{ "cells": [ { "cell_type": "markdown", "id": "42ff7820-3cca-400d-a527-34935f87bb19", "metadata": {}, "source": [ "# Example Reduction of HI Survey\n", "\n", "This notebook goes through the data reduction of position switched observations of the 21 cm HI line. The data used for this tutorial is part of the [New Reference Catalog of Extragalactic HI Observations\n", "](https://greenbankobservatory.org/~koneil/HIsurvey/index.shtml) by [Karen O'Neil](https://greenbankobservatory.org/~koneil/). For more details about how the observations were set up, please refer to the [GBTdocs HI Position Switched (psw) Spectrum tutorial](https://gbtdocs.readthedocs.io/en/latest/tutorials/hi_psw_tutorial.html#) or the [survey article](https://greenbankobservatory.org/~koneil/paps/HIsurvey.html).\n", "\n", "Some basic information about the observations. The observations use position switching, and in some cases, the data is recorded without firing a noise diode, so that it is not possible to derive a system temperature from those records alone. In those cases, we will use observations close in time when the noise diodes where fired.\n", "\n", "## Dysh commands\n", "\n", "The following dysh commands are introduced (leaving out all the function arguments):\n", "\n", " filename = dysh_data()\n", " sdf = GBTFITSLoad()\n", " sdf.index()\n", " tcal = sdf.gettcal()\n", " tcal.get_tcal()\n", " \n", " sb = sdf.getps()\n", " ta = sb.timeaverage()\n", " ta1 = ta.align_to()\n", " ta.average()\n", " p1 = ta.plot()\n", " p1.oshow(ta1)\n", "\n", "\n", "\n", "## Loading Modules\n", "We start by loading the modules we will use for the data reduction. " ] }, { "cell_type": "code", "execution_count": 1, "id": "785f8c3d-9683-4e6f-bca2-abb8e05b3789", "metadata": {}, "outputs": [], "source": [ "# These modules are required for the data reduction.\n", "from dysh.fits.gbtfitsload import GBTFITSLoad\n", "from dysh.log import init_logging\n", "from astropy import units as u\n", "import numpy as np\n", "\n", "# This module is used for custom plotting.\n", "import matplotlib.pyplot as plt\n", "\n", "# We will use this module to compare with published results.\n", "import pandas as pd\n", "\n", "# These modules are used for file I/O\n", "from dysh.util.files import dysh_data\n", "from dysh.util.download import from_url\n", "from pathlib import Path" ] }, { "cell_type": "markdown", "id": "864232b6-f214-4503-bd2e-b85c1e53ca87", "metadata": {}, "source": [ "## Setup\n", "We start the dysh logging, so we get more information about what is happening.\n", "This is only needed if working on a notebook.\n", "If using the CLI through the dysh command, then logging is setup for you." ] }, { "cell_type": "code", "execution_count": 2, "id": "5e920372-398b-4e52-986b-bb460642a8de", "metadata": {}, "outputs": [], "source": [ "init_logging(2)\n", "\n", "# also create a local \"output\" directory where temporary notebook files can be stored.\n", "output_dir = Path.cwd() / \"output\"\n", "output_dir.mkdir(exist_ok=True)" ] }, { "cell_type": "markdown", "id": "6445cf12-0cc9-40e4-b413-a11f31b5a54e", "metadata": {}, "source": [ "## Data Retrieval\n", "\n", "Download the example SDFITS data, if necessary." ] }, { "cell_type": "code", "execution_count": 3, "id": "4503f8b4-3afa-4ad5-9ba1-0a34067fd846", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "07:08:40.110 I Resolving example=survey -> hi-survey/data/AGBT04A_008_02.raw.acs/AGBT04A_008_02.raw.acs.fits\n", "07:08:40.112 I url: http://www.gb.nrao.edu/dysh//example_data/hi-survey/data/AGBT04A_008_02.raw.acs/AGBT04A_008_02.raw.acs.fits\n", "07:08:40.210 I Starting download...\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "7263050a75514e47bdc5f454559dae50", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Output()" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Downloading AGBT04A_008_02.raw.acs.fits from http://www.gb.nrao.edu/dysh//example_data/hi-survey/data/AGBT04A_008_02.raw.acs/AGBT04A_008_02.raw.acs.fits\n" ] }, { "data": { "text/html": [ "
\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "07:08:47.527 I Saved AGBT04A_008_02.raw.acs.fits to AGBT04A_008_02.raw.acs.fits\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Retrieved AGBT04A_008_02.raw.acs.fits\n"
     ]
    }
   ],
   "source": [
    "filename = dysh_data(example=\"survey\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f194d168-1808-4098-9818-3ea164bb0597",
   "metadata": {},
   "source": [
    "## Data Loading\n",
    "\n",
    "Next, we use \n",
    "[GBTFITSLoad](https://dysh.readthedocs.io/en/latest/reference/modules/dysh.fits.html#dysh.fits.GBTFITSLoad)\n",
    "to load the data, and then its \n",
    "[summary](https://dysh.readthedocs.io/en/latest/reference/modules/dysh.fits.html#dysh.fits.GBTFITSLoad.summary)\n",
    "method to inspect its contents."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "0aba826a-c54c-4f0d-ac31-a7c91fb78145",
   "metadata": {},
   "outputs": [],
   "source": [
    "sdfits = GBTFITSLoad(filename)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ff6d5684-85a3-4d50-b716-2235393964e6",
   "metadata": {},
   "outputs": [
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SCANOBJECTVELOCITYPROCPROCSEQNRESTFREQ# IF# POL# INT# FEEDAZIMUTHELEVATION
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2223C2860.0OffOn11.4000001261193.833181.8413
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2273C2860.0OffOn21.4000001261205.947181.2609
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231B1345+1250.0OffOn21.40000012181195.679463.3244
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245B1345+1250.0OffOn21.40000012181203.531162.0587
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247B1345+1250.0OffOn21.40000012181206.976361.4655
248B1345+1250.0OffOn11.37000012181208.040859.6983
249B1345+1250.0OffOn21.37000012181210.721560.7060
250B1345+1250.0Track11.3700001231215.243059.6119
251B1345+1250.0Track11.3700001231215.969259.4175
263U8091213.0OffOn11.42040512301241.133950.6393
264U8091213.0OffOn21.42040512301240.979550.7395
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280U99654524.0Track11.4204051231222.926267.3657
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283U10351891.0Track11.4204051231227.903276.2644
284U90074618.0OffOn11.42040512301248.663238.3497
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286U90074618.0Track11.4204051231250.557436.6787
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291U98035257.0Track11.4204051231266.522156.2884
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294U10351891.0Track11.4204051231255.376464.9184
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298U106292980.0OffOn21.42040512301238.044165.2006
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314U115784601.0Track11.4204051231160.446959.4464
315U115784601.0Track11.4204051231161.004059.5231
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323U119923592.0OffOn21.42040512301125.456554.8251
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      ],
      "text/plain": [
       ""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sdfits.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "265eb1d8-d2dd-4a0b-8e4b-9c638b682fbd",
   "metadata": {},
   "source": [
    "There is a total of 81 scans in this data set, all used a single spectral window (IF), two polarizations (PLNUM), and a single feed (FDNUM).\n",
    "\n",
    "## Data Reduction\n",
    "\n",
    "### Noise Diode Temperature\n",
    "\n",
    "The first step in calibrating the data is to determine the temperature of the noise diode(s).\n",
    "It is a known issue with GBT observations that the values provided are only accurate to $\\sim20\\%$ (see e.g., \n",
    "[Goddy et al 2020](https://ui.adsabs.harvard.edu/abs/2020RNAAS...4....3G/abstract)).\n",
    "The data we are working with contains eight scans of 3C286, a known calibrator source (see e.g., \n",
    "[Perley & Butler 2017](https://ui.adsabs.harvard.edu/abs/2017ApJS..230....7P/abstract)).\n",
    "We use the last two (scans 226 and 227), as the others seem to have problems, to derive the temperature of the noise diode(s).\n",
    "\n",
    "#### Computing the Noise Diode Temperature\n",
    "\n",
    "To derive the temperature of the noise diode(s) we use the \n",
    "[gettcal](https://dysh.readthedocs.io/en/latest/reference/modules/dysh.fits.html#dysh.fits.GBTFITSLoad.gettcal)\n",
    "method.\n",
    "This method is described in more detail in another tutorial.\n",
    "`gettcal` requires a `zenith_opacity` as input.\n",
    "Since this an L-band observation, we use a value of 0.08.\n",
    "The exact value does not have a significant impact on the results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "94c5af71-157b-41bc-88db-bc16740ef84f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "07:08:50.954 I Using Perley-Butler 2017 for 3C286.\n",
      "07:08:50.958 I Will use getps to calibrate the data.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "we used fits returning\n"
     ]
    }
   ],
   "source": [
    "tcal = sdfits.gettcal(scan=226, ifnum=0, plnum=0, fdnum=0, zenith_opacity=0.08)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cd4cf156-1805-41ef-8bf1-a844ed3225a1",
   "metadata": {},
   "source": [
    "The return from `gettcal` is a \n",
    "[TCal](https://dysh.readthedocs.io/en/latest/reference/modules/dysh.spectra.html#dysh.spectra.tcal.TCal)\n",
    "object, which is a child of \n",
    "[Spectrum](https://dysh.readthedocs.io/en/latest/reference/modules/dysh.spectra.html#dysh.spectra.spectrum.Spectrum)\n",
    ", so it has the same methods and properties, plus a `name` and `snu` (the flux density of the calibrator) properties.\n",
    "To get the mean value of the noise diode temperature over the inner $80\\%$ of the spectral window we use the \n",
    "[TCal.get_tcal](https://dysh.readthedocs.io/en/latest/reference/modules/dysh.spectra.html#dysh.spectra.tcal.TCal.get_tcal)\n",
    "method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "2ca32756-7df0-401d-b757-7d33d016a08e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "18.58283\n"
     ]
    }
   ],
   "source": [
    "tcal_0 = tcal.get_tcal()\n",
    "print(tcal_0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6d503b2e-7b8a-4e28-8742-1a5d7aabaa29",
   "metadata": {},
   "source": [
    "Thus the temperature of the noise diode for polarization 0 is $20.49$ K.\n",
    "We do the same for the second polarization."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "3fd63caf-d6bb-4b4a-8934-0663fbb6517a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "07:08:52.550 I Using Perley-Butler 2017 for 3C286.\n",
      "07:08:52.554 I Will use getps to calibrate the data.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "we used fits returning\n",
      "21.63284\n"
     ]
    }
   ],
   "source": [
    "tcal = sdfits.gettcal(scan=226, ifnum=0, plnum=1, fdnum=0, zenith_opacity=0.08)\n",
    "tcal_1 = tcal.get_tcal()\n",
    "print(tcal_1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fcd6294a-2210-4a2b-b3fe-501752703cdf",
   "metadata": {},
   "source": [
    "#### Updating the Noise Diode Temperature\n",
    "It is possible to provide the value of the noise diode temperature using the `t_cal` argument to the calibration methods.\n",
    "An alternative is to update the metadata of the `GBTFITSLoad` object with the new values.\n",
    "Here we use the second approach.\n",
    "First we need to determine where the derived noise temperatures are applicable.\n",
    "This is important as there are two choices for the noise diode temperature, high or low.\n",
    "We start by determining what noise diode was use for scan 226, the one we used to derive the noise diode temperature.\n",
    "We leverage the summary, and its `add_columns` and `scan` arguments.\n",
    "The type of noise diode used is stored in the \"CALTYPE\" column of an SDFITS.\n",
    "We also show the noise diode temperature stored in the metadata, in the \"TCAL\" column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "5bf5094d-899d-42d7-bead-c6b2fcd0277c",
   "metadata": {},
   "outputs": [
    {
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SCANOBJECTVELOCITYPROCPROCSEQNRESTFREQ# IF# POL# INT# FEEDAZIMUTHELEVATIONCALTYPETCAL
2263C2860.0OffOn11.41261200.833380.0265HIGH21.317758
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    "sdfits.summary(scan=226, add_columns=\"CALTYPE, TCAL\")"
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    "So, for scan 226 the \"high\" noise diode was used, and its value is not that far from what we found earlier.\n",
    "Where else was it used?"
   ]
  },
  {
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       "
SCANOBJECTVELOCITYPROCPROCSEQNRESTFREQ# IF# POL# INT# FEEDAZIMUTHELEVATIONCALTYPE
2203C2860.0OffOn11.4000001261185.280682.0246HIGH
2213C2860.0OffOn21.4000001261187.213681.9980HIGH
2223C2860.0OffOn11.4000001261193.833181.8413HIGH
2233C2860.0OffOn21.4000001261195.676681.7788HIGH
2243C2860.0OffOn11.4000001261195.518280.2910HIGH
2253C2860.0OffOn21.4000001251199.935881.6005HIGH
2263C2860.0OffOn11.4000001261200.833380.0265HIGH
2273C2860.0OffOn21.4000001261205.947181.2609HIGH
228B1328+2540.0OffOn11.4000001261207.525773.9844HIGH
229B1328+2540.0OffOn21.4000001261210.960075.1584HIGH
230B1345+1250.0OffOn11.40000012181193.273862.0703HIGH
231B1345+1250.0OffOn21.40000012181195.679463.3244HIGH
244B1345+1250.0OffOn11.40000012181200.954360.9284HIGH
245B1345+1250.0OffOn21.40000012181203.531162.0587HIGH
246B1345+1250.0OffOn11.40000012181204.340860.3930HIGH
247B1345+1250.0OffOn21.40000012181206.976361.4655HIGH
248B1345+1250.0OffOn11.37000012181208.040859.6983HIGH
249B1345+1250.0OffOn21.37000012181210.721560.7060HIGH
250B1345+1250.0Track11.3700001231215.243059.6119HIGH
251B1345+1250.0Track11.3700001231215.969259.4175LOW
263U8091213.0OffOn11.42040512301241.133950.6393HIGH
264U8091213.0OffOn21.42040512301240.979550.7395HIGH
265U8091213.0Track11.4204051231243.480749.8234HIGH
266U82492541.0OffOn11.42040512101241.706550.2801HIGH
267U82492541.0OffOn11.42040512301242.689249.6343HIGH
268U82492541.0OffOn21.42040512301242.545149.7333HIGH
269U82492541.0Track11.4204051231244.943548.0787HIGH
270U85034676.0OffOn11.42040512301270.174160.2485HIGH
271U85034676.0OffOn21.42040512301269.939660.5449HIGH
272U8091213.0OffOn11.42040512301253.578041.4420HIGH
273U8091213.0OffOn21.42040512301253.458641.5517HIGH
274U8091213.0Track11.4204051231255.579339.5541HIGH
275U82492541.0OffOn11.42040512301266.546946.8377HIGH
276U82492541.0OffOn21.42040512301266.373847.0388HIGH
277U82492541.0Track11.4204051231267.942645.1863HIGH
278U99654524.0OffOn11.42040512301221.558267.7182HIGH
279U99654524.0OffOn21.42040512301221.207067.8070HIGH
280U99654524.0Track11.4204051231222.926267.3657HIGH
281U10351891.0OffOn11.42040512301221.295577.4668HIGH
282U10351891.0OffOn21.42040512301220.492477.5922HIGH
283U10351891.0Track11.4204051231227.903276.2644HIGH
284U90074618.0OffOn11.42040512301248.663238.3497HIGH
285U90074618.0OffOn21.42040512301248.554938.4433HIGH
286U90074618.0Track11.4204051231250.557436.6787HIGH
287U90075257.0OffOn11.42040512301234.295648.0209HIGH
288U90075257.0OffOn21.42040512301234.166948.0937HIGH
289U98035257.0OffOn11.42040512301264.961057.9997HIGH
290U98035257.0OffOn21.42040512301264.727658.2483HIGH
291U98035257.0Track11.4204051231266.522156.2884HIGH
292U10351891.0OffOn11.42040512301252.164467.0847HIGH
293U10351891.0OffOn21.42040512301251.813467.3038HIGH
294U10351891.0Track11.4204051231255.376464.9184HIGH
295U106292980.0OffOn11.42040512301233.947266.8551HIGH
296U106292980.0OffOn21.42040512301233.599166.9846HIGH
297U106292980.0OffOn11.42040512301238.358465.0614HIGH
298U106292980.0OffOn21.42040512301238.044165.2006HIGH
299U106292980.0Track11.4204051231241.440563.6131HIGH
300U110174644.0OffOn11.42040512301235.460576.2074HIGH
301U110174644.0OffOn21.42040512301234.772676.3879HIGH
302U110174644.0OffOn11.42040512301241.519774.3514HIGH
303U110174644.0OffOn21.42040512301240.959074.5471HIGH
304U110174644.0Track11.4204051231242.664173.9425HIGH
305U110174644.0Track11.4204051211242.935973.8408HIGH
306U110174644.0Track11.4204051231246.051172.5738HIGH
307U114613122.0OffOn11.42040512301168.053159.8975HIGH
308U114613122.0OffOn21.42040512301167.889459.8843HIGH
309U114613122.0OffOn11.42040512301173.469860.2443HIGH
310U114613122.0OffOn21.42040512301173.311160.2376HIGH
311U114613122.0Track11.4204051231178.120760.3802HIGH
312U115784601.0OffOn11.42040512301156.357358.8036HIGH
313U115784601.0OffOn21.42040512301156.179658.7731HIGH
314U115784601.0Track11.4204051231160.446959.4464HIGH
315U115784601.0Track11.4204051231161.004059.5231HIGH
316U116274864.0OffOn11.42040512301157.806555.3372HIGH
317U116274864.0OffOn21.42040512301157.654955.3107HIGH
318U116274864.0OffOn11.42040512301162.462556.0655HIGH
319U116274864.0OffOn21.42040512301162.319956.0465HIGH
320U116274864.0Track11.4204051231166.798356.5802HIGH
321U119923592.0Track11.4204051231124.476254.3242HIGH
322U119923592.0OffOn11.42040512301125.632054.9128HIGH
323U119923592.0OffOn21.42040512301125.456554.8251HIGH
"
      ],
      "text/plain": [
       ""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sdfits.summary(add_columns=\"CALTYPE\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0737cec7-0349-44b5-a100-b8a4087187cc",
   "metadata": {},
   "source": [
    "All, but a single scan (which we won't use in this tutorial), used the \"high\" noise diode.\n",
    "\n",
    "\n",
    "Now we update all of the rows, with the corresponding noise diode temperature.\n",
    "We have to update both polarizations separately.\n",
    "In this example we only have one spectral window and one beam, but if we had more, we would also need to separate those cases, as each beam can have a different noise diode and the temperature of the noise diode is a function of frequency.\n",
    "\n",
    "To access the metadata, we use the `index` property of the `GBTFITSLoad` object, which is a `pandas.DataFrame` object.\n",
    "Then we select only those rows where \"PLNUM\" is equal to the value we are interested in (e.g., `sdfits[\"PLNUM\"] == 0`)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "6eb7882e-5e36-429b-a90c-2b2e316fafce",
   "metadata": {},
   "outputs": [],
   "source": [
    "sdfits.index().loc[sdfits[\"PLNUM\"] == 0, \"TCAL\"] = tcal_0\n",
    "sdfits.index().loc[sdfits[\"PLNUM\"] == 1, \"TCAL\"] = tcal_1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0a7894e0-6336-4a2c-ad34-10f581091df0",
   "metadata": {},
   "source": [
    "Now we show how to double check that the values where updated:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "fd4e3223-d720-4b92-9834-a3636340fb22",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(142    18.58283\n",
       " 143    18.58283\n",
       " 146    18.58283\n",
       " 147    18.58283\n",
       " 150    18.58283\n",
       " 151    18.58283\n",
       " 154    18.58283\n",
       " 155    18.58283\n",
       " 158    18.58283\n",
       " 159    18.58283\n",
       " 162    18.58283\n",
       " 163    18.58283\n",
       " Name: TCAL, dtype: float32,\n",
       " np.float32(18.58283))"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sdfits[\"TCAL\"][ (sdfits[\"SCAN\"] == 226) & (sdfits[\"PLNUM\"] == 0) ], tcal_0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "e4819719-f01b-4a05-a774-afd3cc3fbbbc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(140    21.632839\n",
       " 141    21.632839\n",
       " 144    21.632839\n",
       " 145    21.632839\n",
       " 148    21.632839\n",
       " 149    21.632839\n",
       " 152    21.632839\n",
       " 153    21.632839\n",
       " 156    21.632839\n",
       " 157    21.632839\n",
       " 160    21.632839\n",
       " 161    21.632839\n",
       " Name: TCAL, dtype: float32,\n",
       " np.float32(21.63284))"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sdfits[\"TCAL\"][ (sdfits[\"SCAN\"] == 226) & (sdfits[\"PLNUM\"] == 1) ], tcal_1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "db8aab5b-1e49-4bf0-9580-6279e726332c",
   "metadata": {},
   "source": [
    "The above shows that the values were successfully updated.\n",
    "\n",
    "Now we can proceed calibrating the data."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "556dc5ee-a48b-4376-b270-1587596f4b26",
   "metadata": {},
   "source": [
    "### Single On/Off Pair\n",
    "We will start by reducing data for a single pair of position switched scans, which used the noise diodes. We will use scan 270. First, we calibrate the data for a single polarization, `plnum=0`. We use the \n",
    "[getps](https://dysh.readthedocs.io/en/latest/reference/modules/dysh.fits.html#dysh.fits.GBTFITSLoad.getps)\n",
    "method of `GBTFITSLoad`, which returns a \n",
    "[ScanBlock](https://dysh.readthedocs.io/en/latest/reference/modules/dysh.spectra.html#dysh.spectra.scan.ScanBlock).\n",
    "Since we are calibrating a single pair of position switched scans, the use of a `ScanBlock` won't be evident, but we will see it when we calibrate multiple pairs of observations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "2016ed5f-7354-4331-b72b-d3524ce2348a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "we used fits returning\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "ScanBlock([])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pssb0 = sdfits.getps(scan=270, plnum=0, ifnum=0, fdnum=0)\n",
    "pssb0"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "91b241e7-35dc-409c-bb19-25cc4e20bd8a",
   "metadata": {},
   "source": [
    "The return is a `ScanBlock` with a single \n",
    "[PSScan](https://dysh.readthedocs.io/en/latest/reference/modules/dysh.spectra.html#dysh.spectra.scan.PSScan)\n",
    "in it. We can extract information from the observations by querying the different attributes of the `PSScan`, like the system temperature in K (`tsys`), or exposure time in seconds (`exposure`)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "17a6d503-f6c6-4004-90c9-f35e0c7176bb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([23.15395387, 23.15939981, 23.1696684 , 23.14617098, 23.16118822,\n",
       "       23.16688336, 23.15221981, 23.14632482, 23.13719172, 23.1660217 ,\n",
       "       23.16490218, 23.16024192, 23.15046162, 23.1557475 , 23.15496778,\n",
       "       23.15356847, 23.15246949, 23.15252061, 23.18794126, 23.16724025,\n",
       "       23.16009429, 23.14933254, 23.15150266, 23.15174903, 23.15595792,\n",
       "       23.15470471, 23.15362478, 23.18724805, 23.15418276, 23.15090147])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pssb0[0].tsys"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "00bbcde5-b2cb-485a-baf8-a1153c7c65a2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([4.77948809, 4.77948809, 4.77948809, 4.77948809, 4.77948809,\n",
       "       4.77948809, 4.77948809, 4.77948809, 4.77948809, 4.77948797,\n",
       "       4.77948809, 4.77948809, 4.77948809, 4.77948809, 4.77948809,\n",
       "       4.77948809, 4.77948809, 4.77948809, 4.77948785, 4.77948809,\n",
       "       4.77948809, 4.77948809, 4.77948809, 4.77948809, 4.77948809,\n",
       "       4.77948809, 4.77948809, 4.77948797, 4.77948797, 4.77948809])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pssb0[0].exposure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "02bac8b0-f9f5-4545-9f0e-fb6d30a93f34",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "271"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pssb0[0].scan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "58b299d8-e168-487e-84c9-d560fd48af19",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pssb0[0].plnum"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "a6c34111-1452-47da-bc37-048d7ed7942c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pssb0[0].ifnum"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0ff582ac-53a9-49f4-bec4-2391eb5e36b1",
   "metadata": {},
   "source": [
    "Notice that the `PSScan` says it has a scan number (`scan`) of 271. This is because `dysh` can tell that the on-source observation has a scan number of 271, and the off-source observation is in scan 270.\n",
    "\n",
    "#### Inspecting Individual Integrations\n",
    "\n",
    "If we want to have a look at the calibrated data, integration by integration, we can use the `_calibrated` attribute of the `PSScan`. This returns an array with rows corresponding to the integrations, and columns to the channel number."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "c35d18de-0b5c-40bf-81a9-1663d6bf96bd",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "masked_array(\n",
       "  data=[[-42.95573792102767, -14.786980497120691, 50.680546568201024,\n",
       "         ..., -0.04795008242404048, 0.05551870961717607,\n",
       "         0.46735376850896293],\n",
       "        [20.79514890484046, -28.012823791386825, -0.16838359164340005,\n",
       "         ..., 0.24525655427112789, 0.5678707470489164,\n",
       "         1.4632634154078992],\n",
       "        [0.9344087650459408, -17.416289751211536, 5.172508938594352, ...,\n",
       "         0.16856129404589837, 0.8176920712587327, 0.30326067864391515],\n",
       "        ...,\n",
       "        [40.22337015566295, -24.03132518007429, -19.72912609017763, ...,\n",
       "         -0.755331043339712, -0.25957163363582336, -1.8308870748472765],\n",
       "        [-126.5365640563671, 39.916026838756395, -42.40570781831775, ...,\n",
       "         0.021537792127780498, 0.5424438514917936, 0.39256936524051056],\n",
       "        [-11.247179829074131, 41.47191291388775, -15.084885206981554,\n",
       "         ..., -0.7265820800954403, -0.4581267483859805,\n",
       "         0.16670335471984124]],\n",
       "  mask=[[False, False, False, ..., False, False, False],\n",
       "        [False, False, False, ..., False, False, False],\n",
       "        [False, False, False, ..., False, False, False],\n",
       "        ...,\n",
       "        [False, False, False, ..., False, False, False],\n",
       "        [False, False, False, ..., False, False, False],\n",
       "        [False, False, False, ..., False, False, False]],\n",
       "  fill_value=1e+20)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pssb0[0].calibrated"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "94146103-75d0-40ff-9a86-dc0a1ef50c09",
   "metadata": {},
   "source": [
    "We can also retrieve the calibrated integrations as \n",
    "[Spectrum](https://dysh.readthedocs.io/en/latest/reference/modules/dysh.spectra.html#dysh.spectra.spectrum.Spectrum)\n",
    "objects using the `getspec` method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "de5f72b3-c427-4dd9-b34f-1b0b0016d58f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "    target: \n",
       "    observer to target (computed from above):\n",
       "      radial_velocity=4686.132814909312 km / s\n",
       "      redshift=0.0157553569969906\n",
       "    doppler_rest=1420405400.0 Hz\n",
       "    doppler_convention=optical)\n",
       "  [1.39229407e+09 1.39229445e+09 1.39229483e+09 ... 1.40479293e+09\n",
       " 1.40479331e+09 1.40479369e+09] Hz> (length=32768))>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pssb0_int0 = pssb0[0].getspec(0)\n",
    "pssb0_int0"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "55f8dc01-d3b5-4d1c-81b0-4b9c1d17a5c5",
   "metadata": {},
   "source": [
    "[Spectrum](https://dysh.readthedocs.io/en/latest/reference/modules/dysh.spectra.html#dysh.spectra.spectrum.Spectrum)\n",
    "objects have a variety of methods, such as \n",
    "[plot](https://dysh.readthedocs.io/en/latest/reference/modules/dysh.spectra.html#dysh.spectra.spectrum.Spectrum.plot),\n",
    "[smooth](https://dysh.readthedocs.io/en/latest/reference/modules/dysh.spectra.html#dysh.spectra.spectrum.Spectrum.smooth),\n",
    "and \n",
    "[baseline](https://dysh.readthedocs.io/en/latest/reference/modules/dysh.spectra.html#dysh.spectra.spectrum.Spectrum.baseline).\n",
    "\n",
    "Here we use `plot` to look a the data, with all default axes and units."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "d43d7906-5821-45a9-8e8d-54727c3a06ef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "32c755ef757648859477e4b7e604bfd4",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox(children=(HBox(children=(Button(description='Clear All Regions', style=ButtonStyle(), tooltip='Clear all …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pssb0_int0.plot();"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "07a60822-e683-4166-9f65-9ea22eafe92b",
   "metadata": {},
   "source": [
    "The y-axis can be adjusted during the call to `plot`, through the `ymin` and `ymax` arguments.\n",
    "\n",
    "You could also trim the edges off the spectrum, and rely on auto-scale.  `pssb0_int0[1000:-1000].plot()`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "662a4ad0-fceb-4d8a-9d85-5519e65906cf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "cd3fdcec51584dd38a612187f2188390",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox(children=(HBox(children=(Button(description='Clear All Regions', style=ButtonStyle(), tooltip='Clear all …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pssb0_int0.plot(ymin=-5, ymax=5, xaxis_unit = \"MHz\");"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab1c334b-6d47-472b-bc49-859784436711",
   "metadata": {},
   "source": [
    "Since this is a single integration, there's not much to see. Let's work on a time average now.\n",
    "\n",
    "#### Time Averaging Integrations\n",
    "\n",
    "Time averaging can be done using the \n",
    "[timeaverage](https://dysh.readthedocs.io/en/latest/reference/modules/dysh.spectra.html#dysh.spectra.scan.ScanBlock.timeaverage)\n",
    "method of a `Scan` or `ScanBlock`. By default time averaging uses the following weights: \n",
    "$$\n",
    "\\frac{T^{2}_{sys}}{\\Delta\\nu\\Delta t}\n",
    "$$\n",
    "with $T_{sys}$ the system temperature, $\\Delta\\nu$ the channel width and $\\Delta t$ the integration time. In `dysh` these are set using `weights='tsys'` (the default).\n",
    "\n",
    "`timeaverage` will return a `Spectrum` object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "3978889d-cb8b-4fb6-ab86-ed0cfff863bf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "    target: \n",
       "    observer to target (computed from above):\n",
       "      radial_velocity=4686.132814909312 km / s\n",
       "      redshift=0.0157553569969906\n",
       "    doppler_rest=1420405400.0 Hz\n",
       "    doppler_convention=optical)\n",
       "  [1.39229407e+09 1.39229445e+09 1.39229483e+09 ... 1.40479293e+09\n",
       " 1.40479331e+09 1.40479369e+09] Hz> (length=32768))>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ps0_spec = pssb0.timeaverage()\n",
    "ps0_spec"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "0c9965ac-ce3d-4d16-b8f2-8b4651d4b608",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6f183155662f45c79fcabff4016bfe91",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox(children=(HBox(children=(Button(description='Clear All Regions', style=ButtonStyle(), tooltip='Clear all …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ps0_spec.plot(ymin=-5, ymax=5, xaxis_unit = \"MHz\");"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "29b9ab8b-c9b1-4e5f-b8c9-a85cd9f63701",
   "metadata": {},
   "source": [
