{ "cells": [ { "cell_type": "markdown", "id": "a6701c44-fcae-4a54-aec2-3a3de1f9624c", "metadata": {}, "source": [ "# Calculating the Barycentric Timing Correction\n", "\n", "When we observe stars using space-based telescopes, such as Kepler and TESS, the arrival time of photons is different to the time they would arrive at the solar system barycenter. For example, If the spacecraft is between the target and the solar system barycenter, the photons will arrive early. So that we can inter compare observations from different facilities, we provide the measurement along with the time those photons would have reached the solar system barycenter. \n", "\n", "`lkspacecraft` provdes a way to calculate this timing correction based on \n", "\n", "1. The time of the measurement **at the spacecraft**\n", "2. The position of the spacecraft at that time\n", "3. The position of the target\n", "\n", "This example shows you how to calculate this for Kepler and TESS." ] }, { "cell_type": "markdown", "id": "893d409f-a906-470c-9b3c-c8c2ddf714d9", "metadata": {}, "source": [ "## Kepler example\n", "\n", "Here we show how to calculate the barycenteric time correction. We'll use `lksearch` to download some Kepler data with which to test. We'll open the `.fits` file using `astropy`." ] }, { "cell_type": "code", "execution_count": 1, "id": "34637c0d-ab43-4114-9772-0c6c8de7921f", "metadata": {}, "outputs": [], "source": [ "from lkspacecraft import KeplerSpacecraft\n", "from lksearch import KeplerSearch\n", "import numpy as np\n", "\n", "from astropy.io import fits\n", "from astropy.time import Time" ] }, { "cell_type": "code", "execution_count": 2, "id": "1f8dc300-2143-4f88-b468-8744331402ca", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Downloading products: 100%|███████████████████████| 1/1 [00:00<00:00, 1.78it/s]\n" ] } ], "source": [ "# Download the file, obtain the local filename\n", "fname = KeplerSearch(\"Kepler-10\", exptime=1800).timeseries[5].download()[\"Local Path\"][0]\n", "# Open using astropy\n", "hdulist = fits.open(fname)" ] }, { "cell_type": "markdown", "id": "55a8aed4-af0e-4f90-861a-2b41c43fc03f", "metadata": {}, "source": [ "We're going to use `lkspacecraft` to create the `KeplerSpacecraft` object" ] }, { "cell_type": "code", "execution_count": 3, "id": "cbf16544-0aae-4e1e-ba7b-e2432282dc35", "metadata": {}, "outputs": [], "source": [ "ks = KeplerSpacecraft()" ] }, { "cell_type": "markdown", "id": "35d778b3-e0d7-402a-a3bd-18bfbeb7f93a", "metadata": {}, "source": [ "Now we'll get the time array out of the data" ] }, { "cell_type": "code", "execution_count": 4, "id": "b5fc143d-80db-4a44-b2c1-d47abb5b2a25", "metadata": {}, "outputs": [], "source": [ "time, time_corr = hdulist[1].data['TIME'], hdulist[1].data['TIMECORR']\n", "k = np.isfinite(time)\n", "time, time_corr = time[k], time_corr[k]" ] }, { "cell_type": "markdown", "id": "8f6255ed-61ff-49d3-9cf6-d694ea36779a", "metadata": {}, "source": [ "SPOC data contains two time extensions:\n", "1. `TIME`: the time of the observations in Barycentric Julian Date (i.e. at the solar system barycenter)\n", "2. `TIMECORR`: the time correction applied to the original spacecraft time, to obtain `TIME`\n", "\n", "`lksearch` only works with spacecraft time. So we need to remove the SPOC applied time correction" ] }, { "cell_type": "code", "execution_count": 5, "id": "dcf7a8dd-ac8f-44fa-8704-ba20f8d4aa8f", "metadata": {}, "outputs": [], "source": [ "t = time - time_corr" ] }, { "cell_type": "markdown", "id": "0b65cbc8-ed14-45d0-8225-2b8c26e8e1ca", "metadata": {}, "source": [ "Kepler times are actually given in Barycentric **Kepler** Julian Date, which is BJD minus a constant. The constant is 2454833, we also store it for you in `lkspacecraft`." ] }, { "cell_type": "code", "execution_count": 6, "id": "00dccb7a-11b7-475f-90e5-0c161ff829aa", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "2454833" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ks.time_offset" ] }, { "cell_type": "code", "execution_count": 7, "id": "3f17a0a3-31be-4b9b-a3c0-a54a65f7faf6", "metadata": {}, "outputs": [], "source": [ "t = time - time_corr + ks.time_offset" ] }, { "cell_type": "code", "execution_count": 8, "id": "cf2e4e31-62be-4f83-ad05-ecb765dc70ba", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([2455276.49115492, 2455276.51158862, 2455276.53202212, ...,\n", " 2455371.11916292, 2455371.13959652, 2455371.16003002])" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "t" ] }, { "cell_type": "markdown", "id": "c95b758f-6260-47ba-a6aa-94fa03933f44", "metadata": {}, "source": [ "We will convert our time from a numpy array to an `astropy.time.Time` object, which will ensure this is a valid time." ] }, { "cell_type": "code", "execution_count": 9, "id": "ac680264-a369-4eba-90b2-29b9a9caf712", "metadata": {}, "outputs": [], "source": [ "t = Time(t, format='jd')" ] }, { "cell_type": "markdown", "id": "8786b595-9932-4ce0-9eee-6517c01af278", "metadata": {}, "source": [ "To calculate the arrival time we will need to know where the target is in the sky, which we can obtain from the fits file." ] }, { "cell_type": "code", "execution_count": 10, "id": "0cec5c00-7b05-4937-8eb8-572b6b530bb2", "metadata": {}, "outputs": [], "source": [ "ra, dec = hdulist[1].header['RA_OBJ'], hdulist[1].header['DEC_OBJ']" ] }, { "cell_type": "markdown", "id": "ef4c41e3-1e33-468c-aea5-0a4b91f0db28", "metadata": {}, "source": [ "Now we have the spacecraft time in julian date and target coordinates, we can calculate the time correction. " ] }, { "cell_type": "code", "execution_count": 11, "id": "20c67ac0-a1af-4edf-8fcd-f3dcf535d6bb", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([-30.41441299, -30.37170985, -30.32899557, ..., 