ExozodiSensitivityEXOSIMS.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "    - "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Autosaving every 60 seconds\n",
      "4.1\n",
      "3.3.4\n"
     ]
    }
   ],
   "source": [
    "#import astropy.convolution as conv\n",
    "#dependencies:\n",
    "%autosave 60\n",
    "%matplotlib inline\n",
    "import astropy.io.fits as fits\n",
    "import astropy.units as u\n",
    "#import photutils\n",
    "import astropy.constants as c\n",
    "import os\n",
    "#import webbpsf\n",
    "#from webbpsf import wfirst\n",
    "import numpy as np\n",
    "#import poppy.utils\n",
    "#import poppy\n",
    "import numpy as np\n",
    "\n",
    "\n",
    "import glob\n",
    "from scipy.ndimage import rotate\n",
    "from scipy.ndimage import shift\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.colors import LogNorm\n",
    "from astropy.visualization import quantity_support\n",
    "quantity_support()\n",
    "%matplotlib inline\n",
    "import matplotlib as mpl\n",
    "import importlib\n",
    "from scipy.stats import lognorm\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import cmasher\n",
    "#print(webbpsf.__version__)\n",
    "plt.rcParams.update({'image.origin': 'lower',\n",
    "                     'font.size': '12',\n",
    "                     'image.interpolation':\"nearest\",\n",
    "                    'pdf.fonttype':42})\n",
    "import astropy\n",
    "import EXOSIMS,EXOSIMS.MissionSim,os.path\n",
    "\n",
    "\n",
    "print(astropy.__version__)\n",
    "print(mpl.__version__)\n",
    "#print(EXOSIMS.__version__)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#convert HabEx target list to csv list of HIP values\n",
    "import pandas as pd\n",
    "mask=[]\n",
    "HIP=pd.read_csv(\"HabEx-Master-Target-List-withCGI-Exozodi-Detection_limits_v5_LBTI_included.csv\"\n",
    "            ,delimiter=\",\")[\"HIP\"].values\n",
    "np.savetxt(\"hip.csv\",HIP,fmt='HIP %s',delimiter=\",\")\n",
    "#HIP.sort()\n",
    "np.savetxt(\"hip_nums_only.csv\",HIP,fmt='%s '+r'\\\\',delimiter=\" \")\n",
    "\n",
    "VMag=pd.read_csv(\"HabEx-Master-Target-List-withCGI-Exozodi-Detection_limits_v5_LBTI_included.csv\"\n",
    "            ,delimiter=\",\")[\"Vmag\"].values\n",
    "HabExDF=pd.read_csv(\"HabEx-Master-Target-List-withCGI-Exozodi-Detection_limits_v5_LBTI_included.csv\"\n",
    "            ,delimiter=\",\")\n",
    "HabExDF=HabExDF.set_index(\"HIP\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Star Number                                           6\n",
       "Vmag                                               2.82\n",
       "dist (pc)                                          7.46\n",
       "Type                                               G2IV\n",
       " EEID (mas)                                         256\n",
       "EEID (AU)                                          1.91\n",
       "HZ inner edge (mas)                                 243\n",
       "HZ outer edge (mas)                                 427\n",
       "Snow line (mas)                                     845\n",
       "IWA (AU)                                           1.12\n",
       "L*/Lsun                                           3.645\n",
       "Surface Brigthness Spectral Correction Factor      0.97\n",
       "Exozodi Detection Limit at IWA (in zodi)         518.37\n",
       "Time to SNR (hours)                                 NaN\n",
       "Exozodi Level measured by LBTI at 11 microns        NaN\n",
       "Unnamed: 16                                         NaN\n",
       "Unnamed: 17                                         NaN\n",
       "Unnamed: 18                                         NaN\n",
       "Unnamed: 19                                         NaN\n",
       "Unnamed: 20                                         NaN\n",
       "Name: 2021, dtype: object"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "HabExDF.loc[2021]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#scriptfile = os.path.join('Bijan-2020-Work/','wfirst_nemati2019_disks.json')\n",
    "#sim = EXOSIMS.MissionSim.MissionSim(scriptfile)\n",
    "#sim\n",
    "# %load sampleScript_coron.json\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "HabEx-Master-Target-List-withCGI-Exozodi-Detection_limits_v5_LBTI_included.csv\n",
      "hip.csv\n",
      "hip_nums_only.csv\n",
      "test_habex.csv\n"
     ]
    }
   ],
   "source": [
    "ls *.csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup EXOSIMS System"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "          { \"name\": \"muf-imager\",\n",
    "      \"QE\": 0.68,\n",
    "      \"FoV\": 9.5,\n",
    "      \"pixelNumber\": 1024,\n",
    "      \"pixelSize\": 1.3e-5,\n",
    "      \"sread\": 0,\n",
    "      \"idark\": 0.00027,\n",
    "      \"CIC\": 0.01,\n",
    "      \"texp\": 2,\n",
    "      \"ENF\": 1\n",
    "    }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Overwriting sampleScript_coron.json\n"
     ]
    }
   ],
   "source": [
    "%%writefile sampleScript_coron.json\n",
    "{\n",
    "  \"staticStars\": true,\n",
    "  \"checkKeepoutEnd\": false,\n",
    "  \"missionLife\": 0.1,\n",
    "  \"missionPortion\": 0.5,\n",
    "  \"OBduration\": 50,\n",
    "  \"minComp\": 0,\n",
    "  \"dMagLim\": 30,\n",
    "  \"pupilDiam\": 2.37,\n",
    "    \"ppFact\": 0.25,\n",
    "        \"fillPhotometry\":true,\n",
    "    \"explainFiltering\":true,\n",
    "    \"catalogpath\":\"hip.csv\",\n",
    "  \"scienceInstruments\": [\n",
    "    { \"name\": \"imager\",\n",
    "      \"QE\": 0.68,\n",
    "      \"FoV\": 9.5,\n",
    "      \"pixelNumber\": 1024,\n",
    "      \"pixelSize\": 1.3e-5,\n",
    "      \"sread\": 0,\n",
    "      \"idark\": 0.00027,\n",
    "      \"CIC\": 0.01,\n",
    "      \"texp\": 2,\n",
    "      \"ENF\": 1\n",
    "    }\n",
    "  ],\n",
    "  \"starlightSuppressionSystems\": [\n",
    "    { \"name\": \"HLC-565\",\n",
    "      \"lam\": 565,\n",
    "      \"BW\": 0.10,\n",
    "      \"IWA\": 0.15,\n",
    "      \"OWA\": 0.42,\n",
    "      \"occ_trans\": 0.2,\n",
    "      \"core_thruput\": 0.01,\n",
    "      \"core_mean_intensity\":  1.4e-11,\n",
    "      \"core_area\": 0.0022,\n",
    "      \"core_platescale\": 0.30\n",
    "    },\n",
    "     { \"name\": \"HLC-565-Disk\",\n",
    "      \"lam\": 565,\n",
    "      \"BW\": 0.10,\n",
    "      \"IWA\": 0.15,\n",
    "      \"OWA\": 0.42,\n",
    "      \"occ_trans\": 0.23,\n",
    "      \"core_thruput\": 0.12,\n",
    "      \"core_mean_intensity\": 1.4e-11,\n",
    "      \"core_area\": 0.0022,\n",
    "      \"core_platescale\": 0.30,\n",
    "               \"ppFact\": 0.125\n",
    "    },\n",
    "      { \"name\": \"HLC-565-TTR-DISK\",\n",
    "      \"lam\": 565,\n",
    "      \"BW\": 0.10,\n",
    "      \"IWA\": 0.28,\n",
    "      \"OWA\": 0.42,\n",
    "      \"occ_trans\": 0.05,\n",
    "      \"core_thruput\": 0.11,\n",
    "      \"core_mean_intensity\": 0.00000000121000,\n",
    "      \"core_area\": 0.0022,\n",
    "         \"ppFact\": 0.125,\n",
    "      \"core_platescale\": 0.30\n",
    "    },\n",
    "        { \"name\": \"HLC-565-MUF-DISK\",\n",
    "      \"lam\": 565,\n",
    "      \"BW\": 0.10,\n",
    "      \"IWA\": 0.165,\n",
    "      \"OWA\": 0.42,\n",
    "      \"occ_trans\": 0.23,\n",
    "      \"core_thruput\": 0.11,\n",
    "      \"core_mean_intensity\":2.8e-11,\n",
    "      \"core_area\": 0.0022\n",
    "    }\n",
    "  ],\n",
    "  \"observingModes\": [\n",
    "    { \"instName\": \"imager\",\n",
    "      \"systName\": \"HLC-565-Disk\",\n",
    "      \"detectionMode\": true,\n",
    "     \"SNR\":5\n",
    "    }\n",
    "  ],\n",
    "  \"modules\": {\n",
    "    \"PlanetPopulation\": \"KeplerLike1\",\n",
    "    \"StarCatalog\": \"HIPfromSimbad\",\n",
    "    \"OpticalSystem\": \"Nemati\",\n",
    "    \"ZodiacalLight\": \"Stark\",\n",
    "    \"BackgroundSources\": \"GalaxiesFaintStars\",\n",
    "    \"PlanetPhysicalModel\": \"FortneyMarleyCahoyMix1\",\n",
    "    \"Observatory\": \"WFIRSTObservatoryL2\",\n",
    "    \"TimeKeeping\": \" \",\n",
    "    \"PostProcessing\": \" \",\n",
    "    \"Completeness\": \" \",\n",
    "    \"TargetList\": \" \",\n",
    "    \"SimulatedUniverse\": \"KeplerLikeUniverse\",\n",
    "    \"SurveySimulation\": \" \",\n",
    "    \"SurveyEnsemble\": \" \"\n",
    "  }\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Imported SurveySimulation (prototype module) from EXOSIMS.Prototypes.SurveySimulation\n",
      "Numpy random seed is: 476738723\n",
      "Imported KeplerLikeUniverse (specific module) from EXOSIMS.SimulatedUniverse.KeplerLikeUniverse\n",
      "Imported TargetList (prototype module) from EXOSIMS.Prototypes.TargetList\n",
      "Imported HIPfromSimbad (specific module) from EXOSIMS.StarCatalog.HIPfromSimbad\n",
      "['HIP 37279', 'HIP 97649', 'HIP 113368', 'HIP 57632', 'HIP 67927', 'HIP 2021', 'HIP 107556', 'HIP 102422', 'HIP 22449', 'HIP 17378', 'HIP 8102', 'HIP 95501', 'HIP 99240', 'HIP 3821', 'HIP 16537', 'HIP 98036', 'HIP 57757', 'HIP 27072', 'HIP 28103', 'HIP 109176', 'HIP 78072', 'HIP 27321', 'HIP 14632', 'HIP 50954', 'HIP 70497', 'HIP 59199', 'HIP 7513', 'HIP 12777', 'HIP 116771', 'HIP 102485', 'HIP 15510', 'HIP 92043', 'HIP 1599', 'HIP 64394', 'HIP 112447', 'HIP 61317', 'HIP 105858', 'HIP 17651', 'HIP 108870', 'HIP 67153', 'HIP 16852', 'HIP 19849', 'HIP 61174', 'HIP 77257', 'HIP 12843', 'HIP 71284', 'HIP 96100', 'HIP 34834', 'HIP 77760', 'HIP 46509', 'HIP 24813', 'HIP 64924', 'HIP 76829', 'HIP 23693', 'HIP 16245', 'HIP 104214', 'HIP 39903', 'HIP 15457', 'HIP 64408', 'HIP 86486', 'HIP 4151', 'HIP 10644', 'HIP 51459', 'HIP 57443', 'HIP 65721', 'HIP 86736', 'HIP 80686', 'HIP 82860', 'HIP 910', 'HIP 47592', 'HIP 5862', 'HIP 109422', 'HIP 73996', 'HIP 29271', 'HIP 53721', 'HIP 25278', 'HIP 97295', 'HIP 48113', 'HIP 95447', 'HIP 88745', 'HIP 86796', 'HIP 7981', 'HIP 29800', 'HIP 25110', 'HIP 97675', 'HIP 40843', 'HIP 45333', 'HIP 104217', 'HIP 64792', 'HIP 3909', 'HIP 15371', 'HIP 56997', 'HIP 114622', 'HIP 32480', 'HIP 47080', 'HIP 49081', 'HIP 80337', 'HIP 73184', 'HIP 84862', 'HIP 78459', 'HIP 12653', 'HIP 18859', 'HIP 58576', 'HIP 32439', 'HIP 89042', 'HIP 79672', 'HIP 22263', 'HIP 8362', 'HIP 15330', 'HIP 7978', 'HIP 99825', 'HIP 3765', 'HIP 35136', 'HIP 107649', 'HIP 12114', 'HIP 38908', 'HIP 42438', 'HIP 105090', 'HIP 81300', 'HIP 75181', 'HIP 98767', 'HIP 26394', 'HIP 3093', 'HIP 49908', 'HIP 84478', 'HIP 43587', 'HIP 3583', 'HIP 23311', 'HIP 56452', 'HIP 72848', 'HIP 40693', 'HIP 13402', 'HIP 100017', 'HIP 114046', 'HIP 54035', 'HIP 27435', 'HIP 544', 'HIP 113283', 'HIP 26779', 'HIP 1475', 'HIP 68184', 'HIP 32984', 'HIP 88972', 'HIP 10798', 'HIP 86400', 'HIP 41926', 'HIP 57939', 'HIP 85235', 'HIP 42808', 'HIP 25878']\n",
      "Imported Nemati (specific module) from EXOSIMS.OpticalSystem.Nemati\n",
      "Imported Stark (specific module) from EXOSIMS.ZodiacalLight.Stark\n",
      "Imported PostProcessing (prototype module) from EXOSIMS.Prototypes.PostProcessing\n",
      "Imported GalaxiesFaintStars (specific module) from EXOSIMS.BackgroundSources.GalaxiesFaintStars\n",
      "Imported Completeness (prototype module) from EXOSIMS.Prototypes.Completeness\n",
      "Imported KeplerLike1 (specific module) from EXOSIMS.PlanetPopulation.KeplerLike1\n",
      "Imported FortneyMarleyCahoyMix1 (specific module) from EXOSIMS.PlanetP[...]el.FortneyMarleyCahoyMix1\n",
      "150 targets imported from star catalog.\n",
      "149 targets remain after nan filtering.\n",
      "149 targets remain after removing requested targets.\n",
      "Calculating target stellar masses.\n",
      "149 targets remain after binary filter.\n",
      "149 targets remain after IWA filter.\n",
      "149 targets remain after integration time cutoff filter.\n",
      "149 targets remain after completeness filter.\n",
      "Imported WFIRSTObservatoryL2 (specific module) from EXOSIMS.Observatory.WFIRSTObservatoryL2\n",
      "    orbitdatapath is none\n",
      "    orbitdatapath (/Users/edouglas/.EXOSIMS/cache/L2_halo_orbit_six_month.p) exists\n",
      "Imported TimeKeeping (prototype module) from EXOSIMS.Prototypes.TimeKeeping\n",
      "OBendTimes is: [36.525] d\n",
      "Limiting delta magnitude for mode syst: HLC-565-Disk inst: imager is 24.515449845125502\n",
      "Loading cached keepout map file from /Users/edouglas/.EXOSIMS/cache/koMap_da43f745971a24b87f3c7be60f944dcd.komap\n",
      "Keepout Map loaded from cache.\n",
      "Loading cached fZ from /Users/edouglas/.EXOSIMS/cache/CompletenessTargetListNematifbc170efeaf49857bf3bdde879a3d964.starkfZ\n",
      "Loading cached fZQuads from /Users/edouglas/.EXOSIMS/cache/CompletenessTargetListNematifbc170efeaf49857bf3bdde879a3d964.fZmin\n",
      "Imported SurveyEnsemble (prototype module) from EXOSIMS.Prototypes.SurveyEnsemble\n"
     ]
    }
   ],
   "source": [
    "scriptfile = os.path.join('sampleScript_coron.json')\n",
    "sim = EXOSIMS.MissionSim.MissionSim(scriptfile)\n",
    "#sim.OpticalSystem?#\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mode should be HLC disk mode, and it is: HLC-565-Disk\n"
     ]
    }
   ],
   "source": [
    "OpSim=sim.OpticalSystem\n",
    "Obs=sim.Observatory\n",
    "TL=sim.TargetList\n",
    "mode=sim.OpticalSystem.observingModes[0]\n",
    "\n",
    "sim.TargetList.fillPhotometryVals()\n",
    "\n",
    "print(\"mode should be HLC disk mode, and it is: \"+mode[\"systName\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.25"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check post-processing gain\n",
    "#assert sim.PostProcessing.ppFact(0) ==1/8\n",
    "f_pp=sim.PostProcessing.ppFact(0)\n",
    "f_pp"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check what stars EXOSIMS dropped:\n",
    "\n",
    "HIP 107556 is a binary (http://simbad.u-strasbg.fr/simbad/sim-id?Ident=hip%20107556&submit=submit%20id)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "107556\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-10-678540c8bea6>:4: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if np.where(TLHIP==name)[0] >=0:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(149, 20)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "TLHIP=[int(name[4:]) for name in TL.Name]\n",
    "exclude=[]\n",
    "for name in HIP:\n",
    "    if np.where(TLHIP==name)[0] >=0:\n",
    "        continue\n",
    "    else:\n",
    "        print(name)\n",
    "        HIP=HIP[np.where(HIP!=name)]\n",
    "        #VMag=VMag[np.where(HIP!=name)]\n",
    "#VMag.sort()\n",
    "HabExDF_NoBinary=HabExDF.drop(index= 107556)\n",
    "HabExDF_NoBinary.shape\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(149, 20)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#drop the binaries\n",
    "HabExDF_NoBinary=HabExDF.drop(index= 107556)\n",
    "HabExDF_NoBinary.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "#define the reference star from Nemati spreadsheet\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Find Habitable Zone Boundaries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "allstars=np.arange(sim.TargetList.nStars)\n",
    "allstars.size\n",
    "\n",
    "\n",
    "EEIDs=sim.TargetList.calc_EEID(allstars,arcsec=True)#\n",
    "\n",
    "EEIDs_AU=sim.TargetList.calc_EEID(allstars,arcsec=False)#\n",
    "\n",
    "innerHZs=sim.TargetList.calc_HZ_inner(allstars,arcsec=True)#\n",
    "\n",
    "outerHZs=sim.TargetList.calc_HZ_outer(allstars,arcsec=True)#\n",
    "\n",
    "innerHZ_AU=sim.TargetList.calc_HZ_inner(allstars,arcsec=False)\n",
    "\n",
    "outerHZ_AU=sim.TargetList.calc_HZ_outer(allstars,arcsec=False)\n",
    "\n",
    "\n",
    "IWA_AU=sim.TargetList.calc_IWA_AU(allstars)\n",
    "\n",
    "\n",
    "#sim.TargetList.calc_HZ_inner?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Number of observable HZ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "74"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "outerHZs[outerHZ_AU>IWA_AU].size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "plt.figure(figsize=[7,4])\n",
    "\n",
    "ax=plt.subplot(111)\n",
    "\n",
    "\n",
    "inner_hist = plt.plot(np.sort(innerHZs)[::-1],np.arange(innerHZs.size),\n",
    "                      #histtype=\"step\",cumulative=-1,bins=np.arange(0.0,.5,.01),\n",
    "                      label=\"inner edge\")\n",
    "\n",
    "inner_hist = plt.plot(np.sort(EEIDs)[::-1],np.arange(EEIDs.size),\n",
    "                      #histtype=\"step\",cumulative=-1,bins=np.arange(0.0,.5,.01),\n",
    "                      linewidth=3,\n",
    "                      label=\"EEID\")\n",
    "ax.plot(np.sort(outerHZs)[::-1],np.arange(outerHZs.size),\n",
    "        #histtype=\"stepfilled\",bins=inner_hist[1],alpha=.5,cumulative=-1,\n",
    "                              linewidth=5,\n",
    "        label=\"outer edge\")\n",
    "plt.fill_between([0,.15],[ax.get_ylim()[1],ax.get_ylim()[1]],alpha=0.25,color=\"gray\")\n",
    "plt.text(.2,60,s='Accessible targets \\n in CGI dark hole',fontsize=12)\n",
    "plt.text(.01,5,s='Behind mask',fontsize=12)\n",
    "\n",
    "\n",
    "ax.set_ylabel(\"cumulative number of habitable zones\")\n",
    "ax.set_xlabel(\"IWA [arcsec]\")\n",
    "ax.set_xticks([0,.15,.2,.3,.4])\n",
    "plt.xlim([.0,.5])\n",
    "plt.legend()\n",
    "#plt.grid(axis=\"x\")iwa\n",
    "plt.title(\"HabEx target stars\")#sim.StarCatalog)#+\", habitable zones after Kaltenegger et al 2018\")\n",
    "#plt.yscale('symlog', linthreshy=200)\n",
    "#ax.set_yticks([0,25,50])#$,100,125,150])\n",
    "#ticks=[0,25,50,75]#,100,100,125,150,175,200,500,1000]\n",
    "#ax.set_yticks(ticks)\n",
    "#ax.set_yticklabels([0,25,50,75,])#100,100,125,150,175,200,500,1000])\n",
    "plt.savefig('n_hz.pdf',bbox_inches=\"tight\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "starmag=sim.TargetList.Vmag\n",
    "resel_area=0.05**2 #as^2/resel"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "## Cumulative Exposure Time Figure\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$[0.5271296,~0.76942806,~1.1555351,~\\dots,~1.9203687,~1.6711229,~0.84856024] \\; \\mathrm{AU}$"
      ],
      "text/plain": [
       "<Quantity [0.5271296 , 0.76942806, 1.15553505, 1.64998341, 1.7094017 ,\n",
       "           1.11881847, 2.13980026, 1.21026306, 1.35611601, 0.54752519,\n",
       "           2.32883077, 0.91625433, 0.89296346, 0.48240817, 2.05479455,\n",
       "           1.63934426, 1.33904657, 2.23181076, 1.75891185, 1.6880486 ,\n",
       "           2.91601868, 1.581111  , 2.43348484, 2.17959895, 2.24047809,\n",
       "           2.02374516, 1.66907744, 2.05704889, 2.20167332, 0.90650876,\n",
       "           2.88129072, 1.28799591, 1.36936283, 2.44458933, 1.2659297 ,\n",