    "The noise is lower, and there are hints of a signal. Let's smooth the data to further reduce the noise. \n",
    "\n",
    "#### Smoothing\n",
    "\n",
    "Smoothing is done with the \n",
    "[smooth](https://dysh.readthedocs.io/en/latest/reference/modules/dysh.spectra.html#dysh.spectra.spectrum.Spectrum.smooth)\n",
    "method. By default it decimates the spectrum, so it only retains independent samples. In this case we smooth using a Gaussian kernel with a width of 16 channels."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "ddff746f-d714-4941-bb84-7db27d70c1b7",
   "metadata": {},
   "outputs": [],
   "source": [
    "ps0_spec_smo = ps0_spec.smooth(\"gauss\", 16)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "6b1abc79-07c6-400d-9ad9-97cfec9075e7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2c09a11738d44dc29f7ced1d1598070a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox(children=(HBox(children=(Button(description='Clear All Regions', style=ButtonStyle(), tooltip='Clear all …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ps0_spec_smo.plot(ymin=-5, ymax=5, xaxis_unit = \"MHz\");"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4de7437a-74bd-4f63-8128-e525b1508029",
   "metadata": {},
   "source": [
    "We have to zoom in further to see the signal. We also limit the x-axis range."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "7168cea5-3dcd-41e4-8748-950babdfe02e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4b5a72c3cdd64242a0fad132edce38e5",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox(children=(HBox(children=(Button(description='Clear All Regions', style=ButtonStyle(), tooltip='Clear all …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ps0_spec_smo.plot(ymin=-0.2, ymax=0.5, xmin=1397, xmax=1400, xaxis_unit = \"MHz\");"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "75b2d337-b27c-4ab0-8864-f31d49a6cf37",
   "metadata": {},
   "source": [
    "#### Polarization Averaging\n",
    "\n",
    "While inspecting the data we saw that there are two polarizations. We can average them together to further reduce the noise by a factor $\\sqrt{2}$. The second polarization can be calibrated following the above steps, but setting `plnum=1`. Here we also demonstrate the use of chaining to do the data reduction. This refers to using multiple commands in a chain, like"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "db12e42f-ff6c-428d-99a0-94c0cef81653",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "we used fits returning\n"
     ]
    }
   ],
   "source": [
    "ps1_spec_smo = sdfits.getps(scan=270, plnum=1, ifnum=0, fdnum=0).timeaverage().smooth(\"gauss\", 16)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "546064ac-c84f-4563-bda1-74d0eb8334db",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "413846ba53624d77a55310ba1c9559ff",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox(children=(HBox(children=(Button(description='Clear All Regions', style=ButtonStyle(), tooltip='Clear all …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ps1_spec_smo.plot(ymin=-0.2, ymax=0.5, xmin=1397, xmax=1400, xaxis_unit = \"MHz\");"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aefb9d3a-f3d9-46e4-898c-5d4ebe36b261",
   "metadata": {},
   "source": [
    "Now we average the smoothed spectra for both polarizations using the \n",
    "[average](https://dysh.readthedocs.io/en/latest/reference/modules/dysh.spectra.html#dysh.spectra.spectrum.Spectrum.average)\n",
    "method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "35af8f07-3cb6-4ce6-830f-04ea03d28fa5",
   "metadata": {},
   "outputs": [],
   "source": [
    "ps_spec_smo = ps0_spec_smo.average([ps1_spec_smo])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "8523b1e2-6a74-458f-a936-5acc696e0e94",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "967871fdefe04c9c83ec12f82f6b1c9a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox(children=(HBox(children=(Button(description='Clear All Regions', style=ButtonStyle(), tooltip='Clear all …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ps_spec_smo.plot(ymin=-0.2, ymax=0.5, xmin=1397, xmax=1400, xaxis_unit = \"MHz\");"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "542c1104-23d2-439d-b39a-d514c451cfbe",
   "metadata": {},
   "source": [
    "#### Statistics\n",
    "\n",
    "Now we will compare the noise properties of the spectra. For this we leverage the ability to slice spectra. First, we replot the spectra over the whole x-range to find a good frequency range where to compute statistics."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "326aabcb-2294-4e61-b4d8-d1ef59947fbb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7c6986d5a9af4108adbcb1af2536082e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox(children=(HBox(children=(Button(description='Clear All Regions', style=ButtonStyle(), tooltip='Clear all …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ps_spec_smo.plotter.reset()\n",
    "ps_spec_smo.plot(ymin=-0.2, ymax=0.5, xaxis_unit = \"MHz\");"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "767d4877-2ea6-414b-98df-a7ec294ea324",
   "metadata": {},
   "source": [
    "We use the range between 1394 MHz and 1398 MHz, and then compute the statistics over this range using the \n",
    "[stats](https://dysh.readthedocs.io/en/latest/reference/modules/dysh.spectra.html#dysh.spectra.spectrum.Spectrum.stats)\n",
    "method of a `Spectrum`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "e149d692-9b63-461a-a053-edf530a3b667",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'mean': ,\n",
       " 'median': ,\n",
       " 'rms': ,\n",
       " 'min': ,\n",
       " 'max': ,\n",
       " 'npt': 656,\n",
       " 'nan': np.int64(0)}"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = slice(1394*u.MHz, 1398*u.MHz)\n",
    "ps_spec_smo[s].stats()   # rms 0.02056197 K"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4d2804d0-56b8-4955-8441-3a911c8d357f",
   "metadata": {},
   "source": [
    "Now for the individual polarizations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "627d0b6f-c429-4f08-9df1-36bd3bd21676",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "({'mean': ,\n",
       "  'median': ,\n",
       "  'rms': ,\n",
       "  'min': ,\n",
       "  'max': ,\n",
       "  'npt': 656,\n",
       "  'nan': np.int64(0)},\n",
       " {'mean': ,\n",
       "  'median': ,\n",
       "  'rms': ,\n",
       "  'min': ,\n",
       "  'max': ,\n",
       "  'npt': 656,\n",
       "  'nan': np.int64(0)})"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ps0_spec_smo[s].stats(), ps1_spec_smo[s].stats()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bde5a1fa-2191-40b9-b6b7-b7a9e484b32a",
   "metadata": {},
   "source": [
    "The individual polarizations had an rms of $\\approx0.0255$ K, and the average an rms of $0.02$ K. Thus, the average has a noise a factor of $0.9\\sqrt{2}$ lower than the individual polarizations. That is $10\\%$ higher than expected.\n",
    "\n",
    "#### Baseline Subtraction\n",
    "\n",
    "Now we will subtract a baseline from the averaged spectrum. For this we use the \n",
    "[baseline](https://dysh.readthedocs.io/en/latest/reference/modules/dysh.spectra.html#dysh.spectra.spectrum.Spectrum.baseline)\n",
    "method. It is important to use a range of frequencies that will not bias the baseline fit. We exclude the range at the low-frequency end of the spectral window, up to 1394 MHz, and the range that contains the spectral line, between 1398 and 1400 MHz. In this case we use a polynomial of order 1 as our baseline model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "2b1ccd20-7f0f-4a78-96b9-81b573a40d9c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "07:08:59.729 I 1000000000.0 Hz is below the minimum spectral axis 1392294069.4697266 Hz. Replacing.\n",
      "07:08:59.741 I EXCLUDING [Spectral Region, 1 sub-regions:\n",
      "  (1392294069.4697266 Hz, 1394000000.0 Hz) \n",
      ", Spectral Region, 1 sub-regions:\n",
      "  (1398000000.0 Hz, 1400000000.0 Hz) \n",
      "]\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "dcacd399e2234529b33b37e58e7e2e98",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox(children=(HBox(children=(Button(description='Clear All Regions', style=ButtonStyle(), tooltip='Clear all …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "exclude = [(1*u.GHz,1.394*u.GHz),(1.398*u.GHz,1.4*u.GHz)]\n",
    "ps_spec_smo.baseline(1, model=\"poly\", exclude=exclude, remove=True)\n",
    "ps_spec_smo.plot(ymin=-0.2, ymax=0.5, xmin=1397, xmax=1400, xaxis_unit = \"MHz\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "a617cb4a-18d0-4576-87fb-3e44d34cdb2a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'mean': ,\n",
       " 'median': ,\n",
       " 'rms': ,\n",
       " 'min': ,\n",
       " 'max': ,\n",
       " 'npt': 656,\n",
       " 'nan': np.int64(0)}"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ps_spec_smo[s].stats()  # rms 0.02056173 K"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "1b5132ef-02ac-4c2e-850a-64bc50165505",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3fdc36dfb3a246d4b18105daab0a8fb4",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox(children=(HBox(children=(Button(description='Clear All Regions', style=ButtonStyle(), tooltip='Clear all …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ps_spec_smo.plot(ymax=0.25, ymin=-0.1, xaxis_unit=\"km/s\");"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a10153ea-929a-45a5-8555-c008957400f5",
   "metadata": {},
   "source": [
    "The mean and median are closer to zero now.\n",
    "\n",
    "### Multiple On/Off Pairs\n",
    "\n",
    "Now that we understand how to process a single pair of On/Off scans, we proceed to calibrate a bunch of them. In dysh this can be accomplished by either, giving a list of scans to the calibration routines, or by selecting the scans based on another property of the data. \n",
    "\n",
    "#### Using a List of Scans\n",
    "\n",
    "First we need to figure out all of the scans for a particular source. For U8503, there is a single pair of On/Off scans, so we need to use a different source. We use 3C286, for which we have scans 220, 221, 222, 223, 224, 225, 226, 227 using OffOn with the noise diodes. \n",
    "\n",
    "We could have figured out which scans using the following pandas inspired python construct:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "080110e2-43ce-41e5-8f5c-73e543996b4e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[220, 221, 222, 223, 224, 225, 226, 227]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scan_list = list(set(sdfits[\"SCAN\"][(sdfits[\"OBJECT\"] == \"3C286\") & \\\n",
    "                     (sdfits[\"PROC\"] == \"OffOn\") & (sdfits[\"CAL\"] == \"T\")]))\n",
    "sorted(scan_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "aa0a0176-1b34-4afe-8458-ddc429d8ba6b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "we used fits returning\n",
      "we used fits returning\n"
     ]
    }
   ],
   "source": [
    "ps0_obj = sdfits.getps(scan=scan_list, plnum=0, ifnum=0, fdnum=0).timeaverage()\n",
    "ps1_obj = sdfits.getps(scan=scan_list, plnum=1, ifnum=0, fdnum=0).timeaverage()\n",
    "ps_obj = ps0_obj.average(ps1_obj);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "de0553e9-e0cf-446a-89fc-684828401aa2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3474a76d84b44ccd861afec0a0859f37",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox(children=(HBox(children=(Button(description='Clear All Regions', style=ButtonStyle(), tooltip='Clear all …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ps_obj.plot(xaxis_unit = \"MHz\");"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e3942f70-07d4-434b-8120-768e0145c81c",
   "metadata": {},
   "source": [
    "Since 3C286 is a continuum source, we can see Galactic HI absorption against the continuum."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "649f68ec-3ac4-4b2c-a77b-19f26f1f3a1d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d648fc8b558a4ae091418c931a4fa9d6",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox(children=(HBox(children=(Button(description='Clear All Regions', style=ButtonStyle(), tooltip='Clear all …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ps_obj.rest_value = 1420405751.786 * u.Hz\n",
    "ps_obj.plot(xmin=-200, xmax=200, ymin=15, ymax=25, xaxis_unit=\"km/s\");"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bb373785-c582-4afc-af21-279aa0e47fe5",
   "metadata": {},
   "source": [
    "#### Using Selection\n",
    "\n",
    "We can do the same by using the \n",
    "[select](https://dysh.readthedocs.io/en/latest/reference/modules/dysh.fits.html#dysh.fits.GBTFITSLoad.select)\n",
    "method before calling `getps`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "d4409448-eee7-4369-9057-6eed4f5c23f8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "we used fits returning\n",
      "we used fits returning\n"
     ]
    }
   ],
   "source": [
    "sdfits.select(object=\"3C286\", proc=\"OffOn\")\n",
    "ps0_obj_b = sdfits.getps(plnum=0, ifnum=0, fdnum=0).timeaverage()\n",
    "ps1_obj_b = sdfits.getps(plnum=1, ifnum=0, fdnum=0).timeaverage()\n",
    "ps_obj_b = ps0_obj_b.average(ps1_obj_b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "ee59956f-c803-483a-b76d-6d1a4bdf81b2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "78f80b58d44e4d6db0a49673dab8df0b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox(children=(HBox(children=(Button(description='Clear All Regions', style=ButtonStyle(), tooltip='Clear all …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ps_obj_b.plot(xmin=1420, xmax=1421, ymin=15, ymax=25, xaxis_unit = \"MHz\");"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0d09beee-5e4b-4853-a690-d4bae6719db5",
   "metadata": {},
   "source": [
    "Once you are done calibrating, you should clear the selection to have access to all the data again."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "8abe9ac7-52b6-4b36-9a23-d026cf436e2a",
   "metadata": {},
   "outputs": [],
   "source": [
    "sdfits.selection.clear()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2c4a4eac-69d9-4e96-8e0d-cb9723d0e371",
   "metadata": {},
   "source": [
    "#### Using Arguments\n",
    "\n",
    "We can accomplish the same by specifying the `object` and procedure type (`proc`) during the call to `getps`. Any of the calibration methods (\n",
    "[getps](https://dysh.readthedocs.io/en/latest/reference/modules/dysh.fits.html#dysh.fits.GBTFITSLoad.getps),\n",
    "[getfs](https://dysh.readthedocs.io/en/latest/reference/modules/dysh.fits.html#dysh.fits.GBTFITSLoad.getfs),\n",
    "[getnod](https://dysh.readthedocs.io/en/latest/reference/modules/dysh.fits.html#dysh.fits.GBTFITSLoad.getnod),\n",
    "[getsigref](https://dysh.readthedocs.io/en/latest/reference/modules/dysh.fits.html#dysh.fits.GBTFITSLoad.getsigref),\n",
    "[subbeamnod](https://dysh.readthedocs.io/en/latest/reference/modules/dysh.fits.html#dysh.fits.GBTFITSLoad.subbeamnod)\n",
    ") \n",
    "can take as argument any of the [columns of an SDFITS file](https://dysh.readthedocs.io/en/latest/reference/sdfits_files/gbt_sdfits.html#data). When used this way, the calibration routine will only use the data that satisfies the conditions, so for example, if we use \n",
    "```\n",
    "sdfits.getps(object=\"3C286\", plnum=0, ifnum=0, fdnum=0)\n",
    "```\n",
    "the calibration routine will only use data that has the column \"OBJECT\" equal to \"3C286\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "57ae4fdf-eb4e-4195-b4cb-9133577cab2e",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "07:09:08.266 W Scan 266 has no matching ON scan. Will not calibrate.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "we used fits returning\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "07:09:09.656 W Scan 266 has no matching ON scan. Will not calibrate.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "we used fits returning\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "19bdf57cbcdd4d47b151c4829f296336",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox(children=(HBox(children=(Button(description='Clear All Regions', style=ButtonStyle(), tooltip='Clear all …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ps0_obj_c = sdfits.getps(plnum=0, ifnum=0, fdnum=0, object=\"3C286\", proc=\"OffOn\").timeaverage()\n",
    "ps1_obj_c = sdfits.getps(plnum=1, ifnum=0, fdnum=0, object=\"3C286\", proc=\"OffOn\").timeaverage()\n",
    "ps_obj_c = ps0_obj_c.average(ps1_obj_c)\n",
    "ps_obj_c.plot(xmin=1420, xmax=1421, ymin=15, ymax=25, xaxis_unit = \"MHz\");"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "010ea361-db22-4ffb-b936-7e4213a79a0d",
   "metadata": {},
   "source": [
    "### Calibration Without Noise Diodes\n",
    "\n",
    "Most of the scans in this example did not use the noise diode. In this case we need to provide a value for the system temperature so that the data can be calibrated. For this particular observation, there is one Track observing procedure associated with the OffOn pairs. For these Track observations, the noise diode was fired, so we can use them to figure out the system temperature.\n",
    "\n",
    "We will work on observations of U11627. For this source the Track scan is 320, and the OffOn pairs are in scans 316, 317, 318 and 319.\n",
    "\n",
    "First we use \n",
    "[gettp](https://dysh.readthedocs.io/en/latest/reference/modules/dysh.fits.html#dysh.fits.GBTFITSLoad.gettp)\n",
    "to figure out the system temperature from the Track scan."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "c722df51-ad3f-4e60-8795-6d9e63b74c64",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "we used fits returning\n",
      "we used fits returning\n"
     ]
    }
   ],
   "source": [
    "tp0 = sdfits.gettp(scan=320, plnum=0, ifnum=0, fdnum=0).timeaverage()\n",
    "tp1 = sdfits.gettp(scan=320, plnum=1, ifnum=0, fdnum=0).timeaverage()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fbfbd52e-c53f-4cb4-bc81-2313280803a4",
   "metadata": {},
   "source": [
    "The system temperature is stored in the `meta` dictionary of each `Spectrum`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "c8e2f2be-ad33-47f3-a3ee-fa8fbcbda7b9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "System temperature for plnum=0: 23.88 K\n",
      "System temperature for plnum=1: 28.39 K\n"
     ]
    }
   ],
   "source": [
    "print(f\"System temperature for plnum={tp0.meta['PLNUM']}: {tp0.meta['TSYS']:.2f} K\")\n",
    "print(f\"System temperature for plnum={tp1.meta['PLNUM']}: {tp1.meta['TSYS']:.2f} K\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "20b67ca9-c555-4024-a56c-1ee34479591e",
   "metadata": {},
   "source": [
    "Now we use these values to calibrate the data. The system temperature is provided for the calibration methods through the `t_sys` argument. It is assumed to be in K."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "f38696bf-c18d-47b2-ab31-9b0d86ff7659",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "we used fits returning\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "07:09:13.758 I Ignoring 2 blanked integration(s).\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "we used fits returning\n"
     ]
    }
   ],
   "source": [
    "sdfits.select(object=\"U11627\", proc=\"OffOn\")\n",
    "ps0_wtsys = sdfits.getps(plnum=0, ifnum=0, fdnum=0, t_sys=tp0.meta['TSYS']).timeaverage()\n",
    "ps1_wtsys = sdfits.getps(plnum=1, ifnum=0, fdnum=0, t_sys=tp1.meta['TSYS']).timeaverage()\n",
    "ps_wtsys = ps0_wtsys.average(ps1_wtsys)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "df80a1cd-3ad0-4a16-a935-05dfa820193c",
   "metadata": {},
   "source": [
    "Picking a clean section of the spectrum to see if the radiometer equation is obeyed. The radiometer() function computed the ratio of measured to expected noise, and should generally be 1.0 or larger."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "5a897e5e-c821-4acd-a39b-ef8244f42783",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.float64(1.0060377308633648)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = slice(1392.5*u.MHz, 1394.9*u.MHz)\n",
    "ps_wtsys[s].radiometer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "b256d7a6-2349-45ba-90ee-4fc479108807",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4e5863aba9224509adffb2a03b9ead82",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox(children=(HBox(children=(Button(description='Clear All Regions', style=ButtonStyle(), tooltip='Clear all …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ps_wtsys.plot(ymin=-0.5, ymax=0.1, xaxis_unit = \"MHz\");"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cfbdbcb1-2a68-4016-9b36-e682613149f2",
   "metadata": {},
   "source": [
    "Now we smooth and remove a baseline."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "aa63147c-040c-41a5-a499-e8a1b55f93fc",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "07:09:15.400 I 1000000000.0 Hz is below the minimum spectral axis 1391602258.4697266 Hz. Replacing.\n",
      "07:09:15.403 I EXCLUDING [Spectral Region, 1 sub-regions:\n",
      "  (1391602258.4697266 Hz, 1393000000.0 Hz) \n",
      ", Spectral Region, 1 sub-regions:\n",
      "  (1397000000.0 Hz, 1399000000.0 Hz) \n",
      "]\n"
     ]
    }
   ],
   "source": [
    "ps_wtsys_smo = ps_wtsys.smooth(\"gauss\", 16)\n",
    "ps_wtsys_smo.baseline(1, model=\"poly\", exclude=[(1*u.GHz,1.393*u.GHz),(1.397*u.GHz,1.399*u.GHz)], remove=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "8177e5cd-f3c2-4dc5-8e1e-c8ea0494038d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0414c11ebd224ebfb2ba2d0264766939",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox(children=(HBox(children=(Button(description='Clear All Regions', style=ButtonStyle(), tooltip='Clear all …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ps_wtsys_smo.plot(ymin=-0.2, ymax=0.2, xaxis_unit = \"MHz\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "bb17097a-e0ab-4dc6-8754-ff92012cde0d",
   "metadata": {},
   "outputs": [],
   "source": [
    "sdfits.selection.clear()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "92d2f5ed-adad-4843-8c7d-eea3a23a1d99",
   "metadata": {},
   "source": [
    "### Combining Off Spectra\n",
    "\n",
    "In some situations we would like to have more flexibility during calibration to specify what will be the Off or reference spectrum used for calibration. In these cases we can use the \n",
    "[GBTFITSLoad.getsigref](https://dysh.readthedocs.io/en/latest/reference/modules/dysh.fits.html#dysh.fits.gbtfitsload.GBTFITSLoad.getsigref) .\n",
    "This function takes as inputs a scan number, or list of them, to be used as On, and a reference spectrum, or scan number (only one in this case). Here we will combine the two Off source spectra from the previous calibration before calibrating the data.\n",
    "We use \n",
    "[gettp](https://dysh.readthedocs.io/en/latest/reference/modules/dysh.fits.html#dysh.fits.gbtfitsload.GBTFITSLoad.gettp) \n",
    "to produce the reference spectrum."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "aaeb17ad-a441-45eb-90ad-dae7b6dcb92e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "we used fits returning\n",
      "we used fits returning\n"
     ]
    }
   ],
   "source": [
    "tp_ref0 = sdfits.gettp(scan=[316,318], plnum=0, ifnum=0, fdnum=0, t_sys=tp0.meta['TSYS']).timeaverage()\n",
    "tp_ref1 = sdfits.gettp(scan=[316,318], plnum=1, ifnum=0, fdnum=0, t_sys=tp1.meta['TSYS']).timeaverage()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9a686d6a-cef0-480c-874b-6bc929ae8286",
   "metadata": {},
   "source": [
    "Now use `getsigref` to do the calibration. Since we specified the system temperature in the previous call to `gettp`, we do not need to provide it again."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "32cfca82-594c-4af8-8cdc-251d1274a478",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "we used fits returning\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "07:09:20.182 I Ignoring 2 blanked integration(s).\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "we used fits returning\n"
     ]
    }
   ],
   "source": [
    "sdfits.select(object=\"U11627\", proc=\"OffOn\")\n",
    "ps0_wtsys_tpr = sdfits.getsigref(scan=[317,319], ref=tp_ref0, plnum=0, ifnum=0, fdnum=0).timeaverage()\n",
    "ps1_wtsys_tpr = sdfits.getsigref(scan=[317,319], ref=tp_ref1, plnum=1, ifnum=0, fdnum=0).timeaverage()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9afe3c26-3549-4331-970d-ff89e493b9c5",
   "metadata": {},
   "source": [
    "Average both polarizations, smooth and remove a baseline like before so we can compare the results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "5410ea19-7db4-45a3-8769-266bfb356eb2",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "07:09:21.101 I 1000000000.0 Hz is below the minimum spectral axis 1391602258.4697266 Hz. Replacing.\n",
      "07:09:21.102 I EXCLUDING [Spectral Region, 1 sub-regions:\n",
      "  (1391602258.4697266 Hz, 1393000000.0 Hz) \n",
      ", Spectral Region, 1 sub-regions:\n",
      "  (1397000000.0 Hz, 1399000000.0 Hz) \n",
      "]\n"
     ]
    }
   ],
   "source": [
    "ps_wtsys_tpr = ps0_wtsys_tpr.average(ps1_wtsys_tpr)\n",
    "ps_wtsys_tpr_smo = ps_wtsys_tpr.smooth(\"gauss\", 16)\n",
    "ps_wtsys_tpr_smo.baseline(1, model=\"poly\", \n",
    "                          exclude=[(1*u.GHz,1.393*u.GHz),(1.397*u.GHz,1.399*u.GHz)], \n",
    "                          remove=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "56d966b0-315e-4d2b-80ff-17a4b1dcf626",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8f97ef1a1aea4b14aa4f24cf0e359416",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox(children=(HBox(children=(Button(description='Clear All Regions', style=ButtonStyle(), tooltip='Clear all …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ps_wtsys_tpr_smo.plot(ymin=-0.2, ymax=0.2, xaxis_unit = \"MHz\");"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "950fc56b-47b1-4b67-94b5-e93d75d9a5cd",
   "metadata": {},
   "source": [
    "Now compare the noise in the end products."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "5a7719e6-d955-4892-9e64-7badad355014",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ratio of rms: 0.9999915516119442\n"
     ]
    }
   ],
   "source": [
    "s = slice(1.393*u.GHz, 1.396*u.GHz)\n",
    "rms_tpr = ps_wtsys_tpr_smo[s].stats()[\"rms\"]\n",
    "rms = ps_wtsys_smo[s].stats()[\"rms\"]\n",
    "print(f\"Ratio of rms: {rms_tpr/rms}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ff450189-7283-4e6c-94e6-fbca0a37fd14",
   "metadata": {},
   "source": [
    "In this case, using a combined reference spectrum improved the noise by an insignificant amount.\n",
    "\n",
    "### Working in Flux Units\n",
    "\n",
    "So far we have calibrated the data to antenna temperature units, but it is also possible to work on flux units. To do so, we must provide a zenith opacity value and specify that we want the data in Jy, using the `zenith_opacity` and `units` parameters. Since this data is observed at 1.4 GHz, we use a small value for the zenith opacity, 0.08. For more details on how to find the zenith opacity for your observations, please see [this guide](https://gbtdocs.readthedocs.io/en/latest/how-tos/data_reduction/calculate_opacity.html), and for background on the conversion between antenna temperature and flux density read [this page](https://www.cv.nrao.edu/~sransom/web/Ch3.html#E48). We also repeat the previous calibration steps."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "b54300a4-1d7d-4ae0-a4f2-a071f954d160",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "we used fits returning\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "07:09:24.211 I Ignoring 2 blanked integration(s).\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "we used fits returning\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "07:09:25.189 I 1000000000.0 Hz is below the minimum spectral axis 1391602258.4697266 Hz. Replacing.\n",
      "07:09:25.190 I EXCLUDING [Spectral Region, 1 sub-regions:\n",
      "  (1391602258.4697266 Hz, 1393000000.0 Hz) \n",
      ", Spectral Region, 1 sub-regions:\n",
      "  (1397000000.0 Hz, 1399000000.0 Hz) \n",
      "]\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "98aa5c339b8e444792437f337b90f9a0",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox(children=(HBox(children=(Button(description='Clear All Regions', style=ButtonStyle(), tooltip='Clear all …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ps0_wtsys_tpr = sdfits.getsigref(scan=[317,319], ref=tp_ref0, plnum=0, ifnum=0, fdnum=0, \n",
    "                                 units=\"flux\", zenith_opacity=0.08).timeaverage()\n",
    "ps1_wtsys_tpr = sdfits.getsigref(scan=[317,319], ref=tp_ref1, plnum=1, ifnum=0, fdnum=0, \n",
    "                                 units=\"flux\", zenith_opacity=0.08).timeaverage()\n",
    "ps_wtsys_tpr = ps0_wtsys_tpr.average(ps1_wtsys_tpr)\n",
    "ps_wtsys_tpr_smo = ps_wtsys_tpr.smooth(\"gauss\", 16)\n",
    "ps_wtsys_tpr_smo.baseline(1, model=\"poly\", \n",
    "                          exclude=[(1*u.GHz,1.393*u.GHz),(1.397*u.GHz,1.399*u.GHz)], \n",
    "                          remove=True)\n",
    "ps_wtsys_tpr_smo.plot(ymin=-30, ymax=80, yaxis_unit=\"mJy\", xaxis_unit = \"MHz\");"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "678f0a09-4e0b-4782-8bba-4f48f8753e07",
   "metadata": {},
   "source": [
    "## Measuring Line Properties\n",
    "\n",
    "`Spectrum` objects provide a convenience function for analysis of HI profiles based on the Curve of Growth (CoG) method by [Yu et al. (2020)](https://ui.adsabs.harvard.edu/abs/2020ApJ...898..102Y/abstract), through the  \n",
    "[cog](https://dysh.readthedocs.io/en/latest/reference/modules/dysh.spectra.html#dysh.spectra.spectrum.Spectrum.cog)\n",
    "method. The implementation of this method can retrieve line parameters in a fully automated way, however, this is likely to work only in high signal-to-noise cases (>10). In low signal-to-noise cases, additional inputs are required to aid the method. In particular, it helps to provide a good estimate of the central velocity of the line using the `vc` parameter and/or restricting the range over which the method is applied, either by slicing the `Spectrum` or using the `bchan` and `echan` parameters.\n",
    "\n",
    "First we apply the method blindly, ignoring the edge channels."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "39407cfa-e673-4dc8-9c73-de422827292c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "07:09:25.446 I Velocity frame: Topocentric\n",
      "07:09:25.449 I Doppler convention: optical\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'flux': ,\n",
       " 'flux_std': ,\n",
       " 'flux_r': ,\n",
       " 'flux_r_std': ,\n",
       " 'flux_b': ,\n",
       " 'flux_b_std': ,\n",
       " 'width': {0.25: ,\n",
       "  0.65: ,\n",
       "  0.75: ,\n",
       "  0.85: ,\n",
       "  0.95: },\n",
       " 'width_std': {0.25: ,\n",
       "  0.65: ,\n",
       "  0.75: ,\n",
       "  0.85: ,\n",
       "  0.95: },\n",
       " 'A_F': np.float64(7.388392022857113),\n",
       " 'A_C': np.float64(3.5634034906080134),\n",
       " 'C_V': np.float64(4.243243714231644),\n",
