From 1797b8e8da5055e110c282ba989fbabc814d0772 Mon Sep 17 00:00:00 2001 From: Martin Kilbinger Date: Mon, 14 Jun 2021 18:08:06 +0200 Subject: [PATCH 1/8] init + utilities --- shapepipe/__init__.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/shapepipe/__init__.py b/shapepipe/__init__.py index 8c01a81bd..21b8404a0 100644 --- a/shapepipe/__init__.py +++ b/shapepipe/__init__.py @@ -8,7 +8,7 @@ """ -__all__ = ['modules', 'pipeline'] +__all__ = ['modules', 'pipeline', 'utilities'] from . import * from .info import __version__, __about__ From 6b3bf9bf81975e3474aba99ad43fa75289f08478 Mon Sep 17 00:00:00 2001 From: Martin Kilbinger Date: Fri, 18 Jun 2021 07:50:21 +0200 Subject: [PATCH 2/8] removed debug print comment --- scripts/sh/psf_residuals.bash | 1 - 1 file changed, 1 deletion(-) diff --git a/scripts/sh/psf_residuals.bash b/scripts/sh/psf_residuals.bash index 1c6d5d488..4929350b6 100755 --- a/scripts/sh/psf_residuals.bash +++ b/scripts/sh/psf_residuals.bash @@ -114,7 +114,6 @@ fi n_skipped=0 n_created=0 FILES=output/*/${runner}/output/${psfval_file_base}* -echo ${FILES[@]} for val in ${FILES[@]}; do base=`basename $val` link_s "$pwd/$val" "$dir_individual/$base" From 11a26adc8ad13f98f75c47fb923c36a482393497 Mon Sep 17 00:00:00 2001 From: Martin Kilbinger Date: Wed, 30 Jun 2021 09:13:08 +0200 Subject: [PATCH 3/8] notebooks --- scripts/jupyter/CFIS_Footprint.ipynb | 136 +++++++++++++++-------- scripts/jupyter/plot_spectro_areas.ipynb | 2 +- 2 files changed, 92 insertions(+), 46 deletions(-) diff --git a/scripts/jupyter/CFIS_Footprint.ipynb b/scripts/jupyter/CFIS_Footprint.ipynb index 5eb139292..d9d13b4ad 100644 --- a/scripts/jupyter/CFIS_Footprint.ipynb +++ b/scripts/jupyter/CFIS_Footprint.ipynb @@ -13,13 +13,12 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 1, "metadata": { "ExecuteTime": { "end_time": "2019-10-08T10:47:15.755022Z", "start_time": "2019-10-08T10:47:13.467478Z" - }, - "collapsed": true + } }, "outputs": [], "source": [ @@ -28,33 +27,29 @@ "import matplotlib.pyplot as plt\n", "import astropy.table\n", "\n", - "import cfis\n", + "from shapepipe.utilities import cfis\n", "\n", "%matplotlib inline" ] }, { "cell_type": "code", - "execution_count": 51, - "metadata": { - "collapsed": true - }, + "execution_count": 9, + "metadata": {}, "outputs": [], "source": [ - "sp_home = '{}/astro/repositories/github/shapepipe'.format(os.environ['HOME'])\n", - "tiles_dir = '{}/aux/CFIS/tiles_202007'.format(sp_home)\n", - "patches = ['P1', 'P2', 'P3', 'P4']" + "tiles_dir = '.'\n", + "patches = ['P1', 'P2', 'P3', 'P4', 'P5', 'P6', 'P7', 'None']" ] }, { "cell_type": "code", - "execution_count": 52, + "execution_count": 3, "metadata": { "ExecuteTime": { "end_time": "2019-10-08T10:47:15.764850Z", "start_time": "2019-10-08T10:47:15.758331Z" - }, - "collapsed": true + } }, "outputs": [], "source": [ @@ -63,13 +58,12 @@ }, { "cell_type": "code", - "execution_count": 53, + "execution_count": 4, "metadata": { "ExecuteTime": { "end_time": "2019-10-08T10:47:15.786757Z", "start_time": "2019-10-08T10:47:15.769213Z" }, - "collapsed": true, "run_control": { "marked": false } @@ -109,9 +103,16 @@ " return ([RAlo, RAhi, RAhi, RAlo, RAlo],[Declo, Declo, Dechi, Dechi, Declo])\n" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Read all tile IDs" + ] + }, { "cell_type": "code", - "execution_count": 64, + "execution_count": 43, "metadata": { "ExecuteTime": { "end_time": "2019-10-08T12:54:03.518299Z", @@ -123,11 +124,15 @@ "name": "stdout", "output_type": "stream", "text": [ - "P1 : 3225 tiles, 806.25 square degrees\n", - "P2 : 1129 tiles, 282.25 square degrees\n", - "P3 : 995 tiles, 248.75 square degrees\n", - "P4 : 1430 tiles, 357.5 square degrees\n", - "all : 6779 tiles, 1694.75 square degrees\n" + "P1 : 3527 tiles, 881.75 square degrees\n", + "P2 : 1514 tiles, 378.5 square degrees\n", + "P3 : 1268 tiles, 317.0 square degrees\n", + "P4 : 3196 tiles, 799.0 square degrees\n", + "P5 : 3087 tiles, 771.75 square degrees\n", + "P6 : 1773 tiles, 443.25 square degrees\n", + "P7 : 231 tiles, 57.75 square degrees\n", + "None : 0 tiles, 0.0 square degrees\n", + "all : 14596 tiles, 3649.0 square degrees\n" ] } ], @@ -156,15 +161,13 @@ " n_tiles = n_tiles + n_tiles_patch\n", "\n", "print('all : ', n_tiles, \"tiles\", end=', ')\n", - "print(n_tiles*0.25, \"square degrees\")\n" + "print(n_tiles*0.25, \"square degrees\")" ] }, { "cell_type": "code", - "execution_count": 56, - "metadata": { - "collapsed": true - }, + "execution_count": 6, + "metadata": {}, "outputs": [], "source": [ "# http://balbuceosastropy.blogspot.com/2013/09/the-mollweide-projection.html\n", @@ -197,10 +200,8 @@ }, { "cell_type": "code", - "execution_count": 57, - "metadata": { - "collapsed": true - }, + "execution_count": 7, + "metadata": {}, "outputs": [], "source": [ "# https://matplotlib.org/1.5.0/examples/text_labels_and_annotations/rainbow_text.html\n", @@ -232,13 +233,22 @@ ] }, { - "cell_type": "code", - "execution_count": 60, + "cell_type": "markdown", "metadata": {}, + "source": [ + "### Create plot" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": { + "scrolled": true + }, "outputs": [ { "data": { - "image/png": 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xbeVwVgoXVhyOh2CMxQIojY2N3RMSEnKB3W5PTUlJYXv37g0rKysLKy0thUKhENtMDscvmZubQ2NjI06ePGk/fPjwRHV1tWN2dnZKJpNVa7Xaf9lstlMAmnk5CI6/w4UVh7MKGGMRAIojIyN3R0VFfcZqtWYrFArZeeedF3T++edHlZaWIiUlhYfzOJw1MDs7i9raWlRWVs4dOnRosqGhgaxWq4ExVjE8PPwBEVVBGJXIb2Qcv4ELKw7nE3CG9LJCQkIuiYuL+6zVas2Xy+XBO3fulO3Zs0e+bds2lp2dvewReQ6HAwaDATqdDhs2bFhzKNBsNmNwcBChoaFQqVRraouIMDw8DLPZjI0bN66pLQCYmZlBf38/cnNz19wWEaGlpQU5OTl+N/pxaGgIwcHBiImJWXNb4+PjGB8fR1pa2pqFuSu/KT09fc2DHhwOBxoaGqBSqRAXF7dm21paWhAaGgqFQoHg4OBlHzc+Po6amhpUVFSYDx06ZGxvbweAYavVekSv178L4BgRzazJOA5nDXBhxeEsAmNMIZVKL1IqlTdYrdZtmzZtkl577bXysrKygOnpaTgcjhW1R0Sw2WywWq2w2+2QyWQICAiATCZbtY1WqxVzc3MgIgQGBiIgIGBNNzur1QqLxQKpVIrg4OA13zhd7YWEhEAqla6pLUAIFdntdo+OipyZmUFYWNia23E4HJidnfVYoVWTyQSHw4HQ0FCPfQ+e+F7tdjvm5uZgs9kQEBCAwMDAVYtch8MBq9UKm80GIpr/n1jNb2VsbAxdXV3o6+ubfP/99+dmZ2f1FovlTYPB8A8ANURkW5WRHM4q4MKKwwHAGAsDcL5SqbyOiC5KTEwMveaaa8Ivu+yykJKSkhVf7IkIU1NT0Gq10Ov1ICIoFAooFApERkau6uZGRDAYDOjv78fExASUSiXUavWabuauYfKdnZ2Ii4tDRkbGirwHi2G329HY2Ii5uTkUFhYiICBgTe0BggCqqqrCeeed5xGR5uLw4cPYu3evR9rq7e2F0WhEfn6+R9obHh5GW1sb8vPz1+wJc3kiOzs7ERUVhczMTISGhq66PbvdDp1Oh4GBAVgsFqjVaqjVagQGBq6qPavVCr1eD51Oh8nJScjlcigUCiQkJKzq4UOn0+HAgQP0xhtvGE6cOOGQSCRtBoPh9dnZ2fcAdPDQIcebcGHF+VTirB21NSYm5srg4OCrQ0JCEj772c8GXnXVVVG7d+9elReDiDA5OYmhoSHo9XqEhoZCqVQiISEBQUFBq7Z1bm4OAwMDGBgYQGRkJJKTkxEbG7smzwMRQa/Xo7W1FbGxsR4RVIDgaamuroZarUZqaqpHcsyICBUVFcjJyfFIqM0dTworIkJlZSWys7M9Zufs7CxqamqQlJSE1NTUNbdHRNDpdGhra0NsbCyysrJWLYZcWCwWDA4Ozv8+U1NTER0dvervnogwPj4+/1Di+j9KTExclUgnInR0dOD999+3/u1vf5tobW21yWSyU8PDw6/Z7fb/IyL9qgzlcJaACyvOpwJnnlRmaGjoZTExMZ93OBxZZWVlkmuuuSbmoosuYgkJCatq13UTGBoawsjICCIjI+fF1FrCfK52XV6QpKQkJCUlrfkmCAg5Ks3NzQgJCUFOTs6aPBfuGAwG1NXVIT8/H7GxsR5pEwAGBwcxNjaGgoICj7XpwpPCChAqk58+fRq7d+/2WB6Y3W5HXV0dpFIptmzZ4pF2iQiDg4Po7OzEhg0bsHHjxjX9Xl1tGgwG9Pb2Ynp6GsnJyVCr1Wv2WBqNRgwPD0Or1SIoKAhKpRJKpXLV/ws2mw3V1dV49913Z996660ZnU43A+ADrVb7NwBHiWh2TQZzPvVwYbXOYYzJAbwAIA8AAfgyhKkmXgWQCqAXwA1ENO7c/0kAFwC4l4iOiGCyx1iYJ5Wbmyu97rrr5JdccklAZmbmmp6oJyYmoNFoMDIyArlcDqVSifj4+DWHqex2OzQaDXp7exEWFoaUlJQ1e6dcmEwmNDU1wWazYdOmTYiMjFxzmy4GBgbQ09OD0tJSj+ZAWa1WlJeXo6yszCOiciGeFlYA0NbWBplMhvT0dI+1SUTo7u6GVqtFaWmpx86F3W5Hb28v+vv7kZGRAbVa7ZHfmsViwcDAAAYHBxEdHY309HSP5J9NT0/PiyypVAq1Wg2lUrkm8TY9PY1jx47hH//4x+T7778/ZzabdWaz+S2DwfB3CPlZvLwDZ0VwYbXOYYy9BGGUzAuMsUAAoQC+A8BARE8wxh4EEE1EDzDGcgB8BcDDAH5PRDeIZ/nKYYyFANiTmJh4PYCLFApF6NVXXx12+eWXh5aUlKz5iXx6ehqDg4PQarUIDw+HWq1GQkKCRzwIFosFvb29GBoaglKpRGpqqkdCc4Bw8+zs7MTw8DA2bdqE1XrnFsMVZhkfH4cnzvFCmpubERoa6pEw2GJ4Q1jZ7XYcPXrUK2JQq9Wira0NpaWlHvM0AkK4ubW1FVNTU8jLy/PYpN2u0GN3dzckEgnS09M9MqIQEPLuNBoNhoaGEBYWNv//uNaHG61WO5+fVVFR4WCMtRkMhtdMJtM7RNS1ZsM56x4urNYxjLFIAHUANronazLG2gDsJaJhxpgSwGEiymaMbQZwK4BHAPyBiD4vht0rgTEWJpFILlWpVF9jjBVeccUVAVdddVXU+eef75HRXq78EY1Gg8DAQKjVaiQmJnpMQExNTaGrqwuTk5NITU2FWq32aHK2VqtFa2sr1Go1Nm7c6NEyBUSE+vp6APBYiMqd2dlZVFVV4fzzz/daPTBvCCtACF8aDAaPJbK7Mz4+jjNnzqC4uNhjAsjF5OQkGhsbER4ejtzcXI8Kw8nJSXR3d8NoNCItLc1jk4YTEYxGIwYHB6HX6yGXy5GUlOQRTy8Rob29He+99571tddem+ju7p6xWq2vjI2N/YGIWtdsPGddwoXVOoYxVgjgtwCaARQAqAFwFwANEcnd9hsnomjn+18A2AXgPiI66HurPxnGWIRUKv1cYmLi12QyWe71118f8sUvfjG8sLDQIzdgh8MBvV6P/v5+mM1mqNVqbNiwYU0J6AuZmJhAe3s7bDYbMjIyEB8f71HxYDab0dDQAMYY8vLyPOb9cuFwOHD69GlEREQgKyvLK8Ln9OnT2LBhg1er1XtLWBERysvLUVxc7BGBvxDXKElPjBhciGukaEdHB7KysqBSqTz+2+zp6YFWq0VKSgpSUlI89jBBRBgbG8PAwAAmJyehVCqRnJzssfD0xMQE/vGPfzhefPFFQ2tr66zNZnt9dHT0JQCNfKQhxwUXVusYxthWAJUAyojoJGPsWQBGAHcuJaz8FcZYVGBg4NXx8fG3BwYGZu3bty/kC1/4QtjmzZs9dtGfmppCf38/9Ho94uLikJyc7HGPwNjYGNrb2yGRSJCZmemVm2J/fz+6u7uxadMmr4gSu92O6upqxMXFeTSPyB2j0YiGhgbs2rXLq9XrvSWsAECv12NgYAAlJSVead9kMuHkyZPIy8tDXFycx9ufm5tDU1MT5ubmkJ+f79HcOUDIn+vt7cXg4CA2bNiAtLQ0j5TmcGGz2TA0NIT+/n5IpVIkJycjMTHRYyJuamoKb7/9Nr344otj9fX1ZofD8beRkZHfAzjDRdanGy6s1jGMsUQAlUSU6lw/D8CDADKwSChQPEsXhzEWExQU9G+xsbH/GRoamvrFL34xbN++faE5OTke68Nut2NoaAh9fX2QyWRITk6GQqHwaDgOAEZHR9HW1oagoCBkZmZ6XLABQujszJkziIiIQG5ursfznQDhfJ06dQqJiYlIS0vzePsuqqqqsHHjRo+OLlwMbworIsKJEyewZcsWjw4UcMdsNuPkyZPIzc31aO6cOyMjI2hqakJqaqpXpmmy2+3o6+tDX18flEol0tPTPSqwACE/sr+/HzqdDnFxcUhLS/NYMVdA8CC+++67tH///rGampo5AG/pdLrfA6jiIuvTBxdW6xzG2DEA/0FEbYyxRwC44hJjbsnrMUR0v2hGusEYiw8NDb1eLpd/NSIiQn3LLbeE33jjjSEZGRke7WdmZga9vb3Q6/VITExEamqqx5/IAaEEQWtrKwIDA5GdnY2IiAiP9+HyUvX09GDLli1eEyMOhwNVVVVQKBReSyYHBG9VY2Mjdu3a5bU+XHhTWAGCoO7r6/Oa1woQ8gArKyuxefNmr3iuAMH709zcjNnZWRQWFno8tAwIv6++vj709vZCrVYjLS3N4w8HDocDOp0OPT09AIDU1FQkJiZ6ND/QbDbjX//6F/bv3z9WUVExxxh7V6vV/g+Eh9yVTdnAOSfhwmqd48yzegFAIIBuALcBkAB4DUAygH4Anycig4g2RoSEhNwol8vvjImJUd5yyy2RN954Y5Cnb96uopg9PT2w2+1ITU2FUqn0yrxzk5OTaG0VcltzcnK84qEChJvqmTNnEBwcjM2bN3vFSwUIN6Tq6mrExsZ6LfznoqamBikpKV4TCe54W1gREY4fP47CwkKPekgWYjabUVlZ6ZWcK3f0ej2ampqQlZWFDRs2eKUP9xIQycnJSE1N9bgHGRC8WL29vRgZGYFKpUJKSorHBaPFYsGBAwewf//+sfLychuAd7Ra7S/Aw4XrGi6sOKLAGJMCuFClUt0TEBCw9bbbbgu77bbbQpKTkz3el81mQ39/P/r7+xEdHY20tDSvhWZmZ2fR0tICi8WC3NxcREd7L3XNdZPzVi6VCyJC+4svIuHoUUSnpwNf+QrgpZvqzMwMamtrUVZW5tXcKhfeFlaAML3K8PAwCgsLvdqPK+equLjYa79vQMi9amhogEQiwZYtW7wm5m02G7q7u6HRaJCZmYkNGzZ45Tdhs9mg0WjQ19eHsLAwpKenr3li9MWwWq1477336Je//OVYfX39tNls/t3ExMSLRKT1eGccUeHCiuNTGGO5CQkJ32SM/dtnP/vZ4K9//evybdu2eeWCaTKZ0N3dDb1eD7VajZSUFK8UmQSEi2ZHRwdGRkaQk5ODhIQErwkDh8OB1tZWTExMoLi4GMFBQcD+/YLgWQ2MCYv7uturw26HxP068dhjwPe+t0rrP56GhgbExsZCpVJ5pf2F+EJYERGOHDmCnTt3enRk6WJMTU2huroa27dv92idq4W4ws+9vb0oKiryqpCzWCxoa2vD5OQkcnNzvebJdI0o7Orqgs1mQ3p6OhQKhVf+j8fHx/Hyyy9bf/3rX0+Mj4/3joyMPG21Wt8kIrPHO+P4HC6sOF6HMRYbHh5+a3h4+B2ZmZnyO++8M/aqq66SeOsmMzExga6uLszOziItLQ0qlcor4T7go3khaWlpSE5O9lpfgCAWa2pqEB8f/2GZgzffBK691mt9nsXQEKBUerxZq9WK48ePY8+ePT7xVgG+EVYA0NfXB5PJBE8OvFgKg8GA+vp67Nq1y2sPEi6MRiNqa2vnyyZ483ubnp5Gc3MziAibN2/2amh1enoa3d3dMBgMSElJQXJyslfCkYBQqf93v/vd9CuvvGIioiPDw8PPADjBQ4XnLlxYcbwCY0wC4DMqleq7YWFhm/7zP/8z8tZbbw3y1sgl4KOlDDIyMhATE+PVC/3Y2BgaGxuRkJCAzMxMr4VEXIyOjqKhoeHsufhGR4H/+i/gD38AvP3/nJwM9PV5pemuri4wxrBx40avtL8YvhJWrmrse/bs8arwdqHVatHV1YWdO3d6vT+bzYaGhgYAQH5+vtcEiIuxsTE0NTUhLi4OWVlZXv2/m5ubQ29vLzQajVdKQrjjcDhw+PBh/PKXvzScOHHCNDc39z/j4+PP80mizz24sOJ4FMZYrFwuvz0oKOiOz3zmM2H33ntvdHFxsdf6cyWkd3R0IDg42GulDNwxm81oamqC1WpFXl6eV5+cgQ/niRseHkZJScnioxeJgPx8oLFxdZ2o1cDevUL4Lzwc5qAgjPT1QZmeDpnFAoSEAEYjsGsXEBQEpKUBW7d+NIS4BlzhsrKyMq/duBbDV8IKEKbniYyMhFqt9kl/XV1dMBqN8FTh3I+DiOZrUm3dutUrI2wX66+3txfZ2dlQKpVe/YzuJSESExOxceNGr4Z1p6am8Mc//tH6zLSmt3wAACAASURBVDPPTM7MzNQMDQ09DqCce7HODbiw4qwZJlzRdiiVyu8EBwfvuOuuuyK/9KUvBXpT4BARhoeH0dnZicjISGRkZHhd4DgcDvT09GBgYAA5OTlITEz0an+AcEE/c+YMZDLZx08b89ZbwDXXeN2ej/Dcc8Cdd3qkKZ1OB61Wi4KCAo+0t1x8KaxmZ2dx+vRp7N692yf9ERHq6uoQHh4OT5crWQqDwYC6ujps2bLFJ6M6LRYLWlpaMDs7i/z8fJ9cAwYHB9Hd3Y3Y2FhkZmZ6pfSECyJCZWUlfvrTnxoqKiqMs7Ozz05NTe0nokmvdcpZM1xYcVYNYyw8PDz8S2FhYXeXlpZG3X///bG7d+/26pMjEUGr1aKjowNyuRyZmZlefzoGhPIJ9fX18+EHb4c7AMEzVl1dDbVa/cl1o/78Z+Dmm71u01l46Ppx8uRJr5alWApfCisA88U8vZns7Y7D4UBFRQWysrIQHx/vkz5NJhOqq6vnc5N8gcFgQENDAxITE5GZmen18CcRYWhoCB0dHYiLi0NmZqbXByaMjY3hhRdeMP/qV7+anpubO6TVan9MRLVe7ZSzKriw4qwYxlhWYmLi92Uy2SVXXnllzMUXXyz1ZlkBFzabDWazGVKpFEFBQT7JVQEEgWOz2RAaGuqzPh0OB2ZnZxE+N4cNBw8iSK8XRAxjYAAImH91HoDkv/7VJ7a5sAcEoPztt0FrDN0REWZmZrzubViM6elpn/Zrs9lgtVo/9mFgdnYWR44cwejoKEJCQlBWVgblGgYLENH85/TVoABA+BwSicSrHp2FWCyW+fPri4cfQBh0YbFYIJPJEBQU5PVz7Jqn86233jL39fWNjIyMPGQ2m18mIqtXO+YsGy6sOMvCGe4rU6lUT6hUquyHH3449vLLL2e+EBqjo6NobW1FWFgYsrKyvDKp7VL9NjY2IiUlBampqT67Ken1ejQ3NWHHkSMI/sEPfNLnqrn3XuCpp9bUREdHBwICArxazX0pfO2xevjhh/Hyyy8jICAAjLGPCHXXtdhgMGB4ePgjx333u9/FD3/4w1X3azAY0NTUhLKyMp89HBDRfLX24uJinwmd6elp1NXVITIy0mtTOy3EPUSoUCiQkZHhk1xBjUaDZ555ZupPf/rTjMVieX58fPwXPEwoPlxYcT4Wxpg0ICDgutjY2B9s27Yt/pFHHoktKirySd9GoxEtLS2QSCTIzc31mWfBbrejubkZU1NTKCoq8kmo0cXg4CB6enqws7wcsu98x2f9rok1XEPESlp34UthRUSQyWRwOFY3q0lGRgYaGhpW7QHq7OyE2WxGXl7eqo5fLa5Rddu2bfPZd0xE82VQvF2N3h338iuuBzJfCNmZmRm88MILlp/97GfGubm5N3U63WNENOD1jjmL4ptHF845B2MsTC6X36NQKPq++tWv/u7kyZM5b731lk9ElclkQm1tLRoaGpCZmYnS0lKfiSqDwYBjx44hPDwcO3fu9Kmo6urqwsDAAHbs2AGZjxKc18waC4VOTEwgIiJCFFHlaxhjePrpp1d9fGdnJx544IFVH5+eno7p6WnodLpVt7EaUlNTkZaWhoqKCpjNvql/yRhDamoqSktL0dLSgubmZtjtdq/3K5FIkJaWhvPOOw9WqxVHjx6FRqOBtx0YYWFhuOuuu4J6enrif/GLX3xl06ZNp1Uq1TuMMd88BXM+AvdYcT4CY0wZHx//YGBg4L5vfvObEXfccUeIrxKKbTYbOjo6oNfrkZ2d7bWqx4vhcDjQ1tYGg8GAwsJCn4UbAeHpurW1FTMzMyguLhaecNvbgexsn9mwajIzgd/8BrjwwlUdXl9fD4VC4dUpeT4OX4cCN27cOD8B8GpxeUNWg8ViwYkTJ7Bz506f5j4BH4bWS0tLff7/1d3djcHBQa9XiV+I2WxGe3s7JicnsXnzZp95zlxzVD700ENjbW1t/Tqd7rsOh+M9Xq7BRxARX/gCAMmJiYl/zsjI0L/00kvWubk58hUOh4P6+/vp4MGD1N3dTXa73Wd9ExHNzMzQsWPHqLW1lRwOh0/7djgc1NDQQLW1tWf3/dJLREKgzb+Xa69d1We32+108OBBn3/f7hw6dMin/RUXFxOEMQerXi677LI1/U51Oh1VVFT4/LdORDQ+Pk4HDx6kqakpn/c9OTlJhw8fpu7ubp9/dqPRSBUVFVRdXU2zs7M+7bu1tZVuvvlmg0Kh6AoKCroRgIT84J6znhfusfqUwxhLS0xM/IlcLr/giSeeiLnqqqskvhw5ND4+jqamJkRGRiInJ8frU3AsRKPRoKOjw6d5GC6ICPX19ZBIJMjLy1vcO/faa8CNN/rUrhVTVycUJ10her0ew8PDPq9d5Y6vPVYdHR0oKirCzMzMmtqZmJhYU2mK+vp6REZGijJgwGg0oqamBiUlJT71HgFC/mRTUxNMJhOKiop8fr3R6XRoaWmBSqVCenq6zxL6AWBoaAiPPvro5JtvvjkxOTnpGkno/fjopxAurD6lMMY2JiYmPhUbG3veT3/605jLLrvMp4Jqbm5u/gKXl5cnygW2oaEBNpsNBQUFPs/xIRKKNwYEBGDTpk1LhzwHB4GkJJ/ahshIwGwWqqonJgIbNgjv7XagsvKj+37968Dzz6+qm9raWiQnJ390eh4f42thBQhlCC666CJUVFSsug2dToe1TA9ls9lQXl7u87CcC9dk0WKIKwAYHh5Ga2vr2dND+QCHwzEfmvRVoWF3tFotHn/8cePrr78+MTU19f3Z2dk/cYHlYcR2mfHFtwuAlMTExNc2b9488u677zrECH319fXRwYMHSaPRiBKOmJ6epiNHjlBPT48o/TscDjpz5gw1NjZ+cv93302ih/qWWm69lWiVYTy73U4HDhwQ5fy74+lQoMlkopSUlDWH+5azNDU1rcnW0dFROn78uGjfwdTUFB08eJCMRqMo/c/OztKxY8eos7NTlHNgMpmourqaTp486fPwIBGRXq+nO++8c1KhUPSGhIR8ATxE6LGFe6w+JTDG1ImJiT+Jioq65Kmnnoq54oorfOqhAoQQQENDw3zYT4yRYFqtFq2trSgoKIAvipouhIjQ6JzPb8nwnzsVFcL8fP6KxQKsIpwyMjKCoaEhUcOAgOc9Vn/5y1/w+c9/3mPtfRJrvX7X19cjKipq1cnwa8UVFvTlyF93HA4HGhsbYbFYUFhYKMo1Sa/Xo7m5GUlJSUhLS/NZnTEXOp0OjzzyiPGNN94Ym5ycfMBsNv+FuDBYE1xYrXMYY5Hx8fE/jIiIuOmpp56Kueaaa3wuqOx2O9rb2zE6OootW7ZALpf7tH9AuAG1trZicnISxcXFPs+tcNHc3Ayr1Yr8/Pzlj3i84w5h5J0/Ul0NlJSs+LC6ujqoVCqfTbOyFJ4UVkTk05uiQqGAVqtdUxtWqxXHjx/Hjh07fD5K0MXk5CRqa2uxbds2hIaGimLD4OAgOjs7UVJSgoiICJ/3b7fb50dE5+fni3KNHBoawve+973Jd955R6vT6f6TiI763Ih1Aq9jtQoYY1LGWC1j7J/O9UcYYxrG2Bnncrnbvk8yxqoZY3t8bGOAXC6/W6VSdTz66KO3t7W1xV177bU+F1WuulCBgYHYvXu3KBcMq9WKU6dOAQC2b98umqjq6uqCyWRamagCABGe5JfNKuqaEREMBoNXclvGx8eh0Wig0Whw0003QSKRQCKRICYmBqdOnVpz0vjH4Ys6Se7cdttta24jICAAOTk5aGpq8oBFqyMqKgoFBQU4deoULBaLKDao1WoUFxejpqbG53W+AEAqlSInJwdFRUVobGz0Wd0td1QqFV588cWoo0ePZl900UVvKJXKI4yxc6Dmix8idizyXFwA3APgZQD/dK4/AuC/FtkvB8CTAEIBvOYj21hwcPC/JSQk9H/729+eFmNYMxGR1WqlhoYGKi8vp+npaVFsIBLyqQ4fPkyDg4Oi2UBE1N/fTxUVFasrLfDzn5Po+VRLLWNjK/444+PjVF1dvfLz8An8/e9/X1Zu0nvvvTd/jCdzrGw2m09yq1xLf3+/x2yvrKykkZERj7W3GnQ6HR09epSsVqtoNlgsFiovL6eOjg7Rcs8cDgd1dnbSoUOHaGwV/1+e4tixY5SXlzeqUCj+BCCB/ODee64s3GO1QhhjagBXAHhhGbtLATiA+TlzvQpjbLtCoai77rrr/ufMmTNJP/rRj8LEyFsYGxtDeXk5wsLCsGvXLlFGHQFCQcJTp06hoKAAGzZsEMUGQMhh6Ovrw9atWz8aKnI4gCefFIprnn8+sHcvcMEFH77u2QOUlQF33+1bg1fi0VvFiC6tVuuVgqCvv/76sva79NJLMTo66vH+pVIpfvWrX3m83aXwpPd3y5YtaGpqWvV0O54gISEBaWlpqKqqEs2OwMBA7Ny5EzMzM6itrRXFDsYY0tPT56vGNzY2+tx7BQC7d+9GfX197DPPPLMvOTm5MT4+/oeMMXFitecY3p+dcv3xDID7ASwMxH+TMXYrgGoA9xLROBE1OX+I5QDu85ZBzlpUvznvvPO2/vrXv47ZvHmzt7r6WBwOB1pbWzE+Pi5qvgSA+QlRxagw7c7k5CRaWlqwc+fOsyeDvf564I03xDHs4+jtBY4fFxZAEIAOh1BywUVAAFBQAKxiglu9Xo/09HTP2OqGSqVa9r51dXX4zGc+43EbfBlm9uRDU2hoKBITE9HT0+OV72a5qNVqmM1m1NXVobCw0GczL7gjkUhQUFCA7u5uVFZWorS0VJSkdteDaU9PD8rLy1FYWLim2mWrgTGGffv2Sa+99tr455577t6nn376yxEREd+fnp5+kYjEU+H+jtgus3NpAfA5AL9yvt+LD0OBCgjeKQmAxwG86CN7guLj45/IyMgY+eCDD0Qdt240GunIkSPU3t4u6hB6h8NBbW1tVFFRIWpIgUgYzr1klenmZhI9jLfU8pOfEHnpOzSbzXTs2DGvtP3cc88tO4wmk8lofHyc3nnnHWptbaWWlhZqbW0lo9FIPT091NTUNL+0tLTQ2NgYaTQaamtrI7PZTGazmfr7+8nhcNDIyAiNj4+Tw+Ggb3zjGz4LBd53330ePX9Wq5UOHjxIFovFo+2uFFc5kra2NlHtICIaGhqiw4cP08zMjKh2+Mv11WAw0B133GFUKBRNAArJD+7L/rjwUYErgDH2YwC3ALABCAYQCeBvRHSz2z6pEASXV6eQDwwMvCQmJuaFb33rW3H33ntviFiT2BIRenp6MDAwIMoT1UJb6uvrAQihDV8PW3bHZrPhxIkT2LRpE+Li4s7eYWoKiI4Wim76I3feCTz3nMebHRgYgMlkQlZW1rL2Hx8fx8svv4yBgQEwxpCZmYnY2FicPHkSDocDjDHExMSgsLAQL7zwAl577TWP27wYmzZtQnNzM+Lj4zEyMgIA2Lp1K6qrq33SPwA89NBDePTRRz3a5uDgIAwGA/JXUUnfkzgcDpw8eRLJycmihvEB4Td45swZFBUViTL4xoV7RKCoqEjUiEBDQwNuvfVWw/Dw8Fs6ne5bRGQUzRg/hAurVcIY2wshYf1zjDElEQ07t98NYDsR7fNSv6rExMT9BQUFpS+88EK0Wq32RjfLYm5uDmfOnEFwcDA2b97s0+kZFmK321FTU4OoqChkZWWJEkJwQUSorq6GQqFAcnLy0jvW1gLf/Cag030YapNIAKlUeAUE4eUKxQHCdplM2AcQtttsHw3VSaXC+9bW1X+IuDjAKRg8SU1NDdLT05d1gxodHcUFF1wwX/eL81HWWn19MYgI5eXlKCoqEqWulDtWqxUnTpxAQUGBqIIGAGZmZlBVVYW8vLzFH5R8yNjYGOrr65GTkwOlUimaHQ6HA/v375976KGHDEaj8d6ZmZlXiAsKAbFdZufqgo+GAv8IoAFAPYC/A1B6oT9ZTEzMAykpKfp33nlHvFlrnYyNjdGhQ4dIo9GIbQpZrVY6fvw4dXd3i20KERG1tbVRfX29uEYMDdGawoFXXulxkxwOx4qqrf/4xz/26Si7c23xFiMjI3Tq1Cmvtb8SXNXZzWaz2KaQyWSiw4cP0/DwsNimkMViocrKSmpoaBB1EnMi4V5w8803TygUiioAWeQH92exFz4qcJUQ0WEi+pzz/S1EtIWI8onoKnJ6rzwFY2ynQqFovf322x9qbW2Nv+yyy0T73ogInZ2daGpqQmlp6YoShr3B3NwcKioqkJycjLS0NFFtAQQvwujoKMQaQDCPUglYLDC5wihvvglMTADj44DBICybNi19vBeS6qemphAREbFsb2JpaanHbVgvhIWFuR64PE5cXBxsNhvGx8e90v5KCA8Px6ZNm1BdXS3qiEUACA4Oxs6dO9HZ2YmBgQFRbQkMDMS2bdsQHByM48ePe7U+2ycRExODP/7xj1Fvvvnm1pycnBMJCQk/Z4yFiGaQPyC2suPL0guAsISEhN/v2LFjrL29ncRmbm6OTp48SfX19aI/JREJidBHjhzxiydIIqKZmRk6dOiQXzxdEwlzwZ06dcpriegrpbOzk3p6elZ0zP333y+6Z8hfl2effdY7XxQRTUxM0IkTJ7zW/kppa2ujxsZGsc0gog895L29vWKbQkRCQvmhQ4dIq9WKbQpZrVb62c9+ZlIoFP1SqbSM/OA+KsbCPVZ+ikwmK1MoFM0/+MEP9p04cSImMzNTVHumpqZw/PhxqFQq0RPDAcBiseDkyZOizA6/GK4cr/z8fAQFBYltDgCgtbUV2dnZHy2TICKjo6Mrzk957733vGTNuc9dd93ltbajoqIglUphMBi81sdKyMzMxNTU1Jqn8PEEMpkM27dvx9DQEPr6+sQ2B9HR0di5cye6urrQ1tbmeigXBZlMhnvuuSe4srIyqaSk5O8KheK/P43eKy6s/AzGWLBCofhNSUnJ3ysrK5O/9rWvBYmZiA0Aw8PDqKmpQVFREcRMlndhsVhQWVmJnJwcjyfvrpampiaoVCrExMSIbQoAQcQEBQUhchUFPL0BEWF2dnZFxWI7OjrQ3d3tRavOfbx5E83JyUHrWgZAeBDGGIqLi9HS0oLZ2VmxzYFUKsW2bdswNDSE3t5esc1BUFAQduzYAZvNhlOnTsFqtYpqT2pqKioqKmK+/e1v35qQkNDCGNsmqkE+ho8K9CMYY9sSEhJevfnmm5OvuOIKidheIUAQMTabDaGhoaKOtHNBRJiZmUFwcPDZBTdFwmq1wmq1ijr8eSEzMzMICQkR3bPowm63w2KxIDQ0FARgDkLhNwLgKhTiAGB2vicATzz+OMoPHAD4NWpJ3n//fa8WJZ2dnUVQUJCoI37dsdvtMJlMoo9YdGdmZgYBAQGizUG6EKvVOv+/5g///xqNBj/5yU/MExMTfxgYGPh/RCTOhJA+hAsrP4AxFhQfH//TDRs23Pz666/HZGRkiG0S7HY7amtrERQUhLy8PL8QVVarFZWVlcjKyvLKlCirwWQyobKyEmVlZX5zYTUYDOjq6vKr5O+uri7YJBJo5HJ8o70drSbT/N9eyMpClEyGB7q60L1wEt6REeCGG3xs7bmBRCLx+lQnrpkDduzY4dV+VkJ7ezvsdjtyc3PFNgWAcK2srKxESkqKX3j0AWBiYgK1tbXYsmWL6OUhAOEcPfnkk6af//znw3q9/noiqhXbJm/ChZXIMMY2JSQkvHX33XdvuO+++0L84cnQbDajuroaarUaqampYpsDQCi4efLkSaSlpYk+EtEFEaGiogKZmZmIj48X25x5Tp48iezsbNFr/7hTVVWF54OD8dLY2IqPDbr00vmkUFdRUJlMNj9KzG63izKXmj9gtVq97rmtrKxEbm6uqMV/3SEinDhxAjk5OYiNjRXbHAAfPvRlZmb6Rc4nIDz0VVVVITU19ePr6fmQlpYW3HDDDQadTvfrkZGR79M6nRZHfD/hp5jo6Oj/SE9PP3LgwIGMBx980C9EldFonPcK+YuocjgcqKqqQlJSkt+IKkDwwkRFRfmVqDIajXA4HH4lqogI09PTeGtiYlXHm81mWCwWzM3NwWazwWq1wmQywWKxwGKxYGZmBk899ZRfhWJ9hS+uGZmZmejo6PB6P8uFMYaioiI0NDSInkvkIiAgANu3b0d7e/t8JX6xCQkJwa5du6DVatHc3CxqUruL3Nxc1NbWxvz7v//7/0tISKhgjPlHkqyH4cJKBBhjYQqF4m8XXnjhk2fOnInLy/Pq7DfLZmxsDDU1NSguLvabpHAiQm1tLRISEvzmqQsQRklqNBq/CUe46OrqwlpDyfuHh6E+cQKx5eW46MwZjC+4eWktFlxcV4drGhqw+/Rp/PUTbiSzs7MIDQ3FtJfqEAUFBeHee+9FT0+PV9r3V26//XafhOhjY2NhMpn8ImncRWhoKNLT09HU1CS2KfO4aks1NTVhcnJSbHMACKP0SktL4XA4UFtbK3otMJdNTz75ZMRLL720ValU1slksj1i2+RpeCjQxzhDf/98/PHHVV/5yldEH/HnYnh4GO3t7di2bRtCQvxndGxzczMcDgf8RXwCgtgrLy/Hli1b/MozZDabcfLkSZx//vmrvuE+OzCAb3V1nbU9KzgY7WbzIkcAMsZg3bMHr2i1+ILbKDIpgP8rKEDS1BS+PTyM193yqpZLvFSKum3bkBgYuOhnmrXbMWGzIUYmQ7BUiurqar/KLfMm8fHx0Ov1PulLo9FgfHzc7/4PT506hbS0NL95EASA6elpVFVVYfv27X7lRe3s7MTo6Ci2bt3qNwN/NBoNrr76akNfX99/j46Ofm+9hAa5sPIhcrn8q7GxsT9+6623Yv3pAtXb2wuNRoNt27ZBrMmcF6O7uxsGgwElJSV+kTzvoqOjAzabze+8VS0tLQgLC1uTZy/g8GHYVnhMEIDH0tJw/xIeIwZhlN9a+EJCAv68oFL8pNUK+fHjH66XlSHAOYL100J5eTnKysq83o/D4cCRI0ewe/duv7pGmM1mVFRU+J1dBoMBDQ0N2Llzp98MagGESdB7e3uxbds2v6m3Z7PZ8O1vf3vqD3/4Q4ter7+SiHzztOBFeCjQB7iH/urq6vxKVHV2dkKn02HHjh1+dWHS6XQYHh5GUVGRX4mqmZkZDA0NCYU3/Qi73Q6tVosNrilsVsn+nJwVH2MBlhRVwNpFFQC8vIhn5vcLikXmVlVh2EceHH/h1ltv9Uk/EokESUlJok/lspDg4GCkp6ejublZbFM+QkxMDLKysvxiKh53kpKSkJ2djcrKSpiX8ED7moWhQalUuldsm9YKF1ZehjGWlJCQcPqxxx67/K9//WuUv9RfISK0tbVhYmICpaWlflOnBhASsFtaWrB161a/souIUF9fj7y8PL+oD+PO0NAQEhMT13y+bk5MxMH8fIT42ed7Pz//rG1/WZDbNTQ3h9MhIfjqV7/qK7NEx5fD+5OTk9Hf3+8XSdDuJCUlYWZmxm+qxLtQKpWIj49HQ0ODX52zhIQE5OXlobKy0q/y5i699FJJVVVVYn5+/l/j4uLuFduetcBDgV6EMbZdpVK9+frrryt27drlN24XIkJzczPm5uZQWFjoVx4hi8WCiooKFBcX+03VcBeDg4MYHR1FYWGh2KacRXl5OUpKSjySH3dvZyeeHhz0gFWeI0EmgwOC90sKYWTYiNWKhb6Al7Kzkd7djd27d/veSJHw5TW8trYWarXar0bCAkJeU01NDc477zy/euhxDb6Ry+XYuHGj2OZ8hPHxcdTV1WHr1q1+VXDVYrHglltumTxy5Mg/9Hr9l4nIP4Z+rgD/+QWuM6Kiom7Nysp6+8SJE4n+JqoaGxtht9v9TlQ5HA5UV1cjNzfX70SV1WpFR0cHNi3I8/EHJicnERAQ4LFBB2+votaUt9HbbBi12TBms0Fvs0G3iKgCgFuVSlRVVfncPjHxZQ2vtLQ0v5jCZSHh4eFITEz0uymQGGMoKCjA0NCQ35RhcBEdHY2ioiJUVVVhenpabHPmCQoKwquvvhp11113/VtCQsJxxli02DatFC6sPAxjTKJQKJ4uLS19pqamJjYlJUVsk+ZxeaocDge2bNniV6IKEJKv4+Pj/aaqujttbW1IT0/3q0RUF319fR6tOdbjJ7kXq8FOhM997nNim+EzbrrpJp+Gy+Vy+XwNMX8jIyMDg4ODfpM75EIqlWLr1q1obGyEaRUjY71JVFQUiouLUVVVhZmZGbHNmYcxhu985zuhv/3tb4sSEhJqGWNZYtu0Eriw8iCMsdCEhIT39+3b9x/vv/9+tD+5VwGgtbUVVqsV+fn5fieqhoaGMD09jczMTLFNOYupqSmMj48jKSlJbFPOwm63Y2xszGPDzYkIc+dwegARISMjAw888IDYpviE5557zud9+mMSOyAImOzsbL9LZAeEJPv8/HxUV1f73SwBUVFR854rf8q5AoCrr75a9sEHH6QkJSWVBwYGXiS2PcuFCysPwRhTJSQk1PzoRz86/9lnn43wp6RrQPC4mEwmFBQU+J2ompqaQnt7O4qLi/3ONlfo1F/mS1yIK2ndU7YxxnCdH8wttlqedt7w77nnHpEt8Q1vvPGGz/tUq9UYHBz0q4RsF4mJibBYLH6XyA4IhVZVKhUaGxvFNuUs5HI5CgoKcOrUqSW9akeOADfdBDz8MGA0+s62/Px8VFVVxW/atOnV2NjYO33X8+rhyesegDG2UaFQHH711VfVe/bs8bu7b09Pz3xhOH8TB3a7HcePH0dBQYHfzEXmjk6nw+DgIEpKSsQ2ZVFOnDiBgoIChIWFeazNZwcH8a3OTo+152to716Mjo76XYK1N7j88svx9ttv+7zfmpoabNy4EdHR/pf+YjQaUV9fj7KyMr+73hERqqursWHDhiWn55qeBr7+daC8HCACJBLA4RAWAJBKhe1EwjaJ5OP3sdsBmQxgTPi7PCK3lwAAIABJREFU3S68d+3jcHzYD5EdNpsFSmUwjEYJTCZhX4lEmA/dPcr6618DX/ual0+YG2azGddee+1ETU3N8yMjIw+RH4sXLqzWCGNss1Kp/OCf//ynsri4WGxzzkKj0aCvrw/bt2/3q9IFLurr6xEeHu53I2YA4SJ49OhRlJaW+mXRSZPJhJqaGo+PgGufnUX2qVMebdOX0N690Gg0Pi1FIBYmkwnBwcE+71ev10Or1SJ/kTIY/sCZM2egUCigVCrFNuUsrFYrjh8/jm3bti16XWltBfys