193.18638174,\n", " 193.22012183, 193.25384805])" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ks.get_barycentric_time_correction(time=t, ra=ra, dec=dec)" ] }, { "cell_type": "markdown", "id": "174d4733-26eb-42f9-99cb-620b0844b179", "metadata": {}, "source": [ "This is the correction that should be applied to the spacecraft time in order to give the light arrival time at the barycenter. Let's compare!" ] }, { "cell_type": "code", "execution_count": 12, "id": "5fe1ac76-c860-4401-bd3a-5d7660138e1e", "metadata": {}, "outputs": [], "source": [ "import matplotlib.pyplot as plt" ] }, { "cell_type": "code", "execution_count": 13, "id": "bd6be79c-ab79-40ab-a57f-ab8c122a0cbd", "metadata": {}, "outputs": [ { "data": { "image/png": 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ykiVLmt/leqJp06aZx+0qOGPm2Hjf0aNHt9KnT28tWbLkhcfLE0XG1waR8PTgwQPzWpMsWTIrWrRo1vnz58P1/duVMjshxfcBblRqxymY5VXMhvDEWhfXbTFexsfHxyz1Z5aBTdOYxWnUqNFz/YqYKejQoQNWrVplsj88MYvDDA6nwwIK2AH7dTF7xP4m7Ms0evRoDBo0yOyPFj9+fOzatQvt2rVD27Ztce7cOWcmhVN4XPG3ZcsWM5XH41OxYsUXZlRmzZplbsPHGRjHY2LbhA8++AA9evQwmR/+3hYtWmDDhg3P1R7VrFnTZLw4DUjMkDGjtXDhQmdNUt26dU0bBWaSuF0KpxbLli3rnDb76aefzP1UrlzZZOM4xVegQAFzHe+Hfaoc2TqeAhPcMbPL+HvvvYfDhw+b38fnQkSdvhORiOPp06fmNZT7WPI1lCUOyR4/xukTJ+wbVKiHWe6e2WHm4r+wI/xPgWRNXmT+/Pkmg8FoukiRIla/fv2sQ4cOvTCzwwh82LBh5jgsX77cunjxovneNfPjauHCheZ6ZkAcWRBe9qpCktnhuJ88eeK8LFOmTFbx4sWd5x8/fmwyUj/++KM5/91335nbPH361HkbPl5mLFatWhXoeJipypEjx0vHzWPbunVrf5cxg1S5cmXneT6url27Pve4mFW5fPmy87ItW7ZYceLEse7fv+/vtunSpbMmTpxovi9cuLDVqFGjYGfrAsvsBHfMAwYMcJ5nxo6XrVixIoij4VmU2RHxj6+xzDxnz57dmV1OClhfA9YDvoctW2aFNmV2PBxrdpj9YK0OMxicL2WWgCtzXHF3eGYw2B16xIgR+Oyzz1ClShXn9cFZrBfeC/qyZcvmb9sO7oXGzWBd2xGwzsfRaJI1NsyiMLPjyHqxbxNX0/z555+v9Zh+/fVXs5+bK57n5a7y5cv33M+y6JtdeB04ztu3b5uxO8bJE1f8OMbJDBAzPa8juGNmMbYDM3Zc6eDavFNExGHTpk3mdaRatWomgx0XwDAAfOXqWKYM/HbuBN59Fx69XUSkwi0jbt+273eHAJfDvvPOO+Y0cOBAtGrVykz5OHoYUa9evcx5vqkyaHA0SuObMKdqAr4BOvBy3jZ9+vTmPL//7bffEB4Ctubn7w7sMsfGrQwgWDzNtGpArsGGq4wZM5p92jgFFhpbAQQ2vRfwMo4zefLkgRbyOabNOIUYXoI6piIijg9gffv2NWUNxFeoLgB6A0jAD3nDhwPlysFuyuyEFIMBvknZcXrNjq3sNH2HtT8uEiVKZAIW1uW4doRl5oT1GuxwHXD3+Hv37pnGjqyDYYaEJ34/fvz45+6f7O7vwozWH3/8YVYk8bG6nrikPDANGzY0wYdrA8vAHhPnpF2X8xPPv8pWJRwnjzVrpwKOk/9PjmxLUEvx2R7gZdsXhOaYRcQznTlzBk2bNjWvWwx0mDlp/yyT81mWLEjAFbbctzICBDqkYMcNcXl5mTJl8P3335viUk6DsH8Ot9OoXr16sO9n2LBhJghiZogFs2fPnjVFwQxsmPFgcOPA7/kmy2JZFt0yuGD2Z9y4cc59zOzCwloGC3zsLFDm8WD2pEuXLs4i5oAKFiyI3r17myJeft2xYwdOnz5tAg0WEXNJvyMzxqlBdgPnY2bBNAuF2R8opMqVK2eOVY0aNbB69WqznJ9tALgEfu/eveY2zMz9+OOP5iuPL9PFnH507bPD/6Pz58/jn3/+CfT3hOaYRcSzXL9+3ZQ/MPvNFhyc8q8PgHn9/6VKheRTpwKHDwO1ar32B/TQpGksN8QpKb5Zc6sN1nowMGGDv9atW+PDDz8M9v2wdmTnzp1mdQ9X7DDrwCxOpUqVTCDFhoUObCS4f/9+fPrppyZA4EogThFx+sixLYhdWI/EAIB/oLVq1cKtW7eQMmVKU/sSVMdNBhEcPwM59hniFE66dOlQp04dsyUJMTBhP6BRo0aZFU7spsvtTgJugRIczKz9/PPPJrjh6ig2hmSwWaJECTPFSLxfBq6ffPKJqa/i+Hm9g+P/iuPkSrzAao9Cc8wi4hkePHhgXsv52uNYlclXjM9Zk8jM84ABQLt2QCjuSRmatF2EtosQkVeg1wbxBJZlYe7cuejXr595vhMnvEcCqBwtGrx69AB69waC+OAYEbaLUGZHREREnsOMOKe32e2ekjN7DIBLXHzYL4wbbqdMichAwY6IiIg4cWUtaxWXLVtmzsd6trqqO1eRVqrEOX7Apd1HZKBgR0RERMBaHHZO5yrUx48fIwqANlwYwX5muXMDn38OvGafL7so2BEREfFgjx49wsSJE80qT0fxcdVndTmZuRDl00/Zj4M9SRBZKdgRERHxUKtWrUK3bt2cDWTfBjCGrTDYg6x/f6BzZ3aoRWSnYEdERMQD63J69Ohh2l0Q25Z+AqCVlxd82rfnTsDsOgt3oWBHRETEQ+tyfJ5t7zCQ29K88w4wejTwNvM77kXBjoiIiAfW5VR71hQwY4YM/wU53AQ6AnU9Dk0KdtzU4sWL8fDhQ7O/lYQ9HW8RiajWrVtntsf55ZdfzHnmbUYDeId1OYMGAR07cmM9uLPIW1otQeIeS9x2YMOGDc9dx20Bunbt6m8/pbFjx4bzCCMHbpHBvcG4Q7lj5/GQHu+Q4r5d3DrC7g1UIzPuK8ZjyB2ZRTwV9zPkB7By5cqZQCch968CcMDLC+906ACcOAF06+b2gQ4p2HFT3Etp+fLl6NSpk9mnSl4N9xfj8eOb5u+///7CQCS4x7t58+bm5190YuBZpEgRcx8v2pE9rB04cMBsdsrHxG0QMmTIYPZV4+OPiHhMud+XK+4Fx2P4thvWHogEZx8rbuScOXNms5eeN4BOAP7gzuTlysGHG3VyI2c3KkB+GQU7bixTpkw4duwYkidnk29x4O7s3NQzOLiRKjcD5Rt+kiRJXvt4cwNOvgk7TsRNOB3n2Zbdz8/PbADK4Ce8MWArVKiQebGcNWuWWY7KTV8ZeA0cyBLGV8MpvsD23GGBZFiIEiWKOYY+PpqpF8/C1VUM8plpvnv3LooB2A/gq7feQvwlS4DVq92yAPmluBGop7tx4wY3QzVfA7p37571yy+/mK/uomTJktYHH3zgPJ86dWprzJgxzvOTJk2y4saNa61du9acnzdvnvX2229b0aJFsxIkSGCVLVvWun37trmuWbNmVvXq1a3BgwdbiRIlsmLHjm21bdvWevDggfP+VqxYYRUtWtTcJ3++SpUq1okTJ/yN6ezZs1b9+vWt+PHjWzFixLDy5s1r7dy503n94sWLrdy5c1tRo0a10qZNa37fo0ePnNf/+++/Vps2bawkSZKY22TLls1atmyZuW7atGnmdy9ZssTKkiWLFSVKFOvkyZPW7t27rXLlylkJEya04sSJY5UoUcLat2+fv+PC54XjxMfqet5x2evgfSxatMjfZRs2bDCX8zG5jp+PJ2PGjFb06NGt2rVrW3fu3LGmT59uxhkvXjyrc+fO1uPHj533c//+fatHjx5WihQpzDEtUKCAue8X4f3x/7BGjRqBXu8YD23cuNHKnz+/5efnZyVLlszq06ePv/8PPsc6duxonmc8vqVKlXI+rp9//tnKkyeP5evray578uSJNWzYMCtNmjTmOZYjRw7znHN19OhR87zh8ytWrFhWsWLFzHNo0KBBz/2f8D75/8vvDxw4EKIx8xj26tXLPA+TJk1q7v9F3PG1QSKvP//806patarz7yAZYH0PWE+jRbOsTz7hE9bytPdvV/rYE0J8f2K0bIcYMWKE+af9kSNHmtPq1atRoEABk21o0KCBuaxmzZq4desWtmzZYo6Da/Ebpzs4xcNaiRYtWiBhwoT4lF03Ady5cwfdu3dHjhw5cPv2bXz00Ufmvjg15O3tbS4rWbIkUqZMiaVLl5pP5Pv373dmX/j7mjZtinHjxqF48eIm29KmDZuYs7ZukLldpUqVzNiYhUiXLp2Zn+anewf+n40YMQKTJ082Y2OW5q+//kKzZs3w1VdfmcfzxRdfoHLlyvjjjz8QO3Zsk2Xh7+VOuszIRI8eHdWqVUPt2rVx/PhxczkvCw8cPx//7NmzzeOsVauWOYasI+InOT4Wjqto0aKoV6+e+RlOqfE48GdSpEiBRYsWoWLFijhy5IjJVAXWXOyff/4xe+IExlGzdP78eXOcOH00c+ZM06+D01x8DgwePNh5+xkzZqB9+/bYtm2bOe/IZPXt2xejRo3CW/ykGT8+hg8fbv7fJkyYYMbFzQcbN26MxIkTm+cFf1+JEiVMrdn69evNced9MivETQqZfeLOx8yQUYIECfD333/7G3tIxszn6q5du7Bjxw5zex5T1m2JRESO1zaemJHlmzorMpmHjVO7NvDFF0Dq1HYP037hFn65SWaHGY2AnyTD6+TIpoRVZqd3795W8uTJzadoB2Y6+LtPnToV6H0xs8FsDbMCDt9884359M1P7IG5cuWKuc8jR46Y8xMnTjSf2K9evRro7ZlJ4id/V999950ZK61atcry9va2jh8/HujPMzPC33fw4MEgjoplxstxODJCxKyVa/YmYNbldQU3s8PzrtkwZs+Yrbl165bzsgoVKpjL6fTp0yaDdf78+eeOZb9+/QIdy4gRI8zvuXbtWpBj/vDDD61MmTJZT58+dV42fvx4f//nfI4xExfY42KWzjX7xMexfft2f7dt2bKl1aBBA/M9x8ts3sOHDwMdjyO76CpgZie4Y2bGyBUzQcwABUaZHbETn8sLFy70l4EuC1i/8G09UybLWr3a8gQ3lNmRkGBWgxmYvXv3mk/cDjlz5kTZsmWRPXt2VKhQAeXLl0edOnXMJ3LX2zDr5LoyidkargRInTq1yZQwm8NPy8wcODI2Z86cMXPLzPDkzp3bfCIPzKFDh8wneUemyFF3c//+ffOphj//xhtvIGPGjC98fKyDYWbJ1aVLlzBgwACTkbp8+bK5T94fxxXR8PgyY+XA4mEWM8eKFcvfZXwcxOwNH0/AY8JPfsxsBcY1WxcUZlL4f+yaZWT2g//n586dQyrupQOYWqfA5MuXz/n9iRMnzDEPmDlhjQ+fE8T/X2b0fH19gzW+1xlzwOcI668cx1QkomAml5nbFStWmPNvPtvioRaz/8xUfvCBR6ywCgkFO6/wpsMXSLt+d1jhm8lPP/2EuXPnmmkGB04FrVmzBtu3bzdTW5zyYeEbA5e0adMG676rVq1qgp5JkyaZ6RQGOwxyHEWrL5sK4vFmx09O3QTEaYjgTCXxNgGnADmFdfXqVTNFxfFFjRrVvCEGVkxrt4Bv9HwsgV3mCCR5zPh/t2/fPn/TeeQaILlyBEac4uFxeF1crv+yyx1/S3zucRrTFf8/KLymCimoYypiN35Y4RTw0KFDzYc9hjO9APTj3xU36hw5EgjwdyT/UbATQnzxe9GLeGTG+hx+UmBNB1ewsBbC9THzUzBPzNAwMGD9B2sbHJmXe/fuOd+Udu7cad5QufyXwQTrWxjoMKCirVu3+vvd/DTNWhp29Qwsu5MnTx5zH+nTpw907Px5fjrn0uigsjsBMVvElums5SBmoph5CgozRMSsSUTGrAjHyKyE47i/DLN2iRIlMvVZ/P8NiMvtWbeTJUsWLFiwwGSCHAEkjyXrnJhhC4msWbOaoIbZNNbnvOj/l7U07AAbWHaH/ycv+/8IzTGL2IEZaNbA8cMIlX3WMydj9uzAV18BL/j7kf9o6bk4sb8Li12ZRXE0GWQGh/0aOL3FN6SFCxfiypUr5s3DgZmQli1bmmJY/jyLhhk4sfiY012cNvn222/NlAULTB1BkgMLoFmUzF4pfANiipZvTCwQJQZYLCrluLi0m1MSLLrlFBTxTZIFrCzQZRbq5MmTJr27cuXKIB8vi2G/++47c398nI0aNXppFoGBHt8suUSbx8GuLN/LMOjj42GBNf/PeEx2795tioGZRQkMg3gGnbyehdhr1641Bef8v2fRcrt27cztOnToYALDzp07mxfeJUuWmP9z/r/y/zwkGGwwsOauywxoWHzO4nRmEHme+FxiAXL9+vXNWDgtyv83BsDE6bzDhw+b8wxWGRQFFJpjFglP/MDCv+PSpUub5y4bYMwCsCZmTGQcMwbYv1+BTjDor1z8KVasmHmzYyDBNxyufOHqGGY/+AbKy1nfw9VPDqzpYeDAgIMrgfhG6VjhwjcSBiacTuHUFd/UPv+cu7H4/2TOKTKukOLvYX3QZ5995px+Ya0QgwveJn/+/KYPDJv9MfBwYHDE6xg4MVvAN+eXfdqfMmUK/v33X5M5atKkiWmn/rJeOpxqYdDFqT7WyPCNOKLi6iS+SHJnY/YAYjDJFWaO+pTAVK9e3UxZMoPSsGFD05SMx/TGjRsmde44BgxqGTyxXotBEINdR/AZUp988onp4cNAjEE0s4t8DjqmSRksM0h2rNpjLRAzhY4sD1dV8fGxFogruByrv1yF9phFwhqnT/khkX+DDO6Zj+wAgCF+wzp14MVgn53w1UsqWLxYpQwPx0+NbJrGF3S+ubvivCg/FfOFl/Uh4h+X5nJ6g3tDiXgSvTZIWGFpAKesHNntXAAmstyAHwC+/hp4NvUuCPL925UyOyIiIhEAs5fMxDJ7yUCHSwlYULDHxwcFPvwQOHpUgc4rUv5LRETEZmyo2rFjR7PYguo8C3RSligBfPMNq/ntHmKkpmBHXsv06dPtHoKISKTFzuKsF5w/f745z0q1rwFU5iado0YBTZtySazdw4z0NI0lIiISzlguy9WPLMpnoMPlGH0AHGWg07o1G16xGZgCnVCizI6IiEg4Yk8w7u+3adMmc549xSexEDlbNmDiRLb3tnuIbkeZnWDSojURcaXXBAkp9oBi3zI2ymSgw574owHs8PVFrk8++a9njgKdMKHMzks4er2wcV54tq0XkYjNsa1IwO04RALDHk+tWrUy+9ZRBQDfsEaHHc6//RbInNnuIbo1BTsvwa0TuCcVu+WyiZm6rYoIG77xNYGvDXyNEAlqOTmbZo4bN848bxI+W2XVKHZseLHBKutz9L4S5vRX+hLcGoA7H7N52OnTp+0ejohEEPzgw27UATeYFXHgtjVsDuh472j8bNoqMTc15n5WKVLYPUSPoWAnGLidAbdDiIi7YYuIfa8LyvRKYLgBcteuXfH999+b82kATODUVfLkwPjxQM2adg/R4yjYCSa+qKklvIiIBIX79HHjWW7gyVC4K4CPudFu27bAZ58B8eLZPUSPpGBHRETkNTG44cbA8+bNM+fZ73ga97PKlOm/AmR2QhbbKAcrIiLyGi0IZs+ejWzZsplAh2vz+gPY7+2NAv37AwcPKtCJAJTZEREReQUXL140BciLFy8253M8y+bkyZkTmDYNyJ3b7iHKM8rsiIiIhDCb89133yFr1qwm0GHWYDB3J48SBXkGD2ZTHQU6EYwyOyIiIsF0/vx5tGvXDsuXLzfn8zzL5uTIk+e/bE4O5nckolFmR0REJBjZnKlTp5raHAY6fgA+BbDT1xc5hg0Ddu1SoBOBKbMjIiIShLNnz6J169ZYtWqVOV8AwFQA2QoWBKZOBbJy7ZVEZMrsiIiIvCCbM2PGDLz99tsm0IkKYCSAbVGjIhu3eti2TYFOJKHMjoiISCArrdq2bYulS5ea84UATAeQibuSM5uTMaPdQ5QQUGZHRETExfz58002h4EOa3M+A7A1WjRkGjsW2LRJgU4kpMyOiIgIgGvXrpkuyD/++KM5nwvATADZCxUCZsxQkBOJKbMjIiIe7+effzbZHAY67II8AMAuX19k535WW7cq0InklNkRERGPdevWLXTv3h2TJ0825zMDmMEVV7lyATNnAtmz2z1ECQXK7IiIiEfauHEjcuTIYQIdLwDdHHtaDRz4X98cBTpuQ5kdERHxKPfu3UO/fv3w5ZdfmvNpnq20Kpk583+1OQXYSUfciYIdERHxGLt370bTpk1x/Phxc74NgFEAYnfrBnz6KRA9ut1DlDCgaSwREXF7jx8/xscff4wiRYqYQCcFgBUAJqZJg9gbNwKjRyvQcWPK7IiIiFv7888/0bhxY+zcudOcrw/gfwDit2kDjBoFxI5t9xAljCmzIyIibrvdw5QpU5AzZ04T6MQFMAvAj8mTI/7PPwMTJyrQ8RDK7IiIiNu5cuUK2rRpg8WLF5vzJZ81CExVu/Z/QU7ChHYPUTwls7N582ZUrVoVKVKkgJeXl/NJ6dC8eXNzueupYsWKz3W8bNSoEeLEiYN48eKhZcuWuH37djg/EhERiShWrFiB7Nmzm/cU32ebd66LFQuppk8H5s1ToOOBbA127ty5Y9KL48ePf+FtGNxcuHDBeXK08XZgoHPs2DGsWbMGy5cvNwEUo3kREfEsd+/eRceOHVG5cmVcunQJ3I98N4BeRYsiyuHDQLNmgBc76oinCdY0VoIECUJ0p8zA7N+/H6lTpw7ydpUqVTKnoESNGhXJkiUL9Lpff/0VK1euxJ49e5AvXz5z2VdffWWe6KNGjTIZIxERcX/79u0zH34dS8o/ADA8ShRE//hjoE8fIAo3gRBPFaxg5/r16xg7dizixmV518sLwjp06IAnT56EWofLJEmSIH78+ChTpgyGDh2KhM9SkDt27DBTV45Ah8qVKwdvb2/s2rULNWvWDJUxiIhIxMT3mhEjRmDQoEFmeXmKZw0C38mUCZg1C8ib1+4hSmQqUK5fv74JOoKjc+fOCA2cwqpVqxbSpk1rlg5++OGHJhPEICdKlCi4ePHic2Py8fExmShe9yIPHjwwJ4ebN2+GynhFRCT8nDx5Ek2aNMG2bdvM+drsmwMgYYcOwOefAzFi2D1EiUzBztOnT0O8sVpoYIDlwGIz7mGSLl06k+0pW7bsK9/v8OHDMWTIkFAZo4iIhL/vv//ezCLw/YaLx78C0DRJEnhNmwZUrmz38CSCiVR9dt566y0kSpQIJ06cMOdZy3P58mV/t2Eakyu0XlTnQ9wT5caNG87T2bNnw3zsIiLy+piJZ4NAZnQY6BQFcAhAs+rV4XX0qAIdCZ1gZ8aMGfjpp5+c53v37m3qZtiC+/Tp0whL586dw9WrV5E8eXJzvnDhwqaeiIVpDuvXrzeZqIIFCwZZ9Myl6q4nERGJ2NgYMFeuXJg1a5Z582J+fmOMGEg7eTKwaBGQOLHdQxR3CXaGDRuG6M/2D2HtDJeNjxw50mRcunEjtRBgP5yDBw+ak2P+ld+fOXPGXNerVy/z5D516hTWrVuH6tWrI3369KhQoYK5fZYsWUxdT+vWrc3mbpy37dSpk5n+0kosERH3KULme0+xYsXM+wTX+W4G8FH+/PDh+0fLllpSLkGzQih69OjW6dOnzfe9e/e2mjRpYr4/evSolShRohDd14YNGywOIeCpWbNm1t27d63y5ctbiRMntnx9fa3UqVNbrVu3ti5evOjvPq5evWo1aNDAihUrlhUnThyrRYsW1q1bt0I0jhs3bpjfy68iIhJxnDt3zipVqpTz/aEeYP3Lt66+fS3rwQO7hyc2C+77d4i3i4gVK5aZSkqVKhVWr16N7t27m8ujRYuGe/fuhei+SpUqZZaqv8iqVateeh9cefXDDz+E6PeKiEjExw7I7IrPOsyYz4qQmydLBq/vvwdeY5GKeJ4QBzvvvPMOWrVqhdy5c+P33383DfyIXYzTpEkTFmMUEREP64Tco0cPTJgwwZzPw807AWSsWhWYOhVIlMjuIYq71+ywRoeFwdxkbcGCBc4GfywSbtCgQViMUUREPMSRI0eQP39+Z6DTk/Whfn7I+PXXwJIlCnTklXhxLgsejksZ2R2ay9C1MktEJPzxrYgfpnv27GmaviZ9tkt5+WzZgNmzgbfftnuIEonfv4OV2Tl8+HCIGgtySov9bkRERF6GMwXVqlUz3fcZ6FTh+w4DHXZC3rNHgY68tmAFO6zPYVFycHGai8vHRUREgsK2Ijlz5sTy5cvhB+BLAMvix0eSxYtZNwE8a3UiEuYFykwvDhw4EDGCuc/Iw4cPX2tQIiLi3pj957Y9n376qXmPyfKsCDlnmTLAzJlAypR2D1E8LdgpUaIEjh8/HqLMjqPxoIiISMBu+A0bNsSWLVvM+VbM6ESJghhDhwK9egFRotg9RPHEYIcbb4qIiLwubjfUrFkzUxrBDTy/5abPadP+V4RcoIDdwxM3Fak2AhURkciJ5Q3snfPuu++aQIe9c/Yz0HnvPeDAAQU6ErGaCoqIiIQE97OqV68e9nBlFYAuAEb6+SHquHFAmzba10rCnIIdEREJM/Pnzzdd99kHJR6AaQBqZM4MzJkD5Mhh9/DEQ2gaS0REQt39+/fRoUMH1K1b1wQ6hQEcZKDTrNl/vXMU6Eg4UmZHRERCFVfvctrq0KFD5nxfAB/HiAHfb74Bmja1e3jigV4p2Pnjjz+wYcMGXL58+bnOyh999FFojU1ERCKZ7777Du3bt8edO3eQ+NmWDxWZxeG0FaevRCJDsDNp0iTzRE6UKBGSJUsGL5fCMn6vYEdExPMwuOnUqROmT59uzpcG8D2AFO3aAaNHqxOyRK5gZ+jQoabjZZ8+fcJmRCIiEqkcPXoU7733Hn799VdTCDoIQP/YsRFlyhSgbl27hycS8mDn33//NQVnIiIizOSwEPnevXtIDuAHAKXy5ftv2uqtt+wensirrcZioLN69eqQ/piIiLiRu3fvomXLlmjRooUJdMoDYDlyqW7dgG3bFOhI5M7spE+f3mwKunPnTmTPnh2+vr7+ru/She2iRETEnVdb8YPvkSNHzCfmIQA+jBcP3tzAs2pVu4cn8hwvi9vNhkBa7mHyAixQ/uuvvxDZ3Lx5E3HjxjW9IOLEiWP3cEREIqw5c+aYJoG3b99G0mfTVmW41cPcuUDq1HYPTzzMzWC+f/u8SttvERHxLA8ePDB7W40fP96cLwngRwDJP/gAGDkS8POze4giYdNU0JEUcl1+LiIi7oUfcrnaau/eveb8h5y6ihULPtOmAXXq2D08kbDZLmLmzJmmXid69OjmlCNHDtNISkRE3MvSpUuRJ08eE+gkAPATgE9z5oQPdypXoCPumtkZPXq0KVBm86iiRYuay7Zu3Yp27drhn3/+QTdW4ouISKT26NEj9O/fH59//rk5XxDAXACpuEv52LFqEijuX6A8ZMgQNA2wv8mMGTMwePDgSFnTowJlEZH/d/78edSvX998kKWuAEZEjw6/b78FGje2e3giYV+gfOHCBRQpUuS5y3kZrxMRkciLfdQaNWpkMvV865gKoHaWLMD8+UDWrHYPTyR8anbYZ2culxgGshwxQ4YMrzYKERGx1ZMnTzBo0CBUrFjRBDq5AOxjoNOoEbB7twIdidRCnNnhFFa9evWwefNmZ83Otm3bsG7dukCDIBERidguX76Mhg0bmtdxagtgrJ8fon39NdCqFZfc2j1EkfANdmrXro1du3ZhzJgxWLx4sbksS5Ys2L17N3Lnzv16oxERkXC1fft20w3577//RgwAEwE0Tpfuv2mrXMzviHhggbI7UoGyiHgavvR//fXX6N69Ox4/fozMABYAyFq7NsDdyuPGtXuIIuFboMw7c9wJvw+KggURkYiNWz20adMGP/7IHsjAewAmR4mC2F98wQ0ONW0lbidYwU78+PHNSqskSZIgXrx4gXZM5qcEXs4iNxERibibeLIc4dixY+YNgF10PkieHF6ctgpkpa2IxwQ769evR4IE7J0JbNiwIazHJCIiYWDBggVo0aIFbt26heTPmgQWK10amD0bSJLE7uGJ2BvslCzJLd/+v6ngm2+++Vx2h5mds2fPhv4IRUTktbAmp1+/fhg1apQ5X4LtQgAk69MHGDoU8HmtbRJFIrwQP8MZ7DimtFxdu3bNXKdpLBGRiOPixYvOdiHUE8Cw2LHhO3MmUKOG3cMTiZjBjqM2J7CCt2jRooXWuERE5DVxuwfuVs4PqLEBTGP7kLffBhYuBNQEVjxIsIMdLk8kBjrcCDRGDHZk+A+zOey9k0s9GUREbMcPpV9++SV69eplprDY+3ghgEzshjxxIhAzpt1DFImYwc6BAwecf0RHjhyBn5+f8zp+nzNnTvTsyQSpiIjYhVn2Vq1amS18qD6AST4+iPXll0D79lpWLh4p2MGOYxUWK/n5iUH9dEREIpbffvsNtWrVwq+//mpe3L8A0Dllyv+WlRcqZPfwRCLPRqBjx441adGAWKD8soaDIiISNubNm4f8+fObQCcFgI0AupQtCy9m5RXoiIcLcbBTv359zGZPhgC4CSivExGR8PPo0SNTU8lCZE5hlQKwH0DRDz8EVq0CEie2e4gikS/YYSFyaTahCqBUqVLmOhERCR+XLl1CuXLlzMbM1BvAmjhxkHTJEuDTT4EoUeweokjkXHr+4MGDQKex+Oni3r17oTUuEREJws6dO822D9ytnMvKZwComSMH2yQD6dPbPTyRyJ3ZKVCgAL799tvnLp8wYQLy5s0bWuMSEZEX4GswO9sz0OFu5XsY6DRtCuzYoUBHJDQyO0OHDjVp00OHDqFs2bLmsnXr1mHPnj1YvXp1SO9ORERCkFnv1KkTJk+ebM7XAjDdxwextaxcJHQzO0WLFsWOHTvwxhtvmKLkZcuWIX369Dh8+DCKFy8e0rsTEZFgOHfuHEqUKGECHYY0wwDMT5oUsTdtAjp0UKAjEgQvi10CPRyXzMeNGxc3btxQ/yARiXA2bdpkVltdvnwZ8QH8CKBCkSIA++ck5/7lIp7pZjDfv0Oc2aE///wTAwYMQMOGDc0fH61YsQLHjh179RGLiEig2z6wZICvtTkB7GWgw0wOG70q0BEJFu9X+YSRPXt2s8x8wYIFpq8DsYZn0KBBIb07EREJxN27d9GkSRN07drV7D/YEMB2Pz+8NXUqMH489+mxe4gi7hvs9O3b1xQpr1mzxt/+WGXKlDFLIUVE5PWcPHnS1EfOmjUL7JTDLjrfv/EGYmzbxj177B6eiPsHO9wEtGbNms9dniRJEvzzzz+hNS4REY/EVa1s43Hw4EGw9/FaAF1Ll4bX/v1Avnx2D0/EM4KdePHi4cKFC4Huip4yZcrQGpeIiMfV53z22WeoWLEi/v33X+QHsI/d6Xv0YASkbR9EwntvrD59+uDixYvw8vLC06dPsW3bNvTs2RNN2dRKRERC5NatW6hbty769etngp6WADZHj443f/wRGDUK8AlxSzQReZ1gZ9iwYcicOTPefPNNU5ycNWtW0/uhSJEiZoWWiIgE3++//45ChQqZBR++7EYPYFLatIjGGkhtriwS/n12eNOzZ88iceLEpj6H9TsMeHLnzo0MGTIgslKfHRGxw9KlS82KK74GcRH5AgCFK1UCZs0C4rOjjoiExvt3iHKjDHbYLZn9dBjcMLsjIiIhw+n/IUOG4OOPPzbniwKYByA5s+ODB2u3cpFQFqJgx9vb2wQ5V69ejdSZHBERu/ATaKNGjfDTTz+Z850AfBErFvy++w6oUcPu4Ym4pRDX7HC1QK9evXD06NGwGZGIiJv67bffUKBAARPoROUmngC+ypwZfnv2KNARiUh7Y8WPH9909nz8+LFpKhg9enR/11+7dg2RjWp2RCSsLV++3GR0+HrzBoBFAPLVqgVMnw7Ejm338EQipTCp2aGxY8e+7thERDwGP09yFevAgQPN98W4WzmApJ98AvTvr93KRcJBiIKdR48emb2x+EebNm3asBuViIgb4GrVFi1aYD53JwfQDsCXsWPD74cfgHfftXt4Ih4jRDU7vr6+pheEiIgE7a+//jL9xxjosH/ORADfZMoEv927FeiIRPQC5Ro1amDx4sVhMxoRETewdu1a5M+f3/QiSwpgA4A2VaoAu3YBmTPbPTwRjxPimh0uOWdvCG4Rwc3qYsaM6e/6Ll26hOb4REQiDdbksK6R2+ewlw73t1oI4I0PPwTYU0f9c0Qix2qsoGp1uFcWU7fBtXnzZnz++efYt2+f2Vx00aJFJnPkwKENGjQIkyZNwvXr11G0aFF88803/nr8cPVX586dsWzZMtMHqHbt2vjyyy8RK1asYI9Dq7FE5HXdu3cPbdu2xXfslwOAOwVOjB4d0WbMAOrWtXt4Im4pzFZjnTx5EqHlzp07yJkzJ95//33U4hLMAEaOHIlx48ZhxowZJshiYXSFChXwyy+/IFq0aOY2XMrJQGnNmjWmgJrFgG3atMEPLAAUEQkH3EaHr2F79+4FczejAHyQOjW8liwBcua0e3giHi/EmR1Xjh9lRue1B+Ll5S+zw/tOkSIFevToYVLCxMgtadKkmD59utl9/ddffzUbke7Zswf58uUzt1m5ciUqV66Mc+fOmZ8PDmV2RORVbd261WSUL1++jAQA5gIoW6YMMHcukDCh3cMTcWvBff8OcYEyzZw5E9mzZzcNBXnKkSOHM3UbWphBunjxIsqVK+e8jA+oYMGC2LFjhznPr/HixXMGOsTbczprFwsBX+DBgwfmALmeRERCauLEiShTpowJdLID2MNAp2tXYNUqBToiEUiIp7FGjx5tppM6depkamgcn2zatWtndkLv1q1bqAyMgQ4xk+OK5x3X8WuSJEn8Xe/j44MECRI4bxOY4cOHm034RERexcOHD02t4LfffmvOsyJnmp8fYk6aBDRltY6IROpg56uvvjJFwk1d/qCrVauGbNmyYfDgwaEW7ISlfv36oXv37s7zzOxoB3cRCY5Lly6ZaSuuSOUE/lC+pqRIAS+25MjP9VciEumDHRYDs1FWQLyM14WWZMmSOV9YkidP7ryc53PlyuW8DdPHrrhnF1doOX4+MFGjRjUnEZGQYAEy6wrPnz8PVgdwGUQVZrjZITmI1xwRsVeIa3bSp0+PuSy8C2DOnDn+loS/Lq6+YsCybt06fxkY1uIULlzYnOdXLknn0nWH9evXm/4WrO0REQktrEssVqyYCXQyAdjNQKdNG77oKNARcbfMDmtd6tWrZ3rkOGp2mM5lUBJYEPSyfWNOnDjhryj54MGDpuYmVapU6Nq1K4YOHWqCKMfSc66wcqzYypIlCypWrIjWrVtjwoQJZuk5a4m4Uiu4K7FERILCbHHv3r0xZswYc54bPXwfJQrijh8PtG1r9/BEJDisV7B3716rUaNGVp48ecyJ3+/fvz/E97NhwwauXX/u1KxZM3P906dPrYEDB1pJkya1okaNapUtW9Y6fvy4v/u4evWq1aBBAytWrFhWnDhxrBYtWli3bt0K0Thu3Lhhfi+/iog4/PPPP+Z1x/Ha1B+wniRObFlbttg9NBGxgv/+/Vp9dtyF+uyISEDHjh0ziy/YFZ6b4kwHUCdvXmDRIkALGkTcs8/O33//bZr7BdaThr+kV69epnhYRCSyW758uakJZKCTBsB2BjpNmgBbtijQEYmEvEPSX4eBTmCRE6OqW7dumduIiERWTHSPGDHCZHT4mlaSjQK9vJCDr23c4yp6dLuHKCJhGexwGwbX3joB8Tp+GhIRiYzu379vXsf69u1rgp42AFbHiYNEK1YA7B8WCtviiEgEX43FlVJcIfUib7zxBk6dOhVa4xIRCTfsEVazZk3T2oIbeY4F0DFDBngtWwZk4kJzEfGIzA73wAoqmOF1vI2ISGTCPl358+c3gU58AKsAdCpfHl7cX0+BjohnBTts0hfUZp/cHLRAgQKhNS4RkTDHZqjFixc3jQIzA9jl2Mjzp5+A+Ax9RMSjprG4Euudd94xxchceeXYoJMrsEaOHInp06dj9erVYTlWEZFQwS7rgwYNMk1LqSKA2T4+iDthAtCypd3DE5FQFqI+OxMnTsQHH3xgOhVzVZaXl5dZdu7r62u6i7Zv3x6RkfrsiHgOdm5nIfIi9ssB0APAiESJEIXnixWze3giEgbv3yFuKsh0L7eF4DYP/NGMGTOiTp06pkA5slKwI+IZTp8+bZaVHz58GH4AvgXQLEcOYOlSIHVqu4cnIhEl2HFHCnZE3N/WrVtRq1YtXLlyBUkAMK9ThPvssRYxViy7hyciEaGDsohIZDVlyhSUKVPGBDq52CiQgc6AAcCCBQp0RDxAiHc9FxGJTDuWc3HFl19+ac7X4R5XUaMiJrsh16tn9/BEJJwo2BERt3T9+nXUq1fPuUp0MICBKVLAm/U53NBTRDyGgh0RcTu///47qlatar6y1elMZnXYB2zxYiB5cruHJyLhzPtVPzFNnjwZ/fr1w7Vr18xl+/fvNyu1RETsxEwOG5wy0OH+5NsY6DRuDGzapEBHxEOFOLPDJZvlypUz1c/cIqJ169ZIkCABFi5ciDNnzphOyiIi4Y0LS8eNG4fu3bubpoGFn624SjpiBNCrlzbyFPFgIc7s8IWkefPm+OOPPxAtWjTn5ZUrV8bmzZtDe3wiIi/18OFD88Gra9euJtBpDmBDzJhIyo08e/dWoCPi4UKc2dmzZ4/ppBxQypQpcfHixdAal4hIsFy+fBm1a9c2fXT46W0kP5SlTfvfjuXZstk9PBGJjMFO1KhRTROfgDg/njhx4tAal4jISx06dAjVq1c3nZHZTmw2gEqlSgHz5wMJE9o9PBGJrNNYbLX+8ccfm/2xiPtjsVanT58+5tOViEh44N5WRYsWNYFOegA7Gehwfz4uNVegIyKvE+x88cUXZiO9JEmS4N69eyhZsiTSp0+P2LFj49NPPw2bUYqIuBQic7dybv1w584dlAWwy9sbWcaPB/73P8DX1+4hikhkn8biKqw1a9aY+XGuzGLgkydPHrNCS0QkLN29exctW7bE7NmcsAI68wNYvHjw5bYPZcrYPTwRiaC0Eag2AhWJFM6dO4caNWpg37595lPaeABtsmQBWIicLp3dwxORCPz+/UodlLkia8OGDWYVBJd5uho9evSr3KWIyAvt2rXLBDpc8clqnAUASlapAvzwA6APKCLyEiEOdoYNG4YBAwYgU6ZMSJo0qSlQdnD9XkQkNHz//fdo1aoVHjx4gLcBLAWQlk0Chw8HokSxe3gi4o7BDncPnjp1qmksKCISVp48eYL+/ftjBDsgcyUoAx9fX8SePBlo2tTu4YmIOwc73t7eZrmniEhYzsM3atQIy5cvN+f7ARiaJAm8lywBChWye3gi4u5Lz7t164bxXOIpIhIG/vrrLxQpUsQEOlEBzOL0eZ488N67V4GOiIRPZqdnz56oUqUK0qVLh6xZs8I3QE8LbggqIvIquPChTp06uHbtGrg/+WIABd57D5g2DYgRw+7hiYinZHa6dOliXpAyZsyIhAkTmiVfricRkVcxYcIElC9f3gQ6+bjqk4HOxx8D7KmjQEdEwjOzM2PGDCxYsMBkd0REXhe3nuFu5f9j92MADQBMiR4d0b/7DtAWNCJiR7CTIEECM4UlIvK6rl69ivfeew/r168354cB6Pvmm/BauhTIlcvu4YmIp05jDR48GIMGDTJt20VEXtUvv/yCggULmkAn1rP6nH5Fi8KLhcgKdETEzszOuHHj8Oeff5qGgmnSpHmuQHn//v2hOT4RcUM///wz6tevj1u3biHNs0aB2Vu0AL75BojKNVgiIjYGO2zZLiLyKrgV3xdffIHevXub70sAmO/lhcRffAF07co27HYPUUTckDYC1UagIuHi/v37aNu2LWbOnGnOtwbwdZw48Js7F6hQwe7hiUgkFKYbgYqIhAQ38KxZsyZ27twJ7mY1BkCn9OnhxQ7JmTLZPTwRcXM+wV2B9fvvvyNRokSIHz9+kBt+skeGiIhrHV/16tVx7tw5xAMwD0C5cuUAZnTix7d7eCLiAYIV7IwZMwaxY8c2348dOzasxyQibmLevHlo1qwZ7t27B+ZvlgHI0KULwBodHyWWRSSC1eyUKVPGbAURLx4/m7kX1eyIhK6nT59iyJAh+JgdkAFUBDDbxwdxudqqVSu7hycibiLUa3Y2btyIhw8fhtb4RMRN3blzB02bNnXuk9cDwIiECRGF50tw/ZWISPhSHllEQs3p06dNfc6hQ4fgB2AigObZswPsiJyGHXVERCJ4sMOOp1xVEZQcOXK87phEJBLatm0batWqhcuXLyMJgEUAilSvDnCPq2c1fyIiET7YKVu2rGkEFhBXZ/Fyfn3y5Elojk9EIoFp06aZHjrc1JMbPSwBkKp/f4A1O94h3pVGRMS+YGfXrl1InDhx6I5ARCKtx48fm27IXLFJ3KN8RtSoiDltGtCA+5eLiESyYCdVqlRIkoQJahHxdNevXzf7W61atcqcHwTgo+TJ4b1kCZA/v93DExFxUoGyiITYH3/8gapVq+L48eOIzmwOgLoFCgCLFgEpUtg9PBERf4I9mV6yZEn4+XF9hYh4sjVr1qBAgQIm0HkDwFYGOo0asT+FAh0RidzBzoYNG9yyoaCIBA8XIYwbNw6VKlUyU1iFAewBkGf48P9WXEVnjkdEJOLRNJaIvBQbinbq1AmTJk0y55uxh07MmIj6449A1ap2D09EJEgKdkQkSFeuXEGdOnWwefNmkwoeCaB7mjTwWrYMePttu4cnIvJSCnZE5IWOHDmCatWq4dSpU+CuM7MBVCpZEpg/H0iUyO7hiYgEi7p9iUiglixZgiJFiphAJx2AnQx02rYFVq9WoCMi7h3s1K5dGyNGjHju8pEjR6Ju3bqhNS4RsbEQediwYahRowZu376NsgB2e3sjy9dfA9y1XKsyRcTdgx3O21euXPm5y7lCg9eJSOR17949NGrUCP251QOATgBWxIuHBMzmdOzIvWHsHqKISNjX7PCTXmD9dnx9fXHz5s2Qj0BEIoTz58+bbM7evXvNC8PXANpmzgywEDl9eruHJyISfpmd7NmzY86cOc9dPnv2bGTNmvXVRyIittm9ezfy589vAp2EbBzIQKdSJWDnTgU6IuJ5mZ2BAweiVq1a+PPPP1GmTBlz2bp16/Djjz9i3rx5YTFGEQlDP/zwA95//308ePAA2QAsBfBWz57AZ58BUaLYPTwRkfAPdrgfzuLFi00B4/z58xE9enTkyJEDa9euNVtKiEjk8PTpU1Ob8xmDGv5tA/je1xdx2DiwGdsGioi4By+LSy88HGuN4saNixs3biBOHHYTEXFvt27dQuPGjbF0KfM4QD8AQ5MkgffixUBhbgQhIuI+799qKijiYU6ePGkaBR49ehRRAUwB0Ch3bjbWAd580+7hiYiEumAFOwkSJMDvv/+ORIkSIX78+PAKYvnptWvXQnN8IhKKNm7caLZ+uHr1KpIDWAygQJ06wPTpQMyYdg9PRMS+YGfMmDGIHTu2+X7s2LFhMxIRCVMTJkxA586d8fjxY+R7FuikHDyYqw4AbzVTFxEPD3aaPStW5IskszoVKlRA0qRJw3psIhIKHj16hK5du+J///ufOd8QwOTo0RF95kyAWR0RETcXoo9zPj4+aNeuHe7fv4/wMHjwYBNcuZ4ys8nZMxxHx44dkTBhQsSKFctsZXHp0qVwGZtIZMDpKn44YaDDyefhXHH1xhuIvn27Ah0R8Rghzl0XKFAABw4cQHjJli0bLly44Dxt3brVeV23bt2wbNky099n06ZN+Pvvv00PIBEBjh07Zv5eN2zYgFjc2BNA3yJF4LV3L5Arl93DExEJNyFejdWhQwf06NED586dQ968eREzQFEje+6EJmaTkiVL9tzlXGY2ZcoU0xDN0dxw2rRpyJIlC3bu3IlChQqF6jhEIpPly5ejYcOGZon5W88aBWZ7/32AU1lRuQZLRMRzhDjYqV+/vvnapUsX52WcXmK7Hn598uRJqA7wjz/+QIoUKRAtWjQULlwYw4cPR6pUqbBv3z5Ti1CuXDnnbTnFxet27NgRZLDDTrE8OWhPL3EX/DscOXIk+vXrZ74vBWC+lxcSjhnDP1pt5CkiHsnnVXp0hJeCBQti+vTpyJQpk5nCGjJkCIoXL276g1y8eNFsSBovXjx/P8PCaV4XFAZMvC8Rd9uxvHXr1pg1a5Y53x7Al3HjwnfuXKB8ebuHJyISeYKd06dPo0iRImZ6yRVXam3fvh2pU6cOtcFV4kaELtNjDH54/3PnzjXbVLwqfurt3r27v8zOm2qmJpEY69W4Y/mePXvMH/U4BjuZMgHskJwxo93DExGJXAXKpUuXDrRxIGtoeF1YYhYnY8aMOHHihKnjefjwIa5fv+7vNlyNFViNj6uoUaOattKuJ5HIvGN5vnz5TKCTAMBqBjoVK/63Y7kCHRGRkAc7jtqcwJa4BixWDm23b982u60nT57cFEf7+vqaHdcdjh8/jjNnzpjaHhFPwCmrEiVKmGle7li+hx9ImLVcvpyfDuwenohI5JrGcizpZqDTvHlzkx1xYFHy4cOHzfRWaOrZs6fZZZ1TV0zTDxo0CFGiREGDBg3Mxl8tW7Y001HczoLZGXaHZaCjlVji7vg3xx3LR4wY4X/H8m+/BZo3t3t4IiKRM9hhcOHI7HDrCNeaGRYKM8BgcWRo4vJ2BjbMGiVOnBjFihUzy8r5vWMbC29vb9NMkKurHM3TRNwZa8waNWpklpf727F80SIglD9wiIi4Ay+L0UsIcBUTMy5hPWUVEbeIF7Ebp3G5Y/kvv/yCaM92LG+oHctFxEPdDOb7d4iDHXekYEcig/Xr16Nu3bpmgUCKZxt55q9bl900tWO5iHikm8F8/w5xgTJXOzVp0sQ0+uPyc9bQuJ5EJPRxerZ8+fIm0CnwrBA5/8cfA3PmKNAREQntPjssTuaKp4EDB5pVUYGtzBKR0MEu4exWPmHCBHO+EYBJ3LH8+++5asDu4YmIuGeww404t2zZglzaSFAkTP3zzz+oU6eO2eSWHyk+A9ArVSp4sVFgzpx2D09ExH2DHXYaVpmPSNjiligsROb2LLEB/ADg3eLFgfnzgSRJ7B6eiEikEuKanbFjx6Jv3744depU2IxIxMMtWbLE9ItioMMdy3cw0GnVCli7VoGOiEh4ZHbq1auHu3fvIl26dIgRI4bpYuwqsK0kROTlmDHlJrUDBgww33PzlXne3kg4dizQqZN2LBcRCa9gh5kdEQn9HcvZEfzHH3805zuyaSZ3LOe0Vblydg9PRMSzgp1mzZqFzUhEPNTZs2dRs2ZN7Nu3z/xBfg2gbebM/+1YniGD3cMTEfG8mh1HF1em2rmVw+XLl81lK1aswLFjx0J7fCJujasbuWM5A51EANYw0KlU6b8dyxXoiIjYE+xwGWz27Nmxa9cuLFy40OxETocOHTIbdYpI8EyaNAllypQxHxhyPmsUWKpnT2DZMm5GZ/fwREQ8N9jhSqyhQ4dizZo1ZgNQB75oc5NOEXl5o8COHTuiTZs25vu6ALb5+SHNd98Bn38OqBO5iIi9NTtHjhzBDz+w64d/SZIkMU3QROTFrly5YhoFbt682TQK/JQfIFKkgNfixUD+/HYPT0TELYU4sxMvXjxcuHDhucsPHDiAlClThta4RNzOwYMHTX0OAx02ClwKoF/hwvDat0+BjohIRAp26tevjz59+uDixYtmX6ynT59i27Zt6NmzJ5o2bRo2oxSJ5ObMmYMiRYqYfeVYdryLjQJbtgQ2bACSJbN7eCIibi3Ewc6wYcOQOXNms20Ei5OzZs2KEiVKmBdyrtASkf/35MkTfPjhh+ZDAnvpVGCg4+2NLF99xQplIGpUu4coIuL2vKxX3OiKvUFYv8OAJ3fu3MgQiZfJ3rx5E3HjxsWNGzcQJ04cu4cjboLPp0aNGuGnn34y53sBGJ4gAaIsWACUKmX38EREIr3gvn+HuEDZgZkdnkTkecePH0f16tXN12gApgBomCMHN74C0qSxe3giIh4lxNNYtWvXxogRI567fOTIkahbl4toRTwbG2wWLFjQBDpvsHEgA506dYDt2xXoiIhEhmCHK0kqV6783OWVKlUy14l4Ks4IM+ivUqWKSakWBbAXQN6hQ4G5c4GYMe0eooiIRwrxNBZrdFybCTpw93POnYl4ort376JVq1bOjTxbc4+rWLHgN2sWUK2a3cMTEfFoIc7scKsILqMNaPbs2WZlloin4XLyYsWKmUCHnx7+B2BiunTw27VLgY6ISGTM7AwcOBC1atUym4Fyiwhat26deaGfN29eWIxRJMLasmWLqWNjZ2Ru5LkAQIkKFQBmeOLHt3t4IiLyKpmdqlWrYvHixThx4gQ6dOiAHj164Ny5c1i7di1q1KgRNqMUiYAmTpxoAn4GOrme1eeU6NUL4FJzBToiIpG/z447UZ8dCYmHDx/igw8+wIQJE8z59wBM9fNDzClTgMaN7R6eiIjHuBnM9+8QZ3aaNWumVVfisS5duoRy5cqZQIcbeQ5jvVqKFIi5bZsCHRGRCCrEwQ6jJ77Ys2Myt444f/582IxMJILZs2eP2ciTdTr8/LCMG3kWKfLfRp758tk9PBERCa1gh/U6DHDat29vVmWlSZPG9NiZP38+Hj16FNK7E4kUpk2bhuLFi5v6tMzPNvKs0qoVsH69NvIUEXG3YIcSJ06M7t2749ChQ9i1axfSp0+PJk2aIEWKFOjWrRv++OOP0B+piE31OR07dsT777+PBw8egAvJd0WJgsxffw18+6028hQRcddgx+HChQtYs2aNOUWJEsV0VubmoOy3M2bMmNAbpYgNLl68iLJly+J//2PnHGAIgEWJEyPOhg1Ax46AF6t2RETE7frscKpq6dKlJq2/evVq5MiRA127dkXDhg2dldCLFi0yn4SZ5RGJjJixZD+pv//+29TnzALwboECAHcsf4M7XomIiNsGO8mTJ8fTp0/RoEED7N69G7lyscOIf6VLl0a8ePFCa4wi4WrKlCmmhxSnsFifsxhApvffB8aPB6JxD3MREXHrYIfTU9zdPFoQL/oMdE6ePPm6YxOxtX8OW2TO8PFBnK++Atq21bSViIgn1OxwCqtFixame7KIO2H9GTOSjv45n3DrhyRJEGfjRqBdOwU6IiKektnhzuapUqXCkydPwm5EIuFs586dZn8r1ufEfVafU6VQIWD+fCBlSruHJyIi4b0aq3///vjwww9x7dq11/3dIrabNGkSSpQoYQKdrGwcyECndWuAGR0FOiIinlmz8/XXX5tpLPbUSZ06NWLGjOnv+v3794fm+ETCBHvmdOnSBd+yVw6AWgCm+/ggNouQ27Sxe3giImJnsKOdzSWyYxanTp062LFjh6nPGcptH5ImhdfChUCRInYPT0REQpl2Pdeu5x5l+/btpj6HDQNZn/MDgMqFC/9Xn5Mihd3DExGRiLDruUhkNXHiRJQqVcoEOtkA7GWgwyXlrM9RoCMi4rZCPI3FlVjstTN37lycOXPG9CZxpcJliWju37+Pzp07Y/LkyeZ8HW7s6euLWKzPYTGyiIi4tRBndoYMGYLRo0ejXr16Jm3EDUHZVt/b2xuDBw8Om1GKvKLTp0+b3coZ6LA+ZziAucmTI9bmzQp0REQ8RIiDnVmzZpnluj169ICPj4/ZNoJvJB999JHpVyISUXCD2rx582Lv3r1IAGAFgL5Fi8KLKwbZR0dERDxCiIMd1jtkz57dfB8rViyT3aF3330XP/30U+iPUCSEuHfb8OHDUbFiRVy9ehV5AewDUKFDB2D9eiBZMruHKCIiETnYeeONN0xrfUqXLp3Z+Zz27NmDqFGjhv4IRUKAwTenVdn4kkHP+wC2Ro2KNDNm/LeRp5+f3UMUEZGIHuzUrFkT69atM9+z6HPgwIHIkCEDmjZtive5M7SITY4ePYr8+fNjyZIlYEjDdoGT06ZFNE6vNm1q9/BERCSy9tlhYzaeGPBUrVoVkZH67ER+c+bMMcH23bt38SaA+QAKVK4MfP89ED++3cMTEREb379DvPQ8oMKFC5uTiB0ePXqEPn36mHYIVBbAjwASc2XgwIGAt1pJiYh4uhAHOyz4TJgwofn+7NmzZmXWvXv3UK1aNbPEVyS8sFj+vffew5YtW8z5vgA+iRsXPj/8ADCrIyIiEpKanSNHjiBNmjRIkiQJMmfOjIMHD5r6CH6i5maKpUuXxuLFi8N2tCLPbNu2DXny5DGBTmwAC9lDJ1cu+HBZuQIdERF5lWCnd+/eZsn55s2bTct9LjWvUqWKmSf7999/0bZtW3z22WfBvTuRV8ISs6+++so8B7kqMCtXArJwngXI27YBb71l9xBFRCSyFignSpQI69evR44cOXD79m1TCMTl5mzaRr/99hsKFSqE69evI7JRgXLkcOfOHRNUs7ElvQdgio8PYo0bB7RrB3ixR7KIiHiKm6FdoMw9r5I9a8bGZoIxY8ZEfJdVLvz+1q1brztukUCdOHHC9M/hdGoUAJ8D6JoiBbwWLFA3ZBERCVKIlqp4BfjkHPC8SFhYtmwZ8uXLZwKdJADY5albqVLwOnBAgY6IiITuaqzmzZs7uyRzJ+l27dqZDA89ePAgJHcl8lKPHz82TSsdtWBscDAPQMqePYHhwwGf1+6cICIiHiDY7xbNmjXzd75x48bP3YZdlEVCa1l5/fr1sWnTJnO+M4BRMWPCb/p0oE4du4cnIiLuGOxMmzYtbEci8gwDHAY6DHhisQiZxciZMwMLFwJZstg9PBERiWTUXlYiDG7cOWLECJQpU8YEOtmeLSt/r3597jSrQEdERF6Jih4kQmCvJtaELV261JznJOkEHx/EHDsW6NBBy8pFROSVKdgR2+3fvx916tTByZMnzW7lXwFo/eab8Jo/HyhQwO7hiYhIJKdpLLEN+1lyq5EiRYqYQCctgO0A2lSq9N+ycgU6IiISChTsiG3dkLnCjx2R2bagKoB9Xl7IO3QosHw58GyzWRERkdelaSwJd8ePHzfTVkePHjXR9nAAPRMlgvfs2UDZsnYPT0RE3IwyOxKu5s6da7ohM9BJCmA9N5ktWhTeBw8q0BERkTDhNsHO+PHjkSZNGkSLFg0FCxbE7t277R6SuHj48CE++OAD1KtXz2wkWxLAAQAle/QANmwAUqa0e4giIuKm3CLYmTNnDrp3745BgwaZlT05c+ZEhQoVcPnyZbuHJgDOnDmDkiVLYhx3JwfQF8Da2LGRnE0CR40CfH3tHqKIiLgxtwh2Ro8ejdatW6NFixbImjUrJkyYgBgxYmDq1Kl2D83jcRPPXLlyYefOnYgHgF10hufMCZ/9+4GaNe0enoiIeABvd5ge2bdvH8qVK+e8zNvb25zfsWNHoD/D1T83b970d5LQ9ejRI/Ts2RPVqlUzDQPzs58OgKotWwL8f0mf3u4hioiIh4j0wc4///yDJ0+eIGlSlrv+P57nlgOBGT58OOLGjes8vfnmm+E0Ws9w+vRpFC9eHF988YU53xXA1mjRkJaZtsmTgejR7R6iiIh4kEgf7LyKfv364caNG87T2bNn7R6S21iyZImZttq1a5eZtloEYEyWLPDbuxdo0cLu4YmIiAeK9H12EiVKhChRouDSpUv+Luf5ZMmSBfozUaNGNScJ3enEvn37YsyYMeY8ex/PAZCmWTMulQNixrR7iCIi4qEifWbHz88PefPmxbp16/ztns3zhQsXtnVsnuLUqVNm2soR6HQHsCVaNKSZNg2YPl2BjoiI2CrSZ3aIy8659QCb1RUoUABjx4412xFwdZaErcWLF5vjfP36dcQHMB1AtaxZgXnzAH4VERGxmVsEO2xUd+XKFXz00UemKJk1IytXrnyuaFlCd9qqd+/e+PLLL835gs+mrVIzwPzqK2VzREQkwvCyuPW0h+PSc67KYrFynDhx7B5OhPfXX3+ZAHMvi44B9AAwLHp0+E2YADRtavfwRETEQ9wM5vu3W2R2JPwsXLgQ77//vnliJXg2bVU1W7b/pq2yZLF7eCIiIu5XoCzh4/79++jSpQtq165tAp3Cz/a2Mk0CuQ+ZAh0REYmglNmRl/rtt99Qv359HDp0yJzvDWBojBjwnTgRaNzY7uGJiIgEScGOvBDLuaZNm4bOnTvj7t27SPxs2qpy9uzA3LlA5sx2D1FEROSlNI0lgeJUVcOGDdGyZUsT6JQFwLxO5TZtgF27FOiIiEikocyOPIdbPTRo0AAnT55EFE5ZceoqThx4c1+runXtHp6IiEiIKLMj/jpPjxgxAsWKFTOBThpu4Amgb5Ei8D58WIGOiIhESsrsiMFmjE2aNMHatWvN+fcATAQQr39/YPBgwEdPFRERiZz0Diam23TTpk1NF+roAL4C8H6yZPCaNQsoU8bu4YmIiLwWTWN5+JYPPXv2RKVKlUygkwPAPgAt330XXkeOKNARERG3oMyOhzpx4oTpnbNvH8MboBOAz319Ee3zz4EuXQAvL7uHKCIiEioU7Hhg75zvv/8eHTp0wO3bt82WD1MBVM+YEZg9G8id2+4hioiIhCpNY3mQ69evm945rM9hoFPyWe+c6s2bA8zwKNARERE3pMyOh9i0aZNZbXX27FnTO2cwgH6xYiEKt3xo2NDu4YmIiIQZZXY8oAi5X79+KF26tAl00gHYBmBAgQKIcvCgAh0REXF7yuy4sePHj6NRo0bOIuSWAMZ6eSHWgAHAwIGAr6/dQxQREQlzCnbctAh50qRJ6Natm9nXKj6ASQBqp0kDfPcdUKyY3UMUEREJNwp23Az75bRu3RpLliwx57mB5wwAKRs3Br7+Gogb1+4hioiIhCsFO25k1apVaN68udn6wQ/AMADdYseG94QJqs0RERGPpQJlN3D//n107doVFStWNIFOFu5cDqBH8eLwZidkBToiIuLBlNmJ5I4cOWJ65xw9etTZCXlklCiI/vHHQJ8+QBQuNBcREfFcCnYiqSdPnmDMmDHo37+/WV6eBMA0AJUzZAC4gWf+/HYPUUREJEJQsBMJnTx5Es2aNcOWLVvM+XcBTAGQpFUrYMwYIFYsu4coIiISYahmJ5ItKZ8yZQpy5MhhAh2GNJMBLI0fH0kWLgQmTVKgIyIiEoAyO5HEpUuXzJLyZcuWmfPFni0pf6tyZWDyZCB5cruHKCIiEiEpsxMJLFy4EG+//bYJdLikfCSAjTFi4K1vvwWWL1egIyIiEgRldiKwGzduoHPnzviOXY8B5ATA77IXLQrMmAGk405XIiIiEhRldiKodevWIXv27CbQ4X9SPwC7fXyQ/bPPuIW5Ah0REZFgUmYngrl3757ZpfzLL7805xnSzARQJEeO//a14lcREREJNmV2IpA9e/YgT548zkCnPYCDXl4o0rcvsHu3Ah0REZFXoMxOBPDgwQMMGTIEI0eONM0CWW48FUDFt94CZs4EWKMjIiIir0TBTgTI5nDzzl9++cWcbwDgKwAJ27YFRo1S3xwREZHXpGksG7M5H374IQoXLmwCHW73sAjAD8mTI+FPPwHcqVyBjoiIyGtTZiciZXOaNgXGjgXix7d7iCIiIm5DmZ1wzuZw407XbM5CRzZn6dL/euco0BEREQlVyuyEk71795pszrFjx8z5+s+yOYmaNPkvm5Mggd1DFBERcUvK7IRTNqdQoUIm0GE2ZwGAH5MlQyJmc7jaSoGOiIhImFFmJ4yzOS1atMDRo0fNeWVzREREwp+CnTDy77//olSpUrhz5w4SA5gAoFayZMDEiUC1anYPT0RExGNoGiuMxLcsfBQjBuoB4JqrWo0bA6zXUaAjIiISrpTZCSvx46NX8eLw2r5d2RwREREbKdgJK15e8GKQ4+2t2hwREREbKdgJS4kS2T0CERERj6eaHREREXFrCnZERETErSnYEREREbemYEdERETcmoIdERERcWsKdkRERMStKdgRERERt6ZgR0RERNyagh0RERFxawp2RERExK0p2BERERG3pmBHRERE3JqCHREREXFr2vUcgGVZ5uvNmzftHoqIiIgEk+N92/E+/iIKdgDcunXLfH3zzTftHoqIiIi8wvt43LhxX3i9l/WycMgDPH36FH///Tdix44NLy8vRLSolUHY2bNnESdOHLuH41F07O2jY28vHX/76NiHDEMYBjopUqSAt/eLK3OU2WHhkrc33njjDURkfNLriW8PHXv76NjbS8ffPjr2wRdURsdBBcoiIiLi1hTsiIiIiFtTsBPBRY0aFYMGDTJfJXzp2NtHx95eOv720bEPGypQFhEREbemzI6IiIi4NQU7IiIi4tYU7IiIiIhbU7AjIiIibk3Bjg2GDx+O/Pnzm47NSZIkQY0aNXD8+HF/t7l48SKaNGmCZMmSIWbMmMiTJw8WLFjg7zbXrl1Do0aNTOOpePHioWXLlrh9+3Y4P5rI5ZtvvkGOHDmcDbsKFy6MFStWOK+/f/8+OnbsiIQJEyJWrFioXbs2Ll265O8+zpw5gypVqiBGjBjm/69Xr154/PixDY/GfY49n8udO3dGpkyZED16dKRKlQpdunTBjRs3/N2Hjn3YPfcduGalUqVKppv84sWL/V2n4x92x37Hjh0oU6aMeb3nbUqUKIF79+45r9fr/etRsGODTZs2mTfUnTt3Ys2aNXj06BHKly+PO3fuOG/TtGlTEwAtXboUR44cQa1atfDee+/hwIEDztvwiX/s2DFzH8uXL8fmzZvRpk0bmx5V5MBO2Z999hn27duHvXv3mheX6tWrm+NI3bp1w7JlyzBv3jzz/8RtRHjsHZ48eWJe7B8+fIjt27djxowZmD59Oj766CMbH1XkP/Y8zjyNGjUKR48eNcd05cqV5gXdQcc+bJ/7DmPHjg102xwd/7A79gx0KlasaN4Hdu/ejT179qBTp07+tj/Q6/1r4tJzsdfly5e5/N/atGmT87KYMWNaM2fO9He7BAkSWJMmTTLf//LLL+Zn9uzZ47x+xYoVlpeXl3X+/PlwHH3kFz9+fGvy5MnW9evXLV9fX2vevHnO63799VdznHfs2GHO//zzz5a3t7d18eJF522++eYbK06cONaDBw9sGb87HPvAzJ071/Lz87MePXpkzuvYh/3xP3DggJUyZUrrwoUL5nm/aNEi53U6/mF37AsWLGgNGDDghbfV6/3rU2YnAnCk6hMkSOC8rEiRIpgzZ45JXXKj0tmzZ5spllKlSjk/CTCVmS9fPufPlCtXznwS2LVrlw2PIvLhJ1UeV2bUmFbmpy5m2XgcHTJnzmymVHi8iV+zZ8+OpEmTOm9ToUIFs3lfwE/IEvxj/6K/C6bsfXz+28JPxz5sj//du3fRsGFDjB8/3kyfB6TjHzbH/vLly+Y1m9OCfN3n8S1ZsiS2bt3q/Bm93r8+bQRqMwYyXbt2RdGiRfH22287L587dy7q1atnakf4Ys858kWLFiF9+vTOmh7+cbji7Rgw8Tp5MU4L8kWGwSPrcnhcs2bNioMHD8LPz8+8qLjii4/jmPKr64u943rHdfJqxz6gf/75B5988om/NL2Ofdgef07h8s2W0yuB0fEPm2PPcgYaPHiwmcbNlSsXZs6cibJly5op3QwZMuj1PhQo2LEZa3f4hHaN4mngwIG4fv061q5di0SJEplCQdbsbNmyxXy6klfHIlgGNswczJ8/H82aNTP1OWLfsXcNeJgpYG0IL+MbgIT98T9x4gTWr1/vryZQwufY8wMvtW3bFi1atDDf586dG+vWrcPUqVPNghZ5fQp2bMQCNEehGQvYHP788098/fXXJgjKli2buSxnzpwm0GGKecKECSbNzPSnK66K4LRXYClo+X/M3jgyZHnz5jXFgF9++aXJpLH4kkGma3aHq7Ecx5RfWUDoyrFaS8f91Y/9xIkTzWW3bt0yhZpcqchPvr6+vs6f1bEPu+PPFXB83QmY1eRqxOLFi2Pjxo06/mF07Pv27WsuC5jhzJIli1n9Rnq9f32q2bEBl3Yy0OGLOT9NpU2b1t/1nDsn10p8ihIlivNTANOhfFNmnYkD74vXFyxYMFweh7vgMXvw4IF5AeKbKz9ROXBFHF9wHHUN/Mp0tOsLD1dHsLYksOkYCd6xd2R0uBqFbwpchRgtWjR/t9WxD7vjzzfcw4cPm8yD40RjxozBtGnTzPc6/mFz7NOkSYMUKVI8137k999/R+rUqc33er0PBaFQ5Cwh1L59eytu3LjWxo0bzaoHx+nu3bvm+ocPH1rp06e3ihcvbu3atcs6ceKENWrUKFN5/9NPPznvp2LFilbu3LnNbbZu3WplyJDBatCggY2PLOLr27evWfV28uRJ6/Dhw+Y8j+vq1avN9e3atbNSpUplrV+/3tq7d69VuHBhc3J4/Pix9fbbb1vly5e3Dh48aK1cudJKnDix1a9fPxsfVeQ/9jdu3DArUrJnz26e765/FzzmpGMfts/9gAKuxtLxD7tjP2bMGLOqjStB//jjD7MyK1q0aOZvwUGv969HwY4N+CIS2GnatGnO2/z+++9WrVq1rCRJklgxYsSwcuTI8dxS9KtXr5one6xYscwfSosWLaxbt27Z8Igij/fff99KnTq1WdLMF+qyZcv6e7G/d++e1aFDB7MslMe9Zs2a5g3X1alTp6xKlSpZ0aNHtxIlSmT16NHDuTxaXu3Yb9iw4YV/F3yDcNCxD7vn/suCHdLxD7tjP3z4cOuNN94wrzv8gLVlyxZ/1+v1/vV48Z/QyBCJiIiIRESq2RERERG3pmBHRERE3JqCHREREXFrCnZERETErSnYEREREbemYEdERETcmoIdERERcWsKdkTEbXDDXO4/xK1VunbtGmr327x5c9SoUQPhbfr06fDy8jKn0Ho8jvsLuA+WuKdTp06hZcuWZlsi7oGWLl06DBo0yOwDGBxsxVepUiXznOHfV2DPJdfT7NmznddzT7XAbvMqO7XzbyFHjhxmGxnuAM9NtENCwY6Im7hy5Qrat2+PVKlSIWrUqGaDwAoVKmDbtm3wFNw5uk6dOjh79iw++eSTYAUpgb0Yu5648zo3bOSLrR2499SFCxfM43EoVaqUv+CH5x3j5f99ypQpUbVqVSxcuPC5++N9jR07NtzGL+GDz4HAnqO//fab2UOLm+0eO3bM7HfGzaQ//PDDYN0vnyt8Xr0I907jc8pxCuzvjft+ud6GwUpIjB49Gv379zd7uPExrF271ry2hYR2PRdxE9yhmp/WZsyYgbfeesvsSM1NTa9evYrIjI+Jm4O+zO3bt80mlXwR5MaKwcUXX4c5c+bgo48+8rcpY6xYsczJLnyjCc7O1q1bt8bHH39sdsM+d+6c2Wi4fv36JuD79ttvnbfjfcWNGzeMRy0RRcWKFc3Jga8NfH5/8803GDVqVJA/yw1hv/jiC+zduxfJkycP9DbMEL7s+cng5kWZRAZiI0aMMM9RZnwyZsyIgQMHmg8t9O+//2LAgAFYtmwZypYt6/w5ZnlCQpkdETfAHZG3bNliXjRKly5tdksuUKAA+vXrh2rVqvl74+SLHNPSTGnzhW/+/Pn+7qtPnz7mBSdGjBjmer7wPHr0yN9t+MKTP39+k1JOlCgRatas6byOOzn37NnTZBdixoxpdmVmOtsVs038JMrfET9+fBOg8EWNeHmnTp1M5oL37fgEx0932bNnN/f55ptvokOHDibAId5/7NixzfdlypQxj5P3w8BvyZIlzqxHwHEQX6gdJwYBjuDCcWKgEzBDxPvu3LmzGSPHnzRpUkyaNAl37txBixYtzFg4nbZixQp/v+vo0aPm2PM++TNNmjTBP//8g9DAY8nxvvHGGyhUqJB5LvDTPMfFT8IiDjdu3ECCBAkQlLt376Jhw4YYP358kMEMp5P4d8rXm6lTp5ppr4By5cplgqV33nnnuUzz8OHDMXPmTJNtYtamW7duaNy4MTZt2mSuX7NmjQmIzp8/jyxZspjn93vvvWeytyGhYEfEDTiyD5xTZ7ARFAYvzAIdOnQIjRo1Mp/+f/31V+f1fKNmOvyXX34x0zd8s2Tq2+Gnn34ywU3lypVx4MABkz3iC50DA5UdO3aYufvDhw+jbt265pPlH3/84fy0yE9oWbNmNbfbunWrmXJ58uSJ8z4YpDCbwxdGvgiSt7c3xo0bZ14Qef369evRu3dvc12RIkWc2ZgFCxaYbM3SpUvNiyJ/tyN9ztuFFo6BL/K7d+82gQ+nEPlY+Tv279+P8uXLm2CGbxqOgJSBWO7cuc0n5ZUrV5rsG8cYVpo1a2aCscCms8QznThxAl999ZWZ8g0Kgw4+l6tXr/7C2zCTOHfuXBOQ8DWFH0B43w4McPj3y79JnvghhR8U+PdBfK0aNmyYCZL4oYYfrvjBgsEOA3X666+/TLDD23FKjR/Orl27ZgKn4NYdGa+5kaiIRBDz5883u7VHixbNKlKkiNWvXz/r0KFD/m7DP/l27dr5u6xgwYJW+/btX3i/n3/+uZU3b17nee7I3KhRo0Bve/r0aStKlCjW+fPn/V3OXZ45HuLOzUWLFn3h7ytZsqSVO3fulzxay5o3b56VMGFC5/l///3XPD7uoO7QrFkzq3r16lZwTZs2zYobN+5zlwe8H46xWLFizvOPHz+2YsaMaTVp0sR52YULF8x4duzYYc5/8sknVvny5f3d79mzZ81tjh8/HqLx8Pd/8MEHLzwf8P+XO5UH534l8vj000/Nc85x8vb2tqJGjervMv49ujp37pyVLl06q2XLlkHe95IlS6z06dP721Wdz9NFixYF+XMDBw40O7cHpUSJElbjxo3N90ePHjX36zpmnnx9fa0CBQo4Hydvs2rVKud9XL582TzelStXWsGlmh0RN8FPVlWqVDHTWTt37jRTKCNHjsTkyZPNpyWHwoUL+/s5nme2xbVuhRmUP//800wTsQaERbIOvC3rQwJz5MgRk6HhNJgrfoJLmDCh8+eZAQlK3rx5n7uMUzFMebPg8ubNm2Zc9+/fN5kTTuGEN9eaAa7+4uPjNJsDp6mIdUTETNqGDRsCrf/hsQ54zEIL36eCKjCVyKldu3b+soLM0vI1oFatWs7LXGvX/v77bzPFzWyNaw1XYJg15XMyYJ0N77948eKBTgcTp6xZSM+/dxbKB4ZZYGZzyTENzWwxp71dOX7eUSvETLBD4sSJTVb1zJkzCC4FOyJuhDU0TO/yxOmqVq1amWWmrsFOUDitxBfNIUOGmLQya1g4HcUiRQfW+rwIX7z4xr9v3z7z1ZXjTT6on3dgXU7A5bPvvvuumSr69NNPTb0BXzC5pJapbDuCHV9fX3/nGVC4XuYIMJiCdxwbTtexliagFxV/vi4Gnpw+ZH2VuBf+DbjW3fDvioXArBUL6Pz58ybQ4YcIrp7