hek_xrs15_example.ipynb

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    "### Example finding flares via HEK and then getting GOES/XRS15 data\n",
    "\n",
    "Note: several ways of doing and plotting this. The following works but there be more \"optimal\" ways.\n",
    "\n",
    "Other resources: [Sunpy doc on goes flare list from HEK](https://docs.sunpy.org/en/stable/generated/gallery/acquiring_data/querying_the_GOES_event_list.html#sphx-glr-generated-gallery-acquiring-data-querying-the-goes-event-list-py) and [Sunpy doc on getting/plotting XRS](https://docs.sunpy.org/en/stable/generated/gallery/acquiring_data/goes_xrs_example.html#sphx-glr-generated-gallery-acquiring-data-goes-xrs-example-py)\n",
    "\n",
    "**Note** that SWPC events are found as described in [https://ngdc.noaa.gov/stp/space-weather/solar-data/solar-features/solar-flares/documentation/readme_solar-features_solar-flares.pdf](https://ngdc.noaa.gov/stp/space-weather/solar-data/solar-features/solar-flares/documentation/readme_solar-features_solar-flares.pdf), i.e. in 1 min 1-8Å data looking for \n",
    "\n",
    "* Start: 4 consecutive 1-min bins > B1 level, all are increasing, and last is 1.4x first bin\n",
    "* Peak: max flux\n",
    "* End: 1/2 of (max+start) flux\n",
    "\n",
    "The resulting flare list is then processed by [ssw latest events](https://www.lmsal.com/solarsoft/latest_events/) to add more info (such as flare locations).\n",
    "\n",
    "---\n",
    "\n",
    "* 22-10-21   IGH\n",
    "* 23-10-21   Fixed axvline needing a datetime, not a string\n",
    "* 25-11-21   Added in info about difference in fluxes from data and catalogue\n",
    "* 10-11-22   Changed to return SSW Latest Events instead of SWPC and also do GOES16 version\n",
    "* 12-11-22   Added in flare list info\n",
    "---"
   ]
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  {
   "cell_type": "code",
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   "id": "8218fe10",
   "metadata": {},
   "outputs": [],
   "source": [
    "#  Import in the usual stuff\n",
    "import glob\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "from sunpy import timeseries as ts\n",
    "from sunpy.net import Fido\n",
    "from sunpy.net import attrs as a\n",
    "from astropy.time import Time\n",
    "from astropy.time import TimeDelta\n",
    "from sunpy.time import parse_time\n",
    "\n",
    "#  Here just assuming it save to your sunpy/data dir but can of course change\n",
    "outdir='/Users/iain/sunpy/data/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "415d7eb2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Let's look for all the X-class flares in 2017\n",
    "trange=a.Time(\"2017-01-01 00:00\",\"2018-01-01 00:00\")\n",
    "# This version only returns the SWPC ones, so same as a.hek.FRM.Name == \"SWPC\"\n",
    "# res = Fido.search(trange,a.hek.EventType(\"FL\"),a.hek.FL.GOESCls > \"X1.0\",a.hek.OBS.Observatory == \"GOES\")\n",
    "# This is ssw latest events, which are the SWPC but with more info, like position\n",
    "res = Fido.search(trange,a.hek.EventType(\"FL\"),a.hek.FL.GOESCls > \"X1.0\",a.hek.FRM.Name == \"SSW Latest Events\")\n",
    "\n",
    "hekres = res[\"hek\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a4dc2406",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div><i>HEKTable length=4</i>\n",
       "<table id=\"table140438749824576\" class=\"table-striped table-bordered table-condensed\">\n",
       "<thead><tr><th>gs_thumburl</th><th>comment_count</th><th>hpc_bbox</th><th>frm_humanflag</th><th>hgc_coord</th><th>event_coordsys</th><th>obs_levelnum</th><th>hpc_coord</th><th>event_npixels</th><th>gs_imageurl</th><th>ar_polarity</th><th>frm_paramset</th><th>hrc_coord</th><th>event_starttime</th><th>ar_mtwilsoncls</th><th>event_type</th><th>intensmin</th><th>fl_fluence</th><th>obs_meanwavel</th><th>frm_url</th><th>skel_chaincode</th><th>bound_chaincode</th><th>noposition</th><th>fl_fluenceunit</th><th>active</th><th>intensmax</th><th>frm_versionnumber</th><th>fl_peaktempunit</th><th>fl_halphaclass</th><th>area_uncert</th><th>obs_dataprepurl</th><th>hpc_geom</th><th>hgc_bbox</th><th>intensmedian</th><th>chaincodetype</th><th>obs_channelid</th><th>event_clippedspatial</th><th>ar_noaaclass</th><th>SOL_standard</th><th>event_avg_rating</th><th>eventtype</th><th>intensunit</th><th>hpc_boundcc</th><th>event_mapurl</th><th>frm_contact</th><th>ar_penumbracls</th><th>intensmean</th><th>bound_ccstartc1</th><th>frm_name</th><th>area_atdiskcenter</th><th>frm_identifier</th><th>obs_observatory</th><th>event_description</th><th>boundbox_c2ur</th><th>obs_firstprocessingdate</th><th>boundbox_c2ll</th><th>frm_institute</th><th>hrc_bbox</th><th>refs_orig</th><th>ar_mcintoshcls</th><th>event_maskurl</th><th>bound_ccstartc2</th><th>gs_movieurl</th><th>event_score</th><th>skel_startc2</th><th>skel_startc1</th><th>fl_efoldtime</th><th>event_expires</th><th>fl_efoldtimeunit</th><th>hrc_boundcc</th><th>event_probability</th><th>intensvar</th><th>frm_daterun</th><th>event_coordunit</th><th>hpc_y</th><th>hpc_x</th><th>search_instrument</th><th>ar_numspots</th><th>kb_archivdate</th><th>kb_archivist</th><th>intenstotal</th><th>sum_overlap_scores</th><th>hgs_boundcc</th><th>intensskew</th><th>obs_includesnrt</th><th>rasterscan</th><th>obs_wavelunit</th><th>kb_archivid</th><th>search_frm_name</th><th>boundbox_c1ur</th><th>ar_noaanum</th><th>area_atdiskcenteruncert</th><th>boundbox_c1ll</th><th>event_importance_num_ratings</th><th>ar_compactnesscls</th><th>skel_curvature</th><th>event_testflag</th><th>event_c2error</th><th>hrc_r</th><th>skel_nsteps</th><th>hgs_y</th><th>obs_title</th><th>fl_peakemunit</th><th>hgs_x</th><th>hcr_checked</th><th>frm_specificid</th><th>event_title</th><th>obs_instrument</th><th>event_c1error</th><th>revision</th><th>hpc_radius</th><th>event_endtime</th><th>event_importance</th><th>event_coord2</th><th>event_coord3</th><th>event_coord1</th><th>search_observatory</th><th>area_raw</th><th>concept</th><th>event_pixelunit</th><th>hgc_boundcc</th><th>fl_peakflux</th><th>hgc_x</th><th>hrc_a</th><th>event_peaktime</th><th>hgc_y</th><th>gs_galleryid</th><th>fl_goescls</th><th>hgs_coord</th><th>ar_zurichcls</th><th>bound_ccnsteps</th><th>intenskurt</th><th>event_clippedtemporal</th><th>fl_peakfluxunit</th><th>fl_peakem</th><th>rasterscantype</th><th>search_channelid</th><th>fl_peaktemp</th><th>hgs_bbox</th><th>area_unit</th><th>obs_lastprocessingdate</th><th>refs [8]</th></tr></thead>\n",
       "<thead><tr><th>str131</th><th>str1</th><th>str120</th><th>str5</th><th>str20</th><th>str12</th><th>object</th><th>str29</th><th>object</th><th>str130</th><th>object</th><th>str57</th><th>str41</th><th>Time</th><th>str1</th><th>str2</th><th>object</th><th>object</th><th>float64</th><th>str82</th><th>str1</th><th>str1</th><th>str5</th><th>str1</th><th>str4</th><th>object</th><th>object</th><th>str1</th><th>str1</th><th>object</th><th>str1</th><th>str186</th><th>str70</th><th>object</th><th>str1</th><th>str3</th><th>str1</th><th>str1</th><th>str30</th><th>object</th><th>str1</th><th>str1</th><th>str1</th><th>str1</th><th>str18</th><th>str1</th><th>object</th><th>object</th><th>str17</th><th>object</th><th>str9</th><th>str3</th><th>str1</th><th>int64</th><th>str1</th><th>int64</th><th>str5</th><th>str110</th><th>str1</th><th>str1</th><th>str1</th><th>object</th><th>str99</th><th>str20</th><th>object</th><th>object</th><th>object</th><th>str1</th><th>str1</th><th>str1</th><th>int64</th><th>object</th><th>str19</th><th>str7</th><th>float64</th><th>float64</th><th>str3</th><th>object</th><th>str19</th><th>str15</th><th>object</th><th>str20</th><th>str1</th><th>object</th><th>str1</th><th>str1</th><th>str2</th><th>str66</th><th>str17</th><th>int64</th><th>int64</th><th>object</th><th>int64</th><th>str1</th><th>str1</th><th>object</th><th>str5</th><th>int64</th><th>float64</th><th>object</th><th>int64</th><th>str1</th><th>str1</th><th>int64</th><th>str4</th><th>str1</th><th>str1</th><th>str3</th><th>int64</th><th>str1</th><th>str19</th><th>Time</th><th>object</th><th>int64</th><th>object</th><th>int64</th><th>str3</th><th>object</th><th>str5</th><th>str1</th><th>str1</th><th>object</th><th>float64</th><th>float64</th><th>Time</th><th>int64</th><th>str1</th><th>str4</th><th>str12</th><th>str1</th><th>object</th><th>object</th><th>str1</th><th>str1</th><th>object</th><th>str1</th><th>str19</th><th>object</th><th>str30</th><th>str1</th><th>str1</th><th>object</th></tr></thead>\n",
       "<tr><td>http://sdowww.lmsal.com/sdomedia/ssw/media/ssw/ssw_client/data/ssw_service_170906_094839_25912/www/ssw_cutout_193_context_thumb.gif</td><td>0</td><td>POLYGON((16.679092 -103.964646,16.679092 -103.964646,16.679092 -103.964646,16.679092 -103.964646,16.679092 -103.964646))</td><td>false</td><td>POINT(115.929197 -8)</td><td>UTC-HGS-TOPO</td><td>None</td><td>POINT(501.171102 -233.008824)</td><td>None</td><td>http://sdowww.lmsal.com/sdomedia/ssw/media/ssw/ssw_client/data/ssw_service_170906_094839_25912/www/ssw_cutout_193_context_full.gif</td><td>None</td><td>AIA20170906_085700_0094.fits,AIA20170906_091102_0094.fits</td><td>POINT(0.580893736757299 245.06493180857)</td><td>2017-09-06 08:57:00.000</td><td></td><td>FL</td><td>None</td><td>None</td><td>9.4e-07</td><td>http://sohowww.nascom.nasa.gov/solarsoft/packages/gevloc/idl/ssw_flare_locator.pro</td><td></td><td></td><td>false</td><td></td><td>true</td><td>None</td><td>None</td><td></td><td></td><td>None</td><td></td><td>01030000000100000005000000483140A289DF7840ECA9D557570375C0483140A289DF78400987DEE2E13960C0D84B5304F8E282400987DEE2E13960C0D84B5304F8E28240ECA9D557570375C0483140A289DF7840ECA9D557570375C0</td><td>POLYGON((84.929197 1,84.929197 1,84.929197 1,84.929197 1,84.929197 1))</td><td>None</td><td></td><td>EUV</td><td></td><td></td><td>SOL2017-09-06T08:57:00L115C098</td><td>None</td><td>9</td><td></td><td></td><td></td><td>Samuel L. Freeland</td><td></td><td>None</td><td>None</td><td>SSW Latest Events</td><td>None</td><td>SolarSoft</td><td>SDO</td><td></td><td>-7</td><td></td><td>-9</td><td>LMSAL</td><td>POLYGON((0.110667 189.114321,0.110667 189.114321,0.110667 189.114321,0.110667 189.114321,0.110667 189.114321))</td><td></td><td></td><td></td><td>None</td><td>http://sdowww.lmsal.com/sdomedia/ssw/media/ssw/ssw_client/data/ssw_service_170906_094839_25912/www/</td><td>0.844000000000000083</td><td>None</td><td>None</td><td>None</td><td></td><td></td><td></td><td>1</td><td>None</td><td>2017-09-06T00:00:00</td><td>degrees</td><td>-233.008824</td><td>501.171102</td><td>AIA</td><td>None</td><td>2017-09-07T07:15:28</td><td>freeland_samuel</td><td>None</td><td>0.887617077621888351</td><td></td><td>None</td><td></td><td></td><td>cm</td><td>ivo://helio-informatics.org/FL_SSWLatestEvents_20170906_101506_117</td><td>SSW Latest Events</td><td>33</td><td>2673</td><td>None</td><td>31</td><td></td><td></td><td>None</td><td>false</td><td>0</td><td>0.580893736757299</td><td>None</td><td>-8</td><td></td><td></td><td>32</td><td>true</td><td></td><td></td><td>AIA</td><td>0</td><td>2</td><td>552.689411461588747</td><td>2017-09-06 09:17:00.000</td><td>None</td><td>-8</td><td>None</td><td>32</td><td>SDO</td><td>None</td><td>Flare</td><td></td><td></td><td>None</td><td>115.929197</td><td>245.06493180857</td><td>2017-09-06 09:10:00.000</td><td>-8</td><td></td><td>X2.2</td><td>POINT(32 -8)</td><td></td><td>None</td><td>None</td><td></td><td></td><td>None</td><td></td><td>131,171,193,211,304</td><td>None</td><td>POLYGON((1 1,1 1,1 1,1 1,1 1))</td><td></td><td></td><td>{&apos;ref_name&apos;: &apos;FRM_URL&apos;, &apos;ref_type&apos;: &apos;unknown&apos;, &apos;ref_url&apos;: &apos;http://sohowww.nascom.nasa.gov/solarsoft/packages/gevloc/idl/ssw_flare_locator.pro&apos;} .. {&apos;ref_name&apos;: &apos;PreviousSSWMovieUrl&apos;, &apos;ref_type&apos;: &apos;html&apos;, &apos;ref_url&apos;: &apos;http://sdowww.lmsal.com/sdomedia/ssw/media/ssw/ssw_client/data/ssw_service_170904_154844_20676/www/&apos;}</td></tr>\n",
       "<tr><td>http://sdowww.lmsal.com/sdomedia/ssw/media/ssw/ssw_client/data/ssw_service_170906_083654_14687/www/ssw_cutout_193_context_thumb.gif</td><td>0</td><td>POLYGON((16.679601 -103.976478,16.679601 -103.976478,16.679601 -103.976478,16.679601 -103.976478,16.679601 -103.976478))</td><td>false</td><td>POINT(116.315059 -9)</td><td>UTC-HGS-TOPO</td><td>None</td><td>POINT(527.43879 -246.825594)</td><td>None</td><td>http://sdowww.lmsal.com/sdomedia/ssw/media/ssw/ssw_client/data/ssw_service_170906_083654_14687/www/ssw_cutout_193_context_full.gif</td><td>None</td><td>AIA20170906_115300_0094.fits,AIA20170906_120302_0094.fits</td><td>POINT(0.612034030781504 244.921810381541)</td><td>2017-09-06 11:53:00.000</td><td></td><td>FL</td><td>None</td><td>None</td><td>9.4e-07</td><td>http://sohowww.nascom.nasa.gov/solarsoft/packages/gevloc/idl/ssw_flare_locator.pro</td><td></td><td></td><td>false</td><td></td><td>true</td><td>None</td><td>None</td><td></td><td></td><td>None</td><td></td><td>010300000001000000050000002FF0DC7B38DA77402EA9DA6E028A78C02FF0DC7B38DA7740523E0455A34159C0E444BB0AE9098540523E0455A34159C0E444BB0AE90985402EA9DA6E028A78C02FF0DC7B38DA77402EA9DA6E028A78C0</td><td>POLYGON((83.315059 1,83.315059 1,83.315059 1,83.315059 1,83.315059 1))</td><td>None</td><td></td><td>EUV</td><td></td><td></td><td>SOL2017-09-06T11:53:00L116C099</td><td>None</td><td>9</td><td></td><td></td><td></td><td>Samuel L. Freeland</td><td></td><td>None</td><td>None</td><td>SSW Latest Events</td><td>None</td><td>SolarSoft</td><td>SDO</td><td></td><td>-8</td><td></td><td>-10</td><td>LMSAL</td><td>POLYGON((0.110676 189.113575,0.110676 189.113575,0.110676 189.113575,0.110676 189.113575,0.110676 189.113575))</td><td></td><td></td><td></td><td>None</td><td>http://sdowww.lmsal.com/sdomedia/ssw/media/ssw/ssw_client/data/ssw_service_170906_083654_14687/www/</td><td>0.986000000000000099</td><td>None</td><td>None</td><td>None</td><td></td><td></td><td></td><td>1</td><td>None</td><td>2017-09-07T00:00:00</td><td>degrees</td><td>-246.825594</td><td>527.43879</td><td>AIA</td><td>None</td><td>2017-11-16T07:00:39</td><td>freeland_samuel</td><td>None</td><td>1.18651003286873413</td><td></td><td>None</td><td></td><td></td><td>cm</td><td>ivo://helio-informatics.org/FL_SSWLatestEvents_20170906_084539_202</td><td>SSW Latest Events</td><td>35</td><td>2673</td><td>None</td><td>33</td><td></td><td></td><td>None</td><td>false</td><td>0</td><td>0.612034030781504</td><td>None</td><td>-9</td><td></td><td></td><td>34</td><td>true</td><td></td><td></td><td>AIA</td><td>0</td><td>4</td><td>582.335428297228759</td><td>2017-09-06 12:10:00.000</td><td>None</td><td>-9</td><td>None</td><td>34</td><td>SDO</td><td>None</td><td>Flare</td><td></td><td></td><td>None</td><td>116.315059</td><td>244.921810381541</td><td>2017-09-06 12:02:00.000</td><td>-9</td><td></td><td>X9.3</td><td>POINT(34 -9)</td><td></td><td>None</td><td>None</td><td></td><td></td><td>None</td><td></td><td>131,171,193,211,304</td><td>None</td><td>POLYGON((1 1,1 1,1 1,1 1,1 1))</td><td></td><td></td><td>{&apos;ref_name&apos;: &apos;FRM_URL&apos;, &apos;ref_type&apos;: &apos;unknown&apos;, &apos;ref_url&apos;: &apos;http://sohowww.nascom.nasa.gov/solarsoft/packages/gevloc/idl/ssw_flare_locator.pro&apos;} .. {&apos;ref_name&apos;: &apos;PreviousSSWMovieUrl&apos;, &apos;ref_type&apos;: &apos;html&apos;, &apos;ref_url&apos;: &apos;http://sdowww.lmsal.com/sdomedia/ssw/media/ssw/ssw_client/data/ssw_service_170905_005339_27220/www/&apos;}</td></tr>\n",
       "<tr><td>http://sdowww.lmsal.com/sdomedia/ssw/media/ssw/ssw_client/data/ssw_service_170907_081802_21496/www/ssw_cutout_193_context_thumb.gif</td><td>0</td><td>POLYGON((16.684203 -104.059908,16.684203 -104.059908,16.684203 -104.059908,16.684203 -104.059908,16.684203 -104.059908))</td><td>false</td><td>POINT(115.760641 -8)</td><td>UTC-HGS-TOPO</td><td>None</td><td>POINT(702.47112 -211.601262)</td><td>None</td><td>http://sdowww.lmsal.com/sdomedia/ssw/media/ssw/ssw_client/data/ssw_service_170907_081802_21496/www/ssw_cutout_193_context_full.gif</td><td>None</td><td>AIA20170907_142000_0094.fits,AIA20170907_143702_0094.fits</td><td>POINT(0.770852672847046 253.236430400911)</td><td>2017-09-07 14:20:00.000</td><td></td><td>FL</td><td>None</td><td>None</td><td>9.4e-07</td><td>http://sohowww.nascom.nasa.gov/solarsoft/packages/gevloc/idl/ssw_flare_locator.pro</td><td></td><td></td><td>false</td><td></td><td>true</td><td>None</td><td>None</td><td></td><td></td><td>None</td><td></td><td>01030000000100000005000000889D29745EE582400BF2B3916B5673C0889D29745EE58240C46169E047735CC0566AF6402B028940C46169E047735CC0566AF6402B0289400BF2B3916B5673C0889D29745EE582400BF2B3916B5673C0</td><td>POLYGON((68.760641 1,68.760641 1,68.760641 1,68.760641 1,68.760641 1))</td><td>None</td><td></td><td>EUV</td><td></td><td></td><td>SOL2017-09-07T14:20:00L115C098</td><td>None</td><td>9</td><td></td><td></td><td></td><td>Samuel L. Freeland</td><td></td><td>None</td><td>None</td><td>SSW Latest Events</td><td>None</td><td>SolarSoft</td><td>SDO</td><td></td><td>-7</td><td></td><td>-9</td><td>LMSAL</td><td>POLYGON((0.110733 189.108861,0.110733 189.108861,0.110733 189.108861,0.110733 189.108861,0.110733 189.108861))</td><td></td><td></td><td></td><td>None</td><td>http://sdowww.lmsal.com/sdomedia/ssw/media/ssw/ssw_client/data/ssw_service_170907_081802_21496/www/</td><td>0.826000000000000068</td><td>None</td><td>None</td><td>None</td><td></td><td></td><td></td><td>1</td><td>None</td><td>2017-09-07T00:00:00</td><td>degrees</td><td>-211.601262</td><td>702.47112</td><td>AIA</td><td>None</td><td>2017-09-08T07:15:31</td><td>freeland_samuel</td><td>None</td><td>1.27837576463177416</td><td></td><td>None</td><td></td><td></td><td>cm</td><td>ivo://helio-informatics.org/FL_SSWLatestEvents_20170907_082206_817</td><td>SSW Latest Events</td><td>49</td><td>2673</td><td>None</td><td>47</td><td></td><td></td><td>None</td><td>false</td><td>0</td><td>0.770852672847046</td><td>None</td><td>-8</td><td></td><td></td><td>48</td><td>true</td><td></td><td></td><td>AIA</td><td>0</td><td>3</td><td>733.648940920688005</td><td>2017-09-07 14:55:00.000</td><td>None</td><td>-8</td><td>None</td><td>48</td><td>SDO</td><td>None</td><td>Flare</td><td></td><td></td><td>None</td><td>115.760641</td><td>253.236430400911</td><td>2017-09-07 14:36:00.000</td><td>-8</td><td></td><td>X1.3</td><td>POINT(48 -8)</td><td></td><td>None</td><td>None</td><td></td><td></td><td>None</td><td></td><td>131,171,193,211,304</td><td>None</td><td>POLYGON((1 1,1 1,1 1,1 1,1 1))</td><td></td><td></td><td>{&apos;ref_name&apos;: &apos;FRM_URL&apos;, &apos;ref_type&apos;: &apos;unknown&apos;, &apos;ref_url&apos;: &apos;http://sohowww.nascom.nasa.gov/solarsoft/packages/gevloc/idl/ssw_flare_locator.pro&apos;} .. {&apos;ref_name&apos;: &apos;PreviousSSWMovieUrl&apos;, &apos;ref_type&apos;: &apos;html&apos;, &apos;ref_url&apos;: &apos;http://sdowww.lmsal.com/sdomedia/ssw/media/ssw/ssw_client/data/ssw_service_170905_064854_21242/www/&apos;}</td></tr>\n",
       "<tr><td>http://sdowww.lmsal.com/sdomedia/ssw/media/ssw/ssw_client/data/ssw_service_170910_093821_2004/www/ssw_cutout_193_context_thumb.gif</td><td>0</td><td>POLYGON((16.69707 -104.071836,16.69707 -104.071836,16.69707 -104.071836,16.69707 -104.071836,16.69707 -104.071836))</td><td>false</td><td>POINT(115.457134 -8)</td><td>UTC-HGS-TOPO</td><td>None</td><td>POINT(942.69654 -135.662304)</td><td>None</td><td>http://sdowww.lmsal.com/sdomedia/ssw/media/ssw/ssw_client/data/ssw_service_170910_093821_2004/www/ssw_cutout_193_context_full.gif</td><td>None</td><td>AIA20170910_153500_0094.fits,AIA20170910_160702_0094.fits</td><td>POINT(0.999937578040974 261.810857373405)</td><td>2017-09-10 15:35:00.000</td><td></td><td>FL</td><td>None</td><td>None</td><td>9.4e-07</td><td>http://sohowww.nascom.nasa.gov/solarsoft/packages/gevloc/idl/ssw_flare_locator.pro</td><td></td><td></td><td>false</td><td></td><td>true</td><td>None</td><td>None</td><td></td><td></td><td>None</td><td></td><td>010300000001000000050000003EA9F6E9F81B89405C7347FFCB2D71C03EA9F6E9F81B89400085798F334D0C4039EE940E96E790400085798F334D0C4039EE940E96E790405C7347FFCB2D71C03EA9F6E9F81B89405C7347FFCB2D71C0</td><td>POLYGON((28.457134 1,28.457134 1,28.457134 1,28.457134 1,28.457134 1))</td><td>None</td><td></td><td>EUV</td><td></td><td></td><td>SOL2017-09-10T15:35:00L115C098</td><td>None</td><td>9</td><td></td><td></td><td></td><td>Samuel L. Freeland</td><td></td><td>None</td><td>None</td><td>SSW Latest Events</td><td>None</td><td>SolarSoft</td><td>SDO</td><td></td><td>-7</td><td></td><td>-9</td><td>LMSAL</td><td>POLYGON((0.110663 189.114741,0.110663 189.114741,0.110663 189.114741,0.110663 189.114741,0.110663 189.114741))</td><td></td><td></td><td></td><td>None</td><td>http://sdowww.lmsal.com/sdomedia/ssw/media/ssw/ssw_client/data/ssw_service_170910_093821_2004/www/</td><td>0.963999999999999968</td><td>None</td><td>None</td><td>None</td><td></td><td></td><td></td><td>1</td><td>None</td><td>2017-09-12T00:00:00</td><td>degrees</td><td>-135.662304</td><td>942.69654</td><td>AIA</td><td>None</td><td>2017-11-16T07:00:45</td><td>freeland_samuel</td><td>None</td><td>0.609450965265191824</td><td></td><td>None</td><td></td><td></td><td>cm</td><td>ivo://helio-informatics.org/FL_SSWLatestEvents_20170910_092246_849</td><td>SSW Latest Events</td><td>89</td><td>2673</td><td>None</td><td>87</td><td></td><td></td><td>None</td><td>false</td><td>0</td><td>0.999937578040974</td><td>None</td><td>-8</td><td></td><td></td><td>88</td><td>true</td><td></td><td></td><td>AIA</td><td>0</td><td>8</td><td>952.408015114614614</td><td>2017-09-10 16:31:00.000</td><td>None</td><td>-8</td><td>None</td><td>88</td><td>SDO</td><td>None</td><td>Flare</td><td></td><td></td><td>None</td><td>115.457134</td><td>261.810857373405</td><td>2017-09-10 16:06:00.000</td><td>-8</td><td></td><td>X8.2</td><td>POINT(88 -8)</td><td></td><td>None</td><td>None</td><td></td><td></td><td>None</td><td></td><td>131,171,193,211,304</td><td>None</td><td>POLYGON((1 1,1 1,1 1,1 1,1 1))</td><td></td><td></td><td>{&apos;ref_name&apos;: &apos;FRM_URL&apos;, &apos;ref_type&apos;: &apos;unknown&apos;, &apos;ref_url&apos;: &apos;http://sohowww.nascom.nasa.gov/solarsoft/packages/gevloc/idl/ssw_flare_locator.pro&apos;} .. {&apos;ref_name&apos;: &apos;PreviousSSWMovieUrl&apos;, &apos;ref_type&apos;: &apos;html&apos;, &apos;ref_url&apos;: &apos;http://sdowww.lmsal.com/sdomedia/ssw/media/ssw/ssw_client/data/ssw_service_170909_034905_13837/www/&apos;}</td></tr>\n",
       "</table></div>"
      ],
      "text/plain": [
       "<sunpy.net.hek.hek.HEKTable object at 0x7fba71c1aa90>\n",
       "                                                            gs_thumburl                                                             ...\n",
       "----------------------------------------------------------------------------------------------------------------------------------- ...\n",
       "http://sdowww.lmsal.com/sdomedia/ssw/media/ssw/ssw_client/data/ssw_service_170906_094839_25912/www/ssw_cutout_193_context_thumb.gif ...\n",
       "http://sdowww.lmsal.com/sdomedia/ssw/media/ssw/ssw_client/data/ssw_service_170906_083654_14687/www/ssw_cutout_193_context_thumb.gif ...\n",
       "http://sdowww.lmsal.com/sdomedia/ssw/media/ssw/ssw_client/data/ssw_service_170907_081802_21496/www/ssw_cutout_193_context_thumb.gif ...\n",
       " http://sdowww.lmsal.com/sdomedia/ssw/media/ssw/ssw_client/data/ssw_service_170910_093821_2004/www/ssw_cutout_193_context_thumb.gif ..."