       "           1.38927484, 2.64410362, 0.54336015, 2.91036072, 2.09438696,\n",
       "           0.74768221, 2.74223042, 1.81862268, 2.13310575, 2.37492085,\n",
       "           0.86320996, 3.2140563 , 2.3839797 , 2.60010395, 1.89465709,\n",
       "           1.28325777, 2.61551886, 1.7468266 , 3.25238514, 0.52297608,\n",
       "           2.99700308, 1.37098975, 3.10045481, 3.21750326, 2.81162138,\n",
       "           1.61759949, 1.91693287, 1.38312593, 2.69784193, 2.64783754,\n",
       "           1.8175209 , 2.2886786 , 2.81214838, 2.25191417, 2.2672308 ,\n",
       "           2.74172916, 2.93312473, 1.52967582, 2.1094079 , 2.15796275,\n",
       "           3.18471336, 2.75532703, 2.27652154, 2.34631634, 2.32666354,\n",
       "           1.12985845, 2.88739157, 3.13283215, 2.87852612, 2.74072723,\n",
       "           2.9354207 , 0.52297608, 2.63388948, 2.36294904, 1.80483699,\n",
       "           1.44175315, 0.98193247, 2.50752268, 1.7053206 , 2.25699677,\n",
       "           1.91668797, 0.8887835 , 2.14899702, 2.58531532, 2.57510719,\n",
       "           2.82485876, 1.91448627, 2.68096504, 2.64177532, 2.08507075,\n",
       "           1.991503  , 1.51011772, 1.80115271, 2.61506281, 1.33666005,\n",
       "           1.11823468, 2.53378372, 2.39923224, 1.07704453, 2.43072453,\n",
       "           2.15331602, 0.59192612, 1.46270113, 2.22189302, 2.37868695,\n",
       "           2.74725294, 1.6589251 , 0.73095853, 0.89557586, 1.85116625,\n",
       "           2.2737608 , 1.30616512, 1.43389735, 1.72651935, 1.87406301,\n",
       "           1.55279503, 2.63527765, 0.49138438, 0.38202932, 2.27652154,\n",
       "           2.05058098, 1.14137878, 1.84162073, 0.53809726, 1.50966182,\n",
       "           1.30650644, 1.65362148, 1.90041804, 1.64998341, 1.83127813,\n",
       "           1.36376038, 1.92036867, 1.67112293, 0.84856024] AU>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "IWA_AU"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/edouglas/anaconda3/envs/python39/lib/python3.9/site-packages/astropy/units/quantity.py:1028: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  out = super().__getitem__(key)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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IrbfeWrDe1KlT+frXv86oUaNYtWoVu+66K5dffnmbPseCBQuYOnUq3bt3Z/vtt99sf2NjY5uOWy1O2GZWagcDQyRdkD4fAEyTdENE3FDFuKwVHnvssYLl8+bN2/T4rrvuKlinvr6+2b3cLR2rUN3tt9+eW2+9tcWEnquhoaFgee7x8u8pzzInbDNrE0l1JH9DugJdJfUA1pMk7NyLpU8DFwOPVDxIsw7ECdvM2uoK4Ns5z08Fro6IhtxKkjYAb0dEts4/VlB75vcuNHFKS/InVLFsccI2szZJE3NDEfXqyx2LWWfgUeJmZmYZ4IRtZlYFHWkwlLVOW//fO2GbmVVYjx49WLZsmZN2JxQRLFu2rE3To/oatplZhQ0ZMoSFCxeyZMmSdh9r9dr1Rdd9s7v/5NeCHj16MGTIkFa/zv/3zMwqrFu3bgwfPrwkx/rxkwuKrnvKXp76Nct8StzMzCwDKtLDltQd+CFwCNAPeAX4ZkQ8ku4/GLgFGAo8CZwZEfMrEZuZWVZNnDiRp157q8X9nzmm8Cpblk2V6mHXAa8DBwLbA1eSTFVYL6k/MCMt6wfMAn5aobjMzMwyoSI97IhYTfMJFmZKeg3YG9gBmB0R0wEkNQBLJY2MiDmViM/MzKzWVeUatqSBwIeB2cDuwDNN+9LkPjctz3/dOEmzJM0qxehKM7OO4jPHnOJT4B1cxRO2pG7A3cAdaQ+6F7Air9oKoHf+a71urpmZdVYVTdiSugB3Au8B49PiRqBPXtU+wKoKhmZmZlbTKpawJQmYDAwEjouIplXXZwN75tTbDhiRlpuZmRmV7WHfCnwEODIi3skpvx/YQ9Jx6Xq6VwHPesCZmZnZ+yqSsCUNA84F9gIWS2pMt7ERsQQ4DrgeeBvYFzipEnGZmZllRaVu65oPaAv7fw2MrEQsZmZmWeSpSc3MzDLACdvM2kTS+HRehLWSpuSUf0LSryS9JWmJpOmSdqpiqGYdglfrypjWrMyzJafs2/5Ve0oVi2XWIuA64HCgZ075B4CJwC+A9cAE4HbgXyodoFlH4oRtZm0SETMAJI0GhuSUP5JbT9IE4HeVjc6s4/EpcTMrtwPYwrwKnnLYrDhO2GZWNpL+mWRuhUtbquMph82K44RtZmUh6YPAI8DXIuLxasdjlnVO2GZWculkSb8Gro2IO6sdj1lH4EFnZtYmkupI/oZ0BbqmUwuvJ1kv4FHgloj4ryqGaNahOGGbWVtdAXw75/mpwNVAALsC35a0aX9E9KpseGYdixN2J+V7qK29IqIBaGhh99WVi8Ssc/A1bDMzswxwwjYzM8sAJ2wzM7MMcMI2MzPLACdsMzOzDHDCNjMzywAnbDMzswxwwjYzM8sAT5xilqMUE8qcsu/QEkRiZtace9hmZmYZ4IRtZmaWAU7YZmZmGeCEbWZmlgFFJWxJF0vaK338CUkLJL0qab+yRmdmZeN2bZYtxfawLwJeSx//G3AjcD1wUxliMrPKcLs2y5Bib+vaPiJWSOoN7AkcEhEbJH2/jLGZWXm5XZtlSLE97NclfRI4CfjftFH3ATaULzQzK7N2tWtJ4yXNkrRW0pS8fQdLmiNpjaTfShpW+vDNOpdie9iXAvcC7wHHpWWfA54qR1BmrVWKCU86ofa260XAdcDhQM+mQkn9gRnAOcCDwLXAT4FPlCRqs06qqIQdEQ8Dg/OKp6ebmWVQe9t1RMwAkDQaGJKz61hgdkRMT/c3AEsljYyIOe2N26yzKnpqUknbAh8EeuXt+kNJIzKziilTu94deKbpSUSsljQ3Le90CXvixIlbrTNu3LgKRGJZV1TClnQ6MIHk1Nk7ObsC8MTJZhlUxnbdC1iSV7YC6N1CHOOAcQBDh3bMPydPvfZWwfKPD+9X4Uiyp6XLXZ1xzv5ie9j/DhwXEb8qZzBmVlHlateNQJ+8sj7AqkKVI2IiMBFg9OjRUeJYzDqMYkeJvwc81p43amlEqaR6SSGpMWe7sj3vZWZFaXe7bsFsktvEAJC0HTAiLe/UPnPMKZs2s9YqNmFfCdyYjv5sq6YRpbe1sL9vRPRKt2vb8T5mVpx2tWtJdZJ6AF2BrpJ6SKoD7gf2kHRcuv8q4FkPODNrn2IT9kvAUcAbkjak20ZJRd+HHREzIuIBYFkb4jSz0mtvu76C5Nr314FT08dXRMQSktvErgfeBvYludfbzNqh2GvYdwJTSe6lfGcrddtqvqQAfgVcGhFL8yt0hsEpZhXUrnYdEQ1AQwv7fg2MbEdsZpan2IS9A3BVRJRjQMhSYB/gr+n73ALcTTIZQzMenGJWUuVs12ZWYsWeEr8dOK0cAUREY0TMioj1EfEGMB44LJ0i0czKp2zt2sxKr9ge9seB8ZK+BbyRuyMiDihxTE2/9lXi45pZc5Vs12bWTsUm7B+lW5ulo0fryBlRCqwH9gaWAy8DHwBuBh6LiBXteT8z26p2t2szq5xi5xK/owTvdQXw7ZznpwJXAy8C3wF2BFaSDDo7uQTvZ2ZbUKJ2bWYVUuzUpCJZeedkoH9E/LOkA4BBETGtmGNsaUQp8JNijmFmpVOKdm1mlVPsKfFrgEOBm4D/SssWAv8PcMM2yya36w7u0Qd+3Ox54zPJ3OVebCSbik3YZwIfjYilkm5Ny14Ddi1LVGZWCWfidm01xOvab1mxt3V1JZnQH94fxd0rp8zMssft2ixDiu1hP0wy5/BFsOna17XAg+UKzMzKzu26A2ppYZH80+OWPcX2sC8GBpOsabs9yS/wYSRzCJtZNrldm2VIsbd1rQSOkbQjSYN+PSIWlzUyMysrt2uzbCmqhy3pLwAR8WZEPN3UqCXNKmdwZlY+btdm2VLsKfEP5hek17s8mtQsu9yuzTJki6fEJU1NH26T87hJPTC7HEGZWfm4XZtl09auYc9t4XEAvwemlzwi65RKPYK1pZGyBrhdm2XSFhN2RFwNIOlPEfGLyoRkZuXkdm2WTcVew35P0nAASYMk3SHpNkmDyhibmZVXWdu1pHpJD0t6W9JiSRPSVfvMrA2KbTw/BA5PH9+Y/nc9MBE4qtRBWefW1tPZnhii1crdrn8IvAnsBPQlWYnvApIldM2slYpN2DtHxIL01/HhJPdsvgcsKltkZlZu5W7Xw4EJEfEusFjSz4HdS3Rss06n2IS9UtJAYA/g+YholLQN0K18oZlZmZW7Xf8AOEnSY8AHgM8CV5bo2B3SxIkTqx2C1bBiE/Z/Ak8D2wAXpmX7A3PKEJOZVUa52/XvgC8BK0kWGrkDeCC/kqRxwDiAoUOHluits+up196qdghWo4oadBYRNwCHAPtHxD1p8d+Bc8oVmJmVVznbtaQuwC+AGcB2QH+SXvYNBeKYGBGjI2L0gAED2vvWZh1WUT1sSV8FfhwRS5vKIuKlskVlZmVX5nbdD9iF5Br2WmCtpNuB64DLSvQeHZrnErB8xd7WdQgwT9JMSSdK6l7OoMysIsrWrtMfAa8B50uqk9QXOAN4plTvYdbZFHtK/CiSEaSPkFzrWixpkqQDyhibmZVRBdr1scC/AEuAV0huGbuoRMc263SK7WETEcsi4paI2A84ENgH+K2keZK+JalX2aI0s7IoZ7uOiL9GxJiI+EBE9I+IL0TEmyUL3qyTKTphA0g6OL0O9RjwBnA6cBrwUZJf6WaWMW7XZtlQ7KCz/wBOAlYAU4ErIuLvOfv/BLxdlgjNrCzcrs2ypdj7sHsAn4+IpwvtjIh1kkaXLiwzqwC3a7MMKSphR8T43OeSdgU2RMT8nDqeRMUsQ9yuzbKlqGvYkn4i6ZPp47NIFrh/XtLZ5QzOzMrH7dosW4o9JX4wyT2UABeT3L+5nGSawcklj8oqptQrXHmyh0xxuzbLkGIT9jYR8Z6knYF+EfF7gHThADPLJrfrGuFFP6wYxSbsv0r6BskkCw8BpI18ZbkCM7Oyc7uuMi/0URk/fnJBwfJT9s3WYjPFJuyzgWuBdcCladl+wN3lCMqqo62ns0t9Wt0qxu3aLEOKHSU+Fzglr+xe4N5yBGVm5ed2XV0e72Gt1aqZzszMzKw6nLDNzMwyoGIJW9J4SbMkrZU0JW/fwZLmSFoj6beShlUqLjMzsyyoZA97Ecni9bflFkrqD8wAriRZ9H4W8NMKxmVmZlbzil38YxvgTGAvoNlyexFxejHHiIgZ6bFGA0Nydh0LzI6I6en+BmCppJGeFtGsfErRrs2scoq9resOYE/gQZLl90ppd+CZpicRsVrS3LS8WcKWNA4YBzB06Nbvn2vp3rtqyNr9ftYplLNdm1mJFZuw/wUYHhHLyxBDL2BJXtkKoHd+xYiYCEwEGD16dJQhFrPOpJzt2sxKrNhr2AuA7mWKoRHok1fWB1hVpvczs0Q52zUAkk6S9IKk1ZLmSvp0Od/PrCMrtoc9FfiZpB+Qd+osIh5tZwyzeX8BAiRtB4xIy82sfMrZrpF0KHADcCLwFLBTe49p1pkVm7Cb1s39Tl55ALsWcwBJden7dQW6SuoBrAfuB74n6TiS+YyvAp71gDOzsmt3u96Kq4FrIuJP6fO/l+CYZp1WsVOTDi/Be10BfDvn+anA1RHRkCbrCcBdwJPASSV4PzPbghK164IkdQVGA/8j6RWgB8mynZdGxDt5dVs1mNRKrzUDdGtlAG1HWdCjNSp2H3ZENESE8raGdN+vI2JkRPSMiDERMa9ScZlZWQwEugHHA58muXXsoyQ/3JuJiIkRMToiRg8YMKCiQZpliacmNbNyaOpF/2dE/CMilgI3AkdUMSazTHPCNrOSi4i3gYUk18PNrAScsM2sXG4HviJpR0kfAC4EZlY3JLPsKnaUuJlZa10L9AdeAt4FpgHXVzUiswxzwjazsoiIdcAF6WZm7eRT4mZmZhnghG1mZpYBTthmZmYZ4IRtZmaWAR50Zh3Sow/8uGrv3fhMv02Px40bV7U4zKxjccKukNbM1WtmZpbPp8TNzMwywD1s6zA+c8wp1Q4BgMZnfl7tEMysygqdVW3vSmLuYZuZmWWAE7aZmVkGOGGbmZllgBO2mZlZBjhhm5mZZYATtpmZWQb4ti6zEnvqtbc2Pe7Vjglz2nsLiJl1LO5hm5mZZYATtpmZWQY4YZtZWUn6kKR3Jd1V7VjMsswJ28zK7Rbg6WoHYZZ1HnRmZmUj6SRgOfAH4IPVjaZ0Jk6cuMX9XlbVysEJ28zKQlIf4BrgYODsLdQbB4wDGDo0OyPjc+8GaPLx4f0K1LRK6shLGXe6hP3oAz8u6fHaukJUqeOoFR31c1mbXAtMjojXJbVYKSImAhMBRo8eHRWKzSxzOl3CNrPyk7QXcAjw0SqHUnZNP9r9Y9XKzQnbzMphDFAPLEh7172ArpJGRcTHqhiXWWZ16oRdK6ez2xpHrch6/FYWE4F7cp7/K0kCP78q0Zh1AJ06YZtZeUTEGmBN03NJjcC7EbGkelGZZZsTtpmVXUQ0VDsGs6yrmYlTJD2WzobUmG4vVjsmMzOzWlEzCTs1PiJ6pdtu1Q7GzMysVtRawjYzM7MCau0a9r9J+i7wIvCtiHisyvFsle+9tC1pz7+PxmeSWbM8zaWZQW31sC8HdgV2Jrkl5EFJI3IrSBonaZakWUuWeLCpmZl1HjWTsCPiyYhYFRFrI+IO4PfAEXl1JkbE6IgYPWDAgOoEamZmVgW1dko8VwAtT0BcRZ4oxLakFP8+fKnFzPLVRMKW1BfYF/gdsB44ETgAuLB6UZmZFV79qdBKXVnQFHevKq9oVesrahWK75R9q7+SXE0kbKAbcB0wEtgAzAGOiQjfi21mZkaNJOx0usJ9qh2HmZlZraqZQWdmZmbWMidsMzOzDHDCNjMzywAnbDMzswxwwjYzM8sAJ2wzM7MMcMI2s5KT1F3SZEnzJa2S9BdJn612XGZZ5oRtZuVQB7wOHAhsD1wJTJNUX82gzLKsJiZOMbOOJSJWAw05RTMlvQbsDcyrRkxmWeeEbWZlJ2kg8GFgdrVjKWTixIkt7muaf7ujLvrT2oVmvE579Thhm1lZSeoG3A3cERFzCuwfB4wDGDq0egsslGJBj0KLa2R1oZDWqvUFPQrJWsy+hm1mZSOpC3An8B4wvlAdr3NvVhz3sM2sLCQJmAwMBI6IiHVVDqkouae+O9u65Fs77d/Zvo9a44RtZuVyK/AR4JCIeKfawZhlnU+Jm1nJSRoGnAvsBSyW1JhuY6sbmVl2uYdtZiUXEfMBVTsOs47EPWwzM7MMcMI2MzPLACdsMzOzDHDCNjMzywAnbDMzswxwwjYzM8sA39ZlZpmxpUU6yq01s3zV+oxg1Yyvre/dURdfaQ0nbDPLlM6ymEatKrTASWdQCwuF+JS4mZlZBriHbWaZVIlTpK15j1o/ZVuL8XmxkdZxD9vMzCwDnLDNzMwywAnbzMwsA5ywzczMMsAJ28zMLAOcsM3MzDLACdvMzCwDaiZhS+on6X5JqyXNl1R7Nw2aWdHcps1Kq5YmTrkFeA8YCOwFPCTpmYiYXdWozKyt3KbNSqgmetiStgOOA66MiMaIeAL4H+C06kZmZm3hNm1WerXSw/4wsCEiXsopewY4sErxmFn7uE13cJWeNrQjTFO6W92h7L333m1+vSKihOG0MQjp08D0iBiUU/YlYGxEjMkpGweMS5/uBrxYyTjz9AeWVvH928pxV1a54x4WEQPKePw2KbZNp+WtbdeFvtOWvufWlPsYPka1jpGr5TYdEVXfgI8Ca/LKLgEerHZsW4h5VrVjcNy1v2U17hJ87rK16ULfaUvfc2vKfQwfo1rHKHariWvYwEtAnaQP5ZTtCXhwilk2uU2blVhNJOyIWA3MAK6RtJ2k/YGjgTurG5mZtYXbtFnp1UTCTl0A9ATeBH4CnB+1ffvHxGoH0EaOu7KyGncplKtNF/pOW/qeW1PuY/gY1TpGUWpi0JmZmZltWS31sM3MzKwFTthmZmYZ4ITdSpIek/SupMZ0q+a94AVJGi9plqS1kqbk7TtY0hxJayT9VtKwKoW5mZbillQvKXK+80ZJV1Yx1GYkdZc0OZ0ve5Wkv0j6bM7+mv3OzSw7nLDbZnxE9Eq33aodTAGLgOuA23ILJfUnGbl7JdAPmAX8tOLRtaxg3Dn65nzv11Ywrq2pA14nmcVre5Lvd1r6Q6PWv/MOS1JvSX2qHYdZqThhd0ARMSMiHgCW5e06FpgdEdMj4l2gAdhT0sgKh1jQFuKuaRGxOiIaImJeRGyMiJnAa8De1Ph3XqskdZF0oaQJkv5J0o6S7kvPXnxP0jZ59b8iabf08S6SngBWAG9L+p2knavxOWqRpG6SHs0rK/r7llSf99oTJU2XdK+kU/P2PSPpWz6rVBpO2G3zb5KWSvq9pDHVDqYVdieZzxnYdK/s3LQ8C+ZLWijp9rTnWpMkDSSZS3s22f/Oq+V7wJEkU5X+GvgyMA34PvBZ4Jq8+t8EFqaPJwB/JpkGcgDwf8APi31jJQ7IK+uS+28uTWpHSxq0+RE2O14XSbtL2kNS17x9n5fUsxWxdZF0UPoD5XJJYyV9uNjXp7qw+Zzurfm+n82J5zzg/5GcOXoK+K6kL+fUHQkcAsyV9Kik0yRtu5XPWCfpMEkXSboq/e9hkgqufSHpAy2UD9nS++TUu1lS77yyPnk/Ug6W9P10G1PMcdPXbfbjqF3aM01aZ9yAfYHeQHfgDGAVMKLacbUQ63XAlJznk4Hv5tX5PXBmtWPdSty9gNEkp54HAvcCv6h2nC3E3o3kD95/Z+k7r7WNJPn2BXYANua2MWAU8Gpe/VVAXfr4DWCbvP8ny1rx3t1JFi5pev5JkrM+G4D/Illx7HngZZJe/P55r5+Y83gY8DegMY3xhbzPsjE9xmTg01uJ6yPpe64B3gHWkyTPlSQT0vTMqfvqFrZ5uZ+vtd83sCrn8d+A/XKefxx4Puf5yvS/9cBVafwrgSnAgQU+455pfPOBB4G7SFZ5m0dy1uqfc+p+OP0+NwJ/B07IO9bKvOdfbGF7G7gQ+GJO3T8Do9LHF5DMJXBzur0BnNOWf0vt3Wplta7MiIgnc57eIelk4AjgP6sUUms0AvnX9PqQ/CGpWRHRSPILHuANSeOBf0jqExErqxhaM5K6kPzhfA8YnxZn8juvAb0jYjmApFURMbdpR0Q8Lyl/cYT/A44H7gFeAfZIyyBJdO/kVpZ0+hbee5u8598j+cO+EXgA+G1EjEqPcznJD8yDcuqfxPuLmXwfeIwkESl9/j2SSyUAq4GjgDNJ1gtfAkwFpkbEa3lx/Igksd9A0kv+Jsm4iAbgv9NjX5DW7Qf8K0mSK/T5ZuaVteb7zp28YyfgTzl1nyrUs42IeSS99GskfQo4HXhA0vKIGJ5TdRLw/YjY7O9p2u5vI/nxDvADYDpJD/9AYIKkXSPiu00vyTvEJOA5Nr/kti3wBWAt74+f+VBEPJ8+/ipwcET8LY3jVpIfE5PS56/mx5qjtGexS5X5O+sGPAJ8tdpxtBBbfk91HPD7nOfbkfxaH1ntWLcUd4H9A0n+aGxf7VhzYhJwO/Bbmvd0MvGd19pGctmgT/r45Lx9A4DFeWUfI+n53AHcBLxFktwmkfyBPi+v/gaSU7iPF9ieoHkP+62c/8drad577wEszTt2bg90UdPnSJ/3yo2dnF4gyaxwpwG/AtaRJPozc+sCXXKedwOW5HwnS3P2PUqyMlqh77Y7sLGt3zfJD9Kp6fY2MChnX9+8OFYViiHn856UV7Ya6NZC/W2A1TnPlwJdc54PIbkMdX2h9yY5I/pCgX8L/wB2zCv7OzAkZ/82eXHk/j9eDpwDHFxg+ywl7GH7GnYrSOor6XBJPdLrLGOBA4BfVDu2XGlsPYCuQNemeIH7gT0kHZfuvwp4NiLmVDPeJi3FLWlfSbul1+92IDkt9VhErKhuxM3cStKTOzIicntzNf2d17CbSU7PEhE/ydt3FPBQbkFE/B/JqduFwD8BS0gG/W1D0vPOnxLyZeDyiPh0/kZyzTW3d7YxfY8A5kTEezn7NpAkzmYkDZc0In3tmpxda0h+tG0mIt6JiDsj4lBgOPBL4LKcKvNJLsk12Q9YnD5eRvMzA9eQLMBSyHs0PyMAW/++H855fj1Jgp9L8uOob86+A9K4mzzeQgxNn/eevOIXgPNbeMm56f4mG0kuTzYdbyEwBvispJsLvN8dJN/ZnpKekvTxlmIj+fE9RdJwkh78LZKGpGcPfkDyo67J/wHvRMRv8jeSH075Pf22K1Xm7wwbyS/Np0lOZy4nORV0aLXjKhBnA0kPNHdrSPcdAswhOUX4GFBf7Xi3FjdwMsmpvdUkv3ankvOrvtobyXXKAN4lOQXetI2t9e88axtJMv4PYFGR9f+5UH2SU8jnt/CabiSnvZueP0FyirRQ3f2Bv+aVbSRJ5BvT7VM5+/YkSfpNz1vsgRZ4ryNJrnc/SHJKexVwbLrvY8Dj+Z8j5/GnSJJp01ZX6HNvoX63MtWty6vbdA37dZIfZT9OP+uC9G/Anjl1HwDOKvA5+pKcPWmxZ0tyrf1p3j8Dk9/D7kJypm8Vydmapv+nG9I2vFNO3THAPi28jyhwrb6tm+cSN7Oall4/PYXklOaeJL22WyJielvrSxoUEYsLvb7A8YaQnHpeW2DfvkD3iPjfnLKdIuIfLRxrFDAwIn6bPj8g97VbiWMQySn1w0kSwa8iouDETZLOBz4ZEaelz9eQnEIWyTXbyyJick7980gGz221frnqpvs/TdIROojkjEkvkh+/s0kSZUNEfCun7rsR8XSBz9+b5IfUiNxjR8TjOc+7kIyG/wzJv5XLc48dEY9L6kvyI3EIyQ/uZyPiVUnX59QdWuj/Qa6IWLC1OkUpVeb35s2bt1JtJL3c40h6k++RjMq+kgK9oa3Uf6uF+vkjiGdsIZai65bz2K2s+0ea90bfznm8F/DHttYvV9207C1g3xY+0/eB+TnPlxVbt4hj35h37NbEkdv73lhgK9k17Ko3TG/evHnL39I/mEtJTmd/LKd8swFCbayfPyDprS3EUnTdch67lXXzB+XlDnwU8EZb65erblp2Esn4g4/mld9CcktafRF1f5hft4hjz23rsYG/klzy+gawC+kYnNytVO3Cg87MrBY9S3Itcl9gn5Ymx2hH/dZcC2ztdcNyHbs1dXtJ2jS4LSL2z9m3LZsPfGtN/XLVJZJBaP8K/ELSHgCSJgOHAgdEcnvY1uoekl+3iGMf2NZjR8ReJAMb+5GMd3iYJOFvExEbImIDJeL7sM2s5kTEGCXTWZ5O8ofzZkm/JPkDv9mo7NbWB+okHcT7I3jznxMRj7ahbjmP3Zq6zwGHkdylkO9fSK4J52pN/XLVBZLR3JK6A7+S9CeSCVIOiAJjDlpTt5zHjojngEvT+/IPJbmv/hZJn4nkDoaS8KAzM6t5OZNtnEAyw9dtEXFZW+tLmseWe6wREbu2tm45j93KuieR3I50PvA/EbExHWR1NMlp3Ysj5/at1tQvV920/mdyPs8FJD3a80hmGmv6kI+2tm65j53zHruRDGA7hWRU+xdj8wlw2swJ28wyI72X/fPA6RHx2VLX70gkXQJcTXJ/9lKSudXXAtdExPfaU7+MdbeW3HJ/lBRdt5zHltSP5NbTM0juC78TuCtKNTI8hxO2mVkHpWR50f1IkuQyklHZLU441Jr65aqbNZLeJelN30nONK25CvXG2/ReTthmZmZt09pLJu16LydsK4Ykkfx72VjtWMzMOiPf1tUJSJon6RuSnpf0tpL1pHtI+oCkmZKWpOUzlbPSjqTHJF0v6fckcyDvKulMSa9KWiXpNSXzqTfV/6KkF9Jj/UI5i9YrWQ/4V5LekvSGpG+m5R+XNEvSyrT8xkp+N2ZmWeGE3XmMJZnScATJ7QlXkPz/v51kLuyhJFPvTch73WkkK071JplI4GbgsxHRm2Sd4L8CSDqGZLm/Y0nmXH8caBop2ptkjeifA4OBDwK/SY//A+AHEdEnjW1aKT+0mVlH4YTdeUyIiNcj4i2S1XZOjohlEXFfRKyJiFVp+YF5r5sSEbMjYj3J7TEbSVaf6hkR/4iIpvsozwX+LSJeSOt+B9gr7WV/jmTGo+9HxLsRsSreX1d8HfBBSf0jojEiCg7aMDPr7JywO4/Xcx7PBwZL2lbSf0uaL2kl8L9AX0ldC70uIlYDJ5Lcj/gPSQ9JGpnuHgb8QNJySctJpooUsDPJdH1zW4jrbJIe/xxJT0v6XLs/qZlZB+SE3XnskvN4KLAIuATYjWSS+z4ky91B8/Vbm41KjIhfRLJe704k8+f+KN31OnBuRPTN2XpGxB/SfSMoICJejoiTgR2BG4B7lTOVoZmZJZywO48vK1mAvR/JteafklyXfgdYnpZ/e0sHkDRQ0lFpQl1Lsuxd0zy5/wV8Q9Luad3tJX0h3TcTGCTpQkndJfVWsiwhkk6VNCAdfb48rV+yuXfNzDoKJ+zO48fAL0lWmnmVZHH2m4CeJLMP/YlkUNiWdCHplS8iOeV9IMm0fUTE/SQ95HvS0+vPAZ9N960imV/3SGAx8DLJereQzicsqZFkANpJEfFuuz+tmVkH4/uwO4H0xv5zIuLX1Y7FzMzaxj1sMzOzDHDCNjMzywCfEjczM8sA97DNzMwywAnbzMwsA5ywzczMMsAJ28zMLAOcsM3MzDLACdvMzCwD/j8y6t+hi0oS0gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 504x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=[7,4])\n",
    "from importlib import reload\n",
    "\n",
    "\n",
    "#drop stars where the IWA is larger then the HZ:\n",
    "IWA_AU[IWA_AU>outerHZ_AU]=np.nan\n",
    "\n",
    "#set to the inner edge of the HZ if the IWA is smaller\n",
    "\n",
    "IWA_AU[IWA_AU<innerHZ_AU]=innerHZ_AU[IWA_AU<innerHZ_AU]\n",
    "plt.subplot(131)\n",
    "plt.hist(outerHZ_AU,alpha=.4,label='outer HZ edge',bins=np.arange(0,10,.5))\n",
    "plt.hist(innerHZ_AU,alpha=.4,label='inner HZ edge',bins=np.arange(0,10,.5))\n",
    "plt.hist(IWA_AU,alpha=.4,label=\"IWA, if inside HZ\",bins=np.arange(0,10,.5),histtype=\"step\",linewidth=3,\n",
    "         color=\"black\")\n",
    "plt.xlabel(\"au\")\n",
    "plt.ylabel(\"n systems\")\n",
    "plt.legend()\n",
    "plt.figure(figsize=[7,4])\n",
    "plt.subplot(121)\n",
    "plt.hist(sim.TargetList.dist,alpha=.4,label='HabEx Targets')#,bins=np.arange(0,10,.42))\n",
    "plt.hist(sim.TargetList.dist[[IWA_AU>0]],alpha=.4,label='Visible HZ',\n",
    "         histtype=\"step\",linewidth=3,         color=\"black\")\n",
    "#,bins=np.arange(0,10,.42))\n",
    "\n",
    "#lt.hist(innerHZ_AU,alpha=.4,label='inner HZ edge',bins=np.arange(0,10,.42))\n",
    "#plt.hist(IWA_AU,alpha=.4,label=\"IWA, if inside HZ\",bins=np.arange(0,10,.42),histtype=\"step\",linewidth=3,\n",
    "#         color=\"black\")\n",
    "plt.xlabel(\"parsecs\")\n",
    "plt.ylabel(\"n systems\")\n",
    "#plt.legend()\n",
    "\n",
    "\n",
    "ax=plt.subplot(122)\n",
    "#plt.hist(np.float_(sim.TargetList.specliste[:,1].flatten()),alpha=.4,label='all systems')#,bins=np.arange(0,10,.42))\n",
    "#plt.hist(np.float_(sim.TargetList.specliste[:,1].flatten())[IWA_AU>0],alpha=.4,label='visible',\n",
    "#         histtype=\"step\",linewidth=3,         color=\"black\")\n",
    "#np.float_(TL.specliste[:,1].flatten())[IWA_AU>0]\n",
    "specs=sim.TargetList.Spec.copy()\n",
    "specs.sort()\n",
    "sp_short=np.array([sp[0:2] for sp in specs])\n",
    "sp_short[IWA_AU>0]\n",
    "\n",
    "hist=plt.hist(sp_short,alpha=.4,label='HabEx Targets', bins=np.unique(sp_short))\n",
    "plt.hist(sp_short[IWA_AU>0],alpha=.4,label='Visible HZ',histtype=\"step\",linewidth=3, bins=np.unique(sp_short),\n",
    "         color=\"black\")\n",
    "\n",
    "\n",
    "# get_xticklabels() actually gets you an iterable, so you need to rotate each label\n",
    "\n",
    "\n",
    "for i,tick in enumerate(ax.get_xticklabels()):\n",
    "    #i=i+1\n",
    "    if i % 2:\n",
    "        tick.set_visible(False)\n",
    "        #tickm.set_visible(False)\n",
    "    tick.set_rotation(90)\n",
    "\n",
    "\n",
    "#,bins=np.arange(0,10,.42))\n",
    "\n",
    "#lt.hist(innerHZ_AU,alpha=.4,label='inner HZ edge',bins=np.arange(0,10,.42))\n",
    "#plt.hist(IWA_AU,alpha=.4,label=\"IWA, if inside HZ\",bins=np.arange(0,10,.42),histtype=\"step\",linewidth=3,\n",
    "#         color=\"black\")\n",
    "\n",
    "plt.ylabel(\"n systems\")\n",
    "plt.legend()\n",
    "plt.tight_layout()\n",
    "\n",
    "#plt.savefig(\"WFIRST-CGI-disks-Uno/visible\"+mode[\"systName\"]+\".pdf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((array([ 1.,  1.,  1.,  1.,  2.,  3.,  4.,  2., 10.,  7.,  8.,  8., 16.,\n",
       "         14.,  3.,  6.,  6.,  1.,  2.,  2.,  3.,  9., 10.,  6.,  4.,  7.,\n",
       "          2.,  3.,  1.,  1.,  5.]),\n",
       "  array([ 0.,  1.,  2.,  3.,  4.,  5.,  6.,  7.,  8.,  9., 10., 11., 12.,\n",
       "         13., 14., 15., 16., 17., 18., 19., 20., 21., 22., 23., 24., 25.,\n",
       "         26., 27., 28., 29., 30., 31.]),\n",
       "  <BarContainer object of 31 artists>),\n",
       " array(['A3', 'A4', 'A6', 'A7', 'F1', 'F2', 'F3', 'F4', 'F5', 'F6', 'F7',\n",
       "        'F8', 'F9', 'G0', 'G1', 'G2', 'G3', 'G4', 'G5', 'G6', 'G7', 'G8',\n",
       "        'K0', 'K1', 'K2', 'K3', 'K4', 'K5', 'K6', 'K7', 'M1', 'M2'],\n",
       "       dtype='<U2'))"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bins=np.unique(sp_short)\n",
    "\n",
    "hist=plt.hist(sp_short,alpha=.4,label='HabEx Targets', bins=np.unique(sp_short))\n",
    "plt.hist(sp_short[IWA_AU>0],alpha=.4,label='Visible HZ',histtype=\"step\",linewidth=3, bins=np.unique(sp_short),\n",
    "         color=\"black\")\n",
    "\n",
    "\n",
    "\n",
    "hist,bins"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'O': 0, 'B': 1, 'A': 2, 'F': 3, 'G': 4, 'K': 5, 'M': 6}\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(TL.specdict)\n",
    "#np.float_(TL.specliste[:,1].flatten())[IWA_AU>0]\n",
    "plt.figure(figsize=[6,4])\n",
    "ax=plt.subplot(111)\n",
    "specs=sim.TargetList.Spec.copy()\n",
    "specs.sort()\n",
    "\n",
    "\n",
    "hist=plt.hist(sp_short)\n",
    "plt.hist(sp_short[IWA_AU>0],alpha=.4,label='visible',\n",
    "         histtype=\"step\",linewidth=3,         color=\"black\")\n",
    "\n",
    "\n",
    "# get_xticklabels() actually gets you an iterable, so you need to rotate each label\n",
    "i=0\n",
    "for tick in ax.get_xticklabels():\n",
    "    i=i+1\n",
    "    if i % 4:\n",
    "        tick.set_visible(False)\n",
    "    tick.set_rotation(90)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$0.15 \\; \\mathrm{{}^{\\prime\\prime}}$"
      ],
      "text/plain": [
       "<Quantity 0.15 arcsec>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sim.OpticalSystem.IWA#IWA.to(u.arcsec).value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$13.602647 \\; \\mathrm{pc}$"
      ],
      "text/plain": [
       "<Quantity 13.60264683 pc>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#find median distance\n",
    "np.median(sim.TargetList.dist[IWA_AU>0])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'EEID (AU)')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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lypRImxYRlDNWkzm0AbJe0b7HAmG098UXX+SCCy6gTp06dO7cmU8++STkc9iZM2cOnTp1ok6dOrRt25a33nqr0jEvvPAC6enptG7dmvfff1/XuP/9739p27Yt+fn5AKxatYomTZrgqRB8wIABAKSlpZGSksL//vc/9u7dy1VXXUWDBg1IT09n9OjRnDlzxnFO69ateemll7joootITk7GYrEwb948WrVqRYMGDZg+fTqtW7dm9erVANhsNsd9bdCgASNHjuTUqVNe59+zZw8DBw4kNTWV9PR0br/9dv03NYqRUhZJKY/av9BavJ0v11BURBCDQfDmmF68OKIb3Zqlkp4SR7dmqbw4oltIZC3s0hl/+WQLW3LOkldQypacszzx8Rbunv9TyByy4cOHc+rUKbKyKsTCT58+zYoVKxg3bhyAy9/mhg0b6N27N3Xr1qVx48Y8/PDDgOeI9d69e+nTpw+pqakMGzbM8TfsztmzZ5k4cSJNmzalWbNmTJkyxZHurCoWi4WRI0cyZMgQ/vCHP4RkzFhDOWM1FXv7pa+f175Hu0MWZnsvuOACsrKyOHv2LFOnTmXMmDEcOXIkpHPYadSoEStWrCA/P585c+bw0EMPsWlTRcbq6NGj5OXlkZOTw9y5c/njH//Izp07/Y57++23c+mllzJ58mROnjzJxIkTmT17Np4KwdetWwfAmTNnKCgo4NJLL0VKyRNPPEFubi47duzg0KFDTJs2zeW8BQsWsHLlSs6cOcOuXbu49957ef/99zly5Ahnz54lJyfHceyMGTNYsmQJa9euJTc3l3r16vGnP/3J6/xPPfUUgwYN4vTp0xw+fJj7778/4HsbC0gpp0kpK+doFBHBYBAMu7gZy+/vz8Yp17L8/v4Mu7hZSPTFnKUznCkuszqkM0JBYmIiI0eOZN68eY5tixYtomPHjh7LGB544AEeeOAB8vPz2bt3LyNHjvQ69rx583jnnXfIzc3FZDIxefJkj8eNHz8ek8nEnj172Lx5M1988YXXerNAeeyxxygsLOT1118PyXixiHLGahr26NIvH8RW+6Uwt4u67bbbyMjIwGAwcPvtt9O+fXs2bAjO4Zs8eTJpaWmOr5tuusll/+DBg7ngggsQQjBw4EAGDRrk8okWYPr06cTHxzNw4EAGDx7MokWLHPv+/ve/u4w/fryjdStvvPEGX3/9NVdccQVDhgypNLcv2rVrx7XXXkt8fDwNGzbk4YcfZu3atZWurUWLFiQmJvLRRx8xZMgQ+vfvT1xcHM8++yxCVPwDe+utt3j++edp3rw58fHxTJs2jY8++shritNsNpOdnU1ubi4JCQkuiyYUiljELp3hiVBLZ4wfP54PP/yQ4mJtBei8efNcng3OmM1m9uzZQ15eHikpKfTr18/ruGPHjqVr164kJyczffp0Fi1aVCnidezYMVatWsWrr75KcnIyjRo14qGHHmLhwoVex120aJHLcywtLc3jcYsXL2bOnDksXryYhIQEP3eh5qKcsZqEc3Rp8wdgMMZO+6Uwt4uaN28eF198seOhsHXrVvLy8oIaa8aMGZw5c8bxtWLFCpf9q1atol+/ftSvX5+0tDQ+/fRTl7nq1atHcnKy43WrVq3Iza34BP3oo4+6jD937lzHvrS0NG677Ta2bt3KI4/4lLSqxPHjx7njjjto1qwZdevWZcyYMZXuQYsWLRw/5+bmurxOSkqiQYMGjtfZ2dncfPPNjnvaqVMnjEYjx44d8zj/yy+/jJSSPn360KVLF955552A7Fcoog1/0hihlM7o378/DRs2ZOnSpezbt48ff/yR3/3udx6Pffvtt9m1axcdO3bkkksuqfSMcsb5b7xVq1aUlZVVei5kZ2dTVlZG06ZNHX/vd911F8ePH/c67siRI12eY84lEXZ2797NxIkTeffdd2tt4b6d2Kv6U3jHObpkA3qNh9TmsdF+KYztorKzs5k0aRJfffUVl156KUajkYsvvrhSMWwoKCkpYcSIEcybN49hw4ZhNpsZPny4y1ynT5+msLDQ4ZAdPHiQrl276hr/559/5p133mHUqFFMnjyZzz77zONxzhEsO0888QRCCH799VcaNGjAkiVLuO+++7ye17RpU5f0aXFxMSdPnnS8btGiBe+88w6XX355pbmys7MrbWvSpAmzZs0CYP369VxzzTUMGDCAdu2UWL0iNmmamkheQanP/aFk3LhxzJs3j507dzJo0CAaN27s8bj27duzYMECbDYbH3/8MbfeeqvL364zhw5VSOMdPHgQs9lMenq6y/YWLVoQHx9PXl5eyBYLFBUVMWLECO6++26GDRsWkjHtSCk5U1xG3rkSyqwSs1GQXieetESzx2djNKAiYzUJ9+hS91GQ+Uj0O2J2WvQJi72FhYUIIRy1VXPmzGHr1q0hncNOaWkpJSUlNGzYEJPJxKpVq/jiiy8qHTd16lRKS0vJyspixYoV3HbbbX7HPn/+PGPGjOGFF15gzpw55OTkMHPmTI/HNmzYEIPBwL59+xzbzp07R0pKCmlpaeTk5PC3v/3N53y33nory5cv57vvvqO0tJSpU6e6OJV33303Tz75pMPxOnHiBEuXLvU6/4cffsjhw4cBLToohAj7EnuFIpzYpTM8EQ7pjHHjxrF69WpmzZrlNUUJMH/+fE6cOIHBYHCkB739rc2fP5/t27dTVFTE008/za233lrp2KZNmzJo0CAeeeQR8vPzsdls7N27t1KZQyDcc8891K9fn+effz7oMTwhpST7ZBE5p4spLrNisdkoLrOSc7qY7JNFYfkQHgqUM1aTsEeXrnpS+x4rTliY6dy5M4888giXXnopjRs3ZsuWLR6jOaGgTp06zJgxg5EjR1KvXj0++OADhg4d6nJMkyZNqFevHhkZGYwePZo333yTjh07Ova//PLLLto76enpgBbZat68Offccw/x8fHMnz+fKVOmsHv37kp2JCUl8eSTT3L55ZeTlpbG999/z9SpU9m0aROpqakMHjyYW265xee1dOnShddee4077riDpk2bUqdOHRo1akR8vCae+cADDzB06FAGDRpEnTp16NevHz/88IPX+X/88Uf69u1LSkoKQ4cO5V//+hdt2sSGzpNC4Qln6QxnQimd4Uzr1q257LLLKCwsrPRcceazzz6jS5cupKSk8MADD7Bw4UKv9Vhjx45lwoQJNGnShPPnzzNjxgyPx82bN4/S0lI6d+5MvXr1uPXWW4NeBHXw4EHmzZvH999/T2pqaiW9sapwpriMghILNimpaywkw3yCusZCbFJSUGLhbHFZlcYPFyJavUR/9O7dW27cuDHSZiic2LFjB506dYq0GYowUVBQQFpaGrt37w67E+Xtd0kI8ZOUsndYJ68m1DMsugj2+WWzSZb/msvsrP0cOVtM09RE7sxsw5CLMkKyYlMRGLuPnaO4zEpdYyEt4o5iRGJFcKi0CfnWZBLNRto3rhNWG4J5fqmaMYVC4ZXly5dz9dVXI6Xk0UcfpVu3brRu3TrSZikUUYNdOmPYxc0ibYoCKLNqAaYUQxFGtJ+NSFIMReRbkx37ow2VplQoFF5ZunQpGRkZZGRksHv3bhYuXBi1BbAKhUJhNmrPpwJbEla0n60ICmxJLvujDRUZUygUXpk9e3bIhB0VCoUi3KTXiSfndDH51mQOlTYhxVBEgS2JfGsyBiFoWCc+0iZ6RDljCoVCoVAoagRpiWbOFmlF/PnWZPKtmoSQQQhS4k2kJpojbKFnlDOmUCgUCoWiRiCEoFWDJM4Wl3HCSWesYZ14UqNYZ0w5Y9HCoQ1hETxVKBQKhaI2IYQgLSmOtKS4SJuiG+WMRQP2NkbWUk2sVWmEKRQKhUJRa1CrKaOBMDfJVigUCoVCEb3odsaEEGYhRKYQ4vby18lCiGR/5yl0EOYm2QqFQqGoubz77rv079/f8VoIwZ49e8Iy15o1a2jevHlYxq7N6HLGhBDdgF3ALODt8s0DgXfCZFftQrUxUigUCkU1cMUVV1SSqwmlgzVhwgTi4uJc2ht179494HEOHDiAEAKLxRISu6J9fr2RsX8DT0spOwL2xk5rgf7eT1EERJiaZCsqaN26NXFxceTl5blsv/jiixFCcODAAY/nTZo0SWltKRQhxGaTLNmcw5DX1tP7uS8Z8tp6lmzOwWaLTnV0RWA89thjFBQUOL5++eWXarchUk5csOh1xroA88t/lgBSykIgMRxGKRThok2bNixYsMDxesuWLRQXF/s858Ybb2TlypXhNk2hqBXYbJK75//EXz7Zwpacs+QVlLIl5yxPfLyFu+f/pBwyL7z44otccMEF1KlTh86dO/PJJ5+Eba45c+bQqVMn6tSpQ9u2bXnrrbcqHfPCCy+Qnp5O69atef/993WN+9///pe2bduSn58PwKpVq2jSpAknTpyodOyAAQMASEtLIyUlhf/973/s3buXq666igYNGpCens7o0aM5c+aM45zWrVvz0ksvcdFFF5GcnIzFYmHevHm0atWKBg0aMH36dFq3bs3q1asBsNlsjvvaoEEDRo4cyalTp7zOv2fPHgYOHEhqairp6encfvvt+m+qH/SupjwA9AIcXW2FEH2A8CSlFTWClza8xG+nfgvrHB3rd+TxPo/rPn7s2LHMmzeP+++/H4C5c+cybtw4pkyZ4vWca6+9lkmTJlFaWkpcnLZU+tNPP+XRRx/l0KFD1K1bl4ceeohHH32Ud999l9mzZ7N+/XrH+UIIdu/eTbt27ZgwYQLJyckcOHCAdevW0blzZz744AMuuOCCIO+AQhFbLPsll/V78igqtXJN3R/on7KJ9QU9WZ3fl6zdeSz/NTc2+zweXgZHvoCmg6D50JAPf8EFF5CVlUWTJk348MMPGTNmDHv27KFp06Yhn6tRo0asWLGCtm3bsm7dOm644QYuueQSevbsCcDRo0fJy8sjJyeH77//nhtvvJHevXtz4YUX+hz39ttvZ9myZUyePJlXXnmFiRMnMnv2bBo2bFjp2HXr1tGmTRvOnDmDyaS5Knv27OGJJ55gwIAB5OfnM2LECKZNm8arr77qOG/BggWsXLmS9PR0du3axb333stnn31Gnz59+Mtf/kJOTo7j2BkzZrBkyRLWrl1Lw4YNmTx5Mn/6059YsGCBx/lHjRrFoEGD+OabbygtLWXjxo2ECr2RsaeAlUKIZ4A4IcQTwIeA9/9gCkUU0q9fP/Lz89mxYwdWq5X//ve/jBkzxuc5KSkpXHzxxWRlVaxynThxIm+99Rbnzp1j69atXHXVVbptWLBgAVOnTuX06dO0a9eOJ598MujrUShijbfX73c4YjNavsyE9JXMaPky19T9geIyK7Oz9kfaxMA5vAy+HQW739C+H14W8iluu+02MjIyMBgM3H777bRv354NGzYENdbkyZNJS0tzfN10000u+wcPHswFF1yAEIKBAwcyaNAgl+cfwPTp04mPj2fgwIEMHjyYRYsWOfb9/e9/dxl//Pjxjn1vvPEGX3/9NVdccQVDhgypNLcv2rVrx7XXXkt8fDwNGzbk4YcfZu3atZWurUWLFiQmJvLRRx8xZMgQ+vfvT1xcHM8++6yL6Otbb73F888/T/PmzYmPj2fatGl89NFHXlOcZrOZ7OxscnNzSUhIcFk0UVV0RcaklCuEEDcAd6LVirUCbpFS/hQySxQ1jkAiVtWJPTo2cOBAOnbsSLNm/j+F21OVV199NaD9UW7fvp3u3btTr1496tWrp3v+W265hT59tNrA0aNH8/DDDwd3IQpFDHLkrFYW0D9lE0mGEgCSDCX0T9nE6vy+jv0xxZEvwFqk/Wwt0l6HODo2b948/vGPfzhqWwsKCirVv+plxowZ3HnnnY7Xa9ascflQumrVKp555hl27dqFzWajqKiIbt26OfbXq1eP5OQKMYVWrVqRm5vreP3oo4/y3HPPeZw7LS2N2267jX/84x8sXrw4ILuPHz/O5MmTycrK4ty5c9hstkrP3hYtWjh+zs3NdXmdlJREgwYNHK+zs7O5+eabMRgq4lJGo5Fjx455nP/ll1/mqaeeok+fPtSrV49HHnmEP/zhDwFdgzd0S1tIKTdJKe+VUg6WUt6tHDFFrDJ27Fg++OAD3n33XcaNG6frnBtvvJFPP/3U8Xrx4sV8+umntGrVioEDB/K///1P9/xNmjRx/JyUlERBQYF+4xWKGKdpqlZqvL6gJ0U2rWlzkS2e9QU9XfbHFE0HgTFJ+9mYpL0OIdnZ2UyaNInXX3+dkydPcubMGbp27YqUoa+vKykpYcSIETz66KMcO3aMM2fOcOONN7rMdfr0aQoLCx2vDx48SEZGhq7xf/75Z9555x1GjRrF5MmTvR7nqW3RE088gRCCX3/9lfz8fObPn1/pHjif17RpUw4fPux4XVxczMmTJx2vW7RowapVqzhz5ozj6/z58zRr1szj/E2aNGHWrFnk5uby1ltvce+994ZMQsSrMyaEeFbPV0isUCiqkVatWtGmTRs+/fRTbrnlFl3ndOzYkbKyMvbt2wfAJZdcwtKlSzl+/DjDhw9n5MiRACQnJ1NUVOQ47+jRo6G/AIUihpnYvw2JZiOr8/sy+eBjvJs3mMkHH2N1fl8SzUbuzGwTaRMDp/lQuHwBtP+T9j3EUbHCwkKEEI7aqjlz5rB169aQzmGntLSUkpISGjZsiMlkYtWqVXzxxReVjps6dSqlpaVkZWWxYsUKbrvtNr9jnz9/njFjxvDCCy8wZ84ccnJymDlzpsdjGzZsiMFgcDxzAc6dO0dKSgppaWnk5OTwt7/9zed8t956K8uXL+e7776jtLSUqVOnujhvd999N08++STZ2dkAnDhxgqVLl3qd/8MPP3Q4d/Xq1UMIgdFo9HvdevAVGWvh9NUe+DNwNdAOuKr8dfuQWKHQz6ENkPWK9l0RNG+//TZff/21S6jdH/boWGlpKe+//z5nz57FbDZTt25dxx9k9+7d2bZtGz///DPnz59n2rRpYboChSI2Gdo9g8z26Q6HbFruPQ5HLLN9OkMu0hdhiTqaD4VLXg9L8X7nzp155JFHuPTSS2ncuDFbtmzh8ssvD/k8AHXq1GHGjBmMHDmSevXq8cEHHzB0qOs1NWnShHr16pGRkcHo0aN588036dixo2P/yy+/7KIzlp6eDmiRrebNm3PPPfcQHx/P/PnzmTJlCrt3765kR1JSEk8++SSXX345aWlpfP/990ydOpVNmzaRmprK4MGD/X6Y7tKlC6+99hp33HEHTZs2pU6dOjRq1Ij4eC0i+8ADDzB06FAGDRpEnTp16NevHz/88IPX+X/88Uf69u1LSkoKQ4cO5V//+hdt2oTow4OU0u8XsBAY4bbtFmCBnvPD8dWrVy9Z6zj4g5TTG0s5rZ72/eAPkbbIhe3bt0faBJ+0atVKfvnll5W2l5WVSUDu37/f5/mffvqpvP7662VJSYm87rrrZFpamqxTp47s3bu3zMrKchz33HPPyQYNGsjmzZvL9957TwJy9+7dUkopx48fL5988knHsd98841s1qxZaC6wBuHtdwnYKCP0zAn1V618hpVjtdrkks2H5U0zsmSv6V/Im2ZkySWbD0ur1RYxm6L9+aWoOufOnZNGo1Hu27cvrPME8/wSUkfOWQhxFqgvpbQ6bTMCp6SUqaFxCwOjd+/eMpTLSmOCrFfg6+e1HpbCqCn2Zz4Saasc7Nixg06dOkXajLBhryU4fPgwiYkxWNcSQ3j7XRJC/CSl7B0Bk0JOrXyGRTE1/flVW1m+fDlXX301UkoeeeQRfvjhBzZt2uSxJixUBPP80lvAvwf4k9u2e4G9AVmoqBqqh2VESUhI4OWXX3YpAFUoFApF9LJ06VIyMjLIyMhg9+7dLFy4MKyOWLDoFX29E/hECPEYkAM0AyxoqcqQIIRoDcwELgVKgI+AB6WUsdXTIJzYe1geyNIcMdU6KaR06dLFUcjpzFtvvcXo0aMBTV9MoVAoFLHB7NmzY6KdnV6dsc1CiPZojlJT4AjwPyllme8zA2ImcLx8/DTgS7To24wQzhH7tOijnLAwsW3btkiboFAoFIpaiN7IGOWO17ow2tIGeF1KeR44KoT4DK0npiKGkFJGZQhYETvoqWNVKMKBzWZzEQBVKAIl2OeXrt86IcQhIcRBT19BzeqZfwF3CCGShBDNgBuAz9zs+KMQYqMQYqOnxqKKyJKQkMDJkyfVP1NF0EgpOXnyJAkJCZE2RVHLSE5OJicnh9LSUvUMUwRFVZ5feiNj7s37mgIPoElehIq1wCQgHzACc4ElzgdIKf8D/Ae0lUghnFsRApo3b87hw4dRjrKiKiQkJNC8efNIm6GoZTRv3py8vDyys7O99iZUKPwR7PNLb83YWvdtQog1aJGrfwU8a+WxDMDnwFvAZUAK8A7wEvBYVcdXVA9mszl0AngKhUJRjRgMBho1akSjRo0ibYqiFqK7ZswDJWh1XqGgPprS/+tSyhKgRAgxB3iOGuaMZZ8sZFbWPpZszqWwxEJyvInhPTKYlNmWVg30q8ErFAqFQqGoGehyxjz0oEwCbgRWhcIIKWWeEGI/cI8Q4u9okbHxwC+hGD9a+Gbnce6dv4kyqw2LTcuyFpRYWLjhEIt/ymHmmJ5ceaH6VKZQxBpCiPlo7eKSgaPAy1LK6F9Pr1AoogK9y0ZauH0lAP9Ac5hCxS3A9cAJNJFZC/BQCMePKNknC7l3/iaKy6wOR8yOxSYpLrNy7/xNZJ8sjJCFCoWiCvwVaC2lrAsMBZ4TQvSKsE0KhSJG0JumfEJKedR9oxCiCdqnwCojpfwZuCIUY0Ujs7L2UWa1OV7fYfiKG4wbWGXtw0Lb1QCUWW3MztrP9OFdI2WmQqEIAimls0idLP+6APgpMhYpFIpQsPPoOdqkJxNnCq/kid7Rd3nZvj1UhtR0lmzOdUTE7jB8xV/NbzPAsIW/mt/mDsNXgBYh+2RzTiTNVCgUQSKEmCmEKAJ+QxPG/jTCJikUiiDJP1/GM8u3ceOMLOZ+dyDs8+l1xiqpeAoh6gI2D8cqPFBYUrFU+gbjBgDs2qj21wCFpSFeUn1og9Zg/NAG/8cqFIqgkVLeC9QBMoGP0RY5uaC0EhWK6EZKySebD3PV39fy7ncHuOOSFtzWO/xSOz7TlEKIQ2jh9kQPAq8NgAXhMqymkRxvoqDcIVtl7cMAwxbsuoKrrBXtjZLjqrLA1Y1DG2DuULCWao3Fxy9TrZQUijAipbQC64UQY4B7cGvnprQSFYroZefRczy1dCsb9p+ie/NU3pnQm4uap1XL3P7+849Bi4p9Cox12i6BY1LKneEyrKYxvEcGCzccwmKTWo1YGZVqxkwGwc09moVu0gNZmiMmrdr3A1nKGVMoqgcTWs2YQqGIcs6dL+PV1bt597sD1E0w8ddbunF77xYYDNXX2s+nM2YXexVCpEspi6rHpJrJpMy2LP4pB4vNCsBC29UOJ8yO2WjgzswQiqa2ztQiYvbIWOvM0I2tUCgAEEI0Aq4CVgDFwDXAKOB3kbRLoVD4RkrJsl9yeW7lDvIKSrjjkpY8dt2F1EuOq3ZbvDpjQognpZTPl7/8s7fmz1LKp8NhWE2jVYNkZo7pWUlnDLSImNloYOaYnqEVfm3RR0tNHsjSHDEVFVMowoFES0m+iVaHmw08KKVcGlGrFAqFV3YdO8dTS7byw/5TXNQ8lVnjenNxi7SI2eMrMuZcsdYi3IbUBq68sBGfPZjJ7Kz9fLI5h8JSC8lxJm7u0Yw7M9uER4G/RR/lhCkUYURKeQIYGGk7FAqFfwpKLPxr9S7mfHuAlAQTL9zcjdsvaYGxGlOSnvDqjEkp73H6+ffVY07Np1WDZKYP76q0xBQKhUKhqCaklCz/9QjPr9zO8XMl3HFJC/7vuo7Uj0BK0hO6l+4JIVKBC9FaFTmQUn4daqMUCoVCoVAoQsHuY+d4euk2/rfvJN2apfLmmF70aFkv0ma5oLc35QTgDaAAcC7kl0Db0JulUCgUCoVCETwFJRZe+2o3b6/fT3K8ieeGd2VUn5YRT0l6Qm9k7HngVillSBqD13ayTxYyK2sfSzbnUlhiITnexPAeGUzKbBueurGqcmiDWgSgqJEIIXoCg4HuQBpwBvgFWCWl3Bg5yxQKRbBIKVnx6xGeX7mDo/nnub13Cx67/kIapMRH2jSv6HXGTMAX4TSktvDNzuOVVlQWlFhYuOEQi3/KYeaYnlx5YaMIW+mEEo5V1ECEEIOAF9AU89cC3wLnyl93At4XQhQAf5FSfh4xQxUKRUDsOV7A1GVb+XbPSbpk1GXmmJ70jLKUpCf0OmMvAVOEENOllKoFUpBknyzk3vmbKC6zVtpnsUksNiv3zt/EZw9mRk+ETAnHKmomdwH3SCl/9HaAEOIS4HFAOWMKRZRTWGLhta/38Pb6fSSajUwf1oXf9W0VlSlJT+h1xh4CmgCPCSFOOu+QUrYMuVU1lFlZ+yizVviyPcUu+hl28L2tE5tkBwDKrDZmZ+2PntWWSjhWUQORUo7QccyPwK3VYI5CoQgSKSWfbjnKcyu3c+TseW7r1ZzHb+hIehSnJD2h1xkbE1YraglLNuc6UpM9xS7ej3sBMxbKMDG69C9skh2w2CSfbM6JHmcsUOFYVV+miBGEEJ4WH5UBOSoDoFBEP3uOFzBt2TbW78mjc9O6vP67HvRqVT/SZgWFLmfM3hZJUTUKyxuFA/Qz7MCMBZOwgbTQz7CDTVYtOlZYavE2RGTQKxyr6ssUscUetBXh7nkMixDiQ+BeKeXZ6jdLoVD4oqhUS0nOztpHgtnIM0O7MLpvS0xGQ6RNCxq90hbPetlVAhwGPpNSHguZVTWU5HgTBeUO2fe2TpRhAqlFxr63dao4Lk63/Ft0oerLFDGElLLSk1sIYUKT63ke+Bvwx+q2S6FQeEZKyWdbjzJ9xXZyz57n1l7Nefz6jjSsE1spSU/o/a/fAbgZ2AAcQmuP1AdYDgwBZgohRkgpPwuLlTWE4T0yWLjhEBabZJPswOjSv1SqGTMZBDf3aBZhS4NE1ZcpYhwppQXYJYS4C/g10vYoFAqNfScKmLpsG1m78+jUtC4zRvWgd+vYTEl6Qq8zZgDukFJ+Yt8ghBgG/E5K2U8IMR54EVDOmA8mZbZl8U85WGzaaspNsoMjNWnHbDRwZ2abSJhXdVRjckXNIR9IirQRCkVtp6jUwhvf7OE/6/aRYDIybUhnxvRrFdMpSU/odcauA0a5bVsBvFf+83zg9VAZVVNp1SCZmWN6VtIZAy0iZjYamDmmZ/TIWgSDe32ZKuhXxCa3A9sibYRCUVuRUvL5tmNMX7GdnDPF3NKzGX++oSON6iRE2rSwoNcZ2wvcg6vDdXf5doB0oDCEdtVYrrywEZ89mMnsrP18sjmHwlILyXEmbu7RjDsz28S2I+aOKuhXRDFCiPfQCvidMQOt0frw3ljdNikUCtifV8i0ZdtYu+sEHZvUYdFdl9KnTc1JSXpCrzN2J/CxEOJxIAdoBliBW8r3Xwg8FXrzaiatGiQzfXjX6JGvcCaUkSxV0K+IbvZ42GYBPkVblHSimu1RKGo1xaVWZq7Zw1tr9xFnMvD0TZ0Zd2nNS0l6Qq+0xSYhRHvgUqApcAT4n5SyrHz/OmBd2KxUVA+hiGQ5O3OqoF8RxUgpn/G1XwjRWUq5vbrsUSiiBZtNsuyXXN5ev58jZ4tpmprIxP5tuKlbU1ZsOVJp+9DuGRiqoHQvpeTL7cd4ZrmWkry5RzOeuKEjjerWzJSkJ3RrKJQ7Xsrhqil4ioBVNZLlyZnTW9CvassUUYAQogFafewEoBsQ+2vmFYoAsNkkd8//ifV78igq1Rab5RWU8sTHW3j+0x0UnC+juMzmsv3TLUd4c0yvoByyA3mFPLN8G9/sPMGFjevw3z/2o2/bBiG9plhAr85YXWAaMBCtPsxxx1U7pBjEWwSsqpEsT85c5iP6VPtVbZkiQpRri90EjEerEzMBL6PJ9igUtYplv+Q6HLFr6v5A/5RNrC/oyer8vo6+yu7bs3bnsfzXXIZdrF+WqbjUyr/X7OHN8pTklMGdGH9Za8y1ICXpCb1XPRPoCTwL1AfuBw4C/wyTXYpw4slpggppique1L4DZL2iOUt6sDtzwhiYM+fNHoUijAghegshXgOOArOBY8C1wAngn1LKI5G0T6GIBG+v3+9wxGa0fJkJ6SuZ0fJlrqn7A4DH7cVlVmZn7dc9x5fbj3HtP9cy4+s93NCtCV89MpA7M9vWWkcM9KcpBwGdpJQnhRBWKeVSIcRGNNFX5ZD5IftkIbOy9rFkcy6FJRaS400M75HBpMy2kVk96SsCZpemCCZaFazOWCAROXs6M7EBFJ9UaU1FVdgAnAQmA4vKBV8RQrivsFQoag1HzhYD0D9lE0mGEgCSDCX0T9nE6vy+Xrfbz/NF9slCnlm+na9/O077RiksmNSPSy+ofSlJTwQi+mrv0VYghEhDK+JvFw6jahLf7DxeSVesoMTCwg2HWPxTDjPH9OTKCxtVr1F6nKZg68f09rEM1B6ocBAtJYANhAGM8SqtqQiWZ4ExwCxgqBDifWAVleUuFIpaQ9PURPIKSllf0JOR9VeTZCihyBbP+oKeAF63N01N9Drm+TIr/16zl3+v3YvZIGp9StITep2xX9Dqxb4CsoA3gAJgV5jsqhFknyzk3vmbHHl2Zyw2icVm5d75m/jswczqj5D5c5qqeyWkHifO7iCiFY8ibUoyQxE0UsppwDQhxABgHJp4dRmQgla8/1XkrFMoIsPE/m144uMtrM7vy+SDj7nUhgEetyeajV47x3y14xjTlm/j0KlihnbP4MnBnWhci1ZJ6kWvMzaJiqL9ycBfgTS0B5jCC7Oy9lFmtTle9xS7KvWiLLPamJ21P/o0x6KxtZHdQXSJjCnJDEXVsEvzCCHuR9NOHAd8LoTYJKWMgl98haL6GNo9g0+3HCFrdx6r8/s6nLBEs5GUBBPnissqbc9sn86QizJcxjl0qohnlm9j9Y7jtGuUwgeT+nLZBenVfj2xgpAyNiPyvXv3lhs3boy0GT7pOvVzCkosgOaIvR/3AmYslGFidOlfHA5ZSryJrc9cF0lTY4dDG+CXBVBwDFIaQ/dR0eEoKqoFIcRPUsre1TBPM2CMlPKlcM0RC88wRe3EZpMs/zWX2VkVemJ3ZrZhcNemrNx6pNL2IRdV6IydL7Py1tp9zFyzB6NB8OA17ZlwWRviTCol6ev5pVtnTAiRCfRAC+E7kFK+UDXzai6F5Y4YQD/DDsxYMAkbSAv9DDscTcILSy3ehlB44ucFFenT7u4tUxUK/wghGkkpj3vbL6XMAV4SQjSWUh6rRtMUiqjAPU4jJRgMgmEXN/MqYfHNb8eZumwbB08VcdNFTXlycCeftWR68SZC601sNtDjowG9OmOvASPR6sWcl0zEZlitmkiONzkiY9/bOlGGCaQWGfve1qniuDh9PnHUrcoMlqoIvKoWS4rQ8I0QYi3wHvCDlNJRTyCEMAB90NKVA4AoqyFQKMKHL9FXb+Kuh04V8eyK7Xy5/RgXNEzm/Tv7cnm70KQkA7UnGPujAb2RsdFAVyllbjiNqWkM75HBwg2HsNgkm2QHRpf+pVLNmMkguLmHf6E8Pasy26YnR7+zVlWBV9ViSREaegB/BP4DtBVC7APOAXWAtsDu8n0PRspAhSIS+BJ9dRd3PV9mZda6fbz+jZaS/PMNHfnD5aFNSQZiTzDHRwt6nbFDQEk4DamJTMpsy+KfcrDYNO98k+zgSE3aMRsNXleh2NGzKvOueT9hEPbXUSCh4S36pSey5StyFo0LCxQxh5SyFHgdeF0I0QJt9WQacBr4tTxNqVDUOtxFX5MMJYysv5rJBx9jdX5fZmftZ9jFzViz8zjTlm3jwMkiBnfTUpJN6iYEnB70l1LUa0+g9geKs525Z4pIjDMhgKIyKxkhSIPqdcYmArOEEAvQVKodlK9ECglCiDuAqUBLNFXsCVLKmJVjb9UgmZljelaKaIEWETMbDcwc09Nv1ErPqsxSp/3ORERCw1f0y19kS0/kLFAtM9X3UuEDKeUhtA+cCkWtx5/o6+HTRdz13kY+33aMtunJvDexD5ntGwaVHtRzjj973MVmAz1eD57spLDMsf9kCNKgep2xXsANaPUT7jVjIelNKYS4FngJuB1NGbtpKMaNNFde2IjPHsxkdtZ+PtmcQ2GpheQ4Ezf3aMadmW10OUdLNuc6HDlfqzLthFRCIxhHxlf0y19kK9Q1Yb6cO+WkKRQKhQveRF/XnesFwJniMtbtyuOx6y9kYv82xJuMQHDpQT3n+BOhdV8gEOjxevBmJxCyNKheZ+wFYIiUcnXAM+jnGeBZKeX35a9rTJqgVYNkpg/vGrSWmN5VmeDdWbPYJJ9szgnMhmDru/xFv5xbLmW94uoMhbomzJtzp5qTKxQKRSU8ib4uPXMlm4s6AnBRs1RmjulFszRXpyaY9KCec3yJ0HoSmw30eD14snNU/c8BQbyhLCRpUL3OWCEQsnSkO0III9AbWCaE2AMkAEuA/5NSBh5TjHICXRWpd1UmhFhCoyotkfzVdXlzhkJdE+bNuftlAVjOA1KtylQoFIpyvIm+CqBHyzQ+uvsyj2m4YNKDes7xJULrSWw20OP14MnOeEPF/9OqpkFB6zmph6eBV4UQTYQQBuevoGatTGPADNwKZAIXo612muJ8kBDij0KIjUKIjSdOnAjR1NXLNzuPc/2rWSzccIiCEguSikL761/N4pudlaWPhvfIwFT+y29flfkPy20eU5R2Z80iDUFLaDiwOzLCGHiUqkUfyHzEu4PjydHTe24g2J27XuPh4ju0bYc2wOb5OJRZDCa1KlOhUCjQtMT+dcfFXN2pEaLc52pcN56/33aRV0cMKtJ/6wt6UmSLB/CbHtRzjsEgeHNML14c0Y1uzVJJT4mjW7NUXhzRzWN9VqDH68GTnSU2EyU2s67r1IMuBX4hhL063PlgAUgppTGomV3HrwecQivYn1u+bQQwRUrZw9M5sahenX2ykOtfzfK4KtJOotlYqdBez3nOeKoZMxkEo/q0rJ6aMb3jhjJN6MtO97kuHgU/zdUcQQT0ngA3vVqFi1FUF6FW4BdCdAamoX0IrI/2HMoCpkkpt4dqHk/E4jNMUfNZt+sE05ZtY19eIdd3acKUmzrRvF6S3/OWbM7hiY+3UFxWuf4r0WzkxRHdKqXvgjknEnizEwjI5lAo8AeeZA0AKeVpIcRhariIbLC9Kv2tyjQaBBIotWhjByuh4ZFAVy4GMm6w6Uh3x8ufY+cehUO6pi67/y7kl6eIfoQQ7YHvgbXAX4BcoBlwM/C9EOISKeXOCJqoUFQbuWeKeW7ldj7dcpTWDZJ49/eXcIUfOSQXuYezxZiNAotN6E4PhiOlGA6Gds9g5a+5rNl1wsVOIGQ263LGpJTZQY0eGHOA+4UQnwFlaGKLK6ph3mpDz6pIb4X2/lZl7ssrrLKERrXj7OjpjcB5crz81ba51411/532pVZS1naeAN6TUv7Jbfs75V1H/gz83t8gQoh4YCZwDVp0bQ/wFynlqhDbq1CEnFKLjbfX72fGV7uRSB4d1IE7M9uSYPad9PIo9wDEGQ3UTTBiNgoy0pIq9a50xp5S9NQH09s5kUMgtISg0xYwGwUdGtdh0oC2VbI5wCKisDIdSAd2AeeBRcDzEbUoxFS1V6WvVZmtGiRXWUIjYgSSsvTkeOlZvekpCqecsNrOQDQHyhOvAN/oHMeEplM2EDgI3AgsEkJ0k1IeqKqRCkW4WL87j6eXbWXfiUIGdW7MUzd1pkV9/ylJ8C1LYTQIpg/vqivF6K/fZTSw7Jdcvt2bR6nV5uFaDUwa0LbK9keNMyalLAPuLf+qkYS6V6U7VZXQiBiBrNr05HjpSXmGK92qiGUaAge87DuI9uHQL1LKQrS6MzsrhBD70fQZvY2vUESMI2eLeW7lDlb+eoRWDZKYM+ESruwYWIeWcCndRyPVca1R44zVBkLZq9IfMdVUPBBtMV9RLn/pzQNZkNgAik+q9KQC0FYgedluE0IEVcMqhGgMdAC2edj3R7SemLRsGRK9bIVCN6UWG3O+3c+/vtqN1SZ5+NoO/HGA/5SkJ8KhdB+tVMe1BuSMlfdwa+YkzKoIgFD1qvSHnqbi1dqn0h+BFvMH0w5p7lCwlAA2EAYwxiuhV0WSEMKbfqIAAl6jLoQwA+8Dc6WUv7nvl1L+B60BOb17967RC5YU0cV3e/J4etk29hwv4JpOjZk6RH9K0hPhULqPVqrjWnU5Y0KIlsACNP0vCaQIIW4FrpdS3lllK2oJoepV6Qs9TcWrtU+lN9wL9sOZRrSnQSlfySpt+oVeVcukmsxEP/tnBzJYue7ie0ApcF+wRikUoeTo2fM8t3I7K349Qov6ibw9vjdXd2pc5XHDoXQfrVTHteqNjL0FrETT4jlZvu1LtCJXRQCEolelL4KVz6hWqrsVkT0N6hIZ0yFiq1om1WjsmoahQAghgLfRBKxvLK+BrZE4yxnYV79N7N+God2jbfVb7BHKe1tmLU9Jrt5NmU3y4DXtuXvgBV5TkoHOHQpZCuc5c84UgYRzJRYsVkm82cCtPZszbUgXTCZ9+vLuUhtJZiMSKC61kJGWFPS9rA4JDr2iryeBhuV1FKeklPXLt5+RUqZV2YogUIKJnuk69XPHIgFfTcVT4k1sfea6yBiZ9Qp8/bxWsC+McNWTmuJ+OAmmZiwSdip8EkrRVyHEVf6OkVJ+rXOsN9EyB9dIKQv0nBOLzzBvcgb2f0rBKpwrQntvv9ubx9Sl29h9vICrOzZi6pAutGzgPSUZ7Nw2mwxalsLbnO40rBPP/x6/yq9Dpme8qvyeVuVa7YRC9PUY0A5NdsI+aGe0FUeKKKKq8hnVQqibgeshmDRoJOxUVCdv+9kvgbb+BhFCtALuAkqAo0I4Hsx3SSnfr5KFUYYvOYOs3Xks/zW3xqygq25CcW+P5Z/n+ZU7WPZLLi3qJzJ7XG+u6ew/JRns3FWRpfA2Z/fEnVxT9wdW5/fllWPjOHGuhGdWbPebyfE2HhCS39NwS3Dodcb+jrZc+6+ASQgxCk2x+sWwWKUImnDLZ4SEUDcDDxexYqciKKSUISlqKRfFrhXhoNokZ1DdVOXellltzP3uAP/8chdlNsnkq9tz7xXeU5KhnDtYPM05uv5nmIQVIaBjgqY1/8qxcXz40yG/zpin8UbV/wKQxBssUf97qleB/x0hxCm0JdmHgHHAU1LKJWG0TREE1SmfYcebjMaNXZvy340HWfnrUcdiBbNRcGO3pjx8bRdaZcaAc6P0yWosQogmUsqjPvb3klL+VJ02RTu1Sc6gugn23n6/7yRPL93KrmMFXHlhQ6YN7RJw7XEk3ldPc5oNFelFIeCauj/wyrFxlJTZPI7hb7x4Q0XpZrT/nuqqihNC9JVSLpFS3iil7CKlvEFKuUQIof5LRRmTMttiNla8rZtkB2ZahzkcMQiNfIadb3Ye5/pXs1i44RAFJRYkmozGBz8c5Hezf2Dpz0dcVo2WWSVLf87l2n+u45udx6tuwKENWm3XoQ1VH0tR29jl/EIIsdttv14F/lqDfQn/+oKeFNniAWqsnEF1E+i9PZ5/ngcXbuaO/3xPYYmVWeN6886ES4JaBBaJ99XTnGU2I/Yydikr+j7Gm/27Kp7GK7GZKbFpMadgr8dmkyzZnMOQ19bT+7kvGfLaepZszsFmC60yjd4C/nwpZV0P2x3F/NVNLBa/VheedMbAVT4jFDpj2ScLuf7VLI8yGnpIMBn4/KEBwa8g9bfaUclS1