{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Fitting the Hipparcos-Gaia Catalog of Acclerations (HGCA)\n", "\n", "Jason Wang (2023)\n", "\n", "We will demonstrate how to fit the stellar absolute astrometry from the [Hipparcos-Gaia Catalog of Accelerations (HGCA; Brandt 2021)](https://ui.adsabs.harvard.edu/abs/2021ApJS..254...42B/abstract) to help constrain the mass and orbital parameters of the companion. The implementation of fitting the HGCA here is based on [Brandt et al. 2019](https://ui.adsabs.harvard.edu/abs/2019AJ....158..140B/abstract), but utilizes the DR3 version of the HGCA. \n", "\n", "## Difference to fitting IAD directly\n", "\n", "This method is an alternative to fitting the Hipparcos IAD and Gaia astrometry. Instead of fitting the individual epochs of Hipparcos data, which also includes needing to fit for the position, proper motion, and parallax of the star, we are only fitting for two differential proper motions: the proper motion difference between Hipparcos and the change in position between Hipparcos and Gaia; the proper motion difference between Gaia and the change in position between Hipparcos and Gaia. This simplifies the fit, but also ignores any detectable curvature in the stellar astrometry seen by Hipparcos. For planet companion cases, the acceleration should be well within the noise of the Hipparcos measurements. \n", "\n", "The benefit of using this technique is that the errors should be more robust. The HGCA catalog inflates the error bars from the Hipparcos and Gaia measurements on a global scale to match the true observed scatter in the data. Additionally, there are bad epochs in the Hipparcos IAD that may not be removed. \n", "\n", "We encourage users to try both to see how similar or different the results are, as the two methods utilize the same underlying data. \n", "\n", "\n", "## Obtain the necessary data\n", "\n", "While `orbitize!` will automatically download the HGCA catalog, you need to obtain two other data files to reconstruct the Hipparcos and Gaia observations for your star. These files tell us when and in what orientation did Hipparcos and Gaia take data of the star for the forward modeling that happens in `orbitize!`. \n", "\n", " 1. The Hipparcos IAD file of the star. Follow the section in the [Hipparocs IAD tutorial](https://orbitize.readthedocs.io/en/latest/tutorials/Hipparcos_IAD.html#Part-1:-Obtaining-the-IAD) on how to obtain this file for your star of interest.\n", " 2. The anticipated Gaia epochs and scan directions in a CSV file that is obtained from the [Gaia Observation Forecast Tool (GOST)](https://gaia.esac.esa.int/gost/). For GOST, after entering the target name and resolving its coordinates, use 2014-07-26T00:00:00 as the start date. For the end date, use 2016-05-23T00:00:00 for DR2 and 2017-05-28T00:00:00 for EDR3. You probably will be fitting the EDR3. The output CSV file is all you need. \n", "\n", "## Setup the `HGCALogProb` Object\n", "\n", "The user just needs to setup the `orbitize.gaia.HGCALogProb` object, which will handle the rest of the HGCA modeling under the hood. To setup the `HGCALogProb` object, we also need to create an `orbitize.hipparcos.HipparcosLogProb` instance, but it will not actually be used in the fitting. It is simply used to handle reading in the Hipparcos IAD to maximize code reuse. \n", "\n", "In the example below, we setup the HGCA fitting for beta Pictoris (HIP 27321).\n" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Using HGCA catalog stored in /home/sblunt/Projects/orbitize/orbitize/example_data/HGCA_vEDR3.fits\n" ] } ], "source": [ "import os\n", "from orbitize import DATADIR, hipparcos, gaia\n", "\n", "# the necessary input data for beta Pic is part of the orbitize! example data!\n", "iad_filepath = os.path.join(DATADIR, \"HIP027321.d\")\n", "gost_filepath = os.path.join(DATADIR, \"gaia_edr3_betpic_epochs.csv\")\n", "\n", "# Create the HGCA and helper Hipparcos object\n", "# we're just going to assume one planet for simplicity\n", "hipparcos_lnprob = hipparcos.HipparcosLogProb(iad_filepath, 27321, 1)\n", "hgca_lnprob = gaia.HGCALogProb(27321, hipparcos_lnprob, gost_filepath)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Setup orbit fit\n", "\n", "After you do this step, the rest of the orbit fit setup is basically the same as a standard `orbitize!` orbit fit. The only differences is that you need to pass the `HGCALogProb` object into the system class (but not the `HipparcosLogProb` because the Hipparcos data is already being handled in HGCA). We also recommend you explicitly set up the system to fit for the dynamical mass of the companions in the system (this should happen automatically with the inclusion of HGCA data, but we recommend being explicit so it is clear what you are fitting for).\n", "\n", "We will also note that that the default prior on the companion mass is a log-uniform prior between 1e-6 and 2 solar masses. If you want a more restricted mass, now is also the time to adjust that. We present one example here, but refer to the Modifying Priors tutorial for further details. " ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "from orbitize import read_input, system, priors\n", "import astropy.units as u\n", "\n", "# read in relative astrometry\n", "astrometry_filepath = os.path.join(DATADIR, \"betaPic.csv\")\n", "data_table = read_input.read_file(astrometry_filepath)\n", "\n", "# set up the system, passing in hgca_lnprob and setting it fit dynamical mass\n", "stellar_mass = 1.75\n", "stellar_mass_err = 0.05\n", "plx = 51.44\n", "plx_err = 0.12\n", "\n", "this_system = system.System(\n", " 1,\n", " data_table,\n", " stellar_mass,\n", " plx,\n", " mass_err=stellar_mass_err,\n", " plx_err=plx_err,\n", " fit_secondary_mass=True,\n", " gaia=hgca_lnprob,\n", ")\n", "\n", "# adjust the prior on mass to be log uniform between 1 and 50 Jupiter masses\n", "mjup = u.Mjup.to(u.Msun)\n", "this_system.sys_priors[this_system.param_idx[\"m1\"]] = priors.LogUniformPrior(\n", " mjup, 50 * mjup\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Run orbitize! \n", "\n", "From here onwards, everything is the same as an regular `orbitize` fit, so we refer to reader to the other tutorials. We recommend using the MCMC sampler, since the orbits are generally too constrained for OFTI. To pick the right number of temperatures, walkers, steps for MCMC, check out papers that performed similar fits (e.g., Section 3.1 of [GRAVITY Collaboration et al. (2020)](https://ui.adsabs.harvard.edu/abs/2020A%26A...633A.110G/abstract) and Section 3.1 of [Hinkley et al. (2023)](https://ui.adsabs.harvard.edu/abs/2023A%26A...671L...5H/abstract))." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Starting Burn in\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/sblunt/Projects/orbitize/orbitize/priors.py:354: RuntimeWarning: invalid value encountered in log\n", " lnprob = -np.log((element_array*normalizer))\n", "/home/sblunt/Projects/orbitize/orbitize/priors.py:463: RuntimeWarning: invalid value encountered in log\n", " lnprob = np.log(np.sin(element_array)/normalization)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Burn in complete. Sampling posterior now.\n", "100/100 steps completed\n", "Run complete\n" ] } ], "source": [ "from orbitize import sampler\n", "\n", "# MCMC parameters\n", "# for demonstration purposes only. You will need to increase these likely\n", "n_temps = 2\n", "n_walkers = 50\n", "n_threads = 1\n", "burn_steps = 1\n", "total_orbits = 100 * n_walkers\n", "\n", "# create the sampler, run it, and save posteriors\n", "this_sampler = sampler.MCMC(this_system, n_temps, n_walkers, n_threads)\n", "\n", "this_sampler.run_sampler(total_orbits, burn_steps=burn_steps, thin=10)\n", "\n", "this_sampler.results.save_results(\"demo_hgca.hdf5\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Plot proper motions from the orbit fit over the HGCA observations\n", "\n", "William Balmer (2023)\n", "\n", "Now, you can plot the result of your fit using the `orbitize.plot.plot_propermotion` function directly, or it's wrapper within the `sampler.results` object. The function is similar to `orbitize.plot.plot_orbits` that you may already be familiar with. These plots show the H-G proper motion (with a large x-axis error bar) in addition to the two \"real\" proper motion measurements from Hipparchos and Gaia.\n", "\n", "Note that the HGCALogprob fits for the time-averaged proper motions, and not the instantaneous proper motions that are shown in this visualization. This is a subtle point, but important, because the data points show over the orbits here are not exactly what is driving the MCMC solver towards the most likely orbits." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Important Note of Caution: the orbitize! implementation of the HGCA \n", " fits for the time-averaged proper motions, and not the instantaneous proper \n", " motions that are being plotted here. This plot is provided only for the \n", " purpose of an approximate check on the fit.\n" ] }, { "data": { "image/png": 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4L8gaRyGEEEKIHUhz+x60TThgg9tgweR77Xvf+96Atj3XX389Bx54IN/61rcYP348mUyG008/nXw+v+0OUoga27Y5+OCDB6zfj+OYBQsWbHT9/vTp0wfsDzB//vz6/p/61Kd46aWXWLx4cf02duxYrrrqKp544on6GJtba+C9IBlHIYQQQgixzbz22mv85Cc/4ec//zmFQoELL7yQz3zmM/zoRz/a1ocmtqmhmaoKmzfmrFmzOPfccznkkEM49NBDuf322ymXy5x//vkAnHPOOYwbN465c+cCcPnll/PhD3+Yr3/96xx33HE89NBDvPDCC9x3330AjBgxghEjRgx4DsuyaG9vZ8899wTeWa2BbUECRyGEEEIIsVU8/vjj5HK5AduiKNrkY1zX5fvf/z7jxo0D4I477uC4447j61//urTzEdvcGWecwfr165kzZw4dHR0ceOCBzJs3r14AZ8WKFQP6hh5++OE8+OCDXHvttVxzzTVMmTKFRx99lH333Xeznvftag1sCxI4CiGEEEKIreKII47gnnvuGbDt2Wef5eyzz97oYyZMmFAPGiGZphfHMUuWLJHAcSc21MVxNsell17KpZdeOuh9Tz755AbbTjvtNE477bR3PP6yZcs22PZ2tQa2BQkchRBCCCHEVpHNZpk8efKAbatWrdpGRyO2Z0NdHEdsPgkchRBCCCF2IH0df9+s7dvaihUrWLNmTX3t1jPPPIOu6/X1XkKI4UECRyGEEEKIHUBbWxvpdIYnH/i3je6TTmdoa2t7D4/q7aVSKc4991z+8z//k0KhwGWXXcbpp59en6b6yCOPMHv27I02XBdCvDckcBRCCCGE2AFMmDCBV199ha6uLvxQccvPCgB84ZRGbDNpNN7W1rbNejhuzOTJkznllFM49thj6enp4fjjj+fuu++u35/P51myZMk2PEKxLQynNY4iIYGjEEIIIcQOYsKECUyYMAEvULS90AvA+97XgmNpQ/7c3/ve9wbd/pGPfKT+Yf28887jvPPO22CfSy65hEsuuWTQx2/sMWLHJoHj8COBoxBCCCHEDsaxNL79mdZtfRhCbDnF5rZcfOfjii2iv/0uQgghhBBCCCF2ZhI4CiGEEEKIbeL6669n8eLF2/owxDCkhvAmtoxMVRVCCCGEEEIMKypWqHgI1jgOwZg7CwkchRBCCCGEEMOKUsltKMYVW0amqgohhBBCCCGE2CQJHIUQQgghhBBCbJJMVRVCCCGE2MF4fsxn5i4B4O7Ze+LYkisQ2xfp4zj8SOAohBBCCCGEGF5kkeOwI5efhBBCCCGEEEJskgSOQgghhBA7oNWvP82j95zMggW/fk+e77zzzuOkk07aYPuTTz6Jpmn09fVt8vF/+tOfOOOMMxgzZgyO47Drrrty/PHH8/Of/1ymF+6E+hOOQ3ETW0YCRyGEEEKIHYxSihd/ewc965Zw3XVzhn3g9b//+78cdthhlEolHnjgAV555RXmzZvHySefzLXXXks+n9/WhyjETm+7DxxvvvlmNE3jiiuuqG9zXZfPfvazjBgxglwux6mnnsq6des2OY5Sijlz5jBmzBjS6TQzZszgH//4xxAfvRBCCCHEu+P58Qa3X/7fE6xf9Wf2OexcXnj+WX7xi3kb7DNclMtlLrzwQo477jh+8YtfcNRRR7Hbbrux1157ceGFF/LnP/+ZpqambX2Y4j3WXxxnKG5iy2zXxXGef/55vvWtb7H//vsP2H7llVfyi1/8gocffpimpiYuvfRSTjnlFP74xz9udKyvfvWrfPOb3+SBBx5g0qRJfOlLX2LmzJn87W9/I5VKDfWpCCGEEEJskf7qqf2UUjx+/zWM3OUADp15NZ2rFnPBJbM5/sJd0TStvt/91+31Xh/qoH71q1/R3d3N1VdfvdF93nzcYucgtXGGn+0241gqlfjkJz/Jt7/9bVpaWurb8/k8999/P7feeisf/ehHOfjgg/nud7/L008/zTPPPDPoWEopbr/9dq699lpOPPFE9t9/f77//e+zZs0aHn300Y0eg+d5FAqFATchhBBCiG1p