{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "21771231-f623-43d0-bea2-399390fd4737",
   "metadata": {},
   "source": [
    "# Reproduction Notebook for Van den Eijnden and Kaper (2026)\n",
    "This notebook contains all the code necessary to perform the data selection, curation, and calculations, and re-create the figures, from Van den Eijnden and Kaper (2026).\n",
    "\n",
    "This package consists of three steps:\n",
    "\n",
    "- Step 1: selecting Gaia counterparts\n",
    "- Step 2: performing the corrections for Galactic rotation (Fig. 1 and 2 in the paper)\n",
    "- Step 3: the statistical analysis from the paper (Fig. 3 to 10 in the paper)\n",
    "\n",
    "We recommend to run all steps in order as shown below to make sure all inter-dependencies operate correctly."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7a66a70d-a215-44f9-85bc-5bc06f6fe380",
   "metadata": {},
   "source": [
    "# Step 0.1: loading all necessary packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d975ea78-662e-41a9-91ef-d0412842c9ef",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/4g/0wm8g7bd7w59_rrcwv_jhv680000gn/T/ipykernel_25027/3849034705.py:26: Pandas4Warning: The 'mode.copy_on_write' option is deprecated. Copy-on-Write can no longer be disabled (it is always enabled with pandas >= 3.0), and setting the option has no impact. This option will be removed in pandas 4.0.\n",
      "  ps.set_option(\"mode.copy_on_write\", True)\n"
     ]
    }
   ],
   "source": [
    "import sys\n",
    "\n",
    "# Astroquery:\n",
    "import astroquery\n",
    "from astroquery.simbad import Simbad\n",
    "from astroquery.vizier import Vizier\n",
    "from astroquery.gaia import Gaia\n",
    "vizier = Vizier()\n",
    "vizier.ROW_LIMIT = -1\n",
    "\n",
    "# Astropy:\n",
    "import astropy\n",
    "from astropy.coordinates import SkyCoord\n",
    "from astropy.coordinates import ICRS, Galactic, LSR\n",
    "import astropy.units as u\n",
    "from astropy.units import cds\n",
    "from astropy.coordinates import ICRS, Galactic, SkyCoord, LSR\n",
    "\n",
    "# Others\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import scipy\n",
    "from scipy.stats import truncnorm, norm\n",
    "import pandas as ps\n",
    "ps.set_option(\"mode.copy_on_write\", True)\n",
    "\n",
    "import os\n",
    "from mpl_toolkits.axes_grid1.inset_locator import inset_axes\n",
    "\n",
    "from zero_point import zpt\n",
    "zpt.load_tables()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "31c98132-84c2-49fe-87a9-52ca72f592b3",
   "metadata": {},
   "source": [
    "List versions of packages:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d7e5b7f0-9a59-4071-90e5-b8542db04ee6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Python:       3.12.13\n",
      "Astropy:      8.0.1\n",
      "Astroquery:   0.4.11\n",
      "Matplotlib:   3.11.1\n",
      "NumPy:        2.5.1\n",
      "SciPy:        1.18.0\n",
      "Pandas:       3.0.5\n"
     ]
    }
   ],
   "source": [
    "print(f\"Python:       {sys.version.split()[0]}\")\n",
    "print(f\"Astropy:      {astropy.__version__}\")\n",
    "print(f\"Astroquery:   {astroquery.__version__}\")\n",
    "print(f\"Matplotlib:   {matplotlib.__version__}\")\n",
    "print(f\"NumPy:        {np.__version__}\")\n",
    "print(f\"SciPy:        {scipy.__version__}\")\n",
    "print(f\"Pandas:       {ps.__version__}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "47b77970-02f9-49a7-9e4c-985a311f2fd0",
   "metadata": {},
   "source": [
    "# Step 0.2: Global inputs:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c41e9de0-1cdf-42d9-8826-d33a45d90f2e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Redoing the Gaia analysis to find the Gaia counterparts of the bow shock driving star if necessary:\n",
    "redo_Gaia_analysis = False\n",
    "Gaia_mag_lim = '20' # magnitude limit of Gaia search\n",
    "\n",
    "# Redoing the correction for the Galactic rotation using the close neighbour approach. Note: this is a slow step, as\n",
    "# for each system, the closest Gaia sources need to be selected.\n",
    "redo_corrections = False\n",
    "\n",
    "# List of sources that are excluded at the Galactic rotation correction step as they have too few close-by neighbours or the \n",
    "# Gaia query raises an error:\n",
    "skip_sources = ['MWP_GaiaDR3_5862417023754683264', 'MWP_GaiaDR3_5862275221161671424', 'MWP_GaiaDR3_5975362259742333696', \n",
    "                '2MASS_J19134842+1724156','UCAC4_546-095094','2MASS_J19394061+2030066','2MASS_J20053813+3639382',\n",
    "                '2MASS_J13081950-6249430','[KCS2016]_J144859.88-594501.2','2MASS_J15445590-5544108','2MASS_J15530776-5428469']\n",
    "gaia_error_sources = ['MWP_GaiaDR3_4063921012088804608','HD_21856','V*_AE_Aur','*_ups_Ori','TYC_8692-53-1','HD_150898',\n",
    "                     'MWP_GaiaDR3_2069492028364032128', 'MWP_GaiaDR3_2066375119057281792']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7d8b51e5-a676-4f07-aa57-1f3887eab2e0",
   "metadata": {},
   "source": [
    "# Step 1: Data selection from both IR bow shock catalogues"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6392552e-857b-41f1-97a7-ef7e0b0d25e5",
   "metadata": {},
   "source": [
    "## Step 1.1: Finding the closest Gaia counterparts of all bow shock driving stars:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "21494692-f0e6-4187-a75a-62019fa5e346",
   "metadata": {},
   "source": [
    "### The MWP data:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae27f9e9-e2b1-486d-b2b0-cdff53df2551",
   "metadata": {},
   "source": [
    "Loading the Milky Way Project catalogue (599 systems)\n",
    "\n",
    "See: https://ui.adsabs.harvard.edu/abs/2019MNRAS.488.1141J/abstract "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e416cc9d-177f-41df-815b-68c6a53c360e",
   "metadata": {},
   "outputs": [],
   "source": [
    "mwproject = vizier.get_catalogs('J/MNRAS/488/1141')\n",
    "mwproject_table = mwproject[1]\n",
    "\n",
    "# Searching for the 'general' source names of the sources in this catalogue\n",
    "# By using the RAJ2000 and DEJ2000 columns, we base our later Gaia search on the 2MASS star assigned by the MWP. \n",
    "ra = mwproject_table['RAJ2000']\n",
    "dec = mwproject_table['DEJ2000']\n",
    "BS_coords_MWP = SkyCoord(ra=ra, dec=dec, unit=(u.deg, u.deg), frame='icrs')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "df602c4e-e455-4560-ad0e-b18ec5825987",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><i>Table length=599</i>\n",
       "<table id=\"table6279536544\" class=\"table-striped table-bordered table-condensed\">\n",
       "<thead><tr><th>MWP</th><th>GLON</th><th>GLAT</th><th>Disp</th><th>HR3</th><th>RelFlag</th><th>K16ID</th><th>K16Arc</th><th>DR2bub</th><th>RAJ2000</th><th>DEJ2000</th><th>Sep</th><th>Jmag</th><th>Hmag</th><th>Ksmag</th><th>R0</th><th>PA</th><th>8um</th><th>Env</th></tr></thead>\n",
       "<thead><tr><th></th><th>deg</th><th>deg</th><th>arcsec</th><th></th><th></th><th></th><th></th><th></th><th>deg</th><th>deg</th><th>arcsec</th><th>mag</th><th>mag</th><th>mag</th><th>arcsec</th><th>deg</th><th></th><th></th></tr></thead>\n",
       "<thead><tr><th>str17</th><th>float64</th><th>float32</th><th>float32</th><th>float32</th><th>str1</th><th>str17</th><th>str17</th><th>str20</th><th>float64</th><th>float64</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>int16</th><th>str1</th><th>str2</th></tr></thead>\n",
       "<tr><td>2G0004000-0128257</td><td>0.4001</td><td>-1.2826</td><td>1.23</td><td>0.222</td><td>R</td><td></td><td></td><td></td><td>267.898703</td><td>-29.254667</td><td>2.09</td><td>9.69</td><td>9.45</td><td>9.38</td><td>4.7</td><td>296</td><td>N</td><td>I</td></tr>\n",
       "<tr><td>2G0005962+0032676</td><td>0.5963</td><td>0.3268</td><td>3.45</td><td>0.180</td><td>C</td><td></td><td></td><td></td><td>266.441487</td><td>-28.257517</td><td>2.93</td><td>12.97</td><td>9.87</td><td>8.10</td><td>39.0</td><td>129</td><td>Y</td><td>I</td></tr>\n",
       "<tr><td>2G0006519+0063256</td><td>0.6520</td><td>0.6326</td><td>1.58</td><td>0.200</td><td>C</td><td></td><td></td><td>MWP2G0006559+0065999</td><td>266.178427</td><td>-28.049631</td><td>1.56</td><td>13.74</td><td>11.40</td><td>10.19</td><td>16.0</td><td>287</td><td>N</td><td>H</td></tr>\n",
       "<tr><td>2G0010561-0015000</td><td>1.0562</td><td>-0.1500</td><td>1.60</td><td>0.205</td><td>R</td><td>G001.0563-00.1499</td><td></td><td>MWP2G0010555-0014901</td><td>267.174020</td><td>-28.110514</td><td>0.64</td><td>12.40</td><td>11.14</td><td>10.44</td><td>6.2</td><td>200</td><td>Y</td><td>H</td></tr>\n",
       "<tr><td>2G0012588-0007656</td><td>1.2588</td><td>-0.0766</td><td>2.01</td><td>0.250</td><td>R</td><td></td><td>G001.2588-00.0780</td><td></td><td>267.221126</td><td>-27.898815</td><td>2.12</td><td>11.36</td><td>10.34</td><td>10.00</td><td>12.5</td><td>337</td><td>N</td><td>FH</td></tr>\n",
       "<tr><td>2G0028152-0013155</td><td>2.8153</td><td>-0.1316</td><td>2.90</td><td>0.273</td><td>R</td><td></td><td></td><td></td><td>268.170850</td><td>-26.590397</td><td>1.92</td><td>14.06</td><td>11.52</td><td>10.32</td><td>18.3</td><td>86</td><td>N</td><td>FH</td></tr>\n",
       "<tr><td>2G0030678-0009803</td><td>3.0679</td><td>-0.0980</td><td>1.15</td><td>0.222</td><td>R</td><td></td><td></td><td></td><td>268.281749</td><td>-26.355453</td><td>0.48</td><td>10.90</td><td>10.33</td><td>10.05</td><td>9.4</td><td>180</td><td>N</td><td>FH</td></tr>\n",
       "<tr><td>2G0032532-0028359</td><td>3.2532</td><td>-0.2836</td><td>1.28</td><td>0.304</td><td>R</td><td></td><td></td><td></td><td>268.565163</td><td>-26.289873</td><td>1.67</td><td>9.68</td><td>9.09</td><td>8.80</td><td>14.4</td><td>103</td><td>Y</td><td>I</td></tr>\n",
       "<tr><td>2G0032835-0014922</td><td>3.2835</td><td>-0.1492</td><td>0.71</td><td>0.175</td><td>C</td><td></td><td></td><td></td><td>268.452951</td><td>-26.195654</td><td>1.89</td><td>14.81</td><td>11.29</td><td>9.67</td><td>11.3</td><td>301</td><td>N</td><td>I</td></tr>\n",
       "<tr><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td></tr>\n",
       "<tr><td>2G3571821-0014382</td><td>357.1821</td><td>-0.1438</td><td>2.25</td><td>0.219</td><td>C</td><td></td><td></td><td></td><td>264.829166</td><td>-31.406647</td><td>3.27</td><td>8.44</td><td>7.59</td><td>7.08</td><td>29.4</td><td>328</td><td>N</td><td>I</td></tr>\n",
       "<tr><td>2G3572956+0075011</td><td>357.2956</td><td>0.7501</td><td>0.95</td><td>0.100</td><td>R</td><td>G357.2959+00.7500</td><td></td><td></td><td>264.018661</td><td>-30.831993</td><td>1.30</td><td>11.08</td><td>10.74</td><td>10.62</td><td>3.5</td><td>180</td><td>N</td><td>FB</td></tr>\n",
       "<tr><td>2G3581326-0058612</td><td>358.1327</td><td>-0.5861</td><td>2.81</td><td>0.388</td><td>R</td><td></td><td></td><td></td><td>265.854800</td><td>-30.834124</td><td>1.29</td><td>14.78</td><td>11.45</td><td>9.85</td><td>39.0</td><td>304</td><td>N</td><td>I</td></tr>\n",
       "<tr><td>2G3582885-0009845</td><td>358.2886</td><td>-0.0985</td><td>3.39</td><td>0.167</td><td>C</td><td></td><td></td><td></td><td>265.468679</td><td>-30.445253</td><td>2.52</td><td>12.25</td><td>9.46</td><td>8.18</td><td>20.5</td><td>55</td><td>N</td><td>I</td></tr>\n",
       "<tr><td>2G3583673+0003653</td><td>358.3674</td><td>0.0365</td><td>2.17</td><td>0.194</td><td>C</td><td></td><td></td><td></td><td>265.383651</td><td>-30.306757</td><td>2.09</td><td>14.59</td><td>11.38</td><td>9.26</td><td>9.4</td><td>302</td><td>N</td><td>I</td></tr>\n",
       "<tr><td>2G3585210+0089852</td><td>358.5211</td><td>0.8985</td><td>2.25</td><td>0.250</td><td>R</td><td></td><td></td><td></td><td>264.634559</td><td>-29.718554</td><td>1.73</td><td>9.49</td><td>9.08</td><td>8.97</td><td>16.5</td><td>313</td><td>N</td><td>I</td></tr>\n",
       "<tr><td>2G3590832-0043645</td><td>359.0832</td><td>-0.4365</td><td>2.74</td><td>0.308</td><td>R</td><td>G359.0835-00.4367</td><td></td><td></td><td>266.284161</td><td>-29.945889</td><td>3.48</td><td>8.37</td><td>8.11</td><td>7.97</td><td>26.9</td><td>160</td><td>Y</td><td>I</td></tr>\n",
       "<tr><td>2G3594809+0027761</td><td>359.4810</td><td>0.2776</td><td>3.71</td><td>0.093</td><td>R</td><td>G359.4811+00.2780</td><td></td><td></td><td>265.823335</td><td>-29.233030</td><td>1.39</td><td>14.32</td><td>10.85</td><td>9.07</td><td>44.0</td><td>230</td><td>N</td><td>I</td></tr>\n",
       "<tr><td>2G3596735-0025096</td><td>359.6735</td><td>-0.2510</td><td>1.27</td><td>0.173</td><td>C</td><td></td><td></td><td></td><td>266.455892</td><td>-29.345324</td><td>1.68</td><td>13.58</td><td>10.29</td><td>8.47</td><td>54.6</td><td>337</td><td>N</td><td>FB</td></tr>\n",
       "</table></div>"
      ],
      "text/plain": [
       "<Table length=599>\n",
       "       MWP          GLON     GLAT    Disp    HR3   ...    R0     PA  8um  Env \n",
       "                    deg      deg    arcsec         ...  arcsec  deg           \n",
       "      str17       float64  float32 float32 float32 ... float32 int16 str1 str2\n",
       "----------------- -------- ------- ------- ------- ... ------- ----- ---- ----\n",
       "2G0004000-0128257   0.4001 -1.2826    1.23   0.222 ...     4.7   296    N    I\n",
       "2G0005962+0032676   0.5963  0.3268    3.45   0.180 ...    39.0   129    Y    I\n",
       "2G0006519+0063256   0.6520  0.6326    1.58   0.200 ...    16.0   287    N    H\n",
       "2G0010561-0015000   1.0562 -0.1500    1.60   0.205 ...     6.2   200    Y    H\n",
       "2G0012588-0007656   1.2588 -0.0766    2.01   0.250 ...    12.5   337    N   FH\n",
       "2G0028152-0013155   2.8153 -0.1316    2.90   0.273 ...    18.3    86    N   FH\n",
       "2G0030678-0009803   3.0679 -0.0980    1.15   0.222 ...     9.4   180    N   FH\n",
       "2G0032532-0028359   3.2532 -0.2836    1.28   0.304 ...    14.4   103    Y    I\n",
       "2G0032835-0014922   3.2835 -0.1492    0.71   0.175 ...    11.3   301    N    I\n",
       "              ...      ...     ...     ...     ... ...     ...   ...  ...  ...\n",
       "2G3571821-0014382 357.1821 -0.1438    2.25   0.219 ...    29.4   328    N    I\n",
       "2G3572956+0075011 357.2956  0.7501    0.95   0.100 ...     3.5   180    N   FB\n",
       "2G3581326-0058612 358.1327 -0.5861    2.81   0.388 ...    39.0   304    N    I\n",
       "2G3582885-0009845 358.2886 -0.0985    3.39   0.167 ...    20.5    55    N    I\n",
       "2G3583673+0003653 358.3674  0.0365    2.17   0.194 ...     9.4   302    N    I\n",
       "2G3585210+0089852 358.5211  0.8985    2.25   0.250 ...    16.5   313    N    I\n",
       "2G3590832-0043645 359.0832 -0.4365    2.74   0.308 ...    26.9   160    Y    I\n",
       "2G3594809+0027761 359.4810  0.2776    3.71   0.093 ...    44.0   230    N    I\n",
       "2G3596735-0025096 359.6735 -0.2510    1.27   0.173 ...    54.6   337    N   FB"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mwproject_table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "db725963-8125-42ce-9f78-8dbb61763feb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# If the Gaia analysis is not repeated, check if the file exists with the output. \n",
    "# If you specified another Gaia magnitude limit for the search than 20, and did not run \n",
    "# this code before with this new limit, the file does not exist and the Gaia search is performed anyway. \n",
    "\n",
    "redo_Gaia_analysis_MWP = False\n",
    "if not redo_Gaia_analysis:\n",
    "    if Gaia_mag_lim == '20':\n",
    "        data_tot_MWP = ps.read_csv('gaia_all_MWP_20_v1.csv')\n",
    "    elif os.path.exists('gaia_all_MWP_'+Gaia_mag_lim+'_v1.csv'):\n",
    "        data_tot_MWP = ps.read_csv('gaia_all_MWP_'+Gaia_mag_lim+'_v1.csv')\n",
    "    else:\n",
    "        redo_Gaia_analysis_MWP = True\n",
    "\n",
    "# Perfoming the Gaia search if requested or necessary:\n",
    "if redo_Gaia_analysis or redo_Gaia_analysis_MWP:\n",
    "    data = mwproject_table\n",
    "    data['done'] = False\n",
    "\n",
    "    for i in range(len(data['RAJ2000'])):\n",
    "        data_i = data[i]\n",
    "        if not data_i['done']:\n",
    "            print(i)\n",
    "            RAdeg = data_i['RAJ2000']\n",
    "            DEdeg = data_i['DEJ2000']\n",
    "\n",
    "            # The Gaia search string:\n",
    "            tot_string = \"SELECT TOP 1 *, DISTANCE(POINT(\"+str(RAdeg) + \", \" + str(DEdeg) + \"),POINT(ra, dec)) AS ang_sep FROM gaiadr3.gaia_source WHERE 1 = CONTAINS(POINT(\"+str(RAdeg) + \", \" + str(DEdeg) + \"),CIRCLE(ra, dec, 3./60.)) AND phot_g_mean_mag < \"+Gaia_mag_lim+\" ORDER BY ang_sep ASC\"\n",
    "\n",
    "            # Launching the Gaia search job:\n",
    "            job = Gaia.launch_job_async(tot_string, dump_to_file=True, output_format='csv', output_file='gaia.csv')\n",
    "\n",
    "            # Saving the data:\n",
    "            data_job = ps.read_csv('gaia.csv')\n",
    "            data_job['recno'] = i\n",
    "            data_job['my_index'] = i\n",
    "            data_job = data_job.set_index(data_job.my_index)\n",
    "            if i == 0:\n",
    "                data_tot_MWP = data_job.copy()\n",
    "            else:\n",
    "                data_tot_MWP = ps.concat([data_tot_MWP, data_job])\n",
    "    \n",
    "            data[i]['done'] = True\n",
    "\n",
    "    # Writing the dataframe to a csv file:\n",
    "    data_tot_MWP.to_csv('gaia_all_MWP_'+Gaia_mag_lim+'_v1.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cf42ccf3-3af8-491d-935d-8385216590cb",
   "metadata": {},
   "source": [
    "### The Kobulnicky + 2016 data:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cf1a3dd1-d810-4c4a-8775-3fac5622dd54",
   "metadata": {},
   "source": [
    "Repeating the steps for Kobulnicky et al. (2016) catalogue (709 systems)\n",
    "\n",
    "See https://iopscience.iop.org/article/10.3847/0067-0049/227/2/18"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "6aa49908-b1db-4559-928c-bf789c9fb2da",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Loading the Kobulnicky catalogue:\n",
    "kobulnicky = vizier.get_catalogs(\"J/ApJS/227/18\")\n",
    "kobulnicky_table = kobulnicky[0]\n",
    "\n",
    "# Searching for the 'general' source names of the sources in this catalogue\n",
    "ra = kobulnicky_table['RAJ2000']\n",
    "dec = kobulnicky_table['DEJ2000']\n",
    "BS_coords_K16 = SkyCoord(ra=ra, dec=dec, unit=(u.hourangle, u.deg), frame='icrs')\n",
    "\n",
    "# Finding the SIMBAD names based on the naming convention of the Kobulnicky et al. (2016) paper\n",
    "SIMBAD_names = []\n",
    "for i in range(709):\n",
    "    SIMBAD_names.append('[KCS2016] J'+''.join(ra[i].split(' ')) + ''.join(dec[i].split(' ')))\n",
    "\n",
    "Simbad.ROW_LIMIT = 1000\n",
    "result_table = Simbad.query_objects(SIMBAD_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "4479007b-968a-43d7-a11c-0d1640aba3d6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><i>Table length=709</i>\n",
       "<table id=\"table6440203616\" class=\"table-striped table-bordered table-condensed\">\n",
       "<thead><tr><th>Seq</th><th>n_Seq</th><th>Name</th><th>RAJ2000</th><th>DEJ2000</th><th>Ref</th><th>Alias</th><th>8um</th><th>Unc</th><th>R0</th><th>PA</th><th>Hmag</th><th>4.5mag</th><th>Ak</th><th>Env</th><th>WISE</th><th>2M</th><th>Simbad</th></tr></thead>\n",
       "<thead><tr><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th>arcsec</th><th>deg</th><th>mag</th><th>mag</th><th>mag</th><th></th><th></th><th></th><th></th></tr></thead>\n",
       "<thead><tr><th>int16</th><th>str1</th><th>str17</th><th>str11</th><th>str11</th><th>str6</th><th>str16</th><th>str2</th><th>str1</th><th>float32</th><th>int16</th><th>float32</th><th>float32</th><th>float32</th><th>str2</th><th>str4</th><th>str2</th><th>str6</th></tr></thead>\n",
       "<tr><td>1</td><td></td><td>G000.1169-00.5703</td><td>17 48 07.01</td><td>-29 07 55.5</td><td>T</td><td></td><td>Y</td><td>C</td><td>26.40</td><td>27</td><td>8.97</td><td>8.72</td><td>0.22</td><td>I</td><td>WISE</td><td>2M</td><td>Simbad</td></tr>\n",
       "<tr><td>2</td><td></td><td>G000.3100-01.0495</td><td>17 50 27.59</td><td>-29 12 46.8</td><td>T</td><td></td><td>N</td><td>C</td><td>6.80</td><td>145</td><td>10.57</td><td>10.31</td><td>0.23</td><td>I</td><td>WISE</td><td>2M</td><td>Simbad</td></tr>\n",
       "<tr><td>3</td><td></td><td>G001.0563-00.1499</td><td>17 48 41.78</td><td>-28 06 37.8</td><td>T</td><td></td><td>Y</td><td></td><td>6.20</td><td>200</td><td>11.14</td><td>9.80</td><td>1.22</td><td>H</td><td>WISE</td><td>2M</td><td>Simbad</td></tr>\n",
       "<tr><td>4</td><td></td><td>G001.2588-00.0780</td><td>17 48 53.40</td><td>-27 53 59.7</td><td>T</td><td></td><td>N</td><td>C</td><td>10.20</td><td>215</td><td>12.28</td><td>11.22</td><td>0.96</td><td>FH</td><td>WISE</td><td>2M</td><td>Simbad</td></tr>\n",
       "<tr><td>5</td><td></td><td>G003.5118-00.0470</td><td>17 53 56.00</td><td>-25 56 50.3</td><td>T</td><td></td><td>N</td><td></td><td>8.60</td><td>135</td><td>10.69</td><td>9.69</td><td>0.91</td><td>I</td><td>WISE</td><td>2M</td><td>Simbad</td></tr>\n",
       "<tr><td>6</td><td></td><td>G003.7391+00.1425</td><td>17 53 43.03</td><td>-25 39 19.2</td><td>T</td><td></td><td>N</td><td></td><td>3.90</td><td>20</td><td>11.48</td><td>9.41</td><td>1.89</td><td>I</td><td>WISE</td><td>2M</td><td>Simbad</td></tr>\n",
       "<tr><td>7</td><td></td><td>G003.8417-01.0440</td><td>17 58 30.64</td><td>-26 09 49.1</td><td>T</td><td></td><td>Y</td><td></td><td>12.60</td><td>25</td><td>10.22</td><td>9.87</td><td>0.31</td><td>FB</td><td>WISE</td><td>2M</td><td>Simbad</td></tr>\n",
       "<tr><td>8</td><td></td><td>G004.3087+00.2222</td><td>17 54 41.06</td><td>-25 07 25.6</td><td>T</td><td></td><td>N</td><td></td><td>10.10</td><td>260</td><td>13.94</td><td>12.10</td><td>1.68</td><td>I</td><td>WISE</td><td>2M</td><td>Simbad</td></tr>\n",
       "<tr><td>9</td><td></td><td>G004.7315-00.3875</td><td>17 57 57.06</td><td>-25 03 53.6</td><td>T</td><td></td><td>N</td><td>C</td><td>10.10</td><td>125</td><td>13.30</td><td>11.77</td><td>1.39</td><td>I</td><td>WISE</td><td>2M</td><td>Simbad</td></tr>\n",
       "<tr><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td></tr>\n",
       "<tr><td>701</td><td></td><td>G357.2959+00.7500</td><td>17 36 04.52</td><td>-30 49 55.6</td><td>T</td><td></td><td>N</td><td></td><td>3.50</td><td>180</td><td>10.74</td><td>10.43</td><td>0.27</td><td>FB</td><td>WISE</td><td>2M</td><td>Simbad</td></tr>\n",
       "<tr><td>702</td><td></td><td>G357.9016+00.5513</td><td>17 38 21.94</td><td>-30 25 39.8</td><td>T</td><td></td><td>Y</td><td></td><td>6.20</td><td>300</td><td>10.59</td><td>9.12</td><td>1.34</td><td>I</td><td>WISE</td><td>2M</td><td>Simbad</td></tr>\n",
       "<tr><td>703</td><td></td><td>G357.9651-00.2206</td><td>17 41 33.68</td><td>-30 47 03.1</td><td>T</td><td></td><td>N</td><td>C</td><td>9.10</td><td>20</td><td>14.35</td><td>11.75</td><td>2.37</td><td>I</td><td>WISE</td><td>2M</td><td>Simbad</td></tr>\n",
       "<tr><td>704</td><td></td><td>G358.1100-00.3104</td><td>17 42 16.33</td><td>-30 42 30.6</td><td>T</td><td></td><td>N</td><td>C</td><td>9.40</td><td>185</td><td>11.14</td><td>9.05</td><td>1.91</td><td>I</td><td>WISE</td><td>2M</td><td>Simbad</td></tr>\n",
       "<tr><td>705</td><td></td><td>G359.0835-00.4367</td><td>17 45 08.21</td><td>-29 56 45.3</td><td>T</td><td>CD-29 13925</td><td>Y</td><td>C</td><td>26.90</td><td>160</td><td>8.18</td><td>7.78</td><td>0.35</td><td>I</td><td>WISE</td><td>2M</td><td>Simbad</td></tr>\n",
       "<tr><td>706</td><td></td><td>G359.2794+00.5232</td><td>17 41 51.04</td><td>-29 16 31.1</td><td>T</td><td></td><td>N</td><td></td><td>5.70</td><td>250</td><td>11.12</td><td>9.85</td><td>1.15</td><td>I</td><td>WISE</td><td>2M</td><td>Simbad</td></tr>\n",
       "<tr><td>707</td><td></td><td>G359.4811+00.2780</td><td>17 43 17.60</td><td>-29 13 58.7</td><td>T</td><td></td><td>N</td><td>C</td><td>44.00</td><td>230</td><td>10.85</td><td>7.34</td><td>3.21</td><td>I</td><td>WISE</td><td>2M</td><td>Simbad</td></tr>\n",
       "<tr><td>708</td><td></td><td>G359.9016-00.3174</td><td>17 46 37.60</td><td>-29 11 07.1</td><td>T</td><td></td><td>N</td><td>C</td><td>6.10</td><td>160</td><td>12.01</td><td>10.25</td><td>1.60</td><td>I</td><td>WISE</td><td>2M</td><td>Simbad</td></tr>\n",
       "<tr><td>709</td><td></td><td>G359.9536-00.5088</td><td>17 47 30.01</td><td>-29 14 24.2</td><td>T</td><td></td><td>N</td><td>C</td><td>11.20</td><td>115</td><td>9.50</td><td>9.33</td><td>0.14</td><td>FB</td><td>WISE</td><td>2M</td><td>Simbad</td></tr>\n",
       "</table></div>"
      ],
      "text/plain": [
       "<Table length=709>\n",
       " Seq  n_Seq        Name         RAJ2000     DEJ2000   ... Env  WISE  2M  Simbad\n",
       "                                                      ...                      \n",
       "int16  str1       str17          str11       str11    ... str2 str4 str2  str6 \n",
       "----- ----- ----------------- ----------- ----------- ... ---- ---- ---- ------\n",
       "    1       G000.1169-00.5703 17 48 07.01 -29 07 55.5 ...    I WISE   2M Simbad\n",
       "    2       G000.3100-01.0495 17 50 27.59 -29 12 46.8 ...    I WISE   2M Simbad\n",
       "    3       G001.0563-00.1499 17 48 41.78 -28 06 37.8 ...    H WISE   2M Simbad\n",
       "    4       G001.2588-00.0780 17 48 53.40 -27 53 59.7 ...   FH WISE   2M Simbad\n",
       "    5       G003.5118-00.0470 17 53 56.00 -25 56 50.3 ...    I WISE   2M Simbad\n",
       "    6       G003.7391+00.1425 17 53 43.03 -25 39 19.2 ...    I WISE   2M Simbad\n",
       "    7       G003.8417-01.0440 17 58 30.64 -26 09 49.1 ...   FB WISE   2M Simbad\n",
       "    8       G004.3087+00.2222 17 54 41.06 -25 07 25.6 ...    I WISE   2M Simbad\n",
       "    9       G004.7315-00.3875 17 57 57.06 -25 03 53.6 ...    I WISE   2M Simbad\n",
       "  ...   ...               ...         ...         ... ...  ...  ...  ...    ...\n",
       "  701       G357.2959+00.7500 17 36 04.52 -30 49 55.6 ...   FB WISE   2M Simbad\n",
       "  702       G357.9016+00.5513 17 38 21.94 -30 25 39.8 ...    I WISE   2M Simbad\n",
       "  703       G357.9651-00.2206 17 41 33.68 -30 47 03.1 ...    I WISE   2M Simbad\n",
       "  704       G358.1100-00.3104 17 42 16.33 -30 42 30.6 ...    I WISE   2M Simbad\n",
       "  705       G359.0835-00.4367 17 45 08.21 -29 56 45.3 ...    I WISE   2M Simbad\n",
       "  706       G359.2794+00.5232 17 41 51.04 -29 16 31.1 ...    I WISE   2M Simbad\n",
       "  707       G359.4811+00.2780 17 43 17.60 -29 13 58.7 ...    I WISE   2M Simbad\n",
       "  708       G359.9016-00.3174 17 46 37.60 -29 11 07.1 ...    I WISE   2M Simbad\n",
       "  709       G359.9536-00.5088 17 47 30.01 -29 14 24.2 ...   FB WISE   2M Simbad"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kobulnicky_table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "d642d125-4a08-45c5-b174-5c6465251449",
   "metadata": {},
   "outputs": [],
   "source": [
    "# If the Gaia analysis is not repeated, check if the file exists with the output. \n",
    "# If you specified another Gaia magnitude limit for the search than 20, and did not run \n",
    "# this code before with this new limit, the file does not exist and the Gaia search is performed anyway. \n",
    "\n",
    "redo_Gaia_analysis_K16 = False\n",
    "if not redo_Gaia_analysis:\n",
    "    if Gaia_mag_lim == '20':\n",
    "        data_tot_K16 = ps.read_csv('gaia_all_K16_20_v1.csv')\n",
    "    elif os.path.exists('gaia_all_K16_'+Gaia_mag_lim+'_v1.csv'):\n",
    "        data_tot_K16 = ps.read_csv('gaia_all_K16_'+Gaia_mag_lim+'_v1.csv')\n",
    "    else:\n",
    "        redo_Gaia_analysis_K16 = True\n",
    "\n",
    "# Perfoming the Gaia search if requested or necessary:\n",
    "# Same code as above, after we selected the targets.\n",
    "if redo_Gaia_analysis or redo_Gaia_analysis_K16:\n",
    "    \n",
    "    # Querying Gaia for those:\n",
    "    data = result_table\n",
    "    data['done'] = False\n",
    "\n",
    "    for i in range(len(data['ra'])):\n",
    "        data_i = data[i]\n",
    "        if not data_i['done']:\n",
    "            print(i)\n",
    "            RAdeg = data_i['ra']\n",
    "            DEdeg = data_i['dec']\n",
    "\n",
    "            tot_string = \"SELECT TOP 1 *, DISTANCE(POINT(\"+str(RAdeg) + \", \" + str(DEdeg) + \"),POINT(ra, dec)) AS ang_sep FROM gaiadr3.gaia_source WHERE 1 = CONTAINS(POINT(\"+str(RAdeg) + \", \" + str(DEdeg) + \"),CIRCLE(ra, dec, 3./60.)) AND phot_g_mean_mag < \"+Gaia_mag_lim+\" ORDER BY ang_sep ASC\"\n",
    "    \n",
    "            job = Gaia.launch_job_async(tot_string, dump_to_file=True, output_format='csv', output_file='gaia.csv')\n",
    "    \n",
    "            data_job = ps.read_csv('gaia.csv')\n",
    "            data_job['recno'] = i\n",
    "            data_job['my_index'] = i\n",
    "            data_job = data_job.set_index(data_job.my_index)\n",
    "            if i == 0:\n",
    "                data_tot_K16 = data_job.copy()\n",
    "            else:\n",
    "                data_tot_K16 = ps.concat([data_tot_K16, data_job])\n",
    "    \n",
    "            data[i]['done'] = True\n",
    "\n",
    "    # Writing the dataframe to a csv file:\n",
    "    data_tot_MWP.to_csv('gaia_all_K16_'+Gaia_mag_lim+'_v1.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "157a0383-4f2a-4da6-bdee-6849ddbdd8df",
   "metadata": {},
   "source": [
    "## Step 1.2: Selecting the sources that pass the Gaia data selection criteria and merging the catalogues"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "15eccee5-17dd-4d28-97e8-6bf162abc217",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-------------------\n",
      "44\n",
      "555\n",
      "599 ; Should be 599\n",
      "-------------------\n",
      "189\n",
      "520\n",
      "709 ; Should be 709\n",
      "-------------------\n"
     ]
    }
   ],
   "source": [
    "# Lists to save the selected and not selected systems:\n",
    "MWP_selected = []\n",
    "MWP_notselected = []\n",
    "K16_selected = []\n",
    "K16_notselected = []\n",
    "\n",
    "# The number of sources per catalogue\n",
    "N_MWP = len(mwproject_table)\n",
    "N_K16 = len(kobulnicky_table)\n",
    "\n",
    "# Starting the the MWP catalogue:\n",
    "N = 0\n",
    "for i in range(N_MWP):\n",
    "\n",
    "    # Checking if the source meets the Gaia quality cuts:\n",
    "    Gaia_quality = False\n",
    "    if (data_tot_MWP['ruwe'][i]) < 1.4 and (data_tot_MWP['parallax_over_error'][i] > 5.) and  (data_tot_MWP['ipd_gof_harmonic_amplitude'][i] < 0.15):\n",
    "        Gaia_quality=True\n",
    "\n",
    "    # Checking the Reliability Flag from the original catalogue:\n",
    "    # We can immediately say it is not selected if it does not have the Reliability flag set to 'R'. \n",
    "    if (mwproject_table['RelFlag'][i] != 'R'):\n",
    "        MWP_notselected.append(i)\n",
    "    \n",
    "    # Testing if the source is not also listed in the K16 catalogue:\n",
    "    if (mwproject_table['RelFlag'][i] == 'R') and not ('G' in mwproject_table['K16ID'][i]) and not ('G' in mwproject_table['K16Arc'][i]):\n",
    "                \n",
    "        # If it is not also listed in the K16 catalogue, check to make sure it is within an arcsecond\n",
    "        # If the Gaia quality is also good enough, we accept the source:\n",
    "        if (data_tot_MWP['ang_sep'][i] * 3600 < 1.) and Gaia_quality:\n",
    "            MWP_selected.append(i)\n",
    "\n",
    "        # If the Gaia quality was too poor or it is too far away, we do not select it:\n",
    "        else:\n",
    "            MWP_notselected.append(i)\n",
    "\n",
    "    # This approach leaves sources that overlap between the catalogues:\n",
    "    # Source that have the okay reliability flag in the MWP catalogue but also have a listed ID for the K16 Arc or star. \n",
    "    if (mwproject_table['RelFlag'][i] == 'R') and (('G' in mwproject_table['K16ID'][i]) or ('G' in mwproject_table['K16Arc'][i])):\n",
    "        \n",
    "        # In these cases, we should first check if both sources have found the same potential Gaia counterpart:\n",
    "        MWP_designations_i = data_tot_MWP['designation'][i]\n",
    "        K16_designations = np.asarray(data_tot_K16['designation'])\n",
    "\n",
    "        # Case A is simple: the same Gaia source is found in both catalogues as the closes counterpart:\n",
    "        if MWP_designations_i in K16_designations:\n",
    "            \n",
    "            # The index in the K16 catalogue:\n",
    "            j = np.argmax(K16_designations == MWP_designations_i)\n",
    "\n",
    "            # Check if either of the distances is small enough:\n",
    "            # If it is close enough in K16, we use that one, even in the case where the MWP distance is also small enough. \n",
    "            # This makes sure the source is selected once and only once, and doesn't show up later in the analysis. \n",
    "            if (data_tot_K16['ang_sep'][j] * 3600 < 1.) and Gaia_quality: \n",
    "                MWP_notselected.append(i)\n",
    "                K16_selected.append(j)\n",
    "            elif (data_tot_MWP['ang_sep'][i] * 3600 < 1.) and Gaia_quality:\n",
    "                MWP_selected.append(i)\n",
