvf/tbvP3222hsbET//v1xxhlnYPLkydYyxugU4uXeq7SseHWr8QbW0kbEeZkoH0+XkT8QokU2KdymS3OvTFNX6sy1PSTUJOWPxar3XKsIGVGhdqn8LurC7kerHKnM5F5FqHCZ65LzBQAZ2taFo5pil6gyWAdcKwY78vbUa8klN041Zc5X7SpU3hnDzyR3P8jHMLwYyTnAampqwre//W088MAD2L17N0aNGoUhQ4agtLQUmzdvxtNPP405c+bg7LPPxsKFC3HmmWcWct/GYczYWSTfqpc1JDUMw2iFj0ajY8eOzfuIsL2G4cVKzgHWiSeeiAkTJuDBBx/Ev/zLvyAS8Towvv3223jsscdw0UUX4Rvf+AbmzJnj62aN4uCss++Wv/v1czd5xjLkVZjsydUdlU7AzLCjdfyTpbmvuLqt8441DBUKkVKlWK6U2LTytmIfiKGwcH0XazNvKz9aXjj7YPlQMUj79wkn8pjy9OpotcUxn4mhXNhdGjUzryrA7TF16mfoWC3o1HTaQVFyzXVjapVSqpSPVQm9L16KNUUh1yPC+vp6bN68ueXfW7ZswcaNG9G3b18cc8wxH2oY3p3IOcB67rnncPLJJ7c559hjj8WCBQtwww034J133sl7c4ZxKAlyFAgA4WbvWLyPsBQo42uzIC0YV1YKuV8WZlUyu5jP2uJkhKVDSJ0NEWJhN1NMlqDuT8DE74s6OlTmoR0NP6pULXtUY2LvUARuNg1+tHth+3ZNWo+SgEwFUirQY+gj2tyDVtbGB9DmoXxdt8eDBV4deRzI8LOKMFcF66WXXsJZZ53V8u/Kyn2V3BUVFVi5cuWHGoZ3J3IOsD4suDqYSCSC4447rl0bMro3nz53iXewv0OUYBiGYeSOTzFergrWxIkTkf2QxK22DMO7E+2uIqytrcWLL76I999/H5lM6yuImTNn5r0xo4iRzY1Fe5Mm75sxKQw+w4187TRJOpcXbuK9Ty+yxRqy/Q25iwGRzB4o4coFs2QIi+PEWJSvESb2Da7HeLTdjg/HjFGhSCmFgt2m61EgU4Nkax6lHDl8U6njNnabLsnsAKibLStI2Hd7+R9VFrJBMnu+3JUj7+NR4tjMOl/rBQCIMFNRVmzSkZ1yfFSwugs///nPnf/mX/7lX1BaSvqgfQjtCrCeeeYZXHLJJaivr0fv3r0ROKhkKRAIWIDVDfl/590rf/fbp3K3TTAMwzCKDz+S3LsCh/ZF/DACgQDefPNNjBgxwvm22hVgXX/99Zg9ezbuuusulJWJhBbjsCXZgx/5pcq4ktNMcqVS4mJBpeMkeuW0tX8uwoeTvYhiE+FXttlw7uOBqMjBEnlcrEWNix0DoFuW5Hp76jajwtLBxTbBVQVjypZco4Mvtl2TqV2UI7U2V33yV6pcCwFYfpFLrhXgZhLqYr2gFKlCKVWKELXU6EgJC75VEXakD1ahqa6uzrnvYa9eLl8urWlXgPXuu+/immuuseCqSJgy9jb5u1+sX9Tq35+cwZWqtj4SPvGF1n/TvQRlwzCMYiWA/D+Ru9cnekVFhdNx35e//OV2B5btCrCmTJmCl156qV2SmVGcBDKiZDniffMle3CJKOHgVRWt5+Ms1woA/wwQN6fUMVX4RRGtawLMTkEVIoo2N6ylTSTspgwwVUopVUqVclGaVB5XNOjNhXGtCmTVWTEHhU6hzDalcuSQi6Sq2FRjZ3p7Di1qXFsHOZmmOlTeuapgTFFyNQllKCsF3QSa3J60lsh5G0YX5OGHH3aa/8ADD7T7tnIOsA5ODPvsZz+Lr33ta3jttddw2mmneTyxPve5z7V7Q0bHMvlTd7Ye6OfewNMwDMPognQxo9HOpqmpCbt378ZRRx3Vavyvf/0rPvrRj/p+ezkHWCwx7Pbbb/eMBQIBpNNdw7fG2EcmwnOismHvpVjZe7xMr/aknnxt9gpyvMKjqpS40I8fIRYhHyIZrxfuvqlCfWLj2RK3K3KmYIVEDpZqvhwiylY6wxWRsmicr0EUHlVppmANnHXFYe6ViAqlbMWICqaQlYFkbWXk6UcVocp9Yrfp2qjZjzwudpsuShXgj7cVQ7YOKlBDZsAtr0qvkdsHX4cajVoOVgtPPvkkrrvuOvTr1w+ZTAYPPvggxo8fDwC49NJL8ec//9n328w5wDrUisHoGKYNvdYzljqWJ+cFUuIDL1is3sGGYRiGkT/f/OY3sWHDBgwcOBAbNmxARUUFvv71r+Piiy/+UN+u9tKuHKxHH30UF154IWKx1sdJiUQCjz/+uNk0tINpJ80v2NrBBFcAkj1KPGNNA/gRYaSRvwCb+nmDN5XLlPLeHACAXbDG+/C5zG0dANJs2+JKOKsqAx3iUKWChSLeCxFV6ceUKoDnWykX9qRQtlgDZ6UMKF8qhvJmclGOXN2tWU6Oq7rDlKNYiKt/So1j3lZabRH7IPpFWYDvQzVqpt5K4vYySi8h23Z1jldeX3QNmftEvKNUux0HNTTiQzPwqHrjOhAia7CxgpENtGEI6LBGNyCZTLa4xY8ePRpr167Feeedh82bN7eymvKTdgVYs2bNwtSpUz1ljnv37sWsWbMswPIT8sQrpSob4h+m6TJ+VtY4wDueIUnrQBvvMfJZmHFJRAeQYKeP6ntBBFjMHiETFR+y6rM3Su5MTHzYi+CIPVBhERypI0IWYKn3vwqOmBEnO/JzRR0RqnEWJKiWOAr2xRp2tANQCe0MlzY3rrgkuasg0qXNjQqCWMCpgudoAY/mWDClW+LkfnQoanJk4OWSuK6uwyIBcvxLHtN2u3u3g2x230++awDFn4M1YMAAvPLKKzj99NMBAH379sXzzz+PiooKvPLKKwW5zXY919lslkZ8//jHP1BeXp73proLZ0cvpuPPJR7r4J0YhmEYRvsp9hysH/7whwiHW4c80WgUP/7xjwvWtscpwDrjjDMQCAQQCAQwadKkVptNp9PYsmULpk6d6vsmDwcyvcT5Wa8S/OKlW1sNTf70XXRqvC+XjlKl4liHXEwny/hcqUo5JJcnhV8bU8fUGtL4kyhb6hhPdiAh40FhBxAUazNlS5adOxzNsaR1wM2UUSkU2mKBHTM6luE7HNUohYIlubseI7F9K6WqV0j0YyJIg0+Hx0kli/vR5kbBlLRCtsRRx49KrWKo1wdTpZTKpJQqpjRFhGwclBYQrNlzbmMFw8ck92Ln6KOPlr/7xCc+UZDbdAqw9lcSbty4EVOmTEHPngfOdqLRKIYNG4bzzz/f1w12R6b0rPAOfmS4nD/txJtaDww2ldAwDMP4ECwH60Npbm7GK6+8Qvsq52s55RRgLVq0z/V72LBhuPDCC1FSIlQXAwAQCBFZReRJBXcLZ82wd410TNguqMbEIq8KRJUKpoUiIq66kty9gaJSW5jVg8y1EnlVGda6Rl3GlojWNexKWLTEUQn0TMFijZfV7QE8CVwpWKrpcZjMV0qVUthYrpRKUJfNhh0uf5VNA1NslFKlcq2YWhVzyMtSt6lUH3W/C6USORt8+mDTQPfhQ3K5ixko4GZ7oNrU+LEGndvJSe6BrPyoclqju7J69WrMnDkTO3fu9PzOD8updtXvV1RUWHBlGIZhGIcJY8eOxSmnnILly5d39lZ84+qrr8YFF1yA7du3I5PJtPrxw88zZwWrb9+++Nvf/oZ+/frhiCOOaLOscffu3XlvrFuQZVeVIqZtaqLDyROGeFdI8avVbIg/neqimVkbqHwt1aKGKU3UMsERVQEo1Wo2Lqr0JLTdTu5moIBWq+gaYn9sDVdlwKXNTc8ItwlIZbxPbknIVfVhjYlVLk3+xpXKPLSMWDKoHKw0qQYDgCBReNTtxYL8MaXrqhw4h2bPChdVSlk6qEpE9jxGHVUwtoYfjZqVxYLOzco9V0rmYNE1vLdoRqNdh5qaGlRWVrbYN/hNzgHWf/zHf7R0lV66dGlBNmMYhmEYhk9YDlabfOELX8ALL7yA4447riDr5xxgVVRU0P83NL9o/pFnbOrIW+jcdCkvm8uSxIPmI7mclI7xN0KqJPfKQNr6po1xmisl3o9y7Vjul1gqj4uZhwaI6ee+X+RuEqq8qgJinObpCFVLtkgh+VOuuTusApDlZQFcqQKAsnDCM6ZyrZQvlUuVmMK1xU+uqBwspaQxVbBE5Y2J1xjzBXOtCnTJn1IqGGuG7KJUueLS/kblWimYWuWiVO2bn19e1b41mFrVyVWERpt897vfxQUXXIDf/va3tK/yNddck9f67fY8y2Qy2Lx5M828/9SnPpXXproL04ZXegd79+j4jRiGYRiHH2bT0CY//vGP8dxzz6GkpAQvvPBCq9SnQCDQOQHWH//4R1x88cV4++23PT18rNnzhyBsddMl/KlIlnuVLaXYqvGAsDXOsis/YbKdFjUNTuqxgxu8amej8qqoUKIKAFWzZ1aI6JBTBfDKQFUBqHKwXFCqFHc/d/OOShG5MEjzCvnVOwD6xDD1pK19uDVIzl8x095W3jFVXatgipKLVxjgjw+WS+6TosShVU7UYc86R4/PTzMVzDV/ykFpkq91ti5zd+9IAcsCrDb5xje+gdtuuw3z589HsAA9e9sVYF155ZUYM2YMVq1ahcGDBxesj49hGIZhGEYhSCQSuPDCCwsSXAHtDLDefPNNPPnkkzj++OP93k/35x81dDgc4y6zGeJ5lYrxF4PyjlL9BdmFs6oWVBeg7DZlvpZS3lxehaq9IHlIZNivlD6iKGUz4ipWKWkFMo1R1WoQuTcsX4gpUgAQ9kERcXF4d6kWBLgq5d4wmvXe42skwd9IrvtmsMcpId64On+K5frknmsFcJXIfR8sj0s1ueb7KCmQL5ir1xRTq9Trw2WNTscUrDapqKjAE088ga9//esFWb9dAdb48eOxefNmC7A+hEz1Du/gR3m1gmrInAmTpNikOEpJi0BKfC84BUcieGNvPvmZqY7EXN7Aah8ssHE8gsuSx48VGQBAQNzJLAlsVDAgjUYdDC0VbA11BOfSfFl9gWrLA3ZUmXt7H0AEl2Ifan8ssFFBoUsg5Uegp5LLXewYXF8f7Da1wWfuCequFgtJ8pyrhswKdryXFqkYrCGzQs3NqDZN5MOJJ753YMTiYxVhsTd7ZqTTadxzzz34xS9+gdNPP92T5H7ffffltX67Aqyrr74a119/Paqrq2nm/f5u1YZhGIZhFD/d0QfrL3/5C8444wwAwKuvvtrqd36kPrUrwNrfb3D27NmtNpPNZi3J/WCI+hH4+7t87mmiFyFZIx11e+Kl+kSQF+8ORqPOF0wu89XFH7tRsRGlPgXDROVwTHJXR4cMpnYBXI1Qc10MRRXKpiFKKh5UUjebC3A1SCV1u9gjZMSLWlkvuCRwsyM4gDdIViTF/lyUJrUGnevDsaY6xpMPHZnuciQJcEsGpmoBbbUfYmOitZdQnyLkOddtdXJXwZja5Xr0mA/WKqdtfv3rXxd0/XYFWFu2bPF7H92CGUd/BeGASmIyDMMwjA7EcrA8vPLKKzj11FNzTmz/61//ipNOOgnhsHu41K4A69hjj23Pn/nC8uXLsWTJElRXV2PkyJFYtmwZxo0bJ+f/5Cc/wS233IKtW7fihBNOwN13341zzjmn5ffZbBaLFi3Cgw8+iNraWnziE5/AAw88gBNOOCHvvQb7H+kZSx7bn84NiCbLKWIeqpLWZR60i62Dg5UCwFNhdL6WUJSIjUSGp6Rp2GWWuPRSieuMTFokhkdzz1tSqo+6qnexJXBpoeNH7gczygS0ChYJsfYyqsqADzOjUa12qVwwkuvjmNzPFCW1D1mUQBUb/jGsGldHSf6U2odU0shrQSl0fiT3S2sOsg+Vg+VHOryLfUNSWJLEHPK4jK7HGWecgerqavTvz7+LD2XChAnYuHEjRowY4Xxb7TYa/eEPf4iqqips2bIF69atw7HHHoulS5di+PDh+PznP9/eZdvkiSeeQGVlJaqqqjB+/HgsXboUU6ZMwaZNmzBgwADP/D/84Q/40pe+hMWLF+Nf//Vf8dhjj2H69On485//jFNPPRUAcM899+D+++/HI488guHDh+OWW27BlClT8Nprr1lDa8MwDMPoRmSzWdxyyy0oKyvLaX4i4e1okSuB7KFOoTnwwAMPYOHChbjuuutw55134tVXX8WIESOwcuVKPPLIIwU71xw/fjzGjh2L7373uwD2uckPHToUV199NebPn++Zf+GFF6KhoQH/8z//0zJ25plnYtSoUaiqqkI2m8WQIUNw/fXX44YbbgAA7NmzBwMHDsTKlStx0UUX0X3E43HE4weaudbV1WHo0KHYs2dPqyTAqR/9hudvs2X8CDET4VeaiSO9nZPj5XyuumhOCzWINXZWjZpVdWGqJx+n+ygRV6Zkf1llBioMSLOkzU1AzA2GRXsTtobKF4qINYjxZ0TcXpSoO2qNmKj0U0ajJWS+mqsMSFljZ1V5FxM5WGw8osxRhUbB1lCKnlJbWG6WUhDLRKNmlpvF1KR9a+T+oayqCJU9QknAu7ZSJ13yxvRjKppRE2sIl4pDhVKwImIN1diZr83nshwsl6bOABAL5Ca71+3N4MgTt3i+L/ykrq4O5eXlOPbubyKYp1CQaW7G2zfdXND9diQTJ050TmB/7LHHMHjwYOfbapeCtWzZMjz44IOYPn06vvWtb7WMjxkzpiVQ8ZtEIoENGzZgwYIFLWPBYBCTJ0/GunXr6N+sW7cOlZWt29VMmTIFTz/9NIB9uWTV1dWYPHlyy+/Ly8sxfvx4rFu3TgZYixcvxm233eYZP6//5Qgf9CYLHDcs17tnGIZhGP5izZ49vPDCCx12W+1Oct9f2ngwsVgMDQ0NeW+KsXPnTqTTaQwcOLDV+MCBA/HGG2/Qv6murqbzq6urW36/f0zNYSxYsKBV4LZfwTqUwF7vY5HuU0rXVK1yKEJ0ZJ5ZAJARVYc09UNZVYntsYts1VZH9/IhjZpFnlRWtP3h8x19sFgal1AFM2J/IYf0DFUZyEhn+MJKBfOjupCtoRrxqtwnhsrBcmnyqxQblfvEmwo7NtAmSpO6vWaRRNiDqGOq9Y16nBLkY1spVc1Zvg82X90Xl4bdfjSGFm9xmaPHWuXoCkDhtebQbkeRzOaWq6Zyu4wD/M///A+uv/56ZDIZ3HTTTbj88ss7e0vtol0B1vDhw7Fx40ZPsvvq1atx8skn+7KxrkwsFkMsJs7SDMMwDKMrUIRVhKlUCpWVlfj1r3+N8vJyjB49Gueddx6OPNJbNNbVaVeAVVlZiblz56K5uRnZbBYvvvgifvzjH2Px4sX4wQ9+4PceAQD9+vVDKBRCTU3rVjM1NTUYNGgQ/ZtBgwa1OX//f2tqalqdr9bU1GDUqFHOe3xqxw9anVFPO/Emz5xwzR76t5mhffmipLow3Cyu3knFIQAEE7nPVxWALhf7ovhJVwa6NHtWFZHEf0pdfyrliB3Lqxws6cJO9qF8n1QTaJYTpW5PKVVh4t+l5gYdGkYrwqTSz3UNBcsN0lWVDq1alPoEoT4FvOqTyvlSeVW8zY1S3XJvaK0qVZXSx5Qtpq61BXtMXVUflybQSfnmZ+85PjUum5UTpFqb+2PN8rWUE3xBKMIA68UXX8RHP/pRHHXUUQCAadOm4bnnnsOXvvSljt2ID7Sr3vTyyy/H3XffjZtvvhmNjY24+OKL8cADD+A73/mOzFvKl2g0itGjR2PNmjUtY5lMBmvWrMGECRPo30yYMKHVfAB4/vnnW+YPHz4cgwYNajWnrq4Of/rTn+SabXHegDmYUnppy49hGIZhHE6sXbsW5557LoYMGYJAINCS83wwy5cvx7Bhw1BSUoLx48fjxRdfbPnde++91xJcAcBRRx2Fd98VBt1dnHbbNFxyySW45JJL0NjYiPr6emqT4DeVlZWoqKjAmDFjMG7cOCxduhQNDQ2YNWsWAGDmzJk46qijsHjxYgDAtddei09/+tP49re/jc9+9rN4/PHH8dJLL+H73/8+gH3u89dddx2++c1v4oQTTmixaRgyZAimT5+e/4abmj1DmQF9+Fx1cUY8r5STu0q7Ub5ZIq3HaW2XC1ZpDcQULMc8DHGLLpOp5xVrAA1wtQtoY9/s9pQ7u0MTXZe1lZqk8qcy9Gqfz5U+WEThkfsQazeSDuRlIV6lp0VSpi4o9Sl3byuVdaMUthD5C5m/Jl5jLL9L7VlVOTK1RflxAXyNxix5XkT1JKs4BIBm4kBf4ui7xZStuHi7lIn3M7tF/voHEuJNzqoZI2RvTR2Yg+Wnk3tdXV2rcZUq09DQgJEjR2L27NmYMWOG5/eudkt+42o0mg/tCrBuv/12fPKTn8RnPvMZlJWVtfhJNDQ04Nvf/jYWLlzo6yb3c+GFF2LHjh1YuHAhqqurMWrUKKxevbolSf2dd95p9aB9/OMfx2OPPYabb74ZX//613HCCSfg6aefbvHAAoAbb7wRDQ0NuOKKK1BbW4tPfvKTWL16dbs8sJ56/8FWR4RTesz0ThIBVjqmrBdIux3x/iRV9f9cm4+zk6G0ekWo7wDyyeSQ79yu+XwjbGG3Vjkun0QqkGLHjy5zFSooURYLCRLwMOsGwC0hnpl+AkBSPIkxMh4UX6DKpoEGaY5J7n6YZTLTThVQuOzDNdmeHTm7NntmQbx6/FXgxY5B1WupWXzVsNe1NKIV7yMWkClj07TMoPdSJh5Tl6NKljzf3JFJ7j4eER5ayLVo0SLceuutnunTpk3DtGnT5HL33Xcf5syZ0yKMVFVVYdWqVVixYgXmz5+PIUOGtFKs3n333TbNxF0544wzsH37dgwYMAAjRozA+vXrC5bf1S4frGAwiEgkgsWLF7eqpqupqcGQIUMOu16E+z1HJuLzrWwagqWkYvAjvOdgfEAPOp4uIVfNQsFSOViJnqq6kKwhOv2wuQAPjlSulfTYIvlW2bCjD1aU5C0JL62g8LBi80OkPyEAhEX1Hst9ioV5YBMSV9NRMl/5YEVFwMO+QFWA5eKDpfahvhR7EqVJ5WvpfCbvuPLdUjBXdBfPLIAHDy5+V3ofbveF+WOpNVQeF6OM+GsBWllkt+laRcgCLJc9A24Blsv+XAMs5tPFcsH27s3goye/3yE+WMO+eacvPlhbb/4Gtm3b1mq/uRR7BQIBPPXUUy0nQolEAmVlZXjyySdbnRJVVFSgtrYW//3f/41UKoWTTz4ZL7zwQkuS+x/+8AffgqAjjzwSzz77LMaPH49gMIiampqcXd1dafcR4aOPPoq5c+fiL3/5C773ve8hGrUefDkhZEnpYECuuJJlBZQ2ZTNlMUy2ohQplogOiCNCJaqoZspsWLXKUcoWWUS21RHFAC6qlEpcdyElHmwWNOmjudyPKlUgVSrkU/ZFpz501Bc5O8BRe5bHe+zYVdxaXFwhqMCLofbHHr+0sFJQSecJhybQIRE0MQWQHfkBOvBiypZ6hGTSP3s9ibeQPGakLYyUGsdfY+y9EXe0Oonn+H6p70iXBh8VrEmTJiEUCmHu3LmYO3duu5bKxW4pHA7j29/+Ns466yxkMhnceOONvipM559/Pj796U9j8ODBCAQCGDNmDEIh/p76+9//ntdttTvAOuuss/CnP/0J5557LiZOnEgT2QzDMAzD6Bz8zMFav359hzm5f+5zn8PnPve5gqw9d+5cfO5zn8PWrVtxzTXXYM6cOejVq1dBbqtdAdZ+m/njjjsOf/zjH/HFL34Ro0ePRlVVla+bKzbCQ4cgHDxIMi3zHhHGe3NJNZQUV1wx71MUErYLKpldwS7QxOkNZH9kdgGqrtCkxQIZkx4LuZuVyk0rFYxNJUd+QFtWD7mX0LuoXdKOwQeLhag4bnNpDq3255Kcr3KAmLVBRKaX549SW9h9zKjWK6oZNbsvUnXL37y1Ics/b1QjaYYyK2VI+wxVSEHuY0joYIlA7sqdeuk2q8baJJFfvW8TQr5mR5vsOWzo0Bws/5zcx44dm7eC1R67Jb/52Mc+hu3bt+Nf//Vf8e1vfxsVFRXUKNwP2nXWdHDaVu/evfHss8/ivPPO86fyzjAMwzCMLsX69evx2muvtTu4Atpnt+Q3ffr0wZYtWwDsK4xrT0FbrrRLwXr44YdRXl7e8u9gMIj7778fZ5xxBtauXevb5oqNn776rdZGoyfc6JkT3dVI/zZxpOjsTWsQRM6MygsSYTQTLlSSu0tulkoRCTXzfad6sh414gZdbBPEXKkcsQpAoYIFRPI7MxpVqBws1hZHKQMuVg8KZbHALr/CQjlSVgNpch9jjjYNQXKbqkpPUeaQCK2MPxkJaW3AYaqgUu5CYh9MFVG5Vgpm9aBUraRQbFxURJ1f50XmmIm3Fkv6T4qvNpXkvjfj/ZLVeWO53xd2v5syHVgE1glGo/X19di8eXPLv7ds2YKNGzeib9++OOaYYz7UbqnQnH/++fjUpz6FIUOGAEDXy8GqqKig47NmzeqwB6krMuPoryAcOBChBAb068TdGIZhGIczfuZg5XpE+NJLL+Gss85q+fd+p4GKigqsXLnyQ+2WCs33v/99zJgxA5s3b+46OViPP/54zi7t27ZtwzvvvINPfOIT7d5YdyBb4r1KzET5Q54VjZqlo6UDKq8qRZRReXHmUEWoSMcc3unSNVXMdylFVJ84Tj5YQjkiipdqAC3XIGPaJFS1v3HInxJKjrJvoLfnoLDFhdmasoBguS0650sZSTLfJ46qIqS5Ug6mpPvme+9LY0bkZYa8RsUK1Y5Jeko54OLpxbzCgLaqC9njJ1oYCbWwkdx1ldemmmIzlP+XUj5Zrhptqu3YTqirkGuS+8SJE/Fh7k/z5s3DvHnz/NqaM1OnTgUAbNiwAddee23BAqycvx4feOABnHzyybjnnnvw+uuve36/Z88ePPvss7j44ovxsY99DLt27fJ1o4ZhGIZhOJD16aeb8vDDDxcsuAIcFKzf/OY3+PnPf45ly5ZhwYIF6NGjBwYOHIiSkhJ88MEHqK6uRr9+/XDZZZfh1Vdf7TC5rysR6NUTgYOqCLPbd3gnjTia/m24XlTO9CJXhELVkgKMEmyY6CD9p8Q4W8I1xYClYAnxJKu8rdLE8V6YkqrKQIoUFsXaRDlyqRYEdBNoentqH2Rcql0O/lgyX0vA/LFcncuZKqKMRlWuWjJDqnGFyqf2x8alC34g9wbOcs8OflcNQgVzNTF12QfLL1KKmaxaJE8Bcz/fNy6ULaI4HhlqoHNrMzzXleWTSS8zh+bctL2Su7d3+/HhiHD/0+FHFWFX5Le//S2+973v4a233sKTTz6Jo446Cj/84Q8xfPhwfPKTn8xrbaccrP3eFDt37sTvfvc7vP3222hqakK/fv1wxhln4IwzzuiQ/j7FQnboYM9Yugc/fkjH1JFC7l/OMnfb8djPZW32GaSc3J1Qt6esF4TzuxPsTjouyz47dT/D/Pcsk9zJuHJ9dzl+VLYQCmptoJK3xRdPjOxbBh9ie+wLnn0xq7kA6JWD+rJlifkA/8JVAZZrIj9DuaKzYFEFaXrt3K+k1GPdK+g9Bm0U1hIq+Z0dw9WRpHVAv/aYnYUq3FC2FezxayQtMJrjKQBv0jW6Mh3pg9VR/PSnP8Wll16KSy65BP/3f/+HeHyfue+ePXtw11134dlnn81r/XYluffr188sGQzDMAyjK9MJVYTFxDe/+U1UVVVh5syZePzxx1vGP/GJT+Cb3/xm3uu3K8Datm0bAoEAjj5633HXiy++iMceewynnHIKrrjiirw3Vaz89PV7Wts0HHW1Z06wTBydiqv3DFG2kmXi6l35aopTApbkLvsgq3G2tnhVBVNCcmfqk+ubms1XifmuTaAJqTR/DlgvQnV7rkeHDBcFq1kkl0dFX8Wow+PhZCjqaDnB1Co/mje7Nkhm+5C97VzMQ8USMdEqhyVfq8dDqT7MAsL1eI+trZSq3iJhnx3ZKeVIsTfrNXQuEbYVbyd4z7khkQ88Yy4tiRTseUn78NrNGR8DrO54RLhp0yZ86lOf8oyXl5ejtrY27/XbpUFffPHF+PWvfw0AqK6uxuTJk/Hiiy/iG9/4Bm6//fa8N2UYhmEYRtfBD6PRrsagQYNaeXbt53e/+x1GjBiR9/rtUrBeffVVjBs3DgDwX//1XzjttNPw+9//Hs899xyuvPJKLFy4MO+NdQcyQ4gPlriaSPXKPXHJNXVHmYeydYQwA5IjvA8WoiuHBZUnxXKUXJs9s/kFrIYOC9WHzhV5S0qxYcoWMx8FgNIIVxdYXlVZOPcSdbWGVJ8c+iAphSKs/EQccFHBFKqpM923eDyU1UOvUJNnTJmVKvsGF3sERZK8npRSJU1kyZufmX4CQF2a50QxewmlpKm1mWrWCP6hpx6ntxPez+p+4b107jsJ3niYvW7YY9ecdnsf5oOfPljdkTlz5uDaa6/FihUrEAgE8N5772HdunW44YYbcMstt+S9frsCrGQyiVhs35v/l7/8ZUtTxo985CPYvn173psyDMMwDMMoJPPnz0cmk8GkSZPQ2NiIT33qU4jFYrjhhhtw9dXeFB9X2hVgffSjH0VVVRU++9nP4vnnn8cdd9wBAHjvvfdw5JE8uj8sedUrPWbHncznpkUOVjT3nA2FEgaYsiWrqYXAxoQVmdriR4GpqiIsUOmzH3lS6oo8IpQtF5sGbRPgHUoIiwVlKEorAIWaEXLIK0mKNWLCUoCpDqryUeUiJcl9LwvxPB2lcrC1VQ6WS5sVVemnYGphUihmLhWKIdGEuDHL1SCmvPUQeWOKOHleWGUhAOxK96Tj7DaVnqdUKdYqZ0+aWzqUBfnrhilp78XLPWOJZre2RnlhSe5tEggE8I1vfANf+9rXsHnzZtTX1+OUU05Bz5498eqrr+LUU0/Na/12BVh33303zjvvPCxZsgQVFRUYOXIkAODnP/95y9Hh4cj0I/8N4cCBN1lA9DcyDMMwjGKiOya57ycajeKUU07B3r178dhjj+Ghhx7CSy+9hHQ6v7SFdgVYEydOxM6dO1FXV4cjjjiiZfyKK65AWZloWnwYEjjh2JznhptF1VEpk4jc9uHU7Fk0FlcCBbWsURWHao0gabIsminL+049rNz8p9i4MiVV9mRM8WINftvaR5Ikwqk2Mi4eVi5ql1pbruEDqoUOy81Kyeo4/nxFHPK7lCrFlC2VTaPaodBGzWrPDqqgi/klwJUt5YOl2svkaqzZFkwNYmqSuj2AK0dKuVP5bkwFe7n+GDr32NKddLw64fWIYq9T9dotBH7mYHVHH6z9rF27Fg899BB++tOfYsiQIZgxYwa++93v5r1uuwIsAAiFQkilUvjd734HADjppJMwbNiwvDdkGIZhGIZPdOMjvnyorq7GypUr8dBDD6Gurg5f/OIXEY/H8fTTT+OUU07x5TbaFWA1NDTg6quvxqOPPopMZt+VQigUwsyZM7Fs2bLDVsV6etdDrSL8qb1neeYEeh9P/zZVJppAE3VH4vpGcllaWW+xHsuuTu4OjYmd1vBhWfUQpdNCMYh4lSZVYafyu1wqFJWixCoX/XByV3PDQm1hV+sxh1Y0gFulWcThsSskSn1iOWyuflxMsVEVgC4qmFJ3lHN5lOSZqbku1Z3q8VDKFlPBWM7dvtvj93Fn2tuPbmjJbjp3T0rkZpG2UHuSXo+uZMYins7m3HPPxdq1a/HZz34WS5cuxdSpUxEKhVBVVeXr7bQrwKqsrMRvfvMbPPPMM/jEJz4BYJ9vxDXXXIPrr78eDzzwgK+bLBbO/9gihENu7SYMwzAMoyBYkjvlf//3f3HNNdfgqquuwgknnFCw22lXgPXTn/4UTz75JCZOnNgyds4556C0tBRf/OIXD9sA61CyJ3pzsDIR0bssJXJ9SJleViQAOald4LlZMk1H5U+RAiOVhiFTD1x6ALr0wnNMF2IPq9qGqvTzo+rQBRcn95TynxK5LRnyhDGn+n1r8yeXKU1xYaqmGjgnyU2quY3C9I0pb0lRa6aqC1l+US/hUK58sJhKF5S9GXPPZ9LNnl2qO93y2phaJas4wR8P1mRZucGr/bF+fwq1v7KQNwdLKVX1af5YMyWNNUd3bZieD37mYHWnJPff/e53eOihhzB69GicfPLJuPTSS3HRRRf5fjvtCrAaGxsxcKC35cuAAQPQ2NiY96aKlmQCSB/4Qgk0kS8o8afxfl4pGQD9hg8lxZGOOJpTAZnLyYRLs2fZVkcFaS6fN8qmIZT/ZZaL04MKpELkiEqZhCqbBtaGJxIp3NGXOsKMqh5LBHUkw5LLlaGoOiJkqKa96qiSBQnquFMRIY9Hszhyki10fIAdMypzVAVv++OWbE+T/h0aMgM8SJMmrcK+gSX4vxs/gszUwTMrRNmR4LYQR0Ya6PjfG71mpUNKaz1j8Q40GvWT7pTkfuaZZ+LMM8/E0qVL8cQTT2DFihWorKxEJpPB888/j6FDh6JXL++xsSvtciaaMGECFi1ahObmAy/4pqYm3HbbbZgwYULemzIMwzAMI0+yPv10U3r06IHZs2fjd7/7Hf7yl7/g+uuvx7e+9S0MGDCgxUA9H9qlYO1PCjv66KNbPLBefvllxGIxPPfcc3lvqmhJZ4CDzPoCexvwv+8sbTXl7Al30D/NhnIvoVdHga5KlYOyLkNxdnErjwgd1pCvTJUQz4YdVDdAHBGqdjZi6QxR2CJhlfCce6K2UohkGx5yJxOiD1LPCL+qZ0cZQaEUqiM7P3BVmhhM5QiS4ymgDaNRMubUVkeMx8Vcl+M99VpSSedMUVKPs2rZw1vDuB2RuzR2Vsaf7Ci1X6Sezn0/mbsCox6PXckeOa9R3dwbPz7z+63G6urq8AB+lvMa+WCtcnLnpJNOwj333IPFixfjmWeewYoVK/Jes10B1mmnnYY333wTP/rRj/DGG28AAL70pS/hkksuQWmpOOo6TJl2ytdbD5Tb42MYhnG4MPp/v9Hq3+lGN7d7o2MJhUKYPn06pk+fnvda7QqwFi9ejIEDB2LOnDmtxlesWIEdO3bgpptuyntjxUi6+n0EDnJyD52QfzfuUMJ7+ZASMVpQtNtJx/hVJbv4Vk2dnS5iVD/mtEjIjnr/QF01qRSsAFVW+CJZsUiGWC9ERYPkoFDSXBo1s3wthWpno1DmpgyV/O6Sg6VMQplBqktOD8Bb6KhcKz9wSS4PivYyJaJVDsvZUiqOepxYrpoyGlUwVUqpcartD8uVUgaru1M8n4k95yzhXM0FgHrijvyBSFAvD3ubbQNcId4u1K5SYscAAJs/8OZgySboHYVVEXYq7crB+t73voePfOQjnvH9PQoNwzAMw+hkLAerU2mXglVdXY3Bgwd7xvv374/t27fnvaliJdT3CISCBxKb0uQ4MNgoGn0ewfMcUkJ9oijlSIynWXqGSnFyyGcShVXIhsXiRA2SdhEuQo5aRJULOrSGyQo1jhmNqptzaeqsUPtjqpTKtXJBKWMqX8UPmwZmNOqa6+PS9scl5yupJF9x6cqqC1WjbFVVyWwJlGKmYEqTqvRTBp9MwVIqk7JeKA97q87rU8JQVFQA9g55VSllpfCXuqPoOFOIB5XsoXP/0cQrFE85soaOPzLuoVb/rqurQzm+Tef6jeVgdS7tCrCGDh2K3//+9xg+fHir8d///vcYMmSILxszDMMwjGJn7P+2zsMt1hys7uSD1VG0K8CaM2cOrrvuOiSTSXzmM58BAKxZswY33ngjrr/+el83WFSEw0DwwEMa2u31S0n35RUowZRSVUi7EuGDlSpRio1YOn8BhVYASh+slMh9iuSeg5VNcmmAqWMB1TDaB4IORqPKnFPmZoVzVyNc1JaUuD1VichQOSVK2YqQF4PKOVLqR5BIgGlhEsrUrn37yz1nS+U+MR8shUvDaGZQqeYCfH9qrsqJYuxO888m5T8VJ3WVrn5cDJVPtqXJm+MEAINjXqVJPR79Y3vp+K64N0fstT2D6Nw+Uf54MAX2/3ZwxazD8DEHqzv5YHUU7Qqwvva1r2HXrl34yle+gkRi34dDSUkJbrrpJixYsMDXDRqGYRiG0Q4syb1TaVeAFQgEcPfdd+OWW27B66+/jtLSUpxwwgmIxQ7vPnzZ+npkAwfyI7IDvWf1qlWOQ+GSU+NlQDuoZ0g1out5O9uL6gSRFZV3AVLVp+ZC5XHRhcWwWJtVBiofLGE5Jua6Paj0NsXtKVWqxEEFc6lQlK15xAbZuKpO9MP9PCjcyF1QOWLsvii1q1HkHLEKOancqebX5HFKOlarMRVR3RflpcWqLXcmebXgESTXCuD5Vurx7xPhFYDMl0rd3vtx7s79j/pyz9iI8l10bkJUzH6QKPOs8/72Pp55mSaugBndj3YFWPvp2bMnxo4d69deip5Av74IBA8EmaKCm5KJCmNC8gylI+KYRnynKl889vlNKp7/OZkPuwSGLJACgCzrLu9Huy7H72tmEirtIoTZa4gcS6aI/QMAREhCPOAWkKlAyg+bBvbh4GLdoEiIL1BlvcCsENTRqBp3OVZTgYaLfYOLSehe8aYrkbYJ3rUbRVK36pXIPpp2p/gHBUtEV0gbBGmx4N23skGoE8nve5PecTYGuJn79hBJ9T1CCXx/zCOe8QnPzafzOxNLcu9c8gqwDMMwDONw48xfeFNhXFTtDsOOCDuVogmwdu/ejauvvhrPPPMMgsEgzj//fHznO99Bz55ckt69ezcWLVqE5557Du+88w769++P6dOn44477kB5+QEZN0DeFT/+8Y/b1Vk7tfUfwMFGox89wTMnGxKJzXF1vucdygb5Gq5J7vSiUr2ZXE7mVGseksy+7w9yX9upqbP0euDD7IgwKBLUldGoC2mh6IUd1Dt1RMiUnLDj8Rk7olLqU4lQHZhNg0pEZwnxClebhiZibaCOE5V9BlOwlFKojkyZrYNSqhRM8VLmnMzSAeD77hfhCeCqKCFO7uMHSW7wKU1kfSgc6BfztsXZvLc/nft2LbdYOLnf+54xdVS56YMBdDye8s4v7+/dW7FWERruFE2Adckll2D79u14/vnnkUwmMWvWLFxxxRV47LHH6Pz33nsP7733Hu69916ccsopePvtt3HllVfivffew5NPPtlq7sMPP4ypU6e2/LtPnz6FvCuGYRiGUXDsiLBzKYoA6/XXX8fq1auxfv16jBkzBgCwbNkynHPOObj33nup99app56Kn/70py3/Pu6443DnnXfiy1/+MlKpFMLhA3e9T58+GDSIl+S6ECrvhdBBSe6BOq9NQ7SWNyGNn+Bw+z5p0VQwcDT4dGnULK0XXNQgYfBJm0A7PkysVY5KiGf5WgAQJM+NsmlQTyNLck+J0vUAy18DUCZa/ORLOMhzi1QeVw+iUKjkbWUpwFZWuVbSPJSoVbIVjVBK2GylzIQc1DiV26VsIZjipcw5e4Z5DhbL2doj2suoPC7WXqa3uD2mIAJAfSr3jvO1Sd4jjLVj6hPluWBl/fj7Ynfcu3YyzU9H3t/NE+WH9K+l47+bfE+rf+8zGr2HzvWdbnpEeN555+GFF17ApEmTPIJJV6IoAqx169ahT58+LcEVAEyePBnBYBB/+tOfcN555+W0zp49e9C7d+9WwRUAzJ07F5dffjlGjBiBK6+8ErNmzaJHh/uJx+OIxw/IvHV1dY73yDAMwzgcmPKb61r9O9VgR4T5cu2112L27Nl45BFvsUFXoigCrOrqagwY0PrcOxwOo2/fvqiurs5pjZ07d+KOO+7AFVdc0Wr89ttvx2c+8xmUlZXhueeew1e+8hXU19fjmmuukWstXrwYt912m2c8UFbauoqw3HsFlO7Jq33SMVFFGGIWBnxfWTLXN5TaQi7glY2ELMJiapAPOU4K1eYmGPVqFK4WC077cFjatWqOKkpCVJFrEIVCKVgqB4vlImXECySszEDJ08X2BgAhkaPHFCx1v1WbFYZq36JUMKZ4uVQcqvkql0kpW2wfGaGSuqhxSqlSFaKsYlBZKShYxWB9ku9DVd0OKfNeJL9T34fOPWGIN19rP7/49NJW/z40uOpwuqmCNXHiRLzwwgudvY0PpV3Nnv1i/vz5CAQCbf688cYbed9OXV0dPvvZz+KUU07Brbfe2up3t9xyCz7xiU/gjDPOwE033YQbb7wRS5YsaXO9BQsWYM+ePS0/27Zty3uPhmEYRnEz+n+/0eqnswn49OPC2rVrce6552LIkCEIBAJ4+umnPXOWL1+OYcOGoaSkBOPHj8eLL77YnrvX5elUBev666/HZZdd1uacESNGYNCgQXj//dZXDalUCrt37/7Q3Km9e/di6tSp6NWrF5566ilEItw0bz/jx4/HHXfcgXg8Lo1TY7EY/10sCgTbzikIJviVXDas2sgQbyaZDyWMPF0qrsTaqoEz24u8IHdSwVSHZIdu1EIiUnlVWVqRp/KnxNoOTYXD4vLGRTVzUbZc2uoolHKUEi/KCFEoVA6Wzqsi1Xsqj0tWjnrXUEqVal3Db0/lT3FFj1X1KQVLtxTyjquKN9V2piepOlRKVWOaf6bFiQHpEWFv3ikAvN10JB3vHfHmbPVSeVxiH+82ek1Ch/fmJqHvN3F1rDkVxqpP3d9qbNj/dy+de8xJvKlzQ8K7v20f9PGMdWgVoY8K1qGpMOp7sKGhASNHjsTs2bMxY8YMz++feOIJVFZWoqqqCuPHj8fSpUsxZcoUbNq0qeWkatSoUUilvGrjc889V1T9jjs1wOrfvz/69+fltAczYcIE1NbWYsOGDRg9ejQA4Fe/+hUymQzGjx8v/66urg5TpkxBLBbDz3/+c5SUKBfNA2zcuBFHHHHEYe9KbxiGcThx4borDxk5vlP20VUZOnRoq38vWrTIcyIEANOmTcO0adPkOvfddx/mzJmDWbNmAQCqqqqwatUqrFixAvPn7zNr3bhxo2/77kyKIgfr5JNPxtSpUzFnzhxUVVUhmUxi3rx5uOiii1qi2XfffReTJk3Co48+inHjxqGurg5nn302Ghsb8Z//+Z+oq6tricD79++PUCiEZ555BjU1NTjzzDNRUlKC559/HnfddRduuOGGdu0zteWd1j5YJ5/omZPuwxW0YEJIR6W5n+K65mBR9Uld7Sj1iXV1ke7nYm2mKLleddEqQqFUyfY33gdEzXVB+yoJZ3syrhQb5YMVJblSrnlcLm1ulO9TU9r7eldO3TGhHDF1RuvQQkkjKpEw45dEyGPaLFriZESeX8jBi8zFOV6hPLbY89UoHOVTImcuSV5725q5z1SDUJ92Jbzu8b1FS5w64c4eI3lVu+PclX5Pgq9xVA9vDtYJp7xL5wLA8xP/Q/7uYCau8X6XpIJx/C2nv84fP20atm3b1qrZc3tEiEQigQ0bNrTqWRwMBjF58mSsW7cuv412QYoiwAKAH/3oR5g3bx4mTZrUYjR6//0HJN1kMolNmzahsXFfW4c///nP+NOf/gQAOP741lciW7ZswbBhwxCJRLB8+XJ89atfRTabxfHHH98SXRuGYRhGrpz280WesSNFm7IOw8cjwkmTJiEUCmHu3LmYO3duu5bauXMn0uk0Bg4c2Gp84MCBTvnWkydPxssvv4yGhgYcffTR+MlPfoIJEya0a0+FpGgCrL59+0pTUQAYNmwYsgfl3EycOLHVvxlTp05tZTCaL+HBgxA+KAcrU+q9upU5WD35U8Ec3lOlfjTq47gKNuzqSFhEualgQgFQF/UB2gRabST3/CnXDye2RlqpTKT/mZqvVDDlbs2qCLV3F1/DjybQpcSPi+UQAUBQ5BGxCjlXHyx6e46X9sylXFXvKTGZqWAqf0qtTdUnUb0XEp+BLk2xldLKcsRU3pjKq2omCqfqFsBUWQAIR7z35ZXqwXTuif130PGaRm/Fd3lMN2Ue/p+LPWM9e3vn9Yh4VdlkpDAedYVm/fr1rRSszuSXv/xlZ28hJ4omwCoKSqLAwTYNpKVNulQ85OK8IhMhX7YJ8aUaE0dfKnHdJZhSx34sQV2dbKhOOSyucQz02PeIXKKLlB3L+J9sXAUOMdXKhyW5q2BMBIDMIFUeMypzTocnUid1E2sDYemgjzvJ2qIbe0Z0GncpHFDJ5UHypKu5KvBiqPZDpeLYldkpxNP89naL9jfsuFgZje5McNNOtka5OCJ8bvNH6PjYY97xjPXrxZPt1fuoOeUN9JpTEfxxijeQYsEVANTXlWDLl709Cg9ln9Ho9z50nm/49Hk3duzYvBWsfv36IRQKoaamdaFATU2NL2bfXQ0LsAzDMAyDMGrVLWSUB5xdET9zsPxQsKLRKEaPHo01a9Zg+vTpAIBMJoM1a9Zg3rx5+W20C2IBlp80NAEHydiBPuQAXrzYZZJ7CZHhhaWDUqpEfilFmYSqtekphlCwxAUyd1hQKpiLsqU+WdRRJVNyxDFeRqg+WaKKqOM9l45H2gxUqE/kNlUiujoKZLepbBrKwrz03MViQY3nuu4/f0NHlcLD9yGaURPVzPl4j7zY5bEmsZZQt6mPKvnjwdQq1nIGAMrBFSVWwFAnjE3VayxBPnD2iJY4I49+j47vbPZ+zqrHdNuePnS8d6lXeauP8yTuQQP20HFjH/X19di8eXPLv7ds2YKNGzeib9++OOaYY1BZWYmKigqMGTMG48aNw9KlS9HQ0NBSVdidsADLMAzDMHzgE8/f1OrfzBurWH2wcj0ifOmll3DWWWe1/LuyshIAUFFRgZUrV+LCCy/Ejh07sHDhQlRXV2PUqFFYvXq1J/G9O2ABlp8Eg/t+9uMgUWRdasYd3zCiUhvkAlSahGZERS4VEhytHvhch4QtiIdaLSHtG7xjronQzGJBNiD2IVFbwdZwUapcYcnKAE9Mjoh9KFUqEuDKissacdLIu1S0uVGqFLN6UMqYeg5Z8rtqGN0zKFRB8mJXe25I8Tcuu+/KeFUlnbvMVUnu1cSA9O1dfenc4f24eWgTyZ8qEc3Oj+lVS8ff2MX9GF/+1zvoOOPQ4Kor0BlHhLkUmM2bN69bHgkeigVYhmEYhkEY9ui36PjWmfM7eCdGMWIBlp/0KANCB64Cg/Xeq7aAqCLMBvlVLLsgVFckfuQtyTY8uafHuLojuCHVnfxLEYOkai6Z5M9LLMavkJk6phSiRJqvzewbVJWeMkJlypFSVVQeF0NbN6g13BoZM5itg1LBnPK4ZHPj3F+oqkpPKUpMrVL5Uwq2b5Ubp/Kqdie9eUtKZVKqVG3Cm+zNWt8AwK44ryLsV+Kt9isZwPf8PrFS2M+6s1sHQqc8fSudV1PXC69N9/6OJbPX7nIzsapr8uafNdR7VcFMY/4Kdc50whGhcQALsAzDMIyiZuT/HBogaa/AT635Ghn98DZqxUhXqyI83LAAy0cy/3gPmcCBpMbgsKM9c7IhUX0mnAmDSVLJJaoIw3GhlPQUV0xsKz4YjX7I8Xtu6yqj0bCDlOaaP8UMPiNcgVHpdUxRSpH8H4C3+AC40sSMQwGtUDSSvJSexPQQ4BWHAFfNlBqnWuiwfUfEi0ypdC45YioHK8wMPkVZK5sLAGnypR3JiipToY4xXzBlFqva6sQCXvU0LXI4lQ8Ze772igpA9dz2iTZ6xlSLmoRQ2PqQx3pwKa/SaxbPV/VebwPn8jKupEVEVXBT3Pt+Ke/LvbQU0Yj3cYoekcKGaXe2Gqurq0P5nNxzu/LCRwXLcCf/ZleGYRiGYXRrxo4di1NOOQXLly/v7K0UDaZg+chT7z/YSkKdMtrbmyoT5TFtICVybByaPacjbv5Y7AJZdNyguWAAz/uSXlpClWL7y0TFpkW7l2zG+/gFHDsKBYiSk1W3p5ztiTqjFKIQa1ANtwpA1YaHqWNKRZAKEbnrYVFmqjy2mCqVFPtQsBws5RiucKnMVHOpo7x4PFgjZIA3ulb5WnVp7gfVJ+JVjlQOVtpBkpbPrXg8WA5WfZJ/gDSK8Z5hr6q6I+5VpABgTzN/PJgnXZ8S7t311vv96DgA/O0LzFQ0dw5VqroEPipYdkTojgVYPnL+mFsRPijJHWUODp+GYRhGl2H0/36Dju+qLqfjoTJv8Jza6/0OyDS5XRzkg585WIY7FmAVkOAe75VmqAcPulJ9+Xgg7X11q3wtpRwp2BtHFTSptV1uMxvK/Z0qc7CUssWmi9wnkOawAM+rCiiVSSgUkTDxShJrpIU6xnoAKtVH+f2wCsUSkfOlqghZNaPKcQoK9YPlM6kqPZ2D5X08lOqj1DhWqUdETwDal4qpVU2iTYLKjUs5qGBx8fH890avCjMgtpfOTYvHqYwoaXVJnoO1Q1QAMlVVPv7i8XjzA+99KRXNkEtFDmFD3PscbNnp9dcCgD49ubIFAJ/85Y2HjHBvN8PIFQuwfCS7czeywYPe7Ed5nWmVoWioWZgv9iJPkWq3I44ZU6UiWHE53nO5inE0Gs2yAMT1qonNVwGdOt4jAVlQJBoHRLI9s2lQRyxhEf+xRPmQSM5VR4QsmVcFMC5J7gp1RMWPNsVxp3iB9HSwMVAJ6sxwUxmNqkN5FqSpYEy9blhgSPq5y9sDeDNk1rYG0AHxNhI0qceuhARjAFCf9D6mss2QeP0O77PbM7armff6KxMXE/GE9zHNbuZBYf1H+PPSt6f3Yrh2D0/YD5Wm8NZFX6e/y4W6ujqUX7Ow3X/vhCW5dyqW5G4YhmEY3ZBANuvLD2BJ7u3BFCwfCfTuhUDwwFVdJkKuboWCFSRHgQBXmbTRqFCqVC9ZcjEnW+U4vFJkzrRYO0D2nYmIRcTRIcJkvmzqrB4QkijvQ6scZX7pkiiv1CRp3+DUXkY9HrnPVeoHw9UUsymT+1ENT4Pm+1Pqk7JNoHMdXx8RoRYyXNrfqNeY2h9TpVQRxPYGntjcv9RrY8COlQGgLsHb8LxT18czdnQvbtOwJ86PMI/qW+sdHFeLX3/m23R+rnzs2Zvz+vvuhiW5u2MBlmEYhnFYM/xHiz1jRxyh5w979G46vnVmF+tHaEeEnYoFWD7y01fubG3TcIbXpkGRVvYN5MJUtsRxhIkRam2XVjmqCt+lc41zOyA6WS3usITYtMp9CpJ8MpXMHlUqHUHZIEQclCNX/GgC7bKuMv5k85VNg25RQ0r5SS4ToFW6OmLEyWwG2toHuy+1Sa67qXZALHdMPaaq2XNtwnubqs1ND5FcHiWKozIlVfYNTPF6by9XSXa8w5tAn3DSu3Q8Xz74oAe2XLLAM66Cq66IVRF2LhZg+cj5p85H+KAjQhxhcqphGEZncOKTXrf0jEoPaKO1jmG0FwuwCkiwMe4Zy/YVWSIFVGxkA2eWtiTmZl1ysNQaLlXPjldNtHWNuPRSeVXMkkHZNIRU5R3N46JTnWwaFEphY9YLal1VRajyuxglQklzMfhUOVjMAFNVHDKlCuAGn64wtUoZiqr8LvYFr5QqpVo2EVWql1CfFLQZuLg91pAZAOrJPhpTXKlqIq2bgH3VhWsnLWk1pnKfBhzrrTgEgLe29/eMhUWLK0VZT+9ntUTlgXZFrNlzp2IBlmEYhtFpnPWr6w8ZUaUKXZ+Tn7rNM/b6ebmniviNNXvuXCzA8pHUtveAwIErtdDJJ3on+ZA/FUqIikNxYaV8sNgFq1pDVRG65GYJayD6mGRZVSAACEWJVeSp60yl+gQcLvVclJlC4lrlyGhO8SeX5cf4kZellLGweEyD5AWiTEJVXpXKRWKo5zbu8FpXHlZNpBeV8uNyMTHdK0xCm4U/FmsGznKqAOCd+j50vHfUq/psr+dfwKpitmfMu0a5aHPTLFSwXr34/Jf/1XtEeO5vr+YbAfDM/1smf3cwW2cdaki6DxZcdTqW5N6pWIBlGIZhHPac9nOv0vSXz3XBoMkoGizA8pHwsUNbJbmne+R+1RwSl8fJnt5cDuV3lQkrjy1+m0y4UIpUUKSwpJian7vNFAAgwxzXVTKqSq2gypYywhIqGMutUM72Sukj6kfEBy8tpaqIrkkyr8oFdpssd6fNNchzoNZQruMu2VN7SaUfAPQiVYfK/VzlRKn8LoaqIuQte/i6Lmu818iVo6N7cE8p5nmVEFWcfWI8v4uqYKId097m3D8LdzdyJ3f1ntu7lx8pjvwfb/PmhkZvdw0AKC3JP0evM48CFVZF2LlYgFVAQnu90nWmVJSR9+TjAXIWko649SJUtgk0/pDnamIJZiOhEvZV4EUCm4BYRBqeUs+J3JPZASDAAj21Z9VPj4ynRE/EkEO7HXU0p447GepoTrUgcTkOVOahjIz4plQtWZhZJkt8B3SwwgIvtYYKpFTg5bIGC5pUIOVydHhcr11iLg8imRVCvx48mZ23O+LjdU08wO1b5m1FA/Bj6MYmHoz1KOOJ6OqIUK3Bjg67LXZE2KlYgGUYhmEcNhz3+F10PJ/+ggA3KwVAvbSMwwMLsPwkk8bBZ1jZiPdqTgkOWXHWkyFqlWsrGnWqQy+c1TEez7d1s5FQYgtRlFzNVGnzZXlCKJLc2X13vHpjalU0wo9NQkJJY6gjQqUysfkuc+W4eF6UyqFUKX576kn3rs0S3wHdMiZD5F2lSClFKU72p5LZ3Y4IVVI9X4MlrsvnULwJ+pR6VZ9EmquQzcKjhb2PjhQqWJMwGmXHgdGoMCtt4MpWWSlX+l4593bPmAquujN2xNd5WIBlGIZhHPYc98Sdrf6dbtZfj1srcm+JM+z7rX2+Mk1unmV5kc3qnA2XNWA+WO3BAiw/iSdbNXPOlHibWYXiXE5KJ0VrjaT3zZEsEzlYQgCQ4+SC1XUNdnUkvBed1KAsy4dqa37Ke6OBiHI8dVjYUQWLhHNPAlcmlS4NgV1QKhMzJQV4foxLY2iAqy1lYZ5LoxQsNs7UJEArSgxlVtog7BGYWSlrnwPofLcEUaWUCapq8N1MktFZwjmgVUtWBFEibBrUGjuaenjG0kIFU6/15rh337R5M4A9Ee2P9edzvil/dzDqGPDQ4Ko90LY6hwRXxYz5YLljAZZhGIZhODCs6l46vvXKGzp4J21jVYSdiwVYfnKIHBva462cSfXryf9UWCwwSwZ9kS4qzZR9A1knLaqplU0Dy82SxVZKHaMLO3o9sHwmlfPl4hGqbk60naEKj5L0HNaQ1YIOdhEqH0cpW0y5UKqKC0qp0vlT3vmqybW6j3xd0YRbvMHSZD6zO9i3Nr+P5cQIVVX6KRWsZ8SrANYluJKm7mOfqHcf1Y296Ny6OF97UI+9nrGm24bQuaGv8yrHHtGEp1WOMuxsbhSJoD7w1oXfyHmuCq4YW6/4mmesrq4O5dctzHmNvLAqwk7FAizDMAyj0xi3+tBju9w9s/Yz7NFveca2zpzfzh3tQx8buu/PODyxAMtPevUADjYaLfdWyKR68KvVQJJfvWd7kqtYlRckDEhdKvJUJSIcGjUrBUuJUk5dZ5SHFV3DrdkzXUFVZwlViq3t2niWN4x2u4xkyoXMtRJ5Y2y+ztNxUMGc+ivx+crDSsEUJV21mDvKNDUh7uKepDePKOxovNpM8pxYvhygFcfdce9nU4kwCS0J19PxvUlvoPH2xXzPvRu4eWhp1CuN9yqN48WpuVf7seCqkISOiDspXp1JIOPYzkysYbQPC7AMwzCMLsWJT3rNQIce+YGYPaCwmyGc8BNvUn2aFNoAwN+/lJ+/Vl7YEWGnYgGWj2RLosiGDuQJBJu9/iyBLM8jUBfkjhfqYmMOU9VFvYPDu0vFoRxXfly5WyUhIJK+VIUia/asXN/VQxoheVUubusKVcnl4ijv2uaGVZrJRs1iDdWUmaHc4JmXFlOC1Nx9+8g9j0vtgz2mKsdJ7aORNJ1Wt6fyu2pJTpR6fSjFke07Lpp+v1dbTsePOqLWM3b8iGo6963XjqLjewFsnXt9qzEWXLXFcUPfx5qz7nP6m1xQKhULrroqluTeuRRNgLV7925cffXVeOaZZxAMBnH++efjO9/5Dnr25EnjADBx4kT85je/aTX27//+76iqqmr59zvvvIOrrroKv/71r9GzZ09UVFRg8eLFCIeL5qExDMMoWoY9enerf0f5aWKbnPL0ra3+nUrp1k1/+4K3R6FRPGzbtg2XXnop3n//fYTDYdxyyy244IILOntblKKJIi655BJs374dzz//PJLJJGbNmoUrrrgCjz32WJt/N2fOHNx++wFH37KyA+/edDqNz372sxg0aBD+8Ic/YPv27Zg5cyYikQjuuqsdjr+BQOtkoIxXAVC5Vqky/lSEm73zWQNogPct/Odv6Ci7+HY+b3foZ6h6JdLkLKXYsIbMALJhMl+pTyIZLMvmqypCsXaarB0VflwuFXkJoS6URriLNV3DQZkBeA6WrLwTbvVMnVFNhaNCbWFKk8rjYj5TgK6UZLj0YFS3p6oZ2b7V7an7OKjMq46pfdQLB3XWf7JZ7KO8jPf6Y+7s1VuOpHP7DKul47W7vBfIicYoAkQJ3lHPL6ZVr0/FoQGdpEF9YJVg61Vdy45B4qPRaFchHA5j6dKlGDVqFKqrqzF69Gicc8456NHD68vW2RRFgPX6669j9erVWL9+PcaMGQMAWLZsGc455xzce++9GDKElwYD+wKqQYMG0d8999xzeO211/DLX/4SAwcOxKhRo3DHHXfgpptuwq233opo1K0sONDY3OpIKTmkj2dONiKSo8VrONGDGGiK74p0qdtRFJvth0monKu+41hw5AfqSNKlRY0IxoKioCBMjwjF2uJLOEQ2HhLmo9q+IfeG0S4BlkqEbkzx9wo7llSBnkr2biBr9wjnHlgC3IBUtaJRsPtYJvbhYjTKjvwAoHeUG7IytOUEf0z/tqO/Z2xgb6/tAqBb6MRI/kLpQN4qR70HynoT2wrREkcRDmVoWxxGzsFVN6E7HhEOHjwYgwcPBgAMGjQI/fr1w+7du7tkgJV/GU0HsG7dOvTp06cluAKAyZMnIxgM4k9/+lObf/ujH/0I/fr1w6mnnooFCxagsfGAN9W6detw2mmnYeDAgS1jU6ZMQV1dHf7617/KNePxOOrq6lr9GIZhGIcHxz1+F/0xgLVr1+Lcc8/FkCFDEAgE8PTTT3vmLF++HMOGDUNJSQnGjx+PF198sV23tWHDBqTTaQwdOjTPXReGolCwqqurMWBA60qRcDiMvn37orqaJ1UCwMUXX4xjjz0WQ4YMwSuvvIKbbroJmzZtws9+9rOWdQ8OrgC0/LutdRcvXozbbiNmeM2JViFrkBwHBtLi2Kpn7j4IsqlzQjTzFa112DpCmEGaX2TnvC4AQAiCgRQx1oxI2Sdv1BFhkCS/h1gTaUdcmjoD/JgxLBLztQrmxeXoS6GsHlhC/L5x74tBKVXqGI/dR2b6CWglh5l5uj4eLHG9NsGT7ZkZKMBNTJVSpZQjpiKqpPp6YqUAAH16eJWjvQk+9wjSGFrN79+bWzo0JPibP0FahGUT/H7X7eyJrbNupL/Lla0zc+8hqCiqQMrHKsJDhYRYLIZYzPsaaGhowMiRIzF79mzMmDHD8/snnngClZWVqKqqwvjx47F06VJMmTIFmzZtavmeHzVqFFIp7+v8ueeeazmx2r17N2bOnIkHH3wwzztYODo1wJo/fz7uvrttyfb1119v9/pXXHFFy/+fdtppGDx4MCZNmoS33noLxx13XLvXXbBgASorK1v+XVdX12UjaMMwDMMd1UcwVETt+Pw8Ijz0O27RokW49dZbPfOnTZuGadOmyfXuu+8+zJkzB7NmzQIAVFVVYdWqVVixYgXmz99nDrtx48Y29xSPxzF9+nTMnz8fH//4x3O/Mx1MpwZY119/PS677LI254wYMQKDBg3C+++/32o8lUph9+7dMr+KMX78eADA5s2bcdxxx2HQoEEeabKmpgYA2lxXRe7ZPj2RDR0YD9Z5u6Yn+/Nz4kxUeRuQuQ6mn0Abxp/k2ZcWC0rIIeMZlUIh3uhZl1ehslhgye9CBVPWBoXK5WSKFABEVasch3YvKlE+TZSmlMrBcrjEVUqVgtkjKAWLNYZW85W1gWwY7UPbH5Y/pR4PuTZ5apUqqF4HTAXbVt+HzlU5eiUkyT0migxceOddnuQOcGf1j/zMmztVUt6MN2Z0UCuZdpCui9AWON2dbdu2tWr2zL4DP4xEIoENGzZgwYIDjbGDwSAmT56MdevW5bRGNpvFZZddhs985jO49NJLnffQkXRqgNW/f3/07+9NtjyUCRMmoLa2Fhs2bMDo0aMBAL/61a+QyWRagqZc2B8V70+QmzBhAu688068//77LdLk888/j969e+OUU05xvDeGYRiG4mPP3kxGC9df0AWe/F4UKcpt42MV4aRJkxAKhTB37lzMnTu3XUvt3LkT6XSapua88cYbOa3x+9//Hk888QROP/30lvyuH/7whzjttNPatadCUhQ5WCeffDKmTp2KOXPmoKqqCslkEvPmzcNFF13Uch777rvvYtKkSXj00Ucxbtw4vPXWW3jsscdwzjnn4Mgjj8Qrr7yCr371q/jUpz6F008/HQBw9tln45RTTsGll16Ke+65B9XV1bj55psxd+7cdkXngeYEAgclMdEqwpAwv1TFYORi37WK0AefSwkTEpytHhzMSuUSrKOQVKpEThrZiMrXUpWITK0KiWpvWcpP9qFsGoIRnnvD1nDNOWLtV1xzsFROFL+93K0e1ItM5SKxqj5V6adgtgllojF0Y5oHDizPTJmB1ovKzK11fT1jyqqAt5AC