9et/ZP2qP/O+j3wWTdN430c+y/pVf2b16xu/gL61PP744+RyuQG3Y445ZpOP+fvf/w7AnnvuWd/2/PPPDxjj8ccfH9LjFsORGsKb2BLbbcbxs5/9LMcddxwzZszgpptuqm9ftGgRQRAwY8aM+rapU6cyYcIEFi5cyGGHHbbBWEuXLqWjo2PAY5qampg2bRoLFy7kzDPPHPQY5s6dy5e//OWteFZCCCGEEJvn7tlvBFxKKT70ofMZPf4Axu3+AQDG7f4BRo8/gJ4l9/O//3XBkGbvjjjiCO65554B25599lnOPvtsAHK5XH372Wefzb333jvoOPvvvz+LFy8GYMqUKYRhODQHLIYtyTgOP9tl4PjQQw/x4osv8vzzz29wX0dHB7Zt09zcPGD76NGj6ejoGHS8/u2jR49+x48BmD17NrNmzap/XSgUGD9+/Ds9DSGEEEKId82x35hA9sQTT/DC889y1Nn31QNETdM44MOf5Vc/vJjfPflrZs6cOWTHks1mmTx58oBtq1atqv+7PxgEaGxsBJLAEGDJkiX1C/yO42wwjhBi29ruAseVK1dy+eWXM3/+/G2+9tBxHBzH2abHIIQQQggB/YX+rqOxdQKpTAtda/5Wvy+VaaGxdQJz5lzHUUcdtc3WDA4WDB511FG0trZyyy238Mgjj2yDoxLD0VAVspHiOFtuuwscFy1aRGdnJwcddFB9WxRF/P73v+fOO+/kiSeewPd9+vr6BmQd161bR3t7+6Bj9m9ft24dY8aMGfCYAw88cEjOQwghhBBia/J9n5WrVlHoWc1j95026D6rVsX4vj+sLnzncjn+67/+izPOOIPjjjuOyy67jClTplAqlZg3bx4AhmFs46MUQmx3geORRx7Jyy+/PGDb+eefz9SpU/nCF77A+PHjsSyLBQsWcOqppwLJ1IcVK1Ywffr0QcecNGkS7e3tLFiwoB4oFgoFnn32WS655JIhPR8hhBBCiK3BcRyefWYh69evxw9i5n5nOQCzL9gV20qms44aNWpYBY39Tj75ZJ5++mluueUWzjnnHHp6emhqauKQQw7hoYce4vjjj9/WhyjeY7LGcfjZ7qqqNjQ0sO+++w64ZbNZRowYwb777ktTUxMXXnghs2bN4re//S2LFi3i/PPPZ/r06QMK40ydOrU+HaK/D+RNN93EY489xssvv8w555zD2LFjOemkk7bRmQohxNApFotcccUV7LrrrqTTaQ4//PAB68bPO+88NE0bcDv66KPfdty77rqLiRMnkkqlmDZtGs8999xQnoYQ4i3Gjx/PQQcdxPvedxBtY/embezevO99B3HQQcltl112GbLn/t73vjdoNfqPfOQjKKU2qD/xVocccggPP/ww69atIwgCurq6mDdvHmeccYa049gJqVgN2W1zbe7ftocffpipU6eSSqXYb7/9+OUvfzng/uuvv56pU6eSzWZpaWlhxowZPPvsswP2mThx4gZ/h2+++ebNPvatabvLOL4Tt912G7quc+qpp+J5HjNnzuTuu+8esM+SJUvI5/P1r6+++mrK5TIXX3wxfX19fPCDH2TevHnbfB2lEEIMhf/3//4ff/nLX/jBD37A2LFj+eEPf8iMGTP429/+xrhx4wA4+uij+e53v1t/zNtlKX784x8za9Ys7r33XqZNm8btt9/OzJkzWbJkCaNGjRrS8xFCDOTY+rDp0yjE9mxz/7Y9/fTTnHXWWcydO5fjjz+eBx98kJNOOokXX3yRfffdF4A99tiDO++8k912241qtcptt93GUUcdxWuvvcbIkSPrY91www1cdNFF9a8bGhqG/oQ3QVOyQnSrKRQKNDU10dnZzciRrdv6cIQQO5j+3zH5fL5ejXBLVKtVGhoa+N///V+OO+64+vaDDz6YY445hptuuonzzjuPvr6+Tfayfatp06bx/ve/nzvvvBOAOI4ZP348n/vc5/jiF7846GM8z8PzvPrX/dWp163rYtSoEVt2gkJsx1zXZenSpUyaNEkuXr8Lm3odt9bvUjE0+t+f++/7PZlM7u0fsJkqlRIXXvwvrFy5csD7v7Gil5v7t+2MM86gXC4P6D162GGHceCBB260/Uz/Of/617/myCOPBJKM4xVXXMEVV1zxbk53q9rupqoKIYR4d8IwJIqiDT5MpdNp/vCHP9S/fvLJJxk1ahR77rknl1xyCd3d3Rsd0/d9Fi1aNKAfrq7rzJgxg4ULF270cXPnzqWpqal+k5ZGQgghABSqXll1q95Icmbjx48f8Pdn7ty5GxzDlvxtW7hw4YD9AWbOnLnR/X3f57777qOpqYkDDjhgwH0333wzI0aM4H3vex9f+9rXtnk/0x1yqqoQQoiNa2hoYPr06dx4443stddejB49mv/+7/9m4cKF9VL5Rx99NKeccgqTJk3i9ddf55prruGYY45h4cKFg1Y37OrqIoqiQfvhvvrqqxs9FumHK4QQYjBDXRxnsIzjW23J37aOjo531Bv+8ccf58wzz6RSqTBmzBjmz59PW1tb/f7LLruMgw46iNbWVp5++mlmz57N2rVrufXWWzfrfLcmCRyFEGIn9IMf/IALLriAcePGYRgGBx10EGeddRaLFi0C4Mwzz6zvu99++7H//vuz++678+STT9an0WwN0g9XCCHEYIa6j2NjY+M2nap8xBFHsHjxYrq6uvj2t7/N6aefzrPPPltfN/nmi6r7778/tm3z6U9/mrlz526zv5syVVUIIXZCu+++O7/73e8olUqsXLmS5557jiAI2G233Qbdf7fddqOtrY3XXntt0Pvb2towDIN169YN2L6pHrpCCCHEcLYlf9va29vf0f7ZbJbJkydz2GGHcf/992OaJvfff/9Gj2XatGmEYciyZcu27GS2AgkchRBiJ5bNZhkzZgy9vb088cQTnHjiiYPut2rVKrq7uxkzZsyg99u2zcEHH8yCBQvq2+I4ZsGCBRvtoSuEEEIMZ1vyt2369OkD9geYP3/+2/4tjON4QLG4t1q8eDG6rm/TKuUyVVUIIXZCTzzxBEop9txzT1577TWuuuoqpk6dyvnnn0+pVOLLX/4yp556Ku3t7bz++utcffXVTJ48mZkzZ9bHOPLIIzn55JO59NJLgWRazbnnnsshhxzCoYceyu233065XOb888/fVqcpxE7L80I+fckTAHzrnpk4jnzkE9uZIZqqurkLJ9/ub9s555zDuHHj6sV1Lr/8cj784Q/z9a9/neOOO46HHnqIF154gfvuuw+AcrnMV77yFT7+8Y8zZswYurq6uOuuu1i9ejWnnXYakBTYefbZZzniiCNoaGhg4cKFXHnllZx99tm0tLRsxRdj88hvESGE2Anl83lmz57NqlWraG1t5dRTT+UrX/kKlmURhiEvvfQSDzzwAH19fYwdO5ajjjqKG2+8ccC6itdff52urq7612eccQbr169nzpw5dHR0cOCBBzJv3rwNigQIIYQQb2eoi+O8U2/3t23FihXo+huTOA8//HAefPBBrr32Wq655hqmTJnCo48+Wu/haBgGr776Kg888ABdXV2MGDGC97///Tz11FPss88+QLL+/6GHHuL666/H8zwmTZrElVdeOWDd47YgfRy3IunjKIQYSjtD77H+c5Q+jmJntbX6OFYqHqeceiuVai/XXjOTI4/8yKAVkYej733ve1xxxRX09fVt8RjSx3H71f/+fOvu35JOb/0+jtVqiU9/5gh5/7eArHEUQgghhNiB/OxnP2Pq1D14Yt41PPW7rzFz5gx22213fvaznw35c3d0dHD55ZczefJkUqkUo0eP5gMf+AD33HMPlUrlHY1xxhln8Pe//32Ij1QIsblkqqoQQgghxA7iZz/7GZ/4xCfYZfz7Ofa4y2hu2ZW+3uW8/PJP+MQnPsFPf/pTTjnllCF57n/+85984AMfoLm5mf/4j/9gv/32w3EcXn75Ze677z7GjRvHxz/+8bcdJ51Ok06nh+QYxfZjqNtxiM0nGUchhBBCiB1AFEVceeUsdhn/fo746LWMHDUVy0ozctRUjvjotewy/v1ceeXniaJoSJ7/M5/5DKZp8sILL3D66aez1157sdtuu3HiiSfyi1/8ghNOOAGAW2+9lf32249sNsv48eP5zGc+Q6lUqo/zve99j+bm5vrXr7/+OieeeCKjR48ml8vx/ve/n1//+tdDcg5i+Ohf4zgUN7FlJHAUQgghhNgBPPXUU6xYsZz99jsdTRv4EU/TdPbd7zRWrFjGU089tdWfu7u7m1/96ld89rOfJZvNDrqPpmkA6LrON7/5Tf7617/ywAMP8Jvf/Iarr756o2OXSiWOPfZYFixYwJ/+9CeOPvpoTjjhBFasWLHVz0MMIxI5DjsSOAohhBBC7ADWrl0LQHPLroPe31Lb3r/f1vTaa6/VW/y8WVtbG7lcjlwuxxe+8AUArrjiCo444ggmTpzIRz/6UW666SZ+8pOfbHTsAw44gE9/+tPsu+++TJkyhRtvvJHdd9+dxx57bKufhxg+1BDexJaRwFEIIYQQYgcwZswYAPp6lw96f29te/9+74XnnnuOxYsXs88++9Sbm//617/myCOPZNy4cTQ0NPCpT32K7u7ujRbPKZVK/Pu//zt77bUXzc3N5HI5XnnlFck4CvEek8BRCCGEEGIH8KEPfYgJE3bl5Zd/glLxgPuUivnLyw8zYcJEPvShD2315548eTKaprFkyZIB23fbbTcmT55cL3azbNkyjj/+ePbff3/+53/+h0WLFnHXXXcB4Pv+oGP/+7//O4888gj/8R//wVNPPcXixYvZb7/9Nrq/2DEks0rVENy29ZltvyRwFEIIIYTYARiGwW233cqqlc/z29/cRGfnKwRBhc7OV/jtb25i1crnue22rw9JP8cRI0bwsY99jDvvvJNyubzR/RYtWkQcx3z961/nsMMOY4899mDNmjWbHPuPf/wj5513HieffDL77bcf7e3tLFu2bCufgRh2ZK7qsCOBoxBCCCHEDuKUU07hpz/9KagO/u8XV/HgD0/n/35xFRqdQ9qKA+Duu+8mDEMOOeQQfvzjH/PKK6+wZMkSfvjDH/Lqq69iGAaTJ08mCALuuOMO/vnPf/KDH/yAe++9d5PjTpkyhZ/97GcsXryYP//5z/zrv/4rcRxv8jFCiK1P+jgKIYQQQuxATjnlFI4++jhOOfVWKtVerr1mJkce+ZEhyTS+2e67786f/vQn/uM//oPZs2ezatUqHMdh77335t///d/5zGc+QyaT4dZbb+WWW25h9uzZ/Mu//Atz587lnHPO2ei4t956KxdccAGHH344bW1tfOELX6BQKAzpuYhtT/o4Dj+akldvqykUCjQ1NdHZ2c3Ika3b+nCEEDuY/t8x+XyexsbGbX04Q6L/HNet62LUqBHb+nCEeM+5rsvSpUuZNGkSqVRqi8fxvJBPX/IEAN+6ZyaOs3PlCjb1Ou4Mv0u3Z/3vz523/5p0evDWLu9GtVrm0itmyPu/BXau3yJCCCGEEDsBxzH53neO29aHIcQWG6qWi5Iy23KyxlEIIYQQQgghxCZJxlEIIYQQQggxrMgax+FHAkchhBBCCCHEsCJTVYcfmaoqhBBCCDHMSFbk3ZHXT4itTwJHIYQQQohhor9lhu/72/hItm+VSgUAy7K28ZGILaWG8Ca2jExVFUIIIYQYJkzTJJPJsH79eizLQtflGv/mUEpRqVTo7Oykubl5yHtXiiE0VFGeRI5bTAJHIYQQQohhQtM0xowZw9KlS1m+fPm2PpztVnNzM+3t7dv6MMS7IMVxhh8JHIUQQgghhhHbtpkyZYpMV91ClmVJpnEHIMVxhh8JHIUQQgghhhld10mlUtv6MIQQok4mzgshhBBCCCGE2CTJOAohhBBCCCGGFVnjOPxI4CiEEEIIIYQYVhRDtMZx6w+505CpqkIIIYQQQgixEXfddRcTJ04klUoxbdo0nnvuuU3u//DDDzN16lRSqRT77bcfv/zlLwfcf/311zN16lSy2SwtLS3MmDGDZ599dsA+PT09fPKTn6SxsZHm5mYuvPBCSqXSVj+3zSGBoxBCCCGEEEIM4sc//jGzZs3iuuuu48UXX+SAAw5g5syZdHZ2Drr/008/zVlnncWFF17In/70J0466SROOukk/vKXv9T32WOPPbjzzjt5+eWX+cMf/sDEiRM56qijWL9+fX2fT37yk/z1r39l/vz5PP744/z+97/n4osvHvLz3RRNyUTfraZQKNDU1ERnZzcjR7Zu68MRQuxg+n/H5PN5Ghsbt/XhDIn+c1y3rotRo0Zs68MRQuyAdobfpduz/vfnazfPI53KbvXxq26Zq7549Dt+/6dNm8b73/9+7rzzTgDiOGb8+PF87nOf44tf/OIG+59xxhmUy2Uef/zx+rbDDjuMAw88kHvvvXfQ5+g/51//+tcceeSRvPLKK+y99948//zzHHLIIQDMmzePY489llWrVjF27NgtOfV3TTKOQgghhBBCiGFFDeENkmDtzTfP8zY4Bt/3WbRoETNmzKhv03WdGTNmsHDhwkGPe+HChQP2B5g5c+ZG9/d9n/vuu4+mpiYOOOCA+hjNzc31oBFgxowZ6Lq+wZTW95IEjkIIIYQQQojhZYgjx/Hjx9PU1FS/zZ07d4ND6OrqIooiRo8ePWD76NGj6ejoGPSwOzo63tH+jz/+OLlcjlQqxW233cb8+fNpa2urjzFq1KgB+5umSWtr60af970ggaMQQuyEisUiV1xxBbvuuivpdJrDDz+c559/vn6/Uoo5c+YwZswY0uk0M2bM4B//+Mfbjru5BQSEEEKIwQx1xnHlypXk8/n6bfbs2e/RmSWOOOIIFi9ezNNPP83RRx/N6aefvtF1k8OFBI5CCLET+n//7/8xf/58fvCDH/Dyyy9z1FFHMWPGDFavXg3AV7/6Vb75zW9y77338uyzz5LNZpk5cyau6250zM0tICCEEEJsK42NjQNujuNssE9bWxuGYbBu3boB29etW0d7e/ug47a3t7+j/bPZLJMnT+awww7j/vvvxzRN7r///voYb/3bGYYhPT09G33e94IEjkIIsZOpVqv8z//8D1/96lf5l3/5FyZPnsz111/P5MmTueeee1BKcfvtt3Pttddy4oknsv/++/P973+fNWvW8Oijj2503FtvvZWLLrqI888/n7333pt7772XTCbDd77znY0+xvO8DdaZCCGEECiFGoLb5jSHtG2bgw8+mAULFtS3xXHMggULmD59+qCPmT59+oD9AebPn7/R/d88bv86y+nTp9PX18eiRYvq9//mN78hjmOmTZv2jo9/a5PAUQghdjJhGBJFEalUasD2dDrNH/7wB5YuXUpHR8eAxf1NTU1MmzZtk4v7N7eAAMDcuXMHrDEZP378uzw7IYQQO4L+GG8obptj1qxZfPvb3+aBBx7glVde4ZJLLqFcLnP++ecDcM455wyY5nr55Zczb948vv71r/Pqq69y/fXX88ILL3DppZcCUC6Xueaaa3jmmWdYvnw5ixYt4oILLmD16tWcdtppAOy1114cffTRXHTRRTz33HP88Y9/5NJLL+XMM8/cZhVVQQJHIYTY6TQ0NDB9+nRuvPFG1qxZQxRF/PCHP2ThwoWsXbu2vvB+c4oBbEkBAYDZs2cPWGOycuXKd3l2QgghxNZzxhln8J//+Z/MmTOHAw88kMWLFzNv3rz637sVK1awdu3a+v6HH344Dz74IPfddx8HHHAAP/3pT3n00UfZd999ATAMg1dffZVTTz2VPfbYgxNOOIHu7m6eeuop9tlnn/o4P/rRj5g6dSpHHnkkxx57LB/84Ae577773tuTfwtzmz77Frjnnnu45557WLZsGQD77LMPc+bM4ZhjjgHAdV0+//nP89BDD+F5HjNnzuTuu+/e4MPMmymluO666/j2t79NX18fH/jAB7jnnnuYMmXKe3FKQgjxnvvBD37ABRdcwLhx4zAMg4MOOoizzjprwLSY94LjOIOuKxFCCLFzq08tHYJxN9ell15azxi+1ZNPPrnBttNOO62ePXyrVCrFz372s7d9ztbWVh588MHNOs6htt1lHHfZZRduvvlmFi1axAsvvMBHP/pRTjzxRP76178CcOWVV/Lzn/+chx9+mN/97nesWbOGU045ZZNjbkkRCCGE2J7tvvvu/O53v6NUKrFy5Uqee+45giBgt912qy+835xiAFtSQEAIIYQQ24/tLnA84YQTOPbYY5kyZQp77LEHX/nKV8jlcjzzzDPk83nuv/9+br31Vj760Y9y8MEH893vfpenn36aZ555ZtDxtrQIBEhRByHE9i+bzTJmzBh6e3t54oknOPHEE5k0aRLt7e0DFvcXCgWeffbZjS7u35ICAkIIIcTGDJc1juIN213g+GZRFPHQQw9RLpeZPn06ixYtIgiCAcUZpk6dyoQJEzZanGFLikD0k6IOQojt1RNPPMG8efNYunQp8+fP54gjjmDq1Kmcf/75aJrGFVdcwU033cRjjz3Gyy+/zDnnnMPYsWM56aST6mMceeSR3HnnnfWv366AgBBCCCG2X9vdGkeAl19+menTp+O6LrlcjkceeYS9996bxYsXY9s2zc3NA/bfVHGGLSkC0W/27NnMmjWr/nWhUJDgUQixXehvdrxq1SpaW1s59dRT+cpXvoJlWQBcffXVlMtlLr74Yvr6+vjgBz/IvHnzBlRiff311+nq6qp/fcYZZ7B+/XrmzJlDR0cHBx544IACAkIIIcQ7NlTpQUk5brHtMnDcc889Wbx4Mfl8np/+9Kece+65/O53v3vPj0OKOgghtlenn346p59++kbv1zSNG264gRtuuGGj+/QXKXuzTRUQEEIIId4piRuHn+1yqqpt20yePJmDDz6YuXPncsABB/CNb3yD9vZ2fN+nr69vwP6bKs6wJUUghBBCCCGEEGJnsl0Gjm8VxzGe53HwwQdjWdaA4gxLlixhxYoVGy3OsCVFIIQQQgghhBBDR4rjDD/b3VTV2bNnc8wxxzBhwgSKxSIPPvggTz75JE888QRNTU1ceOGFzJo1i9bWVhobG/nc5z7H9OnTOeyww+pjTJ06lblz53LyyScPKAIxZcoUJk2axJe+9KUNikAIIYQQQggh3iuqdhuKccWW2O4Cx87OTs455xzWrl1LU1MT+++/P0888QQf+9jHALjtttvQdZ1TTz0Vz/OYOXMmd99994AxlixZQj6fr3/9TopACCGEEEIIId4bssZx+NGUkpdvaykUCjQ1NdHZ2c3Ika3b+nCEEDuY/t8x+XyexsbGbX04Q6L/HNet62LUqBHb+nCEEDugneF36fas//25Yc7jpFLZrT6+65aZc8Px8v5vgR1ijaMQQgghhBBCiKEjgaMQQgghhBBCiE3a7tY4CiGE2HlEUYymaei6ttmPVUoRRwrD3LJrpFEYoxsamrb5zy2EEOLdUbX/DcW4YstI4CiEEGJYCoOIcslHNzQaGjevWJlSilLRI44U2QYHczODR88NcasBtmOQztib9VghhBDvnhTHGX5kqqoQQohhyfciAOJIEceb95c+CmPiKHlMGESb/dxuNRhwDEIIId5jaghvYotI4CiEEGJYit90WTiK4s167JuvKPcHkFt8HJsZtAohhBA7IpmqKoQQYlh6c7eoOFJgvfPHvjnYi+LNCzrfGijGsdqiNZZCCCG2nKxxHH4kcBRCCDEsqTcFcJub9Xtz0Nk/jueFhEGEaRrohoZSoGlgWcZGH/vW4xDbP88NCaMYXQdN0zBNHdM03v6BQoj3lKxxHH4kcBRCCDHsKKUG/HHf3ODtzYGmUlCtBoRBRBBGVCoBUS2LaFk62ayDbb8ROLz1uaIoxkICix1BtRpQrfpEoSKqvc+6rtGQs6UI0k4ijhWFvLutD0O8A8nfgSHIOErkuMUkcBRCCDHsvPXv+uZON33zBwPfj4hRBH6MYeok3TUUGuB5EUp5NDY69azTW59bPmTsGKrVgELeRVHLMmsxKq4FEgUPTdNIpTdjPrTY7sRxTG9vlaobbOtDEWK7JIGjEEKIYefdThftf7jvRwRBRBQrbMfAcUwsy8AwNMIgplTyCPyIajUgk9EwDH2D55biONs/3wsplzwUkEqZZLM2pqmjFBQLLlU3pFBwsWwdw5Ds8o6qkHcJghhD1iwLsUUkcBRCCDHsVCs+ugpwUiaxUnjVENsxSaUtlFJUSj62Y2LZg3/IV0oRBhFRFBOFMbqhYVkGqZSJYSQFxQ1DJ4pjKmUfz43QtIBMxqqXTdB1jTje/FYgw1UcvzHtS9O0nabgj+9HlCs+YaRwHIPGRgddT74HNA0aGh2CMCIMFfm8R3Nz+h29Nv3TqftfU13X0LSd4zXdHpVKHq4XocFm94UV24ascRx+JHAUQggx7JQKPrFXRR+RIQhCKgUfTYP2cU141RDfS24tbdlBH69ihe9H6KaGQmEYOrZt1IPGfo5jEoUxrhsShjHVakh/zKAbSeC4IxTHCWpB9Jvpuo5l6Tt0sBPHCtcN8L0I2zLIZu160NhP13Wam9P0dFeTILPkvW1gEUUxwVv6g2pacnFiZwnItydhGFEu+0ByoSAMt/+f6Z2BrHEcfqSPoxBCiOGn9oddARrJB/GwNqU0DKNNPDDh+REKiCIwTQMNBhTA6WcYepLJTJn4fkgUxYRhEmD1BxhKbb/TVZVKAuj+oFHT3siKxXGM520YUO5IfD/Ed0M0XSOVNrHt5Hr5Wz84mqZBQ6ODBlSrIb4fbnTMgUFj/+up1V7rcIOAUmx7hYKLUuDYBhkpgiTEFtuijONjjz222Y/52Mc+Rjqd3pKnE0IIsZMxzSSgsy0DZehUKwGxSoKdIIhRSqFpWv2/bxZFcTJNNYxJZUwCX2FZxkYza7ZtEIYxpmkkmblQYRoaoOrTVVWsYDvMJAVBTFwrLJSs7UyC4ThWBEGEUqoe6Lw1G7u9SzLIAWGsSKeTta3VakAUxcSxwjR10mmr/n2RSpm4VQPPT7JTg33PhGFcv3BhGHq9lYtSijCMk6nR0Ruvt3jvKJVMK3/rNOxy2cf3Y5miuh1SDNFU1a0/5E5jiwLHk046abP21zSNf/zjH+y2225b8nRCCLFD2dKLbzsV7U3/0TRMQ0+KmcSAUgR+hGUbqFihGQM/3LtukKxNVDGWbYJKMk4bfSpNw3FMwjCip6dKFCbFM+I4CQY0IJ2xMMy3D6x8L6RU9GrTFpNjtlPmBtMj3wtJgLRh0AjJejzbNuqBZRBEO9y6R88NqFQCQBGFBn19VYJgYHa1c22RdMYilbawTINU2iIIkkys54WkUm9UWe0PDmFg0AgDp6m+eVqwBI9vL661xnk7/T9baGBbxoCfrSCIcKvBgCBD0zQMEyrlpIJqNpcURBJiS9x111187Wtfo6OjgwMOOIA77riDQw89dKP7P/zww3zpS19i2bJlTJkyhVtuuYVjjz0WgCAIuPbaa/nlL3/JP//5T5qampgxYwY333wzY8eOrY8xceJEli9fPmDcuXPn8sUvfnFoTvId2OI1jh0dHYwaNeod7dvQ0LClTyOEEDucLb341tbWNjQHNBzVPgAqxYBFFZap43vJ9sBPqqXqb/ps3p8x7M8gGoYOVhJgDpad7BeGEYW8i+9GVCs+pmlgO2aS1YwUpZJHk6ljGHq9yEySfYqJoxhN04jjiELe2+BytmXqjBjd8J5+aA3DiHIpQNPBcTZc2wnUg9sgSDK5vh9i2+YOETz6fsT6rjKVsodtmwRelFyAMHVSKRNNg86OMq7r09sLmbSFricVdS3HwElZlEoeTu17AKgFjaoeJPbrz96iwDCTyrz9mUet9pxicP3TfnVdH3QqeT+34rN+fRm3GuC5ARoahm2QTps0t2TRtKRlTxwpwiAmipPX3nMD0CCXdchmZYrq9ma4FMf58Y9/zKxZs7j33nuZNm0at99+OzNnzmTJkiWDxkJPP/00Z511FnPnzuX444/nwQcf5KSTTuLFF19k3333pVKp8OKLL/KlL32JAw44gN7eXi6//HI+/vGP88ILLwwY64YbbuCiiy6qf72tY6otChzPPffczZp2evbZZ9PY2LglTyWEEDskufi2aar+X4VB/1pDBVqSxfHdsB48vnm6oO+FxKp/jaJGOm1SqU01jaIYwxhYDCaOYwoFj0rZB03DdkyiOEbXNFSsMCwN14uwAoO+3iq6nvR+jGOFoeu1aq06biUk31dF05LsZK7RIQoVvhcRhDHd64q0jsphmkNbjCaKYnwvwnWDenZMxQrfi7Hs5MP5m7Of/cGj76v6ekjH2fi03uHA95NCRqEfJ4FgZmBGNwgiOtYUKBU9dENL0tZardBGrPDcELfqE4ch1WpIOm3geSGmZaBrGm4lpFz2yeUcbMugsSlNXPv+AeqBYBwnmW/fD9/4IOollVo1TUM3NMIwQtN2vGnAW0M94H4bxWKVzrUlCvkqYRTXZx6EZZ+uzojOjiItI7M0Nabr7XaUUlQqAaWij0Lh2CZhGEsGeLujGJqJpZs35q233spFF13E+eefD8C9997LL37xC77zne8Mmv37xje+wdFHH81VV10FwI033sj8+fO58847uffee2lqamL+/PkDHnPnnXdy6KGHsmLFCiZMmFDf3tDQQHt7++ae4JDZosDxu9/97mbtf88992zJ0wghxA5JLr69PfVG5FifZpqsNQRd0zAtnTBI1psl2UUd34uSYNJLvjZMHdM00LUQP4zo667Wp7dlsjZxrOjpruLVCqHkMjZ2ymDd2phCLQi0LR3XjaiUPXINaXwvxLSN2gfQJPtULPpUK0nVVydl0dCUwrIM0s3Jc6xfV8T1Qpb+vYvWtiyZnE1mCLIfUZS0FumfYmvWMqSaVsuo9lZA08g1ODS1vPH9p2nJtFXPi4Dkw3x/EZmh0N8ipb/VSf8aUqVULdNrYL7lA36l4lPscwnDCPWWWj6GqZPNOeQabVw3pHNdkd6eCr4XkcvZYOp4XoCTsojCmJ58Bd9LMlKjR+eSYMTSSWccVBhRqQT45ZBC3kVBkoWtBX66rteDwGrFJ4qSb1TD0NB1vb5+sj8IN4xkHa7jWDtEJndriWtVjyG5wGNZgwfWPd0V1qzopVhwiRXJz07axnYMymWPaldAX4+P64Zo42DEqBzpdPJau25AKm0Rq5hYQXdXmRFt2fqFJzH8DXXGsVAoDNjuOA6O4wzY5vs+ixYtYvbs2fVtuq4zY8YMFi5cOOj4CxcuZNasWQO2zZw5k0cffXSjx5TP59E0jebm5gHbb775Zm688UYmTJjAv/7rv3LllVdimtuuKca7euYgCDj66KO59957mTJlytY6JiGE2KFt6cW3t/6R25EFfphkiEgyOJBkzmIV19trqNrF6DB8o8hLGEVoRvLBwDT0WmAZke+tYFoGtm0S1tb1haHC80N0TaOpOYVp6LjVgDhKprlaloHnh1RLyboqtxrR2JzCsnSaW1JYlkml5FEueQA0N6VpacvgeyFRpKiUfdIZi7ZROZb/owvPC1i7qo+m5gx2OmlCb9lJoLQ11kD2B85KQSZrYZoGjmMSxzE968tAMu222OcSBBEtIzL1IKg/ePT9sJ4J2trZmThWeLXWGBsTBFEyddFIsr+GoVHMe5SLXn0fTU+qoJpWcrEgCmPWryuwanlIFCtcLyLwAwzdwPeT7HBjYwrbNigWPNxKEjS0jcph2gZp06JU9OnrLpPJ2ji2iWlq9PR4lAou3XbyXqXSZj3bmFSqTS4cpGqFd/qFYYznhfXvySCMiCNFJmsP60zueynJNL4x7ffNr0sUxQS1tiiv/KUDt+JjWiYjxzQwojWD7ZhUKgGpKKaxMU3V8nCrET1dJeyUBQoUMWGkaGh0aGywKRQ9wlDRtb6EYxt0rS9uu5MX79wQJxzHjx8/YPN1113H9ddfP2BbV1cXURQxevToAdtHjx7Nq6++OujwHR0dg+7f0dEx6P6u6/KFL3yBs846a8BF4ssuu4yDDjqI1tZWnn76aWbPns3atWu59dZb38lZDol3FThalsVLL720tY5FCCF2KnLxbeMK+SpEFgpFU0uaWCl0TSOO3lhfGMcK10uChUolQNeTKqK6lnwwdT0NsxxQLiUZCQdIZ2zcSkBPTxnN1HAci+a2LIauUa0kRTT02ofZajUkjpJCJ24te1XMu2QbHOJIgaXwvBDb1tF1g0zOJvAjnJSJ5yYBWKXsYxgaqYxNueShGTq93SWaWzOYtfWSnhti2QaWrZPvdeldXyKOwUmb2I5JS0uGdG7wDGUUxrXMV0y5nKxpzGRtjFoxIYAwiDENA7PBwNA18n0ubjkgr7tkG2xs26gXxrEso17cRde1rTbF0vdCXDes98Q0LR1d19F1rX4DcN0Qt+ITVCJ6uksUe5Og3XFMGppTNDSmBgRgcRzT3VmmXPYolTzKZQ/fj2lpSZNOW6AllTRtJ8ky9fVWCfyIbM6iUvIxbZ3AjSiXXEoFHwzIpm3QNAxDwy0n7/GYsY1oegrHMetTogFsx9ggwDZNHdO06wVbXDekUgmSQKbB2Skzj74X4tWyfMmFiYBKOcQt1wpJOcn3ei7roGoZ8r//bR2lggtoNLfY6LqGUuAHIVGYFC9KZ0x0TWHoAZVKyKql3TSPyCTZyaxN06gc6ayDnbLoXFukZ32ZwI/QzR23BY1451auXDkgUHtrtvG9EAQBp59+OkqpDWZovjlruf/++2PbNp/+9KeZO3fuNjlWeJeBIyRTqO6//35uvvnmrXE8Qgix05CLbxvXP92yVPDx3Aiv6gM6saawTbO+3sxOmbX1fBFhpEg5BkqBZRoYuo5hJNVR4yjGMHVSWYtqNaBY9NB1jewYB7fi16fNGbqGZSdr3qIgyW7GCnINdtIHzjFZ31Gk2pjC0AEFKcdmxKgsQS275bkhqbSF5yZr8dZ3FNE1jbbROTwvxnMDojBCIwkywjCma12B3vUVDFOrT4uslCIqJY/erhINTWlGjMpi2xamlayT9KoBbjUJdqvVABXFqEjDrQSoWNWnm/YHOamURSpjoRk6xT4Xt+JjmBphEJFKWxjGG8V/+ouWvNtKq1GtJYbvhoRBhGkZ5BodbGfgx48wSAKBKEymjVYqPr1dSYVb3dDJ5HRs2yQKY4oFD9NMqpr6frI2MZNzCKOYSiVI1py6Ac1tGZqa0vhuiO8l6x59PyKbtTH05PXrWF1FEWPqBoaZrF/tLJawbR2NJOvp+SGGnkx77n/e/rYPmyroYtUqfxqmQbnk4XshhViRzdk7zVq7OFa4lYDgTX0xOzuK9HSVURGksyaWnVys6OuqsCaIyDQ6FApVCnkXQzcYM6GJdCr5vvf9pLJqtRpiGKDipPhTyrbwg5hYKXq6y6TSyfpUatnzKEp+B0RhnPRllYYM2welNui5urXGBWhsbHzbZSBtbW0YhsG6desGbF+3bt1G1x62t7e/o/37g8bly5fzm9/85m2PZdq0aYRhyLJly9hzzz03ue9QedeBYxiGfOc73+HXv/41Bx98MNlsdsD92zKdKoQQw51cfNsITSOMFH4QgwalSoBt6lTKBqEZoxkajmMkAZimo+uQtkyclEnaNglqAYhp6gRuSBQq3IrPqqXd9KyvUip56DpEboBpm2imTiZtkck5lIpJsRzT0okihWXrGIZBW1uGfJ9LEEV0rysQBjHZhhQTd2utF+XoX/fmVgM0DQo9ZUp5F93Q2WViMy0jTLq6ypQLHtVqQK4pRaXisX5tERWDUgbt7VlyTSn8akix4LF+bYFy0aens0T7uGYyOXvAhynd0HFSZn3dThBERIGiVHDJ5GxCv9YawkmClUzWJvRDSgWfSsEjnbOJIoXtGAOKi/RPWe3PSG4utxbYetUQ3wvQDSMJ7koelm1ip0w0koxeGCaFi3RDx3cDPDekpTWDbmikUkmvxTCIk+O0dQI/ptDnEkVR0mJDTz5gNjWn6e2pEEewZlkfvaky2UaHjlV9VIoBVlrHcQxs26JrXZEwSCqlOq0muYYMrhviVUKCIMC0koBPKQ1Nh8ZGO2nVYhmk0xZO6u1fF03TyGQsDEOjVPIIgqherTWV2rHXPcaxolz0iGsXeZy0RW9XmUJfNbmw4Rg0t2Rw0gZuJSQKPUpFl/VdJQp5j+aWNOMmNjJydEOyzlnB2lV5uteXqJQ9TNMgmzEp9imiIMZzQ6quj26ZZLIBpq4olVNUyj7FgottmbS2ZUDTKJVL2/jVEdsL27Y5+OCDWbBgQb0iehzHLFiwgEsvvXTQx0yfPp0FCxZwxRVX1LfNnz+f6dOn17/uDxr/8Y9/8Nvf/pYRI0a87bEsXrwYXdffcWG9ofCuA8e//OUvHHTQQQD8/e9/H3CfzOUXQohNk4tvg9N0HdsxSacMNF0nm0mCG8fSSWcsKmWf1csKydRQx0TTNRqaU8SNKXQ0NC3JRnSuzdOxso9SsUoq4xDGMUEYoymwDKM2PTHAtg3iMMbzw2QKo2Um/eUMDd+LyeaSFg3tY226u0qsXtGH60aYlkm5EpBKWdgpk3TGploJcN2ANUt7CYOQYt4lnbMp9FXJ5lLYhk5V18n3VfEqPtWqV6v6Ck4KujtLqBhyjSkam3XKfVVWLOvFNjUKvVXGTmzCsW1My6ClLZmWp0iKjCRFg5J1fypWFHqqxLHCcsz6tFNdT9Y6Rn5EpNWmjtYyZ2EYk0olwaPn9a8djTeZWXsr3w3oWJ2nkHep1or1pFM2pq2jAbqpk0rFuBUfTaeeYdVMqJb9pMItSYDbNiqHUSt81J85jVVM6CuiOMZ1Q4Iwolz2Cf0Yt+rh2Mn+KKiUPfJ9FeIoxkol00rzPRVWvuaSyto4jk62MY1b8SkGLpat46QMdENLghNdw/NjSn0uHWaR0WNBRRpaKzQ0vvOpYklbjyQz7Psxupa8R07KHNJCRNuSW0nWC2u6RiZrUyq6dK0rYZgGo8Y0MmJUlkLepdTn4nsRMQona7B2ddJvJ45jnJSF74ZYjknXuiJ9PRUqpYAoiImDCBXFOI6VrIU2dLxAEZSquNWAtK3jVSPCIKahyUFv0mhvb8aydZYtrWzrl0e8A6pWOGsoxt0cs2bN4txzz+WQQw7h0EMP5fbbb6dcLterrJ5zzjmMGzeOuXPnAnD55Zfz4Q9/mK9//escd9xxPPTQQ7zwwgvcd999QBI0fuITn+DFF1/k8ccfJ4qi+vrH1tZWbNtm4cKFPPvssxxxxBE0NDSwcOFCrrzySs4++2xaWlq24quxed71b6vf/va3W+M4hBBip7QtLr5FUcT111/PD3/4Qzo6Ohg7diznnXce1157bf05zzvvPB544IEBj5s5cybz5s3b5Nib2yR5YxobbHJZBydlYtkmPV0REKOhUeytsm5Nkd6uEp4fYVkmmq7INaQZt2tTEhTFMT0dRXrXV6iWPSKg6kVkczatI7KMHJ2hu7NCuejipC3SGZvIjygXXCrlAMMMGDO+CV03cJ2AONbw/IimpmSdWzbngOYTBiF9PRVQSQGWTC4JRpYu6aNa8YhiRa4hhW7oVEsBKoaGxjSm5dPTWWFpVxnL0Mk1Opi6Tm9XlSCIWLakC8s0qJR9Aj8inbYolAOqVZ/X/tZJ+7hGJkwZiWaAZVtYtoFpJu+dk0qmnVZKXq3vXUjjm6ZG+m6YTNQzNHQtqbqqlMKrBDgZi0o5wLIMLNsgDKNaIaGkIM3b6euusPL1bvJ9LpViFd9T+F5A6Ec0taZpbs2SbbRxKyGWo5NrSGFaBk0tKUoFj8AL0XWdbM4mm7Wp1oJI00rWgAZ+hFdJMpSxSjLPfjmk2FelWgqSICVtMnpMAyoGv+rT1VXFcnRGjMzh2Ab/eKUTzUjarWSyNipSlAsuhT4Xx0kuAMQKgjDGdX3iMEJpGtVqMiV69NgmKmWfctEjnbVrbT5q7T5qmdr+7K+mgUaSsVQKDF3HthWBF2HaOm41JAiSYH1Hatnhe2F9emoma+N6AWtX5olihWmA70Ys/3sXlZJPoc+tVzYul5LqxHEQYlsGa1YUaGlNoaHR1+sSeBGOrZNpzdUq3CYzD7INKcoVj3SDTW9XhdiPWLOyF8M0aWxJo4hJpS3KJY9M1iaV2jGD9R2NGqKpqps75hlnnMH69euZM2cOHR0dHHjggcybN69eAGfFihUDCpwdfvjhPPjgg1x77bVcc801TJkyhUcffZR9990XgNWrV/PYY48BcOCBBw54rt/+9rd85CMfwXEcHnroIa6//no8z2PSpElceeWVG1Rr3VJhGLJmzZoBrT/eiXf9k3Puuedy4YUX8i//8i/vdighhNjpbIuLb7fccgv33HMPDzzwAPvssw8vvPAC559/Pk1NTVx22WX1/Y4++ugBFWDfbjH+5jZJ3pTAj7Bbk+mjaMl0xb7eKp3rSoTVgMAPsG0LJ2WDCYEXke9OpsE1NNhEKKolnziIsGwL2zHQzOQDZkPOJnCTKa62Y5FKmTQ1OZTLPpVuHz8IyVoOQSUk12Jh2Tb5vIfrBkmVz6KHaehkMxaVosfyv3fS1ZBm7IQmGhodqhUfFcdYtsWIZocwUBimhqZptTYEAbmszWt9VbrXF3EMi8Zmh0yDTVzy6Fqdr0+XrZQ8MhmHtjE50lmLatnFr0as/Gcv1bJP77hGTNtkxKgcYyc2YZpJAKLrGtkGh0rJB5V8kPe9MFmz54VJ2ffWNHGkKBWqGKGBpoOmKex0UtglDONkzaWu4fshgZ9si6KkH146Yw+YalksuKxZ0Udfd4VCvkoqbWJEEWExIp02iENFpeLjBbXANDAIw5gxYxvpWlcmjhWpjE1Ta5psrdCQ70fEYUxY6/dXqa2X8/2IMFYEbkhfdyXJsGox6XSKttFZ0hmHVMpk/dqYVNoELanCWyp6tI7M4lVC7JSOW47o6ymilCKXc5KiOKaOpWsEYQgxxJGG5/oUe6t0rSnQ01liwsQW+rrK5Boc0plk/atp6ZiWUV+DOpj+Nh2gqJYitFphILfqk05bpNL2dj99NYrieqGppB1GxOqlfRRLPtW8S2NLBqJk2m61EiaZ+qxNT1cJv/a9OXJ0I8W+Kr5XZn2HQUOjg2boOLbBiJGNNDQ65JrSyevphpRKLpqu09SUpqkpxZplvZRKHoQ+RhGyWYtSwaNS8UmlTKqeZBzF5rn00ks3OjX1ySef3GDbaaedxmmnnTbo/hMnTnzb4PWggw7imWee2ezjfKf++te/ctBBBxFFb99L9c3edeCYz+eZMWMGu+66K+effz7nnnsu48aNe7fDCiHETmFbXHx7+umnOfHEEznuuOOA5I/Yf//3f/Pcc88N2M9xnM1qPLy5TZIBPM/D895otdDfcqS7s0zsGTQ0Z8jmLOIgwvd81q0oojSFoes0NqfINtg0tWYo9FTp7iyRX1/mtRV9uJWAbINFW3sTzSMzxDHYloHjGFRKHsWCj2lppNMWgR/RsTZZ72ZaBrmcQxQovGqIYXjEelJsJ/AiVq3vRank+W3TpLtUolLxKfRWWb86abVhpgyyaYsxE5rp6axSdX0yaRt0iIKYStEjn68ShxFBNQbDp2tdmWopqbzZ2JZBt3X6eisEfkxJedi9Bk41oqkljZ1K1g3me6rke6qMHNdEtRTQ01Vm7Phmxk1sBpL1d7ZjJOvyTK0+bTSqBWGZrE3n6gJRCBgRkZeccyqIyTQ6KAVhoKi6yZTDYq9L6Ef1NWu6kRSHSWccdB1Wr+hj/doChT6X1hFpUo6JChRt7TmctE1Dc7LerFSo4rsRtpcUnHl9yXrslEU6azOqvYGGxlTy/ZfSk96LtfYM+d4KoR8SBjFuLTvc21UGXRFHGiPHNDFiRAZDSwoMdXeXCaKYbIODpkGp4FLsq5JtcBg3oYmudWUqVRfd0Mg2pEnnDIo9VXw/REUKTSl0PSYOk+IqpaJPHMbkuyt0LOtj1LhG2kblGD2uiVxjiihMXpvQ03EyyXpb3dBrWZNalkMlU4U9L8QwQdN04ijGrUS4lRDdcElnbDIZC9MytssgMrlYoZIg2tZZ8VoP69cWKearjGzPYRga2SaHfE+ZTKOJZRhUKj5+GJNusBg1qoGmlgxrV/TRVS3TtaZAx4qYEaNz7HfIOEa1N9Dcksa0kyJZvV1lyiWfaikg22CjAsg1pZKWOJUAwzAoFlyCQKGpmEopoFyRNY5CbIl3HTg++uijrF+/nh/84Ac88MADXHfddcyYMYMLL7yQE088EcuytsZxCiHEDmlbXHw7/PDDue+++/j73//OHnvswZ///Gf+8Ic/bLCe8sknn2TUqFG0tLTw0Y9+lJtuummjC/i3pEkywNy5c/nyl7+8wfZK0YXAxPciin0mhk7yod71CaMYFSl6e0qMHJXD0HXaRmdpG51j6T+6Wb++hOsGxLHCtMtYKYOW9gYgmfJYyLt4rg+aTaXo44ch1WqIaRhousJyLJqaHBqaU3iVANcPKOY9oigm1+Cg6zoNTQ7Vss+Y8c2Uyx4dawrke6vk+1zSGYuW1gzVaoSKY9C0pJ9iFFMueHSuKlKteIQxjGzL0NNbYeVrXSiSLOCoMY20jc6wflUe343IZJJsSbHPpVK0aR2dwzIMurtKaLpGx6o+mlpSZIopqgWP7s4SY3ZpZsToDCiNdMbGThkEfkQp7xIGEemMje+FmLXA0nJM0jmDUj5p1RGGMY0tKYJQEfoh3etL+OUQyzZJZy28akChxyX0Y3RTw3V9OlcVqJaTD+olw00qqpZDbMegZUQG3/Xxyl6yIDOOqZRC1neUsGutGMZObK4HeJmsXV/7aBg6fhzipCyCUKHFyTrKfG+5VrUzpLUtjV0rWpNO25QKLoGfrHPUNDBMnUrZR9OT9Y+rl/eRTls0tqTI5Ww8N2L9mkI9yxn4Yb31i2HqmIZOQ6NDMe8TuEmQHrgBlbyH5wZM2W8Mo9obCWrrQlHgVZOKr6lMMpX4zcIwxveTKrJJ4Z+ISiUgjpKCMpWyXyugk1Qd3V6CSM8Nk3WNmkY6Y7FmVR8r/tlNKe/R1p6loTlNY6PDmpV5giDCMHQC16djdRHPC2hqStPclCbwI5rasgRhhFvxiVRIteix4h89NDenickAyTrKfJ8LQLbRRsUQqoiGxhSNTSmCIKpP/873VQmqQTKdWG1elkVsI0Pcx3FH1r8EZmOq1eoWjbtVJnmPHDmSWbNmMWvWLF588UW++93v8qlPfYpcLsfZZ5/NZz7zGelRJoQQg9gWF9+++MUvUigUmDp1KoZhEEURX/nKV/jkJz9Z3+foo4/mlFNOYdKkSbz++utcc801HHPMMSxcuBDD2HCt25Y0SQaYPXv2gDUbhUKB8ePHU857aIGZNPNuTIqrlEs+SimclEmp6BO4Ed3rK8QxlAoeqYxJttFm/MRmOtaUccse+b4quqmhGUmxmTVLK4ShoqHZhiCi4saUiy6aCaER49gGZc9DhQrDMDF0RSXv0ddZolj2cVIGo8c0ksmYpDMWqazNmFQTDc0Ory/pplhyKfd5uG5IxqmgmyajxzZgGDqaAt8PyfdWKZdcbNOgO4jwA59qNcK0dNA0MjmTvzyzmmLepWlEhnG7teAHMcW8i1/18aoBucY0o8c10tVZJvRDCr0VLMeiWvIpF3361ldoakmRa0ozclyOTC6DWw2olv2kn56mYUcGtp0UZ9E0jVTGItvg0LW2iF8N6SwXUTpJwFcKkumYdtKuxNChWg2pFjziOKS7o0yl7FHp88g2OFitKfq6K4RBjOVk6OlM7tf0pNKtbmgEfoQiCRSbmlMUuqv4lYDRuzQlvRYbHCzbwK0EVKoBvhdiWRr5Pg+vEmCZJlEc0joyh2nqxJGi2OfiV0PCOCaVtqmWXdJZm/VrS1imTtX1KJaTSrGeHzFmlyb6usoEboRp6Fg5k7amZJ1cueBh6Ek/x3TWqrX60ChXdFQcE0SKfD758FXq8xmzazN7HdBOrimNVw2Sqb1BRCkfJdVhMxa2k7zWpqmj1BvBYCZr09SSplr1qZSCeg/IwI+wHSPpy2npSb9Pa8uq3A61OIrr7WFSGYtCvsKSl9biVUKaWtK0j20iimJWr8gThhGptEkqa9PdVcXOmjSNzNA2Ik2+r0qx4NHbUwZg1LgcxElQWi15LPrDCkaNaSDX5KAZGlFMEpgriOI4ubDTnKKpKUWp4GOYJp1r+igX/eTnq8EmljaO2wUVb34hm3c67o7ub3/7G2eeeSaTJk0a9P61a9duUFfhndiqq4PXrl3L/PnzmT9/PoZhcOyxx/Lyyy+z995789WvfpUrr7xyaz6dEELsEN7ri28/+clP+NGPfsSDDz7IPvvsw+LFi7niiisYO3