    "                K16_notselected.append(j)\n",
    "            else:\n",
    "                MWP_notselected.append(i)\n",
    "                K16_notselected.append(j)\n",
    "                \n",
    "        # Case B: not the same Gaia source has been found due to small differences in the coordinates\n",
    "        # between the catalogues (bow shocks are extended sources, after all, and assigning the driving star \n",
    "        # can be different between catalogues). \n",
    "        # \n",
    "        # In that case, we check which one has good quality Gaia data and continue with that one.\n",
    "        # In a single case, the K16 and MWP cases have different Gaia counterparts which both pass\n",
    "        # the selection: in that case, we can't say which one to use so don't use the system. \n",
    "        else:\n",
    "            wrong_names = {133:137,136:144,274:315,303:360}\n",
    "            # Need to find: index of the one that overlaps:\n",
    "            if mwproject_table['K16ID'][i] in kobulnicky_table['Name']:\n",
    "                j = np.argmax(np.asarray(mwproject_table['K16ID'][i] == kobulnicky_table['Name']))\n",
    "            elif mwproject_table['K16Arc'][i] in kobulnicky_table['Name']:\n",
    "                j = np.argmax(np.asarray(mwproject_table['K16Arc'][i] == kobulnicky_table['Name']))               \n",
    "            else:               \n",
    "                # Cases where slight typos etc. in the source names in the catalogue prevent finding the overlapping sources.\n",
    "                # These were checked manually:\n",
    "                j = wrong_names[i]\n",
    "\n",
    "            # In this statement, also check for the 'C' classification for K16:\n",
    "            Gaia_quality_K16 = False \n",
    "            if (data_tot_K16['ruwe'][j]) < 1.4 and (data_tot_K16['parallax_over_error'][j] > 5.) and  (data_tot_K16['ipd_gof_harmonic_amplitude'][j] < 0.15) and (kobulnicky_table['Unc'][j] != 'C'):\n",
    "                Gaia_quality_K16=True\n",
    "\n",
    "            # If both are bad quality: don't use either\n",
    "            # If both are good quality: also don't use either, since we don't know which to use (this occurs once). \n",
    "            if (Gaia_quality_K16 and Gaia_quality) or (not Gaia_quality_K16 and not Gaia_quality):\n",
    "                MWP_notselected.append(i)\n",
    "                K16_notselected.append(j)\n",
    "                \n",
    "            if Gaia_quality_K16 and not Gaia_quality:\n",
    "                # Only K16 is okay: use that one, if close enough:\n",
    "                MWP_notselected.append(i)\n",
    "                if (data_tot_K16['ang_sep'][j] * 3600 < 1.):\n",
    "                    K16_selected.append(j)\n",
    "                else:\n",
    "                    K16_notselected.append(j)\n",
    "\n",
    "            if not Gaia_quality_K16 and Gaia_quality:\n",
    "                # Only MWP is okay: use that one, if close enough:\n",
    "                K16_notselected.append(j)\n",
    "                if (data_tot_MWP['ang_sep'][i] * 3600 < 1.):\n",
    "                    MWP_selected.append(i)\n",
    "                else:\n",
    "                    MWP_notselected.append(i)\n",
    "\n",
    "# Finally turn to the K16 catalogue:\n",
    "for j in range(N_K16):\n",
    "\n",
    "    # Only consider the ones we have not yet looked at:\n",
    "    if (j not in K16_notselected) and (j not in K16_selected):\n",
    "\n",
    "        # Check the Gaia quality, the quality flag in K16, and the distance between the Gaia counterpart and the source:\n",
    "        Gaia_quality = False\n",
    "        # In this statement, also check for the 'C' classification for K16:\n",
    "        if (data_tot_K16['ruwe'][j]) < 1.4 and (data_tot_K16['parallax_over_error'][j] > 5.) and  (data_tot_K16['ipd_gof_harmonic_amplitude'][j] < 0.15) and (kobulnicky_table['Unc'][j] != 'C'):\n",
    "            Gaia_quality=True\n",
    "\n",
    "        if (data_tot_K16['ang_sep'][j] * 3600 < 1.) and Gaia_quality:\n",
    "            K16_selected.append(j)\n",
    "        else:\n",
    "            K16_notselected.append(j)\n",
    "            \n",
    "print('-------------------')            \n",
    "print(len(MWP_selected))\n",
    "print(len(MWP_notselected))\n",
    "print(len(MWP_selected)+len(MWP_notselected), '; Should be 599')\n",
    "print('-------------------')\n",
    "print(len(K16_selected))\n",
    "print(len(K16_notselected))\n",
    "print(len(K16_selected)+len(K16_notselected), '; Should be 709')\n",
    "print('-------------------')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "87c5f63d-9f37-4d39-91fd-a2d098e57a9c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Bit of code to turn the selected and unselected indices to arrays of True or False for later selection:\n",
    "MWP_bools = []\n",
    "for i in range(N_MWP):\n",
    "    if i in MWP_selected:\n",
    "        MWP_bools.append(True)\n",
    "    else:\n",
    "        MWP_bools.append(False)\n",
    "MWP_bools = np.asarray(MWP_bools)\n",
    "\n",
    "K16_bools = []\n",
    "for j in range(N_K16):\n",
    "    if j in K16_selected:\n",
    "        K16_bools.append(True)\n",
    "    else:\n",
    "        K16_bools.append(False)\n",
    "K16_bools = np.asarray(K16_bools)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "a781a98c-c847-4d7a-bc70-fd74247c6cda",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/4g/0wm8g7bd7w59_rrcwv_jhv680000gn/T/ipykernel_25027/2278126772.py:4: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  data_selected_K16['survey'] = survey_K16\n",
      "/var/folders/4g/0wm8g7bd7w59_rrcwv_jhv680000gn/T/ipykernel_25027/2278126772.py:8: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  data_selected_MWP['survey'] = survey_MWP\n"
     ]
    }
   ],
   "source": [
    "# Selecting the data with good quality Gaia counterparts and merging the dataframes into a single .csv file\n",
    "data_selected_K16 = data_tot_K16[K16_bools]\n",
    "survey_K16 = ['K16']*len(data_selected_K16)\n",
    "data_selected_K16['survey'] = survey_K16\n",
    "\n",
    "data_selected_MWP = data_tot_MWP[MWP_bools]\n",
    "survey_MWP = ['MWP']*len(data_selected_MWP)\n",
    "data_selected_MWP['survey'] = survey_MWP\n",
    "\n",
    "data_selected_both = ps.concat([data_selected_K16, data_selected_MWP], axis=0)\n",
    "data_selected_both.to_csv('gaia_all_combined_20_v1.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "9769dc49-12b3-44c7-a9f4-b1e9d2535f1c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of selected systems with Gaia counterparts; pre-correction for Galactic rotation: 233\n"
     ]
    }
   ],
   "source": [
    "print('Number of selected systems with Gaia counterparts; pre-correction for Galactic rotation:', len(data_selected_both))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bee514ad-45e4-4cac-b555-ddbd2fcd26bf",
   "metadata": {},
   "source": [
    "We note that we confirmed that our Gaia counterparts are the same as those found by Kobulnicky and Chick (2022)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "654aef9b-ebd4-4595-9623-d6831cb347c3",
   "metadata": {},
   "source": [
    "# Step 2: Correcting for Galactic rotation:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "625925fa-9b93-468e-b383-ae8632856d62",
   "metadata": {},
   "source": [
    "### Start here to start from the point where we do the correction of Galactic rotation:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "b76f8bb4-4eed-48f3-9983-0597f762595c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Loading the data from Step 1:\n",
    "filtered_data = ps.read_csv('gaia_all_combined_20_v1.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f9781b3-b4ee-4fd0-b140-2d072a83d844",
   "metadata": {},
   "source": [
    "## Step 2.1: Trying the Galactic rotation curve approach"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3d57a7e2-84db-49b7-b80f-9fc036650f74",
   "metadata": {},
   "source": [
    "In this approach, we assume that for each source, we know where it is in the Galaxy -- based on its l, b, and distance (parallax). That means we can take the rotation curve of the Galaxy and the solar motion with respect to the LSR to calculate the expected motion at the position of the source. By subtracting this from its real motion, we find its peculiar motion. \n",
    "\n",
    "We will find that this works to some degree, but not perfectly: the resulting peculiar motions are on average ~0 in b, but remain negative in l. That means that, when converting to RA/Dec -- i.e., the peculiar motion on the sky -- has a prefential direction ~225 degrees E of N. \n",
    "\n",
    "This result heavily depends on the assumed parameters of the MW rotation curve. In fact, as we'll show, we can find values for the Oort parameters that are not unreasonable, summing up to an okay value, where the mean peculiar motion in b and l is both roughly zero. However, that is very fine-tuned on this sample. \n",
    "\n",
    "In the subsequent attempt, we will therefore instead use nearby sources for each target to measure the mean motion in that part of the MW. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "e7ea7ec5-40a5-4631-88a6-faada414ab24",
   "metadata": {},
   "outputs": [],
   "source": [
    "def local_motion(Galactic_Skycoords, \n",
    "                 A = 12.5*u.km/u.s/u.kpc, B = -12.5*u.km/u.s/u.kpc, \n",
    "                 U = 11.1*u.km/u.s, V = 12.24*u.km/u.s, W = 7.25*u.km/u.s):\n",
    "    \n",
    "    C = 0.211 * u.mas / u.yr / (u.km / u.s / u.kpc) # Unit conversion\n",
    "\n",
    "    pm_l_cosb_0 = np.zeros(len(Galactic_Skycoords))\n",
    "    pm_b_0 = np.zeros(len(Galactic_Skycoords))\n",
    "    \n",
    "    for i in range(len(Galactic_Skycoords)):\n",
    "        l = Galactic_Skycoords[i].l.deg * u.deg\n",
    "        b = Galactic_Skycoords[i].b.deg * u.deg\n",
    "        D = Galactic_Skycoords[i].distance * u.kpc\n",
    "    \n",
    "        pm_l_cosb_0[i] = (C*(A*np.cos(2.*l)*np.cos(b) + B*np.cos(b) \n",
    "                         + (U/D)*np.sin(l) - (V/D)*np.cos(l))).to(u.mas / u.yr).value\n",
    "        pm_b_0[i] = (C*(-1*A*np.sin(2.*l)*np.sin(b)*np.cos(b) \n",
    "                    + (U/D)*np.cos(l)*np.sin(b) + (V/D)*np.sin(l)*np.sin(b)\n",
    "                    - (W/D)*np.cos(b))).to(u.mas / u.yr).value\n",
    "    \n",
    "    return pm_l_cosb_0 * u.mas / u.yr, pm_b_0 * u.mas / u.yr"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7ccda905-df5c-497e-9ec7-888659d5cf3d",
   "metadata": {},
   "source": [
    "**Note**: if the next cell returns the following issue in the zero_point module:\n",
    "\n",
    "\"TypeError: can_cast() does not support Python ints, floats, and complex because the result used to depend on the value.\n",
    "This change was part of adopting NEP 50, we may explicitly allow them again in the future.\"\n",
    "\n",
    "This is caused by NumPy version that is too new. In that case, go to the zpt.py source file and change line 161 from\n",
    "\n",
    "elif not (np.can_cast(inp, float) or np.can_cast(inp, int)):\n",
    "\n",
    "to \n",
    "\n",
    "elif not isinstance(inp, (int, float, np.integer, np.floating)):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "d6f17a69-e54a-41a7-bbce-1d751cc55176",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Zero point corrections:\n",
    "zpt_corr = filtered_data.apply(zpt.zpt_wrapper, axis=1) # follows Lindegren + 2020"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ce6df025-1f00-47b7-8c38-c4d0b27f6f6c",
   "metadata": {},
   "source": [
    "Performing the corrections for different rotation curves and showing diagnostic plots of the result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "c36cf3d2-8a06-4c05-b768-99e480244da6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ICRS_positions = ICRS(ra=np.asarray(filtered_data.ra)*u.degree, \n",
    "                      dec=np.asarray(filtered_data.dec)*u.degree,\n",
    "                      pm_ra_cosdec=np.asarray(filtered_data.pmra)*u.mas/u.yr, \n",
    "                      pm_dec=np.asarray(filtered_data.pmdec)*u.mas/u.yr,\n",
    "                      distance=(1./(np.asarray(filtered_data.parallax) - zpt_corr))*u.kpc)\n",
    "\n",
    "Galactic_positions = ICRS_positions.transform_to(Galactic()) \n",
    "\n",
    "Galactic_corrections1 = local_motion(Galactic_positions) # Standard values\n",
    "Galactic_corrections2 = local_motion(Galactic_positions,\n",
    "                                    A = 15.7*u.km/u.s/u.kpc, \n",
    "                                    B = -13.8*u.km/u.s/u.kpc, \n",
    "                                    U = 10.6*u.km/u.s, \n",
    "                                    V = 10.7*u.km/u.s, \n",
    "                                    W = 7.6*u.km/u.s) # Based on Carritere-Castrillo + 2025\n",
    "\n",
    "Galactic_corrected1 = Galactic(l=Galactic_positions.l.deg*u.deg,\n",
    "                              b=Galactic_positions.b.deg*u.deg,\n",
    "                              pm_l_cosb=Galactic_positions.pm_l_cosb - Galactic_corrections1[0],\n",
    "                              pm_b=Galactic_positions.pm_b - Galactic_corrections1[1],\n",
    "                              distance=(1./(np.asarray(filtered_data.parallax) - zpt_corr))*u.kpc)\n",
    "Galactic_corrected2 = Galactic(l=Galactic_positions.l.deg*u.deg,\n",
    "                              b=Galactic_positions.b.deg*u.deg,\n",
    "                              pm_l_cosb=Galactic_positions.pm_l_cosb - Galactic_corrections2[0],\n",
    "                              pm_b=Galactic_positions.pm_b - Galactic_corrections2[1],\n",
    "                              distance=(1./(np.asarray(filtered_data.parallax) - zpt_corr))*u.kpc)\n",
    "\n",
    "# Plotting: \n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "ax.errorbar(Galactic_positions.l.deg, Galactic_positions.pm_l_cosb, fmt='ks', ms=3, label='uncorrected')\n",
    "ax.errorbar(Galactic_corrected1.l.deg, Galactic_corrected1.pm_l_cosb, fmt='rs', ms=3, label='rotation model 1')\n",
    "ax.errorbar(Galactic_corrected2.l.deg, Galactic_corrected2.pm_l_cosb, fmt='gs', ms=3, label='rotation model 2')\n",
    "ax.set_xlabel(r'$l$ [deg]')\n",
    "ax.set_ylabel(r'$\\mu_l$ [mas/year]')\n",
    "ax.set_ylim(-11, 7)\n",
    "ax.legend(loc=2)\n",
    "plt.show()\n",
    "\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "ax.errorbar(Galactic_positions.b.deg, Galactic_positions.pm_b, fmt='ks', ms=3, label='uncorrected')\n",
    "ax.errorbar(Galactic_corrected1.b.deg, Galactic_corrected1.pm_b, fmt='rs', ms=3, label='rotation model 1')\n",
    "ax.errorbar(Galactic_corrected2.b.deg, Galactic_corrected2.pm_b, fmt='gs', ms=3, label='rotation model 2')\n",
    "ax.set_xlabel(r'$b$ [deg]')\n",
    "ax.set_ylabel(r'$\\mu_b$ [mas/year]')\n",
    "ax.legend(loc=4)\n",
    "plt.show()\n",
    "\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "ax.errorbar(Galactic_positions.pm_l_cosb, Galactic_positions.pm_b, fmt='ks', ms=3, label='uncorrected')\n",
    "ax.errorbar(Galactic_corrected1.pm_l_cosb, Galactic_corrected1.pm_b, fmt='rs', ms=3, label='rotation model 1')\n",
    "ax.errorbar(Galactic_corrected2.pm_l_cosb, Galactic_corrected2.pm_b, fmt='gs', ms=3, label='rotation model 2')\n",
    "ax.set_xlabel(r'$\\mu_l$ [mas/year]')\n",
    "ax.set_ylabel(r'$\\mu_b$ [mas/year]')\n",
    "ax.set_xlim(-15,10)\n",
    "ax.set_ylim(-10,5)\n",
    "ax.plot([0,0],[-100,100], 'k--', zorder=-2)\n",
    "ax.plot([-100,100],[0,0], 'k--', zorder=-2)\n",
    "ax.legend(loc=3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5099873f-7952-472d-9df2-478969a45386",
   "metadata": {},
   "source": [
    "Showing the results in RA and DEC coordinates:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "c7dc6de0-d722-4472-9162-673573c7c77b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ICRS_corrected1 = Galactic_corrected1.transform_to(ICRS()) \n",
    "ICRS_corrected2 = Galactic_corrected2.transform_to(ICRS()) \n",
    "\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "ax.errorbar(filtered_data.pmra, filtered_data.pmdec, fmt='ks', ms=3, label='uncorrected')\n",
    "ax.errorbar(ICRS_corrected1.pm_ra_cosdec, ICRS_corrected1.pm_dec, fmt='rs', ms=3, label='rotation model 1')\n",
    "ax.errorbar(ICRS_corrected2.pm_ra_cosdec, ICRS_corrected2.pm_dec, fmt='gs', ms=3, label='rotation model 2')\n",
    "ax.set_xlim(-10,10)\n",
    "ax.plot([0,0],[-10,10], 'k--', zorder=-2)\n",
    "ax.plot([-10,10],[0,0], 'k--', zorder=-2)\n",
    "ax.set_ylim(-10,10)\n",
    "ax.set_xlabel(r'$\\mu_\\alpha$ [mas/year]')\n",
    "ax.set_ylabel(r'$\\mu_\\delta$ [mas/year]')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "ce8b1ae9-52c5-4c1b-8068-92c1d9fcf773",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save the values of proper motion to a file:\n",
    "data_to_save = np.column_stack((ICRS_corrected1.pm_ra_cosdec.value, ICRS_corrected1.pm_dec.value, ICRS_corrected2.pm_ra_cosdec.value,  ICRS_corrected2.pm_dec.value))\n",
    "\n",
    "np.savetxt(\n",
    "    \"GalRot_results_both.csv\",\n",
    "    data_to_save,\n",
    "    delimiter=\",\",\n",
    "    header=\"pmRA_1, pmDec_1, pmRA_2, pmDec_2\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f9873e99-18a3-4230-afa9-b03fbc5e9838",
   "metadata": {},
   "source": [
    "## Step 2.2: the close neighbour approach"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "14470ff4-a8cc-46c3-8976-1c5f12539e20",
   "metadata": {},
   "source": [
    "We want to know how a star moves with respect to its surroundings. Therefore, we can use the gaia astrometry of all sources within a given 3D distance of a source and measure their mean proper motion. That will allow us to measure whether our target of interest deviates from this value. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f57b953c-e336-42ff-9948-506a96591edc",
   "metadata": {},
   "source": [
    "### Setting up the functions:\n",
    "\n",
    "Here, we separate the function that searches for Gaia neighbours, the one that filters their quality and ranks them by 3D distance, and the functions that calculate the proper motion of the neighbours and the actual peculiar velocity and misalignment angle."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "85fc2f3b-9945-463e-9593-b6badb85221e",
   "metadata": {},
   "outputs": [],
   "source": [
    "def neighbour_search(ra_target, dec_target, name_target, d_target, physical_search_cone, repeat_gaiaquery = False):\n",
    "    \"\"\" \n",
    "    ra_target and dec_target should be in degrees but without a unit\n",
    "    Note: give d_target and physical_search_cone both in kpc but without a unit. \n",
    "    \"\"\"\n",
    "\n",
    "    # Note: since we search in 3D, we have the angular cone size as an upper limit: a star that is further away or closer by than \n",
    "    # the distance of our source, and is closer in 3D than the maximum distance, will have a smaller projected distance than\n",
    "    # a source with the same separation and the same distance as our source. So the angular cone size is the upper limit of the \n",
    "    # search, and we then rank in order of 3D distance. \n",
    "    ang_cone_size = np.round((np.arctan(physical_search_cone/d_target) * u.radian).to(u.degree).value, 3)\n",
    "    \n",
    "    savefile = 'Gaia_per_source/' + name_target + '.csv'\n",
    "    if os.path.exists(savefile) and (not repeat_gaiaquery):\n",
    "        print(name_target+':', 'Gaia search already performed; not repeating', flush=True)\n",
    "        return physical_search_cone\n",
    "    \n",
    "    Gaia.ROW_LIMIT = 100000\n",
    "    coord = SkyCoord(ra=ra_target, dec=dec_target, unit=(u.degree, u.degree), frame='icrs')    \n",
    "    job = Gaia.cone_search(coord, radius=u.Quantity(ang_cone_size, u.deg), \n",
    "                               dump_to_file=True, output_format='csv', \n",
    "                               output_file=savefile, verbose=False)\n",
    "        \n",
    "    return physical_search_cone"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "19925c03-62f0-44c5-8649-2c098294ce3b",
   "metadata": {},
   "outputs": [],
   "source": [
    "def filter_and_sort(ra_target, dec_target, name_target, d_target, physical_search_cone, repeat_filtering = False):\n",
    "    \"\"\"\n",
    "    This function takes the position of a target, its name, its distance, and a search cone, to find all Gaia sources within a requested\n",
    "    distance from the target. \n",
    "    \n",
    "    This function assumes you have already done the search for these Gaia sources with the function neighbour_search(). \n",
    "    \"\"\"\n",
    "    \n",
    "    # Load data from an earlier Gaia query if it exists: \n",
    "    savefile = 'Gaia_per_source/' + name_target + '.csv'\n",
    "    if os.path.exists(savefile):\n",
    "        cone_results = ps.read_csv(savefile)\n",
    "    else:\n",
    "        print(name_target+':', 'No Gaia cone search performed; please run neightbour_search() first or check the target name', flush=True)\n",
    "        return 0\n",
    "\n",
    "    sorted_filename = 'Gaia_per_source/' + name_target + '_filtered_distance.csv'\n",
    "    # If the Gaia query file has already been sorted and filtered, we are done. \n",
    "    if os.path.exists(sorted_filename) and (not repeat_filtering):\n",
    "        print(name_target+':', 'Already filtered and distance-selected; not repeating', flush=True)\n",
    "        return 0\n",
    "        \n",
    "    # Filter data in the same way that we have filtered our actual sources:\n",
    "    filtered_results = cone_results[cone_results['ruwe'] < 1.4]\n",
    "    filtered_results = filtered_results[filtered_results['parallax_over_error'] > 5.]\n",
    "    filtered_results = filtered_results[filtered_results['ipd_gof_harmonic_amplitude'] < 0.15]\n",
    "\n",
    "    # calculate the zeropoint corrections for the filtered cone search results:\n",
    "    zpt_corr = filtered_results.apply(zpt.zpt_wrapper, axis=1)\n",
    "\n",
    "    # Create SkyCoord Objects for the target:\n",
    "    target = ICRS(ra=ra_target*u.degree, \n",
    "                  dec=dec_target*u.degree, \n",
    "                  distance=d_target*u.kpc)\n",
    "\n",
    "    # Create a file name were we will only store the filtered cone search results, and include a column that \n",
    "    # lists there distance to the real target. \n",
    "    \n",
    "    # Note that only the sources that are close enough are saved -- this is to make sure we have a proper complete (down to Gaia\n",
    "    # completeness) sample; otherwise, we may have sources at large physical separation but small angular separation due to very \n",
    "    # different distances, and miss sources that are physically closer (and therefore more likely to represent the movement of\n",
    "    # the Galaxy at that position) but at similar distance and larger angular separation. \n",
    "    \n",
    "    sorted_df_created = False\n",
    "    N = 0\n",
    "    for i in range(len(filtered_results)):\n",
    "        neighbour_i = ICRS(ra=filtered_results.iloc[i].ra*u.degree,\n",
    "                      dec=filtered_results.iloc[i].dec*u.degree,\n",
    "                      pm_ra_cosdec=filtered_results.iloc[i].pmra*u.mas/u.yr, \n",
    "                      pm_dec=filtered_results.iloc[i].pmdec*u.mas/u.yr,\n",
    "                      distance=(1./(filtered_results.iloc[i].parallax - zpt_corr.iloc[i]))*u.kpc)\n",
    "\n",
    "        separations_i = target.separation_3d(neighbour_i).to(u.pc)\n",
    "        if separations_i < physical_search_cone * u.kpc and i != 0:\n",
    "            # i!=0 since the first one is not saved: this is the source we want to get the direction for, so it should be excluded.\n",
    "            N = N + 1\n",
    "            if not sorted_df_created:\n",
    "                sorted_df = filtered_results.iloc[i:i+1,]\n",
    "                sorted_df_all = sorted_df.copy()\n",
    "                sorted_df_all['phys_dist'] = separations_i.to(u.pc).value\n",
    "                sorted_df_created = True\n",
    "            else:\n",
    "                sorted_df_i = filtered_results.iloc[i:i+1,]\n",
    "                sorted_df_i_copy = sorted_df_i.copy()\n",
    "                sorted_df_i_copy['phys_dist'] = separations_i.to(u.pc).value\n",
    "                sorted_df_all = ps.concat([sorted_df_all, sorted_df_i_copy])\n",
    "    if N > 0:\n",
    "        sorted_df_all.to_csv(sorted_filename)\n",
    "\n",
    "    return N"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "b97648f5-20aa-454c-a0a6-a4bb170ffaa5",
   "metadata": {},
   "outputs": [],
   "source": [
    "def pm_neighbours(name_target, savefig=True, use_median=True, show_fig=True):\n",
    "    \"\"\"\n",
    "    This function searches for the file name with all close-by Gaia sources, based on the source name.\n",
    "    If this exists, it calculates the median proper motion of the neighbours. It also plots (and saves) this median \n",
    "    version the number of neighbours included, as a check to see if we converge. \n",
    "    \"\"\"\n",
    "    \n",
    "    sorted_filename = 'Gaia_per_source/' + name_target + '_filtered_distance.csv'\n",
    "    \n",
    "    if not os.path.exists(sorted_filename):\n",
    "        print(name_target+':', 'No filtering and distance calculation performed; please run filter_and_sort() first or check the target name', flush=True)\n",
    "        return None\n",
    "        \n",
    "    data_FD = ps.read_csv(sorted_filename) # FD == FilteredDistance\n",
    "\n",
    "    d_i = np.asarray(data_FD.phys_dist)\n",
    "    pmra_i = np.asarray(data_FD.pmra)\n",
    "    dpmra_i = np.asarray(data_FD.pmra_error)\n",
    "    pmdec_i = np.asarray(data_FD.pmdec)\n",
    "    dpmdec_i = np.asarray(data_FD.pmdec_error)\n",
    "    \n",
    "    zipped = zip(d_i, pmra_i, dpmra_i, pmdec_i, dpmdec_i)\n",
    "    zipped = sorted(zipped)\n",
    "    d_i, pmra_i, dpmra_i, pmdec_i, dpmdec_i = zip(*zipped)\n",
    "    \n",
    "    fig = plt.figure()\n",
    "    ax = fig.add_subplot(111)\n",
    "\n",
    "    if len(d_i) < 3:\n",
    "        return None\n",
    "    \n",
    "    for i in range(2,len(d_i)):\n",
    "        if use_median:\n",
    "            plot_ra = np.median(pmra_i[0:i])\n",
    "            plot_dec = np.median(pmdec_i[0:i])\n",
    "        else:\n",
    "            plot_ra = np.average(pmra_i[0:i], weights=1./np.asarray(dpmra_i[0:i])**2)\n",
    "            plot_dec = np.average(pmdec_i[0:i], weights=1./np.asarray(dpmdec_i[0:i])**2)\n",
    "        \n",
    "        plot_dra_stat = (1./sum((1./np.asarray(dpmra_i[0:i]))**2))**0.5\n",
    "        plot_dra_phys = np.std(pmra_i[0:i])/np.sqrt(i)\n",
    "        plot_ddec_stat = (1./sum((1./np.asarray(dpmdec_i[0:i]))**2))**0.5\n",
    "        plot_ddec_phys = np.std(pmdec_i[0:i])/np.sqrt(i)\n",
    "\n",
    "        plot_dra = (plot_dra_stat**2 + plot_dra_phys**2)**0.5\n",
    "        plot_ddec = (plot_ddec_stat**2 + plot_ddec_phys**2)**0.5\n",
    "        \n",
    "        ax.errorbar(d_i[i], plot_ra, yerr=plot_dra, fmt='ws', mew=1, mec='k', ecolor='k', elinewidth=1.5)\n",
    "        ax.errorbar(d_i[i], plot_dec, yerr=plot_ddec, fmt='ws', mew=1, mec='r', ecolor='r', elinewidth=1.5)\n",
    "    \n",
    "    pmra_corr = plot_ra\n",
    "    pmra_dcorr = plot_dra\n",
    "\n",
    "    pmdec_corr = plot_dec\n",
    "    pmdec_dcorr = plot_ddec\n",
    "\n",
    "    ax.fill_between([d_i[2],d_i[i]], [pmra_corr-pmra_dcorr, pmra_corr-pmra_dcorr], [pmra_corr+pmra_dcorr, pmra_corr+pmra_dcorr], \n",
    "                    color='k', alpha=0.3)\n",
    "\n",
    "    ax.fill_between([d_i[2],d_i[i]], [pmdec_corr-pmdec_dcorr, pmdec_corr-pmdec_dcorr], [pmdec_corr+pmdec_dcorr, pmdec_corr+pmdec_dcorr], \n",
    "                    color='r', alpha=0.3)\n",
    "    \n",
    "    ax.set_xlabel('Distance from target [pc]')\n",
    "    ax.set_ylabel('Sample average proper motion [mas/yr]')\n",
    "\n",
    "    if savefig:\n",
    "        plt.savefig('Figures_per_source/' + name_target + '.pdf')\n",
    "    if show_fig:\n",
    "        plt.show()\n",
    "\n",
    "    return pmra_corr, pmra_dcorr, pmdec_corr, pmdec_dcorr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "cf4a55ba-46a8-47e2-8e27-a5c51dcd744f",
   "metadata": {},
   "outputs": [],
   "source": [
    "def PA_v_calc(pmra_pec, dpmra_pec, pmdec_pec, dpmdec_pec, D, dparallax, PA, dPA = 5.0, return_means=True, Nmc=1000):\n",
    "    \"\"\" \n",
    "    Function that calculates the stellar peculiar velocity and mis-alignment with the bow shock, \n",
    "    and calculates the errors too. \n",
    "\n",
    "    Note that, in this calculation, we need to be careful with the uncertainties on the angles: since these are defined on a \n",
    "    0 to 180 or 0 to 360 axis and repeat, the uncertainties close to the 0/180/360 values can otherwise be unreasonably large. \n",
    "    \"\"\"\n",
    "\n",
    "    # velocity:\n",
    "    vstar = ((((pmra_pec * (u.mas / u.year) / (1.*u.radian)) * (D * u.kpc))**2 + \n",
    "            ((pmdec_pec *  (u.mas / u.year) / (1.*u.radian)) * (D * u.kpc))**2)**0.5).to(u.km/u.second).value\n",
    "\n",
    "    vstar_sim = np.zeros(Nmc)\n",
    "\n",
    "    # varying the input of the velocity calculation, to calculate the uncertainty on the velocity. \n",
    "    parallax = 1./D\n",
    "    parallax_sim = np.random.normal(parallax, dparallax, size=Nmc)\n",
    "    D_sim = 1./parallax_sim\n",
    "    pmra_pec_sim = np.random.normal(pmra_pec, dpmra_pec, size=Nmc)\n",
    "    pmdec_pec_sim = np.random.normal(pmdec_pec, dpmdec_pec, size=Nmc)\n",
    "    PA_sim = np.random.normal(PA, dPA, size=Nmc)\n",
    "\n",
    "    # velocity error:\n",
    "    vstar_sim = ((((pmra_pec_sim * (u.mas / u.year) / (1.*u.radian)) * (D_sim * u.kpc))**2 + \n",
    "                ((pmdec_pec_sim *  (u.mas / u.year) / (1.*u.radian)) * (D_sim * u.kpc))**2)**0.5).to(u.km/u.second).value\n",
    "    dvstar = np.std(vstar_sim) # km/second\n",
    "    \n",
    "    # Calculate angle and mis-alignment: \n",
    "    if pmdec_pec > 0:\n",
    "        theta = (np.arctan(pmra_pec / pmdec_pec) * u.rad).to(u.degree).value\n",
    "    else:\n",
    "        theta = (np.arctan(pmra_pec / pmdec_pec) * u.rad).to(u.degree).value + 180.\n",
    "    if theta < 0:\n",
    "        theta = theta + 360.\n",
    "    \n",
    "    theta_BS = PA\n",
    "    delta_theta = theta - theta_BS\n",
    "    if delta_theta > 180:\n",
    "        delta_theta = abs(delta_theta - 360.)\n",
    "    elif delta_theta < -180:\n",
    "        delta_theta = delta_theta + 360.\n",
    "    elif delta_theta < 0:\n",
    "        delta_theta = abs(delta_theta)\n",
    "\n",
    "    # Errors:\n",
    "    delta_theta_sim = []\n",
    "    for i in range(Nmc):\n",
    "        if pmdec_pec_sim[i] > 0:\n",
    "            theta_i = (np.arctan(pmra_pec_sim[i] / pmdec_pec_sim[i]) * u.rad).to(u.degree).value\n",
    "        else:\n",
    "            theta_i = (np.arctan(pmra_pec_sim[i] / pmdec_pec_sim[i]) * u.rad).to(u.degree).value + 180.\n",
    "        if theta_i < 0:\n",
    "            theta_i = theta_i + 360.\n",
    "        \n",
    "        # mis-alignments:\n",
    "        theta_BS_i = PA_sim[i]\n",
    "        delta_theta_i = theta_i - theta_BS_i\n",
    "        if delta_theta_i > 180:\n",
    "            delta_theta_i = abs(delta_theta_i - 360.)\n",
    "        elif delta_theta_i < -180:\n",
    "            delta_theta_i = delta_theta_i + 360.\n",
    "        elif delta_theta_i < 0:\n",
    "            delta_theta_i = abs(delta_theta_i)\n",
    "            \n",
    "        delta_theta_sim.append(delta_theta_i)\n",
    "\n",
    "    ddelta_theta = np.std(delta_theta_sim) # degree\n",
    "\n",
    "    if return_means:\n",
    "        return vstar, dvstar, delta_theta, ddelta_theta\n",
    "    else: \n",
    "        return vstar_sim, delta_theta_sim"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1cb2b965-015c-4b84-b1d6-6454edf9b8fe",
   "metadata": {},
   "source": [
    "### Perfoming the calculations and the corrections:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "13f1b66b-4f41-41f0-88cd-944ed5653e58",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Loading the identifications for the surroundings where available:\n",
    "surroundings = vizier.get_catalogs(\"J/AJ/164/86\")\n",
    "surroundings_table = surroundings[1]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fd548806-95bc-48cc-a585-6ae855ef8627",
   "metadata": {},
   "source": [
    "The code below is only executed if you set 'redo_corrections' to True at the start of this notebook.\n",
    "\n",
    "**Be Warned**: this step is slow if you run for the first time: the Gaia query for neighbours takes time and returns a large csv file. It sometimes needs to be repeated if there are not enough close-by sources in the first attempted distance. The final set of csv files for all the sources, saved in the 'Gaia_per_source' folder, will amount for 23 GB. Therefore, this folder is included here, but the files are not.\n",
    "\n",
    "You can continue with the output of this step, or run it yourself. But, as said, this will take a while to run. The diagnostic plots are included in the 'Figures_per_source' folder to confirm that the neighbours returned a consistent median Galactic motion."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "207f91eb-8984-4d2d-9006-0e152c8dda32",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Not doing the corrections again\n"
     ]
    }
   ],
   "source": [
    "if redo_corrections:\n",
    "    print('Are you sure? This will be very time consuming, unless the files in the Gaia_per_source exist')\n",
    "    print('You can continue using the output of this step, which is included in this reproduction package')\n",
    "    print('To do so, uncomment the next cell')    \n",
    "else:\n",
    "    print('Not doing the corrections again')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "94d0d401-5e24-4583-a6cc-86ac77d13999",
   "metadata": {},
   "outputs": [],
   "source": [
    "# redo_corrections = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "7fb6edc3-7980-481f-a6e0-d08afef2b77a",
   "metadata": {},
   "outputs": [],
   "source": [
    "count = 0\n",
    "if redo_corrections:\n",
    "\n",
    "    # The zero-point corrections:\n",
    "    distances_zptcorr = (1./(np.asarray(filtered_data.parallax) - zpt_corr))\n",
    "    cone_size_start = 0.02 # in kpc without unit\n",
    "\n",
    "    # The minimum required good quality neighbours. If not met, the cone-size will be increased, up to maximally 100 pc. \n",
    "    Nmin = 50\n",
    "\n",
    "    # Set this to True if you want to repeat the Gaia search and filtering, even though you already did this. (NOT RECOMMENDED)\n",
    "    repeat_analysis = False \n",
    "\n",
    "    # The values that will be saved:\n",
    "    all_pmra_pec = []\n",
    "    all_pmdec_pec = []\n",
    "    all_dpmra_pec = []\n",
    "    all_dpmdec_pec = []\n",
    "    all_dist = []\n",
    "    all_dparallax = []\n",
    "    all_PA = []\n",
    "    all_names = []\n",
    "    all_env = []\n",
    "    all_RA = []\n",
    "    all_DEC = []\n",
    "    \n",
    "    delta_theta_unitsless = []\n",
    "    ddelta_theta_unitsless = []\n",
    "    vstar_unitsless = []\n",
    "    dvstar_unitsless = []\n",
    "\n",
    "    # Loop over the source (233 in total):\n",
    "    for i in range(len(filtered_data)):\n",
    "    \n",
    "        DIST_i = distances_zptcorr[i] # in kpc without unit\n",
    "\n",
    "        # Finding the environment of the source:\n",
    "        GaiaNumber = filtered_data['source_id'][i]\n",
    "        if GaiaNumber in surroundings_table['Gaia']:\n",
    "            k_env = np.where(surroundings_table['Gaia'] == GaiaNumber)[0][0]\n",
    "            env_class = surroundings_table['Env'][k_env]\n",
    "            count += 1\n",
    "        else:\n",
    "            env_class = 'Unknown'\n",
    "\n",
    "        # Find the name, RA, DEC, and parallax error per source:\n",
    "        survey_index = filtered_data['my_index'][i]\n",
    "        if filtered_data['survey'][i] == 'K16':\n",
    "            NAME_i = '_'.join(result_table['main_id'][survey_index].split())\n",
    "            PA_i = kobulnicky_table['PA'][survey_index]\n",
    "        else:\n",
    "            NAME_i = 'MWP_GaiaDR3_'+str(filtered_data['source_id'][i])\n",
    "            PA_i = mwproject_table['PA'][survey_index]\n",
    "        RA_i = filtered_data.ra[i] # in deg without unit\n",
    "        DEC_i = filtered_data.dec[i] # in deg without unit\n",
    "        dPARALLAX_i = filtered_data.parallax_error[i] \n",
    "\n",
    "        # Perform the analysis if the source is not in the list of ones where \n",
    "        # we know (from our trials) that it has insufficient neighbours or the Gaia query raises and error:\n",
    "        if (NAME_i not in skip_sources) and (NAME_i not in gaia_error_sources):\n",
    "\n",
    "            # Check if the search and filter file exists already: \n",
    "            perform_search_filter = True\n",
    "            if os.path.exists('Gaia_per_source/' + NAME_i + '_filtered_distance.csv'):\n",
    "                data_FD = ps.read_csv('Gaia_per_source/' + NAME_i + '_filtered_distance.csv')\n",
    "                if (len(data_FD) > Nmin) or (not repeat_analysis):\n",
    "                    perform_search_filter = False\n",
    "\n",
    "            # If the search and filtering needs to be performed:\n",
    "            if perform_search_filter:\n",
    "                cone_size_i = cone_size_start\n",
    "\n",
    "                # Perform the search and filtering until we have >50 neighbours:\n",
    "                Ni = 0\n",
    "                while Ni < Nmin and cone_size_i <= 0.1:\n",
    "                    print(NAME_i+':', 'starting neighbour search in Gaia with cone size =', np.round(1000*cone_size_i, 1), 'pc', flush=True)\n",
    "                    updated_cone_size = neighbour_search(ra_target=RA_i, dec_target=DEC_i, name_target=NAME_i, d_target=DIST_i, \n",
    "                                                         physical_search_cone=cone_size_i, repeat_gaiaquery = True)\n",
    "                    \n",
    "                    Ni = filter_and_sort(ra_target=RA_i, dec_target=DEC_i, name_target=NAME_i, d_target=DIST_i, \n",
    "                                         physical_search_cone=updated_cone_size, repeat_filtering = True)\n",
    "                    print(NAME_i+':', 'Found ', Ni, 'neighbours within', np.round(1000*cone_size_i, 1), 'pc')\n",
    "                    cone_size_i = cone_size_i + 0.02\n",
    "\n",
    "                # Check if the cone did not get too large:\n",
    "                if cone_size_i <= 0.1:\n",
    "                    print(NAME_i+':', 'accepted; continue', flush=True)\n",
    "                else: \n",
    "                    print(NAME_i+':', 'hit maximum separation limit; continue', flush=True)\n",
    "            else:\n",
    "                print(NAME_i+':', 'not repeating neightbour search and filtering', flush=True)\n",
    "\n",
    "            # If we have enough neighbours, calculate their median motion:\n",
    "            print(NAME_i+':', 'calculating the proper motion corrections', flush=True)\n",
    "            pm_corrections = pm_neighbours(name_target=NAME_i, savefig=True, show_fig=False)\n",
    "    \n",
    "            # Perform the correction:\n",
    "            try:\n",
    "                pmra_corr, pmra_dcorr, pmdec_corr, pmdec_dcorr = pm_corrections\n",
    "        \n",
    "                pmra_pec = filtered_data.pmra[i]-pmra_corr\n",
    "                pmdec_pec = filtered_data.pmdec[i]-pmdec_corr\n",
    "                dpmra_pec = (pmra_dcorr**2 + filtered_data.pmra_error[i]**2)**0.5\n",
    "                dpmdec_pec = (pmdec_dcorr**2 + filtered_data.pmdec_error[i]**2)**0.5\n",
    "                \n",
    "                print(NAME_i, r': Uncorrected: pmRA =', np.round(filtered_data.pmra[i],3), '| pmDEC =', np.round(filtered_data.pmdec[i],3))\n",
    "                print(NAME_i, ': Corrected: pmRA =', np.round(filtered_data.pmra[i]-pmra_corr,3), '+/-', np.round(dpmra_pec,3), '| pmDEC =', np.round(filtered_data.pmdec[i]-pmdec_corr,3), '+/-', np.round(dpmdec_pec,3))\n",
    "                print('')\n",
    "        \n",
    "                all_pmra_pec.append(pmra_pec)\n",
    "                all_pmdec_pec.append(pmdec_pec)\n",
    "                all_dpmra_pec.append(dpmra_pec)\n",
    "                all_dpmdec_pec.append(dpmdec_pec)\n",
    "                all_dist.append(DIST_i)\n",
    "                all_dparallax.append(dPARALLAX_i)\n",
    "                all_PA.append(PA_i)\n",
    "                all_names.append(NAME_i)\n",
    "                all_env.append(env_class)\n",
    "                all_RA.append(RA_i)\n",
    "                all_DEC.append(DEC_i)\n",
    "    \n",
    "                vstar, dvstar, delta_theta, ddelta_theta = PA_v_calc(pmra_pec, dpmra_pec, pmdec_pec, dpmdec_pec, D=DIST_i, dparallax=dPARALLAX_i, PA=PA_i, dPA = 5.0)\n",
    "                \n",
    "                delta_theta_unitsless.append(delta_theta)\n",
    "                ddelta_theta_unitsless.append(ddelta_theta)\n",
    "                vstar_unitsless.append(vstar)\n",
    "                dvstar_unitsless.append(dvstar)\n",
    "                           \n",
    "            except TypeError:\n",
    "                print(NAME_i, 'Skipping due to TypeError')\n",
    "                print('')  \n",
    "\n",
    "    # Save all the csv file:\n",
    "    to_df = {\"Name\": all_names, \"pm_ra_pec\":all_pmra_pec, \"dpm_ra_pec\":all_dpmra_pec, \n",
    "             \"pm_dec_pec\":all_pmdec_pec, \"dpm_dec_pec\":all_dpmdec_pec, \"Distance\":all_dist,\n",
    "             \"dp\":all_dparallax, \"PA\":all_PA, \"delta_theta\":delta_theta_unitsless,\n",
    "             \"ddelta_theta\":ddelta_theta_unitsless, \"vstar\":vstar_unitsless, \"dvstar\":dvstar_unitsless, \"Env\":all_env, \"RA\":all_RA, \"DEC\":all_DEC}\n",
    "\n",
    "    df_comb = ps.DataFrame(data=to_df)\n",
    "    df_comb.to_csv('Corrected_PM_ALL.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2c9fea6e-2b15-45ae-bd2a-dd91ef4c9d2c",
   "metadata": {},
   "source": [
    "## Step 2.3: comparing the two correction approaches:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "ef8d95ed-d9ac-4474-aadd-70d014f764c7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 750x300 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Reproducing Figure 1 of the paper:\n",
    "\n",
    "merged_df_neighbours = ps.read_csv('Corrected_PM_ALL.csv')\n",
    "merged_df_GalRot = ps.read_csv(\"GalRot_results_both.csv\")\n",
    "\n",
    "fig, axes = plt.subplots(1, 3, figsize=(7.5, 3))\n",
    "fig.subplots_adjust(wspace=0.025)\n",
    "\n",
    "ax = axes[0]\n",
    "\n",
    "BINS = np.linspace(-8, 8, 50)\n",
    "ax.hist(np.asarray(merged_df_GalRot['# pmRA_1']), bins=BINS, histtype='step', density=True, \n",
    "        cumulative=False, linewidth=1.2, color='r', label='Rotation curve A')\n",
    "ax.hist(np.asarray(merged_df_GalRot[' pmRA_2']), bins=BINS, histtype='step', density=True, \n",
    "        cumulative=False, linewidth=1.2, color='b', label='Rotation curve B')\n",
    "ax.hist(np.asarray(merged_df_neighbours['pm_ra_pec']), bins=BINS, histtype='stepfilled', density=True, \n",
    "        cumulative=False, linewidth=1.2, edgecolor='k', color=\"0.7\", alpha=1, label='Neighbour approach')\n",
    "\n",
    "ax.plot([0,0], [0, 0.67], 'k--', lw=1, zorder=2)\n",
    "\n",
    "ax.set_ylim(0.,0.88)\n",
    "ax.set_xlim(-7,7)\n",
    "ax.set_xticks(np.linspace(-6, 6, 7))\n",
    "ax.set_xlabel(r'$\\mu_{\\alpha*}$  [mas/yr]', fontsize=10)\n",
    "ax.set_ylabel('Number (normalized)')\n",
    "ax.set_yticks([])\n",
    "\n",
    "ax.legend(loc=2, fontsize=8, fancybox=False, frameon=False)\n",
    "\n",
    "ax = axes[1]\n",
    "\n",
    "BINS = np.linspace(-8, 8, 50)\n",
    "ax.hist(np.asarray(merged_df_GalRot[' pmDec_1']), bins=BINS, histtype='step', density=True, \n",
    "        cumulative=False, linewidth=1.2, color='r')#, label='Error')\n",
    "ax.hist(np.asarray(merged_df_GalRot[' pmDec_2']), bins=BINS, histtype='step', density=True, \n",
    "        cumulative=False, linewidth=1.2, color='b', )#, label='Error')\n",
    "ax.hist(np.asarray(merged_df_neighbours['pm_dec_pec']), bins=BINS, histtype='stepfilled', density=True, \n",
    "        cumulative=False, linewidth=1.2, edgecolor='k', color=\"0.7\", alpha=1)#, label='Error')\n",
    "\n",
    "ax.plot([0,0], [0, 1], 'k--', lw=1)\n",
    "\n",
    "ax.set_ylim(0.,0.75)\n",
    "ax.set_xlim(-8,8)\n",
    "ax.set_xticks(np.linspace(-8, 8, 9))\n",
    "ax.set_xlabel(r'$\\mu_{\\delta}$ [mas/yr]', fontsize=10)\n",
    "ax.set_yticks([])\n",
    "\n",
    "ax = axes[2]\n",
    "\n",
    "ax.plot([0,0], [-100, 100], 'k--', lw=1)\n",
    "ax.plot([-100, 100], [0,0], 'k--', lw=1)\n",
    "\n",
    "ax.errorbar(np.asarray(merged_df_GalRot['# pmRA_1']), np.asarray(merged_df_GalRot[' pmDec_1']), \n",
    "            fmt='rs', elinewidth=1, mew=1, mec='r', ecolor='r', ms=1)\n",
    "ax.errorbar(np.asarray(merged_df_GalRot[' pmRA_2']), np.asarray(merged_df_GalRot[' pmDec_2']), \n",
    "            fmt='bs', elinewidth=1, mew=1, mec='b', ecolor='b', ms=1)\n",
    "ax.errorbar(np.asarray(merged_df_neighbours['pm_ra_pec']), np.asarray(merged_df_neighbours['pm_dec_pec']),\n",
    "            xerr=np.asarray(merged_df_neighbours['dpm_ra_pec']), yerr=np.asarray(merged_df_neighbours['dpm_dec_pec']),\n",
    "            fmt='ko', elinewidth=0.5, mew=0, mec='k', ecolor='k', ms=2, zorder=10, alpha=0.5)\n",
    "\n",
    "\n",
    "ax.set_xlim(7,-7)\n",
    "ax.set_ylim(-7,7)\n",
    "ax.set_yticks([-6,-4,-2,0,2,4,6])\n",
    "ax.set_xticks([6,4,2,0,-2,-4,-6])\n",
    "ax.yaxis.tick_right()\n",
    "ax.yaxis.set_label_position(\"right\")\n",
    "ax.set_xlabel(r'$\\mu_{\\alpha*}$ [mas/yr]', fontsize=10)\n",
    "ax.set_ylabel(r'$\\mu_{\\delta}$ [mas/yr]', fontsize=10)\n",
    "\n",
    "fig.tight_layout(w_pad=0.01)\n",
    "plt.savefig('PAPERPLOTS/methods_3panels.pdf')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e5de9d84-bdba-42b6-a75e-419712c90bae",
   "metadata": {},
   "source": [
    "## Step 2.4: The main observational result: Figure 2 of the paper"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "aa2e38d5-8583-4341-bc02-656503e356ca",
   "metadata": {},
   "outputs": [],
   "source": [
    "def percentage_printer(array, name='Array'):\n",
    "\n",
    "    MEAN = str(np.round(100*np.mean(array),1))\n",
    "    dP = str(np.round(100*(np.percentile(array, 84.) - np.mean(array)), 1))\n",
    "    dM = str(np.round(100*(np.mean(array) - np.percentile(array, 16.)), 1))\n",
    "    \n",
    "    print(name+': '+MEAN+' +'+dP+',-'+dM)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "4d275069-3de0-4227-a864-357445503c71",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "210"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Loading the corrections\n",
    "corrected_pm = ps.read_csv('Corrected_PM_ALL.csv')\n",
    "len(corrected_pm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "4595056c-4bb3-4085-8279-54a75ce1fbac",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Percentages:\n",
      "---------------------------\n",
      "Misaligned: 70.2 +2.2,-1.6\n",
      "Aligned: 29.8 +1.6,-2.2\n",
      "---------------------------\n",
      "Subsonic: 48.5 +1.5,-1.8\n",
      "Supersonic: 51.5 +1.8,-1.5\n",
      "Runaway: 23.5 +1.3,-1.1\n",
      "---------------------------\n",
      "Q1: 40.1 +1.8,-2.0\n",
      "Q2: 30.1 +1.8,-2.0\n",
      "Q3: 8.3 +1.7,-1.7\n",
      "Q4: 21.4 +1.4,-1.4\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/4g/0wm8g7bd7w59_rrcwv_jhv680000gn/T/ipykernel_25027/2099308762.py:175: UserWarning: This figure includes Axes that are not compatible with tight_layout, so results might be incorrect.\n",
      "  fig.tight_layout()\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 600x800 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(6, 8))\n",
    "gs = fig.add_gridspec(2, 2, height_ratios=[1.67, 1], hspace=0.2, wspace=0.2)\n",
    "\n",
    "# Top panel (spans both columns)\n",
    "ax_top = fig.add_subplot(gs[0, :])\n",
    "\n",
    "# Bottom left and right panels\n",
    "ax_bl = fig.add_subplot(gs[1, 0])\n",
    "ax_br = fig.add_subplot(gs[1, 1])\n",
    "\n",
    "ax = ax_top\n",
    "\n",
    "ax.errorbar(np.asarray(corrected_pm['vstar']), np.asarray(corrected_pm['delta_theta']), \n",
    "             xerr=np.asarray(corrected_pm['dvstar']), yerr=np.asarray(corrected_pm['ddelta_theta']),\n",
    "             fmt='wo', ms=5, mew=1, mec='k', ecolor='0.4', elinewidth=0.5)\n",
    "ax.set_xlabel('Stellar peculiar velocity [km/s]', fontsize=10)\n",
    "ax.set_ylabel(r'Misalignment angle $\\beta$ [deg]', fontsize=10)\n",
    "ax.set_ylim(0,180)\n",
    "ax.set_xlim(0,100)\n",
    "\n",
    "ax.plot([0, 110], [30,30], 'r--', lw=2, zorder=1)\n",
    "ax.plot([10, 10], [0,2000], 'r-', lw=2, zorder=1)\n",
    "\n",
    "ax = ax_bl\n",
    "\n",
    "# Calculate the CDF of theta and its error; already calculate those for vstar as well. \n",
    "Nerror = 1000\n",
    "\n",
    "all_vstar_sim = np.zeros((len(corrected_pm['vstar']), Nerror))\n",
    "all_delta_theta_sim = np.zeros((len(corrected_pm['ddelta_theta']), Nerror))\n",
    "for i in range(len(corrected_pm['ddelta_theta'])):\n",
    "\n",
    "    vstar_sim_i, delta_theta_sim_i = PA_v_calc(np.asarray(corrected_pm['pm_ra_pec'])[i], \n",
    "                                               np.asarray(corrected_pm['dpm_ra_pec'])[i], \n",
    "                                               np.asarray(corrected_pm['pm_dec_pec'])[i],\n",
    "                                               np.asarray(corrected_pm['dpm_dec_pec'])[i],\n",
    "                                               D = np.asarray(corrected_pm['Distance'])[i],\n",
    "                                               dparallax=np.asarray(corrected_pm['dp'])[i],\n",
    "                                               PA=np.asarray(corrected_pm['PA'])[i],\n",
    "                                               dPA = 5.0, return_means=False, Nmc = Nerror)\n",
    "    \n",
    "    all_delta_theta_sim[i] = np.asarray(delta_theta_sim_i)\n",
    "    all_vstar_sim[i] = np.asarray(vstar_sim_i)\n",
    "\n",
    "ax_i = ax.twinx()\n",
    "\n",
    "ax_i.hist(np.asarray(corrected_pm['delta_theta']), histtype='step', density=True, \n",
    "          cumulative=False, bins=np.linspace(0,180,10), linewidth=1.5, color='b', zorder=1)\n",
    "ax_i.set_yticks([])\n",
    "ax_i.set_ylabel('Normalized histogram', color='b')\n",
    "ax_i.yaxis.set_label_position(\"left\")\n",
    "\n",
    "# Calculate the percentages:\n",
    "counts_misaligned = []\n",
    "counts_aligned = [] \n",
    "counts_subsonic = []\n",
    "counts_supersonic = []\n",
    "counts_1 = []\n",
    "counts_2 = []\n",
    "counts_3 = []\n",
    "counts_4 = []\n",
    "counts_runaway = []\n",
    "\n",
    "for i in range(Nerror):\n",
    "\n",
    "    delta_theta_unitsless_sim = np.transpose(all_delta_theta_sim)[i]\n",
    "    vstar_unitsless_sim = np.transpose(all_vstar_sim)[i]\n",
    "\n",
    "    Ni = float(len(vstar_unitsless_sim))\n",
    "    \n",
    "    counts_misaligned.append(np.sum(delta_theta_unitsless_sim > 30.) / Ni)\n",
    "    counts_aligned.append(np.sum(delta_theta_unitsless_sim <= 30.) / Ni)\n",
    "    counts_subsonic.append(np.sum(vstar_unitsless_sim <= 10.) / Ni)\n",
    "    counts_supersonic.append(np.sum(vstar_unitsless_sim > 10.) / Ni)\n",
    "    counts_runaway.append(np.sum(vstar_unitsless_sim > 20.) / Ni)\n",
    "    \n",
    "    counts_1.append(np.sum((delta_theta_unitsless_sim > 30.) & (vstar_unitsless_sim <= 10.)) / Ni)\n",
    "    counts_2.append(np.sum((delta_theta_unitsless_sim > 30.) & (vstar_unitsless_sim > 10.)) / Ni)\n",
    "    counts_3.append(np.sum((delta_theta_unitsless_sim <= 30.) & (vstar_unitsless_sim <= 10.)) / Ni)\n",
    "    counts_4.append(np.sum((delta_theta_unitsless_sim <= 30.) & (vstar_unitsless_sim > 10.)) / Ni)\n",
    "\n",
    "print('Percentages:')\n",
    "print('---------------------------')\n",
    "percentage_printer(counts_misaligned, name='Misaligned')\n",
    "percentage_printer(counts_aligned, name='Aligned')\n",
    "print('---------------------------')\n",
    "percentage_printer(counts_subsonic, name='Subsonic')\n",
    "percentage_printer(counts_supersonic, name='Supersonic')\n",
    "percentage_printer(counts_runaway, name='Runaway')\n",
    "print('---------------------------')\n",
    "percentage_printer(counts_1, name='Q1')\n",
    "percentage_printer(counts_2, name='Q2')\n",
    "percentage_printer(counts_3, name='Q3')\n",
    "percentage_printer(counts_4, name='Q4')\n",
    "\n",
    "error_matrix = np.zeros((Nerror, len(all_delta_theta_sim)-1))\n",
    "for i in range(Nerror):\n",
    "    \n",
    "    delta_theta_unitsless_sim = np.transpose(all_delta_theta_sim)[i]\n",
    "    n,bins,patches = ax.hist(delta_theta_unitsless_sim, histtype='step', density=True, \n",
    "                         cumulative=True, bins=np.sort(delta_theta_unitsless_sim), linewidth=0., color='0.5', alpha=0.2)\n",
    "    patches[0].set_xy(patches[0].get_xy()[:-1])\n",
    "\n",
    "    error_matrix[i] = (bins[:-1] + bins[1:])/2.\n",
    "    \n",
    "stds = []\n",
    "for i in range(len(delta_theta_unitsless_sim)-1):\n",
    "    errors_i = np.transpose(error_matrix)[i]\n",
    "    std_i = np.std(errors_i)\n",
    "    stds.append(std_i)\n",
    "\n",
    "n,bins,patches = ax.hist(np.asarray(corrected_pm['delta_theta']), histtype='step', density=True, \n",
    "                         cumulative=True, bins=np.sort(corrected_pm['delta_theta']), linewidth=0., color='k')\n",
    "patches[0].set_xy(patches[0].get_xy()[:-1])\n",
    "\n",
    "bins_min = (bins[:-1] + bins[1:])/2. - np.asarray(stds)\n",
    "bins_max = (bins[:-1] + bins[1:])/2. + np.asarray(stds)\n",
    "\n",
    "ax.step(bins_min, n, 'k-', lw=0.5, zorder=3)\n",
    "ax.step(bins_max, n, 'k-', lw=0.5, zorder=3)\n",
    "ax.fill_betweenx(n, bins_min, bins_max, color='0.50', zorder=2)\n",
    "\n",
    "ax.set_xlabel(r'Misalignment angle $\\beta$ [deg]', fontsize=10)\n",
    "ax.yaxis.tick_right()\n",
    "ax.yaxis.set_label_position(\"right\")\n",
    "ax.set_yticklabels([])\n",
    "ax.set_ylim(0,1)\n",
    "ax.set_xlim(0,180)\n",
    "ax.set_xticks(np.linspace(0,180,7))\n",
    "ax.grid(color='0.8', linestyle='-.', linewidth=1)\n",
    "\n",
    "ax = ax_br\n",
    "\n",
    "ax_i = ax.twinx()\n",
    "ax_i.hist(np.asarray(corrected_pm['vstar']), histtype='step', density=True, \n",
    "          cumulative=False, bins=np.logspace(0,2,11), linewidth=1.5, color='b', zorder=1)\n",
    "ax_i.set_yticks([])\n",
    "\n",
    "error_matrix = np.zeros((Nerror, len(all_vstar_sim)-1))\n",
    "for i in range(Nerror):\n",
    "    \n",
    "    vstar_unitsless_sim = np.transpose(all_vstar_sim)[i]\n",
    "    n,bins,patches = ax.hist(vstar_unitsless_sim, histtype='step', density=True, \n",
    "                         cumulative=True, bins=np.sort(vstar_unitsless_sim), linewidth=0., color='0.5', alpha=0.2)\n",
    "    patches[0].set_xy(patches[0].get_xy()[:-1])\n",
    "\n",
    "    error_matrix[i] = (bins[:-1] + bins[1:])/2.\n",
    "\n",
    "stds = []\n",
    "for i in range(len(vstar_unitsless_sim)-1):\n",
    "    errors_i = np.transpose(error_matrix)[i]\n",
    "    std_i = np.std(errors_i)\n",
    "    stds.append(std_i)   \n",
    "\n",
    "n,bins,patches = ax.hist(np.asarray(corrected_pm['vstar']), histtype='step', density=True, \n",
    "                         cumulative=True, bins=np.sort(corrected_pm['vstar']), linewidth=0., color='k')\n",
    "patches[0].set_xy(patches[0].get_xy()[:-1])\n",
    "\n",
    "bins_min = (bins[:-1] + bins[1:])/2. - np.asarray(stds)\n",
    "bins_max = (bins[:-1] + bins[1:])/2. + np.asarray(stds)\n",
    "\n",
    "ax.step(bins_min, n, 'k-', lw=0.5, zorder=3)\n",
    "ax.step(bins_max, n, 'k-', lw=0.5, zorder=3)\n",
    "ax.fill_betweenx(n, bins_min, bins_max, color='0.50', zorder=2)\n",
    "\n",
    "ax.yaxis.tick_right()\n",
    "ax.yaxis.set_label_position(\"right\")\n",
    "ax.set_ylabel('Cumulative distribution function')\n",
    "ax.set_xlabel('Stellar peculiar velocity [km/s]')\n",
    "ax.set_ylim(0,1)\n",
    "ax.set_xscale('log')\n",
    "ax.set_xlim(1,100)\n",
    "ax.grid(color='0.8', linestyle='-.', linewidth=1)\n",
    "\n",
    "fig.tight_layout()\n",
    "plt.savefig('./PAPERPLOTS/beta_v.pdf')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b03b7cf4-7f86-434d-8631-3071e0419edd",
   "metadata": {},
   "source": [
    "# Step 3: the statistical analysis of the ISM movement"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "93a59aab-0051-4763-a037-e3c370663e8a",
   "metadata": {},
   "source": [
    "## Step 3.1: what to expect, mathematically"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "f22a097b-934b-4c60-b039-cea47f379f30",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/jakobvandeneijnden/miniconda3/envs/astro/lib/python3.12/site-packages/astropy/units/quantity.py:676: RuntimeWarning: invalid value encountered in arccos\n",
      "  result = super().__array_ufunc__(function, method, *arrays, **kwargs)\n"
     ]
    }
   ],
   "source": [
    "alpha_array = np.linspace(0, np.pi, 1000) * u.radian\n",
    "\n",
    "v_star = 50.\n",
    "v_ISM = np.linspace(0, 500, 1000)\n",
    "r_v = v_ISM / v_star\n",
    "\n",
    "# Beta calculation for this grid:\n",
    "beta = []\n",
    "for i in range(len(alpha_array)):\n",
    "    cos_beta_i = (1. + r_v * np.cos(alpha_array[i])) / np.sqrt(1. + r_v**2 + 2*r_v*np.cos(alpha_array[i]))\n",
    "    beta.append((np.arccos(cos_beta_i)).to(u.degree))\n",
    "beta = np.asarray(beta)\n",
    "\n",
    "# The stellar wind correction factors:\n",
    "fcorr = []\n",
    "for i in range(len(alpha_array)):\n",
    "    fcorr.append(1. + r_v**2 + 2*r_v*np.cos(alpha_array[i]))\n",
    "fcorr = np.asarray(fcorr)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4ff4211e-0ce1-499e-a1ff-f380a5784dbb",
   "metadata": {},
   "source": [
    "Plotting the equations for the misalignment angle and correction factor:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "c4bafa99-8569-4d17-9c5d-07cab1ca0f80",
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib.colors import LogNorm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "a084ed07-91ab-4a37-ae68-d7791f680e69",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 800x400 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1, 2, figsize=(8, 4))\n",
    "fig.subplots_adjust(wspace=0.3)\n",
    "\n",
    "ax = axes[0]\n",
    "\n",
    "image = ax.imshow(np.matrix.transpose(beta), cmap = 'magma', origin='lower', extent=[0, 180, 0, 180])\n",
    "\n",
    "levels = ax.contour(np.matrix.transpose(beta), levels=[30, 90, 150], linestyles=['-','-.', ':'], colors='w', origin='lower', extent=[0, 180, 0, 180])\n",
    "\n",
    "ax.set_xticks(np.linspace(0, 180, 7))\n",
    "ax.set_yticks(np.linspace(0, 180, 11))\n",
    "ax.set_yticklabels(np.linspace(0, 10, 11))\n",
    "ax.set_xlabel(r'$\\alpha$ [deg]')\n",
    "ax.set_ylabel(r'$r_v \\equiv v_{\\rm ISM}$ / $v_{\\rm star}$')\n",
    "\n",
    "cbar = fig.colorbar(image, ax=ax, fraction=0.046, pad=0.04)\n",
    "cbar.set_label(r'Misalignment angle $\\beta$ [deg]')#, fontsize=10)\n",
    "\n",
    "ax = axes[1]\n",
    "\n",
    "image = ax.imshow(np.matrix.transpose(fcorr), cmap = 'jet', origin='lower', extent=[0, 180, 0, 180], norm=LogNorm(vmin=0.01, vmax=100))\n",
    "\n",
    "ax.contour(np.matrix.transpose(fcorr), levels=[0.1, 0.999, 10, 100], colors='k', linestyles=['-', '--', '-.', ':'],\n",
    "           origin='lower', extent=[0, 180, 0, 180])\n",
    "ax.contour(np.matrix.transpose(beta), levels=[30, 90, 150], colors='w', linestyles=['-','-.', ':'], \n",
    "           origin='lower', extent=[0, 180, 0, 180])\n",
    "\n",
    "ax.set_xticks(np.linspace(0, 180, 7))\n",
    "ax.set_yticks(np.linspace(0, 180, 11))\n",
    "ax.set_yticklabels(np.linspace(0, 10, 11))\n",
    "ax.set_xlabel(r'$\\alpha$ [deg]')\n",
    "ax.set_ylabel(r'$r_v \\equiv v_{\\rm ISM}$ / $v_{\\rm star}$')\n",
    "\n",
    "cbar = fig.colorbar(image, ax=ax, fraction=0.046, pad=0.04, location='left')\n",
    "cbar.set_label(r'$f_{\\rm corr}$')#, fontsize=10)\n",
    "cbar.ax.yaxis.set_label_position('left')\n",
    "cbar.ax.yaxis.set_ticks_position('left')\n",
    "ax.yaxis.tick_right()\n",
    "ax.yaxis.set_label_position('right')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('./PAPERPLOTS/equations.pdf')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7c27744d-593f-475d-a1ce-5c84a55ed1f0",
   "metadata": {},
   "source": [
    "## Step 3.2: the statistical inferences:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "fd67c7cd-16cf-4779-b410-3145c5b62e2f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "210"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Loading the corrections\n",
    "corrected_pm = ps.read_csv('Corrected_PM_ALL.csv')\n",
    "len(corrected_pm)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab02cf75-ab37-416a-b8c0-42a7a9f34dc0",
   "metadata": {},
   "source": [
    "### The general analysis: no sub-selection in the sample:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "4e1a797d-98dd-469d-b4c6-b9562654e3f3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set the number of repititions for the MC error calculation:\n",
    "# Decrease this number to speed up, if needed. \n",
    "# The paper uses 1000\n",
    "Nerror = 1000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "46e3f5cd-8643-42d7-bf1f-dc8124f01b78",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x400 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1, 3, figsize=(12, 4))\n",
    "fig.subplots_adjust(wspace=0.02)\n",
    "\n",
    "ax = axes[0]\n",
    "\n",
    "# Calculate the CDF of theta and its error; already calculate those for vstar as well. \n",
    "# These are used later as the data, so that we take the measurement uncertainties into account as well. \n",
    "all_vstar_sim = np.zeros((len(corrected_pm['vstar']), Nerror))\n",
    "all_delta_theta_sim = np.zeros((len(corrected_pm['ddelta_theta']), Nerror))\n",
    "for i in range(len(corrected_pm['ddelta_theta'])):\n",
    "\n",
    "    vstar_sim_i, delta_theta_sim_i = PA_v_calc(np.asarray(corrected_pm['pm_ra_pec'])[i], \n",
    "                                               np.asarray(corrected_pm['dpm_ra_pec'])[i], \n",
    "                                               np.asarray(corrected_pm['pm_dec_pec'])[i],\n",
    "                                               np.asarray(corrected_pm['dpm_dec_pec'])[i],\n",
    "                                               D = np.asarray(corrected_pm['Distance'])[i],\n",
    "                                               dparallax=np.asarray(corrected_pm['dp'])[i],\n",
    "                                               PA=np.asarray(corrected_pm['PA'])[i],\n",
    "                                               dPA = 5.0, return_means=False, Nmc = Nerror)\n",
    "    \n",
    "    all_delta_theta_sim[i] = np.asarray(delta_theta_sim_i)\n",
    "    all_vstar_sim[i] = np.asarray(vstar_sim_i)\n",
    "\n",
    "for ax in [axes[0], axes[1]]:\n",
    "    n,bins,patches = ax.hist(np.asarray(corrected_pm['delta_theta']), histtype='step', density=True, \n",
    "                             cumulative=True, bins=len(corrected_pm['ddelta_theta']), linewidth=3, color='k', zorder=10)\n",
    "    patches[0].set_xy(patches[0].get_xy()[:-1])\n",
    "\n",
    "    ax.set_xlabel(r'Misalignment angle $\\beta$ [deg]', fontsize=10)\n",
    "    ax.set_ylim(0,1)\n",
    "    ax.set_xlim(0,180)\n",
    "    ax.set_xticks(np.linspace(0,180,7))\n",
    "    ax.grid(color='0.8', linestyle='-.', linewidth=1, zorder=-20)\n",
    "\n",
    "axes[0].set_ylabel('Cumulative histogram')\n",
    "axes[1].set_ylabel('')\n",
    "axes[1].set_yticklabels([])\n",
    "\n",
    "##### Fitting: zero-centered:\n",
    "# The array of values that we will try:\n",
    "sigma_v_ism_array = np.linspace(0.,50.,101)\n",
    "\n",
    "all_rejected = []\n",
    "for sigma_v_ism in sigma_v_ism_array:\n",
    "\n",
    "    p_values = []\n",
    "    TS_values = []\n",
    "    rejected = 0\n",
    "\n",
    "    error_matrix_18 = np.zeros((Nerror, len(all_delta_theta_sim)-1))\n",
    "    error_matrix_50 = np.zeros((Nerror, len(all_delta_theta_sim)-1))\n",
    "\n",
    "    # Here, the actual comparison happens:\n",
    "    for i in range(Nerror):\n",
    "\n",
    "        # The data: note, we take random iterations of the data as well for each MC iterations:\n",
    "        # This ensures that we also take the errors in the data (and the Galactic rotation correction) into account.\n",
    "        delta_theta_unitsless_sim = np.transpose(all_delta_theta_sim)[i]\n",
    "        vstar_unitsless_sim = np.transpose(all_vstar_sim)[i]\n",
    "        M = len(vstar_unitsless_sim)\n",
    "\n",
    "        # The simulated velocity and angle of the ISM: here, with a zero-centred Gaussian:\n",
    "        v_ism_sim = np.abs(np.random.normal(0., sigma_v_ism, size=M))\n",
    "        r_v_ism_sim = v_ism_sim / vstar_unitsless_sim\n",
    "        # For the angle, as discussed in the paper, we have the measured value as the minimum:\n",
    "        alpha_sim = np.random.uniform(delta_theta_unitsless_sim, np.asarray([180]*M)) * u.degree\n",
    "\n",
    "        # Calculate the resulting misalignment\n",
    "        cos_beta_sim = (1. + r_v_ism_sim * np.cos(alpha_sim)) / np.sqrt(1. + r_v_ism_sim**2 + 2*r_v_ism_sim*np.cos(alpha_sim))\n",
    "        beta_sim = (np.arccos(cos_beta_sim)).to(u.degree)\n",
    "\n",
    "        # Calculate the resulting correcting factor\n",
    "        fcorr = 1. + r_v_ism_sim**2 + 2*r_v_ism_sim*np.cos(alpha_sim)\n",
    "\n",
    "        # Calculate the KS test for this iteration:\n",
    "        TS, p = scipy.stats.kstest(beta_sim, delta_theta_unitsless_sim)[0:2]\n",
    "\n",
    "        # Save the TS value, the p value, and whether it is rejected at 5%.\n",
    "        TS_values.append(TS)\n",
    "        p_values.append(p)\n",
    "        if p < 0.05:\n",
    "            rejected += 1\n",
    "\n",
    "        # Selecting the 23 km/s case for plotting:\n",
    "        if sigma_v_ism == 23.:\n",
    "            n,bins,patches = axes[0].hist(beta_sim, histtype='step', density=True, \n",
    "                                          cumulative=True, bins=np.sort(beta_sim), linewidth=0., color='r', zorder=1)\n",
    "            patches[0].set_xy(patches[0].get_xy()[:-1])\n",
    "            error_matrix_18[i] = (bins[:-1] + bins[1:])/2.\n",
    "\n",
    "    # Plotting the 23 km/s case:\n",
    "    if sigma_v_ism == 23.:\n",
    "        bins_min_18 = []\n",
    "        bins_max_18 = []\n",
    "        \n",
    "        for i in range(len(all_delta_theta_sim)-1):\n",
    "            errors_i = np.transpose(error_matrix_18)[i]\n",
    "            bins_min_18.append(np.percentile(errors_i, 2.5))\n",
    "            bins_max_18.append(np.percentile(errors_i, 97.5))    \n",
    "        \n",
    "        axes[0].fill_betweenx(n, bins_min_18, bins_max_18, color='c', zorder=1, ec='k', lw=0.5) \n",
    "        \n",
    "    all_rejected.append(rejected)\n",
    "\n",
    "### Fitting: off-centered:\n",
    "# the exact same approach, but with different array of values that are tried, and different definition of the ISM movement\n",
    "# for each of the MC iterations:\n",
    "mean_v_ism_array = np.linspace(0.,30.,61)\n",
    "\n",
    "all_rejected_mean = []\n",
    "for mean_v_ism in mean_v_ism_array:\n",
    "\n",
    "    p_values = []\n",
    "    TS_values = []\n",
    "    rejected = 0\n",
    "\n",
    "    error_matrix_11 = np.zeros((Nerror, len(all_delta_theta_sim)-1))\n",
    "    error_matrix_25 = np.zeros((Nerror, len(all_delta_theta_sim)-1))\n",
    "    for i in range(Nerror):\n",
    "        \n",
    "        delta_theta_unitsless_sim = np.transpose(all_delta_theta_sim)[i]\n",
    "        vstar_unitsless_sim = np.transpose(all_vstar_sim)[i]\n",
    "        M = len(vstar_unitsless_sim)\n",
    "            \n",
    "        v_ism_sim = scipy.stats.truncnorm.rvs(a = -3, b = 1000, loc=mean_v_ism, scale=mean_v_ism/3., size=M)\n",
    "        r_v_ism_sim = v_ism_sim / vstar_unitsless_sim\n",
    "        alpha_sim = np.random.uniform(delta_theta_unitsless_sim, np.asarray([180]*M)) * u.degree\n",
    "            \n",
    "        cos_beta_sim = (1. + r_v_ism_sim * np.cos(alpha_sim)) / np.sqrt(1. + r_v_ism_sim**2 + 2*r_v_ism_sim*np.cos(alpha_sim))\n",
    "        beta_sim = (np.arccos(cos_beta_sim)).to(u.degree)\n",
    "                \n",
    "        TS, p = scipy.stats.kstest(beta_sim, delta_theta_unitsless_sim)[0:2]\n",
    "\n",
    "        TS_values.append(TS)\n",
    "        p_values.append(p)\n",
    "        if p < 0.01:\n",
    "            rejected += 1\n",
    "\n",
    "        if mean_v_ism == 12.:\n",
    "            n,bins,patches = axes[1].hist(beta_sim, histtype='step', density=True, \n",
    "                                          cumulative=True, bins=np.sort(beta_sim), linewidth=0., color='r', zorder=1)\n",
    "            patches[0].set_xy(patches[0].get_xy()[:-1])\n",
    "            error_matrix_11[i] = (bins[:-1] + bins[1:])/2.\n",
    "\n",
    "    if mean_v_ism == 12.:\n",
    "        bins_min_11 = []\n",
    "        bins_max_11 = []\n",
    "        \n",
    "        for i in range(len(all_delta_theta_sim)-1):\n",
    "            errors_i = np.transpose(error_matrix_11)[i]\n",
    "            bins_min_11.append(np.percentile(errors_i, 2.5))\n",
    "            bins_max_11.append(np.percentile(errors_i, 97.5))\n",
    "\n",
    "        axes[1].fill_betweenx(n, bins_min_11, bins_max_11, color='r', zorder=1, ec='k', lw=0.5)   \n",
    "    \n",
    "    all_rejected_mean.append(rejected)\n",
    "\n",
    "### legends:\n",
    "axes[0].plot([],[],'-',linewidth=0.5, color='c', label=r'$\\mu=0$ km/s, $\\sigma = 23$ km/s')\n",
    "axes[1].plot([],[],'-',linewidth=0.5, color='r', label=r'$\\mu = 12$ km/s, $\\sigma=\\mu/3$')\n",
    "axes[0].legend(loc=4, fancybox=False, fontsize=10, framealpha=1.0, edgecolor=\"black\", facecolor=\"white\")\n",
    "axes[1].legend(loc=4, fancybox=False, fontsize=10, framealpha=1.0, edgecolor=\"black\", facecolor=\"white\")\n",
    "\n",
    "### Plotting the fit statistic on the right:\n",
    "ax = axes[2]\n",
    "\n",
    "ax.plot([0, 50], [1,1], 'k-.', lw=2)\n",
    "ax.plot(sigma_v_ism_array, 1-np.asarray(all_rejected)/Nerror, '-', color='c', lw=2., ms=2, label=r'$\\sigma$ with $\\mu=0$')\n",
    "ax.plot(mean_v_ism_array, 1-np.asarray(all_rejected_mean)/Nerror, '-', color='r', lw=2., ms=2, label=r'$\\mu$ with $\\sigma=\\mu/3$')\n",
    "\n",
    "full_sample_mean_v_ism_array = mean_v_ism_array\n",
    "full_sample_mean_v_acc_array = 1-np.asarray(all_rejected_mean)/Nerror\n",
    "\n",
    "ax.grid(color='0.8', linestyle='-.', linewidth=1, zorder=-20)\n",
    "ax.yaxis.tick_right()\n",
    "ax.yaxis.set_label_position(\"right\")\n",
    "ax.set_ylabel('1 - rejection rate at p=0.05')\n",
    "ax.set_xlabel('ISM velocity parameter [km/s]')\n",
    "ax.set_ylim(0, 1.03)\n",
    "ax.set_xlim(0,50)\n",
    "ax.legend(loc=1, frameon=True, fancybox=False, fontsize=10, framealpha=1.0, edgecolor=\"black\", facecolor=\"white\")\n",
    "\n",
    "fig.tight_layout(w_pad=-0.05)\n",
    "plt.savefig('./PAPERPLOTS/full_fitted.png', dpi=300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9ee6ce87-98c7-41cd-96a1-245e0926a8fd",
   "metadata": {},
   "source": [
    "### The analysis per velocity quintile"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "99b44150-db79-4350-9b2b-7a5e6ec0ffaa",
   "metadata": {},
   "outputs": [],
   "source": [
    "Nerror = 1000 # same as above is recommended"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "47b2d355-627b-4153-ab17-3bdee57d6239",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9244800420328191 0.22628689343680877 1.631227177000023\n",
      "0.25769750289142296 -0.4025134349879589 0.887298061064793\n",
      "-0.15271029540955702 -0.7886302788445918 0.47940882047189337\n",
      "-0.20544419277173845 -0.7462666007678275 0.3299002697772689\n",
      "-0.07236348098530905 -0.300580229437609 0.16825943897662304\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x400 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1, 3, figsize=(12, 4))\n",
    "fig.subplots_adjust(wspace=0.02)\n",
    "\n",
    "# Only perform the analysis with the mean ISM motion model:\n",
    "mean_v_ism_array = np.linspace(0.,30.,61)\n",
    "\n",
    "# Colors per velocity quintile:\n",
    "colors = ['b', 'r', 'g', 'c', 'm']\n",
    "\n",
    "all_mean_p = []\n",
    "all_std_p = []\n",
    "\n",
    "# Looping over the 5 quintiles:\n",
    "for j in range(5):\n",
    "\n",
    "    # Data selection:\n",
    "    v_max_j = np.percentile(np.asarray(corrected_pm.vstar), 100-j*20.)\n",
    "    v_min_j = np.percentile(np.asarray(corrected_pm.vstar), 0+j*20.)\n",
    "    v_upper_j = np.percentile(np.asarray(corrected_pm.vstar), 0+(j+1)*20.)\n",
    "\n",
    "    Label = str(np.round(v_min_j,0)) + r' $< v_{\\rm star} <$ '+str(np.round(v_upper_j,0))+' km/s'\n",
    "    v_select = np.asarray((corrected_pm['vstar']<v_upper_j) & (corrected_pm['vstar']>v_min_j))\n",
    "\n",
    "    # Velocity selection:\n",
    "    v_corrected_pm = corrected_pm[v_select]\n",
    "    M = len(v_corrected_pm)\n",
    "\n",
    "    all_delta_theta_sim = np.zeros((M, Nerror))\n",
    "    all_vstar_sim = np.zeros((M, Nerror))\n",
    "    \n",
    "    for i in range(M):\n",
    "    \n",
    "        vstar_sim_i, delta_theta_sim_i = PA_v_calc(np.asarray(v_corrected_pm['pm_ra_pec'])[i], \n",
    "                                                   np.asarray(v_corrected_pm['dpm_ra_pec'])[i], \n",
    "                                                   np.asarray(v_corrected_pm['pm_dec_pec'])[i],\n",
    "                                                   np.asarray(v_corrected_pm['dpm_dec_pec'])[i],\n",
    "                                                   D = np.asarray(v_corrected_pm['Distance'])[i],\n",
    "                                                   dparallax=np.asarray(v_corrected_pm['dp'])[i],\n",
    "                                                   PA=np.asarray(v_corrected_pm['PA'])[i],\n",
    "                                                   dPA = 5.0, return_means=False, Nmc = Nerror)\n",
    "        \n",
    "        all_delta_theta_sim[i] = np.asarray(delta_theta_sim_i)\n",
    "        all_vstar_sim[i] = np.asarray(vstar_sim_i)\n",
    "\n",
    "    error_matrix = np.zeros((Nerror, len(all_delta_theta_sim)-1))\n",
    "    for i in range(Nerror):\n",
    "        \n",
    "        delta_theta_unitsless_sim = np.transpose(all_delta_theta_sim)[i]\n",
    "        n,bins,patches = axes[0].hist(delta_theta_unitsless_sim, histtype='step', density=True, \n",
    "                                      cumulative=True, bins=np.sort(delta_theta_unitsless_sim), linewidth=0., color=colors[j], alpha=0.2)\n",
    "        patches[0].set_xy(patches[0].get_xy()[:-1])\n",
    "        error_matrix[i] = (bins[:-1] + bins[1:])/2.\n",
    "\n",
    "    # Plotting the data for this quintile:\n",
    "    bins_min = []\n",
    "    bins_max = []\n",
    "    for i in range(len(all_delta_theta_sim)-1):\n",
    "        errors_i = np.transpose(error_matrix)[i]\n",
    "        bins_min.append(np.mean(errors_i) - np.std(errors_i))\n",
    "        bins_max.append(np.mean(errors_i) + np.std(errors_i))      \n",
    "    axes[0].fill_betweenx(n, bins_min, bins_max, color=colors[j], ec='k', lw=0.5, zorder=1, label=Label, step='post')\n",
    "\n",
    "    # From here, we perform the same analysis as we did before, just on a smaller subset of the data:\n",
    "    all_rejected = []\n",
    "    for mean_v_ism in mean_v_ism_array:\n",
    "\n",
    "        all_fcorr = np.asarray([])\n",
    "        rejected = 0\n",
    "        for i in range(Nerror):\n",
    "        \n",
    "            delta_theta_unitsless_sim = np.transpose(all_delta_theta_sim)[i]\n",
    "            vstar_unitsless_sim = np.transpose(all_vstar_sim)[i]\n",
    "            \n",
    "            v_ism_sim = scipy.stats.truncnorm.rvs(a = -3, b = 1000, loc=mean_v_ism, scale=mean_v_ism/3., size=M)\n",
    "            r_v_ism_sim = v_ism_sim / vstar_unitsless_sim\n",
    "            alpha_sim = np.random.uniform(delta_theta_unitsless_sim, np.asarray([180]*M)) * u.degree\n",
    "            \n",
    "            cos_beta_sim = (1. + r_v_ism_sim * np.cos(alpha_sim)) / np.sqrt(1. + r_v_ism_sim**2 + 2*r_v_ism_sim*np.cos(alpha_sim))\n",
    "            beta_sim = (np.arccos(cos_beta_sim)).to(u.degree)\n",
    "\n",
    "            fcorr = 1. + r_v_ism_sim**2 + 2*r_v_ism_sim*np.cos(alpha_sim)\n",
    "            all_fcorr = np.concatenate([all_fcorr, fcorr])\n",
    "                \n",
    "            TS, p = scipy.stats.kstest(beta_sim, delta_theta_unitsless_sim)[0:2]\n",
    "\n",
    "            if p < 0.05:\n",
    "                rejected += 1\n",
    "                \n",
    "        all_rejected.append(rejected)\n",
    "\n",
    "        # Print the correction factor of the stellar wind parameters for the 12 km/s ISM case:\n",
    "        if mean_v_ism == 12:\n",
    "            n,bins,patches = axes[2].hist(np.log10(all_fcorr), histtype='step', range=(-2, 4),\n",
    "                                          density=True, cumulative=False, bins=M, linewidth=2, color=colors[j], label=Label)\n",
    "            log_fcorr = np.log10(all_fcorr)\n",
    "            print(np.mean(log_fcorr), np.percentile(log_fcorr, 16.), np.percentile(log_fcorr, 84.))\n",
    "    \n",
    "    axes[1].plot(mean_v_ism_array, 1-np.asarray(all_rejected)/Nerror, '-', color=colors[j], lw=2, ms=2, label=Label)\n",
    "\n",
    "# Finishing the figure:\n",
    "axes[0].grid(color='0.8', linestyle='-.', linewidth=1, zorder=-20)\n",
    "axes[0].set_ylabel('Cumulative histogram')\n",
    "axes[0].set_xlabel(r'Misalignment angle $\\beta$ [deg]')\n",
    "axes[0].set_ylim(0, 1)\n",
    "axes[0].set_xlim(0, 180)\n",
    "axes[0].set_xticks(np.linspace(0, 180, 7))\n",
    "\n",
    "axes[1].grid(color='0.8', linestyle='-.', linewidth=1, zorder=-20)\n",
    "axes[1].set_ylabel('1 - rejection rate at p=0.05')\n",
    "axes[1].set_xlabel(r'Mean ISM velocity $\\mu$ [km/s]')\n",
    "axes[1].set_ylim(0, 1.03)\n",
    "axes[1].set_xlim(0, 30)\n",
    "\n",
    "axes[2].grid(color='0.8', linestyle='-.', linewidth=1, zorder=-20)\n",
    "axes[2].set_ylabel('Normalized histogram')\n",
    "axes[2].set_xlabel(r'$f_{\\rm corr}$')\n",
    "axes[2].set_ylim(0,1.6)\n",
    "axes[2].set_yticks([0, 0.2, 0.4, 0.6, 0.8, 1, 1.2, 1.4, 1.6])\n",
    "axes[2].set_xticks(np.linspace(-2,4,7))\n",
    "axes[2].set_xticklabels([r'$10^{-2}$',r'$10^{-1}$',r'$1$',r'$10^{1}$',r'$10^{2}$',r'$10^{3}$',r'$10^{4}$'])\n",
    "axes[2].set_xlim(-2,4)\n",
    "axes[2].set_xticks(np.linspace(-2,4,7))\n",
    "\n",
    "axes[0].legend(loc=4, frameon=True, fancybox=False, fontsize=8, framealpha=1.0, edgecolor=\"black\", facecolor=\"white\" )\n",
    "\n",
    "fig.tight_layout(w_pad=0.5)\n",
    "plt.savefig('./PAPERPLOTS/perbin_fitted.png', dpi=300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a9aa3618-c073-4607-b4f3-d2ff86cfa064",
   "metadata": {},
   "source": [
    "### Analysis per environment:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "af420ee7-d9d8-4438-b773-7bbb9f9e1ed9",
   "metadata": {},
   "source": [
    "We start with a diagnostic plot to show the properties of the different environment sub-samples:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "363db874-0d80-4843-aef4-e285a3140ff4",
   "metadata": {},
   "outputs": [],
   "source": [
    "Nerror = 1000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "78cff1fa-b949-4d5f-9341-7b2ad0a98f6e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAeoAAAKyCAYAAAAaWJ09AAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjExLjEsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvctoD+AAAAAlwSFlzAAAPYQAAD2EBqD+naQAAufRJREFUeJztnQd8FFXXxk8KhBBCCJ1QElpCERRQBEUFu+KLoAIqFlAEG4r6CnZFBcQGAsorNizYFcSCYkEEFSkfSg81IfTQQwgpZL/fc+Msk832bJnZef66bHZ2dufumXvnzD33lCibzWYTQgghhBiS6HA3gBBCCCGuoaImhBBCDAwVNSGEEGJgqKgJIYQQA0NFTQghhBgYKmpCCCHEwFBRE0IIIQaGipoQQggxMFTUhBBCiIGJDXcDTpw4IXPnzpX169fLwIEDpWnTph4/c+zYMfn2229lz5490qFDBznvvPNC0lZCCCEk1ESFM4Xo559/Lg8++KBSzgsXLpT58+dLz5493X5m165dcu6550pCQoJ06tRJKeyLL75YPvjgg5C1mxBCCLHEjLpevXry66+/SkxMjFczaTB69GhJTEyUP//8U+Li4mT16tVy6qmnyjXXXCN9+/YNepsJIYQQy6xRw2Sdmprqk5n8yy+/lJtvvlkpaXDKKadIjx495NNPPw1iSwkhhBCLrlH7Qk5OjuTn50tGRka57W3atJElS5a4/FxhYaF6aJSWlsqBAwekTp06EhUVFdQ2E0IIiWxsNpvk5eVJSkqKREdHW1tRQxCgVq1a5bbjtfaeM8aPHy9jxowJevsIIYRYl5ycHGnSpIm1FXX16tXV85EjR8ptP3z4sHIuc8XDDz8s999/f7n9mzVrJitXrlTr5K6oWrWqxMbGSklJiRQVFdmPf/z4cTUrd0e1atXUnRVm8rjbwms8FxQUuP0cZvjx8fF27/YqVaqoh9YGd6CtaDOWCHBcfRuwzR1YSoCvQHFxsTqW1ga015O/oV4uaD++C/LBa3egbXq5aPJGG/Bwh6Nc0F4c299zo8nb23OD9qKtaIMmb3dAtnq5aPJG2/Eb3OEoF03e3pwbvVx8PTfYLzMzUy1P1axZs9xY8ObcVKYf4hjYVz8WPKGXC75DPxb8OTfO+uFDgwbJc9u22V9X3btXYo8ckZKaNaWofn2pvmmT2n68aVMZ1bq1PDdzptf98Kz/nCVbzt8iLWu2dNve4tJiyT6arf7GvgeOH5CDRQelRpUa0jC+oTRb2kxmvjbTUteIQYMekm3bniv32f37q8jBg1WkRo0S6dr1fvnyy1eCdo3Izc2Vjh07Kv+pYGAqRY0LBk7w5s2by23H69atW7v8HD6jrWnrady4sdSuXdvnduCi5S9JSUkhOU6g8KUNoZJLsNoQqt8aKILdXlyMcVGExQrP4cQo5+bA9u3S8F9lXA5MHnQTiJo5OXIgPl4aNmzo9XfHxMaIrZpNNhU5+X5HyvSGbC7aXOZpVE3kqBxVn43eE+3TcSPhGrF9+wHZtMn5bz56tOx97fPBuEbgBgIEaynV8AlP5s2bJzNmzFB/42LRu3dvmTlzpv2OKCsrSxYsWCD9+vXz+bvDffEhxMhgfNStW5fjhJAw65Kwaqo1a9aoZCcwRYNPPvlEli1bJmeddZZ6AHhzL168WAYPHqxeP//88+o9xE537dpVPv74Y+nVq5dce+21Ph/fk8mRECuD8XHo0CFDzKgJMTLB1iVhHX1Yc9i9e7f6+4EHHlDPeH0Utop/ueSSS6Rt27b21y1btlSx0x999JHKTDZu3Djp37+/WjvxFU9rbYRYGYyP7Oxstb5IRU1I+HRJWBX16aefrh7ugBJ2BA5g99xzTxBbRgghxGj06jVQdu6s6JB78GC81Ks3UJKTnTvrpqSUOb6ZFdqzCCGEmIKdOwtkw4Y5Tt9LT+8jmZnO3zM7VNSEEEIMP2v2NHM2+6zZHVTUhBBCTDFrjvSZs2nDswghhBArwxk1IYQQw5i5Yd7WcGbmTolgE7crqKgJIYQY0swNJZ1pMTO3MyytqJGPmBDienw0atSI44QE1TkMmN1BrEqQdQkVNSHE5cUHZfsICeasORJmzlWoqIOHp2oxhFh9fCBLYI0aNfzK/GdkBvbqJQU7d3q9/9cbNpT9UV2kRiPvZk9RBTmScXZGhe0b/vj3u5yQLulSWVKSeXMVabrE0jNqT+XvCLH6+Ni0aZNK4auVKYwUoKTnaMrXC/qkp8uczEyleDdc7O3nimWDbHCqjDN/zxQrZQ3TE4kOYoVB1iWWVtRajVFCiPPx0aFDB+b5JgEzcUeCmTscusTSihqFwQkhrseHVmeXEBI+XWJpRU3TNyHux8f27dulSZMmEhcXR1ERt6ZuRxN3pJq5nUHTdxChMxkh7scH6lEjRIsQK3pzewudyQghhBg6g5iVZs/hwNKmb0JIZOMqDCv+4MHy+9WrJwXJyS6/J97i8eScPYcXKmpCSETR64pesvNgmXLesXWrNC4qdrpfRp2Tf++IPSSN6ya7jnfesEE+jYoKSKwz45yJr1BRE0IiCihpfazzBj9jniM13tkfc7c3jmI0cwcPKmpCCCEVYMGMCFHUe/fulQMHDlTY3qZNm8p8LSGEEEIqo6iXLl0qgwYNko0bNzp932az+fO1hBBCDBoXTVO3yRT17bffLmeddZZ88cUXkuzGU9LoMIkDIe7HR0ZGBseJRaCp27i6xC9FvX79evnll18kKSlJzEykVQQiJNDjA5WzzBaKtSNva7ibEpGZxegsFj5d4peibtGihezfv9/0irqoqCjcTSDE0ONjz5490qBBA0Pl/NaHX+nRQrGqJ4rkO/lcvRkiyXkiO6pWkcbNm4vVw6gYG20eXeKXon744Ydl+PDh8tprr0mrVq0k6t/4QrPBFKKEuB8fR44ckbp16xo6/EqPu1AsKOnM/ShZ2VzmWDjsilgkhSgcyUB6erqpncni45nejhB346N9+/YUkIWLaNDcbQxd4pei/vHHHwPfEkIIIUGDpm7z4peivvDCCyUSOHbsmNSsWTPczSDEsOMjMzNTeX5Xr1493M0hxNBjJZhUKuFJXl6ebNq0SZm6W7duLYmJiYFrGSEk7JSWloa7CSSIaUABPbuNT6y/RbJHjRol06ZNk+LisoT3VapUkTvuuEOef/55xl0SQogBoLnbwop69OjRMmfOHHnvvfekW7duyuv7zz//lIceekj9PWnSpMC3lBBCKgHCsuDxHcklK71xGAOcRVtAUX/00Ufy7bffyumnn27flpqaKi1btpQrrriCipoQErSY6YPF5WtJO4uV1qPFTad0TpE538wXq8+gATy7MzM970dMrKgPHTqklLIjiKk+fPhwINpFCCFuY6bdxUrrYdw0saSiPu2002TixIny9NNPl9v+0ksvyamnnhqothFCCAmwqRswPtoCivqFF16Qyy67TL788kvp2rWr2rZkyRLZunWrzJ07N9BtJIQQ4gSauq1BtD8fOvfcc1V8Jdajc3NzZd++fepvbMN7hJDIIDrar0sEIcQIcdRNmjSR5557TswMkzgQ4n58dOrUiSIyqJnbnakb0LM7cnSJ14r66NGj6hll77S/XWGm0niEkMhiYL16UpCcbH8dKeFY3pq5NejZHTl4rai1rGPIQuYpA5lZinIUFBQwhSghbsbHli1bVFnbUBSwCUQoVkqBSEGTZJmTmWmp2TOgw1h4x4ohFDUSmjj728wEu9g3IWYfH8iFH6pxEohQLNBHrDd7BpxBh49gjxGvFTUykGn873//kxkzZjjdb/DgweX2NTJVq1YNdxMIMfT4aNq0abibQYhYXZf45Uz27rvvOlXUMHkjragrJW61Yt+EmBmMD5j0YPam9ckY8dCuzNyAsdGRq0t8UtTHjx93+rdWZWfRokXSoEEDMQsoLkIIcT0+EHLZtm1bRkgEGTqKmZvCIOsSnxS13qHEmXMJCnI8++yzgWkZIYREKL7MoDl7Jj4p6oULF6rnc845x/63BspcNmvWTBo1auSTVKdPny6vvPKK7NmzRzp06CAvvviidOnSxeX+R44ckSeeeEK+++47OXjwoDrm7bffLrfddhvPJiEWxIzhWL7MoOkkRnxS1D169FDP69atkzZt2lRaejNnzpR77rlHrWl3795dpSa94IILZO3atZLiYrANHz5c/u///k99Ni0tTb7//nu55ZZbJCkpSQYMGMAzSojFgJI2ezgWIQF3JmvevLn89NNPcuGFF5bbjm2YbcfFxXn1PePHj5chQ4bItddeq15PnjxZvvjiC5k2bZo888wzTj+D0DB4lp9xxhnq9Y033qhm4dhORU2IeXCMm3YXM+0sbhox02Y1d/ti6qaTGPFLUT/22GPKacxRUa9YsUJ+/PFHmTBhglelMtesWSNPPfVUubzC559/vnJKc0W/fv3k66+/VqZuzLrnz5+vkjJMmjSJZ5MQExGouGmzwAIaJKSK+v3335eVK1dW2H7zzTer3MDeKOqdO8vupOvXr19uO14vX77c5edQSnPQoEEq13hsbKxS7ljn7tWrl1uPPL1XHta5Nc/1Y8eOuY2NwzFKSkqkqKjI7vmKz8HL3R3VqlVTbcNxEbaG13j2lMEGDnmaox7ahrV/PLQ2uANtRZsRKoDj6tvgKXwAVhCE4BQXF6tjaW1Aez1lmtPLBe3Hd0E+jpEBjqBterlo8kYb8HCHo1zQXhzb33MD3PUFx3OD9qKtaIMmb3dAtnq5aPJG2/Eb3OEoF03e3pwbvVx8PTca+nPjTT/Uzo2nfpjWJE2iakaV++y2/G1ScKJA6lerLwmxCbL16Fa1Pb1muqS1ipJjdSser2Fqqjp3erlAtvqx4M+58aYf+nKNSEtrKFFRJ/vY5s3VpKQkWho3LpToaJvk5EBONrWfu77Ia0Ss4a4RnsZTWBR1fn6+086PxmpK0N/qPHjt7uIzbNgw+fvvv+WPP/6Qli1byrx589S22rVry3/+8x+XJvYxY8ZU2J6dnS3797u+RU9NTZW6deuq2T/21ZzcsrKylAzcAcc4DOLt27erTtG6dWv1jPV9d6BDtG/fXv2N0BhYDWC9yMvLU5YDd+AmBwkqcKHCZ9u1a6c6565du9z+TtCqVSu1zq9VQ+vYsaPavnHjRo8DQi8XtB++A+gfnn4rsl7p5YJUlcnJyXLgwAElN3fgRk0vF9RIx8U1JyfHYx/UywV9VvO38NReDHq9XNA3cH6Q+37Tpk1uP1unTp1ycsnIyFA58eFEuXfvXref1csFN7haoQz8bk8XCL1cMLYwZnDh8vRbExISlKOmdl4dx4I74FCql4t+LODzGuNGjavw2SGLhsiqQ6tkUItB0q1eNxm4YKDaPr37dKlxbg1x1urh/44VvVzQt/RjwR21atUqJxf9WEAfcYcv14hx49DSk7/g8ss7yN69VWXEiO2SnLxCXnrpPYmPryrjxj3s9vzwGpFsuGuEp/oXlSXK5kdi7t69e6uBiAxlWiIE3LnCHI0f5k1NaiiDevXqqZrWMGdr3HTTTaqutaNXOcDJwQXvk08+Kbceff3118uOHTtkwYIFXs+oMYgxsDDIXMEZNWfUVp5RY39cDDHmcFMR6Bl1vyH9JLtbtvcz6k+iZNZJPW9nZGqqTJo1y7Az6n79Rkp2dsWlOf2MumXL4bJgwQxa3Uxqddu3b5+6aTt8+HBQ6kf4NaOGaRtOY1CMZ511lvohmOGisc4UrDOgIHHn+ttvv5VT1PhOT05hjuna8NrdBQsDz5mDG+6kvSlPho6Bh4Z20rxBf1x0El/Koen3dWyDO3CR0X/WW+c+fefW8KUYg14u6OTe/lZHuTi2IRTnBvhybvRycZS3Oxzlgr7rbfrBcJwbzDT1BLIfZm3Pkg1HnK9R7z1e3sqg9tskUt2JYWi3zVbuOIE6N5Xph5dddnM5x7Hc3Opuncaio8vazGuEOa8RtWrVkmDil6I+5ZRT5J9//pFXX31VhUpBiFdddZXcfffdPuUGHjlypDz88MPSt29fOfPMM1V4FsyAiIvWwHcuWbJEPWDexo3BuHHjlBkDprlffvlFPvvss3JOad7i6a6KECuD8YHZiOYLYtTYaSPGTXvjOMb46MihNMi6xC9FDaAkvXEac8ddd92l1k4xo4bJAOuVc+bMKXcX7+jw9emnn8r999+v1qOwBoR12VGjRskDDzzg8/GD7QBAiJnB+MCaXCBSiPpawtJTOBZjp4mRCLYu8WuNWgOzX6wbO+JPMhSsNzgzZWCNAHcrzsx82t2+r2CNGs4iaDucdAghFcH6LpxksD5d2aIcGWdn+BSKBdKnuA7H6pOebsgkJ1qsdJm5+xO35m7ER8+fX3EfYj4OHjyoLL6GWqNeunSpCpGC56sz/NH9rtYb3K2v+qOk9bAiECHuxwduaEngTN40d0cmMUapR60Ha8hYK0YWMTPPSD15DRJiZTA+EKKE6AxvHXcIsSLFQdYlfinq9evXKycus99tU1ET4n58INwSHq1U1N6bvJ2hmbyZDjQyKTaiokYCBjiBmV1RE0JIIKDJmxhOUSOkClWsXnvtNRULjfAsQggJRRlLI4VkcSZNDKuo4UgG0tPTnb5fCUdyQggph5FDsTiTJoZV1KiQRQghgSphGQllLAkxlKJ2LG9JCCGVKWFptjKWNHkTwytq5PR2h7tCF4QQYnZo8iaGV9SIq3QH16gJIYSQMCrqFStWlHuNFJ/IUvboo4/KfffdJ2bB26pFhFgRjA+U7uM4OQlN3sQZwR4jfilqVK5ypHPnztK8eXO59957VbENM1DZFKSERDIYH+FYxnIMxzJKKBagyZuEQ5cE9NvbtWsnq1evFrOAoh6EENfj49ChQyozWShvao0YjsWZNAmnLokNZKWdqVOnSoqB7n49UVRUFO4mEGLo8ZGdna1KXLpT1P6UsDRbOBZn0iScusQvRe3MHIbSkcgHPHPmTDELla2xS0gkg/HRpUuXoIRimS0ci5Bw6hK/FDVmzo6gihbWqT15hBNCiFmgyZsYAa8V9TXXXCOff/552YdiY9Vrs3P8+PGgFPkmJBLA+MjKypK0tDSpVq2aWBGavIm3YyWYRHu74+zZs9U6NOjfv79EAggrI4S4Hh/5+fkcJ4SEWZd4PaNGlayxY8dK9+7d1euffvrJ5b5MMUoIMTM0eRMj4bWinjJliowYMUKefvpp9fqiiy5yuS8zkxFCzBw3TZM3MaWihmJev369+hv1p6mMCSGRGDeN2fTBg/FO36tXb6AkJxdISorz9wkxjNf30qVLA98SQojhCVTMtJHjpjGbzs2d4/Q9KOnMTOfvEWIoRX366acHviWEEMPDmGlCQg+TXRNCiJcOZDR5k3BARU0IIV46kM2fT7M3CT2WVtRWTeJAiLfjo0OHDhFfZY6hWMTouqRSI3Dv3r1y4MCBCtvbtGkjZiA62ut8L4RYDoyPQNfZdQzFMkI4FkOxiNF1id9e34MGDZKNGzc6fd8soVuFhYXhbgIhhh4f27dvlyZNmkhcXFxEhmIRYgZd4peivv322+Wss86SL774QhXjMCtmuaEgJBxhWKWxpTL4msEy4/MZsjt3d8SFYtHkTcyiS/xS1Eh88ssvv0hSUpKYGa5RE+I+DOu3zb+JdIrM8pU0eROz6BK/DOstWrSQ/ftNMBI9wBk1Ia6JlmhJiE1Qz4SQ8OkSv0bgww8/LMOHD1dr1GZWdgUFBrTHEWIQWtdsLQsuXaCeIwmYvDMy+riNl05P78OYaWIYXeKX6RuOZCA9Pd3p+2ZW3oSQyIYmb2I2/FLUP/74Y+BbQgiJKIwWiuXJeQww+xiJGEXNetOEELOFYnmaSQNmHyNGpFIJT/Ly8mTTpk3K1N26dWtJTEwMXMsIIYQQ4p+iRnD3qFGjZNq0aVJcXKy2ValSRe644w55/vnnA5YcgRASvtKVtaV2RMRMe2vyZp1pElGKevTo0TJnzhx57733pFu3bhIVFSV//vmnPPTQQ+rvSZMmBb6lhJCQlq70RlGbIWbaW5M360yTiFLUH330kXz77bfl6lKnpqZKy5Yt5YorrqCiJoQQQsKpqA8dOqSUsiOtWrWSw4cPi1nA7J8Q4pyi0iLZnLdZPZsRmrxJpOgSvxT1aaedJhMnTpSnn3663PaXXnpJTj31VDEL8fGu16wIsTpbj26VgQsGilmhyZtEii7xS1G/8MILctlll8mXX34pXbt2VduWLFkiW7dulblz5wa6jYQQE8ZNhytmmjNpEmn4pajPPfdcyczMlKlTp8qaNWvUtB9r03fffbcqiWcWjh07JjVr1gx3MwgxJOk102V69+ky7M9hsuFIRWczo8ZNcyZNwqFLDBlHDYX83HPPiZlBSBkhVsJVKJYz9h3fJ29seEM9myUUi5BI1CVeK+qjR4+q5xo1atj/dgX28eVOBB7ke/bskQ4dOsh5553n1edQEGTBggVSvXp16d27t18lN6moidVwFYrljANFB2Tm1pmmCcWiyZuI1RW1lnUMWcg8ZSDztijHrl27lBk9ISFBOnXqpJzTLr74Yvnggw88Vu+aMmWKXHnllUpRY2Y/e/ZsVX7TF0pKSnzanxArkVglUbrW7SpL9i2RvGKHqbQBocmbhItg6xKvFTUSmjj7uzIgcQqUPr4P2cxWr16tvMavueYa6du3r9PPIMkKvMv/+OMPexw3FL4/gioqMmfYCSGhICU+RSZ0mSCDfhskmcXhX3smxKgEW5d4raiRgUzjf//7n8yYMcPpfoMHDy63rytOnDihvMbHjh1rTzl6yimnSI8ePeTTTz91qaihpAcOHFgu2UqjRo28/RmEkAiDJm8S6fjlTPbuu+86VdQweWPG60qJ68nJyZH8/HzJyMgot71NmzYq1MsZ2H/VqlVy7733qln4//3f/0lKSopcdNFFbtfFkZscD40jR46o5+PHj7v11qtatarExsaq2TrumGBm1z5XWlrq9vdVq1ZNoqOj1XEhF7zGs6cC4/Cg12Ly0DasfeChtcEdaCvajJsgHFffBmxzB26WYmJiVO52HEtrA9rraSlDLxe0H98F+eC1O9A2vVw0eaMNWg55VzjKBe3Fsf09N954burPDdqLtqINmrzdAdnq5aLJG233ZA1ylIsmb2/OjV4uWlKG2KhYaZlYMWGRnuMnTp67tBppsrtgt4gclpKkJClq2FAeSk6W4w4RE2n16pXrs5Xph5AL9tWPBdfygX/KO5KbWyaX5s0LJD8/RvburSrx8SekWbNCSU1Nklmz3nH6Xa7OjTf9kNcIXiOAp2tdSBW1vjGODUMnX7RokTRo0MDrylugVq1a5bbjtfaeIwcPHrTfDEDZYub+zjvvqLAw1Mhu166d08+NHz9exowZU2F7dna27N/v2jsGaVHr1q2rMrFh3y5duqjtWVlZ6qbBHXCMwyDevn27kg2qi+F53bp1bj+Hi1r79u3V3wiBw40IZAqZbNmyxe1n69evL02bNlUXcHwW8sCFDksD7n6nllUODnm5ubmyb98+6dixo91pz9PFSi8XtD8tLU1dlD39VoTG6eUCH4Pk5GQ5cOCAkpunqAO9XJCEBxdX3ABqN2Ku0MsF/Rg3h8BTe6F89HJB38D5gXMlqsi5o06dOuXkghtU3FzCiXLv3r1uP6uXy86dO5U/B8Dv9nSB0MsFyhLUjqstM8896STmjJUHV8qEVRPU32M7j5Vn/nlG1slXcqhnT8l+/HG52cXn8Ntg4dLLRT8WMJbcoZcLzqN+LLi6ARs3brgcPZopPXuWyeW557bI4sU1ZeLEpnL66f+ViRNvEJHhLs8vrjnItIgbJuyjHwvoI+7gNYLXCODJwbqyRNm89fzyIk0a3n/22WflkUce8fhdmzdvVgPihx9+UA5kGrfffruaLf/zzz8VPoMLFS54SLKCfXDhQfPhKY61bniPezujhkJDghYoN1fwbpl3y5E2o+54fkfZcskWr2bU1WKqKYX+6P89KotzF0uDlw/LmpKyGfXI1FSZNGuW08+Gakbdr99Iyc4uKwAEMWzYUHFG3aHD1bJ48ftuj8kZNa1ulbW64Wa7efPmKoV2MHJz+DSjXrhwoXo+55xz7H9rYGA2a9bM6/Vi3IliUEJh68FrzLKcUbt2balXr55SzNrsABefnj17yvvvux6MOI6z0psQvnbRcwcuknjoP+ct+uOird4cT0O/r2MbPF149J/1peyodpH1JzWeXi44P97+Vke5OLYhFOcG+HJu9HJxlLc7HOUCJYxHsM6NY9w0YqZLbCWSecS9cxhipjMaZIicK2J7K0sabDqsYqZjjx2W2MOHZbfN5vE3V6YfOsrE2bGysnbblbOerVtPyqWw8OSNja/nJhz9kNcIc14jqvnwnUFX1HD0AjAPaeZCvw8cG6vin2fOnCnDhw9XgwSmU8RGw5ytMW/ePGXug5MauOqqq2Tp0qXlvgtr2unp6ZVqDyFWj5vWg5jpWXEiMBbPOiRS3UDx03QeI1bDL2cyrM1hHdPd+97w/PPPy1lnnaVM3zBnf/zxx9KrVy+59tpr7fvAA3zx4sV2RY1Ya3zmkksukbPPPlv++usvpajnz5/vz08hhJgMxksTq+GXoob52R3eLnvDgQOx06hvDeeRcePGSf/+/ZXJTAMKuW3btvbXWFP++++/1Wfg4IWkJ3Auw9q1r3hrSibEisQePCj1Z85Uz4SQ8OkSv759xYoV5V7DQQZesI8++qjcd999Piv9e+65x+X7UNyOwCv0tttuk8ri7dogIVak6t690nTiRENUxtLM3eDgQddr8/XqDZTk5AJJSWEJWxI6gq1L/K5H7Ujnzp2V1xtinO+66y4xA548UAmxMifi46WgVSuJ37Qp7JWxvDF3AyjpzEzP+xFiJl0S0Pk64lNhyjYLnkJqCLEyhc2aSeY770jbQYPK4p8M6jgGOJMmkaxLYgN5R4H61Eh0YBaC7VJPiFFLWDriWMISoVjVsrKk3YABErdjB+IpJRxwJk3MgKHCs9x5dSOJCOLaEG5lFrRYbEKsHIrluoRlocR7yIZHCJGg6xK/FDVmzo4gxSHWqT15hBsJmr4JcTM+GjaUXUOHSqM33wy5mGjyJmai0Iimb32cs5mhMxkhbsZHUpLs79tX6n/2WcjXqGnyJmbihFGdyZA7+LPPPrMnuocjGUKpfEkTSAgxJgjHSkpNleEiKq93fAhqt/saggUYhkWsgF+KGglHrrjiClUxREtG8uqrr6piHN988429whAhxJwgHOudWbPUjTiKb/iSB91fOIsmxDl+rYAjN/eFF14oO3bsUFWs8EA+7vPPP1+GDRvmz1cSQgghJFAz6pUrV6qZc0JCgn0b/kbublTFIoQQb6HjGCFBUNTIQIaiHI4e3tiG9wghwcdZCUtvcRY3HS5o8iYkCIp61KhRyvN74sSJcvrpp6tty5YtU3m+R48e7c9XEkLCHjdNCDG1okbhbkcuuOCCCttQjvLmm2+ufMsIIRGLtx7egOlBidXxWlH/+OOPEmkwlIwQ9+OjVatWQRkn3pq7AQttEKvrEq8VNby8Iw193WtCrIyzMpYYH0lJSWF1HAOMlSZW1yVeK2rETGu1oLW/XYF9zEBxcXG4m0CIIXBWxhLjIzc3VzmNIo9/IKDjGIlEioOsS7xW1ImJierZZrPZ/3YF9jEDJSUl4W4CIYYeH4jkQB7/QClqQiKRkiDrEq8VNZKaOPvbzMTHuze/EWLlcCyMj0BlGWSsNIlk4oOsS7xW1N26dXP7mhASXMwcjkWTNyEhTiHao0cPiQQKCsKY5YEQE4wPZCHkOCHEPcEeI34lPGnWrJls2rRJhW6YGbOspRMSrvEBJxl/xwljpYlVsAVZl/ilqMeMGSO33HKLTJ48WZW3rFq1auBbRggJaThWoGGsNCFhVNQ33XSTeu7UqZPT9zlTJcR84ViBgrHShBhAUUdiljJCSGCg4xghBlDUH3zwgcyYMcPpe8j1HYlZzAghhBDTKOp3333XqaKGyfu9995zqcQJIZFbxpKx0oQYQFEfP37c6d+gtLRUFi1aJA0aNAhc6wixMGaLm6bJmxADKGp99hVnmVhQCvPZZ58NTMsIIYQQ4puiXrhwoXo+55xz7H9rIBcw4qsbNWpkGrFWr1493E0gxLDhWBgfXbp08bgfTd7E6lQPsi6J9Scj2bp166RNmzbl3issLGR9Z0IsGI5FkzchBkwhGh0dLY899pj99ejRo9UdRVpamqxevVrMguM6OyGk/PhYv36903GCWXRGRh/18Ka2dHp6H9aVJhHL8SDrEr+8vu+//34ZMWKE+hupRKdOnSqffvqpzJ8/Xx588EGZO3eumAGsqRNCXI+PatWqOR0nzDpGSOh0iV+KGt7dH3/8sfr7hx9+kD59+sjVV18t559/vrRs2VLMQlxcXLibQIjTUCwjhGNhfMBKRggJry7xS1HDcSw3N1dq1KihZs9XXnmlvXh2bKxfXxkWEFJGiNlDsYIVjoXxofmeYLnLF8cxzeSdnFxAkzeJeEqDrEv80qqXXXaZDBgwQM444wz59ddf5c0331TbYfrGrNoscI2aEPfjA46jbdu2tXu10uRNSOh1iV/OZK+++qr07NlT9u3bJ19++aU0bNjQngP8qaeeCnQbCSGEEMvi14w6MTFRXnjhhQrb33jjjUC0iRBisDKWrIhFiAkU9dGjR9Uz1qW1v12BfQghkRM3zVhpQkygqDGL1gpvaH+7gvWoCYkc+vUb6VWsNB3HCAmzov7zzz+d/k0IiWyysydJbq77FIlQ0pmZc0LWJkKshNeKulu3bk7/JoSEtoRluMpYEkIMrqjh4e0tdevW9bc9hFgCI8ZNOzqOpaU1lHHjhrvdlyZvQgykqOvVq+f1l5pljVpL4kAIqeg4VlBwXP78M0eOHXM9TmjyJkSCrku8VtSrVq2y/43EJqg7/fDDD6ukJ2Dp0qUyfvz4csU6jA7yGBMSCQQiHMsxBCsnp5qMGNHa6b6cSRMSOl3itaI+5ZRT7H8PHjxYPv/8c1WXWuPss89WtWv1BTu8Yfr06fLKK6/Inj17pEOHDvLiiy96VQMXjBkzRsVz33XXXTJhwgTxFbPM/AkJRTiWYwhWdLRN4uNLpaAgWkpLyxcd4EyakNDpEr/m60grCKXqCLatXbvW6++ZOXOm3HPPPfL444/L8uXLpX379nLBBRfIzp3lixM4Y8GCBfLBBx+orGjIR+wPBQX0wCHEFa1bF8iCBX+rZ0JI+HSJX4q6SZMm8tprr1XYjm1Nmzb1+ntgKh8yZIhce+21kpqaKpMnT5b4+HiZNm2a28/t379fbrrpJnn33XcrlVylatWqfn+WkEhBqy3tGCu9c2dVGT26hXrWYG1pQkKvS/xKIfryyy/LVVddpczfWKPGtH/ZsmVqNj179myvvuPQoUOyZs2acrnBsSCPoh4oo+kOmN7xOOuss6QymKnSF7FuCctgh2O5yjqWlxcrP/98ct0b0ORNSOh1iV/f3rt3b9mwYYOaQWum7osuukgp6WbNmnn1HZp5u379+uW24zXM4K6YNAnJF3KVudxbYBrXm8ePHDming8fPuxWwLhLwvso31lUVGSvIIRKKZ7KmsG5ADceOC5uZPAaz55MJChADqsCOHbsmCopiofWBnegrWjziRMn1HH1bcA2d6CUYUxMjBQXF6tjaW1Aez2tv+jlgvbjuyAfTxVl0Da9XDR5ow14uMNRLmgvju3vudHk7e25QXvRVrRBk7crqiZUlSPnHpHdBbslLjpO0mqkydH8oyInROpXqy/JVcsrQ0d2FuyU5Lw8WW2rLcV160r1DWVhXQXNm4vt3zv5hqmpTtuvl4urc4MwrKio8p89fjxa8vJi5LLLDsjGjdUkMzNBDh+OlcTEeI9y0s5NZfohzin21Y8FT2j9EOcG36EfC+7Avnq56MeCp37IawSvEZouCSZ+3wbAVO2PA5cnt3a8dqUY/vnnH+Vt/tdff/l0BwMTOxzPHNmyZYvs3bvX7W9ETDhm/9nZ2XYnt6ysLMnPz3d7TKzXYxBv375dXQBat26tnrG+7w5c1LBWDzIzMyUlJUUaNGggeXl5qr3uwE0Olh5wocJn27Vrpy50u3btUssF7mjVqpUkJSWpmyDEzHfs2FFt37hxo8eLlV4uaH9aWpq6OHr6rTVr1iwnlxYtWkhycrIcOHBAyc3T8oteLqeddpq6uObk5NhvxFyhlwsuzG3atFHbPbUXykcvF/QNnB/kvt+0aZPLz40bNU5mb5stz658VhpXbywzz50pQxYNkVWHVsmgFoPUwx2jl4+WHPlZDlx2mey87Tbp1LOn2r7luefkeMuW6u/hLtqvlwvGVsuWLdXNjX7fsljp8p9duTJBJkxoJvfdV3Yepk+fI7/+ukIuuOBMj3Jq1KhRObnoxwLGkjsyMjLUchacS3Ee9WPB3Q0YflunTp3K5LJli+pb+rHgjlq1apWTi34soI+4g9cIXiOAp/oXlSXKFibXZygDxGajTGa/fv3s27H2vHXrVlm4cGGFz0ydOlVGjhxZzhVef/eMuxr87c2MGoMYx3Gc0evh3TLvlgMxo+43pJ+s7LKy3Ix6W/42KThR4PWMutHL5WfUCMdKOOUUia1SRe1TrV49eW76dJ9m1MjhjfSgzsCMulq1Upk5c51MnfqFvPHG0+WsS+7gjJpWN6tZ3fbu3SvNmzdXOgg3iRGjqAFmU1dccYVMnDix3B3qgAEDnJbRxIlxvCBinRphYpjde+tYBkWNO2bcLWu1tAkJFhlnZ1QqCxlIn1I+E1mf9HS/wrG0WGkA57Hc3E9c7tut2z0yderNMnHiu/LBB5P9azghFmD37t3KkhQsRR1WbyrMjpE0pW/fvnLmmWcq5Yw7k9tvv92+z9133y1LlixRD+0OSQ/uerCNpTUJCVy5SlCzZtlsZ/r05yhaQsJIWBU1EpVg7RSmb9yJYIY9Z84ctV6kATOFN44khBBCSCTit6LGutzff/+tHDf69++vtsHBKiEhwafveeKJJ9QDZm3H2TJ49dVX3a4noOSms3VpQojr9KDu0NKD1quXRhESYlZFvWPHDunTp4+sXLlSLdRry9xIXHLnnXfKZZdd5vN3OlPSAE4H7tCcewiJlJjpYMRN+2Ly1mKlYcny5OFNCDGoor7vvvtUyAU8s/Uz6NGjR6s1Z38UNSGRQGXLV4aijCUhxAKKGtWzkFVMc7nXOPXUU1UVLbPgahZPiJVN3ikp8fbxgXh1jhNC3BPsMeKXokY8m9YwxKVpIEmBo/I2MrwAETOWsPSljKU/Jm/9+EBSGUKICRV1jx495K233pL//ve/dkUN7+yHHnpIevXqJWYB6+uERFoJS8dYaU+4qi2N8YHMb4mJicyLT0gYdYlfiho1o8877zz5/vvvlSPZjTfeKL/++quaaf/+++9iFjxlWCLEjPgyi3ZXaAPjA1Edbdu2paImJIy6xK8yl6eccory+EaSEhTjQFaW66+/XoVrIVevWaDHOCHuxwdyhXOcEOKeYI8Rv2bUCNnAXfbYsWPFzOjX1wkJVyhWoMKxfHEcc2fy1o8P5iggJPy6xC9FjepDnTt3lhtuuEGuu+460+bLxrp6MPKyEmsQqFCsQIVjBcrkrR8fqLqFAjb6QjiEkPJ4Ki4SFtM3yk1eeOGFqpgGwjcuueQSef/994Ne6ivQeKqgQoiVwfhAARuOE0LcE+wx4teMGjV58Xjuuefkt99+k5kzZ8q9996rimmgwAZeE0ICE47lLhTLXw9v4MrkTQiJoKIcsMvD+xuP4cOHy6233ioffvghFTUhIQrHCrS5mxASYYoa61cfffSRUszwAj/jjDPklVdeCVzrCCEhcRwjhESYon7jjTeUcobZGyUpEZr1+eefqzKVhJDgw5k0IdbBL0X96KOPysCBA9Uadbdu3QLfKkIIIYT4r6h37tzJTEXE0vHSgYyZ9jZu2lfHMXUsmrwJsY6i1kKvatSo4TFmDPsQEikEMl66MjHTvpq71bHoPEaIdRQ1EvMD5PbW/nYF9jEDTOJAzFwZKxTjA8mN4uLiwt0UQiytS7xW1H/++afTv81MdLRf+V4ICWkolr8e3qAyXt4YH8zzTUj4dYnXilrvNPa///1PZsyY4XS/wYMHm8bBrLCwMNxNIMSwHt4YH7t27ZJGjRpxVk1IGHWJX7cB7777rkuT93vvvSdmwSwmemI9MIvOyOijHr7MpNPT+wQsVhrjA/4oHCeEuCfYY8Qnr2+9E5mjQxlynS5atEgaNGggZoFr1MSoGMFxDOOjTZs2Afs+QiKVakZZowb69Spna1dIKfrss88GpmWEmDwMqzKhWIQQ4peiXrhwoXo+55xz7H9rVKlSRZo1a6bWs8zCsWPHWOaSBC0My9dQLH/ipIMZK43xodWer169ekC/m5BI4tixY8ZR1D169FDPGLw0iRES2FAsf8zdgLHShEQ2fmUmo5ImJHChWL6GXwGWqyTEOvhdPWvWrFny2WefybZt26SkpKTce4sXLw5E2wixBEZwHCOEGBe/wrOmTp0qt912m6SkpMjvv/8uF154oVrr/euvv6Rjx46BbyUhhBBiUfyaUU+ZMkXNpnv16iUvvfSS3dN7woQJ8scffwS6jYREHEZzHCOERJii3rJli5x99tnqb+QBRsEOFOIYPny4jB07NtBtJCTioOMY0YMlxH379lEoBqdu3boquskUihpr0lWrVlV/N23aVFasWKFCtnbv3s382cTwhCpeGjBmmnijpBECF+wQH1J5EKaIqKdQK2u/nck0brzxRrn22mvlsssuk59//ln69OkjZgEJWoj1CFe8dE7OTpUSFJjBwxvjA/kROE6CC2bSUNIffPCBUtjEmKxbt05uuOEGdb4cFXWwx4hfinrr1q32vx977DGpU6eOqqh16623yv333y9mgZWBSKjipcHBnP2SbaI4aYwPOoeGDijpzp07h/CIxCy6xC9FnZaWVq6811133aUehJDy8dJ6p7FjNeJFvEwfyjhpQojPihoOY94CxzIzUFBQwBSiJKiY2WkM42Pjxo3SunVrWp8I8TBWDKGoExMTvf5Ss5TFi42t9BI9IRELxge8XDlOSCiYPHmy1K5dW60Dh4Pp06crn4whQ4b4/NlgjxGvvx1r0JEGTgohgcZfpzGjxUljfCCpESHOePPNN5Vj1UMPPRQQAf3yyy/SpEkTnxT1pEmTpH79+nL99ddX+vi//fabKlfpj6IOti7xWlF369ZNIo0TJ06EuwkkxCFYoQjDKimx+WXuNorJWz8+tBwJMTEx4W6O5bj44hslO/twyI6Xmpok8+a97/X+ixYtkqysrIApan/46aefpFWrVgFR1EbWJX7N1z0F5sNcZgYKCwvD3QRiohAsX8tWmh2Mj02bNrHMZZiAkvb3hs8/Khdae+TIEXn33Xdl/fr1yhID5dm8eXP7+//88498/PHHcuDAARVNgJmru/Kp+K758+ervxFZhMli//79y83o8Z1IwDV48GC1bfz48arUMmLT33vvPdm+fbu0aNFCbr75ZmnQoEGFG41PP/1ULeteeumlhtYlfuX6rlevntsHIVYKxeqTni6d4mtIhyoJ6nFAqvts7k5P76MeRjB5E+IrpaWlcu6558qXX34p7du3l+LiYunXr59s3rxZvf/tt9/KGWecIQcPHlTVF7EefNZZZ0lRUZHL70xPT5eePXuqB8zbo0ePVjUmNPA9UOAwl2v7QfGj/kSnTp1k586d6oYA8c9oE246NdBOpMBG/DPWxYcNGyY//vijYU+8XzNqZCJzPEnwDn300UflvvvuC1TbCDFNKBbWpCPB3E2Iv7k1MLvds2ePUqpg1KhRSmHDufiee+6RkSNHyvPPP6/eu+WWW9Rs+/XXX5cRI0Y4/c7u3burhwZm0zBzP/nkk0o59+jRQz1jmzajBsjn8cQTT8i9995r34YZ9VNPPaWSykBfPfjgg0pfYRtA0i7MvCNKUZ922mkVtiFQH4KHcBhTTazC1q07lJL212kMcBZNzA7MzZiZIgEWFO8pp5xiN2vn5OQo8zSUoUZSUpJcfvnlsmDBApeKGgp19uzZqmwyllvxGnk7MjMzlYJ2BmbNeB+OYatWrVI3CXhgmxZiDHM42nP11VfbP9e4cWN7/QojElCf8nbt2snq1asD+ZWEGJqi4samjZMmJFBAKS9cuFBeeOEFlU4aJu1BgwapNePc3Fy1D8zUjr5M//d//+fyO6+77jpZvny5SlONWH54Vn/44YeSl+fEm/Nf9u8vcyDBerZ+GRZmeS2/h9Ye3FjocXwdkYoaXm+oU81wDkIIsR6YqL3zzjv25VHMmDHThpkbwEM8NTW1nLlc/1oP1rLh6AVF3qlTJ7UNs2qY0t3l2NZm2li//s9//uP0u7U83XA4w0xaIzs7W2rVqiURo6ideXXD4w93PDNnzvTpu5CMHo4GWNvo0KGDnHfeeR4/A69CxHUjyBxrGFijIMTo0NxNIhUoXVRP1NaUsTwKpXno0CGlL+DohZhnVFmE+RqW17lz58pnn33m9Ps0BQyFreGshDJmwfAi14DiPf/882XMmDHqWJrixSwaa+gXXnihmmnjPbQHM28cC6byZcuWyamnnioRo6gxc3YkOTlZrVP74vW9a9cuZZJISEhQd01PP/20XHzxxWrB3xlYa0B1LqwvnHnmmUrJw1sP6yJwDCDWwV2cdGVjpYNVtpLmbhKpxMXFKQet/Px8ycjIkA0bNsjhw4fl9ttvV++/9tpr6toORdiyZUsVdgUHryuvvNLp90HB3n333cpz/OKLL1bXfFz/cRw9eB9hYNAFMG3D1P7++++r7WgH1p2h7DF7njhxYrksaFDa0Fko1bxy5Uq1rm5U/FLUeqeAygB3e8SwYXaME4C7LJzIa665Rvr27Vthf5yo4cOHyxVXXGHfhjuyAQMGKI9AuPP7ApM4mJdgxUm7ipdGGNbaQ8dkZ3FjydDtd1AiN3MXxgfWFTlOwgMSkFQ2ttn343nP0KFD7Q5aWPJEXPLff/+tlDRilqEktdSaqAyG7XAeg+J89tlnKyhGeIZj0qYxZcoUpcw3btyoTOiYBSMOW19hDHoCx8RsGTcJWCuHoxoc0DBDhnMZ2obQMH3MNmb8+F5kQ4MOwvvQP/729WCPkShbmBJzY00bAoU5Q+9GD9M3zBdwGvCGvXv3qk4B8znWRLwBZnocG3d8NWvW9Ps3kPCRcXZGUBV1+pTyihrx0Xtq9Zbc3E8qZe6eP9+3z5PIB+uwXbp0UY5TLHNpzvN0JMg6xa8ZNdYiHn74YXUHpV9D8DZzmeayr5lJ9MAJYMmSJV63BYHruGvTHA5cZY3RZ46BUAHuBt0lU69atap6v6SkRHkxandkx48fV6EC7kDOWKzF4Li4F8JrPHuqsoL1Eq22Kcw5WPfHQ2uDO9BWtBk3QTiuvg2eUtzBooG7Qjhr4FhaG9BeT/dyermg/fguyAev3YG26eWiyRttcHQacaRmYtlgSKySKCnxKbLxyEYplVJpmtBUqse4TziSdTRLCksLpWF8Q4mLjpPs/Gy1PaPmyb6Y2krkmM4VI0VSpaBkhvzrMCotWxbIoUOxsn9/FUlIOCFNmhS6nKXMmvWOkq1eLpq8cU4hb3c4ykWTtzfnBucR58Sfc4Pjan0H50k/Ftyh9dnK9EMcA/vqx4In9HLBd+jHgjtcnRtv+mEgrhGe5EmMBfqXY3/0pbpkyBT1TTfdpJTdAw884LeXnOZi7/h5vHbnfq8H5g6sizzyyCPKNOIKrFvAucARmD7cleSERyIcIeAQAY9A3E1p3ou4yXAHHOMwiBGzhwGL8AI8I0uOp8GLLDoAsX8w28BiAJlgncYdSDSA9RZ0JHwWXpi40MEXQAtbcAUc8nBHCKcL3Ggho48mI08XK71c0H7UK8fF0dNvxZ2nXi5IOABfBziHQG7u6HF6D1kmy6Rr3a4yocsEOe/78yS/JF9GtR8l3eufTJLgjAG/DpAtR7fI0NZDpUViC7nl9zKv1Jnn6hwhzxXRt36ciOzZs1F69y6Ty+TJG+Wrr+rK9Okp0qHDUZk69WTWo/IMV78NJmS9XHCDir4HJ0pYhdyhlwuyLWk3pegPnhQuTHxQOrgxhlLA+iCUiqdzAxMkvGO1/RzHgjswFtFvcfGC6VE/FvB5d+jlgmuMfiy4U3z4bXq5oG/px4I7cM3Ry0U/FjB23BGIawR+KzEPWf9e50KpqP0yfeOuEV5+jrlTfQGp5TAgfvjhB+UsoAHnA6xZQwm7AwMKnoRwwX/jjTcquOl7mlFjEOPCgQugKzijNu6M+ryrzpNl3ZcFb0b9icgsnU7pJ6myuWS2bN4c73FGnZo6UmbNmlTumGacUWNWrB0D38MZdXBm1FhPhbc0Td/mMH0vWrSoggUXlmV4uRvK9A0F7elC6gncieKioeWC1cBrzLLcgTtkuOD37t1b5Yx1p6QBjuPoLQjgROAuKbwGBqLeRO54N+Xp2BpopzfH09Dv69gGd0AB6D/r7Ld7MltqaKZHb9DLBRcgb3+ro1wc2+CMI3llyxd5xXmSWXxyxpSTn+N1e3cX7C73OvPIye+xbRKprjNCZIlNNstJWWgKG+Tnx0hm5sn222y7Xf52R7ngQo+HUc+No8UpVP3QUSa+jBu9XBzb4A5HuXjTDwNxjfD2/BNjgP7l2Kc83WyHRVH/97//lTvvvFO53LtK5ebxwLGxStEi7hqe3BgkMCnAK1ALmgfz5s1T5j4tlys8B5FMHdlvUD0Fn/MXrg1ZN/zKXQiWP2FYkRgjjfEBsyxuzKlMCAmfLvFLUSMZ+uOPP67MxxjAjjNaT2Y1DSRoRwUVmL67du2qXO+hhPXhX8hOA9MQFDXMcJhJw0EETmcvv/yyfb9LLrlErfn4QrDvgohxw68CXbIyEmOkMT6wfo71dSpqQlxjyBk16ogi3hlVSiqTcg0OHIhd++ijj9Sd+7hx41Q8tD4mDQoYMXgAa2WaEnd0wPHkTU2IJ3pJPdkpzn0WIjlemhBibPxS1HDkgheps1SivoJMZgh0d4W+UDjWBV588cVKH5MQZ0BJbxD3HsKRbu4mhESIokaYCUIPAqGoCTErkWjuJoREiKJGDtbbbrtNXn31VRVi5cnrmpBIgbNoQsr466+/1IQNfkN6kIfhp59+Us7CiKwJRTu2bt1aYTtyeWMyqX8fugohufBncpd7I2IUNXCVWztMWUkJCTqcRZNQcePFF8thD8llAklSaqq8P2+e1/tPmzZNReo4KmpUN0QtaSyRwuk32EybNk2+++67Cu1ArDMUteP78IdCrg44RJulmJNfivrHH38MfEsIIYTYgZKesyG4kQ96Qlf+42RIExQmMu5hhutYrvjXX39VZSybNGmiIn8Qv4yoIGcgCyOihlzh+P7rr7+ukmvhgaiGiFTUMCkQEoz46EDGSQciVlpv6lafodMYIZUGqYkvvfRSlQcDKXJ///13FU2EilkaqLCFaJ6dO3eqVLII4XWlqP1JuAVguo9YRe2p6IZZnMwYGxqZ8dHuYqX1IVi4XfBUstLKpm6MD1xEOU6IKxAm6ziT9ZRbHSBhFvK6z5kzRyW/QvrUbt26qURW+iqICN9dtWqVyjvvSzswE9enpta/j7/hXwU/K0/f6y3BHiOx/oZUucMsa9TepkIk1grBosPYyfHhLhc+IZi0zZ4926eJHAoEwdkMtaC1azByaGOGDWWqV9RXXnmlV8rUsR3I0aFX1Pr3kZcbtR+QC8QsusSvb1+xYkW510g+D1MGFubvu+8+MQuVzVdOIhMrz6IdxwfWDzE78TbnNbEWztaGUbTi559/dvkZrfoarDV6oFwddYu3ntntfFyj/vvvv9XNAUzgV1xxhRhdl/ilqFE6zxEU0m7evLnce++9ctddd4kZoKImxP34wPogQmyoqEmg0JZGcROorRVrrx2XTYMV+nvaaadJ48aN1Q1FxCpqd3ctWFMwC75U5CGRCx3GXI8Px3J+hFQW1IiAgv7yyy/t/QtOXXPnzpVHHnkkJAI+fPiwqjfesGFDU+iSgClqFMqYOnWqKhhPiJmgqZuQ0IFZ8qRJk2TAgAFKQSMfx9tvvy3169eXO+64IyjH3KtzJsMa9YwZM5S3t1aV0ej4paideXUfOXJEmcdQttIswPU/GEW+rYS34VaBDrvyJvzKn3KVpPz42LJli1pL9KX2NbEGZ555ZoV1Zs3ZeODAgW6vrX379lVr2R9++KGKpcb+8MLW1/FGKJaz7/e2Hfr3UdFRcyZLSkpSCVkQDoa/A0Gwi0L5pagxc3YE3qFYp/bkEW4kzOKdbmRCHW7lLvzKWfUrVyFY9Oz2bnzgAsdxEh6QKaxPiI/nC65mvwi7cufYpYG4aDxc4W3WsDs8zMLxfrBm6hrBHiN+KWp9vWhCzFj9iuZuYnR8SedJIptoX3aGedvZbFoD72EfQowGZtDp6X3sD2YYI4SYBZ9m1JMnT5aSkhKX78OLDingzJLonFgHzqAJIZaYUX/22WdyzTXXuHwf72EfQgghhIRhRr1582aVPcYVqH6yadOmQLSLkIDFRQOaugkhllDUMTExkpeX5zJUA+vTzJ9Nwg3N3IQQyypqZJH54osvXLq64z1mMor8WOhgxkV7Gx+twThpQkik45OiRg5vBIkjKP3mm29WtUS1ohzvvvuujBo1St577z0xC0ziYIxYaH/iozVwe9EuZqfERNukMHa/pDelR3cgxwdyImvjnBASHl3ik6Lu37+/LFu2TG655Rb573//K61bt1aB3liXRkJ15Gm96qqrxCwEK+E7CW18dHrLPrKW1a6CMj6w3EUICa8u8TnhyYQJE5QyRuq3DRs2qAai4PegQYPcZpkxIsi6xBSi5oKOYqEdHzk5OaqIgj61IyHg2LFjUlRUVEEYqLaGGzy8D2trjRo1KtSFgK8Trr2RYq05fvy48TKTIXcqHmaHM2rzQUex0I4PXEg5TsLDxf0uluy9ZbWbQ0Fq/VSZN8v7bGh33nmnfPDBBxUU8ffff68mb3g/KytLfv3113LvI7f3OeecI+vWrZM2bdpIJBBltBl1JBEXFxfuJhBi6PHhLhyTBBco6ZD6jviRsbRHjx4VFLEViQuyLrG0ooZZhhifqlV2SHrzsvIEjIcO7fhAJkKEXEaKiZIQM+oSSyvqYK8rGCWEKlwhVv6EWzkLu2revLHMobNYWMYHzJNt27aV6tWrh74BxPDgRu7QoUP217ip05vCHd8HR48elUjjuBHXqEnoMWIIlT/hVu5CrfTsq7JD+qQ3Vn/Hp5SVpiSEGIvFixdLWlqa/TXyaMyfP9/l+5ozGfENKmoS9lArZ57cdVNOkznzP+HZIcTAeFqjdvb+okWLlDMZ8R4qahJ26MlNCCGuoaImYXMM06CDGCGEuIaKmoQUOoYRQohvMOaCEEKIzyASwDHZiTfvwzM8KSmJ6Wl9gDNqQggxIMgU5k8Skkodzwdee+01v95H1jLHkC3iHksrandJHBC3vGzlX2IrKREjUFw99MlZEPt8NC9GSrw0vERFlUiVKs671P6oWOmT3pShViYbHwkJCUx2EiZ8SedJwkuwEwJZWlG7KzSAuOWjt5YPGbIaiH1O3t/SbeUqPenpfSSTiUkianxESi5mQoJJsIvWWFpRE+9xFuvsCL23CSEk8FhaUaMMG8tcegdjna05PphClBDvxkowsbSirlq1aribYJq4Z86WrTk+UlNTOU4ICbMusbSiRpgAcQ/jnq09PurWrRvuZhBieIKtSywdR43KLoQQ1+Nj3759HCeEhFmXWHpK2b///bJ9+wGn7+Uc2C+xG6IlWmzuv8SG/8MrxigpwT9+4SqcSgupYuUq61JUVCTZ2dkqcQWtT8EH/gDEnOenqKgoqMe2tKLetu052bSpoZgdhkURYl6wvICboRtuuCHcTSEewHkKx3KQpRW1EUOc/IGOXoSYl2bNmqnZGpYZiLGpW7euOl+WU9TTp0+XV155Rfbs2SMdOnSQF198Ubp06RLwzxgBhjgRQpyBi384FAAxB2F1Jps5c6bcc8898vjjj8vy5culffv2csEFF8jOnTsD+hlCCCHErETZbDYP3lLB45RTTpFzzjlHpk2bpl6XlpZK48aNZejQofLMM88E7DOOHDlyRFVvOf/8m106kwUDmKjnz/8kZMcjpDIw4Qkh3rF7925p1KiRHD58OChJtMJm+kb1lDVr1shTTz1VLrH5+eefL4sWLQrYZ9wxc+Zz0rCh+Z3JCCGERC5hU9Saqbp+/frltuM1TNqB+gwoLCxUDw3c9YDc3FyP2WYQloIYObjfw+MPHD9+XM3kPSVpx00EjgujBV7juaDAvTNZVFSUxMfH22c0VapUUQ+tDe5AW9HmEydOqOPq24Bt7oiLi1P1YYuLi9WxtDagvZ6MLnq5oP34LsgHr92BtunloskbbcDDHY5yQXtxbH/PjTdpAPXnBu1FW9EGTd7ugGz1ctHkjbZ7isF0lIsmb2/OjV4uvp4b7Hf06FHZu3evmiXox4I356Yy/RDHwL76seAJvVzwHfqx4M+58aYf8hrBa4RelwTLQB1rtPJgeO3px/r6mfHjx8uYMWMqbO/YsaPP7SWEEEKcsX//frWsGjGKWpsVO85q8dpxxlyZz4CHH35Y7r///nImdOQw3rZtW1CEalTOOOMMWbp0qVilLYE+RmW/z9/P+/K5QO4LX46mTZtKTk6OpYrXGGmcAI6V4MjJ2/292Q9WWnjt165dW4JBbDjj0Vq1aiW//fab9OvXz759wYIFMmDAgIB9BsCUhYcjUNJWugDBnGeU3xuKtgT6GJX9Pn8/78vngrEv9jFKv7HaOAEcK8GRk7f7+/K9jtbeiAjPGjlypLz11ltK0WJtCF7bWA+7/fbb7fvcfffd0rVrV58+Q5xz1113WaotgT5GZb/P38/78rlg7WsljCYXjpXgyMnb/Y3QH8IangWefvppmTRpkjIdtG7dWqZMmSIXXXSR/X2EXS1evFhWr17t9We8Dc8Klis9IZEAxwkhxhgrYVfUGvCuhKeoI/DYhDem5v3pzWc8ge+EgxnWrp2ZxAkhHCeEGEWnGEZRE0IIIaQilq5HTQghhBgdKmpCCCHEwIQ94YkRyczMVJmQkBAFrvmEkPL8/vvvsmLFCvX3tddeG5YavYSYAfhS/d///Z/UqlVLMjIy/PoOKmonrvhff/218uCrU6eO/Pjjj345rBESySDJ0Pr16+Xzzz+XHj16UFET4gTUphg4cKDUqFFDsrOzpVevXvLhhx+Kr9D07TCTnjNnjirivnLlSuW9hwsRIaQ8ffv2lalTp0paWhpFQ4gLDh48KLNnz1YhxtAv3377rccaExE/o0YCfghi4cKFcvnll6u7F0eQPvSjjz6S7du3S9u2bdXdjjZjhnmiZ8+ekpCQoF5fccUVatt1110X8t9CSLBAoMcvv/wi7733nvobz46g+IaWWAjj4frrr3c6ngiJZIqKitRkDXoAY6Bz585OS1x+8sknSgF36tRJrrrqKlUAB8DapFfaKIXpzzJRxMyocceC9KLTp09Xj7/++stp9a1TTz1VZs6cqV4/++yz6uKjVdfBurRWSUmrxpOfnx/CX0FI8Onevbvq+6iM9d133zndBxel5557TtV+x8Xl4osvto8bQqzA119/LS1atFBW1pdeeknWrl1bYR9sa9++vXz//ffq5haZM6GoHaOekS//xhtvlM8++8yuxH3CFiFs2rTJtm3bNvV348aNbePHj6+wz6233mrr2LGjraioSL3evXu3rUaNGrapU6eq1z/88IPtvPPOs+//4IMPOv0eQsxMdna2ep44caKtTp06Fd5ftGgRrjK2ZcuW2bc99NBDtoYNG9pKSkrK7XvmmWfaVqxYEYJWExJaVq9ebduzZ4/6G+Ph/fffr7DPJZdcYrvoootspaWl6vW6detsUVFRttmzZ9v3Wblypa179+629evX+92WiJlRt2zZUlX6cQfWCuChqpm6GzRoIJdeeqnaDmD23rJli0pR+uabb8o777yj9ickkkCVH3fMnTtXVZfr0qWLfVv//v2Vie/vv/+2O8lgjXrPnj3K7Pfpp58Gvd2EhBLMlN1VZYRF6qefflIzZW2W3KZNGznzzDPtOgWWXaS3Hjx4sGzdulXNvPE5S69Re6oTigcUuh68xpq1VgR+/vz58sILL8imTZvkyy+/pLMMsRy4oDje9Gqv8R4UONbb4PXdu3dvycvLUx6thFiJzZs3K78oZzplw4YN6m/4Qp122mlKl2jANwpe4L5gGUWN9WfgmDAdYVj6dWgI+X//+1/I20eIUYDPBvwz9GgXFlSs05xk9I4yhFiNY17olKuvvlo9KkvEmL49oV1o4PWtB68TExPD1CpCjAcuNJgx64E1CiQnJ4epVYRYV6dYRlHjAtOwYUMVy6YH5juYIgghZSAyAuNCi4YA2tp0hw4dKCZCRFSUEfydQqFTLKOoNYcYhJhoZgmst82bN09tJ4SUcc0116hQk9dff129xt8TJ06U8847z6MjGiFWIT4+XuXaePvtt9UYAUuXLlUx14HWKRFT5hLB5hMmTFB/4wKDwPOuXbsqL7yhQ4eq7QcOHFAXGzgAIJYUHnhwjJk1axZzehPLMHbsWHVBgTMMnF4uu+wytX3y5Ml2RQwHy9tuu01OOeUU2bt3r0RHR8sPP/xQwXGGkEhl48aN9ptVxFEjiRZmyvDq1hRxVlaW0in16tVT1ibEXONGV/tcoIgYRQ0ljDsbR5o3b15uMR/mPGQv27Fjh1LiF154oX8B6ISYODkQQq0cueCCC8qtrWFMLVu2TDmW4eLEnPfESmzbts1p2CGKNSEBkAaiHr755hvlx4EJ4tlnnx3wtkSMoiaEEEIiEUutURNCCCFmg4qaEEIIMTBU1IQQQoiBoaImhBBCDAwVNSGEEGJgqKgJIYQQA0NFTQghhBgYKmoSUSDpADLRIVOds9eRitl+dzDaF4zvRLlbfCceWlW9nTt3qtdHjhyRcIK6xlrbnnzyybC2hQQXKmoSFpAbF2kqR48eLQ899JB8/vnn9ny5ABdbXIC0qk3egixBb731lhw+fNjp60jFbL87GO1z/E5/+5AeZHDDd55++unSunVre8Y2bNPKHAYa5KC66667JCcnx+1+yBTXrVs3pbC/+OKLoLSFGAMqahJycIFDSsqnn35alVSsX7++utAgr3RBQYHaBxdbXAxx8SW+U7duXXnjjTeUbK36mwPZhwYPHqxSrIYCpG398MMPpVGjRm73i4uLUzciUNYksokNdwOI9UD+3DVr1siuXbvK1TdGgvuYmBilrB955BG1DbNt1H2FEh85cqTahmIqP/30kyoUgby6V155pdfHxqz99ttvV3/HxsZKWlqaDBw4UOWE14Bp84knnpAxY8bIxx9/rMyfw4cPl9NOO63cd2n7PfXUUzJ79mxV4AKzLhSzqFatWrl9vWkzKrlp+/Tr10/dzACU0XvhhRdk+vTp6j0As+v999+vbnZSUlIqfBdy2iOnt3acQP7uBx54QFUN6tWrl31bcXGxjBgxQoYNGyadO3f26zyhjTjuX3/9pfKL9+3bVxXP8VZO+t/sqg8hxz+KkJx//vn27ysqKlJth3yQq9kfkOv5u+++U/IrLS1Vz48++qjahn4BOUOWuElFTQLkkUZBoJtvvrlCrQF816WXXqrOk1Zi9Msvv1Qz5zPOOEOdN60fEGvAs01Czr59+6Rq1aqqTJweKA9sh7LWLph4xoxBq+8KJYiLau3atVXFmgcffFBuueUWr4+NiyK+Dw/UXUaFHCTZR2k6Dc20CeWSnZ2t2qC/oXDcD9+FG48mTZrIlClT1EVWn0Lfmzbfeuut9gswjnXfffepizzADQ2OAwWggQs+tqEN3piBA/27UU1ID5TyjBkzpEWLFl7/ZkegmB977DF14wHliapEb775ptdy0v9mV30Ichs3bly57/zqq69U+VvUF/YH/O7rr79e3byg5r0mx549e6qbz6ZNm8qLL76oqi+dc845yhSPKmW4gXj88ccrfB8UNb4L/PLLL+pmBe3G+Jg7d65S7sRioCgHIaFkw4YNturVq9tOP/102yuvvGJbsmSJraSkpNw+GzduhKazbd261b7t+++/t9WuXdu2b98++7adO3faqlatalu2bJl6jf3xOXze2Wtn3HXXXbb+/fvbX69atUp95uWXX3b7O7T9HnroIfu2HTt22OLi4mxffvml123++uuvbdHR0ba///7bvs+JEyfUfmD+/PnqOMXFxfb3d+3apbahDaH+3T/99JMtNjbWlpuba982cOBA21VXXeX1b3ZsH2SA79yyZYv9MxMnTrQlJyfbjhw54pWcHL/TWR9asWKFLSoqypaVlWXfdumll9puvvlmp7914cKF6jsKCgoqyAnn4MUXX1RtXLRoUYX333jjDfu2mTNnqm0ffPCBfdvrr79uS0lJKXc8/BbIYf/+/er13Xffbbv22mvL7ZOTk1PuNeTUvn17p+0nkQFN3yTkwDwMc97EiRNl6tSpyqSNtep77rlHmZFdlR3FTAOzcJgUMWPVZq1Yq8P3wZToDXv27FFrgJjt5Ofny/r16506NWl1mj2hLxKP2SBmTQsWLFBmWW/ajBkhaqdjpquBGaOnNUpfCdTvhskb68BYwrjzzjvVTBZ1eDEr9fc8/frrr2rWqzfF33DDDWrGvHLlSjXLD4ScYMaHaf6dd95RfQ2m8B9//FHNXH0Fs3+UzEXbYZ1wRL+mrc3W9SZ3bIO1BJYSzZQN2UEOsESAdu3aqXZiSQDnBeMElhtiLaioSdiU9WuvvWY3hWP9FRd2mAnhIOMM7AeHIXjg6sHF21uHGph8sc4HZdOjRw+pWbOmMrP+9ttvFfatVauWV99Zp06dcq/Rxr1793rd5oMHD0qDBg0kmATyd0OpXHfddUoxQ1HPmjVLrcn37t3b7/MEeeEzeqCscCzcYARSTuhfzz33nAppgtkaNwfnnnuuz98D5d6+fXvJyMhw+r5+aUdTxI7bcBPjqKg1szfAujbADe2QIUPUjQb8Ei666CKf20vMCxU1CTu4QMPxB7OG33//XV1Inc2qGzdurDxiXSlyb4By6dChg1IuGliPrQwIo0lNTS33fZhVe9tmzJCwxusKzEQBFKvmYASlFc7fPWjQILVOvXXrVvXdsCrAv8Df84QbNKy/OsoVSgzrud7IyRFXlhmsJ8Mh7ueff1aKGgrQH77++mv1XbCcQK7aefIXOMOhTfo1dCjwO+64Qz1gBcENBpzlEDaGmy1iDehMRkLOokWLlPOVnu3btyuTbHp6unqtmf70zlK4KG7ZskWmTZtW7rPz589XszhvwAwGFzzNHIvZGrxwK8PkyZPt3wevYzxw8fa2zddee62Sx2effWZ/H+/9888/6m84aOGC/ccff9jfh+nWFwL9u+GgBbMslDWUC8zUlTlPV111lTKLw4ysAQcsmIc1U7cnOTnirA8BKLgBAwYoZzfcaPjrnAVLCn47zOc431C0lQEzdDjeYZaugZsXLbwsISFBOSrCo10LYyTWgDNqEnIw08HFHMoHZsPjx4+rC95ZZ50l9957r9oHHr0wR954440q/AZrgFjLfv3119U+77//vprFrl27Vl3cvE34AK9hfAfMwC1btlRryZjNVSabFcy2MPPCK/eHH35QySo0Ey/WQz21GWu2UPb4rVBuMD1jnw8++EC9D3Pv3XffLVdffbVap8RMGBdtXwjG78asGuu0mPHCnK7hzW92BDKABzTM5zDrol1QyvDIRmIPb+TkiKs+BDDbx2wantiwAFTGGoS+i7Vn3GwgjMpfHM3eAGvYaDduihITE9V6OuQa7KUSYjDC7c1GrMs///xj+/jjj5WH9Nq1ayu8X1RUZJs7d67t7bffVp7EGvCInT17tu2jjz6yez1r5OXlKW/bw4cPO30NDh48qI4JT1x4Bq9fv161Q+PAgQPqM8eOHXPbfr33LzzX8X1//fWX033dtVnv8fvJJ5/YvvrqK9VGRxYvXmz78MMPbcuXL1dtQxvR1lD/bo09e/ao/X/99Veff7Oz9oHMzEzlGY12OpOBOzk5+05XfQj7xMTE2D7//HO3v1Hz+h48eLBt2rRpLuW0d+9etW3NmjVO34eHPLYVFhaW+x3YVlpaql6npqaWa6MGfuM333yjfjPko/+9t956q61bt270+o5wovBPuG8WCDEjq1evVuu+mPUgfpaYB8zMn3/+eWX61mbszsASAdaiAZzOgpWdbNWqVSpeGjHW3q51w9QOi4Vm5seMnkQmNH0TQizD8uXLZdKkScr5C8lp3ClpABNzZZwXvQXKGbnvfXFI01KIksiHM2pC/ASe11hzxVqtY5Y1YkzgsKiFVWmpRwkxOlTUhBBCiIFheBYhhBBiYKioCSGEEANDRU0IIYQYGCpqQgghxMBQURNCCCEGhoqaEEIIMTBU1IQQQoiBoaImhBBCDAwVNSGEEGJgqKgJIYQQA0NFTQghhBgYKmpCCCHEwFBRE0IIIQaGipoQQggxMGFX1OvWrZORI0fKpZdeKitXrvTqMytWrJDhw4dL37595fHHH1d1gQkhhJBIJKyKeuzYsXLVVVdJQkKC/PDDD3LgwAGPn1m8eLF0795dqlSpIv3795dff/1Vzj77bDl27FhI2kwIIYSEkiibzWaTMLF7925p2LChbN++XZo2bSrz58+Xnj17uv3M+eefL0lJSTJr1iz1+vDhw9K4cWMZP368jBgxIkQtJ4QQQiwwo4aS9oWCggL57bfflMlbA0r7wgsvlO+//z4ILSSEEELCS6yYiJycHDlx4oSafetp0qSJmo27orCwUD00SktLlZm9Tp06EhUVFdQ2E0IIiWxsNpvk5eVJSkqKREdHW1tRFxUVqef4+Phy26tXr25/zxkwi48ZMybo7SOEEGJdcnJy1MTR0oq6Vq1a6tnR6Wz//v2SnJzs8nMPP/yw3H///fbXWNdu1qyZrF271u3nqlatKrGxsVJSUqJuBHBDAI4fP65m5e6oVq2aurPCTB53W3iNZ5jv3YEZvnYjAgc5OM3hobXBHWgr2gyrA46rbwO2OQPfe/ToUWVdiIuLk+LiYrVNawPa68mNQS8XtB/fA/ngtTvQNr1cNHmjDXi4w1EuaC+O7e+50eStyaNGjRqqLa7ODdqL99EGTd7uiImJKScX/I1taDuO6Q5HuWjy9ubc6OXi67nBMTFW8BtxTP1Y8Obc+NIPNfRywb76seAJvVzwHfqx4M+5ceyHzvqGFa4RGnq54PfhWFh6xOvKXiMGDXpItm17rmznpEEiV29TfxacKJAd+TskSqKkZc2WsrtgtxwtPirJVZOlTrU6bo+5//h+OVh0UGpUqSEN4xvK5iObxSY2aZzQWOJjyk/2mn0hMvPwydfVtm2T6KIiua1rV5ny9dflrhHOQORRu3btJDExUYKBqRQ17lTq1q2rwrN69+5t347XXbp0cfk5dAo8HIETWs2aNX1uhz+f0UDHDsVxAoUvbQiVXILVhlD91kARivbWrl1bjEAknxuzXyN8wVV7t28/IJs2/euzVGe7SNGmk29WE6VgN2FbDO6sRA7ivyIPYbmwQFcTOSpHyz5bpmtlx4kdIg73JNFZIg33V/yKgkOHpH79+h7br92IBGspNexx1J6YNGmS3H777fbXN910k7z11luyb98+9RphXVDUN998s8/f7WkmYwUgA8iSsqA82Dc4Vnjd8I9gXz/DOqP+8ccf5aWXXrKbpkaNGqXu4G+44Qb1AKtXr1ax0xpPP/20rFq1Slq3bq0e+HvcuHHSo0cPn4/vyUxkBSCD7Oxsu2nT6lAelAX7BseJ0XRJWK/MsOkjKxkYPXq0fXurVq3sf993331y6NAh+2skR5k3b55aX96zZ4/6jgYNGoS45YQQQhzp1Wug7Nzpfo3dGQcPll8zJgZS1FgjxsMd7du3d7odChoPQgghxgBKesOGOeFuRsRh+DVqQgghxMpwUZIQQohPJuy0tIYybtxw6ddvpGRl7bZvpwk7OFBRE0II8cmEHRWFeOJ1kp09STZsKAtNIsGDpm9CCCHEwHBGTQghEY6v3tihNmHXqzdQkpPL2rc1b4e4z0toPSytqJF2z+pABo0aNaIsKA/2jQgeK4H2xt63r4pMn95IPQcCKOnMzLL2ZZydIRtkg5iJKkHuE1TUFgcdDBVfCOXBvmGuseLLLDnQM+T9+6GoU7yeJXsiJcXccdRVqKiDh6ck9FaRgVZoAAn3rQ7lQVmYpW+EM2Y5IeGEdOhwVFatqiH5+TEeZ8mRzokg6xJLz6g9VdWxigw2bdokbdu2tSeWtzKUB2XBvuGZJk0KZerUTTJoUFvJzHR/3eh1RS/ZeXCn12PwYLGHYhteUm+GSHKe5/12xsTI/qiq0ie9aYX34r20oARbl1haUWuly6wugw4dOjDPN+XBvmGgseKNWTscMcuaORulMe++O0Gio/MlPb3UrTkbSnrDxaFfc07OE8l0UhHLkT7pLWVOZqahdYmlFTU6m9WBDFCfllAe7BvGGStGTcVpJXO2kXSJpRU1Td9lMti+fbuq9e2sZrfVoDwoi2D3DSPOlr11/NJmyRwn5aHpO4jQmaxMBqhOhrATQnk4jg/2jcDLw4izZV9nyuwbodUltP0SQgghBsbSpm9CCAk0nkzboTRr+2rSJsaEipoQQgKIkUzbwXD+QrhV1YSqMm7UOOk3pJ9kbc8KSbiVr2FXKQUiA+vVk4LkZLf7eRuCFU6oqAkhhHgNwq2iLohSf2d3y5YNRzYYMuwK9GmSXOnQKyNARU0IIT4wbNhDsnRpVthN296YtWnSjgyoqAkhxAdyc48bwrTNmGbrQEVNCCFeOISlpTWUceOGy5Ej1QwxY+Zs2TpYWlEzwUeZDDIyMigLXZ+gPCgLZw5hOTknZMiQAtm0aaJYfca8LX+bDFk0RD0TCfr109KK2ggVcIwgA1QDIpQH+4Z7CgpiVLUoIlJwokBWHVpFUYRIl1haURcVFYnVgQz27NkjDRo0YM5vysNyfcNVzLMzh7D69Ytk0KA9MnNmA9m7t2rQzdtGMm0P7NVLCnaWVcDakbdV6lerL4NaDJKZW2bK3uN7Q9+eep7DrkIZehVsXWJpRc0UomUyOHLkiNStWzfcp8MQUB7WkoUvMc+owdyt2xGZPbtuxJm3PZWi3LF1qzQuKlZ/V08USYhNkG71usnsbbNDFhetj48uMFjYFetRB5H4eOPcsYZTBu3btw93MwwD5UFZuGLr1ngZODAyx4o3pSj17+Ye3SoDFwwMeVy0Rh+xli6x9IyaEGLtlJ7Bjnmm5zYJBJZW1MeOHZOaNWuK1WWQmZmpPJ2rV68uVofyiDxZBCqlZ3r6MZk+PVOGDcuQDRuqm8q0HWjSa6bL9O7TZdifw0KemcyoYyWYWFpRkzJKS0spCh2Uhzll4YtjmD9ERYnUqFGqns3oEBZIoiRKalSpoZ5J8KGiJoREBOEqhhGps2ZiHFiPmhBCCDEwnFETQkyHMzN3MB3DYN5OTU0SkeGSmjpSbLbdhjZva+FWO3NyxFZS4nH/4uqBX+LwJuxqZ0yM7I+qKn3Sm/r03fEmKE0ZSKioCSGmI9Rmbpi3Z816R9atWyezZk0yvHOdN+FWwcabsKs+6S0NFQ9tVGj6JoQQQgwMZ9SEEMt6cjviyoPbiOZtYh2oqIlER9OwoofyMI4swmHiduXBjVjZcMvDKNjEJkeLj6pnEnwsraiNvs4UKhl06tQp3M0wDJRH+GQRSgcxf2bO7BsnQZKTnj/0DMq5MSPVg6xLLK2oCSHGIZSzZ8Y+EzNhaUVdUFBg+RSikMGWLVukRYsWLFLyb5+gPE6OD8oi9PLQl5T0F5SiDCbNazSX57o8Jw8tf0i2HnV+rB1Vq0if9OaWCLMqKHCemS5QWFpRB7vYt1lkgHznlAXlEaq+EUoHsUA6h4VqrEBJz9lQPrSqV3WRnT40GaUo8yU4ID66QVy+ZO5fLLavC1Xeb2ekdE6ROd/MFysQE+Q+YWlFXbVqYIq/m10GTZv6lmwgkqE8gi8Ls5q4w9k3oKQ3jBBDgPjoP7P2ioyfKEvT02XO74yDrhpkXWJpF8ZgF/s2iwyOHj1KWVAe7BscK95fN+Lj5WiHDlIlLi44FyaTcSLIusTSM+rCwkKxOpABShm2bduWXvCUR1D6hqOp2+gmbldwrOhk0ayZZL7zjtR55JGAydfMFAZZl1haURNCgk8oTN304iaRjCEU9c6dO2Xv3r3SqlUrqVGjhsf9bTab5OTkyMGDB9WaUe3atUPSTkJIeGOhmTmMWJHYcJsLbrrpJpkzZ440adJEKewXX3xR7rjjDpefWb16tVx33XWye/duSUlJkY0bN8p//vMfeffdd6VatWohbT8hJLSzZyPPnAMRVgXiDx4UI4Owq5GpqTJcRKrVqxfu5liCsCrqZ555RhYtWiSbNm2Sxo0byxdffCH9+/eX008/Xc444wynn7n99tuVgl6+fLnytNu6daucdtpp8uqrr8oDDzwQ8t9ACCGuwqqc4U2oVUad8q8PJgZPxvW+rSfJVZK93h9hV5M+naUqiT03fXrwGkaMoajfeustGTZsmFLS4Oqrr5Z27drJ22+/7VJR79mzR6699lq7O3zz5s2lWbNmajshJLLN3JFQHMNIoVYASjrTxxAr5D0nFlDUMHPDfO2okM8880xZsWKFy8899dRTMnr0aLWenZqaKvPmzZO8vDy35nKY2PVeeUeOHFHPx48fd9vhcDMQGxsrJSUlUlRUZPd8xedKS90XWocZHgn8cVysqeM1nj1lsImKirJnPULbqlSpoh5aG9yBtqLNCBXAcfVtcBU+oLVHe7+4uFgdS2sD3ke73aGXC9ofFxen5IPX7kDb9HLR5I024OEOR7mgvTi2v+dGk7cmD2fnSX9u8D7aijZo8vaUEEEvF/yNbWg7foM7HOWiydubc6OXi6/nRkN/bvT9sGrVJImKeqfc5+AuUlpaLPv3V5GEhBPSpEmhbN5cTUpKoqVx40KpUcN9GMu2bXFSUBAj9esXSfv2CfLLL5+o7fpx6mrM6uUC2erHgj/nxrEfOusberk0TEsT+XdGfTw1VUpdLMWl1RKJqimyOW+zlNhKpHH1xhIdFS05+TkSLdHSumZrt+0tKi2yZwNDspF9x/fJgaIDklglUVLi3Wf6Olh0UPYe3yvxMfHSLKGZZB3NUtvdXSM09HLR9x+rXSOc4el3mFZRHzhwQD07OoLVqVNH9u93XW38kksukS+//FLuv/9+NRPPzs6Wxx57TNIwSFwwfvx4GTNmTIXt+Ky7Y+FGoG7dunLo0CG1b5cuXdT2rKwsyc93n/enQ4cOqlNt375ddYrWrVurZ5iL3IEO0b59e/U3QmNg5m/QoIG6GUH6QnfUr19fOdehQ+OzsE6gc+7atcvt7wT4TGJiouTm5sq+ffukY8eOajt8ADwNCL1c0H6cC3RwT78VWZ70ckFqxuTkZNU3IDd3wKdBLxcsf+AiAidD7UbMFXq5YIC1adNGbde3F7/FEQx6vVzQN3B+EIeO5Rt3oF/r5ZKRkaEcJ2EJgiOlO/RywQ2uVigDv9vTBUIvF1x4WrZsqS5cns5NQkKCslRpssBY6N//LklPbyPDhvVR28eNwyplxe+ZPr2RTJ+eIh06HJWpUzfJ5Zd3kL17q8qIEdvlwgsPuT3uk0++JRs3bpcbbrhYOnceaN+O/uzu4orfppcL+pZ+LLijVq1a5eSCSUBSUpIaC+gjjuj7hv4aMXzcOJF588r2efJJyf+3rzgy7t/ny3+6XCnNEW1GSEJsgoxYMkLiY+Nl5rkz3bYXCn7ggjLZTO8+Xd7Y8IbM3DpTutbtKhO6THD72ZlbZsrEtROlVWIreafHOzLg1wFquzfXCL1c8NCw6jVCD64BwSTK5ulWKEhg8ODH//LLL9KrVy/79hEjRsjPP/8sa9eurfAZNLVr165KQX/yySfqrgwnvlu3bnLPPffIIy5i+pzNqDGIoXwxyKw8o8Z3QllAHvh9Vp9Ra/LAAHfMNmS1GTX2x8UQNxm4qWjf/irZs+dLadjQfT/ct6+KzzPq1NSRMmvWpHJygVz1Y8ETwZ5RO+sb+mvEnb17y/R/FbW7GXW/WiLZA40zo079PlVW/rLSpxk1zgdu6KEMITcrXSOcAVngpu3w4cNBqR8RNkWNH4wZ3HvvvSeDBg2yb8c6Nd6bO3duhc/s2LFDdYzvvvtOLrvsMvt2rHP//fffsmTJEq+ODUWNO8NgCZWQSCQjo0/QPLrT0/sY1pvbW/pkZHjlTAZHMSOtUafPS/d5jZqEVqeEzfSNOyzMhL/++mu7ooaC/umnn+Txxx+37weTBcwKMDnCTI67HChsPTAHupsZu8LTXZUVgAxwx4m7VP26pFWhPE5y4YXXSX5+tBw+nC8nTpQGzFHMqE5insKromNiJCEpSfIPH5ZSJ7NPo4dVBRKOk9DqkrB6fT/77LNy8cUXK8XcvXt3mTx5slLGmCFrjBs3ThYvXqzip2G6uOWWW+wmbqxXwJkMM2zEYvtKsB0AzABkgHUXphClPByJiUmUqVOHy6BBbWXDBv9TiJolFtpTeNWxjAxZN3WqtB00SKp7MXOOZHjdsIgzGcDaNNajp06dKr/99ptywEJolt50ACcP/brutGnT1Dr1999/r5wfsC6wcOFCOfvss30+PtZJrA5kACcRyoLycGTPngNy992tZPt2jhM1VrZvl1Z3362e/Sk9GYqYaL/iopN9rwnN60Z5gn399HuNGuZSOGMhjacjSFhiZLhGTYh3xTNyc8vCowJp6oaZe/78yn9vuNaYjbrWrME159BjyDVqzH6xruzKPT5M/mk+48lr0ApABgi1qFevnvKQtDpWlodj+s86dYpl2LCd8sUX9ZQXd6SZun2luE4dyb36aqn3xRdSxUMoU6Rj5XESDl3il6IePny4SvWJkCjEIJoVKuoyGSBWEOeRA85a8vBUfrJuXSjqXbJgQS2fFLWzGXQkUFy3ruwaNkxqLVhARW2hcWJaRY382k8//bRXla4IIdYqoBEpM2hCjIJf8Thw+vKUUYYQQgghYZpRT5o0SW699VZl+oZXNrK96OnZs2cAmkYIMUMBDSOauv0pOWmlOGhiAUW9efNmWb9+vdx2222mdiYjxCoEs060EU3d3pac1INwK8fyko7FNMb9mwI0q07ww6z8CbXyN9yKRKCifvTRR2XUqFEycuRIUzuTEUKIt+UnUfEKIE/3Bvc1HcJagpJEHn4palSKefjhh1WFHUKI+Ty6I83UTUgkE+uvM9k///wjZ511VuBbRAipNFYzdRMSyfilqC+44AIZMGCAjB49WqWfdHQmu/TSS8UMOJYxtCKQAdKwUhbmlUewHMV2764qkyZ9LzVqPC/p6ZxB7y7YLc/884x6tjpmHCfBJNhy8EtRjx8/Xj3fd999Tt/3VF/XKKBilNWBDPypPBapmFEewZo9Hz4cK0uW/MXZsyaP4sPyVc5XAZezGTHjODGzLvHr282iiK3yOyorA/gcwCmQNy6Uh56kpBLp2bOTvQyqmcKtghFqlVQlSXo27Cm/7v5VKW0rw+tGaHWJpaeURUVFYnUgAxRXQX3wcF+MjYDR5RHMeGhHR7G0tIYybNhwJZNwy8KfcKtA0zC+oTx+6uOy/vB6yytqo4+TSNMllZbw0aNHK9xNmCVkC53M6kAGXbp0CXczDIPR5RFMJ7FIdBTzpRSlp7jozCOZcvo3pxu+BGUoMPo4iTRdEutvSa8HH3xQPvvsM6dlLpnwhBBihtjoUMO4aBKyXN/w9l6zZo18+umn6vXChQvlpZdektq1a8u4ccjdYw6OHz8uVgcyQJY5ysJ48oCZOyOjT7lHIM3cmqk7Pb2P/aGPiTaSLIxAakKqvH322+rZ6rBvlCfYY8SvGfWcOXPkl19+kYyMDPUa8dQ9evSQNm3ayBNPPKGSoZiB0tJSsTqQQX5+PmVhQHkE28ztydRtJFkYgWox1aRjckf1bHXYN8oT7DHil6LeuXOntG7dWv2dmJiozN916tSR8847T1avXh3oNhJiCYKZTUyDWcUIMR9+O5NFR5dZzTGLnj17tqqm9dNPPzG2jhCTzqCNHHLFylbEyvilqDWTN3jkkUdUlrInn3xSdu/eLS+88EIg20cIiVCMEHJFSMQqajiYaPTt21dWrVolS5cuVbPr00+vXPgCIVYh2KZuRzN3pBXQ8Cb0KtilKCMl3IpEoKJ+6qmn1EM/w9bPsgkhnmFMtPlDrxhuRQwbnvXcc89JcXFx4FtDCCGEkMrPqDt16iSLFi2SXr16+fNxQixHKFN/RqKZmxAr45eivvLKK+Xaa69V1bPatWtXocSXWcpcVqvGeEjIAPXFma83uPIwo5mbfaM8m/M2y+U/XS4HCg+I1WHfCK0u8etq9Nhjj5V7NmtVKi3EzMpABqwpG3h5hNpRLBizZ/aN8pTYSmTv8b0Bl7MZYd8IrS6xdJnLwsJCsTqQwfbt26VJkyYSFxcnVidQ8jDjDDoQsojk2OjG1RvLiDYjZMr6KbLj2A6xMrxuhFaXWLo+GYuHlMkA6e8oi5N9gvLwXxaRHBsdHRUtCbEJ6tnqcJyUJ9jXT78U9QcffODyPdx5t2jRQjp37ixRUVFiZLhGXSYDLR0s8V8ekWDqNkrf8LY0ZbBipN3FRk+ZNUXiJV7SJd3ScdG8bphgjRrVs5DvW8v1DfLy8tQzKmgdOHBAOnbsKPPmzZMGDRqIUeEs8uSdMdZYjH5jZWR5RIKp2yh9I9zx0a5iozlWKItw6RK/bDjw9j7//PNlw4YNqjY1HpmZmdKzZ0+VUhTrWvXq1ZP7779fjExBQflwFisCGfz999+UhR/y0JehDHSolb70pGP5yVDBvkF5sG8YQ5f4NaN+/fXX5eeff5ZmzZrZt6Wnp8uMGTPkoosukgceeEBeffVVpcwJiVSCNYs2UuEMQkj48WtGnZOT4zTOtEqVKuo90KhRIxacJ4QQQsIxo+7evbvccccdambdsGFDtW3Xrl0ybNgw6datm3r966+/KlM4IZGE3mksWJnFjJZR7KFhwyRr6dKIDLkiJGIV9RtvvCFXXXWViq9s3LixWkiHcxmylH355Zdqn23btsnEiRMD3V5CwooVzd3Hc3MjNuSKkIhV1K1atZJ//vlHfvnlF1m3bp3yCEWJS6xJa96hd999d6DbSggxKd6GXIUr7EqPVUOuiHHxO+EJFPIFF1ygHoREKsGKjw5HXHQ4CXfIFWBJSmI5RX3ixAn5+uuv1Ywapm+Yvf/zn/9ITExMYFtISBixoqmbEBIBinrr1q3Su3dv2bx5szRv3lzNrrds2aJM4t98843aZgaY4KNMBsiqQ1mc7BO7d++TBx+8W3bs2GfpGbTWN0pYe76cPDhWKAtHgt0nomx+pFTBzBm89dZbUr9+ffX33r175dZbb1UNnjPH2DMFJGhJSkqSw4cPS82aNcPdHGIwkMAk0LNoJC0x6wy6T0ZGpZ3JMuqE3/SdPi/dacYxQoyuU/yaUc+fP19lItOUNMDf//vf/yQjIyOQ7SOEBAlvK10x5IqQ8OKXosasGWvUzspfmqnG87Fjxyw/o4YMcNOFG6zq1auL1R3GUlMbyJtvPiRDhx6TDRuqR7Sp21Olq2Pp6ZI5fbpkDBsmkpsrVodjhbJw1zcMp6gvueQSGTp0qLz55pv2NKLZ2dlyyy23qPfMAjKpWR3IICUlxdKy0DuM7dtXLK+/fkD27aucPCLBWazKvn2S8sYb6plwrJTrG7xulCPY10+/FPXkyZOlX79+kpaWZs9Mtnv3bunatau8//77YhasrJz0MjByhbNQc+BAFZk5k/IAVQ4ckAYzZxo6NjqU8dEcK5SFqRQ1ZmCLFy+WBQsWyJo1a5QpHOFZ5513ns/ebytWrFBr23v27JEOHTqoilvJye4HXXFxsbzzzjsq4QrMtZjdn3XWWT7/DpjqrQ5kgBKlKFfqLH+71VKBJiaWSNeuebJkSaLk5cVGlKnbV0oSEyWva1dJXLJEYv8tY2u02OhQxkdbdaw4g7IIrS7xa0EZs2goZOTyvuuuu+TOO+9Uf2ObNsP2Bih75A3H3Uj//v1VfvCzzz7brb0f5cRwrClTpigzOx6PPfaYLF++3OffUVRUJFYHMkBondVkoZm78cjN/cS+PSWlSCZM2KKe/TF1a4/5809+p1kpSkmRLRMmqGdi3bHiDMqiPMHuE37dFmL262qme+DAAa+/B7WrL7vsMpk6dap6fcUVV6jc4Qj7GjHC+a362LFjZePGjbJ+/XqpXbu22gYlH+zFfGJugpFhzMiFNAghkYNPinr27NlO/walpaVqhtyyZUuvvgsz499++00pZQ3EoV144YXy/fffu1TUqHl944032pU0gKd5jRo1fPkpxGIEI8NYJDiMEUIiTFHfcMMNTv8GMF/DuQyOZt6AutUI8WratGm57ajIhThtZ2C2vmPHDjn11FPl2WefVeZurJffdNNNcuaZZ7o8VmFhoXrog9PB8ePH3c7Eq1atqtaisP4A04YWvoTP4cbEHchghBsIHBc5ZfAaz7hBcQeWD+Ljy2ZnaBvkiofWBnegrWgz5Irj6tvgLJwOaO3R3odVBMfS2oD3PeXE0csF7Y+Li1PywWt3oG16uWjyRhvwcIejXNBeHNvVuUlLw3JN2bnOyqomhYXR0rBhocTF2SQ7u5ranpFxTNLSyuShPespKYmSzZvL5NKyZYHUqlV2c6jJ2x1IrauXC/7GNrTd0/qWo1w0ebs7NyhNiapXe7Kz1T61GzZUxz60d69Ex8RI/aZNJa12bTnmIu9BtO7cFaSlSdXduyX28GEpSUqSon+Xt9JqiUQ5ye2wr3Cf7C/cLwmxCdKkehPZnLdZSmwl0rh6Y6kR6/6Gelv+Nik4USD1q9VXn996dKvanl4zXaLEuf9LapNUNVb0coFs9WPBn3Pj2A+1saIfw1a4Rmjo5aIf25FyjfB0boArfeHpd4RUUR89elQ9QyFnZWVV6sBah9I6nP6EuupsmpBGjRol119/vZpZ//XXX2pd+7PPPlOe6M4YP368jBkzpsJ2hJTt37/fZRtTU1Olbt26cujQIbVvly5d1Hb89vz8fLe/D45x6FTbt29XnaJ169bqGbnR3YEO0b59e/U34ptxIwKvbDixYH3MHUg6gxsfdGh8Fg5+kC9qhbv7nQCfgZNMbm6u7Nu3Tzp27Ki2Y5nB04DQywXtR/9AB/f0W5HBRy+XFi1aKEdC3JBBbu7ADZ1eLqeddpq6iOAGULsR0zNu3HARKWvPgAHtZMuWeBk6dJe0aHFcbrmljdo+c+bJ9o4dW7F/79uXJ/fc87L6e8qU+2T58m72cbFp0ya37a1Tp045uSBuHVYgLCMhq5879HJBOdlOnTqp7fjdri4QN993n3o+7bzzJCY/XzaOGCHR+fnS8qGHpKh+fVn1b3SGqzOUsHKlNJswQf2dNXaspD7zjNT96is51LOnZD/+eJlMXXx2+obp6tEhuYNMPXOqXP7T5bL3+F4Z0WaEXJhyodvfOmTREFl1aJUMajFIutXrJgMXDCz7zu7TpUYVF0r+3LKxopcL+pZ+LLijVq1ayhKICzrODVIhw7qHsYCx44j+2mela4ReLnhoRMo1Qo9eLhhjqA4JXLVX042GSiHqTIHCAxs5vrUO5AkIGR0GucGRN1wDaUhXrVolS5YsqfAZdHxc3K6++mr5/PPP7dsHDRqkTsSff/7p9Ywax0bOcn12NUescLeM9mDwpKenK0UdSTPqfv1GSnb2pAqf9TSjhpJ+9FHcjJa/iWzS5B756ac37XJBW9EGI86oR/brJ5OysyV+40aJwjGbNlXPcTt2SGlsrBx3sUQ1LF4kN06kEG2sVk3GvTpTpj73qKxavljyjhyWxJpJUqd+2Yz6SHWRg2XZhAM6o64xp4a0rNdS4qvFy47dO9T21MapLiNK6tWsJ9NfmR6SGTXGCpSMNj6scI3Q0MsFyg/Xz7Zt26r2mPUaEagZNW62of8MlUIUyvWjjz6SmTNnqh+BdeWlS5eqH//JJ5/INddc4/E7cLeDO1GEZ+kVNV5rd1+OJCQkqLsrDBQ9EBDWx12BToGHIxC+N9m40DH04RjaSfMG/XHRSXzJ/qXf17EN7sBg0n/W2W939hl959ZwtHi4Qy8XdHJvf6ujXBzb4O+5ycra7Ta72O7d5eWSmXlyXyhp/Wtgs+11KhdHebvDUS648ODhDb6cm91ZWVJdl3WsWk7OyTaUlEh1F7PMpbqc3Bk1y8zif56VJZmnHP53Dzxrf0Nbu25vfkm+ZB45eZwdx8qUrieaVmkqf37r/KbbGwJ1blz1Q3y/43da4RqhAZnof5+ZrxGecJSLq/b68p0hC8968sknZfTo0epvzGJh8oEpBMobXtnegrVlOJPB1Ap++OEHpahvvvlm+z6TJk2S22+/vdyM+yuY4A4dUq9xZzdr1iw599xz/fkpJAK9u1FUA49AeXajoAYe9OwmhIQDv2bUsNPDVAp+/vlntTaMdR5U1RoyZIjX3/P0008rMzdmyXjg73HjxkmPHj3s+6xevbrcbPmBBx6QlStXqjUlmNnRFqynvvTSSz7/DqsnLdBkAPN/pMiist7dBw/GysyZ9dWz1T27DxYdlJlbZqpnEnljpTJQFuUJdp/w69uxQA9TNxTql19+KU888YTaDkeXRo0aef09MGXPmzdP1q5dq5xqsIDvmM7yvvvus8+eVYNjcSGdqdZHMJNHrnE4GPiDtybHSAYycPS8t3J8tM12o3z7LTy64WRk7fhoOIBNXDsx3M0wDGYfK4GEsgitLvFLUd9xxx0qUQmykGFd+tJLL1Xb4eCF5CO+AgWNhzNcOadhXRqPyuDJecIKQAZw0sA6k7ZObeX46IYNS+SPPz40vDw8lagMRGnK+Jh4aZXYSjblbVIhU1bH7GMlkFAWodUlfilqhEfB4QszWmQT05wJ4JGtX082Op68Qa0AZIAwDXhvWrXMpZ5GjeqYQh6eSlQGgmYJzeSdHu/IoN8GlXMKsyocK5RFuHSJ34b1Cy64oMI25P02E8H21DOLDGDN8MXr06gFNQKRCrS0NNF08ggWWUezZMCvA7z21o50zDhWggVlEVpd4rWi1uKWEXqlj2F2hjfhWUYA4QFWBzLwJbwiEs3dVnAY87Ykpb4cZWFpoWw56j6BhhHLUQYLM46VYEFZhFaXeK2oUUpSU8La32ZX1DR9l8kA2XfgBMiZQuTKw5+SlA3jG8rQ1kPlzY1vyu6C3aYpRxksIrVv+ANlYVDTt97zWv+3maEzWZkMkDrQXYa2SDN3u6sdbRZ5hIKkKknSt1lf+Szrs4AoarPDvkFZmMqZjBAzm7utYOomhEQOfivqDRs2qEQkzupPjxw5srLtIhYn0PHRZqwd7S4EKxDhV4SQCFbUb7zxhoqlRnISVDJxhIqaVBY6jIUmBIsQEqGKGrWg33zzTRk8eHDgW0QIIYSQyilqOJMNGDDAn48SEnaHMauHXQUas4ddERKRirpz587yf//3f+WKZxBSWegwFpqwq0Bj9rArQiJGUX///ff2v5Hn+9prr1WlLlu1alWhoLuW+5sQd9BhjBBCAqiokdPbEVS2ckZJSYmYAasnLdBkgJutcMjCiA5j4ZSH0dh+bLvc/dfd6pmwb+jhOClPsK8XXitqsyhfX7B6BRxNBklJSeFuhmXlYeQQrPySfFmce7IWvNXhWKEswqVLLJ3wpLi4WKwOZJCbmyv16tWTKlWqmLqgRiAcxkItDyOHYNWJqyNXp14tX2R/IfsL94vVCXXfMDKURWh1iaUVdSRaCfyRwb59+1Q8fCguPkY0d4dTHkamVtVacmXTK+XnXT9TUbNvlIPjJLS6xNKKmpVwymTQsWPHcJ8Kw0B5nGRz3mbp/XPvMJ4NY8G+QVmES5dYWlGT0MD4aD9kxvhoQsi/WFpRFxQUSM2aNcXqMti4caO0bt06aHeFZoqPDoU8zBIf3bFZR3n5iZfDLgujYJS+YQQoi4ryMKSi3rJli3z00Ufq+a233lLbvvnmG7noootME9pis9nE6kAGcIQItCyCNYsOdoaxYMnDrJ6slMVJ2DcoC1cE+3rhl6JetGiRSmrStWtXmT9/vl1RL1iwQDZv3iz33ntvoNtJTIaZZtGEEGJk/FLUo0aNkokTJ8ptt91WLivZkCFDpF+/flTUhHgZLx3uWGlCSIQq6pUrV8r111+v/tYr6tTUVNm6dWvgWkdMRaDM3ZFYUMPI8dKEkAhU1NWrV1eB/wkJCeW2r1ixQtWoJtYkUOZumroJIaSSivqqq66SBx98UGbMmGGfUS9fvlyZwvv37+/PVxIS8XgbcmWUspT12tULXiMIIcFV1M8//7z06dNH6tSpI6WlpSqlHrI59ezZU5555hl/vpKYlGCYuyPB1G3UkCtfylIeO3ZM1q1bF5I2EUICrKgRe/zrr7/KwoULZdmyZUpZo0Y1FLVjyUsS2dDcTQghBlTU8Pi+7rrr5JxzzlEPs4K1dqsDGXTp0sWnz0TyLNofeUQqlAXlwb5hDF3il6J++eWX1Rr1BRdcIDfccIMKyapRo0bgW0cMCWfR5itZSQgxL9H+fCg7O1t+/PFHadq0qdxzzz3K03vQoEEyd+5cU1WkOn78uFgdyGD9+vWURQDkoYVgOXt8kpsrZoN9g/Jg3zCGLvFrRh0dHS29evVSj1dffVW+/fZbmTlzpppZJyUlyZ49e8QMcD29TAbVqlXzKItAmLvNEB/trTysAGVBebBveEewrxeVLsqBvN5nnnmmyvm9Zs0aycz07E1qFMySkzzYMkhLSwuJudsM8dHeysMKUBaUB/uGMXSJ34r60KFD8sUXX6iZNHJ8N2/eXGUrw5q1WYC3utWBDAoLC1VHg6XE6vgij15X9JKdB0+uSe/I2yoZdcITG+1LfHRKcopX38e+QXmwbxhDl/ilqK+++mpl7kaY1oABA2Ts2LHSvXt3MRtcoy6TAWJl27ZtW8FzMdDmbiOaun2RhyNQ0hsuLp8WdIMJ4qODIQsrQHlQFqZao8ZsA7PpSy65RGJjLV3SOqKxirmbEEKMjF9a9sMPPwx8S4ghsOIs2t8QLJi6CSHEMIr6888/V8/XXHON/W9XYB9iTjiL9r4KFtajWQ+LEGIYRT106FC7Etb+dgUVNSGEEBJiRQ0vb2d/k8igX7+RkpW1m+ZuQgiJhDXqhg0byu7du31+jxiX7OxJsmFDdcs6jWmhVmlN0mTcqHHSb0g/ydqeVWE/fQhWsMOtfAm58iXsihBiAUXtKvNYcXGxHDhwoLJtIiTkaKFWUTXLMgxld8uWDUecr0BvMHHIFSEkwhX17Nmznf6tBXwvXrxYWrZsKWbB6gk+4OFdWhovgwZdIceOtff585Ho3X3sxDH5c++f6tnqYHwgV4LVx4kG5UFZuCLYYyTKZrPZvN1Zq5CVn58vCQkJ5d6rUqWKSr34/PPPy0UXXSRG5siRIyon+eHDh9WFyKpkZPSpVJx0enofU5u79WScnVEheYkRSJ+Xzhk1IQYn2DrFpxn10aNH1TMUclZWxfU7f9m5c6fs3btXWrVq5VO5TAhn7dq10qhRI0lNTfX5uD7co0RsnHR0tE3i40uloCBaSkujLDmL1hMt0RIfGy8FJQVSKtZOMYvxAUsZZgssUkJ5sG+ET5f4NV8PlJJGTuWBAwcqc3n//v1Vucxp06Z5/XnkFT/rrLNk4sSJfh2/oOBkJSerxUnjkZv7ibRuXSALFvytnn1xGsNj/vxPJNJoXbO1LLh0gXq2Ohgff//9tyXHiTMoD8rCFcEeI5XO/4lZtmMN6lq1ann12WeeeUYWLVokmzZtksaNG6u0pFDYp59+upxxxhluP/vKK6+o/KodOnTwu+1Vq1YVq7NzZ1UZPbqFeiYiOwt2yujlo9Wz1cH4aNGiBccJ5cG+EWZd4peihsn5wQcflM8++0wOHjzotxngrbfekmHDhiklrRX7aNeunbz99ttuFfWKFSvUWvjSpUvl8ssvF3+xUp5yzeTtmBY0Ly9Wfv452dLmbj15xXny866fw90MQ4DxkZzsXWiYFaA8KItw6RK/vn306NGq9vSnn36qHMcWLlwoS5YsUVW0/vvf/3q9Lo14a0eFjNrWUMTuZvDXXnutTJ06VVJSUrw2seOhv9EAWPh3J2DcJeF9WAyKiorsFYQwk/dU1qxatWpqbQ/HxY0LXuPZk4kEa4Hx8WXK8NixY8pJDw+tDe5AW9HmEydOqOPq21C1apJERb0jtWuL1K590qM5KalYzj77iHz2WT3Zvr2a1KlTLLVqlcjmzWVtaNmyQFq2TJJZs96xfwbt0qOXC9qPoi2Qj6eKMmibXi6avBHmh4c7HOUCmZ3/n/OlNLZUqsVVc/vZHXt2qO+vm1xXfceuvbvkYPFByaiZIUlVkuTs+mfL73t/l8PFh8t9rsRWIpvzNpfJJbGlHCo6JPsL90tCbII0qd7E7THxXSem7Zb6hXFSpWUrOREbLYVFhVK7Vm2pWcO180m9dvXUb9TLRZM3ZObpphhywTnx59zExMSocEucIziP6seCN+fGWT/ENnegfTgujoF99WPBE3q54Dv0Y8Ed2FcvF60Njv0Qf+PaAWch/L5Iu0Z4e24gB7QRj9q1a6vjeuqHRrlGRP07Fvw5N+76IXSJ4RT1nDlz5JdffpGMjAz1GuvEPXr0kDZt2sgTTzwhDz/8sMfv0OKtcaL11KlTR/bv3+/yc3feeaece+650q9fP6/bO378eBkzZkyF7Vu2bFFObK6Ag1rdunVVJrbs7Gzp0qWLfY0enu/ugEkenWr79u2qU7Ru3Vo9o2ygO9Ah2rcvC5XKzMxUNyNYu8/Ly1PtdUf9+vWladOmqkPjs7BOXH75YOnd+0IZN264iLg+dk5OnHz+eTW5+upcufLKfTJ48Fg1i54y5T6pU2e423br5YL2w9kQHdzTb8UFTy8XmFkxg0PfgNzc0aRJk3JyOe2001Qs9IjHRkj3+u5Lrg74dYBsObpFBnQcIC0SW8gtv9+its89d659n0EtB1X43J6CPdL7597q78ldJ8tXOV/J9A3TpUNyB5l65lS3x5y9bbZ8mvesLE1qLGsnvavGDhwnc3Jy3PZBgN+oyQU3uJ06dVLb8bs9XeggF1xccRxceOAPgguXp3MDxdysWTP7eXAcC+6Acyf6LW6qsaylHwueshpqckGuBihF/Vhwd3HFb9PLBX1LPxbcgaU6vVzg1AoP3tzcXNm1a1eF/fXnKxKuEVBg+J3urrtALxc8IK/ExER1jj0pTaNcI2L+HQvaZM0VerlgjEG3AVft1RytDRGeZf9QVJS6+9LiCrdu3aoULDomnr2pzYlOgh8Phd+rVy/79hEjRsjPP/+svLkd+eabb5QDGdaytfCwm266STp37iwjR46Ubt26eT2jRmdFu9FxXREJd8tt2/aVI0c+k6Qk53fLaWkFMnZslgwdmi5//51on1HHxAxUDmPezNqMcrfcpkcbKehXINVj3GdYyzqaJYWlhdIwvqHERcdJdn6Z4sGMOq1GmoztPFYe/b9H1X6BnFHXfH63rDsaJ7efd55M++47+8zR0cfDEUe5hGpGrV0ccVHFBdrqM2p8LxQN5KG1y6ozaig/XD9RqxztsfqMeu/evdK8eXNjhGc5C/CGskXyk1tvvVV++ukndXfpDVCU+A7MEPTgtatQK+3O5tFHH7Vv27Fjh+o0GEC///676kiOoFPg4QiEr3Ugd6Bj6E3k2knzBv1x0Um8OZ6Gfl/HNrgDMtB/dvfuOPGU1bWgoExu+/dXUY/09LLt2gXBG/Rywbn19rc6ykUbYN7gKJec/Byv27u7oLxQMo+cnHlBSetfO6IpbJBfku92X428qlWkb2qqxBcV2fspLjzeOqI4yiXY50a7KOE4mowr0w+djUFXOMrEl3Gjl4tjG9zhKBdX/RDf7/idZr9G+HJuIBP97zPbNaKan+cGuGqvL98ZMkWtmbzBI488IgMGDJAnn3xSrTm/8MILXn0HfjBmwF9//bUMGjTIfmGAsn/88cft+8FkAbNCx44dVVUux8pcMGf07NlTJk2a5M9PsZzzmDus5DQWDho3by5zmA6UEBIKRb1+/Xr733379pVVq1YpD2zMdhFa5S3PPvusXHzxxUoxd+/eXSZPnqzWrOEJrjFu3DiVmnT16tX+NNWy+FNX2uyFNQghJBIJiE85Ztj6Wba3YG0a69Hw4P7tt9+UcwVCs/Q2fjh5uFuzwUwba0aEEEKIpRX1559/7vWXOpqn3QEPbjxc4cmD/L333vP6WFZLD+oLqakjpago3lAlJ30FIVbBpt4MkeQ8z/vtqFpFmbr1sAwlISSoinro0KFBUdQkvOZujVmzJvnkxBKKkpNGBEo6030Ui6JPOtejCSEhVtSe4h/NiLdeg5HsOJaYmCBz5nRWSwhEJPd4rkxcM1E9Wx2MD8SiRso4qSyUB2XhimCPEevk0HRCpFyA6DgWOA4UHZCZW2d6te/AevWkwEWKzXgvs+YZfXwgYQShPNg3TKioP/jgA7fvIymJGfCUaMIKQAaIQ0eGISvlPndFYpVE6Vq3qyzZt0Tl/XYHlPQcD1mvzAz7BuXBvmEMXeLXldkxnzeyvOzbt09lcMEduFkUtacsPmYxe/tj8tbipCEDxKojwxAVtUhKfIpM6DJBBv02SDKLI1cJewP7BuXBvmEMXeKXokZiE0dQReuWW25RRTrMgi8ZdYxs9s7NneN3rDRkgKQxWqY5q7PxyEY57/vzpKCENZjZN8pDeVAW4dIlAbs6I1E6EpZMmTJFzALS0lkdyADpBCmLMkqlVKUExbPVYd+gPNg3vCPY18+ALkoivAeVScwCcocHI4G60Ty93aUGhQxwzpB7PZj5an2JjQ52PLS7WOiGKU1l8F2jZMarz8vunRX7coqFJtqh6htmgfKgLFzhTSGqkCvqZcuWOTV9I9+2Vs7MDHiqoBIpnt7uUoNCBqgmFmxZGCk22l0s9LG61WXd6d1lblF1qe5FvHQkE6q+YRYoD8rCFcEeI34p6jPOOKPiF8XGqnzdb775ZiDaRQI4k2aBDUIIMS9+KWrMnh1BeI+zEpPEmDNp4h2RHitNCIlQRV2rVq3At4QQAxLpsdKEEOMTW5kA7+zsbKeza19KXRLvoMmbEEKsiV+KGiUpBw0aJNu3b3f6PhKfkMBCkzchhFgTvxT18OHDpX///nLPPffQDE68Cr0KRsiVtyUnrRxiRQixqKLeunWrPP3001KjRo3At4j4nSY0XF7e4Qq98rbkJCGEWE5Rd+jQQdatW+c0TMtMmCWJg7dpQv3x8oYM2rVrJ3FxcZVoYeRQLStL2g0YIHE7dojVYd+gPNg3jKFL/FLUSGxy6623KtN3y5YtK6RP69mzp5gBo+e39taBrDIzacggEnKeBzr0SlJTxeohWFbuG86gPCiLcOkSvxT15s2bZf369XLbbbeZ2pmssLBQIsGBrDLx0pDBrl27pFGjRpabVTsLvbKyPByhLCgP9g1j6BK/bgMeffRRGTVqlOTm5kpxcXGFh1kwyw1FsGWAPLWUBeXBvsGxwuuGfwT7+unXjPrQoUPy8MMPS0JCgpgZI69R++JAVhnnMcigTZs2fn8+0qA8KAv2DY4To+mSaH+dyf7555/At4Y4OJB94pXZe/58z/sRQggxJ37NqC+44AIZMGCAjB49Wlq1alXBmezSSy8VM3Ds2DHTlrkMpAzgwd+2bVtVptSfOOlAxkj7EhsdjHhob+RhFSgLyoN9w/uxYjhFPX78ePV83333uUwvSozr6W3kOGnGRhNCSAAUNRWxuT29LR12pcPKoVeEEAsU5SCRP5M2K6x4RQgRqyvqDz74wO37N9xwg7/tsSycSRNCCAmYov7vf/9b7nVpaans27dPxZI1aNCAipoQQggJp6LevXt3hW2oS33LLbfIRRddFIh2WYpQxUwTQgix8Bp1cnKyTJ48WS6++GK58847A/W1lsCXohvz5wfHgWzYvcNk6dqlQQ2/8ib0iiUoCSEkiM5kiDvNyckRs+AY/21FBzLIoEqVKrL74O6gh2CZIfRKk0e4+4YRoCwoD/YN7wj29cIvRb1s2TKnpm9U1erSpYuYhXBXBjKCAxlk0LFjR8nZlSPSQcTqYVeaPAhlwb7BcWIUXeKXonZWhzo2Nla6d+8ub775ZiDaRUgFGHZFCLEifilqzJ4dSUxMlJiYGDETBQUFYUsh6o0DWShipiGDjRs3StNGTWWDhCb7mJHR5NG6deuwW1zCDWVBebBveD9WDKeoa9WqJZEArABGdiALRfYxyKBu3bqSl+9lgu0IR5NHOPuGUaAsKA/2De8I9vXCp+pZmzZtkltvvdXl+wjPwj5mAU5DVgcySElJkUNHDoW7KYaSB/sGZcG+wXHiLcG+XvikqJ977jk599xzXb6P9yZMmCBm4cSJE2ExeWdk9PHK7B2KmGnI4PDhwxJfzdpmXkd5hKNvGA3KgvJg3/COYF8vfJqvL1iwQB599FG3inrcuHFiFgoLCw3t6R3ImGlXpSrTmqTJuFHjJL5W5RR1pMRIo0/AKsQyl5QF+wbHiVF0iU+Ketu2bdK4cWOX7zdp0kTtQ4znQOaqVGVUzbL4v4M9DoociewYaUIIMSOxvmYfgyJu1aqV0/ezs7PVPsTYDmRmjZFmWUpCiBXxSVGfd955KqnJ1KlTnb6P93r27BmothELwRhpQggJgKJ+5JFH5Mwzz5Tc3Fy5//77JT09XW3fsGGDvPzyyzJnzhxZsmSJL19pCbxJFco604QQQiqtqE899VSZPXu2DBkyRD799NNy7zVq1Egp6g4dDJ6HMgx440BmVZM3IYQQ9/gcpX3ppZfK1q1bZf78+WomjWTkyOJ0/vnnS1xcnK9fRwghhBA3+JVOpVq1anLZZZepR2VZsWKF/O9//5M9e/ao2ThM6u4c0vLz82XGjBnyxx9/qGwwPXr0kMGDBxs2QYW3nt6V9fJ2FX4VqFKVnsKvzBB6RQghZiSseRIXL16snM+GDh0q/fv3Vwr7iy++UNW5UDLTkdLSUjnllFPkiiuukN69e8uxY8dU3Pbnn38uc+fOlehon/K3hCQ3ubee3pWNmXYVfuWJw8WHZfa22erZHVYJv0KfqFOnjuny1gcDyoLyYN/wjmBfL6JsNptNwgTM5UlJSTJr1iz1GhmhEKc9fvx4GTFiRIX90dQDBw6oC6nG0qVLpWvXrkrpw9HNG44cOaKOi+MFqyiH3oEsN/cTjw5k8+c738dbMs7OCGo96fQp/ilqX0pTfjJ/vn+NI4SQMBJsnRIbzmojv/32m7z11lv2bfihF154oXz//fdOFTXWw/VKGtSvX189Hz161Oc2YIZudQeyuOg4aVy9sew4tkMKSwOfXcdsYVfoE8gyBH8LXy00kQZlQXmwb4Rfl4RVUefk5Kj8qE2bNq2Q3QyOat7y0ksvKeXtbjaNC68+xRvufsChQ4ekatWqLj+H97AOXlJSIkVFRXZz/PHjxz2emJiYsot848aFEh1tk5ycauq5deuTi7mpqQ2V+d7xZkQrr4j3sPaOh9YGVyAV6KFqh2Tv8b0SHxMvzRKaSdbRLKV8G8Y3lKQqSc4/VyNNxnYeK0N/Hyp/H/xb6sTVkVpVa8nmvM3q/ZaJLSU2KlZSW4kcq+v82NX/VcTHU1MlCkpu924pjYuT42lp0jA1tcJv1IAihL8DLCW4cdPkXVxcrB7ucJQLZAbZeXNucEwcG30Cx8ZrgHaiHVlZWZKWllahzKX+3GA/tBVtQD/2lEIQpjEof7QNbcTf2Ia24ze4w1EuWj9EGzwZxPRywbO+De6AfLDfunXrlCxwE60fC96cG00uenl7yomslwv21Y8FT+jlgu+A3Cpzbhz7obO+UZlrhLN+qI0Fd/h7jQBoq14u/pwbyCQvL085FSPVLtrjqR/q5eJrP6xmsGuEM6BLIlJRax3K8WKIE+qps2nAqey1115TpvMaNWq43A+m9DFjxjjNpLZ/v2t7bmpqqip5iJOAfbt06aK2Y7DCqc09ZTP/ESO2S0JCqYwYgfrGpTJz5jrdPsPVhVAPOkT79u3V35mZmaqSU4MGDdTA2LJli8ujIV/3zC0zZeLaidIqsZW80+MdGfDrANlydIsMbT1U+jbr67a1SVXLFPnVqVfLlU2vlN4/91avJ3edLA3iG4icK1K+pSfpcvrpZXJ58kmptmWLpD37rBQ2bizrZs6U4SIVfqMGTESIGNAUQosWLZQjIZY3tm/f7ra9uKHTy+W0005TFxHcAGo3Yq5o166d6ne7du1Sg7ZNmzZqu76dOMeOYNB37NhR/Y2a1egbOD+w5niqGoebSVzgMfBxnIyMDNVn4US5d+9et5/Vy2Xnzp3SqVMntR2/29OFTi8XXHhatmypLlyuzolGQkKCOjdoJ/qh41hwB0I19XKBkygusDinni5oerngPOrHgruLK36bXi7oW5gE4OKOz3oq26uXCzIv4sYE+SLQRxzR943KXCP0csFv048Fd/h7jdAskHq56MeCu2sh0Mtl37596lxB4a5Zs8aj0tTLBe3XjwV3GPkaoccfi64p1qghZHSYb775RjmGaaCM5qpVqzwmTvn444/lpptuUqbzG2+80e2+