       " 'rms': ,\n",
       " 'bchan': np.int64(738),\n",
       " 'echan': np.int64(1108),\n",
       " 'vel': ,\n",
       " 'vel_std': ,\n",
       " 'vframe': 'itrs',\n",
       " 'doppler_convention': 'optical'}"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "line_props = ps_wtsys_tpr_smo[60:-60].cog()\n",
    "line_props"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "414ad747-28cd-45e8-b0da-f64bc466338a",
   "metadata": {},
   "source": [
    "Plot the results along with the central velocity and the line width that encompasses 95% of the total flux."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "284f7d95-b32a-4fb9-9153-8315ef75cb00",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/scratch/psalas/python/dysh-dev/py3.11-venv/lib/python3.11/site-packages/traitlets/traitlets.py:1385: DeprecationWarning: Passing unrecognized arguments to super(Toolbar).__init__().\n",
      "NavigationToolbar2WebAgg.__init__() missing 1 required positional argument: 'canvas'\n",
      "This is deprecated in traitlets 4.2.This error will be raised in a future release of traitlets.\n",
      "  warn(\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "9c90c108d51040d99d0d58220d10606a",
       "version_major": 2,
       "version_minor": 0
      },
      "image/png": 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",
      "text/html": [
       "\n",
       "            
\n",
       "                
\n",
       "                    Figure\n",
       "                
\n",
       "                \n",
       "            
\n",
       "        "
      ],
      "text/plain": [
       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.plot(ps_wtsys_tpr_smo[60:].spectral_axis.to(\"km/s\"), ps_wtsys_tpr_smo[60:].flux)\n",
    "plt.axvline(line_props[\"vel\"].value, c=\"r\", alpha=0.5, ls=\":\")\n",
    "plt.axvline((line_props[\"vel\"] - line_props[\"width\"][0.95]/2).value, c=\"g\")\n",
    "plt.axvline((line_props[\"vel\"] + line_props[\"width\"][0.95]/2).value, c=\"g\")\n",
    "plt.xlim(4500, 5200)\n",
    "plt.xlabel(\"Velocity (km/s)\")\n",
    "plt.ylabel(\"Flux (Jy)\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e6745cd7-1b6e-43c7-a169-ddeea76daa9f",
   "metadata": {},
   "source": [
    "The results are not great. Use CoG again, but providing a range of channels through the `bchan` and `echan` parameters (these can only be channel numbers)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "4bd6a311-5b32-4426-8107-9409cadf5a3d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "07:09:26.137 I Velocity frame: Topocentric\n",
      "07:09:26.139 I Doppler convention: optical\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'flux': ,\n",
       " 'flux_std': ,\n",
       " 'flux_r': ,\n",
       " 'flux_r_std': ,\n",
       " 'flux_b': ,\n",
       " 'flux_b_std': ,\n",
       " 'width': {0.25: ,\n",
       "  0.65: ,\n",
       "  0.75: ,\n",
       "  0.85: ,\n",
       "  0.95: },\n",
       " 'width_std': {0.25: ,\n",
       "  0.65: ,\n",
       "  0.75: ,\n",
       "  0.85: ,\n",
       "  0.95: },\n",
       " 'A_F': np.float64(1.2231434838173203),\n",
       " 'A_C': np.float64(1.0623088645068575),\n",
       " 'C_V': np.float64(3.2400000916666984),\n",
       " 'rms': ,\n",
       " 'bchan': np.int64(738),\n",
       " 'echan': np.int64(1108),\n",
       " 'vel': ,\n",
       " 'vel_std': ,\n",
       " 'vframe': 'itrs',\n",
       " 'doppler_convention': 'optical'}"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "line_props_wrange = ps_wtsys_tpr_smo[60:-60].cog(bchan=line_props[\"bchan\"], echan=line_props[\"echan\"])\n",
    "line_props_wrange"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "66ed8a51-2831-493d-a721-354728d4fd1e",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "07:09:26.329 I Velocity frame: Topocentric\n",
      "07:09:26.330 I Doppler convention: optical\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'flux': ,\n",
       " 'flux_std': ,\n",
       " 'flux_r': ,\n",
       " 'flux_r_std': ,\n",
       " 'flux_b': ,\n",
       " 'flux_b_std': ,\n",
       " 'width': {0.25: ,\n",
       "  0.65: ,\n",
       "  0.75: ,\n",
       "  0.85: ,\n",
       "  0.95: },\n",
       " 'width_std': {0.25: ,\n",
       "  0.65: ,\n",
       "  0.75: ,\n",
       "  0.85: ,\n",
       "  0.95: },\n",
       " 'A_F': np.float64(1.167930500459024),\n",
       " 'A_C': np.float64(1.0831728945459125),\n",
       " 'C_V': np.float64(3.2400000916621514),\n",
       " 'rms': ,\n",
       " 'bchan': np.int64(750),\n",
       " 'echan': np.int64(1100),\n",
       " 'vel': ,\n",
       " 'vel_std': ,\n",
       " 'vframe': 'itrs',\n",
       " 'doppler_convention': 'optical'}"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "line_props_wrange = ps_wtsys_tpr_smo[60:-60].cog(bchan=750, echan=1100)\n",
    "line_props_wrange"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "686f8cf8-116f-4c38-8e95-9eafaac39ec7",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/scratch/psalas/python/dysh-dev/py3.11-venv/lib/python3.11/site-packages/traitlets/traitlets.py:1385: DeprecationWarning: Passing unrecognized arguments to super(Toolbar).__init__().\n",
      "NavigationToolbar2WebAgg.__init__() missing 1 required positional argument: 'canvas'\n",
      "This is deprecated in traitlets 4.2.This error will be raised in a future release of traitlets.\n",
      "  warn(\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "20b356ea78af4b8c9c115999feaf0643",
       "version_major": 2,
       "version_minor": 0
      },
      "image/png": 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",
      "text/html": [
       "\n",
       "            
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       "                
\n",
       "                    Figure\n",
       "                
\n",
       "                \n",
       "            
\n",
       "        "
      ],
      "text/plain": [
       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.plot(ps_wtsys_tpr_smo[60:].spectral_axis.to(\"km/s\"), ps_wtsys_tpr_smo[60:].flux)\n",
    "plt.axvline(line_props_wrange[\"vel\"].value, c=\"r\", alpha=0.5, ls=\":\")\n",
    "plt.axvline((line_props_wrange[\"vel\"] - line_props_wrange[\"width\"][0.95]/2).value, c=\"g\")\n",
    "plt.axvline((line_props_wrange[\"vel\"] + line_props_wrange[\"width\"][0.95]/2).value, c=\"g\")\n",
    "plt.xlim(4500, 5200)\n",
    "plt.xlabel(\"Velocity (km/s)\")\n",
    "plt.ylabel(\"Flux (Jy)\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1e114b5f-c03c-4c7c-a336-298d29532a3f",
   "metadata": {},
   "source": [
    "Much better!"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c54390e3-be6e-47a4-82a0-fe137cc89068",
   "metadata": {},
   "source": [
    "## Putting it all Together\n",
    "\n",
    "Now we can use what we have learned to process all of the observations.\n",
    "We will loop over all the objects, calibrating the data.\n",
    "For the system temperature, we use the last `Track` observation associated with a given object.\n",
    "Since there may have been issues with the previous `Track` observations if they had to be repeated (for example, for U11578 the first `Track` observation did not fire the noise diode).\n",
    "We process \"U8503\" individually, because there is no \"Track\" scan for it but the OffOn observations did use the noise diode.\n",
    "\n",
    "We put all the steps in a function, `process`. This makes it easier to reuse the code, and modify it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "fd05051a-3e16-483e-aa1a-5bb8be82233c",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "we used fits returning\n",
      "we used fits returning\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "07:09:28.096 W Scan 266 has no matching ON scan. Will not calibrate.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "we used fits returning\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "07:09:29.379 W Scan 266 has no matching ON scan. Will not calibrate.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "we used fits returning\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "07:09:30.563 I EXCLUDING [Spectral Region, 1 sub-regions:\n",
      "  (1402171379.4697266 Hz, 1402781731.0322266 Hz) \n",
      ", Spectral Region, 1 sub-regions:\n",
      "  (1405223137.2822266 Hz, 1409800774.0009766 Hz) \n",
      ", Spectral Region, 1 sub-regions:\n",
      "  (1414054924.3916016 Hz, 1414665275.9541016 Hz) \n",
      "]\n",
      "07:09:30.769 I Velocity frame: Topocentric\n",
      "07:09:30.770 I Doppler convention: optical\n",
      "07:09:30.805 W Scan 266 has no matching ON scan. Will not calibrate.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "we used fits returning\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "07:09:31.493 W Scan 266 has no matching ON scan. Will not calibrate.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "we used fits returning\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "07:09:32.370 I EXCLUDING [Spectral Region, 1 sub-regions:\n",
      "  (1392294069.4697266 Hz, 1392904421.0322266 Hz) \n",
      ", Spectral Region, 1 sub-regions:\n",
      "  (1395345827.2822266 Hz, 1399923464.0009766 Hz) \n",
      ", Spectral Region, 1 sub-regions:\n",
      "  (1404177614.3916016 Hz, 1404787965.9541016 Hz) \n",
      "]\n",
      "07:09:32.546 I Velocity frame: Topocentric\n",
      "07:09:32.547 I Doppler convention: optical\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "we used fits returning\n",
      "we used fits returning\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "07:09:33.555 W Scan 266 has no matching ON scan. Will not calibrate.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "we used fits returning\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "07:09:34.324 W Scan 266 has no matching ON scan. Will not calibrate.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "we used fits returning\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "07:09:35.111 I EXCLUDING [Spectral Region, 1 sub-regions:\n",
      "  (1393066964.4697266 Hz, 1393677316.0322266 Hz) \n",
      ", Spectral Region, 1 sub-regions:\n",
      "  (1396118722.2822266 Hz, 1400696359.0009766 Hz) \n",
      ", Spectral Region, 1 sub-regions:\n",
      "  (1404950509.3916016 Hz, 1405560860.9541016 Hz) \n",
      "]\n",
      "07:09:35.293 I Velocity frame: Topocentric\n",
      "07:09:35.294 I Doppler convention: optical\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "we used fits returning\n",
      "we used fits returning\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "07:09:36.616 W Scan 266 has no matching ON scan. Will not calibrate.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "we used fits returning\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "07:09:37.952 W Scan 266 has no matching ON scan. Will not calibrate.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "we used fits returning\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "07:09:38.459 I Ignoring 2 blanked integration(s).\n",
      "07:09:39.199 I EXCLUDING [Spectral Region, 1 sub-regions:\n",
      "  (1409984261.4697266 Hz, 1410594613.0322266 Hz) \n",
      ", Spectral Region, 1 sub-regions:\n",
      "  (1413036019.2822266 Hz, 1417613656.0009766 Hz) \n",
      ", Spectral Region, 1 sub-regions:\n",
      "  (1420000130.6103516 Hz, 1421001107.1728516 Hz) \n",
      ", Spectral Region, 1 sub-regions:\n",
      "  (1421867806.3916016 Hz, 1422478157.9541016 Hz) \n",
      "]\n",
      "07:09:39.382 I Velocity frame: Topocentric\n",
      "07:09:39.383 I Doppler convention: optical\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "we used fits returning\n",
      "we used fits returning\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "07:09:40.471 W Scan 266 has no matching ON scan. Will not calibrate.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "we used fits returning\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "07:09:42.039 W Scan 266 has no matching ON scan. Will not calibrate.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "we used fits returning\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "07:09:43.282 I EXCLUDING [Spectral Region, 1 sub-regions:\n",
      "  (1392593204.4697266 Hz, 1393203556.0322266 Hz) \n",
      ", Spectral Region, 1 sub-regions:\n",
      "  (1395644962.2822266 Hz, 1400222599.0009766 Hz) \n",
      ", Spectral Region, 1 sub-regions:\n",
      "  (1404476749.3916016 Hz, 1405087100.9541016 Hz) \n",
      "]\n",
      "07:09:43.464 I Velocity frame: Topocentric\n",
      "07:09:43.465 I Doppler convention: optical\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "we used fits returning\n",
      "we used fits returning\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "07:09:44.523 W Scan 266 has no matching ON scan. Will not calibrate.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "we used fits returning\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "07:09:45.330 W Scan 266 has no matching ON scan. Will not calibrate.\n",
      "07:09:45.588 I Ignoring 1 blanked integration(s).\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "we used fits returning\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "07:09:46.226 I EXCLUDING [Spectral Region, 1 sub-regions:\n",
      "  (1389683871.4697266 Hz, 1390294223.0322266 Hz) \n",
      ", Spectral Region, 1 sub-regions:\n",
      "  (1392735629.2822266 Hz, 1397313266.0009766 Hz) \n",
      ", Spectral Region, 1 sub-regions:\n",
      "  (1401567416.3916016 Hz, 1402177767.9541016 Hz) \n",
      "]\n",
      "07:09:46.425 I Velocity frame: Topocentric\n",
      "07:09:46.426 I Doppler convention: optical\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "we used fits returning\n",
      "we used fits returning\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "07:09:47.531 W Scan 266 has no matching ON scan. Will not calibrate.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "we used fits returning\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "07:09:48.850 W Scan 266 has no matching ON scan. Will not calibrate.\n",
      "07:09:49.069 I Ignoring 2 blanked integration(s).\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "we used fits returning\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "07:09:50.046 I EXCLUDING [Spectral Region, 1 sub-regions:\n",
      "  (1400235155.4697266 Hz, 1400845507.0322266 Hz) \n",
      ", Spectral Region, 1 sub-regions:\n",
      "  (1403286913.2822266 Hz, 1407864550.0009766 Hz) \n",
      ", Spectral Region, 1 sub-regions:\n",
      "  (1412118700.3916016 Hz, 1412729051.9541016 Hz) \n",
      "]\n",
      "07:09:50.244 I Velocity frame: Topocentric\n",
      "07:09:50.246 I Doppler convention: optical\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "we used fits returning\n",
      "we used fits returning\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "07:09:51.885 W Scan 266 has no matching ON scan. Will not calibrate.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "we used fits returning\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "07:09:53.215 W Scan 266 has no matching ON scan. Will not calibrate.\n",
      "07:09:53.461 I Ignoring 4 blanked integration(s).\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "we used fits returning\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "07:09:54.408 I EXCLUDING [Spectral Region, 1 sub-regions:\n",
      "  (1392556133.4697266 Hz, 1393166485.0322266 Hz) \n",
      ", Spectral Region, 1 sub-regions:\n",
      "  (1395607891.2822266 Hz, 1400185528.0009766 Hz) \n",
      ", Spectral Region, 1 sub-regions:\n",
      "  (1404439678.3916016 Hz, 1405050029.9541016 Hz) \n",
      "]\n",
      "07:09:54.602 I Velocity frame: Topocentric\n",
      "07:09:54.603 I Doppler convention: optical\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "we used fits returning\n",
      "we used fits returning\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "07:09:55.648 W Scan 266 has no matching ON scan. Will not calibrate.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "we used fits returning\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "07:09:56.973 W Scan 266 has no matching ON scan. Will not calibrate.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "we used fits returning\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "07:09:57.460 I Ignoring 1 blanked integration(s).\n",
      "07:09:58.145 I EXCLUDING [Spectral Region, 1 sub-regions:\n",
      "  (1399636888.4697266 Hz, 1400247240.0322266 Hz) \n",
      ", Spectral Region, 1 sub-regions:\n",
      "  (1402688646.2822266 Hz, 1407266283.0009766 Hz) \n",
      ", Spectral Region, 1 sub-regions:\n",
      "  (1411520433.3916016 Hz, 1412130784.9541016 Hz) \n",
      "]\n",
      "07:09:58.343 I Velocity frame: Topocentric\n",
      "07:09:58.345 I Doppler convention: optical\n",
      "07:09:58.623 I Using TSYS column\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "we used fits returning\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "07:09:59.247 I Using TSYS column\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "we used fits returning\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "07:09:59.597 W Scan 266 has no matching ON scan. Will not calibrate.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "we used fits returning\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "07:10:00.415 W Scan 266 has no matching ON scan. Will not calibrate.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "we used fits returning\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "07:10:01.236 I EXCLUDING [Spectral Region, 1 sub-regions:\n",
      "  (1392807006.4697266 Hz, 1393417358.0322266 Hz) \n",
      ", Spectral Region, 1 sub-regions:\n",
      "  (1395858764.2822266 Hz, 1400436401.0009766 Hz) \n",
      ", Spectral Region, 1 sub-regions:\n",
      "  (1404690551.3916016 Hz, 1405300902.9541016 Hz) \n",
      "]\n",
      "07:10:01.433 I Velocity frame: Topocentric\n",
      "07:10:01.436 I Doppler convention: optical\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "we used fits returning\n",
      "we used fits returning\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "07:10:02.433 W Scan 266 has no matching ON scan. Will not calibrate.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "we used fits returning\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "07:10:03.424 W Scan 266 has no matching ON scan. Will not calibrate.\n",
      "07:10:03.660 I Ignoring 2 blanked integration(s).\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "we used fits returning\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "07:10:04.578 I EXCLUDING [Spectral Region, 1 sub-regions:\n",
      "  (1391602258.4697266 Hz, 1392212610.0322266 Hz) \n",
      ", Spectral Region, 1 sub-regions:\n",
      "  (1394654016.2822266 Hz, 1399231653.0009766 Hz) \n",
      ", Spectral Region, 1 sub-regions:\n",
      "  (1403485803.3916016 Hz, 1404096154.9541016 Hz) \n",
      "]\n",
      "07:10:04.773 I Velocity frame: Topocentric\n",
      "07:10:04.779 I Doppler convention: optical\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "we used fits returning\n",
      "we used fits returning\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "07:10:05.820 W Scan 266 has no matching ON scan. Will not calibrate.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "we used fits returning\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "07:10:06.632 W Scan 266 has no matching ON scan. Will not calibrate.\n",
      "07:10:06.874 I Ignoring 4 blanked integration(s).\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "we used fits returning\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "07:10:07.465 I EXCLUDING [Spectral Region, 1 sub-regions:\n",
      "  (1397436018.4697266 Hz, 1398046370.0322266 Hz) \n",
      ", Spectral Region, 1 sub-regions:\n",
      "  (1400487776.2822266 Hz, 1405065413.0009766 Hz) \n",
      ", Spectral Region, 1 sub-regions:\n",
      "  (1409319563.3916016 Hz, 1409929914.9541016 Hz) \n",
      "]\n",
      "07:10:07.646 I Velocity frame: Topocentric\n",
      "07:10:07.647 I Doppler convention: optical\n"
     ]
    }
   ],
   "source": [
    "def process(sdfits, object, results, track=True):\n",
    "    \"\"\"\n",
    "    Function to calibrate the AGBT04A_008_02 observations.\n",
    "    This function was heavily tailored to work with this\n",
    "    observations and there is no guarantee it would work with\n",
    "    other data.\n",
    "    \"\"\"\n",
    "    \n",
    "    o = object\n",
    "\n",
    "    if track:\n",
    "        # Use only the last Track observation for every object.\n",
    "        tp0 = sdfits.gettp(ifnum=0, plnum=0, fdnum=0, object=o, proc=\"Track\")[-1].timeaverage()\n",
    "        tp1 = sdfits.gettp(ifnum=0, plnum=1, fdnum=0, object=o, proc=\"Track\")[-1].timeaverage()\n",
    "    \n",
    "        # Calibrate using the system temperature of the Track scan.\n",
    "        ps0 = sdfits.getps(ifnum=0, plnum=0, fdnum=0, object=o, proc=\"OffOn\", \n",
    "                           t_sys=tp0.meta[\"TSYS\"], units=\"flux\", zenith_opacity=0.08).timeaverage()\n",
    "        ps1 = sdfits.getps(ifnum=0, plnum=1, fdnum=0, object=o, proc=\"OffOn\", \n",
    "                           t_sys=tp1.meta[\"TSYS\"], units=\"flux\", zenith_opacity=0.08).timeaverage()\n",
    "\n",
    "    else:\n",
    "        # Calibrate computing the system temperature from the Off scan.\n",
    "        ps0 = sdfits.getps(ifnum=0, plnum=0, fdnum=0, object=o, proc=\"OffOn\", \n",
    "                           units=\"flux\", zenith_opacity=0.08).timeaverage()\n",
    "        ps1 = sdfits.getps(ifnum=0, plnum=1, fdnum=0, object=o, proc=\"OffOn\", \n",
    "                           units=\"flux\", zenith_opacity=0.08).timeaverage()\n",
    "\n",
    "    # Average polarizations.\n",
    "    ps = ps0.average(ps1)\n",
    "\n",
    "    # Smooth.\n",
    "    ps_smo = ps.smooth(\"gauss\", 16)\n",
    "\n",
    "    # Determine if the Galactic HI line is present, and if so,\n",
    "    # ignore it during the baseline fit.\n",
    "    idx0 = np.argmin(abs(ps_smo.spectral_axis - 1.420*u.GHz))\n",
    "    idxf = np.argmin(abs(ps_smo.spectral_axis - 1.421*u.GHz))\n",
    "    idx0,idxf = np.sort([idx0,idxf])\n",
    "    if idx0 == 0 or idx0 == len(ps_smo.data) - 1 or idxf == 0 or idxf == len(ps_smo.data) - 1:\n",
    "        exclude=[(0,100),(500,1250),(2047-100,2047)]\n",
    "    else:\n",
    "        exclude=[(0,100),(500,1250),(idx0,idxf),(2047-100,2047)]\n",
    "\n",
    "    # Baseline subtraction.\n",
    "    ps_smo.baseline(degree=1, model=\"poly\", exclude=exclude, remove=True)\n",
    "\n",
    "    # Measure line properties using Curve of Growth.\n",
    "    # Ignore 100 channels in each edge, and compute the\n",
    "    # CoG over the inner (750,1250) channels.\n",
    "    cog = ps_smo[100:-100].cog(bchan=750, echan=1250, width_frac=[0.95])\n",
    "\n",
    "    # Save the measured line properties.\n",
    "    # In particular, the object name, flux and its width.\n",
    "    results[\"name\"].append(o.replace(\"U\", \"UGC \"))\n",
    "    for k in [\"flux\", \"flux_error\"]:\n",
    "        results[k].append(cog[k.replace(\"error\", \"std\")].to(\"Jy km/s\").value)\n",
    "    for k in [\"width\", \"width_error\"]:\n",
    "        results[k].append(cog[k.replace(\"error\", \"std\")][0.95].to(\"km/s\").value)\n",
    "\n",
    "measured = {\"name\": [],\n",
    "            \"flux\": [],\n",
    "            \"flux_error\": [],\n",
    "            \"width\": [],\n",
    "            \"width_error\": [],\n",
    "           }\n",
    "\n",
    "sdfits.selection.clear()\n",
    "# Start at object 4 since the previous ones were calibration observations.\n",
    "for o in sdfits.udata(\"OBJECT\")[4:]:\n",
    "\n",
    "    if o ==  \"U8503\":\n",
    "        track = False\n",
    "    else:\n",
    "        track = True\n",
    "    \n",
    "    process(sdfits, o, measured, track=track)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "afdc3697-402e-4180-b1df-dbdbbc64ce2f",
   "metadata": {},
   "source": [
    "### Compare with the Literature\n",
    "\n",
    "We can compare the results obtained here with those listed by the author in [this page](https://greenbankobservatory.org/~koneil/HIsurvey/results_all.html). The results are also provided as a text table. We download this table and parse its contents. We save the flux and $W_{20}$ (the width of the line at 20% of the peak flux) values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "757da219-8f01-4127-b1a2-6eab43127431",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "07:10:07.700 I Starting download...\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5269b37031d345f4b804d03cbbbca705",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "07:10:07.713 I Saved results_all.dat to /home/scratch/psalas/python/dysh-dev/notebooks/examples/output/results_all.dat\n"
     ]
    }
   ],
   "source": [
    "table_file = from_url(\"https://greenbankobservatory.org/~koneil/HIsurvey/results_all.dat\", output_dir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "4b9f2121-43f5-4b69-ad18-05d7d9657fe8",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = {\"name\": [],\n",
    "        \"flux\": [],\n",
    "        \"flux_error\": [],\n",
    "        \"width\": [],\n",
    "        \"width_error\": []\n",
    "       }\n",
    "\n",
    "# Open the file.\n",
    "with open(table_file) as f:\n",
    "    lines = f.readlines()\n",
    "    # Loop over lines extracting the data we are interested in.\n",
    "    # The column numbers are provided in the header of the text file.\n",
    "    for line in lines:\n",
    "        if line.lstrip().startswith(\"\\\\\"):\n",
    "            continue\n",
    "        data[\"name\"].append(\" \".join(line[1:14].strip().split()))\n",
    "        try:\n",
    "            data[\"flux\"].append(float(line[49:53].strip()))\n",
    "        except ValueError:\n",
    "            data[\"flux\"].append(np.nan)\n",
    "        data[\"flux_error\"].append(float(line[54:57].strip()))\n",
    "        try:\n",
    "            data[\"width\"].append(float(line[58:61].strip()))\n",
    "        except ValueError:\n",
    "            data[\"width\"].append(np.nan)\n",
    "        data[\"width_error\"].append(float(line[62:64].strip()))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2d392f50-d42f-4513-97ef-750c4fc8e01f",
   "metadata": {},
   "source": [
    "Now create `pandas.DataFrame` objects to manipulate the measured and literature results. We use `DataFrame` as it provides a convenient way of handling the data. While loading the data, we sort it by \"name\". We will also remove sources that do not appear in both data sets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "bb60ae34-4f42-4181-a3be-d23c6e47723c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert to DataFrame and sort.\n",
    "df_obs = pd.DataFrame.from_dict(measured).sort_values(by=\"name\")\n",
    "df_lit = pd.DataFrame.from_dict(data).sort_values(by=\"name\")\n",
    "\n",
    "# Find sources present in both data sets.\n",
    "shared_names = set(df_obs[\"name\"]) & set(df_lit[\"name\"])\n",
    "\n",
    "# Keep only the sources found above.\n",
    "df_obs_s = df_obs[df_obs[\"name\"].isin(shared_names)]\n",
    "df_lit_s = df_lit[df_lit[\"name\"].isin(shared_names)]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "278755b7-8e6b-4fdd-ba9b-549006843c75",
   "metadata": {},
   "source": [
    "For example, for object UGC 11627 the flux is $2.3\\pm0.0$ Jy km s$^{-1}$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "59f8908a-7cbc-4a42-919d-a7a59cd31589",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         name  flux  flux_error  width  width_error\n",
      "94  UGC 11627   2.3         0.0  126.0          9.0\n",
      "\n",
      "\n",
      "Flux for UGC 11627: 2.3 +- 0.0 Jy km/s\n"
     ]
    }
   ],
   "source": [
    "# With Pandas.\n",
    "print(df_lit_s.loc[df_lit_s[\"name\"] == \"UGC 11627\"])\n",
    "\n",
    "print(\"\\n\") # Blank line.\n",
    "\n",
    "# Without Pandas.\n",
    "idx = data[\"name\"].index(\"UGC 11627\")\n",
    "print(f\"Flux for UGC 11627: {data['flux'][idx]} +- {data['flux_error'][idx]} Jy km/s\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e1ae418c-e49d-4ccb-8336-0d63a19ccb80",
   "metadata": {},
   "source": [
    "Now that we have the results, plot them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "843c7339-0a3c-4df3-8246-b727a4e9ea6d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "084577d566f84228a65b07794d83b836",
       "version_major": 2,
       "version_minor": 0
      },
