{ "cells": [ { "cell_type": "markdown", "metadata": { "tags": [] }, "source": [ "# Estimate disk scale height, disk radius, and disk mass using plons\n", "following the method described in Sect. 4.4. of Malfait et al. 2024a \n", "(https://ui.adsabs.harvard.edu/abs/2024arXiv240813158M/abstract)\n", "\n" ] }, { "cell_type": "markdown", "metadata": { "tags": [] }, "source": [ "### Short overview of the method:\n", "To estimate the disk quantities, we integrate over an array of radii (r_array), with step r_step (set to 0.01 au). \n", "\n", "For each radius r_i in this array, we estimate the disk scale height as the median of the scale heights at certain coordinates (r_i,theta_j)\n", "\n", "Then using the values of r_i and the median scale height, the added mass w.r.t. to total disk mass of the previous integration step is calculated.\n", "\n", "The radius is then defined as the radius where this added mass w.r.t. the total mass is significantly small (see Eq. (9) of Malfait+ 2024a)\n", "\n", "Because the disks are asymmetric, we can estimate these quantities in function of radius in different quadrants, to quantify the assymetry of the disk\n", "\n", "Therefore, this script contains one function were calculations are performed over the full theta range (fullThetaCalculations()), and one over 4 different theta quadrants (thetaRegionsCalculations())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Importing packages" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "tags": [] }, "outputs": [], "source": [ "%load_ext autoreload \n", "%autoreload 2" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "tags": [] }, "outputs": [], "source": [ "import plons\n", "import os\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "# import necessary plons scripts\n", "import plons.ConversionFactors_cgs as cgs\n", "import plons.GeometricalFunctions as gf\n", "import plons.AccrDisk as ad" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Function to calculate scale height and disk mass ifo radius, using the median scale height in all theta directions\n", "\n" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "tags": [] }, "outputs": [], "source": [ "'''\n", "Input:\n", "dumpData input data, used for density and position\n", "run modelName, used to save in correct locations\n", "dump dumpNumber, used to save under correct name\n", "xH calculate quantities at first or second scale height (see Eq.7 and 8 in Malfait 2024), \n", " '2H' used for disk radius and mass estimates\n", "\n", "\n", "Output:\n", "r_array array of radii starting at 'lowerR', ending at estimated disk radius, and increasing with step 'r_step'\n", "SH_array array of scale height estimates at each radius \n", "RhoMax_array array of maximum density value, which is probably at orbital plane, at each radius\n", "totalMassDisk array of total disk mass assuming that disk radius is r_i of corresponding r_array\n", "rel_mass_added array of mass added in this r_step, divided by total mass at previous r_i\n", "\n", "Note that the surface density and optical depth values have not been in used in our paper, mainly because it is a too approximate approach\n", "Sigma_array array of estimated surface density Sigma\n", "SigmaTheor_array array of theoretical surface density Sigma\n", "Tau_array array of estimated optical depth tau\n", "TauTheor_array array of theoretical optical depth tau\n", "\n", "! The output data is saved in a txt file, and can be used to produce plots using the accrDisks_plotsHRM_Plons.ipynb notebook\n", "'''\n", "def fullThetaCalculations(dumpData,run,dump,xH, printOut = False):\n", " # Choose which and how many r and theta values you want to use to calculate scale heights and disk mass and for plot of rho vs h\n", " n_grid = 10000\n", " maxH = 1.5 *cgs.au\n", "\n", " numberOfThetas = 10\n", " thetaMin = 0\n", " thetaMax = 2*np.pi\n", " thetaArray = np.linspace(thetaMin,(numberOfThetas-1)/numberOfThetas*thetaMax,numberOfThetas)\n", "\n", " crit = 0.3 #smalles constant in our case towards which the relative added mass ocnsistently keeps decreasing for all models\n", " lowerR = 0.02\n", " r_step = 0.01\n", "\n", " # Calculate arrays with radii, disk mass, relative addes mass, and scale heights\n", " r_array,totalMassDisk,rel_mass_added,SH_array,RhoMax_array,Sigma_array,SigmaTheor_array,Tau_array,TauTheor_array = ad.get_SH_diskMass_radius(lowerR,r_step,thetaArray,dumpData,maxH,n_grid,run,crit,xH,phiQuadr = False, printOut = printOut)\n", " \n", " '''\n", " Write data for full theta region in txt file infoAccrDisk_wind_00....txt\n", " '''\n", " testLimit = crit\n", " # make directory to save results in\n", " wdir = os.path.join(run,'plotsAnalysis')\n", " !mkdir -p $wdir\n", " # construct txt file with calculated arrays of data\n", " title = os.path.join(run,'plotsAnalysis/infoAccrDisk_wind_00'+str(dump)+'_'+xH+'.txt')\n", " with open (title,'w') as f:\n", " f.write('Model '+str(run)+'wind_00'+str(dump)+'\\n')\n", " f.write('Data analysis accretion disks')\n", " f.write('\\n')\n", " f.write('\\n') \n", " f.write('for rstep '+ str(r_step)+ ' au (=racc) and limit of '+ str(testLimit)+':'+'\\n')\n", " f.write('the estimated radius is: '+ str(np.round(r_array[np.where(rel_mass_added/r_step bow shock side (in front of companion)\n", " thetaMin2 = 1./4. * np.pi\n", " thetaMax2 = 3./4. * np.pi\n", " thetaArray2 = np.linspace(thetaMin2,thetaMax2,numberOfThetas) \n", "\n", " # Towards AGB \n", " thetaMin3 = 3./4. * np.pi\n", " thetaMax3 = 5./4. * np.pi\n", " thetaArray3 = np.linspace(thetaMin3,thetaMax3,numberOfThetas) \n", "\n", " # Negative y --> flow behind companion\n", " thetaMin4 = 5./4. * np.pi\n", " thetaMax4 = 7./4. * np.pi\n", " thetaArray4 = np.linspace(thetaMin4,thetaMax4,numberOfThetas)\n", "\n", " crit = 0.3\n", " \n", " lowerR = 0.02\n", " # sometimes you will see that you need to vary the step a little bit to make sure the algorithm does not get stuck in the first radii steps\n", " r_step1 = 0.01\n", " r_step2 = r_step1\n", " r_step3 = r_step1\n", " r_step4 = r_step1\n", " \n", " '''\n", " Calculate scale heights and mass estimates at each r; and estimate final radius\n", " Make text files with important info to make plots \n", " for different theta regimes\n", " '''\n", " testLimit = crit\n", " # calculate scale height and mass estimates at each r for this setup\n", " r_array1,totalMassDisk1,rel_mass_added1,SH_array1,RhoMax_array1,Sigma_array1,SigmaTheor_array1,Tau_array1,TauTheor_array1 = ad.get_SH_diskMass_radius(lowerR,r_step1,thetaArray1,dumpData,maxH,n_grid,run,crit,xH,phiQuadr=True,printOut=True)\n", " title = os.path.join(run,'plotsAnalysis/infoAccrDisk_theta~0_wind_00'+str(dump)+'_'+xH+'.txt')\n", " ad.writeFile(title,r_step1,testLimit,r_array1,rel_mass_added1,totalMassDisk1,SH_array1,RhoMax_array1,Sigma_array1,SigmaTheor_array1,Tau_array1,TauTheor_array1)\n", " \n", " r_array2,totalMassDisk2,rel_mass_added2,SH_array2,RhoMax_array2,Sigma_array2,SigmaTheor_array2,Tau_array2,TauTheor_array2 = ad.get_SH_diskMass_radius(lowerR,r_step2,thetaArray2,dumpData,maxH,n_grid,run,crit,xH,phiQuadr=True,printOut=True)\n", " title = os.path.join(run,'plotsAnalysis/infoAccrDisk_theta~pi:2_wind_00'+str(dump)+'_'+xH+'.txt')\n", " ad.writeFile(title,r_step2,testLimit,r_array2,rel_mass_added2,totalMassDisk2,SH_array2,RhoMax_array2,Sigma_array2,SigmaTheor_array2,Tau_array2,TauTheor_array2)\n", "\n", " r_array3,totalMassDisk3,rel_mass_added3,SH_array3,RhoMax_array3,Sigma_array3,SigmaTheor_array3,Tau_array3,TauTheor_array3 = ad.get_SH_diskMass_radius(lowerR,r_step3,thetaArray3,dumpData,maxH,n_grid,run,crit,xH,phiQuadr=True,printOut=True)\n", " title = os.path.join(run,'plotsAnalysis/infoAccrDisk_theta~pi_wind_00'+str(dump)+'_'+xH+'.txt')\n", " ad.writeFile(title,r_step3,testLimit,r_array3,rel_mass_added3,totalMassDisk3,SH_array3,RhoMax_array3,Sigma_array3,SigmaTheor_array3,Tau_array3,TauTheor_array3)\n", "\n", " r_array4,totalMassDisk4,rel_mass_added4,SH_array4,RhoMax_array4,Sigma_array4,SigmaTheor_array4,Tau_array4,TauTheor_array4 = ad.get_SH_diskMass_radius(lowerR,r_step1,thetaArray4,dumpData,maxH,n_grid,run,crit,xH,phiQuadr=True,printOut=True)\n", " title = os.path.join(run,'plotsAnalysis/infoAccrDisk_theta~pi3:2_wind_00'+str(dump)+'_'+xH+'.txt')\n", " ad.writeFile(title,r_step4,testLimit,r_array4,rel_mass_added4,totalMassDisk4,SH_array4,RhoMax_array4,Sigma_array4,SigmaTheor_array4,Tau_array4,TauTheor_array4)\n", " \n", " \n", " '''\n", " If you want you can already construct some plots to see the results\n", " '''\n", " if xH == '1H':\n", " fig, axs = plt.subplots(nrows = 1, ncols= 6 , figsize=(35, 5))\n", " else:\n", " fig, axs = plt.subplots(nrows = 1, ncols= 3 , figsize=(14, 5))\n", " \n", " ad.plot_relAddedMass_4theta(axs[0],r_array1,r_array2,r_array3,r_array4,rel_mass_added1,rel_mass_added2,rel_mass_added3,rel_mass_added4,r_step1,r_step3,crit)\n", " ad.plot_totalDiskMass_4theta(axs[1],r_array1,r_array2,r_array3,r_array4,totalMassDisk1,totalMassDisk2,totalMassDisk3,totalMassDisk4)\n", " ad.plot_scaleHeights_4theta(axs[2],r_array1,r_array2,r_array3,r_array4,SH_array1,SH_array2,SH_array3,SH_array4)\n", " if xH == '1H':\n", " ad.plot_surfDensity_4theta(axs[3],r_array1,r_array2,r_array3,r_array4,Sigma_array1,Sigma_array2,Sigma_array3,Sigma_array4)\n", " ad.plot_opticalDepth_4theta(axs[4],r_array1,r_array2,r_array3,r_array4,Tau_array1,Tau_array2,Tau_array3,Tau_array4)\n", " ad.plot_rhoMax_4theta(axs[5], r_array1,r_array2,r_array3,r_array4,RhoMax_array1,RhoMax_array2,RhoMax_array3,RhoMax_array4)\n", " return fig" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Main function for the actual calculations" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "tags": [] }, "outputs": [], "source": [ "'''\n", "input:\n", "run modelName, used to save in correct locations\n", "dump dumpNumber, used to save under correct name\n", "scaleHeightList list with 1H, 2H or one of both, to know for which scale height calculations need to be done\n", "full, quadrants boolians to know if calculations should be done for full thetaregion and/or quadrants\n", "'''\n", "\n", "def main(run,dump,scaleHeightList,full=True,quadrants=True,printOut = False):\n", " '''\n", " Load in data\n", " ''' \n", " setup = plons.LoadSetup(run, \"wind\")\n", " dumpData = plons.LoadFullDump(os.path.join(run, f\"wind_%05d\" % dump), setup)\n", " \n", " # Coordinate transformation: translation to rcomp + rotation such that AGB is on positive x-axis\n", " # Coordinate transformation such that comp is at (0,0) and AGB at -x\n", " dumpData['new_x'],dumpData['new_y'],dumpData['new_r'] = ad.calc_new_position(dumpData['position'][:,0],dumpData['position'][:,1],dumpData)\n", " phi = gf.calcPhi(dumpData['new_x'],dumpData['new_y'])\n", " # print(phi)\n", " dumpData['new_Phi'] = np.where(phi < 0, phi + 2 * np.pi, phi)\n", " \n", " '''\n", " You can use this to test if coordinate transformation is correct\n", " ''' \n", " # testPositionAndTheta(dumpData,run,dump)\n", "\n", " '''\n", " Perform calculations for full theta region and/or 4 different theta regions\n", " make plots and write txt files with results\n", " ''' \n", " for xH in scaleHeightList:\n", " if full==True:\n", " if printOut: print('Calculations full theta region:')\n", " fig = fullThetaCalculations(dumpData,run,dump,xH, printOut)\n", " fig.savefig(run+'plotsAnalysis/Disk_wind_%05d'%(dump)+'_'+xH+'.png', bbox_inches='tight')\n", " if quadrants==True:\n", " if printOut: print('Calculations for different theta regions:')\n", " fig = thetaRegionsCalculations(dumpData,run,dump,xH)\n", " fig.savefig(run+f'plotsAnalysis/Theta_rstep_wind_%05d'%(dump)+'_'+xH+'.png', bbox_inches='tight')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Examples" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true, "jupyter": { "outputs_hidden": true }, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Calculations full theta region:\n", "---------\n", "r 0.02 au\n", "scale height is 0.011401140114011388\n", "mass added [Msun]: 3.4540161426105943e-11\n", "relMass_added/rstep (rico) = 100.0\n", "---------\n", "r 0.03 au\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/matse/codes/anaconda3/envs/plons/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3504: RuntimeWarning: Mean of empty slice.\n", " return _methods._mean(a, axis=axis, dtype=dtype,\n", "/home/matse/codes/anaconda3/envs/plons/lib/python3.10/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide\n", " ret = ret.dtype.type(ret / rcount)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "scale height is 0.009450945094509448\n", "mass added [Msun]: 1.554307264174767e-10\n", "relMass_added/rstep (rico) = 81.818\n", "---------\n", "r 0.04 au\n", "scale height is 0.009000900090009034\n", "mass added [Msun]: 5.200213192485949e-10\n", "relMass_added/rstep (rico) = 73.243\n", "---------\n", "r 0.05 au\n", "scale height is 0.008025802580258031\n", "mass added [Msun]: 8.980441970787545e-10\n", "relMass_added/rstep (rico) = 55.847\n", "---------\n", "r 0.06 au\n", "scale height is 0.008475847584758509\n", "mass added [Msun]: 1.3604985806171728e-09\n", "relMass_added/rstep (rico) = 45.831\n", "---------\n", "r 0.07 au\n", "scale height is 0.010201020102010244\n", "mass added [Msun]: 1.780737211301462e-09\n", "relMass_added/rstep (rico) = 37.495\n", "---------\n", "r 0.08 au\n", "scale height is 0.0118511851185118\n", "mass added [Msun]: 2.1837057612726974e-09\n", "relMass_added/rstep (rico) = 31.497\n", "---------\n", "r 0.09 au\n", "scale height is 0.012976297629762962\n", "mass added [Msun]: 2.477297133394598e-09\n", "relMass_added/rstep (rico) = 26.325\n", "---------\n", "r 0.1 au\n", "scale height is 0.015076507650765061\n", "mass added [Msun]: 2.788158586229551e-09\n", "relMass_added/rstep (rico) = 22.857\n", "---------\n", "r 0.11 au\n", "scale height is 0.017551755175517592\n", "mass added [Msun]: 3.0030751462142103e-09\n", "relMass_added/rstep (rico) = 19.755\n", "---------\n", "r 0.12 au\n", "scale height is 0.02017701770177015\n", "mass added [Msun]: 3.1585058726316876e-09\n", "relMass_added/rstep (rico) = 17.203\n", "---------\n", "r 0.13 au\n", "scale height is 0.023927392739273936\n", "mass added [Msun]: 3.350395658332276e-09\n", "relMass_added/rstep (rico) = 15.432\n", "---------\n", "r 0.14 au\n", "scale height is 0.026252625262526308\n", "mass added [Msun]: 3.3983681047574233e-09\n", "relMass_added/rstep (rico) = 13.535\n", "---------\n", "r 0.15 au\n", "scale height is 0.027977797779777977\n", "mass added [Msun]: 3.442502755468559e-09\n", "relMass_added/rstep (rico) = 12.057\n", "---------\n", "r 0.16 au\n", "scale height is 0.03127812781278128\n", "mass added [Msun]: 3.519258669748794e-09\n", "relMass_added/rstep (rico) = 10.973\n", "---------\n", "r 0.17 au\n", "scale height is 0.03412841284128418\n", "mass added [Msun]: 3.565312218316935e-09\n", "relMass_added/rstep (rico) = 10.005\n", "---------\n", "r 0.18 au\n", "scale height is 0.03735373537353744\n", "mass added [Msun]: 3.5902578904580127e-09\n", "relMass_added/rstep (rico) = 9.153\n", "---------\n", "r 0.19 au\n", "scale height is 0.04215421542154214\n", "mass added [Msun]: 3.5825822990299886e-09\n", "relMass_added/rstep (rico) = 8.369\n", "---------\n", "r 0.2 au\n", "scale height is 0.04657965796579655\n", "mass added [Msun]: 3.6190413583131006e-09\n", "relMass_added/rstep (rico) = 7.795\n", "---------\n", "r 0.21 au\n", "scale height is 0.05018001800180018\n", "mass added [Msun]: 3.5633933204599293e-09\n", "relMass_added/rstep (rico) = 7.128\n", "---------\n", "r 0.22 au\n", "scale height is 0.05318031803180317\n", "mass added [Msun]: 3.446340551182571e-09\n", "relMass_added/rstep (rico) = 6.449\n", "---------\n", "r 0.23 au\n", "scale height is 0.05610561056105611\n", "mass added [Msun]: 3.473205121180653e-09\n", "relMass_added/rstep (rico) = 6.103\n", "---------\n", "r 0.24 au\n", "scale height is 0.059855985598559834\n", "mass added [Msun]: 3.3139365990491646e-09\n", "relMass_added/rstep (rico) = 5.503\n", "---------\n", "r 0.25 au\n", "scale height is 0.0633063306330633\n", "mass added [Msun]: 3.3216121904771878e-09\n", "relMass_added/rstep (rico) = 5.227\n", "---------\n", "r 0.26 au\n", "scale height is 0.06570657065706571\n", "mass added [Msun]: 3.219910604055876e-09\n", "relMass_added/rstep (rico) = 4.823\n", "---------\n", "r 0.27 au\n", "scale height is 0.06818181818181812\n", "mass added [Msun]: 3.1009389369215114e-09\n", "relMass_added/rstep (rico) = 4.438\n", "---------\n", "r 0.28 au\n", "scale height is 0.07043204320432038\n", "mass added [Msun]: 3.008831839785228e-09\n", "relMass_added/rstep (rico) = 4.129\n", "---------\n", "r 0.29 au\n", "scale height is 0.07410741074107417\n", "mass added [Msun]: 2.935913721219005e-09\n", "relMass_added/rstep (rico) = 3.873\n", "---------\n", "r 0.3 au\n", "scale height is 0.07838283828382836\n", "mass added [Msun]: 2.8188609519416454e-09\n", "relMass_added/rstep (rico) = 3.585\n", "---------\n", "r 0.31 au\n", "scale height is 0.08175817581758171\n", "mass added [Msun]: 2.6749436126662046e-09\n", "relMass_added/rstep (rico) = 3.29\n", "---------\n", "r 0.32 au\n", "scale height is 0.08393339333933393\n", "mass added [Msun]: 2.5694042305308802e-09\n", "relMass_added/rstep (rico) = 3.063\n", "---------\n", "r 0.33 au\n", "scale height is 0.08700870087008697\n", "mass added [Msun]: 2.477297133394598e-09\n", "relMass_added/rstep (rico) = 2.869\n", "---------\n", "r 0.34 au\n", "scale height is 0.09023402340234023\n", "mass added [Msun]: 2.346812079118197e-09\n", "relMass_added/rstep (rico) = 2.646\n", "---------\n", "r 0.35 au\n", "scale height is 0.09525952595259521\n", "mass added [Msun]: 2.2796506541229915e-09\n", "relMass_added/rstep (rico) = 2.506\n", "---------\n", "r 0.36 au\n", "scale height is 0.098934893489349\n", "mass added [Msun]: 2.1376522127045564e-09\n", "relMass_added/rstep (rico) = 2.296\n", "---------\n", "r 0.37 au\n", "scale height is 0.10261026102610261\n", "mass added [Msun]: 2.0225183412842035e-09\n", "relMass_added/rstep (rico) = 2.126\n", "---------\n", "r 0.38 au\n", "scale height is 0.10658565856585654\n", "mass added [Msun]: 1.968789201288039e-09\n", "relMass_added/rstep (rico) = 2.027\n", "---------\n", "r 0.39 au\n", "scale height is 0.11131113111311126\n", "mass added [Msun]: 1.8402230448686444e-09\n", "relMass_added/rstep (rico) = 1.86\n", "---------\n", "r 0.4 au\n", "scale height is 0.11626162616261626\n", "mass added [Msun]: 1.7519537434463736e-09\n", "relMass_added/rstep (rico) = 1.74\n", "---------\n", "r 0.41 au\n", "scale height is 0.12256225622562256\n", "mass added [Msun]: 1.6310631784550027e-09\n", "relMass_added/rstep (rico) = 1.594\n", "---------\n", "r 0.42 au\n", "scale height is 0.13261326132613252\n", "mass added [Msun]: 1.5811718341728498e-09\n", "relMass_added/rstep (rico) = 1.522\n", "---------\n", "r 0.43 au\n", "scale height is 0.15729072907290725\n", "mass added [Msun]: 1.5946041191718909e-09\n", "relMass_added/rstep (rico) = 1.511\n", "---------\n", "r 0.44 au\n", "scale height is 0.1707920792079208\n", "mass added [Msun]: 1.469875758466508e-09\n", "relMass_added/rstep (rico) = 1.374\n", "---------\n", "r 0.45 au\n", "scale height is 0.18879387938793873\n", "mass added [Msun]: 1.4852269413225552e-09\n", "relMass_added/rstep (rico) = 1.369\n", "---------\n", "r 0.46 au\n", "scale height is 0.21482148214821478\n", "mass added [Msun]: 1.3528229891891491e-09\n", "relMass_added/rstep (rico) = 1.232\n", "---------\n", "r 0.47 au\n", "!! At this theta direction, you are no longer in the disk or you set your maxH too small!!\n", "scale height is 0.22307230723072308\n", "mass added [Msun]: 1.2779859727659196e-09\n", "relMass_added/rstep (rico) = 1.15\n", "---------\n", "r 0.48 au\n", "!! At this theta direction, you are no longer in the disk or you set your maxH too small!!\n", "scale height is 0.22517251725172505\n", "mass added [Msun]: 1.2434458113398136e-09\n", "relMass_added/rstep (rico) = 1.107\n", "---------\n", "r 0.49 au\n", "!! At this theta direction, you are no longer in the disk or you set your maxH too small!!\n", "scale height is 0.22907290729072904\n", "mass added [Msun]: 1.1225552463484427e-09\n", "relMass_added/rstep (rico) = 0.989\n", "---------\n", "r 0.5 au\n", "!! At this theta direction, you are no longer in the disk or you set your maxH too small!!\n", "!! At this theta direction, you are no longer in the disk or you set your maxH too small!!\n", "scale height is 0.2113711371137113\n", "mass added [Msun]: 1.0304481492121606e-09\n", "relMass_added/rstep (rico) = 0.9\n", "---------\n", "r 0.51 au\n", "!! At this theta direction, you are no longer in the disk or you set your maxH too small!!\n", "!! At this theta direction, you are no longer in the disk or you set your maxH too small!!\n", "!! At this theta direction, you are no longer in the disk or you set your maxH too small!!\n", "scale height is 0.1960696069606961\n", "mass added [Msun]: 9.939890899290487e-10\n", "relMass_added/rstep (rico) = 0.861\n", "---------\n", "r 0.52 au\n", "!! At this theta direction, you are no longer in the disk or you set your maxH too small!!\n", "!! At this theta direction, you are no longer in the disk or you set your maxH too small!!\n", "!! At this theta direction, you are no longer in the disk or you set your maxH too small!!\n", "scale height is 0.19966996699669967\n", "mass added [Msun]: 9.191520735058192e-10\n", "relMass_added/rstep (rico) = 0.79\n", "---------\n", "r 0.53 au\n", "!! At this theta direction, you are no longer in the disk or you set your maxH too small!!\n", "!! At this theta direction, you are no longer in the disk or you set your maxH too small!!\n", "!! At this theta direction, you are no longer in the disk or you set your maxH too small!!\n", "scale height is 0.2046204620462046\n", "mass added [Msun]: 8.615851377956427e-10\n", "relMass_added/rstep (rico) = 0.735\n", "---------\n", "r 0.54 au\n", "!! At this theta direction, you are no longer in the disk or you set your maxH too small!!\n", "!! At this theta direction, you are no longer in the disk or you set your maxH too small!!\n", "!! At this theta direction, you are no longer in the disk or you set your maxH too small!!\n", "scale height is 0.21227122712271213\n", "mass added [Msun]: 7.752347342303777e-10\n", "relMass_added/rstep (rico) = 0.657\n", "---------\n", "r 0.55 au\n", "!! At this theta direction, you are no longer in the disk or you set your maxH too small!!\n", "!! At this theta direction, you are no longer in the disk or you set your maxH too small!!\n", "!! At this theta direction, you are no longer in the disk or you set your maxH too small!!\n", "scale height is 0.21707170717071697\n", "mass added [Msun]: 7.119111049491835e-10\n", "relMass_added/rstep (rico) = 0.599\n", "---------\n", "r 0.56 au\n", "!! At this theta direction, you are no longer in the disk or you set your maxH too small!!\n", "!! At this theta direction, you are no longer in the disk or you set your maxH too small!!\n", "!! At this theta direction, you are no longer in the disk or you set your maxH too small!!\n", "scale height is 0.2256225622562256\n", "mass added [Msun]: 7.291811856622365e-10\n", "relMass_added/rstep (rico) = 0.61\n", "---------\n", "r 0.57 au\n", "!! At this theta direction, you are no longer in the disk or you set your maxH too small!!\n", "!! At this theta direction, you are no longer in the disk or you set your maxH too small!!\n", "!! At this theta direction, you are no longer in the disk or you set your maxH too small!!\n", "scale height is 0.2301230123012301\n", "mass added [Msun]: 6.044528249568538e-10\n", "relMass_added/rstep (rico) = 0.503\n", "---------\n", "r 0.58 au\n", "!! At this theta direction, you are no longer in the disk or you set your maxH too small!!\n", "!! At this theta direction, you are no longer in the disk or you set your maxH too small!!\n", "!! At this theta direction, you are no longer in the disk or you set your maxH too small!!\n", "scale height is 0.23507350735073504\n", "mass added [Msun]: 5.83344948529789e-10\n", "relMass_added/rstep (rico) = 0.483\n", "---------\n", "r 0.59 au\n", "!! At this theta direction, you are no longer in the disk or you set your maxH too small!!\n", "!! At this theta direction, you are no longer in the disk or you set your maxH too small!!\n", "!! At this theta direction, you are no longer in the disk or you set your maxH too small!!\n", "scale height is 0.23732373237323737\n", "mass added [Msun]: 5.488047871036832e-10\n", "relMass_added/rstep (rico) = 0.453\n", "---------\n", "r 0.6 au\n", "!! At this theta direction, you are no longer in the disk or you set your maxH too small!!\n", "!! At this theta direction, you are no longer in the disk or you set your maxH too small!!\n", "!! At this theta direction, you are no longer in the disk or you set your maxH too small!!\n", "scale height is 0.2427242724272427\n", "mass added [Msun]: 5.18102421391589e-10\n", "relMass_added/rstep (rico) = 0.426\n", "---------\n", "r 0.61 au\n", "!! At this theta direction, you are no longer in the disk or you set your maxH too small!!\n", "!! At this theta direction, you are no longer in the disk or you set your maxH too small!!\n", "!! At this theta direction, you are no longer in the disk or you set your maxH too small!!\n", "scale height is 0.2547254725472548\n", "mass added [Msun]: 4.85481157822489e-10\n", "relMass_added/rstep (rico) = 0.397\n", "---------\n", "r 0.62 au\n", "!! At this theta direction, you are no longer in the disk or you set your maxH too small!!\n", "!! At this theta direction, you are no longer in the disk or you set your maxH too small!!\n", "!! At this theta direction, you are no longer in the disk or you set your maxH too small!!\n", "!! At this theta direction, you are no longer in the disk or you set your maxH too small!!\n", "scale height is 0.25067506750675067\n", "mass added [Msun]: 4.202386306842889e-10\n", "relMass_added/rstep (rico) = 0.343\n", "---------\n", "r 0.63 au\n", "!! At this theta direction, you are no longer in the disk or you set your maxH too small!!\n", "!! At this theta direction, you are no longer in the disk or you set your maxH too small!!\n", "!! At this theta direction, you are no longer in the disk or you set your maxH too small!!\n", "!! At this theta direction, you are no longer in the disk or you set your maxH too small!!\n", "scale height is 0.2593759375937594\n", "mass added [Msun]: 3.6650949068812407e-10\n", "relMass_added/rstep (rico) = 0.298\n", "-------------------------------------------------------------\n", "estimate of r of the accretion disk is 0.63 au\n", "Calculations full theta region:\n", "---------\n", "r 0.02 au\n", "scale height is 0.016951695169516955\n", "mass added [Msun]: 3.837795714011771e-12\n", "relMass_added/rstep (rico) = 100.0\n", "---------\n", "r 0.03 au\n", "scale height is 0.014401440144014442\n", "mass added [Msun]: 3.070236571209417e-11\n", "relMass_added/rstep (rico) = 88.889\n", "---------\n", "r 0.04 au\n", "scale height is 0.012451245124512502\n", "mass added [Msun]: 8.059370999424718e-11\n", "relMass_added/rstep (rico) = 70.0\n", "---------\n", "r 0.05 au\n", "scale height is 0.010651065106510722\n", "mass added [Msun]: 2.398622321257357e-10\n", "relMass_added/rstep (rico) = 67.568\n", "---------\n", "r 0.06 au\n", "scale height is 0.010201020102010178\n", "mass added [Msun]: 5.161835235345832e-10\n", "relMass_added/rstep (rico) = 59.251\n", "---------\n", "r 0.07 au\n", "scale height is 0.012151215121512118\n", "mass added [Msun]: 9.345032563618662e-10\n", "relMass_added/rstep (rico) = 51.753\n", "---------\n", "r 0.08 au\n", "scale height is 0.013951395139513965\n", "mass added [Msun]: 1.3106072363350195e-09\n", "relMass_added/rstep (rico) = 42.057\n", "---------\n", "r 0.09 au\n", "scale height is 0.01590159015901597\n", "mass added [Msun]: 1.6349009741690143e-09\n", "relMass_added/rstep (rico) = 34.41\n", "---------\n", "r 0.1 au\n", "scale height is 0.01860186018601864\n", "mass added [Msun]: 1.867087614866727e-09\n", "relMass_added/rstep (rico) = 28.211\n", "---------\n", "r 0.11 au\n", "scale height is 0.020102010201020103\n", "mass added [Msun]: 2.078166379137374e-09\n", "relMass_added/rstep (rico) = 23.897\n", "---------\n", "r 0.12 au\n", "scale height is 0.022877287728772885\n", "mass added [Msun]: 2.258542777695927e-09\n", "relMass_added/rstep (rico) = 20.617\n", "---------\n", "r 0.13 au\n", "scale height is 0.025352535253525352\n", "mass added [Msun]: 2.3640821598312503e-09\n", "relMass_added/rstep (rico) = 17.75\n", "---------\n", "r 0.14 au\n", "scale height is 0.02835283528352834\n", "mass added [Msun]: 2.4178112998274153e-09\n", "relMass_added/rstep (rico) = 15.364\n", "---------\n", "r 0.15 au\n", "scale height is 0.03067806780678071\n", "mass added [Msun]: 2.540620762675792e-09\n", "relMass_added/rstep (rico) = 13.9\n", "---------\n", "r 0.16 au\n", "scale height is 0.03390339033903391\n", "mass added [Msun]: 2.521431784105733e-09\n", "relMass_added/rstep (rico) = 12.123\n", "---------\n", "r 0.17 au\n", "scale height is 0.037128712871287106\n", "mass added [Msun]: 2.586674311243933e-09\n", "relMass_added/rstep (rico) = 11.061\n", "---------\n", "r 0.18 au\n", "scale height is 0.039003900390038995\n", "mass added [Msun]: 2.559809741245851e-09\n", "relMass_added/rstep (rico) = 9.866\n", "---------\n", "r 0.19 au\n", "scale height is 0.04260426042604262\n", "mass added [Msun]: 2.515675090534715e-09\n", "relMass_added/rstep (rico) = 8.839\n", "---------\n", "r 0.2 au\n", "scale height is 0.04560456045604561\n", "mass added [Msun]: 2.5694042305308802e-09\n", "relMass_added/rstep (rico) = 8.28\n", "---------\n", "r 0.21 au\n", "scale height is 0.04830483048304828\n", "mass added [Msun]: 2.456189256967533e-09\n", "relMass_added/rstep (rico) = 7.335\n", "---------\n", "r 0.22 au\n", "scale height is 0.052430243024302374\n", "mass added [Msun]: 2.540620762675792e-09\n", "relMass_added/rstep (rico) = 7.052\n", "---------\n", "r 0.23 au\n", "scale height is 0.05573057305730574\n", "mass added [Msun]: 2.3794333426872975e-09\n", "relMass_added/rstep (rico) = 6.195\n", "---------\n", "r 0.24 au\n", "scale height is 0.058055805580557986\n", "mass added [Msun]: 2.40629791268538e-09\n", "relMass_added/rstep (rico) = 5.896\n", "---------\n", "r 0.25 au\n", "scale height is 0.06300630063006298\n", "mass added [Msun]: 2.3276231005481387e-09\n", "relMass_added/rstep (rico) = 5.395\n", "---------\n", "r 0.26 au\n", "scale height is 0.06518151815181512\n", "mass added [Msun]: 2.2201648205558094e-09\n", "relMass_added/rstep (rico) = 4.894\n", "---------\n", "r 0.27 au\n", "scale height is 0.06833183318331834\n", "mass added [Msun]: 2.2527860841249094e-09\n", "relMass_added/rstep (rico) = 4.731\n", "---------\n", "r 0.28 au\n", "scale height is 0.07275727572757275\n", "mass added [Msun]: 2.1357333148475502e-09\n", "relMass_added/rstep (rico) = 4.293\n", "---------\n", "r 0.29 au\n", "scale height is 0.07710771077107717\n", "mass added [Msun]: 2.1146254384204855e-09\n", "relMass_added/rstep (rico) = 4.077\n", "---------\n", "r 0.3 au\n", "scale height is 0.08003300330032999\n", "mass added [Msun]: 2.0033293627141446e-09\n", "relMass_added/rstep (rico) = 3.719\n", "---------\n", "r 0.31 au\n", "scale height is 0.08370837083708366\n", "mass added [Msun]: 1.968789201288039e-09\n", "relMass_added/rstep (rico) = 3.526\n", "---------\n", "r 0.32 au\n", "scale height is 0.08775877587758776\n", "mass added [Msun]: 1.899708878435827e-09\n", "relMass_added/rstep (rico) = 3.29\n", "---------\n", "r 0.33 au\n", "scale height is 0.09225922592259228\n", "mass added [Msun]: 1.8517364320106797e-09\n", "relMass_added/rstep (rico) = 3.108\n", "---------\n", "r 0.34 au\n", "scale height is 0.0967596759675968\n", "mass added [Msun]: 1.7308458670193087e-09\n", "relMass_added/rstep (rico) = 2.823\n", "---------\n", "r 0.35 au\n", "scale height is 0.1011851185118512\n", "mass added [Msun]: 1.780737211301462e-09\n", "relMass_added/rstep (rico) = 2.822\n", "---------\n", "r 0.36 au\n", "scale height is 0.10553555355535549\n", "mass added [Msun]: 1.6540899527390734e-09\n", "relMass_added/rstep (rico) = 2.554\n", "---------\n", "r 0.37 au\n", "scale height is 0.11033603360336033\n", "mass added [Msun]: 1.5888474256008733e-09\n", "relMass_added/rstep (rico) = 2.395\n", "---------\n", "r 0.38 au\n", "scale height is 0.11453645364536459\n", "mass added [Msun]: 1.554307264174767e-09\n", "relMass_added/rstep (rico) = 2.289\n", "---------\n", "r 0.39 au\n", "scale height is 0.11761176117611756\n", "mass added [Msun]: 1.489064737036567e-09\n", "relMass_added/rstep (rico) = 2.146\n", "---------\n", "r 0.4 au\n", "scale height is 0.12136213621362128\n", "mass added [Msun]: 1.423822209898367e-09\n", "relMass_added/rstep (rico) = 2.011\n", "---------\n", "r 0.41 au\n", "scale height is 0.1256375637563756\n", "mass added [Msun]: 1.3547418870461552e-09\n", "relMass_added/rstep (rico) = 1.877\n", "---------\n", "r 0.42 au\n", "scale height is 0.13193819381938196\n", "mass added [Msun]: 1.4103899248993256e-09\n", "relMass_added/rstep (rico) = 1.917\n", "---------\n", "r 0.43 au\n", "scale height is 0.1359135913591359\n", "mass added [Msun]: 1.2453647091968194e-09\n", "relMass_added/rstep (rico) = 1.664\n", "---------\n", "r 0.44 au\n", "scale height is 0.1401890189018902\n", "mass added [Msun]: 1.20314895634269e-09\n", "relMass_added/rstep (rico) = 1.583\n", "---------\n", "r 0.45 au\n", "scale height is 0.14416441644164418\n", "mass added [Msun]: 1.2549591984818489e-09\n", "relMass_added/rstep (rico) = 1.624\n", "---------\n", "r 0.46 au\n", "scale height is 0.15024002400240022\n", "mass added [Msun]: 1.1283119399194605e-09\n", "relMass_added/rstep (rico) = 1.439\n", "---------\n", "r 0.47 au\n", "scale height is 0.1556405640564056\n", "mass added [Msun]: 1.1283119399194605e-09\n", "relMass_added/rstep (rico) = 1.419\n", "---------\n", "r 0.48 au\n", "scale height is 0.15939093909390933\n", "mass added [Msun]: 1.070745004209284e-09\n", "relMass_added/rstep (rico) = 1.328\n", "---------\n", "r 0.49 au\n", "scale height is 0.1633663366336634\n", "mass added [Msun]: 1.0534749234962311e-09\n", "relMass_added/rstep (rico) = 1.29\n", "---------\n", "r 0.5 au\n", "scale height is 0.16921692169216915\n", "mass added [Msun]: 9.767190092159958e-10\n", "relMass_added/rstep (rico) = 1.182\n", "---------\n", "r 0.51 au\n", "scale height is 0.1761926192619262\n", "mass added [Msun]: 9.268276649338427e-10\n", "relMass_added/rstep (rico) = 1.109\n", "---------\n", "r 0.52 au\n", "scale height is 0.1836183618361836\n", "mass added [Msun]: 9.575300306459369e-10\n", "relMass_added/rstep (rico) = 1.133\n", "---------\n", "r 0.53 au\n", "scale height is 0.18856885688568858\n", "mass added [Msun]: 9.421788477898898e-10\n", "relMass_added/rstep (rico) = 1.102\n", "---------\n", "r 0.54 au\n", "scale height is 0.19306930693069305\n", "mass added [Msun]: 8.443150570825898e-10\n", "relMass_added/rstep (rico) = 0.978\n", "---------\n", "r 0.55 au\n", "scale height is 0.20012001200120014\n", "mass added [Msun]: 8.001804063714544e-10\n", "relMass_added/rstep (rico) = 0.919\n", "---------\n", "r 0.56 au\n", "scale height is 0.206045604560456\n", "mass added [Msun]: 8.404772613685778e-10\n", "relMass_added/rstep (rico) = 0.956\n", "---------\n", "r 0.57 au\n", "scale height is 0.2121212121212121\n", "mass added [Msun]: 8.232071806555249e-10\n", "relMass_added/rstep (rico) = 0.927\n", "---------\n", "r 0.58 au\n", "scale height is 0.21932193219321935\n", "mass added [Msun]: 7.003977178071481e-10\n", "relMass_added/rstep (rico) = 0.783\n", "---------\n", "r 0.59 au\n", "scale height is 0.22742274227422737\n", "mass added [Msun]: 7.829103256584014e-10\n", "relMass_added/rstep (rico) = 0.867\n", "---------\n", "r 0.6 au\n", "scale height is 0.23604860486048604\n", "mass added [Msun]: 7.253433899482246e-10\n", "relMass_added/rstep (rico) = 0.797\n", "---------\n", "r 0.61 au\n", "scale height is 0.2418241824182418\n", "mass added [Msun]: 7.234244920912188e-10\n", "relMass_added/rstep (rico) = 0.789\n", "---------\n", "r 0.62 au\n", "scale height is 0.24542454245424544\n", "mass added [Msun]: 6.332362928119422e-10\n", "relMass_added/rstep (rico) = 0.686\n", "---------\n", "r 0.63 au\n", "scale height is 0.2518001800180017\n", "mass added [Msun]: 6.965599220931364e-10\n", "relMass_added/rstep (rico) = 0.749\n", "---------\n", "r 0.64 au\n", "scale height is 0.2581008100810081\n", "mass added [Msun]: 5.948583356718244e-10\n", "relMass_added/rstep (rico) = 0.635\n", "---------\n", "r 0.65 au\n", "scale height is 0.2677767776777678\n", "mass added [Msun]: 6.428307820969715e-10\n", "relMass_added/rstep (rico) = 0.682\n", "---------\n", "r 0.66 au\n", "scale height is 0.2727272727272727\n", "mass added [Msun]: 6.082906206708655e-10\n", "relMass_added/rstep (rico) = 0.641\n", "---------\n", "r 0.67 au\n", "scale height is 0.2775277527752775\n", "mass added [Msun]: 5.718315613877538e-10\n", "relMass_added/rstep (rico) = 0.599\n", "---------\n", "r 0.68 au\n", "scale height is 0.2817281728172817\n", "mass added [Msun]: 5.641559699597302e-10\n", "relMass_added/rstep (rico) = 0.588\n", "---------\n", "r 0.69 au\n", "scale height is 0.292004200420042\n", "mass added [Msun]: 5.910205399578126e-10\n", "relMass_added/rstep (rico) = 0.612\n", "---------\n", "r 0.7 au\n", "scale height is 0.3031803180318032\n", "mass added [Msun]: 5.296158085336244e-10\n", "relMass_added/rstep (rico) = 0.545\n", "---------\n", "r 0.71 au\n", "scale height is 0.3153315331533153\n", "mass added [Msun]: 4.6053548568141256e-10\n", "relMass_added/rstep (rico) = 0.472\n", "---------\n", "r 0.72 au\n", "scale height is 0.32845784578457854\n", "mass added [Msun]: 5.02751238535542e-10\n", "relMass_added/rstep (rico) = 0.512\n", "---------\n", "r 0.73 au\n", "scale height is 0.34248424842484243\n", "mass added [Msun]: 5.718315613877538e-10\n", "relMass_added/rstep (rico) = 0.58\n", "---------\n", "r 0.74 au\n", "scale height is 0.35546054605460536\n", "mass added [Msun]: 5.334536042476361e-10\n", "relMass_added/rstep (rico) = 0.538\n", "---------\n", "r 0.75 au\n", "scale height is 0.3641614161416142\n", "mass added [Msun]: 4.3367091568333013e-10\n", "relMass_added/rstep (rico) = 0.435\n", "---------\n", "r 0.76 au\n", "scale height is 0.3769126912691269\n", "mass added [Msun]: 4.4710320068237135e-10\n", "relMass_added/rstep (rico) = 0.447\n", "---------\n", "r 0.77 au\n", "scale height is 0.39056405640564057\n", "mass added [Msun]: 4.106441413992595e-10\n", "relMass_added/rstep (rico) = 0.409\n", "---------\n", "r 0.78 au\n", "scale height is 0.39941494149414936\n", "mass added [Msun]: 4.85481157822489e-10\n", "relMass_added/rstep (rico) = 0.481\n", "---------\n", "r 0.79 au\n", "scale height is 0.4067656765676567\n", "mass added [Msun]: 4.6245438353841847e-10\n", "relMass_added/rstep (rico) = 0.456\n", "---------\n", "r 0.8 au\n", "scale height is 0.41704170417041697\n", "mass added [Msun]: 3.837795714011771e-10\n", "relMass_added/rstep (rico) = 0.377\n", "---------\n", "r 0.81 au\n", "scale height is 0.4273177317731773\n", "mass added [Msun]: 3.9145516282920064e-10\n", "relMass_added/rstep (rico) = 0.383\n", "---------\n", "r 0.82 au\n", "scale height is 0.4353435343534353\n", "mass added [Msun]: 3.9721185640021826e-10\n", "relMass_added/rstep (rico) = 0.387\n", "---------\n", "r 0.83 au\n", "scale height is 0.4459945994599461\n", "mass added [Msun]: 3.837795714011771e-10\n", "relMass_added/rstep (rico) = 0.373\n", "---------\n", "r 0.84 au\n", "scale height is 0.4522952295229522\n", "mass added [Msun]: 3.8953626497219473e-10\n", "relMass_added/rstep (rico) = 0.377\n", "---------\n", "r 0.85 au\n", "scale height is 0.45829582958295834\n", "mass added [Msun]: 3.626716949741123e-10\n", "relMass_added/rstep (rico) = 0.35\n", "---------\n", "r 0.86 au\n", "scale height is 0.4654215421542154\n", "mass added [Msun]: 3.5883389926010053e-10\n", "relMass_added/rstep (rico) = 0.345\n", "---------\n", "r 0.87 au\n", "scale height is 0.4739723972397239\n", "mass added [Msun]: 3.415638185470476e-10\n", "relMass_added/rstep (rico) = 0.327\n", "---------\n", "r 0.88 au\n", "scale height is 0.4795979597959795\n", "mass added [Msun]: 3.262126356910005e-10\n", "relMass_added/rstep (rico) = 0.311\n", "---------\n", "r 0.89 au\n", "scale height is 0.4873987398739874\n", "mass added [Msun]: 3.204559421199828e-10\n", "relMass_added/rstep (rico) = 0.305\n", "---------\n", "r 0.9 au\n", "!! At this theta direction, you are no longer in the disk or you set your maxH too small!!\n", "scale height is 0.4578457845784578\n", "mass added [Msun]: 3.089425549779475e-10\n", "relMass_added/rstep (rico) = 0.293\n", "-------------------------------------------------------------\n", "estimate of r of the accretion disk is 0.9 au\n", "Calculations full theta region:\n", "---------\n", "r 0.02 au\n", "scale height is 0.01680168016801686\n", "mass added [Msun]: 3.837795714011771e-12\n", "relMass_added/rstep (rico) = 100.0\n", "---------\n", "r 0.03 au\n", "scale height is 0.014776477647764873\n", "mass added [Msun]: 2.302677428407063e-11\n", "relMass_added/rstep (rico) = 85.714\n", "---------\n", "r 0.04 au\n", "scale height is 0.013126312631263187\n", "mass added [Msun]: 1.0362048427831777e-10\n", "relMass_added/rstep (rico) = 79.412\n", "---------\n", "r 0.05 au\n", "scale height is 0.01222622262226223\n", "mass added [Msun]: 2.552134149817827e-10\n", "relMass_added/rstep (rico) = 66.169\n", "---------\n", "r 0.06 au\n", "scale height is 0.010576057605760608\n", "mass added [Msun]: 5.02751238535542e-10\n", "relMass_added/rstep (rico) = 56.587\n", "---------\n", "r 0.07 au\n", "scale height is 0.011626162616261659\n", "mass added [Msun]: 7.52207959946307e-10\n", "relMass_added/rstep (rico) = 45.848\n", "---------\n", "r 0.08 au\n", "scale height is 0.014101410141014058\n", "mass added [Msun]: 1.0822583913513193e-09\n", "relMass_added/rstep (rico) = 39.746\n", "---------\n", "r 0.09 au\n", "scale height is 0.01590159015901597\n", "mass added [Msun]: 1.3125261341920253e-09\n", "relMass_added/rstep (rico) = 32.525\n", "---------\n", "r 0.1 au\n", "scale height is 0.01762676267626764\n", "mass added [Msun]: 1.5523883663177615e-09\n", "relMass_added/rstep (rico) = 27.782\n", "---------\n", "r 0.11 au\n", "scale height is 0.020327032703270374\n", "mass added [Msun]: 1.74427815201835e-09\n", "relMass_added/rstep (rico) = 23.79\n", "---------\n", "r 0.12 au\n", "scale height is 0.022352235223522428\n", "mass added [Msun]: 1.8901143891507975e-09\n", "relMass_added/rstep (rico) = 20.495\n", "---------\n", "r 0.13 au\n", "scale height is 0.025202520252025126\n", "mass added [Msun]: 2.0378695241402504e-09\n", "relMass_added/rstep (rico) = 18.098\n", "---------\n", "r 0.14 au\n", "scale height is 0.0285028502850285\n", "mass added [Msun]: 2.1702734762736564e-09\n", "relMass_added/rstep (rico) = 16.159\n", "---------\n", "r 0.15 au\n", "scale height is 0.03127812781278128\n", "mass added [Msun]: 2.2086514334137737e-09\n", "relMass_added/rstep (rico) = 14.123\n", "---------\n", "r 0.16 au\n", "scale height is 0.033753375337533746\n", "mass added [Msun]: 2.3141908155490972e-09\n", "relMass_added/rstep (rico) = 12.89\n", "---------\n", "r 0.17 au\n", "scale height is 0.03772877287728774\n", "mass added [Msun]: 2.3276231005481387e-09\n", "relMass_added/rstep (rico) = 11.477\n", "---------\n", "r 0.18 au\n", "scale height is 0.040654065406540686\n", "mass added [Msun]: 2.3487309769752034e-09\n", "relMass_added/rstep (rico) = 10.379\n", "---------\n", "r 0.19 au\n", "scale height is 0.04305430543054304\n", "mass added [Msun]: 2.425486891255439e-09\n", "relMass_added/rstep (rico) = 9.681\n", "---------\n", "r 0.2 au\n", "scale height is 0.04755475547554749\n", "mass added [Msun]: 2.3794333426872975e-09\n", "relMass_added/rstep (rico) = 8.673\n", "---------\n", "r 0.21 au\n", "scale height is 0.05070507050705077\n", "mass added [Msun]: 2.369838853402268e-09\n", "relMass_added/rstep (rico) = 7.951\n", "---------\n", "r 0.22 au\n", "scale height is 0.05460546054605465\n", "mass added [Msun]: 2.3621632619742445e-09\n", "relMass_added/rstep (rico) = 7.344\n", "---------\n", "r 0.23 au\n", "scale height is 0.056780678067806796\n", "mass added [Msun]: 2.3640821598312503e-09\n", "relMass_added/rstep (rico) = 6.846\n", "---------\n", "r 0.24 au\n", "scale height is 0.06023102310231026\n", "mass added [Msun]: 2.425486891255439e-09\n", "relMass_added/rstep (rico) = 6.563\n", "---------\n", "r 0.25 au\n", "scale height is 0.06405640564056403\n", "mass added [Msun]: 2.3276231005481387e-09\n", "relMass_added/rstep (rico) = 5.925\n", "---------\n", "r 0.26 au\n", "scale height is 0.06653165316531649\n", "mass added [Msun]: 2.3410553855471798e-09\n", "relMass_added/rstep (rico) = 5.624\n", "---------\n", "r 0.27 au\n", "scale height is 0.06945694569456944\n", "mass added [Msun]: 2.3218664069771213e-09\n", "relMass_added/rstep (rico) = 5.283\n", "---------\n", "r 0.28 au\n", "scale height is 0.07148214821482142\n", "mass added [Msun]: 2.2163270248417974e-09\n", "relMass_added/rstep (rico) = 4.801\n", "---------\n", "r 0.29 au\n", "scale height is 0.07485748574857484\n", "mass added [Msun]: 2.1798679655586854e-09\n", "relMass_added/rstep (rico) = 4.509\n", "---------\n", "r 0.3 au\n", "scale height is 0.07935793579357936\n", "mass added [Msun]: 2.0628151962813267e-09\n", "relMass_added/rstep (rico) = 4.092\n", "---------\n", "r 0.31 au\n", "scale height is 0.08153315331533151\n", "mass added [Msun]: 2.0877608684224034e-09\n", "relMass_added/rstep (rico) = 3.977\n", "---------\n", "r 0.32 au\n", "scale height is 0.0870087008700869\n", "mass added [Msun]: 1.9649514055740272e-09\n", "relMass_added/rstep (rico) = 3.608\n", "---------\n", "r 0.33 au\n", "scale height is 0.09173417341734175\n", "mass added [Msun]: 1.880519899865768e-09\n", "relMass_added/rstep (rico) = 3.338\n", "---------\n", "r 0.34 au\n", "scale height is 0.09623462346234614\n", "mass added [Msun]: 1.880519899865768e-09\n", "relMass_added/rstep (rico) = 3.23\n", "---------\n", "r 0.35 au\n", "scale height is 0.09818481848184821\n", "mass added [Msun]: 1.7538726413033794e-09\n", "relMass_added/rstep (rico) = 2.924\n", "---------\n", "r 0.36 au\n", "scale height is 0.10141014101410134\n", "mass added [Msun]: 1.6790356248801497e-09\n", "relMass_added/rstep (rico) = 2.723\n", "---------\n", "r 0.37 au\n", "scale height is 0.10471047104710465\n", "mass added [Msun]: 1.6790356248801497e-09\n", "relMass_added/rstep (rico) = 2.651\n", "---------\n", "r 0.38 au\n", "scale height is 0.10876087608760876\n", "mass added [Msun]: 1.5197671027486611e-09\n", "relMass_added/rstep (rico) = 2.343\n", "---------\n", "r 0.39 au\n", "scale height is 0.11326132613261328\n", "mass added [Msun]: 1.561982855602791e-09\n", "relMass_added/rstep (rico) = 2.352\n", "---------\n", "r 0.4 au\n", "scale height is 0.11941194119411941\n", "mass added [Msun]: 1.4314978013263903e-09\n", "relMass_added/rstep (rico) = 2.11\n", "---------\n", "r 0.41 au\n", "scale height is 0.12473747374737477\n", "mass added [Msun]: 1.4161466184703432e-09\n", "relMass_added/rstep (rico) = 2.045\n", "---------\n", "r 0.42 au\n", "scale height is 0.13088808880888092\n", "mass added [Msun]: 1.3317151127620844e-09\n", "relMass_added/rstep (rico) = 1.886\n", "---------\n", "r 0.43 au\n", "scale height is 0.13576357635763578\n", "mass added [Msun]: 1.287580462050949e-09\n", "relMass_added/rstep (rico) = 1.791\n", "---------\n", "r 0.44 au\n", "scale height is 0.1425142514251425\n", "mass added [Msun]: 1.2300135263407724e-09\n", "relMass_added/rstep (rico) = 1.682\n", "---------\n", "r 0.45 au\n", "scale height is 0.14926492649264927\n", "mass added [Msun]: 1.222337934912749e-09\n", "relMass_added/rstep (rico) = 1.644\n", "---------\n", "r 0.46 au\n", "scale height is 0.15286528652865283\n", "mass added [Msun]: 1.1417442249185018e-09\n", "relMass_added/rstep (rico) = 1.513\n", "---------\n", "r 0.47 au\n", "scale height is 0.1563156315631563\n", "mass added [Msun]: 1.070745004209284e-09\n", "relMass_added/rstep (rico) = 1.399\n", "---------\n", "r 0.48 au\n", "scale height is 0.16201620162016203\n", "mass added [Msun]: 1.0457993320682075e-09\n", "relMass_added/rstep (rico) = 1.348\n", "---------\n", "r 0.49 au\n", "scale height is 0.16771677167716775\n", "mass added [Msun]: 1.0457993320682075e-09\n", "relMass_added/rstep (rico) = 1.33\n", "---------\n", "r 0.5 au\n", "scale height is 0.17259225922592258\n", "mass added [Msun]: 9.038008906497721e-10\n", "relMass_added/rstep (rico) = 1.136\n", "---------\n", "r 0.51 au\n", "scale height is 0.17649264926492647\n", "mass added [Msun]: 9.306654606478546e-10\n", "relMass_added/rstep (rico) = 1.157\n", "---------\n", "r 0.52 au\n", "scale height is 0.181968196819682\n", "mass added [Msun]: 8.884497077937251e-10\n", "relMass_added/rstep (rico) = 1.092\n", "---------\n", "r 0.53 au\n", "scale height is 0.18804380438043808\n", "mass added [Msun]: 8.308827720835484e-10\n", "relMass_added/rstep (rico) = 1.011\n", "---------\n", "r 0.54 au\n", "scale height is 0.19306930693069305\n", "mass added [Msun]: 7.829103256584014e-10\n", "relMass_added/rstep (rico) = 0.944\n", "---------\n", "r 0.55 au\n", "scale height is 0.1970447044704471\n", "mass added [Msun]: 7.579646535173248e-10\n", "relMass_added/rstep (rico) = 0.905\n", "---------\n", "r 0.56 au\n", "scale height is 0.20124512451245125\n", "mass added [Msun]: 6.831276370940952e-10\n", "relMass_added/rstep (rico) = 0.809\n", "---------\n", "r 0.57 au\n", "scale height is 0.2069456945694569\n", "mass added [Msun]: 6.217229056699068e-10\n", "relMass_added/rstep (rico) = 0.731\n", "---------\n", "r 0.58 au\n", "scale height is 0.2113711371137113\n", "mass added [Msun]: 6.485874756679892e-10\n", "relMass_added/rstep (rico) = 0.757\n", "---------\n", "r 0.59 au\n", "scale height is 0.2166966696669666\n", "mass added [Msun]: 5.468858892466773e-10\n", "relMass_added/rstep (rico) = 0.634\n", "---------\n", "r 0.6 au\n", "scale height is 0.2275727572757276\n", "mass added [Msun]: 5.35372502104642e-10\n", "relMass_added/rstep (rico) = 0.617\n", "---------\n", "r 0.61 au\n", "scale height is 0.2357485748574857\n", "mass added [Msun]: 5.00832340678536e-10\n", "relMass_added/rstep (rico) = 0.574\n", "---------\n", "r 0.62 au\n", "scale height is 0.241074107410741\n", "mass added [Msun]: 4.6437328139542433e-10\n", "relMass_added/rstep (rico) = 0.529\n", "---------\n", "r 0.63 au\n", "scale height is 0.2487998799879988\n", "mass added [Msun]: 4.240764263983007e-10\n", "relMass_added/rstep (rico) = 0.481\n", "---------\n", "r 0.64 au\n", "scale height is 0.2587758775877588\n", "mass added [Msun]: 4.3942760925434776e-10\n", "relMass_added/rstep (rico) = 0.496\n", "---------\n", "r 0.65 au\n", "scale height is 0.2688268826882688\n", "mass added [Msun]: 3.185370442629769e-10\n", "relMass_added/rstep (rico) = 0.358\n", "---------\n", "r 0.66 au\n", "scale height is 0.27955295529552954\n", "mass added [Msun]: 3.6075279711710644e-10\n", "relMass_added/rstep (rico) = 0.404\n", "---------\n", "r 0.67 au\n", "scale height is 0.2903540354035403\n", "mass added [Msun]: 3.185370442629769e-10\n", "relMass_added/rstep (rico) = 0.356\n", "---------\n", "r 0.68 au\n", "scale height is 0.3020552055205521\n", "mass added [Msun]: 3.089425549779475e-10\n", "relMass_added/rstep (rico) = 0.344\n", "---------\n", "r 0.69 au\n", "scale height is 0.31113111311131114\n", "mass added [Msun]: 2.955102699789063e-10\n", "relMass_added/rstep (rico) = 0.328\n", "---------\n", "r 0.7 au\n", "scale height is 0.3185568556855686\n", "mass added [Msun]: 2.3410553855471805e-10\n", "relMass_added/rstep (rico) = 0.259\n", "-------------------------------------------------------------\n", "estimate of r of the accretion disk is 0.7000000000000002 au\n", "Calculations for different theta regions:\n", "---------\n", "r 0.02 au\n", "scale height is 0.013651365136513776\n", "mass added [Msun]: 0.0\n", "relMass_added/rstep (rico) = 1.0\n", "---------\n", "r 0.03 au\n", "scale height is 0.010801080108010685\n", "mass added [Msun]: 5.756693571017657e-12\n", "relMass_added/rstep (rico) = 100.0\n", "---------\n", "r 0.04 au\n", "scale height is 0.009300930093009484\n", "mass added [Msun]: 2.302677428407063e-11\n", "relMass_added/rstep (rico) = 80.0\n", "---------\n", "r 0.05 au\n", "scale height is 0.00885088508850881\n", "mass added [Msun]: 4.6053548568141254e-11\n", "relMass_added/rstep (rico) = 61.538\n", "---------\n", "r 0.06 au\n", "scale height is 0.008700870087008846\n", "mass added [Msun]: 8.635040356526482e-11\n", "relMass_added/rstep (rico) = 53.571\n", "---------\n", "r 0.07 au\n", "scale height is 0.00990099009900986\n", "mass added [Msun]: 1.285661564193943e-10\n", "relMass_added/rstep (rico) = 44.371\n", "---------\n", "r 0.08 au\n", "scale height is 0.011101110111011003\n", "mass added [Msun]: 1.49674032846459e-10\n", "relMass_added/rstep (rico) = 34.061\n", "---------\n", "r 0.09 au\n", "scale height is 0.012601260126012596\n", "mass added [Msun]: 1.6310631784550021e-10\n", "relMass_added/rstep (rico) = 27.07\n", "---------\n", "r 0.1 au\n", "scale height is 0.014551455145514602\n", "mass added [Msun]: 1.6694411355951196e-10\n", "relMass_added/rstep (rico) = 21.696\n", "---------\n", "r 0.11 au\n", "scale height is 0.016801680168016794\n", "mass added [Msun]: 1.9764647927160615e-10\n", "relMass_added/rstep (rico) = 20.437\n", "---------\n", "r 0.12 au\n", "scale height is 0.018751875187518932\n", "mass added [Msun]: 1.938086835575944e-10\n", "relMass_added/rstep (rico) = 16.694\n", "---------\n", "r 0.13 au\n", "scale height is 0.02160216021602163\n", "mass added [Msun]: 2.2067325355567684e-10\n", "relMass_added/rstep (rico) = 15.972\n", "---------\n", "r 0.14 au\n", "scale height is 0.02445244524452446\n", "mass added [Msun]: 2.2259215141268272e-10\n", "relMass_added/rstep (rico) = 13.876\n", "---------\n", "r 0.15 au\n", "scale height is 0.02685268526852688\n", "mass added [Msun]: 2.2259215141268272e-10\n", "relMass_added/rstep (rico) = 12.185\n", "---------\n", "r 0.16 au\n", "scale height is 0.029852985298529804\n", "mass added [Msun]: 2.475378235537592e-10\n", "relMass_added/rstep (rico) = 11.933\n", "---------\n", "r 0.17 au\n", "scale height is 0.03270327032703276\n", "mass added [Msun]: 2.417811299827416e-10\n", "relMass_added/rstep (rico) = 10.439\n", "---------\n", "r 0.18 au\n", "scale height is 0.036003600360036005\n", "mass added [Msun]: 2.3218664069771216e-10\n", "relMass_added/rstep (rico) = 9.111\n", "---------\n", "r 0.19 au\n", "scale height is 0.039903990399039885\n", "mass added [Msun]: 2.456189256967534e-10\n", "relMass_added/rstep (rico) = 8.791\n", "---------\n", "r 0.2 au\n", "scale height is 0.04455445544554463\n", "mass added [Msun]: 2.475378235537592e-10\n", "relMass_added/rstep (rico) = 8.139\n", "---------\n", "r 0.21 au\n", "scale height is 0.04950495049504943\n", "mass added [Msun]: 2.417811299827416e-10\n", "relMass_added/rstep (rico) = 7.364\n", "---------\n", "r 0.22 au\n", "scale height is 0.053255325532553216\n", "mass added [Msun]: 2.5905121069579446e-10\n", "relMass_added/rstep (rico) = 7.313\n", "---------\n", "r 0.23 au\n", "scale height is 0.05520552055205509\n", "mass added [Msun]: 2.5905121069579446e-10\n", "relMass_added/rstep (rico) = 6.815\n", "---------\n", "r 0.24 au\n", "scale height is 0.058505850585058465\n", "mass added [Msun]: 2.609701085528004e-10\n", "relMass_added/rstep (rico) = 6.424\n", "---------\n", "r 0.25 au\n", "scale height is 0.06180618061806171\n", "mass added [Msun]: 2.648079042668122e-10\n", "relMass_added/rstep (rico) = 6.12\n", "---------\n", "r 0.26 au\n", "scale height is 0.06600660066006604\n", "mass added [Msun]: 2.628890064098063e-10\n", "relMass_added/rstep (rico) = 5.727\n", "---------\n", "r 0.27 au\n", "scale height is 0.0694569456945695\n", "mass added [Msun]: 2.5329451712477684e-10\n", "relMass_added/rstep (rico) = 5.23\n", "---------\n", "r 0.28 au\n", "scale height is 0.07455745574557453\n", "mass added [Msun]: 2.456189256967534e-10\n", "relMass_added/rstep (rico) = 4.827\n", "---------\n", "r 0.29 au\n", "scale height is 0.07785778577857803\n", "mass added [Msun]: 2.5329451712477684e-10\n", "relMass_added/rstep (rico) = 4.741\n", "---------\n", "r 0.3 au\n", "scale height is 0.08055805580558051\n", "mass added [Msun]: 2.2642994712669446e-10\n", "relMass_added/rstep (rico) = 4.066\n", "---------\n", "r 0.31 au\n", "scale height is 0.08370837083708366\n", "mass added [Msun]: 2.3218664069771216e-10\n", "relMass_added/rstep (rico) = 4.003\n", "---------\n", "r 0.32 au\n", "scale height is 0.0862586258625863\n", "mass added [Msun]: 2.2067325355567684e-10\n", "relMass_added/rstep (rico) = 3.665\n", "---------\n", "r 0.33 au\n", "scale height is 0.09015901590159005\n", "mass added [Msun]: 1.9188978570058852e-10\n", "relMass_added/rstep (rico) = 3.088\n", "---------\n", "r 0.34 au\n", "scale height is 0.09420942094209415\n", "mass added [Msun]: 2.1683545784166507e-10\n", "relMass_added/rstep (rico) = 3.372\n", "---------\n", "r 0.35 au\n", "scale height is 0.09780978097809784\n", "mass added [Msun]: 1.938086835575944e-10\n", "relMass_added/rstep (rico) = 2.926\n", "---------\n", "r 0.36 au\n", "scale height is 0.10201020102010191\n", "mass added [Msun]: 1.6886301141651792e-10\n", "relMass_added/rstep (rico) = 2.486\n", "---------\n", "r 0.37 au\n", "scale height is 0.10696069606960684\n", "mass added [Msun]: 1.8997088784358264e-10\n", "relMass_added/rstep (rico) = 2.721\n", "---------\n", "r 0.38 au\n", "scale height is 0.11191119111911191\n", "mass added [Msun]: 1.6694411355951196e-10\n", "relMass_added/rstep (rico) = 2.335\n", "---------\n", "r 0.39 au\n", "scale height is 0.11671167116711674\n", "mass added [Msun]: 1.5926852213148845e-10\n", "relMass_added/rstep (rico) = 2.179\n", "---------\n", "r 0.4 au\n", "scale height is 0.1203120312031203\n", "mass added [Msun]: 1.707819092735238e-10\n", "relMass_added/rstep (rico) = 2.283\n", "---------\n", "r 0.41 au\n", "scale height is 0.12301230123012304\n", "mass added [Msun]: 1.3432284999041193e-10\n", "relMass_added/rstep (rico) = 1.764\n", "---------\n", "r 0.42 au\n", "scale height is 0.12631263126312628\n", "mass added [Msun]: 1.362417478474178e-10\n", "relMass_added/rstep (rico) = 1.758\n", "---------\n", "r 0.43 au\n", "scale height is 0.13381338133813372\n", "mass added [Msun]: 1.4583623713244726e-10\n", "relMass_added/rstep (rico) = 1.847\n", "---------\n", "r 0.44 au\n", "scale height is 0.14161416141614175\n", "mass added [Msun]: 1.2280946284837665e-10\n", "relMass_added/rstep (rico) = 1.531\n", "---------\n", "r 0.45 au\n", "scale height is 0.14806480648064788\n", "mass added [Msun]: 1.419984414184355e-10\n", "relMass_added/rstep (rico) = 1.74\n", "---------\n", "r 0.46 au\n", "scale height is 0.1558655865586559\n", "mass added [Msun]: 1.0170158642131189e-10\n", "relMass_added/rstep (rico) = 1.231\n", "---------\n", "r 0.47 au\n", "scale height is 0.16291629162916293\n", "mass added [Msun]: 1.1897166713436486e-10\n", "relMass_added/rstep (rico) = 1.419\n", "---------\n", "r 0.48 au\n", "scale height is 0.16696669666966704\n", "mass added [Msun]: 1.2280946284837665e-10\n", "relMass_added/rstep (rico) = 1.444\n", "---------\n", "r 0.49 au\n", "scale height is 0.16876687668766868\n", "mass added [Msun]: 1.0745827999232958e-10\n", "relMass_added/rstep (rico) = 1.248\n", "---------\n", "r 0.5 au\n", "scale height is 0.17056705670567046\n", "mass added [Msun]: 1.0170158642131189e-10\n", "relMass_added/rstep (rico) = 1.167\n", "---------\n", "r 0.51 au\n", "scale height is 0.17221722172217227\n", "mass added [Msun]: 1.0745827999232958e-10\n", "relMass_added/rstep (rico) = 1.218\n", "---------\n", "r 0.52 au\n", "scale height is 0.17551755175517553\n", "mass added [Msun]: 1.1705276927735897e-10\n", "relMass_added/rstep (rico) = 1.31\n", "---------\n", "r 0.53 au\n", "scale height is 0.17581758175817572\n", "mass added [Msun]: 1.1321497356334722e-10\n", "relMass_added/rstep (rico) = 1.251\n", "---------\n", "r 0.54 au\n", "scale height is 0.1779177917791778\n", "mass added [Msun]: 1.0553938213532366e-10\n", "relMass_added/rstep (rico) = 1.153\n", "---------\n", "r 0.55 au\n", "scale height is 0.18166816681668146\n", "mass added [Msun]: 1.285661564193943e-10\n", "relMass_added/rstep (rico) = 1.385\n", "---------\n", "r 0.56 au\n", "scale height is 0.18706870687068694\n", "mass added [Msun]: 1.1897166713436486e-10\n", "relMass_added/rstep (rico) = 1.265\n", "---------\n", "r 0.57 au\n", "scale height is 0.19396939693969387\n", "mass added [Msun]: 9.210709713628251e-11\n", "relMass_added/rstep (rico) = 0.97\n", "---------\n", "r 0.58 au\n", "scale height is 0.20177017701770175\n", "mass added [Msun]: 1.1321497356334722e-10\n", "relMass_added/rstep (rico) = 1.178\n", "---------\n", "r 0.59 au\n", "scale height is 0.21002100210020994\n", "mass added [Msun]: 9.402599499328838e-11\n", "relMass_added/rstep (rico) = 0.969\n", "---------\n", "r 0.6 au\n", "scale height is 0.21677167716771678\n", "mass added [Msun]: 1.0745827999232958e-10\n", "relMass_added/rstep (rico) = 1.095\n", "---------\n", "r 0.61 au\n", "scale height is 0.22262226222622267\n", "mass added [Msun]: 7.867481213724131e-11\n", "relMass_added/rstep (rico) = 0.795\n", "---------\n", "r 0.62 au\n", "scale height is 0.2296729672967297\n", "mass added [Msun]: 6.908032285221189e-11\n", "relMass_added/rstep (rico) = 0.694\n", "---------\n", "r 0.63 au\n", "scale height is 0.23552355235523545\n", "mass added [Msun]: 7.483701642322955e-11\n", "relMass_added/rstep (rico) = 0.746\n", "---------\n", "r 0.64 au\n", "scale height is 0.24602460246024593\n", "mass added [Msun]: 6.7161424995206e-11\n", "relMass_added/rstep (rico) = 0.665\n", "---------\n", "r 0.65 au\n", "scale height is 0.2554755475547554\n", "mass added [Msun]: 4.413465071113537e-11\n", "relMass_added/rstep (rico) = 0.435\n", "---------\n", "r 0.66 au\n", "scale height is 