Ys5557LgBnnnlmff/99tuP/fffn913350nn3ySI488cqsdi+M4g66d9N2QUuChSJqBVyyPaslDodOQdcjlUvh+QFBbc9fdWSRVm9ZoWhYtI9PkTaiWQgp9LmGg0PQChqEzYlQOwpjG5gyuG2ClNXrXV0lZGs1taQI/mc7Zvb4IsYahgR8ka+2qBcV6VaTU59LcnCWbszFr2bwJk1pZ/vf1eJpHpZhM18vkNErFpMm55wasXZ1HtyCdM3ELAX09FXQNdMeksTlNy6gMr/11PaViFV3XGT+ljdZROfLd5aTiaa06qm5q2LbDLhNtutaVkp6XPVXMERlyLSm8os/y7jKObdLTlWO3qSMZMTqHZZt4bohf9Ql9g6aWNKmsRbng41YDUmkL2zboXleir6uM6wWAhmXq5JpT+H5MoaePSjUgk7JpanEoFpN1gSpUpBttGprS5FpSlCsBtqOTythJ0OtH6JrCdiwCL8L3Q3QTWkZkiSNFV0eRxtY0vh/R0pYl15hCacnawRgNTVN0dpSToCyMMGyDES0pMo6VTItV4HkBhYKLW/UJQ8WIUTn6uitUqx5uOUJDI9eQIoxiHMdgyeK1OCmDdNpm9NgmWsc00LO2SEl3SadNwlBhpYykqIsdoRsOucYUfb1ViBSGZSTZaxWz7BWftct7GTuxhUmTR9I4IlULHmPiCCrFZDqlnTJJpfvbniSl/X0/CWYzGYdUysb3w6Rwjh/huiGGrmHHBmEQUyXAsg3sWiZyuKhWAqhlaKM4ZPHCVXiVkGyjw4Q9RlDJu5SKPipWtLTlaGhN07Gqj3LRI5dzGDEyi46OZUfke/uoVgJsyyDyFZOnjiKbs/nnP9aT76my/B/dOI6B4Zi0jEzT1JpBI8ncZrM2Ohr5bhffDyn3VvHcCF3T0W3QgHgoegOKrU8yjlts3333Zdq0aVxyySWD3r948WK+/e1vb/a47zpwDIKAxx57jO9+97v86le/Yv/99+eKK67gX//1X+uNLB955BEuuOACCRyFEGIT3u7i24UXXrhVnueqq67ii1/8Yj043G+//Vi+fDlz586tB45vtdtuu9HW1sZrr702aOC4JU2SN6WxJY2ubHRNh0jR3VWkt7eKk7FIN9o4pkHbqBwKnZiIwI8JvJBCl4tfDZIshKmRaksThiGVQlJUJZWzyfdWME0Npbk0tqbB1LHzfpJR0wxaxqQo9LpUSj7lqk+p6KJDUv20xSHWtCTbVumjsSmVtOJwTHw3wNI1LF0j0iGOFKEbEWVj8n1lOlcX8VyfTC6FZRsUI48ojol1yNkGhqFY83ovlYJHjMauk1sYMSpLtewR+hG2qRMZOsTJNMdKNcC2dEaNbaDQl7S+KJU8giikbWQO3TEo9Fbwl/dRKSRFSRpbUjS2pnGrSSsD349wshZ+EFIueLhlHydtkWm0k+xXNUBFIalUKmlZ4kUU+1yiWKGjkWt0SNeyr1GUprHFYcwuLYBGFIKug5O10HQdy9JBh56OCtVqgGHoSZuGvEdc6+vXuapAqtbvz3IMco1p7JSRZPFqlTqrFR/DMrFsnVzWIZdzSKctfDcgiKKkomvJJeWYrF7Wm0wZNRWGDk2tWTI5B01XvPZyJ2hgmCbjJjWTa87glQP6eisopbAMHUNXFHpd/CAk3ZCmsdkkChUYOsVeF88LsNIWVtpAxRrFgsvfX17L2qU95JrTjBidI5O1AWo9I6m9FhpO2iKVNtEMPZlKq+mk0ia6pqHpGikn+bdhasSRStp46DGmaeBWfX7zmz/S2dnBLruM44iPfnibLg3yvTApYKRp2CmDZ3+3lErJw7ZNJu7ZSqXPpVDwsAydxvYMuq6zelkP3evL2LZJtsEmDhVBHJDPV0g3JN+DjQ1pWlpTtQJCIZOmjKSnq8TqFXn6equkUBT6NCrlgOaWNON3bwWlUSq6lPIu+Z5K0oszVCgUBiZRpIhDmaoqdmwf+MAHWLJkyUbvb2ho2KLaCu86cBwzZgxxHHPWWWfx3HPPbVBWFuCII46gubn53T6VEELscDbn4tvWChwrlcqA0uEAhmEQb2L+1qpVq+ju7mbMmDGD3r8lTZI3xU4bOIZDOpW0hyjmPXw3RNc11i3tAy2mY5lB04gMI8c00Doim0xL1XVilWQUVAy5Jgtdt3BskzUr8hS6ypiaxlo3JJNxKJVcWkZkaWxJE3ohlbJHseiRzVk0Nacol5OgsVoJaWpNk2tIYZgaPd1l3FJIvq+Cn7Ixa0VQQi+kqTWLU/Hpy7toWtKOolLyCcMY2zHJpK0koPEiMg1Jf8PQi1i3soiuKyI/BhNKRZ98X5WWtjSmbRD6EUGg8KoepaJPuRKQ90OsLp1Mg00mY9PXVaFcUFQLPrmcQyZrE8WKvi6X3u4qURCTylqMHtdIOmsTBCHr1yYFiQq9VXSSi/FtYxowbZPmERncakgYJC08Ai/CtgwMWyedtam6IaWuMoEXYuqQa0ozfo+RrF7ajenoZHI2pmWSyTmEYYTnhrSOyjBqXA4VKvK9VTJpjTBWlItJZrhU8NBMyDWkqZR8GltSdK4uAooISOcctNBHUxa+GeCbyWsTa1Ct+Di2gd2SJQxDSl1Jq5RSOWbsLo20jMiCFrFiSS+ZjE2u0aZ5dJZQQdf6cjJFmmQ6dK4xhe+GOGmbYsHFdyMKPREpx8TWk8AvjCJ68xWKeQ3TMLBtHS3W6V1XxrR0bMsg05gUcMrmUvXv7zhWSasOLanAatg6dtoklbZIpwxMy6y1IDFA0wiCpC2Khsb/PfE4N/7HHNasXVkfb9zY8dx0w1xOOeVkbCcpyqMnvUDQ9SQQHarprfGbqqg6KZOXnltJsaeKbuhM2nMElXxIseBhmjqpnE3oRZSKVfp6qliWQSZnkU5ZKKUo5gM0wyCTMxk3sZHdp4wi9CO6O8t4VR+3EuBWQ0xbp3lkhihI+oE2tibB6JqleSxbp1rxqZR8XDdA06GhOYVpakm7Dx2q1Z0g5bQDULX/DcW4O7pvfOMbm7x/991336Kq7u86cLzttts47bTTSKVSG92nubmZpUuXvtunEkKIHc62uPh2wgkn8JWvfIUJEyawzz778Kc//Ylbb72VCy64AIBSqcSXv/xlTj31VNrb23n99de5+uqrmTx5MjNnzqyPc+SRR3LyySfXA8O3a5K8OVb8o4e0XiaTdShVfYqFCqEXE7gh5YKL0sCxDXq7KwR+RBDEhF6EYSTr8NxCSBhGFHoAdKqFKqEXoSno6y5hGAZ5VcZaa7Ium2f0Lk1ksg757gpRpPCrJnbKJpW2MUyDTJNF4Cn8IET3oZL3CcIIzXbw4whDT4If0zFpaE6hkcFaW6RU8ulYlqdc9ojDmLbROdat6qNc8NA00FMGbj5Zv6dQqFARa6AFGn1dVVy3A9MwaGxJ0zwiQ67BwXc14jgidAPKJRelNAp9VWzLwE6bxKGir7tKT1eFhqYUI8flaGixKRd93EqE3xtS6nOxUjojRzeRbbEhUmiahpVK+t5Vyz6VoocirlUyVZSqSTY33WCTa0phOQZ+NaSv18Ur+5imTn5dmSUvrKRjVQFd12huy+BXA8rlANPQMTSNVM4G2ySKIhzHJPB8gnJAHEMUxkRxSFBWVIs+6YzJ08/+ngcfvYd/PeWzvG//afhhRGNjMjVRR8et+KBrFPsqlEo+tmXiODrlUoim6RS7SzSOyNDbU6K7s0hvR4k4jmkd3YCTStG3vky1kvQTLPW6BGFIQ1OKfF8FW9fxgxivElAoVHCLAZ4bQaRQmiLStWTtXcUDX6HpSWCbzZioWEepmFSPybpleayUSVOtGqimIEYRRzG6BigIY4Wma0mm065laDU96VdYa+fy9KLfMPebV7PLLu/n2OMup7llV/p6l/PySz/hgos+RceKO/jYkcdi2wZOysKyk2nNmmagG0kvTt3UMPSkZYtm6MlUaUOvtwbRaus6dV2vBZyb7ilbKfv1Kaqv/W0dnauLxHHM+N2aiQJFIV8lVuBYRhLEl4LkfUol71NLS5Zy0cereESaQjc1Ro5pZMLEEbX2JgbjJtn0dZd5/ZX1uNUQ0DAApUGuOYWGorujiG5olAsurhsRRRFtYxoZMbaJhiYHyzAIo5iudSW8yN3s30liG0iuAg7NuDsR13V56aWX6OzsHHCBWNM0TjjhhM0aa4sDxzlz5nDiiSfyqU99akuHEEKInd7mXHzrb1Xxbt1xxx186Utf4jOf+QydnZ2MHTuWT3/608yZMwdIso8vvfQSDzzwAH19fYwdO5ajjjqKG2+8ccB6xNdff52urq7612/XJHlzLH91PblsFkPX8fyYwEumNqo4xi36KA3KhoGT8ajkPZb/rQs3SBrAW46VtFCIwTR1DANCLyKutdEIA0UYeahYEavkw3LnyjwNTTboOlEAuq5AB9MyaR2RQdN1ymUfpWLCMFm3pps6gRdi6Rpr+z88axphqHAcHc0w8Mo+fXkPz0vW+61bVaBa8AjjmJaWDG7JJ4wUugZ22qRUSip1anotk1PyiIHedSV6mx0aW7M4GYvAjYhiDUMzcF2PahCjwrgWaIBhaARRRKm3Qm9niREjG7DTJn6pSqHgohs6qbSNV/QxUjamqZHNWcmU0EpAb75C5MXYtk4UKgIvSDK/PVVUTNILrxJQLFTxKj5eJUrW3WWSqZzVUjL119AUStOwHCMJWgww9WQaaxzHRJEiCKMkYIwUQRgSxxAHUdJLMlb8bOFdrOt9nR/++G7sytgkADU10o6FaRqEQUilHBD6yfRDpRShH6OimCCMsdPJR50oSC48BFGMYxk4WQvD1NFIgqKgGuD6IVFMUg23tt3QNTQ0VBwTExN5Cj+OUCHEKkJP2RiGlvS4dEMMPekRaNkalqVjmEYSHKJhmAamoWPWXo/kgCFWMbECrda6I+nbkTyvYWigacRE3PXw1xi3y/s54shr0bRk1sDIUVM54shr+c2Cm/jq7TdiFHbB0JNKxJqeZEJNx0z6TNoapqZjOAaGodXeEz256RqGrqFbSbbSsCwMHXRTq9+vG8m/DUND03WiKHkddEuje12JNSvyKKVoak2T76rQ29WV9F9M1y4QeCrp8+gHWIZOnLbIr+sELckmpxyLXGMaI1L0rivSHdd+DqoB6zuKFIs+bsVHV8n3iFKKVauK+F5IFCXvnWkb2E7Su7Wp0SZl6oRuiBf4VCo+oRvgF4PN/p0k3ntJG5uhGXdnMW/ePM4555wBf6v7aZr23vVxXLVqFccccwy2bXPCCSfw8Y9/nCOPPBLbtrd0SCGE2Glsy4tvDQ0N3H777dx+++2D3p9Op3niiSfedpxly5ZtsG1TTZI3h+uGqNBHNzWiIEKFMbapgwZxrKGZYBqg/Ihy3qXHS3ro6aaG0jWIkyb1jm1h2zphpIhjBbpOXOtjGIURKIXnRRBrdKcNTFMDXUOFgKXh6Dodr3VBbd0ZGsSahq4lBWMMw6DqBknvP6WwDSM5DstAaTHKj1G6hg6EmLjVWoVSTSVZxkARRxG5BhvLtij0VihVoiSQdDx0XSOIkmm3hqnXgwrT0jFrH+KV0ojDGM8PCGqvg1LUW4FomoaKkq81XQEaCpWsHyVZi6kZSdsL0zTRDIVSGqYOhm4QhRGhH+FHMcSAlkxojeOkv2FS+jbJZtm2QRBGhGFyDsmzaWiGgaElF/pVrFAqTuKyECIAFRMlb0fSD5HkPDvLS1jX+zr7HHYuf33mAf7wuycZ27gX9fyXrtXOkXoAqFQMKrkoYOj92xTEECXxGIaZvJf942iAH4KKo9pB90/z1JPTrb2H/RO8o1gRRnHS7qPqYaZ1NE1HaeD5IQRAJcmW6LqOoSVj6loSzCWBYfLBTdd0altqZx2jlEryqRqAjgLW9C0hX+rkAx++uh409tM0nf32P43/+8VVPPnEb2lv2qP/xUfXNGJN1V4uHb3/rPXaK6bV1l4mO0NtD60WeCbvX+2u/tdaB10z6sccBCG+H6Fi0I3kwkIUKXRLx9ANTLP2XsWKMPkxBJWcu24lYzoZG9vWsQ0zCax1DV0HP4Kq69Uv8jhWUiCKKE7WvJZ9quUAP4iS49WTQHXU6Bx/XpdUUQ6CkKiWaUmKKJXf/peQEDuAz33uc5x22mnMmTNniy7ivtUWB47f+c53iOOYP/7xj/z85z/niiuuYO3atXzsYx/jxBNP5Pjjj6e1tfVdH6AQQuyI5OLbplUKVbwoqDWsT/rCGbW2BJqmofsaUfJpHMMgyU5FCqVBHCTTAC1Dw7RrLQ+Ulnwgj0mmgoYqCWKiJLOlatMENU0nVkm2yTL1JOAL+5u3KwxLSz4sx7UPz8mn6eT/TANNJVM+UYogit8ITBREKukLGEdxfU2pipPsYLUQEoVJ4GcenxQf8rfSa6k28u/4LdvD2m1zvLmmpwK82r/7wxrtTfdtbGzjLf+tj6cUf73/O4zc5QAOnXk1nasW81f3j0z65GeHZL3exnP+G9KBjZWi2dwPVhtbWayoBdU15Zfz8DI0t+w66P4tte1eXE4K5cTJ95tSMVqUfN/GqCS7SX92843Xsb//ZHIp4I1ylv2tC7SkHCmangSysUp6akZERCFQu1BhGBr52vRWpSVXDzSSgF5pOhrJhY0kJq6t8TRqQayh1aYgJ2tAMTXQTfp/zIjjpHgQgA4qfuP4NS05H0USrPd2FDFqhYfM2s+2ZiYBcqQ8hNgZrFu3jlmzZm2VoBHe5RpHXdf50Ic+xIc+9CG++tWv8sorr/Dzn/+cb33rW1x88cUceuihfPzjH+ess84a8obWQgixPZGLb5vmlXy0MPnQGiuVZD2qEbX8FYYJlmOgGUkWLopiVKwwdI0gSD6YYuvEtayQAkwj2R+SAFOFMUF/1kjTkimSYVwLHBVBLXLTFEn2R4coSj6/apqGYemEoULFEbptQJw0P0+qNsZouo6ha0RRTByoegsAXdcIDYVhAZpOlPQcIfCTrOhW7ZO1HVv9+h9Zv+rPHHX2fWiaxvs+8ll+9cOLWf36H9ll8ge39eG95zK5kQD09S5n5KipG9zf27scgH0O3J09JowjjpJpwmgaKkrWqgZ+RBQrolAlwWMUo/Tke1zVArYoqmXmoiTIVLWpu/3BomkYhDHEUUS1EuBVk/WphqFhWxoYSfY1jMJaVJxkzYmT6bi6BkZ/RtfQ0QwNPVK1qw1JgKtiRWxQD2xVGEOYPD6Ma6Ft/1WQ5BQxNA1NV2gquZAU+BEYScbVtsxk2roJhq7jR1vrsowYSqp2sW0oxt1ZfOITn+DJJ59k99133yrjbdW/T3vttRd77bUXV199NevXr+exxx7jscceA+Df//3ft+ZTCSHEdk8uvm1crjmNHidZk5Dkw2AcQ+An0yUNSwc7yWtpKsZAR09pSRP32odQxzFBT9Yc6oaWTH0zk6lsuqGhqZggJAn0VJxU5gyjpHS/SjKKSoFl61iWmeRfdA1NgW5oWLZBEMYQJlPy0EjGAFBJARtiRRwm46O0+riWaRDXpj8aVnIefpA0F1HPPZNMXzQ1lB+DDjo6mqbQbTM5D8tANxSEyQdjTA3b0UlnbJRSeEGyppMoxq3ERHEEQVzL8IBpGWhWMgXRMA0cXRGhEYdJ24dIKQwUYazVMr5gOwZWysRA4VZjAj8mDIKkx6IBtm1BrfJnttEmnbExHYtMxkLXNZRRm5qoAxrJ+rgYiMALk6IzYRgTx0klxfkLbmXULgcwbvcPADBu9w8wctz+vPx/X2PqERl0K2lf0dycJtvooNCoFjxMK1m/l83ZaCo5Nkhec8PUSaUdLDt5fTVdo1r0cb0I0zHINaZoG5OjaUQG4phyOcT3Q1Dguz49HWWqXrKWNp2zk0xaDMQK0zaIY0W54uFWQ0pFH98Pif0QN4iJ/AgtToof6Sr5/kzZJrqlESsNTana66NhmjrZBpt0g4NlG1iGhr7PQSz65VhefuknA9Y4QpLN+8tLDzOqbRwfO+ZjRBG1ixjJ66lBvcCOhp4EWLXMndIUqGS6KJpWW0MKhm1iWwZKB0PTQUG55OJ5IcU+j7Wre6gWApSCTM6isTWbBJe1wC5WSZsOzw2J4xjHschmTUzbwrB1MqlamxYz+UGrlD2KFR+vGhEHEX6k0GKFFsdoWrJuNfAiokAl64z9KPn5TOnompGEnHE9T0oQxcRh8j2laq8BybfbG+tLxfAmxXHetTvvvJPTTjuNp556iv3222+Dtj2XXXbZZo33rgPHarWKUopMJgPA8uXLeeSRR9h777258MILt1r5eCGE2NHJxbc3TNhrFEZsodDJZC0yDRZosHp5X9KuQdcwtKS/XRiE6MrAtpMP45GmEcVJ3z6lIA6TSo22ZYCuY1qgDANTT9ajBWHSoF1FimrJRUVg2MkUOts2cDIGlmGRyiQtA1wvxjI0NEMRRkmiRLP0pECHH6GbWlJkx48IgwhN02kekaGp2aHQ56JpGqmMiR/UPrzrGqW8h++FxERJkKjXWqSEyZj9mVcVJYVUMDVsw8AyDSKSTKWKFIVCMn4SvCaFXay0RlxW+JEi8nx0SyeMIlRFYZo6dsrCzFhEKkYpjVTWxLZMiBWVSoCfpGSxzGSNXhjG6KYiZWkoHKJAkUrpGI4GgQmGRipjo5samayJnU6m/EahIgg8/DBZxxjFisAL8WvrJFVcK78fw+vLX2Tl2lfr2UZIsrzvO+JSfvXDi1nT8xfGte5FseJT7Kli2xrprEOuwQZdkWvMJIF8FEMUo2vJWjtD09At0EwwbA2vEhBGEYZVWzMbRfhlHz9tYhgQBQGaiomDiEpfBdOE1lyaTKODX43w/QjPTQoBeW4StZiGhuPoGJpDGFtJD07PJ/TBdwPiICmwVPUDgihEr0ImbWNaBiqOCANFHOmEYUS+r4pjG6TSFnbG5LzTLudr93yR3y64iX33P42Wll3p7V3OX156mFWrnufaWf+JmTIxSS5cxJFK1qFqOqaZ3GxHx7SSwElp1N6bWnGe/imhClRELVMIXuTjuSG+F1EpefR0V9AMk0yTSa7Voak5je9FBEHy8+RVQ1wvmart5GyyGYtcYwonY5JK20k2VKmkiJMX4nsKn2S6d6rBwDZ1UikbJ22gQgiiEDRIOw62Y6L0mHTGorE5jWWbxHHyMxBFMVqsiAKFHwSEXkSl7FMqeHiVgErVx3dDTFvBI+/97zUh3mv//d//za9+9StSqRRPPvnkgGn+mqa994HjiSeeyCmnnMK//du/0dfXx6GHHopt23R1dXHrrbdyySWXvNunEEKIHZZcfBtc04gUhBbFvEex6qE7oBkm6UYHJ+uQzhlkMzZu3iXf7Sbrmkwj+RDqhui6jpO1kqIwTpLJ0ZQiiCLcEDQtJjS0eqXVTCYpcmM5JnEtoLNTJoapYegGtpPM5zNMnYaUiWVp+NUQtGS9opVKqmRqWY1MQxIUlgsuoa/INtmMGJkh05jCTtmkczYjxuQIwuRDrq5rdK7IJ9VF46RYj2npeF5EtRygawrLNogjhVsN8CphctymhkJHQyXnE8aga/V1oWi1NZ6GTjpjoRkRvlsrEhNGWKaJMkCzNIIwSjJdjolhaUk2TjNw9FrRkrSOZVtUKx5eWSMmaYkA4KSS/nxKAUZyPnEYEUc6xdjFquj1IjkqVoRxf2YxJtYgjkgqi2oKE4Vh6Dz5wo9pbBlPKtNC15q/1b8vUpkWGlrG84c//w+nf/j/Q4s1wjCg7ELgJZmo9vHNSXElRT17FmgKLY6wHDtZaxlDpc8n8EMiFWNaJp4fJhVg/RDVU2vL4ke4bkDsJ2toLUcnbZu4lYgwDImi5Pun6kf1qaBxnMyNjuOYIFBJtheAKOnZqYOuKUI/wvOTFjGuV03q5RhJARi9P7Ota8navTiZwtmS3odzTr6Gxxb8F//3i6vqr0trczsX/+t17DLyIFYs602mX+skxWwApWmoMCaMo3pWrnZ37f+9sQ43DlVS8TZO0vxRlLyuQZAUo4nCWg9KXSOdMwl6Y7o6S0Sxqk1npX4Otm1gWBqxAeWKT6HsEasSppZMjVUKwijJIsYqRtd1bFMn1DSqbkC54ieVYXUNyzGpeD4VN8A0NcJI4brJ+mfdqH0YVv3/ietTWY20Rc4yyLYqWmJFGERUqlIcZ3ugGKKqqlt/yGHr//v//j++/OUv88UvfnGD/s1b4l0Hji+++CK33XYbAD/96U9pb2/nT3/6E//zP//DnDlzJHAUQohNkItvg3O9ABXpuH4SAPT1ekRhFd+PkmDATtHYbNI0qpF0a5bIj7Fsk2JflUJPBc3QaB6RIZW2QFc4aQcVxVSKfhJURHGtb52O5STBYRjEeEGY9AWsVU5VtdRdgIZF0rMuilSy3tFPKoOm0xZhnLSu0DQN34swDJ1cY5p0LvkzW3VjyqUipm2CG7J+bQnDNshkbXq6OuitdGM5BgqFqwK0WMcLAsqBR1NjKy3tY1ARlEs+gRuhohCvklSKjMKQlGMl/SNrgVgchdgpOykIUit1ogO+GyYVKL2QKIxrLRoMND1Zu6abBlYt4xhGCk0li9LSOQfT0kmndSqpkDCIiEOVVKqMkoIpoRdiGmCYJlqtooph6knWNCI5Ak0RKQ0MA90w0IB0GizbwrYNdMCtuhTKPRQK63nsvtMG/wbJtqFrMYaVHHPkK6IgpNwTscrtYcToDKPGJL05lUqm6KazNnam1kNRKdINFg1mCssxqRZ9dEMj15yiZWQOXQPXDSj0uYDCSNs4GZOG1gymptWnQ0IyJTSMk3WtURjjekmw6VVDwiDAD+Kkz2gYE4URfhijQoUfhklvUj8k8hWhSqrURkqhRbXp19SmWGpJhdhYxew55XCu2m0aKzpeoer20drSxuTd98c0TLT+skT14LN2pBpopomBkaxvrFWZjWsVaOuFcWJFRH8AmFSO9f2YKEqyjfXMJEnV3VI5BC2s94DUDQPd0DA0HSdjYZoQxuCXQ7QwTqZnaxoKDSOpe4Nu6NgZE8sysCwNx7HfOCZdw7JMnNr7pmpTDG3HxDBr56pUPfgFahlT/Y2quZqGTa1KLMl03VRVpqqKnYPv+5xxxhlbJWiErRA4VioVGhoaAPjVr37FKaecgq7rHHbYYSxfvvxdH6AQQuzI5OLb4MI4yTzkmtPJ+jFLp5ivYqJhGAqv7NO5KpkGajsm