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#  Four X-class flares in 2017 and lot's of info in the HEK about them\n",
    "hekres"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e1483eb7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><i>HEKTable length=4</i>\n",
       "<table id=\"table140438749825584\" class=\"table-striped table-bordered table-condensed\">\n",
       "<thead><tr><th>event_starttime</th><th>event_peaktime</th><th>event_endtime</th><th>fl_goescls</th><th>hpc_x</th><th>hpc_y</th><th>ar_noaanum</th></tr></thead>\n",
       "<thead><tr><th>Time</th><th>Time</th><th>Time</th><th>str4</th><th>float64</th><th>float64</th><th>int64</th></tr></thead>\n",
       "<tr><td>2017-09-06 08:57:00.000</td><td>2017-09-06 09:10:00.000</td><td>2017-09-06 09:17:00.000</td><td>X2.2</td><td>501.171102</td><td>-233.008824</td><td>2673</td></tr>\n",
       "<tr><td>2017-09-06 11:53:00.000</td><td>2017-09-06 12:02:00.000</td><td>2017-09-06 12:10:00.000</td><td>X9.3</td><td>527.43879</td><td>-246.825594</td><td>2673</td></tr>\n",
       "<tr><td>2017-09-07 14:20:00.000</td><td>2017-09-07 14:36:00.000</td><td>2017-09-07 14:55:00.000</td><td>X1.3</td><td>702.47112</td><td>-211.601262</td><td>2673</td></tr>\n",
       "<tr><td>2017-09-10 15:35:00.000</td><td>2017-09-10 16:06:00.000</td><td>2017-09-10 16:31:00.000</td><td>X8.2</td><td>942.69654</td><td>-135.662304</td><td>2673</td></tr>\n",
       "</table></div>"
      ],
      "text/plain": [
       "<sunpy.net.hek.hek.HEKTable object at 0x7fba6167d430>\n",
       "    event_starttime          event_peaktime     ...    hpc_y    ar_noaanum\n",
       "----------------------- ----------------------- ... ----------- ----------\n",
       "2017-09-06 08:57:00.000 2017-09-06 09:10:00.000 ... -233.008824       2673\n",
       "2017-09-06 11:53:00.000 2017-09-06 12:02:00.000 ... -246.825594       2673\n",
       "2017-09-07 14:20:00.000 2017-09-07 14:36:00.000 ... -211.601262       2673\n",
       "2017-09-10 15:35:00.000 2017-09-10 16:06:00.000 ... -135.662304       2673"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Reduce the table down to just the info we want\n",
    "shrt_res = hekres[\"event_starttime\", \"event_peaktime\",\n",
    "                        \"event_endtime\", \"fl_goescls\", \"hpc_x\", \"hpc_y\", \"ar_noaanum\"]\n",
    "                        # More stuff that could be useful, Heliographic lat lon, and where from\n",
    "                        # \"event_endtime\", \"fl_goescls\", \"hpc_x\", \"hpc_y\", \"ar_noaanum\", \"hgc_x\", \"hgc_y\", \"frm_name\"]\n",
    "shrt_res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "33ffbc9a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2017-09-06 11:43:00.000\n",
      "2017-09-06 12:20:00.000\n",
      "Results from 1 Provider:\n",
      "\n",
      "1 Results from the XRSClient:\n",
      "Source: <13: https://umbra.nascom.nasa.gov/goes/fits \n",
      "13, 14, 15: https://satdat.ngdc.noaa.gov/sem/goes/data/science/ \n",
      "16, 17: https://data.ngdc.noaa.gov/platforms/solar-space-observing-satellites/goes/\n",
      "\n",
      "       Start Time               End Time        Instrument ... Source Provider\n",
      "----------------------- ----------------------- ---------- ... ------ --------\n",
      "2017-09-06 00:00:00.000 2017-09-06 23:59:59.999        XRS ...   GOES     NOAA\n",
      "\n",
      "\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b4af1925d0874e5d91a063eb15fd1e91",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Files Downloaded:   0%|          | 0/1 [00:00<?, ?file/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Now get the XRS data for the second flare (first missing in G15 data)\n",
    "# Want data for +/-10 mins of flare, using astopy time to do this (easier sunpy way?)\n",
    "\n",
    "gtstart=Time(shrt_res[1][\"event_starttime\"],scale='utc')-TimeDelta(10*60,format='sec')\n",
    "gtend=Time(shrt_res[1][\"event_endtime\"],scale='utc')+TimeDelta(10*60,format='sec')\n",
    "print(gtstart)\n",
    "print(gtend)\n",
    "\n",
    "# Search and get the XRS data\n",
    "tflrange=a.Time(gtstart.iso,gtend.iso)\n",
    "rg15 = Fido.search(tflrange, a.Instrument(\"XRS\"), a.goes.SatelliteNumber(15))\n",
    "print(rg15)\n",
    "fg15 = Fido.fetch(rg15,path=outdir,overwrite=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "7feda165",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load data file in, truncate to desired time range, extract out time index and data \n",
    "fg15=glob.glob(outdir+'*g15*20170906*.nc')\n",
    "g15 = ts.TimeSeries(fg15, concatenate=True)\n",
    "\n",
    "tg15=g15.truncate(tflrange.start.iso,tflrange.end.iso)\n",
    "\n",
    "tg_tims=tg15.index\n",
    "tg_x05=tg15.quantity(\"xrsa\").value\n",
    "tg_x18=tg15.quantity(\"xrsb\").value\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f1591f96-f4c0-453d-9760-a8a78dd44105",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "New peak flux  X13.5\n",
      "Catalogue peak flux  X9.3\n",
      "Ratio old/new (should be ~0.7 as GOES15): 0.69\n"
     ]
    }
   ],
   "source": [
    "# What is the peak flux in the GOES data now as data has been reprocessed to remove scaling but catalogue values not changed?\n",
    "# i.e. https://satdat.ngdc.noaa.gov/sem/goes/data/science/xrs/GOES_13-15_XRS_Science-Quality_Data_Readme.pdf\n",
    "max18=max(tg_x18)\n",
    "new_fl_goescls=\"X{0:.1f}\".format(1e4*max18)\n",
    "print(\"New peak flux \",new_fl_goescls)\n",
    "print(\"Catalogue peak flux \",shrt_res[1][\"fl_goescls\"])\n",
    "rat=float(shrt_res[1][\"fl_goescls\"][1:])/(1e4*max18)\n",
    "print(f'Ratio old/new (should be ~0.7 as GOES15): {rat:.2f}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "843cbb0f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# And then plot it all\n",
    "tfl=a.Time(shrt_res[1][\"event_starttime\"],shrt_res[1][\"event_endtime\"])\n",
    "\n",
    "plt.rcParams.update({'font.size': 18,'font.family':\"sans-serif\",\\\n",
    "                         'font.sans-serif':\"Arial\",'mathtext.default':\"regular\"})\n",
    "\n",
    "fig,ax = plt.subplots(figsize=(10, 6))\n",
    "plt.plot(tg_tims,tg_x05,drawstyle='steps-post',marker=None,color='darkblue',lw=2,label='$0.5-4\\;\\AA$')\n",
    "plt.plot(tg_tims,tg_x18,drawstyle='steps-post',marker=None,color='firebrick',lw=2,label='$1-8\\;\\AA$')\n",
    "\n",
    "ax.set_ylabel(\"GOES15 XRS [$\\mathrm{W\\;m^{-2}}$] \")\n",
    "ax.set_xlabel(\"Start Time \"+tflrange.start.iso[:-4])\n",
    "ax.set_yscale(\"log\")\n",
    "ax.set_ylim([5e-8,5e-3])\n",
    "ax.set_xlim([tflrange.start.datetime,tflrange.end.datetime])\n",
    "myFmt = matplotlib.dates.DateFormatter('%H:%M')\n",
    "majorx= matplotlib.dates.MinuteLocator(interval=5)\n",
    "minorx= matplotlib.dates.MinuteLocator(interval=1)\n",
    "ax.xaxis.set_major_locator(majorx)\n",
    "ax.xaxis.set_minor_locator(minorx)\n",
    "ax.xaxis.set_major_formatter(myFmt)\n",
    "\n",
    "# Using sunpy parse_time here to produce datetime version from string\n",
    "ax.axvline(parse_time(shrt_res[1][\"event_peaktime\"]).datetime,color='black',ls='-.')\n",
    "# tfl is a sunpy attrs time range so easy to get datetime out\n",
    "# ax.axvspan(tfl.start.datetime,tfl.end.datetime,color='black',alpha=0.2, label='Flare '+shrt_res[1][\"fl_goescls\"])\n",
    "# Label with the new GOES class\n",
    "ax.axvspan(tfl.start.datetime,tfl.end.datetime,color='black',alpha=0.2, label='Flare '+new_fl_goescls)\n",
    "\n",
    "plt.legend()\n",
    "# plt.tight_layout()\n",
    "# plt.savefig('xflare.pdf')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "896920b1",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2cd10653",
   "metadata": {},
   "source": [
    "### Now try if for a more recent GOES16 flare"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "66e6ed8b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><i>HEKTable length=6</i>\n",
       "<table id=\"table140438750994336\" class=\"table-striped table-bordered table-condensed\">\n",
       "<thead><tr><th>event_starttime</th><th>event_peaktime</th><th>event_endtime</th><th>fl_goescls</th><th>ar_noaanum</th><th>hpc_x</th><th>hpc_y</th><th>hgc_x</th><th>hgc_y</th><th>frm_name</th></tr></thead>\n",
       "<thead><tr><th>Time</th><th>Time</th><th>Time</th><th>str4</th><th>int64</th><th>float64</th><th>float64</th><th>float64</th><th>int64</th><th>str17</th></tr></thead>\n",
       "<tr><td>2022-03-30 17:21:00.000</td><td>2022-03-30 17:37:00.000</td><td>2022-03-30 17:46:00.000</td><td>X1.3</td><td>2975</td><td>483.659832</td><td>308.217828</td><td>84.904748</td><td>13</td><td>SSW Latest Events</td></tr>\n",
       "<tr><td>2022-04-17 03:17:00.000</td><td>2022-04-17 03:34:00.000</td><td>2022-04-17 03:51:00.000</td><td>X1.1</td><td>2994</td><td>-934.086</td><td>200.871276</td><td>96.07453</td><td>12</td><td>SSW Latest Events</td></tr>\n",
       "<tr><td>2022-04-20 03:41:00.000</td><td>2022-04-20 03:57:00.000</td><td>2022-04-20 04:04:00.000</td><td>X2.2</td><td>2992</td><td>789.92574</td><td>-526.891776</td><td>-129.7647</td><td>-34</td><td>SSW Latest Events</td></tr>\n",