      "image/png": 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l0ERERERE9F7JyckYOnQo+vfvr1D89u3bFyEhISx+qcTgDDARERERERXozz//hIeHBx4+fChvMzQ0xLp16+Dl5cWNrqhEYQFMREREREQF2rNnj0Lx6+DggN27d8PCwkLCVESfhkugiYiIiIioQMuXL0edOnWgoaGB+fPn4/Llyyx+qcTiDDAREREREQEARFEEoHh+b5kyZbBv3z6kpKSgRYsWUkUjKhKcASYiIiIiIsTFxWHAgAHYsGFDnmuNGjVi8UulAgtgIiIiIiI1d/78eTRs2BC//vorpk2bhrCwMKkjERULFsBERERERGoqIyMDM2bMQNu2bfHs2TMAQHp6OtavXy9xMqLiwXuAiYiIiIjUUHh4ONzc3BAcHCxv09bWxvfff4+pU6dKF4yoGLEAJiIiIiJSI6Io4ueff8bUqVORnp4ub69duzb8/f3RpEkTCdMRFS8ugSYiIiIiUhOxsbHo3r07xo0bp1D8jhkzBkFBQSx+qdTjDDARERERkRq4d+8enJ2d8eLFC3lbxYoVsW3bNnTv3l3CZETKwxlgIiIiIiI1YGlpCXNzc/nzzp07IzQ0lMUvqRUWwEREREREakBLSwu7d+9GxYoV4ePjg+PHj+OLL76QOhaRUnEJNBERERFRKZOTk4Nr166hRYsWCu2WlpaIjIyEgYGBRMmIpMUZYCIiIiKiUuTp06do3749WrVqhT///DPPdRa/pM7UugCOjIzE2rVrMXDgQFSrVg2CIEBLq/CT4vPmzYMgCBAEAU+fPi3GpEREREREH/brr7+iYcOG+OOPPyCTyeDu7o6kpCSpYxGpDLVeAu3r64uFCxcCAARB+KixDx48wPLly4sjFhERERHRR0lOTsb48ePh6+ur0N60aVPk5ORIlIpI9aj1DLC1tTVmzJiBo0eP4uXLlx81duzYsWjQoAGqV69eTOmIiIiIiD7szz//hK2trULxa2hoiB07duCXX35BuXLlpAtHpGLUegbYzc0Nbm5uHz1u3759OHv2LK5cuQJ3d/diSEZERERE9H7Z2dn4/vvv8f3330Mmk8nbHRwcsHv3blhYWEiYjkg1qXUB/CmSk5MxdepUDBw4EI6OjlLHISIiIiI1FBERAQ8PD1y7dk3epqGhge+++w5z5879qH1tiNQJ/8v4SN999x3i4+OxbNkyqaMQERERkZqaPXu2QvFrbm6OPXv2oHnz5hKmIlJ9an0P8McKDg6Gj48Ppk2bxnt/iYiIiEgya9euRYUKFQAAXl5eCA4OZvFLVAicAS4kURQxZswYVKpUCTNnzvys17Kxscm3PSIigvdqEBEREdEHVapUCb6+vnjz5g369+8vdRyiEoMFcCFt2bIF165dg5+fHw8PJyIiIiKlyMjIwNy5c9G6dWt06dJF4dp/nxPRh7EALoRXr15h9uzZsLe3L5Jdn+/evZtve0Ezw0RERESkfsLDw+Hm5obg4GDs2rULoaGhMDMzkzoWUYnGArgQrly5gri4OCQlJaFz584K12JjYwEAgwcPhq6uLjZs2IBatWpJEZOIiIiISgFRFPHzzz9j6tSpSE9PBwC8ePECixYtgo+Pj8TpiEo2FsAfITw8HOHh4fleu3DhAgAgKSlJiYmIiIiIqDSJjY3F8OHDcfz4cYX2sWPHYvny5RKlIio9WAAXQs+ePSGKYr7XatasiUePHuHJkyeoWrWqkpMRERERUWlx/PhxDB06FC9fvpS3VaxYEdu3b0e3bt0kTEZUevAYJCIiIiIiCaWmpmLcuHHo1q2bQvHbpUsXhIaGsvglKkJqPQMcFBSEsWPHKrTJZDI4ODjIn//222+oXLmysqMRERERkRr4999/4erqqnCbnZ6eHlauXImxY8dCEAQJ0xGVPmpdACclJeH69et52t9ty8jIUGYkIiIiIlIjFStWRGpqqvy5ra0t9uzZg3r16kmYiqj0Uusl0C4uLhBF8b2PmjVrvvc1oqKiIIoi7/8lIiIioo9mbGyM3bt3Q0tLC9OmTcO1a9dY/BIVI7WeASYiIiIiUqanT5/mmThxcnLCgwcPPjjxQkSfT61ngImIiIiIlCEpKQlDhgxBvXr1EBkZmec6i18i5WABTERERERUjP7880/Y2trC19cXycnJGDx4MLKzs6WORaSWWAATERERERWD7OxsLFiwAC1btlSY9ZXJZIiPj5cwGZH6YgFMRERERFTEIiIi0LJlSyxcuBAymQwAoKGhgfnz5+Py5cuoWLGixAmJ1BM3wSIiIiIiKiKiKMLX1xfjx4/Hmzdv5O3m5ubYs2cPmjdvLmE6IuIMMBERERFREYiLi8OAAQMwdOhQheLXy8sLwcHBLH6JVABngImIiIiIioCHhwcCAgLkz01MTLBp0yb069dPwlRE9C7OABMRERERFYFly5ZBV1cXANC6dWuEhISw+CVSMZwBJiIiIiIqAg0aNMCqVauQmpqKqVOnQkODc01EqkZlCuCEhAS8fPkSiYmJMDY2RsWKFVGuXDmpYxERERERKRBFERs2bICTkxMaNWqkcG3cuHESpSKiwpCsAM7MzMTBgwdx/PhxXL58GU+fPs3Tp1q1amjZsiW6du2K3r17Q0dHR4KkRERERERvxcbGYtiwYThx4gRsbGxw8+ZN6OvrSx2LiApJEEVRVOYbxsfHY+nSpdixYwfi4uIgiiIEQcCXX34JU1NTlC1bFomJiYiLi0N0dLT8uqmpKYYNG4ZZs2bBxMREmZGVxsbGBgBw9+5diZMQERER0X8dO3YMw4YNw8uXL+Vts2bNwtKlSyVMRfT51KkOUeqNCStXroSFhQVWrlyJcuXK4dtvv8XZs2eRkJCAJ0+e4M6dO7h8+TJCQkLw9OlTxMfH4/Tp05g5cybKli2LFStWwMLCAqtWrVJmbCIiIiJSY6mpqRg7diy6d++uUPx26dIFkyZNki4YEX00pc4Aa2howNXVFbNnz4a9vf1Hjw8MDMTSpUsREBAAmUxWDAmlpU7fvBARERGVBLdv34abmxvu3bsnb9PT08OqVaswZswYCIIgYTqioqFOdYhS7wEOCgqCra3tJ493dHTEsWPHcPv27aILRURERET0Hzk5OVi5ciXmzp2LrKwsebutrS327NmDevXqSZiOiD6VUgvgzyl+39W4ceMieR0iIiIiov969uwZBg8ejPPnz8vbBEHAtGnTsHjxYvlZv0RU8qjMMUhERERERKogOzsbt27dkj+vUqUKdu3ahTZt2kiYioiKAk/nJiIiIiJ6R40aNbBhwwYAQL9+/RASEsLil6iU4AwwEREREam1tLS0PGf5uru7o0qVKnB2duZGV0SliErMAJ88eRK1atWSOgYRERERqZHs7GwsWLAA9evXR2JiYp7rLi4uLH6JShmVKIBTUlLw6NEjqWMQERERkZqIiIhAy5YtsXDhQjx8+BDjxo2TOhIRKUGxLoGeN29eofqFh4cXZwwiIiIiIgCAKIrw9fXF+PHj8ebNG3n7n3/+idevX6N8+fISpiOi4lasBfD333+PcuXKwdjY+L39UlNTizMGERERERHi4uIwatQoHDhwQKF9yJAhWLt2LYyMjCRKRkTKUqwFsIWFBVq2bInt27e/t9+BAwcwYMCA4oxCRERERGrsjz/+gKenJ549eyZvMzExwaZNm9CvXz8JkxGRMhXrPcDNmzfH1atXP9hPEASIolicUYiIiIhIDWVkZGD69Olo27atQvHbunVrhISEsPglUjPFOgM8ceJEBAYGfrCfs7Mzzp8/X5xRiIiIiEgN9erVCwEBAfLn2tra+OGHHzB16lRoaKjEfrBEpESCyKlXlWFjYwMAuHv3rsRJiIiIiEqHkydPonPnzgCAunXrYs+ePWjcuLHEqYhUizrVIcU6A0xEREREJKVOnTphwoQJyM7OxooVK1CmTBmpIxGRhFgAExEREVGpcOLECTRu3BiVK1dWaP/pp58gCIJEqYhIlUhy48OTJ0/Qpk0bKd6aiIiIiEqZ1NRUjB07Fl27dsXQoUORk5OjcJ3FLxHlkqQATk1NxcWLF6V4ayIiIiIqRYKCgtC0aVP8/PPPAIBTp07Bx8dH4lREpKq49R0RERERlTgymQzLly+Hg4MD7t27J29v3Lgx2rdvL2EyIlJlvAeYiIiIiEqUJ0+ewNPTExcuXJC3CYKA6dOnY/HixdDR0ZEuHBGpNBbARERERFRi7N+/H6NHj0ZCQoK8rWrVqti1axdat24tXTAiKhG4BJqIiIiIVF5SUhI8PT0xcOBAheK3f//+CAkJYfFLRIXCApiIiIiIVN69e/fg7+8vf25oaAhfX1/s27cPJiYmEiYjopKEBTARERERqTw7OzvMmzcPANC8eXPcuXMHnp6ePOKIiD6KZPcAi6Io1VsTERERkYoTRTFPcTtnzhxUrlwZQ4cOhZYWt7Ihoo8nyQxwpUqV5Ge1ERERERHlEkURO3bsQPv27ZGVlaVwTUtLC19//TWLXyL6ZJIUwMbGxhg1apQUb01EREREKur169fo168fhg0bhnPnzmHx4sVSRyKiUob3ABMRERGR5M6dO4eGDRvi4MGD8raff/4ZiYmJEqYiotJGJQtgmUyGX3/9FcuXL8dvv/2GnJwcqSMRERERUTHIyMjAtGnT0K5dOzx//lze3qZNG9y+fRvGxsYSpiOi0kbSAnjz5s0wMzPDyZMn5W0ZGRlo0aIFBg4ciFmzZqFv375wcnJCenq6hEmJiIiIqKjdvXsX9vb2WLVqlbxNW1sbK1aswJkzZ1C1alUJ0xFRaSRpAXzgwAEIgoCOHTvK27Zu3YqbN2+ifv36+Omnn9CqVStcv36dm2YRERERlRKiKMLHxwdfffUV7ty5I2+vW7cubty4gWnTpkFDQyUXKhJRCSfp3yzh4eGoX7++whb3x48fh5aWFo4cOYIJEybg1KlT+OKLL7Bnzx4JkxIRERFRURBFEf369cP48eMVVviNGzcOf/31F2xtbaULR0SlnqQF8KtXr1CpUiWFths3bsDe3h41a9YEAOjo6KBFixZ4+PChBAmJiIiIqCgJgoA2bdrIn5uZmeHYsWPw8fFBmTJlJExGROpA0kPUKleujKioKPnzv//+G3FxcWjVqpVCPy0tLchkMiWnIyIiIqLiMGbMGJw4cQIAsH37dpiZmUmciIjUhaQFsJ2dHX799Vds3boVzZs3x+zZs/PcEwwAYWFh3ASBiIiIqAQKDg6GtbW1wuyuIAjYv38/ypQpo3ArHBFRcZN0CfTcuXOhq6uLUaNGoWHDhjh+/Djs7e0VZoCjoqLw999/w97eXsKkRERERPQxZDIZli9fDjs7O0ybNi3PdQMDAxa/RKR0ks4A169fH1euXMHatWvx4sULNGrUCDNmzFDoc+rUKTRq1Ai9e/eWKCURERERfYwnT57A09MTFy5cAAD8/PPP6NKlC7p16yZtMCJSe4IoiqLUIegtGxsbAG/PxCMiIiIqifbv34/Ro0cjISFB3la1alXs3r0bzs7O0gUjogKpUx3CA9aIiIiI6LMlJSXB09MTAwcOVCh++/fvj5CQEBa/RKQSJF0CTUREREQl39WrV+Hh4YHIyEh5m6GhIdavX4/BgwfzXl8iUhmcASYiIiKiT5KVlYV58+ahZcuWCsVv8+bNcefOHXh6erL4JSKVwgKYiIiIiD7JuXPnsHjxYuTk5AAANDU1sXDhQly6dAm1atWSOB0RUV5qXQBHRkZi7dq1GDhwIKpVqwZBEKCllf+q8MjISHh7e6NFixYoV64cdHR0ULNmTYwZMwZPnz5VcnIiIiIi6XXq1AleXl4AgFq1auHKlSuYN29egf+eIiKSmlr/7eTr64uFCxcCwAeX5wwdOhQXL16EkZERWrRogbJlyyIoKAgbN27Er7/+igsXLqB+/frKiE1ERESkMtatW4cvvvgCc+fOhZGRkdRxiIjeS61ngK2trTFjxgwcPXoUL1++fG/f6tWrY8uWLXjx4gVOnjyJX375Bffu3cPw4cPx+vVrDB06VEmpiYiIiJTv7NmzGD9+PP57gqaRkRGWLVvG4peISgSeA/wOQRCgqamJ7OzsQo9JTEyEiYkJRFHEw4cPYW5u/snvr07nbxEREVHJkJGRgTlz5uDHH38EAOzYsQNDhgyRNhQRFSl1qkPUega4KBgbG8PMzAwAEBMTI3EaIiIioqJz9+5d2NnZyYtfAJg2bRpSUlIkTEVE9OlYAH+mlJQUxMXFAQCqVasmcRoiIiKizyeKItatW4evvvoKISEh8va6devi7NmzMDAwkDAdEdGnYwH8mfz8/JCVlQU7OztUrVpV6jhEREREnyUmJgZdu3bFhAkTkJ6eLm//5ptvcOvWLdja2koXjojoM6n1LtCfKzY2FvPmzYMgCPD29i70uNw19v8VEREBCwuLoopHRERE9FF+//13DBs2DK9evZK3mZmZYceOHejSpYuEyYiIigZngD9RdnY2PDw88PLlS8yYMQOtW7eWOhIRERHRJxFFEWPHjoWrq6tC8dutWzeEhoay+CWiUoMzwJ9o/PjxOHv2LLp06YIffvjho8YWtLtaQTPDRERERMVJEASUKVNG/lxfXx+rVq3C6NGjIQiChMmIiIoWC+BPsHLlSmzcuBG2trbYv38/NDU1pY5ERERE9Fl++OEHnD17FhoaGvD390edOnWkjkREVOQkL4CDgoJw+fJlPHnyBMnJycjOzs5zwDrw9pvJbdu2SZBQ0f79+zFjxgzUrFkTJ06cgKGhodSRiIiIiD5KdHQ0vvjiC2ho/O9uOF1dXRw/fhwVK1aEjo6OhOmIiIqPpAXw3LlzsXTp0nwL3v9ShQL40qVL8PLyQvny5XHy5ElUrlxZ0jxEREREH2v//v0YPXo0vv32W0ybNk3hWpUqVSRKRUSkHIJYmOqzmBgbG0NPTw/r1q1DnTp1YGBgAC2tgmvyGjVqFGseQRCgqamJ7OzsPNfCwsLg5OSEzMxM/PHHH7Czsyvy98+9B7ige4SJiIiIPlVSUhK++eYb+Pn5AQC0tbVx48YNHmtERGpVh0g6A2xsbIymTZuif//+krx/UFAQxo4dq9Amk8ng4OAgf/7bb7+hcuXKcHNzQ3x8PGxsbLBhwwZs2LAhz+uNGDECTk5OxZ6biIiI6GMEBgZi8ODBiIyMlLfp6enh0aNHLICJSK1IWgCvXr0ao0ePxqJFi9CoUSOULVv2vRtKtWrVqkjfPykpCdevX8/T/m5bRkYGACAhIQHA229FCvpmxMXFhQUwERERqYysrCwsXrwYP/zwA3JycuTtLVq0wO7du2Fubi5hOiIi5ZO0AC5Tpgw0NDSwcOHCQvWXyWRF+v4uLi6Fuv8YAKKioor0vYmIiIiK07///gsPDw+FL/Y1NTUxb948zJkz5723nRERlVaS/s03evRovHr1Cm3atEH9+vVhaGjIv4yJiIiIPoMoitixYwcmTJiAlJQUeXutWrWwZ88ehVu9iIjUjaTVZlJSEjp06ICAgAApYxARERGVGocOHcLw4cMV2oYOHYo1a9bAyMhIolRERKpB48Ndis+sWbMQERGBZ8+eSRmDiIiIqNTo2bOnfN8UExMT/Prrr9i+fTuLXyIiSDwDvHHjRjx//hzVq1eHsbExjIyMCtwESxAEREREKDkhERERUcmiqakJPz8/TJo0CWvXrkXVqlWljkREpDIkLYBFUcSXX36Zp62gvkRERET0P3fv3sXFixfzHOtYvXp1HDp0SKJURESqS9IC+GN2Vk5OTi6+IEREREQliCiK8PHxwYwZM5CRkYF69erBxcVF6lhERCpP0nuA7927V6h+V65c4SHtRERERABiYmLQtWtXTJgwAenp6RBFEZ6enkhPT5c6GhGRypO0AG7duvV7i+Ds7GzMnDkTrVu35jm8REREpPZ+//13NGjQQOEEjS+++AKbNm2Cnp6ehMmIiEoGSQvgpKQktGnTJt8i+O+//0azZs2wcuVKGBkZwc/PT4KERERERNJLSUnB6NGj4erqilevXsnbu3fvjpCQEHTu3FnCdEREJYekBfDx48eRnJyM1q1bIzw8XN6+atUq2NnZ4c6dO+jQoQP+/vtvuLm5SZiUiIiISBq3bt1C06ZNsWnTJnmbvr4+Nm7ciCNHjsDMzEzCdEREJYukBbCLiwuOHz+ON2/eoE2bNjh58iTatGmDGTNmQEtLCxs3bkRAQECenaKJiIiISrucnBx4e3vDwcEB//zzj7y9SZMmCAoKwqhRoyAIgoQJiYhKHkkLYABo1aoVAgICkJKSgq5du+LixYtwcnLCnTt3MHLkSKnjEREREUlCEARcv34d2dnZ8uezZs3Cn3/+iTp16kicjoioZBJEFTlg9+rVq+jSpQsyMzNx69Yt1K1bV+pISmdjYwPg7Zl+RERERK9evULDhg2hpaWFXbt28agjIioW6lSHKPUcYH9///de9/T0hI+PDzp06IAffvgBWlqK8XgfMBEREZVW6enpeXZyrlChAo4dOwZzc3OYmJhIlIyIqPRQ6gywhobGB+9VyY2TXz+ZTFYsuVSFOn3zQkRERP8TGBgIDw8PrFq1Cr1795Y6DhGpGXWqQ5Q6A+zp6cnNGoiIiIj+X1ZWFhYtWoQlS5YgJycHX3/9Nezt7VGlShWpoxERlUpKLYB37typzLcjIiIiUln//vsv3N3dcePGDXlbYmIiLl26hEGDBkmYjIio9JJ8F2giIiIidSKKIrZt2wZbW1uF4tfCwgKBgYEsfomIihELYCIiIiIlef36Nfr27YsRI0YgJSVF3j5s2DDcvn0b9vb2EqYjIir9lLoEmoiIiEhdnT17Fl5eXnj+/Lm8zcTEBFu2bEGfPn0kTEZEpD5YABMREREVswMHDqBfv34KbW3btoWvry83vCIiUiIugSYiIiIqZp06dYKlpSUAQEdHB6tWrcLp06dZ/BIRKRkLYCIiIqJiZmhoiD179qBRo0a4ceMGpkyZAg0N/jOMiEjZ+DcvERERURGKiYnByZMn87Tb2dkhKCgIjRo1kiAVEREBLICJiIiIiszRo0fRoEED9OnTB/fv389znbO+RETS4t/CRERERJ8pJSUFo0ePRo8ePfDq1SukpqbCzc0NWVlZUkcjIqJ3cBdoIiIios9w69YtuLm5Kcz46uvrY8SIEdDS4j+1iIhUCWeAiYiIiD6BTCaDt7c3HBwcFIrfJk2aICgoCKNHj4YgCBImJCKi/+LXkkREREQf6fHjx/D09MTFixflbYIgYMaMGVi0aBF0dHQkTEdERAVhAUxERET0Efbt24fRo0cjMTFR3latWjXs2rULLi4u0gUjIqIP4hJoIiIiokLKzs7GypUrFYrfgQMH4s6dOyx+iYhKAEkL4KlTpyIsLEzKCERERESFpqWlhT179qBMmTIwMjKCn58f/P39YWJiInU0IiIqBEEURVGqN9fQ0IAgCGjWrBmGDh2KQYMGoWzZslLFkZyNjQ0A4O7duxInISIiIgAQRTHfjaxyz/s1NzeXIBURUdFSpzpE0hngdevWoWnTprhx4wbGjh2LypUrw8PDA3/88YeUsYiIiIjw4MEDtGzZEn/99Veea66urix+iYhKIEkL4HHjxuHGjRsIDw/HzJkzUbFiRfj7+6N9+/aoWbMmFi5ciEePHkkZkYiIiNSMKIrYunUrGjdujMDAQLi5uSElJUXqWEREVARUYhOs2rVrY8mSJYiKisLZs2cxePBgxMXFYeHChbCwsECbNm3g5+eH1NRUqaMSERFRKfb69Wv06dMHX3/9tbzoffDgAQ4ePChxMiIiKgoqUQC/q02bNti5cydiYmIwZswY5OTk4OLFixgyZAgqVaqE0aNH4+HDh1LHJCIiolLmzJkzaNCgAX777Td5m6mpKQ4ePAhPT08JkxERUVFRuQL4+fPn8Pb2RtOmTbFx40YAQM2aNTF48GDo6upi8+bNaNSoES5fvixxUiIiIioN0tPTMWXKFHTo0AHR0dHy9nbt2iEkJAS9e/eWMB0RERUlLakDAEBGRgYOHToEX19fnDt3DjKZDHp6ehg4cCCGDx+ONm3ayPutW7cOM2fOxJQpU3Dz5k2JkxMREVFJ9vfff8PNzQ2hoaHyNh0dHSxduhSTJk2ChobKzRUQEdFnkLQA/vPPP7Fz50788ssvSEpKgiiKsLW1xfDhw+Hu7o5y5cop9NfV1cW0adNw/vx5nDt3TprQREREVCocOXIEAwYMQEZGhrzNxsYGe/bsQaNGjSRMRkRExUXSAtjR0REAUK5cOYwZMwbDhw9H48aNPziucuXKyMzMLO54REREVIp99dVXMDAwkBfA48ePx7Jly6Cvry9xMiIiKi6Srutp3bo1du/ejejoaPj4+BSq+AWA5cuXIzIyspjTERERUWlWpUoVbN68GV988QUCAgKwdu1aFr9ERKWcIIqiKHUIesvGxgYAcPfuXYmTEBERlS4pKSl4+PAhGjRokOdacnIyjIyMJEhFRKQa1KkO4c4OREREVKr99ddfaNKkCTp27IjXr1/nuc7il4hIfbAAJiIiolJJJpNh6dKlaN68Oe7fv4/o6Gh8/fXX4OI3IiL1pRLHIBEREREVpUePHsHT0xOXLl2StwmCgNq1ayMnJweampoSpiMiIqkotQAeOXJkofppaGigTJkyqF69Olq1aoUmTZoUczIiIiIqLfbu3YsxY8YgMTFR3latWjX4+fnB2dlZwmRERCQ1pRbAW7duhSAIhV56JAgCAKBLly44cOAAdHV1izMeERERlWCJiYkYN24c9uzZo9A+cOBAbNiwASYmJhIlIyIiVaH0JdCOjo4YPnz4e/uIoojU1FRERETg119/xYkTJ7B06VIsWLBAOSGJiIioRLly5Qo8PDzw6NEjeVvZsmWxfv16uLu7y79UJyIi9abUY5A0NDQwZMgQbN++vdBjoqOjUb9+fVSqVKnUb8utTtuPExERFZXMzExYW1srFL+Ojo7w8/ODubm5hMmIiEoGdapDVH4X6MqVK8PR0RGRkZFSRyEiIiIVpKOjgx07dkAQBGhqamLx4sW4cOECi18iIsqjROwCraOjAx0dHaljEBERkYpq3bo1Vq1ahRYtWsDe3l7qOEREpKKUWgCfP38elSpV+uhxN2/eRPXq1YshEREREZUkr1+/xpQpU7B48eI8/zaYPHmyRKmIiKikUGoB/ClHDxw6dAhPnjxB165diyERERERlRRnzpyBl5cXoqOj8ejRI5w7d47n+RIR0UdR6XuAX79+jblz58LY2BgDBw6UOg4RERFJID09HVOmTEGHDh0QHR0NALh48SL27dsncTIiIippVLoALl++PMLCwhAfH49WrVoV+etHRkZi7dq1GDhwIKpVqwZBEKCl9f5J8dOnT8PZ2RlGRkYwMTFB165dcevWrSLPRkRERMDff/8NOzs7rF69Wt6mo6ODH3/8EYMGDZIwGRERlUQlYhOs4uLr64uFCxcCQKHOB9y3bx/c3NygqamJjh07Ii0tDQEBATh37hzOnz+P5s2bF3dkIiIitZCTk4N169Zh5syZyMjIkLfb2NjA398fDRs2lDAdERGVVCo9A1zcrK2tMWPGDBw9ehQvX758b9+kpCSMGTMGAHD8+HEcO3YM586dw4YNG5CRkYFhw4YhJydHGbGJiIhKtejoaHTp0gWTJk1SKH4nTJiAmzdvsvglIqJPJoiiKEodQlXknh+YnZ2d59qmTZswevRodOrUCQEBAfJ2URRRu3ZtPHjwAGfOnEG7du0++f3V6QBqIiKi/Jw5cwaDBg3C69ev5W2VKlXCzp070bFjRwmTERGVXupUhyh1Bvirr77C8ePHP+s1jh49iqZNmxZRosI7e/YsAKBz584K7YIgyNvOnDmj9FxERESlScWKFZGUlCR/7urqipCQEBa/RERUJJRaAKempsLV1RUNGzbEypUr8fTp00KNe/z4MZYtW4b69eujZ8+eCsuhlCUsLAwAUK9evTzX6tevr9CHiIiIPo2trS1++OEHlClTBps2bcLhw4dRsWJFqWMREVEpodRNsEJDQ7FlyxYsWrQIM2bMwMyZM2Fubg57e3vUqVMHpqamMDIyQlJSEuLi4nDv3j1cv34dUVFREEURlStXxsaNGzFixAhlxgYAPHv2DMDbZVjPnj2Dp6cnLCwssHHjRnzxxRcAUOiCnoiIiACZTIZXr17Jf4/mmjp1Kvr27Qtzc3OJkhERUWklyT3AWVlZOHDgADZv3owrV65AJpO9DfPOTsy5sTQ1NeHs7IyRI0eiV69e0NbWLrZc77sHWFtbG9nZ2fj333/x22+/Yfr06QCAv/76C/Hx8Wjfvj2srKxw//79D75P7hr7/4qIiICFhYVarL0nIiL19ujRIwwePBhJSUm4fv06dHV1pY5ERKS21OkeYEmOQdLW1sagQYMwaNAgJCcn4+rVqwgJCcGLFy+QmJiIcuXKoWLFimjUqBFatGgBQ0NDKWIWqH379qhUqRJq1aqFevXqITAwUOpIREREJcbevXsxZswYJCYmAgDmzp2LFStWSJyKiIjUgeTnABsZGaFjx44qv7mFgYEBEhMTkZaWhkaNGiE6Olp+LTU1Vd6nMAr6ZqWgmWEiIqLSIDExEePGjcOePXsU2p8/f46cnBxoaKj16YxERKQE/E1TSFWqVAEAxMTE5LkWGxsLAKhatapSMxEREZUUV65cQaNGjRSKXyMjI/j5+WH37t0sfomISCn426aQcnd/zm+n57///luhDxEREb2VlZWFuXPnwtnZGY8ePZK3Ozo64s6dO/Dw8FDYA4SIiKg4sQAupHbt2gEAAgICFNpFUZS3tW/fXum5iIiIVNWDBw/g6OiIH374ATk5OQDebm65ePFiXLhwgbs8ExGR0rEALqSBAwfC2NgYp06dwpkzZ+TtmzdvxoMHD2BtbY02bdpImJCIiEh1pKeno1WrVrh586a8zdLSEoGBgZg7dy60tCTfhoSIiNSQWhfAQUFBcHBwkD+At2cSvtuWu9mVsbEx1q9fDwDo0qULunfvjnbt2mHMmDHQ0dHBtm3beP8SERHR/9PT08OSJUvkz4cPH47bt2/D3t5ewlRERKTu1Prr19yzB//r3baMjAz5n93d3VG+fHksWbIEFy5cgIaGBjp27IjFixfjq6++UkpmIiKikmLIkCG4du0aOnbsiN69e0sdh4iICIIoiqLUIegtdTqAmoiISo/09HSsWLECkyZNgpGRkdRxiIjoI6lTHaLWM8BERET0ef7++2+4ubkhNDQUDx8+xI4dO6SOREREVCDetEpEREQfLScnB2vWrMFXX32F0NBQAMDOnTtx7NgxiZMREREVjDPARERE9FGio6MxdOhQnDp1SqF9woQJaNu2rUSpiIiIPowFMBERERXakSNHMGLECLx69UreVqlSJezcuRMdO3aUMBkREdGHcQk0ERERfVBKSgpGjRqFnj17KhS/PXr0QEhICItfIiIqETgDTERERO9169YtuLm54f79+/K2MmXKYPXq1fj6668hCIKE6YiIiAqPBTARERG91/PnzxWK36ZNm8Lf3x/W1tYSpiIiIvp4XAJNRERE79W9e3eMHj0agiBg9uzZuHr1KotfIiIqkTgDTERERAoyMjKgq6ur0LZq1SoMHjwYLVq0kCgVERHR5+MMMBEREQEAEhIS4O7ujj59+kAURYVrZcqUYfFLREQlnqQFcHR0dKH7rl27thiTEBERqbfLly+jUaNG8Pf3x/Hjx7Fx40apIxERERU5SQvghg0b4vDhw+/t8/LlS3Tt2hWTJ09WTigiIiI1kpWVhW+//RYuLi54/PixvP3w4cN5ZoGJiIhKOkkL4MzMTPTp0wcjR45EampqnuunTp1Cw4YNERAQgA4dOkiQkIiIqPS6f/8+WrRogSVLliAnJwcAoKWlhR9++AEnTpzg8UZERFTqSFoAh4aGolWrVti6dSuaNGmCv/76C8Dbb6MnT56Mrl27Ijk5GT4+PggICJAyKhERUakhiiK2bNmCxo0by3/3AoCVlRWuXr2KOXPmQFNTU8KERERExUPSArh69eo4f/48Vq5ciUePHsHR0RFz5syBnZ0d1qxZg6ZNmyIoKAhjx46VMiYREVGp8erVK/Tq1SvP6qsRI0YgKCgIzZo1kzAdERFR8RJEFbnBJywsDC4uLnj9+jUAYOTIkVi/fj00NNRno2obGxsAwN27dyVOQkREpdGbN29Qt25dPH36VN5Wvnx5bNmyBb169ZIwGRERSUmd6hCVqC5fv36NuXPn4tWrVzAzMwMA7Nu3D/7+/hInIyIiKj0MDQ0xZMgQ+fP27dsjJCSExS8REakNyQvggIAANGjQAIcPH4aXlxcePHiAEydOQE9PD15eXujXrx/i4uKkjklERFQqzJs3Dy1btsTq1atx8uRJfPnll1JHIiIiUhpJC+AxY8agW7duSE9Px/79+7Fjxw4YGhqiY8eOCA0NRefOnXHw4EE0aNAAJ06ckDIqERFRiZKTkwN/f39kZ2crtGtra+PChQuYNGmSWt1mRETqYcGCBRAEQWG1y7u0tLQK3OE+NjYWkydPhrW1NfT19WFsbAxnZ2fs2LEDMpks3zHR0dGYOHEirKysoK+vDzMzMzg5OcHHxweZmZmf/Xl+++03TJ8+HS1atICenh4EQcD3339fYP/Y2Fhs3rwZXl5esLS0hCAIEARB4daXd0VFRUEQBISFhSEsLEzeP/fh4eGh0P/ChQt5+uT32LVrV573OnPmDDp06AATExPo6OigevXqGDFihMIRfMqgpdR3+49NmzbB2dkZfn5+qFq1qsK1ChUq4NixY1i3bh1mzJgBV1fXPL/EiYiIKK/o6GgMGTIEp0+fRkREBL777juF6yx8iYgU3bhxA506dUJ8fDysrKzg6uqKlJQUXL58GcOGDUOZMmUwYMCAPGM6duyIhIQE1K5dG66urnjz5g0uX76MwMBAdOvWDTVr1vysXL1795b/uTBH0wUEBGDUqFEf/T6ampowNDREz549FdpbtGih8LxSpUrw8vLK9zVEUcTu3buRk5MDJycnhWs7duzAsGHDoKGhgZYtW8LMzAzBwcHYtm0bjhw5gmvXrsHCwuKjc38KSQvgJUuWYObMme/9H3P8+PFwcXGBu7u7EpMRERGVTIcPH8aIESPkm0ouXLgQHTp0gL29vcTJiIhUU1xcHLp164b4+Hj4+Phg3Lhx8mspKSmYN29egWMSEhKwYcMGjBkzRmHMihUroKX1+aXWoEGD4OjoCEdHRxw6dAiLFy9+b/8qVapg4sSJcHR0hJOTE5o3b45Hjx598H10dXVRpUoV7Ny587396tSpU2CfixcvYteuXWjRogVq1aolb8/MzMSUKVMgCAJOnDiBjh07Ani7Umn48OHYuXMnFixYAD8/vw/mLAqSFsCzZs0qVL8GDRrg5s2bxZyGiIio5EpJScHkyZOxZcsWhfZu3bop/EOEiIgULVu2DC9fvsTQoUMVil8AMDAwwKpVq5CWllbgmHeL39wxCxYsKJJs724KfPjw4Q/2b9++Pdq3b18k7/2xfH19ASDPsumwsDAkJCTAxsZGXvwCb1cjTZgwATt37lQ4k764SVoAfwxdXV2pIxAREamkmzdvwt3dHQ8ePJC3lSlTBj/99BNGjBhRqGVzRETqKvd+1bFjxxbYR19fP98x/y2Y1VVqaioOHDgAbW1t9O/fX+Fabh2X3++i3LYKFSoUf8j/J2kB3KZNm0L3FQQB586dK8Y0REREJYtMJoO3tzcWLFigsE9G06ZN4e/vD2trawnTERGpvoiICMTExMDAwABNmzb96DFNmjQp5oTKkZ2djRcvXmDkyJEwMTGBk5MTunbtWug9Iw4dOoTk5GT06NED5cuXV7hmZWWFGjVqICwsDOfPn0fr1q0BvF0C7ePjAwAYOnRo0X6g95C0AL5w4cIH+wiCAFEU+e01ERHROx49eoTBgwfj8uXL8jZBEDBr1iwsWLAAOjo6EqYjIioZ7t+/DwAwNzcvdL3xKWNUXWZmJl69eiW/jWb58uVo1KgRDh8+XKiNvApa/gy83Xl7586d6NGjB9q1awdnZ2dUrFgRwcHBiI6OxtKlSzFs2LAi/TzvI2kBHBkZmW97Tk4OYmNjcfXqVfz444/o0qULvv32WyWnIyIiUl1Hjx5VKH6rV68OPz8/tGrVSsJUREQlS0JCAgCgbNmyxTpGVenq6mLSpEk4cuQIdHR0cOXKFdy4cQPTp0/HnTt30LVrV9y+ffu9X6o+ffoUf/zxB4yNjdG9e/d8+7i4uOD8+fPo06cPzp8/L293dnZG8+bNi/xzvY+k5yDUqFEj34e5uTkcHBwwZcoUnD9/Hv7+/ggICJAyKhERkUoZN24cOnToAODtLqF37txh8UtE9In+O5O7b98+hXNtLS0tJUpWvCpXrozVq1dDX18fmpqaqFChArp06YKLFy+ifPnyCAsLw759+977GrlHH/Xr16/AfZv8/Pzg4OCAZs2a4Z9//kFSUhICAgLw8OFDtGvXrlAbfBUVlT8I0MrKCs2aNcPatWuljkJERCQZURQVnmtoaGDnzp3w9/eHv78/ypUrJ00wIiIV8rFLko2NjQEAycnJCu3m5ubw8vJCnz59ChyTlJT0iSlVX4UKFTB48GAA+OA+TO9b/gwADx8+xPDhw1GjRg35/hRGRkbo1KkTdu7ciezsbEyePBkymaxoP0QBVL4ABoC0tLQCl0sTERGVZllZWfj2228xderUPNcqV66MQYMGSZCKiEg1aWpqfrDPu+fz5m4WGBUVpdDH3t4eO3fuxMqVK/OMt7KyAvD2ds7/fjlZmuTOekdHRxfY58aNG7h37x6qV69e4Cqk/fv3IysrC+3atctzNnLr1q2hp6eHqKioQp1XXBRUugDOzMzE6tWrcePGDVSpUkXqOEREREp1//59ODo6YsmSJVi9ejVOnToldSQiIpVmaGgIAAXOJspkMnkfALCwsICZmRmSkpIQGhpaqPewtLSEmZkZUlJSEBQU9PmhVdSrV68AQOHn9V+5s79ubm4Fzr7nFrYGBgZ5rgmCIG+PjY39rLyFJWkBXKtWrQIfVatWhaGhIaZNmwYAmD59upRRiYiIlEYURWzZsgWNGzfGzZs35e1Lliwp1bMNRESfq3r16gDyn7V8/vw5AKBatWryNkEQ4O7uDgDyHZA/RBAEuLm5AQDWr1//WXlV2dmzZwGgwKOeMjMz5fcH5y6Xzk/uLTr5fcEQHR2N169fA3i7qkkZJC2Ao6KiCnzExMSgXLlyaNeuHQ4ePIhRo0ZJGZWIiEgpXr16hV69emHkyJFITU2Vt3/99dc4fvx4qTlyg4ioOLRu3Rr6+vq4dOkSAgMD5e2iKGLJkiUAgM6dOyuMmT17NkxNTbF+/Xrs3r1b4VpcXFy+7zNnzhyYmppix44d2Lhxo8K1tLQ0LFq0CE+fPi2Kj1Sstm/fnmfpsUwmw6JFi3DlyhXo6+sXeEbv77//jri4ODRu3Bj16tUr8D26du0KADh9+rTCZldZWVny23tsbW0LddxSURBEfpWsMmxsbAAAd+/elTgJERFJ4dSpUxgyZAhiYmLkbeXLl8fWrVvRs2dP6YIREZUg69atw4QJE6ClpQUXFxf5mbPh4eGwtLTE9evXYWpqqjDmypUr6NKlC5KTk2FjY4P69esjPj4ely5dQnp6OlxdXXHkyJECx9SuXRu2trZ48+YNAgMDkZCQgMjIyM8u6saOHStfZv306VM8e/YMVatWld8e2rVrV3z33Xfy/tHR0ejVq5f8+e3bt5GZmYnGjRvLjzLasGGDfFbXxcUFly5dgo6ODnR1deHi4oI7d+7g0aNH0NHRwZ49e9C3b998s7m6uuL333/HqlWrMGXKlPd+jq+//hpbt26FIAhwcHDAl19+iVu3biEqKgqGhoY4d+4c7OzsPv0H9TFEUhn16tUT69WrJ3UMIiJSsrS0NHHChAkiAIVHhw4dxGfPnkkdj4ioxDlx4oTYtm1bsWzZsqK2trZYq1YtccqUKWJcXFyBY548eSKOGzdOrFWrlqirqysaGxuLrVq1Erdt2yZmZ2cXakyFChVER0dHce3atWJGRsZnfw5nZ+c8vxvefXh5eSn0j4yMfG9/AOL58+fl/Xft2iV27dpV1NbWFjU0NEQtLS2xSpUqooeHh3jnzp0Cc7148ULU0tISNTU1xefPnxfqs/j6+opOTk5i2bJlRS0tLbFq1aqil5eXeP/+/U/50XwyzgCrEM4AExGpn8TERDg5OeHvv/+Wt+nq6sLb2xsTJkyAhoZK71dJRERKkp4lw4ukDJiV1YWe9od3u/4Y6lSHaH24CxERERUXY2NjNGjQQF4A169fH/7+/mjQoIHEyYiISBXIckSsPP0Pdl2NQkqmDAY6mvBsURPTOtSGpgb3hfhYJaIAPnLkCO7cuYN58+ZJHYWIiKjIbdiwAVevXkXPnj3h7e0NPT09qSMREdFnSM+SISM7p0hea83ZB9geGCl/npIpw88XIpCZnYMJba2K5D1yRBEaarLJYolYAj106FDs2rWrwPO8Sgt1WnpARKSubt68iWbNmuVpT0pKQtmyZSVIRERERW31mftYc+6B1DEK7fnWsbD6wlAt6pAScWNRTk7RfHtCREQklTdv3uDrr7+GnZ0d9uzZk+c6i18iIqLip/IFcFZWFq5fvy4/QJmIiKikuXnzJho3boytW7cCeHusRVRUlLShiIiI1JBS7wFetGjRR/WPj4/HyZMn8eDBA3h6ehZTKiIiouIhk8ng7e2NBQsWIDs7W95ubW1d6m/rISJSZ2NcLDDMybxIXuu/9wDnGu5kXmT3ADscNSiS1ykJlFoAL1iwAIIg4GNvO+7Zsyd++umn4glFRERUDKKiojB48GBcuXJF3iYIAmbPno0FCxZAW1tbwnRERFSc9LQ1i+yoom+71oWetgZ839kF2qtFTUwtwl2g1WUDLECCXaAdHR0xfPjwD/YTBAHly5dH3bp1UatWLSUkIyIiKhp79uzB2LFjkZSUJG+rXr06du/ejZYtW0qYjIiIShpNDQEzOtXBhLZWxXYOsDpRegFsaWkJLy8vZb8tERFRsUtISMDYsWOxd+9ehXY3NzesX7+e+1kQEdEn09PWRPXyZaSOUeKViHOAiYiISoIff/xRofgtW7YsNmzYAHd3dwlTERERUS6l7gK9Y8eOQi1/JiIiKolmz54tP9PdyckJd+7cYfFLRESkQpQ6A8ylz0REVJrp6+vD398fx44dw8yZM6GpyXu0iIiIVIlSC+CjR4+ievXqsLW1VebbEhERFSlRFLF161bIZDKMHj1a4VrDhg3RsGFDiZIRERHR+yh1CXTPnj2xdu3afK/17t0bPj4+yoxDRET00V69eoVevXph5MiRmDRpEkJDQ6WORERERIWk1AL4fQ4fPoygoCCpYxARERXo1KlTaNCgAY4cOQIAyMjIwMSJEyVORURERIWlMgUwERGRqkpPT8ekSZPQqVMnxMTEyNs7dOiA3bt3S5iMiIiIPgaPQSIiInqP0NBQuLm54e+//5a36erqwtvbGxMmTICGBr9LJiIiKilYABMREeUjJycHa9euxcyZM5GZmSlvr1+/Pvz9/dGgQQMJ0xEREdGnYAFMRET0HwkJCejfvz/OnDmj0D5p0iQsXboUenp6EiUjIiKiz6H0Ajg4OBiLFi366GsAMG/evOKKRUREJGdoaIiUlBT580qVKsHX1xcdOnSQMBURERF9LkEURVFZb6ahoQFBEPK9lhsjv+uiKEIQBMhksmLNJzUbGxsAwN27dyVOQkREkZGRaNSoEdq2bYstW7agQoUKUkciIiIqFupUhyh1BtjLy0uZb1cszpw5gxUrVuDmzZtISUlBpUqV0KFDB8ybNw/Vq1eXOh4REX2CmJgYVKpUSaHN3NwcQUFBsLCwKPDLWyIiIipZlDoDXNLt2LEDw4YNg4aGBlq2bAkzMzMEBwfjwYMHqFChAq5duwYLC4tPfn11+uaFiEgVyGQyeHt7Y/HixTh79iycnJykjkRERKR06lSH8OyGQsrMzMSUKVMgCAJOnDiBCxcu4JdffsG9e/cwZMgQvHr1CgsWLJA6JhERFVJUVBRcXFwwd+5cZGRkYPDgwUhMTJQ6FhERERUjFsCFFBYWhoSEBNSrVw8dO3aUt2toaGDChAkAgL/++kuqeERE9BH27NmDRo0a4cqVK/I2URTx5MkTCVMRERFRcWMBXEi6uroA8t+kK7eNG6QQEam2hIQEuLm5wcPDA0lJSfJ2d3d33LlzB/Xr15cwHRERERU3FsCFZGVlhRo1aiAsLAznz5+Xt+fk5MDHxwcAMHToUKniERHRB1y6dAmNGjXC3r175W1ly5bFnj17sHv3bhgbG0uYjoiIiJSBBXAhaWlpYefOnTA0NES7du3Qpk0bDBgwAHXr1sUvv/yCpUuXYtiwYVLHJCI1s2DBAgiCgCFDhuR7XUtLq8AdjGNjYzF58mRYW1tDX18fxsbGcHZ2xo4dOwo8di46OhoTJ06ElZUV9PX1YWZmBicnJ/j4+CAzM/OzP09SUhJmzJgBKysr6OrqwsjICC1atMCuXbvy9M3KysLvv/+OwYMHw9zcHLq6uihbtixatWoFf39/eb/MzEzMnj0bLi4uePz4sby9ZcuWCAkJQXp6OgRBgCAICkui3xUaGop+/frBzMwMurq6MDc3x5QpU3jPMBERUQmj1GOQSjoXFxecP38effr0UZgFdnZ2RvPmzQv9Orm7rP1XRETEZ+0iTURUWDdu3ECnTp0QHx8PKysruLq6IiUlBZcvX8awYcNQpkwZDBgwIM+Yjh07IiEhAbVr14arqyvevHmDy5cvIzAwEN26dUPNmjU/OdObN2/QokUL3L17F1WrVkX37t2RnJyMCxcuwMvLC7dv38bq1avl/QMDA+Hq6grg7d+rPXv2xOvXr3H58mVcvnwZly5dwsaNGzFnzhysWrVKPk5LSwuLFi3CjBkzkJiYiJkzZ74319WrV9G+fXukpqbC1tYWLVu2xK1bt7B69WqcPHkSf/75J2ePiYiISgjOAH8EPz8/ODg4oFmzZvjnn3+QlJSEgIAAPHz4EO3atcPhw4eljkhE9EFxcXHo1q0b4uPj4ePjg/v372P//v04duwYnj9/jilTphQ4JiEhARs2bMC9e/ewf/9+HD9+HNHR0Zg/fz60tD7vO9Wff/4Zd+/eRZs2bfDgwQMcOHAAp06dwq1bt1CmTBmsWbMG//77r7y/hoYG3NzcEBoair///hv79+/H2bNn8ddff8HQ0BCbNm3C8ePHMWPGDJiZmQEArK2t8eeff2L27NnQ1NTEzJkzoaenh2bNmhWYa9SoUUhNTYW3tzdu376NgwcP4v79++jXrx/Cw8Pxww8/fNbnJiIiIiUSqVAiIiJEbW1t0dLSUszKylK4du7cORGAWLNmTTE7O/uT36NevXpivXr1PjcqEamR+fPniwBELy+vfK9ramqK//2rfsaMGSIAcejQoQW+bmpq6keP+Vw9evQQAYi//vprnmuurq4iAHHfvn2Feq0pU6aIAERPT09RFEXx+PHj4siRI8U3b97I+1y9elUUBEHcs2eP6OzsLAIQL1++rPA6d+/eFQGI1apVy/P3e3R0tKijoyOWL19elMlkH/txiYiIVIY61SGcAS6k/fv3IysrC+3atcszy9G6dWvo6ekhKioKjx49kighEVHh5N5PO3bs2AL76Ovr5ztm3LhxxZarKHbbP3XqFE6ePAkrKysAQExMDACgS5cu2LRpEwwMDAAAMpkMY8aMgYODA9zc3Ap8vfDwcABAo0aNoKmpqXCtUqVKqFWrFl6/fo2QkJDCfEQiIiKSGAvgQsotbHP/8fQuQRDk7bGxsUrNRUT0MSIiIhATEwMDAwM0bdr0o8c0adKk2LK1b98eALB582aFDbXCw8Nx9uxZWFhYoGXLlvmOTU9Px8SJE9GpUyd4enoiLCwMAFCtWrV8+69btw4hISH46aef3pspPT0dAFCuXLl8r5uamgIA/vnnn/e+DhEREakGboJVSLn/+AkNDc1zLTo6Gq9fvwYAVK5cWZmxiIg+yv379wEA5ubmBe4OXRRjPsWQIUMQEBCAQ4cOwcrKCnZ2dnjz5g3Onz+Pxo0bY/fu3dDR0ckzLiQkBO7u7vj7778BAC9fvsTWrVsBAH369MnT//nz55g3bx48PDxgZ2f33ky5M87R0dH5Xn/y5Ml7rxMREZFq4QxwIXXt2hUAcPr0aYXNrrKysjB16lQAgK2t7WftgEpEVNwSEhIAvD3/tjjHfAotLS3s378fkydPxuPHj3HgwAGcPHkSOjo6aN++PSpVqqTQPycnB6tXr0azZs3kxS8AmJmZIS0tDS4uLujcuXOe95k8eTJycnLg7e39wUxNmzaFpqYmrl69ipcvXypcu3jxorwATk1N/ZSPTERERErGAriQWrZsiREjRgAAevfujRYtWqBv376wtrbG3r175TuOEhGVBP+dyd23b5/8LFxBEGBpaan0THFxcXBxcYGvry/279+PhIQEREVFYfDgwVi8eDHatWuHrKwsAG9ncTt16oQpU6YoLJfu06cPXr9+jfLly8PPzy/Pe5w5cwa//PILZs2ahS+//PKDmSpUqIBBgwYhLS0Nffv2RUhICJKTk3HmzBl4eHjIZ4iLc2aciIiIig4L4I+wZcsW+Pr6wtHREXfv3sWRI0eQnZ0NLy8vBAUFfXApHRFRUfvYwiv3vNrk5GSFdnNzc3h5eeW7ZDh3TFJS0iemLJzp06cjMDAQq1atQv/+/WFsbIwaNWpg/fr1aNmyJa5duwY/Pz/89ttvaNCgAc6cOSMfW7lyZWzZsgV//PEHNDU1cejQIVStWlXh9TMyMjBu3DhUr14d06ZNK3SutWvXwsHBAZcuXUKjRo1QtmxZdOjQAaamphg/fjyA4p8dJyIioqKhMvcAv3jxAsHBwXj9+jVq1KiBFi1aSB0pX56envD09JQ6BhERAOTZmTg/7+5cb21tDQCIiopS6GNvbw97e3tERUXh4MGDCtdyd1SOjIyEKIrFMtspiiL8/f0BIN9ly507d8bly5exdOlShbOAAaBnz55YsmQJunbtivj4ePj5+aFVq1Z5XiM8PBwPHjxAzZo10bNnT4Vrubs4T5s2DeXKlcP8+fPRvHlzAICJiQkCAwNx+PBhXLlyBenp6WjcuDHc3d0xf/58AECtWrU++2dARERExU/yAjgqKgrjx49HQEAARFEEAHh5eckLYG9vbyxfvhxHjx6Fk5OTlFGJiFSOoaEhgLfH+uRHJpMp7GBsYWEBMzMzvHjxAqGhoWjQoMEH38PS0lI+JigoqNC7R3+M2NhY+Y7L+e22b2RkBAB49uyZvK1MmTJYs2YNBg0ahNatWyMyMhJLliyBh4fHe98rKioqzxcAua5fvw4AGD16tEK7hoYGevfujd69eyu0BwYGQhAENG7c+P0fkIiIiFSCpEugHz9+DAcHBxw/fhwtW7bEhAkT5EVwLnd3dyQmJuLAgQMSpSQiUl3Vq1cHkP8uxM+fPwegeBSQIAhwd3cH8Pa2jsIQBEF+Vu769es/K29BjIyMoKHx9ldSfrvt57a5uLhAQ0MDzZo1Q3BwMIYOHYqBAwfi5s2bGD9+PGbPnl3ge9ja2kIUxXwfzs7OAIDLly9DFMU8M8T5uX37Nq5duwYnJ6c8G3QRERGRapK0AP7uu+/w8uVL7N27F+fPn8fq1avz9KlWrRrq16+PCxcuKD8gEZGKa926NfT19XHp0iUEBgbK20VRxJIlSwDkXVI8e/ZsmJqaYv369di9e7fCtbi4uHzfZ86cOTA1NcWOHTuwceNGhWtpaWlYtGgRnj59+smfw8DAAC4uLgCAWbNmISkpSb651c2bN7Fr1y4AwKhRo3D69GkEBgbCysoKY8eOxbFjx9C/f/8Pnun7qWJiYvKc8R4REYGBAwdCFEUsWLCgWN6XiIiIip6kS6BPnz4NOzs7DBgw4L396tati9OnTyspFRFRyWFiYoJly5ZhwoQJcHFxgYuLCypWrIjg4GCEh4fD0tISM2fOVBhTsWJFHDlyBF26dMHgwYPh7e2N+vXrIz4+HpcuXQIA2NjYFDhmzJgx+Omnn2Bra4s3b94gMDAQCQkJn70/wpo1a9CqVStcunQJ1atXR0ZGBmxtbXHr1i1kZWVhwIABcHV1ld+DfPjwYWzevBkaGhrQ1NTEsGHD8rxmhQoVsHLlys/KFRwcjB49esDOzg5Vq1bFy5cvcfnyZWRmZmLRokVo06bNZ70+ERERKY+kBXB8fDxatmz5wX7p6enIyMhQQiIiopJn/PjxsLS0xKpVq3Djxg2kpaWhWrVqmDJlCubOnQsTE5M8Y5ycnBAWFgZvb28EBATg8OHD0NPTg52dHby8vODl5VWoMUZGRrCxscGAAQMKdazQ++Su9unduzciIiIAADdu3MBXX32FYcOGYeTIkQobcOWeT5yTk4O9e/fm+5o1atT47AK4du3a6NWrFwIDA3Hjxg0YGhqiTZs2mDRpEjp27PhZr01ERETKJYj/velWiSwsLKCjo4Pw8HB5m4aGBoYMGYLt27cDeLuMz8LCAnp6eggLC5MqqlLkzrjcvXtX4iRERMqRniXDi6QMmJXVxY0/AzF48GA8fvxYft3Y2BiXLl1Cw4YNJUxJRERUuqlTHSLpPcCdO3fG/fv3sWzZsgL7+Pj4ICoqCt26dVNiMiIiKk6yHBHLTt5D08Vn0GrFedT/7hh6zNuOx0/+dx9xq1atcOfOHRa/REREVGQkLYC//fZblC9fHnPmzEG7du2wbt06AG83HPH394eHhwcmTZoEMzMzTJs2TcqoRERURNKzZPjheDh+vhCBlMy3xzdlQwvGDv1QzmUotHT1sWTJEvzxxx+oUaOGxGmJiIioNJF0CTQA3LlzB3379kVERAQEQYAoivJ7vERRRK1atXDw4EE0atRIyphKoU5LD4hIfa04eQ/rL0QUeL137TL4cWhrJSYiIiJSb+pUh0i6CRYANGrUCOHh4Th06BDOnTuHJ0+eAACqVq2K1q1bo2/fvtDW1pY4JRERFZXcWd+ClC1vpqQkREREpG4kL4ABQEtLC/3790f//v2ljkJERMXMQEfzs64TERERfSpJ7wEmIiL1M76tFYY5mud7bbiTOca3tVJyIiIiIlIXkhbAc+fORZMmTXDt2jUpYxARUTEKCQlBaGio/Lmetia+7VoXY10s5LO9BjqaGOtigTld6kJPmzPAREREVDwk3QTL3Nwcurq6uHfvnlQRVIo63XxORKVfTk4OfvrpJ8yePRsWFhb466+/UKZMGYU+754DzMKXiIhIGupUh0g6AxwTE8PzHYmISqFnz56hY8eOmDp1KjIzMxEeHo4ZM2bk6aenrYnq5cuw+CUiIiKlkLQArlixItLS0qSMQERERezQoUNo2LAhzp49K2+rXLkyXF1dJUxFREREJHEB3Lt3b1y6dAmvX7+WMgYRERWBN2/eYPjw4ejTpw/i4uLk7b169UJISAg6dOggYToiIiIiiQvgxYsXo27duujevTuioqKkjEJERJ/hxo0baNy4MbZv3y5vK1OmDLZs2YKDBw+iQoUKEqYjIiIiekvSc4AnTpyI2rVr4/Dhw7CyskKTJk1gYWEBPT29PH0FQcC2bdskSElERAWRyWRYunQpFixYAJlMJm9v1qwZ9uzZAysrHmlEREREqkPSXaA1NAo/AS0IgsI/rkojddp9jYhKh2fPnsHGxgaJiYkA3v69Pnv2bMyfPx/a2toSpyMiIqLCUKc6RNIZ4PPnz0v59kRE9JmqVKmCjRs3YtCgQahRowZ2794NJycnqWMRERER5UvSe4CdnZ0/6kFERNLKb9HQwIEDsXHjRty5c4fFLxFRKbRgwQIIgoAhQ4bke11LSwuCIOR7LTY2FpMnT4a1tTX09fVhbGwMZ2dn7Nixo8DVndHR0Zg4cSKsrKygr68PMzMzODk5wcfHB5mZmZ/9eX777TdMnz4dLVq0gJ6eHgRBwPfff19g/9jYWGzevBleXl6wtLSEIAgQBAFPnz4t9Htu375dPu7KlSsF9vvnn3/g5eWFqlWrQldXF1988QU6dOigcLJCrsjISKxduxYDBw5EtWrVIAgCtLQknd8sEfgTIiKiQrl48SK+/fZb/P777zAxMVG4NmrUKIlSERGRqrpx4wY6deqE+Ph4WFlZwdXVFSkpKbh8+TKGDRuGMmXKYMCAAXnGdOzYEQkJCahduzZcXV3x5s0bXL58GYGBgejWrRtq1qz5Wbl69+4t/3NBhfu7AgICPuv3XFxcHGbOnPnBfqdOnUKPHj2QkZGBpk2bomXLloiNjcWNGzdw9uxZtGvXTqG/r68vFi5cCKBwn4PeYgFMRETvlZmZifnz52PZsmUQRRFjxozB3r17+cuWiIgKFBcXh27duiE+Ph4+Pj4YN26c/FpKSgrmzZtX4JiEhARs2LABY8aMURizYsWKIpnhHDRoEBwdHeHo6IhDhw5h8eLF7+1fpUoVTJw4EY6OjnByckLz5s3x6NGjQr/fzJkzoaenh2bNmuHmzZv59nn58iUGDBgADQ0NBAQEoFOnTvJrqampePz4cZ4x1tbWmDFjBpycnNCiRQueuFBILICJiKhA//zzD9zc3BAUFCRvO3jwIMLCwuQbZhAREf3XsmXL8PLlSwwdOlSh+AUAAwMDrFq1CmlpaQWOebf4zR2zYMGCIsnm7+8v//Phw4c/2L99+/Zo3779J73Xn3/+iW3btmH37t3YvHlzgf3WrFmDxMRE/PjjjwrFL/D2WME6derkGePm5gY3N7dPyqXOJL0HmIiIVJMoiti0aRMaN26sUPxaW1vjzz//ZPFLRETvtWvXLgDA2LFjC+yjr6+f75j/FswllUwmw5gxY+Dg4PDBQvXAgQPQ1dXFiBEjlJROfUk6A9ymTZtC9xUEAefOnSvGNEREBLxdhjVixAgcPXpUoX3kyJH48ccfYWBgIFEyIiIqCSIiIhATEwMDAwM0bdr0o8c0adKkmBMqx7p16xASEoJr1669t9+bN29w//59fPXVVzAyMsKJEydw5swZZGVloWHDhhg0aBCMjIyUlLr0k7QAvnDhwgf7CIIAURR5rxkRkRKcPHkSQ4YMQWxsrLytfPny2LZtG3r06CFhMiIiKinu378PADA3Ny/0v+E/ZYwqe/78OebNmwcPDw/Y2dm9t29kZCREUUT16tUxZMgQ+Pr6KlxfsGABTp48iYYNGxZnZLUhaQEcGRmZb3tOTg5iY2Nx9epV/Pjjj+jSpQu+/fZbJacjIlIvixcvzrMpSceOHbFjxw5UrlxZolRERFTSJCQkAADKli1brGNU2eTJk5GTkwNvb+8P9k1OTgbw9rSF5ORk/Pzzz+jXrx+SkpLg7e2NzZs3o2/fvggLC+MxR0VA0p9gjRo1Crxmbm4OBwcHdO/eHY0bN0aTJk0wevRoJaYjIlIvDg4O8j/r6upi+fLl+Oabb6Chwe0iiIjo4/13Jnffvn0YNGiQ/LmFhQX+/fdfZccqdmfOnMEvv/yCxYsX48svv/xgf1EUAQCvXr3CwoUL5TVP+fLlsWnTJly7dg0hISE4deoUunbtWqzZ1YHK/6vGysoKzZo1w9q1a6WOQkRUqrVv3x6TJk1CgwYN8Ndff2HChAksfomI6KOXJBsbGwP438xmLnNzc3h5eaFPnz4FjklKSvrElKohIyMD48aNQ/Xq1TFt2rRCjSlTpoz8zx4eHnmu9+zZEwA+eC8xFU6JmENPS0srcLk0ERF9vGfPnkFDQyPP0mZvb2+Iogg9PT2JkhERkarR1NT8YJ93l+ZaW1sDAKKiohT62Nvbw97eHlFRUTh48KDCNSsrKwD/ux+2pN4HHB4ejgcPHqBmzZrywjVXSEgIAGDatGkoV64c5s+fj+bNm6NSpUryPtWqVcvzmrm/q1+/fl18wdWISn+1n5mZidWrV+PGjRuoUqWK1HGIiEqFgwcPomHDhvD09EROTo7CNV1dXRa/REQlxIIFCyAIAoYMGZLvdS0trQILydjYWEyePBnW1tbQ19eHsbExnJ2dsWPHDshkMoW+hoaGAN7uVjxx4kRYWVlBX18fZmZmcHJygkwmk/cB3i5tNjMzQ1JSEkJDQwv1WSwtLWFsbIyUlBQ0atQIenp6EAQB33///XvHJScnY8qUKahevTr09PRgZWWF77//HllZWXn6/vPPPxg7diwcHBxQuXJl6OjoYMWKFQCAR48e5fv6uT/jdx+5fatVq4azZ8/mOy4qKgqnTp1SeMTHxwMArl+/jlOnTsk3nKxcubL83ufcPu/KvT/6v8dG0aeRtACuVatWgY+qVavC0NBQvnRg+vTpUkYlIirx3rx5g+HDh6Nv376Ii4vD2bNn8dNPP0kdi4iIlOzGjRuoW7eu/HeAq6srWrZsieDgYAwbNgwHDhxQ6F+9enUAwJEjR7B27VpoamrC1dUVzZo1Q3BwMADgiy++kPcXBAHu7u4AgC1bthQqkyAISExMBACEhoYiMzPzg2PS0tLg4uKC1atXQ0dHB66urkhJScF3332Hvn37yu+tzXXz5k38/PPPePbsGerVq4fevXujfPnyAIBt27Zh586dBb6Xo6MjvLy84OXlJT8OsG/fvnnu8bW1tcX8+fMVPhfwdhbd2dkZAHD58mWIoqgwQ+zi4gIAuHLlSp73DgoKAgDUrVv3gz8T+jBJC+CoqKgCHzExMShXrhzatWuHgwcPYtSoUVJGJSIq0a5fvw5bW1ts375d3mZgYABTU1MJUxERkbLFxcWhW7duiI+Ph4+PD+7fv4/9+/fj2LFjeP78OaZMmZJnjK2tLQAgOzsbU6dOxb179+RjcgvdNm3aKIyZPXs2TE1NsX79euzevTtPhvz06tVLfj9s586dFa6lpaVh0aJFePr0qbxtxYoVCAoKgouLC8LCwvDLL78gPDwctWvXxtGjR7F3716F12jevDnu3LmDJ0+e4Ny5c9i3bx+GDRsG4G2h+s0338hnW/9rxIgR2LlzJ3bu3IkKFSoAAFavXo169erl6WttbY0ZM2bg6NGjePnyZb6vl9/rA8DcuXMRHR0tb7948SIOHToEPT09HkdYVERSGfXq1RPr1asndQwiKkWysrLERYsWiZqamiIA+cPOzk68f/++1PGIiOgzzJ8/XwQgenl55Xs99+/+d82YMUMEIA4dOrTA101NTc13DABRS0tLbNeunTho0CCxbt26IgDR0tJSfP36dZ7XuXz5smhkZCQCEG1sbMQBAwaIHTp0EPX09EQAoqur63vHABDr168vdu3aVSxXrpwIQIyMjJT3rVy5sghAvHbtmsJr7N69WwQgOjo65vv5xowZI9rb24v29vZilSpVRACirq6uCECsXbu2uGjRInnf3J9xrVq15GN0dHREAGLjxo3lbbdu3Srw5wlA1NTUFJ2dnUUA4uXLl/PtN2DAABGAaGJiInbr1k10dnYWtbS0RADizz//nKf/rVu35O9vb28v/5m92/b8+fMCc71LneqQErEJFhERfbzIyEh4eHjg6tWr8jYNDQ3MmTMH8+bNg7a2toTpiIhICrt27QIAjB07tsA+/73XNHfMmjVrcPToUdy4cQNpaWmoVq0apkyZgrlz58LExCTP6zg5OSEsLAze3t4ICAjA4cOHoaenBzs7O/ly4oLG9O7dGzdv3sS9e/cQExMDGxsbDBgwQL7k+O7du4iOjoapqSns7e0VXiN39vjatWtITk6GkZGRwvWwsDBcv35doS0jIwPA2/uEIyIi8uR6+PAhHj58qNB2+/Zt+Z+LYvfqPXv2wMHBAdu3b8e5c+egqamJFi1aYPr06ejWrVue/klJSXk+BwCFttzPRf/DApiIqJQRRRG7d+/GuHHjFI6gqFmzJvz8/ODk5CRhOiIikkpERARiYmJgYGCApk2bfvSY8ePHY8KECR/1nlWrVoWPj89Hj+nSpQtu3ryJ+fPnY+7cuXn6hIWFAcj/vlhTU1N8+eWXeP78Oe7fv5/ns164cAEAkJ4lw4ukDNz96wp6unaHpqYmIiMjFe5nzuXl5QUTExOkpaWhRo0a6NGjR77Ln98n930LoqmpiUmTJmHSpEmFej0XF5c89znTh0l6D/CiRYtw9OjRD/Y7d+4cFi1apIREREQlX0REBIYNG6ZQ/Hp4eCA4OJjFLxGRGrt//z6At+fxFvaYoU8ZowzPnj0DAPkRQj/99BMcHBzkm0jlFrHv3jOcKzLqEZq274UvmrSDVYMm6NKpI2Q5In7+eWO+xS8A+Pr64qeffsKmTZswZ84c1K9fH5MmTcpzmgKpPkkL4AULFuDw4cMf7Hf27FksXLiw+AMREZUClpaWWLBgAQDA2NgYe/fuhZ+fH4yNjaUNRkREksrd4Cn3yJ3iGlPU0rNkSEzLUni8Tni75FhTRxeJaVmYO3curl+/ju+XLEViWhZ09d4u434Zn5hn7PIjfyHo7GEk/X0BmdH/QNDRh2mP2Xhc/qs8favUMMePa3wQFBqG6NeJCAoNw/xF30NPTw9r1qzB/EXf5xmT+0jPkr3vY5FESsQSaH6zQkT0cWbNmoXXr19j0qRJ8uMriIiIAOSZyd23bx8GDRokf25hYYF///1X2bEK9POFCKw590ChLfHq23zHQ6LRaOFpCJZOEMIv45aWDRotPI2YxwkAgNmHQvH9vfL/eUU91Jh5DGJ2FrLiniDxz1/x8uAirIp5gG1X3P/TtwKACliz+yGA3HuAbVHGZSTSAtbgh6Xe8E2ygYaOXp7cE9taYXJ768/9+FTEJJ0BLqyIiAj5eVtERPQ/9+7dw/Dhw5GVlaXQrqmpiR9//JHFLxFRKfaxS5JzVwK9e4sM8HZ5s5eXF/r06VPgmKLY5KkoCdpvC04x6+0mT+U7jUf1yb/AoN7bs3bF7AyFfvm+hpY2dMxqoWKPmdCr1RSJgXuR/iikUO9v0KAdNPQMIWamITP6/ud8FFIypc8AX7p0SeF5TExMnrZcqampuHbtGo4ePYpmzZopIx4RUYkgiiI2bdqEKVOmIC0tDZUqVcIPP/wgdSwiIlIiTU3ND/bR0vrfP/etrd/ORkZFRSn0sbe3h729PaKionDw4EGFa1ZWVgDeniwgiqLK3AesafR2VleWkpDvdVlK/P/3q1Co1zO0aY30h7eQEnYBejUafrC/IAjQKlcZmTEPIEvJ/1xjUk1KL4BdXFwU/sM5deoUTp06VWB/URShqamJWbNmKSMeEZHKe/nyJYYPH47ff/9d3ubt7Y0hQ4bI/6FCRESln6GhIQBAJsv/XlOZTIZy5crJn1tYWMDMzAwvXrxAaGgoGjRo8MH3sLS0lI8JCgoq9O7RRWmMiwWGOZkrtIWHVUHzI94okxaDO/M7KFxLiI9HzWVx0NTUxM3lg/Mcg7Tm7ANsD4xUaBN0ygAA7M2Ag/95vYI03JeFxwDWDm6OLt3yjtHVKhGLbdWO0gtgT09PeQHs6+sLCwsLODo65ttXR0cH1apVQ69evWBjY6PMmEREKikgIABDhw5FbGysvK1ChQrYunUri18iIjWTe5tLdHR0nmvPnz8HAFSrVk3eJggC3N3dsXr1amzZsgVr16794HsIggA3Nzf89NNPWL9+PbZv315E6QtPT1sTetqKs932TRqhcuXKiI6Oxr2QIIWzgI8dOgMAcHBwQFUz0zyv923XutDT1oDv1SikZMpgoKMJC+1Y/AGgapXKMNbX/mCm+/fv4+mTxwCAls3tCjWGVIQoIUEQxKFDh0oZQaXUq1dPrFevntQxiEgFpaamiuPHjxcBKDw6duwoRkdHSx2PiIgkEBcXJ+rr64va2trilStX5O05OTniuHHjRADijBkzFMa8ePFCNDU1FTU0NEQ/Pz+Fa7du3RIBiBYWFvmOASD+/PPPCtdSU1PFhQsXik+ePCnSzzZ//nwRgLh48eIC+yxYsEAEILZu3VrMzMwURVEUExISxNq1a4sAxN27dyv0//7778WQkBD587TMbPHRqxTxxMlTor6+vghAPHbsmPz6ixcvxK1bt4qpqakKr/P06VOxefPmIgDR1dX1g58FgKipqVmozy0VdapDBFGU7vRkDQ0NDBkyRJJvklRR7iz33bt3JU5CRKrkzp07cHd3V/i7QVdXF8uXL8c333wDDQ0usSIiUlfr1q3DhAkToKWlBRcXF1SsWBHBwcEIDw+HpaUlrl+/DlNTxVnQK1euoEuXLkhOToaNjQ3q16+P+Ph4XLp0Cenp6XB1dcWRI0cKHFO7dm3Y2trizZs3CAwMREJCAiIjI1GzZs3P+ixjx45FUFAQgLfn9z579gxVq1ZFlSpVAABdu3bFd999J++fmpoKR0dHBAcHw9LSEk2aNMGVK1fw/PlzdO3aFb///rvCrZe2tra4c+cOrKysUL9+fWhpaeHBgwcIDg4GAHz99dfYvHmzvH9UVBTMzc1haGiIZs2aoVKlSnj+/Dlu3bqFN2/eoE6dOrh48SLMzMwUPkdQUBDGjh0rf379+nUAUJil/u2331C5cuXP+nkVJbWqQ6SsvocMGSJu27ZNyggqRZ2+eSGiwvHx8RF1dHQUZn0bNmwohoaGSh2NiIhUxIkTJ8S2bduKZcuWFbW1tcVatWqJU6ZMEePi4goc8+TJE3HcuHFirVq1RF1dXdHY2Fhs1aqVuG3bNjE7O7tQYypUqCA6OjqKa9euFTMyMj77czg7O+dZ6fTuw8vLK8+YxMREceLEiWLVqlVFHR0d0cLCQly4cKF8RvhdBw8eFL28vMS6deuKpqamopaWlli+fHmxQ4cO4q+//pqnf1JSkjh16lTRzs5OrFSpkqirqyvq6+uLDRs2FBctWiQmJSXl+znOnz//3s8BQIyMjPzcH1eRUqc6RNIZYFKkVt+8EFGhbNq0CaNHj5Y/nzx5MpYsWQI9vYKPdSAiIirp0rNkeJGUAbOyunnu/6Wip051iNI3wSIiosIbOXIkTpw4gZs3b8LX1xft27eXOhIREVGxkeWIWHn6H+x6Z4MqzxY1Ma1DbWhqqMYRTFSysQAmIlIRb968gSAIMDAwkLcJgoBt27ZBEASUL19ewnRERERFLz1LhozsHPnz/x5RlJIpw88XIpCZnYMJbRVPO9DV0uDsMH00yQvgrKws/PLLLzh16hRevnyJzMxM5LcqWxAEnDt3ToKERETF7/r163B3d0fbtm2xadMmhWsVKlSQKBUREVHx+vlCBNace/DBftuuRGLbFcWzeye2tcLk9tbFFY1KKUkL4LS0NLRt2xbXr1+XF72CICgUwLnP393BjYiotMjOzsbSpUuxcOFCyGQyREREoEuXLujRo4fU0YiIiIhKHUnPzli1ahWuXbsGDw8PXLt2DX369AHw9mDp06dPY9y4cdDW1sbIkSPx8OFDKaMSERW5yMhIODs7Y968eZDJZADeHg/34MGHvwknIiIioo8n6QzwL7/8AnNzc+zcuROCIMDQ0BAAYGlpCUtLS7Rr1w69evVChw4dYG9vj6FDh0oZl4ioSIiiCD8/P3zzzTdITk6Wt9esWRN+fn5wcnKSMB0REZHyjHGxwDAnc/nz/94DnGu4k3m+9wATfSxJ/1/z8OFDNG7cWL68WUPjbZx3l0C3adMGjo6OWLdunSQZ8+Pv7w8XFxeYmppCX18fFhYWGD58OOLj46WORkQqLj4+HoMGDYKXl5dC8Tt48GAEBwez+CUiIrWip60JY31t+ePbrnUx1sUCBjpvN7cy0NHEWBcLzOlSV6Gfsb42N8CiTyLpDLC2tja0tbXlz3NngGNjY1GpUiV5e5UqVXD06FGl5/svURQxfPhw7NixA2XKlEGrVq1gZGSEiIgI7NixA9OnT4eJiYnUMYlIRV24cAGenp548uSJvM3Y2BgbN27EwIEDJUxGRESkGjQ1BMzoVAcT2lrxHGAqFpIWwNWqVcP9+/flz2vXrg0AOHHiBIYNGwbgbdF5+/ZtmJqaSpLxXVu2bMGOHTvg4OCAw4cP44svvpBf+/fff1GxYkUJ0xGRKgsLC0ObNm0UVrg4Oztj165dqF69uoTJiIiIVI+etiaqly8jdQwqhSRdAt2yZUuEhIQgJiYGANCjRw9oaWlhypQpWL9+PY4fPw43Nzfcv38fzs7OUkaFKIpYvHgx9PX1ceDAAYXiF3h737KxsbFE6YhI1dWrV0++j4GWlha8vb1x7tw5Fr9ERERESiTpDPCgQYMQFBSEc+fOwd3dHVWqVIG3tzemTp2KCRMmAHhbeFaqVAnLli2TMiquX7+Op0+fYtCgQahSpYqkWYioZFqzZg1iY2OxcOFCNG3aVOo4RERERGpH0gLYyckJf/75p0Lb5MmT4ejoiEOHDiE+Ph7W1tYYOnSo5Eugb926BeBt5sTERPj6+iI8PBzGxsZo164d2rVrJ2k+IlIdL168wM6dOzF9+nSFM8wNDQ1x7NgxCZMRERERqTdJC+CC2NnZwc7OTuoYCnLPIZbJZGjUqBEePXokv7Zs2TL06tUL+/fvV9jUi4jUT0BAAIYMGYIXL16gfPnyGD58uNSRiIiIiOj/SXoP8Ny5c9GkSRNcu3ZNyhiFkntcyaJFi2BoaIirV68iKSkJV65cQe3atfHbb79h6dKlhXotGxubfB8RERHF+RGIqBilpaVh/Pjx6NKlC168eAEAmDhxosKOz0REREQkLUkL4D179iA1NRUODg5SxiiU3J1bExIScPToUTRv3hxGRkZwdHSEv78/AMDHx0dhh1ciUg/BwcH46quv4OPjI2/T1dWFt7c3qlatKmEyIiIiInqXpEugY2Ji0L17dykjFFqZMm+3YW/evDlq1aqlcK1JkyaoXr06Hj9+jIcPH8LCwuK9r3X37t18221sbIomLBEpRU5ODn788Ud8++23yMzMlLc3bNgQ/v7+/G+aiIiISMVIOgNcsWJFpKWlSRmh0CpVqgQABR5ZUrlyZQDA69evlZaJiKTz7NkzdOjQAdOnT1cofqdMmYIbN26w+CUiIiJSQZIWwL1798alS5dKRNFYp04dAEBcXFy+1xMSEgAA+vr6yopERBI5ePAgGjRogHPnzsnbvvzyS5w5cwarVq2Crq6uhOmIiIiIqCCSFsCLFy9G3bp10b17d0RFRUkZ5YNatWoFDQ0N3Lp1C+np6QrX4uLiEBUVBW1tbVhaWkqUkIiU5caNG4iPj5c/7927N0JCQngcGhEREZGKk7QAnjhxImrXro27d+/CysoK9vb2cHNzw7Bhw/I8pD5KpHz58ujZsydevHiBOXPmyDe7ysnJwZw5c5CRkQFXV1fOABOpgcWLF6Nx48YwMDDAtm3bcODAAZQvX17qWKQGFixYAEEQMGTIkHyva2lpKZw9/a7Y2FhMnjwZ1tbW0NfXh7GxMZydnbFjxw7IZLJ8x0RHR2PixImwsrKCvr4+zMzM4OTkBB8fH4Wl/5/qt99+w/Tp09GiRQvo6elBEAR8//33BfaPjY3F5s2b4eXlBUtLSwiCAEEQ8PTp0wLHpKSk4LvvvkPt2rWhp6eHChUqoHv37u89gSI0NBT9+vWDmZkZdHV1YW5ujilTpiAxMTFP39TUVOzbtw99+vRB1apVoaOjAxMTE3Ts2BEnT578uB8IEREVP1FCgiAU+qGhoSFlVFEURTEqKko0MzMTAYh16tQR+/TpI1pbW4sAxC+++EKMior6rNevV6+eWK9evSJKS0RFISsrS8zOzs7T/s8//4gPHjyQIBGps/nz54sARC8vr3yva2pqivn9ar9+/bpoYmIiAhCtrKzE/v37i127dhXLli0rAhD37duX75hy5cqJAMTatWuL/fv3F7t06SIaGRmJAMTIyMjP/jwA5A9BEEQA4uLFiwvsv2PHDoUxuY8nT57k2z89PV20t7eX/57u0aOH2Lx5c1EQBFFTU1M8ceJEnjGBgYFimTJlRACira2t2Lt3b7FGjRoiALFu3bpiQkJCvpkEQRCbNm0qDhgwQGzZsqX88yxZsuTzfkhEREqgTnWIpLtAnz9/Xsq3/2g1atTArVu3MG/ePJw6dQpHjx5FhQoVMHToUCxcuBDVqlWTOiIRFaHIyEh4eHjA1dUVM2fOVLhmbW0tUSqijxMXF4du3bohPj4ePj4+GDdunPxaSkoK5s2bV+CYhIQEbNiwAWPGjFEYs2LFCmhpff4/IQYNGgRHR0c4Ojri0KFDWLx48Xv7V6lSBRMnToSjoyOcnJzQvHlzPHr0qMD+69atw/Xr1+Hg4ICTJ0/C2NgYwNuZ5z59+mDkyJGIiIiAjo6OfMyoUaOQmpoKb29v+X/3mZmZ8PDwwK+//ooffvgBy5cvl/fX1tbGuHHjMG3aNNSsWVPefu7cOXTs2BFz585Ft27d0KBBg0/5ERERUVGTugKn/1Gnb16IVFlOTo7o6+srn+nS1tYWb926JXUsok+aAZ4xY4YIQBw6dGiBr5uamvrRY4pa7md73wzwf+XOzBY0A9ysWTMRgHjq1Kk817p37y4CEA8fPixvu3v3rghArFatWp6VH9HR0aKOjo5Yvnx5USaTFSpf7969RQDivHnzCv2ZiIikoE51iKT3ABMRqZr4+HgMHDgQXl5eSE5OBgBkZWXht99+kzgZ0afZtWsXAGDs2LEF9vnv/hW5Y96dLS6JwsPDAQBNmzbNc83R0RGA4mq03P6NGjWCpqamQv9KlSqhVq1aeP36NUJCQgr1/lZWVgCAmJiYjw9PRETFggUwEdH/u3DhAho2bIhffvlF3mZsbIx9+/Z9cGkmkSqKiIhATEwMDAwM8i0CPzSmSZMmxZyweOWe2pC79PldpqamAIB//vknT/9y5crl+3r5jXmfx48fAwBvkSIiUiEsgIlI7WVmZmLmzJlo06aNwm6yzs7OCAkJwYABAyRMR/Tp7t+/DwAwNzcvcHfoohijqipUqAAg/xnYJ0+eAHi70/V/+7/b9qExBXn58iV+//13CIKAXr16fVxwIiIqNipTAL948QKnT5/G3r17cfXqVanjEJGauHfvHpo3b47ly5fLjzfT0tKCt7c3zp07h+rVq0uckOjTJSQkAADKli1brGNUlYODAwDg8OHDCu0ymQx79+4F8PYYo1xNmzaFpqYmrl69ipcvXyqMuXjxorwAfndMQaZOnYo3b97A09MTNjY2n/MxiIioCEleAEdFRaF79+748ssv0blzZ3h4eGDr1q3y697e3jA1NcWVK1ckTElEpVFwcDCaNGmCoKAgeVvt2rVx7do1zJw5M889gEQl1X9ncvft2yc/Q1cQBFhaWkqUrHjl3sP83Xff4ddff0ViYiIePHiAgQMHygv9d382FSpUwKBBg5CWloa+ffsiJCQEycnJOHPmDDw8POQzxB+aGff19YWfnx+srKywbt264vlwRET0SSQtgB8/fgwHBwccP34cLVu2xIQJE+QzMLnc3d2RmJiIAwcOSJSSiEqrhg0bymeIAGD06NG4detWoe+VJFK2j12SnHvva+6GbrnMzc3h5eWFPn36FDgmKSnpE1Oqjnbt2uGHH35AUlIS+vfvj3LlysHa2hrHjh2TF6b/neleu3YtHBwccOnSJTRq1Ahly5ZFhw4dYGpqivHjx+c75l2XLl3CyJEjUa5cORw9ehRGRkbF9wGJiOijSXoO8HfffYeXL19i79698nvs1qxZo9CnWrVqqF+/Pi5cuCBBQiIqzTQ0NODr64t27dphxYoVcHV1lToS0XsVZlXCu+fz5p5XHRUVpdDH3t4e9vb2iIqKwsGDBxWu5e5cHBkZCVEUS/x9wHPmzIGrqysOHTqEZ8+eoXLlynB3d0dmZiYAoFatWgr9TUxMEBgYiMOHD+PKlStIT09H48aN4e7ujvnz5+c7Jtc///wjv9/34MGDqFOnTjF+MiIi+hSSFsCnT5+GnZ3dBzeYqVu3Lk6fPq2kVERUGqWlpeHUqVPo2bOnQnu1atUQFhbG5c5UIhgaGgJ4ew9rfmQymcIOxhYWFjAzM8OLFy8QGhqKBg0afPA9LC0t5WOCgoJKxYqI+vXro379+gptmzdvBpD/EUkaGhro3bs3evfurdAeGBgIQRDQuHHjPGNiY2PRuXNnxMfHY/fu3WjTpk0RfgIiIioqki6Bjo+PL9TRAOnp6cjIyFBCIiIqjYKDg9G0aVP06tUL586dy3OdxS+VFLmbsuW3C/Hz588BKB65IwgC3N3dAQBbtmwp1HsIggA3NzcAwPr16z8rr6oSRRGbNm2CIAj5LgPPz+3bt3Ht2jU4OTmhUqVKCtdSUlLQrVs3REZGYsWKFfKfHxERqR5JC+AqVaogNDT0vX1EUURISAhq1KihpFREVFrk5ORg5cqVsLOzQ3h4OADAy8sLcXFxEicj+jStW7eGvr4+Ll26hMDAQHm7KIpYsmQJAKBz584KY2bPng1TU1OsX78eu3fvVrhW0H8Lc+bMgampKXbs2IGNGzcqXEtLS8OiRYsUjgxTZcHBwQrPs7KyMG7cOAQFBcHDwwMWFhYK12NiYhAbG6vQFhERgYEDB0IURSxYsEDhWnZ2Nvr374+//voL06ZNw9SpU4vjYxARURGRdAl0586d8fPPP2PZsmWYOXNmvn18fHwQFRWFadOmKTkdEZVkT58+hZeXF/744w+F9kGDBsHAwECiVESfx8TEBMuWLcOECRPg4uICFxcXVKxYEcHBwQgPD4elpWWe36cVK1bEkSNH0KVLFwwePBje3t6oX78+4uPjcenSJQDIc0zPu2PGjBmDn376Cba2tnjz5g0CAwORkJAAT0/Pz/48Y8eOle/CnltQb9q0CceOHQMAdO3aFd999528f3R0tMKZurkz4a6urtDR0QEAbNiwAU2aNJH36dSpE0xMTFCnTh1oamoiMDAQMTExaNKkSb47NAcHB6NHjx6ws7ND1apV8fLlS1y+fBmZmZlYtGhRnqXNPj4+OHHiBPT09PDixQsMGTIkz2vWqVMHs2bN+pQfERERFTVRQs+fPxcrVqwoamhoiG3bthXXrl0rCoIgdu78f+3dd1iT19sH8G/YiEwVUVGZDnC3igMUFQdocdU9wNFapNWfs9aFq46qbVW0jiriqlZx1r0VnFVx4iiixQUqIEN2zvsHb1JjgoKMIPl+rivXBec553nuJzGYO2d5ik2bNon+/fsLLS0tUbFiRRETE6POUIuFk5OTcHJyUncYRJ+8bdu2CXNzcwFA/qhcubI4cuSIukMjKhT79+8Xbdu2FSYmJkJXV1fY2dmJMWPGiLi4uFzbREdHC39/f2FnZyf09fWFqampaNmypVizZo3IysrKU5vy5cuLFi1aiCVLloj09PQC30erVq0U3qfvPnx8fBTqR0VFvbc+AHHixAmFNtOmTRN16tQRZcuWFWXKlBF169YVP/74o0hNTVUZ04MHD0Tv3r2FtbW10NPTExYWFqJjx47i4MGDKusHBAR8MKZWrVoV+LkiIipKmpSHSIR4Z9+hYnbt2jV8+eWXiIyMhEQiUVhxUggBOzs7hISEoH79+uoMs1jIvoG/deuWmiMh+jQlJSVh5MiRWLdunUJ5jx49sHLlSpQrV049gRHRB6VlZiM2MR2WJvow0OW8fCKi4qRJeYhah0ADQP369REREYEdO3bg2LFjiI6OBgBYW1ujdevW+PLLL6Grq6vmKImopDt37hwGDBiABw8eyMuMjIywdOlS+Pr6fvJbuRCVVtlSgYWH72L92YdIyciGkZ42BjW3wbj2NaGtxfctEREVLrUnwEDOnoW9evVCr1691B0KEX2iFi9erJD8uri4YOPGjXBwcFBjVESUlpmN9CxprscXH72PtWFR8t9TMrLx28lIZGRJMbKto8o2+jpa7CUmIqKPovYh0PQfTRp6QFTY4uLiUK9ePTx79gxTpkzBlClTOHqEqAT45cg9LD52v1DPOaqtI0a3q1Go5yQi0mSalIeotQd45syZaNCgAby9vd9b79ixYwgLC8O0adOKKTIi+tRYWFhg8+bN0NbWRosWLdQdDhERERGVQGrdB3j69OnYtWvXB+sdPXoUM2bMKPqAiKjEi4+PR+/evbFt2zalYy1btmTyS0RERES5UmsCnFdSae5zh4hIc5w4cQL16tXDn3/+ieHDh8v3DSXSFNOnT4dEIlG51yyQs6ZGbgu+xcTEYPTo0ahRowYMDQ1hamqKVq1aISgoCNnZ2SrbPHv2DKNGjYKjoyMMDQ1haWkJV1dXBAYGIiMjI08x+7nb41pAe5WPPb41YP7373i8bBAeLeiKx8t88OrAEmQlvcJQV1uVbeY0SsOOWcNgYmKCMmXKoEGDBliyZMl7PyukpaVhzpw5qF+/PoyMjGBiYoJ69eohICBAqa7sOc7tcfTo0TzdNxERlUwlYhGsD4mMjISRkZG6wyAiNcnIyMDUqVOxYMECyJYtiI+Px5IlS/DTTz+pOTqiku/ixYvo2LEj4uPj4ejoCG9vb6SkpODMmTMYMmQIypQpg969eyu16dChAxISElCzZk14e3sjOTkZZ86cQVhYGDp37gwbG5sPXttAV1vlglX//PMP2ro1x8uXL1HB2hZp1rWRGvsIydcPQxJ9BQMnXIapoeI8/l9//RWjR4+Grq4umjdvDmNjY4SFhWHUqFG4ePEiNm7cqHSdhIQEtG7dGuHh4ahYsSI6duyIrKws3L59GytXrsx1hFmLFi1ULqJXuXLlD94zERGVXMWeAJ8+fVrh9+fPnyuVybx58wbnz5/Hnj170Lhx4+IIj4hKmIiICPTv3x9Xr16Vl+nq6mLWrFkYN26cGiMj+jTExcWhc+fOiI+PR2BgIPz9/eXHUlJSVK6vIWuTkJCA5cuXw8/PT6HNggULoKNTsI8Q//vf//Dy5UuMGDECgYGBSM+SIuZ1GhbNnoylixdjwvhx+PPPP+X1nz59igkTJkBXVxfHjx+Hq6srgJxe6rZt22LTpk3o168fvLy8FK4zYsQIhIeHY/jw4ViyZAn09PTkx65cuZJrfMOGDcu1p52IiD5hophJJBKhpaUltLS0FH7O7SGRSISOjo7Ys2dPcYda7JycnISTk5O6wyAqEaRSqVi+fLkwNDQUAOSPmjVrisuXL6s7PCK1CAgIEACEj4+PyuPa2tri3f/aJ0yYIACIwYMH53reN2/e5LtNQSQmJgotLS1haGgo4uPjFY6lpqYKS0tLoa2tLV69eiUvX7ZsmQAg+vbtq3S+kJAQAUB07dpVofzevXsCgGjQoIHIzs7OU2yy5zgoKCjf90VE9KnSpDyk2HuABw0aJJ+fFBwcDHt7+1wXrdHT00PVqlXRrVs3+dLcRFT6xcbGYujQofjrr78Uyr/55hssWrQIZcqUUVNkRJ+e9evXA8jpCc2NoaGhyjZv9xYXpnv37kEqlcLe3h5mZmYKxwwMDNCoUSMcPHgQp0+fRteuXQHkjAYBgM8++0zpfLLPESdOnFAoDwkJAQD4+flBS+uTWPaEiIiKWLEnwOvWrZP/HBwcDFdXV6xdu7a4wyCiEio8PBwdOnRAbGysvKxChQpYs2YNvvjiCzVGRvTpiYyMxPPnz2FkZKQycfxQm0aNGhVJXGlpaQCglPzKWFhYAADu3r2bpzay+q9fv0ZMTAwqVqwIALh8+TIAwNXVFQ8ePMDGjRvx9OlTVKlSBd27d3/vl+snT57EtWvXkJqaiurVq6NLly5wcnLK340SEVGJ80ksgkVEmsPR0RHm5ubyBLhjx44ICgqClZWVmiMj+vTcu3cPAGBra5vr6tCF0Sa/ypcvDyBn/q4q0dHRSsff10ZWX3ZclgA/ePAAAHDz5k34+PjIk2ggZ7XnuXPnYsKECSpjCA4OVvh98uTJGDlyJH7++Wf2JhMRfcLU+hfcx8dHvogFEREAGBkZYdOmTTA2NsbSpUuxf/9+Jr9EHykhIQEAYGJiUqRt8svR0REWFhaIjIzEzZs3FY5FRkbi3LlzAHIWw5Rp2rQpAGD37t1K59uwYYP857fbJCUlAcgZyt2pUyfcvXsX8fHx2Lx5M8qWLYuJEyfi1KlTSrH99ttvuH//Pt68eYP79+9j7ty5MDAwwOLFizF37twC3j0REamTWhPgoKAgDBkyRJ0hEJEaSaVSXLt2Tan8s88+w7///otvv/22yHqgiDTJu++jLVu2KOxtq2q7n6KkpaUln5Pcp08fnDt3DsnJyTh//jy6dOkiH+b8dtxeXl6ws7PD33//jW+//RaPHj3Cq1evsHLlSsydO1feQ/x2G/H/26aZmZlh69atqFGjBszMzNC3b18EBARACIHAwECF2Pr3749vvvkGDg4OMDQ0hIODAyZOnIhly5YBAObNm4eUlJQie26IiKhocQwPEanF48eP0a5dOzRr1gx37txROp7b3EAiTZbfL4RMTU0B/NcTKmNrawsfHx/06NEj1zaJiYkfGWXeTJ06Fd7e3rh165Z8T99mzZohKSkJU6dOBaDYC62rq4uQkBBYWVlh2bJlsLGxQfny5fHNN9+gV69eaNmypVIb2YJ5ffv2hba24l7E3bp1AwCcP38+T/H6+vrC3NwcycnJuHjx4sffOBERqdUnMQf43r17eP78ufw/NyL6tG3fvh1ff/014uPjAeT0uJw7d05hf04iUvZuEqfK2/vz1qhRAwDw8OFDhTouLi5wcXHBw4cP5Sslyzg6OgIAoqKiIIQoslEYenp62L17N44cOYIjR44gMTERtWrVgo+PDzZv3gwAsLOzU2jToEED3L17F3/88QeuX78OXV1dtGnTBl988QWaNGkCiUQCGxsbeX0rKytcv34d1apVU7p+pUqVAACvXr3KU7wSiQT29vb4+++/c527TEREJd8nkQDPnTsX69evR3Z2trpDIaICSEpKwsiRIxVWgwdyeqPS0tKYABN9QNmyZQEg1/8Ps7OzFUZP2Nvbw9LSErGxsbhx4wbq1q37wWs4ODjI21y5ciXPq0d/rHbt2qFdu3YKZWFhYQBUb3lkYmKC4cOHK5QlJSXhxo0bqFWrFoyMjOTltWrVwuHDhxEXF6d0Htlc53e3gHqfly9fAvjvdSAiok8Ph0ATUbE4d+4cGjRooJD8GhkZYc2aNdi2bVuRLrhDVFrIejJV9UA+ffoUAFC1alV5mUQiQf/+/QEAq1evztM1JBIJ+vXrBwDyea/F6dmzZ9i9ezeqVauGxo0b56nN2rVrkZ6eji+//FKh3N3dHQAQGhqq1ObKlSsAgNq1a+fpGvfu3cO///4LAEW2PRQRERU9JsBEVKSysrIwffp0uLm5ybckAXKGYIaHh2PIkCFc6Iooj1q3bg1DQ0OcPn1a3ksK5Cz2NGfOHACAp6enQpsffvgBFhYWWLZsGTZu3KhwTFXPKABMmjQJFhYWCAoKwooVKxSOpaamYubMmXj8+HGB7iUxMRFRUVEKZbGxsfjyyy/x5s0bBAQEKP1tuHv3LlJTUxXKDh8+jEmTJsHc3ByjRo1SOObl5YXKlSvjr7/+ws6dO+XlCQkJmD59OoCcRbhkXrx4gTVr1ihd48mTJ/D19YVUKoW3tzesra0/+r6JiEi9JEK2RGIxmDlzJiQSCfz9/WFhYYGZM2fmqd3OnTtx/fr1Uj8E2tnZGQBw69YtNUdCVDgePHiAAQMGyLc0AXJWf50yZQqmTJkCXV1dNUZH9GlaunQpRo4cCR0dHbi7u6NChQoIDw9HREQEHBwccOHCBVhYWCi0CQ0NhZeXF5KSkuDs7Iw6deogPj4ep0+fRlpaGry9vZW2F3q7Tc2aNdGgQQMkJycjLCwMCQkJiIqKUphvm1937tyBs7MzGjdujOrVqyMpKQmnTp3CmzdvMGzYMJU91hMnTsTq1avRqFEjVKhQAffv38fff/8NAwMD7NmzR2koNQD89ddf6NKlC4QQaNasGSpUqIDz588jJiYGbm5uOHbsmPxv0cOHD2Fra4uyZcuicePGsLKywtOnT3H58mUkJyejVq1aOHXqFCwtLT/6vomISiKNykNEMZJIJEJLS0vcvXtX4XeJRPLBh5aWVnGGqhZOTk7CyclJ3WEQFZq2bdsKAPKHra2tCAsLU3dYRJ+8/fv3i7Zt2woTExOhq6sr7OzsxJgxY0RcXFyubaKjo4W/v7+ws7MT+vr6wtTUVLRs2VKsWbNGZGVl5alN+fLlRYsWLcSSJUtEenp6ge4hLi5O+Pr6CltbW2FgYCBMTU1Fq1atxObNm3Ntc+TIEeHh4SEqVKggdHV1ReXKlcWgQYPEnTt33nutsLAw4enpKSwsLISenp5wdHQUU6dOFW/evFGol5iYKMaOHSuaNGkirKyshL6+vjA0NBT16tUTM2fOFImJiQW6ZyKikkqT8pBi7QEODg4GAHTv3h3GxsbQ0tKCq6srhg4d+t52v//+O86ePcseYKJPzP3799GwYUOkpKRg0KBBWLp0Kef6EmmwtMxsxCamw9JEHwa6H17RmoiIiocm5SHFugq0j4+PUpmDg4PK8redPHkSZ8+eLaqwiKiIODo6YuXKldDR0UHv3r3VHQ4RqUm2VGDh4btYf/YhUjKyYaSnjUHNbTCufU1oa3ENACIiKj6fxDZIRFSyZWRkYOrUqejWrRuaNm2qcEy2Ai0RfXrSMrORniUt8HkWH72PtWH/LXiVkpGN305GIiNLipFtHQt8fgDQ19FirzIREX2QWhPgbdu25WkBDSEEinGkNhHlQ0REBPr374+rV68iJCQEV69ehbGxsbrDIqJC8NvJSCw+dr/Izr8mNAprQqM+XDEPRrV1xOh2NQrlXEREVHqpdRukHj16qNzk/l2LFi1S2iqBiNRLCIHly5ejUaNGuHr1KgAgMjIS8+bNU3NkRERERESqfRJDoMuVK4dy5cqpOwwi+n8xMTEYOnQo9u3bp1Du5+eHyZMnqykqIiIiIqL3+yQS4IkTJ+L+/fsICQlRdyhEGm///v0YPHgwYmNj5WUVKlTA2rVr0blzZzVGRkSFzc/dHkNcbQt8nnfnAMsMdbUt1DnAREREH/JJJMCnTp3CxYsX1R0GkUZ78+YNxo8fj+XLlyuUe3l5Ye3atahYsaKaIiOiomKgq10oC0tN7lQbBrpaCH5rFWif5jYYy1WgiYiomH0SCTARqVdERAR69OiBiIgIeZmBgQEWLlyIESNGQCLhB1giyp22lgQTOtbCyLaO3AeYiIjUigkwEX2QmZmZwpDn+vXrY/PmzXByclJjVET0qTHQ1Ua1cmXUHQYREWkwTpghog+qVKkSfv/9d0gkEowbNw4XLlxg8ktEREREnxz2ABORkhcvXqBChQoKZV27dsXt27dRq1YtNUVFRERERFQw7AEmIrmkpCQMHjwY9erVw4sXL5SOM/klIiIiok9ZsfYAr1+//qPaqfogTkSF69y5cxgwYAAePHgAABg2bBh27drFBa6ISqnp06djxowZ8PHxwbp165SO6+joIDs7G0IIpWMxMTGYN28e9u3bh+joaOjp6aFBgwbw9fXFoEGDoK2tvMDVs2fPMG/ePOzfvx+PHz+GsbExatSogT59+uDrr7+Gnp5ege5n586dOHv2LMLCwnDlyhWkp6dj1qxZmDJlisr6MTEx2L17N8LCwhAWFobIyEgAQHR0NKytrXO9zqVLlxAQEIBz584hOzsbjRo1wpQpU+Dh4aFUNyMjAzt27JDHde3aNWRnZ2PDhg0YMGCAUn1fX18EBwe/9z4lEgmkUul76xARUe6KNQH29fX9qA/TQgh+CCcqIllZWZg9ezZmz56N7OxseXlMTAwSExNhamqqxuiIqKS5ePEiOnbsiPj4eDg6OsLb2xspKSk4c+YMhgwZgjJlyqB3795KbTp06ICEhATUrFkT3t7eSE5OxpkzZxAWFobOnTvDxsamQHF1795d/nNePjMcOHAAw4cPz9c1zpw5Aw8PD2RmZqJ169YwNDTEoUOH0KFDB2zduhVffvmlQv2nT5+ib9++eY7L1dU112ORkZEIDQ2Fm5tbvmImIiJFxZoAt2zZkoksUQny4MEDDBgwAOfOnZOXaWlpYcqUKZgyZQp0dXXVGB0RlTRxcXHo3Lkz4uPjERgYCH9/f/mxlJQUTJs2Ldc2CQkJWL58Ofz8/BTaLFiwADo6Bf840rdvX7Ro0QItWrTAjh07MGvWrPfWr1KlCkaNGoUWLVrA1dUVzZo1w6NHj3Ktn52djaFDhyIjIwOrVq3CV199BQA4ePAgPD09MXz4cHTs2BFly5aVtzEwMICvr6/8GvPmzXtvD++wYcMwbNgwlccGDx6M0NBQlT3HRESUd8WaAJ88ebI4L0dEuRBCYP369fj222+RnJwsL7e1tcXGjRvRvHlzNUZHRCXV/Pnz8eLFCwwePFgh+QUAIyMjLFq0CKmpqbm2eTv5lbWZPn16ocS2efNm+c+7du36YP127dqhXbt2eT7/0aNHcf/+fdSuXVue/AJAx44d4eHhgaNHj2LLli0KCayVlRWCgoLyfI3cpKSkYPv27dDX10fPnj0LfD4iIk3GRbCINExcXBx69+4NX19fheTXx8cH4eHhTH6JKFeytTxGjBiRax1DQ0OVbd5NmD81R48eBZCT8L7Ly8sLAHDkyJEiufaOHTuQnJyMTp06wczMrEiuQUSkKbgNEpEGEULA29sbYWFh8jIzMzOsXLkSvXr1UmNkRFTSRUZG4vnz5zAyMsJnn32W7zaNGjUq4giL1u3btwFA5R7oderUUahT2GTDpgcOHFgk5yci0iTsASbSIBKJBD/++KN8Lr67uzuuX7/O5JeIPujevXsAcqZK5HU9j49pU1I9efIEQM6w5uTkZHTp0gVffvkl3rx5g4oVKwIAHj9+XOjXjY6OxokTJ2BhYSHvaSYioo/HBJhKnenTp0MikcDX11flcR0dnVw/iMXExGD06NGoUaMGDA0NYWpqilatWiEoKEhhheS3PXv2DKNGjYKjoyMMDQ1haWkJV1dXBAYGIiMjo8D3k5iYiAkTJsDR0RH6+vowNjZG8+bNc91WzN3dHRKJJNdHixYtMHXqVPz00084duwYqlatCiBnaw8vLy+Ym5vDxMQE7u7u8iF/7/rjjz/Qq1cvODk5wczMDPr6+qhRowbGjh2LV69eFfieiajkSUhIAACYmJgUaZuSKiUlBUDOEO+DBw9iz549CAkJwYEDB1CmTBmFOoVpw4YNkEql6NmzZ4G3iiIiIg6BLpA7d+6gfv36yMjIeO9eg/RpKIlbeyQnJ6N58+a4desWrK2t8cUXXyApKQknT56Ej48Prl69il9++UVl2x49euDJkyeoUqWKwqqkWlpamDFjhkLd/G7tsWbNGpw4cQJ169aFm5sb9PX1cfXqVfz888/Ytm0bzp49+959NIno0/XuF4hbtmxR2OrH3t4e//zzT3GHVayaN28Oe3t76Onpwc3NDYmJiUV2LdmXnRz+TERUOJgAF8CIESMKpYeP1K+kbu3x22+/4datW2jTpg327dsHAwMDAMDNmzfh4uKCxYsXw9/fHw4ODirjO3/+PNzc3LB161Zoa2urvMbHbO0REBCALVu2oHz58grnGTVqFJYtW4aJEydi48aNBbp3Iipa+R2SLNsTPCkpSaHc1tYWPj4+SE5ORkhIiMo2RZkgFhcjIyMAQGpqKipXrqyQ5D9//lyhTmG5cOEC7t69C1tbW7Ro0aJQz01EpKk4BPojbdq0CadOnULXrl3VHQoVgrxs7eHt7Z1rm9y29ihoL6hssSo/Pz958gvkLLji4eEBIQQuX76s0EY2BPnEiRMAcnp3c+slBj68tUdcXBy2bNmi0MbNzU0h+QUAbW1tTJ48GQBw+PDh/N4qERWz3L4Ue9vbX+LVqFEDAPDw4UOFOi4uLli3bh0WLlyo1N7R0REAEBUVBSFEAaJVvypVqgD4L9l9W0xMDAAU+sgX2eJX/fv3L9TzEhFpMibAH+H169cYO3Yshg4divr166s7HCoEJXVrD319fQCqe2pkZbJE9M2bN/D398fNmzcV6nl5eb136Fxhbu2hpZXzJ0UqleapPhGpj2xUR27rG2RnZyuM/LC3t4elpSUSExNx48aNPF3DwcEBlpaWSElJwZUrVwoetBrJVn9WtdKz7O+uqhWiP1Z6err8y8cBAwYU2nmJiDQdE+CPMHnyZKSmpmL27NnqDoUKQUne2qNdu3YAgFWrVikMt4+IiMDRo0dhb28PNzc3XL16FZ9//jmWL18ur6OtrQ03NzfY2NggJCQEsbGxKq9RWFt7ZGVlISAgAADQqlWrPN4hEalLtWrVAOQs5Peup0+fAoB8kTwg50s3WU/k6tWr83QNiUSCfv36AQCWLVtWoHjVzcPDA0DO9JB37d+/H8B/f7MLw969exEfH4/GjRujZs2ahXZeIiJNxwQ4ny5fvozffvsNU6ZMgaWlpbrDoUJQkrf28PX1Rffu3XH48GE4OjqiZ8+e8PT0RMOGDVG3bl0cOHAAixcvhouLCyIiIhTaZmdn48yZM1i+fDn8/f1ha2urcl5uQbb22Lx5M3x9fdGtWzfY2dlh5cqVqFmzJn7++edCfiaIqLDJFrw7ffq0wt7gQgjMmTMHAODp6anQ5ocffoCFhQWWLVum9PckLi5O5XUmTZoECwsLBAUFYcWKFQrHUlNTMXPmzCLZPqiweXh4wN7eHhEREfj999/l5QcPHsTRo0dhbm6utFBiQciGP7P3l4iokAnKs+zsbNG4cWPh4OAg0tPThRBCBAQECABi1qxZBT6/k5OTcHJyKvB5NJ3sNfHx8VF5XFtbW7z9T3/z5s0CgGjevHmer/ExbT5WZmamGD16tAAgfxgbG4tRo0aJli1bKpRLJBLRvHlz8eeff4ro6GiRkpIirl27Jnx9fQUAoa2tLU6dOqVwfgcHBwFAHD16VGzbtk1+ru3bt4v79+8LAEJXV1dlbKNGjVK4frNmzcSzZ8+K/DkhosKxZMkSAUDo6OgIDw8P0bdvX1G7dm0BQDg4OIhXr14ptTlz5owwNjYWAISzs7Po3bu3aN++vTAwMBAAhLe393vb1KxZU/Tu3Vt06tRJmJmZCQAiKiqqwPfi5+cnXFxchIuLi6hSpYoAIKytreVlM2fOVKj/9OlT+TEXFxehp6cnAIiGDRvKyy5fvqzQ5sSJE0JXV1dIJBLRtm1b0alTJ6GjoyMkEonYsmWLyri6du0qP1/58uUFAGFvby8vW716tVKbmJgYoaOjI3R0dERMTEyBnxsiog/RpDyEq0Dnw8qVK3Hp0iXs2rWrQHvxOTs7qyyPjIyEvb39R5+XCqYkbu0RFxcHb29vREREYOvWrfItl3766ScsXrxYIeYqVapg/fr1aNOmjcI56tWrh6CgIEilUqxfvx7Tp0/H8ePHVV4vv1t7/Prrr/j111/x6tUrhIaGYuzYsahbty7279+Pxo0bF/wJIKIi9d1338HBwQGLFi3CxYsXkZqaiqpVq2LMmDGYMmUKzM3Nldq4urri9u3bmDdvHg4cOIBdu3bBwMAATZo0gY+PD3x8fPLUxtjYGM7OzujduzcqV65c4Hu5ffs2Lly4oFD2+PFjee9yrVq1FI6lp6cr1QeAq1evyn9+92+gu7s7QkNDMW3aNJw7dw5SqRTNmjXDlClT0L59e5VxXb16FY8ePVIoi4yMRGRkJADV6y9s2rQJWVlZ8PT05GgzIqJCxgQ4j2JjYzFp0iS0bdsWXbp0UXc49B6laWuP8ePHIywsDEFBQejVq5f82suWLcONGzdw5swZAEDPnj2xYsUKWFhY5HquMWPGYP369QgNDUVGRob8S5zC2NqjXLly6NKlC+rWrYt69eqhb9++iIiIgK6u7sffPBEVC09PT6Whzh9ibW2NwMDAIm+THydPnpT/nJaZjdjEdFia6MNAV/Vq1zY2Nh+1MnWTJk1UzgPOzburZufF6NGjMXr06Hy3IyKiD2MCnEfjxo1DUlLSe7eTyatbt26pLM+tZ5jypzC39nBxccHDhw+VEuB3t/YoinnAQghs3rwZQM4H1MzMTIWE0tPTE2fOnEGbNm2wdevWD8Yg2ys4MzMTL1++lPe4VKlSBdeuXSuUrT3s7OzQvHlzHDlyBBcuXICrq2ue2hERFYZsqcDCw3ex/uxDpGRkw0hPG4Oa22Bc+5rQ1iq69RqIiOjTwQQ4j/bs2QMjIyOMHz9eoVzWWybrWevWrRuGDx+ujhDp/+Vlaw8zMzP577KtPWJjY3Hjxg3UrVv3g9eQbe0RGxuLK1eu5Hn16PyIiYlBWloaAGDx4sU4ePAgzp49K98P2NjYGEBOMp+XBPzly5fyn9/e2sTJyQn79+8vtK09TExMAOS+cBYRlX5pmdlIzyr+7dAWH72PtWFR8t9TMrLx28lIZGRJMbKtY7HHo6+jlWsPNBERqQcT4HxITEzEoUOHVB67f/8+7t+/rzTHiIrfx27t8csvv2D16tVYsmTJB68h29rj119/xbJly7B27dpCiv4/xsbG0NLSglQqxdy5cwHkrKYqW2FZtg9npUqV8nQ+2V6+9vb28iQVyFnZdOHChTh48KDS6s353dojKysLly9fBpCzqjQRaabfTkZi8bH76g5Dbk1oFNaERn24YiEb1dYRo9vVKPbrEhFR7rgNUh4lJCRACKH0kO17OmvWLAgh8Ouvv6o3UCoVW3sIIbBt2zalnt09e/YgOTkZly5dwvr16wEA3bp1kx8/e/YsDh8+rDSv7cKFC5g8eTIAwN/fX+FYfrf2iIyMxKJFi5CQkKBwnvT0dIwZMwYPHz5E5cqV0bx584+6dyIiIiKiosIeYCp1zM3NMX/+fIwcORLu7u5wd3dHhQoVEB4ejoiICDg4OOD7779XaFOhQgXs3r0bXl5eGDhwIObNm4c6deogPj4ep0+fBqA8R/vtNn5+fvj111/RoEEDJCcnIywsDAkJCRg0aFC+44+Li8Pw4cOxfft2hXIDAwPUrl0b3t7eCA0NRWZmJnr37g1vb295nXv37mHw4MGwsrLCZ599BmNjY0RGRuLy5cuQSqX48ssvMWrUKIXzamtr4/fff0f79u3x9ddfY8uWLTAwMMChQ4cgkUjw22+/yYdbAzmLhY0bNw6TJ0/GZ599hurVq+P169e4evUqnj17BkNDQwQHBxdopXQiIiIioqLABJhKpU91a4/jx49j0KBBePLkibzMxMQETZo0wf3793Ho0CHo6emhUaNGGDx4ML7++muFXuImTZpg6NChOH/+PC5cuIDXr1+jTJkycHV1xZAhQzBo0CCV84Xzs7WHra0t5s6di1OnTiEiIgLXrl1DZmYmqlSpgq+++grjx4+XLxJGRJrJz90eQ1xti/26784Blhnqaqu2OcBERFSySMTH7AFActOnT8eMGTMwa9YsTJkypUDnkvUw5rZKNJVe6enpmDJlChYtWpSzqrSOHrSNzNHis3rYGLw2z6swExFpsmypwKLDdxH81irQPs1tMJarQBMRvZcm5SFMgEsQTfqHR/8RQsDd3T1nqLVEC2ZuA2H8WWdo6RlyCw8ioo+Ql32AiYjoP5qUh3BsDpGaSSQSDBo8FBJ9I5i1HgLTZj2hpWcI4L8tPObsj8Dr1EykZare2omIiP5joKuNauXKMPklIiIlTICJSoDEKi6o9r+tMG3cVeXxNaFRqD/jMH47GVm8gRERERERlSJMgImK2aFDh/D69et3Sjm8mYiIiIioqDEBJiomb968gb+/Pzp27IjvvvtO3eEQEREREWkcJsBExeDq1av47LPPsHz5cgDAhg0bsGXLFvlxP3d7XAtojyEtVG8bMtTVFtcC2sPP3b5Y4iUiIiIiKo2YABMVIalUigULFsDFxQV37tyRl9evXx/16tWT/26gqw1TQ11M7lQbI9ztYaSXs3CLkZ42RrjbY5JXbZga6nJBFyIiIiKiAtBRdwBEpVV0dDR8fHxw4sQJeZlEIsHYsWMxe/Zs6OvrK7XR1pJgQsdaGNnWkVt4EBEREREVMibAREVg27Zt+Prrr5GQkCAvq1KlCtavX482bdp8sL1sCw8iIiIiIio8HAJNVIiSkpLg6+uLXr16KSS/PXv2xPXr1/OU/BIRERERUdFgAkxUiM6dO4fg4GD572XLlsW6deuwdetWWFhYqDEyIiIiIiJiAkxUiNq3bw9/f38AQNOmTREeHg4fHx9IJNznl4iIiIhI3TgHmKiQLViwADVq1MCIESOgo8O3GBERERFRScEeYKKPIIRAUFAQevfuDSGEwjFDQ0OMHDmSyS8RERERUQnDT+hE+RQXF4evv/4aISEhAAA3Nzd8++23ao6KiIiIiIg+hD3ARPlw/Phx1KtXT578AsDcuXORmpqqxqiIiIiIiCgvmAAT5UF6ejrGjRuHtm3b4smTJ/Ly1q1b48KFCzA0NFRjdERERERElBccAk30Abdv30a/fv1w7do1eZmuri5+/PFHjB07Flpa/B6JiIiIiOhTwASYKBdCCCxfvhzjxo1DWlqavLx27drYtGkTGjZsqMboiIiIiIgov5gAE6kglUrRrVs37NmzR6Hc398fP/30E8qUKaOmyIiIiIiI6GNx7CaRClpaWgo9vJaWlvjrr78QGBjI5JeIiIiI6BPFHmCiXEyZMgWHDh1CuXLlsGbNGlSsWFHdIRERERERUQEwASYCcOvWLdSsWRM6Ov+9JXR0dHDgwAGYmppCIpGoMToiIiIiIioMHAJNGk0qleKnn35Cw4YN8eOPPyodNzMzY/JLRERERFRKMAEmjRUdHY22bdvi+++/R2ZmJmbOnIlz586pOywiIiIiIioiTIBJI/3555+oV68eTp48KS+rVKkSsrKy1BcUEREREREVKSbApFESExPh6+uL3r17IyEhQV7es2dPXL9+HW5ubuoLjoiIiIiIihQXwSKNcfbsWQwYMABRUVHysrJlyyIwMBCDBg3iXF8iIiIiolKOPcBU6mVlZWH69Olwc3NTSH6bNm2K8PBw+Pj4MPklIiIiItIATICp1Nu+fTtmzJgBqVQKANDW1sb06dNx5swZ2Nvbqzk6IiIiIiIqLkyAqdTr3bs3PD09AQB2dnY4c+YMAgICFPb8JSIiIiKi0o8JMJV6EokEa9euxXfffYfw8HA0a9ZM3SEREREREZEaMAGmUuX48eOYOXOmUrmVlRWWLFkCY2NjNURFREREREQlAceAUqmQnp6OyZMnY9GiRQCAzz//HF5eXmqOioiIiIiIShL2ANMn7/bt23BxcZEnvwDwzTffICMjo8iuOX36dEgkEvj6+qo8rqOjk+vK0jExMRg9ejRq1KgBQ0NDmJqaolWrVggKCkJ2drbKNs+ePcOoUaPg6OgIQ0NDWFpawtXVFYGBgYVynzt37sT48ePRvHlzGBgYQCKRYPbs2bnWj4mJwapVq+Dj4wMHBwdIJBJIJBI8fvz4vddJS0vDnDlzUL9+fRgZGcHExAT16tVDQECAUt0LFy5gzJgxqFOnDsqUKQNDQ0PUrVsXP/74I9LS0pTq+/r6yuPI7aGlxT95RERERJqMPcD0yRJCYNmyZRg/frxCQlS7dm1s3rwZenp6aoxOtYsXL6Jjx46Ij4+Ho6MjvL29kZKSgjNnzmDIkCEoU6YMevfurdSmQ4cOSEhIQM2aNeHt7Y3k5GScOXMGYWFh6Ny5M2xsbAoUV/fu3eU/52VLqAMHDmD48OH5ukZCQgJat26N8PBwVKxYER07dkRWVhZu376NlStXYsaMGQr1mzZtCgCoXLkyOnToAKlUirCwMEyZMgW7du3C8ePHFYa0u7q65nrtyMhIhIaGws3NLV8xExEREVHpwgSYPkkxMTEYMmQI9u/fr1D+7bff4qeffoKhoaGaIstdXFwcOnfujPj4eAQGBsLf319+LCUlBdOmTcu1TUJCApYvXw4/Pz+FNgsWLCiU1az79u2LFi1aoEWLFtixYwdmzZr13vpVqlTBqFGj0KJFC7i6uqJZs2Z49OjRe9uMGDEC4eHhGD58OJYsWaLwBcWVK1eU6jdt2hTTp09H+/bt5Ul5XFwcPDw88Pfff2PmzJlYsGCBvP6wYcMwbNgwldcePHgwQkNDMWDAgPfGSERERESlGxNg+uT89ddfGDJkCF68eCEvs7S0RFBQUIme9zt//ny8ePECgwcPVkh+AcDIyAiLFi1Campqrm3eTn5lbaZPn14osW3evFn+865duz5Yv127dmjXrl2ez3///n388ccfaNCgAZYvX640FLlRo0ZKbc6dO6dUZmFhgRkzZsDb2xt//vmnQgKcm5SUFGzfvh36+vro2bNnnmMmIiIiotKHE+Lok5GdnY0RI0bgiy++UEh+O3XqhBs3bpTo5BcA1q9fDyCnJzQ37/Zcy9q8mzB/akJCQgAAfn5+BZ6H6+joCAB4/vx5nurv2LEDycnJ6NSpE8zMzAp0bSIiIiL6tLEHmD4Z2traSE5Olv9uaGiIRYsW4ZtvvsnTvFV1ioyMxPPnz2FkZITPPvss321U9ZB+Si5fvgwgZ57ugwcPsHHjRjx9+hRVqlRB9+7d4ezsnOdz/fvvvwCAqlWr5ql+cHAwAGDgwIH5jJqIiIiIShsmwPRJCQwMxJkzZ2Bubo5Nmzahdu3a6g4pT+7duwcAsLW1zXOy/jFtSqoHDx4AAG7evAkfHx+FRcumT5+OuXPnYsKECXk619q1awEAPXr0+GDd6OhonDhxAhYWFiV+hAARERERFT0OgaYS6+XLlxBCKJSZmJjgyJEjOH/+/CeT/AI5KyADOfEXZZuSKikpCUDOUO5OnTrh7t27iI+Px+bNm1G2bFlMnDgRp06d+uB5jh07hj///BPlypXLU8K8YcMGSKVS9OzZs0SuCk5ERERExYsJMJVIW7duhaOjIzZu3Kh0zMHB4ZNNZt7tyd2yZYvCPrUODg5qiqxoyb7IMDMzw9atW1GjRg2YmZmhb9++CAgIgBACgYGB7z3Hs2fPMGjQIAA5c6PLlSv3wevK5lBz+DMRERERAUyAqYRJTEzEoEGD0KdPHyQkJMDf3x9RUVHqDktJfockm5qaAvivJ1TG1tYWPj4+KofzytokJiZ+ZJQlR5kyZQDkbLekra2tcKxbt24AgPPnz+faPiUlBV988QWePn2KOXPm5Gk484ULF3D37l3Y2tqiRYsWBYieiIiIiEoLJsBUYpw9exYNGjTAhg0b5GVCCERERKgxKtXeTeJUeXt/3ho1agAAHj58qFDHxcUF69atw8KFC5Xay1Y7joqKUhoK/qmxsrICAFSrVk3pWKVKlQAAr169UtlWKpWiX79+uHz5MoYNG4aJEyfm6Zqyxa/69+//MSETERERUSnEBJjULjMzE9OmTYObm5tCb2+zZs1w7dq1Erl4UdmyZQHkbM2kSnZ2trwOANjb28PS0hKJiYm4ceNGnq7h4OAAS0tLpKSk4MqVKwUPWo1q1aoFAIiLi1M6Jpvr/O4WUDLfffcd9uzZg06dOuG3337L0/XS09OxZcsWAMCAAQM+ImIiIiIiKo2YAJNa/fPPP3Bzc8OsWbMglUoB5PSuzpgxA6dPn4adnZ2aI1RN1pP57NkzpWNPnz4FoLhNj0QikfdErl69Ok/XkEgk6NevHwBg2bJlBYpX3dzd3QEAoaGhSsdkyb2qRc1++uknLF++HC4uLvjzzz8VetXfZ+/evYiPj0fjxo1Rs2bNjw+ciIiIiEoVJsCkFkIIrF27Fg0aNMCFCxfk5XZ2dggNDcW0adPynOyoQ+vWrWFoaIjTp08jLCxMXi6EwJw5cwAAnp6eCm1++OEHWFhYYNmyZUqLe6nqGQWASZMmwcLCAkFBQVixYoXCsdTUVMycOROPHz8ujFsqUl5eXqhcuTL++usv7Ny5U16ekJCA6dOnAwD69Omj0Gbz5s2YOHEiatWqhX379snnEeeFbPgze3+JiIiI6G0S8alPLixFnJ2dAQC3bt1ScyRFLygoCEOGDFEoGzx4MBYvXgxjY2M1RZU/S5cuxciRI6GjowN3d3dUqFAB4eHhiIiIgIODAy5cuAALCwuFNqGhofDy8kJSUhKcnZ1Rp04dxMfH4/Tp00hLS4O3tzd2796da5uaNWuiQYMGSE5ORlhYGBISEhAVFQUbG5sC3cuIESPkPbGPHz/GkydPYG1tjSpVqgAAOnXqhKlTp8rrP3v2TL54FQBcvXoVGRkZaNiwoXyF7uXLl6NRo0byOn/99Re6dOkCIQSaNWuGChUq4Pz584iJiYGbmxuOHTsGXV1dAEBsbCyqVq2KjIwMtG3bFtbW1irjXrhwIcqXL69QFhsbK4/7yZMnsLS0LNBzQ0RERFTaaVIeAkElhpOTk3ByclJ3GMUiNTVV1K1bVwAQ5ubmYtu2beoO6aPs379ftG3bVpiYmAhdXV1hZ2cnxowZI+Li4nJtEx0dLfz9/YWdnZ3Q19cXpqamomXLlmLNmjUiKysrT23Kly8vWrRoIZYsWSLS09MLfB+tWrUSAHJ9+Pj4KNSPiop6b30A4sSJE0rXCQsLE56ensLCwkLo6ekJR0dHMXXqVPHmzZt8nx+AiIqKUrrGzz//LAAIT0/PAj8vRERERJpAk/IQ9gCXIBr1zQuAGzduYOLEiVi5cmWuPXxU/NIysxGbmA5LE30Y6H54tWsiIiIi+rRpUh5ScidZUqlx+/ZthIeHyxd0kqlbty727dunpqjoXdlSgYWH72L92YdIyciGkZ42BjW3wbj2NaGtlb99j4mIiIiISiImwFRkhBBYtmwZxo8fD6lUijp16qBevXrqDov+X1pmNtKzpPLfFx+9j7Vh/21DlZKRjd9ORiIjS4qRbXP2JNbX0WKvMBERERF9spgAU5F4/vw5hgwZggMHDsjL+vfvj6tXr5bo1Z01yW8nI7H42P0P1lsTGoU1oTmJ8ai2jhjdrkZRh0ZEREREVCS4DRIVur/++gv16tVTSH4tLS0xf/58Jr9ERERERKQ2TICp0Lx58wZ+fn744osv8OLFC3l5p06dcOPGDXh5eakxOiIiIiIi0nTsjqNCceXKFfTr1w93796VlxkaGmLRokX45ptvIJFwEaWSxs/dHkNcbeW/vzsHWGaoq63CHGAiIiIiok8VP83m0Zs3b7Blyxb06NED1tbW0NPTg7m5OTp06ICDBw+qOzy1yc7Oxvz589G0aVOF5Ldhw4a4fPky/Pz8mPyWUAa62jA11JU/JneqjRHu9jDSy1nkykhPGyPc7THJq7a8DhfAIiIiIqJPGfcBzqN169Zh8ODBkEgkaNSoERwcHPD06VOEhoZCCIE5c+bghx9+KNA1PsX9t7Kzs+Hu7o7Q0FAAgEQiwfjx4zFr1izo6empOTr6GNwHmIiIiEizfIp5yMdiD3Ae6erqwt/fHw8ePMDff/+NLVu24PTp0zhy5Ai0tbUxZcoU3LhxQ91hFjttbW1s2LABJiYmsLa2xrFjxzB//nwmv58wA11tVCtXhskvEREREZU6TIDzqH///ggMDISNjY1Cedu2bdGlSxdIpVJs375dPcEVo8zMTKUyGxsb7N27F9evX0fr1q3VEBUREREREdGHMQEuBI6OOQsEPX/+XM2RFK2wsDDUrl0bp0+fVjrWsmVLmJubqyEqIiIiIiKivGECXAj+/fdfAEDVqlXVHEnRyMzMxLRp09CyZUtERkZi4MCBSEhIUHdYRERERERE+cIEuIBevHiBvXv3AgCmTp0KX19flfV0dHRyXQ05JiYGo0ePxj///IOIiAiYmpqiVatWCAoKQnZ2tso2z549w6hRo+Do6AhDQ0NYWlrC1dUVgYGByMjIKPB9PXz4EIMHD4aVlRX09fUxa9YsSKVSAMCTJ09w8uRJpTaXLl1Ct27dUKFCBejq6qJSpUro06cPbt68met1Ll26BC8vL5ibm8PExATu7u44evSoyrp//PEHevXqBScnJ5iZmUFfXx81atTA2LFj8erVqwLfMxERERERlW5cBbqABg0ahA0bNqB+/fq4du0afHx8sG7dOqV6Ojo6yM7OhhBCvsoaAKSmpuLRo0eQSqWQSCQwNjaGm5sbzpw5g8TERGzZsgW9e/dWONfFixfRoUMHJCQkoGbNmqhfvz6Sk5Nx5swZJCUlISoqSmmucn78888/aNasGV6+fAmJRIK3/4loa2sjJCQEXbp0UWhz5MgReHl5ISsrCy4uLqhevTru3LmD69evw9DQEMePH0fTpk0V2pw5cwYeHh7IzMxE69atYWhoiEOHDkEqlWLr1q348ssvFep7eHjgxIkTqFu3LqpWrQp9fX1cvXoVDx48QNWqVXH27FlYW1t/9H0TEREREWkirgJNeRIcHIwNGzbA0dERnp6e+W6fnZ2Nf//9F1KpFFZWVtDT04O1tTX++usvPH36FGPGjFFqExcXh86dOyMhIQHLly/HnTt3sHXrVuzbtw/Pnj1DQEAAdHR0CnRfI0aMwMuXLwFAIfl1dnZGdnY2Nm3apNTmf//7H7KysrBy5UqcP38eW7duxbVr1zBt2jSkpqZi/PjxSvc+dOhQZGRkYOXKlTh27Bj++usv7N27F1KpFMOHD0dycrJCm4CAAMTExCA8PBx79+7F9u3bce/ePfj7+yM6OhoTJ04s0H0TEREREVEpJ+ijnDp1Sujp6QkzMzMREREhAgICBADh4+Ojsr62trZ49+meMGGCACAGDx4shBDCyclJODk5KdR58+bNe9sUtt27dwsACg9zc3Oxfft2kZqaKiwtLYW2trZ49eqVvE18fLwAIMqWLSukUqnC+eLi4gQAYWBgoFB+8OBBAUDUrl1bKQYPDw8BQKxevTpPMT99+lQAEBUqVPiIOyYiIiIi0myq8pDSqtT3AE+fPh0SiaRAc3Nr1KgBQ0ND+dzcOXPmoGvXrgCAkJAQ1KpVS94mNTVV5dxcoWKk+fr16wHk9LjmxtDQUOH3oKAgAMDevXvlPcbDhg3D06dPcz1HUlISxowZg2rVqsHAwACOjo6YPXu20pZGwcHBSkObdXR0kJmZiaVLlyI0NBSNGjVCdna2fCXokydPyld/Tk5OhpaWFiQSifxhYWEBAChTpozCeWXzfDt27KgUr5eXF4CcYdV5oaWV889YNkeZiIiIiIhIlYKNlS3FLl68iI4dOyI+Ph6Ojo7w9vZGSkoKTp8+LU/+Nm3ahDZt2ii027lzJzIzM1GzZk14e3vL5+a+m5xFRkbi+fPnMDIywmeffZanmI4dO4YXL14AACwsLNCyZUvcvn0ba9aswb59+3Dx4kWllahTU1Ph7u6OK1euwN7eHt7e3ggNDcXUqVNx6dIl7Nq1S/4FgJeXFywsLBAXFwcAkEgkaNmypXxubocOHdC8eXMAwN27dwEAVlZW8PHxwYEDBxAbG4sWLVrAwcEBQM7w6Q0bNkAIgX79+inEdfv2bQCAk5OT0n3WqVNHoc77ZGVlISAgAADQqlWrPDyLRERERESkqZgAqyCbZxsfH4/AwED4+/sDAFJSUuDm5oarV69iwIABCkldamoqgJwtg5YvXw4/Pz/5sZSUFBgbGyv0At+7dw8AYGtrm2sP9Lt++OEHADnJ7507d+QLVI0dOxa//PILxo4diz///FOhzYIFC3DlyhW4u7vj0KFD0NPTw+vXr+Hi4oI9e/bgjz/+kN9HhQoVMGfOHHzzzTcAgJUrV+Krr74CABw8eBCenp44f/48gJxVqAGgVq1aWLduHW7evIn27dsjLCwMUqkU1apVw+XLlyGEQKVKlfDLL78oxPXkyRMAOQl0cnIy+vfvD11dXaxfvx4VK1YEADx+/Fjl87B582YcPnwYr1+/xuXLlxEdHY2aNWvi559/ztPzSEREREREmqnUD4H+GPPnz8eLFy8wePBgefKblZWFXr164erVqxg3bhxWrVql0CY0NBQA4ODgoJD8AoCRkZF8mK6MbB9dExOTPMWUlJSEv//+W34NWdIskUgwZ84cWFpaYseOHfLeW5kVK1YAAObNmwc9PT0AgKmpKb777jsAwPLlyxXqV6tWTf5zs2bN5D937NgRzZs3R1ZWFgDgzZs3Cu3q1KmD0NBQ1K9fH+fOncPWrVvxzz//AAC++OILpYW5UlJSAOQM8T548CD27NmDkJAQHDhwQD5cWlbnXRcvXkRwcDB27dqF6OhoNGvWDCdPnkT16tVzfwKJiIiIiEjjMQFWQdXc3MDAQOzfvx8GBgaIjY2Fn58ffH195Y+LFy8CgMJ84Lx4t/f39evXCnNoZcOJ7927J+9BfjeZNDAwUJqbC+QsY/7s2TNYWFjAxcVFXr53715MmzYNAHD+/HkkJSXJjx0/flz+c58+fXDu3DkkJyfj/PnzePDgQa5xHzlyBA0bNoS5uTmuXbuG2NhYeSK7atUqLF26NNfnoHnz5rC3t0ft2rXh5ub2nmcrx6+//gohBF6+fIldu3YhNjYWdevWxaVLlz7YloiIiIiINBeHQL8jt7m5sh7btLQ0eYKsSrly5fJ0HVNTUwBQSD4BQFdXFz4+PkhOTkZISIi8PC0tTf5zYmKi0vlki03J5uYC/82hrV27NoCcHtWxY8di5cqV8jrZ2dm4c+cOGjdurNBGtq+xbM4vAFhaWsp/frvn+vXr1+jduze0tLSwc+dOmJmZYePGjXjz5g3atm2LkydPYvLkyRg4cCDMzMwA5PSKAzlDxytXrizvLQaA58+fK9TJTbly5dClSxfUrVsX9erVQ9++fREREQFdXd33tiMiIiIiIs3EHuB35DY3d/r06RBCqHzs378/T+d+u+e2Ro0aAICHDx8q1ClTpgzWrVuHhQsXKpSXL19e/nNUVJTSqtLR0dEA/pubCyjOs718+TJsbW0Vkl/Z/cnqvf3z7NmzsXv3btjb28PW1hbz589XSMjt7OzkP+/fvx/x8fFo2rSpPMENDg4GAHzzzTewt7dHUlISrly5Im9TpUoVAP8lu2+LiYkBAFhbWysdU8XOzg7NmzdHZGQkLly4kKc2RERERESkeZgAvyO/c3PfbgPk9Kiqkp2djbJly8p/t7e3h6WlJRITE3Hjxo0PXsPR0VG+3VBKSopCMhkZGYlz584BUJybK5tDGxkZiaZNm8pXkAaAhg0bomHDhrm2MTQ0REZGBiIjIxEVFQV7e3tYWVnJ673dO/7o0SMA//XYPn78GMePH4epqSm++OILGBsbA/gvsQX+W/1Z1UrPN2/eVKiTF7LXK7eFs4iIiIiIiJgA5+LdOa5btmxROTf3XW/3wMrI9uh9e4siiUSC/v37AwBWr179wXi0tLTkC3IBQEBAgHxubpcuXeQ9r2/HLUvMw8PD5YtXATmLUp0/f15pj+F3vTs3NzY2Vn5MNmQagPzaN2/ehBACGzduhFQqRc+ePSGEkA9vrlSpkryNh4cHgJzVpd8l61Fv167de+OTycrKwuXLlwFAIUknIiIiIiJ6W6lPgPO6xZBMbnNzbW1t4ePjgx49euTaRiKR4PTp0wgLC5MfE0Jgzpw5AABPT0+Fdj/88AMsLCywbNkybNy4UeHYu6s5A8DUqVPRoUMHAMC+fftgbGyMZs2aISkpCVOnTgWQk4Q+fvwYW7duRWBgoEJ7a2trHD9+HHv27IGenp685/ftubayn6OiouRzc2W9tLIE3NDQUOF57dChA7S1tXH37l0sWbJEPvy5f//+mDp1Kl6/fo2KFSsqrCrt4eEBe3t7RERE4Pfff5eXHzx4EEePHoW5uTl69+4tL4+MjMSiRYsUetsBID09HWPGjMHDhw9RuXJlhTnLRERERERECkQpN2vWLAFA+Pj4qDyura0tdHR05L/fv39fABAmJiYq60dFRQkAwt7eXl527949AUDo6ekJAEJHR0d4eHiIvn37itq1awsAwsHBQbx69UrpfGfOnBHGxsYCgNDX1xcmJiaiffv2wsDAQAAQ3t7eSm1+/vln+bUsLS1Ft27dhLOzswAgAIg7d+4Ie3t7+e8ARK9evURcXJzCeSpXriwAiL///lte5uXlJQAIiUQiXFxcRK9evYSnp6coU6aM/Fx16tRRimn27NkK1zM0NBS1atUSAISurq7YvXu3UpsTJ04IXV1dIZFIRNu2bUWnTp2Ejo6OkEgkYsuWLQp1r169Kn+OmjdvLvr27Su8vLxEpUqV5Nc7cuSIyteMiIiIiIhy5+TkJJycnNQdRrEo9T3Asnm3RTU3F8jZl9fS0hIZGRlYvHgxWrVqhYsXL2L79u3yHsqLFy/KV2p+m6urK27fvg1/f39IpVIkJSXhwoULaNKkCdasWYMdO3YotRk9ejQiIyPh7++PsmXLYv/+/fJhxmPHjoWtrS02bdok33vYyMgIW7Zskc8hBoD4+Hg8ffoU2tra8gW5gP/m3dauXRuxsbHYs2cPzp49i8aNG2PAgAEKdd42efJk7N27V75wVXp6OuLi4vDll1/i3Llz8Pb2Vmrj7u6O0NBQtG/fHpcuXcKpU6fQrFkzHDx4UKH3F8jpgZ87dy5at26NJ0+eYM+ePTh69CgMDAzw1Vdf4dq1a/Jh1URERERERKqU+m2QqlWrBiD/c3N/+eUXrF69GkuWLPngNSQSCfr164dff/0V4eHhOHr0aL5itLa2RmBgIE6cOAEgZ//evLYBcu7NwcEB1apVw4IFCyCRSODi4oK1a9diwoQJiI2NxcWLFxX2ApbNs23atKl8kaq0zGzUa+IKYCGEEAr7/gL/zcnNbW5u+/bt5cOqb9y4kadFrJo0aaJyHvC7TE1NMXHiREycOPGDdYmIiIiIiFQp9T3ArVu3hqGhYZHOzQWASZMmwcLCAkFBQVixYoXCsdTUVMycObPAKxQnJiYiKipKoezChQuoXbs23rx5g4CAAIW5uT4+PhgxYoT8njIzMwHk7Ns7a9YsAICfnx+ypQLzD97BZ7OOYMolCXTNKyEiIgKrVv23OFduc3PftnfvXsTFxaFhw4b5WsGZiIiIiIioOEiEeGdD2VJo6dKlGDlyJHR0dODu7o4KFSogPDwcERERcHBwwIULF5SGJ4eGhsLLywtJSUlwdnZGnTp1EB8fj9OnTyMtLQ3e3t7YvXt3rm1q1qyJBg0aIDk5GWFhYUhISEBUVBRsbGxyjdPZ2RlA7j3Ad+7cgbOzMxo3bozq1asjIiJCPky7fPnyePr0KXR1dRXavHnzBi1atEB4eDjsHRxQr34DnD97Fs+ePUWHjl7YErITS479g7Vh/yXWaf9eR8zWaZCIbLR0bw0DfQMcO3oY2dnZWBO8AX379IGBrrZSfN7e3ti7dy8WLVqEMWPG5P6CEBERERFRifGhPKQ00YgEGAAOHDiARYsW4dKlS0hNTUXVqlXRtWtXTJkyRWFu7NseP36MefPm4cCBA3jy5AkMDAxQv359+Pj4wMfHB9raykngu22MjY1Rs2ZN9O7dG8OHD4eenl6uMX7oH158fDzGjBmDEydOIDo6GlKpVOH4jh070K1bN6V2iYmJmDZtGoI3bUVC/EvoGFeAUZ02MG3aExJt1aPg05/eRULoJqQ/uQNAQM/SDqbNe8PQthFGtXXE6HY1FOq/ePEClStXhhAC0dHRClseERERERFRycUEmNRC1T+8tMxsxCamw9JEHwa62jh69Ch8fHzk85cBwNzcHKtXr1a5RdPbfjlyD4uP3S9wnKoSYCIiIiIi+jRpUgJc6hfB+lRlSwUWHr6L9WcfIiUjG2X0tFE1NRKHF40ExH89v23btkVwcDCqVKmixmiJiIiIiIhKPibAJYwQwOvUTCw+el9hXu6bjGzc1baBmftgvD67BXp6upgZMA3jRo+Sb3f0IX7u9hjiaqtU/u61ZIa62mJkW0elcn2dUr92GhERERERlUJMgEuYuDcZqD/jcK7HTZt0g2mTnHm+uvUc85z8AoCBrrbKxasmd6oNA10tBP9/b7ORnjZ8mttgbPua0NaSqDgTERERERHRp4cJMEFbS4IJHWthZFtHhfnGREREREREpQkTYJIz0NVGtXJl1B0GERERERFRkWACXMJYlNHDuYD2eZqXy7m4REREREREeccEuISRSABTQ13OyyUiIiIiIipkTIBLKM7LJSIiIiIiKlxMgEs4zsslIiIiIiIqHJxESkRERERERBqBCTARERERERFpBCbAREREREREpBGYABMREREREZFGYAJMREREREREGoEJMBEREREREWkEJsBERERERESkEZgAExERERERkUZgAkxEREREREQagQkwERERERERaQQmwERERERERKQRmAATERERERGRRmACTERERERERBqBCTARERERERFpBCbAREREREREpBGYABMREREREZFGYAJMREREREREGoEJMBEREREREWkEJsBERERERESkEZgAExERERERkUZgAkxEREREREQaQSKEEOoOgnIYGxsjMzMT9vb26g6FiIiIiIg0RGRkJHR1dZGUlKTuUIoce4BLECMjI+jq6qo7DCWRkZGIjIxUdxj0Dr4uJRNfl5KLr03JxNelZOLrUnLxtSmZPvXXRVdXF0ZGRuoOo1iwB5g+yNnZGQBw69YtNUdCb+PrUjLxdSm5+NqUTHxdSia+LiUXX5uSia/Lp4M9wERERERERKQRmAATERERERGRRmACTERERERERBqBCTARERERERFpBCbAREREREREpBG4CjQRERERERFpBPYAExERERERkUZgAkxEREREREQagQkwERERERERaQQmwERERERERKQRmAATERERERGRRmACTERERERERBqBCTARERERERFpBCbApOTNmzfYsmULevToAWtra+jp6cHc3BwdOnTAwYMH1R0eveXOnTvQ19eHRCLB7Nmz1R0OAdi8eTPc3d1hYWEBQ0ND2NvbY+jQoYiPj1d3aBrryJEjaN++PczNzaGnp4dq1aph2LBh+Pfff9UdWqkXFRWFJUuWoE+fPqhatSokEgl0dHTe2+bw4cNo1aoVjI2NYW5ujk6dOuHy5cvFFLFmyM/rEhUVhXnz5qF58+YwMzODnp4ebGxs4Ofnh8ePHxdz5KXbx7xf3jZt2jRIJBJIJBK+NoXsY16bhIQETJw4EbVq1YKhoSHMzc3RpEkTLFu2rJiiplwJoncEBQUJAEIikYjPPvtM9O7dW7i5uQmJRCIAiDlz5qg7RPp/rVu3FgAEADFr1ix1h6PRpFKpGDx4sAAgypQpIzp27Ch69uwpGjVqJCQSiYiIiFB3iBpp7dq1AoDQ0tISrVq1Ej179hSOjo4CgChfvrz4559/1B1iqRYQECD/GyX7P0RbWzvX+n/88YeQSCRCR0dHdOrUSbRp00ZIJBKhr68vzp49W4yRl275eV1atWolAAhjY2PRoUMH0bNnT2Fvby8AiHLlyokbN24Uc/SlV37fL2+7d++e0NfXl7ePjo4u4mg1S35fm0ePHolq1aoJAMLGxkb07NlTeHp6Cmtra+Hi4lKMkZMqTIBJycaNG4W/v7+IiopSKD969KjQ1tYWWlpa4vr16+oJjuQ2btwotLS0RNeuXZkAlwArV64UAETTpk3F8+fPFY7dv39fJCQkqCkyzZWeni7MzMyERCIRBw8elJdnZ2cLX19