+sS8eVX/qOOGojHRSmlvgzcxs6y3V7UHgJKoMDxV/bO6AiUZX3K2OhM/jwjXr1/fSsHqLD75yU8iQyyQuiJFEWAB+6oB582bh0mTJrUYjd5///0tv08mk9i0aVNLlWA0GsUvf/lLLF26FA0NDRg6dCjOP/983HzzgauoUCiE//mf/8FVV12FCRMmoEePHqioqGjlm2UYhmF0LMN/tNgzlq3nR8hlA3li/WvTb/WMDau6l99gO8xNiwIfk9zHjh2bt4LVr18/hEKhllSc/dTU1Dil+xQLRRNg9e3bt01T0WHDhiF7kBQ6dOhQj4s749hjj8Wzzz7ryx4PNRoNNhMFQBiKBpNCbSEpW+KiWVfYCQWFXdkoM1DZ5oblYCl/PmlA6tLLR0l9bCNCfRKVgS7+WAoXHywFUx3KhKGoUmFiwq+K4WJAqpoKK6WKraGMRnXTY+/ayliT5Sepcdc12HxpNCoejwR5gykzUKU49o55VaIGxzW213uVCPbaBYCoeC0lGry3WT5gD50LAOun5VZ9x4KrgtMYwtYri8Q8tJPwQ8GKRqMYPXo01qxZg+nTpwMAMpkM1qxZg3nz5vmwy65F0QRYhmEYRvfixJ+yvoP+fC2dRBLo25KqRjzmDQD/fnEnNmr2gc4wGq2vr8fmzZtb/r1lyxZs3LgRffv2xTHHHIPKykpUVFRgzJgxGDduXMsp0/6qwu6EBVh+EggCwQOKQiBJPIAa+dVgslfupYHKgDqYEl5JUdUaJrcxwE2VUjlYTk2g1RrKY4s9fOqTwYdqQbWES2NnFxf25hR/fbBqMIArKErNUPlTTK1yUaoAnmemFCLZzsYhVUrlPpXBqwCq+1InGkmzfcvm1+K+sPkqB0spi0ytahQ+U0nRk69+Lze2Yx5UfQdyI+X+bahV+aCU5EDvBLDDmxvb3Oi975ES/pjGBjSiebf3+Q2R9kPdgkzWreO6WgO5HxG+9NJLOOuss1r+vd/SqKKiAitXrsSFF16IHTt2YOHChaiursaoUaOwevVqT+J7d8ACLD8JBlo5dQbSLEPdLamTfRel+Oc/ssIlVBl/ZslnsjwKVO9RsrY6ZlRHmxn2mKhjQ9VWhx4HqsdaBV7e+akkvzPRmAiUyRdaSSz/D28VDKgveJfkYXXs10iCOtV6RQZYDsd7GRGUuNg0KHsEFuipfahEeXa0qW6vThh89iRHvexxBoC9ooVOeYk3Qb0+zm+vX0+enzSgl7ctzltbeQ7Mnjqu+mREX9Atlyyg44y/nZ974+UR938757nJ5jBVn479Afe2UsUsxgFyPSKcOHFiq3Qdxrx587rlkeChWIBlGIZh+MawB3giedE0SO5O+JjkbrhjAZaf1De2kmkCQa+6kO3HjUZlCx12BCcUqYxoZyN72roczYljGqZWybnqItFJfHJQpcQVtkr6ZxddQWUGKvYRESXtLmswNUhZTqijKDXfBaZWuTSGbms+XUO8cIJZ731UCfFybaJ4KaVKGp6Sx1o9h71Fq5xmpoKJxygilMUdDT09Yz2ivAgikQ7j9//i9Xga+79edee4Ybw92N/f5V6F5Uc0esZq3+FWD36QEep1iTgOZLx9+eFlEBqADzlY//yvH1WEhxsWYBmGYXRjWDDVGeSqbA374bfECvZ11Zl0FR+sYsJesX5SEgWCB3Ih0v3Ji1FdTYjcrABJUAwo+wHXJstsLw4uCHK+tHQQyfZMOnK1NqCtcpQcJxZhCpZKuHW4LFT5UErtognqYu2kyEVipp1KKVF5RExtUTlYKo+LoVQf2eyZKFvNqhGyUKXYbao8LpfGyS5GqgAvKEgl+bMbEsUHTK1irZH2jfP9fbDXm1f1wd4ybP6i122dtbMBgNpdXiWtbDDP+Wrc7p3rTK8Utl7qbbdjtIGPTu6GOxZgGYZhGJ3GaT9fdMiIqOJxYNQqnTy/8bNeawhm0RBt4+ixK/dKPBg/bRrsiNAdC7D8pKEJOPgKv28v7xxxwRtMiVwacvUumz07tNtR60iLBWUSylrUqEI/VRnooKQpskStkgWb6nFithUqT0qoPkwlUgaOLmsoNUONu9hFKDuAMmIBoRQbVyWHIfO7yIusZ5jnONWneDUdU95kBabI3WNrqMdO4WK8qlSpBFHvSkXLpMYEfzxKS3KvbO1VytvfBMlrr7FWBEcif6oXcWHvVd6Ev3zutpz3Z3QMdkTojgVYhmEYBgDguMe9Sk7fwuWtG4XGqgg7FQuwfCR7RDmyoQNXjAFydq2qBRPluRuNKj8p4bGoDT7JmLKDkTlYZDzt2Cc7S65us6ptjUP+lLw9lQtGblM2jM795mQeTCDEx9OkcW9M+UyJXCTVWoeuoTy2iILi4nelkLlg4kUWJeOuHlYs38rFMwtwu4+ygTMxCVWvsR5RrtKBqFIJYSiqVDCmfDaQ5s0A0Jzkn03xuHc8m9IfNh1Zwcfyw9qi2B3bFYFsln4Pua5htA8LsAzDMIxOYdhKr4UEAGy97KYO3kk3JQPdo9ZlDVgOVnuwAMtHsu/8A9nAgSvUwAnDPHMCIt9CrkkumqUiJd5IUn1iFyZCBVNQny75hlaVkmRd2R4ldxd2V22b5Wy55mAxXNdgrUKU6qNysPxQlJiSoyv93FroMFiVHsDVIKUy9ZRVhN4XlMqfcvH02tXMXc6PiHnd1gG+b/UY1TbzfCbWLaBHjCuWMq+NqKR9yhvo3F07eN5Nj3JyH0uS+Ovnb6XzO5KtMy1A8xvLwXLHAizDMAzDN4Yv4y1ttlx9fV7reqsND2BJ8Rw7IuxcLMDykeCA/gge5IOVJc2ekz35Q65ys1QqEl1DNfVUlV8stUJ6WKkbJUOq96GqInRBPSBsXE0NK6mP5II5poIxJUc1U1YqDKtWk87xYidMNXM9KeB9BJV3FM85YgqKUojUGgxWSQcAu9NcUVLPAYNVTwL88egl8qRUf0G2j7pmnvuk1KeBpI9gdR1XFlR+V4oo6R+8SaqeAUSHeB3bAaCJNFnO7tQJmNRolDe2cIat7dKa56M3/Qcd/+vdX233nroEluTeqViA5SOZvj2RCR34sMySJOagCKSQFMnvvb1fJOr0Rx4RSnNTMqba3KgjO7K2spFwWUPtWdsP5H9kx41Xc7dSAHiCuporE/nZXBUkiyCNHT8qM1CX9jdqrgp42G1KqwdxX9ixpNyHspwI5m45wQxW1drqyLQ5m/tH61G99tDx3eL4cQ85Omxs5IHN0P676fj2pDcgy/ThAW5ZqSiYKPV6Sg2r4m7t8sLNIfBVKpVyiDeMzsYCLMMwDKPLM/w/F4vf5F6BfdhhTu6digVYPhKsbUDwIA+FNDEazYjSfIVO9s5v7r4/yHEMjiqY4zFjJlqgN7CyYxBGkkxRCiqDVYdmz8yQEdAKCiutV41/k0I5ipHjtiZxbMXmAvzITilESh2jbX8cW/Yw+wbZskeMs337sYZ6PNSRZAOxadi1l5+T9e/N284wtVB9B76/l9sVUPPbnvy4M/3rvnxxAPjsIfsQ7anKB/D7AgCvnMtb8eRKoFcSW768IK81iv4oUGBO7p2LBViGYRhGuzj2oXs6ewtGB2FVhO5YgOUn6TSQPXD1G4x78z6C5AoWANJlXH5ysTDIit4wTq11hGIjO4KwBskiyV10MUEg5b3RbMTRYsGl2XP+XV0kTLFRoqXM4yJrZIWRJGv8C/DkZpXypdQWtr+ewsBUqXFMbVFql2pd05jwvoBdlSOWK6WUKhdU7pl6bnuQx68xxj8Tmsj9BnhifV04//59vUVLnB0f5Un4WfJQh3sIg9W9fA0X8lWpDkvsiLBTsQDLMAzDMHKkmOwiApm2fAlzX8NoHxZg+Uk60/qyjihKoSZ+hZcu4VfvWXKBLHOZhFKlbBNYc2M5V13EkPkZYVYaTAiFzYccLNrmRlULCpVDNocmSGNNVlUpcsGCwrYixNQn8QQklW0CsxpwbHNDq+nE60NV07nYI6h9MNuEVNLN6oEpTVIFczBTLQnz22O5VmqNMqFCqn1sfXOQZ6zvMbV07p69XNkq7+U1Cd25i9s09B1YR8cbm/l9fGPGQs/YST/LL8/KMIoRC7AMwzAM3xj24BLPWOxIPZ81mH7rIm9vwOE/UlWEwJZLusbxITNZzddgNS/siLBTsQDLR9I1OxEIHJCRQj28V4+BEv6Qp2MiB4tcIAdFPpRs9uyQP6WQqhnJq3I2GnVxU1Uw9UOpJ9JjK/9tuLTbUSaQLnNd1lC4tLlReUsuSpUyGnXZhzIDVaoPyHtA7UN5wbH7rgxFI+Jx+qDJ622lVKYeZaLZc6l37XiSf64MOMJrSgoA7+8mycof8A+QDz6I0iBhxNL7PGOZHqLaNS4MlguYD1ko1DGgcrDvVMxotFOxAMswDMNok9Of8R77AX06ehsdyol3cXd3APjb14vD1sFa5XQuFmD5SPC4YxEMHZB0Un28V6bxfryUTrW5yUS9l9NKqVJihlKUWK5URrwiZINpsrZSzKQJO8lRkmqX9OPK3Q6eNVNWKIVIurOT4ZBozaOUrazDJaNrbhZD+WAxVKVfUHwIu/hPueCqPtWLnChGmJXHAWhKetWqZqFgHVnKGyf3jnkr9VjzZgCIifyuaA9vzlYiwd+4TWG+v1Sjd36PoVztop5ZADJHktwx8dLNJIPYOnO+Z3zYo3fzPyB0lWNARaceBXYA5oPljgVYPhJobETgoC+PIDkODNfzhzzZi3/Ish6FQWJrAAAZZdQo5qdI5bQ8cVJHhA6vINn+hiao5z4XAAI0kVw8Tin+LRyK5p+QzSwZ1AWgCvRCZDxFWvDsm8v3zI6oZLsdh6M5FdiooIkd5SlrA4Xan8tcF9NUdczILBZUgLWriZuHsuKDI3vwYKwuzq0NSmLeNZQNgiqwKO9fn7PB5wk/+SYdz8b54/f25V/LaV0A2DrzppzndjTFolK1iY85WOaD5Y4FWIZhGEYblX5uLSKO/y9vQJZ+n+eZUf86AH+/ttIzdtwTd8rbfOvCb+S4u8OMLNy7vLM1jHZhAZafRGPAQUeE9IUtPqtCzfxdkBIGpAzWXBrQJqHUxFStrY4IyR8ogUIn25MjQiVhqfY3xFRUHpmK40d2oedqscDmZ5gfBrQalyZrMFWrLdLkCXM5CgT08aML7GjONVHepd2ObCRNxtU+VLPn+oT3eF8dBarkd/a8vPNuPzo3UsoT+YPkyC5Swp/beDKMTcQ2wYVgKEPXYEnuwSGNed2WYXQnLMAyDMPoxoz+X6+6s2tbH89YjMd5AICx/+u1TQDa+IMuygmLdeL6mwu6wZHgIViSe+diAZaPpLe+08qmITDqZM8c1c5GqU9BchGbFoaivjR7FkibBvIKUjnMQhigUpPMwVJLsPveRT4XXJs9R1WCP4E1hgaAIGlHo/KnlBrE5rsmqEvbhBxvD+DKlsrjUm14mKIUFmcnLjlpGZHnt6tB5GBFvEpTrz5c9VHWC8zyQOUVHtmPJ67T29tZiq1XePOneHAFZMq8j3WmUXw49Uxh62X55VvZMWA7yMKHHCxfdnJYYgGWYRiGAQAYVnWvZ6z/sYW7PeUd1d0r8ozDAwuwfCR07NEIBQ+yaYh5H95QM8+VSPXg9g1MIZKpMUr1UblPrFWOSNNxsW+QOV8ioZXtLyusDZQpaZasHVANox3MDVWulbRvIPtT1XuqApA2jHZoyKzWiMicL6EcEZsAZdMg1SDyYAcdL4ldqgjrSJ4UwKsZZYsgkT/FYMahgH591NZ7k72P7M3zuBrq+X0JkXzDjDAJrY3y/UWP9NpFJHbwRPQdb/el4wCw9cobWv1bVRx2FbrjMWCbmJN7p2IBlmEYhtGlYMpWNqaPm9nRpgvKVLTorRoycLqglGt0IWprazF58mSkUimkUilce+21mDNnTmdvi2IBlp+EQvt+/kl4V71nSmIw9xEJJoQC0DP3p0iluwSFKkXVJ37RrG8zd39PZMIOipJSLWQSVu6fAlmXykC1ZbE95lelVCZWLQgAYfK8KJUpEuGVZmy+Mh9VipKLD5aqmmM5Wy6KlEI9pj2JV5Vir1C7mBkoAOxq9OZVRYUZaC+RC1ZKcrDiKf4e79Xb25AZAJpYk2Wh1ga2cgUr0Zvvb+tVN9DxXHnzgpud5rOjwC7ZcsboMvTq1Qtr165FWVkZGhoacOqpp2LGjBk48sg2Gl52EkUTYO3evRtXX301nnnmGQSDQZx//vn4zne+g549e9L5W7duxfDhw+nv/uu//gsXXHABACBAvil//OMf46KLLvJv84ZhGEXAsSvuoeNvz77RM8Z8qdIJYZhcq49dLd+qcHTHKsJQKISysn0XDvF4HNlsFtkutsf9FE2Adckll2D79u14/vnnkUwmMWvWLFxxxRV47LHH6PyhQ4di+/btrca+//3vY8mSJZg2bVqr8YcffhhTp05t+XefPn3at8l0GsgeuDLMlnmvkFUVoSLSQCqoermVC6qcKCYkqEpE6YPFhCPHHDGnlBzlB+VD01j2HnVtZ6NypfLdh6sPFnNyV3lByn+KVeqpJssKplYpB3W1tprPUC17mMLGnNkBYHs9V5kH96zzjL27t5zOVdWMbHvNos1NnClVAHr2JMrWEXQqAkfyx6PPs94L0x0fd/NJKxSdEXAV/VGgohNysNauXYslS5Zgw4YN2L59O5566ilMnz691Zzly5djyZIlqK6uxsiRI7Fs2TKMGzcu59uora3Fpz/9abz55ptYsmQJ+vXrmpYhRRFgvf7661i9ejXWr1+PMWPGAACWLVuGc845B/feey+GDBni+ZtQKIRBgwa1GnvqqafwxS9+0aN69enTxzPXMAzDODwY/p+L6fiWL3ft/ocfio8BVl1d6wuMWCyGWMwrIjQ0NGDkyJGYPXs2ZsyY4fn9E088gcrKSlRVVWH8+PFYunQppkyZgk2bNmHAgAEAgFGjRiGV8gb8zz33HIYMGYI+ffrg5ZdfRk1NDWbMmIEvfOELGDhwYH73swAURYC1bt069OnTpyW4AoDJkycjGAziT3/6E84777wPXWPDhg3YuHEjli9f7vnd3Llzcfnll2PEiBG48sorMWvWLHp0uJ94PI54PN7y75YXXn1jq4Sn9ADvlXAgLRzbS12a+vFhWS2olmH5U0KAUXlVzI5IOqg7eFupfcjPCqY+iapF1QOQPeWun01M8RJ9vGVVH0OpTyo3K0Pml4a4QpQUnlLsNpWapHKiWH4X6wvY1tos70v2RBTKEdvf3qSoOIxyZYs5vPeMxclMt8pRlRuXzfLna+9eb7Wfaiiu8uvqz6nHGzk6vA97cAmGPbiE/CaKrXNaJ5er5s3Z8lSX7jtofDhDhw5t9e9Fixbh1ltv9cybNm2a55ToYO677z7MmTMHs2bNAgBUVVVh1apVWLFiBebP39cQfOPGjTntaeDAgRg5ciR++9vf4gtf+EJud6QDKYoAq7q6uiWy3U84HEbfvn1RXV2d0xoPPfQQTj75ZHz84x9vNX777bfjM5/5DMrKyvDcc8/hK1/5Curr63HNNdfItRYvXozbbrvN+4tYBAgeJOuTb+xgkn8QhhLC7LHU4ThQJoDnPq7b2Ygl6LGaWELdFZfjPTGXNnuOCVNM+TgV5hzfxY4B4A2jlbFmqUhyp6aYqo2MeFAjDoUDLkirB3F7YdIaJhV0OyZnx6AqGFPJ78y+gR3FAsBuYd/AzENLovw5jJKEeIA/j/G9fM+BHrkn/ftBsQZRRa9UKXxUsLZt29aq2TNTrz6MRCKBDRs2YMGCA493MBjE5MmTsW7dupzWqKmpQVlZGXr16oU9e/Zg7dq1uOqqq5z30hF0aoA1f/583H03v+LZz+uvv5737TQ1NeGxxx7DLbfc4vndwWNnnHEGGhoasGTJkjYDrAULFqCy8kAz0rq6Ok90bxiGUUwM+57XZBSAa69nynHf9vYtBIDsYF6x+feLuXu84YiPNg2TJk1CKBTC3LlzMXfu3HYttXPnTqTTac9x3sCBA/HGG2/ktMbbb7+NK664oiW5/eqrr8Zpp53Wrv0Umk4NsK6//npcdtllbc4ZMWIEBg0ahPfff7/VeCqVwu7du3PKnXryySfR2NiImTNnfujc8ePH44477kA8HpcRujp7RiQKhA4oWIEEaVfSiyeuqmbPyZ65f7rJ4z2HNjfOKhhrkCxeVfLYj6gtWdUuRjV7JkckVNUCnMxK4Xrsyu6LSpQXV5bsqFK1qEk5HO8pVBNoppQ4Wyw4qHHK1JUpXmofKmG/iahPIfGCVMnvbI33anmS+5A+e+h4I2l+rY479zaU8P2VeY8llfWIalbuRCaArf+en32DUfysX7++lYLVWYwbNy7nI8TOplMDrP79+6N///4fOm/ChAmora3Fhg0bMHr0aADAr371K2QyGYwfP/5D//6hhx7C5z73uZxua+PGjTjiiCPaJX8ahmEUCyOvPcRc85T81zz9GZXX1Sf/xQ1n/LRpGDt2bN4KVr9+/RAKhVBTU9NqvKamplsWmhVFDtbJJ5+MqVOnYs6cOaiqqkIymcS8efNw0UUXtVQQvvvuu5g0aRIeffTRVuWemzdvxtq1a/Hss8961n3mmWdQU1ODM888EyUlJXj++edx11134YYb2nm1Fm5tNOoirWciIiGbiAtZfmHrLAWzVBipMjmsIZs9KysccpUt96GULaZoCBVHJbkzpSnQCZ1O2eehSmZXRpdNSe+DrZQqBVODpHIknjCW36WS3FWiPMtbclHoAH7flXJUvbcXHe9V4lWOykq42lXbzNvOsPuYTHFFr1cPfnzG6FkWx5/P8bapUa1ryn7nNU0tfy2El79TGLuCHifW4pVzby/I2kYb+JiD5YeCFY1GMXr0aKxZs6bFuiGTyWDNmjWYN29efvvsghRFgAUAP/rRjzBv3jxMmjSpxWj0/vvvb/l9MpnEpk2b0NjYujP9ihUrcPTRR+Pss8/2rBmJRLB8+XJ89atfRTabxfHHH99S4WAYhlFMnPQzFsC4JVAx81AAeOvCb7RjR60Z8R88D+vvX62k412VYY9+S/5u68z5HbiTrkl9fT02b97c8u8tW7Zg48aN6Nu3L4455hhUVlaioqICY8aMwbhx47B06VI0NDS0VBV2J4omwOrbt680FQWAYcOG0ZyWu+66C3fddRf9m6lTp7YyGM2bZBI4uFXKn7d6pgQnnEr/NBXiV7EZovoEhdejUohU2gwTHfywehDCgJuhqCu0nFHNFcNkDda8GWgj54V+oYnKUYfHWl2EKmWLVRfGic1AW7Dcp1JhBqoqEVm+lauCFSMqnZrbLNrOsPmy3U6Mq1L1cW/aQJq0RgKAHmINpgCmAnyNVDpIVR8WBIUjwp4ilMEmYsdw3Dsk2Dmat+ZxYc8O3lkjtKdovmq6F5ls/tXRGbcjwpdeeglnnXVWy7/3F4RVVFRg5cqVuPDCC7Fjxw4sXLgQ1dXVGDVqFFavXt0lfazyxV71hmEYXRSe0+SWH3rsD7weVmHH6o1ua8TpM8c+5G019Pa/edsMdRidcEQ4ceLED21dM2/evG55JHgoFmD5SbB1Dlbo+GGeKakov1oNpES+EBsWzpUBIVW5GH8GRP6Ui/ok7ZNcTEIdqhblH4g7rtSnAMnvUp8TquItRDyblFKiSBPlSOUcqQpFpmyVOLa5CQvljaHymVj7m3pSSQdoXyrWDJmpWq4og0/ZWJskFyqjUbXGrlqvwhON8vsSCYs34z+8+V3ZZv46iA/ka6dZs+c6r3EooIMrButZ2N1Qx4AsuOpO+JHkfrhhAZZhGIYBABjxY5ZOoatnhn2/tToW7KWD+L9/qfO9rY57nKeLpHfzoH/rV4rdnsIHBQv+JbkfbliA5SPZ3R8ge5CTe+DIvrn/MbPvBqhiky4RvkqqUXPuSyOrXhFqDeYG72q9Q9QgWUUYcZHjxFQHZUt1TMqI3JssqbxTVYsK5c/ktIZDSxaZ+0RypZiPEwBERV5VY9abGNg7ylWfPXFeHsueL1XNyPKkAKAHaX+j89d4/tQeURnIYFWcABB42+vwnhreQOc2/4PnM0WH19NxlmulOOmO//CMJY/nOViBHfwxzfRQcvfhSaceBSo6odmzcQALsAzDMLopLJAyOg7mjp9pyt1+I28yWeRdXaSaqRofigVYPhLo0xuB4IGrvdSR3ivQrJRERE5P3DueEgqWS79AgAs8Uu1SpujkIpYYXre5D6YcSaVKurOzhd3yloIkf0o5Ybv4MCmlJKwc1Mkzo5r2ujSBVjlY6vFgTaCVUiWbPZPxD+JcCVIO6sxTqneMf0kxpUqhekSyHDgAKC/xKjzVe/iRSWlM9Ig8zqs+pZNCWewfR5Ssw3ozArwdTbiN471Nt7T2vDr+v7hnFo5qxOYv3uwZds05Uu1y3ro+P5uGjyziQeQbt3k9vd66iB9Tdvf8KT+wHCx3LMDykWxZCbKhAwFWIJX7UU9GJL9nydGhS9L6vkXEMMsLdzydYqdOah8qNpLmoQwfOn/4QZi05gGADAlsWOI7oI+52CtBreGCChzkPpi1gXiqwiJBnSW/qwbJDeL40cUgVQVNzKJC7WNXg9eEE+CBaEwkqKsk/FivFNad3dpH6cSf3kHnyvvyah/PWDomTFqH5h5wphqVE7AD4jgxkOwib9wOhrUYqqurQ/lXvX1xC0I2s+8n3zVgOVjtwQIswzCMDuRQtSTWp5P2cYh9Q8C+DbofloPVqdhbykcCNbsQODjJfcgAz5xQszgWEq1y0hGvRKSS2YNJ/kZQa7MLeNWoWeHi7ymTzl3WcEEd7wn1ifk9KqNRdTTndHQoEuWDpDxfGWgqlaMXSSRXSe7q2I/ZJrCGx4BWn9ixH7NdAPRRaojsTzWMbkhwBYWpcaq9T+9Sfvy4tzl3/6nd9d5kdgBo/kD1ufKSzgTxt/O9SsdJWa/5aLqa3x4aQ7QSjnljZVNuru+9X+evhb/cW5h2Owp2FOhKl0xQN4oeC7AMwzC6KMOqvEnSMe91W+H38ejdnrFe4EF1RzN82bfp+Jarr+/gnXRBfExytxwsdyzA8pHM4P7IHJSDlSnzXuFlg8JiQdg0sNY1QZGSkiwT7VuEaTMbV42aVSEJU4+dTUKZPYJsyMyXoMKRyO1SRqMgypa6vaRITGa5WcowMijUJ4Yy4VQwtUrlWqncrHjKO1+tofKZ6omipFQ3tUaC5E+pNXoICwilbDGUUrVnt7doJRTley4p5blPAfLai9eqvCX+vCTjpPl12i3HqcdW/tH/2je9ahALrgAgUe50k0Zn0MWaPR9uWIBlGIZhAACGrSTBlNvJoRPH3ScqCyuLqwG0YTAswCogwUZviXWqP8+VkBYG5MNNKVKyzY2ApaCkVYqIY+PkfMmqcjX5OOVuViq/MIg6o2waVHNdF5Q9AlML1UWoKtlnuWBpkbcUEeoYtYtwbNnDUEaqTKkC3IxGk6Lj+Z5G7ws71JPvQ6mFPcq9Ng2qejKZEm/SqPc2S3tx1a25nh/BBd7z3pcA3OwOqFLFgisAyABbL7sp57U7GjsKbIMsfFCwfNnJYYkFWIZhGN0UGTShY4OmQ1vq7Cfk2HTacMSqCDsVC7B8JLirFsGDjEbTR/fL+W8zYX5FzoxGdQ6WWl0oJeRiX6lgWZHCwuar/QmBgm6P5aoAbXhmUUXD7YOBfY7oXLDcE81k3pJQn1w+z1KiEpGNR0UumMzNElV9DKVssepHPyowE6oSUdyX8jJvZaCq9EslxNpN3vEefRvp3EAA+Ovnb/WM0154L/eia4TKtUNwIY7QOkOlKnn/8PTHMg4PLMAyDMMoAMN++C3PWMgHL8+uAqtwDNcJRapPYfdiCDIZAHkajWb2/b1VEbpjAZafxKLAQT5YTIoIqPYtQizIREkbGdVtR/hdqVwkNq58sIK88wfSRNlilY/OOOZ8UfFDzXVovuyqttAG2uLmpA8WqZBj+VAAEBHVdKrKjqHUILaGUqoULJ9JNWQujfAXWV2TN+eoJMrnhsT+mhLeyEYpVbESXgHYRCr11HPbuJcnM6ZJZWCAG8cjM5DnZgHAyGu87WFevp/4QTXrN+NJt7deI3kCb/bMlDsACDhkv4drw9h8Y8f6Y7kw7AFvsAgAW6/yeogVFVZF2KlYgOUj2ZIosqGDjEbj5EhGfJhG6/i5WryP94sh2UMc+TkcwSlUMCaDJrK2OsaTpeTkA0AdSar7QpPixe0px2raE5G5jwKASG5mAYhqP6mDN++YNOFUa5AoXB1rqmCM3ReXtjoKNbcpyeUdNl89HkllwUEIiUIF1u4IALLk9RR/S3zhDNAtarZeOr/Vv0d8h1fSBXbG8PdrvUeBLLiSt3UlDxAODa7aQzaawduzzaCzS2M5WJ2KBViGYRhdlBFLvcFXL4crppPuyD+QKkZO+bq+36/d1XWVNKN7YQGWjwTiKQRCB6SeTKlXhlFHhMky/lS42DREGsTaPXP/QFbHj0rBCjDhTSyilC2nI0UfLqay6mjOwXpBmZWm6bGJm19EkBhuKruItDKuJfdRKWkpYd/AjveU2qWULaaaKeWuRKiCzeRjSj0eyh6BtTxS+0iJljHsaDndnx9VZoVJKCNDrBsAIBjna+wdkcXfr8szyT0AbLrFAo39FP1RoMJHJ3fDHQuwDMMwOhBPa5fD4FP42BX3yN/ZMWPhyGYzyGbzS3LP9+8PZw6Dt3YHUlvXKsk9M9jbSyIrZASZiJ5mNg18jVSpUBGksSYZc5kLIMusHuT7Ud13ogwoVUtVrrP8KWXpoBLUyRpK1XKxFJB5S+LOMHVGGYoqxYa15wmJ2ws7JMqrptPyvpDnXOWNqQbOTPRV91s9Tk3NJMmdjAHQrr8NpGVPPX/DhJv4c57oQ1op7dES7pvz81OZmgcLz5Rujh0DGl0BC7AMwzC6KMqgc+sVX+vgnRhFSTab/xFf1po9txcLsPykZylwkNFoeI/X3LB5EC8jDKSEAtDLe4WsGjLLVjkOF+pqjYCLouTQzgYAMjFSRajUJ6GwOXRq0dAWNaJiU6h0LNeHjQG6mo6pM0qZUWszZKNs8dgliaKk1Cdl8Emr/YRiFk8KdYzcprrfSlns3dP7XozHuLrT1Mhb1GRIFWG6n/Av2S0qIpOBnE1CVXDlwtuXd41AzI4BO4msDzlYZtPQbizAMgzD6ECGPXxIPlKycG7mJy/U1XSv3976GE01Xgas+bJhtAcLsPwkFgNCBxSsQMJ7hRxMivwf8UywKr2U8NIKKhVM+EExrymVryXzuFibGyU+qS8Ssraq0suo5sbMBNJR5XBpWKwUFJeqOQXLn1LqTizCVRiWE6XMSoNC2mLzpR+Xg5eWau/jB83EUBTgz0Gikc/N1gn16Qiv8WcwLKTMsiQ2f/Fmz7AnuPoQ7CjQyItMpq2k2NywJPd2YwGWYRhGARj2qLdVjq4iyY/jvs3Vp6iLy3CRou77W9eb6ubnEaHhjgVYftLQ2CpBKj34SM+UbEhUF/UW7ShIgiJz6QaANGmrs+8P+DBrf6M8tnT1HpmqVLBI7m2CdFNnPgyXXCShwgSEOsZQPkwuOVFKyWH5Vq7+UwGSWKUUuoR4PNg+5P0WiVxpMl85pauctATJSVMKp8yZY+Mf8Fwr9OKqYCbh3QcbA6Bfj6EMts6cz39XABLl2ZwDDTsGNAx/sQDLMAyjA2HKViGDLuXmbkaj3Z9sJoNsnkeE5oPVfizA8pFsUzOyBykNwWZvP7Kg6rcm8qeYuqPyuJSCJd3ZfTitcFpDqS1EpZOqtOqVyF7Jag2XceVhpdQuMl05pauGxXRZmTeW8xKSGMn5AoAUUZpiYa7uNMS5GsTyyUrF/W52qCJMiRdeWNyX+Nu9PGOxY+rpXPWQJt4VyY+EE37YQMf/No8/TqPm8SBo43e9QRDrIxhGAJsWkrkFbJVz6o187Vfv6djAzY4C28COCDsVC7B8JNCnNwIH2TRkYt5gigUTABCMiyMgGjSpVjRqY3w4SL4r0+LUhM0FxPGesIWQ9g0+pImw+MMlMR/gx0tZ8eGSFsd7IZX0zNaQR3PeIEEdfanAi62tbRpyP95LZviTWxLlLxBmOVHfzF+oIXGslhKmogz1vASHNHrGmneV8kVE6xqUe+9jYA//CN10BV87QK4Q+rwo3nQ+kOxlX46HNZmsrjrKlS4aYDU2NuLkk0/GBRdcgHvvvbezt0OxAMswDKMb4GnBAyBwGCS5G4cnd955J84888zO3kabWIDlI9naOmQPapUTDHqvpjMxYdQmkt8zYeaDII6tEuqYUazN2tyIi5W0spFgF/uOypHTd4BSn9i44xEhLbmXqk/ulhhK1VIXhiwJXCXEx4RyxJQcpkip2wP48V5A5HOo/bHbVMamzXGujkXJfVStcpLby+h4trf3WDLQLB7T93JvvJ4qFa8DIY5lE0Gv+edMPlfBjgJZcAXsKxbZcvX1bjeQIx19FGi0g2wWutm8yxpdizfffBNvvPEGzj33XLz66qudvR2JBViGYRgdyHH3trYVaOsEZ/PXLL/IaD/ZTBbZPI8IVZqEYu3atViyZAk2bNiA7du346mnnsL06dNbzVm+fDmWLFmC6upqjBw5EsuWLcO4ceNyvo0bbrgBS5YswR/+8AenvXU0FmD5SOCIPggcZDSaLfXmVgTjIpkprfrZkFY5Isk9EHFrJM2uyJmxKQAERJqISMnha6g2PA4qmMrXoms47A0AMkSFUcnsSoVhypayWFCwNVSrHJVzxNYoifInQClbTJVSyp0yQo0SFUzdnvocTyS8ayfr+QsyWyaaczd618j2Ecn2fYCtl3qr+kb8+C46/+9f+rpnbNj/x3NCQo2F8cEqlEplGK40NDRg5MiRmD17NmbMmOH5/RNPPIHKykpUVVVh/PjxWLp0KaZMmYJNmzZhwIABAIBRo0YhlfJ+GT333HNYv349TjzxRJx44okWYBmGYRjtY8T9/Ojv79d0zYBq2AM62XjrVTd4xlR7HvPk8olsBvkfEe77+7q6ulbDsVgMsZi3Hci0adMwbdo0udx9992HOXPmYNasWQCAqqoqrFq1CitWrMD8+fsubDZu3Cj//o9//CMef/xx/OQnP0F9fT2SySR69+6NhQsXut6zglM0Adadd96JVatWYePGjYhGo6itrf3Qv8lms1i0aBEefPBB1NbW4hOf+AQeeOABnHDCCS1zdu/ejauvvhrPPPMMgsEgzj//fHznO99Bz549nff40/+7vVUzzCljb/PuKSwUB1FdyBQb12pB9f5iRqOpErG0cBRg90ZV72VIax5AqFJC5ZCd4cljIqvm+LBTOWM6pZQjMuZgPgrwikGVa6XuI7OGcG3ZwyoRVWueFFGZAKA05n3hJFNcWgyLXLVkkjy5KVUay+9jz7+TasZTVHd0TiaeezVjQOW7lWSxZV5uwZEKrgwjV/w8Ihw6dGir8UWLFuHWW291WiuRSGDDhg1YsGBBy1gwGMTkyZOxbt26nNZYvHgxFi9eDABYuXIlXn311S4ZXAFFFGAlEglccMEFmDBhAh566KGc/uaee+7B/fffj0ceeQTDhw/HLbfcgilTpuC1115DScm+SOKSSy7B9u3b8fzzzyOZTGLWrFm44oor8Nhjjznv8bz+lyMcOOjL47QTndcwDKPzoUnjXiutouXYh3hPxLf/7cYO3olRLGzbtq2VgMDUqw9j586dSKfTGDhwYKvxgQMH4o033sh7j12Nogmwbrttnxq0cuXKnOZns1ksXboUN998Mz7/+c8DAB599FEMHDgQTz/9NC666CK8/vrrWL16NdavX48xY8YAAJYtW4ZzzjkH9957L4YMGULXjsfjiMcPNH7ds2cPACCVPeRq/ZW/4qkdP2g19PnJ/IMtUOs1JQWAYIrkn/TgV9JZkY+TKRE5NkwdE2pXRrxSmJCTbeZzs/wuIhsjV1hKTBKVkpkoM8LiS2SEz1GAKChKSFOVgazdTkrlzCkPKzKWaeJzlXdUIOB9sEV6HRJMIQIQjXjXiIvntiSwh47veZ/kIYa9TZMByAebqYXpZv74R8v5BuuGe/eRbRDVuA0hZMmjNeiX/LGum1HnGXt55hw6F/AetUj2iMfJZQ1Bpkk8Tnmu+8olV8jfsbX/7/LLC7KPrsz+++aaPN4eUtl43s2aU9j3vTZp0iSEQiHMnTsXc+fO9WN7eXPZZZd19hbaJltkPPzww9ny8vIPnffWW29lAWT/7//+r9X4pz71qew111yTzWaz2Yceeijbp0+fVr9PJpPZUCiU/dnPfibXXrRo0X57XPuxH/uxH/uxH+eft956y/n7L1eampqygwYN8m2vgwYNyjY1NTnvA0D2qaeeavl3PB7PhkKhVmPZbDY7c+bM7Oc+97k873XXo2gULFeqq6sBgEqR+39XXV3dUrWwn3A4jL59+7bMYSxYsACVByVh1tbW4thjj8U777yD8vLydu957NixWL9+fV7z2O9yGTv434f+/5o1azB06FCPROxKoe4fG8/13/v/W1dX163uI/v/rn4fu9PrVP3+w8bsddr172O+r9Nf/vKXOOaYY9C3b1+n++NCyf/f3p0HNXH+fwB/h3hwiGEIiqLiVVEOOeQQHBAPKmC92o5HPQiKOON9xWrtiPWLkqLYOjo6HbGCeLR4FOvI1I5SKRXoUNQoOnigKJZRsMPVYEFJnt8f/tg2AhLCJoH185phJvvsk83nvQnhIbv7xNwcxcXFePmyhcMGbdStWzfutJr2bsfb2xsZGRnc1A0ajQYZGRlYsWJFu7ff0Zh0gLVp0ybEx8e/tU9hYSFGjBhhpIp009LVExKJpF1vCGKxWKf7v61fc+t0afvvcku3e/bs2SHzNdeu6/Kb7ULJ2NJtoONmFNLrtKX1rbXR67TjZ2zv67Txn/DmJqLmk7m5OS+DorZSqVQoKirilouLi6FUKmFrawtHR0esW7cOMpkMPj4+8PPzw549e1BbW8tdVSgkJh1grV+/vtVjqEOGDNFr23369AEAlJWVoW/fvlx7WVkZPD09uT7l5eVa92toaEBFRQV3f2PS9bj22/o1t06Xtv8ut3S7vQyVr7l2XZf5Ppego2Q01HPYlu29y6/Tlta31kavU/4I/XXaUeXn52P8+PHccuPRHplMhuTkZMyePRvPnz9HTEwMnj17Bk9PT1y4cKHJ0SYhEDHWAefBf4vk5GSsWbOm1WkaGGNwcHCAXC7H+vWvL4uuqalB7969kZyczJ3k7uLigvz8fHh7ewN4PZFZWFgY/vzzzxZPcn9TTU0NJBIJqqur2/UfV0cl9HwAZRQCoecDKKMQCD0f+ZdhP6PkUUlJCZRKJUpKSqBWq6FUKqFUKqFSqbg+I0aMQFpaGgBAJBJhzZo12L59O86dO4eCggJERETAwcGBO/br7OyMsLAwREdHIy8vD9nZ2VixYgXmzJmj8+AKeH3IcOvWrXpdttoZCD0fQBmFQOj5AMooBELPR/7VaT7BioyMxJEjR5q0X758GePGjQPwelCVlJTEHXZk/z/R6MGDB1FVVYXAwEAcOHAATk7/zk9VUVGBFStWaE00unfvXr0mGiWEEEIIATrRAIsQQgghpLPoNIcICSGEEEI6CxpgEUIIIYTwjAZYhBBCCCE8owEWIYQQQgjPaIBlQE+ePMG4cePg4uICd3d3nDp1ytQl8a6qqgo+Pj7w9PSEm5sbEhMTTV2SQbx48QIDBw6EXC43dSkGMWjQILi7u8PT01NrkkAhKS4uxvjx4+Hi4oKRI0eitrbW1CXx5u7du/D09OR+LCwscPbsWVOXxbuvv/4arq6ucHFxwapVq4zyhcnGlpCQAFdXV7i5ueHYsWOmLoe0A11FaEBPnz7lZo5/9uwZvL29ce/ePVhZWZm6NN6o1WrU19fD0tIStbW1cHNzQ35+PqRSqalL49Xnn3+OoqIiDBgwAAkJCaYuh3eDBg3CrVu3BD09SXBwMLZv346goCBUVFSgZ8+e6NJFeF/HqlKpMGjQIDx+/FhQ7zXPnz+Hv78/bt++ja5du2Ls2LFISEhAQECAqUvjTUFBAWQyGXJycsAYw/jx43HhwgXY2NiYujSiB/oEy4D69u2r9bU8dnZ2qKioMG1RPBOLxbC0tAQA1NfXgzEmuP8q79+/jzt37iA8PNzUpRA9Nf5RDgoKAgDY2toKcnAFAOfOncPEiRMFNbhq1NDQgLq6Orx69QqvXr1C7969TV0SrwoLCxEQEABzc3NYWFjAw8MDFy5cMHVZRE80wHqLrKwsTJ06FQ4ODhCJRM1+5L5//34MGjQI5ubmGD16NPLy8prd1tWrV6FWqzFgwAADV902fGSsqqqCh4cH+vfvjw0bNsDOzs5I1beOj3xyuRwKhcJIFbcdHxlFIhGCg4Ph6+uL48ePG6ly3bU34/3799GjRw9MnToVo0aNQlxcnBGrbx2f7zUnT57E7NmzDVxx27U3Y69evSCXy+Ho6AgHBweEhIRg6NChRkzQuvZmdHNzQ2ZmJqqqqlBZWYnMzEyUlpYaMQHhEw2w3qK2thYeHh7Yv39/s+tTU1Oxbt06bN26FdeuXYOHhwdCQ0ObfIF0RUUFIiIicPDgQWOU3SZ8ZLSxscGNGzdQXFyMEydOoKyszFjlt6q9+X788Uc4OTlpzf7f0fDxHF65cgVXr17FuXPnEBcXh5s3bxqrfJ20N2NDQwN+++03HDhwALm5ubh48SIuXrxozAhvxdd7TU1NDXJycjB58mRjlN0m7c1YWVmJ8+fP49GjRygtLUVOTg6ysrKMGaFV7c3YeG7ZhAkT8NFHH8Hf3x9isdiYEQifGNEJAJaWlqbV5ufnx5YvX84tq9Vq5uDgwBQKBddWV1fHgoKCWEpKirFK1Zu+Gf9r6dKl7NSpU4YsU2/65Nu0aRPr378/GzhwIJNKpaxnz55s27Ztxiy7Tfh4DuVyOUtKSjJgle2jT8acnBw2adIkbv3OnTvZzp07jVJvW7XnOUxJSWHz5s0zRpntok/GkydPsmXLlnHrd+7cyeLj441Srz74+F2Miopi58+fN2SZxIDoEyw9vXz5ElevXkVISAjXZmZmhpCQEOTm5gJ4/V2IkZGRmDBhAhYsWGCqUvWmS8aysjL8/fffAIDq6mpkZWVh+PDhJqm3rXTJp1Ao8OTJEzx69AgJCQmIjo5GTEyMqUpuM10y1tbWcs+hSqXCL7/8AldXV5PUqw9dMvr6+qK8vByVlZXQaDTIysqCs7OzqUpuE13yNeqohwdbo0vGAQMGICcnB3V1dVCr1cjMzOw07zWA7s9j46dZd+/eRV5eHkJDQ41eK+GHMM/yNIK//voLarUa9vb2Wu329va4c+cOACA7Oxupqalwd3fnjsUfPXoUI0eONHa5etEl4+PHj7FkyRLu5PaVK1cKKl9np0vGsrIyfPjhhwBeXxUaHR0NX19fo9eqL10ydunSBXFxcRg7diwYY5g0aRKmTJliinLbTNfXaXV1NfLy8nDmzBljl9huumT09/fH5MmT4eXlBTMzM0ycOBHTpk0zRbl60fV5nD59Oqqrq2FlZYWkpCTBXozxLqBnzoACAwOh0WhMXYZB+fn5QalUmroMo4iMjDR1CQYxZMgQ3Lhxw9RlGFx4eLigrwSVSCQd6vxHQ9ixYwd27Nhh6jIM6s1PJUnnRYcI9WRnZwexWNzkDa2srAx9+vQxUVX8EnpGoecDKKMQMgo9H0AZhZKRaKMBlp66desGb29vZGRkcG0ajQYZGRmCmfhO6BmFng+gjELIKPR8AGUUSkaijQ4RvoVKpUJRURG3XFxcDKVSCVtbWzg6OmLdunWQyWTw8fGBn58f9uzZg9raWixcuNCEVbeN0DMKPR9AGYWQUej5AMoolIykDUx7EWPHdvnyZQagyY9MJuP67Nu3jzk6OrJu3boxPz8/9vvvv5uuYD0IPaPQ8zFGGRt15oxCz8cYZWzU2TMS3dF3ERJCCCGE8IzOwSKEEEII4RkNsAghhBBCeEYDLEIIIYQQntEAixBCCCGEZzTAIoQQQgjhGQ2wCCGEEEJ4RgMsQgghhBCe0QCLEEIIIYRnNMAihBBCCOEZDbAIIZ3Wo0ePIBKJIBKJ4OnpadJaxo0bx9WiVCpNWgshxPRogEWIgTX+0W3p54svvjB1iZ3epUuXkJGRodVWU1ODLVu2wNXVFRYWFpBKpfD19cXOnTtRWVmp03bPnDkDsViM0tLSZtcPGzYM69atAwD88MMPyMvLa18QQohgdDF1AYQI3dOnT7nbqampiImJwd27d7m2Hj16cLcZY1Cr1ejSRVi/mq9evULXrl0Ntn2pVAqpVMotV1RUIDAwEDU1NYiNjYW3tzckEgnu3r2LpKQknDhxAsuXL291u9OmTYNUKsWRI0ewefNmrXVZWVkoKipCVFQUAMDW1hY1NTX8BiOEdFr0CRYhBtanTx/uRyKRQCQScct37tyBtbU1fvrpJ3h7e6N79+64cuUKIiMjMWPGDK3trFmzBuPGjeOWNRoNFAoFBg8eDAsLC3h4eOD06dNvraW+vh5yuRz9+vWDlZUVRo8ejczMTG59cnIybGxs8PPPP8PZ2Rk9evRAWFiY1iARAA4dOgRnZ2eYm5tjxIgROHDgALeu8bBdamoqgoODYW5ujuPHj6OhoQGrVq2CjY0NpFIpNm7cCJlMxuVMSUmBVCpFfX291mPNmDEDCxYs0H2HA9i8eTNKSkqQl5eHhQsXwt3dHQMHDsSkSZPw3XffYdmyZTrtk65du2LBggVITk5u8hiHDx/G6NGj4erq2qbaCCHvBhpgEdIBbNq0CV9++SUKCwvh7u6u030UCgVSUlLwzTff4Pbt21i7di3mz5+PX3/9tcX7rFixArm5ufj+++9x8+ZNzJw5E2FhYbh//z7X58WLF0hISMDRo0eRlZWFkpISyOVybv3x48cRExODHTt2oLCwEHFxcdiyZQuOHDnSJNPq1atRWFiI0NBQxMfH4/jx40hKSkJ2djZqampw9uxZrv/MmTOhVqtx7tw5rq28vBzp6elYtGiRTvsEeD3wTE1Nxfz58+Hg4NBsH5FIpPM+iYqKwv3795GVlcXdR6VS4fTp09ynV4QQ0gQjhBhNUlISk0gk3PLly5cZAHb27FmtfjKZjE2fPl2rbfXq1Sw4OJgxxlhdXR2ztLRkOTk5Wn2ioqLYJ5980uxjP378mInFYlZaWqrVPnHiRPbZZ59x9QFgRUVF3Pr9+/cze3t7bnno0KHsxIkTWtuIjY1lAQEBjDHGiouLGQC2Z88erT729vZs165d3HJDQwNzdHTUyrl06VIWHh7OLe/evZsNGTKEaTSaZjM1Ptb169e5tmfPnjEA7KuvvtLqO2rUKGZlZcWsrKzYnDlzdN4njDHm7+/PZDIZt/ztt98yS0tLVlNT02o9hJB3k7BO9CCkk/Lx8WlT/6KiIrx48QLvv/++VvvLly/h5eXV7H0KCgqgVqvh5OSk1V5fX691/pKlpSWGDh3KLfft2xfl5eUAgNraWjx48ABRUVGIjo7m+jQ0NEAikbSYqbq6GmVlZfDz8+PaxGIxvL29odFouLbo6Gj4+vqitLQU/fr1Q3JyMiIjI7U+cdJXWloaXr58iY0bN+Kff/4BoPs+WbRoEdauXYt9+/bB2toahw8fxsyZM2Ftbd3uugghwkQDLEI6ACsrK61lMzMzMMa02l69esXdVqlUAID09HT069dPq1/37t2bfQyVSgWxWIyrV69CLBZrrfvvifZvnowuEom4WhofNzExEaNHj9bq9+Y238ykCy8vL3h4eCAlJQWTJk3C7du3kZ6e3qZt9OrVCzY2NloXEgCAo6MjAMDa2hpVVVUAdN8nc+bMwdq1a3Hy5EmMHTsW2dnZUCgUbc5HCHl30ACLkA6oV69euHXrllabUqnkBj8uLi7o3r07SkpKEBwcrNM2vby8oFarUV5ejqCgIL3qsre3h4ODAx4+fIh58+bpfD+JRAJ7e3v88ccfGDt2LABArVbj2rVrTeavWrx4Mfbs2YPS0lKEhIRgwIABbarRzMwMs2bNwrFjxxATE9PieViA7vvE2toaM2fOxOHDh/HgwQM4OTnpvQ8JIe8GGmAR0gFNmDABu3btQkpKCgICAnDs2DHcunWLO/xnbW0NuVyOtWvXQqPRIDAwENXV1cjOzkbPnj0hk8mabNPJyQnz5s1DREQEdu/eDS8vLzx//hwZGRlwd3fHBx98oFNt27Ztw6pVqyCRSBAWFob6+nrk5+ejsrKSmxOqOStXroRCocB7772HESNGYN++faisrGxy+G/u3LmQy+VITExESkpKG/bav+Li4pCZmQk/Pz/873//g4+PD6ysrHDz5k3k5ubCzc2tzfskKioKQUFBKCwsxMaNG/WqixDy7qABFiEdUGhoKLZs2YJPP/0UdXV1WLRoESIiIlBQUMD1iY2NRa9evaBQKPDw4UPY2Nhg1KhRTeZr+q+kpCRs374d69evR2lpKezs7ODv748pU6boXNvixYthaWmJXbt2YcOGDbCyssLIkSOxZs2at95v48aNePbsGSIiIiAWi7FkyRKEhoY2OTQnkUjw8ccfIz09vclUFbqSSqXIy8tDfHw8du3aheLiYpiZmWHYsGGYPXu2Vq267pPAwEAMHz4cRUVFiIiI0KsuQsi7Q8TePNGDEEKMQKPRwNnZGbNmzUJsbKzWuokTJ8LV1RV79+596zYePXqEwYMH4/r16yb/qpyOWA8hxHRoHixCiFE8fvwYiYmJuHfvHgoKCrB06VIUFxdj7ty5XJ/KykqkpaUhMzNTp5nWG40ZMwZjxowxRNk6Cw8Pp0lHCSEcOkRICDEKMzMzJCcnQy6XgzEGNzc3XLp0Cc7OzlwfLy8vVFZWIj4+HsOHD291m/379+cmBG3p6kljOXToEDf9Q+MVi4SQdxcdIiSEEEII4RkdIiSEEEII4RkNsAghhBBCeEYDLEIIIYQQntEAixBCCCGEZzTAIoQQQgjhGQ2wCCGEEEJ4RgMsQgghhBCe0QCLEEIIIYRn/wcUTenztewZ6wAAAABJRU5ErkJggg==", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots()\n", "h = ax.pcolor(\n", " my_aeff.true_energy_bins, my_aeff.cos_zenith_bins, my_aeff.values.T, norm=LogNorm()\n", ")\n", "cbar = fig.colorbar(h)\n", "ax.set_xscale(\"log\")\n", "ax.set_xlabel(\"True energy [GeV]\")\n", "ax.set_ylabel(\"cos(zenith)\")\n", "cbar.set_label(\"Aeff [m$^2$]\")" ] }, { "cell_type": "code", "execution_count": 20, "id": "3ba3ee5a", "metadata": { "execution": { "iopub.execute_input": "2024-03-22T09:56:43.629679Z", "iopub.status.busy": "2024-03-22T09:56:43.629471Z", "iopub.status.idle": "2024-03-22T09:56:44.010599Z", "shell.execute_reply": "2024-03-22T09:56:44.009937Z" } }, "outputs": [ { "data": { "text/plain": [ "Text(0, 0.5, 'Mean angular error [deg]')" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots()\n", "ax.plot(my_angres.true_energy_values, my_angres.values)\n", "ax.set_xscale(\"log\")\n", "ax.set_xlabel(\"True energy [GeV]\")\n", "ax.set_ylabel(\"Mean angular error [deg]\")" ] }, { "cell_type": "markdown", "id": "c9428ea2", "metadata": {}, "source": [ "We can also easily check what datasets are supported by the different detector information classes:" ] }, { "cell_type": "code", "execution_count": 21, "id": "581e7b8f", "metadata": { "execution": { "iopub.execute_input": "2024-03-22T09:56:44.013124Z", "iopub.status.busy": "2024-03-22T09:56:44.012919Z", "iopub.status.idle": "2024-03-22T09:56:44.016977Z", "shell.execute_reply": "2024-03-22T09:56:44.016343Z" } }, "outputs": [ { "data": { "text/plain": [ "['20131121', '20150820', '20181018', '20210126']" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "EffectiveArea.supported_datasets" ] }, { "cell_type": "code", "execution_count": 22, "id": "7a22fcec", "metadata": { "execution": { "iopub.execute_input": "2024-03-22T09:56:44.019214Z", "iopub.status.busy": "2024-03-22T09:56:44.019015Z", "iopub.status.idle": "2024-03-22T09:56:44.022711Z", "shell.execute_reply": "2024-03-22T09:56:44.022064Z" } }, "outputs": [ { "data": { "text/plain": [ "['20181018']" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "AngularResolution.supported_datasets" ] }, { "cell_type": "code", "execution_count": 23, "id": "4ef00bab", "metadata": { "execution": { "iopub.execute_input": "2024-03-22T09:56:44.024987Z", "iopub.status.busy": "2024-03-22T09:56:44.024791Z", "iopub.status.idle": "2024-03-22T09:56:44.028609Z", "shell.execute_reply": "2024-03-22T09:56:44.027972Z" } }, "outputs": [ { "data": { "text/plain": [ "['20150820']" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "EnergyResolution.supported_datasets" ] }, { "cell_type": "markdown", "id": "d4b49eca", "metadata": {}, "source": [ "If you would like to see some other datasets supported, please feel free to open an issue or contribute your own!\n", "\n", "\n", "For the `20150820` dataset, for which we also have the energy resolution available..." ] }, { "cell_type": "code", "execution_count": 24, "id": "fde43c1f", "metadata": { "execution": { "iopub.execute_input": "2024-03-22T09:56:44.030819Z", "iopub.status.busy": "2024-03-22T09:56:44.030624Z", "iopub.status.idle": "2024-03-22T09:56:56.523920Z", "shell.execute_reply": "2024-03-22T09:56:56.523183Z" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "\r", "20150820_Astrophysical_muon_neutrino_flu...: 0%| | 0/43711022 [00:00" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots()\n", "h = ax.pcolor(\n", " my_aeff.true_energy_bins, my_aeff.cos_zenith_bins, my_aeff.values.T, norm=LogNorm()\n", ")\n", "cbar = fig.colorbar(h)\n", "ax.set_xscale(\"log\")\n", "ax.set_xlabel(\"True energy [GeV]\")\n", "ax.set_ylabel(\"cos(zenith)\")\n", "cbar.set_label(\"Aeff [m$^2$]\")" ] }, { "cell_type": "code", "execution_count": 26, "id": "e8476a17", "metadata": { "execution": { "iopub.execute_input": "2024-03-22T09:56:57.144489Z", "iopub.status.busy": "2024-03-22T09:56:57.144083Z", "iopub.status.idle": "2024-03-22T09:56:59.027858Z", "shell.execute_reply": "2024-03-22T09:56:59.027158Z" } }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots()\n", "h = ax.pcolor(\n", " my_eres.true_energy_bins, my_eres.reco_energy_bins, my_eres.values.T, norm=LogNorm()\n", ")\n", "cbar = fig.colorbar(h)\n", "ax.set_xscale(\"log\")\n", "ax.set_yscale(\"log\")\n", "ax.set_xlabel(\"True energy [GeV]\")\n", "ax.set_ylabel(\"Reconstructed energy [GeV]\")\n", "cbar.set_label(\"P(Ereco|Etrue)\")" ] }, { "cell_type": "markdown", "id": "2fef8db9", "metadata": {}, "source": [ "## Detector model\n", "\n", "We can bring together these properties to make a detector model that can be used for simulations." ] }, { "cell_type": "code", "execution_count": 27, "id": "a07dbed4", "metadata": { "execution": { "iopub.execute_input": "2024-03-22T09:56:59.030586Z", "iopub.status.busy": "2024-03-22T09:56:59.030199Z", "iopub.status.idle": "2024-03-22T09:56:59.033180Z", "shell.execute_reply": "2024-03-22T09:56:59.032604Z" } }, "outputs": [], "source": [ "from icecube_tools.detector.detector import IceCube" ] }, { "cell_type": "code", "execution_count": 28, "id": "7c0549dc", "metadata": { "execution": { "iopub.execute_input": "2024-03-22T09:56:59.035380Z", "iopub.status.busy": "2024-03-22T09:56:59.035185Z", "iopub.status.idle": "2024-03-22T09:56:59.038170Z", "shell.execute_reply": "2024-03-22T09:56:59.037641Z" } }, "outputs": [], "source": [ "my_detector = IceCube(my_aeff, my_eres, my_angres)" ] }, { "cell_type": "code", "execution_count": null, "id": "d1274fdb", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "icecube_dev", "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": "3.9.18" } }, "nbformat": 4, "nbformat_minor": 5 }