       "<tr><td>2022-04-30 13:37:00.000</td><td>2022-04-30 13:47:00.000</td><td>2022-04-30 13:52:00.000</td><td>X1.1</td><td>2994</td><td>914.60724</td><td>264.064164</td><td>94.6502877</td><td>16</td><td>SSW Latest Events</td></tr>\n",
       "<tr><td>2022-05-03 13:09:00.000</td><td>2022-05-03 13:25:00.000</td><td>2022-05-03 13:31:00.000</td><td>X1.1</td><td>0</td><td>-823.57602</td><td>-472.683828</td><td>-120.74303</td><td>-30</td><td>SSW Latest Events</td></tr>\n",
       "<tr><td>2022-05-10 13:50:00.000</td><td>2022-05-10 13:55:00.000</td><td>2022-05-10 13:59:00.000</td><td>X1.5</td><td>3006</td><td>58.1840028</td><td>-415.31331</td><td>-121.66148</td><td>-29</td><td>SSW Latest Events</td></tr>\n",
       "</table></div>"
      ],
      "text/plain": [
       "<sunpy.net.hek.hek.HEKTable object at 0x7fba71dd9d90>\n",
       "    event_starttime          event_peaktime     ... hgc_y      frm_name    \n",
       "----------------------- ----------------------- ... ----- -----------------\n",
       "2022-03-30 17:21:00.000 2022-03-30 17:37:00.000 ...    13 SSW Latest Events\n",
       "2022-04-17 03:17:00.000 2022-04-17 03:34:00.000 ...    12 SSW Latest Events\n",
       "2022-04-20 03:41:00.000 2022-04-20 03:57:00.000 ...   -34 SSW Latest Events\n",
       "2022-04-30 13:37:00.000 2022-04-30 13:47:00.000 ...    16 SSW Latest Events\n",
       "2022-05-03 13:09:00.000 2022-05-03 13:25:00.000 ...   -30 SSW Latest Events\n",
       "2022-05-10 13:50:00.000 2022-05-10 13:55:00.000 ...   -29 SSW Latest Events"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# What do we get for a more recent time range when no GOES15 - basically the same but the GOES Class should correctly match the new unscaled data?\n",
    "\n",
    "trange2=a.Time(\"2022-01-01 00:00\",\"2022-11-01 00:00\")\n",
    "res2 = Fido.search(trange2,a.hek.EventType(\"FL\"),a.hek.FL.GOESCls > \"X1.0\",a.hek.FRM.Name == \"SSW Latest Events\")\n",
    "hekres2 = res2[\"hek\"]\n",
    "shrt_res2 = hekres2[\"event_starttime\", \"event_peaktime\",\n",
    "                        \"event_endtime\", \"fl_goescls\", \"ar_noaanum\",\"hpc_x\",\"hpc_y\",\"hgc_x\",\"hgc_y\",\"frm_name\"]\n",
    "shrt_res2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "42351f48",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-03-30 17:11:00.000\n",
      "2022-03-30 17:56:00.000\n",
      "Results from 1 Provider:\n",
      "\n",
      "1 Results from the XRSClient:\n",
      "Source: <13: https://umbra.nascom.nasa.gov/goes/fits \n",
      "13, 14, 15: https://satdat.ngdc.noaa.gov/sem/goes/data/science/ \n",
      "16, 17: https://data.ngdc.noaa.gov/platforms/solar-space-observing-satellites/goes/\n",
      "\n",
      "       Start Time               End Time        Instrument ... Source Provider\n",
      "----------------------- ----------------------- ---------- ... ------ --------\n",
      "2022-03-30 00:00:00.000 2022-03-30 23:59:59.999        XRS ...   GOES     NOAA\n",
      "\n",
      "\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b8f023b8af684b53899cd20d03bedfb3",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Files Downloaded:   0%|          | 0/1 [00:00<?, ?file/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gtstart2=Time(shrt_res2[0][\"event_starttime\"],scale='utc')-TimeDelta(10*60,format='sec')\n",
    "gtend2=Time(shrt_res2[0][\"event_endtime\"],scale='utc')+TimeDelta(10*60,format='sec')\n",
    "print(gtstart2)\n",
    "print(gtend2)\n",
    "\n",
    "# Search and get the XRS data\n",
    "tflrange2=a.Time(gtstart2.iso,gtend2.iso)\n",
    "rg16 = Fido.search(tflrange2, a.Instrument(\"XRS\"), a.goes.SatelliteNumber(16))\n",
    "print(rg16)\n",
    "fg16 = Fido.fetch(rg16,path=outdir,overwrite=False)\n",
    "\n",
    "g16 = ts.TimeSeries(fg16, concatenate=True)\n",
    "tg16=g16.truncate(tflrange2.start.iso,tflrange2.end.iso)\n",
    "\n",
    "tg2_tims=tg16.index\n",
    "tg2_x05=tg16.quantity(\"xrsa\").value\n",
    "tg2_x18=tg16.quantity(\"xrsb\").value\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "bc96a4c7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "New peak flux  X1.4\n",
      "Catalogue peak flux  X1.3\n",
      "Ratio old/new (should be ~1 as GOES16): 0.94\n"
     ]
    }
   ],
   "source": [
    "# This GOES Class in the flare list should match the actual peak in the data\n",
    "max218=max(tg2_x18)\n",
    "new2_fl_goescls=\"X{0:.1f}\".format(1e4*max218)\n",
    "print(\"New peak flux \",new2_fl_goescls)\n",
    "print(\"Catalogue peak flux \",shrt_res2[0][\"fl_goescls\"])\n",
    "rat2=float(shrt_res2[0][\"fl_goescls\"][1:])/(1e4*max218)\n",
    "print(f'Ratio old/new (should be ~1 as GOES16): {rat2:.2f}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "d687d5bb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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KIiiofA9RDx06VOr2mJgYYmJiCpW1aNGCMWPGMGrUKLp06QJAcHAws2bNKjXJBJg9ezZZWVnccMMNBAUF0blzZ7p06cLatWtZs2ZNmfsr1zTRVEopVSH565yPHz/ex5EEll/8tDvCgO3bq/R8ZT3qHjt2LM8991yhsnHjxvHcc8/Rp08fRo4cid1u591332Xw4MF8+eWXDBgwoMTjTZ8+HYAhQ4Y4y26++WbWrl3LlClTNNGsIE00lVJKVYj2z1TeNHny5FK3F038UlJSGD9+PAkJCfz8888EBwcDcOONN9KjRw9GjBjB7t27CQ8PL3asI0eO8PPPP9O8eXPatGlDcnIyAP379wfg008/Zfz48YWmPlLu0USzAkRkADDA131rlFLKl4YNG+brEALSxevWVdm5qqqPZkXccccd5aq/detWsrOzGTJkiDPJBAgNDeXmm2/mkUceYdOmTc5H6gXNmDEDu93O3r17XfbFP3r0KN9++y2DBg0q/4XUcJpoVoAxZi4wNyEhwXuTgSmllFLKbfktlXa7vdi2/LK8vDyX+06fPh0RYebMmcTFxRXatmrVKh577DGmTJmiiWYFaKKplFKqQkaOHAnApEmTfByJUtC5c2eaNGnClClT+Ne//kVERARgTa30ySefUL9+fc4444xi++3atYtly5bRp08frr/++mLb+/Tpw2uvvcb333/P4cOHadiwodevJZBU63k0lVJKKaXAGl0+YcIENm3aRM+ePXnjjTd49dVXSUhIYNOmTbz++uuEhoYW2y9/7szhw4e7PG5oaCj//Oc/sdlsOqdmBWiiqZRSSqmAcO211/LTTz9Rt25dxowZw7hx46hTpw7z5s3j5ptvdrnP9OnTiY6OdtmamW/EiBGIiC5JWQH66LwCdDCQUkopVXG7du3y2rEvvvhiLr74Yrfru7M0ZevWrUvs36lKpy2aFWCMmWuMGemvI/WUUkoppfyBJppKKaWUUsorNNFUSimllFJeoYmmUkoppZTyCk00lVJKKaWUV+io8wrQUedKKQUXXHCBr0NQSvk5txJNEdnhgXMZY0xbDxzH53QJSqWU0rXOlVJlc7dFsxWwCThcwfM0BDpUcF+llFJKKVUNlefR+XPGmM8qchIRGQp8XJF9lVJK+Sdd61wpVRZ3E80ZwK5KnGcnMLMS+yullPIzAwYM8HUISik/51aiaYwZUpmTGGOWAEsqcwyllFL+RRNNpVRZdHojpZRSFZKSkkJKSoqvw1BK+bEyE00R+T8ReV1E/ikiIUW2fee90JRSSvmzRx55hEceecTXYSg/99RTTyEipb7+/PNPAFq1akXfvn19Gi9AUlIS8fHx1K1bl8OHXY+DvuGGGwgJCeGPP/5wuX3EiBEVvpZ169YRFhbGU089VaH9/Umpj85F5D7gP8B3wKPAnSJyuTHmmKNKjZxETefRVEop5U0rVqwgNTXV6+ex2+1ER0eXWS8uLo7evXtX6lxjxozh9NNPd7mtZcuWlTq2p8XHx/Pmm29y0003ce+99zJr1qxC26dOncrMmTMZO3YsvXr1Krb/Bx98wPvvv0+fPn3KfW6bzcbw4cPJzc2tcPz+pKw+mvcA/zDGrBWRYOAt4BcRudiRbIrXI/RDOo+mUkopb0pNTaV+/fpeP4/dbicmJqbMeklJSZU+V//+/f2itdJdQ4YM4fPPP+fLL79k9uzZDBo0CIDdu3czatQounbtypNPPlloH7vdzvPPP1+plsj//ve//PXXX5UJ3a+U9ei8sTFmLYAxxm6MuQf4GVgoIvUA4+0AlVJKKaV8YeLEicTFxXHvvfeSkpKCMYZbbrmF7Oxspk6dSmhoqLNuVlYW3bp148knn2TYsGE0bdq03Odbv349zz33HOPGjfPkZfhUWYlmkoi0LlhgjHkIWOh46RKWSimllPIKYwwTJ06kZ8+e1KpVi4iICDp27MiLL76IMafaulq1asWIESO4/fbbiYiIoFmzZs5W2GXLltG/f39q1apFrVq1uPTSS1mxYoVb52/cuDGvvPIKhw4d4tFHH2XixIksXryY5557js6dOxeqm5WVRVpaGjNmzODjjz8mJKR8KZLNZuO2226jX79+DB06tFz7+rOyvgs/A8OBQm3Dxpj7ReRN4AwvxaWUUkqpAJKamkpycnKx8tjY2EItgwWNGzeO559/nltvvZURI0Zw4sQJPvnkEx577DEaNWrErbfe6qw7ffp0OnTowBtvvMGhQ4eIj4/np59+4sorr+Tss8/m2WefJTs7m48++ogLL7yQn376iQsuKHuoyT//+U9mzJjB+++/T1RUFBdccAEPPvhgsXq1a9dm69at5U4w87344ots3bqVr7/+GpvNVqFj+KOyvhujSqpjjPm3iLzs+ZCUUkopFWgGDhzosnzhwoUu+27m5uby1ltvceONNzJlyhRn+R133EGDBg348ssvCyWamZmZzJw5k7Zt2wKQl5fHXXfdRc+ePfn1118JDg4GYNSoUZx99tn8+9//Zs2aNW7F/u6779KuXTvS09N58803CQoq/kA4KCjIZbk7/vrrL5555hkmTJhAs2bN2LVrV4WO449KTTSNMTlATv5nEQkCzjfGLHZs3+Pd8JRSSikVCF5++WW6dOlSrNxVGUBoaCiHDx8uNvo6OTmZ2rVrk56eXqi8Xbt2ziQTYM2aNezYsYO7776b48ePF6o7YMAAXnvtNfbt20ezZs3KjH369OnOR/UTJkzg/fffL3Mfd9ntdm677TbOP/98RowIvDHG5W3fjcTqmxnshViUUkopFaC6d+9e7lHnYWFhfPfdd8yZM4fNmzezdetWZ9KYl5dXqG6DBg0Kfd6+fTsAo0ePZvTo0S6Pv3fv3jITzbVr1/L000/Tv39/cnJy+OCDD7jhhhvo379/ua6lJOPHj2ft2rX8/vvvzq4F+deYkZFBcnIydevWrXBrqa9VpCNBjZzSSCmlVGG6BKXyJmMMQ4cOZfr06Zx//vmce+653HnnnVx44YVcfPHFxernPxrPZ7fbAXj22WdLnAO0Y8eOpcaQk5PDsGHDCA0N5Z133iE3N5euXbsyYsQINmzY4NbUUGWZP38+OTk59OzZs9i28ePHM378eHbu3EmrVq0qfS5fqEiiqVMaKaWU0kRTedVvv/3G9OnTGTduHM8884yz3GazcfToUdq0aVPq/vmJWUxMDP369Su0beXKlRw7dozIyMhSj/HEE0+wfv16XnvtNfIXaXn88ccZN24cjz76KG+//XYFrqywV155pdij/cOHDzN06FCGDRvGLbfcQqNGjSp9Hl/R6YmUUkpVSP4653FxcT6NQwWmo0ePAtCpU6dC5ZMnTyYjI6PMkdkJCQk0btyYN998kzvuuMPZ+piWlsbgwYPJzs5mz56Sh5osXbqU8ePHc9555/Hvf//bWf7oo4/yxRdf8O677zJ48OAKrf5TUPfu3YuV5Q8GatOmTbEkubrRRFMppVSF5K9zPmnSJB9HogLRueeeS+3atXnggQfYs2cPcXFxLFy4kBkzZhAREcGJEydK3T80NJS33nqLwYMH061bN+644w4iIiKYPHkyu3fv5tNPPy1xKqKMjAxuvfVWwsLC+PDDDwv1jwwNDeWDDz6gd+/e3H777axbt46oqCi3r2vHjh0sXbqUc889t8xW2UBQPXuW+piIDBCRSVWxDq1SSvmroUOHBtTE0sq/NGzYkHnz5tG2bVueffZZxowZw+7du/n888+55557+Ouvvzh8+HCpx7juuuv48ccfadasGc8++yzjxo2jdu3afPPNNwwZMqTE/UaPHs22bdt49tlnOe2004ptT0hI4IEHHmD79u2MHTu2XNe1ePFihg0bxuLFi8u1X3UlBWfWL7OySDRwwhijCSqQkJBgEhMTfR2GqiLz588nPj7e12EopQJESEiIs99fUStWrKAqGjPsdjvR0dFl1ouLiytxQI3yDxs3buT0008vtY6IrDLGJFRRSED5H53nAp96IxCllFLVS34/suo6GtafuRqB7A02m43Y2NgqOZeqmcqVaDomcB/mpViUUkpVIy+88AKgfTSVUiXTR+BKKaWUUsorNNFUSimllFJeUeFEU0RuEpElInJEROwuXqVPcKWUUkoppQJahebRFJHHgaeBw8BS4HjpeyillFJKqZqmohO23wMsAi4zxuR6LhzfEZFOwGrgDGPMNl/Ho5RSKrAZYzDGICK+DkVVc+