ilHBQuOqJrx2uGMhzOpvP4RfhBxlOm74o0HHcxjWA4W0ZtJQsWTLQ2xctWtR85TQ163WI01isdWNtYnAp2BFxY3wxCdgbg1mfpk2b+jvPOhLavn27eQHhMk+H06dPP5fRYJ0OC3ED4v3wDZbZDH4CDIzj5xlQBReDJwYNDLocL9SsFXgZ1v241gLZKU+ePKZuIU2aNPDxCZ+XXtYVsfCbn8jFMzHQKVWqlPm75uortqhwcBQe8zaso2OhMDMvjuL8gNjWgv16HIsUWHPGYnh+yGLdDutquDjBgTU2vH22bNlMFpZZZmaNVq9e7awP5O1ZH8S/72LFipniadbqMZvMmjNmPFk39MEHH5iMFC/nwovMmTObAC64FOyIuAEuL+fU0Pvvv2+CCb6IsAiW01gBCwznzZuHfPnymReWWbNmmQLbKVOmmOsyZMhgPmUxm8NsANPLXMLsipkivjCyORmLmzmd9PPPPztXcTEzxGCKgQmDH764MrjhuDjNxhcqfkpkMSNT8QxIOL3D8TM1HRh+WuWKMBY/8pOla+FyUBhYrFq1ynwq5DQTM1UBMzLhhatWWOzdoEEDU1jNT+UsFuWx5ptAwExYSHE6j0t3XZee85M4s2EheVMQ98Ig5MSJE+bkyIw4OFZO8W+LfyOOYvrg4N8RV2oxUOH98G+UKyZdp7iZde3Ro4cJpph95WsAp6Ndn4+c9uK0FKeoWYzMqTN+MHDtA8QgjL+Hrx/8sMMsEAv8Q/S3HOzqHhGJsO7fv2/17dvXypMnjyk8jREjhpUpUyZrwIAB1t27d52345/8+PHjrXfeeccUM6ZJk8aaM2eOv/vq1auXKfyNFSuWVa9ePWvMmDHPFbMuWLDAypUrl+Xn52clSpTIqlWrlvO6hw8fWh999JG5bxYaJk+e3KpZs6Z1+PBh5202btxoiqg5hnjx4lkVKlQwBcZBFduOHj3a3Ff06NHN7WfOnGkej+PnAitQZiEjHysfS8DrXrdAOeAYU6dObY6Vq4BFnb///rs5FnzMfByZM2e2unbtaj19+jRE4ylevLjVo0cPf+Ph7+KJ/yc8Tu+++661cOHCEN2viLvy4j/BD41EJDJjHQk/8dux9YGEHFsAsJcPl627YgqfNRWuUwahcb8i7krTWCIiERhrGFjczWkwTglwlR2nHFy7yYYE74tTXayzEPEUCnZERCIoFhaztopYy8BGhSw4ZmsAR1F5SDnaDLxujZBIZKJpLBEREXFr2i5CRERE3JqCHREREXFrCnZERETErSnYEREREbemYEdERETcmoIdERERcWsKdkRERMStKdgRERERt6ZgR0RERODO/g/SFSD6rcKxUgAAAABJRU5ErkJggg==", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots()\n", "ax.plot(t.jd, time_corr * 86400, label='SPOC Time Correction', c='r')\n", "ax.plot(t.jd, ks.get_barycentric_time_correction(time=t, ra=ra, dec=dec), label='`lkspacecraft` Time Correction', c='k')\n", "ax.legend()\n", "ax.set(xlabel='Spacecraft Time [JD]', ylabel='Barycentric Time Correction [s]', title=hdulist[0].header['OBJECT']);" ] }, { "cell_type": "markdown", "id": "38e86c6d-ecd5-4f02-94fa-eda02601148f", "metadata": {}, "source": [ "Looks like our correction is working!" ] }, { "cell_type": "markdown", "id": "37b76b22-9095-4e44-b570-1a10f3255162", "metadata": {}, "source": [ "## TESS Example" ] }, { "cell_type": "markdown", "id": "47a40d04-21df-42e2-9c83-59f3012159e7", "metadata": {}, "source": [ "TESS has a much wider FOV than the Kepler mission, and so there can be a significant difference between the barycentric correction for a target at the center of the field compared with a target at the edge of the field. \n", "\n", "This effect is not currently taken into account in TESScut TPFs. Let's use `lkspacecraft` to fix this." ] }, { "cell_type": "code", "execution_count": 14, "id": "85d69289-8146-4081-9656-d6c8973d53e6", "metadata": {}, "outputs": [], "source": [ "from lksearch import TESSSearch\n", "from lkspacecraft import TESSSpacecraft" ] }, { "cell_type": "code", "execution_count": 15, "id": "b17e6d10-aba5-4276-8330-b4a5d5367954", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Downloading TESScut: 100%|████████████████████████| 1/1 [00:02<00:00, 2.27s/it]\n" ] } ], "source": [ "# Download a TESScut TPF file, obtain the local filename\n", "fname = TESSSearch(\"Kepler-10\", hlsp=False).tesscut[0].download()['Local Path'][0]\n", "# Open using astropy\n", "hdulist = fits.open(fname)" ] }, { "cell_type": "code", "execution_count": 16, "id": "837fdd57-0fe2-4239-bed9-c1225e8fd153", "metadata": {}, "outputs": [], "source": [ "ra, dec = hdulist[1].header['RA_OBJ'], hdulist[1].header['DEC_OBJ']" ] }, { "cell_type": "markdown", "id": "e30b0d47-fa2f-444e-bae7-8fade37b40e0", "metadata": {}, "source": [ "We'll load a new spacecraft this time, TESS!" ] }, { "cell_type": "code", "execution_count": 17, "id": "dc2fce31-a68f-4408-a8ad-7d63650d3486", "metadata": {}, "outputs": [], "source": [ "ts = TESSSpacecraft()" ] }, { "cell_type": "markdown", "id": "198054c5-8a04-424e-8bc9-91382fc51930", "metadata": {}, "source": [ "In TESScut data there is a correction applied, but this is the correction at the middle of the FFI. Let's remove it" ] }, { "cell_type": "code", "execution_count": 18, "id": "83bee026-047d-422d-a895-2ac23f373c54", "metadata": {}, "outputs": [], "source": [ "time, time_corr = hdulist[1].data['TIME'], hdulist[1].data['TIMECORR']\n", "k = np.isfinite(time)\n", "time, time_corr = time[k], time_corr[k]\n", "t = Time(time - time_corr + ts.time_offset, format='jd')" ] }, { "cell_type": "code", "execution_count": 19, "id": "7fc89a8b-303b-43b4-9abd-144ed3bb2e1e", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([155.38559868, 155.38850227, 155.39132197, ..., 148.89153462,\n", " 148.88320421, 148.87483253])" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ts.get_barycentric_time_correction(time=t, ra=ra, dec=dec)" ] }, { "cell_type": "code", "execution_count": 20, "id": "cf0afa7f-552b-443b-9d9b-7f22f2d2c6d2", "metadata": {}, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots()\n", "ax.plot(t.jd, time_corr * 86400, label='TESSCut Time Correction', c='r')\n", "ax.plot(t.jd, ts.get_barycentric_time_correction(time=t, ra=ra, dec=dec), label='`lkspacecraft` Time Correction', c='k')\n", "ax.legend()\n", "ax.set(xlabel='Spacecraft Time [JD]', ylabel='Barycentric Time Correction [s]', title=hdulist[0].header['OBJECT']);" ] }, { "cell_type": "markdown", "id": "dd342eec-f63a-47f7-aa65-8d350d2cb23f", "metadata": {}, "source": [ "This time the stated time correction from TESSCut is incorrect, this is the time correction at the center of the FOV, not at our target. This can make a significant difference, the TESSCut data has photons arriving 40s too early!\n", "\n", "Now you know about this time correction, you can apply it to any work on TESS FFI data." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.9" } }, "nbformat": 4, "nbformat_minor": 5 }