0.26432643264326433\n", "mass added [Msun]: 3.837795714011771e-11\n", "relMass_added/rstep (rico) = 0.377\n", "---------\n", "r 0.67 au\n", "scale height is 0.26957695769576956\n", "mass added [Msun]: 4.2215752854129486e-11\n", "relMass_added/rstep (rico) = 0.413\n", "---------\n", "r 0.68 au\n", "scale height is 0.2764776477647764\n", "mass added [Msun]: 3.837795714011771e-11\n", "relMass_added/rstep (rico) = 0.374\n", "---------\n", "r 0.69 au\n", "scale height is 0.2838283828382837\n", "mass added [Msun]: 2.6864569998082404e-11\n", "relMass_added/rstep (rico) = 0.261\n", "-------------------------------------------------------------\n", "estimate of r of the accretion disk is 0.69 au\n", "---------\n", "r 0.02 au\n", "scale height is 0.019201920192019214\n", "mass added [Msun]: 1.9188978570058856e-12\n", "relMass_added/rstep (rico) = 100.0\n", "---------\n", "r 0.03 au\n", "scale height is 0.01770177017701775\n", "mass added [Msun]: 3.837795714011771e-12\n", "relMass_added/rstep (rico) = 66.667\n", "---------\n", "r 0.04 au\n", "scale height is 0.015301530153015202\n", "mass added [Msun]: 1.5351182856047085e-11\n", "relMass_added/rstep (rico) = 72.727\n", "---------\n", "r 0.05 au\n", "scale height is 0.010801080108010815\n", "mass added [Msun]: 3.645905928311183e-11\n", "relMass_added/rstep (rico) = 63.333\n", "---------\n", "r 0.06 au\n", "scale height is 0.009600960096009671\n", "mass added [Msun]: 7.867481213724131e-11\n", "relMass_added/rstep (rico) = 57.746\n", "---------\n", "r 0.07 au\n", "scale height is 0.011551155115511415\n", "mass added [Msun]: 1.2472836070538253e-10\n", "relMass_added/rstep (rico) = 47.794\n", "---------\n", "r 0.08 au\n", "scale height is 0.013651365136513646\n", "mass added [Msun]: 1.938086835575944e-10\n", "relMass_added/rstep (rico) = 42.616\n", "---------\n", "r 0.09 au\n", "scale height is 0.016051605160516062\n", "mass added [Msun]: 2.686456999808239e-10\n", "relMass_added/rstep (rico) = 37.135\n", "---------\n", "r 0.1 au\n", "scale height is 0.019201920192019214\n", "mass added [Msun]: 3.6075279711710644e-10\n", "relMass_added/rstep (rico) = 33.274\n", "---------\n", "r 0.11 au\n", "scale height is 0.022052205220522043\n", "mass added [Msun]: 4.106441413992595e-10\n", "relMass_added/rstep (rico) = 27.471\n", "---------\n", "r 0.12 au\n", "scale height is 0.0250525052505251\n", "mass added [Msun]: 4.70129974966442e-10\n", "relMass_added/rstep (rico) = 23.926\n", "---------\n", "r 0.13 au\n", "scale height is 0.028502850285028566\n", "mass added [Msun]: 5.334536042476361e-10\n", "relMass_added/rstep (rico) = 21.352\n", "---------\n", "r 0.14 au\n", "scale height is 0.03285328532853286\n", "mass added [Msun]: 5.622370721027243e-10\n", "relMass_added/rstep (rico) = 18.37\n", "---------\n", "r 0.15 au\n", "scale height is 0.03570357035703582\n", "mass added [Msun]: 6.044528249568538e-10\n", "relMass_added/rstep (rico) = 16.492\n", "---------\n", "r 0.16 au\n", "scale height is 0.040354035403540434\n", "mass added [Msun]: 6.581819649530187e-10\n", "relMass_added/rstep (rico) = 15.224\n", "---------\n", "r 0.17 au\n", "scale height is 0.04275427542754272\n", "mass added [Msun]: 6.581819649530187e-10\n", "relMass_added/rstep (rico) = 13.213\n", "---------\n", "r 0.18 au\n", "scale height is 0.04620462046204619\n", "mass added [Msun]: 7.119111049491835e-10\n", "relMass_added/rstep (rico) = 12.504\n", "---------\n", "r 0.19 au\n", "scale height is 0.051155115511551115\n", "mass added [Msun]: 7.38775674947266e-10\n", "relMass_added/rstep (rico) = 11.486\n", "---------\n", "r 0.2 au\n", "scale height is 0.053855385538553985\n", "mass added [Msun]: 7.598835513743306e-10\n", "relMass_added/rstep (rico) = 10.566\n", "---------\n", "r 0.21 au\n", "scale height is 0.057155715571557096\n", "mass added [Msun]: 7.52207959946307e-10\n", "relMass_added/rstep (rico) = 9.469\n", "---------\n", "r 0.22 au\n", "scale height is 0.05970597059705974\n", "mass added [Msun]: 7.426134706612776e-10\n", "relMass_added/rstep (rico) = 8.549\n", "---------\n", "r 0.23 au\n", "scale height is 0.06390639063906393\n", "mass added [Msun]: 7.598835513743306e-10\n", "relMass_added/rstep (rico) = 8.044\n", "---------\n", "r 0.24 au\n", "scale height is 0.06675667566756664\n", "mass added [Msun]: 8.251260785125309e-10\n", "relMass_added/rstep (rico) = 8.033\n", "---------\n", "r 0.25 au\n", "scale height is 0.07110711071107119\n", "mass added [Msun]: 7.771536320873835e-10\n", "relMass_added/rstep (rico) = 7.034\n", "---------\n", "r 0.26 au\n", "scale height is 0.07530753075307539\n", "mass added [Msun]: 7.963426106574424e-10\n", "relMass_added/rstep (rico) = 6.723\n", "---------\n", "r 0.27 au\n", "scale height is 0.08010801080107996\n", "mass added [Msun]: 7.963426106574424e-10\n", "relMass_added/rstep (rico) = 6.299\n", "---------\n", "r 0.28 au\n", "scale height is 0.08445844584458452\n", "mass added [Msun]: 7.944237128004365e-10\n", "relMass_added/rstep (rico) = 5.913\n", "---------\n", "r 0.29 au\n", "scale height is 0.08895889588958904\n", "mass added [Msun]: 7.886670192294189e-10\n", "relMass_added/rstep (rico) = 5.544\n", "---------\n", "r 0.3 au\n", "scale height is 0.09375937593759374\n", "mass added [Msun]: 7.426134706612776e-10\n", "relMass_added/rstep (rico) = 4.962\n", "---------\n", "r 0.31 au\n", "scale height is 0.09900990099009886\n", "mass added [Msun]: 7.809914278013955e-10\n", "relMass_added/rstep (rico) = 4.959\n", "---------\n", "r 0.32 au\n", "scale height is 0.10456045604560468\n", "mass added [Msun]: 7.598835513743306e-10\n", "relMass_added/rstep (rico) = 4.603\n", "---------\n", "r 0.33 au\n", "scale height is 0.10861086108610866\n", "mass added [Msun]: 7.138300028061893e-10\n", "relMass_added/rstep (rico) = 4.145\n", "---------\n", "r 0.34 au\n", "scale height is 0.11371137113711381\n", "mass added [Msun]: 7.675591428023542e-10\n", "relMass_added/rstep (rico) = 4.267\n", "---------\n", "r 0.35 au\n", "scale height is 0.11926192619261924\n", "mass added [Msun]: 6.773709435230776e-10\n", "relMass_added/rstep (rico) = 3.629\n", "---------\n", "r 0.36 au\n", "scale height is 0.12436243624362428\n", "mass added [Msun]: 6.927221263791246e-10\n", "relMass_added/rstep (rico) = 3.578\n", "---------\n", "r 0.37 au\n", "scale height is 0.13141314131413143\n", "mass added [Msun]: 6.735331478090657e-10\n", "relMass_added/rstep (rico) = 3.362\n", "---------\n", "r 0.38 au\n", "scale height is 0.13636363636363624\n", "mass added [Msun]: 6.485874756679892e-10\n", "relMass_added/rstep (rico) = 3.136\n", "---------\n", "r 0.39 au\n", "scale height is 0.14206420642064202\n", "mass added [Msun]: 6.735331478090657e-10\n", "relMass_added/rstep (rico) = 3.154\n", "---------\n", "r 0.4 au\n", "scale height is 0.1474647464746475\n", "mass added [Msun]: 5.756693571017656e-10\n", "relMass_added/rstep (rico) = 2.625\n", "---------\n", "r 0.41 au\n", "scale height is 0.1534653465346535\n", "mass added [Msun]: 6.082906206708655e-10\n", "relMass_added/rstep (rico) = 2.699\n", "---------\n", "r 0.42 au\n", "scale height is 0.15976597659765965\n", "mass added [Msun]: 6.082906206708655e-10\n", "relMass_added/rstep (rico) = 2.628\n", "---------\n", "r 0.43 au\n", "scale height is 0.16591659165916584\n", "mass added [Msun]: 5.392102978186538e-10\n", "relMass_added/rstep (rico) = 2.276\n", "---------\n", "r 0.44 au\n", "scale height is 0.17266726672667268\n", "mass added [Msun]: 5.468858892466773e-10\n", "relMass_added/rstep (rico) = 2.257\n", "---------\n", "r 0.45 au\n", "scale height is 0.18076807680768064\n", "mass added [Msun]: 5.123457278205714e-10\n", "relMass_added/rstep (rico) = 2.07\n", "---------\n", "r 0.46 au\n", "scale height is 0.18736873687368738\n", "mass added [Msun]: 5.430480935326655e-10\n", "relMass_added/rstep (rico) = 2.147\n", "---------\n", "r 0.47 au\n", "scale height is 0.19561956195619556\n", "mass added [Msun]: 4.3558981354033604e-10\n", "relMass_added/rstep (rico) = 1.693\n", "---------\n", "r 0.48 au\n", "scale height is 0.2049204920492049\n", "mass added [Msun]: 4.3942760925434776e-10\n", "relMass_added/rstep (rico) = 1.68\n", "---------\n", "r 0.49 au\n", "scale height is 0.2133213321332133\n", "mass added [Msun]: 4.662921792524302e-10\n", "relMass_added/rstep (rico) = 1.751\n", "---------\n", "r 0.5 au\n", "scale height is 0.22202220222022204\n", "mass added [Msun]: 4.106441413992595e-10\n", "relMass_added/rstep (rico) = 1.519\n", "---------\n", "r 0.51 au\n", "scale height is 0.22907290729072904\n", "mass added [Msun]: 3.6842838854513003e-10\n", "relMass_added/rstep (rico) = 1.344\n", "---------\n", "r 0.52 au\n", "scale height is 0.23507350735073515\n", "mass added [Msun]: 4.0296854997123594e-10\n", "relMass_added/rstep (rico) = 1.449\n", "---------\n", "r 0.53 au\n", "scale height is 0.2403240324032403\n", "mass added [Msun]: 3.262126356910005e-10\n", "relMass_added/rstep (rico) = 1.159\n", "---------\n", "r 0.54 au\n", "scale height is 0.2475247524752474\n", "mass added [Msun]: 3.204559421199828e-10\n", "relMass_added/rstep (rico) = 1.126\n", "---------\n", "r 0.55 au\n", "scale height is 0.2568256825682569\n", "mass added [Msun]: 3.3005043140501225e-10\n", "relMass_added/rstep (rico) = 1.146\n", "---------\n", "r 0.56 au\n", "scale height is 0.26537653765376523\n", "mass added [Msun]: 2.571323128387886e-10\n", "relMass_added/rstep (rico) = 0.885\n", "---------\n", "r 0.57 au\n", "scale height is 0.2736273627362735\n", "mass added [Msun]: 2.897535764078886e-10\n", "relMass_added/rstep (rico) = 0.988\n", "---------\n", "r 0.58 au\n", "scale height is 0.28172817281728174\n", "mass added [Msun]: 2.686456999808239e-10\n", "relMass_added/rstep (rico) = 0.907\n", "---------\n", "r 0.59 au\n", "scale height is 0.289078907890789\n", "mass added [Msun]: 2.2834884498370035e-10\n", "relMass_added/rstep (rico) = 0.765\n", "---------\n", "r 0.6 au\n", "scale height is 0.2938793879387938\n", "mass added [Msun]: 2.2642994712669446e-10\n", "relMass_added/rstep (rico) = 0.753\n", "---------\n", "r 0.61 au\n", "scale height is 0.29987998799880006\n", "mass added [Msun]: 2.2259215141268272e-10\n", "relMass_added/rstep (rico) = 0.735\n", "---------\n", "r 0.62 au\n", "scale height is 0.30333033303330337\n", "mass added [Msun]: 2.0148427498561797e-10\n", "relMass_added/rstep (rico) = 0.661\n", "---------\n", "r 0.63 au\n", "scale height is 0.3102310231023102\n", "mass added [Msun]: 2.1107876427064736e-10\n", "relMass_added/rstep (rico) = 0.688\n", "---------\n", "r 0.64 au\n", "scale height is 0.3156315631563157\n", "mass added [Msun]: 1.8997088784358264e-10\n", "relMass_added/rstep (rico) = 0.615\n", "---------\n", "r 0.65 au\n", "scale height is 0.32073207320732056\n", "mass added [Msun]: 1.4583623713244726e-10\n", "relMass_added/rstep (rico) = 0.47\n", "---------\n", "r 0.66 au\n", "scale height is 0.3249324932493249\n", "mass added [Msun]: 1.6118741998849433e-10\n", "relMass_added/rstep (rico) = 0.517\n", "---------\n", "r 0.67 au\n", "scale height is 0.3286828682868287\n", "mass added [Msun]: 1.4775513498945312e-10\n", "relMass_added/rstep (rico) = 0.471\n", "---------\n", "r 0.68 au\n", "scale height is 0.3313831383138314\n", "mass added [Msun]: 1.419984414184355e-10\n", "relMass_added/rstep (rico) = 0.451\n", "---------\n", "r 0.69 au\n", "scale height is 0.3358835883588359\n", "mass added [Msun]: 1.49674032846459e-10\n", "relMass_added/rstep (rico) = 0.473\n", "---------\n", "r 0.7 au\n", "scale height is 0.3393339333933393\n", "mass added [Msun]: 1.1513387142035309e-10\n", "relMass_added/rstep (rico) = 0.363\n", "---------\n", "r 0.71 au\n", "scale height is 0.34233423342334235\n", "mass added [Msun]: 1.1513387142035309e-10\n", "relMass_added/rstep (rico) = 0.361\n", "---------\n", "r 0.72 au\n", "scale height is 0.34683468346834684\n", "mass added [Msun]: 1.0170158642131189e-10\n", "relMass_added/rstep (rico) = 0.318\n", "---------\n", "r 0.73 au\n", "scale height is 0.3510351035103509\n", "mass added [Msun]: 9.402599499328838e-11\n", "relMass_added/rstep (rico) = 0.293\n", "-------------------------------------------------------------\n", "estimate of r of the accretion disk is 0.73 au\n", "---------\n", "r 0.02 au\n", "scale height is 0.02010201020102017\n", "mass added [Msun]: 0.0\n", "relMass_added/rstep (rico) = 1.0\n", "---------\n", "r 0.03 au\n", "scale height is 0.018451845184518482\n", "mass added [Msun]: 3.837795714011771e-12\n", "relMass_added/rstep (rico) = 100.0\n", "---------\n", "r 0.04 au\n", "scale height is 0.01665166516651657\n", "mass added [Msun]: 1.5351182856047085e-11\n", "relMass_added/rstep (rico) = 80.0\n", "---------\n", "r 0.05 au\n", "scale height is 0.01560156015601565\n", "mass added [Msun]: 2.1107876427064743e-11\n", "relMass_added/rstep (rico) = 52.381\n", "---------\n", "r 0.06 au\n", "scale height is 0.01665166516651657\n", "mass added [Msun]: 7.291811856622365e-11\n", "relMass_added/rstep (rico) = 64.407\n", "---------\n", "r 0.07 au\n", "scale height is 0.018151815181518163\n", "mass added [Msun]: 1.49674032846459e-10\n", "relMass_added/rstep (rico) = 56.934\n", "---------\n", "r 0.08 au\n", "scale height is 0.020102010201020037\n", "mass added [Msun]: 2.648079042668122e-10\n", "relMass_added/rstep (rico) = 50.182\n", "---------\n", "r 0.09 au\n", "scale height is 0.022202220222022267\n", "mass added [Msun]: 3.8186067354417114e-10\n", "relMass_added/rstep (rico) = 41.983\n", "---------\n", "r 0.1 au\n", "scale height is 0.02475247524752491\n", "mass added [Msun]: 4.835622599654832e-10\n", "relMass_added/rstep (rico) = 34.711\n", "---------\n", "r 0.11 au\n", "scale height is 0.02760276027602761\n", "mass added [Msun]: 5.603181742457184e-10\n", "relMass_added/rstep (rico) = 28.684\n", "---------\n", "r 0.12 au\n", "scale height is 0.03045304530453044\n", "mass added [Msun]: 6.082906206708655e-10\n", "relMass_added/rstep (rico) = 23.745\n", "---------\n", "r 0.13 au\n", "scale height is 0.03255325532553254\n", "mass added [Msun]: 6.639386585240362e-10\n", "relMass_added/rstep (rico) = 20.583\n", "---------\n", "r 0.14 au\n", "scale height is 0.034203420342034226\n", "mass added [Msun]: 6.965599220931364e-10\n", "relMass_added/rstep (rico) = 17.759\n", "---------\n", "r 0.15 au\n", "scale height is 0.038103810381038106\n", "mass added [Msun]: 6.888843306651127e-10\n", "relMass_added/rstep (rico) = 14.94\n", "---------\n", "r 0.16 au\n", "scale height is 0.0397539753975398\n", "mass added [Msun]: 7.195866963772071e-10\n", "relMass_added/rstep (rico) = 13.499\n", "---------\n", "r 0.17 au\n", "scale height is 0.04230423042304231\n", "mass added [Msun]: 6.869654328081069e-10\n", "relMass_added/rstep (rico) = 11.416\n", "---------\n", "r 0.18 au\n", "scale height is 0.04575457545754577\n", "mass added [Msun]: 7.023166156641541e-10\n", "relMass_added/rstep (rico) = 10.451\n", "---------\n", "r 0.19 au\n", "scale height is 0.04830483048304828\n", "mass added [Msun]: 7.138300028061893e-10\n", "relMass_added/rstep (rico) = 9.602\n", "---------\n", "r 0.2 au\n", "scale height is 0.05145514551455144\n", "mass added [Msun]: 6.773709435230776e-10\n", "relMass_added/rstep (rico) = 8.351\n", "---------\n", "r 0.21 au\n", "scale height is 0.05400540054005408\n", "mass added [Msun]: 6.831276370940952e-10\n", "relMass_added/rstep (rico) = 7.768\n", "---------\n", "r 0.22 au\n", "scale height is 0.05760576057605764\n", "mass added [Msun]: 6.389929863829598e-10\n", "relMass_added/rstep (rico) = 6.774\n", "---------\n", "r 0.23 au\n", "scale height is 0.06090609060906088\n", "mass added [Msun]: 6.274795992409243e-10\n", "relMass_added/rstep (rico) = 6.237\n", "---------\n", "r 0.24 au\n", "scale height is 0.06255625562556257\n", "mass added [Msun]: 5.967772335288302e-10\n", "relMass_added/rstep (rico) = 5.6\n", "---------\n", "r 0.25 au\n", "scale height is 0.06525652565256518\n", "mass added [Msun]: 5.603181742457184e-10\n", "relMass_added/rstep (rico) = 4.995\n", "---------\n", "r 0.26 au\n", "scale height is 0.06810681068106814\n", "mass added [Msun]: 5.564803785317067e-10\n", "relMass_added/rstep (rico) = 4.726\n", "---------\n", "r 0.27 au\n", "scale height is 0.0715571557155716\n", "mass added [Msun]: 5.679937656737421e-10\n", "relMass_added/rstep (rico) = 4.602\n", "---------\n", "r 0.28 au\n", "scale height is 0.07395739573957402\n", "mass added [Msun]: 4.969945449645243e-10\n", "relMass_added/rstep (rico) = 3.871\n", "---------\n", "r 0.29 au\n", "scale height is 0.07680768076807672\n", "mass added [Msun]: 4.893189535365008e-10\n", "relMass_added/rstep (rico) = 3.671\n", "---------\n", "r 0.3 au\n", "scale height is 0.0793579357935795\n", "mass added [Msun]: 4.70129974966442e-10\n", "relMass_added/rstep (rico) = 3.407\n", "---------\n", "r 0.31 au\n", "scale height is 0.08145814581458133\n", "mass added [Msun]: 4.797244642514713e-10\n", "relMass_added/rstep (rico) = 3.36\n", "---------\n", "r 0.32 au\n", "scale height is 0.0841584158415842\n", "mass added [Msun]: 4.0872524354225357e-10\n", "relMass_added/rstep (rico) = 2.783\n", "---------\n", "r 0.33 au\n", "scale height is 0.08880888088808894\n", "mass added [Msun]: 4.1256303925626533e-10\n", "relMass_added/rstep (rico) = 2.732\n", "---------\n", "r 0.34 au\n", "scale height is 0.09390939093909384\n", "mass added [Msun]: 3.626716949741123e-10\n", "relMass_added/rstep (rico) = 2.345\n", "---------\n", "r 0.35 au\n", "scale height is 0.09645964596459634\n", "mass added [Msun]: 4.0872524354225357e-10\n", "relMass_added/rstep (rico) = 2.575\n", "---------\n", "r 0.36 au\n", "scale height is 0.09960996099609963\n", "mass added [Msun]: 3.262126356910005e-10\n", "relMass_added/rstep (rico) = 2.014\n", "---------\n", "r 0.37 au\n", "scale height is 0.10486048604860487\n", "mass added [Msun]: 3.4732051211806523e-10\n", "relMass_added/rstep (rico) = 2.099\n", "---------\n", "r 0.38 au\n", "scale height is 0.1098109810981098\n", "mass added [Msun]: 3.185370442629769e-10\n", "relMass_added/rstep (rico) = 1.889\n", "---------\n", "r 0.39 au\n", "scale height is 0.11386138613861378\n", "mass added [Msun]: 2.974291678359122e-10\n", "relMass_added/rstep (rico) = 1.733\n", "---------\n", "r 0.4 au\n", "scale height is 0.11881188118811883\n", "mass added [Msun]: 2.83996882836871e-10\n", "relMass_added/rstep (rico) = 1.628\n", "---------\n", "r 0.41 au\n", "scale height is 0.12736273627362732\n", "mass added [Msun]: 2.724834956948357e-10\n", "relMass_added/rstep (rico) = 1.538\n", "---------\n", "r 0.42 au\n", "scale height is 0.1308130813081308\n", "mass added [Msun]: 2.552134149817827e-10\n", "relMass_added/rstep (rico) = 1.42\n", "---------\n", "r 0.43 au\n", "scale height is 0.1344134413441345\n", "mass added [Msun]: 2.4945672141076507e-10\n", "relMass_added/rstep (rico) = 1.369\n", "---------\n", "r 0.44 au\n", "scale height is 0.13801380138013805\n", "mass added [Msun]: 2.1107876427064736e-10\n", "relMass_added/rstep (rico) = 1.145\n", "---------\n", "r 0.45 au\n", "scale height is 0.14296429642964312\n", "mass added [Msun]: 2.3218664069771216e-10\n", "relMass_added/rstep (rico) = 1.244\n", "---------\n", "r 0.46 au\n", "scale height is 0.15046504650465042\n", "mass added [Msun]: 2.0724096855663562e-10\n", "relMass_added/rstep (rico) = 1.098\n", "---------\n", "r 0.47 au\n", "scale height is 0.15871587158715886\n", "mass added [Msun]: 1.6310631784550021e-10\n", "relMass_added/rstep (rico) = 0.857\n", "---------\n", "r 0.48 au\n", "scale height is 0.16381638163816375\n", "mass added [Msun]: 1.9572758141460027e-10\n", "relMass_added/rstep (rico) = 1.018\n", "---------\n", "r 0.49 au\n", "scale height is 0.17011701170117005\n", "mass added [Msun]: 1.5734962427448256e-10\n", "relMass_added/rstep (rico) = 0.812\n", "---------\n", "r 0.5 au\n", "scale height is 0.17581758175817583\n", "mass added [Msun]: 1.3240395213340604e-10\n", "relMass_added/rstep (rico) = 0.678\n", "---------\n", "r 0.51 au\n", "scale height is 0.18136813681368127\n", "mass added [Msun]: 1.5159293070346488e-10\n", "relMass_added/rstep (rico) = 0.771\n", "---------\n", "r 0.52 au\n", "scale height is 0.1858685868586858\n", "mass added [Msun]: 1.2472836070538253e-10\n", "relMass_added/rstep (rico) = 0.63\n", "---------\n", "r 0.53 au\n", "scale height is 0.1915691569156916\n", "mass added [Msun]: 1.1897166713436486e-10\n", "relMass_added/rstep (rico) = 0.597\n", "---------\n", "r 0.54 au\n", "scale height is 0.19846984698469852\n", "mass added [Msun]: 1.0362048427831777e-10\n", "relMass_added/rstep (rico) = 0.518\n", "---------\n", "r 0.55 au\n", "scale height is 0.20747074707470742\n", "mass added [Msun]: 1.1129607570634133e-10\n", "relMass_added/rstep (rico) = 0.553\n", "---------\n", "r 0.56 au\n", "scale height is 0.2152715271527153\n", "mass