WZqrKqVTTRrA3MDKAkMwT4CAj3dSxKKeUPTG4u9uPHwW633icnYz96FAkJweTmYmw2bAcOIMHBJD/5JMH164MIOFZQyVy0CImKIrhOHSQiAtvhw4SfcQb2o0cJ79QJgoMJP/tswtq0QcLCfHy1viEi2O32ElenUcpdubm5BAcH+zoMlyr6070GaO7JQHzsP8DvQNVMXKaUUn7GnppKzubN5GzZwslvvnFrnzzHWuc5Gze63G4yMrBlZDg/Zy1fDkDu1q0ApM+e7dwm4eGY7GyCGzZEgoMJbtSIkAYNiOzTh5CmTQmKiKjIZfm1vLw8MjIyqF27tq9DUdVcWloatWrV8nUYLlU00Xwc+FJEZhtjVnsyoKomIl2AG4AewIM+Dkcppbwud+9eTs6bh23/fnI2bSq1bnCjRkhwMCY7myBHQhTapg0SEkLwnDkYu53Y228nLz2d4Hr1wBhy9+8nuG5dJCSEkKZNMTYbJjsbu2O5wMwVK8jdvLnQeUx2NoCzju3AAbKBk/PnFw4oJITwzp0Jql2b0NatCevYkdCWLZHQUA98Z6pWXl4eycnJhIWFER4ero/QVbkYY8jNzSUtLY3jx4/TokULX4fkUoUSTWPMryJyO7BcRJYBuwB78Wrm9krGV2kicj3whYtNHwMjsR6Z32mMydR/5EqpQJW1bh3pX35ZYutjvuCGDZ0JXPTll5ea/NzqWD4x+sILyxVLzIABzvcmLw9sNozNRl5KCrn79pG7axcn588npGFDcrdvL7yzzUb22rUAZP72W6FNIc2bE9K4MaHt2xPWujXB8fFWC2mQ/87kl5OTw759+3zWV9NutxMZGVnl51WeERwcTK1atWjRogXh4f7Z+69ca507dxLpBcwHSlu3yhhjfN5hQESCAVf/inKBcUCUMeZBR10DtHd3MJCudV6z6FrnqrqxHz3KsZdfLp6sOUT07EnkOecQ2rYtwQ0a+G1CZmw28tLSsB0+bCWjO3eSs3kzuTt2OFtCSyVCcP36RPTqRURCAmEdO/rttVa1pKQkLrvsMl+HoapIdVjrPN8bWInaNcBvxpgUj0XkYcYYO5DuapujtbOxiPyzQPFqEbnLGPNZlQSolFIeYvLyyPjlF07MnOnsP1lU9FVXEXPVVQTXrVvp81XVWucSEkJw3brOmCPPOafQdntaGrk7d5KVmIht/37yTpzAtnv3qQrGYE9K4uS333Ly22+dxSFNmhDRowcRvXsT2rq1Jp9KeUFFE82zgKeMMXM9GUxVM8Z0LPjZ0aLZTac3UkpVJ7k7d3L0pZfIO3rU5fbQdu2oc999hDRs6NHz+sta58G1axPcpQsRXboUKjd5ediPHSN71Sqy160ja+XKQtttBw6QPmcO6XPmOMtiBg0iomdPq9+nn47iVao6qWiieQTI8WQgpRGRyViPtPu62NYaeAXI3/Yt8JAxJqmq4lNKKV+wHT7MkX/9q/iGkBBirr6amIEDvTpa+9577/XasT1BgoIIqV+fkH/8g+h//MNZnrtvH1l//EHm8uXY9uyBAl3I0mfPLjQaPrhxY2KuuYbI3r0Jioqq0viVCgQVTTQ/BIaKyARjjFeXmnQMOroD+NXFtnrAQiAMeBHrekYDZ4lIT2NMuZJhY4yOBlJK+T3b4cMkP/44eamphcpjrr+eWtdeW2UjsLsUaUGsLkKbNSO0WTNqXXcdYLV8Zi5eTMavv1qDpfLynHXtBw+SOnEiqRMnAtaAo6g+fYjo3ZuQBg18Er9S1UlFE83fgauwRp2/A+yk+KhzjDGLKxqYYxDPWOCpUqo9CDQDzjTGbHTs9wfwE3ArMLmi51dKKX9jbDYO3nRTsfLwLl2oN3Zslcez1jH6u7omnPkkKIiovn2J6tvXWZa7ezcnf/iBnG3bsDn6ogLY9u4lbdo00qZNAyCkRQsizz+fiG7dCGneXKcoUqqIio46zytSVPQgQiVGnYtIBPAHVl/QT4BLgG1FH52LyHZgpzGmX5HyTcB+Y8wlFTl/GbGNxJoWiRYtWnTfXbDDuQpoOupc+YrJyeHg0KHFyqOvvJLat9zis+Rm5MiRgO/7aHqbMQb7oUNkrVzJyZ9/xn7wYKn1I/v2Jeriiwnr0MHvE08ddV6zVKdR57d5NIriIrCWubzBGDNTRHYVrSAidYA2wCwX+68GrvRGYMaYScAksKY38sY5lFIKrEe6Ke++S+avhXsOhbRqRfyLL/p9EhMoRISQxo2tfq9XXw1AXkYGmUuWkJWYSPaaNYXqZy5aROaiRfk7E9GrF9GXXUbY6afrPVM1TkUnbP/Y04EUkYY1+Ke0/p9NHV/3u9h2EKgtIrHGmFQX25VSyq/lbNlC8uOPFyoLjo+nwVtv6TQ8fiAoKoro/v2J7t8fAGO3k/3nn2T+9huZS5eeqmgMWcuXO5fflJgYoi68kOgrrtA+nqpGqGiLplcZY/KAoo/ni8pf1DPDxbZMx9dowOOJpogMAAa0c6yKoZRSnpSxaBEp77zj/BzSogX1n36aoOhoH0alSiPBwUR0705E9+7Uuf9+TF4eOX//zcn588lascJZz6Snc3LePE7OmwdYc3lGX3EFkeedp/dXBSS/TDTdlP8nfWmPr8tKVivEMX/o3ISEhBHeOL5SquY68uCD2Pbtc36OveMOoi+91IcRqYqQoCDCzziD8DPOAKxuENlr15L+7bfkrF/vrGc7cIDU998n9f33AavVOurii4m+/HKdTkkFhOqcaJ5wfHW1vGRkkTpKKeXXjN3OwSFDCpXFv/QSoV5edUdVDQkKIqJrVyK6dgUgLz2dkz//TObixdj27nXWsyclcWLGDE7MmAFAaPv2xN5yC2EdOvgkbqUqqzonmnscXxu72NYESDHGnKzCeJRSqkJcTVvUaOpUgsLDfRSR8ragmBhqXXMNta65BoC8zEwyfv6Zkz/8gP3wYWe93K1bSR43DoCQZs2I6N2bmCuuICgmxidxK1VebieaIlLXGHPMm8GUhzEmRUR2At1cbO4KJHrr3NpHUynlKa6mLmo8Y4aOTq5hgiIjiXGsQw9gT07mxKxZZPzyi7OObd8+0mfNIn2WNdlKSLNmxAwcSOS55yIh1bndSAWy8gxdTBKRNSLyuohcIyJx3gqqHL4E+omIc81yEekHdAA+99ZJjTFzjTEjY2NjvXUKpVQNkJeeXijJDK5fnyYzZ2qSqQiuX5+4u+6iycyZNPrkE2L/+U+C6tUrVMe2bx8pEyZw8KabODB4MMdeeomczZupyPzYSnlLef4EWgCcA3QB/gUYEVmHtQTkImCxD6YSegm4BfhZRF7Bmn/zEWAVMK2KY1FKKbfl7tlD0sMPOz8H1a5NwwIjzasDf1/rPFAERUQQfdllRDsmVs9LTyf9m284+cMPmMxMZ72sxESyEq2HeUG1axN53nnUuv56gmrVcnlcpapCuVYGciwL2QPoC1yElXjGYI38zgP+xEo6FwK/GWM8MhjHMWH7rqIrAzm2dQBeAy7EmupoHjDaGJPkiXOXJiEhwSQmeu0JvfIzujKQ8pSUiRMLPRKNPO886tx3nw8jUtVZ7q5dpH/zDZm//15inbBOnYi66CIizzkHCQtzluvKQDWLL1YGqtASlM6drcSzJ1bi2Rc4F8ifj8FmjAnInuwF+miO2Lp1q6/DUVVEE01VWcYYDt5wQ6Gy2sOHE3PFFT6KqHICZa3zQGLy8shes4YTX35J7rZtJdYL69iR2rfcQmpsrCaaNUi1SzQLHUgkCquVczRW62KF1zqvLrRFs2bRRFNVRtbatRx7/vlCZQ3eeouQhg19FFHl1ZS1zqsz+7FjnPzxRzIWLiTv+HGXdaJbt6bpgAE0/7//I6pJkyqOUFWlapVoikgkcB6nHqMnYPX5PAb8BvxqjHnDM2H6J000axZNNFVFuGrFhMAYWb5r1y4AWulcn9WG7cABUj/5hOzVq0usExwdTdt//pO2d9xBiE6jFFD8OtEUkQisR+N9sRLLHkAYcARYDPyKlVxu8EqkfkgTzZpFE01VXnnZ2RwaNqxQWdyoUURdeKGPIlLqFGOzcWT+fOps20ZywfXZi6h//vl0uO8+6nTtWu3/OKrpfJFolmfU+XGsxPIgVovlp8AiY8wmbwTmz3QeTaVUWYzNVizJDIRWzIIWL14MwIWaOFdLEhJCUK9enPPkkwDYs7LYO2sW299/n4wCqxUl//47yQUGGjUbNIimV19N/d69CQoNrfK4VfVSnhbNPMAG/Iij9RJINMZ4ZT3x6kBbNGsWbdFU7io6CXtw48Y0fCPwehJpH83qr7RR5yd372br229z8IcfsKWnl3iMuLPO4rT77qPBhRciQeWZnltVNX9v0cx/bN4XGAf8DzgpIks4lXiuNMbYPByjUkpVK4WSzHr1AjLJVIEvumVLzn7pJc5+6SVMXh7Jy5ZxYN48Dnz7baHEM2XdOlbcfjsAwZGRNLzkEpoNHEj8eecRVGAqJVUzuZ1oGmOWA8uB/xWZ1qgPMAZ4AcgQkWWc6q/5m8cjVkopP1V04E9wo0Y0fPNNH0aklGdIUBDx551H/Hnn0cUxe8KJrVvZMWUK+77+mrysLADsmZkc+PZbDnz7rXPfiEaNaDlkCHW6dqVu9+4ER0T45BqUb1RocVRjjB1Y5nj9t8hE7lcBT2NN4q6LryqlaoS8jAwODR9eqEyTTBXIarVvT5fnn6fL889j8vI4/uef7PvqKw7On0/OsWPOelmHDrH5tdcK7Rtauza1TjuN6Natqd2hA5FNmhDVrBm12rfXVtAAU+lE0DHN0fmcGo3eHQic3u4u6GAgpVRBrpLMJjNn+iYYpXxAgoKo260bdbt146xnnwUg++hR9n75JSlr13Jw/vxC9XPT0jiWmMixEsY5RDZpQp2zz6Zer17Edu5MrdNOIyQ62uvXoTyv3ImmY5qjovNnhmIll1nA71hLUC70WJR+xhgzF5ibkJAwwtexKKV8r2CSGXbmmdQfN853wSjlJ8Lr1aOdY8BYPnt2Nic2byb1r79I37mTrIMHyTl+nORlywrVyzxwgMwDBzgwb16h8ohGjQiPj6fJFVdQq317Ytq0IbxBA4IjIgJqRodA4naiKSJPYyWWPTmVWOYAf3AqsVxmjMnxQpxKKeWXCq5ZHnbGGZpkKlWK4PBw4s46i7izznK53XbyJEdXriR56VLSt23j2OrV2E6ccG7POnSIrEOHSF2/3vUJgoKIaduWWm3bEt2yJcFRUcS0aUN0q1ZEt2pFSFSU6/2U15SnRXMc1vRGKzmVWC4xxmR5IzCllPJ3ufv3kzJxovNz/See8GE0SlV/IdHRNOzbl4Z9+xYqt2VkkPrXXxz59VfSd+zgxKZNnNy9m9A6dcgtuLRmXh7pW7eSvnVryeeIiSGiUSNiO3Uipk0b6nTrRmynToTVqeOlq6rZypNoXg78bow56a1glFKqusjZsoXkxx93fq73zDM+jEapwBYSFUW9Hj2o16OHy+25aWlkHTrEyd27ydi3D1t6OsfXrCH76FFO7tpVaDomW3o66du2kb5tm8tjRTZrRm3HQKU6XbsSd8YZRDZrpo/mK8itRFNElgJPVjTJFJFLgaeMMedWZH+llPInuXv3Fkoya/3f/xHesaMPI/K8lJRcdu48SXx8OE2buu7/NmbMGB9EplRxobVrO0eyl8SelUX6zp2c2LqV9O3bOb5mDWkbNxYaIQ+QuW8fmfv2FT+ACDFt2xLVrBnxF1xA3YQEanfooKsjlcHdFs3eQP1KnCce6FWJ/f2KjjpXqmZLeugh5/s6991H5Hnn+TCa8snKsvPbb0cB+OuvNI4ezWH37gz27s3kxAn31tsIDRUGD25Gq1ZR1K4dwuLFe+jUqRZnnlmb8PBgb4avVIUFR0QQe/rpxJ5+erFtJi+P7ORk0jZv5sSmTRxfu5a0v/8mY98+jN3uqGScLaFHFi0qfOzISGI7dya2c2eiW7YkunVrwuPjrX6hkZFVcHX+y60lKB3LTyYBFX1sHg3UN8YE1G8gXYKyZtElKBXAyQULSHUsuRhz9dXULrAKkL+y2w0PPLCOpUuPlV25XP52fO1UYo2ePevQs2cdYmJCuOaaxoSG6hKF/qS0JSjVKVlHjpC6YQNHV64kbeNGjv/5Z6FBSmWRkBBiWrcmslkzarVrR3SrVtao+bZtCY2NrbLH8r5YgtLdRHMR1gTslWKMuaiyx/AnmmjWLJpoqqP/+x/Zq1c7P/vrXJnGGHbtyuDZZzexbl1aufe/6KL6DBzYhPPOq0dycjYLFiTxxx/HiIoKJikpm6AgYf36NLKz33bscXe5z9G/fwPGju1ATIyu6+FLmmhWTv4gpdS//iJ9xw4yDxzA2Gwc/eMP8nLcn4RHQkMJDg+nVocO1O3WjfAGDajbtSu12rcnJCbGY/H6baKpXNNEs2bRRLNmKzope/3nnyesfXvfBVTEoUNZzJlzkMmTd5Va7803zyIhoQ5hYZVvWUxJSQEgLi4OsB7Lf/XVARYvTmblSmtbnz712bIlnYMHS56gZNy4jtSqFUJUVDA9e9YhKEgHXVQVTTS9yxhDxp49nNi2jRObN5Oxdy8Ze/dycs8eMvfvd/9AQUGExcVR/5xzyDl2jMimTandsSMx7dpRq21bIho2RILLfmisiWY1o4lmzaKJZs125KGHsO3dC0Dj6dPd+qVeFbZtS+fGG1eWWueaaxozdmwHnyZwOTl5/PJLEh99tJvt20vvhdW4cQR33tmKyy9vRHCwJp3epImm72UfO2ZN2bRlC+k7dpC8dCnZycnkHD1aoePV7tSJ7KQk6p97LrmpqUS3akV4vXrU692bet27V3miqc8slFKqDCYnx5lkBsXG+k2SOX36Xl55pfgULTfe2Iyrr27Maad57pGbK3PnzgVgwIABZdYNCwvisssactllDQHIyLBx4YW/uax78GAWTz21iaee2gTA66+fSa9edbV/pwpI4XXrEl63LvUSiud/Ji+PzP37OblnD/asLGv6pj17SN+xw2od3bOn2D5pf1t9p/fPmeP12N2hiaZSSpXhYIEBPw1ee82HkVh27DjJ4MErCpV16RLLBx90q9I4ypNoFhUVFUJiotVt3xhDRoadnTszGD9+C3/9VXiQxf33n1oFpk2bKMaM6cAZZ9QmJEQTTxXYJCiIqObNiWrevNR6eTk55KSmkpuaysndu8nLySHzwAFyU1JI27KFwwsWEN26NezYUUWRn6KJplJKlSKrQPeY0NatCfJgx/yKeOCBdc7pifJ9/nkP2rXzbVyVISJER4dwxhm1+fhjq1Vn48YTDBtWvGvSjh0Z3HHHGgCaNo1gzJgO9OxZRyfTVjVaUFgYEfHxRMTHU6u0qRd98O9EE02llCrFsZdecr6Pf/FFn8Vx8qSNPn0KP2pu2jSCGTN6EhHhH4/yPen002s5Wzz3789k3Li/i42g378/i3vvXQtA7951eOqp06lfP7zKY1VKlcxjiaaI1AEya8La5zphu1I1g73AGsq+nC/zzz9TnK14+RYtuqDGTA3UtGkkH37Y3fl5y5Z0nn12Exs3nnrEvnz5cS67bKnz87Bhzbn11hbExYVVaaxKqcLc7uAiIqEicqeIfFSk/AIR+QtIBtJF5HsRaevpQP2JMWauMWZkbGysr0NRSnlJXno6h++80/k5ugL9ECvLGMOVVy4tlGS2bBnF8uV9akyS6cppp8UwdWoCK1f25auvetGzZ51idaZO3Uu/fktISFjIu+/uICvL7oNIlVLurnUeDiwAzgNyRGSEMcYmIu2BH4BwYD7wF3A9sFREzjLGHPZS3Eop5VVHX3jB+T76iiuqvA+gMYYePRYVKnvuuU7OUdvK6tvZvHkU77xzNgBbt6YzZEjxqZ4++GA3H3ywG4Bnnz2dyy5rqH06laoi7rZo3gecCzwC1DHG5C+I+xQQAUw3xlxpjHkESABswBgPx6qUUlUmd5s1bVBIs2bEFpiovaoUTTKXLeujSWYZ2rePITHxIhITL2L58j489thpxeqMG7eRHj0WcdVVS9mwIY30dPfWd1dKVYy7ieZgYJYx5mVjTCaAiIQBV2MtTflyfkVjzDFgCnCVZ0NVSqmqkZeR4Xwfd9ddVX7+//1vc6HPK1b01TkkyykkJIjrr29KYuJFLFlyIeefX6/Q9kOHshk+fBV9+/7GffetxWbL81GkSgU2d39ztQeKzqx7DhANHDTG/Flk2zagSeVCU0op3zj67LPO96FVvMzkgw+uZ9asA87PK1b01SUZKyk8PJjXXz+LlSv7MmFCF+LjCw8QWrLkGL17/0pCwkL278/0UZRKBSZ3e5MHYz0OL+gSx9cFLurHAhkuypVSyq8Zu53c7dutD8HBVdaXLy/PMGjQH+zbdyrRWbDgPL9OMl8qMPVTdSAi9O5dl++/Pw+A778/xLhxGwvVueaa5QDcdFMz7ryzNdHRNXfQlVKe4O6/oG3A2UXKrsV6bP6ti/r/cOyjlFLVStrUqc73Dd97r0rOabcbevVaVKhsyZILCQ/37/kx4+LifB1CpVx+eSMuv7wRBw5kcvfdf7J//6nZ+T77bB+ffbYPgIsvjuf55ztp9wWlKsDdfzWfA7eIyDUiEiUiDwCdgSPANwUrisjNwKWAfyyyqZRS5XBy3jzn++Datb1+vrS03EJJZoMG4fzxR1+/TzLBWoIyfxnK6qxJk0jmzDmHFSv6cskl8cW2//JLEuecYz1aX7HimA8iVKr6crdF81WsVsqvsFoxBcgB/mmMyQEQkWuBUUBfYDPg+wWBlVKqHLL/+sv5vs4DD3j9fHl5hosv/t35uXfvOkyYcLbXz+splVnr3B8FBQkvvngGYN2bmTP38/LLWwvVueceayWixo0jaNQonNtua8m559YrdiyllMWtRNMYkyMi/YAbsAYBnQCmGWP+LlCtO9Y8m9OAB/NHpyulVHVx9Omnne8jevf2+vl69lzkfN+sWWS1SjIBJk2a5OsQvCYoSLjxxmbceGMzNmxIY/jwVYW2HzyYxcGDWaxZs44JE7rQrVscYWH6aF2potzu5WyMsQOfOV6uvAA8YYwJ+DkidAlKpQKP7cgR5/s699/v1UFANlsevXv/Wqjsq696ee18qnLOOKM2iYkXkZdnePzxv/nxxyOFto8aZbVynnZaDM8914k2baJ9EaZSfsljf34ZYzIKJpki4ruFgb1Ml6BUKvAcGTXK+T7y3HO9dp7ffz9aLMlcvrxPtVypZurUqUwtMHgq0AUFCS+80JnExIuYNasnCQlxhbZv2ZLO4MErSEhYyKBBy5k//zB5ecY3wSrlJ9xu0RSREGAg0Burj+Zq4HNHS2fBei2B94D+WI/RlVLKr2UsWuR8H9qmjdfOM3LkalavTnV+Dg6WaptkAvz2mzW98rBhw3wcSdVr1SqaiRO7Yozh55+T+PDD3WzZku7cvmdPJo8//jePP271MPvqq140bx7lq3CV8hl31zpvgLWm+VlYSSZYg4IeE5ELjTHHHfXuA57Dmsj9d1fHUkopf5PyzjvO9/ULrHHuKWvWpDBixJpCZRMmdKF377oeP5eqWiJCv34N6NevAQC7dmVwww0rsNsLt2Ree+0fAAwb1px//autX8+PqpQnufvo/AWgCzARq0XzTOBRoBXwloiEichXWKPTc4CRxpgLPR+uUkp5lv3Yqelq4v79byTIswM6nn56Y7Ekc+LEszXJDFCtWkXxxx99SUy8iPff71ps+9Spe+nZcxE9ey4kI0PXWVeBz91H5/2A2caYewuU/SUiGcCLwLvANVhzZ95pjDni4hhKKeV3kv7zH+f7qPPP9+ixH3/8b+bPP+z8fNddrbn99pbV9lG5Kp+zz44jMfEicnPzuOyypaSm5jq35eXBhRdaXQ+GDLFWIYqJ0VWIVOBx96e6Aa6XmpwPTABuAf5tjJngqcCUUqoq5B0/DkBo27YePe7bb28vlGQuWHA+cXGhHj2Hqh5CQ4P4+Wfrj5jVq1MYObJwC/f06fuYPt1ahWj8+DPo06e+PlpXAcPdRDMCSHNRnt+r/W1NMpVS1U36N6cWNqs3ZozHjvvjj4f56KM9zs9ff91bk0wFQLduViunzZbHe+/t4qOPdhfaPnr0BgAaNgzngw+60ahRhC/CVMpjPNUZ6XsPHafKicjDIpIjIukFXk18HZdSyvvS55xaKTeoVi2PHNNmy2PMmFNrWSxYcB7NmkV65NgqcISEBHHvvW1ITLyIL7/sVWyqpMOHs7nqqmUkJCzk4YfX+yZIpTzAU4lmbtlV/NbZwEPGmJgCrwO+Dkop5V3GZiPvxAkAat10k2eOaUyhOTJffLEzcXFhHjm2ClwtW0YxcWJXEhMvYvLk4gOIFi1KJiFhIWPG/KUDiFS1U56ex/VEpEWRsvxhkw1cbMMYs6domR86GwjcddSUUi6dmD3b+T7miisqfTxXq/1cckmDSh9X1Sxdu1qP1gFuv301a9eemnf1xx+POFclatQonPfe60rTptparvxbeRLN1x0vVz51UWbKefwqJyIRQAfgERGZBRwExhhjvvNtZEopb8tY4BjfGBKChFW+1bFgktmwYTjffee91YX8RSCvde4PPvigG8YYfv01mYcf3lBo26FD2VxzzXIA2rSJ4tVXz6Jp0wid0UD5HXcTwU+wEsdqR0SuB75wselj4AlgKfAWsBC4FJghIj2MMRurLkqlVFUyNht5KSkAxAwcWOnjvf/+Luf72rVDakSSqaqGiNC3b7xzANHs2Qd46aWthers2JHBwIHLnZ8//7wHeXnQvn20Jp7K59xKNI0xw70chzd9Bbjq5Z9rjMkG+hQo+1ZEFgKXA5poKhWgUt57z/k+5uqrK3WsvDzDxIk7nZ/zp7GpCfLXOa+JS1D6QkhIEIMHN2Pw4GbYbHm8/vp2Pv98X7F6N9640vl+/PgzOPvsWOrU0b7CyjfcGgwkIj3Kc1ARiRCRVyoWkmcZY+zGmHQXr2wR6SIijxTZJRzI8kWsSqmqkfmr4zF3cDBBEZWbPmbQoD+c72fN6lmjWpDWrVvHunXrfB1GjRQSEsTDD7cnMfEiFiw4j06dXM+aMHr0Bvr3X8JVVy1l1qz9nDhRncfuqurI3VHnS0XkaREJLquiiFwArAPur0xgVSQNeFJErhaRIBH5P6wlNr/ycVxKKS8pOAio/lNPVepYCxYcYd++TABCQoRWraIrdbzqZvz48YwfP97XYdR4cXFhfPJJAomJF7F8eR+GDy82NpdDh7L53/+2cNFFv5OQsJBFi5J8EKmqidxNNGcD44DlItLRVQURiRKR/L6OrQGPtWiKyGQRWVTCttYiMltEjjlen4hIvDvHNcbsBIYA/wVOAGOBq40xBz0Vu1LKv5z4/HPn+7AOHSp8nB07TvLYY385Py9efGGl4lLKE0JCghg1qi2JiRexYkVf7rqrtct6Dz+8gYSEhVx++QbefHN1FUepahIxxr0xPiIyDGvQTBgw1hjzWoFtlwCTgVZYrZm3G2NWeSRAkduB94FfjTF9i2yrB6xyxPQGVp/T0cAuoKcxJscTMZQkISHBJCYmevMUyo/Mnz+f+Hi3/oZRfiovK4tDt9wCQOxddxF98cUVO06eoWfPRc7P3357To1cwWXCBGtBuFGjRvk4ElWWEydy2bUrgxdf3MKmTeml1p08+VLuuOOsKopMVSURWWWMSajKc7o9YbsxZirQBVgJvCIiC0XkLBGZBPwINMZq9UzwRJIpIsEi8gRWAluSB4FmwCXGmBeNMc8D1zvivLWyMSilAsuxl15yvo+66KIKH+e++071S7znntY1MskE7aNZndSqFcqZZ8YybVoPFiw4j5EjW5VYd8SIHxF5meuvn8Ps2VvIybFXXaAq4JRrZSBjzG5jTB/gHqA7sAa4HfgV6GqMed4YU+llCxzzW64GngamAvtLqHojsKjgVETGmAXAZsc2pZRyytlwai7Cig7aycszLFt2zPn5n/9sVdmwlKpScXFhjBzZmsTEi/j00w5069bQZb0vv9zKddd9Q3j4a4i8TP/+X3DwYOmtoUoVVe4lKEWkNtZqOlH5RUAOkO25sIgAagM3GGNuBYolryJSB2iD9ei8qNWAV5qGRWSkiCSKSGJSknamVqq6yNm+3fm+4TvvVPg4BR+Z//jjeZUJSSmfq1s3lFWrhmHMwxjzMLm5D/L4471d1l2wYDdNmkxE5GXq1HmLkSN/ZOnSktqBlLKUK9EUkYFY80uOBBYA7bAel18ErBORf3korjSgvTFmZil1mjq+uvopPwjUFpFYD8XjZIyZZIxJMMYkaH89paqP5P/8x/k+uH79Ch3j0UcLr85St67OTagCS0hIEM8+ez7GPIzd/hAZGfcxZ87AYvVSUrKZPHkd5503HZGXiY9/m88+24i74z5UzeHuPJrxIjIT+BKIxBrsc5kxZqejX2RPYCfwuoj8KiJtKxOUMSbPjUfw+ZOGZbjYlun4WrPmGlFKuZT26alVckPbtavQMWy2PH7++dRTjJUr+1Y2LKX8WlCQEBkZytVXt3O2eGZk3Mdrr11E377NC9VNTs7k5pu/IyjoFUReZujQ79i48agmnsrtFs1NWINs5gKdjDEfFdxojFmL1WdzPHAeVuvmA54M1IX82Ev7Kc7zxolFZICITEpNTfXG4ZVSHmTy8kifM8f5uf4zz1ToOO+8c2r1n/nzz61RE7MrlS8yMpT77+/OwoU3YMzDJCXdw803n16s3qefbqRTp4+ciWeLFu/x5purWbXqEFlZlR7KoaoRd9c6twNDjDEzSqpgjMkFHhORb7DWEX8ZeK2k+h5wwvE10sW2yCJ1PMoYMxeYm5CQMMIbx1dKeU7So4863zecOBEJcffXXmGffLLH+b5+/fBKx6VUIKhfP4pp065k2rQrsdnymDNnG08/vZT165ML1du79wT33fdLobK6dSM4diyLLl3iOXToJO3b1yE4WGjVKpYrr2zDGWfUp0OHugQF6R911Zm7v3E7GWOSy64GxpilInIWVuumN+X/1m/sYlsTIMUYc9LLMSil/Jjt0CFsu3c7PwfXrVuh40ybdirJnDjx7MqGpVRACgkJ4rrrTuO6604DICfHztdfb+WzzzYxZ862YvWPHbNWe1671uqScviw1RPu11/38fHHpxZDiI+PxGYz9O/fkgED2nLZZa0QEerVc9XOpPyNW4mmu0lmgfqZgFdn8DXGpIjITqCbi81dAa/NpC4iA4AB7SrY10spVTWO/PvfzveNPv64wsd5/fVTI9YTEupUKqZAEhvr8fGWKoCEhQUzeHBHBg8uvKBgTo6d9PQcUlKyyc62c+JEDocOnWTz5mMsW3aQTZuOsn17qnP+zqQka9jFzJmbmTlzc6FjnXZaHbZsOU5sbDipqacmv+nbtzmRkSF8//1OOnasS0hIEB061KFXr8Y0bBjNaafVoWnTGOrXjyQyMtTL34marWLPkPzHl8D9ItLRGLMJQET6AR3wYouqPjpXyv8de/ll5/uYq68mKLJirR/z5x92vtfWzMJ0nXNVEWFhwdStG0nduqX/mzTGsGNHKkuX7uezzzaydm0SBw8WflC5ZctxgEJJJsCiRXud7zdtsua93bAhmS+/3OryXGecUZ8NG5IZMqQjffo0p1Wr2lx4YTNNQj3A7SUofUlEdgG7XCxBGQ9swJpn8xWs+TcfAbYB5xljPDm3ZzG6BGXNoktQVh9pn35aaABQk5mlzZRWuoSEhc73iYkVX01IKX+UlJTEZZdd5uswysVuz2P37jROnLBWmTYGmjaNYf/+dGdZdrad1NRsTp7MZf/+dDIycklNzWbx4n0cO5bFsWNZnDyZ69b5WreOpXfvxvTq1ZiLL25Bhw51CQsL9tr1eZMvlqCs1i2axpgkEbkQa9DRM1hTHX0NjPZ2kqmU8k/JTzxBzqZNzs+NPvywwsdKSTn1H9HDD7evVFyBSNc6V74QHBxEmzZxxcrj46OKVy5FXp5h/fokDh/OYM6cbaxbl0RUVCg//rirUL2dO1PZuTOV6dNP/V6pVy+Siy9uzoMPJtC9e0NCQ6tn4lkVqkWiaYxpVcq2zcAVVReN9tFUyl8lPfoouTtPTUPUaMoUgqLK959PQf36/e58f8MNTUupWTPpFG+qOgsKErp0aQDApZe2KrY9JSWLn37azfLlB1i3LplfftlDXp71FPjo0Uy++GILX3yxpdA+t97ambPPbsDevWkMGNCWc89tWm1bPz3FI4/ORSQKiAGSTHV4Fu8h+ui8ZtFH5/7tyEMPYdt7ql9W4+nTkeCK/4JfvvwYo0atBeCcc+ry1ltdKh2jUv6mOj4696WsLBvz5u1g0qR1/PDDrnLt27RpDM88cx6nn16PHj0aERJS7lXAK82vH52LSCes/o+/GWM+cJRdDPwXa7J2AbJE5GtgrDFml8ejVUopF5KfeqpwkjltWqWSTIBnnjn1mEyTTKUUQERECIMGncagQac5y7ZuPc7MmZvZvj2FuLhwZs7czP796cX23b8/ndtv/6FQWZMmMYSHB3P//d257rr2NG1aq9h+1Z1biaaI9ALyZ1pd7ii7Cqs/ZArwCXAEaAMMAv4hIuc5HmsrpZTXHBg8uNDnxp9+ioRWfqTokSNWN+/rrmtS6WMFqueffx6AsWPH+jgSpXynffs6jB3b2/n51VdPDRo8eTKHd975k08/3ciJEzns359Odrbduf3AASshve++XwpNaD90aCfuv78b3bs3qoIr8C53WzT/B+wDLjTG5M/18RrWiO8+xhhnRx0RaQ0sBl4CrvFgrH5D+2gq5Xt5GRkcGj68UJmnkswNG9Kc72+/vVWljxeodheYDF8pVVx0dBijR/dk9Oiehcp37Upl0aK9vP/+epYs2V9sv2nT/mbatL+dn9u2jeOmmzpyyy2dads2rlotgetuotkNeDw/yRSRaKAtMKxgkglgjNkpIm8CYzwaqR/ReTSV8q2MxYtJcYx4ztd4xgyP/fIdPnyV832DBrrcpFLKs1q1imX48FiGDz/DWXbwYDpff72NOXO2Fev/uX17Cs8+u5xnn13uLOvRoxE9ejRyDkDy10FH7iaamUBc/gdjzEkRSQNK6skaCtSYQUFKqaph8vI4eOONhcoievSg7ujRHjtHTk6e8/2VV1b/x1ZKqeqhceMY7r77bO6++2zAmi903rydfP75Jr78ckuhR+4AK1ceYuXKQ7zzzp+Fyvv3b0nXrg1o3rwWV1/djhYtalfRFbjmbqI5F3hQRH4xxixxlE0GHhWROcYY53MmETkduA/4ybOhKqVqsrTPPiP9668LldV//nnC2nt2fssPPtjlfP/EEx1LrqiUUl4UHBzEgAFtGTCgLZ9+eqWzfNeuVL77bgdTpmwgMfFwsf1++mk3P/1kdWv5179O9fu88so23g/aBXcTzf8A5wO/isi3wAJgJVYfzK0iMhtrMFBn4CqsAUKPejxapVSNYk9NJenRR8k7dqzwhtBQGk+digR5fnqQDz6wfkGHhwcRHFx9+kEppWqGVq1iuffertx7b1fAavnctSuNl19eyaefbqRXr8YsWFC8//R33+2o6lABNxNNY0yyiPTASjhvAa7GejSe/1v4TsfXbGAO8IgxJmB7ietgIKW8K3fnTpIedf23aoM33ySkkXceaWdlnXo0dc89vvnrXymlyiM4OIi2beN4993+vPtu/0LbDh5MZ8WKQwwc+DW1a4eRllbCQbzI7Xk0jTHpwFhgrIi0Atpj9dsMB04C+4ENxpgMz4fpX3QwkFLekbFoESnvvFOsPKRFC+qNGUNw3bpePf8NN6xwvr/xxmZePZdSSnlb48YxXHNNO4x5GACR+6o8hgotQemYjH2XRyNRStVIxhhOzJhB+uzZxbZF/eMfxN52m1cekbuyf38WANHRwfrYXCmlPKBarHWulAo8eSdOcOT++8k7caLYtri77iLq4ourNJ5t206t5DF7du9Saqp8LVu29HUISik/p4mmUqpK2ZKSSHroIUxWVrFtcf/+N1Hnn++DqOCee/50vq9XL8wnMVQ3uiKQUqos7i5B+UvZtYoxxphLKrCfUioAmbw8Dt1yCyYnp1B5UO3aNHj9dYJiYnwUmTUI6NixXAC6dYv1WRxKKRVo3G3R3A7cjjXS/ABg81pESqmAkrl8OcffeAPshScbjhk0iFo33OAXS6mNG7fR+f7ll8/0YSTVi651rpQqi7vTG40QkeXAJGCBMeY274bl33R6I6Xck71hA8dffbVYeeNp05Aw/3k8vXBhEmDNnVm7duXXSq8pYmO19VcpVbryTG/0gYg0BZ4UkW+NMV96MS6/ptMbKVU6Y7dz8JZbIDe3UHmDCRMIadDAR1G5VnDuzDfeOMuHkVQ/o0aN8nUISik/V945Q54DNgAviUjVzDeilKpWcnfu5OCQIYWSzNq33EKTmTP9LskEmDZtr/N99+5xvgtEKaUCULlGnRtj8kSkJxBhjMnzUkxKqWrIGEPKhAlk/vabsyysc2fqPfGEX/TDLMnEiTud7/05Tn80evRoAMaPH+/jSJRS/qrc0xsZY7KxlppUSikA7CkpHB45slBZndGjiezRw0cRuccY43z/yCPtfRhJ9ZSamurrEJRSfs5j82iKSB0g0xhTfHI8pVTAylyxguMvv1yorOGkSQTHxfkmoHJYtCjZ+f7qqxv7MBKllApMbvezFJFQEblTRD4qUn6BiPwFJAPpIvK9iLT1dKBKKf+TPm9eoSSz1o030mTmzGqRZAK8//4u5/uIiGDfBaKUUgHK3Qnbw4EFwHlAjoiMMMbYRKQ98AMQDswH/gKuB5aKyFnGmMNeilsp5WOH77sP+8GDzs/x48cTWs2WJNy82Vp2sn9//xukpJRSgcDdFs37gHOBR4A6xpj8CdufAiKA6caYK40xjwAJWBO6j/FwrH5DRAaIyCTtn6RqqgODBxdKMhu8+Wa1SzJ37cpwvr/77tY+jEQppQKXu4nmYGCWMeZlY0wmgIiEAVdjrRbkfHZmjDkGTAGu8myo/sMYM9cYM1InK1Y1jcnL48DgwYXKGk+fTkijRj6KqOLee+/UaPMWLaJ8GIlSSgUudwcDtcdKHgs6B4gGDhhj/iyybRvQpFKRKaX8yvG33io0dRFAk5kzfRRN5f3yi7UaUM+edXwciVJKBS53WzSDKb6++SWOrwtc1I8FMlyUK6WqmewNGzgweHChJDOkZctqnWTm5Rnsdmtqo3/8o6GPo1FKqcDlbovmNuDsImXXYj02/9ZF/X849lFKVVO2pCSO3HtvsfL4l18mtEULH0TkOXPmnOpfetVV1e+xv7846yxdslMpVTp3E83PgSdE5HvgJ+BOoDNwGPimYEURuRm4FBjnwTiVUlXE2GwcvOmmYuVhZ55JvbFjkaDqv/rsm29ud74PDtbVgCpK1zpXSpXF3UTzVaxWyq+wWjEFyAH+aYzJARCRa4FRQF9gM/Cap4NVSnmPycnh6AsvkPP334XKoy65hLg77/RRVJ6Xk5PHiRNWT6D/+7+mPo5GKaUCm1uJpjEmR0T6ATdgDQI6AUwzxhT8H6k71jyb04AH80enK6X838lffiF14sTChSI0njoVCQvzTVBeMnXqHuf7e+9t48NIqj9d61wpVRa3l6A0xtiBzxwvV14AnjDG5HkiMKWU96V/+y1pn3xSrLzeU08R3qmTDyLyvokTT01rFBPjsVV4ayTto6mUKovHfssaY3SUuVLVgO3AAY698gq2vXuLbav7n/8QfvbZiARmv8W8PIOxBpszapS2ZlbWsGHDfB2CUsrPubsE5WzgNWPMbwXKgoAzgG1Fk0zHgKBPjDG6eLBSfsKemsrhu+8GW9GZyqD+c88RdtppPoiqaq1Ycdz5fsiQZj6MRCmlagZ3WzQHArOKlNUB1gD9gV88GJPfE5EBwIB27dr5OhSlymQ/etRKMF2oO2YMEWefXbUB+dC4cae6lYeH69/BlTVy5EgAJk2a5ONIlFL+qrKPzgPz+VoZjDFzgbkJCQkjfB2LUiUxOTkce+UVstesKVQeFBtL/CuvEFy7to8i853jx3MBXQ1IKaWqivaEVyrA5GVlceiWW1xua/D664Q0qZmrwyYlZTvfP/lkRx9GopRSNYcmmkoFCGO3c3DIkGLlQbVrU+/JJwlt3twHUfmPL77Y73zfsGGEDyNRSqmaQxNNpao5+9GjJD36KHlpacW2NZw4keC6dX0Qlf/58MPdAMTGhvo4EqWUqjk00VSqGjI5OWQuX07KhAkutzd8//0a2QezJBMmnFpy8rHHAn90vVJK+YvyJJr1RKRFgc/5zSQNipQD1K9cWEqpfMYY8lJTSX3/fbJWrCixXmjr1tR7+mmCIvSxcFFTppxaDah//wY+jEQppWqW8iSarzteRX3qkUiqIWOzYex2jN1OkGOZvtwTJwgKDSVY/7NXlWA7coSsFStcrtpTVK2bbiLmyiuRUH0k7Mr33x9yvv/qq14+jEQppWoedxPNj70aRTWVtnEj31ZykusGffqQe+IEx1evpunVVyMhIeyfMwdjt9P97beJataMyMaNyUlJITclhaDQUGp37EhQWBgmLw9EAnYVl+rM2GwcffZZgmJjqfPAA6XeI/vx49gOHiRz2TKyV6/GnpRU5vGDYmOp//TTNXYEeXmMG7fR+b558ygfRqKUUjWPW4mmMeY2bwfiKyLSCpgInAccBR43xkyrqvMf+fVX5/v933xTaNuqe++t1LGDIyOxZ2YWK2958800/sc/wBgOLVhASEwMsZ07k7phA0Hh4TQdMIDg8HBsmZkEh4UR1bw52cnJBEdFERJ16j9qYwwiQp7NRupffxHbuTNBIYV/pPJsNiQ4uMLJ8MnduwmvXx9bejoRDRuy/cMPydizhza33caxVausxLtTJ6KaNcPYbGTs3Utk06aEREdjS0/n6B9/EN2qldXibAzh9etDUBBBoaFIsGPC7gLJetaRI+QcP07tDh0qFC9YSabt0CFyNloJTvbatZisLExmJikTJ+JcA9FNEhlJzLXXEn3ppQRFaaJUHhs3nnC+Hzu24vdUuXbBBRf4OgSllJ8TU87/9Jw7ijQAmmBN2n7AGHPYk4FVBbGyiz+Bb4EngW5YqxydaYzZWdb+Z7ZubT4eNAiAoNBQ9s+ZQ71evbBlZBASFUVMu3ac2LKF5CVLvHcRqkoFxcaSl5rq9fPEXHstMddco4llJSUkLHS+T0y8yIeRKOWfkpKSuOyyy3wdhqoiIrLKGJNQlecs16hzEYkARgM3A+2LbNsBTAPGF1373I+dA8QC44wxecAKEekFJLuzc3i9enR75RXn57P/9z+PBGXsdo6vXUutdu0ICg9HgoMxdjspa9ciwcFENWtG+o4d7P/mG47+8QdNBgwg+8gR9syc6ZHzq5J5OskMjo+n9k03EdG796kWVuURGRmn1nS/4oqGPoxEKaVqLrdbNEXkNGAe0AY4BCwEDgC5WC2b5zu2bQcGGGM2eSNgTxKRe4ErgB3AYCAJ+I9jickyJSQkmMTERC9G6H0mL4/MgwfJPHCA0Nq1iWnThtwTJwiJjiYvJ4fMAwcIj48n6fffOfzzzwSHh1Ovd2+iWrRg7xdfsHf2bGI7d6bplVdy8IcfMHl5tL3jDnKOHWP9k086zxMSE0OTK64gedkyctPTCY2JwZ6ZSXZyMtGtW3Ny56kG5Eb9+9Pw4oshKIi8nBxiO3cmtnNnspOSSF6yhKCICCIaNsSekUFwVJT1NTLSSsKbNyfryBFiWrXi+Nq1hNaqhT0nh6xDh8jLzSU3NZXU9eup26MHKevXk3ngAMGRkTS+7DJSN2xgxwcfOOOIbtWKsHr1iG7RgpN79nD8yBHCoqIIrl+fzBJaqSU6mogePQiKiiJn+3Yr1h49CO/WjaDISPJOniS4bl0kRGcW87YHHljHb78dBWD58j6EhAT5OKLAo2udV3/aolmz+KJF061EU0RqA6uAOsB9xhiXI81FZCDwHpACJBhjTriqV5VE5HrgCxebPga2AU8BD2L107wI+BLoaozZWtaxAyHRVO6bP38+8fHxvg5DuWHOnIM8+6z1t250dDC//nqhjyMKTHPnWn+TDxgwwMeRqIrSRLNm8edH5yOBlsB5xpiVJVUyxnwtIruAPxz7vFJS3Sr0FVDLRXku8G9gjzHmTUfZDyLyG/APoMxEUynlf9asSXEmmQAzZvT0YTSBTRNMpVRZ3H2WNASYWVqSmc8Y8yfW3JrFF132AWOM3RiT7uKVDWwBYqXwkOhgrAFOSqlqxhjDiBFrnJ+//ro3jRrpnLbekpKSQkpKiq/DUEr5MXcTzbbA0nIcd7ljH3/3E5ANPCMiwSJyOXAu8E3puyml/NEdd5xKMm++uTnNmkX6MJrA98gjj/DII4/4OgyllB9zN9EUoDzzIAUDtjJruXtykckisqiEba1FZLaIHHO8PhERtzrSOUbHX4Q1+vwo1spHNxljdnsodKVUFfn996OsXWvNChAbG8IDD7TzcURKKaXc7aO5CbgEeNfN+hcDmysUUREicjtwB/Cri231sEa/hwEvYl3PaOAsEelpjMkp6/jGmM1AP0/EqpTynfvvX+d8/8MP5/kwEqWUUvncbdGcAQwUkT5lVRSR/sC1wEeVCczxKPsJYHIp1R4EmgGXGGNeNMY8D1wPdAFurcz5lVLVgzGm0MTsL798hk5lpJRSfsLd38YTgI3ANyJyh4iEFa0gImEiMgprlPdK4JOKBuWYGH418DQwFdhfQtUbgUXGGOdixsaYBVitqTdW9PxlxDZSRBJFJDHJjTWplVLe9eKLWwp97ttXp6BSSil/4Vai6XgEnT/lzyQgSUQWich0EZkqIj8Ch4E3gbXANcaY3ErEFQHUBm4wxtyKi/6eIlIHa4L4VS72Xw14ZZ4oY8wkY0yCMSZB51RUyrf27ctk1qwDzs+//abzZSqllD9xe3kSY8wBETkHGA7cAvQCwh2bc4ElWK2PU0xFF1A/JQ1ob4wpbUBRU8dXV62dB4HaIhJrjPH+wtRKqSr3558phUaZz517DpGRuoynUkr5k3Ktg+dopZwMTBaRIKAu1upCHn2G7Fh3PK+MavmTsLtaVz3T8TUa8HiiKSIDgAHt2umoVqV84dChrEJJ5uDBTWncWOfLVEopf1PhHvPGmDxjTLKnk8xyyI+9tNbTspLVCjHGzDXGjIyNjfXG4ZVSpVi0KImrrlrm/NyhQwyPPHKaDyNSSilVknK1aIpIO+A24DljTKaIxGH1hyzqAWPMHA/EV5r8ddRdzcgcWaSOUioA3Hvvn/zxx3Hn57596/Pyy2f6MKKaTZegVEqVxe1EU0TuAV4FQrFW1FmENTF7K2AdkP/bPwF4R0R+MMZkeTLYIvY4vjZ2sa0JkGKMOemNE+ujc6Wq1rFjOVx99TKysk49pEhIiNMk08c00VRKlcWtR+ci0htriqPFwGnGmEVFqjxojLnIGHMRcBdW8ufVeSyNMSnATqCbi81dgUQvnlsfnSvlRcYYTp60sWFDGtdcs4xLL11SKMns2bMOEyd29WGECnStc6VU2dxt0bwfK6m7qqzVdowxn4rIaGAQ8F7lwivTl8D9ItLRGLMJQET6AR2A8V4+t1LKS8aO/ZsffzzictugQU146CF9muAP8tc5nzRpko8jUUr5K3cTzQuAye4s6egwG7i3YiGVy0tYUy39LCKvYM2/+QjW3JrTquD8SikPMMaQmJjCffetIyen5DF8c+b0pmlTV92ylS8MHTrU1yEopfycu4lmPWCXi/IM4BVgd5HyfVgTrnuVMSZJRC4EXgOeccTzNTDaGJPtrfNqH02lPMMYwx9/HGfUqLWl1nvttTM5//x6iEgVRabcceGFOkG+Uqp07iaaR7CSzUKMMZnAaBf1GwOHKhFX0fO0KmXbZuAKT53LHcaYucDchISEEVV5XqWqO5stjyef3MiCBUnY7SXPTNagQTgjRrTi6qsbExysyaW/2rVrFwCtWrXyaRxKKf/lbqL5F3AlVuulOwbiemlIpVQN9vvvR/nhB9d9L/OtWNGXoCBNLquDF154AdA+mkqpkrmbaE4BpovIUGNMqX0fReRurJHgT1YyNqVUAFm8OJmHH95QrPyxx04jPd1G165xdOmiMzkopVQgcTfRnIm1xvkUEbkY+K8xZmvBCiLSBngAuBv40hgzz5OB+hPto6mU+w4cyGTo0ETS0myFyp97rhOXXdbQR1EppZSqCm4lmsYYIyI3YE1XNBy4VUQOYg36EaAR0MzxfgYQ0H0XtY+mUmVLScnh1ltXsX9/8XUbli/vQ0hIhVfAVUopVU24vTKQMSYNGCIiE4CbgT7A6ViTvh8APgGmGWN+9kagSqnq5Z571hZLMp97rhO9e9fRJFMppWqIcq11DmCMWQIs8UIsSqkA8dVXB9iyJd35+f7723Lzzc11eiKllKphyp1oKqVUaW66aWWxJHPo0BY+jEgppZSvaKKplPKYn346UijJfOqpjlx1VWMfRqSUUsqXNNGsAB11rlRxr7yylenT9zk/P/bYaZpkKqVUDaeJZgXoqHOlCps//3ChJDMmJoTrr2/qw4hUVdC1zpVSZdFEUylVKampuTz++N+FyiZP7uqjaFRV0rXOlVJl0URTKVUpl1zyu/P9ZZc1ZODAxrRvH+PDiFRV0bXOlVJl0URTKVVh+/ZlFvr83HOdfBSJ8gVd61wpVRZNNJVSFTZw4HLn+9mze/kwEuUL9957r69DUEr5OU00K0BHnStVvDWzRYsoH0WifKVLly6+DkEp5ed0HbgKMMbMNcaMjI2N9XUoSvnEyZO2Qq2Zn3/ew4fRKF9Zu3Yta9eu9XUYSik/pi2aSqlyKzjK/KabmtGunQ7+qYnefvttQPtoKqVKpi2aSvnIxx/v5t57/yQ1NdfXoZRLWlouv/121Pn5vvu0C4lSSinXNNFUykfeemsHf/xxnEWLkn0dSrlMnLjT+f6uu1oTHCw+jEYppZQ/00RTKR/Lzc3zdQjlMnPmfuf7IUOa+TASpZRS/k4TTaWU2+bPP+x837x5JNHR2s1bKaVUyTTRVEq5Zdu29EKDgJ5/XidnV0opVTpNNCtARAaIyKTU1FRfh6JUlTDGcOONKwuVdepU20fRKKWUqi400awAnUdT1SQZGTYuuuj3QmWPPXaaj6JRSilVnWgHK6VUiX79NZmHHlpfqOzdd8+mR486PopIKaVUdaKJplLKpQceWFdovkyAa69tokmmctK1zpVSZdFEUylVzJYt6cWSTIBBg5r4IBrlr3Stc6VUWbSPplKqkJ9/PsJNNxUe+BMaKnz5ZS9OP72Wj6JS/kjXOldKlUVbNJVShXzzzcFiZcuW9a36QJTf07XOlVJl0URTKeVkjGHJkmO+DkNVE2PGjPF1CEopP6eJplIKAJstj969fy1U1qJFpE5lpErUqlUrX4eglPJzmmgqpVi16jh33vlnoTIRmD27t28CUtXC4sWLAbjwwgt9HIlSyl/pYCBVYxljuOWWeYi8zI03ziUvz/g6JJ+YNWt/sSQT4IsvelV9MKpamTZtGtOmTfN1GEopP6aJpqqxDh48ydSp1trdM2ZsZteumrWkqDGGu+5aw//+t6VQ+SuvnMnKlX1p1SrKR5EppZQKFProvAJEZAAwoF27dr4ORVWCMabIZx8FUsWysuy88so2vvrqgMvt555bFxGp4qiUUkoFIm3RrABd61xVV8YYZs8+UGKSOX/+uYSG6q8FpZRSnqEtmkrVIC+9tJUvvtjvcluLFpHUrx9exREppZQKZNp0oVQNkZSUXWKS2bNnHR1hrpRSyuO0RVOpAJeYeJz//Ocvjh/Pdbn9rrtac8cdrao2KKWUUjWCJppKBbCPP97NW2/tKHF7UBDcdlvLKoxIKaVUTaKJplIBrKQks379MCIignn11TMJDtYR5koppbxDE02lAlRJE9C/+eZZnHtuvSqORgUiXetcKVWWGp1oisjNwHtFiqOBscaYF3wQklKVlpVlZ9q0vUycuLPYtsTEi3wQkQpUuta5UqosNTrRNMZ8Cnya/1lEbgHGAm/7LCilKmHz5hPcfHOiy20XXKCtmMqzdK1zpVRZanSiWZCINAbeBC4zxtSstQhVwHj66U0uy+fNO5cGDXSOTOVZ+euca6KplCqJJpqnPAt8aYxZ7utAlCoPYwwPPLCelSuPk52dV2z7XXe11iRTecVLL73k6xCUUn4u4CdsF5HrRcS4eE0pUKcRMAR4zmeBKlVB27ef5Pffj7pMMgGdI1N5TVxcHHFxcb4OQynlx2pCi+ZXQC0X5QVnrx4CLDLGFB89oZSf2rMngxUrjvO//20psU7z5pFVGJGqaebOnQvAgAEDfByJUspfBXyiaYyxA+llVBsATPF+NEp5zpAhK0tsxYyLC6Vjxxgef7xjFUelahJNNJVSZQn4RLMsIhIE9ADu9HUsSrnrxx8Pl5hkisCCBedXcURKKaVUcdWij6aITBaRRSVsay0is0XkmOP1iYjEl+Pw9YAY4KAnYlXK25YsOcqYMX+XuP2NN86qwmiUUkqpkvl9i6aI3A7cAfzqYls9YCEQBryIdT2jgbNEpKcxJqes4xtjkgBdg0/5tfR0GwsWHGHr1nRmzNhfYj2dkF0ppZQ/8dtEU0SCsSZPf6qUag8CzYAzjTEbHfv9AfwE3ApM9nKYSlWJCRO2M2vWAV+HoZRSSpWLXz46F5EIYDXwNDAVKKkJ50as0eIb8wuMMQuAzY5t3ohtpIgkikhiUlKSN06hFHl5hg0b0sjMtAO4lWSGhGjDvFJKKf/il4kmEAHUBm4wxtwK2IpWEJE6QBtglYv9VwMJ3gjMGDPJGJNgjEmIjy9PV1Clyma3GxYsOELPnosYPnwV48aV3BezIBH4+OPuXo5OKaWUKh9/fXSeBrQ3xhRLMAto6vjqqrXzIFBbRGJ1OUlVnUyduocJE3Y4Py9alMx55xXrnlxIp061eOmlM2jUKMLb4SmllFLl4peJpjEmD3A9d8sp+ZOwZ7jYlun4Gg14PNEUkQHAgHbt2nn60KqG+/bbQ8XKSprGKN+HH3YjJMRfH04opZSqyfwy0XRT/v+sppQ6ZSWrFWKMmQvMTUhIGOGN46uaKSvLjs1W2o+zUv5F1zpXSpWlOieaJxxfXa2xF1mkjlJ+IzvbzrJlxwqVnTiRy9VXL+fEidJ6iyjlX3Sdc6VUWarz87Y9jq+NXWxrAqQYY05648QiMkBEJqWmBk73z6SkDB5//HfWrj3i61CKSUvLZvLkdTRq9A5ffLHZ1+FU2vvv7+Lhhzc4P//vf1vo129JhZLMmJgQgoJ0tLnyjblz5zqXoVRKKVeqbaJpjEkBdgLdXGzuCiR68dxzjTEjY2NjvXWKKvfMM8t4/vnlXH/9N74OpRC7PY/TTvuAkSN/5PDhDO65Z4GvQ6o0V/0w7Xb3HpnfeWcrAM46qzZffdWLWbN6aqKpfEYTTaVUWaptounwJdBPRDrmF4hIP6AD8HlVBJCeXubiQ8UsW3aA5s3f4+2313ghoor580+rJXPbthSPH9sYw4kT7n+f1q9P4qWXVpCWlk1Wlo3Dh0+N90pOzixlz1NmzdpMkybv8vXXW8sdrzt+/TWZefOKJ4xlSU3NJSmp/D8zAMOHt2DYsBY888zp/Pe/nWnePIr69cMrdCylPGHSpElMmjTJ12EopfxYdU80XwKOAT+LyIMiMgaYhTW35jRvn3zz5mPUqvUmiYnFEw5jDBs2JJGba3eW5eTY2bTpKM8/v5x9+04watTP3g7RLzz44CLq1HmLH37Y6Vb9a675mkcfXcxrr7maItU9d921gIMHT3LttXM83h0gO9vOQw+t54knNnLoUJbb+6Wk5HDJJb+X+3zPP9+JIUOaMXRocyIigrniikY0bKhTGSmllPJ/1TrRdKxTfiGwFngGuB/4GrjcGJPtrfPm99FMT88FoEePaWzadJTY2DcReZnvv9/B4MFzOfPMj7n7butRb16eoWXLSZx++kd8992O0g4PwPbtKS4T2Kpw+eWzWLnyYKWOsXNnCpdfPouZMzfx+uursNsNb7/9Z6n7PPror9x66zx27rT6vq5bV/GVl44ePdXyedtt853vV648yO7dpfetzc2188sve0psrS44Mnzp0mOkpOQW2DePxx//m48+2l1sv3HjNhYrK8uDD7bjH/9oyEMPtScuLqzc+yvlTVOnTmXq1Km+DkMp5ceqxahzY0yrUrZtBq6oumhOTW8k0tw5vdG99/5MWpqVmFxxxWxn3Q8+WE92tp1p01yv8PLmm6u57bYzqFXrVBJhjKFz54/Izraza9cIWra0+oLefvt8jhzJ4KuvBpY5b6IxhnnzdtC6dSydOtUv1/XNn7+L5ORMVq4cVq79Cho/fiXz5+9i/vxdzrK8vJL7IWZm5vLSSysrfL7SrFljtWhu3HiUnj0/BeDee8/mP//pVajexo1HiYoKYcCAr1i16jA333w606Zd6dyel2f4/fejJCef+hvmhRc28913h3jllTN56KH1hIYKiYkpAPz++1FCQoRVq1IQAePmzEUNG4Zz+LB1jj59ynfvlKpKv/32GwDDhlX8d4VSKrBVi0SzOvjllz0lbispyQS4775f+Pvvo0yc2N9Zlp1tJzvbeuSelJRJy5ax7NyZwocfWiOVt207TseO9XjwwYWFHi/n5DxAaGgwACtWHOKqq74iJiaUY8dGMWvWFhISGtG+fZ1iMdx//y/8/nvhBZYSEw+Tk2MnLCy4wPHtzJq1hV69GtO2bVwp3w0r7qK++24HR49mUq9e8RmpXCVhs2dvZfny4i2rq1cfZufOVC69tBX9+s2kS5cGTJp0KR9+uJ7//W8F7du7jm3fvlOzXb399p/FEs0BA74q9PnTTzfSuHE048f35Y03VnH//X+5PO7atan061f8kfjatadaTt1NMgEeeqg9jzxi3euwsGr90EEppVQNp4mmH3jvvbWcc05jLr+8NZs3H2fQoDnF6tx++w/O98ZYSVPRPozbt6fQsWM94FRSlZ6eS1jYa846hw/fTXx8FHPmbOPgwZM0bRrDG2+sdhnXG2+somHDaC69tBWNGkXTufNHzsFC6en/5quvttGlSzyJiYe49tr2xMVFcO+9C3jnnT/p3r2hy2P++OMuhgw5vVBZcnKGs6WxqAMH0ouVde9uPaobN643K1YcYsWKQyxcuMcZ29atx10eq6jPP99UZp2XX05k1640Zs3a4tYxPSEmJpjHHjuNnJw84uN1sI9SSqnqSxNNPzF8+HyX5T16TGPgwHYsXLi3UPngwcWnFPnllz3ExUXw00+7+PZb1/1A77lnAY8/fg7XXls8mS3qkUcWO99ff/1phUak33nnT3z66ak+h/Pn7+Kqq9rwzjt/ArBq1WGXx/z0043s3JnKffd1Izo6jN9+28fXX2919sssj2efXe58X9Zo+bfeWs1nnxXuI/nww6WvIZ7P20nmZZc1ZOjQ5gwdempGruuvb+rVcyqllFJVQUx5nukp4NRa59BsBNzn63DKJTw82PlY3pcGDWrP6tWH2bUrzdeh+Nz3359LfHw4I0euZtu2k3zxRS/q1dOBP8r/jRw5EkCnOKrGkpKSuOyyy3wdhqoiIrLKGJNQlefUFs0KcDUYqLrwhyQTrP6XqrCJE7tisxntl6mUUipg6P9oSvnAJZfEExIihIQItWpZf+8FBYkmmUoppQKKtmgq5QPPP9+J48et+TcjIoLLqK2UUkpVT9p84iVNmsQ43/fp08yHkSh/0rBhOF980ZOQkCDi48N1VLlSSqmApoOBKuDUYCCGAa4nV3QtFijP8Ory1q+qc9QHkr14Dr1u/zqHXrd3zqHX7V/n0Ov2zjn0uv3rHB2MMbXKuU/lGGP0VcEXkFjO+pO8Wb8Kz6HX7T8x6XXrdet163Xrdet1e+W6PfHSR+dVq/jkl56tX1XnKC+9bu/Ur6pzlJdet3fqV9U5ykuv2zv1q+oc5aXX7Z36VXWOKqePzitBRBJNFc9H5Q/0umsWve6aRa+7ZtHrrll8cd3aolk5NXWWYr3umkWvu2bR665Z9Lprliq/bm3RVEoppZRSXqEtmkoppZRSyitqdKIpIpNFZFGBz61ExJTx6lvR47vYXkdEDovI8EpcRrn48ppFJN6x/bCIpInIIhHpXdlrqkhcnrhuEfmHiPwmIhkiki4iC8q6nrJ+JjzNl9cdgPf7YhH5XUROiMh+EXldRGLK2CcQ7rdb1x1o97vI8c8SkRwReao8cXibL6870O63iKwoYb9Z7sbhbb687src7xq7MpCI3A7cAfxaoDgJa27MoiKBt4AjwNpKHL/g9jBgJtDA/agrx5fXLCK1gMVAE+A14DgwCvhFRHoaYza4fyXl443rFpE+wPdY86iOxfq3dA/wq4hcYIxZ4WYcXuPL6w7A+30R8BOwCngMaA7cBySIyIXGmDw34/AaX153oN3vIscPAaYAoRWIw2t8ed2Bdr9FRIDTga+BL4ts3l2OOLzGl9dd6ftd1fMp+foFBANPAHmAARa5sc/rgB24wBPHB5oCSxzbDTC8Blzz847tFxYoawRkAJ9Ut+sG1jj+IUYVKGsIHAN+qmwc1f26A/B+rwJ2ApEFyu5xnOfyAL7fbl13oN3vIvuMA7Id53jKE3FU9+sOtPsNtMbN/4sD6X67e92Vvd9e++b44wuIwMruDfAxsK+smwac6bhhH3ji+MClwAkgHXjD3R/uan7N4iif62L/e4AR1em6gTqOf3TjXWz7CjhZmTiq+3UH4P2OAOYDY4qUd3ac79EAvd9uXXeg3W8X+2QDj+M64QqY++3udQfi/Qauchy7l6fjqO7X7Yn77ZVvjr++gDisv84HOz7vcuOmfYOVFDYuUj6cIkmiO8fHeuw0F2gL9C16jAC95vy/mv5d4Ac3prrea6y/MNsDTVwc4zcgtTJxVPfrDrT7Xcr+NzvqDQnE++3udQfq/cbqFpIIfAe0wnWiGXD3u6zrDsT7DTzqKKvt+BztqTiq+3V74n577QfDH19Yg59CCnwu9aYBZzm+wS+72NYGGAq0Kc/xgbAC7/u6+ocegNd8qeOYNwLjsdZmNcA2YEB1vO5SjpEHfF/ROALhugP9fgMtsX5pHwXWAxE14X6XdN2Ber+x+iCnAs0oOdEMuPtd1nUH4v0GpgJpwPtYTxwNsB24MZDvtzvX7Yn7XaMGAxmr43qxTvuluBurCfotF8faAewo7/GNMTnlOH+l+cM1Y/1VBvAskIvVqmsHRgNfi8g/jDELyhFjmbx93UWJNQr3E8fH/1Uijkrxk+uOc3wNuPstInWxftmD1T/pX8aYrErEUSl+ct1xjq8Bc79FpDNWv7hRxph9ItLKQ3FUip9cd5zja8Dcb6zuILWwru0Wx9f7gOkiEmqMmVrBOCrFT647zlG3wve7RiWa5SEikVjZ/zfGGJejzgKNF6853PE1DjjNGHPccb65WH89/Rfw6C+m8qjsdYtIFNbjii7Af40xVTIKsbK8eN2BfL/z/7IPA/4NLBCRG40xJU6B4i+8eN0Bdb9FJBj4CPjdGDPZ2/F5ixevO6Dut8MkINgY83aB43wObADGi8hnxhi756P1HG9dNx643zV6Hs0yXATEAF/4OpAq5K1rPun4Ojv/hxTAGJOClah0lzLmI/SyCl+3iMQBPzqO8SHWY6fqwlvXHbD32xhz3Bgzw/GX/oVYI/Bf9XB83uKt6w60+z0a64+nx0SkvojUxxoIBxDlKKsO/3d667oD7X5jjJlYMNlylGViPVpuCHTyaITe4a3rrvT9rg7/WHzlCiAHq0N0TeGta97v+HrExbYjODoXe/ic5VGh6xaRBsBC4DysvwzvMI5OLdWEt647IO93UY5fyN8CzR3/Kfs7b113oN3vy7BabldgzVOYBKx2bBvt+NzCwzF6g7euO9Dud2nyr9GX1+Mub113pe+3JpolOw9YaYxJ83UgVchb17wBa6qMzi62tQaysH6J+Uq5r9sxge0PwNnAa8aYO6tZkgneu+6Aut8i0lFEdonIPS4218J6rJztyQC9xFvXHVD3G3gI6F/kNdSxbarj8yFPB+kF3rrugLrfItJURP4SkSdcbO7o+LrTY9F5j7euu9L3WxNNF0QkFKvJeI2vY6kq3rxmY8xJrCb2qxydzfPP2Rq4Gpjjq/4vlbjut7GSrTeMMQ96Oi5v8+Z1B+D93gbEAnc5VvTKP1ZL4DrgV2PMCY8G6mHevO5Au9/GmFXGmAUFX1gLbADscJRllXYMX/PmdQfg/d6P9XM+QkRqFzhWc6xZFhYaY/z6DwtvXrcn7rcOBnKtBdYjhD0lVRCRNsC5wFLHaK7qztvX/AjWdE4LReQNrCb++4BMYExFAvaQcl+3iJyOtexXKvCniAwtuo8xZpqX4vUUb193wNxvY4xNRP6F1arzq4hMA+phLcFmgH95P+xK8/Z1B8z9rqrAvEx/n5eghOsehbXoxFIRmYzVYj8KsAH3ejViz/D2dVfqfmuLpmv1HF9La4K+EOsX8IXeD6dKePWajTG7gN5Y67SOxlpx4k/gXB//cq/IdfdxfI3FGqU51cXL33n1ugPsfucn0Ddg/TJ/Fbgf69q8uq6zB3n1ugPtfgcA/X1eMlc/518DA7EGvryI1Y1gGdb1bPRGoB7m1euu7P2W6tetTCmllFJKVQfaoqmUUkoppbxCE02llFJKKeUVmmgqpZRSSimv0ERTKaWUUkp5hSaaSimllFLKKzTRVEoppZRSXqGJplJKKaWU8gpNNJXycyISLiKPichaETkpImkiskpEHhWRCBf1a4lIvIdjCBORpqVsnyIixo3XFBEZ7njf15MxVoSINHLEdFhEckRks4g8JCJSpF6IiDwuIjtEJENE/hCRfpU4nlv1yoj9bhHZJCKZIrJeRG50UaeWiLzmWLP8pIgsEZFLy3GOMBEZJyJbHNe9WkSGlFD3ehH501Fvq2NVoXIRkTqO78nwIuVPlfFztauy5yhS52YRcXuSaRGJcXyf9zq+z7+ISLeK1lMqkOgSlEr5MREJAeYD5wCfAO9h/bu9APgvcLWIXGyMyXbU7461Lu3NwCIPxdAS+NFxviklVHsPWFDg8wXASGAS8FuB8u3AYaylLH264oaIhAM/A62Bt4BdwGXAy0Az4IEC1V/HWpJtEtZ6wrcB34tIX2PMkvIcr5znLSn2h4HxwBfAa8C1wHQRMcaYGY46QVjLy13gOM8OYCgwX0SuMMbMd+Pb9B5wC/ABsBprbePPRKS2Mea9AvH8HzAT+MmxzwXAmyISZYx50Y3zINZ66jOBBi42z8Zae72oPsAdwFwPnCO/ztnAO+4cr4DPgUuBN4DdWMv4LRKRbsaYbRWop1TgMMboS1/68tMXcBPWutKDXGwb7dh2d4Gy4Y6yvh6Moa/jmMPLsc/w8u7jg+/tfY4YryxS/ilgB1o4Pp8G5AHPF6gTCWwFllTgeG7VKyXuOCAd+LRAWRCwGGut42BH2QAXPx8RWAnOH258f8507P9igTIB/gAOcmpluWCsZHlx/rkd5dMccdZx41xNgSWO87n1cwPUdlzLeiDCE+cArgFS8+u4+XPU31F/RIGyBsCxIvfIrXr60legvfTRuVL+7VzH1x9dbHsbyMVq7VTl1xdINsZ8V6R8Jlbi1svx+QasBOvd/ArGmEysVr5zRaRFOY/nbr2SDACii8STh9UK15xTPzN1gLVYCV9+vSxgBVYSWZYmwCqsde3z9zdYCWUjTrUKngu0BCYZY+wF9p/giPOq0k7ieJS/CegCvOlGXPnGAS2AexzXValziMh7wNdYf0C4+vdWkhuBTODj/AJjzBGs+zmwQPcWd+spFVA00VTKv51wfL2z6AZjTAYQY4y5Bax+bJxKChYW7LcmIheLyPciclREckVkv4i8JyJxBepMcfT5u0dEjjtedwELHVU+Kk+/tZIU7aNZ4HMXEZklIidEJElExotIsIjc6ujDmN/HsEuR49URkbcc15QtIhtF5D43+jvehZX0FVXf8dXm+JoAHDHG7CtSb7Xja/dyHs/deiVJKHL+ovEkABhjPjHGnG2Myf8ZQkSCgTOAvWWcA2PMD8aYBGPMpiKbumC1VB4rEs+qIvXWFNlektOxunl0wXrUXyYRaYLVlWG2Mea3ItsWufg5deccpwNPYCXOB0s4byvHz+qUAsUJwHpjTE6R6quBKMdxy1NPqYCifTSV8m/TgAeBl0XkNqz/JH8Glhljsov8pzUbaIzVN/IFYCU4W3O+x3ps+ATWY+BLHfXCsPob5msBPAk8hdWitdJxrDEU72/pad8BvwMPAYOAh7Fa3s7C6iMZBIwFZonI6cYYm4hEY7WwNcdq0dsLXOyofxpWMuKSMeYwVn9RJ0dyehfWI+w/HMVNgf0uDpGfjLQoz/HKcd6SNAWOO/7QKDGeIsePxkpk/gN0xOoj6zZHa1s7rD6F/YHHjTG5BeKBIt8jY0y2iBxzFU8R7xpj3nCcp7mbIT2M1X3hKRfbngfer8A5+uX/eyrlb5QkrO/d9gJlTbF+BosqeD/WlKOeUgFFE02l/Jgx5i8RGQR8CHR2vB4HTorIN8BTxpgtjrrrRGQZVgL5kzFmkeMwD2AlYP0KJKbvOupeR+FEMxLrUeSU/AIRqYWVaC4zxkzDe5YbY250nHMG1n/q/YGzjDF/OcpjsJLN1liPOEdjJZQJxpj1Ba7tBeA/IjLJGLO2HDE8jtXy9JEx5oCjrJYjlqIyHV+jy3m8ytTLj6dokllWPP8F8keBz8L6o6Q87gFecbxfQuHBMrUcX0uKqbTvDy5a+EolIpFYfYAXFLjnBY/3U0XO4WadkxToiuDg7v2oyH1TqtrTR+dK+TlHX76WWH0Fp2K1gEQDQ4C1ItKnjENcBXQv+B+piNQD0oAYF/V/8ETcFeB8pGmMSQWOAFvyk0yHnY6vjR1frwM2AAdFpH7+C6uvHZTRP7AgEbkHeAarL1/Bkd9BWIM4SpJXzuNVqJ4b8eSXuYrna2Ag8D+skeMLHaPf3bXEsf8YrFbmFY7vc348Bc9flMvvTyVch9X/9C0PH7eiBPd+Ptytp1RA0RZNpaoBx2CHmY4Xjrn3HsZKNidSSv8uY4xdRNqIyLNYLaJtOfW405Ujnoq7nA4X+WyjeCz5g03yk5u2WK2wrlocoezHtgCIyGjgJaxRzP9wJLr5TjjOUVRkge3lOZ5b9USkUZHqdmNMUinxRJUUjzHmF8fbOY6+uxOxpsD6UKw5V4OL1D9U5PMfBfZfjTXl1r+xumLkny8Sq+9mQZGu4qmkqx3H9NUfREWl497Ph7v1lAoommgq5acc/erGAKuMMYUedRpjVgM3OQbzXC4i9YwxR0s4zp1YicVmrD6WX2L1A/wXVrJRSJGRw1XJ1SCYsgYfBWP163y6hO1lPYZGRJ7H+j5vBy42xuwpUmUP0MPFrk0cXwv1TXTjeO7WKzogZTfQyhFPXREJN475U0uLx4WZWD8P3bC6ZKzEajEvFF5JOxtjfhCRVMf+OOIBq5V5q/MAVotpXTficZtY88peCnxX5Np9aQ+nWtgLKno/3K2nVEDRRFMp/5WF1Wq5lJL71P2FNdl3pquNjkEcr2KNHL/UGGMrsO1Zj0brG7uAWsaYgpPFIyJ1gEsokPi4IiLjsJK9v7H6sLoabbwauEZEGhVp6evq+LqynMdzt17/Ip/z7/FqrETwbAoPHCoUj4i8BVwOnOaY/ihffp/K/OPdjIuWNhF5FGsgWkdjzPEC5SGO+gXjASvxLPj9Lvb98YDOQCzWgDh/sRr4PxEJKfjvC+v6s7C6dpSnnlIBRftoKuWnHC2LM4C+IjK06HYRqQtcjzUoIn+QQdFHy5FYj1S3FEkyz8ZaVSU/cShN0WP6k2+ALiJyZZHyx7FWzTmjpB1F5BKsltCtwEUlJYWcSvJHFdg3Evgn8LsxZn95juduPWPMgiKvJY5N32ElJs4lHsVaBegerFbP5Y7i3VhdCwYXOfRDjq/fOs6zpOi5HNu3Y82VWXRqrX9hzVbwrePzEuAQcI8jjnyjgJMF6nnC2Y6v/jQ6+0usvs7D8wsc3REGA7MK/Ltzt55SAUVbNJXybw8CPYGpjmTzB6yVS9ph/YcVRuEpfPL7Kt7taIH7TET+AP4pImlYj8/PwFq2L7+VqxZwnJLlH3OoYxqej/3oP8X/Yg0OmS0iE7FaeM/HmoLme8erJC9gtQzOBS51MaXNUmPMDmPMBse8iWMcLaVrsZLMlhRIGtw9XjnquWSMOSoi/wOeciR2vzi+BxcAgwt0fZgA3IrVD7M71hKU/8Ba/eYDY8yvJZ3D4UusJSWfE5FWWMndOVhLUv6IY/S1ow/wY1jLk84TkVnARVirWj1cUv/UCmrn+OqyOwKAiPQHGnpjhgRHd5Zrge3GmGVgDdYTkYXA2yLSBusR+Cise/xM/r7u1lMq4Bg/WJ5IX/rSV8kvrBHmj2M9gjwOZGONvp4ANC5SNxSrFTQDa0LtCKw5Jr8AkrEGHGzAmiJoEFYfyOsc+06hhGX3sFZTSXPs39aNmIdT8jJ/+dv6uvpcoN4uYFFp+zrKGmKtr30Aq6VvC9Z/3FGlxBeJlWibUl7DC9QPw0pqD2C10i3Dao0s1/HKe95S4hesbhU7sR5hrwWud1EvHmsFoyNADtaj+n/jWD7SzZ+9V7GSohysVs6ngDAXdW91HD8L6w+au905R5Fj9C3te4A1rZIBwks5xqKSfo7dOUdp/xaw+sgaYEqR8lislZqSsf6dLADOdrG/W/X0pa9AeuWvVauUUkoppZRH+WOfK6WUUkopFQA00VRKKaWUUl6hiaZSSimllPIKTTSVUkoppZRXaKKplFJKKaW8QhNNpZRSSinlFZpoKqWUUkopr9BEUymllFJKeYUmmkoppZRSyis00VRKKaWUUl7x/8wSqgfyi8oFAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# And then plot it all\n",
    "tfl2=a.Time(shrt_res2[0][\"event_starttime\"],shrt_res2[0][\"event_endtime\"])\n",
    "\n",
    "plt.rcParams.update({'font.size': 18,'font.family':\"sans-serif\",\\\n",
    "                         'font.sans-serif':\"Arial\",'mathtext.default':\"regular\"})\n",
    "\n",
    "fig,ax = plt.subplots(figsize=(10, 6))\n",
    "plt.plot(tg2_tims,tg2_x05,drawstyle='steps-post',marker=None,color='darkblue',lw=2,label='$0.5-4\\;\\AA$')\n",
    "plt.plot(tg2_tims,tg2_x18,drawstyle='steps-post',marker=None,color='firebrick',lw=2,label='$1-8\\;\\AA$')\n",
    "\n",
    "ax.set_ylabel(\"GOES16 XRS [$\\mathrm{W\\;m^{-2}}$] \")\n",
    "ax.set_xlabel(\"Start Time \"+tflrange2.start.iso[:-4])\n",
    "ax.set_yscale(\"log\")\n",
    "ax.set_ylim([5e-8,5e-3])\n",
    "ax.set_xlim([tflrange2.start.datetime,tflrange2.end.datetime])\n",
    "myFmt = matplotlib.dates.DateFormatter('%H:%M')\n",
    "majorx= matplotlib.dates.MinuteLocator(interval=5)\n",
    "minorx= matplotlib.dates.MinuteLocator(interval=1)\n",
    "ax.xaxis.set_major_locator(majorx)\n",
    "ax.xaxis.set_minor_locator(minorx)\n",
    "ax.xaxis.set_major_formatter(myFmt)\n",
    "\n",
    "ax.axvline(parse_time(shrt_res2[0][\"event_peaktime\"]).datetime,color='black',ls='-.')\n",
    "ax.axvspan(tfl2.start.datetime,tfl2.end.datetime,color='black',alpha=0.2, label='Flare '+new2_fl_goescls)\n",
    "\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "40d3a090f54c6569ab1632332b64b2c03c39dcf918b08424e98f38b5ae0af88f"
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}