{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "
\n", "\n", "**This is a fixed-text formatted version of a Jupyter notebook**\n", "\n", "- Try online [![Binder](https://static.mybinder.org/badge.svg)](https://mybinder.org/v2/gh/gammapy/gammapy-webpage/v0.16?urlpath=lab/tree/spectrum_analysis.ipynb)\n", "- You can contribute with your own notebooks in this\n", "[GitHub repository](https://github.com/gammapy/gammapy/tree/master/tutorials).\n", "- **Source files:**\n", "[spectrum_analysis.ipynb](../_static/notebooks/spectrum_analysis.ipynb) |\n", "[spectrum_analysis.py](../_static/notebooks/spectrum_analysis.py)\n", "
\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Spectral analysis with Gammapy" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Prerequisites \n", "\n", "- Understanding how spectral extraction is performed in Cherenkov astronomy, in particular regarding OFF background measurements. \n", "- Understanding the basics data reduction and modeling/fitting process with the gammapy library API as shown in the [first gammapy analysis with the gammapy library API tutorial](analysis_2.ipynb)\n", "\n", "## Context\n", "\n", "While 3D analysis allows in principle to deal with complex situations such as overlapping sources, in many cases, it is not required to extract the spectrum of a source. Spectral analysis, where all data inside a ON region are binned into 1D datasets, provides a nice alternative. \n", "\n", "In classical Cherenkov astronomy, it is used with a specific background estimation technique that relies on OFF measurements taken in the field-of-view in regions where the background\n", "rate is assumed to be equal to the one in the ON region. \n", "\n", "This allows to use a specific fit statistics for ON-OFF measurements, the wstat (see `~gammapy.stats.fit_statistics`), where no background model is assumed. Background is treated as a set of nuisance parameters. This removes some systematic effects connected\n", "to the choice or the quality of the background model. But this comes at the expense of larger statistical uncertainties on the fitted model parameters.\n", "\n", "**Objective: perform a full region based spectral analysis of 4 Crab observations of H.E.S.S. data release 1 and fit the resulting datasets.**\n", "\n", "## Introduction\n", "\n", "Here, as usual, we use the `~gammapy.data.DataStore` to retrieve a list of selected observations (`~gammapy.data.Observations`). Then, we define the ON region containing the source and the geometry of the `~gammapy.cube.SpectrumDataset` object we want to produce. We then create the corresponding dataset Maker. \n", "\n", "We have to define the Maker object that will extract the OFF counts from reflected regions in the field-of-view. To ensure we use data in an energy range where the quality of the IRFs is good enough we also create a safe range Maker.\n", "\n", "We can then proceed with data reduction with a loop over all selected observations to produce datasets in the relevant geometry.\n", "\n", "We can then explore the resulting datasets and look at the cumulative signal and significance of our source. We finally proceed with model fitting. \n", "\n", "In practice, we have to:\n", "- Create a `~gammapy.data.DataStore` poiting to the relevant data \n", "- Apply an observation selection to produce a list of observations, a `~gammapy.data.Observations` object.\n", "- Define a geometry of the spectrum we want to produce:\n", " - Create a `~regions.CircleSkyRegion` for the ON extraction region\n", " - Create a `~gammapy.maps.MapAxis` for the energy binnings: one for the reconstructed (i.e. measured) energy, the other for the true energy (i.e. the one used by IRFs and models)\n", "- Create the necessary makers : \n", " - the spectrum dataset maker : `~gammapy.cube.SpectrumDatasetMaker`\n", " - the OFF background maker, here a `~gammapy.cube.ReflectedRegionsBackgroundMaker`\n", " - and the safe range maker : `~gammapy.cube.SafeRangeMaker`\n", "- Perform the data reduction loop. And for every observation:\n", " - Apply the makers sequentially to produce a `~gammapy.maps.SpectrumDatasetOnOff`\n", " - Append it to list of datasets\n", "- Define the `~gammapy.modeling.models.SkyModel` to apply to the dataset.\n", "- Create a `~gammapy.modeling.Fit` object and run it to fit the model parameters\n", "- Apply a `~gammapy.spectrum.FluxPointsEstimator` to compute flux points for the spectral part of the fit.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Setup\n", "\n", "As usual, we'll start with some setup ..." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "gammapy: 0.16\n", "numpy: 1.18.1\n", "astropy 4.1.dev27293\n", "regions 0.4\n" ] } ], "source": [ "# Check package versions\n", "import gammapy\n", "import numpy as np\n", "import astropy\n", "import regions\n", "\n", "print(\"gammapy:\", gammapy.__version__)\n", "print(\"numpy:\", np.__version__)\n", "print(\"astropy\", astropy.__version__)\n", "print(\"regions\", regions.__version__)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "from pathlib import Path\n", "import astropy.units as u\n", "from astropy.coordinates import SkyCoord, Angle\n", "from regions import CircleSkyRegion\n", "from gammapy.maps import Map\n", "from gammapy.modeling import Fit, Datasets\n", "from gammapy.data import DataStore\n", "from gammapy.modeling.models import (\n", " PowerLawSpectralModel,\n", " create_crab_spectral_model,\n", " SkyModel,\n", ")\n", "from gammapy.cube import SafeMaskMaker\n", "from gammapy.spectrum import (\n", " SpectrumDatasetMaker,\n", " SpectrumDatasetOnOff,\n", " SpectrumDataset,\n", " FluxPointsEstimator,\n", " FluxPointsDataset,\n", " ReflectedRegionsBackgroundMaker,\n", " plot_spectrum_datasets_off_regions,\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Load Data\n", "\n", "First, we select and load some H.E.S.S. observations of the Crab nebula (simulated events for now).\n", "\n", "We will access the events, effective area, energy dispersion, livetime and PSF for containement correction." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "datastore = DataStore.from_dir(\"$GAMMAPY_DATA/hess-dl3-dr1/\")\n", "obs_ids = [23523, 23526, 23559, 23592]\n", "observations = datastore.get_observations(obs_ids)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Define Target Region\n", "\n", "The next step is to define a signal extraction region, also known as on region. In the simplest case this is just a [CircleSkyRegion](http://astropy-regions.readthedocs.io/en/latest/api/regions.CircleSkyRegion.html), but here we will use the ``Target`` class in gammapy that is useful for book-keeping if you run several analysis in a script." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "target_position = SkyCoord(ra=83.63, dec=22.01, unit=\"deg\", frame=\"icrs\")\n", "on_region_radius = Angle(\"0.11 deg\")\n", "on_region = CircleSkyRegion(center=target_position, radius=on_region_radius)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Create exclusion mask\n", "\n", "We will use the reflected regions method to place off regions to estimate the background level in the on region.\n", "To make sure the off regions don't contain gamma-ray emission, we create an exclusion mask.\n", "\n", "Using http://gamma-sky.net/ we find that there's only one known gamma-ray source near the Crab nebula: the AGN called [RGB J0521+212](http://gamma-sky.net/#/cat/tev/23) at GLON = 183.604 deg and GLAT = -8.708 deg." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "scrolled": true }, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "exclusion_region = CircleSkyRegion(\n", " center=SkyCoord(183.604, -8.708, unit=\"deg\", frame=\"galactic\"),\n", " radius=0.5 * u.deg,\n", ")\n", "\n", "skydir = target_position.galactic\n", "exclusion_mask = Map.create(\n", " npix=(150, 150), binsz=0.05, skydir=skydir, proj=\"TAN\", frame=\"icrs\"\n", ")\n", "\n", "mask = exclusion_mask.geom.region_mask([exclusion_region], inside=False)\n", "exclusion_mask.data = mask\n", "exclusion_mask.plot();" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Run data reduction chain\n", "\n", "We begin with the configuration of the maker classes:" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "e_reco = np.logspace(-1, np.log10(40), 40) * u.TeV\n", "e_true = np.logspace(np.log10(0.05), 2, 200) * u.TeV\n", "dataset_empty = SpectrumDataset.create(\n", " e_reco=e_reco, e_true=e_true, region=on_region\n", ")" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "dataset_maker = SpectrumDatasetMaker(\n", " containment_correction=False, selection=[\"counts\", \"aeff\", \"edisp\"]\n", ")\n", "bkg_maker = ReflectedRegionsBackgroundMaker(exclusion_mask=exclusion_mask)\n", "safe_mask_masker = SafeMaskMaker(methods=[\"aeff-max\"], aeff_percent=10)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 2.85 s, sys: 134 ms, total: 2.98 s\n", "Wall time: 3.36 s\n" ] } ], "source": [ "%%time\n", "datasets = []\n", "\n", "for obs_id, observation in zip(obs_ids, observations):\n", " dataset = dataset_maker.run(\n", " dataset_empty.copy(name=str(obs_id)), observation\n", " )\n", " dataset_on_off = bkg_maker.run(dataset, observation)\n", " dataset_on_off = safe_mask_masker.run(dataset_on_off, observation)\n", " datasets.append(dataset_on_off)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Plot off