9vCSxMQA/f2AB5/rPha73Y6bb7558uDBg/+r1+vv8Fdxdc6GAhljwYyxU4yxOsZYE2PsB87tMYyxDxhjHc7XaLdjnmSMVTPGPDI3EWOsVK1WHzx48KBfiiq9Xo+uri6/q1PlYnh4GCaTCWlpaWKbsij9/f2Ii4vzS1EFCOUfvCEefuGH+TPL5R5ngdT77rtPZEu8zx133CGKqAKAuLg4jI2N+VXxS3eys7PR1tbml/YFBAQgPz8fDzygBWM4azlXRBUAGAxAePhH7X/sMcC20ukblolUKsXLL78cdd11130hPj7+fxlj/ndjwzksrCAUfL6QiAoAFAK4lDG2A8CDAA4QUSaAA851MMZcJaXPB/CNtXYeGBh4QVpa2jvl5eUJOauoVu1tjEYjmpubsX37dr+qqO7CbDajtbXV7yqru7Db7eju7vbLZHoXQ0NDa660vhh/GR31eJu+4smMDFgsFrzyyitim+J1xPQWSSQSn85VuFJCQkKgUCjQ398vqh1TU0BdHVBfD9TWAtXVwOnTQH9/DKqq/Gd+Q0/y/e8DAQFAXp7w2T3tU2KM4fnnn4+4/fbbL09ISHibMeZ3Q7X9YybGVeB0AbqKbwQ4FwJwNYC9zu0vATgM4AEIdQVdNQbXdCcPDw+/Ji0t7X+OHDkSk5iYuJamvILZbMbp06dRUlLiN/NBuUNEqKurw6ZNm/yyfAEglDBQqVR+a9/U1BSCg4M9LprtRNBaz7l6fACA4vBwSBiDdsFUN+uVXbt2idr/hg0b0N3dDX+8BgJC+YXy8nIkJSWJ4rE3GoEdO4CWlqX28K9R456mqQkoLBS8WA8/DMTGApdcAmR5oHACYwyPPfZYeHR09PlPPPHEIcbYxUTkN0Maz1lhBQBON2ANgAwAzxPRScaYgoiGAYCIhhljCc73TYyxUADlAFYdJ4iOjr45IyPjuUOHDkX7Y+KmzWZDVVUV8vLyEBERIbY5izIwMIDg4GC/rFcFCN6qvr4+v67erdFoVuytGrJY8OzgIAbNZhBjcBDNu6wJwlNHr5/V2VkJP3DW8vJkIr+/cuWVV4qe3ySXy2E0GmG32/0y1SAgIAAqlQp9fX2i5HDef//HiapPD0TAI498uJ6cDLz+OrBt29rbvueee0LkcvnWBx988ARjbDcR+UWl03NaWBGRHUAhY0wO4A3G2MfObkxEaxqqGRYWdm1mZuZz5eXlflejCvhw+oTk5GTE+emQeZPJhO7ubpSVlYltypL09vZCrVb7ZQjVhVarXfE5vL6xERVTU16ySHyubGwE7d2Lubk5sU3xOmKMBFwIYwwKhQI6nW7JEW5is3HjRpSXlyMlJcXn4s+PLx+i0t8PbN8OREQAF18M/PSnQHr66tv78pe/HBgUFLTp7rvvPsQYO4+IRK8Qey7nWM1DRBMQQn6XAtAxxpQA4Hz1SBJASEjIxcnJyS8cOnTIL0UVAHR0dCAkJAT+VO3dHdckxps3b/Zb0WK329Hf3++3CfWAEAYMCQlZ9jkkIvxHa+u6FlXuqFQqbN++XWwzvIq/JGWrVCoMDw+LbcaSuLxWYuRafe97Pu/y/7N35vFR1ef+/3xnyb5Mtsm+EtawCkZEWURwq6CovYJr1Ra13raK1t7e21ZFUVtt1Vu52lZpXSpevT8VK1atCsGFfSdsCUnInkxmJjOZfXt+f0wmEsgygSTn+0Der1demJkzZz5OzpzzOc/3WVjR0QG89x5QXBw0WTfdBJxuQ/+bb75Zu3Llykl6vf5TIYTiFxe2xkoIkdYZqYIQIhrAAgCHAXwI4PbOzW4HsO5M30uj0VyYlZW1tqysLFnG5T8gWAHY1tYm5Sy7EE1NTdBqtVL3F6qrq0NWVhY0GnmDuU1NTQOKEOy22fDqOZJ3FGLJkiVKSxhydu3apbQEJCYmwmq1SmP0eqKoqAg1NTXDrjE9HZBsdKG02GzA2rVAVFQwJ+vddwe+j3vuuSdyxYoV5+v1+g+UrhZka6wAZALYIITYB2A7gH8R0UcAngawUAhRAWBh5++njRBianZ29rqysrKUwRobMtjY7XaUl5dj+vTpUk12PxGPx4MjR45g4sQ+V2sVJRAIoKamRupoFRBcBhxIflqWpAn4Q0FtZwfDiooKhZUMPTIUVgghkJqaijaJK0m1Wi3S09PR0NAw7O9dWAicYzPBB4V/+7egwSorG9jrfvGLX0Tfddddc/V6/d+FguXmcl6Fw4CI9hHRNCKaTEQTiWhl5+NGIrqUiEZ3/nvasw2EEGOzsrI++fLLL9NkbTbo9/uxc+dOTJ06VcoKwBCHDx9GcXGxFBeD3mhqakJqaqrUGp1OJzQazYA0fmQ0DqEiufjSbAYAlA5GZqzkyHKTkpGRIfVyIBCMWlVVVSkyhmfGjJFlwdNl3rxg1/f6+vBfs2rVqtjrr7/+Kr1e/yelzBVbYzXUCCFyMzIyvvzss8/SR51JZt0Qc/DgQWRnZ0s5WiKExWKB1WqVvhN2VVWVlB3gT6SlpWXA1ZT7JJpaP9TMTEgAANx6660KKxl6XC7Fc3QBBGfgmUwmKWcHhoiKikJCQoJikTUJp3WxgQjIzQU675n6JdTnauHChTfq9fonh1ZdLxpk/jIohRAiNi0tbdcTTzwxZsxgNN0YInw+H9xut/Tl5Xa7HVFRUVKWZIfw+/1wu93SdlkPYbfbER0dPaAlXyuAXwPYN2Sq5OBKAA+f8PtPfvITKQfeDhaPP/64NC1BHA4HoqKipE1FAIJL/U6nU5HzZUtLJJYuvXDY3/dsIiXFhf/7vy1hb+/3+/Gb3/zGvX///jutVutbQyjtFEaM1UkIIVR6vf7Tp59+es4dd9wh7ZqQ0+nEli1bMGvWLKmXABsaGtDW1oYpU6YoLaVPduzYgaKiIiQnJystpVf8fj+++uorzJs3b8CvJSJ8YTbjfYMBfgQbgQaIoBECQgi4AwH8raWl22vuSE+HtnNOhT8QAAkBDYAN7e2o6CFackd6OjRCwO5wwOXxICExEeV2O7bbureWuS4lBQkaDQhAhEoF6tRz3OnElxZLt22nxsZiRnw8/EQgABqVChQIwOTz4f2TljjNF10E3QmVkna7HX/6059QXl7elbisVquRkZGBO++8E0VFRdi4cSNyc3Px5z//GbZOnUTU1ZtJCAEiQiAQgEqlghACgUAAgUCgq8CBiFBbW3tGLRCWL1+OyMhIeDwe/OlPf+r23KJFi5CVlYVt27Zh9+7dXY8/88wzeOihh077PQeT2tpauFwuyHwjCgSHlk+aNEmRHn8dHcB11wGffx78/Wc/C45+cbuBmJjgUGS3G3j33QAMhp4NakYGsGhRcHuVCvB4gNdfD+77XMBuD/6/h4vVasWMGTOMFRUVlxPRzqFT1p0RY3USer3+2WXLli1/4YUX5OyuieCJfOvWrRg1apTUFXYhI3DhhRdKbf5cLhe2bduGOXPmKC2lTwwGA5qbmzFp0qQh2b/T78cumw02nw/5UVEY18udfYAIlU4nqpxOBIigFQIF0dEY3XnGC7X9CC391rlcOGS3gwDEqdWYmZgIdS+pDy1uN/bbbAgAiFWrMSUuDnG9VGgavV7s6byijI2JQc5pzM3buHHjaRnVnmhra8PBgwfh8Xi6lsUiIiLgPaGTfSg3Li8vD3V1dSAijB49utuwYLfbjT179qCjowOjR49GdXU15s6dCyEEjh07hqqqKiQkJGDatGnS5AO6XC7s3LlT6v50QDCP0mg0KpqfVlsbNFR9ZR28/34lbLZ4ZGSkIz8fqKwM9sWaNavngce7dwOtrUHjIQQQERE0XnY7oNEEfxei598jIoL7FiJYnRf6nSj4fFRU8DWh5yMjg7/7/YSyskqUlqYjIiIBPt93z7ndwf/HqKigYXQ6gUAg+LxaDYTuy7Ta755XqYK/q1Tf/a7RBJ93OIKf17RpA/+8O/smNjU3N08PNQ8fakaM1QkkJibeMn369P/+17/+lSTzstXx48dhsVgU77zcH8eOHYPf75f+LvbIkSOIioqStv9XiPLycqSkpEg7QiTE3r17kZubK3X0L8RgGquh4ptvvsGMGTOkvjkBgLKyMlx00UVStyoJBAIoKyvDnDlzpE5N8Hg8+Oabb6TXabfbsX37dsyePVtqnRs2bAjceOONRwwGw3QiGvLxEvIuiA8zQogZ6enpz3/wwQdSmyq73Y7q6mqp+1UBgNfrRW1trfTJ4EQ0ZMOMB5u2tjZpO+qfiN1ulz7vjxOxsbFwOKQZg9YrsrddAILDo7OyshRpvTAQIiIikJubiyrJG2HFxsYiPz8fBw8eVFpKn1xyySWqlStXFun1+neHo1JwxFgBEEJkZGRkfPjJJ5+kJHRWFclIaHjxpEmTpL4rBIDKykoUFhZKr7O1tRUpKSnS63S73VCr1dLrBIJ327IsUZ0NxMbGws6gsjMtLQ0Gg0FpGf2Sl5enSCf2gVJYWIj6+vpuS8kyUlBQgI6ODphMp93ZaFi45557Iq+99trZaWlpK4f6vc55Y9WZrL7utddeS5c9ulJfX4/Y2FikpKQoLaVPPB4PmpubkZeXp7SUfqmtrWWh02g0sohWhVILFOzNd9YRExPDImKVnJws/cUVAKKjo6HRaLqKFWRFrVajsLBQ+qiVEAKTJ0/G/v37pe7ADwCrV69OyMnJ+bFGoxnSZMBz3lilpKT88sYbbxx/2WWXSf1ZeDweVFZWSr8ECARzq4qKiqQuvQaCn6nD4UAigyYzbW1t0htqIJjEHB0drbSMs4qYmBg4nUOeFnLGaDQaqFQqFkOwc3NzWUSt8vLy0NjYKH3UKi4uDpmZmaisrFRaSp9oNBq8//77yampqf8rhBiyE7/cV74hRggxTa/XP/Dss89KWwEYory8HGPGjJF2eHGIULQqNzdXaSn90tDQgJycHBbRFZPJxCIZ3OFwjBirQSYmJobFUiAQbBZqZNDpPyMjAy0tLVI3NQWCOWGFhYU4duyY0lL6pbi4GE1NTdIfq/n5+Xjuuef06enpa4cq3+qcNVZCiBi9Xv/e+++/nyJ7PojZbIbT6RzQ4F2lqK6uRkFBgfTRKiBorDgkrXs8Hmg0GqmrbkI4HA7pm6xyIyIigkUUCPiuC7vsqNVqJCUlsTCBeXl5aGpqgs/nU1pKn6hUKpSUlKC8vFxpKf2ybNky7Zw5cy5MSEi4Yyj2L//Vb4hIT09/deXKlZljx45VWkqfEBHKy8sxceJE6SMrfr8fjY2NLHKWHA4HVCoVok6j99FwYzabpR5ZdCJOp3MkYjXIhL73skdXAD55VgCQk5MjfXUgEDQsOTk5LJYuQ3mgsleHAsCrr76qS0pK+q0QYtCTq89JYxUbG3v91KlTL1++fLncjWEQjKrEx8dD5mrFEHV1dcjKymIRWeHSYgHgswwIjBiroUKr1UqfZwMEdQYCAfj9fqWl9EsouiZ7wjUQrLw7fvw4C60lJSU4ePCg9DcC8fHxeOedd1L1ev2HQohBzbE554yVECIpMTFx9VtvvZXEIQJUUVGBcePGKS2lX4gINTU1KCwsVFpKWDQ1NXXrdi0zI8ZqhOjoaBYJ7ACg0+nQ3t6utIx+EUKw6L0FBA1rWloampqGpXH4GREbG4vU1FQWEbbS0lIsX768IDk5+T8Hc7/nnLFKT0//429/+9tkDheq48ePIzs7W/qOy0Bw3IpOp2PRv8jpdEKtVrPQSkTweDwsjgEg2G+Li1ZOREVFwdXDfEYZ4WKsACAzM5OFWQGCfa1qamqUlhEWo0ePRlVVFYvI5a9+9avYhISEfxdCDFrF1TllrIQQM3Jycq645ZZb5C6tA+Dz+XD8+HHpO5eHqK6uZhWtkn0sTAibzabIwNjThYhYFC5wg1PEKikpiY2xCuWEyb5sBQQjQWq1GlarVWkp/aLVapGdnc3CCEZGRuLPf/5zcnp6+muDtc9z5gwohFDr9fo3X3/99RTZlwCB4ODIvLw8Fp22nU4nvF4vi35QANDc3MxmGdBsNkOn0yktIywCgYD0BRZc4RSxio+PR0fncGzZUalUSExMhNlsVlpKWBQWFqK6ulppGWFRVFSE2tpa6asZAWDhwoWqGTNmTNVqtVcNxv7OGWOl0+n+fdmyZVkcGmz6fD40NDSgoKBAaSlhcfz4cekHGIfwer3w+Xxs8oDa29vZGCu3282iypIjnIyVEAIajYZFsj3wXU8rDuj1ephMJhZmRaPRIC8vj0XUCgD+/Oc/J6WkpLwshDjjk9g5YayEEPq4uLj/WrVqFYs1lePHjyM3N5dFdR0RsUoE5zLIOITVamUTCXS5XCP5VUNEVFQU3G630jLCJiEhARaLRWkZYcFlxiEQNK2c8sLy8/NRV1fHItcqKysLDz74YOpgzBI8J4xVRkbGH5977rnk2NhYpaX0SyAQQG1tLZsIkNFohE6nY7FkCQSHLqenpystIyyICF6vV/pu+yFGIlZDR0REBCtjlZiYyMZYabVaqFQqNp9vbm4u6urqlJYRFhqNBpmZmaivr1daSlg88MAD0YmJiXcIIc6oGaMixkoIsUYI0SqEOHDCY48KIRqEEHs6f6464blnhBA7hBBzT+O9CpOTk+dff/318od/EBy0nJGRweZiymWIMRA0KiaTiU2zTW5dzEciVkOHWq1m0cMoBCdjBQSX2FpbW5WWERaxsbEgIhaDuYFgrlV1dTWLAgGNRoNnn302OSMj47dnsh+lIlZ/A3BFD48/R0RTO38+BgAhRKiJ0xwA9w30jTIyMn7/zDPPsEhY59YLyu/3w2KxsOmxFDIqXKrWrFYri8awIUZaLQw9HC5OQDCB3WazKS0jbDgtBwJAdnY2GhsblZYRFhEREUhKSmJjXBcvXqzS6XQLhBCnvWykyBWGiDYBCHfugRpAAAABGJA7EkKMSk1NnX3llVfK76oQrACLjY1ls5zS2toKvV7PphKMW35VR0cHK2PFqd8WRzQaDYukZeC7CBsXI8gtwpaZmcnGWAG8qhmFEPjd736XnJGR8czp7kO2W/d/F0Ls61wqTAIAIioHEAPgawAvDWRnnKJVAK9eUEBwLAyHwdAhOBorTj2s3G43i6arXOE0jBkI9t7islylUqkQHR0Nu92utJSwiIyMhFarZaM3ISEBfr+fjd6rr75alZSUdIkQ4rQuyDIZq5cAjAIwFUATgN+HniCinxDRdCL6MtydCSGK09LSLrr88stZuCq32w2bzcYm/8fv98NqtbJpBUBE7JbWbDYb4uLilJYRNiNLgUNLZGQkmwRrIHgx5dLPCgCb8TYhsrKy2EWtuLReOCFq9bvTeb00xoqIWojIT0QBAH8BUHom+8vMzHyWU7Sqvr4eOTk5bJbVjEYjUlNT2eh1Op2Iiopio5eI2HUx51TByBFuEau4uDg2EQog2IWdS6NQAEhPT2fTfwsI9gtrbW1lU4Txve99T5WcnDzvdKJW0py1hRAnNkJaAuBAb9uGsS99XFzcRZdddhmPqyiAhoYG5OTkKC0jbFpaWti0LQCC+WtckuyBYIUdl1y7E+FiXDnCzVjFxsaySmBPTExkM4oHCPY2C80S5YBKpUJKSgqbIgEhBFauXJms1+t/MdDXKtVuYS2AzQDGCiHqhRB3AfidEGK/EGIfgEsAPHC6+09KSrr7/vvvT+RykrdarYiMjGSzjEJE7PKVTCYTK2PFbRlwhKFHq9Wy6WYOBCNWnIyVWq2GWq1m9RlzahMBAHl5eWx6cAHBCkG1Wr1ECDGgi7PgUrURLkIIVXp6+vE333wzh0vTSpfLBbVazWYZJRAIwOl0gkPD1RA2mw2xsbFsIiqhu1BOyeAczSAnzT6fDz6fj1Ukk9PnCwRTBrRaLZuGx36/H263m1W/O27HxKuvvup/9913b3e5XH8P9zU8jp6BMW/+/PmxCxYsUFpHWBARysrKcPHFF7P5MtfU1CAQCKCoqEhpKWER+oznzZuntJSwOXjwIFJSUtgst7pcLuzevRsXXnih0lIGxMaNG9kcFyaTCfX19Zg8ebLSUsKmrKwMs2fPZpMrWFtbC4/Hg+LiYqWlhAURYePGjZg7dy6bm8ZDhw4hMTGRTUV5fn6++osvvvg5gLCNFY+jfQBkZ2f/csWKFTxK6xAsqY+JiWFjqgDAYDCwWgbkdocEBJuZcooIer1eVtE1jnAabBwiJiaGTcsFANDpdKz6WQkhEBsby6pIgGMPrvz8/CwhxJhwX3NWGSshhD4mJmbq9OnTlZYSNk1NTWycOxC8Q7LZbKz6K7W3t7MZZBzC4XAgOjpaaRlh4/V6Wd0ccCQiImLEWA0xcXFxrFpEAME2EVwSwoFgkUBHRweLwcwhHnrooRS9Xr8i3O3PKmOVmJh4509+8hM2SevAd93LuRCK/nD6jLl1MAeCeWxqNYvxlgCC+T9ccgS5wjFiFR0dDafTqbSMsFGpVBBCsLrop6SkwGQKd5CJ8ggh2GlevHixSqVSLRFChHVSPquMVWxs7M033HADm7M7xwRls9nMpolpCG4dzAOBACvjCoz0sBoO1Go1qws+wM9YAfz6b8XHx7OLsnGrZtRqtZg7d64awMxwtj9rjJUQIik+Pj49MzOz/40lwWAwIC0tTWkZA4JbPyggWOnDaVmNYw8rn883shQ4xHAz2wBPYxUfHw+r1aq0jLBRqVTQarWsuvJz63IPAMuWLUvR6/XLwtn2rDFWarX6ihtuuIFPzSl4Gitu+Up+vx9CCFYXJW5GEBjJsRqhZ6KiouByuZSWMSA4RoCSkpJYdY3XaDTQaDSsjo0FCxZApVJdHc62Z42xysjI+MF1113Hp4wKQZPCZdYegK5RBJxyf7hV1wE8Z+75/f4RYzXCKXCbbwgEO8ZzSrgHggnhnKoZgeAIIU55VrGxsSgoKIgJZ8TNWWGshBCaQCAwbdq0aUpLCRuPxwONRsPKpHR0dLBrW2C329kZK65LgZyOZa6oVCo2s9YAnsuX3NoXAMGB15yWLwF+SfcAcPPNN+vi4uKu7W+7s8JYAbjwkksuUXP6EptMJnZJ4BaLhdUyIMDTWHGMWI3kWA0ParUaPp9PaRkDhtOED7Vazcq8AvwS7gF+y5cAsHjxYm1CQsIt/W13VhirlJSU71177bWsMqpHquuGB4fDwWrcA8DTWI0sBQ4PGo2GXWUgt+HRAL/WFqE8Uk7Hhlarhd/vZ2Vi8/LyoFarc4UQfZ7szgpjFR0dPXfGjBlKyxgQVquVXfSHYwdzrongnFpwAEFjNbIUOPRwbLnA0VhxTLrnGLWKj49nNagbQGik1Pi+tjkrjJXP5ysoKChQWsaA4BhJcTqd7DRzzFfyeDwsjRWXeXCc4WqsOEV/AJ5tIrjmhnFLup87d26iWq3uM5LD/kwohEjPzs5WKZVf5XK5UFpaiilTpqCkpASPPPIIgGAO1cKFCzF69GgsXLiw21ryE088gVtvvRXjxo3Dp59+2uN++3r9mVJXV4dLLrkE48ePR0lJCV544QUAwI033oipU6di6tSpKCgowNSpU7te8+STT+Lmm2+WTnOIZ599FkKIbr1RnnrqKSxbtgwTJkxQRHNfut99912UlJRApVJhx44d3V7zt7/9DePHj8fYsWMV0X3nnXdCr9dj4sSJXY/t2bMHM2fOxNSpUzFjxgxs27at67mnnnoKN9xwA6ZMmaLY59yT5kcffRTZ2dldx/THH3/cTXNxcTFuu+02xTT3pvvnP/85xo0bh8mTJ2PJkiVob2/veu6vf/0rpk2bptix0RMvvPACJk6ciJKSEjz//POnaLjnnnt6bQb5ySefYOzYsSguLsbTTz89pDpPpL29HTfccAPGjRuH8ePHY/Pmzd0033333WhubpZGczjXmeXLl6OhoUEazeHoSExM7JZ0T0T46U9/iuLiYkyePBm7du1STGtvlJaWRmRkZFza50ZExPoHwFU/+9nPLKQQgUCAOjo6iIjI4/FQaWkpbd68mX7+85/TU089RURETz31FD388MNERFReXk4lJSW0detWqqqqoqKiIvL5fKfst7fXDwaNjY20c+dOIiKyWq00evRoKi8v77bNihUr6LHHHuvSPGnSJNq4caOUmmtra+myyy6jvLw8MhgMXZonT55Mn376qWKa+9J98OBBOnz4MM2dO5e2b9/etX15eTkVFRWRy+VSTHdZWRnt3LmTSkpKuh5buHAhffzxx0REtH79epo7d26X3smTJ9Pnn39OFRUVin3OPWl+5JFH6Jlnnjll25Bml8tFb731lmKae9P96aefktfrJSKihx9+uNu5Y8yYMVRfX6/oMX0i+/fvp5KSErLb7eT1eunSSy+lo0ePdtPw0EMP0T333HPKa30+HxUVFdGxY8fI7XbT5MmTTzkPDRW33XYb/eUvfyEiIrfbTWazuZvm//iP/6Af/vCH0mgO5zrz61//mn7wgx9IozkcHTt27KAtW7Z0bbN+/Xq64oorKBAI0ObNm6m0tHTYdfaHxWKhzMzMI9SHL2EfsUpOTr74oosuUmwQnBCiK+/I6/XC6/VCCIF169bh9ttvBwDcfvvt+OCDDwAA69atw9VXX42kpCQUFhaiuLi4291/iN5ePxhkZmbivPPOAxBc4x4/fny3Ox0iwjvvvINly5Z1abn22muRmJgopeYHHngAv/vd77orLp7bAAAgAElEQVSVdq9btw7f//73ERsbq5jmvnSHIlI96Zk/fz4iIyMV0z1nzpxTuusLIbruLC0WS9fg8HXr1mHp0qVQq9UYNWqUYp9zT5p7I6Q5MjISmZmZimkGetZ92WWXdRUCzJw5E/X19V16rrrqKmi1WkWP6RM5dOgQZs6ciZiYGGg0GsydOxfvv/9+Nw3/9m//1mN0bdu2bSguLkZRUREiIiKwdOlSrFu3bsi0hrBardi0aRPuuusuAMGlSp1O103zzTffjM8//1wazeFcZ37wgx/gyy+/lEZzODo+/fTTbkuu69atw2233QYhBGbOnIn29nY0NTUNu9a+SEhIgFar1fWVwM7eWMXExMwJXbiUwu/3Y+rUqdDr9Vi4cCEuuOACtLS0IDReJzMzsysU3tDQgJSUlK4WADk5OT2Gb3t7/WBTU1OD3bt344ILLuh67KuvvkJ6ejpGjx7dpTktLa0rCVwmzR9++CGys7MxZcqUbts0NDQgPT29q7pOac0n6+6N+vr6bkO5ZdANAM8//zx+/vOfIzc3Fw899BCeeuopAMHPOTc3F0Dw5C+L3hAvvvgiJk+ejDvvvLNrSexEzYA8n3FPrFmzBldeeSWAoO7MzMyuHCsZdE+