DhCXMB/TkpZx+n1aSllPW/7Q00sPsOWbM7hiY+3UFxWubYo0WzkxRHdoi79EyvovbcWq425/8vmn1/uotRi4+6BbbnninYkxgUu3Bro3KHE25zuNWMAY/u18pum/HjTYf7vw1+xSumzZiyQ6wn1gpWgC/jLdXOE9qMQuNZFXABEsApc4Y1wy2fY0SOjYd9+izELgI+tmY59JRYPEhuBOFC+2ihFiyyFcgijGfdPov5e13qqY4l/bUXPvd2w/xRPL93Kb0fPMbCDlpJsk17153kk3ldvczo7YaCtppx6U2ddY0oPf7JVuZ7qXLDir2bMQsUDyd3xslHDGnnXJKqjT+WSzbmOyJs3GY2eYhcL4p4jrvzX5zbjWkaVTmGT7IAEPtmcU2FjoA6Ur9WOevpdhttRihaHUKEIEQaD4M0xvaq8xF9RGV/3tl+bBjz64S98vDmHZmmJvDW2F4M6N8Zp5W7Y5g7X++o+Z+6ZIiRw7nyFzthtvVow9abOunTG5nx7AJvE6yKEBLOBF0d0C+h6qnNhgz9nrA1aNGwtMMBpuwROSCmjrwpOUW3okdHoZ9iBCW11DIBJWr1LbATSMBy8rnbMPlnIZwczGGczYkZSJo3MO5jB9ScLK6KC1eEoBXo9iurGvR1SHafXQbVDqg2Ee4l/uNEjblodwra+5rDfW4vVxrz/ZXPNP9ZSYrFx35Xt+NOVVUtJeiNU72sg907vnBaLjWnLt/HRpsOUlNk8CsL6W4SQEm9iyEUZAb2v1bmwwaczVr5kG6BVyGZU1Bj0yGh8b+uEBSMGqR1nwehdYiMYXS+31Y4V9XKpfC4rVpL+ui2VV3/LqqiXqw5HSemURTvu7ZDcdccCaoekiH481QDlFZTyxMdb+HTLEd4c0wvA7zFVdcj02PHTwdM8tURLSWa2T+eZoV1o2zClSvOGGz3XFei9s1hsXPri15woKHFsO19mY/4PB/l8+zGHIKy//pFN6iYEbFt19t/U25tynrd9Uspx3vYpajZ6ZDQ2yQ6MKp3isWZMgKvERhV1vdz7cm7CqRG7dOvL6c1RCmXqskUfuP5F2LEUOg1TUbEoI5TtkBSxgZ4aICkJe52QLzvW7TrBHf/5HxsOnCYjNYE3x/Tkui5NQpaSDCfhqLGatnybwxF7pPE8r4Kw/vpH9miZxuJNOQHZVp39N/WuppzqtqkJcCvwvpTywZBZEwCxuBKpplEdqym9aZhNymxb6bwpS7Y4nEPwvKDAZBCM6tNSq1Pb+G6Fo9R7QuCpS3+Om6oZCzkhXk05Q0o52en1RCnl206vF0spR4RiLk+oZ1j1M+S19WzJOetSA1Rki3fUAHVrlgrg95jl9/cPuR351kRu3fs3dp1vjQDuueIC7ruqHUmRFOgOED33N9B71/GpVZwvs/FI43nc12gRQmiyF68fH8krx8aRYDbw2/QbHFG5rN15Lv+T7EX7uWeK2ZqbH5Bt/sYM5WpKXTpjUspn3L7uAW5AW1GpqKW0apDMzDE9STQbMbnXAvj5/YwzGfj32F4+HTFvGmYLNxzi+lezKumUeVpQ8LDpQ96Pe4GeQpOUstgkn2zO0Rylz/4M+9Zq3+2OlXvq0hlnTTO7o/X189p3u86Z8zH+xlNEmglur//m9vraarJDUU34qgGy79dzTKjt+KmwI7fvfYld51sDkJpk5rHrO8aUIwb67m+g2AVfr6n7g6P22C4I67zfviDgxRHd6NYslfSUOLo1S+XFEd14c0wvjuafD9g2f2OGcmFDVd7pn4GBIbJDEQYCiSoFO/5XO44hBC56ZslxRm7p2ZwbujZh0cZDrPj1iIsC/+BuTXno2g5+I2LOKUdnLDa3lGP5OAH15fTkKPmq8XKPcl08yrOj5XzM9S/6rhlTsheRxv1JGv15IEWV0FsDFO46IbsdX+f34eeiC1ly5iqamPLolribLcXtaVHPe1PvaCYcNVbxZgPny2yszu9Lx4RsR2TMkyCsrwUBwdpWXQtW9NaMXeW2KQm4A9gecosUVSb7ZCHTlm/jm99OuGy3R5UW/5RTZeFXX8KyNglXdWrEZe3SuaxdOq/e0SPg8fVomJVZXXXKAurL6cnx8lWz5u68ISuf735M8Unv44UyhamcumBRumK1DD01QFIS9jqh31/WmscX/8q6gl4IbPRP2YRJWFlz7pKQ1yJ5W914U7emrNhyhLfX7yf3bDFJZiMSKC61kJGW5HOVoc0mWfJzDv/4chdHzmoRp4zUBAZ0aMjuY+eqdO/c7U2KM3K+zObQHnMXhL2tVwtd96E667+CQW/N2H63TYVokbGnpJTu+6qFQOotXtrwEr+d+i3MFkUHZ4rK2HXsHDY/76vRIOjWLFV3I1lnzpdZ2ZJzFquPdhBCCDo1qUPdRHPA4wP8eOCUY/w6ophO4iACiUSwQ7bknEx0XMclrbUmEPvzCjl+rgT773QdUUxdisgnyXG8EIJGdeI1ocSSc3D+DCSkQbwfofWSc3B0C9r/awFNupXfDKfzPR3jbdyzh+DMQe0jnhCQ1hJS9T1U/Nrl71qijI71O/J4n8d1HRvimrEiYDAVEbElwDCn18ullKFRSPaAqhmrfvTUAAEhrRNy56fs0zy9dCvbcvMxCHB+jIZqDjveFOQTTAbqJJgpLLW4bHfGmy02m+Su9zby1W/Hcf8XYBSC+slmzp23cN5i8zuWXnu90bBOvGM1pT9CXf8VDEEr8NuRUkbWZVTo4nyZld3HKztinpwSm4QjZ88Hpd585Ox5lz9CT+NLKdlxNJ8LG9clLSlwh8y571ddihBIRHngoi5FnMN+HRXHNU1NIK+gBGv5pnMy0XGcHVF+HKA5LXodl/g6mqPj7nz5O8YbCWnAofJ/+6L8dRCcPwPIcqeu/HWMOWMR5DjwjtPrk26vQ9A8VRFN6BU3DYcA6smCEl767DcWbTxMk7oJvHZHD6zSxtvrD4RNZNXX6sbz5SsUfbUO8rTKcNkvuazddcIhsDqq/ioAFpy6gdX5fTlbbOH2S1rw86EzAV+XL3tNBqGVxHgShD28DI58AU0HQfOhHscOWNhWx5ihxGtkrLwVkl+klP7bqYcB9amyMp5WE95izOI241qM2FyU8QFS4k1sfea6gOfpOvVzRzrQm/K+HaOAeLOR4lJrQDVreudwv4Zvdh7nrvd+otTi+dcyzmjgrXG9qt6bMxRpxlCkF2vZis1QRsYijXqG1Q6sNskHGw7yt89+o6jUysTMNky+qj3J8eEvzve3utF5e4nNDEjiDRafqwydx3yj5V+JN2jP6RKbmT8d/HOVVpwGtRrz8DL4dhRYi8CYBJcvqLrzFI4xCT4y5twKyeO45ftDLwWsCApPqwnjKS1vLornQvYg8F4oX8YDpsX8yzLC4SxZJY5wcyA1a3o0zEwG4apTBrRNT/ZZhV1qtVUq/A+KUIjG2gVr7SswExtodWaBOGdV1GZTKBThY/PB0zy1dCtbc/K5tG0Dpg/vQrtGdbSaq805VVL416N0709B3nl7vKHMMba7yrzzXFtzzjrGtDti9vP1KtN7sz03GMX7I19oThNo3498UXXHKRxj+sGXM6ZSkzGGJyfJUL7yxCqF50L2IPBcKF+GEcnlhq30idtZKUJmx74S8u73fuILHxpjkzLbsvinHCy2cgFX6STgWo7ZaODOzDYuq0YLSlwdTD2F/0GhR11fT+TLHtmylAA2EAYwxgcW4XLrQqBQKCLLqcJSXv7sNxb+eIjGdeN5bVQPbrqoKUKIkKjU6x3D3wpC5+3ukTFfyvX2c0fV/9wlMmY/p9Riw2aTXov/vdluNopKdvld8dh0EOybUxHFajpI13vkk3CM6Qev/42dWiE5KE9dNgaORSo9qfCOt9WEVgx8aB3oon7vKaqkF09RqwdMi7ncsBWTkJUicJ4cohKLjWeWbeed31/icQ67hpm3FZtmo4GZY3qyL6/Q4zH2eT2lN+1aY1VyxvxFpPSmD+0RNsr/nKRN9bFUKGIUq02y8MeDvPzZTgpLLPxxQFsmX92eFKeUZChU6vWO4WsFIeDYbq/72l7cljrGQt3K9X86+ASj6q8i3XSaPEs9h33FZVav1+HLdotNEGc0BL7isdEVWq6u3V3a6x/vq1qtV/OhWmqyGmvG9Epb1AVeR5OzMAFlQoiFwGQp5dkw2qcIAD2pPTv2qFIweIpa/csygj5xOytJSfiq9/p653GynZt3u3HlhY347MFMZmft55PNORSWWkiOM3Fzj2YO2311APClNVZQYqHr1M+rpr/mKyKlN41pj7C5RMaUJplCEWv8fOgMTy/dyq+Hz9KvbX2eHdaVDo0rL6Z5e/1+hyNir4saWX+1oy5qdtZ+v86Y3jGGds/g0y1HyNqdx+r8vg4nLMFkKM9SaB9gL03ZQpKhhEtTtjjGEEBm+3Q2Hzzjcy6AGS1fprthr8v53q7Dn+11E4wYDcLFXvuKxyEXZbgO5l7bVe9i+O1V7fW+OVWr9Wo+tFqcMDt681QzgGSgK5CN1jj8+fLt48NjmiJQ9KT2AOJNWlQp2Jope9TqzrkbHfIT3pw/Z4dIyDJuMWaxyVJhk790YasGyUwf3tXjMVOWbPGpReZLawxwRBFDqb/mQG+TcOcIm7easfIom7SWUoaJ39ue4ruStvqdSOXIKWIQPTVR0WDL2eIyXv5cS0k2TInnX3dczNDuGR57Sdpskv15hYD3uqjtR/Lp/dyXNE1N5PeXtwZgzrcHXGurzhT5HMO5ziv3TDFGAySYDZiEIC05DgEcPu27Pis53sSgLo35v49+9XlcQPVd+K9jMxsF04d31bfi0b226/Cyaq/1ChV6nbHrgbZSyvKrZJcQ4vfA3vCYpQgGX6k9O1dd2IipQztXWYH/ygsb8d4f+jD27R8cUhKenL/vbZ2wYsAobRiQ3GZc65IurUq60NOCBfcInK/ooDPeVP0rUe7YHKnXmzf21Pfe3cBLGtN7V4S7vc95IAubpQQDNgxS0t2yhW9pS0GJhZ0/fsXiTa9zUf/BrClqU2ncP7U7RdMlt9ea1ZaKmkEo6qqCmdPd4fr95a2xScmzy7eTf76iHjWvoJQ/L/6V/6zbS+6Z85wrsTDx8jY8cE176iR4lvKxX5N94ZS3uiirTZJXUEpeQSmPfvgLAoG1XPVAb21Vk1TPdV4GAcVnzjvG8zWGEPDYR79iP1RP3ZkeRXt/dWwZaUn6Fe/da7uaD4WCfdVa6xUq9Dpj54GGaFExO+lAScgtUlQJf6m9ULRBsnNZu3RmT7jEp/O3SXbgQ+tAfmf8CoMAo7SFZEUn6Gt/5C06CEEU95dHqGyWEtKkiZ2WJymwtge8RNfsTk95q6RvilpXuld6onJH6vUmTZocjqZzCnieqdwB/fZj3rL8hQKnNOzCDYeov2kpDxo0R07VoilihVDUVQWCN+fv0Q9/QcoKWQG7LSvPZvJjYVe2HzlHu4bJ/PeuS7mwiW99P/s1SVkxztsnhrnUaDnPUbFNVtpmtRkwG4XX2qoeLeqxeNNhj/fP03juY8QZDRSVWCppiXmyV3d9V7lu1987GPhRHGBN/sUu5wE81/wt2ne/GdApi9F8KHR8UBu7+VDo/jw06FuttV6hQq8zNhv4UgjxDyrSlA8B/wmXYYrg8ZXaCzWenL9Es9Hl09jH1kxGGLMwe2tNFCR62x95Iqji/gNZSGspBmyYsXAJ2/mR9o7dlaJrRdscRfw2o5n/lDxJcdkFlYb1F5V7Y099dlqe5BK2O67rOdPbdBEHiKMMo5Agy7jPsJh/2SpkRSw2yfqyjtwTZyLBYEX4SpcqFFFEKOqqAsGX8wcVDkmXhH3MOD6KjYWdiROllMo4EsxGv46Yt2ty1tCyz+N+vYCX2ioTJoP0WFu1+eBpr/fP23jOY5iNgvzztkpaYv1TfnFoiQkgvU4854rLKp1/X4dtDC1dAYfLHSKn2q4OwIX1YUTaF9x/8DGm5d7DNXV/4LWWL5NoKEHmfgWHG+hzpA4vq6gRK9inOWLVXOsVKvT+J3weyAV+B2SU//wyrmrVilqKJ+fv9+9ucPTGDEQnLBACWbDgTE+xiwdMi50cGZ36a60zKcOEQUqHw+cpuna+zMoVf1vDPcalPGwqwSRs2CySnnIrJcJKP8MOTskU6osCXVG5JZtzKbC250fa01PsYkHcc8RRYaNVCoesSL+4HS4rZzfJDoyzPMkfW+Zy7Y23qqiYIibwV1fkT8cqUHw5fwCvtvgbK84M4IZDD5FvTeH36ctoZDrJi0cncjT/fEiuyds++8/ux8eZDEwf3tljbVWfF1YHPJ4AujZL5c7MNjy7fJvjfG9aYinxJv73+FWs3HrExYYpF++mT+7TiD1FsH9OxarE8loue3I50VDC8Mbb+NmWyfDG20gst0kEUusVAT2wcKG3HZJEc7yU86XQxbQhXfjfnnWO/mS+dMKCRe+CBWcqImJlGJBYAtFfa9GH39ueortli+N4T9E1e0rDPVp30pbimNuIxIagFLPvqNyhDYyzLOZ7oTlt9nSsvTbYKiFbNqKlOI5JSIzSwijj14wwZjnG/dHanh25ndiqHDFFjOCvrshbPVKw+HKUckvTGbP/OX4u6kif5K08m/FvWsYfdThqem3xd03gvf7K2z3wVlvlby5P27o6qdvPztrPycKzPrXEWqcnYzIZKtvw48LKDpJzbZcdYxI3XTeem5pfC4eL4dvPAq/1ioAeWLjQK20xCvhZSrlDCNEBmAVYgXullLWjA7ciIFo1SObfY3v51QmrSg2bngUL7lTUlmmO2Le2ri4dA/xF674racu3tAXgXuNSr9IZUDki6Dy3lGAUErOvqFx5jdrDphKHs2d38OKkdpwFE/+x3MRU83sIqTmYfsdVKKIcX/pYPvWmgsST85Jb2oDZJ27hcFkjGpjO8M8Wf+eGuuv5tvBi/n5sXMC2+Loms1Eg8FwDBgR8D/zpi/kbb2L/NvzfR7+4aIlBRf/JOF8fpD05SM66XXGpUHrWtaYrWF2vCOiBhQu9acrngMvKf34F+BEoAGYCV4XBLkUNINjFBN5XHFaWcKg0R4nFZw8v92iVsyMG/qN1euvU7H1BgYrVozbKjy/FiI6uCOVaZc7O3kzrMEaVTqk09q7SFhV9SKUtpLV5CkV1400fy6velAf0SmPYbJLuzVPZmnOW1fl9uT/7MZKNRaw6258yqa2M7Ja4l7PWZO479ITDljijwact7vM3qZtAu0bJ7D5WUOmaLmiYRPapYkqtNpd9BqEtHnDe5m9eX/fPIHA4fb7u6dDuGaz8NZevfjvuciyAUQiuuLCh9/m9OUj+armCrfWK0Roxd7w2Cnc5SIh8KWVdIUQCcARoApQBeVLK+mG20SOqyW7N5Judx31G06YN7cyWnLNeHTVPzdLda7o8bQMwGgSzx/f2qTOmd3znuq5STIwqncIm2YE7DF8x3fwuAisSI0+VTWCh7WrH+GP7tapIUzrpi523Gb22mHLGkz0mg2BUn5aV0p+xqD2mGoXXHmw2yfJfc/XpTXk415O0g93xsEtjeDvOGaMQLlIQAHUTTDwztAvDLm6mu+UPaPVSBoPAbBSYDIJWDZIwCMGe4wUUl7k2tUmJN5KaaCbnjGtNmsmgfQh9a2xvn/fB0/37Q//WIOEdJ90yb/fUZpMs/SWHf3yxi9yz5xFA09QEHr62g9frVvgm2EbhzpwQQrQDugE/SilLhBBJ4LMns6KWEkhky/28e+dv8qiob19x+PjiLZgMUF6KVkkaQo/2mKfaMqOA9/7Qh8vapfu8Nj11av0MOzBhddR1maXF0UC9vihAIDEJsEhJfVHgcq5LVK5cq0wcyGK7oSs7PpOY3JxUo4DuuDpgfmvz9LZqUigiiMEg9OtNuaFXGsP5uObmo+SUNcIsrJSWR8TG9GtJr5b1dDkveue32iRWmyTRbHRxxNyPKyyxUlCiPWeCkffwdf9u7tncdUO57IRzJMtgENzcozk392he6fyQ4jw3wO63KlobuUe8PNgZ0PhRHEHT64xNB35CqxO7vXzb1cAvoTRGCNEe2AJ8JKUcE8qxFdWDp8iWXoX7WVn7fCrq27G4foB0kYY47+TI+WqHZMe5fs2fIwb66tS+t3XCghFDeV2XAEcD9WfKxnpNbSaYDJWd1fKWS72AzzoWVkr5Tu5wmj/sfRHK1fmdo2cCLcVRXGZl8Iz1FQ6x3lZNCkWMolcaY3bWPopKrZhFGbllDRnfYAV3N/qQKTn3sTq/L78cOstzw7tVdl6CnN9dp2trbr5Dd8xbq6FHGs/j7kYfYRa20Mp72J2UuFTY8XewlcLeWdD/w+pzWpzbGe2drT2Typ+bHP0K+i+qsMX52D1vQefHNF0xveNXtT1SmNG7mvJdIcSi8p/tyyF+QOtVGUreQKtHU8QgeiJbvhTu9US17HgTbDUZBWXlLQH8aY+lxAcnhuupFs5kqJh3k+zgqOvqIg7QzbDP0UC9vijwKMFhFHBb7xY+5/WoH5f1CuwpA2FDYGFA3E42l3SoVDfn7BDPv74rvfS0alIoYhQ90hjbcs/y29FzADQx5fFm6xfokrjfcV5VJDS8zX9Po8WYhLWSw+XNzmvq/lB+jq3Svv15BQx5bX1wraKcnRQMQPknXFupFpmqLofFWZrC5qYhbytxlapwPlZaYNtLFbpiesaPcukLQwDHxgE3CyH+TwgxGiiTUh4NlSFCiDuAM8BXoRpTUb14imzda1xKT7HLsc2upeUJb4r6ZrSolvO478e9wMOmD3k/7gXH+BabtkrRVP4wsq9m/IflNhdnzmQQjO3Xiq3PXMf04V0DcsSyTxYyZckWBs9Yz/zvtYYUo/u2ZO7v+xBvqvhz2iQ7MMUykemWsZRhxiINDodwk+zATOswF+cyzhTkCjF7D0xhxGiK5/bbRpFgNgJUcsgsNklxmZUxn0mODP8vXPWkSlEqaiR2uYn1BT0pssUDuMg4SAlDXlvv+BvpkHCQNvG5lY4LVkLD0/xl0oBJaB9UnTW/fNnZP2WT4xz7GPZ9haVWtuScJa+glC05Z3ni4y3cPf8nbDpWlbs4KbilGqqz+KjpIG3FJYAhHoRTfMgQ7ypV0XSQ636s2nXoHT/KpS/0SltcBXwM7ERT4G8JvCGEGCGlrLLzJISoCzyLlvqc6OO4PwJ/BGjZsmVVp1WEGE+RrTjKsGFwFKr7UrjXu1LRV/rRUl6L4aumK1h9M38p2HuvvIA3vt5LqZND6k+MtsoyH249MN/4KZEy6yHHbk+rOsusNmbuacD04Y8EPp9CEQN4knbIOteTr85pkahTRaWM7deKjk3qMH3FDr46F1oJDU/zn7MmM7Hh0so9IPHcUghcdccs0sibx0c49jm3VQq4VZSz/IQhDqT2LMUQr9VqVRfuKy/Be81Y86FaanLbS4BVn3MVQ9IXeldTbgemSSkXOW27DZgupexYZSOE+BeQK6V8SQgxDWjnr2ZMrUSKDL6K86/42xrHJ817jUt5xLQIY7mmlgUjt5c+xSbZASFg/18HVxpbz0pF+3ZvKcyUeBOv/a6HX30zXysmvV339a9meUzB2kk0G3l7fG/mfLufL3cc9zumPYJnsUlSdC5y8GSX8/vh/Nfsa1VnSryJrc9c53GM5HgT93c4xcj0bOp1viqqImdqNaVCD/bVjFm78yr9zaYmmnnvD324qEWa1+PcV12Gan535ynRbKRdo2T2HC90Oc5s1FZ6Wj04XM5jeWqr1M1JvNUn7oXzMeCwADFTkO8JX88vvc7YGaCBlNLqtM2EJm2RVkXjLgbeB3pIKUuVMxa9+JOdkEjOly/P7il28d+46Y5VhRYp+IdlJDOtw1wcAWf0ODx2PEs4wKg+mjRE9snKxe7BNkvPPlnInXM3svt4xcpHfxISvu6VoVw8yCZllZxFb3PYbcsQeY4G7aDpmr1S/h4IYP+Lgx1jdLX95tL70u7sClMcxgnLo8YhU86YQi82m2TRxkP87fOdnCwsxWgQjOzdnOlDu2JyKinwJ6GhV6/M0/zLf81l1rp97Dx2zlFGYcfu8M38Xc9KLYX+0L81n/56lPV7XJ05IcAgBFabZFrGv5mQvtKx7928wUzLvYf0lDg2Trk2tDdTERJC4YzNAPZIKWc4bbsfaC+lnFxF4x5E6315rnxTCmAEdkgpe3o7Tz3Iqhc9jpJRaA8L+2rHCk0tG2XlbX9+FRdW1rxywpuDYRRa6x9/vDSiG7dfEroUtt0e5+v2F5lzjji5O4TXdGrEqq1HKXFfEupEotnodZGDHW/vh7NtVgwIJGa0Y5wjYwAf3NmXiXM30smyw+V6FlszucP4DSZhwyINnLvscepd9+fgbmCIiVZnTAhxHzABTf5ngZRygr9z1DMsfEiplUO88OlvnCwsYXTfljw66ELSkuICGkevXpmecQLVTPN2zqx1+9iam1/1yJii2glKZ0wIkUVFDbABuEcI8RiQAzQDGgPfh8C+/wALnV4/CrQG7gnB2IoQoVd2QjoEFWCh7Wp2lbZwOS7R5Ltey5dqf9PUBF7+fKdPO6ct206/tg2q1GbJjrfVob5q1pzbDnla/ThlyRasHqJYehqGO+Pt/cgQeU62wULrlaRzlsbiFP+1XukiezFt2TbKrDaX6xGyjHTOutTrfZjXWivUVPgiF61TyXVAaBsnKgLit6P5PL1kGxsOnOLiFmnMmXAJ3ZqnBjWWXr0yfwSjmeY4J/0np7Rcf6REX6sob+m8YNN8/s6L4fRhNOCrgH+22+tZ4TCgXCrD0T1UCFEAnJdSngjHfAr/eKohKrFY/cpOWCUkGAVxRoMjsmUvoDcZBIkmfYXqHiUc0BwZ5whZsI6M3msvcFrd6TzfKZnidXGBv7ZDeuQ7fC1y8DeOFQNWDCC11ktbba2Zan4PMxY6Gt5jV2kLRzPzXeVp1+9FJ6wYMEobBiRXGn9matl46osCvrd1Ysv2ulx3sjAkDm5NRUr5MYAQojcQZpVMhSfyz5fx6pe7mfu/A9RNMPHSiG7c1qtFlZTi9eqVhQ0POllDuw/x3yrKm75WILpbzjpkp36Go19oRf57Z0OnR1z7S4ZKzysaHLoI2eD1P4eUcm61WeE677RIzKvQ8LZi0BlfkaESq401j14RsnotO9knC/nvj4dcHDFvjsx732fzyeacgAvivaVIPc33TNlYh7Oit8k4uN5LvRE2T/fC+zhaNCxXprs1J6+YAxuuiv2yAx9aBzLK+DVGITFKG/VFATOtw7QJrJLrX80KauGDwhW1Ijz0SClZ+nMuz3+6g7yCEn7XpyX/d13gKUlP5OrQKwsrHnSyDM2H8uaYXr7Tnt70tfTqbrnokLlhK4FtLwK2CscrFHpevhzI6nKOIigSq7t7sBCiMdAHSMdJiURK+U4Y7FJEAF+irc74kp1IjjN5jWwFS4WTVOEg+VPX16v6b8fftbvP5+KslONPMiP7ZKHLa3/30RPf7DzOXfN+8jmOozE5ODUn1/adkikendiPrZmMMGZh9iKQW1zmW7DXQXnPyyP1evPGnvoBt8Sq6Ugp/4NWmkHv3r11VEHWbvwVz+88eo6nlm5lw/5TdG+eytvje3NR87SQzZ9kNnISTWJiVP0viDeUUWIzO2QpEss1/cKGswSFk5SDS9rz8DI4shTSB4Ghmc/zvG53x0WHzBPlJRJ2x0vvuL7w5NBB9TpHERSJ1aszNhyYD+wGugDbgK7AekA5YzUEvXVh3rSz9ESGAsWbk+RPXR/0qf7b8XXtABkizyX95z5fotnoNwU7K2ufU0WdxmKrpn7v7EAJ8Hgfs08Wcvd7P7nomIFvLTP3ff0MO4ijDKOQCMq4zLiDTZYOlY4DTZ4koBRwec9Lm6WENGlip+VJCqztgcCdY4XCU/F8XkEpT3y8hWU/59CmYQrvfneAOgkm/npLN27vHXxK0pvT57q+Tbp9rwb86WR5i+R4O0+v7pazc+WOMIEwaGr9dscrFHpenhy66naOQuFUBoneyNhzwO+llB8KIU5LKXsIIX6P5pgpagiBtCMKpZiqL3w5SZ4ckGDryHzVYAEYsWHFwELrla6RJ6BNgyQuapHK/R9s9hkFWrI51/EYd7+/H1srWhJJ8HgfZ2Xto9Tiei+cBV3dI3V2nN+rDvIQBrQl9gYhyRd1Kx0XdC3bgSyktRQDWteES9jOj7R37A7EOVYoPBXP24Vbv955ArHzBHf0aclj111IveTgU5K+nD6LVXvdP2UT8QatNCDeYHGkKYt8lBOEDLtz5Qlfzoq383yN53yM3bmKS9Xqw+zfvemS6RlX75zO41ancxRBkVi9zlhLKeWHbtvmAkfRVj8qagDe2hF5a7Jtp8oq8j7w5yA6OyBVKYj3du1CSgRS0+qSkCvTXRwxg4D9J4vYf7LiE6S3KFAg99db705nZ85Z0PU241oX2QqAOJMBm81VyyzdUIAVofXKxMD4HnX5cLPRJfLoy7aCEgtTlmzxnG5snUkZJgxSOqKH4VhkEY2U6y6a0GR5jEKIBMAipayG/9bVT7DaW4HgXjx/uLQxn56t+NDStmEyf72lW5Xn8bVi0n4lzkr4zur5GWlJngetrjqncEZy3J0r+zX52lfV63UfNxLOUVWdyiDR25vyeHnNGMABIcSlwAVoDx5FDSE5vsI3t6cBnXsq2jEZBCnxJoTQdLVG9WnJZw9mhiX1pLdfpb/9vgriwfu1WzB6vQ8A3trA2ftA3jt/k6NWTO/9TYn3/BnJ/V7YBXWFABPWSvfjy4cGMKpPS5f3qm6nqzCY40EYwRTPBZdcz2cPZjq6AfizDWDhhkNc/2oW3+x06jJQXiv2vG28oxco4LWH6CebczzfuNhlClAM/BkYU/7zlIhaFCbskaS/fLIl+N6IOrAXz1+StIVXj/6OG3fNYOf5VtyUuhaAs8VlIZnH3embkL6SGS1f5pq6PyDR/r7sMhLv5g12rKT02i7Jnjrc/Yb2/fCykNjpEbuz0v5P4a2n8nVN4b7e5kPhktdrvFyG3sjYLKA/sBj4J/ANWgXfK2GySxEBhvfIcLQj8lUX5ku0NdTo7Vfpb78/yQlf1w54rMdyx1MUqLjMyqR5G5k1rreu+wta9Knr1M8rpTrd74UFI4bywIsFYyWHzutCikNtHb0sadGHVsAdfVrosg08pBuLtsHcoWAt5c8YGW3TIpL3GpcGtVo0FilfBT4twmZUC6HS3vJHYrlK/j+OjaNExnFH/c95oNH7PJX7J21/iIrnj/hZMWkyCEwGg2cZiQYb4ce/ukZtgqlzCjSy5Cw7cfpnfWVsvzypndd8KHR/XscJTvi6pggWvdckdDljUsqXnH6eJ4RYAyRLKXd4P0sRa0zKbMvin3LC0mQ7WPQ6MHZ7g11Y4O/avaVooaJ26zbjWozYKtXY7TpWwPWvZjFtaGcWG33fXzueUp3De2Tw/vcHkeXnjiqdUqkJOHhfAOCgRZ9K7Y38Xb/PdGODLLCWgrRiRjqcLl/OcVK4V6EpwkZ1aG/tPnaOEwWlAMSJMt5r8yR9UnZQYtMtAKCbpqmJ5BWUek1FXti4DpMGtK0sI9FgI4bvfle5eD7Q1GGgcgreZCeOrYb+H3o+95cnYdsL2s9nt2rfA3HIfF1TBIveaxJ605QuSCkPKkes5tGqQTIzx/Qk0Wx0SVuB5tDoWTEYaiZltsVsrPg13SQ7MNM6zGuEytN+PQ6kr2t3xn2fvU5tlPFr4nykUIvLrExbtp1pQzv7ncOOe6pzUmZb4kyu92KKZSJTLBNdrjfeT5cDT/i6fvs1ek03ts4EYxwIIzaDmR/p7LBvdOlfHGlLZxvPl1ld05yKmMFXJMl5fzAUllj466c7uOFfWY7FKg81fo8+Kdrfk714HghZ8fzE/m1INBu9piInDWjLsIubsfz+/mycci3L7+/PsIubYTj6pWcphkBTh94kHfQc74yt1Pu57qnDQFOJvq6pulKlNZygnDFFzcXejsi91iicdWG+0OMgPnRt+5A4kO7Xbh/DbBQIKu6DM/Y6NaPQ8gRWKRx6XvcalzocF9AiSVtz8ivN4UxPscvjebOz9tOqQTJvju1FnNH7n22cycC/x/YKymF2vn5P1+i1Fq9FHxi/DK56kpMjPmSroaNjvzfn2Srhzrkb+W5PXsB2KiJL01St29P6gp4U2eIBXCJJ9v2BIKVkxa+5XP3KWt5at48RPZvTsUkdAL4r7OFxHq/F8wEytHsGme3THQ7ZtNx7HI6YQ9HeE00HaZEgqBwRCqTOydc4/o53xhDn/Vx3O4JxmHxdUy2p6wonuhqFRyOqyW7twlPTbWdVf3/7q0R5cbq9xqrr1M8dtVvuMhgfWge6tCDy1Ugc8DqWvwbk//xyFyt+PVKpU0BSnJFbejZjUmZbgEptrfQKrwZjl51vdh7nzrkb/fbgBK0B/OwJl+h28qO1UXgwxOozbMnmHJ74eAvFZZVrxhLNRl4c0c1rmtLTKsybLmrK2l0n+G7vSbpk1OWZoV04fLqYV77YyaHTWpQt0HkCJZhG3kDoVhGGomas/V2+z61KzZgiJPh6filnTKHwRbmQKdZSLRU3fhlTfkp01LFBZUfjXuNSHjZ9iEnYsEgD/7Dc5pDgEAL2/3WwY/g2f17pqL0N5Dzw3r7JZBAYhABBJWkLZxkSXw7QlCVbfF6jfSxvizk6P/2ZQ7PJn2ZdotmoW3dMOWORx76aMmt3noskij2S9OaYXh4dGE96XnZMBsGUwZ0Y3bcVf/pgk8dj9M6jUEQrvp5fKk2pUPjiQEVxOtZSOJDlt47NlzSE+6rOeHPFOIGc59yZwD06ZrFJSq02Si2Ve2x6ktzwhJ5aPV+1eMWlnnXLzJTxgGmxxzSsIjYwGARvjunFiyO60a1ZKukpcXRrlsqLI7r5dJCcV2F2S9xNXUOBY5/JIKiXHMfKLUdcVmpOy/g319T9wXFci3qJfucJOYeXwY/3ea67ct/u7dhw2lPVOavDZoVfQr80RaGoSdiL0+2RsdaZjjo2b03F9a7qzD5ZSJmTor738yqvjtTbusrbPn/Cq76uUY/Ir2dJkjKMSC43bKVP3M6ARHkV0YVLb0Sd2Fdh1jeeZUtxezol7OPfGS8wJ2+YYxUm4HOlZlpSXMhSk7rw1bzafTsEvirSPTXpL13pPm/HB+G3V4Pv3RjBxtgKV5QzplD4wl6c7lQzBhXF7vY6tYIS15VdemRBZmXtc9nvzaGSiEoRKL2tq/x1Jbgzsw2zsvbx8aYcl7SQvfbs7fG9WbX1aMC1eJ4kSR4wLeZyw1ZN/b+G644pXCkqtbDn+DkACm0JPJPxJqMbfIpJ2Nh9vgWr8/u6rML0pvkV1ErNqtR1ua903P2WNob79l+ehKSWlY8NpKck+HaMDi+DjZNd5zi8rGoaX0ojLGpQzphC4Q8PulyAQ1T1zsw2XP9qVqVm5u5MG9q5Uq9Ka3nAyZdDZTaISo6P3tZKvvYVlli4/tUsSizWSp0EikqtLPjhoEPnLNCo1aTMtizaeNjRomCT7MC/LCPoE7czKFFeRWQJtv2RlJLPtx3l2eXbKS7TIrl9krdyW/0vMQmbx1WYvjS/Al6pWdXIT9NBsHeWJhsBcOwrbUz3Rtpnt8K53dqKRlup9v3YV3CkxPO83uQsvDlGh5fB+pFgK6kYwxCn7S/YF7zGl9IIixpUzZhCUUU8pQzd5SmMArbm5Lucp7fVU4nT2Hb0tlbytU+i6Z95615jleiqL/OK27jedMf0iPIqIkew7Y/2nShg/JwfuXv+Juommpl8VTsSzUayCnp5bS3kT/MrYMHpQDW83Gk+FJpc43QzSiqcpMsXQGpX131NrtH0tppcU+E4eZrXk5yFL4mLI1+4OmKgzdH9+appfCmNsKhBfRxVKKqInpShVVKpLkpvqydPUSO9nQm87bPHMpwbj/cz7OCUTKG+KKhyY+9ZWfuwOa3Udk7BOjd3h+rv6qAIjEDbHxWXWnn9m93MWrefeJOBqUM6M7ZfKwxC8NvRc2TtzvPcWqhcz+vTLUf8HqObqkZ+Di/T/kgM8Zoz5DyG3XGxR96MSdDuroqasmNrvM/bfKhW72WXmrCP5a0pdtNBsHd2hUNmiNPmso8VyubcioignLFqJvtkYdDaT4roRG/K0L0uSm8vUE9RIz2tq+x42uccy6hwILUCexuCUsxVKrDXW9MGVHtXB0Vg6G1/pKUkjzF9xXZyzhRzS49m/PnGjjSqk+AY680xvfzqeek5RjfenB49OKc4DXGQcWOFs+U8vicHytt257HthfcF+6BB3wr7vImq9l8Ee97SpymmiDmUM1aNeNKF8tSDUBEDOAnBBhvhqkovUH+rHf3pjDnXt1U4kBIpwSgk5ioW2Ot1UAH1Ox/l+GukfeRsMfvzCpm2bBtrd52gY5M6/PeP/ejbtkGlsfSswgxmpaZXvDk9enBOcdpKIbmNd0cpkO3uY+stnFcRrBqNqhmrJvzpQlWpNkdRvdiFYL9+HuYO5f4OpxytmAKpi6pqL1Bfrau+fHgAXz40wGtbqxSPNWeaDZbylk5VKbDXW9PmqSWUIrrw1/5ICMF1/1zHT9mneeqmzqy4v79HRyzk+NLHsu/b/VZwNWOHl0Hhfi0iBvpTnHo1u+JSAWP5C2P5a0VtRinwVxNVVTRXRBFZr2iOmLSCMHK632Nctv5in6spfanMh7WVkxe8/T56qhkL5vcyXL/vSoG/+vHU/ijrXE++OtcXgZY1G35xBn+5sRON6ib4Gy40OKcQjUmuxecu6cV4zUJbaeXj9IxtiIfGV+tLC/qyydtxdvTapohpfD2/1MfSakJPDY0Sv4wR3IRg63W+ipmtWwctkGqXyKjO9z2QmrNgCuyrkoJVRBdDu2d4LKoHSIo3Mntsby5tl169RvlK87mkF0u0Wq/kNvp1xtzPT/GSngzEJm/H2VEaX7UelaasJvTKGCjxS31knyxkypItdJ36OW3+vJKuUz9nypIt1ZPmtQvBXvWk9r1FH58pw88ezKRtenLk7PWAc4rUW020UeA3Vapn/GBSsIrowd7+6NlhXWiYEufYbjIILFYbk97byE0zsliyOcerzEXQeEv7+ZKBcN/X7i5t25Ev9LX8cU8Z6k0h+rLJ23F2lMZXrUelKauJrlM/dxR5+1pdlhJvYusz10XS1KjHV4NsPU2wq5totteeIl286bDH5s2DL2rCw9deGLTTFOoUrEpTRobV248xbfk2Dp8uxmgQWD04XQkmAwM6NAxd30h/aT9fyvrO+0Bf+tDOj/fB7jcqXrf/E1zyun6b9aj924+LS4XSs8F1B1DEHL6eX8oZqyZUzVhoyD5Z6Fft3ld9VnUTK/ZGs8PojHLGqpeDJ4t4Zvk2vvrtOE3qJnC6qJQSi81RO3bOmkwdY6FDdyzRbOTFEd1CsxLS3SlqeiNcubLq4/hzrvTWfikUAeLr+aXSlNXEpMy2mI0Vt3uT7MBM6zAXvSVVQ+MfPWr3dpHSaCAW7FUrfRXunC+z8urqXVzzz7V8v+8kT97YifSUOIcjNqPly0xIX8l9jRYxIX0lM1q+zDV1f6C4zBq63+WmgypWM0JFK6JgxtGTPrSjVOkVEUA5Y9WEqqEJDZ4WQjxs+pD3415wODj2hRDRQCzYGwsOo6L6+Pq3Ywz65zpeXb2b67o04atHrmDSgLYczT8PuOqNifJHmV13DPDfzFuv/IO3VkSBEoxz1XyoFj1TjpiimlCrKasRe5F3VWtoarOKf7Bq95EiFuxVK30VAIdOFfHM8u2s3nGMdo1S+ODOvlzmtEqyaWpipSbeUmoOme5m3oE27m53l++2QnpRgqmKKEc5Y9VMVWUMaruKf1X6OUaCWLA3FhxGRfg4X2blrbX7mLlmD0aD4IkbOvL7y9sQZ3JNnEzs34YnPt7C6vy+vH1iGNfU/YG9Jc3Js6S51Iz5LLUIVHneX1shhaKGEB3/sRS6cK7tccdik1hsWm1PpIvBw0lV+jlGgliwNxYcRkV4+GbncaYt20b2ySIGX9SUKYM7eY1s2fXGTEdWMLHhUpIMJbSKP+roUZlgMvhv5h1M424V1VLUAtTTNYbwVNvj/o/dXttTU9NJsSYmGgv2xoLDqAgth04VMX3Fdr7Yfoy2DZOZP7Ev/dv7Fm61640d+PwNkk5X9Ki8MvVnjqZcy6QBbf0381aRLoXCI0raIoZQWmUasSLDYCfa7Y0V+Q1Q0hZV5XyZlVnr9vH6N3swCMHkq9szsX/llKRPlPSDQhEUqh1SDUHV9miEaiFEdRHt9tpX+gbbzkkRG6wpT0keOFnEjd2aMGVwZzLSfBTbe0NFtxSKkKOcsRhC1fZUEIl+jlUh2u2NdodRETyHT2spyc+3HaNtejLz/tCHAR0aVm1QT3VcetXnFQpFJWr+f+0aRG2u7anNch7VRbQ7jIrAKLFoAqyvfb0bgeCx6y9kYv82xJuMoZ8sUMkKPeMpx05Ri4gKZ0wIEQ/MBK4B6gN7gL9IKVdF1LAoIxaKwcNBbZfzUCgCZe2uE0xbto39eYXc0LUJU27qTDM9KUl/TpB7z0f7z4FKVvizIZSOnUIRA0SLAr8JOAQMBFKBp4BFQojWkTQq2qiNKv6qVY9CoZ+cM8XcM/8nxr+zAYC5f+jDv8f00u+IfTtK6+P47ajKCvnO+9ffButHVhwblxpYyyFfeHLsFIoaTlQ4Y1LKQinlNCnlASmlTUq5AtgP9Iq0bdGGvbZnVJ+WpMSbEEJbPTmqT0s+ezCzxkWIVKsehcI/pRYbM9fs4ZpX1vLNzuP833UX8tmDmQwMpDbMnxPkvN9WqrUnsh9bejZ0/RwD7SWpUNQAoiJN6Y4QojHQAdjmtv2PwB8BWrZsGQHLooPaVNujWvUoFL7J2n2Cqcu2se9EIdd1acxTN3Wmeb2kwAfyJ8jqvN8QBwjNIbMfGypxVrVaU1ELiTpnTAhhBt4H5kopf3PeJ6X8D/Af0DR6ImBerSPShfNKzkOh8MyRs8U8t2IHK7ccoVWDJOb8/pKqRcb9OUHu+yF8DpNS3VfUMqLKGRNCGID3gFLgvgibU+uJhsJ5JeehULhSarHxzrf7mfHVbqw2ySPXdmDSgLYkmEOwStKfE+S+XzlMCkVIiIqaMQAhhADeBhoDI6SUZRE2qVYTLYXzw3tkOBYr2OU8/mG5zaXjQE2V81DEDkKI+kKIT4QQhUKIbCHE78Ixz7d78rjhX+t4cdVvXN4undUPD+T+q9uHxhFTKBQRI5rCCf8GOgHXSCmLI21MbSda+mDWVjkPRczxBlpEvzFwMbBSCPGLlHKbz7N0cvTseaav3M7KX4/Qsn4S70zozVUdG4diaIVCEQVERWRMCNEKuAvtIXZUCFFQ/jU6spbVXjwVzj9s+pD3415wrGS0F86Hk9oo56GILYQQycAI4CkpZYGUcj2wDBgbivGX/ZLLVa+sYfX2Yzx0TQe+eGiAcsQUihpGVETGpJTZgPB7oKLaiKbCedWqRxHldACsUspdTtt+QdNNdCGYFeFt05O5vF06Tw3uTMsGQaySVCgUUU9UOGOK6CPaCudrk5yHIuZIAc66bTsL1HE/MJgV4V2bpTJrXO+q2qhQKKIY5YwpPFKb+2AqFAFSANR121YXOBcBWxQKRQwSFTVjiuhjUmZbzMaKX49NsgMzrcMcjhiownmFopxdgEkI0d5pW3fcRKsVCoXCG8oZU3hEFc4rFPqQUhYCHwPPCiGShRCXA8PQNBMVCoXCL8oZU3iltvXBVCiqwL1AInAcWADcEypZC4VCUfNRNWMKn6jCeYXCP1LKU8DwSNuhUChiExUZUygUCoVCoYggyhlTKBQKhUKhiCDKGVMoFAqFQqGIIEJKXbqDUYcQ4gSQHcAp6UBemMyJBDXteqDmXVNNux6I/DW1klI2jOD8ISPAZ1ik73u4qInXpa4pNojENXl9fsWsMxYoQoiNUsoaI2Nd064Hat411bTrgZp5TbFATb3vNfG61DXFBtF2TSpNqVAoFAqFQhFBlDOmUCgUCoVCEUFqkzP2n0gbEGJq2vVAzbummnY9UDOvKRaoqfe9Jl6XuqbYIKquqdbUjCkUCoVCoVBEI7UpMqZQKBQKhUIRdShnTKFQKBQKhSKCKGdMoVAoFAqFIoLUeGdMCFFfCPGJEKJQCJEthPhdpG2qCkKINUKI80KIgvKvnZG2KRCEEPcJITYKIUqEEO+67btaCPGbEKJICPGNEKJVhMwMCG/XJIRoLYSQTu9VgRDiqQiaqgshRLwQ4u3yv5dzQojNQogbnPbH5PsUi9S055cd9RyLTtSzLHLvVY13xoA3gFKgMTAa+LcQoktkTaoy90kpU8q/Loy0MQGSCzwHvOO8UQiRDnwMPAXUBzYC/61264LD4zU5keb0fk2vRruCxQQcAgYCqWjvyaLyB3Isv0+xSE18ftlRz7HoQz3LImhojUUIkQyMALpKKQuA9UKIZcBY4M8RNa6WIqX8GEAI0Rto7rTrFmCblPLD8v3TgDwhREcp5W/VbmgA+LimmERKWQhMc9q0QgixH+gFNCBG36dYQz2/opea+BwD9Swjgu9VTY+MdQCsUspdTtt+AWL9k+VfhRB5QohvhRBXRNqYENEF7b0BHH9Ee4n99wogWwhxWAgxp/zTWEwhhGiM9re0jZr9PkUbNfX5ZUc9x2IP9SwLEzXdGUsBzrptOwvUiYAtoeJxoC3QDE20brkQ4oLImhQSauJ7lQdcArRC+yRWB3g/ohYFiBDCjGbz3PJPizXxfYpWavK9Vs+x2EI9y8JMTXfGCoC6btvqAuciYEtIkFL+IKU8J6UskVLOBb4Fboy0XSGgJr5XBVLKjVJKi5TyGHAfMEgI4X6dUYkQwgC8h1azdF/55hr3PkUxNfZeq+dYbKGeZeGnpjtjuwCTEKK907buaCHKmoIERKSNCAHb0N4bwFEvcwE1772CGHi/hBACeButcHyElLKsfFdteJ+ihdrw/LKjnmOxhXqWhZga7YyV54A/Bp4VQiQLIS4HhqF5yDGHECJNCHGdECJBCGESQowGBgCfR9o2vZTbnQAYAaP9WoBPgK5CiBHl+58Gfo2Foldv1ySE6CuEuFAIYRBCNABmAGuklO6h8Wjk30AnYIiUsthpe8y+T7FGTXt+2VHPsehFPcsi+F5JKWv0F9qS1SVAIXAQ+F2kbarCtTQEfkQLo54BvgeujbRdAV7DNLRPVc5f08r3XQP8BhQDa4DWkba3KtcEjAL2l//uHQHmAU0iba+O62lVfg3n0UL59q/Rsfw+xeJXTXp+OV2Teo5F6Zd6lkXuvVKNwhUKhUKhUCgiSI1OUyoUCoVCoVBEO8oZUygUCoVCoYggyhlTKBQKhUKhiCDKGVMoFAqFQqGIIMoZUygUCoVCoYggyhlTKBQKhUKhiCDKGVMoFAqFQqGIIMoZUygUCoVCoYggyhlTRDVCiANCiCeEENuFEKeFEHPKW1cghGghhPhYCHFCCHFSCPF6pO1VKBQKhSJQlDOmiAVGA9ehNXHtAEwRQhiBFUA20BpoBiyMlIEKhULhjp8Pk8OEED8LIfKFEHuFENdH2l5F5FDtkBRRjRDiAPCilPLN8tc3Aq8BY4BlQFMppSVyFioUCoVnyp9fBcANaH0dlwPfoD27vgRuBb4CmgJ1ZIw0FFeEHlOkDVAodHDI6edsIANoAWQrR0yhUEQ5r0spDwEIIZ5H+zDZEHhHSvll+TE5kTJOER2oNKUiFmjh9HNLIBfNQWsphFAfKBQKRTTj7cPk3siYo4hGlDOmiAX+JIRoLoSoD/wF+C+wATgCvCiESBZCJAghLo+olQqFQlEZbx8mL4iMOYpoRDljiljgA+ALYF/513NSSiswBGgHHAQOA7dHzEKFQqHwjKcPk28DvxdCXC2EMAghmgkhOkbWTEUkUSkeRSzwo5Tyr+4bpZQHgeHVb45CoVDoxv5hMgNYivZhskgI8Xvgn0Ab4BjwJ0AV8NdSlDOmUCgUCkX48PZh8hPgkwjYo4hCVJpSoVAoFAqFIoKoyJgiqpFSto60DQqFQqFQhBMl+qpQKBQKhUIRQVSaUqFQKBQKhSKCKGdMoVAoFAqFIoIoZ0yhUCgUCoUigihnTKFQKBQKhSKCKGdMoVAoFAqFIoIoZ0yhUCgUCoUigvw/8MJETa0aXiAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=[10,4])\n",
    "plt.subplot(121)\n",
    "plt.plot(TL.dist[IWA_AU>0],TL.MV[IWA_AU>0],\".\",markersize=20,label=\"Visible HZ\")\n",
    "plt.plot(TL.dist,TL.MV,\".\",label=\"all HabEx targets\")\n",
    "plt.ylabel(\"M\")\n",
    "plt.plot([0,22],[4.830,4.83],label=r\"M_\\sun\")\n",
    "#plt.title(np.mean(TL.MV[IWA_AU>0]))\n",
    "plt.ylabel(\"absolute magnitude\")\n",
    "#plt.plot(TL.dist,4.83+np.log10(TL.dist.value/10))\n",
    "plt.legend()\n",
    "plt.subplot(122)\n",
    "\n",
    "scatter=plt.scatter(TL.dist.value[IWA_AU>0],\n",
    "                    sim.TargetList.calc_EEID(allstars,arcsec=False)[IWA_AU>0],\n",
    "                   #c=TL.stellarTeff(allstars).value[IWA_AU>0],#TL.stellarTeff(allstars).value,\n",
    "                   s=60,#alpha=.8,#,1000/10**(TL.Vmag*.4),#,alpha=.5\n",
    "                   cmap=\"viridis\",\n",
    "                label=\"Visible HZ\"\n",
    "                  #)\n",
    "                   )\n",
    "\n",
    "scatter=plt.scatter(TL.dist.value,\n",
    "                    sim.TargetList.calc_EEID(allstars,arcsec=False).value,\n",
    "                   #c=TL.stellarTeff(allstars).value[IWA_AU>0],#TL.stellarTeff(allstars).value,\n",
    "                   s=10,#,alpha=.8,#,1000/10**(TL.Vmag*.4),#,alpha=.5\n",
    "                   color=\"orange\",\n",
    "                    label=\"all HabEx targets\"\n",
    "                  #)\n",
    "                  )\n",
    "plt.legend()\n",
    "\n",
    "#plt.ylabel(r\"$r_s/r_{ez}$\")\n",
    "#plt.yscale(\"log\")\n",
    "plt.plot([0,21],[0,3],label=\"IWA\")\n",
    "plt.legend()\n",
    "\n",
    "\n",
    "plt.xlabel(\"pc\")\n",
    "plt.ylabel(\"EEID (AU)\")\n",
    "#plt.savefig(\"WFIRST-CGI-disks-Uno/eeid.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "#check exozodi surface brightness in magnitudes\n",
    "assert TL.ZodiacalLight.magEZ ==22"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.0098951331240722e-09 1 / arcsec2 149\n",
      "1.0098951331240722e-09 1 / arcsec2 149\n"
     ]
    }
   ],
   "source": [
    "\n",
    "alphaZ=2.34\n",
    "#alphaZ=1\n",
    "fEZ_inner = TL.ZodiacalLight.fEZ(TL.MV,90*u.deg*np.ones(allstars.size),innerHZ_AU,alpha=alphaZ,fbeta=1)\n",
    "fEZ_EEID= TL.ZodiacalLight.fEZ(TL.MV,90*u.deg*np.ones(allstars.size),EEIDs_AU,alpha=alphaZ,fbeta=1)\n",
    "fEZ_outerHZ = TL.ZodiacalLight.fEZ(TL.MV,90*u.deg*np.ones(allstars.size),outerHZ_AU,alpha=alphaZ,fbeta=1)\n",
    "fEZ_IWA =   TL.ZodiacalLight.fEZ(TL.MV,90*u.deg*np.ones(allstars.size),IWA_AU,alpha=alphaZ,fbeta=1)\n",
    "fEZ_outer = TL.ZodiacalLight.fEZ(TL.MV,90*u.deg*np.ones(allstars.size),IWA_AU,alpha=alphaZ,fbeta=1)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "#fEZ_innerFlat = TL.ZodiacalLight.fEZ(TL.Vmag,60*u.deg*np.ones(allstars.size),innerHZ_AU,alpha=1,fbeta=1)\n",
    "#fEZ_outerFlat = TL.ZodiacalLight.fEZ(TL.Vmag,60*u.deg*np.ones(allstars.size),outerHZ_AU,alpha=1,fbeta=1)\n",
    "print(np.nanmean(fEZ_outer),fEZ_outer.size)\n",
    "print(np.nanmean(fEZ_IWA),fEZ_outer.size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-26-2da9b9be1cf5>:41: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "  ax2=plt.subplot(111)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[Text(0, 19.0, ''),\n",
       " Text(0, 20.0, ''),\n",
       " Text(0, 21.0, ''),\n",
       " Text(0, 22.0, ''),\n",
       " Text(0, 23.0, ''),\n",
       " Text(0, 24.0, '')]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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tw+nwYNm1H1ywdJDth+bTkvv+U3t9i17Zg5hX8BZlwWLGdJ3GuO6n43TUvpQjv8D1h0MUCkeUhC4iSgJ+HvniM97asBqfy8WNY07mulGTMBytq493+7gz+N7wSSzZtY12Hh8Tu/eNWwEut+Hkg2tv5/lVS3hj/WoyPR5uHHMyZ/cbHJf2mkISdxuydvUdmFb0Og+VlfkxayfD2wXLjlaYXpHliz6V7Hj0yOrPNSN/3Og+vdPcmHb9J97chpvTe5/R4hhORAEzzJn//is7Sg4TqhrbvW/uO3xRmM+TM65KcnT1tfemc06/EQlpy+t0cfO4Kdw8bkpC2jsWSdxtREXFVg4fWgBRb+uAz5sXs7bap/ejc9ZI9hSvwNZHa004HV7G9bk9Zu1EY9sm81ZdSeGBD5jga8+XFZFekXJ4cCgHFwy4kGG5w+MaQ7yUB4tZuP0t9pRto1f2UCb3/BY+V0ZMjh0ww1z56jNsKar9dGBlOMy7m9ay+dB+BnToFJO2RMtJ4m4jyku/QSknTtyY1O6JKgwGDmj84ZHjdfHYl3h31c0UFi3CoQycDi9nDf8LXZuwzNjCwoU8//ULHKg4QM+sHlw/8npGdh7ZpHY3Fj7JzgMfYNmVdDIqOSdjP7vNzng8eVw/5b90z4zdH6hE2l26lT/OvwnTDhO2g6zYPZcPNj3DL079N+19LR8Xv/7NF/i8YHPU1wylWLZruyTuVkQSdxvh9eUBGieRYkAmISI9b0Xn3HPp1vXimLbnc7fnsgmvURk8SCBcTHZ6nyaNLc/ZNod/LH+cYNWTkZuKNvM/8+/n19Pvb1Ly3lD4BKZ99KaoxxGmj3snhjpIlrP+49up4vmVD1bNYY98WgpZAUwrzGtrH+Xm8Q+16Njbig7yWf7mo49317k1oJSia5JuwiVLeSDE81+u4bMNBfTLbc8tp4yld8fsZIdVTRL3CUZrCxUlQbbLGo3X14PKiq24tMKFG601Tmc6Q4e27Be/MWmejqR5mjYnWWvNM6ufrU7aR4SsEE+vfoZHz37kmMewrIYquTnq3SxNFWErSEHxN9Qd4rKx+HrfohYff3PRAdyGQSD6w4K096UxrVf/FreTKg6VVzL+gX+xv6ySylAYt9Pgb3OX8s6dV3DqoN7JDg+Q6YAnjIO73+XrBd9m5dzpfL1gBgd3v1vrdaUUo8a9QE6H6SjlQikX6RkDGTXuBbzerkmKuja/6acsFP3maWFpYZOO0bfb9zAcdVeDB6+7A+3SUjP5OJRRPf2xLqfDFXX78RiQk1t1MzLS1db66AMnndIzefeq21rdrJJ4euj9hewuLqcyFLk/EzItKkJhrnvqLZpTlC8epMd9Aji0+312bnwY247Ucg6HDrJz48MoHHTodnTFFLc7h5PGPIllVmLrEC5XdpIijs7r9OJxeqgM138aLTetaQ/tjOjzI7bvfYMyfz6mVY7h8KKUwSkjX0jq05ItYTicjOx6Cqv3fI6lj3aLXQ43U3p+u8XH75vTkVN7D2BewWYCpsmRnr3P6eHTmf+PvHbZLW4jlby5YgMhq/6DXgfLK9l+qKRVDJm0nT+jJ7Dd256sTtpH2HaA3dv+FXV/w5nW6pI2ROZeXzbkUjx1Ftj1GB6uGXHsRWcBXM4Mvj1lCVOHP8GgHrMY1e8+Lp2+kS45U+MRcsJcNepndMnsjcfw4TF8uA0vfXJGcOGQWTE5/rPfuYaZoyeR7nbjUA5O7tGXOdfd0eaSNkCaJ3pJWltr0twt/4QTC1KP+wSw4tOpRJviB4oxZyxMdDgtorXm1fWv8sr6VwmaQTI9mVx/0kzO6pu8EqethW3bbDy4kiL/LvKyBtAre0hc2jmy7mVb9fhny7jntU+qh0oADIdict885v90ZouOnfB63EqpWcAsgJ49W/4QhYgdt7cLocCeqNtTjVKKy4deziWDLyFoBfE5fa06iRzyl/DFrrV4nR6m5p0UlwUEtNb8Y9GXPPjJPA5VVNItqx0Pnd+LXnEqt9Kaz3cizDplDF/l7+KVZetwGQ60hm7ZGbx8yyXJDq2a9LhPAEV7P2H7+ofQNWZNKIeHXkN/SU5neUowXp5b+yF/XfYaTmVUT6F77KwfMq5rbB+FfmzBYu774BMqw0d7gGkuF09fcQnfOal1Ve47kWzdX8SS/N30yGnHlP49YvIHLVY9bhnjPgHkdDmTXkPvxePLA2Xg8eXRe+h9krTjaN3BfP627HVCVphKM0BFOPL1gzkP11unsSW01jz4ybxaSRsiTzTe/9EnMWtH1NevUw7fmzicqQN6trpPITKr5ASR0/l0cjqfnuww2ow3Ny0gZIejvrZ459ec3ntsTNoJmCYl/kDU1wqKimPShkg9krijMCv3YYVKcKV3wxGjWhAidsoCe1lT+B9K/IX06jCFQV0uwFlnJkq8+c0gdpRhRg0ErNj1uL1OJx3S09hfXn/lnwG5yV8kuaZiv5+vCneQ5fUyIa8HjjY09zvRJHHXYIcrKN7wL8KVu1HKQNsmaV2mktHrwlb3UamtKiz6kv8uuQJbW1h2kHW732Txlke5dsr7eJyJ+yN7Vu/xzMlfit+s/TSmaZtM6ha7cWelFA+cdxZ3zX6v1nCJz+XiwfPPjlk7LfWPLxZx78cf4jYMbK1p70vjnetuYGBu8xbNEI2TP4k1lGx+gXDFTrDDaCsA2qRy32ICB5cnOzRBZLz3rZW3ErYqqx9fD1sVHK7M56utf09oLNN6nMTk7sPwOSM9fQcKr+Hmh+O+S44vtiudz5wwlv+7dAZ9O+TgNgyGdu7Ey9dcwbmDE7/WYTRf7tjOr+Z8RMA0KQ0GKQ+F2FlSzAX/fgrbPv76MGHL4sNNG/j38qVsPnjg2G9og6THXcUOlxMq3Qp1l8ayQ1Tunocvt8U3gkULHa7Mxx8+XG+7ZQdZt+dNpg/6adxj0FoTMMtxGz7+94wfsGjn18zJX0q6y8uMgdMY3KHlS2dFc8WYkVwxpmkVEhPtia++xF/n5qkmMnSyZGchk3oe+5wEzRBf7l5HflER93+8mIBpYtk2WmsuGT6Sx2d8R4ZeapDEXcW2ApGyaFFmR9pmchYEFbUZDjfo6D04Z5T6JLG2YtcHvLX2j1SEDmM4XEzrcxXnD/kB03q0zoTaUra2eWvjKv6z9kssbfPdYRP4zuCxOB21i5gdqqyI+viXQymKA/4or9S2bM8G7pzzCLbWbCzMIhSuvXrRG9+s4ZS+fblyZOtYJ7Q1kMRdxfDkoAwPut5MAQee9kOTEpOoLcuXR4eMgewv/QbN0QTuNHyM7nldXNvesH8RL6+6j7AVmeFhWSbz81/A0mFmDLsnrm0fj1g+9fj9D17gw61fUxmO3GxdsWc7b29cyfMXzarVxoyhw1i8vaDelMWQZTGpR+O97cpwkB/MeZiKcIBQ2CBsKuouOVcZDvPkkq8kcdcgnz2qKOUgq+93weGi+sJRThyuNNLzWs9NoLbu4rFPke7pjNvIwGn4cDq89O90FqN7XRvXdj/c+PfqpH1E2AqwqOC/hKzo0/USpSJUyYOLHufUF69m2gtXctecB9lZtrdFx1yzr5APtqypTtoAlWaIRTu3sHjnllr7XjVqDAM65pLmitTxUEQeEHrg7HPJ9vkabWdB4arqf2vd8B+cgBl96mVb1Tp63DXrSEJkyCIJszg8OcPIGX4nlXs+xwoU4c4aQFqXKTIlsBVpn9ab75++nG0H51Ee2Ev39mPJzYxPzY6aDlXsbPC1ylAxbl9yygtorblzzgNsObydsB2pHLh0z9fc9P4vefXiv5LpTm/WcRcWbsaMcmOxMhxiwY5NTOkxoHqb1+Xis5tv5aVVK5m9bi0d0tK5ZeKkJo1tV9aYVul2mSilqTvL0ud0ctnwE3M4qrmSn7ij3XU+8j+XhOTtSu9OVv8rE96uaDqHw0n/TmcmtM3uWYPZeGBxve2GcpLpSd586rUHN1NQsrM6aQNoNCEzxHtb5nHF0G8167jtvWm4DIOwXftmvcdwkuOr/8fA63Jxw/gJ3DB+wnG1M7nbMKyq+xZKQecOxew92L6q961Id7vpn9ORWydObtbPcaJK7lBJY3VSWknBciEAzh9yJy6j9g1Ql+Hl3EHfx4jBYgbNtb14V9QbgwErxKaigmYf94IBo3BQv+NkKAcXD47NU6EAXTI6cPPIC/E63SgUab4gA3qUMKZnJteMHss/ZlzC57NuJ80d++JdqSz5PW4hUkDP7OHcPvkp3ln3v+wqWU87byfOHngL43pc2Oj7AmaYw/4KctMz683GiIXe2d2jpFfwGG4G5fRp9nEzPV5evuQ2rnvrXwTNMChwOgz++a2Z5KZlNj/gKG4ZPYOJ3YYye9N8AmaIc/pO5JSeo3AouQXXkORWB6w7tl2XzNsUSXbIf5idpbvpltmF3LSmD4lYts1v5r/DM6sia0K6DSe/mHo+N4yO7YIOWmtu/uBeNhcVVA+XOJQi053BKxc/2uwx7iMs22bF3u1orRndpRcuI/Z/fNqShNfjTjh5xDxu/OFyHMqBx5mW7FBaLdO2+NOX/2Du9kW4DBchK8zUvPH88uQ7cRnHHhp5YMG7PLNqEf6q2RB+M8z9n79Nji+diwaPjlmcSikePeteHlv2PB/nL8S0TMZ3O4kfT7ihxUkbwHA4GN+t+T13ER/Jr8fdUK87Rr1trTXarEAZXpSj9f6dagnTClLs302mN7fReh17y/J5YcV97CxZD0DfnNFcPea35KS1jsWCk604cJiX1z7Lkt2LKQk5KA5pzBpP0noMNzMGnMP3x85s9Dhhy6L/Y7+gMkp510EdOrPw+p/FOnSRIk6cHne0nnWMetuhQ98QLJyLtoKAwtVxBN4eZ6DiMNaYLAu3PsncTX8FNLa2yGs/jlF5lzO083TS3FnV+/nD5Ty84Dr84VIsDTv8nfnycDkvbvkxk7qP4p6J15HXrnPyfpAk84crueeT2ygJHMbSFodCmeg69+6DVoi3N3/M7WOua/Qhl4KSPQ2WfN1dVhLTuEXb1DoGkY/M247h/G2zJJ/A9g/RZmWk/og2CR/6msCOOTE5fmuwaudbfLrpEUJWBSGrEtMOkn9oEW+s/in3f3w6y3a+W73vil0fYtohNJqN5T3ZF8zB0gY2ii92rea6d++jOFCWxJ8mueYWfERFqAyrqoeto97yiyRvu4HH7gHe2PgJ17/3CzT1VwkHGNk5r+XBijavdSTuOAjuWQQ15rYCYJuED62t6oGnvs+3/I2wVbsWhAKUDhC2A7yy6n847I+sRXmgopCQ5afS8lBmptfqTWogaIWZvWlu3GM2bZMPt83lJ3N/za/m/4Glu1fGvc2mWHdwDcEa14VTWUQrXDOgfR+MBj6x7S7bzyNLnyNsh8lJK0VRO8H7nC5GZQ/kmmfe4L635rL9UHGL495bVsbNr7xBl/sfpPcDf+CBOXMJmeax39iKHdxzmPuvfYwz2l/HuZ1v5LGfPk/Af2L8zsZK8odK4sQONvCRVDnQph+V4ML78VAePNjo6xqbVbs+5LT+19MrexgeI42DISfRElLQCvHNwW1xijTCsi3umXs/Gw9tIVCVJJftWcnFg77FzaOuiWvbx9I9owdOhwuzaogjw/BTbGYQOVcKh3Lgdri4a8LNDR7j0+1fVT8FmOkLYDg0hyvTMS0nfdt3ZH+hjz+/v5SKUBi3YfDI3C9567bvcfrg5t38KwsEmfTo4+wvL69+yvFPn81nyY6dvH1jfEsAxEtluZ/rJ/6Mon3FWGbkZ3r1bx+wbukWHp/76yRH13qcsD1uI72hG24K5Wr6PFTL8lO48RFWf34Oqz47nW1r7iUU2B+bIFuoe/ZJUbcfScuWbVXX0RjR9TSyvLmkGzZ1i/gAuB0uBub0bFE8lm2y9cAC1u5+h9JA/VoZz89/mbV711cnbYCAFeS1De+wryK5dZfP7ncBzho3r50Omw7uSjp4PAzO6cf5fU/jyfP/xLCODdfAtrRVa1WcNE+Q7u2L6N+piE52J3YdDFARivxhCFkWFaEw1z77JrbdvIfNXlyximK/v9aj6X7TZP62fNbsaVmtkmT56KWFlBdXVCdtgFAgzPqlW1m3dEsj72xbTtjE7ek+rapgVA0OF55uU5t8c1JrzZaVP+Lg7rewzHJsO0jxgc/ZsPRGLLP+UlKJds7gn+IyfNRMxFpDSEcKZTkNN8O6nAqA0+Hix9Nf4Oz+F5DpCtf7GO8ynFw8sPmLCx8o28zDn07iv8tu4e3VP+HRuVP5dMOfALBtm6uvvpo/v/IolqP++LDTYbBq39fNbjsWOqZ14lfT/0C3jDycDhdOh5PRnUfxzAX/4Inz/sg9k26nZ7vujR5jeo9xUR+ycSjF19v9BM36494l/gBbDhQ1K+Yvtu+oV5EPIlMEV+/e06xjJtu6JZvxV0QfFtm8enuCo2m9TtihEsOXS/rgqwnumo9ZvhuHOwNP18m4cppekKiydD3+sk1ou+a0LhvLrOTQng/p1OOS2Ad+HLpmDeXWKW/w6aZH2XJgIQErQEg7sTBwG14m9LyIvKyjP2+aux2Xn/RzvjXEz1++eo4Pty3GtC1O6jSAn02+gY5p2c2KQ2vNi0uuozx4gJrDMF/m/4ueOeNYPf8Qs2fPpsuFvbBNG4ezdn9BochwJ7+Q16AOQ3nsvGcpCRzGZbhJcx3fPOi+2XlcPewCXvjmXcKWiVLgdDiZOeIiHtu+D6g/fGdpTZq7eY/MD+rUEa/TSaDOmLYCerfPbtYxk6330Dw8PjdBf+2plA6HIq9/cgp5tUYnbOIGMNI6kTbg0ma/31+xNdqoAtoOUFm6vgWRxU7ndoO4ctw/AMgvWsmywnfRaMZ0P49+HaJPF013+fjV1Fu4b8osbK0xWjhnfk/J11SGDlN37Dxs+Vla8BwvPXuAiooKdn++g+5n94U6idtwGIzvGruHUloqy9u+2e+dNfoyTu01gbnbv0IBZ/aeTL/2PTBLl/Oj1z6iMnS0h+xQiuFdO5HXvnlLnd0wYRx/mbewVuJ2Ohz0yM5iap/ezf4ZkumC607jmQdeJxQIVT/e4XQZdO7RkTGnSF38I07oxN1SXl8PomVu5fDgy+iX+ICOoU/OaPrkND0BKqUwYjD9MmRVNjivOWCWVa876N9TzoYnVzLoptFgRwr+t0tvx59Ovx93E55GTBUDc3oxMKd2SdObpoxh0ZYdvLZyHYZDoVDkpPt4ddZlzW6nS2Ymn9x6Ize/8gbr9+0HpThzQD+evPw7Kbu4dVaHTJ5c+AAP3PQ465dtxeFQTD5vNL/4560p+zPFQ/KfnGzFtNZsWDKTQEUBWh/p1SgMZwbDTn4Fpyu2i8KmqrAV4E8fjyJk1V7izWX4OGvIL8n/ysONN95IRUXkvoDDbZA1IAef28eWLzbi88R/2bHWYtO+Q3xVsJNuWZmcNrAPDkdsklGx34/bME6oKnoBfxDDcOBq5lBSaxSrJydP2JuTsaCUYsCYx8jOPQWlnICD9KyTGDTuCUnaNbgMLxeM+B0uhxeFUbUtjY4Z/Rnd43Iuu+wyzj//fNLT03E4HHgMN8H8Cp79w7/aVNIGGNi5A9dMHMkZg/vGLGkDZPt8J1TSBvD6PCdU0o4l6XE3kdYWaH3C1juJhX2lG1m+40XKgwcY1PlMhnW7EKcjkky01nz55ZfMmTOHnJwcvvvd75Kbm5vkiIVIrFj1uCVxCyFEgpw4RaZEm1USKGfBzq9xKMXUvJNo55Eys0I0hSRukRRvb17Mrxc+i6EcgMLWNr879WbO6tPizogQJzy5OSkSbnfZQX698FmCVphKM0ilGSBghfjZvH9ysPLELntaEQxhWg1XFxSiKaTHLRLuw/yltWp6HKFQfFKwnCuGno4/HOKVdUv4eNs3dEpvx/Ujp3JS5x5JiDY2Ptmwjdv/8x4Fh4pxGg5umDyav1xyNh6X/AqK4ydXjUi4gBnCsuvX7bC0TdAKUxEOct5L/0thSRGVZgiHUry5YTl/OOMyvjtsYhIibpmVhXu46PGXq+uKmLbNM1+spKiykpduaP6TvaLtkqESkXCn9RyF21l/fq5DKab3OInnVy9mR8mh6qW/bK3xm2F+Nvc1/OH6y4G1dn/4aFG9eiL+sMnsVRvZV1qepKhEKpPELRJuSMdefGfgNHxONwpwoPA63Vwz/Gz6ZHfl3c2rqxfZrclQDlbvK0x8wC20bu+BqENDbqfB9qITe0xfxIcMlYik+Pnkqzin7wQ+2PoVDqX4Vv/JjOwUqf+S5fVFfY+lbTJT8EnLCb26s37vAaw6dbeDpsmATjlJikqksjaRuC3L5qM31vP2S19jWZrzLh3ChVeMwOU+cRYNTjVKKcZ2GcjYLvUXJrhx1HQW79xCZY1hEYWiS3oWQzt2S2SYMfGzc6byyopvKA8e/XnS3C5unjKG9mnR/0gJ0Zg28eTkfbe9yxdz8wn4I+OMXp+ToaO68Mh/Lo1pvQgRO3/54kMeXTIHt+FEa017XzqvXfp9emd3THZozfL1rn3c/frHLN5WSE66jx+dMZkfnDpRrr82Rh55b6INa/Zxx2WvVCftI3xpLh544gImntI7OYGJYzpYWc6yPfl08GUwtmsvHEpuyYjUJo+8N9HqJbtqrV93hL8yzIrFhY0m7rAV5Js9H7CjaDk5Gb0ZnXcx6W4ZkzxeO0p38u+v/8P6gxvplJ7L1cMuZ1wTFk7omJbBuf1GJCBCIVJLkxO3UmoWMAugZ8+WLSqbSO07+HC6DMLh2snb4zHokNvw0lSVoWKeWHgxZcEDhKxKnA4vn216lJsmv0zXrPitxJG/qYCnH/43G9duYsTYYVx/13Xk9W58rcPWbHvJDu74+B4CZhCNZn/lAf5nwe/4f+Nu5ey+pyc7PCFSUpM/e2qt/6m1Hqe1HpdK5TinndMfw1n/x1SG4qyLBjf4vrmbHqHYv7t6cQDTDhA0y3lt1Y/jFuuqr1Zz8YTLef3ZN1n91RpefvJVvj32Ejat3Ry3NuPt6dUvVCftI4JWkP9b+XTUh3CEEMd2wg8a+tJcPPqfS+jcLRNfmou0dBftO6bx539fTPuODVejW7vnAyxdfy7xwYptVISatyr3sdx/x2/xV/qxrEhCM8MmFWUVPHT3H+LSXiJ8c3BDraR9RNAKUhQ4nISIhEh9J/wYN8CgEZ157Ysb2brhIJZp039oLobR+N8sQzWw8oYGh4r9aQuHw2xYsynqa8sXrWz2cdcdLODRpa/wzcECOqW359bRMzi7z4RmH+94dfDlUBys/5CJ1rpVrOwuRCo64XvcRyil6D8kl0EjOh8zaQOM7Xk5Toen1jaHMujRfjS+OCxbZhgGHq8n6msZ7Roei2/MhkPbmfneg3yx+xtKQxVsObyTe+c/ycvrPmlJqMflquGX4zFq/1xuw83pvU/B50y9h2mEaA3aTOI+XtP73ULPnLG4DB9Ohxe3kU47bxcuG/1wXNpzOBxcev3F9ZK31+fl6tuubNYxH1v+OkGzdm2PgBniseWvE7bNBt4VW9N7nMyNJ11NmtOHz+nF5XBxSs8p3Dnu1oS0n4r2FJfxwNsLuO7J2fxz3nIqgqlXn0XE1wk/j7sltNbsKl7DrpKvaZ+WR//caThU/J62DAaC/Oian7Lgo4W4PS6CgRDfuvxcHnji1zidxz88c+qLP6AoUFpvu9dw8/alv6dLRodYhN0kISvM/or9ZHuzyXA37xNEW7A0fzdn/ukFTMsmYJqku110yExjyX03kJsp5y3VyTzuBFBKkdd+JHntRyakPY/Xw99ffYTdO/awY1shfQb2pnO3Ts0+XvfMjlETtwayvIkdX3YbLvLape60xkSZ+dRbtR6NrwiFCRWXcf/s+fz9mvOSGJloTVrFUEllyXq2Lb+LdZ9fyKbF11C0612a80ngRNGtZ1cmnTqhRUkb4NbRF+E13LW2eZ1uvjNwOj5n9PF0kTwHyirIP1Bcb3vYsnlzxYbEByRaraQnbn/ZFgpW/RR/6Xq0HSIc3M/eLU9yoODFZIdWi7ZNKotWUb7/C6xQapTinNZjJPdOuY723kzchhOv4ebSQadyz6TmjZmL+HIbRpSJkxE+t3w4Fkcl/WrYn/8c2q5980XbAQ7ueJWOPS/DYSS/Zxgsy2fv179HawvQaNsiu9fFtO85I9mhHdO3B0zlgv4nczhQRqY7DbfRwDRHkXRZaV6mDujB/I3bMWuUgPW5ndw8fUwSIxOtTdJ73IHyrRCln6GUIhw8mPiA6tDaYu/aP2Cb5WjLj7YCoMOU7JhNoCQ1Pr46lIMOvixJ2inguZtm0De3PRleN+keFz63kzOH9OHH50xKdmiiFUlYj1sHSrAOrEcHS1CeLIzcIShvFp60PMwoCVprC2crKOgUKN6Atus/QantEKV75uLNavixeSGOV9fsTL554DY+37Sd7QdLGNu7KyPyWnavQ5x4EpK47cpDWNsXgI48yq1D5ZjlezF6TSW399VUlqxH28Hq/ZXDQ/uu52E4k19kPhJX9JrJtulPbDCiTXA4FKcN7p3sMEQrlpChEmvvquqkXU1bWHtXk549gh7DfoHL2wVw4DB8dOjxHboMmJWI0I7JmzUYdP2HVZTDQ0Yn+fgqhEi8xAyVBIob3Z7ZcRKZHSdhWyGUw4lqRQXzHc40cvpdS9HW56uGTDTK4cGT2Y/0jhOTHZ4Qog1KTOJ2uCDKODGO2jfLHHXmHLcW7bqejiezH2V7PsM2K0jvOI60juNQcXyKUgghGpKQxO3I6Y99aFPt4RJl4Mjpn4jmY8KT0QvPgJlJa19rzap9mygo3Uu/7O6MyO2HUrJeoRBtUWISd+4QtBVEFxeAcoC2Udm9cOQOSUTzKa80WMGsD37PjtK9VbWtFf3b5/HEuT8lzSUV9oRoaxKSuJVSOLuORncaBqFKcKehWumwSGv0hy+fZ2vxrloV/TYe2s7DS1/mlyfPTF5gQoikSOhdQGW4Ub5sSdrHQWvNx/lL6pVhDdkm721dnKSohBDJ1Hqmb4ioNLrBtRlNS9ZsFKItksTdyjmUg3Fdh6DqPATkUA5OzhuRpKiEEMkkiTsF/PLkmWS606pLtHqdbrI86fxk0tVJjkwIkQxJrw4ojq1XVhfevezPvL15AZsOFzKkQy8u7D+NDHfySwIIIRJPEneKaOdJ5+rh5yY7DCFEKyBDJUIIkWIkcQshRIqRxC2EEClGErcQQqQYSdxCCJFiJHELIUSKkcQthBApRhK3EEKkGEncQgiRYiRxCyFEipHELYQQKUYStxBCpBgpMiWSriSwj6U73uCwfxf9O0xkRLdzcDpklSQhGiKJO8nK/Tv5ese/KCpfT5fsiQzrMROvOyfZYSXMtkPLeOqrm7G1hWmHWLXrfT7d8gR3TH0ZrzMj2eEJ0SrJUEkS7S9ZwQvzx7Ai/1G27XuHrzY/yPPzR1HmL0x2aAmhtealFXcTsvyYdgiAkFXJoYpCPt/yTJKjE6L1ksSdRJ9+fTthqxzbDgJg2X6CocMs2vDLJEeWGAcrtlMZLqm33bSDrN79XhIiEiI1SOJOkpBZzqGydfW2ayy2H/goCRElntPhRms7+muGJ8HRCJE6JHEnieFwoxo4/U4jLcHRJEf7tG50yuhT7zy4DB+Tel2RpKiEaP2anLiVUrOUUsuUUssOHDgQz5jaBMPhpm/nC3Go2rMnnA4fw3vclKSoEu+acX8l05uLx0jHbfhwObwM6XQKk3pdnuzQhGi1lNb6uN80btw4vWzZsjiE07YEw8W8teTbHCpfh8LA1mF6djyT88a8gNGGpsNZtsmmA4soDeynV84oumQOSHZIQsSFUmq51npcS4/TKqYDWraN4Wh7ozYeVzaXnfw5B0pXUlJZQIfMYeRkDEp2WAlnOJwM6XxKssMQImUkLXFrrfm/5fN4dMknFPkr6Nkuh/tPmcEFA0cmK6SkUErRKWsMnbLGJDsUIUSKSFrifmzpp/zli4/xm5H5uztKi/j+By/gc7k4o8/QZIUlhBCtXlLGJyzb5tGvPqlO2kf4zTAPLXw/GSEJIUTKSEriLg36CVrhqK8VFB9McDRCCJFakpK423l8+JzRZ00MyOmU4GiEECK1JCVxGw4HPzn53HrJ2+d08YupFyQjJCGESBlJuzl585hTSHd7+PPij9hXUUr/nE7cf8oMpvcamKyQhBAiJSR1HveVwydx5fBJyQxBCCFSTtt76kUIIVKcJG4hhEgxkriFECLFSOIWQogUI4lbCCFSjCRuIYRIMZK4hRAixUjiFkKIFCOJWwghUowkbiGESDGSuIUQIsVI4hZCiBQjiVsIIVKMJG4hhEgxkriFECLFSOIWQogUI4lbCCFSjCRuIYRIMZK4hRAixUjiFkKIFCOJWwghUowkbiGESDGSuIUQIsVI4hZCiBQjiVsIIVKMJG4hhEgxkriFECLFSOIWQogUI4lbCCFSjCRuIYRIMZK4hRAixUjiFkKIFNPkxK2UmqWUWqaUWnbgwIF4xiSEEKIRTU7cWut/aq3Haa3H5ebmxjMmIYQQjZChEiGESDGSuIUQIsVI4hZCiBQjiVsIIVKMJG4hhEgxkriFECLFSOIWQogUI4lbCCFSjCRuIYRIMZK4hRAixUjiFkKIFCOJWwghUowkbiGESDGSuIUQIsVI4hZCiBQjiVsIIVKMJG4hhEgxkriFECLFSOIWQogUI4lbCCFSjCRuIYRIMZK4hRAixUjiFkKIFCOJWwghUowkbiGESDGSuIUQIsVI4hZCiBQjiVsIIVKMJG4hhEgxkriFECLFSOIWQogUI4lbCCFSjCRuIYRIMZK4hRAixUjiFkKIFCOJWwghUowkbiGESDGSuIUQIsVI4hZCiBQjiVsIIVKMJG4hhEgxkriFECLFOJu6o1JqFjCr6ttypdTGGi93BA7GMrA4kBhjIxVihNSIU2KMjVSKsVcsDqa01i0/iFLLtNbjYhBP3EiMsZEKMUJqxCkxxkZbjFGGSoQQIsVI4hZCiBQTq8T9zxgdJ54kxthIhRghNeKUGGOjzcUYkzFuIYQQiSNDJUIIkWIkcQshRIqRxC2EECnmmIlbKeVRSj2llNqulCpTSq1USp3XwL4zlVKWUqq8xtepsQ66gbbnKaUCNdrd2Mi+P1RK7VVKlSilnlZKeRIUY3mdL0sp9VgD+ybkXCql7lBKLVNKBZVSz9Z57Qyl1AalVKVS6jOlVIMPDyilcpRSbyqlKqqulSsTEadSapJSao5SqkgpdUAp9apSqmsjx2nydRLDGHsrpXSd/8v7GjlO3M5lIzFeVSe+yqqYxzZwnHiex0ZzTmu4LhuLMRHXZFN63E6gEDgFyALuA15RSvVuYP8vtNYZNb7mNaGNWLmjRruDou2glDoH+BlwBtAb6Av8OhHB1TwvQGfAD7zayFsScS53Aw8AT9fcqJTqCLxB5P87B1gG/LeR4/wdCBH5ua4CHldKDYt3nEB7InfsexN5Kq0MeOYYxzrmdRLjGI/IrtHubxs5TjzPZdQYtdYv1rk+bwe2ASsaOVa8zmODOacVXZeN5cX4X5Na6+P+AtYAl0TZPhNY2JxjtvQLmAfc1IT9XgIeqvH9GcDeJMR7HZFfDNXA6wk9l0R+mZ+t8f0sYHGN79OJ/KEZHOW96UR+OQbW2PY88Pt4xxnl9TFAWUuvkxify96ABpxNeG9CzmUTzuNnwP8k8zzWaW8NcElrvS5rxhhle8yvyeMe41ZKdQYGAt80sMtopdRBpdQmpdR9Sqkm10OJgd9Vtb2okWGFYcDqGt+vBjorpTrEO7g6rgOe01X/cw1I5rmsdZ601hXA1qrtdQ0ELK31phrbVjewb7xNp+Fr84imXCfxsF0ptVMp9UxVzzGapJ/LqqGH6cBzx9g1IeexTs5pldflMfJizK/J40rcSikX8CLwb631hii7zAeGA52I/HX8HnDP8bTRAj8lMuzRncjHlHeUUv2i7JcBlNT4/si/M+Mb3lFKqZ5EPmL9u5Hdknkuof55our7aOfpePaNG6XUScCvaPw8NfU6iaWDwHgiH5vHEjkvLzawb2s4l9cCC7TW+Y3sk5DzGCXntLrrsrG8GK9rssmJWynlIPIxIwTcEW0frfU2rXW+1trWWn8N/Aa4tKlttITW+iutdZnWOqi1/jewCDg/yq7lQLsa3x/5d1m8Y6zhWiLDIA3+YiTzXFape56o+j7aeTqefeNCKdUf+AD4f1rrBQ3tdxzXScxorcu11su01qbWeh+R35+zlVJ1zxm0gnNJ5PpsrFORkPPYQM5pVddlY3kxntdkkxK3UkoBTxEZ4L9Eax1uyvuIjOupJu4baw21/Q0wssb3I4F9WutDCYkq4pi/GFEk+lzWOk9KqXSgH9E/8m0CnEqpATW2jWxg35ir+mj/CfBbrfXzx/n2ZFyjR4bHorWb7HM5BegGvHacb43peWwk57Sa67KxvBj3a7KJg+7/B3wJZBxjv/OAzlX/HgyspZEbHDG8KZANnAN4idztvQqoAAZF2fdcYC8wlMjd37nE6WZFA7GeXBVbZms4l1Xnywv8jkjP4cg5zCXysfKSqm1/AL5s5DgvA/8hckNoStV7hyUgzu5ExjjvieV1EuMYJwKDiHSUOhCZBfFZMs5lQzHWeP2fRO69JO08VrURNee0puuykRjjfk02JbheRP4CBIh89DjydRXQs+rfPav2/TOwr6rhbUQ+3rti9Z/ZSIy5wFIiH4GKq07mWVWv1YqxatuPquIsJTJNxxPvGGu0/QTwfJTtSTmXwP1V/781v+6veu1MYAORu/bzgN413vcL4IMa3+cAs6vi3QFcmYg4gf+p+nfNa7M8WpyNXSdxjvF7QH7VudlD5KZfl2Scy2P8f3urzssZUd6XyPPYYM5pLddlYzEm4pqUIlNCCJFi5JF3IYRIMZK4hRAixUjiFkKIFCOJWwghUowkbiGESDGSuIUQIsVI4hatgorUH19Y4/typVTfZMbUXDXqK8+PwbGeVUr5lVI7YxGbODFI4m7DlFIFSqkz62yrlUCbedxTW5podKQu8bZ4txNHd2itp7f0IFrrmUSeohWimiRuIYRIMZK4RaOUUkOqPvoXK6W+UUp9u8Zr5yul1lUt3bRLKXV3VdGfD4BuNZZi6hbluB2UUm8rpUqVUkuIFAqq+bquqq52XO0opSYopb6oinePUupvSil3nePeqpTarJQ6rJT6e1WxoCOv36yUWl/V1jql1Jiq7d2UUq+ryFJU+UqpO4/zPBYopX5edczDVTW5vTVen6GUWlV1PrYqpc49nuOLtkUSt2hQVZ3hd4CPidQF/wHwolLqyNJKTwG3aK0zidQOn6sjhe3PA3bro0sx7Y5y+L8TqfPQFbih6qshx9OOBfwQ6AhMJrLC0e11jncBkfrYI4HLiRT5QSl1GZFaHtcSKf/5beBQVenOd4gU4e9edcy7VGQZvONxVVVb/YgU3b+3qt0JROqX3EOk6NB0oOA4jy3aEEncYnZV77RYKVUM/KPGa5OIFKP/vdY6pLWeC7xLpGgSQBgYqpRqp7U+rLVubH3Cakopg0h1t19prSu01mtpvMxtk9vRWi/XWn+pI7WvC4gU9Tqlzm6/11oXa613EFmia1TV9puAP2qtl+qILVrr7USSfK7W+jdV52Eb8CRwRVN+3hr+prUu1FoXAQ9y9DzeCDyttZ6jI/XXd+noC5UIAUjiFnCR1jr7yBe1e6fdgEKttV1j23YivU6IJN/ziSzJ9blSanIT28zl6GKrNY/bkCa3o5QaqJR6Vym1VylVCjxEpPdd094a/64k8scJoAeRcpx19SIyJFPzD9wviNRhPh51f94jQ0gNtStEVJK4RWN2Az2qhgqO6AnsAqjqmc4gMowyG3ilap9jlZw8AJhEElbN40Z1nO08TqTk5wCtdTsiCbapBf4LqTPWXmN7fs0/cFrrTK318a74UvfnPTKE1FC7QkQliVs05isidYx/opRyqcgiphcCLyul3Eqpq5RSWTqy8kcpkfFliNQR76CUyop2UK21BbwB3K+USlNKDSWyeHI9zWgns2qfcqXUYOC24/h5/wXcrZQaqyL6q8hKJkuAUqXUT5VSPqWUoZQarpQafxzHBvi+UipPKZVD5A/Kf6u2PwVcr5Q6QynlUEp1r4pdiKgkcYsGaa1DRG7QnUdkwdt/ANfWGH+9BiioGpK4Fbi66n0biKw6sq1qaKHerBIi6/NlEBm2eJbIghYNOZ527gauJFKY/kmOJsem/LyvEhl7fqnq/bOBnKo/NBcSGQvPrzoX/wKi/mFqxEtEbvRuq/p6oKrdJcD1wMNEVmj5nMjwjBBRyUIKQsSYUupjIjNalmmtT6vaVgDcpLX+5DiP9RRwGbBfa90/1rGK1CSJW4gEaG7iFiIaGSoRQogUIz1uIYRIMdLjFkKIFCOJWwghUowkbiGESDGSuIUQIsVI4hZCiBTz/wFt2vzM1vEzcQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax1=plt.subplot(111)\n",
    "import cmasher\n",
    "\n",
    "\n",
    "\n",
    "x_AU_range=np.arange(innerHZ_AU.min().value,np.nanmax(IWA_AU).value,np.nanmax(IWA_AU).value/allstars.size)*u.AU\n",
    "\n",
    "#x_AU_pcrange=np.arange(1/TL.dist.min().value,np.nanmax(1/TL.dist).value,np.nanmax(1/TL.dist).value/allstars.size)*u.AU\n",
    "\n",
    "\n",
    "y_fEZ=TL.ZodiacalLight.fEZ(TL.MV.min(),90*u.deg*np.ones(allstars.size),x_AU_range,alpha=alphaZ,fbeta=1,tau=1)\n",
    "\n",
    "y_fEZmax=TL.ZodiacalLight.fEZ(TL.MV.max(),90*u.deg*np.ones(allstars.size),x_AU_range,alpha=alphaZ,fbeta=1,tau=1)\n",
    "\n",
    "y_fEZ_M5=TL.ZodiacalLight.fEZ(4.84,90*u.deg*np.ones(allstars.size),x_AU_range,alpha=alphaZ,fbeta=1,tau=1)\n",
    "\n",
    "#plt.plot(x_AU_range,-2.5*np.log10(y_fEZ.value))\n",
    "#plt.plot(x_AU_range,-2.5*np.log10(y_fEZmax.value))\n",
    "\n",
    "\n",
    "plt.plot(x_AU_range,-2.5*np.log10(y_fEZ_M5.value),linewidth=5,color=\"orange\")\n",
    "SB_IWA=-2.5*np.log10(fEZ_IWA.value)\n",
    "plt.scatter(IWA_AU,SB_IWA,c=TL.stellarTeff(allstars),\n",
    "            cmap=plt.get_cmap('cmr.rainforest_r') \n",
    "           )\n",
    "lim1=plt.ylim([19,24])\n",
    "plt.ylabel(r\"Exozodi at IWA [m$_{\\rm v}$/as$^2$]\")\n",
    "plt.text(.5,19.7, \"Sunlike Zodi\",color=\"orange\")\n",
    "\n",
    "ax1.set_xlabel(\"IWA separation [AU]\")\n",
    "plt.colorbar(label=\"Stellar Temp [K]\")\n",
    "\n",
    "#plt.savefig(\"WFIRST-CGI-disks-Uno/AU_PC_temp.pdf\",bbox_inches=\"tight\")\n",
    "\n",
    "\n",
    "\n",
    "plt.figure()\n",
    "plt.scatter(TL.dist,-2.5*np.log10(fEZ_IWA.value),c=TL.stellarTeff(allstars),\n",
    "            cmap=plt.get_cmap('cmr.rainforest_r') \n",
    "           )\n",
    "ax2=plt.subplot(111)\n",
    "\n",
    "\n",
    "#plt.plot((1/x_AU_range.value)[::-1],-2.5*np.log10(y_fEZ_M5.value),linewidth=5)\n",
    "\n",
    "\n",
    "ax2.set_xlabel(\"Host distance [pc]\")\n",
    "\n",
    "\n",
    "#plt.colorbar(label=\"Stellar Temp [K]\")\n",
    "plt.ylim(lim1)\n",
    "ax2.set_yticklabels(\"\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading cached fZmax from /Users/edouglas/.EXOSIMS/cache/CompletenessTargetListNematifbc170efeaf49857bf3bdde879a3d964.fZmax\n"
     ]
    }
   ],
   "source": [
    "#use conservative max local zodi so we don't have to worry about observation timing:\n",
    "fZMax = TL.ZodiacalLight.calcfZmax( allstars, \n",
    "                                Obs, \n",
    "                                TL,\n",
    "                                sim.TimeKeeping,\n",
    "                                mode, \n",
    "                                sim.SurveySimulation.cachefname)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Calculate detection limits given lognorm distribution from Ertel et al papers"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)\n",
    "Ertel et al. - 2018 - The HOSTS survey - Exozodiacal dust measurements f.pdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "#s = 5 \n",
    "m=13 #=exp(mu)=median of the fitted distribution (n_zodi...)\n",
    "\n",
    "#m=np.log(m)\n",
    "#\n",
    "#loc=-10\n",
    "s=1.5#|.5\n",
    "\n",
    "mean, var, skew, kurt = lognorm.stats(s,scale=m, moments='mvsk')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\nax = plt.subplot(121)\\nrv = lognorm(s=s,scale=m) #lognorm.pdf(x, s, loc, scale) \\n\\nx = np.linspace(lognorm.ppf(0.05, s,\\n                            #loc=loc,\\n                           scale=m\\n                           ),\\n                lognorm.ppf(0.95, s,\\n                            #loc=loc,\\n                           scale=m\\n                           ), 100)\\n\\n'"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''\n",
    "ax = plt.subplot(121)\n",
    "rv = lognorm(s=s,scale=m) #lognorm.pdf(x, s, loc, scale) \n",
    "\n",
    "x = np.linspace(lognorm.ppf(0.05, s,\n",
    "                            #loc=loc,\n",
    "                           scale=m\n",
    "                           ),\n",
    "                lognorm.ppf(0.95, s,\n",
    "                            #loc=loc,\n",
    "                           scale=m\n",
    "                           ), 100)\n",
    "\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$[1.007827,~1.3930547,~1.1282927,~\\dots,~0.09108497,~0.075838385,~0.049920045] \\; \\mathrm{{}^{\\prime\\prime}}$"
      ],
      "text/plain": [
       "<Quantity [1.00782705, 1.39305471, 1.12829271, 0.89028502, 0.18519376,\n",
       "           0.27944264, 0.07905676, 0.37843179, 0.12467119, 0.38723298,\n",
       "           0.31352758, 0.2299764 , 0.4060797 , 0.3002941 , 0.10012032,\n",
       "           0.2266999 , 0.34244799, 0.29913279, 0.30164316, 0.27197621,\n",
       "           0.41278232, 0.19921681, 0.26094673, 0.1928656 , 0.32346316,\n",
       "           0.1824131 , 0.23621308, 0.20356342, 0.24193029, 0.25168201,\n",
       "           0.16941596, 0.27456657, 0.25860648, 0.18610037, 0.24939625,\n",
       "           0.26999449, 0.20074625, 0.18093309, 0.22364107, 0.17384163,\n",
       "           0.23060749, 0.23757303, 0.17268868, 0.19832521, 0.25013132,\n",
       "           0.20131316, 0.20196336, 0.16244687, 0.20622776, 0.15166474,\n",
       "           0.17686622, 0.23789772, 0.21211306, 0.19157162, 0.18761684,\n",
       "           0.15583844, 0.17980289, 0.08297362, 0.18031861, 0.13110541,\n",
       "           0.19284688, 0.20443951, 0.19191047, 0.09159195, 0.19752264,\n",
       "           0.20172411, 0.17450404, 0.14168009, 0.164051  , 0.16080955,\n",
       "           0.16671593, 0.18135997, 0.14732431, 0.14624091, 0.18067725,\n",
       "           0.1537083 , 0.11278242, 0.09780127, 0.17705466, 0.11053788,\n",
       "           0.14522247, 0.18407167, 0.13350783, 0.13453762, 0.16690957,\n",
       "           0.10695347, 0.14232193, 0.11998837, 0.17673643, 0.17224066,\n",
       "           0.14721181, 0.1271742 , 0.14637117, 0.12298474, 0.11453012,\n",
       "           0.13078013, 0.11391367, 0.14543828, 0.1211631 , 0.15049526,\n",
       "           0.14772488, 0.10981747, 0.15512725, 0.11935052, 0.12775608,\n",
       "           0.13303465, 0.114592  , 0.14668786, 0.14040198, 0.10844365,\n",
       "           0.12137952, 0.14364569, 0.11281301, 0.11764711, 0.14563737,\n",
       "           0.14435922, 0.07631952, 0.11239817, 0.11762153, 0.09388169,\n",
       "           0.1144656 , 0.10090751, 0.11324419, 0.10840592, 0.09216765,\n",
       "           0.11507964, 0.09485794, 0.12049052, 0.09714397, 0.11235392,\n",
       "           0.1050847 , 0.10033748, 0.07677353, 0.09859687, 0.11747304,\n",
       "           0.10273377, 0.0928394 , 0.08901863, 0.06968604, 0.08218617,\n",
       "           0.08770261, 0.08769312, 0.11165286, 0.07621908, 0.09075168,\n",
       "           0.12264063, 0.09108497, 0.07583838, 0.04992004] arcsec>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "outerHZs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"sim.OpticalSystem.Cp_Cb_Csp(TL,\\n                                                          allstars,\\n                                                          0*allstars*u.arcsec**(-2), #neglects local zodi\\n                                                          fEZ_outer,0*allstars,mode[ 'IWA']*np.ones(len(allstars)),\\n                                                          mode,returnExtra=True)\""
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "TL.fillPhotometryVals()\n",
    "\n",
    "#rates:\n",
    "\n",
    "\n",
    "#C_ez=C_F0*fEZ*Omega*core_thruput\n",
    "#cb is the background including detector noise\n",
    "#csp is the speckles\n",
    "cp,cb,csp, extra02as =  sim.OpticalSystem.Cp_Cb_Csp(TL,\n",
    "                            allstars,\n",
    "                            0*allstars*u.arcsec**(-2), #neglects local zodi\n",
    "                            fEZ_outer,0*allstars,mode[ 'IWA']*np.ones(len(allstars)),mode,returnExtra=True)\n",
    "\n",
    "cp,cb,csp, extraInner =  sim.OpticalSystem.Cp_Cb_Csp(TL,\n",
    "                            allstars,\n",
    "                            0*allstars*u.arcsec**(-2),\n",
    "                            fEZ_inner,0*allstars,innerHZs,mode,returnExtra=True)\n",
    "#run for outer HZ surface brightness:\n",
    "cp,cbOuter,csp, extraOuter =  sim.OpticalSystem.Cp_Cb_Csp(TL,\n",
    "                                                          allstars,\n",
    "                                                          0*allstars*u.arcsec**(-2), #neglects local zodi\n",
    "                                                          fEZ_outer,0*allstars,outerHZs,\n",
    "                                                          mode,returnExtra=True)# speckle rate includes post processing, [\"C_sr\"] is the raw speckle rate!\n",
    "#assume everything is at the IWA:\n",
    "cp,cbIWA,csp, extraIWA =  sim.OpticalSystem.Cp_Cb_Csp(TL,\n",
    "                                                          allstars,\n",
    "                                                          0*allstars*u.arcsec**(-2), #neglects local zodi\n",
    "                                                          fEZ_IWA,\n",
    "                                                          0*allstars,\n",
    "                                                          mode[ 'IWA']*np.ones(len(allstars)),\n",
    "                                                          mode,returnExtra=True)\n",
    "cp,cbOuter,csp, extraOuter = cp,cbOuter,csp, extraOuter\n",
    "'''sim.OpticalSystem.Cp_Cb_Csp(TL,\n",
    "                                                          allstars,\n",
    "                                                          0*allstars*u.arcsec**(-2), #neglects local zodi\n",
    "                                                          fEZ_outer,0*allstars,mode[ 'IWA']*np.ones(len(allstars)),\n",
    "                                                          mode,returnExtra=True)'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/edouglas/anaconda3/envs/python39/lib/python3.9/site-packages/astropy/units/quantity.py:1028: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  out = super().__getitem__(key)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(<Quantity [3.89977181e-09] 1 / arcsec2>,\n",
       " <Quantity [3.89977181e-09] 1 / arcsec2>)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ref_star=[TL.Name==\"HIP 2021\"]\n",
    "fEZ_outer[ref_star],fEZ_IWA[ref_star]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$0.007869693 \\; \\mathrm{\\frac{1}{s}}$"
      ],
      "text/plain": [
       "<Quantity 0.00786969 1 / s>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.nanmax(extraOuter[\"C_ez\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.06034925] 1 / s [0.00786969] 1 / s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(<Quantity [0.06034925] 1 / s>,\n",
       " <Quantity [0.60596636] 1 / s>,\n",
       " <Quantity [0.01508731] 1 / s>)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#-TL.starMag(allstars, mode[\"lam\"])\n",
    "print(extraIWA[\"C_sr\"][ref_star],extraIWA[\"C_ez\"][ref_star])#77\n",
    "extraIWA[\"C_sr\"][ref_star],77*extraIWA[\"C_ez\"][ref_star],csp[ref_star] #77"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/edouglas/anaconda3/envs/python39/lib/python3.9/site-packages/astropy/units/quantity.py:477: RuntimeWarning: invalid value encountered in true_divide\n",
      "  result = super().__array_ufunc__(function, method, *arrays, **kwargs)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fd2d1098c40>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def snr(signal,noise,t):\n",
    "    #print((noise*t))\n",
    "    return (signal*t/np.sqrt(noise*t+signal*t)).decompose()\n",
    "plt.scatter(starmag[allstars],snr(extraIWA[\"C_ez\"],extraIWA[\"C_sr\"],100*u.hour),s=10,label=\"0.15 as\")\n",
    "\n",
    "plt.scatter(starmag[allstars],snr(extraOuter[\"C_ez\"],extraOuter[\"C_sr\"],100*u.hour),s=10,label=\"Outer\")\n",
    "\n",
    "plt.scatter(starmag[allstars],snr(extraInner[\"C_ez\"],extraInner[\"C_sr\"],100*u.hour),s=30,\n",
    "            alpha=.6,\n",
    "            label=\"Inner\")\n",
    "plt.ylabel(\"SNR at HZ boundary\")\n",
    "plt.xlabel(r\"m$_{V}$\")\n",
    "plt.legend()\n",
    "#print(extra02as)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "74"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(extraIWA[\"C_sr\"]/extraIWA[\"C_ez\"])[TL.Vmag<3]\n",
    "extraIWA[\"C_ez\"][np.isfinite(extraIWA[\"C_ez\"])].size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'm$_v$')"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "assert mode[\"SNR\"]==5\n",
    "\n",
    "SNR=5#mode[\"SNR\"]\n",
    "\n",
    "#background shouldn't be included here because this is the infinite exposure time case\n",
    "nz_critIWA=SNR*(extraIWA[\"C_sr\"])/extraIWA[\"C_ez\"]\n",
    "nz_critInner=SNR*(extraInner[\"C_sr\"])/extraInner[\"C_ez\"]\n",
    "\n",
    "\n",
    "plt.scatter(starmag,nz_critIWA,label=\"IWA\")\n",
    "plt.scatter(starmag,nz_critInner,\n",
    "                        label=\"Inner\")\n",
    "\n",
    "\n",
    "plt.yscale(\"log\")\n",
    "plt.legend()\n",
    "plt.ylabel(\"n$_{zodi}$\")\n",
    "plt.xlabel(\"m$_v$\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "HabExDF_NoBinary[\"EXOSIMS_nz_critOuter\"] =nz_critIWA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "HabExDF_NoBinary.to_latex(\"HabExDF_NoBinary.tex\",\n",
    "                          header=False,\n",
    "                          na_rep='Below Threshold', \n",
    "                          columns=[\"EXOSIMS_nz_critOuter\"])\n",
    "#HabExDF_NoBinary[r\"\\\\\"]=r\"\\\\\"\n",
    "#HabExDF_NoBinary.to_csv(\"WFIRST-CGI-disks-Uno/HabExDF_NoBinary.csv\",columns=[\"EXOSIMS_nz_critOuter\",r\"\\\\\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "plt.scatter(TL.Vmag,-2.5*np.log10(extraIWA[\"C_sr\"].value/extraIWA[\"C_F0\"].value/.05**2),\n",
    "            c=TL.dist,#\"-2.5*np.log10(extraOuter[\"C_sr\"].value/extraOuter[\"C_F0\"].value/.05**2)-SB_IWA,\n",
    "            cmap=plt.get_cmap('cmr.rainforest_r') \n",
    "           )\n",
    "plt.xlabel(\"\")\n",
    "plt.ylabel(r\"IWA Speckle Brightness [$m_{\\rm V}/as^2$]\")\n",
    "plt.xlabel(r\"$m_{\\rm V}$\")\n",
    "plt.colorbar(label=\"distance [pc]\")\n",
    "plt.savefig(\"IWA_Speckles_vs_mv_\"+mode[\"systName\"]+\".pdf\",bbox_inches=\"tight\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "#sim.OpticalSystem.calc_intTime?\n",
    "\n",
    "def zodi_mag_per_resel(Zodis):\n",
    "    return (-2.5*np.log10(fEZ_IWA.value)) - 2.5*np.log10(Zodis) - 2.5*np.log10(resel_area) - TL.Vmag\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "zIWA=zodi_mag_per_resel((nz_critIWA))\n",
    "\n",
    "#plt.plot(zIWA)\n",
    "#plt.plot(zodi_mag_per_resel(40))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No handles with labels found to put in legend.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'n$_{zodi}$')"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 648x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#assert mode[\"SNR\"]==5\n",
    "SNR=mode[\"SNR\"]\n",
    "plt.figure(figsize=[9,7])\n",
    "b2=[]\n",
    "ndet=[]\n",
    "for i in range(10000):\n",
    "    draws1 = lognorm.rvs(s, scale=m, size=starmag.size)\n",
    "    draws2 = lognorm.rvs(s, scale=3, size=starmag.size)\n",
    "    ndet.append(nz_critIWA[nz_critIWA/draws1>1].size)\n",
    "\n",
    "    nz1o_sorted=np.sort(nz_critIWA)\n",
    "    nz1i_sorted=np.sort(nz_critInner)\n",
    "\n",
    "    # nz1o_sorted2=np.sort(nz_critOuter2)\n",
    "    #nz1i_sorted2=np.sort(nz_critInner2)\n",
    "    plt.plot(nz1o_sorted,alpha=.1,color=\"blue\")#,label=\"Outer\")\n",
    "    plt.plot(nz1i_sorted,alpha=.1,color=\"blue\")#label=\"Inner\")\n",
    "    #plt.plot(nz1o_sorted2,alpha=.1,color=\"black\")#,label=\"Outer\")\n",
    "    #plt.plot(nz1i_sorted2,alpha=.1,color=\"black\")#label=\"Inner\")\n",
    "\n",
    "    b2.append(nz1i_sorted)\n",
    "\n",
    "plt.yscale(\"log\")\n",
    "plt.legend()\n",
    "plt.ylabel(\"n$_{zodi}$\")\n",
    "#plt.xlabel(\"m$_v$\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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TkjuA+4DjwBzwo8CvT6nGHcDXgDcCPwScAn4nydwa/f9LVe3ueXx6OmV+3709+37toA5bOZ69Y0Pn//0F4Ow6q0xrPP8EeA+dF/i/L8k+OldxnQL2AgvAo+tsZ6hjetx1Aj9M50qLOeAgnfs/PrLBtjY8ViZQ56pX9uz7wXW2syXjWVWf6DtW3wV8Bfiv62xrkuP5fdsuzLO593q5G3i4qhar6nngQeCeadRZVVeq6nRVfbWq/m9VfRJ4Fhj4G/olYsvGs8/bgT8FPrMF+75OVZ2rqt+lc5VWrzuBxao6W1XXgNPA0SSH+7exyWN6rHVW1ae6NX67qq4CHwB+Zlz73ax1xnNoWzmeA9wNfLS2wZUk2y7MgR8HXqyqZ3razgODfuse6S7r7bc/yasmWN9ASfbTqX2tm6Vel+RikmeSnEoy7U95em93/59bZ0piu4znMD8gWz2e141V936LCww+TjdzTE/aG9j4hr5hjpVJeS7J15N8pPvXzyDbYjy702pvAD66QdepjOd2DPOh3utljb6rXw/qOzFJbgY+ATxSVUsDuvwn4Bbg1XTOKN4B/Mr0KuRX6UyZvIbOn9y/l+TQgH5bPp5JDtCZunpknW5bPZ4w2nG6Xt+JSfJXgftZf6yGPVbG7SLwU3Smgm6jMzafWKPvthhP4J3AZ6rq2XX6TG08t2OYb+a9Xvr7rn49tfeFSfIyOne7fhe4d1CfqvpKVT3bnY75b8ADdKYSpqKqnqqqS1X1nap6BPgc8NYBXbd8POn8gHx2vR+QrR7PrlGO0/X6TkSSvwx8CvgHVbXm9NUmjpWx6k6XLFTV/6mqb9H5Wfq5JP3jBttgPLveyfonHVMdz+0Y5pt5r5fF7rLeft+qqhuej9uMJKHzCUv7gfmq+t6QqxaQiRV24/vf0vHs2vAHZICtGM/rxqo7j3uIwcfplr5/UXc64Angwara7NtsbNWxujrFNmjfW/5+UOm8bclfAv7tJled3HhW1bZ7AP8G+G1gF50Xa1aAIwP6/XXgm8BP0nnV/g+B902xzg8Bnwd2b9DvLcD+7teHgf8O/NqUanwlcAewk84VOHcBV4DXbsPx/OlubXu2y3h2x2wn8F46f4GtjuNs97ic77b9BvD5dbYz1DE9gTpfQ2cu/1fGeaxMoM7b6XzOwcuAV9G5MujJ7TaePcvP0HldZ0vH87r9TWKjYxjIvcDvdr/xPwZ+odt+gM6fWAd6+v4j4FvAt+lccvXyKdV4kM5v2WvdmlYfd/XXCfxmt8YrdC5jegC4eUp1zgJfoPMn6J/R+eXz5u02nt19/0vgYwPat2w86VylUn2P091lPwss0bmM8tPAXM96/wz41EbH9KTrBH6t+3XvMXp5UJ3rHStTqPMddK4GuwJ8g86Lin9hu41nd9nO7vgcH7DeVMez9+F7s0hSA7bjnLkkaZMMc0lqgGEuSQ0wzCWpAYa5JDXAMJekBhjmktQAw1ySGvD/AMNK5Y92+LadAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from scipy.stats.mstats import mquantiles\n",
    "from astropy.visualization import hist\n",
    "\n",
    "draws= lognorm.rvs(s, scale=5, size=starmag.size)\n",
    "plt.hist(draws/5,bins=\"auto\")\n",
    "nz_critIWA[nz_critIWA<(draws)].size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "z=27. detectionNZ: 51, 95th quantile : 43 - 58, \\sigma= 3.9\n",
      "z=9. detectionNZ: 31, 95th quantile : 23 - 39, \\sigma= 4.2\n",
      "z=3. detectionNZ: 13, 95th quantile : 7 - 20, \\sigma= 3.3\n",
      "z=1. detectionNZ: 4, 95th quantile : 1 - 8, \\sigma= 1.9\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, '$n_{z}$')"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import scipy.stats\n",
    "\n",
    "zrange=[27,9,3,1]\n",
    "fig, ax = plt.subplots()\n",
    "fig2, ax2 = plt.subplots()\n",
    "table_out=open(\"conf_tab_values\"+str(alphaZ).replace(\".\",\"_\")+\"alpha\"\n",
    "            +mode[\"systName\"]+\".tex\",mode=\"w\")\n",
    "detectionNZ=nz_critIWA*f_pp\n",
    "\n",
    "\n",
    "for z in zrange:\n",
    "    ndet={\"detectionNZ\":[],\"nz_critIWA\":[]}\n",
    "    for i in range(10000):\n",
    "        #print(z)\n",
    "        draws= lognorm.rvs(s, scale=z, size=starmag.size)\n",
    "        ndet[\"detectionNZ\"].append(detectionNZ[detectionNZ/(draws)<1].size)\n",
    "        #ndet[\"nz_critInner\"].append(nz_critInner[nz_critInner/(draws)<1].size)\n",
    "        #print(ndet[\"detectionNZ\"])\n",
    "\n",
    "\n",
    "    \n",
    "    for key in [\"detectionNZ\"]:\n",
    "        crit = ndet[key]\n",
    "        quantiles=list(mquantiles(crit,prob=[0.025,0.975]))\n",
    "        print(\"z=\"+str(z)+\". \"+ key\n",
    "              +\": %.0f, 95th quantile : %.2g - %.2g, \\sigma= %.2g\"%tuple([np.mean(crit)]+\n",
    "                                                                           quantiles+\n",
    "                                                                           [np.std(crit)]))\n",
    "        print(str(z)+ r\"& %1.0f & %.2g - %.2g \\\\\"%tuple([np.mean(crit)]+quantiles),\n",
    "              file=table_out)\n",
    "        hist(crit,alpha=z/np.max(zrange)*.8+.1,label=str(z)+\"- \"+key,density=True,\n",
    "             ax=ax)\n",
    "        #ax2.errorbar(z,np.mean(crit),yerr=np.array([quintiles]).T-np.mean(crit),color=\"black\",)\n",
    "        #ax2.plot(z,np.mean(crit),\".\",color=\"black\",markersize=20)\n",
    "        ax2.vlines( z, quantiles[0],quantiles[1],linewidth=5,color=\"k\")\n",
    "        violins=ax2.violinplot(crit, positions=[z], widths=5, \n",
    "                       showmeans=False,\n",
    "                       points=np.size(crit), \n",
    "                       showextrema=False,\n",
    "                       showmedians=True, bw_method=0.5)\n",
    "        \n",
    "\n",
    "        for b in violins['bodies']:\n",
    "            # get the center\n",
    "            m = np.mean(b.get_paths()[0].vertices[:, 0])\n",
    "            # modify the paths to not go further right than the center\n",
    "            b.get_paths()[0].vertices[:, 0] = np.clip(b.get_paths()[0].vertices[:, 0], m, np.inf)\n",
    "            b.set_color('gray')\n",
    "\n",
    "        '''violins=ax2.violinplot(crit, positions=[z], widths=5, \n",
    "                       showmeans=False,\n",
    "                       points=np.size(crit), \n",
    "                       showextrema=False,\n",
    "                       showmedians=True, bw_method=0.5)\n",
    "        '''\n",
    "\n",
    "\n",
    "            \n",
    "        \n",
    "table_out.close()        \n",
    "ax.legend()\n",
    "\n",
    "#ax2.legend()\n",
    "\n",
    "ax.set_ylabel(\"Probability Density\")\n",
    "ax.set_xlabel(\"Number of Detected Systems\")\n",
    "#fig.savefig(\"WFIRST-CGI-disks-Uno/ndet_lognorm_dist\"+mode[\"systName\"]+str(f_pp)+\".pdf\",bbox_inches=\"tight\")\n",
    "\n",
    "\n",
    "\n",
    "ax2.set_ylabel(\"Number of Detected Systems\")\n",
    "\n",
    "ax2.set_xlabel(r\"$n_{z}$\")\n",
    "#plt.ylim([0,1])\n",
    "#fig2.savefig(\"WFIRST-CGI-disks-Uno/ndet_lognorm_bars_\"+mode[\"systName\"]+\".pdf\",bbox_inches=\"tight\",)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[  4.25125324 148.18599131  40.42205401 103.52416973   9.14946823\n",
      "   4.09968218          nan   8.17640849          nan   2.04572996\n",
      " 231.34117468  16.27313945   4.06917121   5.03461566          nan\n",
      "   1.65059298  12.08745897   6.75060564   1.85420039   1.64141153\n",
      "   5.12472662   0.44730476   1.20168488  15.29259572   1.57140604\n",
      "   0.69603469  10.69650893   5.33237446   5.45357409  63.11012381\n",
      "  12.16914948   0.30935227   4.18112518   0.97933969   7.36831629\n",
      "   1.17381429   4.28262941   8.84959746   1.39722881   7.14399934\n",
      "  23.08162642   2.85186998  50.54647442   4.76014285  27.26316684\n",
      "   3.91301808   5.59886633   5.47972633   1.95504345   7.59514144\n",
      "  92.36752237   7.79748104   1.27062823   8.63424895   9.87679276\n",
      "  37.50545784   4.7277298           nan   5.7620669           nan\n",
      "  11.02175759   4.42838849   3.19552282          nan   2.4415988\n",
      "   4.75055445   0.40174967          nan  27.35916787  12.50854523\n",
      "  81.17648491   1.02883043          nan          nan  49.72108367\n",
      "   1.48602939          nan          nan   1.67222668          nan\n",
      "          nan   7.37299761          nan          nan   5.2719019\n",
      "          nan          nan          nan   9.58307375   8.30673791\n",
      "          nan          nan          nan          nan          nan\n",
      "          nan          nan          nan          nan  14.83783423\n",
      "          nan          nan   4.81573194          nan          nan\n",
      "          nan          nan          nan          nan          nan\n",
      "          nan          nan          nan          nan          nan\n",
      "          nan          nan          nan          nan          nan\n",
      "          nan          nan          nan          nan          nan\n",
      "          nan          nan          nan          nan          nan\n",
      "          nan          nan          nan          nan          nan\n",
      "          nan          nan          nan          nan          nan\n",
      "          nan          nan          nan          nan          nan\n",
      "          nan          nan          nan          nan]\n",
      "[2.56870066e+01 1.23846485e+02 1.98125659e+00 1.49167968e+00\n",
      " 8.46818729e-01 1.15195439e+01            nan 2.59087434e+00\n",
      "            nan 1.82708237e-01 1.37195914e+01 6.09456912e-01\n",
      " 3.93639702e-01 2.95078231e+00            nan 2.56956988e+01\n",
      " 1.23298413e+00 4.27182260e+01 3.08108677e+00 2.96817859e+00\n",
      " 1.71751357e+00 2.46372934e+00 6.35280648e+01 2.26277101e+01\n",
      " 2.45870829e+00 9.53049467e-01 4.55961429e-01 1.77259057e+01\n",
      " 1.33001866e+00 5.25714272e-01 6.94282532e-01 1.60299156e+02\n",
      " 5.23038917e+00 2.26889757e+00 6.76410603e-01 2.78137962e-01\n",
      " 3.93372092e+01 8.71198025e-01 2.28893798e+00 1.14438417e+03\n",
      " 2.34070596e+01 4.28065495e-01 1.65615663e+01 4.37732717e+00\n",
      " 5.87785338e+00 1.51142952e+01 3.01787251e+00 1.11145136e+00\n",
      " 2.63881188e+00 1.19553437e+01 1.12163833e+01 2.82259000e-01\n",
      " 2.14266856e+00 2.07112519e+00 2.55449126e+01 5.69949988e+00\n",
      " 3.32543113e+01            nan 1.90686842e+00            nan\n",
      " 1.67945061e+00 3.85985820e+00 5.73040717e+00            nan\n",
      " 2.44710777e+00 2.72762327e+00 1.93361187e+00            nan\n",
      " 1.00157617e+01 1.31667118e+00 3.95885822e+02 1.47635793e+00\n",
      "            nan            nan 3.47737138e-01 1.05547986e+00\n",
      "            nan            nan 1.40309500e+00            nan\n",
      "            nan 2.80480552e+00            nan            nan\n",
      " 1.09161201e+01            nan            nan            nan\n",
      " 3.46408229e+01 2.61120867e-01            nan            nan\n",
      "            nan            nan            nan            nan\n",
      "            nan            nan            nan 7.33830744e-01\n",
      "            nan            nan 1.92159613e+01            nan\n",
      "            nan            nan            nan            nan\n",
      "            nan            nan            nan            nan\n",
      "            nan            nan            nan            nan\n",
      "            nan            nan            nan            nan\n",
      "            nan            nan            nan            nan\n",
      "            nan            nan            nan            nan\n",
      "            nan            nan            nan            nan\n",
      "            nan            nan            nan            nan\n",
      "            nan            nan            nan            nan\n",
      "            nan            nan            nan            nan\n",
      "            nan            nan            nan            nan\n",
      "            nan]\n",
      "[1.09416480e+02 6.44223925e+01 5.74969978e+01 2.77993065e+01\n",
      " 7.96300880e-01 1.88725694e+00            nan 9.36518353e-01\n",
      "            nan 3.60242239e+00 4.69528946e+01 3.14910034e+00\n",
      " 2.84603797e+01 1.08376196e+00            nan 1.26410069e+00\n",
      " 6.05139115e+00 2.66764107e-01 2.05588225e+00 1.21576235e+00\n",
      " 4.28189527e+00 2.06016935e+00 4.62235541e+00 8.16994933e+00\n",
      " 1.93584841e+01 3.58840614e+01 6.52477101e-01 7.67548561e-01\n",
      " 1.17426443e+00 3.56474907e+00 7.51297688e-01 4.21286123e+00\n",
      " 1.49112678e-01 1.80304189e+00 6.63579115e+00 4.25220205e-01\n",
      " 2.01459807e+01 5.21046230e+01 4.09049851e+00 5.06199709e-01\n",
      " 1.14560044e+00 1.82461154e+00 2.27016030e+01 1.20392077e+01\n",
      " 8.15803372e-02 2.66471721e-01 1.16953147e+01 6.32148764e+01\n",
      " 3.28849684e+01 2.76853752e+01 4.20632719e-01 1.69266665e+01\n",
      " 6.68349746e+00 6.76789179e+00 2.09330324e+01 3.02470437e+00\n",
      " 6.57628555e+00            nan 2.19950712e+00            nan\n",
      " 2.14171574e+00 1.93110251e+00 2.03944467e+00            nan\n",
      " 3.55309064e+00 1.26111471e+01 9.14308900e-01            nan\n",
      " 1.15259739e+01 2.68523593e+01 3.19489831e+00 6.08813720e-01\n",
      "            nan            nan 9.61291274e+00 1.35442293e+02\n",
      "            nan            nan 3.10501593e+00            nan\n",
      "            nan 4.93039010e+00            nan            nan\n",
      " 1.65897641e+00            nan            nan            nan\n",
      " 2.33996814e+00 7.71390165e+00            nan            nan\n",
      "            nan            nan            nan            nan\n",
      "            nan            nan            nan 8.78904731e-01\n",
      "            nan            nan 4.75943538e-01            nan\n",
      "            nan            nan            nan            nan\n",
      "            nan            nan            nan            nan\n",
      "            nan            nan            nan            nan\n",
      "            nan            nan            nan            nan\n",
      "            nan            nan            nan            nan\n",
      "            nan            nan            nan            nan\n",
      "            nan            nan            nan            nan\n",
      "            nan            nan            nan            nan\n",
      "            nan            nan            nan            nan\n",
      "            nan            nan            nan            nan\n",
      "            nan            nan            nan            nan\n",
      "            nan]\n",
      "[83.76042918 28.0094254  12.37963369  3.4204021   1.75121806  6.27006247\n",
      "         nan 12.53692226         nan  5.78558384  0.09154803  0.50954262\n",
      "  4.91835372  2.89588324         nan  2.36320856  0.33054419  0.70075099\n",
      "  9.80224198  4.60090598  9.70509581  4.53255874  1.36890278 11.40304335\n",
      "  3.65007185  0.2136236   3.42765486  2.42035598 60.74323623 31.12031118\n",
      " 83.80811177  1.63112643  0.4773806   0.89037175  2.3603322  12.15719995\n",
      "  0.17919605  2.24035588  6.73493259  0.28701676  0.83830831  0.84743476\n",
      "  2.53179588 13.92824967  1.00949001  4.05782396  0.87585139 11.64642801\n",
      "  4.67756559  6.43998862  3.74996045  3.28477202  0.41928018  5.69802449\n",
      "  1.34115918  0.77457725  3.20126358         nan  7.89267316         nan\n",
      " 15.73560274  1.74241394  7.39544199         nan  8.33531613  3.19391788\n",
      "  1.60075846         nan 11.8419168   8.84243866  9.12737691  7.80842116\n",
      "         nan         nan 18.91977753  3.69921081         nan         nan\n",
      "  2.44886126         nan         nan  4.09517911         nan         nan\n",
      " 17.70661922         nan         nan         nan  1.84422706  0.47108067\n",
      "         nan         nan         nan         nan         nan         nan\n",
      "         nan         nan         nan 10.88293434         nan         nan\n",
      "  0.65981853         nan         nan         nan         nan         nan\n",
      "         nan         nan         nan         nan         nan         nan\n",
      "         nan         nan         nan         nan         nan         nan\n",
      "         nan         nan         nan         nan         nan         nan\n",
      "         nan         nan         nan         nan         nan         nan\n",
      "         nan         nan         nan         nan         nan         nan\n",
      "         nan         nan         nan         nan         nan         nan\n",
      "         nan         nan         nan         nan         nan]\n"
     ]
    }
   ],
   "source": [
    "#test that the scale is close the median\n",
    "np.median(lognorm.rvs(s, scale=10, size=10000))\n",
    "(nz_critIWA)[nz_critIWA>0].size\n",
    "\n",
    "for z in [3,3]:\n",
    "    for i in range(2):\n",
    "        draws= lognorm.rvs(s, scale=z, size=starmag.size)\n",
    "        print(detectionNZ/draws)\n",
    "        #ndet[\"detectionNZ\"].append(detectionNZ[detectionNZ/(draws)<1].size)\n",
    "        #ndet[\"nz_critInner\"].append(nz_critInner[nz_critInner/(draws)<1].size)\n",
    "#print(detectionNZ[detectionNZ/(draws)<1])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 1.,  1.,  1.,  1.,  2.,  1.,  4.,  1., 10.,  7.,  8.,  8., 13.,\n",
       "         9.,  1.,  2.,  2.,  0.,  0.,  0.,  1.,  1.,  0.,  0.,  0.,  0.,\n",
       "         0.,  0.,  0.,  0.,  0.]),\n",
       " array([ 0.,  1.,  2.,  3.,  4.,  5.,  6.,  7.,  8.,  9., 10., 11., 12.,\n",
       "        13., 14., 15., 16., 17., 18., 19., 20., 21., 22., 23., 24., 25.,\n",
       "        26., 27., 28., 29., 30., 31.]),\n",
       " [<matplotlib.patches.Polygon at 0x7fd2f308a610>])"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "hist=plt.hist(sp_short,alpha=.4,label='HabEx Targets', bins=np.unique(sp_short))\n",
    "plt.hist(sp_short[IWA_AU>0],alpha=.4,label='Visible HZ',histtype=\"step\",linewidth=3, bins=np.unique(sp_short),\n",