regions" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAekAAAHhCAYAAABKnUb6AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8li6FKAAAgAElEQVR4nOzdd3hU1dbA4d9J7z3UBAi919AtQJDelCogRUEEVFCBK3Yp1it2QPgsKEhHBJFeFQWlSg2BdCCB9J5p+/tjrtGYNmBCJmG9z5NHOGfPnjXjMCvn7LI0pRRCCCGEsD425R2AEEIIIQonSVoIIYSwUpKky5imaVp5x1BRyXsn7jT5zN0eed9uX0nvnSTpspdZ3gFUYBnlHUBFpGmafOZun3zmbo+8b7ev2PdOknTZk5l5QgghboskaSGEEMJKaRV5CZafn5+qU6dOeYdRrIiICIKCgso7jApJ3rvbI+/b7ZP37vbI+3b7IiIiAFRiYmKhF812dzac0lWnTh2OHTtW3mEIIYQQty04OLjIyWNyu1sIIYSwUpKkhRBCCCslSVoIIYSwUhV6TFoIIYR10+v1xMbGkpOTU96hlDsnJycCAgKwt7e3+DGSpIUQQpSZ2NhY3N3dqVOnDnfzxmRKKRITE4mNjb2lmfByu1sIIUSZycnJwdfX965O0ACapuHr63vLdxQkSQshhChTd3uC/tPtvA+SpIUQQlRqMTExdO/enSZNmtCsWTM+/PBDAF5++WVatmxJ69at6dWrF9euXQMgMjISZ2dnWrduTevWrXniiScAyMrKon///jRu3JhmzZrx/PPP5z3H0qVLadGiBa1bt+aee+7h/PnzpRJ7hd5xLDg4WMlmJkIIYb0uXLhAkyZN8v7e9a19XE3JLrX+a3o5c/j5HsW2uX79OtevX6dt27akp6fTrl07Nm/eTEBAAB4eHgB89NFHnD9/nqVLlxIZGcmAAQM4e/Zsvn6ysrI4evQo3bt3R6fTERISwgsvvEDfvn1JS0vL62vLli0sXryYHTt2FIjln+8HQHBwMMeOHSv0MlsmjgkhhLhjrqZkE/lW/1Lrr87z20psU716dapXrw6Au7s7TZo04erVqzRt2jSvTWZmZom3o11cXOjevTsADg4OtG3bltjYWIC8BG1pX5aSJC2EEOKuERkZycmTJ+nYsSMAL774Il9//TWenp7s378/r11ERARt2rTBw8ODBQsWcO+99+brJyUlha1btzJjxoy8Y59++imLFi1Cp9Oxb9++UolXxqSFEELcFTIyMhg6dCgffPBB3pXvwoULiYmJYcyYMXzyySeA+co7OjqakydPsmjRIkaPHk1aWlpePwaDgYcffpinn36aunXr5h2fPn06V65c4e2332bBggWlErMkaSGEEJWeXq9n6NChjBkzhoceeqjA+dGjR7Nx40YAHB0d8fX1BaBdu3bUq1ePS5cu5bV9/PHHadCgATNnziz0uUaNGsXmzZtLJW5J0kIIISo1pRSPPfYYTZo04dlnn807HhYWlvfnLVu20LhxYwBu3ryJ0WgEIDw8nLCwsLwr5pdeeonU1FQ++OCDfM/x9762bdtGgwYNSiV2GZMWQghRqR0+fJhvvvkmb4kUwBtvvMHnn39OaGgoNjY21K5dm6VLlwJw6NAhXnnlFezs7LC1tWXp0qX4+PgQGxvLwoULady4MW3btgXgySefZNKkSXzyySfs2bMHe3t7vL29WbFiRanELklaCCHEHVPTy9miGdm30l9J7rnnHgpbbtyvX79C2w8dOpShQ4cWOB4QEFBoP0De2uvSJklaCCHEHVPSmmaRn4xJCyGEEFZKkrQQQghhpSRJCyGEEFZKkrQQQghhpSRJCyGEEFZKkrQQQohK7U6UqgRYt24dTZs2pVmzZowePbpUYpclWEIIIe6c91tAanTp9edZC545U2wTOzs73nvvvXylKh944AFmz57N/PnzAXOpynnz5uVtaFKvXj1OnTpVoK9Zs2blK1W5fft2+vbtS1hYGG+++SaHDx/G29ubGzdulMrLkyQthBDizkmNhtdSS6+/1zxLbHInSlUuX76c6dOn4+3tDUCVKlVu6+X8k9zuFkIIcdcorFRlYGAgq1atYt68eXnt/ixVef/99/PTTz8V6OfPUpUhISEAXLp0iUuXLtG1a1c6derEjh07SiVeSdJCCCHuCmVZqtJgMBAWFsaBAwdYvXo1kyZNIiUl5V/HLElaCCFEpVfWpSoDAgIYPHgw9vb2BAUF0ahRo3yVsW6XJGkhhBCV2p0oVTlkyBD2798PQEJCApcuXcp7zL8hE8eEEEJUaneiVGXv3r3ZtWsXTZs2xdbWlnfffTfvavzf0Ioqu1URBAcHq2PHjpV3GEIIIYpw4cIFmjRp8teBcliCZU0KvB9AcHAwx44dK3RquVxJCyGEuHMqUEK1BjImLYQQQpSjiIiIIs9JkhZCCCHKUXFLtSRJCyGEEFZKkrQQQghRjoqbwC1JWgghhChHfy7nKowkaSGEEJVaaZWqBOjWrRuNGjXKO/dntauoqChCQkJo2bIl3bp1yyu88W/JEiwhhBB3TO8NvbmWea3U+qvhWoOdw3YW26Y0S1UCrFq1iuDg4HzHZs2axbhx4xg/fjz79u1j7ty5fPPNN//69UmSFkIIccdcy7zGmfGlt1a6xYoWJbYprVKVxTl//jzvv/8+AN27d2fIkCG33dffye1uIYQQd43SKFU5ceJEWrduzfz58/MmfbVq1SqvQMd3331Heno6iYmJ/zpeSdJCCCHuCqVRqnLVqlWcOXOGn376iZ9++invlvZ///tfDh48SJs2bTh48CA1a9bEzu7f36yWJC2EEKLSK61SlTVr1gTMt81Hjx7Nb7/9BkCNGjXYtGkTJ0+eZOHChQB4enr+67glSQshhKjUSqtUpcFgICEhATAn/R9++IHmzZsD5vKUJpMJgDfffJNHH320VGKXiWNCCCEqtdIqVZmZmUnv3r3R6/UYjUZ69uzJ5MmTAThw4ABz585F0zTuu+8+Pv3001KJXUpVCiGEKDP/LM1YHkuwrImUqhRCCGG1KlJCtQYyJi2EEEJYKUnSQgghhJWSJC2EEEJYKUnSQgghhJWSJC2EEEJYKUnSQgghKrXSLFW5du1aWrZsSbNmzZgzZ07e8UWLFtG0aVNatmxJSEgIUVFRpRK7LMESQghxx1zuEYL+Wumtk7avUYP6+/YW26a0SlUmJiYye/Zsjh8/jr+/P+PHj2fv3r2EhITQpk0bjh07houLC0uWLGHOnDmsXbv2X78+SdJCCCHuGP21azS5eKHU+rvQuEmJbUqrVGV4eDgNGzbE398fgJ49e7Jx40ZCQkLo3r17XrtOnTqxcuXK23k5BcjtbiGEEHeNf1Oqsn79+ly8eJHIyEgMBgObN28mJiamwHN8/vnn9O3bt1TilSQthBDirvBvS1V6e3uzZMkSRo4cyb333kudOnUKlKNcuXIlx44dY/bs2aUSsyRpIYQQlV5plaocOHAgR48e5ddff6VRo0Y0aNAgr489e/awcOFCtmzZgqOjY6nELUlaCCFEpVZapSoBbty4AUBycjKLFy9m0qRJAJw8eZIpU6awZcsWqlSpUmqxy8QxIYQQlVpplaoEmDFjBqdPnwbglVdeoWHDhgDMnj2bjIwMhg8fDkCtWrXYsmXLv45dSlUKIYQoM/8szVgeS7CsiZSqFEIIYbUqUkK1BjImLYQQQlgpSdJCCCGElZIkLYQQokxV5LlPpel23gdJ0kIIIcqMk5MTiYmJd32iVkqRmJiIk5PTLT1OJo4JIYQoMwEBAcTGxnLz5s3yDqXcOTk5ERAQcEuPkSQthBCizNjb2xMUFFTeYVRYcrtbCCGEsFKSpIUQQggrJUlaCCGEsFKSpIUQQggrJUlaCCGEsFKSpIUQQggrJUlaCCGEsFKSpIUQQggrJUlaCCGEsFKSpIUQQggrJUlaCCGEsFKSpIUQQggrJUlaCCGEsFKSpIUQQggrJUlaCCGEsFKSpIUQQggrJUlaCCGEsFKSpIUQQggrJUlaCCGEsFKSpIUQQggrJUlaCCGEsFKSpIUQQggrJUlaCCGEsFKSpIUQQggrVaZJWtO0QE3T9muadkHTtHOaps34x/lZmqYpTdP8/nbsXU3Tjmmadn9ZxiaEEEJYu7K+kjYAzymlmgCdgOmapjUFcwIHHgCi/2ysaVrj//3xPmB6GccmhBBCWLUyTdJKqetKqRP/+3M6cAGo+b/T7wNzAPW3h9gCpv8d08oyNiGEEMLa3bExaU3T6gBtgKOapg0CriqlTv+9jVLqHOAC/AwsuVOxCSGEENbI7k48iaZpbsBGYCbmW+AvAr0Ka6uUeupOxCSEEEJYG03TNKVU3h3mMk/SmqbZY07Qq5RSmzRNawEEAac1TQMIAE5omtZBKRVXQl8akPHn3318fMoucCGEEOIOiIiIQNO0zD//rmma25+JukyT9P+S6ufABaXUIgCl1Bmgyt/aRALBSqmEkvr7X9Cuf/49ODhYFdNcCCGEsHpBQUEkJia6FnaurMekuwKPAD00TTv1v59+ZfycQgghRKVQplfSSqmfKWGWtlKqTlnGIIQQQlRUsuOYEEIIYaUkSQshhBBWSpK0EEIIYaUkSQshhBBWSpK0EEIIYaUkSQshhBBWSpK0EEIIYaUkSQshhBBWSpK0EEIIYaUkSQshhBBWSpK0EEIIYaUkSQshhBBWSpK0EEIIYaUkSQshhBBWSpK0EEIIYaUkSQshhBBWSpK0EEIIYaUkSQshhBBWSpK0EEIIYaXsyjsAIYqTpkvjUtIlco25eDl50cSnCTZa0b9bZhuy+e36b6TkpuBo50gLvxbUdKt5ByMW4t9RSqFpWnmHIayEJGlhlcKSw1h1YRW7onZRz7MeTnZOxGfFYzAZGNFwBMMbDcfV3jWvfUJ2Al+c/YKtV7bS0LshVV2qkm3IZsGRBbT2b82EZhMIrhZcjq9I3C2MKSmkbPqO1O+/xxAfD3Z2ODaoj/fIUbiH9ECzt8/X3pSbS9r27aSsXkPOhQsogwE7Pz88Bg7Ae9QoHAIDy+mVCGsgSVrcMSZl4re434hIjcCkTFR3rc69Ne/F3jb/l9buqN0sOLKAsU3GsmXIFvyc/QDzFcaZhDN8de4rtoZvZWnPpfi7+BORGsGU3VPoUasHq/uvJsA9IK+vLH0W2yO2M+fQHKa1nsawhsPyzimlOJtwls2XN3Mt8xo2mg1BHkEMbTiUIM+gO/OmiArDlJmJMS0NzckJWy+vQq92UzZuIv7tt3Hv3o1qr7yMQ506KIOR7OPHSF65khvvvkvA4k9xatQIgJwLF4iZNh3HBvXxfWIKrp06odnbo4uJIWXDBiJHjMR79Gj8npxe4PmMGRnk/PEHpqwsbNzccW7dChsnpzvyXog7R1NKlXcMty04OFgdO3asvMMQJTCajKwJXcO3F77Fxd6FVv6t0NC4knqF8JRwHmrwEJNaTMLF3oXf435n1sFZfPbAZzT2aVxof0oplp5eyt7ovSzuuZhx28cxucVkhjYcWmQMMWkxTNgxgVe7vMp9AfcRlhzGy4dfJjU3lWENh1Hfqz4KxR83/2BT2CYa+TRiftf5VHGpUlZvi6gAlNFIxoEDJH+7mqwTJ7B1d8eUnY1dlSp4P/wwnkMGY+vmBkDy+vUkLv2MwOXLcaxb+C95aT/+SNzCN6i94ivQNKLGjafayy/j0ad3oe0NCQnEPD4F1/vvo8qMGQDooqJI/Oor0n7cjlODBti4u2NMTkYXFYXngw/iM34c9lWrlsn7IcpGcHAwx44dK3yMQylVYX/atWunhHXTGXXqmf3PqHE/jlOnbpxSJpMp3/krKVfUrAOz1MitI1VKTooas22M2h6xvcR+TSaTemL3E+rZ/c+q2QdnWxTLgegDaviW4ep8wnl135r71KZLm5TRZCwYs0Gnlp5aqnpv6K3iMuIse6Gi0jGkpanICRNU+NBhKuX775UxJ0cpZf7sZRw9qmJmzFSXunVX2aGhShcXpy526KhywsNL7Ddp3ToVPmy4ihgzRiWtXlNie31Cggq95x6VfTFUZRw5qkI7d1E3PvxI6eLi87XLjYpScW+8oS7de5/KPn/+9l60KBf/y2WF5jm5khZl6q3f3iIqLYoPu3+Ig61DoW2UUrz9+9ucvnGaG1k32DlsJ3Y2JY/E/BT7EzP2z+DzXp/TpmqbEtsbTUb6f9efHEMOczvOpXedwq9e/vR/Z/6Pn2J/YkXfFfn6OB5/nJvZN7GzsaOhd0O5NV4JKZ2O6EmTcahdm2qvvYpma1tou9StW7nxzru49+mDMuip/uqrJfdtNHK5ew+UXk+DQwcLjFEX5uYnn6K7coXMo0epuWgRrp06Ftk2bft24t98izrr1mJfrVqJfYvyV9yVtIxJi9uilOJY/DHWhq7lQuIFco25+Dr70i+oH0PqD8HT0ZOE7AS2XNnC9oe2F5mgATRNY3bwbLqt60aX6l0sStAAge6BGE1GqrpYdmvP1saWBl4NCEsJKzFBA0xsNpF1oes4n3ieOh51WHVhFesvrcfbyZva7rUxKANv/fYWdT3r8kjTR+gW2M2iOET500VHk7ZtG4abN9Hs7XFs3ASPvn3yxnRTt24FKDZBA3gOHIg+NpaEZcuos2aNRc+t2dpiHxiIKSfbogQN4DV8OJdDQvCfObPYBA3g0bcv2adOk7Tia6r+Zw5KKbKPHyf529VknzqFKTcXWy8vPHr3wmvECEnkVk6StLhlcZlxzNg/g1xDLiMbj2R66+k42jpyNeMqm8I20XdjX55q+xTpunR61e6Fp6NniX3a2thSz6se4WnhFseRZcjC3taedH26xY+5knKF+l71LWpra2PL8IbDWXV+FZdTL1PTrSYfdP+Apr5N89rojXr2xuzl3d/f5UzCGZ5q85TFsYg7Lzc8nPg33yLn7Fk8Bg7AIaguSq8nbcd2brzzDl4jR+A3bRrJq77Ff8bTxSboP3mNGsXNDz/C1svr1oIxmixuqtnagMGAR+9eFrX3HjOayJGj8H5kLNdmzcaYlIT36IfxnzkDG2dn9HHxpH73HeGDh+D98Cj8Z8yQZV9WSpK0uCU3s24yYccERjYayYRmE/L9w67hVoP21doTkx7DtD3TMCkTL3R8weK+G3k34rvL31nc3s3eDb1Jj7Ods+XxZ9+kZ+2eFrdv7d+az89+ztAGQ5kVPKvAF5m9rT196vShY7WOPLrzUfyc/Xi48cMW9y/unOxz54h5fAp+U6YQ8MnH2Dg65p3zfXQiupgY4ubPJ2bSZPQ3buB6zz0W9Wvr6QmaRvruPfiMHWNZMLa3to9U5uHDYGuLrbePRe0datXCPiCAqDFj8RwyGP+nn8732bXz98e5RXP8npxOzNSpqOxsqs6de0sxiTtDdhwTt2Th0YUMqDuAic0nFvmbd6B7IMt7LedaxjVSclIs7rtzjc7kGHKwdJ5Eam4qGhoJ2QkWtTeajOQac+lcvbPFMZ1LPIfRZOS54OeKvdLwdvLmvfvfY+nppeiMOov7F3eGMSOD2GnTqfbKK/iMeyRfgv6TQ2AggYsXo/Q6NE2z6CoaQLOxwcbTk5wLFyyOR9NswGT5lXRO2GU0R0dsXF0sfowhMRHH+vWpUsxVsp2PD7WWLSN9334yjxyxuG9x50iSFha7nnGd3+N+59Hmj5bYtpprNaq6VuXHiB8t7r+uZ11sNBuOxx+3qP26S+voUqMLay5aNhZ4KPYQDrYOONtbfuW95coWgjyDit3l7E91verS0Lshu6N25x27knKFXZG7+DH8R07En8CkLP9iFqUndcsWnFu1KvF2sWZnh+/UaRgSEzGmWz6M4tSwITnnz1nUVul05IaFobt6FWNamkWPyT59GofAQItvSRvT0jDEx+P50IMltrX19MRn4gSSv11tUd/izpLb3SKfKylX2BS2iZj0GBSKALcAHmrwEA28G7Dlyhb6BfXDxd6y3+aH1B/CZ398ht6kx96m5Aky2yK2EVw1mFd/eZUVfVfkbWJSmJ2RO/nl6i982fdLpuyawvpL6xnecHiR7aPTollwZAF9g/qy/tJ6WldpXWI8JmUiLCWMd+57p8S2f+ob1Jefr/6MvY09Ky+s5Gr6VVr6t8TWxpbw1HByDDmMbDSSUY1H4Whb8GpO3D5DYiKZv/yCMTUNGxcXXNoH5+3WlbJ6DVVftGzoxaVtW7CxIWnlKvynPlFie6UUxpRk9LFXyT59GudWrYptn/jFFzg1boydvx83Fi2i2quvFpt8s//4g9zQUABM2dnYOJf8S2bymrWgabhZeMvec9Agbr7/AYbkZJReT8qGDWTs3YcxLQ0bZ2dcgoPxHv0wjvUtm88hSo8kaQHAtYxrvHL4Fa6kXuHB+g8yuN5gAC4kXWDK7inU8ayDl6MXXWt0tbjPMU3GsPjUYsKSw/JNtipMjiGHDZc2sDhkMQdiDjBu+zie7/A8XWt0xdbmr9uOKTkprAldw9rQtSwOWUwt91os6bmEKbuncCnpEuOajiPQ469tFLP0WWyL2MbiU4t5qs1TdA/sTv/v+hOVFkVtj9rFxvRz7M9oaDxQ+wGLX7OHgwenbpzifOJ5nmzzJN0Cu+X9gqKU4o+EP/js9Gfsi97H4p6L821tKm5Pbng4CUuWknHgAK6dOmHr54spPYMb77yDU6uW+EyYiC4yEpeOxc+K/pOtmyvObduQvmOHRUk6++QpVK6O6u++Q8y06dR8fxGuHToUaKeMRpK+/JKUdeupvfpbbFxdiXpkHPHzF1Bl9qwCyVcpReYvv3Btzn+o8dZbpKxbR+oPP+A9vOhfRv+UcWA/Tk2aYOvubuFrdsO+WjVuvLeI9F278Ojbl6ovzMXO1xdjZiYZe/cSNXEiLm3aUuPNN7Bxlc/tnSLrpAWx6bFM2DGBMU3GMLbJ2ALbdOpNelZfWM3HJz9mcovJPN7qcYv77rCqAwFuAXzZ58siZ3nrjXrmHJqDo50jb937FgB7ovbw+ZnPSc5NpmuNrnl7d/9y7Re6BXRjauupBLr/lYwTshNYcW4F31/+nrpedfP27j4ef5x2VdsxodkE2lZtC8DGSxtZfmY5yx5YRi2PWoXGdOrGKWbsn0FKTgpHxxzFyc6y7RZn7p/Jsbhj/Dj0RzwcPAptY1Im5v06j/iseBaHLJZZtf9C1vHjxD71ND4TJuA9Yni+Gdam7GzStm3jxnuLMGVn0/jUSYv7Tfz8C25++CF11q/L28KzMKbMTKIeGYfXiBF4jxpJxk8/c/3FF7GvFYj3iBE4BAWh9Aayjh8jZc1a7KpUoeb7i/KWPRnT07n+8itkHTmC5+DBuHTqiObggD4mhpT1GzCmpVH99ddw7dKFrJMniZ3+JLW+/KLYmDKP/kbMlCl4jRxBNQsngymluNSpM/bVqlHryy+w8yk4QU3pdFx/7XV0UVHU+uLzQsf1xe2RHcdEkUwmkxq2ZZhaeX5liW2f3vu06vJtl0J36SpMbHqs6vptV/X20bdV/0391ZbLW1SOISfvvMFoUAdjDqqx28aqp/Y+pXINuQX6OHvzrFp1fpX64swXatOlTSopO6nY58zWZ6tDMYfUlstb1K7IXepa+rVC2629uFZ1+baLmv/rfHUx8aIyGA1KZ9SpE/En1JyDc9Q9q+9RB2MOqim7p6jNYZster2ZukzVakUr9c25b0psqzfq1YBNA9TxuOMW9S0Kyo2JVaFduqr0n34utl32xVB1vlFjlbb/gMV931y2TEU9+pgK7XqPStu3T5mMBT/zOZcvq/Chw9S1V1/Nt5OeSadTqTt2qugnpqrwBx9S4SNGqKsvvqiy/vij2NcSv+h9FTV5soqcMEHFzpqt0g8dKvC8qT/+qEK7dFVJ69YpY1ZWvnOG1FSV8MWXKrRzFxX39jsqetp0i19v8vdb1PkmTVXutcL/veS9NqNRxTz5pLrx4YcW9y1KJjuOiSL9Hvc7C48s5LvB35V4RRefGU+vjb14//736VG7R4l9f3jiQ3KNucxpP4eDMQdZdWEVocmhNPJuhI2NDREpEXg5eTGq0SgG1RuU77b2nRCfGc/GsI1svryZG1k3AKjlUYthDYYxuP5gPB09ORBzgKWnl/Jt/29LnDy2+NRi/u/M/3Fk9JFiN2/50zfnv+FMwplbGvMWf4l/8000BweqPPdciW0jRj2MKTWVettLnsiolCK8X3+qz5+HKSeXG++8g8rNxWPwIOyrVMGUlUX63n3kXrmC76OP4jNxwh29G5J96hQJny0j++RJXO+9F1t3NwxJyWT+8gtu99yD7xNTcKhZk8s9Qgja/B321auX2OeVPn2x9fGmzrffltg298oVosZPoMG+vWgOJX/ORcmKu5KWJH2Xm31wNm2rtrV4be/IH0aSoctg20Pbim0Xkx7D2B/HsqLPCup41sl3PDotGqMyUt21Og28G/yb8EuN0WRE07QCidhoMjJ+x3jaVm3LM22fKfLLOCUnhT6b+tCzVk8W3LPAoudMyE5g0OZB/PLwL/86/ruNKTuby917ELRxA/Y1S64XnnnkCNGPPkbQpk04NS76VjFA+r593Hz/A4K2fI+maeYdu06eJH3Xboypqdg4O+Hcth0evR4o1ySli71K1tGj5ipY7m64de2Knb9/3vn4N9/EkJRMjXfeLn5i2pkzRI4YSa2vV+Davr1Fzx01bjzeY8fg0cuyzVVE8WRbUFGksOQwHm9p+Rjz3A5zmbhjIktOL2FKyymFXl2Gp4Yzbc80praami9Bg3kN9d/Hkq1FUVfxtja2fNTjI57Y/QRzMuYwqcUkGvn89SWvN+k5EHOAj058hIeDB/3r9rf4Ob0cvcjUZ5KWm8bW8K3sj95Pmi4NZztngqsFM7zhcKq5ypaNhck5dw772rUsStAALh07Yh9Qk2uzZ1Nn/boiSzpmnznD9ZdepuaiRXmJTdM0XNq2Nc/6tiIOATVxCHioyPP+M2cSNX4Cca++RrWXXiz0FwpdTAyxM2ZiX6OGxQkawKlpU/QxMQDo4+LQRUWDMmFfs6bUvy5lkqTvAqFJoawJXcO+6H2k5qbiZAZKGv4AACAASURBVOdEu6rtGNloJHqT3uK9sgF8nX3xdfblyLUjbLm8hRGNRtC5RmccbByIzYjlu7Dv+C3uN2YFz+LBBiWv0awIfJx8+KrPV6w4v4Jpe6dR1aUqtTxqYTAZOBl/kkCPQGa2ncnmK5tJ01m27hXMV9/2Nvb02dSHrjW68kjTR/Bz9iNTn8ne6L0M3TKUnrV78mLHFy26fV4Z6ePiSFm/gZyLF1F6HXZ+/ngO6I9Jr8fWzbKZy2BOtG733U/WiRNEDBuGz7hxeA4YgI2LeTlhbngEyWtWk7b1B6ovXFji/tgVgY2zM7W++Jzrc+cS1iMEr6FDcet2PzbOzhji40n5bjOZv/yC58CBeUu8LKZp5EZEED1lCjmn/zAvzfrfMcegILzHjMa9d2+ZFFkK5HZ3JaY36nn919c5cv0IwxsOZ2C9gfg7+5NlyGJf9D7Whq4lIjWCFzq+wOD6gy3q8/DVwyw5vYSV/VZyNuEsay6u4ULSBXRGHT5OPvSv25/+dftX2qVFBpOB3+N+52b2Text7Gng1YD63ua1o2suruG3uN9Y1G2RRX09vutxziacZeOgjVR3KzhumKnP5OXDL5NlyOLjHh9btNa8sjBmZBL3+utkHDqEZ/9+uHTqZJ71HB1DyoYNGNPT0RwcqL9zh8V9Xp09B5f27bGvUYPk1avJOnIEW29vlF6PMhrxGjoU75EjsK9RowxfWfnIDQ8nec0a83KxnBxsvbxw79Mbz8GDMaWmEjF8BPUP7MfGgtv3Siku9whB6fVUeeYZPPr1zVs+pnQ60vfuJWHZcpwaNqD6woVodnItWBKZ3X0XMpqM6rkDz6npe6arLH1WoW1MJpN6et/Tqv3K9io5O9mifmfum6nWXlxbmqFWGum56arzt53V9YzrJbb9KfYn1XpFa7Uval+x7fRGvZqya4pa/sfy0grT6hkzMlT4sOHq2ksvKUN6RoHzJpNJpe3Zo843bqKS1pRcj1kppQzpGepih45Kd/2v/zeGtDSVGxmpdFevKpNOV2rxV0RREx9VKVu2WNT2+sI31PmmzZQuPr7INsasLBU18VF1/fXXSyvESq242d2yLWgltTtqN9Fp0bzX7b0iC1BomsZb976F0WRk3pF5JfYZlhzGb3G/3dK4693EzcGNcU3H8dyB58jQZRTZzqRMzP91PrU8apVY3tLOxo6Z7Way5uIaDCZDKUdsneLeeAPHhg2oNm8etm4F78homoZ7SAgegwcR/8abGBITS+wzdfNmXDt0yFeW0dbdHYfatbGvUcPikpGVlffYMSR89hnGjKI/twCG5GSSv/0W74cfxr5KlSLb2Tg7U/OjD0nbsRNdVFRph3tXkSRdSa25uIbHWjxW4taTznbO/KfDf9gTtYc/bvxRZLvwlHCm7Z3GCx1fqLS3skvDlJZTaOLbhHE7xnEg5gBGkzHf+fOJ55m2ZxrxWfEs77XcojG7xj6Nqe5ancNXD5dV2FbDkJhI+u49VJ09u8T3puqsWWBjQ9z8+cW2yzp+nITFi/GbPq00Q61U3Lp3x6VdMDFTnsCQnFxoG6XXE/PEE9i6u1NlVsnL3mzd3PB66EHzFqXitslgQSV0NeMq4anh9KhV8lpmgBGNRrApbBOP7XqMgfUGMrLRSBp6NwQgLCWMdaHr2B6xnTnt58hVdAk0TePFji/yY8SPLPtjGW8efZNW/q2wtbElIjWCpJwkutToQgu/FlRxKfpK5J+a+zUnMi2S+7m/DKMvf6nffYd7z54W1Wa28/OjxhsLufrcLK7Pm4/PI2NxDArKO6+/cYOU9etJXrmKGu++i1PjxmUZeoWmaRrVXn2Fm4sWcaVPXzz69cVz0CDs/P0xZWSQvnsPKevXo3Q6qs2fV+Ts+H/yGDSI2Cefoup/5pTxK6i8JElXQjeybhDoHnhLE4261OhCh2odcLF34cl9T3Iz6yYaGr7OvjzU4CE2DdpEVdeqZRh15aFpWt4EuguJFwhPDcdgMjC4/mDaV23P8fjjLD69+Jb6tNFsLC7hWZHlhoXh0sHymdUefftybe4L2DjYEzVmLPaBAdj5+mFKTycnNBSPPn2ovWoljnXrlmHUlYNmY0OVWbPwfuQRUtZvIG7efEypqWgu5gIbgcuXc232bBwCAizu087PD5OFlb5E4SRJV0I2ms0tl0Q0KiMejh5MajGJJ1o9gd6oByiwj7e4NU18m9DEt0m+Y9XdqhOZGmlxdTAwzwdwsHXg2QPPEpkWiVKKGm41eLD+g3QL7HZLy+ismTIY0exv7bVo9vb4TZ2K/zPPkH3qNKb0NDRnZ5xbtMDWo/D900XR7KtWxf/J6fg/Ob3AORsXF0yZmRb3ZcrMRHNxxqTTkRsaiikzExs3d5waN5JZ3xaSd6mCO5dwjjWhazhz8wy5xly8nby5t+a9RKRGkK5Lx93BsrWkp26cYlyzcXl/l+RcdgLdAwnyDGJf9D561+ldYvtDsYc4GneUxJxEHm78MFNaTgHgUvIlvjn/DW/99hbv3PdOXgGRikIXG0vq999jiIsDG1sc69XFxsuL3IgIi/vQ37gBRiM27u5oNja4dixYfUqUHpf27UnfvQeX4GCL2qd89x02Lq5c7t4DO39/bD08MCYnY0xLw2v4cLxHP1xoMQ/xF0nSFVRyTjKzD84mNiOW4Q2H80jTR3CydSIuM47vr3xPjjGHl35+iQ+6f1DiBJxLyZeIzYjl/oDKPd5pTUY1HsX/nfk/7gu4r8jZ92DeiOaZ/c9wT817+KTHJ/n+XzbyacTAegM5fPUwzxx4hv/e/1/aV7N816jyoou9Svz8+WSfPo3HoIE4NW8BJiNZJ06SsW8vyqTwnTABW8/Cq6b9XcqGDXgMHIhmI3Ng7wSvkSOJHDYM/xlP520EU5SsU6dJXPoZ7r164T/j6XzzBXJCL5G8ciURw4ZRa9kyqVNdnKLWZlWEn7t1nXRqbqoasnmIev/Y+8pgNBTaZnfkbtV6RWv139//W2xfOqNOTd45WS07vawsQhVFMJlM6vlDz6vHdz2uMnQF1wIrZV4j3eXbLqrX+l4qU5dZbH+/XP1F3b/m/nxVxqxRzpVwdene+9TNZcsKVHFSSil9QoIK7dJVhfUIKXSNdL62SUnq0j33quwLF8oqXFGIq/95XsXMnKlMhsK/e5RSKicyUl1o1VpdGTwkX4Wwf0re9J261K17sWuu7wayTrqSee/Ye7Sr2o6Z7WYWued0z9o9eazFY3xz/ht2RBS+K1NqbirPHngWB1sHJjafWJYhi3/QNI35XecT6B5Iv039eP/4+1xOvkxKTgqx6bGsOLeC3ht6ozfpWTdgHS72xV+1dK7RmcY+jdkVuav0gjSZzD+lROn1xE6div/TT+E3eXLeLlV/Z+frS60vv8CQkEDM5MlF9mVISiLm8Sl4Dhkis7bvsGqvv4YpNY2YqVPJCb1U4LwuOpqYSZOxcXen9oqvir2T5/XgENxDQkj++uuyDLlCk21BK5jU3FT6buzLDw/9gI9TyWM5z+x/hkOxh2hfvT2D6g7C38WfTH0m+2P2sztqN/2D+jOn/RwZgy5HUWlRrAtdx77offkKbCRkJdCzVk9GNRllUT/7ovex4twKVvRdcfvBZNyEk1/DiW8gJdp8zKsWtB1n/nH1u+2u03bsJHnlSmqv/KbEtpnHTxA9diwuHTrg+9ijuHbqBHZ26K9eJWX9BlI2bsRr2DD8Z86Q/aHLgdLpSPz8c5JXr8E+MBDnFi3Me3eHhpJz/jzGrCzqbfvBomIbuqgoIkc9bN6W1LH4fR0qK6mCVYlsC9/GvQH3WpSgAV7p/Ar9NvUjJDCEHZE7SM1NxdnemXZV2rF1yFZ8nX3LOGJRktoetZndfjaz28/Od3zkDyNp5tfM4n6a+jYlJj3m9gM5uxF+eBaaDIBhn0O1Vubjcafh2BfwcTsYsAiaD72t7pPXrMHrYct+4XBt1xbv0Q+ji4nl5kcfEzNtOphM2Hp54TlwALW/+QbHukEldyTKhObggN/UqfhOmkTGoUPoIqNAmXAePgyVqyNt1y6Lq2E51K6NQ506ZB8/jmuXLmUcecUjSbqCiUmPoalvU4vbezt54+vsay592Gh4GUYmysKtXCVqaCiKuDOWnWz+sXMCt6rwz2GS89/Dzhdhwjao1jz/uZrtzD8dp8KqYWBjB03/UZAlIQyij4A+C5x9oH4IuOT/RTLn3DncPnjf4tfj1q0bSV+tIGjDevMacZMJzbbw4R1RPjR7e9xDQvIdS169Gju/W7vjYufvj1HWUxdKkvRdosgvb2G1arrVJDQplOZ+zUtuDIQmh1LT7W/1lU1GuLQDflsOscfMSVOfDXaOEDwR2o43377WZcHWmfDIpoIJ+u+qNYeHV8M3D0H9B8DBBcIPwE+L4MZ5qBcCjm6QHgc/PgeNB8B9s8DHvJGIys1Fu4XbmZqDIyo31/xnTQNJ0BWC5ux8S2upAUwZGYXOURCSpCucQPdA/kgoeo/tf0rJSSEpO4kqzpZvQSmsw4P1H+Tjkx/zUIOHLLqiXn1xNUEeQcz/dT65+kx8In+hd46RZp1mwOi15uQMcO0k/P5/sLgzjFwJCaEQ2BFqtCk5qBptIKC9+da4SQ8H34EH5pmvrO3+loAzE8y3yD/vbX7umm2x9fNFf/Wqxctt9LGx2PrKcExF49KuHTfeehtTTo5F24cakpPJPnMGp5YtUUrJHIN/kNndVs5oMnIo9hCfnf6MT05+Qq4hlwMxB0jKSbLo8Zsvb6Z7re64ObiVcaSitHWt2ZUMfQZ7o/cW285oMvLS4Zc4fPUwN7NvUs8ziHaRx3C0d+MZHxdGx2zmVNKFvx5Qow0M/hSGLIY1o+H3z6HdBMsDazcBjiw2J+iJ26HliPwJGsDVD3XfbHJazCHj3YfJPLAD927dSdm4yeKnSdm4EY/+/SyPS1gFh8BAnFq2IG3bjxa1T1iyBFsfb8L79uNi02ZcbNuO6EmTSd+3D2U0ltxBJSdX0lbKpEysurCKledX4ufsR4fqHbC3sScsJQydUce4H8exvNdyqrtVL7KPhOwEVl5YyaJui+5g5KK02Gg2vHvfu0zdMxVbzZbutboXaGM0GZm8azInb55kXpd5DGkwBM5thlwTjNnCE5rGrqhdPL3vad669y261PzbxJwGD0D3ubDzJfC9hc0k/BpC0hUY+gX4FJy8pXQ6Ur7bTPLq1ZjS07F38Yf33iQnLhuVk4NLxw64d+tW7FNknTiB/upV3HtYViRGWBe/yZOJfeZZnNu0KXKCn9LpiH16BhkHD+I9+mF8J03CrkoVTFlZZOzbR8Jnn3Hzw48IXLoE++pFf89VekUtoK4IP5V1MxOjyajmHpqrxmwbo84mnC1wPjotWt27+l7VYWUHFZYUVmgf1zOuqwe/f1AtPrm4rMMVZeyPG3+onut7qvHbx6sfw39U0WnRKiYtRu2L2qf6beynWn/dWu2P3v/XA77sr9SZDfn6OB53XN235j4VlxGXv3NdtlKveSt1aZflAZ39TqnXfZQqZCMdY0aGipwwQUWOG6/Sf/5ZmYxGpeLPK/VuQ2VISlCRj01S55s1Vzc/+6zI7rMvhqpL99yr0vbssTwmYXWSN25Soffco1K3bVMmnS7fOUNWlgofNlxdaN5CpWzbVujjTSaTSli+XIU90EvpExLuRMjlprjNTGSdtBVacnoJR64d4bMHPsPJrvAxnaScJMZvH09MegxTW02lR60eONmZtwXdfHkz+6P3M7nlZCY0myBjPJWA3qRnf/R+NlzaQHR6NCZlorprdUKTQ1nRZwWNfBqZG2bchE/awazLYOeQr4+FRxbi5eTF9Nb/KJzwYWuo0sQ8KcwSa8bCjXPw9Ml8h5VSxEyZgp2vH9UXzM8/E/uTDjDsc1TV5sS9Po+U9etx79WLai+/lLd3c25EBClr1pL6/fdUfeklPAdIWdSKLvPXX0n4dDG6qCjceoZg6+GJMSmJtO3bUUoRuGQxrh2K3289/s23MGZmUGPBgjsU9Z0n66QrkCx9FqsurGLtgLVFJmgAHycftgzZwoQdE/jp6k9si9hGrsFcYKN3nd7MCp6Ft5P3HYxclCV7G3t61elFrzq98o79EP4DWy5v+StBA2QlgFu1AgkaYGSjkTy++3GmtJySv2pW3fvhwlZQCkr6hU4piDkCdQvehs46cgTD9esELl5ccKmUkyfoMtE0jeqvvYpzq1bEzZvH5YMHsXF1AaN5ZzOvoUOps2EDDgE1C/QvKh7Xzp1x7dyZnEuXyPr1V4yZmTg2aIDDpUv4jh9XYoIG8J08iSt9+2GcPdui/dwrG0nSVmZH5A7a+LfJv5SmCJqm8XjLx/nwxIdsGbLlDkQnrMmFhHN0dqoKZzaYJ25Va2n+ryG70Pb1veujoZGQnUA112p/nfD432ft+JcQ/GjxT3r8S9BswMG1wKnkb1fjPXp0wRKESkHmTchNh10vw5X9eOnSSfEy4d23PS6Dp6P51sbWy0vKF1ZSTg0b4tSwIQD669dJWLIE9549LXqsnZ8frvd0JW3nTrxHjCjLMK2SzO62MifiT9AtsJvF7TvX6Ex4ajjZRXwxi0ooJxX2v0nuiRU4hO2Biz/AqW9hWTf4cTbkpMHN0EIf6mDrgM6oy3/wyj7oNhcOvA1HloDRUPCBRoP53MF3YMhncHErGHLzTiuTifQDB/AYOLDgY6OPmJP0xsmgTDDwQxi7CY9hY8g8exX7zUOxO7IQTau4Q2/Ccvq4OOwDA9EcCt7tKYpjUF0McfFlGJX1kl9brUy2IbvEYgp/Z6PZ4GTnRI4hp9iSh6KSSLsO3wyB6q3wbTGKqw5O0H6O+ZwuC85tMifFrTPh0e35HpptyCYpJwkvJ6+/DsadNe/R3W4i1O8Jm6fBLx+bNzqp3tLc5vofcPwr8K5jXnLlEwRVm5uv4NuMAUBlZ6PZ2mLr9o+lfiYjbHwUPANg0l7zZif/Y9egLcYLsTBzA2x4FDZOgmFfgpSdrNQ0Ozu4xaVVymjExunu3Ndb/jVYGS9HL25k3bC4fZY+i2x9Nm72sg660jPkwrfDzXtnP7SMPs0fYVv4tr+ujB1coM1YGPe9edx4/xv5Hr4jYgfB1YLxcPAwH9BlwdYZ0Gka2NqZk++j282bj2QlwrEvzT9ZiTBmvfncn0uuur8Au1+BqycA0JycUDodSvePq/QNj0FWEozbmi9BA5jSM7BxdgFHd/OmKmnX4GTJxTdExeYQGIguJgZjSorFj8k+cQKHuvXKMCrrJUnayoTUCmHLlS1YOuv+x4gf6Vqzq1Sxuhuc2wzO3nCfuRBHkGcQDb0bsvny5vztAoKh13w49C6cWg1GAzqjji/OfkEj70bsjNzJqdDvUV8PBP/G0Pkfs72rtYB+78CYdeaffu8U3C40sAMM+hhWDYef3kPLTsKlXTvS9/5v45X48/D9k+Zb8YM+AfeCO96l796NS4f25r/YOUK35+H35eYxbFFp2Xp54Xb//aRs3lxyYyA3LIzcyAjcu3cr28CslCRpK5KhyyDIM4gsfRa/xf1WYnu9Sc+3F79lVCPLKguJCu73/4OOT+SbgT27/Ww+PfUph2IP5W/babp5z+zDH7JrcUt6rurMtbQYLl3ZwY4Dr/Dqzy8wyCWXbxp0RGfS3148jfvB+K2QFA6ftMO7ehTJ77+MWnoffPOgOdl61YIWwwo8VBcbS/bJk3gOGPDXwbrdITcDrp+6vXhEheEzdgxJX3yJ/tq1YtsZMzO5+tws7Pz8uTrnP1x/+RXS9+2/q3YikzHpcqY36tkVtYu1oWu5mHQRN3s3UnWpPLH7Caa3mc4jTR/B0bbgWIzBZOC1X16jmks1OtXoVA6RizsqOwXiz0GD3vkON/RuyEc9PmLm/pl0qt6JkY1G0sq/FZqmkdlyBM9c3cExQy5N7N35zLU5bg4e0CQY1ag/J5POseyPZeyPPcDHPT7G1b7gjO3iKJOJzNAEkndCbmgDVG42xjQdsb81osYHW7CN2AHG3ALLukyZmVx7bhY+EyfkL6pgY2MeB0+KsGwfcVFhObdqhc+jE4kaP4GATz/Jm/n9J6UUCYuXkLB0KbZubvg99RS2nh4Yk5JJ+Gwp8QsW4P/ss3fFWnrZzKQcJWQnMG3PNNwc3BjdeDTdArthZ2OH0WTkv8f+y5rQNXg4eLA4ZHFeXWGDycCh2EN8de4rnO2ceb/b+7c00UxUUCkx8HkveO5CoadTc1PZfHkz60LXkZyTjKOdIynZSTihMb/bfwmpFVLopjZGk5HXf32dhOwEPg351OKNb3LDw4l98ilsnJ3xHj0al+B2YGtH9onjXH/tNTAYqTImBJ/6aTD0/wDzF2/W779z4623cWrWjGrzXi/4fOvGQdMh0PyhW3p7RMWUsnETNxYtwqlRIzyHDMauShWMmZncXPQ+uogIPPr1pcYbbxSYCZ59+jRXn3kWn4kT8XlkbDlFX3qK28xEknQ5ydBlMG7HOEJqhTCt1bRCvxwvJl7k9SOvcy7hHPW96uNs78y1jGvUcKvByEYj6RvUF3sbGYu+K2Qnw/vN4fnogrWg/0YpRZoujeScZMZ8P5Q1Pl0IHPBxsV3rTXoe/P5B5nWZR9uqbUsMRRcVReTYsVSZ+QxeQwsmU0NyMnHzF5C+fTuOPuDSbywYDWT+9hso8Bk/Dq/hwwt+5k0m825pQz+HmiXHISoHk05H+s6dpO/ajTE1FWNqKobERGp/uwrHWrWKfJz+6lUiR4+h5vuLcGlbsT8vsuOYFfrmwjc08GpQZIIGaOzbmNX9V/PSzy9hUiZGNBqBr7Mvge6BdzhaUe6cvc2FLcJ2Q6M+RTbTNA1PR092R+6irUER2GhQiV3b29gzstFI1oSusShJX3t+Ln5Tnig0QQPYeXsTsOg9ssePI2rMKNxMKdjVbYVH3744BwcXfbUecQDsXeVW913GxsEBz4ED8Rw4EKUUEYMGU/Pdd4pN0AD2NWviO+VxklZ8XeGTdHFk4lg50Jv0bLi0gUebP2rR7cXHWz7O4WuHaeLbRBL03az9JPjtM4tmP/985Qf6ZuuhXohFXfcN6svPsT+X2C7n/Hn016/jPWpkiW2dW7XC854W2ET/jM+o4bi0b1/0592QC/vfhA6TS96aVFRa2SdPogwGXDpZNs/Gc9AgMo8cQX/D8mWrFY0k6XJwMv4kVZyr5N9zuRi1PGpRz6sev177tYwjE1bLZIS63cxj04c/KL5tehwZ107g1Wq0xRuDeDt6k6HPKHHpX8rGTXgNH2bx9p1eT75MyplMWD8BdJmFN0q9Bp/dZ95U5Y91sGaMeX12boZFzyEqj5wLF3Dp2MHiuRG2bm44NWmC7vLlMo6s/Mjt7nKQkJ1ATfdbKyAQ4BZAYnZiGUUkrFZSOBz7wrztp8lo3lZz7zz4Yy08sBAa/O1K2ZBrLpSx53VcAmqRFmD5beNUXSqu9q4lfjnqY2Nx7dql2DZ/59S4Cfp0I8rJC+395tB6NDR7CJy9zJuc7HwBYn8H79rQewG4+EF2EpzdBHtfh3uegS5Py9X1XULp9be0XSiA5mCP6Z+b6FQikqTLgYOtA7nG3JIb/k2uMRcH21v78IoK7tiX5oTcZixM2mNe9wzmbTq3PQurhpq36qzVCQw5EHnYXHJywCK6GBPZFbmLPnWKHr/+u12Ru+hco3PJDW83WQ7+BFKizAU6tj5tLraRm27exOTRXVDrH9WQmg+F5ChY9whk3IDeC2/veUWFYufnT9aRoxa3V0qhj47Bzt+/DKMqX3K7uxw09mnM6RunyTHkWNReb9Lze9zvNPZpXMaRCatxajX8tMicnHvN/ytBg3kt8aQ98OTvoM8GFDQeABN/hPFboMEDDKg7gKPXjxKXGVfiUxlNRtaGrrVoUxyH2rXIOXvW4peRc+4c9gE10WxszFuKPjAPph6G7i+CRw3za/hngv6Td23zFqeXdprvEIhKz61bN7JOnkQfV/LnFiD72DGwscGpadMyjqz8SJIuBwHuATTza8bOyJ0Wtd8fvZ9A90AaeDco48iEVcjNgJ1zzVty+hazX7FfA3NiDt1uLo7h99fnw83BjUeaPsJzB58jU1/EWDBgMBp4+fDLONo6Us2lWolj0p5Dh5KyfgNKb9kuZcmr1+A9fHjBE0eXQMir5n27i+Psbd4n/MhSi55PVGy2bq549u9H4hdflNhWmUwkLF+O96hRFo9hV0SSpMvJuKbj+OTUJ8RnFl9+LTE7kQ9OfMD4ZuPvUGSi3J1ZD7W7mm9dl8S3HtR/AE6vLnBqSsspNPZuzLjt4zgUewij6a+tFFNyUnjr6Ft0Xt2ZHZE7yDXkMnHXRAZ/P5hVF1YVmdidGjbEoW5dkr7+usTQss+dI2P/fjyHDs1/4top83h0fctmntN4ACRehpuXLGsvKjS/6dPJ2Le/2M+Y7vp1oidMJDfsMiZdLtmnTllc76CikTHpOywsOYy1oWs5ceME2YZs+m7qy4P1H2Rq66n4Ofvla3s+8Tz/OfQfBtYdSI9aPcopYnHHndkAXZ+2vH3bcbDnNeg0Nd9hTdN4qdNL/BD+A5+e+pQ3jr5B6yqtydJn8fPVn7HVbBlYbyCz28/G2c4ZpRTH44+z6sIq1lxcw9IHllLT7a8JjrrYWFI3bcLG1ZWbH39C5uFfqPrqKzjWrl0gpOxTp4h56imqzZ+Hnbd3/pM3L0Jgx2I3ZcnHzgFqtoOEUPBvWHJ7UaHZ+fpS66uviHn8cTIOHMR7zGjc7r8fzc6O7IsXiXv5FXLOncPW2xuP/v0wxMVzdfYcbNzd8Hv8cTz6WDYPo6KQJH2HZOozmfvTXM4mnGVow6Es6LoAV3tXvr/8PasurGJD2AY6VutI/7r9SdOlsTNyJ3GZcUxtNZWhDYeW/ASi8siIN08Is5R3Hci8WegpTdMY4RWLwwAAIABJREFUWG8gA+sN5FziOU7En2DJqSU80vQRnmrzFHY2dvnaBlcLJrhaMCvOrWDK7ims6rcKl9Rc4l59zVwQY/BgPPr2waVjRxKXLSO8bz8cgupQ5T//wb5qNXQREaSsW0dOaCjV58/HvUf3gkGZDJYn6D/Z2JofJ+4KDgE1CfpuE+k7dpC4bDlXZz6D5uiIKSMDhzp1qPHuu3j06Y1ma/4cqRfmknn4MHHzF5AbHo7/tGnl/ApKjyTpOyDbkM2U3VOo71WfnUN35isr+XTbp5neejrbI7bz7rF3iTsTR4fqHZjYfCL3B9yf70tU3CX+n73zjI6q6hrwc2cy6b2RBoGE3gmhd6SIVOlVioAIKCIWRBQU/VSwYQNFVHzpRQi99yot9BpKKiG9T6ad78cAgoTMDZKQhPusNWuZO/ucu2cc7j5nn12sbMzpVHIx5IKMyP8aHjVYdXkVfav05Y36b+QrO7TGUK6kXGHV/p9p+X9bceneHf+vv3qgIYb7S0PIOnyY259/QfTYcWj8/bD2D8DlxR4EdOyI6lGpNM5+kFgA17UQkHgFnAuWtqhQslHZ2ODSvTsu3buji47mer9++P/fbJw7dHhIVlKpcGzRgvJLFnNz4CCsy5bFpWvXp6D1k0c5ky4C5p6ai4+DD9OaTMuz77NapaZLcBfW9liLCROty7bmuXLPKQb6WcW3DlzbLV/+2m5zxLcFMnWZbL6xmUHVBsmadkj1Ifh8thDnfn3wev21BztWYd55OzZpQlDYGsq88w4qewfKzv8Vl65dH22gAcq3hIx4cyqZHKKOgEkP/qHy5BVKHWlrwnBu1z5PA30/Vh4e+Mz4mMQ5c0vNGbVipAsZrUHL6iurmVBvgsUIRBcbF16u+TJLLy4tIu0UiiWhL8Ox+eaGE5YQwtxnusFIi6IHYg9Qz7seXvbyckrL3szGWSsR1dVyXWS3IYPBoCf7iOU+6KitIHQY7J1pucSpyQh7Z5m/E5nV0xRKF8JgIHX5ctwGDpAlb9+gAZJaRfYR+fnWxRnlV1/I7IraZa657Syv5vbzFZ4nPCGc29mltxatggX8Q8DJF/Z9aVn24PegsTNHg1sgVZuKt723bDVSly7lQqtAUnXpFmUlScJ1wABSlspcYDYeB+lxsOkdsyHOC6PBXPhEr4UGL8vWW6F0oYuMQrK1xbaKvDLKkiTh2K4d2X8fLWTNigbFSBcyURlRVPeQn2hvZ2VHoHMgMZkxhaiVQrFGkqDPAnMp0G3TzJW5/o0uC3b9H/w9D/ovklUJzMbKhhxDjmw1tBcuci3YHlsrW1nyDo0bk3vxorzJre1h8CpIuARzmsKRXyDjFqRFQ/Rx2PcN/NTIXG1s4FLzOb3CM4kpOxuVg0OBxqgdHTHlyP+tF2eUQ89C5nHORSRKb2K+gkycysDL22DjJPi6OvjUMte1trIGJLi6zbx7HrkNnHxkTVnHqw7fHv8WnVEnq8SsUZfL5YxbVPOQka8NSBoNoiA1lO1czRXFzofB7s/Mu2oEIIGkMqdpNXoVrB3lz6lQ6lC7OGNMTEQIIbtoiSEhEbWrSyFrVjQoO+lCJsApgAvJF2TLaw1abqTfwNfBtxC1UigRZMSaXb6SZN5NJ16GmBNwaQN4VIKGo2UbaIAKLhWo6FaRbTe3yZJPdhC0UVfD3dZdlrwuMrLgNZQjdsCGSeYFx6sHYVoqTE+FyZFQuw9sfhf+Gg2G0ttAQSF/NAEBqN3cyD58WJa80OtJ37gRx7alo7aEYqQLmbbl2nIu8RxRGVGy5Dff2Ewtz1r4OMh/+CqUQq5uh//1hIpt4c0LMGYfjDsMr5+Ad25Ao1fgr1FwcmGBph1RcwRfH/+a2MzYfOWupFwhLDiFtuEygtfukLpiJc4FSXuJPAx/vQL9FkKXr6FM9X/c9jaOEDoCRu8Bbar5bLqUROsqFAxJknAb0J/kPxbI8kymb96Cdbly2FYuHYVvFCNdyNhZ2dGjYg++P/m9xR9Yhi6D+WfmM6CqvChGhVJK4hWz8eq/yBy1bf2v8zgra6jVG4ZtNHfJur5P9tRN/Zrycs2XGbp5KPui92ES/xhhIQTh8eHMODSDIZuG4N2tJ+qzV9BeumRx3txr18g6eBCX7t3kKSIEbHwbOn8Jgfl037K2N5/PRx0xG3WFZxKX7t3R37pF4k8/5fsczTlzlvjPPsNrYv51AEoSypl0EfBqnVd5ecvLfHL4EyY3nJxnrnSKNoXXdr5GY9/GtPBv8RS0VCg2HPoRGo4yt6DMD8+K5q5S+7+GCvJ/MwOrDcTHwYfvTn7HZ39/Rvty7YnJjOFo/FHSctNwsXEhxDuEkxkXiG5nYuCIwQT99geuVWrkOZ/u5k2iRo2mzOTJqJ0sNMy4S8xxyE2Hat0ty1rbm137R3/N36ArlFpU9vaU/eUXokaNIvfSZdyHD8Oubl0kScKYmUnyokVkbNqM7loE9o2bgBAFOsMuzkglOeE7NDRUHDt27GmrkS9xmXGsuLyCsIiwe2lVnnae9KrUi+fLP0+WIYt1EevYdH0T/ar0Y3y98agkxcHxzKJNg29rwbi/5Z0367XwTQ14eWv+HbPyQAjBqYRTfPH3F8RkxtAioAWDqg16IBvhWuo1dvw8lZBlp/Ho/iK+g4dhE2y+T+7166QuXUZaWBheEyfi1q+v/JtvnmLucNXqbXnyOanwVVV4LwryWOQqPBsYM7NIXbGClKVLkCQJU64Ow+3bIEnYh9TDoVVrMBpJ++svJI2GMu+/j0PjRk9bbYuEhoZy7NixPFcUsnbSkiQ1A6YDgXfGSIAQQgTlN+5Z589zf/Lz6Z/pGtyVn9v9THmX8pxPPM+c03P43/n/Me/0PPwd/eka3JXV3VcXKIdVoZQSdwq8q8sPCNPYQqX2cPNAgY20JEkcvXUUSZLY1GsTDpqH01yCXIOo8M4i5tb9BKv1O2k1dBemzCyQJFR2drj26kX5lSuxDihgyc6sBFlV0u5h52r+rNo0cPC0LK9QKlE7OuAxfBhugwYSOeJlVLlaykx9H4dGjVA7/pMF4DFqJJm7dhEzaRK+Mz7GqQQHkcl1d88HJgLHgUdUHlC4nwXnFrDqyipWdl2Jr+M/kdq1vWszp90cAA7GHmTy3sk082+mGGgFM7osyz2W/42NE+iyC3yrbH02C84vYFmXZXka6LtIksSYDlPppztD1XfG08y9vvm6vf3juxOtrEFfgDxWIcw1ypV8aQUgae5cVLa2lP39NyTNw54VSZJwatsWKy8voka/gu3KFWj8S2btd7l+1TQhxCYhxG0hRNLdV6FqVoKJzYxl3pl5/Nzu5wcM9L9p6teUD5p8wIcHPiw1dWYV/iO2ruYuWAUhM9680ywgG69vJMQ75IF2lI9CkiT6V+3PssvLUTk4oHJw+G/nfX4hELFTvnzkYXODDSVn+pnHpNWSsmQpPh9+kKeBvh+7WrVw6daVlKXLiki7J49cI71LkqRZkiQ1kSQp5O6rUDUrway8vJKuQV3zNdB3aVeuHQDH4ov32bpCEeFf31x567bMyl05KeYGG8HPFfhWh2IP0T6wvWz5DoEdOBR7qMD3yZPafeH6XkjPPxXsHnfrk5eCQCCF/0b65s3Y1qqJdblysuRd+/cn9a+/ClZopxgh10g3AkKB/wO+uvOSUVj42SQsIozelXvLkpUkid6VexN2NayQtVIoEVhZQ8hLcGSOPPnjf0CljuBYwCIiQLYhG2drZ9nyDhoHDMKA3qQv8L0ewsbJ/DnXTTAXbMmPS5vhxn6o0/+/31ehxJN74QIOjeVH+dtUqIBkY43+dsnshyDLSAsh2uTxKrkn8YWISZhIzEmkvHN52WMquFQgPruALk6F0kujMWZX8PE/8pe7st2crtVSZoT0v3DSOJGSmyJbPl2Xjo3aBo3qCURX56SCXz3zTnpea4jOoxmCIReOzoewceac8cdw6SuUPoReb9HN/W9UGmuE7gksLp8CcqO7XYBpQMs7l/YAHwsh0gpLsZKKhIQKFSZhQo1a1hijMCq9oxX+wcETBv8FC3uajVejMeba3XeJPAxbp0LsSfM57drxULG9eWfqVEb2bVoEtGD9tfX0qNhDlvyGaxv+ew5/8nXY/w2cXwMBDaFMTYg5Br+2B0dvc5EW9yBIuQGnloF3VRi6FsrknaOt8Oyh9vREHy2vgiOAKScHQ1ISVu5uhahV4SHXMvwGnAXuJkIOAX4HehaGUiUZSZIIcg3ixO0TNPKVl593Iv4EQS5KNpvCfXhWMpfEPDofFvcDWxewc4eEC5CdDD41oec88KwMOclwdhX82ADqDTEXOFFZXiB2KN+BL499ybXUawS55v/7M5gMLLu0jKmNpz7+Z4o+BksGQOhwGHf0wQVFxi3Y8r758wY2hYAGMGwDeJWO0o4KTw6Xzp25MWAgXm++icrGcrR/+sZN2Nevj9q1ZHpi5J5JBwshpgkhrt15fQQoVuUR9Knch2WX5EUT6ow6Vl9dTZ/KfQpZK4USh727udjHhNPQ7XtAmHeZky7BmP1Qs6fZWFdoCV1nw+vh5jzrsPGy6lzbqG14tc6rTNw9kaScRydrGE1GZhyega+jL6FlQh/vs6RGmQ10t++gzZSHd/xOPtB7PozYAnGnoVIHxUAr5Il1YCC21auTunKlRVlTbi7JCxbgOqDkxjPINdI5kiQ1v/vHneImpaNZZyHQJagLx+OPczjOcq3huafmUt2jOuVdyhe+YgolE7UV3DoNRr15d/kol7a9OwxcDrfPwWl5i8T+VfvTsXxHBm0cRNjVMHKNuffeE0Jw7NYxxu4YS2R6JF+1+urx066OzDVHdFfplL+cX1147gPYq8SlKjyaMpPfJfGnOWRs3/5IGZNWS8ykSVgHBeHYsuUj5Yo7ssqCSpJUF1gAuGCuNpYMDBNCnCpc9fKnOJcFPXrrKJN2T2Jak2m0Ldf2oYebzqjj59M/s/n6Zv7s9Ccedh5PSVOFYo8Q8FMT6PQFBLWyLH9pM+ydCaPk5yHvj9nPwgsLOZ94nhqeNbBSWXEj7QaSJNGvSj/6VO4jqwd1nuhzzKVLR+4A9wqW5XXZ8E11s7vfLfDx7qlQ6sk5c5boceOwD62P24AB2Navj/bECbJPnSInPBxt+CnsQurhN2sWKuvH/O0WEf+5LKgQIhyoI0mS852/05+gfqWSBj4NmFh/IlMPTMW434innSf+Dv408m1Epj6T1VdXU92jumKgFSwTfRRMBrNbWw6V2ps7TN0682DAWT40929Oc//mRGdEcy3tGnqTnjL2ZajhUeO/NymIPQlu5eUZaDA31KjUAa7vAbeX/tu9FUotdrVqErRhPalr1hA98U2MSUnmPHpJQhMQgJWfL5mHDnN75iw8x43Fyq0UBo5JkjRYCLFQkqQ3/3UdACHE14WoW4klJjOGyXsnE58dz/Caw7FWWXMo7hAxmTH8cuYXrNXWTGsyrUCFJBSeYZKvmdOV5BpLldpcFzv5mmwjfZcApwACnAIeQ8l8yM00N9MoCHZukJvxZPVQKHWoHB3RXbmK2ssT3xkzsK1VEytnZ6Q7O2d9bCxJv87nRv/+BP7+Oxo/v6esccGxtJO+W9A3r2LCSh3LPIjOiGbo5qEMrT6UQdUGob4TZTus5jDAfM635cYWPjn8CT72PtTyKthDVOEZRJgKXmlLkszjigM2TuaI9IKQnWROz1JQyIekn38h99IlKixciMrh4frzGj8/fD78gKT584l6ZQwVVq28Z8BLCvkaaSHEz3f+c7sQ4sD9790JHlO4DyEEb+95m+E1hjO4+uA8ZSRJ4vkKz2NrZcsbu95gU69Nj3/Wp/Bs4BoI8efMZ9NyjLXJZJZv/qZl2SeNLgvOrIQb+8xny7YuUKkdpEZCUoS8Tl25mXBlG7SbXtjaKpRgTNnZJP/+O+VXrczTQN+P+4gRZO7ZS8b27Ti/8EIRafhkkBvd/b3Ma880pxNPk6ZLY2C1gRZlW5dtTZBrEFtvbi0CzRRKNOWagD4776pceXF9D2jszS7yosJkgj2zzAFilzdDcFuoOwACQuHIL2DQms/J5XB6KZRvDi5P2O2uUKpI37gRu5AQrAMs/04kScJt4EBSFi8pAs2eLJbOpJsATQGvf51LO4PMclrPECsuraBv5b6oJHlrn/5V+vPn+T/pEtSlkDVTKNGoVObmErs+hUGrzClZj8Kggz1fFG0zCiFg/QRzU5C8IrIbvAyXt5jzpNeMgx4/Pnqu6GOw6/9g8KrC1VmhxJN1+AhO7eXH9Tg915aYSZMQBgOSVcmp8GjJmlgDjpiNudN9r3RAXgeJZ4jradep611Xtnxd77pcT7teiBoplBoajgZJDatHg16bt4wuG1YON1cmCynCqOjjf5iLqLy05tEpU5U7woDFcGoxrBplLm5yP9nJsP9bWNwXeswpWi+AQonElJODyjF/N/f9SBoNKhsbTNpH/Pspplg6k94D7JEk6Q8hxM2CTi5JUlngT8AHMAG/CCFmS5I0C+gK6IAIYLgQIvXOmFlAG2DSnfuXGIzCiFqS72BQS2oMwkIHIAUFALXG3GQibDzMrg0hQ6HGi+amE9nJcHYlnFxoTl3q8o2ssqD5ce7cOTZv3kx6ejpOTk507NiRWrXyCHIUAg7/BF2+BWsLD8zKz0Ord+H0cvi5hTkwzMELctMh6ihUfQGGrlPqdCvIQu3khDFZfoMYU3Y2Jp0OlZ1dIWr15JF7Jp19p5/0RkmSdt59yRhnwGxsqwGNgXGSJFUHtgE1hRC1gcvAewCSJFW9M64lMK5An6QY4OPgQ0RahGz5iLQIfBx8ClEjhVKFxs5cOvOlMNCmwrLB8EtrWDHM3DFq+Cbo8RNYWa5n/Cj27NlD69atad++PTdumIuZREZG0qlTJ1q0aMGOHTseHHDzgHmHH9hU3g0avwrZiTBqFzSbAFU7m13zr5+EF+cqBlpBNo5t2pC2fp1s+bQNG3Bs2RJJXbJOauU65hcBy4AuwBhgKJBgaZAQIg6Iu/PfGZIkXQD8hRD3R0sd5h/XuRrzjltgrmxWougW3I35Z+fL7iq04vIKugd3L2StFEod3tXghVlPfNqlS5fyxhtv8M0339C7d28097UD/Oqrr1izZg2DBw/miy++4KWX7rjTb501B3nJPf+2dQHv6pAeYy66oqDwmDi1bUP8p5+Sc+4cdjXyX9wJo5GURYvxnjSpiLR7csjdSXsIIeYDeiHEHiHECMw7Y9lIklQeqAcc+ddbI4BNAEKIc4A9sB+Q2fW++NAyoCUJ2QkcjDloUTYiNYK90XtlG3QFhcLk2LFjTJgwge3btzNgwIAHDDSARqOhT58+7N69m3feeYeDB+/8xk16syu+IKg1YNQ9Ic0VnlUkjQavNycS88ZE9LduPVJOmEzc+uhjrNzdcGgm0+NTjJC7k77bLTtOkqTOQCwgOz9CkiRHYBXwxv0lRSVJeh+zS3zR3WtCiNfkzlvcsFJZ8WnzT3lrz1t81eorQn3y7hgUkRrBmO1jeK/he7jYuBSxlgrPCrfStGw5d4ukLB02VirqlnWlabBHnmU+Z82axdSpU6lZM/8CIlWqVOHjjz9m5syZrFmzBpx84fo++UqZTOae0k6+Bf04CgoP4dqjB8bkFG70H4DnmFdw6tIFCQmhzUFyckIbfoqkefMQWi0Bc35CUsndlxYf5DbY6ALsA8pizo92Bj4SQqyVMVYDrAe23F9GVJKkoZhd588JIbJlKWt+umTe/dvd3d0+KenRLfaeFodiD/Hu3nepX6Y+9bzrYaO2QZIkhBCcSTzDzqidTG44mW7B3Z62qgqlkGsJmczacomDEUl0qF4GP1c7tAYjey4loDOYGN0yiH4Nyt4z1nFxcVSvXp0bN27g4mJ50ZiVlUW5cuUIDw+nrLebOTf61QPy8pqvbIcdH8Ere4suRUyh1JOxdx8JX39N7uXL5gsqFRiNqOztce7eHe933kZta/t0lcwHDw8PkpOT77eDjuKOcZZlpB+XO0Z1AZAshHjjvuvPA18DrYQQFs+2H0Vx7YJlNBn58/yf/HHuD7L0WRhMBoQQqFVq7DX2DKo6iJG1R6JRFdBNqKBggdPRqYz44yijWwYxsFEgjjb/OMuEEBy7mcIHa87SNNiTD7pUQ5IkwsLCmDdvHuvXr5d9n169etGvXz/69u1rLlIiBHS20F7SaIA/u0PtPlB/2GN+QgWFB9FeukTUmFexq1ULp06dUDs7IfR61M7OaK9cIXXhIjQBAfh/9SUqe/unrW6e/OcuWJIkeQGjgPL3j7lzNp0fzYAhwBlJksLvXJsCfAfYANvurOYPCyHGyNGluKM36nlzz5tk6jL5pvU31POud39DEk4nnuaHkz8wfsd4ZreZja1V8V3dKZQsUrJ0jFxwjE9frEXHGg9nDUiSRIPy7iwf04S+cw+x8EgkQxoHkpWVhaOjY4Hu5ejoSHb2nYV/q8kwv705z7nZhLx3yEY9rH3dXIil7qDH+XgKCg+hi4wkcuRIykyejEvnzg+9bx8SgtuLLxI75X2iJ06k7I8/lqhCJiA/cCwMcy/p7cCG+175IoTYL4SQhBC1hRB177w2CiEqCiHK3netVBhogE+PfArAL+1/IaRMyAPnf5IkUcerDnPazcHZ2plpB6c9LTUVSiHLj0XRopJXngb6fpxtNXzZpw5zd0dgNAk8PDyIjY0t0L1iY2Nxd3c3/+HgYc5vPr0cfn8Bzq4yVz4D0KbD3/NgbnNz04x+iwoeaKag8Ahuz/oSj6FD8zTQd5GsrfH77P8wpqaSvmVLEWr3ZJC7pLAXQrxbqJqUAmIyY9gRuYMtvbagyedBZKWy4pPmn/D8que5lnqNINegItRSoTRiMgkWHrnJDwNCZMnX9HfBy8mGnRdv07JlSwYPHkxERATBwZYbYERFRXHixAnatm37z0UXf3hlD1zcAEd/NVcVU2vMnbiqdIIXvixYqpaCggX0t26R9fff+H72mUVZSaPBY/hwUhYuytegF0fk7qTXS5JUslqHPAVWXFpBt+Bu2Gssn3vYqG3oWaknyy8vLwLNFEo78RlatHoTdcq6yh7TvnoZjt1Ixs7OjmHDhjF79mxZ47777jsGDRr0sItcrYEaPWDYevggEd65DlNvQ98/oUILxUArPFHSN2/GuUN71DJLgzo99xy5ERH5pmsVR+Qa6QmYDXWOJEnpkiRlSJKUbnHUM8bB2IN0KN9BtnyHwA4ciDlgWVBBwQI5OiP21gWrpGRvrSZHbwRg0qRJhIWF8dtvv+U7ZtGiRSxZsoR33nkn/8lVKrC2f8gwG02C/VcSWXTkJgsP32TflQSMJqU1vULBMSYmogkoK1te0miw8vXBkFj8MoLyQ5a7WwjhVNiKlAayDdk4WzvLlnexcSFbLyv7TEEhX1ztrUnO0qE3mtCo5a2949NzcbUzH8v4+PiwZcsWOnXqxJ49e3jttdcIDf0nz//kyZP88MMPbNu2jc2bNxMgoz3g/eiNJn4/cJ3/Hb6Ji52GWv7mVK/lx6JIyjzD4MaBvNy8AtZWJS+PVeHpIFlbI/R6y4L3IXQ6JOuSFRNhqVVlVSHERUmS8jzoEkKcKBy1SiYOGgdSc1Nly6fmpuJoXbCoWgWFiIRMFh6+yY4Lt0nX6nGwtqJJsAdl3e3Zdj6eF2pZLhRiMJoIC4/h16H/GOKqVaty/PhxfvnlF3r37o2VlRWenp4kJyej1Wp55ZVXOHHiBJ6engXSV6s3MmbhcYwmwQ8DQh5yyZ+JTuPLrZc4GJHIvJdCsdWUrNrKCk8Hm2rVSF6wAK/x8to86OPiMCYkyuo/XZzIN09akqR5QohRkiTtyuNtIYRom8f1IqO45Un/cPIH0nXpTGk0RZb818e/xmQy8VaDtwpZM4XSgM5g4sOws2y/cJt+DQLoUdcfdwdrMnMNbDp7i1/2XsNoMrH37Ta42FvnO9faU7EsOHiDVa/mXSbRaDRy8eJF0tLScHZ2pmrVqlg9ZurKpOWnyNEb+K5/Pawescs3mgRvLAtHAr4boLSpVLCMMBi42vY5ys6bh22Vyhblb8+ejSk9A58PphaBdgUjvzxphBAl9lW/fn1RnIjLjBNNFzcV6bnpFmWzdFmi5dKW4mbazSLQTKGkYzSaxNhFx8Xw3/8WWbn6PGWyc/Ui9JNtInTGNpGlzVtGCCFORqaIkI+3ir+vJxWWuve4kZgp6n28VWTnGizK5ugMov6MreJKfEah66VQOkicN09cHzRIGLXafOVyLl4Ulxo3EdqIiCLSrGDcsWV52rl8D4AkSeqZ36tQlhQlGB8HH7oEdWHS7kloDY9uLK4z6nh337u0Ltuacs7lilBDhZLKmvAYopOz+WlQCPbWee9o7ayt2PpGSwwmEx2+3cuui7cfCMqKSs7mi80XGfHHUWb2rk2D8u6FrveiI5H0rh+AnYygNluNmr6hZVl4uMCt6xWeUdxHjEBTxoeoUaPRx8Q89L4Qgsx9+4h8eSQ+H0zFJqjkpbtacnf/ns9YISxXHCtUipu7G8BgMjBl/xSi0qMYW3cszfyboZJUpOWmkaxN5lzSOZZeWIqnvSezWs7KN59aQeEuPX48wLg2FWlfvYxF2bMxqQyYd4RAd3tuZ+Ti62KLVm8iPkNLz3oBvNQkkPKe8tJW/iudv9vHJz1qUq+cmyz5szFpvLk8nK0TWxWyZgqlBWE0kvjjT6QsWoRdSAiObVqjsrPDcPs2qatXg4Aykyfj2KL501b1kTx2WVAhxPDCUan0YqWy4vMWnxN2NYzvTnzH1APm84+03DRzdxYE9hp7mvk3I02XhqddwYJwFJ49rt7OID5dS9uq3rLka/q7Us3HmVEtK1C5jBNJWTpsrdRU8HSQtaN9kmTlGnC2k78QdbHTkJVrLESNFEobklqN1+uv4THyZdI2bCDnZDhCq0XMGp81AAAgAElEQVTt6orPBx9i37BBnp3fSgpya3f/HzBTCJF65283YJIQovidwBcDVJKKjuU7sv3mdnJNuTT2bUxtr9pUcK5AdY/qXE65zLJLy+i1thdftvqSBj4NnrbKCsWY6JQcKno7olbJf9BU9nEkNlVL++o+BHoUza45L5ztNCRn6Qj2kiefmJmLk23Jqq2sUDxQ2dvj1qcPbn36PG1Vnihy/zV0EkLcC1kWQqTcqUCmGOk8MJqMvL33bZxsnFjVdtVD3a6quFfhwyYf0rF8RybtnsSc9nOo4VHjKWmrUNxRSRIFbVZnElAAm15otK3qTVh4jOzz77DwWNpVs+zSV1B4VpBbOUAtSZLN3T8kSbLD3MVKIQ/2RO8hMSeRGc1m5NuOspFvI94MfZOvjn1VhNoplDQqeDpwIS6dXIM8N7AQglNRqVTw/O85+IakJBJ/mUf0xIlEjR9P3LTpZB89Sn6xLPczoGE51obHkpqtsyibrtWzJjyGAY2UYEoFhbvINdILgR2SJL0sSdIIYBvmPtEKebD04lKGVB8iq19056DO3Ei7wdWUq0WgmUJJpKy7PdV8ndl0Rl7N4fCoVDK0BpoGezz2PU05OcROnUrE853Q3byB03PtcOnWDety5Yj7cBrXu3Un+/hxi/OUcbalX4OyjFl4nGyd4ZFyOTojr/x5nOdr+ODlqKz/FfJHkqTHepVE5JYFnSlJ0mmgHSABM4QQJa/nVxGQlpvGmcQz/PDcD7LkNSoNXYK7sPnGZsa7jS9k7RRKKkObluezjRdoU8UbF/tHL/50BhNfbL7IS00CUT2mv9uUk0PkyFFo/PyouG0ratcHK4S5jxhOxvbtRL/2On4zZ+LYvFm+803uVI3Jq07Ta84hXm9bkXbVy9wrXZqRo2fWlossPx6NzmDifFwaf52MoWUlL4Y0CaRFRc/H/hwKCqWBgkRoXAAMQojtkiTZS5LkJITIKCzFSiop2hTcbd2xVudf8el+fB18lZ20Qr5U9XGiio8TL845wLd961I7j25X6Vo9E5eG42yrYXizCo99r/iZM9H4+uL3xedIqoedbZIk4dy+PVZubkS/9jpB69dh5fHoXbtaJTGzd23Wn47jtwPXmb7uHFV9nMnWGThxMxVHWyuGNinPOx2roFaryNYZWBsey2cbL/C7iy0/DgzBwUYJJlN4NpEb3T0KGA24A8GAPzAXeK7wVCuZWKut0RofXcgkL3RGXYGMusKzgRCCzWdvseDQDa7eziTIyxGd3kT3Hw/g7WzDoEaBtK7iRYbWwKazcaw7FUfXOr5M61qjQJHg92NMTSV9w0aCN2/K00Dfj31oKI5t25C66i88R4/KV1aSJLrW8aNrHT+u3s4kPCqFTzdcYEK7irz+3IMlHe2trejfsBy96gcwedUZxiw8zm/DGshuHKJQenjSLupHzSc3xuJpIHd5Og5oCBwBEEJckSRJXtLmM4a3vTcIuJJyhUpulWSN2R+znx4VexSyZgolCYPRxLurznAuNo1xbSrSsYbPvQ5Rt9K0fLrxAj/tusqCgzcI8nSgaUVPtk5sSRln2/9037SwMBxbtcLKXV40ttuAgcS8/joeo0bKfqBW9Hbkp91XGdw48CEDfT8atYovetViwLzDhIXH0rt+yWqMoKDwJJC7NM0VQtwLz5QkyQoovkuPp4iVyopelXux7NIyWfLX065zOeUy7QPbF7JmCiWJTzZc4FZ6Dn+NbUrXOn4PtHD0cbHl+wH1ODzlOXxdbWld1ZuJ7Sv/ZwMNoL10GfuG8vP27WrWwJiaiikrS/aY5Cwd28/HM0KGS95KrWJ0y2ClVKjCM4tcI71HkqQpgJ0kSe2BFcC6wlOrZNO7cm+23dzG0VtH85XLNeby0aGPGFRtkOLuVrjHzaQs1p6KZc7g+o+s0w3mHtLzXgpl7p4I0rIL1lf3UQiDHsmqgKVqNZoC9fXdcu4Wrap44+Yg7zfftqo3Mak53EySvxBQKFk87Wjsp33//JBrpCcDCcAZ4BVgI0ohk0fibe/NzJYzmbR7Emsj1qI3PfgAS8pJYsO1DQzcMBAHjQMja418SpoqFEcWH4mkV4g/zraWjaWvix1tq3qz4njUE7m3xtsb3c0bsuUNyckIvR61k5PsMQkZuQS628uWV6skAtzsSMzMlT1GQaG0IDcFyyRJ0hpgjRAioZB1KhU08m3ET+1+YtbRWcw+PpsuwV0wmowcijtERGoEEhLe9t5c0F7gpU0v0a9KP16o8AJqldLw/lln2/l4vh8ov6fyi/X8mbM7gpEt/nuHH+euXYka/Qpe48YhaSwvElJXrsK50/NIBeg1rVGryMx9dM50XugMJqzVyr8NhWcPS60qJUmSpkuSlAhcBC5JkpQgSdKHRaNeyaamZ00WdFrAnPZzOJd4juWXl+Ni7cLUxlM5MugIW3pvYWvvrYyoOYKlF5cycfdEco3KbuFZJy1Hj5eT/IIeXk42pOU8GXe3bZUqaAICSAsLsyhrTEsjZckS3PoPKNA9avg5s/9KouyI2tsZWqKSswn0lL/7Vij+FDe3cl4UBx0tLX/fAJoBDYQQ1wEkSQoC5kiSNFEI8U1hK1gaOJt4lrisONb1WEcZhwfrEluprGhbri0t/Fvw7r53mbp/KrNazXpKmioUB+xt1GRqDXjL9CBnag3YP0Z3qyx9FhuubeByymX0Jj2edp50rtAZvw8/JHL4cNTu7ji1bZvnWGNqKlGvjsW5Q3vsatUs0H2bV/QkS2fgZFQqITJaWC4/GkXn2r6y3P8KCqUNS2fSLwED7hpoACHENWDwnfcULKAz6ph9Yjaz28x+yEDfj0at4bMWn3Em8QxnE88WoYYKxY1GFTzYej5etvzmc7doFCS/BKjeqOfLo1/SYWUHDsUeIsgliFqetdCb9IzYMoJxN76AWe9za9p0ol97jayDBxEmE6bsbBLnz+dKy1ZcbtyEnFOnyNi5i4SffkJ/+7bs+6tUEiObV+CjtefyLRUK5jadfxy8wUtNysueX0GhNGFpJ60RQiT++6IQIkGSJGVZK4MtN7ZQ1b0qFd0qWpS1UdvQr0o/llxcwqfNPy0C7RSKI0MaBzJu8QlGtQiyWJQkW2dg9ckY1r8mr6G9zqhj/I7x2FrZsqrbKnwcfB54/7W6r7H66mpGhX/Bj4u+wvNgBPEzZ5F76RIIAWo1Ds2b4z97NrbVqpIbEUHq8hVc69oN74kTcevfT5YegxsHcjYmnUG/HuGbvnUp7/lgO00hBAeuJvHm8nCmvFCNar7OsuZVKN4UZ9e2Je7XvSiLn1gy0vm1rrHc1kaBXVG76BLURbZ81+CudFvdrRA1Uiju1CnrSrCXIx+tO8dH3Wo88sFmMJp4e8Vp2lUrQ4CbvPPar49/jb3Gnq9afZVnkKJGraFvlb542nky4fBkwnqG4VujBlGjX8H3008ecn/b1aiB3Uc18BgxnMiRowCBW//+FvWQJInPetZizp4Ies45SICbHZ6O1iDACNxMzEKlMss8p7SuVHiGseTuriNJUnoerwygVlEoWNJJz03Hw06+K9Ld1p1MfSYmYSpErRSKO98PrMepqFReXxrOtYTMh94/H5vOywuOka7V80kPeWfCablprI1YyweNP7CYRdC2XFvqeNdhfcQ6Yt97D59p0x55Pg1gHRhIufm/cvubb2W7vlV3Uqtc7TXEp2mJS9MSl64lPk1LSrYeb0cb2bnUCgqllXx30kIIJefhP2JrZUu2Plu2fI4hB2u1NSpJqVP8LONsq2HJ6MZ8v/MqveccxNvZhlyDQKs3kpVrwGgSDGpcjrc7VpVd03pdxDpa+LeQvWjsX6U/y5ZMpaFkg1PHDhblrcuVw/n550lduRKvsWMtyn+/4wqrTkTzWc/aNA5yf8BjoDOYWH86llELjjGrT23aVlV20wrPJoolKGRCy4SyK2qXbPmdkTsJLRNaiBoplBTsra0IKeeGtZUanUFQzdeJtlW9Gdq0PL3qB7DsaDRvrzhFlsyc43NJ52jq11T2/Rv4NKDS4Rgce/eUfZbo1q8vaavXWJTbcDqOlSeiWTGmKU2CPR6a39pKRc+QAH4dGsrbK07n6U1QUHgWUPq/FTI9Kvbghb9eIFmbjLtt/k0LhBAsvbhUqUCmAMDaU7F8sv48Pw4KoUH5h3877z5flY/WnWPw/CMsHtkYOwtpWDqjDhu1/PxrSZLwzJCQypeVPcY6OBjDrVv5yggh+Gn3VaZ3q2ExH7xeOTcGNSrHgoM3+Kh7wVK9FIoHJTlY7FEUZRCZspMuZFxtXelRqQdT9k9Bb8y/4MT8s/PRmXS0DGhZRNopFFeikrOZvvYcf77cME8DDeBgY8UXvWpT3sOB/9t4weKc7rbuxGTGyNYhRZuCSQLbgiRyGAxgofpYeFQqGVoDrSp5yZpyQKNyrAmPle0xUFAoTShGugh4s/6b2KnteGX7K5xPOv/AezqjjsUXFtN2eVu+O/EdEakRdF7dme9Pfs+trPx3JAqll4VHbtIrxJ+qPvmnHkmSxHsvVCUsPMZi1bEXgl5g9dXVsoMSV19djVVQBXJOhsvWO/tkODZB+ZcnPX4zhTZVvFDJ7Hnt62JHOXd7LsdnyNZDQaG0oBjpIsBKZcWXrb6khX8LJuyawKCNg/jq2Fd8sP8Dmi5pysyjM6nsVpn1L67nwIADzG4zm0xdJr3W9mL+mfnFuiG5wpNHbzSx8lg0gxoFypL3drKlZWUvwsLz3yXX9aqLjdqGXZGWYySy9Fksu7iMysPGkbpyJUInL+MyZckSXC3kSucaTNjl090rL+w0arR6JeNB4dlDMdJFhFqlZnjN4WzuuZnRtUZjEia23NxCj+Ae7O+/n7nt51LOuRx2VnZUca/Ce43e469uf7H+2np+PfPr01ZfoQhJztKhUkkPFfjIj9BAN67EPzq4KikniWWXllHVvSrv7X+P38/+jtFkzFM2S5/FhF0TaOrflJqhz2NbuQqJv8yzqEPm/gPknDqFS+fO+cq52VsTn661ON9dhBDEZ2hxc1DqJyk8eyhGuohRq9S0KtuKM4lneCv0LaY2mYqjtWOesmUcyvBz+59ZeGEh19KuFbGmCk8Lg0mgLmCwjVqtwmB62OMSmxnLu3vfpeuarpxOOI2/oz+tAlox+8Rsmi5pyjfHv7nnqUnXpbPw/EL6rOtDgGMA7zd6HwC/zz8jLSyMhO9/yHNHnXv9OlFjXiVq9GhMWVlEdHye6DcmknXk7zy9QO2qe7PjQjzpWnlNQY7eSEGtkqhSRn47TAWF0oIS3f0UuJR8iZjMGHpW6mlR1tvem16VerHi0grebfhuEWin8LTxcLAmM9dASpZOdjGPiNuZ+LnYPngtNYLRW0fTu0pvpjSagouNy733ErITmHNqDv87/z8Wnl+Ii40LmfpMWge05uOmH1O/TP17EaxWXl6UX7yI2MnvceW553Dt3Rv7kBCQJBLn/kzOyZOoHB3xm/kFji1aYMrKImPnLm59/DFqFxcCfvgeK/d/gt+8nWxpUdmLxUciGdMqON/PJYTg133XGNI4sFRGCSsoWEIqyeedoaGh4tixY09bjQIz8+hMHDQOjKs7TpZ8XGYcvdb14kD/A8qD6hnhzWXhVPN1ZlRLyz2is3UGmn6+k3Xjm1PW3VweNFufTc+1PXm1zqt0r9j9kWMzdZmM2jaKBmUaMLbuWGytbB8pC5AbEUHKsmXkXo0g98oVEALfjz/GsU3rh36bQggSvp1N5s4dBC5ahNrZHASXmWvg72tJvLniFB91rUH3ev553ksIwTfbr7D9fDwrxjTBwUbZU5RESvsz60nY0NDQUI4dO5bnF6W4u58Ct7JuEeya/w7ifnwdfTGajGTpswpRK4XixOAmgfxx8Aap2ZYDtv44eIP65dzuGWiAjdc3UsmtUr4GGsDR2pGZLWey5qrlAiQANsHB+EyZgvvgwahdnKm4ZTNObdvk+SCWJAmvNyZgV7ceCbO/42xMGm+vOEWTz3bwycYLONpY8caycFrM3MnSvyPvjTOZBHsvJzD8j6PsuBDPHyMaKAZa4ZlF+eU/BdSSGpOpYJGqRmG0WG9ZofQQUs6NF2r5MPyPo8wf2gD3PNzeRpPg4/XnWPp3FNV8nRky/wjVfZ0Z2Kgcyy4tY2LIRFn3KutUlhqeNdhyY4tFo36XlMWL8Rg5EpVD/sFtkiThOfZVLnTuxjhjPQa2rsrut1rj4WguYnI9MZMZ68/z/uqzfLLhAmXd7UjM1OHpaMNLTQLpUdffYpEWBYXSjGKknwJBLkGEJ4TzQtALsuQvJl/EzdYNW3X+rkiF0sV7naoxa+sl2n+9h96hAfQLLYu/mx25BhNzd0fw677rSBKMa1OR0PJu6AwmDkYk0WPOLkxlI6jmVl/2vdqUbcPJ2ydlGWl9fDzaM2cI+PEHWXOviNSj8gzif5WzKd/6QQ9SBU9HfhvWkKTMXAb9eoRa/i681rYSAW52pd5NqqAgB8VIPwVerPQivdb24o2QN7DXWG4xuOzSMvpU7qM8tJ4xVCqJd5+vSr/Qsiw6cpMB8w6TlKlDrZIwCcHY1hUZ37biAw02WlfxZngLT7qstmHgvL9Z9kpjnG0tpy7ZWdmRY8iRpZchLg5NuXKobCyXGM3WGZi55SLLWofikJrwSDkPRxsWj2pM2692M6FdZeW3rqBwB+VM+ing4+BDI99G/Bj+o0XZg7EH2XBtA6m5qfx86md2R+1+ZH6rQumkvKcD73euzpEp7TgwuS121mr+erUZE9tXzrMDlqe9K0g6ape1ZXrYOVn3SMhJeCD6O1/UapB5XBMWHkuD8u642aiRLBzXuDtY06OuP0uOROYrp1AykCTpmVhsFfbnVIz0U2Jak2nsi9nHN8e/ybOmd1xmHKO2jmLMtjHU8KiBk8YJrVHLvNPz6PRXJxacW6D0nH4GWfJ3JJ1r+VIr4NEGVaPW0My/GTUrX2P7hXgSMnLznVMIwdqra2kf2F6WDpqAAHSRkRgzLJfp3Hgmjl4hAeScPo11+fIW5XvXD2DjmThZeigoPAso7u6nhIuNCwueX8D7+9+n/cr29KzUk4a+DbGSrDgRf4K5p+dirbJmVqtZdCzf8YGx55LO8fmRz7mQfIFPm32qBJQ9IwghWPJ3JH8Mb/jI9yNSI4jPjqeuV11WXFnE8zWns/J4NK+2fnQ2wcHYgwiE7BapVm5uODRvRtqaMNyHDM5XNiVbh1/aLXKvReDUto3FuX1dbEmWEdGuoPCsoBjpp4ibrRs/tfuJa2nXWHFpBfNOzyPXmMvllMv0rtybdxq8g5Xq4f9FNTxq8GvHXxmzbQxzT8+VnW+tULLJyDWQlWukmu+DTTf0Jj1rr65l2aVlpOamEugciMFkICojikT1JOJjX+ZV8jbSV1KuMPXAVGY0m1Egl537kCHEvDkJp44d0Hh7A+Zo85iUHLL1BpxtNfi62OJs1CHNmIrKzp4bgwajsrXFrl493Pr1ReP/cH50jt6IrZWy6CwN3M0fLu0u78KuNaIY6WJAkEvQvWpif135i52RO5nSaEq+Y2zUNnza/FP6ru/L8BrDZQWgKZRsTCbBvxtH5RhymLh7Ijn6HF4PeZ2mfk1RSeZTrKScJPqEDeVk7ldM3nuWzn4TiUvVoZIk3JxzuJi5leWXlzO54WSa+zcvkC72ISG49e9H5LDhOH09m1W3JBYduYnJJHC0tSIpU0fvmwd47+BKjHb2eH08HWt/P0w5OWTu3s31nr1waNUS348+QmVnd2/enRdvExLo+p+/KwWF0oJipIsZSy8u5fWQ12XJ+jn6Uc+7Hhuvb6R35d6FrJnC08bJVoMQcCtNi4+LLUIIJu+djIu1Cz+0/eEhr4uHnQdtnT5nT+ZaNlxbwcZrO3BS+WLCQJYxCXtdKAOrf0rHwGaPpY/nmDHEG61I7tuP8sE1mNOvJxWrlseUnc3t738mJ/wU54PqMrVmX7Y1aUVZd3NOtUOTJnhNmEDc9OlEjX6Fsr/OQ2VjgxCChYdvMr1bjf/8XSkolBaUwLFihNagJSI1gqZ+TWWPuZvfqlD6UaskutfzY8md6lzhCeFcTb3KJ80+yfNYJDVbx4KDkbgaWvNZo99wsbXl+47T+aPLNxwatJOvnvuIHaclJi4Px5hHcw4wt83M1hnydOlFJmUzLLU8Cb+uoHH/Lrhs38CtT/+P+FlfknvpEhWWLaHX+oUElHGly/cHyLyvoYbK3h6/zz9H7epC4g/mLIfvd17FztqKJkEeT+LrUlAoFShGuhihNWixsbK5566Ug72Vvez8VoWSz5DG5Vl0JJLbGVqWXlxKvyr90KjzzoPu/8thXO2sWTq6CV2q1aOKexXisuKo4l4FB2sHmlX0ZNnoxiRm5vLZxgv3xmXlGlh8JJIXZu+j6gebCZmxjRrTtvDOylOciU67J/ft9ssMaRzICw2DcOvXl3K/zqPCiuXYVq6M1/jx2NWujSRJrBnbFAno98thYlL/+a1KKhXeb79N8oqVfPLXCdacjGHekPql/gxTQaEgKO7uYoSDtQNag5ZsfbbsM+bb2bfl57cqlHiq+DgxuHE5hv52lGTPPbzT4J085X7YeYXL8RlsmtAC9Z2D7C5BXdgdtZvOQf/0e7bVqPlxYAgtZ+7ilVbBxKdrGfXnMWr5u/DeC1VpFuyJSiWRmJnL8mNRvPK/Y7Sp6s2E5yqx/UI8e95+MGLbmJ5Oxq5deE/+p2Obo62GnwbVY8zCE3SevY/Q8u7ULeuCJElcjs+gkV0ZPE4cYtV7o2R3/VJQeFZQjHQxQqPS0MK/BRuub6BP5T4W5YUQrLu2jvF1x5OYk4hGpcHJ2qlAO3GFkseE5yqhkkzMi8rm+21xDGhoTaCHPbkGEweuJvK/Qzc5HZ3K0KblqeLzTyS4h60HGbqHc5td7a3pVNOXObuvsvZULDO616RTLd8HZDwdbRjbuiIvNSnPK/87xvjFJ2hRyesho6qPjUXj44OVm9sD15sEe+LpaMPM3rW5lpjF9cQsTEIQGuhGi66tsbUyKQZaQSEPFCNdzOhXtR+zjs6iR8UeaFT5l3NcdmkZkemRvL33bWzVtuhNepysnehTuQ8vVnoRTzvPItJaoSiRJInXn6vKgv9Zo9HoGPHHURIyc7FRq6jm58zgxoHcTMpiaJPyD4zL1GdiZ2WX55yda/syfvEJJnWo8pCBvh9HGyt+HhJK8y924uWcRy15kwlUDy8SJUnCx8WWHL2RvqFlH3gv4YQVQv9wQR8FBQXFSBc7Gvs2JsAxgA8PfMiMZjPyDAgymoxM2T+FzTc20zWoK2+FvoWrrTlt5XzSeZZfWk7PsJ583uJzmvrLD0JTKP6kalPJ0GVgr7GnoW8DapSP5P1OPR6S+2DNWVzsHlzkbb+5nQY+DfKcNzNXT5bOSP+GZfN8/34cbczBXfefT9/FqkwZ9LGxmLKzUdk/eGSTozdiq3k4Bzr3ylUcmjSxeF8FhWcRxS9azFBJKr5o+QUpuSkM3zycHZE7MJgM995PzEnkpU0vsfXGVqY0nMInzT+5Z6ABqntUZ3rT6Xzb5lve2/+eEvldCsg15rI2Yi2DNgyi01+dGLVtFN3WdCMiNYKfwn8iS/dwn3FHG6sHKnfdzr7NobhDdAvuluc9tpyLx9vJBhuZhUReahJIVHI2GdoHd8BWHh7Yh4aStmHDA9dvZ2iJuJ1JFR+nB64bEhLI3LcP7blzRI4cReSIl4n74ENywsMLvUiEgkJJQNlJF0PsNfb80PYHNl3fxB9n/2DGoRn4O/qjM+mITI9EIJjfcT4hZUIeOUdImRA+bPwhMw7PYFXXVUrEbAklMSeRsdvH4mztzMhaI2kZ0BK1So0QgkNxh3hr91t0WNWBxZ0XE+gceG9cm6perDsVyxvtKpNjyOGdve8woOoAHK0d87zPqahUaudTD/zfNAn2xEotselsHH1Dyz3wntuAAcR/+inOnTqhdjTfb/nRKDrX9nugI5c+Lo4b/QeA0YiVlxdO7Z4DSSL30iVi3nkXtaMjfl98jk2lSgX5yhQUShdCiBL7ql+/vngWiEqPEifjT4qziWfF5L2TxbzT82SNM5lMostfXcSJ+BOFrKFCYZCpyxQ9w3qKH07+IEwmU54yCdkJovWy1qLhwobi9O3T965fiEsTDT7dKg5EHxED1g8QU/ZNEUaTMe85MrSi4pQN4sedVwqkX81pm0Wzz3eI2+naB66bTCYRO326uD5okDCkpooz0aki5OOt4tKt9HsyuVFR4mL9UHGpcROhT0h4aG6T0ShSVv0lLjVtJnIuXCiQXgrFC6BUv54Ed2xZnnZO2UmXAAKcAghwCsAkTOyI3MFboW/JGidJEi9WepEN1zZQz7teIWup8KRZcnEJFVwqMLbO2Ed6QjztPFnTfQ2jto5i2JZhhJYJpYJLBQwmA6qAA7yx3cjEhiPoX7VfnnNk5hp4deFxmlX05HQeZ8yP4kp8BrYaNX3qB9Bn7kG+6FWbhhXc77Xt8/ngA27NnMWFdh3ZElCfmaMGEWxrQn/rFhk7d3J75izU7m5UWLYMK8+HAxwllQrXni+isrMletx4gjdvQtJY7outUPwQ9x1blBaP3v2fqbBRjHQJIlufjYSEh538ikwBjgGcSThTiFopFAZGk5EVl1bwdZuvLT7YXGxc+K7td/QM60mXoC6k5qZipbLiq7btmLdNYs1eE4GaJJoEe9yby2A0sfPibb7edpl65Vx5r1M1WszcRVxaDr4ueUeA38/CwzcZ0KAsE9pVJtDDgfdWn0GjUtGxRhmcbDUkZOay1hRKrV5VGZd5GsdvpnN1WiqSrQ02FYJQOzkRvG0bKnX+Z+DOnTqRvGgRGTt34dyxg/wvUOGpIIQAg0FZUD1BFCNdgtCoNehNeoQQslekOpPukRWpFIovZxLP4GjtSA0PeXWsfcGYwOMAACAASURBVBx8qO1dGxu1DUOqD7l3vdFLJpYcjWL6unMYjILKZZwwCcHZmDR8XGx5tXUw3er4IUkSfeoHMC3sHHMG179XACUvwqNSWXsqlo0TWgDQo54/3ev6cTAiicPXkriVrsXZVsP8YaHU8HMBuj8wPva9KTi2amnRQN/FbcAAUpctVYx0MUUYjWTu20fKkiVkHzqMMBiQbGxwbNUKtwEDsG/Y4N7zqiTvqoty93w/ipEuQdiobQhwCuB4/HFCfeT1/j0UewiVpGL01tHcSL+BURjxc/Cje8XuvFDhBaV7VjElWZuMn4Nfgcb4OfiRrE1+4JqVWsWQxoEMblSO09FpxKTmoJJgUocqD0Vav/18FUb8cZTXlpzgkx61cP9XcREhBLsu3eadlaeZ1bvOAztuSZJoVtGTZhUt5+bnRkTg1q+v7M9lX78+t7+YKVteoegwJCURNXYsGIy4DRyI/1dfo3Kwx5SWRtr6Ddz6+GM0fn74f/MNakeHp61uiUQx0iWMflX6sezSMllG+nDsYdZFrKO2V20GVRtEba/aqFBxJfUKKy6vYPaJ2Xzc9GPalGtjcS6FwiUhO4HozGiEEPg5+mGttkZr1BZojlxjLjZqmzzfkySJOmVdqVP20W0gbazU/DasAZ9uuEDrWbtoV60MLSp7YmulJjolh+XHohDAdwPq0TT48QvlCIMe1PIfPZJajTAYLAsqFCnGzEwih4/AsU0bvN6Y8MDOWO3qivvgQbj170fc9OlEjx1r7nZmrVSVKyiKkS5hdAvuxq9nfmVv9F5aBrR8pNy5xHOM2zGOxn6N+aX9Lw+85+voS8uAlpxJOMPru15HIGhbrm1hq66QB4diD7H44mJOxJ+ggksFJCRupt8k2DWYc0nnSM9Nx9nG2eI8epOew3GHGVpj6H/Sx8ZKzcfdazKxXWVWHo9m18UEcg3/3959h0dVJXwc/55JI70AoZcACggoVSkKIhaqgCgIiKKyYkMQUFlfG/a2Kgq6umsv9O4qRUF91QVlAUWU3hJ6SQ9pM+f9YwY2kDb4EnIDv8/zzJNw78mZM4eZ/HLvPfccN5UjQniyb3PaN4j7f5+mDKpWndxtWwlt0dyv8jnbtuOKjWHf08+QvnQp7pQUXKGhhLZuTezgwYR36ogpYpYzKVuH336HSk2bFgrogkxgIDUmTiTxjpGkTJ9B3LCbju+rCKe+y+sUd6FGVNTHuXIL1snW7F9jO0/rbOdunmvz3HmF9h/IPGAv+fQS23tOb3s072iJda07uM52mtrJpuekl1VzpQgej8e+suoV231Wdztz40ybmZt5fF92frZdsGWBveTTS+zQfw21+e78UutbumOpHfbFsLJs8mmTumix3T5kqF9lPXl5dvNVV9sNrdvY/a++arO3bbPuzEybd/CgPTJ9ut3at5/dNuB6m7t/fxm3WgpyZ2fbjR062uxt2/wqn7Fypd3Ss1extxLigFupinqcKSXdgqU/PyuglvEt+fuVf2f2ptl0n92dKWunsHDrQuZsnsOE/51Ar7m9CHQFMqP3DCoFFjG/cgHNqzTnkuqXsHDbwjPUegF497d3+feefzO111SuP//6E8YGhASE0KdhHyZ1ncT6w+t54scnSqzr0NFD/G3V3xjebHjZNvo0ibyiK3lJSWSuWFFiOWstSfeNJm/3bhIWLiB+zBhCEhJwhYURWKUKsQMHkjB3DhFXdGXXzbeQn5x8hl6BZH7/PSHnnUdIQoJf5cPatQOPh+zffivjlp19FNIVVNPKTfm458dM7jaZ7Pxsvt/9PWsOrKFZ5WZ0rNmRUS1HERpU+q00AAMbD2Tu5rll3GI5JjUnlffWvcfrV7x+wpSuJ7ukxiWMbzOe+Vvns2DrgkKn3qy1/HLwF2758hb6n9e/wlyyMEFB1HzuWXaPG0/W6uKnrU3+bCoZ331H9YlPEFyz6EF0xhiq3n034ZdeysHXJpVVk895npwc3OnpWI8HgLx9+whOqO/3zxtjCK5fn/z9+4vcX/DIsbw5qS2ga9IVXpO4JjSJa3LCtkXbF9E4rrHfdTSObUxSRtLxf2fkZnA0/ygRwRHFrpokf968LfPoXKcz1cOrl1p26AVDWbF3BS///DL/XPdPeiT0IC4kjvS8dJbsWEJ6bjp3tbyr2Dm5nSq8Y0dqPv8cSXffTXjHDsTceCOhF13kmxZ0E8nTp5E6bz7R1/Un9vrrS62vysg72NqrN/HjxxEQGVlqeSmdOz2d1HnzSZ42jbxduzDBwViPh6gePbwT0Ljdp1SfdedDoCLnVKnHzkanOAbDYsHCnM1zmLZhGjvSdhAWGEZGXgat41szqMkgutTuUuSKXHLqlu1axh0X3uF3+bta3sWE7ybweIfH+TbxWzZkbSAsMIxRrUbRqVanCrt+eMRll9Fw6RJS581n/1NPk7tjB9ZagmrVJPLqa3CFhVH9f/7Hr7oCq1YlvH170hcvJsaPUJeSHV27lsRRowhv144aTzxOaNu2GGPIP3KE1DlzOPz+B9jcXKo99hguPyYu8eTkkP3bekIaNToDrT+76LfuWahuZF3WH15Py/iWfpVfnricHHcOy3YtY3Tr0XSo2QGXcZHrzmXJziW8u+5dPvn9E17r+hrRIf4vwnAuS81JZf6W+Xy/+3sy8zIJCwrj0lqX0rdhX9Jy005pre+qoVVJy02jTbU2tKnWpgxbfeYFREYSN+wm4obddPz0ojGGo+vWkfXjj7hCir6lrCghjRqRt3cfnqwsUv/1L7J/W4/NzSWwSmWievWiUpMmpVciZG/YQOLd91Dj2WeIvPzyE/YFxsVRecQIogcNYkvnLiSNuo86b71Z6ujstC+/pFKzZgTXrl3q8xc8zXymRn075dR2USrmn+BSogHnDWD6xul+vfEOZB3g2ZXPcnX9q5ncbfIJR2bBAcH0btCbT3p+QqOYRoxaNoocd05ZN79C81gPk9dMpsecHvxx5A+GNh3KA+0eYNgFw9iUvImec3uSmpNKRl6G33Vm5mWeE5cdjs37DYDLdfz6p788eblkrljBlq5XkPHtt4Q0Pp+wNq0hMJDEu+5mx+AhHF2ngUul2ffU08SPG1sooAsKjIwkfvx4Mn/4gcx//7vE+vIPH+bQlDeJu3lYieWkGMUN+64Ij3P1FqzSeDwee9386+ycTXNKLXvPV/fYth+3PeEWoKK4PW47culIO/WPqaermWcdj8djn/jxCXvTv26yB7MKr+xkrbWHsg7ZK2ZcYfvP61/s7Sgne3fdu/bh/334dDbV8fJTUuyGdhfb/ORkv8p7cnPthkva2639+tvc3bsL78/Lsynz5tmN7TvYjJUrT3dzzxpHN2ywmzp3sZ68wrd2nszj8dhtAwfaje072Nw9e4osk711m93Sq5c9MOn1P9UezrJbrYqjVbDOMcYYXur8Erctvg2XcXFtw2uLPG20fNdyvkv6jpc6v1Tq9KAu4+K2Zrfx3E/PMahx0Ssqneu+2P4Fvx78lY96fER4UNFTIFYOrcxb3d5i4OcDmb1pNtc3Lvn6qdvjZsbGGbzc5eWyaLJjBURHE3F5F1LmzqPyrcNLLb/vySfxZGZS/5OPcYUX7nsTGEh0374Exsez+/6xNFgwn8DK/i9UczZxp6aSOn8BOZs3Y91uAqvFE33ttYQkJJC2cCHR/fth/BjgZYyhzt//zubOXdjWtx/h7dsTec3VBERF4T5yhNSFn5O9fj1V7rmHuJuG/qm2Wgefhj5TFNJnqQYxDXj3mncZ+81YPvr9IwY1HuSdFtS42JK8hRmbZrDpyCZaxbfimoRr/KqzXfV2ZOdnsyl5E9XCqjFvyzyWJy4nLTeNsKAw2lVrxw2Nb6BWRK0yfnXO9Nkfn3Fvy3uLDehjzo87n1bxrXh19av0a9SPwGKmyLTW8tKql6gXVY/mVfybnetsEnfTTSTdO4qo7tcQVKNGseXcaWmkzJ1H7JAhRQZ0QeEdOnjDf/Ycqtzxl9PdZEfzZGWx/8UXSfviSyI6dyasbVsIcJG7fQc7bxpGpcaNMaGViOx2pd91BsbGEhAVRb1PPyHzxx9JX7QYT1YWrshIonr3ovbkN3BVKnmuBimZQvos1jCmIXP7zmXF3hXM2jSL6Run47EeqodXZ3CTwexK30VaTprf9RljqBlRkw/Xf8g3id9weZ3LGXnhSCqHViYjL4Ovdn7FoM8HcXnty3m0w6PFziNdEVlr+Xnfz94/bpI3ke/Jp2poVfo07EPPhJ4kpidy8OjBEqdqLWhyt8l0nt6ZEUtH8Gj7R2kY0/CE/YnpiUxZO4XtqdsLTet6rgi98ELibr2VncOHU2fKlCJHBrszMtg5/FYCoqKo9oB/66zH3jiY3aNHU3nE7d6jytmzSV/6Fe6UFExYGGFt2hA7+EZCGjYsvbIKwpOZya7bbie4fj0afvlFobMIVceMJmXadA787W+END61AXY2P5+A6GjihgwhbsiQ09lsQSF91nMZFx1rdqRjzY6F9n3y+yccch86pfp2pO7gYNZB5vWbR3xY/An72lRrw6hWo3j0h0cZvWw0b3R7gyBXxV8mMzE9kbHfjCXfk8+gxoO488I7CQoIYmfaTmZtmsVrq1+jT4M+NK/SnACXf8svhgeFc3nty3FbNyOWjCAhOoFmlZthjGHTkU38fvh3+jbqy2PtHzunVyqrfOtwXBHh7Bx2M5VaNCemXz8C4+PxZGWRvmwZaV8uIrByZSrfOdLvNYxDWzQnPyODg6+9RvK06UR260bV+8d4683MJH3ZMnYOH05Yy1bUeO65s2L1pr0TJxLcsCE1nnm6yEtVruBg4m4extHff+fI++9TZeQdfo2sz/7jD1xhYQRE666PsqKQPoc1rdyUmZtmYq1/61N/uf1LDh49yPvd3y8U0MeEBYXxQucXuHfZvXy4/kNGtBhxfJ/b42ZPxh6y8rOIDommWlg1x1/b3pOxh+GLhnN789sZ3GTwCe2tF1WPzrU7s/HIRm5bfBv1ouqdUt1BAUFcXvNyXur8Et8mfcuu9F14rIe+jfryWtfXSp3S9VwRe8MNRPfpQ9qXi0hd+Dnu1FRclSoR2qY1DRYs4OCkSbhCT3H0e14eGd98S4PPFxIUf+J7ObRFC6redRf7nnqKxBEjqPvB+44+ZZublETK9Olk/bwKz9GjBERHE3llN6L79SMgKoq8ffvI+PY7Gn39damft+oP/5W0hQtJmTWLuKGlX0dOnjqN2EEDtcBJGVJIn8Nax7fGZVys2r+KdtXblVr+zbVvclHVi6gbVbfEcoGuQMa0HsOoZaMY3mw46bnpzN48m5kbZ2KxhAeFcyT7CPFh8QxqPIheDXqVyy1GOe4cluxYwvrD68l15xJXKY4eCT1OOPX82I+PMbTpUIY0Lf40XuO4xkxoN4FHfnyE/Zn7qRZerdTnttayJXkLA84bQFBAEFfW8/864LnIVakSMf37EdO/X6F9gZUrk7d7t991pS9bhs3Ops47bxcK6GNMcDDVJ05k99hxHJoyhfhx4wDvpBxpX35JxrLluNPScIWFEXZxO2L69z/jR5Oe7Gz2Pf44Gd9+R3TfvsSPG4srPJz8Q4dInb+Ag5OnUPkvI/BkZxPdq5dfZwQCoqKIuOxSDr0xmdjBg0sM36zVq0lfupQGC+afzpclJ1FIn8OMMdx8wc08/9PzfNj9QyKCI4otu2rfKnam7eTJ7k/6VXeTuCZUC6vGtA3TeP+39+lYqyOvdH2FZpWbAd77iVfsWcHHf3zM9I3TebPbm1QNq3pCHR7rYUvKFtJy0ggNDKVhTEO/ji5z3DkYDMEBRa9d6/a4efvXt5m+cToXVL6ADjU6EBIQwu7M3YxYMoIG0Q14oN0DBLuC2Zqylbe6vVXqc/Zq2Itnf3qWKWun8GSn0vto3aF1ZORlnHWTk5SHqF49SRx5J1XvvdevUckHJ71OSNOmBFUveVpW43IRf/8YdgweQpV77yV17lwOTnqdSs2bE92nNwGVK+NJzyD9q6/Y8uZbxA4aSNUxYzABJ17ysB4PR9euJX/fPggMJKTReYQ0KH5hirz9B0iZPYucP/7A5uYRGF+VqN59CLu43fEjYZubS+KddxEYF0ejZV/jCjvxkkhE587k7dlD4t334MnKotpDD5baL8fUeOEFNrfvwO5x46n+2KMExsae+HqsJePrr9n72OPUfPFFAqtWLaYmOR0U0ue4fo36seHIBm5bfBvPX/Y8DWIanLDfWsuq/asY+81Y6kXVo1W1Vn7XXS+qHpPXTubJjk9ydf2rT9jnMi461upIh5odeOuXtxj51Ug+7vEx4UHhZORmMHvzbGZsnIHFUiW0Cll5WRzIOkDfRn0Z3GQwNSNOXHBhX+Y+Zm6aydzNc0nJScFiiasUx4DzBjDgvAHHj27zPfk88O0DpOel80mPT6gTVeeEeka1HMXCbQu5Y8kdXFLjEvo36k9QQOnXOl3GxfXnX89nf3zG4x0eL/HatNvj5q1f3mJQ40EVdkpPJ6nUpAlBtWqR+vnnxPQrfKRdUG5SEjmbN1P7zSl+1R1crx6VmjZlz4S/krNhA/U++bjQgLKo7teQf+gQu8c/wJ4HH6LmSy9iXC48OTkkf/oZydOm4QoJIbhBA6w7n6O//EJI/QTibrmZyCv/ewbFk5nJviefJP2bb4nq2YOoXr0xwUHkJSay/+mnsB5LjYlPENa2LYfefgdXpUre5woo+r0WVLMmdd9/jy2XdyVn6zYiu3Xz6zUfm/s8ICqKrdd0J7JrV++a3cHB5CUlkTJrNiY4mNpvvE5YG/2RWeaKu4G6Ijw0mcnp4fF47Ae/fWC7TOtib190u/3090/tgi0L7Hvr3rPXzb/O9prTy05ZM8Xevuj2U6q3z9w+duSSkX49//3L77cf/PaB3Zux1/ad29eOXT7Wrtm/5oQJP3al7bJ/+/lvtvO0zvanvT8d3z5702zbaWon+9zK5+zW5K3W4/FYj8djNx3ZZJ/691O209ROduHWhdZaa99Y/YYdsXiEzc3PLbFNPyT9YFt91MqvCWGOycnLsc0/aG4f/PbBYuvPdefaR75/xN666Fabk5/jd91Ssqx1v3knKvnhh2LLuLOy7PYbB9sNl7T3eyIZa61NGjfebmjbzuYdOFBiOXd2tt1+42B7+ONPbH56ut0+ZKjddcdIm7XmxPexJyfHpn7xhd1yTXe778UXrcfjse6MDLvthoF298MP2/z0wmu7ezwem/b1MruxQ0ebtvwbu/HSS2325s1+tX/bDQPt1mv7+v16cxIT7cYOHa211uYdOWIP/fNdmzRuvE0cdZ/dO3GizfzPf06p/6R0JU1mYmwFvlm8bdu2dtWqVeXdjLNGrjuXr3Z+xc/7f+Zo/lEigyLpWrcr7Wu0Jyk9iVsW3cKS65f4NWI7OTuZrjO6MrHjRPo26ltq+V8O/sJD3z1ESEAI1za8lttb3F5s2ZV7V/LAtw/wj6v/wcbkjUxZM4W3r3qb+tH1iyy/JXkLI78ayejWo3np55eY2msqtSNLn0P4qllX0bZaW5677LlSy4L3D95WH7WiS50ubDiygRsa30CPhB5EB0eTmpvK4h2LmbFxBufFnscLl71wTo/aLgtZP/9M0pj7iejcmdghQwht4b233J2RQeqCBSR//AmBtWrhSUkmYdYsv+vd0r0HIQ0bUGdK6UffWavXsGfCBILr1Caodh2qP/5Ysdd13Skp7Bx+K9F9+5KzZTNYih19/d/6V5P4lzsIOf986k/9zK/2py1axO5x42m0fFmx1+ALOvDaa3gys6j+Pw/7Vb/8/7Vt25ZVq1YV+R+v091yXHBAMD0b9KRng56F9tWNqkvdyLp8k/gNV9W7qtS6Zm6aSYAJoEdCD7+e+8IqF5KVl0WdyDolBjR411keedFIJq2exC8Hf+GjHh8VG9AAjWIbMfmKydyy6BZax7f2K6AB2sS34ZvEb/BYj1+npTclb6JKWBUmXTGJ9YfXM2PjDIZ9MYyMvAwigiLoVKsTL3d5+ZycmORMCGvXjgYLF5A6Zw67x4zBnZ6OCQnGk5FJRJcuVH/8cYITEth27bV4jh71a0R43v4D5CUlUfkvI0otCxDaqiVYS+72HdR5550SB14FxMRQ+/VJbL/+BrCWRl8tLXX0dVjr1gQl1IdA/271A4i86iqMy0X64sXEDSt5/mx3Sgops2ZT76MP/a5fypZCWvw2uMlg/vHrP7is1mUlDuCy1rJ4x2IaxzUudvBWoZ/BkpWfReda/k0G0r9Rf15b/RoXV7u40EQgRWlauSmxIbHEhMT4VT/AiBYj+GL7FxzMOujXiO0ZG2cw4LwBADSr3IyJHSf6/VxyehxbpSnutttwp6Zic/MIiI464RaqsFatSPviC2IGDCi1vsyfVgIQ3bPwH65FMcaAMVRq0aLYa8UFBdetS2CVKgTExvg9Ojy0xYWkLVzo962TJiCAoLp1OTR5CmFt2lDpgguKLOdOTSXxrru9U4Q2aFBkGTnzNGpF/HZN/WtoENOA0ctHk5mXWWQZt8fNMyufIT03nfNjz/e77s3Jm7FYmsT5N9tRWFAYoQGhhQaQlaROZB1+P/y73+UbxTYi0BXI9I3TSy27JXkLi3YsYsD5pf/il7JnXC4CY2MJqhZf6B7n2KFDOPzOP3CnlTzbnrWW1FmzCaxa9ZTuw87bs4eQxo39Lh8QE4PN9n91ubDWrfFkZWGzsvwq78nNxZ2cTNUxo9l12+3se/oZcrZuPb4/PzmZw+++y7b+/Qm9sAXx48f53RYpewpp8Zsxhqc7PU2dyDr0nNOTV1a9wsYjGzl09BA703by3m/v0WtuL5Iykni609Os2LsCt8ftV92J6Ym4PW4aV/b/l1ueJ++UZjSrFVGLI9lH/C6flZeFy7iYt2UeUzdMLXay/41HNnLnV3cy4eIJxU7yIs4RfumlhF92GYl/uYP8I0W/H2x+Pvuffob8w4fBWr+XzbQeD+TnE1zf/4ltTEAANtf/kI68spv3lPrOXX6VT1+ylEpNGhM7eDAJc2Z7Z3C7ZTibOnRk02WXsbXbleRs2kztV1+l2l//qolJHEanu+WUBLoCeaT9I9x8wc3M3DSTcd+OIz03ndDAUNpWa3vCNdeYkBh+2PODX/NZr9izgojgCKKCo06pPf7cHnVM+xrtWbB1AfmefAJdpb/1/7X9X3Sq2Ynx7cZz//L7mbFxBoMaD6Jd9XYEugLZmbaTmZtmsubAGh6++OEir+WL8xhjqPbwXzn4+uts7d6DqJ49vCtkVY3Hk5VJxrJlJE+fQch5jaj/2afsuu12Mr//nojOpb+P8/bsAfAuXuFve0JDsYcP+13ek5kJAQEc/uADar7wfImnvD1ZWRx+5x2qjLoX8N6WFT9mDFVHjcKdkuKddzsmxq8pQKV8KKTlT6kbVZdxbccxrm3xp8Zub3E7z//0PBdUvoAqoVWKLbfhyAYW71iMBw9J6Ul+Dexye9zke/JP6T7jw9mHiQiOYNGORfRu0LvEsrnuXD774zMeaPcAdSLrMLPPTH7e9zPTN07nsw2fkefOIz4snj4N+2ikdgVkXC7ix4whdvAQUmbNZN/EJ49PNxrWtg11pkw+fu02dvBgDr39DuEdOpQ4P7i1lkN//ztBdeqQsfwbYgcN9Kstnpxs8g8dwubn+zUZS8qsWUT36UP2hg0ceOFF4h98oMijX3d6Okn33UelZs1OuB8bvEfv5+pSnRWNbsGSMvX2L2+zYOsCnuz0JK3jW5/wV3++J5+vd33Nsyuf5ZH2j7DmwBoCXYGMbTO21Hq/S/qOF396kZDAEGb1mVXqABprLdfOu5Zbmt3C66tf55XLX6Ft9aKPdnLduTz03UO4jIuXu7zs+PnFpWzZ/HyS7rkXV3gYNZ5/Hldw4cGQ1loOvfEGaUuWUHX0aA5NnkLC3DmlnjrO27OHbf2vI7h+PWIHDiJmwHUlls9PTmZ7v/7U+ftbBNWoQdKY+8nbu4fYGwcTeWU377SgBw+RumA+qXPmEtWrF9X+OsGvQWxSfnQLlpSbkReNpGZETZ748QmCAoLoVrcbEUERHD56mC+2f0GN8Bq83OVl2lVvR9O4pgz9YiiX1bqsxLnED2Qd4JkVzzC+7Xje/OVN72noJoNKbMfHv3tnMxtw3gBqR9Zm3Lfj6Fa3G4MaD6JxnPc6+NH8o3y5/Us+/v1jEqITePayZxXQggkMpNak19jz0AS29epN7I03En1tH++0oBkZpC9dSvKnn0FgIPU++ICAuDiSP/yIAy+8QPyECcW+h9wZGSSNuZ/Kt95KRNfL2XXrbQTExhJ5Rdciy+cnJ5N4551E9e5FpaZNAaj7/nscXbuW5M+mcuSjj7BZWbhioom88krqT59GcN2S59kX59ORtJwR1lpW7F3Bz/u8E6VEBUfRtW7XQqO5V+5dyYPfPcjdF93NtY2uPWHhDY/18P3u73l25bPc2PhGhjcfTmJaIsMXD+fmC25mSJMhha5R57pz+XD9h8zcNJMPu39IjYgaABzMOuhd9GPTTNweNyEBIaTkpHBxjYsZ1HgQHWt21JSdcgJrLdm//kryZ1NJX74cT3o6JjSU8HbtiB06hPBLLz1+5OxOSWHXiL8QVLs2Ve66k0oFRntbt5uM//1fDr7yKmFt21Dt0UcxxnD0119JuudeQlteROzgwYRdcgm4XOTv3UvKrFkkz5hJTL++VB07VoO7zjIlHUkrpMVxNhzZwJQ1U1h7cC1X1L3i+NzdyxOXEx0SzR0t7qBbvf/OQ7w7YzeP//g4W1O20q9RP5rENcFi+f3w78zfMp+mcU15ouMTVA8vvKCC2+PmcPZhctw5xIbElrjIiEhBpd2n7MnK4vD775MyYyZB1asTnJCAdbs5uno1AXFxxN08jKjevU+ow5OZSern/yJ52jRyNm4ElwtXeDjRffoQp785bgAADPVJREFUe+MgQho1OhMvTc4whbRUSLszdvNN4jfHV8FqU60NLaq2KLb8tpRtzN0yl90Z3mUL60TWoX+j/iXORiZS1mxeHpkrVpJ/YD8mMJDgho0Ibd6s9J/zeLD5+UVeA5ezi0JaRETEoUoKaV3YEBERcSiFtIiIiEMppEVERBxKIS0iIuJQCmkRERGHUkiLiIg4lEJaRETEoRTSIiIiDqWQFhERcSiFtIiIiEMppEVERBxKIS0iIuJQCmkRERGHUkiLiIg4lEJaRETEoco0pI0xdYwxy40xfxhj1htjRvu2xxljlhpjNvu+xhb4mZeMMauMMV3Ksm0iIiJOV9ZH0vnAOGttU6A9cI8x5gJgAvC1tfY84GvfvzHGNPH9XGfgnjJum4iIiKOVaUhba/daa1f7vk8H/gBqAX2BD33FPgT6+b4PADyABUxZtk1ERMTpztg1aWNMfaAVsBKoZq3dC94gB+J9368HwoDvgbfOVNtEREScKPBMPIkxJgKYDYyx1qYZU/xBsrV2VAn1GCDj2L/j4uJOZzNFRETOuO3bt2OMySywKcJaa+EMHEkbY4LwBvSn1to5vs37jTE1fPtrAAf8qct6hR97JCQklE2jRUREzpCEhAQKZtuxgIayH91tgHeBP6y1rxTYtQC4xff9LcD8smyHiIhIRVTWp7s7AcOAdcaYtb5tDwPPAzOMMbcDu4AbyrgdIiIiFU6ZhrS19nuKH6XdrSyfW0REpKLTjGMiIiIOpZAWERFxKIW0iIiIQymkRUREHEohLSIi4lAKaREREYdSSIuIiDiUQlpERMShFNIiIiIOpZAWERFxKIW0iIiIQymkRUREHEohLSIi4lAKaREREYdSSIuIiDiUQlpERMShFNIiIiIOpZAWERFxKIW0iIiIQymkRUREHEohLSIi4lAKaREREYdSSIuIiDiUQlpERMShFNIiIiIOpZAWERFxKIW0iIiIQymkRUREHEohLSIi4lAKaREREYdSSIuIiDiUQlpERMShFNIiIiIOpZAWERFxKIW0iIiIQymkRUREHEohLSIi4lAKaREREYdSSIuIiDiUQlpERMShFNIiIiIOpZAWERFxKIW0iIiIQymkRUREHEohLSIi4lAKaREREYdSSIuIiDiUQlpERMShFNIiIiIOpZAWERFxKIW0iIiIQymkRUREHEohLSIi4lAKaREREYdSSIuIiDiUQlpERMShFNIiIiIOpZAWERFxKIW0iIiIQymkRUREHEohLSIi4lAKaREREYdSSIuIiDiUQlpERMShFNIiIiIOpZAWERFxKIW0iIiIQymkRUREHEohLSIi4lAKaREREYdSSIuIiDiUQlpERMShFNIiIiIOpZAWERFxKIW0iIiIQymkRUREHEohLSIi4lAKaREREYdSSIuIiDiUQlpERMShFNIiIiIOpZAWERFxKIW0iIiIQymkRUREHEohLSIi4lAKaREREYdSSIuIiDiUQlpERMShFNIiIiIOpZAWERFxKIW0iIiIQymkRUREHEohLSIi4lAKaREREYdSSIuIiDiUQlpERMShFNIiIiIOpZAWERFxKIW0iIiIQxlrbXm34U+rUqWKrV+/frk89/bt20lISCiX5z7bqC9PH/Xl6aO+PH3UlyXbvn27PXz4cJEHzRU6pMuTMSbTWhte3u04G6gvTx/15emjvjx91Jd/nk53i4iIOJRCWkSkaKa8G3AWUV/+STrd/ScZY4xV550W6ktxIr0vTx/15Z+nI+kiGGOGG2N6l1RGb7jTR30pTqT35emjvvzzAsu7AeXNGLMDSAfcQL61tq1v10BjTHdgv7X2qQLlA4BVwG5rbW/ftvuBEYAF1gG3WmuzjTHDga7AUWAvEAQ0BwZaa3PPwMtzjJP7zRhTB/gIqA54gHestZPUZ8UzxlQCvgNC8H52Z1lrH/ftiwH+ibevLHAb0Bj1ZSGl9GN3YBIQAPzTWvu83pPFK+5zXGD/yZ/74agvT4mOpL26WmtbFghogMXW2nvxvoEKGg38cewfxphawH1AW2ttc7wf7htPqudOoLO19hHgJ6BZWbwIhzuh34B8YJy1tinQHrjHGHOBb5/6rGg5wBXW2ouAlkB3Y0x7375JwCJrbRPgIv7b1+rLworsR1+gTAF6ABcAg/WeLFVJn2Mo/LkH9eUpUUgXL9X39fhpGmNMbaAX3iOWggKBUGNMIBAG7CmwL8339aDvay7ev+DPGUX1m7V2r7V2te/7dLwf5Fq+3ed8nxXFemX4/hnke1hjTBTQGXjXVy7XWpviK6e+PElx/QhcDGyx1m7zHdVNA/r6yqkfi1DS57iE35fqy1OgkPZ+OJcYY/5jjLmjlLKvAQ/iPa3j/WFrdwMvA7vwnr5JtdYuKavGVlCF+q0gY0x9oBWw8sw1qWIyxgQYY9YCB4Cl1tqVQAO8v/DeN8asMcb80xije1JLUEw/1gISCxRL4r9/OEopivgcl/i5Fz9Za8/pB1DT9zUe+AXvKZiiyvUG3vR9fznwue/7WGAZUBXvX+TzgJvK+3U55VFcvxXYHwH8B7iuvNtakR5ADLAc7+WYtnhPO17i2zcJeKq821gRHif14w14r0Mf2zcMeKO821gRHid/jkv73Ovh/+OcP5K21u7xfT0AzMV7yqsonYBrfQPNpgFXGGM+Aa4EtltrD1pr84A5QMcyb3jFUVy/YYwJAmYDn1pr55RfEyse6z2d/Q3QHe8RX5L1Hg0CzAJal1PTKpQi+rFOgd21OfHSlRShmM9xsZ97OTXndEgbY8KNMZHHvgeuBn4rqqy19q/W2trW2vp4B4Yts9behPc0d3tjTJgxxgDdKDxQ4pxVXL/5+upd4A9r7Svl2sgKwhhT1TeKG2NMKN4/EDdYa/cBicaYxr6i3YDfy6mZjldcPwI/A+cZYxKMMcF4368Lyq+lzlfc57iE35dyis71W7CqAXO97zMCgc+stYtOpQJr7UpjzCxgNd5TjmuAd053Q89CnfCeTlznuzYI8LC19otybJPT1QA+9I1CdgEzrLWf+/aNAj71hcs24NZyamNFUGw/GmPuBRbjvUvjPWvt+vJrZoWgz3EZ04xjIiIiDnVOn+4WERFxMoW0iIiIQymkRUREHEohLSIi4lAKaREREYdSSIuIiDiUQlpERMShFNIi5cwY4zbGrDXG/GaMWVhgNqyavolySvv5jGK29ztp2cCiyvxijJn651p+evj7OkXORQppkfJ31HrXM28OHAHuAe+88tba6/8f9fbDuy5ykYwxTfH+DuhcnqtmnYbXKXLWUkiLOMu/+e96vPWNMb/5vg8zxswwxvxqjJlujFlpjGl77IeMMc/4jopXGGOqGWM6AtcCL/mO0hsW8VxDgI+BJb6yx+q6zxjzu++5pvm2RRhj3jfGrPNtH+DbfrUx5t/GmNXGmJnGmAjf9h3GmIm+7euMMU1827v42rPWt6xm5Emvs1KB51ljjOnq2z7cGDPHGLPIGLPZGPPiae53EUdSSIs4hG8u6W4UvajD3UCytfZC4CmgTYF94cAKa+1FwHfAX6y1P/rqecB3lL61iDoHAdOBqcDgAtsnAK18z3Wnb9ujeNdKb+HbvswYUwV4BLjSWtsaWAWMLVDPId/2t4Dxvm3jgXustS2By4CjJ7Xp2FmEFr42fWiMqeTb19LX5hbAIGNMHUTOcgppkfIX6luc4DAQBywtosyleJf8w1r7G/BrgX25wLGFNv4D1C/tCY0x7YCD1tqdwNdAa2NMrG/3r3gX67gJ76Ix4F0pasqxn7fWJgPt8Z5O/8HX/luAegWe5tiyhQXb9APwijHmPiDGWpvPiS7Fe3SPtXYDsBM437fva2ttqrU2G+8qX/UQOcsppEXK31HfkWU9IBjf0eRJTAk/n2f/u1KOG/9WtxsMNPGt97sViAIG+Pb1whvIbYD/GGMCfc9/8mo8BljqO1Jvaa29wFp7e4H9OSe3yVr7PDACCAVWHDsN7ufrzCnwvb+vU6RCU0iLOIS1NhW4DxhvjAk6aff3wEAA34jtFn5UmQ5EnrzRGOMCbgAutNbW96352xcY7NtXx1q7HHgQiAEi8F63vrdAHbHACqCTMaaRb1uYMeZ8SmCMaWitXWetfQHv6fGTQ/o7YKiv7PlAXWCjH69V5KykkBZxEGvtGuAX4MaTdr0JVDXG/Ao8hPeUdGop1U0DHvANwCo4cKwzsNtau7vAtu/wnrquBXxijFmHd230V621KcDTQKzvNrFfgK7W2oPAcGCqr10rKBy6JxtToI6jwJdFvM4A3/NPB4Zba3NOrkTkXKH1pEUqAN+gsiBrbbYvcL8GzrfW5pZz00SkDOmajkjFEAYs950GN8BdCmiRs5+OpEVERBxK16RFREQcSiEtIiLiUAppERERh1JIi4iIOJRCWkRExKEU0iIiIg71f63G9cW2Qv1/AAAAAElFTkSuQmCC\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(8, 8))\n", "_, ax, _ = exclusion_mask.plot()\n", "on_region.to_pixel(ax.wcs).plot(ax=ax, edgecolor=\"k\")\n", "plot_spectrum_datasets_off_regions(ax=ax, datasets=datasets)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Source statistic\n", "\n", "Next we're going to look at the overall source statistics in our signal region." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "datasets_all = Datasets(datasets)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "info_table = datasets_all.info_table(cumulative=True)" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/html": [ "Table length=4\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
namelivetimen_onbackgroundexcesssignificancebackground_rategamma_ratea_onn_offa_offalpha
s1 / s1 / s
str7float64int64float64float64float64float64float64float64int64float64float64
stacked1581.736758410930617213.5158.521.1081393207378380.0085349220900465020.100206307501657091.016212.00.08333333333333333
stacked3154.423482418060336531.999999999999993333.029.9338166900580160.010144484460745270.105566041419630481.038412.00.08333333333333333
stacked4732.54699993133551241.90243902439024470.0975609756098437.371273295857450.0088540988657900710.099332887973945211.079018.8533178114086160.05304106205619018
stacked6313.81164062023263652.54132791327913583.458672086720841.988553624767070.0083216495682658320.092409895210210171.0117322.3252827171796650.0447922659107239
" ], "text/plain": [ "\n", " name livetime n_on ... a_off alpha \n", " s ... \n", " str7 float64 int64 ... float64 float64 \n", "------- ------------------ ----- ... ------------------ -------------------\n", "stacked 1581.7367584109306 172 ... 12.0 0.08333333333333333\n", "stacked 3154.4234824180603 365 ... 12.0 0.08333333333333333\n", "stacked 4732.546999931335 512 ... 18.853317811408616 0.05304106205619018\n", "stacked 6313.811640620232 636 ... 22.325282717179665 0.0447922659107239" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "info_table" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.plot(\n", " info_table[\"livetime\"].to(\"h\"), info_table[\"excess\"], marker=\"o\", ls=\"none\"\n", ")\n", "plt.xlabel(\"Livetime [h]\")\n", "plt.ylabel(\"Excess\");" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.plot(\n", " info_table[\"livetime\"].to(\"h\"),\n", " info_table[\"significance\"],\n", " marker=\"o\",\n", " ls=\"none\",\n", ")\n", "plt.xlabel(\"Livetime [h]\")\n", "plt.ylabel(\"Significance\");" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "datasets[0].peek()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Finally you can write the extrated datasets to disk using the OGIP format (PHA, ARF, RMF, BKG, see [here](https://gamma-astro-data-formats.readthedocs.io/en/latest/spectra/ogip/index.html) for details):" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [], "source": [ "path = Path(\"spectrum_analysis\")\n", "path.mkdir(exist_ok=True)" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [], "source": [ "for dataset in datasets:\n", " dataset.to_ogip_files(outdir=path, overwrite=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If you want to read back the datasets from disk you can use:" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [], "source": [ "datasets = []\n", "for obs_id in obs_ids:\n", " filename = path / f\"pha_obs{obs_id}.fits\"\n", " datasets.append(SpectrumDatasetOnOff.from_ogip_files(filename))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Fit spectrum\n", "\n", "Now we'll fit a global model to the spectrum. First we do a joint likelihood fit to all observations. If you want to stack the observations see below. We will also produce a debug plot in order to show how the global fit matches one of the individual observations." ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [], "source": [ "spectral_model = PowerLawSpectralModel(\n", " index=2, amplitude=2e-11 * u.Unit(\"cm-2 s-1 TeV-1\"), reference=1 * u.TeV\n", ")\n", "model = SkyModel(spectral_model=spectral_model)\n", "\n", "for dataset in datasets:\n", " dataset.models = model\n", "\n", "fit_joint = Fit(datasets)\n", "result_joint = fit_joint.run()\n", "\n", "# we make a copy here to compare it later\n", "model_best_joint = model.copy()\n", "model_best_joint.spectral_model.parameters.covariance = (\n", " result_joint.parameters.covariance\n", ")" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "OptimizeResult\n", "\n", "\tbackend : minuit\n", "\tmethod : minuit\n", "\tsuccess : True\n", "\tmessage : Optimization terminated successfully.\n", "\tnfev : 48\n", "\ttotal stat : 122.01\n", "\n" ] } ], "source": [ "print(result_joint)" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(0.1, 40)" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(8, 6))\n", "ax_spectrum, ax_residual = datasets[0].plot_fit()\n", "ax_spectrum.set_ylim(0.1, 40)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Compute Flux Points\n", "\n", "To round up our analysis we can compute flux points by fitting the norm of the global model in energy bands. We'll use a fixed energy binning for now:" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [], "source": [ "e_min, e_max = 0.7, 30\n", "e_edges = np.logspace(np.log10(e_min), np.log10(e_max), 11) * u.TeV" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we create an instance of the `~gammapy.spectrum.FluxPointsEstimator`, by passing the dataset and the energy binning:" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/adonath/github/adonath/astropy/astropy/units/quantity.py:481: RuntimeWarning: invalid value encountered in true_divide\n", " result = super().__array_ufunc__(function, method, *arrays, **kwargs)\n" ] } ], "source": [ "fpe = FluxPointsEstimator(datasets=datasets, e_edges=e_edges)\n", "flux_points = fpe.run()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here is a the table of the resulting flux points:" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "text/html": [ "Table length=10\n", "
\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
e_refe_mine_maxref_dnderef_fluxref_efluxref_e2dndenormstatnorm_errcounts [4]norm_errpnorm_errnnorm_ulsqrt_tstsnorm_scan [11]stat_scan [11]dndednde_uldnde_errdnde_errpdnde_errn
TeVTeVTeV1 / (cm2 s TeV)1 / (cm2 s)TeV / (cm2 s)TeV / (cm2 s)1 / (cm2 s TeV)1 / (cm2 s TeV)1 / (cm2 s TeV)1 / (cm2 s TeV)1 / (cm2 s TeV)
float64float64float64float64float64float64float64float64float64float64int64float64float64float64float64float64float64float64float64float64float64float64float64
0.8590.7371.0024.040e-111.077e-119.177e-122.982e-110.96918.3940.08749 .. 310.0890.0841.15220.687427.9480.200 .. 5.000188.879 .. 679.4413.915e-114.655e-113.502e-123.607e-123.400e-12
1.2611.0021.5881.488e-118.850e-121.095e-112.368e-110.97212.5850.09131 .. 360.0930.0881.16420.513420.7860.200 .. 5.000172.382 .. 615.3051.447e-111.733e-111.349e-121.391e-121.308e-12
1.8521.5882.1605.482e-123.151e-125.786e-121.881e-111.1969.9480.14927 .. 120.1560.1431.51915.286233.6710.200 .. 5.000115.964 .. 242.7346.559e-128.329e-128.190e-138.527e-137.861e-13
2.5182.1602.9362.466e-121.927e-124.811e-121.564e-111.2519.1750.17710 .. 110.1850.1691.63813.945194.4710.200 .. 5.00096.552 .. 175.1893.085e-124.038e-124.361e-134.573e-134.156e-13
3.6972.9364.6569.082e-131.583e-125.741e-121.242e-110.93914.9410.15817 .. 110.1670.1501.28910.703114.5480.200 .. 5.00060.497 .. 213.6798.529e-131.171e-121.436e-131.517e-131.359e-13
5.4294.6566.3303.345e-135.637e-133.034e-129.860e-121.1368.7590.2669 .. 50.2860.2461.7498.14166.2710.200 .. 5.00038.262 .. 80.9483.799e-135.852e-138.896e-149.574e-148.246e-14
7.9716.33010.0371.232e-134.630e-133.620e-127.829e-121.04012.4160.2745 .. 40.2970.2521.6806.85546.9950.200 .. 5.00032.845 .. 80.5281.281e-132.070e-133.378e-143.659e-143.110e-14
11.70310.03713.6474.539e-141.649e-131.913e-126.217e-120.90210.5000.4300 .. 10.4910.3742.0093.36911.3510.200 .. 5.00015.516 .. 38.2724.094e-149.118e-141.951e-142.227e-141.697e-14
17.18313.64721.6361.672e-141.354e-132.282e-124.936e-120.3107.2060.2811 .. 00.3580.3101.1850.9530.9080.200 .. 5.0007.390 .. 42.9775.189e-151.980e-144.705e-155.977e-155.189e-15
25.22921.63629.4196.158e-154.822e-141.206e-123.920e-120.0000.5240.0010 .. 00.2770.0001.1080.0030.0000.200 .. 5.0001.246 .. 18.5691.099e-206.825e-158.662e-181.706e-151.099e-20
" ], "text/plain": [ "\n", " e_ref e_min e_max ... dnde_err dnde_errp dnde_errn \n", " TeV TeV TeV ... 1 / (cm2 s TeV) 1 / (cm2 s TeV) 1 / (cm2 s TeV)\n", "float64 float64 float64 ... float64 float64 float64 \n", "------- ------- ------- ... --------------- --------------- ---------------\n", " 0.859 0.737 1.002 ... 3.502e-12 3.607e-12 3.400e-12\n", " 1.261 1.002 1.588 ... 1.349e-12 1.391e-12 1.308e-12\n", " 1.852 1.588 2.160 ... 8.190e-13 8.527e-13 7.861e-13\n", " 2.518 2.160 2.936 ... 4.361e-13 4.573e-13 4.156e-13\n", " 3.697 2.936 4.656 ... 1.436e-13 1.517e-13 1.359e-13\n", " 5.429 4.656 6.330 ... 8.896e-14 9.574e-14 8.246e-14\n", " 7.971 6.330 10.037 ... 3.378e-14 3.659e-14 3.110e-14\n", " 11.703 10.037 13.647 ... 1.951e-14 2.227e-14 1.697e-14\n", " 17.183 13.647 21.636 ... 4.705e-15 5.977e-15 5.189e-15\n", " 25.229 21.636 29.419 ... 8.662e-18 1.706e-15 1.099e-20" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "flux_points.table_formatted" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we plot the flux points and their likelihood profiles. For the plotting of upper limits we choose a threshold of TS < 4." ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(8, 5))\n", "flux_points.table[\"is_ul\"] = flux_points.table[\"ts\"] < 4\n", "ax = flux_points.plot(\n", " energy_power=2, flux_unit=\"erg-1 cm-2 s-1\", color=\"darkorange\"\n", ")\n", "flux_points.to_sed_type(\"e2dnde\").plot_ts_profiles(ax=ax)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The final plot with the best fit model, flux points and residuals can be quickly made like this: " ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [], "source": [ "flux_points_dataset = FluxPointsDataset(\n", " data=flux_points, models=model_best_joint\n", ")" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(8, 6))\n", "flux_points_dataset.peek();" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Stack observations\n", "\n", "And alternative approach to fitting the spectrum is stacking all observations first and the fitting a model. For this we first stack the individual datasets:" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [], "source": [ "dataset_stacked = Datasets(datasets).stack_reduce()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Again we set the model on the dataset we would like to fit (in this case it's only a single one) and pass it to the `~gammapy.modeling.Fit` object:" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [], "source": [ "dataset_stacked.models = model\n", "stacked_fit = Fit([dataset_stacked])\n", "result_stacked = stacked_fit.run()\n", "\n", "# make a copy to compare later\n", "model_best_stacked = model.copy()\n", "model_best_stacked.spectral_model.parameters.covariance = (\n", " result_stacked.parameters.covariance\n", ")" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "OptimizeResult\n", "\n", "\tbackend : minuit\n", "\tmethod : minuit\n", "\tsuccess : True\n", "\tmessage : Optimization terminated successfully.\n", "\tnfev : 26\n", "\ttotal stat : 29.05\n", "\n" ] } ], "source": [ "print(result_stacked)" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [ { "data": { "text/html": [ "Table length=3\n", "
\n", "\n", "\n", "\n", "\n", "\n", "
namevalueerrorunitminmaxfrozen
str9float64float64str14float64float64bool
index2.600e+005.888e-02nannanFalse
amplitude2.723e-111.240e-12cm-2 s-1 TeV-1nannanFalse
reference1.000e+000.000e+00TeVnannanTrue
" ], "text/plain": [ "\n", " name value error unit min max frozen\n", " str9 float64 float64 str14 float64 float64 bool \n", "--------- --------- --------- -------------- ------- ------- ------\n", " index 2.600e+00 5.888e-02 nan nan False\n", "amplitude 2.723e-11 1.240e-12 cm-2 s-1 TeV-1 nan nan False\n", "reference 1.000e+00 0.000e+00 TeV nan nan True" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model_best_joint.parameters.to_table()" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [ { "data": { "text/html": [ "Table length=3\n", "
\n", "\n", "\n", "\n", "\n", "\n", "
namevalueerrorunitminmaxfrozen
str9float64float64str14float64float64bool
index2.601e+005.887e-02nannanFalse
amplitude2.723e-111.240e-12cm-2 s-1 TeV-1nannanFalse
reference1.000e+000.000e+00TeVnannanTrue
" ], "text/plain": [ "\n", " name value error unit min max frozen\n", " str9 float64 float64 str14 float64 float64 bool \n", "--------- --------- --------- -------------- ------- ------- ------\n", " index 2.601e+00 5.887e-02 nan nan False\n", "amplitude 2.723e-11 1.240e-12 cm-2 s-1 TeV-1 nan nan False\n", "reference 1.000e+00 0.000e+00 TeV nan nan True" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model_best_stacked.parameters.to_table()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Finally, we compare the results of our stacked analysis to a previously published Crab Nebula Spectrum for reference. This is available in `~gammapy.spectrum`." ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plot_kwargs = {\n", " \"energy_range\": [0.1, 30] * u.TeV,\n", " \"energy_power\": 2,\n", " \"flux_unit\": \"erg-1 cm-2 s-1\",\n", "}\n", "\n", "# plot stacked model\n", "model_best_stacked.spectral_model.plot(\n", " **plot_kwargs, label=\"Stacked analysis result\"\n", ")\n", "model_best_stacked.spectral_model.plot_error(**plot_kwargs)\n", "\n", "# plot joint model\n", "model_best_joint.spectral_model.plot(\n", " **plot_kwargs, label=\"Joint analysis result\", ls=\"--\"\n", ")\n", "model_best_joint.spectral_model.plot_error(**plot_kwargs)\n", "\n", "create_crab_spectral_model(\"hess_pl\").plot(\n", " **plot_kwargs, label=\"Crab reference\"\n", ")\n", "plt.legend()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Exercises\n", "\n", "Now you have learned the basics of a spectral analysis with Gammapy. To practice you can continue with the following exercises:\n", "\n", "- Fit a different spectral model to the data.\n", " You could try `~gammapy.modeling.models.ExpCutoffPowerLawSpectralModel` or `~gammapy.modeling.models.LogParabolaSpectralModel`.\n", "- Compute flux points for the stacked dataset.\n", "- Create a `~gammapy.spectrum.FluxPointsDataset` with the flux points you have computed for the stacked dataset and fit the flux points again with obe of the spectral models. How does the result compare to the best fit model, that was directly fitted to the counts data?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## What next?\n", "\n", "The methods shown in this tutorial is valid for point-like or midly extended sources where we can assume that the IRF taken at the region center is valid over the whole region. If one wants to extract the 1D spectrum of a large source and properly average the response over the extraction region, one has to use a different approach explained in [the extended source spectral analysis tutorial](extended_source_spectral_analysis.ipynb)." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "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": "3.7.0" }, "nbsphinx": { "orphan": true } }, "nbformat": 4, "nbformat_minor": 4 }