cOBGbNm2C0WiEw+HAxx9/jLq6um4asrKyYDQaT3ltuH+HwaaqqgppaWm44447MG3aNPzwhz+E3W7vpjkvL08qzUD/15mCgoIe+0Ipqbk/HY2NjQC+a8chi9b+KCkpAYBT74w7YW+sfD5fjtKJ62q1Gnv27EF9fT22bduGAwcO9LotEcHtdndLAlcqP8xms+H666/H888/j4SE74J+a9eu7YpWAUHNXq+3WxK4DJo1Gg1WrVqFlStXnrIdEcHn83VLAleyz1lvn/XJBAKBU5LAZejP9tJLL+G5555DXV0dnnvuua67/dAJ8URk0AsA9957L44dO4Y9e/YgMzMTDz74IAC5NZ/IqlWroNFocPPNNwMI6hZCdCtPV1r3+PHj8Ytf/AILFy7EFVdcgSlTppzSdqO3NhxK/R18Ph927dqFe++9F7t370ZsbOwpeUe9FY8oeez0d53prXhEluO9Nx1RUVFd3fll0dofEyZMiAKQ39vz7I2VWq2OlKXMW6fTYd68efjkk0+Qnp7eFcJsamrqikKEHHjIpNTX13ctq5xIb68fLLxeL66//nrcfPPNuO6667oe9/l8eO+993DjjTd2PZaTk4P6+vquk40smo8dO4bq6mpMmTIFBQUFqK+vx3nnnYfm5mbk5OSgrq4OWq1WUc096e6LrKwsGAyGrt+V1H0ir732Wpf273//+11LUKHPWTa9ofdUq9VQqVT40Y9+xEJziNdeew0fffQR/v73v3ddWHJyctDS0tJ18ZFF91133YVdu3Zh06ZNSE5OxujRo7tpaGtr67FnX7h/h8EmJycHOTk5XZHjG264Abt27TpFc0/jxpTSfCJ9XWeSkpJOMScyaO5LR2RkZFdrC1m09kdBQUGsEKJXYWEbKyHEZUKIMSc9NlYIsfBMBJ4JQghNVFSUoq7KYDB0Ve04nU58/vnnGDduHBYvXozXXnsNQPAkec011wAAFi9ejH/9618gIlRXV6OiogKlpaWn7Le31w8GRIS77roL48ePx4oVK7o9F9Kfk5PTTcv69eul0zxp0iS0traipqYGNTU1yMnJwa5du5CRkYHFixfjgw8+QCAQUExzb7r74sorr8QXX3wBt9utqO6TycrKQllZGQDgyy+/7FomXrx4Md5++214PB6p9ALolpvx/vvvd1XehTS73W40NTVJpRkIVk799re/xYcfftgtsh36HjqdTqk+69BSY21tLd577z0sW7asm4Z33nkH8+bNO+V1559/PioqKlBdXQ2Px4O3334bixcvHlKtAJCRkYHc3FwcOXIEAPDFF19gwoQJ3TS/+eabmDVrljSaw73OzJ49+5TO/EppPpnedJw4T3Lx4sV4/fXXQUTYsmULEhMTu5Y6ZSI7O1udmppa3OsGfWW2n/gDoAJA5kmPZQE4Gu4+BvsHQPasWbOazyDB/4zZu3cvTZ06lSZNmkQlJSVdlXRtbW00f/58Ki4upvnz55PRaOx6zV133UVFRUU0ZsyYrkqr0OOhCrG+Xn+mfPXVVwSAJk2aRFOmTKEpU6bQ+vXriYjo9ttvp5deeumU19x7771UUFAgpeYQ+fn5XVWBREQPPvgg5eXlKaa5L93vvfceZWdnU0REBOn1errsssuIKFg5eO+99yp6fCxdupQyMjJIo9FQdnY2vfLKK/TVV1/ReeedR5MnT6bS0lLasWNH1/ZPPPEEZWVlKfo596T5lltuoYkTJ9KkSZNo0aJF1NjY2E1zUVER5ebmKqa5N92jRo2inJycruPl7rvv7tpehmP6ZC6++GIaP358V3XoyRrmzJlDX3zxBRERNTQ00JVXXtn12vXr19Po0aOpqKiInnjiiSHVeSK7d++m6dOn06RJk+iaa64hk8l0yue2bt06aTSHe5357LPPyGazSaG5J3rS8fjjj3f9dyAQoB//+MdUVFREEydO7FYxLRNbtmyhnJycd6gXbyIozBlOQggLESWe9JgAYCEiRaryhBAzli5d+s+1a9emKvH+p8vGjRt7vIOTmc2bN2PatGmsmm1WVlYiMjKyWzKk7FgsFhw7dgxKF2QMFI7HNMBPd01NDYgIhYWFSksJG7vdjvLy8h6jazLD7dgAgN27d6OwsLDHZUxZqa+vh9Pp7IqCc6C2thYXX3zx5tra2lPDmhhYjlWVEGL+SY/NA1B9OsKEEFcIIY4IISqFEP/R+ViWEOJLIcQ6IUQ4s1OyCgoK+Fzp0XNyMgc4jiwJBAIsNXM8PkYYHoQQrAYaA8Gkam6aucKxM79Go2E3WDwjIwNerze9t+cHMjX1UQDvCSFeBXAMwCgAd3T+DAghhBrAagALAdQD2C6E+BDAbQB+AqAIwC0AXu5nV/qcnBxWM1Z8Ph+7iz3A11hxMylExE7zCMOHjBVS/cHRDHKFq7HipjkiIgIqlarXoE7YZ3AiWgfgMgCxAL7X+e/lnY8PlFIAlURURUQeAG8DuAaAGkCg86ffM4hWq42Ojo5mdRXy+/29lh/LDMcLPsf5ddRZUj/CCL3BzaSc3CKCCxx1czRWarWaXcQKAFQqVa8n6gFd4YloG4BTW/0OnGwAdSf8Xg/gAgDPAHgDgAXATf3tJCIiIipUTs8FjlEUznAzKSPGaoS+4HhscI1YcdTNcdlVpVKxM7BAV455jwyk3UKkEGKVEKJKCGHpfOwyIcS/n46mHh4jIjpORHOIaBERdfS3E5VKpeW2PDXCCCOMMIL8cDSxHGH8OfcqfCBVgf+DYKTpaQD/JCKdECIbwGdEVDIgNUJcCOBRIrq88/dfAgARPTWQ/SQkJPzywQcffHLu3LkDeZmiBAIBuFyubv1pOGCz2bpmVXHB5XJBo9GwWnr1+Xzw+Xysqi8BnscHwE+31+tFIBDoGtXEASKCw+HoGuPFBW7HBgC43W6oVCpwWsnhek38wQ9+0FFTU9NjR4SBXHGWACgmIrsQIgAARNTQaa4GynYAo4UQhQAaACxFGEt/J+P3+z2FhYWsSmLtdjsOHjyI888/X2kpA4Jj6XF5eTn0ej3S0tKUlhI2bW1taG5u7mpmyQWOxwfAT3ddXR08Hg9GjRqltJSw8Xg82L59Oy666CKlpQyITZs24aKLLmJVtHP06FHExcVJ2a28N7i2mPH7/fbenhuIsfKcvL0QIg3AqZMq+4GIfJ1LiJ8imLC+hojKB7ofr9fr5pb0xnU9GeCX/8MxR4Kj5hGGD47HBsfCF4Cnbo45vBzb4nTS65dxIMbqXQCvCSEeAAAhRCaA5xGs6Bu4IqKPAXx8Oq8N4fV67R0dHT4MMAlfSTj27AC+S4rkZKxUKhW7ChmOlUgjDB/cvoNA8MLJTTPA87PmWAnt8/lYpWuECPRxoh7IX+A/EWwGuh+ADsERN00AHjsjdWdGc21trUPB9x8wXEtLOZbxcowOcqzqGWF44Xax52hQuMKxnQ/H3o4OhwNE1Kv3GMhfoBjAYQBPIrh89wER7T9DfWdKY01NjVthDQOC64UzZAg5JUWq1Wp2xmokYjVCX3A8d3BcUuMKx0bOHCNWTU1N0Gg0Tb093+/RLoKsQTBS9Z8AFgH4EYDdQoi/9tXLYRhoqqurG7kKDQNarRZer1dpGQOC47IrRzM4wvDB0aRwvNhzxePxsLr5BYKVjJyqXIGgsSKiut6eD+cbuhzBmYAziSifiC4kojwAFwKYDeDuQVF6erQZDAZeZxnwzP2JiIiAx+NRWsaA0Gg07MwgxyXXEYYPjvlKHI0Vx8ggEDRWERERSssYEByNVWNjI+x2+7Heng/HlNwK4KdEtP3EBzt/v7/zeUUgooDH4+EVkgAQGRnJzqRwNVYcI1YjxmqE3uAaseKomZsZBHjqdrvd7Pr2NTQ0eM1mc1Vvz4dztE8AUNbLc2WdzytGIBBwut2s0qwQFRUFp9OptIwBERkZCW6fM8flS67FDSMMDxzL6TkmVHu9XnZLaiG4RTSdTic7Y1VdXe0A0Njb8+F8Q9W9jZfpfFzRb7lGo6k8evSokhIGTGxsLOz2XnuLSUlUVBRcLpfSMgYERzPIsZJxhOGDo7Hyer3sjJXH42G3PMUxMgjwNLF79uxxA6js7flwjnatEOIS9D4XR9FvTHt7+4YdO3YsnDRpEhubHhMTg46OfkchSkV0dDQaG3s16FLCcfmS291miFBjU676ucDRWHGs+nK73exylVwuF6Kjo5WWMSBCOYPczhvHjh0DgJreng/naG8FsKaf5xXDZrNt/vrrr8133HFHspI6BkJsbCyamnqt1JSS6OhodsuXHHOsuBIqyOB2AeUGV2PFLfrDMQmc45Kaw+FgNyOwpaUFAJqpjwqHfs+CRFQwiJqGgj1btmxhdfWMi4tjtxTIMV+J213QiXCL/oy0iRgeOCYnezwexMfHKy1jQLhcLnYmxW63szMpHR0d7I6NXbt2IRAIfNvXNrxufXqAiCwmk8nLqZIqdBHidCEKhWs5fc4Az2RwjkuYI9WMwwNHY8Uxh8bpdLJbVrPb7YiLi1NaxoCwWCxISEhQWsaA2LJli6O5uXlDX9uwN1YAoNVqDx8+fFhpGQMiLi4ONptNaRkDIjY2Fg4HqwlCLJcwR4zVCL3B0VhxXVbjZqxsNhs7Y2W1WpGYmKi0jAFRVlZmA7Czr23OCmNlsVi+3Lp1K5/wD4DExERYLBalZQwIjmaQq7HituzKsektRzhWfnE0VhyXAh0OBzszyG35kohQUVEBAL32sALOEmNltVo/evfdd01K6xgISUlJMJvNSssYEAkJCbBarUrLGBAxMTHsomwc20RoNJoRYzUMcCwQ4LgUGAgEWEUG/X4/hBCsTLfL5UJkZCSrXNLy8nKoVKryvhLXgbPEWAHYv2fPHi+nu3ydTsfSWHGLsnHsGRYZGcmuZ9hIBebw4PP5WF3wQ3C6eHLsu8UxCdxkMiE5mU0xPwDgvffec7a0tPytv+3OCmNFRKRSqTZ+/fXXSksJG41GAyEEqyUfjjlWXI0Vt4gVxyIBjnAbacNxtqHdbkdsbKzSMgaE1WpllwTO0Vi98847HV6v9+P+tuPzDe2HxsbGN9555x1W4ZTk5GSYTHxWMIUQUKvVrMxgdHQ0OzPIscv9SMRqhJ7gOGCXo7Fqb29nlwRuMpmQlJSktIywMRgMMJlMJiJq62/bs8ZYAdjw8ccfsyql0uv1aG1VtL/qgOGWGxa6u+fU2mLEWI1wtsCxuo7jslp7ezsrk+J2u6FSqVjl3n388cd+p9P5v+Fse9YYKyJy+f3+Ck5zA1NSUmA0GpWWMSCSk5NZGSuA33IgR2Ol1WpHjNUQ00++rJRwNFZWq5WVsfL7/SAiVnlhBoMBaWlpSssYEG+99Zapvb39/8LZ9qwxVgBgNBpfffPNN9ms+6jVakRERLBqB5CUlMRq+RIA4uPjWc1m5JaTAgQjVpyWiDnCsSKQ4/w6h8PBaimwvb0dOp1OaRkDorW1FXq9XmkZYdPR0YG9e/e6AZSHs/1ZZaxcLtfba9assXMq+05PT0dzc7PSMsImOjoaLpeL1dIaxzYR3JLBRyJWQw/HtgXceiuF+oRxurkxGo2sksADgQA7M/jGG294PR7PX/prsxDirDJWROTw+/2fffbZZ0pLCZusrCw0NjYqLWNA6HQ6tLe3Ky0jbHQ6Hbs2Edz6b3GcJckNrsaKUwNIjtV1bW1tSE1NVVpG2BiNRqSkpLAyry+88EK72Wx+Odzth91YCSHGCSE2CyHcQoiHTnquRgixXwixRwix44THs4QQXwoh1gkh+uzZ39zc/Ntnnnmm36x9WYiOjkYgEGBVXp+Wloa2NjYfMcvKQG55YVqtlt0YHm54PB52xopbjhW36jq/3w+Px8PqM25sbERWVpbSMsJm586dsNls+4go7EozJSJWJgA/BfBsL89fQkRTiWjGCY/9FMBPALwC4Ja+dk5E+w8dOtTGKQqUmZmJpqYmpWWETWpqKgwGg9IywkYIwa7p5kjEaoST4RaxCq2acIpMWCwWVsaKWy8oIoLJZEJKSorSUsLmD3/4g7mxsfGpgbxGKFVpIoR4FICNiJ494bEaADNO7hMhhHgGwBoARQDyiOilvvYdExNz16233vrysmXLWGR6EhG7hEmbzYbY2Fg2J02XywW1Ws3mwsTxTpTjEFhOmj0eD4iITV8oruc1LscDEDyvaTQaNkUNPp8PXq+XzXnN4XBg+fLlpoaGhjQiCjuxWLa/BgH4TAhBAP5ERH/ufPxFAG8AsAC4qb+dOJ3OtevXr39q9erVaVwOuG+//RZTpkxhcxI6dOgQEhMT2YR0m5ubYTQaUVJSorSUsHC73di5cydmzZqltJSw2bhxI+bNm6e0jAHBSXNlZSUiIyORm5urtJSwaGtrQ1NTEyZNmqS0lLDwer3YsmULZs+erbSUsNm4cSNmz57NZszRzp07UVRUxKbn1urVq70Oh+O/B2KqAPmS1y8iovMAXAngPiHEHAAgouNENIeIFhFRv3XzROTweDxvv/7662zWJnJzc1FXV6e0jLDJyMhgVc3Irf9WREQEu5wlIQSralFuuN1uREREKC0jbLhFf8xmM5sLPhD8fKOjo9mYKq/Xi46ODjbVgB6PB08//XS72Wz+74G+dliMlRDivs6E9D1CiF5DHETU2PlvK4D3AZSe7nsaDIbHHnvssXYueR+hPCsuTQBDlXZcLqQRERHw+Xzg0oojND6IUwuDiIiIkTyrIcTr9bIzVlwi8AC/fKXm5makp6crLSNs6uvrkZ2dzSZ9ZM2aNR6Xy/U6EQ34jnxYjBURre5MSJ8aMk8nI4SIFULEh/4bwGUADpzBexqdTudaLlErjUaD1NRUNlEgIQRSUlJYVQcmJSWxahMRFxfHqrFpREQEq+pWbnCbu9fR0cGqdUFbWxurpOrGxkZkZmYqLSMsiAi1tbXIy8tTWkpYeDwerFq1ytLW1rbqdF6vRLuFDCFEPYAVAH4lhKgXQiQASAfwtRBiL4BtANYT0Sdn8l4Gg+GxlStXsolaFRYWorq6WmkZYZOdnY2GhgalZYRNamoqKyPIrWN8ZGTkiLEaQrgZK5fLxUav3++Hz+djo9dut0Or1bLRazQaER8fz0bvq6++6nG5XK+dTrQKUMBYEVEzEeUQUQIR6Tr/20pEVUQ0pfOnhIhOyyme9F4mp9P597/97W8snFVcXByEEGwupsnJyWhvb2ezHMgtwpaQkMDmWACCxopbXhgnAoEAq3warVbLZtmH2zIgt15Q1dXVKCoqUlpGWHg8Hjz55JOnHa0C5EteH3QMBsPjjz/+uIVL1KqoqAjHjh1TWkZYCCGQlpaGlpYWpaWERVRUFHw+H5u8JW6jeLj1Chth6OA2yJjTUGAiYrUMaLfb4Xa72SStv/LKKx6n07mGiE47b+SsN1ZEZHK5XK8+//zzLM74er0eVquVzWDmvLw81NbWKi0jbDgtB3IzKiNLgUOHz+djE60C+DXaNBgMbMbCWCwWxMbGsilkqKysRHFxsdIywsJisWDVqlXtRqNxQA1BT+asN1ZAMNfq97//PYtu7EIIjBo1ik3UKiEhAW63m80FVa/Xo7U17MkEisKtY3xUVBSb44Ab3PKrOBmrUJNNLs2Da2tr2fQyc7lcaG9vZ1O9+PDDD1ttNtuviOiMhsueE8aKiJzt7e333H333SwaGWVlZaGtrY1NvkpOTg7q6+uVlhEWKSkpMJlMbNpaJCYmshkgzckEcoObseI0zLi1tRV6vV5pGWHh9/thNBrZ6D127BiKiopY5Nrt2bMH69atO261Wl89032dE8YKAFwu1/rt27fv+de//iV9prUQAkVFRaioqFBaSliEmptyMCsqlQqxsbFsksI5tYgYWQocOjgNMw71iuOydNnU1ISMjAylZYRFQ0MDMjMzWRgVl8sFg8GAnJwcpaX0SyAQwG233WZsaWm5eaBd1nvinDFWANDS0nL73XffbeJw8s/NzYXBYGCRa6XVaqHT6dgMZs7IyGAz9Fqn07HpGM/hZM8Vl8uFqKgopWWEBadlQJ/PB6fTySbR/vjx48jPz1daRlgcPXoUY8aMYXFeeOWVVzwtLS3vEdH+wdjfOWWsiKjOarW++NRTT9mV1tIfQgiMGTMGR48eVVpKWHDqwZWens6mkjE6OhpOp5NFNBAIRgS5dLfnBCdj1d7ezqYCzGAwsFlWM5vNiIqKYhG5dDgcaG9vZ1G5aDQa8cgjj5haW1sfHKx9nlPGCgCMRuNTL730UgsHw5KZmQmr1Qqbzaa0lH5JTEyEz+eD3S69Z0VERAQ0Gg0cDofSUvpFCNFlrjgQFRU1kmc1BDidTjbGitPMPU5tC6qqqlBYWKi0jLA4dOgQxo0bxyJade+997ZbLJYV4cwhDpdzzlgRkae1tfWGJUuWGGVPDhdCYMKECSgvL1daSlhw6sHFqWt8cnIyTCaT0jLCgpMJ5ITT6URMTIzSMsKCS+K63++H1WplEV1zOBxwOBwsRu6YTCZ4vV4WkcD//d//9ZaVlW1xOp1vD+Z+zzljBQBEtLu5ufmFhx9+WPoM5pSUFKjVahYtAjIyMmAymVgkMIeGXnMgOTkZRqNRaRlhMWKshga/3w+NRqO0jH4JjbHhEKloaWlBeno6C61VVVUsquuICOXl5SgpKVFaSr/U1tbi/vvvb21tbV1Gg5xrcU4aKwAwmUyr1q5de/iLL76QvkpwwoQJOHTokPSjY4QQbHKtIiIiEBERwaI6UKfTsakMHDFWgw+X/DqA12iYhoYGZGdnKy2jXzweD9ra2liMsKmvr4dOp5O+GMDv92PJkiUmg8Fw45l0WO+Nc9ZYEVGgtbX12ttvv71N9mhATEwMMjIyWCyz5eTkoKmpCRxGCOXm5rLov6XRaKBSqVj0NYuJiRkxVoOMy+VikbAMAG1tbSyWqzweDxwOB4sly6qqKhQUFEgfrfJ4PKisrMTYsWOVltIvK1eutNfV1f3V5/N9MxT7P2eNFQAQUaPRaLx76dKlZtnvCouLi9HQ0CB9wrVarUZBQQGqqqqUltIvGRkZaG5uZhERSElJYbEcGBMTI/0xyg2Hw8HGWJnNZhYRq1C0ioNZaWpqQl5entJS+uXgwYMYPXq09KN2tmzZgpdffrnaYDD8x1C9xzltrADA6XR+sG/fvn+++OKLUpcyqdVqTJo0Cfv27ZPeCOTl5aGxsVH6qJVarUZycjKL/lupqaksjFVERASLHDtOOBwOFonrbrcbarWaRWPQ+vp6Fo0rQ7lVKpXcl2qj0Qin0yn90mp7ezuWLl1qaG1tXUxEvqF6H7n/WsNEa2vrDx9//PHqsrIyqZOYUlJSEBUVJX01WyhqxWHpkssQaS4J7KEIgOzmnxNcjJXRaGSxDGi1WhERESF9+wq3243m5mbp5wL6/X7s378fkydPljoC6PP5cPXVV5uNRuN9RDSkicAjxgrBWYIGg2HBsmXLWmRPvC4pKUFFRYX0eSz5+flobm6WvqeRTqeD3W6XPsqi0Wig0Wik1wmM9LIabOx2O2JjY5WW0S9cZu7V1NSgoKBAaRn9cvToUYwaNUr6aNWhQ4eQl5cn/TF63333WY8ePfo/HR0d7w71e8n9FxtGiKixqalp8RVXXGGUuVJMq9Vi4sSJ2Lt3r9RRAZVKhTFjxuDIkSNKS+kTIQTy8/NZRK1SU1NZLFvGxsayaBTLBbvdLn3EiohYNAb1+Xwshhjb7XaYzWbplyuNRiOsVqv0jUtffvll9wcffPC1wWD49XC834ixOgEi2tHc3Hz/NddcY5Z5LEdaWhpiYmJw/PhxpaX0SahzvMxGFQhWMtbX10ttVAFAr9ez6GcWExMzYqwGEb/fD61Wq7SMPgmZP9mjK1yS1jl0Lvf5fNi/fz+mTp0qtc6NGzcGfvOb31S1trbeMNj9qnpD7m+BAlgsljcPHDjw14ceekhqNzBhwgTU1NRIbVqEECgpKZG+c7xGo0FKSgqam5uVltInOp0OFotFegMYFxc3YqwGCZ/PxyIZnMMyIBGhpqZG+go7k8kEn88n/ee5b98+FBUVSR1Nra6uxrJly1oMBsMCIhq2/JkRY9UDBoPh52+99da2v/71r9I2DtJoNJg2bRp27dol9dDb5ORkaLVa6U1LUVGR9C0ihBBISEiQvlnoyFLg4MElv6q5uRnp6elKy+iTtrY2JCQkSJ20TkQ4cOAAJk6cqLSUPgmlTshsUi0WCy6//HJjc3PzYiJqHM73FrLf/faEEOIaAI8DCADwAbifiL7ufO4KAC8AUAN4hYie7nw8C8CbADoA3ExEfU42FkLEZmVl7b3vvvuKZs2aJW2c02Uh4O0AACAASURBVOPxwO/3S93nhohgt9sRFxentJQ+sdvtiIqKkjpC4PV64ff7pb44AIDNZpP+7w3Ir9Pr9SIQCCAyMlJpKX0i++cIBL/f0dHRUi9XejweBAIBqb/fgUAADodD6r+3y+XCL37xC3dtbe0PzGbzoM4BDAeuxioOgJ2ISAgxGcA7RDROCKEGcBTAQgD1ALYDWEZEB4UQTwN4A0ARgGwiejmM90lKS0vbunbt2lGXXnqplN9GIsL27duRnZ0tdQ+RiooKBAIBqbvytrW14fjx45g+fbrSUnrF6/Vi8+bNmDNnjtJS+mTjxo2YO3eu1LkXQFDnvHnzlJbRK0eOHEFcXJzU3+2mpiaYzWZMmDBBaSm90tHRgf3792PWrFlKS+kVt9uNb7/9FrNnz5Z2LqTP58O3336LKVOmIDExUWk5PeJ2u3HppZeay8vLHzabza8ooUFKs9AfRGQ7IQktFkDov0sBVBJRFRF5ALwN4JrO59QIRrgCAMI62xOR2WAwXHzTTTfVfvvtt1I6UCEEpk2bhoqKClitVqXl9MqoUaPQ3NwMm63PQKGipKSkwOFwSL2MpdVqodFopG+3MTIzcHCw2WzSz11rampCRkaG0jL6pKKiAqNHj1ZaRp+Ul5dj7Nix0poqIsLevXuRn58vrany+XxYvHhx+6FDh1YpZaoApsYKAIQQS4QQhwGsB3Bn58PZAOpO2Ky+8zEAeBHAnwDcg+CSYFgQUWtra+uc6667rnHPnj1nLnwI0Gq1mD59Onbt2iVtt3OVSoVJkyZJ3SZCCIHRo0ejsrJSaSl9kpmZiaamJqVl9ElcXJzUhRVcsNlsUudYBQIBWCwWqdsshG6WUlNTlZbSK62trfD5fFIPWq6qqoJGo0F+fr7SUnokEAhg2bJlll27dv3RaDT+XkktbI0VEb1PROMAXItgvhXQcySKOrc/TkRziGgREQ3ojE9EdS0tLfOuuuqqZln7MsXHx2PMmDHYuXOntMYlOTkZCQkJUveMSk9PR3t7u9QNLjMyMqQ3VvHx8VJHJzlARCAiqXP+Qt3WZV7yraysRHFxsbQafT4fDh48iEmTJiktpVfa2trQ1NQkrUYiwo9+9CNrWVnZ6waD4TdK62FjrIQQ9wkh9nT+dNl6ItoEYJQQIhXBCNWJ/f9zAAxKNQARVTY1NS1csGBBq6zVY1lZWdDpdDh48KDSUnpl/PjxqK6ulnaZSAiB4uJiVFRUKC2lV6Kjo0FEUpu/+Ph49hGrRx99FEKIrh+VSoXk5GQsXLgQH330Ubdtjxw5goceegjz58+HTqeDEAKPPvroGb2/0+lEdHQ0Hn0UEOK7H5UKSE4GFi4ETpKBDz8E7rgDGDcOiI0FsrKABQuATz45IynoTUTUokXI37+/+7avvQZcfjmQkwNERQFpacCFFwJ/+xtwhhXMj1ZXQ2zc2PWj2rgRyV9/jYV79+KjtrZTtnc6nTCbzcjIyMA/jcau1+2QKG3i0KFDKCgokLYAyWazYf/+/Zg+fbqUif9EhBUrVtj+8Y9/fGAwGH6mtB6AkbEiotVENJWIpgKIEZ23H0KI8wBEADAimKw+WghRKISIALAUwIeDqOFAfX39lXPmzGmV1byMHTsWTqcTNTU1SkvpEY1Gg4kTJ2L37t3SRtaysrJgMpmkNX9AcDmwsXFYK4gHxNlgrEKsXLkSb7zxBtasWYN77rkHu3btwqJFi/DWW291bbN582b84Q9/QF1d3aAVP1it1m75VStXAm+8AaxZA9xzD7BrF7BoEXCCDCxfDnzzTfDxF14A7r8faGgArrwSWLVqEESdIILuvhtRhw4h8ZZbuovYtQtISgLuuw9YvRr41a+A+Pig41u+fBBEACsLCvDGuHFYM3Ys7snKwq6ODiw6cABvtbR02+7o0aMYPXo0HIEA7j16FHGSRf8MBgNsNpu0y2sejwc7duzAeeedJ6XxCwQCuOeee6xvvfXWPwwGwx3D1QC0X0LhZk4/AH4BoBzAHgCbAVx8wnNXIVgZeAzAfw3R+4/PyMho3L59O8mIz+ejr776ilpaWpSW0iv79++nqqoqpWX0SkNDA+3Zs0dpGb3icDjo66+/VlpGn3z55ZcUCASUltEnGzZs6PW5Rx55hADQyd/zHTt2EACaMGFC12NGo5HMZjMREW3fvp0A0COPPHJG2o4ePUp1dXX0yCNEANHJp5sdO4KPnyCDvvji1P3Y7URjxhBptUQm02mK6UFES0sLHV279lQRvXHVVURCEDU1naYIokeqqggbNtB2i6Xb4zusVsKGDTRh69aux+x2O5WVlVEgEKD7Kyoo+5tvaEVFRY+vVwKPx0MbNmwgh8OhtJQe8fv99PXXX1PTGfy9hhKv10vf//7329PS0l5EZ4cDWX7YRKxOhIh+S0QlFIxgXUidPaw6n/uYiMYQ0SgiGox7tJ7e/1Bzc/Osq6++unbjxo2BoXiPM0GtVuP888/HwYMHYbFYlJbTI+PHj0dtba20UY3MzExYLBZpKwRDd48yR9ViYmKk1ne6TJ8+HSkpKd2KHJKTk6HT6Qb1fU6OWJ2qA0hJAU6stZg//9TtYmKAq68GvF5gMFNE6+vrkbxw4akieiM/HyAChuCcND0+HikaDSpPON6OHDkSzDvt6MAf6+vxfHEx4iWKWO3fvx+jRo2SMhJERNi9ezcyMjKkrPh0u9343ve+175hw4b/bm1t/XciSSJVnbA0VjJARDUtLS0zb7zxxuqPPvpIutbnkZGRmDFjBnbt2iWlOVCr1Zg6dSp2794tZed4IQTGjRuHw4cPKy2lV3JyclBXV9f/hgqRkJAgdQuQ06WtrQ1ms3nIq8z6a7XQ1gaYzUA4Murrg/8O1pQUn88Hi8WC5ECgdxEWS1BkRQXw4ovBNcwxY4Di4sERcQJtHg/MPh9SO2cqWq1WOBwOpOj1+NHRo7gsORk3SDQiJjSbVMYhy0SE8vJyREVFYdSoUUrLOQWbzYb58+ebd+zYsVKGRPWeGDFWZwARNbW2tpbeeeedh//+979LN/4mLi4O06ZNw/bt26VMdE5MTEROTo60yfZ6vR4ejwdms1lpKT2SlZWFxsZGaXPVEhISpI2YDgSLxYK2tjY0Nzdj06ZNuPbaaxEIBHDbbbcN2XsGAoHgksIJycIhn9LcDGzaBFx7LRAIAP3J2LsXeO89YPZsoKjoDIV1imjdtw9F9fUQS5b0LuLSS4OJ62PGAD/9KTBnDvDPfwKDEDWy+P1o83jQ7HZjU3s7rj1wAAEAt3VGVw4ePIgJEybgD/X1OOJwYLVEPaxsNhsqKysxefJkKSsVKysr4fV6pWz4ajabcdFFF5nKy8sfMBqNzymtpzfk7ETGCCIyCSFmPvDAAxva2tom/uxnP5NqFoFOp0NJSQm2bduGCy+8ENrOOzpZKCwsxLZt29DU1ITMzEyl5ZzChAkTcODAAcyaNUu6k6BWq0VcXBza29ul7COUkJAgdYJ9uCxYsKDb7zExMVixYgUef/zxXl5x5nR0dJwSrTpJBmJigBUrgL5kGAzAddcB0dHAK4PRLrFTRFdZdl8i/ud/AKsVaGoC1q8HWlqC0a3BkLF3b7ffY1QqrMjJweMFBTAYDNBoNDBHReGx/fvx6/x8FEqy3Ob3+7F7925MmTJFunMxABw/fhwmkwnnn3++dOe7hoYGXHrppcb6+vq7bDbbOqX19MWIsRoEiMgmhLj4ySeffGf//v3zXn755QSZuuempaXB5/Nh69atuOCCC6T6Qoc6x3/77beIj4+Xbv5UYmIiYmNj0dTUJGXzvry8PNTW1kpprOLi4qRchh4oq1evxpgxY6BSqaDT6TB+/Pghz4uxWCyndLdevToY/FGpAJ0OGD8+aJh6w2QKtmRobAz6mjFjBkHY6tVw5+fjaGUlJs2e3beI0tLv/vvWW4Ff/jIYtdq3DzjDJabVo0djTHQ0VEJAp9FgfEwMotVqBAIBHDx4EDNmzMC1R4+iMCoKD+Xm9r/DYeLAgQPIysqS8vtaV1eH+vp6zJw5U7q2Ctu3b8eSJUtampubb/D5fF/3/wplkevTYwwRuVtbW69dt27dC3PnzjW3t7crLakbmZmZXdEhn8+ntJxuREREYOrUqdi5c6d02oBgov2RI0ekzAVLS0uDyWSS8nMTQkCtVks7DSBcSktLsWDBAsyfP3/Yys4tFsspyfClpcGA0fz5wHnn9W+qFiwADh8G3n+/56T206K0FDXFxUhcsqR/ESdz++2AwxHsZ3WmMuLjsSA5GfOTknBefDyiO5cXa2pqkJ6ejs8cDnxuNuOh3Fwcd7lQ6XCg0uGAqfN7Uu92o9LhQGAYl9Fra2vh9XpRdMbrsYNPQ0MDamtrccEFF0jXkPbtt9/2fu973zvW0NAwk4OpAkaM1aBCRGQwGH6zb9++u6dPn26UbTRKdnY28vPzsXXrVukuxDqdDgUFBVKOvImMjEReXp6Uo26EEFKPuElMTDwr8qyGm54iVuFiNgcjVeXlQVN1xRWDp4uI0NjYeHrR21DFnsk0eIJOwO124/jx4yguLsbxzpzSO48cweht27p+/tjQAABYUl6O0du2wTRMpt9isaC6uhpTp06VbomtsbER1dXVKC0tlWpOIRHhl7/8pe2nP/3pVoPBcB4R1SitKVxGjNUQ0NHR8W5VVdWC2bNnN3755ZdStWPIyclBbm4utm7dKl0kIT8/H2q1GjJ2ti8sLERzc7OUS1t5eXk4fvy40jJ6RKfTQbborewQEXw+32kt2ZvNwUjVgQPA//t/wcagg4nZbEZycnLvF2CfDzAae37uj38M/jtz5uCK6uTgwYMYM2YMNBoNrk5JwbsTJpzy8/20NADAb4uK8O6ECUgYBiPhdruxe/duTJ8+XSrjAgQjVceOHZMuRcTpdGLRokXta9as+bvBYJhHRKzKi+X6K59FENEeIcT0pUuXfvHYY48V3XvvvdIktefl5UGlUkmZczV58mRs3rwZcXFxSE9PV1pOFyqVChMnTsS+ffswc+ZMqe46Y2JioNVqzyjKMVTodDocPXpUaRlDjsViwR87jUMoYX/Tpk144oknAACLFy/G5MmTw9pXR0fHaecaLlwYbHy+bBnQ3g68edK4+VmzzqwysLGxEYV9GSObLTjKZskSYOJEID09WMb4wQfAjh3BSsGbbjp9Ab3Q1tYGt9vdFUkrjolBcUzMKdsd6Lwxmq/TYUZCwqDrOBm/34/t27djwoQJ0uWP1tbWoq6uDjNnzpTqGtDY2IjLL7/cVF9f/19ms/llpfWcDiPGagghomYhxIxHH3303a1bt85++eWXE6Ki5PBXOTk5UKvV2Lx5My644AJERkYqLQlA0MDMmDEDmzdvRnR0NBKG4eQXLikpKYiOjkZDQ4N0/WcKCwu7lhpk4mwabdMXZrMZv/71r7s9tmHDBmzYsAFA8PsWrrEym82nndy8c2fw37Vrgz8n89e/npmx8vv9fX8nY2KAH/842A/is8+C7i4+HigpCfayWr58UNotnKzpwIEDKC0tleqGh4iwd+9eZGVlQS9RDy0gmIvW1NSECy64QKoo2qZNm+imm25qbW5uvtHn85Upred0EbLls5yNCCFEcnLyA3q9/j//8Y9/pBQPQYO806W1tRWHDh1CaWmpVB2ArVYrdu3ahQsvvFAa0wcEZ2d98803uOiiixAREaG0nC6ICGVlZZg1a5ZUugDgm2++wYwZM6T6O4bYuHEj5s2bp7SMbuzduxc5OTlISUlRWko3ysvLodPpkJ2drbSUbhw+fBhqtRqjJepVBQTnFDqdTkyZMkVpKd2orKyE0WjEjBkzpElUDwQCeOKJJ+wvvvhilcFguIqI6pXWdCaM5FgNA0RERqPxD4cPH77s4osvrlu7dq00yU16/f9v78yj47qqdP8dlUrWWJIlWaV5sIaSNcuaLMtxQoCEhAxuyOumk5DOACwWIdBNk+SxeK+7w9BNPxY8hgAJpDssCGlCAAdCGgfTSexYlgfJkjVZs2pQzapSqebp3v3+kEpt8gIkVtW9Ffn81qpl15VdZ6tUuve7e+/z7SK0tLTgzJkzSZVZUKlUW/5bydRon5aWBo1Gg4mJCblD+QMYY0nba5WXl5e0JqvJiMvlivt4nO0SjUZhs9mSzmtufX0ddrs96RzCDQYDnE4nWltb5Q5lCyLC5OQk3G43enp6kkZU2e12XHvttWvf+c53nrbb7V3vdFEFcGElKUR0wWq1tv7t3/7ta/fdd587WdzQCwoK0NXVheHhYTgTtGPnStizZw9qamowPDwMUUyePQClpaUgoqTbiVdZWYmVlZWkeq+AjTl6XFi9NaLR6JZNRTKh1+tRXl6eVP5Goiji4sWLaG9vT6q4bDYbtFoturu7kyYuURQxMjKClJQUdHZ2Jk1cJ0+epM7OTtvIyMg9Vqv1ISJKmqTDdkiOd/cqgojWbTbbjb/+9a8f6+zsTBpLBpVKhQMHDmBiYiKpBEN5eTkKCwuTzoahtbUVMzMzCIeTZ5JRamoq1Gp10rmd5+fnJ5VgT2bW1taSLltFRNDr9aiurpY7lD9gbm4OxcXFSdWH6XK5MD09nVTWBZFIBGfPnkV+fj6ampqSog9NFEV84Qtf8N1xxx2TRqOxy+/3/0bumOIJF1Yy8MbS4LPPPpsUKj0jIwP9/f1YXl7GwsJC0giZ2tpaKJVKTE1NJU1Mu3btQmNjI8bGxpImJmCjiX1xcTGpYtq1axcikUjSZdKSEafTmXS9VSaTCQUFBUm1c2xtbQ2rq6tIpn5Vj8eD0dFR9PT0JE0/oc/nw+nTp1FVVZU0xqQ2m23Hlf7eCBdWMhIrDX7mM595+ZZbbnE5/pj/i4SkpaXhwIED8Hq9uHjxYlJcDBljaG5uRjQaxezsrNzhbFFSUoK0tDTo9Xq5Q9kiIyMDubm5sNlscofyB+yUgcyJxuFwID8/X+4wtiAiLC4uJpWAiUajuHjxYlKVtHw+H4aHh9HV1YWsrCy5wwGwIdLPnTuHtra2pBnH9dxzz0Xb29st58+f//BOKv29keT4VF7FENG6xWK59dVXX/1oa2ur9Ve/+pXsc1NSUlLQ3t6O7OxsnDlzJinKXYwxtLe3b02GTxZaWlqwvLwMr9crdyhb1NXVYX5+Xu4w/oCCggJeDvwziKKIUCiUVLtzbTYbVCpVUsU0MTGBvXv3Jo2ACQQCOH/+PDo7O5OmLGkwGDA5OYkDBw4kxVxCh8OBW265xfXpT3/6ZYvF0hwMBl+SO6ZEwu0WkgjG2B61Wv1sZ2fn4QcffDAtGQzlotEogsEgMjMzk+ruMDU1NWnS7YIgIBAIJJUBoN/vR1paWtL0eYiiiEAgkDQXwxherzdpfm6CICAUCiHzTYwt5cLr9SbV734kEkEkEkma94iI4PP5kJGRkTQbDoLBIARBSJrftdOnT9P3vvc97/r6+kfcbvfP5I5HCriwSkJycnLuys3N/fpTTz1V+L73vU/2M5rH48HIyAjq6+uTwsNGFEUMDw+joKAgabZZLy0twePxJI1njcfjwfj4OA4ePJgUzapEtOUZlQzxxEgmH6uFhQWkpaWhsrJS7lAAbGSrVlZWsH//frlDAfDf56GBgYGk6PcKBAI4e/YsWltbk6IvLhQKYWRkBAUFBWhoaJD998zlcuGjH/2o6+TJk8M2m+1OIrLLGpCEyH7R5vz/eDyenxiNxvZ777331N13370ut79UTk4OBgYGYDQaMTk5KXvfVcyd3eFwJE1ZsKamBuFwGCsrydGHmZOTg/T0dKyursodCoCNUi7vs/rTrK6uorCwUO4wAGwI4bm5OTQ0NMgdCoCNzPmFCxfQ2dnJRdWb4HK5MDQ0hNraWmg0GtlF1bFjx8SWlhbbsWPHHrLZbDdcTaIK4MIqaSEii9Vqve7FF1/8+6amJtvRo0cFObOLSqUSPT09SEtLw9DQEOT24IqJq7W1NczNzcm+C44xho6ODiwsLMDtTo55oRqNBrOzs7K/NzEKCwuTRuglG7FSabKUuGw2G7KyspKiTBobDVNdXZ0UszB9Ph/Onj2LtrY22UUVEUGn0+HixYvo6emRfb6qzWbDHXfcsXbvvfeeMhqN7R6P5xlKlhOQhHBhdQUwxnoYYwJj7I7LjmkZYxOMsTHG2PBlx0sZY68wxn7FGHtbZykiovX19X9bWVlp/cQnPvHidddd51xaWornt/K2YIyhoaEBDQ0NGBoakn3nWUpKCrq6uuD1ejE9PS27gFAqldi/fz8uXLiQFA3/2dnZyM7OhtVqlTsUABuGr3b7VXXj+pZxuVxJ0WQMbFysZ2ZmoNFo5A4FwEaZXaFQoKqqSu5Q4Ha7ce7cOXR0dMi+ezMSieDChQtYW1vDwMCArD1VgiDg8ccfD7W1tZleeumlB6xW63VEZJEtIJnhwuptwhhTAPhXAC+/yZffRUQdRNR92bFPAXgIwFMA7r6SNYnIZjab/2JwcPBIf3//8j/8wz/4QqHQlbxUXNizZw/6+/uxsLCA6elpWUuDMSdhIkoKTymVSgWNRoORkRHZYwGSK2uVmZmJUCgEQZB942vSYbPZsGfPHrnDAACsrKygoKAgKbJndrsdZrP5LQ+wTiROpxMjIyPo7u6W3cTV5XJhcHAQarUaHR0dsm5SGRkZQVtbm+OLX/ziD6xWa0MgEDh6NWapLocLq7fPQwB+AeCtpmsUAMTNx7YK39Fo9HWbzab5zne+86+NjY2rx48fl03RpKeno7+/HwqFAkNDQ/D7/XKFsuVzlZmZifPnz8t+4S4pKcHu3bsxPT0taxzAhq9VYWFh0nht7d69m4+3eROSpb9KEAQsLCwkxUBjn8+HycnJpBgNY7PZMD4+jr6+PuTk5MgWBxFhaWkJ4+Pj6O7uRnl5uWyxuFwuPPDAA+vvf//7x6anpw9v+lL5ZAsoieDC6m3AGCsD8BcAnniTLxOA3zHGRhhjH7vs+OMAngTwcQDPbDcGIoo4HI4varXa/R/+8IdP3HrrrWtyjaBhjEGj0aCxsRHnzp2DwWCQLTMSi6WoqCgpvLc0Gg38fn9SDEVuaGjA0tISIhH5vfiKioqSpjSZLMRc6ZPBPmRpaQnl5eWyxxIOh7e8odLT02WNRa/XY3Z2Fv39/bJm8QKBAM6cOQOfz4eBgQHZ+t+ICD/60Y8iTU1N1ueff/7vrFbrfiKS/y4yieDC6u3xDQCPEtGbpUQGiGg/gJsAPMgYOwwARKQjosNEdCsRxW17HxEZLBbL9cePH7+ns7Nz5ctf/rJfrobygoICDAwMYHV1FcPDw7KKmurqatTV1eH06dPw+eS7eWKMYf/+/dDr9bL3oimVSlRXVyeFaeiePXt4A/sbsNlsKCoqkjsMBINBGI1G2UefxOxUNBqNrCW3WK+ZxWJBf3+/rGLTZDLhzJkzqK2tRWtrq2yeWcPDw+ju7nY88sgjPzObzY1ut/vpq73s92ZwYfVnYIw9uNmQPgagG8BPGWNaAHcA+C5j7AgAEJFp808bgKMAeqWILxgM/sZqtdZ/7Wtf+5f6+nr7j3/844gcPU9KpRKdnZ0oKyvD4OCgrGIi1ndw7tw5Wd2+FQoFent7MT09LftOwaqqKtjtdlnFJrAxKFqpVCIQCMgaRzKRLMLq0qVLaGhokNXoMrYDUK1Wo6SkRLY4RFHE6OgoIpEIenp6ZOthijWom0wmDAwMyPY50Wq1uP3229duvfXWcxcuXLjeYrHcTUQuWYJ5B8CF1Z+BiL6z2ZDeQUQ1RFRNRNUAfg7gE0T0AmMsizGWAwCMsSwANwCYlDDGoNPp/NLKysq+z372sz9ubm5efeWVV2S5iygtLd0a5Bw7MclBXl4e+vr6MDk5CYPBIEsMwMYA4q6uLoyMjMjah5aSkoLm5mZMTkr2sfyjqNVqXg7chIiSYkeg0+lEIBCQVcwAG+IuNTVV1qxZKBTC0NAQ8vLy0NraKpsnlMViwalTp1BUVISuri6kpaVJHoPL5cKnPvUpz4EDBxZ/85vffMhsNvcR0bjkgbzD4MIqPqgBnGKMXQRwDsBLRHRM6iCIyGG1Wh+YmZk5cNddd/3Xtdde65SjgTo9PR29vb3Ys2cPTp06BYtFnl23mZmZOHjwIMxmM6ampmTr/8rJyUF7ezvOnTsHOXdzFhYWIjU1VbafR4zi4mLZY0gW1tbWkJeXJ6uhIxFhcnISbW1tssaxuLiIQCCAlpYW2eJYX1/H0NAQ6uvrZRN34XAYIyMjMBgM6O/vR3l5ueTvRzgcxte+9rXgvn37LE8//fTfW61WjSAIv5M0iHcwfKTNDoYxdkCtVv/gve99b/lXv/rVvOLiYsljCIVCGB8fR0pKClpaWmTpUyAizM7OwuVyoaurSzbnZqvVirm5OfT398tWWggGgzhz5gwOHTok6xbt1157TfYYYnHIOdJmenoau3fvljVTtLS0hFAohH379skWg8FgwMrKCvr6+mTbAWgymTA/P4+uri5ZGsOJCGazGbOzs9BoNCgtLZUlhueff1545JFHnD6f7werq6tfJiL5Uu3vUHjGagdDRGesVmvbz3/+8490dHToH374Ya/UPUe7du1CT08PSkpKcPr0aeh0OskzR4wxNDY2orKyEoODg7KNVVGr1aipqcHZs2cRjUZliSE9PR1VVVWYnZ2VZf0YRUVFsjf1JwNy+1cFAgHo9XpZ7RVMJhN0Oh16enpkEVWiKG61DBw8eFAWUeX3+3H27FlYLBYMDAxILqqICL/73e+ora1t9dOf/vRzOp2u1W63f56LqiuDC6sdDhFRIBD4hdVqrXvyySc/29TUZPr85z/vk1pclJaW4tChQ/B4PDh9+rQszdylpaXo7u7G2NiYbL5O5eXlqKiokNVvq7q6Gi6XS1Y/qdLSUphMJtnWTwbcbjcyMzNly9rFGsWbm5tli8FisWBxcRF9fX2yY5s5ZAAAIABJREFUxBAMBjE0NIRdu3aht7dX8my2KIqYm5vDuXPnUFdXh/3790veS/Xqq6+is7PTce+99/5mcnKy32w230VEvAlyG3BhdZVARBG32/2k1Wqtefzxx/+nRqMx/+M//qNfSoGlVCrR0tKC5uZmXLx4EVNTU5JnbrKzszEwMAC73Y6xsTFZxE1lZSVKSkpkE1eMMbS3t2N8fFw21/zc3Fx4PB7ZzVzlxGw2y1oCNBqNSE9Ply1jZrPZMDc3h76+PlnK86urqxgaGkJDQwPq6+sl72NyOBx4/fXXAQCHDx+W3CD29ddfR3d3t+POO+88dvHixQGTyXQbESXHVPt3OLzH6iqFMbYrNzf3E+np6Q8/8MADqocffjhLSs8YIoJWq4VWq0V9fT3KysokPbHFhpfqdDp0dnZCpVJJtnaM5eVlWK1W9PT0yLLFfX5+HtFoVLbemqmpKeTn58sqLuTssTpx4gQOHjwoi6iImU0eOnRIlvUtFgtmZ2dx4MAByfsuRVHE7OwsnE4n9u/fj4yMDEnXDwQCmJqagiAIaGlpkXTGHxHh1VdfxWc/+1mH2Wy+YLFY/o6IpiQL4CqBC6urHMbYLpVK9dGMjIzP3XPPPapHH300W8qJ7eFwGDMzM3C73WhpaZHcENDtdmN0dBSVlZWorq6W/K5Vp9PBaDSit7dX8lIIEWFwcBBNTU2yDJR1uVxYWFhAd3f3n//HCUIuYeV2uzEzM4PeXkns7v4AIsKZM2dQV1cnS7bKZDJtlf+kLnv5/X5cuHABe/bsQUNDg6S/77FxQWazGU1NTZJ6UsV6qB555BGn3W4/YzabHyaiS5IFcJXBhRUHAMAYU2ZnZ9+XmZn5v2+//facz33uc7k1NTWSre92uzE5OYmMjAzs27dP0jEWgiBgcnISoVAIHR0dkp/sDQYD9Hq9LD0ePp8P58+fl2WHHhHhxIkTsu4OlEtYXbp0CSqVCmVlZZKvvby8DK/Xi9bWVsnXXllZgVarlaX8ZzQaMT8/j7a2NklvJIgIJpMJc3NzqKysRE1NjWRN+tFoFM8//7zwhS98YW19fX1wU1DJP4Jhh8OFFecPYIwpUlNTby8sLPxSZ2dn0WOPPVbQ09MjydpEtFUiKC4uRm1traQnX7PZjJmZGezbtw9SW1OYTCYsLCygt7dX8tloOp0OTqcTnZ2dkq4LADMzM8jOzpZtmKwcwoqI8Nprr+Gaa66RXFDGMrQDAwOSr720tASLxSJ5djYcDmNiYgJEhPb2dknPKQ6HA9PT01CpVGhsbJSs7OnxePDkk08Gv/nNb3oikcjzVqv1n4nIKMniHC6sOH8cxlh/aWnpPxcVFbU89thj+bfcckuKFHdaoihCp9NBq9WiuroaVVVVkt3hhUIhXLx4cavRXsqTsN1ux9TUFHp6eiTvuxgZGUFxcbHkAsfn82F8fBz9/f2SrhtDDmHlcDig1+slF7LRaBSDg4OS9xTGZu75fD50dnZK2k9otVoxPT0tuS+U2+3GpUuXwBhDU1OTZBYORqMRX/3qVz3PPfecJxgMfsvlcn03njNqOW8NLqw4fxbGWF1xcfE/paen3/joo4+q7r333jQpsirRaBQLCwuwWCyor69HaWmpJD0RRISVlRUsLCygpaVF0j4Ul8uF0dFRdHZ2StpvFolEMDg4iO7ubsl9fE6dOoWuri7Jm4gBeYTV2NgYysvLJd8FNjY2hry8PFRXV0u2piiKWwbBUo6HiUQimJqaQigUQnt7u2RZ4EAggJmZGfj9fuzbt0+ykuPExAQee+wx5+DgoGN9ff2xQCDwHBHJY5bH4cKK89ZhjBUUFhb+vVKpfOC+++7LevDBB7OkuAsMBoOYm5vD2toaGhoaUFxcLMkJOhAIYHx8HGlpaWhubpas98rn82F4eBgNDQ2S7phzuVwYHx/HwYMHJS3V6HQ6hEIhNDQ0SLZmDKmFVTQaxeuvv47rrrtO0sZpvV4Pm82Grq4uScXN8PAwCgsLUVdXJ9m6sZJ+XV2dZONg5DhHCYKAY8eO0Ze+9CWHTqdbMJvNnwNwgvhFXXa4sOK8bRhj6VlZWXfn5OQ83Nramv/oo48WXn/99Qk/kQQCAczNzWF9fR0ajQZFRUUJX/PyxlMpbSHC4TCGh4dRVFSE2tpayS5KOp0Oq6ur2L9/v2RrRqNRnDp1Ctdee63kuzKlFlY6nQ7BYBAajUayNV0uFy5evChpX5Xf78f58+e3Ms1SEAgEMDExAYVCIdn4rGAwiIWFBTgcDtTX16OkpCThn2GbzYYnnnjC/4Mf/MAXjUaPWSyWrxCR9ENhOX8ULqw424Ixtn/v3r3/TESHbr755owbbrghJdH9G6IoIhQKQRAEpKenS3KxICIEg0GIooiMjAzJer4CgQAASFomCwQCUCgUku6ODAQCUCqVkjdUe71eSUufXq8XWVlZkglIIoLP50NmZqZkn1lBEOD3+5GZmSlZP1U4HEY4HJb0fBAKhRCNRrFr166E92ISEcbHx/GrX/0qtLCw4PR4PP/icrn+nYh8CV2Yc0VwYcWJC4yx3Nzc3AfS09M/dejQIdXDDz+8u7e3N6EXEK/Xi4WFBbjdbtTV1Ulyt7i6uoqpqSmo1WrU19cn/MJBRFhaWoLZbEZ3d7ckvSKCIGw5UkvlteNyuTA/Pw+pdqDGkDJjtb6+jpmZGfT19UmyniiKGBoaQm1trSS7XGOmvysrK+ju7pbkZsDpdGJychIFBQXQaDQJF1V+vx8LCwtwOp2ora1FWVlZQgXr+vo6nn766fC3vvUtdygUOm0ymb4M4Dwv9yU3XFhx4grbUDbXlJaW/q/s7OyOz3zmM3l33nmnMicnJ2FrBgKBrXT83r17UV5entCTnSiKWF5ehsFggEajkaSfYnV1FRMTE2hvb5ekITYUCmFoaAhdXV1I5M/ucjZHbEianZNSWI2NjaG0tFQSsUpEGBsbg0qlQm1tbcLXEwQB4+PjAIC2traE33CEQiFMT08jGAyipaUl4Z9Rj8eD+fl5+Hw+1NXVJfR3PrZL9+tf//raK6+84g2FQt91uVzfJyJnQhbkxB0urDgJgzGmzs/P/4RSqbyvt7c386GHHiq4/vrrE3bSDYVCW145lZWVqKysTGiKPhgMYnp6GqFQCM3NzQnfwh4IBDA8PIyysjLU1NQkXMy53W5cuHAB/f39kvSrGAwGeL1eSUfsSCWswuEwTp8+LVkfWUwEtLe3J3w9v9+PkZERlJeXJ3x6QeymRq/XQ6PRJDxL7XQ6sbCwgGg0uuVUn6j1TCYTfvjDHwaeeuopXzgcHjMajV8B8ArPTr3z4MKKk3A2s1h9JSUln2KMvecv//Iv0z/2sY/lJOoCGolEoNfrodfrsWfPHuzduxeZmZkJWQvYOPlOT08jOzsbjY2NCS3XCYKAqakpBINBSVzibTYbZmdn0d/fn/AyiyiKOHHihKTGmVIJq7m5OSiVSkgxzcBgMGyNSUp0X1VsY0ei3cyJCGazGXNzcygpKUFtbW3CPiOiKMJsNmNpaQkZGRmora3F7t27E7KW3+/H0aNHxW9/+9sOvV6/6na7H/f5fM8SkSshC3IkgQurHQxj7DoA3wCgBLBKRNduHn8fgG8CUAB4ioi+snm8FMAzADwA7iIibwJi2pWamnpbUVHRZ3Jzc2s//vGP5951111piZhPKIoiLBYLlpaWsGvXLtTW1ibs5C/liR+Q7oIGbFyoV1ZW0NfXl/ALtZQCBJBGWAmCgJMnT0oiGKUSwlKOgYrduOTk5ECj0STsxiUSiUCn08FgMKCoqAg1NTUJuSEjIrz++uv47ne/63zttddCoij+h91u/x4RLcR9MY4scGG1Q2GM5QE4DeB9RKRnjBURkY0xpgAwB+C9AFYAnAfw10Q0zRj7CoAfA9gLoIyInkhwjEW5ubn3ZWRkfKypqUn1yU9+suCmm25iiThxOp1OLC4uIhgMoqqqCmVlZQkpSV5eqki0a/zlA2Xr6+sTKnoWFxextraWcB8kqUtmUggrnU63ZRiZSJxO55aLfSJLt+vr6xgbG0v44PLYoGoiwr59+xJWane73dBqtXA6naioqEhYC8H8/Dyefvpp3zPPPOMXBOG0yWT6vwBeJyIx7otxZIULqx0KY+wTAEqJ6H+94Xg/gH8iohs3n38OAIjoXxhjXwXw79gQVpVE9D0J421Tq9UfB3B7d3d32n333Vdw0003sXjfMQYCAeh0OpjNZhQVFaG6ujoh42MikQiWlpZgMplQW1ubsIZ6URQxPz8Pm82Gjo6OhDbxzszMIBAIoKOjI6GiZ2pqCrm5uZKM10m0sIoNmk602FlbW8PFixfR19eXsOZ/IsLCwgLMZjM6OjoSJnS8Xi9mZ2cRDAbR2NiIRGWzzWYztFotFAoFampqEuKLNz09jWeffdb305/+1B8MBhftdvv3wuHwL7hNws6GC6sdCmMsVgJsBpAD4JtE9CPG2B3YyGJ9ZPPffRhAHxF9kjFWhY2M1TqAO+WYMbXZj9VRVFR0L2PsA21tben3339/wS233MLi6TcUKxNqtVowxlBdXQ21Wh138RMOh7GwsACbzYa6urqEGYzGTCArKioS1thORLh06RIikQja2toSJq6CwSDOnj2Lw4cPJzxrlWhhtbKygvX1dTQ3NydsjfX1dVy4cAG9vb0JmzHp8/kwOjq6ZWuQiJsEv9+P2dlZ+Hw+aDSahIyS8vv90Ol0sFgsUKvVqK6ujmu5j4gwMTGBn/zkJ96f/exngUgkMmuz2Z6IRCIvEpE7bgtxkhourHYojLHHAXQDeDeADABDAN4PoB3AjW8QVr1E9JBcsf4xNkVWS2Fh4d+kpqbe0djYmHn//fcX3HbbbSm5ublxW8fj8UCr1WJ1dRVqtRqVlZVxN42MOTSvrq4mzBJCEARcunQJ6+vraGtrS0j2iogwNTWFaDSa0B1nExMTyM/PR1lZWUJeP0YihZUoijh58iQOHDiQsL6g2GzJnp6ehBidiqKIxcVFGI3GhPXzXW5lUF9fD7VaHdfPlSAIMJvN0Ov1ICJUVVWhpKQkbq0ARIQLFy7gmWee8fzyl78MCoIwYbFYnhQE4T8T0afKSX64sNpBMMYeBPDRzac/A5BGRP+0+bV/A3AMG31Vb1oKlDzgtwljbF9BQcGHlUrlh2pra7Pvv//+/CNHjijidbIXBAFWqxV6vR7RaBQVFRUoKyuLaxPw5ZYQVVVVqKqqinuv19raGiYmJrZMTOMt4Ihoa9BsZ2dnQrIXwWAQZ86cweHDhxPaO5ZIYaXX6+H1etHU1JSQ13c4HJiYmEBPT09CMlWx2ZFFRUUJMcONmcKGw2HU19fH1cqAiLC+vg69Xg+Hw4Hi4mJUVlbG7X0SRRHnzp3DM888s/7CCy+EAYyazeYnRFF8mYj8cVmE846FC6sdCmNsH4DHAdwIIA3AOQAfAjCDjeb1dwMwYqN5/U4impIp1CuCMVa/e/fuu3ft2nVXZWWl6v7779/9gQ98IDVe5YNAIACDwQCTyQSVSoWKigoUFhbG7cQfiUSwvLwMo9GIsrIyVFdXx3VnVSzTYDKZ0NzcjMLCwri9doz5+fmthvZEbASYnp5GVlYWqqqq4v7aMRIlrGI7AQcGBhKyY85ms2F6ejohPVWRSASzs7NwuVxob2+Pa+aTiLC6uorFxUUAQENDQ1yzYIFAAEajEUajEZmZmaiqqoqbYBNFEadPn8aPfvQj10svvRRhjJ01Go3fB3CciILbj56zU+DCagfDGHsYwH0ARGzYKnxj8/jN2LBhUAD4dyL6snxRbh/GWE1eXt5fZ2Rk/E1hYeHuI0eOZN18882Z3d3d2842ERGcTidWVlbgdDpRWFiI8vJy5OXlxeVkHY1GsbKyAq1Wi927d2Pv3r1xvZD5fD5MTk5CoVCgubk57hfh2Pb03t7euAuISCSCU6dOJdSmIFHCan5+HgBQX18f99fW6/XQ6XTo7e2Na0M8EcFgMGBxcRF79+5FZWVl3G4kBEHY+pyrVCrs3bsX8SrnRyIRmEwmrKysgIhQVlaGsrKyuHweLRYLfv/739Mvf/lLx9DQkJiSkjJoMpm+jw3jzvD2o+fsRLiw4uwoGGN7GGPvLisr+6tIJNLX2NiY+sEPfjDvhhtuUDY0NGzrQiGKImw2G4xGIzweD4qLi1FeXh6X3hYigs1mw9LSEhhj2Lt3b1xLI7EMR0lJCerq6uKaYbJarbh06RJ6e3vj7vuzvLyMYDCYMKuCRAir2Diga665Jq7vMxFhbm4OLpcLXV1dcRWbLpcLk5OTyM3NRWNjY9zsBkKhELRaLUwmE0pKSlBdXR2XfrNY2X5lZQWBQAClpaUoKyvb9ufP6/Xi5MmT+PWvf+06fvx4JBgMWgKBwNG1tbUXAYwSkbDt4Dk7Hi6sODuWzeb3uszMzPfl5+f/DyLS9Pf3K44cOZL/nve8h6nV6it+7Wg0CovFgpWVFYRCIajVapSWliInJ2fbYsjtdmNpaQkulwvl5eWorKyMy923KIpYWlqCwWBAbW0tKioq4ibc1tbWMDY2htbW1riWHUVR3JohmIg+okQIq9HR0a3PQ7wQBAFjY2NQKpVobW2N28/N5/NhZmYGoVAILS0tcbFQICI4HA5otVr4/X5UVVWhvLx82yIzEonAZrPBZDLB5/OhqKgI5eXl24o5Go3i/Pnz+O1vf+t/4YUXfKurqx5RFI9brdZfAjjF+6U4VwIXVpyrhk1z1K78/Pxb09PTb09PTy++8cYb02677bbca6655oov3JFIBFarFSaTCX6/H0VFRSgpKdl2uTASicBgMMBgMCA7OxvV1dXIz8/f9kU1HA5jfn4eq6uraGxsjJt/T2yWYbxnxjkcDszNzeHAgQNx34UYb2HlcDgwPz+Pvr6+uMUae18rKyvj1m8WCoUwNzeHtbW1rc/AdgmHw1uf19zcXFRXV2/7dyAcDsNiscBsNiMQCKC4uBglJSVQqVRX9LqxrN+xY8ciR48eXZudnY2mpqaeNRqNzxHRK0Rkv+JgOZxNuLDiXLUwxrIAHCouLv4ggPeo1ersI0eOZN10002ZV1pqiUajsNlsMJvNcLvdKCwshFqtRkFBwRXfsRMR1tbWsLy8DK/Xi7KyMpSXl2+7pOL3+7dMPzUaTVwyTYIgYHx8HADQ2toat3LV2NjYVn9bPImnsBIEAadOnYprds1ut2NychLt7e1xafIOh8Nbu1Lj4atGRLDb7VsDtCsqKlBRUbGtUqLP54PVaoXFYkE0Gt0SU1fae2i1WvH73/+ejh49GuuTmnE4HM8HAoFjABb5kGNOvOHCisPZhDFWxBi7vrS09EOCIPTu27dP8YEPfGD3DTfcoKyvr3/bFyBBEOBwOGC1WrG6uorMzEwUFxdDrVZfsSgKh8MwGo1YWVmBUqlERUUFiouLt1Vm8Xg8mJubQzAYjMu2dyKCXq+HVqtFZ2dnXMpL4XAYg4OD6O/vj6snVDyF1dTUFNLT01FbW7vt1xJFEbOzs1hbW8P+/fu3/T2HQiEsLi7CZrOhpqYGFRUV27Kx8Hg8MBgMsFqtyM/PR0VFBXbv3n1FnxtRFOF0OmG1WmG325Geng61Wg21Wn1FPVM+nw8nT57Eiy++6Hr55Zcv75P6DYALvE+Kk2i4sOJw3oTL+rNu2uzPaujv709597vfvbuvr0/R0tLytu7KiQherxdWqxVWqxWCIKCoqAhVVVVXvFPP6/XCYDDAYrFs7Sjcjojxer2Ym5uD3++Pi/O12+3G6OgoqqqqUF1dva3XAjYa8JeXl9Hb2xu3Mlu8hJXT6cSlS5dw8ODBbccWCARw4cIFFBYWYrsbLiKRCObm5mC327dtTCuKIrRaLVZWVrBr1y5UVFRArVZfsag3Go0wm83weDzIz8+HWq1GYWHh285yWq1WnD9/HqdOnfL+9re/9dtsNh+A4xaL5RfgfVIcGeDCisN5CzDGUgF0ZmZmDpSWlh4JBoMt2dnZ6U1NTcrGxsY0jUbzti5aRIRoNAqFQhEXA8xoNArGWFx2oYmiCFEU41bGi0QicdtlFs/XAjayG/Eo2wmCAMZYXH6WoiiCiOK2ozCe71k4HIZSqYyLsI1EIkhJSXlb32dsjuDc3Jw4PT0d0ul0UYVCYSOi13Q63UsABonItu3gOJxtwIUVh3OFMMZUAParVKpDKpXq3dFotEGtVqcePnx41+HDh3N7enri6gXE4VxNBAIBjI6O4syZM+FXX311fXx8nCKRiJMxdsZsNh8novMAFniPFCfZ4MKKw4kjjLE9ALoLCgquTU9Pv04Uxerq6mp23XXXZQ0MDGT19PTEZQcWh7OTiEQimJycxNmzZ4XXXntt7fz58+T3+z0KhWLEarX+LhqNngMwTURRuWPlcP4cXFhxOAlks1erHECPWq2+XqFQXMMYK25sbMS73vUuVX9/f3pXV1fcXKg5nGRHFEXMzc3h3LlzdOLEibXTp08LLpcrmJqaOuFwOI4HAoEhABf5mBjOOxUurDgciWGMpQCoZ4z1lJSUvJeIDiiVyt3t7e04dOhQblNTU5pGo0FNTU3CRrlwOFLgdDoxOzuLmZkZGh0ddZ88eTJktVqjqampc263+7/cbvcpbOzUc8sdK4cTL7iw4nCSAMaYEkAzgNaioqLutLS09mg0WpOWlpZRXV1N7e3tu9rb21WNjY2soaEhrgOhOZztEA6Hsbi4iNnZWUxPTwdHR0e909PTosvlEgA4UlJSptfW1oZ9Pt8EgBFuwsnZ6XBhxeEkMZvZrUoAmszMzOb8/PweImoSRXGPSqVKbWpqQkdHR3ZLS0uGRqNBXV1dXAfzcjjAxi5Wq9Uayz4JY2Nj7vHx8YjBYEAkEvErlcrFYDA4ZrfbLwCYBTBHRB654+Zw5IALKw7nHcqmc3w9AE1BQUFnZmbm/kgkUpeamppVVlaGtrY2ZXt7u2rfvn0KjUaD0tJSnuXi/En8fj/m5+cxOzuLyclJ/+joqG92dhYejyeqUCgsACbtdvu5cDh8CRsCysh35XE4fwgXVpwdA2PsYQB3bT5NBbAPwB4icjLGtAA8AAQAUSLq3vw/pQCe2fzaXUTklTzwOLPZMF8MQKNUKvcVFRX1MMZao9FoaXZ2trK2thZ79+5VVldXZ1ZUVKSVlJSgtLQUpaWlyM7Oljt8ToK4fNySyWSCyWQirVbr02q1ofn5ecFisUAURXdqauq8z+cbcTqdY9gQTwu8kZzDeetwYcXZkTDGbgXwd0R0/eZzLYBuIlp9w7/7CoAfA9gLoIyInpA6VilhjO0CUAOgFEBpbm5udU5OTh1jrEoQhBIiyk5NTU3Ly8ujzWHKaW8UYNuZ28aJP28UTEajkXQ6nU+r1QYNBoNoNptZKBQSBEEIpaam2hljK6FQaNnpdM5Ho1ETABMAHQA7zz5xONuHbzni7FT+GsB/vIV/pwAgbj52fJ2MiEIAZjYfbwpjjBkMBtXExEQJLhNg2dnZdSkpKZXRaLQEQI5SqVTm5uaivLwcVVVVypqamqzLBVhxcTFycnJ4+fEKiUQisNvtbyqY9Hq9YLFYUv6EYDICMGNDNNmIKCLzt8PhXDXwjBVnx8EYywSwAqCOiJybx5YBrAEgAE8S0fc3j1dhI2O1DuBO3nD71tksOaoAxARYiUqlqsnJyalNSUmpikajxaIoZqWkpCgUCkVKSkqKIjU1NSU7O1tUqVSUk5PD8vLyUvLy8hR5eXnKvLy8tNzcXKVKpWLZ2dnIycnZesSeZ2VlxWVsTCIIhULwer3weDxbj8ufr6+vC+vr6yGXyxVxuVzC+vq64HK5yO12w+PxsGAwCEEQxE0EIgorlUoHY8wYDoeXHA7HwhsEk5UbZnI4yQcXVpwdB2PsrwDcTUS3XnaslIhMjLEiAMcBPEREJ2UL8iqFMaYAkA0g57I/t/6ekpKiys7Ozs/IyMhPTU3dnZKSshuAiohyBEHIFkUxc1OobSg1hUKRlZUlZmZmkkKh2Jq9mJKSwi77Oy77Grv8z825iCQIAkRRJFEUIQgCNp/jjcdFUUQkEmEej4eFQiGIoihsiiEBQFihUHhTUlI8ADxE5BIEwRUKhZw+n88RCoVcALzY6OfzvMnfg7wUx+G88+GlQM47GsbYgwA+uvn0ZiIyAfgQ3lAG3DwOIrIxxo4C6AXAhZXEEJGAjezgejxeb9OOIgtAJoCUN3ko/sjx2NfEP/EQ/sjxCDaEUzge3wOHw9lZ8IwVZ0fBGMsFsAyggoh8m8eyAKQQkWfz78cBfIGIjskYKofD4XB2IDxjxdlp/AWA38VE1SZqAEc3m6hTATzLRRWHw+FwEgHPWHE4HA6Hw+HEieTcXsPhcDgcDofzDoQLKw6Hs6NhjGkZYwHGmJcxZmGM/ZAxln3Z17M2v/afcsbJ4XB2BlxYcTicq4FbiSgbQAeATgCfu+xrdwAIAbiBMVYiR3AcDmfnwIUVh8O5aiAiC4CXsSGwYvwNgCcAjOO/Z01yOBzOFcGFFYfDuWpgjJUDuAnAwubzSgDXAfjJ5uMe2YLjcDg7Ai6sOBzO1cALjDEPAAMAG4B/3Dx+D4BxIprGhqlsM2OsU6YYORzODoALKw6HczVwhIhysJGdagRQuHn8HmxkqmLu/CewURrkcDicK4L7WHE4nB0NY0wL4CNE9PvN518G0Azg/wAYxMZw7th4mhxszO0r4wOOORzOlcCd1zkcztXGNwBoAWRgY7zR5X1VGdhoYr8JwIuSR8bhcN7x8FIgh8O5qiAiO4CfYWMQ97eJyHLZYxnAj8HLgRwO5wrhpUAOh8PhcDicOMEzVhwOh8PhcDhxggsrDofD4XA4nDjBhRWHw+FwOBxOnODCisPhcDgcDidOcGHF4XA4HA6HEye4sOJwOBwOh8OJE1xYcTgcDofD4cQJLqw4HA6Hw+Fw4gQXVhyV0NjBAAAADklEQVQOh8PhcDhx4v8BkDDi1ow5oiwAAAAASUVORK5CYII=\n", + "image/png": 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\n", 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" ] @@ -251,34 +261,70 @@ ], "source": [ "all_colours = {'P1':'k',\n", - " 'P2':'b',\n", - " 'P3':'r',\n", - " 'P4':'c',\n", - " 'P5':'m',\n", - " 'P6':'g'}\n", + " 'P2':'b',\n", + " 'P3':'r',\n", + " 'P4':'c',\n", + " 'P5':'m',\n", + " 'P6':'g',\n", + " 'P7': 'orange',\n", + " 'None': 'y'}\n", "colours = {}\n", "for patch in all_colours:\n", " if patch in patches:\n", " colours[patch] = all_colours[patch]\n", " \n", - "mwd_grid(org=150, title='Mollweide projection', projection='mollweide')\n", - "rainbow_text(0, -0.45*(np.pi/2), colours.keys(), colours.values(), size=18)\n", + "mwd_grid(org=150, title='CFIS-DR3 patches (2021)', projection='mollweide')\n", + "\n", + "leg_key = []\n", + "leg_val = []\n", "\n", "for tag in patches:\n", " (xx,yy) = patch_footprint[tag].xy()\n", + " if len(xx) > 0:\n", + " for (x,y) in zip(xx,yy):\n", + " (ra,dec) = patch_footprint[tag].corners_from_xy(x, y)\n", + " s = np.sin(np.radians(dec))\n", + " (xx,yy) = mwd_convert(np.array(ra), np.array(dec), org=150)\n", + " plt.plot(xx, yy, c=colours[tag])\n", + " leg_key.append(tag)\n", + " leg_val.append(all_colours[tag])\n", + "rainbow_text(0, -0.45*(np.pi/2), leg_key, leg_val, size=18)" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "for tag in ['P1', 'P7']:\n", + " (xx,yy) = patch_footprint[tag].xy()\n", " for (x,y) in zip(xx,yy):\n", " (ra,dec) = patch_footprint[tag].corners_from_xy(x, y)\n", " s = np.sin(np.radians(dec))\n", " (xx,yy) = mwd_convert(np.array(ra), np.array(dec), org=150)\n", - " plt.plot(xx, yy, c=colours[tag])" + " plt.plot(ra, dec, c=colours[tag])" ] }, { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [] } diff --git a/scripts/jupyter/plot_spectro_areas.ipynb b/scripts/jupyter/plot_spectro_areas.ipynb index 16cf32cdb..43e14f6c7 100644 --- a/scripts/jupyter/plot_spectro_areas.ipynb +++ b/scripts/jupyter/plot_spectro_areas.ipynb @@ -1352,7 +1352,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.8" + "version": "3.7.9" } }, "nbformat": 4, From ddaf48ba3c48b24645617811c3e4f89802302d81 Mon Sep 17 00:00:00 2001 From: Martin Kilbinger Date: Mon, 12 Jul 2021 13:20:55 +0200 Subject: [PATCH 4/8] config_exp_mccd: Use MCCD inv_opt config file, works for 'outliers' version --- example/cfis/config_exp_mccd.ini | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/example/cfis/config_exp_mccd.ini b/example/cfis/config_exp_mccd.ini index 75530b1ac..1d15551dc 100644 --- a/example/cfis/config_exp_mccd.ini +++ b/example/cfis/config_exp_mccd.ini @@ -144,7 +144,7 @@ SETOOLS_CONFIG_PATH = $SP_CONFIG/star_selection.setools [MCCD] # Path to MCCD config file -CONFIG_PATH = $SP_CONFIG/config_MCCD.ini +CONFIG_PATH = $SP_CONFIG/config_MCCD_inv_opt.ini VERBOSE = False From 1cf19cf4dd7068dfc5c82fa9d11159fd3826c8c5 Mon Sep 17 00:00:00 2001 From: Martin Kilbinger Date: Mon, 12 Jul 2021 13:24:16 +0200 Subject: [PATCH 5/8] CFIS footprint: Added constellations; job_sp: added -c flag for config path, including not on vos --- scripts/jupyter/CFIS_Footprint.ipynb | 76 ++++++++++++++++++++++++++-- scripts/sh/job_sp.bash | 17 +++++-- 2 files changed, 86 insertions(+), 7 deletions(-) diff --git a/scripts/jupyter/CFIS_Footprint.ipynb b/scripts/jupyter/CFIS_Footprint.ipynb index d9d13b4ad..0c1e067a5 100644 --- a/scripts/jupyter/CFIS_Footprint.ipynb +++ b/scripts/jupyter/CFIS_Footprint.ipynb @@ -13,7 +13,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 11, "metadata": { "ExecuteTime": { "end_time": "2019-10-08T10:47:15.755022Z", @@ -26,6 +26,7 @@ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import astropy.table\n", + "from astropy.coordinates import get_constellation, SkyCoord\n", "\n", "from shapepipe.utilities import cfis\n", "\n", @@ -34,7 +35,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -112,7 +113,7 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 16, "metadata": { "ExecuteTime": { "end_time": "2019-10-08T12:54:03.518299Z", @@ -139,6 +140,7 @@ "source": [ "patch_name = {}\n", "patch_footprint = {}\n", + "patch_const = {}\n", "n_tiles = 0\n", "for tag in patches:\n", " path = '{}/tiles_{}.txt'.format(tiles_dir, tag)\n", @@ -146,15 +148,20 @@ "\n", " RA = []\n", " Dec = []\n", + " CONST = []\n", " for ID in patch_name[tag]:\n", " nix, niy = cfis.get_tile_number(ID)\n", " ra, dec = cfis.get_tile_coord_from_nixy(nix, niy)\n", " RA.append(ra.deg)\n", " Dec.append(dec.deg)\n", + " const = get_constellation(SkyCoord(ra, dec))\n", + " CONST.append(const)\n", + " #print(const)\n", " RA = np.array(RA)\n", " Dec = np.array(Dec)\n", "\n", " patch_footprint[tag] = Footprint(RA, Dec)\n", + " patch_const[tag] = CONST\n", " n_tiles_patch = len(patch_name[tag])\n", " print(tag, ': ', n_tiles_patch, \"tiles\", end=', ')\n", " print(n_tiles_patch*0.25, \"square degrees\")\n", @@ -164,6 +171,67 @@ "print(n_tiles*0.25, \"square degrees\")" ] }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "P1\n", + " [('Lynx', 1793), ('Ursa Major', 685)]\n", + "P2\n", + " [('Andromeda', 636), ('Pegasus', 358)]\n", + "P3\n", + " [('Ursa Major', 486), ('Boötes', 326)]\n", + "P4\n", + " [('Ursa Major', 1441), ('Canes Venatici', 1239)]\n", + "P5\n", + " [('Hercules', 1749), ('Boötes', 738)]\n", + "P6\n", + " [('Draco', 1384), ('Hercules', 295)]\n", + "P7\n", + " [('Camelopardalis', 205), ('Ursa Major', 22)]\n", + "None\n", + " []\n" + ] + } + ], + "source": [ + "from collections import defaultdict\n", + "\n", + "def leaders(xs, top=4):\n", + " counts = defaultdict(int)\n", + " for x in xs:\n", + " counts[x] += 1\n", + " return sorted(counts.items(), reverse=True, key=lambda tup: tup[1])[:top]\n", + "\n", + "for tag in patches:\n", + " print(tag)\n", + " top_const = leaders(patch_const[tag], top=2)\n", + " print(' ', end='')\n", + " print(top_const)" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.6707818930041153 0.8598195697432338\n" + ] + } + ], + "source": [ + "print(326 / 486, 1239 / 1441)" + ] + }, { "cell_type": "code", "execution_count": 6, @@ -241,7 +309,7 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 8, "metadata": { "scrolled": true }, diff --git a/scripts/sh/job_sp.bash b/scripts/sh/job_sp.bash index 00bb78ff7..3e7061572 100755 --- a/scripts/sh/job_sp.bash +++ b/scripts/sh/job_sp.bash @@ -16,6 +16,7 @@ ## Default values do_env=0 job=255 +config_dir='vos:cfis/cosmostat/kilbinger/cfis' psf='psfex' retrieve='vos' nsh_step=3500 @@ -36,6 +37,8 @@ usage="Usage: $(basename "$0") [OPTIONS] TILE_ID_1 [TILE_ID_2 [...]] \t 32: shapes and morphology (offline)\n \t 64: paste catalogues (offline)\n \t 128: upload results (online)\n + -c, --config_dir DIR\n + \t config file directory, default='$config_dir'\n -p, --psf MODEL\n \tPSF model, one in ['psfex'|'mccd'], default='$psf'\n -r, --retrieve METHOD\n @@ -73,6 +76,10 @@ while [ $# -gt 0 ]; do job="$2" shift ;; + -c|--config_dir) + config_dir="$2" + shift + ;; -p|--psf) psf="$2" shift @@ -310,10 +317,14 @@ if [[ $do_job != 0 ]]; then echo $TILE >> $TILE_NUMBERS_PATH done - ### Download config files - command_sp "$VCP vos:cfis/cosmostat/kilbinger/cfis ." "Get shapepipe config files" + ### Retrieve config files + if [[ $config_file == "*vos:" ]]; then + command_sp "$VCP $config_dir ." "Retrieve shapepipe config files" + else + command_sp "ln -s $config_dir cfis" "Retrieve shapepipe config files" + fi - ### Get tiles + ### Retrieve tiles command_sp "shapepipe_run -c $SP_CONFIG/config_get_tiles_$retrieve.ini" "Run shapepipe (get tiles)" ### Find exposures From 508837697b199b96dfc630f1449d851c6c31ff16 Mon Sep 17 00:00:00 2001 From: Martin Kilbinger Date: Mon, 12 Jul 2021 13:25:07 +0200 Subject: [PATCH 6/8] Added MCCD config files --- example/cfis/config_MCCD_inv_opt.ini | 116 +++++++++++++++++++++++ example/cfis/config_MCCD_norm_optim.ini | 117 ++++++++++++++++++++++++ 2 files changed, 233 insertions(+) create mode 100644 example/cfis/config_MCCD_inv_opt.ini create mode 100644 example/cfis/config_MCCD_norm_optim.ini diff --git a/example/cfis/config_MCCD_inv_opt.ini b/example/cfis/config_MCCD_inv_opt.ini new file mode 100644 index 000000000..c9fa539ff --- /dev/null +++ b/example/cfis/config_MCCD_inv_opt.ini @@ -0,0 +1,116 @@ +# Configuration file for the MCCD method + +[INPUTS] +INPUT_DIR = . +PREPROCESSED_OUTPUT_DIR = ./output +OUTPUT_DIR = ./output +INPUT_REGEX_FILE_PATTERN = star_split_ratio_80-*-*.fits +INPUT_SEPARATOR = - +MIN_N_STARS = 20 +OUTLIER_STD_MAX = 100. +USE_SNR_WEIGHTS = False + +[INSTANCE] +N_COMP_LOC = 2 +D_COMP_GLOB = 6 +KSIG_LOC = 0.0000 +KSIG_GLOB = 0.000 +FILTER_PATH = None + +[FIT] +LOC_MODEL = poly +PSF_SIZE = 6.2 +PSF_SIZE_TYPE = R2 +N_EIGENVECTS = 5 +N_ITER_RCA = 1 +N_ITER_GLOB = 1 +N_ITER_LOC = 1 +NB_SUBITER_S_LOC = 300 +NB_SUBITER_A_LOC = 400 +NB_SUBITER_S_GLOB = 100 +NB_SUBITER_A_GLOB = 200 + +[VALIDATION] +VAL_DATA_INPUT_DIR = . +VAL_PREPROCESSED_OUTPUT_DIR = ./output +VAL_MODEL_INPUT_DIR = ./output +VAL_OUTPUT_DIR = ./output +VAL_REGEX_FILE_PATTERN = star_split_ratio_20-*-*.fits +VAL_SEPARATOR = - +APPLY_DEGRADATION = True +MCCD_DEBUG = False +GLOBAL_POL_INTERP = False + +# Parameter description: +# +# +# [INPUTS] +# INPUT_DIR : (Required) Must be a valid directory containing the input +# MCCD files. +# PREPROCESSED_OUTPUT_DIR : (Required) Must be a valid directory to write the +# preprocessed input files. +# OUTPUT_DIR : (Required) Must be a valid directory to write the output files. +# The constructed models will be saved. +# INPUT_REGEX_FILE_PATTERN : File pattern of the input files to use. It should +# follow regex (regular expression) standards. +# INPUT_SEPARATOR : Separator of the different fields in the filename, +# ie sexcat[SEP]catalog_id[SEP]CCD_id.fits +# MIN_N_STARS : Minimum number of stars to keep a CCD for the training. +# OUTLIER_STD_MAX : Maximum standard deviation used for the outlier rejection. +# Should not be too low as a hihg quantity of low quality +# stars will be rejected. ie 9 is a conservative rejection. +# USE_SNR_WEIGHTS : Boolean to determine if the SNR weighting strategy will +# be used. +# For now, it needs the SNR estimations from SExtractor. +# +# +# [INSTANCE] +# N_COMP_LOC : Number of components of the Local model. If LOC_MODEL is poly, +# will be the max degree D of the polynomial. +# D_COMP_GLOB : Max degree of the global polynomial model. +# KSIG_LOC : Denoising parameter of the local model. +# ie 1 is a normal denoising, 3 is a hard denoising. +# KSIG_GLOB : Denoising parameter of the global model. +# ie 1 is a normal denoising, 3 is a hard denoising. +# FILTER_PATH : Path for predefined filters. +# +# +# [FIT] +# LOC_MODEL : Defines the type of local model to use, it can be: 'rca', +# 'poly' or 'hybrid'. +# When the poly model is used, N_COMP_LOC should be used +# as the D_LOC (max degree of the poly model) +# PSF_SIZE : First guess of the PSF size. A size estimation is done anyways. +# PSF_SIZE_TYPE : Type of the size information. It can be: fwhm, R2, sigma +# N_EIGENVECTS : Number of eigenvectors to keep for the graph constraint +# construction. +# N_ITER_RCA : Number of global epochs in the algorithm. Alternation between +# global and local estimations. +# N_ITER_GLOB : Number of epochs for each global optimization. Alternations +# between A_GLOB and S_GLOB. +# N_ITER_LOC : Number of epochs for each local optimization. Alternations +# between the different A_LOC and S_LOC. +# NB_SUBITER_S_LOC : Iterations for the optimization algorithm over S_LOC. +# NB_SUBITER_A_LOC : Iterations for the optimization algorithm over A_LOC. +# NB_SUBITER_S_GLOB : Iterations for the optimization algorithm over S_GLOB. +# NB_SUBITER_A_GLOB : Iterations for the optimization algorithm over A_GLOB. +# +# +# [VALIDATION] +# VAL_DATA_INPUT_DIR : (Required) Must be a valid directory which contains the +# validation input data (test dataset). +# VAL_PREPROCESSED_OUTPUT_DIR : (Required) Must be a valid directory where the +# preprocessed input file will be saved. +# VAL_MODEL_INPUT_DIR : (Required) Must be a valid directory which contains the +# saved trained models. +# VAL_OUTPUT_DIR : (Required) Must be a valid directory where to save the +# validation outputs, test PSFs and interpolated PSFs. +# VAL_REGEX_FILE_PATTERN : Same as INPUT_REGEX_FILE_PATTERN but for validation. +# VAL_SEPARATOR : Same as INPUT_SEPARATOR but for validation. +# APPLY_DEGRADATION : Whether the PSF models should be degraded +# (sampling/shifts/flux) to match stars; use True if you +# plan on making pixel-based comparisons (residuals etc.). +# MCCD_DEBUG : Debug mode. Returns the local and global contributions. +# GLOBAL_POL_INTERP : Uses polynomial interpolation for the global model +# instead of RBF kernel interpolation. +# diff --git a/example/cfis/config_MCCD_norm_optim.ini b/example/cfis/config_MCCD_norm_optim.ini new file mode 100644 index 000000000..bcd30b6a3 --- /dev/null +++ b/example/cfis/config_MCCD_norm_optim.ini @@ -0,0 +1,117 @@ +# Configuration file for the MCCD method +# norm_optim + +[INPUTS] +INPUT_DIR = . +PREPROCESSED_OUTPUT_DIR = ./output +OUTPUT_DIR = ./output +INPUT_REGEX_FILE_PATTERN = star_split_ratio_80-*-*.fits +INPUT_SEPARATOR = - +MIN_N_STARS = 20 +OUTLIER_STD_MAX = 100. +USE_SNR_WEIGHTS = False + +[INSTANCE] +N_COMP_LOC = 4 +D_COMP_GLOB = 6 +KSIG_LOC = 0.0000 +KSIG_GLOB = 0.000 +FILTER_PATH = None + +[FIT] +LOC_MODEL = hybrid +PSF_SIZE = 6.2 +PSF_SIZE_TYPE = R2 +N_EIGENVECTS = 5 +N_ITER_RCA = 1 +N_ITER_GLOB = 2 +N_ITER_LOC = 2 +NB_SUBITER_S_LOC = 300 +NB_SUBITER_A_LOC = 400 +NB_SUBITER_S_GLOB = 100 +NB_SUBITER_A_GLOB = 200 + +[VALIDATION] +VAL_DATA_INPUT_DIR = . +VAL_PREPROCESSED_OUTPUT_DIR = ./output +VAL_MODEL_INPUT_DIR = ./output +VAL_OUTPUT_DIR = ./output +VAL_REGEX_FILE_PATTERN = star_split_ratio_20-*-*.fits +VAL_SEPARATOR = - +APPLY_DEGRADATION = True +MCCD_DEBUG = False +GLOBAL_POL_INTERP = False + +# Parameter description: +# +# +# [INPUTS] +# INPUT_DIR : (Required) Must be a valid directory containing the input +# MCCD files. +# PREPROCESSED_OUTPUT_DIR : (Required) Must be a valid directory to write the +# preprocessed input files. +# OUTPUT_DIR : (Required) Must be a valid directory to write the output files. +# The constructed models will be saved. +# INPUT_REGEX_FILE_PATTERN : File pattern of the input files to use. It should +# follow regex (regular expression) standards. +# INPUT_SEPARATOR : Separator of the different fields in the filename, +# ie sexcat[SEP]catalog_id[SEP]CCD_id.fits +# MIN_N_STARS : Minimum number of stars to keep a CCD for the training. +# OUTLIER_STD_MAX : Maximum standard deviation used for the outlier rejection. +# Should not be too low as a hihg quantity of low quality +# stars will be rejected. ie 9 is a conservative rejection. +# USE_SNR_WEIGHTS : Boolean to determine if the SNR weighting strategy will +# be used. +# For now, it needs the SNR estimations from SExtractor. +# +# +# [INSTANCE] +# N_COMP_LOC : Number of components of the Local model. If LOC_MODEL is poly, +# will be the max degree D of the polynomial. +# D_COMP_GLOB : Max degree of the global polynomial model. +# KSIG_LOC : Denoising parameter of the local model. +# ie 1 is a normal denoising, 3 is a hard denoising. +# KSIG_GLOB : Denoising parameter of the global model. +# ie 1 is a normal denoising, 3 is a hard denoising. +# FILTER_PATH : Path for predefined filters. +# +# +# [FIT] +# LOC_MODEL : Defines the type of local model to use, it can be: 'rca', +# 'poly' or 'hybrid'. +# When the poly model is used, N_COMP_LOC should be used +# as the D_LOC (max degree of the poly model) +# PSF_SIZE : First guess of the PSF size. A size estimation is done anyways. +# PSF_SIZE_TYPE : Type of the size information. It can be: fwhm, R2, sigma +# N_EIGENVECTS : Number of eigenvectors to keep for the graph constraint +# construction. +# N_ITER_RCA : Number of global epochs in the algorithm. Alternation between +# global and local estimations. +# N_ITER_GLOB : Number of epochs for each global optimization. Alternations +# between A_GLOB and S_GLOB. +# N_ITER_LOC : Number of epochs for each local optimization. Alternations +# between the different A_LOC and S_LOC. +# NB_SUBITER_S_LOC : Iterations for the optimization algorithm over S_LOC. +# NB_SUBITER_A_LOC : Iterations for the optimization algorithm over A_LOC. +# NB_SUBITER_S_GLOB : Iterations for the optimization algorithm over S_GLOB. +# NB_SUBITER_A_GLOB : Iterations for the optimization algorithm over A_GLOB. +# +# +# [VALIDATION] +# VAL_DATA_INPUT_DIR : (Required) Must be a valid directory which contains the +# validation input data (test dataset). +# VAL_PREPROCESSED_OUTPUT_DIR : (Required) Must be a valid directory where the +# preprocessed input file will be saved. +# VAL_MODEL_INPUT_DIR : (Required) Must be a valid directory which contains the +# saved trained models. +# VAL_OUTPUT_DIR : (Required) Must be a valid directory where to save the +# validation outputs, test PSFs and interpolated PSFs. +# VAL_REGEX_FILE_PATTERN : Same as INPUT_REGEX_FILE_PATTERN but for validation. +# VAL_SEPARATOR : Same as INPUT_SEPARATOR but for validation. +# APPLY_DEGRADATION : Whether the PSF models should be degraded +# (sampling/shifts/flux) to match stars; use True if you +# plan on making pixel-based comparisons (residuals etc.). +# MCCD_DEBUG : Debug mode. Returns the local and global contributions. +# GLOBAL_POL_INTERP : Uses polynomial interpolation for the global model +# instead of RBF kernel interpolation. +# From 446386f956cdf110a10284e9c5b5598b94f38bc2 Mon Sep 17 00:00:00 2001 From: Martin Kilbinger Date: Mon, 12 Jul 2021 15:15:43 +0200 Subject: [PATCH 7/8] 202007 W3 complete --- aux/CFIS/tiles_202007/tiles_W3.txt | 2 + aux/CFIS/tiles_202007/tiles_W3_m2.txt | 212 ++++++++++++++++++++++++++ 2 files changed, 214 insertions(+) create mode 100644 aux/CFIS/tiles_202007/tiles_W3_m2.txt diff --git a/aux/CFIS/tiles_202007/tiles_W3.txt b/aux/CFIS/tiles_202007/tiles_W3.txt index a3fd290f8..d659e3b4a 100644 --- a/aux/CFIS/tiles_202007/tiles_W3.txt +++ b/aux/CFIS/tiles_202007/tiles_W3.txt @@ -210,3 +210,5 @@ 251.286 245.288 237.292 +253.286 +277.282 diff --git a/aux/CFIS/tiles_202007/tiles_W3_m2.txt b/aux/CFIS/tiles_202007/tiles_W3_m2.txt new file mode 100644 index 000000000..a3fd290f8 --- /dev/null +++ b/aux/CFIS/tiles_202007/tiles_W3_m2.txt @@ -0,0 +1,212 @@ +245.292 +230.293 +240.292 +247.291 +257.286 +245.290 +270.282 +271.283 +261.283 +264.286 +243.291 +249.289 +260.285 +253.289 +267.282 +251.289 +235.293 +268.284 +238.292 +256.288 +232.293 +253.287 +265.285 +262.283 +251.288 +242.293 +265.286 +266.283 +260.286 +258.287 +250.290 +228.294 +248.289 +266.286 +262.285 +246.288 +247.288 +239.294 +239.292 +275.282 +234.292 +266.282 +239.291 +261.284 +248.288 +267.285 +242.291 +241.290 +255.289 +267.284 +257.287 +255.286 +235.292 +272.284 +248.291 +256.287 +252.287 +267.283 +234.294 +252.286 +254.285 +244.289 +259.286 +242.289 +244.291 +242.292 +236.292 +262.286 +240.293 +251.287 +246.291 +241.293 +238.293 +247.290 +245.291 +247.289 +278.282 +236.294 +264.284 +237.294 +254.287 +231.293 +257.285 +249.291 +235.295 +271.284 +233.293 +256.285 +269.282 +234.295 +235.294 +246.292 +228.295 +258.285 +260.284 +262.282 +230.295 +240.294 +256.289 +264.283 +255.285 +261.287 +252.288 +268.283 +259.283 +258.288 +268.282 +234.293 +272.283 +237.291 +260.287 +224.295 +248.287 +231.295 +270.283 +259.285 +259.288 +236.295 +226.295 +245.289 +237.295 +243.289 +230.294 +261.286 +269.285 +236.291 +238.294 +258.284 +227.295 +263.282 +265.282 +240.291 +271.282 +241.292 +273.283 +232.294 +263.285 +259.284 +254.288 +260.283 +233.294 +251.290 +249.290 +243.293 +263.284 +231.294 +250.288 +240.290 +239.290 +274.282 +238.291 +247.292 +243.290 +254.289 +262.284 +244.292 +248.290 +252.290 +263.286 +232.295 +225.295 +243.292 +259.287 +261.285 +257.288 +237.293 +276.282 +264.282 +255.287 +265.284 +263.283 +244.290 +255.288 +246.290 +272.282 +229.294 +275.283 +266.284 +266.285 +250.287 +264.285 +258.286 +265.283 +227.294 +233.295 +269.284 +274.283 +233.292 +242.290 +268.285 +254.286 +252.289 +250.289 +273.282 +250.291 +249.287 +229.295 +262.287 +256.286 +270.284 +269.283 +257.284 +236.293 +253.288 +239.293 +241.291 +246.289 +249.288 +253.290 +251.286 +245.288 +237.292 From f5d3407fcdfdd89b18af506c2988153505dbe345 Mon Sep 17 00:00:00 2001 From: Martin Kilbinger Date: Fri, 30 Jul 2021 13:23:52 +0200 Subject: [PATCH 8/8] changes according to PR review --- scripts/python/create_sample_results.py | 60 ++++++++++++++++++------- scripts/python/merge_star_cat_mccd.py | 20 ++++++--- 2 files changed, 59 insertions(+), 21 deletions(-) diff --git a/scripts/python/create_sample_results.py b/scripts/python/create_sample_results.py index 122937be3..8fec90a53 100755 --- a/scripts/python/create_sample_results.py +++ b/scripts/python/create_sample_results.py @@ -52,7 +52,7 @@ def parse_options(p_def): ------- options: tuple Command line options - args: string + args: str Command line string """ @@ -60,14 +60,35 @@ def parse_options(p_def): parser = OptionParser(usage=usage) # I/O - parser.add_option('', '--input_IDs', dest='input_IDs', type='string', - help='input tile ID file specifying sample') - parser.add_option('-i', '--input_dir', dest='input_dir', type='string', - help='input directory name') - parser.add_option('-o', '--output_dir', dest='output_dir', type='string', - help='output directory name') + parser.add_option( + '', + '--input_IDs', + dest='input_IDs', + type='string', + help='input tile ID file specifying sample' + ) + parser.add_option( + '-i', + '--input_dir', + dest='input_dir', + type='string', + help='input directory name' + ) + parser.add_option( + '-o', + '--output_dir', + dest='output_dir', + type='string', + help='output directory name' + ) - parser.add_option('-v', '--verbose', dest='verbose', action='store_true', help='verbose output') + parser.add_option( + '-v', + '--verbose', + dest='verbose', + action='store_true', + help='verbose output' + ) options, args = parser.parse_args() @@ -139,14 +160,14 @@ def read_ID_list(input_ID_path, verbose=False): Parameters ---------- - input_ID_path: string + input_ID_path: str input ID file path verbose: bool, optional, default=False verbose output if True Returns ------- - input_IDs: list of strings + input_IDs: list of str input ID list """ @@ -169,13 +190,13 @@ def create_links(input_dir, output_dir, input_IDs, result_base_names, verbose=Fa Parameters ---------- - input_dir: string + input_dir: str input directory - output_dir: string + output_dir: str output directory - input_IDs: list of strings + input_IDs: list of str tile ID list - results_base_names: list of strings + results_base_names: list of str file base names verbose: bool, optional, default=False verbose output if True @@ -244,8 +265,15 @@ def main(argv=None): input_IDs = read_ID_list(param.input_IDs, verbose=param.verbose) - result_base_names = ['psfex_interp_exp', 'setools_mask', 'setools_stat', 'setools_plot', - 'pipeline_flag', 'final_cat', 'logs'] + result_base_names = [ + 'psfex_interp_exp', + 'setools_mask', + 'setools_stat', + 'setools_plot', + 'pipeline_flag', + 'final_cat', + 'logs' + ] if os.path.isdir(param.output_dir): if param.verbose: diff --git a/scripts/python/merge_star_cat_mccd.py b/scripts/python/merge_star_cat_mccd.py index 03179cfb9..871c6c426 100755 --- a/scripts/python/merge_star_cat_mccd.py +++ b/scripts/python/merge_star_cat_mccd.py @@ -215,11 +215,21 @@ def create_full_cat(input_dir, input_file_pattern, output_path, verbose=False): # Collect columns # convert back to sigma for consistency - data = {'X': x, 'Y': y, 'RA': ra, 'DEC': dec, - 'E1_PSF_HSM': g1_psf, 'E2_PSF_HSM': g2_psf, 'SIGMA_PSF_HSM': np.sqrt(size_psf), - 'E1_STAR_HSM': g1, 'E2_STAR_HSM': g2, 'SIGMA_STAR_HSM': np.sqrt(size), - 'FLAG_PSF_HSM': flag_psf, 'FLAG_STAR_HSM': flag_star, - 'CCD_NB': ccd_nb} + data = { + 'X': x, + 'Y': y, + 'RA': ra, + 'DEC': dec, + 'E1_PSF_HSM': g1_psf, + 'E2_PSF_HSM': g2_psf, + 'SIGMA_PSF_HSM': np.sqrt(size_psf), + 'E1_STAR_HSM': g1, + 'E2_STAR_HSM': g2, + 'SIGMA_STAR_HSM': np.sqrt(size), + 'FLAG_PSF_HSM': flag_psf, + 'FLAG_STAR_HSM': flag_star, + 'CCD_NB': ccd_nb + } # Write file if verbose: