From c0b2dfe866a503c079f62f5aff03f80855593c02 Mon Sep 17 00:00:00 2001 From: Robert Stein Date: Mon, 15 May 2023 02:04:46 -0700 Subject: [PATCH] Try ipac depot for coverage (#294) * Depot * New gitignore --- .github/workflows/continous_integration.yml | 2 + .gitignore | 108 +- notebooks/run_skymap_scan.ipynb | 74459 +++++++++++++++++- nuztf/base_scanner.py | 166 +- nuztf/cat_match.py | 58 +- nuztf/credentials.py | 13 + nuztf/flatpix.py | 34 + nuztf/observations_depot.py | 179 + nuztf/skymap.py | 58 +- nuztf/skymap_scanner.py | 4 + tests/test_neutrino_scanner.py | 3 +- tests/test_skymap_scanner.py | 2 +- 12 files changed, 74330 insertions(+), 756 deletions(-) create mode 100644 nuztf/observations_depot.py diff --git a/.github/workflows/continous_integration.yml b/.github/workflows/continous_integration.yml index d9683234..d574250f 100644 --- a/.github/workflows/continous_integration.yml +++ b/.github/workflows/continous_integration.yml @@ -59,6 +59,8 @@ jobs: SKYVISION_PASSWORD: ${{ secrets.skyvision_password }} FRITZ_TOKEN: ${{ secrets.fritz_token }} DESY_CLOUD_TOKEN: ${{ secrets.desy_cloud_token }} + DEPOT_USER: ${{ secrets.depot_user }} + DEPOT_PASSWORD: ${{ secrets.depot_password }} ZTFDATA: ./ run: | poetry run coverage run -m pytest -v diff --git a/.gitignore b/.gitignore index 55cb531a..6d419e1b 100644 --- a/.gitignore +++ b/.gitignore @@ -1,16 +1,96 @@ -.AMPEL_user.txt -.AMPEL_pass.txt -.EXTCAT_user.txt -.EXTCAT_pass.txt -*.cred -.slack_access_token.txt -.slack_bot_access_token.txt -summary/* -LIGO_candidates/* -LIGO_skymaps/* -LIGO_dat/* -Neutrino_candidates/* +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*$py.class + +# C extensions +*.so + +# Distribution / packaging +.Python +env/ +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +*.egg-info/ +.installed.cfg +*.egg +*.idea + +# PyInstaller +# Usually these files are written by a python script from a template +# before PyInstaller builds the exe, so as to inject date/other infos into it. +*.manifest +*.spec + +# Installer logs +pip-log.txt +pip-delete-this-directory.txt + +# Unit test / coverage reports +htmlcov/ +.tox/ +.coverage +.coverage.* +.cache +nosetests.xml +coverage.xml +*,cover +.hypothesis/ + +# Translations +*.mo +*.pot + +# Django stuff: +*.log +local_settings.py + +# Flask stuff: +instance/ +.webassets-cache + +# Scrapy stuff: +.scrapy + +# Sphinx documentation +docs/_build/ + +# PyBuilder +target/ + +# IPython Notebook +.ipynb_checkpoints + +# pyenv +.python-version + +# celery beat schedule file +celerybeat-schedule + +# dotenv +.env + +# virtualenv +venv/ +ENV/ + +# Spyder project settings +.spyderproject + +# Rope project settings +.ropeproject + +.DS_Store + __pycache__/* -LIGO_cache/* env/* -.python-version +nuztf/data/ diff --git a/notebooks/run_skymap_scan.ipynb b/notebooks/run_skymap_scan.ipynb index ed815625..9f79f517 100644 --- a/notebooks/run_skymap_scan.ipynb +++ b/notebooks/run_skymap_scan.ipynb @@ -4,23 +4,7 @@ "cell_type": "code", "execution_count": 1, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n", - "WARNING: version mismatch between CFITSIO header (v40100) and linked library (v40200).\n", - "\n", - "\n", - "WARNING: version mismatch between CFITSIO header (v40100) and linked library (v40200).\n", - "\n", - "\n", - "WARNING: version mismatch between CFITSIO header (v40100) and linked library (v40200).\n", - "\n" - ] - } - ], + "outputs": [], "source": [ "import logging\n", "from nuztf.skymap_scanner import SkymapScanner" @@ -66,34 +50,99 @@ "\n", "# skymap_file = \"IGWN-GWTC3p0-v1-GW200210_092254_PEDataRelease_cosmo_reweight_C01_Mixed.fits\"\n", "# skymap_file=\"GRB220617A_IPN_map_hpx2.fit\"\n", - "skymap_file=\"glg_healpix_all_bn220617772.fit\"" + "skymap_file=\"https://heasarc.gsfc.nasa.gov/FTP/fermi/data/gbm/triggers/2023/bn230511945/quicklook/glg_healpix_all_bn230511945.fit\"" ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "INFO:nuztf.skymap_scanner:Reading file: /Users/simeon/ZTFDATA/skymaps/glg_healpix_all_bn220617772.fit\n", - "INFO:nuztf.skymap_scanner:Flattening skymap\n", - "INFO:nuztf.skymap_scanner:Summed probability is 100.0%\n", - "INFO:nuztf.skymap_scanner:Event time: 2022-06-17T18:29:00.000\n", - "INFO:nuztf.skymap_scanner:Reading map\n", - "INFO:nuztf.skymap_scanner:Threshold found! \n", - " To reach 95.00% of probability, pixels with probability greater than 6.560151090268943e-05 are included.\n", - "INFO:nuztf.skymap_scanner:Threshold found! \n", - " To reach 95.00% of probability, pixels with probability greater than 6.560151090268943e-05 are included.\n", + "INFO:nuztf.skymap:Downloading from: https://heasarc.gsfc.nasa.gov/FTP/fermi/data/gbm/triggers/2023/bn230511945/quicklook/glg_healpix_all_bn230511945.fit\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 79% [.......................................... ] 1261568 / 1586880" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:nuztf.skymap:Reading file: /Users/robertstein/Data/ZTF/skymaps/glg_healpix_all_bn230511945.fit\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\r", + " 80% [........................................... ] 1269760 / 1586880\r", + " 80% [........................................... ] 1277952 / 1586880\r", + " 81% [........................................... ] 1286144 / 1586880\r", + " 81% [............................................ ] 1294336 / 1586880\r", + " 82% [............................................ ] 1302528 / 1586880\r", + " 82% [............................................ ] 1310720 / 1586880\r", + " 83% [............................................ ] 1318912 / 1586880\r", + " 83% [............................................. ] 1327104 / 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To reach 95.00% of probability, pixels with probability greater than 1.0329590480198429e-05 are included.\n", "INFO:nuztf.skymap_scanner:Checking which pixels are within the contour:\n", - "100%|███████████████████████████████████████████| 196608/196608 [00:00<00:00, 3658372.39it/s]\n", - "INFO:nuztf.skymap_scanner:Total pixel area: 422.7941737751253 degrees\n", + "100%|██████████████████████████████| 196608/196608 [00:00<00:00, 2388076.10it/s]\n", + "INFO:nuztf.skymap_scanner:Total pixel area: 2616.0782921232067 degrees\n", "INFO:nuztf.base_scanner:AMPEL run config:\n", "INFO:nuztf.base_scanner:{'min_ndet': 1, 'min_tspan': -1, 'max_tspan': 365, 'min_rb': 0.3, 'max_fwhm': 5.5, 'max_elong': 1.4, 'max_magdiff': 1.0, 'max_nbad': 2, 'min_sso_dist': 20, 'min_gal_lat': 0.0, 'gaia_rs': 10.0, 'gaia_pm_signif': 3, 'gaia_plx_signif': 3, 'gaia_veto_gmag_min': 9, 'gaia_veto_gmag_max': 20, 'gaia_excessnoise_sig_max': 999, 'ps1_sgveto_rad': 1.0, 'ps1_sgveto_th': 0.8, 'ps1_confusion_rad': 3.0, 'ps1_confusion_sg_tol': 0.1}\n", "\n", - "2022-11-30 12:49:25 DecentFilter:72 INFO\n", + "2023-05-12 00:02:19 DecentFilter:72 INFO\n", " Using min_ndet=1\n", " Using min_tspan=-1.0\n", " Using max_tspan=365.0\n", @@ -117,10 +166,10 @@ " Using gaia_veto_gmag_min=9.0\n", " Using gaia_veto_gmag_max=20.0\n", " Using gaia_excessnoise_sig_max=999.0\n", - "100%|██████████████████████████████████████████████████| 532/532 [00:00<00:00, 158568.06it/s]\n", + "100%|███████████████████████████████████| 3201/3201 [00:00<00:00, 217164.32it/s]\n", "WARNING: TimeDeltaMissingUnitWarning: Numerical value without unit or explicit format passed to TimeDelta, assuming days [astropy.time.core]\n", "WARNING:astroquery:TimeDeltaMissingUnitWarning: Numerical value without unit or explicit format passed to TimeDelta, assuming days\n", - "INFO:nuztf.base_scanner:Time-range is 2022-06-17T18:29:00.000 -- 2022-06-20T18:29:00.000\n" + "INFO:nuztf.base_scanner:Time-range is 2023-05-11T22:38:05.000 -- 2023-05-14T22:38:05.000\n" ] } ], @@ -141,9 +190,9 @@ "outputs": [ { "data": { - "image/png": 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\n", 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" + "
" ] }, "execution_count": 6, @@ -152,14 +201,12 @@ }, { "data": { - "image/png": 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\n", 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", 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" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -179,1235 +226,74127 @@ "output_type": "stream", "text": [ "INFO:nuztf.base_scanner:Commencing skymap scan\n", + "INFO:nuztf.base_scanner:Total chunks: 33\n", "INFO:nuztf.base_scanner:Done.\n", - "INFO:nuztf.base_scanner:Added 1531 alerts found between 2022-06-17T18:29:00.000 and 2022-06-20T18:29:00.000\n", - "INFO:nuztf.base_scanner:This took 8.0 s in total\n", + "INFO:nuztf.base_scanner:Added 65477 alerts found between 2023-05-11T22:38:05.000 and 2023-05-14T22:38:05.000\n", + "INFO:nuztf.base_scanner:This took 421.8 s in total\n", "INFO:nuztf.base_scanner:Commencing first stage filtering.\n", - " 0%| | 0/1531 [00:00\n", + "WARNING:nuztf.base_scanner:Bad API call for source ZTF23aajbqzm: \n", + "WARNING:nuztf.base_scanner:Bad API call for source ZTF23aajcghb: \n", + "WARNING:nuztf.base_scanner:Bad API call for source ZTF23aajblab: \n", + "WARNING:nuztf.base_scanner:Bad API call for source ZTF23aajbpws: \n", + "WARNING:nuztf.base_scanner:Bad API call for source ZTF23aajbwjx: \n", + "WARNING:nuztf.base_scanner:Bad API call for source ZTF23aajcbqb: \n", + "WARNING:nuztf.base_scanner:Bad API call for source ZTF23aajbsmf: \n", + "WARNING:nuztf.base_scanner:Bad API call for source ZTF23aajbnek: \n", + "WARNING:nuztf.base_scanner:Bad API call for source ZTF23aajbiue: \n", + "WARNING:nuztf.base_scanner:Bad API call for source ZTF23aajbgoq: \n", + "WARNING:nuztf.base_scanner:Bad API call for source ZTF23aajciyd: \n", + "WARNING:nuztf.base_scanner:Bad API call for source ZTF23aajcbqc: \n", + "WARNING:nuztf.base_scanner:Bad API call for source ZTF23aajbgow: \n", + "WARNING:nuztf.base_scanner:Bad API call for source ZTF23aajbytt: \n", + "WARNING:nuztf.base_scanner:Bad API call for source ZTF23aajbpxj: \n", + "WARNING:nuztf.base_scanner:Bad API call for source ZTF23aajbwdo: \n", + "WARNING:nuztf.base_scanner:Bad API call for source ZTF23aajbkan: \n", + "WARNING:nuztf.base_scanner:Bad API call for source ZTF23aajbrmx: \n", + "WARNING:nuztf.base_scanner:Bad API call for source ZTF23aajchku: \n", + "WARNING:nuztf.base_scanner:Bad API call for source ZTF23aajbxnf: \n", + "WARNING:nuztf.base_scanner:Bad API call for source ZTF23aajbifd: \n", + "WARNING:nuztf.base_scanner:Bad API call for source ZTF23aajbvyd: \n", + "INFO:nuztf.base_scanner:Saved 29 to fritz group 1563\n" ] } ], "source": [ - "scanner.export_cache_to_fritz()" + "scanner.export_cache_to_fritz() " ] }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "WARNING: UnknownElementWarning: None:27:6: UnknownElementWarning: Unknown element queryType [pyvo.utils.xml.elements]\n", - "WARNING:astroquery:UnknownElementWarning: None:27:6: UnknownElementWarning: Unknown element queryType\n", - "WARNING: UnknownElementWarning: None:28:6: UnknownElementWarning: Unknown element resultType [pyvo.utils.xml.elements]\n", - "WARNING:astroquery:UnknownElementWarning: None:28:6: UnknownElementWarning: Unknown element resultType\n", - "WARNING: UnknownElementWarning: None:36:6: UnknownElementWarning: Unknown element queryType [pyvo.utils.xml.elements]\n", - "WARNING:astroquery:UnknownElementWarning: None:36:6: UnknownElementWarning: Unknown element queryType\n", - "WARNING: UnknownElementWarning: None:37:6: UnknownElementWarning: Unknown element resultType [pyvo.utils.xml.elements]\n", - "WARNING:astroquery:UnknownElementWarning: None:37:6: UnknownElementWarning: Unknown element resultType\n" + "INFO:nuztf.observations_depot:Getting observation logs from IPAC depot.\n", + "100%|█████████████████████████████████████████████| 2/2 [00:01<00:00, 1.76it/s]\n", + "100%|█████████████████████████████████████████████| 2/2 [00:00<00:00, 50.47it/s]\n", + "INFO:nuztf.base_scanner:Found 4335 successful observations in the depot, corresponding to 90.33% of the total.\n", + "INFO:nuztf.base_scanner:Unpacking observations\n", + "INFO:nuztf.base_scanner:Loading from /Users/robertstein/Code/nuztf/nuztf/data/ztf_fields_nested_ipix_nside=128.pickle\n", + "100%|██████████████████████████████████████████| 75/75 [00:00<00:00, 248.44it/s]\n", + "100%|█████████████████████████████████| 12468/12468 [00:00<00:00, 344306.06it/s]\n", + "INFO:nuztf.base_scanner:All observations:\n", + "INFO:nuztf.base_scanner:\n", + " obsjd exposure_time band\n", + "0 2.460077e+06 179 g\n", + "1 2.460077e+06 179 g\n", + "2 2.460077e+06 179 g\n", + "3 2.460077e+06 179 g\n", + "4 2.460077e+06 179 g\n", + "... ... ... ...\n", + "2235 2.460077e+06 179 g\n", + "2236 2.460077e+06 179 g\n", + "2237 2.460077e+06 179 g\n", + "2238 2.460077e+06 179 g\n", + "2239 2.460077e+06 179 g\n", + "\n", + "[2240 rows x 3 columns]\n", + "INFO:nuztf.base_scanner:Observations started at 2023-05-12T04:02:10.000\n", + "INFO:nuztf.base_scanner:2583 pixels were covered, covering approximately 5.4e+02 sq deg.\n", + "INFO:nuztf.base_scanner:1672 pixels were covered at least twice (b>10), covering approximately 3.5e+02 sq deg.\n", + "INFO:nuztf.base_scanner:0 pixels were covered at low galactic latitude, covering approximately 0 sq deg.\n", + "INFO:nuztf.base_scanner:In total, 44.10 % of the contour was observed at least once.\n", + "This estimate includes 0.00 % of the contour at a galactic latitude <10 deg.\n", + "In total, 28.02 % of the contour was observed at least twice. \n", + "In total, 28.02 % of the contour was observed at least twice, and excluding low galactic latitudes.\n", + "These estimates account for chip gaps.\n" ] }, { - "ename": "IndexError", - "evalue": "index -1 is out of bounds for axis 0 with size 0", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mIndexError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m/var/folders/hd/1411jr114w9cvmmnw9mx0xvr0000gn/T/ipykernel_9398/2494418457.py\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mscanner\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot_coverage\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;32m~/Code/nuztf/nuztf/skymap_scanner.py\u001b[0m in \u001b[0;36mplot_coverage\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 742\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mplot_coverage\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 743\u001b[0m \u001b[0;34m\"\"\"Plot ZTF coverage of skymap region\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 744\u001b[0;31m fig, message = self.plot_overlap_with_observations(\n\u001b[0m\u001b[1;32m 745\u001b[0m \u001b[0mfirst_det_window_days\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_days\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 746\u001b[0m )\n", - "\u001b[0;32m~/Code/nuztf/nuztf/base_scanner.py\u001b[0m in \u001b[0;36mplot_overlap_with_observations\u001b[0;34m(self, fields, pid, first_det_window_days, min_sep)\u001b[0m\n\u001b[1;32m 863\u001b[0m \u001b[0msingle_no_plane_pixels\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 864\u001b[0m \u001b[0moverlapping_fields\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 865\u001b[0;31m \u001b[0;34m)\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcalculate_overlap_with_observations\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 866\u001b[0m \u001b[0mfields\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfields\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 867\u001b[0m \u001b[0mpid\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpid\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/Code/nuztf/nuztf/base_scanner.py\u001b[0m in \u001b[0;36mcalculate_overlap_with_observations\u001b[0;34m(self, fields, pid, first_det_window_days, min_sep)\u001b[0m\n\u001b[1;32m 718\u001b[0m \u001b[0mobs_times\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mobs_times\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mfirst_det_mask\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 719\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 720\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlogger\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minfo\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"Most recent observation found is {obs_times[-1]}\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 721\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlogger\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minfo\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Unpacking observations\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 722\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mIndexError\u001b[0m: index -1 is out of bounds for axis 0 with size 0" - ] + "data": { + "text/plain": [ + "(
,\n", + " 'In total, 44.10 % of the contour was observed at least once.\\nThis estimate includes 0.00 % of the contour at a galactic latitude <10 deg.\\nIn total, 28.02 % of the contour was observed at least twice. \\nIn total, 28.02 % of the contour was observed at least twice, and excluding low galactic latitudes.\\nThese estimates account for chip gaps.')" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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EBHJycqQHT/JoERLJY0RBQYHYvn27GD16tAgICBA2Njaic+fO4uOPPxbHjx8Xer2+tE2UlCPy8vLEmTNnRF5eXsnOo0eFgEd/O3r0ju0eOHCg6NGjhxBCiObNm4shQ4YIIYRYs2aNuP5r/6WXXhLPPPOM2bETJ04UNWvWNG1XqlRJfPbZZ7c937Zt24SFhYWIjY017Tt9+rQAxKFDh4QQQrz//vvCwsJCxMXFmfps3rxZqNVqkZiYKK5evSoAsXv37pueo0OHDmLWrFlm+3788Ufh7e1t2gbE2LFjzfocP35cqFQqcenSJSGEEHq9Xvj6+oqFCxcKIYT466+/hIODg8jPzzc7rkqVKuKbb74RQgjRokUL8frrr5u1N2vWTNSrV8+0bTAYRF5enkhOThZRUVHi2LFjIiwsTMTExIi0tDRRXFx86ydQ8sRz0++au0R66CTlntTUVJYtW0afPn1wc3Nj0KBBFBQUMH/+fK5evcrvv//O+PHjqV+/vlzsW/LEMXfuXJYtW8bZs2dvaDt79iytWrUy29eqVSsiIyPR6/V3fI6zZ89SsWJFKlasaNpXs2ZNnJyczM7r5+dnVtanRYsWGAwGIiIicHFxYdCgQYSEhNCtWzfmz59v5hE8ceIEH374oWmOm52dHa+88gqJiYnk5uaa+jVu3NjMtvr161OjRg2Tl27Pnj2kpKTQp08f07jZ2dm4urqajR0TE0N0dLTp+po1a2Y2bosWLcy2VSoVNjY2eHh4UKVKFerVq0dAQAAWFhbExcURFhbG+fPnSUlJoaCg4I6fW4nkTpFJEZJyhxCC06dPs2HDBjZs2MChQ4do0KAB3bp145133qF+/fqylIhE8g9t2rQhJCSEyZMnM2jQoNI257YsWbKE0aNHs2XLFlauXMl7773H9u3bad68OdnZ2XzwwQf06tXrhuOuL8Rqa2t7Q3toaCgrVqzg7bffZsWKFXTq1MlU7Ds7Oxtvb2927959w3H3M4/2+vInFStWJD8/n/T0dFP2rI2NDU5OTjg6OmJrayu/syT3jRR0knKBwWDg77//5tdff2X9+vWkpKTwzDPPMGTIEFatWiXnv0kkt2HOnDnUr1+foKAgs/01atRg3759Zvv27dtHtWrVTCsoaDSa//TW1ahRg8uXL3P58mWTl+7MmTOkp6dTs2ZNU7/Y2FgSEhLw8fEB4MCBA6jVajO7GjRoQIMGDZg8eTItWrRgxYoVNG/enIYNGxIREUFgYOBdX/9LL73Ee++9x9GjR/ntt99YtGiRqa1hw4YkJSVhaWmJv7//La/v4MGDDBgwwLTvwIEDd2WDjY0NXl5eeHl5UVxcTGZmJunp6URGRqJSqXBycsLZ2Rl7e3sZSZDcE1LQScos14u43377jYKCAp577jm+/vpr2rdvL2vCSSR3SJ06dQgNDeWLL74w2z9+/HjTKicvvPAC+/fv56uvvmLBggWmPv7+/vz555/069cPa2tr3Nzcbhg/ODjYdI7PP/+c4uJiXn/9ddq2bWsWArWxsWHgwIF8/PHHZGZmMnr0aPr27YuXlxcxMTEsXryY7t274+PjQ0REBJGRkSYRNXXqVLp27Yqfnx/PP/88arWaEydOcOrUKWbMmHHb6/f396dly5YMHToUvV5P9+7dzWxv0aIFPXv2ZN68eVSrVo2EhAQ2bdrEc889R+PGjRkzZgyDBg2icePGtGrViuXLl3P69GkCAgLu6fWwtLTExcUFFxcXhBBkZ2eTnp7OpUuX0Ov1ODs7S3EnuXse2Iw+ieQBoNfrxV9//SVGjx4tfHx8hKurqxg2bJjYunWrKCwsLG3zJE8YN52ofOmSEDY2jzYhwsZGOe8dcn1ShJGYmBih0WjEv7/2f/vtN1GzZk1hZWUl/Pz8xEcffWTWvn//flG3bl1hbW19w7HXc+nSJdG9e3dha2sr7O3tRZ8+fURSUpKp/f333xf16tUTCxYsED4+PsLGxkY8//zzIi0tTQghRFJSkujZs6fw9vYWGo1GVKpUSUydOtUskWnLli2iZcuWQqvVCgcHB9G0aVOxePFiUzsg1qxZc1P7FixYIAAxYMCAG9oyMzPFG2+8IXx8fISVlZWoWLGiCA0NNUvymDlzpnBzcxN2dnZi4MCB4q233jJLingQGAwGkZWVJWJjY8WJEyfEsWPHRExMjEhPT5cJXY85DyIpQiWEzKuWlC7/9sTl5+fTq1cv+vTpQ/v27e+qMKhE8iC55YLZci1XyUNGCEFOTg7Xrl3j2rVr0nP3mHPL75q7QIZcJaVGWFgYP/zwAytXrjSJuCVLlkgRJyn7+PlJgSV5qKhUKlPGbYUKFUzi7vqwrDEzVyZUSEAWFi5V5syZg0qlMlsjsV27dqaK7Mbba6+9Znbc+vXrqVatGkFBQWzcuPERW31/JCcn8+mnn1KvXj1atWrFlStX+L//+z+SkpL49ttv6dixoxRzEolEch1GcVexYkXq1KlD1apVUavVREdHc+rUKRISEsh/1OsLS8oc0kNXShw+fJhvvvmGunXr3tD2yiuv8OGHH5q2dTqd6XFBQQEjR45kyZIlCCEYMmQIHTt2LNMLRufn57N+/XqWLVvG9u3bad26NePGjaN3797Y29uXtnkSiURSbvi35y4jI4OrV6+SmJiIra0trq6uODs7Y2kpf96fNOQrXgpkZ2cTGhrKt99+e9PsLJ1Oh5eX102PLSgowMLCgvr16wNKtlRBQUGZE3RCCA4cOMCyZctYuXIl7u7uDBgwgAULFpiW25FIJBLJvaNWq03z6oqKikhLSyM1NZXY2FhTSNbBwUGGZJ8QZMi1FBg5ciRdunQhODj4pu3Lly/Hzc2N2rVrM3nyZLMq6A4ODgwePBhvb298fHwYMWJEmfJyJSYmMnPmTIKCgnj22WcB+P3334mIiOC9996TYk4ikUgeAlZWVnh6elKzZk1q1qyJRqPh4sWLnDx5kri4OBmSfQKQHrpHzC+//MKxY8c4fPjwTdtfeuklKlWqhI+PDydPnmTSpElERESwevVqU5/333+fsWPHmiqRlzZCCP744w8WLlzIhg0baN++PTNmzKB79+73nK0jkUgkkntDq9VSoUIFfH19yczM5MqVK5w+fRo7Ozs8PDxwdHSUWbKPIVLQPUIuX77MmDFj2L59+y2FzvDhw02P69Spg7e3Nx06dCA6OpoqVaqY2hwdHR+6vf9FWloay5YtY9GiRaSlpTFkyBDOnj17z8U2JRKJRPLgUKlUODo64ujoSFFREVeuXOHy5cvExsbi5uaGu7t7mZuuI7l3ZB26R8jatWt57rnnTEvqAOj1elQqFWq12jQ/7npycnKws7Njy5YthISEPGqTb0AIwaFDh1i0aBErV66kUaNGjBgxgt69e2NtbV3a5kkkD5QHURtKIilLCCHIyMggNTWVzMxMHB0dcXd3l3PtShlZh66c0aFDB8LDw832DR48mOrVqzNp0qQbxBwotdqAUl+rNCcnhxUrVrBw4UKioqJ4+eWXOXjwIHXq1ClVuySS0iAjI8NsbuvDRqfTlQmvvKT8Y1w31snJiYKCAq5cuUJMTAwWFha4u7vj6uoqS0eVU6Sge4TY29tTu3Zts33GNPPatWsTHR3NihUrePbZZ3F1deXkyZOMGzeONm3a3LS8yaMgLi6OL774gsWLF+Pv78+IESN46aWXysTcPYmkNMjIyOCrr76iuLj4kZ3T0tKSUaNGPRRR5+/vz9ixY83qYZZnHrfreZhYW1vj6+uLt7c36enppKamEh8fj6urK56ennK97HKGnBVZhtBoNOzYsYOOHTtSvXp1xo8fT+/evdmwYcMjt+XEiRMMGDCAwMBAIiMj2bhxI8ePH+fVV1+VYk7yRJObm/tIxRxAcXHxXXsEL1++zJAhQ/Dx8UGj0VCpUiXGjBnD1atXH5KVkvKKWq3GxcWFoKAgatasCcCZM2eIjIwkKysLOTOrfCA9dKXM7t27TY8rVqzInj17Ss0WIQTbt2/n448/Zu/evQwaNIjw8HCqVq1aajZJJJK758KFC7Ro0YJq1arx888/U7lyZU6fPs3EiRPZvHkzBw4cwMXFpVRsu37esKTsodVq8ff3x8fHh9TUVKKiorC2tsbLywtnZ2c5z64MIz9REgoLC/nhhx+oV68e/fv3p3Xr1ly6dIkFCxZIMSeRlENGjhyJRqNh27ZttG3bFj8/Pzp37syOHTuIj4/n3XffNeuflZXFiy++iK2tLb6+vnz99demNiEE06ZNw8/PD2tra3x8fBg9erSpvaCggAkTJuDr64utrS3NmjUz+6O6dOlSnJycWL9+PTVr1sTa2prvvvsOGxsb0tPTzewYM2YMTz/9tGl77969PPXUU2i1WipWrMjo0aPJyckxtaekpNCtWze0Wi2VK1dm+fLl//ncGAwGPvzwQypUqIC1tTX169dny5YtpvaLFy+iUqlYvXo17du3R6fTUa9ePfbv3282zr59+2jXrh06nQ5nZ2dCQkK4du2a6RyzZ8+mcuXKaLVa6tWrx2+//faftpUlNBoNvr6+1K1bF1dXV+Lj4wkPDyc5ORm9Xl/a5klughR0TzDp6enMmzePypUrM3PmTEaNGsWlS5eYOnUq7u7upW2eRCK5B9LS0ti6dSuvv/76DXOgvLy8CA0NZeXKlWZhtI8++oh69epx/Phx3n77bVN5JYBVq1bx2Wef8c033xAZGcnatWvNkqFGjRrF/v37+eWXXzh58iR9+vShU6dOREZGmvrk5uYyd+5cvvvuO06fPk1oaChOTk6sWrXK1Eev17Ny5UpCQ0MBiI6OplOnTvTu3ZuTJ0+ycuVK9u7dy6hRo0zHDBo0iMuXL7Nr1y5+++03FixYQEpKym2fn/nz5/PJJ5/w8ccfc/LkSUJCQujevbuZvQDvvvsuEyZMICwsjGrVqvHiiy+aQu1hYWF06NCBmjVrsn//fvbu3Uu3bt1MQmf27Nn88MMPLFq0iNOnTzNu3Dj69+9fqhGYe8XCwgJPT09q165NhQoVSEtLMxUrLiwsLG3zJNchy5Y8gVy9epXPPvuML7/8krp16zJx4kS6du0qQyASyb+4WSmBxMREFi9e/MhtGT58+B1lux88eJDmzZuzZs0aevbseUP7Z599xptvvklycjIeHh74+/tTo0YNNm/ebOrTr18/MjMz+f333/n000/55ptvOHXq1A3Zj7GxsQQEBBAbG4uPj49pf3BwME2bNmXWrFksXbqUwYMHExYWRr169Ux9xo4dS3h4ODt37gRg27ZtdO/enaSkJJycnBg2bBgWFhZ88803pmP27t1L27ZtycnJITY2lqCgIA4dOkSTJk0AOHfuHDVq1OCzzz67ZVKEr68vI0eO5J133jHta9q0KU2aNOHrr7/m4sWLVK5cme+++46hQ4cCynyyWrVqcfbsWapXr85LL71EbGwse/fuvWH8goICXFxc2LFjBy1atDDtHzZsGLm5uaxYseKmdpUXhBBkZ2eTnJxMRkYGbm5ueHl5ybJV94ksWyK5K1JTU/nkk0/4+uuvadmyJZs2baJ169albZZEInkI3M1/9euFh3H7888/B6BPnz58/vnnBAQE0KlTJ5599lm6deuGpaUl4eHh6PV6qlWrZnZ8QUEBrq6upm2NRnNDpn5oaCjNmzcnISEBHx8fli9fTpcuXXBycgKUxKyTJ0+ahVGFEBgMBmJiYjh//jyWlpY0atTI1F69enXT8TcjMzOThIQEWrVqZba/VatWnDhxwmzf9fYahXRKSgrVq1cnLCyMPn363PQcUVFR5Obm8swzz5jtLywspEGDBre0rbygUqmwt7fH3t6evLw8EhMTOXXqFK6urnh7e0thV4pIQfcEkJKSwrx581i4cCHt2rVj+/btNG/evLTNkkgkD4HAwEBUKhVnz57lueeeu6H97NmzODs73/G0iooVKxIREcGOHTvYvn07r7/+Oh999BF79uwhOzsbCwsLjh49ekMdTTs7O9NjrVZ7w2T6Jk2aUKVKFX755RdGjBjBmjVrWLp0qak9OzubV1991Wy+nhE/Pz/Onz9/R/bfK9d7I422GwwGgNuW88jOzgZg06ZN+Pr6mrU9bmJHq9USEBBAfn6+Sdi5uLjg7e0tC3GXAlLQPcZcuXKFjz/+mK+++or27duzZ88eGjduXNpmSSSSh4irqyvPPPMMCxYsYNy4cWbiIykpieXLlzNgwAAzgXXgwAGzMQ4cOECNGjVM21qtlm7dutGtWzdGjhxJ9erVCQ8Pp0GDBuj1elJSUnjqqafu2tbQ0FCWL19OhQoVUKvVdOnSxdTWsGFDzpw5Q2Bg4E2PrV69OsXFxRw9etQUco2IiLgh0eJ6HBwc8PHxYd++fbRt29a0f9++fTRt2vSO7a5bty47d+7kgw8+uKHNmPgRGxtrdo7HGRsbGypXroy3tzeJiYmcPn1aeuxKASnoHkPS0tL49NNPmT9/Pk899RR//PHHXX1ZSSSS8s1XX31Fy5YtCQkJYcaMGWZlS3x9fZk5c6ZZ/3379jFv3jx69uzJ9u3b+fXXX9m0aROgZKnq9XqaNWuGTqfjp59+QqvVUqlSJVxdXQkNDWXAgAF88sknNGjQgNTUVHbu3EndunXNBNrNCA0NZdq0acycOZPnn3/e7Md/0qRJNG/enFGjRjFs2DBsbW05c+YM27dv56uvviIoKIhOnTrx6quvsnDhQiwtLRk7dux/FsOdOHEi77//PlWqVKF+/fosWbKEsLCwO8qQNTJ58mTq1KnD66+/zmuvvYZGo2HXrl306dMHNzc3JkyYwLhx4zAYDLRu3ZqMjAz27duHg4MDAwcOvOPzlDeuF3YJCQmcOnUKNzc3vL295ZqxjwA5C/4xIi8vj7lz5xIQEMChQ4fYtm0bv//+uxRzEskDRKfTYWn5aP8LW1paotPp7rh/1apVOXLkCAEBAfTt25cqVaowfPhw2rdvz/79+2+oQTd+/HiOHDlCgwYNmDFjBp9++qlp7WgnJye+/fZbWrVqRd26ddmxYwcbNmwwzZFbsmQJAwYMYPz48QQFBdGzZ08OHz6Mn5/ff9oZGBhI06ZNOXnypCm71UjdunXZs2cP58+f56mnnqJBgwZMnTrVLPliyZIl+Pj40LZtW3r16sXw4cPx8PC47TlHjx7Nm2++yfjx46lTpw5btmxh/fr1d1WiqVq1amzbto0TJ07QtGlTWrRowbp160zvi+nTpzNlyhRmz55NjRo16NSpE5s2baJy5cp3fI7yjI2NDQEBAdSsWRO9Xk94eDiXL19+5AW5nzRklutjgMFgYMWKFbzzzju4ubnx8ccfm9Vykkgk98atMs/kWq4SyZ2Tm5tLXFwcOTk5+Pj44O7uLqsq/AuZ5Srhjz/+YOLEiVy5coVZs2bx0ksvyQ+KRPKQcXR0lAJLIrlDdDod1apVIyMjg7i4OFJSUvD19ZUrTzxg5C9/OeXMmTN07dqV5557jr59+3Lu3Dn69+8vxZxEIpFIyiSOjo7UrFkTb29vLl++zLlz58jKyiptsx4b5K9/OSMxMZHhw4fTqFEjqlSpQnR0NJMmTfrPicASiUQikZQ2KpUKNzc3ateujaOjI5GRkURFRZGfn1/appV7pKArJxQUFDBz5kyqVq1qWnpl/vz5uLm5lbZpEolEIpHcFRYWFvj4+FCnTh2srKw4ffo0sbGxMnHiPpBz6MoBO3bsYOTIkeh0OrZu3XpDlXOJRCKRSMojVlZWVKpUCQ8PDy5fvszp06epUKECLi4ucn7dXSI9dGWYhIQE+vXrR+/evRk5ciSHDx+WYk4ikUgkjx1arZaqVatSsWJF4uLiOH/+PHl5eaVtVrlCCroySHFxMfPnz6d69eqoVCrOnTvH6NGjH3ntK4lEIpFIHhUqlQoXFxdq166NTqfjzJkzxMXFodfrS9u0coFUCGWMAwcOMGLECHJzc1m9ejXBwcGlbZJEIpFIJI8MCwsLKlasiKurK5cuXSItLQ0/Pz+cnJxK27QyjRR0ZYS0tDTefvttli9fzjvvvMOECRPkGngSSRklNiOWK7lXHtn53HRu+Dn+98oLEsnjhE6no3r16ly5coWLFy9ia2uLn5+f/G28BVLQlQHWrVvH8OHDady4MeHh4QQEBJS2SRKJ5BbEZsQS9FUQ+cWPrsyCjaUNEaMiHoqo8/f3Z+zYsYwdO/aBj10aPMrr2b17N+3bt+fatWsP1Xs0aNAg0tPTWbt27UMZf+nSpYwdO5b09PSHMv7dMmXKFJKTk1m8eDEqlYrTp08zaNAgdu/ezenTpzEYDDRp0oTjx49Tv379W47Trl07Bg0axKBBgx6Z7f9my5YtvP322xw7duyh14mVc+hKkWvXrjFgwAAGDRrEJ598wsaNG6WYk0jKOFdyrzxSMQeQX5x/1x7By5cvM2TIEHx8fNBoNFSqVIkxY8Zw9erVh2Rl+SEzM5MpU6ZQq1YttFotrq6uNGnShHnz5nHt2rVSs+vixYuoVCrCwsLM9s+fP5+lS5fe87iDBg1CpVKhUqnQaDQEBgby4YcflskSIUlJScyfP5933333hjZ/f38CAwNJTU0FoLCw8KHasnjxYtq1a4eDgwMqleqmgjctLY3Q0FAcHBxwcnJi6NChZGdnm9o7deqElZUVy5cvf6i2ghR0pcbmzZupXbs2V65c4dSpU/Tv31+maEskkgfChQsXaNy4MZGRkfz8889ERUWxaNEidu7cSYsWLUhLSys12/R6PQaDodTOn5aWRvPmzVmyZAkTJkzg4MGDHDt2jJkzZ3L8+HFWrFhRarbdCkdHx/v2AHbq1InExEQiIyMZP34806ZN46OPPnowBj5AvvvuO1q2bEmlSpVu2u7g4EC1atUA5X1+5coVHtSS9Dk5OWZ/eHJzc+nUqRPvvPPOLY8JDQ3l9OnTbN++nY0bN/Lnn38yfPhwsz6DBg3iiy++eCA23g4p6B4xmZmZDBs2jH79+vHhhx+yadMmfH19S9ssiUTyGDFy5Eg0Gg3btm2jbdu2+Pn50blzZ3bs2EF8fPwN3o+srCxefPFFbG1t8fX15euvvza1CSGYNm2aae6Sj48Po0ePNrUXFBQwYcIEfH19sbW1pVmzZuzevdvUvnTpUpycnFi/fj01a9bE2tqa7777Dhsbmxs8HmPGjOHpp582be/du5ennnoKrVZLxYoVGT16NDk5Oab2lJQUunXrhlarpXLlynfkBXnnnXeIjY3l0KFDDB48mLp161KpUiU6duzIzz//zOuvv27q++OPP9K4cWPs7e3x8vLipZdeIiUl5ZZjX716lRdffBFfX190Oh116tTh559/NutjMBiYN28egYGBWFtb4+fnx8yZMwGoXLkyAA0aNEClUtGuXTtAEQQ9e/a8ozFuhbW1NV5eXlSqVIkRI0YQHBzM+vXrb9o3OjqaHj164OnpiZ2dHU2aNGHHjh1mffz9/Zk1axZDhgzB3t4ePz8/Fi9ebNbn8uXL9O3bFycnJ1xcXOjRowcXL168rZ2//PIL3bp1u20fCwsLQBFcHTp0wMbGhlq1arFnz57bHnczhBDs2bOHwYMH4+Xlxd69e01tY8eO5e2336Z58+Y3Pfbs2bNs2bKF7777jmbNmtG6dWu+/PJLfvnlFxISEkz9unXrxpEjR4iOjr5r++6GcinoFi5cSN26dXFwcMDBwYEWLVqwefNmU3t+fj4jR47E1dUVOzs7evfuTXJystkY69evp1q1agQFBbFx48ZHYvfOnTupU6cOly5dIjw8nKFDh0qvnEQieaCkpaWxdetWXn/99RuWBPTy8iI0NJSVK1eaeTU++ugj6tWrx/Hjx3n77bcZM2YM27dvB2DVqlV89tlnfPPNN0RGRrJ27Vrq1KljOnbUqFHs37+fX375hZMnT9KnTx86depEZGSkqU9ubi5z587lu+++4/Tp04SGhuLk5MSqVatMffR6PStXriQ0NBRQREWnTp3o3bs3J0+eZOXKlezdu5dRo0aZjhk0aBCXL19m165d/PbbbyxYsOC2gstgMLBy5Ur69++Pj4/PTftc/51cVFTE9OnTOXHiBGvXruXixYu3nY+Vn59Po0aN2LRpE6dOnWL48OG8/PLLHDp0yNRn8uTJzJkzhylTpnDmzBlWrFiBp6cngKnfjh07SExMZPXq1Tc9z+3GuFO0Wu0tQ5bZ2dk8++yz7Ny5k+PHj9OpUye6detGbGysWb9PPvmExo0bc/z4cV5//XVGjBhBREQEoDx3ISEh2Nvb89dff7Fv3z7s7Ozo1KnTLc+blpbGmTNnaNy48R1dw/vvv8+7777Lhg0bCAoKomvXrly5cmdTEy5cuMD7779PQEAAXbp0Qa/Xs2bNmv8Uk9ezf/9+nJyczOwNDg5GrVZz8OBB0z4/Pz88PT3566+/7njse0KUQ9avXy82bdokzp8/LyIiIsQ777wjrKysxKlTp4QQQrz22muiYsWKYufOneLIkSOiefPmomXLlqbj8/PzRYUKFcT27dvFtm3bRIUKFURBQcFDszcnJ0eMHDlS2NnZiQULFgiDwfDQziWRSB4ceXl54syZMyIvL8+072jCUcE0HvntaMLRO7L5wIEDAhBr1qy5afunn34qAJGcnCyEEKJSpUqiU6dOZn1eeOEF0blzZyGEEJ988omoVq2aKCwsvGGsS5cuCQsLCxEfH2+2v0OHDmLy5MlCCCGWLFkiABEWFmbWZ8yYMeLpp582bW/dulVYW1uLa9euCSGEGDp0qBg+fLjZMX/99ZdQq9UiLy9PRERECEAcOnTI1H727FkBiM8+++ym156UlCQA8emnn5rtb9iwobC1tRW2traiX79+Nz1WCCEOHz4sAJGVlSWEEGLXrl0CMNl8M7p06SLGjx8vhBAiMzNTWFtbi2+//famfWNiYgQgjh8/brZ/4MCBokePHnc0xs24/niDwSC2b98urK2txYQJE4QQymvk6Oh42zFq1aolvvzyS9N2pUqVRP/+/U3bBoNBeHh4iIULFwohhPjxxx9FUFCQ2e9dQUGB0Gq1YuvWrTc9x/HjxwUgYmNjzfbv2rVLVKpUybRtfJ7mzJlj2peSkiI8PT3FxIkTTe/Vtm3biiVLlpj6ZGVlie+++0489dRTwsLCQgQHB4sffvhBZGdn3/bab/U6z5w5U1SrVu2G/u7u7mLBggVm+xo0aCCmTZt2y3Pc7LvmbimXWa7/VtAzZ85k4cKFHDhwgAoVKvD999+zYsUKk+t+yZIl1KhRgwMHDtC8eXMKCgqwsLAwZcdYWlpSUFCARqN54LaeO3eOPn364OjoyIkTJ2TSg0QieSSIu5hX1KJFixu2P//8cwD69OnD559/TkBAAJ06deLZZ5+lW7duWFpaEh4ejl6vN81pMlJQUICrq6tpW6PRULduXbM+oaGhNG/enISEBHx8fFi+fDldunQxzRU7ceIEJ0+eNAujCiEwGAzExMRw/vx5LC0tadSokam9evXq9zTXbM2aNRQWFjJp0iSz1QmOHj3KtGnTOHHiBNeuXTPN/YuNjaVmzZo3jKPX65k1axb/+9//iI+Pp7CwkIKCAnQ6HaCE6AoKCujQocNd22jkXsfYuHEjdnZ2FBUVYTAYeOmll5g2bdpN+2ZnZzNt2jQ2bdpEYmIixcXF5OXl3eChu/41ValUeHl5mTykJ06cICoqCnt7e7Nj8vPzbxl6ND73NjY2d3RN179v3d3dadasGRcuXODMmTM3/a397bffGDZsGLVr1+bEiRPUqlXrjs7zINBqteTm5j7Uc5RLQXc9er2eX3/9lZycHFq0aMHRo0cpKioyK8hbvXp1/Pz82L9/P82bN8fBwYHBgwfj7e2NSqVixowZN7zpHgQ//fQTI0aMYNSoUUyfPl2u9CCRSB46gYGBqFQqzp49y3PPPXdD+9mzZ3F2dsbd3f2OxqtYsSIRERHs2LGD7du38/rrr/PRRx+xZ88esrOzsbCw4OjRo6Z5TUbs7OxMj7Va7Q3TS5o0aUKVKlX45ZdfGDFiBGvWrDHL5MzOzubVV181m69nxM/Pj/Pnz9+R/dfj7u6Ok5OTKSx4/XgA9vb2pnl9OTk5hISEEBISwvLly3F3dyc2NpaQkJBbhgw/+ugj5s+fz+eff06dOnWwtbVl7Nixpv7/DoHfC/c6Rvv27Vm4cCEajQYfH5/b/h5NmDCB7du38/HHHxMYGIhWq+X555+/4bqtrKzMtlUqlUn0Zmdn06hRo5vOa7zVe8/NzQ1QKkDc6fvzetRqNfb29nh7exMZGUlhYaHZH5sePXrw2WefsWzZMho1akS3bt14+eWX6dy58w3XcidcL2CNFBcXk5aWhpeXl9n+tLS0e7qmu6FczqEDCA8Px87ODmtra1577TXWrFlDzZo1SUpKQqPR3PAvzdPTk6SkJNP2+++/z5UrV7h69SpvvfXWA7UtNzeXYcOGMXbsWP73v/8xe/ZsKeYkEskjwdXVlWeeeYYFCxbcsBZmUlISy5cv54UXXjATWAcOHDDrd+DAAWrUqGHa1mq1dOvWjS+++ILdu3ezf/9+wsPDadCgAXq9npSUFAIDA81u//5BuxmhoaEsX76cDRs2oFar6dKli6mtYcOGnDlz5oZxAwMD0Wg0VK9eneLiYo4ePWo6JiIi4ra11NRqNX379uWnn34ym7R+M86dO8fVq1eZM2cOTz31FNWrV7/t/DyAffv20aNHD/r370+9evUICAgwE55Vq1ZFq9Wyc+fOmx5vjBLdbqmr/xrjVtja2hIYGIifn99//h7t27ePQYMG8dxzz1GnTh28vLz+M5nh3zRs2JDIyEg8PDxueP0cHR1vekyVKlVwcHDgzJkzd3SO69+3xvdCjRo18PDwICgoiKKiIpKTkykqKgLA2dmZsWPHcvz4cQ4dOkTFihUZPnw43t7ejBo1ymze253QokUL0tPTzd6Df/zxBwaDgWbNmpn2Gb2SDRo0uKvx75ZyK+iCgoIICwvj4MGDjBgxgoEDB97xm8CIo6PjA/fMnTt3jmbNmnHu3DnCwsLo3LnzAx1fIpFI/ouvvvqKgoICQkJC+PPPP7l8+TJbtmzhmWeewdfX94aMyH379jFv3jzOnz/P119/za+//sqYMWMAJUv1+++/59SpU1y4cIGffvoJrVZLpUqVqFatGqGhoQwYMIDVq1cTExPDoUOHmD17Nps2bfpPO0NDQ00lQ55//nmzFQAmTZrE33//zahRowgLCyMyMpJ169aZkiKCgoLo1KkTr776KgcPHuTo0aMMGzbsPz1Ys2bNwtfXl6ZNm/J///d/nDx5kujoaNasWcP+/ftNnkY/Pz80Gg1ffvklFy5cYP369UyfPv22Y1etWpXt27fz999/c/bsWV599VWzhDwbGxsmTZrEW2+9xQ8//EB0dDQHDhzg+++/B8DDwwOtVsuWLVtITk4mIyPjhnP81xgPgqpVq7J69WrCwsI4ceIEL7300l2XmgkNDcXNzY0ePXrw119/ERMTw+7duxk9ejRxcXE3PUatVhMcHGyWaXo7vv76a9asWcO5c+cYOXIk165dY8iQIYAiYHU6HWq1mjNnzpCVlWV2bN26dfn000+Ji4tj6dKlJCUl0aZNGzZs2GDqk5SURFhYGFFRUYDiSAoLCzOV/alRowadOnXilVde4dChQ+zbt49Ro0bRr18/s6SbAwcOYG1tfcPUhgdNuRV0xuKIjRo1Yvbs2dSrV4/58+fj5eVFYWHhDf/SkpOT7+gf4/3w008/0aRJE7p27cru3bupUKHCQz2fRCJ59Ljp3LCxvLM5Pg8KG0sb3HRud9y/atWqHDlyhICAAPr27UuVKlUYPnw47du3Z//+/bi4uJj1Hz9+PEeOHKFBgwbMmDGDTz/9lJCQEACcnJz49ttvadWqFXXr1mXHjh1s2LDBNEduyZIlDBgwgPHjxxMUFETPnj05fPiwKYx5OwIDA2natCknT540ZbcaqVu3Lnv27OH8+fM89dRTNGjQgKlTp5r9UC5ZsgQfHx/atm1Lr169GD58OB4eHrc9p6urK4cOHWLAgAF89NFHNG3alDp16jBt2jReeOEFvv32W0AJCy5dupRff/2VmjVrMmfOHD7++OPbjv3ee+/RsGFDQkJCaNeuHV5eXmblRkBZBWH8+PFMnTqVGjVq8MILL5g8f5aWlnzxxRd88803+Pj40KNHj5ue53ZjPAg+/fRTnJ2dadmyJd26dSMkJISGDRve1Rg6nY4///wTPz8/evXqRY0aNRg6dCj5+fk4ODjc8rhhw4bxyy+/3JGAnDNnDnPmzKFevXrs3buX9evXm8K2oISA3d3dTSHYxMTEG+aWWlpa0rVrV3777TcSExPNPGuLFi2iQYMGvPLKKwC0adOGBg0amJV7Wb58OdWrV6dDhw48++yztG7d+obyLT///DOhoaGmuZQPC5W4m5mzZZinn34aPz8/5s+fj7u7Oz///DO9e/cGFDd89erVTXPoHjT5+fmMGjWKtWvX8uOPP0qvnETymJCfn09MTAyVK1c2m6gt13KVSB4OQgiaNWvGuHHjePHFFwFlibVBgwbdddj3+qW/cnJyuHDhAtbW1gQEBDyyaVBXrlwhKCiII0eOmOoM3oxbfdfcDeVyYtfkyZPp3Lkzfn5+ZGVlsWLFCnbv3s3WrVtxdHRk6NChvPnmm7i4uODg4MAbb7xBixYtHoqYS05OpmfPngghCAsLk145ieQJwM/RTwosieQhoFKpWLx4MeHh4Q90XFtbW2rUqMHFixc5e/asKdnjYXPx4kUWLFhwWzH3oCiXgi4lJYUBAwaQmJiIo6MjdevWZevWrTzzzDMAfPbZZ6jVanr37m2aR7JgwYIHbseJEyfo1q0bbdq0MVU+l0gkEolEcu/Ur1/fVFbsQWJpaUmVKlVISEjg3LlzBAQE3DJB40HRuHHjOy6UfL88NiHXR826det4+eWXefvtt5k8ebJc8UEieQx5EGEQiURyf1y8eJG1a9cyduzYuzpu6dKltxSHV69e5dKlS/j6+uLh4VHqv+EP4rtGCrq7RAjBvHnzmDFjBsuWLaNXr16lbZJEInlISEEnkTy+ZGdnExUVhbOzMxUrVkStLr080Sd2Dl1pUVBQwKuvvsqOHTvYs2fPXWf9SCSS8on83yuRPH7Y2dlRo0YNoqKiiIyMpEqVKqVWM/ZBfMeU27Ilj5rU1FQ6dOjAmTNnOHz4sBRzEskTgLF6/MNeskcikZQO1tbWVK9eHQsLC86ePUt+fn6p2GH8jrmXFSuMyJDrHRAXF8czzzxD7dq1+eGHHx5JZoxEIikbJCYmkp6ejoeHBzqdrtTn2kgkkgePEIKkpCTS09Px9/d/ZL/zQghyc3NJSUnByckJb2/vex5LCrr/IDIykmeeecaUKfvv9QolEsnjzfVf9BKJ5PEmIyODjIwMPD09zVYuedg4OTnh5eV1X38YpaC7DSdPnqRjx44MGDCAuXPnyn/mEskTjF6vN60JKZFIHl9WrlzJvHnzmD9/Pq1bt37o57OysnogziKZFHELDhw4wLPPPsvEiRN5++23pZiTSJ5wLCwspIdeInkCGDhwIBqNhh49erBs2TKef/750jbpjpCC7ibs2LGDXr16MWfOHF5//fXSNkcikUgkEskj5MUXX8Te3p5+/fqRmZnJkCFDStuk/0QKun+xZs0a+vfvzzfffEP//v1L2xyJRCKRSCSlQNeuXdm4cSM9evQgIyODcePGlbZJt0UKuutYu3YtL7/8MitWrKBHjx6lbY5EIpFIJJJSpF27duzcuZOQkBAMBgPjx48vbZNuiRR0/7B9+3ZCQ0OlmJNIJBKJRGKicePGbNu2jaeffhp7e3uGDx9e2ibdFCnogH379tGrVy8WL14sxZxEIpFIJBIzGjVqxMaNG3n22Wext7fnxRdfLG2TbuCJF3THjh2jS5cufPzxx4SGhpa2ORKJRCKRSMogTz31FL/99hu9evXC1taW7t27l7ZJZjzRS3+dPXuWkJAQ3nvvPV599dXSNkcikUgkEkkZJiQkhB9//JEXX3yRnTt3lrY5ZjyxHrqYmBiCg4MZMWIEEyZMKG1zJBKJRCKRlAN69epFdnY2PXv2ZNu2bbRo0aK0TQKeUEGXkpJCcHAwzz//PB988EFpmyORSCQSiaQcMWDAALKysnj22Wf566+/qF27dmmb9OQt/VVQUECHDh2oWLEiy5cvR61+oqPOEolEIpFI7pH333+fH3/8kUOHDuHm5laqtjxRgk4IwdChQwkPD+fPP/9Eq9WWtkkSiUQikUjKKQaDgRdeeIHU1FS2bduGRqMpNVueKPfUZ599xtatW1m3bp0UcxKJRCKRSO4LtVrN0qVLyczMZNSoUZSmj+yJEXS///47U6dOZe3atfj4+JS2ORKJRCKRSB4DbG1tWbduHevXr+err74qNTueiJDrmTNnaNGiBd988w39+vUrbXMkEolEIpE8Zhw4cIAOHTqwZs0aOnbs+MjP/9h76K5evUq3bt0YPXq0FHMSiUQikUgeCs2bN2fRokW88MILREREPPLzP9YeOiEEXbt2RaPRsGrVKpnRKpFIJBKJ5KEyadIkNm7cyJEjRx7pfP3HWuF89dVXnDp1iiVLlkgxJ5FIJBKJ5KEzc+ZMHB0dmThx4iM972ProQsPD6d58+Zs2bKFp556qrTNkUgkEolE8oQQExND/fr1+emnn+jWrdsjOedj6bbKy8vjxRdfZPz48VLMSSQSiUQieaRUrlyZBQsWMGTIEBITEx/JOR9LD90bb7zBkSNH+Ouvv7C0fCJXN5NIJBKJRFLK9O/fn9TUVDZv3vzQp349dh66jRs38sMPP7B8+XIp5iQSiUQikZQaX3/9NZGRkXz++ecP/Vz3Lej+/PNPunXrho+PDyqVirVr15q1Dxo0CJVKZXbr1KmTWZ/9+/dTv359/P39+f777+/ZluTkZAYPHszXX39NQEDAPY8jkUgkEolEcr84OjqyfPlypkyZQlhY2EM9130LupycHOrVq8fXX399yz6dOnUiMTHRdPv555/N2ocOHcqUKVNYsWIFs2fP5vLly/dky5tvvkm7du3o37//PR0vkUgkEolE8iBp0aIFb775JsOHD0ev1z+089x3TLJz58507tz5tn2sra3x8vK6ZXtOTg4NGzbEw8MDZ2dnsrKy7tqOP/74gw0bNnD27Nm7PlYikUgkEonkYfHOO++wfPlyvv32W1577bWHco5HModu9+7deHh4EBQUxIgRI7h69apZ+9SpU6lRowaOjo40b96cmjVr3tX4hYWFjBw5kg8++ABfX98HabpEIpFIJBLJfaHVavnqq6945513SElJeSjneKBZriqVijVr1tCzZ0/Tvl9++QWdTkflypWJjo7mnXfewc7Ojv3792NhYWHql5OTQ2FhIc7Oznd93jlz5vDzzz9z9OhRmQghkUgkEomkTNKrVy8cHR1ZsmTJAx/7oQu6f3PhwgWqVKnCjh076NChw32f89KlS9SqVYutW7fSqlWr+x5PIpFIJBKJ5GEQGxtLzZo12bJlC61bt36gYz/ysiUBAQG4ubkRFRX1QMYbO3Ysffv2lWJOIpFIJBJJmcbPz48pU6YwYsQIioqKHujYj1zQxcXFcfXqVby9ve97rM2bN7Nnzx7mzp37ACyTSCQSiUQiebiMGzcOvV5/2+og98J9C7rs7GzCwsJM9VViYmIICwsjNjaW7OxsJk6cyIEDB7h48SI7d+6kR48eBAYGEhIScl/nFULw3nvvMWXKFNzd3e/3MiQSiUQikUgeOhqNho8//phZs2aRk5PzwMa97zl0u3fvpn379jfsHzhwIAsXLqRnz54cP36c9PR0fHx86NixI9OnT8fT0/N+Tsu6det47bXXuHDhAlqt9r7GkkgkEolEInlUCCFo3rw5vXv35q233nogY5bLtVyFEDRs2JCBAwcyduzY0jZHIpFIJBKJ5K7YvHkzAwYMICYmBjs7u/ser1yu5bpu3TqSk5N59dVXS9sUiUQikUgkkrumU6dOVKlS5YHNpSt3HjqDwUDDhg0ZMmQIo0ePLm1zJBKJRCKRSO6JrVu3EhoaSkxMDPb29vc1Vrnz0K1du5aUlBReeeWV0jZFIpFIJBKJ5J7p2LEjVatW5auvvrrvscqVh844d27w4MHSOyeRSCQSiaTcs23bNl588UUuX76MTqe753HKlYfuwIEDXLhwgaFDh5a2KRKJRCKRSCT3zTPPPIOPjw+//vrrfY1TrgTd4sWLefnll7G1tS1tUyQSiUQikUjuG5VKxfDhw/nmm2/ub5zyEnI11rE7cOAAdevWLW1zHl+EgJQU5f56VCrw8FDuJRKJRCKRPDCuXbuGr68vBw8epE6dOvc0RrkRdF999RXLly9n//79pW3K44NRvBkMcOWKsp2WBg4OEB2t9AkMhGvXwNkZMjOhalW4etVc8KlUUKsWqMuVw1cikUgkkjLDoEGDcHBw4Isvvrin48uFoBNCUK9ePcaNG8fgwYNL25zyy/XeNyHg3DkoLoZLl8DWFuLioGJFSEgAnQ4cHSEpCXx84PJl8PWFnBxFAGZkKO1OTsotJweaN1fEnfTmSSQSiURyV/z999906dKF+Pj4e0qOKBeC7sCBA4SEhJCQkCDnz90rQsDp04pAu3ZNEWR2dhAbq3jkkpIUEZaSAkFBirjLzgYvL8jKUm52dmBpCUVFyphqNVhYKALPxwfy8qBSJWW/p6fitZOiTiKRSCSS/0QIQd26dZk4cSIDBgy46+PLRYzsu+++o3///lLM3S1CKEItPBx271YeR0fDxYuKB+7MGUVwZWYqIi45WRF1ERFgb6947bKyFO+bt7ci2Hx8FBGXm6ucQ6tV+hw5ogi9EyeU8G1SEvz5pyL2JBKJRCKR3BZjcsR33313b8eXdQ+dXq/H29ubVatW8dRTT5W2OeUHIRTBVlSkCDRQvHKZmYqnLTkZ3N0hMVERb2q1sj8hQRF3588rgk6lUsKw584pXrfkZEXEZWYqnr28PLCxUY5XqZR9mZnKduvWyniBgXKOnUQikUgk/0FSUhIVKlQgMTERd3f3uzq2zP/CHj58mOLiYlq0aFHappQPhFBE16lTkJ8PR49CeroSWhVCmTOXmKgkNyQnlyQ32NkpnjYfH4iKUsRedray/+xZRcQlJSmiLidHEW45OVC9eomYAyWcm5WliLzfflPOlZioiEuJRCKRSCS3xMvLi4YNG/L777/f9bFlXtBt2LCBzp07Y2lpWdqmlG2uF3KnTsH+/UoY9MqVEm+a0XPm6QmRkcr8OEfHkjaDQfGoubmVhGWTkpR+oHjscnMV0ZeTo3jyzp5V2vR6RcgZRWBKClSurGyfPFlimwzBSiQSiURyS7p27crGjRvv+rgyL+g2btxIt27dStuMso0x4eH0aUVgRUUpwik+XgmnZmUp/YwL/yYnK2HQxESlX1JSST+dDlJTISBA8fDVqKGIspwcpU9xcUkW7PHjijg0GJS+Xl6KYHR0VObcZWcrN1tbOHZMEXbr10tRJ5FIJBLJLejatStbt26lsLDwro4r03PoYmNjqVKlCikpKTg7O5e2OWUTo5hLSYHCQiUBwviS+vkpc988PBQRp9MpoVF7e0XE2doqfR0cFHFnZ6eIMVBEm6enMq5WqxwTHa2MlZWlHGOci+frq4xjPMZ4LqN9qalKCNbCQiltYmsLbdrIDFiJRCKRSP6FEIIKFSqwbNkygoOD7/i4Mu2h27hxI61bt5Zi7lYYxVxCgiKaDh1ShFRmpiLAzp4tmfNmFHNZWYqw8vZWxJulpbLP1lY5JiFBmXNnDLdqtcpxycnQsKEScq1YURkjJ0eZixcfr8yd0+uV443h15wc5Vh3d+VcRpvs7BS7y+5/CYlEIpFISgWVSkXXrl3ZsGHDXR1XpgXdpk2b6NKlS2mbUXZJTlYE0okTiqAyeuKMHrdq1ZSQpzEMmp2tJDHExyuiLz1dEV/Z2YrwMiY9qFQlWa8qVUkI9fx5pT0+XsmeNRggLEzxvmVnK4LNwkK59/Ut2XZyUsYTQhGKxhIqycml+/xJJBKJRFIG6dq1K5s2bbqrY8psyFUIgZubG5s3b6Zp06albU7ZQwjYs0fxiGVkKOLIWATY3l4RYvHxihDLzCzx0uXlKf0yMxVvm61tSRKDsa8xScK4okRurnKsMexqDMHm5CjHGgyKcAPzeXoeHiWrU2i1Jft1OiWxwtsbevSQ5UwkEolEIrmOa9eu4eLiQmpqKm5ubnd0TJn9Jb106RKZmZnUrVu3tE1h9erVdOzYEVdXV1QqFWFhYTf0adeuHSqVyuz22muvmfWJjY2lS5cu6HQ6PDw8mDhxIsXFxWZ9PvjgAypUqEDr1q05f/78jcZcn81aWKh44rKzSzxnQijbxtUbkpNZfeoUHX/5Bdfp01F9+CFh588rXjNfX3B0JC02ljd27ybovffQvvMOfm+9xeg1a8hITVVE2z9h19iICLps3Ypu3jw8PvyQiQcOUKzTKWHbf8TfB6tWUeGtt2i9ZAnnw8PhwoWSEihGD6C9vXIN1tbw11/3FHpNTk5m0KBB+Pj4oNPp6NSpE5GRkWZ98vPzGTlyJK6urtjZ2dG7d2+S/+UVXL9+PdWqVSMoKOiesoruh7Nnz9K9e3ccHR2xtbWlSZMmxMbGlhv7p02bRvXq1bG1tcXZ2Zng4GAOHjxo1ictLY3Q0FAcHBxwcnJi6NChZGdnm/X59ttvqVSpEg0aNLjh+EfJ119/jb+/PzY2NjRr1oxDhw6Z2iIiImjVqhUVKlRgxowZpWYjwMKFC6lbty4ODg44ODjQokULNm/ebGov6+8bI/Hx8fTv3x9XV1e0Wi116tThyJEjpnYhBFOnTsXb2xutVktwcPANn/H9+/dTv359/P39+f777x+p/VlZWYwdO5ZKlSqh1Wpp2bIlhw8fLrP2//nnn3Tr1g0fHx9UKhVr1641tRUVFTFp0iTq1KmDra0tPj4+DBgwgISEBLMxSvPzfDv7QVkH9d+/wZ06dSoz9t8tzs7OBAQEcPTo0Ts+pswKuqNHj1KrVi1sbGxK2xRycnJo3bo1c+fOvW2/V155hcTERNNt3rx5pja9Xk+XLl0oLCzk77//ZtmyZSxdupSpU6ea+uzbt49Nmzaxbt06XnrpJUaNGmV+gn9ns8bEKN4utbpk3pwQipgzzl/z8iLH0pLWlSszt3NnozGKEExPh4wMEvLzSbh6lY+ffppTb77J0r592RIZydCtW5UxnZ3RGwx0+e03CvV6/h4yhGUDB7L077+ZunKlMp4Q7Dt1ik3h4awbMICXqlRh1JYtUKWK4qGzs1Pm26nVigD09lYEaEHBXc+nE0LQs2dPLly4wLp16zh+/DiVKlUiODiYnJwcU79x48axYcMGfv31V/bs2UNCQgK9evUytRcUFDBy5EgWLFjAV199xYgRI+46q+heiY6OpnXr1lSvXp3du3dz8uRJpkyZYvZ+L8v2A1SrVo2vvvqK8PBw9u7di7+/Px07diQ1NdXUJzQ0lNOnT7N9+3Y2btzIn3/+yfDhw03tsbGxzJs3j19++YV333231NZqXrlyJW+++Sbvv/8+x44do169eoSEhJCSkgLAqFGj6N+/P+vWrWPdunX8/fffpWInQIUKFZgzZw5Hjx7lyJEjPP300/To0YPTp08DZf99A4r3oVWrVlhZWbF582bOnDnDJ598YjZfet68eXzxxRcsWrSIgwcPYmtrS0hICPn5+aY+Q4cOZcqUKaxYsYLZs2dz+fLlR3YNw4YNY/v27fz444+Eh4fTsWNHgoODiY+PL5P25+TkUK9ePb7++usb2nJzczl27BhTpkzh2LFjrF69moiICLp3727WrzQ/z7ez30inTp3MfoN//vnnMmP/vdCoUaO7EnSIMso777wjhgwZUtpmmBETEyMAcfz48Rva2rZtK8aMGXPLY3///XehVqtFUlKSad/ChQuFg4ODKCgoEEIIsWHDBtGjRw9RWFgoDhw4IJo0aWI+SHKyEMePC/HLL0J89JEQixYJMW+eEAsWCPHNN0J8/LGy/6uvhJgzR4ipU5W2994TYs4cETNjhmL/mDFK+5w5Qrz/vhDLlyt9Fy0SYu5cIRYtEv8LDRUaCwtR9MUXQkydKn4fOVKoVSqRNH68ELNmCfHee2Lh008LB2trUTBjhhBTpogNgwaJHnXrisL33hMHJk4UTSpWVMZbsECxa968kvPOnauc8+efhdixQ4jrnpf/IiIiQgDi1KlTpn16vV64u7uLb7/9VgghRHp6urCyshK//vqrqc/Zs2cFIPbv3y+EECIjI0NUqlRJpKamitTUVOHv7y8yMzPv2I774YUXXhD9+/e/ZXtZt/9mZGRkCEDs2LFDCCHEmTNnBCAOHz5s6rN582ahUqlEfHy8EEKI8PBw0bhxY5GdnS0uXLgg/P39S8X2pk2bipEjR5q29Xq98PHxEbNnzxZCCNGoUSNx8OBBUVhYKLp37y42bdpUKnbeCmdnZ/Hdd9+Vm/fNpEmTROvWrW/ZbjAYhJeXl/joo49M+9LT04W1tbX4+eefTfv8/PzEhQsXRHZ2tmjcuLE4ffr0Q7XbSG5urrCwsBAbN24029+wYUPx7rvvlnn7AbFmzZrb9jl06JAAxKVLl4QQZevzfDP7Bw4cKHr06HHLY8qS/XfKnDlzRO/eve+4f5n20DVs2LC0zbgrli9fjpubG7Vr12by5MnkGtc7RXGt16lTB09PT9O+kJAQMjMzTf+sjf/ejCHE2bNnlwwuhOIJi45W5sxptcr8OYNB8c7FxZVkqqakKN45YwkRe/uSEiOgtDk5KfPegoKUunWenkpiBUB8PBn5+ThYW2N58SJotew/eZI6Xl54urkpx6lUhLRrR2ZBAacvXQIfH0KaNFHsnzWLTl99xexOnUoyZ42Zr6BsJyQo13DunHINKSl37KUrKCgAMPNmqdVqrK2t2bt3L6C8f4qKisxSvqtXr46fnx/79+8HwMHBgcGDB+Pt7Y2Pjw8jRozA3jgH8CFiMBjYtGkT1apVIyQkBA8PD5o1a2YWQijL9t+MwsJCFi9ejKOjI/Xq1QOU97yTkxONGzc29QsODkatVptCGbVr16Zu3bo4OjpSq1atUglnFhYWcvToUbPnWq1WExwcbHquP/zwQ4KDg9HpdKjVakJCQh65nTdDr9fzyy+/kJOTQ4sWLcrN+2b9+vU0btyYPn364OHhQYMGDfj2229N7TExMSQlJZldh6OjI82aNTNdB8DUqVOpUaMGjo6ONG/enJo1az4S+4uLi9Hr9TdEkLRaLXv37i3z9t8JGRkZqFQqnJycgPLxed69ezceHh4EBQUxYsQIrl69amorD/b/m7v10JXJ5ReEEBw9epRp06aVtil3zEsvvUSlSpXw8fHh5MmTTJo0iYiICFavXg0o67NdL+YA03ZSUhIAVlZWbNmyhZSUFJycnNBoNErH62vN2dkpoVYhlOxSlUoJr3p7lwgmHx/lPitLEWpqtSLmqlRR9hsMSsjVw0MRVMZlvVQq0Gq5AkzfuZPhDRsqBYizskgqKMDzn/ArmZlQsyaeFy8q9ltaQkYGVrm5bBkyhJTsbJz0ejSOjopY1GpLSqOkpCj2GZMyvL3h0iVlnl1KimLvf2D8gZo8eTLffPMNtra2fPbZZ8TFxZGYmGh6TjUajenL6Prn3Ph8A7z//vuMHTsWtVr9yH7UUlJSyM7OZs6cOcyYMYO5c+eyZcsWevXqxa5du2jbtm2Ztv96Nm7cSL9+/cjNzcXb25vt27ebJvAmJSXh4eFh1t/S0hIXFxeza/j++++ZN28eOp0OrTF55hFy5coV9Hr9TT+f586dA+DZZ58lNTWVzMzMu15f8WEQHh5OixYtyM/Px87OjjVr1lCzZk3CwsLKxfvmwoULLFy4kDfffJN33nmHw4cPM3r0aDQaDQMHDjTZerPX5PrrGDp0KP369aOwsPCRlreyt7enRYsWTJ8+nRo1auDp6cnPP//M/v37CQwMLPP2/xf5+flMmjSJF198EQcHB6Dsf547depEr169qFy5MtHR0bzzzjt07tyZ/fv3Y2FhUebtvxkNGzbk4sWLXL16FVdX1//sXyY9dHFxcVy7ds30T/9Rsnz5cuzs7Ey3v/76646OGz58OCEhIdSpU4fQ0FB++OEH1qxZQ3R09F3b4OHhUSLmQBE6xcXKMl7h4SWZpC4uiggzrsoALD9zBrvRo7F74w3s3n2Xv06cULxhgYFKggIoma7u7krtOi8vRRDa2oKtLZlaLV2WLqWmpyfThg9X+jo6lmS8JiQoovLMGSUpA0o8f0VFkJmJR04OGuNar+7uStasWq149qpUUebQVaqkjJOdrVzb1au39ND9+zU5cOAAq1ev5vz587i4uKDT6di1axedO3dGfQ8Zs46Ojg/1R+3f9kdERADQo0cPxo0bR/369Xn77bfp2rUrixYtuuvxH7b9cOvPRfv27QkLC+Pvv/+mU6dO9O3b1zTv7G4wTowvy1hbW5cJMQcQFBREWFgYBw8eZMSIEQwcOJAzd7le8qN439wKg8FAw4YNmTVrFg0aNGD48OG88sor9/T+NyblPGp+/PFHhBD4+vpibW3NF198wYsvvnjX30GlZf+tKCoqom/fvgghWLhw4T2NURqf5379+tG9e3fq1KlDz5492bhxI4cPH2b37t13PVZZ+T5ycXGhcuXKHDt27I76l0kP3YULF6hQoUKpPKHdu3enWbNmpm1fX997Gsc4RlRUFFWqVMHLy8ssaw4wZZ55GddKvRnGsh+RkRAbqwivuDhF1MXHl3i7VCqws6O7pyfNxoxRvHAqFb4ajVKP7vx5xSMHisjKzVX2JyYq2a6ZmWRZWtLp44+xt7VlzXPPYXX+vDJ+QgJe9vYciolRvIJRUeDuTnJcnGJ/UJAi3pycFFuqVi1Z8zUrS7kG4/OYlaUIOWMSR0KC0m5chcJYB+8/XhOtVktYWBgZGRkUFhbi7u5Os2bNTO50Ly8vCgsLSU9PN/NWJCcn3/75fgj82353d3csLS1vCK/UqFHDFDIuS/bDrT8Xtra2BAYGEhgYSPPmzalatSrff/89kydPxsvL6wZxV1xcTFpaWqlcw61wc3PDwsLihkzQ0nqu7wSNRkNgYCCghGUOHz7M/PnzeeGFF8rU++ZWeHt73/T9v2rVKqDkOzE5ORlvb29Tn+TkZOrXr//I7LwdVapUYc+ePeTk5JCZmYm3tzcvvPACAQEB5cL+m2EUc5cuXeKPP/4weeeAcvN5NhIQEICbmxtRUVF06NCh3NlvpGrVqsTExNxR3zLpoUtISMDHGDZ8xNjb25t+oAIDA+9ZVBpLmxg/zC1atCA8PNzsDbV9+3YcHBxuPW9CCMUTVlyszG+ztVVCo0ZBZPTMGT1oWVnYV65MYJUqBLq7E+jhgbZ2bcUzFxioHAuKp8/OTsmU/cerlpmWRsf589FYWbE+JASbihWVvgkJ4OVFC09Pwq9eVewPDAQrK7Zfu4aDjQ017exMJVBwdCwRbUbPX3a2Yn9iopKVm5CgXFNUlNJuzLhNSVFud/GaODo64u7uTmRkJEeOHKFHjx6A8iNnZWXFzp07TX0jIiKIjY2lRYsW9/Sa3iv/tt/R0ZEmTZqYPHVGzp8/T6VKlcqc/XDnnwuDwWCa49iiRQvS09PN5oD88ccfGAwGM3FY2mg0Gho1amT2XBsMBnbu3Fkqz/W9YHzey9r75la0atXqtu//ypUr4+XlZXYdmZmZHDx4sExdByh/ary9vbl27Rpbt26lR48e5cp+I0YxFxkZyY4dO24I8ZWXz7ORuLg4rl69avYbXJ7sN+Lj43ND+ZhbUSY9dImJiWb/akqbtLQ0YmNjTU+q8YvIy8sLLy8voqOjWbFiBc8++yyurq6cPHmScePG0aZNG1MdvY4dO1KzZk1efvll5s2bR1JSEu+99x4jR47E2tr6xpMa580VFsLx44pQio0tKR5csWKJcDLeJyaWFAL28lKE09mzpNnZEbt3Lwn/pMtHFBdDWBhelSvjlZ1NZmEhHf/3P3Lz8/lpwAAy/f3J/EcEumdkYJGVRcennqLmnj28/PvvzBOCJIOB99atY2SbNljn5yuCLT9f8bIlJZUUKE5KUhIikpMVz2BysiLmjIWHU1MVr1xGhiI873BS8K+//oq7uzt+fn6Eh4czZswYevbsSceOHQFF6A0dOpQ333wTFxcXHBwceOONN2jRogXNmze/37fEfTNx4kReeOEF2rRpQ/v27dmyZQsbNmwwhQfKuv05OTnMnDmT7t274+3tzZUrV/j666+Jj4+nT58+gOJx6dSpkymUVlRUxKhRo+jXr1+p/WG7FW+++SYDBw6kcePGNG3alM8//5ycnJxSL1twMyZPnkznzp3x8/MjKyuLFStWsHv3brZu3Vrm3zdGxo0bR8uWLZk1axZ9+/bl0KFDLF68mMWLFwPK0kdjx45lxowZVK1alcqVKzNlyhR8fHzo2bNn6Rr/D1u3bkUIQVBQEFFRUUycOJHq1aszePDgMml/dnY2UVFRpu2YmBjCwsJwcXHB29ub559/nmPHjrFx40b0er1pXpmLiwsajabUP8+3s9/FxYUPPviA3r17m36T33rrLQIDA00JTKVt/73i7e1tmhv+nzykbNv7Yvz48WLUqFGlbYaJJUuWCOCG2/vvvy+EECI2Nla0adNGuLi4CGtraxEYGCgmTpwoMjIyzMa5ePGi6Ny5s9BqtcLNzU2MHz9eFBUV3fykSUlCbNumlPtYsEApL2IsQbJokRDffivEwoVKKZCvv1ba/yknYtZ/yhSxpE+fm9vfrZsQixaJXa+8ctN2QMR8+KEQU6YIMXu2uDhmjOhcs6bQWloKN51OjH/qKVE0bpxy3nHjhJg5UylFMnmyEGPGCPHuu8qxc+YI8c47SqmSefMUu999t8TWqVOVtp9/FuLkyTt6TebPny8qVKggrKyshJ+fn3jvvfdM5V+M5OXliddff104OzsLnU4nnnvuOZGYmHhXr/3D5PvvvxeBgYHCxsZG1KtXT6xdu9asvSzbn5eXJ5577jnh4+MjNBqN8Pb2Ft27dxeHDh0y63f16lXx4osvCjs7O+Hg4CAGDx4ssrKySsnq2/Pll18KPz8/odFoRNOmTcWBAwdK26SbMmTIEFGpUiWh0WiEu7u76NChg9i2bZupvSy/b65nw4YNonbt2sLa2lpUr15dLF682KzdYDCIKVOmCE9PT2FtbS06dOggIiIiSsnaG1m5cqUICAgQGo1GeHl5iZEjR4r09HRTe1mzf9euXTf9jh84cKCpJNfNbrt27TKNUZqf59vZn5ubKzp27Cjc3d2FlZWVqFSpknjllVfMyoSVtv33ypdffim6det2R33L5NJfoaGh1KpVi3feeae0TSkdjN65pCQ4dkwJWXp7Kx44Ly9l28VFmYN26ZL5uqsZGcoYFSsqx3t7K6FVLy9l2+i5s7dXjjOGPHW6Eg/b9atPXLkCrq6KJ83NDdLSICCgxBvo66uMq9Uq46lUythxccpjHx9lLOPbzM9POb+Pj2K7SqV48ozrxzZrBnXq3DCPTiKRSCSSJ41Vq1YxZ84cs1VIbsUDmUP3oJfMSUhIKFMh10eKUcwVFSnzypycSrJBa9YsySgtLFSEmr29IuLS0xWRZW+vhGDPnlUSGOLiQKej4MwZClxdlXCscQ6ecU6eTqfsN2a++viUnLNKlRKhl58PDRsqqzv4+ipCLDsbatQoWcu1YkVlrNq1S2yBksenTyvizyj4srIU+7OylESOc+dKrlEikUgkkicYb2/vO55Dd9+C7mEsmZOYmFimY9oPldRUZY5ZZGRJoV9QxFNCAlSvrogfYx05Y5JEbq6ynZOjiCdjIV+DgZOJiTguWoTD+PGczMwEf39lvKwsxWPn4lIyRlAQaDQ3F2y2tooNOp1ik1GEnT1bIhKvF2z/JGqYlhoTQhFv1xdEzswsEXXp6co1X7d0lEQikUgkTyo+Pj4kJyej1+v/s+99C7pPP/2UV155hcGDB1OzZk0WLVqETqfj//7v/wBlzb5GjRpRt25dfHx8SE9P/88xMzIybiiM+cTg6qokB6Snm9ZINYVSfX0VwebrW5JFWr26Irbs7JT6bo6OSo03R0dlPAcHfj53joLiYgqLi/nl4MESsWUUVJmZJduxsYqgNAotg6Gkf3a2Yld6OiQkUFBUpAhKG5uSDNqsrBLBlp2tjJOdrdgHSljV2GYME2dnl3gJhVDCvGVvJoBEIpFIJI8UZ2dn9Hq92Trlt+K+BN3DWjKnqKgIKyur+zGt/CGEEmr86y8ltHn5snkoMjMTLl5UHmdlKdsGg1KLzrjv8mVFBP4jwLKvZRGZXsTPx8NQqVRYWlqy4lgYUQYbLueqOK53IFplS7TQEm6wJUxvT1iRlrCrxYQV2nC82J4odCTmGrhg0HKyyI7wIjtOFupYlVaE06efcjInRxGUvr6KZ9C4zJhKpXj0HBxK5shVqKC0W1gonsH8fGWennGfr68i+IqLb1q+RCKRSCSSJwlLS6UYSXFx8X/3vZ8TPawlc4qLi588QWcMtaaklIg0Y8LAtWvKY5WqxJtm3Aalj7G4cEYGc/bsYfLmzYCyVt2llGTeeOMNvvzyS6rWq0PVt0aZTvvKC4Pp3/MFUjMKMTgbsLJSY+3gSFFGBoWFBuwNBXhUduJK0jUy7axRq8DGSs3/jhwgv6iIXy5fpm6jRsqSYEaM4iw+vkTcVaigeOAqVFA8fMbwrrGPk5MyxrVrys1geHTPvUQikUgkZZBHJujulLtdMqe4uBgL4yT7JwVXV1i/viRU6uSkCLXrQ89qtSKILl9W2qCkHp2fnyKELCzIum7ZMIPBwPDhw5k9ezYFBQVERkaanTZLryffxpacfBXC1hZLK0tyBeDqTmGRHlVRAZqiIjJcXclR26BCRYGlBXuP7EWlUrEqLIyZ7dujUqkUoZadrXjm4uMVb+H1Ga6OjorX8WZ9VKqSsK/BoIRdn9TEGIlEIpFIKBF0RUVF/933fk70sJbMsbCwoAxWU3l4CAF79yri7MQJRaylp5fMnTMYSkqTZGcr7RkZSnt6esm8tH/6zHzqKerb2jJ41SoiIiJYtGgRtra2TJgwgeDgYKysrFCrVBTrDbRp0wF9ThaoLSlGhcqgR61SIwCDAINaYDDA2g0rWb7yO0Dx+iUkxJq8fh23b2fHjh0AzG7ZkreffrrEVqPn0HidWVklma/X97m+3bhurEQikUgkTzCGf6JVd+Lkuq85dA9ryRxLS8s7UqOPDcnJSimQsLASz5tRzNnbl3i5rq/Zdj3GjFajIPL1pU+9evz40gDi4+M5e/Ysf/31F2fOnCE2NpaioiJaNW6BXl/MgYO7cbLVYGNjic7WCmtba2ztrNFYW2KttUSntUZvb0eRvsB0uuu9fsOHDzfLvsn6Z01ZnJ1Lat1ByXUYQ6nG67n+3njdBgNcvSpFnUQikUieaIyhVqOn7nbcd8j1YSyZY2lpeUfx4scCIZTaaxcuKNuXLpUkQTg4KOFV47Zx3lx6esni9tdnqIJSqy4hARwcOHo5FoDp06dz5MgR08L1AEdOHkOlUrH7zz8Y2KMvBXoLhEGPpVpFkV6PXgiEBagcnSA3i4H9h+PpH8T/LZpLVFSUudfv6adxsrPj2549eb5OHSW06uSkCFIPD2VOXU5OSXFjOzsldJyVpVyjECVC1s5OEYFOTsq8Qg+PR/VKSCQSiURSpjBqoTvJK7jvsiUvvPACH3/8MVOnTqV+/fqEhYWxZcuWGxIl7gatVntHKbqPBSkpSjKESlVS5NcYknRwKPFwqdXmws2Icc6dWq0IvIgIZe5ZdjYbI84CcPz4cTQaa8LCwv7pqiYnL5dRo0YRe/kSsxd/woCXOjKwWwvW/LKEjPxc8oqLyC8sJruwELVaTWZeAfVrNWHUhFnExsaae/3i4lj90ks8X7t2SRmU9HRFnOXnK9dWo4Yi6mxtldCypaVyb7xOlUqZB6hWK9eUnq6sTCGRSCQSyRNKbm4uADY2Nv/Z94GsFDFq1CguXbpEQUEBBw8epFmzZvc1npeXl2lh4McaIRRBd/WqEo4MCipZycEoenx8SkKtjo4lWaP29kqBYKMAMnq2atSAuDiKi4o4kxCPhVpNw7oNCAl51qT0DQYDBoOB6dOnM3z4cLPwdm5qHKNe6EhBQiTeNiocDYVoNZa4UIi3tYHk6HAAZs6cSZs2bZg9axYA+yMjS2rOGUunJCQoj6+vZWcUfAaDUvPOmCRhZ6cUKDYYFDFnMMgCwxKJRCJ5oklMTMTZ2Rlra+v/7PtABN2DxsfH546XuijXpKQoN2PR4KQkxYNl9MoZ58QZ56UZ689lZCgC7+xZZQmw+Hilzc5OCd8aDFjm5JC0YAG7J0+jQ0g3NmxYg1qtNk2sHDhwIDY2NoSEhPDnn3+i0+r4YNJ7uGh1FBYW8Of239FkZ2KpFqiz09EU5GKpNrBv3x8AXIi+wDsDXiEuKgqAVRculNSXS01Vig1nZZXM/8vIUGzPzS0RfBkZitfOYFBWkrCxUe6zspQs15QUOY9OIpFIJE8sd7MUapkUdN7e3iQmJpa2GQ8Xo3fuyhXFw5aQoBTZvb7MR3GxInpAEUvG+Wb29or4My79ZTAonrzkZGU91n+W/3KKT4D6TfjfLz8CimfORqtDpVKxefNm7Ozs6NOnD0II6tRvjG+TNuw8oJQj+fPQfhLVFlzOyCPbRkempTWpOQXExF6kUeOWfDjzG7p278v+BYvoUb8+p+LjKbaxUbyKAQFKqNXOTvE6FhQoQk+tVq4xL09ZHuwfV7Jp/lx2tnJva6sIPllgWCKRSCRPMHezFGqZFHRPhIcuNVXxVKlUinjx8VFEnTFBwOh1y8wsWSfVWJrEGFrNyVHaatdWRFJQkHL/T6FetbcXq5Yu5MJFxYvWtGlTcrKzGDVqFMnJyVSoUMGUEn344F5+XjiXxPg4Ro0aRWL8ZX5cNI/BfTrQsWNL1m5ajY1Ww+L/W8XESdNx93BD4+GCS3oGa155hcSxY5UsHHt7xQadTgkbW1lBtWoloWRnZ0WwGZcrM9rs5VXi4TMKvuho6aGTSCQSyRPL3XjoHklh4bvF29ubP/74o7TNeLi4uiq154RQhE1srCLuHB0VT5W3tyLw7OwUL5y3tyLqjEkQcXElHrtz5xRv3T9z0DqtXs3Ws0pCROXKlU2nPHToEL1792b27Nnk5+fzyy+/oFKpsLCwoLi4WCkODHz55Zc0bdrUrByJPjcTR60VumILMBQibG1QxcVhZW+L6vBpXEARnAkJJd43R0dFlKallWTtGrNzExOhVi3lPihIuR5bW0XwGWvrOTvfWKJFIpFIJJInhMTERBlyLdMIAfv2QYMGigBKT1f2G8uT2Noqos7eXinf4eOj7PfzU7xyRhFoTChwdy8Jvfr6kmUMXQL+/v7Y29ub1nJdtWoVdnZ2xMTEkJWleOuKi4sJDg42FQcGaNiwIVFRUdjZ2zNl2kxCh7xKstqSK1bWFNrbkV1UTKKDC2lXMxSxWbWqEj62tlbCpEYPW3GxItqMc+WyshTvo5eXIkStrRUhahR88fElNevUauXaJBKJRCJ5Ain3IdcKFSoQGxtrCgc+dqSmQtOmylJfNjaKkDGKMCFKsj4dHEqEkIODIoBsbBQRaBQ+BoMSorS1NYVeG9eqZTqVhYXFDcINYMeOHTRt2vSmxYHbtGlDp06duHz5Mq99MIcGLduQr9OSYzBg4+qCpZszDsUF5OuLyHB1obhiRYpSr5Bfpx65uflku3gg8vKhSpWSa/H0VASfjY1id3S0IuaiohRhamurPNZq4fx5ZduYGCGRSCQSyRPIpUuX8PX1vaO+ZVLQBQUFUVRURHR0dGmb8nBwc4MtWxTvU1aWItqML5hKpTw21m/TahVRlJWFwd2d4sRECq2tydNqyY6NJcvGhoz8fDJ1OtJi47jq5sHBf8KtcPtVHRo2bGgqDhwREYGFhQU+Pj78+eefzPqnHMmFc6dJK8wns7AQoRJkFRVSEBtLppMTezaso3roC8zcuY/wus05HZ3AGY8qXHTx4VpANeW6KlZUBJylJVSurNzn5ipiLzdXEaTe3orINXoaPT0VcVdcLEuXSCQSieSJpLCwkNOnT1OvXr076l8mBZ1Go6Fu3bocPXq0tE15OKSmKiLHWEDYuPSVsThwYiJ4epISHc2p5GT2FxURYWPD6fR0Dnp4cEij4bizM6f9/YkB4hwdiSwuJtzVnRM5hSQWFAKKdy4iIoIJEyaYhFtkZCQajQaAzp07m4oDJyQkoNfruZaRwcsT3+LCpUsAhO3eicbFCSwt0Lo5o1cZuOLoTE5SEpv2KiHaH/78g/0nTtJyzlS2JKdx2sKBIpVaEabGeYAGgyLgHBwUAZeTo1x3kyaKhzEwUNlnFHdubiW16yQSiUQiecI4ffo0NjY2VKlS5Y76l8mkCIBGjRpx9OhR+vXrV9qmPHgMBkWo5OQoc+SMa7EaRZ69PSQkUOzvz4Xz58nw9qaCnR0GrZbU9HQKPTywLipCLQRqBwdWb9zIilWrAAgODiY2KZE33niDL7/80rSWa1JSEmlpacTFxaHVaoGbLwnm5utDyz49qdWhPWtmzubogf0UZF3DwsEJlUFgaaXGRmuFQetD/MVoVCoVCZdj+OP0cQqLCjkQfpDA2vXIdXGB/X+UhFV1upJaezY2yrVXr66EXqtXV5Y4s7dXRF5AgFKC5Q7dzBKJRCKRPG4cPXqUhg0bolbfme+tTAu6n3/+ubTNeDhcuaKsDmFrW1Ik2JgIYCxRolJBcjJFFSui0unA0RHrnBw0rq7YurhgZWVlWvGh+J9iwWAeYt2/fz9HjhwxCbemTZsCkJ+fDyhLgqlUKtOSYHaOjiTGXKTYUIzWyY5Jc2eScTmePJUgr7iYRRPGcebgQUARjgX5+SbhmJmuzHXbtGElCYmxLB47icrVqinz/nQ6RcBGRSmeN+O8wGPHlDDs2bPKdRu9cbm5JStkgLJfZrtKJBKJ5Ani6NGjNGrU6I77l8mQKyiC7tixY4jHLeQmRMkSWMaac8bVIIyiDsBgQOfigpNKRZFeT8rVqxTY2FCYkUFOdjYZGRnk5uaiUqkYOnQo7777LnZ2dkRFRZlCrHl5eYAi3Pz8/Dh27BigrOWqUqnQ6/U4erihNxhQW1iQn5vLvB0bsLPV4mKrw97GCh9XZywt1VhrVBT8M55i3q3n5sVEnaVJ/56oQkOZEx6ulC9xdFTEnFF8qtWg0cCFC8o8waQk5dqN3svExJLlv2RihEQikUieMB4bQVe7dm3y8vK4cOFCaZvyYElNVYSKsd4cKKLOWLLEYFDEnZMTWisrdEVFaF1ccAsIwJCVhWetWmgKC7GyssLS0pKioiJyc3Np1qwZk6dMITY2lrNnz7Jr1y4iIiJQq9Xo9XoSEhJwcHDAxsYGvV5vqjnXonsn5v25lnm71zFr1xp0zrYUUUiOyCe5MA+VSqCzssBaY8G7S/+PZ158EQtLyxvm5p0/fx5LS0vq1KlDgwYNTJebZSxHAuDiotzb25fUnPP0VBJA3N2VfampiteyuFjJ5E1Lk/PoJBKJRPJEUVRUxMmTJx8PQWdMjDj4T4jvscHNDa5dU2rPVaigiBeDQfFC2dqWrNuanY21Wo2NkxMkJhIfH4/az4+c6GgMOh1WVlZYW1uj1WpxdnbGycmJyPORAMyYMYOnn36aWrVqYTAYUKlUdOvWjbS0NGrUqAFgKglzYsefeLq74unpgoe7E5YaFXp1EcWiiPzCXHLttBjyssgwFFIsinlhwlh6jxxpmptnTKqIj49n7ty5rFq1iqioKOzt7Fka+jIzO3ZUrhcUgQbKdVpaKt46R0dl28lJEXYBAcp+Y6JIRoYUdBKJRCJ5ojhx4gTW1tYEBgbe8TFlVtABPPPMM2zevLm0zXiwXLmieKacnJQsTkdHxTPn7q5ke4Iyx8zHB7Kz8c7JwdneHq1Wi7awEDtPT0RWFvn5+eTl5VFYWEhWVhbZ2dns/XMPAGfOngOUDBkANzc3AgICALh69arJlMaNG5MQc4kfRr7Py9VaMiioNX8v/IHqwgrbqMu8/kwvMg4eJtDTC1+DATdDMc5FhZz7ex+gCMc2bdowc+ZMAH7++WfOnDnD5cuX+Wjy+/Ts1EkRZleuKMJMp1OuNSZGSQQxFh22s1OSIDw8lOfAx0d5XhwcSurRSSQSiUTyhLB582aCg4PvOCECyrig69atG7///jvFxcWlbcqDw+ihu3atZOkuYwkPYyjW3l7xZtnZYZGZiU1eHg4ZGZCejn1REb6enlRycKCSvT2eNja4azS4WVhwMSaG1q2f4pv//Y+3p3+IwWBAq9WSnp7O+vXrUalUpKSkYGVlRXBwMFu3bmX48OFkZGSYzMu/lkZxZgpb1qyhsKCAIePe4uK+fehyM1Gr9FjnZRN1+gygzM27/j4sLMxUv+6vk+GkaO2VuXKVKyvXlZenXJux3lxWFhQVKfPojPuKi5X9xjCtpaXynEkkEolE8oSwceNGunXrdlfHlGlB17RpU9RqNQcOHChtUx4cRg+dUcBlZyvzyJKTlSK8GRnK7Z9sVyd3dzyys7FWqSgsKKBYr0fExZGXm4v+2jXUGRnY5OZilZfHnh9+4NNJb+FqoWLzho0YDAby8vJo27YtkZGRjBo1ivz8fNq2bcuOHTtwdXUlPz+fcxGKR69Vh5b0HNGVZF0Re/86hEqlQqVSs2rfX6Ro1WQY8klU68nNzcXJ1RVXNzdUKhVCCNN6sFFRUQDsPXqAVGtb8ry8laxVlUpZ7svZWRGwVasqYs/JSRF8Rq9dcrLixTOGaY3LokkkEolE8gSQlJTE0aNHefbZZ+/quDIt6CwsLOjSpQsbNmwobVMeHO7uipBRqRRPlJ2dInA8PRWvnKNjyZwxlQqr3FyKdTr0QKaFBXnFxeirVSM9KYmkwkKyLC3JKyggOz+ffBsbklUqUixU5Oblmk55u4zUgwcPkperhHr37fybvi0GsXTCYuIvxZmWC8tIzmJQ02cZ2DSEV9p2BaBR/fokJyUxatQo9Ho97dq1AyAtLY3K/pVJuHQBVUYGNtlZJdeZmalco1HAGkOrKSnK3Dm1WhF9QijC12BQRF5EhJxHJ5FIJJIngt9//50mTZrg4eFxV8eVaUEH0LVrVzZu3FjaZjw4VCpFuBhrzxnrq2VnK4V0MzOV9oQEZVsIdBYWpCUlkalSoff25vzff9N37Fjis7JwsLfH3tsbKycnbHQ6rO3ssHNx5d2F83FydrrpahHnzp3DyspKKQqckEBxcbFZceHbCUAjQgizPtevu9umQSN++r8VFNvbcU2vUoSbMcRaXKw81usV71tCAlSrVlJIOC9Pua9SRXmsUinHyNIlEolEInkC2LhxI127dr3r48q8oOvYsSORkZGPV/kSF5cS4ZaZqXivHByUJAljCRNvb4iM5LfISALHjCEtN5fKFhY4pqezNyKCwsJC/t61i6LCQvJSUrDJzyclKYluISHkHT2Gr5U1WZlZWFpaEB8fz/Tp01GpVFSrVo1atWpRVFTEqFGjyMrKIjg4mCNHjgD/vVwYgJVGY1bvbsKECURGRWFrb8+YSW/T4+XB5Du5EKuyJNLVW7lOHx9FwNrblyxxlpKirCRx/LhSiy4+XkmMyM5Wbt7eigg0ljuRSCQSieQxJj8/n23btt31/DkoB4LOwcGBtm3bsnbt2tI25cFx9SpUqqSIOVtbJdMzM7OkjIlxzpmHB59s2wbAop9+oiA1lQyDgd07dqBSqdj7999o8/JQqVTk5uezes0aCgoK6P/KcJ5p1pr27dtT8M+6rjt27DCd/nYeOGtr6xtKksTFxeHo5Iidgy1vfvUOk7+faap3Z+pz+TIjPppLvZBnsHZzRORmk2WlIctGS5KrG8m5uSRYWXHJwoKUChUQarUSfs7PL1k9IitLEXXZ2crzEh+v3J84IRMjJBKJRPLYs3PnTlxdXalTp85dH1vmBR3Ayy+/zPfff//4rBrh5lZSRDgrSxEtxsxWBwfFS5WcjEEITiQnAxAWG0uHsWOZ8+23XEpMZNSoUVyMj2fCwoU0fv55Wvbrx/pdu8xOYzAYTDH47OxsvLy8bumBs7CwYNCgQeTmKnPvpk+fTps2bUxZq6kpqcz9fjohTzfi4kkliWLmzJlmfS6Gn8TW3h7rzEwq+FckwBpsLQyciLpAmMHAqZwcDhcWcjQ9nSsuLuDqqngnjSFoLy/lsY2NssarwQCRkYqn7uzZR/LSSCQSiURSWnz//fe8/PLLpuL/d0O5EHR9+vQhISGBffv2lbYpDwZj0dwKFRRvnLEWna0tnT76CNWoUaimTSNkzRryCgrueH5bRmYmb7zxBqAUZj537hzXrl2jR48e7NixAyuNFQJxUw+cXq9n27atWPyzNNfx48epVKmSabkwCwsLLuwJp5pVBY5t+BOAS7GXmPT2BC5euqgcs3MXbsXFWLu5YpccT6XCHALsrbF1skdtaUmhvz85lpakW1gQb2+viFmjSLe3V0qUqFRKYeEqVZR7nQ5On5ZhV4lEIpE81iQkJLBp0yaGDRt2T8eXC0Gn1WoZMGAAixcvLm1THgwqlSJYzp+HoCAlxOjoCBkZZFlamroZxdvu3bsJDg5Go9Hc4F2LiIjAwsKC2rVrmwm96tWrk5CQwJIlS3jzzTdJTEzkcuxltDotUOKBmzFjhul8V69eRa/XY2lpiV6vp6Aoh7fffQ3tP8uFbd+xH/vYbCKiYujUqhXbli7j9eGvs/6n5bR5uj2x0RfQGgS2KcloiovITYjnytmzaC0tsfXwwFOrxb9aNdx8fbFMTYUaNZRkiIwMRdzp9YrHTqVSEiLs7BSh5+urhKklEolEInlMWbJkCU8//TT+/v73dLxKlJM45unTp2ncuDHx8fG4lHdvjRDw559KaPH335WQokqlCJucHN48dIgvt2/H08uLXbt2UbVqVSIjI2nZsiVXrlxh3bp1ODs7k5aWRs+ePbGx0eLh4c6OHTuoWrUqGzZsoHv37gBUqFCBuLg4AIKDg9m1axd6vR4LCwv0ej06nY7c3Fw8PT1JTk4mODjYNN9u7MRe9G7Zlg59J9A4MJAjUVHEHThATmQkdpUroxcCg8FAka0tRcLA1eJinDMyMVhbo4+NpbCoCKHXo8rOJsvRkQK9HmEwIPR63Pz8qHLyJI7/LHMGKKIuO1sRcj4+itcyN1cRdM2bQ506JVnBEolEIpE8JhgMBgICAvjss8947rnn7mmMcuGhA6hVqxaNGjXixx9/LG1T7h+VClq1ggMHFA+dEIqQsbcHLy8+bdqUt7s9f0No9Mo/S2AZ56699957ANRp2twsSWHZsmWmU6WkJJseFxcXI4SgatWqplBto0aN8Pf359q1awwbNswshKvOyGb19j8pLCqiRePGRG3YgFajwbVGDTTJyViq1Qi1GkNmJvqCQvTXrpDsbE/WlStkubqS4+5Onq0t2ZUqkVNURJpeT2pREQn5+UScOcOFOnVKljtzdFSEnE6n3CcmKs9JcbEidFNTZekSiUQikTyWbNu2jYKCgnsqV2Kk3HjoAH744Qfmzp3LqVOn7mnCYJni9GlFvKxerQg6W1sl0zMzE4KCaD99BrsjztGkSRMOHz5MkyZNOHbsGHq9Hk83d/p178Pin5eSl5eLVqsjLy+Xxo0bc+TIEWxtbcnNzcXa2hpnJ0cSk5KxtLTEy8uL1atX07hxY7788kvGjx+Pp6cnf/zxB25ubly9epWnn25PVlY6X8ztS6fnWtCyxRQuxKQQWLkyh//6CyEEhowMVBoNhYmJFLu4YDAYyC8qIsXagpxrV9Ba2GAoLEav11NcXIwTkJuXR/Y/XsGioiKEwUBAfDwNNRpU2dnmnjeVShG3mZnKTaWCunXhqaeUxAmJRCKRSB4jevXqRY0aNUxro98L5cZDB0pyRGJiIrt37y5tU+6fGjXg0iVFuNjZKfuEUARLUhJHYi8BcCIsjGee6kDY8eOm+W3fvzuX50K6Y63RoFKpKCjIB+DIkSMEBweTk5NjWuarVm0l9bm4uJi4uDgSExPZu3cvlSpVori4mPj4eM6dO8fp06f/SZCIJyMjh4GvLcHb+zWiLyQzatQoIi9c4PmBA3GuUAHXWrWY//XXaHx8UFtaglqNxtISD70BL1t73C0scbewwEWtxsXCApv8fGytrXHKzMRJpcLDzQ3XvDxsKlYk27i0l1G8GevyZWYq+9XqkpC0RCKRSCSPGbGxsfeVDGHE8r+7lB20Wi2jR4/mww8/pH379qVtzv2hVitlO/z8IDa2ZHms7GzybWzIzsvDy8GBr9+ei5Ww5fmuLzNm6gjyC/LoOm4IwcHBpGek88Ybb/Dll1+ahr0+C7agoIDIyEiz006fPp0jR47QpEkT074ZM2aYvIAAVapUITo6GoPBgLe3903Hys3NxcrCAlVODoXW1hRnZZFflI/BUEimEDgXqlAVF6MuLqagoAD1tWsUZWWRVVRE0aVLFLu6khkVhWXNmgSFhytz5hISlOcgKkqpUWcMQ+fmKiVMUlOVZcOkuJNIJBLJY8KsWbPo1asXlStXvq9xylXIFSA9PR1/f3/Wrl1rWj+03JKUpIRew8IUL1R8vGklhejoaHx9/YhIEyTWaUKRSs23Sz5nw8YVADz99NMEBgby6aef8uabb7Jv3z7OnTuH178SKYKDg8nMzMTe3p7Lly9jZWVF5x4d2LV1L7m5eabEiAYNGhATE0NCQgKOjo7k5OSg1+txdnbmypUrREVFERwcTEZ6Ol/PnEm3nj1RZWWht7Ehv7iYwoICcgsLuFCcQV5xIZ45aooLCikuLkYNuAAFeXkkZWRQ6OxMTmoqxRoNPtnZtKxXD6c//1REnXGt16QkxWNpb6+Eoo3LgdWpo7RLJBKJRFLOuXTpEkFBQRw7doyaNWve11jlKuQK4OTkxLhx45g2bVppm3L/eHoqtdeeekopYSKEKdu1ir8/NoV5+NaoiO7yRZIiTrB12ypGDXkDe1u7G5beunbtGkKY15g7ceIEsbGxTJkyhcTERFQqFR/OeItJE19h2W+zMRgMqFQqnJwcad68OQkJCahUKrKysiguLmbUqFGkpaXRsWNHqlWrRmxsLNk5OVStVQuVhQXY2yMAgxCoHB2xcXHF09ULO1d3hLc7tp6eWDg6UqTVkqrXo7ezQ6VSYaXRYO/tjY1Oh9rLC4tz55RQc0KC8pykpChCzlinz8tLSYy4dq2kbp1EIpFIJOUco3fufsUclENBBzBmzBhOnDjBrn+tjFDuUKmgdWvFS1etmpLpmZ2teKOSksDWFrtLUfg6WrF//3YKCwtIT0nmw1Hv3LD0VkJCAgaDASipMffCCy8AMH78eOrWrYsQgmOxF0nWqnDyq8DzL3dGCEFCQiKXLilz9oQQtGrV6pbFi/V6Pb+tW4eFgwOo1Vip1Wjy8hBCoNfrsSoCayzQ6QvRFhZib2+Pra0tKkdHirOzsXZyIjc1VQnDqtUUFBdzUaslV69XkkSio5VyLqAIvH/mFALg7PzoXhuJRCKRSB4iFy9eZNmyZUydOvWBjHffgm716tV07NgRV1dXVCoVYWFhN/Rp164dKpXK7Pbaa6+Z9Vm/fj3VqlUjKCiIjRs33vacRi/d+++/X/6XA7t6FYKDFU9URoYiaiIilHImeXnoPbzIuXKNbYf2olKp2H1sP0ejo4AS4WbMinFwcADgzJkzACaBZ+foSGy8Uovu6J4jFNvZk6HTsH/vCZMZly/HAsoKExcuXPjX0mDnsbS0RK1Wo1KpWLtxI/qcHIStLYa8PCx9fbG+ehVra2tsrazRZeURn5/N1cI88lJTKSoqwsLCAo2LC7ZaLTYaDUIIioqKSMrLI9rCgms2NorADQwsKWWi0ynz6XQ6ZTsmRplHV95fc4lEIpE88cycOZPevXtTvXr1BzLefQu6nJwcWrduzdy5c2/b75VXXiExMdF0mzdvnqmtoKCAkSNHsmDBAr766itGjBhBYWHhbccbM2YM4eHh5d9L5+6uCDl/f8VDl5UFXl7MWb4c1fjx2A3uz/htvxCfnMCoUaOIS7jMxh2rAWV5LlCKLgNkZWXh7+9PgwYNsLS0RKVSYWlpSU5WJlP/byEVAwO4cCqCvgHP8PXQz4i7EI/nP/PRwsNPAZgyX82XBounbt26GAwGJeM1OppeoaE4OjnhWKMGn86YgbCxQaSnY5mXh87RBWc0ZFmrsHRxRmNlhSori8LCQtSWltjZ22Nna4udnR02NjakpadzycqK4goVlHCrsTRJbi4EBCgCz2BQQq4pKbIenUQikUjKNTExMfz4448PzDsHDyDL9eWXXwYU1+Ht0Ol0eN2ihlhBQQEWFhbUr19fMcrSkoKCAjQazS3Hc3R0ZMKECUyePJn9+/ejVpfL6LHilapVS/E6paUp4k6tJis/39Tl+szVvLw8vv/+e1QqlWnFBwcHB7y9vYmOjubixYsEBgZSXFxsyoANDg5mdJfeZqc1GAwMGzaMZcuW4e/vT4UKFdi7d69Z2PbIkSOmdWTT09NvmT2bl5uLlaUlFrm5WNnaUhR1gUxfF0SxJRbCEmcBFlZWXBECbWEh6sJCijIysHJwwE6rRefkhGtmJumpqbh5eChh5+xs0GpLBJ4x4zUtTXroJBKJRFKumTp1Kn369CEoKOiBjfnIVNDy5ctxc3Ojdu3aTJ48mdzcXFObg4MDgwcPxtvbGx8fH0aMGIG9vf1/jjlu3DhSUlL4/vvvH6bpDx+VCjw8wM0NKlWCrCxmDh3K/55/HkedjujISFMI1NHRESEEQgiCg4PR6/UkJSXRoEED03DXC8B/z4Fr2b4NdnZ2REVH8dZbb5GUlMTWrVu5FHsJB0c7/PwUj11cbDTzPnyBCxciAEhMTDQLw0ZHR+Pk6MiPs2YxdfJkrJKTscnKQhcfj7ujI9XjUqmi1uCGCqfUVJzy8/FPScE/J4f6trY0sramsY0NDXNyqHn1Kp56Pa7W1opXTqVSCi3n5irru2ZmKiIuK0u5l4JOIpFIJOWUPXv2sHbtWmbPnv1Ax30kdeheeuklKlWqhI+PDydPnmTSpElERESwevVqU5/333+fsWPHolar70jMgeL1+/LLLxkwYAA9e/bE3d39YV3Cw8fDQ/FGXb2qeKIiIujTsyeubm50WLSIs2fPkpSUZEpeAEW4GVeHMO63srIiIiKCRYsWmcRXhw4dsHNwYMjU92jfqA7HL17gw8EjOXv2rGlN2Muxl/l62WTGDvuIkKcbMf+T4XjlFdPv6Q70GfsJB49EcOrUKZKSkkhLSyM2Npbtn31Gu7p1EeHhWBQUoPfzw+LAAWy1WmwdHLDIjkal/v/2zjwuqnr9459Z2GdBBWR1FxQQV1xSxDW1UlMrtW511Uy9aT9t0VY183qtbrdu13Y1rdzyVq5ZWpqmIgJqKgICbsiwI8zCOjPn98fjdw6YdVPBAXzer9e8hnPmzJnvwMD58Hk2JVBdTe1IJIlakxw7Rm7t+fNyuxYh2AIDyY0TjqtGQ/t1OjnsmpZGrh33o2MYhmEaEVVVVfjb3/6GxYsXIzg4uE7PfUOCbt26dZgxY4Zje9euXYiJifmfz3vyyScdX3fp0gUBAQEYOnQoMjMz0b59e8djer3+RpYDALjvvvsQExODF154oXE7dcKlKyggUefvD+TlIa64GACw7O9/R/zRo+jduzcAwM3NHRkZGQ7xm5WVBb1eD5PJ5MiBE+IrKysLL7z3HkJ79YDV3QUXjnwNAPj7sr/jaPxR9O5D58w4UYATcXvR0kUFFcqhlGxooW2Ou3v2Q3xiGt5YvhzxR4+iz9U1HPr1VwwNDoaiqAgAoExOBjp0gDI3l4SXEFxKJYWS/f2B9HRqTZKdTeItI4Ped3Y2ibu8PNrOySExV1YGhISQS2c2A97elGtYUEDHMQzDMEwj4d1334VCocDTTz9d5+e+oZDrmDFjcOLECcdN5FfdKH369AEAZGRk3NTzr+Xf//43Nm3ahMOHD9fJ+ZyGnx8VSbRvT0LG0xNfX31Pl9PTsWLcOFzKOAcA8G3ug0uXLiEtLQ0DBgzAm2++idLSUsTcFQtAroBdtmwZAKD011/RTu2KAJUbju47AAC4cOEiXn31VVy6SBWuP+2OQwt4Ar5tIVk8IQWGolrtiW2HEgGQaHz71VeRddUN3BoXB5tSSe5adjatPz0dsFqpGTBAj9ntJM7y80nM5eRQSNVgoDCzGPcl8uWutmyBxSIfb7PRPqORnL0WLW7Pz4RhGIZh6oCsrCwsWbIEH374IVxcXOr8/Dck6LRaLTp06OC4eXh43NSLitYmAQEBN/X8a2nTpg1eeuklzJo1C1artU7O6RQUCiAyEnB1BSIjYb1yBadzcjA2IgK//t//4al+/RD/2usY1b0XcvIMAICuUVE4ePAg3l+xAgCQnnISAJCVmYk3JkxAdmYmAODAvn2QcnNhv5yNzPPnMaRfP3y1aRMmTZqEb7/9FsNjYpB69ixgtUKZkQGVjw8kkwnVbm44k5aG+0aMQNKnn2L2gw8ibvNm3Dd4MJLPnUOZTgdbYaHDUUTLluTIeXnRe1IqyVkD5Jm1wnnT6QCVih6XJOo/J3rPlZfTOXJzSeRaLHSTJKCqCjh4kHPpGIZhmEbD3LlzMWHChD8V2bwZbnn0l8inMhgMuPfee7Fx40aEhYXB398f/v7+yMzMxPr163HPPfegRYsWOHnyJObNm4fg4GDs37+/rt4HKisr0bVrV8ycORNz586ts/M6Bbsd2L8fyM9HcUoKmnl6QiFyyCoqIAUEoNuCBTiZlYWg5s3x4tix+MfWrcguLoZCocCY8HCs+utf0cLLC0X5+Zi2eTN2paXh1BdfoCooCNXnz8MnPByVJSVw9fGBymIBNBqUnD2L5iEhUCiVUFkscNXpoCwrQ4EkoYVaDXdXVyjS01Heti3g5QVjcTH0BQVwCQ6Gxw8/kENnsVB4NSBAFmWlpbQ/MJAEWsuWcl5cbi49Ly+PBJoQhv7+5Mx5epL402gotzAnhxov9+5N9xx2ZRiGYRo4u3btwsMPP4zU1FRHu7C65pYF3Zo1azBlypTf7F+0aBEWL16MrKws/OUvf8Hp06dhsVgQEhKCcePG4ZVXXnE0wq0r9u7di7FjxyIpKQmhoaF1eu7bTm4u8MsvcpsOk4n2a7WwlpbC85VXcE9EBFbdfTdatGuHotxcTNuyBd+dPg3Lu+/CpaLCUREqaTS4UlgIr7Ztcb68HLaWLYHyckgaDWxXz+lSWQm1tzeULi5wUVNqpWtlJdQaDaSrDpx7ZiY8mjdHRWUlqnQ6eNrtsBUVwZ6VBU9/f6hPnpQrVK8Nr0qS7NwVFFBLEotFFm4aDR1TUSHPcvXwcISe4e0tu3Xl5UDbtsD995PDxzAMwzANlCtXrqBr16546aWXfjNUoS65ZUHX0HjmmWdw4MABHD58+A/72DV48vIo/JiQQA6XXk8OnckEBAWh+Ndf0axFCyiCg0kQBQZCunwZV1QqNLdaSVhpNCSE0tIAd3dUKBQwtGqFbJMJlX5+UCuV8PTwgKuLC1TNm0MCHM2IXVxc4OriAteCAiiDgmAzm+Gi10MTHw+1pycq1GqoSkvh6uaGCpUKrtnZJCJLSkioBQTQWrXa2jlxHh60NoVCdtw0Ggq7enrWfiw3l/YplXQuUQms0VChRGAgMHAgV7syDMMwDRJJkjBx4kRYLBbs2LEDinq8XjU5QVdZWYk+ffpg5MiRWL58ubOXc/NIEs14zc2lqtfSUtp/5Qo5VB06kAiyWEj0mM0UwlQo6Lk2G7lbFgsdazAAGg2yq6qQHxwMk5sbVM2aOQScWqWCR3U1PN3coFSrobraNkSlVMLFYoG7jw+kS5eg9PSE+sQJWJs1Q1WzZnApLYVHYSEUkgSr3Q67xQJXT09at2g5EhBA6zIaaT0iv044dAoFvT/hxikU9H7CwmjdRiMJPPFcsxno3JkEXmQkOXoMwzAM08D47LPP8OKLL+LkyZPwq+cUoSYn6ACaZdq7d29s27YNQ4YMcfZybp6aou7cORI2Xl6y4BHHCIdLo6EQZEkJbQcEyHlqNhugVMLq5YVzej0Kvb3h6uYGDzc3uLq4QGk2Q6HVwlpSAoVKBZVSCVeVCkqVCu4qFdyzs1EVGIiywkK4eXhAbTTC1qwZbHY7NEVFkLKyYGzRAioPD7iZzdBVV8sNgQESoS1bknBTqchtFG6cqGa9OiUDRiO5b/n5JPAkid6PaDbs7w8UFgL33UfH/c4EEoZhGIZxFunp6ejRowc2b96MkSNH1vvrNUlBBwAfffQRXn/9dZw8eRItGnOLC0kCDhyg3LJz50j8XB3PBa2WHhfFB6JaVDRmrin6xONaLSx6PS6ZTChv1w4Kkwk2ANUeHkBFBVz1eqhdXKBSqQCFAgqlEi5lZdCr1VAWFKDsaksVhSTBRaeDlJsLTUkJ1DYbCtVqVLZuDV1+PvyysqC4fJleV6mkEGl2Nq1dCDRR9RoSQmFh4eiJtQYGkuC76i46wrJlZeTQuboC/fuzoGMYhmEaFFVVVejfvz8GDBiAd95557a8ZpMVdJIkYdy4cVAqlfj666/rNW5d74iq14IC4OLF2oJNVL+aTHJhQVmZPCJLHCsqRY1GQKFAflQU0pKToQoNhXdpKcrc3WENCIBCqYQkSXIenasrlAoFXPPyAK0WVXY7XJo1g9Jigfpq5aotJQXNFAq4ShKqLBa4arVwP3dOXmPNcV06HTlxOl3t/LmWLUmsajSUP1gzLHttVawIy7ZpA3TqBHTpwnl0DMMwTIPhxRdfxK5duxAfHw83N7fb8ppNVtABQGFhIaKiorBo0aJaEy4aJXl5FH6tqgLOnJFDr9eGYcWPUzhjNXvAiSKFq+1PzrZsCbPVipBu3ZB/+jRyvbyg8vaGh4cHVCoVlEol7HY7XK46di4VFVBenSVrs9mgVCjgUVUFBQClwYBmkgSPkhIoTCZal81G69Lp5Dy4mgUS/v61Cyc8PMhtFPvFuoXAE2FkMTVCowF69eI8OoZhGKbBsHfvXowZMwZHjx5FeHj4bXvdG2os3Njw8fHB+vXr8cwzz+DgwYPOXs6t4ecHhIdT890uXUjw2GzkVlkstG23kzjy8iJhJKY4CGdOq6Xjrg6991QqYbJYkBoXh1Jvb6iVSqhUKlRVVaG8vBwVFRUAqPJVkiSUq9UoLy9HdXU1FAoFXF1dIRmNsHl4wOzmhpzqauRrNKgCZJGp1VLIVKEgR65lS/patCYB5P1KJd1fbYYMSaJjREjVy4uOFU6eQkEFI033fxKGYRimEXHu3Dk89NBDeO+9926rmAOauKADgEGDBuHtt9/G+PHjceHCBWcv5+ZRKEjYdO5MRQXBwSRs1GpZtOl0JNjUatouLaXmu2IGqtFI59JoAI0GQQYDWnt7w+7lBWNZGcrUapjNZse0DYVCgaqqKpSVlaGsrAySJDnEXXV1NTwrKtDc1xcuBw/CIyQE1Z6eKHJ3h6VzZ3n0V24uuWqSJM9qtdnkBsOAPBECoNmubdrQmhUKEqy5ubL7qNHIkyTE94UFHcMwDONkjEYjRo8ejUcffRRTp0697a/fpEOuNZk9e7ajP51GJOM3RiQJOH2aKkCLioBLl0goiTYhAIk60b9N5NnVDM2KcCaAspYtkVJdjRx3dyibNYOLiwvUajWsVisUCgXc3d2hVquhUqmgUCjg4eEBnc0GD4sFGrMZ7hcvoqBdO6guXACio2G/fBlagwFu7u5UBCHGfNntcq5cbq48NUIUbAhhptWSQ9eypRxWtdupD1/NZsP+/vR469Z0bGws59ExDMMwTsFms2Hs2LGwWq3YsWMH1Fcb9N9O7hhBZ7VaMWrUKHh5eeGbb76BUtmIzcma7UwKCuQqUBFWtdtlEQWQoJMkeRaqJMnFFOXlKPHxQbZCgbKWLaFUqVBZXQ1Jo4F7VRVcXFyoKMLFBe5ubmhRUACvkhJy2UTYt7ycRGVODt0DtCarlUQlIDuGhYVAixZ036GDXMRxta0KzGZ5hNi1+XNCxNVsSKxQAN27Ux4dV7syDMMwTmD+/PnYvn07jhw5Ar1e75Q13DGCDqDxG3369MGDDz6Iv//9785ezq0hRF1eHjlaogDBZCI363rFEsIBq3nc1YrTCk9PWJVKePj5wVJRgYorV6Bu0wauJhPcXVygLimRz1laSucUIgyQxdX1+uN5egKnTpGQ0+nk4oa8PNlVrFm0cb0CiWsLKkRVrN1OIVp26RiGYRgnsHbtWsybNw/x8fHo2LGj09ZxRwk6AEhNTUW/fv3w/vvv4+GHH3b2cm4NIeqqqoBjx+QebyYThTSFiyV6uwFy+xJAbuArwrWialaMDDOb6Zz/q6GxaPh7PUfQbqd1uLtT7p/4z8VkIrEmnDhxbE0nTuQE/tHxAInKIUMoX5CrXRmGYZjbxOHDhzF8+HBs27YNQ4cOdepa7jhBBwA//PADJkyYgO3bt2Pw4MHOXs6tIUSd1UoFBUVF9HVBAfVoS00FOnYkUSWElhBtAAkxL6/aLU1q9rETodk/EmzCFavp0tV0BA0GcvMuX6bQqOiNZ7H8vst3be+6mmuomW8n8uxcXYF77pFDvgzDMAxTj6SmpmLgwIFYtGgRnnrqKWcvp+lXuV6PESNG4P3338fYsWNx5MgRZy/n1lAogIgIEjKhoUC7djRVIjQUSEwE2rYloScmNdhsshi7duqE2SyLOS8v2SET+7RaOWQq3DshzoRrJsZ9aTS1569mZ1OuW26u/NpCMAJySxKjkc4rBKfIl6t5vFiLGGmWk0NOY1oaV7wyDMMw9c6FCxcwbNgwPPnkkw1CzAF3qEMnWLFiBRYuXIiff/4ZUVFRzl7OrVMzBPvzz1RckJlJ4i41FWjfHjh5kooRrk6MgKcnuXmiYW9N506rlfeJ4oXrhXCFwwf8fi6dJFEeXWQkcP484OtL+8vL5VCrWINeL7t0JlPtKRI1K2ZrhmbLyoCoKC6OYBiGYeoVg8GAmJgY3Hvvvfj3v//dYCZR3dGCDgD+8Y9/4N///jcOHDiA0NBQZy/n1rk2r85qlXPq0tOBsDByyySJxFx+Pu1LS5MLELy9ZTElGhfr9fKIsaAgCqPWDOFer7pWzJoVDp9OR2to3ZpeVzQ+btmS8uvEeC/h8t1oaLZrV3qfXbrc9m87wzAM0/QpLCxEbGwsevfujVWrVjWojhl3vKADaObaunXrcPDgQbRq1crZy7l1albAFhYCV66QKAoOBrKyahc4iBw30fetWTN5bJck0XF6vXyMyUTiq6Z7J6pma+a2AbVdOtF6xMuLxJwo4PD3J4dQ9JtTKn87tgyo/fxr8/mMRrp3cQGio3m2K8MwDFPnGI1GDBkyBG3btsWGDRuc0mvuj2BBB0CSJMyePRu7d+/GL7/8Av+mELKTJBJ0KSnk0l24QMLH31+uaq0ZTi0vl8Opnp7ypAZJoq87dgROnKBwbU6OnGMnJkGI8V1A7aIKIe5EuFZMqxBh2pohW09P2hZuIHD9ggjgty6d3U5iNDqaXDqudmUYhmHqiLKyMowcORJeXl7YunUrXF1dnb2k39BwvEInolAo8J///Af9+vXD3XffjYKCAmcv6dYRo8IGDaIK0NatSehkZNC9cLvMZhoV5uUltzEpK6Owq5jFGhBAz+vVi0SimMeam0vh2sxMys/Ly5Nz3YSYs1jI0avZ+Fi4bAoFfa3V0uNFRXIenSTJzYtrFmUAvy3AqDlpIjOTCyMYhmGYOqO8vBzjx4+HQqHA119/3SDFHMCCzoFSqcTq1asRERGBgQMHIisry9lLqhsUCiAmhoSamxswahSFT2vmxtWc8Xr2LIVms7NJXJWVUfgzKIj2dexIVbTe3nJladeuVOgQGFh7ikNurnwum40eu17VrBB9QhSKYgelUm4yLCpzRTWuqLDVamVxWFoqh3IZhmEY5hYxGo0YNWoUzGYztm/fDk9PT2cv6XdhQVcDtVqNL7/8ErGxsRgwYADS09OdvaS6Qamk6s/776fQ6qBBlG8m8s88PWWhJdqdtG9PBQxisoTdLjcJFm6ezUbiS7h2RiPd/PwoT87fH0hIkIWaySQ7b6JiVYRQxRoCAki46XR0ji5dKFwsHD6TSW5rIly6mm1OJInyBhmGYRjmFigsLMSQIUPg7u6O3bt3QyfabDVQWNBdg0qlwocffojJkycjJiYGJ0+edPaS6g6lEhg4kJy6nj3lUKiobA0OJoeuWzfg3DkSV3Y7UFIiN/AFyAkTFbJKpeyKCfHn4iKLvvbt6VyBgXJeXc2CDDGFQuTOCdGn1VJPvQsX6P7cORKKpaX0/Ly82q9rsdB7KCkhQSdy8BiGYRjmBsnOzsbAgQPRrl07bNu2rUE7cwIWdNdBoVBg+fLlmDt3LmJjYxEXF+fsJdUdohGxiwuJuhYtqPmw3Q5cukROW14e0Lkziaurs16h1co944R7FhpKjp0I34qKWOH6qVSUnxcYSOfSaGqP/yopIbdO9JIToWCAzlFeLufHtW9P1bAhIfRaIgwL1A7tShJQXQ2cOeOUby/DMAzTuMnMzMSAAQNw1113YcOGDQ02Z+5aWND9AS+88AKWLVuGu+++G3v27HH2cuoOIeoCAym/ztdXriwVTtyZM+TQqVRyJanVKjcD7tSJ3DwfHzlEajZTixSbTa5qNRioJ56oVBWOoHD7VCq6z86mCtoLF2pPmBBrEnl8YqarQkHPVSpJhIrQbno6CUXOpWMYhmFukFOnTmHAgAEYP348Pv30U6jENaoRwILufzBr1ix89NFHuP/++/H11187ezl1h0JBQsjfn9w4Pz9y6ry9SVwJcaTXk5ASs2AtFhKCqakkwAoL6RgRhgVIaImWJu3aARcvyk2GRXuS3Fx6LYDEnb8/Vaj27Cnn5OXmyu5gTg4JSptNDq+KfAazWZ6C0b49OY02G7mIDMMwDPMnOHLkCGJjY/HUU0/hn//8Z4OZAPFnaVhd8RoojzzyCHQ6HR5++GFkZmbi+eefb3Q/6D9EtCHJz6ewZnAwcPQoOXRaLRVJdOtGAqtVK3Lv2rcHfv2V5rOKQgkhtETFqa+vLNJEOFSjuf50CpWKXL+UFBKXQjiazXQu0QevY0dac04OHSdy8iwWej2RmydyABmGYRjmf7Bp0yZMmzYNy5cvx+zZs529nJuCGwvfAMeOHcOYMWMwbNgwfPzxx3Bzc3P2kuoW0Yw4NZXCpMXFQFISVZpmZpJTB5B4O3FCFnOiJYlaTecoKaHjFAo5FOvuLrcnCQyk87ZtS/uUSnLtzp2jeaxCjInGx6Wl1ColLIxCuEYjOX+nTtE5zp2j7Zp5fN7eFE5mUccwDMP8Dna7HUuWLME777yD9evX495773X2km4aFnQ3SE5ODsaOHQtXV1d8++238PX1dfaS6h4xOiw1lYRWYiK5eCJkqlDIfelEY18xaUKhoDy6mgUSGo0cRlUqSQS2a0ciUTQaNhjkMWCdOgGXL9P+3FzaL57n50fnrKigoozjx0lwnjxJIWCFgs7Vty8VTsTG8hgwhmEY5jeUlZVhypQpOHr0KLZv347IyEhnL+mWaJQ5dIsXL0anTp3g5eWFZs2aYdiwYYiPj691THFxsSNU6u3tjWnTpsFcczYogE8//RStW7dG9+7df/P83yMgIAD79+9HcHAwevfujdOnT9fZ+2owiKKJAQNI0N17Lwk50WJEoag9pUGIOZuNhJheT2O4RP86IepUKjpPhw5yI2LRgDgwkM4TEEDzZq1WOjYwkJw/kdd37hy5b/7+VJQRFUVOXc+e5AQaDHTc0aO0Ds6jYxiGYa7BYDAgNjYWBoMBR48ebfRiDmikgi40NBQrVqzAqVOncPDgQbRp0+Y3I7seeeQRJCcnY8+ePdixYwcOHDiAJ5980vH4pUuX8Oabb2Ljxo14+eWXMWXKlD/9+h4eHtiwYQOmTJmC/v37Y+fOnXX6/hoEYnRYbCwJpdhYElJlZXIbEjGlITiYjqk5B1arpVCpaHcSGEiiTLhunTrJjYjFDFedTp72oFTSzdubRGLHjuTC9ehBoVXh8ok8PoOBnufhQc5fWBiNK+N+dAzDMEwNkpKSEB0djcjISPz4449NJtLWJEKuRqMRer0eP/74I4YOHYqUlBSEh4cjISEBvXr1AgB8//33uOeee3D58mUEBgbi9OnTmDJlCn7++Wfk5+djyJAhOH/+/A2/9ldffYWpU6fitddewzPPPNO0iiUEdjsVQtjt5JwlJlKenCSR4FIqqfdbXh45bEql7OaZzXIVrJcXCbFWrei84jktW5ILJ44RfeX0enrdDh1kMSfCvB4eVD3boweJOZtNLpDQ6znsyjAMw/yG//73v/jrX/+KRYsW4bnnnmtS1+xG6dDVpKqqCp988gn0ej26du0KAIiLi4O3t7dDzAHAsGHDoFQqHaHVyMhIREVFQa/XIyIiAkuXLr2p13/ooYewb98+vPPOO3jooYdQWlp662+qoSFGh3XpAri6ksBq1UruAydaiQQEyD3rhHhTKOQJEhYL5d7ZbCTMAAq5urjQc+x2uakxIDc6zsig1ywtlR29ggJy5tLS6Hwi9Cvy9vz8OOzKMAzDACCt8Mwzz2Dq1KlYv3590+tWgUYs6Hbs2AGNRgN3d3e888472LNnD3x8fAAAubm58PPzq3W8Wq1G8+bNkZub69i3atUq5OXloaioCI888shNryU6OhrHjx+HyWRCjx49cOzYsZs+V4NG5NaFh5PQ6taNwqRKJQkys5kEm9FI4s3fn8RcaiqFXPV6Cq2WllJDYjESDCDxp9HIQtBmq93oWKeThV1BgVyUIYok8vKoSMJkovOJ1y8u5gbDDMMwdzAXLlxATEwM9u3bh6SkJIwZM8bZS6oXGrygW7duHTQajeP2yy+/AAAGDx6MEydO4PDhwxg5ciQeeugh5N+EE9OiRQt4eHjc8jp9fX3x3XffYfr06YiJicH777+PJhDN/i0it27QILoPC6Owq7c3ibfLl+leo6FKVIDE39mz9HVAAImtsjIqXsjLI8HVsSMVSuh05MDl5dHXCgWN+0pKogKIkyfpHBoNiT2LhcKzQUGyW6fRUAhXryfRmJ/Poo5hGOYOZOvWrejevTt69uyJuLg4dBS9TJsgDT6HzmQyIS8vz7EdFBR0XQHWsWNHTJ06FS+++CJWr16NZ599FleuXHE8brVa4e7ujs2bN2PcuHH1uuZffvkFkyZNwl133YWVK1dCL2aXNjUkicSSuBUVUW6daFsSEiK3PtHryckzGEi85ebS8wMCSOxpNPI81tJSWfh5e9N2y5bA4cPUW85slitcCwpIQIredzYbhXDFdIn27en1BwygczAMwzBNnqqqKrzwwgtYtWoVPvnkE0ycONHZS6p3GrxDp9Vq0aFDB8ft99w0u92OyspKAEC/fv1QUlKCpKQkx+N79+6F3W5Hnz596n3NMTExOHHixJ0Rgm3ZkvLrWrakkGezZkCbNnQvQqKiYtVoJDGXnk4CLjAQOHiQRJdCIeffBQSQ4NPrKQRrNFK4tX9/EmklJXIfu+BgelyrJTFXWSk3I3Z3l3vdNez/WxiGYZg6omaINTEx8Y4Qc0AjEHTXYrFY8NJLL+HIkSO4ePEikpKSMHXqVGRnZ+PBBx8EAHTu3BkjR47E9OnTcfToURw6dAizZ8/GpEmTEChytuoZEYJ94oknEBMTgxUrVjTNECwg59YFBMhOWI8eJMhE4QRAAk6M71IqSfCJKRQ6HYmuoCAKk3bqRNvHjlHenGh/YrfT1yaT3ObE05OEnosLhYBNJjmXLjCQjikoYFHHMAzTxNmyZcsdE2K9lgYfcr2WiooKPPzww4iPj0dhYSFatGiB6OhovPLKK4iOjnYcV1xcjNmzZ2P79u1QKpWYMGEC3nvvPWg0mtu+5gMHDuCRRx5BeHg4Vq5ciZCQkNu+htuKmDRRXU2CTPSmMxrJNSsvlwWcJJEbJ4TY2bNUbJGfT88T48jE9AhPTwrFWiwkDA0Gel7NdikAuXRlZfL0irZtKUTLYVeGYZgmR0lJCebOnYstW7bg448/vmNcuZo0OkHXWCkpKcG8efPw7bff4t1338Xjjz/e5Eqma3Ftfp3dTgUNnp4k4EJDSZTVzKkzGuVpEGIGq91OwkyEVEU1bHAwiT8vL7lliZcXiTuNhs6t1cr98Pz8yD3093fu94VhGIapU3744QdMmzYNkZGRWLlyJYKDg529JKfAgu42s2PHDkyfPh3R0dH4+OOPEdDUh8cLYZeSQsLqwAESazk5JNZE+xFRPZuRQaJPFEhYLHI7EjHLVa0moWa3k3irKea8vEgAir52Wi05fDExlOvHgo5hGKZJYDKZ8Nxzz2HDhg14++238cQTTzRto+R/0Ohy6Bo79913H5KTk6HVahEZGYkNGzY03dw6QC6cGDiQpjYMH06iKjiYmhObzZRnFxBAwsvLS3bfJIkEnAir+vmReBONioV4s9vlZsYFBRTOFaHWc+conFuj4plhGIZp3Ozbtw9RUVFIT0/HyZMnMX369DtazAHs0DmVb775BjNnzsTAgQPx4YcfNpl5cn9IzVCs1Uq954KDqdpVoyGxJkZ5iVmxGg1VtoqxXmaznIsHyK1PANnNy80lARkSQufr3p0KN9ihYxiGabRYLBZHe7Lly5fjb3/7G5RK9qYAduicyvjx45GcnAwAiIiIwKZNm5q2WwfUbnUSFASMHUvCbPBgyp8TPeRCQki4ieIJnY7Cr4DceFhUxaan0z5/fxJyov+cWi3PkrXbnfu+GYZhmFvi559/Rrdu3XDs2DGcOHECs2fPZjFXA3boGgCSJOGrr77C3Llz0aVLF6xYsQKhoaHOXtbtQ7h2kkQhU6uVcukAcuqMRroFBpJAy84m8aZQULi2pESeHKHX0/Pz86ky1mCg2bDu7vLIMoZhGKbRkJubi+effx5btmzBkiVL8PTTT0OlUjl7WQ0OlrYNAIVCgYkTJyI1NRWdOnVCt27dsHDhQpSLkGJTR7h2/v7k3AUGUhFDWBjlzbVvTyFTNzcSbOHh5L41b05CLzKSQrOtWtF28+Yk4iwWqmytqKDta+b7MgzDMA0Xm82G999/H506dUJlZSVSUlIwb948FnO/Azt0DZBjx45h1qxZKCgowIoVK3DPPfc4e0nOoaZzJxAOXmkp3ev1wOnT1LsuI4OmVFy+TM2Ivb2pKCImRm5uzDAMwzR4EhISMGvWLJSWlmLFihUYMWKEs5fU4GFB10Cx2WxYuXIlXnjhBQwePBjvvvsuWrVq5exlOR8RlvXxIbFXWEjuW2Eh5ckVFZGDp1DQcb6+9DXDMAzT4Lly5QpefvllrFmzBgsWLMCCBQvg7u7u7GU1Cti2aKCoVCrMmDEDaWlp0Ol0CA8Px5tvvonq6mpnL825KBTyfFgRolWpKGQbEEDbSqV8HIs5hmGYBo8kSfj8888RFhaGc+fO4eTJk1i0aBGLuRuAHbpGwoEDB/C3v/0N1dXVeOONNzB27Ng7vucOwzAM0/g5fPgwnnvuOVy6dAnvvvsuJkyYwNe3m4AdukbCwIEDceLECcybNw8zZsxAbGws4uPjnb0shmEYhrkp0tPTMWHCBIwYMQIjR45EWloaHnjgARZzNwkLukaEWq3GzJkzkZGRgdjYWAwZMgSTJk3C+fPnnb00hmEYhvlTFBQUYM6cOYiKioKPjw/S09OxcOFCeHl5OXtpjRoWdI0QrVaL119/HWfPnoWnpyfCw8Px7LPPori42NlLYxiGYZjrUl5ejuXLl6NDhw64cOECkpKS8PHHH8OfJ/jUCSzoGjFBQUFYvXo1jhw5gtOnT6NDhw54++23UVlZ6eylMQzDMAwAwG63OwoeNm/ejC1btmD79u0IDw939tKaFFwU0YTYvXs3nn/+eRiNRixevBiPPPII1Gq1s5fFMAzD3IFIkoTvvvsOr776KoqKirBs2TJMnjyZx3XVEyzomhg2mw1ffvkllixZArVajUWLFmHixIncWZthGIa5LUiShN27d2PhwoU4f/48FixYgKeeeopbkNQzLOiaKNXV1fj888/x+uuvw9PTE4sXL8YDDzzA/xkxDMMw9YIkSdi3bx8WLlyIlJQUzJ8/H7Nnz+Zih9sEC7omTlVVFT777DMsXboUOp0Or776Kh588EF27BiGYZg6QZIk/Pjjj1iyZAlOnz6N5557DnPmzIFOp3P20u4oWNDdIVRWVmLNmjVYtmwZPDw88Morr2DSpEmcY8cwDMPcFJIk4fvvv8eSJUtw9uxZPPvss5g9ezYLOSfBgu4Oo6qqCl988QWWLVsGpVKJ+fPn49FHH+XcBoZhGOZPYbPZsGXLFixfvhwXL17Ec889h1mzZkGr1Tp7aXc0LOjuUKxWK9avX4+33noL+fn5mD17NmbNmgUfHx9nL41hGIZpgFgsFqxZswb/+te/YLVaMXfuXDz55JOcI9dAYEF3hyOqkf75z3/i0KFDmDJlCubNm4cOHTo4e2kMwzBMAyAvLw8rVqzABx98gDZt2uC5557DAw88ABcXF2cvjakBlzw6iZkzZ0KhUODdd9+ttb9NmzZQKBS1bsuXL691zKefforWrVuje/futzzPVaFQYMSIEdizZw8OHz4Mo9GIiIgITJgwAYcPH76lczMMwzCNlzNnzuCJJ55AmzZtcOzYMfz3v/9FYmIiJk+ezGKuAcKCzgl8++23OHLkCAIDA6/7+JIlS5CTk+O4zZkzx/HYpUuX8Oabb2Ljxo14+eWXMWXKlDpbV7du3fDFF18gIyMD7dq1w8iRI3HXXXfhm2++gc1mq7PXYRiGYRomkiTh559/xn333YcePXoAAJKSkrBz504MHjwYCoXCyStkfg8WdLeZ7OxszJkzB+vWrfvd/3C0Wi38/f0dt5r5CUajEd7e3oiKikLPnj1RXl5e52sMCQnBW2+9haysLEyYMAH/93//h7CwMLz99tsoKiqq89djGIZhnIvFYsGqVavQq1cvjB8/Ht26dcOFCxewcuVKHtHVSGBBdxux2+149NFH8fzzzyMiIuJ3j1u+fDlatGiB7t2746233oLVanU8FhkZiaioKOj1ekRERGDp0qX1tl69Xo9nn30W586dw9KlS7Ft2zYEBwfj8ccfR1xcHDj9kmEYpnGTkpKCp59+GkFBQXjvvfcwffp0ZGVlYenSpfD393f28pgbgAXdbeSNN96AWq3G008//bvHPP3009i4cSP27duHGTNmYNmyZZg/f36tY1atWoW8vDwUFRXhkUceqe9lw8XFBZMmTcL+/fuRmJgInU6HkSNHonv37vj4449hNpvrfQ0MwzBM3VBVVYVNmzZh0KBB6N69O65cuYLvvvsOJ06cwMyZM7lqtZHCVa71xLp16zBjxgzH9s6dOzFx4kQcO3bMkTvXpk0bzJ07F3Pnzv3d86xevRozZsyA2WyGm5tbfS/7T2OxWLBhwwZ8+OGHSE9Px6OPPopZs2YhMjLS2UtjGIZhrsOFCxfwySefYNWqVdBqtZg5cyb++te/cruqJgILunrCZDIhLy/Psb1582a8/PLLtWap2mw2KJVKhISE4MKFC9c9T3JyMiIjI5GamoqwsLD6XvYNI0kSEhIS8OGHH2Ljxo3o2bMnZs2ahfHjx8PDw8PZy2MYhrmjsVqt+P777/HRRx9h9+7duPfeezFr1iwMGzaMZ3s3MVjQ3SaKioqQk5NTa9+IESPw6KOPYsqUKb8r1tatW4fHHnsMhYWFaNas2e1Y6k1TXFyMzz//HB9//DEMBgMeeughPP744+jfvz9XRjEMw9xGTp48ibVr12LdunVQq9WYNm0apk+fjuDgYGcvjaknWNA5kWtDrnFxcYiPj8fgwYOh1WoRFxeHefPmYdSoUVi7dq1zF3sDSJKEo0eP4vPPP8eGDRvQvHlzPPbYY3jsscfQpk0bZy+PYRimSZKfn4/169dj7dq1SEtLw/jx4/H4449jyJAhUKlUzl4eU8+w39qAcHNzw8aNGxEbG4uIiAj8/e9/x7x58/DJJ584e2k3hEKhQJ8+ffD+++8jJycHb7zxBhITExEaGopBgwZh9erVMBqNzl4mwzBMo6eyshL//e9/MWbMGAQHB2PLli14+umnkZeXhy+//BLDhw9nMXeHwA4dc9vIz8/Hhg0bsHbtWqSmpmL8+PF47LHHMHToUP6DwzAM8ycRUZC1a9di48aNjijIo48+irZt2zp7eYyTYEHHOIVTp0458jsAYMKECXjwwQcxYMAAFncMwzDXIEkSjh8/js2bN2Pz5s0oKCjAxIkT8fjjj+Ouu+7iPGWGBR3jXKxWK/bv34/Nmzfj66+/hlqtZnHHMAyD34q4vLw8jBkzBg8++CBGjBjBnQSYWrCgYxoMLO4YhrnTYRHH3Cws6JgGCYs7hmHuFFjEMXUBCzqmwVNT3H3zzTeQJAn33HMPRo8ejbvvvhs6nc7ZS2QYhrkhKioqsG/fPuzYsQM7duxAcXExizjmlmBBxzQqbDYb4uLisGPHDmzfvh3p6emIjY3Ffffdh9GjR6Ndu3bOXiLDMMx1yc3Nxc6dO7F9+3bs2bMHPj4+GD16NEaPHo3Y2Fi4u7s7e4lMI4YFHdOoOXfunEPc7d+/Hx07dnSIu759+0KtVjt7iQzD3KFIkoQTJ044/kYdO3YMvXv3dvyNioyM5OpUps5gQcc0GYxGI3bv3o0dO3Zg586dsNvtuOeeezBq1CgMGTIE/v7+zl4iwzBNnJKSEuzfvx+7du3Cjh07UFpaihEjRmD06NEYNWoU/Pz8nL1EponCgo5pkthsNsTHx2P79u3YvXs3Tpw4gc6dO2Po0KEYOnQoYmNjodfrnb1Mhml6SBJQUAD4+ACFhXRfUED7FQrA15e2AXosNRXo1ImOrXk5kiSgqAjo3Ln2YwqF/LzOnWm7oIDO6wS3q7y8HIcOHcJPP/2En376CUlJSQgNDcXw4cMxevRoDBw4EG5ubrd9XcydBws65o6gqKgIP//8s+OPbmZmJnr16uUQeHfddRfnrzDMHyGEWosWsggrKqot2CQJSEsD2rcHMjPpPikJaN0auHIFaNYMuHAB0GrpPBcuAN26Afv3A127AsXFdFy7dsDx40BUFJCVBXh7A3Y7UFIC9O4N7NsHTJgA/PQTbdvt9Fwh6vz86k3cWa1WJCYmOv6WHD58GL6+vo6/JUOGDEFQUFC9vDbD/BEs6Jg7kqysLOzdu9fxR7m4uBj9+/d3/FHu2bMnt0Zh7ixqOmtCoNV87OxZoE8fYONGIDYWOHEC6NULSEwkgaZQkBjTakmEabWA2QwEBdE2QMd4eQEmE32t0wEGAxAWRvclJUBEBAlGPz8gLw8IDqbj4uOBkSOBH38EZs8Gli8Hpk0DfvkFaNWK1njlColIkwno35/O4+ND5yosvCkXT5IkJCcnO/5W7N+/H2q1GoMHD3b8vejYsSPnwjFOhwUdc8cjSRLS0tIcf7D37dsHSZIwaNAgxx/szp0739gf7OuFnQoLZXcjLIycjJoux01ecBjmhrHbgTNn6PMI0GdQkshBy8wkAXXlihz2NBrJLTtyBOjbFzh4kISa2UwCrbSURJRWS25aVhY9bjLJ+7VaQKkk4ebvT/cBAXSv19OabDagogIIDKR1ZWcDAwcC338PDBsGbNkCPPEEsGkT0L07icnQUPl1OncmR/Dhh4HvvgPuu49cQIUC6NBBFoz/43fswoULjr8He/fuhclkQkxMjOPvQbdu3aBUKuvxB8QwNw4LOoa5BpvNhuPHjzv+oB88eBDe3t4YMmQIBgwYgOjoaHTp0gWurq61hVt+Pomy5s3JzWjXDjh2jFyF778HevQA9u4FHngA+OwzYOpUYM8eoGdPujD16kUXHIWCLrRKZe0co6IiWfCJ12UByPwe14ZIw8KAlBR6rKgI0GhI7EgSCaqSEhJogYEkpLRaEmp2O2C10nOio0kIarX0fLOZhJTNRiLQ2xvIySFHLDeX9mk0dL6oKHr9Pn1IGPr60u+Jvz/9zly+TAIvNxeIiaHfmeHDyZEbPpy227en3zPx+mlp8j9HI0eS4Js6FVi9mu43baLXKy2l913TvUtLAzp3hk2SkJqaioSEBBw+fBg//fQTLl26hD59+jgEXJ8+fTgPjmnwsKBjmP9BZWUl4uLi8NNPPyEuLg6JiYmoqKhA165dER0Rgd5duyLa2xthQUFQ6vXA4cPkYuzeTU7Bpk3Agw8CK1dSiOizz4ApU4CvvgJGjCC34667gORkugDqdHTRadeOwlqxsZRjNHQocP48XYwOHaLHS0vpgi2Sx1n03Vlc6wS3aEGiqXlzID0daNuW/okQn6HAQHLelEoScOIcer3squXkkMgym+lxnY4EYceOJLaCgui5ZWUkqrRa+rwajUBICN3bbPSZUyjosfBwElChoXQuLy96zTNn6J+VjAz6Xfn0U+D++yk3btgw+h1q145EnJcXreXsWeDuu0m83X+/7NqtWiWLubFjgW+/BcaNo3+aoqMBgwGS3Y4Lvr5I+OEHJNjtOHr8OI6lpMBms6FHjx7o27cvhgwZgpiYGGi1Wmf8RBnmpmFBxzA3iN1uR0ZGBhISEpBw9CgS4uNx/ORJqFUq9AwKQnRMDKILChA9Zw5ab9kCxZIlwKJFwMyZwEcfUf7PqlV0sYmLI4chMZGchEuX6ALr7U1iLTgYSEggF+/YMbqQHTkC9OsHnD5NCeXHjwNdutDFcdIkyjUKDaULvLc3hdDCwmRRJyoNOcTb+JAkEjfiz7b4GWdk0P358ySqfv2VPjspKfRZSEuTRZrdLostm42el5tLn5HkZDkfzmiU89n8/UmYAfLnxdOTxJ+oFg8JoWN9fWm/QkH/ZKSkkBCz22Vx2Ls3cOAA/Q7s3k0O3NatJMT27CEnLTeXBKNORw5fly702R42DNi2rbYTt2YN8Ne/0vaYMSTyxo1D7pdfIqFNGyTs3IkEqxUJp0+j1GJBVEQEonv3RnSfPoiOjkZ4eDj3rGQaPSzomHolJSUFCxYswP79+2G1WhEeHo6vv/4arVq1AkDjb5599lls3LgRlZWVGDFiBD744AO0bNnScY5t27bhueeeg0KhwNtvv4377rvPWW/nd7FarUhOTiaRFx+PhMREnDp1Ct7e3oj28UH02LGITklB9FtvoeVnn9GF68cfyTkQyd6HDpGDYjbTRdVopJNrNHIYKzOTQrNJSSQAxcVRuBSbNwMPPQTs3ElOnnD/Ll+mC29xMeUSlZbKlYhC7F3bUqIeKwWZP0lNAScqSPV6Ob9NkoCLF+nzItqClJaSUFOpSAwZjbRP5LwpFCTGLBZ6jZISEmNCsNUsalCpSIjp9fQZ7NSJBJoIy3p5Ud5daSmdV4g24aJt3Uqibdcu+qwfOECu2p499PiPP9Ln+cwZ+rzVdOIMBiAyEjh6lP6hSU2lz/Q335DTLcTbtm3AggUoeeEFJPbti4Q1a5Dg7o6ExERkX7mCTu3aIbpvX7pFR6Nr165c0c40SVjQMfVGZmYmevfujWnTpmHy5MnQ6XRITk5G3759Hc01Z82ahZ07d2LNmjXQ6/WYPXs2lEolDh06BIDCnR06dMBnn30GSZIwdepUZGZmUv5aA6e8vBy//voriby4OCQcO4a0s2cRHBCAaD8/RHfvjmhXV/QaOhR6kVt08aJ8Ao2GLrre3nSBNhjoQmow0IU1KYnciq1b5QvbCy8Ar74qi7pp06gqsXdvcvQ6dyZREBFBbkz//rLYAygU16wZfa1WUyiPHb3bw/XyMSWJ8tcyMuj7rtHQz0unk/PChPg3GsmVy86mz4nRSIJM5LeJvDidjsRZcLCcIydJdKwQhZJEx+bkUF5bTo78mfHwoJBudrb8Obz7blm0/fJLbfds1SoqbBCf1717ScTFx9PxOTn0HgVt25IL3auX3GvuzBkSd/HxwPDhKNu8GcfDwpCwZQsSXF2RcOQI0vPz0SYwsJZ469GjB896Zu4YWNAx9cakSZPg4uKCL7744rqPl5aWwtfXF+vXr8cDDzwAAEhNTUXnzp0RFxeHvn37wmg0IioqComJiQCA6OhonDx5stHmtxiNRiQlJckiLzERFy9fRmibNohu1QrRgYGI7tULXauq4CUKI0JC6EIq3BHhWgwZAmzfXivEhG3bKPS0YgWFdteupe2VK4Hx4+m4/v3l8O2PP8piLzycxKJGQ25N9+4Uwmvf/vqOnoAF341zrfN2bZWp1UrfT4CEU+fOco6lJNHnQgg5k4n2a7WymDMY6LjwcBJDej1tC2EnjgkJkYWhJJG4a95cLs4ByDEzGKg1yKVL9PlLSAAmTqTP2WuvAW+8AQwYQHl606dTnuiYMXIeW3w8OXAZGVQclJ4utyUR4hOgf16MRnITrxZfVFmtOJ2Tg4S8PCRkZSEhORnJBgNa6PXo3bs3ovv1Q3R0NHr16gVfX9/b/ZNkmAYDCzqmXrDb7dDr9Zg/fz4OHjyI48ePo23btnjxxRdx//33AwD27t2LoUOH4sqVK/D29nY8t3Xr1pg7dy7mzZsHAHjttdewdOlSKBQKLF26FPPnz3fCO6o/8vPzSeCJcO3RoygoLkaIry/C2rVDmKsrwgYNQmhqKsImTUKrU6egfPhhckCEeJs0iZy66dPl/e+/TxfbRYuAWbOADz8kkSdCVdu3U3FGTbE3dizwww8k8oTzcugQXcSTkyl8e/o00KaN7OSIcG3HjiQEfHzojbHQk7k2962ggETbuXNyHpsQNTabXFhgsZCQP3CAChtSU2UBJAobIiJI/AQHy0LMaCQxl5hILlhWVm0hmJ1NYdfmzeW2IllZ9Jxjx+Sq0p49KWx///30uejVixy4F14AFi6s/blatYo+P+JztWoVPU/805CcXDtf9MwZei85OZBCQ5GTkIA0T0+cPXUKacXFSDMYkFZQgPM5OdB4eaFXt26U83bVfQsJCeHebwxTAxZ0TL2Qm5uLgIAAeHp6YunSpRg8eDC+//57vPTSS9i3bx9iY2Oxfv16TJkyBZWVlbWe27t3bwwePBhvvPGGY19paSmUSmWjdeZuBEmSkJ+fj7S0NLqdOYO0lBSkZWbi3PnzcHFxQUdfX4RFRiKstBRhf/kLwvbtQ9iCBdBv3kxhrhUr6IL7+uvA4sUk6oSYmzKlttibMQN45x3grbeAN9+k3l3r1wP/93+y0/LNNyT6ROL6kSNyHlWnThQ20+nIYVIoKFevfXsKIYt2LD4+tatxm3rvvZoirqCABFpRET125Qp9j0RFM0DuWE3nTeSk5eVRqPKnn0icJSeTeLt8We67NmIECe+AAFkMlpWRA1dSQq8hQqYhIeS0RUSQuLo2XPrCC9S0NyaGHLcnnqDPzYABcjj1gw+Av/0N+M9/6HP1wQfAU09RccL8+fS5EttTpwIbNgDDh8Oydi3SR41C2vr1SNPrkXbsGNLKy3H2/HmYKyrQKjAQYZ06ISwsDGHh4XQfFoagoCDu+8Yw/wMWdEydsG7dOsyYMcOxvXPnTgwaNAiTJ0/G+vXrHfvHjBkDLy8vbNiw4YYEHUNUVVXh3LlzsthLTibBd/48CouK0NLXF2HNmiG0c2cSe5MmISwlBW0nTIDLtm0kyjZsoAvuP/9JF9s33gD+8Q9g6VISeTWdlyVLfrv9xhsk+rZuJQdm2zYSeXFxJDSysmShMXw47Y+MpP2DBpHbNHQohRY7dCBxc63Ya2wi73pTFgoLgepqep/e3iTgFAoSWCKMKfK7lEoKM14bVhVFBhkZJLzi4oCWLeXeb2q1PJHBZqOwaGIiOWsJCfT9zcggwSfC8rt2yVWmQrwJsTZtmlwxunIl/XxrOm5jx1JO5sKF9FmYM4dEXY1/HuzPP49Ls2YhbckSpEVFIW33bqQBOJuRgaziYui8vEi0deyIsIgIh2jr2LEjPDw8nPQDZJjGDws6pk4wmUzIy8tzbPv6+sLHxweLFi3CK6+84ti/YMECHDx4EIcOHfrTIVfmz1FcXOwQemdTUpCWmoq0tDSknz8Pu82G9kFBCPP2RlhUFMKqqxHWuzfCTCb43H03FOJiv2cP5Ub95z/A009TD72HHqLtOXPIsVuwgC7gog2LEHmjR1POlHDyRGK8yKUaP15uN7F9uzxJoHt3arMxeLA801OtJlEH1B7S3tCcPeHCFRRQnlpSEomp9HRaa2mpnJMomvfq9eRs+vtTa46AAHrcYpHbg4gCCFG8YLfLRTEAFbaMGkVhUVEp/XsOm8itFM6ZCM/XFG9CrNVsBzJtGu0X92L/lCnAihUoefxxnF25Eml+fkg7cABpnp5IO3kS6UVFqK6qQru2bRHWoQOJt6vCLTQ0FC1btuRQKcPUAyzomHrjrrvuQvv27WsVRYwbNw4eHh5Yv369oyhiw4YNmDBhAgAgLS0NnTp1chRFMLeOzWbDxYsXZVcvJYVu6ekw5OZC6+mJID8/BOh0CPT2RqCHBwJat0agJCEgKAiBajUCAgPhdeEC5T4lJZEoeO89EnlffUUi8L33KAz3wQckBlevlsXftQ5f//5U2NGpEzlYkyeTaBgzhhozBwdT1aVorjx4MDlKEyfKuV0iX+wWZ3XeMELEia9zc6lNhxiPlZxMYsxqJREm0gQUCnpfqamUq3b6tCz2goPp+Xq9PNtUjLBav56cNhGGzcuj74Nof/P115Rfd+AAhUfXrCHRJZr0bt9OYm7+fGDZMnLSRPhdVKHWaOZbuWkTcgYOhOG775Dj5wfDmTPI0ethSE2FQaFAzuXLMJjNKC4pgU/z5uS0hYXVctvatWvXKCrRGaYpwYKOqTe+/fZbTJw4Ee+//74jh27u3Ln4+eefMWDAAADUtuS7777DmjVroNPpMGfOHADA4cOHnbn0OwaTyYRz584hJycHOTk5MBgMdME2GGC4fBk5BQXIyclBtdUKnacnAlu0QECzZgj09ERgcDAClEoEBgYiQK1GYFgYApKT4TVxIuVzRUTI1ZDC4fvqKxKDn34qO0jTp1NO3+LFcrWkyAv79lsSG1u3Ai++SLmBw4YBp05RGFOnI+FnNJLIE5M0rjcnty6cPbudXDCtlkLFvr70WiKcCtC6JKn2KKuAAFqnwUDFI8eOySJN5IbpdBQ6DQ6Wq5h37CDnc88eeYB9374U9pw9m8Ki48bJjltNJ01sP/EEsHIlKidPRs7bb8MwYABy4uJgcHdHTno6DEolDLm5yLFYYCgoQLHJBKVSCX9fXwT6+yMgIACBISEk7gMDERAQgICAALRt2xbNmze/9Q8hwzB1Ags6pl5ZvXo1/vGPf+Dy5csICwvDa6+9hrFjxzoeF42FN2zYUKuxsL+/vxNXzdTEbrejuLiYRJ7BUFv4Xb4MQ14ecrKzkVNQgOrqaug0GgR6eyPA1xeBWq3s+AEICAyEn0IBbbt20Jw5A82990K1cSOJuX/+k0TIypVyYcfMmeT4zZkjJ+KL+ZxiqgZAuWNnzpAYjIujSRrZ2RSGzMykAo2MDHlA+41U4tbMj/vlF+rD9t13tIacHHLZioooTCxCpUajPO1DzDIVBQpGo6O60zH/FKBjW7WigpOePem1xo6VcxV/+kkWxwsXwr54MSzjxsH8ww8wDR+Ows2bkePr+1tHzWCAobQUxVcLi/x9fRHYsiUCgoNJoF29F2ItMDAQvr6+UKlU9fzJYhimLmFBxzBMnVBT+AnRV9Pxy8nOhiE/HwUFBSivqHA8z9PDA1o3N2h0OmhVKmibNYOmuhraVq2gzcuDtkMHaHJyoI2OhvbXX6Hp1w/a9HRo+/WDNjMTmvBwaLOyoO3aFZpTp6COjqbCgE6dqNJThDO9vUlQeXvLBQc+PrRPFGfU7GMmwrkHD1Kz26QkElwHDtA4LZGfFhdH+YClpRQyLS2VXTqFgoRnUBBsly7B4uYGU0UFTFeuwBQUBHNWFkxeXjBlZMAcHAxTfDxM4eEwHT1K25cvw+ThAXN+PkwuLjAVFcFks8FsNMJc43vo5uqKFs2a1XLUAoODHQKNhRrDNH1Y0DEMc9ux2Wwwm80wmUyOe3GruW02m2EqLYWptBRmkwkmsxmmkhKYKypgKikhcWQ0wlJe7ji3u6srtF5e0Li6QqvRQKNWw8XDA0pJglKthtJuh8rVFUqbDUpXVyiVSigBulcqoXRzg9JqhVKng8Jigc3dHfbsbNi9vWE3GmHXaGAvKoJdr4e9pAR2nQ52kwl2FxfYrVbYJQlWAGaLBabycpgrK2Eym1FWo5rb09MTGg8PaHU6aD08oNHrofXwIDHr6Un7mzWDRqOBVqt13Gpui681Gg1cXFxu/w+RYZgGBQs6hmEaPXa7HRaL5fqC0GSCzWaDzWaD3W6/7u2PHlOpVFCpVLLgq3H7o/0ajeZ3BRi7ZAzD1DUs6BiGYRiGYRo53HqbYRiGYRimkcOCjmEYhmEYppHDgo5hbjMKheK6t7feestxTJs2bX7z+PLly2ud59NPP0Xr1q3RvXt3xMfH3+63wTAMwzQgOIeOYW4zubm5tbZ37dqFadOmISMjA+3atQNAgm7atGmYPn264zitVgsvLy8AwKVLlzB06FB8/vnnyM7OxsKFC3HmzJnb9yYYhmGYBoXa2QtgmDuNa5smb926FYMHD3aIOYFWq/3dBstGoxHe3t6IioqCv78/ymu07WAYhmHuPDjkyjBOJC8vDzt37sS0adN+89jy5cvRokULdO/eHW+99RasVqvjscjISERFRUGv1yMiIgJLly69nctmGIZhGhgccmUYJ/Lmm29i+fLlMBgMcHd3d+z/17/+hR49eqB58+Y4fPgwXnzxRUyZMgX/+te/aj2/qKgInp6e8PDwuN1LZxiGYRoQLOgYph5Zt24dZsyY4djetWsXYmJiHNudOnXC8OHD8Z///OcPz7N69WrMmDEDZrMZbm5u9bZehmEYpnHCgo5h6hGTyYS8vDzHdlBQkMNN++WXXzBw4ECcOHECXbt2/cPzJCcnIzIyEqmpqQgLC6vXNTMMwzCNDy6KYJh6RIx9uh6rVq1Cz549/6eYA4ATJ05AqVTCz8+vrpfIMAzDNAFY0DGMEzAajdi8eTPefvvt3zwWFxeH+Ph4DB48GFqtFnFxcZg3bx7+8pe/oFmzZk5YLcMwDNPQYUHHME5g48aNkCQJkydP/s1jbm5u2LhxIxYvXozKykq0bdsW8+bNwzPPPOOElTIMwzCNAc6hYxiGYRiGaeRwHzqGYRiGYZhGDgs6hmEYhmGYRg4LOoZhGIZhmEYOCzqGYRiGYZhGDgs6hmEYhmGYRg4LOoZhGIZhmEYOCzqGYRiGYZhGDgs6hmEYhmGYRg4LOoZhGIZhmEYOCzqGYRiGYZhGDgs6hmEYhmGYRs7/A80lQDB+V64lAAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" } ], "source": [ - "# scanner.plot_coverage()" + "scanner.plot_coverage()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(scanner.draft_gcn())" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# scanner.check_ampel_filter(\"ZTF23aajchjr\")" ] }, { @@ -1492,9 +74528,7 @@ "metadata": {}, "outputs": [], "source": [ - "import fitsio\n", - "# print(scanner)\n", - "path = \"/Users/robertstein/Data/ZTF/skymaps/GRB220617A_IPN_map_hpx.fits\"" + "# from astropy.io import fits" ] }, { @@ -1503,7 +74537,8 @@ "metadata": {}, "outputs": [], "source": [ - "hdu, header = fitsio.read(path, header=True)" + "# path = \"/Users/robertstein/Data/ZTF/skymaps/grb230430.fits\"\n", + "# res = fits.open(path)" ] }, { @@ -1512,9 +74547,7 @@ "metadata": {}, "outputs": [], "source": [ - "print(header)\n", - "header[\"DATE-OBS\"] = \"2022-06-17T18:29:00\"\n", - "print(header)" + "# res[0].header[\"DATE-OBS\"] = \"2023-04-30T07:47:19\"" ] }, { @@ -1523,7 +74556,7 @@ "metadata": {}, "outputs": [], "source": [ - "fitsio.write(filename=\"/Users/robertstein/Data/ZTF/skymaps/GRB220617A_IPN_map_hpx2.fits\", data=hdu, header=header)" + "# res.writeto(path, overwrite=True)" ] }, { @@ -1536,9 +74569,9 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "nuztf", "language": "python", - "name": "python3" + "name": "nuztf" }, "language_info": { "codemirror_mode": { @@ -1550,7 +74583,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.8" + "version": "3.10.9" } }, "nbformat": 4, diff --git a/nuztf/base_scanner.py b/nuztf/base_scanner.py index 83599784..4edfa22a 100644 --- a/nuztf/base_scanner.py +++ b/nuztf/base_scanner.py @@ -33,9 +33,10 @@ ensure_cutouts, ) from nuztf.cat_match import ampel_api_tns, get_cross_match_info, query_ned_for_z -from nuztf.flatpix import get_flatpix +from nuztf.flatpix import get_flatpix, get_nested_pix from nuztf.fritz import save_source_to_group from nuztf.observations import get_obs_summary +from nuztf.observations_depot import get_obs_summary as alt_get_obs_summary from nuztf.plot import lightcurve_from_alert from nuztf.utils import cosmo from tqdm import tqdm @@ -223,7 +224,10 @@ def plot_ztf_observations(self, **kwargs): self.get_multi_night_summary().show_gri_fields(**kwargs) def get_multi_night_summary(self, max_days=None): - return get_obs_summary(self.t_min, max_days=max_days) + mns = alt_get_obs_summary(self.t_min, max_days=max_days) + if mns is None: + mns = get_obs_summary(self.t_min, max_days=max_days) + return mns def query_ampel( self, @@ -738,7 +742,6 @@ def text_summary(self): def calculate_overlap_with_observations( self, fields=None, pid=None, first_det_window_days=3.0, min_sep=0.01 ): - print(fields) if fields is None: mns = self.get_multi_night_summary(first_det_window_days) @@ -949,17 +952,164 @@ def __init__(self, data): overlapping_fields, ) - def plot_overlap_with_observations( - self, fields=None, pid=None, first_det_window_days=None, min_sep=0.01 + def calculate_overlap_with_depot_observations( + self, first_det_window_days=3.0, min_sep=0.01 ): + mns = alt_get_obs_summary(t_min=self.t_min, max_days=first_det_window_days) + + if mns is None: + return None + + data = mns.data.copy() + + mask = data["status"] == 0 + self.logger.info( + f"Found {mask.sum()} successful observations in the depot, " + f"corresponding to {np.mean(mask)*100:.2f}% of the total." + ) + + self.logger.info("Unpacking observations") + + pix_map = dict() + pix_obs_times = dict() + + # field_pix = get_flatpix(nside=self.nside, logger=self.logger) + nested_pix = get_nested_pix(nside=self.nside, logger=self.logger) + + for i, obs_time in enumerate(tqdm(list(set(data["obsjd"])))): + obs = data[data["obsjd"] == obs_time] + + field = obs["field_id"].iloc[0] + + flat_pix = nested_pix[field] + + mask = obs["status"] == 0 + indices = obs["qid"].values[mask] + + for qid in indices: + pixels = flat_pix[qid] + + for p in pixels: + if p not in pix_obs_times.keys(): + pix_obs_times[p] = [obs_time] + else: + pix_obs_times[p] += [obs_time] + + if p not in pix_map.keys(): + pix_map[p] = [field] + else: + pix_map[p] += [field] + + npix = hp.nside2npix(self.nside) + theta, phi = hp.pix2ang(self.nside, np.arange(npix), nest=False) + radecs = SkyCoord(ra=phi * u.rad, dec=(0.5 * np.pi - theta) * u.rad) + idx = np.where(np.abs(radecs.galactic.b.deg) <= 10.0)[0] + + double_in_plane_pixels = [] + double_in_plane_probs = [] + single_in_plane_pixels = [] + single_in_plane_prob = [] + veto_pixels = [] + plane_pixels = [] + plane_probs = [] + times = [] + double_no_plane_prob = [] + double_no_plane_pixels = [] + single_no_plane_prob = [] + single_no_plane_pixels = [] + + overlapping_fields = [] + + for i, p in enumerate(tqdm(hp.nest2ring(self.nside, self.pixel_nos))): + if p in pix_obs_times.keys(): + if p in idx: + plane_pixels.append(p) + plane_probs.append(self.map_probs[i]) + + obs = pix_obs_times[p] + + # check which healpix are observed twice + if max(obs) - min(obs) > min_sep: + # is it in galactic plane or not? + if p not in idx: + double_no_plane_prob.append(self.map_probs[i]) + double_no_plane_pixels.append(p) + else: + double_in_plane_probs.append(self.map_probs[i]) + double_in_plane_pixels.append(p) + + else: + if p not in idx: + single_no_plane_pixels.append(p) + single_no_plane_prob.append(self.map_probs[i]) + else: + single_in_plane_prob.append(self.map_probs[i]) + single_in_plane_pixels.append(p) + + overlapping_fields += pix_map[p] + + times += list(obs) + else: + veto_pixels.append(p) + + overlapping_fields = sorted(list(set(overlapping_fields))) + + _observations = data.query("obsjd in @times").reset_index(drop=True)[ + ["obsjd", "exposure_time", "filter_id"] + ] + bands = [self.fid_to_band(fid) for fid in _observations["filter_id"].values] + _observations["band"] = bands + _observations.drop(columns=["filter_id"], inplace=True) + self.observations = _observations + + self.logger.info("All observations:") + self.logger.info(f"\n{self.observations}") + + try: + self.first_obs = Time(min(times), format="jd") + self.first_obs.utc.format = "isot" + self.last_obs = Time(max(times), format="jd") + self.last_obs.utc.format = "isot" + + except ValueError: + err = ( + f"No observations of this field were found at any time between {self.t_min} and" + f"{times[-1]}. Coverage overlap is 0%, but recent observations might be missing!" + ) + self.logger.error(err) + raise ValueError(err) + + self.logger.info(f"Observations started at {self.first_obs.isot}") + + return ( + double_in_plane_pixels, + double_in_plane_probs, + single_in_plane_pixels, + single_in_plane_prob, + veto_pixels, + plane_pixels, + plane_probs, + times, + double_no_plane_prob, + double_no_plane_pixels, + single_no_plane_prob, + single_no_plane_pixels, + overlapping_fields, + ) + + def plot_overlap_with_observations(self, first_det_window_days=None, min_sep=0.01): """ """ - overlap_res = self.calculate_overlap_with_observations( - fields=fields, - pid=pid, + overlap_res = self.calculate_overlap_with_depot_observations( first_det_window_days=first_det_window_days, min_sep=min_sep, ) + if overlap_res is None: + self.logger.info("IPAC depot failed, using ztfquery to obtain observations") + overlap_res = self.calculate_overlap_with_observations( + first_det_window_days=first_det_window_days, + min_sep=min_sep, + ) if overlap_res is None: self.logger.warning("Not plotting overlap with observations.") diff --git a/nuztf/cat_match.py b/nuztf/cat_match.py index 19c1ad74..c6b6a420 100644 --- a/nuztf/cat_match.py +++ b/nuztf/cat_match.py @@ -2,12 +2,14 @@ # coding: utf-8 import logging +import warnings from astropy import units as u from astropy.coordinates import SkyCoord +from astropy.utils.exceptions import AstropyWarning from astroquery.exceptions import RemoteServiceError +from astroquery.ipac.irsa import Irsa from astroquery.ipac.ned import Ned - from nuztf.ampel_api import ampel_api_catalog, ampel_api_name @@ -52,6 +54,27 @@ def query_ned_astroquery( return None +def query_wise_astroquery( + ra_deg: float, dec_deg: float, searchradius_arcsec: float = 3.0 +): + """ + Function to obtain WISE crossmatches via astroquery + + :param ra_deg: Right ascension (deg) + :param dec_deg: Declination (deg) + :param searchradius_arcsec: Search radius (arcsec) + :return: result of query + """ + c = SkyCoord(ra_deg, dec_deg, unit=u.deg, frame="icrs") + + r = searchradius_arcsec * u.arcsecond + + with warnings.catch_warnings(): + warnings.simplefilter("ignore", AstropyWarning) + allwise = Irsa.query_region(c, catalog="allwise_p3as_psd", radius=r) + return allwise + + def ampel_api_tns( ra_deg: float, dec_deg: float, searchradius_arcsec: float = 3, logger=None ): @@ -83,8 +106,16 @@ def ampel_api_tns( return full_name, discovery_date, source_group +NUZTF_LABEL = "nuztf_xmatch_label" + + def get_cross_match_info(raw: dict, logger=None): """ """ + + # Cache crossmatch in alert! + if NUZTF_LABEL in raw: + return raw[NUZTF_LABEL] + alert = raw["candidate"] label = "" @@ -164,29 +195,30 @@ def get_cross_match_info(raw: dict, logger=None): # WISE colour cuts (https://iopscience.iop.org/article/10.3847/1538-4365/) if label == "": - res = ampel_api_catalog( - catalog="wise_color", - catalog_type="extcats", + res = query_wise_astroquery( ra_deg=alert["ra"], dec_deg=alert["dec"], - search_radius_arcsec=6.0, - logger=logger, + searchradius_arcsec=6.0, ) if res is not None: if len(res) == 1: - w1mw2 = res[0]["body"]["W1mW2"] + w1mw2 = res["w1mpro"][0] - res["w2mpro"][0] + if w1mw2 > 0.8: label = ( - f"[Probable WISE-selected quasar:W1-W2={w1mw2:.1f}>0.8 " - f"({res[0]['dist_arcsec']:.2f} arsec)]" + f"[Probable WISE-selected quasar:W1-W2={w1mw2:.2f}>0.8 " + f"({res[0]['dist']:.2f} arsec)]" ) elif w1mw2 > 0.5: label = ( - f"[Possible WISE-selected quasar:W1-W2={w1mw2:.1f}>0.5 " - f"({res[0]['dist_arcsec']:.2f} arsec)]" + f"[Possible WISE-selected quasar:W1-W2={w1mw2:.2f}>0.5 " + f"({res[0]['dist']:.2f} arsec)]" ) else: - label = "WISE DETECTIOM" + label = ( + f"WISE DETECTION: W1-W2={w1mw2:.2f} " + f"({res[0]['dist']:.2f} arsec)" + ) else: label = "[MULTIPLE WISE MATCHES]" @@ -219,6 +251,8 @@ def get_cross_match_info(raw: dict, logger=None): if full_name is not None: label += f" [TNS NAME={full_name}]" + raw[NUZTF_LABEL] = label + return label diff --git a/nuztf/credentials.py b/nuztf/credentials.py index 01f3cfcc..8be4341c 100644 --- a/nuztf/credentials.py +++ b/nuztf/credentials.py @@ -39,6 +39,19 @@ def load_credentials(name: str, token_based: bool = False): 'No Credentials for "skyvision" found in environment' "Assuming they are set." ) +try: + io.set_account( + "ipacdepot", + username=os.environ["DEPOT_USER"], + password=os.environ["DEPOT_PASSWORD"], + ) + logging.info('Set up "DEPOT" credentials') + +except KeyError: + logging.info( + 'No Credentials for "DEPOT" found in environment' "Assuming they are set." + ) + try: with warnings.catch_warnings(): warnings.filterwarnings("ignore", category=UserWarning) diff --git a/nuztf/flatpix.py b/nuztf/flatpix.py index 75b2d608..ead2183b 100644 --- a/nuztf/flatpix.py +++ b/nuztf/flatpix.py @@ -16,6 +16,15 @@ def get_flatpix_path(nside: int): return outfile +def get_nested_pix_path(nside: int): + outdir = os.path.join(os.path.dirname(__file__), "data") + if not os.path.exists(outdir): + os.makedirs(outdir) + + outfile = os.path.join(outdir, f"ztf_fields_nested_ipix_nside={nside}.pickle") + return outfile + + def generate_flatpix_file(nside: int, logger=logging.getLogger(__name__)): """ Generate and save the fields-healpix lookup table @@ -30,6 +39,7 @@ def generate_flatpix_file(nside: int, logger=logging.getLogger(__name__)): decs = field_dataframe["Dec"].values flat_pix_dict = dict() + nested_pix_dict = dict() for i, field in tqdm(enumerate(fields), total=len(fields)): ra = ras[i] @@ -44,6 +54,7 @@ def generate_flatpix_file(nside: int, logger=logging.getLogger(__name__)): flat_pix = list(set(flat_pix)) flat_pix_dict[field] = flat_pix + nested_pix_dict[field] = pix outfile = get_flatpix_path(nside=nside) @@ -52,6 +63,13 @@ def generate_flatpix_file(nside: int, logger=logging.getLogger(__name__)): with open(outfile, "wb") as f: pickle.dump(flat_pix_dict, f) + outfile = get_nested_pix_path(nside=nside) + + logger.info(f"Saving to {outfile}") + + with open(outfile, "wb") as f: + pickle.dump(nested_pix_dict, f) + def get_flatpix(nside: int, logger=logging.getLogger(__name__)): infile = get_flatpix_path(nside=nside) @@ -67,3 +85,19 @@ def get_flatpix(nside: int, logger=logging.getLogger(__name__)): field_pix = pickle.load(f) return field_pix + + +def get_nested_pix(nside: int, logger=logging.getLogger(__name__)): + infile = get_nested_pix_path(nside=nside) + + # Generate a lookup table for field healpix + # if none exists (because this is computationally costly) + if not os.path.isfile(infile): + generate_flatpix_file(nside=nside, logger=logger) + + logger.info(f"Loading from {infile}") + + with open(infile, "rb") as f: + field_pix = pickle.load(f) + + return field_pix diff --git a/nuztf/observations_depot.py b/nuztf/observations_depot.py new file mode 100644 index 00000000..d74ede75 --- /dev/null +++ b/nuztf/observations_depot.py @@ -0,0 +1,179 @@ +import json +import logging +import os +from glob import glob + +import numpy as np +import pandas as pd +import requests +from astropy import units as u +from astropy.time import Time +from nuztf import credentials +from nuztf.observations import MNS, coverage_dir, partial_flag +from requests.auth import HTTPBasicAuth +from requests.exceptions import HTTPError +from tqdm import tqdm + +logger = logging.getLogger(__name__) + +username, password = credentials.load_credentials("ipacdepot") + + +class NoDepotEntry(Exception): + """ + No entry in the depot for a given date + """ + + +def download_depot_log(date): + """ + Download the depot log for a given date + + :param date: date in YYYYMMDD format + :return: json log + """ + url = f"https://ztfweb.ipac.caltech.edu/ztf/depot/{date}/ztf_recentproc_{date}.json" + response = requests.get(url, auth=HTTPBasicAuth(username, password)) + if response.status_code == 404: + raise NoDepotEntry(f"No depot entry for {date}") + response.raise_for_status() + return response.json() + + +def coverage_depot_cache(jd: float) -> str: + """ + Return the path to the cached coverage file for a given JD + + :param jd: JD + :return: path to cached coverage file + """ + if (Time.now().jd - jd) < 1: + partial_ext = partial_flag + else: + partial_ext = "" + + return os.path.join(coverage_dir, f"{jd}{partial_ext}.json") + + +def write_depot_coverage(jds: list[int]): + """ + Write the depot coverage for a list of JDs to the cache + + :param jds: JDs + :return: None + """ + for jd in tqdm(jds): + try: + date = str(Time(jd, format="jd").isot).split("T")[0].replace("-", "") + log = download_depot_log(date) + except NoDepotEntry: + log = {} + path = coverage_depot_cache(jd) + with open(path, "w") as f: + json.dump(log, f) + + +def get_coverage_depot(jds: [int]) -> pd.DataFrame | None: + """ + Get a dataframe of the depot coverage for a list of JDs + + :param jds: JDs + :return: Coverage dataframe + """ + # Clear any logs flagged as partial/incomplete + + cache_files = glob(f"{coverage_dir}/*.json") + partial_logs = [x for x in cache_files if partial_flag in x] + + if len(partial_logs) > 0: + logger.debug(f"Removing the following partial logs: {partial_logs}") + for partial_log in partial_logs: + os.remove(partial_log) + + # Only write missing logs + + missing_logs = [] + + for jd in jds: + if not os.path.exists(coverage_depot_cache(jd)): + missing_logs.append(jd) + else: + df = pd.read_json(coverage_depot_cache(jd)) + if len(df) == 0: + missing_logs.append(jd) + + if len(missing_logs) > 0: + logger.debug( + f"Some logs were missing from the cache. " + f"Querying for the following JDs: {missing_logs}" + ) + write_depot_coverage(missing_logs) + + # Load logs from cache + + results = [] + + for jd in tqdm(jds): + res = pd.read_json(coverage_depot_cache(jd)) + results.append(res) + + if results: + result_df = pd.concat(results, ignore_index=True) + return result_df + else: + return None + + +def get_obs_summary_depot(t_min: Time, t_max: Time) -> MNS | None: + """ + Get observation summary from depot + """ + + jds = np.arange(t_min.jd, t_max.jd + 1) + + res = get_coverage_depot(jds) + + if len(res) == 0: + return None + + res["date"] = Time(res["obsjd"].to_numpy(), format="jd").isot + + mns = MNS(df=res) + + mns.data.query(f"obsjd >= {t_min.jd} and obsjd <= {t_max.jd}", inplace=True) + + mns.data.reset_index(inplace=True) + mns.data.drop(columns=["index"], inplace=True) + + return mns + + +def get_obs_summary(t_min, t_max=None, max_days: float = None) -> MNS | None: + """ + Get observation summary from IPAC depot + """ + now = Time.now() + + if t_max and max_days: + raise ValueError("Choose either t_max or max_days, not both") + + if t_max is None: + if max_days is None: + t_max = now + else: + t_max = t_min + (max_days * u.day) + + if t_max > now: + t_max = now + + logger.info("Getting observation logs from IPAC depot.") + mns = get_obs_summary_depot(t_min=t_min, t_max=t_max) + + if mns is not None: + logger.debug( + f"Found {len(set(mns.data['exposure_id']))} observations in total." + ) + else: + logger.debug("Found no observations on IPAC depot.") + + return mns diff --git a/nuztf/skymap.py b/nuztf/skymap.py index ddcf51b7..f7dd3ac1 100644 --- a/nuztf/skymap.py +++ b/nuztf/skymap.py @@ -36,6 +36,7 @@ def __init__( rev: int = None, prob_threshold: float = 0.9, custom_prefix: str = "", + output_nside: int | None = None, ): self.base_skymap_dir = os.path.join(LOCALSOURCE, f"{custom_prefix}skymaps") self.candidate_output_dir = os.path.join( @@ -100,7 +101,7 @@ def __init__( self.key, self.dist, self.dist_unc, - ) = self.read_map() + ) = self.read_map(output_nside=output_nside) t_min = Time(self.t_obs, format="isot", scale="utc") @@ -245,10 +246,14 @@ def get_gw_skymap(self, event_name: str, rev: int): return savepath, summary_path, event_name - def read_map( - self, - ): - """Read the skymap""" + def read_map(self, output_nside: int | None = None): + """ + Read the skymap + + :param output_nside: The nside of the output skymap. + If None, the nside of the input skymap will be used. + + """ self.logger.info(f"Reading file: {self.skymap_path}") @@ -331,7 +336,48 @@ def read_map( f"Please enter the ordewring (NESTED/RING/NUNIQ)" ) - hpm = HEALPix(nside=h["NSIDE"], order=h["ORDERING"], frame="icrs") + # Optionally interpolate to a different nside + if output_nside is not None: + if output_nside != hp.npix2nside(len(data["PROB"])): + self.logger.info(f"Regridding to nside {output_nside}") + + new_prob = hp.ud_grade( + data["PROB"], + nside_out=output_nside, + order_in=h["ORDERING"], + order_out="NESTED", + power=-2, + ) + + new_data = np.array(new_prob, dtype=np.dtype([("PROB", float)])) + + if "DISTMEAN" in h.keys(): + dist = hp.ud_grade( + data["DISTMEAN"], + nside_out=output_nside, + order_in=h["ORDERING"], + order_out="NESTED", + power=0, + ) + + new_data["DISTMEAN"] = dist + + if "DISTSTD" in h.keys(): + dist_unc = hp.ud_grade( + data["DISTSTD"], + nside_out=output_nside, + order_in=h["ORDERING"], + order_out="NESTED", + power=0, + ) + new_data["DISTSTD"] = dist_unc + + data = new_data + + else: + output_nside = h["NSIDE"] + + hpm = HEALPix(nside=output_nside, order="NESTED", frame="icrs") return data, t_obs, hpm, key, dist, dist_unc diff --git a/nuztf/skymap_scanner.py b/nuztf/skymap_scanner.py index a7088b91..001be18d 100644 --- a/nuztf/skymap_scanner.py +++ b/nuztf/skymap_scanner.py @@ -32,12 +32,15 @@ class RetractionError(Exception): class SkymapScanner(BaseScanner): + default_fritz_group = 1563 + def __init__( self, event: str = None, rev: int = None, prob_threshold: float = 0.9, cone_nside: int = 64, + output_nside: int | None = None, n_days: float = 3.0, # By default, accept things detected within 72 hours of event time custom_prefix: str = "", config: dict = None, @@ -60,6 +63,7 @@ def __init__( rev=rev, prob_threshold=prob_threshold, custom_prefix=custom_prefix, + output_nside=output_nside, ) self.t_min = Time(self.skymap.t_obs, format="isot", scale="utc") diff --git a/tests/test_neutrino_scanner.py b/tests/test_neutrino_scanner.py index 446aa8bb..e56804da 100644 --- a/tests/test_neutrino_scanner.py +++ b/tests/test_neutrino_scanner.py @@ -4,7 +4,6 @@ import numpy as np from astropy.coordinates import Distance from astropy.time import Time - from nuztf import NeutrinoScanner from nuztf.ampel_api import ampel_api_catalog from nuztf.base_scanner import cosmo @@ -111,7 +110,7 @@ def test_scan(self): print(repr(res)) # Update the true using repr(res) - true_gcn = f"Astronomer Name (Institute of Somewhere), ............. report,\n\nOn behalf of the Zwicky Transient Facility (ZTF) and Global Relay of Observatories Watching Transients Happen (GROWTH) collaborations: \n\nAs part of the ZTF neutrino follow up program (Stein et al. 2022), we observed the localization region of the neutrino event IceCube-200620A (Santander et. al, GCN 27997) with the Palomar 48-inch telescope, equipped with the 47 square degree ZTF camera (Bellm et al. 2019, Graham et al. 2019). We started observations in the g- and r-band beginning at 2020-06-21 04:53 UTC, approximately 25.8 hours after event time. We covered 77.7% (1.2 sq deg) of the reported localization region. This estimate accounts for chip gaps. Each exposure was 300s with a typical depth of 21.0 mag. \n \nThe images were processed in real-time through the ZTF reduction and image subtraction pipelines at IPAC to search for potential counterparts (Masci et al. 2019). AMPEL (Nordin et al. 2019, Stein et al. 2021) was used to search the alerts database for candidates. We reject stellar sources (Tachibana and Miller 2018) and moving objects, and apply machine learning algorithms (Mahabal et al. 2019) . We are left with the following high-significance transient candidates by our pipeline, all lying within the 90.0% localization of the skymap.\n\n+--------------------------------------------------------------------------------+\n| ZTF Name | IAU Name | RA (deg) | DEC (deg) | Filter | Mag | MagErr |\n+--------------------------------------------------------------------------------+\n| ZTF18acvhwtf | AT2020ncs | {hist_and_new_values['ZTF18acvhwtf']['ra']:011.7f} | {hist_and_new_values['ZTF18acvhwtf']['dec']:+011.7f} | r | {hist_and_new_values['ZTF18acvhwtf']['mag']:.2f} | {hist_and_new_values['ZTF18acvhwtf']['mag_err']:.2f} | {old_flag} \n| ZTF20abgvabi | AT2020ncr | 162.5306341 | +12.1461187 | g | 20.58 | 0.19 | (MORE THAN ONE DAY SINCE SECOND DETECTION) \n+--------------------------------------------------------------------------------+\n\n \n\nAmongst our candidates, \n\nZTF18acvhwtf was first detected on 2018-12-09. It has a spec-z of 0.291 [{hist_and_new_values['ZTF18acvhwtf']['z_dist']:.0f} Mpc] and an abs. mag of {hist_and_new_values['ZTF18acvhwtf']['absmag']:.1f}. Distance to SDSS galaxy is {hist_and_new_values['ZTF18acvhwtf']['ned_dist_new']:.2f} arcsec. [MILLIQUAS: SDSS J104816.25+120734.7 - 'Q'-type source ({hist_and_new_values['ZTF18acvhwtf']['milliquas_dist_new']:.2f} arsec)] [TNS NAME=AT2020ncs]\nZTF20abgvabi was first detected on 2020-05-26. WISEA J105007.28+120846.1 ['G'-type source (0.00 arsec)] [TNS NAME=AT2020ncr]\n\n\nZTF and GROWTH are worldwide collaborations comprising Caltech, USA; IPAC, USA; WIS, Israel; OKC, Sweden; JSI/UMd, USA; DESY, Germany; TANGO, Taiwan; UW Milwaukee, USA; LANL, USA; TCD, Ireland; IN2P3, France.\n\nGROWTH acknowledges generous support of the NSF under PIRE Grant No 1545949.\nAlert distribution service provided by DIRAC@UW (Patterson et al. 2019).\nAlert database searches are done by AMPEL (Nordin et al. 2019).\nAlert filtering is performed with the nuztf (Stein et al. 2021, https://github.com/desy-multimessenger/nuztf).\n" + true_gcn = f"Astronomer Name (Institute of Somewhere), ............. report,\n\nOn behalf of the Zwicky Transient Facility (ZTF) and Global Relay of Observatories Watching Transients Happen (GROWTH) collaborations: \n\nAs part of the ZTF neutrino follow up program (Stein et al. 2022), we observed the localization region of the neutrino event IceCube-200620A (Santander et. al, GCN 27997) with the Palomar 48-inch telescope, equipped with the 47 square degree ZTF camera (Bellm et al. 2019, Graham et al. 2019). We started observations in the g- and r-band beginning at 2020-06-21 04:53 UTC, approximately 25.8 hours after event time. We covered 77.7% (1.2 sq deg) of the reported localization region. This estimate accounts for chip gaps. Each exposure was 300s with a typical depth of 21.0 mag. \n \nThe images were processed in real-time through the ZTF reduction and image subtraction pipelines at IPAC to search for potential counterparts (Masci et al. 2019). AMPEL (Nordin et al. 2019, Stein et al. 2021) was used to search the alerts database for candidates. We reject stellar sources (Tachibana and Miller 2018) and moving objects, and apply machine learning algorithms (Mahabal et al. 2019) . We are left with the following high-significance transient candidates by our pipeline, all lying within the 90.0% localization of the skymap.\n\n+--------------------------------------------------------------------------------+\n| ZTF Name | IAU Name | RA (deg) | DEC (deg) | Filter | Mag | MagErr |\n+--------------------------------------------------------------------------------+\n| ZTF18acvhwtf | AT2020ncs | {hist_and_new_values['ZTF18acvhwtf']['ra']:011.7f} | {hist_and_new_values['ZTF18acvhwtf']['dec']:+011.7f} | r | {hist_and_new_values['ZTF18acvhwtf']['mag']:.2f} | {hist_and_new_values['ZTF18acvhwtf']['mag_err']:.2f} | {old_flag} \n| ZTF20abgvabi | AT2020ncr | 162.5306341 | +12.1461187 | g | 20.58 | 0.19 | (MORE THAN ONE DAY SINCE SECOND DETECTION) \n+--------------------------------------------------------------------------------+\n\n \n\nAmongst our candidates, \n\nZTF18acvhwtf was first detected on 2018-12-09. It has a spec-z of 0.291 [{hist_and_new_values['ZTF18acvhwtf']['z_dist']:.0f} Mpc] and an abs. mag of {hist_and_new_values['ZTF18acvhwtf']['absmag']:.1f}. Distance to SDSS galaxy is {hist_and_new_values['ZTF18acvhwtf']['ned_dist_new']:.2f} arcsec. [MILLIQUAS: SDSS J104816.25+120734.7 - 'Q'-type source ({hist_and_new_values['ZTF18acvhwtf']['milliquas_dist_new']:.2f} arsec)] [TNS NAME=AT2020ncs]\nZTF20abgvabi was first detected on 2020-05-26. WISE DETECTION: W1-W2=0.04 (1.03 arsec) [TNS NAME=AT2020ncr]\n\n\nZTF and GROWTH are worldwide collaborations comprising Caltech, USA; IPAC, USA; WIS, Israel; OKC, Sweden; JSI/UMd, USA; DESY, Germany; TANGO, Taiwan; UW Milwaukee, USA; LANL, USA; TCD, Ireland; IN2P3, France.\n\nGROWTH acknowledges generous support of the NSF under PIRE Grant No 1545949.\nAlert distribution service provided by DIRAC@UW (Patterson et al. 2019).\nAlert database searches are done by AMPEL (Nordin et al. 2019).\nAlert filtering is performed with the nuztf (Stein et al. 2021, https://github.com/desy-multimessenger/nuztf).\n" self.assertEqual(res, true_gcn) diff --git a/tests/test_skymap_scanner.py b/tests/test_skymap_scanner.py index d41c2a0f..2eba460d 100644 --- a/tests/test_skymap_scanner.py +++ b/tests/test_skymap_scanner.py @@ -112,7 +112,7 @@ def test_grb_scan(self): print(repr(res)) # Update the true using repr(res) - true_gcn = "Astronomer Name (Institute of Somewhere), ............. report,\n\nOn behalf of the Zwicky Transient Facility (ZTF) and Global Relay of Observatories Watching Transients Happen (GROWTH) collaborations: \n\nAs part of the ZTF neutrino follow up program (Stein et al. 2022), we observed the localization region of the GRB210927A with the Palomar 48-inch telescope, equipped with the 47 square degree ZTF camera (Bellm et al. 2019, Graham et al. 2019). We started observations in the g- and r-band beginning at 2021-09-27 02:44 UTC, approximately 2.1 hours after event time. We covered 0.1% (1.0 sq deg) of the reported localization region. This estimate accounts for chip gaps. Each exposure was 30s with a typical depth of 20.5 mag. \n \nThe images were processed in real-time through the ZTF reduction and image subtraction pipelines at IPAC to search for potential counterparts (Masci et al. 2019). AMPEL (Nordin et al. 2019, Stein et al. 2021) was used to search the alerts database for candidates. We reject stellar sources (Tachibana and Miller 2018) and moving objects, and apply machine learning algorithms (Mahabal et al. 2019) , and removing candidates with history of variability prior to the merger time. We are left with the following high-significance transient candidates by our pipeline, all lying within the 90.0% localization of the skymap.\n\n+--------------------------------------------------------------------------------+\n| ZTF Name | IAU Name | RA (deg) | DEC (deg) | Filter | Mag | MagErr |\n+--------------------------------------------------------------------------------+\n| ZTF21acdvtxc | ------- | 250.2336698 | +05.3908972 | g | 21.80 | 0.21 | (MORE THAN ONE DAY SINCE SECOND DETECTION) \n| ZTF21acdvtxp | ------- | 250.4636648 | +01.8436867 | g | 21.33 | 0.18 | (MORE THAN ONE DAY SINCE SECOND DETECTION) \n| ZTF21acdvuzf | ------- | 241.8979602 | +19.0755373 | g | 20.82 | 0.17 | (MORE THAN ONE DAY SINCE SECOND DETECTION) \n+--------------------------------------------------------------------------------+\n\n \n\nAmongst our candidates, \n\nZTF21acdvtxc had upper limit problems. PLEASE FILL IN NUMBERS BY HAND!!! WISEA J164056.10+052327.1 ['UvS'-type source (0.03 arsec)]\nZTF21acdvtxp had upper limit problems. PLEASE FILL IN NUMBERS BY HAND!!! [MILLIQUAS: SDSS J164151.27+015037.0 - Likely QSO (prob = 95.0%) (0.21 arsec)]\nZTF21acdvuzf had upper limit problems. PLEASE FILL IN NUMBERS BY HAND!!! \n\n\nZTF and GROWTH are worldwide collaborations comprising Caltech, USA; IPAC, USA; WIS, Israel; OKC, Sweden; JSI/UMd, USA; DESY, Germany; TANGO, Taiwan; UW Milwaukee, USA; LANL, USA; TCD, Ireland; IN2P3, France.\n\nGROWTH acknowledges generous support of the NSF under PIRE Grant No 1545949.\nAlert distribution service provided by DIRAC@UW (Patterson et al. 2019).\nAlert database searches are done by AMPEL (Nordin et al. 2019).\nAlert filtering is performed with the nuztf (Stein et al. 2021, https://github.com/desy-multimessenger/nuztf).\n" + true_gcn = "Astronomer Name (Institute of Somewhere), ............. report,\n\nOn behalf of the Zwicky Transient Facility (ZTF) and Global Relay of Observatories Watching Transients Happen (GROWTH) collaborations: \n\nAs part of the ZTF neutrino follow up program (Stein et al. 2022), we observed the localization region of the GRB210927A with the Palomar 48-inch telescope, equipped with the 47 square degree ZTF camera (Bellm et al. 2019, Graham et al. 2019). We started observations in the g- and r-band beginning at 2021-09-27 02:44 UTC, approximately 2.1 hours after event time. We covered 0.1% (1.0 sq deg) of the reported localization region. This estimate accounts for chip gaps. Each exposure was 30s with a typical depth of 20.5 mag. \n \nThe images were processed in real-time through the ZTF reduction and image subtraction pipelines at IPAC to search for potential counterparts (Masci et al. 2019). AMPEL (Nordin et al. 2019, Stein et al. 2021) was used to search the alerts database for candidates. We reject stellar sources (Tachibana and Miller 2018) and moving objects, and apply machine learning algorithms (Mahabal et al. 2019) , and removing candidates with history of variability prior to the merger time. We are left with the following high-significance transient candidates by our pipeline, all lying within the 90.0% localization of the skymap.\n\n+--------------------------------------------------------------------------------+\n| ZTF Name | IAU Name | RA (deg) | DEC (deg) | Filter | Mag | MagErr |\n+--------------------------------------------------------------------------------+\n| ZTF21acdvtxc | ------- | 250.2336698 | +05.3908972 | g | 21.80 | 0.21 | (MORE THAN ONE DAY SINCE SECOND DETECTION) \n| ZTF21acdvtxp | ------- | 250.4636648 | +01.8436867 | g | 21.33 | 0.18 | (MORE THAN ONE DAY SINCE SECOND DETECTION) \n| ZTF21acdvuzf | ------- | 241.8979602 | +19.0755373 | g | 20.82 | 0.17 | (MORE THAN ONE DAY SINCE SECOND DETECTION) \n+--------------------------------------------------------------------------------+\n\n \n\nAmongst our candidates, \n\nZTF21acdvtxc had upper limit problems. PLEASE FILL IN NUMBERS BY HAND!!! [Possible WISE-selected quasar:W1-W2=0.51>0.5 (0.31 arsec)]\nZTF21acdvtxp had upper limit problems. PLEASE FILL IN NUMBERS BY HAND!!! [MILLIQUAS: SDSS J164151.27+015037.0 - Likely QSO (prob = 95.0%) (0.21 arsec)]\nZTF21acdvuzf had upper limit problems. PLEASE FILL IN NUMBERS BY HAND!!! [MULTIPLE WISE MATCHES]\n\n\nZTF and GROWTH are worldwide collaborations comprising Caltech, USA; IPAC, USA; WIS, Israel; OKC, Sweden; JSI/UMd, USA; DESY, Germany; TANGO, Taiwan; UW Milwaukee, USA; LANL, USA; TCD, Ireland; IN2P3, France.\n\nGROWTH acknowledges generous support of the NSF under PIRE Grant No 1545949.\nAlert distribution service provided by DIRAC@UW (Patterson et al. 2019).\nAlert database searches are done by AMPEL (Nordin et al. 2019).\nAlert filtering is performed with the nuztf (Stein et al. 2021, https://github.com/desy-multimessenger/nuztf).\n" self.assertEqual(res, true_gcn)