zmbUODZOgrv1BH/vlvv1Gym//PI/KSmJdjqjTk0dKbNmTQro3XK/If3k785/+zyj1th+bLvkl+S7nlF/IjLrkO8z6pGpqTLpXx8Es94tW3VGrZeL41iw4ozaGVadUeNY+rFg9Rn1kSNH1A1qsNaow+pMVq9ePbn33nvlscces2/r3Lmzuvt64403XH4OyVagnKdPny4333yz3wv/uDPE3XCgQRlLd+vT6el9Ar427a8zWf1q9WVQi0FqNg4lH2hnsj7p6aZao8aAxmwOd+LulkWsAGVBebBveAcsDNBnEedMBrQZ8e23364U5g8//KDiqidPnlwuf/j69etV6BZAKFZllLQeTzOZYMRNVyZm2l2stL9x0slVk5Wi/nPad1JrpWtFbZU4afQJmKJhqra6oqYsKA/2jfDpEsMo6qefflqZubEGgQf+Rlw0kphorF69WoVeAdytXH/99Wo2/NFHH6mHxn333SeXXHKJhBtPcdOViZn2N1baG2oeE/lzf2BDrgArXhFCiIkVNRxW5s2bJ2vXrlXmRizgw+SoBwpYc0DBOgfyiTtDc64wauGNSCq6YbaQK0IIMTNhVdQaUNB4eFLAcAxArnEj4ilu2ggx04QQQsyHtTM6EEIIIQaHitrgDmSEEEKsjSFM32YnmA5khBBCrA0VtcEIRgiWuzKVSA2KrGP1Ap/mmxBCSACwtKIORJxsoM3ewQrBclWmsqRop+SNHi0zNruuZW21PoE0hVaPoQaUBeXBvuEdwb5eWFpRIxWd1c3esXl5kvzzzz6XpIzkPoFcwoSyYN/gOAmlLnH7/WJhPOWKtULcdHHt2nLgssuk9ty5jI/+t08g4X/t2rVVOKCVoSwoD/aN4OsSb6CitnjcdHHdurLzttskcenScDfFMAMOFaoSExOpqCkL9g2OE6+gog4iWkUXK1N9wwbp1LNn2Yv0dLE66BNauUSrQ1lQHuwbxtAljKP2A8ZNE0IICRWWVtSe6r66dyD7xIMDmev3jURB8+ay5pNP1DMp6xNr1qzxu29EEpQF5cG+4R3Bvl5Yeo3an1Lc7mbT3jqQhTpW2l2ZSlvVqnK8ZUv1LOErTW6oPoHC8mEs024YKAvKg33DO4J9vbC0og50OJY3DmQDe/WSpZm/S/7w4qDHSjuGW/Vx8pmGqakyXERGpqZKfFFRwNtECCGkclBRh5iCnTulcVGxbDBIOcpjx47JunXrZNKsWXSuI4QQA0JFHcC4aaPHTBNCCDEfVNQBjJs2cgYyQggh5sTSXt+EEEKI0eGMOkSe3oQQQog/UFEHwdPbXfjVjrytUj0x8OFX7kKwCCGEmBdLK+r4+PhKz6a1mTTCruDRDZbmbXUbfpXvZ3sLjsVItzot3e7Tx8cqV5DBaaedJtHRXAWhPNg3OFa8g9cN33VJZbC0oo6Kiqr0bFpzIIOSnrOhLOgqo44EJfzKFhPtMdzKHxnExMQE9DvNDOVBWbBvcJwEQ5dUBktPo5CByupABhs3bqQsKA/2DY4VXjcMqks4o/bD7B1JDmS4E4TZO9h3hGaB8qAs2Dc4Tnwl2NdPSyvquLg4v8zeZqk17a0MWrZ0v+5tJSgPyoJ9g+Mk0Lqkslja9F1aWipWBzIoKiqiLCgP9g2OFV43DKpLLK2oXa0rwOSdkdHHpdk7EkzeehmsWrWKa9SUB/sGxwqvG37CNWqDpQuF2VsS8iTj7IwK8dHw9gYHEwMbH62xP8rSKxWEEGJJeOX3woGsnvSSujHLJCbaJrE5UbI0r9BpnLS/IVnHCqtItzrNPe7nTVw0IYSQyIKK2gsHsmTZKWtPHBU5ISLFIhnVAxsn3bh5c5nze2DjowkhhEQGll6jJoQQQowOFbUXcdMxMc7zdhNCCCHBhqZvL+KmsTatzN6EEEJIiOGMmhBCCDEwlp5R6ytGlTN7V+8lEr9TmbyjY2yyofiYZNhOfs5T+JW34VY7qlZRjmQpySlhlUFCQgKrZ1Ee7BscK7xu+Emwqw9G2Ww2nQqyBkeOHJGkpCQ5fPiw1KxZU21Lq15X4grKAqG31tkqxSNcl6n0RMLrVeT8RO/CrT6ZP9/v4xBCCDGmTgkklp5R62fT1YsKZe2/QVdIZVKZ8CuGWxFCCAkUll6jPnbsmN2J7MSJFMvKYPny5XZZWB3Kg7Jg3+A48ZVgXz8trairVq0qVgcySE1NpSwoD/YNjhVeNwyqSyxt+o6NjbU7kSWLdWVQt27dcDfDMFAelAX7BseJP9eNYGLpGXVJScm/sdOfiJVlsG/fPvVMKA/2DY4VXjd8J9jXT0sratRhtjqQQXZ2NmVBebBvcKzwumFQXWJp0/e99z5lj50+KCnSLmanxFVFOckTkvC673dIUbGxktK0aVjjogkhhEQWllbU+/Ydt5u9c2W+JLfsIysyndehJoQQQiyrqHNycmTPnj2Snp7udbC4P59x5MiRauq5Xr2BkpxcICkpFQtyEEIIIeEkrGvUx48fl6uvvloyMjLkxhtvlIYNG8qUKVMC/hlXHDr0lHqGks7MnCPz51vXqYwQQogxCeuMesyYMbJkyRLZvHmzNGrUSGbPni39+vWTrl27yplnnhmwzxBCCCFmJawz6nfeeUeGDh2qFC7o27evnHLKKWp7ID/jimbNHpL09D40eRNCCDEsYZtR79y5U60xd+nSpdx2zIxXrFgRsM+AwsJC9dBA4nQwadIDUq9ePfX37t27nWabQSA7YuTgfl+9enW7+b20tNTt76tWrZqqqILjou4JXuO5oKDA7eeioqIkPj7enpauSpUq6qG1wR1oK9p84sQJdVx9G7DNGfgtR48elYMHD6pjFBcXq2etDWivp7otermg/XFxcUo+eO0OtE0vF03eaAMe7nCUC9qLY/t7bjR5a/LYu3evfbuzc4P2oq1ogyZvd8TExJSTC/7GNrTdUwymo1w0eXtzbvRy8fXcYD9NFvAD0Y8Fb86NL/1QQy8X7KsfC57QywXfoR8L/pwbx37orG9Y4RqhoZdLXl6ekgWKUeBvK10jnJGbm6ueg1XjKmyK+sCBA+q5Tp2yilUaeK29F4jPgPHjxyuTuSMdO3b0q+2EEEKII/v371dVtCJGUeNOBzjeUWl3TYH6DHj44Yfl/vvvt78+dOiQym+9bdu2oAjVTOCOuGnTpsqLPhjl2cwG5UFZsG9wnPgKrLTNmjWT2rVrSzAIm6KGcoBpYceOHeW24zV+cKA+A2BmwcMRKGkqpzIgB8riJJQHZeEK9g3KwhXQTxHlTIY1i7POOkvmzDmZYCQ/P19++uknueiii+zbNm3aZF9/9vYzhBBCSKQQVq/vZ599VoVXwTQN5QsP7vr168uwYcPs+zz33HMqXtqXzxBCCCGRQlgV9XnnnSfz589XRSFeeeUVad++vSxatEhq1Khh36d169bSuXNnnz7jCZjBn3zySafmcKtBWVAe7BscK7xuGPs6GmULlj85IYQQQiqNpctcEkIIIUaHipoQQggxMFTUhBBCiIExRJnLUGeO2bJli4rJRuUtK4H0q0jDiphzx+xuVpQPkpts3LhRWrRoIcnJyRXeR5rBtWvXqtSKyCeP9ImRCnIR7Nq1S1q1aiW1atVymSYxKytLJQtCpEWkgARIq1evVo6rDRo0qPA+3HgwJpBOsmXLli4dhpBaFE6uaWlp9tTEZuSff/5R6TlPP/30Cu/98ccfFdJvNm/eXBo3blxuG8YMZIox065du6DFFwcbXA/XrVunqjW6OqdInIVzn5KSoh7OQIpqJJXCtcbVtdctNgvxxBNP2OLi4mzt2rVTz7feeqvtxIkTtkhn+fLltnPOOcfWuHFj22mnnWarVq2a7dprr7UVFBSU2++RRx4pJ59hw4ZFpHxKSkpsDzzwgC0+Pl7Jo1mzZrZHH3203D5r1qyxtWrVytawYUMlN+yzdOlSW6SRlZVlO/vss221a9dWsqhRo4Zt7NixFfb773//W65vjBgxwlZaWmozMxs3brTdcssttkaNGtmioqJsb7zxRoV93nzzTVtaWpqtRYsWtjZt2thq1aplmz59erl9IAfIQy8f9C+zMW3aNFv79u1tycnJttTUVKf7aL8RfUZ7fPTRR+X2wTjBeGnSpImtQYMGahxhPJmJ9evX22666SbVN6Am33///Qr7ZGdn204//XQ1drp06WJLTEy09ezZ07Zv3z77Prh+4jqq7xu4zvqKZRT17NmzbVWqVLH9/vvv9hORlJRke+WVV2yRzhdffKGUtf7ijME4fvx4+7bPP//cVrVqVduff/6pXq9du1Z1vFdffdUWaUDp4AKC36gNptdee83+Pl7jgnXNNdfYldHNN9+sLl6FhYW2SKJXr162c88915afn1/uvH/99df2fT744AN1U6P1oX/++cdWvXp121tvvWUzM998841SxPjtuIA6U9TPPvusuiBr4IINpa6/acN34AZn5cqV6jXew/fNnDnTZiZwc4Hf8Mwzz7hV1Pq+4QjGBz6LSZA2lvr166fGk5lu7GbNmmV75513bMeOHXOpqAcMGKBubrEP2L9/v7pBuffee+37TJ06VemZdevWqdd//PGH0kO4JvuCZRR1nz59bJdeemm5bUOHDrWdeuqpNivSuXNn2913321/ffnll9uuuOKKcvsMHjxY3SlGErm5ueqGRK+YHcFgwuD8+++/7ds2bdqkts2dO9cWKRQVFdliY2Ntb7/9doWx8p///Mf++vzzz1c3LXpgkcFsKlJwpaidAaWsv8E/66yzbDfccEO5ffr27Wu74IILbGbEk6KGnHAzgrHkyHfffafGydatW+3bli1bprZpkwCzIS4UNW5ycY3Uc8kll9iuv/76ctdZ6Bk90EO9e/f2qQ3mXDjwA6QhdVYeE+sonsqlRQIo9YbEMD/++KM8+OCDar367rvv9iiflStXeiwJZyYWLlyoSt795z//ke3bt8vff/+tyvTpgSxQSk9fXQ1rk0i4766cqtlAkZvExESnufOXL1/usW9Ekiy8BeuVKO+ItXyrymfUqFFy6623Kj+W3r17lysRjN+MNVis02sgYRXWqiNNHo8++qjMnTtXpkyZIj///LOMGzdOXS9xfdXW6VetWhWQvmEZZzKUwXRWHhPChFORXwv8JgI50R966CGllJA/feTIkUr5eJKPVns2UqqMwZkOSnjChAny5Zdfqt8Ih7L//ve/8swzz9hlAaWM2rW+lFM1I/fee688//zzqtBEmzZtZNasWbJ582Z7hTpMKOBs5axvoDYvHKyskuEPMhkyZIi60F5yySX2baje50w+qPEO+Tn2IzMzdepUueWWW+zFkS677DK5+eab5YcffnB5HcHvx3iKtLHTtWtXueKKK1RGMtyYwOHwtttuU85zANdNXD99LctsaUWN2YOz8pjAXYnMSAGKFjNqsHXrVjnnnHPUzPKFF16wlHy0QvK4OYO3Ju70f/31V7nggguUl+uVV17pVBbelFM1I0888YSkp6fLV199JV9//bX07NlT3cQhPa92kcWNjau+oZWejXRwwR0wYIDyfP/tt9/sEQDuSu9CbpGkpMHQoUPtf8PTG/2nf//+9ptbK42d66+/Xt2M4TqCFNbwEMd1FQr6f//7n99lmZ1hGdM3QkqcmfgQigLzn5VAOAUGF8w2nuRTt25diY+Pl0hBM8nhgqNdbKGcMJuEWVyTBRS53iSOmxpcpN2VUzUjUCTXXXedfPzxx2pZBOY8hOcgHE0Dv9lZ32jSpIlpw258VdIDBw5Uy2SoM6APRUIfwmtn8kE/inS0cDZYqgB+M0KV9MuJGEtYLoiksXPixAl1/YQ1QaszgZkyxpJW3TEhIUFt87UsszMif5T9C8pgfvfdd0rAGphFWKE8JszejsD8rTfJQA7ffvttxMvn7LPPVgNIP3g0JazFSfbq1UvNhjDD1IBpD/tdeOGFEkloM2MNxEnjAnTTTTfZt6EPfPPNN8qMC/CMi1Gk9Q1nwPpy7bXXKl8GWF6cXWAhB/QVTT7w6cDrSJOPs+vIvHnz1I084oMBxgeWQ3DTp7+OYDxhXEUKMTExyoIAPxc9iJXWx1trfUMD11dcZ33uGzaLsHPnTlv9+vWV9+qcOXNsw4cPVyEmq1atskU6F110ke3JJ59U4ShfffWVih1FiMC8efPs+2zfvt1Wt25dFXIA+cBTEd6tZot/9IaXXnrJlpKSosIv4KUKD12Ea+3evdu+z4MPPmirU6eO8oiGxyfiKREPGWm89957qj+gb7z77ru2li1bKq9vffw8PHgRzjdo0CDVNxCqVrNmTRWHbGYOHz5sW7hwoXogEmD06NHq78zMTPs+1113nQpNQ6iVti8eCHHU2LBhg5IHPIAhH3j9Ql76fcwAQrPw22677TaVP0D7rcePH7eH6SEyBP0E0Q8YI7iOvPjii+W+B5/H+MK4wfhBnPGoUaNsZuLQoUP23w81+fjjj6u/9X0e3vEJCQnq9+Na+vTTT6soCn30AK6f2AfXDvSN/v372+rVq6eut75gqepZWJuF40xmZqa6M77vvvvk1FNPlUgHd8LTpk1Tpl2cbmTZuf3228s5kwE4Q0A+cK6CfO6//37p0KGDRCKfffaZfPjhh2pGid+I39qoUSP7+5gVvfnmm2o2gL8vv/xyueOOO9TMINL44IMP5NNPP1VmbHjDDx48uEIWtg0bNih/BjiaYekEzndt27YVMwOPXJxTR+Ao9vjjj6u/4SzlGBUAbrjhBjWGNJDB7qWXXlLXGIwreP5i7d9MjBgxwqk38ueff27PUvjLL7+o/gKLFPoBTL/du3evYIXA9QYWTPQp+H1gqclMyyTLli1TvhqOYHyMHj3a/vqLL75QDpiIosESCEzfmqOhvp+9/PLLai0b2e/gNa9ZILzFUoqaEEIIMRvmucUhhBBCLAgVNSGEEGJgqKgJIYQQA0NFTQghhBgYKmpCCCHEwFBRE0IIIQaGipoQQggxMFTUJOLYtWuXqmqDnMOB4N1335XHHnvM/vq5556T1157LSDfTSry+++/q7SdgQIlO2fMmKGqpTlLXqI/LvoNHiiq4I6HH35YJcwJJDimdny0hRANKmpiisxq2gVMX/sWIF8PskThvf/7v/+z7498uijDGKgaxIsXLy6XtQh1ZyOFQCvGQNxoff/995X+HvSNG2+8UfWNn376ScaOHauKjTjmN9cfF/m8kXEMVZDc8eeff6psbYEEx8Sx0Qa0hRCNyMuHSCIOVOKB4kWVmvfee0+l4NNYsGCBSvOJ6jx333232paSkqIS4WuVfQINZlORVLIvUIrRaLz//vtKQSOFI6rA4cYNBVnWrFmjSpo6AyliodjDQfv27dUjEtPUksrBHkFMA+oBv/322+UUNfJxowThW2+9Zd92+PBhZUZEYXdU9sHM6qOPPlIVfZC3G2UtkaNYyz2Mkp+YZSG/NXKc4zjuZlT4npo1a9rzoOP70S5U2Kpfv776PuQ57ty5s2ob6tFec801MmbMGPVZzMaxH3LNa6UQoUxwAzBhwgSVSxk5tZFLGzmjUZEHuZNhTUD1L+Rk1udNhuKBHLAf8i8PHz5cWrVqpd5DfuE777xT5RqeOXOmynOv5epG9TQcF6Z8zWqhyQPycQQzvRdffFH9DYUHpYK2JCcn2/dBXmfkQoYFAiZnvHfPPfeUy3uNur34HvxGbB80aJD6nThnKJ3pDORSxnIDfivyTkOunma9KN0JawuUNMjOzrbfyPkKbhBxs4jvwrl0xuzZs9VNI6pHderUSe666y6pXr26/X1YfHAe0R/QN5ETHJ+ZPn26z+0h1oKmb2IaoARwkdfW7w4dOqTWHW+55ZZy+zmavlFMAsodF0ckzP/jjz/kkUcese9/6623KpPjkCFDVIk6FODABdQVjqZvrF9D8aE4AS7Qw4YNU0oFxU20IgVoT+/evVXb8DtQAKVHjx52Myx+F/bp27evUiTY95133pGLL75YrrrqKlUv+9JLL1VK9ZlnnrEfG4r/3HPPVYoTygsKHDcIKMuo1QLG9+J3Q2ng2L/99pv6Ltxg4FgouRcXF6dkgMeZZ57p9HejoIC2D9oJOXTp0kUpHg0cC8fATUWfPn1k3759ctZZZ6lzpcnivPPOU8UdcGNQpUoV9Rqfg1XEGSh0cdppp6mZP24icHOD4+OGxpN5GscGWJvGjRF+q6+KGiZz3JBALpAtfr+2zKLxwAMPqJuNM844QxVuwE0Njo3SqAA3RLjJQplDFPpAH7z++uuVHAjxSAArgxESFA4ePKhKzaG03siRI21DhgxR26dMmWI755xzbLm5ufb3AUrR4TXKM4ILLrhAlaRz/E5XTJgwwXb22WfbX6P8Ib5D4+qrr1ZlUsG+fftUGcRPPvnE/v66detsUVFR9mPm5eWp9owdO9a+D7ahJN6PP/6oXs+fP1/toy89Om3aNLUN5fU0XnjhBVu7du3sr1GWEvvpueuuu2z9+vVTf6OMK74DZRo11q9fr7ZpJfs+++wzW1JSks1XUAqzVatWqpyhRkxMjCp/qFFYWKhKQOIYACUSExMTVRlBDcgF7YHcnLUHpVdvvfXWcsdGidKmTZu6bNumTZvUd27evNnWs2dP9X2Q1a5du1x+xpkc0E9Q7tVRfji/KB0L/vnnH3Uuc3Jy7PsUFRXZ0tLS1O8FKG942WWXlfvu7t27qzY5gjZo8iIE0PRNTAVmv5i5Tp48WZm77733Xo+fwUz6jTfeUGXoMEPFc61atcrN2PBdmzZtUrM6zAZhRvaGf/75R82KMQPWwOzXsYQowIxKA+vtMH/v3Lmz3D7aDBBg5hgVFSXdunUrt01zNILpGA+UqMQaM2bIeGRlZZWb5ToeWyuxh2NrJnJvwHd/8803yqyPNmB2jGUGzXLg7FhYy4c5W/udMInDkpCUlGTfB7J79NFHXR533rx5ylyPWbT2GzFDxzmCxQDLEI5gxotzDBnDeoDSrjD9Y2lh4sSJXv9mzITRJ/Tr1vguPPRWDfxO+EhoxQjxDOvJ6tWr7b/7oYceKvfdmtWEEE9QURNTAa9drN3ClA0lpa0vuwOmYihO1KCGYsdFf8qUKcpkDBM0zNUw1+LCiYs7zJJ43xugMOD8A9OzHr0i0oB5WQ+UMNbMXe2D92HK1jsX6T+jmZOxpu64tou1eXffCxyP7Ylx48apGyTU6e3Vq5cypW/fvl0pJG9/J9rsKBtnstKDm4Hzzz9f+RboQR9wPJYG6irjvOKGSKsfDJ8CmKd9UdQHDx5U5wDf46rN+E21a9dWNZf1YAkEfc3f302IBhU1MR3amvJtt92mFKQnRQ0nMXwGD3iQ33///Wp9EEpmzpw56mIKZyF9OJa3YIaLmSVmd02bNlXboJQ0x6Vgos24cXNRGU9lTXF740UNhzjIXsMbi4Zjmx3XZXGz5OkzwJffiBk1FLUenBc4C/pCWlqa+hysLpqVRHut3wcx+7jxcza7136D4+/09LsJ0aAzGTEd8ORF+NVTTz3l1f4we8NECuC8BE9jzcmnWrVqkpuba58V7tixQ6ZOnep1W+DkBDPo+PHjyx0PTlTBBh7IMAdDDmi3Bkzfs2bN8vp74EAH866nuHPICt+tAW9lX5UNHN6WLl2qwqYAbpw0T3JX4IYMHuHwItfADBWe9q7AjBqe4vpZ+auvvqr6ji9gFo4HrAmaWfv1118vl0wHzn6JiYnK0oDfozeJw3QOEKeO5RXtc7ix8+QMR4gGZ9TEdGAW7cvsCjNumMsxq8HFdv369SpMBuDCjQsvlDe8mnFhhSczzN/egNk6FDNM5/Pnz1cXbFyssQ4cinhYXPyR1AM3C5hBQiHhJsRb0z2ANzN+Oz6PZ1fhWU8//bRcd9116nfiN+JYkKsv4Mbm8ccfV+cPcsYNBjypgSt5wXQNvwGsbWPpA6ZofE6fLU4PLCVQiFjqwLnGLBqRAlj/18zgvlgbcEMCT274HsBcjfArfbgZQtCwdo8wM8yu8R5uYNq1a2cPG4QSx1o75IXfgHV9yDsUlhcSAdCnjhid4uJi29dff23bu3ev0/fhYat/Pz8/X70+duyYfR9s++OPP9TjyJEjFb5/8eLFtp9++sm2Z88e2+7du+0e5ADeyHhfY9myZbaVK1eW+47Dhw+rz2M7vq9x48a2t956S71XUlKi2uPoaY79NU9heI9jn9LSUvv7+D3ffvttuc/Aa/n777+vIAN4uMNjHF7eOJ4Gfiu+9/jx4/ZtOAa24Zh672z8xm+++cbufe0MtOnnn39W+0Luf/31l23t2rX299Fex/O0YMEC25YtW8ptw2t8z7Zt29Q5waVI8wR39Ru1Yy9ZsqTcuXXkq6++Up7akMmMGTOUh/jy5cttnoCnNby3e/fuXcGTHl76ODY8vHF+0ebMzMxy+0DuOA7OK36XI5D70qVLlRc/fusjjzyiohY0cEwcG22g1zfRE4V/wn2zQIiZgUcv1rm1TGiYRWEdF17k2voqOQlmn4glhjUCs384w2EG7K0VwxNYCoBpfdGiRT59DrN2xIYDnE8kdAkU8MJHbDU80LVZP6IR4CmuxfQjmYu29o3MaUjsQgig6ZuQSoLQHJhVGzVqpNbCcbFFpjAqaecgR/Ydd9yhQsOQKQ0e0zBTBwqsT2vmdF+AYgxW+lD4RmA5Ao6MyAiHNiLKAElYHFOIEuIIZ9SEBACsWyKmGgYqrE1irZq4Bs52mEHCkQ1ruoFcz1+7dq1ShsHK9V4ZEFKItK7wIPfVA51YFypqQgghxMAwPIsQQggxMFTUhBBCiIGhoiaEEEIMDBU1IYQQYmCoqAkhhBADQ0VNCCGEGBgqakIIIcTAUFETQgghBoaKmhBCCBHj8v/n7qXlRaecLAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 500x700 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# No calculations below, just plotting per environment:\n",
    "fig, axes = plt.subplots(2, 1, figsize=(5, 7))\n",
    "\n",
    "ax = axes[0]\n",
    "ax2 = axes[1]\n",
    "\n",
    "colors = ['b', 'r', 'g']\n",
    "labels = ['Isolated', 'H or FB', 'FH']\n",
    "selects = [np.asarray(corrected_pm['Env']=='I'), \n",
    "           np.logical_or.reduce([np.asarray(corrected_pm['Env']=='H'), \n",
    "                                 np.asarray(corrected_pm['Env']=='FB'), \n",
    "                                 np.asarray(corrected_pm['Env']=='FB/H'), \n",
    "                                 np.asarray(corrected_pm['Env']=='FH/B')]), \n",
    "           np.asarray(corrected_pm['Env']=='FH')]\n",
    "\n",
    "for j in range(3):\n",
    "\n",
    "    env_select = selects[j]\n",
    "    Label = labels[j]\n",
    "    \n",
    "    v_corrected_pm = corrected_pm[env_select]\n",
    "    M = len(v_corrected_pm)\n",
    "\n",
    "    all_delta_theta_sim = np.zeros((M, Nerror))\n",
    "    all_vstar_sim = np.zeros((M, Nerror))\n",
    "\n",
    "    for i in range(M):\n",
    "    \n",
    "        vstar_sim_i, delta_theta_sim_i = PA_v_calc(np.asarray(v_corrected_pm['pm_ra_pec'])[i], \n",
    "                                                   np.asarray(v_corrected_pm['dpm_ra_pec'])[i], \n",
    "                                                   np.asarray(v_corrected_pm['pm_dec_pec'])[i],\n",
    "                                                   np.asarray(v_corrected_pm['dpm_dec_pec'])[i],\n",
    "                                                   D = np.asarray(v_corrected_pm['Distance'])[i],\n",
    "                                                   dparallax=np.asarray(v_corrected_pm['dp'])[i],\n",
    "                                                   PA=np.asarray(v_corrected_pm['PA'])[i],\n",
    "                                                   dPA = 5.0, return_means=False, Nmc = Nerror)\n",
    "        \n",
    "        all_delta_theta_sim[i] = np.asarray(delta_theta_sim_i)\n",
    "        all_vstar_sim[i] = np.asarray(vstar_sim_i)\n",
    "\n",
    "    error_matrix = np.zeros((Nerror, M-1))\n",
    "    for i in range(Nerror):\n",
    "    \n",
    "        vstar_unitsless_sim = np.transpose(all_vstar_sim)[i]\n",
    "        n,bins,patches = ax.hist(vstar_unitsless_sim, histtype='step', density=True, \n",
    "                             cumulative=True, bins=np.sort(vstar_unitsless_sim), linewidth=0., color=colors[j], alpha=0.1, zorder=-2)\n",
    "        patches[0].set_xy(patches[0].get_xy()[:-1])\n",
    "\n",
    "        error_matrix[i] = (bins[:-1] + bins[1:])/2.\n",
    "\n",
    "    bins_min = []\n",
    "    bins_max = []\n",
    "    for i in range(M-1):\n",
    "        errors_i = np.transpose(error_matrix)[i]\n",
    "        bins_min.append(np.mean(errors_i) - np.std(errors_i))\n",
    "        bins_max.append(np.mean(errors_i) + np.std(errors_i))      \n",
    "    ax.fill_betweenx(n, bins_min, bins_max, color=colors[j], ec='k', lw=0.5, zorder=1, label=Label, step='post')\n",
    "\n",
    "    error_matrix = np.zeros((Nerror, M-1))\n",
    "    for i in range(Nerror):\n",
    "    \n",
    "        delta_theta_unitsless_sim = np.transpose(all_delta_theta_sim)[i]\n",
    "        n,bins,patches = ax2.hist(delta_theta_unitsless_sim, histtype='step', density=True, \n",
    "                             cumulative=True, bins=np.sort(delta_theta_unitsless_sim), linewidth=0., color=colors[j], alpha=0.1, zorder=-2)\n",
    "        patches[0].set_xy(patches[0].get_xy()[:-1])\n",
    "\n",
    "        error_matrix[i] = (bins[:-1] + bins[1:])/2.\n",
    "\n",
    "    bins_min = []\n",
    "    bins_max = []\n",
    "    for i in range(M-1):\n",
    "        errors_i = np.transpose(error_matrix)[i]\n",
    "        bins_min.append(np.mean(errors_i) - np.std(errors_i))\n",
    "        bins_max.append(np.mean(errors_i) + np.std(errors_i))      \n",
    "    ax2.fill_betweenx(n, bins_min, bins_max, color=colors[j], ec='k', lw=0.5, zorder=1, label=Label, step='post')\n",
    "\n",
    "ax.set_ylabel('Cumulative distribution function')\n",
    "ax.set_xlabel('Stellar peculiar velocity [km/s]')\n",
    "ax.set_ylim(0,1)\n",
    "ax.set_xlim(1,100)\n",
    "ax.set_xscale('log')\n",
    "ax.grid(color='0.8', linestyle='-.', linewidth=1)\n",
    "\n",
    "ax.legend(loc=4, frameon=True, fancybox=False, fontsize=10, framealpha=1.0, edgecolor=\"black\", facecolor=\"white\" )\n",
    "\n",
    "ax2.grid(color='0.8', linestyle='-.', linewidth=1, zorder=-20)\n",
    "ax2.set_ylabel('Cumulative histogram')\n",
    "ax2.set_xlabel(r'Misalignment angle $\\beta$ [deg]')\n",
    "ax2.set_ylim(0, 1)\n",
    "ax2.set_xlim(0, 180)\n",
    "ax2.set_xticks(np.linspace(0, 180, 7))\n",
    "\n",
    "fig.tight_layout()\n",
    "plt.savefig('./PAPERPLOTS/observables_per_env_dist.png', dpi=300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f4a1b6ba-505c-4f7c-aec3-392800602ffd",
   "metadata": {},
   "source": [
    "Then, we again repeat the analysis, now selecting on environment. We also include the case where we set the angle of the incoming ISM to that of the bow shock for cases where the bow shock points towards an HII region."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "307626c4-dc9d-47ce-b06c-56b737da42a7",
   "metadata": {},
   "outputs": [],
   "source": [
    "Nerror = 1000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "35b4780d-d570-4b0c-baee-41ae12247445",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.0295852732167101 0.43686157226211564 1.6486796564158628\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x400 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1, 3, figsize=(12, 4))\n",
    "fig.subplots_adjust(wspace=0.02)\n",
    "\n",
    "mean_v_ism_array = np.linspace(0.,30.,61)\n",
    "dbeta = 0.1 # degrees -> in order to draw the angles, we draw them uniformly from the observed bow shock angle and the angle + 0.1 degrees.\n",
    "\n",
    "##### Low velocities only:\n",
    "colors = ['b', 'g', 'm', 'r', 'm']\n",
    "labels = ['Isolated', 'Facing an HII region', 'Not isolated']\n",
    "selects = [np.asarray(corrected_pm['Env']=='I'), \n",
    "           np.asarray(corrected_pm['Env']=='FH'),\n",
    "           np.logical_or.reduce([np.asarray(corrected_pm['Env']=='H'), \n",
    "                                 np.asarray(corrected_pm['Env']=='FB'), \n",
    "                                 np.asarray(corrected_pm['Env']=='FB/H'), \n",
    "                                 np.asarray(corrected_pm['Env']=='FH/B'),\n",
    "                                 np.asarray(corrected_pm['Env']=='FH')])]\n",
    "\n",
    "all_mean_p = []\n",
    "all_std_p = []\n",
    "for j in range(3):\n",
    "\n",
    "    env_select = selects[j]\n",
    "    Label = labels[j]\n",
    "    \n",
    "    v_corrected_pm = corrected_pm[env_select]\n",
    "    M = len(v_corrected_pm)\n",
    "\n",
    "    all_delta_theta_sim = np.zeros((M, Nerror))\n",
    "    all_vstar_sim = np.zeros((M, Nerror))\n",
    "\n",
    "    for i in range(M):\n",
    "    \n",
    "        vstar_sim_i, delta_theta_sim_i = PA_v_calc(np.asarray(v_corrected_pm['pm_ra_pec'])[i], \n",
    "                                                   np.asarray(v_corrected_pm['dpm_ra_pec'])[i], \n",
    "                                                   np.asarray(v_corrected_pm['pm_dec_pec'])[i],\n",
    "                                                   np.asarray(v_corrected_pm['dpm_dec_pec'])[i],\n",
    "                                                   D = np.asarray(v_corrected_pm['Distance'])[i],\n",
    "                                                   dparallax=np.asarray(v_corrected_pm['dp'])[i],\n",
    "                                                   PA=np.asarray(v_corrected_pm['PA'])[i],\n",
    "                                                   dPA = 5.0, return_means=False, Nmc = Nerror)\n",
    "        \n",
    "        all_delta_theta_sim[i] = np.asarray(delta_theta_sim_i)\n",
    "        all_vstar_sim[i] = np.asarray(vstar_sim_i)\n",
    "\n",
    "\n",
    "    error_matrix = np.zeros((Nerror, len(all_delta_theta_sim)-1))\n",
    "    for i in range(Nerror):\n",
    "        \n",
    "        delta_theta_unitsless_sim = np.transpose(all_delta_theta_sim)[i]\n",
    "        n,bins,patches = axes[0].hist(delta_theta_unitsless_sim, histtype='step', density=True, \n",
    "                                      cumulative=True, bins=np.sort(delta_theta_unitsless_sim), linewidth=0., color=colors[j], alpha=0.2)\n",
    "        patches[0].set_xy(patches[0].get_xy()[:-1])\n",
    "        error_matrix[i] = (bins[:-1] + bins[1:])/2.\n",
    "\n",
    "    bins_min = []\n",
    "    bins_max = []\n",
    "    for i in range(len(all_delta_theta_sim)-1):\n",
    "        errors_i = np.transpose(error_matrix)[i]\n",
    "        bins_min.append(np.mean(errors_i) - np.std(errors_i))\n",
    "        bins_max.append(np.mean(errors_i) + np.std(errors_i))      \n",
    "    axes[0].fill_betweenx(n, bins_min, bins_max, color=colors[j], ec='k', lw=0.5, zorder=1, label=Label, step='post')\n",
    "\n",
    "    all_rejected = []\n",
    "    for mean_v_ism in mean_v_ism_array:\n",
    "\n",
    "        all_fcorr = np.asarray([])\n",
    "        rejected = 0\n",
    "        for i in range(Nerror):\n",
    "        \n",
    "            delta_theta_unitsless_sim = np.transpose(all_delta_theta_sim)[i]\n",
    "            vstar_unitsless_sim = np.transpose(all_vstar_sim)[i]\n",
    "            \n",
    "            v_ism_sim = scipy.stats.truncnorm.rvs(a = -3, b = 1000, loc=mean_v_ism, scale=mean_v_ism/3., size=M)\n",
    "            r_v_ism_sim = v_ism_sim / vstar_unitsless_sim\n",
    "\n",
    "            # For the first and third case, we repeat the normal analysis. For the second case, \n",
    "            # we assume that the ISM originates from the HII region. \n",
    "            if j in [0,2]:\n",
    "                alpha_sim = np.random.uniform(delta_theta_unitsless_sim, np.asarray([180]*M)) * u.degree\n",
    "            else:\n",
    "                # Here, we would normally draw from a uniform distribution. Now, we don't.\n",
    "                max_alphas = []\n",
    "                for k in range(len(delta_theta_unitsless_sim)):\n",
    "                    max_alphas.append(np.min([delta_theta_unitsless_sim[k]+dbeta, 180]))\n",
    "                alpha_sim = np.random.uniform(delta_theta_unitsless_sim, np.asarray(max_alphas)) * u.degree\n",
    "                    \n",
    "            cos_beta_sim = (1. + r_v_ism_sim * np.cos(alpha_sim)) / np.sqrt(1. + r_v_ism_sim**2 + 2*r_v_ism_sim*np.cos(alpha_sim))\n",
    "            beta_sim = (np.arccos(cos_beta_sim)).to(u.degree)\n",
    "\n",
    "            fcorr = 1. + r_v_ism_sim**2 + 2*r_v_ism_sim*np.cos(alpha_sim)\n",
    "            all_fcorr = np.concatenate([all_fcorr, fcorr])\n",
    "                \n",
    "            TS, p = scipy.stats.kstest(beta_sim, delta_theta_unitsless_sim)[0:2]\n",
    "\n",
    "            if p < 0.05:\n",
    "                rejected += 1\n",
    "                \n",
    "        all_rejected.append(rejected)\n",
    "\n",
    "        if mean_v_ism == 12 and (j in [0,2]):\n",
    "            axes[2].hist(np.log10(all_fcorr), histtype='step', range=(-2, 4),\n",
    "                         density=True, cumulative=False, bins=M, linewidth=2, color=colors[j], label=r'$\\mu = 12$ km/s')\n",
    "        if mean_v_ism == 25 and j == 1:\n",
    "            axes[2].hist(np.log10(all_fcorr), histtype='step', range=(-2, 4),\n",
    "                         density=True, cumulative=False, bins=M, linewidth=2, color=colors[j], label=r'$\\mu = 25$ km/s')\n",
    "            log_fcorr = np.log10(all_fcorr)\n",
    "            print(np.mean(log_fcorr), np.percentile(log_fcorr, 16.), np.percentile(log_fcorr, 84.))\n",
    "\n",
    "    axes[1].plot(mean_v_ism_array, 1-np.asarray(all_rejected)/Nerror, '-', color=colors[j], lw=2, ms=2, label=Label)\n",
    "\n",
    "axes[0].grid(color='0.8', linestyle='-.', linewidth=1, zorder=-20)\n",
    "axes[0].set_ylabel('Cumulative histogram')\n",
    "axes[0].set_xlabel(r'Misalignment angle $\\beta$ [deg]')\n",
    "axes[0].set_ylim(0, 1)\n",
    "axes[0].set_xlim(0, 180)\n",
    "axes[0].set_xticks(np.linspace(0, 180, 7))\n",
    "\n",
    "axes[1].grid(color='0.8', linestyle='-.', linewidth=1, zorder=-20)\n",
    "axes[1].set_ylabel('1 - rejection rate at p=0.05')\n",
    "axes[1].set_xlabel(r'Mean ISM velocity $\\mu$ [km/s]')\n",
    "axes[1].set_ylim(0, 1.03)\n",
    "axes[1].set_xlim(0, 30)\n",
    "\n",
    "axes[2].grid(color='0.8', linestyle='-.', linewidth=1, zorder=-20)\n",
    "axes[2].set_ylabel('Normalized histogram')\n",
    "axes[2].set_xlabel(r'$f_{\\rm corr}$')\n",
    "axes[2].set_ylim(0,1.2)\n",
    "axes[2].set_yticks([0, 0.2, 0.4, 0.6, 0.8, 1, 1.2])\n",
    "axes[2].set_xticks(np.linspace(-2,4,7))\n",
    "axes[2].set_xticklabels([r'$10^{-2}$',r'$10^{-1}$',r'$1$',r'$10^{1}$',r'$10^{2}$',r'$10^{3}$',r'$10^{4}$'])\n",
    "axes[2].set_xlim(-2,4)\n",
    "axes[2].set_xticks(np.linspace(-2,4,7))\n",
    "\n",
    "axes[0].legend(loc=4, frameon=True, fancybox=False, fontsize=12, framealpha=1.0, edgecolor=\"black\", facecolor=\"white\" )\n",
    "axes[2].legend(loc=1, frameon=True, fancybox=False, fontsize=10, framealpha=1.0, edgecolor=\"black\", facecolor=\"white\" )\n",
    "\n",
    "fig.tight_layout(w_pad=0.5)\n",
    "plt.savefig('./PAPERPLOTS/per_env_fitted.png', dpi=300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "489cfef0-0d92-4a5b-b464-fe7d2657ad4f",
   "metadata": {},
   "source": [
    "### Step 3.4: compare the measured values with the inferred stellar wind properties:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "c96edf41-99c9-4972-a10e-25132f08ff24",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><i>Table length=70</i>\n",
       "<table id=\"table13339625664\" class=\"table-striped table-bordered table-condensed\">\n",
       "<thead><tr><th>_RAJ2000</th><th>_DEJ2000</th><th>ID</th><th>Vtot</th><th>M</th><th>e_Vtot</th><th>e_M</th></tr></thead>\n",
       "<thead><tr><th>deg</th><th>deg</th><th></th><th>km / s</th><th>1e-10 solMass / yr</th><th>km / s</th><th>1e-10 solMass / yr</th></tr></thead>\n",
       "<thead><tr><th>float64</th><th>float64</th><th>int16</th><th>float32</th><th>int32</th><th>float32</th><th>int32</th></tr></thead>\n",
       "<tr><td>267.0292083</td><td>-29.1320833</td><td>1</td><td>16.3</td><td>2639</td><td>3.8</td><td>1109</td></tr>\n",
       "<tr><td>267.1740833</td><td>-28.1105000</td><td>3</td><td>75.6</td><td>62957</td><td>14.3</td><td>33928</td></tr>\n",
       "<tr><td>269.6276667</td><td>-26.1636389</td><td>7</td><td>12.1</td><td>1411</td><td>1.3</td><td>587</td></tr>\n",
       "<tr><td>268.9000833</td><td>-23.7560556</td><td>11</td><td>31.5</td><td>201</td><td>3.8</td><td>88</td></tr>\n",
       "<tr><td>249.2897500</td><td>-10.5670833</td><td>13</td><td>11.9</td><td>117</td><td>0.1</td><td>45</td></tr>\n",
       "<tr><td>270.2302917</td><td>-22.9602500</td><td>16</td><td>5.0</td><td>673</td><td>3.2</td><td>319</td></tr>\n",
       "<tr><td>272.9975833</td><td>-19.6153889</td><td>26</td><td>8.2</td><td>67</td><td>2.5</td><td>27</td></tr>\n",
       "<tr><td>272.3334583</td><td>-18.6031944</td><td>28</td><td>15.8</td><td>96</td><td>4.4</td><td>38</td></tr>\n",
       "<tr><td>273.5035417</td><td>-18.4298056</td><td>32</td><td>11.5</td><td>227</td><td>4.7</td><td>98</td></tr>\n",
       "<tr><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td></tr>\n",
       "<tr><td>254.6242917</td><td>-43.4215278</td><td>637</td><td>5.0</td><td>3</td><td>3.5</td><td>1</td></tr>\n",
       "<tr><td>256.2162083</td><td>-42.1151111</td><td>648</td><td>23.7</td><td>339</td><td>10.7</td><td>130</td></tr>\n",
       "<tr><td>257.1952500</td><td>-40.5743056</td><td>653</td><td>7.4</td><td>--</td><td>1.6</td><td>--</td></tr>\n",
       "<tr><td>257.0102500</td><td>-40.2734167</td><td>655</td><td>12.3</td><td>23</td><td>3.0</td><td>9</td></tr>\n",
       "<tr><td>260.1595000</td><td>-38.0302500</td><td>673</td><td>12.9</td><td>59</td><td>3.1</td><td>23</td></tr>\n",
       "<tr><td>261.7969167</td><td>-34.2431667</td><td>692</td><td>13.6</td><td>3416</td><td>2.2</td><td>1638</td></tr>\n",
       "<tr><td>264.3772083</td><td>-33.1589444</td><td>694</td><td>9.5</td><td>14</td><td>1.8</td><td>7</td></tr>\n",
       "<tr><td>263.4495833</td><td>-31.2741667</td><td>700</td><td>9.5</td><td>180</td><td>4.8</td><td>84</td></tr>\n",
       "<tr><td>266.8750417</td><td>-29.2400556</td><td>709</td><td>21.5</td><td>317</td><td>6.0</td><td>127</td></tr>\n",
       "</table></div>"
      ],
      "text/plain": [
       "<Table length=70>\n",
       "  _RAJ2000    _DEJ2000    ID  ...         M           e_Vtot        e_M        \n",
       "    deg         deg           ... 1e-10 solMass / yr  km / s 1e-10 solMass / yr\n",
       "  float64     float64   int16 ...       int32        float32       int32       \n",
       "----------- ----------- ----- ... ------------------ ------- ------------------\n",
       "267.0292083 -29.1320833     1 ...               2639     3.8               1109\n",
       "267.1740833 -28.1105000     3 ...              62957    14.3              33928\n",
       "269.6276667 -26.1636389     7 ...               1411     1.3                587\n",
       "268.9000833 -23.7560556    11 ...                201     3.8                 88\n",
       "249.2897500 -10.5670833    13 ...                117     0.1                 45\n",
       "270.2302917 -22.9602500    16 ...                673     3.2                319\n",
       "272.9975833 -19.6153889    26 ...                 67     2.5                 27\n",
       "272.3334583 -18.6031944    28 ...                 96     4.4                 38\n",
       "273.5035417 -18.4298056    32 ...                227     4.7                 98\n",
       "        ...         ...   ... ...                ...     ...                ...\n",
       "254.6242917 -43.4215278   637 ...                  3     3.5                  1\n",
       "256.2162083 -42.1151111   648 ...                339    10.7                130\n",
       "257.1952500 -40.5743056   653 ...                 --     1.6                 --\n",
       "257.0102500 -40.2734167   655 ...                 23     3.0                  9\n",
       "260.1595000 -38.0302500   673 ...                 59     3.1                 23\n",
       "261.7969167 -34.2431667   692 ...               3416     2.2               1638\n",
       "264.3772083 -33.1589444   694 ...                 14     1.8                  7\n",
       "263.4495833 -31.2741667   700 ...                180     4.8                 84\n",
       "266.8750417 -29.2400556   709 ...                317     6.0                127"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load the stellar wind properties from Kobulnicky et al. (2019):\n",
    "\n",
    "vizier = Vizier(columns=['ID', '_RAJ2000', '_DEJ2000', 'Vtot', 'M', 'e_Vtot', 'e_M'])\n",
    "vizier.ROW_LIMIT = -1\n",
    "\n",
    "kobulnicky19 = vizier.get_catalogs(\"J/AJ/158/73\")\n",
    "kobulnicky19_table = kobulnicky19[0]\n",
    "kobulnicky19_table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "f9309067-75e5-4223-84a7-09b705068eed",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Saving the names and K16 indices to match between catalogues:\n",
    "names = []\n",
    "K16_index = []\n",
    "for i in range(len(filtered_data)):\n",
    "    survey_index = filtered_data['my_index'][i]\n",
    "    if filtered_data['survey'][i] == 'K16':\n",
    "        names.append('_'.join(result_table['main_id'][survey_index].split()))\n",
    "        K16_index.append(survey_index+1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "eb764a52-fca6-4b0b-9d4f-85b7fca357e7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAnEAAAHWCAYAAAAYfN/zAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjExLjEsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvctoD+AAAAAlwSFlzAAAPYQAAD2EBqD+naQAAxXJJREFUeJzsnQV4U3cbxd8KFHcr7u7uOnwMZ/hwtjFsQzeGjA3GFOaM4YzBcBju7u7uVhwqtLTN95x/v5smadKmSdomzfntuSNye3Oj99xXzuuh0+l0QgghhBBCXArPhN4BQgghhBASeyjiCCGEEEJcEIo4QgghhBAXhCKOEEIIIcQFoYgjhBBCCHFBKOIIIYQQQlwQijhCCCGEEBeEIo4QQgghxAWhiCOEEEIIcUEo4gzYvHmz5MyZU7Zv355w7wghhBBCiBVQxP2fhw8fyowZM6RkyZISHBxszWtHCCGEEBcgODhYDh06JLdu3bK4zpMnT+TkyZPRrvPq1Ss5ceKE3L9/X5wBlxFxt2/flgsXLli8Pzw8XK5du6bEWGzB+Nhhw4bJ1KlTxdvb2849JYQQQogz8OTJExk5cqTkz59f6tSpIz/99FOUdYKCgqRdu3aSO3duee+996R8+fJSunRpOX/+vNF6P//8s2TNmlU6dOgg+fLlk06dOklISIgkJE4v4pYvXy716tWTokWLSs2aNc2us3v3bsmbN69UqVJF/fvWW2+pN05j7Nixki1btihLz5491f3ff/+9dO/eXbJnzx5vz4sQQgghccv169clQ4YMKnpWpEgRs+tMmzZNtm3bpgJFWO/u3buSMWNG+fDDD/Xr7N+/XwYPHiyLFy+WS5cuqQV/M2nSpAR9Cz10CEM5MQMGDJA2bdrIqVOn5KuvvpLHjx8b3f/8+XMpUKCA9O7dW6ZMmaJCnVDbUN3Lli1T6+C2gICAKNtOliyZpEuXTpInTy5p06bVby9FihSycOFCadKkSTw9S0IIIYTEJWXLllVBnu+++87o9qFDh8qOHTvk+PHj+ttGjx4tq1atknPnzqnrffr0UfcfPXpUv86oUaPk77//VpnChMLpc4e//vqr+vf06dNm71+yZIkSaIi2eXh4SJo0aVTotEuXLuLn5ydZsmSR1KlTqyU6pa7RrVs36dWrl4r+WcqrG9bMIY379OlTpdrx+IQQQoizg/gNAhzIQHl6xn1S7vXr1w5LPWLfTY+3Pj4+arEFRNyWLl0q48aNU8d+lGbNnTtXReg0jh07JpUrVzb6O2T/EDxCcClTpkySEDi9iIsJqOISJUpIqlSp9LdVr15diSuo5saNG8e4DaRWNfAhSJ8+vcUPw+TJk2XChAkO2ntCCCEk4UAUCa4McS3gsidPJc8kzCHbw/He39/f6DYIsPHjx9u0PWTzIORwfF+xYoVKpyJi16BBA/06CNYgLWsIgjcA5VsUcTaCF097IU1fWNPUqzUsWLBApVMtgRDrxx9/rL/+4sULVQyJLwKigIQQQoiz8/LlS8mVK1e0WSpHgQgcBNwcr3ySws5S/EAJlx7+16Mcc31sjMIBBGbgTnHmzBl1PIfoRKNDixYtZO/evWqdJEmSRHGuQEOEdl9C4fKROHSTmoZotRfalhcWNXLRYSlkiw8TRRwhhBBXIj7LgFIm8ZIUHl52bcNDFyYI6DnymLt69Wpp3bq1EnBavXy/fv2kZcuW+rKsPHnyyL1794z+Dte9vLwkR44cklA4fXdqTOBMwtSvRbuO+wghhBBCLIFUKFKopgINQSKt6RGpVQwEMIzGofEBrhn2RAHF3SNxKEL89ttv5erVqyqvDdauXasUOrxe4rLhAktYmGNy/IQQQkhixsPbQzztjPx56GL396GhoXLkyBF1OTAwUAV5Dhw4oOrqYO5v6ILx6aefqlq4K1euyJgxY1RHqibQPvjgA/njjz+kbdu2qn4O1mbr1q2TrVu3SkLi9BYjcE5G7h61atOnT1cvHChUqJB6cbH7tWrVUuoYRYlQz3iBUeQ4fPjwON8/7BuUOmrjmE4lhBDiCsTnsUt7rGXpi0hKO9OpAbowafvsotX7/eLFC7MNjsWKFZPZs2frr+/atUtmzZolN2/eVJE5WIz16NFDpUs1EK2DLxxq59AQOXDgQIv+tfGF04s4KOSdO3dGuX3lypVSsGBB/Zs0ceJE9SagKQH2In379o2X/aOII4QQ4mq4i4hL7LiMT1x04MNhat4X1zCdSgghhFiPp5eHeHral071DKcfa6JqbEjICCGcnA8fPpzQu0IIIYQ4PR5JPByykEgo4gghhBBCXBCnT6cSQgghxPXxRHcq06kOhSLORlgTRwghhFiPSofaKeI8WBNnBNOpNsKaOEIIIYQkJIzEEUIIISR+ulO97OxODWNjgyEUcYQQQgiJczy8PNRi1zaEIs4QplPtqIkrXry4VKpUydZNEEIIIYTYDEWcjbAmjhBCCIl9OtXehURCEUcIIYQQ4oKwJo4QQgghcQ7sRey2GNExEmcIRRwhhBBC4hwPL0+12LUN0TlsfxIDTKfaCBsbCCGEEJKQUMTZCBsbCCGEkFgIDjY2OBymUwkhhBAS53h4cOyWo2EkjhBCCCHEBWEkjhBCCCFxjodXRErVrm2wr8EIijhCCCGEuMbYLVqMGMF0KiGEEEKIC8JInB0WI1jCwsIc+44QQgghiRAPT0+12LsNEglfDRuhxQghhBAS+4kN9i4kEoo4QgghhBAXhOlUQgghhMSb2a9d22BjgxGMxBFCCCGEuCCMxBFCCCEkznFETRtr4oyhiCOEEEJInOPh4YDuVA8mEA3hq0EIIYQQ4oIwEmcj9IkjhBBCrIfpVCcTcc+ePZMHDx5ISEiIZM6cWbJlyyaebmLEB584LC9fvpS0adMm9O4QQgghib87NZw+cXaJuGPHjsnMmTNl8+bNcvnyZaP7MmTIIHXq1JFOnTpJq1atJEmSJLHdPCGEEEIIcaSIO3z4sHzyySeye/duSZ8+vVSpUkXatGkjmTJlkqRJk8qTJ0/k6tWrcuDAAVmxYoX4+vrKhAkTpHfv3m4TnSPEnfjhhx9UJDpNmjTy8ccfJ/TuEEKcHKZTE0jEIerWtGlTad26taxbt04aNWokXl5eFtc/f/68zJs3T0aOHCkHDx6Uv/76y5H7TAhJQMLDw2Xnzp3qJE0rJ2jQoIGUKVOG7wshxCKcnep4rAqR5ciRQ06cOCFLlixRYi46AQeKFSsmkydPluvXr0u7du0cta+EkAQkLCxMfvnlF/X9rl+/vhJw4MWLF1K2bFmpXr26isITQghxokhc8eLFbdo4ztCbNGli098SQpyH169fy7vvviurV6+2uM7+/ftViQUi8DiJ8/BgATIhJBKmU52sO1Wn00lgYKAkT56cdW+EJFLwPe/Zs6eRgMvoW1myF3xbfJJnkoAXN+XOpeXy6llEo9OUKVNUk9OIESMScK8JIc4GRZzjiXXHwY0bN2Tw4MFStGhR1X2aKlUq8fb2lsKFC6vGh3v37sXBbhJCEgrUvy1atEhd9vJOLpUaT5cqzWZKrsKtJUuuWpKvZFep2XqZFK86Sv83Y8aMkYcPH/JNI4QQZxFxW7ZskRIlSshPP/2kxBrO0LWauVy5csmff/4pJUuWlL1798bV/hJCEsDYWqN4tdGSOWf1KOsgdZq3RBfJU7yTuv7mzRuZNWtWvO4nIcQ1InH2LsQGEYcf5W7duknbtm2VgENRM1KpM2bMkMePHyu7gfv37ytLEXjEwQiYEOLa4HuuNSskTZ5RchR4O9r185Xohp9qdXnu3Lnxso+EEOKuWF0TB6uQFClSyOzZs/XdqT4+PtKnTx9l+rtgwQL59ttv1XLhwgV1feDAgXG574QQO8CJF5aYTt7QlQoyZa8qnl7RG3inSJNLUqXLJ/7Pr8nFixdVlN6aBgf4zNFrjpDETUQkzT7fWEbibBRxT58+lYIFC5q1F0F9HLzkNN555x3VqZaY4exUkhiibHfv3rV6fQ9P634uPDwjhZ61NbKaXQkhJPECAWbv2C2PMKZTbRJxqHk7cuSISp1iSoMhMADOnTu3/jomOKBjNTHD2anE1cGkBUTKogNROMxHBs8eHhedLlw8PCyfSQcHPRX/51fVZZzwYZ6ytftCCCEkjkQczDxz5sypZqMOHz5cihQpokZtzZ8/X5YtW6bGbRmKOsxPJYQ4L9amMDGNYdu2bRL48pY8urNHsuSqbXHd2xeXii48VF0eOnSoKq8ghBBAi5EEFHGoa1m8eLGa2ADPKA2cbeOHunLlyuo6bAUyZsyomhsIIa7Phx9+qEQcOLNnglRpNktSps0TZb3H9w7IlRPT9df79+8fr/tJCHFuOHYrgc1+UfuGuahr165Vw+4xkaFhw4aSP39+/TpZs2aV3377LQ52lRCSELRs2VKqVaum6lxfB/rJnpXtJWfh1pKjYIsIs9+XN+T2xeVy//pGEV24+pt+/fqpGlpCEjNoDEI9J8oB2JhDEgIPnWb2RmxCGwCO+ZGs6yGJlUePHqm06unTp2NcF41NmLOM2lhCEjMoMUJzEGpL79y5I65EfB67tMc61a2ppE4afYd7TLwKeSOl56/nMff/2Nfra4GQkBB59epVXGyaEJIAZM6cWXbv3q1SpJaaltDw9MUXX6gaWQo4kpi5du2ayjhpXdVBQUESGhpRC0osQ7NfFxFxmLFoWDdHCHF9cCb9xx9/qMjDtGnT9GIO/pFocLp9+7Z8/vnnagwfIYkRnMg0a9ZMlQrAoUALVsCCK2/evPLll18qE3xCXFrEEUISL+nTp5dBgwapIffa9a5du0qyZMkSetcIiTMwVrJu3bqyfv16/chJQ3Byg5OYevXqKecGYrmxwd6FRGL1KTPy/Xv27LF6ugMhhBCSGFi6dKlRt3WqtLmkUPnOktG3lIS8fiE3zq6R25c2KR/FQ4cOqbrQ7du3s6zABFqMJKCIgw9cbLzfMGOVEEIIcWUwes5whGSJau9Lxbc+F0+vyMNngdLt5Mn907L5784S5O8n+/btUyUGmCVOiFOIOBQtFypUSCZNmmSV4Ltx44a9+0YIIYQkKCtXrtRPLclRsIFUajTe7DxgROXqtpsu6+e01o9m7NWrl1Wzg90F+sQloIirWbOmKuJETYDp2C1zUMQRQghxdebOnau/XLL6+9GKsmx5q0tG3zLy5P5JOX78uJw9e1ZKliwZT3vqAuC1s1fUUhTbJuLQcYY8P+wDEpsT+8aNG+X333/XX+/evbu0adMmQfeJEGcH5qaa0SkhicG4F4spmEIEvLx8xDdfrRi3k6vwW0rEAYyptGTJY+3YO0KiI1ZeAOi8sabrBg7vaMN2Fa5fv6466zp27KiulyhRIqF3iRCnhwcgkpjACQk6TC3h4eVtVWrU0yvSzBbWI9E9nruB1w/NDfZug0TiGVt36jJlysS4XpIkSZR3lCuBMWJwmYegy507d0LvDiGEkHgEEWVMXjBdNOPq0JAAeXL/TIzbeXDzgJFJtrltYmEEO34IDQ2VFStWSKNGjcTT01OGDRsWZZ3//vsvQmCaLKZlYbt27ZKKFSuqoA98AeGXmdDE2pUTZyqbNm2SgIAA9WSqVq0qcQlexOnTp8u8efOUODRXaweTUUQFdu7cKSlTppQuXbrI+PHj9aajM2fOlDVr1kT5uypVqsjo0aOlSZMmki1bNvWcZsyYoUYLzZo1K06fFyGEEOfBUnoTpTYffvihunz+4Ayp2crygfv5o8ty7+oOdRkH+StXroiXl1cc7rVrkRCNDTt37lT6Ae+tlho3h4+Pj7x+/dri/QjwNG3aVG0HGgjGz++++66kS5dO3nvvPXEJEQe12r59e6Mnihbqv/76S+IK2Jq8/fbbqk4NAstc+3fjxo3VF2bv3r1y//59tY/BwcHy7bffqnUqV64sGTNmjPK3vr6+6l/8LRaANHD+/Pkp4gghhCgj6xEjRoi/v79cPrFIsuatLoXKvhvllXkd8Fh2Lo2sF//ggw8o4JzAJ65BgwZqAaNGjbL5cdFtnDVrVpk4caK+bKxbt27y3XffuYaIg1jq06ePGjeCD2fq1KmVczWiXFCjDRs2jJMd3L9/v/p36tSpZu9ftWqVXLhwQbZu3apEGWxQxo0bJ5988on6N1WqVFKqVCm1WAO2BWVNCCGE4FiHevCRI0eqF2PPykFy4+xqKVKhm2QwMPu9eGSuvA6MqBlHUKBfv3588VyIkJAQFexB+hUdxdAPSMFqwPsP7hyGQBwiiGVtgxeCS8gmYn08Fj4nSPHGi4hDuzSeHEKImsiBCsX4kQ0bNsSZiIsJTJEoXry4PqoGsC+IFh49elR1B8UEhnYfO3ZMpVOPHDli1Klq7k3A4s7FqYQQ4k4MHz5cbt26paIx4M7lLWoxR/bs2VWAg8GAuE2nmh57fXx81GIL6CD+/vvvVeYPtXC//fabKrPatm2bXrjBK7BWLePu5CxZsqh/kaa1JOLCwsJUTR4yiaipM8xkYnQhUrQIjNWoUcOmfbf61USaslKlSlE+mHii9+7dk4QCj629kBradeyzNUD09ejRQ51pXbx4Ud+lao7JkyerQeDakitXLjufASGEEGcGB/aff/5ZneBb+s1H7Vu7du2U2X3RokXjfR9dAQ/PyJSq7UvEtvA+pDU4FuPYbCuIqA0dOlTVxiNlOmHCBKlfv75ZyxlDtBm6ljpm8VkoV66cygyWLl1aFi1aJKdOnZJr166pwBEaIyA8mzdvrhacKMRpOhVNA6bgNtyXkJiGI7Xr5oYUm6NatWpWPxYaIQyLX3E2QCFHCCGJGxyo33//fVVWtHbtWrVgtBYiK4jCnDt3TnWdkvgBDY1pDKJftkbhLIESLGQZNSDwHj16ZLSOdt00kGSYKURatnXr1mbTphB4qLlEhPePP/5Qwi627hixamwIDAyM0h2KJ2Hudog7tFfHNXjxED0z3SftPkdjT8iWEEKIawPXAxS1Y1m3bp1ybEDdHAVc/DY2QMCliUOjcbhUIDWuUb16dVm+fLnROki3on7O0n6YszMxByxLhgwZYtN+xkrEIc+fL18+s/eZ3t62bVtZunSpxDWIosGCBCbEWgfq9u3blR1JhQoV4uxxoZyxIN9NCCGEkBhANMrOmji7/94MgwYNUulT1Lyh9h81cRBomJurAZsZpNPR5ILUK/oDYF0C/WENsDmD+4U5y5mTJ08qC5NWrVpJnIm4IkWKqLyutSD/Gx8gTPnpp5/K4MGDlahCgeFXX32lWn7jsrB0wIABakE6Ffl4QgghhDgXjx8/NsoKQjChiQFBHjQygo8++kh5y6KjGIEZRNcwjtOwYRPWY4i8opzqm2++UenVKVOmWG0vgiAY3DRMLdlOnDghb731ltVi0BQPnbWFYwkEPN/w5FF3hxZgrS7v8OHDUqxYMXUZtQh48VFECHdtdJj88ssvFmfWORJNxL148YIO3IQQ4kZgihHSqUil3rlzR1yJ+Dx2aY91dVhnSe0TMQHDVl4Fh0iB7xa63DH34cOHUrNmTTWXHeJPc/2AgMM8+kmTJsV9OhUFnM+fP1cjtcy9eBBZmBWH/K6jomAoHEV40xTDsV6wGEEBIdZDqDI+ZqsxnUoIIYQ498QGZwFdr5j0ACGH0i9E+SDg0CyD7KGtxOrVgGs1OjYsnXEgqNezZ08VinRUrRgEIQx7TRdznR4oOI2v4bhIpSICiIggIYQQQkh0oHcAaVpE4mrXrq384ewRcLGKxMHgFlEx5HMR+TIHujb//vtv5ZGD9ut33nnHrp0jhBBCSOIgIcZuJSQoBUMNnikYQoBaOASkvv76a3Ubmh5s6SWwWsRhkC9SqMjnRgcciGGci4JBijhCCCGJFRS5WztyibgfR48eNepwNdVKhg4eqPGPUxEHJ2GkUq1JV2I9zDJNzLAmjhBC3BtD43diBRi3YG9NmzaywQUYM2aMWuISq18NiDfDmV/RgfXsHerq7LAmjhBCCIkFdo/c8lDbcEU+/PBDmTNnjsO3a7XSKlCggEqRWiPkYIIHTxVCCCGEEHcnXbp08uDBg4QTcYUKFVItsl9++WW066FbE0NeMcyVEEIIIQR4eHg6ZHFFOnfuLHPnzrVpyL3DfOLQCgvzXZgbYnoDrEQMXZEXLFighr1idAQGuyZmWBNHCCGExAJHpENdNJ26fv16NdcdATE0MZh66Q4fPtym4FesRFy7du3UuIlRo0ap3C6G/qLDIigoSPz8/NQ6DRo0iDJWIjHCsVuEEEIIsQaINxj7WgJjvGzBahGHWjgY70ItNmnSRP744w/Zt2+fGjyfPn165ULcsWNHNfgeTQ2oi8MwWUIIIYQQd57Y0KpVK5sG3DtMxGHw66tXr9SwV1iIIJ1oCQySPXPmDEUcIYQQQtzS7NccFy9eVGNCMUs1d+7c0qhRI8mSJYvYSqzSqX369JFMmTJFm7edPHmyTJgwQdXMEUIIIYQQUZnMH3/8UdXDQUuhvwD2bb/88ot0797dppfI6rgkhBvSo2hsQBrVHD/88IN8+umnKmSojZIghBBCCBEMC0B3qV2La0bi1q5dKzNmzJD//vtPNYJeuHBBnj17phpG+/fvL/fu3YtbEYe5qBgfgbmob7/9tpw9e9bofihJRN8g9hYvXqyG0SdmkE7GDNlKlSol9K4QQgghTo+9Rr+OSMcmFFu2bJHBgwerngIN6KSBAwdK7dq1VR+BLcSqQhDz4TZs2CAZM2aUxo0b6/1OoC4HDRokDRs2lGXLlknSpEklscOJDYQQQgixBp1OZ9XY0tgS6zYPFOBt2rRJwsLClJCbOnWqCgXWqVNHVq1apSJ2hBBCCCHGisPTMYsL8tZbb8m0adNk27Zt+ttCQ0Plt99+k127dtncCGpTzjNfvnyyceNGFQIcOnSo1KhRQ9asWSPJkye3aScIIYQQQhIrb7/9tvTu3Vt1o8JfFxlNNDaA6dOnS/bs2eNWxJ0+fVqNjDCkTJkyKo+L2jDYihhSunRpm7stCCGEEJK4QDrR3pRiXKQk44vvvvtOuXzAYgQDEnLlyqUymvFiMQJvk++//97sfaiJMwWmvxRxhBBCCFF4OCAd6qKzUx89eqSib2gOxeIorBZxzZo1k+vXr1u94ZQpU0pihrNTCSGEEGINo0ePluXLl6sytLp166oFGUtMuLIHDx1aJojNvHz5UtKmTSsvXrxQ3buEEEKIsxOfxy7tse5+O0jSJLev+fFlULDkGP6Tyx1znz17Jjt37pQdO3aoBSVqeE00UdehQweb6uISt5kbIYQQQpwDzbDX3m24IOnTpzeanwpRN3/+fJkyZYpy9kiVKpWql4sTEYe5qVCOI0eOVDldawgPD5e///5bmQJzegMhhBBC3Jm7d+/K9u3blZ7Cv/fv35eqVasqmzb47NqCVZK2YMGCylIEnRRdunRRdiIYG2FKcHCwHDhwQI3eyps3r3IiRucqIYQQQtwcTFtwxOKCfPLJJ2rg/cyZM9W/s2fPlufPnyvfuLFjx0qePHls2q5VkbjChQvLsWPHlMUIzH0XLlyobvf19VWRuSRJksjTp0/V7K83b96o23r16iUjRoxQQ14JIYQQ4t54eHiqxd5tuCI5cuRQg+/PnDmjdBF0Ev61N9BlU2PD4cOH1Rww/PvgwQMJCQnR7wwK9GBmlyxZMnEH2NhACCHE1UiIxoZ7P37ikMaG7EO/d7nGBgC5dfLkSX1zA3x2EQTDxCt0r5YtW1bipbEBQ985+J0QQgghVuOIdKiLplM1o2IINQS8KlSoIOXKlZM///xT/v33X1UTF28ijtAnjhBCCIkNHp6earEHe/8+oUCT58qVK1VDw759+9Tc1IoVK8p7772nMpjxOjuViAwYMEAtWpiYEEIIIcTSgIATJ04owYZ+gZo1a0qKFCnEXijiCCGEEBL3YO6pvbNPXXR26q+//honc18p4gghhBASTzVxnm5ZE+cRR+LTNZPLhBBCCCFuDiNxhBBCCIl73DidmqAiDga+AQEBNj1A0qRJHVK8R1yDH374QTV7wL/n448/TvSPSwghhDi1iMNw1vbt29v0AG3btpWlS5fa9LfE9YCYwnw4uFPHt4hLiMclhMQennS5J+5sMWKOc+fOqdFbVapUES8vL4nTdGrJkiXV3NTYgPESr1+/tmW/CCGEJFJ40uWmYGSWvWOzXHTs1uvXr6VJkyayYMECyZkzp/q3e/fuaoqDPcEuq0VckSJFZNSoUbHaOHZq0aJFtuwXcUGQcoeBIQgPD0/o3SGEOBF+fn4ya9YsWbx4sdy/f1/d9ujRI5k+fboKEKRKlSqhd5GQOGP+/PlqUgMEHJgyZYqa1vD2229L+fLl1TiuMmXKxHq7Vkna0qVLK1fh2GLr3xHXAWcR27ZtU2cSMD1++PChuh0/0kjBw53ahvG8hJBEAk7oPvvsM8mVK5eaDwnDU+0kD3O333//fVUGMWPGjITeVRLXoCnB087FRRsbzpw5ox92j+Pk9evXVSQuW7ZsUq9ePTl//nzcReIKFy6sFg18AfHlQw4Xw1ut/TuSuPD395fOnTvLmjVrLEZisbRu3VqFjuOywYVCkRDnA9/LXr16ydy5c41uT5HaV8LDQ+V1wCN1HU1J/fr1k6dPn8rIkSMTaG9JXOPh4akWe7fhimTMmFFOnTqlLq9YsUJq1KihGj/BgwcPJHPmzDZt16ZXAzuQJ08euXfvnrgrcF+Gqq5UqZK4I8HBwdKiRQsjAZcidVYpULq1WpKnymz0eXnnnXeU8HckgYGBKj2DolDts4h/W7ZsKRs3bmRKl5AEZurUqXoB5+HhJcWq9JNWA/ZL28FHpf3Qk9K8z0bJV7KNfn2U7GzYsCEB95iQuKFTp07y999/K82A5juctGhRuZs3b0q1atVs2q6HzoYQxt69e+Xzzz9XaTR3R5ud+uLFC2Vv4S6MHz9eJkyYoC4nTZZGarb6RgqUaSNeXhGR2bDQELlyYpnsWTVS3gS/UrdNnDhRxowZ45DH37x5s4oCPn782OI6ZcuWVQIyb968DnlMQoj1wJoK3z3tBKt22xmSp1hzs+ue2j1VTu78Rl3GbEmUYZDEc+zSHuvhrAmSJkUy+7YV+Fqy9hrnksfcCxcuqABDiRIl5K233lK37dy5U4KCglTTQ7yJuLCwMBX9WLZsmYrIuTPuKOIQhcudO7cqVPbw9JJWH26QrLkrml33wY2DsvL3psiriK+vrzrjiC4Fbw1r166VVq1a6ZsoQMZsBSVV2izy+P5lCXgZkaIB2bNnl3379rn955SQ+AbHh3bt2qnLuYo0kbrtZ1lcF4eh1X/UlpdPrqrrZ8+e1dcPkUQk4mZPdIyI6/m5Wx1zHT6xAQfxb7/9Vnr37q3qnRAeRD7XcDZYypQpbc7xEudm5cqVSsCB/KXesSjgQLa8VSRfibfl+pk1qtkB6dc2bSLTJ7EFZ/UdO3bUC7hCZRpK445fSK5CldX1sLBQOX94taxbMEqePLiq1keDxcGDB+Nsdh0hJCqbNm3SXy5ULnp7Knw3C5btLMe2TtRH2iniiKuzfft2dUJSv359lTbFZUtgHVs+8zaJuHXr1unNf7du3Wp2HZr8Jh4/JyyG4AxIo3D5d2PcBtaBiAPoVh40aFCUdVAjYI1JL1qy0VABSlRuJV0/+Vc8DUwSvby8pWTVNpK3WC35dXQ1eep3XQ4fPix79uyRWrVqxbh9Qkjsfg8sgSYFjbSZisS4frrMkeugXAeBAmux9veDJDBuNnZr27ZtsmTJEtWBCgsRXLYE1ok3EQdPk99//z3adQoUKGDLpomTgTA4JiFYImUa3xi3kTJt5DoQYJoIM32cmED0DSIOeHp6yTu9pxkJOENSpc0sDTuOl8U/RVjc/PbbbxRxhMTx74ElgoOeSqp0OWNY55n+8qtXr9QSm/0iLgCmLdg7ccGFJjZMnDhRLQClBdplR2KTiMufP7/y9iGJH9QcwMPJNBKnCbEXT65Lphylo93GyyfX9Zdh6InaCHOPExOXL1/Wm4QijZouY/QHhdLV2svKGR9JcNArVTxKCHH874ElIMI0cXXj7ErJ6Bv97wTWMbRjSJbM+top1kYRd8WmxoZDhw6p0CBqk1KnTi3ujDs2NqCxAC7TIFeRBtK8d/TjQtb82UruXokQUejMgemhtSkZQ2BRAod3UL3pR9Ky97QY/+aHoaXl4e2zquYGTQ62wnQNIbED31W40+N7m8QnjbTot1VSpjUvAB/dOSIb5rREi4Nqmrp27ZrNsySJEzc2zP/aMY0N3Ua55DF39+7dMmfOHLlz545qEDXkk08+kaZNm8ZPJA7F4vA4GTp0qKqNQ4NDzZo1bdkUcUHQCo2uZHSa3r64VW6cXSd5SzQzu+6102v0Aq5gwYKqrRrdorakZAx5+Sxmj8LwsDB59fyBuoxzFXsek+kaQmIHGttwoj9v3jx5E/xSNi/oIDVa/SyZc5TXr6PThcudy5tl3xrUs0XEEz788EMKOJLo2L9/v2pegFDDNCvTk5RMmTLZtF2bRBzsHaAoURe3cOFCpSwxWxXO3NoYCZJ4wYcPkanBgwer65sW9JAKDYZL8Wq9JHnKjOq2IP9Hcnb/LDm29Tv930H0e3p6xiolYwiEGNKp+PfCsXUS8OqJpEwd8XjmuHh8vQS+eqIu+/j42PwlAa52xkeIM/Ddd98pX9GrV6/Kq2fXZcPstyVj9rKSNXcVCQ8LlXtXt8vLp9f069epU0eGDBmSoPtM4hBtdJa923BRV4f3339ffv7554RPp5qGzGfOnKmGGN+4cUO8vb2lefPmKjoHxYnriRl3TKdqo9e6du0q//zzj/42T6+kkjF7SXX5yb3TEh72Rn9ft27dlHO7vTYfEIJwgQfVmnworfqY/0K8CQ6S38bUknvXj6vrsDbRUsCEkPjj9u3b0qxZM1VGER0NGzZUY/rc6XfU7dKpC7+RNCmS27etwCDJ2nmEyx1zP//8c8mQIYM6hjkST0eEzDEqBWdaOFCWKVNGVq1apcYsobZh7Nix8vz5c8fsLXEaEFFDmmTgwIH628LDQuTR7WNq0QQcRBs+tLNnz3aIT5thqmX/ht9k+fQP5OWziGYHjXs3TspfExvrBRzSuLbUGhBC7AeD71FHjc5yTFExBcO/Yb2wfv16lzooExLbMqTly5erSSZOFYkD2ATm3cHGAR5yOMgiGnfu3Dm5dOmScupHSD1fvnyS2HDXSJwhV65cUZFYzIXTukch8oYNGyb9+/dX3cyOBAcDbFfD08tbCpaqL6nSZBG/exflzpXDRt2wu3btknLlyjl0Hwghth0r0GWO4d8YmZc1a1Y1/Ju4SSTun28dE4nrNNwljrkLFy5UNeAaEHEYhFC7dm1Jntz4dejSpYtN81PtynXiS4gB5DiAo5sInUiYqYmmB3w5wZEjR1Rq9ZdffpHvv/9enPnH5dixYyqiiJCnNteMxAwiXTDmxIJaNzS+QLhPmTIlTl4+fL4gEhGVw1kNamsunYh0h9dAbebq1asp4AhxEhCNL1y4sKpRBYm93Ia4t0/cs2fPVCeqRuXKEZOFnjyJqNU2JCAgwKbHsOkbdOvWLfnss89UCBwjuBo0aKAO4C1btozScVGxYkUZPny4Opg6K3gOLVq0UAKuQoUKqkmDIs424mu0VZ8+fVQNDaJyM2bM0FuPAAwXhsBDzZ6zn6kRQghJnAwYMEAtcYlNIg71Dah/Q0oLB0uInuiAkDMNHToTSAOjbg/pX+0MkTg/sDn56quv5IsvvlARQMymQ/Tt9OnTnJNKCCHORgKN3bp9+7Y62d+yZYt06tTJqJZbmwaE1CfqMhElw/gr1HLjGGMIbKomT56smnRwrPnoo4+stldDoAvbRalZbO5zmIhD6jRJkiQqr402cDwZ5HatoWjRomqxFRgLo4geHZE//vij2XWWLVumXPmxT++++65RAe3x48fl4sWLUf4G6V+8ATt27FCRHczXRCQRMzZpNOk64L3S0jK4zEH3hBDihHh4Riz2biMW7NmzR2VlevToIX5+fsrf1BRYoyVNmlRlE1FOBccNNGmiHAzlQgA1eNWrV5dSpUrJiBEjlM0afN8wP96audzQIOnTpzd73/nz51WTqC1YLeIgdDp37ix169ZVTxTdp9aKOHs7OlBjBbd9vKDmRBxqpJCuRdgShfWVKlVSog77CBCZQcOFKVgPIg4jpObPnx9RePnwoRICeIMgWgkhhBDimpQrV06VSuG4Dq82c6AsB01wGiinwvx31PxPmjRJn7FD3Rq0BTJ2sM2B+IIDx/bt2y0+Php3kOmDCMTlCxcuGN3/9OlT1fyACGGcirhGjRqpBgbYh0CFIoxYvnx5JeiwQLXGBV9//bWKqsEbDCLOlKNHj6owKaJw6PgAEF+DBg1SdW6IykBlY7EEumaREkbtHihZsqQKl7KjkRBCCHEQiKJ5xm8kLqUVwSZDAQfQOIcSMIyM00DEDXXYhiVX0D59+/ZVdfWWSrHGjBmjInsAPojwizMXrILVji1Y/WqgQLxnz55KySK1CjEHcfXrr7+qfyGE4LQNRYr8sqMw5ytkCGrzUA+lCTiAiCFCpqdOnbLqMdA9C3sMvNBffvmlyolbskPBm4V2acOFEEIIIVbWxNm7/N+25KXBgmOzo1ixYoWKskGkaUBTmM7fxnXMQI1upCPq3VCTh0jbN998oy5rCzpXAwMDVS2erZ3aNv0VFCpSlVhQp4aZYEhnQthNmzZN5X1RoIcXAArTVOU62qPMtPhQE2C4z5oIIbxZEOn7999/JVmyZEqIpkuXzuy6KGqcMGGCg/aeEELcD4ztw4GX3ePEHhNpQ8aNG6cszuzlxIkTqn4OGUfDWjfYWZlG27SGzegMfKGHsKCuH5lBR9fb223Sg7AjjBuxwBcMxXsQc1jQYNCmTRtlRRJXBAUFSerUqY1u067jPmtBrR+WmBg9erT6AdLAD5Hph4kQQohlDH9DiRvhwMYGRLLSGFhIOcJZAvXzSJnCeNfU5xQND6hfM0Tze8uYMaNFJw8Mu0dwKKZIG+rxsf1ixYrFap8d7pqH2jIoWExoQJNBXH9Z8SbCUM8Q7YWOi7M8fFCwXcOFEEIIIfGH6XHYx04Rhzp4eN62bdtWlYmZgh6Aw4cjpwGBgwcPKpeLTJkyWWwIhbk1MnjmumKRycQ2YXkCf1NDY+AEE3HIIyMdCZWcJUsWm8ZIxAY0IWC0l2Ed3tmzZ9W/eFHiCrzJ8HVBhyshhBBC4q8mzpGcO3dO2YVAwP3+++9mbapQOw+7MtTha0MP5syZo263BAJa8J/btm2b5M2bV9XvwxEDZWZVqlRRaVakbJHRQ9QOUcDY4rCZJ3hCSJ/iyWNWJkZYoR4NnaNxWROHFx0pzgULFqg8NsZnYcQXxlugRTiunZi1mXAkAta6EEIIcZaxWy9evJDGjRury5jbC684eMchbTl79mx1+/vvv68aNqFbDANP8MTV0qq4HTX/0DmIviFQ1apVK/n000+jfXyIts2bN6vRpOhwhcXIq1evlPsFjpewM7GUjrUGDx1Uj52gKwTjqlDwD1GlhQlRaIgdtqcm7ueff1Zmv4iuQQXDtA/ApV/rFIFy/uSTT5SSRgoXvnKbNm1SpnyJaYgwsQy+VOgQwpmOLSFpQghxJ+Lz2KU91sNVf0ialPZNb3oZECRZW75v9X6HhoaatSdDcAmZPAB9AWFlCurgkA41BOuhaRLz4U27VRMCh0Ti4H1StWpVvYDTGh4gtCDu0OwQ02guS+Dv0AGC7RtiOMbrgw8+kKZNmyrDvBQpUqi8tmmzQ1ykU7GgvZgQQggh0aPz8FCLPcT27729vaPoB1NiU3oFbeFMHrLejsonay/S8uXLpUOHDkrZQoAhBIn7bRVxMBm2BuSbscQXTKcSQgghsUDVtNnbner4mjhXxiGNDQgrap0XyP9u2LBBb7tx/fp1dT8hhBBCCHEyEde+fXtlZKd1pKJQD2lNdGSg4QFdGIQQQghxYzSfOHsX4th0qq+vr2ouQKts//791cQEFBJidAXacR3tUOwMsCaOEEIIce6auMSOwyxGmjVrpgx+Fy1apPxOUAOHrtK4tBdJSFgTRwghhBBLzJ07V3bv3i3WAps0lKTFiYjDkNYkSZKoxRKwd4DVByGEEEJIXI3dcgV2794tp06dUt65MYHJDWgQjTMRt27dOunWrZuaA1axYkVlHYJ/0ZqbGNOlxLWgyTAhhDg5jpi44GLp1H79+kmfPn1iXM+adexOp75+/Vo5HkMxah7B8GvDZAYIOk3cwQmZwi7x8MMPPyizRhgrOuvgalv2yxWeFyGEENdk0KBBarSWo9e1aWIDhsM2b95cDZfHg2EcBW5DAwMWOBhrm0Jn6kcffaQfV5EYMWxswOzWxDyxIbFOQ0isz4sQQpxxYsP9jfMkTcoU9m0rIFB8G3dP1MfcOInEYTwFRmh99913SpyhgQGXtQgG3qSjR4+qBaLOcCB9YoSNDa4JxsHB+mb69Ony4MEDddvDhw9VLSfm5xUqVCihd5EQQhIl7tyd+ujRIyU8zYHMJean2iJKbZqdiujFqFGj5O+//5a6devK1KlTVa2cO5LYZ6dC9CBSBcGDOXF4710VDDfG7N3z589bXKddu3Yyc+bMRPleEkJIQkbi7m2a75BIXPZG3VzumNunTx91bIkOlKQhwFC+fHmrt2tTmwcO6vPnz1ezStG1igfE/NLHjx/bsjnihGACx+jRo5UHoBaxunfvnjRs2FD5/7lapBWf1dq1axsJOO8kySVlmmziYdDthDnAKBWwdMZECLGu3nT8+PHqX0L0uLHZ79ixY1UJz4gRI+TgwYOqBA1ZoTZt2qhj0/79+5U1G64HBATEbSTOEPz5ggULVGQOgu7nn39W0Q53IbFF4vB+/vjjj+qDhno/S5QtW1ZWr16tH6/mzODkomjRovLkyRN1PVOO0lKp4TDJX6q5eHklkSD/J3Lu4Hw5suUHCQ56rtZp1aqVEquEEOvAid3atWtl/fr1aoJPUFCQ+k1EzTBHLzofCRKJ2/y3YyJxDbu43DH3l19+UTPlMRjB9JiLQNiMGTPUv6VKlVLr1qtXz6rt2ixpcYBHjdyyZcuUosQDP3/+XFauXCnuAJoaihcvLpUqVZLExKRJk1R9mCbgPDyTSKYcVSVbvrckReoc+vVOnDihJnTcv3/f6c/48eXQBFzOQnWkw5AtUqhsKyXgQPJUGaVCgyHSfsgW8UmRTt2Gz/G5c+fi6VkQ4rrgIITfQ0zqwckP0kEQcNrBGyd6Xbp00Uf0ifui8/B0yOKKXLp0yaxfnIeHh/ru4H5PT0+lpfz8/BwficNcVNTAoSMVCwRccHCwui9v3rwqMgOrEaTbatSoIe5CYorEIeVo+N4VKN1DCpbrI8lSZFbXdbpw8bu1W07tmSiBL2+r25o0aaLOvJ21wxRiFF8czPCFv9B7n52QtJnyWdze8R2/yu6Vo/XNKzgjIoSYB4ePDz/8UP74448YX6I8efKo9JE1xqckcUbi7m5d5JBIXI4GHV3umPvjjz/KX3/9JTt37pRMmTIZ1Wqjt2DTpk3K7BdNpMuXL5fChQs7tjsVOVzUSEExIszXq1cvvXDDm0Ncn2nTpukvF600UIpU/MjoftSOZc1TR2plKiY7lraR4MBHsmHDBlVnBm9AZwT7pgSciOQuXC9aAQeKV+4ie9eMlfCwN7Jx48Z42ktCXDdybyjgMvrWkBwFWkvKdAUlNOSV+N3eLHevrpTQkJeqzrZp06bKwSCxjmMkJDrTX7h6IOiFuusMGTKo4BimOvTs2VMJOIwubdGihdUCDsQ6Lnn9+nV1NoUHRuQGc1LhHUdcG9hsQP0Dn+QZpWC5fhbXTZYyixQqG+kujfRJfIN07sCBA/XNNEiXojYThtSGGNYfZMxeIsbtIp2aOn1Odfnq1asq0sfibEKigvKZr7766v/XPKR4lQlSpvYPkilHLUme0ldSpy8sBUoPkCpNFkqKNHnVWkgZzZo1iy+nm6ITB6RTba8CS1BSpkypmhcQjUNUGseqcuXKKT2F2wAyYZMnT47Vdq1+NerXr68OkkOGDFE1DojAIDLXqFEj5W+CEHmHDh2Uh9zWrVvVF5w4NxAnEClYMD5N6zjNURAF/0mj/dtcRVvrL//222/67cS16MEcOnzQ8eFHqlNL6eMLgbFw+Gzi8bUqgZCQEP3fBgc8i3H7uvBwCQ6M+OxiG0jVIhVACIk63FurfcteoJVky9vE7EvkkzyzlKg60ej3ws5+OuLqY7fsXVwUT09P6dixo/oOLFmyRKVYEZWzB6vTqQj9oTgVi+Yfhro4zdwXCzqTsGOgbdu2yq6BOC8QJ+Z835Knzh7j3yb1SSte3ikkLDRQ3rx5Y7SduBI9e/bskWbNmsmrV68sroPIHBozcMaPKBxC1xrXzqyV0JAg8U6a3OLf3768Q14HRog9Hx8fVbvgSnUXhMQXixcv1l/OVahDtOsiKpc2c1l58eiEXLx4UZ2MoRSHEHfixo0bMmHCBJVJ+vTTT6V9+/ayfft28ff3V2nUOJ+daqoo0Z2JBREQrYgcNUgQdIl9dqrh2C1XBeIEDQFaJEvr4Ax8FbOhb3DQUyXgQJIkSSRLlixG23U0qB1455139AIuQ9ZCUrnRIClSvqUk9Ukl924ckeM7Zsi5Q0v0Kd4CBQrIZ599pk42VKo18Jmc3POnVKg/2OxjhIeFyqGN3+ivo5EHJyOEkIjIvWGUXes2TZI0raRMG3OzQvrM5ZSIAw0aNJBkyZLp78PkH84vdgNUJM3OdKiLRuIeP34sVapUUVlNBAg0L1J0o1arVk19JzCyNLY4NLkM4YbOih49euiFXWIFnYuwoDh8+LC4KvjRREcnFohviDFw9/IaCQuNSFNa4taFiPo5gFm62nawxMWPMaaCPHsWESHLV7yB9B53UMrX7Ssp02SRJD4pJE+R2tKq/3xp2XeOUdE1vAsxx1dj35pxcmz7LxIWGplmBYGv/GT93B5y79o+dR1p2ZYtWzr8eRDi6pF7bdFOYMN1oValR8PDI094ccJouC2WLLjX2C17F1dk3rx5yr3jn3/+UcJNA9ke1Mjt2LHDpu1aJeJQ4I0auNhi69+R+Cdz5swqtAtCXj+Xi0d+tbhu4Kt7cuVE5PiQ/v37x+m+QYhpxdBe3j7yTt/ZSriZo0TVjlKyaid1GXWZ6AbC2Q/MizWblD2rPpVZ44vJ9iVD5cD6r2Td7O7q+tVTq9U6SZMmVV84b2+bA9WEJDq0yL22IJoAwt4EyMsnp6P9W4i8p/cjTpAAIveG22LJAknsXLt2TR2LNG84Q9KnT6/PhMWJiDt+/Li+eyI22Pp3JGFA04r24bp8fLqc2DnWKLUaHh4q965ukN0rOknI64iOZKQ443poPHx1tEaZYpXaquhbdFSo/77+stZxi46f4cOH628P8n8kp/fOlEMbp8iVkyuVpYjWQQSjX/j2OBMcY0ScKXKP5c8//9Tfd+vC39FG4549PCT+L66oy7BSQDd8XEfviRPixmO3smfPrjeQNxRxKBGCtQjKf2zBOzZRtdgan6J4j7gOmD7x/fff639Qb55bLDfPL5H0mUuJt09qefn4ggQHRc7HhXiDSIfAQDoEZ9Nx8WOMqJhG9vyVY1w/e77IKRowUMT+Yb+++eYbVTyKzzHEneH8V4S0e/furYxLc+fOLc4GnoNmaswDHnEG4EaAzyIiCI/u7pBrZ6ZL/pL9jGYRg5dPz8vZA+P01/EdI+6JTjzUYu82XJGuXbsqVwU4QSAoAWs2ZCrR6ICxdKiLi1MRp/lyxRYWhrsWQ4cOleTJk8vgwYMj7Dl04fLM72SU9XA2jdmiSMPGtcAwtAlBd2lMhL6J9IpD56xhvQ1GhWF59OiRaspBsSmeAxontPQQISRm0JiAWlWt/vnmudnid2uzZM/fUlKlKyRvQl6q64/v7VGHXoBB3506RZQ7EOJO5M6dWx0zESzAqNJ///1X3Q7LrFWrVkVJsTpUxCF6YeuMTMMOJOIavP/++2oGIqJsWOC0roH0KZo6KlSooDo+d+3apTfcRVgYM98MO1UdAZoMNC6dWCNVmwyNdn2sowFBaq7eBsJNE22ogaOAI8S26AK+/zj5A0H+d+TqKfP1tIg0oFSBtabuiyNmn7rq7FTtJAb2V1jwvUHgw9AGyxasnp1KEv/sVEvgg3bv3j2V0798+bKMHDlSZs6cqTf6NAQdrvASxBm6o8ax4SOKbp6zZ8+q691GbZVchczP5w0LfSNzJ9WWBzePq+vo+LFkphjT3FVnwpX2lbgfW7ZsUUbv+NcUfGZxYgj/RpxUEfednXpz12pJkyqlfdvyD5A8td9J1Mfc2MD2OxIjWpgXYuqtt95So0MsgfTlnDlzlFcgxokg4uWIx0f0T6ulWfZbJ2k34F/JWbCq0XrBQa9k7Zz39QIOtQc48yGExC34XcACI1/U+Xz++ecqMg+TeBicMvpGIn7MHdCY4EKRuClTpsiaNZGZoegYNWqUvP3227F+DIo4Nzb7jS3wadPS6j6entI6R1ZplSOrZE+WTPyCQ+S/+w9lye0HEhAWJmfOnJE2bdqozlIYQ9tLr169ZOHChWpqQ+BLP5k3ua7kKVpHipRvJUl9Usr9m8fkzP6FEhz0Up8ixWBuW+sMnAWcbcJ0WPPIw7/z589XdjAsVSDORpEiRdTy7bffKhGHyBsFHNFwhM+bK/nElSxZUgU2NDBuC8cklCWhmQ6lSqiTQ8AB2RZbYDrVTtwhnaql8jRSe3vJjIqlpEjqVFHWvRkQJH2OnJbH/29GwCg2jMpyBBAwqM9EO3Z0wPUaRaPNmzd32RQlxrDgzAxRzYCAgCj3Y14xTIzHjBnDgyRxOpz5u0USLp16Y89ah6RT89Zs7nLH3LVr18oXX3yhSnwMywog5GrWrCkHDhzQT1CKDa4TlyROw5BC+cwKOJAnZXIZVTS/0ZmHo4Ah4tatW9XQYHPedPhioPMH83xjEnDODFrPUceHSK85AQdg64DWdEyVCA6OfroGIYQ4U2ODvYsrsnv3bnn33Xej1IViWgPcHmyd/uSarwaJVwx7X1J5e0lT3+jr3OpmyShZfZKqy+vWrVMTFxwFukhhSox5qEjVpkuXTi/wcOaPbtqiRYuKq4L0PNLQx44dU9e9kyaXivX7S88xu2Tgd5ek64gNUrJqR70XF17fDz74IIH3mhBCrJ2d6oDFRTl06FCU23ASfurUqbi1GIkOFLCfPn1afH19pUmTJsrTCzuVOnVqezdN4mmQdUwY2ssUSZ1Sknt5Rbu+l4eHlEybWh76PVECMH/+/LFO+cU0EBt1dmhawIQFGCcihQohF9vH0EyKnYX169crcQpSpskq3UZulMw5iunvT5cpj+QrXk/K1ekl//zYUvnmzZ49W02jKFYscj1CCCHOw3vvvaesuVDPjAUlMfAnRe12eHi4mqsaryIOUQ/siNapCFNfiDikgqpXr666lLSB6sQ2YjsJwdr1tUHWthASbp0jTUh4uP4yRuzElvgYiO2Mkw8M08/N3vvZSMAZkrdYHanTaoxs/fczdR0/BNOmTYu3/STE1U6QiBPgiHSoi6ZTixUrpuq5P/vsM+nZs6fKUCHwAO2EWjkEIuJVxLVr105F3OA0fOvWLVWsB7Jly6YciDHWCPlfYjuxnYRg7fraIOvYROJwpgDOvXwlfq+DJUsyy9MNXr0JlSPPXqjLCBEjSmtNqBjdbIjcYV13/PFHoS7sGUDajLmlcLno283L1ektO1dMVBMqFi9eTBFHnAZnPEEiCY87j90CGLuFEhiAemdkkuzFJhGH/C1MX+E6DB+gZcuWGd1fvnx5lRKiiHNOYkpVRtedGqYTWXDrnnxcOJ/F9f+9c1+CwiJEH0w+rW1uMOxoi8uDQFzMeo1titocaEXX6g9zFqomnp7Rp62Tp0wvWXKVknvXDqtoJ143R1iqxPbzQQghJHY4QsDZLOJg3lixYkUl4IDpgQO3o06JxB5EvGCSi6HvmO+pdSL+888/KuwK/7OEZv7Nu5ItWVLpmCu7eBq89xAga+77ye9Xb1o97BqF/DgzQTpQq73Dv4j0omC/fv36Dvd6i4tZr/akqM0RHhZq1XphoZFzZTFVwxHERyqbEOJ+uNvYrU8//VRF31B65sh17RZxiGCgIM8S58+fV2lVEjswsgaTCRDhNOT169fSuXNnNZN04sSJ0q9fvwR5afG+awf4by9el8W378s72bNKzuTJ5OHrYCXgrvhHdqJCIMHs0BInTpxQH1gMAzYVsojuYilTpoz6t0CBAg55Doh2aQbNjpw4F9sUtTnwvDUhe+P8DgkNeS3eSS3PHn759I743T6tLnt5eTnsO+eOqWxCSDyA83F7T8pdKJvq5+cnx48fN5r/bQmMlUQTYGyxScRVqlRJ1cHBzqFPnz5GkRJE6aZPn65c5on1wJwWQi26CRD4QPTv31+99l9++WW8v7ypUqVSkbWvv/5aXb8V+Fp+uRIZdTMEQvObb76xuC1YaNStW1fVwWkk8UkvyVJkltcBD+RNSIRYPHnypGqUQUFowYIFbRZHmzZtUmlddH+GhkZEuR48eKDGA2FfrfmSxUcKEnNnMZkiyP+JnNw7XyrU62tx3YObfhadLiJtDdPf8ePH2/34hBBCHMfkyZPVYg0ws4+3iQ1ISWGgMdJd6LBAjRwaGnAAQk0cTFldfeSRtWO3EDmzxz0a5rQQKrBnAWkylJSchTtJRt8a4uHpLS8en5S7V5bI47sRzSMATv5oWYYIgmBGgwlec0Sa0OWCAnm4QDviPTCtIYPzNG5D2tcUPI/BgwerCJu5x8bfPX78WAkqvGYRz7eoFCr/kWTOgefrJeHhb+Thre1y+ejPEvAyQiQWL15c1WIi4hQbZ3jUirVq1Uq5YVsC28SMOzy3hP7MQqzifdM84tp/tEgKlm5itA6+ssd3zpK1cwfgitp/uH7bGwkkhLgPCTGx4crB7ZI6lXmjeGt55e8vBavUc4mJDcHBwUZjt2ICoxRja8dl19gtRNzQGqvV4sBSBNGkn3/+2W184hzxRUCtG7p5Qba8LaRIxVHi4RG1qP3O5X/lyomI4vl8+fJJ9+7dlSgyjGYZUqpUKfnzzz+VG3RcAMNdzDKFmMfrgHRvTHYipiO80mUuLZWbzBAvb2MXa/Am+KXsX9tdAl5cU9chVDFzzloRh1pCiErD9HTyVFkkfZaS8ibEXx7fPSo6XWTkE9FNtH8nJPg6QpxjPqoGZsSWrt5ZUqXzlWd+V+XE7rny4OYJ/f2Y3DB27NgE2mNCiCtCEZc4sHt2Kv4cUYCgoCDJnTu3wzou3OWLAOGRN29eFdFLmiyjVG22Qjy9LDcvnNw5UJ75mR/PgXLPSHe2SGW/evVqm40EHTknEaIP0Vp4CWpUb/GPpM1UwuLf+N3eJUe3fKQuN2rUSDZu3Gj1YxuK4xSpfaV8gwmSo1BD8fSMONMJfPVAzh/8Qy4dnan/m127dkmtWrUkoc/ecDKk7Xt0YHoFhHxCRxAJIa5FQoi4y4d2OCQSV6hyXZeIxMUHnram/9BNCHDwgAiBkZ0m4AzvJ9GDzkytDi5bvhbRCjiQvUCbKG9g61zZZFHNCnK8WR051KSWTC5bTIqkSalvisAYp2vXIqJZ8Q2e24IFC1REDJ8RQwGXOkPRaAUcQIrVJ0UWdXn79u1WNyNcv35dVqxYoS4nTZ5eGnRZJrmKNNULOJAidTap8NZ4KVVrmP62qVOnSkKD0WKokUS6vnDhwhbrUhctWqTmyFLAEUJcAXeenRpX2PRq4ACJTsro7kd9FomZlStX6i+nyVA8xvVTZzAWPd9XKCETSheV4mlTq4N5Mi8vaZ4jqyysUUHqZc2o1vH391cH+/gGaV4Mou/WrZt+sochqdLmjXEbqJFLmSaPuozaAkTcEH3TPNlQxzZu3LgoTQVII2uCr2ilvpIqreXGheJVPlRpVi1l60irEFtBnRuaSBC9RO2hdsaJfzEoGTP46MNICCHujd2zU83x7NkzFU0gMWNY9BgWGhTj+uFhkevkSJ5MGmQzP4w+iaenTCxTVN7aul9eh4XL3LlzZdKkSfFWq4jnhXTm5s2b9bdly11KCpVuILv/i4h2BQc9sWpbIa8jo3eaBYdmdWKpI3Tfvn36y/lLRUwOCQ58KtfOLJFnD8+q1zqpT1rJmreG5CrcTPIUbyMXDv2hIocQSK1btxZnAMK8Xr166n3Dc8a/8GgkhBBXw90nNiSoiENxOGqrAAbe4/p3330XZT3kqWFU27evZWsEEgmiShqP7+6SrLkbR/vyPLobMRwdVMmYLtp10yRJIk19s8iKOw9UVAxTNN5+O/pRTo4C9jOagEueMp10GjxfipZvqqJjp/YtkRdP78rTh0clyP++JE/la3E7L56cF//nV/WNM2ieUM/NpBbCdGIC7FiAp2cS8fRKIgfWfiw3z6+S8LBIc1xw7fRiOZZigmTMXk5/W+/evWXgwIFG63GKASGE2Ie7mf0CuCpAO2XOnFlatmyp/DwR5ECwIFOmTFKkSBGJFxGHHRk+fLjRbabX1Qa9vVXkAOOWiHUdvvAuQ1fno7s7JODldUmZxvxIq9A3AcpqRKNXwdwxbr94utRKxAGkNeGLFtcjlSDUfvnlF/317sOXSsFS9fSRpSoN+8qmxeNFdOFy+fivUqrmRLN1XfBAu3wscjvoeoZPXmwmJsCuZNO8FuL/3LyfHQgOfCL3rmwxiiRjMd0+IYQQYi2oAa9du7ayyIJbwtChQ5UNG+qdtZK0smXLKjeC6IzxHSLiYO2gjYFas2aNKhqfNWuW0To4EKdLly6KlxexDMZowWwWkxhEFyandg+VkjW+kdTpjAvag4MeybkDYyQkKOI9ABl9Yh7BFRAaaaGBUWjxIUbgyXbu3Dl1OV+xmnoBp1GlYR/ZtuJrNZHg7pXVyk6lcIVB4pM8ooYPBPk/kAuHv5VHd3ar6/AihBGutRMTEBFGLSDQBJyXdwrxzd9KsuZuJEl8Mkjgqxty/9oqeXQHfneRDRM4UzL9DLMLihBC7EPn4aEWe7fhKhw7dkwKFSqkL++ZMWOGqmVGWQwymjDQR3ACfrtoPsT1OBNxEBsI/QGYp9apU0d/ndgHIpooqEe0MzjwgRzd3F3SZ6kkGXxrqG5KmP0+uoPOTON5mlsePJaWOaMftbT1QaToQzg3LsQIInuw/oB4Q3o4ICBAf1/pau2irJ8mva+0e3+6LPrpPXX9zuUVcvfqGsmco6YkS5lVAl/dkcf39qtIHYCggqFxdB9w03QnvhCGo7qSpfCVMnV/leQpsxvclkUyZK0sT+7vkzN7R+hfXzQOGKa5CSGE2I+71cR5eXkZ9QegzAzOHaVLl9ZH3r7//nsl9tDk2LVr11g/hk3JZURFbJnxRcwDVY4JCwirasAL7urJqXL5+Hfid3uzXmBkyJBBv87ca7clOJoxXQceP5PTzyOMgDGDFCnbuEilYpvVqlXTpzQR8dNIljKt2b+pUKervDtwtnh5R0QTdeGh4nd7h9y6sFge392rF3Cog0PUt2nTprHaJ3w+8TnVKFr5cyMBZ0hG3+qSq2hXoxQ3IYQQYg8YaI+s1OLFi/W3dezYURnxG1KiRAmbbcBsrhBEfhfO8khhIY1qurRrFzUCQyzj6+urph98++23ZgVyxowZZcSIESoEi7Fm4MqrABl69Kw8+/+4LkMOPH4qw46d1V8fMGBAnPqJaelMLIbi6d71yMkCplSs211G/HxeyteOmib19PRU4vbTTz+1aZ7c7du39XVtKdMWlLSZIhsXzJGjQFv91wEebYQQQhyLu/nEpUuXTmbOnKma5eD52atXL2XAX7lyZf2kJYzbhMm8YYAmXixG0OWIGjlEeHCwRXr1zJkzcvz4cbWjiMyQ2AGz5GHDhqnIFmZoopMFQgQCDtMI8OZrHmgolgwMDJQ9j55Ko60HpKFvZimZNrUEhoXJtgeP5cyLyFFcb731lvTo0SNO3w7DdCZmo0LM4cN5dMd8adL5S0nqk8Ls32XIkhfFlEa3oQMVET1rZsiZdqVqaHNoQdpMZWIUsD7JM0vyVDklyP+WmgNsKZ3KLlVCCLENd0unAthVIcqGjNLu3bvl999/l4sXL6oGQNTLARxzcNzD8d4wCBJnIg4GpKjfgqkvFCSc47UUFGwsED2BJ5mrgHCnZkmhAZGEaFBCgMfF6CcMssebCvGmCThQoUIF+e+//1RtIlKYweHh8t/dh2oxBQJu2bJlKi0ZX6BWEsWb6LgJ9H8qK2Z8JO0/mCGeZhpezh5eLcd3/a0uQ2jhg419tXYIsKWuVEOQqrUGXXiEZx/2wdI22aVKCCEkNkCgwVlBc1dAAObEiROqFg6BLxzrUQ8H6xFMwPrnn3+snnluk4i7cuWKVKlSRe0YDrza2CiAiFyTJk1UFwYGo7sCS5cuVa74ACFORJJu3LghzgxsXCCkMSZq9uzZqhvTNBcPx3+kvONTwGkgoog6AETEjmyfK08eXJO6rYZJ0XJNlZh7eOe87F3/qxzcFDlZQTO0jQ2mXaka+Ew+eBBhrfL04UE16B5dsJaAtcvrwPtR/OjMPR4hhJDYoxMH+MTZXgXmNEC0YRQlFg0cK1EuBWEXm2icTSIO8zi1/C06BjUXfY08efJEO5bL2Rg7dqxawNdffy2hoaEuMY8SrzPGaX311VcqAgoLDkTu0IWK+bUJ+RzQfYOZqZ06dVKC6vr53Wrx8k4i3kmSSXBQZLoXYOD7jh07Yi3ioktvIuW/du1a1fHrd3urshaxxO2LC/WXUZc4ePDgWO0HIYQQ58TPz08Fa27duiX58uVTxxvT6UU47iOriLIw2EzheIpjaXwBBxBk2bDEBrslLUzsEA5EihWEh4fL8uXLHWo/gtQWDqyTJ0+2uA6aApDCnTZtmnqjTCOHEAimC94sQ7DvKEJETZ+rqXp0b+Jf7cPgDCK0ffv2sm7dOhUe1ggLfWMk4JInTy6fffaZSr06ep8RidS4dPRreeZ3NMo6iALeujBPHtxYo67jNUT0khBCSNzUxNm7xIbDhw+r2jPYYCGTAn2C5kDDEioIuIYNG8oXX3yhjp8oV4IFiK0doy41OzVXrlzKqA7dFmhmgIDCSC68cI6ge/fuSnCh0BzbHT16dJR1IAIwIQA5ZUQFUZMH8YDULoB9B1S4KTVr1pQvv/xSfx1vHFp/s2c3b0VBYk+jRo2UiMZUCphDwygaXxh8UaZMmaIEU2wLOa0FwhZ1g/DfCQsNlJM7B0i6LBUla+4mkjRZegl4eUPuX18tQa8ipzl88803qqOIEEJIXJj92jt2K3YibsSIEUoLwIsVQB+g5n3ChAlqcgLAqFD4nEK0wSkC2SOkOqE3DO1BEo2Iw4EZNXEaGCOBJ4toGMQW6rQ0Gwx7gTDDwR8izVBwGTYlIEIHcdC8eXN1W8+ePdXYr/Pnz6vrH330kVpi4rffflNjMRwNuieRJsRZQFyPvHJWw0OkNrHg84HIKsLUQ4YMMVoPr432OjkCRPZgEtymTRt1Fgae+x1RizlwFgYrFkIIIYmDy5cvq6lIhlSsWFE1D2giDgKvQYMGSsBpxyykU0eNGqUEnTNPobJJxAUHB8vVq1f1kThEUuBCHBdAMEYH2nYhCJo1a6a/DenQOXPmKIGHdK81IFqENzumx8Nzx6JhTQ0XRByECwrw3VHEWUtcvDZIj0Lg48uKBe+zKThLw9QM7SSAEEKIc1uMmB57fXx8jKYjGBrponERc8NxYo9MELJ7aHzTggbI8pke+zHxJygoSB27c+eOeU65teAYdPLkSVU3jjQvJhxBJBo6UMQGm+Ka8DBDq2zWrFkloYHfCl5sw3oqzXsF91kLooiIJsZUl4WoX9q0afULRGx0wEsPjSAA4k+b50liFr7jx4836wEXW9BtiqgfPg9ouNHeY/yLukh8oSngCCEkfman2rsAHHvTGhyLLdXMY6wVfvvRMICIHJwbtGwPRJpm+WHa6KCtg/scGagoUqSIivKhGRHgX1hyxWskDhMFYECLuqaEBirWNP2GN1S7z1qsNcOF0DOMGEHJmxNy+/fvV00W8GiD8gewLkG9Her8Bg0apBycSfTRS7y3jkpFw38PIXPNjw7/4iyNEEKIa4GpPGkMjv3monAADQrIsm3dulXVzGN6AjzaELjR6p8h4AzHRQJt4o+puLMV1IWjqQJRP0PBiSwiMkGxyRzaHYlDGBAiDka/CQ0sTkw90rQ3I7qB6baCDwo+OIaLIRAHiCChKBIFkZqA04APHdJ6aKBATp5EDyKXKEB1RESOEEJIwqHTeThkAabHYR8LIk7TApiAhFp51PNv375d1e1rfwPxpDlsaOA6AkKOanRExA2Pb5o51IQm/OHiLRKHEVAYJfHBBx+oF6JSpUqqLs1wxxCdqlGjhsQ1RYsWVQoX9iDahAUoXe2+uEKrsTI0OgZovoDo0EidLrNUrttB0mbIJg/vXpbDO5dKyOtAZeyHkCry4HgtSSR4TbUwN95XgHZwvK59+/Zl9zAhhLgkng4w643d31+/fl3VRmvlXwcPHlQZMjRkanTo0EF5mqIZslixYiqFOnfuXGWT5SjrK5RVaRrFdJuIEGoWYfEi4tCKi5AkQAgQpq6mtG3bNl5EHATQmDFjZPXq1cpOAmBaBNJkcSni0MWIBak+LX179uxZGTdunH6ddn0nSZP2H0uSpJFnCF0+mib//Pax7F4/W0Xt0ISBgkrMTTXF0d2azt5diwkUeC9h0KuJNw2MI0GEc+LEiUrIoQM6ujOv6EB4HNFbR4XJCSGEOCdv3rxRHnDIIOIyji9ocoBAM9QrqEvTJk7BLB/HIBjpO4q6deuqoQLwLzUUccjIwZINlmfxJuLwgsDgNzoc5bUFgYiw5qFDh5Q6xkEeoKYMo5Eg1CCcunXrpt6Ee/fuqVw3vOHiG1iUaCOkWnb/XFp0iepplzJ1Ouk9Yqb4v3wix/euVqlfnBFAmJjiLJ2scd1di6hbnz59jM6MNJIn8ZKUSZPI44DX+igdOqHRHQ1fP1vqMrUUfFyk2wkhhMR9d6q1FC5cWA2eR8YO0TD4kxoa0AOIKmgNrIdmNwSH4DNqa8eoOZDOhak9NAuOWxixhWMZBCOOsZZGPcaJiEPkqWzZshIfoLMQLyTM+bBoGCpZqFu84HgD4P/y119/xXvKDR8OGAaCpMlSSJMOlufGYt9bdh+rRBz4888/zYq4xIhpdBFp5RYtWqiiU0Mq5coig2uVlYaFcomXp6fcfeEvc4+cl9/2nZbXoWGyefNmNZv3559/TqBnQgghxNlFnKZZOnbsKDFRq1YttcQFOO5j6ABq5RENRKMjAiPfffeditLZvF2dFjoiNtfEoQYPNYGwEwEVa7eRgV8si3EbwzsXFL97V9Wbayo6o5sJagnNSBcfDNQt2gMmb0BcwocPkVCElnH2gFQ16gccdYaCbl/MqzXkneL55M929cXbK2rtw8FbD6TN3HUSHBqm9gcdSrE9g3Hk6xRfuLthNCHEsWilQMhKxHXJjvZYR46flVR2lrH4v3olFcuViJf9dgXsHrvlrpjWxGkCDqRIZd0YqRSpIlLO0NEQFYbEdhC8o0BkbODAgSqaaVqXhvswJgsRMISBUUdgD7CA+f3336OkT79vUdOsgANVcmeTPpVLyK/7Tqn9waxbc6PYosNZag1jA4UbIcTVSahIXEKBMV6GM1qjA12rCAbFFoo4B4FokPZm3b56Msb1Q4KD5MGdiC5ac5E4SwIDkT8U96OWDt02sRUw0YGiT+TtjeoJPTwlRersEvYmSIKDnqibEAZGUSgEFEac2cqiRYui2MO0LJFf0qeIPsrXs1IxJeIA6hxsEXGEEELiF3cTcZMmTVLHSWtAlgu14bGFIs5BFiNIqaJmDx2W1y8ekRuXjknewpbnx+7ZNF9eB75Sl9FlGVNaD9tH9AvjxDQTQoAJBOh2eeedd8ReMBhYE3CeXj5StNL7kr90Z0meKouKFj65f0wuHPpN7l/bpq6jjg8dP3DCtjYlaOj39vTp0yjrFM+aIcbt5M2QRlIk8ZbAN6HKVBnpUXPYkpImhBBCHAE0ApwUrMHWEiXWxDmwrgDdlfDOA3kLV5BRP26T5CmNI2o3Lx+X/xZ+LYe2/2t0O+xYIMbatWtn1HEJsQTvOXTgRle+iEYTzILDYq7WK6aaKuw/ooHoAPbwTCJ12s2XzDmrRFkP+3B82zi5enK+ug5vHXNdpaaCTXutYHYcHZ/ULiejG1SMdp3Xb0Il55ezJSZgIQJbEgo5QghJ+Jq4g8cuOKQmrkr5oqyJi4tIHFpzURuG7g5vb/cL8nXt2lWN00BTwI1LR2Vc/4rSosunUqX+u+KdxEfmTxso21b9ZnEeLRb8Pawz8uTJo26Hn42hV41P0iRSs3xpSZ0yuZw4f0Vu3HugbscYEQzRtdUmBJ212oy4/KXeNSvgtNRvmTqfye1LayUk6Knqtvnxxx+jzNHFl9a0zs8aVpy9KiPrVRBPT8sh89Xnrlu1LQjGhKotJLbBBg5CSGJky5YtSiOZA8duTMGCTx2O0bHBZqWl+ZpAuAB4r4waNUpdhojDWIvoREViBL5jEGBoF0aq8OGdy/LXlJ4y69s+4uWdRN6ERHidAZ/kqaVAybckabKUcufqEXl8L2LkB95kvJEwVIaRsqGAG9bzXRnQuY1kTBdx1oTGg20Hj8vHU36RG3cfRJkeERs2bdqkv5y/VKdo1/Xy9pG8xVvLpaMzVR0dxomgY9UQnNnF9GHESC2tJi5txhzy4sldufbkpRJybUsVNPs3QW9C5ac9kTWHmTJlitb015WaF0jcexISQhIOd6uJM2Tfvn3q9w3HPDQwQLTB1xaBhmzZsqljKWrdkYLt37+/xGk6FQ+KWWOo08KoCJi14mCKuiyY5A0dOlTtiDZBwd1C0nhdIG7hwmyKp6eXNOgwUSo3+kB8kkWYzeItuH5up6z+6wN5+vCKug2vJW5fs2aNuv7V4D4ysKv5btD7j55Ira4Dxe9pRK0comJIqwJYcKDTFCI7ODhYCR746mFSBD44WtoTEVR0e4K2gy+Jp1eSaJ/39TP/ypFNI/XGztrEidjUoeEDixQuPj9e3kklLDTi8ZN5R3SotitdUHnEacArbuDKnbLr2j19Chnz5hw1FoUkDJqdzZIlS9Sganzu4Q8J/6Tu3bs7zDicEJKw6dT9xy45JJ1arXxhl0unXrhwQRo3bqxMhTUvOgReUOf+/fffK72watUq1dxw5coVq71ubRpihmhRwYIF9bO+YLILgQDhhogMpikgOpOYwXOFkMXcWHMO0ZjPBuWNg5ChyGjR+1ep9c5wvYADuD9/ibrSe+w2SZkmwvMM4k0TcDmyZJIPOloWxL6ZM8pHXSLnr2L225MnT9QECzhTf/HFF+r9Afj3s88+U7Nte/ToocQbIh+agFPrBEU2TlgiOOipkRjDNrDEJn2JgzP2AUDAIeUMYOY7YMVOqTRtsXy+4YB8u+OYdF24ScpPXaQXcIh6zp49mwLOhQkNDVW/Ffny5VMRZ5z8aOeUOCsdPHiwisiZ2tAQQoirsWzZMiXQDM2Eka3ECFP8BkIzdO7cWapWraqCE9Zik4iDAk6ePLn+OgRbmTJlJEOGiM5C/PDChiIxA484pDvNRds0YVatWjUlmLQDU0bfwlKujmVLjtTpfaVas8HqsmGA9N2m9cXbO/rUdJe3G+ovo7YOUbZ///03iteb4QEUA35RC+fr62s0gur2xYhJEpbAvt2+ECEwAdLqeM+xxPbMCBHCcuXKRezTmwihqXHrub/8vv+0TNl+VDZcvClh4f9/HTNmlI0bN8bb1BDieHAGCgd1TNzQPqMeHp6SNlNBSZ0+t3491Gmi4Qet+oSQxJFOtXdxRR48eGAULDEEwZX79++ry9BRlo7bDhNxKLqHakQEBmfMSIOgjkvj5s2bUWaTuSNIU0JVa5Su0Uk8DdKD5ihbs0uU27JnyRTjY2XOkE6S/L+ZBO8JRBrIkDK5fNqilpydNECe/z5aTkz8QIY1rS6pk0V0wCInX7JkSSMxeunoLAkJem7xse5e2SjPH51TlytXriwPHz5U3bBYYlvHhA5SFHxaM3YEJw4QxWfPnpXq1avH6nGIc4EGHpyZAk+vpFK+/jDpMvq0dBx2SDqPPCHth+6VwhUiazPxvifEPGRCiONQIkxn5+KiIq5evXrKbgR6STs+o/kOWQhkM2vWrKlKixCFi83xzSYRhzQiUoYY5IoFw8gxgF4DczAbNWok7g5Si4aebqnTZYvxb1KZWefm/ztQo+Oe32N58/8PhkbO9Glk92e95NMWtSVPpnRqCkLBrBlkfOt6smN0T8mUOiIdjjmkiJxq71mQ/33ZuayrvHgU0WyhER4eKjfOLpND64fqb/voo4/EXnDmgc8MjHsxR9Wc0EXqFRFg2K2YdsIS1wJzhqdNmxZxxcNDmrz3t1Rq9KmkTOOrXydD1mJSr/2vUqnxGP1t3377bULsLiGE2E2bNm3UtCNMPUJtevr06VXmCtmI+fPnq+DY+fPnZfr06arHIM67U1GvhckBqIPCj6uWEkORMg7KcTVE1pXAG4Q3ShNyTx9ejfFvzK2zaP02GTeghyRNYrnZYP7qyO5Sje87NVbizRxFfDPJV20bSP85EWnR3377TZkJIwWMyNpzv7OyaX5TyZSjomTIVk7CQoPk3tWtSuBpYGoD1oUXm70zPSHcmjRpohZ0raLmEttGWhrpWzROoNiduD6wpdHKLQqWbiO5CjewuG65OkPk0tF/5MXjq7Jt2zZVHIwTR0KI6xEuHmqxdxuuyvjx41V5yJEjR1Q9OozqcczV+gvKl7c8IMASNPt1wMQGFGRb6pSByNUmCiDK9slP15TdiCU2Lhwle//7Xl1GmlPzlRnRu5OMeb+72b+5duee1H1vsDx/6a+/LVeGNHJm0gCj7k5zprmFR/wkTwOClFs06o9wkGzWrJncuHEj2uffpUsXNU4E897iYpi8NqQe4g71Aa40rJ5ED5pZUI8J3u67SnIUiP6E78SOaXJwwwR1+ZdfflH1qIQQ1+tO3XXsmqRKZWd3qv8rqV0+v8t1p8YVNqVTteHlhs0LiJ4g1YWOMhT8J3ZiamzQgPjQxmn4P38gO5Z/aXHdh7dOy6HNEZ14iDp9/fXX+vu+mfmPDJ70k/KD0wgOCZHF67dJ4z7DjAQcKJfHN1oBB5Il8ZZSubLqU1yoU0Jn68mTJ1W6q0iRIlH+BulO1CYh/BudPxtxb1APCiFublm8eLF+vXSZC8W4rXRZCusvY06upe1iMZ0SQgghzsKhQ4eUdsCM8rfffttoMfRqjQ02p1OR10U7LPK8AL5o8DhB5OTvv/9WPif0d4oAnZ8QSWDnykny8tldqfXOCMnkG3FwCn7tLyd3L5Ct/46VN8ERUxNgTdK8eXPllTVs2DB12+wV62XOyg1StmhBSZUiuZy9ckOevoi09ICBIEK0IDDkjVXvY2BwZLeMNrEBZzewfhg4cKCKzGEmLAQ7Ol5Xr47oXIWZMbqSIeaBpa4b4p5YO7Hjtf9jSZkm+lrRIP+Iz7RWCBzd6DZO6CDEedGaE+zdhqsKuBo1aqgGBwRITIch2BpVtEnEIa2FnC5qW7TWWQg4GHZCzKFAHoZ2jih6TwwgegBLDE1pH985Vy1ZcpaQJD4p5NHd8xLyOjKSho5PrfAbhZDoyoSBMoSSml16/nKUx0ANIro2NXZfvCnPAoIkfcpIKxhT7j57KUdvRNS44QOlWcRooB4NkTkt4oZ1kN6FMeGiRYv0whRAPMIzD+85PgPuNq2DiNUTOwzHoV0+uVQyZi8Z7ct35UREFyvghA5CXBeYRNk/scE1Wb58ufTt21fVnyd4OhUdFOhQ1cCILVhFoNYFggOdqoaCwt1BwT9EHESuob+e352zcvfqYSMBh0kNsNzQJiAAFEKiYQStyPnz5zcSWahfQ4PB6dOnVXRMIzg0TDr8+q/ceWrZfHfqxgMS/n8/ujFjxsTYmID25woVKiiHaUMBpwFhj89A27Zt1brEfcFnSbOdMV0wlUFrUjl/cI68em651vHO5R1y79pudRkd8X5+fha3a4vFDSGExAeYJ4/fMEdjk4hDlMUwpYGusSpVquh/mPGvNiGARAIVjjFYSJEiwqV/Ezw9lWszRBAimhDEpsBW49NPP1V2Lto4DkQ7EN2D7ww8+0zZf+WOlPrsV1l59LzR7WHh4fL9+n3y+7bD+g9Xv379YnyrIBK1tGnqpN7SvVhemVS9tIysWExKZkyrXw/PAdG42BgWEvcB5tAw+gUhr1/Kmj9byL1r+4wMrsPDQuXy8SWyaX5kMw+ivByxRojrYrdHnAPSsQkFMpQrV660a8a5w9KpiMJBcKDAHT/IKFRGJEfj4sWLRiKFRIJpA0iRYkG6CWa7mJiA+abWoh3ItOHxACnPevXqS9ZsWeXO7Tuyc+cOZSj4Jixcuk5fLu0rl5B6RfPKjSfP5Z/9p+W2QYQODRTRzWkzHa/btmAumVC1pKRIEvnx6VeygGy74ydDdhyVgNAwFTrGB1armbSnnhCpZHYhJS7QgHDgwAEVlXv19Kas+fNtyZCthGTLW1XCw0Lk9qWtEvAiYsQaQMT5gw8+SNB9JoQQW4FlFn7vSpcurcztDbNyACNLUUoVLyIOBe6I3DRt2lRdh5DDQHUAlYkDOFKC7mIxEt2BCrU/ljzUbI0qmD5mly5dZfCQocqTTgNpp8mTv5L//j9/dcmhs2oxZcKECTGmoAxTp1WzZZQpNcuIp8m+47k0yJVVptQsKx/tOKpuQ+7fXhGHqCS8dUjiArVtiOCjKwvd0ODpg7NqMQWfIXRDI2JMCHFdHDE2y1UnNty9e1dKlSqlLiOjZkrDhpGjM+PNJw4/wtgxhAk1F31cxwQAbbC5O3vtaJE2Sx5nmheaJlSsrefB42jp7PbtO8hXkyabFYRIZ37wfn/Ztm2r0e1aLR0iXA0aWDZa1YBFipYen9OoitTOkcXiuqixa7Bsu9x8FdG1ev36dZtGsGmvDf3hEjf4XCGSD8GPUX6mdjaIvsEAmmlUQlzfJ27r4VuSKpV9j+Xv/1IaVMpNnzh7feJA/fr1VROD4RgkHHTdRcCZgpmlmAf51ltvqVApBJzWuYkDlSUbDggya/2tIMw0Ww/UJg4cNNjiAQ61dojQGYJo3bVr1+S///6zKOBMPb40AZfC20tqZs8c7f4hQtcoT6RlRMWKFenjRSyCMgDY6SC1ipnLsMkBmp0Nov0UcIQQYh678hNHjx6Vn376SY4fP64O9Pny5ZNOnTqpH2V3++GF9QYELQSSKRBvKOTOnTu3SjUjpAqx9+TJE/06GM21d+9eNfj2xx9/tCjqkErVGgaQP0c9XUz1iwULFpIrVyJsSdAAgUG7lkA00JLHV3JvryhpVHNgPQ3D5xgbHy9tP1gL5z7g+5E0aVJ1mRY1hCQ+kPazt93NVS1GNGcPjCnFv6Z+l1988YVN5Uc2izh0RMLsF52omHMJj7Fjx44pKw2kU+ET504g5WP4pmTIklMyZsktT/1uyxO/2+o22IRApME+RJunami0C3FVtmxZNT/NGqPUDBkyWt1MoYk4ZM+j27YmnAw9vrT1n7wOkTuvAiVn6og5b5Y4+SiyUxaRFe3ADKwVZbSKIISQxIU7m/3evn1buXhAqOE4jAALGgqhlVByZG5CUpyJOBS6w7sMNhIwftVMYhEhwrQG2GVA4KHuyl3QBFzewuWl4/uTpUSFBiqdiTfr3PEdsviP0XL1/CEVlYtuusGJEydUVA/CSxvXZQhe4/v3Iwx6r16LWhxpLnJ348Z1/XVESKPrRNWaMAxFFN7H9evXq8