    "         color=\"black\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "13.1924\n"
     ]
    }
   ],
   "source": [
    "## perform random draws to get the distribution versus spectral type:\n",
    "spec_det = []\n",
    "\n",
    "for i in range(10000):\n",
    "    draws=lognorm.rvs(s, scale=3, size=starmag.size) \n",
    "    spec_det.append(np.histogram(sp_short[detectionNZ/(draws)<1],bins=np.unique(sp_short))[0])\n",
    "print(np.mean(np.sum(spec_det,axis=1)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['A3', 'A4', 'A6', 'A7', 'F1', 'F1', 'F2', 'F2', 'F2', 'F3', 'F3',\n",
       "       'F3', 'F3', 'F4', 'F4', 'F5', 'F5', 'F5', 'F5', 'F5', 'F5', 'F5',\n",
       "       'F5', 'F5', 'F5', 'F6', 'F6', 'F6', 'F6', 'F6', 'F6', 'F6', 'F7',\n",
       "       'F7', 'F7', 'F7', 'F7', 'F7', 'F7', 'F7', 'F8', 'F8', 'F8', 'F8',\n",
       "       'F8', 'F8', 'F8', 'F8', 'F9', 'F9', 'F9', 'F9', 'F9', 'F9', 'F9',\n",
       "       'F9', 'F9', 'F9', 'F9', 'F9', 'F9', 'F9', 'F9', 'F9', 'G0', 'G0',\n",
       "       'G0', 'G0', 'G0', 'G0', 'G0', 'G0', 'G0', 'G0', 'G0', 'G0', 'G0',\n",
       "       'G0', 'G1', 'G1', 'G1', 'G2', 'G2', 'G2', 'G2', 'G2', 'G2', 'G3',\n",
       "       'G3', 'G3', 'G3', 'G3', 'G3', 'G4', 'G5', 'G5', 'G6', 'G6', 'G7',\n",
       "       'G7', 'G7', 'G8', 'G8', 'G8', 'G8', 'G8', 'G8', 'G8', 'G8', 'G8',\n",
       "       'K0', 'K0', 'K0', 'K0', 'K0', 'K0', 'K0', 'K0', 'K0', 'K0', 'K1',\n",
       "       'K1', 'K1', 'K1', 'K1', 'K1', 'K2', 'K2', 'K2', 'K2', 'K3', 'K3',\n",
       "       'K3', 'K3', 'K3', 'K3', 'K3', 'K4', 'K4', 'K5', 'K5', 'K5', 'K6',\n",
       "       'K7', 'M1', 'M1', 'M2', 'M2', 'M2'], dtype='<U2')"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "crit\n",
    "ndet#\n",
    "[key]\n",
    "sp_short"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/edouglas/anaconda3/envs/python39/lib/python3.9/site-packages/astropy/units/quantity.py:1028: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  out = super().__getitem__(key)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(-4.8, 17.0)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## make plot versus spectral type\n",
    "\n",
    "plt.figure(figsize=[7,4])\n",
    "from importlib import reload\n",
    "#drop stars where the IWA is larger then the HZ:\n",
    "IWA_AU[IWA_AU>outerHZ_AU]=np.nan\n",
    "\n",
    "#set to the inner edge of the HZ if the IWA is smaller\n",
    "\n",
    "IWA_AU[IWA_AU<innerHZ_AU]=innerHZ_AU[IWA_AU<innerHZ_AU]\n",
    "plt.subplot(131)\n",
    "plt.hist(outerHZ_AU,alpha=.4,label='outer HZ edge',bins=np.arange(0,10,.5))\n",
    "plt.hist(innerHZ_AU,alpha=.4,label='inner HZ edge',bins=np.arange(0,10,.5))\n",
    "plt.hist(IWA_AU,alpha=.4,label=\"Visible HZ\",bins=np.arange(0,10,.5),histtype=\"step\",linewidth=3,\n",
    "         color=\"black\")\n",
    "plt.xlabel(\"au\")\n",
    "plt.ylabel(\"n systems\")\n",
    "plt.legend()\n",
    "plt.figure(figsize=[7,4])\n",
    "plt.subplot(121)\n",
    "plt.hist(sim.TargetList.dist,alpha=.4,label='HabEx Targets')#,bins=np.arange(0,10,.42))\n",
    "plt.hist(sim.TargetList.dist[[IWA_AU>0]],alpha=.4,label='Visible HZ',\n",
    "         histtype=\"step\",linewidth=3,         color=\"black\")\n",
    "\n",
    "#,bins=np.arange(0,10,.42))\n",
    "\n",
    "#lt.hist(innerHZ_AU,alpha=.4,label='inner HZ edge',bins=np.arange(0,10,.42))\n",
    "#plt.hist(IWA_AU,alpha=.4,label=\"IWA, if inside HZ\",bins=np.arange(0,10,.42),histtype=\"step\",linewidth=3,\n",
    "#         color=\"black\")\n",
    "plt.xlabel(\"parsecs\")\n",
    "plt.ylabel(\"n systems\")\n",
    "plt.legend()\n",
    "\n",
    "\n",
    "\n",
    "plt.figure(figsize=[4,3])\n",
    "import matplotlib.ticker as tkr\n",
    "\n",
    "\n",
    "ax=plt.subplot(111)\n",
    "#plt.hist(np.float_(sim.TargetList.specliste[:,1].flatten()),alpha=.4,label='all systems')#,bins=np.arange(0,10,.42))\n",
    "#plt.hist(np.float_(sim.TargetList.specliste[:,1].flatten())[IWA_AU>0],alpha=.4,label='visible',\n",
    "#         histtype=\"step\",linewidth=3,         color=\"black\")\n",
    "#np.float_(TL.specliste[:,1].flatten())[IWA_AU>0]\n",
    "specs=sim.TargetList.Spec.copy()\n",
    "specs.sort()\n",
    "sp_short=np.array([sp[0:2] for sp in specs])\n",
    "#sp_short[IWA_AU>0]\n",
    "\n",
    "p1=hist=plt.hist(sp_short,alpha=.4,#label='HabEx Targets',\n",
    "                 align='left',\n",
    "              bins=np.unique(sp_short),histtype=\"step\",color=\"gray\")\n",
    "p=plt.hist(sp_short[IWA_AU>0],#label='Visible HZ',\n",
    "                        align='left',\n",
    "         histtype=\"step\",linewidth=2, bins=np.unique(sp_short),\n",
    "         color=\"black\")\n",
    "\n",
    "plt.text(17,11,'HabEx Targets',color=\"gray\")\n",
    "\n",
    "plt.text(1,11,'Visible HZ',color=\"black\",)\n",
    "\n",
    "# get_xticklabels() actually gets you an iterable, so you need to rotate each label\n",
    "#ax.set_xticks(mybins)\n",
    "\n",
    "for tick in ax.xaxis.get_minor_ticks():\n",
    "    tick.tick1line.set_markersize(0)\n",
    "\n",
    "for i,tick in enumerate(ax.get_xticklabels()):\n",
    "    #i=i+1\n",
    "    if i % 2:\n",
    "        tick.set_visible(False)\n",
    "        #tickm.set_visible(False)\n",
    "    tick.set_rotation(90)\n",
    "\n",
    "plt.bar(np.unique(sp_short)[:-1],np.nanmedian(spec_det,axis=0),\n",
    "        yerr=np.nanstd(spec_det,axis=0),\n",
    "        color='black',\n",
    "        #align='left',\n",
    "        linewidth=.5,\n",
    "#yerr=list(mquantiles(spec_det,prob=[0.025,0.975])),\n",
    "        alpha=.4,label='Detected if $n_z$=3',width=-1)\n",
    "\n",
    "#,bins=np.arange(0,10,.42))\n",
    "\n",
    "#lt.hist(innerHZ_AU,alpha=.4,label='inner HZ edge',bins=np.arange(0,10,.42))\n",
    "#plt.hist(IWA_AU,alpha=.4,label=\"IWA, if inside HZ\",bins=np.arange(0,10,.42),histtype=\"step\",linewidth=3,\n",
    "#         color=\"black\")\n",
    "\n",
    "plt.ylabel(\"n systems\")\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.tight_layout()\n",
    "plt.ylim([-4.8,17])\n",
    "\n",
    "#plt.savefig(\"WFIRST-CGI-disks-Uno/visible\"+mode[\"systName\"]+\".pdf\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9.0"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.nanmedian(spec_det,axis=0).sum()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#  Find median number of critical exozodi at 5sigma level"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$[7.6566162,~99.244218] \\; \\mathrm{}$"
      ],
      "text/plain": [
       "<Quantity [ 7.65661617, 99.24421828]>"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "nz_detectionNZFin=detectionNZ[np.isfinite(detectionNZ)]\n",
    "nz_critInnerFin=nz_critInner[np.isfinite(nz_critInner)]\n",
    "nz_critInner.size,nz_critIWA.size,\n",
    "scipy.stats.mstats.mquantiles(nz_detectionNZFin,prob=[0.025,0.975])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 880x660 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from astropy.visualization import hist\n",
    "plt.figure(figsize=[4.4,3.3],dpi=200)\n",
    "hist1=hist(nz_detectionNZFin,bins='blocks',linewidth=\".5\",histtype='bar',ec='gray',\n",
    "           color=\"lightblue\",\\\n",
    "          )\n",
    "plt.plot([np.nanmedian(nz_detectionNZFin),np.nanmedian(nz_detectionNZFin)],\n",
    "         [0,hist1[0].max()+5],\n",
    "        color=\"black\",#label=\"Outer HZ\"\n",
    "         linewidth=0.5\n",
    "        )\n",
    "\n",
    "\n",
    "hist1=hist(nz_detectionNZFin,linewidth=\".5\",histtype='bar',ec='gray',\n",
    "           color=\"blue\",bins=100,alpha=.4 \n",
    "          )\n",
    "\n",
    "#plt.ylim([0,hist1[0].max()+15])\n",
    "plt.text((np.nanmedian(nz_detectionNZFin))+4, hist1[0].max()+1,\n",
    "         str(r\"$\\tilde{n_{\\rm Z}}$=%.1f\"%(np.nanmedian(nz_detectionNZFin))\n",
    "             +r\" for $\\alpha$=\"+str(alphaZ))+r\", f$_{\\rm pp}$=%.3f \"%(f_pp),\n",
    "         fontsize=9,color=\"blue\",alpha=.4 )\n",
    "\n",
    "\n",
    "         #\" \\n $n_{sys}$=%.1i\"%(nz_critOuterFin.size)\n",
    "         \n",
    "\n",
    "\n",
    "\n",
    "plt.xlabel(\"n$_{z}$\")\n",
    "#plt.legend()\n",
    "#plt.xlim([0,200])\n",
    "#plt.xscale('symlog', linthreshy=2000)\n",
    "plt.ylabel(\"N$_{stars}$\")\n",
    "plt.minorticks_on()\n",
    "#plt.savefig(\"WFIRST-CGI-disks-Uno/sensitivity_%isigma\"%(SNR)\n",
    "#            +str(alphaZ).replace(\".\",\"_\")+\"alpha\"\n",
    "#            +mode[\"systName\"]+str(f_pp)+\".pdf\",\n",
    "#            bbox_inches=\"tight\")\n",
    "#hist1[0].sum(),hist2[0].sum()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "74"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nz_detectionNZFin.size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 880x660 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from astropy.visualization import hist\n",
    "# If we were to simply plot pts, we'd lose most of the interesting\n",
    "# details due to the outliers. So let's 'break' or 'cut-out' the y-axis\n",
    "# into two portions - use the top (ax1) for the outliers, and the bottom\n",
    "# (ax2) for the details of the majority of our data\n",
    "\n",
    "fig, (ax1, ax2) = plt.subplots(2, 1, sharex=True,figsize=[4.4,3.3],dpi=200)\n",
    "\n",
    "ax1 = plt.subplot2grid((3, 1), (0, 0))\n",
    "ax2 = plt.subplot2grid((3, 1), (1, 0),rowspan=2)\n",
    "\n",
    "fig.subplots_adjust(hspace=0.05)  # adjust space between axes\n",
    "\n",
    "# plot the same data on both axes\n",
    "\n",
    "\n",
    "\n",
    "hist1=hist(nz_detectionNZFin,bins='blocks',linewidth=\".5\",histtype='bar',ec='gray',\n",
    "           color=\"lightblue\",ax=ax1,\n",
    "          )\n",
    "\n",
    "hist1=hist(nz_detectionNZFin,bins='blocks',linewidth=\".5\",histtype='bar',ec='gray',\n",
    "           color=\"lightblue\",ax=ax2,\n",
    "          )\n",
    "\n",
    "# zoom-in / limit the view to different portions of the data\n",
    "ax1.set_ylim(48, hist1[0].max()+2)  # outliers only\n",
    "ax2.set_ylim(0,28 )  # most of the data\n",
    "\n",
    "# hide the spines between ax and ax2\n",
    "ax1.spines['bottom'].set_visible(False)\n",
    "ax2.spines['top'].set_visible(False)\n",
    "ax1.xaxis.tick_top()\n",
    "ax1.tick_params(labeltop=False)  # don't put tick labels at the top\n",
    "ax2.xaxis.tick_bottom()\n",
    "\n",
    "# Now, let's turn towards the cut-out slanted lines.\n",
    "# We create line objects in axes coordinates, in which (0,0), (0,1),\n",
    "# (1,0), and (1,1) are the four corners of the axes.\n",
    "# The slanted lines themselves are markers at those locations, such that the\n",
    "# lines keep their angle and position, independent of the axes size or scale\n",
    "# Finally, we need to disable clipping.\n",
    "\n",
    "d = .5  # proportion of vertical to horizontal extent of the slanted line\n",
    "kwargs = dict(marker=[(-1, -d), (1, d)], markersize=12,\n",
    "              linestyle=\"none\", color='k', mec='k', mew=1, clip_on=False)\n",
    "ax1.plot([0, 1], [0, 0], transform=ax1.transAxes, **kwargs)\n",
    "ax2.plot([0, 1], [1, 1], transform=ax2.transAxes, **kwargs)\n",
    "\n",
    "\n",
    "\n",
    "ax1.plot([np.nanmedian(nz_detectionNZFin),np.nanmedian(nz_detectionNZFin)],\n",
    "         [0,hist1[0].max()+5],\n",
    "        color=\"black\",#label=\"Outer HZ\"\n",
    "         linewidth=0.5\n",
    "        )\n",
    "\n",
    "ax2.plot([np.nanmedian(nz_detectionNZFin),np.nanmedian(nz_detectionNZFin)],\n",
    "         [0,hist1[0].max()+5],\n",
    "        color=\"black\",#label=\"Outer HZ\"\n",
    "         linewidth=0.5\n",
    "        )\n",
    "\n",
    "\n",
    "\n",
    "hist1=hist(nz_detectionNZFin,linewidth=\".5\",histtype='bar',ec='gray',\n",
    "           color=\"blue\",bins=100,alpha=.4 \n",
    "          )\n",
    "\n",
    "ax2.text((np.nanmedian(nz_detectionNZFin))+4, hist1[0].max()-2,\n",
    "         str(r\"$\\tilde{\\mathrm{n}_{\\rm Z}}$=%.1f\"%(np.nanmedian(nz_detectionNZFin))\n",
    "             +r\" for $\\alpha$=\"+str(alphaZ))+r\", f$_{\\rm pp}$=%.3f \"%(f_pp),\n",
    "         fontsize=9,color=\"blue\", )\n",
    "\n",
    "\n",
    "         #\" \\n $n_{sys}$=%.1i\"%(nz_critOuterFin.size)\n",
    "         \n",
    "\n",
    "ax1.set_xscale(\"log\")\n",
    "\n",
    "ax2.set_xscale(\"log\")\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "plt.xlabel(\"n$_{z}$\")\n",
    "#plt.legend()\n",
    "#plt.xlim([0,200])\n",
    "#plt.xscale('symlog', linthreshy=2000)\n",
    "plt.ylabel(\"N$_{stars}$\")\n",
    "plt.minorticks_on()\n",
    "#plt.savefig(\"WFIRST-CGI-disks-Uno/sensitivity_%isigma\"%(SNR)\n",
    "#            +str(alphaZ).replace(\".\",\"_\")+\"alpha\"\n",
    "#            +mode[\"systName\"]+str(f_pp)+\".pdf\",\n",
    "#            bbox_inches=\"tight\")\n",
    "#hist1[0].sum(),hist2[0].sum()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(74,)"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.shape(nz_detectionNZFin>0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "74"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.size(nz_detectionNZFin>0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "countsNez=detectionNZ*extraOuter[\"C_ez\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$[0.0090599987,~0.0030016734,~0.0032532926,~0.0020960892,~0.0077332764,~0.007869693] \\; \\mathrm{\\frac{1}{s}}$"
      ],
      "text/plain": [
       "<Quantity [0.00906   , 0.00300167, 0.00325329, 0.00209609, 0.00773328,\n",
       "           0.00786969] 1 / s>"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(TL.Vmag,extraIWA[\"C_sr\"],)\n",
    "extraIWA[\"C_ez\"][TL.Vmag<3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['HIP 37279' 'HIP 97649' 'HIP 113368' 'HIP 57632']\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(TL.Vmag , detectionNZ)\n",
    "print(TL.Name[TL.Vmag<2.5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Total Observation Time [h]')"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "exp_SNR=4.5\n",
    "\n",
    "#                intTime = np.true_divide(SNR**2.*C_b, (C_p**2. - (SNR*C_sp)**2.))\n",
    "intTime = np.true_divide(exp_SNR**2.*cbIWA,((countsNez)**2. - (f_pp*exp_SNR*extraIWA[\"C_sr\"])**2.)).to(u.hour)\n",
    "#plt.plot(intTime)\n",
    "sel=np.where((outerHZs>mode[ 'IWA'])&(intTime>0))\n",
    "plt.plot(range(intTime[sel].size),np.cumsum(np.sort(intTime[sel],)),\n",
    "         label=\"SNR=%.3g\"%exp_SNR)\n",
    "#plt.plot(extraOuter[\"C_ez\"]/extraOuter[\"C_sr\"])\n",
    "exp_SNR=3\n",
    "\n",
    "#                intTime = np.true_divide(SNR**2.*C_b, (C_p**2. - (SNR*C_sp)**2.))\n",
    "intTime = np.true_divide(exp_SNR**2.*cbIWA,((countsNez)**2. - (f_pp*exp_SNR*extraIWA[\"C_sr\"])**2.)).to(u.hour)\n",
    "#plt.plot(intTime)\n",
    "sel=np.where((outerHZs>mode[ 'IWA'])&(intTime>0))\n",
    "plt.plot(range(intTime[sel].size),np.cumsum(np.sort(intTime[sel],)),\n",
    "         label=\"SNR=%.3g\"%exp_SNR)\n",
    "#plt.plot(extraOuter[\"C_ez\"]/extraOuter[\"C_sr\"])\n",
    "plt.xlabel(\"Number of systems\")\n",
    "plt.legend()\n",
    "plt.ylabel(\"Total Observation Time [\"+str(intTime.unit)+\"]\")\n",
    "#plt.savefig(\"WFIRST-CGI-disks-Uno/exptime\"+mode[\"systName\"]+\".pdf\",bbox_inches=\"tight\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'$\\\\tilde{Z}$=11.9 for $\\x07lpha$=2.34'"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r\"$\\tilde{Z}$=%.1f\"%(np.nanmedian(nz_detectionNZFin))+\" for $\\alpha$=\"+str(alphaZ)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "signal rate[0.04171053] 1 / s\n",
      "raw speckle rate w/o post processing: [0.03284776] 1 / s\n",
      "critical_n zodi w/f_pp:[9.84396438]\n",
      "nzcrit [9.84396438]\n",
      "speckle rate post processed: [0.00821194] 1 / s\n",
      "raw background rate : [0.07069524] 1 / s\n",
      "IWA AU [0.61242478] AU\n",
      "d [pc][3.65016794] pc\n",
      "V-mag[3.6295]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/edouglas/anaconda3/envs/python39/lib/python3.9/site-packages/astropy/units/quantity.py:1028: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  out = super().__getitem__(key)\n",
      "<ipython-input-63-c492079d4cac>:18: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  print(\"V-mag{}\".format(TL.Vmag[ref_star]))\n"
     ]
    }
   ],
   "source": [
    "#-TL.starMag(allstars, mode[\"lam\"])\n",
    "#print(extraOuter[\"C_sr\"][ref_star],extraOuter[\"C_ez\"][ref_star])#77\n",
    "#spot checks\n",
    "ref_star=[TL.Name==\"HIP 8102\"]\n",
    "print(\"signal rate\"+str(extraIWA[\"C_ez\"][ref_star]*10))#77\n",
    "\n",
    "print(\"raw speckle rate w/o post processing: \"+str(extraIWA[\"C_sr\"][ref_star]))\n",
    "\n",
    "print(\"critical_n zodi w/f_pp:\"+str(csp[ref_star]/(extraIWA[\"C_ez\"][ref_star])*5))\n",
    "print(\"nzcrit {}\".format(detectionNZ[ref_star]))\n",
    "\n",
    "\n",
    "print(\"speckle rate post processed: \"+str(csp[ref_star]))\n",
    "\n",
    "print(\"raw background rate : \"+str(cb[ref_star]))\n",
    "print(\"IWA AU {}\".format(IWA_AU[ref_star]))\n",
    "print(\"d [pc]{}\".format(TL.dist[ref_star]))\n",
    "print(\"V-mag{}\".format(TL.Vmag[ref_star]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 37279  97649 113368  57632  67927   2021 102422  22449  17378   8102\n",
      "  95501  99240   3821  16537  98036  57757  27072  28103 109176  78072\n",
      "  27321  14632  50954  70497  59199   7513  12777 116771 102485  15510\n",
      "  92043   1599  64394 112447  61317 105858  17651 108870  67153  16852\n",
      "  19849  61174  77257  12843  71284  96100  34834  77760  46509  24813\n",
      "  64924  76829  23693  16245 104214  39903  15457  64408  86486   4151\n",
      "  10644  51459  57443  65721  86736  80686  82860    910  47592   5862\n",
      " 109422  73996  29271  53721  25278  97295  48113  95447  88745  86796\n",
      "   7981  29800  25110  97675  40843  45333 104217  64792   3909  15371\n",
      "  56997 114622  32480  47080  49081  80337  73184  84862  78459  12653\n",
      "  18859  58576  32439  89042  79672  22263   8362  15330   7978  99825\n",
      "   3765  35136 107649  12114  38908  42438 105090  81300  75181  98767\n",
      "  26394   3093  49908  84478  43587   3583  23311  56452  72848  40693\n",
      "  13402 100017 114046  54035  27435    544 113283  26779   1475  68184\n",
      "  32984  88972  10798  86400  41926  57939  85235  42808  25878]\n"
     ]
    }
   ],
   "source": [
    "#np.savetxt(\"WFIRST-CGI-disks-Uno/HIP_names.txt\", HIP, fmt='%i,',delimiter=\",\")\n",
    "print(HIP)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## "
   ]
  }
 ],
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