added [Msun]: 8.443150570825893e-11\n", "relMass_added/rstep (rico) = 0.418\n", "---------\n", "r 0.57 au\n", "scale height is 0.2223222322232222\n", "mass added [Msun]: 7.675591428023542e-11\n", "relMass_added/rstep (rico) = 0.378\n", "---------\n", "r 0.58 au\n", "scale height is 0.22907290729072904\n", "mass added [Msun]: 9.594489285029426e-11\n", "relMass_added/rstep (rico) = 0.471\n", "---------\n", "r 0.59 au\n", "scale height is 0.23432343234323444\n", "mass added [Msun]: 6.7161424995206e-11\n", "relMass_added/rstep (rico) = 0.328\n", "---------\n", "r 0.6 au\n", "scale height is 0.24122412241224123\n", "mass added [Msun]: 5.3729139996164796e-11\n", "relMass_added/rstep (rico) = 0.262\n", "-------------------------------------------------------------\n", "estimate of r of the accretion disk is 0.6 au\n", "---------\n", "r 0.02 au\n", "scale height is 0.01560156015601552\n", "mass added [Msun]: 1.9188978570058856e-12\n", "relMass_added/rstep (rico) = 100.0\n", "---------\n", "r 0.03 au\n", "scale height is 0.012601260126012596\n", "mass added [Msun]: 7.675591428023542e-12\n", "relMass_added/rstep (rico) = 80.0\n", "---------\n", "r 0.04 au\n", "scale height is 0.012601260126012596\n", "mass added [Msun]: 3.262126356910005e-11\n", "relMass_added/rstep (rico) = 77.273\n", "---------\n", "r 0.05 au\n", "scale height is 0.01170117011701164\n", "mass added [Msun]: 1.1321497356334722e-10\n", "relMass_added/rstep (rico) = 72.84\n", "---------\n", "r 0.06 au\n", "scale height is 0.012451245124512502\n", "mass added [Msun]: 1.9764647927160615e-10\n", "relMass_added/rstep (rico) = 55.978\n", "---------\n", "r 0.07 au\n", "scale height is 0.012901290129012914\n", "mass added [Msun]: 2.648079042668122e-10\n", "relMass_added/rstep (rico) = 42.857\n", "---------\n", "r 0.08 au\n", "scale height is 0.013651365136513646\n", "mass added [Msun]: 3.4732051211806523e-10\n", "relMass_added/rstep (rico) = 35.984\n", "---------\n", "r 0.09 au\n", "scale height is 0.013951395139513965\n", "mass added [Msun]: 3.8761736711518887e-10\n", "relMass_added/rstep (rico) = 28.652\n", "---------\n", "r 0.1 au\n", "scale height is 0.01590159015901584\n", "mass added [Msun]: 4.0872524354225357e-10\n", "relMass_added/rstep (rico) = 23.203\n", "---------\n", "r 0.11 au\n", "scale height is 0.017851785178517845\n", "mass added [Msun]: 4.3558981354033604e-10\n", "relMass_added/rstep (rico) = 19.825\n", "---------\n", "r 0.12 au\n", "scale height is 0.0187518751875188\n", "mass added [Msun]: 4.509409963963831e-10\n", "relMass_added/rstep (rico) = 17.029\n", "---------\n", "r 0.13 au\n", "scale height is 0.021002100210020993\n", "mass added [Msun]: 4.4902209853937726e-10\n", "relMass_added/rstep (rico) = 14.498\n", "---------\n", "r 0.14 au\n", "scale height is 0.02340234023402354\n", "mass added [Msun]: 4.85481157822489e-10\n", "relMass_added/rstep (rico) = 13.551\n", "---------\n", "r 0.15 au\n", "scale height is 0.024902490249024873\n", "mass added [Msun]: 4.70129974966442e-10\n", "relMass_added/rstep (rico) = 11.6\n", "---------\n", "r 0.16 au\n", "scale height is 0.026702670267026916\n", "mass added [Msun]: 4.874000556794949e-10\n", "relMass_added/rstep (rico) = 10.735\n", "---------\n", "r 0.17 au\n", "scale height is 0.029552955295529617\n", "mass added [Msun]: 4.950756471075184e-10\n", "relMass_added/rstep (rico) = 9.832\n", "---------\n", "r 0.18 au\n", "scale height is 0.0313531353135314\n", "mass added [Msun]: 4.547787921103949e-10\n", "relMass_added/rstep (rico) = 8.284\n", "---------\n", "r 0.19 au\n", "scale height is 0.034053405340534\n", "mass added [Msun]: 4.720488728234478e-10\n", "relMass_added/rstep (rico) = 7.918\n", "---------\n", "r 0.2 au\n", "scale height is 0.03735373537353724\n", "mass added [Msun]: 4.4902209853937726e-10\n", "relMass_added/rstep (rico) = 7.004\n", "---------\n", "r 0.21 au\n", "scale height is 0.03945394539453934\n", "mass added [Msun]: 4.3558981354033604e-10\n", "relMass_added/rstep (rico) = 6.362\n", "---------\n", "r 0.22 au\n", "scale height is 0.043054305430543165\n", "mass added [Msun]: 4.4902209853937726e-10\n", "relMass_added/rstep (rico) = 6.155\n", "---------\n", "r 0.23 au\n", "scale height is 0.04620462046204619\n", "mass added [Msun]: 4.4134650711135367e-10\n", "relMass_added/rstep (rico) = 5.704\n", "---------\n", "r 0.24 au\n", "scale height is 0.048604860486048604\n", "mass added [Msun]: 4.547787921103949e-10\n", "relMass_added/rstep (rico) = 5.552\n", "---------\n", "r 0.25 au\n", "scale height is 0.05205520552055194\n", "mass added [Msun]: 4.2791422211231246e-10\n", "relMass_added/rstep (rico) = 4.964\n", "---------\n", "r 0.26 au\n", "scale height is 0.054755475547554805\n", "mass added [Msun]: 4.144819371132713e-10\n", "relMass_added/rstep (rico) = 4.588\n", "---------\n", "r 0.27 au\n", "scale height is 0.05880588058805878\n", "mass added [Msun]: 4.3942760925434776e-10\n", "relMass_added/rstep (rico) = 4.638\n", "---------\n", "r 0.28 au\n", "scale height is 0.06105610561056111\n", "mass added [Msun]: 3.780228778301594e-10\n", "relMass_added/rstep (rico) = 3.837\n", "---------\n", "r 0.29 au\n", "scale height is 0.06345634563456352\n", "mass added [Msun]: 3.799417756871653e-10\n", "relMass_added/rstep (rico) = 3.713\n", "---------\n", "r 0.3 au\n", "scale height is 0.06675667566756677\n", "mass added [Msun]: 3.5883389926010053e-10\n", "relMass_added/rstep (rico) = 3.388\n", "---------\n", "r 0.31 au\n", "scale height is 0.06930693069306929\n", "mass added [Msun]: 3.262126356910005e-10\n", "relMass_added/rstep (rico) = 2.988\n", "---------\n", "r 0.32 au\n", "scale height is 0.07260726072607265\n", "mass added [Msun]: 3.089425549779475e-10\n", "relMass_added/rstep (rico) = 2.752\n", "---------\n", "r 0.33 au\n", "scale height is 0.07455745574557453\n", "mass added [Msun]: 3.051047592639357e-10\n", "relMass_added/rstep (rico) = 2.646\n", "---------\n", "r 0.34 au\n", "scale height is 0.0777077707770778\n", "mass added [Msun]: 2.705645978378298e-10\n", "relMass_added/rstep (rico) = 2.293\n", "---------\n", "r 0.35 au\n", "scale height is 0.08100810081008092\n", "mass added [Msun]: 2.417811299827416e-10\n", "relMass_added/rstep (rico) = 2.008\n", "---------\n", "r 0.36 au\n", "scale height is 0.08430843084308429\n", "mass added [Msun]: 2.4945672141076507e-10\n", "relMass_added/rstep (rico) = 2.029\n", "---------\n", "r 0.37 au\n", "scale height is 0.0889588958895889\n", "mass added [Msun]: 2.3026774284070628e-10\n", "relMass_added/rstep (rico) = 1.839\n", "---------\n", "r 0.38 au\n", "scale height is 0.09270927092709269\n", "mass added [Msun]: 2.1107876427064736e-10\n", "relMass_added/rstep (rico) = 1.658\n", "---------\n", "r 0.39 au\n", "scale height is 0.09555955595559565\n", "mass added [Msun]: 2.0532207069962974e-10\n", "relMass_added/rstep (rico) = 1.587\n", "---------\n", "r 0.4 au\n", "scale height is 0.09810981098109803\n", "mass added [Msun]: 1.784575007015473e-10\n", "relMass_added/rstep (rico) = 1.36\n", "---------\n", "r 0.41 au\n", "scale height is 0.1015601560156015\n", "mass added [Msun]: 1.9572758141460027e-10\n", "relMass_added/rstep (rico) = 1.47\n", "---------\n", "r 0.42 au\n", "scale height is 0.10471047104710465\n", "mass added [Msun]: 1.4391733927544137e-10\n", "relMass_added/rstep (rico) = 1.069\n", "---------\n", "r 0.43 au\n", "scale height is 0.11011101110111012\n", "mass added [Msun]: 1.6886301141651792e-10\n", "relMass_added/rstep (rico) = 1.239\n", "---------\n", "r 0.44 au\n", "scale height is 0.11491149114911482\n", "mass added [Msun]: 1.4583623713244726e-10\n", "relMass_added/rstep (rico) = 1.059\n", "---------\n", "r 0.45 au\n", "scale height is 0.1182118211821182\n", "mass added [Msun]: 1.4775513498945312e-10\n", "relMass_added/rstep (rico) = 1.061\n", "---------\n", "r 0.46 au\n", "scale height is 0.12256225622562263\n", "mass added [Msun]: 1.2472836070538253e-10\n", "relMass_added/rstep (rico) = 0.888\n", "---------\n", "r 0.47 au\n", "scale height is 0.12736273627362746\n", "mass added [Msun]: 1.3816064570442375e-10\n", "relMass_added/rstep (rico) = 0.974\n", "---------\n", "r 0.48 au\n", "scale height is 0.1329132913291329\n", "mass added [Msun]: 1.1129607570634133e-10\n", "relMass_added/rstep (rico) = 0.779\n", "---------\n", "r 0.49 au\n", "scale height is 0.13771377137713786\n", "mass added [Msun]: 1.0937717784933546e-10\n", "relMass_added/rstep (rico) = 0.759\n", "---------\n", "r 0.5 au\n", "scale height is 0.1431143114311431\n", "mass added [Msun]: 1.0170158642131189e-10\n", "relMass_added/rstep (rico) = 0.701\n", "---------\n", "r 0.51 au\n", "scale height is 0.14866486648664878\n", "mass added [Msun]: 9.210709713628251e-11\n", "relMass_added/rstep (rico) = 0.631\n", "---------\n", "r 0.52 au\n", "scale height is 0.1539153915391539\n", "mass added [Msun]: 8.251260785125306e-11\n", "relMass_added/rstep (rico) = 0.562\n", "---------\n", "r 0.53 au\n", "scale height is 0.1578157815781579\n", "mass added [Msun]: 9.402599499328838e-11\n", "relMass_added/rstep (rico) = 0.636\n", "---------\n", "r 0.54 au\n", "scale height is 0.1627662766276627\n", "mass added [Msun]: 8.443150570825893e-11\n", "relMass_added/rstep (rico) = 0.568\n", "---------\n", "r 0.55 au\n", "scale height is 0.16756675667566753\n", "mass added [Msun]: 6.908032285221189e-11\n", "relMass_added/rstep (rico) = 0.463\n", "---------\n", "r 0.56 au\n", "scale height is 0.17476747674767465\n", "mass added [Msun]: 7.675591428023542e-11\n", "relMass_added/rstep (rico) = 0.512\n", "---------\n", "r 0.57 au\n", "scale height is 0.18271827182718264\n", "mass added [Msun]: 6.908032285221189e-11\n", "relMass_added/rstep (rico) = 0.458\n", "---------\n", "r 0.58 au\n", "scale height is 0.18901890189018894\n", "mass added [Msun]: 6.7161424995206e-11\n", "relMass_added/rstep (rico) = 0.444\n", "---------\n", "r 0.59 au\n", "scale height is 0.1968196819681967\n", "mass added [Msun]: 6.332362928119422e-11\n", "relMass_added/rstep (rico) = 0.417\n", "---------\n", "r 0.6 au\n", "scale height is 0.2046204620462046\n", "mass added [Msun]: 6.7161424995206e-11\n", "relMass_added/rstep (rico) = 0.44\n", "---------\n", "r 0.61 au\n", "scale height is 0.20987098709870972\n", "mass added [Msun]: 5.756693571017657e-11\n", "relMass_added/rstep (rico) = 0.376\n", "---------\n", "r 0.62 au\n", "scale height is 0.21782178217821782\n", "mass added [Msun]: 6.908032285221189e-11\n", "relMass_added/rstep (rico) = 0.449\n", "---------\n", "r 0.63 au\n", "scale height is 0.22682268226822686\n", "mass added [Msun]: 4.6053548568141254e-11\n", "relMass_added/rstep (rico) = 0.298\n", "-------------------------------------------------------------\n", "estimate of r of the accretion disk is 0.63 au\n" ] }, { "data": { "image/png": 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piWXLlqFJkyZIT0+HqakpoqKi4O/vj0aNGgGQj4J6/TP4+PigVatWEIlEcHNze+ufVWUnlcpw61kKToXH4cKjBIRFJSNXUrggZG6oC39XK/i7WsLf1QoNqlsKE5YqjJiYGOTn56NPnz6Kfyev/3ueO3cuJkyYgLFjxyq2NW7cWPHfrze79/DwwJw5c/Dll1+WWCD6/vvvMWXKFMXPGU9PT8yZMweTJ09+Y4HIysoKK1asgFgsRq1atdC9e3ecOHECn332GR49eoTt27fj6dOncHJyAgBMnDgRR44cwcaNGzFv3rwix1u+fDmGDRuGTz75BAAwY8YMHDt2rMjopzed19raGmKxWDHKkagiuxaVhAdx6TDSE+PdBk5qO0+FLRAV/CONjY2Fo6OjYntcXJxiVJGDgwNyc3ORlJRUaBRRXFwcWrRoUeKxDQwMFF8Uy8PSWA8AkJSZ95Y9iYjUq4ef49t3ojIz0hPjzmxh+toZ6YlLtd/du3eRk5ODDh06lOt8xsbGiiIAIB+V6+7uDlNT00LbXp8qVBarV6/GunXrEBkZiaysLOTm5pY4IqYk/v7+qFWrFrZv344pU6bgzJkziIuLQ9++fQHIp6Knp6fDxsam0PuysrLw6NGjUp8nNTUVz58/R8uWLQttb9my5RunqwHy7yQzZszAyZMn8eLFC0gkEmRmZiIqKqpU53706BFyc3PRvHlzxTZra2vUrFmzxPd8+OGHCAoKgqenJ7p27Ypu3bqhZ8+e0NX992vfokWLkJGRgatXr8LT0/OtOcry9+L69euYOXMmwsLCkJiYCKlUXuyIioqCr68vvvzyS7z//vu4du0aOnfujPfee0/x/W3o0KHo1KkTatasia5du6JHjx6FWgpoi9TsPJy9/xKn7r3EmftxiE8vfNPR3twAzT1t0MzTBo3creFpawIdHU6D0ZTKcO2vX78+OnToAD8/P3Tp0gWdO3fGBx98ACsrK8TFxeH58+dv/Llw6tQpzJs3D3fu3EFqairy8/ORnZ2NjIwMmJiYFNk/NDQUV65cUUxfBuTThrOzs5GZmQljY+Niz1OnTh2Ixf9+JkdHR9y6dQuAfGqwTCZDjRo1Cr0nJyenyPW7QHh4OEaOHFloW5MmTXDy5MlSn5eoMtlxWT6Ktns9R5gZ6qntPBW2QOTh4QEHBweEhITA398fgHyo8pkzZ7BgwQIAQEBAAPT09BASEqL4MhgTE4N//vkHCxcuVHtGK2P5CKLkTI4gIiJhLe7XQOgIWk0kEpV6mpdQ3ta8U0dH3nbw9b4Wr/d/KKCnV/hLh0gkKnZbwS/7Bc//2y+juGMX2LVrF77++mssWrQIzZs3h5mZGX788UdcunTpjZ+hOAMHDsS2bdswZcoUbNu2DV26dIGtrS0A+VQ2R0fHQn0sCpRl2eayjGoeOnQoXr58iaCgILi5ucHAwADNmzdHbm7pvjso04ekgIuLC8LDwxESEoLjx49j5MiR+PHHH3HmzBnF/8vWrVvjzz//xK5duzBlypS3HlPZvxcZGRno3LkzOnfujF9//RV2dnaIiopCly5dFJ89MDAQkZGR+PPPP3H8+HF06NABo0aNwk8//YSGDRsiIiIChw8fxvHjx9G3b1907NgRe/bsUfrPo6J5npyF43dfIOTOC1x8lIB86b//j00NdNHK2xZtatihuZcN3G2M2RdFQJXh2i8WixESEoILFy7g2LFjWL58OaZNm4ZLly4proUliYyMRLdu3TBixAjMmTMH1tbWOHfuHIYNG1biNVwqlWLWrFmKRYReZ2hoWOK53nS9kEqlEIvFCA0NLVTMAVCoCP1fxV2TlTkvUWWRlp2HgzdjAAAfNVF+5pMyBL3ipaen4+HDh4rnERERCAsLg7W1NVxdXTFu3DjMmzcPPj4+8PHxwbx582BsbIwBAwYAACwsLDBs2DBMmDABNjY2sLa2xsSJE+Hn56dY1UydrBQjiFggIiJhvb/qAvZ+WfLISdJ+Pj4+MDIywokTJzB8+PAirxdMgYqJiVGMulXVkux2dnaIiYlRPH/w4AEyMzNL3P+vv/5CixYtCt39/e+IHn19/UINj0syYMAAfPvttwgNDcWePXuwatUqxWsNGzZEbGwsdHV1C01fepOC3kGvn9vc3BxOTk44d+4c2rRpo9h+4cIFxdSt4t5X8FlXrlyJbt26AQCio6MRHx9fqiwA4O3tDT09Pfz9999wdXUFIG/ufP/+fbRt27bE9xkZGaFXr17o1asXRo0ahVq1auHWrVto2LAhAPmd9tGjR6NLly4Qi8WYNGlSqTOVxr179xAfH48ffvhBMY3/6tWrRfazs7PD0KFDMXToULRu3RqTJk3CTz/9BED+596vXz/069cPH3zwAbp27YrExMRie0dVdI9fpuPwP7E48k8sbj1LKfSal50JOtS2R7uadmjkZg193Qq7hgxVUCKRCC1btkTLli0xY8YMuLm5Yf/+/Rg/fjzc3d1x4sQJtG/fvsj7rl69ivz8fCxatEhxE2HXrl1vPFfDhg0RHh4Ob29vleX39/eHRCJBXFwcWrduXar31KxZE5cvX8agQYMU24q7xrxNaX/WEAnpwI3nyMqTwLuaKRq6Wr39DeUgaIHo6tWrhS5WBX2BhgwZguDgYEyePBlZWVkYOXIkkpKS0LRpUxw7dqxQU8clS5ZAV1cXffv2RVZWFjp06IDg4OAi1Wd1sFSMIOIUMyIS1n9/4ahszp49ix9//BGhoaGIiYnB/v378d5775W4/759+7Bq1SqEhYUhJycHderUwcyZM9GlizBTASoCQ0NDfPPNN5g8eTL09fXRsmVLvHz5Erdv38awYcPg7e0NFxcXzJw5E3PnzsWDBw+KXQ2rLN555x2sWLECzZo1g1QqxTfffFPkru3rvL29sXnzZhw9ehQeHh7YsmULrly5Uqi5qLu7O44ePYrw8HDY2NjAwsKi2GN5eHigRYsWGDZsGPLz8/Huu+8qXuvYsSOaN2+O9957DwsWLEDNmjXx/PlzHDp0CO+9956i983rqlWrBiMjIxw5cgTVq1eHoaEhLCwsMGnSJHz33Xfw8vJCgwYNsHHjRoSFhWHr1q1vfJ+3tze2bNmCRo0aITU1FZMmTVJqqWZTU1MMGzYMkyZNgo2NDezt7TFt2jTFL3PFCQ4OhkQiQdOmTWFsbIwtW7bAyMioSB+f5s2b4/Dhw+jatSt0dXXx9ddflzrX27i6ukJfXx/Lly/HiBEj8M8//2DOnDmF9pkxYwYCAgJQp04d5OTk4ODBg6hduzYA+fc7R0dHNGjQADo6Oti9ezccHBzKNPJLKA/j0nDoViwO3YrBvdg0xXaRCAhwtULnOvbo5OsAD9ui03iISuvSpUs4ceIEOnfujGrVquHSpUt4+fKl4t/SzJkzMWLECFSrVg2BgYFIS0vD+fPnMXr0aHh5eSE/Px/Lly9Hz549cf78+SIrQ/7XjBkz0KNHD7i4uODDDz+Ejo4Obt68iVu3bmHu3Lll+gw1atTAwIEDMXjwYCxatAj+/v6Ij4/HyZMn4efnpyiwv2706NH47LPP0KhRI7Ro0QI7d+7EzZs3SzVl9nXu7u44e/Ys+vfvDwMDg7eOuiJSlzvPU2FprAcny6LfEQqml/Vv7KL2UaWC3qJo164dZDJZkUfBsrgikQgzZ85ETEwMsrOzcebMGdStW7fQMQwNDbF8+XIkJCQgMzMTf/zxR5kaTpdFwRQzjiAiIiqfjIwM1K9fHytWrCjV/mfPnkWnTp1w6NAhhIaGon379ujZsyeuX7+u5qQV2/Tp0zFhwgTMmDEDtWvXRr9+/RQ9YfT09LB9+3bcu3cP9evXx4IFC8r8Zf6/Fi1aBBcXF7Rp0wYDBgzAxIkTS+xDAQAjRoxAnz590K9fPzRt2hQJCQlFekl89tlnqFmzJho1agQ7O7tCq+7818CBA3Hjxg306dOnUPFFJBLh0KFDaNOmDT799FPUqFED/fv3x5MnT4qsklpAV1cXy5Ytw5o1a+Dk5KQoOI0ZMwYTJkzAhAkT4OfnhyNHjuDAgQOK1XVKet+GDRuQlJQEf39/DBo0CGPGjEG1atVK9wf7yo8//og2bdqgV69e6NixI1q1aoWAgIAS97e0tMQvv/yCli1bol69ejhx4gT++OOPYnt5tGzZEn/++SemT5+uWGlNFezs7BAcHIzdu3fD19cXP/zwg2JkUAF9fX1MnToV9erVQ5s2bSAWi7Fjxw4A8sLYggUL0KhRIzRu3BhPnjzBoUOH3lgYqwieJmVi1elH6Bp0Fh0Xn8XikPu4F5sGXR0R2tSww/w+frgyrSP2fNkCn7fxYnGIys3c3Bxnz55Ft27dUKNGDXz77bdYtGgRAgMDAchvvAcFBWHlypWoU6cOevTogQcPHgAAGjRogMWLF2PBggWoW7cutm7divnz57/xfF26dMHBgwcREhKCxo0bo1mzZli8eHG5G8lv3LgRgwcPxoQJE1CzZk306tULly5dKvH3uoEDB2Lq1KmYOHGiYkrq0KFD3zjNrTizZ8/GkydP4OXlpRhpS6Rp0YmZePfnc+gadBaPXhZutP7PsxTcepYCfbGORlatFMnKMrldy6SmpsLCwgIpKSkwNzcv9fviUrPRZN4J6IiAh993Y9NAIhLMez+fx2+jWr59x9eU9dqnbiKR6K0jiIpTp04d9OvXDzNmzCjV/m/6/NnZ2YiIiICHh4fSXzaJSPOE/DebmJGLgzef4/ew5wiNTFJs1xOL0MrbFt38HNHJ114x8lxoFfXarym89muvTp06wcHBAVu2bFHpcfn3gtRt4/kIzPrjDgDA3cYY+0e2VKyYPv23f7Dl70j0qOeIFQMalvkcpb32V+yuaxVcwQ96qUy+CkVF+cFPRFWPssUhbSOVSpGWlvbG3iQ5OTnIyclRPE9NTdVENCLSQtl5Epy4G4f915/idPhLRaNpkQho5mGDXg2cEFjXgd8NidQkMzMTq1evVvRR2759O44fP46QkBChoxEp7XT4SwDynyFPEjIx4tdQbBnWFBKpDL+FPQMA9G/sqpEsFXucbgWnr6sDE315ryMudU9EQpq4+81LbWu7giW7C1a0LM78+fNhYWGheGhqOjIRaY+Hcen49rdbaDz3OEZtu4bjd+OQL5WhrrM5vu1eGxendMD2z5vhoyauLA4BWLlypWLURUBAAP76668S9923bx86deoEOzs7mJubo3nz5jh69GihfYKDgyESiYo8srOz1f1RqIIpmELcunVrBAQE4I8//sDevXs1slARkSpl50nw9+MEAMDS/v4wNdDFpYhEfPvbLRy6FYO07Hy4WBuhhVfRaeLqwBFE5WRprI+M3CwkZebCA5xHTkTCOHDjOX76sL7QMQSxfft2zJw5E7///vsbe7tMnTpVsRgCIB9BxCIREb2NVCrD2QcvsfH8E5y5/1Kx3cnCEO/5O6O3vzN87M3ecISqaefOnRg3bhxWrlyJli1bYs2aNQgMDMSdO3cUK/K9rqC33Lx582BpaYmNGzeiZ8+euHTpEvz9/RX7mZubIzw8vNB7Oe2n6jEyMsLx48eFjkFUbhcfJyAnXwpHC0P0rOcIMwNdDNt0BbuuPsXhf2IBAP0auWisnQ0LROVkZaKHZ8lZSGajaiIijdu5cyeGDRuG3bt3v/WuoYGBAQwMDDSUjIgqu4ycfOy79hTBF57g0csMAPLh/x1r22NoC3c097Rh/8k3WLx4MYYNG4bhw4cDAIKCgnD06FGsWrWq2EbIQUFBhZ7PmzcPv//+O/74449CBSKRSAQHBwe1Zici0pQzr6aXtatpB5FIhPa1qmF6D1/M+uMO0rLzoSMCPmykuRuaLBCVk2IlswxOMSMi4RjpiYWOoHHbt2/Hp59+iu3bt6N79+5qOQfXcSCqHFT5b/VJfAY2XXyCPVefIi0nHwBgaqCLvo1cMKSFG9xsOGL8bXJzcxEaGoopU6YU2t65c2dcuHChVMcoqbdceno63NzcIJFI0KBBA8yZM6dQAem/ytJ/jtd+eh3/PpA6FYxMbVvj31HwQ1u449HLdPz6dxQ6+drD3lxzoyRZIConLnVPRBXBje86Cx2hXNLT0/Hw4UPF84iICISFhcHa2hqurq6YOnUqnj17hs2bNwOQF4cGDx6MpUuXolmzZoiNlQ/BNTIygoWFRbnz6OnpAZA3wXx92XQiqpgyMzMB/PtvV1kymQwXHyfgl7OPcSr832lkHrYmGNzcDR8EVIeZYdmOXRXFx8dDIpHA3t6+0HZ7e3vF9fptiustV6tWLQQHB8PPzw+pqalYunQpWrZsiRs3bsDHx6fY48yfPx+zZs0q1Tl57afilPf6QlSSyIQMRMRnQFdHhJbe//YYEolEmNWrLjrWtoe/i5VGM7FAVE5WxvILRTKbVBORgH4+9RCj2nsLHaPMrl69ivbt2yueF/QKGjJkCIKDgxETE4OoqCjF62vWrEF+fj5GjRqFUaNGKbYX7F9eYrEYlpaWiIuLAwAYGxtDJOJUEqKKRiaTITMzE3FxcbC0tIRYrNxoSolUhqO3Y7HmzCPceJoCQD6NrF0NOwxp4Y42PnacRlYO/71uymSyUl1LS+ot16xZMzRr1kzxvGXLlmjYsCGWL1+OZcuWFXssZfrP8dpPryvv9YXobQpWLwtwsypyE0KsI0K7miX31lQXFojKqWCFikSOICIiAS098aBSF4jatWv3xiHc/y36nD59Wr2BAEWPi4JfFIio4rK0tFSqL02eRIq9oU+x5uxjRMTL+wsZ6OqgX2MXfNrSA+62nEZWHra2thCLxUVGC8XFxRUZVfRfyvSW09HRQePGjfHgwYMS91G2/xyv/fRfyl5fiErrdLj8OiNEIagkLBCV078jiFggIiLSJiKRCI6OjqhWrRry8jhKlKii0tPTK/WdfYlUht+uP0PQifuITswCAFgY6WFIczcMaeEOG1M2slcFfX19BAQEICQkBL1791ZsDwkJwbvvvlvi+5TtLSeTyRAWFgY/Pz+V5AZ47afClLm+ECkjO0+Ci6+Wt29X007gNP9igaicrEzYpJqISJuJxWJ+OSSq5KRSGf68FYOg4/cVK5LZmhpgRFtPfNTEFSYG/EqsauPHj8egQYPQqFEjNG/eHGvXrkVUVBRGjBgBAGXqLTdr1iw0a9YMPj4+SE1NxbJlyxAWFoaff/5Z5fl57ScidboUkYjsPCkczA1Ry8FM6DgK/GlYTpZsUk1EFcCZSe2EjkBEVCH99eAl5h+6hzsx8tWrLI318GVbLwxu7g4jfRYA1KVfv35ISEjA7NmzERMTg7p16+LQoUNwc3MDgDL1lktOTsbnn3+O2NhYWFhYwN/fH2fPnkWTJk00+tmIiMqrYHn7tjXsKlSvMxaIyolNqomoIgiLSoajH1dcISIqcDcmFfMP38PZV0sImxnoYnhrT3zayp0rkmnIyJEjMXLkyGJfK0tvuSVLlmDJkiUqSEZEJKzT9wv6D1Wc6WUAC0TlxmXuiagiGLszDIF+jkLHICIS3IvUbPx0NBx7rj2FTAboiUX4uJkbRr/jA+tXrQGIiIiEEp2YiccvMyDWEaGFt63QcQphgaicLF+NIMrJlyIrV8KhykREREQCyM2XYuP5CCw78QAZuRIAQPd6jpjcpSbcbLgqGRERVQwFq5cFuFrBwqhijWhlgaicTA10oasjQr5UhqTMXBjpc4oHERERkSb99eAlvjtwG49fNaBu6GqJ6T184e9qJXAyIiKiwk4X9B+qYNPLABaIyk0kEsHSWB/x6TlIysyFkyULRESkecFDGwsdgYhI454nZ2HOwTs4/I98xStbU31MCayNPv7O0NGpOE0/iYiIAODO81ScftUbr33NagKnKYoFIhWwMtZDfHoOG1UTkWCsTdlXg4iqjjyJFMHnn2DJ8fvIzJVArCPCkObuGNfJB+ZsQE1ERBWQRCrD1H03IZHKEFjXAb5O5kJHKoIFIhVgo2oiElqvFedxf26g0DGIiNQuNDIJ0/bfwr3YNABAY3crzHmvLmo5VLwv2kRERAW2XHyCG09TYGagi5m96ggdp1gsEKmAlYn8TlUSRxARERERqUVqdh7mH7qH7ZejAMgXCvlfYG18EFCd08mIiKhCe56chR+PhgMAvgmsBXtzQ4ETFY8FIhUoGEGUnMERRERERESq9teDl/hmz008T8kGAPRtVB1TAmtz2XoiIqrwZDIZZvz+DzJyJQhws8KAJq5CRyoRC0QqYKmYYsYRREQkjBk9fIWOQESkcuk5+Zh36C62XZKPGnK1NsbCD+qhmaeNwMmIiIhK58g/sTh+Nw56YhHm9/Gr0KNeWSBSAStj+RSzZPYgIiKBfNioutARiIhU6uKjBEzcfQPPkrMAAEOau+GbwFow1ufXVyIiqhxSsvLw3YHbAIAv23qhhr2ZwInejD9hVaBgilkiC0REJBC/mcfYpJqItIJEKsPSEw+w/OQDyGRAdSsjLPygHlp42QodjYiIqNSiEjIxcc8NxKXlwNPWBCPbewsd6a1YIFIBS2M2qSYiIiIqrxep2Riz/TouRSQCAPo1csH0nr4wNeBXViIiqhykUhk2XXyChUfCkZUngaGeDhZ+UA+GemKho70Vf9qqgNWrBomcYkZERERUNqfD4zB+1w0kZuTCRF+MeX388G4DZ6FjERERldrjl+n4Zu9NXHmSBABo5mmNBe/Xg5uNicDJSocFIhUo6EGUxFXMiEggg5q5CR2BiKhM8iVSLA65j5WnHwEAfB3NsWKAPzztTAVORkREVHoXHsXjk41XkJMvhYm+GFO71caAJq4Vuin1f7FApAIFq5ilZucjXyKFrlhH4EREVNVM5ypmRFQJxaXJp5T9/Vg+pWxQMzdM6167UgzDJyIiKpCSmYfxO28gJ1+KFl42WPhBPVS3MhY6ltJYyVABSyM9xX+nZLEPERFp3js/nRY6AhGRUv5+nIDuy87h78eJMNEXY/lH/pjzXl0Wh4iIqNL57sA/iE3NhoetCdYNaVQpi0MARxCphK5YB2aGukjLzkdSZh5sTA2EjkREVczTV8tAExFVdDKZDKvPPMaPR+9BKgNq2Jti1ccB8OKUMiIiqoT+vBmD38KeQ0cELO5bH8b6lbfMUnmTVzBWxvpIy85no2oiIiKiEmTk5GPCrhs4cjsWANDH3xlze9et1F+miYio6opLzca0324BAEa194a/q5XAicqHP41VxMpYD1GJXOqeiITRyttW6AhERG8UlZCJzzZfRfiLNOiJRZjVqy4+auICkajyNO8kIiIqIJPJMHnvTSRn5qGuszlGv+MjdKRyY4FIRQqWuk/iCCIiEsCGoY2FjkBEVKJzD+Ixats1pGTlwc7MAKs/DkCAW+W+y0pERFXbtstROB3+Evq6OljStwH0dSt/i+fK/wkqCKtXK5lxihkRCeHT4CtCRyAiKkImk2HdX48xeMMlpGTlob6LJf74qhWLQ0REVKk9epmO7/+8CwCY3KUmfOzNBE6kGhxBpCKWxvKVzDjFjIiEcO5hvNARiIgKkUhlmPXHbWy+GAkAeL9hdXzfm6uUERFR6eXkS/DL2cdo4GKFVj4Vo6VCVq4EI3+9hsxcCVp42eDTlh5CR1IZFohUhCOIiIiIiOSy8yQYu+M6jt5+AZEImNatNoa18mC/ISIiKjWZTIbJe27i97DncLIwxIWpHYSOBACY8fs/CH+RBltTAwT1bwAdHe352cYCkYpYFYwgyuAIIiLSvOqWRkJHICICACRl5GL45qsIjUyCvlgHS/o1QPd6jkLHIiKiSuanY+H4Pew5AOB5Sjbi03Nga2ogaKZdV6OxO/QpdETAso8aoJqZoaB5VI09iFTE8tUIokSOICIiAZyc2E7oCEREeJqUiQ9WX0BoZBLMDXWxeVgTFoeIiEhp