sLLtyQUZUiPQJNufUqQHbd9VOXc+XKpSKTLEgnhBDizixcuFAJOPis9unTR6pWrar+xUQm+K+asxaLMxGHeaGIJsEWwzDtAdGClCK6zXDQdycRB/IXrSif/bRdkiVPaSSaSpSvJ2N+3iGThrwll8/sU7cXLVFGhoyYIKXKVZKQ4GDZtW2D/D37N7l04YyyBoFZ7u7du81ateANR9TzwvnzcurUSSlduozFfdqxY4dqbdaAgIvtIPnZs2crQYaavznnrkvNHJnN1sb5vwmVT3Yd14e7+/fvTwFHCCFE3L079fr16yoSB1AzrwV+UHeO0Zb79+9XJSXx0tiAVCCchy3VrSBMaFoL5Q50HvCdkYAzJKlPMuk2MLLODR/DilVriY9PMkmdJq00b/WuzFu6RRo0fkefXv3mm2/MbgtRUI0J48fpZ56ae5+mfD3JzmcVYTSMob0gJDxcem06KJ/tOyVnn7yQsHCdPHsdIvPP35C3V+2Uo37P9IXpEHGEEEKIu5v9hoaG6gciQKwhGGaYatXuixcRh3opRIJM67oMrUegLhMz8IiDWMW8UJA5e34pWqZWtH9ToHhlyZk/Yk7k+bMnJTAgctwW8E6SRMZO+klSpY6oG8N8UnNiGM0jiIyBU6dOSYf2bWXTpo3qQwLQZLJq1Upp26a1Uv9q23Z6bEFQtmzZUl0O1enkn4s3pcXqXVJo7n9S4Z+NMu7Aabn1KlDfAbt27VrlBUYIIYSQSNDoiMlGyFZi7jlMgOvUqSO2YNORvVy5cqoODsX8U6ZMUSFCWAVAMKBbFblfQ5WZGEFkCovmf5PFN59VHbnZchSUO9fOqMsB/q8kRcpURvenTJVaReUWz5+hag8xFaNLly5G66RIkULZL8D1GR4/mJAx4MMPVD1bpsyZ5eGDhxJgIBARFUNtnmFaNbbgLGHp0qXy+eefy88//6y3OTEFH0jMiC1atKjNj0Xcl9h0Jcdkpk0IcS7cOZ06depUfbQNQRikT+fNm6eOzTNnzlR18PFq9gt7EahITAYASK2iiB51cYjaYKyUO6CJuJz5SsiUeadjXH9sv6qqwQHsOHJdH3UzZMOapTJmWEQqEtEvjK8yBW/bmjVrVKFkdFMj4NOGTmJ0vjrKPBfPGR8+CEzMbkWtHGok9+3bp0aKEBIf0BCaENcy+1134J6ktNPsN8D/pTSrmp1mv//H5hwbiusRAULUDT5x6LhEm2z79u2lZMmIlKE7cef6Wbl55aTkKWi5yeDezQt6AZc7bwEVdTNHUFBkjRsEkiGIgM2dO1c53J89G3VEEYCQxhw21M5BvCFC6EjfNWwDw8ixaAfSdOnSUcCReAMnj5oJtdbxbWhlQwgh7oBdhVI4cBsW2bs7i6d/KsO+Xi2eZho+YA2C+zXaduppMf26Y8s6/WXD2kKkq+FgD/EcHQjZzpgxQw2P12C6iSQGdu7cqdL5iE5rEWhMREF6om/fvur3KDoLHUJIwuHO6VSnHLtFjDl5YL38OKatPLhzxeh2v3vX5edxHeXI7oi0KOrgWrTuZPblu3j+tOzbtUXfwYKIG4A3HGrgDAVcpXKlZUDv7tKveyfJnyeyNRkRilatWkXrR0eIK4GTIMz6xXcA005MSwgQmfvqq69UsxEaqwghxB2wKhK3bt066devX6w23Lx5c5k+fbq4C1pN4LE9q9VStExtyeybVx4/vCUXTuxUNWwaufLkk3AzdWwXzp6Ujz/ool/3/fff19u4DBkyRE18AIXy55Nfv5kopYtHNg+MHzFUNm7bJUPGTJAXL1/J1q1bVQctDnyEuDqIJE+bNk1/PVnKzJK7aDPxSZ5enj+6IHcubxFdeKiqk8FvD4RctWrVEnSfCSHGuPPEhrjCqsaGI0eOyC+//BKrDcN6Q/MWS8xoBZvo3OzVq5fVs0F9kiWXRs1aS5nyleX16yDZvX2jHNy7Q38/Rm/t2rVLNQzcu3dPReUgEjOkSyvbVy2WrJnN23fsP3JMWnePENwFChSQS5cuqRq5uILF5SQ+UqiIwAEPDy+p0HCcFK7QXby8ImvgAl89kANrR8jdK1v0n31ErTmDlRDnaWxYs++BQxobWlTP5nKNDV988YWam966detY3eeQSBw6HDEqglimYcOGcvXqVZk1a5b8/vvvcuPGDf19OJDgTULDx3vvvaeG0Ae/DpI1yxeqxVzTCHzWIOC0iQla+ui9ju0sCjhQrWJ5qV2tsuzaf0jtz/bt26VBgwZ864jLYngCCQFXrHKfKOukSJ1N6rT/SzbObS1P7h1Xn33McXa3qTGEEOcEmTRL9bqod8+TJ49N27XPAfb/szkxIgpmsuZmfboTMLcdMWKEDB8+XEXPEE1DrQ4mHnz66af62aiYN4sOU9OoHXxi0I2Kv4WQ03j69Kn+csumjWLcj1bNGikRB9q2bWvU4GCI6YxUQpwNNC2sWLFCn0JFBM4SiMyVqjlIdvzbU12HXyFFHCHOQ7h4qMXebbgS165dUzoAv2W4fODAAaP7cXxHtqF7d8u/bXEq4vADC1sRiAWkFEnEvFT4sWnGfoZdqFDbMESeNGmSqlvr0aOHisxBAKKzDhG76FKySKfGRPq06fSXEXLGYg5rU7/OBk1exWXfNyyxAU06WhQaNXCGKVRz5Cj4lngnTSmhIQHy33//2TQ5hic3hMQN7lgTN2nSJGXmC2DSP3nyZKP7MSgBadR4ndhAIsZuYYnOaDc6EB2DkS9SphBxeCNRMwDxZwrGm2nzUS9euSZZokmnRqxz1cgGRkvLmuJK9QSGQAhoxsWMJLoOOGnA+2YraGKICU9PL0nqk0aJOHw3bXk8Vz25IYQ4H9AJmNaAJkNk2Lp27aq/DwEeS8dna6GIc9DYLUdgKQLwzz//SOfOndXl+UtWSK1qlS1uAweuv5et0l/H+LOCBQuKq3Pu3Dn5448/ZM+ePfLgwQO9uMUMX6StifODkwZzJynRAZscpCHAM7/zMa7/OvCJBPlHjJdDiQdKGWzZT0KI40EbpW0zooy34Ur4+PioBd6tcQFFnAuA0VpZsmRRefXVGzZLm+aNpUmDiG49Q9Bo/O0v0+X23XvqOmbbxrWAc+QkCHPgOSPlvH79+ij3ITqJMxvMa50/f77NhaEkfrAlTQkRh/cVwv3u5S0S+PK+pEjja3H9K8cXik4Xri4PHjxYvvvuO7v3mxDiGNzd7NfPz0/5XGL0pWkWDyVpcPWIdxFXrlw55aCeGKI9zgpUPGbRjhw5Ul3vPWSk9OnaUXp16SB5ckZENo6dOiO/zZov/23aqg/ToskironLdCYMjiHQ0GmogeeVOk16CfB/of8S7N69W3mCwZKFn8PEBUZpYRLDxIkTRacLk/1rh0vdDrPM1sY987sgZ/ZFdrJ+8MEH8by3hBBiHjQ1IGuUOnVqdZwytT+ytSbOKp+42Liqo1PV3hxvYvHascZDzVqfNby23bp1U7NqDfHNmkVC3ryRJ0+fGd2OHDwiEa4Knm+NGjX0nTwZMmaRLn0+kcYtu0jqNOkkMNBftqz9V/6e8Z08uBdhglykSBE5deoUZ2gmMiDmMYkBtaMgY/ayUqrGIMlR6C3x9PSW1wFP5MqJhUrAvQl+pdZ59913ZdGiRQm854Q4LwnhE7d09yOH+MS1q5XZ5Xzixo4dq+adL1myxKHerTZvCVMc0P2lga6L9OnTqxeVUwIcD970efPmqWic1vUK7j/0MxJwaGSAfYkrCzgAx31NwGXJllOmL94t7boNUAIOpEiRSt5p30umL94lefIXUbfB3HX58uUJut/E8fj6+qoueM3C6Mm9E7JjSS9Z/G1RWTqtvCydWkaOb5+sF3BISfz11198KwhxNv7fnWrPgm24IkFBQSra5mjzfZu2hjoViAStoBxpLYyIyp8/v7odBeimXijuCFKN48aNc1jKEeHXr7/+WkXs0LaMVLb2gUDaCW3MiOrZ6jfjTGgzY8GHwydJlmzmC+LTpc8kQ8b8YNQJRBIfmNiwY8cOo3R56JtACXr1QF8Dp0XgcAJgyRuREEISApT8oPTH0dhUE4eQIKJumvswOiCR8sAOYtwNarhWrlwpVatWFXfGGvFmS2MAmhxGjx6tFi0dmzlzZjX2KzGADD8ivSBdhkxSq36LaNcvV6m25MxTUO7cvKK6V+Oy0YIkHFWqVFHRVkxigJHvmjVr1AkkulBx8ogaOPz+EEKcE3fsTtXIli2b7N+/X9q1a6emKCVPnlwMQflQoUKFJF5EHCYKQEhooKC8cOHC+h/QEiVKyKZNm2zZtNsRXz5nrmCQq5nBoh4OJq8gX8Hi4m2QPjYHmh0KFi2lRBzAZxEHdkDj1sQFIs9NmzZVi3YCAxsRdqES4vy448QGDQS2ALKU5jKV3377bfyJOPxonjlzRh1s8aO6atUqqVmzplEbraHIIwmPKxjkmjOD9X9lftqEKf4vI9d7+PCh0TYJIYSQhOSbb75Ri6OxqSYOtVja0PcOHTrIvn37VIhQ4+DBgyr/S+IPiBVzI43Onz+v0q7a/FV0+CFy6sCmZIebwWLRImmXz5+U2zcuR/t3Tx8/lBOHI2oNcFKBNL+2HaZVCSHEudKp9i6ujE6nk5s3byqz+gQTcSiwx5zUV69eyd69e1XHJNIb4Pbt23LkyBE1UorEH3gvDEXciRMnpH79+sqaAc0Q6IwBAQEBqkOmVKlSKoLqTCBCiKYNLIbz5eb+MSVa0blw1o8SGvpGXR4+fLiK5mnbcdaoIyGEEPdi/vz5ak563rx5lekvgKMCjlu2YnOva8WKFeXQoUPqgAmRoIE6lcuXL+sjKSTuePLkiRJlhhMMcB3deUhvb9++PdrmlFatWimjZmcETRopUqRQlzf/t0h+/nqE8oYzJDj4tcz+9StZMu8X/clF//79E2R/CSGERI+99iJ6mxEX5NixY6oBC84LyGBqIOAFIWfrXGmHKq0tW7aoOYctWrRgi38cApH81VdfKTNTrQEAIDwLT63Xr1/LmzcRkamcufJKh669pXa9huKdJKmcOHpQFi+YKadPHFH3Dxo0SHLnzu10kdMMGTLI77//Lu+99566vuzv32X9ygVSr3Eb8c2ZVx49vCvbNyyXly8i0sRaYWi+fPkScK8JIYRYIlwXsdiDvX+fUMBLF1ZssEGCVtJA8KFMmTLKWQH3xZuI++yzz1QnBeZaAox4wkEUIFWHlCq8y1wBiB+IGdTyoWlj/PjxqgXYGdm5c6cSXHCrtpRW1ShfqZr8MnOxJEsW2cqcPUcuadqirfzyw1cye/o0ddunn34q77zzjurydCbgdweRCusIWEkEBryStcvnml0X6dchQ4bE+z4SQgghMfH48WO9z6XpsRblTrbWqXvaKnowFaBz58564TBt2jQZMGCAEhlI62n5XlcA6WCIhLVr18rAgQNtUsPxATqC3377bb2AS5s6lbzftb3M/eFLmfXtBOncspkk84kUzrdv3RD/V1G7M/EB+ujjz6RU2Yrq+rlz59T75oxgbubx48elT58+UXx1QJcuXZT4HjVqlNOJUEIIIc7R2BAaGqqiXfAgtdRUgCwXrEBgAQL3DUdStmxZWb9+vbpseKzCqEgYmWPSTLyJuNOnT0vRokX1kTYIAKhIRENq166txJyjJzbAm+7KlQgfMHPg8W/duqXqxGILzImhkBFZRK2fVovlbHzyySfi7x9RF9agRhU5sf5f+Wr4QHm7QW1p2aie/PzFKDm85h8pXTTCa+bRw/sy49fvzW4LH6KO3fror//777/irCCyO2PGDGUovXXrVpVq1cwTFyxYIJUrV07oXSSEEBIDOvFwyBJbDh06pI7v/fr1U8cSDCLQBJXGsGHDlPMGatbatGkjtWrVcqhFFQIOOIbB1QONhziWIb2KfenatavNRuU2iTiE/gznf0HEYQRX6tSp1XV0XziqfRYdlHjSeIKWJkCgQxb3Q+nCXqJJkyZGj4/0KBouTBdEdwA6Q5Cvzpgxo9oG3OCdDZwhaAbKOX2zypzvJ0qa1FFHC2XPmln++WWKpPj/nMm1q/6VgP8LP1OQbtWYPXu2WYsSZwIDlNFxq0XkUEtA3BdHj7UjhCQ+Hj16JM2aNVMlQ2jowxxmlHtps5jBhg0b5Mcff1TCCsdZrAfBhd8XR4HjFqZaof4N4hB1cQh2ffHFF2pUqa3YJOIgmDA+Ak8S3ZCLFy+WevXq6e+/du2amqPqCBD6hELGrFBzILWIGrHWrVurnPODBw/UfkFxawwdOlTvkmy4TJkyRd2PgwBq4KDWlyxZomqwDGvLnAFEnDR6v9taUiSP/ACaki1zJmnXvKG6HBgQINs3rzW73puQEP1lNEPQGJe4Evje4gSNIo4Q1yDcoLnB5iWWjzl9+nSVSkXZlJbGRMDJULPg+IqxVxjtBzBWtGfPnkbHXUcFIjBdBv6tEJeIyEHf2BOQsKmxAanH6tWrKzEHNYsUn9ZFCBCmdNQgcrwBAE/aHPCrw+PjxxzRQbz4qI9CeBIvEmaK4oXDYomrV6+qIno8H5jDItIIU1wtsmgICu0NO0LjS/hcv35df7lRrZiNlBvWqirzlq1Rl+/dvW12nT07NusvY2A4jXEJIYS4wuxU02Ovj4+PWkyBuT1So3BsgP1WypQpVdrUUBOgRAwizrSMRwsMoXTHUSDIBA9T1OEbAv0BvRJv3anwNUEzA7xNOnbsqGrkAOrSUFdWoUIFiQ8OHz6sDG0NBRcEJooSURDfqFGjGLcxZswY6datm0qroikDNX25cuUyuy7q/iZMmCDxjeEbniRJzG9bUoN5o2FhoVHuDwkJlkUL/tJfx9QNfGgJIYQQZ8f0GD1u3DgVzDEFY0ARoIAmQV0criNbOGfOHJVi1TJ6Wq21BsqrAAI6jhBx0Baotdu4caPZ+1Grp5V4xYuIg5qFNYUp8Bz75ZcI89X4AI0MqMEzRLsOFW0NqLOCGMW28GYniWbgOkZYGaZvcDZgSfA5EkMjwAPHTkmBPNE/5v5jp/SXM2YynmMbGOAvnw8fILduXFPX0aCiTdwwhMPjCSGEOGMkDtOh0qRJo7/dXBQOIFuI8i/UwaEeTbNEQ/YNc7a1CJ7WNKihXTesnbMH+J4iCnf06FGVzTTsK7DncRxi9ov0oqnHCXK80YkhR4HHCTGo7dL2B8R2aoSmvKPDUsg2JtA0AMGHD50tNTx40zUbkL8WLZfOrZpZtNQIDHotf6+IrIM7cmCPpM+QUZIm9ZFjh/fL6uX/yIvnkY0feP3MuUU7a40cXj/ttXT060wIISRuCNd5qMXebQD8xqcxcwwwBWlKRNo0AQfatm2rfG1RSoVMHtZBFtEQzDdFgANNkI4qicIkIjSBOhKbRRwULAxy0WGhDVc3BC8S6tXiGkTBkDY1BDls4KgX3xyo+cNimte2BMQFhBKGstsiLooVK6ZEMfL6py5clnE//CYTPv4wipALefNGBnw+SR49jRRpWzauUYs5EEI2578GnLVGLrrXz97XmRBCSOKhRYsW8t9//6lmRa3sCjX2OHbiOAHQvYosG1Kn6dKl09tuoRzLUSNEISLRPOlobN47mM4i3IgZnbC/gA0IzGgRtsT8ymrVYi6+dwR169ZV3R6GHbEw7YUAcbTiNQR1c1gQ9bHUNIHoJIxo//nnH71/Hc4I0JECK5PYesSVKFFCfdiw3V/nLZZ9R09K305tpUbFsvImNFS27DkgM/5ZLldvRjQyeHl7i49PMpU+NQdEOOoaXR28HiheRWex4esME2OcZRFCCElc6VRr6dChg6o3gyBDxykaHqEZINq0Yzdq0f766y+lY7AOTIFRb49acUeB5s+FCxeq4AI6Y02DJzhWwSIttnjobJj1gIMjDFYRbsSQdczw1KJusO7A+CN0qKJT1F7QxQGxOG/ePJVThkgEEGwIdaKBAUISETFYhsAUGCISY8HQpRrXaCIOosEwcrV582YZOXJklCihBkTu999/H2uxCw87GATG9LbhtfnulzlSoUoN2bV1o1w4d0r27toqVy5FdPnC0BjCF2PGXBn4CKI2E59JSyIf0Tl0IxFCCIn+2BWXj/XXxueSIqV9jxUY8FL6NE4Xq/0OCQmRWbNmqaAKdAm8ZE2bHqEzoDEQjEIjA6YFaWOyHAG0CTpgb9y44dDGBptE3Jo1a1QEB6lUjNeaP3++GlWhAU83hCBRPGgv8GyDUDS3D+g0ATD2RccoojEQJ3BGxt8l1BcBI8mQ+45pbAeEFvYVzSDR1XCZ1nnBOw8CFW3R0YGxWjlzY1j8fTl6aJ9e+CGMjKgVUt6uzM8//6yiiTGBzwTMnJ11Hi4hhLiDiJuxwTEirm+T2Ik4ZwCds8gS/v3332YbG+I1nYrGAS1vDH8xdFwYgjwznI8dAZRxTEBZT506VeITSzVxEJy9e/fWC7hi6VJL14K5pErmDBKu08meh0/k7yu35eqrAHV2gEkJILoaLkTsoOK1dZBSRTcpJlVAzOHsAmaGCM+ibk5rSDh94ohaDEksAg4ROEMBlyZjSclZsL2ky1xWwsPfyJP7e+XO5SUS5H9HtXbDDBrh8SJFiiTofhNCiLui+79hr73bcEVevHih/GsLFy7s0O3aLQVLliypUoZaVAiiBiIhSxZjW4vEBurhkMKDMDD1nNOEXecCuWRpgyrSJm8OyZEyueRKlUI6FcglKxpWlbdzRe87g64Z+NYhRw4BByCWURuHGkSIMaSRkbvXUqJoUkCKG4LWkljBuq4u4BBRRD2DRp5iPaRCg78kW94mkixlNkmROpfkKtxRKjdZKJmy11LroKjV0tQPQgghJC6BD6ul8qoEFXGIDiG/jLmmderUUSlOzCGDB4u7gYYFrRCyYJqU8lnZIuJpxgYkiaenfFWxhGRN7mOUj0c0DQIQNYUIt6L40jDKiegeUqtQ8gMHDlTra6lWDURIBw8erLpv0KWriWmtizUxzBuF1Yo2wSNNxlKSv9T7Zu1WvLx8pHjVCeKdJKIjCePhrPUOJIQQ4lh0Og+HLK5IyZIlVSkUatoR6ELHrOEC37t4S6cilYcokAbmi40dO1ZFhdBBiQLC0qVLi7uBN0YDETdzAk4jqZentM+XQ345d00fas2XL58au2Go1rFeqeyZBZs6dfeRhIRFpGlhqAxBgjpAc3NeIWoQddO8+nDdhvJHpwSt3xo5C7W36JcHvJOkFN98zeX2pUWqDAC1lOg+IoQQkvi7U50FlE4h4IJJEVhM+eOPP6RHjx7x09hAjGviLl26JL6+vkqIof4KrGpYVQqnjTp71ZCDfk+lx66jZu/z9vSUYQ0qSp9qpSRTqohW5CcBQTLrwBn5ZsthefN/MWcozlAThygo5sRpwgZeefBNQxElInmInKLj11lAJBFLbICNyOvXr9Xl6i1WS7IU0XfYPrqzQ07vHakuoxDW3EzcmOD0CkJIYiIhGhv+WPtCktvZ2BAU8FLebx4/++0KOMTFDkIGJrQJMbEhoTD1iTNt7nhjRfXmG4Pu1fzZMsm1B5GpvlldGss7pQoYrZ8xZXIZ3qCSlPTNJJ3mRExkMNTgQUFBKqWNGXGICiKyp4EGlKFDhzrdhx6vn7lpEdaiC486F9YUNDoYPp4tkyicdXoFIYS4CuEOaGyw9+8TGzaLOIyomDhxovKDg4AxtdOIr4kNzgIicQEBAfqD/Y77j6RE+ugF004D0dakUkn5bc0OdbluoVxRBJwhTYvnk4ZF8sjmizfV9RRpfOVN8Ct5Exxh6ovZbPCfQ/eqBqJP5oYDJzQQlZprtrXgDEyba/f43l7JVbhDtOs/ubfXqJMZliO27CchhBDbced0KoAHHSzZkA0zdbaAvy1MgONFxCHiU7t2bVWLhckNOAjD88wQd3PKv3DhgvKrgwkxBO3ia3ekR+E8ktLCyI6nwSGy8kZE9C65TxI5dT0yxdm1YrEYH69r5WJ6EVegdFup8NYouXH2Pzm69Wt59fSGGov2zjvvOH0dnC1pSnwR0OkD7lz+V3IUaCWeXsafP43XAQ/E7/ZWvYDDl8cWEUcIIYTY0/iIIQkVK1ZU7hGmTYa2Zi5tEnFoYEBNEjoEE7uVSGx84vLkyaNELYxlH70OkY/2nZSpVUtL2qTGb87j18EyYO8J8Q+NSAWWzJND9py5or8/X0bzY7wMMVwn5PVL8fL2kQJl2krOQvVlzYzm8uLRZWWBAtuRxAa6fHASgaaOIP/bcmb/Z1K8CrpQU0QRcCd3fyzh4SHqOhoaKOAIISRhcOdI3OLFi5VrB8Z7ORKbRByK5DHM1V0FXHSzUzH6CxYYSPkd8Hsqb63bIy3z+EqVLJFmv2tv3Zeg/zcmZEqbSo5ejoioadx/GRDj499/EbmOd9JI8eKTIr1UafqFbJrXKWL7mTIpO5LElg788ccfVQMHGkke390l+/5rKb553zYy+/W7tUUv4PLmzRsvY9gIIYSYx51r4jw8PFQAwil84qpXr64MaCFUiDFFixZVXjDa3FhE2/6+elsG7T8pQw6ckqXX7+oFXLYMaaVxxRJK3Bmy6NiFGF/WRUcj18mcs7zRfTkL1peUaXPoTYNh/xLblKWzU758eTXqDQ0bIDTkpdy+tFBO7x0hZ/d/Jg9urDMScJs2bVL2LYQQQkh8U79+fTV2y9ElTjaJOFhZzJw5U6WnMOgdlg8wnjVcYpobmpiByD1y5IjKf5vzMEudPJn0b15bdn43XNYfOqNu8/JOImkzRFhlrD17TfZfj5jSYI4jtx7IqtNX1eVkKTNL3uLNjO738PSUtJkiBvci3QubkdjaeLgCDRs2lEOHDqlRJqY1mQBCGh25WEebs0sIISRh06n2Lq5IYGCgKkGDLhg5cqRqNDRcjh07Fr8TGzBRAPVWjRo1Uik7FOUZLh06RN8xmNhBgwNmmkLgtmzZUn97s8ql5OrcyTL1g06Cz+LTVxFp0VKVGkrjth+py/iQdpi1Rv4+cl5CQiNr7nAZEbg2f63WR++KVe6h6uFMeR0Q2fmK7uHEapFRrFgxfbcPag20tLHWxADxyggcIYSQhAQCDlkhHx8f5RyxZcsWo0UbrxkvNXGItKEVFjYjXbp0UfM9TSMhcZH7dfbGBnNATGA9DGwHm46ele0nL8g71crK6+BI/7JUaTLK211GyPkTu+T04c3yKviNDPh3q4xft0+q58uuInr7rt0TP/8IM2Hgm6+GlKkzJMpjPnt4QZ4+OKsue3t7q8kNia0mzhQItd69e8u4ceOUYEUDA5sYCCHEeUCCzt4knasm+UaOHKkWR2OTiIOKxFzOixcvum1zg6XGBnPAggW1W2o+ali4vPvVdGleuZS0qRlZy3bn+hnx9k4iH09eIb990U0O71qhbn/kH6RPnRqSq0hjqdfhD/HyNhbPyLcf2/q1/vr3338vgwYNcsAzJoQQQmzHnbtT4wqbI3Fly5Z1WwFnC4iEaQa1YO2h02rRuHHpuFy7cETyF60oQ75aKueP75QtK/+QwzuXS1hY1KkEKVJnlbCwEDE0Lwny95NDGybIjXNr9Qa/aGkmhBBCSMLx7bffqsYGc8AzLmPGjNKgQQPp1atXrDzjvG0t3EckDlGoxJ6mcxRagwOidojKmRs1Neu7D+Wzn7ZK8hSppXj5umoJfh0oL54+lBfPHso3w96WgFdP1boXj8yTKycWS64ijVQnqv+zW3L70mYJD3ujf7y5c+dKunTp4vmZEkIIIVFx50hckSJFlDUWjs3NmzdXog0laSi1Qm03xmSOHj1atm7dKv/++2/cijh0p86ePVv5j/Xo0UPZPcSUUiQRQMDduHFD1qxZo4oZZ82apR/mjkjc2H5VpfV7Y6Ry3bbinSSphIeHycmDG2Tl3K/0Ak4beh8WGiw3zq6J8tKicBLF/q1bt+bLTgghxClAOZvdPnHimiRJkkRy5cqlfGSTJUumv/3mzZvK8/SLL75QXaqYRnTq1CkpXbp03HWnYiYqhqzPmzdPeZ8g2gNhYbi0a9fOlk27BWg2gMBCwwPUuGGk7u6N8/LLhC7St2kGGdQ2r/Rvnllmfz9Anj2O6FxBd8vu3buVdYZplA1dwiicRK1i+/btE+CZEUIIIcQUiLd3333XSMBpk56qVKkihw8fVnZgaBrFGM84jcTB0DamLgtttiWxvrvS19dXTp48qa4jjRr8+laUCNv+/ftl4cKFKo2NCQSw0PDz81P1iQjNYh1CCCHE2UAGyV6zW2efB24JeOea84ILCQlR88A1b13MpM+WLZvEqYiDfcjXX0d2QLoj1lqMaGBiQnQ1hAi1Hj9+XM0D/eOPP2TJkiVq24jawb4FRoFoVPj9999Vx2lAQICyL9FUPf6eAi7m15kQQkjC4M41cd26dZNKlSopEYpMJXx2b9++rY73OL7DvP7SpUsqKFOtWjWrt+uhc1VZ6yRoFiMYQWaLcED4FE0OsCGBOa252/EGP3v2zOzfa/VxiOLF1iwQUTxN8CS2sVyEEELi7thly2NN+fe5JE9h32MFBb6UkR3Sxct+OxoMAPjss89kz549EhwcrAIzGAbw1VdfSe7cueXRo0eqUxUCL04jcSTu0IQVQqrA8LI5NA3++PFjfVrVWsGG2zShSBFHCCEkLtE5wOwX23BVqlSpohoacdw25zFry3QhijgnQxNWnp4RPSeGAq5U+jTSPk92KZQ2lbwIeSPr7zyUdXcfyptwnbx580ZatGihmh7MzRHVtovpGuh62bdvnzqTAeiORT5ee0xCCCHE0bhzOtU0g+YoRw+KOCfFNMs9qlQh6VYgl9FttbJmlN6F80jfvSfk4etgNej977//lp49exoVTW7cuFEvBuHvh9y7IZjvCg+bIUOGyAcffEAxRwghhNjJp59+qjzfJk+erGreo/N/wzq2uEpQxDmRaEPnaVBQkP66Rstc2aIIOI0CqVPKlIrFpcee4+r6b7/9pkQcomxogPjzzz/l4cOH+vW1DhhTrly5Ih999JFqrFiwYEGsHKMJIYSQmIBHnN0+cS4UiWvcuLGqdYNbB0qdcNnRjh4UcfGIubo0TVRBaGEShjk65c8R7XYrZkwnxdKmkvMv/OXIkSOyfft2+fDDDy16zWTKUVVyF2ktKVLnlED/e3LrwnJ5fHe/ug9nCgjzQvwRQgghjsLd0ql16tRRi2bNpl12JFZ1pyIFB3EQG9AtCUPgxE5sOnxMO1Fhygvh9vRpxCQGS6AW7reqpSWDT9RaN43vz16RWZcjfOXQ3aJZn3h6ekj4/09dPD2TSMVG08Q3X4Mof//gxnY5vGmQhIeFqOsnTpyQMmXKWPEKEEIIcTUSojv1y7+fSzI7u1NfB76UMV1cszs1LrAqEod22Njmatu2basmO7i7TxwibZs3b1ZeMPfv39eLYryecHDWBBwKHavUbCilK1SXsNBQObB7k5w9eUjdd/rZS+m/76TMrVVeUnh7mX2ciHkPEWj7VCCnr1QoXlD+3bRbXS9aebBZAQey5a0nxap8LGf3Rfj/wY8O+0wIIYQ4Al24Ti32bsNV6NOnj8ycOdOqdWfMmKHWj5NIHArfz58/b3Qb/EwmTZqkiuRhYJciRQpVV4Wh623atJFWrVqp8KE7n81g/hnGbMQ0QiNPviIy6ZdFkjtvIaPbTx8/IGM/7iaP/SLE34dF8sqAYvnNbqPDjsNy9nlkJ6uXl6ccnDdV6vcbJS8DAsXLO4U07r5Lkviktrgfb0L8ZeO8WhL2JlDNeH3+/LmK6hFCCElcJEQkbuL8Zw6JxH3eLb1LROLOnz+vsm/WULx4ceUeESeROMz3rFmzplF0CQNbZ8+eraY3GBbx9e3bV5o0aSLNmzcXdwbjNTADDR9ejRSp0kqadJnVHFSM1dIICHgpyZKliLKNUuWqyrd/rJA+7Wuo6Nq/N+5J3yJ5JamJFcjRJ8+NBBx4u1YVyZYpgxJwIH3WMtEKOJAkaSrJkK28PLq9R/z9/VX6VxNxqOGjlxwhhBBbcbfGhmLFiqklLvG21XUYXmSGAk4Dt3fo0EGFEKdOnSruCOw84NmmCbjcBUtL+95jpUKNFuLl7a0E3L4ti2XJzAny5OFtFWkbM7izTF+0Q6VVDSlYpKTUrP+27Ny8Sh4Hh8iuB0/kreyRhoA3/QNlxJGzUfahWc2KRh2uHh7WecAZroe0r4ahGCWEEEJii7s1NpgDAZKrV69GMfEvVKiQZM2aVeJFxCG9amkMFMB9WMddgUWHNgKrcMlq8tm0jZIseUr9/T7JUki9t3tKuWpNZUy/GvLo/g05f+aoHDu4UypUrRtle/Uat1YiDqy980BypEgmL968kXV3Hsp/tx9KsBnbEJ+kSSVNyuSSIpmPBL4Olmd+pyT0TaB4J4ka8dMIfRMkzx6c0F9HaFcTlc4etiaEEEKcmTFjxsg333yjzPkdVRNnk0V/uXLl5MyZM/LLL79EuQ+3w0oDdXLuCKJf8GrT6D38VyMBZ0i6jNmky4eT9ddX/GPe1iNVqkhn5033Hkm7HYel994Tsuzmfb2Ag4I3dIA+cOqCSoW2bVBDXQ8NeSW3L66Idt/vXFolb0IiIm7du3dXuXx00WJhKpUQQog9wCnBEYsrsn79elWCBi/W9957TzVGomauR48eahwX/rUFm0QcLDJGjx4tAwcOVPnerl27Sr9+/aRBgwbKliJXrlyqNs4dgd8bhCwoUKyS5C0UvU1HpTqtJXXajOry4f3bza5z8XxkdMwS8HW7fv26JE+eXF1fuH67vPAPkL5tmurXObv/O3ly37xVzNMHx+TM/in66/CZI4QQQhydTrV3cUV27dol/fv3l6pVqyozfYy5RPPnrFmz1OhLmP3bgs3DMidOnKhGPKVLl06WLVumauBu3LghI0aMUDuriQl3AhFIQ2+1nPliLmj09k4i2XJGdKUGBryS0NBQo/vR0PDf0jn669nytJRseVpI9vztJGueFvrb//vvP0mfPr106tRJXUdDQ98vpknx/LmlfcNaEdsKDZQ9q7rJkc1D5eGtXfLq2VXxu7Vbjmz+WHav7KK6UgHsTypXrmz360EIIYQQkcePH+u7TzNkyCB+fn7qMkqWEAyDfor3iQ2dO3dWC4ks/tfeGHX92SOrXpqXzyPWS5IkqZGlB1Kzf/zwudy/e1NdT5e5shQs84n+/pDXT+XhzTXq8rx585SqHzlypCxZskQVTa7bc1iqvTdUerduLPcePZG9J87BZEfuXlmnFnO89dZbyibGtMGCEEIIsQd3bmzQGew4StIQCEMKFbph69atam55vEbiNG7fvq1yvQcOHBB3B8X/hs0Ap49slRdPI0WdOa6cOywP715Vl3PkLqD+FhYuR/Zvl2H9W8uiOT/9f01PyVW4q9Hf6nSRRsPBwcHqw1C4cGFZsWKF8u0Dl2/dk1HTZkcIuGjIlCmTjB07VtauXeuWUVRCCCFxS7hO55DFFalYsaLky5dPPwwBWiFPnjxqZmrdunWldu3a8Svi0IEKU18MdG3WrJl899136vZbt26piJClQeuJGRT/oxlg2LBh6nromxBZMXeSxfXxGi2ZOV5//cbV8/JWhczSoFxGGdqnhRzau0V/X4HSQyVtxrJGf//ML1I4p0yZUt9BitrE3bt3G3n7mZI6dWplBwOQhkXzwoQJE/S3EUIIIcQxvP/+++rYDFATt2nTJrl8+bJcunRJzSy3FZtFHGqvMF9z+vTpKiyoAVEHZYkaLXd+s7y9IzLV65f8LPN+GiaB/i+M1nn2+L78NK6znNi/wej24NdBEhoa2X6c1CejFK34hfjmbWm0Xnh4qNy7FjnWbMeOHUYdpOXLl1dC7uTJkzJ06FBJliyZuh0RunXr1qlpDJkzZ9bf5uPj49DXgBBCCDFEF+6YJTHg4eGhFpQ+2RP0sknEYYwU0qeYqYquVFNHYhTFY15oYgbtwRiTYc5KJX/+/EZGx2sX/Sjvt8wlP43rIvN/Hi7fjmwtA1rnlf1bl+jXqf9WI6lWvaakS59ef1vSZFmkVM1fJVN2Y++48LBguXx8kgS+uq4P02IxR+nSpVXDBaZuaFG3pk2bqs4YQgghJL7Q4T+dnYu4Zjo1JCREGjVqpLJeYPHixar8qUKFCsrhw1ZsamxA+A9CTeu0MC2Ch2fZ8ePHJTEzYMAAtWgz4czdD3U9ZMgQ9W9wUIDs3bzI7LZGjxkv3Xv0VpefPX0q7du8LXfv3JaQ135yfMd7kjnHW5Iha3Xx9EoqL5+elgc3/5M3wU/V+kh/uutkDEIIIcQVmD9/vgrwYJwlmDx5svz0009qRClsR06fPq2ymLHFpnAM6q8ePbLceYmREiiUd3fgo4c3Bp5rGChvjrcaNtYLOJA+QwaZMXuBZM+RUx91e3hrrZw//JmcPTBcbl+apxdwSJEil16jRoShLyGEEOKsIBUabufiqunUU6dO6UeVQj+hHg4TGvLmzSv169eXc+eibz50qIhD6g4p1dWrV0eJxGHcFoxn69SpY9MOJTaQckXqFXNItRo0w+7Pth0ifN0MyZcvv/y7fI107d5TUqWKOrQeNiRoKtm3b5+0bGlcK0cIIYQQ5wLecGfPRsw5X7VqlVSvXl1fiw5Rp5U8xUs6FenDTz75RFq3bi0dO3ZUogRWI1988YVqdECatVWrVjbtUGIF0Uut89OwHg1vrDkyZswkn439QoZ8MlLmzZkpP/34rb6rBZMZMDWDEEIIcRW0ujZ7t+GKdOzYUT+yFOVmGJCgCbhr165JtWrV4tfsF4INggLDXAMDI5z+Dx06JI0bN1bGs4amtcQYQxF39cplKV2mXLTiD2PMNKDcKeAIIYS4Ghh7au/oUxcdnSpFihSRgwcPKncImPK//fbb6naIOtS141gfryIOQmTcuHFqzBZsLIKCgqRgwYJGgoOYB0IMbcVgyeJ/pHXbDtG+VP8uWmj0t44CliRozND85QghhBASN6BxwbR5oV69enZt0yYRh3TemzdvVHssUqnorCDWg7QqChxVWPXYEfl30d/SoWMXs+uuWPavHD50IM5EHCGEEBIf6MJ1arF3G67Cnj17VP9ArVq1VNoUly2BdRCtixcRd/ToUTUkHQ/au3dvdVkb80RiBo0go0aN0nvDjBszSs6dOyPv9eyrmhrAjRvXZf6cmfLP3/Oi/K0tMOpGCCEkIXG32an//fefLFq0SKZMmaLq4HDZEljHFhHnobOhShAjt2bMmCGzZ89WyhLpOBTtoV3WnPltYkbziXvx4kWMaUn4w2AsF2raYPiH5hAY8Rrimz2HEmr37kYYAmogXx4QEKD/W0IIISQ+jl2OeqyPf/YTn+T2PVZw0Ev5YWCWeNlvV8AmixG4/qMW7vz58ypciGGuCxcuVAbAmBAwbdo0ZTVCokbDUEeopTExb/brr7/Wj8QC9+/dNRJwSJ9++eWXZg2FCSGEEFchPFznkIXYGYkzh7+/vxojMWvWLOVf1q5dO1myJHKsVGLFEWczELxz5syRBQsWqHm0Guj87dmzpzJONo3iEUIIIQl57IrtYw2e+sAhkbhpQ7K5TCRuyZIlqis1Jjp06KACYfHWnWoKGhx8fX3VguHvrurlkhDA5A+pVSz4oGsdo8OHD9evw5o2QgghxLXYuHGj/P333zHWu9k6ecluEYcRW4i+zZ07V0WKsmXLpgRHv379xFWAzx2mKqBbFO2/gwcPVh54CUHq1KmViMO/hrCTlBBCiCujc8DYLFcbu1WtWjVZuXKlcvTo1auXdOvWTbJkyZKwNXHwhMMwV/ibFCpUSHVVlC1bVlasWKEmN+B6gQIFxFX46KOPZO/evdKoUSMlSjG8nhBCCCGOI1ync8hiCzqdTp4+fRrjOo8fP1aCy1HAwePevXsyduxY2bBhg+TOnVv1EcD0NywsLGFE3Nq1a6V79+5KsE2cOFFu3bqlWmkxagupVFcDdWho1OjSpYt89dVXKqoYHByc0LtFCCGEEDt48OCBfPjhh6psCUGnVKlSqUCN6TF+6dKlamQoBtKjrAnlTI4qC4M37LvvviubN29Wjh4lSpSQ/v37S548eWTnzp12bdsmxVWsWDHZunWrisTZ6ltmLRCKf/75pxrlBYGISJkpSOMOGzZMvRiw4oAY+/zzz/Wjv9A0AJFpCooIId6glDEHFtcxSQH1aFDO+fLli9PnRgghhLgLCTE7dffu3SqliUZBCDgMoa9fv75y2YDzAzh9+rR06tRJfv75ZyWujh07ptZBI+GQIUPEkUAkYjwpgl/QNdAv8S7ioCKxxAfocm3WrJnyodMGxhoSGhqqXhC82BCW9+/fV4r39evXyr4DYOgs3jxTtBmkUOXYxsWLF6VMmTIqFZxQlh5sYCCEEEIcQ/v27Y2uQ7tUqVJFzp07p79t+vTpKkr3/vvvq+sVKlSQHj16qFp5R4k4RAQh2tBDAEcKmP2fOnVKTW+KcxF37do12bRpkxI3DRs21F+PDm1dezlw4ICK9mFArDlWr16t3gzsD0KhiBKOHz9ehUIRjUNkDsIMS3Rg7isWeNwVLVpUMmTIIAkBGxgIIYQkRhzh82br39+9e1elUFH/jugcOkY1Dh06FKU7FBOpfvrpJzXcAFE7W8HjIQqILtW6deuqErSWLVuqFKsjsErEIbT4wQcfqGI8CDPtenRo69pLTOlavBkQbhBwGnhcNF9gPFjt2rVjfAxE8H7//Xfx8/OTy5cvy6pVqyyuiw+BYS4dnaSEEEIIib+xW6bHXh8fn2hni0Ok4W+eP3+ugjxNmjTR34e5pvBjNUS7jvvsEXGYbIUgE4Rbrly5lGecOd+4OPWJa968uapN0+ajatejI75mqUJdZ82a1eg2rX0XdW3WgKgh0rWIvlWsWDFaA8HJkyfLhAkT7NxrQgghhNgKBJEhmIaELJwlbty4of5FChMaBsEYLcPn6empSrMM0TpUtdp6e2rgkMJFPb+5mn7DyJ8teFtr5IuJARoQR+vXr5emTZs6hZUI3gBDtA5Zawsg8SJjsYbRo0cbpTyh7E0/TIQQQggx09hgZzpVO64jkJTGIOASXRTOEIwGRSYRpVOaiIO+Qc2aIQ8fPlSZQAwwsIcxY8aoJa6wyWIENWiw4XAGAYeoG3xdDEFaVLvP0eCDgg+O4UIIIYSQmAWYvR5xmogzPQ77WBBx4eFR3YGhGQyzhSi7QlmV4brwdENmLr6yivEq4ooXLx4l9JhQoMsELcOGJn6wGsHEhfLly8fZ46JrBa9DpUqV4uwxCCGEEGI73bt3VzXvJ0+eVFoB0TccvwcNGqRfR/ON69u3r0q34v5Fixap5khnxyYRhwgcfFf++usvSWjQQIFRX0OHDhV/f3+Vc4b3C0Zb2FOMGBN40xGRPHz4cJw9BiGEEJJYQCrVEUtsmDp1qnLUwMgrNA9s375dli9frjSDBjQEgj+I0MEzduHChUrEtWjRQpwdm3zioGbRBAAfNihWRKMyZ85s1EkK7xM0C9gLtoGwptYVmi5dOnX7/v37VVcqQp2oz+vTp48SbShChE8cTPsIIYQQ4hzYIsLMbSM2ZMqUSb799tsY14Nmic6ZIlGJuPPnz6vxVIZjq8xFyBwh4mDwa26OmWEtGl58+MlB5KGpwd5uEmuAeMXiiNlnhBBCCCHxIuIg0GIaEGvaMWorMOu1Fmu7UxyVTsWC7tSEmu5ACCGEuAoIotkZiLP77xMbNok4pE1dcdA9IYQQQtwnnZrYsStchgGuMNdDZE5Lr8JbBTVshBBCCCEk7rA5nIYxEm3atFE1YWhy0Joa0Hjw4YcfqrESaHZIrLAmjhBCCIml2a+dc7fs/fvEhk2RODQQwHulZ8+eqiUXzseGdWmtWrUyGi6bGKHFCCGEEEJcLhJ36NAhVcz/008/qQic6ZB6WH/s3bvXUftICCGEEBcHAxHC7axpMzOAwa2xScRhOkK+fPn04s1UxCHFSusNQgghhGgwneok6VQMi8UIi9evX5sVcYjCOcNc1biEY7cIIYQQ4nIirly5csq/DTVxz58/N7pvwYIFalxFu3btJDHDmjhCCCHEucduJXZsSqfCyHf27NnStGlTNYPM19dXAgMDJXfu3HL79m0ZO3asmqJACCGEEALoE+dEPnG1atWSo0ePqi7VVKlSSerUqaVo0aKyZMkSmTBhgmP3khBCCCGEGGHX2IUiRYrIjBkz7NkEIYQQQtyAcNFJuJ0+b9gGcYCIu379uorEoVMV9XGFCxeW8uXLx8vweUIIIYS4FkynOoGIu3z5svTt21d27txptmv1hx9+kPbt20tihxMbCCGEEOIyIu7+/ftSo0YNefnypbz77rtSuXJlNXIrKChIzp8/r5occDs84jp27CiJvTsVC14LGB8TQgghxDL0iUtgEYeuUwiWAwcOSP78+aPc/91330m/fv1k0KBBaq5q0qRJHbmvhBBCCHHhdKq9ExtoMWJjdyqia4sXL5Y5c+aYFXAAog2NDqiR27Rpk7WbJoQQQgghcSXiLl68qFKnSKdGR5IkSaRDhw5y5MiR2O4LIYQQQhIpNPtNQBF3584d5QNnDVgP6xNCCCGEGNbE2bsQG2ri/P39lamvNcD499WrV5KYYXdqJH5+fnLw4EE1bi08PFzZzdDwmRBCCHESEYeDc2xADV1iht2pooTb1KlTZdmyZfLmzRuj1+f06dMyePBgqVOnToK9R4QQQpwHXXi4WuzdBrGxOxVdqdYMtr97967kyJEjNpsmLsaPP/4on3zyicXQ9ooVK9SCjubx48eLh4dHvO8jIYQQkpiJlYiDOEPUxRratm1r6z4RJ+ePP/6Qjz/+WH/dJ0UGyVeyraRKl0sCX96T62dWSJD/Q3XfF198IcmSJZPRo0cn4B4TQghJaMIdYDFi798nNjx0rBK0C83s98WLF5ImTRpJ7Dx8+FDy5MkjwcHB6nrJGoOkdK2h4uXto18nPOyNnDs4XY5vm6Sue3p6yqVLl6RAgQIJtt+EEEIS5tilPVarAackiU9qu7b1JviVrPy1tNsccx3WnUoImDlzpl7AFSzXRcrVG2Uk4NSHyiuJlKz+kZSo9qG+nhLRO0IIIYQ4Doo4Eiv++uuv/1/ykJLVB0a7bvGqH4inV8TUjlmzZiX6ZhdCCCGWoU+c46GII9Hyww8/SM6cOdWCZpXr16+r2zP6lpbU6XNH+7fJUmaUrLmrqstPnz7Vb0dbsG1CCCHuAUVcAjc2EPfziUMtAxpaTLG2rsFwvQcPHkTZNiGEEEJsgyLORtzFJw6Fo5pdDHpg7t27py4/8zsvYWEh4vX/dKk5dLpwefrgjP66r6+vanIw3DYhhBD3IBz/6cLt3gaJhCKORAusRAztROATCJuZ4MAncuv8WslXsrXFv713baf4P7+pLtevX1+2bt3KV5sQQtwU6DekVO3dBomENXEkViD6qHF0yxfy6tkts+sFvnooh9Z/avbvCCGEEGI/FHEkVtStW1dq1aqlLsPQd92spnJ6z08S5O+nbgsOfCrnDvwh62Y21kfhypQpI++88w5faUIIcWPY2OB4mE4lsQLjs5BOrV27tly4cEFCgp7JiR1fq8XLO5mEhb42Wh/GwGvWrBFvb37UCCHEnUFdtb3zBTifwBhG4kisyZw5s+zdu1eNVjOciWoq4Jo2bSr79++XXLly8VUmhBBCHAzDI8QmMmTIIEuXLlW+cX/++afs3r1bDh48KKGhoZIqVSo5evSoFC5cmK8uIYQQ/fQeLPZg798nNijiiF3ky5dPJk+erC7DwBeecrBcoYAjhBBC4haKOEIIIYTEW2ODvdsgkVDEEUIIISTOgQE8Fnu3QSJhYwMhhBBCiAvCSJyNuMvs1NiAyQ4YQ8ZxWoQQQkxhOtXxeOhoumIX2uzUFy9eULwQQghxCeLz2KU91lud9op30lR2bSs0xF+2/FODx9z/w3QqIYQQQogLwnQqIYQQQuKccF24WuzdBomEIo4QQgghcQ5r4hwP06mEEEIIIS4II3GEEEIIiR+fODvHZtEnzhiKOEIIIYTEOUynOh6mUwkhhBBCXBBG4gghhBAS53DsluNhJI4QQgghxAVhJI4QQgghcQ56GsLDdXZvg0RCEUcIIYSQOAedqXZ3p1LFua+IO336tGzcuFFdbtGihRQpUkR/3+PHj2XdunWSKlUqdV+SJEkScE8JIYQQQqLHrURcUFCQPHjwQDZs2CA5c+bUi7jnz59LxYoVpWrVqnL//n2ZM2eOrF69OqF3lxBCCEk00GLE8bhVY0PlypXlu+++U4LNkPnz50vdunVl0aJFsn37drl06ZKcPHkywfaTEEIISazdqfYuseXs2bPSqlUrKViwoNSsWVOWLl0qiQWnicSFh4fL+vXrZd68eer64sWLo6wTEhIiv//+u+zcuVNSpkwpXbp0kSZNmujvhwA7evRolL8rVKiQtGzZ0uJjX7hwQapVq6Yue3p6SpUqVeT8+fNSpkwZBz07QgghhMQ3Dx8+lDp16igNsHz5ctm9e7d07NhRVqxYoUqnXB2nEXGVKlWSLFmySJo0aWTr1q1m1+nQoYNS1J9++qlKe77zzjsyffp06dmzp7r/1atXKl1qSqZMmaJ97LCwMCXeNLy8vNRthBBCCHHddOqvv/4qSZMmlRkzZqjjfOnSpWX//v0yceJEijhHsnbtWsmWLZtMnTrVrIjbtWuXrFq1Sk6cOKGPkKHGbdSoUdKtWzfx9vZWog5LbEGI9fjx4/rrx44dk/fff9/OZ0QIIYSQhOxO3blzpzRo0MAoUIMM3sKFCyUwMFBSpEjh0m+Q00TiIOCiA80IefPmNUpxtm3bVr788kslwBDJi4nbt2+rNC2ieaGhoeLn5yeDBg2Srl27qu1Crd+9e1e9qaifM0dwcLBaNF68eKH+ffnyZSyeLSGEEJJwaMcsnc6+yFhsCAsNcNg2TI+5Pj4+ajHlzp07UY7nWbNmVc8bGb0CBQqIK+M0Ii4mbty4Ibly5TK6DR2m2n3WiDjU1CHdivy4livXBOSBAwfk33//VfVz3bt3t7iNyZMny4QJE6LcbrpvhBBCiLPz5MkTSZs2bZw+BgIkOM4e2drBIduDFZjpMXfcuHEyfvx4s/X2yNQZolmIJYayKZcRcRBgyZMnN7pNC4PiPmuA4kZ3qjny5csnI0eOjHEbo0ePlo8//lh/HfYkefLkkVu3bsX5FyE+wVkOviSIXqJOMbHA5+U68L1yLfh+uRbIIuXOnVsyZMgQ54+VLFkyuX79utXH6phAFM3Dw8PoNnNROJA5c2blA2uIdh33uTouI+LSpUsnN2/ejHIGAdKnTx9v+2EpZAsBl5jEjgaeE5+X65AY36/E+JwAn5drkVjfL8NasbgWcljim8qVK8u2bduMbkOHKmrh41M7iLv7xJUtW1ZZgbx+/Vp/m9aMgG4TQgghhBBD+vfvL5cvX5Y//vhDRfBgQzZ37lwZMGCAJAZcRsS1a9dO5bZ/+eUXdf3Nmzfy/fffq64TrTaOEEIIIUSjVKlSqhMVteypU6eWWrVqSd++fWXw4MGSGHCadCqKEg8ePKhSpqit0Ex84QOHmjMURcIIuFevXrJgwQKV00ZoGwbBCQlSq9h3S/l4V4XPy7VIjO9XYnxOgM/LteD75fq0b99euVloTRxotEgseOjis784GuDNBssPUzAiA50oGjD0RRoVTQ3ly5ePt3w+IYQQQogz4TQijhBCCCGEWA/DWIQQQgghLghFHCGEEEKIC0IRZwdoWcakCEx5wOguGOO6Gtu3b5cOHTooY9+ZM2eaXWffvn3SuHFjZZaMbmBzs22diUuXLskHH3ygRqlVqFBBdSGZq7fEhI4aNWoov6DWrVvL+fPnxZm5du2afPjhh8pSp1y5cmq+L0ymDUEH9zfffKPuL1y4sPTr1y+K0aWzglnIqIFFt/nVq1eN7kOz05AhQ6RYsWKq2wwND+hQd1bOnTunnofpcvjwYaP18P517txZ/YbAz+rPP/8UZwc2Txh3WLFiRfVejB071mgUIUCTGprT8JtRv3592bx5szgzJUqUMPt+mRrAL126VH1G8ZvRqlUrNcLRmcFYKVhpwKKraNGiap/37t1rtA4qqmCCr/1moHPz0aNHCbbPJJagJo7Enp9//lmXIkUK3T///KM7evSorlmzZrqCBQvqgoKCXOblnDNnjq5OnTrqOaRPn173448/Rlnn7NmzuuTJk+tGjBihO3XqlG7ChAm6JEmS6A4ePKhzRkJDQ3XFixfX/fHHH7qTJ0/q9u/fr6tRo4auZMmSRu/N8uXLdd7e3rrff/9dd+LECV23bt10mTNn1vn5+emclVq1aulmzpyp3pMDBw7oGjdurMuTJ4/u9evX+nU+++wzXcaMGXVr1qxR71H16tV1lSpV0oWFhemcnX79+ulq1qyJGl3d+fPnje7Dcy1durRu7969uk2bNuly5Mih69+/v85ZOX78uHoe+M7cvn1bvwQHB+vXCQgI0OXLl0/3zjvv6I4dO6ZbsGCBLlmyZOoz6azg+1W3bl1dqVKldFu2bNFdvnxZ9/XXX+t+++03/ToXLlxQv42ffPKJev4TJ05Uvxn79u3TOSt37941ep9WrVql3r/Vq1fr18Fl/Gb8+uuv6relR48e6rv24MEDnbNSrlw59RuA3wt8pwYPHqw+YxcvXtSvM27cOPX7j+d86NAh9R0sX768eq+J80MRZwM4IGbNmlV9+DWePn2qfqhwkHUVDL+k+DEyJ+J69uypq1ChgtFtEH6tW7fWOSumguXSpUvqB3n79u1GP269e/fWX3/z5o0uU6ZMSqS6yvPCyQOeFw4o4NWrV0pwG4oAHGSxztq1a3XOzJIlS5Qw2LFjRxQRBzGK2w4fPqy/DScenp6euvv37+ucWcQ9evTI4jrTp09XB9SXL1/qbxs5cqQSqOHh4TpnBPvs4+Oju3nzpsXPZt++fXVlypQxur9Bgwa6Fi1a6FyFDz74QJc9e3aj30icDL333nv667gPx4HPP/9c54w8f/5cfQZXrFhh9DuH7w1O4LUTiZQpU6qghMb169fV30HUEeeH6VQbuHjxojx8+FAaNmyovw3jO5AO2bVrl7gKXl5eMa6zc+dOo+cJkCZx5udpajujpd20ocdIzcGmxvB5YUAyUsWu8rzwnBYvXqxSPkjtAKTqkJI0fF64DyktZ35eN27ckI8++kgZcprzhMNnEN8vpO8MP4NIHe/Zs0ecGaQSkcZq2bJllH3F86pataoyIDV8Xnfv3o2STnYWUIKA7wlmblr6bLrib4Yh+A79888/ypNU+40MCAiQI0eOGD0v3PfWW2857fOCHxrKfVauXKmfWbpkyRI1+gqGtwDTC/DcDJ9X3rx5pUiRIk77vIgxFHE2cOfOHfUvDIgNyZo1q/oBTmzP1dzzhGmiaR2MM4Jo86effqrEDH7QgPYeueL799NPPynhBqPrZcuWqfpEeCa66ucyNDRUOnXqJKNHj5aSJUuaXQfPC8/BdJYyBJ+zPi8M5+7Zs6f8/vvvSmxDyNWuXVs2bNgQ43cLOOvzQr0pnssnn3yi6qeqVKkiX3zxhRI+MT0vDFyHYHB28L3Cvvbu3Vt/271799RviSt9twDM8BF0gKDDidDAgQPVZzB//vwu+5tBjKGIswFEALTojSGI9ISFhUlie67mnidwhecKAYfhxziQai7drvz+QRjs379fiTdE2N555x3x9/d32eeFongcYAYNGhSrzyDAbc76vNB8MmvWLNU4gwabKVOmKMf48ePHu/R3CxFgjD5Mnjy5rF69Wo0yQkMUGmhc+XkZgueDyBQiUhqu+N2C6Hz33XdVlBS/F2ho6N69uxphiSYpV31exBiKOBvInDmz+te06w/XtfsSC3g+5p5nypQp9REgZwUHTBxw1q1bp6Z7JIb3D6k3dBJXr15dRQyQdkPHnKs+LwgBpIHxnBBhhCgFdevWlREjRlj8DKJDElEdZ31eiMSZgtSpYQe0pe+Wdp8zkiVLFhWJQ3cq/kWaFJ3CSD9qkXlLzwtpPMPUsTOC7xPSwejQNMQVv1u7d+9W4m3GjBnq96J48eLyww8/qJOmX3/91WWfFzGGIs4G8GWAiDFs1cYPGA5GqItLTOD5mLak48dBS006K4gQfP/990rAwRLA9ECEebymzws1S670/iGdiLqcwMBAdR01YzjrNnxe+DG+cOGC0z4vHGROnjwpBw4cUItmc4PIKVKsAPv+4MEDffRA+wwCZ/8cGoL9z5Ahg/46ntehQ4dUStnweSFVjpokZwRC1HAMIoAwQ0RHq7uy9JuBz6c5cetMIHoK8YIaRkPwviHy7Uq/GZqoNhXOuI6TIICTW/yGGD6vZ8+eKYscZ31exISE7qxwVQYNGqTsHa5du6Y6ftBVliZNGqduN48OS92psHNAW73W4bRx40Zd0qRJVTehs/Lll1/qUqVKpdu1a5fFdb755htdhgwZlL0IOuumTZumuovPnTunc0bQmYn358WLF+o6OhoHDBigulFv3LihX69Dhw6qy/Phw4fKeqRXr16qy87f31/nCsASxrQ7Fd+vIkWK6Nq2basLDAzUPXv2TFe1alXV8eis4L1CNzT2HZ2msLRBVyc+mxr37t1Tn1N0N6LT8cqVK6ozFdYczgq+L3gesLABjx8/1lWrVk1Xv359/Tpbt27VeXl56X8jYEWCv0FHsTOD9wDfleHDh5u9/4cfftClS5dOdYXjPYXVCH4bYaPijMAxAb9x+A3QbIgWLVqk8/DwMLJO6dy5s7JlQqc3LHDQXYyuW8OuaeK8UMTZCDzHunfvrg788EQqUKCAskdwJeA3hoMGFrSdp02bVl02tN4A+LGCjxBa0VOnTq2bMmWKzll58uSJEgF4T7Tnpi0LFy7UrwfhNnToUGXxgOfl6+urW7Zsmc5ZgQiDBxxsUCC4cVCEX5ep9xZsBVq1aqU+l3huEHTwIHMVzIk4AF+rypUrq+eN59aoUSOnPmHCgb1p06bq+6J9FiECTG1iIHjgFYd1cHIESx9D3z9nBN8TPB/8XuAzhs8bBKmpFQkEhPabMWnSJJ2zA2GKz56hh5ohEG7Dhg1TJ054XtmyZXPqk1mwZ88eZaeE7w2CDBBnU6dONVoHJ4Zt2rRR3ys8txIlSuiOHDmSYPtMYocH/mcanSPWg64s1OZkypTJ5V42FCnDKsUUFC1nzJjR6DYUuT59+lR1OJkrMncWkNZBJ5k5kBIxreNDCgidaHj/nD3Vo4H3Ad2ZplYqhuAziXSKYfrOFcD7geka6JYz9zl7/vy5Sv84e22V4fPBYpqCNAVpb6yDujFXAIcNdKjjc2jp98BVfjM0YD2E742vr2+062m/GfiNjO476EzgtwApVNTDJbbfDHeHIo4QQgghxAVxjdMIQgghhBBiBEUcIYQQQogLQhFHCCGEEOKCUMQRQgghhLggFHGEEEIIIS4IRRwhhBBCiAvi/OY9hBCr2LJli/JRA/C6wvB1AD+v7du3q/Fj8F9zRtasWaMfE4TxRuXKlbN69mqpUqUkX7580a4HX6/NmzersVGY0RodGAN2/fp1adWqlbgq8Erct2+f1K9fP1rfL8wJhXdY2bJl43X/CCGOgZE4QhIJw4YNk4EDB8qiRYv0s0UBhq63b99ejhw5Em/7cvToUTUX1VpWrFih9rtz584yffp0q/+mY8eOypxaY//+/bJp06Yo60KU4TXArMuYmD17tnTt2lVcGcxkxfO9dOlStOudOXNGmjZtKv7+/vG2b4QQx0ERR0giokKFCrJ06VIZNWpUgu5Hr1691BD72Awex36bTtSwBKYBjBgxQt5//32j6OKUKVNk0KBBYg+ISrVu3Vrcgb59+6qpA998801C7wohxAYo4gghDuXGjRty6tSpOE1H/vfff3LlyhXp3bu3w7fdo0cPmT9/vrgDSZMmlS5dusjvv/+uxkkRQlwLijhCYgHmeiJidPv2bbP3r1q1SqWyXIE7d+6o56KlWU+cOCErV67Uz1FEDRnSj5hHq4HbkSbdtm2bvobN3GuAOaANGjTQ3/bs2TOV4t2wYYNcu3bN7n1H5A61cCVKlDCqCUQt2KtXr9Tz0hZzQGRqQtBcTZz2Ohhy69Yt9RioL3z06FGM+4j1DNPa4PTp02Y/P6jtO3bsmNFtSHHu2LFD1QuePXvW7KxPbAv7paVGsd/4jFoCrz3+Bvuh8e6776rZrXjfCCEuho4QYjWXL1/W4WszadKkKPctWrRI3bdly5YEeUXLlCmja968eZTbd+/erfZrzZo1+tsOHjyoy5Ytm65mzZq6R48eqdsGDBigS5kypW7fvn26kiVL6ho2bKjLkCGDrmrVqrpXr17pdu3apStRooS6PVOmTLrChQvrHj58GOXx6tWrp2vbtq3++vfff69LkSKFrmLFimr/8HdY586dO1H+Nm3atLr+/ftH+zxDQ0N1adKkibLesGHDdNmzZ9elTp1aPb62gOPHj6vXYM6cOboOHTroqlWrpqtSpYrOw8NDN3r0aKPtDB48WL0OGiEhIepvkidPrvYbzz9Hjhy6fv36Rbufffr00WXNmlUXHh6uv61Ro0ZqP0aMGKG/7datW+q2X3/9VX8bLuN54LVq0KCBuoz9vX79un6d06dPq7/7888/dW3atFH3Y/3NmzfrVqxYoe7bv3+/fv2tW7fq0qVLp2vWrJnu5cuXRq9nqlSpdH379o32+RBCnA+KOEJiAQ7IOKB26tTJ6PbAwP+1d7ahNb5xHL+wNSUiT4WheZgtTZ5SJJSHIlZIebGVEmFFJA8zGxLlIdpI2opFS8sbpbU3oii1F16ZkLIXwpu94m+auv99fnWd7nPvnJtz/h7+9////dQ657rOfd/nuq/r1P3d7+n6KyguLraH6Z/iR0Vce3u7CZKqqqrg69evqeMQcUVFRUF1dXXw6dMn6+vp6TEBtnv3bhNEiDlAgCF09u3bl/Zdvb29QUFBQdDa2mrtt2/f2nc3NjamHffw4cPgxYsXeYm458+f2zWbmpoGfFZZWRmUlpYO6PcirqKiInjy5Emqv6GhwYQcgiibiGtpabFzu7u704QP4imOO3fu2Hl8N/T19dm8l5SUBHPnzh1w/VevXlmbdaJ9+PDhNKHHeeXl5UF/f3+aiCsrKwsePHhgfawnaxYVcc3NzUFhYWFQU1NjY4+CAGRuhBDJQu5UIXJg0KBBbs6cOeaOC3P27FlzsV24cCH2/Bs3brgjR478sTk/c+aMuc9IfGhtbbWYqDC4SGtqatywYcOsPXnyZLdy5Up39epVd+DAAXOTwsSJE92aNWtcR0dH2vn379/nH0O3bt06a+PehHHjxqUdt2zZMjdr1qy87uHDhw/2OmbMmJzPXbRokVu8eHGqvWfPHhtvZ2dn1nPevXvnhgwZ4kaPHp3qo01SQBy4k0ka8NmyuKb7+vrcqVOnzHXtXbK4radMmeJmzJhh7YsXL1qJmBMnTqSuVVxc7Orr6113d7e5pMPwe1yxYoW9Zz1ZMw+ucBJAdu7caddtbGy0sUdhLv28CiGSg0ScEHlkL758+TIVCN7T0+POnTvnDh065KZOnRp7LrFH2eLpfjWIx7q6Onf79m13/PjxjMcgOqI1wxBsiNdo7Tb6iasLQ1zV0qVLU7XJ5s2bZ3Fr1dXVVj6EWDYSH/4Jft4LCwvzyt6NihdKlMStycaNG11RUZHF4O3du9dKmxDj9z0QfcyZF3GINebWX482ApIYw1WrVqXOe/bsmZ0XFdjUuINo7NzChQuzjgHxhnAj5g5xng2+S4kNQiQPiTghcoQH7Ldv36z+mq/PhqUJEed5/fq1iRYsP5TBoOAuD2csdQTUz549261du9aOJTuQNgKD0hzeegVYe7CY8bpgwQL38ePHvNdrwoQJVpojKgLCIGii4ogHPH9Dhw4d0I9lyYMIwKJVWVmZ6kOskOjBfZNwcPDgQSvMiyUOC1c+eAtcb29vzudS2DZK9D6iIEKxgLGOzB3rOnbsWGszn3EgzrDAffnyxUQbbeYRoUvbW+TCIo559JbQML4vmlDCWOLWnHsLJzJkgt9n3HWEEP9OJOKEyBFvqeLBSPYg2X7nz59PKzpLsVhcYFeuXDFLCa6x0tJSE2w8wClse/nyZTsW9xptitwOHz7crV+/Pq1ILSIR0UMR2rBLL1coBHzt2jUTVDt27EjLOv0ZkLGKUAuLOKD2G25LsiwRLIjYp0+fumPHjuX1PcwjLsFMWa5YDH8FuDtZQ7JNEY+1tbW2XqxbHLiiEV137941Eb969Wrr5xURxx9jZmcFD9Zc/gmI4vuiu1PE3TNjPn36tLnP42oHvnnzxpWXl8feixDi34e23RIiR7CaYa3ioYwblQfw5s2bB7hNN23aZNYz/rDcFRQUWGFaYo+4hqekpCT1nmOJaeKB7WOkdu3a9dN2EMC9NnLkSFdVVWVbUd26dSsvt2QmcKVWVFSkiQzKXYwYMSJlxcNdS7wc8xBXCiMOhC7W0EylXBC53NfPBFcrgtyDWEeMnzx58rv3wFZnHI8bmzmgDVjesEpSn417Ccf3bdmyxTU0NJhgRPADbtdLly7ZtTZs2JDT+I8ePWrubYQ027IR38g6eHCJYxVlPEKIZCERJ0SO4H4rKyszqxauL8RcFB6UfjcBAtwpSot4ylZPrKmpyWLFsNpgreLB6kXczJkzf+oakdiAsEJ4IgiwEv3oTgnZQGRgacMdHIb6Ztu2bbPCv7glEYwch1u4ubk57+/bunWriRNi00aNGpXqx8LV0tJin2ExRaxEBXaukIyCtRUL4/Tp083ayNgR24wjDtzJCDHi4hgbbUDsjh8/3tac9QiDWx6rJmJ3//79ttcr9d9wVd+8eXNAksiPwG+R3x+xiQg5ihl78U68HP9gsE2XECJZyJ0qRJ5V/dlzkm2ewlY1D5mbxE8hKLAMLV++PKPr6/379yZwiI/z7jkyE8NxT5myCXMBKw9WQa7rYewIC7JNr1+/bn1YhDJtN+WD8aMgRLguUDAYa07UlUrWJJ+xqX1XV5dZl7A2UmQ3HAeWKwhD5iXqzsSK1dbWZhay9vb21OcIGMYatqh5GDMJGOH7Dc8DVjSuiXgnCYH4OKxavIa3/MrG9u3b7bt59fA7INuXfj+HHix2iDjWhfhI3jMm3PfE44Xj+zgXV28Ukk74LOx+Z59ZBBtWYeIsPQg65oD4OSFEwvjTNU6E+C9SX18ffP782d5TPJe6Y9SYo14XxWI9XV1dVhDW12Xr7OwMBg8eHHR0dFibQq9tbW0/XCdu/vz5Vgfu8ePHwe+ktrY2mDRpUt7n37t3z8ZNTbrv1Ynz1NXVBdOmTUvVTRO58+jRI6vrR+09IUTykDtViF8AbjPcYMQikflH4gPWFyxglPfAMkWAPq5FLFNYTnAL8spn+YBli0QIrE9cc8mSJe53gTuVemT5QtkOtpFifsJWsThwO2LRw50dV2ZDZAfrKDUOldQgRDIZhJL704MQ4r9If3+/BcXjpgqX56Cf/S7JDvVxb7giOQb3F5mCxEvh6iRmCnGXqTSGEEKI/zcScUIIIYQQCUSJDUIIIYQQCUQiTgghhBAigUjECSGEEEIkEIk4IYQQQogEIhEnhBBCCJFAJOKEEEIIIRKIRJwQQgghRAKRiBNCCCGESCAScUIIIYQQCUQiTgghhBDCJY+/ASQPaAbwFUHhAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "PA = []\n",
    "vstar = []\n",
    "dvstar = []\n",
    "Mdot = []\n",
    "dMdot = []\n",
    "for i in range(len(names)):\n",
    "\n",
    "    # Match the catalogues and plot if possible:\n",
    "    if (K16_index[i] in kobulnicky19_table['ID']) and (names[i] in list(corrected_pm['Name'])):\n",
    "\n",
    "        j = np.where(np.asarray(corrected_pm['Name']) == names[i])[0][0]\n",
    "        k = np.where(kobulnicky19_table['ID'] == K16_index[i])[0][0]\n",
    "\n",
    "        PA.append(list(corrected_pm['delta_theta'])[j])\n",
    "        vstar.append(list(corrected_pm['vstar'])[j])\n",
    "        dvstar.append(list(corrected_pm['dvstar'])[j])\n",
    "        Mdot.append(1e-10 * kobulnicky19_table['M'][k])\n",
    "        dMdot.append(1e-10 * kobulnicky19_table['e_M'][k])\n",
    "\n",
    "sc = plt.scatter(vstar, Mdot, c=PA, cmap='coolwarm', s=80, edgecolor='black', lw=2, vmin=0, vmax=180)\n",
    "plt.errorbar(vstar, Mdot, xerr=dvstar, yerr=dMdot,\n",
    "             fmt='none', ecolor='k', elinewidth=2, capsize=0, zorder=-2)\n",
    "cbar = plt.colorbar(sc, label=r'Misalignment angle $\\beta$ [deg] (this work)')\n",
    "cbar.set_ticks(np.arange(0, 181, 30))\n",
    "plt.yscale('log')\n",
    "plt.ylim(1e-10, 1e-4)\n",
    "plt.xlim(0, 80)\n",
    "plt.ylabel(r'Derived mass-loss rate [$M_{\\odot}$/yr] (KCP19)', fontsize=12)\n",
    "plt.xlabel(r'$v_{\\rm star}$ [km/s] (this work)', fontsize=12)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('PAPERPLOTS/Mdot_vs_vstar.pdf')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "87becb24-b07e-478a-a5e5-2b590fb69b1a",
   "metadata": {},
   "source": [
    "# End of notebook"
   ]
  }
 ],
 "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": "3.12.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