2VYbK23hBCGpwAKStY35vIfp6GiGSSpj05JziKOolhWDUl+FMAbd1ElnTBqMNJgk/fhMg3LJo1IOCPwoCQBqU+xCX5GyDVINDnbaJKiE5HtdYqWwbYNU1iSVcjBraxKLJZ9iyUNFPlYqqeZhmjqGXeXzc87A9zfekNxx0nz7jv+ldeRo0qUAt+ITexFRS5Q0o4/jpFVBrV+i6q8wGyeVJsMIQjfJUlqOQcbSsXyT0E8yUFGg0BVEWjJ1VYUaSkvWehqmjuNYmGbSiN73krWalm3jBUkvTMPQUHGEbZtoVjIlNQ4i7IyD7RjoGQfNVKDX+hkaBpauSGUssjm7VmE2ye7FUUQUZrnpmm9TKPYl6zGN5BhsO6lEW8q76HGGlNGIWwrwq8mUz2oxoFryiaoh+fVVUqaBbZk0tKRpbEkxYnQjhqkn2dZ0Eni4bkDkKxpyDk0tKcZNaiXXlMJ3A1Yt7yVlG9iOSVNrhua2DIah86b+2W+yYQ4jCEKCIMYr+1SqPuW8R1/Bo1x28apJb1LPj3DdsNYbNCJwwyTgDEPCoDbVGoUOBHGMFkdEXoCmwZjWKTiWiWbqeCUf0opU2sRJJesikwxmbT1j/wq/WkuQqL89Tf+xRjFxFOP7ydToahCiPC3JTqqYwI/qU7I1gyRgx0jGM5KgNtmuY1tJD0VT1+tldS07WV9qmgaapuoXbQxDw7KSbUlrDoNUxsKxTJyUQTpjJ/1Sg6R3KkAqY2I7Vv/p1CSB6Jvpen97kdr6zVpYrWkaxeLW6YkrhpYUx3n3zj33XH784x9zzTXXbJXx3nXgOHnyZB599FFOPvlknnjiCa688koAOjs7aWxsfNcH+FZz587lZz/7Ga+++irpdJrDDz+cW265hT333LO+j+u6fP7zn+ehhx7C8zxmzpzJ3XffvclStEoprrvuOr797W/T19fHBz7wAe655x6mTJmy1c9BCCH6ycW3waXTFk3NOXSgpTWNbVusWtaNW/DRbB0/iImjCPP/b+/e46Mo7/2Bf55nbrubewgkgICoVFAUKGqMYrWaY1B7Kki9HatIrVQrKKb1QsvF2zlUT1W0ovzsUaqnUi3nKLW1xYNRezOiIAhURaUotpJwzW0vc31+fzwzkywJISRZcuH7fr22dWdnZ5+Z3Qzzne/zfJ+IP36qIALOORxLg2UZsiubJbNxTsKFY7qASEDhQlaP9OejEwxQOWBDA+OAI2TZ/kh2BMITyClQoUcsJJvk1CBayoRteeBcjvnLztbBVQUK5BhHAQY9wsPxenKcpIqcbNm11U46EI4LAQamKti1axcsK4Vzpi9FfslXWh2HupqP8eYzNyBlNSEWPQrZ2YYcFykEGutMOJ4L25JZK9sWcp5IVxYS4VzWyXRsGynLhZOywTUFRkSBEtGhGgJ2yoKregC4nGqEy2IprisQb7Sg6ypigwwYhoJk0oXnuf78kJ7MNgqGrDwDriP7RnIOpDwXQuWwHQ92o4ArbChcQDU4FE1DTq6AHovCYwJNjZbs1ikYwIRfWJOhuHgwjho2DIah+tOpAJomj6vnygI8qaQMhpnrIpVw4NoO6vcmsfPLBiSaLOzalYDleBgZGYTskQWI5cipbhjT4LoOEg0WLNuBnZLj51IJBXV7E6jbk4Rl2kgmbCiagqLiHOQNkEHjodB1/xIrPxYu8zwPluWiqclEY52JxoYEkkkbqaSDZNJCMu4ilbJgJi2kki5sy/GDOcD1bAhPhSoEPEduK266EElZrEepl1NMyMJKHLoWBGscqioDNX9eDAh/TkvPk+Mtg66pwpNjfV3Xg207EB4DUzhUTYWiKTAMBbEcA3pEhR6RWUJFVaDrTHbZVlUAHhRNhaZxGLoKPapCUxVZiViVRZZ0XYOhKRCMwUpaaGy04NgeDEPOJapHFOgRGRyGF/kCMqiMaOGyjl7/7z97iwfjkL5L0kP8aZAyst0jhOu6eOCBB/Dqq6/i5JNPblUc56GHHjqk7XU5cFywYAH+7d/+DbfeeivOPfdclJWVAZAXQBMmTOjq5lv54x//iJtuugmnnnoqHMfBj370I5x//vn44IMPkJWVBQC49dZb8corr2DFihXIy8vDrFmzcMkll+Cvf/3rAbf7wAMP4NFHH8UzzzyDkSNHYv78+aioqMAHH3yASORQZncihJCOO9w33/qKxr1JOAkGParCdWTF02SjDHyKj8pDQ10Sjg3k5msYflwRolEVibiDHdgHCIFotgGuMJhxG3pEFpHxXA/JlINkgwnHcSFcAVVTEYtpgMblmLOkDdVRYLkywNQNFbkFURgRDVbCBvNceLYNJhhisQjgT96elx/B0GG5yM43sHNHI5oaLGgqR0Tl4ApHdo4ip+awXSi6AstyoUcUNMRlMJJf8hUUDR934OPRkED9viRiWQbyC2KymmhBDIkmC6bl+OPlHIBlIdFkQuEcXOMwEzaScQvxhhSSCRWKqiASk9k2zxVwoips04YrANt0YZk2VF2B4wBQOTzGkIhb8Jgu553kDGqWBs8ViBVwKFyVBW2YHNuoaH4BGiHHSLqOA8WVGS/H8uBZFhptB8l6G0xhUBUFmh4UbdGgaQpURUDT5OdZSdlxUtNkV2LGAE1RkFNgYMRXipCbH4URlYWTrKSN+j1J1HyxD59+uBO7v2yEY7uob0iiblcTBg7Okd+j5cBKMCgag+IwmJ6AYaiIZslCNoCcziGSpaNgYAz5hVn+fJ1dxzlHJMIRiWgoKsqGEIVIJm3Emywk4xYs25XtMx2YpoNU0ka80UIi6cBM2jBTJuykA8uWRXQcx4PrF6RxbQ/ChQwALVm4B/48iuHnM78aThCLhQGY8MccemAKh8IYdMOQN0iiCrKyDGTlajCiBoyIimhMQzRLR1ZMhxbRZOVWJmBEVEQMDYrC4Lkyswm/CI7sispbVTRVVQ7NkJnFWEz1s7rNGVHOOTRdkVlMrZu6mHK7e7ZDMksgMwMSj5y4EZs2bQrjsc2bN6e91pn5cLscOH7rW9/CpEmTsGPHDowb1/yP3nnnnYepU6d2dfOtrFq1Ku35L37xCwwaNAjr1q3D1772NdTX1+Opp57C8uXLce655wIAli1bhjFjxuDtt9/G6aef3mqbQggsXrwY8+bNw8UXXwwAePbZZ1FcXIyVK1fiiiuuaLMtpmnCNJsnkW1ooK4PhJBD0/Lm23nnnZfxm299hSxKw+CZHpyIBzPlIBJVkFeUjYLCGFSFw7ZcGDENnHHkF8ZQVKzJ5UNcDBgUg6brcgyXJ6CpClSDw9AVNCUcNO5NoGFfAhCAHtHgOgLC85CIW+E4Ssfx4DmyIEpE19HgxRFv5IhGNGTlR5CVawB+BVbbkmMmNV3H0ccNRLzBkvM1Oh7MpAVF44gV6cgrisHzgHhDCq4j0Fic06HjkVuQhVhuBApXwDVAcA6uANmFUUT88YoCAslGC1lZGlRNRX5RFIwx1O1NonFvQnb38+RUI67twfE8OCkbrhuBmbTR1GQhFRewkh64yqFHVESzDEBlaKgzYZsOoMjxiAJyCgdF5dA0jixdRXZBBK4tx/lFYiqycyOyCqvlwrXlJPGcC6gKh/DkpO9qREEsokNRZSVZznhaZk8IWajFEzJTlZVtILswiqyYDiOiwohoaUHdwCG5OOrYQow8oRifbtqB7Z/uQWODhS+21yGRcDB4RC4KinP87KWc+iK3IIKc3CiGHF0AxuW0LynTgaYpyMrWuy1obAtjDLGYjpg/jYrjyO6iVsqFaduwTVlEyLJdWCkHpunCsuTUJcmkBcuU2VbHceC4cgylvAEgu3Y6tg3XFn6Xv+Yxf8H/ya7DDJwr4JyDc3kDQDdUaBENWVk6svMMGFEd2Tk6IoYKTVfBOIfK5VymnMv9kO/Tmqu3+tPYqKqca9VzPdmOoKsvk91JEwkLUTD5fRrNl6WeP58kP8RML+k/qKtq173xxhvdur1umcexpKQEe/fuxf/93//BsponVa2pqcHo0a0nqe1O9fX1ABBOlL1u3TrYto3y8vJwndGjR2P48OGorq5uM3Dctm0bampq0t6Tl5eH0tJSVFdXHzBwXLRoEe6+++7u3B1CyBHmcN986yuGHVOAnFg2XEcgJy+CXTsaoakcA4fkIBrTkZ0bQXa+AZUzeIJD1XU59122Dsf2kJUdgaqryMo1oOuKrIAKyIqmKseebAMDS/KQPyCK7DzZq8S2HezZHYeVsMEZw55dcdTvSUAIIBJRoemyOqkAw6CjcpCbE4HtCPzzs71INMSxO2EimbBhRHQUDcpGwaAsNNUnkWxSYCZsOfbPkV2HAAbDkFmYjhhYnI2hR+XBdT0oCoMR1cAVLouFRDXY/nx5WsRDstGBYsjxiJwBisKRlRNBJEuHbihwXAHX8QAmYJsuGvYlkYzbyDUtJBptpOIpJOMuFIUhElHAGQd0D6oix65Fc3RYcRtckVOl5OTGcPTxA5CTq2PvriQYYygZmovsXFl4hkFO9+A4HnL8Y+04HhIJO60Sqew+LMK74IrKoeuKfERlYGXbMijyPOEXnDGhaXIcoqxAyhDN0nHUyEIUleRg6DF7se2DWtT+sx71++JwbQ81nzdAj6qIZcmCN9lGBEcdW4hITHZlTSVtaJ4ix6AexqCFsSArp8DvQAXX9eTYR0fOyWhZrlzmeBDBeFt/bGIq6Xdr9QvxeH6JUwEB07Rl8G4LOK6MIIUnwJic01Q3AE3TYBiya2nEUMH84kUC8rtRFA7NUBHxu5pyhUEIf45Iv3KvrittFuFgDOC89W/dtuXfAmNo9bdAASMhvU+XA8e///3vmDp1KjZt2gTGWBjFByd++Q9kZniehzlz5uDMM8/E2LFjAchgVdd15Ofnp61bXFyMmpqaNrcTLN9/DGR77wGAuXPnorKyMnze0NCAYcOGdWZXCCFHsJKSEpSUlKQtO+2003qoNb1Ddm4EBXnZ0HQFtu0hmiUnlJddGRmMmIbBwwvgmi7ijRZSSQuJBhMN++KIxiJy3kFDBhRZWTo4Y0glZPEUVVdgpRwIAFqLDAfnHNGIjljMQG6eLJSyZ2cTUnELnHMMP64Iu3c2IJmw/ekjZDe7AUVyLKbtCjQ1mOCKC6ZyJJM2cgdko2gox+4djbBSsviLosoMaf6AGHbWf9ah45FfGMOIYwbI7qWW7L4ZydLlnI2enOQ9lbSx658NiEY12d1PCDi2gMo4jNwI8gujiGQbMFNyvCaYnPeSCaD2ywaYKRuRiBzr+eXWvajbmwRXGCIxDdFYAVQFiGZFYKYccAaYpoPCoiwMKM5B4aAs2JYHuByaoaB4aF56N6j9hpSpKkdOjg7HUeG6zdVmWFA0qI2gIRgvqmmKzMyZTjhlhmW5fgCphO+NRDWMGluMggFRfP73faj9Zx0a9yWgKnJeRNUPcrLyZREjAH5AKq9bdKPnK28qijwWLQ9fkD30PJnddT2EvwHbktNmhNNpCOFnEtGcaWyxLcZYOP6P+YE3h/xbAJddW4NMqOfJ6TIUP9OocB5Og6GqMtA+VJbfNVjTlYxmdknfJDw0Z6i7ebukc7ocON5yyy0YOXIkqqqqMHLkSLzzzjvYs2cPfvCDH+CnP/1pd7TxgG666SZs3rwZf/nLXzL6OQdiGAYMgwZYE0K67oMPPsD27dvTem0AwDe/+c0ealHPUnUN2XkRRGMaEnELuXkDUTQ0B54rYKYcqCpHotFCLEdHvh5F/b4EahtMWKaHrDx/jjk/AwIA0SwdEDKb5FiurOrI0Dz/G5rrJQRLGGPIK4hBaZFBGTqiEE0NKezzM5E5+QaycnUMG1WIeKOJpoYUHEeOnczJi0KLKEjGTURiBnLyFHBVbn3YyHwwwZG/PatDx4OBQdMVGIaKpnrZzdW1PWTnGmGgpHIOfXgBbFsGUa4fXAkhUDAwhkhMD2/wJhI2XNfzgwqGWLaORKMFQ5dFTIpLcrF3VxOSTTbMlI3cgigGFGeDcYbdXzaCqwy5+VFwzuXchP6k9lxhyMoxOjR2JsiwqarsFuz6XRltWwY/qiqzXPtvq2VmznH8bpmO57/PlWPmdPm6EAJ5RVkYwQE9wmElcyFcD/mFMeT607Uoql/tFTIDJgRkN1q15wPHtgTBHudKm20UQvhzL3poOZiLc3k82wrQOvJ9BRlPMyWzm8E2Y1lyTOOhcvzpSxhrUUiIENKrdfkvtbq6Gq+//jqKior8f4A4Jk2ahEWLFuHmm2/G+vXru6OdrcyaNQu/+93v8Kc//QlHHXVUuLykpASWZaGuri4t61hbW9vqjn7L9wTrDB48OO0948ePz0j7CSEE6NleG72ZAFA0JAfCE9AiGvSIiqLiHOyqaYQeUWGlHGhCjumLxjSZOcrSACFQUJSNrFwDmp+dCkSz5Vg6M2XL96gy4AnWCeaIYy0urDVdCT8PkN0nBw8vAFM4UgkbmqEiGtOhahxGTIcelYVjPFfATFhIxYFUygLjHJFs2aU0EtHgOkB2roacPFlxs67m4zaPQ8vluq6CMYas3Aia6lPwXIHGuhQiUQ2ekN1OGWMoHJgFMMCx5G11PaKmBQuMMWRl6Ugm5TQQcl5KjgGD5HyYtu0iJxpB4aBs7N0ZR/3uBCzLwd5dCcCVrwsTKCxSkJVrIJalw7JcKClHBmyHGAS0DiBlRQzHkcEPY3I8ZBDktiQzXYoMakwZOAZBJCDAuCKr3+ZFMTJqYO/ORqQSDlRDhWW6UDUFuQXyO5BZzCDb2HcDGZm1lcfM9SukyrGiAp4nu4bK48nC7r0HE5yX5DhGBYwBtu1BUeX4Xse2oOkcuq52KHPoeQJmShaoUTXKNpIDoKqqvU6Xz4yu64al5IuKivDll1/i+OOPx4gRI7Bly5YuN3B/QgjMnj0bL730Et58802MHDky7fWJEydC0zRUVVVh2rRpAIAtW7Zg+/btYdGJ/Y0cORIlJSWoqqoKA8WGhgasWbPmiJ1DjRByePRkr43eTFeZrICZcmBEOCJRDZoux6Ul4hb0iPznK8iuCAFk5xooLM5BTn4E2dkGIlGt9Xb9aQQitotEkwXHcQHI9bwWF8ctxbJ1WZzSE4jEZPCZkx+Fosg5JBWF+ZkThqLiHHAux0d6wkMq6fpj9DSoigI1i0PhCur3JZFMWCgcUIBINIY3n7nhgMciEo1h8ODisF2cM2TnRZBoMuFYHlKJ5gqRkZgWVp5sL2Mmi7JocBw5N2LQJRI6EG2xXvHQXMSydOyuaUCi0YIAwBhHVp4GM+UgJz8CrnBEohyO7cLzGFStc2PTmgNI4c8zKLtfyq6SAoAXricDHiAYHyeE391RYTBTsuqoTLZ5MBlDNKoiElMx5OhC1O2JI9FowbWF7F4LmWlMJW2ZbfSLu/R1QQCpqrJKqet6frdg4QeUzevJYwq0PJ4AwoCzZTERmWXUoSgsDLY9T8Dyi/KoGg9vBLQVlHqeQCJhwXPleFajDwfpJLOoOE7v0+W/1rFjx+L999/HyJEjUVpaigceeAC6ruPJJ5/EMccc0x1tTHPTTTdh+fLl+M1vfoOcnJxwDGJeXh6i0Sjy8vJw3XXXobKyEoWFhcjNzcXs2bNRVlaWVhhn9OjRWLRoEaZOnQrGGObMmYP77rsPo0aNCqfjGDJkCKZMmdLt+0AIIYGe6rXR2w0amgvDkFNxaP4k7IzJ7EQ0piMZt2BEVVlYRXhyLGOOnFNRVflBi84EgYHnZ2SUoNAMWnfbY4whlp0+LEHXFbAcw+8yyKFBQDdUqJoMxIyoJqdPMG0wziFc/0KFy4vyeKOJeNyFrhbgrb+sRW1NLZg//Uc0psH1/AI2AAYPLsaor6T/e8o5Q3ZuBJbptMiSKYecKZPHof0gKSc/gqxcHYlGE6bpQNPkcRdC+IGCA8ZYOEG70sWgKwh4AB7eGGienkGEyw5EN+QUH44jg08I+T0n4rbMtubIiri2n0VOJm3Y/lg7VeOIRrVOlanvzWR2UQmDSBlINh9PGSAebCusVfdh3c8y2rYL25LZXlnNVXZBVdTmsZBBZVXblhlQ7ncnp2wjIX1HlwPHefPmIR6PAwDuuecefOMb38BZZ52FAQMG4IUXXuhyA/f3xBNPAADOOeectOXLli3DtddeCwB4+OGHwTnHtGnTYJomKioq8Pjjj6etv2XLlrAiKwDcfvvtiMfjmDlzJurq6jBp0iSsWrWK5nAkhGTU4e610VfoEQ05+VFwzppHafnXl5quQAiZWbNSLhzLk11TNSWc+40d5GKUMZkZCy5yFYWndcc7GE1TYPtjJbNz04NKeYEug8tsEQkzNnLz8r+jMQ0N+1JwbA8DCopRVFAMxuX4wJaFYSLR5nGabR4nQz0s3So558jOiyK7xbJk3IKZcpBoah6Xa0TVbq1E2hxESsGxDL7n4JgGP46WU0MEwU3QddWxm8dQhisDYdCoaQoiUbXfBY0tBd1+FQXQtJYZRaT9RoPjyVjz8Wzv7yIYcyqrwHqwHVcWcrI9OGgdkSoKQzSW2alOSD/Qi+ZxXLJkCf7zP/8TNTU1GDduHH72s5+1W8RuxYoVmD9/Pj777DOMGjUK999/Py688EIAgG3bmDdvHn7/+9/j73//O/Ly8lBeXo6f/OQnGDJkSLiNvXv3Yvbs2fjtb38bxjWPPPIIsrOzD/SxGdflf20qKirC/z7uuOPw0UcfYe/evSgoKMjIybcj6eVIJIIlS5ZgyZIlHd4OYwz33HMP7rnnni63kRBCOupw99roK8Jqjy36zfEW/6bIDKMBMynnMPQcD5Fcw88qdSz4U/1J5W3bhRFRwyxWR96rqByMpWcsD7QPbW3PMDRkxXQ0NZp+dVBPdqP1K31y5cDVRXuLqF/V1UzJLp5cYW12D+5O8ljKwKejgnkFRSTorinHoHqenHszyIh1dGqU/iQIJLtLcxVYNewaG2SMGeRvhHM592d/DtBJ9+gtQxxfeOEFVFZWYunSpSgtLcXixYtRUVGBLVu2YNCgQa3Wf+utt3DllVdi0aJF+MY3voHly5djypQpeO+99zB27FgkEgm89957mD9/PsaNG4d9+/bhlltuwTe/+U2sXbs23M5VV12FHTt2YPXq1bBtGzNmzMDMmTOxfPnyrh6CTmOCOvp2m4aGBuTl5WHnzj0YOLCwp5tDCOkDXn31VcTjcVxyySX49NNP8Y1vfAMff/xx2Gvj3HPPDdcNzjH19fXIzc3twVZnTrCPH2z8DKNGH4V4oxlml/IKoqjflwzXLSjKQjJhIdFkQTMURKNy/KOi8FZZwLZ4nkBjfQoAkJMXQVNDSo6VzDE61N0y0WSFQWdXAibXlWP5+mrgIoTMLClq2xU7CeltjoRzaV8WfD83fftZGHqs27dvWgks+eU1Hf7+S0tLceqpp+Kxxx4DIKe/GTZsGGbPno0777yz1fqXX3454vE4fve734XLTj/9dIwfPx5Lly5t8zPeffddnHbaafj8888xfPhwfPjhhzjhhBPw7rvv4pRTTgEArFq1ChdeeCH+8Y9/pGUmD6du6d+SSqWwceNG7Ny5U44naOFILSVPCCEdcbh7bfQVQQAij0EwT0YbmbuIBst0IbzmLoeK2rHjxnlzd9WgMAqQPkVHezRDkRPSWzJ47Oz3JbM0nXprr8CYnCqEEEK6VYZTjg0NDWmL25pmz7IsrFu3DnPnzg2Xcc5RXl6O6urqNjdfXV2dNs87IP+tX7ly5QGbVF9fD8ZYOCNEdXU18vPzw6ARAMrLy8E5x5o1azB16tSD7mYmdDlwXLVqFa655hrs3r271WuMsSO2lDwhhHQU3XxrjSlIn6n8ADiXXQyDOfyA9quJ7k83VDi2lVYcpaMBoOpn2DxPIBG3oKoKjAhViCSEkL5g2LBhac8XLlyIu+66K23Z7t274bouiouL05YXFxfjo48+anO7NTU1ba4fFPTcXyqVwh133IErr7wyzIDW1NS06garqioKCwsPuJ3Docv/ws2ePRuXXnopFixY0OogEUIIad+qVatw9dVXY8+ePa1eO5JvvgXBW0diOCOqwnHkxO2Ms0Oq6qlpSph1DJ4fShsjUQ2JuAXHlmO5KHAkhJC+4Ysvvkjrqrp/tvFwsG0bl112GYQQYQHQ3qzLo+5ra2tRWVlJQSMhhHTC7Nmzcdlll2HHjh3+vHXNjyM1aAwwpE9OzljbSUhF4YhlG7JgTtahV2qMxnQ5TjGmHXKFUk1XEMvS5VQY1F2TEEK6jfBExh4AkJubm/ZoK3AsKiqCoiiora1NW15bW4uSkpI2211SUtKh9YOg8fPPP8fq1avTgtiSkhLs3LkzbX3HcbB3794Dfu7h0OXA8Vvf+hbefPPNbmgKIYQceejmW/dQVY5oTOvUHIKcy8xhZyci13Q5t2SmK4oSQsiRJBjimIlHR+m6jokTJ6Kqqipc5nkeqqqqUFZW1uZ7ysrK0tYHgNWrV6etHwSNn3zyCV577TUMGDCg1Tbq6uqwbt26cNnrr78Oz/NQWlra8R3oZl3uU/PYY4/h0ksvxZ///GecdNJJ0LT0fzhvvvnmrn4EIYT0W8HNt2OPPbanm9I77Z88bDE9ByGEEJJplZWVmD59Ok455RScdtppWLx4MeLxOGbMmAEAuOaaazB06FAsWrQIAHDLLbfg7LPPxoMPPoiLLroIzz//PNauXYsnn3wSgAwav/Wtb+G9997D7373O7iuG45bLCwshK7rGDNmDCZPnozrr78eS5cuhW3bmDVrFq644ooeq6gKdEPg+Ktf/Qr/93//h0gkgjfffHO/bkWMAkdCCGkH3Xw7sP3HNwbzIlLcSAgh5HC5/PLLsWvXLixYsAA1NTUYP348Vq1aFfYU2r59Ozhv7u1yxhlnYPny5Zg3bx5+9KMfYdSoUVi5ciXGjh0LAPjnP/+Jl19+GQAwfvz4tM964403cM455wAAnnvuOcyaNQvnnXceOOeYNm0aHn300czvcDu6PI9jSUkJbr75Ztx5551pB+1IRPM4EkIO1VNPPYUbbrgBkUgEAwYMaHXz7e9//3v4/EiYeyzYx48/2o5jjxuKZNyGZTkA5LyN9XsT4byOBUVZPdlUQkgfdSScS/uy4Pu58bJlGZvH8Ylfz6DvvxO6nHG0LAuXX375ER80EkJIZ/z4xz/G3XffTTff2tJGSdW0eR0JIYT0WwICXcxvHXC7pHO6fJUyffp0vPDCC93RFkIIOeLQzbcDYwwdmsuREEIIIZnX5SsV13XxwAMP4Oyzz8bs2bNRWVmZ9iCEEHJgPXHzzXVdzJ8/HyNHjkQ0GsWxxx6Le++9N+3OrhACCxYswODBgxGNRlFeXo5PPvnkoNtesmQJjj76aEQiEZSWluKdd97pUls7Mo8jIYSQ/qc3VFUl6brcVXXTpk2YMGECAGDz5s1przH6F58QQtoV3Hx79dVXcfLJJ7cqjvPQQw91+2fef//9eOKJJ/DMM8/gxBNPxNq1azFjxgzk5eWFxXgeeOABPProo3jmmWcwcuRIzJ8/HxUVFfjggw8QiUTa3O4LL7yAyspKLF26FKWlpVi8eDEqKiqwZcsWDBo06JDbyRiDoqTf31RUDtf1Dn2nCSGEENIlXQ4c33jjje5oByGEHJF64ubbW2+9hYsvvhgXXXQRAODoo4/Gr371qzA7KITA4sWLMW/ePFx88cUAgGeffRbFxcVYuXIlrrjiija3+9BDD+H6668PS5QvXboUr7zyCp5++mnceeedbb7HNE2Yphk+b2hoAABEY3IiZj2iwvMEVE2Ry7N0MCaXE0II6cc8IR+Z2C7plE79y7tx40aMHTu2w2Ny/va3v+H444+HqtI/9IQQ0lJP3Hw744wz8OSTT+Ljjz/GV77yFbz//vv4y1/+EmY3t23bhpqaGpSXl4fvycvLQ2lpKaqrq9sMHC3Lwrp16zB37txwGecc5eXlqK6uPmBbFi1ahLvvvrvVct2QgSJjDNEsvcU2GWLZxqHvNCGEkL5FZKY4DvVV7bxOjXGcMGEC9uzZ0+H1y8rKsH379s58FCGEkG5255134oorrsDo0aOhaRomTJiAOXPm4KqrrgKAcCLiYI6qQHFxcfja/nbv3g3XdQ/pPQAwd+5c1NfXh48vvviiK7tGCCGEkAzpVApQCIH58+cjFuvY3CqWZXXmYwghpF/qbK+N7vLrX/8azz33HJYvX44TTzwRGzZswJw5czBkyBBMnz692z6nIwzDgGFQBpEQQkg64QmIDHQrzcQ2jxSdChy/9rWvYcuWLR1ev6ysDNFotDMfRQgh/c6ECRNQU1ODgQMHdmj9srIybNiwAUVFRd3y+bfddluYdQSAk046CZ9//jkWLVqE6dOno6SkBABQW1uLwYMHh++rra3F+PHj29xmUVERFEVBbW1t2vLa2tpwe4QQQgjpuzoVOL755pvd3AxCCDly9HSvjUQi0SrbqSgKPE9WKx05ciRKSkpQVVUVBooNDQ1Ys2YNbrzxxja3qes6Jk6ciKqqKkyZMgUA4HkeqqqqMGvWrG5tPyGEkCOA8B+Z2C7pFKpWQwghh1lP99r413/9V/z7v/87hg8fjhNPPBHr16/HQw89hO985zsAZEGaOXPm4L777sOoUaPC6TiGDBkSBoUAcN5552Hq1KlhYFhZWYnp06fjlFNOwWmnnYbFixcjHo+HVVYJIYQQ0ndR4EgIIYdZZ3ttBFNVdNXPfvYzzJ8/H9///vexc+dODBkyBN/73vewYMGCcJ3bb78d8XgcM2fORF1dHSZNmoRVq1alzeG4detW7N69O3x++eWXY9euXViwYAFqamowfvx4rFq1qlXBHEIIIeRgRIaqqmakUusRggk6et2moaEBeXl52LlzDwYOLOzp5hBC+pngHFNfX4/c3Nyebk5GBPtYW7sbgwYN6OnmEEL6oSPhXNqXBd/Pd7/xJHSt+2ukWHYS//W7mfT9d0KnpuMghBBCCCGEEHLkoMCREEIIIYQQQki7aIwjIYQQQgghpFcRQj4ysV3SOZRxJISQXmD16tXhtBubN2/Ga6+9hpqamh5uFSGEENJDgsgxEw/SKYccOP75z38GAPz1r39td71du3Z1rkWEEHIE+uEPfwhd1/Hoo4/i0ksvxbJly3D++efjsssu67ZqqoQQQkhfITyRsQfpnEMOHP/whz+guroar7zySrvr3XjjjZgzZw7++c9/hssee+yxQ28hIYQcAXRdBwAsX74c7733Hp577jls3LgRF1xwQThPIiGEEEJITzmkwPHuu++G4zg499xz4bou7rnnngOue9FFF+Gll17Csccei3POOQeTJk3CihUrutxgQgjpjwYNGoRbb70VtbW14Lz51Dxjxgxs3ry5B1tGCCGEEHKIxXEWLlyIn//857j33nuRn5+P7373uwdcd9GiRXj33XcxaNAgbNy4EQsWLMA3vvGNLjeYEEL6o2effRYvvPACnn/+eUybNg3f+MY3cMIJJ2DDhg1QFKWnm0cIIYQcVlQcp/c55K6qjuPghz/8IVzXbXe9oUOHIplMAgBOPvlkvPTSS3jwwQc710pCCOnnBgwYgO9///soLS3FL3/5S9TX1+OJJ57Ahx9+iOeff76nm0cIIYQcXlQcp9c55Ok4brzxRgDA9773vXbXe+ihhzB58mR87Wtfw7hx4/Dll18iEol0rpWEENJPffHFFxg2bFjasvz8fNxxxx091CJCCCGEkNYyNo/jhAkTsG7dOrz66qvYuHEjsrKy8PLLL2fq4wghpE8aMWIECgsLMW7cOIwfPz58WJaFRx99FM8880xPN5EQQgghpOuBY1t3ywOxWAxTp07F1KlTu/oxhBDSL23btg3r16/Hhg0bsH79evz617/Gl19+CQDIzc3t4dYRQgghPSNTU2fQdBydd8hjHPc3YsQIFBUV4bzzzsMPfvAD/Pd//zc2bdqEdevWYfr06d3RRkII6bdGjBiBKVOm4K677sJvfvMbfPHFF/jLX/6CY489Fk888URPN48QQgjpEQIZGuLYibYsWbIERx99NCKRCEpLS/HOO++0u/6KFSswevRoRCIRnHTSSfj973+f9vqLL76I888/HwMGDABjDBs2bGi1jXPOOQeMsbTHDTfc0InWd58uB47btm3Df/3Xf+Gss87Cp59+ih/96EcYP348TjvtNOqaSgghnVBWVoZHHnkEP/3pT3u6KYQQQkjP6CXFcV544QVUVlZi4cKFeO+99zBu3DhUVFRg586dba7/1ltv4corr8R1112H9evXY8qUKZgyZUra1FrxeByTJk3C/fff3+5nX3/99dixY0f4eOCBBw6p7d2ty11VR4wYEd4xD1RXV2P69OntzvNICCEEsCwLuq63Wj5q1Cj87W9/64EWEUIIIf1fQ0ND2nPDMGAYRqv1HnroIVx//fWYMWMGAGDp0qV45ZVX8PTTT+POO+9stf4jjzyCyZMn47bbbgMA3HvvvVi9ejUee+wxLF26FABw9dVXAwA+++yzdtsYi8VQUlJyyPuWKV3OOLaF7pYTQkjHZGdnY/z48ZgxYwYeeeQR/OlPf8Knn36Kn/3sZygvL+/p5hFCCCH90rBhw5CXlxc+Fi1a1Gody7Kwbt26tH+POecoLy9HdXV1m9utrq5u9e93RUXFAddvz3PPPYeioiKMHTsWc+fORSKROORtdKcuZxzpbjkhhHTe66+/jvfffx/vv/8+nnvuOcydOxepVAoAMHnyZCxYsAAnnXQSTjrpJAwZMqSHW0sIIYQcHsKTj0xsF5AFPlsWoWsr27h79264rovi4uK05cXFxfjoo4/a3H5NTU2b69fU1BxSO//t3/4NI0aMwJAhQ7Bx40bccccd2LJlC1588cVD2k536nLgmJ2djRNOOAETJkzA+PHjMWHCBAwZMoTulhNCSAdMmjQJkyZNCp97noctW7Zgw4YN2LBhA9555x38/Oc/x86dO7Fv374ebCkhhBBy+AghIA5xPGJHtwvIyuW9uXr5zJkzw/8+6aSTMHjwYJx33nnYunUrjj322B5pU5cDx0O5Wz569OguN5gQQvozzjnGjBmDMWPG4MorrwyX19bW9mCrCCGEkCNPUVERFEVp9W9wbW3tAccelpSUHNL6HVVaWgoA+PTTT/tu4Hgod8td1+3qxxFCyBGpuLi41UB+QgghpN/qRAXUDm+3g3Rdx8SJE1FVVRUWAvU8D1VVVZg1a1ab7ykrK0NVVRXmzJkTLlu9ejXKysq60upwyo7Bgwd3aTtd0e3FcYK75VdeeSXuv/9+rFq1Cjt27AgntO4Of/rTn/Cv//qvGDJkCBhjWLlyZdrrQggsWLAAgwcPRjQaRXl5OT755JODbvdQ52ghhBBCCCGE9F+VlZX4+c9/jmeeeQYffvghbrzxRsTj8bDK6jXXXIO5c+eG699yyy1YtWoVHnzwQXz00Ue46667sHbt2rRAc+/evdiwYQM++OADAAiTbsE4yK1bt+Lee+/FunXr8Nlnn+Hll1/GNddcg6997Ws4+eSTD+Pep8tIVdW27D9ItCvi8TjGjRuHJUuWtPn6Aw88gEcffRRLly7FmjVrkJWVhYqKirALbVsOdY6W9ijKYTushBBCCCGE9Du9ZBpHXH755fjpT3+KBQsWYPz48diwYQNWrVoVxjbbt2/Hjh07wvXPOOMMLF++HE8++STGjRuH//mf/8HKlSsxduzYcJ2XX34ZEyZMwEUXXQQAuOKKKzBhwoRwug5d1/Haa6/h/PPPx+jRo/GDH/wA06ZNw29/+9suHtWuYSITo04PI8YYXnrppTB9LITAkCFD8IMf/AA//OEPAQD19fUoLi7GL37xC1xxxRVtbqe0tBSnnnoqHnvsMQAyDT1s2DDMnj27zTla2tLQ0IC8vDzs2bMPhYX5Xd43QghpKTjH1NfX9+oB/V0R7GNt7W4MGjSgp5tDCOmHjoRzaV8WfD/fPutn0NVot2/fcpL45Z9n0/ffCf0uNbZt2zbU1NSkVXTNy8tDaWnpAedP6cwcLQBgmiYaGhrSHoQQQgghhJCuCaqqZuJBOqffBY5B3+BDmT+lvTla2ptzZdGiRWkThw4bNqyLrSeEEEIIIYT0mr6qJNTvAsfDae7cuaivrw8fX3zxRU83iRBCCCGEEEK6XZen4+htgjlSamtr08rV1tbWYvz48W2+pzNztACAYRgwDKPrjSaEEEIIIYSEesFsHGQ//S7jOHLkSJSUlKCqqipc1tDQgDVr1hxw/pSWc7QEgjlaujrnCiGEEEIIIYT0dX0y49jU1IRPP/00fL5t2zZs2LABhYWFGD58OObMmYP77rsPo0aNwsiRIzF//nwMGTIkrLwKAOeddx6mTp0azqlSWVmJ6dOn45RTTsFpp52GxYsXp83RQgghhBBCCDk8hCcgvO5PD2Zim0eKPplxXLt2LSZMmIAJEyYAkEHfhAkTsGDBAgDA7bffjtmzZ2PmzJk49dRT0dTUhFWrViESiYTb2Lp1K3bv3h0+P9gcLYQQ0l8cffTRYIy1etx0000AgHPOOafVazfccEO72xRCYMGCBRg8eDCi0SjKy8vxySefHI7dIYQQ0g9RVdXep8/P49ib0DyOhJBM6q65x3bt2gXXdcPnmzdvxr/8y7/gjTfewDnnnINzzjkHX/nKV3DPPfeE68RisXY/8/7778eiRYvwzDPPhD09Nm3ahA8++CDtpt3B0DyOhJBMo3kce7fg+7mi9OGMzeP4/Jpb6fvvhD7ZVbW3cxwPnifAOevppvRp8q4QwjtDitInE+S9xv7Hk3OZSSJHnoEDB6Y9/8lPfoJjjz0WZ599drgsFou1WxysJSEEFi9ejHnz5uHiiy8GADz77LMoLi7GypUrccUVVxzwvaZpwjTN8DnNh0sIIQQAIPxHJrZLOoWuxDPAcVxYlgPLcikd3kmO48I0HViWA9t2Ydvyuet6Pd20Psl1PZim2+p4Oo5Hv9FOksfUgdfHx0pYloVf/vKX+M53vpN2I+G5555DUVERxo4di7lz5yKRSBxwG9u2bUNNTQ3Ky8vDZXl5eSgtLUV1dXW7n0/z4RJCCCF9A2UcMyDINHqeB9P0oKoKVJVi9I4QQsC23fBiPLiQDTJltu3CdQU0jVO2rIMcx4XjBAE3Q3DYhBBwHBeuy6BpCmXIO8jz5O8wCLgdx4OuKz3cqs5buXIl6urqcO2114bL/u3f/g0jRozAkCFDsHHjRtxxxx3YsmULXnzxxTa3UVNTAwCtxoQXFxeHrx3I3LlzUVlZGT5vaGig4JEQQggVx+mFKHDMAF1XoetqeHHpOHIsEQWP7RNCpGVpWwbc8jh6cF0PnufBtgU0TaHg8SBkoC2DRkXhUNXmgNtxvDDjaFkudJ2Cx4PxPAHLcsLnqsr7fBfqp556ChdccAGGDBkSLps5c2b43yeddBIGDx6M8847D1u3bsWxxx7brZ9P8+ESQgghfUPfvuLpxThnMAw1vKiUWR/3IO86stl20G2SQdfVtECbMZkV03V5r0NewFNX4Pa4rhcGjaqqtAq0VZXDMIJl8nj29W6XmRQE2ADAOYdhqFDVvn3z4vPPP8drr72G7373u+2uV1paCgBp0yC1FIyFrK2tTVteW1vb4XGShBBCCOndKHDMME1rzpoFGR7SmiwoJI9Ne5kvzpkfPLKw6yoFj60F3SmB5kxjWxhj0HUKHjtCHk/h38ToH12lly1bhkGDBuGiiy5qd70NGzYAAAYPHtzm6yNHjkRJSQmqqqrCZQ0NDVizZg3Kysq6rb2EEEKOHHKYUmYepHMocDwMWna5lGPKKHhsyfNaduc9eHdJGTwq4Xttm45nS0FADTRnatvTVvBIwXg6x2kOqPtLF2nP87Bs2TJMnz4dqto8amHr1q249957sW7dOnz22Wd4+eWXcc011+BrX/saTj755HC90aNH46WXXgIgf0Nz5szBfffdh5dffhmbNm3CNddcgyFDhmDKlCmHe9cIIYT0C5mKGukap7NojONhoqoKhJDdB23bBWOsW8eTCSHSCsr0lrFqntc80eqBpn8IghzOD5wZ2x/nMiCShXQ82DYOGiAdart6SlCt03X97zN4gQFM/g80jYfddtt6f3OX344dkyB4DILGYMxjbzouPUXe2JA3J/pTEaHXXnsN27dvx3e+85205bqu47XXXsPixYsRj8cxbNgwTJs2DfPmzUtbb8uWLaivrw+f33777YjH45g5cybq6uowadIkrFq16pDmcCSEEEJCnoBgGQjyqGdVp1HgeBhpmgwePc87pGIknieQStpwbA+cAwAD44DCORSNw7bdVl1gg8Cqpy50bduFZbXOrioKh6bxMGvTPB2E7P53KILxo0EBGMbYQQPPYJqUlt0xGWNQFOaPA+y5LoiyCm/r7zJoqfAEzJT8HQByjGI0qkJROYyIBs7ZfkHOoe1LEDyaphtmLftLdq0rWt7Y6OuFcFo6//zz28wsDxs2DH/84x8P+v7938sYwz333IN77rmn29pICCGEkN6DAsfDTNM4LEuEWZ3m4iRtc2wXibgdlg6WcZj877hlwXE8RLM0KEpzkBBkH+U8iK5fVObwBZCplB0WEQFkEBtMpxEUbDFNF7rG4QkRBnydCVAUhUOI5uJDjKHNi3shBJJJOy0oa9kux5EBl2nKbG00qh224xVU6mzZ5Tb4zhiTvSpcx0UibkHhDFA5bMuBZXlwbBe6ocK2XMSydLhecxa1M0FOc+bRCbsB9+WpJrqq5TyXh3pjIxOCccCEEEIIIYcbBY4Z0LJc//462iVQCAEz6cBMyW1xhSES1cJAwrQcOAmZObNSDrJyDBiGCsYYGPwusf70FcGk75GIekjdOTsjCFaBYFoSpcW8lnIsY5Dxa4pbAIBYTOvSVCWqysOg9EDdgJNJOdl9cPxbHnPXlUWLgvkjPQ84HEm24Pu3LTfMKgaVTgEWZkUt04aZcsEZg6oyZOdqgBBIJB24jgfLdP1996CoDEZEhaZ1/k+7q92A+4v0qXS6nnmVU8nIGyWMHXoXact00FhvdqkNhBBCSF+RqUI2VMah8yhwzIBEwkI8biEW09q8MDxYl0DP85BosuA68petRxQYET8oZDKgcJMCmq7CMh04tou6vQmomoJIi0yZwjkYANMP1BJNJhSVIxLRwotXReXdNhedzNjJQDcS0VplqoKKqLquIpmwYVkOhAAsywPndnhx3plMX3vdgC3L8bORMpO4f5CqKHL/DUNNu7jPFNlGmS0Mzl3Bx1kpB/EmM+x6m0rYcFwPwhPgnCGWrcMTMsDUNQUuZ1BUDst00NRkIuIHncFNhAMJtn+gY90yk9vRbsD9TZAB7uy+u66HVMqBmbJhmS4EmscgB39/mq4gYmjQDeWAf4NCCCTjNmzLpfH8hBBCjhwUOfY6FDhmQDJhY9/eBJoaOGJZBgxDAVeau0UKL+i2KbsoCghwJgM4x3GRjNt+8AIYUQ3MZrBtmZ3zhECiyYJpOlA1jmhMh2O7cGwXnuMhFbeg+RnNVEoGTAICriODISOiwra9MKh1HA8m/K6NKk+bPuRQJRImGutMcEUGQMIT0A0VekRBNKqH6wkhwPzKqPX7UmiqT8F1BVRNBrWKymHoKoyoAk1TOxRICiGPl2W6EJ4HM+kXhmEMyWRzZlNR2t+WDCI7tfsHJbO/zWMYhRBwbReOI28eeMGJTAikUg4STaYskCMAParCMFQkEjYScRueK8e7aoYGK+WgqSklt5NykJ2tYU9MR3ZOBLEsPfxuXc8DRFAYSH5UML5TUThULf0GQstKwM2Zt94RPHp+lli0HODOmoMyzhkY73yRKHkDobmbaqLJ8p8HrwtYKRsCAkKWLEIkqkGPyN9sfV0SDXVJOG7LYw2oCgfjDIrfvlTSQYNIQVHkTZVIVG2uLMzkd5WMW/BcAc459MiRl/klhBBCSO9AgWMG1O9NwLZUCL/7Y3aOgWhMh6oAtuVCM1QoqgIEXTdNF4rKwfwLX0CuZxgqbMWVwQ5jsFIuTMtGKunAEx5UVYNjuvA8F1bSRv2+FEzLgQCgaSpUncMTHhgAzuR4woZ9CbgewBmQna0jEtUQiepQVfkZskwNg6IAwu+y6QoB5gFclYVtGBgUf7yXEEDDvgTq9ibRUJ8CA6CqgJl0ZMBjqIhkaVA1FbEsHbEcA4o/Ri8Rt6H6gZxjy+NgJh3ohoJGIduoqhyRmIZoVIMeCcZyIsy8mkmZiXGDYAxCdv0UQDIuvw/TdOA6LqyoAa4koRkqVJWDcw6uyOCCK9z//0OvsOp5Aq4/D6Xs6tpc4VYIGbTb/nQOaeM9Hbl+EPBwAEzhMhPpeFAVDkDAMDgcVyDemIJrC7ieB9d1kIzbiMdtmEkHlmnBsgWE68GxPNm1WeeIRCPIyVMRjUUQy9URixkwYhpiMR2aroIpgOf5NxBMgHH5W1EUWQwGAFzHg+vK46ppClRNAWcA62Q1Wtd18ec//xk7duzA4MGDcdZZZ0HpQLRupmzYfvGgtICxHYxBfrfB9xx8536w3Fb7gy7ElunAc7y04LO+LoGmeguJuAUh0scbarqKRNJCImGDMQ7NYIhEDESjKlSNAeCwUg5sS4619YQ8xq7nwrE92Q3dE1BVBYqiQFEAx/LCG0jRbA1mKtGh/SaEEEL6OpGhqqodvYYgrVHgmAGW6aCoSJUZB8eTgaTpoHFfEq7rQdflRaDws0YQDLYjwBRAU1WoKkNWji67WzKGeIMJz++j1lBnwUxZ4BxwEg5M00FDfRKJuA3LcWAlXdi2A1VTYEQ1RCIq4I97FGBwXQ/JhOkHh3JKB91QEI1p0AwVwnXheQzJhAUr5fhBJ8BUDs7kexSVQ9EUuLaDZJMNKHIfFD+wbNxnyW6ynCOZTKK+LoGsnAjMhI1dNY2Ixy0wAFk5EWRla8jKjUBVZDDpOB6slA0GBtuWwRj2AICAoijQDDk+0UzYSDTafrYWUDQFmsERjepQVC6zegxoajRhJmxoqoJU3Pan75EX51xlUP0uwkFmiQHgKgPAwjFoXGHNXXpVJSw+5PpjSNMrtAKuJ+C5HlwXzd0TgeZMcxBoCk9mkQwFmsqRTLmo2x1HU2MKru1CCAaVA6Yt4LgOAAbHcpFKWmFXX8vyYFo2hCu3CyEr7jLIgFbxA2I1okFTObJzDOQVRFBYFEVWThRZOREoGodwZXEg7ndf1vx9DboxCyHCjKiqNGcmwwyf0jLTJwMizgHG/YDcD75efPFF3HprJbZv/zw8ZsOHj8DDDz+ESy65pM2/J9tykYxb4c2B4DgHAWFLws+mep4I/9t1PLgHGHbMuMy4tgwobUsWIgJj0P3KtImEiX27E/73IgvlqJoGrjBYlot4k4XGeBxNDRYcR45HNSIq7LiDJtXvgm4L2J4HO+nAclw4KQem7cK2BCA82TNAeBBuMP4Rsvu5oSKWbUDTOEwnecDzDiGEEEJIJlHgmAHbPtoJs8FDKm7LLoiuQE5BDJ7tAozBc+JQNAW248K1Hbh+RU/XERCukOOeslRomiYDEzBEohxGTAWgAFxAU+UYtHijDTNlwfEYGDw/O6nAdTwkGkwkG03ougotKscWOqYNzwVsxwbjHE2NKXge4LgemJ/lcxwhX3cAweQyhTNoigLGActyYaYceBBQuALOBVRNhaZzPwMqoGsK9IgcM8g9BtPdCytpy6I+tgdFURDJkmO7dF1FLEtDVnYUWlSDoshuf5qqwPODNK5wmMKDU5dEY10Kru1B1TiMiAx8FduDZTLE60wwhYMrgO14iDeYAGPIyYmAK0wWk7FdWIkkbNOGqivwXAbLdmAl7eZgFTILKrNbLgQ4OBNgjIOpLAxcdF2FasgxqJGoBk1XwRUFqiq7va559y+4/8F7ceftC3HaKWfCMh1YKReuJ8CYrK5bvzeFvXvjMBtN2JYHD4DiR+wMABMCri2DINuRlXRdW2Yq/TgYjAO6rslAmDM4rgXXApKmC7ge3EYTXAB7uYBgABOym2s0piErP4KcHNm1VY/ofgDF5XeuyoBKVRRwVd7Y4IqcBkTTlANm7bDfIs4ZXvn9b/Gd66/GUcNOxYUXzUZ+wQjU7fscmzb9Gt/61rfw/PO/xiVTp/rvZ/BcD8m4Jcf2wS8QFdGg+uMBD1ZcRggBzw0ywJ78b1eEwb7wH44nAMjvPJm0EG8yIQRgGCqSDKjfl4RjubLLr6YgryCCWLYOxhjqG0wkzSa4jotkwoGqyGPmWh4a9iZgJuXv3XFlYAhPdm0FRHiDgoHB8s8Truv6XVsFFM6hKrK4VTxuw9AVWC5lHAkhhBwZaIhj70OBYwZsfusLfKrvhuPILBHgZ2T8C0LH9ABFXuwrjMucFGcyi2gJmYlCMMYJcDwPDLL7INM4DF2RRVBUBY7rj4XUOQxDg6LJi3xVYfAEIDiHyhiYIlviMEBTOJSICithIWW5sJIOXNeF48qCIJ7tgDMGzS+iI9viT/GRcOB5LhgY6ht3I2k1QgiZXXNtB57twfUEFBWIaDnI1gph2U548Q/IQyJkQhCqwsBUDlVVoegMqqpCMxREoyqiMR05+VHoQTVYDsTrTdiWAzCGaJaOaJYhg2yDyyyrpkHVFTAAiaQFM+FAj6hwdAWu68jiM/UmzKQcR6ppClIpF5ZpAwBs00FTg4lkwoJry2BDFvAxkTI9uJYLV8BPH/rfLpffH2McOufguhyvpugcT/z6bmz/8iPcc/dd+PHNjyE7Pwrd0CE8D/EmU2aKk7Y/llGAK4Cqa+Aq989ssotoojGFlOnCsWQ3TU3hAOfgGve79DJ4totU0oZty2q8tunBcR24LgA/iAJk2wWTvw+VA4xxOYelzqBqGhRDQUxXoEZV6FENmsKhGfLGA2cMzOCIqCr0qCKzpYr8zoLuoIwBqqqCczkuT1UVCHj40fw7cdRRp+Lr584DYzJjOXDQaHz93Hl44/X7UHnrD1A64WtQNQ1CCKQSNhgEmKIgElURieky8DbbSB/6ASRjLf67jedgMnuswB9vHGSPbQ/xJlnUCn4X1L21TWhqMOEJD/CAWI4OITxs29KAvbuSqK9LyuJFtgMBBk1VwPwMs/DHQ9q2rHbrODKbzjmHoity3lBFgaYxQOHQOOCy4F9ImfHNzo/AEQJ20oXruGiybDTFKeNICCHkCCGQmaJwndjmkiVL8J//+Z+oqanBuHHj8LOf/QynnXbaAddfsWIF5s+fj88++wyjRo3C/fffjwsvvDB8/cUXX8TSpUuxbt067N27F+vXr8f48ePTtpFKpfCDH/wAzz//PEzTREVFBR5//HEUFxcf+g50EwocM6B2+z7oPNVcNsMPLDzXg0xmMSgAmD+siyn+5aYrAFe+SzCB/btgMzBwlYErCpjCwIP3ewDzAxnO5DrMDzyFfCPgAi4A4coxj1D8cXVc9msUQsBzZKDEGAPTZaEcRVPAITOSniv7mgsBJFJ78cuqH8FxDjw9gKoYuKTsbmRHBwCCNf+dimAkJWDZLoTngjFbdhdlAIcKrjUnrRgHWFBQUgVULgMWI6qCqxyqrkLTAZVrftAmx4jJqUwYVE1eiHueBwgBRVWhKgoURcjPh59ltRyYcQu268J1ZVDhmC5SSReWa8ssnycQXN8LwcJjB7QIUvzg6Ytdm7D9y49w4unT8be3n8HKF1/BiEEny8JHihy/yVUZFGXFdESzIuBZCjxHwLQdmEkbqYTlj+tz5Y0Af9xpwnWax5/6vyfH8WQVVLu5iAuEzDDKLtHyt+AJ2W0SArAA+Vvz5HNw7gd+siqvoilQOQNTVWhRBkVRwfxqvZrqd09W5ffFFdm9VXZzZTAMBaqmAgz48OP12LHjH7jwojlh0Bj+rhnH2JMuxR9euQ0rlr+M8SedCtdyITiT3bqz5PfFNQVcUcAVDsOQmVAjosngVJNdo8OxjIc4zYWZdJA05XjRxoYUbNORbfBkBth1XPzj831o2JuEbTkyewkBJvy/Vy6z7SoXcP2usoD8e2RMyGrHCoPnyDGqHIBwHMRTAsJzAQ/QojqiEQ5dV6BFNRQMiMCyPCQ1B8mkDTPpQThuu/tCCCGE9B+9I3J84YUXUFlZiaVLl6K0tBSLFy9GRUUFtmzZgkGDBrVa/6233sKVV16JRYsW4Rvf+AaWL1+OKVOm4L333sPYsWMBAPF4HJMmTcJll12G66+/vs3PvfXWW/HKK69gxYoVyMvLw6xZs3DJJZfgr3/966HvcjehwDEDGvcloMBrHoflX8PKzKL8by4AMC7H6HEuowHBwot7AH7RFHnRLwBwjUN4HK7nQFgCwvbCC36Fpb8PLdL74Vgv4XdHVYLsjAxwPA+y76InwMDD7CRXuHwvwkRIGJDuTeyE45g4Z/pS5Jd8pdUxqKv5GG8+cwNSJxyH7CEnhM3a3/7NDrQ3zbkDINXO66Gs/Z63jFe8/T5E9R+xjmz44IQQWPvUYgw8ahxOq7gdO/+xAe/+owrFFdeF3Sstf90mALv33wDz29JN7TkUAs1t69Sbbf/RIjm29QvZxTK/YESbbyvwl1f97j188Z6swMsgb6pw+N1xWYssov93woK0L4LiRko4ppJx2cWaBZlQRQmfMya7ADtBZtDx4Nge4DGAC7iOK7uOCw+eI/92ZIZSfjbXZddtVVPAOQfjMiAXfnZXyEo3UIIxoGow9pRBeAKu7SBhuvAcV/79ieAUIHseKLoCrioyMwrA81x4rgfTpIwjIYSQI4Pw5KVxJrZ7KB566CFcf/31mDFjBgBg6dKleOWVV/D000/jzjvvbLX+I488gsmTJ+O2224DANx7771YvXo1HnvsMSxduhQAcPXVVwMAPvvsszY/s76+Hk899RSWL1+Oc889FwCwbNkyjBkzBm+//TZOP/30Q9uJbkKBYwYwt3ncl8xMhK/IAE3I7B9jHrgCuMwNC6fI8Wr+xa0f4HFVAddkFVHh2PAsWZQFHvO73wHuAYaZCeGPU/QzfADCSp7BHRchZBEXz3PDLqTB/womZIAlgj80GUgmTZlpzC/5CoqGj+u+g9dP/HPrX7HrH+/j/G8/CcYYJpxzE/7vlzPxz61/xVHHTerp5h12seyBAIC6fZ9j4KDRrV7ft08Wy7EbNOxGI8CEHw/6v9sWAxLCn7qAn51PXyZvmngt0u0yOGQC8g6LP6TRE0J2KQ1u7Ij9bm4wFnZzBZOZeM7kNhhkQCgACFc0B7BBFprJrukMftDpeeENHAYBz2vedNDsVpjcttwfuYbtHTjDTwghhJCOa2hoSHtuGAYMw0hbZlkW1q1bh7lz54bLOOcoLy9HdXV1m9utrq5GZWVl2rKKigqsXLmyw21bt24dbNtGeXl5uGz06NEYPnw4qqurKXDsT2IFBhQeAWNyXBnzgiImcgwb40xebDJAgPvBmgtP+Bem/jQIXFH8b0hA2LLAh2t7YTdDAGF0KNDi6tnPxvhhYZglBIOco1D2qZQX18H1NRgURQtf86/K/avtIEBl4UsppXlexvbYa9bCzN4ZdqMF8xN94UV9cKUPP2UTPJXdYllQIhTwK3fKTKlMiwbHEH4lzba68bXYH39PmB9MM3+sYnN7RFqWNliJc/jjPHna8WWMhZVGw464TL53/eYHMXDoyRh67JkAgKHHnomBQ0/G+pd/gpKTfwgviOpdQPhfgvCruzI/1cz8uSk1XQc3FCh+qk0AYJ7MZrkQcC00l5ZWAdWfA1B+CMKKvHDkd8gU+Bkw5ndj9f+fyalCwhsdnmyjX9YFwvXnIA2OExNg4a3AICXdHAIJT4RHfqDwkB0pwsaNv8a55zWPcQRkkLdp4wpkR4owuPh48PA1mbVjAASXfWz9RF6YmGdgUIJuuJw134CBnO5Gtrbl99Y85Qxv/lmE4x1F+DsQ4XIIQAGT1XABcL/LNnhwM8X/voDwxpD8DXvNB1Ow5jjUY8GfuF9tNmhE86EM2sHUIHCVBYvUjHTZIYQQQnqfYPqyTGwXAIYNG5a2fOHChbjrrrvSlu3evRuu67YaV1hcXIyPPvqoze3X1NS0uX5NTU2H21hTUwNd15Gfn9+l7XQ3ChwzoPCYfGgwZEbDvxIXYNB0FXpEgRpRwMGg+GPUXNsLx6VxlUHTFcgxgQKM+0Gj48A0hezaZntyYvHgIt6WUzt4QmY+RNDlVbDm8W3+tB9BptHzL+oh/BiMcSiMyaIsXIFwXTh+IMaEvOLlnENRAQ4OHm8APjz4sSgenochxUVgngyyXD+rKbMvzfMdKgoD4xwQDJ7nwHFkUOX44w2BoEciC6u/CgByeJgAPA8Cqt99MQiU/a7AjAEKAw/mTOSQhWX8eRtdPwsk/MBVuPKYwJ9mIri4VzTFDzoZRHixL8fTMfiBMQf+/o/12NXwd5z/zSfDzDNjDBO+Pgv/98uZiKufYVjJyXCC4juuzEAJz4PnyvSuwuXUJ1zl0HQOIyLn24wY3J9zUgWDB8sf/2hbcr7PaMyAEVFlZVkOqKoC1VAhXP/3pXNEDUX21A2mC/GCMEwEYan8Xbryd+d6TFYAduTNDdeV80x6EHKKCb9bp+t6fndND26QYfN/Y8IDzppwFf5Q/Sher7oPJ518KQoKRmDfvs+xaeMK/OOLd/Evp3wf2flReVj9mwnB+1vGS6Ll/4eRfvMjuCchbzD4YWIYn7EWmT75fQU3JoLvlEP+PfD9pmkJ/n5Yi+MkX/PXCfqUQ8jjL794qEx2uVVVmfdnnEEzFEQiGhRdgWoo0FVZXIhx+bcgx5oyuI4AV/2bTYLDSsXxwrqD/90RQgghpH1ffPEFcnNzw+f7ZxtJaxQ4ZkDhwFyoqi6zN6YL13PBOIemy/FXjiMzj1xRoHgAIrLbHBOAEVEA/4JR4XJS8UhE9TNUMkPhpFw4tiMDMduFnXJgpvyCLn5AqGgMelQBZ3JOQ8t04XpyjKMIxmxBQFGaK7syRZHv01U5MbkrA1UIwHHcMMsUydLB9nVs8N2JE4fhK18ZBV1XAeHC8QQcy4VjubLipOXAdeV+uLYL03Lh2C7gCXj+JbpjObBMWclVCMB1Hbi2zDAG8wSCM8Dz4Dl+IOMCXAXA5DQVnAmkkq4fuDIoOgP3qwupisw+McjMomLIi/ZgUnqZwRJ+lkpWgWU86MnIwzkKOeQcjm//4SXkFgxDJFaA3V9+EB6LSKwAuQXD8M7HL+O0snNkUBomdxk8y4bryCDS9QSY58G1XRnkQkBVNRhZKooG5yA7x4DrCTi2TE8VFkVRMCgHOfkR5BVGoUfldC62ZSPeYKKx3oTnODCiepj99sKKn/KYe64skMT8Aj8t52RUNA4mmP+ZDhzbgWkL2KYD23RhO0ElV/k7cy0XpiuDR9cREI6HYaMqUFSSjd9W/Rf+8Mpt4XHJyxmES867FaNHnh5W8fE82Zbm8bl+xhMtMqhpAWXrO5KsObZr0eW0OQfN/Ok8ZLDm30jQGTjj4FwB48LPzHKojPvVf+XfserP7anrMuBTuQJVZ+EYYUWRlX0Vf1ylosuAH0I2RdNUaJHm069/n8NvD4OiMihclcv8sZqGocK0ksDCDv3pEUIIIX1a0BsoE9sFgNzc3LTAsS1FRUVQFAW1tbVpy2tra1FSUtLme0pKSg5p/QNtw7Is1NXVpWUdD3U73Y0CxwwYVJKF/Px8aBENdkpeuOuGClXnMJMWUqZfRVFTkJ1rwIjp0FQGPapC1RQ5PYMnkJ8fgRGVmctU0gZjsniNYzngHDBisqIkANimjaZGC2ZCzkWoawqiWQYUhcGyHKQStqwG6XmwTReqqsCIaVAUBjNlI5V0kEo5SMbl9AJcALH8KPIHRKDrGlzLxe6dcZhxE64rYBhah47FhLLhOPNrJ4bjI11PZrgcT0C4MnBJNqXQuDeFun1J2LYjK08KD4amwfVcpJIOXM8FV1VwIZBosmBaLoQ/H5/nCD+IBITHYLtynkkhBAxDScv6uZYLz//vVMqBovgTwCsKDIMjtzCGWJ6OiKEDENB0FY4f4Hqe8KedkIFlOEegkPvkCQHLthH/+T407KvFy09e2uYx0ZXB+OqZwxGNGX5hFQbFzyq5DmAlbaQaTSTjFpLJFMyEi6ZGE05K7mOiwUQqYSOSpSM3N4IRxw9EbkEUgwbnIJZjpM1taKZUMMaRnRdFLFtmIz3/uLl+pVw5L6QH13Fh+YG7bbtwbE9+b0KEwaaiMGhaFhSNQdN4WIyG+YGm53r+e124rgfHCcbtAq4rMNkZh1t+8B1Ur3kLNTW1yM8txPGjxkPTFAj/+3E9eSPB87OewhP+vItBN1k/i+h393QcB8K/syJEWMsYYEJ271U49IicSkRVAK6oUDUVRoTLiqyGAsWvJgu/eI3CAC+YG1ORvQAihpwzVNUUqLoqu6H7x0UE6fuwj7ispir87r/CcdDYYMHz5Bym0SxNZrj9pCjjDAJ+V2UmK9pyhUHTFOi6AlWVc1c2NqaPxyCEEEL6LQ/7FR/oxu12kK7rmDhxIqqqqjBlyhT5ds9DVVUVZs2a1eZ7ysrKUFVVhTlz5oTLVq9ejbKysg5/7sSJE6FpGqqqqjBt2jQAwJYtW7B9+/ZD2k53o8AxA44+tgg5OdlQFQXRLB3ZuQYcx4OZcOB6HhzTwZ59CaiKgoGDs1AyNB+qyqEbKnbVNAIACgZkoXBgFsyUg0TcQrzJgqIwxGI6OGcwstIDN8YYLFPOl2iZDhzLRSoup3IIuoNGs3VoupwywfMEUnEbruP6U3xwuJaLnTuaULcnAcaEnODeUBDN1gFwDBlRiHh9CrbtIrVxJwBZPbUtwXLGGKyk449P9DN7CgfnAh5nUDiHrsaQmxfDkJFyTKiiysxMENgIyGkzACAa1aFwBtf1YKUcpBKWnGTd8WCZNpJJG67rIZpjICtLQ6LJwt7aOFIpGwwMBQOiiGTraNyXQqLRhGW6yMrRkFuYhcFH50M3NCQa5QTwuQUR6LoKI6LKwF9TOvT9bzj9XezcubPFeDmkdcstKhqIoUOPCtfnTAYOAPxMrwfHcpBI2NhTG0e8KYV4Qwpmwka8PoVU0gGEQF5hDCVH52PwsDwUDspu1T7bcpFoksVUIjENhp/h4gqX80W2sT+eJ8fSyoDShWO6sEwXpunAddLPtLYtwBwHXJXdOhVVzqWZZciReK4foAItxmACALIx/JhvAgAMQ0UkpkFRFf/34q/i/4cQLdOKTLbNk92/w6BdBO1u/66kEDJYDqoJa7oCTVP9rCpLu8HA/LGqtu3B8QPhlttnjEHLUvzfBU8L1vf/TM8TaKxLwjA0GcTn6nKsZYvmMtY8Bljxb0yE43lbsJyO3bAhhBBCSPeorKzE9OnTccopp+C0007D4sWLEY/Hwyqr11xzDYYOHYpFixYBAG655RacffbZePDBB3HRRRfh+eefx9q1a/Hkk0+G29y7dy+2b9+OL7/8EoAMCgGZaSwpKUFeXh6uu+46VFZWorCwELm5uZg9ezbKysp6rDAOQIFjRhQPzkXUyAIYkJ1nIH9AFoQQsFIOFIVDUWSXv6a4Bc/1oOkq8gfEYCZtDCjOAWdA/gA5l0Q0SwdTGBSNQ+EM0ZgORZUXlkL42SJXThWgarL/ZHauAVVVZPbHcmXmLaKGmbKA5/ldDf1gEwwoKM6BJwTMlA0r4SDRlEIibsvxcRrHUaMKoaoKooUnwngkijefueGAxyESiWFgURFcx4PbxpztLXGFIRLVoUfUNi/C43HTb4cIuw6q2Tpi2bJIj+t54IzDiKkwTRdWyoGmcQgBjBjlwYzbaGxIhd0dFUVBdl4EisKgGyqKSnKgGyoa62XQaERURKIaNEOFqiphYNcRw4YNazXg+lAEXV9zNAVZOYbsvuq4MtNq2vBcASspM455hTHk5EXSgkAhBCzTRTIuJ9UwIiqisY4VM5JTWciiTLqhpk1p4jgeLMtp7p4adG915dyEtuUCsMP5LIP98Dw/4PSDMw4ZyGZl62HAGBSmCYvUhFVPm4vWHCwwDMjxsizM2jHm110SzQGopnEoCm93O0xhMBQeBtyOLYNo23LheR4s04FlOuBcjl/WdEWOT27BdTwkE/LvR9dV5ORFZJdVQgghhPQJl19+OXbt2oUFCxagpqYG48ePx6pVq8ICONu3bw97AALAGWecgeXLl2PevHn40Y9+hFGjRmHlypXhHI4A8PLLL4eBJwBcccUVANIL9Dz88MPgnGPatGkwTRMVFRV4/PHHD8MeHxgTmeg8fIRqaGhAXl4etn36T+Tk5MqxTaoCz/NkVzc/Wxior0+iqT7ld0eTF6eMwQ9omn+A8bgls2hRDVobWaIgk+I46VVE5Dg9Hs5r1xFCCDQ1WWE3TyvpIpWSmS6uckAIqLoCI6Lioy1bUfPlzjDj4rkeFEVmTvMKoigZXIyjjjpKZg295sAgIIvTcL+raPsX054n0ORnz2IxDRAyo+bYblomzBNAImGBc4bc3IifXfO7UTouGhtMNNan4LkejIgGMMB1RJjpUjUFRlQG8q67f996v1trMP7vECaZ78hxD7uPtgiQVFWBqvJwHduSQbFtp1eQ5X4xHcZZeLMAkN2hs3KNbm1ryza7rgfXEWGBHsfxZHAeBIJhtRr53chMdsvsHvfb3vHj2TxPIwsLHMllPO13HhxP1/VavJdDaydD2FHNQaTTKhMZ/J5dp/mzGWPIzouE32VnBeeY+vr6g47L6KuCfayt3Y1Bgwb0dHMIIf3QkXAu7cuC7+fi4xZBUyLdvn3bTeE3n86l778TKOOYAbkFURQWZh90vVhMl11GE7Y/VQdHNKa1yoQEF6YHCv4YY/4YqPTgQ2YkgwCjOeCRQU/rbnDBtnRdgWnKyqY5+RHkIIKi4mwIAViWCzNlIxm3UVIyFCUlQ6Ey2XWUc4ZYloH8ATGZrQp0w6+Mczney7ZlUZ1oVAuzbJ4/VtKxXSQTlj+FApNFdvYLsDRNQWFRcxpNVquUGTrFL3wSy9L9gK1l8CGDctcVcN3mNgUBZGcCSRFULg27WTYHIIoiC7G03CZjMjuqG6rMFluuzP753ShbTkei+OP6jANkcLtD8LtTVcDwv+TgdxeMSwyeyzGK/u/TE/D8LLkcEijguCIMumRWXlaF5X510aA7acs7evsLAtmWn9uyrZqmdPgGysGomuL//nT5PViOn4kU8gaO0xww6oYCPaJ1OWgkhBBCjiQy4ZCZ7ZLOocCxB2maAk1ToORwGIYKw2j9dQTdUIPsYXtaXninByVyigf532nvCAPIsOikP9ec43hwHC8MjlpehMturwyKJrsDRmMaGGTXxkx2wwsCR8eRwUFYzVThYZdCVVegaLKQiKErzUFEizFwqibHear7dVcMjnNLQddNVW0OhIIgsq1xdfJYph/Plieolt0xW2NQVRaOcWsP5wyGHxgKIfzslvyutTCoOfzkd8KADnx88Ntsndlt5o+QhPCY/5t2045pMI7Ua56Mc7/2yAC8uwLGtrTsoirH5QZ/O3KMcKYCd0IIIaRfo8ix16HAsYdpWpDd89DW9DHBHIaHeuErs0FBgRHujxHz4Hlokd1q3X00wBlgOx5M00YkEhTkaM5YCg8wIoCuKy1ezyxVlYGe61fubDvQlsV1IhE17P7bUe1d4DcH5YCmNRdjCTNsLSaMP5Te30G2MuhS3BmMMT8D1qm395jmoLy5q+7+2cKgy2tbQWHbMteduCOCbKnWsSGlhBBCCCF9BgWOPSwIHIOxWK27qcoU4cGyje0JMmDBvIXA/sVIgCCIDESiGkTCgRCAqvCwIE/AsmS1m/a6DmaCpikHCRy9w9Ku/ceNtj6erQPyYLqHllleykZJLQPzwMF+o8H76HgSQgghhGQeBY49LH3sntsqQAwyjorSvRfEzd0p296upilhl1BPCKj7XZBnql0Ho2kcpsn8sWRe2rixlpmqw92ugx1PcujomBJCCCFHrqBmQia2SzqHqjX0AkGlVNv20n7MQbEPoGsZx87S9eZ2tXQo4y67myxywv12pRe+aXmsKPNECCGEENJ3BUMcM/EgnUOBYy8QFO8I5mUMtBzfmMniHgdulyzssf+UBs0BWs8EZ0GgHWREW7eLftaEEEIIIYR0J7rC7iWCYMiymrNoPR0Icc7CrqAts3vN3VR7pl1BAZL06UYQBt09FdASQgghhBDSX1Hg2EsEgWPL7F5PZ/ZatqtlN9qeDmhlu+RnB4F2y6kxKONICCGEENLHUV/VXoeK4/QSMrunwHFcWJYLw2A9ntkDmrvRep6AbbvgnMPzmidr7ymyGq0bFskJgsae6tZLCCGEEEK6D03j2PtQaqYXaS5G4yKVsiGECLtl9oZ2maYL05TTcKhqzxagaVkkJ5Wyw+lBtB6a+J4QQgghhJD+jALHXkRVeVrhF6A5aOtJuq6GYwqDbqq9pV2MsbCbqgwme75dhPR2Rx99tD/dSfrjpptuAgCkUincdNNNGDBgALKzszFt2jTU1ta2u00hBBYsWIDBgwcjGo2ivLwcn3zyyeHYHUIIIf2QnMc5Mw/SORQ49jKGoYZdLRWFp81T2JMMQw0zjJqm9HgWFJDdUiOR5nbpukLdVAnpgHfffRc7duwIH6tXrwYAXHrppQCAW2+9Fb/97W+xYsUK/PGPf8SXX36JSy65pN1tPvDAA3j00UexdOlSrFmzBllZWaioqEAqlcr4/hBCCCEk82iMYy/DOUN2thHOk9hbqCpHTk7va5emKdA0pde1i5DebODAgWnPf/KTn+DYY4/F2Wefjfr6ejz11FNYvnw5zj33XADAsmXLMGbMGLz99ts4/fTTW21PCIHFixdj3rx5uPjiiwEAzz77LIqLi7Fy5UpcccUVmd8pQggh/Yt38FV61XaPAD2fNiJt6q1BELWLkP7Fsiz88pe/xHe+8x0wxrBu3TrYto3y8vJwndGjR2P48OGorq5ucxvbtm1DTU1N2nvy8vJQWlp6wPcETNNEQ0ND2oMQQgihrqq9T78OHJcsWYKjjz4akUgEpaWleOedd9pdf8WKFRg9ejQikQhOOukk/P73vz9MLSWEkJ6xcuVK1NXV4dprrwUA1NTUQNd15Ofnp61XXFyMmpqaNrcRLC8uLu7wewKLFi1CXl5e+Bg2bFjndoQQQgghGdVvA8cXXngBlZWVWLhwId577z2MGzcOFRUV2LlzZ5vrv/XWW7jyyitx3XXXYf369ZgyZQqmTJmCzZs3H+aWE0LI4fPUU0/hggsuwJAhQ3rk8+fOnYv6+vrw8cUXX/RIOwghhPQulHHsffpt4PjQQw/h+uuvx4wZM3DCCSdg6dKliMViePrpp9tc/5FHHsHkyZNx2223YcyYMbj33nvx1a9+FY899thhbjkhhBwen3/+OV577TV897vfDZeVlJTAsizU1dWlrVtbW4uSkpI2txMs37/yanvvCRiGgdzc3LQHIYQQQnqffhk4WpaFdevWpY234ZyjvLz8gONtqqur09YHgIqKinbH59DYHEJIX7Zs2TIMGjQIF110Ubhs4sSJ0DQNVVVV4bItW7Zg+/btKCsra3M7I0eORElJSdp7GhoasGbNmgO+hxBCCGmXyOCDdEq/DBx3794N13UPabxNTU3NIY/PobE5hJC+yvM8LFu2DNOnT4eqNhfYzsvLw3XXXYfKykq88cYbWLduHWbMmIGysrK0iqqjR4/GSy+9BEAWp5ozZw7uu+8+vPzyy9i0aROuueYaDBkyBFOmTDncu0YIIaQ/oMCx16HpOLpg7ty5qKysDJ83NDRQ8EgI6RNee+01bN++Hd/5zndavfbwww+Dc45p06bBNE1UVFTg8ccfT1tny5YtqK+vD5/ffvvtiMfjmDlzJurq6jBp0iSsWrUKkUgk4/tCCCGk/8nUeEQa49h5/TJwLCoqgqIohzTepqSk5JDH5xiGAcMwut5gQgg5zM4///wD/uMZiUSwZMkSLFmy5IDv3/+9jDHcc889uOeee7q1nYQQQo5QmcoOUtzYaf2yq6qu65g4cWLaeBvP81BVVXXA8TZlZWVp6wPA6tWraXwOIYQQQggh5IjXLzOOAFBZWYnp06fjlFNOwWmnnYbFixcjHo9jxowZAIBrrrkGQ4cOxaJFiwAAt9xyC84++2w8+OCDuOiii/D8889j7dq1ePLJJ3tyNwghhBBCCDniUMKx9+m3gePll1+OXbt2YcGCBaipqcH48eOxatWqsADO9u3bwXlzwvWMM87A8uXLMW/ePPzoRz/CqFGjsHLlSowdO7andoEQQgghhJAjkxDykYntkk7pl11VA7NmzcLnn38O0zSxZs0alJaWhq+9+eab+MUvfpG2/qWXXootW7bANE1s3rwZF154Yac+l36PhBBCCCGEdF4QN2bicaiWLFmCo48+GpFIBKWlpXjnnXfaXX/FihUYPXo0IpEITjrpJPz+97/fb98EFixYgMGDByMajaK8vByffPJJ2jpHH300GGNpj5/85CeH3vhu1K8DR0IIIYQQQkgf1EsixxdeeAGVlZVYuHAh3nvvPYwbNw4VFRXYuXNnm+u/9dZbuPLKK3Hddddh/fr1mDJlCqZMmYLNmzeH6zzwwAN49NFHsXTpUqxZswZZWVmoqKhAKpVK29Y999yDHTt2hI/Zs2cf+nHsRhQ4EkIIIYQQQkgbHnroIVx//fWYMWMGTjjhBCxduhSxWAxPP/10m+s/8sgjmDx5Mm677TaMGTMG9957L7761a/iscceAyCzjYsXL8a8efNw8cUX4+STT8azzz6LL7/8EitXrkzbVk5ODkpKSsJHVlZWpne3XRQ4EkIIIYQQQo4oDQ0NaQ/TNFutY1kW1q1bh/Ly8nAZ5xzl5eWorq5uc7vV1dVp6wNARUVFuP62bdtQU1OTtk5eXh5KS0tbbfMnP/kJBgwYgAkTJuA///M/4ThOp/e3O/Tb4jiEEEIIIYSQvkkIAeF1f+GQYB7iYcOGpS1fuHAh7rrrrrRlu3fvhuu6YXHNQHFxMT766KM2t19TU9Pm+jU1NeHrwbIDrQMAN998M7761a+isLAQb731FubOnYsdO3bgoYce6uCedj8KHAkhhBBCCCG9i8jQ1Bn+Rr/44gvk5uaGiw3DyMSndVplZWX43yeffDJ0Xcf3vvc9LFq0qMfaSl1VCSGEEEIIIb2LyOADQG5ubtqjrWCsqKgIiqKgtrY2bXltbS1KSkrabHZJSUm76wf/fyjbBIDS0lI4joPPPvvsgOtkGgWOhBBCCCGEELIfXdcxceJEVFVVhcs8z0NVVRXKysrafE9ZWVna+gCwevXqcP2RI0eipKQkbZ2GhgasWbPmgNsEgA0bNoBzjkGDBnVll7qEuqoSQgghhBBCepkW6cFu327HVVZWYvr06TjllFNw2mmnYfHixYjH45gxYwYA4JprrsHQoUOxaNEiAMAtt9yCs88+Gw8++CAuuugiPP/881i7di2efPJJAABjDHPmzMF9992HUaNGYeTIkZg/fz6GDBmCKVOmAJAFdtasWYOvf/3ryMnJQXV1NW699VZ8+9vfRkFBQfcdikNEgSMhhBBCCCGkVxEZGuN4iNM44vLLL8euXbuwYMEC1NTUYPz48Vi1alVY3Gb79u3gvLkT5xlnnIHly5dj3rx5+NGPfoRRo0Zh5cqVGDt2bLjO7bffjng8jpkzZ6Kurg6TJk3CqlWrEIlEAMjxls8//zzuuusumKaJkSNH4tZbb00b99gTmBCHevjIgTQ0NCAvLw+7d+/FgAE9dzeAENI/BeeY+vr6tAH9/Umwj7W1uzFo0ICebg4hpB86Es6lfVnw/ZyXMw8qi3T79h2RQlXjffT9dwJlHAkhhBBCCCG9ihACIgM5R8qZdR4VxyGEEEIIIYQQ0i7KOBJCCCGEEEJ6l0wlBinh2GkUOBJCCCGEEEJ6Feqq2vtQV1VCCCGEEEIIIe2ijCMhhBBCCCGkV+kt03GQZpRxJIQQQgghhBDSLso4EkIIIYQQQnoXIZCRnCOlHDuNAkdCCCGEEEJIr0LFcXofChwJIYQQQgghvQtNx9HrUOBICCGEEEII6VWoOE7vQ8VxCCGEEEIIIYS0izKOhBBCCCGEkN5FhP+Tge2SzqCMIyGEEEIIIYSQdlHGkRBCCCGEENKr0BjH3ocyjoQQcgT65z//iW9/+9sYMGAAotEoTjrpJKxduzZ8/dprrwVjLO0xefLkg253yZIlOProoxGJRFBaWop33nknk7tBCCGknxKeyNiDdA5lHAkh5Aizb98+nHnmmfj617+OP/zhDxg4cCA++eQTFBQUpK03efJkLFu2LHxuGEa7233hhRdQWVmJpUuXorS0FIsXL0ZFRQW2bNmCQYMGZWRfCCGEEHJ4UOBICCFHmPvvvx/Dhg1LCwpHjhzZaj3DMFBSUtLh7T700EO4/vrrMWPGDADA0qVL8corr+Dpp5/GnXfe2fWGE0IIIaTHUFdVQgg5wrz88ss45ZRTcOmll2LQoEGYMGECfv7zn7da780338SgQYNw/PHH48Ybb8SePXsOuE3LsrBu3TqUl5eHyzjnKC8vR3V19QHfZ5omGhoa0h6EEEIIRAYfpFMocCSEkCPM3//+dzzxxBMYNWoUXn31Vdx44424+eab8cwzz4TrTJ48Gc8++yyqqqpw//33449//CMuuOACuK7b5jZ3794N13VRXFyctry4uBg1NTUHbMuiRYuQl5cXPoYNG9Y9O0kIIaRPExAQIgMPihw7jbqqEkLIEcbzPJxyyin4j//4DwDAhAkTsHnzZixduhTTp08HAFxxxRXh+ieddBJOPvlkHHvssXjzzTdx3nnndVtb5s6di8rKyvB5Q0MDBY+EEEJIL0SBIyGEHGEGDx6ME044IW3ZmDFj8L//+78HfM8xxxyDoqIifPrpp20GjkVFRVAUBbW1tWnLa2tr2x0naRjGQYvuEEIIOfK4MPvUdo8EFDgSQsgR5swzz8SWLVvSln388ccYMWLEAd/zj3/8A3v27MHgwYPbfF3XdUycOBFVVVWYMmUKAJnZrKqqwqxZs7qt7YQQQvo3XddRUlKCt2seydhnlJSUQNf1jG2/v6LAMQMYYz3dBEIIOaBbb70VZ5xxBv7jP/4Dl112Gd555x08+eSTePLJJwEATU1NuPvuuzFt2jSUlJRg69atuP3223HcccehoqIi3M55552HqVOnhoFhZWUlpk+fjlNOOQWnnXYaFi9ejHg8HlZZJYQQQg4mEolg27ZtsCwrY5+h6zoikUjGtt9fUeCYAYpCgSMhpPc69dRT8dJLL2Hu3Lm45557MHLkSCxevBhXXXUVAEBRFGzcuBHPPPMM6urqMGTIEJx//vm4995707qVbt26Fbt37w6fX3755di1axcWLFiAmpoajB8/HqtWrWpVMKcjdJ3+eSKEkCNVJBKhwK4XYkIIKi3UTRoaGpCXl4f6+nrk5ub2dHMIIf3MkXCOORL2kRDSs+g8Q0jn0HQchBBCCCGEEELaRYEjIYQQQgghhJB2UeBICCGEEEIIIaRdFDgSQgghhBBCCGkXBY6EEEIIIYQQQtpFgSMhhBBCCCGEkHb1ucDxxRdfxPnnn48BAwaAMYYNGza0WieVSuGmm27CgAEDkJ2djWnTpqG2trbd7QohsGDBAgwePBjRaBTl5eX45JNPMrQXhBBCCCGEENJ39LnAMR6PY9KkSbj//vsPuM6tt96K3/72t1ixYgX++Mc/4ssvv8Qll1zS7nYfeOABPProo1i6dCnWrFmDrKwsVFRUIJVKdfcuEEIIIYQQQkifovZ0Aw7V1VdfDQD47LPP2ny9vr4eTz31FJYvX45zzz0XALBs2TKMGTMGb7/9Nk4//fRW7xFCYPHixZg3bx4uvvhiAMCzzz6L4uJirFy5EldccUVmdoYQQgghhBBC+oA+l3E8mHXr1sG2bZSXl4fLRo8ejeHDh6O6urrN92zbtg01NTVp78nLy0NpaekB3wMApmmioaEh7UEIIYQQQggh/U2/Cxxramqg6zry8/PTlhcXF6OmpuaA7wnW6eh7AGDRokXIy8sLH8OGDeta4wkhhBBCCCGkF+rVXVWfe+45fO973wuf/+EPf8BZZ53Vgy1KN3fuXFRWVobP6+vrMXz4cMo8EkIyIji3CCF6uCWZE+wbnUcJIZlyJJxLCcmEXh04fvOb30RpaWn4fOjQoQd9T0lJCSzLQl1dXVrWsba2FiUlJQd8T7DO4MGD094zfvz4A36WYRgwDCN8vnv3bgCgzCMhJKP27NmDvLy8nm5GRuzZswcAnUcJIZnXn8+lhGRCrw4cc3JykJOTc0jvmThxIjRNQ1VVFaZNmwYA2LJlC7Zv346ysrI23zNy5EiUlJSgqqoqDBQbGhqwZs0a3HjjjR3+7MLCQgDA9u3b++WJqKGhAcOGDcMXX3yB3Nzcnm5Ot+vv+wf0/33s7/sX9GoIzjX9EZ1H+7b+vn9A/9/H/r5/wJFxLiUkE3p14NiWvXv3Yvv27fjyyy8ByKAQkFnDkpIS5OXl4brrrkNlZSUKCwuRm5uL2bNno6ysLK2i6ujRo7Fo0SJMnToVjDHMmTMH9913H0aNGoWRI0di/vz5GDJkCKZMmdLhtnEuh4zm5eX125MtAOTm5tL+9XH9fR/7+/4F55r+iM6j/UN/3z+g/+9jf98/oH+fSwnJhD4XOL788suYMWNG+DyYKmPhwoW46667AAAPP/wwOOeYNm0aTNNERUUFHn/88bTtbNmyBfX19eHz22+/HfF4HDNnzkRdXR0mTZqEVatWIRKJZH6nCCGEEEIIIaQX63OB47XXXotrr7223XUikQiWLFmCJUuWHHCd/QdEM8Zwzz334J577umOZhJCCCGEEEJIv0E5+m5kGAYWLlyYVjCnP6H96/v6+z7S/vV9/X0faf/6vv6+j/19/4AjYx8JyQQmqBYxIYQQQgghhJB2UMaREEIIIYQQQki7KHAkhBBCCCGEENIuChwJIYQQQgghhLSLAkdCCCGEEEIIIe2iwLGFP/3pT/jXf/1XDBkyBIwxrFy5Mu312tpaXHvttRgyZAhisRgmT56MTz75JG2dmpoaXH311SgpKUFWVha++tWv4n//93/T1tm7dy+uuuoq5ObmIj8/H9dddx2ampoyvXsAumcft27diqlTp2LgwIHIzc3FZZddhtra2rR1emofFy1ahFNPPRU5OTkYNGgQpkyZgi1btqStk0qlcNNNN2HAgAHIzs7GtGnTWrV/+/btuOiiixCLxTBo0CDcdtttcBwnbZ0333wTX/3qV2EYBo477jj84he/yPTuddv+3XzzzZg4cSIMw8D48ePb/KyNGzfirLPOQiQSwbBhw/DAAw9kardC3bF/77//Pq688koMGzYM0WgUY8aMwSOPPNLqs3ri+wO6Zx/37NmDyZMnY8iQITAMA8OGDcOsWbPQ0NCQtp2e2sf+fi6l8yidR4Heex4F+v+59Eg4jxLSKwkS+v3vfy9+/OMfixdffFEAEC+99FL4mud54vTTTxdnnXWWeOedd8RHH30kZs6cKYYPHy6amprC9f7lX/5FnHrqqWLNmjVi69at4t577xWcc/Hee++F60yePFmMGzdOvP322+LPf/6zOO6448SVV17ZJ/axqalJHHPMMWLq1Kli48aNYuPGjeLiiy8Wp556qnBdt8f3saKiQixbtkxs3rxZbNiwQVx44YWtvqMbbrhBDBs2TFRVVYm1a9eK008/XZxxxhnh647jiLFjx4ry8nKxfv168fvf/14UFRWJuXPnhuv8/e9/F7FYTFRWVooPPvhA/OxnPxOKoohVq1b1+v0TQojZs2eLxx57TFx99dVi3LhxrT6nvr5eFBcXi6uuukps3rxZ/OpXvxLRaFT8v//3/3r9/j311FPi5ptvFm+++abYunWr+O///m8RjUbFz372s3Cdnvr+umsf9+7dKx5//HHx7rvvis8++0y89tpr4vjjj0/7G+vJfezv51I6j9J5VIjeex4Vov+fS4+E8yghvREFjgew/8XAli1bBACxefPmcJnrumLgwIHi5z//ebgsKytLPPvss2nbKiwsDNf54IMPBADx7rvvhq//4Q9/EIwx8c9//jNDe9O2zuzjq6++Kjjnor6+Plynrq5OMMbE6tWrhRC9ax937twpAIg//vGPYVs1TRMrVqwI1/nwww8FAFFdXS2EkBeFnHNRU1MTrvPEE0+I3NxcYZqmEEKI22+/XZx44olpn3X55ZeLioqKTO9Sms7sX0sLFy5s84Ln8ccfFwUFBeH+CiHEHXfcIY4//vju34l2dHX/At///vfF17/+9fB5b/n+hOi+fXzkkUfEUUcdFT7vLfvY38+ldB6V6Dw6rtXy3nIeFaL/n0v7+3mUkN6Cuqp2kGmaAIBIJBIu45zDMAz85S9/CZedccYZeOGFF7B37154nofnn38eqVQK55xzDgCguroa+fn5OOWUU8L3lJeXg3OONWvWHJ6dOYCO7KNpmmCMpU2aG4lEwDkP1+lN+1hfXw8AKCwsBACsW7cOtm2jvLw8XGf06NEYPnw4qqurAcj2n3TSSSguLg7XqaioQENDA/72t7+F67TcRrBOsI3DpTP71xHV1dX42te+Bl3Xw2UVFRXYsmUL9u3b102tP7ju2r/6+vpwG0Dv+f6A7tnHL7/8Ei+++CLOPvvscFlv2seW+vu5lM6jdB4N9JbzKND/z6VH2nmUkJ5CgWMHBSecuXPnYt++fbAsC/fffz/+8Y9/YMeOHeF6v/71r2HbNgYMGADDMPC9730PL730Eo477jgActzOoEGD0ratqioKCwtRU1NzWPdpfx3Zx9NPPx1ZWVm44447kEgkEI/H8cMf/hCu64br9JZ99DwPc+bMwZlnnomxY8eGbdN1Hfn5+WnrFhcXh22rqalJu9gJXg9ea2+dhoYGJJPJTOxOK53dv47oyDHItO7av7feegsvvPACZs6cGS7rDd8f0PV9vPLKKxGLxTB06FDk5ubiv/7rv8LXess+7q+/n0vpPErn0UBvOI8C/f9ceiSeRwnpKRQ4dpCmaXjxxRfx8ccfo7CwELFYDG+88QYuuOACcN58GOfPn4+6ujq89tprWLt2LSorK3HZZZdh06ZNPdj6junIPg4cOBArVqzAb3/7W2RnZyMvLw91dXX46le/mnYceoObbroJmzdvxvPPP9/TTckI2r+D27x5My6++GIsXLgQ559/fje2rnt0dR8ffvhhvPfee/jNb36DrVu3orKysptb2P36+7mUzqN9S3/fP6D/n0uPxPMoIT1F7ekG9CUTJ07Ehg0bUF9fD8uyMHDgQJSWloZdibZu3YrHHnsMmzdvxoknnggAGDduHP785z9jyZIlWLp0KUpKSrBz58607TqOg71796KkpOSw79P+DraPAHD++edj69at2L17N1RVRX5+PkpKSnDMMccAQK/Yx1mzZuF3v/sd/vSnP+Goo44Kl5eUlMCyLNTV1aXdiaytrQ3bVlJSgnfeeSdte0Eltpbr7F9hr7a2Frm5uYhGo5nYpTRd2b+OOND+Ba9lWnfs3wcffIDzzjsPM2fOxLx589Je6+nvD+iefSwpKUFJSQlGjx6NwsJCnHXWWZg/fz4GDx7cK/bxQPr7uZTOo3QeDbbTk+dRoP+fS4/k8yghPaF33drsI/Ly8jBw4EB88sknWLt2LS6++GIAQCKRAIBWd4wVRYHneQCAsrIy1NXVYd26deHrr7/+OjzPQ2lp6WHag4M70D62VFRUhPz8fLz++uvYuXMnvvnNbwLo2X0UQmDWrFl46aWX8Prrr2PkyJFpr0+cOBGapqGqqipctmXLFmzfvh1lZWVh+zdt2pR20bZ69Wrk5ubihBNOCNdpuY1gnWAbmdId+9cRZWVl+NOf/gTbtsNlq1evxvHHH4+CgoKu78gBdNf+/e1vf8PXv/51TJ8+Hf/+7//e6nN66vsDMvcdBueYYIxdT+5jR/X3cymdR+k82hPnUaD/n0vpPEpID+m5ujy9T2Njo1i/fr1Yv369ACAeeughsX79evH5558LIYT49a9/Ld544w2xdetWsXLlSjFixAhxySWXhO+3LEscd9xx4qyzzhJr1qwRn376qfjpT38qGGPilVdeCdebPHmymDBhglizZo34y1/+IkaNGnXYpuPo6j4KIcTTTz8tqqurxaeffir++7//WxQWForKysq0dXpqH2+88UaRl5cn3nzzTbFjx47wkUgkwnVuuOEGMXz4cPH666+LtWvXirKyMlFWVha+HpSRP//888WGDRvEqlWrxMCBA9ssI3/bbbeJDz/8UCxZsuSwlOjujv0TQohPPvlErF+/Xnzve98TX/nKV8LfRFD9r66uThQXF4urr75abN68WTz//PMiFotlvIx8d+zfpk2bxMCBA8W3v/3ttG3s3LkzXKenvr/u2sdXXnlFPP3002LTpk1i27Zt4ne/+50YM2aMOPPMM3vFPvb3cymdR+k8KkTvPY921z725nPpkXAeJaQ3osCxhTfeeEMAaPWYPn26EKK5TLOmaWL48OFi3rx5aWW2hRDi448/FpdccokYNGiQiMVi4uSTT25VUn7Pnj3iyiuvFNnZ2SI3N1fMmDFDNDY29pl9vOOOO0RxcbHQNE2MGjVKPPjgg8LzvF6xj23tGwCxbNmycJ1kMim+//3vi4KCAhGLxcTUqVPFjh070rbz2WefiQsuuEBEo1FRVFQkfvCDHwjbttPWeeONN8T48eOFruvimGOOSfuM3r5/Z599dpvb2bZtW7jO+++/LyZNmiQMwxBDhw4VP/nJT/rE/i1cuLDNbYwYMSLts3ri++uufXz99ddFWVmZyMvLE5FIRIwaNUrccccdYt++fb1iH/v7uZTOo3QeFaL3nke7ax9787n0SDiPEtIbMSGE6FhukhBCCCGEEELIkYjGOBJCCCGEEEIIaRcFjoQQQgghhBBC2kWBIyGEEEIIIYSQdlHgSAghhBBCCCGkXRQ4EkIIIYQQQghpFwWOhBBCCCGEEELaRYEjIYQQQgghhJB2UeBICCGEEEIIIaRdFDgS0gcxxrBy5cqebgYhhPRZdB4lhJBDQ4EjIYfo2muvBWOs1WPy5Mk93TRCCOkT6DxKCCF9j9rTDSCkL5o8eTKWLVuWtswwjB5qDSGE9D10HiWEkL6FMo6EdIJhGCgpKUl7FBQUAJDdn5544glccMEFiEajOOaYY/A///M/ae/ftGkTzj33XESjUQwYMAAzZ85EU1NT2jpPP/00TjzxRBiGgcGDB2PWrFlpr+/evRtTp05FLBbDqFGj8PLLL2d2pwkhpBvReZQQQvoWChwJyYD58+dj2rRpeP/993HVVVfhiiuuwIcffggAiMfjqKioQEFBAd59912sWLECr732WtoFzRNPPIGbbroJM2fOxKZNm/Dyyy/juOOOS/uMu+++G5dddhk2btyICy+8EFdddRX27t17WPeTEEIyhc6jhBDSywhCyCGZPn26UBRFZGVlpT3+/d//XQghBABxww03pL2ntLRU3HjjjUIIIZ588klRUFAgmpqawtdfeeUVwTkXNTU1QgghhgwZIn784x8fsA0AxLx588LnTU1NAoD4wx/+0G37SQghmULnUUII6XtojCMhnfD1r38dTzzxRNqywsLC8L/LysrSXisrK8OGDRsAAB9++CHGjRuHrKys8PUzzzwTnudhy5YtYIzhyy+/xHnnndduG04++eTwv7OyspCbm4udO3d2dpcIIeSwovMoIYT0LRQ4EtIJWVlZrbo8dZdoNNqh9TRNS3vOGIPneZloEiGEdDs6jxJCSN9CYxwJyYC333671fMxY8YAAMaMGYP3338f8Xg8fP2vf/0rOOc4/vjjkZOTg6OPPhpVVVWHtc2EENKb0HmUEEJ6F8o4EtIJpmmipqYmbZmqqigqKgIArFixAqeccgomTZqE5557Du+88w6eeuopAMBVV12FhQsXYvr06bjrrruwa9cuzJ49G1dffTWKi4sBAHfddRduuOEGDBo0CBdccAEaGxvx17/+FbNnzz68O0oIIRlC51FCCOlbKHAkpBNWrVqFwYMHpy07/vjj8dFHHwGQlfqef/55fP/738fgwYPxq1/9CieccAIAIBaL4dVXX8Utt9yCU089FbFYDNOmTcNDDz0Ubmv69OlIpVJ4+OGH8cMf/hBFRUX41re+dfh2kBBCMozOo4QQ0rcwIYTo6UYQ0p8wxvDSSy9hypQpPd0UQgjpk+g8SgghvQ+NcSSEEEIIIYQQ0i4KHAkhhBBCCCGEtIu6qhJCCCGEEEIIaRdlHAkhhBBCCCGEtIsCR0IIIYQQQggh7aLAkRBCCCGEEEJIuyhwJIQQQgghhBDSLgocCSGEEEIIIYS0iwJHQgghhBBCCCHtosCREEIIIYQQQki7KHAkhBBCCCGEENKu/w/U+eBp6qHOHQAAAABJRU5ErkJggg==", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "figure = this_sampler.results.plot_propermotion(\n", " cbar_param=\"m1\", alpha=0.1, periods_to_plot=3\n", ")" ] } ], "metadata": { "kernelspec": { "display_name": "python3.11", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.11.4" } }, "nbformat": 4, "nbformat_minor": 4 }