2y5F4edTjwAARq+mJt9+nipkJNyNScX03/4BAIzvVAMtvCrGlDdVYoFIRTjFjIiENOfgHaEjEFEV98+zFPRZeQGPXmbA0cIQe75sgWaeNkLHIiKiSubUvThM/11eiBnbwQcdalcDIP85I5T0nHyM2noNOflStKlhh5HtvAXLok4sEKkIm1QTkZC2/B0pdAQiqsLO3H+JfmsuIi4tBzXtzbBvZAvU0JIVXYiISHP+eZaCUduuQSKV4f2G1TGuow/qOlsAAO4IOIJo+m//4HF8BhzMDRHUT7v6Dr2OPYhUxMrk3xFEMpmMTRiJiIioSth1JRpT99+CRCpDCy8brB4UAHNDPaFjERFRJSKRyrD1UiR+PBKOzFwJWnnbYn4fP4hEItRxMgcA3H4uzAiiyIQM7L/+DCIRsGKAP6xf/e6vjVggUpGCJtV5EhkyciUwNeAfLREREWkvmUyGoOMPsPTEAwBAb39nLHi/HvR1OUCdiIhK79bTFEz77RZuPpUXgBq6WmLVxw0VP0/qOMlHED1JyERqdp7Gb0LsuBINAGjtY4dG7tYaPbemsYqhIkZ6Yujr6iA3X4qkjFwWiIhIo27N7Cx0BCKqQiRSGabtv6X40jyqvRcmdq7JEdRERFRqadl5+OloOLb8HQmpDDAz0MXkrjUxoKkbxK9N4bI20YeThSGep2Tj7vNUNNVgf7vcfCl2X5X/rBvQxEVj5xUKb/GoiEgkUowiSmYfIiLSsN1XnwodgYiqiDyJFON3hWHHlWjoiIDve9fFpC61WBwiIiKlTNh1A5suyotD7zZwwomJbTGouXuh4lAB31ejiDS9ktmJuy8Qn54LOzMDdKhtr9FzC4EFIhUqWMksiSuZEZGGzeYqZkSkATn5Eozceg2/hz2Hro4Iyz9qiIFN3YSORURElUxkQgaO3XkBAAj+pDGW9vdHNTPDEvev6yzvQ/SPhvsQbbscBQDo26g69MTaXz7hPCgV+nclMxaIiIiISLtk5Urw+Zar+OtBPPR1dbD644Z4p5b2300lIiLV23pJXnhpW8MO7WpWe+v+BX2INLmSWVRCJv56EA+RCOjf2FVj5xUSC0QqZK1YyYxTzIiIiEh7pGXnYdimq7gckQhjfTHWDW6EFt62QsciIqJKKDtPgl2v+voMala6UagFI4gexKUjO08CQz2x2vIV2HFFXsRq7WMHF2tjtZ+vItD+MVIaZMkpZkQkkANftRQ6AhFpqaSMXHy87hIuRyTCzEAXW4Y1YXGIiIjK7ODNGCRn5sHZ0gjta7199BAAOJgbwtpEHxKpDOGxaWpOKO+3t+tVj8+q0Jy6AAtEKsQm1UQklMR0FqaJSPXi0rLRf+3fuPE0BVbGetj+eTMEuGn3Er9ERKRev/4dCQAY0NS12IbUxRGJRKjjJB9FpIlG1cfvvEB8ek6VaU5dgAUiFWKTaiISytDgK0JHICIt8zQpE31XX0T4izTYmxtg1xfNUdfZQuhYRERUid16moKw6GToiUXo11i5kTkFfYg00ai6qjWnLsAeRCr07xQzjiAiIiKiyuvxy3R8vO4Snqdko7qVEbYNbwZXm6rRf4GIiNSnYPRQNz9H2JoaKPVeTY0gik6UN6cGqk5z6gIsEKnQv1PMOIKIiIiIKqfw2DQMXPc34tNz4WVngq3Dm8HBouSlh4mIiEojJTMPv994BqD0zalfV1AguheTinyJFLpqGtmz5VURq7WPbZVpTl2g6oyV0oCCEUSJGSwQEZFmLe3XQOgIRKQFbj9PQf+1FxGfngtfR3Ps+qI5i0NERKQSe649RXaeFLUczBDgZqX0+91tTGCiL0ZOvhSPXmaoISEQFp2M9eciAABDmrur5RwVGQtEKsQm1UQklAaulkJHIKJK7ubTZAz45RKSMvNQv7oFtn/WDDZKDv8nIiIqjlQqU0wvG9TcDSJR6ZpTv05HRwRfxTQz1fchysjJx9gd1yGRytCzvhM61C7dCmvahAUiFSpoUp2ek4/cfKnAaYioKmn742mhIxBRJXYtKgkDf7mElKw8NHS1xJbhTWHx6sYXERFReV14lICI+AyYGujivQbOZT5OQaNqdfQhmnngNiITMuFsaYS579UtUxGrsmOBSIXMjfRQ8HcoOYvTzIiISuvs2bPo2bMnnJycIBKJ8Ntvv731PWfOnEFAQAAMDQ3h6emJ1atXqz8okRa68iQRg9ZdQlpOPpq4W2PzsKYwN2RxiIiIVOevhy8BAN39HGFiUPZWyAV9iP55ptoRRH/ejMHu0KcQiYDFfevDwqhq/hxkgUiFxDoixV8kTjMjIiq9jIwM1K9fHytWrCjV/hEREejWrRtat26N69ev43//+x/GjBmDvXv3qjkpkXa5FpWEIRsuIyNXghZeNgj+tDFMy/HFnYiIqDjRiZkAgJoOZuU6TsEIojvPUyGVysqdCwCeJ2dh6r6bAICR7bzQ1NNGJcetjPgNQMUczA2RnJmHZ0lZqGFfvr/8RESlNbaDj9ARyiUwMBCBgYGl3n/16tVwdXVFUFAQAKB27dq4evUqfvrpJ7z//vtqSkmkXW4/T8HQDZeRmStBS28brB/SGIZ6YqFjERGRFopOzAKAcq8K5mNvCn2xDtJy8hGdlAk3G5NyHU8ilWH8rjCkZuejfnULjOtYo1zHq+w4gkjFvOxMAQCPXqYLnISIqpJR7b2FjqBRFy9eROfOnQtt69KlC65evYq8vOJHcObk5CA1NbXQg6iqehiXhkHrLyM1Ox+N3Kzwy+BGLA4REZHaRL0aQeRazgKRnlhHMQpJFX2Idl2Nxt+PE2GsL0ZQf3/oiat2iaRCf/r8/Hx8++238PDwgJGRETw9PTF79mxIpf82gJbJZJg5cyacnJxgZGSEdu3a4fbt24Jl9qomLxA9jGOBiIg0p/6sY0JH0KjY2FjY29sX2mZvb4/8/HzEx8cX+5758+fDwsJC8XBxcdFEVKIKJzIhAwN+uYTEjFzUq26BDZ80hrE+B5UTEZF6pGTlISVLfgOvupVRuY9X11k1fYikUhl+OfsYADC+Uw142JZvNJI2qNAFogULFmD16tVYsWIF7t69i4ULF+LHH3/E8uXLFfssXLgQixcvxooVK3DlyhU4ODigU6dOSEtLEySzl538LxVHEBGRJmXlSYSOoHH/XVlCJpMVu73A1KlTkZKSonhER0erPSNRRfM8OQsDfrmEuLQc1LQ3w6ZPmrAhNRERqVVB/yFbU/1yNaguUNdZ3ofoxtPkch3n5L04PI7PgJmhLj5q4lruXNqgQt8uunjxIt599110794dAODu7o7t27fj6tWrAOS/DAQFBWHatGno06cPAGDTpk2wt7fHtm3b8MUXX2g8c8EUM44gIiJSHwcHB8TGxhbaFhcXB11dXdjYFN9Y0MDAAAYGBpqIR1QhJWbkYtD6S3iWnAUPWxNsGd4EVib6QsciIiItV1AgKm//oQINXa0AAGFRyZBIZRDrlG05+nXn5KOHBjR1VUnhShtU6BFErVq1wokTJ3D//n0AwI0bN3Du3Dl069YNgHwVm9jY2EJ9KAwMDNC2bVtcuHChxOOqsw9FQYEoKTMPiRlc6p6INKNXfSehI2hU8+bNERISUmjbsWPH0KhRI+jpcTQE0X9l5ubj0+ArePQyA44Whvh1eFNUMzMUOhYREVUB0UmvCkRWqikQ1bA3g6mBLjJyJbj/omwzh/55loK/HydCV0eEoS3cVZJLG1ToAtE333yDjz76CLVq1YKenh78/f0xbtw4fPTRRwCguHtcXB+K/95Zfp06+1AY6YvhbCmfV8lRRESkKT99WF/oCOWSnp6OsLAwhIWFAZDfAAgLC0NUVBQA+fSwwYMHK/YfMWIEIiMjMX78eNy9excbNmzA+vXrMXHiRCHiE1VoeRIpRm69hrDoZFga62HLsCaK7ypERETqpqoG1QXEOiLUd5FPM7sWlVSmY6z7Sz56qEc9Rzha8GdigQpdINq5cyd+/fVXbNu2DdeuXcOmTZvw008/YdOmTYX2K64PRUk9KAD196HwrsaVzIhIs977+bzQEcrl6tWr8Pf3h7+/PwBg/Pjx8Pf3x4wZMwAAMTEximIRAHh4eODQoUM4ffo0GjRogDlz5mDZsmVc4p7oP6RSGSbvuYnT4S9hqKeDDUMbw7uamdCxiIioColSLHGvukJMwTSza5HJSr83JiULB2/GAACGtfJUWSZtUKEn2k2aNAlTpkxB//79AQB+fn6IjIzE/PnzMWTIEDg4OACQjyRydHRUvC8uLq7IqKLXqbsPhZedKc7cf8kRRESkMXdiKveS7e3atVM0mS5OcHBwkW1t27bFtWvX1JiKqPL74cg97L/+DGIdEVYNDFB8oSYiItKUpyruQQS8ViAqwwiiTRcikS+VoamHNfyqW6gskzao0COIMjMzoaNTOKJYLFYsc+/h4QEHB4dCfShyc3Nx5swZtGjRQqNZX8cRRERERCS09ecisPbV8r0L36+H9rWqCZyIiIiqGqlUhqdJ8hFEqppiBgD+rpYAgIj4DKV6/2bk5GPbpUgAwPDWHD30XxV6BFHPnj3x/fffw9XVFXXq1MH169exePFifPrppwDkU8vGjRuHefPmwcfHBz4+Ppg3bx6MjY0xYMAAwXJzqXsi0jQ/Z979IKJ/Hb/zAnP/vAMAmBJYC+8HVBc4ERERVUUv0rKRK5FCV0ek0l4/lsb68LIzwaOXGbgelYQOtUueQfS63VejkZqdDw9bE3TgjZMiKnSBaPny5Zg+fTpGjhyJuLg4ODk54YsvvlD0pACAyZMnIysrCyNHjkRSUhKaNm2KY8eOwcxMuPn1Xq9GED1NykJ2ngSGemLBshBR1bD3S+FGTRJRxXL7eQrG7LgOmQz4qIkrvmjDO6RERCSMqAT59DJnK6MyL0dfkoauVnj0MgPXSlkgkkhl2HD+CQDg01Ye0FFxHm1QoaeYmZmZISgoCJGRkcjKysKjR48wd+5c6OvrK/YRiUSYOXMmYmJikJ2djTNnzqBu3boCpgZsTPRhaawHmQx4/DJD0CxEVDWM3xkmdAQiqgBepGZj+KaryMyVoJW3LWa/W+eNC3cQERGpU8EKZqpa4v51Dd2Ua1S9/txjRCVmwtJYD+83dFZ5Hm1QoQtElZVIJIKXnXwU0UNOMyMiDTh4K0boCEQksMzcfAzfdBUxKdnwsjPBzwMbQk/Mr3pERCSc6KSCFczUUCB61aj6xtNk5Eukb9w3PDYNPx29DwCY0rUWjPUr9GQqwfBbg5p4vyoQPeJKZkRERKRmUqkM43fewK1nKbA20cfGoU1gYaQndCwiIqriol+NIFJlg+oCPtVMYWagi8xcCe7FppW4X26+FON2hiFXIkWHWtXQr7GLyrNoCxaI1MSrmrxRNUcQEZEmmBrwLghRVbYoJBxHbsdCX6yDtYMC4Gqj+i/iRMpauXIlPDw8YGhoiICAAPz1118l7rtv3z506tQJdnZ2MDc3R/PmzXH06NEi++3duxe+vr4wMDCAr68v9u/fr86PQETlpJhiZq26BtUFdHREaPBqNbPrb1jufumJ+7gbkworYz3Mf9+PU6/fgAUiNVEsdc8RRESkAdemdxI6AhEJ5PewZ/j51CMAwA/v+6GRu7XAiYiAnTt3Yty4cZg2bRquX7+O1q1bIzAwEFFRUcXuf/bsWXTq1AmHDh1CaGgo2rdvj549e+L69euKfS5evIh+/fph0KBBuHHjBgYNGoS+ffvi0qVLmvpYRKQkdY4gAv6dZnYtKrnY10MjE7HqtPxn5LzefqhmZqiWHNqCBSI1KehB9Dg+AxKpTOA0RKTtlp94IHQEIhLAjehkTN5zEwAwoq0X+jTkcvZUMSxevBjDhg3D8OHDUbt2bQQFBcHFxQWrVq0qdv+goCBMnjwZjRs3ho+PD+bNmwcfHx/88ccfhfbp1KkTpk6dilq1amHq1Kno0KEDgoKCNPSpiEgZ2XkSxKXlAFBPk2rgtUbVxYwgysjJx/hdNyCVAX38nRHo56iWDNqkTAWi8PBwfPXVV+jQoQM6duyIr776CuHh4arOVqlVtzKGvq4OcvOlePaqMRcRkbosP/VQ6AhEpGGxKdn4bPNV5OTLeypM6lJT6EhEAIDc3FyEhoaic+fOhbZ37twZFy5cKNUxpFIp0tLSYG3974i4ixcvFjlmly5d3njMnJwcpKamFnoQkWY8TZKPHjIz0IWlsXr64jVwsQQARCZkIj49R7FdJpNh7p93EJmQCScLQ3zXq45azq9tlC4Q7dmzB3Xr1kVoaCjq16+PevXq4dq1a6hbty52796tjoyVklhHBE/bgj5EJTfMIiIiIlJWdp4En2+5iri0HNSwN0VQ/wYQ67CnAlUM8fHxkEgksLe3L7Td3t4esbGxpTrGokWLkJGRgb59+yq2xcbGKn3M+fPnw8LCQvFwcWFzWiJN+bf/kLHa+v5YGOnB51V7l+uvTTNbfvIhtl+OBgD8+GF9LtxQSkoXiCZPnoypU6fi4sWLWLx4MRYvXowLFy7gf//7H7755ht1ZKy0vBQrmWUInISIiIi0hUwmwzd7b+Lm0xRYGeth3eDGMDPkF1+qeP77C6FMJivVL4nbt2/HzJkzsXPnTlSrVq1cx5w6dSpSUlIUj+joaCU+ARGVR1SC+hpUvy7g1TSz0Ej5NLP15yKwOES+pP233WujpbetWs+vTZQuEMXGxmLw4MFFtn/88celviNQVXi9qmQ+ZKNqIlKzc5PbCx2BiDRkw/kn+D3sOXR1RFg5kCuWUcVja2sLsVhc5HeDuLi4IiOA/mvnzp0YNmwYdu3ahY4dOxZ6zcHBQeljGhgYwNzcvNCDiDQj+lWrFXU1qC7wb6PqJOy4HIU5B+8AAL7uWAPDW3uq9dzaRukCUbt27YpdovLcuXNo3bq1SkJpCy87+RSzR1zqnojU7FJEotARiEgDLkckYt6huwDkd0Wbe9kInIioKH19fQQEBCAkJKTQ9pCQELRo0aLE923fvh1Dhw7Ftm3b0L179yKvN2/evMgxjx079sZjEpFwXp9ipk4N3SwBANcikzB1/y0AwOdtPDGmg7daz6uNdJV9Q69evfDNN98gNDQUzZo1AwD8/fff2L17N2bNmoUDBw4U2rcqK5hi9vBleqmH1BIRlcWE3TfQs76T0DGISI3iUrMxats1SKQyvNvACUNauAsdiahE48ePx6BBg9CoUSM0b94ca9euRVRUFEaMGAFAPvXr2bNn2Lx5MwB5cWjw4MFYunQpmjVrphgpZGRkBAsLCwDA2LFj0aZNGyxYsADvvvsufv/9dxw/fhznzp0T5kMS0RtFa6hA5GlrCnNDXaRm5wMABjZ1xdTAWvz9uwyULhCNHDkSALBy5UqsXLmy2NcA+fxgiURSzniVW0GBKDkzD4kZubAxNRA4EREREVVGeRIpRm69hpdpOahpb4b5ffz4xZcqtH79+iEhIQGzZ89GTEwM6tati0OHDsHNzQ0AEBMTg6ioKMX+a9asQX5+PkaNGoVRo0Yptg8ZMgTBwcEAgBYtWmDHjh349ttvMX36dHh5eWHnzp1o2rSpRj8bEb2dTCZTFIjUPcVMR0eE5l42OHr7BXr7O2POu3X5M7KMlC4QSaVSdeTQSkb6YjhbGuFZchYexqWzQERERERlMu/QXVyNTIKZgS5WDwqAsb7SX+GING7kyJGFbiC/rqDoU+D06dOlOuYHH3yADz74oJzJiEjdEjNykZErHzDibKneJtUAMPc9P7zfsDreqVUNOlzVs8yU7kH0uuzsbFXl0FrerxpVP3rJlcyISH22fNpE6AhEpCa/hz3DxvNPAACL+zWAh62JsIGIiIjeoqBBtYO5IQz1xGo/n52ZATrXcYCuuFwljipP6T89iUSCOXPmwNnZGaampnj8+DEAYPr06Vi/fr3KA1Z2iqXu2aiaiNTIxICjCYi0UUR8Bv63T95w86v23ujk++YVoIiIiCqCfxtUq3/0EKmO0gWi77//HsHBwVi4cCH09fUV2/38/LBu3TqVhtMG3lzqnog0oM+qC0JHICIVy8mXYPT2a8jIlaCphzW+7lRD6EhERESloqkG1aRaSheINm/ejLVr12LgwIEQi/8dKlavXj3cu3dPpeG0AZe6JyIiorJYcDgc/zxLhZWxHpb294eYPRWIiKiS0FSDalItpQtEz549g7e3d5HtUqkUeXl5KgmlTdxs5AWi58lZkEplAqchIiKiyuDE3RfYcD4CAPDTh/XhYGEocCIiIqLSU0wxs2KBqDJRukBUp04d/PXXX0W27969G/7+/ioJpU2sTPQAAFIZkJrNAhoRqcfsXnWEjkBEKhKbko2Ju28AAD5p6Y4Otdl3iIiIKpfopFcjiGxYIKpMlO5q+t1332HQoEF49uwZpFIp9u3bh/DwcGzevBkHDx5UR8ZKzUBXDFMDXaTn5CMxIxeWxvpvfxMRkZJ6NXASOgIRqYBEKsPYHdeRlJmHOk7mmBJYS+hIRERESsmXSPE8Wb7iOUcQVS5KjyDq2bMndu7ciUOHDkEkEmHGjBm4e/cu/vjjD3Tq1EkdGSu9glFESZm5AichIm3VYHaI0BGISAVWnnqISxGJMNYXY/lH/jDQVf/SwERERKoUk5INiVQGfV0dVDMzEDoOKaFM6yJ36dIFXbp0UXUWrWVtrI/oxCwkZnCKGRERERXvelQSgk48AADMfrcuPO1MBU5ERESkvMfxGQAAFysj6HCBhUpF6RFEnp6eSEhIKLI9OTkZnp6eKgmlbaxM5NPKkjI4goiIiIiKSs/Jx7idYZBIZehZ3wnvN3QWOhIREVGZ3I1JBQDUcjAXOAkpS+kC0ZMnTyCRSIpsz8nJwbNnz1QSSttYv+o7xClmRKQun7RwFzoCEZXDrAO3EZmQCWdLI8x9ry5EIt5xJSKiyun2c3mByNeJBaLKptRTzA4cOKD476NHj8LCwkLxXCKR4MSJE3B3d1dpOG1RMIIokQUiIlKTqd1qCx2BiMroz5sx2B36FDoiYEm/BrAw0hM6EhERUZndeZ4CAKjDAlGlU+oC0XvvvQcAEIlEGDJkSKHX9PT04O7ujkWLFqk0nLaw5hQzIlKzNgtP4ezk9kLHICIlPU/OwtR9NwEAI9t5o4mHtcCJiIiIyi4zN1/Rg4gjiCqfUheIpFIpAMDDwwNXrlyBra2t2kJpG6tXU8zYpJqI1CU2NVvoCESkJIlUhvG7wpCanY/6LpYY29FH6EhERETlci82DTIZYGdmgGpmhkLHISUpvYpZREREkW3JycmwtLRURR6tZGXMZe6JiIiosI3nI/D3Y/mS9kv7NYCeWOnWkERERBWKov+QI0cPVUZKfxNZsGABdu7cqXj+4YcfwtraGs7Ozrhx44ZKw2kLrmJGROrWtoad0BGISAkP49Kx8Gg4AGB6D1+425oInIiIiKj87rwqELH/UOWkdIFozZo1cHFxAQCEhITg+PHjOHLkCAIDAzFp0iSVB9QG1mxSTURq9svgRkJHIKJSypdIMWH3DeTmS9G2hh36N3YROhIREZFKFDSoZv+hyknpAlFMTIyiQHTw4EH07dsXnTt3xuTJk3HlyhWVB9QGBT2IUrLykC+RCpyGiLTRkA2XhY5ARKW09q/HuBGdDDNDXfzwvh+XtCciIq2QL5HiXmwaAKCOk8Vb9qaKSOkCkZWVFaKjowEAR44cQceOHQEAMpkMEolEtem0hOWrHkQymbxIRESkahcfJwgdgYhKITw2DUEhDwAA3/WsA0cLI4ETERERqcbj+Azk5Ethoi+Gm7Wx0HGoDJRuUt2nTx8MGDAAPj4+SEhIQGBgIAAgLCwM3t7eKg+oDfTEOjA31EVqdj6SMnNhY2ogdCQiIiLSsDyJFON3hSFXIkXH2tXwfkNnoSMRERGpzO1X08tqO5pDR4ejYysjpQtES5YsgYeHB6KiorBw4UKYmpoCkE89GzlypMoDagtrE32kZudzqXsiUgtX3qUhqvBWnnqE289TYWmsh3l9OLWMiIi0CxtUV35KFYjy8vLw+eefY/r06fD09Cz02rhx41SZS+tYmejjSUImErmSGRGpwfHxbYWOQERvcDcmFctPyqeWzepVB9XMDAVOREREpFqKJe5ZIKq0lOpBpKenh/3796sri1azftWoOokrmRGRGsw8cFvoCERUgnyJFJP33ES+VIbOvvboVd9J6EhEREQqJZPJcCemYAQRG1RXVko3qe7duzd+++03NUTRblYFS91zBBERqcG2y1FCRyCiEqw/F4Fbz1JgbqiLue/V5dQyIiLSOs9TspGcmQddHRF87E2FjkNlpHQPIm9vb8yZMwcXLlxAQEAATExMCr0+ZswYlYXTJtavCkTJHEFERERUZTx+mY7FIfcBAN/28EU1c04tIyIi7VPQf8i7mikMdMUCp6GyUrpAtG7dOlhaWiI0NBShoaGFXhOJRCwQlcDKuGAEEZtUExERVQVSqQxT9t1CTr4UrX1s8WFAdaEjERERqUXBCmbsP1S5KV0gioiIUEcOrWdtogeAPYiISD3uzOoidAQi+o9tl6NwOSIRxvpizOvNVcuIiEh7/buCGfsPVWZKF4iobP4dQcQCERGp3rbLURjc3F3t5xk/frzS7/n2229hbW2thjREFdfz5Cz8cPgeAGBSl5pwsTYWOBEREZH6KFYwc+QIosqMBSINKehBxBFERKQOc/+8q5ECUVBQEJo3bw59ff1S7X/u3Dl89dVXLBBRlSKTyfDtb/8gPScfDV0tNfJvk4iISCgpmXl4lpwFgFPMKjsWiDSEq5gRkbbYv38/qlWrVqp9zczMSn3clStX4scff0RMTAzq1KmDoKAgtG7dusT9t27dioULF+LBgwewsLBA165d8dNPP8HGxqbU5yRSh8P/xOLkvTjoiUVY+EE9iHU4tYwqLmUL+CKRCNeuXYObm5uaEhFRZXM7Rt5/yMXaCBZGegKnofJggUhDrF9NMUvLzkeeRAo9sY7AiYiIlLdx40ZYWJR+bvmaNWtgb2//1v127tyJcePGYeXKlWjZsiXWrFmDwMBA3LlzB66urkX2P3fuHAYPHowlS5agZ8+eePbsGUaMGIHhw4dj//79Sn0mIlVKzc7DzAO3AQBftvOGd7XSF0mJhJCcnIygoKBSXdtlMhlGjhwJiUSigWREVFnc4fQyrVHqAtHatWvRq1cvODg4qDOP1jI30oOOCJDK5NPMqplxmVsiUp0/R7fSyHmGDBmi1P4DBgwo1X6LFy/GsGHDMHz4cADyqWxHjx7FqlWrMH/+/CL7//3333B3d1esnOnh4YEvvvgCCxcuVCofkar9dDQccWk58LA1wch2XkLHISqV/v37l3pk6OjRo9WchogqGzao1h6lHsayfft2uLu7o2nTppg3bx5u376tzlxaR6wjguWrUURJXOqeiFQsNjVbsHPn5ubi6dOniIqKKvRQ5v2hoaHo3Llzoe2dO3fGhQsXin1PixYt8PTpUxw6dAgymQwvXrzAnj170L179xLPk5OTg9TU1EIPIlUKi07Glr8jAQDfv1cXhnpigRMRvZ1UKi11cQgA0tLS4OnpqcZERFTZsEG19ih1gejUqVOIiYnB6NGjERYWhhYtWsDLywvjx4/H6dOnIZVK1ZlTK1gZy+djsg8REanasE1XNX7OBw8eoHXr1jAyMoKbmxs8PDzg4eEBd3d3eHh4lPo48fHxkEgkRaai2dvbIzY2ttj3tGjRAlu3bkW/fv2gr68PBwcHWFpaYvny5SWeZ/78+bCwsFA8XFxcSp2R6G3yJVJM3XcLMhnQp6EzWnjbCh2JiIhI7TJz8/HwZToAoI4zC0SVnVI9iKysrPDxxx/j448/Rm5uLk6ePIkDBw5g0KBByMzMRPfu3dGrVy8EBgbCxMREXZkrLflS9xlcyYyItMLQoUOhq6uLgwcPwtHRESJR+Rrx/vf9MpmsxGPeuXMHY8aMwYwZM9ClSxfExMRg0qRJGDFiBNavX1/se6ZOnYrx48crnqemprJIRCqz8fwT3I1JhaWxHqZ1qy10HKIy2bx58xtfHzx4sIaSEFFlceVJEiRSGZwtjeBoYSR0HCqnMjep1tfXR9euXdG1a1esXLkSV69exYEDBzBnzhzcvXsX06dPV2VOrcCVzIhIm4SFhSE0NBS1atUq13FsbW0hFouLjBaKi4srscH1/Pnz0bJlS0yaNAkAUK9ePZiYmKB169aYO3cuHB0di7zHwMAABgYG5cpKVJynSZlYHHIfAPC/brVhY8q/Z1Q5jR07ttDzvLw8ZGZmQl9fH8bGxiwQEVERfz9OAAA08+QqstpAZUtpNWrUCLNnz8aNGzcwZcoUVR1Wq1grehCxQEREqrX8I3+Nn9PX1xfx8fHlPo6+vj4CAgIQEhJSaHtISAhatGhR7HsyMzOho1P4R5hYLO/3IpPJyp2JSBmz/7iDrDwJmnhY48OA6kLHISqzpKSkQo/09HSEh4ejVatW2L59u9DxiKgCuvhIXiBq7sUCkTZQy1rrenp66jhspVcwgigpk02qiUi16jhpfs73ggULMHnyZJw+fRoJCQnlagA9fvx4rFu3Dhs2bMDdu3fx9ddfIyoqCiNGjAAgnx72+p3rnj17Yt++fVi1ahUeP36M8+fPY8yYMWjSpAmcnJxU+jmJ3uR0eByO3XkBXR0Rvn+vbrmnWhJVND4+Pvjhhx+KjC4iIkrPycetZykAWCDSFmWeYkbKszaRF87Yg4iIVO2dRWdwf26gRs/ZsWNHAECHDh0KbS/oHSSRSEp9rH79+iEhIQGzZ89GTEwM6tati0OHDsHNzQ0AEBMTU2hltKFDhyItLQ0rVqzAhAkTYGlpiXfeeQcLFixQwScjKp2cfAlm/XEHADC0hTt87M0ETkSkHmKxGM+fPxc6BhFVMFciEiGRyuBqbQxnS/Yf0gYsEGmQlTF7EBGR9jh16pRKjzdy5EiMHDmy2NeCg4OLbBs9ejRGjx6t0gxEythw7gki4jNga2qAsR19hI5DVG4HDhwo9FwmkyEmJgYrVqxAy5YtBUpFRBXVv/2HrAVOQqrCApEGWSummLFARESVX9u2bYWOQCSY2JRsLD/5AADwv261YGbI6fVU+b333nuFnotEItjZ2eGdd97BokWLhAlFRBXWxcfsP6RtlC4QHTlyBKampmjVqhUA4Oeff8Yvv/wCX19f/Pzzz7CyslJ5SG3BVcyISF3Gd6qh8XOePXv2ja+3adNGQ0mING/eobvIzJUgwM0Kvf2dhY5DpBJSqVToCERUSaRm5+Gfgv5DnrYCpyFVUbpANGnSJEWPh1u3bmHChAkYP348Tp48ifHjx2Pjxo0qD6ktuIoZEanLiLZeGj9nu3btimx7vUGvMj2IiCqTvx8n4MCN5xCJgFm96rAxNRERVTmXHydCKgM8bE3gYGEodBxSEaULRBEREfD19QUA7N27Fz169MC8efNw7do1dOvWTeUBtUnBCKKMXAmy8yQw1BMLnIiItEXd747in1ldNHrOpKSkQs/z8vJw/fp1TJ8+Hd9//71GsxBpSr5Eiu9+vw0AGNjUFXWdLQRORKRaT58+xYEDBxAVFYXc3MI3NRcvXixQKiKqaC6y/5BWUrpApK+vj8zMTADA8ePHFcsOW1tbK72scVVjbqgLsY4IEqkMyZl5cLBggYiIVCNXovlpARYWRX8x7tSpEwwMDPD1118jNDRU45mI1G3b5SiEv0iDlbEeJnauKXQcIpU6ceIEevXqBQ8PD4SHh6Nu3bp48uQJZDIZGjZsKHQ8IqpA/m1Qzf5D2kRH2Te0atUK48ePx5w5c3D58mV0794dAHD//n1Ur15d5QG1iUgk4kpmRKT17OzsEB4eLnQMIpVLyczD4pD7AOR9vyxf/Uwn0hZTp07FhAkT8M8//8DQ0BB79+5FdHQ02rZtiw8//FDoeERUQSRn5uJOjHxwSHMWiLSK0iOIVqxYgZEjR2LPnj1YtWoVnJ3ljRkPHz6Mrl27qjygtrE20UN8eg5XMiMileojQJPcmzdvFnpesBzyDz/8gPr162s8D5G6LTv5AMmZeahhb4qPmrgKHYdI5e7evYvt27cDAHR1dZGVlQVTU1PMnj0b7777Lr788kuBExJRRXApIhEyGeBlZ4Jq5uw/pE2ULhC5urri4MGDRbYvWbJEJYH+69mzZ/jmm29w+PBhZGVloUaNGli/fj0CAgIAyH8hmTVrFtauXYukpCQ0bdoUP//8M+rUqaOWPOXFEUREpA4/vF9P4+ds0KABRCIRZDJZoe3NmjXDhg0bNJ6HSJ0ev0zHpgtPAADfdveFrljpQdhEFZ6JiQlycnIAAE5OTnj06JHiO3V8fLyQ0YioArn4iMvbayulC0TXrl2Dnp4e/Pz8AAC///47Nm7cCF9fX8ycORP6+qobbp2UlISWLVuiffv2OHz4MKpVq4ZHjx7B0tJSsc/ChQuxePFiBAcHo0aNGpg7dy46deqE8PBwmJmZqSyLqli/alTNEUREpEo9l5/DH6NbafScERERhZ7r6OjAzs4Ohoa8k0TaZ96hu8iXyvBOrWpoU8NO6DhEatGsWTOcP38evr6+6N69OyZMmIBbt25h3759aNasmdDxiKiCYP8h7aV0geiLL77AlClT4Ofnh8ePH6N///7o3bs3du/ejczMTAQFBaks3IIFC+Di4oKNGzcqtrm7uyv+WyaTISgoCNOmTUOfPn0AAJs2bYK9vT22bduGL774QmVZVKVgJTOOICIiVQp/kabxc7q5uRXZlpyczAIRaZ1zD+Jx/G4cdHVE+F+32kLHIVKbxYsXIz09HQAwc+ZMpKenY+fOnfD29lbbbAEiqlwSM3JxL1b+vZMFIu2j9Pjo+/fvo0GDBgCA3bt3o02bNti2bRuCg4Oxd+9elYY7cOAAGjVqhA8//BDVqlWDv78/fvnlF8XrERERiI2NRefOnRXbDAwM0LZtW1y4cKHE4+bk5CA1NbXQQ1OsX00xS87M09g5iYjUYcGCBdi5c6fied++fWFtbQ1nZ2fcuHFDwGREqiORyjD3zzsAgI+bucG7mqnAiYjUx9PTE/XqyacsGxsbY+XKlbh58yb27dtX7E0BIqp6Lr0aPVTD3hS2pgYCpyFVU7pAJJPJIJXKl1M+fvw4unXrBgBwcXFR+dzkx48fY9WqVfDx8cHRo0cxYsQIjBkzBps3bwYAxMbGAgDs7e0Lvc/e3l7xWnHmz58PCwsLxcPFxUWlud+EI4iISB0auFhq/Jxr1qxRXD9DQkIQEhKCI0eOIDAwEJMmTdJ4HiJ12HklGvdi02BhpIdxHX2EjkNERCSoi68KRFy9TDspXSBq1KgR5s6diy1btuDMmTOKZe4jIiKKFGrKSyqVomHDhpg3bx78/f3xxRdf4LPPPsOqVasK7ScSiQo9l8lkRba9burUqUhJSVE8oqOjVZr7TaxN9ACwBxERqdauL5pr/JwxMTGKAtHBgwfRt29fdO7cGZMnT8aVK1c0nodI1dKy87DoWDgAYFxHHy5rT1rJ2tpaqZu8rq6uiIyMVGMiIqrIbkQnAwAae1gLG4TUQukeREFBQRg4cCB+++03TJs2Dd7e3gCAPXv2oEWLFioN5+joCF9f30LbateurZjK5uDgAEA+ksjR0VGxT1xc3BuLVQYGBjAwEGY4HFcxIyJ1GLP9OpZ95K/Rc1pZWSE6OhouLi44cuQI5s6dC0BepJdIJBrNQqQOq04/QkJGLjxtTfBxM06vIe2UnJyMw4cPw8LColT7JyQk8BpPVIU9ScgEAE651lJKF4jq1auHW7duFdn+448/QiwWqyRUgZYtWyI8PLzQtvv37yvmQHt4eMDBwQEhISHw95f/YpSbm4szZ85gwYIFKs2iKgUFoiQWiIhIhY7cLnlarbr06dMHAwYMgI+PDxISEhAYGAgACAsLU9w8IKqsniVnYf05+Up9U7vVhh6XtSctNmTIEKEjEFElkJyZi5QseS9dV2tjgdOQOihdICqJOlat+frrr9GiRQvMmzcPffv2xeXLl7F27VqsXbsWgHxq2bhx4zBv3jz4+PjAx8cH8+bNg7GxMQYMGKDyPKpQsMx9IqeYEVElt2TJEri7uyM6OhoLFy6Eqan8TlJMTAxGjhwpcDqi8ll0NBw5+VI09bBGx9rVhI5DpDYFvUWJiN4m8tXooWpmBjDWV1kpgSoQpf+vSiQSLFmyBLt27UJUVBRycwsXOhITE1UWrnHjxti/fz+mTp2K2bNnw8PDQzHFrcDkyZORlZWFkSNHIikpCU2bNsWxY8dgZmamshyqVNCkOjtPiqxcCYz0VTvqioiqJgsjPY2fU09PDxMnTiyyfdy4cRrPQqRK/zxLwb7rzwAA07rXfmNfQyIioqoiMlFeIHKz4eghbaV0gWjWrFlYt24dxo8fj+nTp2PatGl48uQJfvvtN8yYMUPlAXv06IEePXqU+LpIJMLMmTMxc+ZMlZ9bHUz0xdAX6yBXIkViZi6c9Y2EjkREWuDKtI4aO9fZs2dLtV+bNm3UnIRI9WSyf5e1f6+BE+pVtxQ2EBERUQURlZABAHCzMRE4CamL0hPqt27dil9++QUTJ06Erq4uPvroI6xbtw4zZszA33//rY6MWkUkEsGqYCUz9iEiIhVZEnJfY+dq164d2rdvj/bt26Ndu3bFPtq3b6+xPESqdOJuHP5+nAh9XR1M7FJT6DhEldrKlSvh4eEBQ0NDBAQE4K+//ipx35iYGAwYMAA1a9aEjo5OsaNRg4ODIRKJijyys7PV+CmIqEBBg2o39h/SWkoXiGJjY+Hn5wcAMDU1RUpKCgD5SJ8///xTtem0FFcyIyJVW3XmkcbOZWVlBRcXF0yfPh0PHjxAUlJSkYcqpxsTaUqeRIp5h+8CAIa18kB1K34BJiqrnTt3Yty4cZg2bRquX7+O1q1bIzAwEFFRUcXun5OTAzs7O0ybNg3169cv8bjm5uaIiYkp9FBHL1QiKiqqoEBkyxFE2krpAlH16tURExMDAPD29saxY8cAAFeuXBFs6fjKpqBRdRIbVRNRJRQTE4MFCxbg4sWL8PPzw7Bhw3DhwgWYm5vDwsJC8SCqbHZcicbjlxmwNtHHl+28hI5DVKktXrwYw4YNw/Dhw1G7dm0EBQXBxcUFq1atKnZ/d3d3LF26FIMHD37jzxCRSAQHB4dCDyLSjCcFU8w4gkhrKV0g6t27N06cOAEAGDt2LKZPnw4fHx8MHjwYn376qcoDaqOCRtUcQURElZG+vj769euHo0ePIjw8HPXq1cNXX30FFxcXTJs2Dfn5+UJHJFJaek4+lh6XT9Uc28EH5oaab/xOJDSxWIy4uLgi2xMSEiAWl35hldzcXISGhqJz586Ftnfu3BkXLlwoV8b09HS4ubmhevXq6NGjB65fv16u4xFR6WTm5iMuLQcA4M4eRFpL6SbVP/zwg+K/P/jgA1SvXh0XLlyAt7c3evXqpdJw2sr61RQz9iAiIlW5OOUdQc7r4uKCGTNmYNCgQRg2bBh++OEHTJgwAdbW1oLkISqrdX89Rnx6LtxtjDGgqavQcYgEIZPJit2ek5MDfX39Uh8nPj4eEokE9vb2hbbb29sjNja2zPlq1aqF4OBg+Pn5ITU1FUuXLkXLli1x48YN+Pj4lJg9JydH8Tw1NbXM5yeqyqJerWBmYaQHC2PeRNFWSheI/qtZs2Zo1qyZKrJUGVaKKWZ5AichIm1x7mE83m3grNFz5uTkYO/evdiwYQMuXryI7t27488//2RxiCqdl2k5+OXsYwDApC61oCdWeoA1UaW2bNkyAPLpW+vWrYOpqaniNYlEgrNnz6JWrVpKH1ckEhV6LpPJimxTxn9/72jZsiUaNmyI5cuXKz7Df82fPx+zZs0q8zmJSC7yVf8hdy5xr9XKVCB69uwZzp8/j7i4OEil0kKvjRkzRiXBtJn1q4prInsQEZGKTNpzU2MFosuXL2Pjxo3YsWMHPDw8MHToUOzatYuFIaq0Vpx8gIxcCepXt0A3P/YzoapnyZIlAOQFnNWrVxeaTqavrw93d3esXr261MeztbWFWCwuMlooLi6uyKii8tDR0UHjxo3x4MGDEveZOnUqxo8fr3iempoKFxcXlWUgqioKGlS7cnqZVlO6QLRx40aMGDEC+vr6sLGxKXQXQCQSsUBUCrZm8mbeL1K4JCcRVT7NmjWDq6srxowZg4CAAADAuXPniuzHacdUGUQmZGDrJfmqSt8E1irX6AaiyioiIgIA0L59e+zbtw9WVlblOp6+vj4CAgIQEhKC3r17K7aHhITg3XffLdexXyeTyRAWFqZYYbk4BgYGXEiHSAXYoLpqULpANGPGDMyYMQNTp06Fjg6HYJeF66t/VAXzOImIKpuoqCjMmTOnxNdFIhEkEokGExGVzU/H7iNfKkPbGnZo4WUrdBwiQZ06dUplxxo/fjwGDRqERo0aoXnz5li7di2ioqIwYsQIAPKRPc+ePcPmzZsV7wkLCwMgb0T98uVLhIWFQV9fH76+vgCAWbNmoVmzZvDx8UFqaiqWLVuGsLAw/PzzzyrLTUTFK/jd1Y1TzLSa0gWizMxM9O/fn8WhcigoEMWl5SArVwIj/dKvCkFEVJxtw5tq7Fz/nVpMVFndepqCP248h0gEfNNV+f4qRNpGIpEgODgYJ06cKLaVxMmTJ0t9rH79+iEhIQGzZ89GTEwM6tati0OHDsHNzQ0AEBMTg6ioqELv8ff3V/x3aGgotm3bBjc3Nzx58gQAkJycjM8//xyxsbGwsLCAv78/zp49iyZNmpTxExNRaSlGEHGKmVZTukA0bNgw7N69G1OmTFFHnirB0lgf5oa6SM3OR3RSJmrYmwkdiYgqOX1dFu2JlLXgyD0AwHsNnOHrZC5wGiLhjR07FsHBwejevTvq1q1b7imXI0eOxMiRI4t9LTg4uMi2klZRK7BkyRJFvyQi0pzcfCmeJWUBYJNqbad0gWj+/Pno0aMHjhw5Aj8/P+jpFV7ibvHixSoLp83cbExw61kKIhNYICKi8vtg9UXcnxuo9vMcOHAAgYGBRa79JTl06BDat28PIyMjNScjUs5fD17i3MN46It1ML5TDaHjEFUIO3bswK5du9CtWzehoxBRBfIsOQtSGWCkJ4adGXt6aTOlC0Tz5s3D0aNHUbNmTQAo0qSaSsfV2hi3nqWwDxERVSq9e/dGbGws7OzsSrV///79ERYWBk9PTzUnIyo9mUyGH4+GAwAGNnOFCxtuEgGQN5f29vYWOgYRVTCRiullxvydX8spXSBavHgxNmzYgKFDh6ohTtXh+mpoXtSrf2xERJWBTCbD0KFDS70iTHY2V2ukiufYnRe4+TQFxvpijGrPX4aJCkyYMAFLly7FihUr+EsgESkUDGpw5Q0Vrad0gcjAwAAtW7ZUR5YqpWB5wEiOICIiFZj7Xl2NnGfIkCFK7T9w4ECYm7O3C1UcEqkMi4/dBwB80tIdtqYcKk9VW58+fQo9P3nyJA4fPow6deoUmU68b98+TUYjogriSbz8d1Z3Wzao1nZKF4jGjh2L5cuXY9myZerIU2VwqXsiUqXAug4aOc/GjRs1ch4idTl48znCX6TBzFAXn7f2EjoOkeAsLCwKPe/du7dASYiooopKlM964Qgi7ad0gejy5cs4efIkDh48yDsL5VAwxexpYhYkUhnEOhzGS0RlFzD3uEaaVBNVZnkSKZaEyEcPfdHGExbGpWu2TqTNWPgnord5kiAf1ODGFcy0ntIFIktLyyJDUUl5jhZG0BOLkCuRIjY1G86WXOGHiIhInfaGPsWThEzYmOjjk5YeQschIiKq8KRSmWLWi7sNp5hpO6ULRLzLoBpiHRGqWxkjIj4DUQmZLBARERGpUU6+BMtOPAAAfNnOCyYGSn8FItJ6/v7+xTanFolEMDQ0hLe3N4YOHYr27dsLkI6IhBCbmo3cfCl0dURwtDAUOg6pmY7QAaqyf/sQcSUzIiqfYa04GoLoTbZdisLzlGw4mBvi42ZuQschqpC6du2Kx48fw8TEBO3bt0e7du1gamqKR48eoXHjxoiJiUHHjh3x+++/Cx2ViDQk8tX0MhdrY+iKWT7Qdrx9JiA2qiYiVfmmay2hIwAAkpOTYWlpKXQMokIyc/Px86lHAIDRHbxhqCcWOBFRxRQfH48JEyZg+vTphbbPnTsXkZGROHbsGL777jvMmTMH7777rkApiUiT2KC6amEJUEAFTb4KqrJERGXV8oeTGj/nggULsHPnTsXzvn37wsbGBs7Ozrhx44bG8xCVZMvFSMSn58DV2hh9G7kIHYeowtq1axc++uijItv79++PXbt2AQA++ugjhIeHazoaEQmkoEG1OxtUVwksEAnIhSOIiEhFXqbnaPyca9asgYuL/JftkJAQhISE4PDhwwgMDMSkSZM0noeoOBk5+Vhz9jEAYEwHH+hxeDxRiQwNDXHhwoUi2y9cuABDQ3nvEalUCgMDA01HIyKBRL0qELmyQXWVwClmAioYQcQCERFVRjExMYoC0cGDB9G3b1907twZ7u7uaNq0qcDpiOQ2X4xEYkYu3G2M8V4DJ6HjEFVoo0ePxogRIxAaGorGjRtDJBLh8uXLWLduHf73v/8BAI4ePQp/f3+BkxKRpjxJkE8x4wiiqqFUBaJly5aV+oBjxowpc5iqpmAeZ3JmHlKy8mBhpCdwIiKqrN6pWU3j57SyskJ0dDRcXFxw5MgRzJ07FwAgk8kgkUg0nofov9Jz8rH27KveQ+/4sLkm0Vt8++238PDwwIoVK7BlyxYAQM2aNfHLL79gwIABAIARI0bgyy+/FDImEWmITCZTjCByY4GoSihVgWjJkiWFnr98+RKZmZmKRqTJyckwNjZGtWrVWCBSgrG+LmxNDRCfnoOohEz4VbcQOhIRVVKrBwVo/Jx9+vTBgAED4OPjg4SEBAQGBgIAwsLC4O3trfE8RP+1+eITJGXmwcPWBO9y9BBRqQwcOBADBw4s8XUjIyMNpiEiISVm5CItJx8iEVDdigWiqqBUt9IiIiIUj++//x4NGjTA3bt3kZiYiMTERNy9excNGzbEnDlz1J1X63CaGRGpwqD1lzR+ziVLluCrr76Cr68vQkJCYGpqCkA+9WzkyJEaz0P0OvnoIXnvodHveHP0EBERkZIiX/2O6mhuyBVAqwilvy1Nnz4dy5cvR82aNRXbatasiSVLluDbb79VabiqwO3VNLPIV8sHEhGVxaWIRI2fU09PDxMnTsTSpUsL9aMYN24chg8frvTxVq5cCQ8PDxgaGiIgIAB//fXXG/fPycnBtGnT4ObmBgMDA3h5eWHDhg1Kn5e006YLT5CcmQdPWxP0qs/RQ0Qlsba2Rnx8PAD51GFra+sSH0RUtfzboJqjh6oKpZtUx8TEIC8vr8h2iUSCFy9eqCRUVaJYyYxL3RNRJbNp0ybY2tqie/fuAIDJkydj7dq18PX1xfbt2+Hm5lbqY+3cuRPjxo3DypUr0bJlS6xZswaBgYG4c+cOXF1di31P37598eLFC6xfvx7e3t6Ii4tDfn6+Sj4bVW5p2XmK0UNjOrD3ENGbLFmyBGZmZgCAoKAgYcMQUYXyIC4NAOBmzRXMqgqlC0QdOnTAZ599hvXr1yMgIAAikQhXr17FF198gY4dO6ojo1bjFDMiUgUPAZYenTdvHlatWgUAuHjxIlasWIGgoCAcPHgQX3/9Nfbt21fqYy1evBjDhg1TjDwKCgrC0aNHsWrVKsyfP7/I/keOHMGZM2fw+PFjxV1td3f38n8o0grB558gJSsPnnYm6MnRQ0RvNGTIkGL/m4iqNplMhqO35QNAmnhwBGFVofQttQ0bNsDZ2RlNmjSBoaEhDAwM0LRpUzg6OmLdunXqyKjVClYyi+QIIiIqh6Nft9H4OaOjoxXNqH/77Td88MEH+PzzzzF//vy3Tg97XW5uLkJDQ9G5c+dC2zt37owLFy4U+54DBw6gUaNGWLhwIZydnVGjRg1MnDgRWVlZZf9ApBVSs/Ow7lwEAGBsBx+IdUQCJyKqXB49eoRvv/0WH330EeLi4gDIi/K3b98WOBkRaVL4izQ8jEuHvlgHnerYCx2HNETpApGdnR0OHTqEe/fuYffu3di1axfu3r2LQ4cOoVo1zS+zXNkVzOeMSclCbr5U4DREVFlN/+0fjZ/T1NQUCQkJAIBjx44pRpEaGhoqVaiJj4+HRCKBvX3hLx/29vaIjY0t9j2PHz/GuXPn8M8//2D//v0ICgrCnj17MGrUqBLPk5OTg9TU1EIP0j4bz8lHD3nZmaBHPY4eIlLGmTNn4Ofnh0uXLmHfvn1IT08HANy8eRPfffedwOmISJMO3ogBALStaQdzQz2B05CmlHlSvru7O2rWrInu3bujRo0aqsxUpdiZGsBITwypDHiWzDvfRFQ2O69Ga/ycnTp1wvDhwzF8+HDcv39f0Yvo9u3bZZruJRIVHukhk8mKbCsglUohEomwdetWNGnSBN26dcPixYsRHBxcYnFq/vz5sLCwUDxcXFyUzkgVW0pWHtadk/ceGtuxBkcPESlpypQpmDt3LkJCQqCvr6/Y3r59e1y8eFHAZESkSTKZDAdvPgcA9KjnKHAa0iSlC0SZmZkYNmwYjI2NUadOHURFRQEAxowZgx9++EHlAbWdSCR6bZoZVzIjosrj559/RvPmzfHy5Uvs3bsXNjY2AIDQ0FB89NFHpT6Ora0txGJxkdFCcXFxRUYVFXB0dISzszMsLCwU22rXrg2ZTIanT58W+56pU6ciJSVF8YiO1nxRjdRrw7kIpGXnw6eaKbr78QstkbJu3bqF3r17F9luZ2enGDFKRNrv9vNUPEnIhKGeDjrW5vSyqkTpAtHUqVNx48YNnD59GoaGhortHTt2xM6dO1UarqoomGYWzUbVRFSJWFpaYsWKFfj999/RtWtXxfZZs2Zh2rRppT6Ovr4+AgICEBISUmh7SEgIWrRoUex7WrZsiefPnyumPwDA/fv3oaOjg+rVqxf7HgMDA5ibmxd6kPZIyczDhoLeQx3Ze4ioLCwtLRETE1Nk+/Xr1+Hs7CxAIiISwh+vRg91qGUPEwOl17WiSkzpAtFvv/2GFStWoFWrVoWG/vv6+uLRo0cqDVdVuLFRNRGV0/25gYKdOzMzE/fu3cPNmzcLPZQxfvx4rFu3Dhs2bMDdu3fx9ddfIyoqCiNGjAAgvzkxePBgxf4DBgyAjY0NPvnkE9y5cwdnz57FpEmT8Omnn8LIyEiln48qh/XnHiMtJx817c3QrS5HDxGVxYABA/DNN98gNjYWIpEIUqkU58+fx8SJEwtdg4lIe8lkMvx5U14o5vSyqkfpcuDLly+LbUadkZFRYq8IerOCEUSRHEFERGW04VwEPm3lodFzvnz5EkOHDsWRI0eKfV0ikZT6WP369UNCQgJmz56NmJgY1K1bF4cOHYKbmxsAICYmRjGlGZA3yA4JCcHo0aPRqFEj2NjYoG/fvpg7d275PhRVSsmZudhw/gkA+eghHY4eIiqT77//HkOHDoWzszNkMhl8fX0hkUgwYMAAfPvtt0LHIyINCItOxtOkLJjoi9G+FhehqmqULhA1btwYf/75J0aPHg3g36aiv/zyC5o3b67adFVEQQ8iTjEjorL64cg9jReIxo0bh+TkZPz9999o37499u/fjxcvXmDu3LlYtGiR0scbOXIkRo4cWexrwcHBRbbVqlWryLQ0qprW/RWB9Jx81HIwQ9c6DkLHIaq09PT0sHXrVsyePRvXr1+HVCqFv78/fHx8hI5GRBpy8NXooY6+9jDUEwuchjRN6QLR/Pnz0bVrV9y5cwf5+flYunQpbt++jYsXL+LMmTPqyKj1CgpEUYmZb1y1h4ioIjl58iR+//13NG7cGDo6OnBzc0OnTp1gbm6O+fPnK1Y1I1KnpIxcbDwv7z00jqOHiFTCy8sLXl5eQscgIg2TSl+fXuYkcBoSgtIFohYtWuD8+fP46aef4OXlhWPHjqFhw4a4ePEi/Pz81JFR61W3MoaOCMjMlSA+PRd2ZgZCRyIiequMjAzFlGNra2u8fPkSNWrUgJ+fH65duyZwOqoq1p17jIxcCXwdzdHZl6OHiMpDIpEgODgYJ06cQFxcHKRSaaHXT548KVAyItKE0KgkxKZmw8xQF21q2AodhwRQppbkfn5+2LRpk6qzVFn6ujpwtDDCs+QsRCVmsEBEREo7Mra1xs9Zs2ZNhIeHw93dHQ0aNMCaNWvg7u6O1atXw9GRTQ1J/VIy87DpQiQAjh4iUoWxY8ciODgY3bt3R926dTmqnaiKOXhDvnpZZ18HGOhyellVVKoCUWpqaqkPyGWDy8bV2hjPkrMQmZCJADdroeMQUSUTmZgJTztTjZ5z3LhxiuWQv/vuO3Tp0gVbt26Fvr5+sT2DiFQt+MITRe+hTr72QschqvR27NiBXbt2oVu3bkJHISINk0hlOPRPLACgR33e6KuqSlUgsrS0LPUdBGVWraF/udsa4+LjBDyJzxA6ChFVQl9sCdX4UvcDBw5U/Le/vz+ePHmCe/fuwdXVFba2HJZM6pWek4+NF+S9h0a19+ZIByIV0NfXh7e3t9AxiEgAp+7F4WVaDiyN9dDKm9/jqqpSFYhOnTql+O8nT55gypQpGDp0qGLVsosXL2LTpk2YP3++elJWAV6v7vw/fJkucBIiorIxNjZGw4YNhY5BVcTWvyORnJkHT1sTdPPjnU4iVZgwYQKWLl2KFStWsOhKVIVIpDL8eDQcANCvsQv0xDoCJyKhlKpA1LZtW8V/z549G4sXL8ZHH32k2NarVy/4+flh7dq1GDJkiOpTVgHe1V4ViOJYICKiykEmk2HPnj04depUsc1M9+3bJ1Ay0nbZeRL88pd89NCIdl4Qs/cQUZn16dOn0POTJ0/i8OHDqFOnDvT09Aq9xus6kXbad+0pwl+kwcJIDyPbchRhVaZ0k+qLFy9i9erVRbY3atQIw4cPV0moqsjH3gwAEBGfgTyJlFVbIlLKqoGaH7kzduxYrF27Fu3bt4e9vT3vNpPG7Loajfj0HDhbGqG3v7PQcYgqNQsLi0LPe/fuLVASIhJCdp4Ei0PuAwBGtfeChbHeW95B2kzpApGLiwtWr16NRYsWFdq+Zs0auLi4qCxYVeNkYQhjfTEycyWITMhUjCgiIioNIa4Zv/76K/bt28dmpqRReRIp1px5DAD4oq0nb6gQldPGjRuFjkBEAgq+8AQxKdlwsjDE4ObuQschgSldIFqyZAnef/99HD16FM2aNQMA/P3333j06BH27t2r8oBVhUgkgnc1U9x8moKHceksEBGRUjotOavxJtUWFhbw9PTU6DmJ9l9/hmfJWbA1NUDfRrwxRUREVFbJmblYeeohAGB855ow1OPS9lWd0rfdunXrhvv376NXr15ITExEQkIC3n33Xdy/f593kcvJu6BRdVyawEmIiN5u5syZmDVrFrKysoSOQlWERCrDqtOPAACftfbgF1kiIqJyWHn6EVKz81HLwYxTtglAGUYQAfJpZvPmzVN1lirP256Nqomo8vjwww+xfft2VKtWDe7u7kWamV67dk2gZKSt/rwVg4j4DFgY6WFgMzeh4xAREVVaz5KzEHzhCQDgm8BaXPCBAJSyQHTz5s1SH7BevXplDlPV+VSTN6p+wAIRESlpcpeaGj/n0KFDERoaio8//phNqkntpFIZfj4pHwb/SUt3mBqU6R4XERERAVh0LBy5+VI097RBuxp2QsehCqJU364aNGgAkUgEmUxW6BcAmUwGAIW2SSQSFUesOgr6Dj16mQ6pVAYdVnGJqJSGt9Z8L6A///wTR48eRatWrTR+bqp6jt99gfAXaTA10MUnLTyEjkOk9bKzs2FoaCh0DCJSgwsP47Hv2jMAwJTAWrzJRwql6kEUERGBx48fIyIiAnv37oWHhwdWrlyJsLAwhIWFYeXKlfDy8mKT6nJysTKCvq4OsvOkeJbMnh5EVHq1px/R+DldXFxgbm6u8fNS1SOTybD81eihwc3duAQvkZpIpVLMmTMHzs7OMDU1xePH8hUDp0+fjvXr1wucjohUISUrDxN33wAADGjqivoulsIGogqlVAUiNzc3xWPevHlYtmwZvvjiC9SrVw/16tXDF198gaCgIMyZM0fdebWarlgHnrYmAIAHbFRNREqQvBrRqUmLFi3C5MmT8eTJE42fm6qWM/df4tazFBjq6WBYK44eIlKXuXPnIjg4GAsXLoS+vr5iu5+fH9atWydgMiJSlVkHbuN5SjbcbIwxrVttoeNQBaP0Kma3bt2Ch0fRL2ceHh64c+eOSkJVZQXTzNiomogquo8//hinTp2Cl5cXzMzMYG1tXehBpAqvjx4a2NQNNqYGAici0l6bN2/G2rVrMXDgQIjF/64SWK9ePdy7d0/AZESkCodvxWDf9WfQEQGL+9aHCfv50X8o/Teidu3amDt3LtavX6+Yl5yTk4O5c+eidm1WIMtL3qg6Bg9esEBERKX3QUB1jZ8zKChI4+ekqufvx4kIjUyCvq4OPm+j+V5bRFXJs2fP4O3tXWS7VCpFXl6eAImISFXi0rLxv/23AAAj2nohwI0386gopQtEq1evRs+ePeHi4oL69esDAG7cuAGRSISDBw+qPGBVUzCCiCuZEZEy5vX20/g5hwwZovFzUtWz4tQDAEC/Ri6wN2fDXCJ1qlOnDv766y+4ubkV2r579274+/sLlIqIyksmk2HK3ltIysxDbUdzjOtYQ+hIVEEpXSBq0qQJIiIi8Ouvv+LevXuQyWTo168fBgwYABMTE3VkrFJ87F+tZBaXXmTVOCKiknRb+hcOjW0tdAwilQqNTML5hwnQ1RHhi7YcPUSkbt999x0GDRqEZ8+eQSqVYt++fQgPD8fmzZt5I5ioEttxJRon78VBX6yDoH4NoK+rdKcZqiLKNOnQ2NgYn3/+uaqzEAB3GxOIdURIy8nHi9QcOFjwbikRvd3Dlxx1SNrn51Py3kN9GjqjupWxwGmItF/Pnj2xc+dOzJs3DyKRCDNmzEDDhg3xxx9/oFOnTkLHI6IyePQyHbP/kPcKntilBmo6mAmciCqyMpcO79y5gyNHjuDAgQOFHuo0f/58iEQijBs3TrFNJpNh5syZcHJygpGREdq1a4fbt2+rNYc66evqwM1G/iWYK5kREVFV9c+zFJy8FwcdEfBlu6I9UYhIPbp06YIzZ84gPT0dmZmZOHfuHDp37ix0LCIqg5x8CcZsv46sPAlaeNlgWCuOxqU3U3oE0ePHj9G7d2/cunULIpEIsldLKxdMhZJIJKpN+MqVK1ewdu1a1KtXr9D2hQsXYvHixQgODkaNGjUwd+5cdOrUCeHh4TAzq5zVUW87Uzx+mYGHcelo7WMndBwiqgQCXK2EjkCkUqvOPAIA9KjnBA9bTmEnIiJS1o9HwnH7eSqsjPWwpF8DiHXYvoTeTOkRRGPHjoWHhwdevHgBY2Nj3L59G2fPnkWjRo1w+vRpNUQE0tPTMXDgQPzyyy+wsvr3lyCZTIagoCBMmzYNffr0Qd26dbFp0yZkZmZi27ZtasmiCQV9iNiomohKa/vnzTR+zhMnTpT42ooVKzSYhLRNRHwGDt+KAQB82c5L4DRE2s3KygrW1talehBR5XE6PA7rzkUAAH78oD4XeqBSUXoE0cWLF3Hy5EnY2dlBR0cHOjo6aNWqFebPn48xY8bg+vXrKg85atQodO/eHR07dsTcuXMV2yMiIhAbG1to2KuBgQHatm2LCxcu4Isvvij2eDk5OcjJyVE8T01NVXnm8pAvdQ88ZIGIiEpp1LZr+HlAQ42e8/3330dISAgaN25caHtQUBBmzJiBr776SqN5SHusPfsYUhnwTq1qqO1oLnQcIq0WFBQkdAQiUrGXaTmYuPsGAGBwczd09LUXOBFVFkoXiCQSCUxN5SNcbG1t8fz5c9SsWRNubm4IDw9XecAdO3bg2rVruHLlSpHXYmNjAQD29oX/wtvb2yMyMrLEY86fPx+zZs1SbVAVKljqngUiIiqtkDsvNH7OJUuWoFu3bjhz5gx8fX0BAD/99BPmzJmDP//8U+N5SDvEpWZjb+hTABw9RKQJQ4YMEToCEamQVCrDxN03EJ+ei5r2Zvhft9pCR6JKROkCUd26dXHz5k14enqiadOmWLhwIfT19bF27Vp4eqq26VV0dDTGjh2LY8eOwdCw5CFx/10K/m3Lw0+dOhXjx49XPE9NTYWLi0v5A6uIl50pRCIgMSMXCek5sDE1EDoSEVERn3zyCRISEtC5c2ecO3dOsfLN4cOH0aJFC6HjUSW1/nwEciVSNHKzQmN3TmkhEkpWVhby8vIKbTM354g+oopu9dlHOHP/JQx0dbB8gD8M9cRCR6JKROkC0bfffouMjAwAwNy5c9GjRw+0bt0aNjY22Llzp0rDhYaGIi4uDgEBAYptEokEZ8+exYoVKxQjlmJjY+Ho6KjYJy4ursiootcZGBjAwKDiFl2M9MVwtjTC06QsPIxLZ4GIiN7K2lhfkPNOnDgRCQkJaNSoESQSCY4dO4amTZsKkoUqv5SsPGz9OwoARw8RCSEjIwPffPMNdu3ahYSEhCKvq2sxGiJSjUO3YrDwiPx35Bk9fVHDvnIu2kTCUbpA1KVLF8V/e3p64s6dO0hMTISVldUbR+2URYcOHXDr1q1C2z755BPUqlUL33zzDTw9PeHg4ICQkBD4+/sDAHJzc3HmzBksWLBApVk0zaeaKZ4mZeFBXDqaetoIHYeIKri//9dBI+dZtmxZkW2Ojo4wNjZGmzZtcOnSJVy6dAkAMGbMGI1kIu3x69+RSM/JR017M7SvWU3oOERVzuTJk3Hq1CmsXLkSgwcPxs8//4xnz55hzZo1+OGHH4SOR0RvcC0qCV/vDAMADG3hjoFN3YQNRJWS0gWi4qhrVQMzMzPUrVu30DYTExPY2Ngoto8bNw7z5s2Dj48PfHx8MG/ePBgbG2PAgAFqyaQpPvZmOBX+kn2IiKhUFh0Lx4TONdV+niVLlhS7XSwW4/z58zh//jwA+dRfFohIGdl5Emw8L19tZUQ7T+hwKV4ijfvjjz+wefNmtGvXDp9++ilat24Nb29vuLm5YevWrRg4cKDQEYmoGNGJmfhs01Xk5EvRsXY1TO/hK3QkqqRUUiAS0uTJk5GVlYWRI0ciKSkJTZs2xbFjx2BmVrmH03nbsVE1EZXemrOPNVIgioiIUPs5qGraHfoU8em5cLY0Qo96TkLHIaqSEhMT4eHhAUDebygxMREA0KpVK3z55ZdCRiOiEqRk5mHoxstIyMhFHSdzLO3vDzFvslAZVboC0enTpws9F4lEmDlzJmbOnClIHnXxtpcXiB7EpQmchIiodCQSCW7dugU3NzdYWVkJHYcqkXyJFGvPPgIAfN7GE3piHYETEVVNnp6eePLkCdzc3ODr64tdu3ahSZMm+OOPP2BpaSl0PCL6j9x8Kb7cGopHLzPgYG6I9UMaw8Sg0v2KTxUIv4FVUAVL3b9IzUFqdt5b9iYi0rxx48Zh/fr1AOTFoTZt2qBhw4ZwcXEpUswnepND/8QiOjEL1ib66Nuo4qwqSlTVfPLJJ7hx4wYA+aq/K1euhIGBAb7++mtMmjRJ6eOtXLkSHh4eMDQ0REBAAP76668S942JicGAAQNQs2ZN6OjoYNy4ccXut3fvXvj6+sLAwAC+vr7Yv3+/0rmItIFEKsP4XWG48CgBJvpibBjaGA4WJa/8TVQaLBBVUOaGerA3l69edj+Wo4iI6M0ua6hJ9ev27NmD+vXrA5D3rXjy5Anu3buHcePGYdq0aRrPQ5WTTCbDmjPy0UNDW7jDSJ/L8RIJ5euvv1b0j2vfvj3u3buH7du349q1axg7dqxSx9q5c6fi58H169fRunVrBAYGIioqqtj9c3JyYGdnh2nTpil+tvzXxYsX0a9fPwwaNAg3btzAoEGD0LdvX8XiCERVhUwmw/Tf/8HBmzHQE4uw8uMA+DqZCx2LtECZCkRbtmxBy5Yt4eTkhMjISABAUFAQfv/9d5WGq+r8nC0BANejkgXNQUQV38l7cRo/Z3x8PBwcHAAAhw4dwocffogaNWpg2LBhRVagJCrJ+YcJuP08FUZ6YgxqxhVXiCoSV1dX9OnTp8SCzZssXrwYw4YNw/Dhw1G7dm0EBQXBxcUFq1atKnZ/d3d3LF26FIMHD4aFhUWx+wQFBaFTp06YOnUqatWqhalTp6JDhw4ICgpSOh9RZfbj0XBsuxQFkQhY0q8B2tawEzoSaQmlC0SrVq3C+PHj0a1bNyQnJ0MikQAALC0teXFWsQA3eQ+Pa1FJAichoopuyj7NF2Ts7e1x584dSCQSHDlyBB07dgQAZGZmQizmKBAqnTWveg/1a+wCKxN9gdMQVW1jxozBsmXLimxfsWJFiVO+ipObm4vQ0FB07ty50PbOnTvjwoULZc538eLFIsfs0qXLG4+Zk5OD1NTUQg+iymzt2UdYeVr+s/P79/y4sAOplNIFouXLl+OXX37BtGnTCv0C0KhRI94xVrGCAtHVyCTIZDKB0xARFfbJJ5+gb9++qFu3LkQiETp16gQAuHTpEmrVqiVwOqoM/nmWgr8exEOsI8KwVh5CxyGq8vbu3YuWLVsW2d6iRQvs2bOn1MeJj4+HRCKBvb19oe329vaIjY0tc77Y2Filjzl//nxYWFgoHi4u7HNGldfOK1GYd+geAOCbrrUwoKmrwIlI2yjd4jwiIgL+/v5FthsYGCAjI0MloUiuXnUL6OqI8DItB0+TsuBibSx0JCIihZkzZ6Ju3bqIjo7Ghx9+CAMDed80sViMKVOmCJyOKoM1Zx8DAHrUc+TPOKIKICEhodjpXebm5oiPj1f6eCJR4aW2ZTJZkW3qPubUqVMxfvx4xfPU1FQWiahSOnwrBlNfjRj/oq0nvmznJXAi0kZKF4g8PDwQFhYGN7fCfQIOHz4MX19flQUjwFBPjDrOFrgRnYxrUUn88kxEJdrxeTNBzvvBBx8U2TZkyBABklBlE52YiT9vPgcgX9qeiITn7e2NI0eO4Kuvviq0/fDhw/D0LP2/U1tbW4jF4iIje+Li4oqMAFKGg4OD0sc0MDBQ3MAgqqz+evASY3eEQSoD+jd2wZSuHKlN6qF0gWjSpEkYNWoUsrOzIZPJcPnyZWzfvh3z58/HunXr1JGxSmvoaokb0ckIjUzCuw2chY5DRFXcsmXL8Pnnn8PQ0LDYPhWvK1gJh6g46/56DKkMaFPDDnWcim9IS0SaNX78eHz11Vd4+fIl3nnnHQDAiRMnsGjRIqV6jerr6yMgIAAhISHo3bu3YntISAjefffdMudr3rw5QkJC8PXXXyu2HTt2DC1atCjzMYkqumtRSfhiSyhyJVJ083PA9739yj0Sj6gkSheIPvnkE+Tn52Py5MnIzMzEgAED4OzsjKVLl6J///7qyFilBbhZYeP5JwiNZKNqIipZ/7V/4/7cQLWfZ8mSJRg4cCAMDQ2xZMmSEvcTiUQsEFGJEjNysfNqNABgBEcPEVUYn376KXJycvD9999jzpw5AOSri61atQqDBw9W6ljjx4/HoEGD0KhRIzRv3hxr165FVFQURowYAUA+9evZs2fYvHmz4j1hYWEAgPT0dLx8+f/27jwsynJ/A/g9CzPsoLKJLCKCG7nhhua+5HI6mZWWqVnqEc3jQh3TtKN2Kn/WycyTZqXmkprlUlamYhkp7oA7igurgiwKwzrAzPP7A51EUAFh3hnm/lzXXMrDOy/3GH1n5jvP+zwZOHXqFFQqleEqhRkzZqBXr15YsmQJnnnmGfz444/Yv38/Dh06VAuPnsj0XErLxatfn0BBsQ49A1zwyaj2UMjZHKK6U+0GEQBMmjQJkyZNQmZmJvR6Pdzc3Go7F91xd6Hq2FQN8rWlsFPX6D8ZEVGtiI+Pr/TvRNWx4UgCikr0eKKJE0L8G0kdh4juMWXKFEyZMgUZGRmwsbGBvb19jc4zatQoZGVl4d1330VqaiqCgoKwe/duwzIVqampSEpKKnefe9c5jYqKwubNm+Hr64uEhAQAZYtlf/vtt5g/fz7eeecd+Pv7Y+vWrejatWvNHiyRCUvKKsDYNceQU1iCjj7O+GJsMNRK7hJLdava3YZFixZhzJgx8Pf3h4uLS11kons0drKBp5M1buQU4XRKNrr789+ciIjMV0FxKdYfTgBQtvYQp8kTmY7CwkIIIWBrawtXV1ckJiZi9erVaN26dYXt5ati6tSpmDp1aqXfW7duXYWxquza+/zzz1e6/h1RfVJUosPEDSeQnqtFSw8HfD2+C2xVnChAda/av2Xbt2/Hu+++i86dO2PMmDEYNWoUXF1d6yIb3dHRtwFunElFdOJtNoiIqFL/N+IJo/yce3eCeZSlS5fWYRIyV1tPJON2QQl8G9liSJCH1HGI6B7PPPMMRowYgdDQUGRnZ6NLly5QqVTIzMzE0qVLMWXKFKkjElmET8LjEHczDy72Kmx4rQucbK2kjkQWotoNojNnzuD8+fPYtGkTli5dirCwMAwYMABjxozB8OHDYWvLnbZqW7BvA/x8JpXrEBHRA/VraZxLfWNiYsp9HRUVBZ1OhxYtWgAA4uLioFAoEBwcbJQ8ZF5KdHqsPlh2aeKkns2gVMglTkRE94qOjjasL7dt2zZ4eHggJiYG27dvx7///W82iIiM4GTCLXx58BoAYPGItnBztJY4EVmSGr0ya9OmDT744ANcu3YNBw4cgJ+fH2bOnAkPD34SWBc6+pStQxSdlA29/tFTb4nI8nT54Dej/JwDBw4Ybk8//TT69OmDlJQUREdHIzo6GsnJyejbty+GDRtmlDxkXn4+cwPXswvhYq/G88FeUschovsUFBTAwcEBQNnuYCNGjIBcLke3bt2QmJgocTqi+q+guBRvfH8aQgDPdfTCwNbuUkciC/PYH93Z2dnBxsYGKpUKJSUltZGJ7tPa0xHWVnLkFJbgWmae1HGIiAAAH3/8MRYvXowGDRoYxho0aID33nsPH3/8cbXPt3LlSvj5+cHa2hrBwcE4ePBgle4XGRkJpVKJ9u3bV/tnkvEIIbDqj7JPRF/t0RTWVlxok8jUNG/eHD/88AOSk5Oxd+9ew7pD6enpcHR0lDgdUf33f79eRGJWARo7WePfT7eWOg5ZoBo1iOLj4/H++++jdevW6NSpE6Kjo7Fw4UKkpaXVdj4CYKWQo62XMwDwMjMiMhkajQY3b96sMJ6eno7c3NxqnWvr1q2YOXMm5s2bh5iYGPTs2RNDhgypsMPN/XJycjBu3Dj079+/Wj+PjO/ApXRcupkLe7USY7r5Sh2HiCrx73//G2+++SaaNm2Krl27IiQkBEDZbKJ7dxgjotoXeSUTG46UzdRb8lxbONlw3SEyvmo3iEJCQtC8eXN8//33ePXVV5GYmIjff/8dEydOhJOTU11kJPy13X10Yra0QYjIJE3u1czoP/PZZ5/Fq6++im3btiElJQUpKSnYtm0bJkyYgBEjRlTrXEuXLsWECRMwceJEtGrVCsuWLYO3tzc+//zzh95v8uTJGD16tOFNDJmuz/+4CgB4uasPX/QSmajnn38eSUlJOHnyJPbs2WMY79+/v2FtIiKqfZqiEszedgYAMKabD3oFchMokka1F6nu27cvVq9ejTZt2tRFHnqA4DvrEEUlcQYREVX0xqAWRv+Zq1atwptvvokxY8YYLjFWKpWYMGECPvrooyqfp7i4GFFRUZgzZ0658UGDBuHw4cMPvN/XX3+Nq1ev4ptvvsF77733yJ+j1Wqh1WoNX2s0mipnpMcTlXgLJxJuQ6WQ47Un/aSOQ0QP4eHhUWFd0S5dukiUhqj+y9eWYso3UbieXQifhraYO6SV1JHIglV7BtEHH3zA5pAEOvg4AwCupOchu6BY2jBEZHK6GWmR6nvZ2tpi5cqVyMrKQkxMDKKjo3Hr1i2sXLkSdnZ2VT5PZmYmdDod3N3LL8To7u7+wEuXL1++jDlz5mDTpk1QKqv2WcfixYvh5ORkuHl7e1c5Iz2ez++sPTSiYxO4czcWIiIiAEBOQQnGrDmGyCtZsFMpsOzF9rBTV3sOB1GtqdJvX1hYGP7zn//Azs4OYWFhDz126dKltRKMymtkr4afix3iM/MRk5SNvkba0pqIzMMtCRvHdnZ2aNu27WOfRyaTlftaCFFhDAB0Oh1Gjx6NRYsWITAwsMrnnzt3brnnMI1GwyaREcTdzMX+2JuQyYB/SHApJBERkSnKzNNi7JrjiE3VwMnGCutf64L23s5SxyILV6UGUUxMjOHygZiYmDoNRA/W0acB4jPzEZV4mw0iIqo3XFxcoFAoKswWSk9PrzCrCAByc3Nx8uRJxMTEYNq0aQAAvV4PIQSUSiX27duHfv36VbifWq2GWq2umwdBD/RFRNnsocFtPNDM1V7iNERERNK7kV2IMauP4VpmPlzs1fhmYhe09OBOgSS9KjWIDhw4UOnfybiCfRtge3QKdzIjogoGtq7YSDEXKpUKwcHBCA8Px7PPPmsYDw8PxzPPPFPheEdHR5w9e7bc2MqVK/H7779j27Zt8PPjGjem4kZ2IX48dR0AMLm3v8RpiIiIpHctIw9j1xzH9exCeDpZY9OkbvBzqfql+UR1qdprEL322muVbl+cn5+P1157rVZCUeXu7mR2KjkbJTq9xGmIyJSsGN1R6giPJSwsDKtXr8batWsRGxuLWbNmISkpCaGhoQDKLg8bN24cAEAulyMoKKjczc3NDdbW1ggKCqrW+kdUt1YfjEepXiCkWSNOmyciIosXk3Qbz686guvZhfBzscP3U7qzOUQmpdoNovXr16OwsLDCeGFhITZs2FAroahyAW72aGBrhcISHU4nZ0sdh4hMyEtfHpU6wmMZNWoUli1bhnfffRft27fHn3/+id27d8PX1xcAkJqaiqSkJIlTUnVkFxTj2xNl/81C+3D2EBERWbYDl9Ix+qtjuJVfjCeaOOH70BA0cbaROhZROVVeIl2j0UAIASEEcnNzYW391y4kOp0Ou3fvhpsb18WpS3K5DCH+jbD7bBoir2ShU9OGUkciIhMRlWT+l55OnToVU6dOrfR769ate+h9Fy5ciIULF9Z+KKqxDUcSUVCsQ+vGjugV4CJ1HCIiIslsi0rBW9vPQKcX6BngglVjgrlbGZmkKv9WOjs7QyaTQSaTVbprjEwmw6JFi2o1HFXU3d+lrEF0NRMzBgRIHYeIiKiCwmId1h1OAABM7t2s0t3oiIiI6judXmBVxFV8tPcSAGB4e098+Hw7qJTVvpCHyCiq3CA6cOAAhBDo168ftm/fjoYN/5q9olKp4OvrC09PzzoJSX/p0bzsU9iYpNsoKC6FrYqdZyICmnN3KDIh351Mxq38Yng3tMGwJxpLHYeIiMjo4m7m4q3tZxCTlA0A+EevZpgzuCXkcn5oQqaryt2F3r17AwDi4+Ph7e0NuZxdTyk0bWSLJs42uJ5diOPxt9CnBS/rIyJg94yeUkcgAgCU6vT46mDZ1vaTejaDUsHXC0REZDmKSnRYeeAKPo+4ihKdgL1aibeHtsLorj5SRyN6pGpPP7m7YGhBQQGSkpJQXFxc7vtt27atnWRUKZlMhu7+jfB9VAoOX81ig4iIAABv7zyLD559QuoYRPjlbCpSbheioZ0KLwR7Sx2HiIjIaKKTbuPN70/jWkY+AGBAK3f8Z3gbNHbiYtRkHqrdIMrIyMCrr76KX3/9tdLv63S6xw5FD9ejuQu+j0pB5JVMqaMQkYnYFpXCBhFJTgiBVRFls4fGd28KG5VC4kRERETGcehyJiasPwFtqR6uDmos+nsbDAny4Dp8ZFaqPe975syZuH37No4ePQobGxvs2bMH69evR0BAAHbt2lUXGek+3f0bAQAupGpwO7/4EUcTEREZR0RcBmJTNbBVKTAuxFfqOEREREbxZ1yGoTnUp4Ur9s/qjaFPNGZziMxOtWcQ/f777/jxxx/RuXNnyOVy+Pr6YuDAgXB0dMTixYsxbNiwushJ93BztEaAmz0up+fhyLUsDOUCoEQWT8EXIGQCPv/jKgDgxc4+cLZVSZyGiIio7v1xKR3/2BiF4lI9BrRyw4qXO0Kt5AxaMk/VnkGUn58PN7eydW8aNmyIjIwMAMATTzyB6Ojo2k1HD3R3NzNeZkZEABD7n8FSRyALF5V4G8fib8FKIcOkXn5SxyEiIqpzBy6m4x8byppDA1u7Y+XLwWwOkVmrdoOoRYsWuHTpEgCgffv2+OKLL3D9+nWsWrUKjRtzJoux3L3M7PDVLImTEJEpWH1n1ygiqayKKJs9NLx9Ey7GSURE9V5EXAYmb4xCsU6PwW08sPLljlApuXMnmbcarUGUmpoKAFiwYAH27NkDHx8fLF++HB988EGtB6TKdW3WCHIZEJ+ZjxvZhVLHISKJfbj3ktQRyIJdvpmL8As3IZMBk3s3kzoOERFRnYrPzMe0zdEo1ukx9AkP/G90B1gp2Bwi81ftNYhefvllw987dOiAhIQEXLx4ET4+PnBxcanVcPRgTjZWeMLLGaeTsxF5JRMvdOJWwkREJI27O5cNau2O5m4OEqchIiKqO3naUvxjw0nkFpUi2LcBlo1ic4jqj8f+Tba1tUXHjh3ZHJJAD15mRkREErueXYgfT10HAIT29pc4DRERUd0RQuDN707jcnoe3B3V+JyXlVE9U6UZRGFhYVU+4dKlS2schqqnR3MXrPzjKiKvZEIIwW0UiSxY+KxeUkcgC7X64DWU6gVCmjVCB58GUschIiKqMyv/uIo959NgpZDh8zHBcHO0ljoSUa2qUoMoJiamSidjg8K4gn0bQKWUIz1Xi6sZeZzWT2TBrqTnwbeRndQxyMLcyi/Gt8eTAQBT+nD2EBER1V8HLqXjv/vK1nx895kgdOSHIlQPValBdODAgbrOQTVgbaVA56YNEHklC5FXstggIrJgUzZFI+69IVLHIAuz/nACCkt0aOPpiJ4BvNSciIjqpyvpeZixJQZCAKO7+uClLj5SRyKqEzW+YPLKlSvYu3cvCgvLdtASQtRaKKq67v5lL8j/jMuQOAkREVmSfG0p1h9JAFA2e4iziImIqD66nl2IsWuOQVNUio4+zljwdGupIxHVmWo3iLKystC/f38EBgZi6NChhi3vJ06ciDfeeKPWA9LDDWztDgCIiMtARq5W4jRERGQpthxPQnZBCXwb2WJIUGOp4xAREdW6zDwtxq4+htScIvi72uGrcZ2gViqkjkVUZ6rdIJo1axasrKyQlJQEW1tbw/ioUaOwZ8+eWg1Hjxbo7oAOPs4o1QvsiE6ROg4RSeSLscFSRyALoi3V4auDZVvbh/b2h0LO2UNERFS/aIpK8Mra47iWmY8mzjbYOKErGtmrpY5FVKeq3SDat28flixZAi8vr3LjAQEBSExMrLVgVHUvdvYGAGw9kcxL/YgslG9D20cfRFRLdkRfx02NFu6Oaozo2ETqOERERLWqqESHietP4vwNDRrZqbBxQhd4OttIHYuozlW7QZSfn19u5tBdmZmZUKvZUZXC39p6wk6lwLXMfJxIuC11HCKSwOBPD0odgSxEqU6PVRFXAQCTejbjVHsiIqpXsguKMXljFI7H34KDWon1r3VBM1d7qWMRGUW1G0S9evXChg0bDF/LZDLo9Xp89NFH6Nu3b62Go6qxUyvxdDtPAMC3J5IkTkNERPXZ7nNpSMwqgLOtFXdxISKiemXPuVQMWPonIuIyoFbKsWZ8ZwQ1cZI6FpHRVGmb+3t99NFH6NOnD06ePIni4mLMnj0b58+fx61btxAZGVkXGakKRnX2xrcnkrH7bCoWPN0GTjZWUkciIqJ6RgiBlQeuAABe7e4HO3W1X0YQERGZnMw8LRbsOo9fzpRtwBTgZo+PXmiH9t7O0gYjMrJqzyBq3bo1zpw5gy5dumDgwIHIz8/HiBEjEBMTA39//7rISFXQ3tsZLdwdUFSix67TN6SOQ0RGNmdwS6kjkAU4cCkdF9NyYadS4JXuvlLHISIiemy/nEnFoE/+xC9nUqGQy/B6X3/8PP1JNofIIlXro7+SkhIMGjQIX3zxBRYtWlRXmagGZDIZRnX2xrs/X8DWE0kY240v3IksyWtP+kkdgeo5IQRWHChbe+jlbr5wtlVJnIiIiKjmCopLsXDXeXx3smwn6JYeDvjvC+14SRlZtGrNILKyssK5c+cgk3E7W1P0bIcmUCnkOHddg3PXc6SOQ0RGFDj/V6kjUD13PP4WohJvQ6WQYyIbkkREZMbOXc/B35YfwncnUyCTAdP6NseuaU+yOUQWr9qXmI0bNw5r1qypiyz0mBrYqfBUkAeAsi3viYiIasuKP8pmD73QyQtujtYSpyEiIqo+IQTWHIrHiJWHcS0zHx6O1tg8sRvefKoFVMpqvzUmqneqvbpkcXExVq9ejfDwcHTq1Al2dnblvr906dJaC0fV92Jnb/x0+gZ+OHUdbw9tBRsVtx8mIqLHcyYlG3/GZUAhl2FyL643SERE5iddU4R/bTuDiLgMAMDA1u748Lm2aGDHS6aJ7qp2g+jcuXPo2LEjACAuLq7c93jpmfRCmjWCd0MbJN8qxC9nU/F8sJfUkYjICEZ18pY6AtVjn/1etnPZM+094dPIVuI0RERE1bPnXBrm7jiD2wUlUCvlmD+sFcZ08+X7V6L7VLtBdODAgbrIQbVELpfhxc4++GjvJaw/nIDnOjZh4SOyAP8ZHiR1BKqnYlM12HfhJmQyYGqf5lLHISIiqrI8bSne/emvhajbeDri0xfbo7mbg8TJiEwTL7Ssh17s7A21Uo6z13MQlXhb6jhEZARPffKn1BGonlpxoGz20NAnGqO5m73EaYiIiKrmSnoehi0/aFiIekoff+yc2oPNIaKHYIOoHmpkr8azHZoAANZGxkuchoiMIT4rX+oIVA9dzcjDL2dTAZTt8EJERGQOrmXkYfRXR5GYVYAmzjb4dlI3vDW4JReiJnoE/h9ST73ao2wL4j3n0pByu0DiNEREZI5WHrgKIcoW8mzV2FHqOERERI8Un5mPl746ivRcLVp6OGDXtB7o2qyR1LGIzAIbRPVUCw8HPNncBXoBbDiSKHUcIqpjXf0aSh2B6pmkrAL8cOo6AM4eIiIi85CQmY+XvjyKmxotWrg7YNPErmhkr5Y6FpHZYIOoHnvtyaYAgC3Hk5CvLZU2DBHVqY0TukodgeqZzyOuQqcX6BXoinbezlLHISIieqikrAK89NVRpGmKEOBmj02T2Bwiqi6TbhAtXrwYnTt3hoODA9zc3DB8+HBcunSp3DFCCCxcuBCenp6wsbFBnz59cP78eYkSm5Y+gW7wc7FDblEpdkSnSB2HiOpQ6MYoqSNQPZKaU4htUckAgH/24+whIiIybak5hXjpq6NIzSmCv6sdNk/qBhc2h4iqzaQbRBEREXj99ddx9OhRhIeHo7S0FIMGDUJ+/l+LsX744YdYunQpPvvsM5w4cQIeHh4YOHAgcnNzJUxuGuRyGcZ3bwoA+DoyAXq9kDYQEdWZ3y+lSx2B6pEvIq6hRCfQ1a8hOjfl5YtERGS6sguKMW7NcVzPLkQzFztsmdQNrg5sDhHVhEk3iPbs2YPx48ejTZs2aNeuHb7++mskJSUhKqrsk3IhBJYtW4Z58+ZhxIgRCAoKwvr161FQUIDNmzdLnN40PB/sBQdrJa5l5iMiLkPqOEREZOLSNUXYcjwJAPDPfgESpyEiInqwwmIdXlt3ApfT8+DhaI2NE7vCzdFa6lhEZsukG0T3y8nJAQA0bFj2aWZ8fDzS0tIwaNAgwzFqtRq9e/fG4cOHH3gerVYLjUZT7lZf2amVeLGzNwBueU9Un7lyGjXVki//vAZtqR7Bvg3Qozl3fSEiItNUotNj6qYoRCdlw8nGChsmdEETZxupYxGZNbNpEAkhEBYWhieffBJBQUEAgLS0NACAu7t7uWPd3d0N36vM4sWL4eTkZLh5e3vXXXATMC6kKeQy4ODlTFy4UX+bYUSWLHJOP6kjUD2QmafFN8fKdr6c3j8AMplM4kREREQV6fUCb20/gwOXMmBtJcfa8Z0Q6O4gdSwis2c2DaJp06bhzJkz2LJlS4Xv3f8CVgjx0Be1c+fORU5OjuGWnJxc63lNiXdDWwx9ojEAYGn4pUccTUTmaMmei1JHoHrgq4PXUFSiRzsvJ/QKcJE6DhERUQV6vcC7P1/AjujrUMhlWPlyRwT7cr08otpgFg2if/7zn9i1axcOHDgALy8vw7iHhwcAVJgtlJ6eXmFW0b3UajUcHR3L3eq7sIGBUMhl2B+bjpMJt6SOQ0S1bM0hXkJKj+dWfjE2HuHsISIiMl2FxTq8vjka6w4nAAA+fK4t+rV88Ps+Iqoek24QCSEwbdo07NixA7///jv8/PzKfd/Pzw8eHh4IDw83jBUXFyMiIgLdu3c3dlyT1szVHiM7lV1Kt2TPRQjBHc2IiOgvaw5dQ0GxDm08HdGvpZvUcYiIiMpJzy3Ci18dxa/n0qBSyLFsVHs8F+z16DsSUZWZdIPo9ddfxzfffIPNmzfDwcEBaWlpSEtLQ2FhIYCyS8tmzpyJDz74ADt37sS5c+cwfvx42NraYvTo0RKnNz0z+gdArZTjRMJt/HGJO5oREVGZ7IJirD/M2UNERGSa4m7m4tkVh3E6ORvOtlb4ZmJXDO/QROpYRPWOUuoAD/P5558DAPr06VNu/Ouvv8b48eMBALNnz0ZhYSGmTp2K27dvo2vXrti3bx8cHLhI2f08nKwxvntTfPHnNSzZcxG9A10hl/NNAFF9EDV/gNQRyIx9HZmAPG0pWno4YGArTtUnIiLTcehyJqZ8E4VcbSn8XOywdnxn+LnYSR2LqF4y6RlEQohKb3ebQ0DZLKKFCxciNTUVRUVFiIiIMOxyRhVN6eMPB2slLqbl4qczN6SOQ0S15NdzD965kehhNEUlWBtZtobV9P4B/OCAiIhMxraoFIz/+jhytaXo4tcQO6Z0Z3OIqA6ZdIOIap+zrQqhvf0BAB/vi0NxqV7iRERUG+b/cE7qCGSm1kUmILeoFAFu9hjcxkPqOERUj6xcuRJ+fn6wtrZGcHAwDh48+NDjIyIiEBwcDGtrazRr1gyrVq0q9/1169ZBJpNVuBUVFdXlwyAJCCGw/LfLePP70yjVC/y9nSc2TuiCBnYqqaMR1WtsEFmgV3s0hYu9Gkm3CrD1RJLUcYiIAFTvjcSOHTswcOBAuLq6wtHRESEhIdi7d68R09YPmqISrD54DQDwT84eIqJatHXrVsycORPz5s1DTEwMevbsiSFDhiApqfLXnvHx8Rg6dCh69uyJmJgYvP3225g+fTq2b99e7jhHR0ekpqaWu1lbWxvjIZGRlOj0mLP9LJaGxwEouwJi2aj2UCsVEicjqv/YILJAtiolZvRvDgBYtv8y0nP5qQsRSau6byT+/PNPDBw4ELt370ZUVBT69u2Lp59+GjExMUZObt7WRyZAU1SK5m72GPZEY6njEFE9snTpUkyYMAETJ05Eq1atsGzZMnh7exvWGL3fqlWr4OPjg2XLlqFVq1aYOHEiXnvtNfz3v/8td5xMJoOHh0e5G9Uf+dpSTFx/EltPJkMuA94bHoS3BrfkBxhERsIGkYUa1dkHge72yMovxowtp1Cq46VmROZsW2iI1BEeS3XfSCxbtgyzZ89G586dERAQgA8++AABAQH46aefjJzcfGmKSrD60F9rDyn44puIaklxcTGioqIwaNCgcuODBg3C4cOHK73PkSNHKhz/1FNP4eTJkygpKTGM5eXlwdfXF15eXvjb3/72yA8GtFotNBpNuRuZpqISHSZtOImIuAzYWCnw1bhOGNPNV+pYRBaFDSILpVLKsfLlYNiqFDhyLQuf7I+TOhIRPQZzXk+sJm8k7qfX65Gbm4uGDRs+8Bi+SShvfWQCcgpL4O9qx9lDRFSrMjMzodPp4O5efldEd3d3pKVVvqlCWlpapceXlpYiMzMTANCyZUusW7cOu3btwpYtW2BtbY0ePXrg8uXLD8yyePFiODk5GW7e3t6P+eioLpTo9Hh9UzQOX82CnUqBTZO6oj931SQyOjaILFhzN3v833NtAQArDlzF7xdvSpyIiGpq9OpjUkeosZq8kbjfxx9/jPz8fIwcOfKBx/BNwl9yOXuIiIxAJitfW4QQFcYedfy94926dcOYMWPQrl079OzZE9999x0CAwPxv//974HnnDt3LnJycgy35OTkmj4cqiM6vcCsrafw28V0qJVyrBnfGR19Gkgdi8gisUFk4f7ezhPjQsqmbs7aehoptwskTkRElqq6byTu2rJlCxYuXIitW7fCzc3tgcfxTcJf1h/+a/bQ39p6Sh2HiOoZFxcXKBSKCk3+9PT0Ch8G3OXh4VHp8UqlEo0aNar0PnK5HJ07d37oDCK1Wg1HR8dyNzIdQgi8veMsfj6TCiuFDKvGBqNbs8r/exNR3WODiDBvWCu083JCTmEJXt8UDW2pTupIRGRBavJG4q6tW7diwoQJ+O677zBgwICHHss3CWVyi0rw1UHOHiKiuqNSqRAcHIzw8PBy4+Hh4ejevXul9wkJCalw/L59+9CpUydYWVlVeh8hBE6dOoXGjXmZrDkSQuC9X2INC1J/+mIH9G3x4A96iKjusUFEUCsVWPFyRzjZWOF0Sg4W774odSQiqqaPnm8rdYQaq8kbCaBs5tD48eOxefNmDBs2rK5j1ht3Zw814+whIqpDYWFhWL16NdauXYvY2FjMmjULSUlJCA0NBVA2q3PcuHGG40NDQ5GYmIiwsDDExsZi7dq1WLNmDd58803DMYsWLcLevXtx7do1nDp1ChMmTMCpU6cM5yTzsuV4Mtbcudz5w+fbYSjXwyOSnFLqAGQavBrYYtmo9nh13QmsO5yAXoEu6NeSC8MRmYsnm7tIHeGxhIWFYezYsejUqRNCQkLw5ZdfVngjcf36dWzYsAFAWXNo3Lhx+PTTT9GtWzfD7CMbGxs4OTlJ9jhM3b1rD83g7CEiqkOjRo1CVlYW3n33XaSmpiIoKAi7d++Gr2/Z0gapqalISkoyHO/n54fdu3dj1qxZWLFiBTw9PbF8+XI899xzhmOys7Pxj3/8A2lpaXByckKHDh3w559/okuXLkZ/fPR4zqbkYOGu8wCAfz3VAs8He0mciIgAQCburv5mwTQaDZycnJCTk2Oxlxzc9e5PF7A2Mh6N7FT4dWZPuDlYSx2JiKogcP6viHtvSLXuY2q1b+XKlfjwww8NbyQ++eQT9OrVCwAwfvx4JCQk4I8//gAA9OnTBxERERXO8corr2DdunVV+nmm9viNYWl4HJb/dhnNXO0QPqs3G0REFsgSa9+9LP3xm4LsgmL87X+HkHK7EANaueOrccFVWnOQiGquqrWPM4ionNmDW+Dw1UxcTMvFv74/g6/Hd4acbyCIyAimTp2KqVOnVvq9+5s+dxtFVHWZeVqsOXgNAPDmoBZsDhERkdHp7+xYlnK7ED4NbfHxyHZsDhGZEK5BROVYWymw/KUOUCvliIjLwLrDCVJHIiKiWrDiwBXkF+vwRBMnDAnykDoOERFZoJV/XMGBSxlQK+X4fEzZGqhEZDrYIKIKAt0dMG9YKwDA//16EbGpGokTEdGjTOntL3UEMmEptwuw6WjZWh+zB7fgp7VERGR0hy5n4uPwOADAf4YHoY0n1wwkMjVsEFGlxnbzRf+WbijW6TF9SwyKSnRSRyKih5g1MFDqCGTCPt1/GcU6Pbr7NzL7Bc2JiMj8ZORqMXNrDIQARnXyxshO3lJHIqJKsEFElZLJZFjyfFu42KtxOT0P/9p2Bnq9xa9nTmSyOr+/X+oIZKIu38zF9ugUAGU7xXD2EBERGZMQAv/adhqZecVo6eGARc+0kToSET0AG0T0QC72anz6Ynso5TL8dPoGFv10Htz0jsg05RSWSB2BTNTH++KgF8BTbdzRwaeB1HGIiMjCbDiSiD8uZUCllOPTFzvA2kohdSQiegA2iOihejR3ubO7ALD+SCKW/3ZF6khERFRFp5Kzsed8GuSysp3LiIiIjCnuZi7e3x0LAHh7SEu08HCQOBERPQwbRPRIz7RvgoVPl00F/WR/HDYeTZQ4ERHdb3Ab7kpF5Qkh8NHeiwCAER29EODOF+VERGQ8RSU6TN8Sg+JSPXoHuuKV7k2ljkREj8AGEVXJK92bYnr/AADAv388h5/P3JA4ERHda/lLHaSOQCbmj0sZiLySBZVCjpkDAqSOQ0REFubDPZdwMS0XjexU+O8L7bgGHpEZYIOIqmzWgAC83NUHQgCztp7CdyeSpY5ERHeM/OKI1BHIhJTq9IYp/a/2aAqvBrYSJyIiIkvy+8WbWBsZDwD46IW2cHVQS5yIiKqCDSKqMplMhnefCcIz7T1RohOYvf0M3vnhHIpL9VJHI7J4p5KzpY5AJmTryWRcSc9DA1srTO3bXOo4RERkQX6LvYnQb6IBAGO7+aJfS3eJExFRVbFBRNWikMvwycj2mDUgEACw8WgiXl59FBm5WomTERERAOQWleCT8DgAwIz+AXCysZI4ERERWYqfTt/A5I1RKC7VY2Brd8z/WyupIxFRNbBBRNUml8swY0AA1rzSCQ5qJU4k3MbT/zuEmKTbUkcjslgtuAAx3fFFxDVk5hXDz8UOL3fzlToOERFZiG+PJ2H6tzEo1QsMb++JlS93hFrJLe2JzAkbRFRj/Vu544dpPeDvaoc0TRFGfnEEX0RchV4vpI5GZHF++ueTUkcgE3AjuxBfHbwGAJgzpCWsFHyaJyKiurf64DXM2XEWQgAvd/XB0pHt+RxEZIb4fy09Fn9Xe/zweg8MbuOBEp3A4l8vYuzaY0jLKZI6GpFFmbP9jNQRyAT8d98laEv16OLXEINac80HIiKqWzq9wAe7Y/HeL2UbI0zu3QzvDQ+CXM4dy4jMERtE9NgcrK3w+ZiOWDziCdhYKRB5JQuDP/0Te8+nSR2NyGLsiLkudQSS2LnrOdgRXfZ7MH9YK24nTEREdSq3qAT/2HASX/5ZNnP1X0+1wJzBLfn8Q2TG2CCiWiGTyfBSFx/8PP1JBDVxRHZBCSZvjMK/vj+N7IJiqeMREdVrQgi8+9MFAMDw9p5o6+UsbSAiIqrXkm8V4PnPj+C3i+lQK+VY/lIHvN63OZtDRGaODSKqVf6u9tgxpQcm92oGAPg+KgX9P47ADzHXIQTXJiKqKype52/Rfjh1HccTbsHGSoHZg1tKHYeIiOqxEwm3MHxFJC7dzIWbgxpbJ4fg7+08pY5FRLWA7yio1qmUcswd2grbQkMQ4GaPrPxizNx6CuPWHkdSVoHU8YjqpXOLnpI6AklEU1SC93+5CAD4Z//m8HS2kTgRERHVV/sv3MTLXx1DVn4x2ng64sdpPdDe21nqWERUS9ggojrTqWlD/DK9J94cFAiVUo6DlzMx8JMILP/tMopKdFLHI6pXVkVclToCSWRZ+GVk5mnRzMUOE59sJnUcIiKqp345k4rQb6JQrNNjYGt3fB8agsZO/FCCqD5hg4jqlEopx7R+Adg7sxe6+zeCtlSPpeFx6P9xBH45k8rLzohqydLwOKkjkAQupmmw/kgCAGDh39tApeTTOhER1b6dMSn455ZolOoFnmnvic9f7ghblVLqWERUy/hKkozCz8UOmyZ2xf9e6gBPJ2tczy7E65uj8eKXR3H+Ro7U8YiIzI4QAv/+4Tx0eoEhQR7oFegqdSQiIqqHtp5IQth3p6EXwMhOXlg6sj2UXPuQqF7i/9lkNDKZDE+388Rvb/TBjP4BUCvlOBZ/C8OWH8Ira4/j94s3oddzRhERUVX8eOqGYWHq+X9rLXUcIiKqZ7SlOqw4cAVvbT8LIYCx3XzxfyPaQiHnTmVE9RXnBZLR2agUmDUwECM7e2Px7lj8cjYVEXEZiIjLgE9DW4wL8cULwd5wsrWSOiqR2fj9jd5SRyAjyi0qwfu7YwEA0/o1RxMuTE1ERLVEW6rDdydTsPLAFaTmFAEAJvX0w9tDW3Ebe6J6jg0ikkwTZxt8Nroj/pWVj2+OJmLriWQk3SrAe7/E4pPwOLzSvSkm9myGhnYqqaMSmbzzNzTwamArdQwyksW/XkRGrhZ+LnaY2NNP6jhERFQPFJfq8X1UMlb8fgU37jSGPBytMWNAAF7s7M3mEJEFYIOIJOfbyA7zhrVG2MAW+OHUdayLTMClm7lY+cdVrDucgDHdfDGpZzO4Oqiljkpksv65JQZx7w2ROgYZwZ9xGdh8LAkA8P6zQVArFRInIiIic3fhhgZh353CxbRcAICbgxqv922OUZ29YW3F5xkiS8EGEZkMG5UCL3XxwYudvbE/Nh3Lf7uMs9dz8OWf17DhSAJe7uqLyb2bwc3BWuqoRESS0BSV4K3tZwAAr4T4oru/i8SJiIjInJXq9Pjiz2tYtj8OJTqBBrZW+Ge/AIzu6sPGEJEFYoOITI5MJsPA1u4Y0MoNf1zKwLLfLuN0cjbWHIrHpmOJGNPVF/9go4iILNB/frqA1JwiNG1ki7eGtJQ6DhERmbGrGXl447vTOJWcDQAY2NodHzz7BGftE1kwNojIZMlkMvRt6YY+LVzx5+VMfBIeh1PJ2Vh9KB7f3GkUPdO+CVp7OnI3BbJ4a17pJHUEqmO/xd7E91EpkMmA/77QDrYqPoUTEVHN/H7xJqZuikZRiR4O1kosfLoNRnRswnWGiCwcX12SyZPJZOgd6IpeAS4VGkWrD8XD0VqJrs0aIaRZI/QMcEGAu4PUkYmMzsORM+rqs+yCYszdcRYAMPFJP3Rq2lDiREREZK5OJtwyNIeebO6CD59vC0/uhklEYIOIzMj9jaKNRxJw7NotaIpKEX7hJsIv3AQAdGnaEK896YeBrd05s4gsxrD/HeIi1fXYwl3nkZ6rhb+rHd4Y1ELqOEREZKYupeXitXUnUFSiR7+WbvhibDCsFHKpYxGRiWCDiMzO3UZR70BXlOr0OH9Dg8NXs3D4aiaOXM3C8YRbOJ5wCz4NbfFqj6Z4oZM37NX8VSci87QjOgU/nLoB+Z1Ly7hoKBER1UTK7QKMW3sMmqJSBPs2wIrRHdkcIqJy+K6ZzJpSIUc7b2e083bGlD7+SMspwoYjCdh0LAlJtwqw6KcLeO+XWAS42aOtlxOe8HJG2yZOaO3pyCdEIjJ5l9JyMW/nOQDA9P4B6ODTQOJERERkjrLytBi35jhuarQIdLfHmlc6wUbFDxyIqDw2iKhe8XCyxuzBLTGtX3Nsj76OryPjcS0jHxfTcnExLRffnUwBADiolejVwhUDWrmhT6AbGtipJE5O9HjmD2sldQSqZXnaUkzZFIXCEh16Brjgn/0CpI5ERERmKDNPi9fWncC1zHx4Ollj/Wtd4GzL175EVBEbRFQv2aqUGNvNF2O6+iBNU4QzKTk4m5KDs9dzcDolG9kFJfjlTCp+OZMKuQzo6NMAfVu6oXegK9p4OnIHBzI7o7v4SB2BapEQAm9tP4NrGflo7GSNZaPac001IiKqthMJtzBtczRuarRoYGuFDRO6orETF6QmosqxQUT1mkwmQ2MnGzR2ssFTbTwAAHq9wOmUbPwWm479sTdxMS0XJxNv42TibXy09xJcHdToFeCKXoEu6OLXkE+iZBZaL9jLRarrkQ1HEvHLmVQo5TJ8NrojGtmrpY5ERERmRAiBNYfisfjXi9DpBfxd7bBqTDCau9lLHY2ITBgbRGRx5HIZOvg0QAefBnjzqRa4nl2I3y+mI+JSBg5fzURGrhbbo1OwPbrscrQmzjbo6NsAwT7O6ODTAC08HLhILBHVmZik23jvlwsAgLlDWyHYl+sOERFR1eUWlWD2tjP49VwaAODpdp74vxFPwI6bthDRI7BKkMVr4myDsd18MbabL4pL9TiZcAsRcRmIvJqJ2NRcXM8uxPXsQvx0+gYAQCGXwd/VDm08ndC6sSOCmjihrZcTn3SJ6LEl3ypA6DdRKNEJDAnywGs9mkodiYiIzMiZlGxM3xKDhKwCWClkmD+sNcaF+HL5BCKqEr6jJbqHSilH9+Yu6N7cBQCQry3F6ZRsRCWUXYJ29noObuUXI+5mHuJu5mFnzHUAgFwGBLg5oJ23E9p7N0B7b2cEuttDyZ3SyEi4BpH5S9cU4eXVxww7zCx5vi1f0BMRUZXo9QKrD13Dh3suoVQv4OlkjRUvd+Tul0RULWwQET2EnVqJ7v4u6O5f1jASQuCmRosLqTk4f12D8zc0OJOSjRs5Rbh0MxeXbv61U5qtSoG2XncbRk5o4eEIn4a2XGiW6sTCv7eROgI9htv5xRiz5hiSbhXAp6EtNk7oCkdrK6ljERGRGUjPLcIb353GwcuZAIAhQR74vxFt4WTL5xEiqh42iIiqQSaTwcPJGh5O1ujX0t0wnq4pwqnkbJxOyUZMUjbOpOQgT1uKo9du4ei1W4bj1Eo5/F3tEeBuj+au9mjqYgc/Fzs0dbGDPS9Ro8cwYGkE9of1ljoG1UCethTj151A3M08uDuqsWliV7g7Wksdi4iIzMC+82l4e+dZZOYVw9pKjgVPt8GLnb05A5WIaoTvSIlqgZujNQa18cCgOzul6fQCVzPyEJN0GzFJ2Th7PQdX0vOgLdXjQqoGF1I1Fc7hYq+Gv6sdAtztEeDmgAA3ezR3s4erg5pP8vRISbcKpI5ANVBUosOk9SdxOjkbDWyt8M2ErvBuaCt1LCIiMnEptwuwcNcF7I+9CQBo6eGA/73UAQHuDhInIyJzxgYRUR1QyGUIdHdAoLsDRnUuWxtGpxdIuV1wZ/2iXFzLyEdCVj4SMvORlV+MzDwtMvO0OBZ/q9y5VAo5XB3U8HCyhrujGo2dbODnYodmrnZo7soGEpG5upVfjKmbonD02i3Yq5VY/1oXvrAnIqKHKtHpseZQPD7dfxmFJToo5TJM6tUMM/oHcJddInpsbBARGYlCLoNvIzv4NrLDwNbu5b6nKSpBQmY+rmbk4fLNPFxOz8OV9DwkZuWjWKc37KRWGQe1Ek1d7ODd0AZeDWzh1cAGXg1s4OZgDWdbKzjbqmCnUrCJVM+FNGskdQSqhgs3NJi04SSuZxfCTqXA6lc6oa2Xs9SxiIjIRKXnFmHvuTRsPJqIuJt5AIAufg3x/vAgfrhARLWGDSIiE+BobYW2Xs4V3iAWl+qRkadFWk4R0jVFSNMU4frtQly700xKvlWAXG0pzl7PwdnrOQ88v5VCBmdbFRo7WcOrgQ2aOJc1kzydbeDqoIaLvQou9mp+8mTG1r/WReoIVEW/nk1F2HenUViig28jW3w1rhMC+eKeiIjuc1NThF/PpmL3uTScSLgFIcrGG9qp8PbQVniuYxN+AEhEtYoNIiITplLK0cS5rKFTGW2pDgmZBUjMykfK7cI7twKk3C5EVr4WtwtKUFyqR4lOICNXi4xcLc6kPLiR5KBWwtVBDXdHa8Ni3B6O1mhop4K9tRL26r9uSoUMcpkMMhkgl8lgJZfD0UbJFyoSmbThJL4a10nqGPQQOr3Ap/vjsPz3KwCAngEu+N9LHeBsq5I4GRERPcqec6k4Hn8bM/oH1PnuYFcz8rDywFX8cOo6dHphGO/g44yhQY3xQicvPncQUZ1gg4jIjKmVCrTwcEALj8pnHwghUFSix+2CYtzKL8aN7LIm0vXsskbSjewiw9pHJTqBXG0pcrWluJaZX6M8d9dLcncsazI521oBKGsiyQDIZICDtRXc7zSh3Byt4eagRgM7Xgb3uCLiMqSOQA8ghMD+2HR8tPei4bKAiU/6Yc6QllAq5BKnIyKiRynR6fHeL7FIuV2IHTEpCBsYiNFdfGq9hsemavDZgSvYfTbVMFuoo48zhrX1xOAgjwd+YEhEVFvYICKqx2QyGWxUCtiobODpbIOgJk6VHieEgKaoFJl5WqRrtLipKUJqThFuaoqQllOE7MJi5GlLkVdUijytDnnaEuj1gF6IO7ey8zxqvaSHkcsAe7USDtZWcLBWwtpKAbVSbvjTTq1EIzsVXBzUZX/aq+FsawUnm7Kbo40VrPhm26ytXLkSH330EVJTU9GmTRssW7YMPXv2fODxERERCAsLw/nz5+Hp6YnZs2cjNDTUiIkf7WTCLfzfrxdxMvE2AMDJxgoLnm6NER29JE5GRERVZaWQ44Nnn8B7v1xA3M08/PvH89h4JBHz/9YavQNdH/v8567n4NPfLiP8wk3D2IBW7pjWrznaezs/9vmJiKqq3jSIqvvGgoj+IpPJDI0Wf1f7Gp1DW6pDRq4WNzVaZOQW4aZGi5zCEggBCIg7fwKawhKk3/n+TU0R0nO1KC7VQy8ATVEpNEWlNX4ctioFbO40lFRKOdRKBdRWctiplLBTK+FgrYSdWgE7tRI2VgrD8dZWCsP37zaoHNRK2N45TiE3/ZlNHo7WUkd4LFu3bsXMmTOxcuVK9OjRA1988QWGDBmCCxcuwMfHp8Lx8fHxGDp0KCZNmoRvvvkGkZGRmDp1KlxdXfHcc89J8Aj+cju/GL9dTMdPp28YZnZZW8nxWg8/TO7tDyebur00gYiIal+vQFfs9u+JLSeSsXTfJVxOz8Mra4+jnZcTmrs5wKehLXwa2cCnoS1aejjCTv3ot1mxqRos2x+HvefLGkMyGTDsicZ4vW9ztGrsWNcPiYioApkQQjz6MNO2detWjB07ttwbi9WrVz/wjcX9NBoNnJyckJOTA0dHFmMiYxJCQFuqh6aoBJrCUuQWlSC3qBTaUj20pTpoS/QoKtUhr6gUt/KLkZlXbLgsLrugBJrCEuRqa95Uqgq1Ul42E8tKASFgmDUlhIBCLkNDOxUa2KrQ0K7sZqtWQCmXQSGX3/lTBiuFDEq5HFZKOVR3/q5UyKBSyKFUlP3dSi4vO04hNxyvtpIbmll3Z1PV1qV4plT7unbtio4dO+Lzzz83jLVq1QrDhw/H4sWLKxz/1ltvYdeuXYiNjTWMhYaG4vTp0zhy5EiVfmZtPH5tqQ6ZecVI1xQhOikb4RfScCLhtmHNCLkMGNXZGzP6B8LDybybeERUP5hS7ZdCbTz+nMIS/O+3y1h3OAGl+opvpawUMnRu2hB9WriiTws3BLiVffiWlV+MpFsFSL5VgL3n07D7bBqAssbQ0209Mb1/AJq71eyDOiKih6lq7asXDaLqvrG4n6U/URKZu1KdHrlFpdAUlaCopKyxVFyqh7ZUj6ISHfKLyxpM+dpS5GnL/iws0ZXdinUoKNYZvnf3PHnaUphidZTLytaesraSl/vT2dYKWyeHVOtcplL7iouLYWtri++//x7PPvusYXzGjBk4deoUIiIiKtynV69e6NChAz799FPD2M6dOzFy5EgUFBTAyqriLB2tVgutVmv4WqPRwNvbu9qPf+Gu8zh4OQMZudoHznhr1dgRg1q745n2nmhWw1l5RER1wVRqv1Rq8/Gn3C5AVOJtpNwuRFJWAZJuFSA+Mx9pmqJyx7nYqw2vPe51d8bQjP4B3KqeiOpUlWufMHNarVYoFAqxY8eOcuPTp08XvXr1qvQ+RUVFIicnx3BLTk4WAIRKpRJqtVqo1Wpx48YN8e233xq+VqvVIiIiQkRHR5cb++qrr0ReXl65sX/9619CCCGaNWtmGHvmmWeEEEIMHjzYMNaqVStD1nvvX1JSIj777LNyY+fPnxf79u0rN7Zz506RkJBQbmzJkiVCCCEcHBwMYxMnThRCCBEcHGwYu/tv89JLLxnGPDw8hBBCLFiwoNw509PTxaZNm8qNHTp0SJw4caLc2Nq1a0VOTk65sTlz5gghhPD19TWMjRgxQgghxMCBAw1jQUFBQgghpk6dWu7+QgixbNmycmOXLl0Su3fvLjf2008/iStXrpQb+/jjj4UQQtja2hrGJk+eLIQQon379oaxvn37CiGEGDlypGGsSZMmQggh5s+fX+6cWVlZYsOGDeXGjhw5Io4cOVJubMOGDSIrK6vc2Pz584UQQjRp0sQwNnLkSCGEEH379jWMtW/fXgghxOTJkw1jtra2QgghPv7443LnvHLlivjpp5/Kje3evVtcunSp3NiyZcuEEKLc2NSpU4UQQgQFBRnGBg4cKIQQYsSIEYYxX19fIYQQc+bMKXf/nJwcsXbt2nJjJ06cEIcOHSo3tmnTJpGenl5ubMGCBUIIITw8PAxjL730khBCiF69ehnGgoODhRBCTJw40TDm4OAghBBiyZIl5c6ZkJAgdu7cWW5s37594vz58+XGPvvsM1FSUlJubPr06UIIIVq1amUYGzx4sCjQloohw54WarVaqNRq4eXTVJxNyRavTpkuVHfGVGq12HsqXrz+zodCqVIJpVXZ7R+ffC/Gvf+1UFipDLen/rlYjP3fHqFQqoT8zq3N05PEM58dEio7pztjVsKt40AR8sF+YefTWsgUVkKmsBKqxi2E71s/C7ugAQIKKwGFlZBb2wvft34Wzr3GCZ83dhiyJycni23btpV7jL/99ps4c+ZMubGlS5cKACInJ6fSWmks169fFwBEZGRkufH3339fBAYGVnqfgIAA8f7775cbi4yMFADEjRs3Kr3PggULBMqudCx3q+7jD914Uvi+9bPhFvD2btF98W/ipS+PiDUHr4mkrPxqnY+IyJhycnJMovZLpa4fv16vF1fTc8Wag9fE2DXHRMC83Ybni6ZzfhYhH+wXL6w6LObuOCMupmrqJAMR0f2qWvvMfgbRjRs30KRJE0RGRqJ79+6G8Q8++ADr16/HpUuXKtxn4cKFWLRoUYVxS/0khYhMX4mubDZUYbHOMDPq3j/Hf30cl98fWq1zmsqnyHfr+OHDhxES8tcsqPfffx8bN27ExYsXK9wnMDAQr776KubOnWsYi4yMxJNPPonU1FR4eHhUuE9tzSA6m5KDXG0J3BzUcLW3hqONkjvwEZHZMJXaLxVjP/7CYh0upGrQwNYKTRrYQK1U1PnPJCK6X1VrX71ZpPr+F+dCiAe+YJ87dy7CwsIMX999k0BEZKqsFHJYKeRwsK58gWNzblC4uLhAoVAgLS2t3Hh6ejrc3d0rvY+Hh0elxyuVSjRq1KjS+6jVaqjV6sfO+4RX5bsBEhER3c9GpUCwbwOpYxARVYnZ7wldkzcWarUajo6O5W5ERObs1L8HSh2hxlQqFYKDgxEeHl5uPDw8vNzM0HuFhIRUOH7fvn3o1KlTpesPERERERHRw5l9g6gmbyyIiOqbXaduSB3hsYSFhWH16tVYu3YtYmNjMWvWLCQlJSE0NBRA2czPcePGGY4PDQ1FYmIiwsLCEBsbi7Vr12LNmjV48803pXoIRERERERmrV5cYhYWFoaxY8eiU6dOCAkJwZdfflnujQURUX33713n8WIXH6lj1NioUaOQlZWFd999F6mpqQgKCsLu3bvh6+sLAEhNTUVSUpLheD8/P+zevRuzZs3CihUr4OnpieXLl+O5556T6iEQEREREZm1etEgetQbCyIiMn1Tp07F1KlTK/3eunXrKoz17t0b0dHRdZyKiIiIiMgymP0lZndNnToVCQkJ0Gq1iIqKQq9evaSORERERERksVauXAk/Pz9YW1sjODgYBw8efOjxERERCA4OhrW1NZo1a4ZVq1ZVOGb79u1o3bo11Go1WrdujZ07d9ZVfCIii1NvGkRERJZsxxSuuUZERKZj69atmDlzJubNm4eYmBj07NkTQ4YMKXe58L3i4+MxdOhQ9OzZEzExMXj77bcxffp0bN++3XDMkSNHMGrUKIwdOxanT5/G2LFjMXLkSBw7dsxYD4uIqF6TCSGE1CGkptFo4OTkhJycHO5oRkRm6di1LHRtVvn27g9i6bXP0h8/EVkmY9W+rl27omPHjvj8888NY61atcLw4cOxePHiCse/9dZb2LVrF2JjYw1joaGhOH36NI4cOQKgbFkJjUaDX3/91XDM4MGD0aBBA2zZsqVKuVj7icgSVbX2cQYREVE9MHbtcakjEBERAQCKi4sRFRWFQYMGlRsfNGgQDh8+XOl9jhw5UuH4p556CidPnkRJSclDj3nQOQFAq9VCo9GUuxERUeXYICIiIiIiolqTmZkJnU4Hd3f3cuPu7u5IS0ur9D5paWmVHl9aWorMzMyHHvOgcwLA4sWL4eTkZLh5e3vX5CEREVkENoiIiIiIiKjWyWSycl8LISqMPer4+8ere865c+ciJyfHcEtOTq5yfiIiS1MvtrknIrJ0H7/QTuoIREREAAAXFxcoFIoKM3vS09MrzAC6y8PDo9LjlUolGjVq9NBjHnROAFCr1VCr1TV5GEREFocziIiI6oGufg2ljkBERAQAUKlUCA4ORnh4eLnx8PBwdO9e+a6bISEhFY7ft28fOnXqBCsrq4ce86BzEhFR9bBBRERUDzz54QGpIxARERmEhYVh9erVWLt2LWJjYzFr1iwkJSUhNDQUQNmlX+PGjTMcHxoaisTERISFhSE2NhZr167FmjVr8OabbxqOmTFjBvbt24clS5bg4sWLWLJkCfbv34+ZM2ca++EREdVLvMSMiIiIiIhq1ahRo5CVlYV3330XqampCAoKwu7du+Hr6wsASE1NRVJSkuF4Pz8/7N69G7NmzcKKFSvg6emJ5cuX47nnnjMc0717d3z77beYP38+3nnnHfj7+2Pr1q3o2rWr0R8fEVF9JBN3V3+zYDk5OXB2dkZycjIcHR2ljkNEVG0d/hOOmHcGVus+Go0G3t7eyM7OhpOTUx0lM12s/URkiVj7WfuJyPJUtfZzBhGA3NxcAOC2l0Rk1pz+W7P75ebmWuSbBNZ+IrJkrP2s/URkeR5V+zmDCIBer8eNGzfg4OBg2CbzbofNHD9dMOfsAPNLyZyzA+adX4rsQgjk5ubC09MTcrnlLUnH2m9amF865pwdMO/8rP3Gx9pvWsw5vzlnB5hfSqZc+zmDCIBcLoeXl1el33N0dDS7X7i7zDk7wPxSMufsgHnnN3Z2S/z0+C7WftPE/NIx5+yAeedn7Tce1n7TZM75zTk7wPxSMsXab3kfGxARERERERERUTlsEBERERERERERWTg2iB5ArVZjwYIFUKvVUkepNnPODjC/lMw5O2De+c05e31izv8dzDk7wPxSMufsgHnnN+fs9Yk5/3cw5+yAeec35+wA80vJlLNzkWoiIiIiIiIiIgvHGURERERERERERBaODSIiIiIiIiIiIgvHBhERERERERERkYVjg4iIiIiIiIiIyMJZdINo5cqV8PPzg7W1NYKDg3Hw4MGHHh8REYHg4GBYW1ujWbNmWLVqlZGSVlSd7KmpqRg9ejRatGgBuVyOmTNnGi/oA1Qn/44dOzBw4EC4urrC0dERISEh2Lt3rxHTlled7IcOHUKPHj3QqFEj2NjYoGXLlvjkk0+MmLai6v7e3xUZGQmlUon27dvXbcBHqE7+P/74AzKZrMLt4sWLRkz8l+r+22u1WsybNw++vr5Qq9Xw9/fH2rVrjZS2/mLtlw5rv3TMufabc90HWPtNBWu/dFj7pcPaz9pfbcJCffvtt8LKykp89dVX4sKFC2LGjBnCzs5OJCYmVnr8tWvXhK2trZgxY4a4cOGC+Oqrr4SVlZXYtm2bkZNXP3t8fLyYPn26WL9+vWjfvr2YMWOGcQPfp7r5Z8yYIZYsWSKOHz8u4uLixNy5c4WVlZWIjo42cvLqZ4+OjhabN28W586dE/Hx8WLjxo3C1tZWfPHFF0ZOXqa6+e/Kzs4WzZo1E4MGDRLt2rUzTthKVDf/gQMHBABx6dIlkZqaariVlpYaOXnN/u3//ve/i65du4rw8HARHx8vjh07JiIjI42Yuv5h7ZcOaz9rf02Yc90XgrXfVLD2S4e1n7W/Jlj7pav9Ftsg6tKliwgNDS031rJlSzFnzpxKj589e7Zo2bJlubHJkyeLbt261VnGB6lu9nv17t1b8ieKx8l/V+vWrcWiRYtqO9oj1Ub2Z599VowZM6a2o1VJTfOPGjVKzJ8/XyxYsEDSBlF18999srh9+7YR0j1cdbP/+uuvwsnJSWRlZRkjnsVg7ZcOaz9rf02Yc90XgrXfVLD2S4e1n7W/Jlj7pWORl5gVFxcjKioKgwYNKjc+aNAgHD58uNL7HDlypMLxTz31FE6ePImSkpI6y3q/mmQ3JbWRX6/XIzc3Fw0bNqyLiA9UG9ljYmJw+PBh9O7duy4iPlRN83/99de4evUqFixYUNcRH+px/v07dOiAxo0bo3///jhw4EBdxqxUTbLv2rULnTp1wocffogmTZogMDAQb775JgoLC40RuV5i7ZcOaz9rf02Yc90HWPtNBWu/dFj7WftrgrVf2tqvNPpPNAGZmZnQ6XRwd3cvN+7u7o60tLRK75OWllbp8aWlpcjMzETjxo3rLO+9apLdlNRG/o8//hj5+fkYOXJkXUR8oMfJ7uXlhYyMDJSWlmLhwoWYOHFiXUatVE3yX758GXPmzMHBgwehVEpbLmqSv3Hjxvjyyy8RHBwMrVaLjRs3on///vjjjz/Qq1cvY8QGULPs165dw6FDh2BtbY2dO3ciMzMTU6dOxa1bt7gWRQ2x9kuHtZ+1vybMue4DrP2mgrVfOqz9rP01wdovbe23yAbRXTKZrNzXQogKY486vrJxY6hudlNT0/xbtmzBwoUL8eOPP8LNza2u4j1UTbIfPHgQeXl5OHr0KObMmYPmzZvjpZdeqsuYD1TV/DqdDqNHj8aiRYsQGBhorHiPVJ1//xYtWqBFixaGr0NCQpCcnIz//ve/Rn+yAKqXXa/XQyaTYdOmTXBycgIALF26FM8//zxWrFgBGxubOs9bX7H2S4e1n7W/Jsy57gOs/aaCtV86rP2s/TXB2i9N7bfIBpGLiwsUCkWFDl56enqFTt9dHh4elR6vVCrRqFGjOst6v5pkNyWPk3/r1q2YMGECvv/+ewwYMKAuY1bqcbL7+fkBAJ544gncvHkTCxcuNPoTRXXz5+bm4uTJk4iJicG0adMAlBUvIQSUSiX27duHfv36GSU7UHu/+926dcM333xT2/EeqibZGzdujCZNmhieJACgVatWEEIgJSUFAQEBdZq5PmLtlw5rP2u/MbI/iBR1H2DtNxWs/dJh7WftN0b2B2HtrxmLXINIpVIhODgY4eHh5cbDw8PRvXv3Su8TEhJS4fh9+/ahU6dOsLKyqrOs96tJdlNS0/xbtmzB+PHjsXnzZgwbNqyuY1aqtv7thRDQarW1He+Rqpvf0dERZ8+exalTpwy30NBQtGjRAqdOnULXrl2NFR1A7f37x8TEGG1q+F01yd6jRw/cuHEDeXl5hrG4uDjI5XJ4eXnVad76irVfOqz9rP01Yc51H2DtNxWs/dJh7WftrwnW/jKS1f46XwbbRN3dem7NmjXiwoULYubMmcLOzk4kJCQIIYSYM2eOGDt2rOH4u9tdzpo1S1y4cEGsWbNG8u0uq5pdCCFiYmJETEyMCA4OFqNHjxYxMTHi/PnzRs8uRPXzb968WSiVSrFixYpy2xZmZ2ebfPbPPvtM7Nq1S8TFxYm4uDixdu1a4ejoKObNm2f07DXJfz+pdzGrbv5PPvlE7Ny5U8TFxYlz586JOXPmCABi+/btJp89NzdXeHl5ieeff16cP39eREREiICAADFx4kSjZ69PWPtZ+42RnbW/9phz3a9Jftb+usHaz9pvjOys/bWHtV+62m+xDSIhhFixYoXw9fUVKpVKdOzYUURERBi+98orr4jevXuXO/6PP/4QHTp0ECqVSjRt2lR8/vnnRk78l+pmB1Dh5uvra9zQ96hO/t69e1ea/5VXXjF+cFG97MuXLxdt2rQRtra2wtHRUXTo0EGsXLlS6HQ6CZKXqe7vzr2kbhAJUb38S5YsEf7+/sLa2lo0aNBAPPnkk+KXX36RIHWZ6v7bx8bGigEDBggbGxvh5eUlwsLCREFBgZFT1z+s/b7GDX0P1n7W/pow57ovBGu/qWDt9zVu6Huw9rP21wRrvzS1XybEnRXXiIiIiIiIiIjIIlnkGkRERERERERERPQXNoiIiIiIiIiIiCwcG0RERERERERERBaODSIiIiIiIiIiIgvHBhERERERERERkYVjg4iIiIiIiIiIyMKxQUREREREREREZOHYICIiIiIiIiIisnBsEBHVsfHjx0Mmk0Emk+GHH34wm3MTEVHNsfYTEVke1n4yd2wQERnB4MGDkZqaiiFDhtTqeT/99FOkpqbW6jmJiKh2sPYTEVke1n4yZ0qpAxDVF8XFxVCpVJV+T61Ww8PDo9Z/ppOTE5ycnGr9vEREVDWs/UREloe1n+orziAiqqE+ffpg2rRpCAsLg4uLCwYOHFit+7/11lsIDAyEra0tmjVrhnfeeQclJSWG748fPx7Dhw8vd5+ZM2eiT58+tZCeiIhqgrWfiMjysPaTpeAMIqLHsH79ekyZMgWRkZEQQlTrvg4ODli3bh08PT1x9uxZTJo0CQ4ODpg9e3YdpSUiotrA2k9EZHlY+8kSsEFE9BiaN2+ODz/8sEb3nT9/vuHvTZs2xRtvvIGtW7fyiYKIyMSx9hMRWR7WfrIEbBARPYZOnTrV+L7btm3DsmXLcOXKFeTl5aG0tBSOjo61mI6IiOoCaz8RkeVh7SdLwDWIiB6DnZ1dje539OhRvPjiixgyZAh+/vlnxMTEYN68eSguLjYcI5fLK0xfvfdaZSIikgZrPxGR5WHtJ0vAGUREEoiMjISvry/mzZtnGEtMTCx3jKurK86dO1du7NSpU7CysjJKRiIiql2s/UREloe1n8wJZxARSaB58+ZISkrCt99+i6tXr2L58uXYuXNnuWP69euHkydPYsOGDbh8+TIWLFhQ4YmDiIjMB2s/EZHlYe0nc8IGEZEEnnnmGcyaNQvTpk1D+/btcfjwYbzzzjvljnnqqafwzjvvYPbs2ejcuTNyc3Mxbtw4iRITEdHjYu0nIrI8rP1kTmSiunv0EVG1jB8/HtnZ2fjhhx/q7GfIZDLs3LkTw4cPr7OfQUREVcfaT0RkeVj7ydxxBhGREfz888+wt7fHzz//XKvnDQ0Nhb29fa2ek4iIagdrPxGR5WHtJ3PGGUREdSw9PR0ajQYA0Lhx4xrvgGDscxMRUc2x9hMRWR7WfjJ3bBAREREREREREVk4XmJGRERERERERGTh2CAiIiIiIiIiIrJwbBAREREREREREVk4NoiIiIiIiIiIiCwcG0RERERERERERBaODSIiIiIiIiIiIgvHBhERERERERERkYVjg4iIiIiIiIiIyML9P1tdlDFR1BgRAAAAAElFTkSuQmCC", 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", 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", 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "'''\n", "Examples, 2 models, different dumps for the eccentric, different quadrants\n", "for the circular one\n", "'''\n", "model = 'model-v10e50_AD/'\n", "!mkdir -p $model\n", "infile = os.path.join(model, f\"wind.in\")\n", "if not os.path.exists(infile):\n", " !wget -q \"https://raw.githubusercontent.com/Ensor-code/phantom-models/refs/heads/main/Malfait%2B2024a/v10e50_AD/wind.in\" --output-document $infile\n", "setupfile = os.path.join(model, f\"wind.setup\")\n", "if not os.path.exists(setupfile):\n", " !wget -q \"https://raw.githubusercontent.com/Ensor-code/phantom-models/refs/heads/main/Malfait%2B2024a/v10e50_AD/wind.setup\" --output-document $setupfile\n", "dumps = [555,584]\n", "for dumpnumber in dumps:\n", " dump = os.path.join(model, f\"wind_{dumpnumber:05d}\")\n", " if not os.path.exists(dump):\n", " wgetlink = f\"https://github.com/Ensor-code/phantom-models/raw/refs/heads/main/Malfait+2024a/v10e50_AD/wind_{dumpnumber:05d}?download=\"\n", " !wget -q $wgetlink --output-document $dump\n", " main(model,dumpnumber,['2H'],full=True,quadrants=False,printOut=True)\n", "\n", "model = 'model-v10e00_AD/'\n", "!mkdir -p $model\n", "infile = os.path.join(model, f\"wind.in\")\n", "if not os.path.exists(infile):\n", " !wget -q \"https://raw.githubusercontent.com/Ensor-code/phantom-models/refs/heads/main/Malfait%2B2024a/v10e00_AD/wind.in\" --output-document $infile\n", "setupfile = os.path.join(model, f\"wind.setup\")\n", "if not os.path.exists(setupfile):\n", " !wget -q \"https://raw.githubusercontent.com/Ensor-code/phantom-models/refs/heads/main/Malfait%2B2024a/v10e00_AD/wind.setup\" --output-document $setupfile\n", "dumpnumber = 584\n", "dump = os.path.join(model, f\"wind_{dumpnumber:05d}\")\n", "if not os.path.exists(dump):\n", " wgetlink = f\"https://github.com/Ensor-code/phantom-models/raw/refs/heads/main/Malfait+2024a/v10e00_AD/wind_{dumpnumber:05d}?download=\"\n", " !wget -q $wgetlink --output-document $dump\n", "main(model,dumpnumber,['2H'],full=True,quadrants=True,printOut=True)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Other possibilities:\n", "- Do calculations for different dumps throughout simulation to see how disk mass evolves\n", "- Do calculations for different dumps within one eccentric orbit to see how disk properties change depending on orbital phase\n", "- If you want something else, check if there is not yet a function for it in AccrDisk.py in the source code" ] } ], "metadata": { "kernelspec": { "display_name": "plons", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.14" } }, "nbformat": 4, "nbformat_minor": 4 }