fAUAMGDBAjRGWfps2bRITJkwQe/bsES9fvnzvh8bXr1/LX69Dhw7Jy3/77TcBQNSqVUtkZ2cXV+ilWn5el4EDB4rVq1eL1NRUeVlmZqYYOnSoACA+//zz4gq71MvP6/IuDw8P8fnnn8uTLibAhSu/r03z5s0FADFz5kwhlUrl5dnZ2eLKlSvFETK9BxNgypfu3bsLAGLatGnqDkWjJSQkiIoVK4qvvvpK/q0kE2D1kUqlwtraWhgaGorHjx+rOxz6f1evXhUAhLOzs9KxK1euyJMqKj7v+9C4YsUKAUB07NhRoVwqlcp77Y8cOVIcYWqc/CRaMgkJCfKesAcPHhRRZJotr6/LH3/8IQCI0NBQUb16dSbAxeB9r82RI0cEAOHt7V3MUVFecQ4w5YujoyMA4Pnz52qORLNNnjwZqampnPdbQly4cAGPHz9G165dUaVKFXWHQ/9PX18fACCRSJSOycrKly9frDFR7o4ePQoA8PT0VCiXSCTysiNHjhR7XKSaqakpLC0tAfAzgTolJSVh7Nix6NOnD1q0aKHucAjA9u3bAQD+/v5qjoRyk/eZ9USAfNGYqlWrqjkSzXX58mX89ttvmDdvnvzDB6mXbIEeV1dXvH79GsHBwYiIiICpqSk8PDzg4eGh5gg1k6OjI6pXr47bt2/jxIkTaN26NQBAKpUiMDAQADB48GB1hkhvuX37NgDAyclJ6VidOnUU6pD6paSkIC4uDgA/E6jT1KlTER8fj/nz56s7FPp/ly9fhkQiQYsWLXD9+nVs3boVr169gp2dHXr16gUbGxt1h6jxmABTnr148QJ79+6FRCJBt27d1B2ORpJKpfDz84OdnR1GjRql7nDo/z148AAAkJ2djfr16+PRo0fyY/Pnz0e3bt2wdetW6OrqqitEjaSjo4N169ahS5cu8PDwQKtWrVChQgWEh4fj2bNnmDt3LoYMGaLuMOn/PXnyBABgZWWFJ0+eYNCgQbC3t8eKFStQsWJFAODKtiXIhg0bkJmZiSZNmsDa2lrd4Wik8PBwBAYGYtKkSahWrZq6w6H/9+DBA1hYWGDLli34+uuvIZVK5cemTp2KoKAg9OvXT40REodAU56NHTsWycnJGDRoEJydndUdjkZauXIlLl26hIULF0JPT0/d4dD/S0pKAgDMnDkTZcuWxdmzZ5GYmIjQ0FDUrFkTO3fuxNy5c9UcpWZyd3fHiRMnUK1aNZw4cQJ//vkn7t27h0aNGqFZs2bqDo/ekpKSAgAwNDTEH3/8gePHj2P16tW4evUqypQpo1CH1CsmJka+5c68efPUHY5GEkLAz88PVlZW+P7779UdDr0lKSkJaWlp+Pbbb+Hn54eoqCjExsZi6dKlEEJgyJAhuH//vrrD1GhMgClPgoODsWHDBjg6OmLp0qXqDkcjxcbGYtKkSWjbti26dOmi7nDoLUIIADl7/u3ZswfNmjWDsbExWrRogc2bNwMAAgMD5fWo+GzYsAFNmzZF48aNcffuXSQmJuLAgQN48OABPDw8sGvXLnWHSCq0a9cOVlZWaN68ucoh0aQ+WVlZGDBgAF68eIEJEybIpxZQ8Vq9ejXOnz+PefPmwcjISN3h0FuEEEhJSUGzZs0QGBgIGxsbVKhQAd9++y2++eYbpKenY9WqVeoOU6MxAaYPOn36NL7++muYmZlhz549MDY2VndIGmncuHFISkrCL7/8ou5Q6B2y3qlmzZrBzs5O4VijRo1QrVo1vHjxQj5UmorHgwcPMHToUFSvXh2bN29GjRo1YGxsjI4dO2LdunXIysrC6NGjkZ2dre5QCZB/iE9NTUX9+vXx7NkzhIWFwdDQEG/evFGoQ+rz3Xff4ejRo/Dy8sKPP/6o7nA00suXL/HDDz/AxcUF/fv3V3c49A7ZZ4IBAwYoHZNNITx//nyxxkSKOAeY3uvu3bvyN2tISAhq1aql5og01549e2BkZITx48crlP/zzz8AgPXr1yM0NBTdunXD8OHD1RGixrKysgKAXOdgVapUCf/++y9evXoFe3v74gxNo23duhWZmZnw8PCAjo7if3etW7eGgYEBHj58iEePHil9cUHFr0qVKnj9+jWeP38uX/RKJiYmBgA411TNFi5ciBUrVqBBgwbYunUrtLW11R2SRgoNDUVcXBwSExOVVk2XvVcGDhwIfX19LF++nH/fipmVlRUSExNVfiaoVKkSAODVq1fFHRa9hQkw5SomJgaenp6Ij4/Hxo0b0aZNG3WHpPESExNx6NAhlcfu37+P+/fv80sKNZA957IVUd+VkJAAIGduIxUf2WJkqnoNJRIJjIyMkJaWhpiYGH5ALAGcnJxw+/Zt3L59W2nl9Js3b8rrkHps3boVEyZMgI2NDfbv34+yZcuqOySNFxERgYiICJXHTp48CSDncwMVr1q1auHevXsqPxPw80DJwCHQpFJKSgo6d+6MqKgoLFiwgKvVlQAJCQkQQig9AgICAACzZs2CEAK//vqregPVQC1btoSWlhYuX76MtLQ0hWNxcXF4+PAhdHV14eDgoKYINZOZmRkA4MaNG0rHnj17Jv8GXvaNPKmXLOk9cOCAQrkQQl7Wrl27Yo+LcqZC+fj4oFy5cjh48CDfM2rWtWtXlZ8HhBCoXr06ACA6OhpCCDRo0EC9wWogd3d3ADk99e+6cuUKAKB27drFGRK9gwkwKcnKykKvXr3w999/Y9y4cRg7dqy6QyIq0cqVK4euXbvKFyqTLXYllUoxadIkpKenw9vbm9/4FrNOnToBAA4fPqyw2FVmZqb871qDBg24J2MJ0adPH5iamuLQoUM4cuSIvHzVqlW4f/8+atSowZFIanD79m107doVOjo62LdvH2rWrKnukIhKtH79+sHQ0BC///47zp49Ky9//PgxFi5cCCDn7x2pD4dAk5LAwEDs378fBgYGiI2Nha+vr1KdWrVqYeLEicUfHFEJ9fPPPyM0NBS//PILDhw4AGdnZ9y4cQP37t1DxYoVsWjRInWHqHHc3NwwbNgw/P777+jevTuaNm2KypUr4/Lly3j48CHKli2LlStXqjvMUu3KlSsYMWKEQll2djaaNm0q/33nzp2oVKkSTE1NsWzZMgwcOBBeXl7o2LEjUlNTcfz4cejp6WHNmjXQ0uL39oUhP69Lv379EB8fD2dnZyxfvhzLly9XOt+wYcPg6upa5HGXdvl5Xah45ee1kf2fP2LECLi7u6NFixYwMjJCWFgYEhIS0KdPH3Tu3Lm4b4HewgSYlMjmJ6SlpWH9+vUq67Rq1YoJMNFbqlevjsuXL2PatGk4dOgQ9uzZg/Lly2Pw4MGYMWMGqlatqu4QNdLq1avh5uaG1atX4/r167h06RKsrKzg4+ODyZMnw9HRUd0hlmqJiYm4cOGCUvnbZenp6fKf+/fvj3LlymHOnDk4efIktLS00KFDB8yaNQuff/55scSsCfLzusg+E9y6dQu3bt1SeT53d3cmwIUgv+8XKj75fW38/PxQtWpVLFy4EFeuXEFGRgYcHR0xdepUjBo1qlhiptxJBDemJCIiIiIiIg3AsURERERERESkEZgAExERERERkUZgAkxEREREREQagQkwERERERERaQQmwERERERERKQRmAATERERERGRRmACTERERERERBqBCTARERERERFpBCbAREREREREpBGYABMREREREZFGYAJMREREREREGoEJMBEREREREWkEJsBEREREpdzff/+NQYMGwcHBARKJBFOmTFF3SEREasEEmIiIiKiUCwsLw/nz5+Hq6gpTU1N1h0NEpDY66g6AiIiIiIrWd999h1GjRgEAbGxs1BsMEZEasQeYiIiIqJTT0uJHPiIigAkwEVGxePjwISQSCSQSCVq0aKF0/PDhw/Ljvr6+xR9gKXby5MlCfV59fX3lr1Vuj127dgEA1q1bp3Rs+vTphRKHumVlZcHJyQmNGjWCECLXerLn/93HsGHDijFaRbLXcOPGjWqLoaS7evUqJBIJfvrpJ3WHQkRUqDgEmoiomJ07dw7Pnj1DpUqV5GXbt29XY0T0MerXr48GDRqoPFatWjUAgIODA3x8fAAA//zzD8LCwoorvCL3+++/IyIiArt374ZEIsm1npWVlfw5AErf81BaNWzYEN7e3pgzZw6GDh2KcuXKqTskIqJCwQSYiKgY1a9fH7dv38bOnTsxYsQIAEB2djZ27dqFxo0b49KlS2qOkPKqa9euH+zNdXV1haurK4Cc3uDSkvhlZGRgxowZcHZ2hre393vr1qpVC+vWrZP/XpqeB3VKSEjA8+fP31unTJky8i9jPsYPP/yAPXv2YO7cuVi4cOFHn4eIqCThEGgiomJkZmaG1q1bY8eOHfKy06dP48WLF+jevbsaIyPKu5CQEDx//hwDBw5Udygaa8uWLahdu/Z7H4MGDSrQNZo2bQp7e3sEBQUhLS2tkCInIlIvJsBERMWsR48eOHXqFF69egUgJ5moUqUKXFxcVNZ/+vQpvv32W9jZ2UFfXx9WVlYYNGgQHj16pLL+mTNn4O/vj7p168LMzAxGRkaoW7cuZs2ahTdv3qhsc+PGDfTp0we2trYwMDCAtbU12rVrh5UrV6qsL5FIVK4kK5vr7O7urnTs7bm4iYmJ+N///oeqVavCwMAANjY2mDx5coHvfcOGDWjYsCEMDQ1hbW2NgIAAZGVlqaxbkuXn+Z07dy4kEgmGDx+uVP/ff/+FqakpypUrhydPnhRafKtWrQIA9O3bt9DOCQBt2rSBRCLJtYd42bJlkEgkGD16dKFeV+bkyZMwMjJCxYoVcfv2bQA5Kybb2Nhg3rx5MDMzQ7Vq1bB//36EhISgatWqMDExUflv92Pk5334zTffQAjx3sfJkycLHFPfvn0RFxeHkJCQAp+LiKgkYAJMRFTMunbtCqlUij179kAIgZ07d6Jbt24q51Feu3YNDRo0wLJly6Crq4svvvgC1atXx4YNG9CkSRM8ePBAqc348eOxYsUK6OjooEOHDujQoQOSk5Mxbdo0tGnTBunp6Qr1L1++jCZNmmDr1q2wsrJCt27d4OjoiPDw8EL7YP+27OxstGvXDmvWrEG9evXQuXNn6OnpYd++fQW6959//hmDBg3C3bt30b59ezRs2BDz58/HDz/8UOj3UJJ8//33aNWqFVatWqXwHAoh4OPjg8TERKxatQpVqlQplOslJibizJkzsLW1LdDwWlWGDh0KAApDpt8WHBwMABgyZEihXhfIGYnRuXNnlC1bFsePH4eTk5P82JMnT/D777/DxcUF0dHR8PX1xddff41GjRpBX18fc+bMwd9//12g6xf3+zCvZF+2vPv+JCL6ZAkiIipyUVFRAoBo1aqVEEKIli1bik6dOonQ0FABQJw4cUKcOHFCABA+Pj5CCCEyMjKEg4ODACB+/PFHIZVK5ef7448/BADh5eWldK3169eLJ0+eKJRlZWWJgQMHCgBi/fr1Csd8fHwEALFixQqlNvv27VN5PwBE9erVP3ifb5Pdn7GxsWjSpImIiYlROB4WFib/Ob/3/uTJE6Gvry/KlCkjrl27Ji8/f/680NPTU3heC0r2fAUEBOSrXVBQUJ7b5ff5/ffff4WZmZmoWLGiiI2NFUIIsWjRIgFADBkyJF9xfsiBAwcEANGnT5+Pai97HoYOHap0LDU1VZiZmQkTExPx5s0bhWMRERECgPj8888/6rpvk72GGzZsEEIIcebMGVG2bFlRoUIFcfPmTYW61atXFwDEo0ePhBBCtG3bVgAQ06ZNE0IIsWXLFgFA/Pzzz4USU37eh/kRGxsrtm3bJrZt2yYqVKggevToIbZt2yb279//3navX78WEolEWFtbFzgGIqKSgD3ARERq0L17dxw9ehRBQUGoUKEC3NzclOrs2LED//zzD9zd3TFp0iSFHuI+ffqgVatWOHDggNJCOAMHDkTlypUVyrS1teHv7w8AOHTokMKxmJgYAEDz5s2V2nh5eX38TeYiJSUFmzdvhqWlpUL529fP773/+eefSE9Ph4+PD+rVqyev6+Lign79+hX6PQDAjBkzVG7v07Vr1yK53vtUrVoVK1euRExMDL766ivcunULkyZNgoODAxYvXlyo17p+/TqAnMWtCpuBgQH69euHxMREhXnyALB+/XoAwODBgwv1mmfPnoWnpycMDQ1x/PhxODs7K9UxNTWV93bXqVMHAOQrgMueh+jo6ALFUdTvw1u3bqFnz57o2bMnXrx4gZCQEPTs2RN+fn7vbWdiYoLKlSvj8ePHiIuLK3AcRETqxgSYiEgNunfvjoyMDKxduxZdunSBtra2Up1jx44BQK4JVYMGDSCEQHh4uNKxx48fY9myZRg5ciSGDBkCX19fLFiwAADw4sULhbr169cHkLPi640bNwpwV3nTqFEj2Nvbv7dOfu9dNvxU1RcJrVu3/vhg36N+/frw8fFRerRp06ZIrvchvXr1go+PD3bv3g13d3dkZ2dj48aNKFu2bKFeJzY2FgBgYWFRqOeVkQ2Dlg13BnKGc2/atEmeIBeWCxcuwNPTE8nJyVi3bp08uX2XsbGx/GfZ82liYgIAMDIyAgAkJSUVKJaifh+6u7urnCf88OHDD7aVvdbv/u0gIvoUcRskIiI1qFq1Kj7//HNcunQp19WfZT1K//vf//C///0v13PJFtOS+eWXX/D9998jMzNTZf2MjAyF3ydMmICTJ09i37592LdvHywsLNCkSRN06tQJw4YNg4GBQT7u7MM+lPwC+b932QdzKysrpTqqygpDXrZBKm5Lly7FwYMHERMTg++//z7XhdUK4vXr1wAUk8LC1KhRIzRo0ADHjh3D48ePYW1tjRMnTuDff/9Fnz59YGZmVmjXCgwMhL6+PgDgp59+QseOHaGlpdw38PYXVDo6Ogplst8Luthacb8P80OW7MteeyKiTxkTYCIiNVm7di0ePnwIDw+P99br0KHDe5O46tWry3++cOECxowZA2NjYyxbtgydOnVCxYoVoa2tjfv376NGjRoQQii0t7CwwNmzZ3H69GkcOnQIp0+fxpEjR3Dw4EEEBwfj7Nmz0NXVzdM9SaXSD9bJzwf5/Ny7JvjQ83v27Fl5D+2BAwcwY8YMeYJXWGQJaGJi4ke1l/37U5VoygwdOhTfffcd1q9fj0mTJsmHPxf24leVKlXCkSNH4Ofnh1OnTmHRokUYP378R53r3fdVfhXm+7CwyRJfU1NTtVyfiKgwMQEmIlKTOnXq5DrkEgCsra0BAAMGDMCAAQPydE7ZvMnZs2fjq6++Ujj2vqGOWlpacHd3l6/4+uDBA3Tt2hV///03duzYgd69eyvVf7cnGfhvHmNB5ffeK1asmOv1nz17VigxFZSqVb5z8zHP74sXL+Dr6wtjY2P06dMHq1atwoQJEwp9DnCFChUA4KPng8q24ipTpkyudfr374/x48cjODgYo0aNkm851LZt24+6Zm7mzJkDZ2dnrFu3DvXr18fUqVPRoUMHhXnkxSm/78PiInutZa89EdGnjHOAiYhKKNnc1f379+e5TXx8PADA1tZW6diePXvyfB47Ozv5XMyoqCil4xYWFoiNjUVaWppC+blz5/J8jffJ771//vnnAHL2QH7XiRMnCiWmgipXrhyA/16j9/mY53fw4MF4/vw5lixZgmXLlqFx48ZYunQpDhw4ULDA3yGbq3rnzp331nv58qXK8sjISAB47xZK5ubm6NatG+7du4fx48cjOTkZvr6+7+01/hiy4ct2dnZYuHAh0tPTMXDgQJVfPqjDh96HxeH169d49uwZqlatWmTzvomIihMTYCKiEurLL7+EnZ0d/vjjDyxZskRp+Gt0dDTmzp2rUFajRg0AwMaNGxXqHz9+HKtWrVJ5nRUrVih9uE5PT8euXbsAAI6OjkptGjZsiOzsbCxbtkxedv/+fSxatCjvN/ge+b33nj17wsDAAMHBwbh586a8/OLFi/jjjz8KJaaCql27NgDg4MGDSnsxvyu/z+/SpUuxb98+dOvWDT4+PtDR0cGGDRtgaGgIX1/fQuuZB3JWKdbW1saFCxfeW8/NzQ0hISEKQ4OfPXuGbdu2AQBatmz53vayxO+3336DRCIp9NWf3zV8+HB06NAB169fV8u+ux/zPiwOFy9ehBBC5QJzRESfIibAREQllJ6eHkJCQlChQgWMGjUK9vb26NKlC3r37o169eqhevXqmD9/vkKbwYMHw8rKCn/++SecnJzQt29ftGzZEh4eHvDx8VF5nRUrVsDOzg5169ZF79690aNHD9jY2ODkyZNo0qQJunTpotTmm2++AQCMGzcOLi4ucHd3R4MGDeDp6amWe69UqRJmz56NlJQUNGnSBF27doW3tzdatmwp365G3ezt7dG1a1fcu3cP9erVw6BBg+Dr6ytPcN6Wn+f3xo0bmDBhAipWrKjwJUfNmjXx008/ITY2FoMHDy7wHFUZY2NjtGrVCg8fPnzvsPpnz57hyy+/RPXq1dGjRw988cUXcHZ2xuPHj+Hp6Snvtc9NmzZt5CMZWrZsqXJUQ2Fbs2YNzMzM8PPPP+P06dNFfr23fcz7sDjIRlAUxZZoRETqwASYiKgEa9CgAa5fv46xY8dCX18fhw8flu/j6+/vrzSsuVy5crh06RL69euH5ORk7N69G4mJiVi1ahUmTZqk8hozZ86Ej48PsrKysH//fhw7dgyVKlXCggULcOLECfkw0bd1794dy5cvh62tLcLDw/H8+XP89NNPmDJlitrufezYsQgODoajoyMOHjyIq1ev4ocffsCcOXMKLaaCWrt2LQYNGoTY2Fhs2LABwcHBKrexyuvzm5aWhn79+iEtLQ2rV69G+fLlFY77+/ujQ4cOOHDgQKHOBf76668B4L2967t27cLw4cNhYmKCI0eO4MiRI6hYsSKmTJmCkJCQD15DIpHIex19fX0LJe4PqVKlCgIDAyGVSjFo0KCPXujrY3zM+7A4bNmyBRYWFujRo4dark9EVNgkorC+EiYiIiKNkJGRARsbG5iZmeHWrVv5WuArr9LS0mBtbY20tDQ8f/680Pczpg8LCwuDq6srxo4di4ULF6o7HCKiQsEeYCIiIsoXPT09BAQEICIiArt37y6SawQGBuLVq1fo27cvk181mTt3LkxNTfHDDz+oOxQiokLDHmAiIiLKt+zsbNStWxf6+vq4cuVKofQCv3r1Ct9//z2ePn2KgwcPwtDQENeuXYODg0MhREz5ceXKFXz22WeYP38+JkyYoO5wiIgKDRNgIiIiKhEePnwIW1tb6Orqok6dOvj555/le+ISEREVBibAREREREREpBE4B5iIiIiIiIg0AhNgIiIiIiIi0ghMgImIiIiIiEgjMAEmIiIiIiIijcAEmIiIiIiIiDQCE2AiIiIiIiLSCEyAiYiIiIiISCMwASYiIiIiIiKNwASYiIiIiIiINAITYCIiIiIiItIITICJiIiIiIhIIzABJiIiIiIiIo3ABJiIiIiIiIg0AhNgIiIiIiIi0ghMgImIiIiIiEgjMAEmIiIiIiIijcAEmIiIiIiIiDQCE2AiIiIiIiLSCEyAiYiIiIiISCMwASYiIiIiIiKNwASYiIiIiIiINAITYCIiIiIiItIITICJiIiIiIhIIzABJiIiIiIiIo3ABJiIiIiIiIg0AhNgIiIiIiIi0gj/ByJqMKExH7TEAAAAAElFTkSuQmCC",
      "text/html": [
       "\n",
       "            
\n",
       "                
\n",
       "                    Figure\n",
       "                
\n",
       "                \n",
       "            
\n",
       "        "
      ],
      "text/plain": [
       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(dpi=150)\n",
    "# Plot a 1-to-1 line using the observed values.\n",
    "plt.plot(sorted(df_obs_s[\"flux\"]), sorted(df_obs_s[\"flux\"]), \"k--\")\n",
    "plt.errorbar(df_obs_s[\"flux\"], df_lit_s[\"flux\"], \n",
    "             xerr=df_obs_s[\"flux_error\"], \n",
    "             yerr=df_lit_s[\"flux_error\"], \n",
    "             marker=\".\", ls=\"\", ms=5)\n",
    "for n,o,l in zip(df_obs_s[\"name\"], df_obs_s[\"flux\"], df_lit_s[\"flux\"]):\n",
    "    plt.text(o, l, n, ha=\"center\", va=\"bottom\")\n",
    "plt.xlabel(\"Measured Flux (Jy km s$^{-1}$)\")\n",
    "plt.ylabel(\"Literature Flux (Jy km s$^{-1}$)\")\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "864b3ce0-9ac9-4403-a6e5-89245f1f96a9",
   "metadata": {},
   "source": [
    "There are significant differences between the tabulated values and those measured from this data set.\n",
    "At the time of writing we do not understand the origin of the difference. As far as we can tell, processing the data in GBTIDL produces equivalent results as those presented here (the differences in flux measurements are <1 Jy km s$^{-1}$). Stay tuned for an update."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "ae2f0ae1-cfd0-4c83-b2be-2133847acc8b",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "version_major": 2,
       "version_minor": 0
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Ro0dSrGTJkti8eTM6dOggY2ZUmHEFmIiIiIiIVEahUOCXX35B8+bNlYrfNm3a4OrVqyx+KV9xBZiIiIiIiFSmX79+2L59u/RaW1sbvr6+mDhxIrS1tWXMjDQBV4CJiIiIiEhl3NzcpD9XqFABp06dwpQpU1j8kkpwBZiIiIiIiFTGxcUFP/zwAyIiIvDHH3/A3Nxc7pRIg7AAJiIiIiKifPHo0SMYGRmhePHiSvFFixZBW1sbgiDIlBlpKt4CTUREREREeW737t2oV68eBg0aBFEUlY7p6Oiw+CVZsAAmIiIiIqI8k5iYiBEjRqBnz56IiYnBP//8gyVLlsidFhEA3gJNRERERER5JCwsDG5ubrh27ZoUMzIyQsmSJWXMiuh/uAJMRERERERfRRRFrF+/Hg0bNlQqfu3s7BAaGgp3d3cZsyP6HxbARERERET0xeLi4tCvXz8MHjwYCQkJUnz06NE4ffo0rK2tZcyOSBlvgSYiIiIioi9y8eJFuLm5ITw8XIqZm5tj/fr1cHFxkTEzouyxACYiIiIios/27NkzNG/eHElJSVKsefPm2LZtG8qVKydjZkQ54y3QRERERET02cqUKYMJEyYAAARBwIwZM3D8+HEWv6TWuAJMRERERERfZPr06bh9+za8vLzQsmVLudMh+iSuABMRERER0Uelp6dj3bp1SE9PV4rr6Ohgx44dLH6pwGABTEREREREOYqIiEDr1q0xZMgQ/PTTT3KnQ/RVWAATEREREVG29u/fj3r16iEwMBAAMGvWLAQHB8ucFdGXYwFMRERERERKkpOT4e3tja5du+L169dSvE+fPqhbt66MmRF9HTbBIiIiIiIiyb179+Dm5oZLly5JMUNDQyxbtgyDBg2CIAgyZkf0dVgAExERERERAGDbtm0YNmwY3r17J8Xq1KkDf39/1KpVS8bMiPIGb4EmIiIiItJw8fHxGDJkCDw8PJSKXy8vL5w7d47FLxUaXAEmIiIiItJw27Ztw7p166TXJiYm+OOPP9CrVy8ZsyLKe1wBJiIiIiLScEOGDEHHjh0BAE2aNMGVK1dY/FKhxAKYiIiIiEjDaWlpYePGjfD19UVgYCAqVaokd0pE+YIFMBERERGRBjlz5gzmzp2bJV6yZElMmzYNurq6MmRFpBp8BpiIiIiISAMoFAr88ssv8PHxQXp6OmrXro0uXbrInRaRSnEFmIiIiIiokIuKioKzszMmT56M9PR0AMC3336LxMREmTMjUi0WwEREREREhdjhw4dha2uLI0eOSLGKFSti9+7dMDQ0lDEzItXjLdBERERERIVQamoqpk+fnuV53x49euCPP/6AmZmZTJlRbiWlpmPlyXDptVdLKxjoasuYUcHHApiIiIiIqJB5+PAh+vbti7Nnz0oxfX19/Prrrxg+fDgEQZAxO8qt5DQFFh+7K70e7FiZBfBXYgFMRERERFSI7Nq1C0OGDEFsbKwUq1GjBnbs2IG6devKmBmR/PgMMBERERFRIfLXX38pFb+DBg3CxYsXWfwSgQUwEREREVGhsmrVKlSpUgVGRkbYsmUL1q1bh6JFi8qdFpFa0LgCOD4+HpMnT0abNm1QsWJFGBgYwNjYGI6Ojti0aVOW8SdPnoQgCDl++fj4ZHue1NRU+Pr6wtraGgYGBqhYsSLGjRuHd+/e5fclEhEREZEGMzExwa5duxAaGgoPDw+50yFSKxr3DHB0dDTmzp0LU1NT2NjYoEmTJoiLi0NQUBA8PT1x8uRJrFu3Lss8KysrODo6Zonb2dlliSkUCnTv3h0BAQEoW7YsXFxcEBoaikWLFiEoKAiBgYEwMDDIl+sjIiIiIs0QFxeH4cOHo0uXLnB3d1c6Vq9ePXmSIlJzGlcAm5mZ4fjx42jevDl0dP53+VFRUWjatCnWr18Pd3d3tG3bVmmeo6MjNmzYkKtzbNmyBQEBAahZsybOnDkDExMTpKSkoF27dggMDMSiRYswZcqUvLwsIiIiItIgFy9ehKurK+7fv4/9+/fD3t4eVatWlTstIrWncbdAFy1aFK1atVIqfgHAwsICQ4cOBZCxWfjXWLlyJQBgxowZMDExAQDo6enBz89P6TgRERER0edQKBRYtGgRHBwccP/+fQDA27dv8dtvv8mbGFEBoXEF8MdoaWV8OxQKxRe/R1xcHM6fPw9BEODs7Kx0zMHBAaampoiIiEBYWNhX5UpEREREmuXly5fo2rUrxo0bh9TUVAAZv7/OnDkTixcvljk7ooJB426BzsmTJ0+klVknJ6csx8PDwzF16lS8fPkSJUuWRJs2bdC6dess427fvg2FQgFLS0tp9TeTIAioVasWTp8+jZs3b6JmzZr5czFEREREVKicOHECHh4eePbsmRQrW7Ystm3blu3vrkSUPY0ugEeMGIF3797h2bNnCAoKQnJyMry8vNC1a9csY4ODgxEcHCy99vPzQ6tWrfDnn3+iePHiUvzp06cAMm6pBgB/f38sWrQIEydORM+ePVG6dGkAQERERH5eGhEREREVAmlpaZg9ezZmz54NURSleJcuXbB+/XqUKFFCxuyICh6NLoC3bdsmbRIuCAJ8fX0xdepUpTGmpqaYNm0aevXqhSpVqiAuLg6BgYGYMGECTpw4ATc3Nxw5ckQaHx8fDwAwNDQEAMyZMwc3btzAzJkz0bNnTxQpUkRp3KfY2NhkGw8PD4eVldXnXTARERERFRgvXrxAr169EBQUJMV0dXUxf/58jBo1CoIgyJgdUcGk0c8Ax8TEID09HeHh4Zg+fTpmz56NLl26ICEhQRpTr149+Pr6om7dujAyMkLZsmXh5uaGo0ePQk9PD0ePHkVgYGCO53Bzc4ORkRE8PT1VcUlEREREVEgUK1YM0dHR0msrKyucOXMGo0ePZvFL9IU0ugAGMhoHVKlSBTNnzsTs2bNx4MABzJ0795Pzqlevjk6dOgEAjh07JsWLFi0KAEhMTAQA+Pj44O3btxg/fjwASMV15rhPuXHjRrZfXP0lIiIiKtwMDQ3h7+8PQ0NDuLu7IzQ0FA0aNJA7LaICTeML4Pf1798fQMat0bmRudfa+80ILC0tAWTsK5yd58+fAwDKlSv3xXkSERERUeHz/l2ImWxsbHD16lVs2bIFxsbGMmRFVLiwAH5P5g+V3DaoevXqFQDAyMhIilWvXh1aWlqIjIyUni/OJIoibt68CQCoVatWXqRMRERERIXAli1bULFiRVy7di3LMWtra97yTJRHWAC/5+zZswD+18H5Y1JSUqRnf+3s7KS4sbEx7O3tIYoiDh48qDTn9OnTiImJgaWlJbdAIiIiIiK8e/cOAwcORP/+/fHq1Su4urrmulkqEX0+jSuAN23ahEOHDkGhUCjF79+/j1GjRgEAevfuLcWXL1+OFy9eKI1NSkrCqFGjcP/+fVhYWKBHjx5Kx4cPHw4A8PX1RVxcHICMgnnKlCkAAC8vr7y9KCIiIiIqcK5evYqGDRti48aNUiwqKgo3btyQMSuiwk3jtkEKDQ3F4sWLUapUKTRs2BAmJiaIiIjAuXPnkJKSggYNGmDatGnS+Pnz52PMmDGoX78+rKysEBcXh9DQUERFRcHY2Bh//vmntLVRpv79+8Pf3x8HDx5EzZo14ejoiNDQUNy7dw92dnYYO3asqi+biIiIiNSEKIpYsWIFxo0bh+TkZCnetGlTbN++HRUrVpQxO6LCTeMK4H79+gEAQkJCEBoaitevX0NfXx+2trbo06cPfvjhB+jr60vjx4wZg4CAANy6dQs3b95EamoqLC0tMXz4cPz444+oXLlylnNoaWlh79698PPzw+bNm7Fnzx6ULl0a3t7emDVrlrRHMBERERFpljdv3mDIkCHYs2ePFBMEAZMmTcKsWbOgq6srX3JEGkAQRVGUOwn6fDY2NgDAW2SIiIiICoiQkBC4u7vj8ePHUqx06dLYvHkz2rVrJ2NmpK5iE1NhO+uw9PrqjPYwMVT/D0nUuVbRuGeAiYiIiIhUbfPmzXByclIqftu3b4+rV6+y+CVSIRbARERERET5zNHRUdo6U0dHB/PmzcOBAwdQunRpmTMj0iwsgImIiIiI8lnlypWxZs0aVKpUCUFBQfjxxx+hpcVfxYlUjf/VERERERHlodTUVNy/fz9LvHfv3rh58yaaNGkiQ1ZEBLAAJiIiIiLKMw8ePEDz5s3RunVrREdHZznO3UCI5MUCmIiIiIgoD/z555+oV68ezp07h0ePHuHbb78FN1whUi8sgImIiIiIvkJiYiKGDx+OPn36IC4uToqbmZkhNTVVxsyI6EM6cidARERERFRQ3bhxA25ubrh+/boUK1asGH7//Xf07dtXxsyIKDtcASYiIiIi+kyiKOKPP/5Ao0aNlIrfhg0b4vLlyyx+idQUC2AiIiIios8QGxuLvn374rvvvkNiYqIUHzt2LEJCQmBlZSVjdkT0MbwFmoiIiIgol0RRRMeOHXHmzBkpVrx4cWzcuBGdO3eWMTMiyg2uABMRERER5ZIgCJgxY4b02snJCVevXmXxS1RAcAWYiIiIiOgzdOjQAVOmTIG+vj6mTp0KbW1tuVMiolxiAUxERERElIOTJ0+iRo0asLCwUIr/9NNPMmVERF+Dt0ATEREREX0gLS0N06dPR+vWrdG/f38oFAq5U6KPmDlzJgRBwMCBA7M9rqOjA0EQsj32/PlzeHt7o1q1ajA0NISJiQmcnJywfv16pKenZzvn2bNnGD16NKytrWFoaIhSpUrB0dERy5YtQ0pKyldfz99//40JEyagfasWeLSgOx7N64KY0/45jn/+/DlWr14NT09PVK1aFYIgQBAERERE5Pqc69atk+YFBwfnOO727dvw9PREuXLloK+vj9KlS6N9+/Y4evRolrEPHjzAkiVL4ObmhvLly0MQBOjoyLsGyxVgIiIiIqL3PHnyBO7u7lIRcPToUcyfPx8TJ06UOTPKa+fPn4ezszOio6NhbW0NFxcXxMfHIygoCIMHD0aRIkXg6uqaZU6HDh0QExOD6tWrw8XFBe/evUNQUBBCQkLQpUsXVKpU6avy6tGjx3uvsi/c33fgwAEMGzbsi8/35s2bXP39PnToELp164bk5GQ0aNAAzZs3x/Pnz3H+/HkcPXoUbdu2VRq/ceNGzJo1CwBy/ABC1VgAExERERH9v71792LQoEGIjo6WYlWrVkW7du1kzIryw5s3b9ClSxdER0dj2bJlGDlypHQsPj4e06dPz3FOTEwMVqxYAS8vL6U58+fPz5MVzr59+6JZs2ao28AeXcctQuxHVn8BwNLSEqNHj0azZs3g6OiIpk2b4tGjR7k+38SJE2FgYIBGjRrhwoUL2Y55+fIlXF1doaWlhQMHDsDZ2Vk6lpCQgMePH2eZU61aNfz4449wdHSEg4MDSpQokeuc8gsLYCIiIiLSeMnJyZgwYQKWLl2qFPfw8MDKlStRrFgxmTKj/DJv3jy8fPkSgwYNUip+AaBo0aJYuHCh0j7PH855v/jNnDNz5sw8yW3btm0AgNjEVCAXK6ft2rX74g9pzpw5g7Vr12LLli1YvXp1juMWL16M2NhYLFq0SKn4BYAiRYqgRo0aWea4u7vD3d39i/LKL3wGmIiIiIg02p07d9C0aVOl4rdIkSLYsGEDNm/ezOK3kNq0aRMAYMSIETmOMTQ0zHbOhwVzQZWeng4vLy80adLkk4XqX3/9BX19fXz77bcqyi5/qM0KcExMDF6+fInY2FiYmJigZMmSMDU1lTstIiIiIirENm/eDC8vL8THx0sxW1tb7NixA9WrV5cxM8pP4eHhiIqKQtGiRdGgQYPPnmNnZ5fPGarG0qVLce3aNZw9e/aj4969e4c7d+6gYcOGKFasGP79918cOXIEqampqFu3Lvr27VtgPiiSrQBOSUnBrl27EBAQgKCgoGw7lJUvXx7NmzdH586d0aNHD+jp6cmQKREREREVRnfv3sWgQYOUOv2OHDkSCxYsgIGBgYyZUX67c+cOAKBy5cq5bs70JXPUWWRkJKZPn45+/frB3t7+o2MfPHgAURRRoUIFDBw4EBs3blQ6PnPmTBw8eBB169bNz5TzhMpvgY6OjsaPP/4IS0tL9OvXD9u2bUNERATKli2L2rVrw8HBATY2NihTpgyePHmCrVu3wsPDA5aWlpg4caJSQwIiIiIioi9lbW2N2bNnAwBMTU2xe/duLFu2jMWvBoiJiQEAGBsb5+scdebt7Q2FQoG5c+d+cuzbt28BAKdOnYK/vz9WrlyJV69e4f79+xg6dCiePXuGXr16IS0tLb/T/moqLYAXLFgAKysrLFiwAKamppg6dSqOHj2KmJgYPHnyBFevXkVQUBCuXbuGiIgIREdH4/Dhw5g4cSKMjY0xf/58WFlZYeHChapMm4iIiIgKqYkTJ2LChAm4cuUKunfvLnc6pGIfruT6+/tLe+EKgoCqVavKlFn+OnLkCHbu3IlJkyahbNmynxwviiIA4NWrV5gyZQqGDx+O4sWLo3Llyvj9999Rt25d3L17F4cOHcrv1L+aSgvgH3/8ES1atMCZM2dw9+5d+Pr6onXr1jneL25sbIy2bdvCz88P4eHhCAoKgoODA3788UdVpk1EREREBdzr168xZcoUpKamKsW1tLTwyy+/oGLFijJlRnnhc29JNjExAfC/lc1MlStXhqenJ3r27JnjnLi4uC/MUj0kJydj5MiRqFChAsaPH5+rOUWKFJH+3K9fvyzHv/nmGwD45LPE6kClzwCHhoaiXr16Xzy/WbNm2L9/Py5fvpx3SRERERFRoRYUFAR3d3dERERAFEX8/PPPcqdEeUxbW/uTY97fn7datWoAgIcPHyqNady4MRo3boyHDx9i165dSsesra0B/O952IL6HHBYWBju3r2LSpUqSYVrpmvXrgEAxo8fD1NTU8yYMQNNmzaFhYWFNKZ8+fJZ3rNMmTIAMj5oUncqXQH+muL3ffXr18+T9yEiIiKiwis9PR1z5sxBy5YtpYarc+fOxfnz52XOjPKakZERACg1NHtfenq6NAYArKysUKpUKcTFxeG///7L1TmqVq2KUqVKIT4+HqGhoV+ftMwePnyIQ4cOKX1l9ls6d+4cDh06hOfPnwPIKHAzn33OridT5vPRH24bpY64DzARERERFTqRkZFo164dpk2bBoVCASBjBfCXX35Bw4YNZc6O8lqFChUAAM+ePctyLDIyEoDyyqUgCPDw8AAArFmzJlfnEARB2it3+fLlX5WvnOrVqwdRFLP9cnJyApBx14QoikorxC1btgQABAcHZ3nPzA8Eatasme/5fy0WwERERERUqBw4cAD16tXDiRMnpFilSpUQHByMCRMmQEuLvwIXNq1atYKhoSECAwMREhIixUVRhJ+fHwCgY8eOSnMmT54Mc3NzLF++HFu2bFE69ubNm2zPM2XKFJibm2P9+vVYtWqV0rHExET4+vpmu71rYfDtt98CAHx8fJQ+aDh16hR2794NAwMDdOvWTa70ck0QM1t6UYFiY2MDALhx44bMmRARERGph5SUFEydOhULFixQivfu3RurV6+GqampPImRSixduhSjRo2Cjo4OWrZsiZIlS+LKlSsICwtD1apVce7cOZibmyvNCQ4ORqdOnfD27VvY2Nigdu3aiI6ORmBgIJKSkuDi4oK9e/fmOKd69eqoV68e3r17h5CQEMTExODBgweoVKnSV13LiBEjEBoainSFiMth4Uh/9xraxUqgfo0q0NYS0LlzZ0ybNk0a/+zZM6Uu5pcvX0ZKSgrq168PPT09AMCKFStgZ2f30fO2bNkSp06dQlBQEBwdHbMcd3Nzw44dO2BmZoZmzZrh7du3CAkJQVpaGlauXInhw4cD+F+tsnnzZowYMUKaf+7cOQAZz1pn+vvvv6VniFVBpU2wiIiIiIjyw/3799G3b1+l53sNDAywZMkSfPvttwW2YRHl3g8//ICqVati4cKFOH/+PBITE1G+fHmMHTsWPj4+MDMzyzLH0dERN2/exNy5c3HgwAHs2bMHBgYGsLe3h6enJzw9PXM1p1ixYrCxsYGrq2uuthX6lJs3b0rFYqb0t69w8cIrAECNGjWUjiUnJ2cZD0CpeXBedK/eunUrmjRpgnXr1uHYsWPQ1taGg4MDJkyYgC5dumQZHxcXl21e78eSk5O/Oq/PoRYrwAcPHsSIESNw//59uVMpMLgCTERERPQ/q1atgpeXl/S6Vq1a2LFjB2rXri1jVkRfJzYxFbazDkuvr85oDxNDXRkzyh11rlXU4gGI+Ph4PHr0SO40iIiIiKiAGjZsmHQL6HfffYcLFy6w+KUCLV0hYvHRu0qxxUfvIl0h+/plgZavt0BPnz49V+PCwsLyMw0iIiIiKuQEQcDatWvRr18/9OjRQ+50SI0lpaYjOU0hdxqftPjoXawLeaAUWxfyAIIAjGpjLVNWuaMQRWip6WMH+XoLtJaWFkxNTWFiYvLRcQkJCXj16lWO+3ZRVup8WwERERFRfhFFEX/88Qdu3bqFhQsXyp0OFUC/HrmDxcfufnogfbHIP0bAurSRWtYq+boCbGVlhebNm2PdunUfHffXX3/B1dU1P1MhIiIiogIuNjYWQ4cOxc6dOwEADg4O6Nmzp8xZEVFBkq/PADdt2hSnT5/+5DhBEKAGvbiIiIiISE2dO3cO9evXl4pfIGMf17S0NBmzIqKCJl9XgEePHq20EXVOnJyclDYqJyIiIiICAIVCgYULF2LKlClKxW7r1q2xefNm6OhwV0/6PF4trTDYsbLcaXxSds8AA8AQx8pq/wxwk31F5U4hR2qxDRJ9Pj4DTERERIXdixcv4OnpiYMHD0oxLS0t+Pr6YtKkSdDW1pYxO6L8la4Q4fdvGNYG/68IHuJYGVM61YS2lno2mMqkzrWKWmyDRERERET0vmPHjsHW1lap+C1XrhxOnTqFqVOnsvilQk9bS8iy0juqjbXaF7/qTpYC+MmTJ2jdurUcpyYiIiIiNSaKInx8fNCuXTtERUVJ8W7duuHq1atwdHSUMTsiKuhkKYATEhJw6tQpOU5NRERERGpMEARERkZKDVL19PSwZMkS/P333zA3N5c5OyIq6Ng1gIiIiIjUytKlS3H69GkoFArs2LED9evXlzslIiokWAATERERkWxSUlKgq6sLQfjfc41FixZFQEAASpUqhWLFismYHREVNmyCRURERESyuH37Nuzt7bF+/fosx6ysrFj8ElGeYwFMRERERCq3ceNGNGjQAFevXsX333+PsLAwuVMiIg3AApiIiIiIVObt27cYMGAABg4ciPj4eABAYmIiAgICZM6MiDQBnwEmIiIiIpUIDQ2Fm5sb7t69K8XMzMywfv16dOvWTcbMiEhTyLYCnNnanoiIiIgKN1EUsWTJEjRt2lSp+G3WrBmuXLnC4peIVEaWAtjCwgIrV66U49REREREpEKvX79Gt27dMHr0aKSkpADI2OvXx8cHJ0+eRIUKFWTOkIg0iSy3QJuYmGDYsGFynJqIiIiIVOTOnTto06YNIiIipJiFhQW2bt2K1q1by5gZEWkqNsEiIiIionxRsWJFlC5dWnrt7OyMq1evsvglItmwACYiIiKifKGvrw9/f3+Ym5tj/vz5CAgIQKlSpeROi4g0mFp2gU5PT8fu3bvx4MEDWFtbo1u3btDSYq1OREREpM4eP36c5ZneqlWr4sGDBzA2NpYpKyKi/5G1qly9ejVKlSqFgwcPSrHk5GQ4ODjAzc0NkyZNQq9eveDo6IikpCQZMyUiIiKinKSkpGD8+PGwtrbGhQsXshxn8UtE6kLWAvivv/6CIAjo0KGDFPvjjz9w4cIF1K5dG7/99htatGiBc+fOsWs0ERERkRoKDw9Hs2bNsHDhQqSkpMDNzQ1xcXFyp0VElC1ZC+CwsDDUrl0bgiBIsYCAAOjo6GDv3r0YNWoUDh06hNKlS2Pr1q0yZkpEREREH9qxYwfq16+PixcvSjFDQ0O8fv1axqyIiHImawH86tUrWFhYKMXOnz+Pxo0bo1KlSgAAPT09ODg44P79+zJkSEREREQfSkhIwHfffQc3Nze8fftWig8bNgznz59H5cqVZcyOiChnsjbBKlOmDB4+fCi9vn79Ot68eYMWLVoojdPR0UF6erqKsyMiIiKiD/33339wc3PDzZs3pZixsTH++OMP9O7dW8bMiIg+TdYVYHt7e5w9exZ//PEHbty4gcmTJ2d5JhgAbt68iXLlysmUJRERERGJoojff/8d9vb2SsWvvb09rly5wuKXiAoEWQtgHx8f6OvrY9iwYahbty4CAgLQuHFjpRXghw8f4vr162jcuLGMmRIRERFpNj8/PwwfPlxpZ44ff/wRwcHBvOWZiAoMWQvg2rVrIzg4GP3790eHDh0wceJEBAQEKI05dOgQbG1t0aNHjzw5Z3x8PCZPnow2bdqgYsWKMDAwgLGxMRwdHbFp06Yc5x0+fBhOTk4oVqwYzMzM0LlzZ1y6dCnH8ampqfD19YW1tTUMDAxQsWJFjBs3Du/evcuT6yAiIiJSJU9PTxQvXhwAULJkSRw8eBDz5s2Drq6uzJkREeWeIIqiKHcSqhQREYHy5cvD1NQUNjY2sLS0RFxcHIKCghAfH49BgwZh3bp1SnP8/f3h7u4ObW1tdOjQAYmJiThx4gT09PRw4sQJNG3aVGm8QqGAi4sLAgICULZsWTRr1gyhoaEIDw9Ho0aNEBgYCAMDg6+6DhsbGwDAjRs3vup9iIiIiHJr//79WLx4MTZt2oQyZcrInQ5RoRebmArbWYel11dntIeJofp/6KTOtYqsK8ByMDMzw/Hjx/Hy5UsEBwdjx44dOHDgAO7du4dKlSph/fr1OHr0qDQ+Li4OXl5eADK2aNq/fz+OHTuGFStWIDk5GYMHD4ZCoVA6x5YtWxAQEICaNWvi5s2b2LlzJ27evIkWLVrgwoULWLRokUqvmYiIiOhzPH/+PNs73bp06YLDhw+z+CWiAkvjCuCiRYuiVatW0NFRboBtYWGBoUOHAsi43TnT9u3bERMTgw4dOqB9+/ZSfNiwYbC2tsatW7dw/PhxpfdauXIlAGDGjBkwMTEBkLGdk5+fn9JxIiIiInVz9OhR2NrawsXFBS9fvsxyXBAEGbIiIsobGlcAf4yWVsa34/0V3czV4I4dOyqNFQRBih05ckSKx8XF4fz58xAEAc7OzkpzHBwcYGpqioiICISFheXLNRARERF9idTUVEyZMgXt27fH8+fPERkZiYEDB2a5042IqCBjAfz/njx5Iq3MOjk5SfHMNv+1atXKMqd27dpKYwDg9u3bUCgUKFu2rLT6m0kQBOl93p9DREREJKdHjx6hZcuW+Pnnn5HZHkZfXx+dO3fmii8RFSoaXQCPGDECAwYMQLt27WBtbY1Hjx7By8sLXbt2lcY8ffoUQMYt0k+fPkWbNm0wdOhQKBQKlC5dGkBGY63sxgMZDbTs7e2xa9cuAMh2DhEREZFcdu/ejXr16uH06dNSrHr16jh79ixGjBjBApiIChWdTw8pvLZt24bY2FgAGauzvr6+mDp1qtKY+Ph4AIChoSG2b9+O48eP4/jx4xg2bBiKFCmiNObD8QAwZ84c3LhxAzNnzkTPnj2znfMxmR3UPhQeHg4rK6vcXioRERGRkqSkJIwbNw4rVqxQig8cOBBLly6FkZGRTJkREeUfjV4BjomJQXp6OsLDwzF9+nTMnj0bXbp0QUJCQrbj27VrBwsLCzg4OGR7S3R23NzcYGRkBE9Pz7xMnYiIiOiL3bp1C40bN1Yqfo2MjLB582asX7+exS8RFVoavQIMZDS+qlKlCmbOnAlDQ0NMmjQJc+fOha+vL4CMrtGxsbFITEyEra0tnj17Js3NLJSLFi0qxTL/nJiYCADw8fGBj4/PR+d8TE57Z+W0MkxERET0Menp6ejWrRvu3Lkjxezs7ODv7w9ra2sZMyMiyn8avQL8of79+wPIuDU6k6WlJQAgKioqy/jnz58DAMqVK5er8TnNISIiIlIVbW1trF69Wtr9YsyYMTh9+jSLXyLSCCyA32NsbAxAuUHVx7o2X79+XWkMkNE0QktLC5GRkdLzxZlEUfxoV2kiIiIiVXBycsL8+fOxb98+/Prrr9DX15c7JSIilWAB/J6zZ88C+F8HZwBo27YtAODAgQNKY0VRlGLt2rWT4sbGxrC3t4coijh48KDSnNOnTyMmJgaWlpaoWbNmvlwDERERUSZRFLF8+fJsd58YO3as0s4XRESaQOMK4E2bNuHQoUNZNnW/f/8+Ro0aBQDo3bu3FHdzc4OJiQkOHTqEI0eOSPHVq1fj7t27qFatGlq3bq30XsOHDwcA+Pr6Ii4uDgCQkpKCKVOmAAC8vLzy/sKIiIiI3vPq1Su4uLjg+++/h7u7O9LS0uROiYhIdhrXBCs0NBSLFy9GqVKl0LBhQ5iYmCAiIgLnzp1DSkoKGjRogGnTpknjTUxMsHz5cvTv3x+dOnWCs7MzEhMTcfz4cejp6WHt2rXSMzSZ+vfvD39/fxw8eBA1a9aEo6MjQkNDce/ePdjZ2WHs2LGqvmwiIiLSIKdOnYKHhweePn0KAAgKCsLChQsxceJEmTMjIpKXxq0A9+vXD6NHj0aFChUQGhqKv/76C5cvX4atrS3mz5+PkJAQ6VngTB4eHvj333/RtGlTnDx5EhcuXECHDh0QEhICR0fHLOfQ0tLC3r17MWPGDBgYGGDPnj1ITk6Gt7c3Tp48Ke0RTERERJSX0tPTMWvWLLRu3VoqfgGgY8eOGDx4sIyZERGpB0EURVHOBEJDQxEUFIQnT57g7du3SEtLQ3YpCYKAtWvXypChesrcBimnbZKIiIhIszx9+hQeHh44deqUFNPV1cXcuXMxZsyYLHesEZH6i01Mhe2sw9LrqzPaw8RQV8aMckedaxVZb4H28fHBzz//nG3B+yEWwERERETZ279/PwYOHIjXr19LsSpVqsDf3x+NGjWSMTMiIvUiawG8dOlSlChRAkuXLkWNGjVQtGhR6Oho3GPJRERERF8kOTkZkyZNwm+//aYUd3Nzw++//57lsS4iIk0na7VpYmKCBg0aoE+fPnKmQURERFQg3b59G8uXL5deGxoaYunSpRg8eDAEQZAxMyIi9SRrAfzrr79i+PDh8PX1ha2tLYyNjaGtrZ3j+BYtWqgwOyIiIiL1VrduXfzyyy/w9vZG7dq1sWPHDtSqVUvutIiI1JasBXCRIkWgpaWFWbNm5Wp8enp6PmdEREREVLCMHj0aenp6GDRoEHeaICL6BFkL4OHDh+PVq1do3bo1ateuDSMjIz4DTERERJSNa9eu4eeff8aGDRugr68vxQVBwIgRI2TMjIio4JC12oyLi0P79u1x4MABOdMgIiIiUluiKGLVqlXw9vZGcnIySpcunaXpFRER5Y6sG8JNmjQJ4eHhShu1ExEREVGG6Oho9O7dGyNGjEBycjIAYMmSJbh165bMmRERFUyyrgCvWrUKkZGRqFChAkxMTFCsWLEcm2AJgoDw8HAVZ0hEREQkjzNnzqBv37549OiRFCtVqhQ2b96MGjVqyJgZEVHBJWsBLIoiypYtmyWW01giIiKiwk6hUOCXX36Bj4+PUgPQtm3bYvPmzbCwsJAxOyKigk3WAvjhw4e5Hvv27dv8S4SIiIhIDTx//hz9+/fHkSNHpJi2tjZmz56NiRMnQktL1qfXiIgKPFl/iub2+ZXg4GDUq1cvf5MhIiIiktHRo0dha2urVPxWqFABgYGBmDx5MotfIqI8IOtP0latWn20CE5LS8PEiRPRqlWrz1otJiIiIiponjx5gufPn0uvu3fvjitXrsDBwUHGrIiIChdZC+C4uDi0bt062yL4+vXraNSoERYsWIBixYph8+bNMmRIREREpBoDBw6Eu7s79PX1sWLFCuzatQtmZmZyp0VEVKjIWgAHBATg7du3aNWqFcLCwqT4woULYW9vj6tXr6J9+/a4fv063N3dZcyUiIiIKG+93+AKyNjxYtWqVbhw4QK8vLwgCIJMmRGRutDX0cLoNtbSl74OH4X4WoIoc3vlwMBAdO7cGUZGRli/fj1++eUXnDp1CkWLFsWCBQswdOhQOdNTWzY2NgCAGzduyJwJERERfY7ExESMGzcOiYmJWL9+vdzpEBHlOXWuVWTtAg0ALVq0wIEDB9CpUyd07twZAODo6IgNGzagcuXKMmdHRERElHfCwsLg5uaGa9euAQDatGmDfv36yZwVEZHmUIs1dEdHRxw8eBDFihWDvr4+Vq1axeKXiIiICg1RFLF+/Xo0bNhQKn4BYO3atZD5ZjwiIo2i0hXgbdu2ffT4gAEDsGzZMrRv3x4//fQTdHSU0+NzwERERFTQxMXFwcvLK8vvQd7e3vj555/5rC8RkQqp9BlgLS2tT/6Qz0wnu3EfNovQZOp8Xz0RERFluHjxItzc3BAeHi7Fihcvjg0bNqBLly4yZkZElH/UuVZR6QrwgAED+CknERERFXqiKOK3337DxIkTkZqaKsWdnJywdetWWFpaypgdEZHmUmkBvGHDBlWejoiIiEjlUlNT0aNHD+zfv1+KaWlpYfr06fDx8YG2traM2RERaTbZu0ATERERFSa6urqoUKGC9NrS0hJbt26Fk5OTjFkRERHAApiIiIgozy1cuBDBwcGoUKEC1q9fjxIlSsidEhERgQUwERER0Vd5+fIlSpQoodTnxMDAAMeOHUPx4sXZ/4SISI2oxT7ARERERAXRP//8gxo1amDFihVZjn1YFBMRkfxYABMRERF9puTkZIwZMwYuLi548+YNxo4diytXrsidFhERfYJKC+CGDRsiICDgq95j3759aNCgQR5lRERERPR57t69CwcHByxevFiK6ejoKO31S0RE6kmlBXBCQgJcXFxQt25dLFiwABEREbma9/jxY8ybNw+1a9fGN998g+Tk5HzOlIiIiCirrVu3ws7ODqGhoVKsTp06uHjxInr27CljZkRElBuCKIqiqk6Wnp6ONWvWwNfXF1FRURAEAZUrV0bjxo1Ro0YNmJubo1ixYoiLi8ObN29w69YtnDt3Dg8fPoQoiihTpgxmzJiBb7/9Flpamn33to2NDQDgxo0bMmdCRERU+L179w4//PADNmzYoBT38vLCwoULYWhoKE9iRERqSJ1rFZUWwJlSU1Px119/YfXq1QgODkZ6enpGMu81ishMS1tbG05OThg6dCi6d+8OXV1dVaerltT5LxUREVFhcvXqVbi6uuL27dtSzMTEBGvXruWqLxFRNtS5VpGlAH7f27dvcfr0aVy7dg0vXrxAbGwsTE1NUbJkSdja2sLBwQFGRkZypqiW1PkvFRERUWFx5swZtGrVSunxq6ZNm2Lbtm2oVKmSfIkREakxda5VZC+A6cuo818qIiKiwiI1NRVOTk44c+YMBEHApEmTMGvWLN6RRkT0Eepcq2j2g7REREREH6Grq4tt27ahRo0aOHToEPz8/Fj8EhEVYDpyJ0BERESkDtLT03H27Fk0a9ZMKV6pUiXcuHFD4xtwEhEVBvxJTkRERBovKioKHTp0gJOTE0JCQrIcZ/FLRFQ48Kc5ERERabRDhw7B1tYWx44dQ3p6Ovr27Ys3b97InRYREeUDFsBERESkkVJTUzFx4kQ4OzvjxYsXUtze3p4rvkREhRSfASYiIiKN8+DBA/Tt2xfnzp2TYvr6+vjtt98wbNgwCIIgY3ZERJRfWAATERGRRvnzzz/x3XffITY2VorVrFkTO3bsQJ06dWTMjIiI8hvv7yEiIiKNkJiYiOHDh6NPnz5Kxe+QIUNw4cIFFr9ERBqAK8BERESkESZMmIDff/9del2sWDH8/vvv6Nu3r4xZERGRKnEFmIiIiDTCtGnTULp0aQBAw4YNcfnyZRa/REQahivAREREpBFKly6NLVu24MCBA/j555+hp6cnd0pERKRiLICJiIio0Ll48SLi4uLQunVrpXjbtm3Rtm1bmbIiIiK58RZoIiIiKjQUCgUWLVoEBwcHuLm54dmzZ3KnREREaoQFMBERERUKL1++RNeuXTFu3Dikpqbi5cuX8PT0hCiKcqdGRERqgrdAExERUYF34sQJeHh4KK34WlpawsfHB4IgyJgZERGpE64AExERUYGVlpaG6dOno02bNkrFb9euXXH16lW0aNFCxuyIiEjdcAWYiIiICqQnT57Aw8MDQUFBUkxPTw/z58/HDz/8wJVfIiLKQvYC+Pnz59i0aRMuXbqEN2/eIC0tLdtxgiDg2LFjKs6OiIiI1NG+ffswaNAgvHnzRopVrVoVO3bsgJ2dnYyZERGROpO1AD579iycnZ3x9u3bTzao4Ke4REREBADJyckYM2aMUvHr4eGBlStXolixYjJmRkRE6k7WAnjMmDGIi4tD7969MWzYMJQvX56b0hMREdFH6evrY9u2bWjevDn09PSwYsUKDBgwgB+WExHRJ8laAF+9ehV169bFjh075EyDiIiICpgmTZpg7dq1aNy4MapXry53OkREVEDI2gXazMwM1tbWcqZAREREauzdu3fw8vLCgwcPshwbMGAAi18iIvosshbAPXr0wIULF5CSkiJnGkRERKSGrly5ggYNGmDVqlVwc3NDamqq3CkREVEBJ2sBPHfuXJQvXx49evTAzZs35UyFiIiI1IQoili2bBkaN26MO3fuAADOnz+P33//XebMiIiooFPpM8BVqlTJElMoFDh9+jQOHDgAQ0NDlCxZMtsmFoIgIDw8XBVpEhERkUzevHmDIUOGYM+ePVJMEARMnjwZw4cPly8xIiIqFFRaAD98+PCjxxMSEvDo0SPVJENERERqJTg4GO7u7njy5IkUs7CwwObNm9G2bVsZMyMiosJCpbdAKxSKr/rKKwkJCfD390fPnj1Rrlw56OnpwczMDB06dMDBgwezjD958iQEQcjxy8fHJ9vzpKamwtfXF9bW1jAwMEDFihUxbtw4vHv3Ls+uhYiIqKBLT0/HnDlz4OTkpFT8dujQAVeuXGHxS0REeUbWbZDksnPnTgwaNAiCIMDOzg6Ojo6IjIzEkSNHcPjwYfj5+WHy5MlZ5llZWcHR0TFL3M7OLktMoVCge/fuCAgIQNmyZeHi4oLQ0FAsWrQIQUFBCAwMhIGBQb5cHxERUUHx7Nkz9OvXD8ePH5diOjo68PPzw7hx46ClJWu7EiIiKmQ0sgDW1dXFyJEjMX78eFSqVEmKHzt2DB06dICPjw+6dOmCOnXqKM1zdHTEhg0bcnWOLVu2ICAgADVr1sSZM2dgYmKClJQUtGvXDoGBgVi0aBGmTJmSh1dFRERU8Lx69QqnT5+WXleqVAn+/v5o3LixjFkREVFhpZEfq3p4eGDZsmVKxS8AtGnTBt26dYNCocBff/31VedYuXIlAGDGjBkwMTEBAOjp6cHPz0/pOBERkSarU6cOfvvtNwBA7969cfnyZRa/RESUb2QtgH19fbFv375Pjjt27Bh8fX1VkBFgbW0NAIiKivri94iLi8P58+chCAKcnZ2Vjjk4OMDU1BQREREICwv7qlyJiIgKGlEUs8SGDh2KgwcPYseOHTA1NVV9UkREpDFkLYBnzpyptM1BTo4ePYpZs2blf0IAHj9+DAAoX758lmPh4eGYOnUqhg4diqlTpyo9r/S+27dvQ6FQoGzZstLqbyZBEFCrVi0A4N7HRESkUXbs2AFHR0ckJCQoxQVBQIcOHbLdBpGIiCgvFYhboPOyA/THvHz5Ev/88w8EQUD37t2zHA8ODoafnx/WrFkDPz8/tGnTBq1bt8br16+Vxj19+hRAxtYNAODv7w97e3vs2rULAFC6dGkAQERERH5eDhERkVpISEjA0KFD4ebmhtOnT8Pb21vulIiISEMViAI4PDwcRYsWzffzZG5RNGDAANjY2EhxU1NTTJs2DVevXsXbt2/x9OlTbN++HeXKlcOJEyfg5uam9D7x8fEAAENDQwDAnDlzcOHCBcycORMAUKRIEaVxH2NjY5PtV3h4eF5cMhERUb66fv06GjVqhDVr1kgxf39/6cNiIiIiVVJ5F+jAwECl11FRUVlimRISEnD27Fns27cPjRo1yte8Nm7ciM2bN8Pa2hpLly5VOlavXj3Uq1dPem1kZAQ3NzfUr18fdevWxdGjRxEYGIgWLVpk+95ubm6YN28ePD098/MSiIiI1IYoilizZg1Gjx6NpKQkKd6oUSP4+/vD0tJSxuyIiEhTqbwAbtmypdIzPocOHcKhQ4dyHC+KIrS1tTFp0qR8yykwMBBDhw6Fqakp9u3bh2LFiuVqXvXq1dGpUyfs2bMHx44dkwrgzNXqxMREAICPjw98fHykeZnPPuVmVfvGjRvZxt9foSYiIlInMTExGDp0KP7880+l+Pjx4/HTTz9BT09PpsyIiEjTqbwAHjBggFQAb9y4EVZWVmjWrFm2Y/X09FC+fHl079493wq+27dvS8/77tq1CzVq1Pis+VWrVgUAPHv2TIplfqqdUyfp58+fAwDKlSv32fkSERGps3PnzsHNzQ0PHz6UYiVKlMCmTZvQsWNH+RIjIiKCDAXwhg0bpD9v3LgRjo6OWLdunarTAJBRiHbs2BHR0dHYsmULWrdu/dnv8erVKwAZt0Vnql69OrS0tBAZGYnY2FilTtCiKErdnzO7QRMRERUGy5Ytg7e3N9LS0qRYq1atsGXLFpQtW1bGzIiIiDIUiCZY+SE+Ph5dunTBgwcPMH/+fLi7u3/2e6SkpEjPL9vZ2UlxY2Nj2NvbQxRFHDx4UGnO6dOnERMTA0tLS9SsWfPrLoKIiEiNlChRQip+tbS0MHv2bBw5coTFLxERqQ1ZC2BPT084Ojqq/LxpaWno06cPLl68iPHjx2PcuHEfHb98+XK8ePFCKZaUlIRRo0bh/v37sLCwQI8ePZSODx8+HADg6+uLuLg4ABkF85QpUwAAXl5eeXU5REREasHNzQ1DhgxBuXLlcPLkSfj4+EBbW1vutIiIiCSCKIqi3Emo2m+//QZvb28YGBigT58+Sk25MtWoUUNqvFWpUiU8ffoU9evXh5WVFeLi4hAaGoqoqCgYGxsjICAgSyGvUCjQuXNnHDx4EGXLloWjoyNCQ0Nx79492NnZITg4WNom6UtkPhOdU5MsIiKi/JSWlobk5OQsDR0TEhKQmJiI4sWLy5QZERHJTZ1rFZU/A6wOYmJiAGSs4m7atCnbMU5OTlIBPGbMGAQEBODWrVu4efMmUlNTYWlpieHDh+PHH39E5cqVs8zX0tLC3r174efnh82bN2PPnj0oXbo0vL29MWvWrK8qfomIiOT0+PFjuLu7w9LSEv7+/kofJBcpUkTa756IiEjdqHQFePDgwV88VxAErF27Ng+zKdjU+VMVIiIqvPbs2YPBgwcjOjoaALB69Wp89913MmdFRETqRJ1rFZWuAL/fAfp9giAgpzo88xgLYCIiIvkkJSVhwoQJWLZsmVL84sWLLICJiKjAUGkBfOLECaXXaWlpmDx5Mq5duwZ3d3e0atUKFhYWADL21T1+/Dj8/f1Rv359+Pn5qTJVIiIi+n+3b9+Gm5sbrly5IsWKFi2KFStWYMCAAfIlRkRE9JlUWgA7OTkpvZ45cyauX7+OoKAgNGrUKMv4AQMGYMSIEXByckJISAhatWqlqlSJiIgIwKZNmzBixAjEx8dLsXr16mHHjh2oVq2ajJkRERF9Plm3QVq7di3atWuXbfGbyd7eHu3bt8eaNWtUmBkREZFme/v2LQYMGABPT0+l4veHH37AmTNnWPwSEVGBJGsX6NevX0NL69M1uJaWFl6+fKmCjIiIiCg+Ph6NGjXC7du3pZiZmRnWr1+Pbt26yZgZERHR15F1Bbh69eo4cuTIR7uD3bhxA4cPH0b16tVVmBkREZHmKlq0KLp06SK9dnR0xNWrV1n8EhFRgSfrCvDYsWPh6emJJk2aYODAgXB0dJSaYD1//hxBQUHYuHEjkpKSMG7cODlTJSIi0ih+fn4IDg5Gu3btMGPGDOjoyPorAxERUZ5Q6T7A2Vm0aBGmTp2K5ORkCIKgdEwURRgYGGDOnDkYO3asTBmqJ3XeW4uIiAqWq1evok6dOlkeS0pNTYWurq5MWRERUUGlzrWK7AUwkLHl0c6dO3Hx4kXpWd/SpUujYcOG6N27t7QqTP+jzn+piIioYEhPT4efnx9mzpyJefPmYfz48XKnREREhYA61ypqUQDT51Pnv1RERKT+IiMj0a9fP5w4cQIAoKOjg5CQENjb28ucGRERFXTqXKvI2gSLiIiIVO/AgQOwtbWVil8AKFeuXK52ZiAiIirICsS/dHfu3EFgYKDcaRARERVoKSkpGD9+PDp16oRXr15J8d69e+PKlSto2LChjNkRERHlvwJRAP/8889o1aqV3GkQEREVWOHh4WjWrBkWLlwoxQwMDLB69Wrs2LEDJiYmMmZHRESkGtzTgIiIqJDbsWMHvvvuO7x9+1aK2djYwN/fH7Vr15YxMyIiItUqECvARERE9GX2798PNzc3peJ36NChOH/+PItfIiLSOCpdAfb19YUgCBg5ciTMzc3h6+ubq3lXrlzJ38SIiIgKqY4dO6JVq1Y4ceIEjI2NsXr1ari6usqdFhERkSxUug2SlpYWBEFAWFgYqlWrJr3OTQqCICA9PV0FWRYM6txanIiI1EtkZCS+++47LF26FFWqVJE7HSIiKuTUuVZR6Qrw+vXrAQBlypSRYs2aNcOQIUM+Ou+PP/7A6dOn8zU3IiKigi4mJgaHDx9Gnz59lOJly5ZFQECATFkRERGpD5UWwJ6enlliVatWzTb+vpMnT7IAJiIi+oizZ8/Czc0Njx8/RokSJdC6dWu5UyIiIlI7bIJFRERUgCkUCsybNw/NmzfHo0ePIIoiPDw88Pr1a7lTIyIiUjuyboP0559/olKlSp8cJ4pirp4TJiIi0iTPnz/HgAEDcPjwYSmmra2N77//HqampvIlRkREpKZU2gTrS71+/Rrv3r1DxYoV5U5Fbajzg+VERJT/jh49in79+uH58+dSrHz58ti+fTuaNWsmY2ZERKTp1LlWUekt0D/99BN27NiBS5cuITY2NtfzihcvzuKXiIgIQGpqKqZMmYL27dsrFb/ffPMNrly5wuKXiIjoI1R6C/S0adMgCIL0unjx4qhatar0ZW1tLf3ZzMxMlakRERGpvUePHqFv3744c+aMFNPT08OiRYswYsQIpX9jiYiIKCuVFsAzZ85EREQEnjx5In2dPXsWZ8+eBQClf7hNTU2VCuKqVauiX79+qkyXiIhIrfj4+CgVv9WrV4e/vz/q1asnX1JEREQFiOzPAMfFxWUpijO/7ty5g4iICGlsenq6jJmqF3W+r56IiPLH69evUa9ePURERGDgwIFYunQpjIyM5E6LiIhIiTrXKrJ2gQYAY2Nj1KpVC5UrV8bly5eRlJSEx48fIyoqCpGRkRBFEUWKFIGdnZ3cqRIREcmqePHi2LZtGx49esS7ooiIiL6ArCvAixcvRmhoKC5duoTbt28jPT0dOjo6sLGxgb29Pezt7dGoUSPUrl0bWlrcsvh96vypChERfR1RFLFp0yaYm5uja9eucqdDRET0WdS5VpF1Bdjb2xuCIKBq1arw8/NDs2bNUL9+fRgaGsqZFhERkWzevn2LESNGYMuWLTAzM8OVK1dQoUIFudMiIiIqFGRdAba0tMSzZ88AAPr6+rC1tVVa+a1evbpcqak9df5UhYiIvkxoaChcXV1x7949Kda1a1fs27dPxqyIiIg+jzrXKrKuAD99+hQvX77EpUuXcOnSJVy+fBn79+/HsmXLIAgCjI2N0aBBA6ko/uabb+RMl4iIKF+IooglS5ZgwoQJSE1NleLNmzfHihUrZMyMiIiocJG9C/SHXr16hatXr2Lfvn3Yt28fHj9+LB1jF+j/UedPVYiIKPdevXqFwYMH459//pFigiBg2rRpmDZtGnR0ZO9XSURE9FnUuVZR6b+qMTExOW559OTJEzx9+hRJSUnS+MzaPHNPYCIiosIkMDAQ7u7uePr0qRQrW7YstmzZglatWsmYGRERUeGk0gK4ePHiSq/fX3w2MzND7dq1YW1tjapVqyr9r7m5uSrTJCIiylfp6en46aefMGvWLCgUCineqVMnbNiwASVLlpQxOyIiosJLpQWwiYkJqlWrlqXArVq1KotcIiLSGImJidiyZYtU/Orq6mLu3LkYM2YMt/0jIiLKRyotgN+8eaPK0xEREaklIyMj7NixA02aNEG5cuXg7++PRo0ayZ0WERFRocfOGkRERDKoX78+9u3bh6ZNm8LY2FjudIiIiDQC77MiIiLKR/fu3UOrVq1w+/btLMc6dOjA4peIiEiFWAATERHlk+3bt8POzg4nT56Eq6ur0k4HREREpHosgImIiPJYfHw8hgwZAnd3d7x9+xYAcPXqVezdu1fmzIiIiDQbnwEmIiLKQ9euXYOrqytu3bolxUxMTLBmzRr07t1bxsyIiIiIK8BERER5QBRFrFq1Cvb29krFb+PGjXH58mUWv0RERGqABTAREdFXio6ORu/eveHl5YXk5GQpPnHiRAQFBaFy5coyZkdERESZeAs0ERHRV7h06RJ69uyJR48eSbFSpUph8+bNaN++vYyZERER0Ye4AkxERPQVjIyM8OrVK+l127ZtcfXqVRa/REREaogFMBER0VeoXr06VqxYAW1tbfj5+eHQoUOwsLCQOy0iIiLKBm+BJiIi+gyJiYkwNDRUig0YMABNmjRBtWrVZMqKiIiIcoMrwERERLmQmpqKyZMno379+tLevu9j8UtERKT+1GYF+MWLF7hy5Qpev36NihUrwsHBQe6UiIiIAAAPHz5E3759cfbsWQDAyJEjsWnTJpmzIiIios8l+wrww4cP0bVrV5QtWxYdO3ZEv3798Mcff0jH586dC3NzcwQHB8uYJRERaapdu3ahfv36UvELAOfPn0d0dLSMWREREdGXkLUAfvz4MZo0aYKAgAA0b94co0aNgiiKSmM8PDwQGxuLv/76S6YsiYhIEyUmJmLEiBHo1asXYmJipPigQYNw6dIlmJmZyZccERERfRFZb4GeNm0aXr58ie3bt8PV1RUAsHjxYqUx5cuXR+3atXHy5EkZMiQiIk0UFhYGV1dX/Pfff1LMyMgIq1atgoeHh4yZERER0deQdQX48OHDsLe3l4rfnNSsWROPHz9WUVZERKSpRFHEunXr0LBhQ6Xi187ODqGhoSx+iYiICjhZC+Do6GiUL1/+k+OSkpKQnJysgoyIiEiT/fDDDxgyZAgSEhKk2JgxY3D69GlYW1vLmBkRERHlBVkLYEtLS6VP2LMjiiKuXbuGihUrqigrIiLSVO3atZP+bG5ujn379uHXX3+Fvr6+jFkRERFRXpG1AO7YsSPu3LmDefPm5Thm2bJlePjwIbp06aLCzIiISBN169YNP/zwA1q0aIGrV6+ia9eucqdEREREeUgQP2y7rELPnj2Dra0tXr9+jVatWqFbt24YPXo0nJ2d0a9fP/z777/Yvn07SpYsiWvXrqFUqVJypap2bGxsAAA3btyQORMiooLp1atX0NLSgrm5uVI8JSUFWlpa0NGRtU8kERFRgaXOtYqsBTAAXL16Fb169UJ4eDgEQYAoihAEAUDG7c9VqlTBrl27YGtrK2eaaked/1IREam7U6dOwd3dHQ0bNsSePXukf3eIiIjo66lzrSL7x9u2trYICwvD7t27cezYMTx58gQAUK5cObRq1Qq9evWCrq6uzFkSEVFhkJ6ejtmzZ2P27NlQKBTYt28fli1bhh9++EHu1IiIiEgFZC+AAUBHRwd9+vRBnz595E6FiIgKqYiICHh4eCAwMFCK6erqcvWXiIhIg8jaBEsuCQkJ8Pf3R8+ePVGuXDno6enBzMwMHTp0wMGDB3Ocd/jwYTg5OaFYsWIwMzND586dcenSpRzHp6amwtfXF9bW1jAwMEDFihUxbtw4vHv3Lj8ui4iIcvDPP//A1tZWqfi1srLCmTNn8P3338uYGREREamSrAWwj48P7OzscPbsWZWed+fOnejbty/+/vtvWFhYoEePHqhTpw6OHDmCjh074ueff84yx9/fH87Ozjh9+jScnJxgZ2eHAwcOoFmzZjhz5kyW8QqFAt27d8eMGTOQkJAAFxcX6OrqYtGiRWjdujWSkpJUcalERBotOTkZ3t7ecHFxwZs3b6S4u7s7QkND0aBBAxmzIyIiIlWTtQDeunUrEhIS0KRJE5WeV1dXFyNHjsT9+/dx8eJF+Pv7IzAwEEeOHIG2tjZ8fHyU9ieOi4uDl5cXACAgIAD79+/HsWPHsGLFCiQnJ2Pw4MFQKBRK59iyZQsCAgJQs2ZN3Lx5Ezt37sTNmzfRokULXLhwAYsWLVLpNRMRaZq7d+/CwcEBv/32mxQrUqQI1q1bhy1btsDY2Fi+5IiIiEgWshbAUVFRqFu3rsrP6+HhgWXLlqFSpUpK8TZt2qBbt25QKBT466+/pPj27dsRExODDh06oH379lJ82LBhsLa2xq1bt3D8+HGl91q5ciUAYMaMGTAxMQEA6Onpwc/PT+k4ERHlvdevX6Nhw4YIDQ2VYnXr1sXFixcxaNAgPvdLRESkoWQtgEuWLInExEQ5U8jC2toaQEZxnuno0aMAgI4dOyqNFQRBih05ckSKx8XF4fz58xAEAc7OzkpzHBwcYGpqioiICISFheXLNRARabrixYsrdXYeMWIEzp49i5o1a8qYFREREclN1gK4R48eCAwMxOvXr+VMQ8njx48BAOXLl5diN2/eBADUqlUry/jatWsrjQGA27dvQ6FQoGzZstLqbyZBEKT3eX8OERHlrZkzZ6Jr167YtWsXli9fDkNDQ7lTIiIiIpnJWgDPnj0bNWvWRNeuXfHw4UM5UwEAvHz5Ev/88w8EQUD37t2l+NOnTwEAFhYWePr0Kdq0aYOhQ4dCoVCgdOnSADK218huPJDRQMve3h67du0CgGznEBGpm6TUdPx65I70lZSaLndK2RJFEbt27UJ6unJ+Ojo62LdvH3r06CFTZkRERKRuZN0HePTo0ahevTr27NkDa2tr2NnZwcrKCgYGBlnGCoKAtWvX5ms+mVsUeXp6wsbGRorHx8cDAAwNDbF9+3YcP34cx48fx7Bhw1CkSBGlMR+OB4A5c+bgxo0bmDlzJnr27JntnJy8n8f7wsPDYWVl9QVXSUSUO8lpCiw+dld6PdixMgx0tWXMKKvo6GgMGTIEf//9N3x9fTFt2jS5UyIiIiI1JusK8IYNG7B582a8ffsW6enpuHDhAvz9/bFhw4Zsv/LTxo0bsXnzZlhbW2Pp0qU5jmvXrh0sLCzg4OCQ7S3R2XFzc4ORkRE8PT3zKl0iKgBmzpwJQRAwcODAbI/r6Ojk2Izp+fPn8Pb2RrVq1WBoaAgTExM4OTlh/fr1WVY6Mz179gyjR4+GtbU1DA0NUapUKTg6OmLZsmVISUn56uuJi4vDjz/+CGtra+jr66NYsWJwcHDApk2bsoxNTU3FP//8g/79+6Ny5crQ19eHsbExWrRogW3btuX6nOvWrYMgCBAEAcHBwUrHTp8+jXr16uHvv/8GAEyfPh16enqoXLkyxo4di9jY2K+7YCIiIip0ZF0BPnHihJynlwQGBmLo0KEwNTXFvn37UKxYMaXjRYsWRWxsLBITE2Fra4tnz55JxxISEqQx748HIDX48vHxgY+Pz0fn5OTGjRvZxnNaGSaigu/8+fNwdnZGdHQ0rK2t4eLigvj4eAQFBWHw4MEoUqQIXF1ds8zp0KEDYmJiUL16dbi4uODdu3cICgpCSEgIunTpkqXz/edQpCSiQ+sWCLt5E+XKlUPXrl3x9u1bnDx5Ep6enrh8+TJ+/fVXaXxISAhcXFwAZPy8+uabb/D69WsEBQUhKCgIgYGBWLVq1UfP+ebNG0ycODFrLgoF5s2bh2nTpil9GFCsWDG0aNEC169fx6+//oqDBw/izJkzWXoxEBERkeaStQB2cnKS8/QAMhpWZT7vu2vXLtSoUSPLGEtLS8TGxiIqKkpqepXp+fPnAIBy5copjQeUO0l/ag4REZBR9HXp0gXR0dFYtmwZRo4cKR2Lj4/H9OnTc5wTExODFStWSPuWZ86ZP38+dHS+7sf928v/4snNm2jdujUCAgKkR1WuX7+Oxo0bY/HixRg5ciSqVq0KANDS0oK7uzsmT56s9HPzv//+g4ODA37//Xd07doVnTt3zvGcEydOhIGBARo1aoQLFy4AyPi52r9/f6k7fyZnZ2cEBARAS0sLKSkp6NevH/7880/89NNP+OWXX77q2omIiKjwkPUWaLk9f/4cHTt2RHR0NNavX4/WrVtnO+5jXZuvX7+uNAYAqlevDi0tLURGRma5BU8UxY92lSYizTZv3jy8fPkSgwYNUip+gYy7RhYuXCitrGY35/3iN3POzJkzv/oDt+SnGdu2eXl5KfVpqF27Ntq2bQtRFHHp0iUp3qJFC2zdujXLh4Z16tTB0KFDAQA7d+7M8XxnzpzB2rVrMW/ePKlvwrlz52Bra5ul+C1dujT2798PLa2Mf9L09PSwZMkS6OnpYd26dVAoFF9x5URERFSYaGwBHB8fjy5duuDBgweYP38+3N3dcxzbtm1bAMCBAweU4qIoSrF27dpJcWNjY9jb20MURRw8eFBpzunTpxETEwNLS0vuR0lEWWQ+TztixIgcx3y4nU/mnA8L5rwkaOtm/G82zyxnxkqUKJGr98puv/X3paenw8vLC02aNIG7uztEUQQAjB8/Hi9evJDGNWnSBADQqFEjaGsrN+eysLBAlSpV8Pr1a1y7di1XeREREVHhp5EFcFpaGvr06YOLFy9i/PjxGDdu3EfHu7m5wcTEBIcOHcKRI0ek+OrVq3H37l1Uq1Yty+rx8OHDAQC+vr6Ii4sDAKSkpGDKlCkAkGWVhogoPDwcUVFRKFq0KBo0aPDZc+zs7PItN4NK9QBk/Nx7v6FWWFgYjh49CisrKzRv3jxX75XdfuvvW7p0Ka5du4bffvsNAJSKXgDQ19fHypUrpYLf1NQ02/cxNzcHkPGoCxERERGgoQXwsmXL8O+//8LAwAAvXrzAwIEDs3zNnTtXGm9iYoLly5cDADp16oSuXbuibdu28PLygp6eHtauXSvdepepf//+cHZ2xs2bN1GzZk24urrCxsYGgYGBsLOzw9ixY1V6zUSk/u7cuQMAqFy5co7dofNizpcwqtMWXbt9g8OHD8Pa2hq9e/dGx44dUb9+fdSpUweHDh2Cnp7eJ98nOTkZW7duBQD07Nkzy/HIyEhMnz4d/fr1g729PYD/7Z0OADVq1MD58+cxfPhwlCxZEgCUGhO+78mTJx89TvmvsHVC//vvvzFhwgQ4ODjAwMAAgiBgzpw5OY5//vw5Vq9eDU9PT1StWlXqaB4REfHR81y4cAGdOnWCmZkZjI2N0bJlyyy3/mdKSUmBv78/Ro0ahQYNGkjf0y1btmQ7fuDAgVIeOX19+DsNEVFhImsTrJyeuc2OIAg4duxYnpw3JiYGAJCUlJTt9h1ARoOuSZMmSa89PDxQvHhx+Pn54eTJk9DS0kKHDh0we/ZsNGzYMMt8LS0t7N27F35+fti8eTP27NmD0qVLw9vbG7NmzcpyCyMRUebPJmNj43yd8yUELW2s37wNP82Yil9//VVaxS1WrJi0PVxu/PTTT3j8+DFatmyJjh07Zjnu7e0NhUKh9CFkpj59+mDdunVSB/0GDRpAW1sbp0+fxsuXL6WCGABOnTolFcCZnfep4FDHTugA0KNHD+nPufnA6cCBAxg2bNhnnSMoKAht27ZFamoqWrVqBUNDQxw6dAgdOnTAjh070KtXL6XxkZGR6Nu3b67zcnR0zPFYeHg4goODc303BxFRgSTKSBCET35paWlJ/0v/U6tWLbFWrVpyp0FEHzFjxgwRgOjp6ZntcW1tbfH9H8Pbtm0TAYjNmjVTGrd9+3YRgPRlZWWVZY6Dg0Oe5x+TkCJWnLhfrDhxv1hu1HaxSVMH0dzcXNyxY4cYExMjPnz4UBwxYoQIQGzSpImYkpLy0fc7duyYqK2tLRYvXlx88uRJluOHDx8WAYidOnVSijs5OYkAxKCgoCxz+vXrJwIQW7RoIV69elWMi4sTDx8+LJYrV04sUaKECED08/P7um8EfbHP/W9AFEXx9evXYsmSJUUA4rJly5SOvXv3Thw7dqzo7++f45wVK1ZkmTNjxoxs/859rr59+4rLli0TL1++LE6bNk0EIM6ePTvH8YcPHxZHjx4t7ty5U4yMjBQrVqwoAsgxl7S0NNHa2loEIK5evVqKHzhwQAQgmpubi2/fvlWa8+zZM3HgwIHimjVrxLCwMNHT01MEIG7evPmzr2/gwIFZzk1E9CXUuVaRdQX4wYMH2cYVCgWeP3+O06dPY9GiRejUqROmTp2q4uyIiL7O596SnLlf7du3b5XilStXhqenJ969e4ddu3ZlOyez10B+iT6xDhH/ncb69evRp08f6dzLly/Hf//9h6CgIGzevBmDBw/Odv6tW7fQq1cvaGtrY/fu3Vm6UiclJaF///4QBAH//vsv/v33X3Tq1OmTeS1ZsgT37t1DYGAgbG1tpXjdunXRs2dPzJgxI99Xxylv5aYTemJiYo5zcuqEnhe2bdsm/XnPnj2fHN+uXTulJpmfcvToUdy9exc1a9bEd999J8WdnZ3Rtm1bHD16FP7+/vj222+lYxYWFli/fn2uz5GT+Ph4/PXXX9DX10fv3r2/+v2IiNSVrA95VKxYMduvypUro0mTJhg7dixOnDiBbdu2ZenATESk7j7sTJyd9/fnrVatGgDg4cOHSmMaN26MDRs2YMGCBVnmZ3ZUfvDggdQtOa+JooiEsFMAkO1ty5mxkydPZjv/xYsX6NSpE6Kjo7F27Vq0aNFC6XhsbCy++eYbPH/+XLqGb775Bm3btoWzs7PUxXn8+PFwdnbGmTNnpLlmZmYICQnBrl274O3tDS8vL6xevRpnzpyRPkioUqXK130DSKXUtRO6KmQ+5+vs7JzlWOYHQu8348xLu3fvxrt379C5c+ccG8sRERUGsq4A54a1tTUaNWqEJUuWSJ2ViYgKAiMjIwDIsWFPenq60i+aVlZWKFWqFF68eIH//vsPderU+eQ5qlatKs0JDQ3Ndffoz6GIj4GYltFAKPP52/cVK1YMQEbDnw8lJCRIW875+fmhX79+SscvXLgANzc33L9/Xymempqape/DuXPnACDLvwVaWlro0aOH0vOZABASEgJBEFC/fv3cXCapAXXuhK4KN2/eBADUqlUry7HMPbUzx+S1jRs3Asho4klEVJgViDZ/iYmJOd4uTUSkripUqAAg+y7EkZGRAJS3AhIEAR4eHgCANWvW5OocgiBI+5hndqvPa4KeISBk/HPx33//ZTmeGStTpoxSPD09Ha6urrhw4QJ++OEHTJ48WTqmUCiwcOFCODg4KBW/Tk5OiIiIgCiK0peTkxOAjOZAoijim2+++WTOly9fxtmzZ+Ho6JjrBl0kP3XuhK4KT58+BZBxW/O7d+/QrVs39OrVCwkJCVI39E91kP4ST548wYkTJ2Bubp6rRw+IiAoytS6AU1JS8Ouvv+L8+fOwtLSUOx0ios+S2cE1MDAQISEhUlwURfj5+QHIekvx5MmTYW5ujuXLl2fZxuTNmzfZnmfKlCkwNzfH+vXrsWrVKqVjiYmJ8PX1/apfmrX0DGBQIWP1adKkSUrPG1+4cEG6/bR79+5K80aMGIH9+/ejT58+0p6+APDy5Ut06dIF48ePR1paWsY5tLQwa9YsHDt27LN+3kdFRWVZeQ4PD4ebmxtEUcyzZz9JNdS5E7oqxMfHA8i4xfvgwYPYt28fdu3ahQMHDqBIkSJKY/LS5s2boVAo0Lt371xtZ0ZEVJDJegv0x57LSklJwYsXL6RbBydMmKCqtIiI8oSZmRnmzZuHUaNGoWXLlmjZsiVKliyJK1euICwsDFWrVsXEiROV5pQsWRJ79+5Fp06d0L9/f8ydOxe1a9dGdHQ0AgMDAQA2NjY5zvHy8sJvv/2GevXq4d27dwgJCUFMTAwGDBjwddfSZigS/56GwMBAVKtWDU2bNkVsbCyCg4ORmpoKV1dXuLi4SOP37NmD1atXQ0tLC9ra2lJzrKioKAQGBio1MbK0tMTWrVulld7PceXKFXTr1g329vYoV64cXr58iaCgIKSkpMDX1/ezttsj9fHhSq6/v7/SVj9WVla4d++eqtNSKQcHB1hZWUFPTw/NmzfP10Z3mR9i8fZnItIEshbAHzZ6eZ+WlhbMzc1Rv359DB8+PMvKAhFRQfDDDz+gatWqWLhwIc6fP4/ExESUL18eY8eOhY+PD8zMzLLMcXR0xM2bNzF37lwcOHAAe/bsgYGBAezt7eHp6QlPT89czSlWrBhsbGzg6uqKsmXLftV16JWshENnzmPZovk4dOgQAgICoKenBzs7OwwaNAhDhw5VKloyV+UUCgW2b9+e4/t26dIF69evR4kSJb4or+rVq6N79+4ICQnB+fPnYWRkhNatW2PMmDHo0KHDF70n5Z3C1AldFTKfsU9MTETZsmWVivyoqCilMXnl3LlzuH37NipXroxmzZrl6XsTEakjWQtghUIh5+mJiFSiY8eO2XZP/phy5cph2bJl+T7nc1SoUBG///57rsYOHDgQAwcOzBKPjY1F/fr1ERERgfnz52PUqFGfLJJy6i4NZBRG/v7+ucqJVC8vO6E3btwYDx8+zFIAf9gJvSA/B2xpaYmrV69Kxe77Mm/1/3ALsa+V2fwqs/8AEVFhp9bPABMRkTzSFSIWH72rFFt89C7SFV+31ZKJiQn+/PNPnDlzBqNHjy7QxQp9Wm46oWeOAf7XCT0uLi7bhmvZyeyEHh8fj9DQ0K9PWkaZ3Z+z6/R8/fp1pTF5ITk5WfoA6cMO7UREhRULYCIiQlJqOmITU6WvnwLCsC5Eufv+upAH8Ps3TBqTlJp9UQNk/GI9ZsyYbFdnGzRokC/bNZH6KSyd0FWlbdu2AICDBw9mOfbvv/8CANq1a5dn5/vnn38QHR2NRo0aoXr16nn2vkRE6kzWAtjX1xf79u375Lhjx47B19dXBRkREWmmlSfDYTvrsPT1YfGbaW3wA2nMypPh2Y65e/cumjZtisWLF2Po0KEID89+HBV+haUTuqq0bdsWVlZWCAsLwx9//CHFDx48iKNHj8LMzAyurq55dr7M25+5+ktEGkWUkSAI4qBBgz45btKkSaKWlpYKMio4atWqJdaqVUvuNIgKlRkzZogARE9Pz2yPa2trizn92IyKihLHjBkjWltbiwYGBqKxsbHYokULcd26dWJaWlq2cyIjI8VRo0aJVatWFQ0MDMSSJUuKzZo1E5cuXSomJyd/9fXs3r1bHD9+vNi0aVNRX19fBCDOnj0727GLDt8Wy32/WTTv8L1YtHZrUce0jAhABCBaem0QK07cn+Vr0eHb4rt370QfHx+xWrVqor6+vmhkZCR9nzK/Pvx+Xrt2TezVq5dYsmRJUU9PT6xUqZLo7e0txsTEZMkrPj5e3L59u9ijRw/R0tJS1NXVFU1NTcX27duLBw4c+OrvEeW/JUuWiABEHR0dsW3btmLfvn3FmjVrigDEqlWriq9fv84yJygoSCxWrJgIQLSxsRFdXV3F9u3biwYGBiIA0cXF5aNzqlevLrq6uoqdO3cWTU1NRQDigwcPvvpavLy8xMaNG4uNGzcWLS0tRQBiuXLlpJivr6/S+MjISOlY48aNRT09PRGAWL9+fSl26dIlpTknTpwQdXV1RUEQxDZt2oidO3cWdXR0REEQRH9//2zz+uabb6T3K1GihAhAtLKykmJr1qzJMuf58+eijo6OqKOjIz5//vyrvzdERO9T51pF1iZYucVmWUSkzs6fPw9nZ2dER0fD2toaLi4uiI+PR1BQEAYPHowiRYpkWbU5f/48OnTogJiYGFSvXh0uLi549+4dgoKCEBISgi5duqBSpUpflVePHj2kP+fmWdvE+5fw5lDum2ilpaSgTZs2OHfuHEqVKoVSpUrhyZMnSmNGjhyJBQsWSK9Pnz6Ndu3aISEhAfXq1UPz5s1x6dIl/Prrrzh48CDOnDkjdfUFgJ07d2LQoEEQBAF2dnZwdHREZGQkjhw5gsOHD8PPzw+TJ0/Odc6keoWlEzqQ8WzuuXPnlGIRERHS6nKNGjWUjiUnJ2cZDwCXL1+W/vxh9+qWLVsiODgY06dPx5kzZ6BQKNC0aVP4+Pigffv22eZ1+fJlPHr0SCkWHh4u3X3h7OycZc7WrVuRlpaGjh07olSpUjldMhFRoVMgCuDw8PA8b/tPRJQX3rx5gy5duiA6OhrLli3DyJEjpWPx8fGYPn16jnNiYmKwYsUKeHl5Kc2ZP3++UmfcL9W3b180a9YMzZo1w+7duzF79uwcx3q1tELl5PY4VDkdTZo2xfW0svh1VB+kx71QGjfEsTJGtcnourtiya84d+4c6tSpg8TExCz7shYvXhyLFi2Cnp6eFBs2bBgSEhIwd+5caQ/klJQU9OvXD3/++Sd++ukn/PLLL9J4XV1djBw5EuPHj1f6QODYsWPo0KEDfHx80KVLF9SpU+eLv0+U/wpLJ/TMjuRJqel4EZeMUsb6MNDNudN1pUqVIIqf3zjO3t4+2+eAc/KxbSVz4u3tDW9v78+eR0RU0Km8AA4MDFR6HRUVlSWWKSEhAWfPnsW+ffvQqFEjVaRHRPRZ5s2bh5cvX2LQoEFKxS+QsV/nwoULkZiYmOOc94vfzDkzZ87Mk9y2bdsm/XnPnj0fHWugq41vunTEN10yipR0hYg/ftRBzHuLU0McK2NKp5rQ1spYTf57118AgFu3biE1NVUa5+DggCJFiuDo0aM4cOAAunXrBiBj9ez69esoX748xo8fL43X09PDkiVLsHfvXqxbtw5z586FllZGiwoPD49st2dp06YNunXrht27d+Ovv/5iAUwqka4QseDwbWw6/RDxKekoqqeNAQ6VML59dem/CyIiUm8qL4BbtmypdCveoUOHcOjQoRzHi6IIbW1tTJo0SRXpERF9lk2bNgEARowYkeMYQ0PDbOd8WDCrE20tAcUMdBHzXmxUG2vpl/zY2FjpNs7M4lcQBEyZMgUzZ87EwoULcfToUZw4cUIqgMPCwgAAtra2WfaHtbCwQJUqVXDr1i1cu3YN9erV+2SOmfu/ZrdnKuWfpNR0JKdp5qNJi4/eVWoQF5+SjpUnw5GSppDujNBE+jpaH10JJyJSJyovgAcMGCAVwBs3boSVlRWaNWuW7Vg9PT2UL18e3bt3h42NjSrTJCL6pPDwcERFRaFo0aK53tbn/Tl2dnb5nGH+0dXVRVpamvTawsICW7ZsQZs2bQAA5ubmAIDbt29LY5KSkgAApqam2b7n+3NyUwA/fvwYgPI2OpT/Vp4Mx+Jjdz89UIOsDX6AtcHZd07XBKPbWMO7XTW50yAiyhWVF8AbNmyQ/rxx40Y4Ojpi3bp1qk6DiOir3blzBwBQuXLlXDWZ+tI56qhIkSIoUaIEXr16BScnJ+zcuVOpkU5mM6z3938tUaJEltj7spuTk5cvX+Kff/6BIAjo3r37F18HERERaRZZ9wEmIirIYmJiAADGxsb5OkddOTo6AgB69uypVPymp6dj+/btADJ6OWRq0KABtLW1cfr0abx8+VLpvU6dOiUVwO/Pycm4cePw7t07DBgwgHcIERERUa7J2gXa09NT+gWKiKig+nAl19/fH3379pVeW1lZZemQrEpJqek4E/4aAHD63iskpaZ/0fN6N2/eRNMGttLrkSNHYs+ePZg+fTosLCzQvn17vHjxAlOmTJEK/fe/NyVKlEDfvn2xZcsW9OrVC0uXLkXlypVx9uxZDB48WFpR/tTK+MaNG7F582ZYW1tj6dKln30d9HW8WlphsGNludOQxYfPAGd6vzu6JtLX4XoKERUcshbA69evl/P0RERKPveW5Mz9at++fasUr1y5Mjw9PfHu3Tvs2rUr2zkf7v2Zn5LTFDj34P8L4PuvkZymyF0B/MH2LYMHDcLFM0HStnRt27bFTz/9hGnTpqFPnz7SOAMDA6xfvx59+/bNstK9ZMkS3Lt3D4GBgbC1/V8xXbduXfTs2RMzZsz46Op4YGAghg4dClNTU+zbtw/FihX79HVQnjLQ1dbYhkdTO9eEga4WNr7XBdrToRLGsQs0EVGBUSD2ASYiUoUPOxNn5/39eatVy2j68uEenI0bN0bjxo3x8OHDLAVwZufiBw8eQBRFtX0O+P79+3j+4rlS7NGjR7h48SKcnJyk2JQpU+Di4oLdu3fj6dOnKFOmDDw8PJCSkgIAqFKlitJ7mJmZISQkBHv27EFwcDCSkpJQv359eHh4YMaMGdnOyXT79m3ped9du3ahRo0aeXa9RLmhrSXgR+caGNXGOlf7ABMRkfphAUxE9P+MjIwAZDzDmp309HSlDsZWVlYoVaoUXrx4gf/++y9Xe9FWrVpVmhMaGprr7tGqtHPnTnz33XdISU5Rih8/cQJN7OpmGV+7dm3Url1bKbZ69WoAyPb6tLS00KNHD/To0UMpHhISAkEQUL9+/Sxznj9/jo4dOyI6OhpbtmxB69atP/u6iPKKga42KhQvIncaRET0BWQvgFNTU7Fz504cOnQIL1++REpKCsQPbrsDMm5NPHbsmAwZEpGmqFChAoDsuxBHRkYCUN5yRxAEeHh44Ndff8WaNWuwZMmST55DEAS4u7vjt99+w/Lly9WqC35CQgLGjBmDNWvW/H9EeXW6Zs2auXofURTx+++/QxAE9OzZM1dzLl++jLNnz8LR0REWFhZKx+Lj49GlSxc8ePAACxYsgLu7e67ek4iIiOhDshbAiYmJaNOmDc6dOycVvYIgKBXAma/V9TZBIio8WrVqBUNDQwQGBiIkJETao1wURfj5+QEAOnbsqDRn8uTJ2LhxI5YvXw57e3v069dPOvbmzZtszzNlyhRs2rQJ69evh729PYYPHy4dS0xMxPz58zF48GCUK1cury8xRzdu3ICrqytu3LghxQQtAaIi6weS77ty5YrSnr2pqakYPXo0QkND0b9/f1hZWSmNj4qKgiAIKF26tBQLDw+Hm5sbRFHEzJkzlcanpaWhT58+uHjxIsaPH49x48Z9+UUSERGRxhPE7JZbVWTOnDmYPn06+vfvj5EjR2L+/PnYvXs3bt++jYcPH2Lv3r1Ys2YNBg4ciMmTJ6NixYpypap2Mrf9eP+XVSL6ekuXLsWoUaOgo6ODli1bomTJkrhy5QrCwsJQtWpVnDt3Dubm5kpzgoOD0alTJ7x9+xY2NjaoXbs2oqOjERgYiKSkJLi4uGDv3r05zqlevTrq1auHd+/eISQkBDExMXjw4AEqVar0VdcyYsQIhIaGIl0h4nJYONLfvYZ2sRKoX6MKtLUEdO7cGdOmTcPevXvh5uaGpKQkaW7RokWRkpKC1NRU6JW2ArR0ULecCX5ftRJ2dnbSOAsLC5iZmaFGjRrQ1tZGSEgIoqKiYGdnh+PHj0tNvzIdPHgQ3bp1g729PcqVK4eXL18iKCgIKSkp8PX1xbRp05TG//bbb/D29oaBgQH69OmT7YehNWrUwKRJk77qe0VERER5R61rFVFGderUEatUqSIqFApRFEVx4MCBopaWltKYY8eOidra2uK6devkSFFt1apVS6xVq5bcaRAVSv/++6/Ypk0b0djYWNTV1RWrVKkijh07Vnzz5k2Oc548eSKOHDlSrFKliqivry+amJiILVq0ENeuXSumpaXlak6JEiXEZs2aiUuWLBGTk5O/+jqcnJxEADl+eXp6iqIoiuHh4aKxsfFHx2Z+nThxQukc06dPF2vXri0aGRmJRYoUEevUqSP+9NNPYmJiYrY53b9/X3R1dRXLlSsn6unpiebm5qKzs7N48ODBbMfPmDHjkzk5OTl99feKiIiI8o461yqyrgAbGRnB2dkZf/31FwBgyJAh2LBhA9LS0pQ+5XdycsLbt28RGhoqV6pqR60/VSEitRKbmArbWYel11dntIeJoa7SmB07duD777/Hxo0b0alTp1zPIyIiIvqQOtcqsu5crqurC13d//0yldmB9flz5a03LC0tcefOHZXmRkRUGKQrRCw+elcp9tPejNui3+fq6op79+5JxS8RERFRYSRrE6zy5csrFbbVq1cHAPz7778YPHgwgIzmM5cvX87yzB0RUWGUlJqO5DRFnr3f4qN3sS7kgVJs55VX0NO9ggmdlLcugl4RxCamSi/jklKVDn/4OpO+jhb3QiUiIqICQdYCuHnz5li9ejWioqJgYWGBbt26wdvbG2PHjkViYiIqVaqELVu24M6dO9z2gog0wsqT4Vh87O6nB36lLRciseVC5GfNaT7vRLbx0W2s4d2uWl6kRURERJSvZL0Fum/fvmjYsKG0v6+lpSXmzp2LuLg4jBo1Ci4uLtixYwdKly6NefPmyZkqERERERERFXCyrgA7OjrizJkzSjFvb280a9YMu3fvRnR0NKpVq4ZBgwbxFmgiIiIiIiL6KrJ2gaYvp86d1Yjoy+XVM8ABAQH4/vvvIdp0hEmjb7IcH+JYGaPaWH/1eQA+A0xERETK1LlWkXUF2MfHB//++y9WrFiBJk2ayJkKEZFaMNDV/qpiMikpCRMmTMCyZcsyAi/WAaIIE/vu0pghjpUxpVNNaGsJObwLERERUeEk6zPAW7duRUJCAotfIqI80q1bt/8VvwCKFjHE3P5OSmNGtbFm8UtEREQaSdYCOCoqCnXr1pUzBSKiQsXb21v6s62tLS5dugQ3NzcZMyIiIiJSH7LeAl2yZEkkJibKmQIRUaHi7OyMCRMmICkpCb/88gsMDAyU9vYlIiIi0mSyFsA9evTA+vXr8fr1axQvXlzOVIiICpwrV66gTJkyKF26tFJ83rx5EATe4kxERET0IVlvgZ49ezZq1qyJrl274uHDh3KmQkRUYIiiiKVLl6Jx48YYMGAAFArlrtEsfomIiIiyJ+sK8OjRo1G9enXs2bMH1tbWsLOzg5WVFQwMDLKMFQQBa9eulSFLIiL18ebNGwwePBh79+4FABw+fBjz58/HxIkTZc6MiIiISP3JWgBv2LBB6fWFCxdw4cKFbMeyACYiTRccHAx3d3c8efJEillYWKBhw4YyZkVERERUcMhaAJ84cULO0xMRFQjp6en4+eefMWPGDKXbnZ2dnbFx40aUKlVKxuyIiIiICg5ZC2AnJ6dPDyIi0mCRkZHo16+f0geGOjo6+PnnnzF27FhoaX26lYO+jhZGt7FWek1ERESkiWQtgImIKGcHDhyAp6cnXr58KcUqV64Mf39/2Nvb5/p9DHS14d2uWn6kSERERFSgsAAmIlJDjx8/houLC9LS0qRYnz59sHr1apiYmMiYGREREVHBxfvgiIjUUIUKFTBr1iwAgKGhIdasWQN/f38Wv0RERERfQW1WgF+8eIErV67g9evXqFixIhwcHOROiYhIVhMnTkRERARGjhwJGxsbudMhIiIiKvBkXwF++PAhunbtirJly6Jjx47o168f/vjjD+n43LlzYW5ujuDgYBmzJCLKPwkJCZg7dy5SU1OV4tra2lixYgWLXyIiIqI8ImsB/PjxYzRp0gQBAQFo3rw5Ro0aBVEUlcZ4eHggNjYWf/31l0xZEhHln+vXr6NRo0aYPHkyZsyYIXc6RERERIWarAXwtGnT8PLlS2zfvh0nTpzAr7/+mmVM+fLlUbt2bZw8eVL1CRIR5RNRFLF69Wo0atQIN2/eBJBxx0tISIjMmREREREVXrIWwIcPH4a9vT1cXV0/Oq5mzZp4/PixirIiIspfMTExcHV1xbBhw5CUlCTFJ0yY8FnbGxERERHR55G1CVZ0dDSaN2/+yXFJSUlITk5WQUZERPnr3LlzcHNzw8OHD6VYyZIlsWnTJjg7O8uXGBEREZEGkHUF2NLSEv/9999Hx4iiiGvXrqFixYoqyoqIKO8pFAr88ssvcHR0VCp+W7dujatXr7L4JSIiIlIBWQvgjh074s6dO5g3b16OY5YtW4aHDx+iS5cuKsyMiCjvvHjxAp06dcLEiRORlpYGIKPD85w5c3D48GGUKVNG5gyJiIiINIOst0BPnToVO3fuxJQpU3DkyBF069YNABAVFYVt27bh33//xfbt21GqVCmMHz9ezlSJiL7Y3r17cejQIel1+fLlsW3bNjg6OsqYFREREZHmEcQP9x1SsatXr6JXr14IDw+HIAgQRRGCIADIuP25SpUq2LVrF2xtbeVMU+1k7gt648YNmTMhok8RRRE9evTAnj178M0332Dt2rUwNzeXOy0iIiKifKHOtYqsK8AAYGtri7CwMOzevRvHjh3DkydPAADlypVDq1at0KtXL+jq6sqcJRHRlxMEAWvXrkXnzp0xZMgQ6UM+IiIiIlIt2VeA6cuo86cqRJrs77//xvnz5/Hzzz/LnQoRERGRLNS5VpG1CZacHjx4gCVLlsDNzQ3ly5eHIAjQ0cl+QfzkyZMQBCHHLx8fn2znpaamwtfXF9bW1jAwMEDFihUxbtw4vHv3Lj8vjYhkkJSUhJEjR6JHjx6YO3cudu/eLXdKRERERPQBWW+B9vX1Rb169eDi4vLRcceOHUNISAimT5+eZ+feuHEjZs2aBQC5vh3Rysoq26Y1dnZ2WWIKhQLdu3dHQEAAypYtCxcXF4SGhmLRokUICgpCYGAgDAwMvu4iiEgt3Lp1C66urrh27ZoUGz16NLp06QI9PT0ZMyMiIiKi98laAM+cORMDBw78ZAF89OhR/PLLL3laAFerVg0//vgjHB0d4eDggBIlSnxyjqOjIzZs2JCr99+yZQsCAgJQs2ZNnDlzBiYmJkhJSUG7du0QGBiIRYsWYcqUKV95FUQkJ1EUsXHjRowcORIJCQlSvF69etixYweLXyIiIiI1UyBugVYoFHn+nu7u7pg3bx66du2K4sWL5/n7r1y5EgAwY8YMmJiYAAD09PTg5+endJyICqa3b9+if//+GDRokFLxO2rUKJw9exbVqlWTMTsiIiIiyo7sXaBzIzw8HEWLFpU7jVyLi4vD+fPnIQgCnJ2dlY45ODjA1NQUERERCAsLQ82aNWXKkoi+1KVLl+Dm5oZ79+5JMXNzc6z/v/buOyqq428D+LP0IkVURIoNsIAFMTZEwdgQDMYWUaOoMTFqfrZEo7GAJZbYEqOxxdgVk2gssXfAAkbExK6osYCV3mGZ9w/evXHdpVfh+ZyzR3buzL0ze7kevjtt48Y8R7SUhtQMOVafiZDej3a3hZ62ZhnWiIiIiKh8KPUAODAwUOn9s2fPVNIUkpOTcfHiRezfvx+tWrUqjerlKiIiAtOnT8fLly9Ro0YNdO7cGe+//75Kvtu3byMrKwtWVlZS76+CTCaDg4MDzp8/jxs3bjAAJnqHCCHwww8/YMqUKcjIyJDSXV1dsWPHDtjY2JRh7f6TlpmFH07eld6PcK3HAJiIiIgIZRAAu7u7Ky06dfToURw9ejTH/EIIaGpqYurUqaVRvVwFBwcjODhYej9//nx06tQJv/32m9Iw6qdPnwIALCwsAAABAQFYtmwZvv76a/Tt2xc1a9YEADx58qQUa09ERSWTyRAeHi4FvzKZDDNnzsTMmTNzXEWeiIiIiMqPUv+LbejQoVIAvHnzZtja2qJ9+/Zq8+ro6MDGxga9e/eW9pIqC6amppg5cyb69euH+vXrIz4+HoGBgZg8eTJOnz4NHx8fHD9+XMqflJQEANDX1wcAzJs3D9evX4e/vz/69u0LAwMDpXy5yandERERsLW1LWrTiKiAVq5ciYsXLyI+Ph7bt29Hp06dyrpKRERERJRPpR4Av7mK8ubNm+Hq6opffvmltKtRIE5OTnBycpLeV6lSBT4+PmjRogWaNWuGEydOIDAwEB07dlRb3sfHB4sWLYKvr28p1ZiIikNWVpa037dClSpVsG/fPpiZmaFGjRplWDsiIiIiKqh3YhXo8qphw4bw9PQEkL1XsYJiwa6UlBQAwIwZM5CQkICvvvoKAKQVY/OzsNf169fVvtj7S1Synj59ivfff1/t1mcNGzZk8EtERET0DirTANjX1xeurq5lWYUis7OzAwBERUVJaVZWVgCyF/hS5/nz5wAAa2vrEq4dERXGwYMH0bx5c5w9exZffPEFbt68WdZVIiIiIqJiUKYB8MaNGzFixIiyrEKRvXr1CkD2sEiFhg0bQkNDA5GRkYiLi1PKL4TAjRs3AAAODg6lV1EiylN6ejomTZqEnj174vXr1wCyR2xs3769jGtGRERERMWBQ6CLID09XdrCydnZWUo3NjZG69atIYTAkSNHlMqcP38esbGxsLKy4hZIROXIvXv34OLiguXLl0tp+vr6+PnnnzF37twyrBkRERERFZd3IgC+c+dOjnsFl4ZVq1bhxYsXSmmpqakYN24c7t+/DwsLC/Tp00fp+Oeffw4AmDNnDuLj4wFkB8zffPMNAGD06NGlUHMiyo+dO3fC2dkZly9fltKaNGmCv/76C5988onSIlhERERE9O56JzauXLBgAbZs2QK5XF5s5wwLC8OYMWOU0uRyOdq2bSu9/+OPP1CrVi0sXrwYEyZMQIsWLWBra4v4+HiEhYXh2bNnMDY2xm+//SZtbaQwZMgQBAQE4MiRI2jcuDFcXV0RFhaGe/fuwdnZGZMmTSq2thBR4SQlJWHcuHEqK9F//vnnWLZsmbSVGRERERFVDO9ED3BJiI+PR0hIiPRSeDMtLS0NADBhwgS4u7sjKioKBw4cwIkTJ6Cvr4/PP/8c4eHhahfy0tDQwL59++Dn5wc9PT3s3bsXaWlpmDhxIs6cOcM/rInK2KNHj9CqVSul4NfExAS//vorVq9ejUWLFkEmk2HYsGFqy2tpaeXYM/z8+XNMnDgRDRo0gL6+PkxMTODm5oaNGzfm+EVeVFQUxo8fD3t7e+jr68Pc3Byurq5YuXIl0tPTi9zehw8fYvjw4bCysoKOjg6sra0xcuRIREZG5lhm3759cHNzg7GxMQwMDODk5IQVK1YgKysrxzKpqamYP38+mjdvDkNDQxgbG6NZs2bw8/NTyevv7y9tM6XudeLEiSK3m4iIiOhN70QPcElwd3eHECJfeSdMmIAJEyYU+Bo6Ojrw9/eHv79/gcsSUcmqWbMm9PT0pPdt2rTBzp07Ua9evSKdNzQ0FB4eHoiJiYG9vT28vb2RlJSEoKAgjBgxAgYGBhgwYIBKme7duyM2NhYNGzaEt7c3EhMTERQUhHPnzqFnz56oW7duoet0P+IeunXqiFevXqFhw4Zo27Ytbty4gQ0bNuDgwYMIDQ2FjY2NUpnvv/8eEydOhLa2NlxcXGBkZIRz585h/PjxCA0NxbZt21SuExsbi06dOiE8PBw1a9aEh4cHMjMzcePGDaxduxazZ89WW7/27dtLK+q/ydLSstBtJiIiIlKnVAPgOXPmQCaTYezYsTAzM8OcOXPyVS48PLxkK0ZElY6uri527dqF9957D6NHj8bcuXOhra1dpHNGR0ejZ8+eiImJwcqVKzF27FjpWFJSEmbNmpVjmdjYWPz0009K6wMkJSVh8eLF0NIq2n/V0yZ/hVevXmHMmDFYuXIlZDIZhBD48ssvsXz5cnz55Zf49ddfpfyRkZGYMmUKtLW1cerUKWmUS1RUFDp37ozt27dj0KBB0j7oCmPGjEF4eDhGjRqFFStWQEdHRzoWFhaWY/1GjhyZY087ERERUXEq1QBYMdxtwIABMDMzk97npyeWi9AQUVG8fPkSNWrUUEqzt7fH3bt3YW5uXizXWLRoEV6+fInhw4crBb8AYGhoiKVLlyIlJSXHMm8vjmdoaFjkESRZack4fuwI9PX18e2330r/l8pkMsyfPx/bt2/Hnj17EB0dDTMzMwDA3r17kZGRgYEDBypN8ahVqxbmzZuHvn37Yv369UoB8N27d7Fz5044OTnhp59+goaG8gybN1fKJyIiIiorpRoAb9y4EUD2H1EK7du3xyeffJJruZ9//hnnz58v0boRUcWUlZWFxYsXY86cOQgMDETLli2VjhdX8AsAW7ZsAQCVBfbe9Pb8f0WZtwPm4pIRE4msrCzY2trC1NRU6Zienh6cnZ1x5MgRBAYG4sMPPwQA3Lx5EwBUPisg+/9sADh9+rRS+u7duwFkr3D/dvBLREREVF6UagDs6+urkmZnZ6c2/U1nzpxhAExEBfb8+XMMHToUx44dAwAMGDAAYWFhMDY2LvZrRURE4NmzZzA0NFQbOOZVpqR6SEVm9gJabwe/Cope39u3b0tpqampOZZR5I+Li8Pz589Rs2ZNAJC2kHJ1dcX9+/exbds2REZGwsrKCn369IGjo2OOdTxz5gyuXr2KlJQU1KlTB7169YKDg0PBGkpERESUD5V2ESwiqtiOHz+OIUOG4Pnz51JaRkYGHj9+nGswVlh37twBANSrVy/fUzYKU6agNPWzg/2oqCi1xx8/fqxyvHr16jmWUeRXHFcEwPfv3wcAXLt2Db6+vlIQDWRPf1mwYAGmTJmitg6bN29Wej99+nSMGzcOy5YtY28yERERFasy/cvit99+y9ewPyFEvldsJqLKLSMjA9OmTUP37t2Vgt/evXvjypUrJRL8AtkrIAMoUO9yYcoUlJaZJaqamSEiIgLXrl1TOhYREYELFy4AAJKTk6V0xX7o+/btUznf1q1bpZ/fLJOQkAAgeyi3l5cXbt++jZiYGOzYsQNVqlTB1KlTcfbsWaVz2dvbY/Xq1bh79y6Sk5Nx9+5dLFiwAHp6evjhhx+wYMGCIraeiIiISFmZBsB9+/bN11DBpUuX4sGDB6VQIyJ6l/37779wc3PDwoULpS/NdHV1sWrVKuzevVsavluS3u7JDQgIUNrbVt12PyVbHw188ukoAICPjw8uXLiAxMREXLx4Eb169ZKGOb9Zb09PT9SvXx9//fUXvvjiC/z77794/fo11q5diwULFkg9xG+WUXzepqam2LVrFxo0aABTU1MMHDgQfn5+EEJg5cqVSnUbPHgwPv/8c9jZ2UFfXx92dnaYOnUqVq1aBQBYuHAhkpKSSuyzISIiosrnnRhbVq1aNdSpU6esq0FE5dju3bvh5OQk9WgCQMOGDRESEoIxY8YUeIhxQfObmJgA+K8nVKFevXrw9fVF3759cywTHx9foGsV1JRp0+Ht7Y3r169Le/q2a9cOCQkJmDlzJgDlXmhtbW3s3r0bFhYWWLVqFerWrYvq1avj888/x0cffYSOHTuqlDEwMAAADBw4EJqamkrX7927NwDg4sWL+arvsGHDULVqVSQmJiI0NLTwDSciIiJ6yzsRAE+dOlXtH49ERADw448/ol+/ftKQYiA7iLp8+TKaN29eqHO+HcSpo9if19/fH15eXgCAhw8fKuVp06YNtm3bJq2S/CZ7e3sA2QtQ2dvbQ19fHyYmJnBzc8PGjRshl8vVXjcqKgrjx4+Xypibm8PV1RUrV65Eenq6Sn4dHR3s27cPx44dw+TJkzFq1CgsX74c4eHhUjvr168v5f/jjz+wfft22NjYSG1s164d9u3bh82bN+PRo0eQyWSoW7cugOzFxjIzMwEAa9askXq7nzx5AuC/lf9fv36tVK/U1FTMnz8fzZs3h6GhIYyNjdGsWTP4+/vD1tZWaisAhISEYNKkSWjSpAkMDAygr6+Ppk2b4ttvv1Wab6wwbNgwpZ53dS/OLyYiIqp83olFsM6ePcteACLKUe/eveHv74/o6GhUqVIFq1evxscff1ykc1apUgUAcgxC5XK52m2F4uPj8c8//6Bp06Z5XiM6OhoymQwZGRlIS0uDt7c3kpKSEBQUhBEjRsDAwAADBgxQKhMaGoru3bsjNjYWDRs2hLe3NxITExEUFIRz586hZ8+eqFrTSu31unbtiq5duyqlnTt3DoDylkd9+vSRflb0hHt6esLb2xsJCQn4559/0KhRIxgaGgIADh8+jBs3bgDI3m/5bYovJt7cAio2NhadOnVCeHg4atasCQ8PD2RmZuLGjRtYu3atlFdxHxTzki0tLdG9e3dkZWXh3LlzmDFjBvbu3YtTp07ByMhIOv+b+xe/LSIiAsHBwejQoUOOeYiIiKhieicCYCKi3FhbW2PTpk3w9/dHQECA1LNaFLVr1wagfiXkyMhIAICNjY1Sev369XHjxg2sX78eK1asyPX80dHR+OCDD6S5s126dMEvv/wCAEhKSsKsWbPUlunZsydiY2Px008/YfTo0dKxpKQkLF68WOqxzY+oqCjs27cPtWvXRqtWraT0gQMHon379mjfvj327NmDuXPnSsd++eUXpKWloV+/flKalZUVvLy8cPDgQXTt2hV37tzBv//+Kx0PCwsDADRu3FhKGzNmDMLDwzFq1CisWLECOjo60rE//vhDOr9ie6i2bdvC398f3bp1k4Ly6OhodOnSBX/99RfmzJmDxYsXS+cYOXIkRo4cqbbdw4cPR3BwcJG/JCEiIqJ3kHgHtG3bVmhoaJR1NcoVBwcH4eDgUNbVICp1cXFx4u+//1Z7LDMzs9iuEx0dLfT19YW2trYIDg6W0rOyssTYsWMFADFlyhQhhBB+fn4CgBgwYIAwMzMTGhoaYuvWrVIZTU1NAUAAELa2tkIIIaZMmSIAiIEDBwozMzMBQKxevVqpDq9fvxazZ88Wjx8/ViozfPjwXOsem5wu6nz9p/R69OyVuH//vlKe58+fCxcXFwFAbNiwQeUct27dEsnJyVLb5s6dK44ePSoMDAxE1apVxatXr5Typ6amCktLSyGTyUSNGjUEAPH48WMRExMjWrVqJQCIH3/8UQghxJ07dwQAYWNjIxITE5XO8+TJE9GuXTsBQHh7e+faTiGE2L9/vwAgateunWdeIYRITEwUVapUEbq6uiImJiZfZYiIiKhgynOswh5gInpn/PXXX/Dx8UFqairCw8Ol1YgV8jNvN7+qVq2KRYsWYdy4cXB3d4e7uztq1KiB8PBw3Lx5E3Z2dvj666+Vyujp6WHfvn3w9PTEkCFDsHDhQjRp0kRpGLViG6YtW7YAACZNmoQxY8bA09MTo0ePxvfffw8nJyckJibi3LlziI2NxdChQ5XK5Gf7uDc9i4pE25ZOaNWqFerUqYOEhAScPXsWycnJGDlyJEaMGKFSZuPGjVi/fr00zHvNmjWYOXMm9PT0sGvXLlSrVk0pv66uLtauXYtevXpJw6A/+eQTXL16Fc+fP0eHDh0walT2atSK+dCPHz+GhYUFWrVqBQsLC0RGRuLy5ctITExEo0aNsH79+jzbpujtf/bsWb4+iz179iAxMRF9+vRRGcJOREREFR9XACGick8IgeXLl8PFxQURERF4+vQphg8fXuL7g//vf//DoUOH4ObmhtDQUPz+++9IS0vDpEmTEBoaqnZbJVdXV9y4cQNjx45FSkoK9u7dKx3bsGED9uzZg4iICDx79gyGhoZo2bKlUpmMjAzs3bsXISEhcHR0xIoVK2BpaalURjEsOL/MzWti6NChePHiBfbv34/z58+jVatW2LFjR45BZpcuXeDs7CwFlvHx8Rg6dCjCw8NV5hEr9OzZE0FBQdDT0wMAnDlzBsbGxpg5cyaOHj0KbW1tAMDly5cBZC9UZWtri0uXLuHXX3/F+fPnYWlpiTlz5iA0NBTm5uZ5tu3Ro0cAVIej52Tz5s0AgCFDhuQrPxEREVUs7AEmonLt1atXGDZsGA4ePCilaWhooGXLlsjKyirWXl91evTogR49ehSojLW1tdKet1paWpDL5VJP6507dwBkb5GkmM/6dpm3qSuTX6ZVq2Ljxo0FKtOlSxe4unVCrxETcGzbSrj2GYG165dCTzv3z9vFxQU1a9bEv//+i4iICFhbW6vkuX//PoDszzYgIEBaxVkul+PevXvQ1dVVWtAqN4p50/nZKeDx48c4ffo0zMzM4Onpma/zExERUcVSqgGwYvheQalbVZSIKr6zZ89i0KBB0qJTQPYqwNu3b4e7u3vZVayIFKsiv7mPbnGVkWcJ/HDirlLaDyfuYrpXY2hqFCxwTsvMQsiD7K2Lzt9/jbTMrDwD4PxQ7JU8duxYeHl5Yf78+TA3N8fhw4fx+eefY+rUqWjTpg3c3NxyPc/Jkyfx66+/olq1apgyZUqe1926dSuysrLQv39/pUW3iIiIqPIo1QBYsS9jQQkhClWOiN5Ncrkcc+fOxdy5c5GVlSWle3l5YdOmTSpzf4siNUOOtMysvDPmcQ4ASJdnIS4lI8d8imPJ6dl75mYJKOXf/dsufOL739DcevVtceXaTaUy8iyR6zV+OHEXv5x7oJT2y7kHkMmAcZ0Ltjp2fGpGru9zkvX/I9PjUzLU1lX+/xmMTUyxbtM2qRff88N+mPLoCWZMnYLlP6yAU2uXHK/xLCoKQ96YG/32nGR1FF/CcvgzERFR5VWqAXDHjh0ZyBJRrp48eYLBgwcjMDBQStPW1saiRYswYcKEYv8/ZPWZCPxw8m7eGXMRG5w9pHd/eCTOzz6mclwR8DX//2MpEREAgMv3IqU0AEiLfAHDJp0h0lOQfOc8Hkcnq5QJe6tMfm0IfoANwQ/yzpiLDotO5ytfZGwKAKDb8kBoGat+WREZnx3Mv675HpznnVQ6lhGbPa/6zxOBObYzKz0Vz3dORfqzSCxYsCBfw5lDQkJw+/Zt1KtXD+3bt89XO4iIiKjiKdUA+MyZM6V5OSJ6xwgh4OXlhb///ltKs7W1RUBAAN57770yrFkeNPKxnqDGf0OHtcysAACZcS+UsuhaNoSuZUNkxj1H8p3zSse0qlr+f5nn7/yoGE3Dqsh4+RCaxjVUjmlVyQ6As1IS1JYVIguvDixG+rN7aNOjP6ZOnZqvayoWvxo8eHAha01EREQVAVeBJqJyQyaT4YcffpCCu4EDByIsLKx8B78ANLT1s38QOQylFlnQ0NaT3mqZ1oKGgSlEejLSXz7M1zW0qlpml8lIRfrziCLWuGxpV8teGCsrVTXIzUpNAgDItNTP0Y0+vhYp90Kgb9sK/cb55+t6aWlpCAgIAAB8/PHHhagxERERVRRcBZqIyhV3d3d8++23qFmzJoYPH17iPZ2j3W0xwrVekc6xf28Shp5aj/fMZdjn103pWFRkJBovAhrZ18P5N459k+KLn378AR/o3sR3fp8plfn334dovgawMTPAlTfKTEseitUrV6Bz1hWs8hujti7q5gADwCeu9Qo1B7hp8HbpfdDXnWCsp51nuaY79fE4Hjg2sSOs1KwCfcApGUMGHkBbg5cIeOvzOnbkMD5aBbRu0RRH3jr2w7Il8LtyEO+1ao39h4+hqnGVfLXjwIEDiImJQatWrdCwYcN8lSEiIqKKiQEwEZWZnTt3okOHDipb5UybNq3U6qCnrVnklY17enSFvr4+zgcH4VpYqDTHVAiB6csXZ+fx9ISJ/n/Bo//M6QjYvhU/r12NDi5tlXomM5Oze0Y1ZFAqM3vWDOzasQ3bt26Gq0tbfP7559KxlJQULF68GL7DhkMmq6c03/cT13r4xrPgq0C/zVhPW6k+OVFcxlhfff7+vb3xtaUljh4+hFNH/kTv3r0BZK90vXjBPADA4EEDlcru2LED/jOno1GjRjhy+BCqVTPJd70Vw5/Z+0tEREQyIYQo60pQwTk6OgIArl+/XsY1ISq4pKQkfPHFF9i0aRM6duyIU6dOlfh+viXtxx9/xLhx46ClpQV3d3fUqFED4eHhuHnzJuzs7BASEgIzMzOlMsHBwfD09ERCQgIcHR3RpEkTxMTEIDAwEKmpqfD29sa+fftyLNOwYUM4OTkhMTER586dQ2xsLB48eICqNa2UFpC66tctX4GrwpgxYxAWFgZ5lsCVmxGQJ76GplF1tGhUH5oaMnh5eWHmzJlS/qioKCmIBYArV64gPT0dLVq0kLYb+umnn+Ds7Czl+fPPP9GrVy8IIdCuXTvUqFEDFy9exPPnz9GhQwecPHkS2trZdX7x4gVsbGyQnp6Ozp07q91bGACWLFmiskL4ixcvYGWVPef66dOnMDc3z/fnQERERIVTnmMV9gATUam6evUqBgwYgNu3bwMAAgMDsXDhQkyfPr2Ma1Y0//vf/2BnZ4elS5ciNDQUKSkpsLGxwaRJkzBjxgxUrVpVpYyrqytu3LiBhQsX4vDhw9i7dy/09PTQunVr+Pr6wtfXN19ljIyM4OjoiAEDBsDS0hIp8qK15caNGwgJCVFKkye8wl+XXgEAGjVqpHQsLS1NJT+QHQgrxMfHKx3r2bMngoKCMG/ePISEhOCvv/5CnTp18Nlnn2HatGlS8AsAycnJSE9PB5C9929O/P39VQLg7du3IzMzEz169GDwS0REROwBfleV529ViNQRQmD16tWYNGkS0tLSpPS2bdti586dqFu3btlVroKJS8koUg9wcZ+HiIiIKpfyHKtwFWgiKnExMTHo168fxo4dKwW/MpkMU6dORWBgIIPfckieJfDDCeX9kX84cVfa05iIiIjoXcQh0ERUos6fP4+BAwfi0aNHUpq5uTm2bduGrl27lmHNKo/41IwCl1G3mvQv5x5AJkOBV5MGAF0tjSIvNkZERERUVAyAiahEZGVlYdGiRZg5cybk8v8mpXbt2hVbtmyBhYVFGdaucumw6HSxnWtD8AOlFabza3xne0zs2qDY6kFERERUGBwCTUQl4tatW/Dz85OCX01NTSxcuBBHjhxh8EtEREREZYIBMBGVCAcHByxcuBAAUKdOHQQFBeHrr7+Ghgb/2yEiIiKissEh0ERUYiZOnIiMjAx89tlnarcBotIR9HUnGOsVbPVmdXOAAeAT13qFngNMREREVNYYABNRkT18+BDz58/Hjz/+CF1dXSldJpPh66+/LsOaEQAY62kXePui6V6NIZNBab7vJ6718I1nY2hqyIq7ikRERESlgl/JE1GR/P7773BycsL69esxderUsq4OFRNNDZlKT++4zvYMfomIiOidxgCYiAolJSUFo0ePRv/+/REXFwcA+P777xEeHl62FSMiIiIiygGHQBNRgd24cQMDBgzAtWvXpDQjIyOsXbsWTk5OZVcxIiIiIqJcsAeYiPJNCIENGzbgvffeUwp+W7ZsibCwMAwcOLAMa0dERERElDsGwESUL/Hx8Rg0aBBGjhyJlJQUKX3ixIk4f/487OzsyrB2RERERER54xBoIsrTX3/9hQEDBuD+/ftSWrVq1bBp0yb07NmzDGtGRERERJR/DICJKE9///23UvDr5uaG7du3w8rKqgxrRURERERUMBwCTUR5Gj58OAYOHAgNDQ34+/vj5MmTDH6JiIiI6J3DHmAiUiGEgEz2336vMpkMa9aswZgxY+Dq6lqGNSMiIiIiKjz2ABORJDMzE35+fvj0009VjhkbGzP4JSIiIqJ3GnuAiQgA8OTJEwwaNAhBQUEAgE6dOmHw4MFlXCsqS7paGhjf2V7pPREREdG7jAEwEWH//v0YPnw4oqOjpbSlS5dK836pctLT1sTErg3KuhpERERExYZ/2RJVYmlpaRg/fjx69eqlFPwOGjQIZ86cYfBLRERERBUKe4CJKqm7d+9iwIABuHLlipRmYGCAVatWwdfXV2kRLCIiIiKiioABMFEltG3bNowePRqJiYlSWrNmzbBr1y40atSoDGtGRERERFRyOL6RqBKRy+UYNmwYhgwZohT8jhkzBhcvXmTwS0REREQVGnuAiSoRTU1NGBgYSO9NTU2xYcMG9OnTpwxrRcWNqzcTERERqScTQoiyrgQVnKOjIwDg+vXrZVwTetekpKSgTZs2qFKlCnbu3Ik6deqUdZWIiIiIqAIpz7EKuwXoneHv7w+ZTIZhw4apPa6lpZXjwk3Pnz/HxIkT0aBBA+jr68PExARubm7YuHEj5HK52jJRUVEYP3487O3toa+vD3Nzc7i6umLlypVIT08vcnv++OMPTJ48GS4uLtDT04NMJsO8efNyzP/8+XOsW7cOvr6+sLOzg0wmg0wmw5MnT9Tmf/jwoZTnzZeBgQH++ecf1K1bVyn4PXPmjNr8b7+2bNmicq3jx4+jW7duqFq1KnR0dFC7dm2MHDkSjx49KvLnRERERERUXDgEmiq80NBQeHh4ICYmBvb29vD29kZSUhKCgoIwYsQIGBgYYMCAASplunfvjtjYWDRs2BDe3t5ITExEUFAQzp07h549e6Ju3bpFqtebw47zs+Ly4cOHMWrUqHyf/6+//gIAGBsbo3fv3irHXVxclN5bWFjA19dX7bmEENi2bRuysrLg6uqqdGzjxo0YMWIENDQ00KFDB5ibmyM8PBwbNmzAvn37cPHiRdja2ua73kREREREJYUBMFVo0dHR6NmzJ2JiYrBy5UqMHTtWOpaUlIRZs2blWCY2NhY//fQTRo8erVRm8eLF0NIq+qMzcOBAtG/fHu3bt8eePXswd+7cXPNbWVlh/PjxaN++PVxdXdGuXTv8+++/KvnkcjkWLlwotS0hIQGTJk1Cs2bNcj1/o0aNsGnTJrXHzp49iy1btsDFxQX169eX0tPT0zFp0iTIZDIcOnQI3bt3BwBkZWXhk08+waZNm+Dv74+tW7fmem0iIiIiotLAAJgqtEWLFuHly5cYPny4UvALAIaGhli6dClSUlJyLPNm8Kso4+/vXyx127Fjh/Tz3r1788zftWtXdO3aNdc8UVFR+Pjjj3Hq1CkpTQiBy5cv5xkA52bz5s0AgI8//lgp/caNG4iNjYWjo6MU/AKAhoYGxo0bh02bNkk90UREREREZY1zgKlCU8xXHTNmTI559PX11ZZ5O2Au744cOYLmzZsrBb8A0KJFCwwfPrzQ501OTsbvv/8ObW1tfPTRR0rHdHV1Aagfwq1Iq169eqGvTURERERUnBgAU4UVERGBZ8+ewdDQEC1btixwGWdn5xKuYfFIT0/HlClT0KNHD7x8+VJK79GjBwAgNTUV/v7++Oyzz/D111/jwIEDyMrKyvf59+zZg4SEBHh6eqJatWpKx+zt7VGnTh3cuHEDp0+fltKzsrKwcuVKAChS8E1EREREVJw4BJoqrDt37gAA6tWrl69Fpgpbpqz17dsX4eHh0ns9PT18//336NatG+rXr4+bN29i9uzZ0vHvvvsOzZs3x969e/O1kFdOw5+B7JW3N23ahF69eqFLly5wc3NDjRo1EB4ejqioKCxYsAAjRowochuJiIiIiIoDe4CpwoqNjQWQvQpySZYpK4qtmN4Mfhs3bozQ0FCMGjUKenp6mDBhAkJDQxETE4OXL1/i4MGDcHBwwNWrV+Hl5ZXndk5PnjzBqVOnYGJigg8++EBtHnd3d5w+fRq1a9fG6dOn8euvv+LOnTtwdnZGu3btiq29RERERERFxQCYKry3e3IDAgKU9rW1s7Mro5oVjY6OjtL7kSNH4tKlS2jatCkAoFatWli+fDlatWoFU1NTVK9eHZ6enjh79iyqVauGGzduICAgINdrKLY+6t+/vzTf921bt25F27Zt0apVK9y+fRvx8fE4fPgw7t+/jy5duuRrgS8iIiIiotLAAJjeGQUdkmxiYgIgexugN9WrVw++vr7o27dvjmXi4+MLWcvSZ2lpiZ07d2L9+vUwNDTMM3/16tUxZMgQAMDJkydzzZvb8GcAuH//Pj755BPUqVMHO3bsQIMGDWBkZAQPDw9s2rQJmZmZmDhxIuRyeQFbRURERERU/CptAPzgwQOsWLECPj4+sLGxgUwmy3Nv12PHjsHNzQ1GRkaoWrUqvLy8cPny5RzzZ2RkYM6cObC3t4eenh7q1KmDL7/8EomJicXdnEpBU1Mzzzxv3sMGDRoAAB4+fKiUp02bNti0aROWLFmiUt7e3h5A9u+HEKIItS1eQghcvXpV7bGgoCD4+PgU6HyKXu+oqKgc84SGhuLWrVuoXbs2OnbsqDbPrl27kJGRgS5duqg8P506dYKenh4ePnyodr9iIiIiIqLSVmkD4M2bN2P8+PHYtWsXnj59mmf+gIAAeHh44Pz583Bzc4OzszMOHz6M9u3b48KFCyr5s7Ky0Lt3b/j5+SE5ORne3t7Q1tbGsmXL8P777yM1NbUkmlWhValSBQBy7E2Uy+VSHgCwtbWFubk54uPj8c8//+TrGnZ2djA3N0dSUhLCwsKKXuliEBcXh4EDB6Jly5Y4f/68yvG3h0Lnx6tXrwBA6fN6m6L3d9CgQTn2visCW3U9zzKZTEp//vx5getIRERERFTcKm0A3KBBA0yZMgX79+9X2jpGnfj4eIwePRoAcPDgQfz55584efIkfvrpJ6SlpWHEiBEq28ps27YNBw8eROPGjXHjxg38+uuvuHHjBjp27IhLly5h2bJlJda2iqp27doA1PdaRkZGAgBsbGykNJlMhsGDBwMA1q9fn69ryGQyDBo0CACwatWqItW3OISGhqJFixbYtWsX5HI5Bg4ciJiYmCKf98SJEwCQ41ZP6enp0vxgxXBpdUxNTQFA7RcMUVFReP36NYDs+chERERERGWt0gbAgwYNwqJFi/DBBx+o7G36tp07dyI2Nhbdu3dHt27dpPRRo0bB3t4et27dwqlTp5TKrF69GgDg5+cnzSvV0dHB/PnzlY5T/nXq1An6+voIDAzEuXPnpHQhhPS5Kva+VZg2bRrMzMywatUqbNu2TelYdHS02ut88803MDMzw8aNG7FmzRqlYykpKZgzZw6ePHlSHE3KUVZWFpYsWYL27dvjwYMHUrqtrW2eKzcr/PLLLypDj+VyOebMmYPg4GDo6+vnuEfvgQMHEB0djRYtWsDBwSHHa3h5eQHInh7w5mJXGRkZ+PLLLwEATk5O+dpuiYiIiIioxAkSQggBQGhqaqo91q9fPwFA/PDDDyrHxo0bJwCIKVOmSGlxcXFCQ0NDyGQyERsbq5Q/KytLmJqaCgDixo0bha6vg4ODcHBwKHT5d9WKFSsEAKGlpSW6dOkiBg4cKBo3biwACDs7O/H69WuVMkFBQcLIyEgAEI6OjmLAgAGiW7duQk9PTwAQ3t7euZZp2LChGDBggPDy8pLu3YMHD4rcltGjR4s2bdqINm3aCCsrKwFAWFtbC2dnZ2FiYiIASC8NDQ0xZcoU0bp1a6mMjo6OACBatGghpV2+fFk6v5ubm5DJZKJZs2aif//+wtvbW9SpU0cAEDo6OuK3337LsW4ffPCBACCWLl2aZztGjhwpAAiZTCbatWsn+vbtK+rWrSsAiCpVqoiQkJAif1ZERERE9O4oz7EKA+D/l1sA7ODgIACI48ePqxxbt26dACB69uwppYWGhgoAwsrKSu35XFxcBADx+++/F7q+5fmXqqQdOnRIdO7cWRgbGwttbW1Rv359MWnSJBEdHZ1jmcePH4uxY8eK+vXrC11dXWFiYiI6duwoNmzYIDIzM/NVpnr16qJ9+/ZixYoVIi0trcjtcHNzUwpyc3pZW1uLwMBA8eDBgzzznj59Wjr/li1bhJeXl6hbt64wMjISWlpawsrKSnz88cfi6tWrOdbrxYsXQktLS2hqaorIyMh8tWXz5s3C1dVVGBsbCy0tLWFtbS18fX3FnTt3ivoxEREREdE7pjzHKrkve0wAIC2SZWFhgadPn2Lo0KGwtbXFmjVrULNmTQBQGhL7Zn4gewGtZcuW4euvv0bfvn3VlqH869Gjh8pQ57xYW1tj5cqVJV6mIM6cOSP9nJiShunzvsPKxd8iKyNNSvf29sYvv/wiDdMXBViZesiQIbnO381JjRo1kJGRUaAyQ4cOxdChQwt8LSIiIiKi0sQAOB+SkpIAAPr6+ti5cydOnTqFU6dOYdSoUTAwMFDK83Z+AJg3bx6uX78Of39/9O3bV22ZnDg6OqpNj4iIgK2tbeEbReWCPEtgybHbWH/6NjJlzrAauxUJYQeRHLILSxZ/hy+++KLA+x8TEREREZF6DIALqGvXrrCwsED9+vXh4OCgtBhTTnx8fLBo0SL4+vqWQg2pLKRmyJGWmZV3xrf8cOIufjn3AJBlP4oaugYwadcfH/v6YuhAV8SnZhbofLpaGtDTznu/ZCIiIiKiyogBcD4YGhoiLi4OKSkpaN68udI2PMnJyVKeN/MD2SsGA8CMGTMwY8aMXMvk5Pr162rTc+oZprKx+kwEfjh5t9jOd+BOMg7MPlbgcuM722Ni1wbFVg8iIiIiooqk0m6DVBBWVlYAgGfPnqkce/78OYDs+aL5yZ9TGSIiIiIiIipZDIDzQbEP6o0bN1SOXbt2TSkPADRs2BAaGhqIjIxEXFycUn4hhHSe3PZXJSIiIiIiouLFIdD50KVLF/z+++84fPgwxo0bJ6ULIXD48GEA2XODFYyNjdG6dWtcvHgRR44cwYABA6Rj58+fR2xsLKysrNC4cePSawSVqNHuthjhWi/H40lJSfjqyy8RsGuXlObu7g73sYvwy7mHKvk/ca2HcZ3tC1wPXS1+p0VERERElBP+tZwPPj4+MDExwdGjR3H8+HEpfd26dbh79y4aNGiA999/X6nM559/DgCYM2cO4uPjAQDp6en45ptvAACjR48updpTadDT1oSJvrba1/1b1+Du0ho7t/wCkZYEkZYEUwMd/G/UJ5ju5YAx7rYw1MleuMpQRxNj3G3xjWfjHM+X24sLYBERERER5UwmCrKxaAUSFhaGMWPGSO9DQkIAAG3atJHS/vjjD9SqVQsAsH37dgwZMgSamprw8PBASkoKTp06BW1tbZw8eRKurq5K58/KyoKXlxeOHDkCS0tLuLq6IiwsDPfu3YOzszOCg4OlbZIKQ7EIVk6LZFHZE0Jg5cqV+Oqrr5Ceni6lt2/fHjt27EDt2rWltNQMOV7Ep8HcWJdBLBERERG908pzrFJpA+AzZ86gU6dOueZ58OAB6tatK70/cuQI5s+fjytXrkBDQwMuLi6YO3cu3nvvPbXl09PTMX/+fGzduhVPnjxBzZo10a9fP8yePRtGRkZFqn95/qUi4PXr1xgxYgT2798vpclkMnzzzTfw9/eHlhZnHxARERFRxVSeY5VKOwTa3d0dQohcX28GvwDg4eGBwMBAJCQkIC4uDocPH1YJfv39/SGTyTBs2DDo6OjA398fERERSEtLw6NHj7BixQoYGxurrdPz588xceJENGjQAPr6+jAxMYGbmxs2btwIuVyutkxUVBTGjx8Pe3t76Ovrw9zcHK6urli5cqVSr2NhPXz4EMOHD4eVlRV0dHRgbW2NkSNHIjIyMscyly5dQu/evVGjRg1oa2ujVq1a8PHxkRYMy6mMp6cnqlatCmNjY7i7u+PEiRNq8+7cuRMfffQRHBwcYGpqCl1dXTRo0ABffvklXr9+XeQ2F1VQUBCcnJyUgl8LCwscP34c8+bNY/BLRERERFRGKm0PcEnx9/fH7Nmz4evri02bNqkc19LSglwux9sfe2hoKDw8PBATEwN7e3u0aNECSUlJCAoKQnx8PAICApQW03J0dERKSgpiYmIQGxuLhg0bonnz5khMTERQUBASEhJUerAL6t69e2jXrh1evXqFhg0bwtHRETdu3MCtW7dgYWGB0NBQ2NjYKJU5fvw4PD09kZmZiTZt2qBOnTq4desW/v77b+jr6+PUqVNo27atUpmgoCB06dIFGRkZ6NSpE/T19XH06FFkZWVh165d6Nevn1L+Ll264PTp02jatClsbGygq6uLK1eu4P79+7CxscH58+fLbIupjIwMNG7cGBEREVJa9+7dsWXLFpibm5dJnYiIiIiISlN57gGGoGLl5+cnAAhfX1+1xzU1NcXbH/vr169FjRo1BACxcuVKpWOJiYli0qRJIiAgQCm9YcOG0rl++uknlTJ+fn7i8ePHRWqLl5eXACDGjBkjsrKyhBBCZGVliYkTJwoAon///iplHBwcBACxdu1apfRZs2YJAMLV1VUpPTMzU9jb2wsAYt26dVL64cOHBQBhZmYmEhISlMoEBgaKly9fqpxn7NixAoAYPHhwkdpdVOfPnxeamppCS0tLfPfdd0Iul5dpfYiIiIiISpODg4NwcHAo62qoxR7gYlaYHuCvv/4a3333HYYPH45ffvlF7XlTUlKUFs2qXr06Xr9+nWuZokhISJCGF0dGRsLU1FQ6lpqaijp16uD169d48eIFzMzMAACxsbGoWrUqqlSpgvj4eMhkMqlMTEwMzMzMoKenh5SUFCn96NGj8PDwQOPGjVX2We7atStOnDiB9evXY+TIkXnWOSoqCpaWlqhRowZevHhRxE+gaNatW4fmzZsrLapGRERERFQZlOce4Eo7B7g82bJlCwAorUr9trdXjI6LiwMAjB07tkTqdOfOHWRlZcHW1lYp+AUAPT09ODs7Qy6XIzAwUErX0dGBTCaTXm9SvK9evbpSumKer4eHh0odPD09AUBp66ncaGhk/zpnZWXlK39RpaenY/r06Xjw4IHKsc8++4zBLxERERFROcMAuIxFRETg2bNnMDQ0RMuWLfNdJjMzEzKZDM7OziVSr9TUVABQCX4VFL2+t2/fltIMDAzg4uKChIQEbN68WSn/ihUrAADDhw9XSlf0+jo4OKhco0mTJkp5cpOZmQk/Pz8AgJubW575i+r+/ftwdXXF/PnzMXDgQGRkZJT4NYmIiIiIqGi4HG0Zu3PnDgCgXr16Kr2meZVR9LiWBEVPbVRUlNrjjx8/Vnt8zZo16NatG4YNG4a1a9eidu3auH37Nm7evIkJEyZg1qxZSvmfPn0KIHuV5MTERAwePBja2trYsmULatasCQB48uSJ2jrs2LEDx44dQ1xcHC5fvozHjx+jYcOGWLZsWeEbng+7du3CZ599hvj4eADZe0j/8MMP+Oqrr0r0ukREREREVDQMgMtYbGwsAOS4NVJuZRRDfkuCvb09zMzMEBERgWvXrkm9sUB2D/SFCxcAAMnJyUrlmjRpguDgYPTp0wcXLlyQ8jk5OcHNzU1lC6CkpCQA2UO8jxw5Im0dNHDgQDRv3lwpz9tCQ0OVeprbtWuHPXv2wMLCoihNz1FycjImTJiA9evXK6V/9tlnuQ5fJyIiIiKi8oFDoMuJt3tyAwIClObT2tnZlWp9NDQ0pKDOx8cHFy5cQGJiIi5evIhevXpJQ6Pfrvfx48fRokULVK1aFVevXkViYiLOnTuHzMxM9O7dGz/++GOO13RxcYGtrS0aN26MDh065FnH77//HkIIvHr1Cnv37sWLFy/QtGlTXLp0qfANz8G1a9fQqlUrpeDX2NgYAQEBWLt2LQwMDIr9mkREREREVLwYABezgg5JNjExAZC96vKb6tWrB19fX/Tt2zfHMiW92NPMmTPh7e2N69evw8XFBUZGRmjXrh0SEhIwc+ZMAMo913FxcRgwYAA0NDTwxx9/oFmzZjA0NISLiwt2794NTU1NTJ8+XerBBgBDQ0MA2atcW1pa4t69e7hx4wbMzc2l3mVFnpxUq1YNvXr1wrFjx5CSklKsc3KFEFi/fj1atWqlNBe5devWuHLlitLezEREREREVL4xAC5mmpqaeeZ5cxhwgwYNAAAPHz5UytOmTRts2rQJS5YsUSlvb28PIHsV4pLcxUpHRwf79u3DsWPHMHnyZIwaNQrLly9HeHi41M769etL+Q8dOoSYmBi0bdtWZfGsBg0awNbWFgkJCQgLC5PSraysAADPnj1Tuf7z588BANbW1vmqb/369eHi4oKIiAiEhIQUqK3qxMXFwcfHB5999pm0KBgATJ48GUFBQUptJyIiIiKi8o9zgItZlSpVAAByuVztcblcrhQc2trawtzcHC9evMA///yDpk2b5nkNOzs7aGpqQi6XIywsLN+rRxdW165d0bVrV6W0c+fOAYDStf/9918AOffYGhkZAfgvsAWyV38+dOiQ2pWer127JuXJL0WPdE4LZxXEo0ePsG/fPul9jRo1sGXLFrVbNhERERERUfnHHuBiVrt2bQDqV0+OjIwEANjY2EhpMpkMgwcPBgCVxZVyIpPJpGHQq1atKlJ9CyMqKgr79u1D7dq10apVKyldEdhfu3ZNpWc6NTUV9+7dAwDUqlVLSu/SpQsA4MiRIyrXOXToEACoBN85yczMxOXLlwGgWBbCatq0Kb7//nsAwPvvv4/w8HAGv0RERERE7zAGwMWsU6dO0NfXR2BgoNRLCmTPJZ0/fz4AoEePHkplpk2bBjMzM6xatQrbtm1TOhYdHa32OtWrV4empiY2btyINWvWKB1LSUnBnDlzitwLGh8fjwcPHiilvXjxAv369UNycjL8/PyU5jx3794dmpqauH37trTvL5Dd9pkzZyIuLg41a9ZEu3btpGNdunSBra0tbt68iZ9//llKP3LkCE6cOIGqVasqzbONiIjA0qVLleYRA0BaWhomTZqEhw8fwtLSEi4uLkVqu8KoUaPw66+/4tixY7C0tCyWcxIRERERUdmQiZKcRFpJ/fjjjxg3bhy0tLTg7u6OGjVqIDw8HDdv3oSdnR1CQkJgZmamVCY4OBienp5ISEiAo6MjmjRpgpiYGAQGBiI1NRXe3t5Kw3EdHR2RnJyM169fIyEhAQ0bNoSTk5O06nJsbCwePHiAunXrFrodt27dgqOjI1q1aoU6deogISEBZ8+eRXJyMkaOHKm2x/rbb7/FjBkzAAAtWrSAra0trl27hlu3bkFbWxu///47vL29lcqcOXMG3bp1Q2ZmJt5//33o6enh6NGjkMvl2Llzp1IAHB4ejhYtWkBXVxctW7ZEnTp1EBcXhytXriAqKgr6+vrYv3+/1LOcXydPnsR3332HvXv3Ql9fvxCfFhERERERAdmxCgBcv369jGuihqAScejQIdG5c2dhbGwstLW1Rf369cWkSZNEdHR0jmUeP34sxo4dK+rXry90dXWFiYmJ6Nixo9iwYYPIzMxUyuvg4CAcHBxUylSvXl3Y2NgIAOLjjz9Wex1NTU2R061/9uyZmDBhgrC3txe6urpCW1tb6OnpCW1tbWFiYiLc3NzEjh07lMpERkaKcePGCTs7O6GnpyeMjY2FqampMDAwEFpaWsLc3Fz069dP/PXXXzm2PSQkRHTv3l0YGxuLKlWqiA4dOoijR48KIYSIi4sTkydPFnZ2dkJbW1vo6OgIU1NTUb16dWFoaCh0dHREvXr1xKeffiru3Lkj3NzcBIAcXxkZGdJ1MzIyxDfffCNkMpkAIGxsbISpqakwMjISbm5u4vjx42rru2PHDtG/f3/RuHFjYWJiInR0dIS9vb2YNGmSePXqVY7tJCIiIiKq6BSxSnnEHuB3VG7fqvj7+2P27Nnw9fXFpk2bVI5raWlBLperzNMNDQ2Fh4cHYmJiUK++HZydWyA1JRlBQUGIj49HQECAyrY/oaGh6N69O2JjY9GwYUM0b94ciYmJCAoKQkJCQpF7oRMTE9G2bVtcv34d1tbWaNOmDRISEnDmzBmkp6djwoQJWL58uVIZd3d3nD17Fn379pUWJXvTL7/8Ag0NDTx69AgDBw7E+fPnlY63a9cOZmZmOHr0KLKysrBr1y7069dPKU+XLl1w+vRpNG3aFDY2NtDV1cWVK1dw//592NjY4Pz58/levZqIiIiIqCIpzz3AXAWaAGTPNe7ZsydiYmJg4TEGWc09cVNHE0Nd6mLHTmvM9vfLsUxsbCx++uknjB49WjqWlJSExYsXK235VBirV6/G9evX8f777+PgwYPQ09MDkL3QVps2bfDDDz9g7NixsLOzUym7ZMmSHIPvP/74AyNGjFCZSzxz5kzMmTMHQPY85B49emDUqFHw8PBQCqb9/PwQEBCA6tWrS2lyuRzjx4/HqlWrMHXqVJX53EREREREVLa4CNY7TAggLiVD5ZWakb0FU7o8S+1xhTfT5nw7Hy9fvoRh0y7Qbe4JAEhKl2P1mQj8cPYRZs1bCLeuPdSWGTzEF4OGjVQ6lqmhg4lfT4dRtZpq65ZfioXERo8eLQW/ANCkSRN06dIFQghp5ef8mDFjBmQyGfr06aMS/DZs2BDz58+XFvby8PBAly5dEB0djYCAADx//hwTJ05EgwYN0K1bN9ja2sLNzQ0bN26EXC6HpqYmpk+fDgA4duwYgOwVs8ePHw97e3vo6+vD3Nwcrq6uWLlyJdLT0wv0Wajzxx9/YPLkyXBxcYGenh5kMhnmzZuXa5mEhARMmjQJtWvXhp6eHuzt7TFv3jxkZGSo5L19+zbGjBmDtm3bolatWtDR0YG5uTk+/PBDBAcHqz2/v78/ZDJZjq8TJ06olHnw4AFWrFgBHx8f2NjYQCaTFfnLEyIiIiKit/EvzHdYdHI6ms8+ppIeGxwBANgfHonzao7Ls7KHPr9Z9snaXwAARi28VPJvCH6ADcEPVNIVZU5qtFBbD3XGd7bHxK4N8pUXAHR1dQFAabVpBUXam72wubl165bKwl2Ghobo2LEjDh8+DE9PT6XVqwHA09MTJ06cwK5duzBlyhTExMTA3t4e3t7eSEpKQlBQEEaMGAEDAwMMGDAAGhrZ3yllZWWpDA/39vaWhoefO3cOPXv2LNLwcADo06eP9LO6z+htKSkpcHd3R1hYGGxtbeHt7Y3g4GDMnDkTly5dwt69e5XOc+nSJaxevRrW1tZwcHBAjRo18PTpU+zfvx8HDhzAhg0bMGzYMLXXat++vdqeeXWraW/evBmzZ8/OdzuIiIiIiAqDATAhIyYK8qQYyLT1oGOhGrDkWaambYnVrWvXrvj111+xbt06fPDBB9DR0QEA3Lx5EydOnICtrS06dOigtuzGjRvx8uVLaGhoICkpCbt27UJKSop0vEWLFggICMDEiRMBAA4ODirnaNKkCQDg9OnTkMvlWLlyJcaOHSsdT0pKwqxZswBk70Ps55c9VLxdu3YlPjwcAAYOHIj27dujffv22LNnD+bOnZtr/sWLFyMsLAzu7u44evQodHR0EBcXhzZt2mD//v3YuXMnBg0aJOVv164drl69imbNmimdJygoCJ07d8YXX3yBDz/8UNoD+k0jR47MMTh+W4MGDTBlyhS4urrCxcUl319qEBEREREVBANgQmb0UwCAlknNfPe+FaZMYQwbNgyHDx/Gnj17YG9vj9atWyMxMRGnT59GixYtsG3bNikofptiLq86jRs3xoULF6Crq4unT7PbYmFhIS0M1q9fP2zZsgU1a9YEkD2/d/jw4UrBLwDs27cPr1+/xvbt2zF58mQ8fvwYDRs2hJWVFV6+fInhw4crBb9Adq+zv79/YT8SJTt27JB+3rt3b575FXtGL1y4UPrcTExMMHPmTHz88cf46aeflAJgW1v1X2506NABnTp1wrFjx3D+/Hl4enoWoRXAoEGDlK5LRERERFQSGAC/w8wMdHDBr5tK+oJ5F7HoHODtZInVao5XWyKDXA5c/f9jv/8ajZG/AxbVq6q9zieu9TCus71SmqKMs50ljqq5Rk50tQo27VxLS0safrx8+XI8evQIAGBkZISuXbvCwsJCpcz777+PCRMm4L333oOZmRnu3buH5cuXK62I3bp1a2l4dVJSEgAo7f+7e/duDBw4EM2bN5fSxowZo3Kt0NBQbN68WXrfrl077NmzBy1atAAAlYC5LF2/fh1RUVEwMzNDmzZtlI716NEDAHDx4kUkJCTAyMgoz/O9OdybiIiIiOhdwAD4HSaTASb62irpetqaAAAdTQ21xxUUxwx0sn8NbKoZope7LTaff4ikdDky7wTh6R+LMGsRMAvZvYH37t1TKqOpIcv1GkUVHR0Nb29v3Lx5E7t27ZLm1H733XeYO3cujh8/jsDAQGhr/1eHmTNnIi0tTVo0q1mzZti4cSPS09OVekxz07hxY3To0AH//POPlNayZUuVfN9//z2+//57vH79GsHBwfjyyy/h4OCAmJgYGBoawtnZuYifQPG5ceMGgOy2vc3MzAyWlpaIjIzEnTt31Lb1TSdPnsSpU6egr6+PVq1aqc1z5swZXL16FSkpKahTpw569eqldpg5EREREVFpYQBcARV0SLKJiQkAIDEhAVM8GmFcZ3u8iE/Dw1tV8YvxMyQmJmL37t1qy8THxxdPpXMwefJknDt3Dhs3bsRHH30kXXvVqlX4559/EBQUhK1bt2LEiBEAgNevX2P48OEwMDDAzp07lT6LKVOmSAGwXP7fatSGhoYAsheIUuRXBIu3b98GkN3bmdvnWq1aNfTq1QtNmzaV9j2rW7duuVrQ6c2h3kB28B4QEIAlS5bA1dUVNWvWRGRkJJ48eaISAD969AizZs1CRkYGIiIiEBISAm1tbaxdu1YaJv62N3vGAWD69OkYN24cli1bJvUeExERERGVJv4VWgFpamrmmefNBZgaNMhelfnhw4cAsnuQa1czQMf2Lti0aROWLFmiUt7ePntI9IMHD6R5s8VNCCEFrIohum9SpJ05cwYAEBgYiObNm+PAgQPYtWsXfvnlF6X8b65InJaWJv1sZWUFAHj27JnKNZ48eQIASlsw5aZ+/frSZ1Oegl9Adaj3jBkzEBISgkWLFgEADAwMlPK9KTo6Gps3b8aOHTsQEhICIyMj7NmzB0OGDFHJa29vj9WrV+Pu3btITk7G3bt3sWDBAujp6eGHH37AggULSqqJRERERES5YgBcAVWpUgWAci/nm+RyuZQHyB7abG5ujvj4eKUhv7mxs7ODubk5kpKSEBYWVvRKvyE1Q47lx+9gzq/nkZqaCuC/Xto3KeapPnv2DHPmzEGnTp2kXk4AOHz4sFJw/urVK+nnN78AUAzLVfT6vunx48cAlOcHA0BAQIDS3rZvBteKvMWxz29JGjRoEIyNjTF48OA88zo5OUEIgdTUVISHh6NHjx744IMP1C7mNXjwYHz++eews7ODvr4+7OzsMHXqVKxatQpA9gJc6oJsIiIiIqKSxgC4AqpduzYAICoqSuVYZGQkAMDGxkZKk8lkUhD09j65OZHJZNKqvYrAprikZWbhh5N3sSHkGSDL/hVVF5gr0v7++2/4+flJizFpaWlhyZIl+PXXX5V6YY8fPy79/ObK0V26dAEAHDlyROUa165dA6DaA1yvXj34+vqib9++SumZmZn4999/AZS/APjNod4AsG7dOsTFxcHHxwcAkJycrJRPHV1dXTRv3hy7du1Cjx49MHv2bJw+fTpf1x82bBiqVq2KxMREhIaGFqUpRERERESFwgC4AurUqRP09fURGBiIc+fOSelCCMyfPx+A6pDiadOmwczMDKtWrcK2bduUjkVHR6u9zjfffAMzMzNs3LhR2l5HISUlBXPmzJGGEBeGho4e9Gpn78M7depUpfnGly5dklZ1fv78uZRuaWmJpUuXYtKkSUrzTENCQjB9+nS11+nSpQtsbW1x8+ZNZGVlSb3DR44cQXh4OADluc4REREIDg7G999/rzQ8PC0tDZMmTZLq8+LFixIbHl4YuQ31Bv77HK2trfN1vo8//hgAsH379nzll8lk0rZK6r6cISIiIiIqaQyAK6CqVati0aJFyMjIgLu7O7p27YpBgwbB0dERq1atgp2dHb7++mulMjVq1MC+fftgaGiIIUOGoEmTJvDx8UH37t3Rvn17AJAWd3q7jJGREUaPHo1GjRrBx8cHPXv2hKWlJfz8/JCZmVm0tnT+DKZVqyIwMBANGjRA79690alTJ7Rt21alh3XAgAGYPn06xo8fD0tLS/Ts2RMDBw5E69at4eLighcvXkjbGr05PFxTUxM///wztLW1pYC1Z8+e+OCDDyCTyWBsbIyEhASpxzkhIQFfffUVLCws0K9fPwDZAV29evXw448/Ql9fH6ampkhOTi724eFFoRjqffPmTZVjMTExiIyMhKampjQnPC/GxsYAUKAvORTD0N8cgk9EREREVFoYAFdQ//vf/3Do0CG4ubkhNDQUv//+u9RDGRoaCjMzM5Uyrq6uuHHjBsaOHYuUlBTs3bsXISEhaN26NTZs2IA9e/bkWiYjI0Mq4+joiBUrVsDS0rJI7dCpUReBF0Lx2WefQU9PDwcPHkRgYKDS3rP6+vpYv349du7cCXd3d3zyySeoVq0aQkJCsHv3bty5cweurq7YtGkTZs6cCUC1B9Ld3R1//PEHgOx9bc+ePYt27drhyJEj+OSTTwD8Nzy8Xr16WLBgATp16iT1piYnJ0NPTw+ffvoprl69imHDhgEo/uHhReHg4IBatWohOjoaISEhSscOHToEAGjbtm2+9gAGsvcMBqB2L2Z17ty5I+3jXJ62hyIiIiKiSkTQO8nBwUE4ODiUdTVKRGxyuqjz9Z/SKzY5Xen4xIkTBQABQDg6Oopr167l+9zR0dFCX19faGtri+DgYCk9KytLjB07VgAQU6ZMUSrz4sULYWZmJjQ0NMTWrVuVjl2+fFkAELa2tmrLABCrV69WOpacnCxmz54tHj9+nO9654efn58AIObOnZtjHn9/fwFAdOrUSaSnZ3+usbGxomHDhgKA2LZtm1L+efPmib///lvlPCdOnBD6+voCgPjzzz+l9BcvXoiff/5ZJCcnK+V/8uSJaNeunQAgvL2982wLAKGpqZlnPiIiIiIqf8pzrCITohxNUqR8UwxHvn79ehnXpHjJswS+PXgTv5x7IKWNaF8P070aQ1Mje0GrtLQ0tG/fHu+99x6WL1+uskJzXn788UeMGzcOWlpacHd3R40aNRAeHo6bN2/Czs4OISEhKj3kwcHB8PT0REJCAhwdHdGkSRPExMQgMDAQqamp8Pb2xr59+3Is07BhQzg5OSExMRHnzp1DbGwsHjx4gLp16xbug/p/Y8aMkYZZP3nyBE+fPoW1tbU039fLy0vq9Qaye6rbt2+P8PBw2NnZwdnZGcHBwYiMjISXlxcOHDigtHCYk5MTrl69Cnt7ezRp0gRaWlq4e/euNDf6008/xbp166T8Dx8+RL169VClShW0atUKFhYWiIyMxOXLl5GYmIhGjRrh7NmzMDc3V2pHWFgYxowZI71X9FC3adNGSvvjjz9Qq1atIn1eRERERFTyynOswgD4HVWef6kKKjVDjrTM7CHNP5y4qxT8KnziWg/jOttDV0sDetqaSE5OlvatLYzDhw9j6dKluHTpElJSUmBjY4MPP/wQM2bMQNWqVdWWefLkCRYuXIjDhw/j6dOn0NPTQ/PmzeHr6wtfX1+1+y+/XcbIyAgNGzbEgAEDMGrUKKXVqAvD3d0dZ8+ezfG4r6+vtFiYQnx8PGbNmoXdu3fjxYsXsLGxwdChQzFt2jRoa2sr5d2zZw/279+P0NBQPH/+HPHx8TAxMUHLli3x6aefSnOgFRISEjB79mwEBQXh0aNHiImJgYaGBuzt7dGvXz9MmDBB7RDrM2fOoFOnTrm2tTi+MCAiIiKikleeYxUGwO+o8vxLVVDLj9/BDyfv5ivv+M72mNg1f4s0VTapGXK8iE+DubEu9LRVg3EiIiIiotJQnmMVLoJVAfn7+0Mmk0kLMb1NS0tLaZjrm54/f46JEyeiQYMG0NfXh4mJCdzc3LBx40allZPfFBUVhfHjx8Pe3h76+vowNzeHq6srVq5cWSx74WbGPcerg9/jyaqhmOzZBNbW1hg5cqS0p7E6CQkJmDRpEmrXrg09PT3Y29tj3rx5yMjIyLHMpUuX4OnpiapVq8LY2Bju7u44ceKESr4zZ85AJpPl+dqyZUuR254f2cPGb6Cp/1F0XHwaTf2P4tuDNyDP4ndbRERERERv0irrClD5ERoaCg8PD8TExMDe3h7e3t5ISkpCUFAQRowYAQMDAwwYMEClTPfu3REbG4uGDRvC29sbiYmJCAoKwrlz59CzZ88iDVvNiInEs61fISslHlpm1mjs9B4yXj3Chg0bcPDgQYSGhsLGxkapTEpKCtzd3REWFgZbW1t4e3sjODgYM2fOxKVLl7B3716VLwCCgoLQpUsXZGRkSPsoHz16FN27d8euXbuUhvpaWFjA19dXbX2FENi2bRuysrLg6uqaY7veHPZdVG8PG8+QC6wPeoAsAYzrbF/k8yuGnRMRERERvesYABMAIDo6Gj179kRMTAxWrlyJsWPHSseSkpIwa9asHMvExsbip59+wujRo5XKLF68GFpaef+KjXa3xXCXOlixYgV+OHkXRu/1ko7FnFyPrJR4tPb0wdHfNkNPWxO6Whr48ssvsXz5cnz55Zf49ddflc63ePFihIWFwd3dHUePHoWOjg7i4uLQpk0b7N+/Hzt37sSgQYOk/HK5HJ988gnS09Oxbt06fPrppwCAI0eOoEePHhg1ahQ8PDykvWsbNWqkMq9W4ezZs9iyZQtcXFxQv379HNu8+kxEvod9F9aG4AfYEKw6n7qgOOyciIiIiCoKDoEmAMCiRYvw8uVLDB8+XCn4BQBDQ0MsXboU3t7eOZZ5M/hVlPH394e1tXWe146LfoUBfbzh980URJ/agLjQ/9+PNy0ZKfcvQ1tHD4e2/gRTAx3oaWtCJpNh/vz5MDc3x549exAdHa10vjVr1gAAFi5cKC0yZWJiIq2G/NNPPynlP3HiBO7evYvGjRtLwS8AeHh4oEuXLoiOjkZAQECe7QCAzZs3AwA+/vjjfOUnIiIiIqLSwwCYAECar/rmVjRve3u7IUWZtwPmgjhx4gSaN2+OY8eOZSeILBj9GwQge/gzRBbs7W1RzUx5ZWY9PT04OztDLpcjMDBQSr9+/TqioqJgZmamtIUOAPTo0QMAcPHiRSQkJCjVAcgOeN/m6ekJADh+/HiebUlOTsbvv/8ObW1tfPTRR3nmJyIiIiKi0sUh0ISIiAg8e/YMhoaGaNmyZYHLODs7F+q6p06dQrdu3fDmQuS9e/fG8pVr4LbiEkRm9gJaJiamassr9uq9ffu2lHbjxg0AQOPGjdXmt7S0RGRkJO7cuSO1VVHGwcFBpUyTJk2U8uRmz549SEhIQK9evVCtWrVc8452t8UI13p5njM/8to6qqh0tfg9GRERERFVDAyACXfu3AEA1KtXL8fVoYujzNvc3NzQsWNHnD17Frq6uli2bBlGjx6N+NRMAICmvjEA4NmzZ2rLP378GED2KtQKT58+BZC9UBUAfP/99wgICMCSJUvg6uqKmjVrIjIyEk+ePJEC4DfLJCYmYvDgwdDW1saWLVtQs2ZNANn7+ealIMOf9bQ1i21hqelejaGlKcPGcw+QIRfQ1pRhePt6+NqjETQ1CndviIiIiIgqIgbAhNjYWACAsbFxiZZ5m6amJrZv3w4fHx/8+OOPcHJyUjquZWYJDT0jPLgfgWvXrkm9sUB2D/SFCxcAZA89VkhKSgLw33DtGTNmICkpCYsWLYKrqysMDAyU8r1d5siRI9i/fz8AYODAgWjevLlKfnWePHmCU6dOwcTEBB988EGBP4ui0NSQ4RvPxpjUtQH3ASYiIiIiygXHNpLk7Z7cgIAApX1t7ezsCn3u1NRU/PnnnyrpVlZWCAoKUgl+s+ujAaMW2XNwfXx8cOHCBSQmJuLixYvo1asXTE1N1db7TYMGDYKxsTEGDx6cr3q6uLjA1tYWjRs3RocOHfJVBoC09VH//v2hq6ub73LFSU9bE7WrGTD4JSIiIiLKAXuAK6CCDkk2MTEBAKWFoYDs4c2+vr5ITEzE7t271ZaJj4/P8/y3bt3CgAED8M8//+DkyZPo1KlTrvl1tTQw/v/nrmZ2nIHA1cn4888DcHFxkfLUrl0bM2fOxPjx45V6oQ0NDQFk7wUMAOvWrcO6deuk44reYkW+t8tYWlri3r170jHF8Os386vD1Z+JiIiIiMo/BsAVkKZm3j2Ab+7P26BB9h6vDx8+VMrTpk0btGnTBg8fPlQJgO3tswPUBw8eQAihNugWQmDz5s0YO3asFHgOHjwYV69eRY0aNXKsm562ptK+s5M99+P48eM4fvw44uPj0ahRI/j6+mLHjh0AoLTfrpWVFYCc5w0/f/4cAJS2Z7KyssLVq1fVllGX/22hoaG4desWateujY4dO+aYj4iIiIiIyhYD4AqoSpUqAAC5XK72uFwul4YPA4CtrS3Mzc3x4sUL/PPPP2jatGme17Czs5PKhIWFqawenZCQgNGjR2P79u1K6QMGDMh13nBqhhyrz0RI70e720JPWxNdu3ZF165dlfKeO3cOAJSurVjJ+ebNmyrnjomJQWRkJDQ1NaWgX1Hm0KFDald6vnbtmtJ51VH0/g4aNKjQC4IREREREVHJ4xzgCqh27doAlFdHVoiMjAQA2NjYSGkymUyaI7t+/fp8XUMmk2HQoEEAgFWrVikdu3z5MpydnZWC32rVqmH//v1Yvnx5rnNk0zKz8MPJu9IrLTNLbb6oqCjs27cPtWvXRqtWraR0BwcH1KpVC9HR0QgJCVEqc+jQIQBA27ZtYWRkJKV36dIFAHDkyBGV6yjKvB18K6SnpyMgIAAAMGTIkBzbRUREREREZY8BcAXUqVMn6OvrIzAwUOolBbKHJM+fPx8A0KNHD6Uy06ZNg5mZGVatWoVt27YpHYuOjlZ7nW+++QZmZmbYuHEj1qxZAyEEvv/+e7Rr105pHm3Hjh0RHh5eqNWR4+Pj8eCB8h63L168QL9+/ZCcnAw/Pz+lXleZTIZRo0ZJbcrIyAAAxMXFYe7cuQCA0aNHK52vS5cusLW1xc2bN/Hzzz9L6UeOHMGJEydQtWpVDBgwQG39Dhw4gOjoaLRo0SLXXmIiIiIiIip7MiGEKOtKUME5OjoCAK5fv672+I8//ohx48ZBS0sL7u7uqFGjBsLDw3Hz5k3Y2dkhJCQEZmZmSmWCg4Ph6emJhIQEODo6okmTJoiJiUFgYCBSU1Ph7e2Nffv25VjG0NBQZbug8ePHY+nSpfmalwwAcSkZaD77mPT+N5/aaNvSCa1atUKdOnWQkJCAs2fPIjk5GSNHjlTbY52cnIz27dsjPDwcdnZ2cHZ2RnBwMCIjI+Hl5YUDBw6oDFU+c+YMunXrhszMTLz//vvQ09PD0aNHIZfLsXPnzhwDYG9vbxw4cABLly7FpEmT8tVGIiIiIqKKLK9YpSwxAH5H5eeX6vDhw1i6dCkuXbqElJQU2NjY4MMPP8SMGTNQtWpVtWWePHmChQsX4vDhw3j69Cn09PTQvHlz+Pr6wtfXV20g++TJE3Tr1k1p3q2Ojg5GjRqFJUuWQEdHJ9/tejsAPjuuFfynf42zZ88iKioKurq6cHJywqhRozBw4MAczxMfH49Zs2Zh9+7dePHiBWxsbDB06FBMmzYN2traasuEhoZi1qxZuHDhArKystCiRQvMmDED3bp1U5v/5cuXsLS0hBACjx8/Rq1atfLdTiIiIiKiiooBMBW78vZL9erVKzg5OeHp06fw8vLCpk2bUL169QKf5+0A+KpfN5joqw9YiYiIiIio/ClvscqbOAeYikX16tWxY8cOLFu2DAcOHChU8CvPEvjhxF2ltB9O3IU8i9/REBERERFR0XEbJCqwP//8EzKZDF5eXkrpHTt2LNI+uAsP38Qv55QXvPrl3APIZMDk7g2hp52/ecRERERERETqsAeY8i0tLQ0TJ07EBx98gKFDh+Lx48fFdu7UDDk2nX+o9tiG4Af48eRdtceIiIiIiIjyiwEw5cvdu3fh4uKC77//HkD21kiffvppsZ3/RXwaMuQ5D3VOSpcX27WIiIiIiKhyYgBMedqxYwecnZ0RFhYmpTVt2hTLli0rtmuYG+tCW1OW43FDHQ5/JiIiIiKiomEATDlKSkrCiBEjMHjwYCQmJkrpo0ePRkhICBwcHIrtWnramhjmUlftsU9c6+F/ne2L7VpERERERFQ5cREsUuvq1asYMGAAbt++LaWZmJjg559/Rr9+/UrkmlN7NEaWyJ7zq/CJaz1849kYmho59w4TERERERHlB3uASYkQAj/99BPatGmjFPy2bdsW4eHhJRb8AoCmhgzj3urpHdfZnsEvEREREREVCwbApCQlJQXLly9HWlqalDZ16lQEBgaibt26ZVcxIiIiIiKiImIAnE9169aFTCZT+7K2tlZb5tixY3Bzc4ORkRGqVq0KLy8vXL58uZRrXjAGBgbYtWsXtLW1YW5ujqNHj2LBggXQ1tYu66oREREREREVCecAF5Cvr69KmpmZmUpaQEAABg0aBE1NTXTv3h0pKSk4fPgwTp48idOnT6Ndu3alUd1CcXZ2xq+//oq2bdvCwsKirKtDRERERERULBgAF9CmTZvyzBMfH4/Ro0cDAA4ePIhu3boBANasWYPRo0djxIgRuH79OjQ0ym8H/IcffljWVSAiIiIiIipW5TcCe4ft3LkTsbGx6N69uxT8AsCoUaNgb2+PW7du4dSpU2VYw/JLV0sD4zvbSy9dLf6KEhERERFR8WAPcAk4ceIEAKBHjx5K6TKZDD169MDdu3dx/PhxdOnSpSyqV67paWtiYtcGZV0NIiIiIiKqgBgAF9DSpUtx9+5d6OrqwsnJCf369YORkZFSnhs3bgAAHBwcVMo3adJEKQ8RERERERGVDgbABfTVV18pvZ88eTICAgKUenOfPn0KALCwsMDTp08xdOhQ2NraYs2aNahZsyYA4MmTJ6VXaSIiIiIiIuIc4Pzq06cPjh49iqioKCQkJCAkJAQ9e/bE69ev8eGHH+LOnTtS3qSkJACAvr4+du7ciVOnTmH9+vW4cuUKDAwMlPLkxdHRUe0rIiKi+BtJRERERERUgTEAzqdly5ahW7dusLCwQJUqVdC6dWscOHAAbm5uSEpKwsKFC9WW69q1KywsLODi4qJ2SDQRERERERGVDg6BLqKJEyfi7NmzOHnypJRmaGiIuLg4pKSkoHnz5oiKipKOJScnS3ny4/r162rTHR0di1BrIiIiIiKiyoc9wEVkZ2cHAEpBrpWVFQDg2bNnKvmfP38OALC2ti6F2hEREREREZECA+AievXqFQCgSpUqUppiqLO6lZ6vXbumlIeIiIiIiIhKBwPgIjp+/DgAwNnZWUpTrAh9+PBhpbxCCCmta9eupVRDIiIiIiIiAhgA58vhw4dx8eJFlfRDhw5h6dKlAICxY8dK6T4+PjAxMcHRo0elABkA1q1bh7t376JBgwZ4//33S77iREREREREJOEiWPkQEhKC2bNno06dOnBycoKuri5u3bqFv//+G0D2Qli9e/eW8puYmGDVqlUYMmQIPD094eHhgZSUFJw6dQo6OjrYsGEDNDT43QMREREREVFpYgCcD926dcO9e/dw6dIlBAYGIiEhAcbGxujevTvGjh2LDz74QKXM4MGDUa1aNcyfPx9nzpyBhoYGunfvjrlz5+K9994rg1YQERERERFVbjIhhCjrSlDBKbZBymmbJCIiIiIiorJQnmMVjsMlIiIiIiKiSoEBMBEREREREVUKDICJiIiIiIioUmAATERERERERJUCA2AiIiIiIiKqFBgAExERERERUaXAAJiIiIiIiIgqBQbAREREREREVCkwACYiIiIiIqJKgQEwERERERERVQoMgImIiIiIiKhSYABMRERERERElYJMCCHKuhJUcEZGRsjIyICtrW1ZV4WIiIiIiEgSEREBbW1tJCQklHVVVLAH+B1laGgIbW3tApWJiIhARERECdWIior3p3zj/SnfeH/KN96f8o33p3zj/Sn/eI9UaWtrw9DQsKyroRZ7gCsRR0dHAMD169fLuCakDu9P+cb7U77x/pRvvD/lG+9P+cb7U/7xHr1b2ANMRERERERElQIDYCIiIiIiIqoUGAATERERERFRpcAAmIiIiIiIiCoFBsBERERERERUKXAVaCIiIiIiIqoU2ANMRERERERElQIDYCIiIiIiIqoUGAATERERERFRpcAAmIiIiIiIiCoFBsBERERERERUKTAAJiIiIiIiokqBATARERERERFVCgyAK4i6detCJpOpfVlbW6stc+zYMbi5ucHIyAhVq1aFl5cXLl++XMo1r1gePHiAFStWwMfHBzY2NpDJZNDS0sq1TEHvQ0ZGBubMmQN7e3vo6emhTp06+PLLL5GYmFjczalwCnJ/zpw5k+MzJZPJMGPGDLXleH8KJzk5GQEBAejbty+sra2ho6ODqlWronv37jhy5EiO5fj8lI6C3h8+P6UrKSkJ06ZNQ+fOnVGnTh3o6enB2NgYrq6u2LJlS47l+PyUjoLeHz4/Ze/WrVvQ1dWFTCbDvHnz1Obh8/MOE1Qh1KlTRwAQvr6+Kq+JEyeq5N+5c6eQyWRCS0tLeHl5iffff1/IZDKhq6srzp8/XwYtqBj8/PwEAAFAyGQyAUBoamrmmL+g90EulwsvLy8BQFhaWor+/fsLW1tbAUC0atVKpKSklGTz3nkFuT+nT58WAIStra3a52r37t0qZXh/Cm/jxo3SfWnZsqUYMGCA6NChg3Sf5s+fr1KGz0/pKej94fNTuh4/fiwACFNTU9G+fXvx0UcfCQ8PD2FoaCgAiOHDh6uU4fNTegp6f/j8lL1OnTpJfy/MnTtX5Tifn3cbA+AKQhEA50dcXJwwNTUVMplMHD16VEpfvXq1ACAaNWok5HJ5SVW1Qtu+fbuYMmWK2L9/v3j16lWuAVZh7sPmzZsFANG4cWMRGxsrhBAiLS1NdOzYUQAQ3377bck1rgIoyP1R/AHi6+ub7/Pz/hTetm3bxNixY8WDBw+U0k+cOCE0NTWFhoaG+Pvvv6V0Pj+lq6D3h89P6UpMTBSnTp0SGRkZSulRUVGibt26AoA4fvy4lM7np3QV9P7w+Slb27ZtExoaGuLDDz9UGwDz+Xn3MQCuIAoSAK9Zs0YAEB4eHkrpWVlZwt7eXuU/Yiq83AKswtyHtm3bCgAiICBAKT04OFgAENbW1sXbgAquuANg3p+S0adPHwFAzJo1S0rj81N+qLs/fH7Kj/nz5wsAYvLkyVIan5/yQ9394fNTdmJjY0XNmjXFp59+Ko0aezsA5vPz7uMc4EroxIkTAIAePXoopctkMint+PHjpV6vyqag9yE+Ph6hoaGQyWTw8PBQKuPi4gJTU1M8efIEN2/eLOGakzq8PyXH3t4eAPDs2TMpjc9P+aHu/hQU70/J0dDI/lMvKytLSuPzU36ouz8FxftTfKZPn46UlJQc5/0CfH4qAgbAFczSpUvx+eefY/z48di4cSMSEhJU8ty4cQMA4ODgoHKsSZMmSnmo5BT0Pty+fRtZWVmwtLSEiYmJUn6ZTCadh/eueEVERGD69On47LPPMH36dJw6dUptPt6fkvPo0SMAgI2NjZTG56f8UHd/FPj8lK3Hjx9j9erVAAA3Nzcpnc9P+ZDT/VHg81O6Ll++jNWrV2PGjBkwNzfPMR+fn3cfA+AK5quvvsLatWuxYsUKjBgxAvXq1ZO+qVJ4+vQpAMDCwgJPnz5F586d8dlnnyErKws1a9YEADx58qTU617ZFPQ+vJkfAAICAtC6dWvs3r0bAHjvSkhwcDDmz5+P9evXY/78+ejcuTPef/99vH79Wikf70/JePnyJQ4cOACZTIbevXtL6Xx+yoec7o8Cn5/SN2bMGAwdOhRdu3aFvb09/v33X4wePRoffPCBlIfPT9nJz/1R4PNTerKysjB69GjUr18f48ePzzUvn593HwPgCqJPnz44evQooqKikJCQgJCQEPTs2ROvX7/Ghx9+iDt37kh5k5KSAAD6+vrYuXMnTp06hfXr1+PKlSswMDBQykMlp6D34c38ADBv3jxcunQJ/v7+AMB7V8xMTU0xc+ZMXL16FQkJCXj69Cl27twJa2trnD59Gj4+Pkr5eX9KhmKLiKFDh8LR0VFK5/NTPuR0f/j8lJ0dO3Zg69atOHHiBNLT0zFnzhysXLlSKQ+fn7KTn/vD56f0rV27FpcuXcKSJUugo6OTa14+P+8+BsAVxLJly9CtWzdYWFigSpUqaN26NQ4cOAA3NzckJSVh4cKFast17doVFhYWcHFxUTuUg0pHYe6Dj48PqlSpAl9f3xKuXeXk5OSEOXPmoFmzZqhSpQosLS3h4+ODEydOQEdHBydOnEBgYGCO5Xl/im7z5s3YunUr7O3t8eOPP+aYj89P2cjt/vD5KTuxsbGQy+WIiIjArFmzMHfuXPTs2RPJyclq8/P5KV35uT98fkrXixcv8M0336Bz587o1atXgcry+Xk3MQCu4CZOnAgAOHnypJRmaGgIAEhJSUHz5s0RFRWFc+fOQV9fX/oPWJGHSk5B78Ob+QFgxowZSEhIwFdffQUAvHelpGHDhvD09ASQ83MF8P4UVWBgID777DOYmppi//79MDIyUjrO56ds5XV/csLnp3RoaGigfv368Pf3x9y5c3H48GGlL8L5/JStvO5PTvj8lIyvvvoKCQkJWL58eb7y8/l59zEAruDs7OwAAFFRUVKalZUVAPUrdj5//hwAYG1tXQq1q9wKeh9yy59TGSoZBX2uAN6fgrh9+7Y0n3T37t1o1KiRSh4+P2UnP/cnN3x+SteQIUMAZA+9VeDzU36ouz+54fNT/Pbv3w9DQ0NMnjwZHh4e0mvbtm0AgC1btsDDwwNr164FwOenImAAXMG9evUKAFClShUpLbfV5q5du6aUh0pOQe9Dw4YNoaGhgcjISMTFxSnlF0LkuiohFS91zxXvT/F4/vw5evTogZiYGGzcuBHvv/++2nx8fspGfu9Pbvj8lC5jY2MAygvs8PkpP9Tdn9zw+SkZ8fHxOHr0qNIrIiICAHD37l0cPXpU2qaIz8+7jwFwBafYh8zZ2VlK69KlCwDg8OHDSnmFEFJa165dS6mGlVdB74OxsTFat24NIQSOHDmiVOb8+fOIjY2FlZUVGjduXMI1r9zS09OluVdvPle8P0WXlJSEnj174sGDB1i8eDEGDRqUY14+P6WvIPcnJ3x+St/FixcB/LcCLcDnpzxRd39ywuenZMTGxkIIofLy8/MDAMydOxdCCHz//fcA+PxUCILeeYcOHRIXLlxQST948KDQ09MTAMSePXuk9NjYWGFiYiJkMpk4duyYlL5mzRoBQDRo0EDI5fJSqXtFB0BoamqqPVaY+7Bp0yYBQDg4OIi4uDghhBBpaWmiY8eOAoCYN29eyTWmAsrt/qxcuVI8f/5cKS0lJUWMGjVKABAWFhYiKSlJ6TjvT+FlZGQIT09PAUB89dVXeebn81O6Cnp/+PyUrs2bN4sjR46o/M5HRESIxo0bq9w3Pj+lq6D3h89P+eDn5ycAiLlz5yql8/l59zEArgAUD2idOnVEr169xEcffSSaNWsmAAgAYuLEiSpltm3bJmQymdDS0hI9e/YUnTt3FjKZTOjo6IigoKAyaEXFcPnyZdGmTRvppbgHb6ZFRkZK+Qt6H+RyufDw8BAAhKWlpfjoo4+EnZ2dACCcnZ1FcnJyaTb3nVOQ+1OnTh2hpaUlWrVqJXx8fISnp6ewsLAQAISxsTHvTzFbvny5ACD09PTE0KFDha+vr8prwYIFSmX4/JSegt4fPj+la/z48QKAMDc3F56enmLgwIGiQ4cOQkdHRwAQLVu2lP7oVuDzU3oKen/4/JQPOQXAQvD5edcxAK4Azp07JwYPHiwaNGggqlatKrS0tISZmZno3r272L9/f47lDh8+LDp06CCqVKkijI2NhYeHh7h06VIp1rziOX36tBRU5fR68OCBUpmC3oe0tDTh5+cn6tevL3R0dISNjY2YOHGiiI+PL+HWvfsKcn+WL18uunTpIqytrYWhoaHQ0dER9erVE59//rm4f/9+jtfg/SkcxR8aub3c3NxUyvH5KR0FvT98fkrXpUuXxPjx48V7770nLCwshLa2tqhSpYpo1aqVWLx4sUhNTVVbjs9P6Sjo/eHzUz7kFgALwefnXSYTQoj8DJUmIiIiIiIiepdxESwiIiIiIiKqFBgAExERERERUaXAAJiIiIiIiIgqBQbAREREREREVCkwACYiIiIiIqJKgQEwERERERERVQoMgImIiIiIiKhSYABMRERERERElQIDYCIiIiIiIqoUGAATERERERFRpcAAmIiIiIiIiCoFBsBERERERERUKTAAJiIiIqpk/vrrLwwdOhR2dnaQyWSYMWNGWVeJiKhUMAAmIiIiqmTOnTuHixcvwtXVFSYmJmVdHSKiUqNV1hUgIiIiotL1v//9D+PHjwcA1K1bt2wrQ0RUitgDTERERFTJaGjwT0Aiqpz4vx8RUQX18OFDyGQyyGQytG/fXuX4sWPHpOPDhg0r/QpWYGfOnCmWz9XMzAwymQzR0dG55pszZw5kMhnmzp2rcmzTpk2QyWTw9/cvVB2Kqy0AIJPJSr23MTMzEw4ODnB2doYQQm2eYcOGQSaTYdu2baVat3fJlStXIJPJ8N1335V1VYiIioQBMBFRJXDhwgVERUUppf3+++9lVBvKL3t7ewDAkydPcs33+PFjAICdnV2J1+ltiuDxzJkzpX7t/Pj5559x8+ZN+Pv7QyaTlXV13lktWrSAt7c35s+fj9evX5d1dYiICo0BMBFRBde8eXNoaWnhjz/+kNLkcjn27t2LVq1alWHNKC+KgFYR4OYktwC4d+/euHnzJr744ovir2A5l56ejtmzZ8PR0RHe3t5lXZ0SFRsbi1u3buX6evToUZGuMW3aNMTFxWHBggXFVGsiotLHAJiIqIIzNTVFp06dsGfPHiktMDAQL1++RJ8+fcqwZpQXRQ9wUQJgExMTNGrUCNWrVy/+CpZzu3fvxrNnzzBkyJCyrkqJCwgIQOPGjXN9DR06tEjXaNu2LWxtbbFx40akpqYWU82JiEoXA2Aiokqgb9++OHv2rDR0cffu3bCyskKbNm3U5o+MjMQXX3yB+vXrQ1dXFxYWFhg6dCj+/fdftfmDgoIwduxYNG3aFKampjA0NETTpk0xd+5cJCcnqy3zzz//wMfHB/Xq1YOenh6sra3RtWtXrF27Vm3+nOaPKuY6u7u7qxx7c/5qfHw8JkyYABsbG+jp6aFu3bqYPn16kdu+detWtGjRAvr6+rC2toafnx8yMzPV5i0odT3AVlZWkMlkWL58uZT2+PFjVKtWDVWrVgWQPbxdMb9b8crPHOD8tkUx7Fkmk2Hz5s0AgE6dOuX7egkJCfjiiy9Qq1Yt6OnpoU2bNrhw4UJ+PpICWbduHQBg4MCBhSp/5swZGBoaombNmrhx4waA7BWT69ati4ULF8LU1BS1a9fGoUOHsHv3btjY2MDY2Fjt71VhFOQZ+fzzzyGEyPVVHMPUBw4ciOjoaOzevbvI5yIiKgvcBomIqBL48MMPMXr0aOzfvx/Dhg3DH3/8gT59+qidE3n16lV07doVL1++RIMGDfDBBx/g8ePH2Lp1K44ePYoLFy6gfv36SmUmT56MS5cuoVmzZujevTsyMjJw5coVzJo1CwcPHsTZs2ehq6sr5b98+TJcXV2RmpqKtm3bom3btnj27BnCw8Nx5coVjBo1qljbL5fL0bVrV9y4cQMdO3aEvr4+/v77bxw8eBDffvttodu+bNkyfPnll9DX10e3bt2QlZWFRYsW4dChQ8VS77d7gF+8eIHIyEgAQFhYGAAgLi4OCQkJSl9m1K1bF76+vgCAe/fu4dy5c3leqyBtcXV1lX4ODg5GREQEunfvDgsLCyndyclJ7XWysrLQo0cPPHr0CC4uLrh79y5CQ0PRvXt33L59G7Vq1cqzrvkRHx+PoKAg1KtXD7Vr1y5w+cDAQPTs2RNVqlTBqVOn4ODgIB17+vQpfv75Z7Rp0wbHjh3DsGHDIJfL4erqivPnz2P+/Pno3bs33nvvvULXv7Sfkfxyd3fHvHnzcPDgQQwePLhM6kBEVCSCiIgqpAcPHggAws3NTQghRMeOHYWXl5cIDg4WAMTp06fF6dOnBQDh6+srhBAiPT1d2NnZCQDi22+/FVlZWdL5du7cKQAIT09PlWtt2bJFPH36VCktMzNTDBkyRAAQW7ZsUTrm6+srAIg1a9aolDl48KDa9gAQderUybOdb1K0z8jISLRu3Vo8f/5c6fi5c+eknwva9qdPnwpdXV1hYGAgrl69KqVfvHhR6OjoKH2uhfXq1SsBQLi7uwshhDh06JAAIOzs7ETjxo2FEEJcu3ZNABCDBw9We46NGzcKAMLPzy/H6xSlLYp7efr06TzbA0AAEB4eHiIlJUUIIYRcLhf9+/cXAMTChQvzPEd+HT58WAAQPj4+eeZVtGHr1q1CCCGCgoJElSpVRI0aNcS1a9eU8tapU0cAEP/++68QQojOnTsLAGLWrFlCCCECAgIEALFs2bIi1b8wz0hBvHjxQvz222/it99+EzVq1BB9+/YVv/32mzh06FCu5eLi4oRMJhPW1tZFrgMRUVngEGgiokqiT58+OHHiBDZu3IgaNWqgQ4cOKnn27NmDe/fuwd3dHd98841SD7GPjw/c3Nxw+PBhPHv2TKnckCFDYGlpqZSmqamJsWPHAgCOHj2qdOz58+cAABcXF5Uynp6ehW9kDpKSkrBjxw6Ym5srpb95/YK2/ddff0VaWhp8fX3RrFkzKW+bNm0waNCgYqm3Ylizogc4LCwMOjo6GDlyJG7fvo2kpKRiWQG6NNrypu+++w56enoAsvejVfRmXr58udiu8ffffwMAGjVqVKBy58+fR48ePaCvr49Tp07B0dFRJY+JiYnUq9ykSRMA//V4K66X17ztvJT0M3L9+nX0798f/fv3x8uXL7F79270798fo0ePzrWcsbExLC0t8eTJkzy35yIiKo8YABMRVRJ9+vRBeno6fvnlF/Tq1QuampoqeU6ePAkge8i0Ok5OThBCIDw8XOXYkydPsGrVKowbNw4jRozAsGHDsHjxYgDAy5cvlfI2b94cQPaqsv/8808RWpU/zs7OsLW1zTVPQdv+119/AYDaLxI6depU+Mq+xc7ODk+fPgWQHQA3b94crq6uyMrKwpUrV6RASzFcujBKqy0AoK+vj6ZNmyql2djYAECxBlQvXrwAkL2Xcn6FhISgR48eSExMxKZNm6Tg9m1GRkbSz1WqVAGQHRgCgKGhIYDsec5FUdLPiLu7u9p5wg8fPsyzrOIzffu5JiJ6F3AOMBFRJWFjY4P33nsPly5dynH1Z0UwNWHCBEyYMCHHc729D+jy5cvx9ddfIyMjQ23+9PR0pfdTpkzBmTNncPDgQRw8eBBmZmZo3bo1vLy8MHLkSKl3sLjkFfwCBW+74o//N+e9KqhLKyx7e3tcunQJL1++RFhYGDw9PdGiRQtoaWkhLCwMr169AlC0HuDSagsA1KhRQyVNR0cHAIpt8TAge240oBys5mXlypXSXPXvvvsOHh4e0NBQ7St488sjLS0tpTTF+6K2pbSfkYJQBPuKz5iI6F3CAJiIqBL55Zdf8PDhQ3Tp0iXXfG8vaPS2OnXqSD+HhIRg0qRJMDIywqpVq+Dl5YWaNWtCU1MTd+/eRYMGDSCEUCpvZmaG8+fPIzAwEEePHkVgYCCOHz+OI0eOYPPmzTh//jy0tbXz1aasrKw88xQkWChI20uDIrC9evUqHj58iNatW8PAwACOjo64fPmyFKAVJQAuTeoWXisJpqamALIXw8qvWrVq4fjx4xg9ejTOnj2LpUuXYvLkyYW6/tu/8wVVnM9IcVMEviYmJmVyfSKiomAATERUiTRp0iTHYZ0AYG1tDQD4+OOP8fHHH+frnIr9hefNm4dPP/1U6Vhuwyk1NDTg7u4ubV90//59fPjhh/jrr7+wZ88eDBgwQCX/2z3JwH9zJYuqoG2vWbNmjtePiooqljoB/w1t3r9/PwCgdevW0r/nz5+HhYUFTE1NUa1atUJfo7TaUpoUPc0FGVY9f/58ODo6YtOmTWjevDlmzpyJ7t27K82LLk0FfUZKi+IzVdebT0RU3nEOMBERSRTzPQuyjU9MTAwAoF69eirHFEFbftSvXx+ffPIJAODBgwcqx83MzPDixQukpqYqpRfX/rEFbbtii5ugoCCVY6dPny6WOgH/9ezu378fxsbG0iJLrVu3xq1bt3Dnzp0izf8FitYWxfDlnPZ7LiuKObS3bt3KdxnF8OX69etjyZIlSEtLw5AhQ9R+8VIW8npGSkNcXByioqJgY2NToPnVRETlBQNgIiKS9OvXD/Xr18fOnTuxYsUKleHFjx8/xoIFC5TSGjRoAADYtm2bUv5Tp05h3bp1aq+zZs0alT/g09LSsHfvXgDqF3Rq0aIF5HI5Vq1aJaXdvXsXS5cuzX8Dc1HQtvfv3x96enrYvHkzrl27JqWHhoZi586dxVIn4L/P4t9//0XLli2lIcStWrWCXC7H48ePizz8uShtUSxgdfHixSLVobi5uLhAU1MTISEhhSo/atQodO/eHX///TemT59ezLXLW2GekdIQGhoKIYTaBdOIiN4FDICJiEiio6OD3bt3o0aNGhg/fjxsbW3Rq1cvDBgwAM2aNUOdOnWwaNEipTLDhw+HhYUFfv31Vzg4OGDgwIHo2LEjunTpAl9fX7XXWbNmDerXr4+mTZtiwIAB6Nu3L+rWrYszZ86gdevW6NWrl0qZzz//HADw1VdfoU2bNnB3d4eTkxN69OhRJm2vVasW5s2bh6SkJLRu3RoffvghvL290bFjR2lLnOJQrVo1aT6rYvgzkD2c3cDAAIDy/N9Xr15h2LBh0uvnn38GAOzdu1cp/c2e0aK0pXfv3tDU1MT8+fPRt29fTJkyBVOnTsWJEyeK6RMoHCMjI7i5ueHhw4f5WtlYnQ0bNsDU1BTLli1DYGBg8VYwD4V5RkqDYkRASWxXRkRUGhgAExGREicnJ/z999/48ssvoauri2PHjkn7+I4dO1ZlWHO1atVw6dIlDBo0CImJidi3bx/i4+Oxbt06fPPNN2qvMWfOHPj6+iIzMxOHDh3CyZMnUatWLSxevBinT5+WhqK+qU+fPvjpp59Qr149hIeH49mzZ/juu+8wY8aMMmv7l19+ic2bN8Pe3h5HjhzBlStXMG3aNMyfP7/Y6gT819v3ZgCsqakJZ2dnAMoBcGJiIjZv3iy9zp07ByB7Ea0309/ey7mwbWnSpAl2796NVq1a4ejRo1i8eDEWLVqE4ODgYml7UXz22WcAUOgeeSsrK6xcuRJZWVkYOnRogRbUKqrCPCOlISAgAGZmZujbt2+ZXJ+IqKhkoqjLFBIRERGVQ+np6ahbty5MTU1x/fr1UluBuqI6d+4cXF1d8eWXX2LJkiVlXR0iokJhDzARERFVSDo6OvDz88PNmzexb9++sq7OO2/BggUwMTHBtGnTyroqRESFxh5gIiIiqrDkcjmaNm0KXV1dhIWFsRe4kMLCwtCyZUssWrQIU6ZMKevqEBEVGgNgIiIiIiIiqhQ4BJqIiIiIiIgqBQbAREREREREVCkwACYiIiIiIqJKgQEwERERERERVQoMgImIiIiIiKhSYABMRERERERElQIDYCIiIiIiIqoUGAATERERERFRpcAAmIiIiIiIiCoFBsBERERERERUKTAAJiIiIiIiokqBATARERERERFVCgyAiYiIiIiIqFJgAExERERERESVAgNgIiIiIiIiqhQYABMREREREVGlwACYiIiIiIiIKgUGwERERERERFQpMAAmIiIiIiKiSoEBMBEREREREVUKDICJiIiIiIioUmAATERERERERJUCA2AiIiIiIiKqFBgAExERERERUaXAAJiIiIiIiIgqBQbAREREREREVCn8Hz67T/5TkGSqAAAAAElFTkSuQmCC",
      "text/html": [
       "\n",
       "            
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       "                
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       "                    Figure\n",
       "                
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       "                \n",
       "            
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       "        "
      ],
      "text/plain": [
       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(dpi=150)\n",
    "# Plot a 1-to-1 line using the observed values.\n",
    "plt.plot(sorted(df_obs_s[\"width\"]), sorted(df_obs_s[\"width\"]), \"k--\")\n",
    "plt.errorbar(df_obs_s[\"width\"], df_lit_s[\"width\"], \n",
    "             xerr=df_obs_s[\"width_error\"], \n",
    "             yerr=df_lit_s[\"width_error\"], \n",
    "             marker=\".\", ls=\"\", ms=5)\n",
    "for n,o,l in zip(df_obs_s[\"name\"], df_obs_s[\"width\"], df_lit_s[\"width\"]):\n",
    "    plt.text(o, l, n, ha=\"center\", va=\"bottom\")\n",
    "plt.xlabel(\"Measured Width (km s$^{-1}$)\")\n",
    "plt.ylabel(\"Literature Width (km s$^{-1}$)\")\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "40ecf549-8a5e-4b73-b949-a01cfbb5de46",
   "metadata": {},
   "source": [
    "The widths agree much better, since they do not depend on the flux calibration."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}